bio-based and applied economics 3(3): 185-186, 2014 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-15285 editorial feeding the planet and greening agriculture: challenges and opportunities for the bio-economy the 2007/08 commodities crisis and repercussions in the following years have shown the vulnerability of the global food economy to shocks coming from extreme weather events, financial or energy market disruptions. the repeated food shortages, exacerbated by agricultural policies aimed at reducing the domestic impacts of the world food crisis, raised concerns about the potential devastating impacts of a new era of global food scarcity on the world’s poor and on the world’s natural resource equilibrium. the estimated significant increases of the people to be fed in the next thirty years and the expected nutritional improvements for a relevant share of the world population will imply a very substantial rise in the demand for agricultural production. to meet the increased food demand resulting from population and income growth, agricultural production will need to be 60% higher in 2050 than in 2006 (fao, 2014). because there is little scope for expanding agricultural land, except for some regions in africa and south america, demand for agricultural land and water is expected in the next years to grow. in the past decades, agriculture has satisfied increases in food demand by means of productivity improvements. increasing agricultural productivity growth is considered as a key factor for the improvement of food security in the next decades (baldos and hertel, 2014). however, recent evidence about a decreasing trend in yield growth of major crops increases concerns about the ability of agriculture to feed world population (fao, 2014). climate change and the reduction of water availability are likely to sharply affect productivity in agriculture and forestry, especially in regions where malnutrition is most prevalent. on the other hand, agriculture and forestry are key target sectors of climate change mitigation policies, in that their contribution to the reduction of greenhouse gas emissions could be substantial. in this rapidly evolving scenario, the eu reformed its policies with the objective of developing a sustainable bio-economy, that is, an economy that satisfies the increase in food and energy needs, while ensuring the sustainable use of biological resources, biodiversity and environmental protection. agriculture plays a key role in the “greening” of the whole economy. recent reforms of the common agricultural policy include greening measures, such as the introduction of greening rules farmers must follow to get a part of the cap payments. the challenges and opportunities for the bio-economy and the new policy demand in this new scenario were the overall topic of the third aieaa conference held in alghero (italy) 25-27 june 2014. the conference included more than 80 papers addressing a range of research and policy issues such as: food and nutrition security issues in specific areas; the modeling and measurement of climate change impacts on bio-based productions; risk management strategies for climate change; innovations in the bio-technology industry and implications for the bio-based supply chain; cost of the policies aimed at mitigating 186 editorial the effects of climate change on the bio-economy. the bae editor invited the speakers of the aieaa conference to submit their papers for this bae special issue. four papers published on this issue were selected after regular peer review from those presented during the third aieaa conference; they represent only an essay of the topics covered by the conference. in particular, the paper by justus wesseler deals with the introduction of new biotechnologies in the agri-food industry, namely the genetically modified engineered crops, and analyzes the responses of the food producers, food processors, food retailers and consumers industry. davide marino and co-authors address the issue of the assessment of the costs and benefits of natura 2000, the european network of protected areas aimed at the protection of biodiversity, by analyzing two italian case studies. the paper by joseph cooper and benoit delbecq deals with the new us income entitlement programs for agribusinesses, referred to as “shallow loss”, introduced by the us 2014 farm bill: more specifically, they propose an approach to assess the sensitivity of the farmers downside risk protection to marginal changes in the deductible of shallow loss program scenarios. finally, the paper by pisani and burighel uses a social network analysis approach to assess the transnational cooperation projects promoted by the local action groups in a specific italian region. baldos, u.l.c., hertel, t.w. (2014). global food security in 2050: the role of agricultural productivity and climate change. the australian journal of agricultural and resource economics 58: 554-570. fao (2014). the state of food and agriculture 2014, fao, rome, 2014. margherita scoppola president of aieaa issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(3): 231-233, 2012 the bio-based economy: a new development model donato romano president associazione italiana di economia agraria e applicata (aieaa) there is no agreed-upon definition of the concept of the ‘bio-based economy’ (or bioeconomy). one of the most widely accepted definitions (european commission, 2012b) describes it as an economy encompassing the sustainable production of renewable biological resources and their conversion into goods and services for final, as well as intermediate, consumption. as such, it encompasses not only traditional economic activities, such as agriculture, fishing, aquaculture and forestry, but also recently developed industries, such as bio-technologies and bio-energy. overall, in 2009 the bioeconomy in europe accounted for 1 trillion euros value added, with an approximate market size of over 2 trillion euros and around 21.5 million jobs (clever consult, 2010). the prospects for further growth are more than promising: according to the oecd (2009), by 2030 on average the use of biotechnologies is estimated to contribute up to 35% of the output of chemicals and other industrial products that can be manufactured using biotechnology, up to 80% of pharmaceuticals and diagnostic production and some 50% of agricultural outputs across oecd countries. the bioeconomy, thanks to its strong innovation potential, encompasses a large part of the task of addressing global challenges, from the contributions of industrial biotechnology through environmental applications to climate change issues, improved health outcomes, and feeding global populations with better yielding crops and better delivery of nutrients and vitamins in foods. in short, the bioeconomy holds at least some of the cards to ensure long-term economic and environmental sustainability. however, technological solutions per se are not a guarantee of success. indeed, the challenges above call for a profound change in the policy environment as well as the research sector. addressing global challenges requires a move from sectoral policy frameworks and governance mechanisms to a more integrated approach (europabio, 2011). the very crosscutting nature of the bioeconomy offers a unique opportunity to address in a comprehensive and systemic manner inter-connected societal challenges. this ambitious approach is fully embedded in the eu commission’s strategy “europe 2020” (european commission, 2010), which calls for building a bio-based economy by 2020 as a key element for supporting an economy based on knowledge and innovation, as well as in “horizon 2020” (european commission, 2011), the new eu framework programme for research and technological innovation (2014-2020), and in the recent eu commission communication on “innovating for sustainable growth: a bioeconomy for europe” 232 d. romano (european commission, 2012a). the overall objective of all of these frameworks is a refocusing of the european development model, promoting a bio-based economy to foster economic growth and job creation. at the same time research activities and the higher education system should be reoriented. this is already happening at different scales as witnessed, for example, by the launching of the above-mentioned eu horizon 2020 research programme, the blossoming of international research cooperation frameworks (such as the eu-lac bioeconomy working group), the birth of new institutes/departments focusing on the bioeconomy (see, for example, the bioeconomy institute at iowa state university) or networks of institutions/researchers (such as the bioeconomy network at michigan state university, the bioeconomy science center at the university of aachen, the international consortium on applied bioeconomy research) as well as new postgraduate programs (such as the master of sciences in management of bioeconomy, innovation and governance at the university of edinburgh). all of these initiatives share the common view that research and education must be re-oriented towards a more comprehensive model based on the ’convergence‘ of different disciplines, acknowledging that while a deep disciplinary background remains vital, robust cross-disciplinary education/research is essential to address complex issues. the italian association of agricultural and applied economics (aieaa) is part of this process. the reasons for establishing aieaa are rooted in the challenges above, as well as the implied changes in the topics/methods in the field of agriculture and applied economics (viaggi et al., 2012; sckokai, 2012; schmid et al., 2012) and the activities carried out by aieaa over the past year or so reflect these reasons. the idea of launching bio-based and applied economics (bae), the aieaa’s official journal, is deeply rooted in the awareness of those changes. this is also the reason for which the theme of the first aieaa conference, held in trento on 4-5 june 2012 was “towards a sustainable bioeconomy: economic issues and policy challenges” aimed at discussing challenges and opportunities offered by the bio-based economy, specifically focusing on what research, innovation and policy can do to foster economic growth and provide an alternative development model to address global challenges. the papers included in this issue of bae are all from that symposium and provide an overview of the wide range of topics debated in trento1 (cf. http://www.aieaa.org to download the full set of papers and presentations), focusing on some of the most important bioeconomy issues, such as biofuels, innovation, gmos and product differentiation. in particular, esposti deals with the evolution of the knowledge and innovation system implied by the biotechnology revolution in agriculture (though many of his remarks are also applicable to other bioeconomy sectors). the paper, commencing from a conventional science-based approach, highlights the emergence of some system failures and the need for a new conceptualization and design, discussing policy implications at the eu level. moschini et al. make a thorough assessment of the state of the art of the economics of biofuels, analyzing the pivotal role played by some critical policies in the sector’s performance over the last decade and providing an overall assessment of the impact of biofuels on the economy and on the environment. sckokai and varacca analyze product and brand 1 the papers published in this issue were submitted to the journal in answer to a call open to a selection of the papers presented at the conference. they underwent the regular double blind peer review process adopted by the journal before final acceptance. 233the bio-based economy: a new development model competition in the italian breakfast cereal market, demonstrating the presence of patterns of substitution within products sharing the same brand and similar nutritional characteristics. finally, mora et al. analyze the socio-economic drivers affecting the use of gm animals in livestock and pharmaceutical industries and review the risks and benefits implied by the adoption of gm animals from the point of view of the life sciences. references clever consult bvba (2010). the knowledge based bioeconomy (kbbe) in europe: achievements and challenges. brussels. esposti, r. (2012). knowledge, technology and innovations for a bio-based economy: lessons from the past, challenges for the future. bio-based and applied economics 1(3): 231-264. europabio (2011). building a bio-based economy for europe in 2020, europabio policy guide. brussels. european commission (2010). communication from the commission “europe 2020 a strategy for smart, sustainable and inclusive growth”, com(2010) 2020 final. brussels. european commission (2011). proposal for a council decision establishing the specific programme implementing horizon 2020 the framework programme for research and innovation (2014-2020), com(2011) 811 final. brussels. european commission (2012a). innovating for sustainable growth: a bioeconomy for europe. brussels. european commission (2012b). commission staff working document accompanying the document “communication on innovating for sustainable growth: a bioeconomy for europe”. brussels. mora, c., menozzi, d., kleter, g., aramyan, l.h., valeeva, n.i., zimmermann, k.l., pakki reddy, g. (2012). factors affecting the adoption of genetically modified animals in the food and pharmaceutical chains. bio-based and applied economics 1(3): 309325. moschini, g., cui, j., lapan, h. (2012). economics of biofuels: an overview of policies, impacts and prospects. bio-based and applied economics 1(3): 265-292. oecd (2009). the bioeconomy to 2030: designing a policy agenda. oecd, paris. schmid, m., padel, s., levidow, l. (2012). the bio-economy concept and knowledge base in a public goods and farmer perspective. bio-based and applied economics 1(1): 47-63. sckokai, p. (2012). agricultural and applied economics: what is this? bio-based and applied economics 1(1): 13-27. sckokai, p., varacca, a. (2012). product differentiation and brand competition in the italian breakfast cereal market: a distance metric approach. bio-based and applied economics 1(3): 293-308. viaggi, d., mantino, f., mazzocchi, m., moro, d.,, stefani, g. (2012). from agricultural to bio-based economics? context, state of the art and challenges bio-based and applied economics 1(1): 3-11. bio-based and applied economics 5(1): 1-4, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-18336 editorial biorefineries in the bio-based economy: opportunities and challenges for economic research loïc sauvée, davide viaggi guest editors 1. background and objectives this special issue of bio-based and applied economics (bae) is devoted to the topic of biorefinery, with a focus on third generation biorefinery. as a broad technological definition, biorefinery is intended as the conversion of all kinds of biomass (organic residues, energy crops, aquatic biomass etc.) into a wide range of bio-based products, such as fuels, chemicals, power and heat, materials, food and feed (demirbas, 2010). in this special issue we approach the biorefinery concept with an eye to defining a new business model involving the complete valorisation of biomass in energy, food, feed, biomaterials and bio-based chemicals. the development of this industry opens important opportunities for many countries in a context of sustainability and competitiveness. industrial initiatives are growing rapidly in brazil, the usa, italy, france, and sweden. biorefineries are increasingly at the core of the bioeconomy vision at the eu level and worldwide, as highlighted by the documents of the recent world bioeconomy summit (german bioeconomy council, 2015a; 2015b; 2015c). their future development is connected to key developments, including sustainability concerns and the need for decoupling from food production. the evolution and the increasing relevance of biorefineries underscore the need for new economic and organisational knowledge about the concept of biorefinery and its practical applications. this also challenges researchers to provide new theoretical approaches, as well as empirical studies, including cross-country comparisons, scenario building, business model assessment, and analyses of impacts on economic growth and employment. the objective of this bae special issue is to address some of the main challenges that the development of biorefineries will face in europe, and the world over, from economic, strategic and organisational perspectives. biorefineries are challenging economic research from several perspectives, some of which are explicitly in the background and form the motivation of this special issue. firstly, the role that biorefineries will play in global, national and local sustainability transitions, including considerations of state vs. industrial group strategies, is an open issue. this topic can be approached through international comparisons of the business models used by biorefineries: levels of horizontal/vertical integration, the role of cooperatives, processors and distributors, and the role of public-private partnerships (ppp) and public procurement. a second topic concerns instruments that are used for chain coordination related to biorefinery, in particular contracts and chain management approaches. a third issue that is of particular importance for the economy is that of territories and the local/regional anchoring of biorefineries, the role of farmers and farmers organisations 2 l. sauvée, d. viaggi in the development of territorial biorefineries, and the role of cooperatives and professional bodies. finally, when developing research on biorefineries, it is also of paramount importance to take into account the institutional environment, legal framework, international negotiations and public perceptions of the development of biorefineries. as a complementary view, an analysis and design of dedicated biorefinery policies and interactions with related existing policies (e.g. agricultural policy, waste policy, etc.) is also to be considered. we will see that these topics are emphasised both directly and indirectly in the following articles, with more theoretical and empirical developments on the issue of business models, consumption and impacts of/on the institutional environment. we will first present the overall content of these articles. second, we will relate them to the wider scope of the question of biorefinery and to paths for future research. 2. the papers in this issue the three papers in this issue are very different in scope and method and provide examples of different and complementary issues in addressing the topic of biorefinery. ceapraz, kotbi and sauvée (2016), in their article, put forward the underlying business models of the new generations of biorefineries, and more specifically that of the territorial biorefinery, in opposition with the port biorefinery. they highlight that the concept of territorial biorefinery does not reach a consensus among scholars. their article provides the key findings of several theoretical approaches, from the socioeconomics of proximity to governance of territorial assets, industrial and territorial ecology and sociotechnical transition. they suggest that a clarification of what constitutes the territorial biorefinery from a business model point of view and an identification of its main characteristics should be made explicit in order to facilitate the manner in which practitioners study, develop and set up businesses of this kind. bonfiglio and esposti (2016) investigate the impact on the economy of sardinia (italy) as a result of a new biomass power plant fed by locally cultivated cardoon. the cardoon in question also allows for the production of biopolymers. in their article, the impact is assessed at an economy-wide scale using two multi-regional closed input-output models that make it possible to take into account the entire supply chain activated and the supralocal effects generated by trade across local industries. the effects are computed under alternative scenarios simulating different levels of substitution of existing agricultural activities with the new activity (cardoon). their results show positive and locally significant impacts in terms of value added and employment. however, these impacts are substantially influenced by the degree of substitution. the results also suggest that there are specific territorial areas that are more sensitive to negative effects induced by substitution. the article by sivashankar (2016) assesses diesel vehicle owners’ willingness to pay (wtp) for jatropha biodiesel in sri lanka, and the factors affecting their decisions. the study was carried out in the kandy region among diesel vehicle users. the wtp was assessed using a contingent valuation method (cvm). the factors affecting wtp were estimated using probit regressions. the mean wtp for biodiesel by the diesel vehicle users was 0.74 euro/litre for lower bound levels. the median wtp was 0.85 euro/litre. elderly respondents with higher education are less likely to pay for biodiesel. married respondents with higher income are more likely to pay higher prices for biodiesel. 3biorefineries in the bio-based economy 3. outlook and perspectives for future research on biorefinery the articles in this special section, though limited in number, underscore the diversity of effort needed for the economic analysis of biorefinery; in particular, they hint at a need for contribution on the part of the economic literature in at least three directions. the first is a conceptual one, focusing on understanding the logic of biorefinery and its connection with theoretical views and business models. the second concerns the modelling of the economics of biorefinery and its effects. the third concerns the need for rigorous assessments of the potential market and non-market role of emerging biorefinery solutions. a common feature of the state of the art concerns the fact that that the biorefinery concept is largely addressed using ideas taken from other fields, and the level of specificity and adaption of the tools used remains rather low. however, as this special issue also demonstrates, the development of the biorefinery in the biobased economy opens up opportunities and needs for future research in trying to meet the specificities of the concerned processes. in this section we identify a few of them. first, although the biorefinery is one of the building blocks of the bio-based economy of the future, it is undoubtedly not the only one. the potential of the biorefinery to expand, especially in rural areas, and to become a significant part of the bio-based economy is directly related to its ability to articulate economic aspects with environmental and social dimensions, as well as to match the needs of related sectors. the importance of integrated approaches toward sustainability assessment brings into question the methodologies being used in the various research communities. indeed, as we have seen in this issue, the veritable novelty of the biorefinery concept is the new representation that it provides of the functioning of the economy. the so-called ‘ecological economics approach’ already introduced a vision whereby natural and industrial processes are intertwined: the complementarities in terms of material, energy and information flows highlight the need for an even more expanded system approach. systemic in spirit, the biorefinery is notwithstanding a business model that should be economically efficient. researchers have shown that the categories of products that could emerge from biorefineries range from mass products to intermediary and even niche products, in the various categories of food and feed, bioenergy, biomaterials and biochemical products. besides the traditional dichotomy dilemma, between market size (large or small)/market price (low or high), the opportunities of the biorefinery model in terms of future developments and impacts for local producers (and thus rural development) are extremely diverse. future research should investigate how to reach optimal solutions in the trade-offs between relevant dimensions. the biorefinery also brings into question the topic of customer needs and customer behaviour, either at the industrial market level or at the consumer market level. we have seen in the past that the controversy faced by gmo products on the market found some resonance in the first generation biofuels with respect to the food versus non-food uses of land. on the contrary, innovative bio-based products open up possibilities of wider consumer and industrial acceptance. yet, what remains unclear is the pathway for change in these behaviours towards products that are sourced through renewable resources. in addition, technologies avoiding trade-offs with food production may yield other sources of concern for citizens (e.g. use of wastes). 4 l. sauvée, d. viaggi from a technological perspective, the economic efficiency of different conversion technologies is one of the questions to be addressed. researchers in the social sciences could provide valuable insights into developing results not exclusively from a static economics efficiency and optimisation angle, but more from dynamic process perspectives. indeed, the ability of players to develop innovative triple helix models of collaboration between research organisations, institutions and private companies is a key enabling factor in the process. the diversity of open innovation models to back up third generation biorefineries will necessitate research in organisational and institutional economics in parallel with the more traditional approaches of economic optimisation of alternative conversion technologies both within the biomass sector and between other resources (solar, oil etc.). a final issue that researchers must take into consideration is the question of territory. the development of biorefineries has deeply redefined the link between the processing unit and the space in which this economic activity is embedded. once again, in line with the systemic reasoning that is at the base of the biorefinery concept, the way farmers belong to and participate (or not) in the design of the bio-based sectors is crucial. research into participatory approaches, design thinking and the conception of innovative biorefinery models that integrate multi-scale analysis provide promising opportunties for the research community. a few references cherubini, f. (2010). the biorefinery concept: using biomass instead of oil for producing energy and chemicals. energy conversion and management, 51(7), 1412-1421. colonna, p., tayeb, j., & valceschini, e. (2015) les nouveaux usages de la biomasse. le déméter, paris. chertow, m., & ehrenfeld, j. (2012). organizing self‐organizing systems. journal of industrial ecology, 16(1), 13-27. demirbas a. (2010). biorefineries. for biomass upgrading facilities. springer-verlag, london. german bioeconomy council (2015a). bioeconomy policy (part i). synopsys and analysis of strategies in the g7. office of the bioeconomy council, berlin. german bioeconomy council (2015b). bioeconomy policy (part ii). synopsis of national strategies around the world. office of the bioeconomy council, berlin. german bioeconomy council (2015c). global visions for the bioeconomy – an international delphi-study. office of the bioeconomy council, berlin. kokossis, a. c., & yang, a. (2010). on the use of systems technologies and a systematic approach for the synthesis and the design of future biorefineries. computers & chemical engineering, 34(9), 1397-1405. langeveld, j. w. a., dixon, j., & jaworski, j. f. (2010). development perspectives of the biobased economy: a review. crop science, 50(supplement_1), s-142. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 3(2): 175-184, 2014 doi: 10.13128/bae-14704 an addendum to: a meta-analysis of hypothethical bias in stated preference valuation gianluca stefani1,*, riccardo scarpa2, ginevra v. lombardi1 1 department of economics and management, university of florence, florence, italy 2 economics department, waikato management school, hamilton, new zealand abstract. a recent study published by murphy et al. (2005) reported results of a meta-analysis of hypothetical bias using 28 valuation studies. the authors found a median ratio of hypothetical to actual values of 1.35 but they did not investigate the ratio of variances of the hypothetical and actual value distributions, which is of great relevance in joint stated and revealed preference analysis. we propose an addendum to murphy et al. (2005) to provide some insights on the distribution of the scale factor across 23 studies for which relevant data is available. we distinguish three types of dispersion parameters reported in the literature. we find that the ratio of real to hypothetical standard deviations of marginal distributions of wtp is about 0.6. keywords. contingent valuation, experiments, scale identification, meta-analysis, stated preferences jel codes. c9, h41, q26, q28 1. introduction stated preference methods are widely used in nonmarket valuation of environmental goods. however, they have been criticised for a number of reasons revolving around the issues of credibility and reliability of hypothetical responses (cummings et al. 1997, diamond & hausman 1993, green et al. 1998, to name but a few in the context of contingent valuation). the difference between responses in hypothetical and real payment settings, known as hypothetical bias, is an issue that has given rise to a fierce debate among scholars and has motivated much research effort. a key question in the ensuing research agenda has been the estimation of a calibration factor (cf). this is the ratio between hypothetical (stated preference) and actual (or revealed preference) values. using cf, values elicited with hypothetical choices may be corrected to obtain value estimates similar to those obtainable from revealed preference studies. a methodological approach that has generated much attention in this area of applied research has been the use of meta-analysis. at least three literature reviews or meta-analysis studies have investigated the scope and extension of cf. harrison and rutstrom (2008) 1 corresponding author: gianluca.stefani@unifi.it. short communication 176 g. stefani, r. scarpa, g.v. lombardi using 35 observations report a cf ranging from 0.75 to 26. list and gallet (2001) analyse 29 studies with a total of 174 observations of willingness to pay (wtp) and willingness to accept (wta) estimates. according to their study hypothetical values are about three times larger than real ones, with cf being larger for wta rather than wtp or when the values are elicited for public rather than private goods. little and and berrens (2004) expanded the dataset of list and gallet to include 17 additional observation. the cf from their dataset ranges from 2.93 to 3.34 with a median value of 3.13. murphy et al. (2005), drawing on the study by list and gallet, proposed a new meta-analysis focussing on wtp estimates and including only observations that employ the same mechanism to elicit hypothetical and real values. the authors selected 28 studies that yield a total of 83 observations for which the distribution of cf is skewed with a mean value of 2.60 and a median value of 1.35. the authors found mixed results about the determinants of cf. students and group setting seem to widen cf, while discrete choice format and valuation of private goods would have the opposite effect. in a cautionary note, the authors warn that results are sensitive to model specification and that the choice of explanatory variables is affected by the lack of a theory explaining hypothetical bias. all four studies purport the ratio between hypothetical and actual values as a key factor in criterion validity of stated preference estimates, under the assumption that values elicited from revealed preference data are closer to the truth. our point of departure is the observation that distributions of ratios of value estimates are not completely characterised by location parameters alone (such as the mean or the median). dispersion parameters are also of crucial importance, especially in the context of joint preference estimation from merged revealed and stated preference data (e.g. hensher, louviere and swait, 1999). in this context there are good theoretical reasons for the existence of a difference in error scale from different data sources, which has been corroborated by much empirical evidence (louviere, 2001)2. similarly, cameron et al. (2002) state that “what would be most valuable for predicting actual demand behavior from stated preference choice data would be some means of using common underlying systematic preference parameters, […] mapping the dispersion parameter from the particular stated preference method into the likely corresponding dispersion parameter for a revealed preference choice context. this might allow prediction of the distribution of wtp for real market choices.” for example, the results from one of the first papers addressing the impact of real vs. hypothetical treatments on values elicited with contingent valuation (cv) of public goods (cummings et al., 1997) were indeed questioned with respect to the assumption of equal variance across treatments two years later (haab et al. 1999). we define as inverse relative scale factor (irsf) the ratio of standard deviations of real over hypothetical value distributions: σ σ σ σ = =irsf r h r h 2 2 (1) 2 according to louviere (2001) “experimental manipulations, differences incontexts, actions taken by managers, and the like impact not only distributions of response means but also variances of these distributions”. 177a meta-analysis of hypothethical bias where σr and σh are standard deviations with subscripts referring to “real” and “hypothetical” distributions, respectively. we named the ratio inverse relative scale factor since the scale factor is usually defined as μ = 1/σ (adamowicz , louviere and williams, 1994) and the relative scale factor as sf = μr/μh whilst our index is given by irsf = μh/μr. the aim of this note is to provide a first estimate of the distribution of the irsf from a subset of 23 studies out of the original 28 considered by murphy et al. (2005), for which relevant data on scale is available. our focus is on deriving estimates of the irsf rather than exploring the determinants of hypothetical bias, therefore the note should be considered as an “addendum” rather than a “comment” to the original paper by murphy. the remainder of this note is set out as follows. section 2 illustrates alternative measures of dispersion of wtp. section 3 deals with data and estimation procedures. sections 4 and 5 provides a summary of findings and regression results while section 6 concludes. we provide an assessment of the empirical distribution of the sf across a sample of stated preference studies finding that differences of variances are mild, a result similar to that provided for cf by murphy et al. (2005). we found that cf and sf are correlated and that factors that affect the former also tend to affect the latter. 2. alternative measure of dispersion of wtp depending on the estimation framework adopted, different measures of dispersion are reported in the studies we reviewed. so, we provide a simple model that helps clarifying the differences among alternative measures. let us start with a simple linear-in-the-parameters random wtp model: β ε=wtp x ' +i ii (2) a first important distinction we make is between the marginal or unconditional variance of wtp and the variance of the error term of the model: ( ) ( )=   +  var wtp e var wtp x var e wtp x( | ) |x x (3) or ε β( ) ( ) ( )= +var wtp var var xx (4) equation 4 decomposes the unconditional variance of wtp into two terms. the first terms is the variance of the error term and the second term is the variance of the conditional mean of wtp with respect to a vector of covariates x. it is clear that the ratio of unconditional variances will be always different from the ratio of the error term variances unless x is fixed in the hypothetical and real treatment or the ratio of the varx(xβ) is the same of the ratio of the var(ε): σ σ σ σ ≠ ε ε wtph wtpr h r 2 2 2 2 (5) 178 g. stefani, r. scarpa, g.v. lombardi further measures of dispersion can arise as some researchers calculate fitted wtp for different representative persons. then, drawing from the asymptotically joint normal distribution of the maximum likelihood parameter estimates, they build up a sampling distribution of fitted wtp estimates following the procedure originally set out by krinsky and robb (1986) to estimate confidence intervals for elasticities. then the distribution reflects the estimation precision for all of the parameters in the model and not only the error precision, and it shows how estimation efficiency affects the range of plausible values for wtp for a representative subject. in the case of the linear model 2, which is estimated using ols, it is well know that the asymptotic estimator of var(b|x) is given by:  σ( ) ( )= ′ − var bx x x| u 2 1 (6) therefore the variance of estimated wtp for a representative subject (at the mean values of x, x is: σ( ) ( )= ′    − var bx x x x x x| u ' 2 1 (7) which again is different from either var(ε) and its estimator  σ ε ε= −n k 'ˆ u 2 or from (4). 3. data and estimation we supplemented the dataset employed by murphy et al. (2005)3 by recording measures of dispersion for the wtp distribution irrespective of the form in which the measures were provided by the authors. we were able to collect data from 23 out of the original 28 studies providing 67 observations4. in addition we retrieve 4 more observations from two studies surveyed by little and berrens (2004). overall, our dataset includes 25 studies and 71 observations. in the augmented dataset available, measures of dispersion can be classified according to both the type of measure of dispersions outlined in the previous section and the format the dispersion is provided with. we classified different formats for dispersion measure into 4 groups as follows. 1) standard deviation. studies based on experimental auctions and open-ended elicitation formats generally provide data on standard deviations of wtp values distributions. 2) confidence interval. most dichotomous choice and some open ended cv methods provide confidence intervals for the estimates of the mean wtp. as the sample size is similar for real and hypothetical treatments, the width of confidence intervals is proportional to the standard deviation of wtp. therefore we maintain that the ratio of the sizes of confidence intervals is a close proxy for irsf. 3) sigma. studies that employ dichotomous choice data often use probit or logit models to explain outcome probabilities. in such cases it is possible to recover the stand3 both dataset, bibliography and description of variable have been made available by murphy on its own webpage at: http://faculty.cbpp.uaa.alaska.edu/jmurphy/meta/meta.html 4 the five excluded studies are: blumenschein, et al. (2001); boyce, et al. (1989) ; duffield and patterson (1992); murphy, et al. (2002) and sinden (1988). 179a meta-analysis of hypothethical bias ard deviation σ of the underlying distribution from the inverse of the estimate of the parameter of the bid variable5 as described in cameron and james (1987). actually, in the case of logit models the inverse of the parameter of the bid variable gives κ σ π= 3 / that is the dispersion of the error term in the logistic regression. however, since κ is a linear function of σ the constants cancel out and the probit and logit ratios are equal: κ κ σ σ =r h r h (8) 4) scale factor. finally, a single study (carlsson and martinsson, 2001) carried out using multiple choices, reports directly the corresponding scale factor. from each of the studies employed here a irsf value is obtained by dividing the measure of dispersion of the real or actual subsample by the one estimated from the hypothetical subsample. table 1. observations classified according to format and type of dispersion. format type of distribution total error parameters marginal confidence interval 1 5 8 14 scale_fact 1 0 0 1 sigma 14 0 0 20 stand. dev 0 8 34 36 total 16 13 42 71 most of the observations in the dataset are estimates of marginal wtp distributions, formatted either as standard deviations or confidence intervals. only 16 observations provide an estimate of error distribution followed by the group of observations where the measure provided is a function of model parameters standard errors through krinskyrobb type procedures. 4. results: summary statistics overall, the mean value of the irsf in our sample is 0.67 with a standard deviation of 0.41. however, the irsf distribution is quite different across the three distribution types confirming their different nature. all means and medians are smaller than 1, consistently with theoretical expectations. haab et al. (1999) state that real experiments control more effectively for sources of variability, therefore the distribution of elicited values is likely to be less dispersed than in hypothetical settings. 5 a single study that directly provides the scale factor for a multinomial logit model also belongs to this group. 180 g. stefani, r. scarpa, g.v. lombardi table 2. summary statistics of irsf across distribution types. form type of distribution total error parameters marginal min 0.45 0.04 0.07 0.04 max 1.85 1.40 1.51 1.85 mean 0.90 0.67 0.58 0.67 med 0.82 0.69 0.54 0.66 st.dev 0.39 0.50 0.35 0.41 it is worth noticing that with a similar number of observations, measures of irsf based on all model parameters distributions are more dispersed than those based on model error distribution. across all types of distribution there are observations with irsf values higher than 1, a result that mimics what was found by murphy for the cf. kernel density estimates of the three distributions of irsf are plotted in figure 16. figure 1. density estimates of irsf. 0.0 0.5 1.0 1.5 2.0 0. 0 0. 5 1. 0 1. 5 2. 0 irsf de ns ity error marginal parameters irsf shows an overall weak and positive correlation (r=0.58) with the inverse of cf (icf), which is the ratio of actual vs hypothetical mean. it is the irsf obtained from marginal distributions of wtp that shows the highest correlation with icf. interestingly, irsf from error distributions does not seem to be correlated with icf. however, this results is likely to be affected by the presence of outliers as it can be seen from figure 2. 6 a normal kernel density estimate was employed. the bandwidth parameter was selected with sheater and jones (1991) formula. 181a meta-analysis of hypothethical bias table 3. correlation between icf and irsf. ρ ci error -0.13 ( -0.56 , 0.36 ) parameters 0.66 ( 0.18 , 0.89 ) marginal 0.81 ( 0.67 , 0.90 ) all 0.58 ( 0.41 , 0.72 ) figure 2. icf vs irsf. 0.0 0.5 1.0 1.5 2.0 0. 0 0. 5 1. 0 1. 5 irsf in v c f error marginal parameters 5. results: regression analysis to try and explain hypothetical bias we also regress irsf on icf and on the explanatory variables used by murphy et al. (2005) (tab. 4). this allows us to see if there is any further marginal and significant effect besides icf. table 4. regression results: marginal distribution type only. estimate estimate std. error t value (intercept) 0.15 0.06 2.33 icf 0.81 0.09 8.61 choice -0.08 0.09 -0.82 private -0.12 0.09 -1.28 student 0.02 0.06 0.28 within 0.19 0.09 1.95 calibrate 0.19 0.08 2.46 multiple r-squared: 0.77. adjusted r-squared: 0.73. 182 g. stefani, r. scarpa, g.v. lombardi we include in the regression only observations derived from marginal distributions of wtp due to limitations on the degrees of freedom for the error and parameter groups both with less than 20 observations each. . an r-squared of 0.77 is obtained and the only statistically significant coefficients are those for icf, within sample (at the 10% level) and calibrate. within might give irsf closer to 1 either because of carry over effects7 or simply because the hypothetical and the real treatment groups are identical. this finding is consistent with evidence of a larger variance in responses in between subject experimental designs (louviere, 2001). the calibrate variable refers to either ex-ante calibration techniques such as budget reminder or cheap talk scripts or ex-post calibration such as using lab experiments to calibrate field data or other uncertainty adjustments (murphy et al., 2005). the calibration techniques is likely to mitigate the erratic behaviour observed in hypothetical treatments. however, as in the case of the cf, for the sf we also lack a comprehensive theory that explains hypothetical bias. so, the causality of significant parameters should be interpreted with caution. 6. conclusions our point of departure is the observation that most meta-analyses on discrete choice contingent valuation studies comparing real and hypothetical choice settings ignore the role of scale factor. yet, the issue of estimation efficiency (bias and mean square error) is likely to be as important as the bias question in comparing stated and revealed preferences. building on murphy et al. (2005) our study provides some insights on the distribution of the inverse relative scale factor across 25 stated preference studies. the results show that, on average, the irsf is about 0.6-0.7 and is correlated with the ratio between real and hypothetical average wtps. however, there are important differences in the distribution of the irsf depending on which type of wtp distribution is considered: marginal wtp distribution, wtp model error distribution and wtp estimate distribution considered as non linear function of model parameters distribution. 7. acknowledgments the authors would like to thank, with the usual disclaimer, james murphy for the comments provided on various versions of this paper. 6. references adamowicz, w., louviere, j. and williams, m. 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(2000). stated choice methods: analysis and applications. canbridge university press, cambridge (uk) murphy, j., allen, g., stevens, t. and weatherhead, d. (2005). a meta-analysis of hypo184 g. stefani, r. scarpa, g.v. lombardi thetical bias in stated preference valuation. environmental and resource economics 30: 313-325. murphy, j. and weatherhead, d. (2002). an empirical study of hypothetical bias in voluntary contribution contingent valuation: does cheap talk matter? paper prepared for the world congress of environmental and resource economists monterey, ca. sheater, s. and jones, m. (1991). a reliable data-based bandwith selection method for kernel density estimation. journal of the royal statistical society 53: 683-690. sinden, j.a. (1988). empirical tests of hypothetical bias in consumers’ surplus survey. australian journal of agricultural economics 32: 98-112. bio-based and applied economics 4(2): 149-163, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-16219 labour constraints on choosing profitable products for part-time farmers in swiss agriculture laure latruffe1,2,*, stefan mann3 1 inra, umr1302 smart, f-35000 rennes, france 2 agrocampus ouest, umr1302 smart, f-35000 rennes, france 3 agroscope reckenholz tänikon, switzerland date of submission: october 14th, 2013 abstract. in this paper we suggest that low labour availability on part-time farms may limit part-time farmers’ choice of production enterprises, and as a consequence those farmers may be forced to engage in less profitable enterprises. this proposition is illustrated by a conceptual framework based on the assumption that products with high returns to labour are labour intensive. an empirical analysis for the period 19962004 based on aggregated yearly data from the swiss farm accountancy data network (fadn) did not fully confirm the proposition. this may suggest the existence of a joint determination of the choice of production enterprise and of the choice to work off the farm. in addition, our assumption that highly profitable enterprises are labour intensive may not hold for specific products or contexts. keywords. part-time farms, enterprise choice, labour, returns to labour, switzerland. jel codes. q12 1. introduction several papers have investigated the reasons underlying the choice of specific production enterprises in agriculture. moran and anderson (1988) analysed the determinants of changing from dairying to beef farming in new zealand in 1975-1983. more recently, gillespie and mishra (2011) examined the selection of agricultural production enterprises among five of them (beef, dairy, crops, hogs, broilers) in the united states (us). some articles have also investigated the issue in developing countries, such as okon et al. (2012) and ojo et al. (2013) in nigeria or tamirat (2013) in ethiopia. most of the articles relied on the use of a multinomial probit to evaluate the determinants of the probability of choosing a specific production enterprise. by contrast, gillespie and mishra (2011) evaluated the extent of engagement in a specific enterprise, by using tobit equations (one for each of the five enterprises considered) where the dependent variable was the share of * corresponding author: laure.latruffe@rennes.inra.fr. mailto:laure.latruffe@rennes.inra.fr 150 l. latruffe, s. mann value produced by the specific enterprise within the total farm production value. these studies reported significant effects of sociological characteristics (age, education, household size) and structural characteristics (farm size, location) of the holding on enterprise selection. only gillespie and mishra (2011) studied the role of off-farm work on the choice of enterprises. the authors considered the number of off-farm hours spent by the farmer as a proxy for off-farm commitment. a broader definition of off-farm commitment is gainful activities carried out off the farm, not only by the farm head, but also by other members of the farm family (lund, 1991). in addition, as noted early by salter (1936), the extent of off-farm occupation may be measured either in terms of labour quantity supplied off the farm or in terms of income stemming from off-farm work. this author provided the first definition of part-time farming as “the combination of a small amount of farming with an occupation not connected with the farming”. using farm-level data in 2003, gillespie and mishra (2011) showed that in the us a greater time spent off farm by the farmer induced a higher probability to choose beef production over crop or dairy production. the authors explained this results by the low capital and labour requirements in beef production enterprise (most beef farmers being lowinput cow-calf or stocker farmers), as “farmers choose a production enterprise based on labour availability and requirements (and other farm resources)”. in this paper we also follow the idea that labour requirements may limit part-time farmers’ choice of production enterprises, but we go a little further. we suggest that, as a result, part-time farmers may be forced to engage in less profitable enterprises. this idea is based on the assumption that products with high returns to labour are labour intensive. this suggestion is supported by kingwell’s (2011) proposition that “profitable farming systems are complex and timeconsuming to manage” when considering farmer’s annual labour, land use or enterprise diversity, and revenue and expenditure diversity for a sample of australian farms. we demonstrate our idea with a simplified conceptual framework in section 2, and we provide an empirical application for swiss farms, for which the methodology and data are described in section 3. section 4 presents the results and section 5 concludes. 2. conceptual framework the conceptual framework presented below demonstrates the idea that in some situations part-time farmers may not produce highly profitable products, but instead may be constrained to produce on their land mainly products with low returns to labour. the underlying assumption, as explained above, is that products with high returns to labour are labour intensive: this means that they require that a substantial level of labour input is used, a requirement that part-time farms may not be able to meet due to constrained own labour supply. the notion of a substantial amount of labour input that needs to be allocated to a specific production activity deals with the technical nature of agricultural production processes. there is a proportion of factor requirements that are fixed, which may occur, firstly, when entering a new production activity, and secondly, during the production process of specific activities. firstly, as shown by mann et al. (2003), entering a production activity requires investment decisions in at least two respects. in order to start a new produc151choosing profitable products for part-time farmers in swiss agriculture tion activity, not only capital investments become necessary, but human capital has also to be invested, so that technologies and the organisation of labour are known to the farmer. labour investments and other fixed factor requirements often contribute to the persisting phenomenon of economies of scale (hallam, 1991; shah, 1992; langlois, 1997). mann et al. (2003) additionally showed by internationally comparing exit rates from production enterprises, that conservative farmers like swiss farmers tend to consider entering a new production process more as an investment compared with more flexible farmers as, for example, dutch farmers. secondly, labour requirements differ across agricultural products. for example, pig breeding is a labour intensive activity and requires a substantial labour time in order to become acquainted with the numerous complicated cycles and processes of piglet production (knap et al., 2001). once the business is running, a substantial number of hours have, at several stages of the breeding process, to be spent in order to keep animals healthy and to result in the desired number of piglets, independent of holding size. by contrast, an example with a relatively low level of labour requirements is the production of spelt. producers who are familiar with grain production generally have little additional investments to do to enter the production of spelt. our conceptual framework is mainly graphical, but is based on a theoretical objective program of farmers who may work or not off farm, and who have the possibility to produce two products with different returns to labour and different labour use requirements. we assume that product 1 has higher returns to labour than product 2, but necessitates a level of labour input that is above a specific threshold contrary to product 2. using a simplified framework, the objective program for farmers is as follows: max π = p1f1(x1,l1) + p2f2(x2,l2) + px(x1 + x2) + ωlo (1) on x1, x2, l1, l2, lo subject to ( ) = >f x l l l, 0 for 1 1 1 1 1 (2) ( ) ( )∂ ∂ > ∂ ∂ p f x l l p f x l l , , 1 1 1 1 1 2 2 2 2 2 (3) t = l1 + l2 + lo (4) l1 ≥ 0 (5) l2 ≥ 0 (6) lo ≥ 0 (7) where π is the total (on-farm and off-farm) profit; f1, f2 are the production functions of respectively product 1 and product 2; 152 l. latruffe, s. mann l1, l2 are labour hours devoted to the production of respectively product 1 and product 2; x1, x2 are other factors devoted to the production of respectively product 1 and product 2; p1, p2, px are the prices of product 1, product 2 and the other inputs, respectively. 1l is the labour input that needs to be allocated to the production of product 1; it is a specific threshold below which product 1 cannot be produced. t is the total time endowment; lo is the time allocated to off-farm employment; ω is the off-farm wage. constraint (2) represents the requirement of a substantial level of labour input (i.e. above the threshold 1l ) for the production of product 1, while constraint (3) shows that the marginal returns to labour for product 1 is greater than the one for product 2, therefore representing the larger returns to labour for product 1. constraint (4) is the time constraint. the case of a farmer producing both products is depicted on figure 1. the horizontal axis shows the labour allocation to both products; the length of the axis representing the total time available to the farmer (t). the left, respectively right, vertical axis represents the profit generated from the production of product 1, respectively of product 2. both production technologies p1f1 and p2f2 are depicted, with the production technology of product 2 starting at l2= 0 and the production technology of product 1 starting at =l l1 1 the larger returns to labour for product 1 than for product 2 (formalised by constraint (3)) are represented by a greater slope of the production technology of product 1 than the slope of the production technology of product 2. the farmer’s objective is to maximise his/her total profit; the latter is maximised at point a, that is to say where the marginal labour productivities of both products are equal. from the farmer’s objective program above, the optimal point a is represented by the following kuhn and tucker condition: p1 ∂ f1 l1 *( ) ∂l1 = p2 ∂ f2 l2 *( ) ∂l2 =ω − µ0 (8) μ0 being the lagrange multiplier of constraint (7). at point a the total profit generated is π1 * + π2 * which is greater than the maximum profit that could be generated if the farmer was producing product 1 only (π1 max) or product 2 only (π2 max). for this reason the farmer produces both products. this is the case of a full-time farmer, that is to say all time is allocated to production: t = l1 * + l2 *. however, a farmer may be part-time farmer that is to say may also work off farm, as represented by figure 2. here the farmer’s off-farm labour allocation is lo * and both products are still produced: t = l1 * + l2 * + lo *. in this case the kuhn and tucker condition is: ω ( ) ( )∂ ∂ = ∂ ∂ =p f l l p f l l1 1 1 * 1 2 2 2 * 2 (9) 153choosing profitable products for part-time farmers in swiss agriculture figure 1. graphical representation of labour allocation for a full-time farmer. profit from product 2 profit from product 1 p2f2 p1f1 π1* π2* l1* l2* t 1l π1*+π2* π1*+π2* π2 max π1 max a source: the authors however, when a farmer allocates a large part of his/her time off farm, only product 2 may be produced, as depicted by figure 3. in this case, the farmer is constrained in his/her time left for production. the profit from producing product 2 only (π2 max) is greater than any other combination (production of product 1 only, or production of both products). thus, in this case, the farmer is better off not producing product 1 at all, even though this product is more profitable than product 2. the kuhn and tucker conditions for product 1, respectively product 2, are: λ ω ( )( )− ∂ ∂ =p f l l1 1 1 * 1 (10) ω ( )∂ ∂ =p f l l2 2 2 * 2 (11) λ being the lagrange multiplier of constraint (2). we acknowledge that this is a simplified framework. firstly, in this framework only production aspects (technologies and prices) are considered since it is a farm profit maximisation framework. by contrast, several studies have shown that there exist other determinants of labour demand, such as farmer’s or farm household’s age, education and com154 l. latruffe, s. mann position, as well as macroeconomic conditions (baum et al., 2006; benjamin and kimhi, 2006; dupraz and latruffe, 2015). to account for household’s characteristics, the household’s utility could be maximised instead of the farm profit, and total time endowment (t) figure 2. graphical representation of labour allocation for a part-time farmer allocating little time off farm. profit from product 2 profit from product 1 p2f2 p1f1 π1* π2* l1* l2* t 1l π1*+π2* π1*+π2* π2 max π1 max a lo* source: the authors figure 3. graphical representation of labour allocation for a part-time farmer allocating much time off farm. profit from product 2 profit from product 1 p2f2 p1f1 π1* π2* l2* t 1l π1*+π2* π1*+π2* π1 max π2 max lo* source: the authors 155choosing profitable products for part-time farmers in swiss agriculture would be augmented by the time endowment of family members. also, members of the farmer’s household may engage in off-farm activities, thereby providing additional income but constraining the availability of on-farm labour. in addition, leisure and consumption are not considered in our framework. in a utility maximisation framework, they would be additional decision variables of the household’s programme. leisure would enter the time constraint (4), and an additional constraint would be needed, namely the budget constraint including consumption and profit (benjamin and kimhi, 2006). farmer’s and household’s preferences for leisure and consumption could be integrated in the specification of the utility function. secondly, in our framework farm labour does not include hired labour nor contract labour. the latter could nevertheless be used by part-time farmers to fulfil the demand for labour particularly in peak periods (errington, 1998). this would extend the time endowment on the farm, or allow the farmer to spend additional time off farm. distinguishing between the different types of labour implies considering that they are not substitutable. thirdly, risk neutrality is assumed here while some authors have reported that farmers may be risk averse. in this case, off-farm income could act as insurance, reducing the variability of the farmer’s income as noted by barlett (1991) and mishra and goodwin (1997). in addition, the choice of the production enterprise may be guided by the variability of the income generated by each enterprise. the choice of producing only one product or two products may also be explained by risk considerations: diversifying the production portfolio may reduce production risk, while in the absence of risk, specialisation may be more profitable. similarly to gillespie and mishra’s (2011) indication as regard their framework, ours also “simplifies a complex issue”. our framework notably assumes that farmers are risk neutral and that there is perfect substitutability across all types of labour. however, it shows that in certain situations profitable enterprises may not be operated by part-time farmers due to labour constraints. 3. methodology and data the above conceptual framework shows that farmers who allocate a large part of labour off farm may not be able to produce highly profitable products, in other words part-time farmers may concentrate on products with low returns to labour. we now provide an empirical application based on data from the swiss farm accountancy data network (fadn) dataset for the 9-year period 1996-2004. as explained by roesch (2012), the fadn provides bookkeeping information for a rotating non-random sample of about 3,500 farms and “weighted extrapolation can be used to apply the results to the swiss agricultural sector with its approximately 50,000 farms”. despite fadn data being farm level data, we carry out our empirical analysis with data aggregated at the production enterprise level. we consider 16 different production enterprises with a sufficient number of observations: pig fattening, pig breeding, suckler cows, milk, proteinseeds, sunflower, rapeseed, wheat, sugar beet, spelt, maize, triticale, barley, oats, rye, and potatoes. as we consider a 9-year period, our sample should include 144 observations (16 observations per year). however, there are in total 141 observations in the sample used for the empirical analysis, as no data were available for sunflower in three years (1996-1998). 156 l. latruffe, s. mann our objective is to test whether the engagement of part-time farms in production enterprises depends on the profitability of these enterprises, based on the assumption that products with high returns to labour are labour intensive. for this, we carry out a regression where the dependent variable is the share of the country’s agricultural production for a specific enterprise j (j=1,...,16) that is produced by part-time farms, while the explanatory variable is the average financial returns to labour for the production enterprise j. if the sign of the explanatory variable’s coefficient is (significantly) negative, this would indicate that the more profitable the enterprise, the less it is produced by part-time farms, and would give support to our proposition. in order to calculate the explanatory variable, which is the average financial returns to labour for the production enterprise j, one issue is to obtain the labour input allocated per enterprise. the latter is not available in the fadn data and needs to be calculated. for this, we use the standard labour requirements (slr) calculated for each product, under typical swiss conditions, by the “labour economics” research group from the swiss federal research station art (schick and stark, 2007). however, it is expected that these “theoretical” figures may diverge from “real” figures that is to say from the farm’s actual labour use per enterprise. in fact, standard labour requirements were also calculated by art for the total farm (flr), and comparing them with the observed figures of total labour used per farm available in the fadn data (l) shows some discrepancy. the comparison can be made with the following ratio: =l l flr (12) which takes the value of 1 when what is observed on a farm (as recorded in fadn) exactly matches art theoretical calculations, and is strictly larger than 1 when art calculations underestimate farms’ labour use. in the present case, the observed (fadn) labour use on swiss farms (l) tends to be higher than that estimated by art (flr) since the average of the ratio l for our sample is 2.33. we account for this discrepancy in the calculation of labour use for single enterprises. more precisely, we apply the discrepancy coefficient l to the measure of standard labour requirements for each enterprise on each farm provided by art (slr), in order to obtain the “real” labour use for single enterprises (r): = ×r l slr (13) then for each farm i in the fadn sample, financial returns to labour (p) for each production enterprise j are calculated as the returns from the production ( π ) divided by r: = π p rji ji ji (14) 157choosing profitable products for part-time farmers in swiss agriculture finally, average financial returns to labour in the sample were calculated for each enterprise j by: ∑ ∑∑ ( ) =p p w a n w aj ji ji ji i j ji ji ii (15) where wji is the fadn extrapolation weight of each farm i involved in the production activity j; aji is the amount produced by each farm i in the production activity j; nj is the number of farms involved in the production activity j. the use of aggregated data is motivated by the issue we wish to test. an investigation of the choice of enterprises by farms would simply rely on using of a multinomial model (or on tobit models as in gillespie and mishra, 2011) applied to farm level data, and for example on comparing the model results for part-time farms to the model results for full-time farms. however, our question goes further than the choice of enterprise, since we wish to investigate whether the choice is related to the profitability of enterprises. using such multinomial methodology for our question would therefore imply defining a priori categorising enterprises based on their profitability. another possibility with farm level data would be to use the financial returns to labour as a dependent variable and the part-time status as an explanatory variable. however, this would imply estimating as many equations as there are enterprises. the advantage of using the methodology described above is that it can give a straight answer to our question and enables to consider the average profitability of each enterprise within the sample, that is to say the average profitability that could be expected when engaging in this enterprise. in order to test the sensitivity of our proposition to the definition of profitability, two regressions are performed, differing by the proxy of financial returns to labour (pj) used as the explanatory variable. in a first regression, we use the revenue produced per unit of labour, and in a second regression we use the gross margin (that is to say the revenue reduced by the variable costs for crops and livestock) per unit of labour. another explanatory variable included in the regression is a livestock enterprise dummy, which represents the categorisation of the 16 enterprises into livestock and crop activities, since there could be any systematic bias for part-time farms to either of them. two additional variables are included in order to account for the fact that part-time farmers may invest more intensively in substitutes for their own time (namely capital and hired labour): the sample’s average capital to labour ratio for each production enterprise j, and the sample’s average share of hired labour for each production enterprise j. finally, time dummies are included. due to the panel characteristics of our data (16 observations per year) we use a panel data model with random effects, the breusch-pagan test indicating that this specification is preferred over the pooled ordinary least squares (ols) specification (wooldridge, 2002). as explained above, part-time farming may be measured in terms of hours spent off farm or in terms of income generated by off-farm activities. while our conceptual framework is based on the notion of time and not on the notion of income, there is no information regarding off-farm hours in the swiss fadn. for this reason, our categorisation 158 l. latruffe, s. mann is based on the share of household income stemming from off-farm activities: full-time farms are those where less than 50 per cent of income stem from off-farm activities, and part-time farms are those where more than 50 per cent of income stem from off-farm activities. this definition of part-time farms is standard in analyses about swiss farms, as for example in roesch (2012). table 1 presents descriptive statistics of the variables used in the regressions. during the period 1996-2004 and for the 16 enterprises outlined above, on average 10 percent of the country’s agricultural production was produced by part-time farms, with a minimum of 6 percent and a maximum of 41 percent for specific enterprises. on average the revenue per labour unit for the 16 enterprises considered was 216,270 swiss franks, while the respective figure for the gross margin per labour unit was 37,095. the average capital to labour ratio was 443,279 swiss franks and on average 22.5 percent of the labour used was hired. table 2 presents averages (over the period 1996-2004) of the variables of interest per enterprise. figures show that the enterprise for which the average share of production provided by part-time farms is highest is suckler cows (22.4 percent of the country’s production). this can be explained by lower labour requirements in grazing systems, in line with gillespie and mishra’s (2011) observation in the us in 2003. by contrast, the lowest average share of production provided by part-time farms is for pig breeding (5.2% of the country’s production), confirming our suggestion above that this is a labour intensive activity. the high average revenue per labour unit and gross margin per labour unit for this production seems to support our idea that labour intensive enterprises may be highly profitable. however, table 2 shows that a high share of production produced by part-time farms is not always associated with a high revenue or gross margin to labour, which does not support our proposition. it should however be kept in mind that table 2 presents average figures for the whole period 1996-2004 and not yearly figures. in addition, we do not expect an exact relationship but we want to test with econometrics the strength of the relationship. table 1. descriptive statistics of the aggregated data used: 16 enterprises in 1996-2004. variable mean standard deviation minimum maximum share of the country’s agricultural production produced by part-time farms 0.100 0.059 0.006 0.413 revenue per labour unit (pj) (swiss franks) 216,270 234,673 4,339 1,161,250 gross margin per labour unit (pj) (swiss franks) 37,095 53,756 1,375 282,226 livestock enterprise (dummy) 0.255 0.438 0 1 capital to labour ratio (swiss franks) 443,279 71,907 45,942 610,288 share of hired labour 0.225 0.029 0.146 0.282 number of observations: 141. source: authors’ calculations based on swiss fadn data for 1996-2004. 159choosing profitable products for part-time farmers in swiss agriculture table 2. descriptive statistics of the aggregated data used: yearly averages per enterprise. enterprise share of the country’s agricultural production produced by part-time farms revenue per labour unit (swiss franks) gross margin per labour unit (swiss franks) pig fattening 0.078 290,501 95,933 pig breeding 0.052 335,129 175,042 suckler cows 0.224 134,832 121,697 milk 0.073 56,813 43,203 proteinseeds 0.107 145,308 2,854 sunflower 0.104 121,663 2,128 rapeseed 0.086 135,521 1,880 wheat 0.076 78,457 4,664 sugar beet 0.074 1,004,197 71,130 spelt 0.140 95,753 3,450 maize 0.109 242,275 7,652 triticale 0.076 162,993 5,287 barley 0.082 160,881 5,136 oats 0.113 128,326 4,453 rye 0.100 153,908 5,283 potatoes 0.101 182,231 32,078 number of observations: 141. source: authors’ calculations based on swiss fadn data for 1996-2004. 4. results table 3 and table 4 present the regression results of the share of the country’s agricultural production that is produced by part-time farms, for 16 specific enterprises in 19962004. results in table 3 are for the regression including in the explanatory variables the revenue per labour unit as the proxy for the financial returns to labour in each production enterprise. results in table 4 are when the proxy for the financial returns to labour is the gross margin per labour unit. the wald tests show that both models are highly significant. the breusch-pagan tests conclude to a random effect panel specification. in both models the dummy for livestock enterprise, the capital to labour ratio and the share of hired labour have no significant effect. as regard the main variable of interest, results in table 3 show that the coefficient for the revenue per labour unit is significant (at 5 percent) and negative. this indicates that the share of part-time farms in a specific production is influenced by the returns to labour in this specific production. the more revenue per labour unit is generated by a product, the less part-time farms engage in its production, giving support to our proposition. by contrast, table 4 shows that this effect is not confirmed when returns are proxied by gross margin, as the coefficient of the gross margin per labour unit is not significant. we have investigated whether the effect of the returns to labour on the dependent variable differed depending on the type of activity, namely livestock or crop, by including in the regression 160 l. latruffe, s. mann the returns to labour interacted with the livestock enterprise dummy. however, the coefficient for this cross term was not significant. table 3. explaining the share of part-time farms in the country’s agricultural production, using the revenue as the proxy for financial returns. explanatory variable coefficient p-value significance revenue per labour unit (pj) -4.88 e-08 0.041 ** animal enterprise (dummy) -8.52 e-03 0.757 capital to labour ratio 3.36 e-09 0.965 share of hired labour -5.28 e-01 0.114 constant 0.240 0.006 *** wald test (chi2) 114.6 0.000 *** breusch-pagan test (chi2) 26.9 0.000 *** r2 0.224 number of observations 141 dependent variable: share of agricultural production produced by part-time farms in 16 different production enterprises. results for time dummies not shown. estimated using random effects since the breusch-pagan test rejects the null hypothesis of a pooled ols model in favour of a random effect specification. *, **, *** indicate significance levels at 10, 5 and 1 percent respectively. source: authors’ calculations based on swiss fadn data for 1996-2004. table 4. explaining the share of part-time farms in the country’s agricultural production, using the gross margin as the proxy for financial returns. explanatory variable coefficient p-value significance gross margin per labour unit (pj) -8.86 e-08 0.669 animal enterprise (dummy) 1.11 e-03 0.972 capital to labour ratio -6.10 e-09 0.934 share of hired labour -5.17 e-01 0.120 constant 0.230 0.005 *** wald test (chi2) 118.5 0.000 *** breusch-pagan test (chi2) 29.7 0.000 *** r2 0.199 number of observations 141 dependent variable: share of agricultural production produced by part-time farms in 16 different production enterprises. results for time dummies not shown. estimated using random effects since the breusch-pagan test rejects the null hypothesis of a pooled ols model in favour of a random effect specification. *, **, *** indicate significance levels at 10, 5 and 1 percent respectively. source: authors’ calculations based on swiss fadn data for 1996-2004. 161choosing profitable products for part-time farmers in swiss agriculture 5. conclusion the literature has little investigated part-time farmers’ choices of production enterprises on their farm. such choices may be more constrained than those made by full-time farmers due to limited time availability, as suggested by gillespie and mishra (2011) for us farms. a further idea, that we developed in this paper, is that part-time farmers, due to their constraint on farm labour supply, may not engage in highly profitable enterprises. this may be the case for example, as shown by our conceptual framework, when such enterprises are labour intensive. this conceptual framework, as well as the empirical application for swiss farms using fadn data for 1996-2004 and revenue per labour unit as the proxy for financial returns, illustrate this idea that there may be difficulties to enter attractive activities within agriculture if off-farm employment constitutes an important part of the household income. this may help explain the low profitability of part-time farming, as shown for example by roesch (2012). using fadn data for 2005-2010 the author showed that the probability of being a profitable farm is lower for part-time farms. our proposition, that part-time farmers tend to engage in products with low financial returns to labour, has been confirmed with our empirical results when revenue per labour was used as the proxy of financial returns to labour. however, the relationship was not strongly significant (at 5 percent only) and the coefficient was found to be very close to zero, indicating that the effect is negligible. in addition, the proposition was not confirmed when gross margin per labour was used instead. this suggests that our proposition, which was illustrated by our conceptual framework, is not supported by our data. one reason may be that this proposition of constraint posed by part-time jobs on profitable enterprises, may hold only in specific contexts. another reason may be that our assumption that highly profitable enterprises are labour intensive could be questioned, in particular for specific products. hence, further research may be necessary. for example, although part-time farms may be constrained in their choice of enterprise as we suggest, the consequence in terms of residual profit may not be negative. this may depend on the proxy used for profitability and further analysis is therefore required. the role of government support could also be investigated, as it may influence the choice of production by part-time farmers. in addition, one could question whether the causality is unique. while we assumed here that the part-time status determines the choice of enterprise, the reverse may also hold. indeed, farmers may be forced to engage in a low profitable enterprise for structural reasons, such as difficult soil and climatic conditions in the farm location, or low land availability and high land prices (this is the case in switzerland, see giuliani, 2002). based on our assumption that high profitable enterprises are labour intensive and inversely, those farmers forced to choose low profitable enterprises would have an excess of labour, an excess that they would supply off the farm. there may thus be a mismatch between the availability of “excess supply of labour in agriculture” (schultz, 1945) and the availability of other factors, a mismatch which is balanced by offering labour outside of the farm. this suggests that there is a possibility of joint determination of the choice of production enterprise and off farm status, which would require specific econometric modelling in future research. 162 l. latruffe, s. mann acknowledgements the authors would like to thank pierre dupraz for his valuable comments, as well as the reviewer who pointed out the issue of reverse causality. references baum, s., cook, p., stange, h. and weingarten, p. 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(2002). econometric analysis of cross section and panel data. massachusetts institute of technology. bio-based and applied economics 4(3): 261-277, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-16377 a critical assessment of the implementation of cap 20142020 direct payments in italy stefano ciliberti*, angelo frascarelli department of agricultural, food and environmental sciences, university of perugia, italy date of submission: july 7th, 2015, accepted october 14th, 2015 abstract. the common agricultural policy (cap) reform of 2014-2020 is characterized by a strong mandate of member states regarding the 1st pillar. this paper’s objective is to elucidate the possible impacts of italian choices on direct payments and their coherence with the specific objectives of the cap, which was established by the expert group on monitoring and evaluating the cap (egmec): food security and sustainable food production. thus, an online survey was disseminated among italian cap experts in the spring of 2015. the results from a sample of 25 respondents show that italian direct payments may positively contribute to viable food production by improving agricultural competitiveness. in addition, the combined effect of general direct payment schemes and italian choices is to ensure sustainable food provision or, at least, to successfully allow the exploration of this new route in cap history, which most likely represents future challenge for european agriculture. keywords. common agricultural policy, direct payments, italy, evaluation, likert scale jel codes. q18 1. introduction the common agricultural policy (cap) is currently organized into two pillars, with the first being related to direct payments and common market organizations (cmos) and the second being related to rural development policy. historically, the first pillar is the most important in financial terms, and it currently consumes more than 60% of the overall cap resources (erjavec et al., 2011; henke and coronas, 2011). the current direct payments system, which is known as the single payment scheme (sps), has been redesigned by the cap reform 2014-2020, which has a few similarities to the swiss scheme (european parliament, 2010). the debate over the cap 2014-2020 began several years ago. after an extensive public discussion, the european commission (ec) began an inter-institutional debate by means * corresponding author: stefano.ciliberti@unipg.it. 262 s. ciliberti, a. frascarelli of the ‘the cap towards 2020’ report, which defined the challenges that were encountered in reform (greer and hind, 2012; swinbank, 2012). in that text, the european union (eu) attempted to respond to new economic, social, environmental, climate-related and technological challenges by identifying new objectives and new policy instruments that could improve the socio-economic conditions of european farmers (huang et al., 2010). in particular, analyses on future agriculture developments show an increase in production in addition to greater sustainability, which is better known by the slogan “sustainable intensification” suggested by buckwell (2014). the main result of the inter-institutional debate, which is known as the trilogue, was the increase in national flexibility to implement the cap (european parliament, 2015). most importantly, the governments needed to select the optional payments to be activated and their annual ceilings. these decisions represented a crucial turning point for orienting the political actions of every member state (ms) because national flexibility should guarantee coherence between the national socio-economic targets and the policy instruments to be improved. it follows that the cap 2014-2020 could offer an opportunity for the creation of a better-targeted policy action due to the first pillar funds (erjavec et al., 2011; westhoek et al., 2013). because the last cap reform was characterized by a strong mandate to the mss to manage direct payments, this paper’s objective is to assess whether and how italian choices concerning the first pillar are consistent with the following general objectives: food security and sustainable food production. it follows that the paper attempts to provide a sort of ex ante evaluation of the consistency between the policy objectives of the first pillar of cap 2014-2020 and the manner in which it has been implemented in italy after the government made final decisions regarding direct payments. against this backdrop, the following research questions have been addressed in this work: 1. are the italian decisions on direct payments consistent with the objective to enhance food security? 2. are the italian decisions on direct payments consistent with the objective to ensure sustainable food production? 2. theoretical and policy framework the direct payments system of the cap provides income support for european farmers. the existence of such payments is justified by the need to provide income stability and compensation for higher production standards with regard to consumer protection, animal welfare and environmental conservation than many non-european countries (uthes et al., 2011). in recent years, the most relevant innovation in terms of farm income support tools has been the introduction of decoupled payments by the eu beginning with the 2003 fischler reform of the cap (moro and sckokai, 2013). this policy change was expected to make farmers’ production decisions more market-oriented because their subsidy revenue maximization objectives could become profit-maximizing objectives as well and could induce efficient/productive farms to exit unprofitable businesses or reallocate resources to other sectors, leading to aggregate productivity gains for the sector as a whole (kazukaus263implementation of cap 2014-2020 direct payments in italy kas et al., 2010). in sum, the decoupling of subsidies was aimed to reduce the efficiency losses associated with coupled subsidies (kazukauskas et al., 2014). 2.1 a short review of direct payments decoupled payments are generally assumed to not distort market outcomes because they are considered to be essentially lump sum transfers, which do not produce any market distortions (o’donoghue and withaker, 2010). if these payments are non-distortionary, a government could use them in domestic policies without affecting either domestic or international markets. however, several theoretical avenues exist through which direct payments could alter behaviour and, by extension, market outcomes (kazukauskas et al., 2014). as a matter of fact, beginning with their introduction, the central research issue on decoupled payments has regarded their true non-distorting nature and their impact on farm choices (moro and sckokai, 2013). critics of the decoupled payment system argue that although such payments are not directly tied to production requirements, they may continue to have important effects on production. hennessy (1998) asserted that direct payments often have purposes in addition to income support. in fact, in a very unstable world market, in which prices and yields fluctuate considerably from year to year, risk-averse producers may benefit considerably from income stabilization. this income-stabilizing attribute has a corresponding insurance effect, which may affect optimal decisions. in addition, there is a wealth effect on optimal decisions, namely, that the higher average income that arises from the support policy may affect producer decisions. risk attitudes and relaxing farmers’ credit constraints are not the sole means by which decoupled instruments may influence production choices (goodwin and mishra, 2005). decoupled support may impact the decision to exit the market, which would produce distortions in the supply. within this framework, one justification could be that these payments are taken as rewards for the multifunctional role of agriculture (moro and sckokai, 2013). moreover, whereas input suppliers usually capture a great part of the coupled support, decoupled payments are capitalized in land values/rents so that support to actual farmers depends on the share of land they own. consequently, increases in the price of land inhibit the conversion of agricultural land to other uses as well as the entrance of young farmers into the agricultural sector due to the increased capital outlays required to purchase a farm (patton et al., 2008). various studies have shown that the cap subsidies impact farm sector productivity as well. theoretical studies suggest that subsidies may have a positive impact on farm production because they generate a selection process in which the less productive farms exit; however, at the same time, they generate a negative impact on farm productivity because they distort the production structure of recipient farms, which leads to allocative inefficiency (kazukauskas et al., 2010). nevertheless, for the decoupled subsidies, the link to farm activities is weaker; in fact, farms receive cap decoupled payments irrespective of their production decisions. therefore, the subsidies are less likely to induce allocative and technical inefficiency (rizov et al., 2013). in particular, kazukauskas et al. (2014) show that decoupling policy had positive and significant effects on productivity but that this did not lead to high cost product switching behaviour. on the contrary, it incentivized spe264 s. ciliberti, a. frascarelli cialization in more productive farming activities that are less “painful” for farms in the short term (lower requirements of capital, knowledge and technology) and that may produce positive results sooner. certain economists have also argued that expectations of future programme payments may influence farmers’ current production decisions (o’donoghue and whitaker, 2010). nevertheless, predictions of the precise nature of long-term changes to the cap are speculative because such changes will largely depend on the division of power between reformist and more conservative mss (erjavec et al., 2011). for instance, pressure from the net contributor mss to reduce cap spending has recently increased; therefore, there has been considerable pressure for a re-nationalization of the first pillar, in which all mss would be required to co-finance cap direct income support from national funds. concurrently, in the context of the wto trade liberalization agenda, the discussion centres on the distortionary impact of subsidies on agricultural markets and how the effects differ by types of subsidy. uthes et al. (2011) analysed the impacts of abolishing direct payments showing that although rich regions, with a moderate dependence on direct payments and either a relatively competitive agricultural sector or a highly diversified sector, will find a means to cope with such a relevant policy change, poor regions, with less favourable conditions for agriculture and insufficient marketing, processing and sales structures as well as a high dependence on direct payments, will be most severely affected. overall, it must also be noted that the introduction of the decoupling policy coincides with a period of relevant increase in uncertainty. specifically, in the past decade, developments in world markets, which have led to increasing volatility of global food prices and rising food security concerns, have also led to calls for maintaining agricultural support, stimulating farm investments and adopting productivity-enhancing modern technologies (rizov et al., 2013). consequently, the debate on cap post-2013 has focused on the contrast between food security arguments and the provision of environmental services. although the solutions proposed by the so-called “productionist frame” is to maintain a strong first pillar to increase productivity and stimulate public goods provision, proponents of the “environmental frame” argue that the cap needs to be re-focused and should consider both food production and the provision of environmental services as an integral part of european agriculture by means of better targeted income support and innovation incentives (candel et al., 2014). it is indeed not surprising that representatives of farmers, whose core business is to produce food, deploy the productionist frame and that environmental ngos primarily use the environmental frame. it follows that the fractured consensus among stakeholders regarding food security and the provision of public goods has generated strong disagreement about the appropriate course of action and how a future cap should facilitate such actions. because agricultural and, increasingly, environmental interests are traditionally the most dominant interests in european agricultural policy formation, eu institutions have attempted to respond to new economic, social, environmental, climate-related and technological challenges by identifying new objectives and new policy instruments that could improve the socio-economic conditions of european farmers (huang et al., 2010). therefore, the need for better targeting of support, which would improve spending quality and remunerate farmers for the public goods that they provide, led to an innovative scheme of direct payments (westhoek et al., 2013). 265implementation of cap 2014-2020 direct payments in italy 2.2 the new direct payments: aims and structure the cap 2014-2020 will address a set of challenges, with a few being unique in nature and a few being unforeseen, which press the eu to make strategic choices regarding the long-term future of its agriculture; these are: 1) guarantee viable food production and 2) promote the sustainable management of natural resources and actions to mitigate climate change. based on these main targets, certain priorities have been acknowledged for each pillar. the intervention logic for the first pillar involves some specific objectives that must be achieved by direct payments (egmec, 2015): a. contribute to farm incomes and limit farm income variability in a manner that includes minimal trade distortion; b. improve the competitiveness of the agricultural sector and enhance its value share in the food chain; c. maintain market stability; d. meet consumer expectations; e. provide public goods; f. pursue climate change mitigation and adaptation. the need for improving the effectiveness of the public resources spent requires a clear link between policy decisions and cap targets (grant, 2010; van ittersum et al., 2008). obviously, these choices must also be related to the national context and priorities and, therefore, should be adopted after a deep analysis of the primary sector’s socio-economic indicators. article 110 of the horizontal regulation1 proposed the establishment of a common monitoring and evaluation framework that includes a set of indicators to measure the performance of the cap. thus, the expert group on monitoring and evaluating the cap (egmec), which assists the ec in the preparation of legislation and in policy definition, has provided a set of result indicators. table 1 shows a selection of indicators that are referred to as the first pillar and that can be used to create an ex ante evaluation of the results of national decisions on direct payments schemes (ciliberti and frascarelli, 2013; van ittersum et al., 2008). the new direct payments system will preserve certain features of the current sps (tranter et al., 2007). farmers must own or obtain entitlements and possess eligible hectares as well as observe the cross compliance rules. the new scheme is composed of an income support component (the basic payment and young farmers’ scheme) and a public goods provision component (greening) (overmars et al., 2013). as shown later, mss are able to activate other optional payments (table 2). these policy choices determined the financial ceilings for each payment because the greening percentage has solely been established directly by the eu2. the basic payment, the greening and young farmers’ scheme must necessarily be activated by each ms. the basic payment scheme ceiling is obtained by deducting from 1 regulation (eu) no 1306/2013 of the european parliament and of the council of 17 december 2013 on the financing, management and monitoring of the common agricultural policy and repealing council regulations (eec) no 352/78, (ec) no 165/94, (ec) no 2799/98, (ec) no 814/2000, (ec) no 1290/2005 and (ec) no 485/2008. 2 regulation (eu) no 1307/2013 of the european parliament and of the council of 17 december 2013 establishing rules for direct payments to farmers under support schemes within the framework of the common agricultural policy and repealing council regulation (ec) no 637/2008 and council regulation (ec) no 73/2009. 266 s. ciliberti, a. frascarelli the national ceiling the amounts that are utilized for the other (mandatory or optional) payments. the payment for agricultural practices that are beneficial for the climate and the environment shall receive a fixed percentage, 30%, of the annual ceiling. to receive table 1. first pillar objectives and result indicators. general objectives specific objectives result indicators viable food production enhance farm income share of direct payments in agricultural income variability of farm income improve agricultural competitiveness share of value added for the primary producers in the food chain share of exports in world markets share of high value-added products in exports maintain market stability commodity price compared with that of the rest of the world commodity price volatility commodity price volatility compared with that of the rest of the world meet consumer expectations share of organic area in total uaa share of organic livestock in total livestock sustainable management of natural resources and climate action provide environmental public goods share of (permanent) grassland in agricultural land share of arable land share of ecological focus area (efa) in agricultural land climate change mitigation and adaptation net greenhouse gas (ghg) emissions from agricultural soils source: 6th meeting of the egmec (2015). table 2. the architecture of direct payments 2014-2020. payment mandatory/ optional financial ceiling basic payment scheme mandatory residual (68%-18%) redistributive payment optional 30% max payment for agricultural practices that are beneficial for the climate and the environment (greening) mandatory 30% payment for young farmers mandatory 2% max payment for areas that have natural constraints optional 5% max coupled support optional 13% max + 2% (support protein crops) small farmers’ scheme optional 10% max (sourced from direct payments scheme) source: regulation (eu) 1307/2013. 267implementation of cap 2014-2020 direct payments in italy this payment, the farmers must implement three standard measures3. the young farmers’ scheme shall receive a percentage of the annual national ceiling that is not higher than 2%; it provides a payment to farmers with specific features. with regard to the optional payments, the coupled support scheme could be used to maintain levels of production in certain sectors or in certain regions where specific types of farming or specific agricultural sectors experience difficulties and are particularly important for economic, social and/or environmental reasons. because italy allocated more than 5% of its available payment amount to granting the specific supports to article 684 for the 2014-2020 period, it may decide to use the maximum percentage (13%) of the annual national ceiling. this percentage may be increased by up to 2 percentage points in those mss that decide to support the production of protein crops. payment for areas that have natural constraints could be granted to farmers whose holdings are either fully or partly situated in disadvantaged areas, which are designated by mss. to finance this payment, a maximum of 5% of the annual national ceiling could be used. the redistributive payment could receive up to 30% of the amount that is available for direct payments. if italy adopts this option, no more than the first thirty hectares of each farm will receive a supplement, which could attain a maximum of 65% of the average payment per hectare. finally, if introduced, the small farmers’ scheme must replace other direct payments. to finance this payment, mss shall deduct the amounts to which the small farmers would be entitled from the other direct payments funds. 2.3 the application of direct payments in italy the italian budget for direct payments for 2013-2019 totals 27,090 million €, or nearly 3,800 million € every year. the main national choices of the italian government are summarized in table 3 and are described in detail below. first, to address a number of legal loopholes that have enabled a limited number of companies to claim direct payments although their primary business activity is not agricultural, the reform tightened the rule on active farmers.  italy extended the so-called “negative list” to include further business activities that should be excluded from receiving direct payments (covering airports, railway services, water works, real estate services and permanent sports and recreation grounds). in addition, the government established criteria to identify active farmers, with flexible requisites for farmers in mountain areas and selective conditions for other areas. regarding the minimum requirement for receiving direct payments, italy decided not to grant direct payments to a farmer when the total amount of direct payments claimed is less than 250 € (regardless of the farm size) in 2015-2016 and less than 300 € after 2017. 3 these include crop diversification (which involved cultivating at least two or three crops, depending on the amount of arable land that is owned), permanent grassland (which does not allow farmers to plough the designated, environmentally sensitive areas), and ecological focus areas (efas) (which involves maintaining at least 5% of arable land that is recognized as an efa). 4 council regulation (ec) no 73/2009 of 19 january 2009 establishing common rules for direct support schemes for farmers under the common agricultural policy and establishing certain support schemes for farmers, amending regulations (ec) no 1290/2005, (ec) no 247/2006, (ec) no 378/2007 and repealing regulation (ec) no 1782/2003. 268 s. ciliberti, a. frascarelli the italian government decided to apply the so-called irish model for internal convergence, which is based on the calculation of the initial unit value (iuv). in practice, the ‘value’ that is carried forward from 2014 is spread across the ‘number’ of entitlements that is allocated to the farmer in 2015. this iuv forms the basis of all subsequent convergence calculations for the value of those entitlements for each year of the scheme. all entitlements held under the basic payment scheme are subject to convergence. in simple terms, those who hold entitlements with an iuv that is above the basic payment scheme national average will observe the value of their entitlements decrease over the five years of the scheme, whereas those with entitlements with an iuv that is below 90% of the national average will observe the value of their entitlements increase gradually over the five years of the scheme. those who hold entitlements that have an iuv between 90% and 100% of the national average value will observe no change. in addition to the standard level of convergence outlined above, a further test is applied whereby all farmers must achieve a minimum entitlement value of 60% of the national average by 2019. if a farmer does not reach 60% under standard convergence, then the value of his entitlements will be increased in equal increments to ensure that the level is reached by 2019. the allocation of national resources across seven components of direct payments was as follows (table 3): the basic payment scheme received 58% of resources, greening received 30% of the budget (as established by reg. 1307/2013), the young farmers’ scheme table 3. italian choices on direct payments. decision national choice active farmer (exemption threshold to be an active farmer) <1250 € for other areas; <5000 € for mountain areas. minimum requirements for receiving direct payments <250 € direct payments in 2015-2016; <300 € direct payments in 2017 type of regionalization/model of internal convergence national/irish model basic payment scheme 58% of national budget redistributive payment no greening (amount of payment) 30% of national budget (calculated as 30% of payment entitlements held by the farmer) areas that have natural constraints no young farmers’ scheme 1% of national budget (value: 25% of the average value of payment entitlements) coupled support 11% of national budget (of which: 25.1% for beef, 20.8% for milk, 16.4% for olive oil, 14% for cereals, 8.3% for protein crops, 5.3% for rice, 4% for sugar beet, 3.5 for sheep, 2.6% for industry tomato) small farmers scheme (max. payment) yes (<1,250 €) degressivity and capping (% reduction of direct payments) 50%, if dir. paym.> 150m €; 100%, if dir. paym.>500m €; salary costs deducted. source: our elaboration on european parliament (2015). 269implementation of cap 2014-2020 direct payments in italy received 1% of national funds, the coupled payment received 11% of funding and the small farmers’ scheme was activated. the redistributive payment and the payment for areas with natural constraints were not activated. regarding the greening payment, the amount received by every farmer who implements the three standard greening measures will be calculated as an annual fixed percentage of the value of the entitlements activated by the farmer under the basic payment scheme. regarding the young farmers’ scheme, it is given (for a maximum period of five years) to farmers who participate in the basic payment scheme, who are aged no more than 40 years in the year when they first submit an application under the basic payment scheme and who are establishing an agricultural holding for the first time or establishing such a holding during the five years preceding the first submission of the direct payments scheme. in italy, the young farmers’ payment will be calculated as 25% of the value of the entitlements activated by the farmer under the basic payment scheme multiplied by the number of entitlements activated by the farmer (no more than 90 hectares). with regard to the voluntary coupled payment, nearly 400 million € are provided annually to the livestock, arable lands and the olive oil sectors. in particular, nearly 50% of the coupled support budget is allocated to animal production (milk, beef, sheep and goat), 35% to arable crops (cereals, protein crops, tomato, sugar beet, and soybean), and the remaining funds incentivize olive oil production. the small farmers’ scheme establishes that farmers receive an annual payment of no more than 1,250 €, regardless of the farm size.  italy chose a method to calculate the annual payment whereby farmers would simply receive the amount they would otherwise receive. this method will be a considerable simplification for the farmers concerned and for the national administration because participants will be exempted from greening and cross-compliance sanctions and controls.  finally, in italy, the amount of direct payments support that an individual farm holding receives (not including the greening payment) is reduced by 50% for amounts above 150,000 € (i.e., degressivity) and by 100% for those above 500,000 € (i.e., capping); to consider employment, salary costs are deducted before the calculation is performed. 2.4 methodology the cap reform has offered an important opportunity to every ms to better adapt economic and financial instruments (direct payments) to their policy targets (erjavec et al., 2011). therefore, national policymakers’ choices on direct payments should be based on rational and objective criteria. as previously noted, this article’s objective is to evaluate whether and how the decision made by the italian government may affect the main objectives of the cap 2014-2020. according to this purpose, the following methodology was adopted: 1) a questionnaire was implemented and an on-line survey was disseminated to nearly one hundred cap experts using soscisurvey software during the spring of 2015; 2) a 7-point likert scale was adopted to allow respondents to evaluate the potential impacts of italian choices on direct payments 2014-2020 using appropriate indicators established by the expert group on monitoring and evaluating the cap (egmec, 2015) for the common monitoring and evaluation framework of the cap (ciliberti and frascarelli, 2013); 270 s. ciliberti, a. frascarelli 3) 25 questionnaires were collected, and descriptive statistics were provided to elucidate the possible impacts due to italian choices for the first pillar as well as to evaluate coherence with cap specific objectives concerning food security and sustainable food production. the survey was distributed to both academic representatives (professors and researchers) and stakeholders (both private and public), who were involved using contacts taken from different sources (personal contacts, institutional websites, and scientific papers). this choice allowed thorough information and evaluations to be obtained not only from the theoretical perspective but also considering the real implications and impacts of the italian direct payments scheme on the primary sector. as shown in table 4, the response rate was 25%, and the respondents are distributed among the different positions/roles with the sole exceptions of politicians. the academic world is sufficiently represented by professors (28% of the sample); private interests in the agricultural sector (private managers, private employers and stakeholders total 40% of the sample) and, to a lesser extent, public controllers (8% of the sample) are sufficiently represented as well. finally, the remaining respondents (“others” are 28% of the sample) represent various positions/roles (e.g., consultants, researchers and agronomists). table 4. characteristics of respondents: position/role (n=25). position/role % professor 28.0 other (consultant, researcher, and agronomists) 24.0 stakeholder 16.0 private manager 12.0 private employer 12.0 public manager 4.0 public official 4.0 politician 0.0 the low rate of response is not surprising because the issue is also not deeply known by the stakeholders, particularly regarding the consistency between decisions and policy objectives. the absence of politicians and the scarcity of answers from agricultural professional organizations represents a disadvantage. however, this scarcity is because policy makers have a lower opinion of completing a survey and most likely do not prefer to publicly evaluate the results of their decisions; these are very often strongly affected by the path dependence. in contrast, the fact that more than 50% of respondents are professors and researchers may be because, on the one hand, of the complexity of the issue (higher for policymakers and stakeholders than for scholars) and on the other hand, of the greater propensity to complete an on line survey. nevertheless, the prevalence of answers from the academic world may have certain positive implications for the reliability of the results because they, in contrast to politicians, usually are not influenced by conflicts of interests and can therefore offer a more objective evaluation of the implications of the national choices on direct payments. 271implementation of cap 2014-2020 direct payments in italy 3. findings and discussion 3.1 findings experts were requested to evaluate italian choices on direct payments for 2014-2020 to test the effectiveness and consistency of the cap first pillar compared to its specific objectives. as previously noted in the conceptual section, the need for viable food production and sustainable management of natural resources are the main general objectives of cap reform 2014-2020. these two main goals generate many specific objectives; the means by which these latter could be achieved is verified by means of ad hoc result indicators that have been established by the egmec. respondents provided a subjective assessment by means of a 7-point likert scale based on their own knowledge regarding the new direct payments scheme in italy. overall, the evaluations provide an interesting picture of the possible consequences of the application of direct payments for the italian primary sector as well as highlight certain incongruences between the cap targets and their application in one of the most important primary sectors for the eu-28. descriptive statistics of the survey are reported in table 5. concerning the first general objective (viable food production), four specific objectives were adopted. with regard to the purpose of enhancing farm income, respondents criticize the capability of direct payments to both increase the share of direct payments of agricultural income (mean = 3.2; sd = 1.47) and limit the variability of farm income (mean = 3.68; sd = 1.47). in sum, more than 60% of respondents believe that the reform of decoupled payments will fail to increase farm incomes in italy. similar (negative) results concern the ability of the new direct payments scheme to maintain market stability, although the new common market organizations also contribute to realizing this objective. in detail, a majority of experts negatively evaluate the impacts of the decoupled aid reform on i) stabilizing the prices of italian agricultural commodities compared with those of the rest of the world (mean = 3.44; sd = 1.39), ii) limiting the price volatility of italian agricultural commodities (mean = 3.48; sd = 1.53) and iii) limiting the price volatility of italian agricultural commodities compared with that of the rest of the world (mean = 3.36; sd = 1.41). in addition, it must be noted that a large share of respondents (28% of the sample) claim to “have no knowledge” of such tricky issues due to the difficulty of expressing justified and reliable opinions. as regards the third specific objective, experts very positively assessed the impact of italian choices on improving agricultural competitiveness. in fact, many experts note that in the future, the share of high value added products of italian agricultural exports may increase (mean = 4; sd = 1.32), perhaps following and strengthening the current positive trend of high-quality italian foodstuffs exported all over the world. conversely, regarding the impacts of new decoupled payments scheme on i) increasing the percentage of value added for primary producers in the food chain (mean = 4; sd = 1.61) and on ii) increasing the share of italian exports in world agricultural markets (mean = 4.04; sd = 1.43), there is strong uncertainty (i.e., the share of those who say they “have no knowledge” total 12% and 36%, respectively). finally, with respect to the aptitude to meet consumer expectations, cap experts evaluate the direct payments reform in italy very positively. in particular, due to the greening payment and the internal convergence of the basic payment that will support extensive farming, the new scheme of direct aids could truly enhance high quality produc272 s. ciliberti, a. frascarelli table 5. ex ante evaluation of italian choices on direct payments for 2014-2020: descriptive statistics (n=25). general objectives specific objectives result indicators negative (%) have no knowledge (%) positive (%) mean1 sd1 viable food production enhance farm income increasing the share of direct payments in agricultural income 76.0 0.0 24.0 3.20 1.47 limiting variability of farm income 60.0 4.0 36.0 3.68 1.31 improve agricultural competitiveness increasing the percentage of value added for primary producers in the food chain 44.0 12.0 44.0 4.00 1.61 increasing the share of italian exports in world agricultural markets 28.0 36.0 36.0 4.04 1.43 increasing the share of high value-added products in italian agricultural export 32.0 24.0 44.0 4.00 1.32 maintain market stability stabilizing the price of italian agricultural commodities compared with that of the rest of the world 48.0 28.0 24.0 3.44 1.39 limiting the price volatility of italian agricultural commodities 48.0 28.0 24.0 3.48 1.53 limiting the price volatility of italian agricultural commodities compared with that of the rest of the world 52.0 28.0 20.0 3.36 1.41 meet consumer expectations increasing the share of organic area in total utilized agricultural area (uaa) 24.0 16.0 60.0 4.52 1.50 increasing the share of organic livestock in total livestock 20.0 24.0 56.0 4.32 1.28 sustainable management of natural resources and climate action provide environmental public goods increasing the share of permanent grassland in agricultural land 44.0 24.0 32.0 3.84 1.31 increasing the share of arable land 64.0 16.0 20.0 3.16 1.25 increasing the share of ecological focus areas (efa) in agricultural land 20.0 16.0 64.0 4.56 1.29 climate change mitigation and adaptation limiting the greenhouse gas emissions from agricultural soils 24.0 16.0 60.0 4.36 1.25 1 1 = very negative; 2 = fairly negative; 3 = somewhat negative; 4=have no knowledge; 5= somewhat positive; 6=fairly positive; 7= very positive. 273implementation of cap 2014-2020 direct payments in italy tion by increasing the share of the organic area among the total utilized agricultural area (mean = 4.52; sd = 1.5) as well as by increasing the share of organic livestock among the total livestock (mean = 4.32; sd = 1.28). the second main general objective of cap 2014-2020, the sustainable management of natural resources and climate action, entails two specific objectives that must be achieved according to the egmec: provide environmental public goods and foster climate change mitigation and adaptation. regarding the first specific objective, on the one hand, the cap experts positively evaluate the manner in which direct payments reform may increase the ecological focus areas (mean = 4.56; sd = 1.29) as well as limit the increase in the share of intensive arable farming (mean = 3.16; sd = 1.25). on the other hand, the experts do not predict a relevant increase of the share of permanent grassland of agricultural land (mean = 3.84; sd = 1.31). finally, with regard to the potential impact of limiting greenhouse gas emissions from agricultural soils, the new decoupled payments scheme was evaluated to be capable of (at least) beginning to confront this large challenge (mean = 4.36; sd = 1.25), most likely due to the introduction of new direct payments components (e.g., greening) that for the first time in cap history, aims to foster the provision of public goods instead of solely enhancing production. 3.2 discussion experts’ evaluations concerning the potential impact of italian direct payments on result indicators established by the egmec offer the opportunity to test the consistency between the policy decisions and the specific objectives of the first pillar. the descriptive statistics obtained by questionnaires collected in italy provide interesting results. although it is difficult to isolate and evaluate the effects of the new direct payments scheme in a real multifaceted sector, in which, among other things, world market dynamics are increasingly influencing farmers’ outcomes, the results may represent a preliminary test of the capacity of the italian government to make decisions that are consistent with cap 20142020 targets to confront the challenges of the 21st century. considering the results of the empirical study, the research questions are discussed below. regarding the first research question, the findings highlight that the reform of direct payments is not able to enhance (or at least preserve) farm income in a very challenging economic framework, in which italian farmers are now exposed to unpredictable price volatility and global competition. in fact, italy is subjected to a clear reduction in direct payments budget (due to external convergence processes aiming to equilibrate the cap among the eu-28) as well as to the internal convergence processes that will determine the decrease in direct support received by traditional italian production (e.g., milk, olive oil, and arable crops), whose income strongly depends on direct aid. at the same time, the new direct payments regime cannot maintain stability in a turbulent world agricultural market. this finding is definitely consistent with the new cap paradigm, which has progressively shifted from a protectionist approach towards a market-oriented approach since the 1990s and in which market stability tools have been progressively discarded or at least deeply reshaped. moreover, experts assess the decisions on direct payments made by the italian government as partially suitable to improve agricultural competitiveness. more precisely, 274 s. ciliberti, a. frascarelli whereas there is much uncertainty regarding the capabilities of decoupled aid to foster an increasing value-add for primary producers as well as regarding the share of italian foodstuffs exports in world markets, evaluators consider italian choices on the direct payments scheme adequate for increasing the share of value-added products in agricultural exports. in sum, although all these potential effects are difficult to attribute to new cap reform on decoupled aids alone, the survey shows that this type of support may at least maintain the current positive trend of italian foodstuffs exports in world markets. another interesting topic is the capability of the italian direct payments scheme to meet consumer expectations, namely, to foster farmers’ action or to change their attitudes to fulfil market requests. the findings show that the reform of decoupled payments in italy may significantly improve the manner in which the agricultural production is able to satisfy consumers’ wants due to the incentivisation of both organic farming and livestock, which are widely perceived as synonymous with high quality and safe production. to summarize, the findings highlight that the new direct payments scheme is not able to directly enhance farm income or protect farmers from world market turbulence due to the external and internal convergence processes and the ongoing dismantling of old market policies. at the same time, new decoupled aids could positively contribute to viable food production by improving the agricultural competitiveness of italian farms, supporting increasing exports of high value-added foodstuffs on the one hand and fulfilling consumer expectations (for instance, through high quality organic farming) on the other hand. these results confirm that in accordance with european trends, the implementation of direct payments in italy no longer strives to directly support farm income, but seeks to foster italian farm competitiveness to make them able to confront world market challenges. furthermore, because certain key targets of the cap 2014-2020 reform process were the provision of public goods and the sustainable management of agro-ecosystems, experts were also requested to assess the potential effects of the new italian direct payments scheme on these issues. therefore, with regard to the second research question, the evaluations appear to reveal that italy could succeed in containing intensive crop farming and increasing the share of ecological areas in utilized agricultural areas. these results may be due to the introduction of the greening payment on the one hand and to the combined effect of the internal convergence (that finally fosters extensive farming) and the decision to assign a relevant portion of coupled support to mountain livestock on the other hand. overall, primarily for the same reasons previously noted, the application of direct payments in italy may help limit greenhouse gas emissions from agricultural soils. in sum, the findings show that italian choices for direct payments are capable of ensuring sustainable food provision or at least positively exploring this new route along the old cap history, which is smartly summarized by the slogan “public money for public goods,” and that which may represent the serious future challenges for european agriculture beyond 2020. 4. conclusions cap reform 2014-2020 entails a deep revision of direct payments. in a general economic, financial and policy framework where public funds represent scarce resources, european institutions and mss must carefully manage such a relevant policy, which involves millions of farmers across the eu-28. to realize a reform that is more targeted to 275implementation of cap 2014-2020 direct payments in italy recipients and to foster effective public spending, for the first time in cap history, each ms received a strong mandate to manage the first pillar. obviously, this new approach involves italy, where policymakers are strongly involved in making relevant decisions concerning the new direct payments scheme. this paper offered a sort of ex ante evaluation of the ability of these italian choices of direct payments to match the general and specific objectives of the cap 2014-2020 reform. the egmec result indicators allow experts to assess whether and how newly decoupled support helps italian agriculture achieve these targets. although, on the one hand, the survey mainly involved scholars rather than politicians or agricultural professional organizations and, on the other hand, such an evaluation may be tricky and generic because it is usually very difficult to separate and clearly distinguish the effects of policy implementation from the impacts of markets dynamics, the findings show a discrete ability of the new italian direct payments scheme to pursue the cap-specific objectives, mainly ensuring viable food production and fostering the sustainable management of natural resources. findings (reported in table 5) have shown that the evaluators considered that direct payment reform 2014-2020 will have specific impacts in italy, which are described below: 1) a slight but negative effect on farm incomes caused by both the decrease in the italian budget for direct payment and the regionalization of direct aids; however, such negative effects are counterbalanced by the so-called “irish model” of internal convergence that may lessen the economic shock due to the redistribution of payments across farms and regions; 2) an improvement in farm competitiveness, increasingly influenced by a strong liberal and market-oriented approach and by peculiarities of the italian reform of direct payment (e.g., the “irish model”, coupled supports). consequently, italian farms will need to rely on their main strengths (e.g., high-quality and high value-added products) instead of on direct aids to acquire a strong position in a competitive world market, enhance their incomes and indirectly contribute to viable food production; 3) no enhancement of global agricultural market stability because direct payments are not allocated to regulate market functioning, in contrast to the common market organization (reg.eu 1308/2013), which is properly devoted to such an issue; 4) a positive impact on the agro-ecosystem due to the increased ecological area and a decrease in greenhouse gas emission; in particular, italian choices have not significantly modified the “environmentally friendly” approach of the direct 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(2013). the provision of public goods by agriculture: critical questions for effective and efficient policy making. environmental science & policy 32: 5-13. issn xxxx-xxxx (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(1): 3-11, 2012 from agricultural to bio-based economics? context, state of the art and challenges davide viaggi1, francesco mantino2, mario mazzocchi1, daniele moro3, gianluca stefani4 1 università di bologna, bologna, italy 2 istituto nazionale di economia agraria, roma, italy 3 università cattolica del sacro cuore, piacenza, italy 4 università di firenze, italy the world economy is experiencing dramatic changes. the key issues for the future appear to be increasing human demands (food, energy, environmental public goods) that will put greater pressure on natural resources, exacerbating old scarcities and leading to new ones (water, biomasses, environmental quality). agriculture has gone through an innovation process attaining long-term productivity growth, but has also become less central to the production of bio-based products, though remaining a key sector. disciplines related to bio-based industries and rural issues are searching for a better understanding of their potential role in future research and policy-making, and, finally, in contributing to society’s ability to face the major challenges ahead. while major changes have already occurred in these disciplines in recent decades, the on-going trends and perspective scenarios seem to involve further challenges, as witnessed by the changing aim and scope of scientific research and publications, as well as university curricula. the variety of academic literature in the field is increasing remarkably and, for some issues, such as bioenergy and biotechnology, the number of contributions has been growing exponentially. in this context, the concept of bioeconomy (or bio-economy, or bio-based economy) has emerged as a key strategy to match human needs while facing resource efficiency requirements, based on the sustainable exploitation of biological resources. actually the definition of the term ‘bioeconomy’ is still a matter of discussion (see schmidt et al., 2012). on the policy side, after having proposed several different definitions in recent years, the eu communication on the bioeconomy (european commission, 2012a) does not provide a clear-cut definition. the accompanying working document (european commission 2012b) states that “the bioeconomy encompasses the production of renewable biological resources and their conversion into food, feed, bio-based products and bioenergy. it includes agriculture, forestry, fisheries, food and pulp and paper production, as well as parts of chemical, biotechnological and energy industries. its sectors have a strong 4 d. viaggi, f. mantino, m. mazzocchi, d. moro and g. stefani innovation potential due to their use of a wide range of sciences (life sciences, agronomy, ecology, food science and social sciences), enabling and industrial technologies (biotechnology, nanotechnology, information and communication technologies (ict), and engineering), and local and tacit knowledge.” based on this delimitation, the eu bioeconomy accounts for an annual turnover of 2.046 billion euro (of which 965 come from the food sector and 381 from the agricultural sector) and 21,5 million employees (of which 4,4 million are employed in the food industry and 12 million are employed in the agriculture sector) (clever consult bvba, 2010). besides its economic weight and potential, the bioeconomy represents a major challenge for policy and research. the recent becoteps (2011) white paper emphasizes that a “successful bioeconomy needs coherent and integrated policy direction”, with key areas of action including: investment in research, encouraging innovation, strengthening entrepreneurship in the bioeconomy, providing a skilled workforce, guaranteeing an innovationfriendly regulatory framework which balances both risks and benefits, and a good two-way communication with the public embedded in r&d projects to ensure societal appreciation of research and innovation. several of these challenges are already taken up in the draft documents on the horizon 2020 research and innovation program of the eu (european commission, 2011). in this context, the italian association of agriculture and applied economics (aieaa) is launching a new journal, “bio-based and applied economics” (bae). the main questions behind this initiative are: why a journal on bio-based economics? and, why is it launched by a scientific society of agricultural economists? we will try to answer these questions by briefly reviewing current trends in the evolution of academic responses to past, recent and emerging research needs in the field of agriculture economics and its interaction with the closest fields of economics, building on this examination to single out relevant challenges for future research. as expected, a systematic review of all issues related to the evolution of agricultural economics and the potential emerging field of the “bio-based economics” is too wide to be addressed in a single article. we rather focus on some key trends and exemplary cases, mainly in order to kickoff the debate and set the stage for a research forum in this broad field, which is what this journal aims to be. 1. evolution of agricultural economics both in the us and europe, agricultural economics arose from the fields of agronomy and economics, as the first scholars mainly focused on farm management (nou, 1967; olsen, 1991). the agronomic ascendant is linked to the peculiarities of agricultural production, that is usually conceived as a process, namely “a set of tasks with a certain length in time that unfold along the time dimension, at given dates, with characteristics defined by agronomic techniques” (romagnoli, 1990). the firm and the organisation of production were the economic themes at the core of the discipline as is illustrated by the corresponding entry in the new palgrave dictionary of economics: “agricultural economics arose in the late 19th century, combined the theory of the firm with marketing and organization theory, and developed throughout the 20th century largely as an empirical branch of general economics” (runge, 2008). 5from agricultural to bio-based economics? later, the interests of the discipline widened to the economics of the agricultural sector and the related policies as witnessed by the renaming of the journal of farm economics, which became the american journal of agricultural economics in 1968. driven by the contraction of the agricultural sector in developed countries and the quest for a wider field of investigation, the discipline has never ceased to enlarge its scope outside the boundary of agriculture. runge (2008) identifies seven broad areas in which agricultural economists have made “distinctive contributions” since the 1970s. the list substantially mirrors the structure of us-based reference texts such as the handbook of agricultural economics (gardner and rausser, 2001): • technical change and returns to human capital investments; • environmental and resource issues; • trade and economic development; • risk and uncertainty; • price determination and income stabilization; • market structure and the organization of agricultural businesses; • consumption and food supply chains. as the issue of trade and economic development gained momentum in the 1970s, the focus shifted to overall regional development of rural areas worldwide, highlighting topics such as the linkages between agriculture and non-agricultural sectors in rural development, the competition for the use of land and environmental externalities of agriculture. by the late 1980s further areas were added, notably food industry and policy, biotechnology, agricultural research, farming systems and environmental issues (bellamy, 1991). overall, the changing scope of the discipline may be traced back to three main characteristics of the agricultural (and forestry) production processes: their biological nature, the presence of land as a basic (and scarce) resource and the horizontal division of labour that increasingly affected agriculture in the 20th century. land provides the link between agricultural and rural economics. in the countryside a large portion of the soil is used by agriculture but agriculture is not the sole economic activity that takes place in rural areas. integrated development of rural areas is often based on a coherent network of primary, secondary and tertiary activities that exploits the specific potentiality of places and communities. in agriculture, the heterogeneity of land is the source of “location specific factors” (nerlove, 1996) that affect human capital through the role of contextual knowledge of soil and environmental conditions that is shared by farming communities (ray, 1998). thus, social and technological factors contribute to extend the scope of agricultural economics towards rural economics. rural economics, however, has gained wider and more complex dimensions over time, including relations with other sectors. the process of the horizontal division of labour and the increased importance of processes located downstream of agriculture is the source of the growing interest of many agricultural economists in food economics. the division of labour and the related productivity gains and the increasing size of markets explain how most of the activities and functions that were once performed at the farm level have been increasingly carried out in other sectors of the economic system. this is also at the roots of the agribusiness (davis and goldberg, 1957) and agri-food system approaches (malassis, 1973). 6 d. viaggi, f. mantino, m. mazzocchi, d. moro and g. stefani 2. tracing connections between agricultural economics and the bioeconomy: selected examples several examples may be used to qualify the new bioeconomy challenges and to trace their connections to agricultural economics. if one considers the demand side of markets for bio-based products, especially food, the main challenge for the future is clearly that of securing safe food for an increasing population in a profoundly changing world. according to fao estimates, in 2030 the average daily per-capita consumption will reach 2850 kcal while the additional annual food energy production required to meet global needs will be about 2,000,000 billion kcalories. of course, the steady and unpredictable growth of emerging economies, in primis china and india, will likely put an even stronger pressure on food demand, especially if associated with a transition of diets towards animal products. at the same time, the steadily growing demand for bioenergy, although motivated by the need to control for greenhouse gas emissions, will compete for the use of renewable resources (mainly land). traditional economic determinants (prices and income), although still important, have been losing relevance in explaining food consumption in advanced economies; other factors have been gaining more and more relevance. even the traditional framework of a fully rational utility-maximizing food consumer has been challenged, since choices often appear to be attributable to irrational or purely instinctive behaviour as witnessed by the growing relevance of unhealthy diets, obesity or overreaction to food scares. in this context, the role of information is, of course, crucial: information provision becomes a key element in consumer reactions, and the lack of information is an explanation for behaviour. the existence of ‘uncertainty’ related to food choices (uncertainty regarding product attributes and quality, food safety, health consequences, etc.) will worsen the problem and boost the attention given to relatively new fields of investigation, such as behavioural economics. the ramification of the interest of economists in underexplored areas is even more apparent when one looks at the growing number of multidisciplinary works involving scientists from the biological sciences, which fit very well with the title and aims of this journal. it is not necessary to go as far as neuroeconomists (or even neuromarketing experts) do and look at the interaction between brain functions and consumer choices (mirja, 2010). this is especially the case when looking at the relationship between food consumption and health; recognising that it is bi-directional (hence extremely difficult to model!) leads to very interesting pathways even in ‘standard’ economic research (strauss, 1998; shogren, 2005). an example on the supply side is the issue of innovation. agriculture and the bioeconomy are presently dominated by projections concerning production needs of food, fibres, bioenergy and biomaterials (biodegradable plastics, bio-based polymer, biopharming), and related trade-offs. agriculture will have to face the challenge of securing enough supply without impoverishing natural resources. one route to meet these competing goals is either to increase arable land beyond current levels or to increase yields. increasing yields can be reached through technological progress, the rate of which has been slowing down in recent years; one possible solution may be the growing research effort in agricultural biotechnology, mainly devoted to contrasting abiotic and environmental stresses, also allowing to reintroducing crops to marginal areas without resorting to sowing previously uncultivated land. food energy supply will also greatly benefit from more attention towards ‘waste’: this 7from agricultural to bio-based economics? will imply a greater efficiency in food processing and marketing, reducing losses along the supply chain, as well as in the purchase, preparation, consumption and disposal of food. once again, biotechnologies at both the agricultural and the processing level may help in reaching such objectives. a guiding issue in the study of innovation is the discrepancy between the speed of development of new technologies and the factors hampering innovation potential in the agricultural and food sectors (brander, 2009). while one could affirm that “there is little doubt that technological innovation is the most important economic force underlying improvement in the human condition and that more inputs are being provided to the innovation process than ever before” (brander, 2009), the study of innovation adoption has always emphasized the complexity of the process. this is made even more relevant by the recent emphasis on the interaction between consumer concerns and the production of innovation itself, as witnessed by the gmo debate in the eu. more generally, a bioeconomy is characterised by forms of technical progress that may loosen up the constraints of relative resource scarcity (quadrio curzio et al., 2011) as agricultural innovations did in the 1970s. however, biotechnology can also compete with other more traditional agricultural activities for the use of scarce resources as in the case of first generation biofuels that reallocate land towards non-food production with potential impacts on food prices (mitchell, 2008). on the policy side, recent contributions on the most appropriate policy to build a bioeconomy (see the europabio, 2011) outline the need to move from a number of sectoral and separated policies and funding mechanisms to a more integrated and holistic approach. this implies coordination among policies in different areas such as climate change, energy security, renewable feedstock supplies, research and innovation, agriculture, environment and trade. this quite ambitious approach is fully embedded in the europa 2020 strategy and the on-going debate on the 2014-2020 financial perspectives. the cap is mentioned as a central component of the bioeconomy strategy (european commission, 2012a), paving the way for new potential areas of reform for the cap and related research challenges. nowadays, one key research area is the role of the cap in promoting the sustainable management of natural resources and in the provision of environmental public goods. in this regard, the manner in which policy instruments are designed and targeted is crucial in understanding policy efficiency and effectiveness. this comes on the heels of at least two decades in which a number of societal concerns have dominated the agriculture policy agenda (swinnen, 2008) leading to a long lasting reform process, which began at the end of the 1980s and the early 1990s under several pressures, including market surpluses and international trade liberalization agreements. this is also confirmed by the current debate on the cap reform for the 2014-2020 period. a major point of connection between agriculture and the bioeconomy is in the field of public policy and institutional arrangements that regulate innovation, production processes and the allocation of intellectual property rights. in the case of gene technology, for example, it is the very nature of living things that makes the allocation of property rights problematic (cipr, 2002). plant genetic resources are often available because generations of farmers contributed to their conservation and development. how this contribution should be rewarded or protected is a debated issue (cipr, 2002). to some extent this debate echoes property rights issues in environmental and food economics. most environmental goods provided by nature are considered in legal systems to be based on roman 8 d. viaggi, f. mantino, m. mazzocchi, d. moro and g. stefani law, res nullius or res communis, that is things common to all and usable by all citizens (brans, 2001, p. 36-37). similarly, geographical indications in the food system are considered to be the property of communities rather than of single individuals (moran, 1993). a major cross-cutting issue is that of societal coordination and decision-making, bringing together the different roles of human beings as consumers, agents of the production process and “citizens”. the debate about some of the key components of the bioeconomy, (i.e. biotechnology), has drawn attention to the issue of wider societal coordination. while this is often simplistically narrowed down to a mere problem of communication, it actually calls for a stronger focus on the interface between economics, psychology, sociology and political science in studying how institutions evolve in responding to external drivers. in this context, a growing area of attention for research is social innovation, in the wider sense of studying innovation in social structures and institutions. a further stimulating area of interaction lies between this broad field and that of rural governance. the concept of “rural governance” itself has only recently been thoroughly examined and developed in the literature. goodwin (1998) highlights the existence of an incomprehensible lack of interest in rural studies regarding the modalities with which rural areas are governed. this appears in sharp contrast with what has instead taken place in other fields of the social sciences, where issues relating to governance have long since assumed a certain theoretical importance. this is also in contrast with policy concerns. the recent barca report (2009), for example, puts governance at the centre of the reform for the new cohesion policy 2014-2020. altogether, we can argue that technological factors are still at the core of the recent trends towards the expansion of agricultural economics research, namely the path towards bio-based economics, with a common distinguishing focus on technologies based on biological processes. production processes based on the biological means of production without (extensive) use of land are widespread in the field of fisheries and aquaculture, or in microbiological production of algae, yeasts or drug substances. compared to the previous broadening of the subject, e.g. towards studying fisheries or forestry, the bioeconomy is much wider in scope, as shown by the definitions mentioned in the introduction. besides being broader in the range of sectors involved, it encompasses both primary, secondary and tertiary activities (such as the agri-business sector), but it draws particular attention to innovation and dynamic aspects of such activities and broadens the concept of the consumer to better account for a variety of human needs and their interaction. on the other hand, being so broad in scope, the bioeconomy does not share all the technological peculiarities of agriculture that, in turn, determine the patterns of production organization in the sector (such as the role of family vs. capitalistic farms). indeed, certain biotechnological processes, such as those involved in bioplastic production, are akin to the production processes in the chemical industry and have similar economies of scale. 3 discussion and future challenges the opening question was: why does an agricultural economics association launch a journal on bio-based economics? the answer to this question can be largely found in the analysis of the contents of the evolving field of agricultural economics and the emerging area of the bioeconomy, which has allowed to emphasize several connections and simi9from agricultural to bio-based economics? larities, particularly in relation to the distinguishing character of dealing with biological resources. at the same time, several emerging areas of research in agricultural economics already address issues that are included in the definition of the bioeconomy. this is, on the other hand, not exclusive of agricultural economics, as areas such as biotechnologies, bioenergy and innovation are already largely addressed by environmental and applied economics, as well as by industrial and organization studies. projecting this consideration into the future would require a further discussion – based on the above – that could be structured around three main questions: a) are we really witnessing a move towards bio-based economics? b) are we able to define this field of research with some precision? c) what are the key directions for further research in this field? the answer to the first question is a (partial) yes. though it may be too early to identify a new field of research and education, there appears to be scope for this area to emerge and it is also up to the academics to develop and shape such disciplinary area, as it is already the case in the policy sphere. this is emphasized by the growing number of initiatives targeting the bioeconomy as a research subject (e.g. conferences such as: icabr, iaae 2012, aieaa 2012) and the policy attention given to the concept of the bioeconomy. the answer to the second question could be either a (qualified) yes or a (even more qualified) no. the impression from the literature is that while a reductionist vision of the bioeconomy (i.e. based on a list of sectors) seems to be the most straightforward, a definition based on key technological or institutional characteristics remains a problematic issue. it is interesting to note that, in this field, academic research and policy development have initiated a debate. how this debate is likely to lead to the foundation of a separate branch of economic analysis is still unclear. this is a challenge for a newly founded journal, but at the same time provides ground for scientific discussion and is hence a stimulating context to start with. this leads to the third question – maybe the most difficult one – about future research directions. it would be too easy to conclude that this question is too broad and that answering it is beyond the scope of this paper. as a first step it could be argued that there are at least two directions for attention. the first is the large bulk of specific research fields related to individual issues. consumer sciences, markets, property rights, and innovation, are but a few examples. attention to individual sectors appears to be even more telling in this respect if we consider the economic and social aspects of bioenergy, biotechnologies and biomaterials. the second challenges the real meaning of the broad concept of bioeconomy for research and for policy making. what is the added value of the concept of bioeconomy as a whole and how could this comprehensive approach help economic analysis and policy design, besides the common issue of biological resources and the importance of strengthening links between different sectors? this is likely the most difficult but also the most interesting question, and likely the one to which researchers should pay particular attention in the years to come. references barca, f. 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(2008). the perfect storm. the political economy of the fischler reforms of the common agricultural policy, ceps, brussels. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(1): 73-90, 2013 modelling the adoption of automatic milking systems in noord-holland matteo floridi1, fabio bartolini2,*, jack peerlings3, nico polman4, davide viaggi1 1 department of agricultural science, university of bologna 2 department of agriculture, food and agri-environmental, university of pisa 3 agricultural economics and rural policy group, wageningen university 4 regional economy & land use group, lei wageningen university abstract. innovation and new technology adoption represent two central elements for the business and industry development process in agriculture. one of the most relevant innovations in dairy farms is the robotisation of the milking process through the adoption of automatic milking systems (ams). the purpose of this paper is to assess the impact of selected common agricultural policy measures on the adoption of ams in dairy farms. the model developed is a dynamic farm-household model that is able to simulate the adoption of ams taking into account the allocation of productive factors between on-farm and off-farm activities. the model simulates the decision to replace a traditional milking system with ams using a real options approach that allows farmers to choose the optimal timing of investments. results show that the adoption of ams, and the timing of such a decision, is strongly affected by policy uncertainty and market conditions. the effect of this uncertainty is to postpone the decision to adopt the new technology until farmers have gathered enough information to reduce the negative effects of the technological lock-in. ams adoption results in an increase in farm size and herd size due to the reduction in the labour required for milking operations. keywords. real options; dairy livestock; automatic milking systems; investment; uncertainty; common agricultural policy jel codes. q18, q12, q16 1. introduction and objectives innovation and new technology adoption are two central elements in the continuity of enterprises and industries. generally, innovation is an important driver of economic growth and rural development. research, innovation adoption and diffusion play a central role in meeting the 2020 eu challenges (european commission, 2010) and are a key part of the common agricultural policy (cap) reform process. in particular, within post 2013 * corresponding author: fbartolini@agr.unipi.it. 74 m. floridi, f. bartolini, j. peerlings, n. polman, d. viaggi cap objectives, innovation is expected to contribute to the promotion of farm competiveness as well as to the sustainable use of natural resources and the balanced development of the rural areas (european commission, 2011a, 2011b). the eu dairy sector is facing several challenges in the near future. the expected soft landing of the milk quota and the opening of the market will likely cause an increase in competition with respect to milk production and the need to reduce production costs. in addition, in recent years, milk prices have shown very large fluctuations (oecd-fao, 2012), which imply uncertainty for investment choices. finally, the reduction in household labour availability and the need to reduce production costs have encouraged the adoption of labour-saving technologies (sauer and zilberman, 2012). automatic milking systems (ams) that automate all the functions of the milking process provided by conventional milking systems (cms) are a labour saving technology in dairy farming. this new technology is considered to be one of the main innovations in the dairy sector (meskens et al., 2001). by the end of year 2009 more than 8,000 dairy farms worldwide had adopted ams (de koning, 2010), and this number is rapidly increasing (heikkila et al., 2012). the majority of the adopting farmers (90%) are located in the northern europe (sauer and zilberman, 2012). one of the areas with a high adoption rate is the netherlands, where more than 10% of the farmers have already applied this technology (steeneveld et al., 2012). ams became commercially available in the early 1990s. the technology was mostly developed in the netherlands and was thought to be applied on large family dairy farms. in fact, ams are a complete automation of the milking process and are composed of: a milking machine, laser sensors, robotic arms and gate systems for controlling cow access with the aim of saving labour allocated to the milking operation (rotz et al., 2003). ams allow milking at any time of the day. as a result the milk yield increases about 10-15% on average (steeneveld et al., 2012) by increasing the milking frequency from two to three times per day (castro et al., 2012). ams have the potential to significantly reduce the production costs or indeed to change the capital-labour ratio (steeneveld et al., 2012). in fact, by replacing cms with ams, the estimated saving is 20 to 30% (mathijs, 2004; bijl, et al., 2010; sauer and zilbermann, 2012) of the labour allocated to the milking activities. other authors have highlighted that there is no statistical difference in labour use between ams and cms but they have found differences in task and work flexibilities (steeneveld et al., 2012). recently, steeneveld et al. (2012) quantified the capital cost of ams at 12.71 € per 100 kg of milk instead of 10.10 € per 100 kg of milk for cms. the economic literature has highlighted the positive effect of the cap in promoting the process of innovation and investment. both direct payments and rural development program (rdp) payments affect the decision to invest in agriculture, ensuring liquidity and reducing investments or participation costs (see for example: sounding and zilberman, 2010; bartolini et al., 2011). recently heikkila et al., (2012) found that increasing investment payments by 1% increases the probability to adopt robotics and automation in dairy farms by about 2%. this paper seeks to assess the impact of alternative levels of subsidies in the form of co-financing of investments (measure 121 of rdp) on the adoption of ams on dairy farms in the netherlands, under uncertainty regarding labour costs, single farm payment levels and milk price. under measure 121, the dutch rdp 2007-2013 provides investment 75modelling the adoption of automatic milking systems in noord-holland subsidies to farmers as high as 35-50% of investment costs, with milking machinery being one of the eligible investment categories (regiebureau, 2012). in order to achieve this objective, the paper uses a dynamic farm-household model which is able to simulate the timing of the adoption decision and how such a decision will affect the allocation of productive factors between on-farm and off-farm activities. the model implements a real options (ro) approach that represents the farmer’s choice about the optimal timing of investment, when investment is affected by uncertainty. the model is used to simulate the decision to replace a traditional milking system with ams. the simulations consider uncertainty in labour costs, milk prices and the level of the single farm payment (sfp) after 2013. in this paper the methodology presented by was et al. (2011) was applied to the case study area of noord-holland, a province of the netherlands. unlike in the paper by was et al. (2011) in this study ams adoption is simulated in a very competitive region. these characteristics of the region affect the farm structure including off-farm allocations of household labour and hence the potential profitability of innovation adoption. furthermore, also unlike was et al. (2011) in this case the current policy framework (2009 health check) is considered, including sfp and cross-compliance, measures following from the nitrate directive and the abolishment of the milk quotas in 2015. the paper is structured as follows: section 2 we describe the conceptual framework; in section 3 we discuss the methodology and present the case study to which the empirical methodology is applied. section 4 provides a discussion of the results, and is followed by the conclusion. 2. conceptual framework the study of the adoption and diffusion of innovation plays a central role in the agricultural economics literature (sauer and zilberman, 2012). one of the first studies was conducted by griliches (1957) who explained the diffusion of innovation by means of an imitation process. earlier works on this issue described innovation diffusion as an s-shape function (rogers, 1962), where the new technology is firstly introduced by a group of innovators, then followed by earlier adopters, then by the early and late majority, and finally by laggards. davis (1979) observed farm heterogeneities and differences in learning and farm skills, and he applied a threshold model to explain pattern of innovation adoption and diffusion. later, other studies applied models based on the expected utility function which added elements of risk attitude, size effects and human capital, and learning effects as drivers of technology adoption (sauer and zilberman, 2012). a large body of literature has investigated the effect of risk attitudes when innovation decisions are affected by uncertainty in future variables (just and zilberman, 1983). literature in this field has highlighted that technological progress contributes significantly to the reduction of farmers’ risk exposure and otherwise farmers’ risk attitude plays an important role in determining the adoption and diffusion of innovations (kim and chavas, 2003; sauer and zilberman, 2012). other authors have considered uncertainty without assumptions about risk attitudes (or using risk neutral agents) developing the ro model (dixit and pindyck, 1994). the ro model allows to quantify the value of the option to delay investment decisions until further information about the state of nature (as well as market and other prices) has been collected or shown (trigeorgis, 1988). the ro model, as differentiated from 76 m. floridi, f. bartolini, j. peerlings, n. polman, d. viaggi capital budgeting tools, allows to improve the analysis of investment decisions when the outcome is affected by irreversibility and uncertainty (schwartz and trigeorgis, 2004). a growing body of literature has investigated the adoption of ams on dairy farms. generally speaking, the majority of the models are normative in nature (steeneveld et al., 2012) and focus on measuring the profitability of the adoption (hyde and engel, 2002; dijkhuizen et al., 1997; rotz et al., 2003). was et al. (2011), in applying a ro model to polish farmers, found that future conditions such as market prices and expectations about the cap strongly affect the profitability of ams adoption. large farms with large herds have a higher expected profitability, hence higher probability to adopt ams. recently, sauer and zilberman (2012) highlighted the effect of a sequence of innovations connecting the expansion of the herd size to the adoption of the new technology. the authors found that larger scale of milk production and larger herd size positively affects the ams adoption, hence confirming that larger farms are likely to find the adoption of ams more profitable. this paper addresses the decision to adopt ams using a ro approach. as highlighted by the literature, ams adoption is strongly affected by uncertainty in relevant decision variables. elements that determine uncertainty are mainly those classified with labour availability and market conditions (cost of household labour allocated off-farm, agricultural output prices, and the cost of hired labour). furthermore, they can be associated with variables related to the financial management of the investment such as loan rates, loan accessibility, and the amount and certainty of sfps and rdp payments. under conditions of uncertainty and investment irreversibility the ro approach allows for the calculation of the net present value (npv) increment due to the option to delay the ams investment until a later period, when the farmer has access to more information about the exogenous uncertain variables determining investment profitability (sauer and zilberman, 2010). following the model presented by was et al. (2011) the adoption of ams could be presented as a two-period model (figure 1). figure 1. timing of ams adoption new technology adoption choice delayed lock-in t1 =first period t2= second period new technology adoption 1 2a 2b choice delayed strategy 1 strategy 2 source: adapted from was et al. (2011) 77modelling the adoption of automatic milking systems in noord-holland we assume that a discrete choice about the decision to adopt a new technology can be undertaken in two separate periods: either the first or the second period. when a farmer invests during the first period he is locked-in by the investment in the second period (strategy 1), where the lock-in situation is a consequence of the high investment and sunk costs and by the irreversibility of the investment (carruth et al., 2000). otherwise, a farmer can delay the investment decision to period 2 when unknown future variables are revealed or he has obtained more information about the uncertain variables. given the information obtained in period 2 the farmer can decide to invest in ams in period 2 (strategy 2a). otherwise, the farmer can choose to further delay the investment in ams (strategy 2b). in order to operationalise this modelling framework, we apply a model developed by was et al. (2011) and modified by bartolini and viaggi (2012). following was et al. (2011), the optimal strategy for the farmer will be the one with the highest npv of cash flow over both periods: , where is the net present value of the cash flow in strategy 1 and is the net present value of the cash flow in strategy 2, as depicted in equations 1 and 2: (1) (2) where: cft = cash flows of a year t, with t = t1 the number of years belonging to the first period and t = t2 the number of years belonging to the second period; k = cost of investment; i = discount rate; γ = probability of the ams having a favourable state of nature; = cash flow in year t of period 2 when stochastic variable values are favourable to ams adoption; = cash flow in year t of period 2 when stochastic variable values are unfavourable to ams adoption. inn = subscript means ams adoption. the ams adoption is subject to uncertainty in the second period. this assumption implies stochastic cash flow values during the second period. following dixit and pindyck (1994) we assume that the annual cash flows can follow a brownian motion with drift, so that , where dcft is the instantaneous value of the cash flow; is the expected cash flow value; μ is drift (percentage), σ is the volatility (percentage), and dz is a wiener process with a mean of zero and independent increments. 78 m. floridi, f. bartolini, j. peerlings, n. polman, d. viaggi under such an approach, it is possible to differentiate two values of cash flows: one favourable to ams investment ( ), and one unfavourable ( ). these two values are obtained assuming that the random variable generated from the wiener process can have positive or negative values in order to allow for adding or removing the same amount from the expected value at any time in the period t2. this approach helps to maintain a constant expected value, and to change only the amount of uncertainty in the second period. 3. parameterisation of the model the empirical analysis is presented in three steps. the first step is the identification of the representative farms to be simulated, the second is the construction of the farm household model and the final step is the modelling of uncertainty. 3.1 identification of the representative farms the model has been constructed for four representative farm households specialised in dairy production, in the case study area of noord-holland, a province of the netherlands. the representative farms were obtained by applying a cluster analysis to the capire2 database, which contains interviews with 300 farm households located in noordholland. a subsample of 149 farm households which indicated dairy farming was their main farming specialisation were selected for the cluster analysis. for each farm, information was asked about farm and farmer characteristics (farm structure, herd size, crop rotation, household information about income from off-farm labour, the amount of offfarm labour, off-farm capital investment and information about sfp and rdp payments received). no information concerning the current milking system was asked during the interview. applying a cluster analysis3 to the 149 farms, four groups of livestock farms were identified. the main characteristics of the groups resulting from the cluster analysis and the frequencies in the database are presented in table 1. the clusters generated represent four different dairy livestock systems. the characteristics of cluster 1 are the smallest herd size, with only 26 dairy cows and a low usable agricultural area (uaa), respectively composed of 18.40 ha of land owned and 9.35 ha of land rented-in. the average age of the farm owner is higher than for the other clusters (54 years). the other three clusters (clusters 2, 3, 4) are composed of younger farmers compared to the first cluster and are also differentiated by herd size and farm size. cluster 2 has the largest herd size with 213 dairy cows, and a high use of labour, mainly provided by hired labour. finally, clusters 3 and 4 are characterised respectively by medium herd size (106 dairy cows) and small herd size (62 dairy cows). these two clusters are quite homogeneous with 2 cap-ire is the acronym of a 7th framework program project entitled assessing the multiple impacts of the common agricultural policies on rural economies. further information is available on: http://www.cap-ire.eu/ default.aspx. 3 a non-hierarchical k-means cluster analysis was applied. the variables used for the cluster analysis are the herd size and the on-farm labour used, expressed in full time equivalents (both household and hired labour). the best clustering was obtained by the one with the highest calinski/harabasz pseudo-f value. http://www.cap-ire.eu/default.aspx http://www.cap-ire.eu/default.aspx 79modelling the adoption of automatic milking systems in noord-holland respect to the age of the farm owner and the amount of labour used. cluster 3 has a higher use of land compared to cluster 4. table 1. group characteristics and frequencies cluster age of the owner dairy cows (#) household labour (# full time equivalent) no household labour (# full time equivalent)) land owned (ha) land rented-in (ha) sfp received per year (€/ per farm) frequency (%) cl1 54.4 27 1.47 0.33 18.4 10.0 8,873 23.18 cl2 49.0 213 1.79 2.71 94.1 42.4 59,451 4.64 cl3 47.5 106 1.84 0.43 50.1 19.7 29,236 26.49 cl4 48.0 62 1.76 0.33 34.8 12.1 21,259 45.70 3.2 building of the farm household model the empirical analysis was conducted using a dynamic farm household model with the objective to maximise the net present value of the cash flow over the next 20 years. ams is an innovation with investment characteristics. a dynamic model instead of a static one with a 20 year time horizon was applied because it is better capable of simulating cash flows. this allows to simulate the effects on profitability of liquidity constraints, credit and savings (see, for example, viaggi et al., 2011, for a discussion of the dynamic investment model). following dixit and pindyck (1994) the ro model simulates the farmers’ investment behaviour under uncertainty whilst considering risk neutral agents. in this paper we applied the model provided by was et al. (2011) and bartolini and viaggi (2012) which allows to simulate investment behaviour taking into account the allocation of resources (mainly capital and labour) to off-farm and on-farm activities. as depicted in figure 1, a two-time period model is applied. in order to investigate the effect of cap reform on adoption, the first period (t1) includes the years between 2010-2013, and the second (t2) includes the 2014-2030 period, consistently with the expected policy reform becoming active in 2014. the farm household model allows to simulate the profitability of investment in ams, considering the connections between livestock activity, crop cultivation and labour allocations among such activities, return on capital invested off-farm and off-farm income. the household has been assumed to maximise the whole household npv, subject to consumption and leisure constraints. with reference to equation 1 and 2, the cash flow in year t (cft) is equal to the sum of on-farm income ( ) and off-farm income ( ) minus possible loan repayments ( ). formally: . on-farm income is obtained by summing crop production incomes ( ), milk production income ( ), the possible rdp received for investment in ams (rdpt), and the sfp received (sfpt), minus the cost of external labour hired ( ). accordingly, on-farm income is calculated as: . θ in front of a variable means that the variable has a stochastic distribution in the second period. off-farm income is obtained by summing financial income (fint), pen80 m. floridi, f. bartolini, j. peerlings, n. polman, d. viaggi sions received by household members (penst) and income obtained by allocating household labour to off-farm activities (oint). formally, off-farm income is calculated as . then, with reference to equation 1 (ams adoption during t1), the cash flow of a year in the first period (t1) and in the second period (t2) are respectively: and , where the superscript i indicates that the farm has adopted ams. the calculation of the on-farm profit in the two periods is as follows: (3) (4) with reference to equation 2 (decision to adopt ams postponed to the second period), the cash flow of a generic year in the first period (t1) and in the second period (t2) is: and where: (3’) (4’) (4”) where: = area of crop c in time period j; yc = yield of the crop c; = amount of milk sold in period j cc = production cost of crop c; cm = milk production cost; 81modelling the adoption of automatic milking systems in noord-holland = cost of land rented-in in year t; = milk quota rent; = crop prices in year t; = milk price in year t; γ = probability to have favourable conditions for ams4; ; = favourable and non-favourable milk prices respectively in the second period; ; = favourable and non-favourable sfp respectively in the second period; ; = favourable (low) and non-favourable (high) labour cost respectively in the second period. in the model, rotation constraints, livestock housing capacity and manure and slurry spreading constraints are included. finally, a liquidity constraint has been applied in order to force the farm model to obtain a loan and to pay interest on the loan, when cash is insufficient to finance the ams. 3.3 modelling uncertainty the objective of the paper is to assess the impact of alternative levels of subsidies in the form of co-financing of investments (measure 121 of rdp) on the adoption of ams on dairy farms in the netherlands, when choice is affected by uncertainty in relevant context parameters. then effects of certainty in rdp co-financing investment measure with uncertainty in relevant stochastic parameters (the amount of sfp received by the farm, milk prices, and labour costs) on adoption decision have been assessed. we assume that during period t1 (first period) the farmer knows the first two moments (expected value and variability) of stochastic parameters in the second period. formally, uncertainty can be governed by a wiener process: , where st2 is the expected value for a year belonging to the second period (t2); se is the average or known value during the first period; σ is the maximum oscillation (known during the first period) and dz is a random variable uniformly distributed with a minimum value of 0 and a maximum value of 1. through a monte carlo approach, dz has been simulated as a n x m matrix of random values, where m represents the times at which each stochastic parameter changes during the second period, and n represents the number of samples generated by the monte carlo simulation. this general approach has different specifications depending on the stochastic parameter considered. concerning the sfp parameters, se is the expected value of the sfp during the second period, which is equal to half of the current sfp5, and σ is the maximum 4 the set of equations refers to a situation in which all three stochastic parameters simultaneously turn favourable or non-favourable. however, as explained in the results section, empirical analysis is undertaken developing one model for each stochastic parameter. 5 this value is equal to the average between the current value and sfp equal to zero. 82 m. floridi, f. bartolini, j. peerlings, n. polman, d. viaggi oscillation with a value equal to se. under this assumption, we simulate that farmers expect a reduction in the current spf amount and that the sfp can take values between the current value (2010-2013) and zero for each year in the second period. with regard to milk price, we have the following specification: se is the average net producer milk price in the years 2007-2009 (lei, 2009) and σ was calculated using the forecasted prices for the period 2009-2018 in the oecd-fao agricultural outlook 2009 report. the oecd-fao agricultural outlook 2009 expected annual reductions in milk prices and we have added an annual drift (μ) which was calculated as the annual percentage reduction to obtain the forecasted price level from the oecd-fao agricultural outlook 2009 report. finally, the labour costs se used are the 2009 labour costs obtained from eurostat, while σ was calculated using the forecasted labour costs in the scenar2020 report (european communities, 2007). due to expected trends regarding increases in labour costs, we have added an annual drift (μ) which was calculated as the annual percentage increase to obtain the forecasted price level in 2020 (european communities, 2007). such a specification allows for the determination of a random value of the price/cost variables with a uniform distribution and a maximum value seμ + σ and a minimum value seμ – σ. following this notion, and referring to equation 3, 4”, the variables used in the simulation can be summarised6 for sfp: and ; for milk prices: and ; and for labour costs: and . the expected value and the uncertainty simulated in the model are presented in table 2. table 2. descriptive statistics of the stochastic parameters parameter expected value t2 (se) uncertainty at t2 (σ) drift (μ) sfp cluster 1 4,437 € per farm 4,4371 € per farm sfp cluster 2 29,725 € per farm 29,7251 € per farm sfp cluster 3 14,618 € per farm 14,6181 € per farm sfp cluster 4 10,629 € per farm 10,6291 € per farm milk prices 0.34 € per litre 0.0432 € per litre -0.0052 € per litre per year labour costs (external) 15.35 € per hour 3.07 € per hour 0.0048 € per hour per year the expected value corresponds to the first moment, and the sum of the expected value, plus or minus σ represents the price level in the second period. the uncertainty is simulated by running a single model for each independent stochastic parameter. table 3 presents the comparison of the main characteristics of ams and cms used in the simulations. 6 note that the macron indicates a favourable ams adoption situation, and vice versa the underscore indicates an unfavourable ams adoption situation. 83modelling the adoption of automatic milking systems in noord-holland table 3. descriptive statistics of the milking system characteristics milking system features cms ams capacity (# of cows) current number 50 production (litre of milk per year) 8,118 8,682 variable costs per milking system (€ per year) 17,446 21,500 labour needed per cow ( in hours) 35.74 31.89 investment cost (€) 0 241,443 investment life time (years) 20 8 the data shown in table 3 were collected mainly from zucchi et al. (2004) and hogeveen and heemskerk (2006). in order to adapt data to the dairy farming in noordholland, the data has been discussed with, and corrected by, experts. in this paper we have simulated the single-stall ams (capacity of 50 cows). ams adoption requires less labour compared to cms. as depicted in table 3 ams will reduce the milking activity tasks by about 4 hours. the potential reduction in labour use allows an increase in milking frequency which in turn increases milk yields. 4. results the results of the model are presented in tables 4, 5, and 6 for labour costs, milk prices and sfp respectively, with each result having been parameterised on a different level of rdp support (measure 121). these values correspond to the percentage of ams investment costs covered. the average values obtained by all interactions (n=100) using the monte carlo approach are presented in the tables. in addition, the percentage of adoptions in each period over the total number of interactions (n) for each cluster are presented. for period 1, we indicated the percentage of situations in which an immediate adoption is more profitable than a delay until period 2. for each cluster, the average net present value and the average value of option are presented. the npv is the net present value of the cash flows when the adoption is undertaken in period 1. the value of the option is the increment of npv obtained by delaying the decision to adopt the ams after 2014, hence having the possibility to adapt to the new economic conditions. in this case, the model allows for the adoption of ams if the state of nature becomes favourable to the ams (strategy 2a), or the use of cms if the state of nature becomes unfavourable to the ams (strategy 2b). under uncertainty in context parameters the npv is rather stable for the different percentages of cost coverage by rdp, but differs strongly across clusters (see also table 5 and table 6). cluster 1 has the lowest npv, whilst cluster 2 has the highest npv and clusters 3 and 4 have similar npvs. this result is clearly connected to farm size, but is not straightforward as npv accounts for additional profits that are not necessarily proportional to the original farm size. the results also confirm the threshold effect identified by davis (1979). the option value is positive for all clusters, except for cluster 2 for which, when rpd cost coverage is higher than 25%, immediate adoption is most profitable. for cluster 1, the investment is profitable only with a high co-financing rate. with 75% of investment co-financed, cluster 1 delays the investment until the second period, 84 m. floridi, f. bartolini, j. peerlings, n. polman, d. viaggi for 97% of the interactions. a similar pattern can be seen for clusters 3 and 4. the option value has a negative correlation with the rdp coverage percentage because higher public support for investment reduces the advantage of delaying the investment. the possibility to postpone the investment is the best choice for clusters 1, 3, 4 for every value of rdp coverage, and no ams investments are made during the first period. ams will be adopted in the second period if the rdp covers at least 75% of its cost. this implies that without rdp in these clusters the ams will never be adopted, even with more certainty about labour costs (hence higher probability that high labour costs will make it profitable in some conditions). by increasing the rdp cost coverage, the value of the option decreases significantly. uncertainty about labour prices has a strong negative effect on the adoption of ams in the first period, in particular for cluster 2 where the adoption never takes place in 2010. results with uncertainty in milk prices are presented in table 5. the choice to adopt ams under uncertainty in milk prices follows the same tendencies as uncertainty in labour costs. for all clusters, it is profitable to delay the decision when the farmer has more information about future prices in at least one interaction per level of rdp coverage. for this reason, the value of the option is always positive. cluster 1 delays the decision for each level of rdp, and the rate of adoption in the second period increases with a higher cost coverage by rdp. for cluster 2 adoption is always profitable, however the decision is delayed to the second period for an investment cofinancing rate lower than 75%. with the highest co-financing rate the adoption is profitable table 4. results with uncertainty in labour costs (€ per farm) cluster variable rdp cost coverage (%) 0 25 50 75 1 npv 434,863 458,636 495,687 551,360 value of option 154,968 106,379 41,385 6,702 adoption t1 (% of n) 3 t2 (% of n) 97 2 npv 8,491,980 9,019,733 9,547,485 10,144,900 value of option 284,790 adoption t1 (% of n) 100 100 100 t2 (% of n) 3 npv 1,161,421 1,298,166 1,434,911 1,571,656 value of option 415,547 278,802 142,057 223 adoption t1 (% of n) 2 t2 (% of n) 98 4 npv 873,474 964,637 1,055,800 1,146,964 value of option 303,570 212,407 121,243 37,907 adoption t1 (% of n) t2 (% of n) 100 85modelling the adoption of automatic milking systems in noord-holland in the first period. clusters 3 and cluster 4 adopt ams only with a 50% or higher rdp coverage and the investment will be made in the second period. the results with uncertainty in sfp are presented in table 6. under uncertainty in the first pillar of the cap, timing of the investment in ams follows the same trends as with the other stochastic parameters, especially with uncertainty in labour costs. by increasing the percentage of rdp cost coverage, the rate of adoption increases, but the decision is taken in the first period only in clusters 2 and 3, when the rdp measure covers more than 25% of investment cost in cluster 2 and 75% in cluster 3. this implies that uncertainty in sfp is relevant for postponing investment. the optimal strategy in clusters 1 and 4 implies delaying the decision until the second period, when more information is available regarding the amount of sfp to be received after 2014, before making a decision regarding ams adoption. in this case the number of adoptions increases with higher rdp support. 7. concluding remarks in this paper the model developed by was et al. (2011) was applied to representative dairy farms in noord-holland, a province in the netherlands. labour saving technologies, such as ams, are the main innovations on these dairy farms, and are driven by an increasing demand for labour flexibility and the need to reduce production costs. the paper addresses the impact of uncertainty on the adoption of ams. the results show that table 5. results with uncertainty in milk prices (€ per farm) cluster variable rdp cost coverage (%) 0 25 50 75 1 npv 434,863 458,636 495,687 551,360 value of option 200,510 179,823 169,391 110,343 adoption t1(% of n) t2(% of n) 34 100 100 100 2 npv 8,491,980 9,019,733 9,547,485 10,144,900 value of option 731,308 413,162 120,223 adoption t1(% of n) 100 t2(% of n) 100 100 100 3 npv 1,161,421 1,298,166 1,434,911 1,571,656 value of option 569,909 410,118 307,530 247,842 adoption t1(% of n) t2(% of n) 2 76 100 100 4 npv 873,474 964,637 1,055,800 1,146,964 value of option 427,348 337,380 301,612 264,152 adoption t1(% of n) t2(% of n) 2 80 100 100 86 m. floridi, f. bartolini, j. peerlings, n. polman, d. viaggi the adoption of ams is strongly affected by uncertainty in future policy and in market conditions, and its main effect is the postponement of the adoption. the results highlight that the quality and availability of information play a central role in the process of innovation adoption especially when innovation is irreversible, has sunk costs or its adoption generates lock-in effects. the results confirm the findings of was et al. (2011) where uncertainty in milk prices compared to other sources of uncertainty (sfp and labour costs) results in higher option values, hence lower adoption in the first period. the results also confirm previous findings that there are thresholds for ams adoption, with mainly large dairy farms finding it profitable (rotz et al., 2003). indeed, the results highlight the role of labour costs and labour flexibility in determining the replacement of cms by ams, which is driven by the aim of reducing the use of hired labour. as also pointed out by earlier studies on the adoption of ams, even farms that are smaller than the threshold are likely to adopt ams (sauer and zilberman, 2012). this effect can be explained by the expectation of high wages from off-farm labour as compared to on-farm labour returns. as previously highlighted by the literature, there are strong interconnections between changing agricultural policy and farmers’ behaviour (viaggi et al., 2013). our results confirm this and highlight that agricultural policies strongly affect the adoption rate. while changes in agricultural policy directly affect the profitability of the innovation, and hence its adoption, the literature has emphasised that reducing public support will also affect the demand for other productive factors, causing a general reduction in the profitability table 6. results with uncertainty in sfp (€ per farm) cluster variable rdp cost coverage (%) 0 25 50 75 1 npv 434,863 458,636 495,687 551,360 value of option 155,632 108,086 33,984 7,033 adoption t1 (% of n) t2 (% of n) 100 2 npv 8,491,980 9,019,733 9,547,485 10,144,900 value of option 284,790 adoption t1 (% of n) 100 100 100 t2 (% of n) 100 3 npv 1,161,421 1,298,166 1,434,911 1,571,656 value of option 415,557 278,812 142,067 adoption t1(% of n) 100 t2 (% of n) 4 npv 873,474 964,637 1,055,800 1,146,964 value of option 303,327 212,164 121,000 37,687 adoption t1(% of n) t2(% of n) 100 87modelling the adoption of automatic milking systems in noord-holland of agricultural activities (bartolini and viaggi, 2013). the results show a positive effect of co-financing investment measures in increasing both the adoption rate and the time of the adoption. this is particularly true for large farms that are more exposed to uncertainty. the results also highlight the positive effects of sfp on the profitability of ams and its adoption by mainly providing liquidity and reducing risk exposure. the results underscore the need to reinforce (or build) links between investment support measures and uncertainty reducing measures (such as insurance). such measures are suitable to prevent excessive exposition to risk for those farmers with the strongest intention to invest and encourage a more timely reaction by farmers with funding opportunities. altogether, the results suggest that there is a need to develop a coherent policy framework, in which, besides milk quota abolishment, measures to support and promote investment/innovation are needed to increase farm competitiveness and ensure the continuity of farm activity. in particular, the abolishment of milk quotas could affect milk price fluctuations, and in turn increase the uncertainty in milk prices with the effect of further delaying investment in ams. the literature highlights that innovation adoption is negatively affected by uncertainty when famers are risk adverse. as pointed out by sauer and zilberman (2012), while the average milk price positively affects ams adoption, milk price variability negatively affects adoption. our results, even with risk neutral agents, confirm such findings, where uncertainty has the effect of delaying investment decisions. the paper addresses the combined effect of uncertainty and differences in farm structure in affecting farmers’ innovation behaviour and reactions to the alternative design of measure 121 of the rdp. hence, the methodology applied emphasises differences between farmers, rather than providing a territorial representativeness of the case study area. however, the results obtained by each cluster could be representative for different production scales and input uses. the paper has several limitations that are connected to the simplification of the process of innovation adoption and to the data used. concerning the first point, the modelling strategy adopted imposes a particular stylisation of the decision process in the direction of not considering the effect of learning on the adoption rate. the economic literature has found that differences in farmers’ skills or quality of extension services affect the profitability of an innovation (tsur et al., 1990). in addition, we have assumed a two-period model: relaxing such assumptions and allowing the adoption to be made in more than two periods, with learning effects, could improve the quality of the results. with respect to the data used, sensitivity analyses could be added to analyse the effects of uncertainty in other decision variables (e.g. investment costs, off-farm wages). in addition, the simulation could account for the effects of correlation between different uncertain variables, in order to increase the model’s quality and the robustness of the results. acknowledgments financial support from the fp7 project cap-ire (assessing the multiple impacts of the common agricultural policies on rural economies) is acknowledged and greatly appreciated. the authors are solely responsible for the content of the paper. this work does not necessarily reflect the view of the european union and in no way discloses the com88 m. floridi, f. bartolini, j. peerlings, n. polman, d. viaggi mission’s future policy in this area. the authors wish to thank the two anonymous referees and the associated editor for their fruitful comments on an earlier version of this paper. references bartolini, f. and viaggi, d. 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(2004). analisi zooeconomica dell’impiego degli ams. negli allevamenti dei bovini da latte in italia. bologna: avenue media. issn xxxx-xxxx (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(1): 47-63, 2012 the bio-economy concept and knowledge base in a public goods and farmer perspective otto schmid1,*, susanne padel2 and les levidow3 1 fibl research institute of organic agriculture, switzerland 2 the organic research centre, england 3 les levidow, open university, milton keynes, uk abstract. currently an industrial perspective dominates the eu policy framework for a european bio-economy. the commission’s proposal on the bio-economy emphasises greater resource-efficiency, largely within an industrial perspective on global economic competitiveness, benefiting capital-intensive industries at higher levels of the value chain. however a responsible bio-economy must initially address the sustainable use of resources. many farmers are not only commodity producers but also providers of quality food and managers of the eco-system. a public goods-oriented bio-economy emphasises agro-ecological methods, organic and low (external) input farming systems, ecosystem services, social innovation in multi-stakeholder collective practices and joint production of knowledge. the potential of farmers and smes to contribute to innovation must be fully recognised. this approach recognises the importance of local knowledge enhancing local capabilities, while also accommodating diversity and complexity. therefore the bio-economy concept should have a much broader scope than the dominant one in european commission innovation policy. socio-economic research is needed to inform strategies, pathways and stakeholder cooperation towards sustainability goals. keywords. bio-economy, public goods, european union, agro-ecology, sustainable development, rural development jel-codes. q20; q57 1. introduction in february 2012 the european commission announced its «strategy for a sustainable bio-economy to ensure smart green growth in europe». the strategy and action plan was called «innovating for sustainable growth: a bioeconomy for europe». the goal is a more innovative, low-emissions economy which reconciles demands for sustainable agriculture and fisheries, food security and the sustainable use of renewable biological resources for industrial purposes, while also ensuring biodiversity and environmental protection. the plan focuses on three key aspects: developing new technologies and processes for the bio* corresponding author: otto.schmid@fibl.org. 48 o. schmid, s. padel and l. levidow economy; developing markets and competitiveness in bio-economy sectors; and pushing policymakers and stakeholders to work more closely together (european commission, 2012a; 2012b; 2012c; 2012d). eu strategy gives great importance to the bio-economy concept, so it is worth reflecting on what the concept really means. across various scientific publications, papers, communications and comments of recent years, quite different definitions have been used for the bio-economy or the bio-based economy. the term bio-economy (synonymous with bioeconomy in this article) is used on its own, albeit with different meanings, as well as in conjunction with other terms like «innovation» or «knowledge base», e.g. knowledgebased bio-economy (kbbe). when looking at more recent statements of the european commission, it seems that the bio-economy concept has had multiple meanings. such diverse definitions make it difficult to understand which one is favoured by the european commission’s strategy. this paper will analyse those different definitions in order to identify aspects that are most relevant for a perspective favouring public goods, agro-ecological methods and farmers’ knowledge. as we will demonstrate, there is a need to broaden and further develop the bio-economy concept along lines that give farmers’ knowledge and public goods a more prominent role. 2. defining the bio-economy 2.1 definition of the bio-economy concept by the european commission according to an early definition, the bio-economy is «the sustainable, eco-efficient transformation of renewable biological resources into food, energy and other industrial products« (dg research, 2005). this definition was used for the knowledge-based bioeconomy (kbbe) in the framework programme 7 research agenda, especially in theme 2 cooperation: food, agriculture & fisheries and biotechnology (dg research, 2006). later the bio-economy concept was taken up for the innovation union, the economic growth model of the european union, stating that the bio-economy represents only one but a very important sector (european commission, 2010a). the bio-economy is meant to achieve policy objectives that were listed in the first draft of the «european strategy and action plan towards a sustainable bio-based economy by 2020» (european commission, 2010c). these objectives are in particular: • reinforcing european leadership and creativity in the biosciences; • optimising innovation and the systems for knowledge transfer; • research into safe, nutritious and affordable food; • making rural and coastal economies more sustainable; • improving the efficiency of agricultural, food and industrial production and distribution systems; • maintaining the competitiveness of european industry and agriculture; • building low-carbon industries; • reducing emissions of ghg and waste. 49the bio-economy concept and knowledge base in a public goods and farmer perspective in order to prepare and to concretize the bio-economy strategy, the european commission undertook several activities, such as an on-line consultation in spring 20111. furthermore, as mentioned in the commission staff working document accompanying the document «a bioeconomy for europe», two independent experts groups produced crucial background information on the social, environmental and economic impacts of the bioeconomy, as well as on skills (2012c: 8). the skills were identified as a key element to support the development of the bio-economy in europe. several conferences and workshops on the bio-economy concept were organised in 2010-2011 by the european commission and by european technology platforms, with the commission’s sponsorship. the result of these activities appear in the commission’s february 2012 communication entitled «innovating for sustainable growth: a bioeconomy for europe» (european commission, 2012c). this is one of the operational proposals of the europe 2020 strategy and its flagship initiative, «an innovation union», also contributing to other flagships such as «a resource efficient europe». the multi-annual financial framework for 20142020, and some of its key proposals, such as horizon 2020 and the common agricultural policy (cap) post-2013, duly takes into account the bio-economy (european commission, 2012c). the europe 2020 strategy calls for a bio-economy as a key element for ‘smart, sustainable and inclusive growth’ in europe (european commission, 2010d). advances in bio-economy research and innovation uptake are expected to allow europe to improve the management of its renewable biological resources and to open new, diversified markets in food and bio-based products. establishing a bio-economy in europe holds a great potential: it can maintain and create economic growth and jobs in rural, coastal and industrial areas, reduce fossil fuel dependence and improve the economic and environmental sustainability of primary production and processing industries. the commission’s 2012 bioeconomy strategy and its action plan aim to promote a more innovative, resource-efficient and competitive society that reconciles food security with the sustainable use of renewable resources for industrial purposes, while ensuring environmental protection (european commission, 2012c). for the term ’bio-economy’, the european commission’s public communication uses the following definition in their bioeconomy newsletter february 2012: the term ‘bioeconomy’ means an economy using biological resources from the land and sea as well as waste, including food wastes, as inputs to industry and energy production. it also covers the use of bio-based processes to green industries (european commission, 2012b). this definition emphasises biological resources and biomass as inputs to other industries, while omitting food as a major output, which was in the 2005-2006 definitions for fp7. the strategy and bioeconomy action plan 2012 has been further developed since its first draft in 2010. research and innovation are key components of the cross-cutting nature of the bio-economy, which is meant to address inter-connected societal challenges – including food security, natural resource scarcity, fossil resource dependence and climate change – in a comprehensive manner, while also achieving sustainable economic 1 http://ec.europa.eu/research/consultations/bioeconomy/consultation_en.htm 50 o. schmid, s. padel and l. levidow growth. a key priority, for example, is «improving the knowledge base and fostering innovation for producing quality biomass (e.g. industrial crops) at a competitive price». this complements the pervasive emphasis on «enabling and industrial technologies», e.g. life sciences, biotechnology, nanotechnology and ict (european commission, 2012c: 4, 6; also 2012d: 5, 14). in the communication «innovating for sustainable growth: a bioeconomy for europe», the bioeconomy action plan 2012 rests on three main pillars: 1. investments in research, innovation and skills aimed at ensuring substantial eu and national funding, in synergy with cohesion funds and cap, as well as private investment; 2. reinforced policy interaction and stakeholder’s engagement, through the creation of a bioeconomy panel, a bioeconomy observatory and regular stakeholders conferences that will contribute to enhancing synergies and coherence throughout the whole value chain; 3. enhancement of markets and competitiveness in bio-economy sectors by a sustainable intensification of primary production, a cascading use of biomass and waste streams as well as mutual learning mechanisms for improved resource efficiency (european commission, 2012c). eu intervention «is essential to provide the level of excellence and critical mass for research and innovation, which will play a fundamental role in the development of the bioeconomy» (european commission, 2012b). we will next look how the eu’s bio-economy concept is seen by different stakeholder groups and how it has evolved. then we will explore linkages of the bio-economy concept with a public goods perspective on farming, social innovation, knowledge systems and rural development. 2.2 towards a broader definition in recent years the scientific and political arena has had lively discussions on the concept of ‘bio-economy’. basically there are two main views – one from an industrial perspective, and one from a public goods perspective – each promoting different futures for agricultural systems and farmers’ roles. a bio-economy perspective on further industrialising agriculture has come from the oecd and multinational companies. according to an oecd report on the bio-economy to 2030 – designing a policy agenda, the application means «transforming life science knowledge into new, sustainable, eco-efficient and competitive products» (oecd, 2008). a common definition has also been developed by several european technology platforms through the becoteps project: the bio-economy is the sustainable production and conversion of biomass, for a range of food, health, fibre and industrial products and energy. renewable biomass encompasses any biological material to be used as raw material (epso, 2011: 5). biomass including organic waste (broadly defined) is sought as a substitute for fossil fuels, and as a plant-based resource whose economic value must be extracted and trans51the bio-economy concept and knowledge base in a public goods and farmer perspective formed. plant structures are seen as a barrier which must be overcome, especially through genetic changes. such views have resonance among some european commission staff who see genetically redesigned biomass as an el dorado, i.e. as a cornucopia linking renewable resources with intellectual property (original interview quotes in levidow et al., 2012). also outside europe there are political and commercial initiatives linked to the bioeconomy and bio-based products such as in the united states, japan, china, india and brazil. in particular in the usa many research institutes and companies are dealing with biomass and biofuels (for references see usda national library, http://www.nal.usda.gov). in september 2011 a report of the usda was published on bio-based economy indicators, which proposes indicators to assess various aspects of growth, profitability and uncertainty in the bio-economy. the definition of the bio-economy in this report is relatively narrow and relates to the «production and distribution of bio-based products». a definition of bio-based products was provided by the us congress in the farm security and rural investment act of 2002. congress later modified the definition in the food, conservation, and energy act of 2008, stating: the term ‘bio-based product’ means a product to be a commercial or industrial product (other than food or feed) that is— (a) composed, in whole or in significant part, of biological products, including renewable domestic agricultural materials and forestry materials; or (b) an intermediate ingredient or feedstock (usda, 2011). however, focusing on bio-mass and bio-technology (in particular, based on genetic modification) constrains the development of the bio-economy by omitting industrial and economic sectors that produce, manage and otherwise exploit biological resources and related services, supply or consumer industries (such as agriculture, food, fisheries, forestry) and their associated industries (european commission, 2009a). dominant industrial definitions have been criticised as too narrow, especially by downgrading the output of agriculture to biomass and/or emphasising novel food. the dominant perspective promotes food ‘quality’ as defined by measurable compositional characteristics, as in «functional food» (levidow et al., 2012). this perspective neglects the contribution that agriculture makes to quality-food production (including speciality and traditional foods), the strong scientific advancement of traditional agronomic and food science, the contribution of farmers to rural development through social and organisational innovations, as well as public goods and the multiple ecosystem and social services that agriculture is delivering (e.g., cooper et al. 2009). the standing committee on agricultural research (scar) financed a third foresight exercise which makes a similar criticism: in the kbbe concept, the human factor disappears, industry is considered the main player of the bio-economy and rural territories are only mentioned as beneficiaries. in other words, the framework built around kbbe covers only a part of what agriculture is and should be. on this regard one can detect several contradictions with recent eu elaboration around agriculture and rural development (freibauer et al., 2011: 7). to ensure the long-term economic growth of the bioeconomy, the definition should be non-restrictive. a former agricultural commissioner, franz fischler, defined the knowledge-based bio-economy more broadly as: 52 o. schmid, s. padel and l. levidow production paradigms that rely on biological processes and, as with natural ecosystems, use natural inputs, expend minimum amounts of energy and do not produce waste as all materials discarded by one process are inputs for another process and are re-used in the ecosystem (european commission, 2010b). this definition explicitly considers recycling within the production process itself, not only the output. 2.3 synergies and inconsistencies between different approaches a more detailed literature analysis shows that there are tensions among different definitions, concepts and emphases of the ‘bio-economy’, even within european commission documents. the tensions are illustrated by the following examples: • a broad concept is being promoted at the public-relations level, e.g. emphasising ‘local and tacit knowledge’ and social sciences (european commission, 2012a: 1). the importance of social innovation, public goods and farmers is emphasised in the staff working document accompanying the bioeconomy strategy (2012d), yet this is not the case in the bioeconomy action plan (2012c). but a narrower definition apparently guides r&d priorities, especially for horizon 2020, the successor to framework programme 7. • definitions favouring biomass production and transformation emphasise capitalintensive inputs for agriculture and biomass processing technologies. these favour the upper levels of value chains, while devaluing the knowledge and capabilities of farmers, who become mere recipients of lab knowledge and its products. • the potential of farmers and smes to contribute to innovation is not fully recognised in all relevant documents. rarely mentioned is the importance of local knowledge and local capabilities to better accommodate diversity and complexity. • the commission’s definitions also seek to feed expanding global markets for agriinputs and outputs, thus reinforcing pressures on renewable resources. • bio-economy has been linked with the concepts of eco-efficiency and/or resource efficiency, which likewise have diverse meanings: unstated is which efficiency, by what means, and for what aims. • policy documents sometimes mention public goods – the concept itself and/or specific examples. but only some definitions of the bio-economy give priority to maintaining public goods. and that priority conflicts with a definition that emphasises industrial biomass development. a wider working definition of bio-based economy was used in the report of one of the external expert group of dg research of eu commission on social, economic and environmental implications of a bio-based economy (menrad et al., 2011: 6). the report looked more at synergies between the different concepts: the bio-economy covers, in principle, all production systems involving biophysical and biochemical processes, and thus includes all of the life sciences and related generic technologies necessary to make useful products. it responds to the innovation needs of a broad range of farming systems, including fish farming, supports developments such as low input organic and conservation agri53the bio-economy concept and knowledge base in a public goods and farmer perspective culture and fisheries, precision farming and production in urban and peri-urban environments. it includes the promotion of healthy food derived from sustainable systems that exploit technology to produce efficiently while maintaining our environment and protecting biodiversity. applications of biotechnology in agriculture and industry, such for bio-refineries, bio-energy and bio-chemicals, are an integral part of the bio-based economy. it also includes novel forms of land and sea usage (such as those enhancing ecosystems services and other public goods) as well as the use of materials currently considered as wastes . on those grounds, as the report further argues, the european bio-based economy has to be broadly defined: • one based on the full range of ecosystems, land and sea resources, biodiversity and biological materials (plant, animal and microbe), food processing, and food consumption; • encompassing existing sectors of agriculture, forestry, fisheries, food, biotechnology and chemical industry and • contributing to the sustainable growth and production of food, feed, energy and renewable materials as well to the development of rural and coastal areas (menrad et al., 2011: 6). a partially broader bio-economy concept appears in the european commission communication of february 2012 on the «bio-economy for europe» and especially in the related staff working document (2012d), which incorporates views from the public consultation and the expert impact assessment. the latter document mentions that the bioeconomy strategy will support ecosystem-based management and that it will seek synergies with the common agriculture policy (cap) common fisheries policy (cfp), integrated maritime policy (imp), the water framework directive (wfd) and the marine strategy framework directive (msfd). mentioned also are the eu environmental policies on resource efficiency, sustainable use of natural resources, protection of biodiversity and habitats and provision of ecosystem services. however the full potential of an integrated bio-economy needs to be developed more through linkages with public goods and a more prominent role for farmers. 3. concepts of food quality and public goods: linkages with the bio-economy although the ‘bio-economy’ concept dates only from 2005, relevant components in the agriculture sector have a very long history. a lot of knowledge, understanding, traditional technologies and infrastructure have been developed, creating value not only through commodities but also through ecosystem and social services including food cultures. agriculture is intertwined with public goods which depend on farmers’ collective knowledge. the concept of multi-functional agriculture is widely established in europe (e.g. piorr and müller, 2009) but is ignored in the current bio-economy concept of the european commission. european agriculture has an economic opportunity by producing food of high quality, which is a central plank of the common agricultural policy. in this context the european commission refers broadly to food quality: beyond food safety and nutritional value, food 54 o. schmid, s. padel and l. levidow quality includes ‘farming attributes’ – e.g., production method, type of animal husbandry, use of processing techniques, place of farming and of production, etc. (european commission, 2009b). the common agricultural policy recognises also the co-production of public goods in particular in the context of the rural development programme (pillar ii). this public goods perspective should be taken into account in an integrated, more broadly defined bio-economy. what do we mean by public goods related to the bio-economy? what definition of public goods is most relevant here? what do public goods imply for the bioeconomy and its priorities? 3.1 defining environmental and social public goods in agri-forest sectors industries that rely on biological processes and resources, such as those in the bioeconomy, have intense interactions with the environment throughout the production cycle. such processes therefore are fundamentally intertwined with public goods, which can be either enhanced or undermined. true public goods exhibit two defining characteristics; they are both: • non-excludable: if the good is available to one person, then others cannot be excluded from the benefits that it confers. • non-rival: if the good is consumed by one person, then it does not reduce the amount available to others (mankiw, 2010). when referring to the bio-economy (including the sectors of agriculture, forestry, fisheries, food, feed, chemicals and bio-energy) the most significant public goods are: • environmental public goods: agricultural and forested landscape; farmland and forest biodiversity; water quality and availability; soil functionality, climate stability (greenhouse gas emissions, carbon storage), air quality, resilience to flooding and fire; • social public goods: food security and food culture; rural vitality; animal welfare and health (adapted from cooper et al., 2009). given the relationship between the sectors in the bio-economy and environmental public goods, activities within the bio-economy sectors that generate negative externalities can directly undermine the productivity of the bio-economy. for example if the ecological value diminishes over time, such as soil fertility or resilience to flooding, this will directly undermine the bio-economy and with that also the whole economy. public goods provide a direct social function. the european public places a high value on public goods, showing widespread concern for environmental issues especially with regard to biodiversity loss, mitigation of climate change, water and air pollution, and in particular the depletion of natural resources, including soil quality. there are also a number of secondary social and economic benefits that depend, partially or wholly, on the existence of the public goods provided by agriculture (cooper et al., 2009). therefore including public goods in the bio-economy concept may ensure real, continuous shortand long-term economic growth based on ecological, social, and economically sustainable systems. 55the bio-economy concept and knowledge base in a public goods and farmer perspective 3.2 farming systems and public goods all types of farming systems and methods can provide public goods if the land is managed appropriately. however there are significant differences in the type and amount of public goods that are provided by different types of farms and farming systems in europe. already a number of specific farming systems, and the practices employed within them, are particularly important for the provision of public goods. these include more extensive livestock and mixed systems, the more traditional permanent crop systems and organic farming systems. there may also be a large potential for highly productive farming systems to adopt environmentally beneficial production methods and thereby to provide public goods (for more information refer to cooper et al., 2009). building and maintaining a farming landscape with diverse assets, able to perform multiple functions (ecosystem services) and therefore enhance public goods, will help to open up more options for adaptation to environmental and socio-economic changes. this approach will require coordinated and long-term planning, so there is a need to combine research on agro-ecological, social and institutional aspects at different scales (schmid et al., 2009). agro-ecological methods draw upon local natural resources, ecological processes and farmers’ knowledge of them. bio-diverse farming systems with lower dependence on external resources avoid the endemic stresses of monoculture systems and climate change, thus reducing the risk of epizootic and zoonotic diseases and food-related disorders. agro-ecological, low-input and organic farming methods are important for maintaining and linking on-farm resources – e.g. soil fertility, plant genetic diversity and bio-control methods. furthermore, by re-linking production and consumption patterns, this further reduces dependence upon external inputs, thus moving towards greater self-sufficiency (niggli et al., 2008: 29; schmid et al., 2009). for those reasons, many civil society organisations have argued that ‘agro-ecological forms of production must be defined as the standard form of production in the eu’ (food sovcap, 2010). in the food sector there is a need to balance innovation (new knowledge) with tradition (old existing knowledge). many european quality food labels are based on traditional products and are preferred by consumers because of this. in the past, technology in food and farming has created not only economic growth but environmental and public health risks. in the recent history of the food sector, there are several examples where technological development has caused major food scandals. the most prominent example is bse, which prompted the european union to move towards ensuring a high level of food safety, as expressed in a white paper on food safety (european commission, 1999). therefore consumers have good reasons to seek re-assurance in traditional food products, rather than trust that all techno-industrial development in the food sector is always good for them. a broader, systems-based approach to agri-food innovation in the bio-economy can help to turn research into economic growth whilst at the same time providing public goods. a more diverse, robust bio-economy is less vulnerable to external shocks such as erratic climatic behaviour or resource scarcities – e.g. fertile land, freshwater, phosphorous and biodiversity. such an approach will create more resource protection, resource-use efficiency, business diversification and agricultural resilience. these offer added value and a more equitable distribution of wealth (padel et al. 2010). 56 o. schmid, s. padel and l. levidow farmers are being encouraged to increase productivity, which can have different meanings. from a public goods perspective, it can mean fewer external inputs needed for the same output and/or higher-quality outputs, e.g. through eco-functional intensification (niggli et al. 2007). practices protecting natural resources depend upon farmers gaining a greater share of value added in the agro-food chain. also the commission’s communication on agricultural productivity and sustainability suggests the need for greater productivity: the increase in output must go hand in hand with improved economic viability for primary producers who have suffered a declining share of value-added in the food chain over the past decade. without greater farm profitability, ecological sustainability will become even more challenging (european commission, 2012e: 3). according to that document, moreover, «innovative solutions should be adapted to the whole supply chain as well as the growing bio-based economy. solutions should be sought for bio-refinery and recycling and the smart use of biomass…» (european commission, 2012e: 8). such solutions include on-farm agro-ecological recycling of organic matter to produce energy and other inputs for agriculture (niggli et al, 2008: 34; schmid et al., 2009: 59). short food-supply chains provide means to remunerate agro-ecological methods via greater market demand, alongside closer relations between producers and consumers. these relations depend on consumer knowledge and trust of agricultural production methods and/or ‘quality’ labels (karner, 2011; knickel et al., 2008; levidow, 2008). such supply chains thereby provide financial and social incentives for methods that conserve or even enhance natural resources. moreover, a participatory approach to knowledge and innovation with regards to the delivery of public goods from agricultural practices can add value to the bio-economy and help create economic instruments that promote an appropriate balance between private and public goods. at the farm, watershed, district and national scales, new methods may be needed to assess and improve the performance of farming systems in relation to the multiple functions of agriculture, as outlined in the iaastd synthesis report (mcintyre et al., 2009). as an economic community and a society, europe is in a unique position to achieve a bio-economy that provides both market and public goods. both can be achieved through innovation, especially the wider development and application of agro-ecological knowledge. of particular importance becomes social innovation (explained below). the common agricultural policy (cap) has opportunities to promote public goods through support measures, both directly and indirectly. for example, climate change can be mitigated by reducing energy inputs and enhancing soil organic carbon, which also improves soil fertility. the post-2013 cap aims to promote «improvements in energy efficiency, biomass and renewable energy production, carbon sequestration and protection of carbon in soils based on innovation» (european commission, 2010e: 5). likewise, «mitigation action should relate to both limiting emissions in agriculture and forestry from key activities such as livestock production, fertilizer use and to preserving the carbon sinks and enhancing carbon sequestration with regard to land use, land use change and the forestry sector» (european commission, 2011a: 11; also european commission, 2012e: 8). farmers need support measures «in adopting and maintaining farming systems and practices that are particularly favourable to environmental and climate objectives, because 57the bio-economy concept and knowledge base in a public goods and farmer perspective market prices do not reflect the provision of such public goods» (european commission, 2011: 5). such public goods can be provided by agro-ecological methods, which therefore warrant greater support measures. on-farm energy production, via organic recycling of bio-wastes, provides an extra means to reduce external energy inputs. 3.3 social innovation and knowledge base social innovations are widely referred to in the context of health and education and feature prominently in innovation union documents (e.g. european commission, 2012c: 7, 2012d: 18-19). social innovations aim for the empowerment of groups facing common problems and address dysfunctional markets by deploying non-monetary resources and rules of partnership and collaboration (bepa, 2009, 2010). such activity has clear relevance to the bio-economy, especially for public goods. as outlined in a report by the scar working group on agricultural knowledge and innovation systems (akis), a distinction is necessary between science-driven research and innovation-driven research (eu scar, 2012: 101). in the latter perspective, innovation is seen as a bottom-up, interactive social process, rather than top-down from science to implementation. even very technical innovations are socially embedded in a process involving clients and advisors. often partners are needed to implement an innovation. as innovation is a risky business and benefits from the exchange of ideas, learning and innovation networks have proven to be an adequate vehicle for empowering groups of farmers to investigate new options to make their business more viable or sustainable (eu scar, 2012: 9). social innovation refers to more than social aspects of the innovation process or the aim that innovations should also be sustainable in the sense understood by corporate social responsibility (freibauer et al., 2011: 90). social innovation also highlights the fact that social problems need innovative approaches. such problems include rural development in europe’s lagging regions with declining populations, decreasing (governmental) service levels and (sometimes) uncompetitive agriculture. agro-food systems have developed many forms of social innovation (eu scar, 2012: 50-60). in poor neighbourhoods of big cities with high rates of unemployment and obesity, social innovation with urban farming and food projects can contribute to a better quality of life (eu scar, 2012: 101; karner, 2011). a sustainable development of the bio-economy requires, first of all, not only a broader definition of the term «bio-economy» but also a wide understanding of the necessary knowledge base. in its above report the scar akis working group recommends building on models of joint knowledge-production, spanning the boundary between knowledge generators and users (eu scar, 2012: 32, 42). this implies that expertise is sought in multiple forms from academics, practitioners, businesses, land managers and the public, all of whom can make valuable contributions to the knowledge base. scientific and nonscientific knowledge can be mutually enriching. the joint production of knowledge model underlines the need to move from ideas about one-way «knowledge transfer» to processes that will facilitate «knowledge exchange». indeed, «this evolution towards a knowledge exchange approach should enable greater 58 o. schmid, s. padel and l. levidow participation in comparison with a knowledge transfer approach» (eu scar, 2012: 75). such a joint participatory model for knowledge-production should overcome the boundaries between knowledge generators and users, while respecting and benefitting from a transparent division of tasks. it recognises the importance of local knowledge and leads to the enhancement of local capabilities, while also accommodating diversity and complexity (padel et al. 2010: 58; padel et al., 2011). here farmers and smes have an important role. 3.4 new structures and partnerships for providing public goods as a multi-functional activity, agriculture has a fundamental role in the economy, especially in the bio-economy by producing food as well as delivering public goods and services. european national governments seem unlikely to restore their previous role as leading investors in agricultural research. therefore we need new structures and partnerships for the direction and delivery of public agricultural research that reconsider the public goods aspects of the knowledge and technology outputs required. by ‘public goods’ in research outputs we mean those that are largely in the public domain and whose consumption is non-rival and so available to different uses and users. according to technology platform organics, this means: the creation of a green low-waste production chain, that is also able to secure food supply in the context of climate change and growing population can span from improved management systems that minimize inputs at the land/sea level and throughout the supply chain. farmers’ collective knowledge of natural resources, ecological processes and product quality, can be used as a basis to minimise dependence on external inputs and gain societal support. shorter agro-food chains based on consumers’ trust and greater proximity to producers can also be seen as a basis of a low-waste production chain, whilst addressing consumer demands for high quality food, taking into account animal welfare (tp organics, 2011). the bio-economy directly links innovation to economic growth. it is therefore important that the knowledge base of the bio-economy is non-restrictive. in order to increase productivity whilst maximising the efficiency of resource use and minimising the impact on the environment, innovation is needed not only in scientific research and technological development, but also in all areas of the bio-economy. this has to involve many stakeholders – in particular farmers, foresters, fishermen, advisory services – and all industries involved in the supply chain, as well as consumers and society at large. according to a linear model of innovation, however, a new technological development is patented with the expectation that its products will be purchased by farmers (eu scar, 2012: 15-17); this model marginalises the potential for innovation and thus for greater economic development of entire industries and sectors, thus restricting the bio-economy. to enhance public goods, europe needs policy support measures for a broader bio-economy: ‘societal interests (or public goods related demands) tend to be – by definition – not adequately addressed through market demand and demand-driven approaches’ (eu scar, 2012: 41). farmers, processors and other actors throughout the food chain are experimenting regularly and are generating innovations, as they have done since agriculture began (hoffman et al., 2007). farmers bring experience from their lifelong work on one complex farm experiment, which includes a largely tacit body of knowledge. this requires the uti59the bio-economy concept and knowledge base in a public goods and farmer perspective lisation of group approaches, and encouragement of producer ownership of the problems and solutions. these experiments in social innovation take many forms – e.g. communitysupported agriculture, short food-supply chains, and territorial labels – bringing consumers closer to producers, especially through better knowledge of agro-ecological production methods. public knowledge systems are needed to help promote those innovations and thus the public goods that they generate (levidow et al., 2012). extension (or advisory) services can play an important role, as increasingly recognised in europe. however, these advisory services have declined in most eu member states over the past decades; at the same time, agricultural research has become more distant from farmers’ knowledge. this process should be reversed. the post-2013 cap plans to promote farm advisory services, mainly so that farmers can fulfil their statutory obligations relevant to sustainable development (european commission, 2011). these services could also help to integrate agro-ecological methods with closer relations between producers, retailers and consumers. only when there is cooperation among producers and all other actors along the supply chain will the european bio-economy fulfil its potential. involving all levels of supply chains in the knowledge-base could lead to a better-managed system addressing the problems set out in the eu bioeconomy action plan. farmers and smes have been a major source of innovation and knowledge in the food and farming sector in the past. their potential to drive innovation for the future needs to be recognised and supported. 3.5 bio-economy and rural development rural development can be promoted through a bio-economy. this linkage is mentioned by the european commission’s communication on the «bio-economy for europe», as well as the related staff working document: the bio-economy can significantly contribute to the future development of rural and coastal areas because it will promote both supply and demand actions with regional dimension, such as the creation of supply chains for residues and waste as feedstock for bio-based industries, setting up of a network of small-scale local biorefineries or developing aquaculture infrastructures (european commission, 2012d: 18). this aim could be supported by future cohesion policy as well as by the reformed cap. both in pillar i and pillar ii of a revised cap there will be more possibilities to support the sustainable production of biomass for purposes other than food and feed. examples are coupling farmers’ area payments for specific desired products for energy or material use with specific sustainability requirements (menrad et al. 2011: 93). to what degree new biomass processing and bioenergy plants will create new employment and income will depend on policies, which could favour either more large-scale centralized businesses or else more decentralised systems with stronger involvement of farmers. along the latter lines, a bio-economy more oriented to public goods could create additional opportunities for rural development, such as by: • enhancing the landscape value and quality of life in rural areas as basis for other agricultural activity such as agro-tourism and eco-tourism, including its economic value for rural development. 60 o. schmid, s. padel and l. levidow • supporting green-care entrepreneurship: green care refers to the utilisation of farms – farm animals, plants, gardens, forest, and landscape – as a base for promoting mental and physical health and quality of life for a variety of client groups. • linking agriculture with energy production by recycling bio-waste at farm level, thus reducing input costs and ghg emissions. • building short food-supply chains that remunerate farmers for agro-ecological methods. • enhancing resilience of bio-diverse agro-food systems through in-built protection from threats of epizootic disease. • creating attractive employment for professionals in the field of agriculture, horticulture, food processing and nursing services. 4. conclusions currently an industrial perspective dominates the eu policy framework for a european bio-economy. a broad concept is being promoted at the public-relations level, but a narrower one apparently drives the eu’s r&d priorities. the latter perspective is promoted by stakeholders who foresee further industrialising agriculture as an opportunity for a ‘gold rush’, opening up a boundless cornucopia of ‘renewable resources’. the commission’s proposal on the bio-economy emphasises greater resource-efficiency, largely within an industrial perspective on global economic competitiveness, benefiting mainly capitalintensive industries at higher levels of the value chain. however a responsible bio-economy must initially address the sustainable use of scarce natural resources – such as soil, water and biodiversity – many of which are public goods. farmers should be seen not only as commodity producers but also as providers of quality food, as managers of the agricultural eco-system and landscape and as contributors to rural development. a public goods-oriented concept of the bio-economy emphasises agro-ecological methods, the organic and low (external) input food and farming sector, ecosystem services and social innovation. therefore the bio-economy concept should have a much broader scope than the dominant one expressed in the european commission innovation policy. we need integrated, comprehensive and sustainable approaches towards innovation by carefully developing future systems of natural resource use, both within and beyond agriculture. this also needs multi-stakeholder partnerships involving a broad range of civil society groups, including farmers, scientists, smes and consumers in addition to representations of various sectors of bio-based industries. the potential of farmers and smes to contribute to innovation in the food and farming sector must be fully recognised. they can contribute to a joint production of knowledge. the knowledge base of the bio-economy needs a move away from the classical topdown ’knowledge transfer’ towards processes that facilitate ‘knowledge exchange’. this approach recognises the importance of local and tacit knowledge, which encompasses different types of expertise, enhances local capabilities, and accommodates diversity and complexity. an important feature is social innovation in multi-stakeholder collective practices and knowledge-production. in sum, special efforts are necessary to ensure that the eu initiative for a «knowledge based bio-economy» will become a contribution to sustainable development, espe61the bio-economy concept and knowledge base in a public goods and farmer perspective cially to ensure the delivery of societal benefits and public goods. as an academic article has argued, «proposed solutions to environmental sustainability challenges are often orientated towards the partisan agendas of dominant stakeholders and myopic technological fixes, while marginalising other civil society actors and critical insights from social science» (diedrich et al., 2011: 937). socio-economic research is needed to inform strategies, pathways and stakeholder cooperation towards sustainability goals. acknowledgement the authors 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(2009). rural landscapes and agricultural policies in europe. berlin heidelberg: springer-verlag. schmid, o., padel, s., halberg, n., huber, m.; darnhofer, i., micheloni, c., koopmans c., bügel, s., stopes, c.; willer, h., schlüter, m. and cuoco, e. (2009). strategic research agenda for organic food and farming. technology platform organics. ifoam eu group. brussels, <http://www.tporganics.eu>. tp organics (2011). response to the consultation on the “bio-based economy for europe: state of play and future potential”. brussels: technology platform organics. usda (2011). biobased economy indicators. office of the chief economist, office of energy policy and new uses (oepnu), u.s. department of agriculture. prepared jointly by oepnu and the center for industrial research and service of iowa state university. oce-2010-2. issn xxxx-xxxx (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(1): 29-45, 2012 the experience economy as the future for european agriculture and food? johan swinnen, kristine van herck* and thijs vandemoortele licos centre for institutions and economic performance university of leuven (ku leuven), leuven, belgium centre for european policy studies (ceps), brussels, belgium abstract. as recently as a century ago, one out of two people in europe was employed in the agricultural sector. today agriculture represents only a small fraction of total employment in most eu member states. what makes this decline in agricultural employment even more striking is that this evolution has occurred despite substantial eu subsidies to support farmers’ income. given the apparent ineffectiveness of government support in keeping agricultural employment steady, it is worth considering which farming activities are likely to be successful in the economy of the 21st century. we argue in this paper that a potential growth path for european agriculture is the «experience economy» in which consumers are willing to pay premium prices for products and services that provide additional intangible ‘experiences’. we discuss the growth potential of the «experience economy» in the agricultural sector and conclude that it is worthwhile to consider the experience economy as a pathway for future farm growth. keywords. agriculture, experience economy jel-codes. q10, q13, q18, r11 1. introduction raising the question whether europe will be without farmers may sound ridiculous at a time when the european union (eu) has almost 12 million ‘farmers’ and when the issue of being able to feed the world population is at the top of the international policy agenda. however, as we will argue in this paper, the question is not only relevant, it also has major implications for the current debate on the future of the eu’s common agricultural policy (cap) and even for the more broader debate on the eu 2020 strategy. just a century ago, four out of five households in the world were engaged in the agricultural sector. today, agriculture represents only a marginal share of total employment in the developed world. in his fascinating treatise a world without agriculture peter timmer (2009) discusses the world economy’s structural transformation and how it is propelling the global economy – and especially the rich countries – toward «a world without agriculture». a similar process is taking place in europe. statistics on agricultural employment and gross value added (gva) indicate that the agricultural sector is (slowly) ‘disappearing’. in * corresponding author: kristine.vanherck@econ.kuleuven.be. 30 j. swinnen, k. van herck and t. vandemoortele the eu 27 the share of the agricultural sector in total gva and employment has decreased to respectively 1,7% and 5,4% in 2010. in many european countries, these numbers are even lower. spain is a good illustration. as recently as the 1960s, 50% of the population was employed in the agricultural sector. the decline has been dramatic: currently only 4.6% of the population is working in agriculture (figure 1). what makes this spectacular decline in agricultural employment even more striking is that this evolution has occurred despite large subsidies under the cap. in the period 2005-2010, the eu spent roughly €50 billion per year on supporting farmers – and including support through market regulations, up to €77 billion in 2010 (oecd, 2011). it is evident that government support is ineffective in sustaining farmers’ income at levels sufficient to keep agricultural employment steady. in fact, evidence suggests that, paradoxically, farm subsidies may increase the outflow of farmers instead of reducing it; see swinnen and van herck (2010) for a review1. 1 evidence from the oecd countries and inside the eu suggests that the outflow of labor from agriculture has been strongest in those countries and sectors that received most government support for the agricultural sector. in fact, over the past two decades there is actually a negative correlation between the change in agricultural support and the change in agricultural employment in the oecd countries – which is inconsistent with the notion that agricultural support has a significant positive impact on agricultural employment in the long run. there are several (potential) reasons for this: goetz and debertin (1996; 2001) find that farm payments accelerate capitallabor substitution and stimulate the takeover of farmers seeking to exit. petrick and zier (2010) find that large farms benefited more from farm payments, at the cost of smaller farms. berlinschi et al. (2012) argue that subsidies allow further skill improvements of farmers’ offspring which enables them to earn higher returns in (and switch to) other economic sectors. figure 1. share of agricultural employment in total employment (%; 1960-2010) 0   5   10   15   20   25   30   35   40   45   50   1960   1965   1970   1975   1980   1985   1990   1995   2000   2005   2010   sh ar e   of  a gr ic ul tu ra l  e m pl oy m en t  ( % )   spain   france   italy   netherlands   united  kingdom   source: ilo (2010), eurostat (2010) 31the experience economy as the future for european agriculture and food? the nature of farming is also changing. farmers are increasingly turning into business managers and policy debates are emphasizing farmers’ role as managers of the landscape and the environment (lans et al., 2004; rise, 2009). these changes have important policy implications in responding to the demands of modern society. most of the debate on cap reform has centered around the question how much of the budget should be allocated to agricultural policy in the eu after 2013, and how this budget should be spent, i.e. whether the current system of «single farm payments» with cross-compliance requirements is to be continued, changed, or abolished. in this paper we take a different perspective. we focus on the question which farming activities are likely to be successful in the economy of the 21st century – with or without government support. in other words: will europe be without farmers or not? and if not, how will these european ‘farmers’ look like in the 21st century? in this perspective, we focus on one major element of change that appears to have major implications for future economic success – and thus also for the success of farming in the 21st century: the shift from a service economy to an «experience economy». our approach is different from van der ploeg et al. (2000) and others who analyze the role of rural development and multifunctionality in the european agricultural sector. our analysis focuses on changes in (private) consumer demand as a main driver behind these changes, namely the rapid increase in the demand for experience – related products and services2. 2. the next revolution: towards an «experience economy»? 2.1 what is the «experience economy»? our society has evolved from an agrarian economy – which dominated the world for thousands of years – to an industrial economy in the 19th and early 20th century, and to a service economy in the late 20th century. since the end of the 20th century, our society has started to move in a new direction: the «experience economy». consumers in affluent societies have begun to take the quality offered by the service economy for granted and are expecting something extra – «experiences» – from the goods and services they purchase. this evolution is described by pine and gilmore (1999) and jensen (1999), who show that the share of consumers’ income spent on commodities and goods is declining, while the share of income spent on leisure activities and entertainment is increasing. the authors argue that the «experience economy» is taking over and will become the main value-generating element for firms in the 21st century. pine and gilmore (1999) call the key attribute in this evolution «experiences», which they describe as «memorable, but intangible offerings which allow the consumer to enjoy events or the consumption of a good in a personal way». jensen (1999) defines it as «stories», which are «symbolic value statements that reinforce the consumer’s identity and communicate his beliefs and goals». common to both approaches is that each underlines the importance of adding authenticity, feelings and emotions to products and services in order to satisfy the demands of the 2 in addition, we discuss the shift from a service to an experience economy from an economy-wide perspective, whereas van der ploeg et al. (2000) focus exclusively on the agricultural sector. 32 j. swinnen, k. van herck and t. vandemoortele post-materialistic consumer who is today – and will be even more so in the future – not just searching for high-quality services and products, but also for a story to which they can emotionally relate. an illustration is the rapid increase in popularity of pop and rock concerts and many sports events. for example, the number of visitors to one of europe’s most famous rock concerts, «rock werchter» in belgium, increased from 5,000 visitors in 1978 to 320,000 visitors in 2010. this is no exception: all over the world hundreds of thousands tickets for concerts by famous singers, bands and performers sell out in minutes on the internet. at first sight this is remarkable: with improved technology and declining costs of purchasing music, the quality of music is better and the price lower when listening at home. the same holds for sports events: the view is typically much better on tv. however, concerts and sports events are able to offer an experience that listening or watching at home cannot provide: a great atmosphere and a sense of belonging and togetherness – short: «the experience». in order to more precisely relate the concept of «experiences» to consumer theory in economics, we draw on the work of tirole (1988) and ronnen (1991) on modeling quality and andreoni (1989) and besley and ghatak (2007) on modeling a «warm glow» component in consumers’ preferences3. we define products and services as consisting of three components, each adding value to the product or service. the first component is the physical good or service. the second component comprises the good’s or service’s quality characteristics. «experiences» are the third component. if present, this third component adds value to the commodity or service for consumers who care about these experiences, and thus increases these consumers’ willingness to pay for the product or service. «experiences» – in contrast to «quality» – have an intangible impact on consumers. all else equal, there is no tangible difference between consuming a fair-trade product or a traditional product at the consumer’s level, but a consumer may draw a «warm glow» from this experience feature by believing that his consumption ensures a fair share for poor farmers in developing countries4. in short, consumers value «experience products» for the attached story (jensen, 1999). formally, define consumer i’s indirect utility from consuming product (or service) j as vi (pj, bj, qj, ej), where pj is the price of product j, bj is the value of the physical good or service which also encompasses the quantity consumed, qj is the product’s quality, and ej is the “experience” embodied by the product. this indirect utility function can be further specified in diffe3 the «warm glow» effect was originally coined by andreoni (1989), who argued that the internal motives for charitable giving are more important than many people had acknowledged. in the warm-glow view of philanthropy, people are not giving money to save the whales; they are giving money to feel the glow that comes with being the person who helps to save the whales. 4 note that our definition of «experiences» is therefore different from the concept of «experience characteristics» as defined by the economic literature on information asymmetries and externalities. this literature distinguishes between «search», «experience», and «credence» characteristics of products (nelson, 1970; darby and karni, 1973). search attributes are those that consumers can ascertain in the search process prior to purchase, while experience characteristics can only be discovered after purchasing and using the product, and credence qualities cannot be evaluated in normal use. these three meta-characteristics may apply to tangible or intangible characteristics, and are therefore not useful in distinguishing between quality and experience components. 33the experience economy as the future for european agriculture and food? rent ways. for example, a quasi-linear, unit-demand specification could have the following form (see swinnen et al. (2012) for a formal derivation)5: vi (pj, bj, qj, ej) = bj + ϕiqj pj + γif(ej). as before, bj is the value of the physical good or service and is identical among consumers. here it is independent from quantity consumed as we specify a unit-demand function. ϕi is a consumer-specific parameter, with distribution g(ϕi), which measures consumer i’s quality preferences – a higher ϕi refers to a higher preference for the product’s quality qj. ϕiqj is the central component of the indirect utility function specifications used in the vertical differentiation literature (tirole, 1988). the fourth component, γif(ej), represents consumers’ valuation of the “experiences” embedded in product j, where f(ej) is the (concave) valuation function of these experiences. consumers who value these experiences more have a higher γi. to illustrate the components, consider apples: qj represents the taste of the apple and/ or the absence of harmful pesticide residues, and f(ej) could represent the consumer’s appreciation of buying the apple at the local farmers’ market, or of knowing that the apple has been produced in an environmentally friendly way, etc. for a different example, consider a harley davidson motorbike: qj is quality (speed, etc.) and safety of the vehicle, and f(ej) the feeling that comes with driving a harley davidson or belonging to the community of harley davidson motorcyclists. from these examples, it is clear that consumers have different reasons to value different “experiences”. for example, in the case of “environmental friendliness”, the experience is valuable to consumers because it represents the private provision of a public good6. in the case of the harley davidson example, the valuation of the experience – “belonging to a community” – may be driven by the interdependence of consumers’ utility functions, i.e. so-called “veblen-effects” or “conspicuous consumption” (for more information, see e.g. bagwell and bernheim, 1996 and references therein). however, despite this clear theoretical distinction, the distinction between experiences and quality characteristics may be less clear in practice and at least some characteristics may have both quality and experience components. for example, “organic” may refer to healthier products (a quality feature) and/or cultivation under more environmentally friendly conditions (an experience feature). 2.2 markets of experience jensen (1999) identifies six markets in the experience economy: (1) the market for adventures, where people pay to participate in adventures (e.g. hot-air balloon travels, exotic trips, festivals where people dress up as soldiers or medieval knights, …); (2) the market for togetherness, friendship and love, which comprises of movies, novels and art5 although standard in the literature, this specification only serves as an illustration here, and, as any functional specification, has its limitations. 6 remark however that this does not imply that increasing demand for public goods is necessarily reflected by an increasing demand for private goods that (claim to) supply these public goods. 34 j. swinnen, k. van herck and t. vandemoortele work, but also theme parks, concerts, sport games, etc. – and to some extent also restaurants and bars where people go for food and drinks (services), but also for conviviality; (3) the market for caring, which targets people who have a need to receive or provide care, to show compassion and to help others or nature. consumers also experience a good feeling or “warm glow” by engaging in charity; (4) the market for self-definition, where products and services become means of self-definition. individuals tell a story about themselves by the way they dress, the places they visit, and the events they attend. for example, purchasing a harley davidson is not only buying a motorbike but also the identity that goes along with it; (5) the market for peace of mind and tradition, which targets consumers searching for peace of mind and tradition. one example of this interest in tradition is “rural romanticism”, which potentially explains the growing interest in organic food products and farmers’ markets; and (6) the market for convictions, where people pay for products that are consistent with their ethical beliefs. those consumers value the concept “animal welfare” and support environmental organizations such as greenpeace. 2.3 importance of the “experience economy” experiences are of course nothing new and have, for example, always been at the heart of the entertainment industry, from sports events to movies and pop concerts. however, it appears that demand – and with it economic performance – has grown substantially. companies have started to add and “wrap” experiences around their traditional products and services to make these more attractive, allowing better differentiation from their competitors and higher prices.7 coffee is a good example to illustrate how adding experiences to a basic product may provide firm-specific growth opportunities. pine and gilmore (1999) analyze the revenue distribution of a cup of coffee through the supply chain in the late 1990s (figure 2). companies that traded the basic commodity, the coffee bean, got a price of $1 per pound, which translated into one or two cents per cup of coffee. when manufacturing companies ground, packaged and sold the same beans in a grocery store, the price increased from 5 up to 25 cents per cup, depending on the type of beans. when the coffee was served in a local coffee shop, the price per cup varied between 50 cents and $1, but when it was sold in a starbucks coffee shop – which explicitly advertises “experience” as one of its assets – the price would range between $2 to more than $5 per cup. hence, providing an additional and distinct experience to a cup of coffee allows the starbucks coffee company to charge the consumer a substantially higher price for the same product. yet, despite this disproportional rent distribution, the success of starbucks and similar companies has only increased. while starbucks initially grew in the us – a country not known for its highquality coffee in the 1990s – the growth of starbucks shops outside the us has been the most remarkable aspect of its expansion, at least from an experience perspective. the success of starbucks outside the us was illustrated by its recent opening of a shop in ant7 the computer repair firm “geek squad” is an example of a company that “wraps” an experience around an existing service. the “geek squad” employees are all dressed as special agents: they wear white shirts and black ties and have badges in order to identify themselves as “geek squad” agents. by adding this “show” to their service, they provide such a distinctive computer repair service that satisfied clients buy t-shirts and pins with the company’s logo. 35the experience economy as the future for european agriculture and food? werp (belgium). despite the fact that it is easy to get a decent cup of coffee for a fraction of the starbucks price at other nearby places in the city, people queued for hours when the starbucks coffee company opened the doors of its first shop in antwerp. figure 2. price of coffee offerings 0 $ 1 $ 2 $ 3 $ 4 $ 5 $ 6 $ commodity good service experience source: pine and gilmore (1999) figure 3. annual growth in employment and nominal gross domestic product (gdp) per economic offering (%, 1959-1996)* -0.7% 0.5% 2.3% 2.7% 5.3%5.6% 6.4% 7.9% 8.5% 8.9% -1% 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% commodities goods all offerings services experiences c om po un d a nn ua l g ro w th r at e in e m pl oy m en t a nd n om in al g d p (% ) employment nominal gdp * the authors base their estimates for employment and nominal gdp growth of the category «experiences» on admissions to recreational events (movies, concerts, sports, etc.). source: pine and gilmore (1999) 36 j. swinnen, k. van herck and t. vandemoortele since the experience economy is a broad concept, it can be measured in various ways. pine and gilmore (1999) estimate that the experience economy –measured by admissions to recreational events such as movies, concerts and sports games – was the fastest growing sector with an annual growth of 8.9% in nominal gdp and 5.3% in employment (figure 3). in contrast, commodity output in the us grew annually only at 5.6% and employment even shrank from the 1960s through the 1990s. manufacturing output grew at an annual rate of 6.4% and also employment grew slightly (0.5%). the services sector’s output and employment grew respectively by 8.5% and 2.7% per year. 3. agriculture and the experience economy to analyze its relevance and potential for the european agricultural sector, we look at several different, yet admittedly imperfect indicators. indicators of the experience economy are imperfect because there is no agreement on its definition and because the indicators may capture both quality characteristics and experiences. for example, some people buy ‘organic’ for health-related reasons (a quality characteristic), while others buy ‘organic’ for its lower environmental impact (an experience characteristic). however, without detailed studies on consumer behavior – which are currently not available – it is not possible to determine which share of consumers buy a product for its quality or its experience characteristics. nevertheless, these indicators may be useful. as an introductory indicator, we first compare the agricultural sector as a whole with two other sectors that are more closely associated with the experience economy, namely the recreational, cultural and sports (rcs) sector, and the hotel and restaurant sector. in 1995, the agricultural sector represented 2.7% of gva and 5.0% of total employment – approximately the same as the hotel and restaurant sector but considerably more than the rcs sector. however, by 2010 agricultural employment dropped to only 3.3% of total employment (a 34% decrease). in the same period, the employment share of the hotel and restaurant sector grew to 5.1% (a 19% increase) and the employment share of the rcs sector even more, from 1.8% in 1995 to 2.3% in 2010 (a 28% increase). moreover, agriculture fell below the rcs sector. gva generated by rcs activities had an average annual increase of more than 5%, while gva generated by the agricultural sector remained approximately the same over the past decade. the gva of the rcs sector overtook agriculture’s gva in 2004, and the gap has been widening ever since (figure 4). however, as mentioned, this indicator is imperfect. one reason is that the experience economy has grown inside the agricultural and food system as well. 3.1 experience standards in the agricultural and food industry compared to fifteen years ago, all agricultural and food products are subject to a range of new product and process standards. many of these are safety and minimum quality standards, but increasingly they also relate to experience concepts such as animal welfare and the environment. until 20 years ago, issues such as ecology, global warming, animal welfare and genetic engineering were relatively unimportant in the marketing of products and services, but because consumer concerns about these issues have been on the rise, new «experience standards» have been imposed and increasingly affected agricultural production. 37the experience economy as the future for european agriculture and food? the private sector appears to have taken the lead in introducing experience standards. most retailers impose private standards on their suppliers, and these private standards are frequently more stringent than their public counterparts. this holds particularly for experience standards. these private experience standards are closely related to, and in most cases part of a company’s ‘corporate social responsibility’ (csr) strategy8. one specific example of an experience standard which has been rapidly adopted by the private sector is free-range eggs. despite the frequently demonstrated absence of quality differences between eggs from free-range and battery hens (van den brand et al., 2004), consumers are willing to pay more for free range eggs. in belgium for example, the market share of free range eggs – with a price premium of 20 to 40 percent – was as high as 90% in 2011 (vlam, 2011). also in this particular example, private retailers have taken the lead in adopting private standards to address consumers’ animal welfare concerns. while eggs from battery cages are officially prohibited only from 2012 onwards, several retailers 8 csr is defined by the european commission as «a process, whereby companies integrate social, environmental, ethical and human rights concerns into their business operations and core strategy in close collaboration with their stakeholders». although this process is not new, it was characterized by stellar growth in the past decade and is currently ranked as the number one priority of managers in the global retail and consumer goods sector (the consumer good forum, 2011). there is an extensive literature dealing with various aspects of csr, from the economic justification of csr to its implications on firm performance (hartmann, 2011). one specific strand of this literature relates to our concept of «experience» and analyzes firms’ incentives to compete for «ethical» consumers (e.g. bagnoli and watts, 2003; besley and gathak, 2007), in which ethical consumers derive utility from buying products from firms that adhere to certain ‘experience’ standards such as fair trade or animal welfare standards. figure 4. gross value added (gva) of the agricultural sector vs. the recreational, cultural and sports (rcs) sector in the eu-15 (billion €; 1998-2008) 100 110 120 130 140 150 160 170 180 190 200 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 g ro ss v al ue a dd ed (b ill io n €) agriculture recreational, cultural and sporting (rsc) activities source: eurostat (2010) 38 j. swinnen, k. van herck and t. vandemoortele have already decided to stop selling and using these eggs in their own private label products. colruyt was in january 2006 the first retailer in belgium to remove these eggs from its shelves, and less than three years later all belgian retailers had followed (vilt, 2008). certification practices are another example. there exists a variety of initiatives and certification schemes related to environmental preservation, fair trade (see below) and other issues. here again the private sector does pioneering work, in collaboration with ngos. an example of such a certification scheme is the «rainforest alliance» (ra) certification which aims at promoting good farm management practices for resource conservation, environmental management, and improved labor conditions. the ra certification scheme has been widely adopted by large multinational companies. by 2007 ra-certified bananas accounted for approximately 28% of total us banana imports. all banana plantations owned by chiquita and 84% of the bananas purchased by chiquita from latin america are ra-certified (fao, 2009). kraft recently committed to use ra coffee beans in the production of several of its coffee brands. unilever – acquiring 12% of the world’s black tea – committed to buy all its tea ra-certified9. similarly, the forest stewardship council (fsc) aims at halting deforestation by promoting sustainable forest management. between 2004 and 2008, its certified area almost tripled, and in 2009 it certified approximately 116 million hectares of forests (2.9% of the global forest surface). in some countries a significant share of the forests are certified (e.g. croatia: 95%; poland: 76% – marx and cuypers, 2010). 3.2 fair trade products initiatives such as the ra certification scheme are closely related to the «fair trade» (ft) concept. consumers buy ft products mainly because these allow small and poor farmers in developing countries to receive a higher price for their products, enabling them to improve their lives, send their children to school, etc. in this sense, ft products fit within the market for care and charity as consumers get a «warm glow« when buying these products – they draw utility from knowing that they support small and poor farmers by buying ft products. additionally, ft products also fit in the market for convictions as by buying ft, consumers reject the purchasing practices of traditional firms. to date, the ft concept so far is mainly applicable to famers in developing countries and therefore less relevant for european farmers10. yet, the evolution of ft is a good indicator for consumers’ interest in experience products. ft products are the fastest growing segment of food sales. global sales of ft certified foods reached nearly €2.9 billion in 2008, with tea, cocoa, coffee and bananas enjoying the highest growth11. on average, global ft product sales expanded by 40% annually over the period 1997-2007 (fao, 2009). in the uk, where most detailed data are available from, the sales of ft products increased from around £30 million in 2000 to £1,170 billion in 2010 – a dazzling increase of 49% per year (figure 5). 9 source: <http://www.rainforest-alliance.org/>, 10 june 2010. 10 recently, ft products have originated from the eu as well. for example, in response to the recent dairy crisis, ft milk brands have emerged. 11 source: <http://www.fairtrade.net/>, 26 september 2010. 39the experience economy as the future for european agriculture and food? a recurrent comment is that despite strong growth, the market share of ft retail sales has remained quite low. aggregate data seem to confirm this: ft retail sales make up only slightly more than 1% of total food retail sales. however, this is misleading as ft products are concentrated in some specific commodities. for example, the sales of ft bananas in the uk increased from 5% of all banana retail sales in 2004 to 30% in 2008 (figure 6). in 2009, ft coffee retail sales represented more than 15% of total coffee retail sales in the uk and ft tea retail sales amounted to 9% of total tea retail sales. furthermore, uk consumer awareness of ft products has risen from 20% in 2002 to 70% in 2008. the number of uk consumers buying ft products has followed a similar growth pattern – from 9% in 2006 to 25% in 2009. 3.3 organic food products organic sales in the uk have grown from around £100 million in 1995 to more than £1,700 million in 2010, an average increase of roughly 30% per year in sales value (figure 7)12. although the share of organic products in total retail sales is limited, data show that this differs strongly by country and – as with fair trade – by product category. organic products account for 1.6% of total food retail sales in the uk and 4% in the us. in 12 the economic crisis may have temporarily slowed or even reversed this trend, but this reinforces the argument that economic growth is related to the growth of experience products and services. figure 5. fair trade sales and market penetration of fair trade in the uk (mio £, 1998-2010) 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 200 400 600 800 1000 1200 1400 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 m arket penetration (% ) fa ir t ra de r et ai l s al es ( £ m ill io n) fair trade retail sales market penetration source: website: <http://www.fairtrade.org.uk/>, 30 september 2010 40 j. swinnen, k. van herck and t. vandemoortele figure 6. share of selected fair trade products in total retail sales in the uk (%, 2004-2009) 0 5 10 15 20 25 30 2004 2005 2006 2007 2008 2009 sh ar e of fa ir tr ad e pr od uc ts in to ta l r et ai l s al es (% ) coffee tea bananas source: own calculations based on defra (2009), euromonitor (2010) and fairtrade foundation (2010) figure 7. organic food sales and market penetration of organic food sales in the uk (sales in mio £, market penetration in %; 1995-2010) 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0 500 1000 1500 2000 2500 1995 1997 1999 2001 2003 2005 2007 2009 m arket penetration (% ) u k sa le s o f o rg an ic p ro du ct s ( m io £ ) uk sales of organic products market penetration source: soil association (2011) 41the experience economy as the future for european agriculture and food? france, organic milk sales has grown from 2.8% of total milk retail sales in 1999 to more than 7% in 2007 (figure 8). the share of land allocated to organic farming increased in all eu member states between 2000 and 2007. in some countries, such as austria and sweden, a substantial share of total farmland is used for organic production. retail companies and outlets specializing in organic products have emerged. in the us, the organic retailer whole foods has grown from 19 employees and one outlet in 1980 to 57,200 employees and 296 outlets in 2009. it is currently the ninth largest food and drug store in the us13. another example is bio-planet owned by the belgian retailer colruyt. 3.4 direct sales of food products figure 9 shows the rapid increase in the number of farmers’ markets in the us. in the period 1994-2009, the number of farmers’ markets, where consumers buy food directly from farmers, increased from 1,755 markets in 1994 to 5,274 in 2009. also in the uk, farmers’ markets have gained in popularity and their number increased from close to zero in 1995 to approximately 550 markets (with 230,000 stallholders) in 2006 (farma, 2006). 3.5 summary in the agricultural sector, many different products and services can provide an experience to the consumer, ranging from products where the experience is only one aspect in the consumer’s decision (e.g. organic products for which also quality and health concerns play a crucial role) to products and services where the experience feature is the most important driver (e.g. fair trade products). a frequent critique of initiatives reviewed here is that these forms of «experience farming» are marginal in terms of total production and that therefore their impact is marginal and irrelevant for the majority of farmers as well. our documentation of the economic importance of «experience farming» shows that it is no longer a marginal phenomenon, but growing rapidly in importance, especially within specific product categories and regions. admittedly, figures on the past growth of the «experience economy» do not allow to make projections about future growth, and more detailed analyses of the main drivers affecting both demand and supply for experience products and services are needed. potential factors affecting the market for experience products and services are demographic changes, changes in purchasing power, production limitations and policy initiatives. for example, the financial crisis or specific policy measures such as abolishing subsidies may affect organic production. 4. conclusions and implications for eu policy in this paper, we have argued that a new type of economy is emerging: the «experience economy». typically products and services sold in this experience economy contain, 13 source: http://www.wholefoodsmarket.com/, 9 august 2010. 42 j. swinnen, k. van herck and t. vandemoortele figure 8. share of organic milk in total retail sales of milk in france (%; 1999-2007) 1.8% 2.2% 2.8% 3.1% 3.5% 3.8% 4.2% 4.4% 4.8% 5.2% 6.6% 7.1% 7.7% 2.8% 3.5% 4.4% 4.7% 5.3% 5.7% 6.3% 6.4% 7.0% 7.8% 9.8% 10.4% 10.8% 0 2 4 6 8 10 12 1999 2001 2003 2005 2007 2009 first semester 2011 m ar ke t v ol um e / v al ue (% ) percentage of the total market volume percentage of the total market value source: agence bio (2008) figure 9. number of farmers’ markets in the us (1994-2009) 1755 2410 2746 2863 3137 3706 4385 4685 5274 0 1000 2000 3000 4000 5000 6000 1994 1996 1998 2000 2002 2004 2006 2008 2009 n um be r of fa rm er s' m ar ke ts source: usda-ams marketing services division 43the experience economy as the future for european agriculture and food? aside from the physical product/service and its quality features, an additional «experience». these additional experience characteristics positively affect consumers’ willingness to pay for the product or service, and are intangible at the consumer level – they create a «warm glow» feeling – but may have tangible consequences at other levels, e.g. by affecting positive or negative externalities. we have documented that throughout europe, and specifically its agricultural sector, the experience economy is growing, creating more income and more jobs. this is in strong contrast to traditional agricultural activities where employment decreases and its relative contribution to economic development declines even further. hence it appears that there may be (at least) two diverging future growth patterns for the eu agricultural sector. one pattern is the large-scale, labor extensive and capital intensive production of agricultural commodities as inputs for the food, feed, and fuel industry. incomes will have to be raised through increasing productivity, cutting costs, and possibly increasing prices with increased demand for feed and food, and the growing integration of the agricultural production system in bio-energy production – all driven by increasing food and fuel prices in the long run. here it is crucial for eu policy to support this system with investments in r&d and innovation (swinnen and van herck, 2010). another potential growth path for european farmers is the experience economy where consumers are willing to pay a price premium in exchange for various ‘experiences’. note that these two growth patterns should not necessarily co-exist within the same region and/or sub-sector and that the relative importance is endogenous to the characteristics of the region and/or sub-sector. a frequent critique of current initiatives in the agricultural sector is that these forms of «experience farming» are marginal in terms of total production and therefore only have a marginal impact on the eu agriculture and food system. however, the rapid growth of for example organic and fair trade sales suggests that the experience economy may be a future growth area for the agri-food system. while total food sales of these products are still relatively small compared to the total food market, this is no longer the case in specific product markets where they have gained substantive shares. what is promising for farmers is that experience characteristics not only relate to consumption or product characteristics per se, but also to production characteristics. this, therefore, may offer substantial growth perspectives and a growing comparative advantage for european farmers in the medium term. from a policy perspective, the question arises whether local, national and eu-level policy can play a role in stimulating farmers to increase the value of their agricultural production by focusing on ‘experience’ aspects of their production processes. at the eu level, some policy support is already incorporated in particular rural development programs. examples are programs that stimulate co-operation for developing new products (axis 1) and programs that encourage the development of tourist and craft activities (axis 3). hence, in the light of the discussion on the cap budget after 2013, it appears useful to discuss whether more resources should be oriented towards programs that aim at assisting farmers and the agricultural system as a whole to reorient itself towards what appears to be a growth area for the future: the experience economy. these programs could help farmers to accumulate the appropriate human capital and to develop the institutional infrastructure to make the transition towards the experience economy, which can be an effective way to increase their (farm) incomes. of course, the question applies here as 44 j. swinnen, k. van herck and t. vandemoortele well to what extent specific support policies can drive this type of change and whether the key factors of a successful transition are not more general policies which stimulate skill enhancement, innovation and entrepreneurship. finally, it is important to acknowledge the important role that the private sector can play in stimulating growth of the experience economy. over the past years, the introduction of various experience standards in csr strategies of large, international enterprises has been a catalyst for rapid growth of some specific experience products. acknowledgements the authors wish to thank david harvey, louise knops, guilia meloni, mara squicciarini and seminar participants in brussels (ceps workshop) and zurich (eaae conference) for valuable comments. further, the authors gratefully acknowledge the comments and suggestions by the anonymous referees and the editor. references agence bio (2008). chiffres 2007: l’agriculture biologique française. publication de l’agence française pour le développement et la promotion de l’agriculture biologique. paris. andreoni, j. 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(2004). effects of housing system (outdoor vs. cages) and age of laying hens on eggs characteristics. british poultry science 45(6): 745-752. van der ploeg, j.d., renting, h., brunori, g., knickel, k., mannion, j., marsden, t., de roest, k., sevilla-guzmán, e. and f. ventura (2000), rural development: from practices and policies towards theory, sociologia ruralis 40(4): 529-543. 46 j. swinnen, k. van herck and t. vandemoortele vilt (2008), vilt newsletter, 03 october 2008. vlam (2011). eierbesteding in 2011. marketingdienst vlaams centrum voor agroen visserijmarketing (vlam), brussel. bio-based and applied economics 4(1): 33-53, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-14705 innovation in european food smes: determinants and links between types francesca minarelli1,*, meri raggi2, davide viaggi1 1 department of agricultural sciences, university of bologna, bologna, italy 2 department of statistical sciences, university of bologna, bologna, italy date of submission: 10th july 2014 abstract. the food sector has traditionally been considered one with the lowest research and development expenditure to value added ratio. in recent decades, however, the business environment has become more demanding in terms of technological inputs for reasons related to food safety, quality and also the globalisation of the food market. this provides a strong incentive to innovate, especially for small and mediumsized enterprises (sme) seeking to remain in business. most businesses operating in the food sector belong to the sme category which, based on the literature, tends to have a low level of research capacity. this study seeks to identify determinants of the types of innovation adopted and associations between them by analysing a sample of european food smes. for this purpose a non-parametric analysis, namely the classification tree technique, is carried out. the main finding is that due to the technological factors inherent in the food industry, a tight linkage exists between product, process and market innovation. moreover, the study shows that collaboration between competitors encourages smes to engage in market, process and business model innovation. conversely, synergy with suppliers and customers supports product innovation. keywords. food smes, innovation, sme network jel codes. o31, o32 1. background and objectives the food sector has traditionally been considered to be a low-tech sector in comparison with other sectors (christensen et al., 1996; garcia-martinez et al., 2000). there are numerous reasons for this. first, innovation in the food industry does not usually make use of scientific inputs as innovation in the sector tends to be more incremental than radical; this is also related to the observation that consumers are typically conservative and reject radically novel food products (garcia-martinez et al., 2000). second, the food industry is * corresponding author: francesca.minarelli@unibo.it. 34 f. minarelli, m. raggi, d. viaggi mostly characterised by small and medium-sized enterprises (smes) (schiemann, 2008), a size typology that is often deemed to lack the internal resources necessary to undertake innovation. this occurs in spite of the fact that various studies have demonstrated the contribution of smes to the main innovations of the twentieth century (although this has been observed mostly in non-food related sectors) (oakey, et al., 1988; rothwell and zegveld, 1982; rothwell, 1994). recently, however, changes in food demand have introduced new requirements for the food industry, hence generating the need for new technological inputs and a growing interest in the promotion of innovation in the food sector as well (traill et al., 2002). the technological needs of the food sector are increasing as a result of the introduction of new technology as a means of increasing food safety and quality (traill et al., 2002). in particular, the food industry has increased the use of technological inputs to meet emergent economic social requirements (baregheh et al., 2012) and to keep pace with the globalisation of demand in the food market (grunert et al., 1997). this scenario not only boosts the role of innovation in improving competiveness, but also explains the growing interest shown by scholars in analysing innovation behaviour in the food sector (traill et al., 2002). hence in order to become competitive it is necessary for smes to develop the capacity to innovate, that must be maintained in the future, along the whole process of innovation (gellynck et al., 2007). the literature reveals that several topics related to innovation have already been thoroughly explored, notably with regard to both large firms and smes, but mostly without focusing specifically on the food sector. a review of the literature suggests that success in innovation lies in the understanding of the local contest (ebbekink et al., 2012; van der borgh et al., 2012) and is determined by factor combinations in connection with several firm characteristics, such as type of sector, country etc. in particular, in the food sector, the literature points to the importance of networking for food smes and how firm size influences innovation behaviour (colurcio et al., 2012; s o’reilly et al., 2003; olsen et al., 2012; bhaskaran, 2013; minarelli et al., 2014). innovation is defined in the literature as the process of bringing new ideas to the market. it starts with the strategic goals, then develops through product development, process development, marketing development and organisational development, or combinations of them (earle m.d., 1997). prior research has highlighted that in order to identify the determinants of innovation it is necessary to distinguish types of innovation (knight, 1967; rowe and boise, 1974; downs and mohr, 1976). joseph schumpter is often recognised as the first economist to focus on the importance of innovation for industries (rogers, 1997). he defined five types of innovation, namely: the introduction of a new good or a change in the quality of an existent product; the introduction of a new production process; the introduction of product into a new market; the acquisition of a new source of supply of raw materials; and the implementation of a new industrial organisation. thereafter, the oecd’s oslo manual (oecd, 1997 2nd edition) concentrated on the first two types due to the greater ease in defining and measuring: technological product innovations and technological process innovations. the connection between product and process innovation is also stressed in the literature in terms of existing relationships, due to the fact that a linkage exists between technical products and the processes implemented to generate these products. in fact, among 35innovation in european food smes: determinants and links between types researchers, changes in the product systems have significant consequences for a firm´s manufacturing system and for technical and administrative processes and the dynamics involved in their adoption at the industrial, environmental and organisational level are different. (abernathy and utterback, 1978; daft, 1978; tornatzky and fleischer, 1990; jansen et al., 2006; kimberly and evanisko, 1981; light, 1998). moreover, as several academics suggest (utterback and abernathy, 1975; hayes and wheelwright 1979 a, 1979 b; kim et al. 1992), changes in the product system also have significant impacts on a firm’s business model. once the firm introduces a new product, a change must be considered not only in the technical processes, but also in the administrative ones. to the best knowledge of the authors, there is a lack of studies available addressing the topic of the determinants of different types of innovation pursued by food smes. in many cases the literature tends to focus both on process and product innovation (triguero et al. 2013, capitanio et al. 2010, avermaete et al. 2004, de jong and vermeulen, 2006) without analysing them separately and without considering possible interactions/links between them. moreover, other types of innovation, such as market and business organisation innovation, are often neglected. given these premises, the objective of this study is twofold: • first, to investigate the link between different types of innovation (in product, process, market and business models) introduced by food smes; and • secondly, to identify the specific determinants of each type of innovation (in product, process, market and business models) by food smes. the analysis is carried out based on information collected through a survey of 381 food smes located in 6 european countries. survey data are analysed through non-parametric techniques, (the classification tree analysis), to establish which firm characteristics affect the innovation realised (distinguishing product, process, market and business model innovation) over the years 2011-2013 and the existence of relationships between the different types of innovations. in light of the limited amount of studies available related to the food sector and the increasing demand for research on this topic, this study provides mainly an empirical contribution to the description and understanding of innovation in food smes and similar low tech sectors. moreover, to the best knowledge of the authors, the classification tree analysis has never been used before in this field. hence, we also provide insights into the potential and limitations of this methodological approach. the remainder of the study is organised as follows: chapter 2 describes the methodology, chapter 3 illustrates the results, and a discussion and conclusions are provided in chapters 4 and 5. 2. methodology 2.1 overview the paper attempts to test for the existence of relationships between innovation types and explores the determinants of single types of innovations in a sample of european food smes. accordingly, the analysis includes two separate steps. first, a classification tree 36 f. minarelli, m. raggi, d. viaggi analysis was performed making use of the four innovation variables: product, process, market and business model innovation. second, the classification tree analysis was computed individually for each innovation type, entering the following potential explanatory variables: innovation strategy, firm size, and collaboration typology. information was collected through a web survey carried out in 6 eu countries in 2012-2013. in the following we discuss the data collection process and the methodology used to identify significant determinants. 2.2 data collection the present work makes use of data collected through a web-survey developed for the european commission-funded project netgrow (www.netwgrow.eu). the survey respondents represented food and drink smes in six eu countries (belgium, france, sweden, ireland, italy and hungary). the sme definition adopted is the one provided by the european commission (2009), namely ‘firms with less than 250 employees’. in particular, the class micro-enterprises identifies firms that have up to 10 employee, the class small-enterprises those that have up to 50 employees and medium-enterprises up to 250 employees. the respondents were identified through national databases, where available. generally, sme owners or managers were the target interviewees for each company. the contact details of the firms were not readily available from databases in all of the countries. accordingly, in those countries where contact details were difficult to locate, some missing contacts were collected by way of internet searches or telephone calls. in addition, in several cases, telephone calls were needed to obtain the contact details of a suitable respondent within the firm. depending on the country, respondents were approached directly by e-mail or through an initial telephone call, followed by an e-mail. the e-mail provided the web-link to the survey and a personalised cover letter explaining the project. additionally, reminders to some respondents were sent via e-mail. the data were collected between october 2012 and april 2013 using an online questionnaire. five hundred and sixty-two (562) smes completed the questionnaire during this period. around 36% were from france, 18% from belgium, 13% from italy, 12% from sweden, 10% from hungary and 10% from ireland. not all of the smes that initiated the questionnaire process actually completed it. the percentages of smes having completed the survey in each country are as follows: 78% in belgium, 76.5% in hungary, 72% in sweden, 70% in ireland, 65% in italy and 54% in france. finally, 381 of the questionnaires were usable for data analysis. table 1 provides the percentage distribution of smes participating in the survey in europe. in the questionnaire, each of the four variables capturing product, process, market and business model innovation was re-coded from a likert scale, into a dummy variable for analysis purposes (0= no innovation, 1= 1, 2, 3 to 5, 6 or more innovations in the last two years, 999= missing values). the definition of innovation provided in the questionnaire was stated as follows: • innovation in products: new products or services; • innovation in processes: new processes; 37innovation in european food smes: determinants and links between types • innovation in new markets: new types of customers or new geographical markets; • innovation in business models: new business model or management tools. it should be noted that the concept of innovation in the web-survey is rather broad, and strongly related to what is perceived as ‘new’ by the firm. this approach is not suitable for some types of analyses. for example, it is not suitable for studies seeking to assess the spread of new ideas or evaluate the impact of innovation. however, the approach can be useful to analyse factors that influence a firm’s propensity to innovate and hence was adopted for the aim of this study. the variables entered in the classification tree analysis to identify the determinants of each innovation type are: innovation strategy, firm size and collaboration typology. this study distinguishes innovation strategy, according to miles and snow (1978), based on the type of innovation strategy adopted, namely: prospectors, analyzers and defenders. while a prospector type company seeks opportunities and responds rapidly to changes in the external environment, analysers focus on efficient and full analyses of directional strategies and how best to compete. defenders, for their part, will focus on maintaining existing markets and competing on price and quality rather than being at the forefront of innovation (laforet, 2008). there are different outcomes in terms of innovativeness. in fact, prospectors seek to exploit new products and new markets, whereas defenders try to protect their existing market. for their part, analyzers combine the two behaviours by quickly following prospectors into new products and markets while at the same time protecting their niche market of products and customers. the reason for the adoption of these categories as explanatory variables in this paper is their prominence in the literature with regard to explaining innovation behaviour. in the present analysis innovation strategy is entered as a multinomial variable consisting of three categories: prospector, analyzer or defender. many scholars have investigated the issue of innovation relating to firm size, without conclusive results (cohen and mowery, 1987; amato et al., 1981) or highlighted the variation of the effect of size depending on the sector (acs and audretsch,1987; acs and audretsch, 1991; cohen and klepper, 1996). for example, large firms in low-tech industries have an advantage over small firms, but no difference exists in high-tech industries (acs and audretsch, 1991). recent findings have also reported that firm size has an impact on innovation type in lowtech industries (wagner and hansen, 2005; karantininis, 2010 ). in many cases, the literature reports different results because the existence of a table 1. distribution of smes in european countries. size country belgium france hungary ireland italy sweden micro-sized 31% 31% 45% 25% 40% 58% small-sized 49% 40% 40% 30% 44% 38% medium-sized 20% 29% 15% 45% 17% 4% total per country 100% 100% 100% 100% 100% 100% 38 f. minarelli, m. raggi, d. viaggi relationship between size and innovation depends on a combination of factors or typologies of innovation. for example, maietta (2014) found that very small size significantly influences innovation in products but not innovation in process. moreover, different outcomes in the literature in terms of existing relations between size and innovation are due to the use of different definitions for firm categories in the sample or to the different target populations. the inclusion of ‘collaboration typology’ as a determinant is motivated by several studies. collaboration between chain network members is considered as an important factor for enhancing the innovation competence (gellynck and kühne, 2010). also, the existence of a significant interaction between the type of innovation introduced and the type of actors present in the network in which the firm is involved is well recognised among scholars. in fact, gemunden et al. (1996) demonstrate that horizontal collaboration, namely collaboration between smes and their suppliers and customers, prompts product innovation in the high-tech sector. they also show that collaboration with consultants and universities fosters process innovation. a summary of dependent and explanatory variables, as well as their coding and frequency of answers in the sample is provided in table 2. in table 3 frequency of collaboration, expressed as a percentage, with suppliers, customers, competitors, and public and private research institutions per country are reported. due to the structure of the survey, in which information on collaboration between firms and actors are aggregated for resource type, it is not possible to express firms’ engagement in collaboration as a percentage. collaboration with suppliers is the highest percentage in all countries, followed by collaboration with customers. differences in behaviour from different countries sampled are more evident for horizontal collaboration (competitors) and research. 2.3 methodology various studies have investigated determinants of innovation through parametric models (triguero et al.2013; bhattachary and bloch, 2002). such approaches are suitable only if the relationships between explanatory and dependent variables follow the imposed functional form. however, data frequently do not match with underlying distributional hypotheses. in these cases, a non-parametric model can be more suitable than a parametric one for the identification of significant relationships and for explaining response variables. in this paper, due to the features of the dataset, the understanding of determinants is carried out by means of a non-parametric technique. in particular, a classification tree analysis is applied, using the chaid algorithm (chi-squared automatic interaction detector) (kass 1980). this technique has been used in different contexts, with only few examples related to the agri-food sector, either focusing on firm decisions (viaggi et al., 2011) or consumer behaviour (bozkir and sezer, 2012). the aim of the classification tree procedure is to divide the population into subgroups based on the best predictor of the dependent variable. the best split is determined by checking whether there is any statistically significant difference, by computing 39innovation in european food smes: determinants and links between types the chi-square test, between respondent variable categories and the independent variable. the first split cuts where the stronger association occurs i.e. on the variable that shows the lowest p-value. once the first level of the tree is concluded, the procedure starts again table 2. list of variables used in the classification tree analysis. code coding frequency (%) variable description type of variable missing value (%) product innovation 1= 1 or more innovation 0= no innovation 80 20 number of new products realised in the last 2 years dummy 7.6 process innovation 1= 1 or more innovation 0= no innovation 56 44 number of new processes realised in the last 2 years dummy 14.7 market innovation 1= 1 or more innovation 0= no innovation 70 30 number of new markets realised in the last 2 years dummy 13.6 business model innovation 1= 1 or more innovation 0= no innovation 38 62 number of new business models realised in the last 2 years dummy 22.6 innovation strategy prospector analyzer defender 28 18 54 prospector: first to market analyser: seldom first to market but fast follower defender: focus on niche market multi nomial 1 firm’s size micro small medium 37 41 22 micro (less than 10 employees), small (10 to 50 employees), medium (50 to 250 employees) multi nomial 0 vertical collaboration 1= at least one collaboration 0= no collaboration 85 15 collaboration with suppliers and clients dummy 11.8 horizontal collaboration 1= at least one collaboration 0= no collaboration 74 26 collaboration with competitors and research institutions dummy 17.6 table 3. collaboration with different actors per country. country actor suppliers customers competitors universities & research institutions private research organisations total belgium (%) 36 25 12 20 7 100% france (%) 36 26 7 18 13 100% hungary (%) 32 26 19 17 6 100% ireland (%) 29 23 8 23 17 100% italy (%) 31 28 12 16 13 100% sweden (%) 37 28 17 11 7 100% 40 f. minarelli, m. raggi, d. viaggi by attempting to split each of these groups into smaller sub-groups until associations are statistically significant. the chaid method tends to be more flexible than conventional statistical models and accordingly is more suitable for the analysis of the surveyed sample. this technique makes it possible to recognise the main characteristics explaining the variations with respect to the target variable, in this contest expressed by the four types of innovation implemented in the last two years by smes: product innovation, process innovation, market innovation and business model innovation. first, the four innovation types were used as an input in the decision tree analysis to highlight the existence of some degree of interaction among them. innovation in products was used as the dependent variable in this case. innovation in process, markets and business models was used as independent variables. this choice is motivated by the prominent role of product innovation in the literature and also in the answers to the survey (it is the most frequent type of innovation in the sample). moreover, the literature highlights a strong relationship between innovation in product and process; in addition, some studies suggest that when innovation in product occurs, changes in process and administrative organisation can also be expected (utterback and abernathy 1975; hayes and wheelwright 1979 a, 1979 b; kim et al. 1992). classification trees were also computed using each innovation as a dependent variable in order to investigate innovation determinants. the variables considered as determinants are all derived from the questionnaire and are described in table 1. the analysis was performed by means of ibm spss statistics 21. 3. results the first aim of this study is to investigate the link between different types of innovation. in this regard, of the 381 european smes surveyed, 282 declared to have innovated in products, 70 declared not to have innovated in products and 29 smes have not provided any answers. 228 smes have innovated in markets, 184 in processes and 110 in business models in the last two years. conversely, 101 smes have not produced any innovation in market, 141 smes have not produced any innovation in process and 185 smes have declared no innovation in business models. these data are expressed also in percentage in table 4. as it can be notice in table 4, the majority of smes innovate in products, table 4. frequency of sme innovation. number of respondents frequency smes with innovation frequency smes with no innovation % smes with innovation % smes with no innovation in product 352 282 70 80 20 in process 325 184 141 56 44 in markets 329 228 101 70 30 in business models 295 110 185 37 63 41innovation in european food smes: determinants and links between types then market and process, conversely, only 37% reported to have introduced innovations in business models. the classification tree in figure 1 reports the association identified between the four types of innovations determined by computing the chi-square test. the results highlight a relationship between the four types of innovation. the main association identified is the one represented by the linkage between innovation in product and innovation in market. in fact, the latter variable represents the first figure 1. relationship between the four types of innovation. 42 f. minarelli, m. raggi, d. viaggi category where the split is made. two groups are created: smes that introduced innovations in markets and smes that did not introduce innovations in markets. as can be seen in the first group, 91% of firms also introduced a product innovation. this group is further split into two groups based on innovation in process, where there is a higher percentage of smes innovating in products among those innovating in processes (93%), with respect to smes that do not innovate in processes, among which innovation in products falls to around 84%. at the level of the second group originated from the first split second branch, smes that do not introduce any market innovations are also split based on innovations in processes. two groups are identified: smes that innovate in processes and those that do not. it should be noted that 86% of the smes that innovate in processes (without have innovations in markets) introduced an innovation in products. on the contrary, only 37% of those smes that do not innovate in either processes or markets had introduced a new product into the market. the final split occurs only for this last group and it is characterised by innovation in business models. the division is once again between smes that innovate and those that do not innovate in business models. eighty per cent (80%) of smes introducing an innovation in their business models also introduced new products, while this figure is only 34% for the smes that did not innovate in business models. the main outcome from this analysis is that the introduction of the different types of innovation occurs simultaneously in the majority of the cases, the introduction of a new product type implies the adoption of new business models or new markets or new processes. the second objective of this study is to identify determinants of each type of innovation. the analysis through chaid demonstrates that innovation in products is primarily explained by the innovation strategy (figure 2). smes that innovate in products are highly differentiated between two groups based on the innovation strategy in place: prospectors and defenders/analyzers. the first branch (full sample) of the tree reports that 80% of smes introduced innovations in products. the percentage is higher (94%) for prospectors, but falls to less than 75% for the other innovation strategies. the second significant variable is the vertical collaboration that splits the analyzer and defender into two groups based on the fact that smes either engage or not in vertical collaboration. it can be noted that 81% of smes involved in collaboration with suppliers and customers introduced innovation in products in the last two years (20112013). this amount is considerably higher with respect to smes that did not have any collaboration, i.e., 51% of whom innovate in products. the group of smes engaged in collaboration is then split further based on a third significant variable, namely firm size. small and medium-sized firms introduced more innovations in products compared with the micro-sized firms. in the classification tree analysis computed for innovations in processes, the main determinant of innovation is once again the innovation strategy (figure 3.). this distinguishes two groups: one with a lower percentage of innovation in processes (48%) and another with a higher percentage of innovation in processes (79%). the first group is characterised by analyzers and defenders and the second group by prospectors. the first of these two groups is further split into two groups based on horizontal collaboration. around 61% of smes having horizontal collaboration with competitors and 43innovation in european food smes: determinants and links between types research institutions innovate in processes, while this figure falls to less than 30% for those not involved in horizontal collaboration. in the classification tree analysis computed for innovation in markets, the main determinant of innovation is once again the innovation strategy (figure 4). once again, two groups are identified, but with a different combination compared with the types of innovations discussed previously: prospectors/analyzers are merged together and, in this group, almost 80% of smes introduce innovation in markets. on the contrary, only 62% of defenders introduced innovation in markets and this group is furfigure 2. determinants of innovation in products. 44 f. minarelli, m. raggi, d. viaggi ther split into two groups based on the existence of collaboration. the first group collects smes with horizontal collaboration; among these, 73% introduce market innovations. the second group includes smes that do not collaborate and a minority of smes that innovate in markets (only 46%). distinctive behaviour is observed for the innovation in business models. this is the only respondent variable that is not affected by innovation strategy. on the contrary, the classification tree analysis of innovation in business models (figure 5) shows horizontal collaboration as the main significant determinant. the percentage of smes in the sample that introduced innovation in business models is about 37%, in contrast to the much higher share of smes innovating in the previous innovation types. here, the first split divides smes into two groups: those that engaged in collaboration with competitors and suppliers and those that did not. within the first group, 42% of smes introduced innovations in business models, whereas only 20% of those not having any collaboration have introduced innovation in business models. the figure 3. determinants of innovation in processes. 45innovation in european food smes: determinants and links between types last split is made within this last category, introducing firm size as a second determinant of innovation in business models. firm size can influence the introduction of new business models in firms that do not engage in horizontal collaboration. specifically, two groups are created from the size category, micro/small firms and medium firms. by comparing these two groups it is noted that around 66% of medium-sized firms introduced innovations in business models, versus 15% of micro and small firms. a summary of the findings illustrated above is provided in table 5. we found that there are important differences and similarities in the characteristics of the firms that adopt the four types of innovation. first, the connection between smes that seek to enhance innovativeness and firm innovation strategy is emphasised. basically, the results highlight that the group with the greatest number amount of firms willing to improve their innovativeness are those adopting the prospector strategy (figures 2, 3 and 4). prospector smes seek to enhance their innovativeness in all innovation types, with the exception of the adoption of new business models. figure 4. determinants of innovation in markets. 46 f. minarelli, m. raggi, d. viaggi second, the collaboration with other actors in the food chain, such as competitors, suppliers and customers represents a common determinant of all smes that innovate. in particular, as also noted in the literature, it is not only the existence of relationships between firms that fosters their innovativeness, but also the type of actor participating in the collaboration that influences the firm’s innovation objective. in fact, horizontal collaboration seems to have a significant influence on the achievement of innovation in processes, markets and business models. conversely, smes that seek to achieve innovation in products are mostly focused on vertical collaboration. smes not at all engaged in collaboration tend to pursue innovation to a lesser extent. this feature is common to all types of innovation, but mostly stressed in process and market innovation. in fact, the lack of horizontal collaboration significantly reduces innovation output. finally, size only impacts those smes that innovate in products and business models (figures 2 and 5). figure 5. determinants of innovation in business models. 47innovation in european food smes: determinants and links between types table 5. determinant comparison for types of innovation determinants innovation in products innovation in processes innovation in markets innovation in business model size micro + no sign. no sign. small ++ no sign. no sign. medium ++ no sign. no sign. ++ innovation strategy prospector ++ ++ ++ no sign. analyzer + ++ no sign. defender + + no sign. collaboration: horizontal 0 no sign. 1 no sign. ++ ++ + (-) collaboration: vertical 0 + no sign. no sign. no sign. 1 ++ no sign. no sign. no sign. source: own elaboration ++ positive strong association: the percentage of independent variables is above the percentage of the respondent variable which is above 50%. + positive association: the percentage of independent variables is above 50% but below the percentage of the respondent variable. negative association: the percentage of independent variables is below 50% and below the percentage of the respondent variable. + (-) the percentage of independent variables is above the percentage of the respondent variable, but below 50%. 4. discussion of the 381 european smes surveyed, 285 indicated having undertaken innovation in products, 228 in markets, 184 in processes and 110 in business models in the last two years. this result is in contrast with findings from the literature in which the most common type of innovation pursued in the food sector is innovation in processes (triguero et al., 2013; alfranca et al., 2002; capitanio et al., 2010; galizzi et al., 1996; grunter et al., 1997). a result of this kind can usually be explained by the tendency for firms in the food sector to engage in incremental rather than radical innovation, notably due to conservative consumer preferences with regard to food. the difference between the literature and the results from our sample, in which the majority of smes declared to have introduced new products in the years 2011-2013, may be due to a degree of bias in the sample because of the self-selection process in the sampling modalities (no stratification was imposed and available databases may under-represent micro-firms) and the questionnaire approach , in particular the request for respondents to access a web-survey. the result may be a sample biased towards more marketing and communication-oriented firms, most likely also focusing on product and market innovations. this represents a difficulty often observed in studies involving surveys. another underlying issue, highlighted in the extensive work of the oslo manual, is that types of innovation are difficult to define (rogers, 1998). there may well be different interpretations of what qualifies as an innovation and indeed there is a high degree of subjectivity in distinguishing types. findings from academic studies (simonetti et al. 48 f. minarelli, m. raggi, d. viaggi 1994) have shown that a large majority, around 96%, of innovations can be classified as product or process innovations, hence falling into a so-called “grey zone”, depending on the type of definition adopted. only 4% of innovations can be unequivocally classified as product or process. this result implies that the distinction between types of innovations is not straightforward and that it depends on the perception of innovation. this may have affected the answers of the participants in this study, and, in particular, the multiplicity of innovations declared by several firms. we did not, however, find any clear and explicit distortion or difficulty due to definition issues, likely in part because the distinction between innovation types, though difficult to define, is now well established among practitioners. yet it should be noted that differing perceptions, or indeed definitions, of innovation among innovating actors can lead to a degree of confusion or misunderstanding among academics and operators. the main finding of this study is that, in the majority of firms, there exists a coexistence between types of innovations. the decision tree (figure 1) illustrates this concept by reporting which type of innovations are associated and the level of importance. the decision tree makes it possible to recognise the degree of intensity of this association, which can be identified through the level of the tree branch. the level of importance of associations between innovations in products and markets is stronger than the innovations in processes, which is displayed at the third level of the decision tree, and business models which are found at the fourth level of the tree. this is not in line with findings from the literature in other industrial sectors where the linkage between product and process innovation is more important (utterback and abernathy 1975; hayes and wheelwright 1979 a, 1979 b; kim et al. 1992). at the same time, however, by focusing on individual analyses carried out on determinants of innovation type, our findings show that each type of innovation has different determinants. this is consistent with the literature, whereby scholars distinguish innovation types on the basis of differing processing generations (utterback and abernathy, 1978; daft, 1978; tornatzky and fleischer, 1990). prospectors are those that mostly innovate with respect to all of the three types of innovations: product, process and market innovations. defender strategies are mainly adopted by smes that pursue innovation in markets in order to increase their competitiveness by placing their products in new geographical areas or by addressing new potential customers. the literature stresses that the likelihood of a firm engaging in collaboration is influenced by the innovation objective (e.g. product, process, and market innovations) (gooroochurn et al., 2007). findings from this study demonstrate that through their collaboration with competitors and research institutions smes are encouraged to undertake market, process and business model innovations. conversely, synergy with suppliers and customers tends to support product innovation. these results are in line with findings in the literature that underscore that collaboration between customers and suppliers increases product innovation, not only in the food sector, but also in the high-tech industry (gemunden et al.,1996). finally, innovation in business models shows a distinctive behaviour compared with other innovation types. different dynamics are involved in the innovation process with 49innovation in european food smes: determinants and links between types respect to business models that are not dependent on the type of innovation strategy adopted by the company, but have more to do with horizontal collaboration, size of the firm and business models adopted by other firms within their networks. in fact, innovation in business models is a type of innovation that more likely can be shared with competitors and is influenced by the size of the company in the majority of medium-sized firms that introduce innovations of this type. finally, some weaknesses can be identified in the use of high aggregated categories in the questionnaire; in particular, the questionnaire considered collaboration between firms and competitors, and research institutions as a whole. yet, other sector findings show that innovation in processes requires intensive collaboration between universities and consultants (swann, 2002). in the same line, a more detailed specification of different types of collaboration would be needed for a better explanation of these connection with innovation. the methodology used in this paper proved to be very practical for explorative purposes, thanks to the fact that it can be applied without pre-defined assumptions about the functional form of relationships among variables. however, this also has limitations in terms of consistency with theory and understanding/interpretation of the direction of causality. for example, the association between different types of innovation may derive from very different stories, and possibly with a different sequence of innovations. likewise, a firm’s collaboration can be either a determinant of, or an action purposely oriented to support, an innovation the determinants of which are to be found elsewhere. 5. conclusion the analysis carried out suggests, as a main finding, the existence of a tight relationship between different types of innovation in the food sector. the study also highlights a relationship between types of collaboration and types of innovation. the practical contribution of this study is to provide information on which type of factors are more likely to affect innovation with regard to the innovation objective, and in particular which factors should be fostered in relation to the type of innovation that smes want to pursue. first, even if a linkage exists between innovation types, different factors must be targeted in relation to the type of innovation that the firm or the policy-maker seeks to enhance. second, policies should explicitly take into account the interconnection between different types of innovation. third, different types of collaboration and network types would need to be tailored to the type of innovation sought. the latter point also highlights the relevance of further connecting smes with research institutions and activities specifically aimed at supporting sme involvement in the innovation process, which represents the core of european policies for the enhancement of competitiveness. given the limitations of the methodology, further research could be carried out by using other statistical techniques on the same sample to better connect empirical findings with theoretical insights. in addition, potential for further studies is highlighted by the weaknesses discussed in the previous section as well as by its empirical insights, including the limited number of observations. the distinction between different types of innovation may be an important issue, yet greater priority should perhaps be placed on clarifying their connections. furthermore, the issue of collaboration as a determinant of innovation has been treated in a 50 f. minarelli, m. raggi, d. viaggi simplified manner here, but has proved to be very important. consequently, further studies focused on disentangling in greater detail the effect of different forms of collaboration should be considered, in particular sme collaboration with universities and other similar stakeholders. this work also suggests that further studies should seek to better understand innovation-related interactions where innovation types prioritised by firms can also change in relation to either different stages of a firm’s life cycle and/or the product life cycle in food companies. acknowledgments we acknowledge funding from the european commission, 7th framework programme through the project netgrow (enhancing the innovativeness of food smes through the management of strategic network behaviour and network learning performance, 7 fp(kbbe), project number 245301, www.netgrow.eu). this work does not necessarily reflect the views of the european union and 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(2005). innovation in large versus small companies: insights from the us wood products industry. management decision 43(6):837-850. bio-based and applied economics 5(1): 47-62, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-15379 the territorial biorefinery as a new business model ion lucian ceapraz, gaëlle kotbi, loïc sauvée picar-t research unit, institut polytechnique lasalle beauvais, 19 rue pierre waguet, 60000 beauvais, france date of submission: 2015 25th, january; accepted 2016 13th, april abstract. the transition toward more sustainable industries opens the way for alternative solutions based upon new economic models using agricultural inputs or biomass to substitute oil-based inputs. in this context different generations of biorefinery complexes are evolving rapidly and highlight the numerous possibilities for the organization of processing activities, from supply to final markets. the evolution of these biorefineries has followed two main business models, the port biorefinery, based on the import of raw materials, and the territorial biorefinery, based on strong relationships with local (or regional) supply bases. in this article we focus on the concept of the ‘territorial biorefinery’, seen as a new business model. we develop the idea of a link between the biorefinery and its territory through several relevant theoretical approaches and demonstrate that the definition of ‘territorial biorefinery’ does not achieve, from these theoretical backgrounds, a consensus. more importantly, we emphasise that the theoretical assumptions underlying the different definitions used should be made explicit in order to facilitate the manner in which practioners study, develop and set up businesses of this kind. keywords. territorial biorefinery, innovation, business model, industrial and territorial ecology jel codes. o33, q16, r11 1. introduction and objectives in the context of the energetic transition and the emergence of a new bioeconomy, the issue of defining innovative business models to support this fundamental change is crucial for policy makers and researchers alike. considering this policy background, the objective of this article is to identify the relevant theoretical contributions to the understanding of the territorial biorefinery as a new business model. underlying this objective is the importance of developing innovative research capable of providing insights and recommendations at the policy level. first, we empirically characterise the concept of ‘territorial biorefinery’ as a new means of biomass development based on the “doubly green” chemistry (in the sense of * corresponding author: lucian.ceapraz@lasalle-beauvais.fr 48 i.l. ceapraz, g. kotbi, l. sauvée nieddu, 2010; octave and thomas, 2009) and the principles of territorial and industrial ecology applied to this industry. second, we identify a theoretical corpus proposed for the understanding of this field. the corpus of the socio-economics of proximity (boubaolga and zimmermann, 2004; torre and filippi, 2005) and its developments for agricultural and food sectors (requier-desjardins, 2003) make it possible to identify the different approaches of the territory. third, we highlight the definition of the territory, not as a passive registration of economic activity, but rather as an endogenous variable resulting from a socio-economic process of building territorialized assets. understanding biorefinery as a new concept assumes therefore that we should consider all the dimensions of its roots. from these preliminary remarks we distinguish two possible theoretical frameworks. the first focuses on the various forms that biorefineries can take in a given territory (second section), from the passive biorefinery to the socially constructed biorefinery. the second framework immediately places the territorial biorefinery as a source of profound rupture and originality (third section). the biorefinery is thus no longer only a concept to be understood, but also an object to be invented and built as the conditions of its appearance and development are not given a priori. in section 5 we provide a synthesis of the approach toward developing the territorial biorefinery as a conceptual object. in section 6 we provide concluding comments regarding the interests and limitations of the article. 2. biorefinery, plant refinery, territorial biorefinery: what empirical definitions? 2.1 definitions according to naik et al. (2010), “the term ‘biorefinery’ was initially established by nrel1 (1990) or the utilization of biomass for production of fuels and other bioproducts”. the technological objective of biorefineries is to split biomass and recover the essential components, namely carbohydrates, proteins and fats. these raw materials are then processed and transformed, by way of various technologies, into different final products. as wagemann et al. (2012) outlines, “a biorefinery is characterized by an explicitly integrative, multifunctional overall concept that uses biomass as a diverse source of raw materials for the sustainable generation of a spectrum of different intermediates and products (chemicals, materials, bioenergy/biofuels), allowing the fullest possible use of all raw materials components”. an initial definition proposed by the international energy agency (iea) in its bioenergy task 42 describes biorefinery as “… the sustainable processing of biomass into a spectrum of marketable products and energy”2. according to cherubini (2010), “a biorefinery is a facility (or network of facilities) that integrates biomass conversion processes and equipment to produce transportation biofuels, power, and chemicals from biomass” the territorial biorefinery (hereafter, tb), as a new concept, is put at the crossroads of several theoretical approaches. before evaluating the concept in terms of existing theories, 1 national renewable energy laboratory “located in golden, colorado, is the united states’ primary laboratory for renewable energy and energy efficiency research and development” (wikipedia, 2015). 2 http://www.iea-bioenergy.task42-biorefineries.com/en/ieabiorefinery.htm. 49the territorial biorefinery as a new business model it is necessary to clarify the framework. we initially provide an empirical definition of the object of “biorefinery” followed by the definition of the “territorial biorefinery”. in 2011 the iar3 competitiveness cluster proposed the following definition: “a biorefinery is an industrial complex, located on the same site, which turns agricultural and forest biomass into a variety of bio-based products (food, feed, chemicals, biomolecules, agro-materials) and bioenergy (biofuels, electricity, heat) as part of a sustainable development strategy. so it is both the transformation of the vegetal plant by valorising all its components and the integration of the components of an industrial site to achieve an original “industrial metabolism” and an “industrial symbiosis” (beaurain and brullot, 2011). two large biorefinery models (europabio, 2011; european commission, 2012) have emerged: (i) a model of the ‘port-biorefinery’ which is strongly connected to global flows of raw materials, and the economic logic of which is based on threshold effects, specialisation, and economies of scale; and (ii) the ‘territorial biorefinery’, which is strongly connected to its surrounding territory and the economic logic of which is based on a more diverse and more thorough valuation of various biomasses of agricultural origin. these two types of biorefineries have developed a strong reputation in europe. the first model focuses on aggregated value chains based on low-cost imports of vegetable raw materials. it is logically located near major communication routes (ports, channels etc.) to achieve an agglomeration of resources (colletis et al., 1999) and economies of scale. the territorial biorefinery strongly integrates value chain actors according to logic of proximity (in the sense of the economics of proximity), resource requirements (colletis et al., 1999) and complementarities between actors. these second generation biorefineries are built on the synergies between public-private stakeholders (farmers, local professional communities, etc.), researchers and different communities that enable the transformation and the development of a territory. thus, local resources and territorial strategic assets interact in terms of localization and geographical proximity with the presence of local actors. 2.2 the territorial biorefinery approach: territorial engineering and the territorial project as ‘action tools’? the territorial biorefinery puts forward its distinctive features, notably geographical proximity, institutional proximity4 (linked to the existence of a “territorial project”5) and organizational proximity (multiple and multi-level interactions between local actors in an 3 in the french context, iar means “industries & agro-resources”; which is a competitive cluster of global importance (or ‘pôle de compétitivité’ (i.e. competitiveness cluster) that brings together large and small firms, research bodies and educational establishments, all working together in a specific region to develop synergies and cooperative efforts around a shared theme” (www.competitivite.gouv.fr). it has been launched in 2005. 4 “based on the adherence of actors to a common space of representations and rules of action directing collective behavior, this institutional proximity has more or less influence on the conformity of different modes of coordination between actors, and therefore on the emergence of patterns of localized productive coordination. “(colletis et al., 1999, pp. 27-28). 5 the territorial project design stage is crucial in the process of territorial development, as it broadens the scope of possible actions and the possibility for action of the actors” (lardon et al., 2005). the territorial engineering is seen as “the set of concepts, methods, tools and devices available to actors in the territories, to support the design, implementation and evaluation of regional projects”, (lardon et al., 2005). 50 i.l. ceapraz, g. kotbi, l. sauvée “eco-systemic logic” of industrial and territorial ecology). another distinctive feature of the tb is its relationships, which are developed within a given territory. indeed, territorial engineering6 could be applied to the biorefineries insofar as they all have the attributes of territorial projects. according to piveteau (2011), territorial engineering is synonymous with some forms of territorial organisation. there is a link with territorial projects characterised by hybrid forms of control (development councils and elected bodies) and an ascending construction which claims external support: technical and financial support from the state, regions and the european union. according to bayrand and sergeant (2007), the use of the territorial engineering concept is all the more necessary for the development of territories that involve the cooperation and consultation of local actors and territorial development actors. these actors employ complex procedures in relation to new territorial projects that may be located on territories that are increasingly competitive with each other. this concept “makes use of different tangible and intangible resources, which make up the territory to accompany the process of territorial development” (lenormand, 2011; see also lamara, 2009). regarding the actors, the concept of territorial engineering mobilizes “not only the local development actors, politicians, residents and local leaders, but all the players facing the challenges of territorial development” (lardon et al., 2005). to do so the emergence of a project on a territory (for example a biorefinery) implies the coordinated mobilization of various public and private engineering skills around territorial projects, which is a territorial intelligence. related to the territorial development of a biorefinery, territorial engineering can accomplish the mission to support “projects for the establishment or expansion of private companies” but also “interventions for the maintenance of jobs”. one can also add any “design approach and co-construction of a project to which the concerned community is associated without necessarily being main carrier of the project” (bayrand and sergeant, 2007). the development of this type of biorefinery is born from the logic of economic incentives as a result of the transition from a socio-technical system to another through the innovation and learning-by-doing of economic players at several geographic scales. these could be public-private partnerships following a ‘bottom-up logic involving local authorities and private actors with democratic legitimacy or ‘top-down’ policies, according to the economic and socio-political conditions at stake. 3. the territorial biorefinery: approaches by the conceptualization of the territorialization 3.1 overview the territorial rootedness of a biorefinery in a given territory can be approached initially from the role of the territory in the location of the economic activity. the contributions of the concept of proximity provide an expanded role to the territory, which acquires 6 territorial engineering (“ingéniérie territorale”) is seen as “the set of concepts, methods, tools and devices available to actors in the territories, to support the design, implementation and evaluation of regional projects”, (lardon et al., 2005). 51the territorial biorefinery as a new business model a status of an endogenous variable (camagni, 2002). institutional and competitive changes in the agro-industrial sector incite to shed light on the question of the role of territorial assets in building the competitive advantage of firms localized in situ. over the past 15 years the research on firm organisation and strategy was highly relevant to this question (bencharif and rastoin, 2007; brechet and saives, 2001; depret and hamdouch, 2007) and has led to several approaches concerning the spatialization of productive activities. 3.2 the territory as a passive registration of agribusiness and agricultural activity scientific research approaches dealing with space and territory in business strategy are not uniform. lauriol et al. (2008) distinguish two major trends. the first stream is interested in the spatial dimension of strategies. strongly influenced by the work of economists, this stream of thought mainly deals with the role of productive activities according to the characteristics and attributes of a given territory. since these attributes are not mobile, firms define their spatial location based on real or perceived territorial benefits, resulting in a certain spatial localization of firms. space is seen in this work as a largely external dimension to the firm, the choice of which is guided by an optimal choice of spatial localization given the strategic choices of the biorefinery system. by this we mean that location decisions should be considered as strategic and “immobilizing a large amount of resources and involving an important group of industrial actors” (serrano et al., 2015). the localization choice could have a significant importance when referring to environmental footprint and when taking into account “transportation and logistics activities because of the supply chain procurement” (serrano et al., 2015). the approach concerning the optimal location of a facility (in this case a biorefinery) is related to location science or facility location which is a field addressed by operations research (op) (melo et al., 2009). according to melo et al. (2009), “the facility location decisions play a critical role in the strategic design of supply chain networks” and “the optimal location of a new facility is determined with respect to cost, profit, distance, service time, market coverage, or some other desired attribute” (bowling et al., 2011). the theoretical framework of facility location is derived from the area of industrial organisation and uses “specific geographic information in location-allocation problems” (tittmann, 2010). several examples can be mentioned when locating a biorefinery using a geographic resource estimation. authors like perlack et al. (2005), walsh et al. (2000), graham et al. (2000) have proposed a model of the optimal location of biorefineries through the use of “feedstock input based on the marginal cost of an energy crop feedstock delivered to the site” (tittmann, 2010). a second stream focuses on how firms are spatially distributed within a given industry. for lauriol et al. (2008), the logic of spatial activities and firms cannot be reduced merely to a firm’s individual choice of location. according to sierra (1997), a territory is not reducible to its spatial or localization dimension but is an entity that operates as a complex spatial organisation and as an economic, political and social mode of organisation between a set of economic agents anchored locally. indeed, there are many favourable effects (‘spillover effects’), for example related to knowledge, know-how etc., which lead to an aggregation process of activities or agglomerations. these activities may involve aggregations of firms in the same industry or different industries, but these companies are look52 i.l. ceapraz, g. kotbi, l. sauvée ing for positive network externalities that it is those of logistics, applied or basic research, services, etc. the logic of competitiveness clusters, or marshallian districts, are prominent examples. the competitive poles or clusters concept has been widely used in the academic literature when related to the localization of firms in a common geographical area. the concept has been widely popularized by michael e. porter in its seminal work “the competitive advantage of nations” (porter, 1998) where a cluster is seen as “a spatially concentrated group of firms competing in the same or related industries that are linked through vertical and horizontal relationships”. 3.3 the territory as an endogenous variable: the contribution of socio-economics yet a territory is also seen as a spatially built entity the constitution of which is based on the intentional combination of individual and/or collective actions, and the mobilization of specific resources (rallet and torre, 2005; torre and filippi, 2005; réquier-desjardins et al., 2003). one of the key concepts of these approaches is the notion of activation. activation is defined as the finalized interaction of an actor with a tangible or intangible resource (registered within a territory or mobile) (réquier-desjardins et al., 2003). the territory is then no longer a passive provider of resources, but rather a place of active construction on behalf of the economic and institutional actors (local authorities, for example). these actors intentionally participate through their interactions in building competitive advantages related to the territory. consequently, the dimension of intentionality of the actor acquires a particular resonance when addressed to the strategic approach linked to the territory. this conception of territory, as a built entity, broadens the scope of strategic issues faced by firms, such as how best to build and maintain territorialized assets over time, and how to better coordinate these resources at the local or regional level, including for firms operating in several countries, or at the global level. this dimension of coordination and asset control refers to the issue of governance and its relationship to the geographical space. 3.4 the governance of territorial resources governance, and more precisely the territorial governance, is strongly linked to the performance of clusters (de langen, 2004) and to the coordination of activities between local actors. two important attributes of clusters should be mentioned, namely the network attribute and the spatial attribute (or the territorial attribute) (berthinier-poncet, 2015). in the case of france, territorial governance is defined as “a complex institutional process combining cognitive and political dimensions, in which institutional proximity appears as a precondition of collective action and so organizational proximity at the micro-level of coordination” (carrincazeaux et al., 2008). questioning the role of territory in agribusiness activities within the new competitive and institutional context requires the consideration of a complementary perspective, namely that of governance (or more generally of the organisation) of strategic assets. as a corollary we issue the question of the articulation of two often disjointed concepts: the concept of the value chain and the territory concept seen as a basis for a strategic asset. the study of agro-industrial group strategy shows that this construction is contingent on searching for a competitive advantage (kotbi and sauvée, 2010) and the goal of competi53the territorial biorefinery as a new business model tive advantage varies greatly from one group to another (kotbi et al., 2011). in a context of the globalization of markets, the agribusiness enterprise considers the increasingly strategic assets in terms of a portfolio, where the vertical governance related to the territory is substituted by the global governance of the industrial group. this mode of governance of the territory is more horizontal and flexible, and cannot escape either the institutional and competitive environment of each region or the heavy constraints of the productive dimension typical to any agricultural activity. each agribusiness group (enterprise) helps define a unique combination of territorial assets, a territorial value chain, given its internal and external situation and its objectives for building a competitive advantage. the sources of competitiveness and/or attractiveness of regions reside mainly in the specific attributes or characteristics (colletis et al., 1999) largely specific to local conditions (such as adequate soil and climatic conditions, the density of producing farms, and logistical conditions, camagni, 2002). 3.5 the global value chain approach the approaches focused on the global value chain (hereafter gvc) provide a good starting point for understanding the global strategies of firms, articulating both an organisational and a spatial dimension. initiated in the early 1990s by the american sociologist gary gereffi (gereffi et al., 2001), these approaches have found practical application to agri-food sectors (bencharif and rastoin, 2007; ghersi and rastoin, 2010). for gereffi, the global value chain consists of four elements: the sequence of activities, the mobilized geographical space, the institutional environment and the governance structure. the approaches in terms of the global value chain (gvc) lead to the identification of typical configurations defined primarily by the characteristics of the modes of governance of these gvc: the market, the network, the captive network, and the hierarchy (gereffi et al., 2001). concerning the biorefinery and its market, there are new challenges with respect to the integration of its output into existing global value chains and in this respect can describe several classes of relationships (king et al., 2010): a) “bio-based products that directly replace molecules in existing value-chains”; and b) “bio-based products that are novel or that cannot easily be integrated into existing value chains”. in other words, this question puts forward the articulation between existing and new value chains and the possible flexibility between these chains. renewed by the works of dicken et al. (2004), coe et al. (2004, 2008), dicken starts from a critique of gereffi noting that the spatial dimension of gvc is treated in fairly abstract terms and is incomplete. the spatial scale the gvc approach is basically between a centre and a periphery that organises the international division of labour based on skills. on the contrary, for dicken the territory must be addressed in relationship with the gvc and its configuration of activities. the interface between global production networks (dicken et al., 2004) and the spatial level is validated by the so-called “strategic linkage”. this interface is strongly inserted in the institutional and competitive context locally and regionally. the quality of this coupling, including its ability to create and maintain a tension for the in-situ actors, explains the choice of spatial configurations of firms and their durability over time, hence their territorialisation. this concept, 54 i.l. ceapraz, g. kotbi, l. sauvée which is significant to dicken, is also found in the work of réquier-desjardins et al. (2003) on the location of agrifood activities and las7 (or ‘localized agrifood systems’). 4. the territorial biorefinery: approaches through the organisational and socio-technical break (transition) 4.1 the approach of industrial and territorial ecology the emergence of the territorial biorefinery can also be understood as a potentially sharp break (transition) with the existing model of traditional oil refinery. territorial and industrial ecology (hereafter tie) is based on four principles: localization, closing of flows, diversity and gradual evolution (beaurain and brullot, 2011). designed by engineers, and focusing on technology from the outset, the approach of industrial and territorial ecology emphasizes two radically opposed visions (beaurain and brullot, 2011). these authors point out that the first approach, developed by allenby (1992), is mostly positive, with a scientific principle of weak sustainability while the second approach, that of ehrenfeld (2004), is more social, with a normative principle of strong sustainability. while these approaches have in common a cyclical conception of how natural ecosystems function, the approach developed by allenby (1992) is positioned “in highly restrictive conditions of competition” (that of perfect competition) as highlighted by beaurain and brullot (2011: 317). ehrenfeld paves the way for the consideration of human factors and industrial actors, as is also the case for the authors beaurain and brullot and the economy of proximity. we have classified industrial ecology as an institutionalist approach of the economy and thus providing a richer approach to the process. thanks to this approach it is possible to consider the emergence of radically new economic systems in a much more integrative way (figuière and metereau, 2012a, 2012b). this approach takes into account all the activities and actors at all levels of the socio-economic system. in this way, the industrial and territorial ecology approach calls for a profound transformation of the organisation of the territory, from the point of view of its territorial metabolism (balance of flows of input and output materials and energy through the territory) and its relations with public and private actors. tie approach emphasizes the territorial governance practices presented in the previous section. the organisational and human dimension of industrial and territorial ecology is based on the study of current practices and the emergence of new practices such as: i) the exante, in terms of intentionality, coordination of actors, ii) the implementation of new governance modes based particularly on the effects of experience made possible by collective learning mechanisms, both technological and organisational, iii) the conception of a shared repository of values, and iv) the creation of organisational and institutional proximity in addition to the geographical proximity related to the territorialisation (beaurain and brullot, 2011). in terms of methods, the tie has its own territorial engineering, which includes all the resources used to design, plan, implement, monitor and evaluate the collective 7 in french literature, las is translated by the term syal (“systèmes agroalimentaires localisés”; réquierdesjardins, 2010)). 55the territorial biorefinery as a new business model schemes to identify and characterise the flows of energy and matter and its synergies (including the optimization, the description tools of the metabolism, the conception of an ecological or territorial footprint, the approaches of an environmental assessment, and several multi-criteria approaches of performance or risk evaluation, etc.). the theoretical contribution of tie is also based on the creation of new forms of territorial development. the idea here is to focus on the potential forms of territorial development induced (or made possible) by the implementation of industrial and territorial ecology approaches and question their potential for structuring or territorial planning and their sustainability criteria for integration conditions. through the study of two cases beaurain and brullot (2011) show that the tie “becomes a structural element of the strategy for the economic development of the territory”. in this sense the public and private actors are sharing a common goal to fight air pollution in the first case and economic decline in the second. according to all particular territorial specificities, tie can be seen as a consistent development strategy involving various environmental approaches, including the rebalancing between urban and industrial activities/or rural areas in order to organise economic clusters around local resources. 4.2 the biorefinery in the dynamics of socio-technical transition the socio-technical transition approach (geels, 2002),8 which encompasses the notions of technological niches9, socio-technical systems10 and the socio-technical environment11 distinguishes breakthrough innovation that occurs once these multilevel interactions between actors have been triggered. these sociotechnical niches can enable the development of production systems via a form of transition that disseminates innovation (lopolito et al., 2010). regarding the socio-technical regime, there is a multitude of institutional rules of the actors that allow us to understand the dynamics of innovation. the socio-technical system “is a grammar, that is to say, a set of rules defined for a set of products, qualifications and procedures [...] embedded in institutions and infrastructure” (kemp, 1994; geels, 2002, 2004, 2005; rip and kemp, 1998). the last element that characterises the socio-technical transition is indicated by the socio-technical environment which “represents the upper level and consists of institutions, social, political and cultural norms guiding the existing socio-technical system” (kemp, 1994; geels, 2002). according to coenen et al. (2013) the transition refers here to the changes between different socio-technical configurations that include not only new technologies but also the changes that occur in the markets and for the consumer and institutional actors (geels, et al., 2008). the interaction between the socio-technical transition and the geog8 for more details on the socio-technical transition approach, see geels (2002, 2004, 2005). 9 niches act as incubation rooms for radical innovations, nurturing their early development. niches may take the form of small market niches, with specific selection criteria that are different from the existing regime. these can be r&d projects, but also experimental projects, involving heterogeneous actors, e.g. users, producers, public authorities” (geels, 2002, 2004, 2005). 10 “societal functions are fulfilled by socio-technical systems, which consist of a cluster of aligned elements, e.g. artifacts, knowledge-user practices and markets, regulation, cultural significance, infrastructure, maintenance networks and supply networks” geels (2002, 2004, 2005). 11 “…the socio-technical landscape, which refers to aspects of the wider exogenous environment that affect sociotechnical development” geels (2002). 56 i.l. ceapraz, g. kotbi, l. sauvée raphy of innovation offers a new dimension for understanding the concept of transitional space. the analytical framework often presented simply for the trajectory of technological change, did not sufficiently take into account how this transition is “trapped” within a local area or region (mccauley and stephens, 2012; smith and olesen, 2010). the integration of space and geographical proximity was recently assessed by a number of authors (markard and truffer, 2008; coenen et al., 2013; spath and rohracher, 2010; truffer and coenen, 2012), who substituted the idea of understanding a “sustainable socio-technical transition” with the idea of a “regional transformation.” 5. what theoretical approaches for the territorial biorefinery: an attempt to synthesize the territorial biorefinery is fundamentally a specific mode of using biomass resources. the foundations of the territorial biorefinery, seen as a new business concept, are based, according to the desire of its designers, on the idea of a transition within the logic of production. it is part of a broad socio-technical transition, allowing for the passage from the petrochemical model to the model of renewable carbon molecules. we are in the presence of a new way of organising production and processing, affecting a multitude of value chains in the energy, material, chemical, and food sectors, etc. a second transition that is brought forward by the territorial biorefinery is the significant reduction in ghg12 of economic activities. with regard to the specific case of biorefinery, it is therefore important to introduce a new dimension into the economic calculation of costs. the costs are not added ex post, as in conventional approaches impact on ghg emission levels of various productive activities, but ex ante, in the design of chains value. a third break in the logic of production methods is based on the idea of a total valuation of the plant through its circularity of processes. in the conception of the territorial biorefinery, each component is considered from the standpoint of its productive purposes, but this logic goes further by establishing a principle of circularity in the transformation of the product, each sub-product being directly or indirectly reintroduced into the economic circuit. putting forward the conceptualization of territorial biorefinery therefore constitutes questioning the very object of its foundations: the theoretical foundations that govern its definition, the degree of departure from the existing model that this new valuation model assumes and the position of the researcher vis-à-vis this conceptual object. on this point we use the terminology of gavard-perret et al. (2012) which distinguishes between the “constructivism and methodological knowledge” to describe the relationship of the researcher to the object, and the “constructivism and object knowledge” to refer to the constructed nature of the studied object (gavard-perret et al., 2012, p.90). from an initial basic definition of the territorial biorefinery, we synthesised and identified two dimensions that seem essential for the approach of the territorial biorefinery as a conceptual object: the underlying theoretical approach and the situation of the researcher with regard to its object. we have seen that it seems possible to identify a first difference between the theories of territorial anchorage theories which place the territory as a major dimension in the definition of the territorial biorefinery as a concept, and also theories of disruption, plac12 greenhouse gases. 57the territorial biorefinery as a new business model ing the territorial biorefinery as one element in an overall transition from a petrochemical system to a renewable carbon-centred system (colonna, 2013). in terms of epistemological position, we join the approach proposed by david (2012) who emphasizes an original reading of the different research approaches that can overcome the traditional dichotomy between positivism and constructivism. on one level david distinguished at first the contribution of research to the construction of reality: it may have implications for the action with a construct of reality. instead, the research can be placed in a situation of intervention and transformation, more or less directly linked to this reality. thereafter, david questioned the degree of contextualization of research in a classic, inductive approach of the existing. yet the research approach can also place the concrete project or its idealized representation as a starting point for research, and consequently put itself in a situation of designing the organization of activities ex ante. this epistemological and methodological reflection seems particularly fruitful for us to question the concept of territorial biorefinery. indeed, beyond the diversity of theoretical approaches that can be mobilized to address the object of territorial biorefinery, two questions remain open: the epistemological presuppositions of the theoretical approaches and the researcher’s position relative to the concrete reality on the ground. according to the main theoretical approaches developed in this article, the definition of the territory and, more importantly, its role for the biorefinery, differs widely. it is possible to sketch, along a continuum, the situation of these theories. at one end of the continuum, the territory serves simply as an optimisation function for the costs. at the other end, the bt is seen as the active development of territorial assets and relationships by actors. in between, we find theories combining local conditions and a global (meaning geographically integrated) configuration of activities. considering the position of the researcher with regard to the object under examination, we find here the classical opposition between positivism and constructivism. indeed, we suggest on this point that the researcher should also make explicit his/her positioning: is the researcher a neutral observer of the reality, providing an in-house model of the optimisation of the territorial biorefinery? or is the researcher involved in one way or another in the changes that occur? we have seen that the bt as an ex ante designed business model introduces a new role for the researcher, being actively concerned by its object, as in the research-action models. 6. conclusion we have seen that the concept of territorial biorefinery can refer to different theoretical approaches that we have schematically grouped into two broad categories: approaches centred on territorial assets and the degree to which they are rooted in the local context, and approaches focused on the model of the territorial biorefinery, seen as a major sociotechnical transition. the demonstration of this diversity of theoretical approaches reflects a certain lack of consensus with regard to what actually constitutes a territorial biorefinery as a basis for a new business model. these divisions also reflect a diversity of epistemological issues, positivist, or constructivist, or of action research. we believe that it is useful, either from the point of view of research or for the practitioners involved in their development, to make them explicit and to identify how the coupling between theoretical and epistemological issues helps to define precisely what the territorial biorefinery should in fact be. 58 i.l. ceapraz, g. kotbi, l. sauvée to conclude, a key issue seems to crystallize the importance of the definition of the tb, namely its scale levels. the issue of territorial scales and related integration (in their economic, strategic, organisational and eco-systemic dimensions) characterises the tb as a concept and we have seen that it is not independent from the way the micro, meso and macro scales are operationalized by the various theoretical approaches. future research on this topic should focus on the active development of territorial assets and their activation by partners (institutions as well as companies), at these micro and meso levels as this is the main component of the specificity of the territory for a biorefinery that is anchored in its local supply base. similarly, the dynamic aspects, i.e. the capacity of a given set of actors in a territory to learn and improve themselves in the long run and to create ultimately a competitive and sustainable business model is also an important field of investigation. eventually the territorial biorefinery could create one of the building blocks of the bioeconomy of the future. acknowledgements this article is part of the project amontbioraf pivert, financed by the genesys program from ite pivert (institut de transition energétique picardie innovations végétales enseignements et recherches technologiques). references allenby, b.r. 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(eds) (2012). biorafineries roadmap. society for chemical engineering and biotechnology, druckerei schlesner kg, berlin. walsh, m., perlack, r., turhollow, a.f., de la torre ugarte, d., becker, d.a., graham, r.l., slinsky, s.e. and ray, d.e. (2000). biomass feedstock availability in the united states: 1999 state level analysis. tech. rep., department of energy and oak ridge national laboratory. bio-based and applied economics 3(3): 271-283, 2014 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-13551 performance and productivity changes in microfinance banks in south-west nigeria musa a. olasupo1,*, caroline a. afolami2, adebayo m. shittu2, a.a.a. agboola3 1 development finance office, central bank of nigeria, abeokuta. ogun state, nigeria 2 department of agricultural economics and farm management, federal university of agriculture, abeokuta, ogun state, nigeria 3 department of mathematics, federal university of agriculture, abeokuta, ogun state, nigeria abstract. the nigerian microfinance sub-sector is yet to attain the desired level of global best practice. this paper thus investigated the performance and productivity changes of mfbs in south-west nigeria, from 2006 to 2010, having had the microfinance policy launched in 2004. from the use of relevant accounting ratios, the study revealed that only 16% of the sampled mfbs met the recommended maximum par value of 5% in 2006. it was also revealed that 31% of the sampled mfbs reported a debt/equity ratio above the recommended value of 2 in 2006, while 32% had gearing of over 2 in 2010. the malmquist productivity index revealed that the mfbs experienced fluctuating performances in their productivity changes, with pure technical efficiency improvements in 2007 and 2009. overall, the performance and productivity changes experienced by the mfbs depicted a sub-sector with huge potentials and hence require nurturing to achieve its goals. keywords. microfinance banks, performance, malmquist productivity index, productivity changes. jel codes. o16 1. introduction microfinance institutions (mfis) in sub-saharan africa comprises a variety of diverse and geographically dispersed institutions that offer financial services to low-income clients: non-governmental organizations (ngos), non-bank financial institutions, cooperatives, rural banks, savings and postal financial institutions, microfinance banks (mfbs) and an increasing number of deposit money banks (dmbs). mfis provide a range of financial services (loans, savings, micro-insurance, micro-leasing, funds transfer, pension services etc.) to poor households. their worldwide growth in numbers has had a positive impact by providing the poor with microfinance services and has helped create an encouraging socio-economic environment for many households of these developing countries. * corresponding author: maolasupo@cbn.gov.ng. 272 m.a. olasupo, c.a. afolami, a.m. shittu, a.a.a. agboola the nature of these institutions is quite different from traditional financial institutions (deposit money banks) as they are smaller in size, limited in their services towards the poor households and often provide small collateral-free group loans. the basic operational objectives of mfis revolve around two approaches or paradigms namely the “institutionist” and the “welfarist paradigms”. the “institutionist paradigm”, which affirms that mfis should generate enough revenue to meet their operating and financing costs and the “welfarist paradigm”, which includes a focus on poverty alleviation and depth of outreach along with achieving financial sustainability. brau and woller (2004) posited that an efficient mfi management should promote these two objectives. vetrivel and kumarmangalam (2010) submitted that the fundamental problem is not so much of unaffordable terms of loans as the lack of access to credit itself. the lack of access to credit for the poor is attributable to practical difficulties largely due to the discrepancy between the mode of operation followed by financial institutions and the economic characteristics and financing needs of low-income households. they concluded that microfinance institutions (mfis) worldwide have shown that micro enterprises loans can be profitable for borrowers and lenders alike, making microfinance one of the most effective poverty reducing strategies. lafourcade et al. (2005) reported that mfis in africa are dynamic and growing. in their study, they confirmed that african mfis are among the most productive globally, as measured by the number of borrowers and savers by staff member among other positive indices. they also noted that african mfis face many challenges. technological innovations, product refinements, and ongoing efforts to strengthen the capacity of african mfis are needed to reduce costs, increase outreach, and boost overall profitability. these further underscore the need to increase the service delivery capacity of these mfis amidst the enormous potential in the market. in recognition of the important roles of microfinance in the overall development of the nigerian economy, the federal government of nigeria launched a microfinance policy for nigeria in the year 2005. the microfinance policy, regulatory and supervisory framework for nigeria was one of the key innovations adopted to diversify the supply axis of the financial market with a major policy thrust of significantly enhancing the latent capacity of the poor for entrepreneurship through the provision of microfinance services to enable them engage in economic activities and be more self-reliant, increase employment opportunities, enhance household income and create wealth (cbn, 2005). mfbs are expected to empower the economic active poor in the grassroots especially those that do not have access to the conventional banks. the major economic activity of these targeted clients is agriculture or agro-allied as they are mostly involved in various nodes of the agricultural value chain. the agriculture and agro-allied activities covered by the mfbs include: crop production, animal husbandry, cassava processing, feed milling etc. cbn (2012) gave the breakdown of the sectoral funding by mfbs as: agriculture and forestry (6.31%); mining and quarrying (0.65%); manufacturing and food processing (3.10%); real estate and construction (5.27%); transport and commerce (74.60%); and others (10.07%). hence, a meaningful effort to improve their economic prowess could help jump-start the agricultural revolution. kulik and molinari (2004) suggested that for mfis to meet this huge potential, it is critical that they achieve financial self-sustainability with little or no dependence on donor 273performance and productivity changes funds. the further stated that despite the excellent prospects of the sector, with low loan default rates and untapped demand for financial services by low-income groups, only 1% of the mfis are self-sustainable and average adjusted return on asset (roa) is still negative across all continents. the also highlighted the mfi’s lack of access to recent technology as a prime reason for their poor performance. this submission was buttressed by frankiewicz (2003), who posited that information technology (it) can be a strategic tool for microfinance in africa. it can facilitate more efficient and effective collection, processing and use of data; it exposes microfinance institutions to offer new products and better customer service; it enables greater outreach; and facilitate integration with the rest of the financial sector. the global financial crisis of 2007 also had a significant effect on microfinancing worlwide as microfinance institutions experienced liquidity crunches; increased cost of funds and foreign exchange; reduced disposable income for loan beneficiaries and an increase in portfolio arrears (kruijff and harstenstein, 2013). the nigerian microfinance sub-sector was not too exposed to these adversities due to the stringent regulatory requirements that guides their operations. microfinance banks are not involved in foreign exchange transactions; they get little or nothing as grants or foreign aids; their exposure to credit risks is also limited due to their low gearing. however, they were not exempted from the rising portfolio in arrears due to the general reduction in the disposal income of their beneficiaries. this paper will seek to determine the performance and productivity changes of microfinance banks in south-west nigeria over the period of 2006 to 2010 in the light of the central bank of nigeria’s (cbn) revocation exercise of september 2010 that revoked the operating licenses of 224 mfbs after the target examination conducted on 820 mfbs. the result of the examination showed that the affected banks were ‘terminally distressed’ and ‘technically insolvent’ and/or had closed shop for at least six months. some of the factors adduced for their failure among others include: high level of non-performing loans, resulting in high portfolio at risk; gross undercapitalization in relation to their level of operations; poor corporate governance and incompetent boards; high level of nonperforming insider-related credits, and other forms of insider abuse; heavy investment in capital markets with resultant diminution in the value of investment after the meltdown; poor asset-liability management owing to portfolio mismatch etc. hence an investigation into the operational performance of the mfbs will provide further insight into their level of preparedness as catalyst in the financial intermediation process of the economic active poor in the country. 2. research methodology thapa (2007) posited that sustainability in microfinance can be financial, managerial or organizational but that financial sustainability dominates as a measure of efficiency, profitability and productivity. the institutionality and financial sustainability of any mfi lies in its ability to cover all operational expenses from income earned through financial services after making proper adjustments for inflation, subsidies etc (natilson et al., 2001; rosenberg, 2009; dzene and asiedu, 2010). shah (1999) and natilson et al. (2001) suggested repayment rates, operating cost ratio, portfolio quality as additional measures upon which mfis performance can be hinged. performance measurement is defined as 274 m.a. olasupo, c.a. afolami, a.m. shittu, a.a.a. agboola the process of developing indicators to assess progress towards certain predefined goals and reviewing performance against these measures. it is also the use of statistical evidence to determine progress toward specifically defined organizational objectives. natural measures of performance often involves productivity ratios of output(s) to input(s) with larger values of this ratio associated with better performance. performance measurement is often done in comparison to either the previous performances or to the performance of similar units or certain benchmarks in the industry. performance measurement using ratio analysis is the most widely used technique in financial analysis. an accounting ratio is a proportion or fraction or percentage, expressing a relationship between one item in a set of financial statements and another item in the same financial statements. ratio analysis is the most important device for interpreting the performance of organizations from their financial statements. the essence of any interpretation of financial statements is comparison (comparison of current with past figures of the same firm and with its budget or forecast, and comparison with the performance of similar firms in the industry). in order to facilitate this comparison, it is customary to express figures in ratios or percentages, so that a disparity in size between two firms does not prevent comparison of their results. production is the transformation of inputs into outputs or it could be defined as any activity that creates present or future utility. it may also be equivalently described as a process that transforms inputs into outputs (frank, 2008). data envelopment analysis (dea) was proposed by charnes et al. (1978) and banker et al. (1984). the use of dea in this study is appealing due to the fact that: dea can easily accommodate both multiple inputs and outputs unlike the stochastic frontier production function; dea does not require the imposition of a specific functional form on the model. this allows the technological frontier to be constructed without imposing a parametric functional form on technology or deviations from it (inefficiencies); and it permits the construction of a surface over the data, which allows comparison of one production method (or best producer) with others, in terms of a performance index. there are three alternatives for measuring the productivity changes. these alternatives include: fisher index, tornqvist index and the malmquist index. lovell (1996) remarked that given a set of panel data, one may use dea-like linear programs and a malmquist total factor productivity (tfp) index to measure productivity change. the malmquist approach does not require the assumption of efficient production, but instead identifies the ‘best-practice’ firms in every period, which gives an efficient production frontier, and measures each decision making unit’s (dmu’s) output relative to the frontier. according to grifell-tatjé and lovell (1996), the malmquist index has some advantages relative to other productivity indices. for example, it does not require input prices or output prices, which makes it particularly useful in situations where prices are misrepresented or non-existent. it also does not require the profit maximization or cost minimization assumption and hence makes it useful in situations where the objectives of producers differ, are unknown or not achieved. färe et al. (1994) showed that the malmquist productivity index can be decomposed into two components – technical efficiency change and technical change. the value of this decomposition is that it provides insight into the sources of productivity change. the main disadvantage of the malmquist index is the necessity to compute distance functions. there are many different methods that could be 275performance and productivity changes used to measure the distance function, which makes up the malmquist productivity index. one of the more popular methods has been the dea-like linear programming method suggested by färe et al (1994). the malmquist productivity index can be used to identify productivity differences between two firms or one firm over two-time periods by calculating the ratio of the distances of each data point relative to a common technology. given period t+1 technology as the reference technology, the malmquist (output-orientated) tfp change index between period t (base period) and period t+1 can be written as: m (xt+1, yt+1,xt , yt) ={dt (xt+1, yt+1) dt+1 (xt+1, yt+1) / dt (xt , yt) dt+1 (xt, yt)}1/2 (1) where the notation d represents the distance function, x and y are inputs and outputs respectively, and the value of m is the malmquist productivity index. a value of m greater than one (i.e. m >1) denotes productivity growth, while a value less than one (m<1) indicates productivity decline, and m=1 indicates no productivity change. this represents the productivity of the production point (xt+1, yt+1) relative to the production point (xt, yt). calculation of the malmquist index for adjacent periods includes four different distance functions – dt(yt, xt), dt(yt+1, xt+1), dt+1(yt, xt) and dt+1(yt+1, xt+1). the function in equation (1) can further be broken down into its components: efficiency change = dt+1 (xt+1, yt+1) / dt (xt, yt) (2), and technical change = [(dt (xt+1, yt+1) / dt+1 (xt+1, yt+1) (dt (xt , yt) / dt+1 (xt, yt)]1/2 (3) the efficiency change in equation (2) represents the change of technical efficiency (effch) between time t and t+1, which is the change in the relative distance of the observed production from the maximum potential production. the technical change (techch) is the geometric mean of the two productivity indexes, representing shift in production technologies between time t and t+1. in addition, the technical efficiency change can be further broken down into: pure technical efficiency = dt+1 (xt+1, yt+1) / dt (xt , yt) (4) scale efficiency change = dt+1(v) (xt+1, yt+1)/ dt+1(c) (xt+1, yt+1) * dt(v) (xt+1, yt+1)/ dt(c) (xt+1, yt+1) 1/2 dt+1(v) (xt, yt)/ dt+1(c) (xt, yt) dt(v) (xt, yt)/ dt(c) (xt, yt) (5) the scale efficiency change component in equation (5) is actually the geometric mean of two scale efficiencies. the first is relative to the period t+1 technology and the second is relative to period t technology. the extra subscripts of v and c, relate to the vrs and crs technologies, respectively. 276 m.a. olasupo, c.a. afolami, a.m. shittu, a.a.a. agboola 2.1 study data and sources this study took a census of all the 86 microfinance banks in ogun, oyo and ondo states and observed their operation activities from 2006 to 2010. mfbs for this study were limited to unit mfbs (mfbs with minimum capital requirement of n20 million) to create a fair platform to assess the operations of firms on a similar operational level. 2.2 performance measurement using accounting ratios the performance indicators of the mfbs were measured around key operational indices like portfolio quality, financial management, efficiency and productivity and also profitability and sustainability. there are many ratios used in banking and microfinance but this study will adopt ratios based on the toolkit referred to as “seep ratios”. seep (small enterprise education and promotion network) has published a framework that advocates industry standard ratios for the monitoring of microfinance institutions in credit operations. the framework builds on a consensus of practitioners, donors (including cgap {consultative group to assist the poor}), evaluators and others in the microfinance industry (including the mix {microfinance information exchange. 2.3 malmquist productivity index (mpi) there are many different methods that could be used to measure the distance function, which makes up the malmquist productivity index. in the empirical part of this study, following similar input and output choices by martinez-gonzalez (2008) in the estimation of the efficiency scores of mfis, deap computer program was used to construct malmquist indices using dea-like methods (coelli, rao and battese, 2005). since the mfb’s objective is on achieving meaningful increase in outputs, within the context of a given level of inputs, the malmquist (output-orientated) tfp change index between period t (base period) and period t+1 can be written as: m (xt+1, yt+1,xt , yt) ={dt (xt+1, yt+1) dt+1 (xt+1, yt+1) / dt (xt , yt) dt+1 (xt, yt)}1/2 (6) where, m = malmquist productivity index, and d = distance function x and y = inputs and outputs respectively across time period t to t+1. where x1 = mfb’s operating expenses (n); x2 = mfb’s salaries and wages (n); y1 = mfb’s gross loan portfolio (n); y2 = mfb’s total savings. the choice of the output oriented malmquist index was motivated by the underlying assumption that microfinance is all about service delivery whose primary objectives are better measured in terms of its output vis-à-vis outreach, financial sustainability and welfare impact. 277performance and productivity changes 3. results and discussion in general, the sampled mfbs total savings mobilized grew from n3.672 billion ($22.95 million) in 2006 to n4.720 billion ($29.50 million) in 2010, while women savings represented 63% and 57% of the total savings in 2006 and 2010 respectively. the quantum of loans sought by the mfbs’ clients was n3.877 billion ($24.231million) in 2006, it grew to n5.275 billion ($32.969 million) in 2008 and reached n7.298 billion ($45.612 million) in 2010. credit gap (variance between loans applied for by beneficiaries and loan approved by the bank) of between 17% and 20% were observed over the study period. the mfbs outstanding loans as a percentage of loans disbursed was 21% in 2006, 22% in 2007, 21% in both 2008 and 2009, and 19% in 2010. the total annual loans disbursed to agricultural related activities grew marginally by 1% in 2007, but made significant leaps by recording growth figures of 20%, 21% and 5% in 2008, 2009 and 2010 respectively. the combined total assets of the sampled mfbs grew by 13% in 2007, 12% in 2008, 8% in 2009, and 7% in 2010. the combined total liabilities of the sampled mfbs rose by 11% in 2007, 12% in 2008, 8% in 2009 and 7% in 2010. 3.1 performance indicators for mfbs the study attempted to compare the ratios of the sampled mfbs with the global average for mfis reported by the central bank of nigeria (cbn). however, it should be noted that the reported global average was aggregated for all countries irrespective of the degree of development of their microfinance sub-sector and hence it might show huge variance with the ratios obtained from the nigerian microfinance sub-sector that is still at the infancy stage. 3.1.1 portfolio at risk (par) this is the outstanding principal amount of all loans that have at least one installment past due for one or more days. the amount includes the unpaid principal but excludes the accrued interest. it measures the potential for future losses based on the current performance of the portfolio. however, the average annual par of the sampled mfbs was 17%, 18%, 17%, 18% and 17% for 2006 to 2010 respectively. only 16% of the sampled mfbs met the recommended maximum par value of 5% in 2006. the value dropped to 14% in 2007, further declined to 8% in 2008, slumped to 6% in 2009 and increased marginally to 7% in 2010. the high par value recorded by the mfbs might be due to inadequate loan tracking mechanism, weak loan recovery practices and the lack of institutional support to enforce loan repayment. 3.1.2 portfolio to assets this ratio measures how much of the asset base of the mfbs that are invested in high performing loan portfolio. the average annual portfolio to asset ratio of the sampled mfbs were 57%, 55%, 58%, 66% and 69% for 2006, 2007, 2008, 2009 and 2010 respectively. the study also revealed that 34% of the sampled mfbs had a portfolio to asset ratio of below 40% in 2006, the percentage of mfbs rose to 37% in 2007, slumped to 23% in 2008, 278 m.a. olasupo, c.a. afolami, a.m. shittu, a.a.a. agboola rose marginally to 26% in 2009 and crashed to 22% in 2010. the nigeria microfinance sub-sector is still assumed to be at its infancy stage and following the learning curve principle of perfection through repeated trials, more mfbs are expected to have higher portfolio to asset ratio with time. 3.1.3 debt to equity ratio (leverage) leverage reflects the mfbs capital strength at a point in time. it depicts the portion of equity and debt that the mfb uses to finance its assets. increased debt becomes meaningful only if the resultant increase in earnings outweighs the cost of financing the debt. the study revealed that the annual average debt/equity ratios of the sampled mfbs were 2.65 in 2006, 2.21 in 2007, 2.20 in 2008, 2.15 in 2009 and 2.15 in 2010. the study also showed that only 31% of the sampled mfbs reported a debt/equity ratio of above 2 in 2006. the percentage of mfbs declined to 26% in 2007, rose to 29% in 2008, 30% in 2009 and 33% in 2010.the increasing trend from 2008 showed a better efficient matching of debts and equity by the mfbs and hence depicted a more efficient financial management system. 3.1.4 cost per client this ratio measures the quantum of the mfbs operating expenses (excluding the cost of funds or provisions for bad loans) that is required to serve a client. it connotes the expensive nature of the operations of the mfbs. the study showed that the sampled mfbs incurred an average of n4,214.17 ($26.36) per client in 2006, n4,350.00 ($27.19) in 2007, n4,863.95 ($30.40) in 2008, n4,560.07 ($28.50) in 2009 and n4,283.84($26.77) in 2010. this average cost per client is considered quite high as a significant number of the clients falls within the borrowers’ range of n5,000.00 to n50,000.00 ($31.25 to $310.25). this high cost per client ratio could adversely affect the mfbs efficiency and productivity. 3.1.5 borrowers per loan officer it depicts the productivity of loan officers in serving the client caseload. the ratio is not expected to be too high as it might lead to client overload for each loan officer, resulting in poor customer satisfaction, ineffective loan monitoring and high default rate. the study showed an annual borrowers/loan officer ratio of 232 in 2006, and it continually rose to 266 in 2007, 269 in 2008, 289 in 2009 and 313 in 2010. 3.1.6 return on assets (roa) roa depicts the management of the mfbs assets to maximize profit. it indicates the profitability of the mfbs before leverage. it measures the amount of profit the mfbs make per naira of its assets. the study revealed an average roa value of 8% in 2006 (i.e the mfbs made an average of 7.81 kobo for each naira worth of asset. the average roa value was 7%, 7%, 8% and 9% for 2007 to 2010 respectively. the high rate of returns on assets as compared to the global average could be adduced to the high interest rates charged by the mfbs. due to the short duration of the credit facilities provided by the mfbs, most mfbs charge between 2% to 5% monthly on their credit facilities. however, the roa is quite small compared to the average cost of capital that ranged between 18% and 22% over the study period. 279performance and productivity changes 3.1.7 return on equity (roe) it measures the rate of return on the shareholders’ equity of the mfbs. it shows the mfbs’ efficiency at generating profits from every unit of shareholders’ fund. the study reported annual average roe value of 26% in 2006, 21% in 2007, 19% in 2008, 21% in 2009 and 24% in 2010. the study further revealed that 60% of the sampled mfbs had roe ratio of above 15% in 2006, the valued declined to 57% in 2007, 56% in 2008 and 2009 and rose to 63% in 2010. overall, the various ratios depicted an evolving sub-sector with enormous amount of potentials. the revelations from the sampled mfbs might not deviate much from the industry’s average and hence the regulatory authorities must keep a tab on the activities of the mfbs through the formulation of policies that will create conducive environment for their growth. table 1. average performance of sampled mfbs (2006 to 2010) using ratio analysis. year 2006 2007 2008 2009 2010 mean global average portfolio at risk >30 days (%) 17.47 17.65 17.01 18.06 17.33 17.504 4.6 portfolio/asset 57.4 55.21 58.14 65.96 69.03 61.148 na debt/equity 2.65 2.21 2.2 2.15 2.15 2.272 2.9 cost per client (n) 4,214.17 4,350.00 4,863.95 4,560.07 4,283.84 4,454.406 na borrowers per loan officer 232 266 269 289 313 273.8 238 returns to asset (%) 7.81 7.31 6.84 7.77 8.61 7.668 1.5 returns to equity (%) 26.09 21.33 19.49 20.52 23.63 22.212 7.1 source: author’s computation (2013). na = not available. 3.2 malmquist productivity index as earlier stated, an attractive feature of the malmquist index is that it decomposes into pure technical efficiency change and technological change. table 2 summarized the decomposed mean annual mpi or total factor productivity (tfp) over the study period. the mpi is decomposed into technical efficiency change and technological change of period t+1 relative to period t. the efficiency change is further divided into pure technical efficiency change and scale efficiency change (changes due to input-output combination efficiencies). the mfbs experienced fluctuating performances in their productivity changes. table 2 revealed improvements in the mfbs pure technical efficiency of 1.006 and 1.01 in year 2006-07 and 2008-09 while they suffered deterioration in their pure technical efficiency with values of 0.91 and 0.834 in periods 2007-08 and 2009-10 respectively. however, there were improvements in their scale efficiency in years 2006-07, 2008-09 and 2009-10, while they only suffered scale efficiency deterioration in year 2007-08 with a value of 0.967. the mfbs suffered technological decline throughout the periods with values of 0.96, 0.985, 0.658 and 0.46 in years 2006-07, 2007-08, 2008-09 and 2009-10 respectively. this trend 280 m.a. olasupo, c.a. afolami, a.m. shittu, a.a.a. agboola is quite disturbing as the sharp declines in years 2008-09 and 2009-10 showed the mfbs are not leveraging on available technology to improve on their efficiency. technological innovations will help simplify the operations of mfbs as they struggle to balance their operational objectives of outreach and financial sustainability. the observed technological decline from 2006 to 2010 corroborates the submissions of oladejo and olowookere (2011) and moya musa et al. (2012) on the poor deployment of technology among mfis. overall, the mfbs experienced total factor productivity improvements only in year 200607, while there were deteriorations in years 2007-08, 2008-09 and 2009-10. figure 1 showed trend in mean annual technical and technological changes of the sampled mfbs and corroborated the earlier assertion that the mfbs had better technical efficiency changes than their technological efficiency changes. it further underscores the relevance of technology in the modern business environment and mfbs should be encouraged to leverage on available technology to ease their operations and enhance their accounting and loan tracking proficiencies. table 2. summary of the malmquist productivity index for the (2006 to 2010). concept 06-07 07-08 08-09 09-10 efficiency change 1.142 0.88 1.445 1.661 technological change 0.96 0.985 0.658 0.46 pure efficiency change 1.066 0.91 1.01 0.834 scale efficiency change 1.071 0.967 1.431 1.993 total factor productivity change 1.097 0.867 0.951 0.764 author’s computation (2013): figure 1. trend in mean annual technological (tech) and technical changes (eff) for mfbs (20062010).   281performance and productivity changes 4. summary and conclusion in assessing the performance and productivity changes of mfbs in south-west nigeria as an indicator to the happenings in the nigerian microfinance sub-sector, the study noticed a steady growth in the operations of the mfbs but also revealed a lot of opportunities for improvements. the performance indicators of the mfbs using relevant ratios indicated gross inconsistencies in the mfbs performance as benchmarked against global standards. though the nigerian microfinance sub-sector is still emerging, huge deviations from global standards indicated the enormous effort required to mainstream mfbs towards best-practices. the study revealed an annual average par < 30 days of between 17.01% and 18.06% as compared to global par < 30 days value of 4.6%. par is crucial to the mfbs’ survival as it measures the potential for future losses based on the current performance of the loan portfolio. mfbs should be encouraged to tighten their loan tracking and recovery mechanisms. regulatory authorities could also assist the mfbs to establish enforce-able loan recovery systems. the annual average portfolio to assets ratio of the mfbs ranged from 55% to 69%. the mfbs reported an average leverage of 2.32, which though lower than the reported global average of 2.9 but very close to the 2.4 average reported for africa. the average cost per client ratio of the mfbs was n4,454.41 ($27.84), a value perceived as being too high considering the fact that significant number of the clients still borrow between the n5,000 to n50,000 ($31.25 to $310.25) range. the return on assets (roa) and return on equity (roe) of the mfbs were quite impressive as compared with reported global averages but this could be highly connected to the high interest rates currently being charged for the services of the mfbs. the average interest rate among the sampled mfbs ranged from 3% to 6% per month. the malmquist productivity index showed inconsistencies in the technical and technological changes as the mfbs had more pronounced changes in their technical productivity changes than their technological productivity changes. it was revealed that the mfbs had no technological productivity improvements as the mfbs experienced technological productivity decline throughout the study period of 2006 to 2010. this positive correlation between performance and technology amplifies the relevance of technology to the overall performance and sustainability of mfbs in nigeria. in this age of information technology, it is expedient for mfbs to collaborate and evolve operational softwares that will ease their customer tracking, loan monitoring, operational performances and provides cost effective and efficient means of achieving their business objectives. mfbs should be encouraged to leverage on available technology and improve their service delivery for better efficiency and profitability. overall, the mfbs had alternating advancements and deteriorations across all forms of the constituents of their total factor productivity changes but had the best trend in their scale efficiency changes over the period. mfbs are advised to pay attention to each constituent of their total factor productivity as advancements in some areas and deterioration in other areas will continue to hamper advancements in their overall tfp. 282 m.a. olasupo, c.a. afolami, a.m. shittu, a.a.a. agboola 5. recommendation the study depicted the nigerian microfinance sub-sector as an emerging one with huge potentials to achieve its desired goals and objectives as an engine for sustainable economic growth and development. based on the findings of this study, the following recommendations were made: 1. the high mean par ratio of 18% found in the study for the sampled mfbs portends great danger for the sub-sector. the cbn should establish a credit bureau for mfbs. this will aid mfbs effort in loan tracking and handling of default cases towards reducing their high portfolio at risk (par). regulatory authorities should also institute enforce-able loan recovery systems to mitigate against cases of deliberate default. 2. the malmquist productivity index showed technological decline among the sampled mfbs over the study period. mfbs should invest in technology by leveraging on available business solution applications and also invest in research and development. efficient use of it will aid their customer profiling, loan monitoring and recovery and ultimately help reduce their par. 3. mfbs are critical to the success of the financial inclusion policy of the cbn as they are closer to the financially excluded populace. the central bank of nigeria (cbn) should invest in the development of robust operational softwares for the management information system (mis) and operational efficiency of 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(2010). role of microfinance institutions in rural development. international journal of information technology and knowledge management 2(2): 435-441. bio-based and applied economics 5(1): 27-45, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-15283 an estimation of the willingness to pay for biodiesel: a pilot study of diesel consumers pathmanathan sivashankar1,*, jeevika weerahewa2, gamini pushpakumara3, lakshman galagedara4 1 postgraduate institute of agriculture, university of peradeniya and sabaragamuwa university of sri lanka 2 department of agricultural economics and business management, university of peradeniya, sri lanka 3 department of crop science, university of peradeniya, sri lanka, 4 grenfell campus, memorial university of newfoundland, corner brook, nl, canada date of submission: december 28th, 2014; accepted 2016 27th, april abstract. sri lanka’s energy policy presumes that the country will be meeting 20% of its energy requirements by non-conventional renewable energy resources by 2020. this study attempts to assess diesel vehicle owners’ willingness to pay (wtp) for jatropha biodiesel and the factors affecting their decisions. the contingent valuation method (cvm) was used to elicit the wtp for the non-marketed biodiesel, which leads to a hypothetical allocation. for diesel vehicle users, a single bid approach was used at rs.121/= per litre (€ 0.83). the study was carried out in kandy region among diesel vehicle users. the factors affecting wtp were estimated using probit regression and wtp was estimated using nonparametric estimation techniques. the mean wtp for biodiesel by the diesel vehicle users was rs.109 per litre (€ 0.74) for lower bound levels. the median wtp was rs.124/= per litre (€ 0.85). elderly respondents with higher education are less likely to pay for biodiesel in both samples. married respondents with higher income are more likely to pay higher prices for biodiesel. keywords. willingness to pay, jatropha biodiesel, probit regression, diesel consumers jel codes. q21, q41, q42 1. introduction the debate over energy and economic development has received growing attention among economists and policy makers in the post millennia era. almost all past economic recessions were linked to oil price fluctuations, either directly or indirectly (hamilton, 2011; killian and vigfusson, 2014). hence, energy conservation and efficient utilization of energy and energy sources play a crucial role for policy makers. dwindling fossil fuels * corresponding author: sivashankar.p@hotmail.com 28 p. sivashankar et al. have been threatening the growth prospects of many countries including sri lanka. as sri lanka’s energy needs have been increasing over the years, its dependence on imported crude oil to meet energy demand has increased. sri lanka uses diverse energy sources. the main source is imported petroleum, followed by renewable sources, including hydropower, biomass, solar, and wind (energy balance, 2010). among them, hydropower is widely used for electricity generation. commercially traded forms dominate the local energy market through petroleum and electricity while biomass is also traded in smaller shares. this heavy dependence on imported crude oil has made the vision of achieving sustained growth in developing countries dubious (igberaese, 2013; osman and nour, 2011; rodriguez and sanchez, 2005). even for developed countries, economic growth has been limited due to the dependence on fossil fuels (rodriguez and sanchez, 2005). developed countries, having understood the constraints on fossil fuels and depleting natural resources, have begun to embrace the concept of bio-based economy. their governments promote and invest heavily in the research and development of bio-based products with subsidies at different levels of the supply chain, from the field to the consumer (erma, 2007; steenblik, 2007; josling et al., 2010; world bank, 2010). the concept of bio-based economy is still new to many developing countries. the eu defines bio-economy as the economy that “encompasses the production of renewable biological resources and their conversion into food, feed, biobased products and bio-energy. it includes agriculture, forestry, fisheries, food and pulp and paper production, as well as parts of chemical, biotechnological and energy industries” (eu commission, 2012). in the literature, there is not yet agreement on a common definition, but studies have emphasised the importance of bio-economy, its challenges, and potential and how it has moved further away from agricultural economies (roman, 2012; viaggi et al., 2012). bio-based products are commercial or industrial products composed of the whole or part of a biological origin from agriculture, forestry, or renewable biological material (usda, 2006). similar definitions have also been proposed by the eu commission (2012) and cen (2014). renewable energy is a key component of the biobased economy. biomass (45.80%), petroleum (40.90%) and hydropower (11.10%) are the main energy resources used in sri lanka (energy balance, 2010). the use of renewable energy sources such as wind power and solar electricity is estimated to be insignificant in comparison to these three sources. electricity and petroleum products are the main forms of commercial energy, and an increasing amount of biomass is also commercially grown and added to the national energy supply. demand for energy in sri lanka is on the rise. however, the share of electricity generation from non-conventional renewable energy resources is as low as 4%. in 2005, the then government of sri lanka’s target was to achieve 10% by 2015. the national policy, viz, mahinda chintanaya (mahinda’s vision) in 2005 targeted 20% of contribution from non-conventional renewable energy sources by the year of 2020. biofuel is an energy source/combustible fuel that is produced from plants and other biobased products. two different types exist, viz, bioethanol and biodiesel, which are not yet locally produced on a commercial scale. accordingly, it is essential to study the prospects of biodiesel options in sri lanka. globally, the usa (corn) and brazil (sugarcane) account for about 89% of global ethanol production (www.dnv.com, 2010). the eu currently represents 90% of global bio29a pilot study of diesel consumers diesel production and consumption. due to the competitive advantage in land size and technology, larger economies have reached greater milestones in innovation in biofuels. due to its environmental merits, the biofuel share in the automotive fuel market will grow rapidly in the future (demirbas, 2008). moreover, the combustion of bio-diesel fuels produces fewer emissions than diesel (mittelbach and remschmidt, 2004). given this context, this study attempts to measure the willingness of sri lankan consumers to consume biodiesel produced from a bio-based, non-food agricultural crop, jatropha (physic nut). is the sri lankan public ready to consume biodiesel instead of conventional diesel? what are their perceptions about biodiesel consumption and investment on this bio-based sector? what is the price they are willing to pay for this environmentally friendly product and what factors affect their intention to consume biodiesel? this study attempts to answer these questions. the study measures the acceptance of biodiesel by diesel consumers in sri lanka, and estimates the demand for biodiesel through positive willingness to pay (wtp) and factors affecting their choices. although wtp research has been carried out on other bio-based products in sri lanka, this is the first of its kind on the estimation of wtp for biodiesel in the country. the organization of the rest of the paper is as follows. section 2 discusses the relevant literature on renewable energy and bio-based products and the demand estimation for biodiesel. section 3 posits the theory for wtp estimation and the methodology followed. section 4 and 5 present the results and the emerging discussion, respectively. a conclusion is provided in section 6. 2. review of literature 2.1 bio-based products as renewable energy sources. bio-based products are products that have a biological origin, be it from agricultural land, the livestock industry, forestry, fisheries, or even from microbes (usda, 2006; eu commission 2012; cen, 2014). bio-based products include, but are not limited to, adhesives, construction materials and composites, fibres, paper and packaging, fuel additives, paints, plant inks, solvents etc. (usda, 2006). most of these products are environmentally friendly in nature and fetch higher market prices. these industries are also heavily subsidized in many countries. among these bio-based products, biofuel production is particularly common the world over. the most common biofuel types are biodiesel and bioethanol. biodiesel is produced from oil crops like rapeseed (brassica napus), sunflower (helianthus annuus), jatropha (jatropha curcas l.) and soybean (glycine max), while bioethanol is produced from starch crops like sugarcane (saccharum officinarum), wheat (triticum aestivum) and corn (zea mays). with the onset of the 2008 food crisis, it has been claimed that the usage of food crops in biodiesel production is a threat to food security (tenenbaum, 2008; iufost, 2010; jideani et al., 2011; popp et al., 2014). the current trend is biodiesel extraction from non-edible, oil-bearing trees such as jatropha, pongamia (milletia pinnata), castor (ricinus communis) and neem (azadirachta indica) (lele, 2008). in addition to these plant varieties, further research is focussed on biofuel production from algae (khola and ghazala, 2012; pitman et al.,2011; hossain et al., 2008; park et al., 2011). this could be a 30 p. sivashankar et al. remedy for the challenges faced by land limitations and the food security issues related to biofuel production. the benefits from biofuel production include a reduction in carbon emissions, job creation, poverty alleviation, and an improvement in the socio-economic conditions of the rural people, especially the rural poor (francis et al., 2005; tomomatsu and swallow, 2007; and pushpakumara et al., 2008). the multidimensional long-term benefits of biofuels have created a growing interest in biofuel production in developing countries. among other energy crops, jatropha has been extensively produced in developing countries on the asian, african, and latin american continents. like other energy crops, jatropha’s contribution to mitigate greenhouse gas (ghg) emissions has been emphasised by tomomatsu and swallow (2007). if oil prices continue to rise, alternative fuels will become an economic necessity for small economies. 2.2 demand for biodiesel demand estimation for these types of products through a surrogate market indicates whether there is a market for these bio-based products. wtp for biodiesel or bioethanol could be affected by many reasons. studies reveal that consumers value the environmental benefits of green energy (roe et al., 2001; shrum et al., 1995). in terms of ethanol as an alternative fuel, the interaction between intended purchases of e10 blended fuel and environmental, political and national security benefits have also been addressed using the contingent valuation method (cvm) in a simultaneous latent variable framework (bhattacharjee et al., 2008). the study revealed that males with liberal ideologies who were familiar with ethanol had higher wtp. jeanty and hitzhusen (2007) used a cvm estimate wtp for air pollution reduction from using biodiesel in diesel engines. the study focuses on valuing the benefits of biodiesel such as a reduction in co2 emissions by 75%, reductions in fine particulates by 47%, sulphur emissions by 100%, and volatile organic compounds by 56%. li et al. (2009) used a mixed-mode cvm survey to estimate wtp for increased research and development in support of replacing fossil fuels in the united states where wtp was higher for females, people with liberal political ideologies respondents with higher incomes, and those who considered energy issues to be important. solomon and johnson (2009) conducted a case study of michigan, wisconsin, and minnesota residents to determine how these residents valued climate protection through the potential purchase and consumption of cellulosic ethanol, using a multi-part, split-sample cvm. jeanty and hitzhusen (2007) present the results of a contingent valuation study in two ohio regions to estimate willingness to pay for air pollution reduction from using biodiesel in diesel engines. the double bounded parametric formulation was used to estimate mean wtp. gracia et al. (2009) suggests that determinants of wtp heterogeneity are not limited to the buyers’ socio demographic characteristics. it is also determined by buyers’ knowledge on biodiesel, their fuel purchasing habits and factors of the behavioural model. behavioural model factors include attitudes, subjective norms, and perceived behavioural control, where all three factors affects the intention (azjen, 1991). the common message of these studies is that the consumers are willing to pay a higher price for biodiesel. in greece, 90% of people believe that climate change is related to fossil fuel consumption, whilst only half think that biofuels could be an effective solution (savvanidou et al., 31a pilot study of diesel consumers 2010). although biofuels are not new, usage remains low in certain western countries and public opinion of biofuels is still divided. according to savvanidou et al. (2010), eighty per cent of respondents who owned vehicles were willing to use biofuels, of which 44.8% were willing to pay more for them (€ 0.06 per litre). in particular, the highly educated are more likely to pay this amount whereas the general public is willing to pay more if research organisations manage the introduction of biofuels rather than government or industry. there is a need to educate people on the positive impacts of biofuels through campaigns (savvanidou et al., 2010). marra (2010) revealed that, in general, consumers were willing to pay for reductions in greenhouse gas emissions through purchases of e85 and that these amounts vary from one market segment to the next. no such study has been conducted on the wtp of sri lankans for biodiesel. this study is the first of its kind targeting sri lankan diesel vehicle users. 3. methodology 3.1 demand estimation in this section, the theoretical background and the methods used for estimating the demand for jatropha biodiesel is discussed. demand has been estimated using two techniques. the first method is the parametric estimation of demand (hanemann, 1984). the second method is non-parametric estimation of demand for biodiesel. studies show that nonparametric estimation can be used as it does not assume the normal distribution of data and at times it is the best method for demand estimation of non-marketed products (turnbull, 1976; kristrom, 1990; haab and mcconnell, 1997; vaughan and rodriguez, 2001). 3.2 theoretical and empirical estimation of wtp theory of demand suggests demand is a function of price (p). in addition to price p, lancaster (1971) argues that demand is also a function of other attributes of the product (a) and income (i) of consumers. farrow et al. (2011) expands this theoretical model to include the psychological characteristics (c) of the decision maker. these characteristics include the standard theory of planned behaviour variables: attitudes, beliefs, norms, and perceived behavioural control (azjen, 1991; armitage and conner, 2001; daigle et al., 2002). the general theoretical model is as follows: wtp = f (p, a, i, c) (1) welfare change cannot be directly measured; indirect utility function and minimum expenditure function provide the theoretical basis for welfare estimation. in stated preferences, welfare change is measured by a change in these functions. cvm can be viewed as a direct measure of welfare change. wtp is the amount of income that compensates an individual for a welfare change. an individual’s wtp for environmental benefit is the amount that must be taken away from the individual’s income while keeping his or her utility unchanged (huang, 2011; freeman iii et al., 2014): 32 p. sivashankar et al. v(y-wtp, p, q1) = v(y, , p, z, q0) (2) where v is indirect utility function, y income, p is a price vector, z is a vector of socioeconomic variables, and q0 and q1 are the environmental quality at status quo and improved levels respectively. according to this model, choice (the decision to purchase fuel) is based on the attributes of the fuel (environmental, fuel security) and the individual’s psychological characteristics, specifically their beliefs (perceptions) about the environment (e.g., global warming), fuel security (dependence on foreign sources of fuel), as well as attitudes towards new technologies, products and their prices. behavioural theories suggest that attitudes are an important determinant of behaviour (ajzen and fishbein, 1988; ronis et al., 1989; azjen, 1991; daigle et al., 2002; sutton, 2004). from equation (2), wtp function can be obtained as in equation (3). wtp = f (y, p, z, q0, q1) (3) equation (3) underlies the estimation of a valuation function that depicts the monetary value of a change in economic welfare that occurs for any change in environmental quality. the residuals of the respondents, ε, are assumed to have a standard normal distribution ε~n[0, 1]. that is, the distribution of the residuals of the socio-economic variables, denoted by x, follows a standard normal distribution with zero mean and a common variance, unity. given that our response variable is a binary response, with the cumulative density function being normally distributed, a probit model for wtp can be developed. the latent variable wtp* is linked to the observed binary variable wtp through the relationship below: wtp 1 if wtp* > 0 0 if wtp* ≤ 0 ⎧ ⎨ ⎪ ⎩⎪ the probit model can be derived from the following equation: wtp* = φ(x’jβ + εj) (4) as equation (4) denotes, wtp*, an unobserved continuous variable, can be written as the function of vector, xj with a cumulative normal distribution with the predicted probabilities. for a given covariate, the probit function provides the probability area of the standard normal distribution. mathematically, the probit function results from the inverse of the cumulative density function (φ) of the standard normal distribution. accordingly, it can be denoted as in equation (5) (razzaghi, 2013). wtp = φ-1(wtp*) = (x’jβ + εj) (5) wtp = (x’jβ + εj) (6) wtp* is an unobserved variable. to use it as an observed variable the variable wtp is applied. here, equation (5) serves as the link function (probit link) between actual wtp* 33a pilot study of diesel consumers and estimated wtp. denoting the willingness to pay determinants as a vector, x, then for each respondent j=1…. n in the sample, the latent variable, wtp, can be written as in equation (6) for a single bounded model. β is the vector of parameters estimated (long, 1997). the level of wtp is investigated using the probit regression to model the respondents’ responses against attitudinal variables and several socio-economic variables. this is a common approach in contingent valuation studies to test the validity of wtp results by examining how well the model corresponds to the economic theory in which individuals with higher incomes are expected to have a higher than average wtp. similarly, individuals with low incomes are expected to have a lower than average wtp. equation (7) presents the empirical equation for the unobserved continuous variable, wtp* , which determines the value of wtp in the study. the respective variable names and units are presented in table 1. wtp* = β0 + β1x1 + β2x2 + β3x3 + β4x4 + β5x5 + β6x6 + β7x7 + β8x8 + β9x9 + β10x10 + ε (7) table 1. variables used in the probit regression. variable definition type expected sign x1 age continuous + x2 gender (0=female, 1=male) dummy +/x3 education continuous + x4 marital status (0=single, 1=married) dummy +/x5 employment (emp) (0=unemployed, 1=farmer, 2=government, 3=self employed, 4=private, 5=other) categorical +/x6 income continuous + x7 refuel cost continuous + x8 vehicle age continuous + x9 knowledge on ghg (0 = no idea, 1=heard, 3= know categorical + x10 knowledge on climate change (0 = no idea, 1=heard, 3= know categorical + note: for dummy variables, the first level is chosen as the reference level. 3.3 nonparametric estimation the parametric approach to derive the wtp measure requires a distribution assumption (hanemann, 1984; bishop and heberlein, 1979), which may result in inconsistent estimates when the distribution is miss-specified. in order to overcome this potential problem, turnbull (1976) suggested a distribution free lower bound mean estimate. the turnbull lower bound mean (lbm) estimate is calculated as (haab and mcconnell, 1997; vaughan and rodriguez, 2001): 34 p. sivashankar et al. lbm turnbull( ) = p1b1+ i=2 m ∑pi bi − bi−1( ) (8) the variance of the lbm: var lbm( ) = i=1 m ∑ pi 1− pi( ) bi − bi−1( )2 n (9) the relationships between wtp responses and characteristics of respondents in the model are hypothesized in line with the economic theory. thus, variables of income, education, refuelling costs, age of vehicle, and knowledge of greenhouse gases and global warming are expected to have a positive relationship with wtp amount for biodiesel. the gender and age characteristics cannot be directly assumed to have a positive relation with the amount of wtp. since there is scepticism involved in expecting which direction the gender and age variable will direct the amount of wtp, predictions are not formulated. 3.4 data collection a survey was conducted to measure the wtp for diesel vehicle users and the factors affecting their decisions. the sample included one hundred randomly selected diesel vehicle users from kandy district. the data collection was conducted face to face through an interviewer administered questionnaire, behind the city centre premises leading to the car park. the reason for selecting this location is that it is the centre of the city and is connected to the two main car parks in the central town: the city centre car park and central market car park. the sampling unit for diesel vehicle users were individuals because in this case the driver or the decision maker is an individual who is deemed to be the economic agent, not the family. since there is no biodiesel in the market of any kind, the conditions of the study were clearly stated to the respondents to elicit answers with less bias. two types of wtp questions were asked. first, the respondents were asked whether they would like to pay more for jatropha biodiesel. for this question respondents had to select either yes or no. a subsequent another question followed, irrespective of their answer to the first wtp question. this question directly asked respondents the price they would be willing to pay for a litre of biodiesel. this was an open question, hence allowing respondents to provide their preferred price. as a result, possibility of a jatropha biodiesel market had to be explained to the respondents, minimizing the bias, in order to create the surrogate market for biodiesel. also, more generally, the concept of biodiesel had to be introduced to the respondents. the production and availability of biodiesel and how it can be used in the diesel engine, the modification needed for the engine, mileage comparisons, and the benefits and constraints had to be explained to the respondents. after doing so, respondents were asked whether they would be willing to pay more for a litre of biodiesel compared to the conventional diesel price of rs. 121 per litre. once respondents had answered either yes or no, another open question was 35a pilot study of diesel consumers posed to determine how much respondents are willing to pay. this is an open question, so respondents were free to indicate any amount. this exercise was particularly time consuming as this was the first study of its kind in sri lanka, conducted to elicit wtp for biodiesel using a cvm instrument. the questionnaire for diesel vehicle users consisted of mainly two parts. the first, consisting of thirteen questions, focused on the socio-economic information of the respondents. categorical and open-ended questions were included to determine the background information of the respondents. the second part captured the respondents’ knowledge of biofuels. knowledge of jatropha biodiesel and their wtp for jatropha oil were included in this section. it also included a series of questions related to the respondents’ perceptions of renewable energy development in sri lanka, and knowledge of greenhouse gases and global warming. 4. results 4.1 sample composition and information table 2 describes the summary of the demographic variables of the sampled 100 diesel vehicle users. on average, the respondents were educated above 11th grade in the secondary school level (i.e. ordinary level). the mean age of the sample is 40 years and the average monthly income is around rs. 28,500. the range of income was from zero to one hundred thousand sri lankan rupees. on average they spent around rs. 15,885 to refuel their vehicles with diesel, with a range of rs. 500 to rs. 270,000 per month. this is mainly due to the presence of respondents who do hiring services and transportation services for the finished and intermediary goods. table 3 depicts the level of awareness of renewable energy sources, knowledge of greenhouse gases and climate change and, more specifically, awareness of jatropha biodiesel production. the majority of the respondents have basic knowledge of renewable energy sources and all of them were supportive of the development of renewable energy sources in sri lanka. respondents were more familiar with, and knowledgeable of, climate change phenomena than greenhouse gases. this is plausible as climate change is more often cited in local newspapers in the context of floods and droughts. knowledge of biofuels and jatropha biodiesel production is limited among the respondents. diesel vehicle users were asked whether they would pay more for biodiesel than conventional diesel. seventy-two per cent (72%) of the respondents were not willing to pay a higher price for biodiesel. the majority of the sample responded negatively (72%) and only 28% said they would be willing to pay more for biodiesel above the current conventional diesel price of rs 121 per ltr. for the open-ended wtp question, 7% of the respondents did not indicate a price. the highest price reported was rs. 140/litre from one respondent whereas around 9% of the respondents were willing to pay between rs. 130 to 140 per litre. around 22% were willing to pay rs 120/litre and another 16% of them were willing to pay rs.125/litre. approximately 4% were willing to pay less than rs. 90/litre. these figures were used to elicit the non-parametric wtp for biodiesel. the mean wtp for biodiesel by the diesel vehicle users was rs.109 per litre for lower bound levels. the median wtp was rs.124 per litre. 36 p. sivashankar et al. table 2. summary profile of the diesel vehicle users. variable category per cent (%) variable category per cent (%) gender male 96 marital single 14 female 4 status married 86 education below 11th grade(o/l) 18 religion buddhism 97 up to 11th grade 34 islam 2 up to 13th grade 36 christianity 1 above secondary education 12 occupation unemployed 5 household (size) n=89 2 4 farmer 1 3 25 government 19 4 23 student 2 5 26 self employed 50 6 7 private 19 7 2 other 4 8 2 times refuelled less than once a month 1 monthly income (rs.) mean 28,494.68 once a month 1 minimum 0.00 once every two weeks 18 maximum 100,000.00 once a week 32 std. dev 17,685.802 more than once a week 48 age (years) mean 40.09 refuel cost/month (rs.) mean 15,885.00 minimum 19.00 minimum 500.00 maximum 71.00 maximum 270,000.00 std. dev 11.81 std. dev 31640.46 table 3. summary of perceptions and awareness of bio-energy issues. variable category percentage knowledge of renewable energy sources yes 81% no 21% development of renewable energy in sri lanka against 1% supportive 99% familiarity with greenhouse gases (ghg) no idea 56 % heard about it 36% know about it 8% familiarity with climate change no idea 12% heard about it 64% know about it 24% knowledge of biofuels never heard about it 39% have heard about it 32% know what it is 14% know its origin 6% know its benefits 9% knowledge of jatropha biodiesel production yes 36% no 64% 37a pilot study of diesel consumers 4.2 regression results for factors affecting wtp for jatropha biodiesel according to the probit model, only income, age of the respondent, age of the vehicle and education are significantly affecting the decision to use jatropha biodiesel. the pseudo r2 (i.e. mcfadden r2) is around 0.29 (table 4). it is the measure of proximity of the model to the observed data. this suggests that around 29% of the variation is captured from the model and the rest is misclassified and captured by the error term. since the probability value for the model is 0.043, at a 5% significance level, the hypothesis that all coefficients are equal to zero is rejected. it is noteworthy to mention that this type of regression using survey data usually yields low r2 values. when incomes increase respondents are more likely to opt for biodiesel. older people are less likely to pay for biodiesel. similarly when education levels increase compared to the primary education level, respondents are less likely to pay more for jatropha biodiesel as an option for the substitution of conventional diesel. this is evident up to the secondary education level. beyond that it does not posit a significant relationship. similarly, with the additional increase in a vehicle’s age, people are less likely to opt for biodiesel. this is in contrast table 4. probit regression results for factors affecting wtp for biodiesel. dv=wtp(1/0) coefficient pvalue marginal effect age -0.039* 0.098 -0.010 gender: male -0.772 0.446 -0.26 education (ref: grade v) upto o/l -1.710** 0.014 -0.543 upto a/l -1.239** 0.043 -0.446 above a/l -1.423 0.125 -0.490 marital status: married 0.904 0.190 0.174 occupation (ref: unemployed) government 0.134 0.894 0.051 self employ -0.923 0.322 -0.266 other 1.047 0.423 0.3924 income 0.000027* 0.100 7.16e-06 refuel cost 7.32e-07 0.950 1.93e-07 vehicle age -0.00742* 0.083 -0.002 ghg (ref: no idea) heard -0.138 0.745 -0.043 know 0.1242 0.415 -0.170 climate change(ref: no idea) heard -0.138 0.833 -0.036 know 0.124 0.892 0.036 constant 2.261 0.205 probit regression lr chi2(17) = 28.14 prob> chi2 = 0.043 log likelihood = -34.258556 pseudo r2 = 0.2911 **,*significant at 5% and10% significance level respectively. 38 p. sivashankar et al. with the expectation that owners of brand new vehicles will be hesitant to use biodiesel. this may be due to the fact that brand new vehicles are better at energy conservation; hence people may be showing a preference for biodiesel as a result. older vehicle users might also have the perception that biodiesel is a new concept and thus it is not applicable to older vehicles. it was expected that perceptions of global warming and climate change might have an impact on decisions, but those variables were not significant, even at a 10% significance level. the regression coefficients and their magnitudes cannot be directly used for interpretation. thus, marginal effects are used to interpret the outcomes of limited dependent variables. a one year increase in age decreases the likelihood of paying more for biodiesel by 1%. respondents educated up secondary education level are 54% less likely to pay more for biodiesel than people who have education levels below secondary education level. the latter is the base reference level for education categories. compared to the reference level, the respondents who are educated up to an advanced level are 44% less likely to pay more for biodiesel than the conventional diesel price. people with an educated higher than the advanced level are 49% less likely to pay more for biodiesel compared to the respondents who are educated below ordinary levels. a oneunit increase in income would increase the likelihood of paying more for biodiesel by a very minimal percentage (0.000716%). people with older vehicles are 0.2% less likely to pay more for biodiesel than people with newer vehicles. 4.3 nonparametric estimation of wtp as mentioned in the methodology, nonparametric elicitation of wtp has been used in many studies to avoid the assumption of normality. in short, this is calculated based on the cumulative density changes for each price. lbm for diesel vehicle users is rs 109.46 per litre. the median wtp was rs. 124.43 per litre. the kristrom mean was rs. 112.42/ litre. though different mean estimations provide different estimates, they are calculated at different boundaries, viz, lower boundary, middle, and the upper boundary. 5. discussion the present study looked at the wtp for biodiesel from jatropha, a bio-based renewable energy product. it hypothesized, in general, that with higher income and education levels diesel vehicle users would be willing to pay more for biodiesel. hence, they will be willing to pay a higher price for biodiesel than for conventional diesel. other variables that were considered to have a positive association with higher wtp for biodiesel were age, awareness on greenhouse gases, awareness of climate change, age of vehicle, and monthly refuelling costs. gender, marital status and employment levels were not assumed to have an impact on wtp for biodiesel as the directions of associations could be either way for these variables. though there were significant variables, the outcomes were rather mixed, supporting only a few hypotheses. interestingly, some variables that were expected to have an effect were not significant. out of the significant coefficients, age, education, and age of vehicle had a negative association with the wtp for jatropha biodiesel. income was also significant, but had a very marginal effect on wtp for biodiesel. about 72% of 39a pilot study of diesel consumers the sample was not ready to pay an additional price for biodiesel. past studies also show a mixed response, but mostly favour a positive wtp. most of the studies have been focused on europe and north america (radics et al., 2015). in anderson (2012) and petrolia et al. (2010), there were positive responses for a premium price for ethanol and blended ethanol, respectively. a sizable number of respondents are willing to pay more for biofuel. even though consumers did not have a clear picture of the benefits of using them, they still preferred to pay a higher price. this is in contrast to the present study. though consumers are in favour of paying a premium, their preferences are heterogeneous (fimereli and mourato, 2009). to understand the results of the study, it is essential to understand the background of biodiesel production in sri lanka. however, this is not the main objective of this article. although sri lankans are aware of biodiesel, it is not commonly compared to petroleum, hydropower or even electricity. jatropha biodiesel production is in its infancy in sri lanka. unlike the african nations and other asian countries that have invested in jatropha production for biodiesel, sri lanka has not made any huge investments. only a few preliminary studies have been conducted on jatropha biodiesel production. furthermore, the jatropha cultivations that were started with the aegis of non-governmental organisations have not provided promising results up to now. meanwhile, jatropha plantations can only be cultivated on non-agricultural lands, to avoid negative impacts on food security. in many households, jatropha has been used as a fence crop. initially, it is unlikely to produce sufficient jatropha oil to be used as biodiesel. but, a blend with certain percentage of jatropha biodiesel mixed with conventional diesel can be used. moreover, local production has proven that the oil content of jatropha seeds is not up to the expectations. sivashankar et al. (2014) show that under local conditions, at status quo, biodiesel production is not economically feasible. yet they stress that if conditions were to improve, i.e. seed yield increases or biodiesel being sold at higher prices than the conventional diesel price (i.e. rs. 121 per litre) the venture could become feasible. herath et al. (2008) show that jatropha cultivation and biodiesel production is financially feasible in sri lanka and the by-products can be also sold with a benefit cost ratio of 1.92 and 24% internal rate of return. jatropha cultivation, oil extraction and biodiesel processing are protected by the government given the tax regime and farmers are better off compared with customers (sivashankar et al. 2014). although, there is an absence of direct government involvement in jatropha biodiesel production, through the tariff regime jatropha farmers and oil processors are literally protected from any foreign substitutes. the main issue is: does sri lanka have enough land to cultivate jatropha? will it be affordable? at present, it is highly unlikely that sri lanka will be able to produce higher levels, but with improved jatropha varieties such as those used in india, sri lanka too can expect to have higher yields. unlike the african experience, jatropha is not currently cultivated for any commercial purpose. consequently, there are no value chains in which stakeholders could potentially intervene. given the environmental concerns, it is plausible to understand why consumers are willing to pay for biodiesel. however, any biodiesel product in the market in sri lanka is going to be a new product and a new concept. thus, it would take some more time to materialize the repercussions of biodiesel in a country like sri lanka. studies in other countries have been able to analyse at least a primary level biodiesel production line. sumisaki and yabe (2007) study the introduction of bioethanol in rural japan, which is similar 40 p. sivashankar et al. to this study. there, consumers’ wtp is positive, but it is plausible that many diesel vehicle users are not willing to pay a higher price for the new product. furthermore, vehicles are considered to be costly in sri lanka, as is the case in other developing nations. given these factors, rational consumers would logically hesitate to use any new fuel type, despite its merits. the availability factor is a major issue in transportation. the fuel has to be available in the country and limiting fuel availability only to metropolitan cities would not be enough. with education, people tend to look for more benefits and decision factors when they purchase any good. open remarks from interviewees collected while the survey was conducted, showed that many educated people and people who have recently purchased a vehicle, or rather who own brand new vehicles, are reluctant to pay a premium for biodiesel. they are comfortable with conventional diesel even if the price is high. they have their own reservations on biofuel usage. even people with higher education and who work in white-collar jobs have their own scepticism when it comes to biodiesel. in contrast, almost all the respondents are in favour of renewable energy development in sri lanka. though in the questionnaire the pros and cons of biodiesel were clearly mentioned compared to diesel, many people still preferred diesel. on the other hand, less educated people are comfortable with biodiesel as long as its price is kept lower than the price of diesel. the results of this study are not ground breaking, but posit that further consideration should be given to biodiesel generation. the findings are not sufficient to form any policy implications based on this study alone, especially given the relatively small sample size. yet, this study has added to the literature on the estimation of demand for biodiesel in developing countries, where there is a lack of demand estimation studies on biodiesel. since the product is still not available on the market, a surrogate market condition had to be created. data collection was time consuming in this study, as before asking about jatropha biodiesel, the benefits had to be explained to questionnaire respondents. only after that the fuel customers were able to gauge and compare diesel with biodiesel and come up with a wtp amount. this had an impact on the decision to proceed with a fairly small sample size. besides this, the information burden on the interviewees might have had a non-neutral impact on the interviewee wtp. this is another reason to focus the study on the level of wtp for biodiesel rather than on the real wtp value. though it was obtained, due to the nature of the product and the biases that cannot be eliminated, it is possible that the real wtp value is misleading. inter alia, non-representativeness of the sample for the entire sri lankan population could also have led to distortions in wtp for biodiesel. as gunatilake (2003) suggests, biases from both the respondents as well as the enumerator could lead to distortions in the respondents’ answers. consequently, only the biases that are under control of the enumerator can be mitigated to a certain extent through proper training and research design as is the case in many contingent valuation studies. 6. conclusion the main objective of this research is to assess the willingness of diesel vehicle users in sri lanka to pay for jatropha biodiesel. the factors affecting these decisions were identified. the mean wtp for biodiesel by the diesel vehicle users was rs. 109 per litre (€ 0.74) for lower bound level. the median wtp was rs. 124 per litre (€ 0.85). older respondents with higher education are less likely to pay for biodiesel. married respondents 41a pilot study of diesel consumers with higher income are more likely to pay higher prices for biodiesel. it is recommended to use a larger sample of diesel vehicle users for further studies. choice card experiments would yield better results as the respondents would have the opportunity to directly see the information and pictures related to biodiesel, while listening to the enumerator’s description. however, since the experiences of jatropha biodiesel production are country specific, any support to major policy changes should take in to account the effect it will have on the overall economy. this could be done using a general equilibrium approach. this study posits that at least before pumping heavy investments into different types of renewable energy sources, there should be substantive consideration given to the acceptance of those technologies. at least, jatropha can be used for production of other biobased products that might be feasible. moreover, the state should consider how jatropha investments could stabilize the rural economy. in this respect, studies should be conducted to evaluate the possibility of using jatropha to improve rural livelihoods. acknowledgement the authors acknowledge the support for this study provided by nsf grant no: rg/2009/biofuel 002. the authors also acknowledge the anonymous reviewers of this manuscript for their comments aimed at improving the study. references ajzen, i. 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(2012). from agricultural to bio-based economics? context, state of the art and challenges. bio-based and applied economics 1(1): 3-11. world bank (2010). analysis of the scope of energy subsidies and suggestions for the g-20 initiative. washington d.c.: the world bank. available at http://documents. worldbank.org/curated/en/2010/06/17457082/analysis-scope-energy-subsidies-suggestions-g-20-initiative. accessed on 13th february, 2016. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(2): 213-229, 2012 consumers’ awareness and attitudinal determinants of european union quality label use on traditional foods wim verbeke1, zuzanna pieniak1, luis guerrero2, margrethe hersleth3 1 ghent university, department of agricultural economics, belgium 2 irta-monells, finca camps i armet, spain 3 nofima, norway abstract. this study analyses european consumers’ awareness and determinants of use of pdo, pgi and tsg labels in six european countries (italy, spain, france, belgium, norway and poland) using data from a cross-sectional survey with 4,828 participants. the study confirms a higher awareness of pdo (68.1%) as compared to pgi (36.4%) and tsg (25.2%). awareness is higher among men and people aged above 50 years. consumers’ use of a pdo, pgi or tsg label is triggered by the belief that the label signals better product quality. quality beliefs are shaped by an interest in getting information about product quality through the quality label. interest in the origin of foods is a stronger direct and indirect driver of label use than interest in support for the local economy, but both motivations are not directly related to tsg-label use. differences in the role of determinants are small between the three labelling schemes and between countries with versus without a strong tradition of quality labels in their agricultural and food quality policies. apart from building general awareness and favourable quality perceptions of the quality schemes and their respective labels, efforts to stimulate consumers’ interest in origin and getting information about product quality through eu quality labels are recommended. keywords. consumer, eu food quality policy, pdo, pgi, traditional food products, tsg jel-codes. d12; d83; q13; q18 1. introduction “a constantly increasing number of consumers attach greater importance to the quality of foodstuffs in their diet than to quantity.” this statement as mentioned in the european council regulation ec 510/2006 (european commission, 2006: l 93/12) was one of the main justifications for introducing the three european union (eu) quality schemes related to geographical indications and traditional specialities as the cornerstone of the eu agricultural product quality policy. the three quality schemes are commonly known with their acronyms pdo (protected designation of origin), pgi (protected 1 corresponding author: wim.verbeke@ugent.be. 214 w. verbeke, z. pieniak, l. guerrero and m. hersleth geographical indication) and tsg (traditional speciality guaranteed), from which the pdo and pgi (geographical indications) schemes are the most widely used with more than 500 registered products each by the end of 2011 (european commission, 2011). the pdo scheme covers agricultural products and foodstuffs which are produced, processed and prepared in a specific geographical area using recognised know-how. for pgi, at least one of the stages of production, processing or preparation has to take place in a specific geographic area. tsg highlights the traditional character of products, either in their composition or means of production, and hence, does not strictly refer to geographical origin (european commission, 2012). the three schemes are used as a means of product differentiation for otherwise often unbranded or generic agricultural products (verbeke and roosen, 2009) and have built on a long history of regional and traditional specialities, especially in southern european countries (teuber, 2010). belletti and marescotti (2011) stress the positive rural development potential of origin products – if well-embedded in a comprehensive rural development policy – through the fact that these create favourable economic, social, cultural and environmental effects. according to sylvander and barham (2011), geographical indications have gradually become part of the global economy and managed to become firmly embedded in it. food and agricultural quality policies are quite diverse across europe. becker (2009) identified several european regional clusters based on the focus in their food qualityenhancing policies, which included geographical indications as well as collective quality marks, quality assurance schemes and organic production. specifically, france, italy and spain were classified as countries that are clearly pdo/pgi oriented, in contrast with belgium, norway and poland, which were classified as rather food-quality-assurance scheme oriented, and “catching up with respect to pdo/pgis” (becker, 2009: 128). the three quality schemes aim at providing consumers with clear and succinct information regarding the product origin or speciality character, in order to enable consumers to make the best possible choices, i.e. choices in line with their preferences. many studies have focused on consumer issues related to geographical indications (for an overview, we refer to carpenter and larceneux (2008), verbeke and roosen (2009) and aprile et al. (2012) or traditional specialities (e.g. guerrero et al., 2010; vanhonacker et al., 2010; almli et al., 2011). whereas several studies support the idea that consumers value geographical indications and the traditional character as quality signals on food products (e.g. caporale and monteleone, 2001; van der lans et al., 2001; espejel et al., 2008; hersleth et al., 2011), several others report that consumer valuation cannot be taken for granted (e.g. bonnet and simioni, 2001; loureiro and umberger, 2007) and is often limited to particular market segments (tregear and giraud, 2011). although an increasing trend towards consumer awareness of and interest in pdo/pgi/tsg food products has been acknowledged (arfini et al., 2011), further consumer research aiming at improving our understanding of consumer reactions towards geographical and traditional speciality labels in europe has been recommended (chrysochou et al., 2012). the objective of the present study is, first, to describe european consumers’ awareness and second, to investigate attitudinal determinants of european consumers’ interest in and use of geographical (pdo, pgi) and traditional speciality (tsg) labels when purchasing traditional foods. the data for this study have been collected through a crosssectional pan-european consumer survey in six countries, namely italy, spain, france, 215consumers’ awareness and determinants of eu quality label belgium, poland, and norway. the choice of countries was informed by their geographical location across southern, central and northern europe, and by the differences in those countries’ focuses in terms of agricultural and food quality policies, most notably the different importance of products with a geographical indication or a traditional character in their policies. this paper will first develop and present the research framework for the study based on insights from previous consumer research with respect to quality labels. next, materials and methods will be presented, including data collection and modelling approaches. finally, findings will be presented and discussed, based on which conclusions and policy implications are set forth. 2. research framework the study validates a research framework with consumers’ use of quality labels as the behavioural response or outcome variable of interest (figure 1). the assumption is that quality labels fail to have an impact on behaviour and food choice unless they are used by consumers (verbeke, 2005; grunert and wills, 2007), which makes it relevant to gain more insight in determinants of quality label use. the framework of the study is informed by a classical stage model of consumer decision-making (solomon et al., 2006) in which interest in getting information about product quality through a pdo/pgi/tsg quality label is hypothesised to trigger attitude formation (i.e. perceived quality and distinctiveness in this study) and a subsequent behavioural response (i.e. label use in this study). two motivations are assumed to fuel consumers’ interest in getting information, namely interest in the origin of foods and interest in support for the local economy in food production (e.g. lusk et al., 2006). the first motivation refers to the fact that consumers might prefer products from certain regions or countries since they are believed to be simply better (i.e. more tasty, safer, healthier, more sustainable) (e.g. loureiro and umberger, 2007; resano et al., 2007; dekhili et al., 2011). the second motivation refers to consumer ethnocentrism or a so-called economic support dimension as identified by van ittersum et al. (2007). consumers might prefer products from their own region or country, e.g. because of loyalty to it and/or animosity towards others, or because of a related preference to support the local economy rather than remote or foreign economies. interests or motivations are hypothesised to influence label use both directly and indirectly through perceived quality as signalled by and inferred from the label information (van der lans et al., 2001). quality has been identified as one of the explicit goals of traditional food chains (together with traditionalism) that effectively matter to consumers (molnar et al., 2011). quality labels may generate positive associations or beliefs about product quality (carpenter and larcenaux, 2008; resano et al., 2007). two perceptions of product quality are included in the study, namely the belief that the quality label signals better or superior quality, and the belief that the quality label signals a distinct product character (perceived distinctiveness). the hypothesis is that a quality label as an information cue may perform better in triggering label use if it communicates something meaningful and relevant from the consumer perspective, such as better or superior quality and/ or distinctiveness. the research framework is operationalised for pdo, pgi and tsg separately. the overall study was performed within the broader context of traditional food consumption (guerrero et al., 2009, 2010). 216 w. verbeke, z. pieniak, l. guerrero and m. hersleth figure 1. conceptual framework: determinants of use of quality labels (pdo, pgi, tsg) interest  in  origin of  foods interest  in  getting   information  about   product  quality   through  a (pdo,  pgi,  tsg)  label interest  in  support  for   the  local  economy in  food  production use  of  the (pdo,  pgi,  tsg) label belief  that  the  quality   label  signals  better   quality belief  that  the  quality   label  signals  a  distinct   product  character note: the thickness of arrows indicates a consensus on the strengths of paths as obtained from the sem analyses performed in this study 3. materials and methods 3.1 research approach and sampling quantitative descriptive data were collected through a cross-sectional consumer survey with samples representative for age, gender and region in italy, spain, france, belgium, poland, and norway. the age range of the population was defined as 20-70 years. total sample size was 4,828 with around 800 participants in each of the six considered european countries. participants were randomly recruited from the representative tns european online access panel in line with the national population distributions with respect to age, gender and region. all contact and questionnaire administration procedures were electronic and web-based. data collection was performed during the period from october 25 until november 9, 2007, as part of the pan-european truefood (eu fp6) consumer study (vanhonacker et al., 2010). participants were asked to complete a self-administered structured electronic questionnaire. the master questionnaire was developed in english and translated in the national languages using the procedure of back-translation. following back-translation, the questionnaire was extensively pre-tested by the researchers through personal interviews with 15-20 participants in each of the countries in order to identify and eliminate potential problems and to ensure linguistic equivalence. fieldwork started after editing, correcting, electronic programming and additional pre-testing of the electronic version of the questionnaire. the average time for completing the total questionnaire ranged from 29’33” in france to 33’36” in poland. detailed socio-demographic characteristics of the national and pooled samples are provided in table 1. gender is equally distributed, which reflects that the population was 217consumers’ awareness and determinants of eu quality label intentionally not restricted to the main responsible person for food purchasing. age distributions, mean age and mean household sizes match closely with the national census data for the respective countries. table 1 also presents a proxy of socio-economic class, which was a subjective assessment (self-estimate) of the household’s financial situation reported on a 7-point interval scale ranging from “1 = difficult” over “4 = moderate” to “7  =  well off ”. the sample is slightly biased towards higher education and towards participants who reported to belong to the ‘moderate-well off ’ socio-economic classes, which may be attributed to the use of an electronic survey method. table 1. selected socio-demographic characteristics of the pooled and national samples pooled sample n=4,828 norway n=798 belgium n=826 france n=801 spain n=800 italy n=800 poland n=803 gender (%) female male 49.2 50.8 49.1 50.9 49.4 50.6 51.9 48.1 47.4 52.6 47.3 52.7 50.2 49.8 age (years) < 35 35-55 >55 mean s.d. 34.1 46.4 19.5 41.5 12.8 34.1 47.5 18.4 41.4 12.5 28.5 46.4 25.1 43.7 13.3 33.7 46.4 19.9 41.4 12.8 35.5 47.4 17.1 40.7 12.3 35.0 45.8 19.2 41.2 12.8 37.9 44.8 17.3 40.6 12.8 household size (number) mean s.d. 2.9 1.3 2.6 1.3 2.7 1.3 2.7 1.2 3.1 1.3 3.2 1.3 3.0 1.4 financial situation (%) difficult moderate moderate well off education lower secondary upper secondary higher education 24.6 32.1 43.3 8.6 38.8 52.6 24.8 31.5 43.7 9.8 47.9 42.3 17.8 28.6 53.6 8.1 35.2 56.6 35.5 32.5 32.0 9.0 37.5 53.5 18.9 36.2 44.9 9.4 24.4 66.2 29.8 32.8 37.4 12.4 61.4 26.2 21.3 31.0 47.7 2.6 26.7 70.7 3.2 measurement and scaling consumers’ awareness of the european food quality certification schemes was measured by asking the question “have you ever heard of food products with pdo (protected designation of origin)/pgi (protected geographical indication)/tsg (traditional speciality guaranteed)?” on three separate binary “yes”/“no” scales. interest in getting information about product quality through a pdo, pgi and tsg label as an information cue was measured by asking participants “to what extent would you like to be informed about the specific quality of a traditional food through a pdolabel (protected designation of origin)/a pgi-label (protected geographical indication)/a 218 w. verbeke, z. pieniak, l. guerrero and m. hersleth tsg-label (traditional speciality guaranteed)?” on a 7-point interval scale ranging from “1 = not at all” to “7 = very much”. belief that pdo, pgi and tsg signals better quality on a food product was measured using the question “to what degree do you consider a pdo-label (protected designation of origin)/a pgi-label (protected geographical indication)/a tsg-label (traditional speciality guaranteed) as signalling better quality food?”, which was to be answered on a 7-point interval scale ranging from “1 = not at all” to “7 = very much”. belief that pdo, pgi, tsg signals a distinct character of a food product was measured by asking participants “to what degree do you consider a pdo-label/a pgi-label/a tsg-label as signalling a distinctive character of traditional food?” on a 7-point interval scale ranging from “1 = not at all” to “7 = very much”. use of pdo, pgi and tsg as an information cue in consumers’ food purchasing decision-making was measured using the question: “to what extent do you consider a pdo-label (protected designation of origin)/pgi-label (protected geographical indication)/tsg-label (traditional speciality guaranteed) when making food purchasing decisions?” to be answered on a 7-point interval scale ranging from “1  =  not at all” to “7 = very much”. the questions probing for awareness, interest in getting information, beliefs and label use were asked for pdo, pgi and tsg in this order without randomising question items or label schemes. finally, interest in the origin of foods was measured by the statement “it is important to me that the food i eat on a normal weekday has the country or region of origin clearly marked”, whereas interest in support for the local economy in food production was assessed by the statement “it is important to me that the food i eat on a normal weekday has been produced while supporting the local economy”, both on a 7-point likert scale ranging from “1=i totally disagree” to “7=i totally agree”. 3.3 statistical analyses completed questionnaires were edited by the field research agency in order to ensure accuracy and precision of the response prior to coding and transcription of the data in spss 16.0. given the large sample sizes and very low numbers of missing responses, pairwise deletion was used as the method for treating missing values. the research framework has been tested for pdo, pgi and tsg using structural equations modelling (sem) by means of lisrel 8.72. with the use of sem the examination of all the relationships between constructs and items is performed simultaneously, which is a substantial advantage compared with single equation modelling (bollen, 1989). due to the large sample size the χ² may not be the most appropriate measure of goodness-of-fit (browne and cudeck, 1993). therefore, three other indices will be reported: the root mean square error of approximation (rmsea), the goodness of fit index (gfi) and the comparative fit index (cfi). values below 0.08 for rmsea (browne and cudeck, 1993) and above 0.90 for gfi and cfi (bollen, 1989) suggest an acceptable fit of the model. given the differences in food quality policies and marketplace provenance of products with geographical and traditional speciality labels in the different countries involved in the study, we will empirically validate the model separately for the countries with a strong tradition of using this type of quality labels (italy, spain and france) versus the countries 219consumers’ awareness and determinants of eu quality label without a strong emphasis on using these quality labels in their agricultural and food quality policies (belgium, poland and norway). 4. empirical findings 4.1 consumers’ awareness of pdo, pgi and tsg two thirds (68.1%) of the total sample reported to be aware of pdo, whereas the claimed awareness of pgi and tsg amounted only to 36.4% and 25.2%, respectively (figure 2). relatively more men to women were aware of pdo, pgi and tsg (table 2). in line with the market presence of the quality schemes, french, italian and spanish consumers were significantly more aware of pdo, whereas relatively few belgian, norwegian and polish consumers claimed to be aware of pdo. additionally, relatively more italian and relatively few belgian and polish consumers claimed to be aware of pgi. relatively more italian and polish consumers were aware of tsg, in contrast with belgian and french consumers. consumers above 50 years of age were significantly more aware of pdo, pgi and tsg than the younger ones. no significant differences in the awareness of pdo were found between people with different education levels, whereas relatively more consumers with upper secondary school education claimed to be aware of pgi and tsg. household size did not significantly associate with the awareness of pdo, pgi or tsg on food products. associations between self-reported financial situation and awareness of quality labels are statistically significant (0.01<p<0.05) but small. figure 2. consumers’ awareness of protected designation of origin (pdo), protected geographical indication (pgi) and traditional speciality guaranteed (tsg) in belgium, france, italy, norway, poland and spain; total n = 4,828; n = 800 per country (%) 0   10   20   30   40   50   60   70   80   90   100   belgium   france   italy   norway   poland   spain   %   pdo   pgi   tsg   220 w. verbeke, z. pieniak, l. guerrero and m. hersleth ta bl e 2. c on su m er s’ aw ar en es s of p d o , p g i a nd t sg b y de m og ra ph ic s an d co un tr y aw ar en es s o f pd o te st st at ist ic * pva lu e aw ar en es s o f pg i te st st at ist ic pva lu e aw ar en es s o f ts g te st st at ist ic pva lu e to ta l sa m pl e ye s n o ye s n o ye s n o g en de r ( % ) f em al e m al e 47 .6 52 .4 53 .0 47 .0 12 .5 0. 00 1 42 .7 57 .3 52 .8 47 .2 46 .2 6 <0 .0 01 47 .2 52 .8 49 .9 50 .1 2. 62 0. 05 6 49 .3 50 .7 c ou nt ry (% ) b el gi um f ra nc e it al y n or w ay p ol an d s pa in 11 .7 23 .7 23 .1 8. 5 9. 6 23 .4 28 .7 1. 2 2. 6 33 .7 31 .8 1. 9 17 57 .4 <0 .0 01 7. 7 13 .5 37 .8 14 .2 12 .7 14 .0 22 .5 18 .4 4. 4 17 .9 18 .9 18 .0 95 7. 82 <0 .0 01 9. 6 11 .8 22 .3 17 .0 22 .6 16 .7 19 .6 18 .2 14 .6 16 .4 14 .6 16 .5 14 3. 4 <0 .0 01 17 .1 16 .6 16 .6 16 .5 16 .6 16 .6 a ge c at eg or y (% ) < 3 5 yr 3 555 y r > 5 5 yr m ea n ag e (y ea rs ) 32 .3 49 .0 18 .7 41 .9 8 37 .7 47 .0 15 .4 40 .5 2 16 .0 7 1. 46 <0 .0 01 <0 .0 01 30 .1 49 .2 20 .1 42 .8 36 .3 47 .5 16 .2 40 .8 23 .8 2 2. 10 <0 .0 01 <0 .0 01 29 .3 48 .2 22 .5 43 .7 35 .7 48 .4 15 .9 40 .8 33 .6 1 4. 32 <0 .0 01 <0 .0 01 34 .0 48 .3 17 .6 ed uc at io n (% ) l ow er se co nd ar y u pp er se co nd ar y h ig he r e du ca tio n 9. 1 38 .6 52 .3 7. 6 39 .1 53 .4 3. 24 0. 19 8 9. 0 42 .7 48 .3 8. 3 36 .6 55 .1 21 .5 7 <0 .0 01 9. 2 42 .0 48 .7 8. 3 37 .7 53 .9 9. 92 0. 00 7 8. 6 38 .8 52 .7 h ou se ho ld si ze (# ) m ea n 2. 89 2. 87 2. 37 0. 67 2. 92 2. 86 1. 46 0. 15 2. 94 2. 86 0. 10 9 0. 09 2 2. 88 fi na nc ia l s itu at io n (% ) d iffi cu ltm od er at e m od er at e m od er at e w el l o ff 25 .7 31 .8 42 .5 22 .3 32 .7 44 .9 6. 59 0. 03 7 22 .7 32 .2 45 .1 25 .7 32 .1 42 .2 6. 18 0. 04 5 22 .4 31 .2 46 .4 25 .4 32 .4 42 .2 7. 3 0. 02 6 24 .7 32 .1 43 .3 *t es t st at is tic s us ed : c hi -s qu ar e as so ci at io n te st fo r g en de r, co un tr y, a ge c at eg or y, e du ca tio n, f in an ci al s itu at io n; in de pe nd en t sa m pl es t -t es t fo r m ea n ag e an d m ea n ho us eh ol d si ze 221consumers’ awareness and determinants of eu quality label 4.2 determinants of consumers’ use of pdo, pgi and tsg three multi-group analyses have been performed in order to identify the determinants of use of the three quality labels. participants who were not aware of the pdo label (n = 1,541), the pgi label (n = 3,071) and the tsg label (n = 3,612) have been excluded from the sem analyses. as a result, the lisrel model analyses were performed with a sample of n = 3,287 participants who were aware of pdo; n = 1,757 who were aware of pgi; and n  =  1,216 who were aware of tsg. first, inter correlations between the constructs of the research model were checked, as for example presented in table 3 for the pdo model. all correlation coefficients across the three models were significant but below 0.80, thus (severe) multicollinearity is not a concern in the present data (tabachnick and fidell, 2001). table 3. construct correlation matrix for pdo (n = 3,287) construct 1 2 3 4 5 6 1. use of the pdo label 1.00 2. interest in getting information through pdo 0.50 1.00 3. belief that pdo signals better quality 0.64 0.49 1.00 4. belief that pdo signals distinct character 0.62 0.50 0.74 1.00 5. interest in support for the local economy 0.28 0.30 0.20 0.21 1.00 6. interest in origin of foods 0.33 0.37 0.23 0.25 0.53 1.00 note: all correlations are statistically significant at p<0.01 (two-tailed) the proposed model performed relatively well for the three schemes. the pdo model fitted the data best with χ² = 91.89 and 14 degrees of freedom (p<0.001); the rmsea value of 0.058; the gfi 0.99 and the cfi 0.99, which are satisfactory goodness-of-fit indices (table 4). additionally, the goodness-of-fit statistics indicated that the presented research framework was acceptable as well for the pgi model (satorra-bentler χ² = 85.09, df = 14; rmsea=0.076; table 5) and tentatively acceptable for the tsg model (satorra-bentler χ² = 78.23, df = 14; rmsea = 0.087; table 6). consistent results are obtained across the three models and two sets of countries. first, interest in the origin of foods fuels consumers’ interest in getting information about product quality through a pdo/pgi/tsg-label. this motivation is much stronger associated with interest in getting information through a label than the relationship with interest in support for the local economy in food production. a notable exception is the pgimodel for belgium, norway and poland, where the relation of both motivations with consumers’ interest in getting information through a pgi-label is nearly equal. second, direct relations between interest in origin or support for local economy and label use are small for pdo and pgi, and insignificant for tsg. also the direct relation between interest in getting information through the labels and label use is rather small. third, consumers’ 222 w. verbeke, z. pieniak, l. guerrero and m. hersleth ta bl e 4. d et er m in an ts o f c on su m er s’ us e of p ro te ct ed d es ig na tio n of o rig in (p d o ) l ab el pa th fr om to ita ly, s pa in , f ra nc e (n =2 ,3 08 ) be lg iu m , n or w ay , p ol an d (n =9 79 ) m ot iv at io ns in te re st in o rig in o f f oo ds in te re st in g et tin g in fo rm at io n th ro ug h pd o 0. 31 0. 24 in te re st in su pp or t f or th e lo ca l e co no m y in te re st in g et tin g in fo rm at io n th ro ug h pd o 0. 13 0. 14 in te re st in o rig in o f f oo ds u se o f t he p d o la be l 0. 10 0. 11 in te re st in su pp or t f or th e lo ca l e co no m y u se o f t he p d o la be l 0. 07 0. 06 in te re st in g et tin g in fo rm at io n th ro ug h pd o be lie f t ha t p d o si gn al s b et te r q ua lit y 0. 49 0. 42 in te re st in g et tin g in fo rm at io n th ro ug h pd o be lie f t ha t p d o si gn al s d ist in ct c ha ra ct er 0. 15 0. 20 in te re st in g et tin g in fo rm at io n th ro ug h pd o u se o f t he p d o la be l 0. 15 0. 14 pe rc ei ve d qu al ity be lie f t ha t p d o si gn al s b et te r q ua lit y be lie f t ha t p d o si gn al s d ist in ct c ha ra ct er 0. 67 0. 62 be lie f t ha t p d o si gn al s b et te r q ua lit y u se o f t he p d o la be l 0. 30 0. 40 be lie f t ha t p d o si gn al s d ist in ct c ha ra ct er u se o f t he p d o la be l 0. 29 0. 22 g oo dn es sof -fi t s ta tis tic s: χ ²(1 4) = 9 1. 89 , p <0 .0 01 ; r m se a = 0 .0 58 ; n n fi = 0 .9 82 ; c fi = 0 .9 92 ; g fi = 0 .9 88 . o nl y si gn ifi ca nt c oe ffi ci en ts (p <0 .0 5) a re s ho w n; re po rt ed c oe ffi ci en ts a re d ire ct e ffe ct s on ly . 223consumers’ awareness and determinants of eu quality label ta bl e 5. d et er m in an ts o f c on su m er s’ us e of p ro te ct ed g eo gr ap hi ca l i nd ic at io n (p g i) la be l pa th fr om to ita ly, s pa in , f ra nc e (n =1 ,1 48 ) be lg iu m , n or w ay , p ol an d (n =6 09 ) m ot iv at io ns in te re st in o rig in o f f oo ds in te re st in g et tin g in fo rm at io n th ro ug h pg i 0. 37 0. 24 in te re st in su pp or t f or th e lo ca l e co no m y in te re st in g et tin g in fo rm at io n th ro ug h pg i 0. 12 0. 20 in te re st in o rig in o f f oo ds u se o f t he p g i l ab el ns 0. 07 in te re st in su pp or t f or th e lo ca l e co no m y u se o f t he p g i l ab el 0. 10 0. 13 in te re st in g et tin g in fo rm at io n th ro ug h pg i be lie f t ha t p g i s ig na ls be tte r q ua lit y 0. 55 0. 47 in te re st in g et tin g in fo rm at io n th ro ug h pg i be lie f t ha t p g i s ig na ls di st in ct c ha ra ct er 0. 13 0. 16 in te re st in g et tin g in fo rm at io n th ro ug h pg i u se o f t he p g i l ab el 0. 17 0. 10 pe rc ei ve d qu al ity be lie f t ha t p g i s ig na ls be tte r q ua lit y be lie f t ha t p g i s ig na ls di st in ct c ha ra ct er 0. 69 0. 66 be lie f t ha t p g i s ig na ls be tte r q ua lit y u se o f t he p g i l ab el 0. 39 0. 34 be lie f t ha t p g i s ig na ls di st in ct c ha ra ct er u se o f t he p g i l ab el 0. 27 0. 29 g oo dn es sof -fi t s ta tis tic s: χ ²(1 4) = 8 5. 09 , p <0 .0 01 ; r m se a = 0 .0 76 ; n n fi = 0 .9 85 ; c fi = 0 .9 87 ; g fi = 0 .9 88 . o nl y si gn ifi ca nt c oe ffi ci en ts (p <0 .0 5) a re s ho w n; n s = no t s ig ni fic an t; re po rt ed c oe ffi ci en ts a re d ire ct e ffe ct s on ly . 224 w. verbeke, z. pieniak, l. guerrero and m. hersleth ta bl e 6. d et er m in an ts o f c on su m er s’ us e of t ra di tio na l s pe ci al ity g ua ra nt ee d (t sg ) l ab el pa th fr om to ita ly, s pa in , f ra nc e (n =6 17 ) be lg iu m , n or w ay , p ol an d (n =5 99 ) m ot iv at io ns in te re st in o rig in o f f oo ds in te re st in g et tin g in fo rm at io n th ro ug h ts g 0. 33 0. 25 in te re st in su pp or t f or th e lo ca l e co no m y in te re st in g et tin g in fo rm at io n th ro ug h ts g 0. 14 0. 16 in te re st in o rig in o f f oo ds u se o f t he t sg la be l ns ns in te re st in su pp or t f or th e lo ca l e co no m y u se o f t he t sg la be l ns ns in te re st in g et tin g in fo rm at io n th ro ug h ts g be lie f t ha t t sg si gn al s b et te r q ua lit y 0. 55 0. 53 in te re st in g et tin g in fo rm at io n th ro ug h ts g be lie f t ha t t sg si gn al s d ist in ct c ha ra ct er 0. 14 0. 15 in te re st in g et tin g in fo rm at io n th ro ug h ts g u se o f t he t sg la be l 0. 09 0. 10 pe rc ei ve d qu al ity be lie f t ha t t sg si gn al s b et te r q ua lit y be lie f t ha t t sg si gn al s d ist in ct c ha ra ct er 0. 71 0. 69 be lie f t ha t t sg si gn al s b et te r q ua lit y u se o f t he t sg la be l 0. 42 0. 34 be lie f t ha t t sg si gn al s d ist in ct c ha ra ct er u se o f t he t sg la be l 0. 28 0. 34 g oo dn es sof -fi t s ta tis tic s: χ ²(1 4) = 7 8. 23 , p <0 .0 01 ; r m se a = 0 .0 87 ; n n fi = 0 .9 65 ; c fi = 0 .9 83 ; g fi = 0 .9 82 ; n s = no t s ig ni fic an t o nl y si gn ifi ca nt c oe ffi ci en ts (p <0 .0 5) a re s ho w n; n s = no t s ig ni fic an t; re po rt ed c oe ffi ci en ts a re d ire ct e ffe ct s on ly 225consumers’ awareness and determinants of eu quality label belief that pdo/pgi/tsg signal better quality emerges as the strongest driver of label use. on one hand, this quality perception is strongly influenced by interest in getting information about product quality through the label, and its relationship with label use on the other hand is further reinforced by a strongly associated belief that the quality label signals a distinct product character. fourth, the role of perceived distinctiveness as moderator and determinant of label use is significant but somewhat weaker. only few differences between both sets of countries are observed. the path from interest in the origin of foods, over interest in getting information through a quality label, to label use is stronger in countries with a tradition of quality labels (italy, spain, france). paths involving interest in support for the local economy are similar – whether significant or not – across countries for the pdo and tsg model, but somewhat stronger in the pgi model for countries without a tradition of quality labels (belgium, norway, poland). finally, there is also a tendency that the role of perceived distinctiveness is more prominent in countries without a tradition of quality labels, which is most apparent for the pgi and tsg models. 5. discussion and conclusions the present study assessed european consumers’ awareness of pdo/pgi/tsg and it proposed and validated a research framework for analysing consumers’ use of those labels when making food purchasing decisions. the study faces specific strengths owing to its quantitative and cross-cultural approach with large consumer samples, but also some limitations owing to the use of single item constructs, the non product-specific nature of the study and the sampling procedure (on-line consumer panel). framing was limited to traditional foods, which is a very broad product category (guererro et al., 2009). it remains to be investigated whether the framework holds equally for quality labels on meat or dairy products versus olive oil versus alcoholic drinks, for example. despite the use of nationally representative samples with respect to age, gender and region, the use of an online survey method may have introduced some bias towards higher educated and financially better off participants. first, consumers’ awareness of geographical indications, especially pdo, is very high in the countries with a strong tradition of using this quality scheme. awareness of pgi is at a comparable high level as pdo in italy, but much lower than pdo in france and spain. this finding is remarkable since the market presence of pgi in terms of the number of registered products is at a comparable level as for pdo in france and spain, whereas the number of registered pdo products largely outweighs the number of pgi products in italy (arfini et al., 2011). apparently, the pgi products have been much more successful in building consumers’ awareness in italy than in france and spain. awareness about products with tsg is highest in poland, which coincides with the fact that poland has the largest number (nine by the end of 2011) of registered tsgs among the countries in the study. despite the fact that france and norway had no registered tsgs at that time, awareness levels around 20-25% are reported. our findings confirm a high level of consumers’ awareness of pdo in the countries with a tradition of geographical indications, which in our study are located in southern (italy and spain) and western (france) europe (arfini et al., 2011), but also indicate a considerable gap with consumers’ awareness of pgi and tsg, as well as substantial differences in awareness between countries with versus without a tradition of geographical indications in their agricultural and food quality 226 w. verbeke, z. pieniak, l. guerrero and m. hersleth policies. while some of the observed differences corroborate with market presence and quality policies in the respective countries, others are not straightforward in their interpretation and may require additional research. in addition, it should be noted that objective knowledge about the attributes guaranteed by geographical indications is low, even among italian consumers (aprile et al., 2012). second, with respect to socio-demographics, a higher awareness is reported among men and older consumers for each of the schemes. in comparison, dekhili et al. (2011) reported a higher use of official cues including the french aoc (appellation d’origine contrôlée) among women and older consumers, versus a higher use of origin cues (country and region of origin) among men for the case of olive oil. bonnet and simioni (2001) found a positive age and income effect on willingness-to-pay for camembert cheese. loureiro and umberger (2003) reported stronger support in terms of willingness-to-pay for country-of-origin labelled beef among female and wealthier consumers. income effects are small in our sample where income was measured as a self-estimate of wealth. if willingness-to-pay for products with quality labels is effectively higher among females, wealthier and older consumers, it is paramount to ensure that also awareness of these cues is higher within these consumer segments. however, the findings from our study do not suggest patterns that are consistent with this logic. hence, additional targeted efforts towards creating higher consumers’ awareness of pdo/pgi/tsg among segments with a higher possible willingness-to-pay are recommended. third, findings from this study confirm the prominent role of motivations and quality perceptions in shaping consumers’ use of the respective labels in food purchasing decisions. the structure of attitudinal determinants of label use is consistent across the three labelling schemes. the exception of non-significant direct paths from origin and local economy support in the tsg-model is consistent with the positioning of the tsg scheme, which is not directly related to geographical origin or local produce. apart from a potentially stronger role of consumer ethnocentrism in the case of tsg in countries without a strong tradition of quality labels, only minor differences are detected between the motivational structures of consumers in countries with a different emphasis on quality labels in their agricultural and food quality policies. the study herewith indicates that similar triggers (e.g. product positioning and marketing communications) can be used to stimulate consumer interest in and use of pdo, pgi and tsg across europe. apart from building general awareness, highest success can be expected from policy and communication efforts that: stimulate consumers’ interest in the origin of foods, trigger interest in getting information about product quality through the quality schemes and labels, and build favourable perceptions about quality and distinctiveness of products with pdo, pgi or tsg labels. acknowledgement this study was supported by truefood (traditional united europe food), an integrated project financed by the european commission under its sixth framework programme (eu pf6 contract no. food-ct-2006-016264). the information in this document reflects only the authors’ views and the european commission is not liable for any use that may be made of the information contained therein. 227consumers’ awareness and determinants of eu quality label references almli, v.l., verbeke, w., vanhonacker, f., naes, t. and hersleth, m. 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(2009). market differentiation potential of country-of-origin, quality and traceability labelling. the estey centre journal of international law and trade policy, 10: 20-35. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 3(1): 63-81, 2014 doi: 10.13128/bae-12827 consumer willingness to pay for food safety: the case of mycotoxins in milk paolo sckokai1,*, mario veneziani2, daniele moro1, elena castellari1,3 1 dipartimento di economia agroalimentare, università cattolica del sacro cuore, piacenza, italy 2 dipartimento di economia, sezione di economia agroalimentare, università degli studi di parma, parma, italy 3 college of agriculture and natural resources, university of connecticut, storrs-mansfield, ct, usa abstract. mycotoxins contamination in food is a serious source of health risks. this paper evaluates the italian consumers’ perception of the mycotoxins’ risk through their willingness to pay (wtp) for a hypothetical bottle of milk obtained by cows fed with, inter alia, maize certified for the “good practices” (gps) that reduce this risk. therefore, a web-based stated choice experiment (sce) has been carried out involving a representative sample of 973 italian consumers and the wtp has been measured using the panel data version of a random parameters logit (rpl) model. results show that italian consumers are willing to pay a 29% average price premium for “reduced-mycotoxins” milk. this premium increases for consumers between 44 and 54 years of age, who are students, have completed tertiary education, are economically well-off and shop fairly infrequently. keywords. food safety, mycotoxins, willingness to pay, choice experiments, mixed logit jel codes. c35, c93, d12 1. introduction food safety is one of the most relevant determinants of consumer food demand. when food products are perceived unsafe, because of the occurrence of a food safety scandal (e.g., bovine spongiform encephalopathy (bse) crises, avian influenza, dioxin in meat, foodborne pathogens), their demand drops and recovery to pre-scandal levels may be slow and partial. public institutions have strived to maintain and promote food safety through regulation: in 2002 the european union (eu) has established the european food safety authority (efsa) as the central agency aiming to improve eu food safety, ensure a high level of consumers’ protection and restore and maintain confidence in the eu food supply (efsa, 2014). moreover, the eu common agricultural policy (cap) places a high value on food safety, recognising its nature of public good produced by a multifunctional agriculture, such that future agricultural support could be heavily linked to these issues: 59% of the respondents to a recent survey of european citizens (euro* corresponding author: paolo.sckokai@unicatt.it. http://dx.doi.org/10.13128/bae-12827 64 p. sckokai, m. veneziani, d. moro, e. castellari barometer, 2010) deem ensuring safe and high quality food one of the priorities of the cap. furthermore, since consumers value food safety, private incentives in ensuring food safety might arise; thus, a better understanding of consumers’ risk perception and valuation may help both private firms and public agencies to design and implement actions aimed to enhance food safety. since 1979 the eu has been managing the rapid alert system for food and feed (rasff), a tool to exchange information about measures taken as a response to serious health risks detected in food or feed products (rasff, 2013). the annual rasff report provides comprehensive statistics on notifications by eu member states concerning detection, in their own territory or at the eu borders, of these potential health risks due to a number of hazards. mycotoxins were the second most important hazard, accounting for roughly 15% of the total number of notifications in 2012 (i.e., 525 out of 3516), just behind pathogenic micro-organisms (roughly 17% of notifications) (rasff, 2012). while some 39% of the mycotoxins notifications constitute border rejections of nuts, nut products and seeds, some 6% refer to cereals and bakery products while almost 15% can be ascribed to feed. nonetheless, the number of notifications reporting a contamination by mycotoxins has declined sharply (-16.8%) due to a reduction in the occurrence of those by aflatoxins (afs) which were down 17.3% year on year. however, five instances of high concentration of af m1 have been recorded renewing a concern last experienced back in 2007. the latter can be ascribed to af b1, found in european maize used as feed for milk cows, which transferred to milk as af m1 (galvano et al., 1996). in fact, the severe drought affecting the 2012 maize growing season in south-eastern europe has been instrumental to raising the concentrations of afs in this crop and area.1 although afs have been considered a minor threat to the safety standards of the upstream phases of the agricultural/food marketing chain (i.e., before the agricultural commodity reached the storing and processing stage of its marketing chain (battilani et al., 2012)), recent studies have increasingly “…recognise[d] that good agricultural practise[s] (gap[s]) represent the primary line of defence against the contamination of food products by inherent plant toxins and mycotoxins” (speijers et al., 2010:4).2 moreover, this awareness has spread along the whole supply chain of maize and animal feed produced out of it, which appear particularly prone to afs contamination, spurring the “… implementation of good manufacturing practise[s] (gmp[s]) during the handling, storage, processing and distribution of cereals for food and animal feed” (speijers et al., 2010: 4). a major concern for private firms along the supply chain is to inform the consumer that their final products are derived from agricultural commodities or food raw materials employing gaps/gmps. “safer” food products will then command a higher price, to remunerate all the actors in the marketing chain for their good practises (gps), compared to conventional ones, if the consumer values the risk of falling ill from eating unsafe 1 climate change, raising temperatures and declining rainfalls are likely to transfer a problem typical – especially at the farm production stage – of tropical and sub-tropical regions to previously unaffected areas of the globe (battilani et al., 2012). 2 note that the european commission has compiled a recommendation (17 august 2006) collecting all the gaps aimed to prevent and/or reduce the incidence of fusarium toxins in cereals and cereal products (european commission, 2006). 65consumer willingness to pay for food safety: the case of mycotoxins in milk food.3 since several attributes related to food safety may not be observable but mostly are communicated by the producer through labelling and advertising, the existence of information asymmetry in this market segment turns some of the products’ attributes into credence attributes. if the safety-related products’ attributes can be identified correctly, and the information asymmetry largely overcome, consumers may be willing to pay a (higher) price premium for a food item produced by a marketing chain applying, at each stage, the relevant gps.4 therefore, the price premium also provides a measure of the value placed by consumers on the associated health risks. given the production of “safer” food items is more expensive than that of traditional ones, the food industry and its suppliers may be interested in gauging consumers’ interest and willingness to pay (wtp) for a product with these characteristics ahead of the official release on to the market. in presence of hypothetical yet technically feasible products, stated preference (sp) methods (i.e., contingent valuation (cv), stated choice experiments (sces), auctions) are commonly employed to elicit consumer preference for products defined on the basis of the levels of a bundle of attributes (enneking, 2004) and to acquire or calculate consumers’ wtp by modelling individual choices (i.e., choice modelling, grounded on random utility theory (quagrainie et al., 1998)). nonetheless, the hypothetical nature of choices submitted to consumers in a sce, which translates into consumers not being required to make an actual choice involving a monetary outlay, may lead to wtp estimates biased upwards. moreover, consumer wtps may vary according to the number and type of attribute levels the consumer is confronted with in the experiment (i.e., “context dependency”, mørkbak et al. (2012)). however, the joint evaluation of multiple product attributes gives rise to welfare measures, of which wtp is arguably one, which have generally smaller variances – relative to their means – compared to those obtained from cv (adamowicz et al., 1998). furthermore, considering different levels of the attributes eliminates the part-whole bias (hanley et al., 1998; enneking, 2004). empirical studies on consumer valuation of and preference for “safer” food products include enneking (2004), brown et al. (2005), aizaki and sato (2007), goldberg and roosen (2007), nakamura et al. (2009), tonsor et al. (2009), tonsor (2011) and mørkbak et al. (2012). those focused on “safer” milk comprise wang et al. (2008) and wolf et al. (2011).5,6 3 khlangwiset and wu (2010) collect evidence on the cost and percentage reduction in afs levels in food and agricultural products due to the implementation of gaps and gmps. it is evident that applying both types of gps implies additional, sometimes significant, costs for farmers and feed processors. nonetheless, the existing attempts to quantify the economic costs of, especially, gaps seem to disregard the reduced income which a farmer cultivating a high yield/value crop like maize in rotation with a low(er) yield/value output such as alfalfa or clover is likely to experience, compared to a farmer producing a (maize) monoculture. 4 wang et al. (2008) report that hosono (2005), employing japanese scanner (i.e., revealed preference) data, unveiled a price premium of 12% on purchase of milk and milk products carrying a hazard analysis critical control point (haccp) label. it should be noted that the haccp certification is the signal of enhanced food safety most frequently analysed in the applied literature investigating consumer valuation of food safety concerns. 5 in compiling this list, we have tried to focus on food safety concerns triggered mainly by food contaminations due to pathogens. nonetheless, we recognise that because of, inter alia, genetically modified and cloned crops or animals, animal illnesses or traceability and country-of-origin issues, additional food safety concerns can arise in some consumers. hence, food safety is a multifaceted concept subject to different meanings across consumers. 6 aizaki (2012) provides a systematic review of, inter alia, studies employing sces to value consumer wtp for safer milk. unfortunately, all the relevant references discussed therein are in japanese but the salient evidence of these contributions is reported and also used for the discussion of the present results. 66 p. sckokai, m. veneziani, d. moro, e. castellari in this paper, the consumers’ perception and economic valuation of mycotoxins’ risk in food is investigated estimating, in the context of a sce, the wtp of a sample of italian consumers for a “hypothetical” bottle of milk obtained by cows fed with, inter alia, maize certified for farmers and processors applying gaps and gmps such that a lower risk of mycotxoin contamination could be expected. the wtp has been measured using the panel data version of a random parameters logit (rpl) model (train, 2003) on data collected with a web-based survey distributed in the summer of 2009. to the best of our knowledge, and mainly drawing from aizaki (2012), the only papers explicitly considering consumer valuation of gaps/gmps, as measures to maintain/improve food safety, are nakamura et al. (2009) on the production of bottled and carton apple juice, aizaki and sato (2007) on valuing gaps-certified tomatoes and aizaki et al. (2004) that estimate the attitudes towards beef derived from cattle, inter alia, fed “… in accordance with hypothetical food safety measures” (aizaki, 2012:9). therefore, this work contributes to the literature adding further evidence on a fairly unexplored food safety aspect. 2. mycotoxins and gaps/gmps mycotoxins are naturally occurring secondary metabolites produced mainly by moulds of the aspergillus, penicillum and fusarium genera: aspergilli develop at high temperatures and most frequently at the storage stage of an agricultural commodity or food marketing chain while fusaria thrive in presence of plentiful water and high humidity levels, largely at the growing stage (e.g., speijers et al., 2010). whilst moulds can be considered as plant pathogens, the ingestion of their toxin can result in acute and chronic disease in animals and humans (speijers et al., 2010). mycotoxins like afs and ochratoxin a are known to be carcinogenic (williams et al., 2004) and have been defined “… the main chronic health risk related to food …” (speijers et al., 2010:6). mycotoxins are particularly dangerous for human health because they cannot be destroyed either by (animal) digestion or by heating and/or refrigeration while cooking: if they are present in the raw agricultural commodity, they remain virtually unchanged in the food chain. serious health concerns arise from afs in maize, due to its prevalence in animal feed, and especially in cows’ daily rations such that afs can contaminate frequently consumed products like milk and dairy products. for instance, in 2003, very high concentrations of af m1 were detected in italy in milk bottles and in grana padano cheese, one of the most famous italian protected denomination of origin (pdo) dairy product. agronomic and technological research aimed to curtail the risk of afs concentrating in maize suggests preventing or containing the damages, on the growing crop, due to high temperature, water stress and insects’ diffusion to limit the initial infection by the concerned moulds (battilani et al., 2012). in turn, tillage and fertilisation practises, crop rotation and hybrid choice, planting and harvesting date/technique, limited cropping density and adequate irrigation are gaps which have proven to limit the occurrence of afs in maize (battilani et al., 2012 and specific references therein; speijers et al., 2010). in particular, planting maize in rotation with cotton, wheat and soybeans appears to minimise the soil populations of the fungi producing afs (e.g., abbas et al., 2004). eeckhout et al. (2013) suggest that wheat, hence possibly maize, cultivated after, inter alia, alfalfa and clover or beets is a rotation plan subject to a low risk of fusarium contamination. nonetheless, the effects of crop 67consumer willingness to pay for food safety: the case of mycotoxins in milk rotation may only be limited in the short term. appropriate fertilisation, delivering sufficient levels of nitrogen, and tillage practices, which removing plant debris from the field limits the fungal inoculum, may appropriately prepare the soil to the early planting of hybrids which do not mature in very hot and dry months or, as bt-maize, appear to be more resistant to fungal infection.7 throughout the growing season, (sprinkler) irrigation, weed and pests control (i.e., against the european corn borer in maize)8 appear crucial activities to control fungi diffusion in maize hence the possibility of afs development. among the gmps, reducing the time intervening between harvesting and drying; proper sanitation of the relevant machinery and storage areas; segregating the product according to moisture and protein content; storing in a clean, well maintained and sanitised, cool, dry and ventilated premise; fumigation with phosphine or essential oils to control insect pests and mould infections as well as using appropriate filtering systems at the mills seem to effectively impede the development/spread of mycotoxins in processed products (speijers et al., 2010; eeckhout et al., 2013). 3. theory safety is a food “attribute”. according to lancaster’s consumer theory (lancaster, 1966), goods are considered a bundle of attributes, and consumers’ preferences are stated over attributes. in this context, goods’ characteristics can be evaluated using discrete choice models, where choices are made among mutually exclusive finite alternatives within an exhaustive choice set. mcfadden (1974) proposed the econometric framework for discrete choice analysis in the context of random utility models. for an individual i the (indirect) utility obtained from a good j, uij, can be decomposed in a deterministic part, vij, related to the k observed good’s characteristics (including price), and in a stochastic part, εij, accounting also for unobserved variables u v f x ,ij ij ij jk ik ijε β ε( )= + = + (1) where xjk is the level of attribute k in good j and βik is the individual preference parameter for the kth characteristic (i.e., the deterministic part of individual utility is a function of product’s characteristics). the choice rule is utility maximization: good j is chosen by individual i among all alternatives iff u u h jij ih≥ ∀ ≠ (2) different assumptions on the structure of the stochastic component lead to a variety of specifications. in the so-called mixed logit (ml) model the stochastic part εij is decom7 resistant hybrids have not been developed and commercialised yet and may also require appropriate and unique crop management practises which might become ineffective whenever peculiar environmental conditions develop in the field. moreover, note that bt maize hybrids are not permitted in several european countries (battilani et al., 2012). 8 currently, no commercially admitted fungicide can be legally employed to limit the biosynthesis of afs by the two most aflatoxigenic fungi. only biocontrol agents such as natural oils from thyme, lemongrass and other herbs may limit the afs content in maize (battilani et al., 2012). 68 p. sckokai, m. veneziani, d. moro, e. castellari posed as εij = ηij + uij where ηij is an additive random term that can be related to attributes and alternatives and can account for, inter alia, correlation and heteroscedasticity, while the uij term is an i.i.d. random component with an extreme value distribution. in our study, we have employed the rpl, where a ml specification is obtained by allowing the set of individual preference parameters βi to be distributed across individuals according to a statistical distribution, βi ~f(β|μβ, σβ) characterized by mean μβ and variance-covariance matrix σβ. the rpl model has become the standard reference for sc studies because of its ability to account for preference heterogeneity and its flexibility in accommodating a variety of model specifications (mcfadden and train, 2000). then, the probability pi that individual i may choose alternative j, conditional on a given set of values of the βi parameters, is given by p j l exp expi i ij i v v h ij i ih i∑ ββ ββ( ) ( )≡ = ββ ββ ( ) ( ) (3) the rpl-ml specification can be also generalized to panel data (i.e., each sampled individual i makes repeated choices), assuming that parameters are constant across time/ choices. if t is the number of repeated choices made by each individual, by integrating the product of the t conditional probabilities, we obtain the probability of choosing alternative j as p j l f d,i ijt i i t t 1 ∏∫ ββ ββ µµ σσ ββ( )( ) ( )= ββ ββ = (4) the rpl-ml specification does not require the independence of irrelevant alternatives (iia) property to be fulfilled, thus it does not restrict substitution patterns as in the multinomial logit model; therefore, the ratio of the probabilities of two alternatives, j and h, depends also on attributes of alternatives other than j and h. in order to evaluate consumer wtp for product attributes, we need to consider that in the random utility model each preference parameter represents the marginal utility of the attribute, that is, u xk kβ∂ ∂ = . mean wtp estimates for attribute k in a rpl can be calculated as βk/βprice and can be considered representative for the entire sample in presence of jointly insignificant σβ, implying absence of preference heterogeneity (wolf et al., 2011).9 nonetheless, because of effects coding all the relevant variables in the model, the formulation for the calculation of mean wtp for attribute k, applicable to dummy coded variables, needs to be amended as wtpk = 2βk/βprice (lusk et al., 2003; tonsor, 2011; wolf et al., 2011). furthermore, given the individual-specific nature of the preference parameter vector β due to expected significant preference heterogeneity, the individual (simulated) estimate of the wtp for any attribute k is given by (greene et al., 2005) 9 note that the present calculation of the mean wtp for attribute k does not feature the usual negative sign because the price variable employed in this study is, in fact, minus the original price variable. 69consumer willingness to pay for food safety: the case of mycotoxins in milk e wtp r l r l ˆ 1 2 ˆ ˆ 1 ˆ s i k ikr i price r ir r r ir r r, , ,1 1  ∑ ∑ ββ ββ ββ ββ( ) ( )   = = = (5) where r is the rth draw out of the total number of r replications.10 to provide useful insights into which socio-demographic variables may be fruitfully employed to segment the potential market in order to secure the highest price premium, the average wtpk|g for each socio-demographic group g can be calculated by averaging wtpi,k over the number of individuals in each group. similarly, to obtain a synthetic measure of the wtpk, the sample mean estimate is obtained averaging wtpi,k across all individuals in the sample. 4. methods in a sce individuals are requested to choose among alternatives. when products’ characteristics are not available on the market (i.e., hypothetical new products) a sce is the only available approach to investigate consumers’ preferences. a number of applications in agricultural and food marketing studies have been implemented in recent years (e.g., burton et al., 2001; burton and pearse, 2002; west et al., 2002; james and burton, 2003; alfnes, 2004; rigby and burton, 2005; alfnes et al., 2006; mtimet and albisu, 2006; loureiro and umberger, 2007; jaeger and rose, 2008; gracia et al., 2009). to evaluate consumers’ perception of mycotoxins’ risk in milk, a sce was conducted in july 2009 on a representative sample of 973 italian consumers relying on a web-based survey administered by lightspeed research ltd.. the survey was introduced by a statement describing mycotoxins, their potential health effects and the role of some gps in reducing the risk of contaminations in milk. the first section of the questionnaire collected information on consumers’ shopping habits: frequency of grocery shopping and milk purchase, preferred type of milk and attitude towards mycotoxins’ labelling. in the second section of the survey, consumers were required to choose among three one litre bottles of milk differing for heat treatment (uht, fresh pasteurised and fresh high quality (hq)), fat content (whole, semi-skimmed and skimmed) and price. a further attribute, mycotoxins level measured the lower risk of mycotoxins contamination due to the use of gps maize in cow breeding, compared to the higher one associated with employing maize from conventional practises (cps) for animal feeding. table 1 lists the attributes and their levels.11 10 the procedure outlined here estimates the utility coefficient of each attribute from a model cast in the “utility space”. this modelling choice may yield counterintuitive distributions of the wtp due to the wtp originating from the ratio of two of the model’s coefficients. in particular, the distribution of the wtp may have a very long right tail. to overcome this issue, a re-parameterisation of the model in the “wtp space”, such that the (marginal) wtp for each attribute is directly estimated, has been recently proposed in the literature (train and weeks, 2005; scarpa et al., 2008). empirical applications of the latter approach appear to provide distributions of the wtp with slimmer tails (train and sonnier, 2005; sonnier et al., 2007). 11 a referee of this journal pointed out that it is recommended practice to administer consumers choice sets which feature individual specific “status-quo” options modelled according to, in this case, preferred heat treatment and fat content (kontoleon and yabe, 2003; rose et al., 2008). this would increase consumers’ familiarity 70 p. sckokai, m. veneziani, d. moro, e. castellari table 1. milk attributes and their levels in the sce. attributes acronym¥ levels heat treatment ht_ uht (a)† – fresh pasteurised (b) – fresh high quality (hq) (c) fat content fc_ whole (a)† – semi skimmed (b) skimmed (c) mycotoxins level ml conventional† – reduced price (€/litre) pr 1.05 – 1.15 – 1.25 – 1.35 – 1.45 – 1.53 – 1.55 – 1.58 – 1.63 – 1.68 – 1.73 – 1.78 – 1.83 – 1.88 source: own elaboration. note: in italics, the attributes’ levels for the “status quo” alternative; ¥, acronym employed for the heat treatment and fat content attributes in conjunction with the letters representing the levels, to identify the β parameters in equation (6) and the related estimates in table 4; †, denotes the base levels for effects coding the product attributes with a value of -1. the full factorial experimental design based on the attributes and levels in table 1 produces 3·3·2·14 = 252 alternative treatments. to reduce the dimension of the experiment while allowing the main consumer responses to be identified, a d-optimal experimental design selects only 13 alternative treatments which are constructed considering only the linear individual effects for the attribute variables. three choice sets were submitted to each participant, thus three choices were made, providing a balanced panel of observations. each of the three choice sets administered was composed of three alternatives, the first always being the “status-quo” alternative (i.e., fresh hq, whole, cps, 1.58 €/l) and the remaining two being randomly selected, without replacement, within a set of 13 possible alternative treatments. the “status-quo” alternative, constant across choice sets, is always included to help to scale the utilities among the various choice sets. often, the “status-quo” alternative is specified as the “no choice” option in market penetration studies. because this is not the main focus of the present work, the “status-quo” alternative is defined according to consumer demand data and to determine choices with respect to the product modern retailers were keener to promote on their premises, at the time the experiment took place (i.e., fresh hq). moreover, fully characterising the “status-quo” alternative prevents respondents from providing no information at all through the “no choice” option (haaijer et al., 2001). an example of a choice set is given in table 2. the third section of the questionnaire collected the socio-demographic characteristics of the respondent: age, location, gender, marital status, education level, employment status and position, number of household residents (including the respondent) and household income level. the summary statistics of the sample appear in table 3.12 the sample of respondents features a few more females than males (55.5%) and a limited number of very young (18-24 years of age) consumers (8.9%), while the 35-44 year with the “status quo” option reducing further the extent of a possible bias. while this is an attractive experimental design, we were not able to implement such a complicated framework when we selected the surveying partner. future experiments may be designed following this suggestion. 12 note that the respondents who are not responsible for their household’s grocery shopping and who have either never gone shopping or never consumed milk have been removed from the sample. 71consumer willingness to pay for food safety: the case of mycotoxins in milk old are the relative majority (21.8%). responses have been collected mainly from residents in the south of italy (35.9%), married (72.5%) and either employed (52.4%) or retired (23.6%). moreover, the largest share of our sample of consumers earns an income in the 20,000-40,000€ range (37.4%), has completed higher education (57.5%), purchases and consumes milk more than once a week (51.7% and 40.0%, respectively). nonetheless, only 18.8% of the respondents consumes milk every day. 5. results the rpl model has been estimated using the econometric software nlogit 5.0.13 the set of explanatory variables includes both product attributes x and socio-demographic characteristics z; randomness is assumed for all the attribute parameters: the parameters for the attributes heat treatment, fat content and mycotoxins level are modelled as following a normal distribution while the one for price a triangular distribution. moreover, we constrain the triangular distribution for the price coefficient to spread over negative values in order to limit the possibility that, upon calculating the wtps for the product’s attributes, the distributions of wtps are not well behaved because 0 is in the domain of the triangular distribution (daly et al., 2012).14 the socio-demographic characteristics included in the final specification of the model have been selected evaluating the significance of the likelihood ratio (lr) test for the model with a single demographic variable (or group of mutually exclusive effects coded variables accounting for the same sociodemographic characteristic) being superior to the model with the sole product attributes. this model selection procedure has identified gender, age (continuous) and frequency of 13 nlogit 5.0 fits this model employing a maximum simulated likelihood estimator. crucial features of the estimator include the nature and number of the discrete points in the integration space. following best practice, halton sequences are selected, and 1000 draws are employed. the latter have been selected by following the estimates’ “robustness checks” suggested in hensher and greene (2003) and have been carried out by verifying the stability of the ratio of the estimated mean to the standard deviation of the model’s random parameters when the model is estimated with 25, 50, 100, 250, 500, 1000 and 2000 points. 14 imposing this constraint implies that a model with correlated random parameters cannot be estimated and that the scale parameter for the price coefficient is set to be equal to the absolute value of the related β coefficient (mean) (greene, 2012:n-545). while ignoring the correlation between random parameters has been suggested to lead to correlation among the implied wtp distributions (scarpa et al., 2008), we maintain the constraint to obtain better-defined wtps. table 2. example of a choice set. milk (1 litre) 1 2 3 heat treatment fresh hq (c) uht (a) fresh pasteurised (b) fat content whole (a) semi skimmed (b) semi skimmed (b) mycotoxins level conventional conventional reduced price (€/l) 1.58 €/l 1.15 €/l 1.63 €/l choice (tick the box)    source: own elaboration. 72 p. sckokai, m. veneziani, d. moro, e. castellari milk consumption as the sole socio-demographic variables statistically significant, one at a time, in the model.15 moreover, these variables could be included also simultaneously in the estimated rpl16 resulting in a lr test for joint significance of 76.39 which, being chi15 the results from the model selection procedure are available from the authors upon request. 16 since in rpl models the effect of socio-demographic variables is identified by means of interacting these variables with the product attributes for every choice in the experiment’s choice set, it is possible that the model associated with an experiment with numerous treatments is too big to be estimated. because the choice set of this experiment comprised only 13 alternatives, a model with multiple socio-demographic explanatory variables could be estimated and its results are presented in table 4. although the coefficients for the socio-demographic variables of interest are not presented to conserve space (but are available from the authors upon request), we can report that roughly 31% of the interactions between socio-demographic variables and product attributes are statistically significant, at conventional levels. table 3. main summary statistics for the sample composition. characteristics % characteristics % gender education level female 55.5 none/elementary† 2.9 male† 44.5 middle 11.5 age college 57.5 18 – 24† 8.9 tertiary 28.2 25 – 34 17.1 income 35 – 44 21.8 up to 10,000€ 6.2 45 – 54 18.3 10,001€ – 20,000€ 18.4 55 – 64 15.7 20,001€ – 40,000€ 37.4 older than 65 18.0 40,001€ – 70,000€ 16.9 geographical area over 70,001€ 3.1 north – west† 26.1 i prefer not to disclose† 18.1 north – east 18.8 number of household residents centre 19.2 2† 35.3 south 35.9 3 31.9 marital status 4 25.4 married 72.5 more than 4 7.5 not married† 27.5 purchasing frequency employment status once every 15 days† 2.1 home duties 10.5 once a week 16.6 looking for new employment 5.1 more than once a week 51.7 looking for my first employment 1.3 every day 29.7 employed 52.4 frequency of milk consumption retired† 23.5 once a month† 5.5 student 7.1 once every 15 days 9.7 once a week 26.1 more than once a week 40.0 every day 18.8 source: own elaboration based on 973 questionnaire responses. notes: †, denotes the base levels for effects coding the socio-demographic variables with value -1. 73consumer willingness to pay for food safety: the case of mycotoxins in milk squared distributed with 39 degrees of freedom, is statistically significant at the 1% level.17 all explanatory variables, except price and age, have been introduced in the model using sets of mutually exclusive effects coded variables.18 thus, the final preferred specification of the individual’s utility, in each choice situation, is (tonsor et al., 2009) u z xij i i j ij 'ββ γγ µ( )= + + (6) where the vector ; ; ; ; ;i i ht a i ht b i fc b i fc c i ml i pr, _ , _ , _ , _ , ,ββ β β β β β β=   collects the individual-level values of the coefficients estimated for the xj vector of product attributes presented in table 1; γ’ is the vector of estimated coefficients for the vector of socio-demographic characteristics (age (continuous), the relevant effects coded variables for gender and frequency of milk consumption) of the individuals zi.19 model estimates (means and spread coefficients) appear in table 4. focusing on the β coefficients, it is interesting to note that the attributes price and (reduced) mycotoxins level are the sole to be statistically significant, at the 1% level, in their ability to explain the mean probability of purchasing a bottle of milk. moreover, the coefficient for the price attribute records the largest estimated beta, suggesting that consumers are very sensitive to changes in the price of a bottle of milk. furthermore, because this variable has been included in the model as minus the price levels in the choice experiment, it appears that a decline in price is associated with an increased probability of purchasing. consumers are more likely to purchase a litre of fresh pasteurised milk compared to an uht one, on average and ceteris paribus, while the coefficient for hq milk is not statistically significant. somewhat surprisingly, respondents seem to dislike skimmed, compared to whole, milk, on average and ceteris paribus. this may be due to consumers’ unfamiliarity with the product which is also fairly difficult to find on display at the preferred point of sale. all the included attributes contribute to explain the strong heterogeneity, among respondents and around the mean levels of probability, as reflected in all the estimated spread coefficients (i.e., standard deviations (σβ) or scale parameters) being statistically significant at the 1% level. overall, the model performs quite well given the mcfadden pseudo r2, a measure of goodness-of-fit in discrete choice models, reaches 0.665 such that the χ2 test for joint model’s significance (with 50 degrees of freedom) strongly rejects the null that the estimated model does not explain respondents’ choices. 17 this testing procedure is in line with the one carried out in greene et al. (2006:88). 18 effects coding, contrary to dummy coding, allows to distinguish the effects that the base level of an attribute (i.e., the level associated with the excluded dummy variable in case of dummy coding) and the overall or grand mean have on the level of recorded utility (hensher et al., 2005; tonsor, 2011; wolf et al., 2011). for the choice of the base levels employed in effects coding the attributes and demographic variables please refer to the notes to table 1 and table 3, respectively. 19 a referee of this journal pointed out the value of allowing for “… sources of observations-specific influence on the variance of the unobserved effects in the choice models …” (greene et al., 2006:89). while we agree this could be a valuable exercise to gain further insights into individual behaviour in choice models, the many challenges introduced by difficult convergence, somewhat limited gains in terms of overall model’s explanatory power (adjusted pseudo r2), sign changes and extreme values of the calculated wtps (greene et al., 2006) suggested we leave this more demanding analysis for a future research effort. 74 p. sckokai, m. veneziani, d. moro, e. castellari 6. discussion the focus of the paper is on the evaluation of consumers’ attitude towards mycotoxins’ risk; to this extent the wtp, that is the price premium that consumers are willing to pay to obtain a product with a reduced mycotoxins’ risk, has been computed. because of table 4. estimated parameters for product attributes for the rpl model. parameter¥ estimate βht_a 1.6648** (0.7483) βht_b -0.1802 (0.5566) βfc_b 0.4620 (0.6448) βfc_c -2.0705** (0.8302) βml 1.2548*** (0.3455) βpr 7.9813*** (2.2621) σht_a 1.1320*** (0.2437) σht_b 2.1672*** (0.1948) σfc_b 0.9917*** (0.2185) σfc_c 2.1002*** (0.3515) σml 1.7388*** (0.1504) σpr † 7.9813*** (2.2621) diagnostics log likelihood -2576.6639 model joint significance ~ χ2(50) 10253.4888*** mcfadden pseudo r2 0.6655 n° points in the halton sequence 1000 lr test for demographic variables ~ χ2(39) 76.3854*** source: own elaboration using nlogit 5.0. note: ***, significant at the 1% level, **, significant at the 5% level, *, significant at the 10% level; estimated standard errors in parentheses; ¥, please refer to table 1 for the acronyms representing the attributes and employed to identify the estimated parameters; †, because the price random parameter is assumed to be distributed according to a triangular distribution, this standard deviation coefficient is, in fact, a scale parameter. demographic variables employed to estimate the model: gender, age (continuous), frequency in consuming milk; all the (set of ) demographic variables included in estimation determined a significant lr test for individual (joint) significance against the attribute only model. 75consumer willingness to pay for food safety: the case of mycotoxins in milk the existence of preference heterogeneity and the t = 3 repeated choices made by respondents, we can compute a wtp for each of the individuals in the sample according to (5) and then average it out across the sample and socio-demographic groups. the sample average wtp for lower risk of contamination is 0.44 €/l, which corresponds to a premium of 28.8% on the average milk price employed in the experiment (1.53 €/l). this premium is almost six times the (weighted average) one wang et al. (2008) estimate, for a haccp certified milk sold in china, using a cv method and more than 50% higher than the premium wolf et al. (2011) calculate for a gallon of us carrying a food safety claim (+18.5%).20 moreover, it is larger than the (average) 23% premium ennekin (2004) quantifies, employing a conditional logit model, for the quality and safety improvements of a brand of liver sausages in germany. the present findings are in line with the premium wolf et al. (2011) determine, relying on a sce and rpl model, for a half a gallon of us milk endowed with a generic enhanced food safety attribute. concerning the evaluation of food safety due to the implementation of gaps/gmps, our evidence supports the existence of a price premium which is around three times the one aizaki and sato (2007) attribute to citizens of sendai for purchasing tomatoes produced in kumamoto following the same practices. in table 5, the average wtp for socio-demographic groups are reported. trying to summarise the results, we focus on those representing larger deviations with respect to the sample average. it is interesting to note that, contrary to their statistically significant contribution in explaining consumer choices through a rpl, gender and frequency of milk consumption do not give rise to group-average wtps which are markedly different from the estimated sample average. on the contrary, respondents in the 45-54 and 65 and above age ranges do display average values of the wtp for (reduced) mycotoxins levels different from the overall sample one. nonetheless, the former group has a higher than sample average wtp (0.52 €/l) while the latter has a lower than sample average one (0.38 €/l). similarly, segmenting the market according to the employment status of the individuals, consumers who are either looking for their first employment or retired have a (markedly) low wtp (0.28 and 0.36 €/l, respectively) as opposed to students who have the highest wtp for (reduced) mycotoxins levels. respondents who either have not completed any education or have abandoned school after the completion of the elementary level have the lowest average wtp (0.36 €/l), while consumers who have completed their tertiary education have the highest (0.48 €/l), which is slightly above the sample average wtp. somewhat similarly, it is found that the respondents reporting the lowest income (and not reporting their income at all) have the lowest average wtp (0.34 €/l), while the two highest income classes are associated with higher than sample average wtps (0.52 and 0.48 €/l, respectively). nonetheless, it is interesting to note that the richest respondents do not have the highest wtp. lastly and quite surprisingly, consumers who purchase milk only once every 15 days do record a wtp for (reduced) mycotoxins levels of 0.52 €/l while those who shop more frequently have a lower – also than sample average – wtp. finally, the wtp for those who purchase milk only once a week is only 0.38 €/l. 20 because some of the relvant literature is in japanese, we resort to aizaki (2012) to report that iwamoto (2004) and hosono (2003, 2004) unveil a positive wtp for a haccp compliant litre of milk, with the ones for the latter studies being 6 to 5 times larger than the one for the former. 76 p. sckokai, m. veneziani, d. moro, e. castellari 7. conclusions european statistics show that one of the most serious sources of health risks related to food is mycotoxins. in this paper, we have evaluated the italian consumers’ perception of the health risks associated to the presence of high levels of mycotoxins. in particular, we have calculated the wtp for a hypothetical bottle of milk obtained by cows fed with maize certified for being produced employing the gps that reduce mycotoxin contamination. therefore, a web-based questionnaire has been distributed to a representative sample of 973 italian consumers who were required to make hypothetical choices among choice sets composed of three products. responses have been analysed relying on the panel data version of a rpl model to determine consumer wtp. the results show that italian consumers are willing to pay a moderate average price premium (29%) for “reduced-mycotoxins” milk and this table 5. average wtp for the reduced mycotxins level attribute across groups (€/l). characteristics characteristics gender education level female 0.42 none/elementary 0.36 male 0.44 middle 0.40 age college 0.42 18 – 24 0.46 tertiary 0.48 25 – 34 0.42 income 35 – 44 0.40 up to 10,000€ 0.34 45 – 54 0.52 10,001€ – 20,000€ 0.46 55 – 64 0.44 20,001€ – 40,000€ 0.44 older than 65 0.38 40,001€ – 70,000€ 0.52 geographical area over 70,001€ 0.48 north – west 0.42 i prefer not to disclose 0.34 north – east 0.44 number of household residents centre 0.46 2 0.44 south 0.42 3 0.42 marital status 4 0.44 married 0.44 more than 4 0.46 not married 0.44 purchasing frequency employment status once every 15 days 0.52 home duties 0.40 once a week 0.38 looking for new employment 0.48 more than once a week 0.44 looking for my first employment 0.28 every day 0.42 employed 0.46 frequency of milk consumption retired 0.36 once a month 0.40 student 0.50 once every 15 days 0.46 once a week 0.44 more than once a week 0.44 every day 0.42 source: own elaboration. 77consumer willingness to pay for food safety: the case of mycotoxins in milk premium increases for consumers between 44 and 54 years of age, who are students, have completed tertiary education, are economically well-off and shop fairly infrequently. despite the estimated price premium might indeed cover the additional costs of implementing the gaps and gmps deemed instrumental to reduce mycotoxins contaminations, it should be acknowledged that its value could represent an overestimate of the health risks due to mycotoxins contamination (wolf et al., 2011). because mycotoxins may be an elusive concept to the general public, the description of the health risks provided in the questionnaire might have generated a perceived risk higher than the actual risk. for instance, due to the higher incidence of mycotoxins in warmer and more humid climates and because of the related notifications in foreign countries (i.e., china and the middle east area), consumers may have attributed some of the concerns related to country-oforigin to the occurrence of a mycotoxin contamination (tonsor, 2011). likewise, because consumers may consider gps a general proxy for safer food, the associated wtp may represent the price premium for the more comprehensive concept of “safer milk”.21 the analysis shows that the risks from mycrotoxins contamination of food exist and are perceived differently among italian consumers. on the one hand, the results of this study can demonstrate to policy makers that the adoption of gps is valued by consumers and may be economically viable even without public subsidies.22 on the other hand, the food industry can benefit from these findings since they can provide insights into the market opportunities related to the introduction of a “mycotoxin free” product. however, further analyses, extended to other european countries and to different products, are necessary to evaluate differences among eu states and across products categories. acknowledgments the authors would like to thank a referee and the editor of this journal for their comments which have contributed significantly to an improved quality of the paper. nonetheless, the usual disclaimer regarding errors and omissions applies. this study was carried out when mario veneziani was research assistant at the dipartimento di economia agroalimentare, facoltà di scienze agrarie, alimentari e ambientali and dipartimento di scienze economiche e sociali (dises), facoltà di economia e giurisprudenza, università cattolica del sacro cuore, piacenza, italy whose allround support is gratefully acknowledged. references abbas, h.k., zablotowicz, r.m. and locke, m.a. 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(1974). frontiers in econometrics. new york: academic press. issn xxxx-xxxx (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(1): 13-27, 2012 agricultural and applied economics: what is this? paolo sckokai università cattolica del sacro cuore, piacenza, italy (paolo.sckokai@unicatt.it) abstract. the scientific domain of «agricultural and applied economics» is an open issue. in this paper, we analyse the jel codes of the articles published in seven major agricultural economics journals in the period 2000-11, in order to define which disciplines and which research areas are the «core business» of today’s agricultural and applied economists. a special attention has been given to the sub-sample of the italian studies, since a new scientific society in this area (aieaa) has been recently founded by a group of italian economists. keywords. agricultural economics, applied economics jel codes. a11, q10 1. introduction the «italian association of agricultural and applied economics» (aieaa), the new scientific society of which this review is the official journal, has chosen a name that may look rather strange, at least at a first glance. one may ask: what is agricultural and applied economics? how is its scientific domain defined? these are of course legitimate questions, which need an appropriate answer. it is well known that the founders of aieaa have a long record of activity in the field of agricultural economics. thus, why using two adjectives («agricultural and applied») to define its scientific domain? a first answer can be found in the aieaa «mission statement»: a new scientific society related to the themes of agriculture and food is motivated by the radical change in the research objectives and in the methodologies characterizing the agricultural economics profession. this reflection on the new research agenda of agricultural economists is not new and is common to most international scientific societies in this area. in the last few years, several presidential addresses of the major agricultural economics associations have tackled the issue of redefining the role of the profession in a changing environment (see among others: kinsey, 2001; thompson, 2001; brandt, 2003; leon, 2005; buccola, 2006; doering, 2007; mittelhammer, 2009). this debate has led to some relevant changes: the most striking example is the american agricultural economics association, which has recently redefined its mission statement and changed its name in agricultural and applied economics association. thus, the new italian association (whose name is clearly not brand new!) seems to be in line with an international trend that aims to redefine the research mission of agricultural economists. 14 p. sckokai why is this needed? it is well known that the role of agriculture in modern economic systems has radically changed: while its role as sector producing a key item for human life remains central, its share in terms of total value added and employment has become extremely small as compared to a few decades ago. the same is true for the role of agriculture in the food supply chain: the share of raw materials in the value of food items sold to final consumers is becoming smaller and smaller, since all services related to food production are gaining importance (processing, preparation, retailing, logistics, catering, marketing and communication…). this obviously means that studying the economic problems related to food means studying all the elements of the supply chain, their vertical relationships, and their relationships with the rest of the economy, both at the national and at the international level. issues like competition and market power in the food supply chain, distribution of the valued added among agents, identification of the consumer needs, food safety and food quality are clearly at stake in the research agenda. the same is true for a range of issues having an international dimension, like world trade and prices of agricultural and food commodities or the contribution of agriculture and food production to economic development in the developing world. another area of new research potentials is the relationship between agriculture, food and the natural environment. the environmental sustainability of agricultural and food production, given the massive use of key resources like land and water, the new climate change challenges and the contribution of agriculture to the production of renewable energy are research themes that show the strict linkages between agriculture, food, resource use and environmental protection. thus, agricultural economists are facing new research challenges and an enlarged research agenda, which implies new methodological approaches, often interlinked with other scientific disciplines: from development economics to regional economics, from food technology to sociology, from management to industrial organization, from environmental and resource economics to energy economics. the objective of their research is also increasingly finalized to the definition of new public policies concerning the traditional areas of agriculture, food, trade and rural development, but also the new areas of environment and energy. thus, the dialogue between researchers and policy-makers becomes another essential element of the profession. the above considerations, largely elaborated by the aieaa mission statement, are already a good rationale for enlarging the scope of a scientific society related to the themes of agriculture and food. thus, the adjective «applied» has the role of synthesizing the interrelationships between traditional agricultural economics and a number of ‘sister’ disciplines. but nobody can deny that such adjective is rather vague and is not able to identify the key research trends in the ‘enlarged’ agricultural economics discipline. in his presidential address, mittelhammer (2009) has already discussed how it is difficult to identify the domain of «applied economics», since different definitions may apply. nonetheless, comparing the content of a set of general economics vs. agricultural economics journals, he concludes that the diversified research interests of agricultural economists are perfectly in line with a broad definition of «applied economics», that he summarizes as follows (page 1174): it is a richly varied collection of approaches for analyzing real-world economic issues that encompasses all methods of analysis capable of being applied to economic data or issues, utilizes knowl15agricultural and applied economics: what is this edge of relevant history and institutions, and involves professional judgment, all of which is either combined with or guided by established economic theories or else transcends the existent body of economic theory and may contribute to and expand that body of theory. nonetheless, defining precisely which disciplines and which research areas are the «core business» of today’s «agricultural and applied economists» remains an open issue. for this reason, this paper, building on the work of mittelhammer (2009), analyses the content of the major agricultural economics journals, trying to identify the most relevant areas and trends in published research. a special attention is given to the contribution of researchers working in italian institutions. the paper is organized as follows: in section 2 we present the methodology and the data used in the analysis, in section 3 we discuss the results, while in section 4 we draw some conclusions. 2. methodology and data in order to identify research areas and recent trends in agricultural economics research, we have analysed the journal of economic literature (jel) codes of all articles published in the period 2000-2011 in some selected journals. most researchers in the broad field of economics are familiar with the jel codes. this system classifies all research outputs in economics using a four-digit classification (american economic association, 2012). the first digit (alphabetical letters) defines twenty primary areas: the most important for agricultural economics is the area q (agricultural and natural resource economics; environmental and ecological economics). the second digit defines a general topic inside the primary area (for example q1 stays for «agriculture»), while the third digit defines a specific research area (for example q18 stays for «agricultural policy; food policy»); the fourth digit is not used in the present system (it is 0 for all research areas), but is available for future refinements of the classification. in total, 804 codes are available. all pieces of economic literature available in the jel online database econlit (articles, books, book reviews, collective volume articles, working papers and dissertations) are classified with at least one jel code, although it is very common that each item is classified with more than one code, in order to facilitate the online search. in terms of journals, we have chosen to analyse seven major international agricultural economics journals: agricultural economics (ae); american journal of agricultural economics (ajae); australian journal of agricultural and resource economics (ajare); canadian journal of agricultural economics (cjae); european review of agricultural economics (erae); food policy (fp); journal of agricultural economics (jae). why this selection? the 2010 edition of the web-of-science (wos) journal citation report (jcr) includes 14 journals in the category «agricultural economics and policy», but the above seven are by far the most important and the most relevant for the profession. in fact, they are included in the wos database since its establishment, they are the first seven in terms of citation indexes (impact factor and 5-year impact factor), although their relative ranking has changed over the last few years. for example, fp has taken the lead as most cited journal in the field, thanks also to its more pronounced interdisciplinary nature; the citation indexes of the other journals tend to fluctuate over time, with a clear increasing 16 p. sckokai trend for all of them, except for the cjae, which seems to have lost some appeal. finally, all these journals, except fp, are official journals of one of the major scientific societies in agricultural economics: the international association of agricultural economists (of which ae is the official journal), the european association of agricultural economists (erae), the us agricultural and applied economics association (ajae), the uk agricultural economics society (jae), the canadian agricultural economics society (cjae) and the australian agricultural and resource economics society (ajare). thus, they should closely reflect the evolution of the «core business« of the profession, since the members of the above societies consider their official journals as reference outlets for their research. of course, these seven journals do not cover the whole scientific production of agricultural economists, since their diversified research interests often lead them to publishes in specialised journals of other disciplines and sub-disciplines. this is probably the major limitation of the present analysis, which would be much more accurate if we could analyse publications of a selected number of authors that we can define «agricultural economists». unfortunately, this type of authors’ classification is not available in any bibliographic database, and, even if one has available a list of members of the major scientific societies in the field, carrying out a name by name search is likely to be an impossible task. the second limitation is linked to how the jel codes are chosen. only a subsample of our seven journals (and, in general, of all economic journals included in econlit) publish the jel codes in the front page of each article; in this case, the jel codes are chosen by the authors/editors and reflect their personal view of the topic(s) covered by the article. for all the other journals, the jel codes are provided by the econlit database managers, probably based on the content of the abstract and/or the keywords of each article. thus, the classification is subjective and not homogenous among articles/journals, and this may lead to some inconsistencies. moreover, since the main objective of econlit is to facilitate the online search, there is no limit in the number of jel codes attached to each article, and the order by which they are proposed does not imply any priority. thus, each article cannot be uniquely classified in a given research area, namely a unique jel code. for this reason, in our analysis each article is counted as many times as its jel codes, thus contributing to the share of all research areas for which it has been classified. again, this may have created some imbalances in our results. finally, in some cases the jel codes define a very specific research area, with no ambiguity, (i.e. q22, defined as «renewable resources and conservation: fishery; aquaculture»), while in other cases they define a very wide and comprehensive research area (i.e. q18, defined as «agricultural policy; food policy»). this implies that the latter are used to classify very different type of articles, that often have very little in common in terms of objectives, methodology and results, even though they share the same code. again, this may create some imbalances in the results, with some comprehensive jel code being overrepresented. in table 1, we provide a classification of the articles considered in this analysis by journal and year. this data was downloaded at the beginning of september 2011, thus the last year is largely incomplete, especially for some journals, like the ajae, for which data tend to be uploaded rather late in econlit. concentrating on the years 2000-10, for which all articles are available, one can clearly see that the total number of articles published each year in these seven agricultural economics journals has experienced a decline in the 17agricultural and applied economics: what is this years 2002-05, and an increase in the most recent years. in fact, if one compares 2000 with 2010, several journals have experienced a significant increase in the number of published articles (ae +36%, ajare +59%, fp +54% and jae +26%), the erae has remained stable, while the two north american journals have experienced a sharp decrease (ajae -19% and cjae -39%). despite this reallocation, the ajae remains by far the leading journal in agricultural economics in terms of number of published articles, since it represents one third of all the articles considered in the analysis. ae and fp contribute respectively for 18 and 14% of the total articles, while the other four journals have a share ranging between 7 and 10%. in table 2, we present the number of articles with at least one author working in an italian institution. globally, the share of italian articles is 1.9%, but such share is increasing over time: it is 1.3% in the 2000-05 period and it becomes 2.5% in the 2006-11 period. this trend is clearly encouraging, although, in absolute terms, the contribution of italian researchers remains rather small. 3. results 3.1 the most relevant research areas in agricultural economics the classification of the 3,897 agricultural economics articles by their jel codes is provided in tables 3 and 4 and is expressed in terms of shares with respect to the corresponding totals. of course, since each article carries more than one jel code, it can be table 1. total number of agricultural economics articles considered in the analysis   ae ajae ajare cjae erae fp jae total 2000 53 118 22 49 25 43 27 337 2001 63 127 25 42 22 34 28 341 2002 29 124 26 35 25 33 33 305 2003 43 131 23 24 22 33 24 300 2004 46 127 24 22 24 37 29 309 2005 68 111 22 28 25 35 24 313 2006 71 105 31 38 23 40 33 341 2007 64 108 28 35 21 38 35 329 2008 76 107 28 22 25 61 30 349 2009 64 135 33 32 24 62 36 386 2010 72 96 35 30 23 66 34 356 2011* 38 14 25 22 20 75 37 231 total 687 1303 322 379 279 557 370 3897 source: econlit note: the acronyms in each column represent the following journals: agricultural economics (ae); american journal of agricultural economics (ajae); australian journal of agricultural and resource economics (ajare); canadian journal of agricultural economics (cjae); european review of agricultural economics (erae); food policy (fp); journal of agricultural economics (jae). *incomplete 18 p. sckokai counted several times and the sum of the shares is always higher than 1. considering the whole sample, it turns out that, as expected, the most important jel codes are those that have a rather general and comprehensive definition of the corresponding research area: 23% of the articles are classified as q18 (agricultural and food policy), 22% as o13 (contribution of agriculture, natural resources and energy to economic development) and 21% as q12 (microeconomic analyses at the farm level). thus, papers are almost equally distributed among these three categories, which represent the most important research areas in agricultural and applied economics. considering q18 first, researchers in agricultural economics seem to have a special focus on problems related to government intervention in the agricultural and food sector. this is not surprising, since most countries, and especially developed countries, have a long history of intervention in this area, with a variety of instruments: from agricultural price support to direct payments to farmers, from traditional trade policies (tariffs and subsidies) to non-tariff barriers to trade, from food safety legislation to food quality promotion, from food labelling to animal welfare regulation. thus, there is a wide set of studies that can potentially hit one or more of these issues. although for different reasons, it is also not surprising that many agricultural economics studies deal with the contribution of agriculture and some related sectors to economic development (o13): in developing countries the primary sector is still extremely relevant in terms of contribution to value added and employment, and can play a key role in development strategies. finally, the third area of research by importance (q12: microtable 2. number of agricultural economics articles with at least one author working in an italian institution   ae ajae ajare cjae erae fp jae total 2000 1 1 1 5 1 9 2001 1 1 1 3 2002 1 1 2 2003 1 2 3 2004 1 1 1 3 2005 1 1 1 1 1 5 2006 3 1 4 2007 1 2 1 2 1 1 8 2008 2 2 4 3 11 2009 3 2 3 2 1 11 2010 1 2 4 7 2011* 3 1 1 3 8 total 10 14 0 4 18 12 16 74 source: econlit note: the acronyms in each column represent the following journals: agricultural economics (ae); american journal of agricultural economics (ajae); australian journal of agricultural and resource economics (ajare); canadian journal of agricultural economics (cjae); european review of agricultural economics (erae); food policy (fp); journal of agricultural economics (jae). *incomplete 19agricultural and applied economics: what is this ta bl e 3. j el c od es o f a gr ic ul tu ra l e co no m ic s ar tic le s by jo ur na l, 20 00 -2 01 1 (s ha re s ov er th e re sp ec tiv e to ta l i n % ) a e a ja e a ja re c ja e er a e fp ja e to ta l q 18 a gr ic ul tu ra l p ol ic y; f oo d po lic y 17 .0 17 .5 16 .1 22 .4 28 .0 41 .5 30 .0 23 .1 o 13 ec on om ic d ev el op m en t: a gr ic ul tu re ; n at ur al r es ou rc es ; e ne rg y; e nv iro nm en t; o th er pr im ar y pr od uc ts 46 .1 11 .8 11 .5 4. 7 3. 9 48 .3 17 .3 22 .3 q 12 m ic ro a na ly sis o f f ar m f irm s, fa rm h ou se ho ld s, an d fa rm in pu t m ar ke ts 32 .9 16 .5 17 .7 15 .3 26 .5 16 .9 28 .6 21 .3 q 16 a gr ic ul tu ra l r & d ; a gr ic ul tu ra l t ec hn ol og y; b io fu el s; a gr ic ul tu ra l e xt en sio n se rv ic es 26 .6 14 .0 11 .8 19 .5 15 .4 22 .8 14 .3 18 .0 q 11 a gr ic ul tu re : a gg re ga te s up pl y an d d em an d a na ly sis ; p ric es 19 .5 15 .6 9. 9 14 .0 14 .7 18 .7 13 .0 15 .8 q 13 a gr ic ul tu ra l m ar ke ts a nd m ar ke tin g; c oo pe ra tiv es ; a gr ib us in es s 11 .2 13 .3 7. 5 22 .2 16 .8 16 .9 10 .8 13 .8 q 17 a gr ic ul tu re in in te rn at io na l t ra de 10 .2 7. 8 8. 7 16 .4 15 .1 10 .1 12 .2 10 .4 q 15 la nd o w ne rs hi p an d te nu re ; l an d re fo rm ; l an d u se ; i rr ig at io n; a gr ic ul tu re a nd en vi ro nm en t 13 .1 7. 1 18 .9 11 .1 7. 5 5. 7 12 .2 9. 9 d 12 c on su m er e co no m ic s: em pi ric al a na ly sis 6. 1 7. 7 5. 3 8. 7 13 .6 11 .5 8. 6 8. 4 l6 6 fo od ; b ev er ag es ; c os m et ic s; to ba cc o; w in e an d sp iri ts 5. 8 5. 0 2. 8 12 .9 12 .9 12 .6 8. 1 7. 7 o 15 ec on om ic d ev el op m en t: h um an r es ou rc es ; h um an d ev el op m en t; in co m e d ist rib ut io n; m ig ra tio n 7. 9 3. 8 1. 9 0. 5 1. 1 18 .7 2. 2 5. 8 f1 3 tr ad e po lic y; in te rn at io na l t ra de o rg an iz at io ns 3. 1 4. 1 4. 7 9. 5 9. 0 5. 9 8. 1 5. 5 q 25 re ne w ab le r es ou rc es a nd c on se rv at io n: w at er 4. 7 3. 2 16 .8 7. 9 2. 5 1. 4 2. 2 4. 6 o 18 ec on om ic d ev el op m en t: u rb an , r ur al , r eg io na l, an d tr an sp or ta tio n a na ly sis ; h ou sin g; in fr as tr uc tu re 10 .6 1. 7 1. 9 1. 1 0. 0 9. 5 3. 0 4. 3 d 18 c on su m er p ro te ct io n 1. 5 3. 0 0. 6 4. 0 5. 4 12 .2 3. 2 4. 1 p3 2 c ol le ct iv es ; c om m un es ; a gr ic ul tu re 6. 8 2. 0 3. 1 2. 6 3. 6 7. 0 2. 7 3. 9 d 24 pr od uc tio n; c os t; c ap ita l, to ta l f ac to r, an d m ul tif ac to r p ro du ct iv ity ; c ap ac ity 6. 8 3. 0 5. 6 2. 1 5. 0 0. 7 4. 9 3. 8 i1 2 h ea lth p ro du ct io n 2. 3 3. 0 0. 0 1. 1 2. 5 13 .8 0. 8 3. 7 q 28 re ne w ab le r es ou rc es a nd c on se rv at io n: g ov er nm en t p ol ic y 3. 2 3. 5 12 .1 2. 9 3. 9 0. 4 3. 5 3. 7 q 58 en vi ro nm en ta l e co no m ic s: g ov er nm en t p ol ic y 1. 3 3. 4 10 .9 6. 1 3. 6 0. 5 4. 9 3. 6 q 14 a gr ic ul tu ra l f in an ce 3. 8 5. 4 1. 6 5. 3 2. 5 0. 5 2. 7 3. 6 o 19 in te rn at io na l l in ka ge s t o d ev el op m en t; ro le o f i nt er na tio na l o rg an iz at io ns 4. 4 2. 3 0. 6 2. 1 3. 6 7. 0 3. 0 3. 3 q 22 re ne w ab le r es ou rc es a nd c on se rv at io n: f ish er y; a qu ac ul tu re 1. 2 4. 2 5. 6 1. 6 3. 9 2. 7 3. 8 3. 3 q 24 re ne w ab le r es ou rc es a nd c on se rv at io n: l an d 4. 1 3. 2 4. 3 2. 6 2. 2 0. 9 3. 8 3. 1 f1 4 c ou nt ry a nd in du st ry s tu di es o f t ra de 3. 1 2. 5 2. 2 4. 5 2. 9 2. 9 3. 0 2. 9 i3 2 m ea su re m en t a nd a na ly sis o f p ov er ty 4. 4 1. 9 1. 6 0. 0 1. 1 6. 8 0. 5 2. 6 so ur ce : e co nl it n ot e: t he a cr on ym s in e ac h co lu m n re pr es en t th e fo llo w in g jo ur na ls : a gr ic ul tu ra l e co no m ic s (a e) ; a m er ic an j ou rn al o f a gr ic ul tu ra l e co no m ic s (a ja e) ; au st ra lia n jo ur na l o f a gr ic ul tu ra l a nd r es ou rc e ec on om ic s (a ja re ); ca na di an j ou rn al o f a gr ic ul tu ra l e co no m ic s (c ja e) ; e ur op ea n re vi ew o f a gr ic ul tu ra l ec on om ic s (e ra e) ; f oo d po lic y (f p) ; j ou rn al o f a gr ic ul tu ra l e co no m ic s (j a e) . 20 p. sckokai ta bl e 4. j el c od es o f a gr ic ul tu ra l e co no m ic s ar tic le s by y ea r, 20 00 -2 01 1 (s ha re s ov er th e re sp ec tiv e to ta l i n % ) 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 * to ta l q 18 a gr ic ul tu ra l p ol ic y; f oo d po lic y 18 .7 22 .9 22 .3 23 .0 21 .7 26 .2 26 .1 24 .0 22 .3 24 .9 23 .9 20 .8 23 .1 o 13 ec on om ic d ev el op m en t: a gr ic ul tu re ; n at ur al r es ou rc es ; e ne rg y; e nv iro nm en t; o th er p rim ar y pr od uc ts 23 .7 25 .8 21 .0 22 .7 21 .7 17 .6 21 .1 22 .8 24 .6 22 .0 21 .1 23 .8 22 .3 q 12 m ic ro a na ly sis o f f ar m f irm s, fa rm h ou se ho ld s, an d fa rm in pu t m ar ke ts 10 .4 18 .5 19 .0 15 .7 22 .3 25 .2 24 .6 22 .8 18 .6 28 .8 25 .6 22 .9 21 .3 q 16 a gr ic ul tu ra l r & d ; a gr ic ul tu ra l te ch no lo gy ; b io fu el s; a gr ic ul tu ra l ex te ns io n se rv ic es 13 .4 16 .4 16 .4 22 .7 20 .1 16 .6 18 .2 23 .4 19 .2 16 .1 18 .0 15 .6 18 .0 q 11 a gr ic ul tu re : a gg re ga te s up pl y an d d em an d a na ly sis ; p ric es 20 .8 14 .7 17 .0 12 .3 11 .0 15 .0 14 .1 14 .3 19 .8 13 .0 19 .9 17 .3 15 .8 q 13 a gr ic ul tu ra l m ar ke ts a nd m ar ke tin g; c oo pe ra tiv es ; a gr ib us in es s 15 .4 13 .8 11 .5 10 .7 12 .6 10 .9 9. 7 14 .0 12 .9 21 .0 14 .9 18 .2 13 .8 q 17 a gr ic ul tu re in in te rn at io na l t ra de 13 .6 14 .1 15 .1 8. 0 6. 8 8. 0 10 .3 10 .0 9. 7 9. 8 11 .2 6. 5 10 .4 q 15 la nd o w ne rs hi p an d te nu re ; l an d re fo rm ; l an d u se ; i rr ig at io n; a gr ic ul tu re an d en vi ro nm en t 7. 1 7. 3 8. 5 7. 7 10 .7 9. 6 11 .7 10 .0 10 .6 10 .4 10 .7 15 .2 9. 9 d 12 c on su m er e co no m ic s: em pi ric al a na ly sis 3. 6 6. 5 5. 2 8. 7 6. 1 8. 6 5. 6 5. 5 12 .6 10 .4 11 .5 18 .2 8. 4 l6 6 fo od ; b ev er ag es ; c os m et ic s; to ba cc o; w in e an d sp iri ts 3. 0 3. 8 3. 9 5. 7 4. 2 9. 9 11 .4 7. 9 8. 3 10 .4 11 .0 13 .0 7. 7 o 15 ec on om ic d ev el op m en t: h um an re so ur ce s; h um an d ev el op m en t; in co m e d ist rib ut io n; m ig ra tio n 7. 1 6. 7 4. 9 5. 0 6. 5 7. 3 7. 0 5. 5 6. 6 4. 7 2. 8 5. 6 5. 8 f1 3 tr ad e po lic y; in te rn at io na l t ra de o rg an iz at io ns 4. 7 4. 7 8. 9 6. 0 4. 2 5. 1 7. 3 3. 3 6. 0 5. 7 6. 5 2. 6 5. 5 q 25 re ne w ab le r es ou rc es a nd c on se rv at io n: w at er 6. 8 4. 7 5. 6 4. 7 2. 6 2. 6 5. 3 3. 6 5. 4 3. 6 5. 6 5. 2 4. 6 o 18 ec on om ic d ev el op m en t: u rb an , r ur al , re gi on al , a nd t ra ns po rt at io n a na ly sis ; h ou sin g; in fr as tr uc tu re 2. 7 3. 8 1. 0 2. 0 5. 2 5. 4 7. 0 7. 3 5. 2 4. 7 3. 1 4. 3 4. 3 21agricultural and applied economics: what is this 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 * to ta l d 18 c on su m er p ro te ct io n 2. 7 2. 1 2. 3 2. 0 5. 5 7. 7 4. 4 5. 2 6. 0 4. 1 3. 1 4. 8 4. 1 p3 2 c ol le ct iv es ; c om m un es ; a gr ic ul tu re 6. 2 3. 5 4. 6 2. 3 1. 6 5. 4 3. 2 4. 0 4. 0 5. 4 3. 1 2. 6 3. 9 d 24 pr od uc tio n; c os t; c ap ita l, to ta l f ac to r, an d m ul tif ac to r p ro du ct iv ity ; c ap ac ity 2. 7 1. 8 3. 6 2. 7 2. 3 4. 2 5. 3 5. 8 2. 3 3. 6 6. 2 5. 6 3. 8 i1 2 h ea lth p ro du ct io n 1. 2 2. 3 1. 6 2. 7 1. 9 5. 1 2. 6 4. 6 6. 9 2. 6 5. 9 8. 7 3. 7 q 28 re ne w ab le r es ou rc es a nd c on se rv at io n: g ov er nm en t p ol ic y 5. 0 5. 3 6. 2 4. 0 1. 0 3. 2 3. 5 4. 0 4. 3 3. 1 2. 5 1. 7 3. 7 q 58 en vi ro nm en ta l e co no m ic s: g ov er nm en t po lic y 0. 0 0. 0 0. 0 2. 3 7. 1 3. 5 4. 7 3. 3 6. 6 4. 4 6. 7 4. 8 3. 6 q 14 a gr ic ul tu ra l f in an ce 5. 0 4. 4 4. 6 3. 7 3. 2 2. 2 1. 8 3. 3 4. 9 5. 4 2. 5 1. 3 3. 6 o 19 in te rn at io na l l in ka ge s t o d ev el op m en t; ro le o f i nt er na tio na l o rg an iz at io ns 3. 3 5. 0 3. 9 2. 3 2. 6 2. 6 3. 2 3. 0 4. 0 3. 9 4. 5 0. 4 3. 3 q 22 re ne w ab le r es ou rc es a nd c on se rv at io n: fi sh er y; a qu ac ul tu re 4. 7 4. 1 3. 6 2. 3 2. 9 2. 2 3. 8 2. 4 3. 2 2. 3 4. 2 3. 0 3. 3 q 24 re ne w ab le r es ou rc es a nd c on se rv at io n: la nd 1. 2 3. 5 3. 6 2. 3 5. 8 3. 5 2. 9 3. 0 5. 7 2. 8 1. 4 0. 0 3. 1 f1 4 c ou nt ry a nd in du st ry s tu di es o f t ra de 3. 6 1. 2 2. 0 1. 0 2. 9 2. 9 3. 8 0. 9 3. 2 5. 4 5. 1 1. 3 2. 9 i3 2 m ea su re m en t a nd a na ly sis o f p ov er ty 5. 0 3. 2 0. 7 2. 3 1. 3 4. 2 2. 1 1. 5 3. 7 1. 6 3. 1 3. 0 2. 6 so ur ce : e co nl it *i nc om pl et e 22 p. sckokai economic analyses at the farm level) can be considered the evolution of traditional agricultural economics, since researchers have always dedicated a special attention to the specificities of agricultural production (price volatility, weather risk, biological cycles,…) and of the organisation of the farm business. other relevant research areas for agricultural economists, interesting more than 10% of the articles, are: 1. agricultural technology, research and extension (q16), in which the new topic of agricultural commodities cultivated for biofuel production plays a key role; 2. demand, supply and market price analysis (q11), in which researchers address issues like demand and supply elasticities as well as commodity price volatility; 3. agriculture in international trade (q17), where trade policies (tariffs, subsidies, sanitary and phytosanitary measures,…) are studied, with a special focus on the role of agriculture in international trade negotiations in the world trade organization (wto) context; 4. agricultural and food markets and marketing (q13), in which a special attention is given to the functioning of the food supply chain and the industrial organisation analysis of the different actors of the chain (farmers, processors, retailers). however, one should not just look at the most relevant research areas in terms of shares of total articles, mainly for two reasons: first, this ranking may be biased by the wide definition of some jel codes (see above); second, and more important, some ‘niche’ areas, interesting a lower share of articles, may reveal new topics and new trends that are gaining importance. if one considers all the jel codes reported in tables 3 and 4 (all those interesting at least 2.5% of the articles considered in this paper), one of the most interesting elements is the presence of research areas outside the area q, which is by definition the area of agricultural economics. among these research areas, the most important is by far the area o13 (contribution of agriculture to economic development), which has been discussed before. the second one by importance is d12 (consumer economics), since researchers are dedicating an increasing attention to the analysis of factors influencing consumers’ choices for food products. the third one is l66 (food and beverage industry), which includes all industrial organization studies concerning the food industry; this jel code is often linked with q13, the code specific to supply chain studies in the area q. this kind of linkages between codes in area q and codes in other jel research areas are relevant also for production studies (q12 and d24, the specific code for efficiency and productivity studies), demand studies (q13 and d12) and trade studies (q17 and f13, the code related to international trade organizations). on the contrary, the linkages between agriculture and other sectors of the economy tend to be classified in areas outside q, like o15 (role of human resources, migration, and income distribution for economic development) and o18 (relationship between urban and rural areas, regional economics, transportation and infrastructures). finally, studies very specific to the agricultural sector are those related to land use, that are classified under q15, and to water use (q25), the most important environmental jel code that we find among agricultural economics articles. 23agricultural and applied economics: what is this 3.2 the jel codes by journal in table 3, the jel codes of agricultural economics articles are disaggregated by journal, and one can easily appreciate that there are strong differences among journals in terms of the relative weight of the most represented research areas. starting from the ajae, the most important journal in terms of number of published articles, one can note that, while the ranking of the different areas is in line with the totals, their shares tend to be significantly smaller. this is true for almost all jel codes considered in table 3, which means that the articles published in the ajae tend to cover a wider spectrum of topics, although many of them have a low share and can be considered as ’niche’ research areas. some relevant differences with respect to the whole sample concern the area o13 (agriculture and economic development), which is rather under-represented, and the area q14 (agricultural finance), to which the ajae dedicates more space than all the other journals. the two european journals, the erae and the jae, have a similar profile, with a strong emphasis on agricultural and food policy (q18) and on the microeconomic analyses at the farm level (q12). these two codes are often linked together, especially when the articles address issues related to the common agricultural policy (cap) of the european union (eu), whose role is extremely relevant for the farm business in europe. other areas that are intensively covered in these two journals are trade policies and industrial organization of the food industry, as well as consumer economics (mainly in the erae) and land use studies (mainly in the jae). the two journals with a stronger international focus, ae and fp, also have a similar profile, with a strong emphasis on articles dealing with the relationship between agriculture and economic development (o13), the issues related to agricultural technology, r&d and biofuels (q16), as well as on agricultural market and price analyses (q11). fp is also strongly focused on agricultural and food policies, while ae hosts many studies concerning microeconomic analyses at the farm level. the two remaining journals, ajare and cjae tend to be rather focused on national problems; however, in terms of topics, the former frequently hosts articles dealing with environmental economics and policies, while the latter is especially targeted to trade and supply chain issues. now the question is: are these differences the results of specific editorial policies? it is of course difficult to answer this question. in general, all the seven journals analysed here have recently revised their «aims and scope», typically enlarging the list of their topics of interest. thus, all these journals are diversifying their content, although the actual composition is clearly related to the outcome of the peer review process and its impact on the potential submissions, since authors tend to submit in journals where they have already found some related articles. 3.3 the jel codes over time given the objectives of this analysis, the evolution of the jel codes of agricultural economics articles is clearly a key issue, since it allows us to identify positive or negative trends in terms of research topics. focusing first on the main research areas discussed in 24 p. sckokai section 3.1, at the beginning of the period the topics related to agriculture and economic development were by far the most relevant in agricultural economics journals. over time, we observe a strong increase in the share of agricultural and food policy articles, which peaked in 2005-06, and slightly declined in the last few years. this is likely to be linked with the major agricultural policy reform processes that took place in the years 2002-2005 in several developed countries, which generated a relevant flow of articles analysing their impact. since we expect another relevant wave of reforms in the near future, this is likely to happen again in the next few years. as discussed above, these articles are often developed through microeconomic analyses carried out at the farm level. this is probably one of the reasons linked to the trend of the corresponding jel code (q12), which follows rather closely that of q18: a strong increase till the years 2005-06 and a slight decline in the last few years. a similar trend characterises also the articles dealing with agricultural technology, r&d and with the issue of biofuels. this is not surprising, since the debates on genetically modified organisms (gmos) and on the use of agricultural raw materials for producing renewable energy has been especially hot in the middle of the decade, and, as for the policy issues, are likely to come back again in the near future. some other important jel codes show the opposite pattern: a decline in the middle of the period and a recovery in the last few years. this is the case, for example, of research areas like market and price analysis and industrial organisation of the food sector. the recent interest in these areas is clearly linked to the dramatic increase in commodity price volatility and to the consequences of the economic crisis. in this context, issues like the transparency of food pricing, the market power of food processors and retailers and the distribution of the value added along the supply chain are becoming increasingly important. the research area that shows the sharpest increase over time is the empirical analysis of consumer preferences (jel code d12). this is due to the development of research tools in experimental economics and choice modelling dealing with consumer preferences for food, as well as to the increasing importance of issues related to the linkage between food and health, like obesity or functional foods. in this area, agricultural economists are playing a leading role in terms of addressing these hot empirical issues. finally, the share of the articles linked to environmental economics and policy tends to fluctuate over time, with a clear increasing trend only for the research area related to land use, irrigation and the relationship between agriculture and the environment (q15). 3.4 the contribution of italian studies table 5 presents the jel codes of the agricultural economics articles with at least one author working in italian institutions. given the rather low number of articles, we have grouped the articles in two sub-periods. as compared to table 4, two of the most relevant research areas are the same as in the general sample: agricultural and food policies (q18) and microeconomic analyses at the farm level (q12). once again, these two jel codes are often linked to the same article, and their sharp increase over time clearly shows an increasing interest by italian studies in this wide research area. the rather high share of articles dealing with trade problems and policies (q17 and f13) is a rather peculiar feature of italian studies. this is likely to be linked with the presence of a group of very active italian researchers, with a strong reputation in this field, 25agricultural and applied economics: what is this that have proposed some significant contributions to the literature. the same happens for consumer economics studies, whose share is well above the average of the general sample, since again a group of italian researchers is very active in this area. italian studies are well represented also in the areas related to the industrial organisation and supply chain analyses of the food sector (q13 and l66), while their contribution is rather marginal, as compared to the general sample, in areas like price analysis (q11) and agriculture and economic development (o13). finally, one the most striking evidences concerning italian studies is their virtual absence from all areas concerning environmental economics and policies. of course, this conclusion is subject to all qualifications mentioned in section 2, since, for example, agricultural economists interested in environmental issues tend to publish in specialised journals, mainly for two reasons: a) there are several specialised journals related to environmental and natural resource economics; b) some agricultural economists are leaders in topics related to the relationships between agriculture, food and the environment. 4. concluding remarks this paper has analysed the jel codes of the articles published in the period 2000-11 in seven major agricultural economics journals, in order to define which disciplines and which research areas are the «core business» of today’s agricultural and applied economists. a spetable 5. jel codes of italian agricultural economics articles (shares over the respective total in %) 2000-05 2006-11 total q18 agricultural policy; food policy 16.0 34.7 28.4 q17 agriculture in international trade 28.0 24.5 25.7 q12 micro analysis of farm firms, farm households, and farm input markets 16.0 24.5 21.6 f13 trade policy; international trade organizations 20.0 20.4 20.3 d12 consumer economics: empirical analysis 20.0 18.4 18.9 q13 agricultural markets and marketing; cooperatives; agribusiness 16.0 18.4 17.6 q16 agricultural r&d; agricultural technology; biofuels; agricultural extension services 20.0 14.3 16.2 o13 economic development: agriculture; natural resources; energy; environment; other primary products 0.0 24.5 16.2 l66 food; beverages; cosmetics; tobacco; wine and spirits 12.0 14.3 13.5 p32 collectives; communes; agriculture 0.0 10.2 6.8 d18 consumer protection 12.0 4.1 6.8 q11 agriculture: aggregate supply and demand analysis; prices 4.0 8.2 6.8 d24 production; cost; capital, total factor, and multifactor productivity; capacity 8.0 4.1 5.4 o19 international linkages to development; role of international organizations 0.0 6.1 4.1 j43 agricultural labor markets 8.0 2.0 4.1 source: econlit 26 p. sckokai cial attention has been given to the sub-sample of the italian studies, since a new scientific society in this area (aieaa) has been recently founded by a group of italian economists. the results concerning the general sample confirm the wide spectrum of interests of agricultural and applied economists, that includes some rather diversified research areas: from agricultural and food policies to the microeconomics of farmers’ behaviour, from development issue to commodity market and price analysis, from the industrial organisation of the food sector to agricultural technology, innovation and research, from agrienvironmental economics and policy to consumer behaviour, from trade problems to water and land use issues. in general, this wide spectrum of interests is in line with the definition of applied economics recently proposed by mittelhammer (2009) as the mission of the aaea, the recently renamed us society of agricultural and applied economists. of course, this ’enlarged scope’ of research in agricultural and applied economics also implies a new working style of researchers, in which the collaboration and integration with several related disciplines, the adoption of new research methodologies and a close dialogue with the policy makers become increasingly important. now the question becomes: is this retrospective analysis sufficient to define future research trends in agricultural and applied economics? answering this question is very difficult, for a number of reasons. the most important one is that, given the timing of the peer review process, there is already a significant lag between paper submission and publication, ranging, on average, between 1 and 2 years. this means that the research output we observe in scientific journals is likely to be linked to ideas and projects developed several years before, while any article related to today’s hot topics in terms of social demand and/or policy relevance is likely to be published within the next 2-3 years. thus, for example, it is very likely that articles covering issues like agricultural policy reform in the eu or commodity price volatility will increase in the near future. nonetheless, other areas in which we should expect an increasing attention are those related to methodological improvements. for example, based on the jel codes analysed in this paper, areas in which research is likely to be very active are, among others, farmers’ behavioural modelling, vertical relationships in the food supply chain, price transmission mechanisms, econometric analysis of farm level data, policy evaluation modelling, experimental economics and consumers’ choice modelling. this list, although not exhaustive, may be considered a starting point for the future research agenda in agricultural and applied economics. the above general approach to the agricultural and applied economics profession as well as the most promising research trends are well developed also in the mission statement of the aieaa, whose scopes seem in line with the trend of the other major scientific societies in the field. this should become a strong incentive for italian agricultural and applied economists wishing to play a role in the international arena. in fact, the analysis concerning italian studies has shown that, while in terms of topics they are fairly in line with the general international trend in published research, their quantitative contribution remains rather small (only 2.5% of the published articles in the last 6 years), despite an encouraging increasing trend. this is clearly an important problem for the italian agricultural and applied economics profession, which should dedicate some of its research and training efforts to develop the capacity of publishing a higher number of articles in international peer reviewed journals, especially through its younger members. this seems to be one of the most important missions of the newly founded aieaa. 27agricultural and applied economics: what is this finally, one should bear in mind that this analysis carries some important limitations, discussed in section 2. the most important one is certainly the fact that agricultural and applied economists, given their diversified research interest, publish also in journals that are not included in the isi category «agricultural economics and policy», whose domain is also rather ambiguous (for example, in the well-known database «scopus», no category of this type exists). this makes our analysis incomplete by definition, and further research efforts aiming to include a broader spectrum of journals would certainly be valuable. references american economic association (2012). journal of economic literature (jel) classification system, <http://www.aeaweb.org/jel/jel_class_system.php>, accessed 6 february 2012. brandt, j.a. (2003). aaea: adapting to meet member needs. american journal of agricultural economics 85: 1095-1104. buccola, s. (2006). the organization of economics. american journal of agricultural economics 88: 1123-1134. doering, o.c. (2007). the political economy of public goods: why economists should care. american journal of agricultural economics 89: 1125-1133. kinsey, j.d. (2001). the new food economy: consumers, farms, pharms, and science. american journal of agricultural economics 83: 1113-1130. léon, y. (2005). rural development in europe: a research frontier for agricultural economists. european review of agricultural economics 32: 301-317 mittelhammer, r. (2009). applied economics – without apology. american journal of agricultural economics 91: 1161-1174. thomson, k.j. (2001). agricultural economics and rural development: marriage or divorce? journal of agricultural economics 52(3): 1-10. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(2): 151-174, 2012 modelling economic returns to plant variety protection in the uk chittur s. srinivasan1 university of reading, uk abstract. this paper attempts an empirical assessment of the incentive effects of plant variety protection regimes in the generation of crop variety innovations. a duration model of plant variety protection certificates is used to infer the private appropriability of returns from agricultural crop variety innovations in the uk over the period 1965-2000. the results suggest that plant variety protection provides only modest appropriability of returns to innovators of agricultural crop varieties. the value distribution of plant variety protection certificates is highly skewed with a large proportion of innovations providing virtually no returns to innovators. increasing competition from newer varieties appears to have accelerated the turnover of varieties reducing appropriability further. plant variety protection emerges as a relatively weak instrument of protection. keywords. intellectual property rights, plant variety protection, appropriability, economic returns jel-codes. q16, c41 1. introduction crop variety innovations have been one of the key determinants of agricultural productivity growth (evenson and gollin, 2001). over the last hundred years, plant breeding research has taken place in an institutional setting – that is innovations have been the result of investments in plant breeding made by public and private sector institutions. in the uk, the post-1985 period has been a period of significant institutional change in the organisation of agricultural research, with greater emphasis being placed on the role of the private sector, especially for “near market” research. the response of private sector investment to institutional and policy change can be expected to be significantly influenced by the appropriability of economic returns from innovation afforded by existing intellectual property rights (ipr) regimes (e.g., the effectiveness of plant variety protection systems in the case of crop varieties). the incentive effects of ipr regimes and their impact on the generation of innovations are, therefore, a major concern for policy. 1 corresponding author: c.s.srinivasan@reading.ac.uk. 152 c.s. srinivasan these concerns have been further sharpened over the last decade by two developments – the first is that a number of studies including a major study by defra (defra, 2003) have highlighted concerns about the sharp slowdown in the growth of total factor productivity (to about 0.26% per annum) in uk agriculture since the mid-1980s. these studies have explored several potential causes of this slowdown in tfp growth including the role of domestic and foreign agricultural innovations. the key policy question that arises is whether incentives for innovation provided by current institutional arrangements for agricultural research are adequate. a considerable amount of anecdotal evidence appears to be available regarding the declining enthusiasm of the private sector for conventional plant breeding on account of low appropriability (e.g., monsanto’s decision to withdraw from conventional plant breeding in the pbi). the second key development is the increasing importance of innovations based on agricultural biotechnology. the application of biotechnology to agricultural innovations calls for investments of a much higher order of magnitude than that required for conventional plant breeding. with the private sector playing a dominant role in this area, stronger forms of protection appear to be required for stimulating innovations based on biotechnology. any policy designed to encourage agricultural innovation needs to take into account the emergence of agricultural biotechnology. although the uk and other eu countries have been pioneers in establishing ipr systems for plant variety innovations (and these systems have become well established over the last four decades), there have been no systematic studies on the impact of ipr regimes and other institutional arrangements on the generation of agricultural innovations. this paper will focus on appropriability issues. much of the current debate regarding the appropriate ipr regime for promoting agricultural innovations (e.g., plant variety protection versus patents) revolves around the question of appropriability of economic returns afforded to innovators under different regimes. this paper will attempt a quantitative estimation of the returns appropriated by innovators in the uk from variety innovations using duration models derived from the behaviour of economic agents. this will provide an empirical basis for assessing the incentives for innovation provided by ipr systems over the period of the study, which has hitherto been lacking. the analysis will address the question of whether there is an economic case for stronger ipr regimes to stimulate crop variety innovations. the empirical application will be to crop variety innovations in the uk protected through plant variety protection over the period 1965-2000. 2. renewal model plant variety protection certificates are seldom marketed or traded and hence their private value is usually not observed. using the model developed by schankerman and pakes (1986), we will attempt to infer the value of plant variety rights from the economic responses of pvp certificate holders. in almost all countries with pvp legislation, certificate holders must pay an annual renewal fee in order to keep the certificate in force. if it is assumed that certificate holders make their renewal decisions based on the value of returns they obtain from the renewal, then the data on renewal of pvp certificates and renewal fee schedules contains information on the private value of pvp 153modelling economic returns to plant variety protection in the uk rights2. such a renewal model implies that protected plant varieties for which protection is more valuable (e.g. because it commands a larger market share) will be protected by payment of renewal fees for longer periods of time. a breeder will not renew protection for a variety for which he sees no commercial potential. the estimates of the private value of pvp certificates derived from renewal models can be used to supplement the data on the number of pvp certificates as a measure of inventive output. it is also possible to estimate how the average value of pvp certificates differs across crop groups or over time. if the distribution of the value of pvp certificates is highly skewed and dispersed, then the number of certificates granted alone may not be a good indicator of the value of breeders’ innovations. following the assumptions of the schankerman and pakes (1986), it is assumed that each cohort of pvp certificates is endowed with a distribution of initial returns, which decay deterministically thereafter. the model allows both the distribution of initial returns and the decay rate to vary over time. it is assumed that certificate holders choose the lifespan of the certificates so as to maximise the discounted value of net returns (i.e. current returns minus renewal fees). schankerman and pakes show that for a given schedule of renewal fees, these assumptions imply a sequence of renewal proportions over age for each cohort. the proportion of pvp certificates renewed in each year depends on parameters, which determine the distribution of initial returns and the decay rates. their model estimates a vector of parameters, which makes the renewal proportion predicted by the model as close as possible to the ones actually observed. let us consider the case of a plant breeder who holds a pvp certificate. let j denote the cohort year of the pvp certificate and t its age so that t + j represents the year (in which renewal decisions are made). in order to keep the certificate in force, the breeder has to pay an annual renewal fee, which generally varies with the age of the certificate. renewal fees are periodically revised, and once revised, apply to all renewals irrespective of the cohort of the certificate. let the sequence of renewal fees (in real terms and taking into account periodic revisions) at different ages be denoted by {ctj}. a breeder who pays the renewal fee earns the return to protection in the following year, which can be denoted by rtj. it is assumed that rtj is known with certainty at the time the pvp certificate is granted. the breeder has to maximise the net value of discounted returns by choosing the optimal age at which to stop paying the renewal fee. given an assumed functional form for the distribution of initial returns, the model derives the relationship between the predicted renewal proportions and the vector of parameters of the distribution of initial returns and the decay rates. the functional form, which was found to best fit the sequence of the renewal propor2 renewal models by their very nature can estimate only the private value of pvp certificates, which can be appropriated by the ipr holder. as schankerman and pakes (1986, p. 1069) observe: “it should be emphasised that these estimates refer only to the private value of patent rights. we cannot address the broader question of social benefits of patent protection with the present model of renewal behaviour. the social benefits must encompass both the private value and gains in consumer surplus created by the additional r&d effort which is stimulated by patent protection (these latter gains, of course, continue after the patent has expired)”. the selfreproducing nature of seed implies that breeders have considerable difficulty in appropriating returns from their innovations. the renewal model used in this paper also estimates only the private returns that can be appropriated by the plant breeder or the certificate holder. 154 c.s. srinivasan tions, was the lognormal distribution3. if r0j (initial returns) follows a log-normal distribution, then:   using a lognormal functional form for the distribution of initial returns, the model yields the following estimation equation4:   where dτj = 1 -δτj and δτj is the decay rate of initial revenues of cohort j in each time period. ptj = proportion of certificates of cohort j renewed at time t. schankerman and pakes (1986) allow for inter-cohort differences in the distribution of initial returns, by allowing cohort-specific variation of μ, but maintaining a common value of σ. this is equivalent to letting cohorts of pvp certificates differ by a proportional rescaling of the initial returns of all certificates in a given cohort. they also allow decay rates to vary across decades. thus, if the renewal data span three decades (decade1, decade 2 and decade 3) then:   where it is assumed that:   and   and   are dummy variables such that:   positive values of β1 and β2 indicate a decline in the rate of decay during decade 2 and decade 3 relative to decade 1. 3 the other distributions that have been commonly used in patent renewal models are the weibull and paretolevy distributions. 4 for a derivation of the estimating equation please see schankerman and pakes (1986). 155modelling economic returns to plant variety protection in the uk the estimation of the value of pvp certificates was based on the above equation. the equation was estimated using non-linear least squares. one modification made in estimating the value of pvp certificates was that instead of allowing cohort-specific values of μ, the value of μ was allowed to vary only across three time periods in order to reduce the number of parameters to be estimated. 3. description of data the estimation of the private value of pvp certificates was attempted for agricultural and ornamental crops in the uk over the period 1965-20005. the uk has been a pioneer in the provision of iprs for plant variety innovations. with legislation enacted in 1964, the uk has four decades of experience in the implementation of plant variety protection. among eu countries, it has been one of the leading issuers of pvp certificates. thus, the data for the uk are able to provide fairly large cohort sizes for agricultural and ornamental crops. there are two important reasons why we have not included the post-2000 grants in the dataset used for estimation of the model. the first is that from the year 2006, the uk stopped levying renewal fees for pvp certificates. this implied that after 2006, pvp certificate holders would have no incentives to surrender their certificates before the full term (unless they are not able to “maintain” the variety for other reasons) based on a comparison of renewal costs and returns. the second reason is that given the average length of survival of wheat varieties from 5-7 years, the inclusion of grants over the period 2000 to 2006 would have led to a large increase in the proportion of censored observations in the dataset which would have significantly increased the standard errors of the estimates of the duration model. a key element of this study was assembling a comprehensive dataset on plant variety protection certificates issued in the uk since inception of pvp legislation to 2000. this dataset was put together from the information contained in various issues of the plant varieties and seeds gazette published by defra6 over this 36 year period. the dataset covers all species/genera of plants that have been protected in the uk and contains 13,365 records (including both grants and unsuccessful applications). using this database it was possible to derive for each cohort the proportion of pvp certificates renewed at different ages. there are three important components of the total cost of obtaining a pvp certificate. these are (a) application fee (b) examination fee for dus7 testing and (3) annual renewal fee for keeping the certificate in force. while the application fee is a one-time fee, the examination fee has to be paid for each year or growing season over which the variety is tested and the renewal fee has to be paid each year. data was provided by defra on pvp application, testing and renewal fee schedules for the period 1964-2000. data on renewal fees is essential for the application of renewal models to pvp certificates. it must be noted that pvp fee schedules are periodically revised. when a schedule is revised, the revised fees apply to all pvp certificates renewed after the revision, irrespective of the cohort to which they belong. 5 data was collected for agricultural, horticultural and ornamental crops. however, estimation of the private values of horticultural crops could not be undertaken because the cohort sizes were too small. 6 department of environment, food and rural affairs in the uk and its predecessors. 7 distinctness, uniformity and stability. 156 c.s. srinivasan the fees applicable in nominal terms were converted into real terms (2003 = 100) using a gdp deflator. certain key features of pvp costs that emerge from the data are as follows: a. application fees, examination fees and renewal fees are in general higher for agricultural crops than they are for horticultural and ornamental crops. cereals tend to have the highest costs and ornamentals the lowest. the costs are set at a relatively high level for cereals probably because cereal varieties are expected to have a higher volume and total value of sales in relation to other crop-groups. at the same time, the fee schedule itself can influence the number of varieties offered for protection. pvp grants in the uk (and most other countries) are dominated by grants for ornamental species. one factor responsible for this may be the relatively low pvp fees that are set for ornamentals, which allows varieties with limited commercial potential also to be offered for protection. b. application and examination fees constitute only 25-35% of the total discounted cost of obtaining a pvp certificate and keeping it in force for a period of 20 years or the statutory maximum period. for a variety for which protection remains in force for the full term, renewal fees constitute a major portion of the cost. c. unlike some other eu countries, renewal costs in the uk do not increase with age. in absolute terms, the renewal fees are fairly modest. for the year 2000 cohort, the annual renewal fee was on average £ 435 for agricultural varieties, £ 320 for horticultural varieties and £ 175 for ornamental varieties. the discounted costs of obtaining a pvp certificate for a cereal variety and keeping it in force for 20 years are under £ 10,000. in spite of the modest levels of pvp fees, a large number of pvp certificates do get surrendered before their full term. this suggests that there may be a large concentration of pvp certificates very with little private economic value. d. renewal fees in nominal terms have increased significantly since mid-sixties, but in real terms these costs have remained remarkably stable (figure-1). renewal fees have in fact declined in real terms after a spurt in the mid-1990s. over the entire period, renewal fees for cereal varieties in real terms have only increased from £ 300 to about £ 470 per annum (in 2003 prices). e. since 1995, breeders in the uk have had the option of obtaining eu-wide protection through the community plant variety office8 (cpvo) instead of obtaining national protection. initially the expectation was that the cost of obtaining and maintaining eu-wide protection through the cpvo would be higher than the cost of obtaining national protection in individual countries, but would still provide considerable savings in transaction costs to the breeder in relation to the cost of securing national protection separately in several countries. however, at present while application and examination fees for eu-wide protection are higher than those for national protection, the renewal costs set by the cpvo are lower than those set by several eu 8 the community plant variety office issues pvp certificates valid in all the countries of the eu against a single application made by the breeder. this substantially reduces the transaction cost faced by the breeder for obtaining protection in several countries. the system of eu-wide grants has not replaced national pvp grants. it is still possible for a breeder to apply for protection in individual eu countries under the relevant national pvp law. a breeder’s decision on whether to seek protection separately in one or more countries or to seek eu-wide protection will depend on the commercial potential of the new variety. eu-wide protection cannot be held simultaneously with national protection – if eu-wide protection for a variety is obtained through the cpvo, then national protection has to be surrendered. the grants made by the cpvo are not reported as national grants. 157modelling economic returns to plant variety protection in the uk countries for national protection. for instance, the annual renewal cost for a wheat variety protected in the uk is £ 475 whereas it is only 200 euros in the cpvo. this may encourage breeders in the uk to give up national level rights to secure eu-wide rights. this may also account for the decline pvp applications in the uk over the last 5 years for certain genera/species. it is likely that in the eu, in the course of time, national level protection will remain relevant only for those varieties, which have no potential market outside a country. table 1. data for survival analysis by crop group (cohort range 1965-2000) crop group total number of pvp certificates number of expired/ surrendered/ terminated certificates number of valid certificates as at 31/12/2000 (censored cases) percent censored agricultural 2313 1794 519 22.44 horticultural 1262 983 279 22.11 ornamental 3556 2584 972 27.33 overall 7131 5361 1770 24.82 figure 1. trends in average nominal and real renewal fees for pvp certificates in the uk (2003 £) figure-1:trends in average nominal and real renewal fees for pvp certificates in the uk (2003 £) 0 100 200 300 400 500 600 700 800 900 19 66 19 68 19 70 19 72 19 74 19 76 19 78 19 80 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 year r en ew al fe e (£ ) nominal renewal fee real renewal fee the data used for the analysis is described in table 1 and table 2. 158 c.s. srinivasan table 2. mean and median survival durations crop group mean survival duration (years) median survival duration (years) agricultural crops 6 4 horticultural crops 9 5 ornamental crops 8 5 test statistics for equality of survival distributions of different crop groups test statistic df significance log rank 154.42 2 .0000 breslow 112.22 2 .0000 tarone-ware 128.02 2 .0000 as in most other countries with pvp legislation, pvp grants are dominated by ornamental crops. grants for ornamentals account for 50% of all grants, while agricultural crops (which include cereals) account for only 32%. the remaining 18% is accounted for by grants for horticultural crops (mainly fruit species). the proportions are similar when we consider the currently valid grants. it is somewhat surprising that an ipr instrument which is primarily intended to encourage innovation in agricultural crops (food crops and industrial crops) has its greatest impact on the generation of ornamental varieties. as discussed later, this may be partially explained by the differences in the appropriability regime for agricultural and ornamental crops. figure 2. kaplan-meier product limit estimates of survival function of pvp certificates of different crop groups   survival functions of pvp certificates by crop groups years 403020100 cu m ul at iv e su rv iv al p er ce nt ag e 1.2 1.0 .8 .6 .4 .2 0.0 -.2 crop group ornamental ornamental-censored horticultural horticultural -censored agricultural agricultural -censored 159modelling economic returns to plant variety protection in the uk there are statistically significant differences in the survival patterns of varieties in different crop groups (table 2). ornamental and horticultural varieties survive for significantly longer durations than agricultural varieties. the kaplan-meier survival functions for pvp certificates of the three crop groups for all cohorts from 1965-2000 are plotted in figure 2. the survival function for ornamentals completely dominates the survival function for agricultural varieties – that is, at any age the proportion of ornamentals surviving is greater than agricultural varieties. for all crop groups, the mean/median survival duration of pvp certificates is considerably less than the maximum duration of protection allowed under the legislation of 20-25 years. only 40 to 60% of pvp certificates for agricultural crops survive for more than five years and less than 30% survive for more than ten years. less than 3% of the certificates survive for the full term. the average agricultural variety survives remains protected for only for 6 years. the fact that on average ipr royalties are collected by breeders over a relatively short time span has important implications for the appropriability of returns from variety innovations. there are significant differences in the survival pattern of varieties within crop groups across decades. for the purpose of analysis we have divided the entire period into three time periods 1965-1980, 1980-1990 and 1990-2000. the data for these three time periods is summarised in tables 3 and 4 and the kaplan-meier survival functions are plotted separately for each time period in figure 3. the mean and median period of survival of varieties within crop groups has steadily fallen from the 1960s to the 1990s. this shows table 3. data for survival analysis by crop group and time periods crop group total number of pvp certificates number of expired/ surrendered/ terminated certificates number of valid certificates as at 31/12/2000 (censored cases) percent censored cohort time period 1965-80 1683 1648 35 2.08 agricultural 439 433 6 1.37 horticultural 416 396 20 4.81 ornamental 828 819 9 1.09 cohort time period 1980-90 2399 1944 455 18.97 agricultural 782 710 72 9.21 horticultural 468 359 109 23.29 ornamental 1149 875 274 23.85 cohort time period 1990-2000 3049 1769 1280 41.98 agricultural 1092 651 441 40.38 horticultural 378 228 150 39.68 ornamental 1579 890 689 43.64 total for all crop groups and cohort time periods 7131 5361 1770 24.82 160 c.s. srinivasan that the turnover of varieties quickened over the three decades. the declining mean period of survival in the context of an increase in the number of pvp grants indicates that new varieties have been faced with increasing competition over time. the relative patterns of survival have remained the same, with the survival function for ornamental varieties dominating that of agricultural varieties in all the three time periods. table 4. mean and median survival durations crop group mean survival duration (years) median survival duration (years) cohort time period 1965-80 1980-90 1990-2000 1965-80 1980-90 1990-2000 agricultural crops 7 6 4 7 4 3 horticultural crops 8 9 6 6 6 4 ornamental crops 8 9 6 5 6 6 test statistics for equality of survival distributions of different crop groups test statistic df significance log rank 154.42 2 .0000 breslow 112.22 2 .0000 tarone-ware 128.02 2 .0000 figure 3. kaplan-meier product limit estimates of survival function of pvp certificates of different crop groups by time periods   cohort range : 1965-1980 years 403020100-10 cu m ula tiv e s ur viv al pe rc en ta ge 1.2 1.0 .8 .6 .4 .2 0.0 -.2 crop group ornamental ornamental-censored horticultural horticultural -censored agricultural agricultural -censored 161modelling economic returns to plant variety protection in the uk 4. estimation of the renewal model the results of the estimation of the renewal model for agricultural crops are presented in table 5. the results of three sets of regressions are presented. regression (1) presents the results of a model, which allows for no cohort-specific variation in the distribution of initial revenues (μj = μ for all j). regression (2) allows µj to vary across time periods. that is, µ1 represents the value of μ in the period 1965-1979, µ2 the value in the period 1980-1989   cohort range 1980-1990 years 3020100-10 cu m ula tiv e s ur viv al su rv iva l p er ce nt ag e 1.2 1.0 .8 .6 .4 .2 0.0 crop group ornamental ornamental-censored horticultural horticultural -censored agricultural agricultural -censored   cohort range 1990-2000 years 121086420-2 c um ul at iv e s ur vi va l p er ce nt ag e 1.2 1.0 .8 .6 .4 .2 0.0 crop group ornamental ornamental-censored horticultural horticultural -censored agricultural agricultural -censored 162 c.s. srinivasan and µ3 the value in the period 1990-2000. regression (3) allows μ to vary as in regression (2) but in addition allows variation in the decay rates across the three time periods. table 5. regression results of renewal models for pvp certificates-agricultural crops* model parameters (1) (2) (3) μ 7.95 (0.80) μ1 7.70 (0.38) 7.66 (0.44) μ2 6.93 (0.25) 6.90 (0.27) μ3 6.60 (0.21) 6.61 (0..20) σ 3.05 (1.02) 1.97 (0.35) 1.93 (0.14) δ 0.36 (0.10) 0.26 (0.04) 0.26 (0.052) β1 0.0107$ (0.0165) β2 -0.0001$ (0.0208) δ1** 0.25 δ2** 0.26 r2 0.78 0.85 0.857 df 333 331 329 note: μ1 = relates to 1965-1979 μ2 = relates to 1980-1989 μ3 = relates to 1990-2000 β1= relates to 1980-1989 β2= relates to 1990-2000 * figures in parentheses are standard errors. **δ1 = 1-(1-δ)*exp(β1) and δ2 = 1-(1-δ)*exp(β2) $ not significant at 5% or 10% level of significance. 4.1 agricultural crops the key parameters of the model μ, σ and δ all have the right signs and are statistically significant9. the high values of r2 indicate that the lognormal distribution fits the 9 the only parameters not significant at the 5% level of significance are the parameters β1 and β2 which allow the decay rates to vary across the three time periods (regression (3)). 163modelling economic returns to plant variety protection in the uk data reasonably well10. an f-test clearly rejects the hypothesis that all the µs are equal, that is, there is no inter-cohort variation in the distribution of initial returns. the mean value of the distribution of initial returns from a cohort of varieties is given by � e µ + 0.5σ 2   in a lognormal distribution. taking figures from regression (3), we find that the mean value of initial returns of pvp certificates in the uk has steadily declined over the three time periods from £ 13,663 during 1965-1979 to £ 6,389 1980-1989 to £ 4,781 1990-2000. the decline in mean values has taken place alongside an increase in the number of pvp certificates issued (except in the post-1995 period when the number of certificates issued annually has not increased – possibly owing to the increasing use of eu-wide protection by breeders)11. for any given sequence of decay rates, a decline in the mean value of the distribution of initial returns implies that the present value of returns from the average protected variety has declined. if returns to titleholders accrue in the form of royalties linked to the volume of seed sales over the life12 of a variety, then it can be seen that the present value of returns depends on the sequence of market shares obtained by the variety. a decline in the mean value of initial returns then suggests that the (cumulative) market share obtained by the average protected variety has declined. this may be due to competition from a larger number of varieties and/or an accelerated turnover of varieties in the post-pvp period. in this paper we have not examined the relationship between pvp and the introduction of new varieties. but in general, in most european countries, the postpvp period has seen a proliferation of varieties along with a much quicker turnover of varieties. the declining mean value of initial returns, however, does not preclude the possibility of a few varieties in the tail of the distribution acquiring very large market shares. the degree of skewness in the distribution of initial returns is illustrated by the ratio of the mean to the median value. for the log-normal distribution this is given by  . this ratio is 6.4 for the uk. the distribution of initial returns is, thus, skewed to the right and rather sharply so. the estimated rate of decay (regression(2)) is 26%, which suggests that pvp certificates tend to lose value fairly rapidly. when we allow decay rates to vary, we find that decay rate declined in 1980-1989 compared to 1965-1979 but (marginally) increased in 1990-2000 relative to 1965-1980. however, it must be noted that the co-efficients β1 and β2 which produce these effects are not significant. an increase in the decay rate could be expected as the result of the changes associated with the introduction of the eu-wide system of protection through the cpvo. after the cpvo was established in 1995, breeders had an incentive to switch from national protection to eu-wide protection through the cpvo, provided their varieties had market potential in several eu countries. but for acquiring eu-wide rights, national rights have to be surrendered or kept 10 r2 is computed as 1 -(residual sum of squares/corrected total sum of squares). in the case of non-linear regression r2 is not bound by 0 and 1. 11 in our analysis, we have grouped together all agricultural crops. consequently, the mean value of the distribution of initial returns has not been separately estimated for different agricultural crops. it must be noted that the potential market share of a variety does depend on crop. in the case of crops where varieties are adapted for being grown over large areas (“widely-adapted varieties”), the potential market share of a variety may be larger compared to a protected variety in a crop where varieties are locally oriented. however, the potential market share of a new variety depends not only on the general adaptability of varieties in a crop, but also on the degree of competition from competing varieties. the potentially larger market share of widely adapted varieties could be offset by competition from a larger number of varieties. 12 the reference here is to the “protected” life of a variety, or the duration for which a variety remains protected. 164 c.s. srinivasan suspended. this may lead to a jump in the decay rates reflecting only the upsurge in the “surrender” of certificates at the national level owing to breeders switching to eu-wide protection. however, the impact of such switching is not reflected in the data. 4.2 ornamental crops table 6. regression results of renewal models for pvp certificates-ornamental crops* model parameters (1) (2) (3) μ 7.92 (0.79) μ1 8.15 (0.97) 8.21 (1.06) μ2 8.49 (1.12) 8.40 (1.12) μ3 7.93 (0.95) 7.98 (0.95) σ 2.48 (1.03) 2.30 (1.32) 2.05 (1.34) δ 0.32 (0.082) 0.35 (0.100) 0.38 (0.11) β1 0.098$ (0.052) β2 0.019$ (0.0416) δ1** 0.325 δ2** 0.377 r2 0.75 0.756 0.763 df 375 373 371 note: μ1 = relates to 1965-1979 μ2 = relates to 1980-1989 μ3 = relates to 1990-2000 β1= relates to 1980-1989 β2= relates to 1990-2000 * figures in parentheses are standard errors. **δ1 = 1-(1-δ)*exp(β1) and δ2 = 1-(1-δ)*exp(β2) # a very small positive value $ not significant at 5% or 10% level of significance. the results of the renewal model for ornamental crops are presented in table 6. the three sets of regressions are the same as that for agricultural crops. the results for ornamental crops are a very similar to those for agricultural crops. again, the key parameters of the model μ, σ and δ all have the right signs and are statistically significant (though β1 and β2 are not significant in regression (3)) the mean value of the dis165modelling economic returns to plant variety protection in the uk tribution of initial returns increased from £ 30,069 in 1965-1979 to £ 36,360 in 19801989 before declining to £ 23,890 in 1990-2000. the interesting feature of these results is that the mean values of ornamental varieties are 2-3 times the mean values for agricultural varieties. there has been a steady upward growth in the number of certificates in the uk, especially in the late 1980s and till the mid-1990s. therefore, as in the case of agricultural crops, the number of new varieties or innovations produced every year has increased over time, but the value of the average innovation has decreased. the number of new varieties protected and the mean value of initial returns appear to have moved in opposite directions. this again may be the result of a larger number of varieties competing for market share. the distribution of initial returns is skewed to the right in the case of ornamentals as well with a mean to median ratio of 8.17. the decay rate of 32% (regression (2)) for ornamentals is higher than that for agricultural varieties, suggesting that ornamental varieties lose value faster. when we allow for variation in decay rates (regression (3)), we find that the decay rate increases marginally to 32.5% in 1980-89 and to 37.7% in 1990-2000. it must be noted, however, that the co-efficients β1 and β2 are not significant at the 5% level of significance. if β1 and β2 are not significantly different from zero then the decay rate has not changed over the decades. 5. private value of pvp certificates the parameters of the renewal model estimated for agricultural crops and ornamentals can be used to derive the private value of pvp certificates i.e. the net returns that are appropriated by the titleholder. the present value of a single pvp certificate denoted by v is given by:   where rt ct is the net return from holding a pvp certificate during age t, i is the discount rate, δ is the decay rate and t is the optimal life span of the pvp certificate based on the renewal rule discussed earlier (i.e. the certificate will be renewed only if rt > ct). the assumption of a lognormal distribution for the initial returns (r0) for a cohort of certificates leads to a distribution of v. the estimates of the parameters μ, σ and δ are used to generate the distribution of v by simulation. to do this, 50,000 random variables were drawn from a lognormal distribution with the estimated values of μ and σ and v was calculated for each one of them using the decay rate, the renewal fees applicable in any given year and the renewal rule. from this derived distribution of v, the quantiles of the private value of pvp certificates could be derived. tables 6 and 7 present for agricultural crops and ornamentals respectively the distribution of the private value of pvp certificates for three different cohorts in constant 2003 pounds. the key feature of the value distribution for both agricultural crops and ornamental crops is the sharp skewness. there is a high concentration of pvp certificates with 166 c.s. srinivasan very limited private economic value13. for agricultural crops, the median value of a pvp certificate was £ 2,762 for the 1975 cohort, £ 856 for the 1985 cohort and only £ 275 for the 1990 cohort. there is a sharp rise in the value of pvp certificates in the third quantile, but most of the value of pvp certificates is concentrated in the tail of the distribution, especially in the top 1%. for agricultural crops only 1% of the protected varieties were worth more than £ 130,000 for cohorts in the 1990s. similarly, for ornamentals, the median value of a pvp certificate was £ 3,598 for the 1975 cohort, £ 5,768 for the 1985 cohort and £ 2,782 for the 1990 cohort. the top 1% of the certificates had a private 13 the assumption of a constant and deterministic decay rate, which implies that the returns (rt) obtained by a breeder tend to monotonically decline over time, tends to bias the estimated private value of pvp certificates downward. indeed, here we measure the minimum private returns to holders of pvp certificates in the circumstances. in the case of plant variety protection, returns to the titleholder generally accrue in the form of royalties linked to the volume of seed sales over the life of the protected variety. the royalties obtained by the titleholder are likely to be related to the rate of producer adoption, i.e., it will depend on the sequence of market shares obtained by the variety. a typical new variety may take some years to reach peak market share, after which market share may decline. the sequence of market shares generally follows an inverted-u pattern. the sequence of returns obtained by the titleholder may also follow a similar pattern. the assumption of a constant decay rate may be more appropriate to industrial process inventions where the licensing income obtained by a patent holder may decline over time as competing innovations become available. the incorporation of the adoption and diffusion pattern of protected varieties in the renewal model may significantly increase the mean value of the distribution of the private value of pvp certificates. while we have not attempted to do so in this paper because of the difficulties that it poses in estimation, it can be seen from tables 7 and 8 that even a significant increase in the private value distribution is unlikely to affect our conclusion that that private economic value of the bulk of pvp certificates is very modest. table 7. estimates of the private value distribution of pvp certificates for agricultural crops in the uk (all values in constant 2003 uk £) 1975 cohort 1985 cohort 1990 cohort estimated parameters of the renewal model μ 7.66 6.90 6.61 σ 1.93 1.93 1.93 δ 0.2649 0.2569 0.2650 value distribution mean 24,436 11,525 7,911 minimum 0 0 0 maximum 3,829,475 1,705,080 1,337,739 percentile 25 380 0.30 0 percentile 50 2,762 856 275 percentile 75 13,735 5740 3,777 percentile 95 96,609 46,390 32,447 percentile 99 376,988 182,444 130,363 μ = mean of the distribution of initial returns from pvp certificates on agricultural crop varieties σ = standard deviation of the distribution of initial returns δ = decay rate of initial returns note: value distribution estimated by simulation 167modelling economic returns to plant variety protection in the uk value in excess of £ 445,000. the inescapable conclusion is that the bulk of pvp certificates generate only very limited privately appropriable returns14. the highly skewed distribution of private value of pvp rights is consistent with the results of studies of the values of patent rights for industrial products15. interestingly, the mean value of private returns appropriated from ornamental varieties is 1.5-3 times that appropriated from agricultural crop varieties. this has interesting implications for the proportion of the market value of seed appropriated by pvp titleholders in the case of agricultural and ornamental crops. for an accurate assessment of this proportion, we need to estimate the private value of a cohort of pvp certificates and the market value of the seed sold of varieties included in the cohort over the life of the varieties. the private value of a cohort of pvp certificates can be estimated by multiplying the mean value of the cohort (from tables 6 and 7) with the number of certificates issued in that year. in the uk, in 1990, a total number of 389 grants were made, of which 161 related to agricultural crops and 198 to ornamental 14 it must be clarified that the private value of pvp certificates estimated by the renewal model reflects the returns attributable to the holding of iprs alone. the results only suggest that the ‘pure’ private returns to holding iprs (and that too in the form of pvp and not patents) are modest. to generate returns from a new variety, iprs have to be combined with other complementary assets such as production, marketing and distribution capabilities. the factors, which affect the distribution of returns between the innovator and the owners of the complementary assets, are discussed later. 15 based on an extensive survey of uk industry, taylor and silberston (1973) concluded that only a very limited number of patents generate substantial licensing income for the patent holders. schankerman and pakes (1986) too found that the bulk of patents in uk, france and germany had very little economic value. table 8. estimates of the private value distribution of pvp certificates for ornamental crops in the uk (all values in constant 2003 uk £) 1975 cohort 1985 cohort 1990 cohort estimated parameters of the renewal model μ 8.21 8.4 7.98 σ 2.05 2.05 2.05 δ 0.3893 0.3250 0.3374 value distribution mean 33,808 52,274 27,896 minimum 0 0 0 maximum 6,280,324 9,664,581 5,213,735 percentile 25 566 883 287 percentile 50 3,598 5,768 2,782 percentile 75 17,325 26,916 14,086 percentile 95 127,955 197,593 106,039 percentile 99 376,988 182,444 130,363 μ = mean of the distribution of initial returns from pvp certificates on ornamental varieties σ = standard deviation of the distribution of initial returns δ = decay rate of initial returns note: value distribution estimated by simulation 168 c.s. srinivasan crops (the remaining 50 related to horticultural crops). the private value of the 1990 cohort of protected agricultural varieties was thus £ 1.273 million, while the 1990 cohort of ornamental varieties was valued at £ 5.5 million (current prices). we do not have the estimated value of the seed sales of varieties included in the 1990 cohort over the life of the varieties. however, it may be seen that the private value of the 1990 cohort of agricultural varieties constituted just 0.04% of the value of agricultural crop output of £ 3,088 million in that year, while the private value of the cohort of ornamental varieties constituted 1.08% of ornamental crop output of £ 506.4 million16. thus, the private value of a cohort of ornamental pvp certificates constitutes a much larger proportion of the value of output than a cohort of agricultural crop certificates. as the seed market value is generally related directly to the value of the crop17, these figures suggest that titleholders for ornamental varieties appropriate a larger proportion of the seed market value than titleholders of agricultural crop varieties. the absence of farmers’ exemption (plant-back rights) in the case of ornamentals and the ease of detecting ipr infringements are probably the factors that increase the appropriability of returns from protected varieties of ornamentals. better appropriability of returns in the case of ornamentals may also explain the large number of grants for ornamentals in most countries18. at the same time, the loss of revenue to breeders on account of farmers’ exemption may be an important reason explaining the lower proportion of seed market value appropriated by breeders of agricultural crop varieties19. the low average value of pvp certificates may appear to be somewhat surprising, especially against the background of large profits made by multinational seed companies. however, low average private values of ipr holdings and the highly skewed distribution of private value are not unusual in the literature and are not unique to pvp certificates. a large number of studies on the private value of patent rights (a much stronger form of ipr protection) for different sectors of the economy have found very similar results. (schankerman and pakes, 1986; pakes, 1986; schankerman, 1998; sullivan, 1994). it must also be noted that inventors and plant breeders can and do use various methods to protect their innovations, including iprs (patents or pvp certificates), trade secrets, different forms of first mover advantage etc. (levin et al., 1987; cohen, nelson and walsh, 1996). the decision to seek ipr protection for an invention or a new plant variety depends on the costs and benefits of these alternative benefits. for example, in the case of hybrids, trade secrets based protection of parental lines may provide adequate protection for a new variety. the private value of iprs represents only the incremental returns that could be generated by holding iprs, above and beyond what could be earned using the alternative means (schankerman, 1998). the private value of pvp certificates, therefore, does not constitute the entire value of the returns to innovation in plant breeding. 16 the figures for the value of agricultural and ornamental crop output are from oecd (2000). the value of agricultural and ornamental output, of course, includes the output of varieties not included in the cohort. the comparison is, therefore, intended only to illustrate the difference in appropriability between agricultural and ornamental crops. 17 the seed market value is estimated at less than 10% of the value of crop output in the case of agricultural crops (oecd, 2000). for ornamental crops, it is estimated to be between 10-15% of the value of output. 18 in most countries with pvp legislation, grants for ornamentals account for 50-80% of all grants made. 19 over the last decade, plant back rights for farmers have been circumscribed in the eu. only “small farmers” are allowed the privilege of using the saved seed of protected varieties without payment of royalties to the titleholders. our calculation relates to 1989 when payment of royalties on saved seed of protected varieties was not a widespread practice. 169modelling economic returns to plant variety protection in the uk in order to assess the relative importance of patent protection, relative to other methods of appropriating returns from invention, the “equivalent subsidy rate” (esr) is often used. it is the ratio of the total value of patent rights to the r&d used to produce those patents. it is an approximation to the cash subsidy that would have to be paid to r&d performers to yield the same level of r&d if patent protection were eliminated. schankerman (1998) finds that the esr for patents ranges from 15-24% and these estimates are very similar to those found in a number of other studies on patent renewal models. an esr of 25% indicates that patent protection generates as much as a quarter of the total private returns to r&d. but the finding also implies that 75% of the private returns to r&d must come from sources other than patents. this finding is consistent with survey evidence that finds that firms rely on many methods to appropriate rents from inventions (levin et al., 1987) and that the importance of patent protection varies greatly across industries (taylor and silberston, 1973). we do not have the data necessary for the estimating the esr in the case of pvp certificates. while the estimated private value of a given cohort of certificates can be obtained as described earlier, we do not have the r&d cost of producing the cohort because these costs may be spread over the years leading to the development and protection of new varieties. however, for a rough comparison it is possible to compare the private value of the 1990 cohort of pvp certificates with the aggregate national agricultural r&d expenditures for that year. this comparison is shown in table 9. such a comparison, no doubt, ignores the fact there is always a lag between r&d expenditure and pvp output. it also ignores the fact that not all the private value of pvp certificates in a country accrues to nationals, while research institutions and companies may also receive the value of varieties protected abroad. moreover, the aggregate agricultural r&d expenditures do not relate to plant breeding alone. nevertheless, the comparison does show that the private value of a cohort of pvp certificates constitutes a small fraction (2%) of the annual agricultural r&d expenditures. in the literature on the evaluation of returns from agricultural research, estimates of rates of return of 30% and above are quite common (see, for instance, the survey by alston et al., 1998)20. the implication is that the private value of pvp certificates appropriated by the titleholders constitutes a relatively small portion of the overall returns from agricultural r&d. the low private values of pvp certificates also reflect the fact that iprs by themselves do not ensure the capture of value (teece, 1987; rausser, scotchmer and simon, 1999). in order for the innovator to appropriate returns from his/her innovations, iprs have to be combined with a range of complementary assets21. in the case of innovations in plant breeding, the key complementary asset is a marketing and distribution network that can reach the innovation to farmers. our concern here is with the distribution of privately appropriable returns from an innovation between the innovator, imitators and the owners 20 these estimates relate to the total social returns from r&d and not to private returns alone. these studies also vary considerably in the elements of agricultural research expenditure that they include in the analysis. 21 complementary assets are assets with which the innovation must be combined in order to make the innovation useful and valuable to the consumer. teece (1987) distinguishes between three types of complementary assets. “generic assets” are general purpose assets that do not need to be tailored to the innovation in question. “specialised assets” are those with unilateral dependence between the innovation and the complementary asset. “co-specialised assets” are those with bilateral dependence. 170 c.s. srinivasan of complementary assets (seed producers and distributors)22. the key determinants of this distribution are (1) the regime of appropriability and (2) market structure in the ownership of complementary assets. 22 it must also be noted that iprs and the relevant complementary assets can be brought together in myriad ways. these range from contractual modes (e.g., arms-length licensing by an innovator with independent suppliers or distributors) to “integrated” modes (e.g., where the innovator and the owners of complementary assets merge). in general, contracting rather than integrating, is likely to be the optimal strategy when the innovator’s appropriability regime is a strong one and the complementary assets are in competitive supply (that is, there is adequate capacity and a choice of sources). in the seed industry, contractual modes are rare – there are few instances of independent plant breeders licensing their varieties to large seed firms; invariably, the plant breeding operation is also owned by the seed production and distribution firm. the predominance of the integrated mode suggests that the appropriability regime for innovators is weak and the complementary assets may not be in competitive supply. table 9. private value of pvp certificates and agricultural r&d expenditure agricultural crops ornamental crops horticultural crops number of pvp grants made in 1990 161 198 50 estimated mean discounted private value of pvp grants of 1990 cohorta 7911 27,896 27,896 total estimated value of the whole cohort of 1989 (= mean value x number of grants) £1.273 m £5.5 m. £1.395 r&d expenditure on agriculture by business enterprises £98.25 m public sector agricultural r&d expenditure £290.80 m total agricultural r&d expenditureb £389.05 m private value of pvp rights as a percentage of total agricultural r&d expenditure 8.168/389.05 =2% note: (a) as we do not have the mean value estimates of grants for crops which are not agricultural or ornamental crops, we have taken the mean value of agricultural crops (table 7) or the mean value of ornamental crops (table 8), whichever is higher, and multiplied it by the total number of grants for all crops. choosing the higher of the two values implies that the estimated discounted private value of the cohort is biased upward. however, this only reinforces the conclusion that the private value of pvp certificates constitutes a small portion of the returns from agricultural r&d; (b) the figures of agricultural r&d expenditures shown in the table do not relate to plant breeding. alone. however, it should be noted that the figures exclude r&d expenditures on ‘agro-chemicals’ and ‘food and beverages’. sources: (i) oecd basic science and technology statistics 1999 (r&d expenditure by business enterprises on ‘agriculture, fisheries and forestry’). (ii) oecd basic science and technology statistics 1999 (government budgetary outlays on r&d for the socio-economic objective ‘agriculture’). r&d expenditure/outlay figures in current prices have been adjusted to 2003 base using a gdp deflator. 171modelling economic returns to plant variety protection in the uk the regime of appropriability refers to environmental factors, excluding firm and market structure that govern an innovator’s ability to capture profits generated by an innovation. the appropriability regime depends on (a) the efficacy of the legal system to assign and protect intellectual property (scope and breadth of iprs, efficacy to enforcement etc.) and (2) the nature of technology – whether the protected innovation is a product or a process, whether the knowledge embedded in the innovation is codified or tacit (these characteristics affect the ease of imitation). in the case of new plant varieties, both these factors make for a weak appropriability regime. plant varieties are selfreproducing and lend themselves easily to imitation and modification. besides, pvp law generally allows for researchers’ exemption and farmers’ privilege that reduce the flow of rents to the certificate holders. enforcement of pvp rights is also rendered difficult because the consumers (farmers) are large in number and widely dispersed. teece (1987) also notes “access to complementary assets, such as manufacturing and distribution, on competitive terms, is critical if the innovator is to avoid handing over the lion’s share of profits to imitators and/or to the owners of complementary assets that are specialised or co-specialised to the innovation” (p. 197). there is considerable evidence of market power in seed production and distribution23. at the global level the top ten seed companies account for approximately 40% of the commercial seed market valued at us $ 15 billion (rafi: 1997). the four-firm concentration ration (cr-4) is approximately 21%. the world seed market is relatively fragmented compared to the agro-chemical industry where the top ten firms account for 82% of global sales. but concentration at the national level is high in most developed countries and in some developing countries. given the location-specificity of plant varieties and the limited movement of protected varieties across countries, national levels of concentration may be more relevant in assessing market power. in the united states in 1980, the four largest corn seed firms accounted for 57% of the market. by 1997, the cr-4 ratio had risen to 69% (goldsmith, 2001). with the recent extensive merger and acquisition activity, the dupont-pioneer and the monsanto-dekalb-holden complexes now influence nearly 90% of the corn market (hayenga, 1998). the soyabean market is less concentrated as pioneer and monsanto have only 19% of the market share each. the cr-4 ratio is 47%. the cotton seed market is the most concentrated. at one stage (when the takeover of delta and pineland co. by monsanto was in the offing) it appeared that monsanto alone would have 84% share with the cr-4 ratio above 90%. 23 the market power exercised by seed companies is not attributable entirely to variety ownership. this can be seen most clearly in the case of private seed companies in developing countries that operate in the absence of an ipr regime. in many developing countries, which have opened up the seeds sector to private and/or foreign investment, private companies have acquired significant market shares in several crops within a relatively short span of time (morris: 1998). it is true that the private seed industry in developing countries has tended to focus on hybrids (which have an inbuilt technological protection). but it is important to note that many of the varieties sold by private companies are publicly-bred hybrids with no ipr protection. while hybrids protect a firm against replication of seed by farmers, they do not provide any protection against imitation (as the varieties are themselves in the public domain). a large part of private sector activity in developing countries has also been based on the multiplication and distribution of self/open pollinated public sector varieties, which are again not protected by any form of iprs and are easy to replicate. for instance, in india, it is estimated that nearly 50% of commercial seed in wheat (a selfpollinated crop) is now supplied by private firms, which continue to thrive even in the absence of any ipr-based barriers to entry. the profits of these companies cannot be attributed to variety ownership (as the varieties are in the public domain) but must be seen as returns to the complementary assets discussed above. 172 c.s. srinivasan therefore, a weak appropriability regime and the existence of market power in the ownership of complementary assets may mean that the incremental return appropriated by the innovator on account of iprs are low. in the above discussion we have treated the innovator and seed producer as separate entities. but even when the innovator and the seed producer constitute a single entity, it is conceptually possible to distinguish between the returns that accrue on account of ipr ownership and the returns that accrue to the ownership of complementary assets. the renewal model is able to infer the incremental private value accruing to the innovator on account of owning a pvp certificate. the fact that even pvp certificates owned by large companies are seldom held for the full term (when annual renewal costs are only in hundreds of pounds) is an indication that a marginal calculus is being applied – that is, incremental returns from pvp are being compared with the renewal costs. the weakness of pvp as an ipr measure has certain other implications as well. if plant breeders have stronger alternative modes of protecting their varieties, they will switch to them and the use of pvp is likely to decline. this trend is most sharply visible in the u.s.24 where the number of pvp certificates issued every year has declined from about 300-400 to about 60-70 in the late 1990s, while the number of utility patents issued for plant varieties has steadily increased. this trend is not yet very apparent in european countries possibly because of the legal uncertainties surrounding the patentability of plant varieties25. as alternative modes of protection become available, the switch away from pvp can be expected in other countries as well. thus, while developing countries currently in the process of enacting pvp legislation are worried about the monopoly profits that plant breeders may reap, in developed countries the use of pvp is likely to decline because it facilitates only limited appropriability. the increasing importance of biotechnology based innovations in agriculture may accelerate this trend as such innovations appear to call for stronger forms of protection. it is noteworthy that genetically modified varieties of agricultural crops are being protected by utility patents (and not through plant variety protection) in countries like the us, japan and australia where such protection is available. 6. conclusion in this paper we have used a renewal model to estimate the distribution of private value of pvp grants. the most striking feature of the value distribution of pvp grants is 24 it must be noted that us pvp law is considerably weaker than it is in eu countries. the us law explicitly allows plant-back rights to farmers, i.e., farmers can use the seeds obtained from the harvest of a protected variety for resowing their own land. in the eu farmers have to pay a royalty to the breeder or titleholder even when they use the seeds obtained from the harvest of a protected variety, though an exception is made for the category of small farmers. in the us, “farmers’ privilege” has been considerably circumscribed by judicial decisions, and farmers are no longer allowed to sell up to 50% of the seed obtained from the harvest of protected variety as “unbranded” seed (“brown bagging” is no longer permitted). it is also important to note that if a variety is protected by patents (rather than by a pvp certificate), then farmers’ privilege and researchers’ exemption do not apply. 25 the european patent convention does not allow plant varieties to be protected. however, the position may change as the result of the european union’s “directive on the protection of biotechnology inventions (directive 94/44ec) which contains specific provisions on the patentability of genetically engineered biological material including plants and animals. however, there are still a number of unresolved issues (see eratt et al.: 2000). 173modelling economic returns to plant variety protection in the uk their sharp skewness, which indicates that there is a large concentration of pvp rights with very little private economic value. pvp emerges as a relatively weak ipr measure, which allows the private appropriation of only a small 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(1987). the competitive challenge: strategies for industrial innovation and renewal. cambridge, massachusetts: ballinger publishing company. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 3(1): 21-43, 2014 doi: 10.13128/bae-11151 exploring the willingness to pay for forest ecosystem services by residents of the veneto region paola gatto*, enrico vidale, laura secco, davide pettenella1 dipartimento territorio e sistemi agro-forestali, università di padova, agripolis, legnaro (pd), italy abstract. forests produce a wide array of goods, both private and public. the demand for forest ecosystem services is increasing in many european countries, yet there is still a scarcity of data on values at regional scale for alpine areas. a choice experiment survey has been conducted in order to explore preferences, uses and the willingness of the veneto population to pay for ecosystem services produced by regional mountain forests. the results show that willingness to pay is significant for recreation and c-sequestration but not for biodiversity conservation, landscape and other ecosystem services. these findings question the feasibility of developing market-based mechanisms in veneto at present and cast light on the possible role of public institutions in promoting policy actions to increase the general awareness of forest-related ecosystem services. keywords. payments for ecosystem services, choice experiments, multi-nomial logit, latent class models. jel codes. q23, q56 1. introduction the evidence was provided long ago that, in addition to wood and non-wood products, italian forests deliver ecosystem services (es) like soil protection, recreation and landscape amenity (di bérenger, 1965). today, driven also by global forces, this list has expanded to include services such as climate mitigation, biodiversity conservation and effects on water quality and quantity (croitoru et al., 2005; tempesta and marangon, 2004; gios et al., 2006; goio et al., 2008). most of these services are, for different reasons, public goods and are therefore enjoyed by the population free of charge. some are provided through uncompensated mandatory instruments; in 1923, for example, italian legislation imposed strong limits on felling in all mountain forests in order to protect soils from erosion. similarly, a constraint to preserve landscape amenity was enforced in 1985. other services like recreation and climate mitigation are public goods mostly because of a poor enforcement of property rights. when the provision of es is not rewarded through suitable mechanisms, forest owners do not include them in their management objectives unless constrained by command-and* corresponding author: paola.gatto@unipd.it. http://dx.doi.org/10.13128/bae-11151 22 p. gatto, e. vidale, l. secco, d. pettenella control policies. as a result, in the best cases forest management regimes rarely achieve a social optimum. in the worst cases, forest owners cease management activities and abandon their forests. this results in a general environmental degradation and the occurrence of negative externalities, like loss of landscape quality or biodiversity (croitoru et al., 2005). increasing the revenues of the benefit providers and improving forest management from the perspective of society is therefore essential if good levels of forest and environmental quality are to be secured. since it has been shown that traditional command and control measures may not always give the best results (merlo et al., 2000), achieving these objectives requires new policy tools. one solution could be to identify forms of marketing for the es, under the umbrella concept generally known as payments for ecosystem services (pes) (wunder et al., 2008; gómez-baggethun et al., 2010). an essential step in designing pes mechanisms is the assessment of the values at stake (millennium ecosystem assessment, 2005; teeb, 2010; uk national ecosystem assessment, 2011), which can aid the process of turning the good into a product by providing a basis for the definition of its price. this assessment process involves determining if, and to what extent, ‘consumers’ of forest es perceive their value, whether a willingness to pay (wtp) exists, who is willing to pay, for what and how much. assessing the value of forest ecosystems and incorporating them into appropriate policy mechanisms are the objectives of the eu-funded newforex project. the project involves several universities and research institutes throughout europe and aims to provide more accurate es values for the most important types of forest regions in europe (mediterranean, atlantic, boreal, central-european and mountainous) and identify new tools for the remuneration of service providers. this paper presents the results from the initial phases of the project, focused on assessing the existence and extent of wtp for forest es. geographically, the evaluation is targeted at the alpine areas of the veneto region, north-eastern italy, taken as case-study for the mountainous forest region investigated by newforex. despite the vast literature at the european level on forest values and consumer characteristics/perceptions and demand for forest es, attempts to estimate different forest es and their trade-offs are still rather scarce on a regional scale and especially in the alpine context. this research seeks to contribute to filling this gap, mostly with an operational goal, in the sense that the evaluation effort is aimed at producing values and indications to aid the design of appropriate pes mechanisms supporting es provision. the paper is organised as follows: a literature review introduces es valuations in the alps; the methodology section presents the method used to assess the value of mountain forest es, i.e. choice experiments (ce), and the related econometric models; a results and discussion section follows and, lastly, the conclusions provide some reflections on the scope for pes development in the light of the wtp results. 2. evaluation of forest es in the alps: a brief overview a review of the studies in alpine areas shows a wide variability in the focus – i.e. the type (or types) of es – scale – from local to regional to national – and nature – use and/ or non use values – of the es under evaluation. because of this variability, framing the results of the studies within any analytical scaffolding or attempting a cross-comparison of 23exploring the willingness to pay for forest ecosystem services values is a rather arduous task. one simple key to reviewing the studies and their results is the type of es evaluated; another is to focus on the studies’ methodological implications. both of these approaches have been used here. to our knowledge, studies on biodiversity values in the alpine region are very limited. soliva and hunziker (2009) evaluated biodiversity protection in switzerland. getzner (2000) focused on specific protected areas such as the hohe tauern national park in austria. scarpa and menzel (2005) measured the wtp for the implementation of new biodiversity protection programmes. studies on the recreational services generated in alpine mountain areas have been published by scarpa and thiene (2005) and by scarpa et al. (2007), who analysed choice patterns and determinants of demand for different climbing destinations in the north-eastern alps. as regards landscape, a study published by tangerini and soguel in 2004 found that the landscape attribute considerably affects the final price of real estate properties in the swiss alps: positive changes in landscape are appreciated both by locals and tourists, while a loss of landscape quality linked to the development of tourism infrastructure can negatively affect real estate values in the opinion of the residents. the issue of assessing the value of watershed protection in the alps has long challenged environmental economists. due to the large presence of steep slopes and unstable soils, this is a crucial service; however, it is also the most difficult one to evaluate, given the complexity of the cause-effect relationships between forest management and downstream water uses (hamilton, 2008). pettenella et al. (2006) measured the economic value produced by an appropriate forest management affecting the quality and regularity of water flows. croitoru (2007a) provided values for the watershed protection services in mediterranean countries, including alpine countries such as italy, france and slovenia. notaro (2001) evaluated watershed protection, relating the value of fresh water in rivers to the level of fishable species. instead of measuring values for single services, other authors have attempted to measure the overall value of forest es. examples are the papers published by croitoru (2007a; 2007b), who focused on the total economic value of mediterranean areas; or by goio et al. (2008), who measured the value of the traditional production – i.e. timber – in the alpine forests of trento province and compared it to the value of es, showing the much higher economic value of the latter. lastly, reviewing previous es valuation works, grêtregamey et al. (2008) provided a broad perspective on values of es for the alpine area, reporting on evaluations for scenic beauty, recreation, biodiversity, avalanche protection and c-sequestration: in some cases the authors provided values for single es, and in others aggregated values. the review confirmed the wide variability among values, even when studies dealt with similar es. there are many reasons for this variability, including the distribution of environmental risks and uncertainties like natural hazards (grêt-regamey et al., 2012). the authors concluded by highlighting how difficult is to distinguish, in the values, the effect of actual preference heterogeneity from that of the methods used to estimate them. analysing the published studies from a methodological perspective, a pattern can be discerned in the approaches used, following the general evolution of the discipline: especially in the field of recreation values, older studies focused on use values and mostly used travel costs (merlo, 1982) or hedonic pricing (tangerini and soguel, 2004). more recent 24 p. gatto, e. vidale, l. secco, d. pettenella contributions (scarpa and menzel, 2005; scarpa and thiene, 2005; scarpa et al., 2007) have merged travel cost with ce, aiming at a greater reliability of the evaluation process. however, approaches have also been used based on provision costs (croitoru, 2007a; pettenella et al., 2006), benefit transfer (croitoru, 2007b; goio et al., 2008) or on mixed approaches (notaro, 2001). only recently, the focus of researchers has shifted towards a better understanding of consumer behaviour as a basic component of environmental services demand. methods like ce have been developed in order to overcome the limits of contingent valuation (cv) (hausman, 1993; diamond and hausman, 1994) and have also focused on the alpine context: to our knowledge, however, these methods have so far only been applied here at a local scale and, more especially, never to all of the most important forest es at the same time. 3. methodology 3.1. choice experiment background the use of ce in environmental economics dates back more than fifteen years, with the first proposals by louviere (1992), adamowicz (1995), boxall et al. (1996) and hanley et al. (1998). since then, the method has spread and many authors now consider it preferable to cv approaches. while cv targets the study of wtp for a specific event like a policy change, ce considers complex goods, such as environmental resources, as made up of single attributes, each one representing specific conditions of the good itself. combinations of different attributes can be created, each one reflecting a certain status of the resource or simulating the results of a policy change. the person interviewed can compare and choose one of the policy alternatives within a choice set, usually composed of different scenarios, plus the status quo (sq). hence, instead of having to answer to a complex bidding question as in cv, the respondent has to select one out of a certain number of choice sets (louviere, 1992) corresponding to the preferred policy alternative. ce models have their roots in the random utility model (train, 2003), which states that the utility uijt which a given individual i gets from the alternative j in the choice situation t can be divided into a deterministic part vijt and a stochastic term εijt. the deterministic part is generally specified as linear and may be written as a product of the vector describing the situation under study (1): uijt = vijt + εijt = β’xijt + εijt (1) where xijt is the vector of attributes linked to the individual i who chooses the alternative j within the choice situation t, while β’ is the vector of the betas. the respondent in a ce choice set will maximise his/her utility by choosing the scenario (or alternative) j among the other k within the choice set if the scenario j has higher utility than the others. hence, the probability of choosing the alternative j over the other k may be described as: prob(j|c) = prob(uijt> uikt) = prob{[(vijt-vikt)>(εikt-εijt)]; j,k ∈c; j≠k} (2) where c represents the complete set of choices. 25exploring the willingness to pay for forest ecosystem services to estimate equation (2) the error distribution must be assumed, usually gumbeldistributed and independently and identically distributed (iid), hence the probability of choosing j is given by ∑ = µβ µβ ∈ p e eijt x x k c ' ' ijt ikt (3) where µ is the scale parameter (usually set at 1 to keep constant error variance). appendix 1 presents the characteristics of the models used in more detail, i.e. multi nomial logit (mnl) and latent class models (lcm), while the following sections describe the steps taken for the application of the methodology, including the study area, attribute selection and description, statistical design of the pilot and full survey and characteristics of the sample and target population. 3.2 study area and attribute selection forests in veneto are mostly located in the northern part of the region, in the mountainous areas of the alpine range, where, according to the last forest census (ministero delle politiche agricole alimentari e forestali, 2005), they are an essential component of landscape, covering 436,000 ha out of 784,000 ha of the regional territory. mountain areas and their forests are common destinations for recreational activities: 4.8 million day-visits were registered in the mountainous areas of veneto in 2012 (regione veneto, 2013). forests also play an essential role in protecting steep slopes from soil erosion and contribute towards biodiversity conservation: as many as 146,000 ha of forests in the region are in the natura 2000 network (ministero delle politiche agricole alimentari e forestali, 2005). in addition, given that only 33% of the regional annual forest growth is harvested (pierobon et al., 2011), forests also represent a significant carbon sink. based on these considerations, a preliminary selection was made of forest-based es for defining the ce attributes. the initial list comprised landscape, biodiversity conservation, carbon sequestration, recreation, soil erosion and landslide prevention. an expert consultation process was set up in order to verify the suitability of this preliminary selection and to identify the most effective way to present the attributes and their levels to the public. more than 30 experts were recruited, in three fields: i) forest ecology and silviculture; ii) natural science and landscape ecology; and iii) environmental economics. the experts were first interviewed individually, then invited to a joint discussion in three thematic focus groups. this complex procedure was implemented in order to ensure that the final choice of attributes and levels was based on the broadest possible consensus, attempting to avoid any bias related to the experts’ selection process. agreement was reached on the following attributes: i) forest structure view; ii) carbon sequestration; iii) important species for biodiversity conservation; iv) landscape; v) forest recreation; and vi) costs. the meaning of each attribute and the reasons for its choice are briefly discussed below: i. forest structure view: an on-going debate amongst forest managers is focused on which forest structure (coppice, or different types of high forests) would be most appropriate in relation to more multi-functional forest management. while the pre26 p. gatto, e. vidale, l. secco, d. pettenella sent forest structure, i.e. monoplane high forest (representing the sq in the choice set) is primarily for timber production, it is argued that other forest structures – including coppice or multi-plane high forests – have higher aesthetic values. this attribute refers to the view enjoyed by a visitor walking in the forest when looking at different forest structures. it is completely different from the ‘landscape mosaic’ attribute, which is the perception of landscape on a larger scale, on which the forest structure has no influence whatsoever (a ‘patchwork’ of forest types in the landscape would have the same effect regardless of their structure, which impacts only on a finer scale of observation). ii. carbon sequestration: the capacity of offsetting carbon emissions through appropriate forest management is an issue that has received a great deal of attention from the regional authorities (pierobon et al., 2011). the focus group experts suggested expressing the attributes by making reference to the percentage of the resident population whose c-emissions would be offset by the growing forest. the experts estimated that, with the current trends in forest area dynamics and management, forests in the region will be able to offset the emissions of up to 5.5% of the current resident population (sq). iii. important species for biodiversity conservation: forests in the region are home to a large number of species of fauna and flora. the disappearance of forest ecosystems associated to land use changes driven by urban and tourist developments, and, in addition, simple alterations of the delicate mature forest ecosystem equilibrium due to various human disturbances will strongly affect the number of species living in the forest areas. although residents in the region are not very familiar with biodiversity, the attribute was considered because of the general increasing demand for experiences in the wild, with the desire to see a return of the typical flora and fauna of the alps. the biodiversity attribute was designed according to the popular concept of biodiversity communicated by the media, i.e. the number of endangered alpine species that would be lost or gained depending on the implementation of specific forest conservation policies. the focus group experts estimated that, continuing with present trends, as many as 50 species would be lost in the next ten years (sq). iv. landscape mosaic: the typical alpine landscape is composed of a balanced mosaic of forest and open areas (pastures, meadows and rocks). the abandonment of crops and dairy farming activities coupled with the lack of appropriate policies is leading towards a rapid natural expansion of forests with a shift to a more ‘closed’ landscape (bonsembiante and merlo, 1999). active forest and pastureland management could maintain the desired balance of forests and open areas, while it has been estimated that 5% of the present proportion of open areas will be lost within the next 10 years due to the natural expansion of forests on abandoned farmland (sq). v. forest recreation: recreation in forests is typically envisaged by the ordinary visitor as the possibility of having family outings and picnics in open areas under the shade of forest trees. a smaller percentage perceive forest recreation as the opportunity for trekking or hiking, which is made possible by the availability of an adequate network of forest paths. this attribute is therefore expressed through the provision by forest authorities of recreational facilities such as picnic tables, car parks and path signs. the sq is represented by no provision of recreational facilities. 27exploring the willingness to pay for forest ecosystem services vi. costs: the experts identified the best payment vehicle as an annual regional tax paid by each household to support the application of a regional forest policy producing the desired level of attributes in ten years’ time. the wide range of cost levels was suggested by the economists in the focus group in order to have less repetition among the alternatives, at the same time keeping the number of choice sets as low as possible. the experts advised against including in the survey the issues related to the effects of forests on soil erosion and landslide prevention that had been included in the initial list. serious flooding had occurred in some areas of the region in the winter of 2010, damaging many crops, and residential and commercial properties. farmers and households who had suffered damage were still waiting for compensation from the regional authorities. the population was therefore still too sensitive about this issue and, especially, highly critical of any kind of soil conservation policy implemented by the regional government. since soil erosion and landslide prevention is a very important service produced by forests, its exclusion from the list of attributes and therefore from the evaluation exercise represents a limitation of this work. the final list of attributes, with their levels, is presented in table 1: the list shows that our ce is based on both continuous variables and dummies. 3.3 statistical design of pilot and full surveys after the utility function specifications with the variables reported in table 1, we built the statistical design with ngene©®, following bliemer and rose’s (2009) approach. the ce exercise was run in two stages: a pilot and a final survey (sandor and wedel, 2001; bliemer and rose, 2005; ferrini and scarpa, 2007). we first created the statistical design for the pilot survey in order to estimate preliminary betas (priors). the priors, estimated with multi nominal logit (mnl), were used to develop the final statistical design for the main survey (street et al., 2005). in the pilot, we drafted, by way of ngene, a dz-efficient design, using priors equal to zero, rather than an orthogonal design. the benefit of this procedure is the possibility of combining dummy coded and continuous variables in the same design, as well as weighting the maximum and minimum values of continuous variables. the pilot version of the questionnaire was submitted to 74 people in six provinces in veneto. out of these, 16 were dropped from the pilot analysis because they either refused to answer or provided answers that were considered strategic according to a set of specific set of control-questions; the priors were therefore estimated with the remaining 58 respondents. based on the pilot ce answers, we estimated the mnl model betas to be used as priors in the final statistical design using the utility function. lastly, we checked the presence of dominant alternatives, finding limited dominant effects in the estimated design, and a similar distribution in the choice frequencies from the questionnaire, with a higher ratio in those having higher probability of being chosen in the estimated design. during the ce, respondents were asked to answer six choice tasks out of the twelve resulting from the ce statistical design, which was designed according to d-efficiency as proposed by bliemer and rose (2009) (appendixes 2 and 3). we opted to have two blocks in order to reduce the answering load on the respondents; hence, to split the choice tasks 28 p. gatto, e. vidale, l. secco, d. pettenella homogeneously, a further attribute, i.e. the block column, was introduced in the statistical design and balanced against the other attributes. 3.4 sample and target population the target for the ce survey is the resident population of the veneto region, the potential payers of the regional tax proposed as payment vehicle. six hundred and thirtyseven (637) people participated in the full survey. data was collected through face-to-face interviews, from july to october 2011. in order to maximise representativeness, we designed the survey sample using two main strata, namely the place of residence – whether the person lived in the mountain areas or not – and the size of the municipality of residence: four levels were used in this table 1. attributes of the ce survey. attribute levels forest structure view from forest paths icons description a= thick stand forest (coppice); b= even-aged forest – sq; c= uneven-aged forest; d= uneven-aged forest with dead trees model code viewa viewc viewd carbon sequestration by forests in terms of % of carbon-neutral veneto resident population icons description level 1= 5.5% of residents – sq; level 2= 7% of residents; level 3= 8.5% of residents; level 4= 10% (all the forest area is left to natural evolution) model code co2 7% co2 8.5% co2 10% change in number of important species for biodiversity conservation icons description level 1= -50 species – sq; level 2= -25 species; level 3= 0 species lost; level 4= +2 species gained from surrounding regions. model code bio -25 bio 0 bio +2 (continued) 29exploring the willingness to pay for forest ecosystem services case, based on the number of inhabitants: i) up to 5,000; ii) from 5,000 to 10,000; iii) from 10,000 to 100,000; and iv) the seven provincial capitals in the region. the geographical distribution of the sample covered 63 municipalities, i.e. 10.3% of the regional total. chi-squared was used to test similitude of the sample population to the target one. the difference did not exceed 5% for gender balance, age class and household size. vice versa, for two characteristics – i.e. income and education – comparisons were not possible due to information gaps. italians are very reluctant to reveal information on their income and this information is considered sensitive by the public authorities and therefore not disclosed. hence, the income distribution in the target population was unknown. for the sample, information on income was collected according to four broad categories of annual net income: i) up to 30,000 €; ii) from 30,000 to 60,000 €; iii) from 60,000 to 120,000 €; and iv) over 120,000 €. education, on the other hand, was not considered in the statistical design and the proportion of people in the sample without formal education proved to be lower than in the target population. therefore, one potential bias of the research could be the failure to reach people with little education. the interviews were carried out using a questionnaire in four parts. the first part focused on attitudes towards, and frequency of use, of mountain es. the second part introduced the attributes and attribute levels and thoroughly explained them, to ensure that the respondents had sufficient background information to correctly make their choicattribute levels landscape mosaic icons description level 1= -10% of grassland; level 2= -5% of grassland – sq; level 3= 0% of grassland; level 4= +2% of grassland model code land-10 land 0 land +2 forest recreation icons description level 1= no service – sq; level 2= tourist facilities; level 3= path signs; level 4= tourist facilities and path signs model code recrst recrs recrsst cost levels= 0 (sq) 25, 50, 75, 100, 125, 150, 175, 200 €/household/year attributes and levels representation have been defined with reference to the following works: nielsen et al., 2007 for forest structure view; mogas et al., 2006, for c-sequestration; lethonen et al., 2003, christie et al., 2006, jacobsen et al., 2008 for biodiversity conservation; tempesta and thiene, 2004, tempesta and marangon, 2004, grêt-regamey et al., 2008 for landscape; christie et al., 2007 for recreation. table 1. (continued). 30 p. gatto, e. vidale, l. secco, d. pettenella es, and ended with the six choice tasks of the ce. the third part was a debriefing section aimed at understanding the respondent’s impressions of the questionnaire. the last part collected socio-demographic data. 4. results and discussion in general, 57.9% of respondents stated that they visit mountain forests at least once a year. however, only 29.4% chose a forest area in the region as destination, the remainder preferred destinations outside the region. as many as 97.5% of visitors to the regional forests made only day-trips, without an overnight stay. one aspect that emerged from the pilot survey was that the majority of respondents had a poor awareness of the es produced by forests. therefore, after having introduced the ce attributes and let the respondent become familiar with the issues, we initially asked what level of each attribute the respondents would choose in the absence of an environmental tax. even in these conditions, they tended more frequently to select the less costly alternatives, generally related to the sq. the results are reported in table 2. only some respondents chose the higher level (l4) on biodiversity maintenance, carbon sequestration and tourism infrastructure. moreover, the ‘landscape mosaic’ attribute was chosen with an opposite trend to that documented in the literature, where open areas have been found to be more desirable than forest areas (tempesta and thiene, 2004): in this survey, landscapes with more forest area were preferred to those with more open areas and meadows. table 2. attribute level choice before and during ce: frequency table. attribute before ce ce choices differences l 1 l 2 l 3 l 4 l 1 l 2 l 3 l 4 δ l 1 δ l 2 δ l 3 δ l 4 chi-test view 9.1 33.0 34.4 23.5 16.6 50.7 15.5 17.2 7.5 17.7 -18.8 -6.4 4.1e-06 co2 22.5 19.4 18.2 39.9 44.8 13.8 16.3 25.1 22.3 -5.6 -1.9 -14.7 1.8e-06 bio 14.8 11.0 33.2 41.1 54.3 16.5 13.8 15.4 39.5 5.5 -19.4 -25.6 3.3e-29 land 31.8 26.5 26.3 15.5 17.7 45.8 19.4 17.1 -14.1 19.3 -6.8 1.6 5.6e-05 recr 17.4 14.6 27.0 41.1 47.5 15.8 19.5 17.2 30.2 1.1 -7.5 -23.8 1.1e-14 note: serial non-participants were excluded in the compilation of this table. the columns ‘before ce’ were calculated by ngene, while ‘ce choices’ were calculated, according to the methodology reported in the ngene manual, by summing the frequency of the chosen alternative containing the specific attribute level. the sq levels are in grey. see table 1 for the attribute code meanings. l = level. l1 l4 has the same order as the attribute levels reported in table 1. once the cost attribute was introduced (see ‘ce choices’ columns), the frequency of the choice of sq increased dramatically. this strongly affected the model outputs, especially in terms of attribute significance. the estimation of a general model for the veneto region population was the first step in data analysis. applying the basic mnl model (mcfadden, 1974), we initially obtained rather weak results (table 3) at the aggregated level. model 1.1 (all respondents) shows the 31exploring the willingness to pay for forest ecosystem services general unwillingness to pay for the majority of attributes, except for recreation facilities and path signs (recrsst variable). alternative specific constant (asc) represents the hidden characteristics that the respondent does not see in the choice task. a significant and negative asc means they want to change the present sq. slight improvements to model 1.1 have been obtained by introducing dummy variables. for example, 61 respondents to the full survey – i.e. those always opting for the sq and also answering positively to some control questions – were classified as either ‘serial non-participants’ (von haefen et al., 2005) or even ‘protesters’ (meyeroff and liebe, 2008). when the vector of the chosen attributes is multiplied by a dummy for the ‘non-protester’ respondents (i.e. excluding the 61 serial non-participants and protesters, hereinafter ‘protesters’) as in model 2.1, the results show a general willingness to change from the present situation (asc < 0) and a better fitness of the model, with the majority of attributes significant at 5%. table 3. mnl model outputs. variables mnl model 1.1 (all respondents) model 2.1 (dummy for non-protest) model 3.1 (dummy for nonprotest & users) model 4.1 (dummy for nonprotest & nonusers) model 5.1 (interaction with edu & dummy for non-protest) asc 0.045 -0.684*** 0.457*** 0.307*** -0.065 viewa -0.003 0.106** 0.103* -0.200** 0.008** viewc 0.030 0.130** 0.145** -0.105 0.014*** viewd -0.005 0.086* -0.034 -0.001 2e-5 co2 0.052 0.112** -0.143*** 0.297*** -0.001 bio 0.003 0.006** 0.019*** -0.015*** 0.001*** land -0.005 -0.010* 0.001 -0.013 -0.823 recrst 0.048 0.119** -0.050 0.204** 0.004 recrs 0.011 0.065 0.291*** -0.400*** 0.011** recrsst 0.223*** 0.441*** 0.211*** 0.270*** 0.025*** cost -0.009*** -0.010*** -0.013*** -0.006*** -0.012*** obs. 3822 3822 3822 3822 3822 log-l -4172.47 -4172.47 -4172.47 -4172.47 -4172.47 r-sqrd 0.05928 0.09962 0.08795 0.06130 0.09186 adj. r-sqrd 0.05792 0.09833 0.08694 0.06003 0.09055 note: p-value : * = 0.10, ** = 0.05, *** = 0.001. see table 1 for the attribute code meanings. following the idea of use and non-use values, we also tested the differences between the ‘users’ and ‘non-users’ of mountain areas (model 3.1 and 4.1 respectively) (adamowicz et al., 1998). as expected, people with a direct interest in a given good also have a 32 p. gatto, e. vidale, l. secco, d. pettenella higher propensity to pay for it; in model 3.1 we see an increasing interest in biodiversity maintenance and in even-aged managed forests, although the respondents’ main attention is focussed on what they ‘use’ more: tourist facilities and path signs. nevertheless, the present state of the environment does not encourage the mountain users towards a high willingness to change, as the asc is positive and significant. non-use values is another crucial piece of information for the policymaker targeting es provision at regional scale. model 4.1 shows the presence of a low wtp by mountain non-users, though limited to recreation infrastructure and carbon sequestration. finally, the role of education (number of years of schooling) was tested in model 5.1: the differences from model 1.1 highlight the role of education in the general wtp. in a further step, the models were re-estimated after recoding co2, bio and land in dummy coded variables taking the sq for each variable as a reference level. the results obtained showed a non-linear pattern, explaining only average respondents’ behaviour on a single level. in general, the recoding highlighted the respondents’ propensity to pay greater attention to the extreme levels of the attributes. there is a rather important difference between mountain users and non-users on biodiversity conservation, landscape and path maintenance. the former are more willing to pay for what they regularly visit or, in other words, for the things they get more utility from. the latter care more for what they know from the mass media (i.e. carbon sequestration co2 10%) or for what they may possibly use in the future (i.e. recreational facilities – recrst). in model 5.2 the role of education is tested through the interaction with the number of years of school attended by a given respondent. people with higher education generally consider in a proper way the attributes (an example of this interaction is visible in the attributes view and recr in table 4, where the first is considered in model 5.2 and not in model 2.2; for recreation, the respondents understand that the benefits coming from recrsst derive from the sum of recrst and recrs). lastly, lcm has been estimated (table 5), with results in line with the major previous findings. the lcm model displayed two groups of people differing by their willingness to support the changes. group 1 is characterised by those inclined to leave forests to natural evolution (viewd) and to support carbon sequestration policies and an abundant supply of recreational infrastructure, while considering actions for biodiversity conservation, maintaining the open landscape and providing path signs superfluous. group 2 shows the opposite inclination and hence favours having more path signs, landscape maintenance and coppiced forest. nevertheless, in general the idea of paying for a change from the sq does not hold, as asc is positive and significant. this behaviour can be explained by looking at the λ values: group 2 comprises mountain users and highly educated people, while group 1 includes the non-users and the poorly educated. after the mnl estimations, we calculated the wtp marginal values as the ratio between the coefficient attributes and the negative coefficient of prices as reported above (tables 6 and 7). a different approach was only needed for model 5.1, as education level differed throughout the sample so the attribute coefficient had to be weighted with the sample frequency (hidrue et al., 2011; martinez-cruz, 2012). the overall wtp for es provision in veneto can be calculated as the positive marginal variation of each attribute within the given model; the values range from a minimum of € 48 (model 1.1 in table 6) to a maximum of € 313 per household per year (this last figure is the total positive wtp for all of the variables in model 2.2 in table 7). 33exploring the willingness to pay for forest ecosystem services 5. conclusions the research sought to understand if and to what extent the population of the veneto region was aware of the es produced by the regional forest area and, especially, if it was prepared – and willing – to pay for them. information on values and wtp provides a solid basis for the further development of policy tools to support the provision of es, especially if they were to be modelled according to a pes approach. since forest policies in the veneto region are designed and implemented on a regional scale, our target population was rather broad, being represented by all residents of the region. a further source of complexity was due to the attempt to estimate four es – carbon sequestration, biodiversity, recreation and landscape (the last at two different scales) – at the same time. this complexity is both a strength and a weakness of the approach. the strength lies in the improvement of information on es values in the alps, whereas the works published to table 4. mnl model outputs for recoded variables. variables mnl model 1.2 (all resp.) model 2.2 (dummy for protest) model 3.2 (dummies for non-prot. & user) model 4.2 (dummies for non-prot. & nonuser) model 5.2 (interaction with edu & dummy for non-protest) viewa 0.042 0.239*** 0.201** -0.228** 0.015** viewc 0.159** 0.175** 0.032 0.194* 0.013** viewd -0.167** -0.098 -0.076 -0.351*** -0.010** co2 +7% 0.294*** 0.417*** 0.176 0.464*** 0.030*** co2 +8.5% -0.317*** -0.502*** -0.383*** -0.203* -0.035*** co2 +10% 0.263*** -0.074 0.109 1.014*** 0.004 bio -25 0.007 0.094 -2e4 0.302** 0.007 bio 0 0.151** 0.508*** 0.451*** -0.465*** 0.034*** bio +2 -0.055 0.267** 0.182* -0.734*** 0.014** land -10% -0.090 -0.252*** -0.021 -0.083 -0.016*** land 0% 0.085 0.434*** 0.369** -0.266* 0.029*** land +2% -0.103 -0.156** -0.171* -0.062 -0.011** recrst 0.013 -0.174** -0.190* 0.271** -0.011* recrs 0.048 0.240*** 0.169** -0.285** 0.015** recrsst 0.289*** 0.417*** 0.302*** 0.452*** 0.030*** cost -0.009*** -0.017*** -0.010*** -0.004*** -0.014*** obs. 3822 3822 3822 3822 3822 log-l -4172.47 -4172.47 -4172.47 -4172.47 -4172.47 r-sqrd 0.06599 0.10818 0.08756 0.10277 0.10139 adj. r-sqrd 0.06403 0.10631 0.08564 0.10077 0.09950 p-value : * = 0.10, ** = 0.05, *** = 0.001. 34 p. gatto, e. vidale, l. secco, d. pettenella date in the literature were undertaken at a site-specific scale with samples selected among the users of the good or the service. the weakness lies in the difficulty of choice tasks, which were challenging for the respondents, who sometimes faced options that they did not completely understand and for which they therefore opted for a no-change solution. we used multi nomial models and latent class models. yet not all the models tried were successful in explaining whether wtp exists and what its determinants are. accordingly, the results must be viewed with caution. where the models showed higher attribute significance, we obtained interesting insights into the es values. in general, our results showed that most residents perceive some forest es as something they already have rights to, or which at least should be provided without any cost to the beneficiaries. this was the case for landscape quality and biodiversity conservation, for which wtp was very low, while people were more prone to pay for some recreational benefits. however, when the models focused on explaining the behaviour of non-users, the presence of some option and existence values emerged for specific es, showing a perceived potential scarcity table 5. latent class model. variables group 1 (prob. 53.3%) group 2 (prob. 46.7%) asc -1.114*** 1.489*** viewa -0.112 -0.111** viewc -0.050 0.018 viewd 0.722*** 0.061 co2 0.785*** -0.052 bio -0.026*** 0.007* land -0.065*** 0.013** recrst 0.030 -0.063 recrs -0.677*** 0.244*** recrsst 1.777*** 0.035 cost -0.051*** -0.004** prob. model λ1 λ2 (reference) constant 1.142*** 0 protest 32.032 0 mount. user -1.014*** 0 education -0.050** 0 obs. 3822 log-l -2968.79 r-sqrd 0.29296 adj. r-sqrd 0.29045 p-value : * = 0.10, ** = 0.05, *** = 0.001. see table 1 for the attribute code meanings. 35exploring the willingness to pay for forest ecosystem services table 6. wtp marginal values (€/unit). variables mnl model 1.1 (all respondents) model 2.1 (dummy for non-protest) model 3.1 (dummy for nonprotest & users) model 4.1 (dummy for nonprotest & nonusers) model 5.1 (interaction with edu & dummy for non-protest) asc           viewa 0 21.28 0 -62.22 14.46 viewc 0 25.98 20.91 0 24.18 viewd 0 0 0 0 0 co2 0 11.21 -10.30 46.20 0 bio 0 0.62 1.37 -2.41 0.87 land 0 0 0 0 0 recrst 0 23.69 0 63.33 0 recrs 0 0 41.96 -124.62 19.68 recrsst 48.43 88.00 30.33 84.05 44.35 see table 1 for the attribute code meanings. table 7. wtp marginal values for recoded variables (€/unit). variables mnl model 1.2 (all resp.) model 2.2 (dummy for non-protest) model 3.2 (dummies for non-prot. & user) model 4.2 (dummies for non-prot. & nonuser) model 5.2 (interaction with edu & dummy for non-protest) viewa 0 27.74 38.71 -104.46 23.30 viewc 32.71 20.29 0 0 20.79 viewd -34.44 0 0 -160.17 -16.39 co2 +7% 60.48 48.46 0 211.95 46.27 co2 +8.5% -65.39 -58.31 -73.86 0 -55.76 co2 +10% 54.07 0 0 462.49 0 bio -25 0 0 0 137.81 0 bio 0 31.02 58.96 86.77 -212.40 52.75 bio +10 0 31.05 0 -334.87 21.28 land -10% 0 -29.27 0 0 -24.96 land 0% 0 50.37 71.08 0 45.55 land +2% 0 -18.18 0 0 -18.18 recrst 0 -20.25 0 123.48 0 recrs 0 27.87 32.57 -130.27 24.22 recrsst 59.52 48.38 58.22 206.27 46.96 see table 1 for the attribute code meanings. 36 p. gatto, e. vidale, l. secco, d. pettenella among residents in the region, 83% of whom, after all, live in the urbanised po plain, at a two-three hours driving distance from alpine areas. with the exception of the field of recreational services, these results pose a serious challenge for the development of pes tools, whose foundations rest on the existence of a demand and the related wtp by consumers. the possibility of implementing market mechanisms for biodiversity or landscape conservation seems far in the future, if not even in question by itself. overall, the study has shown a widespread lack of appreciation by the average respondent for the role that forests play at present – and will play in the future – in the provision of ecosystem services. in this regard, initial policy actions could focus on increasing general environmental awareness, as a more advanced knowledge on forestrelated services is certainly a pre-requisite for the introduction of 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(2008). taking stock: a comparative analysis of payments for environmental services programs in developed and developing countries. ecological economics 65(4): 834-852. 40 p. gatto, e. vidale, l. secco, d. pettenella appendix 1. models used in the ce equation (3) in section 3.1 can be estimated through a multi-nomial logit (mnl) regression only if independence for irrelevant alternatives (iia) holds, which means that the ratio of choice probability of a given alternative is not affected by any other alternative, because the utility is set up systematically with the statistical design (ben-akiva and lerman, 1985). nevertheless, iia is seldom respected. the average wtp for a given attribute is calculated with the ratio between the beta parameters of interest and the beta parameters of cost (train, 2003), as follows: β β =wtp c a c (4) where c is a constant term. only in the case of effect-coded variables, (4) has to be multiplied by a constant term equal to 2 (bech and gyrd-hansen, 2005). in order to overcome this problem, we also used the latent class model (lcm) (boxall and adamowicz, 2002), in which the presence of a certain number of segment s in the population sample, which has s segments in total, is considered. thus, the utility function of an individual i who belongs to a particular segment s and chooses the alternative j within the choice situation t, can be written as: uijt\s = vij\s + εijt\s = βs’ xijt + εijt\s (5) where βs is the vector that explains the homogeneity within the segment s and the heterogeneity among the segments s. assuming the same error distribution as in (3), the probability that individual i belonging to segment s chooses the alternative j is given by: ∑= µ β µ β ∈ p e eijt s x x k c \ ' ' s s ijt s s ikt (6) nevertheless (6) provides only partial information on the individual choices. the likelihood of an individual i belonging to a given segment s within a finite number s depends on socio-economic characteristics, personal knowledge and attitudes, in general z, thus membership likelihood function may be represented as: mis = λs zi + ξis (7) where λs is a segment of the vector λ and ξis is the error term. assuming iid of the error term, the probability of the individual i to belong to segment s is: ∑ = µ λ µ λ ∈ p e eis z z k c s s i s h i (8) 41exploring the willingness to pay for forest ecosystem services where (h=1,2,..s) are the segment-specific parameters that have to be estimated for each individual. the sum of all the segments is one, so the s-th varies from zero to one. when (6) and (8) are put together, we obtain the probability of the individual i belonging to segment s and choosing alternative j within the choice situation t; this can be written as: ∑ ∑= ⋅ = ⋅ µ β µ β µ λ µ λ ∈ ∈ p p p e e e eijts ijt s is x x k c z z k c \ ' ' s s ijt s s ikt s s i s h i (9) 42 p. gatto, e. vidale, l. secco, d. pettenella a pp en di x 2. s ur ve y st at is ti ca l d es ig n a lte rn at iv e 1 a lte rn at iv e 2 c ho ic e sit uat io n fo re st st ru ct ur e c ar bo n se qu es tr atio n ex tin ct io n ra te g ra ss la nd op en a re as re cr ea tio n c os t fo re st st ru ct ur e c ar bo n se qu es tr atio n ex tin ct io n ra te g ra ss la nd op en a re as re cr ea tio n c os tb lo ck 1 d 5. 5% -5 0 sp ec ie s -1 0% to ur ist fa ci lit ie s & pa th si gn s 75 € c 8. 5% 10 sp ec ie s 0% to ur ist fa ci lit ie s 15 0€ 1 2 b 10 % 10 sp ec ie s 2% to ur ist fa ci lit ie s & pa th si gn s 25 € c 5. 5% -2 5 sp ec ie s -1 0% pa th si gn s 12 5€ 2 3 c 10 % -5 0 sp ec ie s 0% pa th si gn s 25 € a 7% 10 sp ec ie s -5 % to ur ist fa ci lit ie s & pa th si gn s 20 0€ 1 4 a 7% 0 sp ec ie s 2% n o se rv ic e 12 5€ b 10 % 0 sp ec ie s -1 0% pa th si gn s 50 € 1 5 d 10 % 10 sp ec ie s -5 % n o se rv ic e 75 € b 5. 5% -5 0 sp ec ie s 0% to ur ist fa ci lit ie s & pa th si gn s 75 € 1 6 c 5. 5% -2 5 sp ec ie s -5 % to ur ist fa ci lit ie s & pa th si gn s 20 0€ a 8. 5% -5 0 sp ec ie s 0% to ur ist fa ci lit ie s 50 € 2 7 d 7% 0 sp ec ie s 0% to ur ist fa ci lit ie s 15 0€ a 10 % -2 5 sp ec ie s -5 % n o se rv ic e 25 € 2 8 c 8. 5% -2 5 sp ec ie s -1 0% n o se rv ic e 50 € d 10 % 10 sp ec ie s 2% pa th si gn s 10 0€ 2 9 b 8. 5% 0 sp ec ie s 2% pa th si gn s 10 0€ d 7% -2 5 sp ec ie s -1 0% to ur ist fa ci lit ie s 25 € 1 10 b 5. 5% -2 5 sp ec ie s -5 % to ur ist fa ci lit ie s 10 0€ c 8. 5% 0 sp ec ie s 2% to ur ist fa ci lit ie s & pa th si gn s 10 0€ 2 11 a 8. 5% -5 0 sp ec ie s 0% pa th si gn s 50 € b 5. 5% 0 sp ec ie s -5 % n o se rv ic e 17 5€ 2 12 a 7% 10 sp ec ie s -1 0% to ur ist fa ci lit ie s 17 5€ d 7% -5 0 sp ec ie s 2% n o se rv ic e 75 € 1 43exploring the willingness to pay for forest ecosystem services appendix 3. alternative choice frequency: difference between estimated and real choices choice situation estimated real differences alt 1 alt 2 sq alt 1 alt 2 sq δ alt 1 δ alt 2 δ sq 1 33.67 30.94 35.39 34.91 22.01 43.08 1.24 -8.93 7.69 2 59.93 15.34 24.72 65.20 19.44 15.36 5.27 4.09 -9.36 3 37.88 26.71 35.41 60.69 16.67 22.64 22.81 -10.04 -12.77 4 16.53 51.29 32.18 14.15 47.17 38.68 -2.38 -4.12 6.49 5 43.75 23.66 32.59 22.64 27.36 50.00 -21.11 3.69 17.41 6 23.32 31.58 45.10 16.30 44.83 38.87 -7.02 13.25 -6.23 7 24.68 38.99 36.34 21.00 52.98 26.02 -3.67 13.99 -10.32 8 33.49 35.30 31.21 23.51 26.65 49.84 -9.98 -8.65 18.63 9 24.07 44.83 31.10 22.33 53.14 24.53 -1.74 8.32 -6.58 10 18.45 47.67 33.88 15.67 28.53 55.80 -2.78 -19.14 21.92 11 31.94 17.33 50.73 37.30 17.87 44.83 5.36 0.54 -5.90 12 33.50 20.44 46.06 16.04 30.19 53.77 -17.46 9.75 7.71 note: the choice situation of block 2 is shaded in grey issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(1): 49-72, 2013 greenhouse gases mitigation policies in the agriculture of aragon, spain mohamed taher kahil, josé albiac1 agrifood research and technology center, cita-government of aragon, spain abstract. climate change is an important threat to human society. agriculture is a source of greenhouse gases (ghg), but it also provides alternatives to confront climate change. the expansion of intensive agriculture around the world during recent decades has generated significant environmental damages from pollution emissions. the spatial distribution of emissions is important for the design of local abatement measures. this study makes an assessment of ghg emissions in an intensive agricultural area of aragon (spain), and then an economic optimization model is developed to analyze several ghg mitigation measures. the results indicate that adequate management of manure, emissions limits, and animal production restrictions are appropriate measures to abate pollution. economic instruments such as input and emission taxes could be only ancillary measures to address nonpoint pollution problems. suitable pollution abatement policies should be based on institutional instruments adapted to local conditions, and involve the cooperation of stakeholders. key words. ghg emissions, economic modelling, mitigation policies, effectiveness, abatement costs jel codes. q54, d78, c61 1. introduction the impacts of climate change on the human society and the environment have been the subject of widespread discussions and social concern during recent decades. several studies indicate that arid and semiarid regions will sustain large negative effects from climate variability. suitable climate conditions for cultivation are expected to move northwards in the northern hemisphere, resulting in more frequent and severe droughts in the southern region. recent climate change projections for the end of the twenty-first century indicate that in arid and semiarid regions including the south of europe and the mediterranean basin, there would be large reductions in water availability, huge increase of water withdrawals, pressures on food production systems with falling crop productivity, and harmful damages to aquatic ecosystems (fao, 2011). climate change projections for the end of the twenty-first century in spain indicate that temperature and evapotranspiration increase by about 4 °c and 21%, respectively, 1 corresponding author: maella@unizar.es. 50 m.t. kahil, j. albiac while precipitation decreases by 17%. these changes reduce crop yields, increase the water requirements of irrigation, and intensify the spread of pests and diseases (cedex, 2010). water scarcity would worsen in most spanish basins, with water availability falling up to 40 percent in some basins and drought recurrence increasing by a factor of ten (lehner, 2005; iglesias, 2009). the increase of the greenhouse gas (ghg) concentrations is largely determining the current and projected climate changes in the earth (houghton, 2001). anthropogenic global ghg emissions have risen 70% between 1970 and 2004, due to fossil fuel use, land use changes, and agriculture intensification (ipcc, 2007). the stern review estimates that without the implementation of ghg mitigation policies, the impacts of climate change could result in damages equivalent to 5% of global gdp per year, while the costs of mitigation policies could be limited to around 1% (stern, 2007). agriculture is a significant source of ghg emissions and the main source of non-co2 emissions such as methane (ch4) and nitrous oxide (n2o). the sources of these emissions are nitrogen fertilization in cultivated soils, large animal production facilities, and nitrogen pollution loads in rivers and water streams. agriculture accounts for around 14 percent of global anthropogenic ghg emissions (ipcc, 2007). but agriculture is also a source of low cost alternatives to mitigate climate change, by adequate management of forests, woody vegetation and soils to enhance carbon sequestration, and by using good agricultural practices to reduce ghg emissions. in spain, ghg emissions from agriculture are close to 39 million t co2eq, which represent approximately 11 percent of spain anthropogenic emissions. about 46 percent of all agricultural ghg emissions are from soil fertilization management. about 33% of emissions are methane from enteric fermentation from livestock, and 21% are nitrous oxide and methane from manure handling and storage. land use, land use change and forestry (lulucf) activities may improve the total ghg emission budget by 8% (29 million t co2eq), by using reduced emissions and carbon sequestration measures (marm, 2011a). the european union (eu) is committed by the kyoto protocol to reduce ghg emissions by 8% in the commitment period (2008-2012), over the 1990 baseline. member states are required to limit or reduce their ghg emissions in the sectors covered by the eu emission trading scheme (eu-ets), and they could increase their emission budget through the flexible mechanisms and the lulucf activities (eea, 2010). consequently, the emission limits established for the regulated sectors and the lulucf activities are an indirect mechanism to place emission limits on diffuse sectors not covered by the euets, such as agriculture and transport. the emission reduction target for the diffuse sectors in the eu is a 10% reduction in 2020 compared to the 2005 baseline. this overall emission reduction target is shared by all european countries with different individual targets depending on their emission level. spain is required to reduce its ghg emissions from diffuse sectors by 10% (oj, 2009). climate change mitigation issues have been recently included in european agricultural and environmental policies. the purpose is to provide policy instruments to reduce emissions from agricultural sources without damaging the economic viability of production activities. these policies are basically the common agricultural policy (cap), the integrated pollution prevention and control directive (ippc), the water framework directive (wfd), and the nitrates directive (nd). 51greenhouse gases mitigation policies in the agriculture of aragon, spain the cap shapes the evolution of european agriculture. recent reforms of the cap promote the sustainability of agriculture, the protection of the environment, and better animal health and welfare conditions. the main changes to protect the environment and curb climate change are the agro-environment measures, the decoupled income support and cross-compliance, the compulsory modulation, and the setting of farm advisory systems. the cap after 2013 would include climate specific measures to further explore agriculture potential to mitigate ghg emissions and adapt to the adverse impact of climate change (ec, 2009 and 2010). the ippc directive is oriented to reduce pollution emissions from industrial and agricultural sources. this directive regulates industrial and agricultural activities with a high pollution potential, such as the energy, chemical and livestock sectors. these production activities follow an authorization procedure with minimum requirements included in all permits, especially pollution emissions. the directive includes livestock facilities for intensive rearing of poultry and pigs. the main recommendations to reduce pollution from livestock facilities are the use of the best available technologies to prevent pollution, the efficient use of energy, setting emission limits, and measures for waste management. the wfd regulates water resources in europe. the objectives are to achieve the good ecological status of all water bodies, protect all continental, coastal and subsurface waters, and advance the sustainable use of water. the directive promotes water pricing policies, the combination of emission limits and water quality standards, and also the participative management of basins. the improvement of water management could have a positive effect on ghg emissions. the directive is considered a key tool for climate change adaptation and mitigation. the nd aims to protect water quality across europe by preventing nitrates from agricultural sources to pollute ground and surface waters. the main measures are the  identification of vulnerable zones to nitrate pollution, the design and implementation of good farming practices, and the setting of fertilization limits. the objectives are the abatement of nitrate pollution in water bodies and mitigation of ghg emissions generated by excessive nitrogen fertilization and manure surplus. moreover, the use of good farming practices such as reduced tillage, crop rotations, soil winter cover, and catch crops enhances carbon sequestration by soils and vegetation (smith, 2004). at the spanish level, the government has developed a strategy for climate change (marm, 2007). the strategy sets up an emission limit for the whole spanish economy for the period 2008-2012, when ghg emissions should not exceed 37% over the 1990 baseline, and provides guidelines for the design of mitigation measures by sector. the strategy calls for the cooperation of regional governments and local administrations to reduce ghg emissions and comply with the spanish commitments. the objectives for the agricultural sector are to reduce ghg emissions from cultivation and livestock sources, but without a specific reduction target for the whole sector, and to increase carbon sequestration by 2% for the period 2008-2012. the measures to mitigate agricultural ghg emissions are to improve information and knowledge on production processes, reduce nitrogen fertilization and better manure management, promote organic farming, biodigestion of manure, improve the energy efficiency in irrigated agriculture, increase the land area of energy producing crops, and renew agricultural machinery. 52 m.t. kahil, j. albiac the purpose of this study is to contribute to the ongoing policy discussion about ghg mitigation in agriculture. the study analyzes first the ghg emission sources linked to agricultural production activities in an intensive agricultural area of aragon (spain). then, the cost-effectiveness of several ghg mitigation measures at local scale is evaluated. specific local measures to mitigate ghg emissions are important for policy design. the study is organized as follows. first, a review of the economic literature about ghg mitigation policies is presented in section 2. section 3 describes the methodology and the study area, and section 4 presents the results. section 5 concludes with the summary and policy recommendations. 2. literature review there are several studies in the economic literature about climate change that analyze ghg emissions released by agricultural activities, and the cost-effectiveness of policies to mitigate emissions (de cara et al., 2005; neufeldt and schäfer, 2008; pérez et al., 2009; macleod et al., 2010; lengers and britz, 2012; kahil and albiac, 2012). the economic impacts of mitigation policies are important determinants of whether or not policies are implemented. smith et al. (2007) indicate that economic constraints may limit global agricultural ghg mitigation to around 35 percent of the total biophysical potential for mitigation. some studies estimate ghg abatement costs and the welfare implications of abatement measures (de cara et al., 2005; macleod et al., 2010). other studies consider also the indirect effects of these policies and the trade-offs between environmental externalities (neufeldt and schäfer, 2008). different methodological approaches have been used, such as partial or general equilibrium models, econometric models, or more integrated models that couple a socioeconomic core with environmental and agricultural sector components (povellato et al., 2007). the studies evaluate abatement policies such as emission trading, inputs taxes, and emission standards. a single instrument is not sufficient to mitigate emissions, rather a combination of adequate regulatory instruments is highly recommended to achieve climate stabilization in a cost-effective way. results indicate also the importance of identifying the main emission sources, and then designing policy instruments adapted to local conditions. the design of instruments should consider the biophysical, socioeconomic and political heterogeneity of producing areas, in order to achieve the collective action of stakeholders (macleod et al., 2010; kahil and albiac, 2012). some studies point out that the inclusion of agriculture in ghg regulatory systems could contribute to climate stabilization in a cost-effective way, and provides also opportunities to reduce ghg mitigation efforts in other sectors where abatement costs are higher (de cara et al., 2005; pérez et al., 2009). the main hypothesis behind these results is that the response of agricultural pollution to policy intervention is similar to the response of urban and industrial pollution, ignoring the nonpoint characteristics of agricultural pollution. agricultural pollution is linked to an important problem of information and knowledge, because of the impossibility of identifying the agents generating the emissions, the precise spatial location of sources, and the amount of emission loads at the source. several issues arise in the design and implementation of policies to control nonpoint pollution. these issues are linked to uncertainties about sources and controlling factors, 53greenhouse gases mitigation policies in the agriculture of aragon, spain the complexity and randomness of biophysical processes, and the strategic behavior of stakeholders facing abatement measures. all these factors make the measurement of emissions, and the design and enforcement of policies to control ghg emissions from agriculture costly and complicated (tomasi et al., 1994; weersink et al., 1998; shortle and horan, 2001; albiac, 2009). lengers and britz (2012) indicate the difficulty to measure agricultural ghg emissions and implement economic instruments for mitigation. their empirical results in dairy farms in germany show that abatement strategies of farmers and the related costs would depend on the particular performance indicator for emissions chosen by the regulator, creating incentives for free-riding. defra (2009) indicates that the problem of implementing economic instruments to mitigate ghg emissions is the high transaction costs for small farmers. abatement policies could entail negative impacts on agricultural income and rural development, or could produce unintended environmental damages (neufeldt and schäfer, 2008). smith et al. (2007) present a review of policy and technological constraints to implement ghg mitigation measures in agriculture. they point out that suitable policies must provide benefits for climate, but they also emphasize that their effective implementation requires that the outcomes from these policies are sustainable from the economic, social and environment perspectives. in a previous work by kahil and albiac (2012), several policy instruments to mitigate ghg emissions from cultivation are analyzed. results indicate that standards limiting nitrogen fertilization and investments in irrigation modernization are appropriate mitigation measures to abate emissions. another finding is that economic instruments are not very well suited measures to abate agricultural nonpoint pollution. results show also the importance of forests as carbon sinks, and the potential of reforestation. however, reforestation could have negative effects on water balances in watersheds under acute water scarcity in arid and semiarid regions. 3. methodology the study analyzes agricultural activities in aragon, a region located in the middle ebro basin in north-eastern spain. the region has an important irrigated area covering 450,000 ha, and a large swine herd close to 5 million heads. agriculture in aragon releases almost 3.6 million t co2eq of ghg, representing 20% of the total emissions of the region, which is above the percentage of emissions in spain (11%). the ghg emissions from livestock are 1.9 million t co2eq, including methane (ch4) from enteric fermentation, and nitrous oxide (n2o) from manure management. the ghg emissions from crop cultivation are close to 1.7 million t co2eq, of nitrous oxide (n2o) from crop and soil management (eaccel, 2011). agricultural pollution is a negative externality that causes harmful environmental and socioeconomic impacts. the control of nonpoint emissions from agriculture is very different from the control of point emissions from industrial and urban activities. the main features of diffuse pollution are the lack of knowledge and randomness of biophysical processes, and the asymmetric information between regulators and polluters. these features impose important difficulties for regulation, which make the task of finding adequate policy measures quite complex. perman et al. (2003) analyze the avail54 m.t. kahil, j. albiac able policy instruments to control agricultural pollution. these instruments are classified into three groups: command and control instruments (input or output control, emission or concentration limits, etc), economic instruments (input or output based taxes, subsidies for control practices, emission trading, etc), and institutional instruments (management organizations, liability rules, education and social programmes, etc). the present study considers alternative abatement measures from the three groups of instruments. the study analyzes the cultivation and livestock activities in four counties of aragon: barbastro, cinca medio, hoya de huesca and monegros (figure 1). this area includes about 138,000 ha of crop land and more than 2 million heads of swine. the main cultivated crops are barley (77,000 ha), alfalfa (20,000 ha), corn (16,000 ha) and wheat (10,000 ha) among field crops, and olives (2,300 ha) and vineyards (1,900 ha) among fruit trees (gobierno de aragón 2009). figure 1. map of the study area.   3.1 economic model the analysis of agricultural ghg mitigation measures is based on the development of a regional economic optimization model that includes cultivation and livestock activities. the programming technique is linear programming, and the main advantage of using this 55greenhouse gases mitigation policies in the agriculture of aragon, spain procedure is the possibility to introduce a large set of biophysical and economic information in a suitable level of disaggregation. linear programming models have been used in the literature to analyse climate change issues (de cara et al., 2005; schneider et al., 2007; povellato et al., 2007). these models can simulate policy responses at a detailed activity level, and taking into account the spatial heterogeneity of production processes. the model maximizes the social welfare from cultivation and livestock production activities, subject to technical and resource constraints. a leontief production function technology is assumed with fixed input and output prices, where farmers are price takers. the optimization problem is stated as: max c x c x e. . .i i j ji j 1 5 1 28 ∑∑ λ( ) ( )[ ]+ − == subject to: x di i s 1 28 ∑ ≤ = [1] w x w x d. .mi i i mj j j wm 1 23 1 5 ∑ ∑+ ≤ = = [2] o x o x d. .mi i i mj j j om 1 28 1 5 ∑ ∑+ ≤ = = [3] x x.i n in n 1 5 ∑α= = ; 1n n 1 5 ∑α = = ; 0nα ≥ [4] x j plj ≤ lj [5] x x, 0i j ≥ ; i 1,...,28= ; j 1,...,5= the first equation is the objective function of social welfare from production activities, which is defined by the difference between farmers’ net income and environmental damages (perman et al., 2003; koundouri and christou, 2006; esteban and albiac, 2011). farmers’ net income is equal to the sum of the net income for each crop i (i=1,... , 28) and each animal category j (j=1,... , 5). the net income ci and cj of each activity are equal to revenue minus direct and indirect costs, and depreciation. environmental damages are approximated by a linear function of ghg emissions e, where the unit emission cost λ is 56 m.t. kahil, j. albiac assumed to be equal to 25 €/t co2eq which is the average price of the emission allowance in the european trading scheme (marm, 2010a). the decision variables in the optimization problem are xi and xj, corresponding to crop area and livestock herd numbers. the land use constraint [1] represents the available land area ds of rainfed and irrigated crops. the water constraint [2] represents the monthly water availability dwm. the coefficients wmi and wmj are the monthly water requirements of each irrigated crop i (i=1,..., 23) and each animal category j (j=1,.., 5), respectively. the labor constraint [3] represents the monthly labor availability dom. the coefficients omi and omj are the monthly labor requirements of each crop i and animal category j, respectively. the aggregation constraint [4] forces crop production activities xi to fall within a convex combination of historically observed crop mixes xin. the index n indicates the number of years of the observed crop mixes. variables αn are non-negative weights assigned to each historical observed crop mix and are determined endogenously during the optimization process. once the weight variables are determined, the optimal aggregate supply response will be determined as a weighted sum of the corresponding historical crop mixes (önal and mccarl, 1989 and 1991). when performing an analysis at regional-level instead of at individual farm-level, there is an aggregation problem because farms in a region are different. ideally, a model could include a component for every individual farm, but this is unfeasible for a regional model (hazell and norton, 1986). the convex combination approach solves the aggregation problem using theoretical results from linear programming. the optimum solutions of linear programs (e.g. crop production decisions at the farm level) occur at corner solutions (extreme points). önal and mccarl (1989 and 1991) using the mathematical programming theory of dantzig and wolfe (1961) prove that the optimum solution of an aggregate linear program is formed by stacking the optimum solutions of the farm level models. therefore, this approach eliminates the need for full information about micro-level input-output data and extreme points of the individual farm problems. rather, what is needed is only a set of observed aggregate supply responses, namely historical crop mixes that characterize the decision space of the aggregate producer, which can easily obtained from publicly available statistics and other data sources. the convex combination approach embodies individual farm resource constraints, crop rotation considerations, farmers’ conservative behavior (risk aversion) against price, yield uncertainty, and a variety of natural conditions (chen and önal, 2012). there are other procedures to address the aggregation problem in regional models, such as the representative farm approach (day, 1963) and positive mathematical programming (howitt, 1995).2 the livestock facilities constraint [5] represents the available number of places lj for each animal category j. plj is the maximum number of heads per place in one year (1 head for sows, 2.3 heads for fattening pigs, 1 head for cattle). in order to account for the quasifixed nature of livestock-related capital, the variation of animal numbers for each animal 2 the representative farm approach requires that all individual farms have the same production possibilities, the same type of resources and constraints, and the same level of technology and managerial ability. also, the matrix of technical coefficients has to be identical for all farms, and the availability of resources and the coefficients of the objective function have to be proportional (day 1963). these requirements are very demanding and implausible for regional models. 57greenhouse gases mitigation policies in the agriculture of aragon, spain category does not exceed ±15 percent of the initial animal numbers in the corresponding animal category (de cara et al., 2005). biophysical and economic information specific to the study area has been collected and introduced in the model: land and water use, fertilizers, labor, and revenues and costs of crop and livestock production activities (table 1). this information has been taken from a large number of primary and secondary data sources (mema 2006, gobierno de aragón 2009, marm 2009, orús et al. 2011, kahil and albiac 2012). table 1. parameters of the model. parameters value unit total crop area 138 103 ha irrigated area/total crop area 60 % rainfed area/total crop area 40 % field crop area/total crop area 95 % fruit trees area/total crop area 5 % flood irrigation area/ irrigated area 52 % sprinkler irrigation area/ irrigated area 46 % drip irrigation area/ irrigated area 2 % water use 567 mm3 mineral nitrogen use 16,640 t n organic nitrogen use 3,080 t n nitrogen leaching 5,900 t n irrigation water price 0.05 €/m3 nitrogen fertilizers price 1 €/kg n swine place numbers 875 103 places cattle place numbers 50 103 places swine nitrogen excretion 8.3 kg n/place cattle nitrogen excretion 48.7 kg n/place crops net income 39 106 € livestock net income 28 106 € an important component of the empirical model is the estimation of ghg emissions generated by cultivation and livestock production. the method used to assess agricultural ghg emissions follows the approach of the intergovernmental panel on climate change (ipcc, 1996). this method combines the use of emission factors per activity unit, and the regional-specific data on crop area, fertilizers use, animal numbers, nitrogen excretion, manure management systems, and historical spanish emission data. the model simulates different policy scenarios, and provides results on social welfare, environmental damages, farmers’ net income, inputs use, pollution loads, crop area, and swine herd size. results show the abatement potential and cost, welfare effects, and environment impacts of each mitigation measure. this information is used to classify measures by their cost-effectiveness. the model has been developed using the gams package (general algebraic modelling system). 58 m.t. kahil, j. albiac 4. results 4.1 assessment of agricultural ghg emissions the study area contributes significantly to the final agricultural production of aragon, as well as to employment and rural development (marm, 2009). however, these activities generate environmental damages by releasing ghg emissions, nitrogen loads in rivers and streams, and manure surplus, which contribute to global warming and the degradation of water resources. the net income of farmers in the study area is close to 67 million euros, with 39 million from cultivation activities and 28 million from livestock. field crop cultivation is the main activity generating 31 million euros of net income from the large cultivated area (131,000 ha). by county, monegros generates the largest net income (30 million €), while net income in barbastro, cinca medio, and hoya de huesca ranges between 10 and 14 million euros (table 2). table 2. ghg emissions and farmers’ net income from agricultural production activities. barbastro cinca medio hoya de huesca monegros total study area n2o direct emission (103 t co2eq) 16 12 20 45 93 n2o indirect emission (103 t co2eq) 10 7 12 27 56 n2o manure management (103 t co2eq) 3 8 5 9 25 ch4 manure management (103 t co2eq) 63 88 45 246 442 ch4 enteric fermentation (103 t co2eq) 15 32 17 47 111 total emissions (103 t co2eq) 107 147 99 374 727 crop net income (106 €) 9 8 5 17 39 livestock net income (106 €) 4 6 5 13 28 total net income (106 €) 13 14 10 30 67 emission intensity (kg co2eq/€) 8 11 10 13 11 agricultural activities in the study area contribute to ghg emissions through four main gas-emitting processes: direct and indirect nitrous oxide (n2o) emissions from agricultural soils, nitrous oxide (n2o) emissions from manure management, methane (ch4) emissions from manure management, and methane (ch4) emissions from enteric fermentation.3 emissions in the four counties are estimated at 727,000 t co2eq, which represent 20 percent of the agricultural emissions in aragon. manure management is the main source of emissions (65%) because of the large concentration of swine herd in the area. 3 direct n2o emissions from agricultural soils occur through the nitrification and denitrification of nitrogen in soils from mineral and organic fertilizers, while indirect n2o emissions arise from nitrogen leaching and run-off from agricultural soils (ipcc, 1996). 59greenhouse gases mitigation policies in the agriculture of aragon, spain monegros generates around half of emissions in the area (374,000 t co2eq). in terms of emission intensity, monegros is the most intensive (13 kg co2eq/€), followed by cinca medio, hoya de huesca and barbastro.4 the difference in emission intensity by county is related to the heterogeneity of production systems, land use and management practices. one factor is the prevailing irrigation technology used in each county, which influences the emission intensity. sprinkler and drip irrigation are more efficient than flood because they use less nitrogen and water to produce the same output, so both water percolation and pollution loads are smaller. also the extent of rainfed crop area influences emission intensity, because rainfed land is used to dump manure surplus and there is no strict control on these fertilization loads. the study area is quite intensive in emissions (11 kg co2eq/€) compared to the emission intensity of the whole agricultural sector in aragon (3 kg co2eq/€) (kahil and albiac 2012). 4.2 policy scenarios policy measures for ghg mitigation have been widely studied in the literature during the last decade (ipcc 2007). measures are linked to improved or optimized fertilization, such as adjusting nitrogen inputs to crop requirements, better use of organic fertilizers, setting organic fertilization standards, using more precise manure spreading technologies, and the promotion of no-till cultivation methods. for livestock, recommendations are limits on livestock densities, production quotas, improving the feeding and housing of animals, and better on-farm manure storage and processing. several mitigation measures recommended in the literature are evaluated by their cost-effectiveness. three types of measures are considered: economic instruments (emission tax, inputs-based taxes), emission limits (ghg emissions limit, limit on nitrogen leaching in irrigation water returns), and control of inputs and production (standards limiting nitrogen fertilization, improved feed of swine, reduction of the swine herd, reduction of irrigation water) (tables 3 and 4). the baseline scenario represents the current conditions of land use, inputs use, yields, and revenues and costs. social welfare in the baseline scenario is 49 million euros, the difference between farmers’ net income (67 million €) and environmental damages (18 million €) from ghg emissions (727,000 t co2eq). agricultural activities create large pressures on water resources, with water extractions around 600 mm3 and nitrogen leaching loads around 6,000 t n. the high livestock density in the area generates also a large amount of manure with nitrogen loads around 10,600 t n. only 30 percent of this manure is used in crop fertilization (marm 2011b), and therefore this manure surplus results in large ghg emissions and considerable pollution loads on water bodies and streams. economic instruments the first scenario evaluates the first best measure of taxing ghg emissions by 25 €/t co2eq.5 this measure achieves the maximum social welfare (+37% over the base4 the emission intensity is measured in kilograms of ghg emission loads per euro of net income. the higher is the value of the emission intensity, the more emission intensive is the production within an area. 5 the emission tax is the first best measure that achieves the maximum social welfare and the optimum level of 60 m.t. kahil, j. albiac table 3. social welfare, net income and land use under each scenario.* scenarios welfare (106 €) farmers’ net income (106 €) environmental damages (106 €) crop area (103 ha) swine herd (103 heads) baseline 49 67 18 134 2,050 economic instruments emission tax (te=25 €/t co2eq) 67 49 18 136 1,940 nitrogen tax (tn=0.5 €/kg n) 48 58 17 114 2,050 nitrogen tax (tn=1 €/kg n) 48 51 17 111 2,050 water tax (tw=0.02 €/m3) 48 57 18 119 2,050 water tax (tw=0.05 €/m3) 47 43 18 117 2,050 emission limits limit on ghg emissions (10%) 49 65 16 130 1,769 limit on nitrogen leaching (10%) 48 65 17 100 2,050 control of inputs and production fertilization standards 55 71 16 134 2,050 improved feed 46 64 18 134 2,050 swine herd reduction (15%) 48 64 16 134 1,746 reduction of irrigation water (25%) 43 61 18 118 2,050 * for nitrogen, water and emission tax instruments, social welfare is set equal to farmers’ net income minus environmental damages, plus taxes. table 4. ghg emissions, inputs use and pollution loads under each scenario. scenarios ghg emissions (103 t co2eq) water use (mm3) nitrogen fertilization (t n) manure surplus * (t n) nitrogen leaching (t n) baseline 727 567 19,720 7,500 5,900 economic instruments emission tax (te=25 €/t co2eq) 700 569 19,900 7,100 6,000 nitrogen tax (tn=0.5 €/kg n) 694 505 16,890 9,400 4,700 nitrogen tax (tn=1 €/kg n) 690 497 16,300 9,600 4,500 water tax (tw=0.02 €/m3) 706 492 16,800 8,100 5,050 water tax (tw=0.05 €/m3) 709 437 18,240 7,600 5,200 emission limits limit on ghg emissions (10%) 655 549 19,080 6,700 5,700 limit on nitrogen leaching (10%) 677 503 13,140 8,800 3,950 control of inputs and production fertilization standards 653 567 10,751 2,200 2,700 improved feed 726 567 19,720 4,650 5,900 swine herd reduction (15%) 655 558 19,720 6,300 5,900 reduction of irrigation water (25%) 711 506 17,470 7,800 5,200 * manure surplus is the difference between manure availability from livestock (swine and cattle) and manure use in crop fertilization. 61greenhouse gases mitigation policies in the agriculture of aragon, spain line) with the desirable level of ghg emissions. manure surplus falls by 5 percent, with a decrease in livestock production and a slight increase in crop production. the reason of the decrease in livestock production is that the ghg marginal abatement costs are lower for livestock than for crop production. proposals of taxing agricultural emissions have been strongly rejected by farmers in previous experiences in countries such as denmark, ireland and new zealand, given the substantial losses in income and employment resulting from the tax instrument (kasterine and vanzetti, 2010). in addition, taxing individual farmers would result in very low levels of pollution abatement because farmers equate their individual marginal costs of abatement with their individual marginal benefits of abatement. this is a non-cooperative solution or nash equilibrium. when there is cooperation among farmers, the condition for efficient provision of public goods applies (in this case pollution abatement), and then each farmer equates the individual marginal costs of abatement with the total marginal benefits of abatement (perman et al., 2003). the cooperative solution requires the support of stakeholders. therefore, the pollution abatement efforts should focus on nurturing this collective action by providing the right institutional setting to support it (ostrom, 2010). the second scenario considers taxing nitrogen fertilizers with two tax rates following the “polluter pays” principle. tax ranges have been chosen taking into account the findings from the literature, which indicate that relatively high nitrogen taxes are needed to abate pollution (martínez and albiac, 2006). the implementation of this measure reduces the use of nitrogen fertilizers and abates ghg emissions up to 5 percent for a high tax rate (1 €/kg n). this measure has a positive effect on nitrogen leaching, which falls 24%, but the high tax reduces strongly farmers’ net income causing the abandonment of crop cultivation. the nitrogen use-based tax could increase manure surplus given the fall of crop area where manure is spread. the third scenario considers taxes on irrigation water. water taxes increase water prices towards “full recovery costs” and spread the water scarcity signal to any potential water markets that may appear. this water pricing is the measure of choice for water savings promoted by the wfd, and in fact the high tax rate (0.05 €/m3) saves 75 mm3 of water. however, water pricing results in very low abatement of ghg emissions (2-3%) and very high losses of net income (15-36%). the tax instrument is quite ineffective to abate emissions and looks politically unfeasible. taxes would be opposed by farmers because irrigation water is at present a common pool resource (rival and non-excludable good) and not a private good. taxing water is equivalent to introduce water markets, and the current institutional setting of water allocation would have to be dismantled in order to introduce taxes or water markets, and the common pool resource would have to be transformed into a private good. the implication is that water pricing doesn’t seem to be the best instrument to allocate irrigation water and abate emissions.6 emission. however, implementation of the first best measure is almost impossible because of the complexity of biophysical processes and the heterogeneity of pollution functions among agents (perman et al. 2003). 6 bosworth et al. (2002), cornish and perry (2003), and cornish et al. (2004) present compelling results from the literature and from field cases that show the difficulties of allocating irrigation water through water pricing. see martínez and albiac (2004 and 2006) for arguments on the ineffectiveness of water pricing in abating nonpoint pollution. 62 m.t. kahil, j. albiac emission limits the first scenario limits ghg emissions by 10 percent, which is the emission reduction target for the spanish diffuse sectors. net income losses under this scenario are quite small and social welfare remains unchanged, while manure surplus falls by 11%. however, the measurement, monitoring and enforcement of emission limits at the source are quite complicated tasks and entail very large transaction costs (shortle and horan, 2001). since observing pollution at the source is impossible or very costly, an alternative is to design measures to control ambient pollution (segerson, 1988). so, the alternative is to control nitrogen leaching in irrigation water returns, which generates indirect nitrous oxide (n2o) emissions. the control of nitrogen content of water returns gives an indication on the use of nitrogen fertilizers, and could be used to estimate the overall direct and indirect n2o emissions at the source. this measure is feasible because there is already a network of pollution measurement stations in the main irrigation districts in the ebro river basin (che, 2010). under this measure, a limit of nitrogen leaching is set up at 39 kg n/ha, which is 10 percent less than the baseline scenario. this measure cuts ghg emissions by 7% and reduces nitrate pollution by 30 percent, but manure surplus increases 17%. there is a small decline of farmers’ net income and social welfare, and also a fall in cultivated area. limiting nitrogen leaching in irrigation return flows is a cooperative solution, because farmers choose the pollution abatement level that could be reached collectively by equating their marginal individual costs from abatement to the total marginal benefits of abatement. this finding is in agreement with the results by esteban (2010), which indicate that the best instruments to abate agricultural pollution are those that induce farmers’ cooperation based on measurable pollution load limits, within the appropriate institutional setting. control of inputs and production one problem derived from crop production is the overuse and mismanagement of nitrogen fertilizers. recent estimations in aragon indicate that nitrogen fertilization from mineral and organic sources exceeds crop requirements by 24%. the consequence is a nitrogen surplus of 42,000 t n (marm, 2011b), which damages the environment. in addition, estimations of nitrogen content from manure reveal that the nitrogen available could cover up to 80% of the nitrogen requirements of crops. this would reduce the use of mineral fertilizers, saving costs, solving manure disposal problems, and abating pollution. in aragon, farmers do not take advantage of the large amount of manure available, because of the high costs of transportation and spreading, the difficulties of management, and the uncertainty of the impact on crop yields (orús et al., 2011). several studies in the ebro river basin indicate that an adequate manure use could achieve satisfactory yields for crops such as corn, wheat and barley, substituting mineral fertilization partially or completely, and achieving lower nitrogen losses (daudén and quilez, 2004). the transport and spreading costs of manure are lower than the costs of mineral fertilizers, when the distance from the manure source to the application site is small.7 since nitrogen can readily change form and is highly mobile in soil, a strategy for 7 the average cost of applying manure is estimated at about 0.8 €/kg n when the distance to the application site is less than 33 km (iguácel et al., 2010). 63greenhouse gases mitigation policies in the agriculture of aragon, spain reducing ghg emissions from nitrogen fertilization is to improve its overall use efficiency. this would reduce the amount of nitrogen in the soil available to pollute the environment. one important factor affecting nitrogen use efficiency is the application rate. nitrogen losses are often enhanced where available nitrogen from mineral and organic fertilization exceeds crop requirements. therefore adjusting the application rates during the growing season with precise estimations of crop nitrogen requirements and nitrogen content of all sources would reduce the losses of all forms of nitrogen (orús et al., 2011). in the study area, nitrogen available from manure, biologic fixation, and organic matter is around 14,000 t n, which covers the entire nitrogen requirements of field crops that are 12,000 t n. however, some additional mineral fertilization is required for other nutrients. nutrient balance results indicate that there is a large nitrogen surplus in cinca medio and monegros, while additional potassium is needed. in barbastro and hoya de huesca, there is a deficit of nitrogen and potassium that requires additional mineral fertilization (table 5). the fertilization standard scenario combines mineral and organic fertilization at adequate rates supplying enough nitrogen to meet expected yields. this scenario increases net income up to 71 million euros (+6% over the baseline), while social welfare goes up to 55 million € (+12% over the baseline). ghg emissions fall 10 percent and nitrate pollution is cut by half. manure surplus falls significantly down to 2,200 t n (-70%). this measure is “win-win measure” that enhances income while reducing pollution, benefiting both farmers and the environment. however, its implementation requires the support of extension services for effective control mechanisms and changes in the current environmental regulation. the second scenario considers the improvement of swine herd feeding, a recommendation of the ippc directive. the improvement of swine feeding consists in matching feeds more closely to animal requirements at various production stages, thus reducing the nitrogen excretion of animals and the nitrogen content of manure. the cost of this measure for farmers is 2.6 €/place/year, using the best available technologies (marm, 2010b). the impact of this measure on social welfare is slight, and its emission abatement potential is quite low. however, there is an important decrease in manure surplus (38%) with positive ancillary effects on ammonia and phosphorus pollution abatement. moreover, the improved feeding of swine could be linked with other measures such as better animal housing to enhance pollution abatement. the reduction of the swine herd size is another measure to mitigate ghg emission from livestock that could be imposed in the future by the new cap. this measure is also recommended by the spanish strategy for climate change to abate pollution from livestock. the measure could enhance the balance between cultivation and livestock activities in some regions with high livestock densities. the reduction of the swine herd by 15% reduces ghg emissions by 10%, and achieves the reduction target of the european union for the spanish diffuse sectors. manure surplus is reduced by 16%, lessening the risk of pollution. the economic impact of this measure is slight, with decreases in social welfare and farmers’ net income by 2 and 5%, respectively. the last scenario is the reduction of water supply by the water authority. a large reduction of 25% results in less cultivated area and water savings amounting to 130 mm3 (-23%). this measure reduces the land area of water-intensive crops such as alfalfa, rice and corn, but maintains the area of fruit trees. this is the strategy followed by the ebro 64 m.t. kahil, j. albiac basin drought plan (che, 2007), where the basin authority reduces water supply and farmers adapt their land use to water scarcity. the net income losses are only 9% and the ghg abatement potential is very limited (3%). nitrogen leaching falls 12% while manure surplus increases by more than 1%. 4.3 evaluation of policy scenarios the policy measures are evaluated by comparing the welfare effects and the abatement costs sustained by farmers under each measure. the first best measure of taxing ghg emissions increases social welfare by 18 million euros (+37%). nevertheless, this measure is not feasible because it requires information on the emission loads from each individual parcel, and the cost of this information is prohibitive (martínez and albiac, 2006). besides, taxing ghg emissions has unintended negative effects in the area, since the measure increases nitrogen leaching and the use of irrigation water. another problem is that the measure does not induce farmers’ cooperation, which is a necessary condition to avoid policy failure in abating agricultural nonpoint pollution (byström and bromley, 1998). among second best measures, standards limiting nitrogen fertilization improve social welfare by 6 million euros (+12%) achieving a considerable abatement of ghg (-10%). the main obstacle for implementing quantitative limits on nitrogen fertilization is the difficulty to verify farmers’ compliance. the solution is to give the responsibility of control to the irrigation user associations, coupled with measurements of the water quality of return flows from irrigation districts by the basin authority. this collaboration between irrigation districts and the basin authority is a feasible option to achieve the collective action of farmers. the control of nitrogen fertilization sought by the nitrates directive was initially based on spreading information and voluntary compliance, and more recently farmers have been required to keep an individual nitrogen balance book. enforcement ta bl e 5. n ut rie nt b al an ce in th e st ud y ar ea .a n ut rie nt s ba rb as tr o c in ca m ed io h oy a de h ue sc a m on eg ro s to ta l s tu dy a re a n b p 2 o 5c k 2o d n p 2 o 5 k 2o n p 2 o 5 k 2o n p 2 o 5 k 2o n p 2 o 5 k 2o c ro p re qu ire m en ts (t ) 2, 01 8 1, 00 5 2, 08 5 1, 60 5 95 3 2, 53 7 2, 75 7 1, 32 8 2, 78 3 5, 68 0 3, 25 1 8, 02 5 12 ,0 60 6, 53 7 14 ,4 29 n ut rie nt a va ila bi lit y (t) 1, 84 5 1, 51 8 91 4 2, 93 2 2, 36 1 1, 71 3 1, 98 8 1, 66 6 1, 06 2 7, 13 6 5, 68 1 3, 25 2 13 ,9 00 11 ,2 26 6, 94 0 n ut rie nt b al an ce (t ) -1 73 +5 13 -1 ,1 71 +1 ,3 27 +1 ,4 08 -8 24 -7 69 +3 38 -1 ,7 21 +1 ,4 56 +2 ,4 30 -4 ,7 73 +1 ,8 40 +4 ,6 89 -8 ,4 89 a es tim at io n is b as ed o n w or k by o rú s et a l. (2 01 1) . n ut rie nt b al an ce is c on si de re d th e ph ys ic al d iff er en ce b et w ee n nu tr ie nt a va ila bl e fr om m an ur e, b io lo gi c fix at io n an d or ga ni c m at te r to t he a gr ic ul tu ra l s ys te m a nd t he u pt ak e of n ut rie nt b y fie ld c ro ps . a p os iti ve v al ue in di ca te s a su rp lu s w hi le a n eg ativ e on e in di ca te s a de fic it. b n itr og en , c ph os ph or us , d po ta ss iu m 65greenhouse gases mitigation policies in the agriculture of aragon, spain is based on inspections of farms drawn by chance, where noncompliant farms are penalized in their cap payments. however, control is limited to cultivation areas located over aquifers or streams declared officially vulnerable to nitrate pollution. the usefulness of this fertilization control mechanism remains to be seen because it ignores cultivation over the whole basin, rainfed lands, and very polluting crops that are not receiving subsidies such as vegetables or fruit trees. also, this control mechanism does not promote cooperation among farmers (kahil and albiac, 2012).8 the measure of limiting ghg emissions at the source by 10% achieves the ghg reduction target of the european union for the spanish diffuse sectors, and maintains social welfare unchanged. this measure could be acceptable from a social point of view, but requires enforcement mechanisms and entails transaction costs. the reduction of the swine herd is another measure that reduces ghg emissions (-10%) with a slight loss of social welfare (-2%). this measure could be a suitable instrument to lessen large swine concentrations and the ensuing pollution in selected counties. all measures, except fertilization standards, show large abatement costs for farmers, between 28 and 3,000 €/t co2eq. this range is well above the abatement cost threshold of 20 €/t co2eq recommended by the european climate change program for ghg mitigation (eccp, 2003). table 6. ghg abatement potential and cost of measures. scenarios ghg abatement potential (t co2eq) ghg abatement cost (€/t co2eq) fertilization standards 74,000 -54 limit on ghg emissions (10%) 72,000 28 limit on nitrogen leaching (10%) 50,000 40 swine herd reduction (15%) 72,000 42 nitrogen tax (tn=0.5 €/kg n) 33,000 273 reduction of irrigation water (25%) 18,000 333 nitrogen tax (tn=1 €/kg n) 37,000 432 water tax (tw=0.02 €/m3) 16,000 625 emission tax (te=25 €/t co2eq) 27,000 667 water tax (tw=0.05 €/m3) 21,000 1,143 improved feed 1,000 3,000 the implementation of fertilization standards increases farmers’ net income and achieves the highest ghg abatement. this cost-effective and win-win measure shows that there is a considerable mitigation potential in agriculture. this result is in agreement with macleod et al. (2010), which find that the improved management of fertilization achieves a considerable ghg abatement in the agriculture of the uk at a negative cost. 8 the current organic fertilization limit established by the nd is 210 kg n/ha in cultivation areas outside vulnerable zones, with no limits on mineral fertilizers use. the organic limit is excessive compared to average nitrogen requirements of crops in aragon which are below 100 kg n/ha (orús et al., 2011). 66 m.t. kahil, j. albiac economic instruments for mitigating agricultural ghg emissions are taxes on irrigation water, nitrogen fertilizer or emissions. although they follow the “polluter pays” principle, their implementation has an important barrier. farmers will oppose this type of measures rather than cooperating, because they sustain very high abatement costs under these taxes, and policy failure would be quite likely. the empirical results in the study area indicate high abatement costs, between 273 and 1,143 €/t co2eq, achieving very small pollution abatement, below 5%. these results confirm the ineffectiveness of economic instruments to control agricultural nonpoint pollution. certainly, properly designed economic instruments promote behavioural changes in agents. esteban (2010), de cara et al. (2005) and pérez et al. (2009) analyze these behavioural changes in agriculture following the economic theory of demand and supply of private goods. esteban (2010) shows how non-uniform water taxes induce heterogeneous farmers to adopt advanced irrigation technologies which abate pollution. de cara et al. (2005) and pérez et al. (2009) state the advantage of using market-based instruments to reduce agricultural ghg emissions. however, the policy evidence seems to question these results. shortle (2012) reviews several agricultural emission trading schemes in the united states, canada and new zealand. most of these schemes are from the united states with demonstration pilot projects subsidized by the administration. emission trading takes place between urban or industrial point sources which have pollution limits, and agricultural nonpoint pollution sources without pollution limits. shortle acknowledges that the outcomes from these emission trading experiences are unconvincing, and explains the reasons for the difficulty of emission trading as a policy to control agricultural nonpoint pollution. emission trading is an economic instrument with serious difficulties to be effective because it is market based. ghg emissions abatement is a public good requiring collective action through the cooperation of farmers and other stakeholders. under these circumstances, economic instruments such as taxes and subsidies are likely to fail (ostrom, 2010). the measure of improving the feeding of swine results in very large abatement costs, with a quite small abatement potential. however, this measure involves other positive environmental effects such as abatement of water pollution by nitrate and phosphorus. therefore, these effects may compensate the abatement costs of improved feeding and advance its feasibility. the results of evaluating the measures for mitigation of ghg emissions in the agriculture of aragon indicate that a combination of institutional instruments and command and control instruments could result in significant cost savings compared to economic instruments. however, these measures face a number of obstacles, such as high transaction costs, difficulties to enforce quantitative limits, and problems with the current environmental legislation. these shortcomings can be overcome by promoting the involvement of private and public stakeholders, and this will be the key driver to change the farmers’ behavior in addressing climate change mitigation. 5. conclusions climate change is a negative externality from the economic activities that is already damaging the natural environment and its ecosystems, and could endanger seriously the 67greenhouse gases mitigation policies in the agriculture of aragon, spain human well being in the coming decades. the large growth in wealth and population since the industrial revolution, based in accelerated technological developments, have largely increased atmospheric ghg concentrations. these emissions are changing the energy balance of the earth and the climate system, and threatening with a future of higher temperatures, lower precipitations in arid and semiarid regions, rises of the sea level, and more severe and frequent extreme events (ipcc, 2007). agriculture is an important sector for implementing climate change mitigation policies. agriculture is a significant source of ghg emissions and the main source of nonco2 emissions. besides, climate change would also have large harmful effects on agricultural activities, especially in arid and semiarid regions. the design of adequate mitigation policies for the agricultural sector is needed, and these policies require the cooperation of farmers through the right institutional setting. this study evaluates the ghg emissions from an intensive agricultural area in aragon, located in the middle ebro basin. emissions are estimated at 727,000 t co2eq, which represent 20% of the agricultural emissions in the aragon region. the emission intensity in the area is 11 kg co2eq/€, well above the average emission intensity of the agriculture in aragon. this type of information is important because the spatial dimension of emissions contributes to the adequate design and implementation of ghg mitigation policies adjusted to local conditions. the analysis of ghg mitigation measures in agriculture indicates that there is not a unique preferred measure, and that no single instrument can address the mitigation of ghg emissions. a combination of suitable measures is needed to contribute towards climate stabilization while achieving cost-effectiveness. the choice of measures depends on the objectives of decision-makers, but also on the availability of biophysical and economic information. local characteristics, economic and environmental effects, and social acceptability have to be considered in the design of measures. policies have to be legitimate, because enforcement cannot be achieved without the support of stakeholders. inappropriate ghg abatement measures could indirectly increase the nitrogen loads into water resources, through increased leaching and runoff from crop cultivation and manure surplus. a comprehensive nutrient management planning is needed to reduce nitrogen pollution loads, by considering the whole nitrogen cycle, sources and sinks when implementing measures to reduce ghg emissions. in the case of aragon, more attention has to be paid to manure management in order to make a better use of this waste. manure could eventually become a resource if properly managed, reducing at the same time its ghg emissions. empirical results show that a combined and balanced organic and mineral fertilization of crops is a good second best policy, achieving almost the same welfare (82%) than the first best policy. although manure management is an important aspect of the nitrates directive, its achievements are quite poor, and manure regulation needs revision to be more effective and adapted to local conditions. the results of this study indicate that economic instruments that follow the “polluter pays” principle are quite ineffective in abating agricultural nonpoint pollution. economic instruments display very high abatement costs for farmers and mostly negative welfare effects. the alternative is using institutional instruments, where stakeholders cooperate for a better land use planning and protection of environment assets. cooperation is needed 68 m.t. kahil, j. albiac for a reasonable allocation of resources and for achieving significant nonpoint pollution abatement efforts. the empirical findings challenge the design of mitigation policies using pure economic instruments, and call for policy efforts focused on nurturing stakeholders’ collective action and on supporting the necessary institutional setting. this study shows some measures that could induce farmers’ cooperation in abating emissions. also, preparation for climate change calls for more coordination between environmental policies, adaptation to spatial conditions, and enforcement supported by the involvement of local stakeholders. acknowledgments support for this research was provided by projects gobierno de aragón-la caixa galc-001/2010 and inia rta2010-00109-c04-01. references albiac, j. 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(1998). economic instruments and environmental policy in agriculture. canadian public policy 24(3): 309-327. bio-based and applied economics 4(3): 199-200, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-17940 editorial innovation, productivity and growth: towards sustainable agri-food production the expected sustained growth of world demand for food, feed, and energy raises concerns about the ability of global agricultural production to ensure adequate supply growth, while avoiding major turbulences on agricultural markets and still providing healthy and environmentally safe products. how these major challenges can be dealt with is, at once, both a political and scientific question. the scientific community is expected to provide rigorous and sound evidence on the nature and size of these challenges and to put forward effective and viable solutions. in this respect, a first major task for agricultural and applied economists is to investigate the actual dynamics of agricultural productivity growth worldwide and the differences emerging between developed and developing countries. efforts should also be devoted to deepening the available knowledge on the impact of agricultural productivity on overall economic growth and, consequently, on income distribution, food consumption and international trade. finally, applied economists are asked to investigate which kind of innovations can better contribute to achieving a sustainable increase of agri-food production and which policies and institutional settings may encourage these innovations. specifically, in the eu context, attention inevitably involves analysis of the common agricultural policy (cap) and its recent reform. whereas agricultural innovation and productivity growth apparently represent some of the key objectives inspiring this reform, a critical review of its actual contents and implementation raises major doubts on its capacity to lead european agriculture in the desired direction. the aim of the fourth conference of the italian association of agricultural and applied economics (aieaa), held in ancona, june 11-12, 2015, was to provide a scientific contribution to these issues by expanding the knowledge base on the fundamentals of agricultural productivity growth and innovation, and also by promoting a critical debate on the underlying theoretical and methodological issues and policy implications. the conference included about 50 papers addressing a range of research and policy issues such as: long-term patterns of worldwide food production and consumption; measuring and explaining agricultural productivity growth and gaps; the impact of agricultural productivity growth and innovation on income growth and poverty; sustainable knowledge intensification and innovation in agricultural production; design, implementation and evaluation of policies fostering sustainable agricultural innovation; the assessment of social, economic and environmental impacts of new bio-economic production and processes. the bae editor invited some of the speakers to submit for publication of their own papers on this bae issue. the papers published on this issue were among those presented during the fourth aieaa conference, although representing only an essay of the topics covered by the conference. the paper by keith fuglie deals with the issue of the measurement of technical and efficiency change; he proposes a growth accounting 200 editorial approach to measure the agricultural total factor productivity, which takes into account the animal feed inputs employed in the sector. the findings of the paper show that using these more complex measures could produce rather different results from those obtained by studies based on partial productivity indexes, and provide us with a more optimistic view about agricultural productivity growth. parthena chatzinikolaou and co-authors provide an assessment of the ecosystem service in a traditional italian cultural landscape, by using a framework suitable to be translated in a multi-criteria evaluation process. the paper by ciliberti and frascarelli deals with the implementation of the new common agricultural policy (cap) in italy and in particular aims at assessing to what extent the choices made by italy about cap implementation are consistent and effective in pursuing the objectives of the new cap. finally, the paper by davide viaggi provides an overview of state of the art of the literature and a discussion of the impacts of research and innovation on productivity, in the light of the specific features of the bio-economy; the study highlights a number of new challenges in this research area. margherita scoppola president of aieaa bio-based and applied economics 4(2): 179-197, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-14511 credence goods, consumers’ trust in regulation and high quality exports nadia cuffaro*, marina di giacinto uniclam, dipartimento di economia e giurisprudenza, cassino (italy) date of submission: may 9th, 2014 abstract. we analyze the impact of the effectiveness of internal regulation for the development of internal and export markets for credence goods, focusing on food products, particularly for a developing country which is an exporter (or a potential exporter). in the model, since goods of actual different quality can be sold as high quality goods, expected quality is a function of consumers’ beliefs about the effectiveness of regulation. foreign consumers, who cannot observe foreign regulation as closely as domestic ones, may partly base their expectations on the level of development of the exporting country. low effectiveness, negative stereotype and low consumers’ trust may cause a failure in the market for high quality, and there may be a trap of underdevelopment and no high quality exports. the main policy implications are that increasing the effectiveness of regulation improves export prospects; standard setting and enforcement by external actors, such as supermarkets, or ngos in the case of certain niche markets, is likely to be beneficial. keywords. credence goods, standards, trade and development. jel codes. l15, f13, o12 1. introduction the perception consumers have of the effectiveness of regulation on product quality and safety in a country is generally important for the development of internal and especially export markets. such perception and trust become crucial when consumers cannot really evaluate some or all of a product’s attributes, especially process attributes: it may be prohibitive to find out whether a product is actually “environmentally friendly”, “organic”, or simply completely safe. therefore consumers’ notion of quality and demand will be related to their trust in regulation. the article discusses the development of the market for goods in the presence of information asymmetry and uncertainty about product quality: specifically when it would be difficult and/or costly for consumers to evaluate certain product characteristics before consumption, and there is no learning or very slow learning even after consumption. * corresponding author: cuffaro@unicas.it. mailto:cuffaro@unicas.it 180 n. cuffaro, m. di giacinto the classification of goods on the basis of consumers ability to evaluate their quality before consumption, or on the basis of experience, or not even after consumption as search, experience and credence (trust) goods is originally due to darbi and karni (1973). ever since a large literature has developed on the functioning of the markets for credence goods and services (dulleck et al., 2011; emons, 1997; 2001; wolinski, 1983; 1995).the existence of the market for such goods is strongly dependent on quality guarantee by a third party, which defines the standard and/or monitors compliance. in food markets many products can be considered experience or credence (caswell and mojduszka’s, 1996), examples of the latter are many health/safety related attributes, process attributes such as environmental impact (environmental “friendliness”) or “bio” food, use of gmos, ethical characteristics such as various specification of “fairness” . asymmetric information problems occur since consumers know with certainty only what the producers’ quality claims are or what the label says, i.e. real product standards are unobservable to consumers. a significant literature has developed accordingly in agricultural economics (unnevehrk et al., 2011; costa et al., 2009). most contributions have analyzed jointly the economics of credence (or experience) goods and labelling, in general (marette et al., 1999; mccluskey, 2000; anania and nistico, 2004; zago and pick, 2004; roe and sheldon, 2007; mccluskey and loureiro, 2005; crespi and marette, 2001) or focusing on specific mechanisms /attributes, including geographical indications (menapace and moschini, 2012), genetically modified organisms (fulton and giannakas, 2004; moschini and lapan, 2005), organic farming (dabbert et al., 2014) ethical products (chang and lusk, 2009) . models have concentrated on the welfare implications of different labelling schemes, often assuming fully credible certification. for example zago and pick (2004) consider the welfare impact of labeling policies for credence agricultural goods, assuming a fully credible certification system. menapace and moschini (2012) develop a model for an experience good where, as in shapiro (1983), firm reputation offers a possible solution to the market failure identified by akerlof (1970) and where geographical indications (gis)1 and trademarks are complementary means for signalling quality. they postulate a fully credible trademark system and a fully credible certification scheme for gis (i.e. there is no counterfeit product on the market and all certified products meet the requirements established by the certification schemes). roe and sheldon (2007) model the market for a credence good, with costly certification and perfect monitoring to analyze how different forms of labelling impact the size and distribution of surpluses. the trade implications of asymmetric information on quality have been less discussed, with notable exceptions (e.g. bureau et al., 1998)2; the issue of consumer preferences for country of origin, as lusk et al. (2006) remark, has also been relatively neglected, whereas 1 gis provide labels typically accessible to a large number of firms producing similar, competing, products, each with its own distinct trademark. in the eu gis schemes are based on the notion of a quality-geography nexus, and require the definition of a code of rules. usage rights over a gi are granted to all producers within a designated production area who comply with the product specification. 2 bureau et al. (1998) consider the case of the dispute over hormone-treated beef between the eu and the united states, and conclude that the positive effect of trade liberalization on welfare may be offset by the increase in imperfect information about product quality. 181credence goods, consumers’ trust in regulation and high quality exports a wealth of evidence on the topic has accumulated in the business and marketing literature (as discussed in section 3). the model we present (i) considers the market for a credence good with imperfect monitoring, as in anania and nistico (2004) who focus on the credibility of regulation, i.e. counterfeits in these markets can be sold as high quality goods3; (ii) extends the analysis to trade; (iii) includes a country of origin effect. although the term regulation usually refers to governmental standards, the term standards and regulation will be used here indifferently to refer to all standards, public or private, involving certification. the term “effectiveness of regulation”, unless better specified, indicates the scope of regulation i.e. to what extent standards meet consumers demand for product quality and safety; the quality and relevance of the standards in terms of meeting the defined objectives; the efficacy of the monitoring system in ensuring that producers actually meet the standard. the latter two characteristics also indicate to what extent consumers can trust regulation, e.g. the probability that a product labeled “environmentally friendly” actually is environmentally friendly. in the formal model however the regulation parameter is kept relatively simple, it is the probability λ of being caught cheating on quality, in order to make the model more tractable when the analysis is extended to trade. the article is organized as follows: sections 2 briefly reviews credence goods and the relationship between standards and trade; section 3 introduces the assumptions on consumers’ expectations about quality; section 4 presents a model on the relationship between consumers’ trust and the internal and export markets for credence goods4. there are two development dimensions of the problem. first, regulation may often be less effective in developing countries, as discussed in section two. second, foreign consumers may partly base their expectations about product quality on the level of development of the producing country as a proxy for the effectiveness of regulation, i.e. on general notions about the relationship between regulation on quality and income level. hence developing country exporters may suffer from a specific “trust” problem regarding the effectiveness of internal regulation, which may hamper high quality exports: low effectiveness of internal regulation could have an heavy impact on foreign demand for high quality credence goods, in general, but more so for a developing country. 2. credence attributes, standards and trade the information environment for different product attributes may be search, experience, or credence in nature: consumers can learn about the quality level prior to purchase (search), after purchase and use (experience), or not at all (credence). credence attributes can obviously be of a very different nature, but, restricting the discussion to goods, there are two major classes that have received increasing attention: (i) attributes that have health/safety consequences; (ii) consumers’ demand/(willingness to pay) for attributes that are of “altruistic” nature, 3 this topic is also extensively treated in the eco-label literature (costa et al., 2009; engel 1998; mason, 2006). 4 for a discussion focused on organic and fair trade agricultural products see cuffaro and liu (2008). 182 n. cuffaro, m. di giacinto i.e. related to concern for “others”, typically to the production processes (fairness of distribution, the environmental cost of production, the use of child labour, the animal welfare standards applied). an important example is the demand for “fair trade.” standards are increasingly important for trade for several reasons: first, the shift from mass markets to markets with differentiated products and niches serving consumers with relatively high incomes, who increasingly demand high quality, safety and “credence”, attributes; second, the trend towards outsourcing for cost reduction; third, the significant decline of tariff barriers, implying that differences in product and process standards gain importance for trade flows and in the trade liberalization arena. as standards increasingly address process issues, the role of regulation and standards depends also on how much consumers “trust” regulation, i.e. to what extent they believe that a product marked “high quality” is actually a high quality good regulation may be ineffective for several reasons. for example, in many countries firms apply to independent labeling agencies for a license to use a particular label stating that their product is environmentally friendly, socially responsible or safe. these ecolabeling programs are often applied to products where consumers would generally be individually unable to determine the actual environmental friendliness (e.g. the biodegradability of a product) and the firm’s compliance is gauged by random monitoring. but when monitoring is random, certification must be viewed as noisy. furthermore, the certifying party cannot be certain that the firm always uses an environmentally friendly technique, nor that the monitoring scheme is able to perfectly detect any violations. even if the certifying process is perfectly able to evaluate a product’s compliance with the test’s standards, standards may not be perfectly correlated with “environmental friendliness”5 (engel, 1998; mason, 2006). in addition, certifiers have mixed incentives: the incentive to maximize the number of clients, the incentive to maintain their reputation. in other words, third party verification does not automatically guarantee impartiality or absence of conflicts of interest.6 finally, enforcing a process standard may be a very difficult problem in the context of value chains coordination across borders.7 there are important agribusiness and development related dimensions of the standards topic. first the tendency of standards to become a strategic instrument of competition in differentiated product markets and the shift from performance (realized or “search” characteristics of the product) to process standards has been very pronounced in agribusiness (reardon et al., 1999) and this was associated with an increase in the scope and stringency of public standards and the upsurge of initiatives on collective private standards. second, many strands of literature have shown that there is a relationship between the development stage and effectiveness of public standards: their scope8, their quality and rel5 in the mason (2006) model of ecolabeling, the certifying test is subject to two types of errors: there are some green sellers that would fail the test and some brown sellers that would pass the test. 6 evidence on opportunist behavior in the certification systems in the eu is reported in jahn et al. (2005). 7 an example is the safety crisis within the us toys industry in relation to production in china (bbc, 2007). 8 the scope of standard depends on income and development related factors. stephenson (1997) provided a description of the situation at the beginning of the 1990s, showing for example that the number of national standards in developing countries, including large latin american countries, for which data were available, was at least ten times lower than the corresponding number in the us and also the proportion of mandatory standards was comparatively low. 183credence goods, consumers’ trust in regulation and high quality exports evance in terms of meeting the defined objectives and the effectiveness of the monitoring system in ensuring that producers actually meet the standard. in agricultural economics the literature on the “privatization” of agribusiness standards in the 1990s9 has indicated that in developing countries private standards for quality and safety of food products were a strategic response first and foremost to missing or inadequate public standards10 (jaffee, 2003; henson and reardon, 2005; humphrey, 2008; reardon et al. 2009). developing countries standards have been much discussed also in the agricultural economics trade literature, often with the prior that the ‘‘standards as barriers’’ case is more likely to apply to developing country exporters. for example disdier et al. (2008) construct an inventory of sps and tbts measures and use a gravity model to show that they do not significantly affect bilateral trade between oecd members but significantly reduce dcs and ldcs exports to oecd11. in particular the spread of private standards has been investigated on the basis of the concern that they could lead to the exclusion of the least developed countries and the poorest farmers, who are unable to comply with stringent requirements due to a lack of technical and financial capacity (graffham et al., 2007; maertens and swinnen, 2007; reardon et al., 2001; swinnen and vandeplas, 2011). finally an indirect indication that standards are lower is provided by a vast literature on value chains coordination by multinationals, pointing out that one of the main advantages for developing countries is the upgrading of standards (cuffaro and liu, 2008). for example a madagascar case study (bart et al., 2009) shows the benefits, even for very poor farmers, of the integration in global value chains, through a monopsonistic marketing company, controlling and enforcing the standards imposed in private protocols by the clients –mainly european supermarketsrelated to search and credence characteristics. 3. consumers’ expectations about quality we formulate three main assumptions regarding consumers’ expectations about quality. first, expected quality is a function of consumers’ beliefs about the effectiveness of regulation. second, domestic and foreign consumers may hold different beliefs. domestic consumers know the effectiveness of internal regulation and the incidence of cheaters and base their expectations on such incidence. foreign consumers base their expectations on 9 in agribusiness privatization has occurred in two distinct ways: on the one hand large firms, mostly supermarkets and large processors and especially multinationals, created private standards generally meeting or exceeding the stringency of public standards and insured their implementation through vertical co-ordination. on the other hand, ngos have provided the standards and the monitoring and enforcing mechanism for many credence products with “ethical” attributes, occupying a fast growing market segment of products originating in the poor countries (reardon et al., 1999). 10 although developing country situations are heterogeneous, public infrastructure, governance structures, and institutions are generally poorer, which translates into a relative lack of public standards especially for non tradable and traditional products (perez-aleman, 2011; reardon et al., 1999). also, in general (piore, 2003; amsden, 1989; lall, 2000) and in agribusiness international standards are strongly based on ideas and practices developed in advanced countries and their adoption in developing countries occurs in a context that can be far from the technological frontier and/or traditional local customs. 11 although findings on trade effects can be more complex (sheperd and wilson, 2013). 184 n. cuffaro, m. di giacinto the percentage of imports from the country which failed border quality inspection, which is in turn linked to the effectiveness of internal regulation in the exporting country, but they are also influenced by a country of origin stereotype. their trust in the regulation of product quality increases with the level of development of the exporting country. the second assumption is based on the idea that since foreign consumers cannot observe regulation in each country of origin of their imports as closely as domestic consumers, they may partly base their expectations about product quality on general notions about the relationship between regulation on quality and income level. in general what foreign consumers can observe about the effectiveness of regulation in exporting countries is a very loose indicator of such effectiveness. for example jaffee and henson (2004) report that over a typical three year period the us food and drug administration (fda) undertakes inspections of all domestic firms that produce lowacid canned foods, yet the same inspections are undertaken on just 3% of foreign facilities exporting such products to the united states. even after substantially increasing resources for the inspection of food imports, the fda still inspects only 1 to 2% of the more than six million consignments of food and cosmetic products imported each year. regulatory oversight for certain products and markets is more stringent on domestic, rather than imported supplies (world bank, 2005). although the dramatic transformation of the world economy within the last decade (global outsourcing and “hybrid” products) has made the issue more complex, marketing and business research shows that consumers do use country of origin as a quality signal especially when information about quality is ambiguous (reviews of this literature include: bilkey and nes, 1982; verlegh and steenkamp, 1999; pharr, 2005; rosenbloom and haefner, 2009; papadopoulos and heslop, 2014) and more specifically this research has also shown that negative evaluations by consumers on the basis of country images constitute significant market barriers for firms from less developed countries (baughn and yaprak, 2014). country of origin is regarded as a cognitive cue, viz., an informational stimulus about or relating to a product that is used by consumers to infer beliefs regarding product attributes such as quality, and since it can be manipulated without changing the physical product, it is an extrinsic cue like price, brand name and retailer reputation. the cognitive processes underlying the effects of country-of-origin on product evaluation may be explained through different hypothesis, some of which are especially relevant for credence attributes. for example research on the role of stereotypes suggests that these may be used as a heuristic basis for judgements especially when the amount of attribute information is large and difficult to integrate or when other information is lacking. thus, subjects who learn that a product is originating in a country with a reputation for high quality may use this knowledge as a basis for evaluation without considering information about the product’s specific attributes, especially if evaluating the information is difficult (hong and wyer, 1989); maheswaran (1994) examines consumer expertise and attribute information as moderating the effects of country of origin, and shows that all types of consumers use country of origin evaluations when attribute information is ambiguous. in the agricultural economics literature most studies – surveys, choice experiments, and experimental auctionsfind that consumers prefer and are willing to pay a premium for many foods with country of origin labels, although the premium varies substantially across studies, products, countries, and experimental method (grebitus et al., 2010) 185credence goods, consumers’ trust in regulation and high quality exports product/country images contain widely shared cultural stereotypes. for example, consumers recognize that the production of high-quality technical products requires a highly trained and educated workforce; hence, they perceive that such products are of better quality when produced in developed countries (verlegh and steenkamp, 1999). in a review of countryof-origin effects on product evaluation, bilkey and nes (1982) point out that several studies found a hierarchy of biases, including a seemingly positive relationship between product evaluation and degree of economic development. han and terpstra (1988) show specifically that products with a country-of-origin label from a developing country were rated inferior to those with an industrial country-of-origin label and head (1993) reports that a ‘made in germany’ label evokes the concepts of reliability, precision and punctuality. liu et al. (2001) provide empirical evidence of a ‘level of development’ factor in the market for organic foods. verlegh and steenkamp (1999) evaluated the findings of past country-of-origin studies within the marketing and business literature for the period 1980-1996 and found that the country-of-origin effect is strong especially for perceived quality and that one factor closely related to the evaluation of products in general is the level of development: the country-of-origin effects are significantly larger when products from more developed countries are compared with products from less developed countries. this finding supports the notion that consumers believe that products from ldcs are lower in quality, and associated with a larger risk of bad performance and dissatisfaction (cordell, 1992).12 roth and romeo (1992) argue that consumers’ evaluations are based on the match between product and country: consumers prefer a country as an origin for specific products when they believe that there is a match between its perceived “strengths’’ and the skills that are needed for manufacturing the product under consideration: a strong positive match would exist when the country is perceived as being very strong in an area that was also an important feature for a product category. actually in the case of credence goods an important issue is the effectiveness of regulation, which in turn depends on good general and dedicated institutions. this is the “skill” required and consumers may establish a positive association with the level of development just as for the case of high quality technical products. 4. credence goods, trust and the market for high quality 4.1 the model the model we formulate analyses the impact of the effectiveness of regulation on the development of the market for high quality credence goods, i.e. a market where goods of actual different quality can be sold as high quality goods (examples are goods labeled environmentally friendly, or bio, or safe for children). the effectiveness of regulation is measured by the probability λ of being caught cheating on quality, internal consumer know that measure, and expected quality depends on it. figure 1 shows the domestic market before trade. with perfect regulation only high qual12 also, there is anecdotal evidence that in some poor countries some producers unlawfully package their products with a country of origin label different from their own, a “better” country of origin. 186 n. cuffaro, m. di giacinto ity producers participate in the market and the supply function is s0 d. the demand function is dd 0 with the equilibrium price pe d . if λ < 1 , the supply function shifts to s d 1 which represents the sum of product offered by cheaters and high quality producers. as consumers are aware that λ < 1, the demand curve rotates towards dd 1 (the analytical form of the demand function is such that its vertical intercept is equal expected quality), the equilibrium price decreases and consumers surplus is reduced. figure 1. domestic market.   p d d 0 s d 0 p d e s d 1 p' d e d d 1 0 q the analytical results presented in this section show that the equilibrium price is increasing in expected quality, and therefore in λ: better regulation on quality, here intended as the ability of regulators to exclude cheaters from the market, results in higher prices for high quality credence products. furthermore both consumer and producer surplus are strictly increasing in expected quality and therefore in λ. extending the basic idea of figure 1 to trade, let’s assume that country/area a is a large exporter (country a has a comparative advantage based on factor endowment) and world price is formed on the internal market of a as a result of the interaction between internal demand dd plus the demand for imports from the rest of the world di and supply in a, sd (the same result could be obtained summing the excess function of this large country to the market of rest of the world). with perfect regulation (λ=1) the internal supply and demand functions are sd o and dd 0 and the demand for imports from the rest of the world is d0 i , in country a total demand is dt 0 and the equilibrium is e0. a lower λ would reduce the expectations of internal and foreign consumers about quality in country a. internal supply, internal demand, the demand for imports and total demand for the high quality product rotate (dotted lines in figure 2a) and the new equilibrium is e1. a negative country of origin stereotype, linked to the level of development of the exporter would instead only rotate di 0 . the model’s results show that the equilibrium price is strictly increasing in λ and also strictly increasing in the level of development parameter. 187credence goods, consumers’ trust in regulation and high quality exports figure 2. trade of credence goods.   figure 2 trade of credence goods                      s   0  i            sd0   e  0   s  d  1                    d   0  i                                         d  1  i                          e  1                                      d  d  1          d  d  0                                  d   t  0    d  t  1   export supply       (b) rest of world   import demand     (a) country/region a     in both cases if there were a minimum price of high quality, known to consumers, below that price there would be no supply and no demand (as consumers know that the good cannot be high quality) i.e. there may be no high quality production and export. analytically, in analogy with the model of anania and nisticò (2004), we assume that markets are competitive and there are high quality producers and low quality producers who try to cheat. there are nh identical high quality producers and nl identical low quality producers, with marginal cost functions β=c qh h h β=c ql l l with βh>βl. each high quality producer produces a quantity such that β=p qh h depending on the probability λ of being caught cheating, a fraction λ( )−1 of low quality products is sold on the h market, therefore the expected marginal revenue of cheaters is p λ( )−1 hence each low quality producer offers on the h market a quantity such that λ β− =p q(1 ) l l 188 n. cuffaro, m. di giacinto the aggregate supply in the high quality market is: β λ λ β = + − − s p n p n p ( ) (1 ) (1 ) h h l l (1) assuming for simplicity that there is a continuum of mass 1 of producers of each type, nh=nl=1, equation (1) becomes: β λ β = + −s p p p ( ) (1 ) h l 2 (1bis) the first term in the right hand side of equation (1bis) reflects the supply from high quality producers and the second term that from low quality producers that cheat. on the demand side13 it is assumed (mussa and rosen, 1978; tirole, 1988) that consumers agree on the order of preferences, they prefer a higher quality for a given price but have different intensity in their taste for quality, represented by a parameter θ, a real positive number. they have net utility u=θe(k)-p if they buy a good of expected quality e(k), where k is a random variable which can take two possible values, associated respectively to high or low quality, at price p. although this framework implies a tradeoff between quality and price, it can be applied also to a context in which consumers are only interested in high quality –in the sense of a product with the specified standard, e.g. “bio”but quality is probabilistic and there is a tradeoff between the likelihood of getting the unwanted “attribute” and price. willingness to pay for a quality e(k) is given by θe(k), and increases with θ and e(k). demand is equal to the number of consumers with parameter θ such that θe(k)≥p derivation of the demand function14 uses the ‘threshold’ consumer with a taste parameter θ is indifferent to buying or not buying a unit of product of expected quality e(k) at price p,  ( ) 0θ − =e k p implying that  ( ) θ = p e k demand is d p( ) = m 1− p e k( ) ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ (2) where m is the total number of consumers. domestic consumers are aware of the measure of the effectiveness of regulation λ and know also the other parameters of the supply function. they expect high quality with probability 13 the discussion of demand is mostly based on a simple model with vertical differentiation of products (mussa and rosen, 1978; tirole, 1988). this demand function has been extensively used in the literature marette, crespi, and schiavina (1999); crespi and marette (2001); moschini and lapan (2005); zago and pick (2004). 14 if θ is distributed in the economy according to a cumulative distribution function f(θ), f(θ) is the fraction of consumers with a taste parameter lower than θ. if only one quality k is offered at price p, demand is equal to the number of consumers with parameter θ such that θ(e)k≥p . d(p)=m [1-f(p/e(k))] where m is the total number of consumers. 189credence goods, consumers’ trust in regulation and high quality exports π β β λ β = + − p p p / (1 ) h h h l 2 (3) this probability is one if regulation is perfectly enforced (λ=1) and expect low quality with probability π λ β β λ β = − + − p p p (1 ) / (1 ) l l h l 2 2 (4) this probability is zero if regulation is perfectly enforced (λ=1). assuming that there is no learning or very slow learning on the part of consumers because of the credence nature of the attributes considered, in these type of markets in equilibrium products of different qualities can be sold as high quality products, in the sense of products that respect the specified standard. k is a random variable which can take only two possible values, kh and kl, with probabilities πh and πl. assuming kh =1 and kl=0 expected quality e(k) is equal πh β β λ β β β λ β = + − = + − e k p p p( ) / (1 ) (1 ) h h l l l h2 2 expected quality e(k) is increasing in λ and it is equal 1 with λ=1. abiding by the general functional form of equation (2), and setting the mass of consumers m=1 domestic demand can be specified as follows: β β λ β = − + − d p p( ) 1 (1 ) l l h 2 (5) 4.2 results analytically the equilibrium price satisfies β λ β β β λ β + − = − + − p p p(1 ) 1 (1 )h l l l h 2 2 (6) solving equation (6) for p we obtain the following equilibrium price: 190 n. cuffaro, m. di giacinto p* = βhβl βl +βlβh + (1−λ) 2βh + (1−λ) 2β 2 h = βhβl 1+βh( ) βl + 1−λ( )2βh⎡⎣ ⎤⎦ = e(k) βh 1+βh (7) the equilibrium price is increasing in e(k) and therefore in λ: better regulation on quality , here intended as the ability of regulators to exclude cheaters from the market, results in higher prices for high quality credence products. the equilibrium quantity is q* =1− p e(k) = 1 1+βh consumer surplus is β+ e k( ) 2(1 )h 2 ; producer surplus is β β+ e k( ) 2(1 ) h h 2 and they are both strictly increasing in e(k) and therefore in λ. the model described by equations (1)-(6) may also give insight on trade in two distinct cases. considering a world with two regions, a and b, where a is “developing” and b is “developed”, the first case is when there is no internal production in region b; in the case of food this could be because of climate, and therefore it is not an irrelevant case. supply in a is described by equation (1bis), demand is the sum of demand in a and b. the latter depends also on how foreign consumers’ expectations are formed. the second is a specific category of credence goods: some credence “ethical” products such as “fair trade” products, which by definition are exported only by developing countries. in this case there would be no internal production in region b. supply in a could be described by equation (1bis), demand in a is solely the demand for imports and it depends on how consumers in region b form expectations about regulation and quality in region a. generally speaking foreign consumers have less information than internal ones, but in this case they will likely assume that the incentive and/or ability of national regulators in any developing exporter to “exclude” part of the supply from the market is low. therefore, without alternative mechanisms of regulation, the situation is the same as in figure 1 with λ “low”, and the demand for imports would be “low” like in the case of d1 d. the development of these markets requires alternative forms of regulation: indeed for ethical products such as “fair trade” regulation is provided by supranational non profit organizations. for trade in the general circumstances – there is internal supply in both countries – it is assumed that consumers are aware of the country of origin of the product and the traded product is a perfect substitute for the domestic one, except for consumers’ expectations about quality. supply reflects factor endowment and regulation, country a (developing) has a comparative advantage based on factor endowment. the values of kl and kh are the same for foreign and internal consumers; in country b the supply function is =s p p c ( ) (8) 191credence goods, consumers’ trust in regulation and high quality exports if consumers in b cannot distinguish between domestic production and imports, with trade expected quality becomes some average of the expectations about quality in b and a (as in bureau et al., 1998). low expectations about quality of imports from a will shift downward internal demand for a credence good in b, reducing consumer surplus and the demand for imports. lets’ consider instead the case where there is a country of origin label. for prices below the autarchy equilibrium price in b import demand from country b is = − −d p e k p c 1 ( ) i b (9) where eb is now depending on the expectations of consumers of country b about quality in region a. = − + d p e k c e k c 1 ( ) ( ) i b b (10) and the inverse function is ( ) ( ) ( ) ( ) = + − + p ce k e k c ce k e k c q b b b b (11) ( ) ( )+ ce k e k c b b is increasing in eb(k) (its first derivative in eb(k) is strictly positive for positive values of c). consumers in the importing country are likely to form expectations on the quality of imports on the basis of several factors. they may observe that there are imports which fail border quality inspection: the simplifying hypothesis adopted here is that the rate of failure is the same as the value of πl in equation (4). however, consumers in any importing country will probably be very uncertain about the conditions of supply for every exporting country and about the technology of border quality inspections (which can be limited and/ or variable). therefore consumers in b may, as implied by the literature discussed in paragraph 3, be influenced by a country of origin stereotype linked to the level of development. foreign consumers expect high quality imports from a in line with probability π δπ=h i h (12) δ≤ ≤0 1 and low quality imports from a in line with probability πi l π π π δ π= − = + −1 (1 )l i h i l h (13) 192 n. cuffaro, m. di giacinto here δ is increasing in the level of development – it is an index of reliability or positive country stereotype – hence (1–δ) is the negative stereotype, which amplifies the perception of low quality formed through the incidence of import control failures. hence both the actual effectiveness of internal regulation in a and the country stereotype influence expectations. equating import demand(10) and export supply (1bis minus 5) the equilibrium price is p* = 2βhβlcδ c βl +βh (1−λ) 2⎡⎣ ⎤⎦ βh +δ (1+βh )[ ]+βlβhδ (14) it is immediate to see that the equilibrium price p* strictly increasing in λ. moreover, an easy computation proves that the first derivative of p* with respect to δ is strictly positive, i.e. the equilibrium price p* is strictly increasing in δ, too. moreover, if a credence attribute is related to safety a change in λ may cause a sudden and more than proportional drop in consumers’ confidence, depending on the nature of the problem, causing severe damage to the sector involved, as illustrated by several major food safety crises during the last decades. in such crises the adverse effects on health and on consumers’ confidence were often amplified by a combination of poor communication about risks, mismanagement of crisis responses on the part of governments and private companies and by the media (world bank, 2005). the developing exporter whose internal regulation on product quality has recently been most scrutinized is undoubtedly china. china however is not an exporter that can be easily “abandoned” by importers. smaller countries could be much more damaged by a national stereotype problem15. indeed the world bank (2005) remarks that international buyers and consumers are likely to be more tolerant and patient with core and long-standing suppliers that have established a national image in which they have confidence, and conversely, that small countries and niche products are probably far more vulnerable to loss of markets and reputation in the face of safety or other quality problems. 5. conclusions there are several important implications of the trust and stereotype problem as represented here for an exporter, especially a developing country. first, low effectiveness of regulation causes failures in the market for high quality credence goods. second, there may be a trap of low levels of development/effectiveness of regulation and failure in high quality exports. therefore, strategies to increase the effectiveness of regulation, such as improving legislation and monitoring are crucial to improve export prospects. an important challenge 15 an illustration of the possible impact on a small exporter is given by the cyclospora crisis and the change in the us import demand for raspberry from guatemala to mexico, a case in which the industry never recovered (world bank, 2005); a similar sequence is quoted in chisik (2003) for colombia’s garment industry. 193credence goods, consumers’ trust in regulation and high quality exports is to increase the supply and quality of public standards and their associated monitoring mechanism. however, the literature discussed and the model suggest that if a developing country is not well prepared to achieve high levels of effectiveness of regulation and/or if there is a strong country of origin prejudice, linked to the level of development, standard setting and enforcement by external actors, such as supermarkets may be beneficial. this trust effect has been important for the growth of high quality food exports in many developing countries. if the standard on a credence attribute is established and monitored by separate, non national entities such as ngos, there obviously is no divergence between domestic and foreign consumers’ expectations about quality and the national prejudice problem may be bypassed. trust will be based on the ngo reputation and the perceptions consumers have about ngos incentives and efficiency in monitoring compliance with standards acknowledgements the authors thank giovanni anania and three anonymous referees for their comments and suggestions. references akerlof, g.a. 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(2004). labeling policies in food markets: private incentives, public intervention, and welfare effects. journal of agricultural and resource economics 29(1): 150-165. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(3): 269-296, 2012 economics of biofuels: an overview of policies, impacts and prospects giancarlo moschini1, jingbo cui, harvey lapan department of economics, iowa state university, ames, ia 50111, usa abstract. this paper provides an overview of the economics of biofuels. it starts by describing the remarkable growth of the biofuel industry over the last decade, with emphasis on developments in the united states, brazil and the european union, and it identifies the driving role played by some critical policies. after a brief discussion of the motivations that are commonly argued in favor of biofuels and biofuel policies, the paper presents an assessment of the impacts of biofuels from the economics perspective. in particular, the paper explains the basic analytics of biofuel mandates, reviews several existing studies that have estimated the economic impacts of biofuels, presents some insights from a specific model, and outlines an appraisal of biofuel policies and the environmental impacts of biofuels. the paper concludes with an examination of several open issues and the future prospects of biofuels. keywords. biodiesel, biofuel policies, ethanol, greenhouse gas emissions, mandates jel-codes. q2, h2, f1. 1. introduction the production and use of biofuels – ethanol and biodiesel – has experienced a remarkable growth over the last decade. according to the us energy information agency (eia) data, total world biofuel production increased nearly six-fold over the 20002010 period, from 315 thousand barrels per day to 1856 thousand barrels per day (figure 1). three countries/regions have been leading this development: the united states, brazil and the european union (eu). ethanol has been the leading biofuel in the unites states (from corn) and in brazil (from sugarcane), whereas biodiesel is the preferred biofuel in europe (rapeseed oil is the most important feedstock). whereas both types of biofuels have experienced a similar upward trend in recent years, ethanol remains the dominant type of biofuel. in 2010 ethanol accounted for three-fourth of world biofuel output (when expressed in energy equivalent units). ethanol production increases have been particularly impressive in the united states, where the annual rate of growth over the period 2000-2010 was more than double that in brazil. indeed, the united states surpassed bra1 corresponding author: moschini@iastate.edu. 270 g.c. moschini, j. cui and h. lapan zil as the world’s largest ethanol producer in 2006, and by 2010 it produced 57.1% of world’s ethanol output. whereas the development of this industry draws on roots established long ago, especially in brazil, its recent boom has been heavily influenced by critical policies that are promoting the production and consumption of biorenewables in general, and biofuels in figure 1. world production of biofuels (thousand barrels per day). panel a: ethanol; panel b: biodiesel 271economics of biofuels: an overview of policies, impacts and prospects particular. the rapid developments that have affected biofuel policies and biofuel industries have generated considerable debate, and a number of unresolved issues remain. the purpose of this paper is to provide a brief introduction to economic analyses of biofuels. needless to say, a comprehensive assessment of models and results in this setting goes beyond the scope of this paper, is probably premature at the current state of knowledge, and will therefore not be attempted. we start with a cursory review of the salient attributes of the biofuel industry growth, with emphasis on the policy drivers. given the primary importance of ethanol in the current biofuel industry, and the dominant role assumed by the united states in ethanol production, our presentation privileges us issues and policies, although some context is given for the other major players (brazil and the eu). this is followed by a discussion of the main economic questions that arise in the context of biofuels. specific attention is given to the basic analytics of biofuel mandates, the review of several existing studies that have estimated the market impacts of biofuels, the insights from a specific model, and an appraisal of biofuel policies and the environmental impacts of biofuels. the paper concludes with an examination of several open issues and the future prospects of biofuels. 2. boom of an industry a number of countries have experienced recent rapid increases in both supply and demand of biofuels, but the us ethanol sector stands out for the rate of growth experienced in the last decade, which has made the united states the leading producer of biofuels worldwide. in the united states ethanol has been produced from corn for more than three decades. the production trend depicted in figure 2 shows a slow albeit steady growth up to the beginning of the new millennium, and a markedly increased growth rate over the last decade. in 2011 ethanol output reached 13.9 billion gallons, an 80-fold increase relative to the 1980 level. throughout this period, a few federal policies have played a key role in the development of this industry (tyner, 2008). an initial stimulus came from the $0.40/gallon subsidy (technically, an excise tax exemption) established by the energy tax act of 1978. in various forms, the federal subsidy was active until it was allowed to expire at the end of 2011. it increased early on to reach $0.60/gallon with the tax reform act of 1984, but has been gradually adjusted downward since 1990. the subsidy (by then a blender tax credit) was last decreased to $0.45/gallon as of january 2009, and it was finally phased out at the end of december 2011. the desire to keep the subsidy for domestic production only motivated the introduction of a $0.54/gallon duty on ethanol imports (to supplement the out-of-quota bound ad valorem import tariff of 2.5%). this specific import duty also expired at the end of 2011. many other federal (yacobucci, 2012) and state programs exist that provide biofuel incentives. whereas the federal subsidy undoubtedly supported the earlier growth of the us ethanol industry, environmentally-led regulations also played an important role. in particular, the 1990 amendments to the clean air act introduced a 2% oxygen requirement for gasoline (duffield and collins, 2006). as concerns eventually arose as to the groundwater contamination potential of methyl tertiary butyl ether (mtbe), an early favorite oxygenate gasoline additive and octane enhancer, and following bans by some states (led by california), the gasoline refining industry began phasing out mtbe in the early 2000s. etha272 g.c. moschini, j. cui and h. lapan nol emerged as the most viable oxygenate substitute for mtbe, which fostered a valuable market niche for ethanol as a key gasoline additive.2 2.1. the us renewable fuel standard a major change in the policy context for us biofuels in general, and for ethanol in particular, was affected by the energy policy act of 2005 which first introduced a renewable fuel standard (rfs). the law established quantitative mandates for the minimum amount of biofuel to be included in the us transportation fuel. these quantitative mandates were expanded considerably by the energy independence and security act (eisa) of 2007 (schneff and yacobucci, 2012). the current rfs, after eisa, envisions the total amount of biofuel to increase to 36 billion gallons by 2022. to qualify as “renewable” for the purpose of the rfs, biofuels must achieve at least a 20% reduction in greenhouse gas (ghg) emissions, relative to the conventional fuel it replaces, based on a lifecycle analysis. the us environmental protection agency (epa) has determined that most biofuels (including corn-based ethanol) meet this carbon reduction require2 mtbe was completely phased out in 2007, after achieving a maximum use of about 3.3 billion gallons (of gasoline equivalent) per year over the period 1999-2002. owing to ethanol’s lower energy content, to replace that amount of mtbe would require about 4.7 billion gallons of ethanol. figure 2. evolution of the us ethanol industry note: mtbe quantity expressed in ethanol energy units. billion gallons $/gallons 273economics of biofuels: an overview of policies, impacts and prospects ment. furthermore, eisa specifies a number of nested requirements as to how the overall biofuel mandate is to be achieved. first, the largest category is that of “advanced biofuels,” which are defined as biofuels that achieve at least a 50% ghg emission reduction. this category, from which corn-based ethanol is excluded, encompasses a variety of biofuels, including sugarcane ethanol and biodiesel, and it is supposed to grow to 21 billion gallons by 2012. a portion of the advanced biofuel mandate is reserved for biodiesel, which is to achieve 1 billion gallons from 2012 onward. the largest portion of the advanced biofuel mandate is reserved for cellulosic biofuels, identified as reaching a ghg emission reduction of 60%, which is envisioned to grow to 16 billion gallons by 2022. from all this it follows that corn-based ethanol is implicitly capped to a maximum of 15 billion gallons (from 2015 onward). also, a portion of the advanced biofuel mandate is unspecified and can be met by a variety of biofuels (including sugarcane ethanol, biodiesel and cellulosic biofuels). this unspecified portion of the advanced biofuel mandate is to grow to 4 billion gallons by 2012. the epa is responsible for implementing the rfs. to do so, prior to each year the epa estimates the total volume of transportation fuel expected to be used. based on that, it computes the blending percentage obligations (the “standards”) that are needed to meet the quantitative requirements of the rfs. for the current 2012 year, these standards require a total blending ratio of 9.23% for total renewable fuel, with corn-based ethanol accounting for as much as 7.104% of the expected consumption of transportation fuel (schneff and yacobucci, 2012). this rfs percentage is then used to determine the individual renewable volume obligations (rvos) that pertain to “obligated parties” (e.g., blenders and fuel refiners). to enforce such quantitative obligations the epa has developed a renewable identification number (rin) system.3 the epa has authority to waive specific rfs requirements under certain conditions. it has now used this authority repeatedly for cellulosic biofuel, the mandated quantity of which has been reduced owing to the lack of sufficient commercial capacity for its production. indeed, the mandate for cellulosic biofuel is emerging as a very controversial feature of the rfs, as many question whether the ambitious cellulosic biofuel mandates are actually feasible. the fact that the epa has drastically reduced the cellulosic biofuel mandate for the first three years of its implementation is significant. for example, eisa originally envisioned 500 million gallons of cellulosic biofuels for 2012, but the epa has reduced this year’s requirement to 8.65 million gallons. it is apparent that the current size of the us ethanol industry owes much to the quantitative mandates of the rfs. the change in rate of expansion of the industry in the mid2000s was concomitant with the expectation of such mandates, formalized in 2005 (figure 1). the rfs provided a floor for the size of the market, and the announced schedule of increases of this mandatory market for ethanol provided a secure environment for the construction of new ethanol plants. as figure 3 shows, the expansion of the industry capacity not only mirrors the schedule of mandated production, but it also shows that the 3 rins are identifiers assigned to ethanol batches at production and must follow such ethanol through the marketing chain. rins are “separated” from ethanol only when the latter is blended with gasoline, and can then be used by obligated parties to show compliance with their volume obligations. blenders can meet the rin requirement by buying a sufficient amount of ethanol to satisfy their rvos or, alternatively, by buying rins from other obligated parties who are using more ethanol than what they are mandated (mcphail, westcott and lutman, 2011). 274 g.c. moschini, j. cui and h. lapan total plant capacity is converging to the maximum of the rfs mandate for corn-based ethanol (to 15 billion gallons by 2015). an unresolved issue concerning the prospects for the ethanol industry is the socalled “blend wall.” this concept refers to the possible limit posed on the use of ethanol in transportation fuel that arises because of regulations and current infrastructure. ethanol is blended with gasoline to be used as fuel, and most ethanol is used as a 10% component of gasoline, the so-called e10 gasoline blend available at retail refueling stations. the epa has actually recently approved use of blends up to 15% for vehicles produced in 2001 or later, but the so-called e15 blend is not yet being marketed pending the resolution of a number of practical issues. a higher-ethanol blend that can contain from 51% to 83% ethanol, the so-called e85 fuel, can be used by flexible-fuel vehicles. but the limited size of the flex-fuel vehicle fleet (about 3% of all us vehicles), the comparative scarcity of e85 pumps at refueling stations, and the apparent lack of price competitiveness of e85 fuel,4 are currently seriously limiting the effectiveness of this avenue for biofuel use expansion (celebi et al., 2010). finished motor gasoline consumption in the united states was estimated by the eia to be 8.736 million barrels per day in 2011 (about 134 billion gallons for the year), down about 6% from the pre-recession consumption level of 2007. given that the penetration rate of ethanol in gasoline consumption is thus effec4 because ethanol contains only about 70% of the energy of standard gasoline, e85 should sell at a substantial discount relative to gasoline or e10, which does not appear to be the case (doe, 2012). figure 3. growth of the us ethanol industry: capacity expansion 275economics of biofuels: an overview of policies, impacts and prospects tively capped at 10%, at current fuel consumption levels the blend wall is therefore at about 13.4 billion gallons of ethanol. 2.2. biofuels and biofuel policies in brazil and the european union outside of the united states, the most significant developments for biofuels have happened in brazil and the eu. brazil was for the longest time the world’s leader in biofuel production. significant investments in this sector began with the establishment in 1975 of proalcool, the brazilian ethanol program to promote domestic energy production as a response to the oil price boom of the 1970s. public support through various programs, including price controls, subsidized credit and lower taxes for ethanol-powered vehicles, played an important role initially, but price controls were phased out in 1999 (martinesfilho, burnquist and vian, 2006; miranda, swinbank and yano, 2011). brazilian ethanol is efficiently produced from sugarcane. most plants produce both sugar and ethanol (with some discretion on the mix of output), and can be energy self-sufficient when bagasse (crushed sugarcane stalks) is used to generate heat and electricity (valdez, 2011). two types of ethanol are produced and marketed: hydrous ethanol (a stand-alone fuel for dedicated engines) and anhydrous ethanol (to be blended with gasoline). brazilian ethanol production has grown steadily, to about 7.4 billion gallons in 2010, although brazil was overtaken by the united states as the world’s largest producer in 2006. the usefulness of dedicated-engine vehicles was severely tested in the late 1980s when hydrous ethanol suffered widespread shortages (a supply crisis brought about by the competitive pressure of cheap oil). flexible-fuel vehicles, introduced in 2003 and currently accounting for the vast majority of new vehicles sold in brazil, marked the beginning of renewed consumer interest in ethanol as transportation fuel. it also allows the government to influence ethanol consumption by means of mandatory blending percentage of anhydrous ethanol with gasoline. the current mandate specifies the range of 18% to 25% (the lower end of this range was revised down from 20% in 2011 to deal with tight ethanol availability). ethanol continues to benefit from various credit subsidies, and from preferential tax treatment at both federal and state levels. ethanol also enjoys a 20% import tariff, which was temporarily suspended in 2010 and 2011 (usda, 2011b). brazil’s support for biodiesel, produced mostly from soybeans, is more recent. there is a biodiesel mandate for blending with diesel fuel, currently set at 5%, and a biodiesel import tariff of 14%. in the eu the goal of increasing biofuel consumption has been a key ingredient in the pursuit of the kyoto ghg emission targets. the 5.75% target for the biofuel share of transportation fuel, set in 2003 and to be achieved by 2010, was not mandatory and apparently not very effective. the current overarching ambition, articulated in the 2009 energy and climate change package, is summarized by the so-called 20/20/20 objective: 20% ghg emission reduction, a 20% increase in energy efficiency, and a 20% share of renewable energy in the eu total energy consumption, all by 2020 (dixson-declève, 2012). a part of this eu legislation is the renewable energy directive, which establishes a target of 10% of renewable energy in transportation fuel use (european union, 2009). whereas the overall aspiration of the 20/20/20 objective is for the eu in the aggregate, the 10% target of renewable fuel for transportation is mandatory for all eu individual countries, although member states are granted flexibility on instruments and modalities to pursue the target. in order to 276 g.c. moschini, j. cui and h. lapan count towards the 10% goal, allowable biofuels have to meet certain sustainability criteria (usda, 2011a). for example, biofuels must achieve a 35% reduction in carbon emissions relative to fossil fuels, a saving that is to increase to 59% in 2017. the eu biofuel sector has experienced rapid growth over the last decade. biodiesel production in the eu relies on a variety of feedstock, the most important of which is rapeseed oil (more than half of the total). biodiesel production increased steadily from 1,065 mt in 2002 to 9,570 mt in 2010. but, according to the european biodiesel board, in 2011 the eu domestic production of biodiesel is expected to decline. of some note is the fact that the eu biodiesel capacity utilization has been low in recent years (about 44% in 2010 and 2011). ethanol production has also expanded, from 292 million liters in 2000 to 3,703 million liters in 2009. the favorite feedstock for ethanol production in the eu is sugar beet, but wheat, corn, rye and barley are also being used (usda, 2011a). as noted, biofuel imports are more important for the eu than for the united states or brazil. the import tariff for biodiesel is 6.5%, but it is considerably higher for ethanol: euro 102 per thousand liters for denatured ethanol and euro 192 per thousand liters for undenatured ethanol.5 apparently, most eu member states permit only use of undenatured ethanol for blending, thereby implicitly enforcing the higher tariff rate (usda 2011a). 3. why biofuels and biofuel policies? three reasons are routinely cited to rationalize biofuel production and biofuel support policies: energy security, environmental impacts, and support for agriculture and rural development. whereas fossil fuels have emerged as the dominant supply of energy since the industrial revolution, efforts to find other sources of energy have a long history. a major motivation is the fact that the stock of fossil fuels is fixed (although its size is uncertain) and we will therefore approach depletion at some point in the future.6 this consideration implies that fossil fuel prices should be expected to rise over time, on average, as per the insight of hotelling’s (1931) seminal contribution, although the outlook for the intermediate run suggests price levels well below recent record-high spikes (smith, 2009). alternative energy sources should, therefore, become competitive as time goes by. the global concern about the scarcity of oil is further heightened at national levels and articulated as an “energy security” issue, a reflection of the anxieties (especially in importing countries) brought about by recurrent shocks, price spikes and the general volatility of the oil market. in the united states, the goal to decrease dependence on foreign energy sources is routinely articulated at the policy level (council of economic advisers, 2008).7 5 at may 2012 average prices and exchange rates, these tariffs amount to about 22% and 42%, respectively, of the us ethanol price. 6 when that is likely to happen remains an open question, and indeed non-conventional petroleum sources might turn out to be the most competitive substitutes for conventional oil for many years to come (aguilera et al., 2009). for example, a major recent development in the united states is the drastic decline in the price of natural gas (at a 10-year low in february 2012, a mere 23.8% of the price level in october 2005), which is attributed to the shale gas boom enabled by the (controversial) use of modern hydraulic fracturing (fracking) technology. 7 petroleum accounts for the largest share of us energy sources (37% in 2010 according to the eia), and only about one third of it is domestically produced. 277economics of biofuels: an overview of policies, impacts and prospects what makes biofuels potentially very attractive among alternatives to fossil fuels is the fact that they are renewable, and they are liquid. similar to other renewable sources (e.g., electricity from hydro or wind), not only do they overcome the exhaustible nature of fossil fuels, but also hold the promise of mitigating ghg emissions. despite the fact that burning biofuels contributes to carbon emissions, just like burning gasoline, the carbon emitted is (at least partly) simply recycled (having been absorbed from the atmosphere by the feedstock used to produce biofuels). this environmental impact, and its potential benefits in the context of climate change concerns, was much touted earlier on as a justification for bioefuel support policies (rajagopal and zilberman, 2007), but, as discussed further below, has emerged as a controversial feature. the intermittent nature of many renewable energy production platforms, and the lack of simple ways to store renewable power, continue to be major drawbacks for renewable energy sources (heal, 2010). unlike other renewable energy sources, however, biofuels consist of a liquid fuel that can readily be used for transportation, and this fact is of paramount importance in explaining the enthusiasm for biofuels production. one of the obvious economic impacts of biofuels is to increase the demand for agricultural output, beyond the traditional uses for food and feed. the resulting price effects positively impact incomes and returns in agriculture, and thus biofuels can play a positive role in the longstanding perceived need (especially in developed economies) to support agriculture. in particular, there is interest in the potential of biofuels to help with rural economic development, by spurring investment and employment in rural areas with sluggish economic activity. the need for biofuel policies, although commonly taken as implied by the foregoing comments on the potential positive attributes of biofuels, from an economic perspective requires a distinct argument. ultimately, the case must be made that there exist market failures that impede a desirable allocation of resources, and that the policies under consideration actually improve upon the market outcomes that would otherwise prevail.8 externalities that affect the environment, quite clearly, should take center stage in this context. in particular, carbon emissions, which are thought to be a primary cause of global climate change, are presumably not optimally priced (despite a panoply of taxes and regulations that affect them), as evidenced by the stated objective of most countries to reduce their level. the pursuit of energy security can similarly be related to a number of possible market failures. repeated attempts to exercise market power by opec constitute prima facie evidence that the competitive conditions that may lead to optimal resource allocation are not met. the unequal distribution of oil wealth around the globe, and the nature of oil extraction and exploitation, make this resource prone to political control (tsui, 2011), which further weakens the efficiency of the market in this setting. a related issue is the rationalization of a portion of national defense expenditures to protect access to foreign oil, which can be sizeable (delucchi and murphy, 2008). ultimately, from a given country’s perspective, the national “energy security” argument ascribes benefits to reducing oil imports, which typically also bring about national welfare gains from terms-of-trade effects (lapan and moschini, 2012). 8 a somewhat higher standard would require these policies to be at least as effective, vis-à-vis the stated goals, as alternative energy policies that could be implemented instead. 278 g.c. moschini, j. cui and h. lapan 4. assessing the impacts of biofuels considerable work has been done to assess the impacts of biofuels. at its basic level, one of the attributes of the development of biofuels is to affect a fundamental change in the demand for agricultural output. traditionally, most of the demand for agricultural output has been driven by food demand, either directly or indirectly (e.g., feed used in animal production). at the aggregate (and global) level, the dynamics of agricultural markets has thus been driven by expanding demand stemming from a growing world population and changing diets (towards more animal protein, which require more resources to produce), and by an expanding supply due to productivity increases and some increases in arable land. the recent development of the biofuel industry adds a potentially significant non-food component to total demand. to illustrate how biofuels might affect agricultural and energy markets, and in view of the fact that mandates are emerging as perhaps the most important policy instrument in this setting, consider the following (extremely stylized) representation of how a biofuel mandate might work. agricultural output can be used to produce either food or biofuel. transportation fuel can come from gasoline (obtained from refining oil) and/or biofuel. there is a mandate which specifies that at least a given amount xb of biofuel must be used.9 assume further that biofuel and gasoline are obtained in fixed proportion from the agricultural output and oil, respectively, and that there are no other costs in the production of these two products. if the mandate is binding (i.e., without it the market provision of biofuel would be strictly less than xb,) then the competitive equilibrium in the agricultural and energy markets can be represented as follows: (1) sc(pc) = dc(pc) + xb / α (agricultural market equilibrium) (2) βso(po) + xb = df(pf) (fuel market equilibrium) (3) αpb = pc (zero profit condition in biofuel production) (4) βpg = po (zero profit condition in oil refining) (5) pf · df(pf) = pg[df(pf) – xb] + pb · xb (zero profit condition in fuel blending) where s denotes (upward-sloping) supply functions, d denotes (downward-sloping) demand functions, p denotes prices, and the subscripts are as follows: c = food, f = fuel, o = oil, g = gasoline and b = biofuel. furthermore, the production coefficient α denotes the quantity of biofuel produced by one unit of agricultural output, and the production coefficient β denotes the quantity of gasoline produced by one unit of oil (and units of measurement are presumed adjusted such that gasoline and biofuel have the same energy content and thus are perfect substitutes from the consumers’ perspective). without biofuel mandates (e.g., xb = 0), under the maintained assumption that no biofuel would be produced in such a case, the price of transportation fuel is simply the price of gasoline, which is in a constant relation with the price of oil and it is determined by the fuel market equilibrium: df(pg) = βso(βpg). similarly, the price of food is determined separately in the agricultural market equilibrium: dc(pc) = sc(pc). with a positive 9 following lapan and moschini (2012), the mandate here is represented in terms of a total quantity. obviously, the mandate could alternatively be cast as a fraction of fuel consumption, without affecting the conclusions to be discussed. 279economics of biofuels: an overview of policies, impacts and prospects and binding biofuel mandate xb > 0, the price of food is still determined by the equilibrium condition (1), and clearly dpc / dxb > 0 the price of biofuel is linked to the price of food by (3), and thus dpb / dxb > 0. given that the mandate xb > 0 is exogenous and binding, and the price of biofuel is determined in the agricultural market, the conditions in (2), (4) and (5) determine the prices of blended fuel and of gasoline/oil. the implication of the biofuel mandate for the energy market is that of reducing the price of gasoline/oil, dpb / dxb < 0. note that a mandate has simultaneously two distinct effects: it is a subsidy to biofuel and a tax on gasoline. the impact of the mandate on the blended fuel price, on the other hand, is indeterminate. one should expect that increasing a binding mandate raises the price of fuel (which, as implied by (5), is a weighted average of the prices of gasoline and biofuel), and thus reduces total consumption. but if the derived supply of ethanol is more elastic than the derived supply of gasoline, then over some domain an increasing ethanol mandate may in fact lower the price of fuel and raise total fuel consumption (de gorter and just, 2009b; lapan and moschini, 2012). the foregoing makes it clear that, as a consequence of biofuel production expansion, agricultural prices rise and agricultural output expands. the general belief is that the relevant supply function is rather inelastic, and so the price effects could be sizeable and larger than the output effect. in the longer run, a number of market adjustments are set into motion. supply can expand because of new investments in agriculture, perhaps more land brought into production, and increased productivity by renewed r&d efforts. all of that can neutralize some of the price increase effects, but the fact that biofuels essentially shift rightward the total demand for agricultural output leaves little doubt as to the nature of the final effects. this formulation, of course, is too simplistic to provide a sufficient articulation of the important economic impacts of biofuels in real-world settings. the demand and supply in the agricultural and energy markets are affected by many other policies beyond biofuel mandates (e.g., fuel taxes, biofuel subsidies, farm support programs, trade restrictions, environmental regulations), which impact resource allocation at the national level as well as trade flows. also, the type of feedstock used in biofuel production will matter, as will the geographical distribution of biofuel production. to get a firmer grasp on the estimated economic impacts of biofuels, including environmental and welfare effects, more comprehensive models are desirable. numerous modeling efforts are now available that study various features of biofuel production, and the key policies believed to be responsible for the expansion of the biofuel sector. although a simple taxonomy is perhaps reductive, roughly speaking they differ as to whether they adopt a computable general equilibrium (cge) approach or a partial equilibrium (pe) approach. although other cge models dealing with bioenergy exist (kretschmer and peterson, 2010), a modeling framework that has been used extensively in this setting is provided by the global trade analysis project (gtap), originally a cge model of agricultural trade. a series of papers have extended and adapted this model to make it suitable for analyzing biofuels, including the addition of a module that separates global land use in several agro-ecological zones. some of the most significant published gtap studies are summarized in the appendix. not surprisingly, the specific results that one gets depend on the orientation of each modeling endeavor. in general it is found that: rising oil prices were an important element in the “biofuel boom,” but the role of various support poli280 g.c. moschini, j. cui and h. lapan cies has also been critical; the impact of the biofuel expansion on composition of agricultural production is significant, especially the increase of corn acreage in the united states and the increase of oilseed area in the european union; the cumulative nature of us and eu policies matters considerably and the analysis of these policies should be done jointly rather than separately; land-use changes are not insignificant with crop cover rising at the expense of pastureland and commercial forests; the policy of biofuel mandates reduces the transmission of price volatility from the energy sector to the agricultural sector, but might exacerbate the impact of agricultural supply shocks; explicitly incorporating by-products in the analysis is important and can considerably change the magnitude of some variables of interest; although the estimated indirect land use change (iluc) is lower than that suggested by other studies, the carbon benefits of biofuels relative to gasoline may be negligible. one of the alternatives to a cge approach is provided by pe, multi–commodity, multi-country/region models of the agricultural sector. one example is the food and agricultural policy research institute (fapri) model utilized by some analysts at iowa state university and the university of missouri. some studies that rely on versions of the fapri model are summarized in the appendix. one of the results is to emphasize the role of oil prices in determining the development and long-run size of the ethanol industry: at oil prices in the range of 60-75 $/barrel, the corn-based ethanol industry is forecasted to grow to beyond 30 billion gallons/year. a well-known application of this model was the estimation of iluc effects (searchinger et al., 2008), which argued that corn-based ethanol actually worsens ghg emissions. but, in another application, dumortier et al. (2011) make the case for much lower levels of carbon emissions due to iluc effects. a number of other studies are available, both for cge and pe; without any claim of an exhaustive coverage, some of these studies are included in the appendix as well. cge models are attractive because they can link the agricultural sector to the rest of the economy, and account for feedback effects. gtap models also link domestic agricultural sectors across countries by trade and in principle can represent bilateral trade flows. gce models are also attractive for studying iluc effects because they typically model competition for land across alternative uses in an explicit fashion. conversely, pe models often rely on reduced-form specifications that sacrifice the structural internal consistency of cge models in exchange for more disaggregated coverage in the product space, and sometimes a more detailed representation of the policy instruments at work. evaluating results across models with such structural differences is inherently very difficult. in any event, even a casual comparison of the results summarized in the appendix would suggest that they are hardly definitive. for example, pe extrapolations of the future size of the us corn-based ethanol industry appear suspect. another issue that has been noted is that cge models seem to predict much lower agricultural price effects, due to biofuel expansion, than pe models (kretschmer and peterson, 2010), and indeed such price effects are often not explicitly discussed in the gtap models reviewed in the appendix. 4.1. appraising biofuel policies assessing the economics of biofuels cannot be divorced from the assessment of the policies that have been critical to support the expansion of this industry. as noted earlier, 281economics of biofuels: an overview of policies, impacts and prospects a myriad of policies have been implemented at various junctures and/or jurisdictions to support biofuels, and an assessment of biofuel policies per se might be desirable. a recent comprehensive review that focuses on policy evaluation is provided by de gorter and just (2010). the specific normative evaluation of policy tools, of course, depends critically on the welfare criterion that is used, which in turn depends on the market failures that are presumed. as noted earlier, multiple objectives/market failures are routinely invoked to rationalize biofuel policies, but their explicit characterization is often missing in empirical studies. for example, large partial equilibrium commodity models are notoriously ill suited for welfare evaluation. some useful conclusions, however, can be gotten from conceptual studies and parameterization of smaller models. one result of some interest is that outright subsidy to biofuel production, such as the blending tax credit implemented in the united states until its expiration at the end of 2011, have questionable impacts. in particular, de gorter and just (2009b) show that the introduction of such a subsidy in a setting where the mandate is binding leads to a decrease in the price of fuel (i.e., the blend of gasoline and ethanol) and thus acts as a consumption subsidy. ceteris paribus, this effect increases consumption, which tends to increase (rather than reduce) ghg emissions (one of the stated objectives of biofuel policies). a particularly interesting result in this setting emerges from the comparison of a subsidy-only policy (a price instrument) and a mandate-only policy (a quantity instrument). lapan and moschini (2012) show that, perhaps counter-intuitively, the optimal ethanol mandate yields higher welfare than the optimal ethanol subsidy. thus, the equivalence between a price instrument and a quantity instrument that one typically expects in competitive models without uncertainty does not apply here. the main reason is that, as shown in lapan and moschini (2012), a biofuel mandate, per se, is fully equivalent to the combination of an ethanol subsidy and a gasoline tax that are revenue neutral. in the typical setting of interest for biofuel policies, where multiple objectives are relevant, these two effects are both desirable. thus, in a second-best context where a full set of instruments is not available, biofuel mandates are preferable to biofuel subsidies. a distinct role for production mandates is to stimulate investments in the construction of biofuel production plants by providing assurance as to the size of future demand. the growth of the us corn ethanol capacity depicted in figure 3 certainly lends support to this perspective. to be effective in this role, however, mandates need to be credible, and this credibility might be called into question by the possibility of waivers envisioned by current us policies. a case in point is the rfs mandate for cellulosic ethanol has now been modified and largely waived for three consecutive years. some implications of a waivable mandate in this setting are explored by miao, hennessy and babcock (2011). insofar as a relevant objective of biofuel policies is to support farm incomes, a critical element relates to how they interact with pre-existing agricultural support policies. this is a challenging task because it entails comparing second-best policy instruments, which are typically difficult to rank from a welfare perspective, and because of the many and disparate potentially active farm policies that would need to be explicitly represented in a coherent model. an earlier study by gardner (2007) concluded that the us ethanol subsidy reduces the deadweight loss of farm programs that are contingent on corn price (e.g., the loan deficiency payment). de gorter and just (2009a), by contrast, find that the annual rectangular deadweight costs – which arise because they conclude that ethanol would not 282 g.c. moschini, j. cui and h. lapan be commercially viable without government intervention – dwarf in value the traditional triangular deadweights costs of farm subsidies. 4.2. some insights from a specific model the study by cui et al. (2011), which generalizes in a number of significant ways the framework outlined in equations (1)-(5), can help to provide some insights into the modeling of biofuel policies and the market impacts of biofuels for the case of the united states. the root of this model is provided by the theoretical analysis of lapan and moschini (2012) (an earlier version was presented in lapan and moschini, 2009), who build a simplified general equilibrium (multimarket) model of the united states that links the agricultural and energy sectors of this country to each other and to the rest of the world. among other things, the model makes the oil price endogenous (in addition to corn price), thereby relaxing an undesirable feature of many models in this setting that treat the oil price as exogenous. in cui et al. (2011) this model is extended to account for petroleum by-products and it is parameterized to make it suitable for calibration and simulation. the model’s components are: us corn supply equation; us food/feed corn demand equations (exclusive of ethanol use), rest of the world (row) demand for corn imports, us oil supply (production) equation, us fuel demand equation, us petroleum by-products demand equation, row oil export supply equation. the model treats the ethanolproducing segment as a competitive industry with free entry, with ethanol production represented by a fixed-proportion technology and with explicit recognition of valuable byproducts arising from this process (e.g., distillers dried grains with solubles). refining of oil is also represented as a competitive industry with oil converted (in fixed proportions) into unblended gasoline and other petroleum by-products (e.g., heating oil). gasoline is blended with ethanol to produce “fuel.” having accounted for the fact that ethanol and gasoline have different energy content per volume unit (one gallon of ethanol is equivalent to 0.69 gallons of gasoline), ethanol and gasoline are treated as perfect substitutes to satisfy fuel demand.10 the welfare function includes an accounting of the externality costs of carbon emissions, from the point of view of the united states, and also accounts for how changes in the terms of trade impact us welfare. upon calibration of the parameters, to reflect consensus on production coefficients and elasticity estimates, the model is well suited to evaluate the positive and normative impacts of a variety of policy interventions related to biofuels. the policy scenarios considered are: (i) the status quo characterized by the (then active) blending subsidy for ethanol and fuel tax (on both ethanol and gasoline); (ii) the laissez faire (no taxes nor subsidies); (iii) the no-ethanol policy (tax on fuel but no subsidy for ethanol); (iv) the first-best policy combination, which in this setting consists of oil import and corn export tariffs and a carbon tax; (v) the second-best policy consisting of optimally chosen fuel tax and ethanol subsidy; (vi) a restricted second best in which the only active policy instrument is the ethanol subsidy; and (vii) a restricted second best in which the only active policy instrument is the ethanol mandate. 10 the model does not consider trade in ethanol on the presumption that the $0.54/gallon ethanol import duty, in place till the end of 2011, effectively acted as a prohibitive tariff. 283economics of biofuels: an overview of policies, impacts and prospects among the estimated market impacts, it is found that the status quo policy leads to an 11.6% increase in corn production and a 53% increase in the price of corn (relative to the no ethanol policy benchmark). the corn ethanol industry also owes its very existence to status quo policies: in the no ethanol policy scenario the industry virtually disappears. but it is important to note that, in the laissez faire scenario, the model shows a sizeable ethanol industry, more than half the size of the current industry. this result highlights a critical feature of the institutional setting. that is, the current fuel tax is levied in volume terms (about $0.39/gallon when accounting for both the federal tax and the average of state taxes) and thus, when viewed in energy terms, it is implicitly much higher for ethanol than it is for gasoline (e.g., as modeled, the current fuel tax is effectively a $0.39/gallon tax on gasoline but a $0.57/gallon tax on ethanol). given such a volume tax on fuel, an ethanol subsidy (or an ethanol mandate) is desirable to level the playing field, even absent any environmental advantage that ethanol might have relative to gasoline. two of the reasons invoked to rationalize biofuel policies are to ameliorate the environment (by reducing co2 emission) and to lessen us dependence on foreign oil. with respect to the latter, it is found that the first-best solution (relying on an optimal import tariff) would reduce oil imports by about 20% relative to the laissez-faire (but the status quo policy only reduces oil imports by 4.6% relative to the no ethanol policy). as for the impact on emissions, firstand second-best policies are essentially equivalent, both reducing carbon emission by 8.5% relative to the laissez-faire scenario. but the status quo situation actually leads to more emissions than the “no ethanol policy” scenario. as noted by de gorter and just (2009b), the ethanol blending subsidy ends up working as a consumption subsidy for final consumers, which, ceteris paribus, leads to an expansion of fuel consumption that translates into higher (not lower) carbon emissions levels. the welfare impacts of the various policy scenarios considered are also illuminating and show that all of the ethanol support policies considered improve us social welfare (relative to both the laissez faire and the no ethanol policy benchmarks). a major reason for this result is due to the favorable terms of trade effects of the various policies; because the united states is a “large country” in the oil and corn markets, policies that reduce oil imports and/or corn exports have price effects that are beneficial to the united states. in addition to this overall welfare impact, the analysis can shed some light on the distributional effects associated with ethanol support policies. to consider this issue in more concrete terms, figure 4 illustrates the components of the welfare effect of the status quo as compared with the no ethanol policy scenarios. not surprisingly, it turns out that there are clear winners and losers from these policies. the biggest beneficiaries of the status quo ethanol policies are corn producers and fuel consumers. corn producers benefit from the increased price of corn (which however penalizes users of corn for food and feed), and fuel consumers benefit from the reduced equilibrium gasoline/fuel price induced by the ethanol subsidy. users of petroleum by-products experience a welfare loss because the price of petroleum by-products increases with the subsidized increase in ethanol use (this arises because less oil is refined, which, owing to the fixed proportion technology, tightens the supply of these by-products). figure 4 also illustrates the point mentioned earlier, that the subsidization of ethanol production in the status quo scenario actually worsens the externality of carbon emissions. it also shows that the monetary value of the externality amelioration is minor, compared with the other welfare effects. 284 g.c. moschini, j. cui and h. lapan 4.3. environmental impacts a major motivation for biofuels has been the expectation that they might provide a cleaner source of transportation fuel. on an energy equivalent basis, biofuels typically produce lower ghg emissions relative to gasoline, although major differences exist across types of biofuels and processes. for example, the performance of corn-based ethanol is sensitive to the energy used to power ethanol refineries (wang, wu and huo, 2007). a necessary condition for a net positive environmental impact is that biofuel production, viewed from the perspective of life cycle analysis (lca), yields more energy than the fossil energy used in its production.11 among current (so called “first generation”) biofuels, the evidence from a vast literature available suggests that brazilian ethanol produced from sugarcane leads to the greatest carbon savings. to evaluate the environmental footprint of biofuels, lca takes a system-wide approach that is meant also to account for all the energy/carbon associated with the production of inputs used in biofuel production (miranowski, 2012). but the traditional implementation of lca essentially assumes that one unit of biofuel (in energy equivalent terms) substitutes for one unit of fossil fuel, which is unrealistic from a number of reasons (rajagopal, hochman and zilberman, 2011). the general concern is that of “carbon 11 this attribute has actually been disputed by some for the case of corn-based ethanol, but it is now generally accepted as true (farrell et al., 2008). figure 4. welfare effects of the status quo ethanol policy (changes relative to “no ethanol policy”), $ billions (source: cui et al., ajae 2011) note: c.s. = consumer surplus; p.s. = producer surplus. 285economics of biofuels: an overview of policies, impacts and prospects leakage,” whereby reduced emissions in an activity or country are partly or wholly offset by increased emissions elsewhere. a specific instance in our context relates to “indirect land use” effects that arise because of market adjustments to large-scale biofuel production. for example, diverting corn to ethanol production in the united states might bring new marginal land into production elsewhere because of the increased overall demand for agricultural output. such indirect land use change (iluc) effects can dramatically change the assessment of the environmental impacts of biofuels (searchinger et al. 2008; fargione et al. 2008). whereas there is no doubt that they are plausible, accounting for iluc effects is a non-trivial matter. dumortier et al. (2011) show that the results of the model used by searchinger et al. (2008) can be very sensitive to parametric and model assumptions. several studies in this area have gravitated toward the use of cge analysis. using gtap, hertel, tyner and birur (2010) find that, to jointly meet the biofuel mandate policies of the united states and the eu, coarse grains acreage in the united states rises by 10%, oilseeds acreage in the eu increases dramatically, by 40%, cropland areas in the united states would increase by 0.8%, and about one-third of these changes occur because of the eu mandate policy. the us and eu mandates jointly reduce the forest and pasture land areas of the united states by 3.1% and 4.9%, respectively. hertel et al. (2010), by a fuller accounting of market-mediated responses and by-product use, find lower estimates of iluc effects, about one-fourth the value estimated by searchinger et al. (2008). still, the estimated iluc effects are enough to completely eliminate any positive carbon emissions effect from corn-based ethanol. one consequence of the emerging fuller picture on the environmental effects of biofuel production is the incorporation of iluc effects into regulatory standards. for example, the epa accounts for international iluc in assessing the ghg emissions reduction of various biofuels to meet the rfs requirements (epa, 2010). according to the epa, corn ethanol still achieves a 21% ghg reduction compared to gasoline and thus meets the minimum requirements established by the rfs; also, sugarcane ethanol qualifies as an advanced biofuel since it achieves an average 61% ghg reduction compared with baseline gasoline. although the eu has acknowledged the desirability of including iluc into its biofuel sustainability standards, this has not yet been implemented. environmental issues related to iluc are not limited to the potential for indirect carbon emissions caused by biofuel expansion. more generally, the concern is that a massive expansion of biofuel production is bound to put additional stress on limited global land and water resources. the resulting intensification of production practices, and the stimulus to use marginal land, may lead to increased soil degradation, increased pollution, and may have adverse consequences for wildlife habitat and biodiversity. 5. conclusion and prospects for the future if the rapid development that biofuels have enjoyed over the last decade is to be sustained, several challenges will have to be overcome. in the united states, expansion of the corn-based ethanol beyond the limit envisioned by the rfs mandate (15 billion gallons by 2015) appears unlikely, and the dynamics of ethanol-plant capacity construction (figure 3) is consistent with this interpretation. a related but distinct issue is represented by the so-called “blend wall,” where the current infrastructure might make it difficult to increase 286 g.c. moschini, j. cui and h. lapan the fraction of ethanol in transportation fuel beyond 10% in volume terms. because this blending ratio is essentially already achieved by current production levels, it is clear that this blend wall is an issue that needs resolution if the contributions of advanced biofuels (including cellulosic ethanol) are to meet the ambitious targets set out by the rfs mandates. and this, it seems, is not the greatest challenge facing so-called “second-generation” biofuels.12 commercial production of second generation biofuels is lagging behind the (perhaps overly optimistic) expectations embedded in the rfs mandates. critical technological feasibility issues are still being sorted out, and the (efficient) solution to the logistical challenges of the feedstock provision, and the scalability of pilot plants into commercially viable entities, remains fraught with challenges. after a careful review of the state of knowledge on the key production economic issues of second generation biofuels, carriquiri, du and timilsina (2011) conclude that the cost of cellulosic ethanol is two to three times larger than the price of gasoline, and the cost of biodiesel from microalgae many times higher still. renewed interest in the next generation of biofuels is also justified by a number of controversies that continue to surround the development of biofuels. two of the major are: (i) the actual contribution that biofuels can realistically have towards ameliorating ghg emission vis-à-vis climate change concerns; and, (ii) the impact of large-scale biofuel production on food prices, i.e., the “food vs. fuel” debate. as discussed earlier, the contribution of biofuels to reducing carbon emission, while positive, is limited, as is the ability of biofuel to significantly reduce the use of fossil fuels. in particular, some argue that biofuels are inherently ill-suited for that purpose. jaeger and egelkraut (2011), for example, conclude that, for the purpose of reducing fossil fuel use and ghg emissions, us biofuels are 14-31 times as costly as other alternatives strategies (such as gas taxes and promotion of energy efficiency. when considering biofuel policies from the perspective of carbon emissions, the recurring gold standard of an ideal policy response is a “carbon tax.” to make such a policy operational, of course, one would need to assign a monetary value to the social cost of carbon pollution.13 it is useful to note, at this juncture, the considerable uncertainty that surrounds this parameter. tol (2009) surveys 232 published studies and finds that the mean of these estimates corresponds to a marginal cost of carbon emissions of $105/ tc (metric ton carbon) (this is equivalent to $28.60/tco2). but the standard deviation of these estimates is rather large: $243/tc ($66/tco2) (all of these figures are expressed in 1995 dollars). the us national highway traffic safety administration (nhtsa), in calculating their proposed corporate average fuel economy (cafe) standard, relies on an earlier survey (tol, 2008) and pegs the global social cost of carbon at $33/tco2 (in 2007 dollars) (nhtsa, 2009). a lower social cost of carbon is presumed by parry and small (2005), who use $25/tc (expressed in year 2000 dollars), which is equivalent to $6.8/tco2 (but they also account for external congestion costs of 3.5¢/mile, and an exter12 such biofuels include cellulosic ethanol made from lignocellulosic biomass from crop residues (e.g., corn stover) and whole plant biomass from would-be specialized energy crops (e.g., switchgrass, miscanthus, and fastgrowing trees such as poplars). biodiesel from alternative feedstock, such as lipids from microalgae, are also considered promising avenues for second generation biofuels. 13 the recently proposed new eu energy tax directive (voted down in april 2012), for example, sought to include a common component of euro 20/tco2 in the tax rate of all energy products. 287economics of biofuels: an overview of policies, impacts and prospects nal accident cost of 3¢/mile). the widely cited “stern review” (stern, 2007) provides the much higher estimate of approximately $80/tco2. this is mostly due to the choice of a low discount rate for future economic damage from climate change, an assumption questioned by some. by using a more conventional discount rate, hope and newbery (2008) find that the (global) carbon cost from the stern report could be reduced to the range of $20-$25/tco2. another influential study (nordhaus, 2008) suggests an estimate of about $8/tco2. quite clearly, much remains to be understood in this setting. although this is not a problem unique to biofuel, it is nonetheless central to the design of firstand second-best biofuel policies. harnessing the energy of the sun by means of crops, which ultimately is what is attempted by biofuels, has to deal with an overarching constraint: land is scarce, and land used for biofuel production is not available for food production. as discussed earlier, one of the expected impacts is a rise in food prices. whereas such price increases might be tolerable for developed economies, indeed can be viewed as quite consistent with a longstanding commitment to support the agricultural sector, this pecuniary externality clearly has distributive implications that might be undesirable for less developed countries. in a world where 850 million people are deemed undernourished (fao, 2011), the price of food is, objectively, a serious obstacle to food security for a significant share of the world’s least affluent population. how much responsibility one ought to put on biofuels in this setting depends on their actual impact on food prices, an issue that is somewhat unresolved. qualitatively, as discussed earlier, it is clear that food prices will increase, and it is believed that biofuel production may impact food prices more than energy prices.14 concerns about the food-price effects of biofuel expansion were heightened by the commodity price hike that culminated in the 2008 food crises. although no single nor simple explanation for this phenomenon (and similar price booms experienced in the past) appears possible (carter, rausser and smith, 2011; wright, 2011), it seems clear that a sizeable increase in the diversion of basic staple commodities to biofuel production, that materialized over a short period of time, had the potential to have a very significant effect on commodity prices, especially at a time when the stocks (relative to production and demand) had been running at historically low levels (wright, 2011). timilsina and shresta (2011) review a number of recent studies that have tried to estimate the likely impact of biofuel expansion on commodity and food prices. although the magnitude of estimated price effects turns out to fall in a fairly wide range and, not surprisingly, to also depend on assumptions and modeling framework (e.g., partial equilibrium models appear to suggest larger price effects that cge models), there is considerable evidence of a significant impact of biofuel production on commodity and food prices. as the full extent of biofuel mandates in the united states, eu and elsewhere is realized over the next few years, and global demand rebounds from the great recession, earlier concerns about the food price effects of biofuel and their implications for food security (runge and senauer, 2007) might resurface in a heightened fashion.15 14 for example, in the simulation results presented in cui et al. (2011), the status quo scenario relative to the no ethanol policy scenario shows a 53% increase in the corn price and only a 6% decrease in the gasoline price. 15 this paper was written in may 2012, before a major drought materialized in the united states. the steep commodity price increases, triggered by the expected harvest shortfall caused by the 2012 drought, underscore the importance of the issues briefly outlines in this paragraph. 288 g.c. moschini, j. cui and h. lapan the role that international trade can play in the path toward fulfilling biofuel mandates (in the united states, the eu and elsewhere) remains to be clarified. comparison of production and consumption data of total biofuels from the eia suggests a fairly sizeable but still somewhat limited extent of international trade in biofuels. for example, for the two most recent years with available data (2009-2010), the eia shows net exports from brazil (about 16% of production), net exports from the united states (about 2% of production), and net imports in europe (about 23% of consumption). for the eu these figures might need to be supplemented by the consideration that a large fraction of feedstock used in biodiesel production (either as vegetable oils or as oilseeds that are crushed in the eu) is imported. a critical consideration in that setting refers to the impact of sustainability standards. for example, the fulfillment of the unspecified portion of advanced biofuels (i.e., apart from cellulosic biofuels and biodiesel) of the rfs mandates in the united states, which is set to reach 4 billion gallons by 2022, may well have to rely on sugarcane ethanol produced in brazil. yet the prospect of the united states importing sugarcane ethanol from brazil to meet low-carbon standards, while exporting corn-based ethanol to brazil (as observed in 2011), is perplexing. also, lack of international harmonization of sustainability standards, and lack of uniform guidelines and institution for the certification and enforcement of these standards, holds the potential for such standards to become serious impediments to trade. the plethora of biofuel programs and subsidies can easily create situations ripe for trade conflicts (de gorter, drabik and just, 2011). still, insofar as reducing carbon emission is a global problem, the contribution of biofuels would be maximized by efficient production and full exploitation of comparative advantages. acknowledgements an earlier version of this paper was presented at the 1st aieaa conference ‘towards a sustainable bio-economy: economic issues and policy challenges’. 4-5 june, 2012, trento, italy. this research was partially supported by the u.s. national institute of food and agriculture through a policy research center grant to iowa state university. references aguilera, r.f., eggert, r.g., lagos, c.c.g. and tilton, j.e. 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(2012). biofuels incetives: a summary of federal programs. crs report for congress r40110, congressional research service: washington, d.c., january 11. appendix in this appendix we summarize some contributions that have studied the economics of biofuel with emphasis on their market impacts. the purpose is to allow a quick comparison of the models’ methodology, key questions, and conclusions. the studies covered here are not meant to provide an exhaustive list of relevant studies to date: some are omitted for space reasons, others are omitted because they are discussed elsewhere in the text. for another comparative review of some recent biofuel models, see fonseca et al. (2010). a1. some gtap studies dealing with biofuels 1. birur, hertel, and tyner (book chapter, 2009) research questions: study the impacts of biofuel growth in the eu and us (driven by the oil price shock, replacement of mtbe by ethanol, and ethanol subsidy) on world food markets main conclusions: the share of us corn utilized by the biofuel sector more than double from 16% in 2006 to 38% in the projected 2010. trade balance for petroleum products improves by about $6 billion which is largely offset by deterioration in us agricultural trade balance. the us corn acreage rises by 10%, the eu oilseeds acreage rises by 12%. 2. keeney and hertel (ajae, 2009) research questions: study the importance of the acreage response and bilateral trade 293economics of biofuels: an overview of policies, impacts and prospects specification in predicting global land use change. main conclusions: nearly 30% of the five-year output response to a shock of marginal ethanol demand is expected to come from yield gains. 3. hertel, tyner, and birur (energy journal, 2010) research questions: study the interacted impacts, on global markets, of biofuel mandates in the us and eu (15 billion gallons of ethanol by 2015 in us and 6.25% of total fuel as renewable fuel by 2015 in eu). main conclusions: us oilseed output falls by 5.6% in the presence of a us-only mandate, due to the dominance of ethanol in the us biofuel; when the eu policies are added, us oilseed production actually rises. us exports of coarse grains are reduced by nearly $1 billion. eu exports of coarse grains, oilseeds and other food products are sharply reduced. coarse grain acreage in the us rises by 10%, oilseed acreage in the eu increases dramatically by 40%. cropland area in the us increases by 0.8%, and about one-third of these changes occur because of the eu mandate. the us and eu mandates jointly reduce the forest and pasture land areas of the united states by 3.1% and 4.9%, respectively. us, eu and world welfare decline. 4. taheripour et al. (biomass and bioenergy, 2010) research questions: study the effects of incorporating biofuel byproducts into the gtap framework by comparing the impacts of the us and eu biofuel mandate policies with and without the byproducts. main conclusions: the model with by-products shows smaller changes in the production of cereal grains and larger changes for oilseeds in the us and eu, and the reverse for brazil. prices change less in the presence of by-products, e.g., coarse grains in the us increase 13.0% with the presence of by-products instead of 19.8% in the absence of by-products. the model with and without byproducts predicts sharply different changes in trade flows. incorporating byproducts alters the land use consequences of the joint us and eu mandates, e.g., coarse grain area in the us increases 7.1% when accounting for byproducts instead of 11.3% (without byproducts). with byproducts, less of forest and pasture land are converted to cropland. 5. beckman et al. (erae, 2012) research questions: study how energy price volatility transmits to commodity prices, and how energy policy affects the volatility of agricultural commodity prices in the presence of us and eu biofuel mandates. main conclusions: the imposition of the us and eu mandates reduces the susceptibility of agricultural markets to energy volatility, but increases the supply-side impacts. us markets are more vulnerable than the eu to supply-side shocks due to their product-specific mandates. 6. hertel et al. (bioscience, 2010) research questions: provide a comprehensive analysis of market-mediated changes in global land use in response to the expansion of us-grown maize for ethanol. main conclusions: the associated ghg release due to indirect land use change (iluc) is 294 g.c. moschini, j. cui and h. lapan 800 grams of co2 per megajoule (mj), which is roughly a quarter of the estimate of emissions reported by searchinger et al. (2008). a2. some card/fapri studies dealing with biofuels 7. elobeid et al. (agbioforum, 2007) research questions: study the long-run potential, and impacts on food markets, of corn ethanol. main conclusions: given an exogenous crude oil price of $60 per barrel, the estimated long-run corn price is $4.05 (a 58% increase). the corn-based ethanol production reaches 31.5 billion gallons per year, with corn area and total corn production increasing by about 20%. soybean price declines. 8. tokgoz et al. (review of agricultural economics, 2008) research questions: study the impact of drought and oil price spikes on ethanol production. main conclusions: the projected long-run ethanol production increases by 55% in response to an increase in oil prices of $10 per barrel. the increase of corn acreage is partly offset by a decline in the acreages of soybean and wheat. a us crop shortage similar to the 1988 drought raises the price of corn by 44% (because the rfs mandate prevents ethanol production from dropping substantially). 9. searchinger et al. (science, 2008) research questions: study the environmental impacts of biofuels through emissions from iluc. main conclusions: accounting for emissions from iluc, corn-based ethanol, instead of producing 20% savings, nearly double ghg emissions over 30 years and emits 177 grams co2 per mj. the portion of these emissions due to the iluc is around 104 grams co2 per mj. 10. hayes et al. (journal of agricultural and applied economics, 2009) research questions: study the impacts, on agricultural markets, of four different scenarios: high energy price, high energy price without biofuel tax credits, low energy price without biofuel supports, and high energy price with tax credits but without e-85 bottlenecks. main conclusions: the baseline scenario with the price of crude oil set exogenously at $75 per barrel projects 32.9 billion gallons of corn-based ethanol production by 2022. the high energy price scenario (price of crude oil set at $105 per barrel) increases ethanol production by 50% relative to the baseline. removing the biofuel tax credits from the high crude oil price scenario leads to an ethanol production decline of 35%. in the scenario with a low crude oil price ($75) and no biofuel supports, ethanol production drops by 72% relative to the baseline. in the last scenario, corn-based ethanol production reaches 39.8 billion gallons. 11. fabiosa et al. (land economics, 2010) research questions: study global land-allocation effects of biofuels by contrasting two exogenous shocks on ethanol demand: i.e., a 10% increase in u.s demand, and a 5% increase in world demand. 295economics of biofuels: an overview of policies, impacts and prospects main conclusions: higher us demand for ethanol only translates into a us ethanol production expansion, with little global ethanol expansion. this us expansion however has strong global effects on land allocation. in contrast, an expansion in non-us ethanol demand mainly affects the world ethanol market and land used for sugarcane production in brazil and, to a lesser extent, in other sugar-producing countries. the brazilian expansion has a small impact on land uses in most other countries. 12. dumortier et al. (applied economic perspectives and policy, 2011) research questions: provide a sensitivity analysis of ghg emissions from iluc for the model used in searchinger et al. (2008) main conclusions: emissions from iluc are around 63 grams of co2 per mj, which are less than two-thirds of the level calculated by searchinger et al. (2008). a3. a few other studies dealing with biofuels 13. al-riffai, dimaranan, and laborde (ifpri working paper, 2010) approach: cge model. research questions: study the impact of the eu and us first-generation biofuel policies with different trade policy scenarios, i.e., mandate policy, and mandates along with trade liberalization. main conclusions: the baseline projects that, in 2020, the us produces 1.25 million tons oil equivalent (mtoe) biodiesel and 17.8 mtoe ethanol, while the eu produces 9.10 mtoe biodiesel and 1.87 mtoe ethanol. in the 1st scenario, the eu and us mandates together will lead to a 58.1% increase in us ethanol, and 49.8% increase in eu ethanol. biodiesel production expands by 115% in the us and by 44.3% in the eu. this leads to a reduction in oil demand and a slight decline in the world oil price (2%). with trade liberalization in place, there is a 18% increase in us ethanol production but a 55% decline in eu ethanol production. the world oil price displays a smaller decline. these two policy scenarios improve us terms of trade (0.36% and 0.21%). 14. khanna, ando, and taheripour (review of agricultural economics, 2008) approach: multi-market equilibrium model. research questions: study the welfare impact, for the united states, of a carbon tax ($25 per tc) and ethanol subsidy, assuming pollution externality from fuel consumption and ethanol demand as a gasoline substitute driven by the vehicle miles traveled (vmt) by consumers. main conclusions: the fuel tax of $0.387 per gallon and the then-current ethanol subsidy of $0.51 per gallon reduce carbon emissions by 5% relative to the no-tax, laissez faire situation. the second best policy of a $0.085 per mile tax with a $1.70 per gallon ethanol subsidy could reduce gasoline consumption by 16.8%, thereby reducing carbon emissions by 16.5% (71.7 million tc). 15. chen et al. (nber working paper, 2011) approach: multi-market equilibrium model with endogenous land allocation. research questions: study the welfare implications of four policy scenarios, for the unit296 g.c. moschini, j. cui and h. lapan ed states, in the context of both corn-based ethanol and cellulosic ethanol: a carbon tax, rfs mandate alone, rfs mandate with biofuel subsidies, and carbon tax with the rfs and subsidies. main conclusions: the business as usual (bau) scenario projects 50.7 billion liters of corn ethanol production. compared with the bau, a carbon tax of $30 per ton of co2e raises ethanol consumption by 10.5%, leading to an expansion in corn acreage by 7.5%, an increase in corn price by 7.5%, and a decline in ghg emissions by 0.57 billion metric tons (2%). the rfs alone results in a reduction in ghg emissions by 0.32 billion metric tons. subsidies in the 3rd scenario make cellulosic ethanol more competitive than corn ethanol, converting cropland (including corn land) to miscanthus acreage. thus, corn ethanol production falls to 33.4 billion liters while cellulosic production increases to 103.1 billion liters, resulting in a further decline in ghg emissions relative to the bau scenario. when the carbon tax is applied together with the rfs and subsidies, land uses for miscanthus and for switchgrass (hence the cellulosic ethanol production) further increase, leading to a 5.3% reduction in ghg emissions relative to the bau. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 3(2): 159-174, 2014 doi: 10.13128/bae-13246 adoption intensity of soil and water conservation practices by smallholders: evidence from northern ghana paul kwame nkegbe1,*, bhavani shankar 2 1 department of economics & entrepreneurship development, university for development studies, ghana 2 centre for development, environment and policy, school of oriental & african studies, uk abstract. soil and water conservation practices are being promoted in ghana as a way of sustainably managing the environment to support agricultural production. despite the important role the adoption of the practices plays in conserving the environment, very few studies have been conducted to analyse the factors influencing their intensive adoption. this study analyses the determinants of intensity of adoption of soil and water conservation practices using data from a cross-section of smallholder producers in northern ghana. count data models are used for the analysis. the empirical results show that access to information, social capital, per capita landholding and wealth play an important role in smallholder producers’ decision to intensively adopt soil and water conservation practices. keywords. conservation practices, adoption intensity, count data models, underdispersion, ghana jel codes. q12, q18, q56 1. introduction a greater majority of the populations in developing countries depend on the natural environment for their subsistence (barbier, 2010), and most of these countries, like ghana, are predominantly agrarian relying heavily on earnings from agriculture exports. however, the agricultural sector has long been identified as a cause of environmental degradation and this trend is expected to continue in the next half century (millennium ecosystem assessment, 2007). as a result, a major challenge confronting these countries is how to maintain the natural resource base while at the same time supporting agricultural production. the people of northern ghana are predominantly peasants depending almost exclusively on renewable natural resources for their livelihoods and survival, but they are confronted with deteriorating soil conditions which tend to put a major strain on their livelihoods (irg, 2005). while about 69.0 percent of the total land area of ghana is said to be prone to erosion estimated at a cost of 2.0 percent of gdp (mofa, 2007) and about 35.0 percent estimated to be vulnerable to desertification (irg, 2005), the majority of the areas under this * corresponding author: pnkegbe@uds.edu.gh full research article 160 p.k. nkegbe, b. shankar classification are located in the three northern regions of ghana (irg, 2005; mofa, 2007). the upshot of this is that farmers have devised and continue to devise various ways of managing the scarce natural resources, sometimes with external support such as ngos offering incentives, so they can continue to produce. some of the conservation practices promoted are grass stripping, composting, stone and soil bunds construction among others, but the scale of penetration of the individual practices among farmers is believed to be inadequate. even though a lot of studies have been conducted on technology adoption in developing countries’ context, only a few account for intensity of adoption. while decision to adopt is usually a binary one (i.e. adopt or not adopt), intensity of adoption goes on to look at the extent to which the various techniques are adopted. in analysing the adoption decisions of multiple soil and water conservation practices, count data models are employed in which the number of practices adopted serves as a measure of intensity of adoption (see, for example, isgin et al., 2008; lohr and park, 2002; sharma et al., 2011). the objective of this study is to identify the determinants of multiple technologies adoption by smallholder producers in northern ghana using data collected from 445 farm households between november 2009 and march 2010 in the three northern (viz. northern, upper east and upper west) regions of ghana. most of such studies mentioned above account for overdispersion, where the variance of the count-dependent variable is greater than the conditional mean. the current study is somewhat unique in the sense that it accounts for underdispersion (i.e. the variance of the count-dependent variable is less than its conditional mean) in analysing intensity of adoption of soil and water conservation practices among smallholder farmers in northern ghana. in the presence of underdispersion both overand equi-dispersion models will yield unreliable estimates. the rest of the paper is organised as follows. section 2 presents a brief review of the literature on intensity of adoption. while section 3 discusses the empirical methods employed, section 4 describes the survey data and variables used. section 5 presents and discusses the main results of the study, and concluding remarks are provided in the last section. 2. intensity of adoption studies various options exist for measuring intensity of adoption. some of these include count data models, tobit and double hurdle models. for example, isgin et al. (2008), lohr and park (2002), rahelizatovo and gillespie (2004), ramirez and shultz (2000), and sharma et al. (2011) employed count data models to explain intensity of adoption of various technologies. studies such as arslan et al. (2013) and alene et al. (2000) have employed the tobit model to examine intensity of adoption of various technologies. in using this approach, either the proportion of land under the given technologies or the proportion of farmers adopting such technologies is used as the dependent variable. this implies that attention is not paid to how many of the technologies are adopted. a number of other studies (see, for example, beshir, 2014; caviglia-harris, 2003; gebremedhin and swinton, 2003) have considered factors affecting both the decision to adopt and the degree or intensity of adoption of technologies or conservation practices using double hurdle models. these usually involve a first stage probit model and a second stage truncated model to assess the degree (or intensity) of use. other studies (for 161adoption intensity of soil and water conservation practices by smallholders example, mbaga-semgalawe and folmer, 2000) use an integrated socio-economic model of adoption to examine a first stage perception of erosion, a second stage adoption of improved soil and water conservation measures, and then a poisson regression model to analyse a third stage adoption effort (or level of adoption) of improved conservation measures in which selectivity bias is accounted for using the heckman two-stage approach. two methodological issues, however, arise from such integrated studies. first, it is not clear whether the use of the poisson model to analyse the level of adoption decision is appropriate since it is not known whether there is equidispersion in the dependent variable. the second issue relates to the use of the inverse mills ratio as a variable in the poisson model to account for selectivity. it is argued that such a procedure is inappropriate since the model is nonlinear (greene, 2006; terza, 1998). the use of the number of practices adopted as a measure of adoption intensity is underpinned by a number of assumptions and these are discussed by lohr and park (2002), isgin et al. (2008) and sharma et al. (2011). one of the assumptions is that provided a farming household derives a greater utility from the last adopted technology, there is no limit to the number of practices or technologies adopted. adopting greater number of conservation practices is seen to be better where marginal benefit of adopting is at least equal to the marginal cost. it is acknowledged that conditions that make the marginal benefit of conservation adoption to be greater than the marginal cost will include situations where the risk of environmental degradation is very high (like in the study area) and where farming households can efficiently use the practices. another assumption is that the adoption decision of the farming household for any one conservation practice does not rule out the adoption of the other available practices, but as noted by isgin et al. (2008, p.232), the adoption of a given technology might not be independent of another since the effects of certain technologies might be complementary. however, isgin et al. (2008) still believed adoption of most technology components in their sample was independent due to variable needs and conditions of producers, and most especially because only 12.0 percent of the 153 correlation coefficients turned out to be greater than a threshold level of 50.0 percent and statistically significant. given this observation, there is even a stronger reason in this study to believe the adoption of conservation practices is independent since none of the 15 correlation coefficients is up to the 50.0 percent threshold; indeed, only one is 40.3 percent (refer to appendix 1). it is also noted that a serious limitation of count data models is that they do not have very sound theoretical basis, and there is very little guidance on the appropriate functional form, but they remain the best when modelling number of technologies adopted. 3. empirical methods2 to investigate factors influencing the intensity of adoption of soil and water conservation practices in the study area, the number of conservation practices adopted by each 2 these methods are based on the economic model of utility maximisation by households known as the agricultural household model (proposed by singh et al. (1986)), details of which are presented in nkegbe, p.k. (2013). soil conservation and smallholder farmer productivity: an analytical approach. journal of management and productivity 3(2): 92-99. 162 p.k. nkegbe, b. shankar farming household defines the dependent variable; it is thus a discrete nonnegative integer-valued count variable. following the studies that have employed count data models to explain the intensity of adoption of various technologies, thus, the number of conservation practices adopted is interpreted as defining the intensity of adoption. the number of conservation practices at any given yi which is an integer count variable, can be said to come from a poisson distribution and can thus be modelled using the basic poisson model as (cameron and trivedi, 1998; greene, 2008; maddala, 1983; winkelmann, 2008): prob(υi = yi|xi) e−λiλi yi yi! ,λi ∈! + , yi = 0,1,2,… (1) from equation (1), the λi = e(yi|xi) = var(yi|xi) and the mean is usually defined λi = exp(xiβ) where xi is a vector of characteristics specific to household i, and β is a vector of unknown parameters to be estimated. the marginal (or partial) effects in the poisson model are given by: ∂e yi xi( ) ∂xi = λiβ . (2) this marginal effect, as in other count data models, is interpreted as the unit change in the intensity of adoption variable resulting from a change in the explanatory variable (cameron and trivedi, 1998, p.80). even though the basic poisson model is attractive for use in empirical studies, it has a major shortcoming of assuming equality between the variance of the count-dependent variable and its conditional mean, known as the equidispersion condition (cameron and trivedi, 1998; greene, 2007a; greene, 2007b; winkelmann, 2008). but in most empirical studies the count-dependent variable has been observed to exhibit overdispersion, implying the variance is greater than the conditional mean, due largely to the preponderance of zero observations of the dependent variable in such data sets. as a result, most empirical applications (including, gale, 1998; isgin et al., 2008; kim et al., 2005; lohr and park, 2002; rahelizatovo and gillespie, 2004) have employed a negative binomial model, which is suitable for modelling overdispersion. however, an initial inspection of the count-dependent (i.e. the intensity of adoption) variable in this study as a crude guide shows the variance is less than the mean, a result that is most likely emanating from the fact that only about 12.0 percent of the observations on the dependent variable is zero (see table 1). this implies that the model(s) to be used should be capable of handling underdispersion, and this is what makes the current study different from other previous studies. to model the apparent underdispersion, the gamma model is employed since the flexibility in that model allows for handling underor overdispersion, besides equidispersion (cameron and trivedi, 1998; greene, 2007b; winkelmann, 2008).3 3 it is noted that the generalised poisson model could also be used, but it has the shortcoming of the range of the 163adoption intensity of soil and water conservation practices by smallholders the gamma count model is derived from the gamma distributed renewals proposed by winkelmann (1995), and has been discussed by cameron and trivedi (1998), greene (2007b), and winkelmann (2008). if waiting times between any two events are distributed as continuous, two parameter gamma variate, then the density for inter-arrival times can be stated as (greene, 2007b; winkelmann, 2008): f τ α ,λi( )= λi α γ α( )τ α−1e−λiτ ,τ ≥ 0,α > 0,λi > 0 (3) with α being a shape parameter and λi = e xi 'β being a location parameter. using a laplace transformation of a gamma distribution (as detailed in winkelmann 2008, p.56) with parameters λi and αj and arrival time of the jth event being qj = τ1 + τ2 +…+ τj the density function of qj can be written as: f j qj α ,λi( )= λi α j γ α j( )qj α−1e −λiqj ,qj ≥ 0,α > 0,λi > 0 (4) to derive the count data distribution, the cumulative distribution function below must be evaluated: fj t α ,λi( )= λi α j γ α j( )u α j−1e−λiu du,α > 0,λi > 0 0 t ∫ = 1 γ α j( ) uα j−1e−u du 0 λit ∫ , j = 0,1,2,… = g(αj,λit) (5) it is noted that the integral above is an incomplete gamma function normally approximated numerically (greene, 2007b). if j events occur in a fixed (0,t) then it gives the twoparameter distribution function: prob(j events) = g(αj,λit) g (α(j+i),λit) (6’) where g(0,λit) = 1 if the period is normalized to t = 1 the distribution for the counts of events, especially in a cross-sectional framework, is given by: random variable being dependent on an unknown parameter, thereby violating one of the standard conditions for consistency and asymptotic normality of maximum likelihood estimation procedure (cameron and trivedi, 1998). 164 p.k. nkegbe, b. shankar prob(j events) = g(αj,λi) g(αj+α,λi). (6) in the model, α is the dispersion parameter with α<1 and α>1 denoting overand underdispersion, respectively, while α=1 reduces the model to the basic poisson model hence equidispersion. the conditional mean and variance functions are e yi xi( )= jg α j,λi( ) j=1 ∞ ∑ and var(yi |xi )= j2[g(α j,λi )−g(j=1 ∞∑ α j +α iλi )]− e(yi |xi ) 2 , respectively. as noted by greene (2007b), because the conditional mean has no closed form, an approximation usually used is e(yi |xi) ≈ λi/α so that it readily reduces to the poisson model if α=1. this then leads to the marginal effects function: ∂e yi xi( ) ∂xi = λi α β . (7) finally, the model that is estimated is of the form: ( )( )prob y = y x = f x ,x , xi i i i p i fc i si (8) where: yi = number of conservation practices (count) adopted by household i; xi p = personal and household characteristics; xi fc = farm/plot and cropping characteristics; and xi si = socio-economic and institutional variables. the variables are explained in the following section. 4. survey data and variables the data for the study are obtained from a survey of 445 households in the three northern regions of ghana. the survey covered production activities for 2008/2009 agricultural year and was undertaken between november 2009 and march 2010. the households were drawn using a multi-stage sampling procedure which involved identifying a district in each of the regions, randomly selecting 5 communities from each district and finally randomly selecting 30 households from each community.4 the households are smallholder producers growing cereals like maize, millet, sorghum and rice; and other crops like groundnut, cowpea and soy bean under rainfed conditions. these crops are produced mainly for home consumption, but surpluses are marketed to meet other household needs. 4 six households were dropped from an original sample of 451 due to incomplete responses. detailed explanation of the sampling approach can be found in nkegbe, p. k. (2011). resource conservation practices: adoption and productive efficiency among smallholders in northern ghana. unpublished phd dissertation, department of agricultural and food economics, university of reading. , 165adoption intensity of soil and water conservation practices by smallholders farmers in this study were asked which conservation practices they adopted. the responses formed the basis for the construction of the dependent variable. the soil and water conservation practices examined are stone bund, soil bund, grass strip, agroforestry, cover crops, and composting. the numbers of conservation practices adopted by the sample are shown in table 1. table 1. distribution of counts of conservation practices adopted practice counts frequency percent 0 53 11.9 1 156 35.1 2 114 25.6 3 74 16.6 4 41 9.2 5 7 1.6 total 445 100.0 from table 1, 11.9 percent of the sampled households did not adopt any of the soil and water conservation practices and thus have a zero count while only 1.6 percent of the sample adopted five of the conservation practices, with no household adopting all six. the majority of households (35.1 percent) adopted one practice and the average number of practices adopted among the sample was 1.81 (see also table 3). the types of conservation practices adopted by the households are also shown in table 2. while the most adopted practice is stone bund, the least adopted is cover crops with 56.6 percent and 8.8 percent of the households, respectively, adopting each of them. the variables hypothesised to influence the probability of the intensity of adoption of conservation practices have been classified as personal and household characteristics, farm or plot and cropping characteristics, and socio-economic and institutional variables. sixteen variables, excluding the ueast (upper east region) variable since that region is used as the reference location in this study, were included in the count data models. table 3 presents the definition and descriptive statistics of the variables. table 2. distribution of adoption of conservation practices by households practice frequency percent stone bund 252 56.6 soil bund 248 55.7 grass strip 140 31.5 agroforestry 67 15.1 cover crops 39 8.8 composting 59 13.3 166 p.k. nkegbe, b. shankar the personal and household characteristics considered here include age of household head, gender of head, average education of household members, own labour use, per capita landholding, wealth (house type) and number of plots cultivated. age could have positive or negative effect on the decision to intensively adopt conservation practices. because conservation practices in the study area mostly do not involve construction of permanent structures on the land, it is not likely there will be gender dimensions to adoption as land is mostly owned by males and the presence of permanent structures is interpreted as laying claim to the land. thus it would have been more difficult for females to adopt conservation practices if construction of permanent structures were involved. the effect of average level of education of adult household members on adoption is mixed. while it could have positive effect on adoption of conservation practices, it might offer alternative livelihood opportunities in off-farm activities thereby increasing the opportunity cost of labour and competing with labour use for agricultural production (scherr and hazell, 1994). own labour use is proxied by household size as in most adoption studies. the effect of household per capita landholding, which is used to capture the effect of population pressure (lapar and pandey, 1999), could go either way. it could either provide evidence supporting the malthusian view of negative effect of population pressure on natural resource conditions, or provide evidence in support of the boserupian hypothesis that population pressure induces households to embark on agricultural intensification thereby adopting land enhancement technologies which ultimately leads to improved natural resource conditions. given the high poverty levels in the study area, wealthy farming households are expected to have higher probability of adopting soil and water conservation practices. housing type is used to proxy the effects of wealth on adoption. number of plots operated by the households is expected to positively impact the intensity of adoption of soil and water conservation practices, as conditions on different plots may require the use of different conservation practices. perception of degradation, type of soil, slope and location are the factors considered under farm/plot and cropping characteristics. it is expected that the more farm households perceive their fields to be degraded in the form of erosion on plots the more likely they will adopt practices to minimize the adverse effects of degradation on their farming activities (mbaga-semgalawe and folmer, 2000). this variable is an index with 1 being less degraded and 4 being highly degraded, and where a farmer cultivates more than one plot an average index is used. also, farm households will adopt fertility enhancing practices, especially in northern ghana where the soils are less fertile, to reduce soil infertility induced yield losses. the variable on major soil type is also an index ranging from 1 to 4, with 1 being most fertile. it is expected that the steeper the slope the more likely farm households will adopt conservation practices. a very steep slope is given an index of 4 with a flat topography taking 1. for this variable also, an average index is taken for farmers with more than one plot. the upper east region is the most degraded of the three regions and so households in that region should be more likely to intensively adopt soil and water conservation practices than those in the northern and upper west regions. finally, the socio-economic and institutional variables hypothesised to affect adoption decisions of farm households are related to access to information and social networks.5 5 security of tenure, an important institutional variable, has generally been reported to have positive effects on 167adoption intensity of soil and water conservation practices by smallholders farm households’ access to information, which is expected to have positive effect on adoption intensity, is proxied by contacts with extension (public or private) staff. farmers’ social networks generally will facilitate adoption through information flow and group action, especially involving engagement in mutual labour sharing arrangements. effects of social capital in this study are measured by membership in farmer association and total man-days of mutually shared labour received in the 2008/09 agricultural production year. distance between homestead and plot can affect adoption of conservation practices in either way depending on the type of practice. for example, adoption of composting will be less likely for plots that are very far away from the house as preparation of the compost might require the use of bulky kitchen waste and dung from compound kraals. conservation investment in southern ghana (abdulai et al., 2011a; besley, 1995; goldstein and udry, 2008). but this might not be the case for northern ghana as the predominant type of ownership remains communal and rather complex (abdulai et al., 2011a), as against the increasingly individualised type of ownership in the south. as a result, including a variable to capture tenure security might amount to a misspecification, thus no provision is made for security of tenure. table 3. variables definition and descriptive statistics variable definition mean s.d. dependent variable numcon number of conservation practices adopted (counts) 1.81 1.21 explanatory variables personal and household characteristics hhage age of household head (in years) 53.24 15.42 hhgend dummy for gender of household head (1 if male, 0 if female) 0.91 0.28 avedu average level of education of all adult household members (in years) 5.95 2.88 hh_size household size 7.86 2.72 adland landholding per economically active member of household (in hectares) 0.52 0.41 house index for the type of house/dwelling (3-12) 4.63 1.47 noplots number of plots cultivated by the household 2.58 1.48 farm/plot and cropping characteristics per_deg average index for perception of degradation on all plots (highest = 4) 2.06 0.51 soildex average index for major soil type on all plots (1 = most fertile) 2.24 0.68 slopedex average index for type of slope on plots (1 = flat) 1.72 0.56 north dummy for location, 1 if in northern region and 0 otherwise 0.33 0.47 uwest dummy for location, 1 if in upper west region and 0 otherwise 0.33 0.47 socio-economic and institutional variables exntact number of contacts with extension officers in the 2008/09 agricultural year 2.53 4.51 memfa dummy for membership in farmer association (1 if member, 0 otherwise) 0.60 0.49 shlab total self-help labour for 2008/09 agricultural year (in man-days) 40.86 55.01 distfh distance of plot from homestead (in km) 1.58 2.04 168 p.k. nkegbe, b. shankar 5. results and discussion from the results in table 4, extension contacts in the previous production season, membership in farmer association, engagement in mutual labour sharing, wealth, number of plots cultivated, index for soil type and slope are positive and significant determinants, while landholding per economically active member of household and being located in the upper west region are negative and significant determinants of the intensity of adoption decision across both models. the results can therefore be said to be reasonably uniform across the two (i.e. poisson and gamma) count data models. however, a number of formal tests of dispersion confirmed the existence of underdispersion by the crude check. first, results in table 4 indicate two of the statistics developed by cameron & trivedi (1990), and discussed by greene (2007b), for testing for dispersion in the poisson count data model, g(μi) and g(μi 2) are -8.307 and -8.162, respectively. the two statistics have limiting chi-squared distributions with one degree of freedom under the null hypothesis of equidispersion, thereby giving a critical value of 3.841 at the 0.05 level. clearly, the absolute values of both statistics exceed the critical value. the equidispersion assumption is therefore rejected, and the fact that both values are negative indicates underdispersion and not overdispersion. as a result, the gamma model is used to model farmers’ intensity of adoption of soil and water conservation practices in northern ghana. as shown in table 4, the results of the gamma count model confirm that there is underdispersion in the data since the dispersion parameter is greater than one and statistically significant. the rest of the discussion of the results is thus based on the chosen gamma count data model. three factors under the category personal and household characteristics are statistically significant determinants of the decision to intensively use soil and water conservation practices. the results show that larger landholding per economically active member of the household reduces intensity of adoption; as this variable increases by one additional unit, households in the sample decrease the number of soil and water conservation practices adopted by about 0.4 (as shown by the marginal effect of the variable). this observation can be explained in two ways. first, it is likely that households with larger per capita landholding are labour constrained and so are not able to mobilize the required labour for implementing soil and water conservation practices. however, this point seems not to be in operation in the study area as the evidence suggests household labour use on farm does not have any effect on the intensity of adoption of conservation practices. the second point, which appears more plausible for the area, is the explanation that as pressure increases on land reflecting in smaller landholding per economically active member of household, adoption of conservation practices becomes more intensive thereby providing more evidence for the boserupian thesis of population-induced agricultural intensification, as reported by pender et al. (2004) and tiffen et al. (1994). household wealth, proxied by type of house, impacts intensity of adoption positively. the existence of wealth effects in the intensive adoption of the conservation practices points to imperfections or failures in the credit market in the study area, a situation that is pervasive in developing countries. consistent with expectations, an additional plot cultivated by the sampled households would lead to the adoption of over 0.1 more soil and water conservation practices. the result on number of plots is similar to the one obtained by deininger and jin 169adoption intensity of soil and water conservation practices by smallholders table 4. results of count data models variable poisson gamma count coefficient marginal effect coefficient marginal effect constant -0.2088 (0.3273) -0.3777 (0.6293) 0.0142 (0.2232) 0.0291 (0.4785) hhage -0.0033 (0.0025) -0.0060 (0.0049) -0.0029 (0.0019) -0.0059 (0.0041) hhgend -0.0732 (0.1432) -0.1324 (0.2752) -0.0655 (0.0992) -0.1343 (0.2132) avedu -0.0023 (0.0134) -0.0042 (0.0256) -0.0019 (0.0100) -0.0039 (0.0214) hh_size 0.0079 (0.0160) 0.0142 (0.0307) 0.0068 (0.0112) 0.0138 (0.0240) adland -0.2125* (0.1254) -0.3845 (0.2484) -0.1913* (0.1083) -0.3922* (0.2356) per_degr 0.0579 (0.0740) 0.1048 (0.1428) 0.0496 (0.0513) 0.1018 (0.1106) exntact 0.0247*** (0.0063) 0.0447*** (0.0142) 0.0220*** (0.0033) 0.0453*** (0.0087) memfa 0.3002*** (0.0827) 0.5431*** (0.1820) 0.2616*** (0.0585) 0.5365*** (0.1397) shlab 0.0029*** (0.0007) 0.0052*** (0.0016) 0.0026*** (0.0005) 0.0053*** (0.0012) house 0.0506** (0.0250) 0.0915* (0.0502) 0.0445*** (0.0172) 0.0913** (0.0384) noplots 0.0633 (0.0406) 0.1145 (0.0800) 0.0558** (0.0297) 0.1145* (0.0647) soildex 0.0848 (0.0543) 0.1534 (0.1071) 0.0757** (0.0370) 0.1553* (0.0814) slopedex 0.0867 (0.0649) 0.1569 (0.1271) 0.0766** (0.0388) 0.1570* (0.0850) distfh 0.0144 (0.0179) 0.0262 (0.0345) 0.0129 (0.0120) 0.0264 (0.0259) north -0.0683 (0.1065) -0.1236 (0.2050) -0.0525 (0.0753) -0.1077 (0.1622) uwest -0.4859*** (0.1187) -0.8790*** (0.2705) -0.4326*** (0.0908) -0.8870*** (0.2182) g(μi)a -8.307 g(μi 2) -8.162 alphab 1.937*** log likelihood -659.359 -636.570 chi squared 87.768*** 45.578*** aic 3.040 2.942 bic 3.196 3.107 ***, **, *, stand for values statistically significant at 0.01, 0.05, and 0.1 levels respectively; figures in parentheses are standard errors; a g(•) are dispersion tests values in poisson model; b is the dispersion parameter for gamma count data model. 170 p.k. nkegbe, b. shankar (2006) who reported that fragmentation, for which number of plots was used as a proxy, had a positive effect on investment in land improvements in ethiopia. under the farm or plot and cropping characteristics category, three variables are statistically significant. the intensity of use of soil and water conservation practices and perception of soil fertility are positively and significantly correlated. perceiving a problem of soil infertility, ceteris paribus, results in the adoption of about 0.2 more conservation practices. this contrasts the finding of bekele and drake (2003) who observed that farmers in their sample in ethiopia were inclined to conserve more fertile plots. similarly, and in consonance with the findings of amsalu and de graaff (2007), bekele and drake (2003) among others, the slope variable has a significantly positive effect on intensity of adoption of soil and water conservation practices. this implies that as households perceive their plots to be steeply sloped, the higher the probability of them adopting soil and water conservation practices more intensively. while location variables, north and uwest, have a negative effect on intensity of use of conservation practices, only the latter’s effect is statistically significant. being located in the upper west region could bring about almost 0.9 decrease in the number of conservation practices adopted. sharma et al. (2011) and isgin et al. (2008) who also employed count data models found evidence of regional effects in the adoption intensity of pest control measures among their sample of uk cereal farmers and precision farming technology among us farmers, respectively. a number of socio-economic and institutional variables have statistically significant effect on the adoption intensity decisions of households. obtaining technical advice from extension officers in the previous production season has a positive effect on adoption intensity, a result that highlights the importance of extension service in promoting sustainable agricultural production practices in developing countries. this result is consistent with that of kim et al. (2005) who reported positive effect of contact with extension staff on the intensity of adoption of best management practices among beef cattle producers in louisiana, and that of lohr and park (2002), also in the us, reporting positive effects of both number of personal information sources and secondary information outlets on the intensity of adoption of insect management portfolios. membership in farmer organization and engagement in mutual labour sharing arrangements, representing social capital, both have positive effect on intensity of adoption and are statistically significant at the 0.01 level. while total self-help labour marginally increases intensity of adoption, the farmer association membership variable increases the number of conservation practices adopted by over 0.5. these results, besides confirming the findings in adoption studies like abdulai et al. (2011b), bandiera and rasul (2006), caviglia-harris (2003), and munasib and jordan (2011) reporting positive effect of social networks on adoption, are consistent with that of ramirez and shultz (2000) who found belonging to a farmer organization positively affected the intensity of adoption of integrated pest management technologies among their sample from costa rica. as farmers belong to associations they learn from others, and especially the influential individuals within their group, even if they do not have direct contact with extension staff. thus they will have access to information which enhances intensity of adoption. 171adoption intensity of soil and water conservation practices by smallholders 6. concluding remarks soil and water conservation practices have been promoted in ghana, especially in the northern parts, as a way of sustainably managing the environment to support agricultural production. despite the important role that the adoption of the practices plays in conserving the environment, very few studies have been conducted to analyse the factors influencing their intensity of adoption. in this study, thus, the determinants of intensity of adoption of soil and water conservation practices in ghana are examined using data from a cross-section of 445 smallholder producers in northern ghana. the empirical results confirm the important role access to information plays in stimulating and sustaining adoption of soil and water conservation practices since the number of contacts with extension officers remains a positive and significant determinant in both models. this implies that adoption could be enhanced if the capacity of extension staff in soil and water conservation practices is built and their outreach further increased. further, the importance of wealth (with type of house used as its proxy variable) in the intensity of use of conservation practices highlights the need to increase smallholders’ access to credit as a way of both promoting and intensifying the adoption of conservation practices. social capital, in the form of belonging to farmer association and engagement in mutual labour sharing arrangements, is important in the decision to intensively adopt soil and water conservation practices. it is thus important that this strength of social capital is harnessed in the formulation of rural development policies to improve, or at worse maintain, the condition of the environment for sustainable production. for example, since access to information is considered vital to the decision to intensively adopt conservation practices, a rural development policy that promotes formation of strong and vibrant farmer associations will strengthen farmer-to-farmer knowledge sharing. the presence of location effects in the intensity of adoption decision of smallholders in northern ghana implies that different strategies should be employed for different regions if policy makers aim at promoting widespread diffusion of soil and water conservation practices. also, regional differences in terms of geography should be factored into any promotional strategies since different practices appear useful to different locations. the results show the upper west region must particularly be targeted since intensity of use decreases for households located in that region relative to those located in the upper east. on the methodological front, the study yields almost uniform results across the poisson and gamma count data models, and this demonstrates the robustness of the estimates obtained. it is important to point out that the current study could suffer a couple of limitations. first, one of the assumptions underpinning the study is that employing more measures means greater intensity of adoption, implying that a producer with several uniform plots might adopt the same practice on all plots whilst another with a single plot with different conditions might adopt more than one practice, in which case the former will be considered adopting less than the latter. again, the count data on  adoption considers whether the producer had one of the practices applied on a plot in the specific production year, but some measures could have been established on the plot from previous production 172 p.k. nkegbe, b. shankar season(s), so that adoption could potentially pose an endogeneity problem.6 in the light of these, thus, such issues should be taken into account in looking at the results of the study and may provide hints for further research in this field. acknowledgements the authors gratefully acknowledge two anonymous referees and the editor for their insightful and valuable comments which helped improve earlier versions of this paper. that notwithstanding the usual disclaimer applies. references abdulai, a., owusu, v. and goetz, r. 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(2008). econometric analysis of count data, 5th ed. berlin heidelberg: springer-verlag. appendix 1. spearman’s correlation coefficients between adoption methods methods stone bund soil bund grass strip agroforestry cover crops composting stone bund 1.0000 soil bund -0.0131 1.0000 grass strip 0.1633*** 0.0777 1.0000 agroforestry 0.1529*** 0.0463 0.3508*** 1.0000 cover crops 0.1269*** -0.0438 0.0981*** 0.4028*** 1.0000 composting -0.0322 0.1083*** 0.0063 -0.0534 -0.0040 1.0000 *** stands for values statistically significant at the 0.01 level. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(2): 175-198, 2012 efficiency and farm size in philippine aquaculture. analysis in a ray production frontier framework xavier irz1, james r. stevenson mtt agrifood research finland, economic research unit, finland abstract. we investigate the existence of an inverse relationship (ir) between farm size and technical efficiency in philippine brackishwater pond aquaculture. the study is motivated by the exemption of fish ponds from the comprehensive agrarian reform laws and suggestions in the literature of inefficient management of fish farms. the analysis of technical efficiency is based on the estimation of a multi-product ray production function estimated in a stochastic frontier framework. there is some evidence of an ir but of only limited strength. hence, it is unlikely that agrarian reform is the key to unlocking the productive potential of brackishwater aquaculture in the philippines. keywords. aquaculture, inverse relationship, ray production function, efficiency, land reform, philippines jel-codes. q15, o13 as global production of capture fisheries stagnated over the last decade, output from aquaculture expanded steadily, making aquaculture one of the fastest growing food-producing sub-sectors globally (ahmed and lorica, 2002; fao, 2002). this spectacular development has sometimes been described as a blue revolution, with the underlying idea that aquaculture has the potential to solve some aspects of the world’s chronic hunger and malnutrition problems (coull, 1993). while there is no arguing with the increase in aquaculture production, it is however necessary to acknowledge that this development has generated a number of social, environmental and economic problems. hence, questions have been raised about the ecological impact of aquaculture, in particular with regard to biodiversity (jana and webster, 2003; tisdell, 2003) and mangrove destruction (primavera, 2000); about the equity of its development (primavera, 1997; alauddin and tisdell, 1998; coull, 1993) and about its food security benefits (naylor et al., 2000; primavera, 1997). aquaculture development in the philippines fits the global picture described above. yap (1999) reports that aquaculture output in the country grew at the average annual rate of 5.4% in the 1990s and that its share of total fisheries production keeps increasing. yet, its development has had a detrimental effect on mangroves, resulted in the salinisa1 corresponding author: xavier.irz@mtt.fi. 176 x. irz, j.r. stevenson tion of previously productive agricultural land, generated conflicts over the use of natural resources (yap, 1999) and some have even argued that it has been responsible for the marginalisation of some coastal communities and an increase in the rate of unemployment (primavera, 1997). against this background, the aim of this article is to address one equity aspect of aquaculture development in the philippines that relates to the distribution of fishpond holdings.2 we investigate whether there is evidence of an inverse relationship (ir) between farm size and technical efficiency in brackishwater aquaculture in order to evaluate the case, on efficiency ground, for reform of the existing tenurial system, land redistribution, or other policies aimed at improving the functioning of the land market. the study is motivated first by a common perception that the vast areas of philippine brackishwaters3 represent a valuable resource that is not exploited optimally and is not contributing fully to the development process of coastal areas. we believe that it will make a contribution to an important and ongoing policy debate that emerges from the fact that, while the philippines adopted several land reform laws in the late 1980s, aquaculture ponds have so far been exempted4. as a result, the distribution of holdings in brackishwater aquaculture remains very unequal as indicated by a gini coefficient of 0.72 for the two regions that form the focus of our study5. naturally, large fishpond owners and leaseholders believe that agrarian reform would, if anything, only worsen the severe problems of poverty and inequality in the communities where fish farming represents an important activity. yap (1999) cites a telling extract from the newsletter of negros prawn producers and marketing cooperative: the implementation of the (land reform) law is liable to cause widespread strife among the landowners…. there is no showing that land reform will enliven the plight of the poor. without undermining their capabilities, it is also doubtful whether they (the farmers) can put up the necessary capital to maximize land use. having been used to having a landlord on whom to call in times of need, this plunge to independence may have a crippling effect. this view stands in sharp contrast with the common belief in agriculture that small farmers tend to achieve higher productivity and efficiency levels than large farmers, i.e. that there usually is an ir, as hypothesized in sen’s seminal paper (1962)6. besides, the experience of thailand, where the extremely dynamic prawn industry is supported by relatively small farmers (yap, 1999), suggests that there is no particular impediment to the development of a competitive aquaculture sector based on smallholders. we therefore believe that testing the ir in philippine brackishwater aquaculture will generate important policy insights; in particular, a strong ir would suggest that institutional changes leading to a more equal size distribution of holdings could increase both equity and efficiency. 2 although it is not always specified, our study relates only to brackishwater pond aquaculture. 3 yap reports that there are 239,323 hectares of brackiswater fishponds in the philippines. the electronic data that we obtained from the bureau of agricultural statistics gives a total harvested area of 415,272 hectares in year 2000. 4 the most recent one is the comprehensive agrarian reform law (carl) of 1988 that imposes land redistribution with a five hectare retention limit set on all agricultural land. 5 source of data: bureau of agricultural statistics’ inventory of fishponds from 1997. 6 a recent review of the ir literature is fan and chan-kang. it concludes to the lack of consensus on the validity of the ir hypothesis. 177efficiency and farm size in philippine aquaculture our analysis is based on a sample of 127 farms in two of the three main regions for brackishwater aquaculture in the philippines and investigates the level and determinants, including farm size, of their technical efficiency. although our focus lies primarily with the analysis of a policy issue, we also believe that the article makes a modest methodological contribution to the agricultural economics literature by establishing two methods to measure the explanatory power of the inefficiency effect variables in the widely used composed error model of battese and coelli (1995). this is useful in the empirical section to establish by how much inefficiencies would decrease and output rise if aquaculture land was distributed more equally in the philippines. the paper is organized as follows. the next section develops the conceptual framework, emphasizing the advantages of the estimation of a ray production function over alternative approaches. section three presents the estimation strategy and proposes an approach to quantify the explanatory power of the inefficiency effect variables in the econometric model. the remaining sections discuss the data and empirical model, present the empirical results, and offer conclusions. 1. measuring the efficiency of polyculture farms. a conceptual framework 1.1 choice of approach the ir literature started with the simple observation that yields, defined as output per unit of surface area, differed according to the size of farms. however, output per hectare is only a partial productivity indicator which cannot satisfactorily measure overall farm productivity (jha, chitkara and gupta, 2000), and economic optimality usually differs from yield maximisation. to address this concern, albeit only partially, one can investigate the relationship between gross margin and farm size, but this again fails to account for the input of primary factors when comparing farm performance. there is also a concern that gross margins depend on the price environment in which farms operate (coelli, rahman and thirtle, 2002). there is therefore a strong case to investigate the ir within the confines of production economics, which can accommodate the multi-dimensional aspect of farm production. aquaculture production in the study area involves the polyculture of prawns, fish (tilapia and/or milkfish) and crabs, which are produced simultaneously in the same ponds so that the inputs (e.g. feeds, labour) with the exception of the fry and fingerlings, are non-allocable. this introduces a first linkage among the different outputs of the aquaculture farm. second, it is necessary to recognize the possibility of output jointness as it is likely that the different species interact with each other in the aquaculture pond. for instance, biologists and aquaculture experts often consider that the association prawn/ tilapia tends to reduce the rate of prawn mortality because tilapias, through their filtering activity and consumption of organic matter lying at the bottom of the pond, improve the bacteriological quality of the pond water (corre et al., 1999). we therefore conclude that the production process relies on a truly multiple-output technology, and that it is not possible to specify different production functions for each output. the most common approach to measure efficiency in a multi-output setting involves the estimation of dual cost, revenue or profit functions (löthgren, 2000). however, this group of methods relies on relatively restrictive behavioural assumptions of econom178 x. irz, j.r. stevenson ic optimization, such as static revenue or profit maximisation, that are not expected to hold in developing country aquaculture as farmers are likely to adopt complex livelihood strategies in the face of multiple market failures. for instance, prawn production in the philippines, though profitable on average, is also inherently risky due to the presence of diseases that are not easily controlled but have the potential to wipe out entire harvests. this type of risk is unfortunately not insurable due to the poor development of insurance and credit markets. furthermore, at a more practical level, estimation requires data on prices of inputs and/or outputs with a minimum of variability, but such data is unfortunately not easy to obtain at a given point in time because input and output markets are relatively well integrated within regions. hence, a primal approach seems better suited to the analysis of efficiency and productivity for this particular study. in a primal setting, a transformation function can be re-written so as to express one output as a function of the input vector and the quantities of all other outputs, but the resulting efficiency scores then depend on the particular output that is chosen as dependent variable, with no guarantee of consistency of the resulting efficiency rankings of farms for alternative formulations of the model. the efficiency literature has addressed the problem by developing models based on input and output distance functions (coelli and perelman, 2000; morrison-paul, johnston and frengley, 2000; brümmer, glauben and thijssen, 2002) or ray production functions (löthgren, 2000). both types of functions appear equally satisfactory from a theoretical point of view, but the ray production function seems superior for the problem considered in our paper for two reasons. first, the output distance function is linear homogenous in outputs, which is imposed globally through the use of a logarithmic functional form that cannot accommodate zero values7. in response to that problem, a common practice consists of replacing zero values with small numbers (see morrison-paul, johnston and frengley, 2000 as well as fousekis, 2002 for two examples) but this seems highly unsatisfactory as the logarithmic function goes asymptotically to minus infinity at zero. battese (1997) explores this problem in the context of a cobb-douglas production function to conclude that it can seriously bias the parameter estimates. given that most farms in our sample do not produce all four outputs, this problem represents a major obstacle to the estimation of an output distance function from our data. the second issue with the distance function arises from the fact that its value is unobservable so that the estimation equation is derived indirectly by exploiting the homogeneity properties of the distance function. however, it is feared that the resulting estimable equation leads to possible endogeneity problems (grosskopf et al., 1997; löthgren, 2000). by contrast, no homogeneity restriction needs to be imposed on the ray production function, which can therefore be represented by non-logarithmic functional forms and hence accommodate zero values. furthermore, it is also believed that the endogeneity problem highlighted above for the distance function does not apply to the ray production function (löthgren, 2000). hence, we choose to pursue our investigation of efficiency of aquaculture farms in the philippines based on the estimation of a ray production function. 7 in fact, all the published papers on distance functions of which we are aware use a transcendental logarithmic functional form, in order to impose homogeneity while conferring sufficient flexibility to the parametric function. 179efficiency and farm size in philippine aquaculture 1.2 theoretical model the main insight of löthgren (2000) is to express the output vector y of dimension m in polar coordinates:   (1) where denotes the euclidian norm of vector y (  ), θ(y) is an (m-1) vector of polar coordinate angles of the output vector y, and the m functions mi: [0, π/2] m-1 → [0,1] define the coordinates of the normalized output vector. this is illustrated in the two-output case in figure 1. the output vector of farm c is expressed in terms of its norm, oc/ocr, and a single angle θc measuring the relative proportions of fish and prawn outputs, i.e. the output mix. the two functions mf and mp of the polar-coordinate angle θc simply define the (regular) coordinates of the normalized output vector ocr obtained by radial projection of vector oc on the circle of radius 1. the (m-1) polar coordinate angles are obtained recursively as in löthgren (1997), from which the coordinates of the normalized output vector can easily be recovered. this set up allows us to represent any technology by a multi-output ray production function f(x,θ(y)) as follows:   (2) this function gives the maximum norm of the output vector that the firm can produce, given a vector of inputs x and the existing production set p(x), and assuming that any increase in production would involve a proportional increase in all individual outputs. figure 1. graphical presentation of the ray production function   mp(θc)   mf(θc)   cr      •   θc   1   •   cd   •  c           p   •   p’   prawn   output     fish  output     o   1   180 x. irz, j.r. stevenson hence, any technologically feasible input-output combination (x, y) is defined by the inequality. in terms of figure 1, the value of the ray production function is simply equal for farm c to the ratio ocd/ocr. under the assumption of strong input disposability, the ray function is positively monotonic in inputs (löthgren, 2000). the usefulness of the ray production function in measuring efficiency derives from its relation to the output distance function. it follows from equation (2) that, for any observed output vector y:   (3) this is indeed observed in our graphical example, as ratio oc/ocd is obviously equal to oc/ocr divided by ocd/ocr. this relationship is most important because we know that virtually all the properties of a multi-output technology can be recovered from the distance function. for instance, brummer, glauben and thijssen (2002) use it to characterize technological change and productivity growth, while kim (2000) derives measures of output substitutability from it. equation (3) therefore implies that the same can be done from the ray production function. for our purpose, it is sufficient to recognize that output elasticities are easily derived from the ray production function as:   (4) this expression gives the percentage change in all outputs resulting from a one percent change in input j and is expected to take a positive value (fousekis, 2002). alternatively, appendix 1 demonstrates that because the ray production function entertains some duality with both the maximum revenue and minimum cost functions, this elasticity can be interpreted as the revenue elasticity or the scale-adjusted cost share of input j. the scale elasticity follows immediately (löthgren, 2000):   (5) this elasticity should be compared to unity to establish whether the firm operates under decreasing, constant or increasing returns to scale. 2. estimation strategy 2.1 the econometric model the estimation of firm-level efficiency scores from a ray production function follows the stochastic frontier methodology initially proposed by aigner, lovell and schmidt 181efficiency and farm size in philippine aquaculture (1977). accordingly, a scalar-valued composed error term is introduced in the empirical ray production function8:   (6) where β is a vector of parameters to be estimated; v is a symmetric random variable that is independently and identically distributed across individuals; and u is a non-negative random variable. this specification recognizes the fact that production is first affected by random shocks and measurement errors, which are captured by the disturbance term v. however, the productive performance of farms is also determined by the quality of managerial decisions and it is likely that some farmers make mistakes, i.e., that they are technically inefficient. this is formally captured by the random variable u that describes the deviation of the norm of the observed output vector y from the maximum achievable norm, which is conditional on the exogenous shock v. given a parameterisation of the ray production function and distributional assumptions on the random terms, equation (6) can be estimated by the maximum likelihood methods that have now become commonplace in the stochastic frontier literature9. we adopt the specification of battese and coelli (1995) who relax the assumption of identically distributed inefficiency terms by considering that ui is obtained by truncation at zero of a normal variable n(µi; σu 2) where10:   (7) the term zi denotes a vector of potential determinants of inefficiencies, including farm size, while δ is a vector of parameters to be estimated. note that because the inefficiency effects enter the model in a highly non-linear way, there is no identification problem when using the same variable in the specification of the ray production function and as an inefficiency effect.11 the likelihood function is derived algebraically as in battese in coelli (1993) and it can then be maximized numerically to produce estimates of both the ray production function and the vector of parameters δ. further, while the individual inefficiency levels are not directly observable, the method allows for calculation of their predictors by applying the procedure first proposed by jondrow et al. (1982). as the expressions for these predictors are presented in battese and coelli (1993) only for the multiplicative model, while ours is additive, they are worth reporting here. first, the conditional expectation of the inefficiency term u given a total residual e=v-u is derived from 8 notice that the error term is introduced in an additive rather than multiplicative way because, as explained earlier, we do not want to use a logarithmic functional form due to the ‘zero value’ problem. 9 see coelli, rao and battese (1998) for an introductory presentation of this literature and kumbhakar and lovell (2000) for a more detailed and technical one. 10 the individual subscript i was ignored up to this point for notational clarity. 11 an example of a stochastic production frontier where land appears both as an input and as an inefficiency effect is ngwenya, battese and fleming, cited on page 212 of coelli, rao and battese (1998). the issue of identification is also discussed in battese and coelli (1995) where a time trend is used to capture both technological change and inefficiency change over time. 182 x. irz, j.r. stevenson the expression of the conditional density function of u given e presented in full in battese and coelli (1993)12:   (8) where:   (9 a and b) and f(.) and f(.) denote the density and distribution functions for the standard normal random variable. these expressions express mathematically that the random variable u, conditional on e, is simply obtained by truncation at zero of the normal variable n(m*,s* 2). the farell output-oriented efficiency score follows immediately:   (10) where denotes the fitted output norm and is the estimated residual. 2.2 quantifying the strength of the inefficiency effects next we turn to the issue of quantifying the explanatory power of the inefficiency effects introduced in vector z, which is motivated by our primary aim of exploring the robustness of any potential ir by introducing farm size as an efficiency effect. this problem has been largely ignored in the literature, as the only attempt at tackling it of which we are aware is pascoe and coglan (2002). their procedure simply involves regressing the estimated technical efficiency scores against the vector of inefficiency effect variables z by ols. this approach is ad hoc and seems unsatisfactory because it fails to recognize the highly non-linear way in which the inefficiency effects enter the model. from equation (7), it is evident that the mean of the normal variable truncated at zero to model inefficiencies is a linear function of the z variables but this implies that the relationship between predicted efficiencies (10) and these variables takes a complex non-linear form. we therefore prefer to investigate this question differently. a first approach compares the full specification of the model to a restricted one where the inefficiency effect variable zk is dropped from vector z. the comparison is based on the decomposition of the total variance term e into its random shock and inefficiency components u and v (coelli, 1995): γ*=γ/[γ+(1-γ)π/(π-2)] (11) 12 in the remainder of this section, we again omit the farm subscripts for notational clarity but note that e, u, µ*, z, zk, y and te are all farm-specific. 183efficiency and farm size in philippine aquaculture where parameter γ=σu 2/(σu 2+σv 2). this quantity γ* measures the variation in production not accounted for by physical factors that is attributed to inefficiencies rather than random shocks. hence, the difference between this quantity for the full model and the restricted model gives us directly a measure, in percentage terms, of the explanatory power of the inefficiency effect variable zk. we would also like to be able to measure the strength of the relationship between any zk variable and technical efficiency by calculating a standard elasticity but, once again, the literature seems to have ignored this issue. from equations (8) and (9), one can derive the responsiveness of the conditional predictor of u to a change in any inefficiency effect variable zk:   (12) using this expression in equation (10) defining the efficiency score, one obtains:   (13) this elasticity gives the percentage change in efficiency resulting from a unit percentage change in variable zk. note that it depends not only on the parameter estimates but also on the data so that it can be estimated at any sample point or at the sample mean. the empirical section of the paper uses this expression to derive what we call the technical efficiency elasticity of farm size. 3. data and empirical model two main regions of the philippines for brackishwater pond aquaculture were selected for this particular study. the northern central luzon region has brackishwater fish ponds in the four provinces of pampanga, bulacan, bataan and zambales. the western visayas region is located in the central philippines, and includes the provinces of iloilo, capiz, negros occidental and aklan. the sample was stratified by farm size and by province, based on census data from 1997 provided by the bureau of agricultural statistics. production and socio-economic data were then collected by interviews with farm operators and caretakers (salaried supervisors). a total of more than 150 farms were initially surveyed but several observations were dropped because of inconsistencies and/or missing values, so that our analysis is based on a sample of 127 individuals. table 1 presents the summary statistics of the production variables. the farms in the study area are relatively large, with an average surface area of more than eleven hectares, and land is unequally distributed, as indicated by a gini coefficient of 0.67. the main intermediate input corresponds to the seeds13, followed by the feeds and, finally, fertilizers. 13 this means the “fry” for prawns, “juveniles” for crabs and “fingerlings” for milkfish and tilapia. 184 x. irz, j.r. stevenson this cost structure reflects the extensive nature of brackishwater aquaculture in the philippines, as even in semi-intensive production systems, the feeds account for the major share of cash costs. also, the substantial cost of fertilizers reveals that farm operators attempt to bolster the natural productivity of aquaculture ponds, while the production process in intensive aquaculture relies solely on the provision of feeds from an external source for the growth of the cultivated species. finally, the summary statistics also suggest that labor represents an important cost of production, as, on average, the total wage bill exceeds the cost of any individual intermediate input14. table 1. summary statistics* variable mean standard deviation minimum maximum outputs (kg)         milkfish 4,356 1,098 0 80,000 tilapia 674 230 0 25,600 prawns 691 202 0 22,240 crabs 311 79 0 8,000 inputs         land (ha) 11.5 1.9 0.1 130.0 labor (man days) 1,160 220 187 26,312 feeds (pesos) 95,259 39,617 0 4,420,893 fert (pesos) 33,578 5,867 0 403,260 seeds (pesos) 183,770 46,518 0 4,140,000 *all variables are expressed on a per year basis. with respect to outputs, milkfish is the dominant production in volume. this is not surprising as the polyculture production system described here represents a recent evolution of the traditional milkfish monoculture system (chong et al., 1984). the average milkfish yield of less than 500kg per hectare confirms the extensive nature of production. the volumes produced of the other species appear relatively small compared to that of milkfish but the relative importance of the species is different in value terms. given that prawns fetch a price nearly ten times as high as that of milkfish per weight unit, they actually represent the dominant production in terms of revenue share15. this price differential is explained in part by the fact that milkfish and tilapia are consumed domestically, while an important proportion of the prawns are exported to the high-income markets of japan and the united states. however, notice that crabs, which are also exclusively sold on domestic markets, also receive high prices and are therefore important productions in 14 the wage rate for farm labor is approximately 150 php/day in central luzon and 100 php/day in the western visayas. 15 the average prices per kilogram for our sample are 45php for milkfish, 31php for tilapia, 412php for prawns and 210 php for crabs. 185efficiency and farm size in philippine aquaculture economic terms. finally, we note that, although not apparent in table 1, the farms in the sample choose different associations of species. first, a large majority of farms (82) practice the polyculture of at least two species, hence justifying our earlier discussion on multi-product technologies. and second, the association of all four species is only adopted by a relatively small fraction of the sample farms (11), implying that there is a large number of zero output values in the sample. we choose a quadratic functional form as a first step in estimating the output ray function defined in equation (6):   (14) where the vector w includes each of the (m-1) polar coordinate angles θ(y) and the k inputs, and d is a regional dummy taking a value of unity for the farms located in the western visayas16. the quadratic production function is a flexible functional form in the sense that it can serve as a local second-order approximation to any unknown production function. this specification therefore gives flexibility to the model which can accommodate zero values on both inputs and outputs. the empirical specification includes the three following inputs: land and labor, defined as in table 1 as the total surface area of the aquaculture farm and the number of man days of labor used on the farm; and intermediate inputs, expressed in value terms, and hence representing an aggregate of the feed, seed and fertilizer inputs. on the output side, all four productions were used to define the three polar coordinate angles for tilapia, crabs and prawns. the last step in specifying the model involves choosing the inefficiency effects z, which should include variables susceptible of influencing the adoption of particular management practices or the determinants of their adoption (irz and mckenzie, 2003). given the focus of this paper on the ir, farm size is included as it is our aim to establish whether small and large farms adopt different management practices that lead to differences in efficiency. it is also possible that management differs across regions, and we therefore include the regional dummy as well as inefficiency effect. other variables, such as training and experience of the operator, probably have an influence on efficiency but our data unfortunately does not allow for their inclusion. 4. empirical results 4.1 partial productivity indicators we start our analysis of the ir by investigating the relationship between farm size and land productivity as, although imperfect, partial productivity indicators have played an important role in the development of the ir literature. table 2 presents the results of three ols regressions relating a measure of land productivity to farm size and, in order to account for possible regional effects, the regional dummy d. the first regression uses the 16 we introduce the regional dummy because the preliminary ols regressions discussed below suggest that there might be technological differences between the two regions. 186 x. irz, j.r. stevenson crudest measure of land productivity, i.e. harvest weight per hectare, and the results seemingly indicate a significant and positive relationship between farm size and productivity. however, it makes little sense to add weights of species that fetch widely different prices and the second regression tackles this problem by measuring land productivity in terms of revenue per hectare. the regression has a surprisingly large explanatory power, as indicated by a r-squared value of 0.42 and reveals a significant and negative relationship between farm size and revenue per hectare. the coefficient of the farm size variable is an elasticity and indicates that a 10% increase in farm size results in a 2.2% decrease in revenue per hectare. finally, the coefficient of the regional dummy is also negative and significant, indicating that farms tend to be substantially less productive in the western visayas than in central luzon. the last regression accounts for differences in use of intermediate inputs when comparing farms as it measures land productivity by gross margin per hectare17 but, although it confirms the results of the previous regression, its explanatory power is too low to draw definite conclusions. table 2. farm size and partial productivity   dependent variable regressors log(harvest weight per hectare) log(harvest value per hectare) gross margin per hectare constant 6.52 11.60 90,94 (37.32) (71.78) (6.78) log(farm size) 0.87 -0.22 -8,95 (11.91) (-3.30) (-1.60) western visayas -0.85 -1.69 -63.53 dummy (-4.04) (-8.69) (-3.93) r2 0.55 0.42 0.13 note: t-values in parenthesis the difference in results between the first two regressions imply that, on a per hectare basis, larger farms tend to produce more in weight but less in value terms than smaller ones. hence, it is likely that larger farms tend to choose output combinations with greater emphasis on lower value species (tilapia, milkfish). the difference in results between the last two regressions is more difficult to interpret. it could indicate that smaller farms make a more intensive use of intermediate inputs, or rely more on family labour, than larger farms. we conclude from this preliminary analysis that there is only weak evidence of an inverse relationship between land productivity and farm size. 17 note that for this regression, the dependent variable is the level of the gross margin and not its logarithm. this is so because some farms have negative gross margins, which prohibits the use of a log-log functional form for this regression. 187efficiency and farm size in philippine aquaculture 4.2 specification tests and the structure of the technology the stochastic ray production frontier described above was tested against simpler alternatives in order to gain some insights into the structure of the technology and inefficiencies. a second objective is to define a more parsimonious specification as the full model requires estimation of a relatively large number of parameters given the sample size18. the results of likelihood ratio tests are presented in table 319. table 3. specification tests   log-likelihood lr statistic critical value outcome null hypothesis     5% 1%   1 no inefficiencies -113.0 39.2 8.8 12.5 reject 2 no inefficiency effects -104.3 21.9 6.0 9.2 reject 3 no regional effects -94.1 1.6 6.0 9.2 accept 4 no farm size effect -100.0 13.4 3.8 6.6 reject 5 input-output separability -195.2 203.8 16.9 21.7 reject first, we test the composed error specification against the hypothesis of absence of inefficiencies by comparing the log-likelihood of our model against that obtained by standard ols regression. the likelihood ratio statistic of 39.2 exceeds by far its critical value and we therefore conclude to the presence of substantial inefficiencies across our sample farms20. this implies that the modelling of the technological relationship between inputs and outputs as a stochastic ray production function rather than a deterministic one is strongly supported by the data. the second test investigates the explanatory power of the inefficiency effect variables. it is also strongly rejected, implying that the regional dummy and farm size variables have, jointly, a statistically significant influence on efficiency. the third test considers the null hypothesis that the regional effects, introduced into the model through the regional dummy in the ray production function and in the inefficiency effect component of the model, are inexistent. the hypothesis is accepted, which stands in sharp contrast to the results obtained earlier based on partial productivity indicators. there is no inconsistency here, however, because the ray production function can accommodate possible differences in output mix across regions, while partial productivity indicators fail to do so21. next, the explanatory power of farm size on inefficiencies is tested and the null hypothesis of no farm-size effect is strongly rejected. we conclude from these four tests that the regional dummy variable can be dropped from the speci18 the total number of parameters in specification (14) is equal to 34, for a sample size of 127. 19 the test statistic is lr=-2*{ln(l(h0))-ln(l(h1))}, where l(h0) and l(h1) denote the values of the likelihood function under the null and alternative hypotheses (coelli, rao and battese, 1998). 20 note that the null hypothesis includes the restriction σu=0. as this parameter is necessarily positive, the test statistic follows a mixed chi-square distribution, the critical values of which are found in kodde and palm (1986). 21 in terms of figure 1, the efficiency of farm c is measured radially, which means that this farm is implicitly compared to farms with a similar output mix. by contrast, gross margin or revenue per hectare measures fail to account for possible differences in output combinations when comparing farms. 188 x. irz, j.r. stevenson fication of the model, while farm size as an inefficient effect should be retained. the last test investigates whether inputs and outputs are separable by comparing our model to a restricted version where the parameters of all cross-terms between inputs and polar coordinates angles in (18) are set equal to zero. the null hypothesis is rejected at any sensible level of significance, which implies that it would not be possible to aggregate consistently the four outputs into a single index. this is why the ray production frontier is used rather than a frontier production function, which requires output aggregation prior to estimation. altogether, we conclude from this series of tests that there are substantial inefficiencies among the sample farms, which are partially explained by farm size, while the regional dummy can be dropped from the model’s specification. further simplification of the specification is not possible as the tests indicate that the technology is truly multi-product and the relationship among inputs and outputs is a complex one. table 4. estimated stochastic ray production frontier parameter estimate t-ratio ray frontier α0 0.569 0.68 αa 0.018 0.01 αl 0.965 0.83 αi -0.890 -1.40 αθt -0.685 -0.64 αθc 0.417 0.36 αθp -0.858 -1.87 βaa 0.047 1.27 βll 0.001 0.08 βii -0.092 -13.76 βθtθt 0.140 0.20 βθcθc -0.320 -0.48 βθpθp 0.448 2.00 βal 0.034 0.98 βai 0.403 11.72 βaθt -0.689 -0.69 βaθc 0.588 0.34 βaθp 0.998 1.88 βli -0.218 -5.16 βlθt 0.103 0.11 βlθc -0.920 -0.86 βlθp -0.031 -0.09 βiθt 0.843 1.53 189efficiency and farm size in philippine aquaculture βiθc 0.580 0.69 βiθp 0.368 1.14 βθtθc 0.120 0.22 βθtθp 0.084 0.27 βθcθp 0.066 0.24 inefficiency model δ0 -2.356 -2.26 δa 2.292 4.43 variance parameters s2=su 2+sv 2 1.052 3.02 g=su 2/s2 0.944 32.46 log-likelihood -94.147   subscript notations: a =  land input, l =  labor inputs, i =  intermediate inputs, (θt, θc, θp) =three polar coordinate angles corresponding to tilapia, crabs and prawns respectively; α0 and δo are the constant parameters. the results of the maximum likelihood estimation for our preferred specification are presented in table 4. we note that many of the coefficients present relatively low levels of statistical significance but this should be expected as there is a high level of collinearity among the covariates22. the individual parameters of the technology are not directly interpretable and we therefore compute in table 5 the elasticities of the production ray function at the sample mean, together with their standard errors. most straightforward to interpret are the input elasticities described in equation (4). first, there is a significant and positive relationship between land input and production, as a one percent increase in farm size results in a 0.58% increase in all outputs. hence, land stands out as a key production factor which can be explained by the extensive nature of the technology. second, the elasticity with respect to intermediate inputs is also highly significant, with a one percent increase in that aggregate resulting in a 0.36% increase in production. finally, the elasticity with respect to labor is very small, negative and not statistically significant, which means that the model fails to capture a positive relationship between labor input and production. there are several possible explanations for this negative result. it is difficult to measure labor input properly, in particular as far as farm operators are concerned and the labor variable presents a high degree of collinearity with the other inputs. we also note that the finding of a negative and/or insignificant labor elasticity, although paradoxical, represents almost an empirical regularity (whiteman, 1999). the scale elasticity is obtained by summation of all three input elasticities to give a value of 0.92, with a standard error of 0.11. hence, the technology exhibits slightly decreasing returns to scale at the sample mean but the hypothesis of constant returns to scale cannot be rejected. on the 22 this is not unusual when using flexible functional forms. for instance, in the full translog specification of his model, löthgren (2000) reports only five significant coefficients (5%) from a total of 21 in the specification of the technology. 190 x. irz, j.r. stevenson output side, the elasticities of the ray function with respect to the polar coordinate angles are more difficult to interpret. we conclude, however, that the representation of the technology that we obtain appears reasonably consistent with theoretical expectations. table 5. elasticities of estimated ray production function at sample mean elasticity w.r.t. estimate t-ratio land 0.58 4.51 labor -0.03 -0.43 intermediate inputs 0.36 7.90 qt 0.03 0.12 qc 0.08 0.34 qp 0.61 7.37 4.3 inefficiencies and the inverse relationship the large t-ratio on parameter g in table 4 confirms that inefficiencies are statistically significant. the mean efficiency score for the sample is equal to 0.3723, which is very low and implies that the sample farms could potentially increase production 2.7 times without any increase in inputs or change in technology. this finding suggests that there is considerable room for managerial improvement of the farms in the study area and represents an empirical validation of yap’s contention that many brackishwater ponds are underdeveloped and under-productive (yap, 1999). it can also be explained by the fact that extensive production systems have not been the focus of much research and extension activity in the philippines. the interviews carried out with farmers confirmed that formal extension services are simply not regarded as an important source of technical information by the operators of extensive farms. finally, it is also necessary to recognize that the extensive production systems considered here are intrinsically complex and offer numerous opportunities for farmers to make mistakes. this is so because these systems are open, due to the frequent exchange of the pond’s water, which limits the farmer’s control of the production process. furthermore, the production process depends on the natural productivity of the pond, which itself relates to populations of various plankton and filamentous algae species that are difficult to manage and sensitive to temperature, salinity, soil conditions and the chemical and nutrient composition of the culture water (arfi and guiral, 1994)24. the situation is very different in intensive production where the growth of the target spe23 in the additive model presented here, the predicted efficiency scores can take negative values, which is theoretically impossible. we therefore replaced negative values by zeros when that occurred (in only a few cases) prior to calculating this average. 24 we are thankful to pierre morrisens for this idea. 191efficiency and farm size in philippine aquaculture cies depends primarily on the feeds brought from outside of the farm and the pond has little biological function beyond the provision of oxygen to the fish/crustaceans (kautsky et al., 2000)25. figure 2 presents the frequency distribution of efficiency scores and indicates a high level of heterogeneity within the sample. the distribution is very flat, as reflected by a standard error of 0.26, and is spread over the whole possible range, from a minimum of zero to a maximum of 0.97. we now turn to the direct analysis of the ir by investigating whether these large variations in technical efficiency scores are related to farm size. the likelihood ratio tests already demonstrated the existence of a significant relationship between farm size and efficiency, which is confirmed in table 4 by the large t-ratio of parameter δa. furthermore, note that this parameter takes a strictly positive sign, indicating that larger farms in our sample are less efficient than smaller ones. hence, we conclude to the existence of a statistically significant ir in philippine brackishwater aquaculture. we would like, however, to go further in identifying the strength of this relationship, which cannot be established directly from the parameter estimates and we now implement for that purpose the two approaches discussed in the methodological section. our first suggestion was to compare the full model to a restricted version where farm size is dropped as an inefficiency effect. we find that for the full specification, inefficien25 an analogy with agriculture might be useful here. extensive aquaculture, like organic farming, seems to be management intensive while intensive aquaculture, like conventional farming, tends to rely on the application of standard technological packages that leave little initiative to the farmer. figure 2. frequency distribution of efficiency scores 0 5 10 15 20 25 0-0.1 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9 0.9-1 efficiency score n um be r o f f ar m s 192 x. irz, j.r. stevenson cies account for 86% of the total variance term, implying that the bulk of the variation in production not accounted for by physical factors is attributed to inefficiencies rather than random shocks (i.e., what pascoe and coglan, 2002, refer to as luck). for the restricted model, where farm size is dropped from the z vector, inefficiencies account for only 73% of the total variance term. we therefore conclude that variations in production not accounted for by inputs are attributable to random shocks for 14%; farm size for 13%; and unexplained inefficiencies for 73%. this implies that the ir, although statistically significant, appears to be of only limited quantitative importance. next, we compute the efficiency elasticity of farm size corresponding to equation (13) and obtain a value of -0.137 at the sample mean. this indicates that a 10% increase in farm size decreases the level of farm-level efficiency by a modest 1.4% for the average farm and confirms the previous result of an ir of only limited strength. when farm-level efficiency is predicted by the mode of the distribution of u given e, the efficiency elasticity at the sample mean takes the same value at the three-digit level. the results are therefore robust to the choice of predictor used to infer farm-level efficiency scores. from a methodological point of view, it is also interesting to compare our results to those obtained by application of the procedure suggested by pascoe and coglan (2002) to quantify the explanatory power of the inefficiency effect variables. when regressing by ols the predicted efficiency scores against the logarithm of farm size, we obtain results that are simply inconsistent with the first-stage maximum-likelihood estimation. the estimated efficiency elasticity of farm size at the sample mean is 0.24, with a t-ratio of 3.52, and the r-squared for this regression is only 9%. clearly, the sign of the elasticity is inconsistent with that of parameter δa in table 4. furthermore, these results suggest that farm size explains only 7.7% (=0.09*0.86) of the variation in outputs not accounted for by physical inputs, while we find a value almost twice as large. hence, we conclude that this procedure, which is not consistent with the underlying model of efficiency measurement, can lead to erroneous conclusions regarding both the direction and the strength of the relationship between inefficiency effect variables and efficiency scores. we therefore believe that our methodological contribution is important in deriving the policy implications of the popular battese and coelli (1995) model. finally, we simulate the production effect of applying the comprehensive agrarian reform law, with its five hectare retention limit, to fish ponds. this is done by considering that large farms are broken done into units of five hectares, while farms smaller than the limit are not affected. we compute the adjustment in efficiency for each farm via equations (8-10), and the production effect is found by multiplying the change in efficiency by observed production. in accordance with the previous results, we find that this hypothetical land reform would increase production of the sample farms only marginally (3.5% for prawns, 2.5% for milkfish, 2.3% for crabs and 3.4% for tilapia). 5. discussion and conclusion this paper uses a stochastic ray production function in order to investigate a potential inverse relationship in philippine brackishwater aquaculture, based on a cross-section of 127 farms. the estimated multi-product technology is not separable in inputs and outputs, implying that our approach is superior to the estimation of a stochastic production 193efficiency and farm size in philippine aquaculture function, which requires the aggregation of outputs into a single index. returns to scale are slightly decreasing at the sample mean but the crs hypothesis cannot be rejected. the distribution of efficiency scores is spread out over the whole possible range with an average value of 0.37, which is extremely low. large potential productivity gains are therefore achievable in the study area, without any change in the technology, output mix or input combination. is land redistribution or an improvement in the functioning of the land market a key to achieving these efficiency gains? our analysis reveals that it is probably not the case. we find that there clearly exists a significant inverse relationship between farm size and productivity, but that the strength of this relationship is limited. farm size explains only 13% of the variability in outputs not accounted for by physical inputs, against 73% for unidentified factors, and 14% for random shocks. the elasticities that we derive indicate that when farm size doubles, efficiency decreases by 14%. while substantial, that percentage is small in view of the very low average level of efficiency in the sample. it is therefore likely that application of the land reform laws to brackishwater fish ponds, which so far have secured exemptions via intense political lobbying by the pond owners and lease holders, does not constitute a panacea to unlock the productive potential of these areas. there might be legitimate reasons, on equity grounds, to call for the removal of these exemptions, but the efficiency case for this policy carries only limited weight. we know that the cost of implementing land redistribution programs is always high, and that is likely to be particularly true in the philippines where issues of corruption, weak law enforcement and slowmoving bureaucracy in coastal areas are 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(1999). rural aquaculture in the philippines, rome: fao. appendix 1. dual properties of the ray production function the revenue maximisation problem can be written in terms of the ray function as:   (a.1) denoting by λ the lagrange multiplier, the first order conditions are:   (a.2)   (a.3) re-arranging (a.2), multiplying by yj and summing over all outputs gives:   (a.4) 197efficiency and farm size in philippine aquaculture the first term of this sum can be re-written as  , but since the function θm is homogenous of degree zero in y,   further, using (a.3), equation (a.4) reduces to:   (a.5) this expression means that the lagrange multiplier is simply the unit value of the norm. applying the envelop theorem to the original problem (a.1) therefore gives us:   (a.6) hence, the elastictities of the revenue function and output ray function with respect to any input k are equal and are expected to be positive. we also use (a.5) to rewrite (a.2), from which it follows that the marginal rate of transformation between two outputs is:   (a.7) suppose all the derivatives of the ray function with respect to the angles are equal to 0. this implies that the previous ratio pj/pi is simply equal to yj/yi, which can only be the case if the ppf is perfectly approximated in the plane (yi;yj) by a circle. hence, the restriction that all derivatives of the ray production function with respect to the (m-1) angles are equal to zero means that the ppf is a perfect sphere of dimension m. the ray function also shares some dual properties with the minimum cost function. we proceed as before to rewrite the lagrangian of the cost minimisation problem:   (a.8) the focs are:   (a.9) 198 x. irz, j.r. stevenson   (a.10) proceeding as before it follows that , implying that:   (a.11) the elasticity of the ray function with respect to any input xk is therefore interpreted as the scale-adjusted (optimal) cost share of that input. bio-based and applied economics 4(1): 17-32, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-15088 organisation as a key factor in localised agri-food systems (lafs) corrado giacomini, maria cecilia mancini* department of economics, university of parma, via kennedy 6, 43125 parma, italy date of submission: april 28th, 2014 abstract. most studies of localised agri-food systems (lafs) focus on the localised concentration of members and firms, and pay less attention to organisational factors, particularly those of an exogenous nature. this paper focuses on the role of organisation in a lafs, assessing the efficacy of eu organisational measures aimed at strengthening the concentration of supply from the recent cap reform 2014-2020. the paper has three sections. part one describes the evolution of the concept of lafs. part two examines the leading role played by institutions in organising relationships between firms in a lafs. the example provided is that of the measures affecting the organisation of supply introduced by the recent cap reform, 2014-2020. it makes particular reference to the distretto del pomodoro da industria – nord italia (‘industrial tomato district – northern italy’). part three describes how organisational factors can lead to the creation of a lafs, while the criterion of proximity is necessary but not sufficient. keywords. agricultural policy, organisation, contractualisation, agri-food, place. jel codes. q18, l23, o18 1. introduction various concepts can be used to interpret the economic development of a specific area. these include: marshall industrial districts, localised production systems (lps) and clusters. these three models have three common characteristics: 1. external economies; these are determined by the proximity of players in the production system; 2. knowledge, in other words, the skills and know-how of individuals and firms, is not transferred outside; 3. cooperative relationships exist between firms and the market. the concept of ‘industrial district’ was introduced in the 19th century by marshall (1890), who analysed the advantages of external economies for small enterprises localised * corresponding author: mariacecilia.mancini@unipr.it. 18 c. giacomini, m.c. mancini in a specific area. he found that external economies are generated by the local concentration of small firms and by the presence of a plurality of actors linked through production and trade, and thus recognised the role of the social and institutional environment of the firm (cresta 2008). marshall’s work resulted in the creation of a new scenario for local development policies, which can be used to create favourable conditions for local firms. external economies, in fact, result from industrial and organisational resources in a given area, as well as intangible variables relating to un-codified innovation and the quality of social capital. mutual trust between clients and suppliers of semi-finished goods, worker know-how and lower informational asymmetry between supply and demand are all inherent elements. building on marshall’s work, becattini (1989) more recently defined an industrial district as “a socio-economic entity characterised by the joint presence in a circumscribed area, set by nature and history, of a community of people and population of firms”1. accordingly, for both marshall and becattini, the local factors underpin historical relationships that assist in the development of external economies that lead to a positive environment which strengthens the competitive capacity of firms, mostly small, and system. the second concept, localised production systems (lps) (courlet and pecquer 1992; courlet et al. 1993; courlet 2002), is widely agreed to be somewhat similar to marshall’s districts. lps can be defined as a set of firms, sufficiently near to each other and reciprocally linked, which produce goods directly or indirectly for the same end market. lps are characterised by the geographical and/or organisational proximity of member firms and by a common historical or cultural link. this affects the role of firm activity and resources in the production system, and in part explains the strength of the ties and trade between internal activities and resources and their reproduction and evolution over time. the concept of cluster is also widely considered to be a reworking of industrial districts. porter (1990) finds that the competitive advantage of an area lies in the close link between area and economy, and thus the presence of a cluster of firms localised in a restricted area. porter identifies clusters as “geographic concentrations of interconnected companies and institutions in a particular field. clusters encompass an array of linked industries and other entities important to competition.” these three approaches have all been used in agri-food economics to describe and classify the production dynamics of the territorial groups of small agricultural and food firms2. an example is the concept of localised agri-food systems (lafs), which appeared in french literature in 1996 as syal, or systèmes agro-alimentaires localisés. these systems are defined as “production and service organisations (agricultural and agri-food production units, marketing, services and gastronomic enterprises, etc.) linked by their characteristics and operational ways to a specific territory. the environment, products, people and their institutions, know-how, feeding behaviour and relationship networks combine within a territory to produce a type of agricultural and food organisation in a given spatial scale” (cirad-sar 1996; muchnik and sautier 1998). the key words in this definition are ‘organisation’ and ‘specific territory’. 1 the recent volume edited by becattini, bellandi and de propris (2009) contains a series of studies on the evolution of the concept of industrial district and is of particular interest. 2 there is a great deal of italian literature on agricultural and agri-food districts, including iacoponi (1990); favia (1992); cecchi (1992); becattini (2000); sassi (2009); brasili and marchese ( 2012). 19organisation as a key factor in localised agri-food systems (lafs) ten years later, muchnik (2006) updated the definition of lafs describing them as “models of agri-food development based on the valorisation of local resources, more respectful of the environment, more respectful of diversity and agri-food product quality, more respectful of dynamics of local development and today’s challenges in the countryside”. the two definitions, ten years apart, highlight the evolution in the concept of lafs, resulting from attempts to theorize values expressed in the contexts in which lafs operate (courlet 2002; requier-desjardins et al. 2003; correa et al. 2006; boucher 2007; perriercornet 2009; muchnik 2010; requier-desjardins and colin 2010; fournier and muchnik 2010; sforzi and mancini 2012; torres salcido and muchnick 2012). initially, by focusing on spatial proximity of players in the production model, lafs production paradigm was similar to cluster theory (fournier and muchnik 2010). but more recently, the concept has been more closely linked to the local characteristics of products, people, institutions and social relationships that connect food and place. this research focused on the relationship between lafs and the qualification of local products, where collective action often seeks the designations of origin (do). lafs is also a model that is capable of evolving according to the emergence of new requirements for rural development, such as sustainability, multifunctionality and product quality etc. lafs is thus also “a process under construction, a spatial area comprising relationships between players sharing interests in one or more rural agri-food sectors” (boucher 2007). this definition, among other things, also helps to explain why the concept of lafs has a dual interpretation. on one hand, a lafs is a physical object; a group of agri-food activities with a local collocation. at the same time, it also constitutes an approach or method of analysis of development processes for local resources that is useful for the formulation of development policy (muchnik et al. 2008; muchnik 2009; fournier and muchnik 2010; fourcade et al. 2010). the state and other institutions play a key role in development processes and can offer incentives for firm aggregation in several ways. they can provide infrastructure for connections to markets and promote and regulate types of organisation through which firms can interact formally or informally within the system. in some cases, institutions are so deeply involved in the functioning and organization of lafs that they constitute the very “matrix” (courlet et al. 1993; aubert et al. 2001). interventions on the part of the european union (eu) are particularly relevant. since the early 2000s, the eu has regulated supply and distributed support to various supply chains such as tomatoes, oilseeds, fruit, vegetables and oil. this has impacted on local areas, where producers are encouraged to organise. today, support is given to all sectors, as border protection and price support is no longer available, and competition is a global phenomenon. today’s competition plays out between production systems, rather than between firms, as was the case in the past. the models in the recent reform of common agricultural policy 2014-20203 are contract systems, producer organisations (po) and interbranch organisations (ibo). these organisations enhance a sense of belonging to area and have a clear impact at the local level as they tend to act in the joint interests of their members. consequently, this paper analyses the role of public and private institutions in the formation of a lafs. in fact, the interfirm organisation underpinning the creation and develop3 see regulation (eu) n. 1308/2013 of the european parliament and the council of 17 december 2013 establishing a common organisation of the markets in agricultural products and repealing council regulations (eec) n. 922/72, (eec) n. 234/79, (ec) n. 1037/2001 and (ec) n. 1234/2007. 20 c. giacomini, m.c. mancini ment of a lafs requires governance to be able to lead member firms to establish reciprocal relationships in the space where they are located (benko and lipietz 1992). the paper is divided into three sections. part one describes the evolution of the concept of lafs. part two examines the leading role played by institutions in organising relationships between firms in a lafs. the example provided is that of the measures affecting the organisation of supply introduced by the recent cap reform. it makes particular reference to the distretto del pomodoro da industria – nord italia (‘industrial tomato district – northern italy’). part three describes how organisational factors are a key determinant, whereas the proximity factor is a necessary but not sufficient condition. 2. area and organisation in the formation of lafs a good number of the many success stories of localised agri-food systems concern concentrations of small agri-food firms that process local agri-food products in limited areas (perrier-cornet 2009). the key dimension of the system is always the ‘area’, or the fact that the firms are physically close to one another, and this is what allows for the development of positive externalities thanks to the synergies of joint interest and information flows between firms (requier-desjardins and colin 2010). in comparison with other production sectors, agri-food systems present unusual characteristics that affect both product characteristics and the aggregation of firms. raw materials, whether they are vegetable or animal, are living organisms which closely reflect the natural resources present in their production area and, in general, wherever they are found. the french term terroir is particularly useful to express the idea of place in agrifood economics. terroir includes cultural and social aspects along with natural resources which affect the end agri-food products. a key aspect is savoir faire, or the knowledge and skills belonging to human resources in the area, as well as the synergy derived from sharing these. through these interconnections, the characteristics, the history and the name of the agri-food product are closely linked to the place, and make up the reputation of the product as well as the place. the cultural and social value of local production and the role of collectivity in production know-how have given rise to a rich field of research in france. since the 1980s, researchers (sylvander and lassaut 1994; bérard and marchenay 1995; letablier and delfosse 1995; torre 2002) have studied the influence of place on agri-food products and especially typical or specialty products. typical products feature three types of characteristic; the specific nature of local resources; the history and tradition of production and the collective dimension with the presence of shared local knowledge (sylvander 1995; de sainte maire et al. 1995; bérard and marchenay 1995; allaire and sylavander 1997; barjolle et al. 1998; casabianca et al. 2005). in this light, place is the explanatory variable of the production model of a geographically concentrated set of firms. lafs success stories in underdeveloped and developing countries (latin america and africa) also tend to focus on the link between firms within a given area. in this case, the perspective is production models for local development policy (bom-konde et al. 1995; cerdan and sautier 1998; boucher and requier-desjardins 2005a; boucher and requierdesjardins 2005b; requier-desjardins et al. 2003; mancini 2013). muchnik, cañada and torres salcido (2008) widen the approach with their suggestion that the geographical area of a lafs is not necessarily one continuous geographi21organisation as a key factor in localised agri-food systems (lafs) cally bounded space4. it can also be an ideal reference area that is not spatially continuous, but divided, but which still constitutes a single entity on the basis of common identity reference points (bonnemaison et al. 1999). this is important given that that production activities are often spread over different areas and the ‘localness’ of a lafs is provided by a sense of belonging and the shared nature of interests of those taking part in social networks and economic organisations, which do not necessarily coincide with geographical boundaries. according to this approach, lafs depends not on an area but rather on the system of relationships between firms, which may not lie within a single area. geographical proximity is a potential advantage of local concentration, but it is organisational proximity which gives a lafs the dynamism necessary to meet global challenges (rallet and torre 2004; torre 2000). this organisational proximity is enhanced by a sense of belonging and the common nature of interests. the lafs can, in fact, be analysed as the outcome of cooperation between firms having common interests, localised in one area, which are organised and which agree on production and sales standards in order to gain market advantage over other producers who may or may not be in the same area and who do not follow the rules of the lafs. geographical proximity is central to the coordination of members, and may or may not have local boundaries. it is necessary to establish the extent to which geographical limitations can be overcome without jeopardising the very foundations of the system, the sense of belonging and the common nature of interests (rallet 2002)5. institutions play a key role as they can provide incentives for horizontal (locallybased) development by providing the regulatory framework which legitimises behaviours and choices. these horizontal relationships constitute the “organisation capital” of the lafs, which gradually grows along with relationships between firms, with institutions taking an active role in their development (aubert et al. 2001)6. the set of regulations, the collaboration between lafs members and the legal framework underwritten by local authorities, form the basis of the formal and/or informal governance of the lafs, which is almost always informal, in relation to its aim (torre 2000). 3. eu policy for the aggregation of agri-food firms direct eu intervention in farm prices was halted in 2003 by the fischler reform, (johan and swinnen 2008). in the reform of agricultural policy, 2014-2020, the novelty is that the responsibility for the support and stabilisation of agricultural income is given to three tools. these tools are in existence for some time: on the supply side, there are po in all sectors and ibo which regulate relationships within the chain. the third tool is that of contract systems, namely the formalisation of trade negotiations and the regulation of relationships within the chain covering all affected parties. state intervention extends 4 see fournier and muchnik (2010) who write: “geographical proximity ( “concentration”) is not a sine qua non for the development of a syal”; although they referr to different geographical scales for possible development later in the work, they do not consider a scale larger than a region. 5 the use of itc has increased the area and changes the geographical form of organisation of firms (cappellin 2000). 6 examples of public institutional intervention include codes of specifications and the recognition of do products. see also menard (2000). 22 c. giacomini, m.c. mancini these contracts to all interested operators when signed by pos and ibos representing the majority of operators in an area (danel et al. 2012). these innovative measures, which will strengthen the contractual position of farmers, are contained in cap 2014/2020 and from article 152 onwards in regulation 1308/2013, ‘single cmo’. this alters the position of the ec with respect to supply aggregation, previously promoted and supported within the tight limits of certain cmo (hops, tomatoes, oilseeds, fruits and vegetables, and oil) in compliance with monopoly legislation. today, pos and ibos are extended to all sectors and are no longer primarily a tool for the distribution of aid, as they have been to date for most member states. the clearest example are the operational programmes of the fruit and vegetable pos. from now on, pos and ibos are to become a tool for organising supply, regulating the market and increasing the contractual power of farmers. a report by the french agriculture ministry (malpel et al. 2012) states that pos are associations of farm producers who come together to organise the trade of their produce, to obtain technical assistance, and constitute structures providing storage and processing, and supporting product promotion and enhancement. rio and nefussi (2001) identify certain necessary conditions for setting up and developing ibo. these include the presence of operators active in the same product, or family of similar products, supply chain, in a defined region or country, who draw up a joint strategy on the basis of democratic decisions, hence benefitting from a broad delegation of power by public authorities. coronel and liagre (2006) define an ibo as a private organisation recognised by the state which groups together upstream and downstream operators of the same supply chain. the aim is to make contractual policy choices that develop chain performance and protect its interests, while at the same time ensuring equality between members. the definition by rio and nefussi includes an interesting aspect not mentioned by coronel and liagre in that it specifies that area is an essential factor for an ibo. this is necessary for a public authority to delegate power. indeed, it is what makes it possible for the ibo to extend the contractual rules stipulated by the ibo to non-members of the same area. the ibo translates the strategy agreed upon by various supply chain actors into collective contracts regulating behaviour within firms and in the market. such agreements form the guidelines for supply chain relationships and impose a hierarchical order, which is subordinated to the collective agreements expressed in contracts (coronel and liagre 2006). perrier-cornet and sylvander (2000) write that do chains7 can be analysed as processes of economic cooperation in a given place, between operators organised amongst themselves by way of shared rules, leading to a collective comparative advantage8 from which each individual benefits. in this light it is clear why the success stories involving lafs concern dos and/or specialty products and why the entrepreneurs in these chains are often organised as ibos. such products can only be managed collectively, and the ibo is the most useful type of organisational structure for this (giacomini et al. 2012). the 7 this is true of all typical products. 8 torre (2000) notes that pdo producers who commit to meeting the standards imposed by a code of specifications are helping to build a localised system based on cooperative relationships and a joint strategy for product enhancement. for the link between système localisé de production et d’innovation and quality food production, see also allaire and sylvander (1997). 23organisation as a key factor in localised agri-food systems (lafs) function of protecting the collective ownership of the food product by all members of the chain is often realised by an ibo constructing ‘entry barriers’9. this can lead to a quasimonopoly rent10, in the marshall sense of the term in that supply chain operators share profits according to interbranch agreements. describing do product supply chains (pdo, pgi, etc.) from the point of view of organisational structures along the lines of williamson (1991), perrier-cornet and sylvander (2000) classify them as a hybrid form11. the chains are based on cooperation between members, and defined by long-term contractual relationships that do not affect their autonomy or ownership rights. williamson finds that in hybrid forms relationships between parties are regulated, or rather governed, by principles of authority, and some decision making powers are transferred to third party institutions12. in short, hybrid forms have the function of mediating between the market and entrepreneurs who have entrusted to themselves the governance of the collective interest resulting from the relationships developed in the relevant area. perrier-cornet and sylvander note that for do products, and in general for all lafs where there is governance capable of promoting and defending collective interests, the third party institution to which power is entrusted could be the ibo. the institution is, in fact, required to mediate between operators in the various phases and also directs planning of output in terms of quality and quantity; it produces information on company behaviour within the chain and on market trends, promotes trading partnerships and protects chain interests with regard to public institutions and competitors. it can also activate entry barrier mechanisms. accordingly, in this scenario, the relationships along the chain are concretised in contractual relationships underpinned and mediated by interbranch agreements. these are the main functions of the ibo which, ultimately, act as collective contracts that can intervene in numerous areas: adapting supply to demand, fixing sales terms through example contracts, developing criteria and quality control procedures, gathering and transferring information on market trends, and promoting and valorising products. trading relationships between firms are regulated by formal or informal contracts, but the collective interests implicit in the chain relationships, especially where strengthened by the physical proximity of firms, encourage the chains to organise and adopt rules imposed on its individual links through some sort of hybrid organisation, such as an ibo. for example, by way of reg. no. 261/201213, the ec recently intervened to prevent a milk crisis like the one experienced in 2009. this states that formal written contracts strengthen the responsibility of chain operators and raise awareness of the importance of market signals, improve price transmission and match supply to demand, as well as pre9 reputation or name is the real common good of pdo products, and belongs to the collectivity. 10 for pdo products this can be seen as profit from local quality and as additional income from the local ‘anchoring’ of production (perrier-cornet and sylvander 2000). 11 hybrid structures are between the market and an organisation (williamson 1991) and consist of ‘governance structures’ which manage transactions. they are characterised by being able to exploit goods owned by autonomous members, without this leading to a single integrated firm (menard 1997). 12 see menard (1997), and for governance of ‘hybrid organisations’ of do supply chains see raynaud and sauvée (2000). 13 regulation (eu) n. 261/2012 of the european parlament and of the council of 14 march 2012 amending regulation (ec) n. 1234/2007 of the council as regards contractual relations in the milk and dairy products. 24 c. giacomini, m.c. mancini venting unfair competition. in order to ensure a fair standard of living for milk producers through strengthened contractual power, the ec assigns pos the task of negotiating terms, including prices, with dairies, whilst ibos are given the responsibility of regulating the supply of pdo and pgi cheeses. the ec also emphasises that ibos ‘can play a useful role in facilitating dialogue among the various stakeholders in the supply chain and in promoting best practice and market transparency’, and encourages member states to encourage all interested parties to become members of an ibo. 4. the role of eu policy and the distretto del pomodoro da industria – nord italia14 industrial tomato production and processing have been carried out in the provinces of parma and piacenza15 in northern italy for over a hundred years. indeed, this activity has been rooted in agricultural and industrial innovation and widespread dissemination and training since the early years of the 20th century. in these two provinces, and nearby areas of the po plain, a scientific approach to farming was popularised among farmers, and training and services to farmers were promoted by local institutions. a noteworthy example is that of16 the stazione sperimentale delle conserve alimentari (ssica) (http:// www.sica.it), a state-funded research centre founded in parma in 1922 for the development of the food industry. from that time it has been an important element in the continuity and expansion of agri-food in the area, which is known as the ‘italian food valley’ thanks to the presence of numerous specialty products, including parmigiano-reggiano cheese and prosciutto di parma ham17. history and the accumulation over time of collective elements have always characterised the tomato processing industry in parma and piacenza. the local identity of the area has always been strong, and has been the premise for external economies for firms involved in the dissemination of innovation, trade partnerships and joint cultural growth etc. this has increased the competitive advantage of the area compared with the tomato industry in southern italy, which is markedly less advanced. it is, however, to be noted that towards the end of the 1970s the local proximity of the different phases proved to be insufficient and parma and piacenza were unable to set up and maintain a system that could be used by all individual farms and companies for a collective goal. 14 the distretto del pomodoro da industria – nord italia was analysed in a qualitative-quantitative study. the historical evolution from birth to today was studied qualitatively and data 2006-2012 was used to show current quantitative dimensions of the system. 15 at the end of the 1990s, surface area given over to industrial tomato growing in the two provinces accounted for almost 15% of the entire surface in italy, and 65% of that in the region of emilia-romagna. 16 only in the early years did the tomato processing industry in parma and piacenza have the characteristics of a rural agro-industry as described by requier-desjardins (2002), in other words carry out post harvest activities serving local farms which were mainly household units. but it very quickly took on the characteristics of industrial activity requiring significant capital endowment, and produce was sold on italian markets and overseas. 17 the area of distretto del pomodoro da industria – nord italia includes the distretto del prosciutto di parma dop (pdo parma ham district) (mora and mori 1995; giacomini et al. 2010) and the lafs of the cheese formaggio parmigiano reggiano dop (giacomini et al. 2012). apart from the distinction between lafs and district in terms of the characteristics outlined in the introduction, the two localised systems differ significantly from distretto del pomodoro da industria – nord italia as they cover a smaller area, especially the pdo parma ham district, and in that the products are typical specialities. but in both of these localised systems, the organization of inter-firm relationships promoted by the consortium and its governance is a key factor. 25organisation as a key factor in localised agri-food systems (lafs) as a result of repeated market crises from over-production, the ec introduced reg. no 1151/78, providing aid per tonne for fresh tomatoes destined for processing. the aid is based on contracts signed between tomato producers and processors, or between their associations. the contracts fixed amounts, times and prices, for which a minimum level was fixed by the ec each season. at first, this regulation ushered in a period of stability, but producers soon began to apply for increasing levels of aid, and output shot up. accordingly, the ec introduced fixed production quotas for each processing plant. for the purposes of the present discussion, one of the most interesting innovations was that support was given only to those processing firms that had signed contracts with pos18, and also paid the minimum price for produce within the allowed quota. hence, the pos benefited directly from the aid and received the payments which were passed on to their producer members. this ec’s intervention followed on from direct action by the italian ministry of agriculture to encourage production aggregation. it encouraged producers in parma and piacenza, and nearby areas, to form pos and at the same time persuaded the parma and piacenza processing industry to reduce its number and increase the size of the existing processing plants. some of the biggest plants were, and are today, owned by cooperatives recognised as pos19. this agricultural policy and legislative framework underpins the growth of a po system which, in northern italy, has developed with relative efficiency. it is based on large firms specialised in tomato production and processing, and encourages the creation of better more efficacious relationships between different phases of the supply chain. the aggregation of supply in the pos and the rationalisation of the processing industry in northern italy, centred on the production basin of parma and piacenza, made it clear to farmers and processors that market equilibrium was in the best interests of all parties. consequently, in the early 2000s, representative farmer and industry organisations reached interbranch agreements for industrial tomatoes for all of northern italy, fixing the quantities that factories would buy, as well as prices and quality characteristics. the reform of the fruit and vegetable cmo of 2007 (reg. no. 1182/2007) brought about a fundamental change in public interventions aimed at industrial tomato production, as well as other processed fruits and vegetables. aid was no longer provided, even under quota limits by volume of output processed, and payments were decoupled as per the fischer reform of 2003 for all other sectors. however, given that about 50% of earnings from tomato production is derived from eu support, the new regulation allowed member states the option of reaching complete decoupling gradually, in order to prevent abrupt changes from destabilizing the entire system. through the new regulation, coupled support at 50% could be maintained for three years until the 2010/2011production season, which was opted for by italy. clearly, decoupling could have removed the motivation for industrial tomato producers and processors to draw up interbranch agreements, which were mainly reached to qualify for financial support. instead, decoupling allowed the industry to count on low18 between about 1985 and 1995 industrial tomato producers and other fruit and vegetable producers formed producers associations which were similar in form and function to po. it was with reg. no. 2200/96, the new fruit and vegetable cmo, that the associations became po. 19 an example is copador, a cooperative and po in the province of parma. it is the largest in the province and one of the largest in europe in 1999 it already had a capacity of over 250 thousand tonnes of fresh tomato (giacomini and mancini 2000). 26 c. giacomini, m.c. mancini er fresh tomato prices, and producers to accept lower prices. this led firms and pos to strengthen the existing organisational structures across northern italy and particularly in parma and piacenza (arfini et al. 2007). a preliminary series of meetings in 2007 led to the setting up of an association called distretto del pomodoro da industria – nord italia20. members were pos and processing firms from parma, piacenza and the neighbouring province of cremona. between 2007 and 2011 this association was active in gathering and disseminating production and processing data to members. it also provided opportunities for technical and commercial contact between members and operators in other areas of northern italy. the distretto was originally based in parma and covered three provinces: parma, piacenza and cremona. subsequently it expanded to include the whole of northern italy, thus including pos and processing firms in the regions of emilia-romagna, lombardy, veneto and piedmont (table 1). table 1. industrial tomato cultivation area of members of the distretto del pomodoro da industria – nord italia. year area tomato surface (ha) 2007 parma, piacenza and cremona provinces 15,173 2008 parma, piacenza and cremona provinces 16,530 2009 parma, piacenza, cremona and mantova provinces 21,410 2010 emilia-romagna, lombardy, veneto and piedmont regions 37,944 2011 emilia-romagna, lombardy, veneto and piedmont regions 34,784 2012 emilia-romagna, lombardy, veneto and piedmont regions 32,472 2013 emilia-romagna, lombardy, veneto and piedmont regions 29,175 source: authors’ elaboration on distretto del pomodoro da industria – nord italia data (www.distrettopomodoro.it) this expansion was one of the reasons for the distretto becoming an inter-regional interbranch organisation in 2011, which was formally recognised by the region of emilia-romagna and subsequently by the ec (www.distrettopomodoro.it). in 2013 the ibo distretto del pomodoro da industria – nord italia had the following members: eighteen pos, 10 of which account for most of the fresh tomato output (95%) in the area and 6 of which own their own processing plant , processing over 40% of the tomatoes of the district; twenty-one processing firms, many with a turnover exceeding 100 million euro and in no case under 20 million euro; representative organisations of farmers and industry; and, lastly, local authorities with advisory functions and chambers of commerce of provinces involved and research bodies. in 2013 the distretto processors received about 2 million tonnes of fresh tomatoes, constituting 98% of the industrial 20 the term distretto was not used in the same sense as becattini (1987). here the term expresses the wish that the organisation might constitute an instrument to help local economic development. 27organisation as a key factor in localised agri-food systems (lafs) tomatoes produced in northern italy and over 50% of the pan-italian total production. this represents, almost entirely, tomatoes from po members. accordingly, the high level of pos and processor representation means the distretto is a key component of the interbranch agreement. to date, the agreement has been signed outside the context of the ibo21 but by the same parties. it sets the reference price for the season for industrial tomatoes grown in northern italy, as well as quality standards and coefficients for calculating the final price, the amounts of fresh tomatoes to be grown and supplied to firms represented by their branch associations. the distretto, in its role as ibo, makes use of model supply contracts to be used by each po and processor in order to close sales to processors. for combined grower-processor cooperatives, a supply commitment is made and the price is not fixed beforehand. the example contract contains a series of rules approved by the distretto whereby signatories are required to ensure the traceability of tomato batches and to label the end product as ‘made in italy’. they are also required to inform the po, and thus the ibo, of the amounts delivered to each factory every day and of payments. if payment is not made, the distretto can halt supply to the firm and exclude it from the next season’s negotiations until the debt is paid; or, where established in the interbranch agreement and in the example contract, sanctions can be imposed. the steady nature of supply in terms of quantity and quality, and the ibo behaviour regulations, have had a significant impact on production choices. more than 50% of tomato production is used for higher value added products such as pulps, juices, sauces and cubed tomatoes, which has increased the reputation of outputs from the area. the areas also has a good reputation for customer service among italian and international supermarket chain buyers and the multinational firms that use tomato concentrate in their products. almost 75% of the po members’ growing area, and the biggest processors in emilia-romagna, are located within the distretto, and exports of processed tomatoes from emilia-romagna reached 330 million euro in 2011 (canali 2012). the “organisational capital” of the lafs distretto del pomodoro da industria – nord italia consists of rules drawn up by the distretto on the basis of eu policy. the distretto itself, acting in its role as ibo, governs the collective interest of member firms (courlet et al. 1993; courlet 2002; aubert et al. 2001). hence, the factor that aggregates the lafs distretto del pomodoro da industria – nord italia is not so much the geographical proximity of the firms scattered across northern italy, but rather the organisational proximity, which is supported by a common historical and cultural background, particularly in the provinces of parma and piacenza. 5. concluding remarks unlike many lafs described in the literature, the distretto del pomodoro da industria – nord italia covers a large area, which is also highly developed. it includes nearly all of 21 up until now, the price of fresh tomatoes for a current season in northern italy has been set without input from the ibos, and only by the pos and the processing industry, represented by its own professional organisation. due to monopoly legislation, current cmo regulations on fruits and vegetables (reg. n.1182/2007) prohibit ibos from setting prices, particulary where the organisations are dominant in the area. 28 c. giacomini, m.c. mancini the regions comprising the po plain and is one of the most industrialised areas of europe. the product is something of a commodity; moreover tomatoes are hardly a characteristic specialty of northern italy. on world markets, in fact, tomato products (paste, pulp, sauces etc.) tend to invoke the image of southern italy. yet the economies of the provinces of parma and piacenza are characterised by the food industry22, and the local history of cultural and social development has played a key role in the formation of the distretto. in its current form, the factor which aggregates the lafs distretto del pomodoro da industria – nord italia is not geographical proximity of firms scattered all around northern italy, but is the result of the organisational background previously developed in response to eu agricultural policy rather than geographical proximity of different players in the chain (rallet and torre 2004; torre 2000). it is significant that in emilia-romagna, the region where most of the firms in the distretto are concentrated, the cooperative movement has been historically strong in farming sectors such as dairy, pig farming and tomatoes. eu tomato support was thus absorbed by a chain which was already familiar with some type of organisation. about 40% of output was already processed by cooperative owned plants, forcing non-cooperatives to rationalise and increase in size and decrease their numbers to better absorb the concentrated supply from growers. the tomato industry lafs in northern italy was formed thanks to a common culture based on the farming industry and the cooperative approach to farm management, accompanied by its efficient institutional structure. the same cannot be said for the other half of italy’s tomato output, (concentrate, canned tomatoes, pulp and sauces) from the south of italy23, which is subject to eu intervention and policy in the same way as the north. in southern italy the absence of an industrial tomato lafs has prevented the formation of external economies generated by cooperative relationships between firms and the spread of innovation. the result has been little rationalisation, and yet there is still a high number of firms (100 in 2011). there is, therefore, fierce competition and fresh tomato prices are lower. indeed, there have been cases in the past where the ec minimum price was not respected24. in the distretto del pomodoro da industria – nord italia on the other hand, price stability and stable ibo agreements between farmers and the industry have allowed bigger firms to focus on value added and brand promotion and enhancement. the distretto del pomodoro da industria-nord italia is thus a different type of lafs from those based on typical specialty products from restricted areas or those arising in limited rural areas of developing countries. its key characteristic is the horizontal structure of relationships between firms, enabled not so much by geographical proximity, as by the intervention of institutions, particularly the distretto, in its role as ibo. 22 the two provinces were home to agri-food industries from the early years of the 20th century. 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(1991). comparative economic organisation: the analysis of discrete structural alternatives. administrative science quaterly 36 (2): 269-296. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(3): 313-329, 2012 factors affecting the adoption of genetically modified animals in the food and pharmaceutical chains cristina mora1*, davide menozzi1, gijs kleter2, lusine h. aramyan3, natasha i. valeeva3, karin l. zimmermann3, giddalury pakki reddy4 1 department of food science, university of parma, italy 1 2 rikilt, wageningen university & research center, the netherlands 3 lei, wageningen university & research center, the netherlands 4 agri biotech foundation, india abstract. the production of genetically modified (gm) animals is an emerging technique that could potentially impact the livestock and pharmaceutical industries. currently, food products derived from gm animals have not yet entered the market whilst two pharmaceutical products have. the objective of this paper is twofold: first it aims to explore the socio-economic drivers affecting the use of gm animals and, second, to review the risks and benefits from the point of view of the life sciences. a scoping study was conducted to assess research relevant to understanding the main drivers influencing the adoption of gm applications and their potential risks and benefits. public and producers’ acceptance, public policies, human health, animal welfare, environmental impact and sustainability are considered as the main factors affecting the application of gm animal techniques in livestock and pharmaceutical chains. keywords. genetically modified (gm) animals, socio-economics, life sciences, acceptance, sustainability jel codes. q57, q18, d11. 1. introduction the production of genetically modified (gm) animals is an emerging technique that could potentially impact the livestock and pharmaceutical industries. most gm animals have been developed for research in private or university laboratories; rodents, but also rabbits and pigs, are genetically modified to study action and function of gene mechanisms. apart from those gm animals developed for recreational purposes (e.g., the first gm animal commercialized was glofish®, a gm zebra fish with a fluorescent gene to glow in the dark under uv light), some gm animals are also being produced to improve livestock production, such as those developed to increase growth, to be disease resistant or to increase the quality of their products (meat, milk, etc.). other applications, such as enviropigtm, were created to reduce the environmental impact of farming (e.g., reduc* corresponding author: cristina.mora@unipr.it. 314 c. mora et alii ing phosphorus pollution). finally, genetic engineering can be used for bio-medical and human health applications, like gm livestock (cows, goats, sheep, pigs, chickens or rabbits) developed for producing pharmaceutical proteins from milk, egg white or other fluids (e.g., blood), human antibodies, animal tissue or organs for use in human transplants, or xenotransplantation (houdebine, 2009; laible, 2009; murray et al., 2010; vàzquez-salat et al., 2012). although the economic analyses of potential costs and benefits of gm crops are widely described and used, there is little analysis of gm applications in the animal and pharmaceutical chains. this is because genetic modification of animals has proceeded much slower than crops, for a variety of reasons, such as socio-economic, technical, human health, environmental and animal welfare factors. this paper examines the main drivers affecting the development and adoption of transgenic animals from a socio-economic and life science point of view. a scoping study was conducted to assess research relevant to understanding the main drivers influencing the adoption of gm applications. europe has had a leading role in the development of cloned and gm animals throughout the ‘90s. notable examples include dolly, the sheep that was the first animal created by cloning through transfer of a cell nucleus from a differentiated cell to an egg cell at the roslin institute in scotland. another example is herman the bull, developed by the dutch biotechnology company gene pharming europe. genetic modification was applied so that subsequent generations of female offspring would produce the protein lactoferrin through their milk, which can be used for food, nutraceutical, and pharmaceutical purposes. other experimental animals have been developed at european institutions, including genetically modified fish and chicken, with specific advantages and benefits to food production and other areas of application. despite considerable european innovations occurring in the area of gm animal technology, many of the current activities in the field of gm food animals take place outside the eu, in particular regions like the far east, north and south america, and australianew zealand. it can be envisaged that some of these animals will possibly find their way into the european food supply chain through imports from overseas, in particular given that the eu is the world’s largest international trading block for food commodities. in a more general sense, improvements in animal biotechnology (including but not limited to genetic modification of animals) are expected to result in economic benefits for farmers, processors and consumers. for instance, the development of gm fish species growing faster than non-gm ones is expected to reduce farming costs, e.g. feeding costs, while providing economic advantages for consumers in terms of lower prices (menozzi et al., 2012). however, the distribution of these benefits depends on many factors like the type of technology (cost reducing or quality enhancing applications), market structure and competitiveness (concentration ratio, suppliers’ market power, etc.), information transparency (labelling and traceability programs, etc.), price elasticity, consumer acceptance, etc. besides the direct economic effects, other externalities should also be considered in the overall economic evaluation. in particular, transgenic animals could provide substantial benefits to consumers in the form of safer food produced by healthier livestock, improved products including food with additional health benefits, and, in a more general sense, a cleaner environment through reduction of the environmental footprint of livestock farming (laible, 2009). on the other hand, the application of animal biotechnology should be 315factors affecting the adoption of genetically modified animals properly controlled so as to prevent unintended environmental damage or increased risks to human health, as well as animal health and welfare. the objective of this paper is twofold: first it aims to provide insight into the socioeconomic drivers affecting the adoption of genetically modified (gm) animals in the food and pharmaceutical production chain (feed industry, breeding industry, primary sector, processing industry, and pharmaceutical industry). second, it aims to review the risks and benefits from the point of view of the life sciences on issues like public health, animal health, animal welfare, environmental safety, sustainability, and agro-biodiversity. 2. material and methods scoping studies aim to map rapidly the key concepts underpinning a research area and the main sources and types of evidence available (arksey and o’malley, 2005). scoping study guidelines have been developed to provide suggestions on how to identify relevant papers (keywords, journals, web sources, etc.) for the socio-economic dimensions. strict limitations on the use of search terms were avoided in these guidelines in order to identify relevant studies more clearly and study a selection at the outset and reporting stages. the process is not linear but iterative, requiring researchers to engage with each stage using reflection and, where necessary, to repeat steps to ensure that the literature was covered comprehensively. the kind of terms that it was appropriate to search for was a key question for which all partners involved in the scoping study were asked to provide feedback. an initial list was provided as suggestions (table 1), and new terms were added iteratively. table 1. keywords applied in the socio-economic search biotechnologies-related keywords methods-related keywords animals-related keywords use-related keywords gm animals ge animals transgenic animals clone gm food traceability labelling identity preservation animal welfare intellectual property rights stem cell dna nucleus transfer biotech genetic trait revenue cost, benefit price economic effects cost-benefit analysis supply chain analysis willingness to pay added value food safety costs livestock economics net present value fish, salmon, carp, tilapia pig, sow, swine sheep, goat cow horse rabbit chicken bees food meat feed milk pharmaceutical vaccine medical nutraceutical 316 c. mora et alii several sources were considered in the analysis of the literature: electronic databases, reference lists, key journals and existing networks. the search strategy for electronic databases (e.g. internet, cd-rom, etc.) was developed from the research questions and definitions of keywords and key concepts. it was important to check the reference lists and bibliographies of studies found through the database searches to ensure they had been included in the scoping exercise. another important step was the hand-searching of key journals; this helped to identify studies missed in database and reference list searches. finally existing knowledge and networks could generate information about research. contacting relevant national or local organizations working in the field, eu projects and/or eu support researches with a view to hand-searching libraries and/or identifying unpublished work thus improved the analysis. papers in english were preferred in the study; however, relevant publications in other languages (e.g., italian, dutch, spanish, etc.) were included in the research as well, provided there was an abstract in english covering the main information included in the paper (subject, method applied, main results, etc.) or, alternatively, the main information had been translated into english for charting and reporting. a “data charting form” was defined to collect and standardize all the information of the relevant papers. this form included general information about each study (e.g., year, aim of the study, source, etc.) and specific information (e.g., genetic modification, economic effects, governance issues, methodology applied, main results, factors affecting the adoption of gm technologies, geographical location, outcome measures, data source, secondary results, etc.). in this way, the main characteristics of each study analyzed were shown in the form of a table or graph. these data formed the basis of the analysis. a total of 145 studies were collected from different sources. 3. socio-economic factors affecting the introduction of gm animals a third of the selected studies involved food chains and only in a relatively smaller proportion pharmaceutical chains (30%); about a half of the studies were reviews of transgenic applications and only one third empirical or econometric analysis. this shows the large number of reviews about potential applications of gm animals, rather than actual economic data. many studies were published between 2002 and 2003 (30%), as well as in more recent years (25% after 2007). the type of animals involved was mostly bovine (44% of the studies), fish (30%) and swine (30%), showing a marked interest of research in these species. the review of methods to evaluate the economics of gm animals shows that, although there is great potential of gm applications to improve the performance of animal production chains and pharmaceutical products in theory, the applications ready for the market are very limited (mora et al., 2011). empirical research on economic factors, such as costs and benefits, affecting the introduction of gm applications in animal and pharmaceutical products compared to gm crop products is substantially lacking. for gm applications in animals, most of the economic analyses are focused on gm applications related to introduction of gm hormones or gm vaccines in animals. the economic analysis of gm applications in animals themselves (e.g. introducing foreign dna into germline) are lacking to a great extent. besides, most of the studies are not at the chain level, but at the farm or laboratory level. a wide variety of methods and techniques are used to 317factors affecting the adoption of genetically modified animals analyze the economic advantages and disadvantages of gm applications including quantitative economic models, scenario analysis with simulation models, econometric analysis, and qualitative telephone interviews. from the literature studied, the main factors affecting the (future) application of gm animals techniques to livestock and pharmaceutical chains range from public and producers’ acceptance to public policies. other factors, such as environmental sustainability, human health effects, animal welfare and ethical concerns are also involved and will be analysed in the following sections. 3.1 public acceptance public acceptance is generally considered as a “condicio sine qua non” for any development of transgenic animals in food and pharmaceutical chains. the uncertainty of consumers’ reaction is the largest issue in assessing the potential of animal biotechnologies worldwide (caswell et al., 2003). the framework suggested for adopting technology, therefore, takes the consumer as a starting point. consumers’ attitude (positive vs. negative) and concerns (health, food safety, unnaturalness, ethical, environmental, animal health and welfare, etc.) are fundamental factors to understanding gm adoption and public perceptions of gm technology. these issues have been the focus of several studies (novoselova et al., 2007; frewer et al., 2011). many studies show that public acceptance of gm application is lowest where food or animals are involved (gaskell et al., 2000; aerni, 2004). the fact that plant applications received higher support than animal applications has been reported by a research carried out in the u.s. (knight, 2006). a fao global pool reports that 62% of all respondents worldwide opposed the application of biotechnology to increase farm animal productivity. another example is a survey performed for the pew initiative on food and biotechnology, which indicates that 65% of consumers disagree with the idea of creating transgenic fish to improve efficiency of production (logar and pollock, 2005). the end-user acceptance of biotech varies considerably by application area and by world geography. medical and pharmaceutical biotechnology related to gm animals is generally accepted by most, due to perceived personal benefits for patients carrying strong interests and willingness to take high risks. so in the pharmaceutical sector the level of acceptance for gm animal applications is higher, ranging from 83% in developing countries to 70% in japan (devlin et al., 2009), because of the expected advantages and the different array of political actors (vàzquez-salat and houdebine, 2013). the final user of gm animal food-related applications is the consumer. in countries where food security is not a priority, consumers acceptance of gm animals is expected to be lower, especially for those applications offering economic advantages, like accelerated growth. only a few applications, such as enviropigtm or pigs with omega-3 fatty acids, offer non-economic advantages (vàzquez-salat and houdebine, 2013). fish biotechnology shows the lowest acceptance rate. the low tolerability for gm fish may stem from several factors, including environmental concerns. if geographic differences are considered, consumers’ acceptance is higher in developing countries where the requirement for enhanced food production might be met by application of this technology (devlin et al., 2009). different cultural values were also reported for gm animals resistant to common diseases, such as mastitis. 318 c. mora et alii american animal welfare organisations believed that application of gm would result in a welfare improvement for mammals, whilst their european counterparts consider it to be an excuse to worsen housing conditions and veterinary interventions, with a negative impact on animal welfare (vàzquez-salat and houdebine, 2013). another study shows that disease-resistant animals were the most accepted among livestock-derived products by u.s. consumers, while the least accepted were animals producing tastier and tender meat, those producing human organs for xenotransplantation, and those providing increased outputs (knight, 2006). some empirical studies analyze consumer acceptance of specific gm products, e.g. reporting a higher consumer preference of conventional over gm pork (novoselova et al., 2005). in this case, the negative perception of gm pork may be compensated by improvements in quality, increased animal health and welfare (greger, 2011), a lower impact on the environment, less residues and a price discount (novoselova et al., 2005). increased animal welfare has the most positive effect on consumer choices, whereas improvement in environments receives the lowest utility. this means that, according to this study, consumers trade off gm applications with significant benefits, included price discount. in other words, they have an interest in gm products as long as they bring them different benefits and they are substantially cheaper. the amount of monetary compensation is also dependent on gm application (novoselova et al., 2005). price discount is the most quoted personal benefit for accepting gm salmon (kuznesof and ritson, 1996, grunert et al., 2001, bennet et al., 2005). other benefits associated with gm salmon consumption are health benefits, resulted from higher omega-3 intake (lutter and tucker, 2002; qin and brown, 2006; smith et al., 2010) and environmental benefits, from reducing the need for chemical usage (bennet et al., 2005) or using less fodder (grunert et al., 2001). low consumer acceptance results in high price discounts required by consumers to buy gm salmon, or premium price to avoid this product (kaneko and chern, 2005, chen and chern, 2004, chern and rickertsen, 2004, grimsrud et al., 2002). consumer acceptance in the u.s. is higher than in europe, which leads to a lower price discount required than for european consumers (chern and rickertsen, 2004). other important factors, like environmental sustainability, human health effects, animal health and welfare and ethical concerns may also affect consumer acceptance of gm fish. in this context, a study conducted within the pegasus project analysed 71 papers containing data on public perceptions of agri-food applications of genetic modification (frewer et al., 2013). these papers were published between 1994 and 2010, reporting on data collected between 1990 and 2008, and were amenable to formal meta-analysis. the results indicate that consumer intention to use the products of gm animals was lower than for gm plants or for gm applications in general, independent of region. among europeans, there was less intention to purchase and a lower acceptance for the products derived from gmos than in asia and north america. similarly, results show that north american and asian consumers had more positive attitudes to gm applied to agri-food production compared to europeans. north americans perceived more benefits associated with gm overall when compared to europeans and asians. however, benefit perception increased with time in all of the regions for which analysis was possible. this effect occurred independent of whether the target of the application was focused on gm animals, plants or generic applications. north american, south american and asian participants perceived fewer risks than europeans. risk percep319factors affecting the adoption of genetically modified animals tion increased with time, almost equally compared to benefit perception increase, independent of region and of target organism. in contrast, ethical and moral concerns were greater in north america and asia compared to those in europe. 3.2 producers’ acceptance like consumers, producers may also have concerns about the adoption of a new technology. uncertainty surrounding the way the technology will perform in the future, concerns related to increased dependency on input suppliers, expectations of higher input prices, problems related to coexistence at the production stage and segregation along the supply chain, uncertainty of the results and of the likely consumer acceptance, are among the main producers’ concerns cited in the literature reviewed (melo et al., 2007; novoselova et al., 2007; areal et al., 2012). it is also clear that producer acceptance will depend on the benefits expected from the gm application (reduction of feeding costs, increase yields, etc.) and on how costs and benefits are distributed across the chain. it is often argued that the costs of technology adoption occur in one stage of the chain, while the benefits are perceived in another stage (novoselova et al., 2007). initially, the methods for animal transgenesis, such as microinjection technology (i.e. dna transfer via direct microinjection into a pronucleus or cytoplasm of embryo), were highly inefficient, but recent scientific advances have overcome many of these technical difficulties (houdebine, 2009). however, it has been suggested that gm animal applications for food production are more technically difficult to develop than the pharmaceutical ones, mostly because of difficulties in selecting the appropriate target genes and because of increased welfare concerns, especially regarding growth-related transgenesis (vàzquez-salat and houdebine, 2013). moreover, the long reproductive cycles of large animals, such as cows, is considered as a major limiting factor, since projects involving such animals require significant investment over extended periods of time (vàzquez-salat and houdebine, 2013). compared to mammals, avian species are easy to raise and have short reproductive cycles and high egg production; they are therefore particularly suited to more efficient production of commercially valuable and biologically active proteins in egg white for pharmaceutical and industrial use (li and lu, 2010). similarly, the high research attention placed on transgenic fish is explained by technical factors, i.e., a higher production of eggs that can be more easily manipulated (aerni, 2004), as well as by economic reasons, since fish farming is a rapidly growing market (menozzi et al., 2012). it has also been suggested that existing structural differences in different production chains will also have an effect in the adoption of gm animals. the strong vertical integration and the powerful role of multinational companies in sectors like pharmaceuticals may facilitate the adoption of a new application (vàzquez-salat and houdebine, 2013). the commercial release of transgenic animal products into food chains may also require new boundaries, e.g., segregation and other handling measures required to guarantee coexistence (areal et al., 2012). this implies additional costs on the production chains while also creating new objects of governance requiring specific regulatory attention (bloomfield and doolin, 2011). the production of high-value products from transgenic livestock, e.g. lactose-free milk, could also affect the structure of agricultural industry with new niches and segmented markets (melo et al., 2007). 320 c. mora et alii in the specific case of aquaculture, it has been suggested that a company that produces a new growth-enhanced salmon may not just face scepticism from consumers, but may also be shunned by the fishery industry itself. american aquaculture producers have been described as reluctant to accept gm fish, and aquaculture producers’ association in norway reassured the consumer that they will not use gm salmon in their farms (vazquezsalat and houdebine, 2012). established local fish producers might fear new competition from transgenic fish and a radical change in the market structure of the sector. if transgenic fish become widely grown because of their higher efficiency, and if special broodstock are required to produce fry for on-growing to adults, which cannot be used as broodstock, a dependency on input suppliers is created. depending on the arrangements made for seed supply, this dependency may become more or less oppressive for fish farmers (beardmore and porter, 2003). in turn, retailers, who wield most market power in the food business and value consumer concerns more strongly than producers’ innovative strategies, may be unwilling to buy transgenic fish and run the risk of being ostracized by their customers. companies may also be afraid of anti-gmo campaigns by activist groups which might negatively affect the public image of the brand (aerni, 2004). the picture varies considerably if we consider the pharmaceutical sector. biopharming is the production of pharmaceutical compounds in plant and animal tissue in agricultural systems and it is considered as the next major development in both farming and pharmaceutical production (kaye-blake et al., 2007). for biomedical applications, gm animal technology not only enjoys the greatest public acceptance due to perceived personal benefits – such as obtaining cheaper drugs produced more quickly – overriding other ethical concerns (devlin et al., 2009), but also commands supreme economic incentives. for pharmaceutical firms the use of transgenic animals for producing proteins and other pharmaceutical compounds in milk and other animal tissues, promises a method for reducing production costs and increasing yields. however, due to the high costs, the production of transgenic animals such as pig, goat, sheep and cattle must bring an elevated profit in order to be an economically feasible investment. drugs produced by animal bioreactors, although highly valuable, are often targeted to a small community of patients which makes these applications less attractive to multinational companies’ investment (vàzquez-salat and houdebine, 2013). nonetheless, the production of high-value pharmaceutical substances is the principal and most promising application for animal transgenesis (melo et al., 2007). so it is not surprising that the recombinant protein atryn® (human antithrombin-iii) produced in transgenic goats’ milk was approved in the eu in 2006 (houdebine, 2009) and the ruconesttm (rhucin® outside the eu), a recombinant c1-inhibitor produced by a gm rabbit, in 2010 (vàzquez-salat and houdebine, 2013). 3.3 policy implications public policies affect the profitability of private r&d investment through mechanisms that include direct public funding of research, intellectual property rights legislation, regulatory policies, financial and tax policies, education policies and other policies covering the environment and industry (caswell et al., 2003). several documents have been produced to provide insights into the governance of products derived from transgenic animals (gavin, 2001; kleter and kok, 2010). food safety and environmental risk 321factors affecting the adoption of genetically modified animals assessments are considered fundamental steps to deal with these new technology applications. recently, a review was carried out on behalf of the european food safety authority (efsa) to define environmental risk assessment criteria for gm fish to be marketed in the eu (cowx et al., 2010). it has also been argued that, as decisions made by one country may affect the others, different approaches towards decision-making should be harmonized as much as possible (le curieux-belfond et al., 2009). intellectual property rights (i.e. patents, trademarks and copyrights) influence a firm’s incentive to invest in r&d by enhancing a firm’s ability to capture rent and profits resulted from the innovation (caswell et al., 2003). in the case of biotechnology and transgenic animal in particular, this is a very difficult issue. the transgenic animals’ patent debate is not confined to technical and legal arguments and has extended over ethical and political issues, including public opinion. many products of nature (like specific antibiotics, microorganisms, protein etc.) have been successfully patented protecting the innovators right to reproduce. but it is debatable whether a naturally occurring substance can be patentable, as it lacks novelty and inventive steps. however, if a product of nature is enriched, purified or modified in an industrially useful format, it is then patentable. biological materials which previously existed in nature are patentable provided they are purified from their natural environment and confirm to the general patentability principles regarding novelty, non-obviousness, utility and sufficiency of disclosure (daneshyar et al., 2006). the future of private industry funding for biotechnology r&d will be influenced by the regulations in force. for instance, multinational companies in the pharmaceutical industry were believed to be unwilling to invest in gm applications until they are accepted by regulatory agencies such as the u.s. food and drug administration (fda) or the european medicines agency (ema) (vàzquez-salat and houdebine, 2013). in particular, environmental and food safety regulations are expected to affect the profitability of r&d by i) increasing the costs of developing new technology by: extending the time necessary to bring a product to market and ii) increasing the cost of meeting stricter standards (caswell et al., 2003). regulatory policy and industry practices associated with transgenic livestock must be transparent and effectively communicated to achieve consumer acceptance (kochhar and evans, 2007). strict control of an animal or a herd starts at the level of identification. reliable and permanent identification is already available in the livestock industry in many forms, such as ear tags, ear tattoos, external electronic transponders, subcutaneous electronic transponders, etc. (gavin, 2001). segregation measures along the supply chain to guarantee coexistence of gm and non-gm animals and derived products may impact producers’ willingness to adopt the technology (areal et al., 2012). therefore, the impact of heavy regulatory procedures may be stronger in the breeding sector, where the abilities of small and medium enterprises to efficiently comply with it can be limited, than in the pharmaceutical sector, where the market is highly harmonised and shaped to absorb the administrative regulatory burden (vàzquez-salat and houdebine, 2013). labelling and information policies could be a solution in helping consumers to make a deliberate choice and in helping producers to differentiate their products. assuming that gm animals and derived products will be properly labelled in the eu once approved and commercially available, it is unclear whether the food obtained from gm animals will have to be labelled on other markets. the u.s. fda is now debating whether gm salmon should be labelled (u.s. food and drug administration, 2010), mostly for environmen322 c. mora et alii tal and allergenicity reasons, although this would lead to a different solution compared to food from gm crops. labelling regulations will lead to extra costs, including the costs of traceability (novoselova et al., 2007). monetary costs associated with tracing and labelling biotech-derived animals and their products have to be taken into account, especially in countries like u.s. where such regulations are not in force for gm crops. other costs that might be necessary to meet the regulatory requirements (e.g., segregation with physical containment for gm fish) will also have to be considered. the costs of complying with regulations will likely reduce the private profitability of the technology, but the public will benefit from reduced risk. thus, the balance between the costs and benefits of the regulation will determine the social cost-effectiveness of the regulation (caswell et al., 2003). finally, it has been argued that gm animals will likely face similar regulatory challenges in the u.s. and eu for their strict regulations in both pharmaceutical and food sectors. however, it is not clear yet if these regulations will also be applied in other countries where investment is high (e.g., argentina and china), or if a more favourable regulatory framework will offer a competitive advantage (vàzquez-salat and houdebine, 2013). in this context, china, where regulatory requirements for the approval of gm animals and derived products are already in place, is expected to take the lead thanks to a favourable policy environment and steady investments in this field. 4. life science factors affecting the adoption of gm animals as explained above, the pegasus project also explored the factors of gm animals producing food, feed or pharmaceuticals which have an advantageous or disadvantageous impact from a life science perspective. the outcomes were summarized in a project report (kostov et al., 2011). from a general, overall review of the literature and risk assessment guidance documents [including the codex alimentarius and scientific panels of the european food safety authority (efsa)], different categories of factors were identified. these were human health, animal health and welfare, the environment, sustainability and agro-biodiversity. human health considerations include the potential effects on consumers of gm animal-derived foods as well as humans, such as farmers, coming into contact with the animals. for the safety of foods produced from gm animals, internationally harmonized guidelines have been published by the fao/who codex alimentarius (codex alimentarius, 2009). this is an international organization representing nations of the world which sets internationally recognized standards and codes of conduct for food quality and safety. the scientific panels of efsa on genetically modified organisms and on animal health and welfare recently published guidance on the assessment of food and feed safety as well as animal health and welfare, which expands upon the codex alimentarius’ (efsa, 2012). a central role in the approach recommended by codex alimentarius and the efsa gmo panel is the comparative assessment of gm products with conventional non-gm counterparts with a history of safe use, in addition to the molecular characterization of the introduced genetic material. the focus of the additional tests is on the differences identified by this comparative analysis. commonly considered items include the occurrence of unintended effects alongside targeted modification, potential toxicity and allergenicity (of the introduced or altered components), nutritional value, and horizontal gene transfer. 323factors affecting the adoption of genetically modified animals additional considerations include, for example, the potential transfer of zoonotic pathogens from the animal (acting as a reservoir) to humans and the safety of the vectors used for the transformation of the gm animal (e.g. viruses) (codex alimentarius, 2009; efsa, 2012). among the advantages identified are the ability to produce enhanced quantities of food (food security) or food with increased quality characteristics, as well as new or ameliorated pharmaceuticals for the cure of patients. as a disadvantage, potential human health impacts linked to the use of this technology have to be assessed before the product can be marketed (kostov et al., 2011). the impact on the health and welfare of the gm animals themselves are also a focus of attention. this includes the health of founder animals, selected further for desirable traits and absence of other adverse symptoms and used for commercial production as well as the first generations after genetic modification. the approach is comparative in this case too, and compares the impact of the genetic modification of the gm animal versus the health and welfare of non-gm animals. moreover, health and other phenotypic characteristics of the gm animal compared to a non-gm animal may also serve as an important indicator for potential adverse effects on both consumers and people coming into contact with the animal. welfare includes the ability of the animal to express its normal behaviour, among other things, and is linked to animal health. an advantage of the use of gm technology in animals is the ability to enhance resistance against parasites and diseases, while the disadvantages include possible suffering of the animals during the genetic modification process (including that of surrogate dams) as well as potentially adverse effects on the offspring (kostov et al., 2011). the potential environmental impact of gm animals straddles a wide range of issues, of which two important ones are 1) the possible effects on wild populations, such as introgression or replacement (once the gm animal is released into the environment) and 2) the impact on the eco-system as a whole. these effects can be caused by either or both of two factors; the behaviour of the gm animal itself once released into the environment (e.g. after escape) and the production systems used for raising the gm animal as compared to conventional systems. the possible advantages identified include the decreased environmental burden of more efficient production systems as well as diminished requirements for space and inputs (e.g. for rapidly growing farmed fish). the possible disadvantages identified include possible disruption of ecosystems and loss of biodiversity of wild populations (kostov et al., 2011). with regard to the issues of sustainability, this relates to the ecological footprint of the production system for raising the gm animal (and whether this has changed as compared to conventional production). agro-biodiversity relates to the animal breeds that are available to breeders for creating new breeds with desirable characteristics. a possible advantage of gm animals in this respect is that this technology widens the genetic resources available to the breeders for improvement of animal characteristics (such as disease resistance). on the other hand, there may be a loss of agro-biodiversity of commercially used breeds (e.g., if less competitive than gm animals) as well as issues related to the privatization of genetic resources (e.g., patenting) (kostov et al., 2011). the advantages and disadvantages from life science perspectives have been further explored in depth in three case studies, growth-enhanced salmon, dairy cattle producing human lactoferrin through their milk, and rabbits producing humanized polyclonal 324 c. mora et alii antibodies. these case studies include aspects of terrestrial and aquatic animals, as well as food and pharmaceutical applications (kostov et al., 2011). growth-enhanced gm salmon, which is to be used in aquaculture, does not grow bigger than conventional cultured salmon but reaches its marketable size within a shorter time span. the possible advantages identified include nutritional benefits for consumers if fish becomes more affordable and hence is consumed in greater amounts by certain segments of the population (leading to increased uptake of omega-3 fatty acids). another envisaged advantage is decreased environmental burden caused by aquaculture systems employing gm fish owing to less feed inputs required and less waste for the same outputs. possible disadvantages are animal health issues, such as skeletal deformations observed in some studies on experimental gm fishes and enhanced stress under oxygen-deprived conditions caused by increased need for oxygen. an environmental issue, and a possible disadvantage, which has received a lot of attention surrounding the potential market introduction of growth-enhanced salmon, is the effect of escape of such fish into the wild on natural salmon populations. because of this, one company seeking market approval in the usa has proposed to grow this salmon in tanks in land-locked facilities instead of the conventional aquaculture practice employing pens in open waters (kostov et al., 2011). with regard to the recombinant human lactoferrin protein (naturally occurring in human mother’s milk) produced through the milk of gm dairy cattle, it is noted that this product may have different purposes. for example, lactoferrin’s antibacterial properties may strengthen the animal’s defence against certain bacterial infections, such as mastitis. because of its antibacterial properties, it may also find applications in human medicine, after purification from the bovine milk. moreover, because of its iron-binding capacities, the bovine form of lactoferrin has been used as an ingredient for baby and infant foods. the human version of this protein could help consumers to avoid allergic reactions. depending on the application chosen, the products could thus fall under different categories, each covered by a different legislation (besides gmo regulations), such as dietary supplements, human or veterinary medicine, or foods for medicinal or particular nutritional uses. a possible advantage of the gm dairy cattle producing recombinant human lactoferrin is the improved health of humans and animals, while possible disadvantages include animal health and welfare effects on the first generation of offspring and their dams (so-called “large offspring syndrome”, which may occur at high frequencies as a result of cloning techniques for creating the gm animals) (kostov et al., 2011). with regard to the production of humanized polyclonal antibodies in rabbits, this aims at the application of antibodies for “passive immunization” of human subjects against the antigens, such as pathogens, with which the rabbits have been challenged so as to trigger the production of antibodies neutralizing the antigen. these antibodies contain a range of molecules with slightly different structures that recognize distinct parts on the antigen, to which they bind, forming an antibody-antigen complex that can be further neutralized by specialized cells of the host’s immune system. replacing the rabbit’s own polyclonal antibodies with a humanized version helps to prevent possible reactions against rabbit-derived proteins when antibodies purified from serum of immunized gm rabbits are used in human subjects. a wide range of antigens can be used to challenge the gm rabbits so as to trigger the production of antibodies recognizing these antigens. this provides a flexible production platform that can be employed against a great variety of dis325factors affecting the adoption of genetically modified animals eases to be treated with passive immunization, and is also envisaged as a possible advantage for human health. a possible disadvantage is the environmental consequences of a hypothetical escape of these animals into the wild. it is considered that gm animals used for production of pharmaceuticals will have to be kept in highly contained facilities under disease-free conditions, so that the animals would be unlikely to be able to cope with natural conditions in the hypothetical event of escape (kostov et al., 2011). the case studies above show that a number of generalizations are possible on potential issues relating to food and feed safety, animal health and welfare, environmental safety, sustainability and agro-biodiversity. but at the same time each specific case also raised case-specific concerns and envisaged benefits from the life-science perspective. 5. conclusions the production of transgenic animals, which could potentially have a big impact on the livestock and pharmaceutical chains, has proceeded much slower than genetic modification of crops. improvements in animal biotechnology are expected to result in economic benefits for farmers, processors and consumers. beside the direct economic effects, other externalities, both positive and negative, should be considered in the overall evaluation. the interest in gm development in aquaculture is stronger than for terrestrial animals. there are several reasons for this; faster growth rates in fish and improved feed conversion rates that may result in a cost reduction, and thus lower market prices, which also explain why the economic impact of the introduction of gm fish could be significant. the case of growth-enhanced gm fish shows that benefits for producers, arising from increased growth rates and food conversion rates, may lead to a reduction in costs and, without a full transmission of these advantages to consumers, to an increase in gross margin. at the same time, environmental and human health risks should be considered in depth in the overall evaluation of the transgenic fish introduction. in fact, serious ecological concerns associated with gm fish farming may make necessary physical containment strategies, which may potentially limit the economic attractiveness of gm fish. biopharming is a new territory for the agricultural and pharmaceutical industries, and presents novel challenges for government regulators and others. due to the high cost, the production of transgenic animals such as pig, goat, sheep and cattle must bring an elevated profit in order to be a feasible economic investment. for this reason, the production of high-value pharmaceutical substances, which correspond to a market worth billions of dollars, is currently the principal and most promising application for animal transgenesis. however, the financial commitment required during the protracted development phase has halted many attempts at commercial exploitation and, at present, only two drugs produced in this way have reached the market. given the rapid development of these technologies and the intense gm debate of the 1990s, some governments are beginning to produce a regulatory response to the marketing of gm animals. experts argue that the distinction between the u.s. and eu approaches, which in the past accompanied the development of gm crops, might be less marked in the case of gm animals (vàzquez-salat et al., 2012). both players are going to face stakeholders’ adversity, e.g. from animal welfare organizations, and a lower positive pressure from multinational companies. the regulatory strategy adopted by these global play326 c. mora et alii ers will affect their ability to exploit the commercial potential of biotechnologies as well as international trade. in this context international bodies, such as fao, world health organization (who) and world organization for animal health (oie), will have an important role in providing forums for neutral discussion and encouraging harmonization in the food sector (vàzquez-salat et al., 2012). a review of these issues in general and for the three case studies in particular (growthenhanced salmon, dairy cattle producing recombinant human lactoferrin, rabbits producing humanized polyclonal antibodies) shows that at present it is not possible to make generalizations on the possible advantages and disadvantages of gm animals from a life science perspective. so should one of these be introduced for possible marketing in europe, a case-by-case approach will need to be followed for the assessment of these issues. acknowledgments an earlier version of this paper was presented at the 1st aieaa conference ‘towards a sustainable bio-economy: economic issues and policy challenges’. 4-5 june, 2012, trento, italy. this research has been supported by the pegasus (public perception of genetically modified animals – science, utility and society) project which is funded by the european commission through the seventh framework programme (grant agreement n. 226465). the following people contributed to the research described in this paper: p. rüdelsheim, g. smets (perseus, belgium); g. dimov, t. dzhambazova, k. kostov (agrobioinstitute, bulgaria); l.m. houdebine (inra, france); a. merigo, s. pancini, g. sogari (university of parma, italy); j. bartels, m.j. reinders, i. van den berg, x. zhang (lei, the netherlands); j. van dijk, m. groot, m. noordam (rikilt, the netherlands); i.a. van der lans, a.r.h. fischer (wageningen university, the netherlands); s. bremer, m. kaiser (university of bergen, norway); g. rowe (institute of food research, u.k.); b. salter, n. vàzquez salat (king’s college of london, u.k.); m. brennan, l.j. frewer, m. raley (university of newcastle, u.k.); k. millar (university of nottingham, u.k.). in particular, we gratefully acknowledge leadership and coordination of prof. lynn frewer. the information contained in this paper reflects only the authors’ opinions and the sole responsibility lies with the authors. the european commission is not liable for any use of the information contained therein. references aerni, p. 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(2012). the current state of gmo governance: are we ready for gm animals? biotechnology advances 30(6): 1336-1343. bio-based and applied economics 4(3): 301-320, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-17437 short communication a tribute to giovanni anania: scholar, mentor, friend this section reports the contributions to the special session chaired by jo swinnen held at the 29th international conference of agricultural economists, milan, 9-14 august 2015 to honor the memory and contribution of giovanni anania. giovanni suddenly and unexpectedly passed away on 15 july 2015. giovanni has been for many of us a warm colleague and friend, and an example of moral integrity, leadership and commitment to any endeavor he engaged in. those of us who had the privilege of working closely with giovanni count our collaborations with him as among the most stimulating and rewarding of our careers. he was mentor of many students and especially in italy he was reference for various generations of agricultural economists. giovanni began his career as an outstanding economist getting a post-graduate diploma at the portici centre in naples and earning the phd from the university of california at davis. he quickly rose through the ranks and became full professor and head of the department of economics at the university of calabria, italy. giovanni was a great scholar and participated in or coordinated many national and international research projects. he published many books and articles, receiving the european association of agricultural economists (eaae) quality of policy contribution award. giovanni provided several relevant scientific contributions mostly in the fields of agricultural policy analysis and international trade. he significantly contributed to the profession as an active member of national and international associations and recently served as president of the eaae. he also played an important role in the policy debate both at the national and international level through consulting activities with european commission, fao, ictsd, and oecd. last, but not least, giovanni was also a great organizer. he was ready to do whatever it takes to organize successful scientific events, defining stimulating agendas and planning memorable social outings, such as the iatrc conferences in isola capo rizzuto and capri. the special session in honour to the memory of giovanni has been organised in two parts: the first part focuses on giovanni’s contributions to agricultural economics, while the second one on giovanni’s contributions to scientific associations and institutions. we are very grateful to the colleagues and friends of giovanni who contributed to that session and to the hundreds of agricultural economists who participated. margherita scoppola president of aieaa 302 a tribute to giovanni anania tribute with respect to his work on trade policies and agreements mary bohman (ers-usda, us), colin carter and alex mccalla (university of california, davis, us) we write these words to help us celebrate the life of giovanni anania both as a talented and internationally recognized applied agricultural economist, but more importantly as a warm, caring, ever positive human being. we were blessed with the opportunity to work with him as a scholar, enjoy him as a conscientious, understanding and collaborative colleague, and as a loving friend whom we loved in return. indeed, he was a true and loyal friend and a loving husband to his wife margherita. we shall all miss him but are much better off for having known him. we focus on two facets of giovanni: the man-the wonderful human being and the scholar -whose work will be his lasting legacy. giovanni the man we all first encountered giovanni at davis as a fellow student, as a co-author and as his major professor. we all watched as he did extremely well in our program and was always intellectually curious and challenging to be around. once you met giovanni you were hooked for life. you wanted to be his friend and he reciprocated in kind. giovanni was the heart and soul of his davis graduate student co-hort. he and margherita hosted large, italian style dinners at their graduate student apartment with tables, chairs and dishes they borrowed from their close friends ila and mondi temu to seat everyone. giovanni’s leadership among students portended his future leadership roles that we all have benefited from. he was always fair, seeking to see all sides of the issue, careful in his judgements and sensitive to those on the other side. he didn’t have a discriminatory bone in his body. after he left davis all of us counted him as a valued colleague and forever friend. giovanni was constantly engaged internationally with the international agricultural trade research consortium (iatrc), a member since 1986 when he attended as a graduate student. he was a regular and active participant, with the aaea, iaae and the european association of which he was the current president. all three of us marveled at his organizational prowess as we worked as a team to put on two wto conferences in calabria and capri, both cosponsored by the iatrc. we were all thrilled when giovanni went out of his way to attend our 50th anniversary celebration of the uc davis phd program this past march. he came, he said because he was a life time fan of the uc davis program. as usual he broke up the house as he helped recall some of the non-academic happenings of his decade. for all of us it was our last chance to enjoy being giovanni’s friend. we are proud that he was such a distinguished ambassador for the uc davis program. alex mccalla had the special pleasure interacting with giovanni closely over an extended period as he worked through a complex theoretical and modelling topic in inter303a tribute to giovanni anania national trade. in his phd dissertation acknowledgement he wrote: “working with alex mccalla was always stimulating and encouraging. the lessons i learned from him go far beyond the matter of this dissertation. i only hope that i will be able to establish with my students the kind of relationship that i had with him”. that relationship lasted 30 years until july 15th and will always be cherished. we have abundant evidence that he was loved by his students. we shall all miss him, but we will be sustained by remembering the good times. giovanni the scholar giovanni came to davis with a firmly established concern for the economics of agriculture and the welfare of rural people in southern italy. his early writings were concerned with market imperfections and the impacts of discriminatory policies. at davis he chose to work on international trade issues because he saw things like targeted exports subsidies, embargoes and trade preferences as real issues in agricultural trade but as he tried to model them he found traditional spatial trade models did not properly deal with the possibility of countries being both importers and exporters of the targeted product. his thesis addressed and provided a modelling approach to deal with the issue. this was the beginning of his life time interest in multilateral, bilateral and unilateral trade policies, analysis of their impacts and critical role in trade negotiations. in the early 1990s, giovanni (and alex mccalla) published a paper showing the importance of alternative assumptions regarding arbitrage behavior when modeling discriminatory trade policies. they found the previous literature was quite restrictive due to the use of simple arbitrage rules. they applied their more realistic model to the us-ussr grain embargo in the late 1970s and concluded that this type of export embargo is unlikely to be effective. in this same time period, giovanni examined the domestic and international impacts of the u.s. export enhancement program (eep) for wheat. eep used targeted in-kind subsidies to expand u.s. exports and was designed specifically to compete with subsidized exports from the european community (ec). with his co-authors, mary bohman and colin carter, professor anania found that eep could not be welfare-improving for the u.s. it was in this early work that giovanni demonstrated his tremendous skill for modeling complicated international commodity markets. he had a talent for seeing the core economic drivers instead of getting lost in the obscure details. this was one of the central themes of his research program throughout his career. just a few years ago he published an insightful paper on calibrating mathematical programming spatial models. in a 2000 oecd report, giovanni identified the major agricultural trade policy concerns of oecd non-members. the insights from his research were used to supplement oecd documents that considered how emerging and transition economies were affected by the implementation of the uruguay round agreement. professor anania assisted the oecd in understanding the issues at stake for emerging and transition economies in multilateral trade negotiations and the resulting policy implications. in a paper entitled “agricultural export restrictions and the wto: what options do policy-makers have for promoting food security?” giovanni examined the likely trade, food security and development implications of various options for disciplining agricultural export restrictions. this paper was published in 2013 by the international center for 304 a tribute to giovanni anania trade and sustainable development and it was a thorough analysis of the implications of various options for disciplining agricultural export restrictions. giovanni was the leading economics expert on the world market for bananas. he conducted a number of influential studies on regional trade deals between the eu (the largest importer of bananas) and banana exporters and the resulting impacts on trade in bananas and the overall competitiveness of the industry. continuing his scholarship in complex linkages between international and domestic markets, last year, with margherita scoppola, giovanni published a timely paper on the importance of assumptions made about market structure and firm behavior in empirical trade policy analysis. recognizing that market structure matters in agricultural trade, they incorporate imperfectly competitive markets in a spatial modeling framework. their paper will serve as a roadmap for other trade researchers trying to include realism in their models. we hope by now we have documented what an amazing guy giovanni was. he was a first class applied economist with a clear sense of what the real issues were and a great loving friend who you really wanted to hug. we shall miss him. to margherita and his family our thoughts are with you. may you be comforted, as we will be, by remembering how fortunate we all were to have been able to share him for a while. peace. 305a tribute to giovanni anania giovanni anania: shaping the future of european agricultural policy1 jean-christophe bureau (agroparistech paris) and alan matthews (trinity college dublin, ireland) introduction in today’s session we are remembering giovanni anania’s contribution as a scientist, but for many who are present we remember him even more for the person he was. jeanchristophe first came to know giovanni through mutual friends at davis, whereas i came to know him relatively recently. giovanni and i were participants in an eu fp6 project tradeag coordinated by jean-christophe that began in 2005. subsequently, we both participated in the eu fp7 project agfoodtrade also coordinated by jean-christophe. during one of those projects giovanni invited us to hold a project meeting in an agriturismo in calabria. i remember well from that visit both giovanni’s love of food and of his region. then in my time as president of the eaae 2011-2014 giovanni was the association’s vice president and we worked closely together until giovanni succeeded me as eaae president in august 2014. giovanni’s work on eu agricultural policy can be characterised in a number of ways. he early on recognised the inadequacy of analyses of eu agricultural policy which looked at the domestic market alone as though this existed in isolation from the outside world. the growing importance of international agricultural trade flows, the creation of new rules governing agricultural protection and support in the 1994 uruguay round agreement on agriculture and giovanni’s own interest in international agricultural policy issues meant that he always approached the analysis of eu agricultural policy with an international perspective in mind. his approach to eu agricultural policy was also informed by a thorough understanding of local and sectoral issues. giovanni saw no contradiction between devoting time to better understanding the development of agriculture in his local region of calabria and analysing the rules governing international agricultural trade. although he strongly believed in the importance of rigorous scholarship in academic research, he also insisted on the importance of communicating the results of that research to policy-makers and using that research to influence policy. giovanni was not only a modeller but also someone who could use the results and think about the bigger picture. he was always willing to patiently repeat his explanation of complex economic issues, and 1 this is an extended version of the tribute which was delivered by alan matthews at the special session organised to honour the memory and contribution of giovanni anania to the agricultural economics profession at the 29th international conference of agricultural economics, milan, 9-14 august 2015. we would like to thank many friends and colleagues of giovanni who contributed to the preparation of this tribute: filippo arfini, federica demaria, fabrizio de filippis, tassos haniotis, jonathan hepburn, koen mondelaers,, krijn poppe, luca salvatici and margherita scoppola. 306 a tribute to giovanni anania was known for starting this repetition with a gentle “now once again…”. indeed, he recognised that the research-policy interaction was a two-way street, and some of his more important papers were originally prompted by a request to explore a policy question, as we will see. the italian debate on the cap giovanni’s earliest work related to the structural problems of agriculture in the calabria region and his master’s thesis was on different issues related to part-time farming in italy. as a result of this interest he participated in the well-known arkleton trust study on farm household adjustment in western europe in 1992 which strongly highlighted the role of pluriactivity in contributing to farm household income on smaller farms.2 giovanni participated in this project with a number of colleagues who would become important collaborators in his later work, including fabrizio de filippis. de filippis was interested in agricultural policy analysis, and particularly the common agricultural policy, to which he had been introduced by michele de benedictis, while giovanni had returned from his phd studies in the united states with a strong background in agricultural trade policy, influenced by alex mccalla. this project began a fruitful collaboration over the following decades. at the beginning of the 1990s the debate in italy on shaping the future of the cap was in full swing. an early outcome of giovanni’s collaboration with de filippis was a book which they jointly edited on the gatt agreement and european union agriculture, in italian, published in 1996.3 this book was the final outcome of a research project financed by the italian national research council which also included many other younger italian agricultural economists. it was during this project that the second iatrc meeting was held which led to the publication of the book edited by anania, carter and mccalla, agricultural trade conflict and gatt new dimensions in north american european trade relations.4 during the period 1997-2000 giovanni was a member of the inea research team osservatorio delle politiche agricole dell’unione europea led by de filippis which produced a number of reports on eu agricultural policy developments. in the 1997 volume giovanni wrote the chapter concerning international trade and the gatt negotiations.5 in 2000 giovanni put together a team of young italian researchers to examine the state of the art in quantitative modelling of the cap, a project which was also supported by the italian national institute of agricultural economics. the names of the people that giovanni gathered to work on this project are well-known in the profession today in both 2 anania, g. and gaudio, f., farm differences, family strategies and agricultural structural changes. a synthesis of the results of the baseline survey in calabria (italy), in structural policies and multiple job holding in the rural development process, arkleton trust & department of land economy, university of aberdeen, aberdeen (gb), 1991, pp. 264-282. 3 anania, g. and de filippis, f. (eds), l’accordo gatt in agricoltura e l’unione europea, franco angeli, milano, 1996. 4 anania, g., carter, c.a and mac calla, a.f. (eds), agricultural trade conflict and gatt new dimensions in north american european. trade relations, westview press, 1994. 5 anania, g., le implicazioni dell’accordo gatt del 1994 per le politiche agricole dell’ue, in aa.vv., rapporto sulle politiche agricole dell’unione europea nel 1997, inea, roma, 1998,pp. 15-41. 307a tribute to giovanni anania academic and policy circles. the overall objective of the research program was to provide a comprehensive analysis of modelling issues and applications related to the cap. the results of the study were published in 2001 in a volume the contribution of some quantitative research to the evaluation of their effects on the italian agriculture.6 giovanni’s contribution to the project was, not surprisingly, a chapter on modelling agricultural trade liberalisation and its implications for the european union. this paper remains a superb overview of the state of play of global agricultural trade models as of the 1990s. his critique of existing studies is thorough, exhaustive and compelling. his conclusion was that the efforts to model agricultural trade and trade policies, taken as a whole, were not fully satisfactory and left much to be desired. giovanni’s recommendations for improvements were based on the observation that effective solutions already existed to many of the problems he identified, but that “greater care and attention must be paid to tailoring models to answer the specific questions addressed, and abandoning once and for all the claim that, once it has been set up, a model can be used to simulate any change in the policy scenario whatsoever”. his manifesto for improved modelling practice consisted of five points, and these principles also underpinned his own modelling work particularly on bananas as we will see: first, make use of a model which has a structure and specific features which are coherent with the question to be addressed. second, think about integrating the use of different models instead of trying to adapt a model to do things it was never designed to do. third, model the functioning of market and trade policy instruments more effectively and more realistically. fourth, strive for more effective coordination and greater cooperation between modelling efforts, through joint projects and the sharing of information on models and data bases. fifth, put effort into the construction of reliable data bases, which supply the information needed to model both market agents’ behaviours and policies. indeed, these are precisely the directions that simulation modelling has taken, and giovanni was both prophetic and prescient in identifying these needs. during the period 2000-2002, giovanni was the national coordinator for a “scientific research program of national importance” under the italian ministry for university and research on the topic wto negotiations on agriculture and the reform of the common agricultural policy of the european union. this project resulted in the book, published in italian, reform of eu agricultural policy and the wto negotiations.7 the chapters in 6 anania, g. (ed), valutare gli effetti della politica agricola comune. lo “stato dell’arte” dei modelli per l’analisi quantitativa degli effetti delle politiche agricole dell’unione europea, nis, napoli, 2001 (with filippo arfini, piero conforti, pasquale de muro, pierluigi londero, luca salvatici, and paolo sckokai). most chapters were translated in english and are available on line: http://econpapers.repec.org/paper/agsineawp/14804.htm; http://econpapers. repec.org/scripts/search.pl?ft=conforti; http://econpapers.repec.org/scripts/search.pl?ft=arfini; http://econpapers. repec.org/scripts/search.pl?ft=sckokai; http://econpapers.repec.org/scripts/search.pl?ft=anania, accessed 12 october 2015. 7 anania, g. (ed), la riforma delle politiche agricole dell’ue ed il negoziato wto. il contributo di alcune ricerche quantitative alla valutazione dei loro effetti sull’agricoltura italiana, francoangeli, milano, 2005. 308 a tribute to giovanni anania this book, many of which were published in english in international journals, applied the modelling methodologies which had been described in the earlier project to italy. many of those involved recall the satisfaction of being able to apply empirical models to policy questions and to make a contribution to the policy debate. giovanni also contributed to the 2004 book edited by de filippis written in the wake of the partial decoupling introduced by the fischler mid-term review of the cap, towards the new cap: the reform of june 2003 and its application in italy.8 giovanni was again the national coordinator of an italian scientific research program of national importance, together with luca salvatici, margherita scoppola and fabrizio de filippis, on european union policies, economic and trade integration processes and wto negotiations during the years 2008-2010. we have remarked that an important feature of giovanni’s approach was his capacity to link academic rigour with political debate and dissemination of research results. all the researchers working in the projects that he coordinated were strongly encouraged in take part in dissemination and participate in the political debate. the link with inea in these projects with its institutional relationships with the italian ministries of agriculture and foreign affairs and with the italian government gave giovanni the opportunity to offer support to italian policy-makers on these issues. during this period giovanni and fabrizio were often consulted with regard to the italian position on the cap reform process, especially for the cmos that were most important for italy (e.g. olive oil). also as part of his insistence on linking scholarship with the real world, between 2007 and 2009 giovanni was a member of the steering committee of “gruppo 2013”, an italian think tank active on themes related to cap, markets, and international relations coordinated by de filippis and sponsored by coldiretti, the principal italian farmers’ organisation. in 2008 he prepared a paper for this group with alessia tenuta on the effects of regionalisation of aid in the single payment scheme on its spatial distribution in italy.9 european interventions on cap around the same time as the inea project on cap modelling got under way, giovanni took part in the first of many discussions on shaping the future of the eu’s agricultural policy. the macsharry reform of the cap in 1992 had shown that change in the cap was possible, albeit with strong prodding from external pressures such as the need to be able to respond to criticisms from trading partners in negotiating the uruguay round agreement under the gatt. in 1996 an important conference in cork, in which giovanni participated as an invited expert and panel member, issued the cork rural development declaration which set out a ten-point rural development programme for the union.10 in 1997 dg agri had published the report of the influential expert group chaired by allan buckwell towards a common agricultural and rural policy for europe.11 this opened up a 8 de filippis, f., verso la nuova pac. la riforma del giugno 2003 e la sua applicazione in italia, quadernidel forum internazionale dell’agricoltura e dell’alimentazione, 2004. 9 anania, g. and tenuta, a., il futuro dei pagamenti diretti nell’health check della pac: regionalizzazione, condizionalità e disaccoppiamento”, in de filippis, f. ed, l’health check della pac. una valutazione delle prime proposte della commissione, quaderni del gruppo 2013,edizioni tellus, roma, 2008, pp. 29-39. 10 http://ec.europa.eu/agriculture/rur/leader2/dossier_p/en/dossier/cork.pdf, accessed 12 october 2015. 11 http://ec.europa.eu/agriculture/publi/reports/buckwell_en.pdf, accessed 12 october 2015. 309a tribute to giovanni anania vision of transforming the cap from a policy of generalised direct support payments to a policy with specific targets for market stabilisation, environmental and cultural landscape payments, rural development incentives and transitional adjustment assistance. it was an important milestone in the evolution of the cap and it opened the way for further reflections on the direction of reform. in december 2000 another expert working group on the future of the cap and its implications for rural europe co-chaired by winfried von urff and françois colson started as a joint initiative of the ‘akademie für raumforschung und landesplanung’ (arl, academy for spatial research and planning, arl) and the ‘délégation à l’aménagement du territoire et à l’action régionale’ (datar). the group was sometimes referred to as buckwell ii as it included some of the experts involved in the preparation of the buckwell report, and giovanni was also a member. the vision for sustainable rural economies in an enlarged europe produced by the group proposed a shift in funding from pillar 1 to pillar 2 of the cap while recommending a more territorial, bottom up approach to the development of rural areas through pillar 2.12 giovanni prepared a paper assessing the extent of pressure for a change of the cap to be expected from wto in the light of the doha ministerial declaration in 2001. the papers were completed in summer 2002, just after commissioner fischler proposed his mid term review in july 2002, so it is hard to assess the influence of this report. it certainly fed into the demands for a stronger pillar 2 which characterised the evolution of the cap during the following decade. another area of giovanni’s involvement with the cap was his early contribution to helping to formulate priorities for future research. already in 1998, he took part as an expert in a workshop on research activities priority setting for the 5th eu framework programme of rtd. in 2003 he participated in a workshop to review the draft work programme for “scientific support to policies” for the 6th eu framework programme of rtd. in 2006, he was a member of the group that undertook the first foresight analysis in the field of agricultural research in europe for the standing committee on agricultural research (scar).13 the major task of the expert group was to review the available foresight studies relating to eight “major driving forces” which were to be considered together in the formulation of four future scenarios of the agro-food system evolution. giovanni prepared the background paper on economy and trade. giovanni later presented an extended version of this background paper at a workshop on “reflections on the common agricultural policy from a long run perspective” organized by the commission’s bureau of european policy advisers in brussels in february 2009.14 these reports (the fourth in the series was published in 201515) played an important role in the research planning / agenda setting process of the scar. 12 anania, g. et al., policy vision for sustainable rural economies in an enlarged europe, akademie für raumforschung und landesplanung (arl) & délegation à l’aménagement du territoire et à l’action régionale (datar), studies in spatial development, n. 4, hanover, 2003. 13 foresighting food, rural and agri-futures, european commission, directorate-general for research, february 2007 (with t. gaudin, coordinator, j. cassingena-harper, k. cuhls,l. downey, j. leyten, j. e. olesen, y. schenkel, m. walls and p. raspor). 14 anania, g., the eu agricultural policy from a long run perspective: implications from the evolution of the global context, working paper 09/4, research project on “european union policies, economic and trade integration processes and wto negotiations” financed by the italian ministry of education, university and research (scientific research programs of national relevance), university of calabria. 15 https://ec.europa.eu/research/scar/index.cfm?pg=foresight4th, accessed 12 october 2015. 310 a tribute to giovanni anania he was a keen observer of the most recent cap reform. he was an invited speaker at the conference on the public debate on the cap post-2013 organised by the dg agri in 2010, and he participated in many organised sessions to discuss the reform at meetings of the italian and european associations of agricultural economists, taking a critical but even-handed view of the commission’s proposals. many of us will remember his technicolour slide presentations in which he dissected with exemplary precision the main elements of the reform. in his writings on the cap, giovanni had the rare gift of being able to maintain an appropriate balance between positive and normative analysis. in his interactions with farmers’ unions and policy makers, he always liked to be wholly independent from the most popular positions of stakeholders. some cap analysts heavily emphasize a normative approach to what is wrong with the cap according to the economics textbook: this is correct but often irrelevant in the public debate. other analysts accept too readily the status quo on the argument that it is the best (or least bad) possible policy given the political constraints. giovanni was always realistic and pragmatic in his analysis but never gave up the dream of a better policy. his last contribution on this topic is a magisterial summary of the 2013 cap reform, written together with maria rosaria pupo d’andrea, which is the opening chapter of a book edited by jo swinnen on the political economy of the recent cap reform.16 his final paragraph is worth quoting in full: “it should be clear by now why an overall assessment of the reformed cap remains difficult. the cioloş reform brought positive innovations in the cap as well as innovations which have brought the robust, consistent path outlined by the previous reforms since 1992 to a grinding halt. those who hoped for a significant step forward along the same path, with the reform identifying a clear set of consistent strategic goals pursued by the cap, a more targeted distribution of support and a significant portion of the financial resources devoted to increasing the market competitiveness of farms and promoting the production of public goods, probably have good reasons for being disappointed. those who hoped the financial resources allocated to eu policies for agriculture and rural development would not be severely cut (as feared at the beginning of the decision process), and for the reformed cap to bring as few changes as possible, are probably quite satisfied by the final result.” in other words, cap reform remains unfinished business. sadly, with giovanni’s much too early death, he will no longer be here to help to shape its future. research and policy advice on eu banana policy i now turn to giovanni’s research and policy work on eu banana policy. giovanni started to work on bananas in 2004. the initial stimulus came from an italian consulting company cogea which had been commissioned to undertake an evaluation study on the banana cmo for the eu. giovanni was one of a number of economists who were engaged as consultants on this study. the eu had been required to restructure its banana import 16 anania, g. and pupo d’andrea, m.r., the 2013 reform of the common agricultural policy, in swinnen, j. ed., the political economy of the 2014-2020 common agricultural policy: an imperfect storm, brussels, centre for european policy studies and london, rowman and littlefield international, 2015, 33-86. 311a tribute to giovanni anania arrangements in 1992 following the introduction of the single market which made the previous system of national import quotas inoperative. this import regime had been successfully challenged at the wto by a group of latin american banana exporters and the us. during the negotiations to start the doha round in 2001, the eu had been granted a waiver until 2006 after when it was required to introduce a tariff-only import regime for bananas. the council adopted this regime in november 2005 to start in 2006 but it was immediately challenged at the wto and once again the eu found itself as a defendant in a wto banana case. giovanni had been struck by the existence of different tariff rate quotas (trqs) applied by the eu to imports of bananas from different groups of countries: this was, in his view, a good example of the need to use a spatial model as the most adequate tool to properly model bilateral trade policies. in this context, he developed the first version of his spatial model for bananas to analyse the impact of the 2006 cmo reform.17 subsequently, when the eu found itself yet again as a defendant at the wto, giovanni was thus the main source for the eu to know what would be the consequences for eu production and agricultural income of different options with respect to border protection in the bananas dossier. he assisted both in person and with his spatial model in the negotiations, not directly at the negotiating table, of course, but supporting in the background. without his modelling support, it would have been much more difficult to assess the impact of the choices made. his work on bananas led to a series of first-class papers, including one in food policy for which he was awarded the european association of agricultural economists quality of policy contribution award in 2010.18 this work was not only policy-relevant but also contributed to methodological breakthroughs. one of the problems with spatial trade models is that they typically show a discrepancy between the observed and optimal (equilibrium) quantities. that is, there is typically a divergence between the realised quantities of the produced and consumed commodities and their trade flows, and the production, consumption and import-export patterns generated by the model for the same year. previous researchers had tended to either ignore these discrepancies or to make ad hoc adjustments. in a 2011 paper in economic modelling with quirino paris and sophie drogué, giovanni proposed a calibration procedure in which the calibrated models generate solutions that exactly reproduce quantities produced and consumed as well as trade flows.19 however, he continued to worry about another dimension in which he felt his spatial model was unrealistic. he was conscious that perfect competition (assumed in his spatial model) was a heroic assumption particularly when dealing with trade in bananas. two anti-trust reports by the eu commission reporting evidence of non-competitive behaviour by banana traders were the final “push” to tackle this problem and, together with margherita scoppola, they further developed the spatial model to compare the results of 17 anania, g., the 2005 wto arbitration and the new eu import regime for bananas: a cut too far?, european review of agricultural economics, 33, (4), 2006, pp. 449-484. 18 anania, g., eu economic partnership agreements and wto negotiations: a quantitative assessment of trade preference granting and erosion in the banana market”, food policy, (35), 2010, pp.140-153; anania, g., the 2006 reform of the eu domestic policy regime for bananas. an assessment of its impact on trade”, journal of international agricultural trade and development, (4), 2, 2008, pp. 255-271. 19 paris, q., drogué , s. and anania, g., calibrating spatial models of trade, economic modelling, 28, 2011, pp. 2509-2516. 312 a tribute to giovanni anania trade policy change simulations under different market structures.20 the most important innovations from the modeling point of view were the inclusion in a spatial model of both upstream and downstream market power by traders and the consideration of a range of different oligopolistic structures instead of focusing only on cournot competition. a further insight was that, in combination with the two step calibration procedure developed in the earlier paper, it was possible to derive an estimate for the degree of market power in the banana market from the observed trade outcomes. in the run-up to the geneva agreement on trade in bananas in december 2008 which resolved the disputes between the eu and the latin american banana exporters and the us, tensions had also arisen among developing countries over a broader issue in the doha round negotiations, namely, the extent and pace of tariff reduction on tropical and preference products. while there was a general agreement that tariff reductions should be deeper on tropical products, this was resisted by those countries which benefited from special preferences and which would lose by deep reductions. work at the international centre for sustainable trade and development (ictsd) in geneva had identified that the dispute really revolved around a handful of products, including bananas. they invited giovanni to geneva to talk to the wto delegates of the countries mainly concerned and to present his modelling work on bananas. his even-handed and dispassionate treatment helped to allay some of the concerns and was part of the process in helping the break the deadlock which resulted in the geneva agreement in december 2008. i think it speaks volumes about giovanni’s ability to undertake and present his research in an independent, rigorous and yet fair-minded way that when ecuador was concerned about the impact on its banana exports to the eu of the conclusion of an eu free trade agreement with the central american countries, it was to giovanni that they turned, even though he had been the main economic advisor to the commission a few years earlier during their wto dispute with the eu. all of this work on bananas was disseminated to a wider policy audience in a fruitful relationship with the ictsd during those years.21 giovanni’s most recent work on bananas was as an expert for the consulting company commissioned by dg agri to undertake an evaluation of the eu’s agricultural trade relationships with the african, caribbean and pacific (acp) countries. giovanni’s specific contribution was to examine the role of trade preferences in the development of cameroon’s banana exports to the eu. again highlighting the way in which policy advice and scientific research continually interacted throughout giovanni’s professional career, this work was the stimulus for his contributed paper the role of trade policies, multinationals, shipping modes and product differentiation in global value chains for bananas. the case of cameroon accepted for this conference. alas, giovanni will not be here to present it. 20 anania, g. and scoppola, m., modeling trade policies under alternative market structures, journal of policy modeling,36, 2014, pp. 185-206. 21 anania, g., how would a wto agreement on bananas affect exporting and importing countries?, issue paper no. 21, international centre for trade and sustainable development, geneva, 2009, pp. 1-38; anania, g., the implications for bananas of the recent trade agreements between the eu and andean and central american countries”, policy brief no. 5, 2010, pp. 1-5; anania, g., implications of trade policy changes for the competitiveness of ecuadorian banana exports to the eu market, issue paper no. 10, international centre for trade and sustainable development, geneva, 2011, pp. 1-35. 313a tribute to giovanni anania conclusion in conclusion, we remember a scholar of the utmost integrity, which cost him dearly in his professional career. he had a very strong sense of right and wrong. while very serious and committed on the important issues, he was very relaxed and warm and a great companion once the important issues were addressed. giovanni had the happy knack of bringing people together and making things happen. he was always well-prepared and well-briefed, and always constructive. for all of those who worked with him, he was the most important point of reference both as a source of intellectual stimulation and as a guide to personal conduct. we greatly miss giovanni, and our thoughts are with his wife margherita at this time. 314 a tribute to giovanni anania european association of agricultural economists (eaae) imre ferto (corvino university of budapest, hungary) and alan renwick (university college dublin, ireland) giovanni was a very warm and passionate colleague and friend who served the eaae for many years. as president he was dedicated to making the eaae a strong and leading organisation for the benefit of all agricultural economists across europe. his presence, inspiration and dedication will be missed by all within the eaae. giovanni first became active in the eaae through his membership of the programme committee for the ix congress held in warsaw in 1999. following this he was elected to the eaae board and served between 2002 and 2008. during this time he played an instrumental role in the development of the association. in particular, he commented in detail on the renewal of the constitution process that took place in 2003 and led the introduction of the eaae prizes. in 2011 he joined the board again but this time as vice-president before becoming president at the 2014 conference in ljubljana. since becoming president he began both internal and external initiatives to strengthen the association. internally he began the process of strengthening the relationships between the eaae and its members, in part by reinvigorating the role of the liaison officers. externally his initiatives included the signing of an mou with the australian association and he had begun discussions with the uk agricultural economics society. giovanni had an ongoing desire to see the eaae generally more connected globally and in particular to be represented at international meetings. before becoming president, for example, he was responsible for organising sessions at the iaae conference in durban and at the aaea and waea meetings. throughout his association with the eaae, he was also an avid supporter of the phd workshops and saw them as having a key role to play in developing the next generation of european agricultural economists. it is perhaps fitting that one of his last engagements for the association was speaking at the workshop held in rome in june of this year. whilst he made many contributions to the professional development of the eaae, he also contributed in many other ways. anyone who met him at eaae events benefited from his warmth and enthusiasm and through this he added much to the camaraderie of the association. giovanni’s personality shone through in everything from his wide selection of jumpers through to his famously multi coloured slide presentations! whilst his departure has left a massive hole at the helm of the eaae, all those on the board are determined to continue on the course he established, but he will be sorely missed. 315a tribute to giovanni anania associazione italiana di economia agraria e applicata (aieaa) giovanni cannata (università del molise, italy) giovanni anania accompanied my professional and personal life for more than 30 years. it has been a privilege for me to know him, sharing experiences with him and enjoying his friendship and professional knowledge. it is hard to speak in his honor without being moved by warm memories and emotions, but i will try. and i will try also to avoid any rhetoric tone. i would like to recall a few aspects of giovanni’s life with specific reference to his italian academic and professional career, considering that other colleagues already talked about his outstanding achievements at international level. specifically, i’d like to recall also giovanni’s civil engagement as a man of the south, a son of calabria, the land he came from and he honored a lot. giovanni’s professional life is very consistent with the topic of icae 2015 where his presence is deeply felt, and this is why we wish to pay him a tribute “agriculture in an inter-connected world”. the topic of interdependence between agriculture and the economy and society is central in his research since the very beginning, and this is why we met some thirty years ago. giovanni knows how to master even the most sophisticated analytical tools with great simplicity and to share them with colleagues and collaborators, but never forgetting why these tools are developed for, which is providing answers to relevant problems. he is the quintessence of an applied economist. this is true not only in his well-known research on international trade or european agricultural policies, but also in his “italian” researches focusing on the transformations of agriculture, public intervention in southern italy as well as the labour market in agriculture and pluriactivity or the analysis of some production sectors such as the citrus fruit, olive oil, and dairy sectors. likewise remarkable is giovanni’s good common sense in applying quantitative approaches to the study of territorial systems and to the analysis of business structures, thus drawing implications for the future. providing insights for future directions in real life settings featured his works on europe and international trade as well as on his italian works, especially as far as calabria is concerned. calabria towards the future. the analysis of the implications of these researches contributes a lot in strengthening our collaboration in a particularly fertile season for research in italy, that of the so-called targeted research programs, when the government, via the national research council, promoted multi-year projects concerning specific objectives, like the ipra (increase of agricultural resources productivity) or the raisa (advanced research for the innovation of agricultural systems). these were the good old times for research, in italy, unfortunately gone away. with the collaboration of other colleagues from the university of calabria, giovanni remarkably contributed first to solve some issues in territorial analysis (and i greatly benefited of his help and advise) and later to build up a think tank on international trade and policy. 316 a tribute to giovanni anania referring to institutional service, fundamental is giovanni’s contribution to many organizations such as inea, the italian national institution of agricultural economics (recently shut down by an unfortunate decision of the italian government) and istat, the italian national institute of statistics. when inea regional offices were established, giovanni was its great catalyst in calabria. this office is a sort of grass-root think tank, a knowledge lab well rooted in the local environment but looking at the global. a gym for the many youths who began a research career, some more successful than others, but all of them holding a huge debt of gratitude to giovanni. considerable is also his contribution to istat, where giovanni actively participates to the modernization of agricultural statistics and to the design of agricultural census. giovanni is also the promoter of a number of initiatives aimed at aggregating scholars and experts. for instance, i remember the support he gave me during my presidency of the italian association of agricultural economics or his contribution to the rossi doria association and, more recently, to the establishment of aieaa, the italian association of applied agricultural economics, whom he is a co-founder. furthermore, i underline his role as one of the most active member of the group 2013, an interesting experience where not only producers, but also stakeholders at large and policy-makers make use of scholars’ independent advices and analyses. even in this case giovanni’s role is key, walking in the footpaths of a glorious tradition of agricultural economists that can be traced back to ely’s institutional school. besides his active and constructive contributions to congresses, workshops and seminars, i would like to mention also an activity where giovanni is specially good at, that is mentoring of younger colleagues, probably a legacy of his experience at the portici centre. it is worth mentioning his enthusiastic, friendly, informal contribution to the summer schools for phd candidates and post-docs, organized by sidea first and later on by aieaa. giovanni offers his experience and knowledge to the young participants by stimulating them with provocative questions such as: “i got my phd: what shall i do with it?” when the aieaa is established, giovanni provided precious suggestions on its statute design, so that it has to be firmly research-oriented, more open than other italian associations to the international debate, and focusing on the “analysis of agricultural economics and policy in a multidisciplinary context”. giovanni’s curriculum is so outstanding both from research and profession service viewpoints, and well known by all of you, as proven by the fact that many of you all elected him to the eaae presidency, that what i just said are probably only minor aspects. therefore, i would like to turn now to something that probably not everybody in this room is aware of, that is his civil engagement. giovanni is one of the best examples of a scholar who was educated in his own home region, specialized abroad and then decided to come back in his own region to contribute to its development, despite the offer of positions in more prestigious academic institutions. giovanni is an example of social investment aiming at building human capital in an underdeveloped region. before concluding, let me recall some activities showing his engagement with and dedication to the development of his home region, the special relationship with calabria featuring his whole life, just the way they were told me. 317a tribute to giovanni anania giovanni is a member of the editorial board of “meridiana” (1989-1995), a multidisciplinary journal founded in 1987 by a group of scholars looking at the italian “mezzogiorno” in a very open-minded way, well beyond the received cultural stereotypes. he always looked at the problems of “mezzogiorno” (inequality and geographical disparities, political and social regulation, environmental policies, etc.) addressing a demand of knowledge whose coordinates were scientific rigor and policy relevance. in a conference significantly entitled “public choices, private strategies, and economic development in calabria. knowledge to make decisions”, giovanni wrote: “universities are often rightly criticized for their limited ability in offering to the surrounding territory the results of their researches. the awareness of this limit brought the department (of economics, ed.) to open its doors and to decide to disseminate, mostly to a non-academic audience, the results of those researches more directly concerned with the public and private choices particularly relevant for the economic development of calabria. a conference and a volume are just the first step to make available what we do to ones who, with different roles and responsibilities, are in charge of making decisions, which are relevant for the regional development. we hope that our readers will share our view about the usefulness of what we have done so far, pushing us to do more and better in the future.” as an example of his civil commitment, let me tell you that giovanni actively supports the cooperative breeding farm “valle del bonamico”, helping in solving its organizational and marketing problems. the cooperative is active within an initiative of the diocese of locri-gerace, devised to organize unemployed young people in the valleys “bonamico” and “careri” and to offer them labour opportunities as an alternative to a fate of marginalization and probably mafia enrolment. employment and entrepreneurship as a possible remedy to underdevelopment and social deviance in calabria. solidarity and trust-building as an antidote to the prevailing individualism and familistic closure. giovanni enthusiastically joins, as a funding partner, also the cooperative “terre grecaniche”, whose mission is to rebuild local communities able to plan their own future. the cooperative’s goal is the improvement of productive use of local resources and the development of farming in the “grecanica” area (or bovesìa: a part of the province of reggio calabria, where the spoken dialect is a derivation of ancient greek). the cooperative allocates part of its revenue to the creation of microcredit projects and solidarity initiatives in developing countries as well as in projects aimed at contrasting school drop-out in the “grecanica” area. finally, giovanni is also engaged in local political life: he gives always, when required, his personal contribution at local and regional level in informal terms, offering his knowledge derived from years of studies on the issues of the agricultural sector and, more generally, of economic development. giovanni was to me a colleague with first class academic and human talents. giovanni was a partner in academic fights. giovanni shared with me fruitful research programmes. giovanni was a leader for fellows and colleagues. giovanni is, and i’m sure will still be, a reference and a fraternal friend to many of us. 318 a tribute to giovanni anania european commission josé manuel (cuqui) silva rodriguez we had many wonderful contributions on giovanni anania the academic and on his countless friends. i would like to make a small contribution in between professional and personal, because behind that great agricultural economist who was giovanni also was a great person. negotiations in the eu banana sector were always complex. i think that this is the product in which there are more differences of opinion differences between the dollar area producers and acp producers, but also differences between european countries. differences between the very liberals, such as germany, and those with production, as spain or france, or those who want their former colonies to continue to export to europe. there are also differences between those who want to spend to sustain the sector, and those who refuse any budgetary effort. so when in 2005 we had to propose an external tariff on bananas, you can imagine that this was anything but easy to agree in coreper. each ambassador had a different opinion. we did not look like easily reaching a common position, but we had to do so before the start of the ministerial meeting of the wto in hong kong, as i am referring to the month of december 2005. i still remember very well the date the agreement came at the beginning of an evening on the first friday of december 2005 (i had to catch a flight the same evening…). fortunately, we negotiators from the commission had a very effective weapon we had agreed to commission an external study on the banana sector, a study led by giovanni. today, it sounds normal that the commission requests external academic studies, but at that time this was still very new. in fact this study pioneered the collaboration between dg-agri and the academic world. as negotiator, i felt very well protected and supported, not only by colleagues who were with me in the meetings aldo, mary, tassos, elisabetta or nicolas among others but above all i felt supported by the factual arguments provided to me by the study done by giovanni. and although no one particularly liked the numbers produced by giovanni, no one was also able to contest them – and if nobody seems happy with an agreement, but can live with it, this is a sign of a good agreement! the study on the banana market also allowed me to meet giovanni. i am a person who is interested in people above all. meeting giovanni was a wonderful gift. his intelligence, his humour, his humanity and especially that look of his, will always accompany us. death, margherita, although sure, is always unwelcome. fortunately, somehow we sense that people do not leave us completely; and i think the smile of giovanni remains alive among us. 319a tribute to giovanni anania european commission tassos haniotis (dg agriculture) i am the last to speak in this session we would all have liked never to have taken place. i will thus not focus on giovanni’s academic contributions, already mentioned, but raise instead three more personal aspects in our professional relationship. but before i do so, i would like to start from something cuqui already mentioned – negotiating with “a number” and bring some personal relevant background information. i started my career in the commission with cuqui (in a market unit), in a job more related to my passport than my field of academic expertise (trade). but i soon moved into an analytical unit, and started working on developing a market model at a time a myth was circulating in the corridors of dg agri, one stemming from a former director’s general statement that claimed “don’t give me numbers, give me room for manoeuvre”! yet everything that cuqui said about his ability to better negotiate as a director general was stemming exactly from the fact that he had numbers the solid numbers that giovanni had produced. and it is evidence of the monumental change of mentality that has taken place in dg agri since then, with giovanni’s work fitting perfectly well in solidifying this change. i don’t exactly remember when i first met giovanni. mary mentioned to me earlier that he was in the icae in buenos aires, the first one that i also went to. but i do not recall meeting him there. i do recall though when i first came across his name. it was during these “modelling years” of mine, when out of curiosity i was leafing through the ajae annexes for names of europeans that received, like myself, their phd degree from us universities. this is when i first noticed giovanni’s name, and remembered it especially since he worked with my idol of the time, alex mccalla. in person i believe i first met him at the icae in sacramento in 1997. since then, we often met, especially during my fischler years and beyond. we developed a pretty close relationship based on what we broadly agreed on that numbers matter, that policy concepts matter, and that trade distortions are real issues for policy making. from this professional relationship, i would like to mention three areas one where we always agreed, on where we often disagreed, and one more personal. we always agreed on trade, on the necessary path of trade reform, on the speed of reform which has to be the right one to allow smooth adjustment, and on the importance of always looking at the big picture. giovanni was a pragmatist, and it was this pragmatism that gave him, as a trade modeller, the capacity to put things into perspective taking the real world as his starting point. after all, models are supposed to be a representation of reality, and not the other way round. we often disagreed on income support, and more specifically on its logic. as many academics do, giovanni considered that the target of income support should be meanstested. i believed, and continue to do so, that family income should not be an item for farm policy since taxation (a national responsibility in the eu) is there to address differences in the level of wealth, and implementation complexities would make any such meas320 a tribute to giovanni anania ures counter-productive. i always considered this debate trivial; more important is for me the debate about the logic of greening, or voluntary coupled support and it is exactly in these areas that i will miss giovanni’s critical point of view the most. his contribution to jo swinnen’s book on the recent cap reform shows how pertinent the policy questions he raised in this book are going to be in the very near future. but i would like to finish with a more personal note, one stemming from the common interest we developed in recent years on something that united us beyond academics. we both came from a very old part of this old continent of ours, a part with so many similarities. very often, such similarities are drawn from our problems, and from the fact that their analysis does not always point to a “lysis” (solution), but sometimes even to paralysis. yet our similarities are much deeper and different than this. scanning the internet you will fast discover that calabria is the region first occupied by two tribes which ancient greeks called “oenotrians” and “itali”. the first name comes from the greek word for wine, whose production was of interest to giovanni. and the “itali” are a reminder that the origins of your country may come more south that some would like to believe. but this part of italy also goes by the name of magna grecia, indicating the long historical links between our countries. and the name comes as a more pertinent reminder that this region of the world is where trade, and especially trade in food items (giovanni’s main area of academic interest), has been from ancient times a factor that unites people, reflecting and promoting their cultures and diversity. in this part of the world, in the two corners of the italian boot, a dialect is still today spoken by a few thousand people a dialect called by some medieval, or better, byzantine greek, also known by the name of griko. on numerous occasions giovanni invited me to visit together this part of italy, and i will always regret that i will never have the chance to do it with him. i will certainly do it for him, though. and i am sure that, from wherever he is, he will have learned how to tell us in this dialect “ steo ettù ma ‘sà “ “i am here with you”! for me, the only thing that is left to say is to use the very word we have in modern greek to bid farewell to a friend: addio! bio-based and applied economics 5(2): 153-174, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-20087 innovation and marketing strategies for pdo products: the case of “parmigiano reggiano” as an ingredient maria cecilia mancini*, claudio consiglieri department of economics, università degli studi di parma, via j. kennedy 6, 43125 parma, italy date of submission: 2015 30th, september; accepted 2016 8th, june abstract. typical products can contribute to socio-economic development of their place of origin if they are able to take part in the logic of the global market. the aim of this research is to examine one type of innovation, the use of pdo products as ingredients, as a part of a strategy to re-launch pdo products which have a mature market. the evolution of the concept of innovation is discussed; innovation is then considered in relation to product life cycle and information asymmetry. there is then a case study on the use of pdo parmigiano reggiano cheese as an ingredient in industrial processing. this case shows that even incremental innovation can have serious effects for the market when it is applied on production phases which lie outside the direct control of the pdo producers. in order to protect the consumer as well as the pdo producer, it would be advisable for new legislation to regulate in more detail innovation involving products bearing origin certificates. keywords. incremental innovation, pdo, information asymmetry, product life cycle, ingredient, mature market, typical products jel codes. m31, q13, o30 1. introduction although the integration of typical production systems in global trade circuits is associated with various risks, such as the commercialisation of tradition (barthel, 1996; lindholm, 2008), new power relations in the typical supply chain (rangnekar, 2004; dupuis and goodman, 2005; arfini et al., 2010) and exclusion of poorer farmers from global value chains (shapiro, 1983; prost et al., 1994; mancini, 2013), overcoming traditional dichotomies, such as that between typical vs. standardized production systems or local vs. global, is increasingly noted by scholars and is becoming widespread on the market. murdoch and miele (1999) and rastoin and vissac – charles (1999) state that the development of typical products goes hand in hand with globalization. in fact, because *corresponding author: mariacecilia.mancini@unipr.it 154 m.c. mancini, c. consiglieri typical products can be strong contenders in national or export markets, they are exposed to the challenges of the global market and their success requires a wide range of strategies, including innovation. innovation clearly involves obstacles on both the demand and supply sides, as well as many benefits, particularly when the territorial quality of a product is certified. eu certifications pdo/pgi/tsg1, for instance, institutionalize three main factors: the specific nature of local resources used in the production process; the history and tradition of production techniques; the collective dimension and the presence of locally shared knowledge (bérard and marchenay, 1995; barjolle et al., 1998; casabianca et al., 2005; rocchi and romano, 2006). these three factors are institutionalized in a code of specifications, approved by eu; so whatever type of innovation is subsequently proposed, formal amendment to the code has to be authorized by the commission. in spite of this, however, innovation occurs, especially in sectors where the market for the certified product is saturated and new outlets are required. this study discusses innovation as a strategy for the re-launch of pdo/pgi/tsg2 products where the market is mature, and focuses on some critical aspects. it focuses on the particular type of innovation which is becoming increasingly common; the use of pdo products as ingredients in industrial processing. the study starts with a literature review on the topic of typical products and innovation (section 2). this is followed by the definition of a theoretical framework for the discussion of the concept of innovation in relation to typical product life cycles and information asymmetry (section 3). there is then a case study on the use of pdo parmigiano reggiano cheese as an ingredient for industrially processed foods, as a strategy to face the mature market of the pdo product. this is supported by testing of the physical and chemical qualities of industrial products containing processed parmigiano reggiano. there is a discussion of possible consequences of such innovation on the market (section 4) and final remarks are then made (section 5). 2. literature review on the relationship between typical products and innovation for the sake of this research, we define a typical product as “a product which presents unique quality attributes which are the expression of the specific nature of the territorial context in which the production process takes place” (belletti et al., 2006). a typical product thus derives its unique qualities from being closely linked to a territory physically and anthropically3. because it involves the economic sustainability of production in a significant part of european rural areas, the relationship between typical products and innovation is of 1 pdo, protected designation of origin; pgi, protected geographical indication; tsg, traditional speciality guaranteed, reg. (ec) 1151/2012. 2 for the sake of simplicity, we use the term pdo below to refer to pdo, pgi and tsg. 3 in this literature review, contributions both on typical and traditional products were considered. in fact, according to regulation (eu) n. 1151/2012 of the european parliament and of the council of europe 21 november 2012 on quality schemes for agricultural products and foodstuffs, traditional refers to “the proven usage on the domestic market for a period that allows transmission between generations; this period is to be at least 30 years”. the sharing of concepts such as “people”, “place” and “time” provide a close link between “traditional” and “typical”. 155innovation and marketing strategies for pdo products interest to policy makers as well as the academic community. the european commission encourages these producers to become more competitive through innovation, including modern techniques of production, management, and marketing and promoting nutritional and health aspects of these products (e.g. ec, 2007). but in spite of this, various studies have shown that the relationship between typical products and innovations is complex and often problematic. in cases where the traditional and territorial nature of products is institutionalized in certifications such as pdo, there are limitations on innovation that can be made. in fact, codified rules on characteristics of production techniques and the product impact on the level of innovation and time required to adopt it in such supply chains (marty, 1998). for these products, innovation mainly pertains to product innovations, such as packaging innovations and changes in product composition, product size and form or new ways of using the product. process innovations are less common, given their impact on the authentic identity of the product and its production process (kühne and gellynck, 2009). nevertheless, according to kühne et al. (2010), feasible applications may also relate to improving the production process in order to assure quality and traceability. moreover, although innovations, in particular organizational ones, can be valuable for typical products, they can meet with resistance on the part of different actors in the supply chain (kühne and gellynck, 2009) where small and medium enterprises are not always receptive to changes. consumer perception is also a key issue. a good understanding of consumer perceptions, expectations and attitudes towards innovations in traditional food products is crucial for the successful introduction of innovations (linnemann et al., 2006). according to guerrero et al. (2009), the degree of acceptance of innovations applied to traditional products is closely dependent on type of innovation. consumers are particularly positive towards packaging innovations because they do not modify the core characteristics of the traditional food product and provide sought-after benefits, e.g. longer shelf life. innovations meet consumer approval when they increase safety levels or are associated with clear tangible benefits (bruhn et al., 1992; caporale and monteleone, 2004; cayot, 2007; guerrero et al., 2009) which enhance nutritional value or improve the nutrient profile of products, e.g. reducing salt, saturated fat or sugar content (guerrero et al., 2012). but product innovations with implications for the sensory properties are rejected (cayot, 2007; kühne et al., 2010) and compromises on taste for health are not welcomed by consumers (verbeke, 2006). khune et al. (2010) argue that consumer attitudes towards innovation in traditional products are segmented and vary between countries and within countries. nowadays, the complex relationship between typical products and innovation, on both supply and demand side, is accompanied by the challenges of mature markets where re-launch strategies are required. in recent years, on mature pdo product markets, it has become increasingly frequent to use pdo products as an ingredient of industrially processed foods. but although there is a large amount of literature on the relationship between local production systems and mass-produced industrial systems, the function of innovation, and particularly the use of pdo products as an ingredient, has been very little studied, and little is known about threats to the commercial success of this strategy. this raises new research questions on the potential paths to overcoming the dichotomy between the two production systems, and raises the need for policy discussion on 156 m.c. mancini, c. consiglieri processing of pdo products outside the certified production system. this paper aims to examine this field and form a starting point for such a debate. 3. aim and theoretical framework as stated above, the aim of this research is to examine one type of innovation, the use of pdo products as ingredients of industrially processed food as a strategy to re-launch pdo products which have a mature market. it aims to focus on possible consequences on the market and identify medium long term critical aspects. the concept of innovation is considered in its evolution and therefore discussed according to product life cycle with particular reference to typical agri-food products. the relationship between innovation and information asymmetry is then discussed. 3.1 innovation and product life cycle according to the oecd oslo manual (2005): “innovation is the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organisation or external relations”. this definition takes into account progress in understanding the innovation process and its economic impact, as well as the field of non-technological innovation and the linkages between different innovation types. the definition is the outcome of a great deal of literature that has been studying the meaning of innovation, as well as comprehensive classification, for decades. in fact, elsewhere, it is widely agreed that innovation follows invention, where invention is the discovery of something new (myers and marquis, 1969; trott, 2012). as “innovation is not a single action but a total process of interrelated sub-processes” (myers and marquis, 1969), it is not only about physical change but can involve the introduction of a new good, a new method of production, the opening of a new market, the conquest of a new source of supply of raw materials, the introduction of a new organization (schumpeter, 1934) and/or new management tools or services (trott, 2012). innovations vary in the degree of newness to an adopting unit, and this variation is captured by the notion of radicalness (dewar and dutton, 1986). incremental innovation introduces relatively minor changes to the existing product, exploits the potential of the established design, and often reinforces the dominance of established firms (nelson and winter, 1982; ettlie et al., 1984; dewar and dutton, 1986; tushman and anderson, 1986). radical innovation, on the other hand, is based on a different set of engineering and scientific principles and often opens up whole new markets and potential applications (dess and beard, 1984; ettlie et al., 1984; dewar and dutton, 1986). radical innovation often creates great difficulties for established firms (cooper and schendel, 1976; rothwell, 1986; tushman and anderson, 1986) and can be the basis for the successful entry of new firms or even the redefinition of an industry. the distinction between the two types of innovation is not however one of hard and fast categories. instead, there is a continuum of innovations that range from radical to incremental (hage, 1980). although radical and incremental pertain to distinctions along a theoretical continuum of the level of new knowledge embedded in an innovation, the middle values of this continuum are difficult to interpret. a helpful 157innovation and marketing strategies for pdo products contribution comes from garcia and calantone (2002), who on the basis of existing literature identify a third category of innovation between radical and incremental; “really new”. the classification is made according to two pairs of factors: macro/micro perspective and marketing/technological discontinuity. the macro-perspective aims at measuring how the characteristics of the innovation are new to the world, the market or the industry, while micro-perspective is identified when innovativeness of the product is related to the firm (or the customer). the second pair of factors – marketing and technological discontinuity – depends on the forces from which discontinuities may originate (figure 1). “product innovation may require new marketplaces to evolve and/or new marketing skills for the firm. similarly, product innovation may require a paradigm shift in the state of science embedded in a product, new r&d resources and/or a new production process for a firm. some products, of course, may require discontinuities in both marketplace and technological factors.” (garcia and calantone, 2002; p.119). so, radical innovations have discontinuities along both macro/micro and levels as well as marketing/technology sublevels. really new innovations have discontinuities along a single level, macromarketing or macrotechnology, but not both, and on one dimension of the sublevel macromarketing or macrotechnology. incremental innovations have discontinuities only along the micro level. the potential of innovation varies according to the product and the phase of the product life cycle. product life cycle (plc) theory identifies a set of common stages in the commercial life of products, shown as a curve divided into four phases: introduction, growth, maturity and decline (see among others buzzel, 1966; polli and cook, 1969; kotler and scott, 1998). each stage has a duration and curve depending on different factors (cox 1967; rink and swan 1979) which have different influence in the different phases (day, 1981). literature has also shown that many products have a life cycle that differs from the standard one (cox, 1967; swann and rink, 1982). these include agri-food prodfigure 1. product innovativeness. source: authors’ elaboration on garcia and calantone (2002). 158 m.c. mancini, c. consiglieri ucts, which show a primary cycle and a second cycle. the first corresponds to the usual plc, while the second cycle is shorter and less intense (kotler and scott, 1998). in other words, the fourth decline stage does not end the product life cycle but leads into a recycle, by means of extrinsic changes of the product, without there being any modifications in intrinsic attributes of quality, nutrition or taste and smell (pilati, 2004). for typical products, the plc is also much longer than the few years normal for other products, and it is thus likely that consumption models will alter during the cycle. the renewal stage which takes place when the market of the product is in advanced maturity and about to decline is different for typical products (figure 2). although there is a great deal of literature on innovation, minarelli et al. (2015) found that there are few studies addressing innovation in the food sector. their analysis of the food sector suggests that a determinant of innovation is collaboration, in particular when smalland medium-enterprises (smes) collaborate with universities and other similar stakeholders4. they argue that “further studies should seek to better understand innovation-related interactions where innovation types prioritised by firms can also change in relation to either different stages of a firm’s life cycle or the product life cycle in food companies” (p.50). this study aims at reducing this gap by focusing on an innovation aimed at relaunching a mature pdo product which may undergo unexpected changes when adopted by actors external to the pdo system. 4 the impact of geographical proximity between food producers and universities or public research laboratories is analysed by maietta (2015). figure 2. life cycle of an agri-food product. source: kotler and scott (1998). 159innovation and marketing strategies for pdo products 3.2 innovation and information asymmetry the use of a pdo product as an ingredient of an industrially processed food is an interesting strategy not only for pdo producers but also for manufacturers of the food, given that this is enriched by the pdo product and its reputation. it allows both types of producer to increase frequency of use by customers and/or to find new market outlets, thus halting or reversing falling price trends. the strategy can be particularly effective where the ingredient and the final product are quality brands from differing market segments. the benefits of such innovation may however be subject to limitations due to the information asymmetry of the agri-food market. information asymmetry exists on a market where not all agents have the necessary information available to make an optimum allocation of resources (akerlof, 1970; klein and leffler, 1981, shapiro, 1983, stiglitz 1987). because consumers are not able to verify credence attributes, the intangible quality attributes of a product, agri-food markets are particular affected by highly asymmetric levels of information between producers and consumers on the quality of products (anania and nisticò, 2004). search attributes of a product can be identified before purchase and experience attributes during consumption, but credence attributes cannot be verified even after consumption (darby and karni, 1973). in recent years, consumers have increasingly started to search for products combining recognizable material characteristics with an intangible content meeting ethical, cultural and health consciousness needs. however, although it is important for the consumer to know as much as possible about intangible attributes it is not necessarily in the producer’s interests to supply full and precise information (boccaletti and moro, 1993). information asymmetry thus encourages moral hazard for producers, who may place on the market products of lower quality than what is claimed, while the consumer accepts a different level of risk from what is claimed. in the long term, however, consumers will meet information from sources other than producers, such as consumer associations, magazines and journals etc., and become aware of the moral hazard. adverse selection will occur, in that consumers realize they have been misled and stop buying high quality products. in the long term, this will lead to a decrease in the number of products offered for sale and a loss of collective wellbeing (grazia et al., 2008). there is thus a pressing need for institutions and private actors to provide information in order to prevent this loss of social well-being (shapiro, 1983). intervention is required to ensure that markets are transparent, by way of measures such as the introduction of quality standards, regulations on labelling and advertising, recognition and registration of brands, supervisory authorities, production guidelines etc. for pdo products, the italian law in 2004 laid down that the use of a pdo as an ingredient is subject to authorization of the product consortium. law d.l. 297/2004 states “that the reference to a protected name in the labeling, presentation and advertising of products made, processed or transformed, is not punishable when authorized by the consortium for the protection of the protected name…”5. more recently, regulation eu 1151/2012 of the european parliament and european council 5 in the absence of a recognized protection consortium, authorization may come from the ministry of agriculture, food and forestry. 160 m.c. mancini, c. consiglieri on the regimes of quality of agricultural products and food extends protection to pdo/ pgi products used as ingredients, banning the evocation, misuse and imitation of the name in the list of ingredients of processed products where the product is not present (art. 13). 4. a case study from the dairy sector: pdo parmigiano reggiano cheese 4.1 the parmigiano reggiano supply chain parmigiano reggiano is one of the most representative pdo products of the longstanding italian gastronomic tradition. its history dates back to the thirteenth century when benedictine monks began producing it in emilia. in the late eighteenth century, cheese dairies were introduced, making it possible for small producers to process milk into parmigiano reggiano cheese (de roest and menghi, 2000). in the twentieth century there was strong growth thanks to the foundation of the consorzio del formaggio parmigiano reggiano (cfpr) in 1934, whose mission has always been to protect the typical nature of the product, the designation and the brand. in 2013, parmigiano-reggiano pdo production stood at 1.12 billion euro (1.97 billion euro consumer turnover). the cheese was made in 340 dairies covering 3,100 farms. it absorbed about 15% of national milk output and employed 20 thousand people, rising to 50 thousand along the whole supply chain6. the actors of this supply chain are milk producers, dairy owners, wholesalers-agers and traders; all members of cfpr. for many years, the supply chain was able to ensure adequate income for mountain farms, where sale of milk to cooperative dairies was the only source of income, as well as for hill and flatland farms. but since the 1980s, global competition has severely damaged mountain farming and many farms have been forced to close (arfini and mancini, 2013). today, the parmigiano-reggiano supply chain is no longer able to provide the same level of economic and social support to disadvantaged rural areas as in the past. 4.2 the parmigiano reggiano market parmigiano reggiano is traditionally and most frequently used to add flavor to food. as a hard cheese, it is mainly grated and used with pasta, the first course in an italian meal. parmigiano reggiano and grana padano represent a specific market segment within the overall cheese market because of the way they are used (de roest and menghi, 2000). but in the last few years, sales on the italian market have fallen. an increase in stocks on the supply side has led to a fall in market price. time analyses of the trend of prices on the wholesale market of 12 month matured parmigiano reggiano cheese show that prices are sensitive to output quantity, which is typical of a commodities’ market, even though this pdo cheese should behave like a niche product, with a degree of price stability (arfini and mancini, 2013). the problem is also due to the policy of large retailers which currently sell about 70% of parmigiano reggiano at promotional prices (pugliese, 2010; giacomini, 2010). 6 agricoltura (2014), la filiera del parmigiano reggiano. supplemento 56. 161innovation and marketing strategies for pdo products on the demand side, consumption of parmigiano reggiano on the italian market is today in a context of economic crisis7 which has decreased purchasing power of italian households and has led to a decrease in food consumption in real terms. a key factor in domestic demand for parmigiano reggiano in the current period is the price difference with its main competitor, grana padano. this is a similar cheese with a long ripening period, but it is produced using more industrial techniques. if the retail price difference between these two cheeses rises, then some consumers – particularly those living outside the parmigiano reggiano production area – will switch over to grana padano. in other words, demand varies according to the absolute price level of parmigiano reggiano cheese and to the price difference between it and grana padano cheese (de roest and menghi, 2000; giacomini, 2010; cersosimo, 2011). stagnation in consumption is also a result of changes in diet in italy, as hard ‘grana’ cheese is being replaced by lower calorie fresh cheeses8. finally, the degree of penetration of parmigiano reggiano in the domestic market is very high. data show that around 60% of italian households consume parmigiano-reggiano (arfini et al., 2006) and nearly 100% consume parmigiano reggiano and/ or grana padano (rama, 2010). the frequency of consumption is also high, mainly due to the type of consumption: on average, among customary consumers, parmigiano reggiano is consumed 5 times per week; 60% of these consumers use parmigiano reggiano daily. the parmigiano reggiano consortium and producers have responded to these problems by rationalizing supply in a supply regulation plan approved by the ministry for agriculture. the plan was based on the eu regulation 261/2012 as regards contractual relations in the milk and milk products sector, and its key element was the continuation of “parmigiano reggiano milk quotas” given to farmers. the aim was to regulate supply and re-balance the relationships of strength between farmers and dairies in the supply chain (giacomini and manfredi, 2013). another measure adopted by the cfpr to rationalize supply on the domestic market is promotion of exports, for which it has renewed financial support. in 2014, a total of €4 million was spent9. finally, cfpr has been promoting technical and marketing innovation for years. the parmigiano reggiano supply chain now collaborates with external actors, such as food manufacturers, working towards new packing and consumption models. vacuum packing of pieces of cheese for longer periods of storage, and individually packaged portions for snacking, were introduced as far back as the 1980s. in the 1990s grated cheese was launched in response to requirements for time saving; more recently, the strategy of co-branding is meeting new types of demand. 4.2.1 parmigiano reggiano pdo as an ingredient an increasingly successful type of innovation of pdo products, including parmigiano reggiano, is the use of the product as an ingredient in industrially processed food. an example is the co-branding scheme of 2007 between the cfpr and mcdonalds for a 7 in 2014, final demand for parmigiano reggiano fell by 3% compared to 2013, and in 2013 it had fallen by 1% compared to 2012 (sole24ore, 2014). 8 mark up (2008), “mercati. i formaggi 2008”, ottobre; mark up (2013), “il formaggio fresco resiste alla crisi”, luglio; agricoltura (2014), la filiera del parmigiano reggiano. supplemento 56. 9 www.parmigianoreggiano.it 162 m.c. mancini, c. consiglieri parmigiano-reggiano burger. this successfully combined mcdonalds, an emblem of the global market, with the consolidated reputation of parmigiano reggiano, a product representing the gastronomic culture of a place10. the co-branding of this innovative product brought added value deriving from the synergy between the reputation of the two brands and the taste preferences of two types of consumer. other products using parmigiano reggiano as an ingredient include filled pasta containing the cheese, and crisps flavoured with parmigiano reggiano and black pepper11. the spread of such products suggests that consumers are appreciating the guarantee of the typical nature of the pdo ingredient associated with new uses. earle (1997) and martinez and briz (2000) classify this as an incremental innovation. pdo parmigiano reggiano is also being used as an ingredient in processed dairy products made by melting cheeses. these are processed cheese slices or wedges12, used in cooking, traditionally perceived by the consumer as made from reject pieces of various cheeses, and thus as low value added products. in fact, processed cheeses have an average price 30% lower than the average cheese price. in 2013, processed cheeses accounted for 7% volume of the italian cheese market, or about 530 million euro, and their penetration was 85% for cheese slices and about 50% for wedges13. the use of a pdo product as an ingredient gave parmigiano reggiano producers entry to a new market segment and, at the same time, the opportunity to update consumer experience of processed dairy products. today on the italian market there are italian and overseas brands of processed cheese products which feature pdo parmigiano reggiano as an ingredient14, and their advertising often cites benefits of enrichment with the nutritional characteristics of pdo parmigiano reggiano. 4.3 product characteristics and production techniques the product specification code defines parmigiano reggiano as: “a hard cheese, slowly matured, produced with cow’s milk, raw, partially skimmed in a natural process” and states that: “the milk… must come from cows whose diet is based on the use of fodder obtained in the area of origin.” parmigiano reggiano is produced exclusively in the area defined by the code of specifications15 where the cows’ diet is fodder produced in the area. silage and fermented foods are not permitted. strict feeding regulations are the main reason for lower milk 10 the burger was sold at a higher price than others and was on sale for a limited period of time (13 months) (reitano and pantano 2009). the characteristic of scarcity influenced the consumer to consider this product rare (walchli, 2007; geylani et al. 2008). 11 the main brands of pasta using co-branding with parmigiano reggiano are barilla and fini, and the crisps are produced by kettle. 12 processed cheese products enriched with parmigiano reggiano pdo include various brands of cheese spread (parmareggio, boni, margi, etc.); their market value however is residual. 13 assolatte, (2014) relazione annuale settore lattiero caseario anno 2013. editoriale il mondo del latte, milano. 14 the same type of innovation involves the pdo grana padano production system. grana padano is used as an ingredient for the production of stuffed pasta and processed cheeses. therefore, parmigiano reggiano and grana padano are competitors in this segment too. 15 the provinces of parma, reggio emilia, modena and part of the provinces of mantova and bologna, plains, hills and mountains between the po and the river reno. 163innovation and marketing strategies for pdo products yield per cow, and higher production costs than for industrial milk, and competitor cheeses such as grana padano. production standards define the method of processing milk into cheese as well as the area of production. these methods are the core of the scheme because they ensure that traditional methods are followed. one of the main provisions, for example, is that no preservative except salt can be used in the processing phase. during the long phase of ripening, which has to be at least 12 months, the main constituents of the cheese are transformed; particularly important nutritionally are the protein transformations. the milk protein is ‘digested’, or decomposed into smaller components right down to amino-acids. this gives parmigiano reggiano its distinctive taste and makes it more digestible. 4.4 the survey in the industrial production of processed cheese, cheeses are added to other ingredients, including water and emulsifiers16, then heated and mixed to a stable homogenous emulsion. in traditional processes, the mixture was heated to between 75 and 100°c, but with modern technology the sterilization temperature (121°c) can be reached by way of steam injection heat exchangers. given that the advertising of slices and wedges highlights the characteristics of pdo ingredient products and aims to differentiate them from competitor products which do not contain pdo ingredients, this study aimed to establish whether the industrial process, particularly the heating process, impacts on the quantity and/or quality of the characteristics of the pdo ingredient. we examined nine processed cheese products (wedges) on the end market, of which three contain pdo parmigiano reggiano as an ingredient. the three products containing parmigiano reggiano represent the universe of such products on the italian market17. for these three, we examined consumer advertising for explicit claims of a direct link between product characteristics and pdo ingredient characteristics. the claims were in fact found: the advertising cited particularly “naturalness”, “genuineness”, “goodness” and “nutritional quality” of parmigiano reggiano18, and implied that these qualities were transferred from the pdo ingredient to the processed cheese. for the aims of the research, these descriptions were codified into observable product characteristics. the terms “natural” and “genuine” are applied to unadulterated products which retain the characteristics of their natural factors. these characteristics can be observed in parmigiano reggiano cheese, as it is the outcome of a strictly artisan process but it is 16 emulsifying agents (citrates and/or sodium polyphosphates) are essential, as without them, the mixture loses water through evaporation and the fat separates, leaving a rubbery mass of lumps. 17 the other six products (not containing parmigiano reggiano) are those brands available on the shelves of coop, conad and esselunga. market share of these three retailers is nearly 40% of the total market. 18 company wesbites contain the following statements about wedges enriched with pdo parmigiano reggiano: “[they] are a completely new type of processed cheese, enabled by the outstanding natural and genuine qualities of parmigiano reggiano”; “…parmigiano reggiano, with its special nutritional qualities, is the only cheese ingredient; “all the authentic goodness of our parmigiano reggiano, the only cheese ingredient, can be found in our soft slices; they are delicately flavoured and perfect for adding flavor to dishes every day as well as for making toasted cheese sandwiches. 164 m.c. mancini, c. consiglieri more difficult to define and observe such characteristics in industrially made products such as processed cheeses, where the production process necessarily affects the natural and genuine qualities of the ingredient. an analysis of consumer perception of these qualities and their relationship to industrial processing would be necessary, but lies beyond the scope of this research. “goodness”, on the other hand, is the presence of characteristics which meet consumer taste. the product specification for parmigiano reggiano says the cheese is “fragrant, delicate, full flavour but not peppery”, but here again, the verification of whether the characteristics are retained in the processed cheese would require consumer perception evaluation and analysis techniques. these too lie outside the scope of this research. this research was however able to focus on “nutritional quality”. the next section looks at the combination of nutritional elements. 4.4.1 data analysis table 1 reports the ingredients shown on the label for each of the nine products examined19. it shows that three products contain pdo parmigiano reggiano in a percentage between 19.6 and 30%. all products of course contain the essential raw ingredients: milk, water, milk proteins, whey or rennet, and in some cases, butter and cream. emulsifiers are the traditional phosphates (e452 – e339) and citric acid (e330 – e331). currently, the amount of polyphosphates used in industrial food process19 the products have been conventionally called 1,2,3,4,5,6,7,8,9. table 1. ingredients shown on the labels of the processed cheese wedges. brand ingredients 1 parmigiano reggiano pdo 25%, water, whey, milk protein, emulsifier: sodium citrate, acidity regulator: citric acid, thickening agent: carrageenan. 2 parmigiano reggiano pdo 19,6% (milk, salt, rennet), pasteurized fresh whole milk, water, whey concentrate, cream, butter, milk protein, emulsifiers sodium citrate and potassium citrate, acidity regulator: citric acid. 3 milk, parmigiano reggiano pdo 30% (milk, salt, rennet) cream, milk protein, emulsifier e331, acidity regulator e330. 4 cheese, water, whey powder, butter, milk protein, emulsifier: sodium citrate; acidity regulator: citric acid; stabilizing agent: carrageenan. 5 cheeses (milk, salt, rennet), water, whey concentrate and / or powder, butter (cream and / or whey), milk protein, emulsifiers: sodium polyphosphates, sodium citrate. 6 milk (40%), cheese, cream, milk protein, emulsifiers (e331), acidity regulator: citric acid. 7 leerdammer cheese 100%, water, cream, emulsifiers e452, e339. 8 cheeses, water, whey concentrate and / or powder, butter, milk protein di latte, emulsifiers: sodium polyphosphates, sodium citrate. 9 cheese (milk, milk enzymes, salt, rennet) 43%, water, butter, whey powder, emulsifier: sodium citrate, acidity regulator: citric acid. source: product labelling. 165innovation and marketing strategies for pdo products ing is undergoing drastic reduction because it is now known that excessive ingestion of phosphorous harms human health by eliminating calcium from the body and weakening muscles and bones (travia, 1979; messa, 2008; cozzolino et al., 2009; cupisti and d’alessandro, 2011). only two of the sample products use polyphosphates, but they are not products using the pdo ingredient. as noted above, we examined the combination of nutritional elements; it was evaluated and compared by measuring the protein, carbohydrate and fat contents. proteins vary between a minimum of 10% and a maximum of 14.7%; carbohydrates between 3% and 6.5% and fats between 13.1% and 21% (table 2). 4.4.2 results the research question for this test is: “does the presence of the pdo parmigianoreggiano ingredient enrich the “nutritional elements” of the end product enough to justify consumer advertising claims?” the three main constituents (protein, carbohydrates and fats) are found to be distributed evenly around the average values with relatively low levels of standard deviation, regardless of the presence or absence of the pdo ingredient. in other words, the three variables are distributed around the average values with no appreciable differences (figure 3). in order to verify possible nutritional differences between processed cheeses containing pdo and other ‘standard’ products, a t-test of hypothesis for the difference between the two group means was run, and results are shown in table 3. the t-test verifies whether the mean values of the three main constituents are equal across the pdo-enriched (group a) and traditional (group b) processed cheeses. the calculated values of the t-test statistics are compared with the tabulated critical values of the student t distribution at 95% confidence level, with 7 degrees of freedom. because the t-test is known to be sensitive to the number of observations, which in this application is rather limited, the parametric testing is complemented with a non-parametric analysis – less demanding on the data table 2. percentage composition of protein, carbohydrates and fats of the processed cheese wedges protein % carbohydrates % fats % 1 14 5 15 2 11.4 5.5 13.1 3 14.0 4.3 16.0 4 11.5 6.5 18.5 5 12 4 17 6 14.7 4.4 18 7 12 3 17 8 14 4.6 16.5 9 10 4 21 average 12.6 4.6 16.9 dv_std 1.60 1.00 2.23 source: product labelling. 166 m.c. mancini, c. consiglieri – carried out employing the mann-whitney (1947) u statistic (table 3). both the parametric t-test and the non-parametric mann-whitney (1947) u statistic provide the same result with respect to the level of statistical significance of the difference between the two sample means for every constituent. therefore, the statistical analyses undertaken here suggests that there are no statistically significant differences between the two group means for proteins and carbohydrates, while the mean for fats is statistically significant at the 5% level. but although this is statistically significant, it isn’t particularly relevant in nutritional terms. in fact, the legal classification of cheeses on the basis of fat content of dry matter (art. 53 law no. 142 of 1992) specifies three categories: full fat cheese containing > 35% fat; light or semi-fat cheeses with fat content between 20% and 35% and low-fat cheeses with <20% fat content. these classifications are broad, in that fat content of light or semi-fat cheeses can vary between 20 and 35%. table 4 shows that all samples fall into the ‘full-fat’ category, so internal variations have little nutritional relevance. note also that the percentages shown in table 4 are calculated for about 250 gr., which is an extremely large portion of processed cheese. more detailed observations can be made on the ‘functional’ components of the pdo ingredient. the proteins contained in high quantities (33%) in parmigiano reggiano are known to be of excellent biological quality. this is thanks to the amino-acid composition which includes essential amino-acids, and because they are easily digestible thanks to the proteolytic enzymes from the milk and the milk bacteria. the average content of free amino-acids is in fact 23.2% of proteins with a minimum of approximately 19% and a maximum of over 27%. the content of free amino-acids is directly proportional to the length of the ripening period up to 15 months, after which it stabilizes. with continued ripening, the amino-acids increasingly metabolize and are eventually freed. the second aspect, the digestible nature, is given by the long ripening period when the main constituents are transformed. the transformation of proteins is the most interfigure 3. distribution around average value of variables (protein, carbohydrates, fats). source: authors’ elaboration. 167innovation and marketing strategies for pdo products esting from the nutritional point of view, because the action of the proteolytic enzymes breaks the long casein chains up into peptides. the casein is thus much more easily digestible. a growing number of researchers (gobetti et al., 2002; fitzgerald and maisel, 2003; phelan et al., 2009) are focussing on the nutritional content of peptides in dairy products. certain peptides have been found to have important functional qualities, such as positive anti-oxidant effects and antithrombotic effects on the cardiovascular system, effects on the immune, gastrointestinal, and nervous systems as well as possible anti-tumor effects. these are termed bio-active peptides. but naturally, given that they comprise short sequences of amino-acids, peptides are sensitive to anything that alters protein structure, including heat, acidity and enzyme reactions. so it is clear that using cheeses ripened over a long period and thus rich in bio-active peptides in heattreated end products may compromise or ‘flatten’ the table 3. statistical analyses of group means. protein % carbohydrates % fats% groups a and b (n=9)       average 12.6 4.6 16.90 standard deviation 1.60 1.00 2.23 group a (n=3) average 13.1 4.9 14.70 standard deviation 1.5 0.6 1.50 group b (n=6) average 12.4 4.4 18.00 standard deviation 1.7 1.2 1.60   t-test for h0 :µa −µb =0 h1 :µa −µb ≠0 ⎧ ⎨ ⎩ t(7) 0.654 0.708 -2.923** student t critical value at 95% confidence 2.37 mann-whitney u test for h0 :µa =µb h1 :µa ≠µb ⎧ ⎨ ⎩ -0.264 -1.037 2.334** source: authors’ elaboration using stata 12. notes to table 3: group a with pdo; group b without pdo; ** significant at the 5% level. table 4. fat content as a percentage of dry matter. fat % 1 44.1 2 46.6 3 43.7 4 50.7 5 51.5 6 48.5 7 53.1 8 47.0 9 60.0 source: authors’ elaboration. these percentages are calculated proportionally using the formula h2o% = 100 (proteins + carbohydrates + fats) for each sample. 168 m.c. mancini, c. consiglieri functional properties of the pdo ingredient (lund, 2003). in fact, it has been noted that industrial processing affects negatively a series of micro-constituents and/or functional substances and makes their presence less significant (korhonen et al., 1998)20. we therefore find that the nutritional properties of the pdo ingredient are compromised by the melting process and it is not true, as is sometimes claimed, that they are completely transferred to the end product. 5. discussion from the point of view of both parmigiano reggiano producers and the food industry, this type of innovation can be classified as incremental (figure 1). for the actors in the parmigiano reggiano supply chain, the use of the product as an ingredient changes nothing at macro level; there is no use of alternative technology and the structure of the sector is unaltered. only at micro-level producers are given the opportunity to increase sales to the food industry. the use of the product as an ingredient does not alter the macro level for the food industry either. technologically and commercially, no innovation is taking place in the sector. it is a micro-innovation at the level of the individual company, in that using the pdo widens the range of products it can offer on the market. what is unusual in this case is that there is an indirect impact on the parmigiano reggiano production system and reputation of the typical product which is caused by an innovation adopted by the food industry, external to the parmigiano reggiano production system. it has been shown that as the pdo product can be heavily processed, some of its characteristics may not survive in the end product. these are credence attributes which cannot be perceived by the consumer, and if consumer advertising uses the reputation of such characteristics which have in fact been modified by processing, there is a discrepancy between communication and physical features of the product. the theory of information asymmetry holds that consumers in the future will have access to alternative sources of information, which will help them to find about credence attributes and become newly aware of certain facts. there will then be adverse selection whereby consumers will stop buying the product. this could negatively affect various different actors. in the first place, the food industry could lose market share, and there could be negative effects on brand reputation. secondly, pdo producers could also lose market share and see their reputation harmed. thirdly, cfpr, which encourages and permits the strategy, could suffer. like many typical product consortia (mancini, 2012), cfpr has played a key role in protecting and developing parmigiano reggiano, but if it loses consumer trust, decades of activity in promoting it could be undone. finally, there will be debate on pdo legislation which permits pdo products to be used as ingredients without effectively protecting the consumer from information asymmetry. this case shows that even incremental innovation can have serious effects when it is applied on production phases which lie outside the direct control of the pdo producers. the 20 note, however, that this research covers only one of the quality aspects advertised for parmigiano reggiano as an ingredient and it would be useful to examine the other important characteristics, the ‘authentic’, genuine’, ‘natural’ and ‘good’ qualities, and their effect on the end product. 169innovation and marketing strategies for pdo products presence of food manufacturers impinging on the traditional relationship between the consortium (parmigiano reggiano producers) and consumers is causing the consortium to lose control of the supply of information, and this could have negative effects on the entire pdo production system. asymmetry of information could compromise the success of the relaunch of mature pdo products, and in general hinder collaboration between typical product systems and industrial systems which is, at present, a promising avenue for the economic development of many rural areas. but as reputation is a necessary condition for food companies to continue using typical products and exploit their name to enhance their own products, collaboration between these two systems depends on the typical product maintaining a strong reputation for excellence. in today’s market, where the integration of local and global characteristics is finding increasingly favourable response from consumers, it is therefore in the interests of both production systems to work for the break-down of the dichotomy. 6. final remarks typical products will be competitive and will also contribute to socio-economic development of their place of origin if they are able to take part in the logic of the global market. this study focused on innovation, a competitive strategy, with regard to pdo products which have a mature market. it examined the case of parmigiano reggiano pdo cheese used as an ingredient to enrich industrially processed cheese, and found that where innovation is carried out by actors external to a pdo system, it can lead to a loss of control over consumer information by pdo producers, which has potential negative repercussions on pdo product reputation. in order to prevent this, there needs to be effective collaboration between holders of the intellectual property of the designation and those who use its reputation. to date, italian legislation fails to ensure that consumers are correctly informed, even though it makes compulsory the consortium to allow the use of the product as an ingredient. european legislation is also lacking, as it does not prevent adverse selection which in the long term can lead to a loss of collective well-being. as boisvert (2006) says, quality labels, such as pdo, can be used as instruments for policies promoting the preservation of cultural heritage when they are associated with relevant ‘smart’ rules. it is necessary today for typical production systems to collaborate with the food industry in order to hold their place on global markets. to this end, extensive policy discussion is necessary in order to create legislation to regulate more closely innovation involving products bearing origin certificates, particularly their use as ingredients and, at the same time, preserve their distinctive nature and protect the consumer as well. acknowledgement the authors wish to thank mario veneziani for his precious collaboration. references akerlof, g. 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(2007). the effects of between partner congruity on consumer evaluation of co-branded products”, psychology and marketing 24: 947-973. bio-based and applied economics 4(2): 103-123, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-15301 the common agricultural policy as a driver of water quality changes: the case of the guadalquivir river basin (southern spain) gloria salmoral1,2,*, alberto garrido1 1 water observatory of the botín foundation and ceigram, research centre for the management of agricultural and environmental risks, technical university of madrid, 28040, madrid, spain 2 centro de prospectiva estratégica – ceproec, instituto de altos estudios nacionales, quito, ecuador date of submission: january 2nd, 2015 abstract. several studies have analysed the effects of european environmental policies on water quality, but no detailed retrospective analysis of the impacts of the common agricultural policy (cap) reforms on observed water quality parameters has been carried out. this study evaluates the impact of the cap and other drivers on the concentrations of nitrates and suspended solids in the guadalquivir river basin (southern spain) over the 1999-2009 period. the most important drivers that are degrading both water quality indicators are exports from upland areas and agricultural intensification. water quality conditions have improved in regions where there has been abandonment and/or deintensification. the decoupling process has reduced the concentration of nitrates and suspended solids in a number of subbasins. although agricultural production and water efficiency in the basin have improved, high erosion rates have not yet been addressed. keywords. common agricultural policy, freshwater quality, nitrates, suspended solids, panel data. jel codes. q18, q25 1. introduction water quality conditions play a critical role in present and forthcoming water sustainability. by 2015, the ecological status, as defined by the water framework directive (wfd), of almost half of europe’s surface water bodies is likely to be poor, with pressure from diffuse agricultural pollution becoming a growing concern (eea, 2012). achieving more efficient and equitable water management objectives at catchment level, not only relates to the actual water resource itself, but is influenced by water related policies * corresponding author: gloria.salmoral@upm.es, gloria.salmoral@gmail.com. mailto:gloria.salmoral%40upm.es?subject= mailto:gloria.salmoral@gmail.com 104 g. salmoral, a. garrido and the application of scientific knowledge, for instance, the blueprint report (european commission, 2012a) emphasizes the need for better implementation and deeper integration of water policy objectives with the common agricultural policy (cap). during the period 1999 to 2009, two cap reforms were implemented: agenda 2000 and 2003 cap. the objectives of agenda 2000 were based primarily on the convergence of cereal support prices with world markets and the introduction of decoupled area payments for herbaceous crop growers (mapa, 2002). the 2003 cap reform introduced a more radical change with the establishment of the single payment scheme (sps) for all crops, decoupled from production and conditional upon cross-compliance. “cross-compliance is a mechanism that links direct payments to compliance by farmers with basic standards concerning the environment, food safety, animal and plant health and animal welfare, as well as the requirement of maintaining land in good agricultural and environmental condition (gaec)”1. cross-compliance involves 18 statutory management requirements (e.g. the nitrates directive) and a number of measures for ensuring gaec (e.g. control of soil erosion and soil organic matter content). under both reforms, farmers could participate in agri-environmental measures (aem) (e.g. organic farming, crop and farming extensification and set aside) as part of rural development programmes (ojec, 1999) and some of these aem became mandatory with the introduction of cross-compliance. based on the changes introduced by the cap reforms, we analyse their effects on existing water quality conditions. several differing results were reached with respect to the expected effects of the 2003 cap reform on changes of nitrate pollution. on the one hand, no significant changes were found in water quality status when crop pattern changes after the reform do not differ significantly in nutrient requirements, as in the midi-pyrenees (france) (belhouchette et al., 2011). the concentration of nitrates in this case, would not decrease if there is not a big reduction in subsidies in the event of non-compliance (ibid). however, decoupled income support can lead to reductions in nitrate pollution as a result of more diversified production patterns and extensive management practices with the introduction of less nitrogen-intensive crops and the reduction of the cultivated area of the most nitrogen intensive crops (gallego-ayala and gómez-limón, 2009; cortignani and severini, 2012). volk et al. (2009) highlighted that the most effective way to mitigate nitrate pollution would be to pursue management practices that move away from conventional farming and towards eco-farming practices, and to convert arable land to pastureland. on the other hand, decoupled direct subsidies, together with higher agricultural prices, can stimulate intensification and crop development linked to trade liberalization (martínez and albiac, 2006; sieber et al., 2013). these potentially conflicting outcomes are an obstacle to the attribution of the observed environmental effects to policy reforms. another environmental concern is soil erosion. some studies have identified this to be a consequence of intensification promoted by subsidies coupled to crop production (boardman et al., 2003), particularly for traditional olive orchards in spain (de graaff and eppink, 1999). the sps still favours more intensive olive orchards since the amount of this payment at farm level was, until 2014, dependent on historical production during the 1999-2002 period (de graaff et al., 2011). nevertheless, there is no specific eu legal basis 1 http://ec.europa.eu/agriculture/envir/cross-compliance/index_en.htm. http://ec.europa.eu/agriculture/envir/cross-compliance/index_en.htm 105the common agricultural policy as a driver of water quality changes for soil protection in europe, and the soil framework directive proposal was withdrawn in 2014, although the european commission is working on the thematic strategy for soil protection (european commission, 2006), e.g. soil protection as an integral part of the gaec (european commission, 2012a). in europe, no ex-post evaluation of the implications of cap implementation for observed surface water quality has yet been performed. considering the failure to fulfil the goals of the wfd and the existing environmental concerns in europe, this study proposes an approach for determining the implications of the cap reforms for nitrates and suspended solids during the period 1999 to 2009, utilizing a specific case study in the guadalquivir river basin (southern spain). our approach aims at understanding the relationships between water quality parameters and factors such as agricultural production, natural environment and economic policy indicators. 2. materials and methods 2.1 site of study the guadalquivir river basin (grb), located in southern spain, was selected for the case study due to existing water quality concerns, as well as the importance of agricultural production in the basin. the main pressure on surface water bodies in the grb is diffuse pollution (50%), followed by point pollution (37%) (grba, 2012). emerging water quality problems caused by diffuse pollution are related to land and water use in the basin, i.e. agricultural production on hillsides aggravates water runoff and soil erosion (blomquist et al., 2005). there is a high risk of erosion in the basin as a result of olive orchard production (gómez, 2008; taguas et al., 2011; taguas et al., 2013). erosion rates are in excess of 50 t ha-1 year-1 for 20% of the olive orchard extension in the upper and middle part of the grb (rga, 2008). in 2009, cropland accounted for approximately 2,650,780 ha (not including pastureland), with 31% of the area under irrigation. olive orchards are the principal crop in the case study site and account for 56% of the total cropland area, followed by wheat (13%) and sunflower (9%) (magrama, 2012). the grb has a surface area of 57,530 km2, and the climate in the basin is mediterranean with precipitation ranging from 289 mm to 743 mm over the 1998-2009 period. the hydrographic network is configured around the axis of the guadalquivir river, which is 655 km long and has a mean annual discharge of 7022 hm3. estuarine water bodies are to be found downstream, including the doñana national park, which is a high-biodiversity area and a wildlife shelter for migratory european and african birds (fernández-delgado, 2005) (figure 1). 2.2 subbasin delineation and water quality dataset based on a 100 × 100 m digital elevation model (dem) (cnig, 2011), the drainage area of each monitoring station is calculated. a total of 89 water quality monitoring stations are located within the grb, which cover a total of 12,619 km2. this accounts for 22% of the grb. the drainage area identifies the area upstream of the monitoring station that is related to the existing water quality conditions. arcgis 9.3 (esri, 2009) is used 106 g. salmoral, a. garrido to map out the subbasin boundaries that are delineated by means of a two-step process. first, if a monitoring station is located at the mouth of the subbasin, the delineated subbasins provided by grba (2012b) are selected. second, if a monitoring station is situated inside a subbasin, the bottom boundaries are delineated using the dem. the 89 delin eated subbasins are then classified depending on the dominant type of irrigated agriculture according to the criteria of geographic proximity, production orientation and economic importance (rga, 2010a): olive orchards (‘olive’), mountainous areas (‘mountain’), intensive crops in coastal areas (‘coast’) and semi-intensive crops (‘semi-intensive’) (figure 2). a water quality dataset is created for each monitoring station, including the water quality indicator (nitrates and suspended solids) and the year and month for the period from september 1998 to august 2009 according to data available from the grba (2011), though the series are not complete for all stations (table 1). 2.3 water quality trends in order to understand our water quality dataset, we identified the annual2 water quality trends. in general, the water quality parameters in the grb have remained stable dur2 in our study, the annual scale refers to the period comprising the agricultural season from the beginning of september until end of august. figure 1. guadalquivir river basin. 107the common agricultural policy as a driver of water quality changes ing the study period. but most of the significant trends for both nitrates and suspended solids are indicative of a degradation of the water quality conditions. for nitrates, the prevalent existing annual trends indicate an increase (linear or minimum) in the concentration of nitrates (figure 3). the group of nitrate-increasing trends occur mostly within figure 2. study subbasins classified by dominant type of agriculture. source: own elaboration based on monitoring station location (grba, 2011), digital elevation model (cnig, 2011) and main irrigated regions (rga, 2010a). table 1. descriptive statistics for monthly observations at 89 sampling stations over the 9/19988/2009 period and by subbasin classified according to dominant type of irrigated agriculture. subbasin classification no. sampling stations nitrates suspended solids no. observations p10 p50 p90 no. observations p10 p50 p90 (mg l-1) (mg l-1) mountain 13 916 1 2 7 827 3 8 30 olive 41 2201 1 5 21 2551 4 25 156 coast 3 158 1 5 26 210 11 41 191 semi-intensive 32 2307 2 12 33 2377 9 53 280 *p10: 10th percentile; p50: 50th percentile (median), p90: 90th percentile. 108 g. salmoral, a. garrido ‘semi-intensive’ areas in the south-eastern and mid-guadalquivir valley and the river estuary, dominated by annual crops. regarding the behaviour of suspended solids, most trends show a quadratic component (15 out of 89 subbasins), and particularly a u-shaped curve (14 subbasins). nevertheless, despite the acute concentrations of suspended solids in the basin, only five monitoring stations recorded reductions in the concentrations of suspended solids. the concentration of suspended solids is improving in the east, upper and middle part of the basin. the concentration of suspended solids (both linear and quadratic) is worsening across the subbasins classified as ‘olive’ in particular and in ‘semi-intensive’ areas along the guadalquivir river axis (figure 3). figure 3. linear and quadratic trend regressions with significant coefficients (p<0.05) for median annual values of concentrations of nitrates and suspended solids. the linear trend provides the slope (b) of the time variable (h), with yt = a + bht + εt, where yt is the concentration of either nitrates or suspended solids, ht is the time trend (t=1,...,t) and εt is the error term. the quadratic trend equation yt = a + bht + cht 2+ εt is used to check for convex or concave trend behaviour. the classification of dominant agricultural areas across the basin is also shown. 2.4 subbasin characterization the main factors related to surface water quality conditions for each subbasin i and agricultural season t, characterized by 21 variables over the period september 1998 to august 2009 are as follows: 1) climatic and physical environmental characteristics (precipitation, slope, erosion, soil permeability, exportno3 and exportss), 2) urban point sources (population density), 3) agriculture structure and productivity measures (biomassrainfed, biomassirrigated, shannon, % drip and ncons), and 4) economic and policy indicators (agenda 2000 reform, 2003 cap reform, subsidiesrainfed, subsidiesirrigated, % coupling, vz ratio, crop price index, pricen and pricefuel) (see table 2 for further details). arcgis 9.3.1 (esri, 2009) was used to adapt geographical information from biophysical (i.e. basin) or administrative (i.e. municipal, province) level to subbasin level. 109the common agricultural policy as a driver of water quality changes the mean annual precipitation, slope, erosion rates and soil permeability were calculated for each subbasin based on georeferenced information (cnig, 2011; grba, 2012b; sgpyusa, 2012). the categorical erosion and soil permeability variables were weighted by the area of each classification. exports of nitrates (exportno3) and suspended solids (exportss) from upland areas were also considered as factors affecting the environmental quality. they were included as the concentration of the water quality indicator for the closest upstream subbasin. although this study focuses on diffuse pollution, we also estimated point source pollution using population density based on the annual population of each municipality (ine, 2013). for agricultural structure and productivity measures, total aboveground biomass in dry weight was used as an indicator of agricultural intensification since it can aggregate all agricultural biomass generated within a region. the study area includes 129 crop species. the total aboveground biomass was calculated separately for rainfed (biomassrainfed) and irrigated (biomassirrigated) systems by totalling the agricultural (t) and residual (t) production and dividing by the subbasin area (ha). agricultural production comprises the economic or agricultural parts (grain, fibre, fruit or tuber). residual production refers to the crop residues that remain in the field after the crop is harvested. supplemental material a details the calculation process for crop area and total aboveground biomass, distinguishing between rainfed and irrigated systems and annual and woody crops. secondly, crop diversity was measured using the shannon index (shannon index), which spatially and temporally characterizes crop area allocation to different species. a greater shannon index value is indicative of more diversified agricultural areas. thirdly, irrigation system modernization is included as the percentage of drip irrigation (% drip) per type of agricultural classification (‘olive’, ‘semi-intensive, ‘coast’ and ‘mountain’), sourced from the andalusian regional government (rga 2010c). % drip is interpolated and extrapolated between 1997 and 2008 to obtain the annual observations for the 1999-2009 period. finally, since data on nitrogen fertiliser applications are not available at subbasin level, the average consumption of nitrogen (ncons) at subbasin level was estimated by multiplying the average nitrogen rates in spain (kg ha-1) by the total cropland area per subbasin (ha) and dividing by the total area of each subbasin (ha). for the economic and policy indicators, the study looks at the effects of the cap, the eu nitrates directive and crop, fertiliser and fuel prices. agricultural subsidies (subsidiesrainfed and subsidiesirrigated in € ha-1) were used as the first cap policy indicator for the 1999-2009 period, which considers agenda 2000 (2000-2006) and the 2003 cap reform (2007-2009) changes. a one-year lag for both variables (l.subsidiesrainfed and l.subsidiesirrigated) was also considered, since farmers’ behaviour might also be influenced by subsidies from the previous agricultural season. a total of 32 crops were eligible for subsidies. the second cap policy indicator was the average percentage of coupled subsidy (% coupling). calculations for both cap indicators are detailed in supplemental material b. agenda 2000 introduced some voluntary agricultural aems for farmers (e.g. strip zones and organic farming) and the 2003 cap reform considered gaec as a requirement for cross-compliance. however, no information is available at subbasin level regarding the level of participation to aems during agenda 2000 and gaec during cross-compliance implementation. since we cannot characterize the aems and gaec in quantitative terms, a dummy variable characterizes agricultural policy implementation after the 2000/01 agri110 g. salmoral, a. garrido ta bl e 2. c ha ra ct er iz at io n of s ub ba si ns b y ag ric ul tu ra l s ea so n ov er th e pe rio d fr om 9 /1 99 8 to 8 /2 00 9. va ria bl e cl as sifi ca tio n va ria bl e u ni ts av ai la bl e da ta c lim at e an d ph ys ic al en vi ro nm en t pr ec ip ita tio n m m m on th ly ra st er d at a (s g py u sa , 2 01 2) sl op e % d ig ita l e le va tio n m od el w ith a g rid c el l s iz e of 1 00 m x 1 00 m (c n ig , 20 11 ) er os io n t h a-1 c la ss ifi ca tio n: 0 -5 , 5 -1 2, 12 -2 5, 2 550 , 5 010 0, > 20 0 e ro sio n ra te s ( g rb a , 2 01 2b ) pe rm c la ss ifi ca tio n: 1 : v er y lo w, 2: lo w, 3 : m ed iu m , 4 : h ig h; 5: v er y hi gh s oi l p er m ab ili ty (g rb a , 2 01 2b ) ex po rt s o f n o 3 an d ss fr om u pl an d ar ea s ( ex po rt n o 3, ex po rt ss ) m g n o 3 l-1 m g ss l -1 w at er q ua lit y in di ca to r c on ce nt ra tio n fo r t he n ea re st h ea dw at er su bb as in (g rb a , 2 01 1) u rb an p oi nt so ur ce s po pu la tio n de ns ity po pu la tio n pe r k m -2 a nn ua l p op ul at io n by m un ic ip al ity (i n e, 2 01 3) a gr ic ul tu re st ru ct ur e an d pr od uc tiv ity m ea su re s to ta l a bo ve gr ou nd b io m as s f or ra in fe d cr op s ( bi om as s ra in fe d) a nd irr ig at ed c ro ps (b io m as s ir rig at ed )1 t h a-1 l an d us e m ap s f or y ea rs 1 99 9, 2 00 3 an d 20 07 (r g a , 2 01 0; m a rm , 20 09 ). a nd al us ia n irr ig at ed c ro p in ve nt or ie s f or y ea rs 1 99 6 an d 20 02 (r g a 19 99 ; 2 00 3) i rr ig at ed c ro p lo ca tio n in 2 01 03 c ro p yi el ds (m a rm , 2 01 2) h ar ve st in de x (s ee s up pl em en ta l m at er ia l a t ab le a 1) r es id ue to p ro du ct ra tio (s ee s up pl em en ta l m at er ia l a t ab le a 1) c ro p di ve rs ity (s ha nn on in de x) m un ic ip al c ro p ar ea d at a (m a g ra m a , 2 01 2a ) m od er ni za tio n of ir rig at io n sy st em (% d rip ) % i rr ig at io n m et ho d (s ur fa ce , s pr in kl er a nd d rip ) a re a by ir rig at ed ag ric ul tu re c la ss ifi ca tio n (‘o liv e’, ‘s em i-i nt en siv e, ‘c oa st’ a nd ‘m ou nt ai n’ ) fo r y ea rs 1 99 7 an d 20 08 in a nd al us ia (r g a , 2 01 0b ). c on su m pt io n of n itr og en (n co ns )1 kg h a-1 a ve ra ge n at io na l n itr og en c on su m pt io n ra te s ( m a g ra m a , 2 01 2b ) 111the common agricultural policy as a driver of water quality changes va ria bl e cl as sifi ca tio n va ria bl e u ni ts av ai la bl e da ta ec on om ic a nd po lic y in di ca to rs ag en da 2 00 0 re fo rm d um m y va ria bl e w ith v al ue o f 1 a fte r 2 00 0/ 01 a gr ic ul tu ra l s ea so n 20 03 c a p re fo rm d um m y va ria bl e w ith v al ue o f 1 a fte r 2 00 6/ 07 a gr ic ul tu ra l s ea so n a gr ic ul tu ra l s ub sid ie s f or ra in fe d cr op s ( su bs id ie s ra in fe d) a nd ir rig at ed cr op s ( su bs id ie s ir rig at ed )1, 2 € ha -1 a gr ic ul tu ra l s ub sid ie s p er u ni t o f p ro du ct io n (€ t-1 ) o r c ul tiv at ed a re a (€ h a-1 ) b ef or e 20 06 /0 7 ag ric ul tu ra l s ea so n (s ee s up pl em en ta l m at er ia l b ta bl e b1 ) p er ce nt ag e of d ec ou pl ed p ay m en ts , r ef er en ce p er io d an d su bs id ie s pe r u ni t o f p ro du ct io n (€ t-1 ) o r c ul tiv at ed a re a (€ h a-1 ) a fte r 2 00 6/ 07 ag ric ul tu ra l s ea so n (s ee s up pl em en ta l m at er ia l b t ab le b 1) c ro p yi el ds (m a rm , 2 01 2) pe rc en ta ge o f c ou pl ed su bs id y (% co up lin g) 1 % p er ce nt ag e of d ec ou pl ed p ay m en ts (s ee s up pl em en ta l m at er ia l b t ab le b1 ) n itr at e vu ln er ab le z on e ra tio (v z ra tio )1 n itr at e vu ln er ab le z on e an d cr op a re a aff ec te d by e u n itr at es d ire ct iv e (b o ja 1 99 9; b o ja 2 00 1) cr op p ric e in de x1, 2 n at io na l c ro p pr ic es (m a g ra m a , 2 01 2b ) fu el p ric e (p ric e fu el) eu ro l -1 a nn ua l f ue l p ric e (a sa ja , 2 01 1) (u pa , 2 01 2) n itr og en e le m en t p ric e (p ric e n ) eu ro t-1 n n itr og en e le m en t p ric e. a nn ua l f er til ise r p ric es (m a g ra m a , 2 01 2b ) 1 m un ic ip al c ro p ar ea d at a (m ag ra m a , 2 01 2a ) i s al so r eq ui re d fo r th e ca lc ul at io n in o rd er t o de te rm in e cr op a re a at s ub ba si n le ve l ( se e su pp le m en ta l m at er ia l a ). 2 a o ne -y ea r l ag w as a ls o co ns id er ed a s an e xp la na to ry v ar ia bl e. 3 p ro vi de d by th e g ua da lq ui vi r r iv er b as in a ut ho rit y. 112 g. salmoral, a. garrido cultural season (agenda 2000 reform) and a second dummy variable accounts for agricultural policy changes after the 2006/07 agricultural season (2003 cap reform). the eu nitrates directive implementation is accounted for by the ratio of nitrate vulnerable zone per subbasin area (vz ratio). the extension of the vulnerable zone was divided by the subbasin area and multiplied by the proportion of crop area affected by the eu nitrates directive within the vulnerable zone. a crop price index was calculated based on 2000 current prices and weighted by crop production. a one-year lag for crop price index (l.crop price index) was also considered, since agricultural practices might also depend on prices from the previous agricultural season. the prices of the nitrogen element (pricen) and fuel (pricefuel) were also considered for the subbasin characterization. pricen was calculated as the weighted price of each fertiliser based on nitrogen element content. 2.5 significant correlations between variables under study a pairwise correlation analysis was performed between all independent and dependent variables under study in order to better understand their behaviour. independent variables that present constant values between subbasins and significant correlations (ρ>0.5, p<0.001) with other explanatory variables were excluded from the subsequent analysis of panel data regressions. 2.6 fitting panel data models 2.6.1 model formulation panel data analysis for n units (subbasins) over t periods (agricultural seasons) is applied in order to explain the variation of the median (p50) of both physicochemical indicators taking into account the variables described in section 2.4 that characterize the water quality status. through panel data analysis we can model time series processes while accounting for heterogeneity across geographical units (i.e. subbasins characterized by monitoring stations). the general regression model for analysing panel data is formulated as follows: yit = zi´α + x´it β + εit i= 1,..., n; t=1,..., t, (1) where xit is the itth observation of each explanatory variable. heterogeneity is controlled by the intercept zi´α, where zi includes a constant term and a set of individual or groupspecific variables (greene, 2012). the error component model includes the unobservable unit effects (λi) and the remainder disturbance (uit): εit =λi + uit. (2) the fixed effect (fe) model assumes λi to be fixed, independent of uit and identically distributed (iid) (0, σ2 u). this model requires estimating n separate λi that, together with the intercept zi´α, comprise a dichotomous variable (νi) for each unit. fe only analyses the 113the common agricultural policy as a driver of water quality changes impact of variables that vary over time, since time-invariant variables are absorbed by νi. the random effect (re) model assumes λi to be random, where λi ~ iid (0, σ2 λ), uit~ iid (0, σ2 u) and λi to be independent of uit (baltagi, 2008). however, fe or re regression residuals often have attributes that ordinary least squares (ols) cannot handle. feasible generalized least squares (fgls) or panel corrected standard errors (pcse) can be used to deal with heteroskedasticity3 (het), contemporaneous cross-correlation4 (ccc) and first-order autocorrelation5 (ar (1)). driscoll and kraay standard errors (dkse) is applied when the autocorrelation is a moving average type (ma) (hoechle, 2007), which represents the average value of a variable over a given period of time. we opted for the pcse or dkse models, since our dataset does not always have the same number of observations per subbasin and fgls requires rectangularized datasets. 2.6.2 model selection stata 12 statistical software (statacorp, 2011) was used for model fitting. we set up models on three different scales: 1) the whole basin including all subbasins (‘total’), 2) a group of subbasins selected according to the dominant type of irrigated agriculture: ‘olive’, ‘semi-intensive’, ‘coast’ and ‘mountain’, and 3) a group of subbasins selected according to actual water quality time trends: ‘increasing’, ‘decreasing’ and ‘minimum’. regressions for the ‘maximum’ classification were not carried out because there were fewer observations (less than 10). since the analysis comprises two water quality parameters and different subbasin classifications, we used the following abbreviation: [water quality parameter] [subbasin classification]. variables were log-transformed as ln(variable+1) and standardized to have a mean of zero and a standard deviation of one. regressions were firstly run for pooled ols, and we discarded the explanatory variables with a variance inflation factor greater than 4. then panel re and fe ols were run. after running the re model, we conducted the breusch and pagan lagrange multiplier test (breusch and pagan, 1980) to verify the absence of re with the null hypothesis that error variance across units is zero. after running fe regressions, we tested the null hypothesis that all dichotomous variables are zero (ho: ν1 = ν2 = νi = 0) by means of a f test (snedecor and cochran, 1983). the selection of re or fe depends on whether the individual error component (uit) and explanatory variables are correlated. this is identified using the hausman test (hausman, 1978). we chose the pcse model when disturbances were assumed to be heteroskedastic across panels or heteroskedastic and contemporaneously cross-correlated across panels with or without ar (1). heteroskedasticity was detected in the fe residuals with the modified wald statistic for groupwise heteroskedasticity (greene, 2012). to test the crosssectional correlation, we performed the pesaran test (pesaran, 2004) after running fe with the null hypothesis that the error term is independent across sections. finally, autocorrelation was tested under the null hypothesis of no serial autocorrelation (wooldridge, 2002). 3 the error variance of each panel is not constant. 4 observations of some panels are correlated with other panels during the same period of time. it refers to the error correlation of at least two or more panels. 5 autocorrelation occurs when errors are not independent with regard to time. 114 g. salmoral, a. garrido we opted for the dkse model when heteroskedasticity, cross-correlation panels and ma were detected’ ma was identified if the coefficient of determination presented larger values for the dkse model than for pcse. explanatory variables with a pairwise correlation coefficient greater than 0.5 were excluded from the regressions. we only kept variables with significant coefficient estimators. the wald test checked that the removed variables were not significant in the final panel model regression according to the null hypothesis that coefficients are zero. 3. results and discussion 3.1 main correlations between variables under study we first analysed the correlations between all the examined independent and nonindependent variables before performing the panel data regressions. the significant pairwise correlations that are reported in this section refer to p<0.001. under farmers’ control, larger fertilization rates (ncons) are correlated to larger levels of nitrates and sus pended solids in rivers (ρ>0.5). besides, when the price of nitrogen fertilisers (pricen) and fuel (pricefuel) increases, ncons decreases (ρ>-0.1). pricen and pricefuel are larger after both the agenda 2000 (ρ>0.5) and 2003 cap reforms (pricen: ρ>0.8, pricefuel: ρ>0.6). both pricen and pricefuel are highly correlated (ρ>0.9). the modernization of irrigation systems is also significant after both agricultural reforms (ρ>0.1) (see supplementary information c and table c1). these results show that, aside from a few significant policy changes, there are other factors that occur in parallel, which may have an effect on farmers’ behaviour, with consequences for water quality indicators, i.e. the increase of price of nitrogen fertilisers and fuel. for the agricultural production systems, both larger values of agricultural intensification (biomassrainfed: ρ>0.2 and biomassirrigated: ρ>0.5) and greater crop diversity (shannon index: ρ>0.2) degrade the level of the two water quality indicators. as a result, we can expect agricultural regions with a greater variety of crops to be more intensified, as the shannon index indicates with its positive correlation to biomassirrigated (ρ>0.3). for erosion concerns, specific soil conservation practices should be considered with a greater variety of crops, e.g. vegetation filters, contour tilling. more frequent tillage practices or herbicide control of weeds reduce surface cover and roughness (vanwalleghem et al., 2011) and can lead to higher erosion risks. in terms of the impacts of policy measures, we found larger concentrations of nitrates and suspended solids are positively correlated with a larger degree of coupling to production (% coupling: ρ>0.4), agricultural subsidies (subsidiesrainfed: ρ>0.1, subsidiesirrigated: ρ>0.3), and implementation of nitrate vulnerable zones (vz ratio: ρ>0.3). furthermore, we found a greater probability of runoff or leakage for nitrates in the vulnerable zones that can also be explained by the greater soil permeability in these zones. nevertheless, policy makers and farmers need to bear in mind that alongside controlling nitrogen pollution, additional measures need to be applied in vulnerable zones to restrict erosion rates, as the positive sign of the ratio of vz to concentrations of suspended solids suggests. concentrations of nitrates are higher after the 2003 cap reform (ρ>0.07), whereas the concentration of suspended solids decreases (ρ>-0.07) after the agenda 2000 reform. the reduction of 115the common agricultural policy as a driver of water quality changes erosion rates after the agenda 2000 reform in our study is consistent with fleskens and de graaff (2010). they also found positive environmental outcomes of cross-compliance and aem policy instruments with respect to soil conservation. 3.2 panel data analysis modelling results for the median annual measurements of nitrates (table 5) and suspended solids (table 6) indicate whether panel data analysis is required instead of pooled regression when there are relevant random effects or fixed effects between subbasins. models fitted with fixed effects (‘minimum no3, ‘decreasing ss’ and ‘increasing ss’) require a dichotomous variable for each control entity for all time-invariant differences between subbasins, and only assess the net effect of predictors changing over time. pricen and pricefuel were not considered in the panel data regressions because of their high correlation (ρ>0.5, p<0.001) with the agenda 2000 reform and the 2003 cap reform. additionally, erosion was not included either in the analysis because of its correlation with slope (ρ>0.6, p<0.001) (see supplementary information c and table c1). for the total study area (‘total’) and ‘olive’ subbasin sample, the concentration of nitrates increases with nitrate export from upland areas (exportno3), biomass intensification (biomassrainfed and biomassirrigated), population density (population density) and soil permeability (perm). as a result, more agriculturally intensified regions are related to larger concentrations of nitrates, where, as in previous studies, export from upland areas can be a major contributor of excess nutrients (king and balogh, 2011). besides, there is a greater probability of nitrate leakage for more permeable soils, as the positive sign of perm in ‘total’ and ‘olive’ (as well as in ‘no trend’) indicates. in ‘mountain’, concentrations of nitrates are larger with greater agricultural intensification, lower agricultural subsidies for irrigated areas (biomassirrigated and subsidiesirrigated) and before the 2003 cap reform. the negative sign of the 2003 cap reform might represent the effect of a shrinkage of agricultural areas by nearly 30% during the study period (from 41,675 ha to 29,130 ha), with this explanatory variable concealing drivers not directly related to cap measures. deintensification has occurred in ‘mountain’ with a decrease of irrigated biomass, particularly with respect to industrial crops (from 7.7 to 4.9 t ha-1) and cereals (from 12.2 to 10.4 to ha-1). however, after the reform, there has been some intensification with respect to fodder (from 11.5 to 17.1 t ha-1), as well as with the expansion of irrigated olive orchards (from 390 to 1,520 ha). technical progress (i.e. irrigation of spanish farms) makes it possible to maintain or even increase crop yields in areas with less propitious physical conditions (bakker et al., 2011). as hatna and bakker (2011) reported, processes of abandonment, intensification and expansion can be found at the same time and are more likely to occur in dry, warm and accessible areas. this study also shows that the nitrate content of receiving water bodies can be reduced through irrigation system modernization (negative sign of % drip in ‘increasing no3’). the concentration of nitrates drops with modernized systems because nitrogen losses in the irrigation return flows are reduced through the development of more efficient irrigation systems (lecina et al., 2010; barros et al., 2012). similarly, the reduction in the area of vulnerable zones (negative sign of vz ratio in ‘increasing no3’ and ‘semi-intensive no3’) is related to higher concentrations of nitrates. as a result, the whole alluvium area 116 g. salmoral, a. garrido should be considered as a nitrate vulnerable zone when considering mitigation measures (arauzo et al., 2011; arauzo and valladolid, 2013). besides, the observed negative trend with respect to the concentration of nitrates in ‘decreasing no3’ is correlated with the implementation of the decoupling process (% coupling), as well as with lower irrigated subsidies (subsidiesirrigated). these results would be in line with the more extensive mantable 5. panel data regressions for nitrates (no3) considering all the subbasins (total), by type of agriculture (olive, coast, semi-intensive and mountain) and by existing time trend (decreasing, increasing, minimum and no trend). the regression models include significant variables (p<0.05) only. dependent variable no3 subbasin classification explanatory variables total olive coast semiintensive mountain decreasing increasing minimum no trend exportno3 0.21*** 0.14** 0.27*** 0.25** 0.23*** precipitation -0.19* perm 0.10*** 0.15* -0.13*** 0.11*** population density 0.09*** 0.15*** 0.75*** 39.65*** 0.07*** biomassrainfed 0.32*** 0.25*** 0.35*** 0.24*** biomassirrigated 0.37*** 0.38*** -0.61* 2.85*** 0.21*** shannon 0.13* 0.09*** ncons 0.71*** 0.23*** % drip 0.35** -0.38*** vz ratio -0.06*** -0.52*** 3.69** price index 0.09* l.price index -1.02* subsidiesrainfed -0.09* 0.30*** subsidiesirrigated 0.08* l.subsidiesrainfed -0.48* l.subsidiesirrigated 0.17* % coupling 0.76*** -0.98*** 0.15* agenda 2000 reform 0.07* -0.45*** 0.62* 2003 cap reform -0.11* -1.71*** 0.41*** 1.62*** -0.27** -0.95*** -19.21*** intercept 0.21*** 0.14** 0.27*** 0.25** 0.23*** no. subbasins 89 41 3 32 13 5 3 14 71 no. observations 873 334 30 260 137 35 70 51 717 r2 0.49 0.46 0.76 0.49 0.28 0.53 0.54 0.70 0.57 model dkse with re pcse with re pooled ols dkse with re pooled ols pooled ols pooled ols pcse with fe pcse with re het het het het het het ma ma ccc ccc ccc ccc ccc * p<0.05, ** p<0.01, *** p<0.001. ols: ordinary least squares, pcse: panel corrected standard errors, dkse: driscoll-kraay standard errors, fe: fixed effects, re: random effects, ar(1): ar(1)-type autocorrelation, ma: autocorrelation with moving average, ccc: contemporaneous cross-correlation, het: heteroskedasticity. 117the common agricultural policy as a driver of water quality changes agement practices applied under decoupled income support (piorr et al., 2009; cortignani and severini, 2012). consequently, in the light of our results, the new 2014 cap reform should mitigate diffuse pollution thanks to total decoupling from production. for the total basin (‘total ss’), we find that erosion rates increase with export of sediments from upstream (exportss), biomass intensification (biomassrainfed and biomassirrigated), agricultural subsidies in irrigated areas (subsidiesirrigated), modernized irrigated systems (% drip) and after the 2003 cap reform. exportss is related to the negative sign of terrain slope (slope) (as in ‘semi-intensive ss’, ‘mountain ss’, ‘minimum ss’ and ‘no trend ss’), since sediments are dragged from upstream and accumulated downstream (gómez, 2008) table 6. panel data regressions for suspended solids (ss) for all subbasins (total), by type of agriculture (olive, coast, semi-intensive and mountain) and by existing time trend (decreasing, increasing, minimum and no trend). the regression models include significant variables (p<0.05) only. dependent variable ss subbasin classification explanatory variables total olive coast semiintensive mountain decreasing increasing minimum no trend exportss 0.23*** 0.17*** 1.57*** 0.34*** 0.95*** 0.29*** 0.19*** slope -1.18** -0.11*** population density 0.19*** 0.28*** 0.21*** 0.13* 0.26* 0.20*** biomassrainfed 0.29*** 0.45*** 0.32*** biomassirrigated 0.10*** shannon 0.19*** ncons 0.08*** % drip 0.19*** -0.20*** vz ratio 0.16* l.price index 0.09*** 0.11*** 0.30** 0.10*** subsidiesrainfed 0.20* subsidiesirrigated -0.24*** -0.36*** -0.60*** -0.45*** -0.16*** % coupling 0.23*** 0.17*** 1.57*** 0.34*** 0.95*** 0.29*** 0.19*** agenda 2000 reform -0.29*** 4.77** -0.46*** -0.51*** -0.44*** -0.26*** 2003 cap reform -1.18** -0.11*** intercept 0.19*** 0.28*** 0.21*** 0.13* 0.26* 0.20*** no. subbasins 89 41 3 32 13 5 3 14 71 no. observations 803 330 31 291 133 39 28 134 638 r2 0.60 0.52 0.86 0.62 0.49 0.86 0.89 0.74 0.58 model pcse with re pcse with re pooled ols pcse with re pcse with re pcse with fe panel fe pcse with re pcse with re het het het het het het het het ar(1) ar(1 ccc ccc ccc p<0.05, ** p<0.01, *** p<0.001. ols: ordinary least squares, pcse: panel corrected standard errors, fe: fixed effects, re: random effects, ar(1): ar1-type autocorrelation, ccc: contemporaneous cross-correlation, het: heteroskedasticity. 118 g. salmoral, a. garrido (see also table c1 in supplementary information c). more efficient irrigation systems are associated with higher concentrations of suspended solids, probably consistent with the expansion of new irrigated areas on steeper slopes in the basin (gómez-limón and riesgo, 2012). in contrast, lower concentrations of suspended solids are found after the agenda 2000 reform (as well as in ‘semi-intensive ss’, ‘mountain ss’, ‘minimum ss’ and ‘no trend ss’) perhaps because of the positive soil conservation effects of applied aem. our study also highlights that a greater variety of crops (positive sign of shannon index in ‘olive’ and ‘no trend ss’) might be related to more frequent tillage practices or herbicide control of weeds. it is worth highlighting that soil management practices have been proven to be unsustainable in the spanish olive sector worsened by frequent tillage, and the dependence on external sources of farm income (gomez et al., 2008; junta de andalucía, 2008; xiloyannis et al., 2008). as farmers do not notice the economic costs of inappropriate soil management much, they do not feel obliged to adopt soil conservation practices (ibáñez et al., 2014). payments to farmers found in breach of not fulfilling the cross-compliance requirements under the new cap (2015-2021) would be reduced immediately. the decreasing trends of suspended solids (‘decreasing ss’) in subbasins are explained by improving water quality conditions upstream (exportss), lower biomassrainfed, larger subsidiesrainfed and with the implementation of the decoupling process (% coupling). as with the nitrate models, the decoupling process seems to be a useful tool for reducing erosion risks. this negative trend can also be explained by the reduction of the total agricultural area in this group of subbasins. by contrast, the ‘increasing ss’ trend is supported by the 2003 cap reform and a higher price index from the previous season (l.crop price index). higher crop prices may result in a greater production intensity (kirchner and schmid, 2013; renwick et al., 2013), which may lead farmers to make production decisions without evaluating the subsequent environmental consequences (boardman et al., 2003). both fertiliser and fuel prices increased after the 2003 cap reform which incurred additional costs for farmers which probably resulted in the reduction of sustainable soil conservation practices, e.g. cover crops, contour tilling. 4. conclusions this study analyses diffuse pollution in the grb resulting mainly from land use cover and agricultural practices, focusing on the effects of the cap reforms from 1999 to 2009. the study identified significant correlations between all the examined independent and non-independent variables before performing the panel data regression. this enabled us to understand the existing relationships that later proved to be consistent with the results of the panel data regressions. the observed relationships of nitrates and suspended solids with the natural environment, agricultural sector characteristics, urban areas and economic policy factors should not be extrapolated because other basins will have different features. however, a better understanding of these correlations is useful for improving the coordination of policies related to water and land management. it is not easy to discern the effects of point and diffuse sources on the water quality status. in general, exports of both nitrates and suspended solids from upland subbasins and the intensification of agricultural systems increase the concentration of both water 119the common agricultural policy as a driver of water quality changes quality indicators. in regions where agricultural abandonment and/or deintensification have taken place (i.e. ‘mountain no3’ and ‘decreasing ss’), the water quality conditions have improved. the decoupling of agricultural subsidies through the cap reform and the reduction of subsidies for irrigated land is also related to the improvement of both water quality indicators. therefore, in the light of our results, the new 2014 cap reform will perhaps bring about environmental benefits in terms of reduced diffuse pollution and erosion risks thanks to the decoupled support scheme. however, it is worth noting that there was a missed opportunity to create political synergies between the wfd and the cap as the cap 2014 gaec did not establish measures to control irrigation, i.e. water abstraction permits, water meters and reporting on water use (european court of auditors, 2014). although some improvements in the concentration of suspended solids were observed in the basin, concentrations were found to increase in more productive areas with better water efficiency and larger subsidies for irrigated land after the 2006/07 agricultural season. the impact of intensification is particularly significant for erosion rates in ‘olive’ and under irrigated conditions. erosion rates were found to be larger in intensified agricultural regions because of poor soil management practices. there is a mismatch between the regulations concerning the wfd and soil protection, since the wfd does not provide any guidance on achieving good ecological status, specifically for sediment standards (rickson, 2014) with many watercourses in europe failing to meet the standard of ‘good ecological status’. potential weaknesses and limitations due to the assumptions made in this study include the calculation of irrigation modernization and nitrogen consumption, since they underestimate variability across subbasins. secondly, information regarding the level of participation in aems during agenda 2000 and gaec during cross-compliance implementation, would help to characterize this in quantitative terms. improvements could be achieved based on those calculations. while our modelling framework included some of the most important income support measures and a key price index, post-2007 price volatility in international and european agricultural markets may interfere with our causation hypotheses. an intra-annual assessment would determine whether short-term commodity prices affect the physicochemical status of surface water bodies. finally, low r2 values suggest, particularly in the ‘mountain’ nitrates model, that additional explanatory variables, e.g. livestock load, are required to explain a larger proportion of the variance of the water quality parameters. despite the technical and political difficulties that appeared during the negotiation of the post-2014 cap, conditionality measures, greening components and rural development programmes in the 2014 cap reform may well offer great opportunities for improving the water quality conditions encountered in the grb. acknowledgements funding for this work was provided by the botín foundation and water observatory team. we would like to thank two anonymous reviewers and editor whose thoughtful comments were very helpful in improving this manuscript. 120 g. salmoral, a. garrido references arauzo, m., valladolid, m. 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(2008). semi-intensive olive orchards on sloping land: requiring good land husbandry for future development. journal of environmental management 89: 110-119. doi: 10.1016/j.jenvman.2007.04.02 http://dx.doi.org/10.1016/j.scitotenv.2013.05.057 http://dx.doi.org/10.1016/j.scitotenv.2013.05.057 http://dx.doi.org/10.1016/j.landusepol.2013.01.002 http://dx.doi.org/10.1016/j.geoderma.2013.04.011 http://dx.doi.org/10.1016/j.geomorph.2011.06.018 http://dx.doi.org/10.1016/j.landusepol.2008.08.005 http://dx.doi.org/10.1016/j.landusepol.2008.08.005 http://dx.doi.org/10.1016/j.jenvman.2007.04.02 http://dx.doi.org/10.1016/j.jenvman.2007.04.02 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 3(1): 1-20, 2014 doi: 10.13128/bae-13622 does the “green box” of the european union distort global markets? klaus mittenzwei1,*, wolfgang britz2, christine wieck2 1 norwegian agricultural economics research institute, oslo, norway 2 institute for food and resource economics, university of bonn, germany abstract. the vast majority of domestic support to farmers in the european union (eu) is notified in the world trade organisation (wto) green box as decoupled payments. the eu considers this support to minimally distort production and/or trade. as demonstrated in the literature, this claim is questionable. this paper aims at analyzing this claim using a global, spatially differentiated, partial equilibrium simulation model with endogenous land supply functions. comparing a complete elimination of the eu green box measures with a baseline scenario, the model results indicate only small distortionary effects in production and trade. hence, eu support notified to the green box seems to be compatible with the general requirements of the green box. the finding seems to result from the assumption of unchanged eu border policies. keywords. modeling, agricultural policies, domestic support, green box, trade liberalization, wto. jel codes. f13, q11, q17, q18 1. introduction a major achievement of the uruguay round agreement on agriculture (uraa) was the establishment of common rules for international agricultural trade and the establishment of quantitative constraints for domestic support measures to agriculture (josling and tangermann, 1999), which were placed into three ‘boxes’ according to their distortionary effect. however, the extent to which notified support measures do in fact comply with the specific criteria set out for the three boxes remains unclear. that question is especially relevant for the european union (eu) which by now shields the overwhelming part of its domestic support from reduction commitments by notifying it under the socalled “green box”. the single farm payment (sfp) of the eu alone amounts to about 40 bio € per year and pays to farmers, on average, close to 300 € per hectare of farmed land (european commission, 2011: figure 8). can such expensive support measures really only minimally distort trade and/or production as required to satisfy the green box requirements? there is both a legal and an economic approach to this question. the * corresponding author: klaus.mittenzwei@nilf.no. http://dx.doi.org/10.13128/bae-13622 2 k. mittenzwei, w. britz, c. wieck legal approach would carry out an assessment of whether the measure in question complies with the policy-specific criteria set out in the uraa. albeit swinbank and tranter (2005) raise doubts as to whether the sfp complies with the requirements for the green box; we will not consider legal questions in the following. there are so far no comparable cases in the dispute settlement mechanism of the world trade organization (wto) which would provide information beyond the arguments of swinbank and tranter (2005) whether the eu’s green box payments, including the sfp, meet the criteria for notification in the green box (rude 2001). focusing on economic issues, theoretic approaches such as dewbre et al. (2001) or viaggi et al. (2011) highlight possible pathways regarding how fully decoupled payments, which are not linked to current production, still might impact allocation decisions. this can happen if decoupled payments affect risk attitude, reduce the costs to finance investments, or if farmers expect updates of entitlements to decoupled payments depending on their current production program. in all cases, the theory suggests higher production levels and thus a potential impact on trade. quantitative approaches focus either on the trade implications of potential decoupling scenarios of eu direct payments (e.g. banse et al., 2008; boulanger et al., 2010; brockmeier et al., 2008; anderson et al., 2010) or on eu domestic production effects (bhaskar and beghin, 2009; schokai and moro, 2009; balkhausen et al., 2008; rude, 2008; gohin, 2006). where simulation models are used, usually little production – and thus trade – and welfare effects are found. however, the pathways described by dewbre et al. (2001) are often not considered in these studies, as producers are assumed to be risk neutral, and neither changes in costs for finance nor expectations about future policy changes are considered. econometric studies which estimate impacts of decoupled payments on production should implicitly capture also effects not covered in simulation models; their findings generally also suggest limited allocative effects. that holds also for the so-called “wealth” effect. this effect regards the asset structure of farm households and how these assets, including land, are affected by the re-design of agricultural policies (femenia et al., 2010). the objective of this paper is to analyze the global trade, eu trade and welfare impacts that may result from the abolishment of the eu green box measures. to accomplish this objective, we use the detailed, spatially differentiated, partial equilibrium, multicommodity model capri2 that is, due to its endogenous modeling of input markets (such as land) and comprehensive coverage of policy instruments, uniquely suited to analyze this kind of question. we first link the policy instruments specified in the model (such as the sfp, remaining coupled payments, agri-environmental payments or less favoured area (lfa) support payments) to the detailed categories of the wto-notification boxes to develop indicators that reflect the official notification of eu domestic support. thereafter, we implement a scenario in which all green box measures are abolished. our research adds to the existing literature by accounting for potential production and trade effects that may result from eu green box support in a modeling framework that considers both in detail eu green box support including pillar ii measures and the 2 common agricultural policy regionalised impact model. further information can be found in britz and witzke (2011) and under: www.capri-model.org. 3does the “green box” of the european union distort global markets? global integration of eu agricultural markets. this allows for a realistic and up-to-date assessment of the trade impact of the eu green box policies considering the multiple distortionary policy effects present in current markets. we specifically focus on interactions between eu green box payments and the land market. here, kilian and salhofer (2008) and kilian et al. (2012) show in their theoretical and empirical analysis that the sfp capitalizes on land values over time. we thus explicitly consider that fact by allowing for an endogenous land supply, while modeling the interplay between land farmed and premium entitlements. however, due to the nature of our simulation model, we neglect the pathways described by dewbre et al. (2001). the paper proceeds as follows. the next section elaborates on the topic of trade distortion and green box payments, while section 3 provides an exposition of the capri model, and section 4 presents an overview on the scenario implementation. section 5 contains the modelling results followed by a discussion and conclusion in the last section. 2. market distortions and the wto green box the uraa classifies domestic agricultural policies into three categories that differ according to the extent to which they distort production and/or trade. trade-distorting policies are notified in the amber box (or aggregate measurement of support – ams), for which spending limits apply, while trade-distorting policies that underlie productionlimiting programmes are grouped in the blue box. these two boxes thus are sector specific exemptions from the general wto “agreement on subsidies and countervailing measures” (scm) rules, introduced to prevent the failure of the uruguay round and thought of as an intermediate step towards trade liberalization. indeed, the so-called “peace clause” did not allow to open dispute settlement cases against notified measures under the boxes until 2004. policies are placed in the green box, and thus thought to comply with general wto rules, if they “meet the fundamental requirement that they have no, or at most minimal, trade-distorting effects or effects on production” (wto 1994: 59). the green box is further divided into twelve categories for which policy-specific criteria apply. for seven of the twelve categories, the policy-specific criteria requires that (a) the amount of the payment may not be related to, or based on, the type or volume of production and/or that (b) no production shall be required in order to receive such payments.3 although these categories, with their policy-specific criteria, may simplify the assessment as to whether a given policy instrument meets the overall green box requirements, they provide little help to evaluate the abovementioned fundamental requirement. one reason might be that the economic concept of market distortions is seemingly intuitive, while its practical applicability is less straightforward. as we only analyze changes in the eu green box in the following, we keep all other policies unchanged.4 3 see agreement on agriculture. part of annex 1a «multilateral agreements on trade in goods» of the agreement establishing the world trade organization. marrakesh declaration of 15 april. marrakesh. the seven categories are: decoupled income support, income insurance and income safety-net programmes, natural disaster relief, structural adjustment assistance through resource retirement, structural adjustment through investment aid, regional assistance programmes, and other direct payments. 4 concerning empirical studies, and especially when agricultural sector models are involved, the non-intervention situation required to assess the size of market distortions might have the character of a structural break for 4 k. mittenzwei, w. britz, c. wieck the basic green box requirement has so far been at stake in only one wto dispute settlement case (wto, 2004), relating to us support to cotton. after the end of the peace clause, six new dispute settlement cases regarding agricultural products and policies have been filed to the wto since january 2004, compared to nineteen cases in the period between 1996 and 2003. however, none of the new cases address green box measures. figure 1 shows the development of the three types of support for the eu between 1995 and 2008 where the green box is split between the seven categories that must not be related to production and the remaining five categories which encompass, for example, agri-environmental measures. figure 1. support to agricultural producers in the european union as measured by the wto by type of support, 1995-2009 (mill euro). source: authors’ calculations based on wto (var.). the numbers clearly show that the eu has continuously reduced its trade-distorting domestic support since the wto-agreement came into place in 1995. as an effect of the 2003 cap-reform to decouple direct payments, domestic support has been shifted almost completely from the blue box to the green box. it is now mainly channelled through green box categories for which the relevant policy-specific criteria state that neither the payment amount nor the payment eligibility must be related to production. the green box share of total support from the eu has increased further since 2009 as most remaining coupled support regimes are integrated in the sfp. at the same time, eu countries gain some flexibility as part of the cap 2013-reform to introduce national coupled payment programs financed under the cap. member states have not yet come forward with detailed program which the model’s parameters are not longer valid (lucas, 1976). we therefore refrain from simulating in here also a fully liberalization scenario where also eu border protection for agricultural products is removed. 5does the “green box” of the european union distort global markets? implementation measures. in any case, these programs are still restricted and could fall at least partially under de-minimis, i.e. agreed upon thresholds under the scm up to which subsidies are allowed. recently, the eu has declared that it will no longer provide export subsidies. formally, the eu has still considerable amber box support based on guaranteed market prices (or administrative prices). the resulting ams is measured using world market prices from the mid-eighties. any possible new agreement would update these prices; at least at current price levels, the ams would basically altogether vanish. that leaves indeed only the green box payments as wto relevant support and motivates the focus of our analysis. 3. overview on capri modeling system and modeling of sfp 3.1 overview the capri model (britz and witzke, 2011) is a global comparative-static deterministic partial equilibrium model with a strong focus on europe, consisting of a detailed supply module for selected countries and a global market module in which the detailed supply module is integrated. the supply module, covering only the eu, norway, turkey and western balkans, comprises independent aggregate non-linear farm programming models covering approximately 50 crop and animal activities at either regional (nuts 25) or farm type level. each non-linear programming model maximises regional or farm type agricultural income with explicit consideration of the cap instruments of support at given prices, subject to technical constraints for feeding, young animal trade, fertilization, set-aside, a land supply curve and production quotas. the farm type level (gocht and britz, 2011) provides for the whole eu a consistent disaggregation from the regional level to about 1850 farm type models differentiated by farm specialization and economic size. prices for agricultural outputs are endogenous based on a sequential calibration (britz, 2008) between the supply models and the global market model. the latter is a global spatial multi-commodity model covering 77 countries or country aggregates in 40 trade blocks and about 50 products.6 the armington approach (armington, 1969), assuming that the products are differentiated by origin, allows simulating bilateral trade flows and related bilateral, as well as multilateral, trade instruments, including tariff-rate quotas. capri has been intensively used for the assessment of the different common agricultural policy (cap) reforms (see e.g. britz et al. 2012; oecd, 2011), but also to address trade policy questions: piketty et al. (2009) and burrell et al. (2011) have used capri for the assessment of bi-lateral trade liberalization between eu and mercosur countries, whereas renwick et al. (2013) used capri in conjunction with a land use model to investigate the removal of pillar i and a possible wto compromise with a focus on land abandonment. 5 the “nomenclature d’unités territoriales statistiques” (nuts) refers to spatial administrative units in the eu context where the layers of nuts 1, nuts 2, and nuts 3 are usually distinguished with nuts 1 referring to the highest administrative level below state level. 6 raw commodities in the supply model correspond roughly to the products of the market model. for the dairy sector and the oil seed sector, however, there is processing of raw commodities into products for final demand. 6 k. mittenzwei, w. britz, c. wieck 3.2 policy instruments and wto policy indicators for the eu, the programming models cover in rich detail the different coupled and decoupled subsidies of the first pillar (pillar 1) of the cap, as well as major ones from pillar 2 (i.e., lfa support, agri-environmental measures, natura 2000 support). the interaction between payment entitlements and eligible hectares for the sfp (including the single area payment schemes – saps) as well as the different sfp implementation rules at member state level are considered explicitly in the model and explained in detail in the next two sections. decoupled payments are thus simulated in capri relatively closely to their definition in existing legislation. the remaining payments like possibly coupled payments for suckler cows, sheep and goats, as well as national “specific support programs” under article 68 of council regulation (ec) no. 73/2009 are also implemented closely to their specific eligibility rules, and accordingly allocated to different production activities. the rather high disaggregation regarding production activities of the regional farm model template and the resolution by farm types inside of nuts 2 regions clearly provides an implementation advantage. currently, more than 30 coupled payment schemes are differentiated, in addition to decoupled income support (sfp). the schemes cover almost all payments within the first pillar of the cap.7 as mentioned in the previous paragraph, important payments of the second pillar of the cap are covered as well. each premium scheme is defined by four attributes: (1) the groups of production activities covered; (2) payment rates for each group of activities; (3) the way the premium is implemented (per ha, per head, per slaughtered head or per main output coefficient); and (4) possible ceilings in values and/or physical limits, such as the maximum number of hectares or heads eligible. the numerical attributes of the premiums can be differentiated in a hierarchical manner from eu over member states, and sub-regional eu differentiation (nuts 1, nuts 2), and finally to farm types in nuts  2 regions, and are typically stored as a time series. during models runs, payments are endogenously updated subject to national value ceilings so as to ensure that they do not violate those ceilings. this is done by first calculating the amount of payments based on eligible acreage/animals and actual payment rates. this amount is compared to the national ceiling. if the national ceiling is overshot, payment rates are reduced using a flat percentage reduction rate. the regional programming models are then solved again to adjust the amount of eligible acreage/animals. this procedure is repeated until the national ceilings are no longer violated. the market model endogenously captures market interventions and subsidized exports as a function of market and administrative prices as well as support to consumers and processors, respectively. border protection is based on specific and ad-valorem tariffs which can be defined bi-laterally. tariffs and per unit quota rents under bi-lateral and multi-lateral tariff rate quotas (trqs) are explicitly modelled. these tariffs are endogenous in the model as 7 premiums to arable land (“grandes cultures”), durum wheat premiums in traditional regions, durum wheat premiums in established regions, rice premiums, premiums to pulses, premiums to energy crops, silage premiums in sweden and finland, suckler cow premiums, direct premiums to dairy cows, extensification premiums to bulls, steers and suckler cows, payments to sheep and goats, and supplementary payments to sheep and goats, to olives, to fruits and vegetables, to the wine sector, to tobacco, to cotton, to starch potatoes; different type of nordic aid premiums in northern sweden and finland as well as complementary national premiums in the new member states during the transition period. 7does the “green box” of the european union distort global markets? they can change dependent on minimum import price regulations. care is given to capture the different trade preferences with respect to tariff reduction and trqs granted by the eu under the everything-but-arms agreement and other preferential trade agreements. capri contains information regarding the eu’s domestic support notifications to the wto and calculates endogenously whether some elements of product-specific ams qualify for the de-minimis rule. if this is the case, that ams is taken out of current total ams. besides ams, capri calculates blue box and green box support. in addition, overall trade-distorting support (otds) consisting of ams, de-minimis support and blue box support is calculated. based on a comparison of those policies included in capri and those notified to the wto, we expect that capri mirrors all blue box payments and the majority of total green box payments. based on value, almost all payments in capri belong to the green box. the two most important blue box payments are coupled payments to suckler cows and cereals, the latter related to national programs. what is missing in capri are very specific payments such as natural disaster payments and more general payments such as general services, public stockholding, structural adjustment and member states’ specific programs such as gas rebates. we also expect capri to correctly reflect the market price support component of ams, and de-minimis support. 3.2 micro-economic analysis of the single farm payment consider the following optimization problem: x ,z maxπ = p ' y −w 'x + s 'z s.t. y = f x ,z( ) z ≤ z [λ] (1) where y and x are vectors of marketable outputs and inputs with prices p and w while z are non-marketable inputs with given farm endowment z, with attached per unit subsidies s, and f is a general multi-input multi-output production function. the first order condition (foc) of the above problem reveals that the marginal value of the product must be equal to per unit costs, which are either equal to the market price w or the opportunity costs λ minus the subsidy s: p '∂ f ∂x −w = 0 (2) p '∂ f ∂z −λ + s = 0 (3) given the fact the sfp covers by now all crops and is also paid to idling land, the sfp may be appropriately considered to be a uniform premium s as in the model above. as the subsidy rates are not entering the foc for the marketable inputs (2), they will not change 8 k. mittenzwei, w. britz, c. wieck optimal input use and output quantities; therefore, they will have no allocative effects. for the case of land, equation (3) states that the return to land for each crop including the sfp must be equal. accordingly, an absolute change in s will provoke the same absolute change in the opportunity cost λ. next, we extend the model in (1) to a utility maximization problem (see (4)) for the farmer (e.g., accounting for risk, preferences for certain output or input mixes). as long as the first derivative towards any of the variables p, y, w, x in the model above does not depend on the per unit subsidy, i.e. if the subsidy sum enters as a separate additive term in the utility function as in (4), we arrive at the same basic finding, i.e. the subsidy will only change the opportunity costs of the fixed endowments: x ,z maxu = g(p, y ,w ,x ,z)+ s 'z s.t. y = f x ,z( ) z ≤ z [λ] (4) the discussion around possible production effects of such per unit subsidies thus relates to extensions of the decision problem in (4) through the inclusion of premium s into the utility function g. as dewbre et al. (2001) points out, there are several such extensions. if u is related to income and risk, the farmer’s attitude towards risk might depend on its income, such that there is no additive relation as in (4) since s is an argument of g(.). equally, the prices paid by farmers, e.g., interest on credits, might depend on their income or on their asset value which might depend on λ, and thus indirectly on s. and finally, an endowment such as land in the case of the sfp might not be considered fixed. the subsidy s would be attached to x in that case, and then clearly provoke an allocative effect. however, in the case of the sfp, even considering land as a marketable input would not change the picture from (4) for increases of s as the subsidy is upper-bounded by the number of entitlements. hence, at an optimal program where the entitlements are binding, an increase in s cannot change the optimal allocation. in the following, we will use the deterministic model capri where land is considered a marketable input while explicitly considering premium entitlements which are assumed to be non-tradable across nuts2 regions. 3.4 modelling land supply and total agricultural land use the capri model comprises land supply and transformation functions at the regional level and for different farm types within a region which allows for the endogenous supply of arable land and grassland in response to a change in marginal land rents. the behavioural functions for land supply were parameterised based on the results of van meijl et al. (2006) and golub et al. (2006). in addition, some adjustments based on geographical information system (gis) analyses and simulation experiments using the dynaclue8 model, were necessary in order to capture the regional resolution of capri. the 8 a dynamic version of the ‘conversion of land use and its effects’ model. further information can be found in verburg et al., (2010). 9does the “green box” of the european union distort global markets? core of the farm programming model reaction when cap support is reduced, results from the interaction between the land supply function and subsidies to land (the sfp and the support to lfa under pillar 2) which together account by far for the largest share of cap spending. figure 2. effects of the sfp on land markets   land supply land rent source: britz et al. (2012). to obtain the full sfp, a farmer must not only possess one hectare of land in good agricultural and environmental condition but must also have a (tradable) payment right (the following description is cited from britz et al., 2012). figure 2 depicts the impact on the land rent, and it helps illustrate the reactions to changes in the sfp. for simplicity, we did not graphically capture changes in other cap support. in the absence of the sfp, the amount of land under agricultural cultivation is determined by the intersection of the marginal returns in agriculture to land (mr) and the land supply from other sectors (t0 in figure 2). the sfp, as a subsidy to land use in agriculture, shifts the mr curve upward according to the size of the subsidy. without further restrictions, agricultural land use would be expanded to a new intersection between the two curves. because of the introduction of the entitlements, such an expansion does not occur, as the old and the new mr curves are identical beyond the entitlement point, where the subsidy cannot be claimed. accordingly, immediately after its introduction, the subsidy is capitalised into the payment rights. however, each year some agricultural land is lost to non-agricultural uses such as building, infrastructure, recreation and other needs for society. according to eurostat (2012), the share of agricultural land converted to non-agricultural uses (soil sealing) has been 0.2% p.a. in 38 european countries between 2000 and 2006. therefore, the land supply curve shifts to the left over time. depending on the slope of the land supply curve and the speed and size of the shift, the economic rent linked to the sfp will be partially re-distributed from the entitlements to land. once the new intersection between the mr and the 10 k. mittenzwei, w. britz, c. wieck land supply curves is to the left of the original entitlements, at that point (t1), the subsidy will be fully capitalised in the land rent. because the land supply curves are rather steep in many countries and due to a continuous decline in agricultural land as observed in almost all eu member states, as we assume in our 2020 baseline (i.e., more than fifteen years after the introduction of the policy reform called “mid term review), the capitalisation only occurs on land and not on payment rights. this interpretation follows a theoretical (kilian and salhofer 2008) and empirical analysis carried out by kilian et al. (2012) and is further supported by the present legislative text (european commission, no year), which states that entitlements that are not claimed for two consecutive years, will be withdrawn. femenia et al. (2010) argue that there may be large wealth effects for farmers generated from the capitalization of farm subsidies in land prices if farmers are also the owners of the land. 4. baseline and counterfactual scenario while the model is calibrated to the base year 2004, a reference run or baseline is composed for the year 2020. the baseline captures developments in exogenous variables. these include policy changes foreseen in current legislation, population growth, gdp growth and agricultural market development for the year 2020 (table 1). it relies on a combination of three information sources (for a detailed description see britz and witzke, 2011): (1) most importantly, the aglink-cosimo baseline, (2) analysis of historical trends and (3) expert information (blanco fonseca et al., 2010). table 1. exogenous drivers considered for the baseline construction. exogenous drivers value inflation 1.9% per annum growth of gdp per capita 2.0% nominal per annum for the eu, 10.5% for india, 1.5% for usa, 4% for russia, 1.5% for least developed countries and acps, and 1% for rest of the world demographic changes eurostat projections for europe and un projections for the rest of the world technical progress 0.5% input savings per annum (affecting exogenous yield trends), with the exemption of n, p, k needs for crops where technical progress is trend forecasted domestic policy national decisions on coupling options and premium models, with their expected implementation date for the 25 eu member states (25 different premium schemes, compliation by massot martí, 2005) common market organizations supply and demand shifted according to the expert forecasts (european commission, 2005) trade policy final implementation of the 1994 uruguay round agreement plus some further regional trade agreements, such as nafta world markets supply and demand forecasts (fao, 2005) source: own compilation. 11does the “green box” of the european union distort global markets? the share of green box payments covered by capri in the 2020 baseline is considerably higher compared to the 2004 base year as the national envelopes now include several policy instruments that were either not in place or accounted for differently in 2004. in 2020, the national envelopes include: the sfp, the remaining coupled payments and national support under article 68. equally, the ex-ante data base covers a major part of pillar 2 spending, such as agri-environmental schemes, less favoured area (lfa) supports, and natura 2000 payments. the counterfactual scenario is composed of all elements of the baseline in addition to a complete removal of all eu agricultural support notified in the wto green box and captured by capri. the construction of the scenario hence allows us to study how production and trade are influenced by the eu green box support. it is important to keep in mind, however, that all other policy instruments, in particular market access, remain unchanged. that is, we evaluate the potentially trade-distorting effects of the eu green box support in the presence of all other eu policy instruments. 5. model results this section presents the most important results of the counterfactual scenario. the main finding is a small overall change in production volumes indicating some coherence between the classification of the eu green box support and the support’s production-distorting impact. compared to the baseline, the elimination of total green box support in capri leads to a less than 4 per cent reduction in the net production of major commodities such as cereals, oil seeds, meat, eggs and dairy products (table 2). similarly, there are even smaller changes in the demand volumes of these commodities. the changes in traded volumes are also limited, although the percentage changes may be quite high as the absolute volumes are low. imports increase across all commodities, while exports show a decrease. for instance, there is a 19 per cent increase in eu cereals imports, while eu exports decrease by 14 per cent. the percentage changes are even more pronounced for products based on feed-concentrates like pork and egg, while they are smaller for products based on roughfodder such as beef and dairy. therefore, if measured in percentage changes, the tradedistorting impact of the eu green box support seems to be greater than its productiondistorting effect. total demand in the eu decreases with increasing market prices. since the decrease in production is higher than the reduction in demand and lower exports does not offset the increased gap between demand and production, imports increase. however, the still limited increase in imports in absolute terms might be caused by the fact that border protection for major products is still rather high and tainted by instruments which prevent a rapid reaction of imports to changes in eu prices such as trqs and flexible levies linked to minimum border prices. some relative trade effects appear quite high, because the absolute numbers are small. generally, table 3 reveals that trade effects seem to be larger than production effects, and the relative effects are quite significant for most sectors except dairy. a closer look at the import flows reveals that, in general, imports to the eu grow mostly from regions with significant exports to the eu in the baseline. this result is straightforward, as the relative 12 k. mittenzwei, w. britz, c. wieck competitiveness between the world regions are unchanged in the counterfactual scenario, and is also caused by the armington assumption on the imperfect substitution of imported goods from different destinations. one exception is sugar, where africa, the largest exporter to the eu covering more than 50 percent of total exports in the eu, enjoys a smaller increase (1.2 per cent) than its trade competitors in australia and new zealand (3.6 per cent) middle and south america (3.3 per cent). a reason for this might be the eu trade policy for sugar with tariff rate quotas and bilateral trade arrangements. the removal of the eu green box leads to ambiguous land use change across the regions in europe (figure 3). dark red coloured regions experience the largest reduction in land use (41.2 per cent compared to the baseline), while dark green coloured regions are characterized by virtually no change or even a slight increase. the reduction of agricultural land in most regions in the eu is less than 5 per cent. broadly speaking, regions at the boundary of the eu experience a larger reduction in land use than regions in the middle of the eu. table 2. market balance for the eu in 2020 by type of scenario (1000 tons, percentage change compared to baseline in italics). baseline elimination of green box prod. a imp. b exp. b dem. c prod. a imp. b exp. b dem. c cereals 306 662 10 912 30 602 286 972 301 539 12 987 26 274 288 252 -1.7 19.0 -14.1 0.4 oilseeds 32 364 18 562 1 073 49 853 31 359 19 352 966 49 745 -3.1 4.3 -10.0 -0.2 meat 44 903 690 2 696 42 897 44 134 757 2 262 42 629 -1.7 9.7 -16.1 -0.6 beef 7 772 217 411 7 578 7 709 219 372 7 556 -0.8 0.9 -9.5 -0.3 pork 23 420 20 1 801 21 639 23 018 26 1 528 21 516 -1.7 30.0 -15.2 -0.6 eggs 7 187 15 299 6 903 7 075 21 242 6 854 -1.6 40.0 -19.1 -0.7 dairy products 70 985 243 2 892 68 336 70 547 249 2 827 67 969 -0.6 2.5 -2.2 -0.5 milk powder 865 5 76 794 853 5 73 785 -1.4 12.3 -3.9 -1.1 cheese 10 278 30 565 9 743 10 188 31 553 9 666 -0.9 3.3 -2.1 -0.8 oils 18 542 5 341 6 743 17 140 18 333 5 390 6 604 17 119 -1.1 0.9 -2.1 -0.1 sugar 15 548 6 387 1 373 20 562 15 504 6 482 1 348 20 638 -0.3 1.5 -1.8 0.4 notes: a net production, b excluding intra-eu trade, c demand composed of human consumption, processing, feed use, and change in intervention stocks. source: capri modelling system. 13does the “green box” of the european union distort global markets? figure 3. regional land use change (percentage change relative to baseline). source: own map based on capri-results. table 3. imports to the eu in 2020 by geographic region a and type of scenario (1 000 tons, percentage change compared to baseline in italics). non-eu europe a africa north america b middle and south america asia australia and new zealand baseline cereals 3 539 2 5 415 1 917 10 35 meat 45 9 20 373 19 231 dairy products 106 47 6 24 77 sugar 372 3 765 999 1 233 19 elimination of green box cereals 4 571 29.2 3 12.7 6 105 12.8 2 258 17.8 12 19.4 44 27.9 meat 50 10.7 10 18.5 30 44.9 421 12.7 23 18.4 232 0.3 dairy products 110 4.4 49 2.8 6 -2.2 25 1.8 77 0.7 sugar 382 2.6 3 810 1.2 1 032 3.3 1 238 0.5 19 3.6 notes: a non-eu europe: switzerland, norway, western balkans, russia, ukraine, belarus, kazakhstan, morocco, turkey, tunesia, algeria, egypt, israel, and middle east; b north america: usa, canada and mexico. source: capri modelling system. 14 k. mittenzwei, w. britz, c. wieck for instance, reductions in land use are particularly high in southern spain, southern italy, greece, the baltics, north-eastern finland, parts of sweden, and scotland. on the contrary, regions in central france and central germany show almost no change in agricultural area use. the removal of the eu green box support decreases production mainly by a reduction of agricultural land use, although there are also direct impacts on production from removing remaining coupled support (suckler cows, sheep and goats; smaller national coupled programs) and removing the agri-environmental programs. as a result, eu market prices increase as do crop and animal output yields which are due to both intensification and a change in the underlying cropping pattern. table 4 shows that eu producer prices increase by up to 5 per cent compared to the baseline, while the effect on consumer prices is somewhat lower. as market prices increase, the eu loses its relative competitiveness to other actors in the world market, hence leading to reduced exports. the same is true for countries outside the eu like norway, turkey and the western balkans. the model results underscore the fact that the major part of the otds and the current total ams of the eu in its current definition are rather detached from the price and quantity developments in eu agriculture (table 5). as administrative prices remain unchanged, only changes in quantity for those products for which ams is calculated may potentially trigger changes in ams. about half of blue box payments are tied to land use in eu agriculture. eliminating the green box support thus has the effect of reducing blue box payments through a decrease in overall land use. however, production activities still benefitting from coupled (blue) support show lower decreases or even slight increases compared to those not directly supported. equally, some non-exempt measures such as processing aid are reduced since productions is lowered. table 4. commodity prices for the eu in 2020 by type of scenario (euro per ton, percentage change compared to baseline in italics). baseline elimination of green box consumer price producer price consumer price producer price cereals 2 523 135 2 529 0.2 140 3.7 oilseeds 2 401 290 2 417 0.7 305 5.2 meat 5 440 1 758 5 518 1.4 1 822 3.6 beef 7 975 3 029 8 057 1.0 3 106 2.5 pork 5 388 1 418 5 446 1.1 1 472 3.8 eggs 2 949 672 2 996 1.6 710 5.7 dairy products a 1 683 270 1 712 1.7 286 5.9 skim milk powder 2 014 1 974 2 092 3.9 2 031 12.3 cheese 4 823 3 354 4 906 1.7 3 435 2.4 oils 3 004 1 237 3 031 0.9 1 263 2.1 sugar 6 793 449 6 795 0.0 451 0.5 note: a producer price for cow and buffalo milk. source: capri modelling system. 15does the “green box” of the european union distort global markets? the overall welfare effects of eliminating green box support are quite limited. rather, welfare is distributed differently between farmers, the tax payer and indirectly, via the price changes, consumers. the decomposition of total welfare into consumer surplus, producer and processor surplus, and taxpayer costs indicates that consumers remain largely unaffected (-8 208 mio €), while taxpayers gain (+47 672 mio €) at the expense of producers and processors (-40 626 mio €) (table 6). table 6. decomposition of welfare for the agricultural sector of the eu in 2020 by interest group and type of scenario (mio euro, percentage change compared to baseline in italics). baseline elimination of green box consumer surplus 7 691 839 7 683 631 -0.1 producer and processor surplus 117 310 76 684 -34.6 taxpayer costs 43 702 -3 970 -109.1 budget costs 51 221 3 746 -92.7 tariff revenues -7 520 -7 716 2.6 total welfare 7 765 448 7 764 285 -0.0 source: capri modelling system. table 5. wto policy indicators a for the eu in 2020 by type of scenario (in mio euro). otds curr. tot. ams mps non-ex. dir. pay. de minimis blue box green box baseline total of commodities 12 764 9 344 8 591 753 698 2 722 47 213 primary commodities 7 889 4 497 4 479 18 671 2 722 47 213 cereals 5 001 4 497 4 479 18 473 32 15 319 meat 61 61 fodder 958 958 20 336 dairy products 1 375 1 375 838 536 secondary commodities 3 500 3 473 3 274 199 27 elimination of green box total of commodities 12 413 9 236 8 493 743 676 2 501 primary commodities 7 581 4 418 4 401 17 662 2 501 cereals 4 919 4 418 4 401 17 469 32 meat fodder 956 956 dairy products 1 353 1 353 828 526 secondary commodities 3 478 3 464 3 265 199 14 notes: a otds: overall total direct support calculated as sum of mps, non-ex. dir. pay., and blue box; curr. tot. ams: current total ams calculated as sum of mps and non-ex. dir. pay.; mps: market price support; non-ex. dir. pay.: non-exempt direct payments. source: capri modelling system. 16 k. mittenzwei, w. britz, c. wieck the intuition is straightforward as a large drop in budget expenditures on agriculture foremost benefits taxpayers. not astonishing given the decoupled nature of the biggest part of the removed subsidies, producers are mostly unable to avoid the budget cut through farm management adjustments such as changes in the optimal mix of production activities as their loss compares to about 85 percent of the taxpayer’s benefit. moreover, the budget costs in the counterfactual scenario are lower than the revenues from agricultural import tariffs such that the taxpayers’ surplus turns slightly positive. 6. discussion and conclusion the removal of the green box results in a slight decrease in eu domestic production. less supply is available on the eu market, which in turn drives up market prices to a certain extent. with respect to the international position of the eu food sector, this leads to a decrease in exports and an increase in imports, but these effects are overall small. the main uncertainty in this impact chain relates to the supply reaction induced by the sfp reduction. this depends on the calibration of the land supply function as a somewhat different shape or elasticity of the function leads to change in the supply reaction. as mentioned above, in the calibration of the land supply function, we follow van meijl et al. (2006) and golub et al. (2006). thus, the main finding of the simulation indicates that eu support under the green box distorts the absolute volumes of production and trade only to a minor extent. for small trade volumes, the relative changes can be significant for specific commodities. thus, from an economic point of view, eu support notified to the green box seems to be compatible with the general requirements of the green box. that does not preclude, of course, that those measures may be green box-incompatible for legal reasons. the literature does not report examples from model simulations involving the complete dismantling of the eu green box measures, likely because that scenario is politically unlikely. in any case, such a scenario may provide a useful supplement to theoretical studies on the production effects of the sfp and empirical studies focusing on single farms. exercises with other large-scale models would be of potential interest. in general, our results support the segment of the literature that finds the production effects of sfp payments to be limited (e.g. schokai and moro, 2009; rude, 2008; gohin, 2006). in addition, our results seem consistent with anderson et al. (2006) who find that a reduction in domestic support in general provides much fewer welfare improvements than an improvement in market access. however, both our analysis and other studies using large scale models could face a similar criticism. most of them assume a risk-neutral, profit maximizing farmer and do not consider possible feedbacks of green box payments on input prices. unlike our analysis, it seems that other studies neglect the effects of entitlements. the standard assumption in the widely used gtap model (urban et al. 2012) is that land can only be used in agriculture. the cge analysis based on that model, which assigns the sfp (as we judge correctly) as a factor subsidy to land, will hence find no allocative effect according to equation (3). assigning parts of the sfp as a subsidy to land or capital will generate allocative effects in the analysis. such an implementation of the sfp contradicts its actual implementation, and if introduced to capture the pathways described e.g. in dewbre et al. (2001), is at odds with the micro-economic assumptions and market descrip17does the “green box” of the european union distort global markets? tions underlying the cge, which assume profit maximization and do not consider a link between wealth and credit prices. analyses with partial equilibrium models such as aglink-cosimo or ag-memod of the sfp seems to generate results which simply reflect the so-called coupling factors introduced (see balkhausen et al. 2008), so that we do not consider them as a suitable benchmark for comparison. banse et al. (2008) combine, rather loosely, a cge model, the partial equilibrium model esim and the supply side of capri. the scenario which is closest to our analysis encompasses full trade liberalization with the removal of direct payments, but also a partial lift of cross-compliance and changes to pillar ii. accordingly, results are far more directly comparable. still, they find, similar to the analysis presented here, only limited production effects from domestic support. the strongest effects are reported for land rent changes, but the article does not provide a decomposition with regard to the underlying drivers (trade liberalization or reduction in domestic support). our results demonstrate that the green box support effects are overlaid by the distortionary effects of multiple other policy instruments applied in the eu. the continuously high level of eu border protection and its specific implementation (trqs, minimal border prices) insulate eu markets to a certain extent on the import side from world markets. removing subsidies allow eu market prices to increase, which partly offsets the reduction in output. consequently, the limited trade effects mainly result from the fact that output is slightly reduced and that eu exports decrease with increasing eu market prices. at the same time, land supply functions are in most eu regions inelastic, so that a land subsidy reduction impacts mostly land rents and thus land owner incomes, but only has a small effect on allocative decisions. from a trade policy point of view, our study addresses the question of how to judge the potential production and trade distortionary effects of agricultural policies in the context of wto dispute settlement procedures. should they be assessed in the presence of other policy instruments, or should they be assessed in comparison to a hypothetical nonintervention and unbiased market scenario? our model results underscore the intuitive finding that less border protection would give rise to a larger effect on production and trade of the complete elimination of green box measures. they thus support a well-known result in economic theory that the distortionary effect of a measure is influenced by the presence of other existing measures. this is, of course, exactly the idea of the blue box which links supply boosting support instruments with supply control measures to reduce their distorting effects. to the best of our knowledge, the few wto disputes that have addressed the distortionary effects of agricultural policies to date have not utilized largescale sector models. in this respect, our study indicates that the results from detailed agricultural sector models, like capri, may well be useful as an input for those disputes. an issue for future research remains the point raised by femenia et al. (2010) indicating that ignoring the downside risk aversion of farmers, if they are also the owners of their land, may lead to an understatement of production effects. acknowledgements the authors gratefully acknowledge very supportive comments from three anonymous reviewers and the editor. 18 k. mittenzwei, w. britz, c. wieck references anderson, k., valenzuela, e. and van der mensbrugghe, d. (2010). global welfare and poverty effects: linkage model results. in: anderson, k., cockburn, j. and martin, w. (eds), agricultural price distortions, inequality and poverty, washington dc: world bank, march 2010, 49-85. anderson, k., martin, w. and valenzuela, e. (2006). the relative importance of global agricultural subsidies and market access. world trade review 5(3): 357-376. armington, p.s. (1969). a theory of demand for products distinguished by place of production. international monetary fund staff papers 16(1): 170-201. banse, m., helming, j.f.m., nowicki, p., van meijl, h. 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(2006). the impact of different policy environments on agricultural land use in europe. agriculture, ecosystems and environment 114(1): 21-38. verburg, p., van berkel, d.b., van doorn, a.m., van eupen, m. and van den heiligenberg, h.a. (2010). trajectories of land use change in europe: a model-based exploration of rural futures. landscape ecology 24(2): 217-232. viaggi, d., meri r. and gomez y paloma, s. (2011). farm-household investment behaviour and the cap decoupling: methodological issues in assessing policy impacts. journal of policy modeling 33(1): 127-145. wto (var). domestic support notifications of the european communities/european union. geneva: world trade organization. wto (1994). agreement on agriculture. part of annex 1a “multilateral agreements on trade in goods” of the agreement establishing the world trade organization. marrakesh declaration of 15 april. marrakesh. geneva: world trade organization. wto (2004). united states – subsidies on upland cotton. report of the panel. wt/ ds267/r. 8 september 2004. geneva: world trade organization. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(3): 237-255, 2013 commodity futures markets: are they an effective price risk management tool for the european wheat supply chain? cesar revoredo-giha1, marco zuppiroli2,*1 1 scotland’s rural college (sruc), land economy and environment research group, food marketing research team, edinburgh, uk 2 università degli studi di parma, dipartimento di economia, parma, italy abstract. the instability of commodity prices and the hypothesis that speculative behaviour was one of its causes has brought renewed interest in futures markets. the paper analyses the european wheat futures markets (feed and milling) and the chicago board of trade’s wheat contract as a comparison. although the main purpose of the paper is to analyse whether futures markets are still useful for hedging (considering the demands from different market participants), implicitly this can be seen as testing whether the increasing presence of speculation has made futures markets divorced from physical markets. the results indicate that hedging with futures markets is still a viable alternative for dealing with price risk. this is particularly true in short period hedges (e.g. merchants and processors), where the basis seems to have been affected by the observed price instability. keywords. futures markets, wheat, hedging, commodity prices, price risk. jel codes. g13, q14, g01 1. introduction the relatively recent instability of commodity prices has brought back the interest on futures markets and their use for hedging as a device to reduce vulnerability to risk. as pointed out by lence (2009), vulnerability to risks is amongst the most important problems faced by commodity producers in developing and developed countries. furthermore, this renewed interest has extended use of futures and options contracts to the area of food security, as they have been proposed as a way in which importing countries could manage price volatility (sarris et al., 2011). as it is well known futures markets perform several functions: they provide the instruments to transfer price risk, they facilitate price discovery and they are offering commodities as an asset class for financial investors, such as fund and money managers who had not previously been present in these markets. * corresponding author: marco.zuppiroli@unipr.it. 238 c. revoredo-giha, m. zuppiroli commercial participants use futures contracts to hedge their crops or inventories against the risk of fluctuating prices, e.g., processors of agricultural commodities, who need to obtain raw materials, would buy futures contracts to guard against future price rises. if prices rise (i.e., both cash and futures prices), then they use the increased value of the futures contract to offset the higher cost of the physical quantities they need to purchase. however, hedgers are not the only agents operating in futures markets, as one can also find non-commercial participants, who do not have any involvement in the physical commodity trade in contrast to commercial participants, such as farmers, traders and processors. these are called “speculators” and they buy and sell futures contracts in order to obtain a profit. it’s a matter of fact the massive increase in trading in commodity derivatives over the past decade; commodity derivatives include futures and options traded on organised exchanges as well as the forwards and the options traded over the counter. trading in commodity derivatives also increased along with the rapid expansion of the presence of the commodity index traders (united nations, 2011). this paper focuses on the usefulness of futures prices for hedging against price risk, paying particular attention to the hedging performance in recent years. in addition, the work can be considered as an indirect test of whether the increasing presence of speculation in futures markets has made them divorced from the physical markets, and therefore, not useful for price hedging. the reason behind this is because from all the reasons mentioned behind the increasing volatility in commodity prices (e.g., increasing demand for biofuels, draughts, china’s increasing demand for food), speculation, as applied by index funds, seems to be the only one that might imply a divorce between futures and physical markets, all the other reasons can be considered as movements on the fundamentals within the commodity markets. the paper is structured as follows: first, we provide a brief overview of the discussion of how events in futures markets are affecting commodity price volatility. this is followed by the empirical part of the paper where the data and the methods are explained. the next section presents the results of the different tests and the last section offers some conclusions. 2. evidence on volatility and speculation the purpose of this section is to briefly review evidence on both the rise in volatility in commodity prices, focusing on the wheat market, and the effects of speculation on futures markets. 2.1 volatility in wheat prices most of the studies on the behaviour of commodity prices in recent years assert that food price volatility has increased. in fact, figures 1 to 3, which present the evolution of wheat spot prices in three eu countries (france, italy and uk) during a 25 years interval and in chicago, show increasing dispersion in commodity prices since 2007. to quantify the increasing dispersion observed in commodity prices table 1 was constructed. it presents the volatility of the returns (i.e., first differences of logarithmic nomi239european commodity futures markets nal prices) of the four spot prices presented in figures 1 to 3 and it considers three periods 1988-97, 1998-2005 and 2006-12. as shown in table 1 the volatility of the returns increases over time in all the markets. it is important to note that while table 1 shows that recent volatility (i.e., since 2007) increased since the 1980s, gilbert and morgan (2010) pointed out in the past that there have been also periods of high volatility and the recent levels could return to historical levels over the coming years. figure 1. france and italy: evolution milling wheat spot prices, 1998-2012. 0.00 50.00 100.00 150.00 200.00 250.00 300.00 350.00 27 /0 3/ 19 98 27 /0 7/ 19 98 27 /1 1/ 19 98 27 /0 3/ 19 99 27 /0 7/ 19 99 27 /1 1/ 19 99 27 /0 3/ 20 00 27 /0 7/ 20 00 27 /1 1/ 20 00 27 /0 3/ 20 01 27 /0 7/ 20 01 27 /1 1/ 20 01 27 /0 3/ 20 02 27 /0 7/ 20 02 27 /1 1/ 20 02 27 /0 3/ 20 03 27 /0 7/ 20 03 27 /1 1/ 20 03 27 /0 3/ 20 04 27 /0 7/ 20 04 27 /1 1/ 20 04 27 /0 3/ 20 05 27 /0 7/ 20 05 27 /1 1/ 20 05 27 /0 3/ 20 06 27 /0 7/ 20 06 27 /1 1/ 20 06 27 /0 3/ 20 07 27 /0 7/ 20 07 27 /1 1/ 20 07 27 /0 3/ 20 08 27 /0 7/ 20 08 27 /1 1/ 20 08 27 /0 3/ 20 09 27 /0 7/ 20 09 27 /1 1/ 20 09 27 /0 3/ 20 10 27 /0 7/ 20 10 27 /1 1/ 20 10 27 /0 3/ 20 11 27 /0 7/ 20 11 27 /1 1/ 20 11 27 /0 3/ 20 12 eu ro /t on ne france italy source: la depeche agricole and ager borsa merci di bologna. note: figures correspond to standard milling wheat in rouen (france) and bologna (italy). figure 2. uk: evolution of feed wheat spot prices, 1988-2012. 0 50 100 150 200 250 03 /1 0/ 19 88 03 /0 4/ 19 89 03 /1 0/ 19 89 03 /0 4/ 19 90 03 /1 0/ 19 90 03 /0 4/ 19 91 03 /1 0/ 19 91 03 /0 4/ 19 92 03 /1 0/ 19 92 03 /0 4/ 19 93 03 /1 0/ 19 93 03 /0 4/ 19 94 03 /1 0/ 19 94 03 /0 4/ 19 95 03 /1 0/ 19 95 03 /0 4/ 19 96 03 /1 0/ 19 96 03 /0 4/ 19 97 03 /1 0/ 19 97 03 /0 4/ 19 98 03 /1 0/ 19 98 03 /0 4/ 19 99 03 /1 0/ 19 99 03 /0 4/ 20 00 03 /1 0/ 20 00 03 /0 4/ 20 01 03 /1 0/ 20 01 03 /0 4/ 20 02 03 /1 0/ 20 02 03 /0 4/ 20 03 03 /1 0/ 20 03 03 /0 4/ 20 04 03 /1 0/ 20 04 03 /0 4/ 20 05 03 /1 0/ 20 05 03 /0 4/ 20 06 03 /1 0/ 20 06 03 /0 4/ 20 07 03 /1 0/ 20 07 03 /0 4/ 20 08 03 /1 0/ 20 08 03 /0 4/ 20 09 03 /1 0/ 20 09 03 /0 4/ 20 10 03 /1 0/ 20 10 03 /0 4/ 20 11 03 /1 0/ 20 11 03 /0 4/ 20 12 g b p/ to nn e source: agricultural and horticultural development board (ahdb). note: figures correspond to east anglia feed wheat. 240 c. revoredo-giha, m. zuppiroli figure 3. chicago: evolution of wheat spot prices, 1988-2012. 0 200 400 600 800 1000 1200 1400 01 /0 1/ 19 88 01 /0 7/ 19 88 01 /0 1/ 19 89 01 /0 7/ 19 89 01 /0 1/ 19 90 01 /0 7/ 19 90 01 /0 1/ 19 91 01 /0 7/ 19 91 01 /0 1/ 19 92 01 /0 7/ 19 92 01 /0 1/ 19 93 01 /0 7/ 19 93 01 /0 1/ 19 94 01 /0 7/ 19 94 01 /0 1/ 19 95 01 /0 7/ 19 95 01 /0 1/ 19 96 01 /0 7/ 19 96 01 /0 1/ 19 97 01 /0 7/ 19 97 01 /0 1/ 19 98 01 /0 7/ 19 98 01 /0 1/ 19 99 01 /0 7/ 19 99 01 /0 1/ 20 00 01 /0 7/ 20 00 01 /0 1/ 20 01 01 /0 7/ 20 01 01 /0 1/ 20 02 01 /0 7/ 20 02 01 /0 1/ 20 03 01 /0 7/ 20 03 01 /0 1/ 20 04 01 /0 7/ 20 04 01 /0 1/ 20 05 01 /0 7/ 20 05 01 /0 1/ 20 06 01 /0 7/ 20 06 01 /0 1/ 20 07 01 /0 7/ 20 07 01 /0 1/ 20 08 01 /0 7/ 20 08 01 /0 1/ 20 09 01 /0 7/ 20 09 01 /0 1/ 20 10 01 /0 7/ 20 10 01 /0 1/ 20 11 01 /0 7/ 20 11 01 /0 1/ 20 12 u s ce nt s/ b us he l source: chicago mercantile exchange (cbot). note: figures correspond to soft red winter wheat. table 1. wheat spot price volatility by period. cash market 1988 1997 1998 2006 2007 2012 chicago 0.280 0.313 0.495 rouen n.a. 0.168 0.286 bologna n.a. 0.142 0.196 east anglia 0.180 0.217 0.233 source: own calculations based on data from ahdb, cbot, la depeche agricole and ager borsa merci di bologna. note: the volatility indicator was computed as the standard deviations of annualized (i.e., multiplied by √250, where 250 approximates the trading days in the year) logarithmic daily nominal spot price returns. 2.2 speculation in futures markets from all the possible reasons behind the surge in commodity prices (e.g., draughts, use of food crops for biofuels), the only one that could imply a break in the relationship between the futures and spot market is the increasing presence of speculation on the futures market (e.g., bohl and stephan (2012) for a recent literature review on the issue). a number of authors – e.g. gheit (2008); masters (2008); masters and white (2008) – have asserted that speculative buying by index funds in commodity futures and over– the–counter (otc) derivatives markets (i.e., trading is done directly between two parties, without any supervision of an exchange) created a ‘‘bubble,’’ with the result that commodity prices, and crude oil prices, in particular, far exceeded fundamental values at the peak (irwin, et al., 2009. p. 377). furthermore, according unctad (2009): 241european commodity futures markets “financial investors in commodity futures exchanges have been treating commodities increasingly as an alternative asset class to optimize the risk-return profile of their portfolios. in doing so, they have paid little attention to fundamental supply and demand relationships in the markets for specific commodities. a particular concern with respect to this financialization of commodity trading is the growing influence of so called index traders, who tend to take only long positions that exert upward pressure on prices. the average size of their positions has become so large that they can significantly influence prices and create speculative bubbles, with extremely detrimental effects on normal trading activities and market efficiency. under these conditions, hedging against commodity price risk becomes more complex, more expensive, and perhaps unaffordable for developing-country users. moreover, the signals emanating from commodity exchanges are getting to be less reliable as a basis for investment decisions and for supply and demand management by producers and consumers.” (unctad, 2009, p. iv). in contrast with the aforementioned view, irwin et al. (2009) considered that fundamentals offer the best explanation for the rise in commodity prices. four of their points are worth noting: first, the arguments of bubble proponents are conceptually flawed and reflect misunderstanding of how commodity futures markets actually work, as they state that the money flows that go into futures and derivatives markets pressures the demand for physical commodities, when that money only operates in the futures market.2 second, a number of facts about the situation in commodity markets are inconsistent with the existence of a substantial bubble in commodity prices such as the fact that the available data do not indicate a change in the relative level of speculation to hedging. third, the available statistical evidence does not indicate that positions for any group of investors in commodity futures markets, including long–only index funds, consistently lead futures price changes and fourth, there is a historical pattern of attacks upon speculation as scapegoat during periods of extreme market volatility. it is clear that if futures market prices follow factors that are not related to fundamentals, one should expect futures and spot prices to become divorced or less correlated. this disassociation would necessarily bring a reduction in the effectiveness of hedging spot price risk using futures markets. as it is well known the correlation between both prices (futures and spot) is fundamental for the traditional minimum variance calculation of the optimal hedging ratio (ederington, 1979; sanders and manfredo, 2004). therefore, one could say that, if after computing the hedging ratio and the hedging effectiveness measures one finds that hedging in futures markets is still a useful tool for risk management, then it means that both markets are still related and the financialization of futures markets has not broken that link. this is the topic of the work of the next section. 2 note that there are at least two ways in which futures markets can affect the physical markets: the first one is through arbitraging between the two markets. the second way is through the use that commercial entities make of futures prices for pricing their products (e.g., processors selling flour for future delivery). clearly, the latter strategy makes sense only if the entities believe that the two markets are related. as regards the former reason, note that arbitrage will force both prices (futures and spot) to converge at the delivery time. 242 c. revoredo-giha, m. zuppiroli 3. data and methodology 3.1 data description the futures market price data used for the analysis was from the feed wheat contracts from the london international financial futures and options exchange (liffe) and for milling wheat contracts from the marché à terme international de france (matif). in order to provide a comparison wheat contracts (i.e., the deliverable varieties correspond to milling wheat) data from the chicago mercantile exchange group (cbot) were also used. for liffe and cbot contracts the data comprised the period 1988 until 2012, while for matif contracts the data were available only since 1998. as hedging performance requires the contemporary evaluation of cash price changes, spot prices from east anglia (uk), rouen (france), bologna (italy) and chicago (usa) were also collected. 3.2 methodology the methodology of the paper is based on carter (1984) and it comprises two parts: first, it explores the efficiency of wheat future markets, and second, hedging effectiveness is addressed for the periods before and after 2006, i.e., two periods of very different volatility levels. the choice of studying the efficiency of the markets and not only the hedging effectiveness is due to the fact that if the markets do not operate efficiently (e.g., they are thin) then they cannot be useful for hedging. 3.2.1 efficiency analysis the efficiency analysis of the studied futures markets comprised three complementary analyses: (1) price efficiency; (2) market unbiasedness; and (3) forecasting predictability of futures markets. as regards price efficiency, in this paper it is limited only to information conveyed from historical prices, i.e., only the so-called “weak price efficiency” was tested (fama, 1970).3 it consisted of studying the autocorrelation of the returns (i.e., first difference of futures price logarithms) and verifying that they show low autocorrelations (i.e., because past information of returns would be not useful to predict future returns). the market unbiasedness is associated to the theory of “normal backwardation” and the possibility that speculators would perceive profits for absorbing the taking risks (i.e., an inefficient market should give a structural advantage to the long positions taken by speculators with respect to the short positions taken by hedgers). according to carter (1984) this characteristic is typical of thin markets where the hedgers, interested in transferring the risk to other agents, would accept returns favouring in the long-run the buyers of the contracts. market unbiasedness was tested using the implication simulating a trade routine, such as the long position taken by speculators in futures market, should earn them positive 3 other notions of efficiency, i.e., semi-strong or strong, would have required either availability of public information on the market fundamentals (e.g., supply-demand sheets, ending stocks, stock to use ratio) or private information. 243european commodity futures markets profits over time (in contrast to hedgers who are supposed to be continuously net short and making losses equivalent to the price insurance they pay for their reduction in price risk). in this paper, following carter, we used the trading routines designed by cootner (1960) and gray (1961). the gray’s trading routine assumes that the speculator takes a net long position all the year round. if the annual harvest is immediately hedged, the price at harvest time must be low enough to induce speculators to invest on the long side of the hedge. futures prices must rise continuously over the postharvest life of the contracts in order to insure profits for speculators as a whole. the hypothetical gray’s trading routine involves purchasing the futures contract closest to maturity buying it on the first trading day in the delivery month of the preceding futures contract. then, every contract is sold on the first trading day of its own delivery month. in contrast to gray’s routine, cootner noted that hedgers were not always net short, in fact, when commitments to deliver at fixed prices are larger than commitments to buy, hedging may be net long. therefore, during the period of declining interest on shorthedging, prices must fall. under this condition a rational behaviour of speculators is to be long not for all the months but only for a part of the year (being short the other periods). to apply cootner’s routine, information on price seasonality was extracted from the data. this allowed us to adapt the trading routine to the actual price dynamics determining the months which are better for taking long and short positions. the forecasting ability of futures markets of the spot price at the delivery time comprised two aspects: first, whether futures prices were good predictors of spot prices at the delivery time (considering the average futures prices at the planting month and the average spot prices at the delivery month) was measured using the mean squared errors of the prediction divided by the average spot price at the delivery month (i.e., the coefficient of variation).4 the second aspect was to observe the sign of the prediction error to verify whether there was an apparent bias (i.e., whether the errors were all positive or negative). 3.2.2 hedging effectiveness analysis the optimal hedging ratio was computed using equation (1) (ederington’s, 1979; leuthold et al., 1989; sanders and manfredo, 2004), where δpst the change in the spot price at time period t, δpft the change in the futures price at time period t, β is the hedging ratio α is the intercept of the regression and εt is the regression error) and the r2 values give the proportionate reduction of price risk attainable (i.e., the measure of hedging effectiveness, hull, 2008). δpst =α +βδpft + εt (1) myers and thompson (1989) found that the model with the prices in levels provided a poor estimation of the ratio (since the variables are normally non-stationary), instead the estimation of a model such as (1) provided reasonably accurate estimates (myers and thompson, p. 859). 4 a coefficient of variation was used instead of just the mean square prediction error to allow for a comparison of the results for the studied markets (given the fact that the each of the studied markets are in different currencies). 244 c. revoredo-giha, m. zuppiroli to test the stability of the hedging ratios over time slope dummy variables were introduced for the years 2006 until 2012. the augmented model with dummies is given by (2): 2( ) δpst =α +βδpft + γ i ⋅di ⋅δpft i=2006 2012 ∑ + εt (2) where di the dummy variable that takes the value of 1 in year i and 0 otherwise, the γ i are the coefficients associated to the slope dummy, so the hedging ratio corresponding to year i is equal β γ( )+ i furthermore, in equation (2) β represents the coefficient for the period before 2006. note that a model such as (1) allows us to consider hedging for different future markets users along the wheat supply chain, e.g., for merchants and processors one would consider hedging on short intervals such as 7-days or 30-days, in contrast to farmers that might be interested on hedging over considering longer intervals such between planting and harvesting.5 as regards the hedging model for wheat farmers, this needs to take into account that growing season for wheat is a lengthy one (generally 10 to 11 months), and moreover, the cultivation calendar differs in all the studied countries. in the uk, the cultivation of winter wheat begins approximately between mid september to 3rd week october; in a normal season harvesting starts mid-august ending at the beginning of september. in france, and mostly in italy, cultivation starts later than uk. it begins between october and mid november and finishes at the end of june (italy) or july to early august (france). for the us the cropping calendar is approximately the same as in northern europe, i.e., planting in september and harvesting in july. therefore, for italy and the us, the post-harvest price should be taken during july while for uk and france during august would be better. table 2 presents the information used for computing farmers’ optimal hedging. it was considered that the farmer opened the hedge at the month of the planting time and the hedging was lifted after nine, ten or eleven months depending on the country (see table 2). table 2. parameters adopted for farmers’ hedging by countries country exchange contract delivery month planting time (month of year t) post-harvest time (month of year t+1) us cbot september september july italy matif september / august (*) october july france matif september / november (*) october august uk liffe november october august notes: (*) the september contract is available on matif only until 2007. for merchants and processors it was assumed that the hedging was not “seasonally specified”. this was due to the fact that merchants and processors usually hedge their 5 in this paper only price risk is considered and ignores production risk in the computation of the optimal hedging ratios for farmers. 245european commodity futures markets physical (spot) positions all over the year holding position in the futures market for less than 10-11 month. therefore, the lengths of the hedging were assumed to 30, 60 and 90 trading days. these intervals imply, approximately, one month and a half, three months and four months period respectively. finally, such as in carter (1984) a very interval hedging was included (7 trading days, i.e., approximately 10 calendar days). 4. results and discussion 4.1 results from the efficiency analysis 4.1.1 price efficiency analysis figures 4 to 6 show that the autocorrelation coefficients for the returns for time lags from 1 to 12 (each lag represented with a different colour) working days for each contract. the values of the coefficients are relatively low (concentrated between -0.2 and 0.2) for all the contracts and markets, suggesting that the current returns are relatively independent from the past information and therefore price efficient in fama’s sense. furthermore, in comparative terms, the matif market (see figure 6) seems to perform better in terms of price efficiency than the other two markets as its autocorrelations are closer to zero. figure 4. cbot autocorrelation coefficients for wheat futures returns, 1988-2012. -­‐1.00 -­‐0.80 -­‐0.60 -­‐0.40 -­‐0.20 0.00 0.20 0.40 0.60 0.80 1.00 m ar ch -­‐1 98 8 ju ly -­‐1 98 8 de ce m be r-­‐ 19 88 m ay -­‐1 98 9 se pt em be r-­‐ 19 89 m ar ch -­‐1 99 0 ju ly -­‐1 99 0 de ce m be r-­‐ 19 90 m ay -­‐1 99 1 se pt em be r-­‐ 19 91 m ar ch -­‐1 99 2 ju ly -­‐1 99 2 de ce m be r-­‐ 19 92 m ay -­‐1 99 3 se pt em be r-­‐ 19 93 m ar ch -­‐1 99 4 ju ly -­‐1 99 4 de ce m be r-­‐ 19 94 m ay -­‐1 99 5 se pt em be r-­‐ 19 95 m ar ch -­‐1 99 6 ju ly -­‐1 99 6 de ce m be r-­‐ 19 96 m ay -­‐1 99 7 se pt em be r-­‐ 19 97 m ar ch -­‐1 99 8 ju ly -­‐1 99 8 de ce m be r-­‐ 19 98 m ay -­‐1 99 9 se pt em be r-­‐ 19 99 m ar ch -­‐2 00 0 ju ly -­‐2 00 0 de ce m be r-­‐ 20 00 m ay -­‐2 00 1 se pt em be r-­‐ 20 01 m ar ch -­‐2 00 2 ju ly -­‐2 00 2 de ce m be r-­‐ 20 02 m ay -­‐2 00 3 se pt em be r-­‐ 20 03 m ar ch -­‐2 00 4 ju ly -­‐2 00 4 de ce m be r-­‐ 20 04 m ay -­‐2 00 5 se pt em be r-­‐ 20 05 m ar ch -­‐2 00 6 ju ly -­‐2 00 6 de ce m be r-­‐ 20 06 m ay -­‐2 00 7 se pt em be r-­‐ 20 07 m ar ch -­‐2 00 8 ju ly -­‐2 00 8 de ce m be r-­‐ 20 08 m ay -­‐2 00 9 se pt em be r-­‐ 20 09 m ar ch -­‐2 01 0 ju ly -­‐2 01 0 de ce m be r-­‐ 20 10 m ay -­‐2 01 1 se pt em be r-­‐ 20 11 m ar ch -­‐2 01 2 lag  1 lag  2 lag  3 lag  4 lag  5 lag  6 lag  7 lag  8 lag  9 lag  10 lag  11 lag  12 source: own calculation based on data presented in section 3.1. figure 5. liffe autocorrelation coefficients for wheat futures returns, 1988-2012. -­‐1.00 -­‐0.80 -­‐0.60 -­‐0.40 -­‐0.20 0.00 0.20 0.40 0.60 0.80 1.00 no ve mb er -­‐1 98 9 m ar ch -­‐1 99 0 no ve mb er -­‐1 99 0 m ar ch -­‐1 99 1 no ve mb er -­‐1 99 1 m ar ch -­‐1 99 2 no ve mb er -­‐1 99 2 m ar ch -­‐1 99 3 no ve mb er -­‐1 99 3 m ar ch -­‐1 99 4 ju ly-­‐ 19 94 jan ua ry -­‐1 99 5 m ay -­‐1 99 5 no ve mb er -­‐1 99 5 m ar ch -­‐1 99 6 ju ly-­‐ 19 96 jan ua ry -­‐1 99 7 m ay -­‐1 99 7 no ve mb er -­‐1 99 7 m ar ch -­‐1 99 8 ju ly-­‐ 19 98 jan ua ry -­‐1 99 9 m ay -­‐1 99 9 no ve mb er -­‐1 99 9 m ar ch -­‐2 00 0 ju ly-­‐ 20 00 jan ua ry -­‐2 00 1 m ay -­‐2 00 1 no ve mb er -­‐2 00 1 m ar ch -­‐2 00 2 ju ly-­‐ 20 02 jan ua ry -­‐2 00 3 m ay -­‐2 00 3 no ve mb er -­‐2 00 3 m ar ch -­‐2 00 4 ju ly-­‐ 20 04 jan ua ry -­‐2 00 5 m ay -­‐2 00 5 no ve mb er -­‐2 00 5 m ar ch -­‐2 00 6 ju ly-­‐ 20 06 jan ua ry -­‐2 00 7 m ay -­‐2 00 7 no ve mb er -­‐2 00 7 m ar ch -­‐2 00 8 ju ly-­‐ 20 08 jan ua ry -­‐2 00 9 m ay -­‐2 00 9 no ve mb er -­‐2 00 9 m ar ch -­‐2 01 0 ju ly-­‐ 20 10 jan ua ry -­‐2 01 1 m ay -­‐2 01 1 no ve mb er -­‐2 01 1 m ar ch -­‐2 01 2 lag  1 lag  2 lag  3 lag  4 lag  5 lag  6 lag  7 lag  8 lag  9 lag  10 lag  11 lag  12 source: own calculation based on data presented in section 3.1. 246 c. revoredo-giha, m. zuppiroli figure 6. matif autocorrelation coefficients for wheat futures returns, 1998-2012. -­‐1.00 -­‐0.80 -­‐0.60 -­‐0.40 -­‐0.20 0.00 0.20 0.40 0.60 0.80 1.00 se pt em be r-­‐1 99 8 no ve mb er -­‐1 99 8 jan ua ry -­‐1 99 9 m ar ch -­‐1 99 9 m ay -­‐1 99 9 se pt em be r-­‐1 99 9 no ve mb er -­‐1 99 9 jan ua ry -­‐2 00 0 m ar ch -­‐2 00 0 m ay -­‐2 00 0 se pt em be r-­‐2 00 0 no ve mb er -­‐2 00 0 jan ua ry -­‐2 00 1 m ar ch -­‐2 00 1 m ay -­‐2 00 1 ju ly-­‐ 20 01 se pt em be r-­‐2 00 1 no ve mb er -­‐2 00 1 jan ua ry -­‐2 00 2 m ar ch -­‐2 00 2 m ay -­‐2 00 2 ju ly-­‐ 20 02 se pt em be r-­‐2 00 2 no ve mb er -­‐2 00 2 jan ua ry -­‐2 00 3 m ar ch -­‐2 00 3 m ay -­‐2 00 3 ju ly-­‐ 20 03 se pt em be r-­‐2 00 3 no ve mb er -­‐2 00 3 jan ua ry -­‐2 00 4 m ar ch -­‐2 00 4 m ay -­‐2 00 4 ju ly-­‐ 20 04 se pt em be r-­‐2 00 4 no ve mb er -­‐2 00 4 jan ua ry -­‐2 00 5 m ar ch -­‐2 00 5 m ay -­‐2 00 5 ju ly-­‐ 20 05 se pt em be r-­‐2 00 5 no ve mb er -­‐2 00 5 jan ua ry -­‐2 00 6 m ar ch -­‐2 00 6 m ay -­‐2 00 6 se pt em be r-­‐2 00 6 no ve mb er -­‐2 00 6 jan ua ry -­‐2 00 7 m ar ch -­‐2 00 7 m ay -­‐2 00 7 se pt em be r-­‐2 00 7 no ve mb er -­‐2 00 7 jan ua ry -­‐2 00 8 m ar ch -­‐2 00 8 m ay -­‐2 00 8 au gu st-­‐ 20 08 no ve mb er -­‐2 00 8 jan ua ry -­‐2 00 9 m ar ch -­‐2 00 9 m ay -­‐2 00 9 au gu st-­‐ 20 09 no ve mb er -­‐2 00 9 jan ua ry -­‐2 01 0 m ar ch -­‐2 01 0 m ay -­‐2 01 0 au gu st-­‐ 20 10 no ve mb er -­‐2 01 0 jan ua ry -­‐2 01 1 m ar ch -­‐2 01 1 m ay -­‐2 01 1 au gu st-­‐ 20 11 no ve mb er -­‐2 01 1 jan ua ry -­‐2 01 2 m ar ch -­‐2 01 2 m ay -­‐2 01 2 lag  1 lag  2 lag  3 lag  4 lag  5 lag  6 lag  7 lag  8 lag  9 lag  10 lag  11 lag  12 source: own calculation based on data presented in section 3.1. 4.1.2 market unbiasedness analysis as explained in the methodology before implementing the trading routines it is necessary to estimate the seasonality of futures prices for each of the markets. figure 7 presents the seasonality analysis using nearby futures prices. as regards the seasonality for cbot prices, on average, prices approximately have a decreasing trend from january to july and rose steadily thereafter. the seasonality in matif prices is similar to the observed for cbot prices. in contrast, liffe seasonal pattern is not that clear and it shows an approximate a two step seasonal pattern: one between may and june (decreasing) and another between october and november (increasing). figure 7. average monthly price indexes of wheat futures. 93 94 95 96 97 98 99 100 101 102 103 104 jan feb mar apr may jun jul aug sep oct nov dec pr ic e in de x months cbot matif liffe source: own calculation based on data presented in section 3.1. 247european commodity futures markets table 3 reports the results from implementing gray’s trading routine (i.e., ‘long only’) and cootner’s trading routine (i.e., ‘long and short’). the table shows the average profits per trade that could be earned, before brokerage fees. cootner’s routine shows profits higher than gray’s routine for all the markets. table 3. results of trading routines in wheat futures. exchange speculative market position dates price at beginning and ending dates number of trades average profit / loss per trade t-ratio liffe long only 1/11/89 -1/03/12 £/t. 110.5-164.8 112.00 £/t. -1.01 -0.06 liffe long and short 1/11/94 – 1/11/11 £/t. 107.1-147.8 102.00 £/t. 0.39 0.03 matif long only 1/09/98 – 1/03/12 €/t. 118.9-214.5 73.00 €/t. 2.36 0.11 matif long and short 2/11/98 – 1/11/12 €/t.124.3-187.8 78.00 €/t. 2.74 0.14 cbot long only 1/03/88 – 1/03/12 $/bu. 3.2-6.6 120.00 $/bu. -0.06 -0.08 cbot long and short 1/03/88 – 1/12/11 $/bu. 3.2-6.0 143.00 $/bu. 0.06 0.10 source: own calculation based on data presented in section 3.1. note: ”long only” refers to gray’s trading routine and “long and short” to cootner’s trading routine. while in cbot and liffe markets the gray’s routine show losses, cootner’s routine in those markets resulted in profits. in the matif market, both routines showed a profit (slightly higher in the case of cootner’s (€2.74) than gray’s (€2.36)). however, it should ne noted that in none of the cases the average profits were statistically different from zero (using a t student test) at 95 per cent significance. therefore, conclusion from table 3 is that none of markets show a systematic bias in favour of speculators. 4.1.3 forecasting ability analysis with respect to the forecasting ability of the futures markets, figure 8 presents the results of the analysis (i.e., in terms of the forecasting errors). it shows that the prediction power in all the markets6 was between 60 and 70 per cent when the entire sample is considered (first set of columns in figure 8). if the sample period is broken down into 19892005 and 2006-11, it is clear that the prediction errors worsen during the period 2006-11. on the possible bias of the errors, figures 9 to 11 show that the sign of the errors were both positive and negative without indicating any clear bias. 6 for the matif exchange, the prediction test was carried out considering the spot prices from the rouen market. 248 c. revoredo-giha, m. zuppiroli figure 8. coefficient of variation of the prediction errors. source: own calculation based on data presented in section 3.1. note: data for matif exchange are available since year 1999 instead of 1989. figure 9. cbot – forecasting errors of futures markets by contract. -400.0 -300.0 -200.0 -100.0 0.0 100.0 200.0 300.0 400.0 500.0 600.0 se p 89 se p 90 se p 91 se p 92 se p 93 se p 94 se p 95 se p 96 se p 97 se p 98 se p 99 se p 00 se p 01 se p 02 se p 03 se p 04 se p 05 se p 06 se p 07 se p 08 se p 09 se p 10 se p 11 us c en ts / bu sh el source: own calculation based on data presented in section 3.1. 249european commodity futures markets figure 10. liffe – forecasting errors of futures markets by contract. -80.0 -60.0 -40.0 -20.0 0.0 20.0 40.0 n ov 8 9 n ov 9 0 n ov 9 1 n ov 9 2 n ov 9 3 n ov 9 4 n ov 9 5 n ov 9 6 n ov 9 7 n ov 9 8 n ov 9 9 n ov 0 0 n ov 0 1 n ov 0 2 n ov 0 3 n ov 0 4 n ov 0 5 n ov 0 6 n ov 0 7 n ov 0 8 n ov 0 9 n ov 1 0 n ov 1 1 g b p / t on ne source: own calculation based on data presented in section 3.1. figure 11. matif – forecasting errors of futures markets by contract. -200.0 -150.0 -100.0 -50.0 0.0 50.0 100.0 se p 99 se p 00 se p 01 se p 02 se p 03 se p 04 se p 05 se p 06 se p 07 n ov 0 8 n ov 0 9 n ov 1 0 n ov 1 1 eu ro / to nn e source: own calculation based on data presented in section 3.1. note: to evaluate matif futures price forecasting power, spot price from rouen were used. 250 c. revoredo-giha, m. zuppiroli 4.2 results from the hedging effectiveness analysis tables from 5 to 9 provide the results of the analysis of the hedging effectiveness of futures contracts for all the studied markets. as mentioned different operators along the supply chain have their own particular hedging needs and this was taken into account by considering different hedging lengths. as equations (1) and (2) were estimated using time series, in order to avoid spurious associations, the series used (i.e., the price differences) were tested for unit roots using the phillips–perron test (phillips and perron, 1988), which considers that the process generating data might have a higher order of autocorrelation. in addition, the test is robust with respect to unspecified autocorrelation and heteroscedasticity in the disturbance process of the test equation. all the series were found stationary, and therefore, it was possible to estimate equations (1) and (2) by ordinary least squares. the results for the farmers’ hedging exercise are presented in table 5. they show that when the entire sample is used, the performance of the european exchanges, in terms of the variance reduction that farmers could have attained through hedging is better than in the chicago market. thus, a us farmer hedging 39 per cent of his wheat using the chicago wheat futures would have reduced his price risk only by 14 per cent; whilst the reduction using the european exchanges ranged from 40 per cent (for the case of spot prices from bologna and the matif futures markets) to 73 per cent (for the east anglia spot prices and the liffe futures markets). however, the results for the entire sample hide the dramatic changes in the hedging ratios since 2007 for all the cases. as shown in table, the optimal ratios changed significantly during the period 2006-07 to 2010-11. it is clear from table 5 that if farmers had computed their hedging ratios based only on historical price information; the errors on the strategy would have been significant. in this sense, probably the most appropriate strategy for computing hedging ratios would have been that proposed by myers and thompson (1989), which consists of incorporating additional relevant information (e.g., supply and demand conditions). the results for hedges for lengths of 7, 30, 60 and 90 days are presented in tables 6 to 9 for the different exchanges and spot markets. these are supposed to represent other supply chain operators such as merchants or processors, who do not need to hedge in a specific season of the year, as in the case of farmers. as shown in the tables, the short-term hedges report a substantive reduction in price risk with r2 that are, in general, high (with more than 75 per cent of price risk reduction). furthermore, for all the studied markets, the performance of the short-term hedges improves with the increase of the hedge length. in fact, the 7 days hedges are relatively low, particularly if wheat from bologna is hedged using matif. in contrast with the results from the farmers’ hedging exercise, the inclusion of the slope dummy variables do not improve much the r2 of the hedging regressions (despite the fact that in many cases the coefficients are statistically significant), i.e., the changes in the hedging ratios add little to the reduction in price risk. 251european commodity futures markets 5. conclusions the primary aim of this paper has been to study whether hedging in futures markets is useful instrument for price risk reduction for commercial entities operating with commodities along the wheat supply chain. thus, the focus was on two european wheat futures markets, liffe and matif, in addition of cbot market for comparison purposes. the evaluation comprised two stages: first, the efficiency analysis of the futures markets, which consisted of three sub-analyses: price efficiency, market unbiasedness, and the forecasting ability; and second, the effectiveness for hedging. as regards the efficiency analysis, the results indicate that the increasing participation of speculative investors mentioned in the recent literature, have not reduced the market price efficiency. the same result was found with respect to the market unbiasedness. in this respect, the test show that holding a speculative position showed that the average profits during the last 20 years were not statistically different from zero. the forecasting performance of futures markets showed that the prediction capacity of in the three markets was modest with a coefficient of variation of the error that was between 25 to 40 per cent. table 5. estimates of hedging ratios and effectiveness for farmers’ hedging. cases coefficients r2 slope dummies for years with high variability obs. α β 2006-07 2007-08 2008-09 2009-10 2010-11 cbot chicago farmer’s hedging 10.25 0.39 0.14 448 (2.36) (8.55) with year dummies 11.94 0.86 0.67 0.19 -1.67 -0.42 2.15 -0.73 448 (3.92) (13.51) (1.85) (-18.29) (-5.39) (10.81) (-3.93) liffe east anglia farmer’s hedging -5.79 1.01 0.73 330 (-7.58) (29.95) with year dummies -6.12 1.12 0.87 -0.15 6.73 -1.62 0.39 -0.85 330 (-10.26) (23.41) (-2.53) (8.72) (-8.79) (5.30) (-9.01) matif rouen farmer’s hedging -8.71 0.82 0.64 249 (-7.71) (21.10) with year dummies -7.71 1.26 0.70 -0.47 2.93 -0.54 -1.41 249 (-6.73) (8.69) (-3.13) (4.65) (-2.57) (0.00) (-3.35) matif bologna farmer’s hedging -10.61 0.78 0.40 286 (-9.67) (13.81) with year dummies -11.26 0.78 0.46 -0.10 2.60 -0.05 0.78 0.27 286 (-8.77) (4.41) (-0.51) (2.82) (-0.20) (2.91) (0.63) note: the numbers in parenthesis below the coefficients are the t-statistics. 252 c. revoredo-giha, m. zuppiroli table 6. cbot – chicago: estimates of effectiveness of short-term wheat hedging. cases coefficients r2 slope dummies for years with high variability obs. α β 2006 2007 2008 2009 2010 2011 2012 7 days hedge 0.06 0.86 0.72 6,297 (0.37) (128.01) with year dummies 0.10 0.84 0.73 -0.08 0.07 0.01 0.14 -0.08 0.09 0.06 6,297 (0.61) (63.47) (-2.04) (2.83) (0.76) (4.62) (-3.44) (3.70) (0.91) 30 days hedge 0.33 0.85 0.77 6,274 (1.11) (145.77) with year dummies 0.26 0.86 0.78 0.12 0.04 -0.03 0.21 -0.21 -0.02 0.11 6.274 (0.86) (72.05) (3.37) (2.04) (-1.82) (7.76) (-9.73) (-0.79) (1.26) 60 days hedge 0.44 0.92 0.80 6,244 (1.10) (158.84) with year dummies 0.22 0.92 0.81 0.17 0.04 -0.03 0.42 -0.16 -0.02 0.15 6,244 (0.56) (79.54) (4.79) (1.99) (-1.84) (14.41) (-7.75) (-0.79) (1.18) 90 days hedge 0.54 0.94 0.81 6,214 (1.13) (161.34) with year dummies 0.41 0.93 0.81 0.11 0.03 -0.04 0.18 -0.01 0.16 -0.36 6,214 (0.83) (80.89) (3.00) (1.68) (-2.65) (5.44) (-0.68) (6.32) (-4.48) note: the numbers in parenthesis below the coefficients are the t-statistics. table 7. liffe – east anglia: estimates of effectiveness of short-term wheat hedging. cases coefficients r2 slope dummies for years with high variability obs. α β 2006 2007 2008 2009 2010 2011 2012 7 days hedge 0.03 0.55 0.32 6,101 (0.65) (53.07) with year dummies 0.00 0.35 0.34 0.22 0.33 0.39 0.27 0.33 0.24 0.57 6,101 (0.08) (21.45) (2.69) (10.49) (11.09) (6.23) (10.23) (7.79) (5.45) 30 days hedge 0.04 0.80 0.65 6,078 (0.56) (106.93) with year dummies -0.07 0.60 0.68 0.30 0.32 0.38 0.29 0.31 0.17 0.45 6,078 (-0.99) (48.12) (4.46) (15.16) (16.28) (8.66) (13.91) (6.80) (8.14) 60 days hedge 0.00 0.89 0.78 6,048 (0.04) (145.41) with year dummies -0.06 0.77 0.78 0.24 0.15 0.22 0.20 0.15 0.15 0.25 6,048 (-0.70) (70.11) (4.56) (8.43) (12.34) (6.31) (8.15) (7.32) (3.79) 90 days hedge -0.05 0.95 0.86 6,018 (-0.54) (192.66) with year dummies 0.01 0.88 0.86 0.04 0.06 0.12 0.13 0.05 0.12 0.04 6,018 (0.08) (96.53) (0.99) (4.29) (8.59) (3.88) (3.51) (7.03) (0.55) note: the numbers in parenthesis below the coefficients are the t-statistics. 253european commodity futures markets table 8. matif rouen: estimates of effectiveness of short-term wheat hedging. cases coefficients r2 slope dummies for years with high variability obs. α β 2006 2007 2008 2009 2010 2011 2012 7 days hedge 0.04 0.72 0.48 3,670 (0.51) (58.40) with year dummies 0.03 0.54 0.50 0.22 0.35 0.32 0.21 0.15 0.01 -0.05 3,670 (0.36) (14.33) (2.55) (7.58) (6.90) (2.74) (3.21) (0.15) (-0.53) 30 days hedge 0.07 0.93 0.85 3,647 (0.68) (141.03) with year dummies -0.09 0.82 0.85 0.16 0.21 0.12 0.10 0.08 -0.01 0.25 3,647 (-0.84) (39.42) (3.42) (8.60) (5.03) (2.37) (3.26) (-0.52) (4.68) 60 days hedge 0.12 0.95 0.91 3,617 (1.05) (191.33) with year dummies -0.06 0.92 0.91 0.04 0.07 0.03 0.10 0.05 -0.05 0.10 3,617 (-0.51) (60.41) (1.22) (3.96) (1.50) (2.36) (2.61) (-2.43) (2.48) 90 days hedge 0.15 0.99 0.94 3,587 (1.28) (241.48) with year dummies 0.25 0.95 0.94 -0.01 0.04 0.07 0.22 0.06 -0.02 0.03 3,587 (1.98) (79.64) (-0.29) (2.74) (4.65) (6.47) (3.87) (-1.42) (0.89) note: the numbers in parenthesis below the coefficients are the t-statistics. table 9. matif – bologna: estimates of effectiveness of short-term wheat hedging. cases coefficients r2 slope dummies for years with high variability obs. α β 2006 2007 2008 2009 2010 2011 2012 7 days hedge 0.09 0.35 0.18 3,670 (1.13) (28.85) with year dummies 0.11 0.34 0.19 0.07 0.02 -0.03 -0.04 -0.05 0.12 -0.07 3,670 (1.27) (8.92) (0.78) (0.33) (-0.57) (-0.52) (-1.02) (2.49) (-0.67) 30 days hedge 0.15 0.70 0.57 3,647 (0.94) (70.20) with year dummies -0.07 0.71 0.59 0.19 0.02 -0.19 -0.09 0.04 0.15 -0.12 3,647 (-0.45) (22.51) (2.72) (0.50) (-5.16) (-1.44) (1.10) (3.81) (-1.53) 60 days hedge 0.04 0.81 0.71 3,617 (0.20) (93.43) with year dummies -0.29 0.92 0.73 0.07 -0.03 -0.31 -0.37 -0.12 0.12 -0.30 3,617 (-1.45) (35.70) (1.29) (-0.86) (-10.05) (-5.17) (-3.74) (3.45) (-4.69) 90 days hedge 0.00 0.90 0.78 3,587 (0.01) (114.28) with year dummies -0.13 1.00 0.80 -0.06 -0.06 -0.27 -0.10 -0.13 0.11 -0.40 3,587 (-0.54) (45.09) (-1.26) (-2.27) (-9.85) (-1.49) (-4.78) (3.53) (-6.58) note: the numbers in parenthesis below the coefficients are the t-statistics. 254 c. revoredo-giha, m. zuppiroli with respect to the hedging effectiveness analysis, the results can be divided into: first, farmers’ hedging, and second, by other supply chain members. for farmers, although some of the results indicate a significant reduction in the price risk (e.g., liffe-east anglia and matif-rouen), it is clear that the instability of the period 2006-07 to 2010-11 affected significantly the estimation of the optimal hedging ratios. furthermore, the introduction of dummy variables to control for the variability shows an important improvement in the reduction of the price risk. this is common to all the markets. therefore, one can conclude that price volatility affected significantly the hedging effectiveness for farmers. the results for the other participants of the wheat supply chain (i.e., short hedging) show, for most of the cases, higher price risk reduction than that observed for farmers’ hedges (although an exception are the 7-day hedges for wheat from bologna being hedged at the matif market). in addition, the inclusion of dummies in the regression to estimate the optimal hedging ratio do not increased much the r2 (such as in the case of the farmers’ hedge), showing that short-term hedges were not much affected by the increasing price volatility. the above results imply that the studied futures markets are not only still efficient but may also be a useful tool for the reduction of price risk (e.g., they might be useful for food security purposes). however, it is important to stress that the analysis carried out in this paper is only valid for the regions where the exchanges are and cannot be extrapolated to other regions without careful evaluation. acknowledgements an earlier version of this paper was presented at the 2nd aieaa conference “between crisis and development: which role for the 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(ed.). safeguarding food security in volatile global markets. rome: food and agriculture organization of the united nations. unctad (2009). trade and development report 2009, geneva. united nations (2011). price volatility in food and agricultural markets: policy responses. united nations, june. bio-based and applied economics 4(3): 279-300, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-17555 review article research and innovation in agriculture: beyond productivity? davide viaggi department of agricultural sciences (dipsa), university of bologna, viale fanin, 50 40127 bologna (italy) date of submission: october 5th, 2015: accepted november 15th, 2015 abstract. studies on the effects of research and innovation in agriculture have been largely characterised by efforts to make a connection between expenditure and productivity. a number of issues have challenged the ability of productivity to measure the effects of research, namely, in recent years, increasing efforts towards improving the environmental performance of the farming sector. besides environmental concerns, however, a number of recent concepts have emerged that are shaping the current research and policy agenda and which could result in a revision of the productivity concepts used to evaluate research impacts. the objective of this paper is to discuss these issues and their implications for studies on the impact of research and innovation. we address, in particular, the following issues: a) the development of the of bioeconomy and related concepts such as the circular economy, resource efficiency and bio-refinery; b) the connection with entrepreneurship and eco-innovation; c) changing tools in research assessment, in particular the widespread use of life cycle assessment (lca); and d) the evolving concepts of sustainability and ecosystem services. we argue that while the traditional notion of productivity, intended as output/input ratio, maintains (and may be strengthens) its role on the aggregate, a more analytical interpretation of the pathways towards research impacts is needed, as well as a broadened view of productivity and its determinants. keywords. agriculture, research, innovation, productivity, bio-refinery, circular economy, bioeconomy jel codes. q16 1. introduction and objectives the link between research, innovation and productivity has gained growing attention in the last decade. the productive focus of agriculture in the mid-twentieth century appeared rather straightforward. yet the environmental crisis of the late twentieth century brought a wider variety of issues to the forefront. at the outset of the twenty-first cencorresponding author: davide.viaggi@unibo.it. 280 d. viaggi tury, the dilemma in eu agriculture appeared to be whether to move towards more traditional, organic farming or to modern biotechnology-based agriculture (de wilt et al., 2001). now, however, the scenario is more complex. on the one hand, different agricultural technologies and sustainability pathways co-exist (worldwide at least); on the other hand, attention has moved from choosing between existing technologies to a focus on new technological developments through innovation. at the same time, the concept of sustainability is changing: initially it was focused mainly on environmental effects, then enlarged to economic and social sustainability and resource efficiency. the recent decade has refocused attention on feeding the word while preserving resources for future generations, which is the “mantra” of the current debate and recent events (see expo 2015), with a clear renewed attention on productivity. policy has followed and promoted this change in focus. an example is the evolution of the common agricultural policy (cap) from providing incentives to production through market support, to focusing more on income support, to a mix of sustainability and income support, and to the present aggregate of productivity, resource efficiency and climate change concerns. the relationship between research, innovation and productivity has taken on a growing policy focus in the eu (in particular with the eu2020 strategy), with an increasing focus on competitiveness, innovation and resource efficiency. this has also affected subsequent policy implementation, including the cap 2014-2020. in this programming period, and in particular in the rural development component, innovation has a prominent role, including new connections with research, especially through the new instrument represented by the european innovation partnership (eip). the focus of agricultural research in the last two centuries has followed the needs expressed by society and policy, with deep changes over time (spierz, 2014). in recent years, increasing attention to public good production by agriculture (environmental, working quality etc.) has affected both the focus of research and the breadth of impacts that would be necessary to measure to account for these effects. a general definition of productivity is the ability of production factors to produce output (latruffe, 2010). this can be measured through a ratio between output and input. however, a wide body of literature has treated productivity measures in agriculture addressing both conceptual and practical issues in implementing this concept. the literature includes simple measures of partial productivity (e.g. relating output to individual inputs as well as measures accounting for more than one input (e.g. multiple factor productivity). a more comprehensive measure of productivity is total factor productivity (tfp), which is a ratio of the (monetary) aggregation of all outputs and the aggregation of all inputs. this concept often follows a time series approach to account for productivity improvements based on changes in tfp (coelli, 2005; latruffe, 2010). fuglie (2015) shows that tfp has a major role in accounting for the growth of agricultural production over time, though with a differing weight in different time periods and in different regions. productivity indexes have been discussed and extended to account for a number of issues, including the growing variety of inputs and outputs related to environmental and resources concerns. several multi-output, resource-saving, or dynamic specifications are now attempting to tackle these issues. this has led, among others, to proposing extensions such as environmentally adjusted tfp or even green tfp (e.g. chen and golley, 2014). 281research and innovation in agriculture: beyond productivity? besides measuring productivity, the economic literature has attempted to investigate the factors affecting productivity and its changes. this has been done largely through econometric models using productivity measures as dependent variables and various influencing factors as explanatory variables. among such factors, research and innovation have a prominent role. in particular, the research to date has focused on seeking to make a connection between research expenditure and productivity (alston et al., 2010, 2011; wang et al., 2013). research and innovation are indeed potentially key explanatory variables of changes in productivity over time. through the provision of new knowledge (increasing the knowledge stock), embedding this knowledge in new technologies (enlarging the technology set) and the diffusion of innovation in the economic system, research is expected to directly affect the relationship between input and output and hence improve productivity. in this way, the role of research and innovation can be seen as a way of escaping (or at least modifying) trade-offs among goods, given limited resource availability, by enhancing technical possibilities. yet as research is costly, this implies a trade-off between present consumption and investment in technology development. methods used to assess this connection may utilise econometric techniques on large data sets of (relatively simplified) information, as well as case study type approaches (alston et al., 1995; gaunand et al., 2015). altogether, the link between research, innovation and productivity is conceptually straightforward, but much less easy to streamline in practice. this is partly due to technical issues, such the practical difficulties in proving numerically the linkage between research and productivity change, in part due to data limitations. further conceptual difficulties are due to the complexity of the system connecting research, innovation and productivity. this is underscored by, among others, the long-standing debate about the difficulty in matching demand and supply of innovation, the on-going discussion regarding the functioning of knowledge and innovation systems, and the growing discussion on the multiple directions (functions and objectives) of agriculture and related research. there are also potential counterintuitive effects of research, such as the negative impacts of highly codified public research on local enterprises (maietta, 2015). making this linkage has also become more difficult over time due to the increased variety of technology pathways (conventional, integrated, organic and more) used in agriculture and of the goods (private, public, intermediate) produced by agriculture. these issues have been exacerbated in recent years by the emerging focus on the bioeconomy and renewed attention devoted to resource efficiency and productivity. the concepts related to the bioeconomy emphasise the complexity of technical and economic linkages that can transmit the effects of research (viaggi et al., 2012; swinnen and weersink, 2013). finally, worldwide trends, including the opening of various economies and climate change, have given additional weight to uncertainty and dynamics, with a special emphasis on non-linear and “non-trend” dynamics and fundamental uncertainty in the outcome and relevance of any technology change and policy action. it is now rather well established that the assessment of the impacts of research (especially public research) should deal with multiple dimensions, covering not only economic, but also environmental, health, social, and policy impacts. furthermore, it is accepted that innovation results from the activities and interactions of multiple actors. understanding the impact of a research activity/organisation increasingly requires paying attention to the 282 d. viaggi intermediaries as well as the beneficiaries (i.e. the innovation network) of the research results (gaunand et al., 2015). while some of the issues above have already been covered extensively in the literature, others are rather new and the scientific community is still in the process of identifying the challenges ahead. the objective of this paper is to discuss the link between research, innovation and productivity in agriculture in light of selected emerging economic and sustainability concepts, mainly, but not exclusively, linked to the development of the bioeconomy. the paper addresses, in particular, the following issues in relation to the link between research and productivity: a) the concept of ‘bioeconomy’ and related concepts such as the circular economy, resource efficiency and bio-refinery; b) the connection with entrepreneurship and eco-innovation; c) changing tools in research assessment, in particular the widespread use of life cycle assessment (lca); and d) the evolving concepts of sustainability and ecosystem services. in relation to these fields, this paper attempts to identify implications for the concept of productivity and for the understanding of the linkage between research and productivity, in particular seeking to identify developments that may challenge the current notion of productivity. this reasoning is then used to develop implications for future research. the paper does not intend to focus specifically on the measurement of productivity (though this is clearly an important issue in this context and is implicitly or explicitly addressed at several points in the paper). rather, it focuses on the connection between objectives/ mechanisms and the effects of research. moreover, while recognising that the issue has relevance for all scales of analysis, the focus of this paper is implicitly more on the micro to meso level and closer to the research level, compared with many productivity-oriented studies. in addition, it tends to be more on the threshold between decision support and assessment methods, largely because the paper seeks significant inspiration from trends in evaluation practice and decision making concerning research and innovation funding. we do not restrict attention to a specific field of research; while at the same time not excluding research in the social sciences, however, the paper focuses more on “classical” technological research. the paper is organised in three main sections beyond this one. in the next section (2), we review the literature on the four issues listed above and their implications for tracing the effects of research on productivity; in section 3 we discuss the implications of the findings from the literature for future research and evaluation practices. concluding remarks are provided in section 4. 2. research and productivity: selected issues 2.1 changing paradigms in economic organisation: bioeconomy, circular economies and biorefinery the concept of the bioeconomy, based on the sustainable exploitation of biological resources, has emerged as a key strategy in many countries to meet human needs while taking into account resource efficiency requirements. after having proposed several dif283research and innovation in agriculture: beyond productivity? ferent definitions in recent years, the eu communication on the bioeconomy and the accompanying working document (european commission, 2012a; european commission, 2012b) defines the bioeconomy as encompassing “the production of renewable biological resources and their conversion into food, feed, bio-based products and bioenergy. it includes agriculture, forestry, fisheries, food and pulp and paper production, as well as parts of chemical, biotechnological and energy industries. its sectors have a strong innovation potential due to their use of a wide range of sciences (life sciences, agronomy, ecology, food science and social sciences), enabling and industrial technologies (biotechnology, nanotechnology, information and communication technologies (ict), and engineering), and local and tacit knowledge.” based on this delimitation, the eu bioeconomy accounts for an annual turnover of more than 2 billion euro (of which almost half from the food sector) and more than 20 million employees. besides its economic importance and potential, the bioeconomy strategy provides major challenges for policy and research. several of these challenges are already taken up in the work programmes of the seventh framework programme and the horizon 2020 research and innovation program of the eu. the definition of the term ‘bioeconomy’ is still a matter of discussion (see schmidt et al., 2012), in part due to the fact that different countries have been using different definitions. for example, the oecd and the usa definitions are more focused on biotechnology than that of the eu. different views are also linked to the use of the term bioeconomy in different branches of research, with different normative visions of technology. in particular, there is an emerging tension between an industrial/process vision of the bioeconomy and a territorial/landscape vision. in spite of this diversity, a common feature of the different visions of the bioeconomy is the central role of knowledge, research and innovation. from the point of view of tracing the impact of research, the bioeconomy goes in the direction of making the problem more “analytical” and more difficult. first, it tends to promote a stronger decomposition of material flows, and increasing the potential for re-combinations. in addition, it tends to promote cross-discipline and cross-sector “contaminations”, making it more difficult to identify the flows of effects. finally, working with biological materials tends to increase the degree of uncertainty about the characteristics of input and output, as well as of the outcome of research processes and their applications. a final and perhaps more important issue is that, given the knowledge-based nature of the bioeconomy, the impact of research critically depends on the stock of available knowledge and how it is mobilised to exploit newly produced knowledge. a symptom of the complexity and difficulty in providing clear-cut judgements is that the development of the bioeconomy is accompanied by a mix of opposite expectations about its effects and its role in ensuring sustainability. pfau et al. (2014) identify different visions: (1) the assumption that sustainability is an inherent characteristic of the bioeconomy; (2) the expectation of benefits under certain conditions; (3) tentative criticisms in light of potential pitfalls; and (4) the assumption that the bioeconomy impacts negatively on sustainability. this clearly hints at the potential opposite (ambiguous) effects of the bioeconomy on sustainability. a concept largely embedded in the bioeconomy discourse is that of bio-refinery. biorefinery is the sustainable processing of biomass into a spectrum of bio-based products and bioenergy. it is based on the idea of a stepwise treatment of biomass starting from the 284 d. viaggi highest added-value components down to energy (cascading approach) in such a way as to make the most out of limited biomass resource availability. bio-refinery in europe is promoted as one of the key economic organisational concept in the bioeconomy, in particular for non-food chains. the research may focus on different issues in this field. one key focus is the extraction of an increasing number of products from the same initial biomass; however, the qualifying feature of bio-refinery is the ordering of extraction, in such a way as to maximise the value of production prior to the destruction of the biomass. this also goes in the direction of a higher level of disaggregation of biomass in more simple and basic components that can later be used as the building blocks of a variety of products. the degree of pureness of the obtained materials seems to have an added value here. at the same time, in many cases, the actual value of such products is uncertain due to the fact that they are “far” from final products, new to the market (or new substitutes of existing ones) or due to quality issues (presence of residues etc.). moreover, technologies developed by new research may yield a displacement of effects from one step to the other or change the structure of downstream processes after changing a step in the process. finally, research can also lead to modified economic relationships, e.g. new products may have the potential to transform by-products into main products, justifying the process based on market forces, with implications in the allocation of effects. this effect is potentially more important as the number of potential extractions from the same raw material grows. as in all processes using biomass, which are often characterised by high transport costs per unit, spatial location is an essential feature of industrial organisation and the economic viability of processes. an emerging concept closely connected to the bioeconomy is that of the circular economy, which relates to the idea that the economy should rely less on external raw materials and more on the re-use of resources that are already in the system. chertow and ehrenfeld (2012) define the circular economy and discuss the process of the organisation of circular economies in industrial symbioses. at present, the circularity of the economy (measured by the degree of circularity intended as the share of materials that flow back into the antropic system) is rather limited. hass et al. (2015) provide an estimation of the degree of circularity of the global and the eu-27 economy for 2005. they show that there is a global flow of roughly 4 gigatonnes per year (gt/yr) of recycled waste materials; this is of moderate size compared to 62 gt/yr of processed materials and 41 gt/yr of outputs. biomass has an important role in this process as it accounts for 19 gt/yr with only a 3% (7% in the eu-27) degree of circularity. one of the reasons for this is that biomass is largely used for energy purposes (including food), which makes it non-recyclable. here, however, the definitions and the system considered are of significant importance; if biomass is produced sustainably (that is, without damaging soil or water resources and without depleting ecological carbon stocks), it can be considered renewable and the emitted co2 as well as waste flows can largely be recycled into new primary biomass within ecological cycles (jordan et al., 2007). closing the cycle for biomass-related industries hence involves closing the cycle of nutrients (nitrogen and phosphorous), reducing waste from production and consumption, changing dietary patterns towards less meat demanding diets. as a result, both agriculture 285research and innovation in agriculture: beyond productivity? and food are heavily affected by the process of getting to a more circular economy, or, looking at the other side of the coin, can directly contribute to it. the concept of the circular economy has implications not only in terms of the organisation of the economy, but also in relation to the role of technologies and, hence, research. indeed, a growing focus of research is now related to closing the cycles of key materials, e.g. nutrients. this implies considering the degree of closeness of an economy as a key indicator of performance of the system or a change thereof as an important impact indicator for research. at the same time, the focus on circularity questions traditional productivity measures if they do not qualify the origin of raw materials (recycled or newly extracted). in economic terms, this implies investigating the different economic values attached to external versus re-used resources. resources are re-used at a cost; external resources have a cost affected by extraction costs and scarcity rents. both costs may change depending on the quantity allocated to a given source or another. this hints at least at the idea that the degree of closeness of an economy may not tell the full story in economic terms, as actual values may be not proportional to the degree of closeness/openness. instead, the value of resources in classical productivity measures can actually be more informative about the degree of optimal closeness of an economy, except in the case of externalities, market distortions or information failures. when market prices are not considered to be good estimates, shadow price approaches may help to investigate the actual degree of scarcity of resources (e.g. halvorsen and smith, 1984). finally, an issue related to impacts in the circular economy and bioeconomy (i.e. using living organisms) is that scarce resources may in fact change over time, depending on the availability of resource stocks and technology. a pertinent example is the growing attention to the increasing scarcity of phosphorous in the field of agriculture. hence, eventual indicators of circularity may require the flexibility to change over time to account for critical issues. another issue that is typical of living organisms is their inherent variability, which should be taken into account in both evaluation and policy design (carillo e maietta, 2014). 2.2 entrepreneurship and eco-innovation the role of the different actors in research and innovation is another issue that is changing dramatically. in a simplified (older) vision of research and technology uptake, the market and policies played major roles. the market was seen as a promoter of private research and the main determinant of technology uptake, whereas the public sector, for its part, was a research provider and promoter of targeted technology uptake, e.g. through subsidies. the transfer of innovation from science to the industry, initially thought of as a sort of linear process, has been over time supported by specific extension policies aimed at smoothing and encouraging this one-way flow. this naive idea of technology transfer has been widely challenged over time, notably by investigating the number of actors, institutions and mechanisms that provide a bridge between research and the farming sector and considering feedback loops between the farming sector and research. the broad-based literature on agricultural knowledge and innovation systems (akis) indicates that this has become increasingly important in agriculture, especially in relation to the bioeconomy (esposti, 2012). relevant categories go beyond single extension units and farmers. networks are becoming a more and more 286 d. viaggi important category in this field. networks also support a dynamic view of the innovation process, in which different types of actors can get involved at different stages and innovators need to continuously reflect and re-position themselves with respect to their environment. this view of the innovation process also implies that facilitators and innovation monitoring and evaluation methods can play a major role. in addition, it implies a rethinking of policies to ensure that they are not excessively aimed at determining innovation direction, but rather support facilitation activities and the emergence of innovation initiatives (klerkx et al., 2010). accounting for higher and growing complexity also requires adequate approaches for research and evaluation. a pertinent example is that of the impact pathways evaluation approach (douthwaite et al., 2003). participatory approaches in different steps of the research to innovation processes have become particularly evident in the context of eu initiatives. the process started with the eu technology platforms at the outset of the seventh framework programme and has become paramount in the context of the new horizon 2020 programme, focusing on the contribution to competitiveness through research and innovation and promoting a multiactor approach in research projects. in this context, one of the most relevant phenomena in recent decades, which was also largely supported by the development of the bioeconomy, is the role of entrepreneurship in research and innovation. entrepreneurship can take different forms, from brokerage of innovation as a specific business activity, to the financing of innovation, up to a sort of “innovation entrepreneurship”. moreover, the process of developing ‘entrepreneurship’ activities and attitudes by researchers is increasingly being promoted. the role of entrepreneurship has attracted attention in the field of biotechnology for at least two decades with the emergence of the term “bio-entrepreneurship” (schoemaker and schoemaker, 1998). the concept includes a wide range of typologies, ranging from researchers developing enterprises to exploit their knowledge to entrepreneurs investing in life science research and development companies. this concept is now widespread and increasingly used in specific educational programmes involving university students and researchers (see e.g. uctu and jafta, 2015). this pathway also draws attention to the process of building research objectives and priorities. a specific point concerns the growing role of businesses in building innovation strategies and, through these, guiding the development of oriented applied research. as this approach becomes more important, the building of circular connections between research and business seems to be giving additional weight to business, which is providing a vision for the future, while research has more and more the (limited) aim of buttressing this vision with much needed knowledge. from the point of view of impact evaluation, a key issue in recent years is the incorporation of collective values (e.g. public goods concerns such as environmental and resource issues) into market strategies by the private sector. this can be viewed as the progressive merging of private and public type values. this is witnessed by a process of strategic choice on the part of industry, awareness and related behavioural changes by consumers and the appropriate functioning of markets and marketing, including the communication of values and the transmission of information about products and processes. in the incorporation of ‘green’ concerns into private business, a key concept is that of eco-efficiency. the concept attempts to reconcile economics and the environment. the 287research and innovation in agriculture: beyond productivity? world business council for sustainable development (wbcsd) in 1992 defined eco-efficiency as being “ …achieved by the delivery of competitively-priced goods and services that satisfy human needs and bring quality of life, while progressively reducing ecological impacts and resource intensity throughout the life-cycle to a level at least in line with the earth’s estimated carrying capacity” (schmidheiny, 1992, cited in govindan et al., 2014). the green economy has been put forward as an even more positive and environmentally focused way of seeing the economy. it is intended as an economy seeking to reduce environmental and ecological impacts and that fosters sustainable development without degrading the environment. it also incorporates the idea of fairness. an interesting area of attention linking research and the green economy is provided by eco-innovation, which also offers examples of the articulated interplay between firm strategies, their economic context and inter-firm relationships. the exploration of the factors of eco-innovation effort is considered to be at the heart of new research directions in the new millennium (rashid et al., 2014), driven in particular by four eco-innovation drivers: regulatory push, technology push, market pull, and firm strategies. in the context of this trend, the connection between vision, research objectives and the impacts of technological innovation is becoming of central importance. for example, björkdahl and linder (2014) explain how and why a shared environmental vision can accelerate environmental innovation. specifically, they emphasize that a shared environmental vision can lead to an increase in the number of application areas and in increased market sales based on existing green solutions. however, they also show that the efficacy of the shared vision is dependent on a good match between the environmental problems faced and the core competencies of the firm. cuerva et al., 2014 found that the factors driving eco-innovation are different from other types of innovations. based on a questionnaire carried out in the spanish food and beverage sector, they found that technological capabilities such as r&d and human capital foster conventional innovation, but not green innovation. on the contrary, the implementation of quality management systems (qms) and differentiation contribute only to the adoption of green innovations. one of the findings of this study is that greater implementation of voluntary certification schemes would be more effective in enhancing ecoinnovation than public subsidies. furthermore, attention to collaboration and the needs of consumers are positively associated with eco-innovation. triguero et al. (2013), studying eco-innovation in europe at the firm level, found that those entrepreneurs who give importance to collaboration with research institutes, agencies and universities, and to the increase in market demand for green products, are more active in all types of eco-innovation. greater attention to existing regulations shape eco-product and eco-organisational innovations while expected regulations and access to subsidies and fiscal incentives do not have any significant effect on decisions to eco-innovate. policy, management and communication instruments are playing a key role here as promoters of change. for example, quality management and certification schemes have been at the core of a wide field of research in recent decades and are increasingly the subject of analysis. new communication technologies may have a key role in boosting these connections, e.g. by improving the awareness of consumers and their ability to choose, or providing information, for example, about the quality and eco-friendliness of products. examples include apps that help search for non-gmo, organic, or in-sea288 d. viaggi son food1. the role of these technologies in shaping awareness, preferences and future demand-supply interaction is still largely unexplored. 2.3 emerging methods for measuring the impacts of research: towards a diffuse life cycle assessment approach? another strategy for understanding the implications of current trends in the analysis of the links between research and productivity is to look at instruments for measuring the impacts of (new) technologies and, indirectly, of research. in this perspective, a dominant role is currently played by life-cycle assessment (lca). lca is an assessment method focusing on impacts generated by each unit of product (or, more appropriately, functional unit) along its lifecycle, from “cradle to grave”. the basis of the method is a compilation of the inventory of inputs and outputs, notably with reference to key resources (e.g. energy, water) or pollutants (e.g. ghg, nitrogen). besides the inventory phase, lca is being expanded to include the evaluation of differences among technology alternatives, including the use of multi-criteria analysis and the linkage with economic performance, e.g. using lifecycle costing, along the life cycle of a given product. lca has been used for more than two decades as an environmental assessment tool and is increasingly used to support marketing messages. it is increasingly used as the basis for the selection of products in ‘green procurement’ and the inclusion of products in various national and regional eco-labelling schemes, i.e. european flower, german blue angel, nordic swan eco-label etc. it is now widely promoted for early evaluation of research and innovation processes. in particular, lca was already applied in the seventh framework programme of the eu (tilche and galatola, 2008) and is now regularly required in the calls of horizon 2020. in principle, lca responds to the basic idea of productivity, though expressed in a reverse way, i.e. aiming at minimising the unit of input and emission per unit of product. notably, however, it tends to maintain a multidimensional and rather broad (and diverse) view of such a ratio, depending on the environmental indicators measured for input and output. from the point of view of the linkage between research and productivity, lca addresses the need to better account for the broader impacts of technological research, hence considering a potentially wide range of effects on complex systems. in addition, it seeks to account for environmental effects along the value chain of a given product (broken down in different key phases), hence being able to explicitly account for displacement or compensatory effects at different stages of the chain. it also takes into consideration by-products and recycling. furthermore, lca makes it possible to support prescriptions regarding the steps in the process where intervention/research is more urgent due to higher criticalities in terms of impacts. for its characteristics, lca appears especially suitable to address the impacts of innovation and research applied to the emerging issues discussed above (bioeconomy, circular 1 http://grist.org/list/this-powerful-app-brings-organic-farming-into-the-candy-crush-age/ http://foodtank.com/news/2013/10/twenty-three-mobile-apps-changing-the-food-system 289research and innovation in agriculture: beyond productivity? economy and bio-refinery). in fact, several papers already report applications referred to these issues. for example, mattila et al (2012) discuss the methodological aspects of applying lca to industrial symbioses and, more generally, to circular economies. the literature is also developing on the specificities of the application of lca to bio-refinery systems (sandin et al., 2015). lca is also at the forefront of the measurement of eco-efficiency, which to date has been using different methods, ranging from simple indicators to modelling. lca and dea are arguably amongst the most used (govindan et al., 2014; lin et al., 2012). an example of the application in agriculture is available in picazo-tadeo et al. (2012). yet lca applications have also experienced difficulties and the above-referenced literature emphasise a number of open issues. while databases are now more and more available for standard applications of well-established technologies, coefficients needed for new technologies or for rapidly evolving product chains are often not readily available. conceptual issues related to the boundaries of the system remain open, especially for not yet well-structured or evolving production chains. moreover, lca yields comparable results only when the functional unit is perfectly comparable, which weakens the potential for comparing different products or processes yielding different results in terms of services. applications to the concepts of bioeconomy and biorefinery emphasise the issue of the attribution of impacts internally to the system considered; at the same time, as the process is purposely designed to yield multiple products, the issue of finding a common functional unit or to allocate common effects to different products is also highlighted. sandin et al. (2015) explore how the choice of the allocation methods influences results and in which decision contexts the choice is particularly important, by testing six allocation methods in a case study of a bio-refinery using pulpwood as feedstock. the results indicate that the choice of allocation method deserves careful attention, particularly in consequential studies and in studies focussed on co-products representing relatively small flows. an issue of specific interest for this paper is that lca tends to focus attention on environmental/resource use, whilst economic and social impacts remain more difficult to account for. notably, recent attention has been devoted to the use of life cycle costing (lcc), which is an economic assessment tool considering all projected significant and relevant cost flows over a period of analysis expressed in monetary value. it can be applied to a physical asset life or to the life cycle of a product/services in analogy to lca. notably, this is gaining attention for use in public procurement, another area of innovation in which the purchasing power of public institutions is used to provide incentives towards environmentally friendly technologies (iisd, 2009; dragos and neamtu, 2013). from an economic point of view, two key nodes remain open issues, in particular in evaluating the impact of research. first, the way in which research can impact the production process includes several variables (e.g. uptake, organisation of the production process, concentration etc.), which makes it necessary to estimate potential impacts based on a number of assumptions. the use of lca and location (including related limitations) must also be considered in the light of the different types of chains addressed: i.e. short, long, and global. concepts connecting the measurement of impacts and trade relationships are also emerging, such as virtual water and water footprint. second, the “engineeristic” impact in terms of changes in flows needs to be given an economic value. this also implies assumptions about, for example, the location of impacts. 290 d. viaggi taking the case of the use of water resources, abstraction can have unit costs of a different order of magnitude depending on the source used. it should also be acknowledged that the distribution of impacts across sectors can be non-neutral. an issue related to the economics of lca information is its role in decision-making. on this issue, sandin et al. (2014) note that, “particularly in inter-organisational r&d projects, the roles of lcas tend to be unclear and arbitrary, and as a consequence, lca work is not adequately designed for the needs of the project considered. there is a need for research on how to choose an appropriate role for lca in such projects and how to plan lca work accordingly” (sandin et al., 2014, p. 97). similarly, considering the connection with the work on the measurement of eco-efficiency, govindan et al. (2014), draw attention to the need to better connect these studies to supply chain management. 2.4 from performance to positioning: sustainability and ecosystem services the wider context in which the above issues have been developed has been characterised by the widespread use of the concept of sustainability as the increasingly important aim of agriculture and food systems and, related to that, of sustainability-oriented technology change. without entering into the debate with respect to the definition of sustainability, it is clear from the literature that it is to a large extent related to socially constructed notions. on the one hand, the literature points out and advocates the need for the political process to define sustainability (schepers, 2014). on the other hand, it is claimed that sustainability is a continuous social learning path and that such a transformation should be “profound (e.g. affecting moral standards and value systems), transversal (e.g. requiring the involvement of individuals as well as collective action) and counter-hegemonic (e.g. requiring the exposure and questioning of stubborn routines)” (wals e rodela, 2014). it is also important to emphasise that the problems encountered are not only related to providing adequate definitions, but also touch upon the empirical conceptualisation and practical measurement of sustainability, including its relationship with globalisation and development literature (olson et al., 2014). a good example of the practical problems faced in the measurement of sustainability is given by the approaches towards the assessment of environmental sustainability of agriculture, somehow the component of sustainability better studied, as compared to economic and social sustainability. this issue has been largely addressed in the literature through the use of indicators (bockstaller et al., 2008). as the number of indicators developed is both high and varied, the literature has also formulated a number of proposals for composite indicators and integrated sustainability assessments (rodrigues et al., 2010a; 2010b). moreover, a number of methodologies have been developed relying not only on more or less complex (sets of) indicators (singh et al., 2009), but also on modelling (ness et al., 2007). the literature notes the contradiction among the different requirements expressed towards these tools, which are expected to be at the same time specific yet broad, tailored but standardised (ness et al., 2007). there is also demand for composite indexes, whilst recognising that simple aggregate indexes can provide misleading information to decisionmakers (singh et al., 2009). in most cases, data availability remains the clearest criterion for decisions about the individual indicators and tools to be used. bockstaller et al. (2008) 291research and innovation in agriculture: beyond productivity? conclude that, as the data available at the regional level are usually limited, several simple indicators should be used, at least at this level. only when more detailed information is available can indicators based on operational models be useful. in experimental studies, when possible, it is suggested to use both measured indicators and model-based indicators. in recent years, concepts such as resilience and vulnerability have increasingly accompanied or replaced that of sustainability. the detailed consideration of each of these terms would require the examination of a wide body of literature, which is beyond the scope of this paper. notably, all of these issues have found noteworthy parallel use in ecology, environmental economics and development economics, highlighting the importance of dynamics and the relevance of “potential” effects/changes. another feature of these concepts is their attempt to be comprehensive, which is at times pursued at the cost of difficulties with accurate definitions and measurement. the most direct implication for productivity measures arising from this field of study is a push for a stronger consideration of sustainability in both output and resource concerns. it should, however, be emphasised that expectations regarding easy corrections of traditional productivity measures may be overambitious. one of the consequences of the increasing uncertainty in the different interpretations of ‘sustainability’ and the difficulty in the measurement of outcomes/impacts of processes, accompanied by the quest for more comprehensive concepts, is the move towards proxies able to measure the positioning towards the future, rather than the direct measurement of outcomes/impacts either exante or ex-post. as a consequence, also in relation to the measurement of productivity and of the effects of research, the consideration of proxies, or of a limited number of selected issues (most likely measuring pressures), could remain the more realistic option. a concept closely related to sustainability assessment is that of ecosystem services (es). according to teeb (2010), es are the direct and indirect contributions of ecosystems to human well-being. they are most often categorized into four types (mea, 2003; teeb, 2010): provisioning, regulating, habitat and cultural services. in contrast to other approaches, the es approach takes ecosystems directly into account and links them to the uses that human beings can make of the services they provide. the approach has gained a broad consensus and has increasingly been adopted in policymaking. the use of es presents several advantages, among which, notably for the scope of this paper, to include in the same framework services directly linked to “traditional” productivity measures (provisioning) and those that relate to other ecosystem roles in human life, thus making explicit the various trade-offs, synergies and relative weights. on the other hand, es make it possible to link issues related to the economics of sustainability with the ecosystem context and to cast sustainability issues in a territorial and landscape dimension (van zanten et al., 2014). of significant importance is also the fact that the concept seems to be particularly suitable for policy communication. on the other hand, a number of studies show that the understanding of es, and their inherent trade-offs, require an enlargement of the system considered and hence re-introduces, and even emphasises, the trade-offs between detail and comprehensiveness. furthermore, es does not solve data issues, which remain a key driver of the choice of specific indicators and their measurement strategies, nor do they address the problem related to the attribution of economic values to es services, at least for the part represented by public goods-type services and externalities (viaggi, 2015). 292 d. viaggi another issue linked to sustainability is related to the strategies currently being used to achieve improved product sustainability. far from traditional end-of-pipe approaches, current strategies directly address process and product design, value chain organisation or even producer habits. this is the focus of a wide body of literature on alternative technology options in agriculture, but it is now also of increasing importance for food production. in this regard, van der goot et al. (2016) list several technological strategies, including: avoiding dilution, minimizing drying, focusing ingredient production on functionality rather than purity, tailoring ingredient production to specific applications rather than for general use, developing smaller and more flexible fractionation processes which should also be located in the vicinity of the application, and using milder process conditions for less refined ingredients. in other words, sustainability concerns are increasingly embedded in the whole product design and chain/system functioning. similar approaches are advocated for most of the issues connected to sustainability, including the promotion of the circularity of the economy. these considerations have implications in particular for the measurement of impact in research aimed at improving sustainability, which in fact tends to require an analysis (and awareness) of entire processes and a comprehensive view of the related economic dimensions. 3. discussion, implications for research and the way forward each of the issues and perspectives illustrated above has potential specific implications for the relationship between research, innovation and productivity and for the measurement/evaluation of the impacts of research. the implications can be broadly organised into three interconnected topics: a) measurement of productivity; b) parameters able to explain changes in productivity, especially due to research; c) ways of making the connection between research efforts and productivity change. as far as the measurement of productivity is concerned (point a) the emergence of environmental and resource problems, and the new areas of concern illustrated above, have primarily brought to the attention (even more strongly than before) the need to expand the range of output indicators and the range of resources to be considered as input. besides this, the current trends seem to introduce novelties in the quality of the effects sought, which are increasingly represented by: a) soft effects, amenable to interpretations and flexibility in the social construction of related values (and hence prices), and; b) effects that are more valued for their potential than for their actual observable effects. altogether, while the classical measures of productivity remain key references at the aggregate level, the emerging attention to circular economies and resource efficiency brings into question, in particular, the application at intermediate and lower levels of aggregation (chains, firms, small territorial units). here, the widespread diffusion of lca signals (and partially responds to) the need to develop more functional measures of the effects of (and in) different steps in the chain, and brings directly to attention the technological and economic inter-linkages among different processes. lca is itself, however, challenged by the emphasis on circularity and by the social construction of values related to impacts. indeed, the discrepancy between more accurate technical measures of impact 293research and innovation in agriculture: beyond productivity? (or at least pressures) and the limited ability to valuate their effects in economic terms is still very high. it is likely that the new issues emerging from the development of the bioeconomy will further contribute to this discrepancy, making the effects of research even more difficult to measure objectively. these reflections also lead to question the extent to which economics can actually play a role in measuring productivity effects in light of current technological development. on the one hand, many issues addressed above, such as circularity in the economy, can already be incorporated into the measures currently being used, if prices work well, particularly with respect to scarcity. on the other hand, it is becoming more difficult to account monetarily for the preferences of final consumers, who are becoming less stable and more driven by perceptions, expectations and information distortions (especially for services that are less and less related to basic needs or for those that are new for consumers). it is also becoming difficult to identify the economic mechanisms that are determining the impact of research, starting with the use of new knowledge and the adoption of new technologies. in this context, the traditionally clear distinction between private and public goods and the category of externalities (and the related instruments for economic valuation), are also weakening due to the “marketization” of environmental and social values, the increasingly explicit socially (or policy) driven construction of values and preferences and the growing number of cases of goods that are somewhere between private and public. these trends expose the system to the instability of preferences over time, which may lead to difficulties in prediction, overlapping and double counting. they also require (and partially ensure) greater attention to information and communication, the embedding of technology change in participatory processes, and the awareness of the processes leading to the construction of values. similarly to output measurement, the interpretation and understanding of determinants of productivity (point b), and among them research, also require effort to account for an increasing number of parameters that contribute to the explanation of productivity changes. this includes accounting better for a larger number and diversified quality of input, more complex interplays among sectors and larger geographical interactions. a specific element requiring attention is the strengthening of the connection between research and innovation efforts and the stronger linkages with human capital and entrepreneurship attitudes. changes in the way research is performed must also be better analysed. research is becoming increasingly analytical, cross sector and taking on multiple methods of interaction (in interdisciplinary, multidisciplinary and transdisciplinary contexts). it is also necessary to take into account the multiple number of actors, which also imply different ways of understanding the directions of interaction among different pieces of knowledge. in this framework, information about research expenditure, even when available, becomes less and less satisfactory in accounting for research efforts, without considering the increasing difficulties in linking expenditures to the specific objectives of research (and hence expectations about the direction of its impacts). furthermore, specific attention should be paid to understanding how to account for the existing stock of knowledge. past research expenditure is less and less a good way for accounting for the increased stock of very diversified knowledge; this issue in itself deserves to be the subject of more focused research, considering improved proxies and a 294 d. viaggi better understanding of mechanisms taking into account spatial dynamics, including flows of knowledge and technology. the connection between research and its effects (point c), is possibly the most puzzling of the three topics listed above, and its evolution is still less studied compared with performance measurement and its potential determinants. in addition, recent literature shows that addressing this link is not only a matter of measuring items, but more widely of understanding and codifying cause-effect relationships among factors affecting the direct and feed-back loops between research, technology adoption and performance change. relevant issues to be accounted for include scope and scale of the evaluation, i.e. what is the right bundle of products/issues to be addressed and at what geographical level and process detail. bioeconomy research is a good example of the need to better understand how to address these issues: innovation tends to be increasingly analytical in improving processes as its effects are becoming “finer” and there is a tendency toward a greater disaggregation of biomass into smaller blocks. but some concerns do exist (among which sustainability, counteracting this trend). the joint pathway of the circular economy and biorefinery is leading towards the need for more analytical and more comprehensive approaches. yet their effects and, arguably, the perception of their effects has become more ambiguous and difficult to measure. a more specific issue relates to time lags for the effects of research. most of the literature emphasises that long time lags are necessary to allow research to impact on productivity (alston, 2010; wang, 2013). however, the evolution of the bioeconomy and the explicit focus on innovation in research programmes (see horizon, 2020) may be expected to push for reducing such lags, or, more likely, to diversify even more the lags among the various pieces of research. this variety of potential time lags is also visible from having a simultaneous quest for very applied research supporting short-term innovation and a very open “blue sky” (in the words of the eu commission) and fundamental research. in addition, given the status of continuous change and the different technologies that interact, the role of both indirect and unexpected effects, which may in fact occur at different points in time, are more prominent. another economic issue, stemming in part from the evolution in governance/legislation and the existence of more sophisticated technologies, is the growing complexity of property rights in research results. this makes the use of research results and the appropriation of their benefits more distributed, while the participation in the exploitation and the potential for alternative uses of knowledge also become wider whilst, at the same time, requiring more flexible ways of collaboration. given this complexity of chain effects, one of the strategies for future understanding of the impact of research would be to look at shorter chains of effects, i.e. between research and adoption/organisation, or between adoption and performance, rather than trying to address the long way between research and final changes in the system, with the related causality problems. altogether, one could argue that, while the productivity of research should remain and even become a more important focus of research, the real priority issue is not the measurement of impacts (though it is to some extent), but rather the mechanisms making the link between research and its impacts, which could also be considered as a good (or the best) proxy of impact itself, or at least as a measure of potential impact. 295research and innovation in agriculture: beyond productivity? several pathways for further research crosscutting the simplified three points illustrated above can be identified. these include, notably, the following: 1. there is a need to identify improved representations of goods, technologies and their change that can be used to better account for the current trends in technologies that break down biomass and recombine its compounds; one direction could be in representing input as bundles of attributes (elements) and the output as potential attributes rather than products. 2. there is also a need to better investigate new institutional mechanisms, especially the role of entrepreneurship in research, knowledge exploitation and the social construction of successful technologies. this connects with the notion of business models and the “shape” (or, better, “non-shape”) of enterprises (which, metaphorically, can be better represented by an amoeba rather than any other constant form concept) becoming more and more a bundle of loosely connected rights and values; this trend is emphasised by the growing complexity in technology ownership and the exploitation of innovation. 3. a better study of the interplay between consumers and producers, beyond simplified market mechanisms, is also needed. on this issue, new awareness building and marketing strategies (social networks) and thoroughly informed/aware communities (yet often limited in their intepretation ability) connecting demand and supply, boosted by new technologies (e.g. smartphones) call for the need for collaboration and linkages between studies on production and studies on consumption in order to directly deal with uncertainty about prices and market shares. this is particularly relevant for new technologies for which the market has yet to be developed, in which frequently changing or new policy measures are also often observed. 4. new communication technologies have a key role in several of the processes mentioned above; though they are playing a disruptive role in changing societies, their role as means and promoters of change in economic behaviour linked to innovation and exploitation of research is still insufficiently studied. 5. there is also a need for studies on tools and strategies to deal with uncertain futures. a specific issue related to emerging technologies and their interplay with awareness and market building is the difficulty in predicting futures; trends are less and less a relevant indication for the future, while a breakthrough of new solutions may be more relevant. on this point, economic valuations using potential trend-breaking scenarios may require greater attention with respect to on past-based expectations. 6. the investigation of new potential tools to measure productivity and link with research is also needed. the existing tools are for the most part well-established in the literature; sometimes they are seen to be novel when “discovered” by different disciplinary fields or when new variants become available, but in the last couple of decades there have mostly been incremental innovations rather than radical innovations in this field. contamination among existing tools could be a promising pathway and is already under way, e.g. studies proposing combinations of lca and dea analysis, or lca and multi-criteria analysis. 7. this goes hand by hand with the further development of studies to better understand research and technology impact pathways in this changing context; a distinguishing feature of future studies could be found in a more extended use of impact 296 d. viaggi pathways approaches, to better account for the larger set of determinants discussed in this paper. 8. an investigation into the new role of policy and related mechanisms is also needed. not only regulation and policy-defined prices contribute to reveal and signal preferences through the value chain; the building of new markets is becoming a stronger policy issue that has a broad range of implications. indirect policy mechanisms, such as those helping to reveal preferences related to public goods or facilitating coordination, are becoming more important, yet continue to be poorly understood. an increased emphasis on policy measures related to awareness, information, education and knowledge management may also result in the need for different tools to study policies, e.g. more qualitative and systemic tools. 9. information and data needs remain a key issue for researchers in the field of the evaluation of research impacts on productivity. gaps primarily involve productivity measures, in particular “non-conventional” components of productivity, such as environmental improvements considered in multi-output productivity specifications or input-saving approaches. data gaps are also very much relevant for research expenditure and potential intermediate explanatory variables (e.g. akis actors). however, it should be emphasised that this issue cannot be restricted to complaints regarding insufficient data availability. rather, data users and providers should collaborate in coconstruct data sources taking into account forward looking needs. in addition, greater attention should be given to emerging sources of data due to the digitalisation of huge amounts of information and related opportunities for data mining and linkages of databases (still largely unexploited). 4. conclusions this paper has investigated the implications for research, innovation and productivity studies of selected bioeconomy-related issues, namely: a) the concepts of bioeconomy, circular economy, resource efficiency and bio-refinery; b) the connection with entrepreneurship and eco-innovation; c) changing tools in research assessment, in particular the widespread use of lca; and d) the evolving concepts of sustainability and ecosystem services. we argue that the “traditional” idea of productivity intended as an output/input ratio maintains (and may strengthen) its role on the aggregate, though the current trends in research are more focused than they were in the past on creating “potential” rather than straightforward changes. furthermore, we find that, while the role of research in productivity change is likely increasing, it is becoming somewhat more difficult to link changes in productivity to specific research actions. as a result of the above, the understanding of the interconnections and pathways between research and productivity are becoming more relevant than final productivity measures themselves, though much more difficult to trace than in the past. policy and practitioners are requiring improved ways of performing ex-ante and ex-post analyses that are better connected with decision making and capable of managing interplays between aggregate and disaggregate levels. this is also in line with a growing emphasis on incorporating sustainability concerns into private (both firm and consumer) behaviour while dealing with globalised markets and global environmental and social concerns. 297research and innovation in agriculture: beyond productivity? this highlights a number of new challenges for research in economics, especially related to agriculture, food and the bioeconomy. a key issue concerns the need for methodological developments, which may find their basis in the improved knowledge of new research processes and new technologies, as well as in a better understanding of surrounding societal change. it also requires economists to take up the “procedural” and cultural challenges of an increased involvement in the agriculture and bioeconomy innovation system, while at the same time guaranteeing objective analyses and robust independent judgements, suitable for evidence-based support to decision-making. acknowledgments the author wishes to thank ornella maietta and laurens klerkx for their useful comments and suggestions on intermediate versions of this paper. an earlier version of this paper was presented at the 4th aieaa conference, ancona, 11-12 june 2015. the usual disclaimers apply. references alston, j., norton, g.w. and pardey, p.g. 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(2013). technology and the future bioeconomy. agricultural economics 44 (s1): 95-102. doi: 10.1111/agec.12054. bio-based and applied economics 3(3): 205-227, 2014 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-14628 a multi-region approach to assessing fiscal and farm level consequences of government support for farm risk management joseph cooper1,*, benoît delbecq2 1 economic research service, usda, washington dc, usa 2 brechbill farms, auburn, indiana, usa abstract. the 2014 u.s. farm act has new programs for providing producers with commodity support payments covering “shallow losses” in revenue. we develop an approach to examine the sensitivity of the farmer’s downside risk protection to marginal changes in the deductible in shallow loss program scenarios. the copula approach we use simultaneously considers price and yield correlation across all u.s. counties producing several major field crops. we find that average payments under the shallow loss program scenarios are elastic with respect to the program’s payment coverage rate. to empirically assess where shallow loss is likely to most benefit producers, we map at the county level the ratios of expected shallow loss payments to crop insurance premiums for corn, soybeans, cotton, and winter wheat. as tail dependencies among individual crop yield densities may vary spatially, we propose a method for grouping counties in a t-copula that allows for heterogeneity in tail dependencies. keywords. 2014 u.s. farm act, copula, nonparametric yield density, shallow revenue loss jel codes. q10, q18, c14 1. introduction the 2014 u.s. farm act (more precisely, the agricultural act of 2014) was signed into law in early february, 2014, after approximately three years of hearings. many policy recommendations for title i of the 2014 farm act – historically the main section dealing with commodity support – revolved around the debate over whether federal farm support programs should focus mainly on protecting farmers against deep losses in revenues (or yields) or also include protection against shallow losses (shields and schnepf, 2011). in its current form, u.s. federal crop insurance offers revenue coverage levels ranging from 50% to as high as 85%, i.e., deductibles from 50% to 15%. therefore, it covers deep losses in crop revenue but the deductibles leave producers exposed to potential for out-of-pocket * corresponding author: jcooper@ers.usda.gov. 206 j. cooper, b. delbecq loss (i.e., shallow loss). one possible legislative response is for federal crop insurance to be complemented by shallow loss coverage in the farm act legislation. in fact, several characteristics of the average crop revenue election program (acre) in title i of the 2008 farm act fitted the general definition of a shallow loss coverage program. in particular, acre could trigger a revenue payment with state level crop revenue falls as low as 10% relative to the benchmark revenue (in addition, a farm level trigger with no deductible needed to be met). however, the acre program cannot be truly considered a pure shallow loss program. in addition to the 10% state level deductible, the acre program limited payments to 25% of benchmark revenue, hereby fully covering state revenue losses when state revenue was between 67.5% and 90% of expected revenue, but with the payment at the ceiling when revenue was below 67.5% of expected revenue. hence, even with the cap on payments, a portion of the deep losses are covered. in addition to acre, the 2008 farm act’s supplemental revenue (sure) program was a whole farm standing disaster assistance program that covered some of the deductible in the federal crop insurance program in case of disasters. a true (albeit conditional) shallow loss program, sure, expired in september 2011. title i of the 2014 farm act allows the farmer to choose to enroll in agricultural risk coverage (arc), a shallow loss program. in the analysis in this paper, we examine the earlier but similar arc that was included in legislation that u.s. senate passed in 2012. that arc, with an 11% deductible (i.e., the coverage rate is 89%), and payments capped at 10% of benchmark revenue, is purely a shallow loss program, with a per acre payment rate fully covering revenue losses when revenue is between 79% and 89% of expected revenue. presumably, the seemingly arbitrary 89% coverage rate was not chosen based on any general principles for farm risk management, but as a result of a budgetary scoring exercise. this choice of coverage rate begs the question of what the impacts of different coverage rates would be on program payments.1 in fact, the arc that was actually passed by congress has a coverage rate of 86 percent of expected (or benchmark) revenue, demonstrating the tweaking of program parameters during the development of program proposals. the main goal of this paper is to develop an approach to examine the sensitivity of average payments as well as the farmer’s downside risk protection to marginal changes in the deductible in shallow loss program scenarios, based on one-percent increments over the 15% to 5% deductible range (i.e., revenue coverage rates in the range of 85 to 95%). given that the program that we examine has a payment ceiling of 10% of benchmark revenue, our analysis covers a program that would provide support for actual revenues in the range of (75 to 85)% to (85 to 95)% of benchmark revenue, i.e., losses between (5 to 15)% and (15 to 25)% of benchmark revenue are examined. to analyze how the payment distribution changes with the coverage rate requires an estimation approach that can differentiate over small increments in the coverage rate, such as the kernel-based approaches in goodwin and ker (1998) and cooper (2010). however, these approaches have never been simultaneously applied to more than one region. since the federal government needs to concern itself with national level impacts as well 1 in addition to arc, the 2014 farm act has shallow loss support in the form of the supplemental coverage option (sco) for program crops besides cotton and the stacked income protection (stax) program specifically for upland cotton. a farmer’s participation in individual-arc precludes participation in sco. for the sake of brevity in our discussion of shallow loss support, we focus on arc. 207an approach to assessing consequences of government support for farm risk management as regional implications, minimizing aggregation bias requires using county level data (the lowest aggregation level available nationally). and as county yields are spatially correlated, to produce unbiased national level figures we require a simulation approach that can simultaneously account for correlation across all counties yet allow for examination of marginal changes in the coverage rate with the kernel-based densities (or parametric densities, for that matter). we conduct our analysis for all counties that grow corn, soybeans, winter wheat, and upland cotton (the latter not an arc crop but included here nonetheless for academic interest) for which the national agricultural statistics service (nass) of the u.s. department of agriculture (usda) reports data. to date, the only published approaches to estimating farm act support that address the spatial correlation across multiple regions use block bootstrap approaches (e.g., cooper, 2010; dismukes et al., 2011). these approaches work by simply drawing with replacement vectors of a year’s worth of historical data. since each random draw is a cross-section of all regions included in the analysis, historical correlations between the regions are maintained. however, by imposing no other assumptions on the data, the empirical distribution for each region is only defined in 1/t probability increments, where t is the total number of data points for each region in the analysis. since us county level data is relatively sparse before 1975, we had approximately 35 years of county level data for a broad coverage of the us. this is equivalent to estimating a density in 2.87% increments, which is not sufficiently defined for addressing incremental changes in the shallow loss coverage rate. in response, this paper uses copula approaches with nonparametric price and yield distributions that can simultaneously estimate revenue distributions across all counties reporting yields for each of the four crops using empirical distributions that are defined over arbitrarily small probability increments. in considering spatial relationships in yields, imposing only the historic correlations on the simulated marginal densities may be overly restrictive by not considering possibilities for tail dependencies in yields (i.e., extreme yield events may occur simultaneously across groups of counties). as tail dependencies among individual crop yield densities may vary spatially, we propose a method for grouping counties in a t-copula that allows for heterogeneity in tail dependencies. using this approach, we compare payments and their impacts on farm revenue for county and farm level implementations of arc. next, we compare arc support payments and their revenue impacts to those under the existing acre program. finally, we generate maps to assess how the relative size of arc payments to federal crop insurance varies regionally. 2. background 2.1 general background before moving to the discussion of the mechanics of specific shallow loss programs, we provide a more general background on government policy for farm risk management in the us, and contrast it with that in the eu, focusing on policies covering field crops. in this paper, we use the term “farm risk management” as a convenience. that is, the term is not intended to imply the purpose of the government support we examine, e.g., whether it is to augment mean and/or higher moments of income. historically, the risk manage208 j. cooper, b. delbecq ment agency (rma) of the usda oversees multiple peril crop insurance programs that address within season yield and/or revenue risk, where the base price is calculated from futures price falling in the crop year the policies cover. the farm services administration (fsa) of the usda traditionally oversees policies in title i of the farm act, with price targets that are fixed for the life of a farm act (typically 4-5 years), and starting with the 2008 farm act, policies with revenue garantees based on interseasonal calculations. these general policy differences between federal crop insurance programs and title i commodity support programs suggest that, at least to some extent, these two sets of programs have different policy goals. while farm risk management policy in the eu is very differentiated between countries, all significant farm risk management policy in the us is managed by the federal government. the latter means that program rules do not differ across the us, but which is not to suggest that all qualifying crops are insurable under the federal crop insurance in all regions and up to the same coverage levels (rma, 2014a). while eu support has largely moved to lump sum supports, such as single area payments, with the 2014 farm act, such support (i.e., direct payments) is eliminated in the us, save for some transitionary cotton support while new rules are implemented. see the chapters on titles i and xi in ers (2014) for an overview of commodity support in the 2014 farm act, cafiero et al (2007) for a detailed discussion of risk management policy in the eu, and capitano (2010) for a detailed comparison of eu-us differences. in the not too distant past, title i of the farm act addressed price risk (e.g., the marketing loan program and the counter-cyclical payment program), and the multiple peril federal crop insurance programs addressed yield risk, although fsa does administer ad hoc disaster assistance that historically has been yield-based (an exception was the marketing loss assistance of the late 1990s). today however, rma’s revenue-based crop insurance policies cover more acres than do yield-based insurance policies. with the 2008 farm act, fsa began administering revenue-based programs – acre and sure, and while these programs have expired, the 2014 farm act has new revenue support programs administered by fsa – the arc program – and supplemental coverage option (sco) and stacked income protection plan (stax), which are administered by rma (effland, cooper, and o’donoghue, 2014). stax, which only applies to cotton, is essentially a substitute for price-based and lump sum support that was adminstered by fsa under title i of the 2008 farm act, suggesting some bluring of the traditional risk management niches of rma and fsa. with lump sum support eliminated in the u.s., all support is now counter-cyclical to low yields, prices, or revenues (while noting that arc and price loss coverage [plc] in title i of the 2014 farm act are paid to base [historic] acres), which is a marked distinction to the eu, with its continued use of lump sum support. another distinction between the two regions is the higher emphasis on crop insurance in the us. federal crop insurance is administered by the government, including rate setting, but is delivered to farmers by private companies. federal crop insurance is supported by the government via premium subsidies, support for administrative and operating expenses, and sharing of underwriting risks. in recent years, it has become a larger budgetary item than title i support. in fact, crop insurance in the u.s. has become widespread enough, with 119 million insured hectares in 2014 (rma, 2014b), that even in the aftermath of the 2012 drought that resulted in massive yield shortfalls in major field crops in much of the country, ad 209an approach to assessing consequences of government support for farm risk management hoc disaster payments were not made for these crops. at least in some eu countries, crop insurance covers only “exceptional events” (capitano et al, 2010), while in the us, traditional federal crop insurance can cover up to 85% of expected yields or revenue depending on the crop and region, and the new shallow loss programs providing protection for losses between the chosen coverage rate under the traditional insurance program and a higher “shallow” rate, albeit at different levels of yield aggregation. at what coverage rate level systemic risk in yields taper off and idiosycratic risk begins to predominate is difficult to say, but it seems safe to say that federal support in the us can cover multiple sources of idiosycratic (e.g. hail) and systemic (e.g., drought) risk. 2.2 agricultural risk coverage the arc program is complex and we will only describe its principle properties here. under the senate’s arc program, a qualifying producer would make a one-time choice for the life of the next farm act to receive the revenue support based on farm or county level benefit calculations.2 the arc revenue payment (denoted as arcijt) to producer i of crop j in period t as defined in the 2012 proposed legislation is: arcijt = max{ 0, min[(0.10 ∙ brijt ), (arcgrijt − acrijt )]} ∙ pe ∙ aijt (1) where: brijt is the benchmark revenue, calculated as the 5-year olympic moving average (an average that removes highest and lowest values) yield per eligible acre of crop j for farm or county i times the 5-year olympic moving average of the national marketing year average price. if average yield for the individual is less than 60% (70% in 2013 crop years or later), then 60 (70)% of the applicable “transitional yield” is used in its place;3 arcgrijt is the agricultural risk coverage guarantee revenue, calculated as 89% of the benchmark revenue (br). acrijt is the actual crop revenue, calculated as county or farm yield for crop year t times the higher of the u.s. average midseason cash price for marketing year t or the crop’s marketing assistance loan rate; pe is the percentage of eligible acres planted and is 65% for the farm level payment and 80% for the county level payment. the prevented planting rate is 45% in either case;4 aijt is the total of eligible acres for farm or county i. in the case of arc, eligible acres are all acres planted to crop j. to the extent possible, the proposed legislation calls for making separate arcgr calculations for irrigated and nonirrigated acres. note that unlike the 2008 farm act, the 2 this paper examines the senate version of arc. the version actually passed into law with the 2014 farm act differs in some parameters and provisions, but not in the manner that is germane to the focus of this paper. 3 the “transitional yield’ is defined as per the risk management agency (usda) and generally mirrors average county yield. 4 a functionally equivalent statement to equation (1) is arcijt = min{ (0.10 ∙ brjt ), max[0, (arcgrjt − acrjt )]} ∙ eeij ∙ aijt. 210 j. cooper, b. delbecq proposed 2012 senate bill does not give the farmer the choice between enrollment in the revenue-support program or the “traditional” price-based supports, and the latter are eliminated. arc payments are subject to total limits per recipient and spouse, as well as limits based on adjusted gross income (as defined in federal tax code). the arc program does not cover cotton, which has its own support option under title xi of the proposed bill. nonetheless, the benefit of the research process is that we can still model cotton support under arc. title xi of the 2014 farm act also has two shallow loss options, which are designed to complement federal crop insurance, and while we cannot address these here for the sake of brevity, our analysis approaches can be extended to those as well as to other federal crop insurance policies. there are a number of differences between the arc and acre programs that are too numerous to cover here. besides differences in the coverage rate and the maximum payment rate, these include the omission of the farm to aggregate yield ratio and the double trigger. unlike under acre, the farmer enrolling in arc does not receive a percentage of direct payments, which are eliminated in the proposed legislation as well in the 2014 farm act. like acre, arc payments in the 2012 senate bill are made to planted acres, but total acres receiving payments are limited to acres planted on the farm over the 2009 to 2012 crop years (plus some allowed acreage adjustments) under arc instead of base acres on the farm under acre. unlike the acre revenue guarantee, the arc revenue guarantee has no floor or ceiling on how much it is allowed to move from year to year, but on the other hand, calculation of average prices uses a longer time frame under arc than under acre. a more detailed description of the acre program is available in cooper (2010). 3. methodology for estimating the density function for arc payments for the simulation of arc payments, we need to generate the distributions of market year price and county or farm yields. however, the procedure for doing so is considerably complicated by the fact that price and yields are temporally correlated with each other, and yields across regions are spatially correlated. hence the estimated distributions must take these correlations into account or measures of the variability of payments and their impacts on revenue variability will be incorrect. we estimate the density function for payments based on: 1) estimates of price and yield densities for a particular base year; and 2) an empirical method for imposing the historical correlations on this simulated data. the appendix provides a schematic of the general steps in the methodological approach. 3.1. modeling the price-yield relationship using price and yield deviates our focus is on estimating the distribution of payments for a given reference crop year t, given that at pre-planting time in t, season average prices and realized yields are stochastic. as such, sector level modeling that separately identifies supply, demand, and storage is unnecessarily complex and would divert the focus of this article. a convenient way to address our questions is to model prices and yields as percentage deviations of realized prices and yields at the end of the season from the expected values at the beginning of the season when planting decisions are made. 211an approach to assessing consequences of government support for farm risk management the benefit of our reduced form approach is that it is computationally tractable and transparent. in principle, program payments may affect the farmer’s production decisions. one potential limitation of our reduced form approach for generating the price-yield distribution is that it assumes that the price distribution does not shift in response to possible non-random switches in planted crop acreages (both within and across crops) due to availability of a new program. future research can focus on adapting the model to allow for potential price shifts due to the support. one could develop a model with supply functions for the principal crops, where supply is a function of the first and second moments of revenue per acre, and with downward-sloping farm-level demand curves. a recursive application of this model could find the market clearing prices at planting time associated with potential supply shifts induced by the support payments. a structural model with carryover stocks could permit payment analysis across years. while the academic literature is rich with papers on price estimation for commodities (for an overview see goodwin and ker, 2002), few express prices in deviation form. one example that does is lapp and smith (1992), albeit as the difference in price between crop years rather than between pre-planting time and harvest within the same crop year. as price deviation was measured between years, yield change was not included in their analysis. paulson and babcock (2008) provide a rare example of analysis of within-season price-yield relationship in an examination of crop insurance. like them and cooper (2010), we re-express the historical price and yield data as proportional changes between expected and realized price and expected and realized yield within each period, respectively. for the model, the realized county, state, and national average yields, yit , are detrended to 2011 terms to reflect the proportional change in the state of technology between that in time t and that in 20115. we detrend yield based on the standard practice of using a linear trend regression of yit = f(t).6 the expected value of yit, or e(yit), is calculated from the fitted trend equation. based on historical yield shocks, δyit = y e y e y it it it ( )( ) ( ) − we generate the detrended yield distribution, yit d as y e y y 1it d i it2011 ( )( )= + ≠ 2011, (2) where index i corresponds to all geographical units for which nass has provided data over the study period for the corresponding crop. price is transformed into deviation form, δpt as the difference between the expected and realized (harvest time) price, or δpt = p e p e p t t t ( )( ) ( ) − using the short time series of available yield and price data to calculate yi d and δp results in discontinuous distributions that are inadequate to investigate subtle incremen5 the crop subscript is omitted. 6 we examined the results across multiple counties using various forms of trend regressions (e.g. loess and fully flexible fourier), and found the simulation results to not be highly sensitive to the model specification. 212 j. cooper, b. delbecq tal changes in the shallow loss coverage rate. therefore, we start by simulating continuous distributions of yi d and δp. 3.2. generating the distribution of yields and prices like deng, barnett, and vedenov (2007) and goodwin and ker (1998), we utilize the nonparametric kernel-based probability density function (härdle, 1990; silverman, 1986) for generating a smoother yield density than that which would be supplied by a block bootstrap. while a parametric density function such as the beta could be used as an alternative, the nonparametric density function allows more flexibility in modeling the density functions. the downside is the lower level of fit of nonparametric densities relative to the parametric densities, but then, given our relatively low sample size of years, we do not attempt to test best parametric versus nonparametrics fits. the kernel function, as applied to our notation and omitting the geographical subscript i, is f y th k y y h ˆ 1 l d t t l d t d 1 ∑( )= − = l = 1,…,l. (3) where yl d are the yield points for which the density function is estimated. it allows us to generate values of y d distributions that approach a continuous function as l approaches infinity. equation (2) gives support to generating yield values over the observed range of detrended yields, i.e., the (l x 1) vector y d is drawn over the y ymin ,maxd d( )( ) ( ) interval. the function k(.) is a gaussian kernel (ibid.).7 the optimal bandwidth h for smoothing the density is calculated according to equation 3.31 in silverman (1986), which is a common choice for single mode densities such as those being evaluated here.8 we simulate a yield distribution for each {crop, geographical unit} combination by taking n = 10,000 draws of yield values, denoted as y d* from each estimated kernel density. the draws are generated using a table-based inverse cdf approach combined with interpolation (e.g., derflinger et al., 2009). that is, we first construct tables of the yield values and their associated probabilities from the estimated kernel densities. then, for each randomly chosen probability, the closest pair of probability values spanning the random probability draw are looked up in the table along with their associated pair of yield values. more precise approximations of the continuous distributions are constructed by linear interpolation between these two {probability, yield} points from the tables. the simulated price deviations are generated using the same kernel approach, again with 10,000 draws from the inverse cdf. yields and prices generated from a kernel-based density function can be expected to have a lower standard error than the actual data given the smoothing of the density (but greater than with a parametric functional form). we bring the standard error of the kernel-generated yields back to the level of the actual data by assuming that any difference between the kernel yield and the actual yield is normally generated noise with mean zero, 7 we found the estimated density of program payments to be insensitive to the choice between gaussian and biweight kernels. 8 the bandwidth h = 0.9/n0.2×min[s(!y),z(!y)/1.34], where !y is the (nx1) variable for which the density is to be estimated, s(!y) is the standard deviation, and z(!y) = yi – yj, is the inter-quartile range, where yi and yj are the 75th and 25th percentile values of the values of !y sorted in ascending order. 213an approach to assessing consequences of government support for farm risk management and add this noise to each y d* . this approach and its application to generating farm level yields y fd* is discussed in more detail below. 3.3. imposing the historical correlations on the simulated densities of course, as drawn, the simulated national, state, and county detrended yields, and the simulated price deviation for each crop, being i.i.d., do not have the same pearson correlation matrix as the historical (actual) data, even if these have the correct means and variances.  the historical correlations between these m+1 data vectors need to be imposed on their simulated counterparts, but without changing their respective means and variances, where m is the number of yield vectors in the model.  to do so we rely on a copula-based approach (nelsen, 2006). a block bootstrap would automatically maintain the historic relationship between the marginals. the downside of the copula relative to the block bootstrap is that by imposing the copula we may not well capture the true relationships between the marginal densities. on the other hand, as noted earlier the block bootstrap produces insufficiently smooth densities given low degrees of freedom for yield data in many counties particularly to analyze marginal changes in support payments (plus, there is the question of whether pre-1970s yield data is relevant to contemporary yield analysis). given r the n-by-(m+1) matrix of simulated data vector, the basic outline of the copula process is to: (1) generate a n-by-(m+1) matrix of uu ni u0 1ni { }= ≤ ≤ that follow a desired multivariate distribution (in our case the meta-t distribution), which is defined over a target correlation matrix, c, and a vector of potentially different degrees of freedom parameters ν and (2) use an inverse probability density function (pdf) approach to find ur r* 1 ( )= − where u 1− is a loose mathematical notation. r* will have (approximately) the same target inter-variable dependence relationship parameters as the historical data. the matrix u can be thought of as the structure of the association between the marginal distribution functions. formalized first by sklar (1959, 1973), copulas are multidimensional functions that couple multivariate distribution functions with their univariate marginal distribution functions. therefore, a copula can be used to convert a set of uncorrelated variables (e.g., our simulated yield and price distributions) to a multivariate distribution with a dependence structure defined by the target inter-variable relationship parameters of the chosen functional form. the t-copula is of particular interest when modeling crop yield distributions because it has the ability to capture lower and upper tail dependence. this is a desirable feature if extremely low (crop failure) or high yields are likely to occur contemporaneously at neighboring locations. a downside of the t-copula is that it has symmetric tail dependencies.9 the t-copula is the unique copula of a random vector x that has a multivariate t distribution with ν degrees of freedom and univariate t-distributed marginal distributions, each with the same degrees of freedom parameter, ν (e.g., demarta and mcneil, 2005). there is an inverse relationship between ν and extreme value (or tail) dependence (embrechts et al., 2002). because all marginal distributions share a common degrees of 9 archimedean copulas (e.g. clayton and gumbel) have asymmetric tail dependencies but do not appear practical for application to the large number of marginal distributions that we consider. 214 j. cooper, b. delbecq freedom parameter, the tail dependence imparted by the t-copula is highly symmetrical. given the geographical extent of our analysis, this constitutes a non-negligible limitation to generating multivariate yield densities. we address this issue by relying on the so-called grouped t-copula described in daul et al. (2003) and demarta and mcneil (2005). the grouped t-copula is similar to the t-copula except that subgroups of the univariate t-distributed marginal distributions of random vector x are now allowed to have different degrees of freedom parameters, resulting in varying levels of asymptotic tail dependence. in this paper, we estimate different degrees of freedom parameters for groups of counties classified by farm resource region. these regions were constructed by the economic research service to represent “geographic specialization in production of u.s. farm commodities” based on physical (climate, soils, topography, water), and socioeconomic farm characteristics (heimlich, 2000). we form two other groups, one with all state-level yield marginal distributions and the other one with the national yield and price marginal distributions. in total, we estimate 11 degrees of freedom parameters for corn and winter wheat, 10 for soybeans, and 8 for cotton. for each crop j included in the analysis, we impose historical correlations on the m i.i.d. simulated detrended yield distributions and the price deviate distribution following steps outlined in demarta and mcneil (2005). first we estimate nonparametrically the (unknown) marginal distributions of the actual data y y pd , , ,d m d 1{ }= by so-called pseudo-likelihood (genest et al., 1995). this method consists in extracting the “probabilities” associated with each value of the actual data to derive the empirical marginal distribution functions, f̂di as follows: f d t ˆ ( ) 1 1 1d d d t t 1 i i t, ∑= + { }≤ = (4) where 1 is the indicator function, which takes value one when the condition between brackets is met and zero otherwise. note that if the matrix of actual data, d , is not full rank, i.e., d < number of columns of d then it is bootstrapped to ensure full rank. this entails that, at this stage, t may be greater than the number of years of the historical data (t=10,000 in the present analysis). using equation (4), we can form the matrix of marginal distributions of the historical data, vv̂ { }t t t(1, )= ′ ∈ , with  v v v f y f y f p, , ˆ , , ˆ , ˆ t t t m d t d d t m d ,1 , 1 ,1 ,m1( )( ) ( )( ) ( )= =+ . the unknown parameters that uniquely define the grouped t-copula are the correlation matrix and, in our particular case, the degrees of freedom associated with each of the m+1 marginal distribution functions. in the context of the t-copula, lindskog et al. (2003) suggests a method-of-moments estimator for the correlation matrix. following this procedure, we first construct the empirical kendall’s tau rank correlation matrix of the actual data, ĉτ which is an unbiased and consistent estimator of the true kendall’s tau matrix. second, we transform this matrix into the corresponding pearson correlation matrix, ĉ using the result of lindskog et al. (2003) who prove that there is a direct correspondence between kendall’s tau and pearson correlation’s coefficient for the family of elliptical distributions, to which the multivariate-t belongs: c cˆ sin 2 ˆπ = τ . (5) 215an approach to assessing consequences of government support for farm risk management daul et al. (2003) show that the equality cannot be maintained in the case of meta-t distributions in which degrees of freedom parameters differ across groups of univariate marginal distributions, but the approximation error is small. we use the spectral decomposition-based approach proposed by higham (2002) to force the obtained correlation matrix to be positive semi-definite and contain all ones on the diagonal in the event that it does not meet these requirements. next, we estimate a separate degrees of freedom parameter for each of the m+1 univariate marginal distributions in v̂ by maximizing the likelihood function of the t copula (e.g., bouyé et al., 2002). with the estimated degrees of freedom parameters and correlation matrix in hand, we can now build matrix u . the multivariate t distribution is a mixture of a multivariate random normal distribution (mvn) and the root of a univariate inverse gamma distribution (ig). generalizing this mixture construction, we simulate the grouped t-copula following demarta and mcneil (2005). we start by generating n=10,000 draws of a (m+1)dimensional mvn distribution, z with mean vector from the historical yield and price data and correlation matrix ĉ we then construct m+1 perfectly dependent10 wi variates by applying the inverse cumulative distribution function (cdf) of each univariate ig(νi/2,νi/2) to the same n=10,000 random draws of a uniform distribution u(0,1). the next step is to calculate the so-called multivariate meta t random vector (embrechts et al., 2002), x as: w zx i i i m1 ( 1){ }= ≤ ≤ + (6) where each vector of xi is a univariate t distribution with νi degrees of freedom, the correlation amongst which is ĉ finally, the matrix of marginal distributions of the grouped t-copula, u is formed by applying the cdf of the univariate t distribution with νi degrees of freedom to the corresponding vector xi. we then generate discrete correlated simulated county, state and national yield, and national price distributions, r* by using the same table-based inverse cdf functions for the kernel marginal densities discussed earlier, in which the “probabilities” from the grouped t copula are used to find the corresponding price and yields from the nonparametric distributions in r (that is, the p-values from the grouped t copula are matched with same p-values in the linearly interpolated tables for the kernel density, and the associated price and yield values for the latter p-values are looked up in these tables). spearman rank correlations are maintained throughout the successive steps. the copula approach above imposed the historical correlations on the simulated densities for 1,171 corn counties, 1,017 soybean counties, 734 winter wheat counties, and 117 upland cotton counties. 3.4. generating the farm level yield distribution in general, farm level yields with adequate time series and relevance to specific regions are not available from the usda. one approach to developing farm level yield is to infer it from federal crop insurance premiums in conjunction with information from 10 perfect dependence in this case means that kendall’s tau is equal to one (demarta and mcneil, 2005). 216 j. cooper, b. delbecq nass on county yields, using the assumption that the premiums are actuarially correct, that the nass county yields have the same distribution as the county yields for the crop insurance participants, and that the difference between county and farm level yield is distributed normally with mean zero (coble and dismukes, 2008). these first two assumptions are strong and are hard to test in general, but cooper et al. (2012) suggest some empirical evidence for the third. we select the inflation factor, kiα such that the actual production history (aph) indemnity calculated from our yield distribution is equal to the aph premium: n p e y ymin max ( ) , 0i k i aph i k ni k n 1 ,2008 2 ki ∑ω θ{ }( )− − α − ’ where y y h y yˆ ( ˆ ) ( ˆ )ni k in k in ki i k i k2 2 0.5 α σ σ( )( ) ( )= + ⋅ − , y e y yˆ ( )(1 ˆ )in k i in k ,2010= + , hin a n(0,1) random variable, y( ˆ )i kσ the standard deviation for ŷi k , i kω is the rma base premium rate for the crop and county, pi aph the aph price, and the coverage θ is .65.11 for each county-crop combination, we generate the simulated farm-level yields by adding a normally distributed random shock with mean zero and standard deviation kiα our simulated county-level yield data to generate simulated farm-level yields. another approach to generating the county to farm noise could be to use to use a “rule of thumb” potentially based on analysis of actual farm level yield data. the risk management agency maintains farm level data on farmers enrolled in the programs, but the length of the time series on this data has been relatively low, but is growing and may be sufficient within a few years to make this approach a feasible alternative to inferring the standard error of yields from crop insurance premiums. see cooper et al. (2012) for additional discussion of this topic. 4. data data on county, state, and national planted yields for corn, wheat, and soybeans are supplied by the national agricultural statistics service (nass) of the u.s. department of agriculture. we assume that each farmer’s benchmark yield for the purpose of arc calculations is simply the county average yield. for each crop, we follow ther risk management agency’s (rma) definitions of the expected and realized prices as used in their revenue-based insurance policies. for example, for realized price pt for corn, we use the average of the daily november prices (october prices starting in 2011) of the december chicago board of trade (cbot) corn future in period t. for the expected value of price pt, or e(pt), we utilize a non-naive expectation, namely the average of the daily february prices of the december cbot corn future in period t, t = 1975,…,2011. 11 according to a personal communication in 2012 with the chief actuary of the usda’s risk management agency, the base premium rate is the appropriate crop insurance rate from which to infer the farm level standard deviation of yield. here we assume that the producer does not choose enterprise units, but if one wanted to account for these, i kω be scaled by a ratio of the premium under some average choice of enterprise units versus that under the basic option. this ratio would be less than one, thus lowering kiα . 217an approach to assessing consequences of government support for farm risk management by definition, the senate’s arc’s actual crop revenue (acr) is calculated using the midseason average national cash price. based on an examination of monthly cash prices and sales volumes over the last 37 years for corn, soybeans, and winter wheat, we find that the mid-season price is on average 97% to 98% of the season average price. therefore, we assume a 0.02 basis value between the two. we convert p2011 to the cash price using the basis defined as the median difference between pt and the nass season average price in t over the ten years prior to 2011. 5. discussion of the payment simulation results tables 1 and 2 present the simulation results for arc payments per acre, gross revenue per acre, and total gross revenue with arc payments for the 2011 crop year for corn, soybeans, winter wheat, and upland cotton, assuming that producers have chosen the county-level option or the farm-level option, respectively (noting that upland cotton is not included as an arc eligible crop in the proposed legislation). the results are weighted by planted acres for all counties for which nass reported county level data over 1975 to 2010. to conserve space, the lower bound of the 95% confidence intervals for payments per acres (section a in the tables) is not shown, but the values are close to zero. similarly, the upper bound of the confidence intervals are not shown for gross revenue plus the payments (section c), because these are the same as in the gross revenue only case (section b). to preserve the impacts of spatial correlation in the reported national-level statistics, the data in the (number of counties) ×10,000 matrix of simulation results is summed vertically through each of the 10,000 columns to derive the 1×10,000 draws of the national level impacts. as can be seen in the first output column of tables 1 and 2, average payments per acre tend to be relatively low, and not exceeding $5.08 per acre in any scenario given 2011 price and yield assumptions. the farm-based average payments are larger than the countylevel ones despite the farm-level program paying to a lower share of planted acres than its county-level counterpart. the difference between the farm and county payments can be attributed, at least in part, to differences in risk between farmand county-level yields (and we expect payments to be increasing in yield variability). hence, without an empirical analysis like this paper, one would not be able to say which program would provide greater benefits. section d of tables 1 and 2 presents two measures of downside risk reduction. we show that arc would produce relatively low decreases in the coefficient of variation (cv) of revenue (last column), with the maximum change being a 4.18-percent decrease in the case of wheat with the farm-level trigger. however, cv is limited in informational value in the case of the asymmetric distributions assessed here. alternatively then, the tables also provide the change in the lower bound of the empirical 95% confidence interval of revenue in moving from the case of gross revenue (section b of the tables) to gross revenue plus the payment (section c). this measure of downside risk reduction ranges from 1.35% to 3.89% depending on the scenario, with the latter being for winter wheat with the county option (see table 2). hence, while the average benefits that would be provided by the arc program would appear small for 2011, its impact on reducing downside revenue risk would not appear trivial, particularly for winter wheat producers. in general for 2011, 218 j. cooper, b. delbecq ta bl e 1. s im ul at ed a rc p ay m en ts p er a cr e, g ro ss r ev en ue p er a cr e, a nd t ot al g ro ss r ev en ue w ith a rc p ay m en ts , 2 01 1 cr op y ea r, fa rm -le ve l t rig ge r. c ro p a . a rc re ve nu e pa ym en t p er a cr e b. g ro ss re ve nu e pe r a cr e sk ew c . r ev en ue p er a cr e w /a r c d . % c ha ng e c -b sk ew m ea n ($ / ac re ) u pp er bo un d, 95 % c i ($ ) c oe ff. o f va ria tio n % o f tim e pa ym en t is m ad e m ea n ($ / ac re ) lo w er bo un d, 95 % c i ($ ) u pp er bo un d, 95 % c i ($ ) c oe ff. o f va ria tio n m ea n ($ / ac re ) lo w er bo un d, 95 % c i ($ ) c oe ff. of va ria tio n lo w er bo un d, 95 % c i c oe ff. o f va ria tio n c or n 4. 64 12 .0 4 2. 70 15 .2 4 84 4 63 6 11 55 0. 38 0. 23 84 8 64 6 0. 37 1. 70 -2 .9 0 41 .0 8 so yb ea n 5. 08 10 .4 1 2. 10 17 .6 2 58 0 42 9 77 1 0. 43 0. 33 58 5 43 9 0. 42 2. 36 -3 .6 7 28 .2 7 w in te r w he at 3. 28 5. 96 1. 61 22 .1 2 24 9 17 1 36 7 0. 57 0. 55 25 3 17 7 0. 55 3. 42 -4 .1 8 14 .8 9 c ot to na 4. 77 10 .6 3 2. 09 28 .4 3 78 3 42 2 12 32 0. 66 0. 66 78 7 43 2 0. 65 2. 46 -1 .9 0 6. 60 a n ot e th at u pl an d co tt on is n ot in cl ud ed a s an a rc e lig ib le c ro p in th e se na te ’s 20 12 fa rm b ill le gi sl at io n no r i n th e 20 14 f ar m a ct . ta bl e 2. s im ul at ed a rc p ay m en ts p er a cr e, g ro ss r ev en ue p er a cr e, a nd t ot al g ro ss r ev en ue w ith a rc p ay m en ts , 2 01 1 cr op y ea r, co un ty -le ve l t rig ge r. c ro p a . a rc re ve nu e pa ym en t p er a cr e b. g ro ss re ve nu e pe r a cr e sk ew c . r ev en ue p er a cr e w /a rc d . % c ha ng e c -b sk ew m ea n ($ / ac re ) u pp er bo un d, 95 % c i ($ ) c oe ff. o f va ria tio n % o f tim e pa ym en t is m ad e m ea n ($ / ac re ) lo w er bo un d, 95 % c i ($ ) u pp er bo un d, 95 % c i ($ ) c oe ff. o f va ria tio n m ea n ($ / ac re ) lo w er bo un d, 95 % c i ($ ) c oe ff. of va ria tio n lo w er bo un d, 95 % c i c oe ff. o f va ria tio n c or n 2. 30 11 .5 7 5. 22 6. 56 84 4 63 6 11 55 0. 38 0. 23 84 6 64 4 0. 37 1. 35 -1 .4 5 19 .6 7 so yb ea n 2. 25 9. 85 4. 73 12 .0 0 58 0 42 9 77 1 0. 43 0. 33 58 2 43 7 0. 43 1. 97 -1 .4 0 8. 16 w in te r w he at 2. 63 6. 89 2. 23 9. 33 24 9 17 1 36 6 0. 57 0. 52 25 2 17 8 0. 55 3. 89 -2 .9 2 7. 31 c ot to na 2. 75 12 .6 2 5. 42 7. 41 78 3 42 2 12 32 0. 66 0. 66 78 5 43 2 0. 66 2. 26 -0 .9 4 1. 83 a n ot e th at u pl an d co tt on is n ot in cl ud ed a s an a rc e lig ib le c ro p in th e se na te ’s 20 12 fa rm b ill le gi sl at io n no r i n th e 20 14 f ar m a ct . 219an approach to assessing consequences of government support for farm risk management if implemented, the farm-level program would tend to provide higher mean benefits than the county program across all crops. it would also yield marginally greater reductions in downside revenue risk than the county-level program for corn, soybeans, and cotton, while this conclusion is reversed for winter wheat. table 3 has the same output format as tables 1 and 2, but for the acre program from the 2008 farm act. the difference in design between acre and arc are big enough that a priori assessments of the empirical differences between these two programs are difficult to make. average payments under acre were lower than for both farm-level and county-level arc across all crops. based on the percent change in the lower bound of the empirical 95% confidence interval between gross revenue and gross revenue plus the payment, arc’s downside revenue risk protection was superior to acre’s for all crops but cotton for which acre is dramatically better. when considering the coefficient of variation as a measure of risk, while we reach the same conclusion, it is worth noting that the superiority of acre over arc for cotton is not as pronounced. assuming she or he had a choice, a farmer would prefer arc over acre given yield and price conditions similar to 2011. however, under the acre program, farmers still received 80% of the (fixed) direct payments, as well as marketing loan benefits, albeit at a 30 percent reduction in marketing assistance loan rates, in addition to the revenue payment. note that the 95% confidence intervals in tables 1 to 3 are nonparametric and account for asymmetry, but of course, the mean and coefficient of variation do not. to provide further information on the (a)symmetry of the revenue distributions, we include the skewness measure (third moment) for gross revenue and the percentage change in skewness of gross revenue plus the payments relative to the base gross revenue distribution to tables 1 to 3. a value of zero for this measure means that a distribution is symmetric. for our revenue distributions, the average skewness is positive, showing that the right tail of gross revenue is long relative to the left tail, a not surprising result based simply on the fact that the distribution of revenue is truncated at $0. the average percentage change in skewness is positive, demonstrating that with the addition of the payments, the distributions have become increasingly skewed to the right, which is as one would expect adding the payments to do if they work as planned. the arc program proposed by the senate is only one option among many commodity support programs that could be designed to protect farmers against shallow losses in crop revenue. while arc in its 2012 senate version covers losses between 11% and 21% of benchmark revenue, one could envision lowering or increasing the coverage rate. we investigate what happens to average arc payments per acre when letting the coverage rate vary from 85% to 95%, which corresponds to losses ranging between (15% to 25%) and (5% to 15%) of benchmark revenue, respectively. resulting farmand county-level payments are represented on figure 1. average payments as a function of the coverage rate tends to be relatively linear although a couple of cases exhibit small positive second derivatives. all functions are elastic with respect to the coverage rate. moving from a coverage rate of 85% to 95% (or reducing the deductible from 15% to 5%), average payments increase by a minimum of $0.60 for the farm-level cotton program to $1.44 for the farmlevel soybean program. in percentage terms, the most dramatic increased is observed for the county-level soybean program with 69%. not only does the level of coverage matter to the farmer but it also impacts the total cost of the program to the government. american 220 j. cooper, b. delbecq ta bl e 3. s im ul at ed a cr e pa ym en ts p er a cr e, g ro ss r ev en ue p er a cr e, a nd t ot al g ro ss r ev en ue w ith a cr e pa ym en ts , 2 01 1 cr op y ea r.a c ro p a . a c re re ve nu e pa ym en t p er a cr e b. g ro ss re ve nu e pe r a cr e sk ew c . r ev en ue p er a cr e w /a c re d . % c ha ng e c -b sk ew m ea n ($ / ac re ) u pp er bo un d, 95 % c i ($ ) c oe ff. o f va ria tio n % o f tim e pa ym en t is m ad e m ea n ($ / ac re ) lo w er bo un d, 95 % c i ($ ) u pp er bo un d, 95 % c i ($ ) c oe ff. o f va ria tio n m ea n ($ / ac re ) lo w er bo un d, 95 % c i ($ ) c oe ff. of va ria tio n lo w er bo un d, 95 % c i c oe ff. o f va ria tio n c or n 1. 20 11 .9 6 9. 83 2. 79 84 4 63 6 11 55 0. 38 0. 23 84 5 64 2 0. 38 1. 07 -0 .7 0 7. 26 so yb ea n 1. 04 9. 27 10 .1 6 4. 22 58 0 42 9 77 1 0. 43 0. 32 58 1 43 6 0. 43 1. 74 -0 .6 8 3. 77 w in te r w he at 0. 76 4. 67 7. 39 2. 73 24 9 17 1 36 6 0. 57 0. 52 25 0 17 4 0. 56 1. 94 -1 .0 6 3. 11 c ot to n 2. 23 26 .4 1 6. 17 4. 52 78 3 42 2 12 32 0. 66 0. 66 78 5 44 6 0. 66 5. 68 -0 .7 9 1. 98 a ac re c al cu la te s re ve nu e lo ss es a t t he s ta te le ve l b ut a ls o in cl ud es a fa rm le ve l t rig ge r. ta bl e 4. s im ul at ed a ve ra ge p ay m en ts a nd d ow ns id e ri sk r ed uc tio n fo r fa rm -le ve l ( fa rc ) a nd c ou nt yle ve l ( ca rc ) a rc a nd a cr e g iv en t hr ee c as h pr ic e sc en ar io s. % c as h pr ic e c or n so yb ea ns w in te r w he at c ot to n av g. p ay m en t ( $/ ac re ) % c ha ng e c i lo w er b ou nd av g. p ay m en t ( $/ ac re ) % c ha ng e c i lo w er b ou nd av g. p ay m en t ( $/ ac re ) % c ha ng e c i lo w er b ou nd av g. p ay m en t ( $/ ac re ) % c ha ng e c i lo w er b ou nd 80 % 8. 49 3. 52 8. 69 4. 66 4. 84 5. 98 6. 17 3. 59 fa rc 10 0% 4. 64 1. 70 5. 12 2. 39 3. 28 3. 46 4. 77 2. 46   12 0% 3. 01 1. 02 3. 42 1. 32 2. 43 2. 14 3. 97 1. 66 80 % 5. 05 3. 62 6. 18 5. 71 4. 95 7. 88 4. 42 4. 06 c -a rc 10 0% 2. 30 1. 35 2. 25 1. 97 2. 63 3. 89 2. 75 2. 26   12 0% 1. 31 0. 69 0. 97 0. 69 1. 60 1. 98 1. 92 1. 21 80 % 8. 48 7. 69 8. 02 11 .1 4 2. 99 9. 04 8. 51 17 .5 1 a c re 10 0% 1. 20 1. 07 1. 04 1. 74 0. 76 1. 94 2. 23 5. 68 12 0% 0. 29 0. 15 0. 14 0. 21 0. 28 0. 46 0. 70 1. 07 221an approach to assessing consequences of government support for farm risk management farmers planted 92.3 million, 75.2 million, and 41.1 million acres of corn, soybean, and winter wheat in 2011. based on our estimates, increasing the coverage rate from 85% to 95% would raise the cost to the government by $267 million and $209 million under the farmand county-level options, respectively. the arc payments presented up to this point were estimated under the assumption that cash prices, used to calculate actual revenues, were at the 2011 levels. in table 4, we report what would happen to payments if market prices were 80% and 120% of their 2011 levels based on our simulations. we first observe that average program payments per acre and downside risk protection, measured by the percent change in the lower bound of the confidence interval of average revenue, both have a negative (as expected) and nonlinear relationship with cash prices. furthermore, while downside risk protection is higher for acre than arc at low price levels (relative to 2011), the drop off in this metric as cash prices increase is much more rapid for acre than arc. at relatively higher cash prices, average payments per acre and downside risk protection remain comparatively higher for the farm-level arc program (f-arc). at lower prices, c-arc has more impact on reducing downside risk than f-arc, for all crops considered in this study. more broadly speaking, it would seem that when benchmark revenue is determined at an aggregate level (e.g., county for c-arc or state for acre), average payments and downside revenue protection tend to skyrocket as cash prices fall. with a farm-level trigger (as in f-arc) the rate of change in these two metrics is noticeably slower. that is, figure 1. average national arc payments as a function of the coverage rate factor in the acr’s “guarantee revenue”. 222 j. cooper, b. delbecq payments remain more “under control” with the more targeted f-arc as prices drop. when taking all these observations into account, it would seem that c-arc is an intermediate between acre and f-arc, with more moderate increases in payments as prefigure 2. ratio of federal rp crop insurance premiums to farm level arc payments for a representative farmer in each county. 223an approach to assessing consequences of government support for farm risk management vailing cash prices decrease compared to the former, and a higher likelihood of no payments being made than the latter. we hypothesize that shallow loss support like arc is likely to be of relatively greater interest to farmers in less risky production regions. figure 2 shows maps of county level the ratio of the (pre-subsidy) federal rp insurance premium to average farm level arc payments for a representative farmer for each county in the data set. rp is farm revenuebased crop insurance product, and we assume the producer chosen coverage rate is 70%. when actuarially correct, the rp premium is equal to the mean rp indemnity payment. since the rp insurance covers deep (or at least deeper) losses and the arc covers shallow losses, we presume a priori that rp insurance premiums will be larger relative to average arc payments the riskier the production region. this hypothesis is visually affirmed by the maps in figure 2. for corn and soybeans, the ratio tends to be lower for counties in the corn belt, and higher in riskier areas such as the south east or far north. for winter wheat, the ratio is clearly higher in texas than the generally less risky production areas in kansas. as the premium subsidy on rp is expressed as a percentage of the total premium, the results in these maps show that in riskier production regions, federal commodity support in the form of arc payments will tend to be lower relative to federal commodity support in the form of insurance premium subsidies 6. conclusions the 2014 farm act provides farmers with options for “shallow loss” revenue support, including a variant of the arc program discussed in this paper. the choice of the deductible for determining the arc payment was in flux over the negotiations period leading to the 2014 farm act, and may be so again in negotiations over the next farm act, given the significant impact this choice can have on support payments. the main goal of this paper is to develop an approach for examining the sensitivity of the farmer’s downside risk protection to marginal changes in the deductible in shallow loss program scenarios. in particular, this paper develops an approach with nonparametric price and yield distributions that can simultaneously estimate revenue distributions across all counties reporting yields for four major crops using empirical distributions that are defined over arbitrarily small probability increments. we find that average payments are elastic with respect to the revenue program’s coverage rate. in addition, using this approach, the paper compares payments and their impacts on farm revenue for county and farm level implementations of arc. we find that based on our estimates of expected payments and their impacts on downside revenue risk, producers are likely to prefer the farm-level implementation of the revenue support program to the county-level versions, as least for the 2012 senate proposal arc. furthermore, our maps show a tendency for shallow loss support to be a greater proportion of total commodity support (defined here as arc payments plus federal crop insurance support) in primary production regions. for this analysis, no attempt was made to adjust the price deviates for exogenous variables (e.g., changes in interest rates) that may have caused a shift in the distribution of price deviates over time. an econometric approach for accounting for the effects of these variables on price deviates is addressed in cooper (2010). future analysis can seek to apply information from that approach to re-centering the price distributions as modeled here. 224 j. cooper, b. delbecq while the empirical analysis in this paper is for a u.s. policy subject, the methods discussed here are general and apply to a wide variety of subjects for which the analysis requires that systemic risk across large geographic regions be addressed. for example, in the eu, the second pillar of the common agricultural policy reform for 2014-2020 offers a new risk-management toolkit including insurance schemes for crops, animals, and plants. the methods discussed in this paper could be directly applied to assessing the budgetary costs and impacts on farm revenues in the eu of possible insurance programs in a manner that accounts for systemic weather risks within and across eu member countries. but the methods discussed here have applications beyond crop insurance. many models that seek to empirically assess how producers respond to changes in risk require the simulation of prices and yields. since weather impacts can be systemic, correct analysis of how producers may respond across a wide region to these impacts require that the simulated yields across the producers maintain the correlations of the actual historic data. the approach demonstrated here can impose these relationships across thousands of representative farmers, or sub-regions. furthermore, the same approach can be used to simulate correlated weather data across a large number of regions, which is useful in modeling multi-region (e.g., counties, provinces) impacts of risk management strategies for climate change. based on historical precedent, negotiations over the next farm bill will include discussion of what is the overall strategy for handling farm risk, and what is the government’s role in this strategy. does the addition of revenue supports to the farm act, which started with the 2008 farm act and continues into the 2014 farm act, complicate the message of which risks – e.g., downside revenue risk in general, idiosyncratic risk, systemic risk, price risk, yield risk – are to covered, and if so, why? that is, what risks should be partially borne by the government, and in what forms does the support get delivered to producers? with the 2008 farm act, and continuing into the 2014 farm act, risk support niches provided by fsa and rma have shifted. before 2008, rma-managed programs covered revenue risk and fsa programs addressed price risk. now, fsa also has a program that manages revenue risk. with the 2014 farm act, risk/income support programs for cotton producers under fsa purview (in title i) have been transferred to rma albeit in another form (in title xi). these evolving roles for administrative agencies beg the question of how the government might rebalance its resources on reducing yield risk, price risk or revenue risk, or some combination of these. can these ends be achieved while avoiding program overlap and in ways that increase transfer efficiency and also in ways that reduce costs of adapting to climatic variability? economics cannot easily answer the normative aspects of some of these questions, but the empirical approach presented here can help inform the debate over the government’s role in risk management. acknowledgments an earlier version of this paper was presented at the 3rd conference of the associazio ne italiana di economia agraria e applicata (aieaa), 25-27 june 2014 at alghero, italy. the views expressed are the authors’ and do not necessarily represent those of the economic research service or the us department of agriculture. 225an approach to assessing consequences of government support for farm risk management references bouyé, e., durrleman, v., nikeghbali, a., riboulet, g., & roncalli, t. 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(1973). random variables, joint distribution functions, and copulas. kybernetika 9(6): 449-460. u.s. department of agriculture (2008). 2008 farm bill side-by-side. washington, d.c.: economic research service, http://www.ers.usda.gov/farmbill/2008/2. u.s. senate (2012). s. 3240 agriculture reform, food and jobs act, agriculture committee, u.s. senate, june 21st, 2012. http://www.ag.senate.gov/download/?id=ced10412bf5e-46a8-bdef-e2594584ac9b. 227an approach to assessing consequences of government support for farm risk management appendix. schematic of the general steps in the methodological approach discussed in section 3. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(3): 257-275, 2013 structural and economic dynamics in diversified italian farms cristina salvioni1, elisa ascione2,*, roberto henke21 1 department of economics, university of chieti-pescara, pescara, italy 2 inea, rome, italy abstract. objective of this work is to investigate the structural change and economic dynamics of farms pursuing diversification and differentiation strategies in italy. the analysis was performed on a panel of data built on the basis of information collected by the italian fadn between 2003-2009. for the purpose of the analysis, we divided the population of commercial italian farms into a five-fold farm typology based on size and the extent of diversification and differentiation strategies adopted by the farms. in detail, farms are defined as differentiated when they make use of a system of quality certification, while they are defined as diversified when they take up nonfarming activities (agritourism, social farms etc.). the findings show that conventional farms remain by far the largest category within the population of italian commercial farms, while only 13% of the total commercial farms are classified as differentiated and/or diversified. farms adopting product differentiation strategies are found to have an income growth path similar to that of conventional farms. yet the category of diversified farms is the only one showing an upward trend with regard to income per worker in the observed years, while farms relying entirely on agricultural products appear to perform poorly in terms of labour productivity. keywords. income diversification; farm profiles; farm policy; italian agriculture. jel codes. q010; q120; q180. 1. introduction after world war ii, italian agriculture was affected by a wave of modernisation associated with the paradigm of productivism. during this period, the farming system underwent a major structural change encompassing a rapid decrease in the number of farms (associated with a large increase in average farm size), as well as an increase in the dependence on purchased inputs (fabiani, 1986). these phenomena were particularly intense in the northern plains, while farms in the southern and mountainous areas primarily conserved many of the traits associated with traditional, “lagging behind” agriculture: small holding sizes, a low level of technological equipment and aged and pluriactive holders. any attempt at diversification (e.g., pluriactivity), was originally interpreted by the supporters of productivism solely as a strategy for survival, which was the prerogative * corresponding author: ascione@inea.it. 258 c. salvioni, e. ascione, r. henke of marginal, small or residential farms (bowler et al., 1996). however, over the last three decades, product differentiation and income diversification strategies have proved to be not only a survival strategy but also a means of developing attributes that were interpreted, in the view of productivism, as backward, but for which consumers were willing to pay a premium price (gil et al., 2000; loureiro and hine, 2001). in other words, differentiation and diversification strategies proved to be a source of competitive advantage and were effective in improving the profitability of the farms. this, in turn, explains why farmers have increasingly adopted these strategies. farmers have constantly tried to diversify their production either to reduce the agronomic and climatic risk as well as the price risk on the output or to create new and interweaved sources of income originating from on-farm activities. this tendency has been enhanced by the renewed role of agriculture and rural areas in society due to agenda 2000 (european commission, 1997), as well as by the increased perception of farmers as the primary natural resource managers and landscape stewards. the growing integration of agriculture into the rural economy has contributed to the creation of more opportunities to diversify farms even in fields unrelated to farming. in this process, food market segmentation and the variety of services offered by farms have also been key in explaining the interest in the diversification of farm activities. at the same time, rural development policies, the so-called “second pillar of the common agricultural policy (cap)”, became pivotal in widening the realms of intervention of multifunctional diversification to the production of externalities and public goods and also to stimulating other economic activities with indirect social and environmental effects (european commission, 1997; oecd, 2005). cap policies have been increasingly addressing the production of public goods and services in agriculture and improving the environmental function of the agricultural activities through a process of “greening” of the cap (matthews, 2012). however, the extent to which this process has been successful and thorough in recent years is a matter of further discussion (mardsen and sonnino, 2008; zahrnt, 2009). in summary, recent decades have seen a proliferation of product differentiation and income diversification initiatives. product differentiation(moving farm production toward agricultural goods with unconventional characteristics (e.g., organic farming, pdo and pgi products) results in a deepening2 of farming activities (van der ploeg and roep, 2003). yet income diversification replaces agricultural products with other outputs that compete for the allocation of production factors while broadening the scope of the business (oecd, 2009). the aim of this work is to investigate the diffusion of income diversification and product differentiation strategies in italian agriculture and to compare the structural change and economic dynamics of farms pursuing these strategies to those of farms that have not adopted them. previous related literature has focused attention either on the diffusion of the diversification strategies among farmers and the characteristics of diversified farms over a limited period of time (belletti et al., 2003; henke and salvioni, 2008; meert et al., 2005) or on the role played by idiosyncratic characteristics of farmers with respect to the 2 according to the original definition, deepening refers also to activities such as processing and marketing of agricultural products. 259structural and economic dynamics in diversified italian farms adoption of diversified activities (aguglia et al., 2009; esposti and finocchio, 2008; jongeneel et al., 2008; mcnally, 2001). we contribute to this literature by analysing the performance of conventional compared with diversified farms over time. the analysis was performed using the data collected by the italian farm accountancy data network (fadn) survey between 2003 and 2009 (last available year when the analysis has been performed). this time period is justified on the basis that before the year 2003 the participation to the survey was voluntary, as a consequence the sample was biased due to self-selection. from year 2003 onward the survey is conducted on a random sample that is representative of the population of commercial italian farms. the italian perspective on these matters is relevant for many reasons. regarding product differentiation, italy has long founded its strategy of agricultural development and competitiveness on quality attributes and the ties between production and origin3 (sylander et al, 2000; treagar et al., 2007; carbone and henke, 2012). regarding diversification, recent studies have focused on two aspects that are related to the potential for on-farm income diversification in italy: the wide range of territorial features and the large spectrum of farm sizes and types, which enable new activities for farmers and their family members (belletti et al., 2003; di iacovo, 2003; ieep, 2009). the paper is structured as follows: in the first section, we review the new diversification trajectories that have emerged in italy in recent decades; in the second section, we describe the data and the farm typology used to segment the population of italian commercial farms in mutually exclusive categories that take into account the extent of differentiation and diversification activities in farms. the results of the application of this typology are presented in the following sections: in section 4 we compare the structural and economic characteristics of italian commercial farms by farm type, while in sections 5 we follow their evolution between 2003-2009. finally, section 6 draws some conclusions and sketches possible future work. 2. paths of diversification in agriculture 2.1 a glance at the farm income problem one of the traditional problems affecting the primary sector is the low level of income caused by a limited endowment of financial capital, a low rate of return on farm assets, the inelasticity of either supply and demand and, ultimately, the technological changes that progressively worsen the price-cost squeeze (gardner, 1992; cochrane, 1958; tweeten, 1979). for example, in italy from the 1970s onward, the percentage of agricultural to total real net value added at factor costs (also referred to as factor income) has slowly increased from 20% to 40% (henke and salvioni, 2011). in spite of that, only a third of the farms has been able to remunerate the factors of production at a level equal to or slightly higher than the opportunity cost. moreover, this level of remuneration clearly decreases if one considers the remuneration levels net of the public support ensured by the european union. 3it is worth recalling that italy has a high number of protected designation of origin (pdos) and protected geographical indication (pgis) (244), the first in europe, with a share of 22%. 260 c. salvioni, e. ascione, r. henke the remedies suggested by the economic theory to overcome the farm income problem in the view of productivism have been growth and specialisation. more recently, farms have begun to tackle this problem by adopting different diversification strategies. diversification in agriculture has been discussed in a recent debate on the methods and potential results of transition and reorganisation processes underway in agriculture (van der ploeg, 2002; van der ploeg and roep, 2003; wilson, 2007). according to this literature, the move toward multifunctionality and diversification in western agriculture can be interpreted as a reaction to the dominant socio-technological regime of the agri-food system, i.e., large-scale production, the gradual replacement of natural capital by financial and man-made capital, a tendency toward mono-crop agriculture, the abandonment of artisan processes and products, the centralisation and vertical integration by which costs are externalised (e.g., environmental, social), and the concentration of control and profits. these aspects of industrialisation carry negative effects for the quality of the natural environment (e.g., pollution, loss of biodiversity) and the food produced, as well as a loss of autonomy for those involved in agricultural production and a growing subjugation to the logic of the agri-industrial system. likewise, other practices such as on-farm sales can be viewed as instruments for reducing dependence on the industrial and financial system, increasing employment and value added in agriculture, and regaining autonomy. this process of change garners strong support from social movements that are concerned with the relocation of food products, environmental issues or social equity. after decades of success of the modernist paradigm that has pushed farms into production intensification and specialisation, the lack of sustainability of this process (stressed by the economic crises and the long-standing process of agriculture economic decline) has stimulated a search for new activities that lead to other and differentiated sources of income that are sometimes off-farm but are increasingly on-farm, however of a non-agricultural nature (wilson, 2007 and 2008). the result of this process has been that of a co-existence of different models of agricultural development that have been catalogued as post-modernist, neo-modernist, or multifunctional, according to the specific aspects highlighted within this more generalised decomposition of the dominant model (ravenscroft and taylor, 2009). 2.2 offand on-farm diversification one possible trajectory of the diversification process (figure 1) is off-farm income diversification (arkleton trust, 1983; evans and ilbery, 1993; marsden, 1990). following this strategy, farm households decide to allocate part of their labour resources to off-farm employment (pluriactivity) with the goal of maximising and stabilising family incomes, which is referred to as the sum of onand off-farm family income4. pluriactivity has been a very successful model in italy, as shown by many studies in the 1980s and 1990s (de benedictis, 1990; fabiani, 1991; saraceno, 1985). these stud4 pluriactivity is only one way to deal with low returns on agricultural activities. other diversification strategies involving the off-farm deployment of resources other than labour are the leasing of land and buildings, as well as renting out of machinery. 261structural and economic dynamics in diversified italian farms ies focused on the persistence of pluriactivity in agriculture as a new mode of organising production factors between agriculture and other off-farm activities that would ultimately ensure the survival of primary activities, especially in remote and marginal areas that otherwise would have been abandoned. however, pluriactivity functions successfully where economic and social conditions for off-farm work for the family members exist and are prosperous. in other words, the success of pluriactivity directly depends on the nonagricultural context (fabiani, 1991). the analysis of the non-agricultural context in which farmers operate is beyond the scope of this paper; accordingly, this topic is not investigated. another possible path that farms can follow to counteract a decline of income is onfarm diversification. this process occurs when a farm business undertakes a new activity that is intended to increase returns through resources available on the farm. the current literature investigates several forms of on-farm diversification (oecd, 2009). three main forms of on-farm diversification can be identified: agricultural output diversification, product differentiation and non-agricultural income diversification. figure 1. offand on-farm diversification. 1    2   diversification on-farm agricultural output diversification product differentiation (e.g., organic, pdo) non-agricultural income diversification (e.g., agritourism, natural resource management, energy production) off-farm pluriactivity agricultural output diversification is intended to thwart uncertainty. more specifically, the goal of output diversification is to reduce the risk of the return by selecting a mixture of agricultural activities with net returns that have a low or negative correlation (heady, 1952). product differentiation (chamberlin, 1933) refers to those strategies in which farmers produce goods with specific and unique quality attributes. the profit-maximising strategy is not to minimise costs but rather to create a differentiated product that provides the firm with a degree of market power. the differentiated product may even cost more to produce but can increase profits if customers are willing to pay more to purchase it. differentiation makes high quality product an imperfect substitute for other standard goods; thus, con262 c. salvioni, e. ascione, r. henke sumers of the quality goods are more loyal and producers are less susceptible to the activity of competitors. this means that farmers producing high quality products operate in niche markets where they may be able to charge higher prices for their high quality products than perfect competition would allow. the diversification path can also take the direction of a new business that does not necessarily or entirely relate to the agricultural business, for example agri-tourism, energy production (e.g., photovoltaic, wind-powered) or natural resource management. in these cases, diversification can be interpreted as a process that widens the range of production possibilities of the farm business beyond agriculture but that utilises the same production factors and structures. in this paper, we focus on the two latter forms of on-farm diversification, namely product differentiation and non-agricultural income diversification (henceforth income diversification). 2.3 on-farm diversification in italy product differentiation strategies are widely used by italian farmers. as in other mediterranean countries, food quality differentiation in italy has been primarily based on the territorial linkages of production. this is partly explained by the fact that this type of product differentiation is perceived as a means to foster rural development, for example by increasing cross-sectoral interactions and enhancing civic pride (tregear et al., 2007) and has received support from both national and eu policy makers. an early piece of evidence on the quality orientation of italian farmers can be found in the rapid increase in the number of territorial food labels over the last 20 years. a second piece of evidence is the wide diffusion of organic farming. previous works have highlighted the extent and the relevance of on-farm income diversification in italy (belletti et al., 2003; henke and salvioni, 2008). in particular, the taxonomy of the activities has revealed the relevance of the integration of downstream activities such as processing and direct sales as well as quality certification. limited to a few but significant cases are the more innovative activities that move farms away from the primary activities, such as tourism, recreational services, etc. however, one specific feature captured by the analyses is the co-existence of activities within the same farm: “complex” systems of activities and functions are arising that cast farms in a new light and imply a reorganisation of the use of factors, entrepreneurial skills and territorial relationships. the latest italian agricultural census (2010) defined income diversification as the presence of other gainful activities alongside agricultural production. according to this restricted definition, only 5% of the total farms are diversified. however, even within this small category of farms, there is a wide spectrum of diversification profiles that feature farms with several gainful activities that interact with each other and farms with a complex allocation of production factors. the amendment to the cap has also contributed to the enhancement of diversification in italy due to the generous financial support granted to farms that choose to diversify along this path. in particular, the rural development policy with agro-environmental programmes and investment plans has ensured the acknowledgment of diversified and multifunctional farms to units that have favoured the production of public goods and that 263structural and economic dynamics in diversified italian farms introduce green measures and sustainable investments in green technology, animal welfare and food quality improvement. 3. methodology and data this study utilises data collected by the fadn survey. the fadn is an annual sample survey, carried out with a uniform methodology at the eu level; since 2003, the sample has been randomly selected in full compliance with the requirements of statistical representativeness (cagliero et al., 2011).the field of observation consists of commercial farms, i.e., those with european size units (esu) over 4800 euros. the fadn survey provides information on the presence of differentiation and diversification activities on farms. for example, information is provided on whether the farm makes use of organic farming or low-impact techniques and if it produces landscape or bio-diversity conservation services, as well as agri-tourism, commercial (direct selling) or social (e.g. educational, therapeutic) services. in addition to these indications, fadn provides information on the use of protected designation of origin (pdo) and protected geographical indication (pgi) or whether the farm produces traditional products. from 2008 onward, the survey also recorded information regarding the value of total production due to a) pdo, organic and other products covered by quality certification products, and b) agri-tourism, on-farm recreational activities, on-farm processing (wine, cheese, etc.) and other services (e.g., educational, green care). this information can be used to sort farms into homogeneous groups in terms of their efforts in the areas of product differentiation and income diversification. to provide a consistent basis for the description of the main characteristics of distinct groups of farms and compare their income evolution over time, in this article we make use of a farm typology that divides the fadn farms by size, and the extent of product differentiation and on-farm income diversification (salvioni et al., 2013). the proposed typology builds on a simple two-step methodology. first, we selected all farms that recorded a total output (to) of less than 15000 euros and defined these farms as micro farms5. in the second step, we sorted the remaining non-micro farms into four groups according to the magnitude of their efforts in terms of income diversification and product differentiation. the first group covers farms with a total output larger than 15000 euros and with at least 30% of the total output originating from non-farming goods and services6. this group of farms adopted income diversification strategies, henceforth called diversified farms. the second group, referred to as differentiated farms, covers farms with at least 30% of the total output originating from the production of quality-certified products. the third group, hereinafter referred to as differentiated and diversified (d&d), includes farms for which output from both product differentiation and income diversification was above 30% of total output value. finally, the fourth group includes all the other 5 the separation of micro farms from the rest of the population under analysis is primarily because these farms play more of a welfare and environmental role rather than a productive role with respect to the rest of the population, as will be demonstrated in the following sections. 6 the cut-off values used to define different groups of farms were based on expert judgment (salvioni et al., 2013). the 30% threshold was intended to focus on farms where on farm diversification strategies play an important role in income generation. 264 c. salvioni, e. ascione, r. henke non-micro farms, i.e., farms with total outputs larger than 15000 euros and with less than 30% of the total output related to the use of strategies of either income diversification or product differentiation (in this way the 100% of the fadn farms is reached). we refer to this latter group as conventional farms. it is worth noting that the term conventional in this article is not used in opposition to organic or other alternative farming practices. rather, conventional refers to a farm that is producing only agricultural products and of standard quality. this classification of farms into different categories may be sensitive to changes in the thresholds defined above and to the year in which the differentiation and diversification efforts were measured. this does is not an issue in our application because the aim of this paper was not to understand whether income diversification and product differentiation emerge as important dimensions to explain differences between farms but rather to study the structural and economic characteristics of farms that adopted product differentiation and income diversification strategies and to compare these to conventional farms, i.e., to farms that have not adopted these two strategies. we first present the main structural and economic characteristics of the different farm types observed in 2008. we then compare the economic performance over time of the identified groups of farms by making use of a seven-wave balanced panel7 of more than 3000 farms for which continuous records are available from 2003 to 2009. we focused our analysis of the characteristics of non-micro farms observed in the year 2008 because this was the first year in which information regarding the involvement in income diversification was collected. 4. structural and economic characteristics by farm type in this section, we present the main structural (table 1 and figure 2) and economic (table 2) characteristics of the italian commercial farms by the farm types described in the previous section. the data refer to year 2008; this was the first year in which the fadn survey started to collect the information used to discriminate between farms via the extent of differentiation and diversification activities. focusing on only one year allowed us to exploit the information gathered from the entire sample8 that in year 2008 covered 11,234 commercial italian farms. most importantly, after weighting the sample observations using an appropriate system of weights, our results can be extended to represent the entire population of commercial italian farms. micro farms comprise 15% of italian commercial farms. as expected, their size (in terms of both total and utilised agricultural area (respectively taa and uaa) is the smallest group of the population under study. the small size also reflects a very small number of annual working units (awu). regarding production, micro farms are relatively more specialised in the production of permanent crops (grapes, olives and fruit) and cereals, oil and protein seed (cop) crops than commercial farms, while micro farms are relatively de-specialised in more labour-intensive productions such as horticultural crops and livestock. 7 for more detailed information about the construction of this panel, see henke and salvioni (2013). 8 this will not be possible when we analyse the balanced panel, as already mentioned in section two. 265structural and economic dynamics in diversified italian farms micro farms recorded a median farm net value added per unit of labour (fnva/ awu) that is half the value recorded in the entire sample. as expected, this group of farms presents the lowest results in terms of labour productivity. however, these findings cannot simply be interpreted as a symptom of the poverty of the farm household; rather, this low level may be due to the fact that they are hobby, residential or retirement farms. micro farms are not oriented toward farm income maximisation but instead towards the maximisation of other non-economic variables such as social welfare of the farm household (perali et al., 2005). in many of these cases, farm income is likely used by these farm households to complement other sources of off-farm income. it is also interesting to note that the average financial aid received under pillar 1 in percentage of the farm net value added is above the value recorded for the entire population. in other words, pillar 1 support is an important share of farm income in this group of firms. however, the average percentage ratio of pillar 2 payments to farm net value added is below the population average, which suggests low participation with the agri-environmental and other payment schemes under pillar 2. the same applies to the investment subsidy scheme as noted by the null value of the percentage ratio of cap payments related to capital assets under pillar 2. these latter results may result from different causes. on the one hand, these farms may have little access to pillar 2 financial aid due to the difficulties and the lack of competence in following the procedure to make a request for this kind of support; on the other hand, this result could be due to the inability of pillar 2 to solve the structural and economic problems of this group of farms. finally, the low percentage of payments related to capital assets may be the result of a lower propensity to invest; conversely, the farm household may easily find the financial sources needed to cover their smaller investments. the analysis needed to test these hypotheses is beyond the scope of this study. table 1. structural characteristics by farm type – 2008 (weighted data). micro conventional diversified differentiated d&d total number of farms % 15 71 6 5 2 100 awu (n.) median 0.7 1.0 1.1 1.0 1.2 0.9 taa (ha) median 5.0 11.0 7.5 11.1 12.5 7.4 uaa (ha) median 4.5 9.8 6.6 10.4 11.8 6.5 source: our elaboration of the fadn data. of all italian commercial farms, 71% are conventional. these farms make little or no use of diversification and differentiation strategies. their size is large both in structural and financial terms. their median endowment of total and utilised land as well as their working units are above the median of the farm population. the same applies to their economic results. these farms feature a relatively strong specialisation in the production of intensive and industrial products such as horticultural crops and livestock. regarding public support, it is interesting to note that on average they receive financial aid under pillar 1 of the cap is in line with the aid received by other non-micro farms, while support from pillar 2 is relatively lower. this result was largely expected because direct payments under pillar 2 of the cap are primarily associated with the adoption of multifunctional 266 c. salvioni, e. ascione, r. henke and diversification activities. however, conventional farms attract more payments related to assets. this was also expected because this support mostly takes the form of investment subsidies primarily associated with the modernisation axis of pillar 2. diversified farms cover 6% of the total population of italian commercial farms. their median land size reveals that most farms in this group are fairly small, with total and utilised land respectively equal to 7.5 and 6.6 ha. these values are significantly closfigure 2. specialisation by farm type 2008 (weighted data). 0%   10%   20%   30%   40%   50%   60%   70%   80%   90%   100%   micro   conventional   diversi;ied   differentiated   d&d   total   cop  crops   horticultural  crops   permanent  crops   livestock   mixed   source: our elaboration on fadn data. table 2. economic characteristics by farm type – 2008 (weighted data). micro conventional diversified differentiated d&d total fnva/to % median 48.9 58.4 69.2 56.7 56.7 56.0 defl. fnva/awu € median 6,622 18,245 17,832 16,597 19,493 13,142 pillar 1/fnva % mean 30.7 25.8 14.5 36.7 16.8 27.1 pillar 2 direct payments/fnva % mean 1.2 5.1 3.5 6.9 7.4 3.6 pillar 2 non-direct payments/fnva % mean 0.0 0.1 0.0 0.1 1.4 0.0 source: our elaboration on fadn data. note: financial data are deflated using the gdp deflator 2005=100. note: pillar 1 and 2 median payments are always equal to zero; as a result, we report the mean values. note: pillar 2 non-direct payments include the investment subsidies not included in the pillar 2 direct payments. 267structural and economic dynamics in diversified italian farms er to those observed among micro rather than non-micro farms. the median labour use of these farms is above the values for the entire farm population. the higher use of labour in these farms can be due to a substitution of internal labour with contracted labour because owners work mainly for non-farming activities. in terms of production, diversified farms specialise in permanent crops. the median income per worker for these farms is similar to that recorded for other non-micro farms, and it is interesting to note that diversified farms present the highest farm net value added to the total output ratio (fnva/to). this ratio is a proxy for farm profitability: an increase in this indicator reveals that a larger share of revenues is used to cover the remuneration of inputs (labour, land and capital), while the share of costs due to raw and intermediate goods decreases. in situations of farms adopting income diversification strategies, the relatively high value of profitability is likely the result of the uptake of non-agricultural activities with returns higher than farming. it is also interesting to note that the mean percentage of the financial aid received under pillar i over the farm net value added is significantly lower than the value recorded in all other farm types. the mean percentage ratio of pillar 2 direct payments to farm net value added is the lowest among non-micro farms, while the percentage of non-direct payments under pillar 2 (mainly investment subsidies) over the farm net value added is on average close to zero. the overall result is that farms that adopted income diversification strategies are less dependent on cap support than other farms. differentiated farms cover 4.6% of the total population of italian commercial farms. differentiated farms have median large endowment of total and utilised land. the use of labour in these farms is in line with that observed in the rest of the non-micro farm population. differentiated farms present strong specialisation in the production of horticultural and permanent crops, while they are relatively under-specialised in livestock production. the median labour productivity in these farms is lower than that of the rest of the non-micro farms, and the median index of profitability in differentiated farms is lower than diversified farms. regarding public support, differentiated farms receive an average level of financial aid under pillar 1 and pillar 2 (especially in the form of direct payments) that is substantially larger than that for other farm types. these results suggest that the types and levels of support provided by the cap meet the specific policy needs of farms that adopted product differentiation strategies. finally, the d&d farms cover 2% of the total commercial farms and is composed of farms that have the largest endowment of both land and labour. d&d farms are relatively more specialised toward livestock production than the rest of the population. d&d farms feature the highest median labour productivity in the population while, as observed for the differentiated farms, the median index of profitability is lower than the value recorded for the other farm types. this latter result can be partly attributed to the recent crisis experienced in the livestock production sector. regarding public support, this group of farms receives relatively little support under pillar i, while the mean percentage of pillar 2 direct and non-direct payments over the farm net value added is the highest in the population. all in all, this evidence suggests that d&d farms are large users of pillar 2 benefits, which is likely due to a larger interest in agri-environmental measures or a higher propensity to invest than the other farm types. 268 c. salvioni, e. ascione, r. henke 5. farms’ growth paths we will now analyse the growth paths that have been followed by the previously identified farm types using the information contained in the balanced panel of farms for which continuous records are available from 2003 to 2009. to this end, we calculated the average annual percentage changes in the median values of the main structural and economic characteristics by farm type: land, labour, value added to total output ratio, value added, and value added per unit of labour and per hectare of utilised land (table 3). table 3. average annual percentage change in the median structural and economic characteristics by farm type (2003-2009, balanced panel data). micro conventional diversified differentiated d&d uaa -1.17 0.12 -1.18 -0.56 2.17 total land -0.85 0.10 -2.18 0.45 0.99 awu -2.21 -2.80 -2.63 -1.58 -0.87 fnva/to 4.31 0.51 2.06 1.76 -2.33 fnva/uaa -0.09 -2.46 5.23 3.15 -3.85 fnva/awu 4.25 0.69 6.10 1.30 -5.91 source: our elaboration on fadn data. note: the financial variables have been deflated using the gdp deflator 2005=100. regarding the structural characteristics, the data indicate that all farm groups, with the exception of conventional and d&d farms, experienced a reduction in utilised land. it is worth noting that diversified farms were the group that most intensively disinvested in farmland. given their decrease in total land, the reduction in cropped area does not reflect a change in land use; this reduction instead signals the progressive substitution of non-agricultural activities to farming. the other group in which we observed negative changes both in total and arable land is that of micro farms; this evidence suggests a progressive marginalisation of this group of farms likely leading to the exodus of many of these farms from the commercial segment of the sector and, eventually, from agriculture. the negative changes in labour observed in all farm types confirm that italian commercial farms as a whole continue to experience a process of labour restructuring. this reduction is particularly intense in conventional, d&d and micro farms. the relatively large changes in land and labour observed in both differentiated and diversified farms suggest that these two groups of farms are still searching for an optimal resource allocation that would allow them to increase their efficiency and profitability. this is partly because their transition toward high quality agricultural production and non-agricultural activities often requires a change in the set of heuristic rules that enable the farm to perform daily business. when farmers move toward diversification, these rules need to evolve (nelson and winter, 1982); in other words, farmers have to search for solutions to threats and opportunities implied by the new diversified activities. most importantly, it is not always easy for farmers to build an optimal portfolio of farm and non-farm activities, especially when the latter requires highly specific labour skills that are very different 269structural and economic dynamics in diversified italian farms from the skills required for farming. for example, over-diversification may cause the loss of control over the farm’s competence leveraging and combinative capabilities and hence its organisational coherence (dosi et al., 1992; pavitt, 1998). regarding economic results, we analyse the farm net value added to the total output ratio; this indicator, a proxy of farm profitability, increased in all farm types, with the exception of d&d farms. this common positive trend is partly due to the increase in agricultural prices experienced at the end of the decade. the increase in profitability is larger in the groups of diversified and differentiated farms than in the conventional farms. recalling that farms adopting product differentiation are less specialised than the conventional farms in cops, i.e. in the group of crops that most benefitted from the recent agricultural price bubble, this result may be interpreted as a signal that the farms that adopted product differentiation strategies have been able to reduce their dependence on off-farm inputs and have been able to increase the share of the value added kept in the farm and not passed it to the rest of the supply chain. regarding farms adopting income diversification strategies, an increase in profitability can be interpreted as the result of the substitution of more profitable non-agricultural activities for farming. finally, it is worth noting the large increase in profitability in micro farms; the noteworthy specialisation in the production of cops by micro farms is likely driven by the agricultural price bubble. the income per unit of land declined in the d&d, conventional and micro farms. however, regarding the diversification and differentiation groups, land productivity increased. keeping in mind that these two latter groups of farms decreased their farmland allowance over time, their increase in land productivity is partly due to the fact that they have been able to concentrate farming in the most productive land, while reducing it in marginal land, and partly to the uptake of activities more profitable than standard farming. the income per unit of labour shows an increase in diversified, differentiated and micro farms but remained relatively stable in conventional farms. finally, the income per unit of labour declined in d&d farms. labour productivity is a very important indicator of economic performance because it provides a simple measurement of the efficiency with which inputs are used to produce goods and services. income per labour unit may also provide information about the living standards of farmers. this explains why it is often proposed to assess whether the agricultural policy objectives have been achieved (hill, 1991). given the importance of this indicator for the evaluation of the economic performance of farms, in the following section we provide more insight into evolutionist dynamics by farm type. focussing on labour productivity, figure. figure 3 reports the scatter plots of income per labour unit by farm type. these graphs are useful to understand the degree to which the distribution of labour productivity is stretched or squeezed by farm type. conventional farms present the most stretched distribution, which is likely because they are not specialised for specific productions and are instead active in a wide range of crop and livestock productions, each associated with different income levels. overall, in farms adopting income diversification and/or product differentiation strategies, the distribution of income per unit of labour is more squeezed than in conventional farms despite the fact that there are farms with very high values of income per labour unit within each of these groups. finally, the income per unit of labour in micro farms is primarily concentrated in a small region that is very close to the horizontal axis, i.e., to zero income per labour unit. 270 c. salvioni, e. ascione, r. henke the differences in the levels of income by farm type can be more easily perceived using figure 4, which also displays the evolution of the median values of labour productivity by farm type. the results show that, though it increases over time, the income per labour unit in micro farms remained significantly lower than that obtained in the rest of the sample. among the non-micro farms, those following both product differentiation and income diversification strategies are higher at the beginning of the observed period, but exhibit a sharp drop in income per labour unit in more recent years. this result is likely due to their specialisation in livestock production, a sector that experienced a drop in income in the most recent years under observation. the rest of the non-micro farms presented an increase in income per unit of labour over 2007 and 2008, which is likely due to the effect of the agricultural commodity price bubble that began in 2007. it is interesting to note, first, that farms using product differentiation strategies had better results than the conventional farms over 2004-2008. second, it is also interesting that the farms that adopted income diversification strategies experienced a progressive increase in labour productivity and, contrary to the rest of the non-micro farms, did not incur a labour productivity reduction in 2009. this suggests that the uptake of non-agricultural activities shelters farms from the temporary shock to farm income caused by weather or price shocks. consequently, the uptake of these activities may help to stabilise the total (agricultural and non-agricultural) farm income. it is also interesting to note that farms that adopted income diversifigure 3. scatter plot of income per unit of labour by farm type (2003-2009). 0 10 0 20 0 30 0 0 10 0 20 0 30 0 2003 2005 2007 2009 2003 2005 2007 2009 2003 2005 2007 2009 micro conventional diversified differentiated d&d total eu ro (0 00 ) source: our elaboration on fadn data. 271structural and economic dynamics in diversified italian farms fication recorded median levels of income per unit of labour below those recorded for the rest of the non-micro farms at the beginning of the observed period. however, in the most recent years, the results for the farms that adopted income diversification are in line with those of other non-micro farms. in other words, diversified farms are characterised by a positive and rapid dynamic that has allowed them to overcome their income problems and to approach the level of income per unit of labour achieved in the other non-micro farms. finally, we compare the annual growth rates of income per worker for the total farms to those recorded in farms that specialised in permanent crops (table 4). table 4. annual growth rate of median fnva/awu in farms that specialised in the production of permanent crops by farm type (2003-2009). conventional diversified differentiated d&d total agriculture 0.7 6.1 1.3 -5.9 permanent crops 1.8 8.7 3.2 6.8 source: our elaboration on fadn data. the focus on these farms is due to the relative specialisation of income-diversified and product-differentiated farms in the production of these crops. in the entire population of non-micro farms, as well as in farms that specialised in permanent crops, the average annual income growth rate was lower in the conventional farms than in the diversified and differentiated farms. these results suggest that income diversification and product differentiation strategies may be a solution to the low farm income problem. however, we leave the counterfactual analysis of how labour productivity would have grown, if income diversification and product differentiation strategies had not been adopted, for future research. figure 4. median of income per unit of labour (fnva/awu) by farm type (euros). source: our elaboration on fadn data. 272 c. salvioni, e. ascione, r. henke 6. conclusions in this paper we used the information collected by the italian fadn to provide evidence regarding the structural and economic characteristics, as well as performance over time, of farms that adopted product differentiation and diversification strategies. for this purpose, we divided the population of commercial farms into a five-fold farm typology. the methodology proposed is relatively simple and shows significant limits such as the arbitrary cut-off points used to classify farms. however, results produced are interesting and seem to reflect quite effectively and realistically the different behaviour of the identified groups of farms. our first finding is that only 13% of the total italian commercial farms have adopted product differentiation and/or income diversification strategies. this result is clearly in line with the figure produced, on this matter, by the latest italian agricultural census. this low percentage was expected because product differentiation and income diversification are innovative strategies with respect to conventional farming, i.e., farms that specialise in the production of standard agricultural products. another interesting outcome is related to the dynamics of structures and economic results by farm type. farms adopting product differentiation strategies are fairly stable in terms of structure and demonstrate a relatively better economic performance than conventional farms. this suggests that the experience gained over time in producing and marketing products with attributes for which consumers are willing to pay a premium over the standard price are leading these farms toward increasing levels of profitability. however, in the last few years, these farms appear to follow an income per unit of labour growth path that is similar to that of conventional farms. in other words, weather and agricultural price shocks cause the same types of temporary farm income shocks in all farms that rely substantially on agricultural production (even those with high-quality products). we also find that the group of farms that adopted income diversification strategies is experiencing a process of deep structural change, as well as increases in both profitability and income per unit of labour. these phenomena suggest that farms that adopted income diversification strategies are still looking for an optimal resource allocation that would allow them to further increase their efficiency and profitability over time. the relatively low income per unit of labour in diversified farms in the first years of the study, as well as the downward trend in d&d may be partly due to the fact that it is not always easy for a farmer to build an optimal portfolio of both standard agricultural and innovative activities as well as agricultural and non-agricultural activities while maintaining the organisational coherence of the farm. it is also worth noting that the category of diversified farms is the only one showing an upward trend in income per unit of labour in the last observed years, while farms relying entirely on agricultural products appear to perform poorly after 2008. this suggests that the new non-agricultural sources of income contribute to stabilising the overall farm income per unit of labour in diversified farms. because farm income stabilisation is a cap objective, effective direct income stabilisation tools or indirect ones (like diversification measure in pillar 2) seem to be highly desirable. that means, for pillar 2, more flexibility at the farm level for access to pillar 2 support, a better timing of payments and, more generally, the elimination of the rigidities often imposed by the rules of the current rural development plans. this, in turn, would favour a larger uptake of income diversification 273structural and economic dynamics in diversified italian farms strategies, thus accelerating farm income stabilisation. when new and more recent data become available, it will be possible to assess whether the observed trend toward stabilisation is a permanent trait of income diversification rather than the result of a temporary shock to the income of diversified farms. the analysis also demonstrates that micro farms often play only an ancillary role in terms of total household income generation. however, micro farms have been found to receive a larger share of payments compared to their income than non-micro farms. given the large number of micro farms operating in italy, future research is needed to better understand what role these farms play (e.g., productive, welfare or environmental) and what type of policy action can be designed to fulfil the objectives that are relevant to the needs and opportunities of micro farms. acknowledgements an earlier version of this paper was presented at the 2nd aieaa conference “between crisis and development: which role for the bio-economy”, 6-7 june, 2013, parma, italy. the authors wish to thank two anonymous referees and the editor for their helpful suggestions and comments. references aguglia, l., henke, r. and salvioni, c. 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(2009). public money for public goods: winners and losers from cap reform. working paper n. 08/2009, brussels: ecipe. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(3): 277-300, 2013 how rural is the eu rdp? an analysis through spatial fund allocation beatrice camaioni1, roberto esposti2, antonello lobianco2, francesco pagliacci2,*, franco sotte1 1 the national institute of agricultural economics (inea), rome, italy 2 università politecnica delle marche, dept. of economics and social sciences, ancona, italy abstract. although representing less than 20% of total cap expenditure, the rural development policy (rdp) 2007-2013 is supposed to support rural areas which are facing new challenges. currently, many eu rural areas are experiencing major transformations and the traditional urban-rural divide seems outdated (oecd, 2006). going beyond dichotomous definitions and approaches, the paper applies at eu nuts 3 level a new composite and comprehensive measure of rurality and peripherality (the peripherurality indicator, pri): the higher this index, the more rural and peripheral a given region is. within a principal component analysis (pca) approach, this indicator takes into account both conventional socio-economic indicators and the relevant geographical characteristics of the region. on the basis of this analysis, the paper also puts forward a clusterisation of nuts 3 regions across europe and assesses the correlation between the rdp expenditure intensity, the pri and the different regional clusters. this analysis is aimed at assessing the coherence of rdp fund allocation with the real characteristics of eu rural space. keywords. rurality and peripherality, eu rural development policy, multivariate analysis jel codes. o18, q01, r58 introduction: the scope of the paper this paper aims to investigate the links between the degree of rurality in eu nuts 3 regions and the allocation of rural development policy (rdp) expenditures throughout this area. rural regions still play a key role within the eu economy and society, even though the relative dominance and major vitality of its urban space, from mega cities to the network of its medium-sized cities, has been repeatedly pointed out (espon, 2005). moreover, eu rural space faces new challenges and new opportunities which are due to ongoing major transformations and an increasing heterogeneity, especially after the enlargement of the eu towards eastern countries. * corresponding author: f.pagliacci@univpm.it. authorship may be attributed as follows: first section to sotte, second and fifth sections to pagliacci, third section to camaioni, fourth and sixth sections to esposti, seventh section to lobianco. 278 b. camaioni, r. esposti, a. lobianco, f. pagliacci, f. sotte with regard to this evolutionary pattern, the traditional urban-rural divide can be considered largely outdated (oecd, 2006). a new geography of eu rural space has emerged and new definitions and taxonomies are needed. although in previous studies on eu rural space major geographical issues were substantially ignored (copus, 1996; ballas et al., 2003; bollman et al., 2005; vidal et al., 2005; copus et al., 2008), a new representation of eu rural geography necessarily implies a proper consideration of how conventional rural features (e.g., low density, key role of agriculture, etc.) combine with geographical features (e.g., remoteness, integration with urban areas, etc.). in order to achieve this new representation of eu rural space, the present paper puts forward a new composite and comprehensive peripherurality indicator (pri), linking together both conventional rural and geographical features. such a multidimensional approach can help in defining different typologies of rural areas across the eu and, in turn, it could also support policy makers in better framing and targeting the eu rdp. the rdp is the second pillar of the common agricultural policy (cap) (funded by the european agricultural fund for rural development, eafrd). it supports the implementation of rural development programmes across the eu. at present, for the 2007-2013 programming period, the rdp is aimed at supporting rural areas which are facing new challenges by promoting their economic restructuring, enhancing the sustainable management of natural resources, helping regions to meet future social, economic and environmental challenges (sotte, 2009; esposti, 2011). the analysis of the current spatial allocation of rdp expenditure can help in assessing how these declared objectives match the real characteristics of the eu regions and their true degree of rurality. the most disaggregated territorial level at which the analysis can be performed is eu nuts 3 level. at this level, the spatial allocation of current rdp expenditure not only depends on the top-down ex-ante political decisions taken by the eu and/or the member states, but also by the bottom-up capacity of each territory (nuts 3 region) to attract and use these funds. the paper is organised as follows. the second section briefly summarises the in-depth debate about the definition of eu rural areas, ranging from the most “conventional” typologies proposed by the oecd (2006) and eurostat (2010). the role of a multidimensional (i.e., multivariate) approach and the relevance of often neglected geographical aspects is then stressed. in in the third section, the available data for a more thorough and comprehensive analysis of peripherality and rurality across europe, together with some critical issues, are presented. the fourth section briefly presents the adopted methodology, a combination of multivariate techniques (principal component analysis and cluster analysis) through which a composite peripherurality indicator (pri) is computed. the fifth section provides the main results of the analysis by showing how this pri is distributed across the eu space. a clusterisation of rural regions is also illustrated and discussed. the sixth section shows the allocation of rdp funds across nuts 3 regions, by emphasizing the links between this distribution, the computed pri and the identified urban-rural clusters. the final section concludes the paper, suggesting some possible directions for future research. concept, definition and classification of the rural space in the eu the concept of rurality according to its relevance among eu main priorities and policies, rural development has become one of the major topics in agricultural economics as well as in other social 279how rural is the eu rdp? an analysis through spatial fund allocation sciences. nevertheless, the concept of rural development remains a “disputed notion, both in practice, police and theory” (van der ploeg et al. 2000, p. 404). the lack of a strong and common theoretical foundation still affects most of the literature on the concept and definition of “rural” itself and on the consequent taxonomy. therefore, before providing taxonomies about the eu rural areas and assessing the allocation of eu rdp funds in this respect, the concept of “rural” has to be explicitly defined and discussed. in focusing on the proper definition of rurality, many studies pointed out the relevance of the major evolution which has affected agriculture and rural areas in both developing and developed countries over time (johnston, 1970; timmer, 1988; saraceno, 1994; basile and cecchi, 1997; romagnoli, 2002, sotte, 2003; sotte et al., 2012). those transformations, both from the historical and the geographical perspective, call for different approaches in classifying rural areas as well as in defining rural development policies. in this work, we follow the evolutionary concept of rurality and of rural development suggested in sotte (2003) and sotte et al. (2012). in the 50s and 60s, due to the still crucial role of agriculture, the concept of “rurality” and the identification and classification of rural areas were mainly based on sectoral variables (e.g., the share of the agricultural employment) (the so-called agrarian rurality model). since the 70s, the importance of agriculture in eu regions has fallen steadily. thus, the agrarian rurality model has been progressively replaced by the industrial rurality framework. this decline in agricultural activities was accompanied by rapid rural depopulation and urbanization (basile and cecchi, 1997). therefore, in the industrial rurality framework, rural areas were mainly defined and classified according to demographic criteria (i.e., population density). despite these generalized demographic trends, some rural regions still experienced successful development patterns, often based on manufacturing, thanks to other favourable conditions (economic dynamism, social mobility and cohesion, etc.) (esposti and sotte, 2002). mostly following these cases of “rural success”, in the 90s another form and concept of rurality emerged (the post-industrial rurality). two major elements characterise rural areas within this new model. first, the territorial dimension of rurality has now become relevant, especially in terms of a stronger integration across the rural space and between rural and urban territories. within this integration, the role assigned to the rural regions consists in supplying the society with a whole set of services associated to public goods, either environmental goods (e.g., clean air and water, biodiversity…) or “cultural” goods (e.g., landscape, historical heritage, agricultural traditions, etc.). the second element is that, given this large set of possible services, many different forms of rural-rural and rural-urban integration has emerged and may co-exist. polymorphism has thus become one of the key feature of the rural space within the post-industrial rurality, e.g., in post-industrial societies. together with the current co-existence of the three different models of rurality across the eu27, this polymorphism clearly affects how rural areas can be defined and classified. while none of the conventional measures (based on sectoral or demographic indicators) can capture these complex and polymorphic features, it seems increasingly evident that a proper definition and classification needs to be multidimensional and that it is still useful that these conventional indicators remain included within such multidimensionality. 280 b. camaioni, r. esposti, a. lobianco, f. pagliacci, f. sotte indicators, measures and typologies of rural areas: towards a multidimensional approach as mentioned, the debate about the concept of rurality inevitably opens the debate about how to properly define rural areas. actually, a univocal and homogeneous definition of rural areas is still lacking at international level (montresor, 2002; anania and tenuta, 2008). for example, the european commission (ec) does not provide any formal criteria to identify those areas where rural development policies are to be implemented: each member state is autonomously in charge of defining its own rural areas. this lack is due to the considerable differences in terms of demographic, socio-economic, and environmental conditions occurring across the eu rural space (european commission, 2006; hoggart et al., 1995; copus et al., 2008). moreover, it may also be attributed to the lack of homogeneous and comparable statistical information at territorial level which may foster the identification of a common statistical definition of rural areas (bertolini et al., 2008; bertolini and montanari, 2009). nevertheless, since the 90s, significant steps forward in providing a homogeneous definition have been taken and some general criteria are now widely accepted. the most well-known urban-rural typologies are those adopted by the oecd (1994; 1996; 2006) and the ec (eurostat, 2010). both follow a similar and simple approach, based on demographic density and on the presence of major urban areas (thus recalling the aforementioned industrial rurality model). according to this oecd-eurostat methodology, nuts 3 regions in eu27 member states are classified as predominantly urban (pu), intermediate (ir) and predominantly rural (pr). however, this approach suffers from a major drawback. it measures “rurality” using a single indicator (i.e., demographic density) and, then, this indicator is collapsed into a discrete ordinal variable that distinguishes only three typologies of rurality/urbanity. such measure seems too rough to capture the evident and increasing polymorphism observed across eu27 rural areas. actually, the emergence of a post-industrial concept of rurality makes the measures just based on density outdated and insufficient. recently, the oecd (and even the fao) has launched new research strands in order to put forward new and more comprehensive measures of rurality based on a qualified set of variables (faooecd report, 2007; the wye group, 2007). this is the underlying idea of multidimensional approaches to define and classify rurality. they consist in using a wide set of variables, usually ranging from socio-demographic (e.g. population density) and sector-based variables (e.g., the share of agriculture within the economy) to territorial/geographical features (e.g., land-use, remoteness, integration with the urban space, etc.). a thorough review of these multidimensional approaches can be found in copus et al. (2008). many of these works have identified the major typologies of rural areas across europe, by applying multivariate statistical approaches and by taking into account a broad list of socio-economic indicators. some of these analyses focus on either single (auber et al., 2006; buesa et al., 2006; kawka, 2007; lowe and ward 2009; merlo and zaccherini, 1992; anania and tenuta, 2008) or a few eu member states (barjak, 2001; psaltopoulos et al., 2006), while other works analyse the rural space across the whole eu. to mention a few, terluin et al. (1995) analyse lessfavoured areas in the eu15; copus (1996) analyses nuts 3 regions in the eu12 comparing aggregative and disaggregative methods (factor analysis and k-means cluster analysis) 281how rural is the eu rdp? an analysis through spatial fund allocation with about 45 socio-economic indicators. ballas et al. (2003) apply factor analysis and cluster analysis to eu27 nuts 3 regions and also suggest a sort of peripherality index. bollman et al. (2005) move from the original oecd urban-rural typologies and suggest an additional grouping of rural areas (leading, middle, lagging regions). vidal et al. (2005) analyse the spatial features of rural areas in the eu12, according to demographic, economic, sector-based and labour market variables. combining socio-economic and geographical features the present paper pursues the abovementioned multidimensional approach in order to analyse eu rural areas and at the same time suggests some further improvements in this direction. while the relevance of different (and conventional) socio-economic features in characterising rural areas is again stressed, an additional set of indicators covering geographical features is also proposed. the underlying idea is that geography matters when defining rural areas, as, according to the post-industrial rurality model, rurality and its different possible forms also have to do with the degree and quality of integration of a given area with the surrounding space. on the basis of this key idea, the paper adds a set of spatial/geographical variables to a more conventional set of indicators expressing rurality and its evolutionary stage (agrarian, industrial, post-industrial). few studies have concentrated on a link between the economic and geographical features, which are explicit in defining rural areas (cecchi, 1999; ballas et al., 2003), even though this has never been done at the nuts3 level across the whole eu27 space. in the present analysis, the four aforementioned dimensions of rurality are linked together by computing a composite and comprehensive indicator. it expresses the idea that rural areas can be defined according to an evolutionary combination of conventional features (e.g., population density and the role of agriculture) and indicators of their integration (or exclusion) with respect to the surrounding space. these are the major thematic areas considered here: • socio-economic indicators (population-based approach); • the role of agriculture (sector-based approach); • land use and landscape features (territorial approach); • accessibility/remoteness over different territorial scales (geographical approach). in particular, regional accessibility is considered as a key variable in this study. despite the rapid increase in the use of information and communication technologies (ict) and the efforts made to reduce the digital divide, remoteness still remains a major feature of many eu rural areas. several regions, although rural in a traditional sense, are closely integrated with (and provide many services to) the surrounding urban space. these spatial and geographical issues are included in this analysis taking into account two different perspectives: the distance from major urban areas and some indexes of potential accessibility. to achieve a synthetic indicator of this spatial dimension, a distance matrix between the centroids of all the eu nuts 3 regions is firstly computed. as remoteness usually refers to the distance from some major cities, for each region the distance from major eu urban areas is taken into account. in particular, the concept of mega (metropolitan economic growth area) is here used. megas are the most important urban areas among 282 b. camaioni, r. esposti, a. lobianco, f. pagliacci, f. sotte the european fuas (functional urban areas), according to population, transport, tourism, industry, knowledge economy, decision-making and public administration (espon, 2005). five typologies of megas are identified: global nodes (or global megas), category 1 megas, category 2 megas, category 3 megas and category 4 megas. secondly, regional remoteness is expressed according to the multimodal potential accessibility indexes. these indicators take into account the presence of physical infrastructures connecting regions, thus reducing travel times and costs. all of them measure how easily people living in one region can reach people located in other regions. both the multimodal accessibility index (measuring the minimum travel time between two regions by combining road, rail and air networks) and the air accessibility index (taking into account only the air network) are considered here. both indexes are computed by summing the population in all other european regions weighted by the travel time needed to reach them (espon, 2005)2. the combination of these two dimensions (distance from megas and accessibility indicators) provides a detailed and somehow original representation of the real eu geography, where remoteness and peripherality do not only depend on geographical distance from major urban areas but also on the endowment and quality of infrastructures allowing integration between the rural and the urban space. the dataset following such multidimensional approach, 24 variables are collected in order to identify the heterogeneity of eu rural areas (table 1). the variables refer to the aforementioned four different thematic areas: i) socio-demographic features (7 variables); ii) structure of the economy (7 variables); iii) land use (3 variables); iv) geography (7 variables). although most of these variables are conventional in multidimensional approaches to rurality (except the fourth area), some comments are needed for the variables “population” and “average sgm”. in the former case, it is worth noticing that all adopted variables are expressed in relative terms in order to make them independent on regional size that shows a remarkable heterogeneity in the sample. for instance, the group of variables “socio-demographic features” are meant to express the demographic structure and dynamics (as in the case of “population variation”) regardless the regional size. nonetheless, the lack of any variables expressing the regional size would prevent from separating those regions whose specificity is, in fact, being dominated by the presence of large towns or urban agglomerations. therefore, the “population” variable turns out to be necessary to isolate this group of highly urban regions and identify their main characteristics. variable “average sgm” is included among the second group of variables (“structure of the economy”), although, in fact, it does not represent the relevance of the agricultural sector within the regional economy but the characteristics of the regional farms both in terms of economic performance and size. therefore, it seems needed to distinguish “agricultural” regions due to a relatively underdeveloped economy from highly devel2 in order to avoid distorting “edge” effects, in computing accessibility and distances european regions bordering the territory covered by espon have also been taken into account. 283how rural is the eu rdp? an analysis through spatial fund allocation oped regions where agriculture still maintains a relevant share within the economy due to highly competitive farms. in the latter case, the strength and relevance of the agricultural sector within a given rural region is often caused by a strong integration with the urban space; this is not necessarily true in the former case (von thünen, 1826). these variables are collected at eu27 nuts 3 territorial level. although the nuts 2010 classification is currently in force (commission regulation (ec) no 105/2007), eurostat data have not yet been fully updated. therefore, for the purpose of the current work, the nuts 2006 classification (commission regulation (ec) no 1059/2003) is adopted. thus, the original sample size is composed of 1303 nuts 3 regions. however, further adjustments in the sample have been made to exclude specific regions. in particular, regions far from the european continent have been excluded (the nuts 3 regions belonging to the french departements d’outre-mer and to the spanish and portuguese atlantic islands). thus, the final sample is made up of 1288 nuts 3 regions. the nuts 3 level allows a detailed representation of eu rural space. previous studies mainly focused on the nuts 2 level (see, for instance, shucksmith et al., 2005) which is, in fact, too large a scale to be representative in terms of rural features: most nuts 2 regions usually include both urban and rural space. an even smaller scale (e.g., the lau 2 level) could improve the analysis further but it is unfeasible given the current data availability for all eu member states. nonetheless, working at nuts 3 level may still lead to some practical problems. firstly, some of the adopted variables (table 1) are not available at nuts 3 level for all eu countries. secondly, even when available in principle, several variables show a large amount of missing values. missing observations have been replaced with data observed at the closest higher territorial aggregation that is either nuts 2 or nuts 1 level. a third issue concerns the considerable size heterogeneity of nuts 3 regions in the eu27. in fact, nuts 3 regions in peripheral and more sparsely-populated countries tend to be larger than nuts 3 regions in more central areas. a final issue about the nuts 3 territorial scale has to do with its appropriateness for policy analysis. in particular, it may be debatable whether this scale is appropriate when analysing fund allocation for those policies whose decisions are taken at a higher level (eu or country level). this is the case of the rdp studied here. this issue will be discussed in more detail in next sections according to the time coverage of the available data, the analysis focuses on the last observed year, ranging between 2006 and 2010. as most of the selected variables are structural, it is reasonable to assume that they are not significantly influenced by the negative economic trend which has started in 2008. in addition, two variables are included to express the main long-term dynamics within the eu in terms of population growth (2000-2010 variation) and the change in multimodal accessibility (2001-2006 variation). a new composite measure: the peripherurality indicator (pri) the 24 variables described in table 1 are expected to capture the heterogeneity of rurality and of its evolutionary stages across eu space. the passage from such multidimensional set of features to a composite measure of rurality, and then to rural typologies, is here obtained following a 3-step methodology. first of all, conventional principal component analysis (pca) is applied to the 24 elementary variables. this technique reduces the dimension of the problem to be 284 b. camaioni, r. esposti, a. lobianco, f. pagliacci, f. sotte table 1. variables adopted in the analysis grouped in 4 thematic areas. variable definition year source mean standard deviation so ci ode m og ra ph ic fe at ur es population resident population (000) 2010 eurostat 386.00 462.34 population variation average annual variation (in %) of the resident population 20002010 eurostat 0.15 0.74 net migration rate ratio of the difference between immigrants and emigrants with respect to the average population, including statistical adjustments 2010 eurostat 1.22 5.36 density ratio of the resident population on the total surface of a given area (in km2) 2010 eurostat 456.23 1056.67 unemployment rate unemployed population (aged 15-64) as % of the total economically active population 2009 eurostat 8.36 3.82 young-age dependency ratio ratio of the number of people aged 0-14 with respect to the number of people aged 15-64 2010 eurostat 22.45 3.71 aged dependency ratio ratio of the number of people aged 65+ with respect to the number of people aged 15-64 2010 eurostat 29.02 6.39 st ru ct ur e of th e ec on om y gva agriculture (%) share of gva from sector a (nace classification rev. 2) on the total 2009 eurostat 2.94 3.36 employment agriculture (%) share of employment in sector a (nace classification rev. 2) on the total 2009 eurostat 7.22 9.43 employment manufacturing (%) share of employment in sectors c-e(nace classification rev. 2) on the total 2009 eurostat 18.84 8.06 employment services (%) share of employment in sectors g-u(nace classification rev. 2) on the total 2009 eurostat 66.43 12.36 per capita gdp gdp in euro per inhabitant (pps) 2009 eurostat 21,945 9,465 average farm size average agricultural area (in ha) per agricultural holding 2007 eurostat (farm structure survey) 42.74 52.57 average sgm average standard gross margin (in esu) per agricultural holding 2007 eurostat (farm structure survey) 41.13 42.13 285how rural is the eu rdp? an analysis through spatial fund allocation investigated, while preserving most of the original statistical information (everitt and hothorn, 2010)3. after the extraction of the principal components (pcs), it is possible to compute a standardised score for each statistical unit (i.e., for each of the 1288 eu nuts 3 regions under study). the second step consists in using these pc scores as input for a conventional cluster analysis (ca). the 1288 regions are grouped according to the extracted pcs and respective scores, in such a way that the units in the same cluster are more similar (in terms of rurality and peripherality) to each other than to those belonging to other groups (kaufman and rousseeuw, 1990). considering the specific dataset and problem under study, a hierarchical cluster analysis is performed here, as this approach seems more suitable for properly handling outliers and it does not require the ex ante definition of the 3 due to these properties, the use of the pca to obtain a composite measure of rurality is not new in this literature (nui maynooth et al., 2000; ocana-riola and sánchez-cantalejo, 2005; vidal et al., 2005; nordregio et al., 2007; bogdanov et al., 2007; monasterolo and coppola, 2010). variable definition year source mean standard deviation la nd u se artificial areas (%) share of total surface which is covered by artificial areas (urban fabric, industrial and commercial units…) 2006 corineeurostat 12.88 17.18 agricultural areas (%) share of total surface which is covered by agricultural areas 2006 corineeurostat 51.31 20.73 forests (%) share of total surface which is covered by forests and other semi-natural areas 2006 corineeurostat 32.82 21.90 g eo gr ap hy (s pa tia l d im en sio n) air accessibility the index is calculated by summing the population in all the other eu nuts 3 regions, weighted by the travel time to go there by air. values are standardised with the eu average (eu27=100) 2006 espon (project 1.1.1) 92.94 37.55 multimodal accessibility the index is calculated by summing the population in all the other eu nuts 3 regions, weighted by the travel time to go there by road, rail and air. values are standardised with the eu average (eu27=100) 2006 espon (project 1.1.1) 95.65 38.54 multimodal accessibility change relative variation (in %) of the multimodal accessibility index 20012006 espon (project 1.1.1) 10.11 12.22 distance from mega1 distance from closest mega1 (centroid) authors’ elaboration 264.95 257.70 distance from mega2 distance from closest mega2 (centroid) authors’ elaboration 203.48 174.76 distance from mega3 distance from closest mega3 (centroid) authors’ elaboration 153.42 140.80 distance from mega4 distance from closest mega4 (centroid) authors’ elaboration 108.86 85.05 table 1. continued. 286 b. camaioni, r. esposti, a. lobianco, f. pagliacci, f. sotte number of clusters4. both the pca and the ca stress the multidimensional characteristics of rurality in europe. nonetheless, they are still unable to provide a comprehensive and univariate (i.e., synthetic) measure of rurality of any given eu region. therefore, the third methodological step consists in using the pc scores to compute a composite peripherurality indicator (pri). to do this, an ideal region with “extreme” urban features is identified. this ideal region represents a sort of urban benchmark across the eu and it is defined on the basis of the two eu global megas: paris and london (espon, 2005). for each extracted pc, the average score is computed for the two nuts 3 regions of paris and london, thus representing the scores for this ideal eu benchmark. secondly, the statistical “distance” between any nuts 3 region and this ideal urban benchmark is computed as the euclidean distance over the k-dimensional space of the pcs extracted5: ∑ ( )= − ∀ = ∀ =pri y y i n p k, 1,..., and 1,...,i ip ubpp 2 (1) where n = 1, …., n indicates the set of regions under consideration, yip represents the i-th region’s score for the p-th pc and yubp represents the urban benchmark’s score for the p-th pc. by construction, the greater the pri the more rural and/or peripheral the i-th region is. the pri captures both a socio-economic and a geographical (spatial) distance from “urbanity”: therefore, here it is called the peripherurality indicator (pri). main results: the eu rural space principal components and cluster analyses table 2 (upper part) shows the results of the extraction of the pcs6. following the guttman-kaiser criterion7 six pcs should be extracted. the same indication emerges from 4 when studying the urban-rural typologies, both hierarchical and partitioning approaches have been adopted: copus (1996) and vidal et al. (2005) applied partitioning methods; buesa et al. (2006) and dimara and skuras (1996) adopted aggregative (hierarchical) clustering approaches. 5 the euclidean distance is used to compute the pri from the selected pcs because this distance metric is more sensitive to extreme values (therefore, it highlights more extreme rural/urban features) than other distance metrics, e.g., the manhattan distance. the adopted metric implicitly assumes a sort of complementarity among the k different “dimensions” (in this case, different pcs) over which the distance is computed. one can argue that this complementarity might overemphasize those features of rurality/urbanity that are redundant across these different dimensions. in the present case, however, the euclidean distance is computed on pc scores and pcs are orthogonal by construction, thus they do not contain redundant information. for this reason, it seems preferable to apply this distance calculation to the extracted pcs rather than to the original 24 elementary variables where, definitely, a significant redundancy can be observed. 6 it is preliminarily helpful to test whether the selected variables are suitable for pc extraction. the kaisermeyer-olkin (kmo) test is applied on the original variables. this is a test of sampling adequacy calculated as the ratio between the sum of squares of all correlations of the variables and the same sum plus the sum of all bivariate partial correlations. if this ratio is low all variables do not share much variance and the pc extraction becomes less meaningful. the kmo test ranges from 0.0 to 1.0. according to kaiser (1974), scores lower than 0.5 are unacceptable, [0.5, 0.6) are miserable, [0.6, 0.7) are mediocre, [0.7, 0.8) are middling, [0.8, 0.9) are meritorious, [0.9, 1.0) are marvellous but satisfactory values should be greater than 0.5. in the present case, the kmo test on the variables under study is fully satisfactory (.738). 7 the guttman-kaiser criterion suggests choosing those principal components which are able to explain at least 70-80% of the cumulative variance. 287how rural is the eu rdp? an analysis through spatial fund allocation the analysis of the eigenvalues (pc with eigenvalues greater than 1). however, there is a substantial drop in all indicators between the 5th and the 6th pcs. thus, in order to make the interpretation easier, only the first 5 pcs are extracted. they account for 67.46% of total variance, with each of them explaining at least 5% of total variance and showing an eigenvalue greater than 1.5. to provide an interpretation of these 5 pcs, the respective factor loadings are reported in the lower part of table 2. factor loadings are the correlation coefficients between the original variables and the pcs. factors are regarded as not significant (and not shown in table 2) when they are smaller than |.15|. the sign and the magnitude of these factor loadings allow an economic interpretation, and hence labeling, to be attributed to the extracted pcs. pc1 – economic and geographical centrality: this refers to both geographical and economic variables. it is positively related to accessibility indexes, share of employment in services, per capita gdp, share of artificial areas and demographic density. it is negatively related to the distance from megas and the relevance of the agricultural sector. thus, pc1 sums up most of the characteristics of “urbanity” in terms of both economic centrality and accessibility. pc2 – demographic shrinking and ageing: this pc mainly refers to socio-demographic features. it is positively related to the aged dependency ratio, whereas it is negatively related to the annual population variation, young-age dependency ratio and net migration rate. this pc thus captures two interrelated social phenomena, demographic shrinking and population ageing, which are deeply affecting many rural regions across europe. pc3 – manufacturing in rural areas: this pc is positively linked to the share of employment in manufacturing activities and to the share of agricultural areas. the youngage dependency ratio is also positively related to the pc, while a negative factor loading is observed for the unemployment rate. this can be explained by the fact that, across europe, manufacturing regions usually show a better performing labour market. pc4 – land use: forests vs. agricultural areas: this pc captures land use characteristics, by distinguishing agricultural regions from regions covered by forests. the average farm size also shows a negative factor loading. regions with the highest scores for this pc are mountain regions (e.g., the alps, the pyrenean region and northern scandinavia), while the lowest scores are observed in the north-western european plain areas. pc5 – urban dispersion: this pc is positively related to demographic density, % of artificial areas on the total, % of employment in manufacturing activities. thus, positive values for pc5 are associated to urban and industrial areas. however, pc5 is also negatively related to annual population variation, net migration rate and the young-age dependency ratio. thus, it captures a sort of declining “urbanity”, or urban dispersion associated to industrial decline. on the basis of the selected pcs, a standardized score can be assigned to each nuts 3 region. moving away from these factor scores, regions can be clustered applying a hierarchical ca (the agglomerative algorithm agnes is used)8 that generates the whole hierarchy of clusters. finally, seven clusters of homogeneous regions are identified. cluster centres for the five pcs are reported in table 3 (upper part). according to these results, 8 agnes is the acronym of agglomerative nesting. the algorithm is included into the ‘cluster’ package of software r (r version 2.15.2 has been used). 288 b. camaioni, r. esposti, a. lobianco, f. pagliacci, f. sotte the clusters can be interpreted and labelled as follows: i) peripheries; ii) nature-quality regions; iii) cities; iv) remote regions; v) mixed-economy regions; vi) shrinking regions; vii) manufacturing regions. table 2. pc extraction (eigenvalues and variance explained of the first 7 pcs) and factor loadings (only significant values, ≥ |.15|, are reported). variable pc1 pc2 pc3 pc4 pc5 pc6 pc7 pc extraction eigenvalues 7.61 2.82 2.08 1.90 1.78 1.20 0.91 % of variance 31.71 11.74 8.66 7.92 7.44 4.99 3.79 cumulative % of variance 31.71 43.45 52.11 60.03 67.46 72.46 76.24 pc factor loadings so ci ode m og ra ph ic fe at ur es population -0.302 -0.175 population variation -0.348 -0.401 net migration rate -0.201 -0.327 density 0.176 -0.237 -0.317 0.350 unemployment rate -0.346 -0.231 young-age dependency ratio -0.270 0.199 -0.234 -0.286 aged dependency ratio 0.388 0.194 st ru ct ur e of th e ec on om y gva agriculture (%) -0.287 employment agriculture (%) -0.290 employment manufacturing (%) 0.381 0.274 0.326 employment services (%) 0.272 -0.290 -0.268 per capita gdp 0.248 0.165 average farm size 0.412 -0.201 -0.214 average standard gross margin 0.383 -0.283 la nd u se artificial areas (%) 0.217 -0.186 -0.301 0.343 agricultural areas (%) 0.403 -0.479 forests (%) 0.541 -0.229 g eo gr ap hy (s pa tia l di m en sio n) air accessibility 0.314 multimodal accessibility 0.322 0.151 multimodal accessibility change 0.162 distance from mega1 -0.280 -0.168 -0.183 distance from mega2 -0.296 distance from mega3 -0.293 -0.157 distance from mega4 -0.209 -0.226 -0.229 source: authors’ elaboration. 289how rural is the eu rdp? an analysis through spatial fund allocation the eu geography emerging from the ca can be illustrated through the territorial distribution of the seven clusters (figure 1) as well as through the distribution of the number of nuts 3 regions, resident population and total area across clusters (table 3, lower part). nevertheless, a detailed description of the characteristics and territorial distribution of clusters goes beyond the scope of the present study. here, our main interest is the representation of eu space that the cluster output generates. table 3. defining typologies: cluster centres according to the 5 pcs and size in terms of number of regions, population, total area. clusters pc1 pc2 pc3 pc4 pc5 1. peripheries -3.25 -0.65 -0.68 0.08 -0.43 2. nature-quality regions -0.10 -0.07 -0.41 1.43 -1.40 3. cities 3.42 -1.47 -1.29 -0.15 0.97 4. remote regions -6.33 -0.89 0.00 -0.77 1.89 5. mixed-economy regions 1.10 -0.01 0.85 -1.06 -0.72 6. shrinking regions 0.38 4.09 -1.70 -1.10 0.46 7. manufacturing regions 0.54 0.42 1.16 1.10 0.53 clusters no. nuts 3 regions population (000 inhab.) area (km2) % nuts 3 regions % population % area 1. peripheries 204 74,965 1,516,377 15.84 15.08 35.22 2. nature-quality regions 140 42,546 774,287 10.87 8.56 17.98 3. cities 185 133,075 118,173 14.36 26.77 2.74 4. remote regions 77 27,065 411,722 5.98 5.44 9.56 5. mixed-economy regions 315 132,382 927,612 24.46 26.63 21.54 6. shrinking regions 91 10,774 93,351 7.07 2.17 2.17 7. manufacturing regions 276 76,370 463,983 21.43 15.36 10.78 total 1,288 497,177 4,305,504 100 100 100 source: authors’ elaboration. the pri in order to achieve a composite measure of rurality, the pri is computed applying (1) to the regional factor scores obtained from pca. the pri is expressed as the distance from an urban benchmark: thus, the greater this indicator, the more rural and/or peripheral is the given region. in figure 2, the values of the pri are mapped for the whole set of 1288 nuts 3 regions. as expected, the lowest values are observed in capital-city regions and, more generally, in the urban space. on the contrary, the highest values are observed for mediterranean regions, and for regions located in central-eastern europe and in northern scandinavia. looking at figure 2 from a more general perspective, the pri shows a wide range of variation, from very urban contexts (e.g., eu capital cities) to deep rural and remote conditions (peripheral eu areas). therefore, this indicator draws a relatively new geography of eu regions, by including different but interdependent features in a single and comprehensive measure. 290 b. camaioni, r. esposti, a. lobianco, f. pagliacci, f. sotte figure 1. territorial distribution of the seven clusters. source: authors’ elaboration. figure 2. pri values across eu nuts 3 regions. pri source: authors’ elaboration. 291how rural is the eu rdp? an analysis through spatial fund allocation although groups of regions with relatively homogeneous values can be identified, a large variability in the values of pri is observed when considering the seven clusters previously identified. table 4 shows the average values (arithmetic means) and the standard deviations of the pri across clusters. on average, the pri ranges from 11.32 (“cities”) to 18.50 (“remote regions”). some clusters share very similar pri average values. although standard deviations are generally low, it is debatable whether the difference observed in the average pri has any statistical significance or not. to answer this question, an anova (analysis of variance) was performed. it suggests that no statistical difference in the value of the pri occurs between mixed-economy regions and manufacturing regions and between manufacturing regions and nature-quality regions. these clusters somehow represent a sort of middle ground between “urbanity” and “rurality”. while some clusters clearly identify the urban space and others clearly show rural and peripheral features, “intermediate” clusters identify the combination of urban and rural features in a well-integrated continuum, representing one of the key features of european space9. table 4. pri across clusters: averages and standard deviations.   pri mean standard deviation 1. peripheries 16.74 0.78 2. nature-quality regions 15.46 0.74 3. cities 11.32 2.03 4. remote regions 18.50 0.91 5. mixed-economy regions 14.99 0.85 6. shrinking regions 16.28 1.06 7. manufacturing regions 15.18 0.76 source: authors’ elaboration. the allocation of rdp funds across the eu space in order to finally analyse the spatial allocation of the rdp funds at the adopted territorial scale, thus investigating its relationship with the aforementioned definitions of (periphe)rurality, data have also been collected for the expenditure of the european agricultural fund for rural development (eafrd). we consider data on total eafrd real expenditures, taking into account the total real payments as registered ex post by the eu bureaus. payments at individual (anonymous) beneficiary level are then aggregated at nuts 3 level, according to the nuts 2006 classification (about 1300 regions). years 2007 to 2009 are considered. by themselves, these expenditure data do not allow directly representing the different support across regions due to their considerably different size. therefore, the analysis on 9 this feature is particularly evident in some eu countries or macro-regions; for instance, the north-eastern and central italian regions (esposti and sotte, 2002). 292 b. camaioni, r. esposti, a. lobianco, f. pagliacci, f. sotte fund allocation is performed here by means of three indexes expressing the expenditure intensity: 1. rdp expenditure per unit of utilised agricultural area in ha (€/uaa); 2. rdp expenditure per agricultural annual working unit (€/awu); 3. rdp expenditure per thousand euros of agricultural gross value added (€/.000 €). data on utilised agricultural areas (uaa) and annual work units (awu) are collected from the eurostat farm structure survey (2007). data on agricultural gva are taken from eurostat national accounts (the average value for years 2007 to 2010 is considered). according to the declared objectives, the final step of the present analysis consists in linking these findings with the territorial allocation of the eu policy and funds dedicated to rural space, defined as the rdp. this link may eventually show to what extent the rdp is “rural”, that is, to what extent its funds prevalently go to more rural regions. this research question is not new as previous studies have already investigated the territorial allocation of eu rdp funds (shucksmith et al., 2005; crescenzi et al., 2011). however, these works have , at the most, considered the nuts 2 level, and the allocation of rdp support did not concern real expenditure but only the ex ante allocation of funds (as established by political decisions taken at eu and national levels), or the reconstruction of real expenditure based on some sample observations (e.g., fadn data). moreover, these investigations limited their attention to the eu15. therefore, what is new in the present analysis is the higher level of territorial disaggregation (nuts 3 level) and coverage (eu27), and the nature of the expenditure data. the latter are the total real payments as registered ex post by the eu bureaus aggregating individual beneficiaries at nuts 3 level. a further novelty is that, while previous studies prevalently linked eu support to the degree of rurality expressed through conventional indicators (mostly the oecd-eurostat urban-rural typologies), here rurality is measured though a comprehensive and continuous indicator, the pri. it can be argued that the nuts 3 territorial scale might not be appropriate for this kind of policy analysis, that is to say, for investigating the distribution of policies whose ex ante allocation decisions are taken at a higher territorial and institutional level (eu, nuts 0 or nuts 1 level). in fact, this is the main reason why working at nuts 3 level with real expenditure data may offer greater insight than previous works. the expenditure observed at this territorial scale does not only depend on ex ante top-down political decisions but also on the bottom-up capacity of territories to attract and really use these funds. this kind of policy evaluation, therefore, does not only concern political decisions but also has to do with the real implementation of policies across space. with this implementation, the underlying higher-level political decision is only one of the factors involved. the other contribution is the capacity and the specific features of individual territories (nuts 3 regions) which are likely to affect the expenditure they really receive. as discussed in previous sections, the allocation of the overall rdp funds for the period 2007-2009 is investigated here by looking at the rdp expenditure intensity. this indicator allows comparability despite size heterogeneity across the regions. figures 3-5 map expenditure intensity per uaa, per agricultural awu, per thousand euros of agricultural gva. the values show a remarkable heterogeneity, but the overall picture also significantly changes with the three indicators. for example, rdp expenditure intensity per unit of 293how rural is the eu rdp? an analysis through spatial fund allocation figure 3. 2007-2009 rdp expenditure per unit of utilised agricultural area (€/uaa). source: authors’ elaboration. figure 4. 2007-2009 rdp expenditure per agricultural annual working unit (€/awu). source: authors’ elaboration. 294 b. camaioni, r. esposti, a. lobianco, f. pagliacci, f. sotte uaa is particularly low in the plain regions of northern france and in spain. conversely, rdp expenditure intensity per agricultural gva (in thousand €) is particularly high in the regions of eastern european countries due to their lower agricultural gva levels. looking at the territorial distribution of these expenditure intensities, a preliminary remark concerns the particularly high values observed in a few cases. analysing in detail the rdp expenditure intensity at nuts 3 level, some outliers can be detected: they mainly refer to urban areas, where uaa and awu are quite small but expenditure is still significant as several rdp beneficiaries are located in these regions. this implies “artificially” high levels of expenditure intensity. thus, according to the distribution of these indexes (and by considering very high thresholds), these outliers have been eliminated from the dataset. after excluding these outliers from the dataset, the number of the observations under investigation is of 1273, 1271 and 1284 regions, respectively. table 5 reports the value of the expenditure intensities per urban-rural eurostat typology and per cluster. when the eurostat typologies are considered, the evidence fully confirms the expectations: pr regions are more supported than pu regions, both in terms of expenditure levels and intensities. thus, the eu rdp seems properly targeted towards more rural areas. however, when the expenditure distribution across the seven clusters is considered, a more complex pattern emerges. in terms of expenditure per unit of uaa, “shrinking regions”, “nature-quality regions”, “manufacturing regions” and “peripheries” are more supported than other clusters. on the contrary, both “remote regions” and “mixed-econfigure 5. 2007-2009 rdp expenditure per unit of agricultural gross value added (€/.000 €). source: authors’ elaboration. 295how rural is the eu rdp? an analysis through spatial fund allocation omy regions” receive much lower support. the expenditure per agricultural awu tends to be higher in the “shrinking regions” and “nature-quality regions”, while, once again, “remote regions” receive less support than all the other clusters. this evidence is at least partially reversed in the case of expenditure per agricultural gva: in this case, “remote regions” receive higher support, due to the lower value of agricultural gva registered in these areas. the differentiated support intensity emerging for the clusters demonstrates that the allocation of rdp expenditure across eu space is much more articulated and controversial than it appears to be by simply looking at the eurostat urban-rural typologies. table 6 shows the correlation coefficients between the rdp expenditure levels and intensities and two alternative indicators of rurality: a quite conventional and frequently adopted indicator (i.e. population density); a multidimensional composite indicator taking explicitly into account the spatial dimension, i.e., the pri. firstly, it is worth noticing that pri expresses rurality in the opposite direction to density: the greater the pri, the greater the degree of rurality. table 6 provides contradictory evidence if we look at expenditure levels. a higher density (lower rurality) implies lower expenditure, but the same occurs with a higher pri (higher rurality). therefore, these indicators are apparently not concordant in capturing rurality and the consequent rdp expenditure allocation. when considered in terms of expenditure intensity, however, the correlations seem to be more coherent. whatever indicator of rurality we consider, it does not seem to be statistically correlated to rdp expenditure per uaa. on the contrary, the expenditure per agricultural awu is lower in more rural regions regardless of the indicator we adopt. the opposite is found in the case of rdp expenditure per agricultural gva: support is higher in more rural regions, although this correlation is not statistically significant when rurality is expressed simply by population density. table 5. 2007-2009 rdp expenditure per urban-rural typology and cluster (regional averages). total expenditure (000 €) expenditure per uaa (ha) expenditure per awu expenditure per agric. gva (in .000 €) urban-rural typology predominantly rural (pr) regions 19,130 130.76 3,048.21 154.72 intermediate (ir) regions 10,611 111.33 2,997.10 117.72 predominantly urban (pu) regions 5,786 101.07 2,625.86 89.82 clusters 1. peripheries 23,393 135.55 1,801.96 137.13 2. nature-quality regions 19,059 147.76 4,720.54 180.06 3. cities 4,304 120.35 3,273.43 96.04 4. remote regions 14,389 60.44 533.27 124.88 5. mixed-economy regions 11,933 69.89 2,067.80 76.20 6. shrinking regions 6,934 152.09 7,797.13 242.67 7. manufacturing regions 9,851 141.76 2,714.20 127.04 source: authors’ elaboration. 296 b. camaioni, r. esposti, a. lobianco, f. pagliacci, f. sotte table 6. pearson correlation coefficients between rdp expenditure and different indicators of rurality (p-values in parenthesis).   density pri total expenditure -0.051 (0.066) -0.094* (0.001) expenditure per uaa 0.033 (0.245) -0.023 (0.416) expenditure per awu 0.091* (0.001) -0.073* (0.009) expenditure per agri gva -0.009 (0.760) 0.090* (0.001) *: statistically significant at 5%. source: authors’ elaboration. some concluding remarks the aim of this paper is to analyse the distribution of rdp support across the eu27 space and, in particular, to assess to what extent this supposedly “rural” policy really supports rural regions more than non-rural, or urban, ones. answering these empirical research questions, however, brings to light a preliminary and preparatory conceptual and practical issue, that is, how to properly define “rurality”. in this regard, the paper tries to go beyond the conventional definition of urban-rural typologies proposed by the oecd (1994; 1996; 2006) and eurostat (2010). a multidimensional approach is suggested in order to capture the multiple features and the considerable heterogeneity within the eu rural space. while remoteness and peripherality still represent major weaknesses for many rural regions, it is true that there are many examples of rural areas showing high integration with the urban space and good economic and social performance. at the same time, the urban space may itself encounter serious difficulties and several urban areas show a clearly declining tendency. in the middle, a wide and heterogeneous intermediate space can hardly be interpreted according to the rough urban-rural dichotomy as it presents both dimensions, both manufacturing activities and agricultural specialisation, both good performance and declining trends. rurality must therefore be measured with a composite and comprehensive indicator (the pri is proposed here) and at an appropriate territorial scale (nuts 3 is considered here). by computing the pri at nuts 3 scale, the analysis of the degree and characteristics of rurality suggests a more complex geography at eu level. following this “geography”, the analysis of the spatial allocation of rdp expenditure may also provide unexpected evidence. on the basis of the results obtained, the eu rdp seems less “rural” than stated in the political intentions. in relative terms (per unit of land and, above all, of labour), urban and central regions tend to be more supported than strongly rural and peripheral ones. in fact, this is not a completely new and surprising result. although on a different geographical scale and coverage and using different policy data, previous findings have already questioned a clearly positive link between the degree of rurality and the amount of support delivered through the eu rdp (shucksmith et al., 2005, crescenzi et al., 2011). 297how rural is the eu rdp? an analysis through spatial fund allocation in fact, assuming this is one major purpose of this policy, the rdp does not apparently induce any real redistributive effect from the urban to the rural space throughout the eu. therefore, the present paper confirms that empirical evidence seriously challenges the territorial targeting of this eu policy and that a further research effort is needed to understand the main forces behind this spatial allocation and to analyse more thoroughly the rdp expenditure by looking at the spatial allocation by single axes and measures. nonetheless, the evidence provided here goes beyond the policy issue of better targeting the rural policy to the rural space. it also questions how the rural space itself is defined and identified within the eu. urban-rural typologies and, more generally, dichotomous or discrete variables, but also univariate indicators (such as population density), do not provide an accurate enough representation of eu geography and, therefore, of the rural space. the multidimensional nature of rurality involves socio-economic characteristics, the structure of the economy, remoteness and peripherality. this multidimensionality also implies that rurality is naturally heterogeneous in its characteristics, especially across a quite diverse space like the eu27. all these features must be captured by composite and comprehensive indicators in order to allow a more accurate and insightful analysis of the link between policy support and the degree and nature of rurality. acknowledgments an earlier version of this paper was presented at the 2nd aieaa conference “between crisis and development: which role for the bio-economy”, 6-7 june, 2013, parma, italy. this study is part of the wwwforeurope research project funded by the european community fp7/2007-2013 under grant agreement n° 290647. the authors would like to thank two anonymous referees and the editor for their useful suggestions on a earlier version of the paper. references anania, g. and tenuta, a. 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(1826). die isolierte staat in beziehung auf landwirtshaft und nationalökonomie. pergamon press, new york (english translation by wartenberg c.m. in 1966, p.g. hall, ed.). in memory of giovanni anania it is with deep sadness that we announce the untimely and sudden death of our friend and colleague prof. giovanni anania on wednesday, 15 july. prof. anania was one of the founders of the aieaa and indeed one of its most active promoters. an assiduous researcher at the international level, prof. anania was also full professor at the university of calabria (italy) and president of the european association of agricultural economists (eaae). we remember him for his scientific commitment, his moral integrity and his extraordinary human qualities. the next issue of bae will include a section with notes by colleagues in memory of giovanni. bio-based and applied economics 4(3): 201-234, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-17151 accounting for growth in global agriculture keith fuglie economic research service, u.s. department of agriculture, washington, dc date of submission: august 21st, 2015; accepted october 4th, 2015 abstract. rising prices of agricultural commodities have renewed concerns about constraints to agricultural productivity. to assess productivity trends, total factor productivity (tfp) is generally preferred to partial productivity indexes as an indicator of technical and efficiency changes because it is more closely related to the unit costs of production. but measuring tfp is demanding of data, and developing comprehensive and comparable indexes of international agricultural tfp has been challenging. this study proposes a growth accounting approach, using fao data on quantity changes in inputs and outputs and aggregating input changes using cost shares derived from other sources, as a consistent way of constructing agricultural tfp indexes for world agriculture. this produces aggregate growth rates for agricultural output, input and tfp at the country, regional and global levels. results suggest that the rate of agricultural tfp growth accelerated in recent decades, especially in developing countries. most regions of the world now rely on productivity-based growth rather than resource-based growth to raise agricultural output. keywords. growth accounting, technical change, total factor productivity jel codes. q16, o13 1. introduction the reversal of the long-run decline in global prices for agricultural commodities that occurred in the first decade of the 21st century raised concern that the rate of productivity growth in world agriculture may have slowed. if so, this would pose serious challenges to meeting projected future growth in the global demand for food and exacerbate environmental degradation as more resources would be converted to produce food. in fact, there is evidence of a slowdown in the rate of growth in cereal grain yields (alston et al., 2009). however, single-factor productivity measures like crop yield mix the effects of technical change with intensified use of other inputs like fertilizer and irrigation. since total factor productivity (tfp) accounts for the contributions of all inputs to production, it is a better indicator of technical or efficiency improvements and more closely associated with changes in production cost. * corresponding author: kfuglie@ers.usda.gov. 202 k. fuglie tfp is the real output produced by a firm or industry over a period of time divided by the real input used by that firm or industry over the same period of time (where real input refers to the combined use of land, labor, capital and material resources employed in production). it is often difficult to provide meaningful definitions of real output or real input due to the heterogeneity of outputs produced and inputs used. however, it is possible to provide meaningful definitions of the output growth and input growth between any two periods of time using index number theory (caves et al., 1982). using information on output and input quantities and prices alone from two or more points in time, one can derive indexes of output and input growth, with the difference in their growth being defined as the growth in tfp. the measurement challenge for tfp growth is whether information is available to represent output and input quantities and prices at a sufficiently disaggregated level to account to quality differences in these measures. most of the readily available data for measuring tfp growth is found in high-income countries, which is why most agricultural tfp studies have focused on these countries. from a series of studies compiled by alston et al. (2010), it appears that the growth rate of agricultural tfp may have slowed in a number of developed countries (namely, australia, the united kingdom, south africa and possibly the united states), lending further support to the productivity slowdown hypothesis. for most countries, and for the world as a whole, estimates of agricultural tfp can only be approximated. while there are limitations with the coverage of output data, the larger challenge is with the input data. while the fao provides quantity information on several of the major inputs used in agricultural production, data on their costs and prices is mostly lacking. arnade (1994) used data envelope analysis (dea) to get around the lack of input price data, solving linear programming problems to trace out a world agricultural productivity frontier and determine the distance of each country to that frontier. in this framework, shifts in the frontier over time (defined over all or a group of countries) represents technical change, whereas the distance of a particular county to the frontier represents technical inefficiency of that country. together, changes in technology and efficiency sum to the rate of growth in tfp. coelli and rao (2005) give a summary of studies that have used this approach and provide updated estimates comparing agricultural tfp among 93 countries over 1980-2000. however, the dea method is sensitive to the “dimensionality” issue: as more or fewer countries or inputs are added to the analysis, results change. it is also sensitive to outliers. further, as coelli and rao (2005) point out, the solutions to the linear programming problems provide shadow values for the inputs, which in many cases appear to be implausible, at least at the country level (implying, for example, that the marginal values of land, labor and/or other inputs are often zero). another approach to dealing with the lack of input prices (or actually, lack of input cost shares), was proposed by avila and evenson (2010). they used input cost shares estimated from agricultural censuses in brazil and india to impute cost shares for other developing countries (specifically, they applied india cost shares to asia and africa and brazilian cost shares to latin america, making some adjustments based on relative input intensities per hectare). applying these cost shares to input quantities from fao, they estimates agricultural tfp growth rates for 78 developing countries for 1961-1980 and 1981-2000. unlike many of the dea models which found that agricultural tfp growth was apparently negative – even for countries experiencing the green revolution of the 1970s and 1980s – avila and evenson (2004) reported positive and accelerating tfp growth for these developing countries. 203accounting for growth in global agriculture nin-pratt and yu (2010) used the avila and evenson (2010) cost shares to set upper and lower bounds on the input shadow values in a dea application (which they called “constrained” tfp) to generate agricultural tfp estimates for 63 developing countries over 1967-2006. they found significant differences between estimates of constrained and unconstrained tfp growth and their constrained tfp growth results were closer to those of avila and evenson (2010). findings from these studies suggest the average rate of agricultural tfp growth for developing countries had risen since the 1980s but there remained large variation among countries. this first result, of higher average tfp growth in recent decades, seems at odds with the productivity slowdown hypothesis, but these estimates typically lag at least a decade from the present and rarely have complete global coverage. the present study uses growth accounting to construct tfp indexes for agriculture world-wide. the approach is similar to what avila and evenson (2010) proposed, but with a much broader and representative set of input cost shares for constructing agricultural input indexes. the analysis includes industrialized, developing and transition countries for a near-complete global coverage. it extends my earlier work (fuglie 2008, 2010b, 2012) which has steadily developed and improved upon this approach. in addition to providing updated evidence on agricultural tfp growth (extending the estimates through 2012), the principal methodological innovation in this paper is to include a comprehensive measure of animal feed in the aggregate input index. most studies of international agricultural productivity have ignored animal feed as an input in agricultural production, using the fao measure of net agricultural output (which subtracts from gross output the portion of crops kept on farms for use as feed) to net out animal feed. however, this measure does not account for by-products from food manufacturing and other industries that are processed into animal feed nor crops that are imported for use as feed. the present study extends the approach suggested by nin et al. (2003) to construct a more complete estimate of animal feed using the fao commodity balance sheets. it is expected that the revised estimates of aggregate agricultural inputs will be higher (and thus agricultural tfp somewhat lower) as a result of this more complete accounting of inputs used in agriculture. more complete coverage of inputs provides better estimates of tfp and improves our ability to track growth in productivity. but as the input quantities are generally not adjusted for quality, these estimates should be considered as “raw tfp” (avila and evenson, 2010). in particular, labor inputs are measured in terms of the number of economically active adults employed primarily in agriculture. part-time employment and the improvement in schooling and skills are not factored in. many of the national tfp studies from industrialized countries have gone to great lengths to account for the rising quality of labor and other inputs in measuring the change of total inputs used in agriculture (e.g., see ball, 1985; ball et al., 1997). controlling for changes in input quality will generally raise the estimate of input growth and therefore lower the residual between output and input growth. we should expect that the raw tfp growth estimates reported here should be somewhat higher than the tfp growth estimates from these national studies. another point regarding the interpretation of tfp growth is that since it is estimated as a residual between output and input growth, it will tend to reflect not just pure technical change but also economies of scale, improvements in technical and allocative efficiency, and changes in the quality of natural resources like soil, water and climate. the latter is especially important in light of the impact of climate change on agriculture. if climate 204 k. fuglie change is negatively affecting agricultural productivity, say due to the effects of increased heat stress on crop yields, then estimates of tfp growth would indicate the net effect of the gains from technical, efficiency and scale improvements against the losses in productivity from natural resource degradation. a final point to keep in mind is that simply measuring tfp growth does not tell us anything about the causes of this growth. however, in order to have an explanation for tfp growth, it is first necessary to have a good measure of it. the next section of the paper describes in detail the methods and sources of data used to construct agricultural tfp indexes for each country, region and the world as a whole. this is followed by a discussion of some of the main findings on trends in global agricultural productivity growth during 1961-2012. the final section concludes and offers some suggestions for future research. 2. methods and data 2.1 measuring total factor productivity growth here, i sketch out the procedures used to construct internationally comparable measures of agricultural tfp growth relying primarily on fao data on agricultural inputs and outputs, and supplementary information on production costs from other studies. define total factor productivity (tfp) as the ratio of total output to total inputs in a production process. let total output be given by y and total inputs by x. then tfp is simply: tfp = y/x (1) changes in tfp over time are found by comparing the rate of change in total output with the rate of change in total input. expressed as logarithms, changes in equation (1) over time can be written as: = − d tfp dt d y dt d x dt ln( ) ln( ) ln( ) (2) which simply states that the rate of change in tfp is the difference in the rate of change in aggregate output and input. agriculture is a multi-output, multi-input production process, so y and x are vectors. when the underlying technology is represented by a constant-returns-to-scale cobbdouglas production function and where (i) producers maximize profits so that the output elasticity with respect to an input equals the cost share of that input and (ii) markets are in long-run competitive equilibrium so that total revenue equal total cost, then equation (2) can be written as: ∑ ∑= − − − − tfp tfp r y y s x x ln ln lnt t i i t i ti j j t j tj1 , , 1 , , 1 (3) where ri is the revenue share of the ith output and sj is the cost-share of the jth input. total output growth is estimated by summing over the growth rates for each output com205accounting for growth in global agriculture modity weighted by its revenue share. similarly, total input growth is found by summing the growth rate of each factor of production, weighted by its cost share. tfp growth is just the difference between the growth of total output and total input. one difference among growth accounting methods is whether the revenue and cost share weights are fixed or vary over time. paasche and laspeyres indexes use fixed weights whereas the tornqvist-thiel and other chained indexes use variable weights. allowing the weights to vary reduces potential “index number bias.” index number bias arises when producers substitute among outputs and inputs depending on their relative profitability or cost. in other words, the growth rates in yi and xj are not independent of changes ri and sj. for example, if labor wages rise relative to the cost of capital, producers are likely to substitute more capital for labor, thereby reducing the growth rate in labor and increasing it for capital. for agriculture, index number bias in productivity measurement appears to be more significant for inputs than outputs. cost shares of agricultural capital and material inputs tend to rise in the process of economic development while the cost share of labor tends to fall. commodity revenue shares, on the other hand, appear to show less change over time. to reduce potential index number bias in tfp growth estimates, cost shares are varied by decade whenever such information is available. for outputs, however, base year prices (or equivalently, base year revenue shares) are fixed, since these depend on fao’s measure of constant, gross agricultural output (described in more detail below). the base period for output prices is 2004-2006. a key limitation in using equation (3) for measuring agricultural productivity change is a lack of representative cost share data for most countries. for the present study, direct estimates of cost shares were assembled for 22 countries representing about two-thirds of world agricultural output. for another set of countries where input prices are not available or market-determined, (sub-saharan africa and transition economies of the former soviet union and eastern europe) econometric estimates of production elasticities were used in place of cost shares. for remaining countries, representing about 25% of world agricultural output, cost shares are approximated by applying cost shares from a “like” country. the section below on “input cost shares” provides details on the data sources and assumptions. the framework outlined above provides a simple means of decomposing the relative contribution of tfp and inputs to the growth in output. using g(z) to signify the annual rate of growth in a variable, the growth in output is simply the growth in tfp plus the growth rates of the inputs times their respective cost shares: ∑ ( )( ) ( )= + = g y g tfp s g x j j j j 1 (4) i call equation (4) a cost decomposition of output growth since each sj g(xj) term gives the growth in cost from using more of the jth input to increase output.1 it is also pos1 strictly speaking, input prices are held constant when estimating total input growth, so any increase in cost comes from using more quantity of the input and not from changes in its price. if input and/or output prices actually change between any two periods over which tfp growth is estimated, this would affect the distribution of the economic gains in tfp but not the measure of tfp growth itself. for example, if output prices fell between the two periods, some of the gains in tfp would be passed on to consumers in the form of lower food prices. if fertilizer prices increased between two periods, some of the gains in tfp would be distributed as higher payments for fertilizers. in competitive equilibrium, any tfp benefits that are retained by the farm sector will be capitalized into the price of sector-specific capital inputs (land) so as to maintain the zero profit (total cost= total revenue) condition. 206 k. fuglie sible to focus on a particular input, say land (which i will designate as x1), and decompose growth into the component due to expansion in this resource and the yield of this resource: ( ) ( )= +g y g x g y x 1 1 (5) this decomposition corresponds to what is commonly referred to as extensification (land expansion) and intensification (land yield growth). we can further decompose yield growth into the share due to tfp and the share due to using other inputs more intensively per unit of land: ∑( ) ( ) ( )= + + = g y g x g tfp s g x x j j j j 1 2 1 (6) i call equation (6) a resource decomposition of growth since it focuses on the quantity change of a physical resource (land) rather than its contribution to changes in cost of production. figure 1 gives a graphical depiction of the growth decomposition described in equation (6). the height of the bars indicate the growth rate of real output. growth in real output is first decomposed into growth attributable to agricultural land expansion (extensification) and growth attributable to raising yield per hectare (intensification). finally, yield growth itself is decomposed into input intensification (i.e., more capital, labour and fertilizer per hectare of land), and tfp growth, where tfp reflects the efficiency with which all inputs are transformed into outputs. improvements in tfp are driven by technological change, improved technical and allocative efficiency in resource use, and scale economies. the decomposition of output growth into these components is both intuitively appealing and has some direct policy relevance: land expansion and input intensification figure 1. growth in output, yield and tfp. 207accounting for growth in global agriculture are strongly influenced by changes in resource endowments and relative prices. for example, increasing population density or higher crop prices can induce more intensive use of existing farmland and investments in land improvement (boserup, 1965). but in the short run, the ability to raise yield through intensification is largely confined to existing technology. changes in tfp, on the other hand, are driven by changes in technology and allocative efficiency. yield growth resulting from incremental improvements to technology can be sustained over the long-run through investments in research and development (r&d). 2.2 data fao’s 1961-2012 annual time series of crop and livestock commodity outputs and land, labor, livestock, farm machinery, inorganic fertilizers and animal feed inputs are the primary source used to construct the national, regional and global quantity measures. in some cases these data are modified or supplemented with data from other sources (such as national statistical agencies) when they are considered to be more accurate or up-todate, as described below. output for agricultural output, fao publishes estimates of annual production of 198 crop and livestock commodities by country since 1961. fao also aggregates production into a measure of the gross agricultural output using a common set of global average commodity prices from 2004-2006 and expresses this in constant 2005 international dollars. fao excludes production of animal forages but includes crop production that is used for animal feed and seed in estimating gross agricultural output. the fao also provides a measure of output net of domestic production used for feed and seed. however, the net production measure does not exclude imported grain that may be used as feed or seed, or grain that is exported and used in another country for these purposes. because current (or near current) prices are fixed to aggregate quantities and measure changes in real output over time, the fao gross agricultural output is equivalent to a paasche quantity index. the set of common commodity prices is derived using the gearykhamis method. this method determines an international price pi for each commodity which is defined as an international weighted average of prices of the i-th commodity in different countries, after national prices have been converted into a common currency using a purchasing power parity (pppj) conversion rate for each j-th country. the weights are the quantities produced by the country. the computational scheme involves solving a system of simultaneous linear equations that derives both the pi prices and pppj conversion factors for each commodity and country. the fao updates these prices every five years and recalculates its index of gross production value back to 1961 using its most recent set of international prices. see rao (1993) for a thorough description and assessment of these procedures. i use the fao value of gross agricultural output in constant 2005 international dollars as the basis for a consistent measure of output for each country and the world. however, due to the influence of weather and other factors, agricultural production is volatile from year to year, and it can be difficult to disentangle short-run fluctuations from 208 k. fuglie long-term trends. to relieve the data of some of these fluctuations, i smooth the output series for each country using the hodrick-prescott filter (setting λ = 6.25 as recommended for annual data by ravn and uhlig, 2002). even with smoothing there is still considerable curvature in the output series, although much of the year-to-year fluctuation in output has been removed from the data. i assume that the smoothed series provides a better indicator of productivity trends and that annual variation around this trend is primarily due to short-term disturbances like weather. inputs inputs are divided into six categories: farm labor, agricultural land, two forms of capital inputs – farm machinery and livestock, and two types of intermediate inputs – inorganic fertilizers and animal feed. the primary source of information is fao, which published annual estimates beginning in 1961 (and for farm labor beginning in 1980) for each country, except for former soviet socialist republics (ssrs) for which data begin in 1992. i extend the time series for each of the ssrs back to 1980 from shend (1993) and further to 1965 using lerman et al. (2003). farm labor is the total number of adults (males and females) who are economically active in agriculture. fao currently publishes farm labor estimates and projections for each country of the world from 1980 to 2020, although previously fao also published estimates for 1961-1979. fao estimates are used for each country except china, nigeria and transition economies (former soviet union and eastern europe). estimates are backcast from 1980 to 1961 using the agricultural labor force growth rates from the 2006 version of fao labor force statistics, which included estimates from 1961 onward. for china, agricultural labor estimates are from the statistical yearbooks of the national bureau of statistics of china. for nigeria, labor force estimates are from fuglie and rada (2013), who determined that fao farm labor force estimates for this country were grossly undercounted. to derive more plausible estimates, fuglie and rada (2013) used fao data (2006 version) for 1961-1966 and then extrapolated them to the present assuming a 2% annual growth rate. for transition economies, national agricultural statistical sources are used, as reported in eurostat for the baltic countries and eastern europe, cisstat for russia, belorussia and moldova, the international labor organization’s laborsta for ukraine, and the asian development bank for asiatic former soviet republics. pre-1992 labor estimates for these countries are from shend (1980) and lerman et al. (2003). agricultural land is the area in permanent crops (perennials), annual crops, and permanent pasture. cropland (permanent and annual crops) is further divided into rainfed area and area equipped for irrigation. the areas of rainfed cropland, irrigated area and permanent pasture are then aggregated into a quality-adjusted measure that gives greater weight to irrigated cropland and less weight to permanent pasture in assessing agricultural land changes over time (see the next section on land quality). however, for agricultural cropland in sub-saharan africa total area harvested for all crops is used rather than the fao cropland series (fuglie and rada, 2013). for china i use sown crop area (national bureau of statistics of china) for cropland, given unreasonable discontinuities in the cropland series of both the fao and chinese government sources (fan and zhang, 2002). for new zealand, fao cropland series prior to 2002 fails to reflect changes in a consistent definition over time. for cropland, i use the area in grain, seed, fodder and horticul209accounting for growth in global agriculture tural crops from statistics new zealand (2003) for 1961-2001, and fao data from 2002 onward. for similar reasons, for cropland in indonesia prior to 1990 national estimates from the badan pusat statistik as described in fuglie (2010b) are used. farm machinery is the total metric horse-power (cv) of major farm equipment in use.2 it is the aggregation of the number of 4-wheel riding tractors, 2-wheel pedestrian tractors, power harvester-threshers, and milking machines, expressed in “40-cv tractorequivalents.” the average cv per machine is assumed to be 40 cv per 4-wheel tractor, 12 cv per 2-wheel tractor, 20 cv per power harvester-thresher, and 1 cv per milking machine. however, due to insufficient information no adjustment is made for differences across countries or over time in farm machinery sizes within these categories, except for china, which reports farm machinery inventories in power units (national statistical bureau of china). the fao reports continuous time series data for 4-wheel tractors, harvest-threshers and milking machines, but not 2-wheel walking tractors. for many developing countries, particularly in asia, 2-wheel tractors have been a major component of farm mechanization. for 2-wheel tractors, fao reports numbers in use for 1970s but then discontinued this series until recommencing it in 2002. for interim years, i collected national farm machinery statistics on 2-wheel tractors in use from the agricultural censuses of china, japan, south korea, taiwan, thailand, philippines, indonesia, indian, bangladesh, pakistan, and sri lanka, and interpolated between census years. these countries constitute most of the global use of 2-wheel tractors in use on farms. presently, fao farm machinery statistics only extend to 2009 (and for many countries they may not extend past 2005).3 to extend estimates of farm machinery to 2012, national statistics on the number of tractors and combine-harvested from more recent years were collected for a number of countries: bangladesh (hassan, 2013), china (national statistical bureau of china, 2014), europe (eurostat), india (singh et al., 2015), japan (ministry of agriculture, forestry and fisheries), russia (russian federation federal state statistics service, 2015), and the united states (national agricultural statistical service, 2014). for remaining missing data, farm machinery stocks were extrapolated using the average growth rate from the three most recent years of available data. livestock capital is the aggregate value of animals used for breeding, milking, egg laying, wool production, and to provide animal traction. to approximate livestock capital, total inventories of animals on farms, measured in “cattle equivalents” are used.4 inven2 this measure of capital stock is based on physical inventories. an alternative is to estimate capital stock as the sum of accumulated past investments with depreciation (perpetual inventories). larson et al. (2000) used the perpetual inventory method to estimate agricultural capital stocks for 62 countries over 1967-1992. this is a promising effort but coverage remains incomplete. 3 in addition to the number of farm machines in use, fao reports the value of gross capital stock of farm machinery and equipment annually from 1975 to 2007. this value is computed by multiplying the number of 4-wheel tractors, harvester-threshers, and milking machines in use by a fixed unit price and adding $35 of hand tools per agricultural worker. thus, the two measures, of machines in use and the value of gross capital stock of farm machinery, are highly correlated. however, 2-wheel tractors are not included in the fao estimate of gross capital stock. 4 the fao agricultural capital stock series makes a distinction between livestock “fixed assets” and livestock “inventories” simply by treating 85% of value of farm animals as fixed assets (breeding stock) and the rest as pure inventories. since we are primarily interested in the growth rate of livestock capital, it makes no difference which measure is used since they are directly proportional to one another. 210 k. fuglie tories include dairy cows, other cattle, water buffalo, camels, horses, other equine species (asses, mules, and hinnies), small ruminants (sheep and goats), pigs, and poultry species (chickens, ducks, and turkeys), with each species weighted by its relative size. the weights for aggregation are based on hayami and ruttan (1985, p. 450): 1.38 for camels, 1.25 for water buffalo, dairy cows and horses, 1.00 for other cattle and other equine species, 0.25 for pigs, 0.13 for small ruminants, and 12.50 per 1,000 head of poultry. fertilizer is the amount of major inorganic nutrients applied to agricultural land annually, measured as metric tons of n, p2o5, and k2o nutrients. the source of the data is the international fertilizer association, except for small countries, which is from fao. animal feed is the total amount of crop (except fodder), animal and fish products used for feed, measured in tonnes of dry-matter (dm) equivalents. data on commodities used for animal feed are from the fao commodity balance sheets. in addition to total dm, total metabolizable energy (me)5 and total crude protein (cp) of animal feeds were estimated.6 parameters for the dm, cp and me mcal/kg (for ruminants) for each type of feed are from the national research council (1982). see appendix table a1 for details. table 1 shows how the composition of global animal feed evolved over the three decades between 1976-1980 and 2006-2010 (reported in 5-year average annual quantities). there was a significant shift toward greater use of oilcrops and oilcrop meals (or cakes) in feed, contributing to an overall rise in the protein content of animal feeds. over these three decades, the amount of cp in the global feed mix rose by 84%, while total dm and me increased by 58 and 56%, respectively (dm and me are highly correlated with each other). by 2006-2010, cereal grains (including processing by-products such as brans and distiller grains) contributed about 64% of total me and 41% of total cp. the share of animal and fish products (whey, milk, meat and fish products) in global animal feeds declined over time. while these six inputs account for the major part of total agricultural input usage, there are a few types of inputs for which complete country-level data are lacking, namely, use of chemical pesticides, seed, veterinary pharmaceuticals, energy, and services from farm structures. however, more detailed input data are available from several of the national studies from which input cost shares are derived (see section below on input cost shares). to account for these inputs, i assume that their growth rate is correlated with one of the six input variables just described and include their cost with the related input. for instance, services from capital in farm structures as well as irrigation fees are included with the agricultural land cost share; the cost of chemical pesticide and seed is included with the fertilizer cost share; costs of veterinary medicines are included in the animal feed cost share, and energy costs are included in the farm machinery cost share. so long as the growth rates of the observed input and its unobserved counterparts are similar, then the model captures the growth of the unobserved inputs in the aggregate input index. 5 metabolizable energy is total energy of feed consumed after accounting for energy in feces, urine and gasses. 6 for a few small countries the fao commodity balance sheets do not report feed utilization data. for these countries, regional average amounts of dm, cp and me per livestock unit are multiplied by the number of livestock units in that country to estimate total feed. all of these countries are estimated to use substantially less than one million tonnes of feed per year and the total feed use of these countries combined amounts to less than 0.005% of global feed use. 211accounting for growth in global agriculture table 1. the changing composition of global animal feed (average annual quantities). million metric tons (dry matter) % change share of total 1976-80 2006-10 1976-80 2006-10 quantity cereals (grain & bran) 644 891 38.4 0.72 0.63 oilseeds (crops, meal, oil) 93 278 199.0 0.10 0.20 roots and tubers 49 58 19.0 0.05 0.04 other crops 30 71 139.6 0.03 0.05 milk, whey & butter 65 91 40.4 0.07 0.06 meat & fish (meat, meal, oil) 15 19 23.5 0.02 0.01 total 896 1,409 57.3 1.00 1.00 metabolizable energy cereals (grain & bran) 1,878 2,613 39.2 0.72 0.64 oilseeds (crops, meal, oil) 259 794 206.6 0.10 0.20 roots and tubers 137 162 18.1 0.05 0.04 other crops 67 147 120.7 0.03 0.04 milk, whey & butter 211 278 32.0 0.08 0.07 meat & fish (meat, meal, oil) 50 63 26.5 0.02 0.02 total 2,601 4,058 56.0 1.00 1.00 crude protein cereals (grain & bran) 73.9 100.1 35.4 0.55 0.41 oilseeds (crops, meal, oil) 35.5 108.6 205.9 0.27 0.44 roots and tubers 2.7 2.6 -3.8 0.02 0.01 other crops 3.4 11.4 238.5 0.03 0.05 milk, whey & butter 9.9 12.8 29.9 0.07 0.05 meat & fish (meat, meal, oil) 8.0 9.7 21.4 0.06 0.04 total 133.4 245.3 83.9 1.00 1.00 source: feed quantities from fao commodity balance sheets; feed composition from national research council (1982). 2.3 land quality the fao agricultural database provides time-series estimates of agricultural land by country and categorizes this as either permanent pasture or cropland (which is further divided in arable and permanent crop land). it also provides an estimate of area equipped for irrigation. the productive capacity of land among these categories and across countries can be very different, however. for example, some countries count vast expanses of semi-arid lands as permanent pastures even though these areas produce very limited agricultural output. using such data for international comparisons of agricultural productivity can lead to serious distortions, such as significantly biasing downward the econometric estimates of the production elasticity of agricultural land (peterson, 1987; craig et al., 1997). 212 k. fuglie in this study, because i estimate only productivity growth rather than productivity levels, differences in land quality across countries is less of an issue. the estimates depend only on changes in agricultural land and other inputs over time. however, a bias might arise if changes occur unevenly among land classes. for example, adding a hectare of irrigated land would likely make a considerably larger contribution to output growth than adding a hectare of rain-fed cropland or pasture. to account for the contributions to growth from different land types, i derive weights for irrigated cropland, rain-fed cropland, and permanent pastures based on their relative productivity and allow these weights to vary regionally. in order not to confound the land quality weights with productivity change itself, the weights are estimated using country-level data from the beginning of the period of study (i.e., using average annual data from 1961-1965). i first construct regional indicator variables (regioni, i = 1,2,…5, representing developed and former soviet bloc countries, asia-pacific, latin america and the caribbean, west asia and north africa, and sub-saharan africa). i then regress the log of agricultural land yield in a country (its total output y divided by the sum of cropland and pasture area) against the proportions of agricultural land in rain-fed cropland (rainfed), irrigated cropland (irrig), and permanent pasture (pasture). multiplying the land-use proportions by the regional indicator variables allows the coefficients to vary among regions: ∑α ( ) + = = ln y cropland pasture rainfed region * i i i 1 5 ∑ ∑β γ( ) ( )+ +pasture region irrig region* * i i i i i i (7) the coefficient vectors α, β and γ provide the quality weights for aggregating the three land types into an aggregate land input index. countries with a higher proportion of irrigated land are likely to have higher average land productivity, as will countries with more cropland relative to pasture. the estimates of the parameters in equation (7) reflect these differences and provide a ready means of weighting the relative qualities of these land classes. the regression estimates show that, on average, one hectare of irrigated land was between 1.1 to 3.0 times as productive as rainfed cropland, which in turn was 10-20 times as productive as permanent pasture. the results give plausible weights for aggregating agricultural land across broad quality classes. in fact, this approach to account for land quality differences among countries is similar to one developed by peterson (1987), who derived land quality weights by regressing average cropland values in u.s. states against the share of irrigated and unirrigated cropland and long-run average rainfall. he then applied these regression coefficients to data from other countries to derive an international land quality index. the advantage of my model is that it is based on international rather than u.s. land yield data and provides results for a larger set of countries. the effects of this land quality adjustment on global land use change are shown in table 2. when summed up using unadjusted data, between 1961 and 2012 total global agricultural land expanded from 4,429 million ha (mha) to 4,930 mha, or by about 11%. when adjusted for quality, “effective” agricultural land expanded by 28%, or nearly three times the rate of growth in raw area. the reason is that irrigated area expanded much faster than oth213accounting for growth in global agriculture er types of land and when weighted for its greater productivity, it implies a much greater expansion in “effective” agricultural land. for the purpose of tfp calculation, accounting for the changes in the quality of agricultural land over time increases the growth rate in total agricultural inputs and commensurately reduces the estimated growth in tfp. table 2. global agricultural land use changes between 1961 and 2012 (millions of hectares).     developed countries transition countries developing countries world (millions of hectares) rainfed cropland 1961 357 272 567 1,196 2012 309 219 734 1,262 % change -14% -19% 30% 6% irrigated cropland 1961 33 11 100 145 2012 50 23 242 315 % change 51% 110% 141% 118% permanent pasture 1961 885 358 1,845 3,089 2012 774 380 2,199 3,353 % change -13% 6% 19% 9% total agricultural land 1961 1,276 641 2,512 4,429 2012 1,133 622 3,175 4,930 % change -11% -3% 26% 11% (millions of hectares of rainfed cropland-equivalents) quality-adjusted agricultural land 1961 505 329 884 1,718 2012 482 305 1,419 2,206 % change -4% -7% 60% 28% source: agricultural land area from fao, with adjustments made for china, indonesia and new zealand based on national data sources. cropland includes fao’s measure of arable land and land under permanent crops except for sub-saharan africa, where cropland equals total area harvested. cropland for china is total sown area. land quality adjustments reflect the average productivity of different land types relative to rainfed cropland and are derived from regression analysis (see text). this adjustment for changes in different classes of land allows us to further refine the resource decomposition of output growth in equation (6) to isolate the contribution of irrigation apart from expansion in agricultural area to output growth. letting x1 be the quality-adjusted quantity of land (and for simplicity, dropping the region subscripts on the land quality parameters), then a change in x1 is given by δx1 = αδ(cropland) + βδ(pasture) + (γ-α)δ(irrig). (8) the first two right-hand-side terms indicate the expansion in land area (with growth in pasture area adjusted for quality to put in on comparable terms with cropland expan214 k. fuglie sion). the third term isolates the contribution of irrigation expansion: (γ-α)*100% gives the percent augmentation to yield, holding other factors fixed, from equipping a hectare of cropland with supplemental irrigation. dividing equation (8) by x1 converts the expression into percentage changes so that it shows the respective contributions of changes in rainfed cropland, pasture area and irrigation to output growth. combined with equation (6), the resource decomposition expression shows the contributions to agricultural growth from expansion of agricultural land, extension of irrigation, intensification of other inputs per hectare, and improvements in tfp: ∑θ α θ β θ γ α( )( ) ( ) ( ) ( ) ( )= + + − + + = g y g x g x g x s g x x g tfp c c p p w w j j j j 1 1 1 2 1 (9) where θc, θp and θw are the shares of quality-adjusted agricultural land in crops (x1c), pasture (x1p), and irrigated area (x1w), respectively (note: x1 = x1c + x1p + x1w). the first two terms [θcαg(x1c) + θpβg(x1p)] give the share of output growth attributable to land expansion (holding yield fixed), while the third term [θw(γ-α)g(x1w)] indicates the share of output growth due to the extension of irrigation (holding other inputs fixed). the fourth term of equation (9) gives the contribution to growth of input intensification and the last term the contribution of growth in total factor productivity. input cost shares the fao (and supplementary) quantity data allow us to calculate the growth rates for six categories of production inputs (land, labor, machinery capital, livestock capital, and material inputs represented by fertilizer and feed), but to combine these into an aggregate input measure requires information on their cost shares or production elasticities. for this i draw upon 19 studies that have estimated nationally or regionally representative cost shares or production elasticities for agricultural inputs (see appendix table a2 for a list of these studies and the cost shares derived from them). these costs shares are assumed to be representative of not only those nations but also for other countries in the same region. for instance, the cost shares from india were applied to other countries in south asia, the cost shares for indonesia were applied to other countries in southeast asia and the pacific, the cost shares for mexico were assigned to other countries in central america and the caribbean, and the cost shares for brazil were applied to other countries in south america as well as the north africa-west asia region. these assignments were based on judgments about the resemblance among the agricultural sectors of these countries. countries assigned to the cost shares from brazil tended to be middle-income countries having relatively large livestock sectors, for example. for agricultural capital, some of these studies only reported an aggregate cost share for all capital services. to partition capital services into machinery and livestock capital services, i used the average proportions of capital stock in machinery, livestock and tree capital for low, middle and high income countries reported in butzer et al. (2012), and assigned the cost share of capital services from trees to land. while the lack of direct observations on input cost shares for most countries introduces uncertainty in the tfp estimation, the countries for which cost shares are observed represent about 65% of the global agricultural economy. this proportion rises to threefourths when sub-saharan africa and the former soviet union are included – regions where econometrically-estimated production elasticities are used in place of cost shares. 215accounting for growth in global agriculture thus, countries to which input cost shares were imputed represent only one-quarter of world agricultural output. another argument in support of this approach is that there is a significant degree of congruence among the cost shares reported for these country studies. for the developing countries for which cost shares data are available (india, indonesia, china, brazil and mexico), farm-supplied inputs (land, labor, and livestock capital) account for between 60 and 90% of total costs, while inputs supplied by industry (machinery, or fixed capital, and purchased materials such as fertilizers and processed animal feed), accounted for a far smaller share of resources. the cost share of inputs supplied by industry rises with the income of a country, and accounts for a third or more of total costs in the more highly industrialized countries. the use of modern inputs in transition countries, on the other hand, fell sharply after reforms were initiated in the early 1990s. these patterns of input use is reflected in cost shares estimated or imputed for these countries. of perhaps greater concern is that some of the cost shares are becoming out of date. while the model attempts to adjust cost shares for each decade, it is still dependent on the information available from other published studies. if cost shares are unavailable for recent years, the model uses the last available data. in the case of china, the last nationally comprehensive input cost shares for which we have estimates is for 1992. in china’s rapidly changing agricultural sector, we should expect that the use of costs modern industrialize inputs (and their share of total cost) to continue to rise, and tfp may be overestimated if the input index is not capturing the full extent of this transformation. continued effort to extend and update national estimates of agricultural costs of production is necessary to undertake global productivity assessments like the one described here. country and regional productivity using the methodology and data described above agricultural tfp indexes are estimated for nearly every country of the world on an annual basis beginning in 1961 (and since 1965 for the independent states of the former soviet union). however, some countries have dissolved or are too small to have complete data. for the purpose of estimating long-run productivity trends, some national data are aggregated to create consistent political units over time. for example, data from the nations that formerly constituted yugoslavia are added together to make comparisons with productivity before yugoslavia’s dissolution. similarly, data were aggregated for czechoslovakia, ethiopia and the former soviet union (tfp series for individual ssr’s begin in 1965). because some small island nations have incomplete or zero values for some agricultural data, three composite territories were constructed by adding up available data for island states in the lesser antilles, micronesia, and polynesia. altogether, the countries included in the analysis account for more than 99.7% of fao’s global gross agricultural output. the only areas not included in the analysis that have significant agricultural production are the west bank and gaza. in addition to individual countries, data are aggregated and tfp indexes estimated at the regional level. input and output quantity aggregation is straight forward since they are all measured in the same units (although not adjusted for quality differences in the inputs). regional cost shares are the weighted averages of the national cost shares for the countries in a region. appendix table a3 provides a complete list of countries included in the analysis and the regional groupings. 216 k. fuglie 3. results: growth in agricultural productivity 3.1 sources of growth in global agriculture table 3 provides summary findings on productivity measures for the global agricultural economy as a whole over the past five decades and for the entire 1961-2012 period. the first two columns of results show average annual growth rates of total agricultural outputs and inputs and the remaining columns indicate growth rates in four measures of productivity: changes in tfp, labor productivity, land productivity, and cereal grain yield per hectare harvested. the average growth rate of world agricultural output remained remarkably stable over time, rising by 2.8% per year in the 1960s and between 2.1% and 2.5% per year in every subsequent decade. the source of output growth, however, shifted from being primarily input-driven to productivity-driven. annual growth in total inputs fell from 2.8% in the 1960s, to between 1.5% and 1.7% in the 1970s and 1980s, and to less than 0.8% since 1991. table 3. productivity indicators for world agriculture (average annual growth rate in percent). period gross output total input total factor productivity output per worker output per hectare of land cereal yield per area harvested 1961-1970 2.79 2.79 0.00 1.13 2.44 2.88 1971-1980 2.29 1.74 0.56 1.55 2.12 2.08 1981-1990 2.10 1.49 0.62 0.59 1.76 1.88 1991-2000 2.17 0.63 1.54 1.92 2.06 1.56 2001-2012 2.52 0.84 1.68 2.83 2.59 1.53 1961-2012 2.23 1.30 0.93 1.25 2.01 1.94 gross output: fao gross production value in constant 2004-2006 international dollars. total input: author’s aggregation of agricultural land, labor, capital and material inputs (see text). tfp: the difference between output growth and total input growth, based on author’s estimation. output per worker: fao gross production value divided by number of persons working in agriculture. output per hectare: fao gross production value divided by total arable land and permanent pasture. cereal yield: global production of maize, rice and wheat divided by area harvested of these crops. the average annual growth rate in series y is found by regressing the natural log of y against time, i.e., the parameter b in ln(y) = a + bt. offsetting the declining growth in inputs to keep output growth from falling has been productivity. annual tfp growth rose from a global average of 0% in the 1960s to about 1.7% since 2001. the growth in global agricultural tfp has been generally lower than growth in either land or labor productivity. this reflects an intensification of capital and material inputs in agriculture, which raise land and labor productivity but may not affect tfp. also, since the number of workers in agriculture expanded faster than agricultural land area, the growth rate in labor productivity tended to be lower than growth in land productivity. however, at the global level the agricultural labor force is now declining in 217accounting for growth in global agriculture absolute terms (while agricultural land is still expanding), so the rate of labor productivity growth now exceeds the rate of land productivity growth. in the most recent period of 2001-12, output per worker grew by 2.8% per year while output per hectare grew by 2.6% per year. the growth rate in cereal yield per area harvested, which has been used as a harbinger of slowing productivity growth, has actually remained fairly stable since 1990, averaging at least 1.5% in annual decadal growth rates. this is below the nearly 2.9% rate of yield growth achieved in the 1960s, but does not appear to indicate a persistent slowdown in yield growth. note that land productivity (output per hectare of land in agriculture) has generally grown more rapidly that crop yield per area harvested. the main reason for this in increased land use intensity. while yield of individual crops is generally calculated on the basis of area harvested, land productivity is based on total output in a calendar year from the area designated as agricultural land. increased land use intensity has come about from greater use of multiple cropping and less cropland in fallow or devoted to fodder crops. globally, cropland intensity (total area harvested divided by total area designated as cropland) gradually increased from about 0.74 in the 1960s to 0.78 in the 1990s, but then grew more rapidly reaching 0.85 by 2012. the decomposition of global output growth into contributions from inputs and tfp is depicted in figure 2. the height of each column gives the average annual rate of output growth by decade. over the entire 50-year period, total inputs grew at about 60% the rate of output growth, implying that improvement in tfp accounted for about 40% of new output. however, the rate of input growth declined over time, and tfp’s contribution to output growth increased. by the 2001-12 period, tfp accounted for two-thirds of the growth in global agricultural production. figure 2, panel a shows the contributions of various inputs to global agricultural growth according to their share of total costs (see equation 4), and the residual, or tfp. increased use of material inputs, especially fertilizer, was a leading source of agricultural growth in the 1960s and 1970s, when green revolution cereal crop varieties became widely available in developing countries (these crop varieties were more responsive to fertilizers, especially when grown under irrigation). fertilizer and animal feed use also expanded considerably in the soviet union during these decades, where they were heavily subsidized. the exceptionally low rate of input growth in global agriculture during the 1990s was due primarily to the rapid withdrawal of resources from agriculture in the countries of the former soviet bloc when these countries underwent a transition from a centrally planned to market economies. by the early 2000s agricultural resources in this region had stabilized, and there was a recovery in the global rate of input growth compared with the 1990s. also in the 2000s, the world’s agricultural labor force began to shrink for the first time in modern history. while the size of the agricultural labor had been falling for decades in industrialized countries, this is now also the case in china and latin america. south asia may also soon enter into a period where the absolute size of its farm labor force declines, if it hasn’t already (rada, 2013). in low income countries (especially those in sub-saharan africa), the number of persons primarily employed in agriculture continues to rise although the share of the labor force working in agriculture is falling. 218 k. fuglie figure 2. sources of global agricultural growth. panel a: input cost decomposition panel b: resource decomposition. 219accounting for growth in global agriculture the line shows the average annual growth rate in gross agricultural output during the period specified. the shaded components of the bar show the contribution of an input or productivity to total output growth. in panel a, the growth rate of an input is weighted by its cost share. panel b shows the growth rate in agricultural land (and the contribution of irrigation to raising effective land area) and the growth rate in yield, which is further decomposed into growth due to input intensification (inputs per area) and total factor productivity (tfp). figure 2 panel b shows the resource decomposition of global agricultural growth slightly differently. instead of by input cost as in panel a, it shows the relative contribution of growth in land and water (irrigation), input intensification and tfp to growth (see equation 9). the rate of expansion in natural resources (land and water for irrigation) has diminished over time while the rate of growth in resource yield has risen. the underlying source of resource yield gain has shifted markedly from input intensification to improvement in tfp. as expected, the inclusion of animal feed as an explicit input in production raised the growth rate of inputs and reduced the growth rate of tfp. however, the changes were not substantial and did not affect the general pattern of tfp growth acceleration world-wide over the study period. over the entire 1961-2012, tfp growth without animal feed data (where feed use was assumed to grow at the same rate as the size of the livestock herd) averaged 1.01% per year, compared with 0.94% per year with animal feed inputs measured directly. the most significant effect of including animal feed was on tfp growth rates estimated for the former soviet union (fsu), especially the pre-transition era (19611991). without including animal feed inputs, agricultural tfp in fsu remained virtually unchanged between 1961 and 1991, but with animal feed inputs it regressed by 23%. it would imply that during the soviet era, agricultural growth was largely achieved by increasing levels of inputs but with declining marginal (and average) productivities in the use of those inputs. 3.2 sources of agricultural growth by world region the same kind of growth analysis shown above for global agriculture can be carried out at the regional or country level. figure 3 shows the contribution to agricultural growth from land, labor, capital, material inputs, and tfp for industrialized market economies, developing countries, and transition economies of the former soviet union and eastern europe. in industrialized economies (panel a), the average annual rate of output growth fell from around 2% in the 1960s and 1970s to less than 1% in the last three decades. this slowdown in agricultural growth partly reflects engel’s law, where per capita food demand is satiated and the growth in food demand reflects the growth in population, which is declining in these countries. labor and land (and in recent decades, capital and materials as well) are being withdrawn from the agricultural sector. the fact that output is able to continue to expand in the face of these resource withdrawals is entirely due to tfp. the increase in the productivity of the resources remaining in the sector has been rapid enough to offset the decline in the amount of resources used. the high rate of tfp growth enabled the agricultural sectors of these countries to remain internationally competitive, and developed countries as a whole were net exporters of food. 220 k. fuglie figure 3, panel b indicates that improved productivity performance in developing countries was the proximate cause of the acceleration in global agricultural tfp growth after 1990. during the 1960s and 1970s, annual tfp growth averaged less than 1% for these countries, but since 1990 their agricultural tfp growth doubled to nearly 2% per year. for developing countries as a group, agricultural labor declined in absolute numbers over the 2001-12 period. this was primarily due to the exit of nearly 100 million chinese farm workers to the non-farm sector. this trend is likely to accelerate in the coming decade not only in china but in other developing countries as well, as structural transformation moves workers out of agriculture. sub-saharan africa, however, is expected to continue to experience growth in the size of its agricultural labor force at least through 2020, according to fao projections. during the era of central planning (pre-1991), today’s transition economies (figure 3 panel c) experienced agricultural tfp regression. all agricultural growth achieved in this region during this period was due to resource expansion, especially the rapid growth in material inputs like fertilizers and feed. inputs supplied to agricultural were often at highly subsidized rates. when the soviet bloc broke apart in 1991 and these countries began to move toward market economies, agriculture underwent a sharp contraction as subsidies were withdrawn from the sector. agricultural output growth resumed in the 2000s, and most of this renewed growth can be attributed to improvement in tfp. more region-specific information on agricultural output tfp growth is provided in table 4. these estimates show considerable heterogeneity in agricultural tfp growth rates among regions, which is even more pronounced if tfp is compared at the national level – not shown but available from economic research service (2015). the outstanding productivity performers over the past few decades have been brazil and china. both are large agricultural producers (china has by far the largest agricultural sector in the world, accounting for 24% of gross agricultural output in 2013 according to fao, and brazil was the fourth largest, after india and the united states). tfp growth over the past several decades enabled china to remain largely self-sufficient in foodstuffs during a period of rapidly rising domestic food demand (due to population and per capita income growth) despite virtually no new land for agriculture. for brazil, rapid tfp growth since the 1980s enabled it to move from a food deficit country to become a major exporter of agricultural commodities. besides these countries, southeast asia, south asia, and north africa have also accelerated their agricultural tfp growth, achieving an average annual growth rate of over 2% since 2001. a number of industrialized regions (southern europe, south africa, northeast asia, and north america) have maintained agricultural tfp growth rates averaging at least 1.9% per year since 2001. these estimates are generally higher than those reported by country studies of agricultural tfp growth in these regions (ball et al., 2010). but recall that the tfp estimates reported in national studies typically make adjustments for quality changes in inputs, particularly labor, while the estimates reported in table 4 do not. adjusting an input for quality changes usually increases the share of output growth that is “accounted for” by growth in that input (e.g., adding one more skilled worker to the agricultural labor force raises output by more than adding one more unskilled worker). so, the tfp estimates in table 4 should be interpreted as including not only the effects of technical change, but also the effects of using inputs of higher quality. 221accounting for growth in global agriculture figure 3. sources of agricultural growth in industrialized, developing, and transition economies. panel a: industrialized countries. panel b: developing countries. panel c: transition economies. 222 k. fuglie table 4. agricultural output and total factor productivity growth in global regions. region 1961-1970 1971-1980 1981-1990 1991-2000 2001-2012 output tfp output tfp output tfp output tfp output tfp developing countries 3.15 0.61 2.97 0.85 3.42 1.06 3.61 2.00 3.42 1.96 sub-saharan africa 3.01 0.17 1.07 -0.12 3.17 0.90 3.21 1.11 3.25 0.60 latin america and caribbean 3.05 0.80 3.32 1.33 2.27 0.90 3.15 2.02 3.19 2.00 caribbean 1.70 -0.93 1.98 0.35 0.62 -0.52 -0.51 -0.26 0.39 -0.09 central america 4.64 3.00 3.72 1.82 1.36 -1.79 2.96 2.68 2.21 1.90 andean 2.97 1.44 2.82 1.08 2.79 0.47 3.20 1.78 2.66 1.46 northeast (brazil, mainly) 3.56 0.24 3.86 1.07 3.45 2.95 3.58 2.38 4.10 3.23 southern cone 1.80 0.49 2.87 2.57 1.13 -0.88 3.17 1.26 2.76 0.79 asia (except west asia) 3.28 0.74 3.11 1.02 3.64 1.41 3.71 2.55 3.49 2.61 northeast asia (china, mainly) 4.79 0.93 3.32 0.69 4.45 1.79 5.04 3.94 3.52 3.09 southeast asia 2.63 0.48 3.92 1.85 3.34 0.42 2.96 1.36 4.00 2.53 pacific 2.52 -0.04 2.34 0.21 1.58 -0.65 2.06 0.51 2.16 0.92 south asia 2.02 0.57 2.66 0.81 3.32 1.21 2.66 1.03 3.63 2.04 west asia-north africa 2.87 1.33 3.02 1.52 3.56 1.35 2.79 1.47 2.49 2.13 north africa 2.62 1.28 1.57 0.34 4.19 2.52 3.26 1.58 3.49 2.70 west asia 2.98 1.13 3.65 2.09 3.30 0.75 2.60 1.50 2.01 1.86 industrialized countries 2.06 0.76 1.94 1.62 0.72 1.14 1.36 1.94 0.56 2.00 europe, northern 1.55 0.93 1.36 1.26 0.51 1.40 0.37 1.38 0.11 1.44 europe, southern 2.11 1.43 1.96 1.87 0.69 1.13 1.32 1.88 -0.40 1.92 japan-s. korea-taiwan 3.52 1.67 2.45 1.95 1.19 1.34 0.06 2.06 -0.28 2.02 australia-new zealand 2.90 0.91 1.69 1.59 1.49 1.18 3.22 2.79 0.67 1.35 canada-usa 2.06 0.47 2.29 1.55 0.68 1.00 1.96 1.95 1.10 1.96 south africa 3.18 -1.12 2.55 0.95 1.21 2.97 1.54 3.01 2.55 2.62 transition countries 3.54 -0.69 1.29 -0.56 0.80 0.25 -3.61 0.41 1.39 1.30 eastern europe 2.67 -0.16 1.73 0.32 -0.03 0.60 -1.33 0.04 -1.18 0.08 russia-ukraine-belarusmoldova-kazakhstan 0.76 -1.33 1.41 0.44 -5.43 1.00 2.35 2.39 central asia & caucasus 4.71 1.93 0.55 -1.20 0.11 1.60 3.98 2.02 baltic countries 0.93 -0.97 1.09 0.49 -6.00 -1.75 1.87 1.90 world 2.79 0.00 2.29 0.56 2.10 0.62 2.17 1.54 2.52 1.68 the average annual growth rate in series y is found by regressing the natural log of y against time, i.e., the parameter b in ln(y) = a + bt. regions experiencing persistent low growth in agricultural tfp include sub-saharan africa, eastern europe transition economies, southern cone countries of south america, the caribbean and pacific island nations. all of these regions show a growth trend in agricultural tfp of substantially less than 1%. sub-saharan africa is the most critical case, given its large population, rapid population growth, and heavy dependence on agriculture as a source of livelihood. the fact that agricultural tfp growth has remained low for this 223accounting for growth in global agriculture region means that it has remained poor and food insecure, and increasingly dependent on food imports. 4. conclusions the principal advantage of a tfp measure of productivity growth is that it clearly distinguishes between resource expansion, resource substitution, and technical or efficiency improvements in resource utilization as sources of economic growth. growth in tfp is more likely to be associated with lower unit costs of production, and, in long-run equilibrium, changes in market prices of output, than partial productivity indexes. the limitation of tfp is that it is subject to error if outputs and/or inputs are not fully or appropriately measured or if procedures for aggregation are biased. this paper seeks to move toward plausible indexes of international agricultural tfp by constructing a more complete accounting of the inputs employed in the sector. specifically, the paper develops an explicit measure for animal feed inputs, something which most previous studies of international agricultural productivity have ignored. animal feed inputs are composed of much more than the portion of crops retained on farms and fed to animals. it includes many by-products of food manufacturing, such as oilseed cakes, distiller grains, milling brans, sugar and molasses, whey, animal slaughter waste, and fish meal. the paper proposes three ways of aggregating these diverse feed sources into a single quantity measure of feed input: dry-matter weight, metabolizable energy in mcal, and tonnes of crude protein. it turns out that the growth in dry-matter weight and metabolizable energy are highly correlated, while the growth of crude protein has been more rapid, implying quality improvement in the overall animal feed mix over time. one direction for future work could be to develop a quality-adjusted measure of feed input that combines energy and protein (and perhaps other nutrients). as expected, inclusion of animal feed in the measure of total agricultural inputs led to higher growth in measured agricultural inputs and thus lower growth in agricultural tfp for the world economy over 1961-2012, although the differences were not substantial. results did not alter the central finding from my previous analyses using this approach (fuglie, 2008, 2010b, 2012) that there has apparently been a significant acceleration in global agricultural productivity growth since the 1990s. this is in contrast to evidence – based primarily on partial productivity indexes like crop yield – that since around 1990 the rate of agricultural productivity has significantly slowed in most of the world (alston et al., 2009; alston and pardey, 2014). the evidence for accelerated productivity arises from the fact that the real growth of agricultural output has not fallen while growth rates for most agricultural inputs have declined. it suggests that much of the rapid growth in crop yield and land and labor productivity observed in the 1960s and 1970s was due to factor substitution (especially fertilizer for land and capital for labor), and once the growth of other factors is taken into account, the real gains in efficiency during these decades were not exceptional. the present analysis suggests, though, that the global trend is hardly uniform. at least three general patterns of agricultural growth are evident: 1. in industrialized market economies, the agricultural output growth rate is slowing while input growth has turned negative. tfp growth offset the decline in resources to keep output from falling in absolute terms. 224 k. fuglie 2. the dissolution of the soviet union in 1991 imparted a major shock to agriculture in transition economies as they began the adjustment from centrally-planned to market-oriented economies. in the 1990s, agricultural resources sharply contracted and output fell. agricultural inputs stabilized in the early 2000s and agricultural growth resumed in former soviet states but not yet in eastern european. agricultural tfp growth, which was negative during the soviet era, turned positive following economic reforms. 3. in developing countries, agricultural productivity growth doubled from around 1% per year during 1960-1990 to around 2% per year during 1991-2012. two large developing countries in particular, china and brazil, have sustained exceptionally high tfp growth in recent decades. several other developing regions, including southeast asia, central america, and north africa, also registered accelerated tfp growth in the 1990s and/or 2000s. very recently, agricultural tfp growth in india has also accelerated. the major exception is the developing countries of sub-saharan africa, where long-run tfp growth has remained below 1% per year. despite these generally optimistic findings on agricultural productivity growth, the next several decades present major challenges to maintaining present rates of improvement. the prospects for a general slowdown, even though it may not have yet occurred, are probably inevitable. one source of a slowdown, as alston and pardey (2014) argue, is if global investments in agricultural r&d are not sufficiently robust to create new productivity-enhancing technologies and offset technological obsolescence. stagnant or declining spending on public agricultural r&d in industrialized countries, which has been a key source of major scientific advances for world agriculture, may put future productivity growth in agriculture at risk. another source of a slowdown is likely to emerge from natural resource degradation, particularly from the warming of the climate. the effects on agriculture from climate change may be positive in some areas in the short term, but are likely to turn increasingly negative over time. having a robust measure of agricultural tfp growth as outlined in this paper provides a promising means of tracking these developments and for analysing their causes and consequences. nonetheless, the measurement of world agricultural tfp continues to suffers from some serious limitations, so caution is warranted in its interpretation. information on farm investments in new capital is incomplete, leading to deficiencies in the measurement of agricultural capital stock and capital services. heterogeneous quality of inputs, especially land, may introduce serious measurement errors when aggregating across national and regional frontiers. the labor input, which is always hard to measure in agriculture where much of the work is done by unpaid family members, could be mismeasured if hours worked per capita changes, not to mention skill levels. a broader issue in agricultural productivity measurement is the consumption of unpaid (but socially valuable) environmental resources. agriculture imposes significant costs on the environment in the form of greenhouse gas emissions, soil and water quality degradation, consumption of scarce and non-renewable water resources, and loss of biodiversity. how agricultural productivity growth affects these costs is not well understood (although evidence being assembled by the oecd (2014) suggests that in many cases agricultural productivity growth is conserving of environmental as well as market resources). insufficient 225accounting for growth in global agriculture understanding of the environmental inputs and outputs associated with agricultural production (and how to value them) represents a serious limitation to using any standard productivity index to judge questions of long-term sustainability of agricultural systems. the important question about the usefulness of tfp is not so much whether it is complete as a productivity index, but rather does it convey more meaningful information than commonly used alternatives like crop yield? the same informational deficiencies that plague tfp also affect the interpretation of other available measures as indicators of the rate of technical change. the growth accounting approach proposed in this paper seems to provide additional and useful insights on the nature and sources of economic growth that partial productivity indexes lack. efforts to construct tfp also point the way to what needs to be done to strengthen them. acknowledgements the author would like to thank ornella maietta, nicholas rada and davide viaggi for their comments on an earlier draft of this paper. nicholas rada also contributes to building the ers database on international agricultural productivity. the views 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(eds.), productivity growth in agriculture: an international perspective. wallingford, uk: cab international, pp. 73-108. 229accounting for growth in global agriculture appendix table a1. composition and price of animal feedstuffs. feed type price (2005 $/t) dry matter (%) crude protein (%) dietary energy (mcal/kg) wheat 158 89 14.2 3.45 rice (milled equivalent) 279 89 7.6 3.47 barley 119 88 11.9 3.27 maize 142 89 9.6 3.40 rye 112 88 12.1 3.24 oats 114 89 11.8 3.02 millet 181 90 11.6 3.34 sorghum 154 90 11.1 3.40 cereals, other 142 89 9.6 3.40 brans 158 89 15.2 2.74 potatoes 30 23 2.2 0.84 cassava 30 37 1.5 1.31 yams 30 23 2.2 0.84 sweet potatoes 30 37 1.5 1.31 roots & tubers, other 30 37 1.5 1.31 sugar cane 33 15 1.2 0.41 sugar beet 43 11 1.5 0.38 molasses 440 94 9.7 2.91 sugar (raw equivalent) 440 100 0.0 4.30 pulses (beans, peas) 556 89 22.6 3.31 oilcrops 274 90 39.2 3.67 vegetable oils 274 100 0.0 8.85 soybean meal 274 90 42.9 3.37 groundnut meal 274 93 48.1 3.39 sunflowerseed meal 274 90 23.3 1.75 rape & mustard meal 274 92 35.6 3.08 cottonseed meal 274 93 37.9 2.46 palm kernel meal 274 90 42.9 3.37 copra meal 274 92 20.7 3.34 sesameseed meal 274 93 45.5 3.15 oilseed meal, other 274 90 42.9 3.37 vegetables 188 92 21.6 2.35 fruits 349 89 4.6 2.67 cocoa beans 1,038 87 11.8 2.18 meat, meat meal & offal 1,322 94 51.4 2.93 animal fats, including butter 274 100 0.0 9.92 whey 312 93 13.3 3.33 milk, excluding butter 312 12 3.3 0.70 eggs 892 94 51.4 2.93 230 k. fuglie feed type price (2005 $/t) dry matter (%) crude protein (%) dietary energy (mcal/kg) fish 411 50 32.7 1.86 fish meal 411 92 65.5 3.20 oilcrops include soybean, cottonseed, groundnuts, rapeseed, sesame, sunflower, coconuts, palm kernels and other. sources: feed composition from national research council (1982); prices are fao global average commodity prices from 2004-2006, except for the following: prices for roots and tubers have been adjusted downward to reflect feed quality; wheat price is used for bran; soybean price is used for all oilcrops and meals, vegetable oils, and animal fats; fish and fish meal price are assumed to be 1.5 times the price of soybean. table a2. agricultural input cost shares. source study country/ region and period of study input input cost shares input shares applied to 1961-70 1971-80 1981-90 1991-00 2001-10 2011-12 industrialized countries               usa (1948-2011) economic research service (2014), based on ball (1985) labor 0.219 0.170 0.155 0.196 0.203 0.133 usa land 0.190 0.212 0.187 0.171 0.147 0.245 livestock capital 0.111 0.114 0.115 0.102 0.091 0.077 fixed capital 0.112 0.117 0.155 0.113 0.112 0.100 crop materials 0.175 0.193 0.222 0.274 0.298 0.274 livestock materials 0.192 0.194 0.166 0.143 0.150 0.170 canada (1961-2006) cahill and rich (2012) labor 0.345 0.406 0.303 0.431 0.349 0.349 canada land 0.035 0.023 0.022 0.016 0.016 0.016 livestock capital 0.009 0.007 0.005 0.004 0.005 0.005 fixed capital 0.146 0.147 0.162 0.087 0.085 0.085 crop materials 0.223 0.211 0.279 0.262 0.328 0.328 livestock materials 0.242 0.206 0.229 0.200 0.217 0.217 australia (1978-2009) ^ zhao et al. (2012) with butzer et al. (2012) decomposition of total capital stock labor 0.176 0.176 0.093 0.088 0.099 0.099 australia and new zealand land 0.349 0.349 0.600 0.661 0.541 0.541 livestock capital 0.182 0.182 0.110 0.085 0.136 0.136 fixed capital 0.137 0.137 0.096 0.065 0.081 0.081 crop materials 0.115 0.115 0.074 0.076 0.105 0.105 livestock materials 0.041 0.041 0.026 0.025 0.039 0.039 japan (1880-1985) van der meer and yamada (1990) labor 0.388 0.351 0.313 0.313 0.313 0.313 japan land 0.288 0.224 0.200 0.200 0.200 0.200 livestock capital 0.024 0.028 0.026 0.026 0.026 0.026 fixed capital 0.113 0.165 0.195 0.195 0.195 0.195 crop materials 0.077 0.107 0.117 0.117 0.117 0.117 livestock materials 0.110 0.125 0.149 0.149 0.149 0.149 231accounting for growth in global agriculture source study country/ region and period of study input input cost shares input shares applied to 1961-70 1971-80 1981-90 1991-00 2001-10 2011-12 korea-taiwan (1914-1971; 1971-2007) 1961-70 is average for korea and taiwan from hayami et al. (1979); 1970+ from kwon (2010) using korea data labor 0.374 0.558 0.349 0.208 0.156 0.156 south korea and taiwan land 0.417 0.227 0.392 0.506 0.519 0.519 livestock capital 0.020 0.004 0.009 0.010 0.012 0.012 fixed capital 0.010 0.016 0.040 0.080 0.122 0.122 crop materials 0.130 0.097 0.105 0.098 0.096 0.096 livestock materials 0.049 0.097 0.105 0.098 0.096 0.096 united kingdom (1952-2005) thirtle, piesse and schimmelpfennig (2008) labor 0.327 0.164 0.136 0.137 0.137 0.137 united kingdom land 0.084 0.126 0.179 0.216 0.216 0.216 livestock capital 0.031 0.052 0.050 0.060 0.060 0.060 fixed capital 0.183 0.199 0.202 0.204 0.204 0.204 crop materials 0.220 0.281 0.235 0.176 0.176 0.176 livestock materials 0.155 0.178 0.199 0.209 0.209 0.209 europe, northern except uk (1972-2002) ^ ball et al. (2010); capital decomposition from butzer et al. (2012) labor 0.339 0.339 0.251 0.243 0.229 0.229 northern europe except united kingdom land 0.043 0.043 0.082 0.082 0.080 0.080 livestock capital 0.020 0.020 0.026 0.017 0.014 0.014 fixed capital 0.075 0.075 0.111 0.141 0.143 0.143 crop materials 0.243 0.243 0.254 0.251 0.265 0.265 livestock materials 0.280 0.280 0.276 0.265 0.268 0.268 europe, southern (19732002) ^ ball et al. (2010); capital decomposition from butzer et al. (2012) labor 0.539 0.539 0.403 0.388 0.443 0.443 southern europe land 0.073 0.073 0.112 0.136 0.089 0.089 livestock capital 0.019 0.019 0.022 0.016 0.012 0.012 fixed capital 0.072 0.072 0.094 0.130 0.121 0.121 crop materials 0.141 0.141 0.207 0.148 0.139 0.139 livestock materials 0.155 0.155 0.161 0.182 0.195 0.195 south africa (1947-1992) schimmelpfennig et al. (2000) labor 0.232 0.210 0.166 0.161 0.161 0.161 south africa land 0.129 0.143 0.169 0.144 0.144 0.144 livestock capital 0.043 0.018 0.010 0.035 0.035 0.035 fixed capital 0.252 0.230 0.237 0.239 0.239 0.239 crop materials 0.246 0.279 0.275 0.274 0.274 0.274 livestock materials 0.098 0.120 0.143 0.147 0.147 0.147 developing countries & regions             sub-saharan africa (1961-2008) fuglie (2011) labor 0.248 0.248 0.248 0.248 0.248 0.248 sub saharan africa land 0.315 0.315 0.315 0.315 0.315 0.315 livestock capital 0.308 0.308 0.308 0.308 0.308 0.308 fixed capital 0.024 0.024 0.024 0.024 0.024 0.024 crop materials 0.055 0.055 0.055 0.055 0.055 0.055 livestock materials 0.049 0.049 0.049 0.049 0.049 0.049 mexico (1960-1991) fernandez-cornejo and shumway (1997) labor 0.257 0.240 0.119 0.115 0.115 0.115 central america & caribbean land 0.505 0.352 0.179 0.225 0.225 0.225 livestock capital 0.089 0.161 0.315 0.263 0.263 0.263 fixed capital 0.089 0.161 0.315 0.263 0.263 0.263 crop materials 0.031 0.027 0.017 0.045 0.045 0.045 livestock materials 0.029 0.059 0.056 0.090 0.090 0.090 232 k. fuglie source study country/ region and period of study input input cost shares input shares applied to 1961-70 1971-80 1981-90 1991-00 2001-10 2011-12 brazil (1970, 1985, 1996, 2006) unpublished estimates provided by nicholas rada, calculated from brazilian agricultural censuses labor 0.434 0.434 0.443 0.415 0.373 0.373 south america, north africa and west asia land 0.342 0.342 0.159 0.115 0.083 0.083 livestock capital 0.096 0.096 0.090 0.070 0.053 0.053 fixed capital 0.071 0.071 0.110 0.177 0.161 0.161 crop materials 0.027 0.027 0.120 0.112 0.255 0.255 livestock materials 0.030 0.030 0.078 0.111 0.076 0.076 china (1952-1992) fan and zhang (2002) labor 0.443 0.396 0.413 0.333 0.333 0.333 china, mongolia, and north korea land 0.250 0.209 0.178 0.258 0.258 0.258 livestock capital 0.210 0.222 0.207 0.190 0.190 0.190 fixed capital 0.021 0.021 0.087 0.074 0.074 0.074 crop materials 0.038 0.064 0.084 0.121 0.121 0.121 livestock materials 0.038 0.039 0.031 0.023 0.023 0.023 india (1956-1987; 19802008) * evenson et al. (1999); rada (2013) labor 0.406 0.419 0.564 0.554 0.505 0.505 south asia land 0.314 0.210 0.173 0.181 0.267 0.267 livestock capital 0.213 0.269 0.123 0.115 0.052 0.052 fixed capital 0.003 0.010 0.024 0.043 0.065 0.065 crop materials 0.014 0.042 0.066 0.047 0.044 0.044 livestock materials 0.050 0.050 0.050 0.060 0.067 0.067 indonesia (1961-2006) fuglie (2010a) labor 0.370 0.538 0.476 0.388 0.392 0.392 southeast asia and pacific land 0.219 0.195 0.188 0.306 0.329 0.329 livestock capital 0.327 0.166 0.221 0.160 0.120 0.120 fixed capital 0.018 0.020 0.004 0.010 0.015 0.015 crop materials 0.033 0.048 0.054 0.045 0.046 0.046 livestock materials 0.033 0.033 0.057 0.091 0.098 0.098 transition countries & regions             ussr, european (1965-1990; 1992-1999) lerman et al. (2003); cungu and swinnen (2003) labor 0.104 0.104 0.104 0.190 0.190 0.190 european states of the former soviet union and formerly communist countries of eastern europe land 0.257 0.257 0.257 0.230 0.230 0.230 livestock capital 0.183 0.183 0.183 0.170 0.210 0.210 fixed capital 0.043 0.043 0.043 0.090 0.090 0.090 crop materials 0.143 0.143 0.143 0.070 0.070 0.070 livestock materials 0.270 0.270 0.270 0.250 0.210 0.210 ussr, asia (1965-1990; 19921999) lerman et al. (2003); cungu and swinnen (2003) labor 0.194 0.194 0.194 0.190 0.190 0.190 irrigationdependent asian states of the former soviet union land 0.210 0.210 0.210 0.230 0.230 0.230 livestock capital 0.054 0.054 0.054 0.300 0.270 0.270 fixed capital 0.113 0.113 0.113 0.090 0.090 0.090 crop materials 0.379 0.379 0.379 0.070 0.070 0.070 livestock materials 0.050 0.050 0.050 0.120 0.150 0.150 * evenson et al. (1999) and rada (2013) do not report a cost share for animal feed for india. to derive the feed cost share for india, i estimated total feed costs from fao commodity balance sheets on feed utilization and divided this by fao gross agricultural output (both valued at fao international prices for 2004-2006). i then subtracted the feed cost share from the livestock capital cost share reported in the evenson et al. (1999) and rada (2013) studies so that the input shares sum to 1.00. ^ when studies did not report fixed capital separately from livestock capital, average capital component shares for high-income, middle income, and low-income countries from butzer et al. (2012) were used to divide total capital into these components. cost shares in italics are extrapolations using estimates from the nearest period available. 233accounting for growth in global agriculture source: compiled by author from sources listed. eldon ball, shenggen fan, jorge fernandez-cornejo, oh-sang kwon, nicholas rada, david schimmelpfennig and colin thirtle kindly provided additional, unpublished data. table a3. countries and regional groupings included in the productivity analysis. sub-saharan africa (ssa) (developing) central eastern horn sahel southern western nigeria cameroon burundi djibouti burk. faso angola benin   car kenya ethiopiab c. verde botswana côte d’ivoire congo rwanda somalia chad comoros ghana   congo, dr seychelles sudanb gambia lesotho guinea   eq. guinea tanzania mali madagascar g. bissau   gabon uganda mauritania malawi liberia   sao tome niger mauritius sierra leone   & principe senegal mozambique togo     namibia     swaziland     zambia           zimbabwe     latin america & caribbean (lac) (developing)  n. america africa northeast andes s. cone c. america caribbean developed developed brazil bolivia argentina belize bahamas canada south africa fr. guiana colombia chile costa rica cuba usa   guyana ecuador paraguay el salvador dom. rep.     suriname peru uruguay guatemala haiti       venezuela honduras jamaica       mexico l. antillesa       nicaragua puerto rico           panama trin. & tob.     asia-pacific west asia & north africa developed south asia se asia pacific ne asia west asia north africa japan afghanistan brunei fiji china bahrain algeria korea, rep. bhutan cambodia micronesiaa korea, dpr iran egypt taiwan nepal indonesia n. caledonia mongolia iraq libya singapore sri lanka laos png   israel morocco   bangladesh malaysia polynesiaa   jordan tunisia   india myanmar solomon is.   kuwait   pakistan philippines vanuatu   lebanon syria   thailand   oman turkey   viet nam   qatar uar           s. arabia yemen 234 k. fuglie oceania europe developed europe former soviet union (transition) developed northern southern transition baltic e. europe cac australia austria cyprus albania estonia belarus armenia n. zealand belgium-lux. greece bulgaria latvia kazakhstan azerbaijan   denmark italy czecholithuania moldova georgia   finland malta slovakiab   russia kyrgyzstan   france portugal hungary   ukraine tajikistan   germany spain poland   turkmenistan   iceland   romania   uzbekistan   ireland sweden yugoslaviab       netherlands switzerland         norway uk         cac = central asia & caucasia. a composite territories composed of several small island nations. b statistics from the successor states of ethiopia (ethiopia and eritrea), sudan (sudan and south sudan), czechoslovakia (czech and slovak republics), and yugoslavia (slovenia, croatia, bosnia, macedonia, serbia and montenegro) were merged to form continuous geographical coverage since 1961. issn xxxx-xxxx (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(1): 109-124, 2012 positive mathematical programming approaches – recent developments in literature and applied modelling thomas heckelei*, wolfgang britz and yinan zhang university of bonn, germany abstract. this paper reviews and discusses the more recent literature and application of positive mathematical programming in the context of agricultural supply models. specifically, advances in the empirical foundation of parameter specifications as well as the economic rationalisation of pmp models – both criticized in earlier reviews – are investigated. moreover, the paper provides an overview on a larger set of models with regular/repeated policy application that apply variants of pmp. results show that most applications today avoid arbitrary parameter specifications and rely on exogenous information on supply responses to calibrate model parameters. however, only few approaches use multiple observations to estimate parameters, which is likely due to the still considerable technical challenges associated with it. equally, we found only limited reflection on the behavioral or technological assumptions that could rationalise the pmp model structure while still keeping the model’s advantages. keywords. positive mathematical programming, estimation of programming models, farm and sector models, policy impact assessment, review jel-codes. c61, q12, q18 1. introduction positive mathematical programming (pmp) is an approach to calibrate (agricultural) programming models by introducing non-linear terms in the objective function such that optimality conditions are satisfied at observed levels of decision variables. applications of pmp date back almost 25 years (kasnakoglu and bauer, 1988), but a more widespread use and subsequent discussion in the literature required a general motivation offered by howitt (1995). a key factor of success was the ability of pmp-type models to generate solutions with realistic diversification of production activities and smooth supply responses without adding weakly justified constraints to the model formulation. the development of pmp coincided with an increased attractiveness of programming models as their explicit technology representation facilitates interdisciplinary research on agrienvironmental interaction connecting economic and bio-physical aspects of agricultural systems. heckelei and britz (2005) as well as henry de frahan et al. (2007) review the use * corresponding author: thomas.heckelei@ilr.uni-bonn.de. 110 t. heckelei, w. britz and y. zhang of the approach in applications and the methodological development until that time. paris (2011, p. 340-419) provides a recent pmp introduction in the context of a more general and highly interesting textbook on ‘symmetric programming’. heckelei and britz (2005) specifically focus on parameter specification and simulation behavior of pmp models and criticize the weak empirical justification and consequently arbitrary model response implied by many of the early applications. they argue in their conclusions that the future use of pmp-type approaches should avoid the arbitrary specification of parameters and dual values of constraints by incorporating prior information in the calibration step, for example on supply elasticities or land values, or go one step further and estimate parameters of non-linear programming models using multiple observations. the authors acknowledge, however, the considerable methodological challenges related to the latter. this paper aims at assessing the progress with respect to the empirical foundation of pmp-type approaches since the review by heckelei and britz (2005). for this purpose we not only look at journal articles that are likely more advanced, but also review the empirical specification and calibration of larger scale programming models regularly used in agricultural and environmental policy analysis. apart from the empirical foundation, we also like to add another dimension to the assessment by looking at limits and advances of the economic rationale behind pmp-type methodologies thereby picking up an issue already raised by heckelei (2002) and heckelei and wolff (2003). despite the fact that we mostly talk about quantitative methodological issues here, we will ourselves rarely use a formal mathematical exposition. the length limit of this paper precludes a suitable technical treatment of the issues considered. consequently, we run the danger of a less than desirable level of precision at times. we try to accommodate this as much as possible by trying to refer to the literature in a targeted fashion such that the reader may dig into the technical details with the consultation of the references. the paper comprises two main sections: next, the literature since 2005 is reviewed focusing on methodological advances and distinguishing the three areas calibration, estimation and rationalisation of pmp-type models. the subsequent section reviews some programming models regularly applied in evaluating policies affecting the agri-environmental system with a focus on how pmp is applied and how its application interacts with the remaining structure of the model. finally, we summarize and conclude. 2. recent methodological advances of pmp-type approaches in the next two subsections we stick to parameter specification issues and distinguish here between the two ways heckelei and britz (2005) suggested to improve the empirical base of pmp models: using (1) exogenous information on supply responses and shadow prices of resources in calibrating the models to a base year observation on activity levels and (2) estimating programming model parameters in an econometric sense using multiple observations. the final subsection then briefly addresses issues related to the theoretical rationale of pmp models wondering if advances have been made to improve upon the original ad-hoc introduction and motivation of non-linear parameters to the objective function. 111positive mathematical programming approaches 2.1 calibration in the early or ‘standard’ approach of pmp a first phase added constraints to the original linear program (lp) forcing production activities to observed levels. heckelei and britz (2005) argue in their conclusion that such a first phase is neither necessary nor advisable as shadow prices of resource constraints are set arbitrarily by this procedure and the calibration can be done directly based on the first order conditions of the non-linear model. furthermore, they strongly recommend the use of prior information such as estimated supply elasticities to define the non-linear terms of the objective function as they dominate the simulation response of the model to changing economic conditions but no information on this is contained in just one observation. the first publication to be mentioned in this context since 2005 is henry de frahan et al. (2007). they calibrate a large sample of belgian farm models individually, thereby preserving farm heterogeneity in an agricultural sector analysis. their pmp approach skips the original first step and directly calibrates the models based on the first order conditions of the non-linear constrained optimization model. the constraints relate to milk and sugar quotas but land is assumed to be tradable between farms moving this input into the variable cost component using farmland rental values from the database. the shadow prices of the quotas are approximated by the gross margin differential between the quota products and the next best alternative crop (henry de frahan et al. 2007). non-linear cost terms in the objective function follow the ‘traditional’ pmp quadratic functional form in activity levels with only diagonal terms determined by the so-called “average cost function” approach (heckelei, 2002, p. 11) a modified pmp calibration approach is introduced by kanellopoulos et al. (2010) in the context of the newly developed farm systems simulator (fssim). it does use a first step with calibration constraints for technical reasons, but sets the shadow price of land to the observed average gross margin, thereby acknowledging that land is the only binding constraint in the base year and consequently captures all returns to fixed factors. a newly introduced parameter then allows for a continuous shift between linear and nonlinear cost function parameters while keeping marginal cost constant at the level satisfying the calibration condition. this parameter is directly related to the supply elasticities of the simulation model. for its specification the authors use exogenously given supply elasticities and, alternatively, by an iterative procedure optimizing the forecasting ability across two time periods. the latter outperformed the «standard pmp» approach based on an out-of sample test. a unique feature of this pmp application is that fssim is formulated in rotations rather than using the typical single production activities. mérel and bucaram (2010) point out that most studies using prior information on supply elasticities – which seems to be the most frequently used calibration approach according to our review of models presented below – perform a ‘myopic’ calibration, i.e. they ignore the change of resource shadow prices when translating the elasticity information to model parameters, a problem already addressed by heckelei (2002, p. 12 and p. 57). the special contribution of mérel and bucaram (2010) is, however, the very rigorous treatment of the question under what conditions a programming model can be exactly calibrated to exogenous own-price supply elasticities. they derive necessary and sufficient conditions for calibrating models with leontief and ces production functions both combined with quad112 t. heckelei, w. britz and y. zhang ratic objective function entries. merel et al. (2011) extend the approach to a generalized ces formulation and hint at the possibility of incorporating more than one constraint. it is not clear to us if the two technically impressive contributions by mérel and bucaram (2010) and merel et al. (2011) may be adapted to the case where not only own price, but also cross price elasticities are available. when looking at the non-myopic calibration effort by britz and adenäuer (2009), a question of perhaps more practical relevance is what happens if the conditions for exact calibration are not fulfilled. are there any insights to be gained on how closely the model may be calibrated to the given elasticities? or similar to the discussions of flexible functional forms in behavioral regression models: what are the requirements for the parameterization of constrained programming models to be able to represent any model consistent behavior up to a certain degree of flexibility? this would certainly be also relevant for the estimation of programming models to which we now turn to. 2.2 estimation the first steps to move into the direction of econometric estimation of pmp models or, more generally, programming models were already made by paris and howitt (1998) who introduced the generalized maximum entropy (gme) principle to the specification of pmp models. the first use of multiple observations was britz and heckelei (2000) and an indication of statistically consistent estimation could be provided by heckelei and wolff (2003) when using optimality conditions of programming models as estimating equations. buysse et al. (2007b) introduce the term «econometric mathematical programming» (emp) for this and consider it a relevant and likely enlarging field for the analysis of multifunctionality issues in agriculture as new appropriate datasets become available. they do cite heckelei and britz (2005), however, who concluded that the estimation of programming model parameters in the context of larger, policy relevant models might be methodologically quite challenging and had not yet been shown. buysse et al. (2007a) put emp to practice analyzing the reform of the common market organization in the eu’s sugar sector. they use a sample of 117 belgian sugar beet producing farms across 9 years to estimate parameters of a cost function quadratic in activity levels by employing gme on the first order conditions of the farm programming models. allowing some parameters to vary between farms they can add restrictions to calibrate each individual farm model to a reference period. sugar beet quota rents are estimated simultaneously with the cost function parameters using the prior assumptions that quotas are binding and rents cannot be negative. the authors acknowledge that the assumption of binding quotas considerably simplifies an already complex estimation problem as the algorithm does not need to identify the status of the constraints. the issue of binding or non-binding constraint is addressed methodologically by jansson and heckelei (2009) in the context of a spatial equilibrium trade model. a weighted least squares criterion estimates optimality conditions including complementary slackness conditions. prices and trade cost are simultaneously recovered incorporating the primal and dual side of the model. a simulation exercise shows that this approach is superior in a mean square error sense to previous two-step algorithms to calibrate spatial trade models. they conclude by suggesting that the general approach could also be applied to pmp type models. 113positive mathematical programming approaches arfini et al. (2008) perform a cross sectional estimation of a quadratic cost function using 50 sample farms from the emilia-romagna region. unlike other farm level pmpmodels, the calibrated programming models not only incorporate this cost function but also separately estimated linear, ’farm level‘ demand functions for the products, thereby endogenising prices. calibration of individual farm models is achieved by using specific ’deviations‘ of costs and prices being interpreted as the ’distance‘ of the farm to costs and prices at the regional level (page 10). we cannot really decide if an endogenous price formulation makes sense in their empirical setting, but the combination of pmpcalibrated farm programming models with a product demand functions formulation is a useful tool in general. paris (2011, p. 397-400) – also referring to a 2005 paper in italian by arfini and donati – focuses on the cost function estimation employed by arfini et al. (2008) within a pmp chapter of his book on symmetric programming. he stresses that this approach of specifying the total variable cost function is similar to the estimation of a dual cost function in econometric models. the difference is seen in the additional structure assumed (‘known’) and the often negative degrees of freedom when estimating pmp models. in our view the dissimilarities go further and the connection is not fully worked out as a dual cost function returns the minimum cost at given input prices subject to a technology being able to produce the given output quantities. even if one interpreted activity levels in pmp models as equivalent to output quantities, then the typical pmp non-linear cost function still misses the consistent connection to variable inputs and corresponding prices as determined by an underlying technology. this lack of rationalisation certainly applies to almost all pmp type applications beyond the papers by arfini, paris and co-authors and we devote an extra sub-section to it below. jansson and heckelei (2011) provide probably the most extensive estimation exercise up to this date, estimating 217 regional agricultural programming models with 23 crop production activities for the eu. here, a bayesian highest posterior density estimator replaces the typical gme applications in the field allowing for a less computationally demanding and – with respect to the employed prior information – a more transparent estimation of the cost function parameters. dual values of resource constraints (land, quotas) are estimated using average gross margins as a basis for formulating an imprecise prior reflecting limited knowledge before the estimation. despite the advantages of the bayesian estimation compared to gme, the authors still had to assume that resource constraints are binding to render the estimation exercise feasible. so far, all estimation exercises in the field lack the implementation of statistical inference for the estimated parameters. buysse et al. (2007a) argue that this is not their prime interest as the models’ calibration is the main objective (p. 33). jansson and heckelei (2011) similarly state that the empirical content of the parameters is higher compared to previous calibration exercises based on one observation (p. 149). the key issue here is however the highly demanding nature of the computations required for hypothesis tests in the context of these complex models. conceptually, means exist with bootstrapping gme models or simulating bayesian posterior distributions (jansson and heckelei, 2011b). another issue – not really restricted to the estimation of programming models – is the so far seemingly arbitrary decision on what model variables are treated as random. for example, whereas jansson and heckelei (2011) treat observed acreages, prices, yields, 114 t. heckelei, w. britz and y. zhang and input requirements as random, arfini et al. (2008) implement the same variables as deterministic. the specification in this regard is not discussed in either of the two and seems to perhaps reflect how much the authors identify themselves more with the econometric (random observations) or programming branch (deterministic observations) of the production economics literature. gocht (2009, p. 51ff) combines the estimation of a pmp-type model with the reoccurring problem of estimating input allocations to production activities from farm accountancy data. he argues that a simultaneous use of observed activity levels in an optimisation model and observed aggregate input cost categories offers more information for the estimation of input coefficients compared to previous approaches. the general claim is confirmed by an out-of-sample-test for belgian farm data. the specification of a calibrated farm group model is an automatic side effect of the, admittedly, challenging estimation exercise. 2.3 rationalisation of pmp models the above mentioned calibration or estimation papers leave an important question mostly unanswered: what is the economic or technological rationale behind the non-linear terms in the objective function of the simulation model? this question was raised more generally before by heckelei (2002, p. 51ff) but was not discussed by heckelei and britz (2005). the answer seems central for a proper use of pmp based models. a key argument for their application instead of econometric ones is the facilitated analysis of agri-environmental interactions by explicitly simulating farm management (use of fertilizer, plant protection, tillage, irrigation, etc.). under the assumption of a leontief technology, input use increases linearly with the production activity levels and determines their gross margins. if the non-linear pmp terms in the objective function are seen to relate to input use, for example caused by heterogeneous land quality or rotation effects, then they also imply a discrepancy between average and marginal input application rates (and a deviation from a leontief technology). an environmental indicator using the average rates (input coefficients) would consequently be inconsistent with the model structure. the same discrepancy holds for the calculation of economic indicators based on the leontief input coefficients. are we able to motivate the non-linear costs in a way which preserves the assumption of a leontief technology for land and intermediate inputs? let’s assume the relation of (opportunity) costs and activity levels (x) not accounted for either by linear constraints (ax ≤ b) or the variable cost entries in the objective function (c), can be expressed by a non-linear capacity constraint (heckelei, 2002, p. 30) as f(x) = 0. the capacity constraint might be understood as a non-linear aggregation of labor and capital requirements of the activities bounded by a fixed labor and capital stock. this interpretation is inviting if the linear objective function covers only the difference between revenue (r) and variable costs and labor/capital are not bounded by the linear constraints. the resulting model is: max x π = (r − c)'x s.t. ax ≤ b λ[ ] f (x)= 0 µ[ ] . 115positive mathematical programming approaches its first order optimality conditions differ from those of the usual pmp approach only by the shadow price μ of the capacity constraint (l for lagrangian): ∂l ∂xi = ci + a i 'λ + ∂ f ∂xi µ ≥ ri ⊥ xi ≥ 0 ∀i for all i. one could equivalently define a function g(x) for which holds that ∂g ∂xi = ∂ f ∂xi µ + f x( ) ∂µ ∂xi for all i, remove the capacity constraint and add g(x) to the objective function. consequently, a non-linear objective function could be rationalised with a capacity constraint. however, at this point we are not sure if there exists a functional form for the capacity constraint such that g(x) becomes quadratic as often assumed in pmp. for sure it is possible to simply stick to an explicit constraint formulation and doole et al. (2011) used this to calibrate total milk production on farm as a quadratic function of herd size. leaving the behavioural model of profit maximisation behind and not longer assuming non-linearities in the technology, the mean-variance risk model constitutes a fitting and rather straightforward rationalisation of the quadratic non-linear objective function entries (heckelei, 2022, p. 41). cortignani and severini (2009) and severini and cortignani (2011) develop a pmp approach that additionally takes risk aversion behavior into account to evaluate insurance schemes. petsakos et al. (2011) refrain from extra non-linear costs terms such that remaining quadratic objective function entries represent a covariance matrix of gross margins. the authors apply gme to adjust this matrix to perfectly calibrate the model and interpret the resulting matrix as the true covariance matrix. 3. review of some pmp based models this section discusses pmp type models with a focus on those designed for repeated application over a longer time horizon and for which documentation was available. nevertheless, a fully transparent selection rule could not be applied and the selection likely depends on our subjective and limited overview. for each model, a pre-selected list of attributes were collected as far as possible from papers and websites and afterwards verified by the authors of the models1 which also added missing information (see table 1). while focusing on pmp based models, we would like to mention, that according to our literature review, the only larger regularly applied model in the european arena not using pmp appears to be aropaj (decara et al., 2005). besides aropaj, the programming models of the mccarl school (schneider et al., 2007: fasom; schneider and schwab, 2006: 1 we would like to thank (in alphabetical order) for filling out the questionnaire and clarifying further questions to the model: filippo arfini, university parma/it, fipim; ali ferjani, art, tänikon/ch, silas-dyn; horst gömann, vti, braunschweig/de, raumis; john helming, lei, the hague/nl, dram; lucinio judez, university madrid/es, promapa; robert mac gregor, agriculture and agri-food canada, ottawa/can, dram. 116 t. heckelei, w. britz and y. zhang eufasom; havlik et al., 2011: globiom) are other large-scale systems not relying on pmp. and even in a fasom inspired model, pmp is now used: pasma (schmid and sinabell, 2007), replaces the quadratic terms by a step-wise linear function, a combination of pmp with linear programming and convex combination constraints (mccarl, 1982). 3.1 model types the review reveals that pmp based models cover a wide range of approaches which might be roughly categorised by three types. the first group comprises bio-economic farm models, typically being quite rich in the technology description and comprising many different activity variants for producing one output. application of pmp to these model types is relatively new; ffsim (kanellopoulos et al., 2010) is taken here as an example. a possible explanation for the limited use could be that researchers dealing with only a few model instances continue to use traditional approaches to model calibration by changing manually coefficients and employing a rich constraint set. the second strand of models are price exogenous models for aggregate agents, either farm type groups as in farmis (offermann et al., 2005), fipim (arfini et al., 2003), promapa (júdez et al., 2008) and capri-farm (gocht and britz, 2011) or regional models as in raumis (cypris, 2000), silas-dyn (mann et al., 2003), capri-reg (britz and witzke, 2008) and dram (helming, 2005). most farm type groups models use single farm records as the source, in europe often data from the farm accounting data network (fadn). exemptions are fipim which adds data from iacs, a geo-referenced data base set up for the control of the direct payments claims of the cap, and capri-farm which uses the farm structure survey. most models integrate crop and livestock activities and seem to comprise both animal feed and crop nutrient requirements. silas-dyn seems to be the only recursive-dynamic model in that group and the only one using a capital stock constraint. finally, we have two large scale north-american systems in the third group of models which incorporate price endogeneity for outputs: cram (horner et al., 1992) from canada and reap (johansson et al., 2007) for the u.s.. both combine by now pmp with a spatial equilibrium setup following takayama and judge (1971) to incorporate price feedback directly in the model structure. alternatively to the takayama-judge approach, capri and some models covering member states such as raumis or silas-dyn are linked to market models in a more or less coherent way (cf. britz, 2008) to allow for price feedback. compared to price exogenous model applications, the price feedback clearly dampens the allocation response in the overall model chain. dram is a national approach where regional models are linked to allow for clearing of the manure market. this feature can be switched on in raumis on demand. 3.2 pmp specification in the models most models using regional/national data also seem to be linked to some outlook activity, i.e. are used for ex-ante policy assessment and thus face the question if and how to update their pmp terms to a future point in time whereas models at the farm level generally do not project the model specification into the future. models with a national or regional 117positive mathematical programming approaches focus such as dram, promapa or swap incorporate specific features such as manure trade or explicit consideration of irrigation activities (promapa). the latter, as well as the use of different intensity variants (capri, promapa, dram) or different crop rotations (reap and fssim) might require additional pmp terms to steer substitution between the variants/crop rotations rendering myopic calibration even more questionable. some of models reviewed were designed from the beginning to use pmp, other switched to pmp during their lifetime and might have changed the model structure as a consequence, for example raumis dropped flexibility constraints used in earlier versions. in most of the reviewed models, the constraint set is small and the number of decision variables is large so that the allocation response depends to a large extent on the pmp parameters. in most of the models, explicit consideration of capital and labor is missing such that their allocative impact must be captured by the pmp terms. the importance of the non-linear terms for simulated changes in crop areas and herd sizes and thus output quantities might even be higher than at first glance or as the model documentations might suggest. many of the constraints mentioned in the model documentations often directly steer other endogenous variables apart from crop acreages and herd sizes. animal nutrient requirements and other constraints related to feeding often determine the feed mix, nutrient crop requirements often endogenously drive the fertilizer mix. labor constraints do not impact directly the allocation if the model structures allows for buying labor, but rather define hired labor use. the same holds for restrictions, for example relating to stable places if the model comprises investment possibilities. however, in some models these restrictions are not symmetric as they are not matched with dis-investments or off-farm labor activities. these observations underline that the allocation response in the second strand of pmp based models rests to a large extent on the pmp parameters. fipim, however in the newest estimated version operating for three european regions (arfini and donati, 2011), and capri (jansson and heckelei, 2011) are the only models reviewed for which pmp parameters are estimated (at least partially). all remaining (and capri for the case of some (perennial) crops not covered by the estimations and for livestock) seem to rely on exogenous elasticities, and all but two seem to employ a myopic calibration method (heckelei, 2002: 12) which neglects the effects of changing dual values on the simulation response. here merel et al. (2010) develop an easy-to-use correction to at least account for the effect of one major constraint such as land. the two exemptions are a variant of the swap model used by merel and specific sub-modules in reap where crop rotations are employed in conjunction with a cet transformation. here, several sets of transformation elasticities are introduced in sensitivity experiments. from these, the set which came closest to the elasticities from another model was chosen. in many cases, the sources of the exogenous elasticities used in the calibration step are not found in scientific papers or model descriptions available. related to the question of parameter derivation is the question of how dual values of resource constraints are generated, as they might impact the range of stability. interestingly, despite the criticism and straightforward alternatives to the biased estimates of duals derived in the so-called first stage of the original pmp formulation that are offered in the literature (júdez et al., 2001; heckelei and wolff, 2003; heckelei and britz, 2005), many models still rely on it. ta bl e 1. o ve rv ie w o n re vi ew ed m od el s m od el ba sic m od el ty pe ti m e a ct iv iti es fe ed c ro p nu tr ie nt s po lic y in st ru m en ts pm p te rm s ri sk sh ad ow p ric es la nd in ve st m en t la bo ur ra u m is a gg re ga te re gi on al pr og ra m m in g m od el s, c s ea 31 c ro ps 16 li ve st oc k en do g fe ed m ix fr om in di vi ua l pr od uc ts n ,p ,k pr od uc tio n qu ot as , d ire ct pa ym en ts , d ec ou pl in g, se tas id e, st oc ki ng ra te li m its , m in im um la nd u se a nd a gr oen vi ro nm en ta l r eq ui re m en ts m fix ed co nt in uo us re -in ve st m en t co st s c on st ra in ts d riv e hi re d la bo ur fa rm is a gg re ga te fa rm ty pe m od el s c s ea 27 c ro ps 22 li ve st oc k en do g fe ed m ix fr om in di vi ua l pr od uc ts n ,p ,k pr od uc tio n qu ot as , d ire ct pa ym en ts , d ec ou pl in g, m od ul at io n, se tas id e, st oc ki ng ra te li m its , m in im um la nd u se re qu ire m en ts m fix ed co nt in uo us re -in ve st m en t co st s c on st ra in ts d riv e hi re d la bo ur c a pr i a gg re ga te re gi on al an d fa rm ty pe m od el s c s ea 37 c ro ps (a ll ag r. la nd us e) 16 li ve st oc k en do g fe ed m ix fr om 8 bu lk s n ,p ,k , m in /m ax m in er al c ou pl ed a nd d ec ou pl ed pr em iu m s, pr od uc tio n qu ot as , a bc su ga r m ar ke t r eg im e, se tas id e, gr ee ni ng in st ru m en ts of c a p e (a nn ua l cr op s) m re st on ly a bc su ga r b ee t re gi m e la nd /q uo ta re nt s ex og en ou s, re st 1s t la nd su pp ly cu rv e, im pe rf ec t su bs tit ut io n ar ab le a nd g ra ss la nd s d ra m li nk ed a gg re ga te re gi on al m od el s c s 16 a ra bl e/ 3 fo dd er c ro ps 8 liv es to ck dr y m at te r fr om ro ug ha ge cr op s f or c at tle he rd pr od uc tio n qu ot as fo r s ug ar , m ilk a nd st ar ch p ot at oe s, di re ct pa ym en ts , d ec ou pl in g, m an ur e po lic ie s m 1s t fix ed ff si m bi oec on om ic fa rm ty pe m od el s (t yp ic al fa rm s) c s n ,p ,k m m ea nva ria nc e fix ed pr o m a pa a gg re ga te fa rm ty pe m od el s c s 31 c ro ps (o f w hi ch 5 fo dd er ) 4 liv es to ck en do g fe ed m ix c ou pl ed a nd d ec ou pl ed pr em iu m s, pr od uc tio n qu ot as , se tas id e, m od ul at io n re nt al v al ue o f la nd e xo ge no us , re st 1 st fix ed , i rr ig at ed an d no n -ir rig at ed c ra m ta ka ya m aju dg e ty pe m od el w ith ex pl ic it pr od uc tio n fu nc tio n fo r ag ric ul tu re 12 c ro ps 6 liv es to ck fix ed fi pi m c s 11 c ro ps 3 liv es to ck e fix ed re a p ta ka ya m aju dg e ty pe m od el w ith ex pl ic it pr od uc tio n fu nc tio n fo r ag ric ul tu re c s ea pr ede fin ed ra tio s fix ed a nd c ou nt er -c yc lic al pa ym en ts , t ar ge t p ric es , lo an ra te s, lo an d efi ci en cy pa ym en ts , a nd d om es tic , ag rie nv iro nm en ta l p ro gr am s m fix ed , d iff er en t so il ty pe s i n re gi on s sw a p 12 c ro ps fix ed si la sd yn a gg re ga te re gi on al pr og ra m m in g m od el s rd ea 37 c ro ps in cl ud in g al pi ne g ra zi ng ac tiv iti es 17 li ve st oc k fe ed m ix fr om 55 fe ed ty pe s, su m m er / w in te r r at io n m in (n ,p ,k , m g) a nd m ax (n ,p ) sw iss d ire ct p ay m en ts sy st em an d ec ol og ic al re qu ire m en ts , pr od uc tio n qu ot as ,a nd en vi ro nm en ta l i m pa ct s m 1s t fix ed in ve st m en t ac tiv iti es (b ui ld in gs / m ac hi ne ry ) c on st ra in ts d riv e hi re d la bo ur a bb re vi at io ns : c s: c om pa ra tiv e st at ic ; r d : r ec ur si ve d yn am ic ; e a : e xan te ; m : m yo pi c ca lib ra tio n ag ai ns t ex og en ou s el as tic iti es ; e : e st im at ed ; n : n itr og en ; p : p ho sph at e; k : k al iu m ; 1 st : du al s fr om fi rs t s ta ge p m p n ot e: e m pt y ce lls im pl y m is si ng in fo rm at io n ta bl e 1. c on tin ue d m od el o th er re st ric tio ns in te ns ity va ria nt s en v. in di ca to rs m ai n da ta so ur ce s c ov er ag e re so lu tio n o th er fe at ur es w eb sit e, m od el d oc um en ta tio n so ftw ar e g ra ph ic al u se r i nt er fa ce ra u m is n b al an ce n at io na l a cc ou nt s o f a gr ic ul tu re fa rm st ru ct ur e su rv ey c al cu la tio n da ta g er m an y 30 0 n u ts 3 na tio na l l in ka ge po ss ib le e .g . o f m an ur e tr ad e ht tp :// w w w. vt i.b un d. de /n o_ ca ch e/ en / st ar ts ei te /in st itu te s/ ru ra l-s tu di es /r es ea rc har ea s/ po lic yim pa ct -a ss es sm en t/v tim od el lin gne tw or k/ ra um is. ht m l fo rt ra n fa rm is n b al an ce g er m an re gi on na l s ta tis tic s a nd fa d n g er m an y fa rm ty pe g ro up s ht tp :// w w w. vt i.b un d. de /e n/ st ar ts ei te / in st itu te s/ ru ra l-s tu di es /r es ea rc har ea s/ po lic yim pa ct -a ss es sm en t/v tim od el lin gne tw or k/ fa rm is. ht m l g a m s c a pr i pr od uc tio n qu ot as , se tas id e, pr em iu m en tit le m en t, c a p gr ee ni ng in st ru m en ts , g h g em iss io ns 2 fo r c ro ps a nd an im al s n ,p ,k b al an ce s, a m m on ia a nd g h g e m iss io ns , lc a e ne rg y re gi on al st at ist ic s a nd f ss c al cu la tio n da ta ex te rn al o ut lo ok s ( a g li n k c o si m o ) eu 27 /n or w ay / tu rk ey /w es te rn ba lk an s ~1 80 0 fa rm ty pe s / re gi on al m od el s se qu en tia l c al ib ra tio n w ith g lo ba l m ar ke t m od el , s pa tia l d ow nsc al in g w w w. ca pr i-m od el .o rg g a m s ow n de ve lo pm en t, ja va d ra m m ax im um a m ou nt o f n a nd p fr om o rg an ic an d m in er al fe rt ili ze r pe r c ro p pe r r eg io n 8 fo r d ai ry co w s, 2 fo r ar ab le c ro ps a m m on ia em iss io ns , n b al an ce d ut ch f a d n a gr ic ul tu ra l c en su s h an db oo ks n et he rla nd s 66 re gi on s m an ur e tr ad e be tw ee n re gi on s g a m s ff si m m an y o w n su rv ey s, te ch ni ca l c oe ffi ci en t ge ne ra to r, cr op g ro w th m od el s eu ro pe xx ty pi ca l f ar m s g a m s pr o m a pa ir rig at ed a nd no nirr ig at ed cr op s sp an ish f a d n sp ai n 14 0 fa rm ty pe s (s pe ci al isa tio n x si ze x re gi on ) g a m s fo rt ra n a c c es s c ra m fl ex ib ili ty b ou nd s st at ist ic s c an ad a, a gr ic ul tu re an d a gr i-f oo d c an ad a, fo r t he m ed iu m t er m p ol ic y ba se lin e, u sd a , f or in te rn at io na l d at a c an ad a 29 c ro p pr od uc tio n re gi on s u p to 4 m ar ke t r eg io ns fi pi m 3 re gi on s i n ita ly fa rm g ro up s re a p yi el d fu nc tio ns fr om e pi c , cr op ro ta tio ns u sd a p ro du ct io n da ta er s co st d at a u s 45 p ro du ct io n re gi on s c et fu nc tio ns fo r til la ge p ra ct ise a nd cr op ro ta tio ns g a m s sw a p m on th ly ir rig at io n w at er c al ifo rn ia 33 re gi on s ht tp :// sw ap .u cd av is. ed u/ si la sd yn m in 7 % e co lo gi ca l c om pe ns at io n a re as 4 in te ns ity le ve ls n ,p ,k , m g ba la nc es a m m on ia a nd g h g e m iss io ns lc a e ne rg y a gr ic ul tu ra l i nf or m at io n sy st em ec on om ic a cc ou nt s f or a gr ic ul tu ra l sw iss f a d n d at ab as e sw itz er la nd 8 zo ne s a nd 3 p ro d. re gi on s in te ra ct io n w ith s w iss m ar ke t m od el a nd li nk ag e to s w iss a gr ic ul tu re l ife c yc le a ss es sm en t ( sa lc a ) lp l a bb re vi at io ns : c s: c om pa ra tiv e st at ic ; r d : r ec ur si ve d yn am ic ; e a : e xan te ; m : m yo pi c ca lib ra tio n ag ai ns t ex og en ou s el as tic iti es ; e : e st im at ed ; n : n itr og en ; p : p ho sph at e; k : k al iu m ; 1 st : du al s fr om fi rs t s ta ge p m p n ot e: e m pt y ce lls im pl y m is si ng in fo rm at io n 120 t. heckelei, w. britz and y. zhang unfortunately, the review of these longer standing applied models contributes little (if at all) to the question of how to economically rationalise the introduction of non-linear terms on the objective function. many models do not explicitly consider capital and labor, suggesting that the pmp terms are related to the two primary factors. that interpretation is explicitly used by arfini and donati (2011), but hard to defend for models such as raumis, farmis or silas-dyn where labor and capital are explicitly handled via constraints (fixed factors) or through the objective function (variable factors). 4. summary and conclusions heckelei and britz (2005) considered the typical pmp application up to this point to have an insufficient empirical base and suggested to either use exogenous information and/or multiple observations. our literature review and discussion first looks at papers published since this earlier review to see if things have changed. the use of prior information in calibration such as exogenous elasticities or price data for the dual values or resource constraints has clearly increased. rising awareness of the problem can also be inferred from the fact that some calibration approaches explicitly evaluate the resulting simulation behavior. a highly technical set of papers discusses the required conditions that need to be fulfilled for programming models to be exactly calibrated to a set of exogenous own price elasticities. applications of what the literature termed econometric mathematical programming (emp), i.e. the estimation of programming model parameters based on multiple observations, are still few (regarding number of papers and independent groups engaged in it). while the use of estimators differs probably for good reasons depending on the available data and prior information, divergence in other core assumptions, for example regarding which data are treated as deterministic or stochastic, might be a sign of the still emergent status of that research field. researchers still face considerable computational challenges for large-scale applications preventing, for example, to relax the assumption that constraints are binding for all observations. moreover, full statistical inference on estimated parameters is not beyond the conceptual state yet. a research gap we consider at least as important relates to the lack of a clear rationale, i.e. a combination of behavioral and technological assumptions for the use of typical pmp from model parameterizations. the only exemption is given by papers relying on a meanvariance risk analysis where the quadratic part of the objective function is rationalised by the covariance matrix of uncertain returns. a previously discussed and recently employed alternative of non-linear (capacity) constraints is shown to be completely equivalent to the non-linear objective function entries as long as certain functional restrictions are satisfied. other behavioral or technological assumptions which would completely move away from typical pmp formulation but still allow for a differentiated analysis of factors affecting agri-environmental interactions could not be identified. the second part of our review deals with pmp based programming models build for repeated use in policy evaluation exercises. even though most of these rather diverse models (from small scale bio-economic to national or international scope) are european models, also large-scale, price endogenous, north american models use pmp in different variants. apparently, the share of (n)lp based models drawing on other calibration 121positive mathematical programming approaches methods has considerably decreased. the models maintained by groups which are also involved in emp development are often at least in part parameterized by econometric estimations. most other applications calibrate their models against exogenous elasticities. early approaches criticised as leading to arbitrary allocation response such as the so-called «standard approach» are abandoned. however, calibration is still mainly done in a myopic way ignoring feedbacks with resource constraints and despite the fact that easy alternatives exist. equally, many models still use the original first phase of pmp leading to biased shadow prices of binding resources. the recent developments in the pmp literature clearly move towards a better understanding and improvement of related model specifications. a more solid empirical foundation of models regularly applied in evaluations of agricultural policies or those affecting agriculture can be identified and clearly support an increased reliability of the results. further improvements in coverage and quality of empirical approaches are still desirable, but the still limited economic rationalisation of pmp-type approaches remains an at least important deficiency. progress in this area is needed to increase not only scientific acceptability, but also the trust in and understanding of this modeling approach in the policy process. this seems rather important given the increasing relevance of national and global issues requiring sound economic models with technology rich specifications of farm and aggregate agricultural systems. acknowledgements the research for this paper was partially funded by the european 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(1971). spatial and temporal price and allocation models. journal of international economics 3(3): 304-304. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(3): 235-268, 2012 knowledge, technology and innovations for a bio-based economy: lessons from the past, challenges for the future roberto esposti1 department of economics and social sciences, università politecnica delle marche, piazzale martelli 8 60121 ancona (italy) abstract. the paper presents an evolutionary perspective on how agricultural knowledge and innovation systems (akis) have adapted over time to new challenges and technological paradigm and trajectories. starting from a conventional science-based approach and the robust empirical evidence supporting it, the analysis highlights the emergence of some system failures and the need for new conceptualization and design of the akis. particularly concentrating on developed countries’ agenda, we then discuss how, along this evolutionary pattern, bioeconomy emerges as the convergence of traditional sectors as a result of these new technological trajectories. finally, some implications for the eu policies are drawn. key-words. agricultural knowledge and innovation system, productivity, r&d, bioeconomy jel-codes. q16, o30 1. introduction the object of this paper is the agricultural knowledge and innovation system (akis), that is, “the set of agricultural organisations and/or persons, and the links and interactions between them, engaged in the generation, transformation, transmission, storage, retrieval, integration, diffusion and utilisation of knowledge and information, with the purpose of working synergistically to support decision making, problem solving and innovation in agriculture” (röling and engel, 1991; see also poppe, 2012b). the aim of this paper is to analyze the conceptual and organizational evolution of this system and its gradual adaptation to progressively changing contexts and scenarios. in this respect, the paper also aims at discussing the main implications of this evolution in terms of institutional and policy changes. the structure of the paper pursues this objective by firstly reviewing the contribution that the literature has attributed to the akis in determining, over the last century, a remarkable growth of factor productivity in agriculture (section 2), then drawing attention to the criticisms that have gradually emerged in that respect, particularly considering the new challenges and the new technological paradigm that the akis is now facing (section 3). this evolution results in a substantial widening of the scope of the system, from the strict boundaries of the agricultural sector to the broader contours of the so-called bioeconomy (section 1 corresponding author: r.esposti@univpm.it. 236 r. esposti 4). paying particular attention to eu policies, the final section outlines the steps that have been taken and the initiatives that are being proposed to build an eu-level knowledge and innovation system in accordance with the afore-mentioned evolutionary process. 2. an institutional success: the long-term agricultural productivity growth last century experienced a remarkable growth of agricultural production at a global level (alston et al., 2010a). such a strong increase in agricultural supply counterbalanced the large growth of global food demand, thus allowing for stable and relatively declining agricultural prices (alston et al., 2009b, 2010b), and it has been almost entirely generated by a major increase in agricultural factor productivity (fuglie, 2010). table 1 reports the growth rate of land and agricultural labour productivity during the second half of the last century. land productivity growth was initially quite homogeneous between developing and high-income countries. then, a significant drop in growth rates was observed in the latter during the last decades of the period, while it continued to remain stable in developing countries (pardey and pingali, 2010). labour productivity growth, on the contrary, does not show any decline, and it is much higher in highincome countries due to a more intense loss of agricultural labour force. a significant part of this productivity growth can be attributed to massive capital intensification, that is, the increase of capital endowment per unit of agricultural labour and land. however, capital intensification has been just one of the drivers of factor productivity growth. the other major driver has been technological progress (fuglie, 2010). total factor productivity (tfp) provides a measure of productivity growth that does not depend on the intensification of some production factors. therefore, it expresses that part of growth that can be exclusively attributed to a purely technological component.2 we may observe (table 2) that agricultural tfp growth rates continued to rise in both developed and developing countries up to the last decade of the last century. then, a slight decline occurred in the last period, but this can be almost entirely attributed to developed countries and especially to the top producing areas worldwide, that is, north-america and europe. here, the drop in the tfp growth rate is remarkable and has been widely emphasized and investigated (also known as agricultural productivity slowdown) (alston and pardey, 2009). however, it remains true that world agriculture really experienced a huge productivity step forward during the second half of the last century. worldwide, in about 50 years, land productivity increased by almost 150%, agricultural labour productivity by almost 75%, and agricultural tfp by about 55%. a sort of “slow magic” (pardey and beintema, 2001) occurred which can be identified in the continuous and incessant scientific and technological progress that brought innovations into agricultural production. this in turn allowed world agriculture, or its richer part, to escape the trap of food shortage: using the words of alston et al. (2009a), mendel (the capacity to improve crop yields) eventually prevailed on malthus (the food shortage induced by population pressure). 2 regardless how it is actually calculated or estimated, total factor productivity (sometime also called multi factor productivity) is an index that expresses the ratio between an aggregate index of outputs and an aggregate index of inputs. therefore, tfp growth expresses the increase in aggregate output production obtained from a given level of aggregate input use: it measures that part of output growth not explained by a higher factors’ use (ball and norton, 2002). 237knowledge, technology and innovations for a bio-based economy unquestionably, both major drivers of productivity growth (capital intensification and technological change) have been considerably favoured by policies that strongly promoted agricultural production either through price support or direct coupled payments. in the eu, in particular, the common agricultural policy (cap) provided the farmers with robust incentives to invest in new capital and to introduce technological innovations, together with increasing technical prescriptions that oriented the direction of this capitalisation and innovation process. accordingly, the gradual shift observed since the eighties from these agricultural policies towards progressively decoupled or extensification-oriented support may have played a role in the observed productivity slowdown. nonetheless, the influence of agricultural policies (and of the cap, in particular) on agricultural technological change and productivity may be more complex and not unidirectional3 and is well beyond the scope of this paper that only focuses on akis policies, i.e., those policies that explicitly promote agricultural research and innovation.4 table 1. land and agricultural labour productivity: average. annual growth rate (%), 1960-2005 land productivity agricultural labour productivity 1960-1990 1991-2005 1960-1990 1991-2005 world 2.03 1.82 1.12 1.36 high-income countries 1.61 0.72 4.26 4.18 middle-income countries 2.35 2.30 1.51 2.02 low-income countries 2.00 2.39 0.46 1.03 china 2.81 4.50 2.29 4.45 usa 1.81 1.50 3.64 1.54 source: alston et al. (2010a). table 2. avg. annual growth rates (%) of agricultural total factor productivity (tfp), 1960-2007 1960s 1970s 1980s 1990s 2000s world 0.49 0.63 0.92 1.54 1.34 developed countries 1.21 1.52 1.47 2.13 0.86 transition countries 0.67 -0.26 0.25 0.73 1.92 developing countries 0.18 0.54 1.66 2.30 1.98 usa and canada 0.86 1.37 1.35 2.26 0.33 europe (exc. fsu) 1.17 1.31 1.22 1.63 0.59 source: fuglie (2010). 3 for instance, esposti (2007) shows how a support coupled to agricultural production may, in fact, reduce agricultural labour productivity as it maintains within the sector the labour force that would otherwise move to other sectors. 4 see oskam and stefanou (1997) for a more extensive view on this issue. 238 r. esposti 2.1. the role of public r&d starting with the fifties and sixties of the last century, most researchers and analysts identified the key engine of the above-mentioned productivity growth in a sequence of major mechanical, chemical and biological innovations and in the r&d investments that have generated or induced them. early empirical studies supported the idea that relevant and appropriate r&d investments were the cause of those technological innovations that had a direct impact on agricultural productivity (evenson, 2001). however, in the case of agricultural innovations, this research and innovative effort combined with other major factors that enabled or favoured their adoption and diffusion. in particular, two other driving forces have been the increasing amount of human capital embodied in agricultural labour force (education) (huffman, 2001) and the (mostly public) provision of a set of services and institutions aimed at informing farmers about the existence of new technological solutions, as well as facilitating the learning process concerning their suitability and appropriate application (extension) (evenson, 2001). of this “knowledge triangle” (research, extension and education),5 the r&d component (and public research, in particular) has been largely considered prevalent and hierarchically dominant because it generated those results that eventually activated the other components. table 3 (upper part) reports the growth rate of public agricultural r&d in real terms. continuous growth in agricultural r&d investments over time is evident in both developed countries and developing or emerging countries (beintema and stads, 2008, 2010; beintema, 2010). however, growth rates are regularly declining in developed countries and they are not entirely compensated by the higher growth of developing countries. at least until the last decade of the last century, the declining but still positive growth of agricultural r&d expenditure accompanied an increasing productivity (tfp) growth. then, when the growth of r&d expenditure continued to decline, the tfp also started slowing down. whether this declining growth rate can also be observed in private agricultural r&d is more questionable. first of all, collecting comparable data on private agricultural r&d is challenging since it is difficult the define the boundaries of what “agricultural” r&d is across different countries and periods (esposti, 2011).6 secondly, most of the evidence and discussion about the declining agricultural r&d growth rate has to do with public budget cuts observed, in real terms, in many countries in recent years. nonetheless, despite the different patterns observed for private r&d with respect to public r&d (huffman and just, 1999), the arguments and implications about the r&d declining growth rate are usually extended to both components of the agricultural research effort. in particular, for both public and private research, the link between agricultural r&d investments and pro5 despite the recent conceptual and institutional developments that will be more extensively discussed in section 4, the prevalent representation of the akis remains the so-called “knowledge triangle” whose vertices are research, education and extension (oecd, 2012). the eu adopts a slightly different version, the three components being research, high education and innovation (european commission, 2011). 6 for instance, alfranca and huffman (2003) report quite different shares of private r&d on total national agricultural research for european countries: 60% for the uk, 25% for italy, 10% for germany and spain. pardey et al. (2006b), however, present slightly different data: in 2000, the private share was 71% in the uk, 54% in germany, 54% in all oecd countries. with reference to 2000, kirschke et al. (2011) indicate that the average private share is 54% in developed countries and only 6% in developing countries. 239knowledge, technology and innovations for a bio-based economy ductivity growth has been repeatedly and carefully investigated and confirmed by rigorous econometric models and estimates over the last decades and in recent years (alston et al., 2000; evenson, 2001; alston et al., 2011). in general terms, it can be concluded that this empirical literature emphasizes this cause-effect link between agricultural r&d expenditure and productivity growth so strongly that the existence of this direct link was seldom questioned. table 3. cross-country comparison of public agricultural research expenditure (in real terms) and of agricultural research intensity (ari) (public and private r&d), 1976-2005 1970s 1980s 1990s 2000s avg. annual r&d growth rates (%)a world 4.5b 2.0 1.7 1.5b high-income countries 2.5b 2.1b 0.2b 1.1b low and middle-income countries 7.0b 2.2 3.3 1.9b usa 3.2 2.4 2.0 0.9c france 3.9 -6.8 germany 1.0 2.4 total oecd 1.9 0.4 china 4.8 6.7 india 6.2 7.0 agricultural research intensity (ari)d,g high-income countries 1.94 3.01 4.19 5.38 low and middle-income countries 0.44b 0.53b 0.62 0.69 usae 1.68 2.64 2.65 chinae 0.41 0.35 0.40 indiae 0.18 0.24 0.34 italyf 0.75 1.20 francef 3.50 3.70 germanyf 4.10 4.10 spainf 1.10 1.50 ukc 5.40 6.20 a source: asti database b source: author’s elaborations on pardey and beintema (2001) c source: author’s elaborations on pardey and alston (2012) d source: pardey and pingali (2010) e source: pardey et al. (2008) f source: esposti et al. (2008) g ari data may be not consistent with data reported in the upper part of the table, as this latter only concerns public agricultural r&d. a reliable cross-country comparable ari, however, must include both private and public r&d. 240 r. esposti 2.2. a certain idea of the akis this strong cause-effect relationship between investment in agricultural research (but also extension and education) and productivity growth postulates an underlying idea of the akis. in essence, this relationship was interpreted as a sort of “reduced form” specification of that complex and continuously evolving institutional process that handles and affects the creation, adoption and diffusion of agricultural innovations. of this complex process, r&d and productivity performance are, somehow, the initial and the final stages, respectively. though apparently this idea can be considered as an expression of the socalled linear model of innovation, it really wants to grasp the fact that, far from being a simple linear process, technological innovation is rather the product of a system. the numerosity and complexity of subjects, agents and relations involved in this institutional process not only make them a “system” (namely, the akis), but it also implies that an increase in r&d effort “upstream” can be converted into an improvement in the productivity performance “downstream” only if this “system” works correctly and effectively.7 nonetheless, the pivotal role of r&d tends to postulate a top-down flow of knowledge. according to this idea, innovation is essentially science-based, i.e. a “ready-to-use” solution offered by science in favour of “downstream” applications, including agriculture. therefore, this literature, more or less explicitly assumes or accepts a science-based supply-side idea of the akis (ss akis) (falk et al., 2010). this idea has found its major justification in the nature and intensity of technological growth experienced by global agriculture. a progress mostly made of process innovations that has enabled generalized yield increase (or reduction in factor use per unit of production), across many different applications and contexts. these process innovations mostly consisted in ready-to-use technology packages flowing top-down (from the research to the field), to be adopted in full without (or with a limited) specific adaptation or learning effort: new agricultural machinery, new chemical fertilizers, new active ingredients for weed control and pathogenic biological agents, and new crop varieties with higher yields or resistance. though the validity of this interpretation originally concerned the historical experience of developed countries, this ss perspective was applied also to less developed countries with the introduction of strongly science-based and essentially global innovations; that is, innovations coming from public research centres of international reputation (for example, international agricultural research centres, iarcs) (maredia and raitzer, 2010) or from few high-level public or private research centres of technologically leading countries. this global agricultural r&d can be considered as the classic example of an ss akis that delivers key innovations from few centres of worldwide excellence into applied research and adapts them to the specific needs at the national or local level and, eventually to the farmers’ adoption. 7 given its systemic nature, drawing sectoral boundaries of this complex set of interacting agents is difficult. eventually, agricultural is just one link along the food chain and this is particularly true in developed countries. the knowledge and innovation system, therefore, should be extended to include other relevant subjects along the food chain like, for instance, food industries and large retailers. though some of these non-agricultural agents are implicitly included in the akis (industries supplying agricultural production factors, for instance, are major funders of private agricultural r&d), most of the literature reviewed and critically discussed here maintains its primary focus on the agricultural sector. for the sake of simplicity and of space limits, this preference is also maintained here. 241knowledge, technology and innovations for a bio-based economy at the same time, this prevalent ss perspective has dictated a coherent policy agenda. a proper policy did not only have to strongly focus on research but it was also expected to find solutions to the problems arising from this configuration, such as providing public research, stimulating and orienting private research, granting intellectual property rights on agricultural knowledge and innovations to some extent, stimulating technological spillovers across sectors and territories. 3. success, failures and new challenges 3.1. the lessons we learned the main lesson that can be learnt from the contribution of the ss akis to the global agricultural productivity growth is that it represents a case of institutional success. it depends on appropriate incentives, norms and regulations, that is, on the smooth functioning of what we can consider, in broad sense, formal and informal institutions.8 this smooth functioning is expressed by the ability of the system to effectively manage that peculiar “good” represented by scientific and technological knowledge. we can summarize this success in terms of a properly designed akis that continuously generates new knowledge that can be gradually incorporated “downstream” (from research to production) in actual technological innovations but which can still continue to be diffused as public good.9 from an ss perspective, the origin of this entire institutional process is the production of scientific knowledge, largely unincorporated and “free” which acts as a pure public good. this knowledge can result in some large-scope technological solutions. these are sometimes incorporated in proprietary forms (e.g., patents) that make scientific knowledge assume the character of a private (or club) good (oehmke et al., 1999). in fact, however, the scope of these new technologies is so wide that it enables a host of specific (or sectoral) technological applications with little or no reciprocal rivalry. in practice, by virtue of this almost unlimited application potential, at this stage scientific knowledge tends to maintain a certain degree of public-good nature.10 in the case of agriculture, at the origin of this process, there were revolutionary scientific results and theories (in mechanics and thermodynamics, chemistry, biology) that produced broad-spectrum technological solutions (for example internal combustion engine, industrial synthesis of chemicals, genetic hybridization), the so-called general purpose technologies (gpt),11 which were later “appropriated” by the agricultural sector through some specific applications (ruttan, 2008). these sector-specific technology applications become forms of knowledge with a higher degree of incorporation, that is of a more private nature. this is the case of conventional agricultural technological innovations strictu sensu that characterized the produc8 for a detailed discussion on these different types of institutions see parto (2003). 9 good examples of the reconstruction of the institutional success underlying the akis, especially in the usa experience, can be found in pardey and beintema (2001), huffman and evenson (2006), alston et al. (2010c). 10 the most emphasized example of the relevance of broad-spectrum public scientific and technological knowledge is the contribution of non-profit international agricultural research centres, iarcs, to the so called green revolution of the 1960s and 1970s (gardner and lesser, 2003; dalrymple, 2008; brooks, 2011). 11 “new and evolutionary growth theorists point out that the level of spillovers varies among sectors and technologies. they are thought to be greatest where there is a pervasive cluster of technologies or general purpose technologies” (van meijl and soete 1995, p. 112). 242 r. esposti tivity “miracle” of the last century: new fertilizers or pesticides, new agricultural machinery, new plant varieties (pardey and pingali, 2010). parallel to this “downstream” movement of scientific knowledge towards production applications, and to this gradual change in the nature of scientific and technological knowledge, another form of largely non-scientific, practical and contextual, and diffused knowledge is produced. it has a limited degree of incorporation and maintains a high degree of public nature though often “local” (that is, it concerns specific contexts, applications, conditions, etc.). a “cloud of knowledge” which is made up of information, training, and learning about a given technological solution, its scientific basis and its potential applications, but also of cases of successes and failures and of opinions and beliefs around it. if the core of this cloud is the prerogative of research and technological development, its larger edge is actually determined by a more composite set of stakeholders. despite this inherent complexity, the clearest support to this success, in terms of empirical evidence (decades of empirical studies on the relationship between agricultural r&d and productivity growth), are the high social returns to “knowledge” investments. generalized high returns have been mostly found for public agricultural research, but also for private research, as well as for extension and education (alston et al., 2000; evenson, 2001; huffman, 2001; huffman and evenson, 2006; alston, 2010). alston et al. (2000) present a survey of more than 1800 estimates of the social rate of returns to research and extension investment obtained over time starting from 1953. they report quite high average returns of more than 80%. although the authors stress the wide variability of estimates, it remains true that widespread high returns are considered a well-established and robust evidence. evenson (2001) analyzes a large number of studies and estimates of the returns to investments in agricultural research reaching similar conclusions that are generalizable to different countries and contexts. a main implication derived from this generalized empirical evidence is that the overall level of investment in these activities (mostly research and extension) is lower than the table 4. estimated annual marginal internal rates of returns (%) to agricultural r&d and extension r&d only extension only r&d+extension alston et al. (2001) – various countries mean 99 85 48 highest 5,645 636 430 lowest -7 0,0 negative evenson (2001) – various countries median 49 41 45 highest 285 215 119 lowest negative 0 0 alston et al. (2011) – 48 usa states, various methodologies mean 10-23 highest 12-29 lowest 8-15 243knowledge, technology and innovations for a bio-based economy socially desirable level (the underinvestment hypothesis; esposti and pierani, 2006). even very recent studies, which take into account all the possible methodological complications in estimating these returns, confirm that “agricultural productivity growth is worth many times more than the annual spending on agricultural r&d (including extensions)” (alston et al., 2011, p. 1225). therefore, “most states (or countries) substantially underinvest in agricultural r&d” (alston et al., 2011, p. 1225). 3.2. some critical evidence: where and why does the system fail? the underinvestment hypothesis has been revitalized by the slowdown in the growth of agricultural r&d expenditure that emerged in the last decades, especially in developed and technologically leading countries (huffman and just, 1999). on the one hand, this slowdown or decline in research expenditure reinforces the idea of underinvestment. on the other hand, it seems to provide a strong argument to explain the productivity slowdown observed in several developed countries. there are two implicit assumptions behind this interpretation. the first assumption is that the slowdown is real, that is, the computed tfp growth rate correctly measures the actual productivity performance. the second assumption is that the primary objective of agricultural r&d is to improve productivity performance as indicated by tfp growth. however, since the main target of agricultural r&d is actually shifting from strict productivity performance to other objectives (environmental quality and protection, risk reduction, etc.), measuring the returns to r&d based only on the basis of its impact on agricultural tfp may be misleading, and other evaluation methods should be preferred (alston et al., 1998). if we accept these assumptions, however, there is an apparent contradiction between high returns to agricultural research, education and extension, and a slowdown in expenditure. this is one of the empirical facts that progressively brought out some more critical views on the real contribution of the ss akis to productivity growth and regarding the fact that such contribution has to be intended as a clear institutional success. another clear contradiction concerns the relevance of agricultural technological spillovers, that would indicate a strong public nature of agricultural knowledge and innovation, and the lack of convergence in agricultural productivity, that would indicate, on the contrary, its prevalent private (or non-public) nature. in support of a prevalent public-good nature, there is a large literature (see johnson and evenson, 1999, schimmelpfennig and thirtle, 1999; esposti, 2002) showing the relevance and extent of agricultural technological spillovers both across sectors and countries. a consequence of technological spillovers and, more generally, of the public nature of knowledge, should be convergence in agricultural productivity. as they may rely on the same technology base, it is reasonable to expect that agriculture in different countries and territories tends towards common productivity levels. the empirical evidence, however, does not entirely support this convergence hypothesis. 12 12 it should be clarified, however, that a prevalent public nature of agricultural knowledge does not necessarily imply productivity convergence for two major reasons, both related to the heterogeneous agricultural conditions across countries and regions (esposti, 2011). first of all, a permanent productivity gap may persist even with the same technology, simply because one country/region has a better environmental endowment (weather conditions, soil quality, etc.). secondly, a given technological solution, though public, may have been designed for 244 r. esposti table 5 shows the results obtained by ball et al. (2010) for the long-term multilateral comparison of agricultural tfp across the usa and eu countries. no undisputable path of convergence emerges. some countries recover at least part of the productivity gap (as in the case of spain), some others do not (as in the case of italy).13 table 5 also shows the results obtained by ball et al. (2002) who compared agricultural tfp across the usa states and the results presented by pierani (2009) concerning long-term agricultural tfp convergence across italian regions. in both cases we can conclude that, although weak catching-up processes may be observed, absolute tfp convergence is never actually achieved. pierani (2009) concludes that convergence occurs but it is actually conditional: regions with lower initial tfp levels do catch up, but they converge towards different long-term tfp levels. this comparison, across countries and regions, would reveal that there is no clear empirical support which gives the idea that agricultural productivity growth is based on a stock of knowledge and technological innovation behaving as a public good. on the contrary, it is rather evident that, besides significant technological spillovers, the generation of new knowledge and innovations maintains its specificity and remains an exclusive access for some countries (or territories). this implies that countries that produce new knowledge or innovation according to their own specific needs and objectives (leader countries) always retain a productivity advantage compared to countries that mostly adapt to their own needs technological solutions produced elsewhere (follower countries) (pardey et al., 2008). table 3 (lower part) compares the intensity of agricultural research (agricultural research intensity, ari) (beintema and elliott, 2011) across developed and developing countries. this indicator is the ratio of annual expenditure in agricultural r&d to agricultural value added or gdp. a sharp difference (pardey et al., 2006a, 2006b) emerges between the leader countries, i.e., those having a permanently higher tfp and showing a more research-intensive agriculture, and follower countries. as discussed in esposti (2011), the presence of a public component together with a non-public component of agricultural knowledge and technological innovations may explain the apparent contradiction between large and persistent technological gaps and widespread technological spillovers. thus, it explains the persistence of countries with a leader strategy (and akis) together with countries with a mostly adaptive follower strategy (and akis). this failure to exploit all potential non-rival uses of the available stock of knowledge and technological innovation has also its counterpart in the comparison across different agricultural productions (esposti, 2000; alston et al., 2010c). some productions may remain largely excluded from the benefits induced by new technological solutions and, as a consequence, also territories or countries with a strong production specialization.14 some specific conditions and, thus, may be more effective in some countries/regions than in others. 13 for further details and evidence on agricultural tfp comparison, see: craig et al. (1998), mccunn and huffman (2000), ball et al. (2001), ball and norton (2002; part i), ball et al. (2004), liu et al. (2011). a broad empirical literature can be found on agricultural productivity convergence across regions and countries, in both italian and eu cases. however, this literature mostly concentrates on partial factor productivity (agricultural labour productivity, in particular) and not on tfp convergence which is of major interest here (sassi, 2009). 14 according to the discussion above about the role of country/regional heterogeneity in preventing convergence, the lack of absolute convergence may not be necessarily intended as a system failure caused by the incomplete public nature of agricultural knowledge. in fact, knowledge can be public but with different effectiveness in heterogeneous conditions. in this sense, we may still conclude that there is a “failure to exploit all the potential non245knowledge, technology and innovations for a bio-based economy table 5. multilateral tfp comparison across countries, usa states and italian regions lowest tfp/ highest tfp (initial year) lowest tfp/ highest tfp (middle year) lowest tfp/ highest tfp (final year) italian tfp/highest tfp spanish tfp/ highest tfp ball et al. (2010) (usa&eu countries, 1973-02) 0.39 0.55 (1988) 0.57 initial year = 0.63 middle year = 0.56 final year = 0.57 initial year = 0.44 middle year = 0.77 final year = 0.84 ball et al. (2002) (usa states, 1960-96) 0.35 0.32 (1978) 0.32 pierani (2009) (italian regions, 1951-02) 0.36 0.47 (1980) 0.4 an immediate interpretation of these possible failures of the traditional ss akis focuses on the already mentioned underinvestment hypothesis. this failure is, in turn, determined by the inadequate management of the public-good nature of agricultural knowledge and innovation. like all goods with a prevalent, or relevant, public-good nature, the provision (i.e., the investments) of agricultural research falls short of the optimal level that would be indicated by high social returns.15 this interpretation evidently applies to public agricultural r&d whose main purpose is to generate knowledge with a high degree of “publicity”, but it may also be valid for private r&d (for which high rates of return are observed, as well; alston et al. 2000; evenson, 2011) to the extent that its results are not entirely appropriable. according to this interpretation, the observed reduction in the agricultural r&d investment rate in many countries would express a tendency to act as free-riders, i.e., to benefit from technological solutions developed in other contexts only focusing, whenever possible, on adaptation to their own specific conditions. it is a sort of the tragedy of the commons involving the international dimension of akis especially with regard to the high-level research (pardey et al., 2008).16 this tendency would also explain the diminishing returns of these investments (alston, 2010). it tends to produce just additive and incremental knowledge and innovations, less adoptable and adaptable in different contexts than those in which they are produced. from this perspective, productivity slowdown and lack of convergence in agricultural productivity (both across countries or regions and different agricultural productions) are just results of this progressive deterioration of the global and largely public components of the akis. rival uses” not because of the public/private nature of knowledge but because of the irreducible heterogeneity of agricultural conditions. 15 “these institutional failures continue to impose very large opportunity costs on individual states and the nation as a whole” (alston et al., 2011, p.1276). 16 this argument, combined with the observed reduction of public agricultural r&d growth rate, should lead to a reduction in technological spillovers. however, the empirical evidence provided so far does not support this interpretation. it may be just a matter of time as it takes years for observed spillovers to respond to the decline in r&d growth. as for tfp and r&d returns calculation, major measurement problems may also arise as to how technological spillovers are computed and attributed to agricultural productivity (johnson and evenson, 1999). 246 r. esposti nonetheless, his interpretation of system failures does not raise substantial doubts about the validity of the ss akis or about its capacity to cope with future challenges. its major limits or failures depend on the public nature of knowledge and innovation and not on where this new knowledge and innovation comes from, that is, on its strongly sciencebased and supply-side design. however, we can also put forward a less immediate, and conventional, interpretation of these system failures. it consists in questioning the classical ss design of the akis. the key argument of this critique is that most of the large productivity gains observed in the past decades at both global and local levels are not due to contributions of science and research that have been then transferred and adapted “downstream” towards productive uses. looking inside the “black box” of agricultural innovations, it turns out that this role has often been over-emphasized by focusing on few successful cases. but what really underlies the “miracle” of agricultural productivity growth, the actual engine of agricultural innovation, is what has been previously termed as the “cloud of knowledge”. therefore, the knowledge and innovation that the akis is expected to produce, diffuse and adopt is not necessarily scientific knowledge of academic rank or knowledge embedded in some proprietary technology. more often, and more critically, it is a widespread collective and practical knowledge and, though sometimes tacit, informal and local, it tends to be free and public to any possible extent.17 in this respect, the biggest institutional failure of an ss akis rather lies in having focused its attention (and most resources) on a limited portion of the process, the generation and application of that kind of scientific-technological knowledge, and on a conventional and limited idea of innovation. the productivity slowdown itself can be interpreted as a support to this analysis. despite the huge and still growing investments in agricultural research, the outcome in terms of productivity is decreasing simply because not enough attention and effort have been directed to those factors that really gave impetus to productivity. 3.3. new challenges and diverging agendas in the last decade, the need for a critical review of the design and the organization of the traditional ss akis has been strengthened by the new emerging challenges for global agriculture. on the one hand, the main challenge of the last century returns with a new urgency: the ability to produce enough food to feed a growing and more demanding population (alston et al., 2009b, 2010b). this is the never-ending challenge of agricultural technology, that of food security (“to feed the world”) (alston and pardey, 2009; freibauer et al., 2011). but, compared to last century, today global agriculture faces a different landscape (beddow et al., 2010; kirsten, 2010; maracchi, 2010). the above-mentioned productivity slowdown, the strong food consumption growth in emerging countries such as china, india and russia (more than one-third of the 17 some sentences taken from galiay (2010) clearly express the sense of this critique. “lessons from case studies on aks governance: […]overall, a failure to incorporate diverse values/norms in a common and shared vision”, “partial in scientific advice, insufficient in risk assessment, insufficient in communication and dialogue”, “lack of inclusiveness in framing issues and lack of sense of urgency”. for a critical view of the traditional akis design, see also glover (2012) and ritter (2012). werrij (2009) offers a further perspective on the failures of an akis strongly focused on high-level research and on the linear model of innovation. he also analyses the implications for a proper agricultural research policy within developed countries. 247knowledge, technology and innovations for a bio-based economy world population), and the recent turbulence in agricultural commodity markets all these factors confirm that the challenge of food security has not been definitively won in the last century and now it tends to assume new dimensions (huffman and evenson, 2006; kirschke et al., 2011; glover, 2012; pardey and alston, 2012; ritter, 2012). again paraphrasing alston et al. (2009a), we can argue that malthus is getting his revenge on mendel: after a century in which agricultural technological progress (symbolized by crop genetic improvement) was able to meet the challenge of an increasing food demand, there are now legitimate doubts as to whether this success can be repeated. in fact, a large number of people worldwide did not win the challenge of food security in the past and are likely to suffer the greatest consequences of a renewed food shortage even in the future (sadler, 2010). but the real novelty with respect to the previous century is that this challenge can now be won only under specific conditions (and constraints). the main condition is environmental sustainability. not only must the growth in supply obtained through further technological innovations be compatible from an environmental perspective, but agriculture is also expected to actively contribute to sustainable growth by playing a role with respect to the global environmental issues of the century (renewable energy, climate change mitigation, etc.) (msangi et al., 2009). in fact, this first condition leads to a second fundamental requirement. the agriculture of the future must necessarily be multifunctional, i.e., it must have the ability to produce other non-food goods and services, of public or collective interest, in addition to food. these include environmental services that bring us back to sustainability. in affluent societies, in particular, post-industrial agriculture is expected to produce landscape and aesthetic values, cultural and recreational services, physical and mental health services, etc. moreover, since agriculture is the first link in the food chain, it is expected to ensure food safety and food quality, i.e., health, nutritional, environmental and ethical safety of food as well as to ensure food origin and provenience. sustainability and multifunctionality, however, require knowledge and innovation of a different nature compared to the more conventional challenge of food security: more product innovations (or functional innovations, as discussed below) than process innovations; organizational and marketing innovations and not just technological innovations. therefore, more complex innovations18 and knowledge is required. no longer simply “mendel against malthus” but “much more than mendel” (a wider idea of agricultural innovation) against “much more than malthus’ (more extensive needs to be met). moreover, further productivity growth strictu sensu and sustainability and functionality can easily come into conflict. the actual risk is that, due to the difficulties encountered in defining an agenda that reconciles both these needs,19 two different and diverging agendas eventually emerge (pardey et al., 2006b, p. 2). an agenda for the new scarcity which mostly concerns agriculture, people and countries for which the challenge of food security remains prevalent (lele et al., 2010; beintema and elliott, 2011; kirschke et al., 2011, p. 39); an 18 see below the concept of system innovation. 19 several proposals have been put forward at various institutional levels, and especially by those international institutions dealing with these issues at global level (fao, wb, ifpri, etc.), in order to establish a strategy for an akis able to reconcile these potentially diverging needs; for example, that of sustainable intensification (house of lords, 2011, ch. 1-3). 248 r. esposti agenda for the post scarcity which mostly concerns more affluent countries in which food security seems secondary to the challenges of sustainability and multifunctionality.20 given these diverging agendas, a spontaneous reorganization of the akis inevitably requires a diverging akis design. for a strategy that still focuses on the challenges of new scarcity, the basic idea is that of an ss akis with a strong global/international component and a greater attention to strengthen the diffusion of the benefits to countries, territories and agriculture hitherto excluded (akis/rd, 2000; rivera et al., 2005). for a strategy primarily focused on the challenges of post scarcity, a substantial rethinking and reorganization of the akis emerges as a priority.21 the higher complexity of innovations, required by agricultural sustainability and multifunctionality, depends on two aspects. first of all, these innovations are often intended to tackle very specific local issues and, even when issues are actually global (for instance, reduction in ghg emissions), the solutions must still be “local”, that is, strongly placebased and tailored. secondly, these innovations potentially involve a larger number of stakeholders. as they concern not only quantitative food production22 but also many other aspects related to food (quality, safety, origin, etc.) and to non-food functions, the validity and acceptability of these innovations often depend on the proper involvement and contribution of all these stakeholders. these features imply a redesigned akis that is able to take advantage of new technological paradigms and opportunities, providing bottom-up together with top-down flows of knowledge, and which is pulled from the demand-side rather than exclusively pushed by the supply-side (ritter, 2007; hall, 2007; moreddu, 2012). 4. looking for a new model23 4.1. new technological paradigm and trajectories beside the new challenges, another major driver of this reorganization of the akis is the gradual emergence of a genuinely new technological paradigm and of new technological trajectories it generates (freibauer et al., 2011). broadly speaking, the technological paradigm underlying the conventional ss akis was characterized by the progressive introduction of process innovations to meet the main need of that agricultural model: to produce more with fewer factors of production, i.e., to increase the productivity of agri20 a major impulse to this shift of the agenda towards post-scarcity issues has been given by the evolution of the agricultural policy in developed countries. in the eu, in particular, the evolution of the cap in the last two decades towards more conservative and low-impact objectives has significantly affected the creation and adoption of technological and organizational innovations by farmers and by all other relevant subjects (oskam and stefanou, 1997) and has therefore contributed to this need for a redesigned akis. unfortunately, for a long period, this policy evolution did not coordinate with akis policies. section 5 focuses on this aspect. 21 for instance, pardey et al. (2012) highlight that in 1985 69% of the research expenditure of the usa state experiment stations (saes) was concentrated on projects aiming at improving productivity. since then, this share has continuously been dropping down to 56% in 2007 (last observed year). 22 as already mentioned, even if we limited our attention to innovations aimed at a purely quantitative increase in food production, they may still involve a larger number of subjects than farmers alone: food industries, retailers, etc. nonetheless, the focus here is primarily on agriculture and, therefore, on farmers’ innovations. 23 henceforth, the analysis will prevalently focus on those affluent countries (like the eu) whose agricultural sectors are mostly oriented towards post-scarcity agenda. 249knowledge, technology and innovations for a bio-based economy cultural inputs. the technological trajectories developed along this paradigm were mainly those of varietal innovations, animal genetics, chemistry, pharmaceuticals and plastics for agricultural use, and agricultural machinery. over the past two decades, a new technological paradigm has appeared. in fact, the new gpt that are currently becoming predominant, or are expected to prevail in the next few decades (ict, microelectronics and nanotechnology, modern biotechnology, neuroscience, robotics, advanced materials, photonics) show an agricultural application potential of substantially different nature. in particular, they facilitate new innovative dimensions in addition to process innovation: product innovation and, above all, function (or functional) innovations. it is worth noticing that the introduction of a new agricultural business or function is mostly the outcome of an organizational or marketing innovation, and not so much of a technological innovation. however, these innovations often have a technological “activation”, i.e., a technology component that enables or facilitates these new solutions. the new paradigm thus takes advantage of the capacity of the new gpt to improve this non-technological innovation potential. in this sense, such gpt are also known as key enabling technologies (ket): while not central to the innovative solution they still enable them (european commission, 2010b, p. 131).24 therefore, the new paradigm opens a new space for functional innovation. figure 1 depicts this innovative hyperspace (the agricultural innovation hyperspace). in the conventional technological paradigm, most innovations were process innovations and were intended to increase productivity, strictly intended as tfp (type i productivity). the advent of new technologies and of the hyper-consumer progressively expands the innovation space in the direction of product innovation (the food innovation space), a large ideal innovative space where new food products may find peculiar innovative combinations of functionality, convenience and naturalness (esposti, 2009). but these same technological solutions also facilitate the organizational and managerial innovations that constitute the space of functional innovation through some combinations of sustainability and multifunctionality. all these latter innovations contribute to improve agricultural performance in terms of production of goods and services of private or collective utility and, eventually, in terms of agricultural income. but here productivity growth remains more elusive, difficult to measure and to compare. we can thus refer to this performance improvement as growth of functionality25 or, to keep the analogy with the standard notion of productivity, of type ii productivity. 24 “kets reflect the enabling nature of general purpose technologies that support widespread industrial deployment and provide significant economic improvement over existing complementary technologies” (van meijl and soete, 1995, p112)”; “most general purpose technologies play the role of “enabling technologies”, opening up new opportunities rather than offering complete, final solutions” (bresnahan and trajtenberg, 1995, p. 84). for instance, agrotourism, direct selling, organic agriculture have been among the most impacting innovations in eu agriculture in the last two decades. strictly speaking, they are not technological innovations or, at least, they are not process innovations. they are product and, above all, functional innovations with a prevalent organizational and marketing content. nonetheless, they have been facilitated by new technological solutions. for instance, agrotourism and direct selling have been strongly favoured by the advent of the web and, more generally, of the ict (information & communication technologies). also, energy production from agricultural biomass is taking advantage of modern biotechnologies. 25 the european commission, for instance, refers to “soil functionality” when dealing with technological innovations that improve the whole productive capacity of soils (not only food, but also environmental functions) (european commission, 2012a). 250 r. esposti this new innovative space26 is made up of continuous incremental improvements, problem-solving adaptations, tailored solutions, often drawn from (or along with) the final users, farmers or food producers, i.e., the demand-side of the akis. therefore, these technological solutions are not produced as a ready-to-use invariable technological package. any technological solution tends to be rather just a temporary stage in the continuous improvement and adaptation, within the networks of users, of an innovative idea originally developed for the solution of a specific problem and then diffused and made “collective”. therefore, we move from a one-way closed-space ss paradigm to a multi-directional open space paradigm that could be called permanent-beta network (pβn). this paradigm shift is the eventual consequence of the change in the nature, in the frequency and in the direction of movement of the “object” of the system, that is, scientific and technological knowledge. its nature evolves from well-delimitated final technological innovations to more “liquid” knowledge and solutions. its frequency shifts from a discrete release of new technological innovations towards a continuous flow of incremental adaptations. as will be discussed in section 4.3., its prevalent direction of movement changes from an unidirectional movement, mostly generated by scientific knowledge, to a multidirectional and non-hierarchical movement across all agents and stakeholders involved. this shift in technological paradigm gradually but deeply changes the fundamental properties of the knowledge and innovations system itself and it is expected to change its design, organization and functioning. moreover, opening this potential innovation space towards a variety of new products and functions inevitably expands the traditional sectoral boundaries. this widening of sectoral boundaries, however, does not depend on the fact that food production, particularly in affluent societies, necessarily embraces all the other links along the food chain. the argument here is that, even though the focus remains on agriculture, the boundaries themselves of “agriculture” are becoming larger. this expansion, in particular, makes agriculture overlap with, and therefore converge to, other sectors. this convergence of traditional sectors in broader and inclusive combination favoured, if not induced, by the new technological paradigm as well as by the new challenges, is now largely identified as the bio-based economy or bioeconomy.27 in this dynamic and evolutionary perspective, bioeconomy is thus a stage of this evolutionary process from the old to the new technological paradigm and from the old to the new challenges; it is the innovative hyperspace represented in figure 1.28 as a consequence, the 26 in this respect, it seems inappropriate to think about future agriculture in terms of a choice between two contrasting trajectories; typically, biotech vs. organic agriculture (neubauer, 2010). what really characterizes the future perspectives of agriculture is the occupation of all this innovation space, which is the combination and coexistence of all the viable technological trajectories. 27 several more or less concordant definitions of bio-based economy or bioeconomy have been proposed (european commission, 2012e; danish presidency of the council of the european union, 2012). for more details, see http://ec.europa.eu/research/bioeconomy. however, it seems largely agreed that the concept itself of bioeconomy comes from the recent technological evolution: “the bioeconomy consists of all industries that use biological processes to produce products: food, fiber, green chemicals, pharmaceuticals, biofuels and energy. agriculture and fermentation were the key elements of the traditional bioeconomy. the modern or new bioeconomy is based on our expanding knowledge of molecular and cell biology and takes advantage of information technology and nanotechnology” (from the call for papers of 128th eaae seminar) (http://www.economia.uniroma2.it/ icabr-conference/sarea.php?p=15&sa=192). 28 in this respect, bioeconomy is intended here in a wider perspective compared to conventional definitions. conventional definitions mostly focus on the convergence of sectors whose production is based on biologi251knowledge, technology and innovations for a bio-based economy analysis of the knowledge and innovation system must necessarily expand its traditional sectoral scope (akis) towards these more inclusive and dynamic boundaries: the knowledge and innovation system for bioeconomy (kisb). figure 1. the agricultural innovation hyperspace p r o c es s in n o v a ti o n s “old” paradigm, ss akis type i productivity (tfp) function innovations product in novatio ns “new” paradigm, akis? type ii productivity food innovation space sustainablity mult ifu nctio nalit y n at u r al n es s convenience functionalit y cal processes (therefore with a strong emphasis on biotechnology). here, in bioeconomy we also include those activities that are linked to these biological processes because they share with them the same resource base, in particular land. for instance, agrotourism, strictly speaking, is not a bio-based activity. nonetheless, it is a landbased activity that can not be separated from bio-based activities that constitute conventional agriculture. for a discussion on a wider definition of bio-based economy or bioeconomy, see also schmid et al. (2012). 252 r. esposti 4.2. is agricultural tfp obsolete? the prevalence of process innovations in the “old” technological paradigm implied that the agricultural innovation performance could be captured by a measure of productivity growth, i.e., tfp growth. tfp was intended, and still is, as a proxy of technological level and its growth as a proxy of technological progress. gradually moving towards the new paradigm, however, the identification and implementation of a univocal appropriate measure expressing an innovative performance is more challenging. along with “traditional” process innovations that increase the amount of product obtainable from a given amount of production factors (type i productivity), product and functional innovations mostly result in a performance improvement of different nature (type ii productivity): new and better products but also more and better non-market goods and services which can be hardly measurable or observable. under these latter circumstances, the usual construction of aggregate indices of output and input to perform the calculation of tfp may face serious measurement difficulties. this is not a genuinely new problem in the literature. the intense debate on the so-called productivity paradox29 (brynjolfsson, 1993), developed especially in the nineties, concerned the problems encountered in tfp calculation whenever relevant quality improvements of both factors of production and products or services were observed. in this respect, the slowdown in tfp growth registered in usa and eu agriculture in recent decades (table 2) could be considered as “our” productivity paradox. whenever the sector began to move towards food safety & quality, environmental sustainability and multifunctionality, thus providing superior performance in terms of consumer satisfaction and social utility, the conventional measures of productivity reported a slowdown. evidently, passing from the “productivity slowdown” to the “paradox” emphasizes that what may appear as a declining performance, or even failure, of the knowledge and innovation system may be, at least in part, an artefact due to measurement errors or, to be more precise, to new and larger measurement errors induced by the evolution of the system itself. this also implies a more cautious interpretation of what emerges from tfp calculation and comparisons across space and time: the decline of tfp growth may be not necessarily an indicator of failure. it may rather (or also) be an indicator of structural changes in the nature of the system. two different approaches can be identified to cope with this methodological challenge. the first solution is to remain focused on the calculation of tfp as a primary and univocal proxy of productivity. of course, this calculation must be adapted to take into account these product and function innovations. this implies a multi-output specification of the production process that admits quality heterogeneity, production of non-market services of collective interest (in essence, positive externalities) as well as of negative externalities (oskam and stefanou, 1997), and also admits that the production of all these market and non-market goods and services may show some degree of time-varying jointness (or non-separability) (oskam and stefanou, 1997; oecd , 2001). serious doubts can be raised on whether the neoclassical production theory (on which the concept and the calculation/estimation of the tfp is based) (chambers, 1988), 29 the productivity paradox is given by the massive introduction of ict in most sectors, and mostly in services that apparently did not generate any impact in productivity figures. 253knowledge, technology and innovations for a bio-based economy is flexible and adaptable enough to properly take into account all these aspects. several adjustment and extensions have been proposed within this production theory in this respect.30 nonetheless, it has still to be demonstrated that such complex and highly datademanding adaptations really enable empirical productivity analysis with the same accuracy and comparability of the conventional tfp calculation. given these difficulties, an alternative option consists in recognizing that the traditional tfp calculation (as well as the underlying production theory), though valid, is not able to take full account of these kinds of innovation. in other words, productivity performance is made up of two different elements (see figure 1): type i productivity (i.e., conventional tfp) capturing the productivity gains arising from process innovations; type ii productivity (or functionality) expressing performance improvement resulting from product and functional innovations. this second productivity type can be measured, in levels and growth rates, through a battery of indicators that accompany the traditional tfp. therefore, an overall assessment and comparison of productivity performance will eventually be multicriteria. the literature on this second line of research is at an early stage (ball and norton, 2002, part iii; esposti, 2008), but it represents a promising line of methodological and empirical study. 4.3. towards a new model: from akis to kisb the analysis carried out so far emphasizes the need of a re-definition and re-design of the akis mostly due to the substantial change in the nature and dynamics of the “object” of the system (knowledge and innovations). however, it remains difficult to illustrate in details which structure and features the system is actually going to assume along this evolutionary process. eventually, the final outcome of this evolutionary process depends on how the involved agents and institutions will behave and adapt to the new context and how policies will accompany and condition this transition. this evolution still being largely in progress, incomplete, and country-specific, it seems only possible to outline some of its general characteristics and to provide some general guidelines for its proper design. an uninterrupted international debate on purposes, limitations, needs and challenges of the akis started in the sixties (bergeret, 2012, p. 9). this debate determined an evolution in the conceptualization of the akis and induced its progressive reform. figure 2 outlines, very schematically and synthetically, the main stages of this reform process.31 conceptually, the key driver of this debate is the gradual emergence of the socalled knowledge system thinking (röling, 1992), namely the belief, dictated by the evidence, that agricultural innovative performance is the final outcome of complex systemic interactions between different actors and institutions involved in the production and dis30 for instance, a recent paper by zuniga gonzales (2012) proposes a bio economic-oriented tfp (btfp) where the tfp calculation is extended and adjusted to include production of other non-agricultural products (biofuels) obtained from farming activities. kim et al. (2012) analyse farm-level productivity performances within a multi-output framework taking explicitly into account complementarities, scope economies and non-convexities associated to the diversification of farm activity. this approach may be interesting to evaluate the performance of multifunctional farms. 31 for more details on the knowledge system thinking and on the progressive shift from aks to ais and akis, see dockes et al. (2011), poppe (2012b) and eu scar (2012). 254 r. esposti semination of knowledge and in its incorporation into innovative technological solutions (knickel et al., 2009). even within this systemic logic, however, a top-down (ss) conceptualization of the akis was initially prevalent. at the top of the system there is scientific research whose production of knowledge and innovation flows downstream, through education and extension, up to final applications in agricultural production (from lab to field; ltof). the rise of the knowledge system thinking has progressively challenged this view in favour of an interpretation that emphasizes a stricter coordination and integration between the components of the “knowledge triangle” (research, education and extension) that eventually generates those various forms of knowledge and technological solutions to be finally transferred to producers. this was the original conceptualization of the aks (agricultural knowledge system) (poppe, 2012b) that essentially remains a top-down and supply-side representation, although not necessarily science-based (from lab, classrooms and meeting rooms to field, lcmtof). in fact, the next stage consisted in questioning the supply-side dominance in favour of a more active role of the demand-side (namely, farmers or final users). the peer interaction between users and the institutions of knowledge creation and diffusion eventually converts this knowledge into actual innovative practices (knowledge&innovation system thinking). this evolution determined a new conceptualization, that of the ais (agricultural innovation system) (the world bank, 2011), and, then, of the akis (agricultural knowledge and innovation system) (deschamps, 2011; bergeret, 2012, p. 11-12; oecd, 2012) that emphasized the bi-directional interaction between supply and demand-side and the combination of top-down and bottom-up knowledge and information flows (from lab to field, from field to lab, ltof-ftol). this view is essentially non-hierarchical, but based on the quantity and quality (i.e., intensity) of the interactions and flows of knowledge occurring within this system. this element is exalted further in the last stage of this conceptual and organizational evolutionary path. according to this perspective, the system is not an articulation of conceptual interacting components (as in the knowledge triangle: research, education, extension), but rather a network of real heterogeneous, autonomous but interdependent evolving subjects whose interaction is itself specific and dynamic. these subjects go beyond the traditional boundaries of the system, since in this context also consumer organizations, pressure groups and lobbies, opinion movements may become relevant. in short, the system is made of a wider range of stakeholders. what really structures and drives the system, therefore, is not some ex-ante allocation of functions and resources across conventional categories (research, extension, education), but the actual behaviour of these stakeholders, their choices, conflicts and cooperation. the system actually becomes an actively participated network operating both on a local and on a global scale (from stakeholder to stakeholder, stos). 32 this change in the conceptualization of the akis, and the consequent need to reform it accordingly, comes from the new challenges and paradigms outlined above. as they induce sectoral convergence, it is necessary to switch from a strictly agricultural perspective 32 a clear example of this evolution is the concept of linsa (learning and innovation networks for sustainable agriculture) (crepe, 2011; solinsa, 2012). see also paffarini and santucci (2009). 255knowledge, technology and innovations for a bio-based economy to a system open to all traditional sectors now converging into bioeconomy: from akis to kisb (guillou, 2012). moreover, while the conventional ss akis was based on an idea of knowledge codified into stable forms, thus allowing appropriate institutional arrangements to regulate its public/non-public nature, in the new paradigm, knowledge is a good with a much more complex nature and dynamics. an exemplary expression of this evolution is the concept of system innovation. this is a more complex and articulated idea of innovation which incorporates/hybridizes its different implications, the strictly technological content but also its organizational content as well as its social and environmental implications. system innovation inevitably and directly involves not only the supplier-user relationship but also consumers, citizens, agricultural-rural communities and institutions, etc. (geels, 2005). a kisb with a strong network structure is the natural counterpart of this idea of innovation (eu scar, 2012).33 it is a system where a top-down structure (ss kisb) should be replaced by a network structure (pβn kisb). this latter structure is eventually able to generate those complex system innovations by fostering not only research, dissemination and education, but also other critical processes for a successful innovation, that is, participation, experimentation, training and learning by doing and interacting.34 designing the kisb as a network, however, does not immediately imply a well-functioning system. there are serious risks associated to a network structure. first of all, what is expected to be a well-functioning network in the conceptual design may eventually function as a highly fragmented and disorganized system, in practice, not able to selforganize and self-coordinate its activities (klerkx and proctor, 2013). in essence, it may behave as a system incapable of designing new technological trajectories because it produces and circulates incoherent fragments of knowledge and innovation instead of producing and circulating system innovations. this new perspective may also be helpful to better understand what happened in eu agriculture in the last two decades. most of the major successful novelties (for instance, agrotourism, organic agriculture, direct selling, agroenergy) are not usually considered as innovations because of their non-technological nature. however, they can be definitely regarded as product and function innovations and fit the concept of system innovation for which technology is often just a facilitating factor. for the most part, these innovations were generated spontaneously within local and specific contexts which then spread across the whole agricultural sector. in analyzing these successful cases, the role of public policy should not be understated. several policy interventions played a major role in favouring these product and functional innovations. the evolution of the cap of the last two decades, in particular, contributed a lot. as the attention here focuses on akis/kisb policies, however, we must acknowledge that these successful novelties had little to do with the research and innovation policy. research, as well as extension and education, certainly made their contribu33 “system innovations are multi-factor, multi-actor and multi-level (multi-scaled) and can be only understood in terms of historical co-evolutionary process which link-up all these actors, factors and levels” (geels, 2005). on system innovation, see also barbier (2010) and verguts et al. (2010). 34 this idea of complex innovation (or system innovation) finds various expressions though all based on the same idea. for example, hall (2011) proposes the concept of agricultural innovation networks to meet new challenges (“an increasingly complex agenda”). along the same line, we find analogous or similar concepts: collective intelligence, social innovation, multi-actor (or participative) innovation, innovation and learning networks (deschamps, 2011; bergeret, 2012; cristiano, 2012; eu scar, 2012; klitgaard, 2012; poppe, 2012b). 256 r. esposti tion but not following a pre-determined strategy imposed by some pivotal institution. this contribution emerged gradually and spontaneously and accompanied the emergence of these innovations. therefore, strictly speaking, it is not an institutional success, but rather the success of a system of permanent and high-quality relations. therefore, these successful innovations are the result of the proper functioning of a network. at the other extreme, consider a case that can be definitely regarded as a failure in the recent experience of many national kisb within the eu: the case of genetically modified (gm) crops. the national systems have invested significant resources in public (public universities hold several biotechnology patents) and private (clusters of highly innovative biotech firms) research, in high-level education and training (many university degree programs dedicated to modern agro-biotechnology), in information, dissemination and technical assistance (the remarkable effort of supplier firms, often multinational corporations, to inform and convince about the validity and viability of these technological solutions). yet, all this effort has been almost completely lost in terms of final outcomes, that is, productive applications and productivity performance. it is definitely a system failure. is it an institutional failure? actually, institutions and agents made their deliberate choices within a legal framework (e.g., patent protection) and a set of incentives that were not fundamentally different compared to other countries where the advent of gm crops was more successful. it was rather a network failure. many relevant stakeholders (e.g., consumers) as well as fundamental aspects of a proper network participation, discussion, and collective decision making were neglected. most of the key actors and stakeholders operating within the network did not coordinate their choices and actions, and their interactions were not intense enough to avoid that all the innovative effort was dispersed and lost within the network due to actors’ different needs, views and beliefs. 5. some final considerations: a new policy design for the kisb at this stage of the analysis, therefore, it is difficult to argue how the new system (the kisb) should be structured. as mentioned, its mostly spontaneous evolution is still in progress and depends on many heterogeneous interacting stakeholders. moreover, properly outlining and designing a well-functioning network is more challenging than organizing a strongly hierarchical system. nonetheless, the role of policies in this respect remains crucial especially within sectors (like agriculture) where policies, even when they do not explicitly focus on research and innovation, still strongly affect the behaviour of agents within the akis/kisb. therefore, to conclude the present analysis we attempt to draw some general conclusions about the most appropriate policy to induce, govern and influence the depicted evolutionary trajectory of the kisb. the attention concentrates on the eu policy. not only because it directly concerns those developed countries whose agriculture and bioeconomy is of primary interest here, but mainly because the eu seems an exemplary case of the attempt to move in a direction consistent with the depicted evolution of the kisb and of the difficulties encountered in designing coherent and effective policies in this respect. the ambition to build a common eu-wide kisb (european commission, 2011) encounters two serious and mutually reinforcing coordination problems. the first coordination problem concerns the struggle of any european policy to harmonize and, gradually, 257knowledge, technology and innovations for a bio-based economy orient heterogeneous and specific national (and sometimes regional) policies (materia, 2012; poppe, 2012a). this harmonization effort, though needed, can not disregard heterogeneity, that is, the fact that there is no one-fits-all model of the kisb to which all eu countries and regions should easily converge. the second problem concerns the difficult coordination of the two main sectoral eu policies that directly or indirectly affect the kisb, namely the cap and the eu research policy. if we consider the current design of these european policies, this latter coordination problem is less visible at present since the cap contains only a limited number of measures (and resources) for the kisb, which are mostly concentrated in axis 1 of the second pillar (sotte, 2009).35 on the contrary, inspired by the lisbon agenda, the current research policy already incorporates a number of ideas and initiatives that clearly focus on some key aspects of the depicted evolution of the system. in particular, under the seventh framework programme (fp7) (i.e., the main line of research funding by the eu), one of the ten key areas, “food, agriculture and fisheries, and biotechnology”, is specifically aimed at building a european knowledge based bio-economy (kbbe).36 nevertheless, this eu research policy design inevitably maintains a top-down (ss) perspective. 35 sotte (2009) analyses the budget allocation of italian regions within their rural development programmes across the different measures. funds allocated to measures concerning innovation, education and training, extension, dissemination and technical assistance are just 6% of the total budget; quite close to funds allocated to generational turnover (5%), much less than funds spent on agro-environmental measures (32%). 36 the kbbe budget is about 2 billion euro of research funds, almost 4% of the total fp7 budget. figure 2. evolution of the knowledge system thinking in the conceptualization of the akis ltof r&d education farmers lcmtof stakeholder 1   (s1) stos extension education extension r&d farmers lof-­‐ftol education extension r&d farmerss2 s3 s4 sn 258 r. esposti a more demand-side and bottom-up perspective should be provided by the cap and, in particular, by its second pillar measures.37 improving this demand-side of the kisb and ensuring a closer matching with the supply side is one of the main purposes in reforming these eu policies for the period 2014-2020. the eu research policy continues along the line that has already been defined in the previous period. “europe 2020” contains an ambitious programme of research funding (“horizon 2020”) (european commission, 2011) in which the kbbe maintains a central position (european commission, 2012a; danish presidency of the council of the european union, 2012) (http:// ec.europa.eu/research/bioeconomy).38 this research policy, however, is now adopted within a new framework, the innovation union initiative (http://ec.europa.eu/research/innovation-union/) (european commission, 2010a). it is one of the key initiatives inspired by “europe 2020” and its ambition is to make different eu policies (e.g., research and agricultural policies) converge towards the common goal of increasing the innovation capacity within the eu, in all countries and all sectors. the innovation union initiative provides over 30 different actions. of major interest here, it is the creation of thematic european innovation partnerships (eip) to favour innovations on specific sectors and issues. one of these eip concerns agriculture: the european innovation partnership (eip) for agricultural productivity and sustainability (eipa) (matthews, 2011). though there is still an incomplete information on how this new instrument will work, the most recent communications of the eu commission (european commission, 2012a; 2012f) clarify its objectives, design and functioning. on this basis, epi-a appears to be a real step forward in order to build a kisb in accordance with the discussed evolution, the new challenges and technological trajectories. on four aspects, in particular, the eip-a seems to fully capture the evolution of the system (european commission, 2012a; 2012f). first of all, it clearly acknowledges that agricultural innovations are expected to improve not only productivity in conventional sense (tfp) but also the performance with respect to other agricultural functions (soil functionality) (european commission, 2012a, p. 4). secondly, and related to the former point, the eip-a seems consistent with a wider scope of the system moving from the strictly sectoral boundaries of agriculture to the bioeconomy, as clearly emphasised in many of the declared areas of innovative actions (european commission, 2012a, p. 8-9). thirdly, the eip-a emphasises that the agricultural knowledge and innovation system has a prevalent network structure, weakly hierarchical and with many heterogeneous agents involved (european commission, 2012a, p. 6). as a consequence, the current unsatisfying performance of the system should be intended as a network failure.39 finally, the initiative clearly aims at building the missing bridge between the eu research policy and the cap (european commission, 37 the potential contradiction between eu research policy on agriculture and the eu rural development policy emerges more clearly by looking at the actual figures. neubauer (2010) reports data on the research projects funded under the sixth framework programme in the areas of “agricultural biotechnology” and “organic agriculture”. the former area has received total funding which is about four times the funding received by the latter. the second area, however, received much more support within the second pillar of the cap (sotte, 2009). 38 in particular, agricultural issues are mostly framed within the objectives of food safety and sustainable agriculture. the resources dedicated to the kbbe during the entire period should amount to 4.5 billion euros, more than double the budget for the kbbe in the fp7, but still just about 5% of the total budget of horizon2020. 39 “the scientists do not know what the farmers want and the farmers do not know what science does” (matthews, 2011). 259knowledge, technology and innovations for a bio-based economy 2012a, p. 7), that is, at coordinating and matching the top-down and bottom-up initiatives to eventually generate real innovative behaviours and choices. nonetheless, the novelty and these remarkable strengths of the eip-a initiative may remain just good intentions if not accompanied by an appropriate design and implementation. at the moment, some weaknesses clearly emerge in this respect. the first problem concerns funding. the eip-a by itself will have a very limited funding, as it is rather expected to mobilize and orient resources made available within horizon2020 and the cap (second pillar). in relative terms, however, these latter resources are still marginal. within the current cap reform proposals, this novel initiative will involve only a small portion of the budget (4.5 billion euros corresponding to just over 1% of the total cap budget). these resources will be dedicated to agricultural (or bioeconomy) research and will be managed with the clear objectives, rules and procedures of the eu framework program for research (european commission, 2012a,b). therefore, it may just represent a transfer of money from a eu policy to another, not necessarily a coordination between them. eventually, the eip-a might not be strong enough to counterbalance the institutional inertia of both eu research policy and the cap. on this latter aspect, a stronger contribution to reduce this inertia can be expected from the reform of the cap’s second pillar. the current proposal identifies the transfer of knowledge and the impulse to innovation as one of the six key horizontal priorities (european commission, 2012d). on the actual contribution of this rural development policy to the eip-a initiative, however, other potential weaknesses can be identified. on the one hand, the cap remains jealous of its strictly sectoral boundaries. therefore, it is willing to accept, at least apparently, the integration with the research policy, but shows greater difficulty in opening to those other sectors that, together with agriculture, converge in the bioeconomy. while the eu research policy claims the kbbe, the cap’s emphasis on innovation remains almost exclusively confined within the traditional agricultural sector. on the other hand, the rural development programmes are expected to bring all these interventions, coordinated by the eip-a, into the local contexts where they may not meet similar and coordinated interventions at the national and local scale. more generally, the “local” kisb might not be ready to integrate this top-down impulse coming from the eu within their own bottom-up effort (fieldsend, 2012). the eu commission seems aware of these potential weaknesses of the eip-a and proposes an instrument to overcome the funding issues as well as the institutional inertia of the two eu policies the eip-a is expected to coordinate. this instrument is the creation of operational groups (og) that should transfer in practice all the strengths of the eip-a initiative. og should behave as innovation networks involving all the relevant stakeholders and working on all the possible relevant areas of the bioeconomy. funded by both horizon2020 and the second pillar of the cap, og will be asked to reduce the gap between scientists and farmers, thus improving the innovative performance of the eu agriculture and bioeconomy. while acknowledging that these og may be of strategic relevance to make the eip-a effective, however, there is still little information on how they will be brought together and how they will function in practice. therefore, only next years will show whether the reformed design and implementation of the involved eu policies will really follow the declared objectives and will be able to meet the abovementioned expectations. 260 r. esposti acknowledgements an earlier version of this paper was presented at the 1st aieaa conference ‘towards a sustainable bio-economy: economic issues and policy challenges’. 4-5 june, 2012, trento, italy. the author wishes to thank two anonymous referees and the editor for their helpful suggestions and comments on an earlier version of the paper. the usual disclaimers apply. references akis/rd (2000). strategic vision and guiding principles. agricultural knowledge and information systems for rural development (akis/rd), rome: fao. alfranca, o., huffman, w.e. 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(2012). total factor productivity and the bio economy effects. selected paper prepared for presentation at the international association of agricultural economists (iaae) triennal conference, 18-24 august, foz do iguaçu, brasil. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(2): 125-150, 2012 tools for integrated assessment in agriculture. state of the art and challenges wolfgang britz1, martin van ittersum2, alfons oude lansink3, thomas heckelei1 1 bonn university, institute for food and resource economics, germany 2 wageningen university, plant production systems group, the netherlands 3 wageningen university, business economics group, the netherlands abstract. the increased interest in integrated assessment (ia) of agricultural systems reflects the growing complexity of policy objectives and corresponding impacts related to this sector. the paper contemplates on the status of quantitative tools for ia in agriculture, drawing on recent european experiences from the development and application of large-scale integrated modelling systems which are both multi-dimensional/disciplinary and covering multiple spatial scales. specific challenges arise from the numerous roles of agriculture with societal relevance, the heterogeneity of farms and farming systems across a geographical region and the multitude of environmental impacts of interest associated with agricultural production. conceptual differences between typical bio-physical and economic models as well as deficiencies regarding validation and uncertainty analysis require continued efforts to improve the tools. keywords. integrated assessment, quantitative tools, multi-scale analysis, agricultural systems, model validation jel-codes. c63, q10, q18, q57 1. introduction porter and rossini (1980) introduced the term “integrated impact assessment” (iam) for activities which, based on quantitative and qualitative approaches, inform policy processes about economic, social and environmental consequences of changes in policy instruments. the term “integrated” stresses both the interdisciplinary nature and the importance of coherence and consistency of this activity. they raised in their seminal paper issues such as the need to “integrate component contributions from professionals of diverse disciplinary backgrounds”, of “validation of analytical techniques” and of “evaluation of study approaches”, issues which are still relevant today as our paper will show. based on newer developments, we prefer however to distinguish between “integrated assessment” (ia) and “impact assessment” (ima), where the former describes a 1 corresponding author: wolfgang.britz@ilr.uni-bonn.de. 126 w. britz, m. van ittersum, a.o. lansink, t. heckelei scientific activity and the latter a formal evaluation procedure of legislative proposals in public administrations. following the integrated assessment society (tias) we define integrated assessment as “the scientific ‘meta-discipline’ that integrates knowledge about a problem domain and makes it available for societal learning and decision making processes” (tias, 2011). other definitions, such as the one by rotmans and asselt (1996) see it as a process of combining and communicating interdisciplinary knowledge on complex phenomena. the term “impact assessment” has gained importance in recent years due to the fact that the eu and some national governments require a formal assessment of new legislative proposals (eu commission, 2009). following the famous brundtland report of 1987 (un, 1987), impact assessment addresses three pillars of economic, environmental and social sustainability, and consequently is “integrated” or “interdisciplinary” by nature. from this follows a recently increased policy relevance of quantitative ia tools. in addition to this ‘pull’ effect, scientists themselves increasingly acknowledge the fact that global challenges related to agriculture cannot be usefully analysed by sticking to separate, disciplinary approaches. the aim of our paper is to reflect on the status of tools for integrated assessment in agriculture drawing on recent experiences from the seamless project (system for environmental and agricultural modelling linking science and society; van ittersum et al., 2008), the development and application of the capri model (common agricultural policy regional impact; britz and witzke 2008) as well as related european projects, and to derive key challenges and research questions for the future. accordingly, our focus is on quantitative large-scale approaches in the european research community focusing on agriculture which integrate components operating on different spatial scales and assessing both economic and environmental impacts. seamless aimed to develop concepts and procedures for ex-ante policy impact assessment, and, connected to that, an integrated framework called seamless-if for multi-level and multi-dimensional analysis including flexible model chains. it also developed some new model components such as apes (agricultural production and externalities simulator, donatelli et al. 2010) and fssim (farming systems simulator, louhichi et al. 2010a,b) and generated the infrastructure to link some existing ones such as capri. the paper is organized as follows. in the next section we will discuss features of agriculture and agricultural policies which underline the need of a specific approach and specific tools in this area. expanding on that, section 3 focuses on farm heterogeneity as a key issue in ia of agricultural systems and discusses the current available approaches, covering micro and macro approaches and their linkage. the subsequent section is devoted to environmental impact assessment, specifically how to properly capture technology and technology choice and adoption. section 5 discusses challenges in component linkage including information technology (it) questions, followed by a section on model calibration, validation and uncertainty analysis. then we conclude and mention some aspects and challenges not covered by the paper. 2. ia of agricultural systems: specific challenges agriculture differs from other economic sectors in terms of its environmental and economic dimensions. from an environmental viewpoint, an important distinctive fea127tools for integrated assessment in agriculture ture of agriculture is its strong dependency on land as production factor. consequently, the impact of agricultural production on the environment, the landscape and land-use is much more prominent than the impact of other economic sectors and in that respect only comparable to forestry. more generally, agriculture has substantial interactions with soil, water and air as well as with habitats (eea, 2007: 294-305). a second distinct attribute of agricultural production is its direct reliance on production with living species in open biological systems, introducing inter alia questions of animal and plant health, animal welfare and bio-diversity into agricultural impact assessment, a feature again shared with forestry. the pre-dominantly open production systems based on biological processes more often cause non-point-source environmental externalities compared to other economic sectors. mass and nutrient flows are harder to manage than in non-agricultural production processes and nutrient and agro-chemical losses to the environment are to a certain extent unavoidable. emissions of nutrients or agro-chemicals into soil, air and water are subject to complex biological transformation processes and show a high spatial and temporal variability. technical solutions to reduce emissions applied in other sectors such as spatial confinement combined with the use of filters is usually infeasible in agriculture. accordingly, environmental externalities can often not easily be separated from the production of market output. a specific further challenge is given by the multitude of environmental impacts of interest – such as emissions to ground and surface water of nitrogen and phosphorus compartments, ammonia emissions and emissions of gases relevant to climate change (galloway et al., 2008). from an economic perspective, the agricultural sector also differs in several ways from most non-agricultural sectors. it is characterized by an atomistic structure, with many small family operated enterprises and considerable farm heterogeneity due to cross-farm differences in management quality as well as natural and infrastructural location factors. albeit agriculture’s contribution to gdp in the european union as a whole is small (1.8% in 2008, eurostat, 2010: 100), it is still a core economic activity in rural regions with important upand downstream linkages, especially in the new member states (eu commission, 2008: 103ff). accordingly, a strict separation between rural development policies and agricultural policies is not possible, as acknowledged by integrating rural development related policy instruments (pillar ii) in the common agricultural policy (cap) of the european union. beyond its role in the rural economy, farming also shaped landscapes and settlements over centuries, forming cultural heritage and generating touristic attractiveness (daugstad et al., 2006). from a wider perspective, agriculture is increasingly integrated in what is termed the “bio-economy” (eu commission 2012) which requires a multi-sector perspective in ia. the role of the agricultural sector in the overall economy is fundamentally different in developing countries, where agriculture typically provides food security in subsistence settings as well as a large share of employment and corresponding income to the rural population. at the same time, food expenditures constitute a major part of urban household budgets. with agriculture’s growing integration into international markets, spill-over effects of agricultural policies on developing country markets regularly occur. according to the eu’s policy coherence for development approach (eu commission, 2007), assessment of european agricultural policies should hence cover impacts on developing countries, reflecting the fact that the eu is world-wide the largest importer of agricultural and 128 w. britz, m. van ittersum, a.o. lansink, t. heckelei food products from developing countries. the recent food price crisis has underlined again the importance to assess the impact of developed countries’ (agricultural) policies on developing countries and that a careful distinction between households that are netconsumer or net-producers of food is necessary. another more recent specific challenge for ia of agriculture vis-à-vis other economic sectors is the dual role it plays in managing greenhouse gas emissions. on the one hand, agriculture is a major emitter of gases contributing to global warming such as n2o and ch4. on the other hand, agriculture may contribute to the solution of the global warming problem by carbon sequestration or the production of renewable energy (lee et al., 2007). at the same time, climate change will have profound impacts on agriculture and will require adaptation to maintain agriculture’s production capacity for food, feed, fibre and energy (schmidhuber and tubiello, 2007; quiroga and iglesias, 2009). in the european union, agriculture is one of the few sectors for which most relevant policies are defined and managed at the eu level. impact assessment of eu agricultural policies is thus inherently pan-european and the relevance of assessments requires addressing the whole eu with its diversity of farms and farming systems. at the same time, it faces the challenge that not all policies are uniformly implemented across the eu, such as the agri-environmental measures under pillar ii of the cap which are programmed and implemented by national/regional governments using a rather diverse portfolio of instruments. finally, many developed countries including the eu provided income support to agriculture over decades by shielding the sector from world markets through trade policy instruments and, often, guaranteed minimum prices. the eu’s opening of the domestic agricultural sector to international markets in the past two decades still causes adjustment processes, for example the change of private and public risk management under an increasing volatility of prices. parallel to opening domestic markets, the income support in the form of direct payments is increasingly linked to management requirements. in order to receive the full payment, farmers have to comply with certain practices relevant for the environment, animal/ human health, or animal welfare (for the newest suggestions on “greening”, see eu commission 2011). these types of measures not only increase the need for ia per se but also require reflecting farm heterogeneity in the analysis as the measures’ impacts at the national or eu level strongly depend on the distribution of farm characteristics – an aspect focused on in the next section. summarizing, it is evident that the agricultural sector differs in several aspects from other sectors of the economy, asking for specific tools to evaluate policies impacting on the agricultural sector. science has responded to that challenge by developing and applying a highly specialized and diverse set of quantitative approaches and tools. these are differentiated by resolution in space (plot, farm, regions, country, globe), by system components and processes they focus on (bio-physical, economic or social), by time horizon and other attributes. the integrated view in impact assessment requires tools aiming at a more holistic analysis of policies, such as the integrated assessment tools developed in seamless and sensor (sustainability impact assessment: tools for environmental, social and economic effects of multifunctional land use in european regions; helming et al., 2008). 129tools for integrated assessment in agriculture 3. farm heterogeneity as a core challenge in agricultural ia 3.1 farm heterogeneity and scaling-up the foregoing discussion showed that many policy issues nowadays require an integrated assessment, i.e. an assessment that accounts for the interrelated economic, environmental and social effects at country, regional, farm or even plot levels. therefore, integrated assessments need methods for scaling up economic, environmental and social variables from the field/farm level to higher aggregation levels (region, country). scaling methods should account for (i)  heterogeneity in time and space, (ii)  existence of ecological and economic feedback loops (e.g. endogenous prices), and (iii)  the non-linearity of many functional relationships (van gardingen et al., 1997; wossink et al., 2001). figure 1. overview of ia concept decision  making   know-­‐how   risk  a-tude   further  preferences   state  of  nature   endowment   herds   machinery/buildings/   installa?ons   labor   produc?on  rights   equity/access  to  credits   irriga?on  water   soil   climate   slope   plots   farm   markets   ground-­‐  and  surface  water   bio-­‐diversity   policy  environment   standards/taxes/subsidies   social  &  cultural   environment   ghg  emissions   erosion   soil  degrada1on   carbon  sequestra1on   nutrient/pes1cides   run-­‐off  and  leaching   …   input  demand   output  supply   prices   marke?ng   opportuni?es   aggregate  level   plot  level   farm  level   heterogeneity in time and space can be accounted for by invoking the hierarchy concept (ewert et al., 2009) according to which individual agents and their situations are the building blocks of an agro-economic system, in figure 1 highlighted by the central position of the farm as core decision unit. quantification of an agro-economic system requires that individuals are characterized by their attributes and that rela130 w. britz, m. van ittersum, a.o. lansink, t. heckelei tionships governing interactions among individuals are described (weston and ruth, 1997). each individual (i.e. farm operated by a farmer) can be characterized at time t by the attributes: state of nature (st), fixed endowments (bt), and technology set (φt). also, farmers may generally differ in whether and how they evaluate different objectives such as profit, risk, preferences for on-farm work or specific farming system or management practices and future utility e.g. expressed by discount rates. the state of nature at time t is determined by market and institutional mechanisms at higher economic scales that determine prices and market conditions for primary factors, inputs and outputs, the supply of new techniques and policy constraints, incentives and disincentives. the values of all attributes and the farmer’s objectives at time t determine yt, representing the actual farmer’s choices on the use of inputs and the production of outputs, which determine the farm’s impact on the environment. antle and mcgucking (1993) included scaling-up in a general spatio-temporal model by statistical aggregation. their method accounts for nonlinearity in agricultural production and assumes that the characteristics of individual farms in the population (st, bt, φt) induce a joint distribution y(st, bt,φt) of the aggregate outcomes, e.g. input use and environmental impacts. an expected value for aggregate output, input and environmental indicators is obtained by integrating over the joint distribution of st, bt and φt, i.e. if yt is the measure of interest we get. economic feedback to lower scales may take place through prices of input and outputs determined in regional or world markets. regional and world-wide economic models of market processes are common (i.e. partial equilibrium models, computable general equilibrium models) but often have to be redefined in order to incorporate the behaviour of micro level (field/farm) simulation tools (just, 1993: 38). scaling up should also account for ecological feedback mechanisms if aggregate environmental impacts of agriculture affect e.g. the technology set and its effectiveness and efficiency, such as agriculture’s impact on climate change, nutrient deposition or the development of resistance of pests to pesticides as a result of continued pesticides application. 3.2 current approaches to capture farm (including location) heterogeneity this section describes three categories of models that account for farm heterogeneity in time and space, i.e. farm type models, regional models and hybrid approaches. the approaches are summarised in table 1. 3.2.1 farm scale and farm type models this category of models consists of a diverse set of (non)-linear programming and econometric models that have in common that they model the behaviour of a single farm or farm type. among the eu wide (non)-linear programming models that capture farm heterogeneity are aropaj, fssim and the farm type models in capri. aropaj (de cara and jayet, 2000; de cara et al., 2005) represents the supply and onfarm consumption of agricultural products for the european union (eu) based on 1307 representative farm-types, aggregated from several fadn (farm accouncy data network) farms and differentiated by fadn region, specialization, economic size, and altitude class. 131tools for integrated assessment in agriculture each farm type is represented by an independently solved linear programming model. results can be aggregated to the regional (120 regions within the eu), national and european levels. aropaj represents most crop and animal activities and their interactions at farm level (mixing farming system like multiple crops with cattle breeding for example). moreover, it includes several greenhouse gas emission (ghg) indicators related to agricultural activities. fssim is a non-linear programming model template developed within seamless, covering the most important annual and permanent crop activities and various livestock activities (louhichi et al., 2010a; louhichi et al., 2010b). fssim’s farm typology extends the existing eu typology (decision 85/377/eec, 1985) which classifies farms according to their income and specialization with the farm’s land use and intensity of farming (andersen et al., 2007). furthermore, a spatial allocation procedure was developed to georeference farm types allowing the aggregation of model results at farm type level to both natural (territorial) and administrative regional level (elbersen et al., 2006; kempen et al., 2010). the fssim model template, as with aropaj, is applied to an “average farm” constructed by averaging input and output data of fadn farms of the same type according to the farm typology. detailed information on crop management stems from surveys and the fadn records and generates feasible input-output combinations, currently available for 109 farm types in 12 major eu production regions. the capri farm type models (gocht and britz, 2010) are described below in the context of the capri regional models. econometrically estimated farm type models usually consist of a set of input demand and output supply equations based on duality theory. fadn data have been employed in numerous studies to estimate such models. specific econometric techniques account for farm heterogeneity in the data. fixed-effects, random effects and the hausman-taylor model (baltagi, 1995; gardebroek and oude lansink, 2003) assume farm-specific intercepts in each of the supply and demand equations (oude lansink and peerlings, 2001). applications in europe cover both instruments from the cap (e.g. oude lansink and peerlings, 1996; boots et al., 1997) or environmental ones (e.g. oude lansink and peerlings, 1997). generalised maximum entropy (gme) estimation (oude lansink, 1999) and the now more often preferred bayesian methods allow for estimating a full set of farm-specific parameters also in cases where the number of observations is smaller than the number of parameters to be estimated by introducing prior information regarding model parameters. gardebroek (2006) showed that bayesian random coefficient models are superior to classic random coefficient models as they allow for incorporating prior information and avoid estimation problems in panels with a small time series component. most farm scale models regularly applied to policy impact assessments assume either profit maximizing behaviour (aropaj, duality based approaches) or take also risk attitudes into account (fssim). 3.2.2 regional models regional models for agriculture are usually comparative static supply models of the agricultural sector in a country, where typically administrative regions are treated as representative farms. this regional `farm` pursues a large number of arable crop and live132 w. britz, m. van ittersum, a.o. lansink, t. heckelei stock activities and produces marketable outputs and intra-sectorally produced inputs. since the lowest resolution level is the region (usually level iii of the nomenclature des unités territoriales statistiques (nuts), i.e. about 1300 regions for eu 27 which represent bigger cities or smaller regions with a population size between 150000 and 800000 inhabitants), the extent to which heterogeneity is represented is limited. two regional models which integrate economic and environmental aspects are raumis (gömann et al., 2007) and dram (helming, 2005). both are to a large extent comparable to capri (see below for a more detailed description), being comparative-static and employing positive mathematical programming (pmp) to steer the allocation. a specificity of dram is a differentiation of the dairy herd by milk yield. raumis uses a full cost approach where investment goods are depreciated by operating hours and labour can be sold and bought. both models can be solved at national level to simulate trade in manure. 3.2.3 hybrid models integrating farm level and regional level models hybrid models integrate farm level models into regional level models. two examples of hybrid models are capri and seamless-if. the capri model (britz and witzke, 2008; gocht and britz, 2010) consists of a supply and a market module. the supply module comprises independent aggregate non-linear programming models representing approximately 50 crop and animal activities of all farmers at either regional (nuts ii) or farm type level. the farm type layer provides for the whole eu2 a consistent dis-aggregation from the regional level to about 1850 farm type models differentiated by farm specialization and economic size. prices for agricultural outputs are rendered endogenous based on sequential calibration between the supply models and a global, spatial multi-commodity model. capri allows for modular applications as e.g. regional supply models for a specific member state may be run at fixed exogenous prices without market feedback. the farm type model layer may be switched on or off, in the latter case turning capri into a regional model. another important feature of capri is its ability to spatially scale down results to clusters of 1x1 km grid cells, covering crop shares, crop yields, animal stocking densities and fertilizer application rates and allowing for linkage with the bio-physical model dndc (britz and leip, 2009). the seamless integrated framework shares many of the characteristics of the hybrid capri model; capri is integrated within seamless-if. in seamless-if, the farm level model represented by fssim has a richer underpinning of crop management practices and environmental impacts than the farm level model in capri. using a link to an agronomic component, fssim allows the introduction of new activities making technological innovation scenarios possible. however, due to data limitations, it only covers a few representative farm types so far. for those, an explorative link between fssim and the regional programming models in capri is made by the module expamod (dominguez et al., 2009). 2 versions until spring 2012 do not break-down bulgaria and romania to individual farm types due to missing fadn data. 133tools for integrated assessment in agriculture table 1. overview of approaches to model farm heterogeneity, scaling up and feedback loops type examples major data sources major properties representative bioeconomic farm model fssim own surveys, fadn crop rotations, parameterized from crop growth model; explicit consideration of location factors such as soil; current and future practices; focus on important farming systems; risk attitude representative farm type models aropaj fadn focus on current practices, no rotations; typically profit maximizing behaviour assumed duality based econometric models fadn or similar single farm records only implicit representation of technology, simulation of all farms in samples; typically profit maximizing / cost minimizing behaviour assumed regional programming model raumis, dram regional statistics implicit / explicit representation of interaction between farms at regional level; focus on current practices; no crop rotations; often calibrated based on pmp hybrid capri, seamless regional and global statistics, fadn supply side: regional or farm type models; link to global multicommodity model allows for endogenous prices based on sequential calibration 3.3 limitations of current approaches as discussed above, integrated assessment of agricultural and environmental policies has to capture heterogeneity at the field/farm level and requires methods for scaling up field/farm level responses to regional and higher levels in order to capture economic and ecological feedback loops. the three approaches discussed, i.e. farm level models, regional models and hybrid models differ in the way they represent heterogeneity, scaling up and feedback loops. farm level models allow for modelling the behaviour of individual farms. however, in practice only a limited number of representative farms are modelled, due to a lack of information on individual farms and in order to preserve the empirical tractability. farm level models do not scale up responses and do not account for feedback loops at higher levels of aggregation (e.g. prices, environmental impacts). models that operate at regional levels are more suited for representing aggregate behaviour of a region and some models account for feedback loops, such as exchange of agricultural inputs between regions and price adjustments in markets of inputs and outputs. however, regional models poorly address the heterogeneity at the farm and field level. instead, each region is considered as one ‘farm’. hybrid models combine the strengths of farm level and regional level approaches. they account for heterogeneity at the level of representative farms and account for economic 134 w. britz, m. van ittersum, a.o. lansink, t. heckelei feedback loops (i.e. prices are determined within the model), that typically need iterative procedures to ensure consistency in the feedback loop. generally, all operational approaches mentioned above are of a comparative-static nature and do not assess structural change. the conceptual approach of updating farm type weights (number of farms in certain classes) in baseline and scenario simulation for future years was developed in the seamless project (zimmermann et al. 2009b) and the empirical analysis was performed (zimmermann et al. 2009a), but it was not implemented in simulation. finally, ecological feedback loops are presently not accounted for in any of the models discussed. 3.4 challenges for future research current approaches for modelling heterogeneity among farms have several shortcomings that can be addressed by future research. first, current approaches generally lack a proper account for environmental impacts emerging through spatial relations or interactions between different farms, such as those related to the occurrence of pests and diseases, green and blue ecological corridors or hydrology and nutrient emissions. clearly, including such spatial interactions is complicated and requires a thorough understanding of the mechanisms themselves and the involvement of the proper disciplines. furthermore, current approaches for modelling heterogeneity suffer from a limited availability of data on e.g. environmental impacts, management, local climate and geographical conditions. accordingly, spatial variability in location factors (soil type, climate, slope, surrounding land cover, accessibility etc.) is typically not explicitly (aropaj, capri farm types) or only partially (fssim) covered. bio-physical processes and interaction between farm management and the environment are however strongly depending on these location factors, and are often highly non-linear. collecting the necessary data is very time consuming and costly and integrating this information in models adds to the model complexity. although spatially referenced data on soil, climate and land use are increasingly available, thanks to e.g. satellite information, these data generally miss a link to on-farm management practices. also, it is still not known whether the benefits of a greater resolution outweigh the costs of collecting and integrating more data. a more general intriguing question for future research is consequently, what the minimum complexity level of modelling is for various agricultural and environmental policy issues? and finally, dynamics and structural changes are so far often not covered. here, agent-based models have clear advantages compared to traditional equilibrium models (happe et al. 2006), but current concepts are limited to regional case studies due to resource and data requirements and empirical validation is still not sufficiently developed (zimmermann et al., 2009b). 4. modelling technology and technology adoption to quantify environmental impacts and economic modernization environmental impacts of agriculture are strongly related to bio-physical characteristics of agricultural production processes and management decisions. therefore, an integrated assessment of policies at an aggregate level, in order to feed bio-physical models and approaches with an appropriate level of detail, requires a detailed technology and 135tools for integrated assessment in agriculture agro-management representation at the micro level for determining environmental effects of policies. the latter is generally not needed for determining economic impacts in the narrow sense, i.e. without attempting to value externalities, where dis-continuities at the farm level are smoothed at the aggregated level. also, many integrated assessments aim to assess future changes, sometimes with a time horizon of decades. hence, knowledge of current and future production technologies is relevant. however, data on current and future production technologies are usually not available; even for current activities realized on farms, basic information such as the amount and timing of fertilizer use on specific crops are typically not available from official statistics. there are three interlinked approaches to overcome this missing data problem: • own data sampling. seamless conducted own surveys to sample the necessary, relatively detailed, data on agricultural management in ca. 15 regions in the eu (zander et al., 2009). • use of engineering information as for example found in farm management handbooks. • statistical estimators which combine own data and engineering information with sectoral statistics or farm accounting data to derive process and region specific attributes consistent to observed aggregated quantities, for example on total fertilizer or feed use. it is obvious that limited data availability at farm and regional level introduces a high uncertainty about technical coefficients of models. 4.1 new technologies and their adoption one main challenge in integrated assessments lies in incorporating technologies, i.e. elements of the production set, which are currently not yet or rarely used by farmers, or are even not yet fully developed, usually referred to as “alternative activities” (van ittersum and rabbinge, 1997; hengsdijk and van ittersum, 2003). some examples of alternative activities are no-tillage systems, precision agriculture technologies, technologies with more targeted input application which might increase yields or low-input alternatives such as organic farming. if their process details are spelled out in details, then their performance as measured by economic, social and environmental indicators can be evaluated. however, even in win-win situation where no obvious dis-advantages to farmers from implementing innovative processes are visible, the adoption by farmers might be slow. positive mathematical programming (howitt, 1995) and variants thereof used to overcome the normative character of programming based approaches (for example in fssim and the capri supply models) cannot deal with alternative activities if the farmers’ choices with regard to them are still unobserved. promotion of alternative activities, is however an often proposed measure to mitigate negative externalities from agriculture or to strengthen positive externalities. impact assessments then need to quantify the impact of policy measures such as subsidies on the implementation of alternative activities. defining which farm management options will be chosen under certain future conditions (policy and market environment, climate change etc.) seems to be a core question for many agrienvironmental policy assessment studies and can so far hardly be answered with the tools discussed by us; hence future research should address this. new methodologies such as 136 w. britz, m. van ittersum, a.o. lansink, t. heckelei agent based modelling describing knowledge diffusion (berger, 2001) and belief formation (hegselmann and krause, 2002) in the farming population might be linked to existing tools to improve simulation of adoption processes. to capture different agro-management options for one type of production such as cropping wheat, different production activities need to be formulated such as fertilization through chemical fertilizers only or combined use of chemical and organic (e.g. manure) fertilizers. most classical aggregate programming models working at the regional or farm type group level include only one production activity variant characterized by current average input and output coefficients. higher detail in technology is the domain of socalled bio-economic models (e.g. brown, 2000; janssen and van ittersum, 2007) where the economic model is linked to bio-physical process models (see e.g. jame and cutforth, 1996) describing e.g. the interaction between soil, climate, farm management, crop growth and water and the nutrient cycle. fssim (louhichi et al. 2010b) provides an example of such a bio-economic model. it can be linked to a crop-growth model (belhouchette et al., 2011) which delivers inter alia crop rotation related environmental indicators such as nutrient surpluses to fssim. estimation of biotic stresses from pests, weeds and diseases is generally not possible with crop growth models. here, expert-based rules are generally applied (e.g. dogliotti et al., 2004). a key challenge with respect to alternative activities refers to the decision on how many and which must be identified to adequately capture future options and secondly how to assess these in where crop growth or other bio-physical models crop growth models are not available or tested. there are often many activities which may theoretically be relevant, and due to non-linear relationships between inputs and outputs these could all be relevant for inclusion in programming models. 4.2 temporal scales the currently available agro-technology rich programming models are typically comparative static in nature with a medium-term planning horizon, whereas many environmental process models are formulated (recursive) dynamically. biophysical processes usually take a long time until steady state solutions are achieved or until variation has been captured adequately. therefore, crop growth models often perform simulation over decades, assuming no-change in farm management regarding the timing or rates, but typically taking stochastic variation of weather into account. the long-term simulations often target accumulation or depletion processes of nutrients in soils and their feedbacks on crop growth and environmental indicators (tittonell et al., 2010; dogliotti et al., 2004; hengsdijk and van ittersum, 2003). typically, averages over the simulation period are then used to parameterize the economic models. so far, there is little scientific work on integrating changes in farm management over time, which are underlying e.g. past yield increases, with the dynamic bio-physical feedback processes as described in crop-growth models (cf. barbier and bergeron, 1999). it thus remains challenging to consistently simulate processes across different time scales. to summarize, technologically rich simulation models are necessary to spell out environmental impacts, though also economic and social ones, which is challenging both due to low data availability and with respect to consistent links to market models. specifically, more research on the simulation of adoption of alternative technologies and the underly137tools for integrated assessment in agriculture ing spatial-dynamic processes is desirable to improve ia by including technological innovations which are promising from an engineering point of view. 5. modules, models, tools and data 5.1 challenges in combined model usage the coherent application of different model components in an impact assessment remains a challenge, even if tools such as seamless-if have generated technical infrastructure for combined application of components. in most applications, the linkage is uni-directional bottom-up or top-down. that easily leads to inconsistencies if, to take a classical example, the supply response to price changes in a detailed supply model at the bottom of the chain is different from the market model at the top used to derive market clearing prices. that consistency issue is found in all applications where components show overlap in endogenous variables, and clearly reaches way beyond the question of differences in reactions to price changes in combined tool use. there are three ways for achieving consistency – with their specific pros and cons. firstly, the components can be merged into one simultaneously solved model. however, that solution proves often hard or even infeasible from a computational point of view, especially if the components involved work on different spatial and temporal scales or employ different numerical solution strategies. it is for instance clearly impossible to embed the actual simulations with a fully specified crop growth model into a bio-economic farming model based on constrained optimization. it is also quite challenging to debug and to systematically analyse the outcome of the evolving super-models. secondly, the components can be sequentially linked so that the e.g. supply behaviour of the market model is updated based on the results of the supply model such as in capri (britz, 2008). that approach was further explored in seamless, to link capri and gtap (global trade analysis project, a global economic data base with a matching computable general equilibrium model template) (jansson et al., 2009). the iterative solution requires however a rather stringent it integration and might fail if not all components show rather smooth, convex reactions to changes in the update process. and thirdly, modules in components can be parameterized such as to summarize results or the behaviour of some other component. an example is the approach adopted in seamless to summarize simulations of a specific farming activity with a crop growth model into a vector of input/output coefficients and eventually a co-variance matrix of yields (in fssim). an example from the economics domain is expamod (dominguez et al., 2009) in seamless where allocation responses of farm type models are extrapolated to the regional scale. meta modelling is also an often applied strategy to summarize the behaviour of a model and to avoid the high computational load of performing simulations with e.g. complex bio-physical models for a large number of locations and technological alternatives (e.g. in capri-dynaspat, britz and leip, 2009). it has been applied in sensor to build a tool which only consists of meta-models and hence no longer requires the original components in applications (sieber et al., 2008). in seamless, meta-modelling was to a large extent avoided, to keep the full functionality offered by the individual components. 138 w. britz, m. van ittersum, a.o. lansink, t. heckelei the experiences gained both from the work on expamod, from linking caprigtap (jansson et al., 2009; britz and hertel, 2009), from combining economic and landuse cover change modelling (britz and verburg, 2010), as well as from sequential calibration in capri underline again the crucial role of harmonized data bases (cf. janssen et al., 2009) for combined model application. harmonization encompasses common classifications for different dimensions such as time, space, products or processes as well as numerical consistency where required. in capri, the data underlying the market and the supply models are fully harmonized enabling a swift combined application. in many other cases, applications suffer both from differences in definitions, numerical inconsistencies or incomplete coverage of the data underlying the components. we might thus conclude (again) that integrated impact assessment requires increased efforts to harmonize data bases of tools from different domains. that harmonization requires the combined expertise of modellers and those responsible for official statistics. 5.2 how much and what type of software is needed in ia? the components underlying the assessment must be operated in an it environment, and especially seamless investigated into novel it approaches to host and link components (cf. rizzoli et al, 2008; wien et al., 2010) and promoted the use of a declarative approach, i.e. an approach that describes components and models as well as their relations in a formal way outside the procedural software code implementing the linkage. integration of components from different disciplines provides a challenge due to diverging traditions in it use. economic modellers often rely on algebraic modelling languages (amls) which offer a compact, declarative way to code economic models and a transparent link to performing solvers for different problem formats (britz and kallrath 2012), or use statistical packages for estimation and simulation of econometric models. the community of agent based models has developed its own libraries in object oriented programming languages (cf. luna and stefansson, 2000)). seamless has invested in building similar libraries for crop-growth models (donatelli et al., 2010). seamless started with a far reaching vision to develop a generic approach allowing to link components, bringing together tool developers from different domains to exchange knowledge and visions about concepts and technical realizations. for those involved, it was a beneficial process which led to a broader, better informed view on existing solutions in the different domains as well as cost and feasibility of harmonization in it across those domains and automated tool usage. a possible conclusion is the fact that the existing diversity in technical realization reflects, at least to a certain extent, comparative advantages. some core functionalities offered by the specific solutions in use are very hard to replace by generic approaches – licensing of and building interfaces to performing numerical solvers for constrained non-linear optimization provides an example from economic modelling. additionally, the investments of the different communities into coding their models and into human capital to efficiently use the underlying software platforms lead to large sunk cost. it solutions for combined component use must be capable of integrating the existing diversity of tools. therefore, seamless did not reprogram larger pre-existing components such as capri or gtap in another language compatible with the open modelling interface (openmi), but rather devel139tools for integrated assessment in agriculture oped openmi compliant wrapper applications which call these components (wien et al., 2010; gijsbers et al., 2007). based on the experiences with seamless two conclusions can be made. first, better and more harmonized documentation of components across disciplines is needed for combined applications. at least for those outputs and inputs subject to linkage with other components, clear definitions of units used, underlying product and process classification, and clear spatial reference must be provided to avoid errors and to ease communications in-between modelling communities and with the client. secondly, fully automated linkage across components from different domains is very hard to achieve, and given the dynamics in component development, costly to maintain even with advanced approaches such as ontologies (janssen et al., 2009). nevertheless, the paradigm of exchangeable components promoted in seamless could well open the door for further improvements of modelling in other domains as well. 6. model calibration, validation and uncertainty analysis 6.1 model calibration, evaluation and validation ia models are computerized tools to analyse complex real world problems in their social, economic, environmental and institutional dimensions. technically, ia models often consist of interlinked sub-models, using outputs from one sub-model as inputs to another. in the scientific process of their development each model and preferably the entire model chain must be calibrated and evaluated or validated. model calibration is the procedure of parameter adjustments to reproduce the response of the object system within a range of accuracy specified by some performance criteria (refsgaard and henriksen, 2004; scholten, 2008); it aims at matching simulation results and measurements (observations). model validation is the substantiation that a model possesses a satisfactory range of accuracy for the intended application of the model (refsgaard and henriksen, 2004; scholten, 2008) and therefore generally requires to specify the purpose of the application. often the terms ‘calibration’ and ‘validation’ have different (operational and sometimes even conceptual) meanings across different disciplines. in biophysical science, model calibration typically refers to the process of tuning the model parameters, each within their theoretically or empirically valid domain such that the simulated values best fit the observed values according to some defined statistic (for example minimum root mean square error – wallach et al., 2011). generally this is done for observations from an experiment in one or several years. economists would typically term this process parameter estimation and use the word ‘calibration’ in contexts where the number of observations is not sufficient to identify all model parameters. consequently, calibration of complex economic models often implies the use of an exact calibration procedure adjusting parameters of a behavioural specification to reproduce observed historical data. an example of such a procedure for constrained optimisation models is positive mathematical programming (howitt, 1995). biophysical models are typically evaluated or validated by simulating selected processes and comparing the results against an independent experimental dataset, not used in the calibration exercise. if the validation exercise leads to confidence in the 140 w. britz, m. van ittersum, a.o. lansink, t. heckelei model, it is then often used for simulations in similar conditions without a calibration procedure, while for dissimilar situations new calibrations must be performed. if the model has been calibrated and validated for a representative set of conditions in a particular region, it is used in regional studies with regional input data (therond et al., 2011). a complicating factor in regional analyses can be that the biophysical model does not include all major processes that determine production or environmental impacts in the farming reality of a specific region. for instance, cropping system models generally do not consider pests and diseases, while these are important determinants of farming and regional yields. then, usually an extra calibration step is used to empirically correct for such factor(s) (e.g. supit, 1997; wolf et al., 2010 within the european crop growth monitoring system). for (agricultural) economic models, subsequent validation of the model against an independent dataset is rare and not the general practice. this is partly due to the fact that real human (economic) systems rarely allow performing experiments. consequently, there is often just one historical data set for the model domain, i.e. the data set already used for calibration. models are then often used for forecasting based on the assumption that the description of the processes, including the calibrated parameters, also hold for the future. nevertheless, economic models are sometimes tested against out-of-sample historical data either from the same system used for calibration but a different period of time or a similar system for the same time period. examples in our context are kanellopoulos et al. (2010) who used such a set-up to test the quality of predictions of a bio-economic farm model, whereas heckelei and britz (2000) assessed different specifications of regional supply models regarding their performance in forecasting observed reactions to policy changes. such simulation experiments can be seen as a test of the validity of the calibrated structural parameters across the time or spatial domain. they also do require, however, out-of-sample data on all exogenous drivers of the considered tool whose acquisition might be costly or prohibitive for complex ia models. additionally, the trade-off between setting observations aside for out-of-sample tests and a more robust estimation of parameters due to a larger sample needs to be taken into account. therefore, such procedures have been rarely used for the system models considered here, but we nevertheless plea for more ex-post analyses as part of model ‘validation’ exercises. in a model chain, independent calibration and validation of individual model components is adequate as long as no feedbacks exist between the components. if feedbacks do exist then also the combined models in the model chain must be calibrated and validated adding to the complexity of the task. to our knowledge, examples of such calibration and validation exercises are rare in general and in the agricultural system domain they do not exist in the literature. despite these limitations, larger modelling systems such as capri have been increasingly applied in policy relevant contexts3. the required acceptance for this development could be interpreted as the outcome of an ‘extended peer review’ (van der sluijs, 2002) created by many iterations of applications, publications and user feedback. such a type of validation might be the only one currently feasible for complex ia modelling tools as a whole. 3 for a list of projects and publications with capri applications, see <http//www.capri-model.org>. 141tools for integrated assessment in agriculture 6.2 uncertainty analysis given the complexity of the problems addressed and the complexity of the models themselves, ia models are subject to various types and sources of uncertainties which may have important implications for their reliability and acceptance. proper calibration and model validation may take away some of these uncertainties, but models may still reproduce observed data for the wrong reasons or may reproduce historical data while not making proper forecasts. to become useful tools, therefore, an assessment of uncertainties in ia models is essential. uncertainty analysis may be defined as the assessment of uncertainty in model results due to incomplete knowledge of model parameters, input data, boundary conditions and the conceptual model. ideally, the combined effects of these uncertainties are taken into account. furthermore, the uncertainty originating from the decision context (exogenous factors) may be included (scholten, 2008). sensitivity analyses can be regarded as a method contributing to uncertainty analysis. uncertainty analysis in ia models has received considerable attention within the scientific literature. an important body of literature has focused on typologies of uncertainties. one such typology, based on others, is proposed by walker et al. (2003). they discriminate between statistically quantifiable uncertainty, uncertainty in the scenario definition (scenario uncertainty) and uncertainty due to an imperfect understanding of the underlying problem (recognised ignorance). these three types of uncertainties can pop up at different places in an ia model, i.e. in the model boundaries (what is endogenous and exogenous to the model), model structure (equations) and its technical implementation (code), model inputs and model parameters. all these uncertainties will likely accumulate in the model output. however, it is unclear if these uncertainties increase or decrease actual quantitative errors. a second topic in the literature refers to tool catalogues and guidelines for selecting appropriate methods (van der sluijs et al., 2003) and frameworks for the systematic assessment of uncertainties (e.g. krayer von krauss and janssen, 2005; janssen et al., 2005). since ia models are often developed with the aim to provide scientific input to decisionmaking processes, they can also be characterised as “science-policy interfaces” (van der sluijs, 2002; watson, 2005) or “bridge building tools between science and policy” (rotmans and van asselt, 2001). this function can only be satisfied if the information supplied by and through the model meets the information requirements of the policy design process. in practice, much of the science and literature has focused on uncertainty from a modeller’s perspective and generally uncertainty analysis has been treated much more extensively in biophysical models, such as cropping system models (e.g. wallach et al., 2011; payraudeau et al., 2007) than in bio-economic and economic models (e.g hertel et al., 2007). bio-economic farm models typically contain very large numbers of technical coefficients varying by site. this renders the uncertainty analysis of relevant model outputs difficult because uncertainty information for all model parameters is rarely available. therefore, uncertainties are often only assessed with respect to econometrically estimated parameters using standard errors for draws in monte carlo analyses. a broader assessment requires use of subjective distributions to include parameters for which no empirical uncertainty distributions are available. as written above, parameters are not the only source of uncertainty and the number and complexity of uncertainties inherent to ia modelling in agriculture suggests to take a 142 w. britz, m. van ittersum, a.o. lansink, t. heckelei more user-oriented approach, where the type of uncertainty analysis and resulting information is defined by the final model (result) users (iiasa, 2002; cec, 2004; gabbert et al., 2010). this helps to focus the uncertainty analysis on the relevant model outputs. 7. summary and conclusions ia tools for agriculture are developed and used to inform policy processes about social, economic and environmental impacts of legislative proposals. this paper has identified some key scientific challenges in that respect. firstly, impacts in all three sustainability dimensions depend to a large extent on attributes which show a high variability across farms, both relating to location factors, farm management and further attributes such as farm size. capturing farm heterogeneity while at the same time modelling interactions across time and regional scales remains a challenge. such interactions encompass market interactions, social interactions such as belief formation regarding alternative technologies, and environmental interactions such as for example development of pests and diseases in landscapes. secondly, spelling out environmental impacts, though also economic and social, asks for a detailed technology description which can often only be achieved by close integration of bio-physical and economic models. challenges here are manifold: (a) bio-physical models often work on field scale, whereas economic models typically represent averages of administrative regions; (b) bio-physical process models have typically both a high temporal resolution (often days) and cover long simulation horizons based on recursive-dynamic simulations, whereas most technology rich programming models are comparative static with a medium term horizon; (c) assessing alternative technologies requires identification of relevant future options which may be numerous and selections are often subjective, whereas their simulation requires availability of models; (d) last but not least, data availability regarding farm management is low, and an inclusion at least of some basic attributes such as fertilizer application rates and timing, animal housing systems and manure management would be highly beneficial. thirdly, combined application of tools and models also calls for combined model calibration and validation. different disciplines have different traditions in that respect. a potentially promising activity is combined ex-post validation exercises, also to increase the common understanding about calibration and validation. at the same time, more research on how to assess and communicate uncertainties in combined tool use is necessary. from the technical side, more focus on the implementation of quality assurance in the coding process (incl. documentation) of ia tools seems to be beneficial. component and tool linkage while maintaining flexibility in software use for components from different disciplines and domains remains a challenge. there are many other important aspects and challenges in iam which could not be covered by our paper, and we will mention a few. from an institutional viewpoint: how can we ensure maintenance of existing tools without losing scientific impetus and competition? what is the impact of tool use in ia on policy processes? how to understand and improve the policy-science interface, i.e. the interaction between scientists involved in the ia and various levels of administrations, stakeholders and decision makers? and, there are further scientific challenges, for instance on how we can improve knowledge 143tools for integrated assessment in agriculture about alternative ways to present agricultural systems by computerized, independent components which are integrated into tools. how much detail is needed and what is the trade off with flexibility? how to address policies and developments affecting agriculture jointly with other sectors while maintaining detail in representing the agricultural system? there are many promising, so far more case study type, approaches such as regional cges or multiplier analysis where it remains to be seen if they can be successfully expanded to pan-european type assessments (britz et al. 2011, viaggi et al. 2010). iam is a growing research field of high societal relevance with many remaining challenges. it offers not only agricultural economists, but all agricultural scientists ample opportunity to demonstrate advantages of an interdisciplinary and theme-focused approach to research. it also promotes a healthy balance between further specialization in different fields of agricultural sciences and deepened interaction between these fields. references andersen, e., elbersen, b. godeschalk, f. and verhoog, d. 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(2009b). modelling farm structural change for integrated ex-ante assessment: review of methods and determinants, environmental science & policy, 12 (5): 601-618. bio-based and applied economics 5(1): 5-26, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-15247 analysing the economy-wide impact of the supply chains activated by a new biomass power plant. the case of cardoon in sardinia andrea bonfiglio, roberto esposti* department of economics and social sciences, università politecnica delle marche , piazzale martelli 8, ancona (italy) date of submission: 2014 18th, december; accepted 2015 21st, june abstract. this study investigates the impact on the economy of sardinia (italy) generated by a new biomass power plant fed by locally cultivated cardoon. the cardoon also serves the production of biopolymers. the impact is assessed at an economy-wide level using two multiregional closed input-output models, which allow us to take into account the entire supply chain activated and the supra-local effects generated by trade across local industries. the effects are computed under alternative scenarios simulating different levels of substitution of existing agricultural activities with the new activity (cardoon). results show positive and locally significant impacts in terms of value added and employment. however, these impacts are substantially influenced by the degree of substitution. results also suggest that there are specific territorial areas that are more sensitive to negative effects induced by substitution. keywords. multiregional i-o model, biomass energy, supply chains, cardoon, sardinia. jel codes. r11, r15, q42 1. introduction this study concerns a project for the construction of a biomass power plant at porto torres in sardinia, an italian region. the plant, having a power of 135 mwt, is expected to use about 250 thousand ton/year of biomass (straw) deriving from the local cultivation of cardoon (cynara cardunculus) for the production of electricity and thermal energy. moreover, the project contemplates the extraction of vegetable oil from the cardoon for the production of biopolymers as an alternative to petroleum-based plastics. the objective of the project is thus twofold: reducing the use of fossil fuels and stimulating local economy by cultivating cardoon. * corresponding author: r.esposti@univpm.it. 6 a. bonfiglio, r. esposti the present paper is aimed at assessing the local and supra-local economy-wide impacts deriving from the implementation of the power plant and, in particular, from the consequent activation of the cardoon supply chain within the specific agricultural and economic conditions of sardinia. the methodology adopted consists of multiregional input-output (i-o) models that compute impacts in terms of value added and employment at both local and supra-local levels. therefore, only socio-economic impacts are of interest here, while environmental implications, which can be particularly relevant in such a case, are not considered. impacts derive from all the backward linkages that the biomass power plant will activate to guarantee its functioning, including the provision of biomass, and from the production and sale of oilseeds. they also come from possible substitution of existing agricultural activities that the introduction of the cardoon might cause in local economies. impacts are computed under alternative hypotheses about the land use associated with the new cardoon production. a scenario assumes that the cardoon will be established on uncultivated and suitable land (currently unutilized or that will be abandoned within few years). a further scenario hypothesises that all the land required by the cultivation of cardoon replaces pre-existing agricultural production. another and more realistic scenario considers a mixed case where only a part of the land to be allocated to cardoon is unutilized while the remaining part substitutes existing activities. the analysis of biomass power plants is a relatively recent topic. however, there is already a quite wide and well-established literature on the assessment of their overall impacts. most of the relevant studies concentrate on what seems to be the most important concern underlying renewable energy, i.e. overall environmental implications (ucsusa, 2011). though such implications are beyond the scope of this paper, there are two major aspects faced by those studies that are also relevant to the present study. a first aspect regards the need for assessing all processing stages, from production of raw materials to distribution of energy. in environmental studies, this assessment is carried out by adopting a life cycle assessment (lca) framework (heller et al., 2004). a second aspect is concerned with the need for a spatially explicit analysis, based on the consideration that both magnitude and direction of impacts are not only localised but also differentiated across space (höltinger et al., 2014). moreover, in these studies, there is an increasing awareness that all impacts have to be carefully taken into account for a proper evaluation. they are environmental as well as social and economic impacts with a focus on the entire production process, on the one hand, and on the local/spatial implications, on the other hand (madlener and vögtli, 2006). with reference to this kind of integrated socio-economic and environmental assessment (ministry of agriculture and land – british columbia, 2007), an increasing number of studies, evaluating economic impacts of biomass power plants, have been recently carried out. following the abovementioned directions, these studies take the whole “from-biomass-to-energy” supply chain into account and explicitly consider local specificities and the consequent differential impacts (kaffka et al., 2011). to perform such economic assessment, regional multisectoral models have been consistently adopted. some studies use computable general equilibrium (cge) models (deloitte access economics, 2014), while others employ multiregional input-output (i-o) models. among the latter, we can mention, in particular, the impact analysis for planning (implan) i-o models (timmons et al., 2007). both cge and i-o models provide rich details in terms of local/regional interdependences and, therefore, of differ7economy-wide impact of a biomass power plant ential impacts. one of the main differences is that i-o models disregard possible impacts on prices. however, in the case of local medium-size plants, one may reasonably suppose that these impacts are not significant and can be therefore neglected. since no major price impacts are expected in the case under study, we decided to adopt a multiregional i-o approach. this is a well-established and standardised methodological framework. nevertheless, it was applied in a non-traditional way by assessing impacts associated with scenarios related to different degrees of crop substitution. this approach can be assimilated to sensitivity analyses performed by previous studies to assess the robustness of results (abdoulmoumine et al., 2012). however, the novelty here is that the use of i-o methodology goes beyond the mere sensitivity analysis as it looks at policy implications of a biomass power plant in sardinia induced by different levels of crop substitution. the rest of this article is organised as follows. the next section shortly describes the area under investigation. section 3 illustrates the methodology adopted, the data used and how alternative scenarios have been modelled. section 4 provides results under different scenarios whereas the last section offers some concluding remarks and suggestions for further research. 2. the area under study the area under investigation is the northwest portion of sardinia, italy. in particular, the project for the construction of a biomass power plant involves the municipality of porto torres, where the production of bioenergy and biopolymers will be carried out, and eight main local districts that have been identified as possible areas of cultivation of cardoon (figure 1). these districts are: anglona, gallura, goceano, marghine-barbagia, mejlogu, montacuto, nurra-sassarese-romangia (where porto torres is located) e planargia-montiferru. these areas comprise about one hundred municipalities, which were regrouped on the basis of common historical, geographical and productive characteristics. the localisation of these areas is motivated by the need to rationalise the costs of transferring cardoon to the biomass power plant (and thus to reduce environmental impact related to road transportation), take advantage of strong socio-economic relationships between the northern part of sardinia with the industrial site of porto torres and contrast negative dynamics of the agro-food sector in these areas. some general indicators about socio-economic characteristics of the various local districts provide interesting evidence on the substantial diversity across the areas under consideration (table 1). some territories, especially the coastal ones, reveal a significant degree of urbanization (nurra sassarese romangia) and a presence of manufacturing industries (marghine barbagia and gallura). most territories, however, are characterized by low population density, depopulation, aging and high incidence of agricultural activity, though residual and incapable of ensuring the same productivity and profitability of other sectors. a common aspect is that agriculture in these areas has undergone a profound transformation in recent decades characterized by extensification and progressive abandonment. the different territorial characteristics affected the identification of the areas potentially available for the cardoon. a study based on land suitability classification (fao, 1976) distinguishes the available land in five different classes of suitability characterized by different levels of productivity and profitability. it concludes that the area that can be allo8 a. bonfiglio, r. esposti cated to the cultivation of cardoon amounts to more than 72 thousand hectares, 11% of total available area and corresponding to a potential quantity of biomass of 825 thousand tons, far beyond the needs of the plant (250 thousand tons). the study also identifies the figure 1. the area under study, sardinia, italy (municipal administrative level) 1    2   porto torres gal   ang   mon  nsr   mej   pla   mar   goc   table 1. main socio-economic indicators of the local districts involved in the project, sardinia, italy. district pop. (2011) employees (2008) var. % farms 2000-10 uaa per farm (2010) var. % uaa 2000-10per km2 var. % 2001-11 % of pop. % in agr. % in manif. anglona 46.1 -0.2 28.1 8.0 7.2 -50.6 29.9 5.5 gallura 29.5 -1.0 41.0 4.7 16.3 -50.5 26.5 -6.9 goceano 24.5 -9.6 30.0 16.9 9.8 -47.6 30.3 -12.5 marghine barbagia 40.1 -7.2 38.4 7.5 26.5 -44.5 33.5 15.0 mejlogu 24.9 -9.1 31.6 17.2 13.6 -52.1 39.3 5.4 montacuto 23.7 -6.2 32.1 9.7 9.0 -31.2 41.5 21.3 nurra sassarese romangia 138.8 4.0 33.5 4.3 8.2 -47.7 13.4 -1.5 planargia montiferru 39.8 -3.2 31.3 15.3 6.8 -49.0 27.5 11.2 source: authors’ own elaboration on istat (2001a,b; 2012a,b,c,d); 2008 data are estimated. 9economy-wide impact of a biomass power plant quantity of biomass attributed to each district and that of oilseeds obtainable by cultivating cardoon, which was estimated as 11% of biomass (table 2). the area to be used for the cultivation of cardoon can be indirectly estimated by dividing planned production by average productivity levels. it amounts to over 22 thousand hectares, of which about 70% concentrate in the districts of nurra sassarese romangia, mejlogu and montacuto. table 2. area and production of cardoon in the local districts involved in the project, sardinia, italy. district area production biomass oilseeds ha % tons % tons % anglona 1,482.2 6.7 17,340 7.0 1,907 7.0 gallura 750.4 3.4 7,163 2.9 788 2.9 goceano 1,066.3 4.8 10,732 4.3 1,181 4.3 marghine barbagia 2,163.3 9.8 21,175 8.5 2,329 8.5 mejlogu 4,741.9 21.5 54,762 22.1 6,024 22.1 montacuto 3,544.3 16.1 35,786 14.4 3,936 14.4 nurra sassarese romangia 6,791.6 30.8 86,163 34.7 9,478 34.7 planargia montiferru 1,529.7 6.9 15,129 6.1 1,664 6.1 total 22,069.7 100.0 248,250 100.0 27,308 100.0 source: authors’ own elaboration. 3. methodology: impact evaluation 3.1 the multiregional closed i-o models the i-o methodology allows the assessment of the overall impact generated in an economy by a shock in the final demand (miller and blair, 2009). it is commonly used to assess the socio-economic benefits produced by a given agricultural project or investment (bonfiglio and esposti, 2014) and can be very useful whenever the objective is to evaluate the overall impacts generated by linkages along supply chains. the application of this methodology in the field of bioenergy is not new. see for instance english et al. (2004), madlener and koller (2007), perez-verdin et al. (2008), herreras martínez et al. (2013) and wang et al. (2013). in this study, estimation of the impacts generated by the cardoon supply chain was made by using two closed 37-sector 9-region i-o models: a traditional demand-driven model and a mixed-variable one. the regions analysed are the eight districts where the cardoon will be cultivated in addition to the rest of sardinia. unlike the traditional model, the mixed-variable i-o model assesses the impact that a variation of output, rather than of final demand, produces on a given economy. this is possible by making the output of given sectors and the relevant final demands exogenous and endogenous to the model, respectively. in spite of some differences in terms of construction and a higher complexity associated with mixed-variable models, the mechanism through which the effects propagate through10 a. bonfiglio, r. esposti out the sectors is the same so making the results perfectly comparable (more simply, the relevant impacts can be added). the only difference lies in the nature of initial shock, which is the final demand in a traditional model while it is the output in a mixed-variable model. both models are closed with respect to the household sector. this allows us to estimate three kinds of effects: direct, indirect and induced effects. the former coincide with the initial variation of final demand (or output in the case of a mixed-variable model) whereas indirect effects derive from the existence of backward linkages among sectors. supposing an increase in the final demand (or output) of a given sector, these effects are those which are indirectly produced by the initial increase. this variation causes an increase in purchases from other sectors, which are therefore forced to adjust their level of production to a higher level of input demand. in addition, induced effects come from increases in labour income that translate into an increase in household consumption and, in turn, in the output of sectors which adjust to a higher level of final demand. finally, both models present the benefits of a multiregional approach, particularly the capability of capturing spatial effects among industries located in different regions, i.e. interregional spillover and feedback effects. the former are changes in exporting regions induced by regions that purchase inputs from outside to satisfy internal requirements, while the latter are those effects that return to the importing regions since they can also be exporting regions for others. for instance, the region that has been initially involved by a variation of final demand (or output) could purchase inputs from another region. this latter could increase the level of production to satisfy external requirements. this increase is part of spillover effects. moreover, the exporting region could in turn purchase inputs from the importing region. the change in output that this brings about in the importing region represents a feedback effect. since the traditional i-o model is well known in literature, here we shortly present only the mixed-variable one. consider an economy made by two regions (r, t) and three sectors (i,j = 1, 2, 3), of which two are intermediate sectors and one is an institutional sector represented by households. now suppose that we want to estimate the impact of an output variation in sector 1 of both regions. the model can be formulated as follows: y1 1 x2 1 y1 2 x2 2 y ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ =m−1n x1 1 y2 1 x1 2 y2 2 yx ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ with m = −1 −a12 11 0 −a12 12 −k1 1 0 1− a22 11( ) 0 −a22 11 −k2 1 0 −a12 21 −1 −a12 22 −k1 2 0 −a22 21 0 1− a22 22( ) −k2 2 0 −h2 1 0 −h2 2 1− h3( ) ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ n = − 1− a11 11( ) 0 a11 12 0 0 a21 11 1 a21 11 0 0 a11 21 0 − 1− a11 22( ) 0 0 a21 21 0 a21 22 1 0 h1 1 0 h1 2 0 1 ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎧ ⎨ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪⎪ ⎩ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ (1) 11economy-wide impact of a biomass power plant where yi r indicates the final demand of sector i in region r, xi r the output of sector i in region r, y is the total labour income and yx the labour income paid by other institutions for services offered by households. looking at matrices m and n, aij rr a regional input coefficient, i.e. products of sector i in region r purchased as inputs from sector j in region r and aij rt an interregional trade coefficient expressing products of sector i in region r purchased as inputs from sector j in region t. in other words, it represents exports from sector i in region r to sector j in region t, or rather imports of sector j in region t from sector i in region r. yet, hi r is a labour income coefficient, expressing the share of labour income on sector i’s output and ki r is a consumption coefficient, expressing the share of income paid by households for purchasing commodity i produced in region r. to determine the output impact on the overall economy generated by a variation of output of sector 1 in both regions δx1 1 and δx1 2 the matrix m n1− is multiplied by the vector δx1 1 ,0,δx1 2 ,0,0⎡⎣ ⎤⎦ −1 . to calculate the impact in terms of value added, model (1) must be modified as follows: y1 1 v2 1 y1 2 v2 2 v3 ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ = mv̂-1( )-1 n x1 1 y2 1 x1 2 y2 2 yx ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ (2) where vi r is value added of sector i in region r, v3 equals value added received by households for services offered to households themselves (domestic services), v 1,v ,1,v ,v2 1 2 2 3= v i r is value added of sector i in region r per output unit and v3 is value added received by households for domestic services per labour income unit. note that the model does not give information on variation of value added in sector 1 whose output is exogenous. to find the corresponding change in value added, the following formula, based on a linear relationship between output and value added, is applied: δv1 r = δx1 r v1 r . analogously, for assessing the impact in terms of employment, model (1) becomes: y1 1 e2 1 y1 2 e2 2 e3 ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ = mê-1( )-1 n x1 1 y2 1 x1 2 y2 2 yx ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ (3) where ei r is employment of sector i in region r, e3 equals employment in the household sector for services offered to households themselves (domestic services), e 1,e ,1,e ,e2 1 2 2 3= ei r is the employment of sector i in region r per output unit and e3 measures employ12 a. bonfiglio, r. esposti ment in the household sector for domestic services per labour income unit. like income model, even employment model does not give information about sector 1 whose output is exogenous. to find the relevant change in employment the following formula is applied: δe1 r = δx1 r e1 r . 3.2 data and regionalisation procedure the starting point for the construction of the multiregional i-o models is a 2008 37-sector i-o table of sardinia (italy) produced by irpet (regional institute of economic planning of the tuscany).1 the table, evaluated at basic prices, was first aggregated into 35 sectors to guarantee correspondence with sectoral and territorial detail of the available employment data.2 next, it was disaggregated into the territories under study using a three-stage regionalisation procedure (bonfiglio, 2006). the first stage consists in applying a location quotient to estimate regional input coefficients3 and total imports of each region from the rest of sardinia. we used the augmented flegg et al. location quotient4 (aflq) (flegg and webber, 2000) since it shows to be superior in comparison with other location-quotient-based techniques. its superiority is mainly related to its capability to minimise the differences between estimated and true multipliers and thus to estimate more reliable impacts (bonfiglio and chelli, 2008; bonfiglio, 2009). this is due to its specific properties. in fact, it has been designed to solve an important drawback of traditional location quotients, i.e. the overestimation of i-o coefficients (and so underestimation of regional imports), through better modelling of regional and sectoral specialisation. a more reliable estimation of regional imports is particularly important in multiregional i-o models where trade relationships between regions play a significant role. the aflq was calculated using 2008 estimated employment data at a municipal level (then aggregated at a district level), distinguished by sector. given the lack of official information on employment disaggregated at municipal and sector levels for the year 2008 (i.e. the reference year of the i-o table), data on employment, with the exception of agriculture, have been estimated using 2001 data on employees of local units coming from the last available census (istat, 2001a). data on non-agricultural workers were then updated to reflect 2001-2008 changes in levels of working-age resident population at a municipal 1 in italy, there are no official regional i-o tables. for this, they are generally derived starting from the national i-o table published by istat and by integrating all existing superior data at a regional level to increase their reliability. as to sardinia, it was decided to use an i-o table built by irpet, which has a long and recognized experience in the construction of i-o tables at both regional and multi-regional levels, which are perfectly consistent with the system of istat/eurostat territorial economic accounts. 2 specifically, sector “manufacture of chemicals and chemical products” has been aggregated with sector “production of pharmaceutical, chemical-medicinal and botanical products” and sector “publishing, audiovisual and broadcasting activities” with sector “telecommunications”. 3 the location quotient was directly applied to sectoral flows, which have been preliminarily regionalised using employment ratios at a sectoral level. 4 the aflq is based on the use of a parameter that allows reduction in the tendency of i-o coefficients, derived through location quotients, to overestimate local production. in this study, we chose a value of 0.36, since it has the highest probability of being the most reliable in different regional contexts (bonfiglio, 2009). 13economy-wide impact of a biomass power plant level.5 about the agricultural sector, the 2001 figures have been replaced with information on family and continuous non-family employees resulting from the 2010 census on agriculture (istat, 2012b). employees were finally adjusted by a nonlinear optimization technique6 using 2008 data on total employees related to 45 labour local systems (istat, 2012d) and employees by sector resulting from regional economic accounts (istat, 2012c) as constraints.7 the second stage uses a gravity model to allocate total imports among regions. the model assumes that the probability of attraction exerted by a given region is an indirect function of the distance from other regions8 and a direct function of its ability to attract flows of goods and services, approximated by its sectoral size in terms of employment. the first two phases are repeated recursively for all districts under consideration then obtaining a preliminary version of a (35-sector) x (9-region) i-o table of sardinia. the last stage consists in inserting all available superior data and applying a nonlinear optimization technique to remove possible discrepancies with the starting i-o table. this technique was applied to the entire multiregional table including the quadrants of primary payments, distinguished in value added and other payments, and of final demand, disaggregated into household consumption and other components. as regards primary payments, value added at a municipal level has been preliminarily estimated using employment coefficients (ratios between municipal workers and regional workers operating in given sectors) adjusted using the simple location quotient. estimates were then adjusted by applying a nonlinear optimization technique constrained to information on value added by sector resulting from the regional i-o table and superior data on value added at a provincial level distinguished by macro-sectors (istat, 2011). the value added by municipality and by sector thus obtained was then aggregated by district and added into the table as superior data. labour income, which is a part of value added, was estimated starting from regional economic accounts, since the relevant information is missing in the regional i-o table. specifically, it was calculated by multiplying sectoral value added reported in the regional table by the ratio between labour income and value added resulting from economic accounts. it was therefore assumed that the incidence of labour income on value added is the same in all districts. the other components of value added were calculated as a residual. to estimate the remaining payments, the ratio between sectoral value added by district and total sectoral value added was used. regarding the quadrant of final demand, household consumption was initially derived by multiplying value added by the ratio between household consumption and value add5 as for the year 2008, we used istat pre-census data on resident population in municipalities. 6 non-linear optimization procedures used in this study adopt a cross-entropy method (golan et al., 1994), which is based on the minimization of the entropic distance between the initial matrix of estimates and the final matrix under given constraints in terms of accountancy and related to availability of superior data. 7 2008 istat data on employment of sardinian local labour systems slightly differ from those resulting from regional economic accounts. the difference is about 9 thousand employees in less. to ensure consistency between the two information sources and thus ensure convergence of the optimization procedure, sectoral data from economic accounts have been adjusted to make the total coincide with that of local labour systems. 8 the distances between districts used in the gravity model were calculated as geodesic distances between centres of each area. we adopted geodesic distances rather than other commonly used distances such as simple straight lines in order to take account more appropriately of higher distances due to shape of the earth. for the calculation of distances, we used geographic data at a municipal level, which come from the enea archive (enea, 2002). 14 a. bonfiglio, r. esposti ed by sector resulting from the regional i-o table, while the other components of final demand were estimated using value added ratios. the result of this optimization procedure is a balanced table, consistent with the regional one, from which sectoral outputs by district can be easily derived by adding up columns. outputs were then used to convert intersectoral flows into input and trade coefficients. in order to estimate both the impacts related to the production of biomass for the power plant and those associated with the production and use of oilseeds, we also added two new sub-sectors connected with the cultivation of cardoon in each district involved: the biomass and the oilseeds sectors. therefore, the final multiregional i-o table is a 37-sector table. the addition of new sectors requires the computation of the relevant coefficients (regional input coefficients, interregional trade coefficients, employment, labour income, value added and household consumption coefficients). in this regard, we used the balance sheet for the whole production of cardoon provided by the project and distinguished by territorial area. it provides data about revenues from the sale of oilseeds and biomass, direct payments, purchase of seeds, fertilisers, chemicals, mechanical and transport services, financial costs and profits. regional input coefficients have been derived by dividing purchases of inputs by outputs, which equal total revenues. value added coefficients have been calculated by dividing the sum of financial costs and profits by outputs. as the balance sheet does not distinguish between the two products, biomass and oilseeds, we estimated the coefficients of these two sub-sectors by applying output (revenues) ratios to the coefficients of the cardoon. moreover, it was assumed that the inputs necessary for the cultivation of cardoon are purchased locally within each district. this implies that interregional trade coefficients (extra-local purchases or sales) of these new sectors in each district are null.9 the relevant employment and labour income coefficients are zero because the project assumes that the cultivation of cardoon will rely on agricultural contractors. impacts in terms of labour income and employment related to the cultivation of cardoon are thus produced indirectly through purchases of mechanical services from the agricultural sector. also the corresponding coefficients of household consumption are null since the project does not contemplate the direct sale of products from the cultivation of cardoon to consumers. as regards oilseeds sub-sector, the project establishes that the relevant output is entirely used to produce bio-polymers. we modelled this activity by including the expected intermediate sales from oilseeds sub-sector of each district to sector “rubber and plastics” located in the district of nurra sassarese romangia within the multiregional i-o table. 3.3 alternative scenarios the hypothesis that maximizes impact (corresponding to the full-additional scenario) assumes that the cardoon will be cultivated on unutilized and suitable land and therefore 9 in the cases where a sector producing inputs necessary for the cultivation of cardoon was not present in a district, it was supposed that inputs are purchased from an existing sector of the closest district. in these cases, interregional trade coefficients are not null. 15economy-wide impact of a biomass power plant will not cause any substitution of other crops. in this case, the benefits of the cardoon supply chain will be full since the cardoon adds to existing agriculture. however, this hypothesis is hardly tenable for most local districts. in fact, according to 2010 agricultural census data, only in the district of gallura there is potentially available area that can be used for cultivating cardoon (that is, unutilized agricultural area plus set-aside land) (table 3). in all other districts, in order to assure adequate provisions to the biomass power plant, it would be necessary to replace existing crops. on average, about 33% of the area that is necessary for the cardoon should be subtracted from other uses. table 3. available unutilized agricultural area and required cardoon area in the various districts of sardinia (italy), 2010. area available area (ha) utilized agricultural area to be replaced unutilized agricultural area set-aside total ha % of area for cardoon anglona 1,176.22 0 1,176.22 305.99 20.6 gallura 2,230.51 757.52 2,988.03 0 0.0 goceano 411.93 74.73 486.66 579.68 54.4 marghine barbagia 988.05 152.68 1,140.73 1,022.52 47.3 mejlogu 2,479.14 0 2,479.14 2,262.77 47.7 montacuto 1,083.76 587.48 1,671.24 1,873.06 52.8 nurra sassarese romangia 4,866.44 1,603.49 6,469.93 321.68 4.7 planargia montiferru 561.27 80.28 641.55 888.16 58.1 total 13,797.32 3,256.18 17,053.50 7,253.90 32.9 source: authors’ own elaboration on istat (2012b). the full-additional scenario supposes that when the biomass power plant will be operative, after a limited initial period where alternative biomass will be imported, all necessary and suitable land for feeding the plant will be available owing to progressive release of abandoned land. it is evident that this process of progressive release might not be complete when the plant will become operational, thus making substitution necessary. in order for substitution to occur, however, there should be convenience in cultivating cardoon. one possible reason could be related to higher profitability compared with other crops. however, such convenience can be hardly represented within the adopted approach, since, in the i-o models we used, agricultural sector aggregates several activities. moreover, prices are included within i-o flows and are supposed to be constant. nonetheless, there could be other important reasons of convenience in cultivating cardoon. one relates to supply contracts, which are likely to be stipulated with farmers in order to assure constant provision to the industrial site of porto torres. these contracts will fix prices and quantities over given periods, so reducing the risk of market volatility. this risk reduction could induce farmers to replace even crops that are averagely more profitable but par16 a. bonfiglio, r. esposti ticularly subject to price volatility. convenience could also be explained with the relative simplicity in cultivating cardoon. in fact, several operations can be easily mechanized and can be attributed to contractors specialized in offering mechanical services. this process of productive deactivation could be favoured by progressive ageing of farmers operating in the area under study. under these hypotheses, substitution can be effectively modelled by the approach adopted (see section 3.4). crop substitution can have compensating effects with respect to the gross benefits generated by the cardoon supply chain. in principle, these effects can even offset all gross benefits and, therefore, they have to be carefully taken into account in performing impact evaluation. to measure both gross and net impacts associated with a different degree of crop substitution, three scenarios were simulated: 1. full-additional scenario (no substitution): it is based on the hypothesis that the cardoon adds to existing agriculture by occupying unutilized land suitable area. 2. partial-substitution scenario: it assumes that a part of the area, which is necessary for the cultivation of cardoon, will be taken from already used agricultural area. this represents the most realistic hypothesis. 3. full-substitution scenario: it supposes that all the area to be allocated to cardoon comes from currently used agricultural area. simulating different levels of substitutions also allows the identification of a breakeven point, i.e. that level of substitution at which the benefits of the cardoon supply chain are offset by the implicit (or opportunity) costs generated by the replacement of agricultural activities. for lower costs there are still benefits whereas for higher costs there will be increasing losses. a graphical illustration of these scenarios may be helpful to clarify the key differences among them (figure 2). this representation only considers value added (va) impacts and assumes that the value added per hectare associated with the cardoon is lower than that related to other agricultural activities. if taa (total agricultural area) is higher than uaa (utilized agricultural area) and the available area for new activities (taa – uaa) is larger than uac (utilized area for cardoon), we have the full-additional scenario where all the va impacts generated by the cardoon (represented by a solid area) are additional and correspond to the gross (net) social benefit generated by the project. a second and more general case occurs whenever taa-uaa>0 but uac>(taauaa), identifying the so-called partial-substitution scenario. in this case, the new activity generates an economy-wide impact in terms of va that is partly additional and, for the remaining part, competitive with the benefits produced by the agricultural activities replaced by the cardoon, so generating a loss that compensates gross benefits. the net social benefit can thus be either positive or negative. the larger the competitive land (i.e. the greater the area subtracted by the cardoon and measured by uaa−(taa-uac)), the lower the net social benefit. a special case occurs when the additional va equals the lost social va thus making the net social benefit equal to zero. the break-even point measures that degree of land substitution that makes this special case occur. a third case arises whenever taa equals uaa. in such a circumstance, all the uac replaces units of uaa and we have the full-substitution scenario where there might be a social loss produced by the replacement of existing agricultural activities with the cardoon. 17economy-wide impact of a biomass power plant figure 2. diagram describing the three scenarios adopted. 1    2   uaa uac taa uva gross (net)social benefits legend: uvac = va of cardoon per hectare uvao = va of other agricultural activities per hectare taa = total agricultural area (ha) uaa = utilized agricultural area (ha) uac = utilized area for cardoon (the area in hectares is measured by segment uac-taa) uvao uvac uva 3. full-substitution scenario uvao uvac uaa=taa uac social loss 1. full-additional scenario ha ha 2. partial-substitution scenario uvao uvac taa uac uaa social loss uva ha gross social benefits € € € the level of substitution simulated in these scenarios is measured as a percentage of reduction in the output of the agricultural sector, corresponding to the value of the uaa potentially allocable for the cultivation of cardoon in replacement of existing activities. reduction in output was estimated by multiplying the hectares used for the production of cardoon by the average unit value of agricultural output. the latter, which is a measure of land productivity (in value) and differs in the various districts, was obtained by dividing agricultural output, deriving from the multiregional i-o table, by the uaa surveyed in 2010. 3.1 modelling impacts estimation of impacts in terms of value added and employment was made by applying the two multiregional i-o models described previously. under the full-additional scenario, we used a traditional demand-driven model to assess the impact produced by requirements for inputs (such as biomass, machinery maintenance, water, waste disposal, transport, etc.) that are necessary to guarantee the functioning of the plant. it is assumed that the power plant purchases inputs only from the sectors of the district where it operates (nurra sassarese romangia), except for purchases of biomass, which, instead, comes from various districts. furthermore, it was supposed that only a share of total fixed and 18 a. bonfiglio, r. esposti variable expenses made by the power plant concern the local economy and thus produce local impacts. project expenses addressed to local sectors and necessary for managing the power plant were allocated to corresponding i-o sectors using istat ateco 2007 classification (istat, 2009). they were modelled as positive changes in final demand of the sectors involved. we also adopted a mixed-variable model to evaluate the impact generated by the production and sale of oilseeds, modelled as a positive change in the output of oilseeds sub-sectors related to the cultivation of cardoon in each district. total impact of the cardoon supply chain is thus the sum of these two impacts. under substitution-based scenarios, we used the mixed-variable model to estimate the effects of a decrease in agricultural output corresponding to the level of substitution. this impact offsets that estimated under the full-additional scenario. the parameters used and related to agriculture are average values represented in the i-o table. this hypothesis seems consistent since how farmers will react in terms of land allocation is unknown. as already remarked, they are likely to substitute worse activities in terms of profitability, provided that there will be substitution, but could even replace more profitable activities for reasons related, for example, to market volatility and risk aversion, technical simplicity and ageing. moreover, the activities replaced could be more or less integrated with the rest of economy, thus having different i-o coefficients. therefore, the average coefficients represented by the i-o table allow us to take account of these extremes avoiding unrealistic hypotheses about the kind of agricultural activities that could be replaced.10 in this way, however, the effects generated by substitution and estimated by the model could overestimate the actual impact; hence, they should be considered as an upper limit of potential effects induced by substitution. 4. results the impacts induced by the cardoon supply chain on the local economy under different scenarios significantly differ across districts. with reference to value added impacts, in the case of no substitution, the project generates an annual increase of 20.4 mio €, equivalent to an average of 0.07% of 2008 value added of sardinia (table 4). most impact, however, is absorbed by the district of nurra sassarese romangia. this is because, in this area, there are most project operations related to the functioning of the biomass power plant in addition to the cultivation of cardoon. the other districts are instead involved only by the provision of biomass and oilseeds. nevertheless, this project can be particularly important for less developed areas, such as mejlogu and montacuto, as indicated by the relevant and relatively higher shares of local value added generated by the cardoon supply chain. under the hypothesis of partial substitution, about 30% of the benefits generated in terms of value added would be lost. goceano is the district with the highest decrease in benefits. in this district, the loss of value added equals 27% of initial benefits produced by the cardoon. all the other territories would continue to benefit from the cardoon, par10 in any case, within an i-o model, the replacement of agricultural activities having average characteristics can also be justified economically by assuming redistribution of the components of value added due to a reduction in profits, which become lower than the profits related to the cardoon, and a corresponding increase in other components such as labour costs, net taxes and depreciation. in this way, coefficients remain unaltered but there are incentives to substitution. 19economy-wide impact of a biomass power plant ticularly gallura and nurra sassarese romangia, owing to a higher availability of unutilized land. it is interesting to note that gallura would lose about 3% of benefits although there is no substitution in this district. this occurs for the spatial interdependence among regions: a decrease in production in a region can generate negative effects in other regions because of reciprocal sectoral linkages. table 4. impacts produced by the cardoon supply chain in terms of value added, sardinia, italy. area benefits per scenario break-even point (% of hectares replaced) substitution elasticity of impact full additional (000 €) % va (2008) partial substitution (% of full impact) full substitution (% of full impact) anglona 856.1 0.21 77.8 -7.5 93.0 1.075 gallura 463.9 0.08 97.2 -34.5 74.4 1.345 goceano 494.5 0.27 -26.9 -134.2 42.7 2.342 marghine barbagia 971.6 0.24 70.0 35.3 154.6 0.647 mejlogu 2,508.7 0.64 15.7 -78.0 56.2 1.780 montacuto 1,602.0 0.40 30.4 -32.8 75.3 1.328 nurra sassarese romangia 9,166.0 0.22 93.9 4.0 104.1 0.960 planargia montiferru 653.4 0.28 6.6 -62.0 61.7 1.620 rest of sardinia 3,766.1 0.02 90.8 69.5 327.9 0.305 sardinia 20,482.3 0.07 71.4 -2.2 97.8 1.022 source: authors’ own elaboration. under the full-substitution scenario, the value added impact generated by the cardoon supply chain would be neutralised, also producing a loss that is equivalent to 2% of initial benefits. most districts would be penalized. this is particularly true for goceano, where the loss of value added is 134% of the initial benefits produced by the cardoon in this district. the areas that instead would maintain benefits are marghine barbagia, which preserves 35% of benefits, nurra sassarese romangia, which restrains 4% of positive impacts, and the rest of sardinia, with about 70% of benefits.11 the break-even point indicates that the overall benefits of the cardoon supply chain would be neutralised in correspondence to about 98% of substitution.12 in other words, only if in each district 98% of the hectares that are necessary for the cultivation of cardoon were taken from already cultivated land, the benefits of the cardoon supply chain would be eroded completely. for lower values, there will be net benefits. 11 the capability of the rest of sardinia to maintain, in the case of substitution of agricultural activities, a high percentage of impacts produced by the cardoon supply chain is related to the fact that this area is only indirectly affected by the substitution that happens in the districts. 12 since there is proportionality between levels of substitution and reduction in value added/employment (this can be easily observed in figures 3 and 4), the break-even point can be calculated by dividing the impact estimated under the full-additional scenario by the reduction in value added/employment associated with a decrease by 1% in agricultural output due to substitution (i.e. slopes of the straight lines represented in figures 3 and 4). 20 a. bonfiglio, r. esposti at a district level, it can be observed that the break-even point is mostly under 100%. as expected, it is higher in those cases where there are higher net benefits. in marghine barbagia, nurra sassarese romangia and the rest of sardinia the break-even point is higher than 100%. this apparently odd result is an indicator of how far we are from offsetting the benefits and, therefore, of how large these benefits are. for instance, a value of 154.6% (i.e. the result obtained for marghine barbagia) means that if the hectares replaced in each district were 54.6% larger than the hectares that are actually required for cultivating cardoon, local benefits would be eventually offset. this is consistent with the result that, in those districts, a part of benefits remains even under the full-substitution scenario. substitution effects and the break-even point can also be displayed graphically. it can be noted that, as the level of substitution increases, the area of net benefits tends to shrink proportionally (figure 3). the critical area (that is, that degree of substitution at which benefits are entirely offset and there are losses) is particularly small. in fact, the break-even point is collocated almost at the end of the straight line that displays the reduction in value added due to increasing substitution. the so-called “substitution elasticity of impact” indicates the percentage of reduction in benefits due to 1% of substitution of agricultural activities (table 4).13 it is also a measure of the degree of sensitivity 13 this elasticity is calculated by dividing the loss of benefits induced by 1% of substitution by the benefits generated by the full-additional scenario (and then multiplied by 100). figure 3. benefits and losses in terms of value added generated by the cardoon supply chain in sardinia, italy. 0 5 10 15 20 25 0 10 20 30 40 50 60 70 80 90 100 m io € % of substitution losses for substitution full-additional scenario benefits break-even point area of net benefits source: authors’ own elaboration. 21economy-wide impact of a biomass power plant of the economy to substitution. the higher this indicator, the higher the level of sensitivity of the economy to possible replacement of agricultural activities for the cardoon. as regards value added, the substitution elasticity of impact for all sardinia is 1.022. this value indicates that per each percentage point of reduction in agricultural output due to substitution (or per each 1% of hectares that are replaced), the overall value added generated by the cardoon supply chain diminishes by 1.022%. in the case of full substitution, the reduction in benefits would thus amount to 102.2%. this means that the benefits obtained under the full-additional scenario are entirely offset (100%) and an additional loss (2.2% of those benefits) is also generated. the substitution elasticity of impact ranges from 0.305, in the rest of sardinia, to 2.342 in goceano, which therefore reveals to be the most sensitive to possible substitution. with reference to employment, full-additional scenario generates an increase in employment of about 400 employees, which corresponds to 0.06% of total employment (table 5). also in this case, most impact concentrates on the district of nurra sassarese romangia, although benefits, relatively to local economy, are again higher and more significant in less developed areas. on the contrary, substitution causes higher negative impacts than those recorded for value added. this can be explained by relatively higher employment multipliers (lower productivity) and relatively lower value added multipliers (low profitability) that characterize agriculture in the districts considered. in fact, under partial substitution, 40% of overall benefits are lost or rather about 160 jobs are no more created. the areas that show the highest employment impact are in particular gallura, nurra sassarese romangia, the rest of sardinia and, to a lower extent, anglona and marghine barbagia with just 18% of the benefits estimated. the other areas, especially goceano and planargia montiferru, experience losses. table 5. impacts produced by the cardoon supply chain in terms of employment, sardinia, italy. area benefits per scenario break-even point (% of hectares replaced) substitution elasticity of impact full additional (employees) % empl. (2008) partial substitution (% of full impact) full substitution (% of full impact) anglona 11.4 0.17 51.5 -135.0 42.6 2.350 gallura 8.5 0.06 93.8 -104.5 48.9 2.045 goceano 5.4 0.15 -149.4 -361.0 21.7 4.610 marghine barbagia 20.8 0.21 18.3 -74.5 57.3 1.745 mejlogu 24.6 0.38 -74.2 -267.2 27.2 3.672 montacuto 20.1 0.27 -31.0 -150.2 40.0 2.502 nurra sassarese romangia 212.8 0.25 92.3 -37.8 72.6 1.378 planargia montiferru 12.6 0.25 -113.6 -269.6 27.1 3.696 rest of sardinia 75.1 0.02 90.6 68.6 318.1 0.314 sardinia 391.3 0.06 60.0 -55.8 64.2 1.558 source: authors’ own elaboration. 22 a. bonfiglio, r. esposti in the case of full substitution, on the contrary, there would be a net loss of more than 200 employees. all areas would be penalized except for the rest of sardina that will maintain about 69% of employment impact. in line with these results, the breakeven point is 64%, by far lower than that of value added. it indicates that it is sufficient to replace less than two-thirds of the agricultural area necessary for cultivating cardoon to see the benefits of the cardoon supply chain disappear. in all districts, the break-even point is under 50%, with the exception of marghine barbagia (57%) and nurra sassarese romangia (73%). graphically, the break-even point related to sardinia is collocated about in the middle of the straight line, which describes the decrease in employment caused by increasing substitution. for this, two separate areas are clearly identified: an area of net benefits (to the left of the break-even point) and a more limited area of net losses (to the opposite side) (figure 4). finally, as expected, the substitution elasticity of impact is higher than that of value added and equals 1.558. it varies from 0.314, in the rest of sardinia, to 4.610 in goceano (table 5). these results can be very helpful for policy makers in evaluating more correctly the socio-economic implications of the construction of a biomass power plant in sardinia. firstly, they measure possible negative effects in addition to potential positive impacts for the economy. in fact, they indicate to what extent crop substitution induced by the introduction of the cardoon in sardinia will produce compensating effects with respect to the figure 4. benefits and losses in terms of employment generated by the cardoon supply chain in sardinia, italy. 0 100 200 300 400 500 600 700 0 10 20 30 40 50 60 70 80 90 100 e m pl oy ee s % of substitution losses for substitution full-additional scenario benefits break-even point area of net benefits area of net losses source: authors’ own elaboration. 23economy-wide impact of a biomass power plant gross benefits generated by the cardoon supply chain. compensation in terms of value added may vary from 30% to 100% with possible but restrained reductions in the current levels of value added, while that related to employment may go from 40% to full offset and a relatively significant reduction. secondly, they give important information about the sensitivity of different areas to potential substitution effects. in other words, they assist policy makers in identifying territorial contexts where possible negative impacts induced by the introduction of the cardoon could be more significant locally. from this point of view, goceano as well as planargia montiferru and mejlogu are the areas that would suffer to a greater extent from the replacement of existing agricultural activities with the cardoon in terms of both value added and employment. 5. concluding remarks this study assessed the economy-wide impact produced by the supply chains, in particular that of the cardoon (biomass and oilseeds), activated by a biomass power plant within the local economy of sardinia, an italian region. for this aim, two multiregional closed i-o models were adopted. the areas considered are eight districts where the cultivation of cardoon will be introduced. three different scenarios were analysed in relation to the degree of substitution of existing agricultural activities: full-additional, partial and full-substitution scenarios. under the hypothesis of no substitution, results show positive and locally significant impacts in terms of value added and employment, though limited in comparison with the overall economy size. they are the sum of direct, indirect and induced effects generated by intersectoral and interspatial linkages of the sardinia economy. in addition, findings show that in the case of a partial substitution, which represents the most realistic scenario, most benefits will be maintained especially in terms of value added. with reference to employment, however, negative effects would be higher. full substitution would exacerbate these impacts, by producing net losses in most districts in terms of value added, and in all districts in terms of employment. from a territorial standpoint, results also show that there are specific areas that are more sensitive to negative effects induced by substitution of existing agricultural activities with the cardoon. the present analysis offers valuable policy implications since it gives objective and empirical support to public choices concerning the suitability or desirability of private investments and, eventually, the need and the extent of a public contribution in this respect. in particular, this study provides policy makers with useful information to assess the impact that the implementation of a biomass power plant can have on the economy of sardinia. in fact, it takes into account not only its potential benefits but also those opportunity costs (substitution effects) that this project can generate. nonetheless, results should be taken with caution. firstly, they can be affected by the data used, in particular the coefficients of the adopted multi-regional i-o models. the latter represent estimates, as far as they can be representative, of real economic flows recorded in a given period and may therefore change over time. secondly, and more importantly here, results depend on the approach we adopted to compute economy-wide impacts, which suffers from some limitations. in particular, two aspects should be stressed. a first aspect is concerned with the hypothesis that the cardoon will replace “average” agricultural 24 a. bonfiglio, r. esposti activities, represented, within the i-o model, by coefficients of the entire agricultural sector, which aggregates activities having different production characteristics. this assumption may overestimate negative impacts induced by substitution. a second aspect is related to the hypothesis that the introduction of the cardoon and possible substitution of other agricultural activities do not generate any effect on prices as the i-o approach is based on constant prices. this assumption may be consistent only with small changes within the economy and, therefore, with small impacts generated by the new investment. although overall results seem to confirm this, there is however the possibility that effects are locally overestimated since results show relatively significant impacts related to some specific areas. finally, it is also worth reminding that an important concern about this kind of power plants and, therefore, a major challenge for policy makers is about their negative social and environmental implications in addition to overall socio-economic impact. from this point of view, the adopted approach is not helpful unless it is being appropriately adapted and extended towards an integrated assessment of all impacts, including the environmental ones, generated by this kind of investments. further research is therefore needed to cope with these data and methodological limitations by suggesting suitable extensions and modifications of the approach here adopted. acknowledgments although this paper is common to both authors, the authorship can be attributed as follows: sections 3 and 4 to bonfiglio; sections 1, 2 and 5 to esposti. this paper develops a study commissioned and funded by an italian company involved in the energy sector. the authors wish to acknowledge the financial and scientific support given by the company that made the present analysis possible. they also wish to thank two anonymous referees and the editor for their helpful comments and suggestions on an earlier version of the paper. the usual disclaimers apply. references abdoulmoumine, n., kulkarni, a., adhikari, s., taylor, s. and loewenstein, e. 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(2013). a simulation analysis of the introduction of an environmental tax to develop biomass power technology in china. journal of sustainable development 6(1): 19-31. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(2): 131-149, 2013 heterogeneous preferences for water rights reforms among smallholder irrigators in south africa stijn speelman1,*, prakashan chellattan veettil2 1 department of agricultural economics, ghent university, belgium 2 department of environmental and resource economics, institute of economic growth, delhi, india abstract. in the light of growing water scarcity appropriate institutional arrangements are needed to complement technical interventions, in order to ensure more efficient use and allocation of water in agriculture. a theoretically interesting institutional intervention is the installation or improvement of water rights, but the benefits of such intervention and their distribution are insufficiently researched. this paper contributes to the water rights literature by applying a state-of-the-art valuation method to a case study in south africa. using a latent class choice modelling approach the heterogeneity in the benefits generated by changes in water rights is investigated. two segments could be distinguished in the sample population. while one of the segments has a lot to gain from a water rights reform, benefits for the other seem rather limited. furthermore they clearly differ in preference for specific improvements. such considerations should be taken into account in policy design. keywords. choice modelling; latent class model; water rights reforms; smallholder irrigators; south-africa jel codes. q15, q58 1. introduction increasing population growth, economic activity and development pose increasing stress on water resources worldwide. in this context, understanding is growing that technical solutions alone are not sufficient to deal with this problem and that appropriate institutional arrangements are needed to complement the technical interventions and to ensure more efficient water use and allocation (kemper, 2001; brennan, 2002; bruns et al., 2005). water (use) rights, water pricing, laws regulating water use and enforcement mechanisms are examples of such institutional arrangements (kemper, 2001). the theoretical rationale for establishing water rights is based on arguments of efficiency. araral (2010) for example states that only when water rights are clearly defined, pareto optimal outcomes are possible and molle (2004) explains that clearly defined water rights will reduce uncertainty and conflicts. 1 corresponding author: stijn.speelman@ugent.be. 132 s. speelman , p.c. veettil in this study a choice experiment (ce) considering hypothetical water rights configurations was conducted for studying the gains associated to water right improvements in south africa. this approach is similar to the one taken in frija et al.(2008), speelman et al.(2010a,b) and veettil et al. (2011). the current study uses the same dataset as speelman et al. (2010a,b). nevertheless, speelman et al. (2010b) mainly focused on the average benefits of such reforms. some interaction terms were introduced in the rank ordered logit model to get a first insight into the effect of socio-economic variables, but the number of interactions that can be included is limited2. in speelman et al. (2010a) a different approach was therefore taken: the population was divided in a number of segments according to a number of socioeconomic variables. such segmentations allow to see the effect on all the attributes, however it is not shown whether the effects in the different segmentations reinforce or counter each other. as a consequence the potential to develop targeted policies is limited when using this approach. therefore in the current article the data from the choice experiment are analysed using a latent class modelling approach. this adds a focus on the heterogeneity in benefits between groups of respondents to the previous literature. understanding is not only gained on the aggregate or average economic value associated with changes in the water rights framework, but also on the distribution of the benefits among clearly identified groups of respondents (boxall and adamowicz, 2002). studying the potential of water right improvements for smallholders in south africa is relevant in the light of their poor production performance and of the weak cost recovery at smallholder irrigation schemes under a general context of water scarcity. moreover these smallholders consist of a heterogeneous group (van averbeke et al., 2011; denison and manona, 2007), justifying the focus on the heterogeneity in benefits. the aim of the study is therefore to reveal the economic value for smallholders of the benefits generated by improving the water rights framework and to show how these benefits are differing across beneficiaries. given the context described above, such information is highly relevant for policy-makers. the second section reviews the literature on the importance of water rights. in the third section the relevance of choosing the smallholder irrigation sector in south africa for this case study is explained by assessing the current water rights framework and the smallholder sector. the fourth section describes the different steps in the methodology, with an additional focus on the importance of accounting for heterogeneity. then the results are presented and discussed. finally conclusions are formulated, highlighting the policy relevance. 2. literature review from a new institutional economics perspective, water rights are seen as an institution that serves as a source of incentives for individual and group behaviour governing water use. they serve as a mechanism for avoiding externalities in the use of water and they generate incentives for efficient resource use and for avoiding depletion and overexploitation (narain, 2009). the way water rights are defined will structure the incentives and disincentives which members of society face in their decisions regarding water own2 as a consequence this approach only allows to see the effect of some socio-economic variables on some specific water right attributes, not on the entire set. 133preferences for water rights reforms among smallholder irrigators in south africa ership, use and transfer. moreover, a well-defined set of rules is necessary to allow market transactions to take place (qureshi et al., 2009). as indicated by challen (2000, 2002) and wichelns (2004), the potential inefficiency of water rights is linked to the transaction costs associated with the making of decisions over the use of water. in general ill-defined property rights create higher transaction costs (information search, negotiation, monitoring) and limit the value people assign to a resource (randall, 1978; ostrom, 2000; heltberg, 2002; linde-rahr, 2008). this confines the incentives for resource users to sustainably manage a resource (matthews, 2004; hodgon, 2006; yandle, 2007). the focus of empirical work on property rights has thus far mainly been on explaining the role and functioning of property rights over natural resources, and in part on their emergence. there is however a need for research that quantifies the benefits of improving rights (brennan, 2002; dinar and saleth, 2005; linde-rahr, 2008; irimie and essmann, 2009; araral, 2010) and that identifies how these benefits differ within the population. transaction costs theory offers an interesting framework to study policy changes (challen, 2002; crase et al., 2002). in contexts where respondents face policy related transaction costs, such as those originating from ill-defined water rights, choice modelling and standard contingent valuation surveys can be used to estimate willingness to pay to reduce transaction costs, in this way evaluating the institutional settings (mc cann et al.,2005). some applications of this approach to evaluate potential changes in a prevailing institutional structure were recently developed for the case of water rights (e.g., crase et al., 2002; herrera et al., 2004; frija et al., 2008; speelman et al., 2010a,b; veettil et al., 2011). 3. case study background 3.1. water rights in south africa in south africa the national water act (republic of south africa, 1998) abolished the previous system of water rights and entitlements, which were linked to the land rights. a new system of administrative limited-period and conditional authorizations to use water, called licenses, was installed (nieuwoudt, 2002). in the new system ownership of water is held by the state, implying that the authorizations only concern usufruct rights. to use water, a person must be issued with a licence by the department of water affairs (dwaf). this water use licence will specify the water user, the property or area where the water may be used, the specific use authorized, and in most instances conditions of use (republic of south africa, 1998). although the new water rights system is not yet made fully operational, several shortcomings have already been identified (tewari, 2009). while quantities will be specified in the water use license, they are not guaranteed or enforced (republic of south africa, 1998). in this way water supply is not perceived as reliable by farmers. furthermore louw and van schalkwyk (2002) and tewari (2009) have criticized the provisions made in the national water act regarding transferability. market forces are generally believed to ensure efficient allocation of water (bjornlund and mckay, 2002; nieuwoudt and armitage, 2004; zekri and easter, 2007; brooks and harris, 2008; tewari 2009). in south africa however a water management agency has the responsibility over the trade in water use 134 s. speelman , p.c. veettil authorizations. this administrative procedure3 proposed in the water act seems to create unnecessary transaction costs certainly for transfers occurring among irrigators within the same irrigation scheme. according to donohew (2009) the potential negative externalities of such transfers are limited. the administrative procedure might therefore limit the potential efficiency gains from water right transfers (shi, 2006; donohew, 2009; tewari, 2009). finally a five year review period of the licenses is foreseen. this should allow government to take timely measures to maintain the integrity of the water resources, to maintain a balance between available water and water requirements, or to accommodate changes in water use priorities (dwaf, 2004). backeberg (2006) and tewari (2009) explain that this short review period will have a negative effect on farmers’ investment decisions. because the conditions (for instance the volumes of abstractions) attached to licenses may change at each review, this gives farmers the impression that the licenses are insecure (nieuwoudt and armitage, 2004). 3.2. the south african smallholder irrigation sector smallholder irrigation is considered as an important rural development factor in south africa. it is believed to create employment opportunities, generate income and enhance food security (perret, 2002). nevertheless performance and economic success of the smallholder irrigation schemes have been very poor and fall far short of the expectations of planners, politicians, development agencies and the participants themselves (van averbeke et al., 2011). in a context of increasing water scarcity, the smallholder irrigation schemes suffer from poor cost recovery of water service costs (perret and geyser, 2007) and poor water use efficiency (yokwe, 2009). huge investments are made to rehabilitate existing schemes (perret and geyser, 2007). in these revitalization programmes there also is attention for institutional solutions (denison and manona, 2007). there are however inequalities and significant class-based differences among the households engaged in smallholder irrigation, generating different farming styles with divergent interests (denison and manona, 2007; cousins, 2010). it is thus important to take this heterogeneity into account in the formulation and evaluation of reform policies, because too much emphases on common interest will certainly lead to policy failures. 4. methodology and data 4.1. designing the choice experiment for the analysis of the benefits of improving the water rights system in south africa a choice experiment was developed. choice experiments are a survey-based technique for modelling preferences for goods, where goods are described in terms of their attributes and the level these take. respondents are presented with various descriptions of the good, 3 in the national water act it is stated that permanent transfers, constituting trade in water licenses, will be subject to all requirements for license applications. this means that the water management authority has to approve every transfer. one of the criteria that will be used in the evaluation is that a balance should be maintained between the interest of the parties involved in the trade and the general public interest (dwaf, 2004). 135preferences for water rights reforms among smallholder irrigators in south africa differentiated by the attribute levels and they are asked to select their preferred alternative. choice experiments enable to value multidimensional interventions in a system (hanley et al., 2001, burton et al., 2007; rigby et al., 2010). in our study, they will be used to determine willingness to pay (wtp) for improvements in the water rights system. these wtp values reflect the potential benefits obtained by water users from changes in the water rights system. because they are relying on the choice between a series of alternatives, instead of asking for respondents’ wtp explicitly, choice experiments are considered a reliable way of eliciting wtp values (hanley et al., 2001). the design of a choice experiment involves the specification of the attributes and their levels. based on literature review (louw and van schalkwyk, 2002; perret, 2002; nieuwoudt and armitage, 2004; backeberg, 2006; pott et al., 2009, tewari, 2009) and expert knowledge4, three water rights dimensions are chosen as attributes. these characteristics do not consist of operational-level rights5 but rather of so called “property rights dimensions”. yandle (2007) describes how these dimensions can be used to assess the quality of a property right. attenuation of the right with respect to one of these dimensions is expected to lead to a lower quality property right, reducing the value for the owner (crase and dollery, 2006). in our case “duration”, “transferability” and “quality of title” are considered. “duration” refers to the period of time for which the operational-level rights are guaranteed or the time until the rights regime is renegotiated. if duration increases, rights holders will have more incentives to use a resource sustainably (backeberg, 2006; yandle, 2007). “transferability” considers if transfers of water rights are allowed and which procedures are used for transfers. the “quality of the title” dimension describes the capacity of the title to adequately define the resource and how much of a resource rights holders may extract. this dimension is related to the reliability of the supply. theoretically it is expected that rights with longer duration, where transferability is least restricted and where supply is guaranteed will be preferred. to be able to economically value attribute changes, it is necessary to also include a pricing vehicle in the choice experiment. here water charge is included for this purpose. the next step in the specification of the attribute space is the stipulation of the attribute levels that will be used in the experiment. for duration two levels are included ‘5 years’ and ‘10 years’. for transferability the levels considered are ‘no transfer’; ‘agency based transfer’ and ‘market transfer’ and for “quality of the title”, two levels are used in this study: ’no guaranteed supply’ and ‘guaranteed supply’. finally three water charge levels are used: 0.06, 0.09 and 0.12 r/m³. a more elaborated discussion on the choice for these levels can be found in speelman et al. (2010a). table 1 gives an overview of the different attributes and their levels. after the selection of the attributes and the determination of the levels, the choice sets, which will be presented to the respondents, need to be constructed. in this case all possible combinations of four attributes, two with two different levels and two with three different levels would provide 36 possible water right definitions. this is called a full factorial design. clearly, it would not be feasible for respondents to choose among 36 profiles. therefore, as described in speelman et al. (2010a) a three stage procedure was used 4 the attributes were discussed with officials from the dwaf and the water research commission 5 operational level rights determine the actions a property rights holder must, may, or can not take with regard to a resource 136 s. speelman , p.c. veettil generating three blocks of three choice sets each containing four choice options. finally a graphical representation was used for the attributes and their levels because it was expected that part of the respondent population would be illiterate. 4.2. importance of accounting for heterogeneity farms are heterogeneous with regard to a range of factors like input endowments, ownership structure, location, farm management and crop mix. as a result policy interventions will have differential effects on them. this will be reflected in differences in their preferences and consequently also in their behaviour. agent-based models specifically try to capture this heterogeneity (straton et al., 2009), but also in choice models acknowledging this is very important. understanding the extent and form of the heterogeneity will promote usefulness of the results for policy formulation (ruto et al., 2008). a proper representation of heterogeneity improves the insight in the factors underlying respondent behaviour and willingness to pay, and how the benefits and costs of policies are distributed across beneficiaries (colombo et al., 2009). furthermore researchers have found that when heterogeneous preferences are not properly accounted for, valuable information is discarded and inconsistent estimates and biased welfare measures are obtained (boxall and adamowicz, 2002; birol et al., 2006; carlsson et al., 2003; hynes et al., 2008; bujosa et al., 2010). provencher and bishop (2004) showed in a simulation study that the models accounting for heterogeneity outperform the ones that assume homogeneity of preferences (in the absence of specification errors). unfortunately, most environmental studies so far assumed homogeneity of preferences (hanley et al., 2003), implying that all respondents are assumed to have the same taste for attributes. this is a very serious limitation in many environmental related issues where there is wide variation in preferences for attributes. for the case of changes in water rights attributes, veettil et. al (2011) for example have reported wide variations in preferences among farmers. to our knowledge, the study by veettil et al. (2011) up to now was the only one which relaxed the homogeneity assumption in the property right preference elicitation process by assuming perfect heterogeneity. however assuming perfect heterogeneity is not that useful for policy formulation, because policy makers usually are interested in policies targeted towards specific sections of the population. table 1. attributes and levels used in the choice sets in south africa. attributes levels transferability not transferable agency based transfer market transfer duration 5 year 10 year quality of the title guaranteed quantity no guaranteed quantity price* 0.06 r/m³ 0.09 r/m³ 0.12 r/m³ *the average exchange rate at the time of data collection was 1r=0.13us$. 137preferences for water rights reforms among smallholder irrigators in south africa 4.3. econometric model in this study a latent class model (lcm) is used for the econometric analysis of the data collected. this type of model, which ce practitioners have started employing most recently, allows accounting for preference heterogeneity (birol et al., 2006). the underlying theory of the lcm posits that individual behaviour depends on observable attributes and on latent heterogeneity that varies with factors that are unobserved by the analyst. this heterogeneity can be captured through a model of discrete parameter variation. thus, it is assumed that individuals are implicitly sorted into a set of q classes, but the class membership of any particular individual, whether known or not to that individual, is unknown to the analyst (greene and hensher, 2003). in lcm, the population thus consists of a finite and identifiable number of groups of individuals (i.e., segments or classes), each characterised by relatively homogenous preferences. these classes, however, differ substantially in their preference structure. this approach can accommodate preference heterogeneity while allowing for the number of classes to be determined endogenously by the data. in the lcm, belonging to a class with specific preferences is probabilistic, and depends on the social, economic and attitudinal characteristics of the respondents (birol et al., 2006). in this paper we follow greene and hensher (2003) and employ a standard lcm specification which assumes a random utility model. this model has two parts, an observable component (βsxnjt) and an unobservable random component εnjt|s. thus, the utility that an individual n obtains from selecting alternative j in the tth choice set is unjt|s = βsxnjt + εnjt|s (1) where u is the utility obtained by individual, β is a vector of parameters of segment s, x is a vector of attributes from the ce and ε is a random component following a type 1 extreme distribution. it is assumed that the systematic component (βsxnjt) of equation (1) can be decomposed into two components. the first relates to the specific attributes of the choice made. the second captures individual specific characteristics (i.e., socio-economic and attitudinal variables). the choice probability for an individual n, given that he belongs to segment s, of selecting an alternative i from a choice set of j alternatives, for a specific choice activity is as follows: prnit/s = eβ 's xnit i=1 i ∑ eβ 's xnjt ⎛ ⎝ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟ (2) the probability that an individual belongs to a specific segment can be expressed as follows: prns = ea 's zn s=1 s ∑ ea'szn ⎛ ⎝ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟ (3) 138 s. speelman , p.c. veettil where zn is a vector of individual-specific variables and as a vector of segment specific utility parameters to be estimated. the unconditional probability that any randomly selected respondent chooses an alternative is obtained by combining the conditional probability in (2) with the segment membership probability in (3), resulting in following equation (4). prni = s=1 s ∑ ea 's zn s=1 s ∑ ea'szn ⎛ ⎝ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟ t=1 t ∏ eβ 's xnit j=1 j ∑ eβ 's xnjt ⎛ ⎝ ⎜ ⎜⎜ ⎞ ⎠ ⎟ ⎟⎟ (4) in the lcm the heterogeneity in preferences is thus incorporated through the systematic component of utility (βsxnjt). there also exist models such as the covariance heterogeneity model, which incorporate heterogeneity in the error components (colombo et al., 2009). a major advantage of the proposed lcm is its ability to enrich the traditional economic choice model by including psychological factors. this integrated modeling strategy also offers an opportunity to merge various social psychological and economic theories in explaining behaviour (boxall and adamowicz, 2002). hence the proposed lcm is well suited for our case study. the parameters in equation (4) are estimated using maximum likelihood estimation procedure using limdep 9.0 nlogit 4.0. estimation requires the number of segments s to be determined in advance. we therefore run models with 2, 3 and 4 segments and employ various statistical criteria to select the “optimal” number segments. the log likelihood, ρ2, akaike information criterion (aic) and the bayesian information criterion (bic)6 are used to determine the optimal number of segments (ruto et al., 2008; colombo et al., 2009). once the parameter estimates have been obtained, a measure of economic value can be derived for each water right attribute using the formula given in equation 5 below (hanemann, 1984). these ratios, which are often referred to as marginal implicit prices can also be interpreted as a marginal rate of substitution (mrs) between water rights attributes and money: the coefficient βm gives the marginal utility of income and is the coefficient of the price attribute, and βk is the coefficient of the water rights attributes: wtp= βk/βm (5) this wtp for attribute changes and the 95% confidence intervals were estimated for the different segments of the lcm using the wald procedure (delta method) in limdep 9.0 nlogit 4.0. 6 the model has the most optimal fit when these criteria are minimized. the akaike criterion is specified as −2lnl+jδ, where lnl is the log-likelihood of the model at convergence, j is the number of estimated parameters in the model, and δ is a penalty constant which equals 3 ; the bayesian criterion is specified as −lnl+(j/2)*ln(n) where lnl is the log-likelihood of the model at convergence, j is the number of estimated parameters in the model and n is the number of observations 139preferences for water rights reforms among smallholder irrigators in south africa 4.4. data the choice experiment was conducted in april 2008 in the limpopo province of south africa. seven irrigation schemes were identified from the national database of smallholder irrigation schemes. the sample included both larger irrigation schemes with over 100 farmers and smaller schemes with only 30-40 farmers. in addition differences in cropping patterns, reflecting varying degrees of water scarcity, were covered7. within the schemes about 30% of the farmers were randomly selected from a list of active farmers. in total 134 questionnaires were completed, providing 402 choice sets. besides the ce experiment, there was a structured questionnaire collecting detailed information regarding farming activities, alternative income sources and other relevant institutional aspects of water management. 5. results in table 2 some socio-economic and farm characteristics of the respondents in the sample are presented. the average age of the farmers in the sample was 58 years. they have a low education level (average of 5.6 years of schooling) and own small plots (average of 1.2 ha of irrigated land). this picture of an aging and low educated farming population, cultivating small plots at smallholder irrigation schemes in rural south africa was described by several authors (perret, 2002; hope et al., 2008). also the finding that a majority of the famers was female (62%) is in line with these studies. on average the respondents produce 4 crops. maize is the main summer crop while spinach, beans, beetroots, cabbages and tomatoes were the most important winter crops in the sample. the monetary income generated by irrigated agriculture is quite low. for most of the households in the sample, cropping is not their primary income source. as in most rural areas of south africa, pensions and child allowances are more important income sources (perret, 2002; hope et al., 2008). the non-farm income of the respondents is highly variable. also the income share from irrigated farming among the farmers was highly varying, ranging between 1% and 100% with an average of 29%. higher dependency on income from irrigated farming was generally associated with a lower overall income level, as it generally reflected an absence of other income sources. it must be noted that most production is subsistence-oriented. farmers were furthermore questioned about their trust in water management institutions. for three levels of water management institutions8 they could indicate their degree of trust on a 4 point likert scale. the three scores were then combined in an overall trust score. the average trust score of 2.4 indicates that overall trust level is in between “trust somewhat” and “do not trust very much”. a majority of the farmers (61.2%) did already experience water shortages and a small majority (53.7%) already experienced conflicts about water. finally, in irrigation schemes the distance from the water intake often has an impact on the quantity of water available and on the supply reliability. the situation of the plots of the respondents within the irrigation scheme was therefore also recorded. it is observed that more 7 in the drier parts of limpopo most farmers are limited to growing maize during the wet summer season. in other parts of the province production is more diversified, with the majority of the farmers producing maize in summer and a wide variety of crops in winter. 8 see speelman et al., 2010a for more details 140 s. speelman , p.c. veettil head-end farmers are included in the sample (42.5%), compared to middle (28.4) and tailend farmers (29.1). possibly this is because we sampled among active farmers and given the worse water supply situation the percentage of unused plots in the middle and tail-end parts of the schemes might be higher. table 2. selected sample population characteristics. mean (st. dev ) min-max continuous variables 57.8 (13.2) 27-85 farmers’ age (years) education (years) 5.6 (4.5) 0-15 household size 6.4 (2.7) 1-15 # crops cultivated 4 (2.5) 1-11 irrigated plot size (ha) 1.2 (0.4) 0.4-4 income from irrigation (r/year) 1993 (2798) 0-16504 income share from irrigation (%) 29 (24) 0-100 non-farm income (r/year) 16672 (16708) 0-94320 trust in water management institutions 2.4 (0.9) 1-4 categorical variables gender (% male) 37.3 occurrence of water conflicts (% no) 46.3 occurrence of water shortage (% no) 38.8 field situation % head %middle %tail 42.5 28.4 29.1 the first step in the latent class model approach is determining the number of segments. table 3 presents the statistical criteria used for this decision. the log likelihood and ρ2 statistics improve as more segments are added, supporting the presence of multiple segments in the sample. the 2-segment solution provides the best fit to the data since, although aic statistics decreases, bic starts to increase again as more segments are added to the model. improvements in the other criteria are also much smaller from 2 to 3 and 3 to 4 segment models. for comparison table 4 first provides the results of the conditional logit. these results were also presented in speelman et al. (2010a,b). all the coefficients in the conditional logit model are highly significant and all the signs are as expected a priori. all of the water rights attributes are significant factors in the choice for a water right specification, and a positive change in any single attribute increases the probability that a particular specification is selected. in other words, the respondents prefer water rights with longer duration, guaranteed supply, which are transferable. the sign of the price coefficient indicates that the effect on utility of choosing a choice set with a higher price level is negative. when the price attribute is used as the normalizing variable, the most important attribute is guaranteed supply. 141preferences for water rights reforms among smallholder irrigators in south africa table 3. criteria to determine optimal number of segments. # segments log likelihood ρ2 aic bic 1 -1051.5 0.371 1.752 1.773 2 -948.8 0.432 1.600 1.667 3 -932.9 0.441 1.592 1.706 4 -925.4 0.446 1.598 1.758 table 4. results of the conditional logit and latent class models. cl lcm segment 1 segment 2 duration 0.096*** (0.014) 0.126*** (0.033) 0.112*** (0.011) quality of title 0.628***(0.038) 3.19*** (1.211) 0.318*** (0.029) price -0.048***(0.015) -0.140*** (0.039) -0.031** (0.013) agency based transfer 0.230***(0.050) -0.093 (0.112) 0.355*** (0.037) market transfer 0.360***(0.051) 0.902 (0.106) 0.509*** (0.046) model statistics pseudo ρ² 0.371 0.432 log l -1051.469 -948.868 segment function lcm : respondents’ social and economic characteristics constant 1.221 (1.340) trust in water management institutions -0.629 ** (0.298) farmers ‘ age -0.014 (0.016) gender -1.020 ** (0.455) income share from irrigation -0.327 (0.934) occurrence of water conflictsa -0.181 (0.479) a dummy variable indicating whether respondents have already experienced conflicts regarding irrigation water. ***1% significance level,** 5% significance level, *10% significance level with two-tailed tests. next the results of the 2-segment lcm are reported in table 4. the upper part of the table displays the utility coefficients from water rights attributes, where the lower part reports the segment membership coefficients. the segment membership coefficients for the second segment are normalized to zero allowing identification of the segment membership coefficients for the first segment (birol et al., 2006). these coefficients are then interpreted relative to this normalized segment. for segment 1 the utility coefficients for all attributes except the transferability attributes are significant and the segment membership coefficients reveal that having trust in the water management institutions and being male decreases the probability that a respondent belongs to the first segment. the other factors do not signifi142 s. speelman , p.c. veettil cantly affect membership. for the second segment all attributes are significant determinants of the choice, and except for the price attribute, higher levels of these attributes increase the likelihood that respondents in segment 2 choose a particular water right specification. the effect of a higher price is significant and is again negative for this segment. the relative size of each segment is estimated by inserting the estimated coefficients into equation (3). this generates a series of probabilities of each respondent belonging to either one of the two segments. based on their largest probability score the respondents are assigned to one of the segments. it is found that 36.5% of the sample belongs to the first segment and 63.5% belongs to the second segment. in table 5 the descriptive statistics for a number of social, economic and farming characteristics of each segment are reported. a significant higher proportion of respondents in the second segment is male, they are better educated, have more trust in water management institutions and are spatially located closer to the schemes’ water intake. on average they have both higher non-farm and farm incomes, but for irrigation income the difference with segment 1 is not significant. age, household size and occurrence of water conflicts are also not significantly differing between the two segments. table 5. profiles of respondents belonging to the two segments. segment 1 (n=49) segment 2 (n=85) gender (% male) *** 22 45 farmers’ age 56 (11.5) 60 (14.1) household size 6.7 (3.0) 6.2 (2.5) education** 4.4 (4.2) 6.3 (4.5) field situation head % 35 47 middle % 35 25 tail % 30 28 occurence of water conflicts (% no) 46 47 trust in water management institutions *** (average score) 2.2 (0.8) 2.6(0.9) non-farm income ***(r) 12551.5 (9633.5) 19048.2 (19323.4) income from irrigation 1732.2(2757.5) 2140.8 (2826.3) # crops cultivated 3.9 (2.6) 4 (2.6) occurence of water shortage (% no) 38 39 t-tests and pearson chi square tests show significant differences (*) at 10% significance level; (**) at 5% significance level, and (***) at 1% significance level. finally table 6 presents the implicit prices and their confidence intervals for attribute changes for respondents of both segments. for the qualitative attributes the implicit prices reflect the wtp for a level change, for example smallholders in the first segment are willing to pay 0.2283 r/m³ for water if there would be a shift from non-guaranteed to guaranteed supply. for the duration which is included as a quantitative attribute the implicit 143preferences for water rights reforms among smallholder irrigators in south africa price is the wtp for a unit increase in this attribute. it can be observed that for most attributes wtp for changes is clearly lower among respondents of the first segment. only for guaranteed supply a substantial wtp is found for respondents of this segment. table 6. segment specific willingness to pay for changes in water right dimensions and 95% confidence intervals (figures are in 0.01r/m³). segment 1 segment 2 duration 0.903 (0.165; 1.641) 3.590 (0.499; 6.680) quality of title 22.830 (3.925; 41.735) 10.246 (1.730; 18.762) agency based transfer -0.663 (-2.208; 0.882) 11.410 (1.765; 21.055) market transfer 0.646 (-0.857; 2.149) 16.373 (2.750; 29.996) 6. discussion in comparison with the earlier studies by speelman et al. (2010a,b) the lcm approach allowed us to identify that the smallholder population consists of two distinct groups with a different preference structure. this information can help policy makers to target their interventions. this finding is also in line with the observation of denison and manona, (2007) who claimed that smallholder irrigators in south africa are heterogeneous, with different farming styles making it impossible to design one-fit-all policy interventions. a first clear difference between the two segments in our lcm is observed with respect to the attitude towards a shift to agency based water right transfers. this is revealed in table 4 and table 6. the difference could possibly be explained by differences in the level of trust in the water management institutions between the two segments. this effect of trust was also suggested in speelman et al. (2010a), where a segmentation was made based on trust level. the respondents in segment 1, which have a significantly lower trust in the water management institutions, dislike the idea of a system of agency based transfers, while for the respondents in segment 2, preference is clearly positively influenced by such a system, resulting in a positive wtp. while for respondents of the second segment the ongoing water pricing reforms9 and the water right reforms can thus go hand in hand, this does not seem the case for those of the first segment. for policy makers this emphasizes the need to acknowledge the complementarity between different institutional reforms as identified by veettil et al. (2011) and earlier by saleth and dinar (2005). to make this complementarity work for irrigators of the first segment water management institutions should gain the trust from these water users. an obvious way to do this is by improving the functioning and service delivery of these institutions. also a recognition 9 the introduction of water charges is one of the ongoing reforms in the south-african water sector (dwaf, 2004) 144 s. speelman , p.c. veettil in the institutional structure of the traditional governance systems managing the access to and the use of water resources in the rural communities might be a crucial issue to increase local trust (kapfudzaruwa and sowman, 2009). a second important finding is that respondents from the first segment are more sensitive to the price attribute. when considering their socio-economic profile, this is in line with expectations, because several characteristics (income levels, gender, field situation, education level) seem to indicate that the respondents in segment 1 are socio-economically worse off than those in segment 2. therefore the higher price coefficient and the lower levels for most other attributes may well reflect the limited capacity of these poorer farmers to pay for water, a problem already identified by backeberg (2006) and perret and geyser (2007). since the wtp for attribute changes is a reflection of the benefits for farmers generated by these changes, it is shown that water right reforms clearly generate less benefits for farmers of the first segment. policy makers will have to design other interventions to lift up this poorest, more subsistence oriented segment of the smallholder population. denison and manona, (2007) for example propose that interventions to reduce the market dependency for inputs and outputs would be an interesting intervention for this type of farmers because it reduces external cash exchange and supports risk reduction. in contrast with their low wtp for other attribute changes, respondents of the first segment have a high wtp for guaranteed water supply. these farmers rely more on irrigated agriculture for their income and livelihood. as a consequence they seem to be more risk averse with respect to failures in water availability. in their typology of smallholders denison and manona (2007) distinguish a similar type of smallholders and suggest that interventions to reduce risk are best suited for this type of farmers. this is confirmed by our findings because guaranteeing water supply is such a risk reducing intervention. given the heterogeneous risk profile of the two segments a potentially interesting intervention would be the introduction of security differentiated water rights. lefebvre et al. (2012) showed that such a system offers interesting opportunities in terms of risk allocation. from a government perspective this would probably also be a less costly measure compared to guaranteeing water supply for all. finally a plausible explanation for the larger preference for water rights with longer durations among respondents in segment 2 could be the positive relationship between education and investments in productivity. this relationship implies that better educated people are more inclined to make investments to increase productivity, but as explained by backeberg (2006) typically this type of investment decisions is negatively affected by a short duration of the licenses. 7. conclusions better institutional arrangements to coordinate use and resolve conflicts are highly needed for the water sector in many countries (brennan, 2002). in this light, there is a growing understanding that irrigation water rights are important and that a lack of effective water rights systems creates problems for the management of water supplies (matthews, 2004; bruns et al., 2005). this paper contributes to the water rights literature by applying a latent class valuation of water rights reforms to a case study of smallholder irrigators in south africa. we assess 145preferences for water rights reforms among smallholder irrigators in south africa the economic value for smallholders of the benefits generated by improving their water rights. as ruto et al. (2008) indicate it is also important to understand what underlies differences in values that people place on institutional changes. in our study we aim to understand who benefits from which reforms. our latent class modelling approach allows us to capture and explain the heterogeneity in the preference structure of smallholder irrigators. in general our results are in line with theoretical expectations (bruns et al., 2005; hodgson, 2006) regarding the benefits of water rights reforms: for smallholders there are positive and significant economic benefits associated with the improvements of the water rights. nevertheless two segments could be distinguished. while one of the segments has a lot to gain from a water rights reform, benefits for the other seem rather limited. to stimulate this latter segment other policy interventions might be more suitable. this confirms the findings of denison and manona (2007), who in their report on the revitalization of the smallholder irrigation sector in south africa identified several types of farmers and suggested that different policy initiatives would be needed for each of them. furthermore the segments clearly differed in their preference for individual changes in water right dimensions. the difference in wtp for guaranteed supply for example reveals an heterogeneity in risk profile of the segments and could be a reason to consider security differentiated water rights. the demonstrated heterogeneity in benefits therefore shows the importance of targeted policies moreover by using the knowledge concerning heterogeneity in the formulation of policies, acceptance of/ and support by stakeholders can be increased. acknowledgement the corresponding author is post-doc fellow of the research foundation flanders. references araral, e. 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(2007). water reforms in developing countries: management transfers, private operators and water markets. water policy 9: 573-589. issn xxxx-xxxx (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(1): 65-80, 2012 economics of food security: selected issues silvia saravia-matus, sergio gomez y paloma and sébastien mary european commission – joint research centre – institute for prospective technological studies (ipts) , seville, spain abstract. the present article reviews selected key challenges regarding food security from both an academic and policy-oriented angle. in the analysis of the main constraints to achieve food access and availability in low and high-income societies, a detailed distinction is made between technological and institutional aspects. in the case of low-income economies, the emphasis is placed on the socio-economic situation and performance of small-scale farmers while in high-income economies the focus is shifted towards issues of price volatility, market stability and food waste. in both scenarios, productivity and efficiency in the use of resources are also considered. the objective of this assessment is to identify the type of policy support which would be most suitable to fulfil the increasing food demand. innovation programmes and policies which integrate institutional coordination and technical support are put forward as strategic tools in the achievement of food security goals at regional and global level. key words. food security, technology and innovation policies, small-scale farmers, market stabilization jel codes. q18, q16, q01 1. introduction «food security exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life. the four pillars of food security are availability, access, utilisation and stability. the nutritional dimension is integral to the concept of food security» (world summit on food security, rome, november 2009). the concept of food security has been present in the policy agenda for many years now and in this particular context, it appears to have evolved into two yet complementing themes as the world is said to be in a status of post-food surplus disposal (world food programme, 2007). on one hand, there is the urgent issue of under-nourished people that are mainly located in rural areas of low-income countries, where local access to food, technology and natural/agri-resources is the major constraint. on the other hand, highincome countries feel threatened by volatile food markets (e.g. meeting g20 agriculture ministers, 2011) and are primarily concerned with long-term strategies that will guarantee * corresponding author: silvia.saravia-matus@ec.europa.eu. 66 s. saravia-matus, s. gomez y paloma and s. mary food availability and affordability in the medium and long run as the competition on limited resources continues to increase. from an academic outlook, the discussion on food security touches on several aspects. one relevant subject is that of how to increase agricultural productivity where the role of technology adoption, the declining effects of the green revolution, the potential benefits of incorporating biotechnology, among others, are assessed (pingali, 2007; otsuka and kalirajan, 2005; estudillo and otsuka, 2004; pingali and traxler, 2002; evenson, 2001, 2003; lipton, 2001). another thematic trend in the scientific literature is concerned with the macroeconomic analysis of price volatility, trade and market stability (gilbert and morgan, 2010; apergis and rezitis, 2011). likewise, the effects of the demand for biofuels, farmland acquisition/investments and the food price crisis on small-scale farmers have also been well-documented topics (swinnen and squicciarini, 2012; dauvergne and neville, 2010; deininger, 2008; ivanic and martin, 2008). the aim of this paper is to explore the two main policy angles to food security briefly introduced above and assess their related obstacles under the light of academic findings. for this purpose, section 2 examines the most pressing food security challenges from the respective view of low and high-income countries. section 3 discusses potential opportunities and focuses on initiatives which may contribute to reducing hunger and malnourishment while also securing food availability and environmental sustainability at a global level. this section concludes by highlighting the strategic relevance of integrating institutional and technological innovations to achieve food security goals in the short and long term. 2. analysis of food security challenges in high and low-income countries about one billion people globally do not have adequate food to meet their basic nutritional needs (fao, 2010a) and according to the ifad 2011 rural poverty report, the under-nourished mainly belong to the poorest households in rural areas. if we take into consideration that over 80 percent of rural households are said to depend directly on farming and agriculture (ifad, 2011), the development of the rural and agricultural economy becomes pivotal to reduce poverty and hunger worldwide. in addition, there is evidence that the proportion of farmland cultivated in small holdings has been growing since 1960’s, particularly in africa and asia (lipton, 2006). sen (1998) and others (tomlinson, 2011; smith et al., 2000) argue that the dynamics of income earning and of purchasing power may indeed be the most important component of food insecurity and starvation. in this respect, smith et al. (2000) analyse fiftyeight developing countries with high prevalence of food insecurity. their findings indicate that there was little correlation between national food availabilities and food insecurity. in their study, food availability is measured in terms of daily per capita dietary energy supply and balance while food insecurity is defined following the guidelines established by fao. interestingly, the group of countries which exhibited the highest severity of food insecurity were those with high poverty, yet, high food surpluses. the latter is consistent with the view that poverty is the most widespread cause of food insecurity for the selected countries in the time period covered by this study (1990’s). the emphasis in the academic literature is thus placed on food access (or the capacity of households to fulfil their minimum dietary needs) as the main limiting factor of food security. 67economics of food security: selected issues however, the current policy debate on food security is strongly dominated by the issue of food availability and affordability, particularly in high-income societies (although some low-income countries have also taken protective measures such as banning of grain exports to maintain national stocks at times of food price pikes). reports from both international organisations and the agricultural industry have claimed (and worked under the statement) that in order to meet the food requirements of the nine billion population of 2050, an expansion of food production of 70 per cent is needed (with a base reference of 2006). while pretty et al. (2010), fao (2009a; 2009b) and godfrey et al (2010) work with the «70%» figure, others such as tilman et al. (2011) have forecasted that a much higher increase in global crop supply will be needed, i.e. 100-110 per cent increase from 2005 to 2050. it is worthwhile to dwell on the origins of the rather quoted estimate of 70 per cent increase of food production. tomlinson (2011) offers a critique by arguing that this «70 per cent» figure does not correspond to an increase in actual tonnes of production or yields but the aggregate volume produced within the crop and livestock sectors, which is calculated by multiplying the different quantities by the price of each commodity. in this estimation, fruit and vegetables are excluded and if the weight of the actual production was used, the figure would, for instance, be reduced by 6 per cent. another fundamental issue is that the 70 per cent estimate does not account for wasted food or matters related to unequal food distribution and access. regarding food waste, it is estimated that the loss may rise to almost one third of harvested crops. the average current global edible crop harvest is said to be about 4600 kcal per person per day, but harvest and distribution losses along with post-consumer waste cause the loss of 1400 kcal (smith, 2000 and lundqvist et al., 2008; cited by tomlinson, 2011). if reductions on food waste could be effectively implemented, it is plausible to assume that the «70 per cent» level estimations could be lowered. despite the limitations of this quite fragile figure, it cannot be ignored that feeding a growing population with limited resources and in a sustainable manner is undoubtedly a challenge and it is clearly justified to plan ahead and to be proactive in designing preventive measures. yet, a balance must be sought since the focus on the estimation of future food requirements should not lessen the importance of addressing the particular challenges of the food vulnerable or the (semi-)subsistence / small-scale farmer located in lowincome areas. it is therefore necessary to establish a common starting point for an analysis based on institutional coherence, technology transfer and support aimed at achieving food security for low and high-income societies. in sub-section 2.1, food (and nutrition) insecurity is assessed at the rural sector and farm level of low-income countries. the emphasis on the rural areas of developing countries is based on the fact that notwithstanding the changing demographic trends (increasing displacement to urban centres), nowadays the majority of the food-insecure and poor still belong to the rural economies and are highly dependent on farming. in sub-section 2.2, the food security challenges from a high-income society perspective are addressed. instability brought about by volatile food markets in recent years is discussed along with an examination of key drivers for future demand and supply. in addition, the less obvious aspect of over-nourishment is also briefly discussed. 68 s. saravia-matus, s. gomez y paloma and s. mary 2.1 food security in low-income countries: the rural and farm level perspective a natural starting point of analysis of the food access question is that of rural and agrarian economies in low-income areas and the ability of their population to secure higher income, improve production and enhance livelihoods. (semi) subsistence farmers often rely on their production to secure only partially their household consumption, which is far from reaching the nutritional balance required for a healthy life. therefore, the focus of this section is now on to the main technological and institutional obstacles preventing rural farm households from meeting their dietary needs. first, the technological and physical (including the pressures on the fragile natural resource base) constraints are taken into consideration before moving on to the institutional barriers. 2.1.1 technological, scientific and physical constraints african countries are said to currently achieve less than 30 percent of their potential yield (world bank, 2010; deininger, 2011). this would imply that substantial increases in cultivated land may not be an absolute requirement and forest cover need not be substantially reduced in order to effectively increase agricultural output (a positive announcement, particularly in terms of climate change impact reduction). in fact, it is estimated that «in developing countries, 80 percent of the necessary production increases would come from increases in yields and cropping intensity and only 20 percent from expansion of arable land» (fao, 2009a). in other words, the current technology level in the agricultural sector of most low-income areas has substantial room for improvement without necessarily expanding arable land area. this, however, entails that technology must be not only developed and adapted to the needs of non-temperate climates in low-income countries but that an effective diffusion mechanism must be in place along with timely access to agricultural inputs. likewise, agricultural practices must be adapted so that a sustainable use of natural resources is ensured. each of these obstacles is next explored in detail. regarding the development and access to yield-improving technology which is tailored to the specific agro-ecological setting of low-income countries several aspects must be considered. according to pingali (2007) recent developments in genetically modified crops are promoted by large multinationals that focus on relatively few (tempered weather-based) crops excluding crops in tropical, arid, marginal or stress-prone environments. simultaneously, other academics are in favour of greater precaution in the application of biotechnology outside the contexts of high-income countries and temperate climate and advocate for more research on its unknown effects, especially in tropical regions (mcaffee, 2004; serageldin and persley, 2000). currently, many low-income countries do not have the technical and regulatory capacities to assess the benefits and costs of modern biotechnology in their domestic agriculture and eventually to monitor the inclusion of transgenic crops in their agriculture (fao, 2009a). in the meantime, academic findings suggest that the positive effects of the green revolution technology on the yields of the main three cereals (maize, wheat and rice) have started to stagnate or decline. adlas and alchot (2006) analyse long-term yield growth of rice in various ecosystems and states of india between 1967 and 1999. their findings indicate that yield growth (of areas where adoption of modern varieties and irrigation coverage were nearly complete) slowed down during the late green revolution period (i.e. after 1985). pingali (2007) argues that the decline in the productivity 69economics of food security: selected issues growth rates of the three primary cereals may be attributed to: 1) degradation of the land resource base due to intensive cultivation 2) declining infrastructure and research investment and 3) increasing opportunity cost of labor (mainly arising from the off-farm sector). fao (2009a) has also recorded a (at global level) decline of the rate of growth in yields of the major cereal crops from 3.2 percent per year in 1960 to 1.5 percent in 2000; although the decrease may be higher in climate or resource-stressed areas. the challenge for technology is to reverse this trend and to re-focus agricultural research on the particular physical and technological constraints of farmers in low-income countries in order to close the yield gap while attempting to promote a sustainable use of natural resources. other obstacles are related to the elimination or substitution of agricultural practices which hinder sustainable and productive processes. the latter are mainly related to the adequate management and use of land, water, pesticide and fertilizer. for instance, the reduction of land degradation would require not only the introduction of minimum/zero tillage and the prevention of soil erosion but also a balanced nutrient and water access. in the case of tropical agriculture, efforts should be targeted to securing sustainable alternatives to the «slash and burn» system. water management is also of great importance. irrigated agriculture covers one fifth of arable land and contributes nearly 50 percent of crop production (fao, 2009a). water scarcity could turn into a major problem if deforestation rates are not controlled or if irrigation systems are not efficient (i.e. water logging and salinity resulting from excessive water use and poorly designed drainage systems) (ali and byerlee, 2001). adequate pesticide use and research on potential effects are pending tasks for most agricultural sectors in low-income areas. according to ruttan (2000) there are shortcomings (some of which may be even unforeseeable at this point) in the use of pesticides and pathogen resistant crop varieties since an appropriate assessment of long-term impact for tropical and other non-tempered environments has not been fully undertaken. clearly, uncontrolled use of chemical methods to deal with plant or animal pests may induce to the evolution of more resistant pathogens which nowadays are able to spread worldwide due to international travel and trade. as a consequence, there will be a substantial need to constantly update and replace pesticides in order to deal with environment specific constraints (ruttan, 2000). supply, access and use of macro nutrients/fertilisers constitute another risk factor for agricultural production worldwide. the scarcity of macro nutrients especially nitrogen and phosphorous is acknowledged in both policy and scientific literature. undersupply of nitrogen and phosphorus poses a critical constraint to yields in least developed regions. in the case of humid regions, nitrogen is leached to surface waters and groundwater. inefficient n input to agriculture (too little or too much) leads to land degradation (scar, 2011). phosphorus is the major non-renewable and non-replaceable input to agriculture. grain yields are highly sensitive to phosphorus deficiency. phosphorus is mainly lost from cropland by erosion and washed into rivers and the sea where it becomes lethal to coastal and marine ecosystems leading to the loss of freshwater as well (scar, 2011). a recent paper published by keyzer (2010) addresses the issue of upcoming scarcity of phosphorus. the author argues that the shortage of this macro-nutrient in the next 80 or 100 years (due to lack of recycling) will affect not only yields but production costs in agriculture and other industries. in other words, phosphorous scarcity will have an impact on rising food prices, growing food insecurity and widening inequalities between rich and poor countries. 70 s. saravia-matus, s. gomez y paloma and s. mary another issue to consider in the development of yield-improving technology is the impact on biodiversity. a potential trade-off between protecting biodiversity through traditional agriculture and securing the highest yields possible per hectare may arise for farmers in low-income countries. currently, a dozen species of animals provide 90 percent of the animal protein consumed globally and just four crop species provide half of plant-based calories in the human diet (fao, 2009a) but in (semi)subsistence farming, households rely in a great diversity of plants and crops to fulfil very basic needs. although the yield potential and yield gaps for rainfed crops per country and agro-ecological zone have been calculated by the international institute for applied systems analysis (world bank, 2010), one key challenge is to learn how to exploit the technical advantages of different farm structures (i.e. size, production mix, input mix, food chain coordination, etc.) and their economic potentials within each agro-ecological zone while protecting the biodiversity. in this respect, the challenge is to incorporate a joint techno-economic and ecological dimension to the management of natural resources and biodiversity in agricultural practices. rural infrastructures, harvest equipment and storage facilities also represent a handicap for the agricultural sector in low-income countries. inadequate or absent harvest equipment and storage facilities constitute key factors in the high percentage of output losses at farm level. in the case of african tropical agriculture, the percentages of harvest losses are estimated above 30 per cent and in the case of sierra leone, estimations indicate up to 40 per cent (maffs, 2009; nsadp, 2009). transport and communication costs besides restricting the ability of rural producers to engage in more effective trading arrangements also contribute to high percentages of harvested output losses, due to long travel distances and/or poor roads and vehicles (holloway et al., 2000; renkow et al., 2002). likewise, fuels prices also increase production costs through the increased prices of imported factors of production (such as pesticides, herbicides or fertilisers). the reduction in harvest losses and the physical connection of remote areas to sale points or local development poles (airports, harbours, and country capitals) is therefore a key issue for improving rural livelihoods. the development of an adequate network of rural infrastructures is an essential prerequisite for the improvement of food commercialisation and for the viability/sustainability of food producers located in isolated regions. furthermore, according to ccafs report (2010) changes in the mean and variability of climate will affect the hydrological cycle, crop production and land degradation, particularly in regions where the most of the world’s hungry are (i.e. sub-saharan africa and south asia). moreover, the report underscores that climate change (through variable rainfall and temperature) has the potential to transform food production, especially the patterns and productivity of crop, livestock and fishery systems. currently, small farmers in high-risk areas do not have wide access to monitoring or information services, which could help them prepare for climate variability and extreme events. in this respect, barrios et al. (2008) analyse the impact of climatic change on agricultural production for the case of sub-saharan africa between 1961 and 1997. their results indicate that changes in country-wide rainfall and temperature have been major determinants of agricultural output in the region. in fact, the authors’ simulations indicated that if rainfall and temperatures had remained at their pre-1960’s means then the agricultural output gap between sub-saharan africa and other developing countries by the end of the 20th century would have been approximately one third of its actual magnitude. 71economics of food security: selected issues 2.1.2 institutional constraints during the 2008 food price rise, small semi-subsistence farmers were not found to be supply responsive (fao, 2009b). according to fao (2009b) this behaviour is partly explained by simultaneously increasing production costs (i.e. fuel and fertilisers) faced by small producers during this period. likewise, it was emphasised that the occurrence of a price shock (even if it is the highest in many years) does not create enough incentives to increase production in face of the downward price trend that has dominated the agricultural scene in the last decades. in any case, it should be stated that most (semi)subsistence farmers are only marginally integrated in the market systems and as stated by evenson and gollin (2003) their participation largely depends on the relative difference between prices and costs. in other words, transaction costs play an important role in the decision to self-consume or engage in trading (de janvry et al., 1991), especially as they are mostly household-specific. moreover, given that smallholders are likely to follow both risk-reducing and coping strategies (ellis 2000), timely access to market information, credit and extension services can allow them to benefit from market opportunities. however, resource constraints (at national and local levels) and the limited existence or absence of socio-economic mechanisms (such as farmer associations, producer cooperatives or integrated food chains) which would enable a faster and more efficient access to this type of institutional support and ultimately increase market participation, constitute important practical obstacles. another institutional constraint is related to issues of contract enforcement (benham and benham, 2000; dorward, 2001). this is essential to foster economic interaction and organization not only within the agricultural sectors but across other sectors (mainly industrial) as a way to promote agri-business activities. the latter is also relevant when it comes to land access and management in low-income countries, particularly in the light of the recent and increasing trend of large-scale investment in farmland (also referred to as land grabbing). both policy documents and academic articles have pointed out to the potential negative effects of these land transactions for small-scale farmers (deininger, 2011; hallan 2011). in this respect, major considerations include the adequate valuation of land, the respect for traditional/informal property rights, the impact on local labour market and the ability to flexibly reallocate land in case an investment fails (world bank, 2011). lastly, as stated by von braun and meinzen-dick (2009) it is in the long-run interest of investors, host governments and the local people involved to ensure that any land arrangement is properly negotiated, practices are sustainable and benefits are shared. the latter also implies an adequate evaluation of water (and other resources) management and distribution. finally, the agricultural productivity of smallholders also depends on the adequate provision of public goods. access to education and health services for the rural population are seen as key aspects to increase agricultural productivity (yúnez-naude and taylor, 2001; appleton and balihuta, 1996). in the case of africa, particular attention should be given to the aids pandemic, which will entail dramatic changes to the composition of rural communities (fao, 2010a). civil conflict and war periods are also factors which erode the livelihoods systems of both urban and rural populations. 72 s. saravia-matus, s. gomez y paloma and s. mary 2.2 food security in high-income countries: the global and market level perspective the outlook given to food security in high-income countries is mainly concerned with sustainability, long-term availability, and consequently, affordability of food. for example, the global food security programme, which is the uk’s main public funders of food-related research and training, is aimed at «meeting the challenge of providing the world’s growing population with a sustainable and secure supply of safe, nutritious and affordable high quality food. that food will need to be produced and supplied from less land and with lower inputs and in the context of global climate change, other environmental changes and declining resources» (global food security, 2011). in other words, the focus from this viewpoint is to meet the rising demand for food in ways that are environmentally, socially and economically sustainable while keeping affordable prices. 2.2.1 technological, scientific and physical constraints higher competition on limited resources such as energy, land, macro-nutrients or fresh water, leads to higher costs for the environment and for food production and manufacturing. although farmers in high-income countries maintain high productivity in the use of agricultural inputs, the transport and retailing practices need to become more efficient and effective in both in their provision of safer and healthier food and in the reduction of food waste. although evidence on waste estimation is relatively weak, arguably, there is a significant proportion of food grown which is lost or wasted after farm gate before and after consumption. the latter is said to be equivalent to 30 per cent of food grown and if this estimated total amount of food waste could be halved in 2050, it would correspond to a 25 per cent increase of today’s production (global food security, 2011). hodges et al. (2011) estimate that from the 222 million tonnes of edible food supply in 2008 in the usa 9 per cent (19.5 million tonnes) were lost at the retail level and 17 per cent (37.7 million tonnes) at the consumer level. the total proportion of food lost was thus 26per cent or 57.1 million tonnes. their estimate for on-farm and between the farm and retailer was of 3 per cent, reaching an overall figure of 30 per cent food loss. similarly, hall et al. (2009) estimate for the usa a food waste per capita of 1400kcal which corresponds to one third of the average current global edible crop harvest. the authors also highlight that food waste contributes to excess consumption of freshwater and fossil fuels which, along with methane and co2 emissions from decomposing food, impacts global climate change. food waste under their calculations would account in the usa for more than one quarter of the total freshwater consumption and approximately 300 million barrels of oil per year. the resulting environmental degradation calls for increasing efficiency not only in the agricultural sector but in the sub-sequent steps of food manufacturing, including consumption. recently, the increase in demand for bio-fuels has potentially exerted pressures not only on world prices for agricultural commodities but on land use, i.e. how much planting area could be diverted from producing other crops to those used as feedstock for the production of bio-fuels (fao, 2009b). this issue is also connected to land grabbing/acquisition in low-income countries, as many projects foresee the cultivation of sugar cane, maize or palm oil to produce bio-fuels. to illustrate this, in 2008 the total area under biofuel crops was estimated at 36 million hectares, more than twice the 2004 level (world 73economics of food security: selected issues bank, 2010). according to fao (2009b) the development of the bio-energy market will also determine how far it will be possible to meet the growing demand with the available resources and at affordable prices. 2.2.2 institutional and market aspects volatility refers to variations in economic variables over time and the emphasis is placed on whether these variations are predictable or unpredictable (gilbert and morgan, 2010). the variations in prices become problematic when they are large and cannot be anticipated and, as a result, create a level of uncertainty which increases risks for producers, traders, consumers and governments and may lead to sub-optimal decisions (fao et al., 2011b). volatility in prices for many agricultural products is connected to a range of factors. lower global stocks (associated to higher transport and storage costs), high fuel prices, poor harvests in export countries (many of these related to major climatic events), rising demand for bio/agro-fuels and increased demand for meat and milk products are all elements which may influence price volatility. likewise, the un special rapporteur on the right to food has underscored the emergence of a speculative bubble on food commodities (de schutter, 2010). these different aspects affecting food/agricultural price volatility deserve some additional remarks. for instance, stocks play a key role in equilibrating markets and smoothing price variations (fao, 2009b). since the 1995 high price situation, global stock levels have on average declined by 3.4 percent per year (particularly in cereals) and uruguay round agreements are said to have been instrumental in reducing stock levels in major exporting countries. the global economy is also strongly vulnerable to any major climatic event affecting one or several of the main «grain belts». one example is the impact of the extreme drought which affected russia in 2010, followed by floods in australia (soares et al., 2011). fuel prices not only affect the prices of agricultural inputs but have been an important determinant in the increase of bio-fuel production. bio-fuels are as well considered an important factor in the determination of agricultural prices. for example, out of the increase of nearly 40 million tonnes in total world maize use in 2007, almost 30 million tonnes were absorbed by ethanol plants alone. yet, a more permanent effect of the food and financial crisis of 2008, according to the world bank (2010), was that it prompted some food import-dependent countries to reconsider their policies to reduce vulnerability from what is considered to be an «undue-dependence» on imports, while other export countries have relied on export bans as protectionist measures of national food levels. another type of issues mainly related to over-nourishment, obesity and diet-related ill heath have started to get further attention in both policy and academic papers (lipton, 2001; global food security, 2011). according to tomlinson (2011) who quotes fao reports (2006) the world food economy is being increasingly driven by the shift of diets and food consumption patterns towards livestock products. in high-income countries, meat consumption is projected to increase from 90.2 kg/person/year in 1999 to 103 kg/ person/year in 2050 while consumption of dairy products is also expected to increase from 214 to 227 kg/person/year for the same time period. the result is a highly obese population with increasing health problems (lipton, 2011; hodges et al., 2011). consequently, food availability and affordability must also contemplate parallel programmes of 74 s. saravia-matus, s. gomez y paloma and s. mary public health and consumer education along with the improvement of production and retailing processes. 3. possible opportunities and policy alternatives this section now addresses the technological and institutional opportunities as well as policy alternatives which could be considered in order to address food security challenges concerning both access and availability. the discussion is structured in two parts: (i) technology (which increases productivity and reduces environmental damage) and institutional coordination (to integrate small scale farmers) (ii) market stabilization and the role of governments and international agencies (to promote transparency in foreign land investment, research on climate change and technology, provision of market information). 3.1 technology & institutional coordination: research and access beyond the farm level it has been recognized that farming in low-income countries has on average the largest room for technical improvement at global level (world bank, 2010; deininger, 2011). technology which adapts to the needs of low-income agricultural areas is therefore of key relevance to achieve general food security objectives. however, the necessary resources to promote research on productivity enhancement (which include genetic improvements or bio-technology) and sustainable management are lacking in low-income countries. the application of improved technology (adapted to the circumstances of non-temperate or environmentally marginalized zones) could increase average yields two to threefold in many parts of africa, and twofold in the russian federation (government office for science, 2011). a primal initiative in the reduction of food insecurity is based on supporting agricultural research for areas with high potential for technical improvement and performance. the spread of current best practices (in terms of extension services and technology adoption) to reduce yield gaps may be consequently expected to play a crucial role in improving food security both in its access and availability dimensions. for the purpose of dissemination and adoption of new technology, the establishment of public-private partnerships, producer associations and cooperatives which support not only training but guarantee the well-functioning of food chains and access to inputs and services for small and medium producers (i.e. mainly credit, insurance, veterinary services) have proven successful tools (lipton, 2006; world bank 2006). the same principle applies for the adoption of environmental friendly practices in the context of low-income countries. the widespread adoption of any of these practices will take place if they are tied to the improvement of rural livelihoods and not only the preservation of natural resources. in the case of recycling of macro nutrients such as phosphorous, further support might be required. in other words, the results of agricultural research to improve productivity must include a dissemination and adoption strategy which is compatible with farmers’ utility and profit maximisation decisions. successful stories in agricultural sectors of low-income areas (which entailed an increase in rural employment, output and value added) illustrate that technology adoption is sustainable when tied to market opportunities (world bank, 2006; lipton, 2006). these experiences illustrate that it is possible for small scale farmers who are organized in a cooperative or association to invest and smooth 75economics of food security: selected issues supply when there is guaranteed access to and stable demand from (domestic or international) markets. traditionally, a well-established food chain with straightforward contracts becomes a strategic tool and government support varies from funding research and setting up networks of producers (preferably in different stages of the production and export processes) to introducing regulations which enhance international trade opportunities. financial and insurance schemes would also play relevant roles. similarly, agricultural research can also play a role in exploring production models which combine agro-forestry and fishery activities for small and medium farmers. the latter is expected to not only maintain and use different natural resources in a sustainable manner but also to diversify agricultural livelihoods. diversification both on-farm and offfarm is recognised as suitable strategy to reduce risk and increase food access (ellis, 2000). for this reason, opportunities through the participation in vertically integrated agri-business structures could also be of relevance to increase rural incomes. overall, it is essential to undertake technology improvement programmes in institutional settings which guarantee incentives for the adoption of sustainable food production practices. in other words, technology adoption which increases productivity in a environmentally friendly manner will take place when rewards are easily quantifiable. in the context of a market-economy, pre-requirements include the improvement in market information and transparency (fao, ifad, oecd, unctad, wfp, world bank, wto, ifpri and un hltf, 2011) which may reduce risk aversion in agricultural production. risk-reducing strategies are one of the main differentiators between small/medium family based farms and large commercial farms, particularly in low-income economies. the primacy and the gap between the two types of farming also depend on a number of factors including technology level, production mix and agrarian management. one way forward is to promote pro-poor investment in agricultural sectors. falcon and naylor (2005) propose to focus on crops produced and consumed by those who are food insecure. as argued above, private-public partnerships are innovative mechanisms to organise input supply and smooth output production among small and medium farmers. 3.2 market stabilization and the role of governments and international organizations in the attempt of reducing price volatility, it is crucial to highlight the importance of creating a world trading system and incentive structures for the agricultural sector that not only maintains stable food prices but keeps agricultural producers motivated to stay in business and invest in updated technology and sustainable agricultural practices. adequate price signals are therefore essential to push investment both in highand low-income countries’ agriculture. simultaneously, global markets have to function effectively for an increasing number of countries to join in active international trade and have access to a stable supply of imports (fao, 2009a; fao, 2011a). in the same line, export bans at times of food stress (which exacerbated the 2007-2008 food price spike) should be avoided and thus food selfsufficiency as a viable option to contribute to global food security should be further revised in both high and low-income countries (foresight, the future of food and farming 2011). countries may consider combined measures to be better prepared for future shocks to the global food system, through coordinated action in case of food crises, reform of trade rules and joint finance to assist people affected by a new price spike or localised disaster. 76 s. saravia-matus, s. gomez y paloma and s. mary in other words, the focus should be not only on increasing food supply and availability but also on access of the world’s poor to the food they need to live active and healthy lives (fao, 2009a). increased opportunities (through the diversification of farm-household income) in the rural areas would also contribute to reverting or decreasing the pressure of migration on urban centres (it is estimated that by 2050, 70 percent of the world population will reside in urban areas). the reversal or containment of this trend would also bring positive effects in terms of urban pollution or increase in the number of settlement in environmentally risky disaster-prone zones (landslides, floods). a complementing measure would also include incentives which relieve pressures on the future balance between supply and demand. for example, support waste reduction programs can be undertaken (via education, campaigns). in parallel to waste reduction, it is also possible to affect people’s diets via taxation, campaigns or regulatory actions. in particular, it is important to better assess the needs and the associated consequences of food assistance in the long run. in extreme situations, food aid allows ensuring short-term food security. however, it has been criticised for generating potential perverse effects on long-term development of markets and private agents. in order to limit such negative effects, agencies and donors can inform market agents about their intentions for food aid distribution and limit the duration of such activities (wfp, 2005). international organizations may also contribute in setting coordinated institutional frameworks to reduce other specific food security threats. for instance, joint efforts could be increased in order to provide timely and accurate information on climate change risks and forecasts of natural disaster and their impact on agricultural production. another important role relates to the emerging trend of farmland investment contracts in low-income countries. in this respect, land contracts could be publicly available as a way to certify their transparency and fairness in the allocation of property rights (hallam, 2011). their involvement could promote investments which not only protect local land rights but also provide technological spill over to smaller farmers as well as market access/opportunities. 3.3 concluding remarks in summary, food security at the rural or farm level is directly linked to the attainment of the 1st of the millennium development goals: eradicate extreme poverty and hunger (united nations, 2009). but, assuming a continuation of current trends in key factors such as agricultural production and income, the number of food-insecure people would not significantly decrease over the next ten years (shapouri et al.; usda, 2010). therefore, simply planning for an increase in overall production which does not focus on distribution and access is inadequate. special attention is clearly needed in areas where highest productivity increases are possible, thus promoting an integrated course of action to deal with food access and availability. to conclude, it is essential to re-focus and coordinate food security research agenda and policy making in order to deal with both high-income and low-income perspectives on food security through the creation of initiatives which address both technological and institutional constraints. as fao (2009a) states, adequate supply of food at the aggregate level, globally or nationally, does not guarantee that all people have enough to eat and that hunger will be eliminated. but if the global food trade scenario creates incentives 77economics of food security: selected issues and opportunities for individuals in low and high-income countries to effectively engage in sustainable production and consumption patterns an important step towards securing food access and availability is likely to be made. acknoledgements the authors would like to thank jacques delincé for valuable comments. the views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the european commission. references agrimonde (2010). scénarios et défis pour nourri le monde en 2050. s. paillard, s. treyer, b. dorin (coordinateurs). versailles: editions quae. ali, m. and byerlee, d. 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(2001). the determinants of nonfarm activities and incomes of rural households in mexico, with emphasis on education. world development 29(3): 561-572. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(2): 191-207, 2013 what is going to change in eu rural development policies after 2013? main implications in different national contexts francesco mantino*1 national institute of agricultural economics, rome, italy abstract. this paper will address the changes the cap post‐2013 may bring to its second pillar in eu countries. after the presentation of the legislative package put forward by the european commission, a debate emerged in some countries on how to define rural development strategies for the period 2014‐2020. the paper will discuss positive innovations and main challenges of the new rural development policies with respect to what happened in the 2007‐2013 period. in particular, the paper intends to focus on the following issues: 1) which relevant changes have been introduced in the rural development framework; 2) how these changes can influence the preparation of the next programming period 2014-2020, looking more in depth at three countries (italy, spain and france); 3) what lessons can be drawn from this reform and the initial implementation in three countries in terms of institutional changes and their likely success and failure. this analysis concludes that the success or failure of the 2014-2020 reform of rural development will significantly depend on what types of transaction costs and incentive systems will be brought about within the programming system. these two factors in turn will strongly depend on the way the different actors will perceive the different costs and incentives, their expectations on the role of rural development programmes in a context where budget for agriculture is shrinking everywhere, and finally their capability to build new strategic alliances and cooperation at every level (national, regional and local) with other economic and social actors. future policy scenarios might bring new and heavy constraints to rural development policies and consequently might also reduce the opportunities of institutional innovations. keywords. common agricultural policies, rural development, institutional changes. jel codes. q18 1. introduction after the publication of the proposals by the european commission, in october 2011 (ec, 2011), the discussion and the negotiations on the reform of the common agricultural policy (cap) have brought about over the previous months quite different results for the two pillars. we can assume that, after the conclusion of the tri-lateral discussion among the european parliament (ep), the european commission (ec) and the european council (ec) and the final agreement which has been recently reached, the new regulation concerning the 2nd pillar can be considered as definitive. * corresponding author: mantino@inea.it. 192 f. mantino the main objectives of this paper are as follows: 1) to explore which relevant changes have been introduced in the rural development regulation; 2) how these changes can influence the preparation of the next programming period 2014-2020, by examining more in depth three countries (italy, spain and france); 3) what lessons can be drawn from this reform and the initial implementation in terms of institutional changes and their likely success and failure. this analysis will be carried out in a context where a political agreement has been already reached with the irish presidency on the multiannual financial framework (mff), thus concluding the negotiations on the eu’s medium-term financial framework until 2020. the actual mff regulation and the accompanying inter-institutional agreement including various declarations by the parties must be voted in the parliament in the autumn 2013 once the council has adopted the draft mff regulation. the overall mff ceiling and the allocation for the cap pillars 1 and 2 as agreed by the european council in february 2013 were not changed in the final agreement. in this scenario most of member states and regions have already started preparing their programming documents, which are rural development plans (rdps) and for operational programmes (ops) deriving from the structural funds (sfs): that is, european social fund (esp) and european regional development fund (erdf). 2. main changes in regulations concerning rural development and sfs the new set of regulations2 introduced different types of changes, which are becoming as evident as the preparation of the programming documents is going forward. the main changes can be summarised as follows: 1. the introduction of a common programming frame including cohesion policies and rural development which aims to strengthen integration between them and a more flexible programming system for rural development; 2. a new emphasis on innovation in agricultural systems; 3. more emphasis on cooperative approaches in specific fields as food chain projects, entrepreneurial networks and local development projects; 4. more selective targeting to rural areas and beneficiaries: e.g. lfa, small farms, farms not exceeding a certain size, new comers (start-up initiatives), etc. we can add to these specific changes in the rural development regulation all those relevant changes in the framework of the 1st pillar that have relevant impacts on the pro2 two regulations are crucial for rural development: • the new regulation concerning common rules for erdf, esf, eafrd and emff com(2011) 615 final/2, a regulation of the european parliament and of the council laying down common provisions on the european regional development fund, the european social fund, the cohesion fund, the european agricultural fund for rural development and the european maritime and fisheries fund covered by the common strategic framework and meef; • the specific regulation on rural development com(2011) 627 final/2, a regulation of the european parliament and of the council on support for rural development by the european agricultural fund for rural development (eafrd) 193what is going to change in eu rural development policies after 2013? gramming and management of rdps. among those it is worth mentioning at least two: a) the first one concerns the greening rules, which will have relevant effects on the design of agro-environmental measures, especially on the new baseline levels related to the future farmer’s commitments; b) the second change concerns the broader transfer of decisions on the modalities of implementing the 1st pillar at the country level, with the consequence of allowing higher integration among the rural development measures and the regionalised support to be provided via the new direct payments. the common programming frame is represented by a set of thematic objectives underlying the europe 2020 strategy and a common programming document: the partnership agreement (pa). this document should define a shared strategy (among the different funds) and the coherent modes for coordinating actions that are individually financed by each fund. the pa should also define the contribution of rural development policy to the common set of objectives of europe 2020. an integrated pa is certainly the biggest challenge of the new programming period, due to the traditional separation between funds in the concrete implementation of past eu policies. in fact, both rural development and fishery policy have got, since agenda 2000, a relative autonomy from the cohesion policy, producing separate programmes and even different programming cycles, with very few relations between them (excluding objective 1 regions in the 2000-2006 period). in the period 2007-2013 this separate approach has been confirmed by the existence of two different national documents: the national strategic frame (nsf) for sfs and the national strategic plan (nsp) for eardf. the pa 2014-20 is strongly different from the nsp 2007-2013, with regard to the potentiality of strategic guidance of rdps. there are several elements that contribute to strengthen the strategic function of the ap 2014-20 in comparison with the previous period: • the need to set precise targets for each thematic objective of the europe 2020 strategy; • the need to explain how (economic, social and environmental) conditionality is to be fulfilled before the programme approval; • the demonstration of a sufficient administrative capability for all institutions which are responsible of the programmes’ management; finally, the need to describe how coordination is going to be ensured by the different institutions, in order to implement effective actions in the different territories (urban, rural, coastal areas) and in favour of specific target groups. integration of eu funds under a common framework can provide new opportunities for public choice to avoid duplications and policy overlapping, and to strengthen synergies among different policy instruments. integration might also provide new opportunities for local private and public potential beneficiaries in mixing different sources of funding for their investment needs. but at the same time integration of eu funds will be possible only if policy makers and administrations responsible of the different funds agree to cooperate in more effective ways than in the past. this is not easy for two different reasons. first, agenda 2000 and the consequent separate programming system brought about different working rules and strategies which in turn increased over time the lack of communication between cohesion and rural development policies. second, in each sector policy makers and public administrations usually consider their set of financial resources and policy instruments as their own fenced area where only they can take allocation decisions. for 194 f. mantino these reasons policy makers and public administrations are not used to collaborate in policy design and implementation. this implies that the new common frame will raise the need of coordination and also the transactions costs related to the coordination system (meetings, committees, human resources involved, etc.). the introduction of a common framework is joined to a more flexible system of organising the rdp. this is also presented by ec as a an element of simplification that was introduced by the reform. this new system is based on six priorities (instead of four) and a menu of measures that can be combined without any restrictions (instead of being ex-ante grouped in four axes). in any case, moving from a logic based on measures to a result-oriented approach will not be costless. first, in public administrations structures the logic will continue to be measure-based because they are still strongly organised according to expertise in single measures and are used to design future planning by measures and not by results. the new approach would consequently require a reorganisation of the present technical and administrative rdp delivery structures in a very different way. second, even farmers’ organisations and local delivery agencies are used to express their policy demand as a list of policy measures and not as a specific set of economic and social needs to be fulfilled by policy strategies. in both cases there is a demand strongly influenced by the past and present ways in which rural development policy has been supplied until now, consequently causing a strong path-dependency mechanism. main stakeholders are used to deal with rdp measures already in place, which they have practiced for years and they know very well how to manage in farm advice. innovation in the agricultural system is another key issue and one of the horizontal principle that next generation of programmes should pursue, through a better transfer of the research outputs to the farm system and a more effective use of more traditional policy measures as vocational training and extension services. innovation will also be considered as a criterion to allocate funds among potential beneficiaries of rdps. innovation does not only depend on technologic novelties, but also on social and organisational progresses in the farm system (dwyer, 2013). a concrete signal of the real consideration addressed to innovation by future rdps will be given both by the enforcement rules of this principle (e.g. in selection criteria used to assess applications for funds) and also by the amount of financial resources that will be allocated to the european partnership for innovation groups, vocational training and extension services. innovation groups are not completely new in rural development. they have been introduced in the present phase 2007-13 through ad hoc measure concerning cooperation for development of new products, processes and technologies in the agriculture and food sector and in the forestry sector, that aimed to promote the cooperation between primary producers in agriculture and forestry, the processing industry and/or third parties (article 29 of the ec regulation no. 1698/2005). but the diffusion of such groups has been quite limited and unevenly diffused at regional level. the challenge will be the mainstreaming of this approach, that did not get so great success in the present rdps. as the case of leader initiative has shown, mainstreaming an innovative approach does not always bring to success. moreover, innovation overcomes regional administrative borders and this might be a factor driving towards a multi-regional programming, especially in those countries with a regionalized institutional setting. in any case, an effective selection and setting up of innovation partnerships raises new costs of coordination and information, which should be taken into account in policy design of future rdps. 195what is going to change in eu rural development policies after 2013? the new regulations (both the common one and that specific for rd) put a strong emphasis on cooperative approaches promoted by farmers, agro-industrial entrepreneurs and local non-farm actors. article 36 of the rd regulation focuses on different forms of intermediate institutions promoting cooperation: food chains (short and long ones), entrepreneurial networks, leader-like partnerships, etc. these forms are now quite diffused in rural areas, especially in those countries we are examining here (italy, france and spain). the real challenge with the operational support of food-chains is the demonstration of the value added generated for primary producers (the farm sector strictu sensu), a condition to be verified before and after the conclusion of the project. as diverse evidences in this field have already shown, enhancement of primary producers’ value added usually depends on their bargaining power and the inter-professional contracts that are set up between farmers associations and agro-industry, rather than on ex-ante criteria imposed by the state and/or regional administrations. instead, food chain projects usually focus on structural investments and do not take into account contractual arrangements and the general organisation of the chain, that is the crucial issue in many cases. with regard to the leader-like approaches, now called in the common regulation “community-led local development” (clld), the real novelty concerns the opportunity of using all funds to finance rural development projects and extending this approach to cover even urban, peri-urban (rural-urban fringe) and finally coastal areas. in this case previous experience can facilitate the extending of clld approach to areas and public administrations different from agriculture and rural policies. those who are unfamiliar with clld might prefer simpler and more direct instruments to support local development initiatives. instead, multi-fund approach covering all types of areas is strongly advised by ec services, who recently published guidelines to implement clld in all european areas. in effect, multi-fund approach could offer more opportunities for mixing funds to cover non-sector interventions and consequently focusing eafrd on more sector needs in agriculture and agro-industry and even in capacity building of local communities. multi-fund approach in local development is not new, but it requires time and human resources to be invested in information, coordination and learning processes both at level of regional administrative structures and local communities. after twenty years of leader programmes we can assume that this capital of information, coordination and learning capabilities has been accumulated in those administrative structures dealing with rural policies and in rural communities, but the same cannot be said for the other structures and urban communities. this is the reason why this approach is perceived as burdensome and complex by the “new comers” of local development. every policy reform of rural development is in reality carried out in three different phases: 1) the preparation of the reform principles in the regulations; 2) the definition of the policy strategy by programmes at national and/or regional level; 3) the definition of more operational criteria for applications’ eligibility and selection by management authorities. it is worth recalling that innovative principles, although introduced by eu regulations, can be hampered by inefficient and ineffective policy strategies and operational rules. this means that every reform might eventually fail when concrete rules are set up, because relevant stakeholders contribute to make strong resistances to the process of reform and institutional change. looking at the 2014-2020 reform of rural development, there are two fundamental factors which can affect the ways the reform is translated into policy strategies and operational criteria: 196 f. mantino • first, the general policy scenario for next period, including the relations between rural development and cohesion policies within the partnership agreement, the role of national policies and, last but not least, the redistributive effects of the reform of the 1st pillar of cap; • second, the perception of the reform by main stakeholders, their responses (acceptance or resistance) to the institutional changes which the reform implies. this will contribute to explain success and failure factors of strategies of rural development in the next programmes. we are going to examine the policy scenario in the third paragraph and then the role of success and failure factors in the fourth one. 3. the new scenario for designing the future rural development in europe: new relations with cohesion policies, 1st pillar of the cap and national policies as already mentioned, the first steps in building the future program strategy (in every european country) is the preparation of the pa. this document is more complex than in the previous programming periods, not only because of the multi-fund and multi-goal strategy, with all characteristics we have already stressed in the previous paragraph, but also for the different policy constraints which should be taken into account (mantino, 2013). the first set of rules and constraints is represented by the package of eu regulations and the ec position paper containing country-specific recommendations on the future use of eu funds, to be considered in the pa and rdps design (see the left-hand side of the figure 1). ec services prepare the position paper for each country by taking into account of the progresses and lessons coming out of the 2007-13 programmes. on the basis of these results, the ec position paper would provide country-specific guidelines on priorities and governance of eu funds. this is a new institutional step in the whole decision-making process: in the past ec services evaluated national strategies after the programme proposal was conceived and officially submitted by member states. this implies the european commission is going to play a major role in influencing national and regional strategies in the programming phase 2014-2020. the pa should also take into account of the national reform programme (right-hand side of the figure 1), where strategies have been defined for economic development, in agreement with the european commission. in conclusion, rural development policies, while should be conceived and implemented with a regional and territorial logic, will be constrained by european strategic guidelines and implementing rules. the development of rural areas in the future programmes cannot follow a separate approach, it should be part of the whole set of development strategies which are being planned in each country. this will be increasingly true in many fields, e.g. the case of research, renewable energies, climate change, water and irrigation infrastructures, digital services, etc. this new frame somehow forces the european funds working together and also affects internal governance of cohesion and rural development policies, both at national and regional level, via the programming role of the state, the regional and local communities (mantino, 2010). future strategies for rural development will be also influenced by the financial frame for 2014-20. when we look at the current draft budget allocations that have been recently 197what is going to change in eu rural development policies after 2013? circulated by ec concerning the rural development in the 2014-20 period, total resources devoted to rural development decrease from 95,5 billion € of the 2007-13 period to 84,7 billion € (10,6 billion €, 11,1% at 2011 constant prices). most of the bigger countries (especially poland losing almost 29%, then germany, spain and romania) seem to lose in the global allocation, with the exception of italy and mainly france (figure 2). in particular, italy will gain in the rural development budget, and in the cohesion policy. the current estimate of the ministry of economy is that this country will move from the present 28,7 billion € to 29,5 billion in the 2014-20 period. also, italy is going to receive, according the mff agreement, a specific endowment of 500 million € for interventions in rural areas to be funded by erfd. to understand the more general financial frame for the future rural development strategy, even the future financial budget for national agricultural policies will be crucial. rural development policies are more and more policies funded by eu. this role increased over time for two reasons: 1. the decreasing availability of national resources for the support of the agricultural sector. this is quite evident in italy, for example, where financial resources devoted to own agricultural policies are shrinking over time, especially after the beginning of the recent financial and economic crisis (2008); figure 1. programming system of eu funds (cohesion and rural development) in 2014-2020. source: information drawn from the regulations package. 198 f. mantino 2. the broad range of measures included into the menu. previous reforms of cap enlarged the panoply of available measures to member states, so as to include also typologies of instruments belonging to the tradition of the 1st pillar (e.g. risk management measures). looking at the italian case, the global budget for agricultural policies was strongly reduced in the central years of crisis (figure 3). this is due to the reduction of both national and regional budgets for agricultural policies, and in particular to the package envisaged by the financial stability programme. while national policies were progressively being cut, eu policies3 were raising both in absolute and relative terms, so as to become a stable source of public support. it is worth recalling here that in the next future the macro-conditionality related to financial discipline and the stability and growth pact will affect in a relevant way the opportunities to finance policy support in agriculture and rural areas. the move from the pre-crisis years (2006-8) to three last years (2009-11) was really astonishingly critical for agricultural policy in italy: national policies devoted to agriculture were globally reduced by almost 1/3 and regional policies by 1/5 of their initial budget. unfortunately, similar data are not available for other european countries, so we can use state aid expenditures as a proxy of national expenditures in agricultural sector from the data published by the european commission services (dg competition) on the ec 3 eu policies also include the national quota of co-funding that is a sort of constrained allocation for each member state, to be added to eu quota for cohesion and rural development policies. figure 2. resource allocation in 2014-20200 period by member state in comparison with 2007-2013 (european commission, 2013). figure_2 pagina  1 0 2000 4000 6000 8000 10000 12000 14000 16000 million  €  2007-­‐13 million  €  2014-­‐20 199what is going to change in eu rural development policies after 2013? web scoreboard, providing time series of state aid by sector and objective since 1992 to 2011 (at 2000 constant prices). when we look at the time series of state aid for agriculture in eu-27 member states, we can confirm that national financial support has strongly decreased since the second half of the 90s, then after the last eu enlargement to the central-eastern countries there was a new temporary increase and finally a further decrease has taken place since 2008 (figure 4). in this context, pa and rdps resources will be under heavier political pressures from the various stakeholders, in order to counterbalance, to some extent, the cuts of national resources and will be allocated to cover partly previous interventions benefitting from national funds. this will drive policy-makers (national and regional) to design generic and multi-scope strategies, with the consequence of shaping non-focused policy objectives. the third element of the policy scenario come from the potential impacts of the 1st pillar reform, in particular from the redistribution effects linked with the regionalisation of the direct payments (dp). recent analyses of different scenarios for italian agriculture (pupo d’andrea and de vivo, 2013) highlight that: a) regions with the present greatest share of direct payments (lombardia, veneto and puglia: 36,5%), unless a conservative scenario is being adopted, will be beneficiaries of lower shares; b) a redistribution in favour of mountain and hill areas will take place in almost all regions; c) a relevant redistribution will occur across different types of farm, even in the most conservative scenario: reduction of dp for specialist cereal and other field-crops farms and for specialist livestock farms in northern italy; reduction of dp for specialist fruit, citrus fruit, olive farm types in southern italy. in conclusion, rural development programmes will be substantially affected by three components of the future scenario: figure 3. public expenditures for agriculture and rural development in italy: regional policies, national policies, eu policies. 2005 2006 2007 2008 2009 2010 2011 )*00) 1.000,0) 2.000,0) 3.000,0) 4.000,0) 5.000,0) 6.000,0) 7.000,0) 8.000,0) 9.000,0) eu)policies na:onal)policies regional)policies bi lli on &€ source: annuario agricoltura italiana, inea. 200 f. mantino 1. they will be more framed in the complex set of general objectives and constraints provided by europe 2020 economic policy and the related national reform programmes. this is going to raise a new awareness of complementarities with structural funds and searching for synergies between them ; 2. in many countries, due to financial cuts of national and regional expenditures, rdps are going to become the only and one structural policy addressed to agricultural sector, with the consequent generation of increasing pressures and demand by the agricultural stakeholders; 3. pressures are also coming by regions and areas losing from dp regionalisation, to compensate for the cuts of 1st pillar with further allocation of rdps’ resources. 4. policy strategies in some european countries strategic choices that member states are making at the present moment concern the number of programmes and the division of responsibilities between central government and regions, the thematic priorities and the territorial priorities. the state of the art is quite different according to which member state is considered. here we focus on three countries (italy, france and spain), where the discussion on possible options for rural development strategies is more advanced than in others4. 4 information used here derive from interviews with responsible officials of rural development policies in each country. figure 4 – state aid for agricultural sector in eu-27, million €. figure_4 pagina,1 ,,1992 ,,1993 ,,1994 ,,1995 ,,1996 ,,1997 ,,1998 ,,1999 ,,2000 ,,2001 ,,2002 ,,2003 ,,2004 ,,2005 ,,2006 ,,2007 ,,2008 ,,2009 ,,2010 ,,2011 8000 10000 12000 14000 16000 18000 20000 eu27 eu27 source: european commission, dg competition, state aid scoreboard 2013. 201what is going to change in eu rural development policies after 2013? italy in this country there will be 21 regional rdps and also two national programmes: one addressed to risk-management measures and one to the national rural network. the justification for the risk management national programme lies in the complex management at regional level and in related scale economies at national level. other priorities requiring a stronger coordination at national level are innovation and knowledge transfer in agriculture, irrigation infrastructures and interventions in agri-food sector with interregional scope. the ec position paper outlines the need for a greater role of ministry of agricultural policies, but national programmes for these thematic priorities seem unrealistic, given the present distribution of functions between state and regions and the bargaining power of these two institutions. food chains will be one of the main priorities of pa in the context of enhancing the competitiveness of agriculture and forestry. short and long food chains will be supported through the instrument of the integrated projects, which have been adopted widely in the present italian rdps. a common approach is going to be proposed by pa to avoid disparities and failures in the different regions. territorial priorities have to be included in the strategic design, both at national and regional level. rural territories will be defined with the same approach adopted in the 2007-2013 period, when they were identified in four main typologies. within these typologies, the most remote areas represent a relevant priority for the erdf5 and eardf. there will be a national programme funded by erdf addressing this category of areas. even rdps can assume territorial priorities, in particular with a stronger emphasis on mountain areas. this option will not be considered by all italian regions, only some of 0ly (emilia-romagna, marche and friuli venezia-giulia). the new role of mountain and most remote areas will enhance the territorial dimension in the national and regional programmes. this undoubtedly represents a step forward with regard to the 2007-2013 programming period. even the future leader approach is going to be more territorially targeted, with a more selective approach to the choice of the eligible territories. a multi-fund logic is going to be implemented by the italian authorities, although with relevant differences in relation to the region. spain in this country the programming structure will be still based on 17 rdps plus two national programmes (the national rural network; irrigation infrastructures). the nature of coordination is going to become more formalised through specific instruments than in italy. first, the central government is going to prepare an intermediate programming document (the national framework), placed between pa and rdps, aiming at setting detailed guidelines to the design and enforcement of measures at regional level. second, the government is going to appoint a national management authority with the task of coordinating all 5 a specific earmarking of 500 million € has been approved by european council for these areas within the mff agreement. 202 f. mantino regional authorities and promoting synergies with cohesion policies in several crucial topics for national policies, such as water management, soil erosion, flood prevention and forest fire fighting. a stronger effort in this direction is recommended by the ec position paper (2012). spain does not put any particular emphasis on territorial priorities, leaving the regions and autonomous communities free to decide what territorial strategy has to be adopted. on the whole, spain does not seem to adopt relevant changes in the strategic programming with regard to the present period. france this country, after the beginning of the new government by mr. hollande, is more oriented towards a relevant devolution of responsibilities in the design and the management of the european programmes. this trend is also confirmed for rural development, with the option of 26 regional rdps and only one national programme for the rural network. as in the spanish case, this devolution is somehow requiring more coordination efforts at national level, ensured by a national framework providing strong guidance to each measure. the regionalised option is still under discussion, but the representatives of the french ministry of agriculture state that this is going to prevail as the most likely policy scenario. territorial strategy in france is linked to the leader approach and to macro-regional contexts, especially in mountain areas. leader approach encompasses urban and periurban areas with a multi-fund logic. multi-regional programmes underpin territorial strategies for five massif areas (alpes, central massif, jura, pyrenees and vosges) and for four river basins. in conclusion, france is going to give a relevant impulse to the regionalisation process of rural development, while it is maintaining his territorial approach with a broader use of leader method and cohesion policies addressed to interregional areas. looking at three countries, we can summarise the following strategies: • in italy there is a confirmation of the regionalised organisation of rural development and cohesion policy, but at the same time the central level is trying to foster an increasing focus on territorial priorities. some intervention, due to scale economies and greater effectiveness, is being managed at central level; • in france, there is a strong political pressure towards a more regionalised model, but with a reinforced coordination at the central level by the ministry of agriculture; • in spain, which remains a regionalised country, this model will be strengthened through more coordination by central level. thus, coordination has emerged as the focal point and one of the major institutional weakness in all decentralised countries, not only in italy and spain (mantino, 2010). this has been stressed in ec position papers concerning the use of european funds in the next programming period and will be emphasized in the future, because coordination is going to involve not only administration of agriculture, but also administrative bodies responsible of other policies, and can generate strong resistance in a world fenced by policy specialization and sectoral lobbies. 203what is going to change in eu rural development policies after 2013? 5. the success and failure factors in the next rural development programmes the recent reform of rural development does not involve radical changes in the present basic framework of rules. we could use in this case the concept of «incremental institutional change» introduced by douglass c. north (1990)6. now the question is the following: what is the likely degree of success of the most relevant aspects of this reform? this question is particularly important for all those novelties introduced by the recent reform (integration of funds, concept of innovation, cooperative approaches, etc.). the results of this reform strongly depend on three key institutional factors: • the effects of the reform on perceived transaction costs; • the system of incentives which the reform foresees and is going to put in place in the national/regional programmes with the aim to compensate the new transaction costs arising from the reform; • the presence of agents of change who can efficiently exploit the new system of incentives and give a support to main novelties of the reform. most of new rules of this reform contribute to change of transaction costs of designing and implementing the future pa and rdps. the involvement of different funds, instruments and sector administrations, as a consequence of the integrated approach required by this reform, contributes to increase three types of costs: 1. coordination costs, linked to a common programme for all funds (pa) and to the need for a consistency between different programmes and integrated projects; 2. information costs, linked to increased complexity of new rules and the need for acquiring information on the modes the other policies usually operate; 3. learning costs, linked to the new rules and to the exchange of expertise in different fields of interventions. the size of these costs can generate serious constraints in managing and accessing future policies, both for public administrations and for individual beneficiaries, as we have already mentioned in the first paragraph. in this respect, transaction costs are evident in the process of coordination of different funds in the preparation of the pa. information costs arise, for example, for experts in one type of policy to access the complex frame of rules pertaining to other type of policies. learning costs are related to new rules are going to emerge over time in preparing and implementing a very complex document like pa or local integrated projects and food chain projects. rural development regulation, however, does not only contribute to raise transactions costs, but also to reduce them in two different ways: first, by introducing specific incentives aiming to take into account transaction costs; second, by simplifying some of the previous rules that have been burdensome both for public administration/advisory service and for final beneficiaries. some example of the latter can be found either in the definition of common rules between categories of investment which are supported by structural funds and eafrd or in the use of standard costs to prepare the individual application for agricultural investment. both types of simplifica6 this does not mean that institutional changes are not relevant in the reform, but simply that it is going to make «…adjustment to the complex of rules, norms, and enforcement that constitute the institutional framework» (north, 1990: 83). 204 f. mantino tions have been strongly requested by public administrations and farmers’ organisations during the preparation of the reform proposals. which are the main incentives for coordination of different funds? actually, regulations do not foresee incentives, but simply prescriptions for policy makers and administrations which are responsible of funds’ management: they have to set up mechanisms and structures with the specific task of coordination. regulations state that effective structures and mechanisms should be submitted to the european commission either in national documents (pa) or in regional programmes. these prescriptions do not automatically ensure that they will be effective: these mechanisms can be only formally created, but their substantial compliance with the coordination principle can be hardly verified. if the main stakeholders follow their own “utility function” (as d.c. north would say) and respond to their interest groups, there will be no incentive to cooperate in the real world. but for agricultural stakeholders there are several implicit incentives in cooperating with other economic and social actors in contributing to the preparation of the partnership agreement. these incentives are clearly evident now, while the pa is being prepared in the different european countries. first, there is an opportunity of giving voice to agricultural sector’s needs also to non-agricultural policy makers that usually neglect them because of the traditional separation among sectors in the public administration. in principle, there is no opposition from funds other than eafrd to cover financial needs of those territories where agriculture has still a relevant economic and social role. second, there is a good argument in favour of cooperation among funds everywhere public interventions cannot be designed and implemented only by one fund: e.g. water resources, climate change, biodiversity, renewable energy and last but not least, food chains. incentives to coordinate become more visible at the local level, where local strategies and/or pressure groups express a demand for integrated policies, independently from the source of funds. this is the case, for example, of food-chain projects, where changes in competitiveness not only depend from agricultural investments but also from research and territorial infrastructures (e.g. logistic platforms). in any case, cooperative approaches at the local level, being they either under the form of food-chain projects, or farm networks, or integrated projects promoted by groups of farmers and public-private partnerships, are all projects entailing higher transaction costs in comparison with the single and more traditional application for public funds. in that respect, the regulation on rural development allows, in all those cases, to cover transaction costs deriving from information, animation, coordination, monitoring and running costs of the local “agency” or group of farmers involved in the project. the new regulation opens this opportunity to different types of project, other than the local action plans managed by lags: now collective projects focusing on agri-environment, climate change, renewable energy, forestry, etc. are envisaged. these collective forms of project design can also contribute to reduce the costs of local selection and monitoring for public administration, while it entails more impact at territorial level. on the contrary, these forms might be perceived as more costly by some part of public administration more prone to path-dependency and less open to institutional change. incentives to coordinate can also be placed in a national strategy allocating financial resources to specific areas under the condition to implement a place-based policy. this is 205what is going to change in eu rural development policies after 2013? the case of the italian strategy for the most remote areas7, promoted by the ministry of economic development, the ministry of agricultural policies and the ministry of labour and social services, whose main objective is combining different policies and funds (european, national and regional). this strategy will be carried out under the coordination of the ministry of territorial cohesion and can work only under the condition of a multilevel collaboration. for the other institutions (either national or regional) the main incentives to cooperate are in the political ownership of the initiative and its potential results and visibility. the new programming system, as we have earlier mentioned, implies a series of transactions costs due to the result-oriented logic and the introduction of a quantified target system. a good example of incentive for public administration is given by the performance reserve aiming to provide financial rewards to most effective and efficient programmes. this reserve has been used in the previous period for cohesion policies and programmes, with some relevant results in italy because it was linked, in the convergence regions, to the fulfilment of important targets in the infrastructure and services sector. the incentive systems in this experience largely depended from two key factors: a) the quality of targets defined to measure the programme performance; b) the capability of managing and strengthening the target system at eu and national level. the introduction of the performance reserve also in the field of rural development policies needs to evaluate if the two conditions can be sufficiently developed there. looking at the past experience in rural development it must be outlined that a strong investment in human resources, methods and technical assistance should be done in the initial years of the programming period. so, in this case the possibility of extending this instrument to rural development is highly controversial. the reform of rural development has also contributed to reduce transaction costs via some form of simplification of implementing rules: it is the case of the introduction of standard costs to justify simplified procedures for the expenditures accounting and the payment control. this change can be relevant both for public administrations and for final beneficiaries. but it is worth saying that still a certain amount of work has to be done in the process of simplification and harmonisation of rules among the different funds. in conclusion, the success or failure of the 2014-20 reform of rural development will significantly depend on what types of transaction costs and incentive systems the new programming system will bring about. these two factors in turn will strongly depend on the way the different actors will perceive the different costs and incentives, their expectations on the role of rural development programmes in a context where budget for agriculture is shrinking everywhere, their positions with regard to non-agricultural institutions and stakeholders within a common framework where all funds should collaborate to pursue the europe 2020 strategy and, finally, their capability to build new strategic alliances and cooperation at every level (national, regional and local) with other economic and social actors. future policy scenarios might bring new and heavy constraints to rural development policies and consequently might also reduce the opportunities of institutional changes. in that case, pa and rdps will prevail with extensive strategies: policy-makers and public administrations will be driven to design generic and multi-scope strategies, with the consequence of shaping non-focused policy objectives (mantino, 2012). 7 these types of areas are generally far from urban centres and from main agglomerations of services. 206 f. mantino but at the same time this scenario might force towards different policy strategies. this reform, when carefully examined, can offer relevant opportunities: a) to counterbalance transaction costs via specific incentives (e.g. financial performance reserve for most performing programmes or support to collective projects for running and animation costs); b) to reduce transaction costs via more simplified procedures (e.g. simplified payment rules through the direct cost system); c) to reduce transactions costs via some reorganisation process of the delivery structure (e.g. grouping technical and administrative structures by rd objectives, leaving apart traditional organisation by measure). in that case, pa and rdps will prevail with more focused strategies: this means more focused on few and selected strategic topics and more territorially oriented. it is likely that both type of strategies (extensive and more focused) will be put in place, although it is very hard saying which will prevail at the end. policy strategies will be defined within the new institutional framework, where innovative and conservative attitudes coexist. as d.g. north says, «…the actual institutional framework is in fact a mixed bag of institutions that promote such productivity-raising activities and institutions that provide barriers to entry, encourage monopolistic restrictions, and impede the low-cost flow of information» (1990:64). references dwyer, j. (2013). transformation for sustainable agriculture: what role for the second pillar of cap? biobased and applied economics 2(1): 29-48. http://www.fupress.net/ index.php/bae/article/view/12174 european commission (2011). proposal for a regulation of the european parliament and of the council on support for rural development by the european agricultural fund for rural development (eafrd), bruxelles, com(2011) 627/3. european commission (2012). position of the commission services’ on the development of partnership agreement and programmes in italy for the period 2014-2020, bruxelles. http://ec.europa.eu/regional_policy/what/future/pdf/partnership/it_position_ paper.pdf european commission (2012). position of the commission services’ on the development of partnership agreement and programmes in france for the period 2014-2020, bruxelles. http://ec.europa.eu/regional_policy/what/future/pdf/partnership/fr_position_paper_fr.pdf european commission (2012). position of the commission services’ on the development of partnership agreement and programmes in spain for the period 2014-2020, bruxelles. http://ec.europa.eu/regional_policy/what/future/pdf/partnership/es_position_paper.pdf mantino, f. (2010). the reform of eu rural development policy and the challenges ahead, notre europe policy paper, no. 40, paris. http://www.eng.notre-europe.eu/0112327-the-reform-of-eu-rural-development-policy-and-the-challenges-ahead.html mantino, f. (2012). the eu proposals for rural development after 2013: a good compromise between innovation and conservative choices?, notre europe policy brief, paris. 207what is going to change in eu rural development policies after 2013? http://www.eng.notre-europe.eu/011-3041-the-eu-proposals-for-rural-developmentafter-2013-a-good-compromise-between-innovation-and-conservative-choices.html mantino, f. (2013). la programmazione dello sviluppo rurale 2014-2020: il position paper e l’accordo di partenariato. agriregionieuropa 32(9), marzo. http://www.agriregionieuropa.univpm.it/dettart.php?id_articolo=1037 matthews, a. (2013). the cap budget in the mff agreement, in cap reform.eu. http:// capreform.eu/the-cap-budget-in-the-mff-agreement north, d. c. (1990). institutions, institutional change and economic performance. cambridge university press, new york. pierangeli, f. (2013). quadro finanziario pluriennale 2014-2020: una prima analisi degli impatti agriregionieuropa 32(9), marzo. http://www.agriregionieuropa.univpm.it/ dettart.php?id_articolo=1046 pupo d’andrea, m.r., de vivo c. (2013). la regionalizzazione degli aiuti diretti, inea. http://www.rica.inea.it/pac_2014_2020/pac_italia.php viaggi, d. (2012). rural development in the post-2013 cap: huge opportunity or devil in the details? intereconomics 47(6), november/december. bio-based and applied economics 5(1): 63-81, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-16367 the instability of farm income. empirical evidences on aggregation bias and heterogeneity among farm groups simone severini*, antonella tantari, giuliano di tommaso università della tuscia, dafne, viterbo, italy date of submission: 2015 29th, june; accepted 2016 16th, february abstract. this paper analyses the instability of farm income experienced by a constant sample of italian farms over the period 2003-2012. it assesses the extent of the aggregation bias due to the use of aggregated vs. single farm data and estimates the level of farm income variability in several groups of farms for the whole period and for two sub-periods. differences between groups and periods are assessed by means of non-parametric tests. results suggest that analyses based on aggregated farm data are likely going to strongly underestimate the extent of income variability faced by farmers. income variability levels differ among farm groups and have significantly increased over the considered time. this has policy implications regarding the risk management tools recently introduced within the rural development policies and how these should be targeted on the farms that more need them. keywords. farm income, income instability and variability, aggregation bias, farm heterogeneity, risk management policies jel codes. q12, g320, q18 1. introduction farming is a risky business because forces (such as weather) beyond the control of farmers affect their income (mishra and sandretto, 2002). farm income stability has been one of the goals of agricultural policies both in the us and the eu (mishra and sandretto, 2002: 209). this is because income instability negatively affects farmers’ well-being and decisions, their ability to expand operations and repay debt and, in turns, this can also have secondary effects on agribusiness firms and creditors (mishra and el-osta, 2001; mishra and holtausen, 2002; mishra and sandretto, 2002; vrolijk and poppe, 2008; vrolijk et al., 2009). while a large number of studies focuses on price and/or yield and revenue instability (looking often at single crops or very specialised farms), there are not many analyses specifically focused on the stability of the whole farm income. this seems an important * corresponding author: severini@unitus.it 64 s. severini, a. tantari, g. di tommaso knowledge gap because “… farmers are ultimately concerned more about their net incomes than about prices and costs” (mishra and sandretto, 2002: 219). the lack of empirical evidences on income instability in the eu may become a constraint for monitoring and designing the set of tools that have been introduced by the recently reformed common agricultural policy (cap) to support farmers to cope with risk (matthews, 2010; meuwissen et al., 2011; tangermann, 2010)1. this paper tries to fill this gap investigating the level of income variability of a large constant sample of italian farms over the period 2003-2012. this allows to assess the level of farm income variability in the whole considered sample and in several farms grouped according to production orientation, size and economic performances as well as of two consecutive periods of time. the paper aims at identifying the information that are useful for designing and targeting income stability policy tools focusing on two aspects. on the one hand, it assesses whether it is preferable to use variability indexes that account only for down-side risk (i.e. movements of farm income on the left side of the distribution) (horcher, 2005) or common variability indexes considering both sides of the distribution. on the other hand, the paper assesses how important it is to account for the heterogeneity existing within the farm sector. this issue is relevant because of two main reasons. first, as it is well known, focusing on regional or national aggregates is expected to generate aggregation bias that underestimates the level of variability experienced by farmers (coble et al., 2007). this aspect is relevant also to correctly assess the extent of the compensations of income losses that the recently introduced cap income stabilization tool will pay to farmers. this is especially because it is still not very clear how the reference income and deviations from it will be assessed provided the existing heterogeneity in data availability and quality among eu member states. second, because farms strongly differ in terms of several dimensions, different farm groups can face different levels, types and evolution of variability. the paper contributes to the existing literature because, based on our current knowledge, empirical evidences based on individual farm income data on the use of down-side risk indicators, on the extent of the aggregation bias and on whether income variability has increased over time are scant in the agricultural economics literature. furthermore, the analysis is innovative because, to test for differences in terms of income variability between farm groups and periods, it uses non-parametric approaches that are less affected than parametric tests by the presence of outliers – a situation that is often encountered when using individual farm data. the results of the analysis could also feed the policy debate regarding whether the instability of farm income is a relevant policy concern and the introduction of the new cap risk management measures is justified. the analysis also provides insights on how to target intervention on the farm groups where it is more needed. the next section casts the analysis into the previous literature on farm income variability while the following one describes data and methodology. section 4 presents the 1 regulation (eu) no 1305/2013 of the european parliament and of the council of 17 december 2013 (o.j.e.u. l 347 of the 20.12.2013) establishes risk management measures that cover: (a) financial contributions to premiums for farm insurances (art. 37); (b) financial contributions to mutual funds (art. 38); (c) an income stabilisation tool, in the form of financial contributions to mutual funds, providing compensation to farmers for a severe drop in their income (art. 39). 65the instability of farm income obtained empirical results while the last paragraph critically discusses the main findings of the analysis, underlines its weaknesses and identifies possible areas for further research. 2. literature review on farm income instability several risks affect farm businesses but most of the analyses focus on the business risk that is generated by the aggregate effect of production, market and other sources of business specific risks (hardaker et al., 2007). while many analyses have been focused on single farm risk sources (e.g. yield risk or price risk) or single production enterprises (e.g. milk production), what is relevant is the interaction among the many elements generating business risk (oecd, 2009). this is because the overall risk a farmer is facing depends on the interactions among the different production activities carried on-farm and on the evolution of different parameters (e.g. product prices and yields). furthermore, in the eu, farm incomes are supported by mean of direct payments that represent around 30% of farm income (european commission, 2011) and have been claimed to reduce income variability (agrosynergie, 2011; cafiero et al., 2007; el benni et al., 2012). these elements support the idea that, to evaluate the business risk a farmer is facing, it is needed to account for the variability of the overall income of his/her farm over time (mishra and sandretto, 2002; oecd, 2009). in almost all the agricultural economic literature, analyses on farm risk refer to income (agrosynergie, 2011; el benni et al., 2012; el benni and finger, 2013; european commission, 2011; finger and el benni, 2014; meuwissen et al., 2008; oecd, 2003, 2009; vrolijk and poppe, 2008; vrolijk et al., 2009). however, the variability of the economic performances of firms can also be analysed by using cash flow indicators2 (plewa and friedlob, 1995). some authors have supported the idea that cash flow can be used to do so because of two main reasons. first, cash flow is better observable and harder to manipulate under generally accepted accounting principles. second, it is closer to liquidity management and can be a good indicator for the analysis of the firm’s survival: a company that is short on cash could fail and be technically bankrupt despite it has a large amount of accounts receivable on its balance sheet. despite this, cash flow has not been used yet in the agricultural economic literature apart in few cases (meier, 2004). because of this, we have decided to focus on farm income to allow for the comparability of the results with those of previous analyses. in order to assess the level of instability farmers are facing, it is preferable to have farm-level time-series because the aggregation of data from different farms generates aggregation bias. at higher levels of aggregation, poor incomes in some farms are offset by good incomes in others thereby reducing the overall variability (coble and dismukes, 2008; finger, 2012; oecd, 2009)3. several authors conclude that using aggregated data can severely underestimate the farm level risk (coble et al., 2007; coble and dismukes, 2008; kimura et al., 2010; popp et al., 2005). despite this, empirical evidences on the extent of aggregation bias in the case of farm income variability are scant. 2 we thank one of the anonymous reviewers for suggesting us to consider this branch of literature. the use of cash flow seems a very promising and innovative direction for future research developments. in particular, it could be very interesting to compare income variability with cash flow variability. 3 however, this phenomenon is reduced when systemic risk is pervasive and relevant. 66 s. severini, a. tantari, g. di tommaso this paper focuses on business related risks and considers only farm income. this seems coherent with the sectorial nature of cap. however, other analyses, such as mishra and sandretto (2002), have investigated the instability of the income of farm households, i.e. considering also off-farm income. mishra and sandretto (2002) showed that the variability of the incomes of farm families has not diminished over the considered 7 decades. however, their analysis relies on national-wide data and does not provide evidences about differences within the sector4. farm level analyses in the us focus more on the decomposition of household income variability by income sources than on the level of income variability per se (mishra and el-osta, 2001; mishra et al., 2002). empirical evidences based on single farm data are not abundant also in europe. the analyses by vrolijk and poppe (2008) and vrolijk et al. (2009) represent relevant pieces of literature on this issue. these rely on large samples of farms, have been developed in a considerable number of eu countries and allow for comparison between countries and types of farming. however, differences between countries and types of farms have not been subject to statistical testing. finally, the focus in the eu is in most of the cases on farm business income (i.e. off-farm incomes are not considered) because of data availability constraints and the agricultural policy orientation of the analyses. however, this is not the case of recent analyses developed in switzerland where the national farm data network also collects data on off-farm incomes (el benni et al., 2012; finger and el benni, 2014). in order to assess and to manage risk, it has been found that, in some cases, it can be preferable to consider down-side risk other than common variability indexes (miller and leiblein, 1997). this is because farmers are generally more concerned with movements of farm income on the left side of the distribution (horcher, 2005). however, indexes considering both sides of the distribution could perform equally well whenever, for example, the distribution of income over time is symmetric. thus, the use of one type of variability index or the other should be chosen on the basis of the specific situation under study. 3. data and methodology 3.1 data having to assess the variability of farm economic results over the years, there is the need to select farms that have been in the samples for a reasonably long period of years. the analysis is based on data from all individual farms that belonged to the whole italian sample of the farm accountancy data network (fadn) during all years of the decade 2003-2012. thus, the dataset is made by a balanced panel because the considered farms do not change over the 10 years. these are 2404 farms for 24040 observations. referring to a constant sample of farms and the same time interval allows for better comparing results among farm groups and sub-periods because this avoids that some of the reported differences may be due to changes in farm composition5. the resulting num4 mishra and sandretto (2002) use individual farm data in the second part of their paper to verify that off-farm income has contributed to the farm household income stability. 5 the use of an unbalanced panel dataset, while increasing the number of observations, could generate comparability problems. this is because farms refer to time intervals of different length and to different periods (e.g. at the beginning or at the end of the interval of time). 67the instability of farm income ber of farms is large enough also for comparing groups of farms selected within the sample. this is important because the considered farms have been grouped according to types of farming, economic size and productivity level (european commission, 2010) (table 1). 7 types of farming (tf) have been considered to account for production orientation and specialisation. economic size refers to small, medium and large farms defined by mean of the european size unit (esu) classes provided by fadn. finally, farms have been also classified into four groups according to the level of a partial productivity index calculated as the ratio between farm income and the amount of labour used on farm in terms of annual work units (awu) (european commission, 2010). unfortunately, the choice to have only farms belonging to all considered 10 years has driven us to have a not randomly selected sub-sample. this has two important consequences. on the one hand, the selected sub-sample cannot be considered representative of the whole farm population. on the other hand, the statistical weights provided by fadn annually for each sampled farm cannot be used for reporting the results to the farm population. however, despite these limitations, it is important to note that the distribution of the farms within the sub-sample is very similar to the distribution of farms within the whole sample when grouped by types of farming, macro-regions and altimetry zones (table a1 and a2 in the appendix). the finger and kreinin (1979) similarity indexes computed on the two samples show a level of similarity that is never below 90%6. this suggests that the sub-sample does not provide an incomplete representation of the italian farming sector. 3.2 income definition and treatment of trends the focus is on the fadn variable farm net income (fi) that is the remuneration to fixed factors of production of the family (work, land and capital) and remuneration to the entrepreneur’s risks (loss/profit) in the accounting year (european commission, 2007). fadn is a business oriented database, thus it provides data on the income coming from the farming activities but it does not provide data on off-farm income earned by farm family members. however, it includes returns from nonfarm-based production activities such as, for example: hiring out of equipment, agro-tourism and forestry activities. fi is net of taxes linked to the farming activity but does not deduct personal taxes that are very much dependent on the overall amount of income (both on and off-farm) of the family members. the size of the fi depends, among others, on the relative amount of factors owned by the family provided that fi is net of the wage, rent and interest paid to third parties. thus, if a farmer decides to use a large amount of external factors, this implies that (ceteris paribus) the remaining fi declines and, in some cases, it is likely that it becomes also more variable7. 6 the finger – kreinin index has been originally developed to compare the structure of the export of products of two countries. it sums the shares of all products considering, for each product, the minimum value between the two series. thus, it assumes a value of 100% in the case of complete similarity, while it tends to zero as long as similarity declines. 7 the choice to use external factors is a management decision and farms indeed differ because they use different management strategies. the choice of obtaining labour, land and capital from third parties affects farm economic performances, their variability over time and, in turns, the wellbeing of farm families. thus, it seems logical that different management strategies have different implications also in terms of the income risk farmers face. 68 s. severini, a. tantari, g. di tommaso as shown in the previous paragraph, farm income has been the economic variable used to assess the farm risk by almost all the analyses developed on the eu farm sector. this is because farm income describes better than other variables (e.g. revenues) the economic performances of a specific farm provided that, at the end, farmers are interested in how much the resources they use on-farm are remunerated and how this remuneration varies over time. the fact that the mean or expected value of farm income has a trend or a cyclical behaviour does not necessarily imply risk: an economic variable may follow well-defined patterns that are known to farmers (oecd, 2009). trends in income may occur because, for example, prices generally increase over time due to inflation and trends are pervasive in crop yields. for this reason, it has been chosen to eliminate the impact of inflation and to assess the variability around the trend (if existing). the original fi series have been first deflated by using the gdp deflator and later standardised (dividing each value by the 10 year average) to have all series centred around 1. the series have been then explored to identify linear trends by pooling all farms into 7 types of farming (tf) (i.e. farms with a similar production pattern) (european commission, 2010) (table 1). the trends have been estimated by using a robust regression approach based on two weight functions (huber weights and bi-weights) to account for the presence of outliers (finger and hediger, 2008; huber, 1964; maronna et al., 2006). because in all types of farming but specialised granivore farms significant linear trends have been identified, the deflated fi series have been detrended in those 6 cases8. 3.3 variability indexes the detrended series have been used to calculate two variability indexes in each farm. these are standard deviation (v1) and semi-standard deviation (v2) of farm income over the decade9. for a generic i-th farm, this latter index has the following structure: v 2i = tfii ,t<fi i t ∑ fii,t − fi i( ) 2 m (1) 8 because we worked with short time-series, it was considered not possible to get sound results from models able to explore the possible cyclical nature of the series. however, the use of linear trend appears still valid provided that estimation results are satisfactory. furthermore, while the cyclical nature of price is very important, this is not necessarily the case for income series. this is because farm revenues come from different activities (i.e. diversified farms), they are also influenced by the evolution of unitary production levels (e.g. yields) and, in some cases, there is a natural hedge between the two (i.e. negative correlation between price and yield). furthermore, incomes are also influenced by the evolution of production costs. in the considered decade, the prices of some commodities have shown some cyclical path (e.g. cereal prices higher than usual for two or three years) (figure a1 in the appendix). however, the examination of the evolution of farm income by types of farming does not provide evidences of clear cyclical behaviour (table a3 in the appendix). this is also the case of field crop farms because, in the same period in which cereal prices have been high, costs have also reached high levels due to the extraordinary high prices of some required inputs, noticeably, chemical fertilisers (figure a2 in the appendix). 9 standard deviation has been calculated on standardized data, so it can be directly interpreted as a coefficient of variation of the absolute income. this allows for the direct comparability between farm groups and periods. 69the instability of farm income it differs from v1 because it considers only data from those years (m < t) in which income levels are below the average value fii,t < fi i that, in our case, is calculated over the 10 year observations (t=10). thus, this index focuses only on the down-side risk (i.e. adverse income conditions). however, v1 could be a good proxy of v2 and has a very desirable property: it relies on a larger number of observations than v2. this latter aspect seems relevant in our case because only 10 years of data are available. variability indicators v1 and v2 have been obtained for each farm of the sample. the mean and median levels of variability have been analysed by grouping farms according to the three dimensions previously described: type of farming; economic size; farm productivity (table 1). the disaggregation by types of farming is interesting because some production industries are inherently more risky than others and because diversification can reduce risk. furthermore, the relative level of support provided by the cap direct payments strongly differs in italy among farms with different production patterns and economic size, and this source of income has been found to stabilise farm income (cafiero et al., 2007; el benni et al., 2012; enjolras et al., 2014; oecd, 2009; severini and tantari, 2013). referring to productivity level is also relevant because the same relative level of income variability can have more severe implications for low productivity farms than for high productivity farms. this is because owned resources already receive a low remuneration and this generally implies a limited capability to accumulate cash reserves to cope with low income years. the distributions of v1 and v2 within the groups and within the whole sample have been explored calculating common dispersion indexes (standard deviation and coefficient of variation) and testing for normality by means of the shapiro-francia test (shapiro and wilk, 1965). the results of these tests allow to reject the normality of the distributions. this has suggested to focus on median values (other than on mean values) of each considered group and to rely on non-parametric tests for comparisons among variability indexes, groups and periods. correlations between v1 and v2 have been calculated for the whole sample and for each group using the spearman’s test and the related rho correlation coefficient (hauke and kossowski, 2011). differences between farm groups have been tested by means of both kruskal-wallis test and wilcoxon rank-sum test (kruskal and wallis, 1952; mann and whitney, 1947). the first allows to test for the presence of at least one inequality among groups. when the results of this test allow to reject the null hypothesis, the pairwise comparisons between groups has been developed by using wilcoxon rank-sum tests. this latter has been also used to test for differences between variability indexes calculated in the periods 2003-2007 and 2008-2012. 3.4 aggregation bias in order to have a reference level to assess aggregation bias, v1 has been calculated not just on individual farm data but also on the average income of different groups of farms. the average income of the j-th group of farms (e.g. a specific type of farming) in the t-th year is: 70 s. severini, a. tantari, g. di tommaso afi j ,t = i j n j ∑ fii,t nj (2) where nj is the number of farms belonging to the j-th group. the average over time of such aggregated income indicator is: afi j = 1 10 t=1 10 ∑afi j ,t (3) thus, when using the average income of the j-th group of farms10, v1 is calculated as it follows: v1j = t=1 10 ∑ afi j ,t − afi j( ) 2 10 (4) the levels of variability calculated as in (4) are referred to as aggregated data in table 2. the other columns of table 2 refer to basic statistics of the variability figures calculated on each individual farm of the sample. 4. empirical results while the median levels of standard deviation (v1) are higher than those of the semistandard deviation (v2) (table 1), the level of the two variability indicators are very much correlated. in all groups it is possible to reject the null hypothesis that v1 and v2 are not correlated according to the spearman’s test at 1% significance level. the rho correlation coefficient calculated on the whole sample is 0.889 and is greater than 0.79 in all groups (table 1). these results allow to conclude that, the two indicators provide a very similar representation of the relative income risk of the considered individual farms. this supports the hypothesis that there is not an apparent advantage to rely on a down-side risk indicator (v2) in similar circumstances. because of this, the presentation of the empirical results will focus on v1 only even because, as previously noted, v1 is calculated on a larger number of observations than v2. the median of v1 for the total sample (0.637) suggests that farmers are faced with relatively high levels of income variability (table 1). however, there is a high dispersion of v1 levels within each group as testified by the large values of standard deviation (sd) and coefficient of variation (cv) of v1 (table 2). this is particularly the case of specialist field crop farms and, to a lesser extent, of specialist grazing livestock farms and specialist granivore farms (table 2). 10 v1 has been also calculated on the average income of all farms of the sample (i.e. not just in each specific group of farms) by using the same approach. this figure is reported in row total sample of column aggregated data of table 2. 71the instability of farm income the comparison of the v1 calculated on individual farm data with those calculated on aggregated data allows to assess the extent of the potential aggregation bias. v1 calculated on aggregated data are always way lower than the medians of the values of v1 calculated on individual farm data. on the whole sample, the variability calculated on the aggregated data is less than ¼ of the median value of farm level variability (table 2). similar results are found even when the sample is subdivided according to types of farming: aggregation bias seems relatively lower for specialist field crop and for specialist granivore farms. however, in all cases the medians v1 calculated on aggregated data do not exceed 3/5 of those calculated on individual farm data (table 2). this means that the extent of the aggregation bias is not substantially reduced even when farms are grouped according to their production specialisation. despite the large dispersion of variability within each farm group, it is possible to say that groups differ. variability is lower in specialist grazing livestock farms (tf 4) and specialist horticulture farms (tf 2), while higher in specialist granivore farms (tf 5) (table 2). table 1. sample size, median values and correlation between standard deviation (v1) and semistandard deviation (v2) of farm income within each group and the whole sample of farms. sample size standard deviation semi standard deviation number v1 v2 spearman's rho^ types of farming (tf) code specialist field crops 1 572 0.659 0.534 0.911 *** specialist horticulture 2 276 0.584 0.507 0.790 *** specialist permanent crops 3 715 0.665 0.550 0.895 *** specialist grazing livestock 4 493 0.572 0.484 0.887 *** specialist granivore 5 84 0.844 0.739 0.796 *** mixed cropping 6 161 0.688 0.564 0.879 *** mixed livestock and mixed crops-livestock 7 103 0.658 0.567 0.904 *** economic size (esu classes) small (classes 1, 2 and 3) 699 0.715 0.595 0.895 *** medium (classes 4, 5 and 6) 1595 0.604 0.509 0.887 *** large (classes 7 and 8) 110 0.686 0.589 0.828 *** productivity levels (fi per unit of labour) low 601 0.857 0.708 0.873 *** low-medium 601 0.642 0.530 0.880 *** medium-high 601 0.549 0.479 0.880 *** high 601 0.545 0.457 0.867 *** total sample 2404 0.637 0.530 0.889 *** correlation between v1 and v2: ^ in all groups it is possible to reject the null hypothesis that v1 and v2 are not correlated according to the spearman’s test at 1% significance level (***). source: own elaborations on a constant sample of the whole italian fadn farms for the period 20032012. 72 s. severini, a. tantari, g. di tommaso according to the kruskal-wallis test, there is at least one inequality among types of farming. the results of wilcoxon rank sum test show that specialist granivore farms (tf 5) face a level of variability that is significantly higher than in the other groups. specialist horticulture farms and specialist grazing livestock farms are in the opposite situation (table 3). the high variability found in specialised granivore farms is consistent with the findings of previous studies (vrolijk and poppe, 2008; vrolijk et al., 2009; european commission, 2011) and can be explained by the nature of these farms: high specialisation and a limited importance of cap direct payments. similarly, the low income variability of specialist grazing farms is consistent with the findings of vrolijk and poppe (2008). on the contrary, the finding that also specialist horticulture farms are facing a relatively low level of income variability is not fully consistent with previous studies and contrasts with the common believe that horticulture crops are affected by high business risk. in this case, it is possible that diversification and other risk management tools already used by such farms (e.g. crop insurance) are effective tools to stabilise income. differences exist also among farms of different size. the median v1 in small farms is greater than in large farms and almost double than in medium farms (table 4). this result is coherent with what has been found by the european commission (2011). despite the dispersion of values within the groups, kruskal-wallis tests suggest that there is at least one inequality among these groups. furthermore, the wilcoxon rank sum tests show that income variability of medium size farms is lower than that of the other two groups of farms with a statistical significance of 1%. this means that small farms are facing a relatively higher level of income variability. as suggested by vrolijk and poppe (2008), this could be caused by the fact that income of small farms is in many cases very low, so that table 2. income variability levels (v1) by type of farming and in the whole sample. v1 has been calculated on aggregated data (i.e. sum of individual farm data) and on individual farm data. aggregated data^ v1 mean median sd^^ cv^^ description code specialist field crops 1 0.400 1.297 0.659 8.535 6.58 specialist horticulture 2 0.091 0.689 0.584 0.479 0.69 specialist permanent crops 3 0.120 0.909 0.665 1.523 1.67 specialist grazing livestock 4 0.099 0.787 0.572 1.780 2.26 specialist granivore 5 0.469 1.343 0.844 3.426 2.55 mixed cropping 6 0.153 0.982 0.688 1.320 1.34 mixed livestock and mixed crops-livestock 7 0.233 1.111 0.658 1.699 1.53 total sample 0.150 0.980 0.637 4.401 4.49 types of farming (tf): of v1 individual farm data ^ as specified in (4), figures in the column aggregated data refer to the v1s calculated on the averages of the incomes of the farms belonging to each type of farming as well as of all farms in the sample (total sample). ^^ sd and cv of v1 should be considered with caution because v1 is not-normally distributed. source: own elaborations on a constant sample of the whole italian fadn farms for the period 20032012. 73the instability of farm income small changes in revenue (or costs) can cause high relative changes in income. however, despite that the median value is higher in small than in large farms, the results of the wilcoxon test do not support the hypothesis that there are significant differences between these two groups of farms while it suggests that income is more stable in medium size farms than in the other two groups (table 4)11. income variability levels also differ among farms with different levels of productivity (i.e. farm income per unit of labour). the highest variability can be found in the low productivity farms (table 5). similarly, income variability in farms belonging to the next 11 in very large farms most of the labour is provided by non-family members who receive salaries accounted for as explicit costs. this makes income of those farms relatively small if compared with the total revenues generated. thus, also in this case, small changes in revenue (or costs) can cause high relative changes in income. the opposite can be said for medium size farms where a large share of the labour is often provided by family members. table 3. significance of the wilcoxon rank sum test between couples of types of farming. types of farming (tf) code 1 2 3 4 5 6 7 specialist field crops 1 *** *** *** specialist horticulture 2 *** *** *** * specialist permanent crops 3 *** *** specialist grazing livestock 4 *** *** ** specialist granivore 5 *** *** mixed cropping 6 mixed livestock and mixed crops-livestock 7 types of farming codes: ^ significant at 1% (***), 5% (**) and 10% (*) according to wilcoxon rank sum tests. source: own elaborations on a constant sample of the whole italian fadn farms for the period 20032012. table 4. income variability levels by economic size classes and in the whole sample. mean, median, standard deviation and coefficient of variation of v1. mean median sd^ cv^ medium large small 1, 2 and 3 699 1.404 0.715 7.90 5.63 *** medium 4, 5 and 6 1595 0.789 0.604 1.06 1.35 ** large 7 and 8 110 1.049 0.686 2.94 2.80 total sample 2404 0.980 0.637 4.40 4.49 of v1 wilcoxon rank sum test results^^:sample size esu classes ^ sd and cv values should be considered with caution because v1 is not-normally distributed. ^^ significant at 1% (***), 5% (**) and 10% (*). source: own elaborations on a constant sample of the whole italian fadn farms for the period 20032012. 74 s. severini, a. tantari, g. di tommaso group (i.e. low-medium productivity) is significantly higher than in those belonging to the other half of the distribution (table 5). the fact that the income is less stable in farms already facing productivity problems suggests that these farms are even more endangered by income variability than other farms. this is because a low level of productivity often results in the under-remuneration of farm resources and does not allow to accumulate cash reserves to be used in low income years. variability has increased over time. the medians of v1 calculated over the period 2008-2012 in the whole sample and in each farm group are always greater than those calculated in the previous period (2003-2007) (table 6). this result is also supported by the analysis of the mean and the median values of v1 calculated over 6 overlapping sub-periods (table 7). in both cases, there is a slight increase of the values of v1 moving from the first to the following periods, except for the last one. the largest increment is found in specialist horticulture farms (tf 2). according with the results of the wilcoxon rank sum tests, there are statistical significant differences between the two periods in all tf but specialist permanent crops farms (tf 3) that showed the lowest increase (table 6). significant increases can be found also in the three size groups. large farms suffered the greatest increase, while small farms the lowest (table 6). however, it is important to note that income variability increased the most in the farms with the highest levels of productivity while the less in those with the lowest levels of productivity (table 6). this shows an improvement of the relative condition of low productivity farms. 5. conclusions the paper has provided empirical evidences regarding the five research issues described in the introduction. first, in the considered case, there is not an apparent and relevant advantage to account only for down-side risk to assess the relative level of income risk faced by farmers. this supports the hypothesis that simple variability indexes, such as sd and cv of farm income, could be used under similar circumstances. second, a table 5. income variability levels by classes of productivity levels (fi per unit of labour) and in the whole sample. median and coefficient of variation (cv) of v1. median cv 25-50% 50-75% 75-100% low 0 25% 0.857 5.01 *** *** *** low-medium 25-50% 0.642 1.34 *** *** medium-high 50-75% 0.549 1.86 high 75-100% 0.545 0.70 total sample 0.637 4.49 productivity classes wilcoxon rank sum test results: of v1 ^significant at 1% (***), 5% (**) and 10% (*) according to wilcoxon rank sum tests . source: own elaborations on a constant sample of the whole italian fadn farms for the period 20032012. 75the instability of farm income large potential aggregation bias arises when using aggregated data. this confirms that it is preferable to use individual farm data to avoid underestimating the income risk farmers are facing. third, italian farms seem affected by a not negligible level of income variability and this is the case even if variability is calculated on deflated and de-trended series. fourth, significant differences among farm groups have been found in terms of levels and evolution of income variability. this implies that not all farms face the same level of income risk and urge policies supporting their participation to risk management measures. in particular, income variability seems relatively lower in specialist horticulture and table 6. variability levels in the periods 2003-2007 and 2008-2012 for the whole sample and for each farm group. means and medians of v1 and relative change over time of the medians. change in median values mean median mean median % code specialist field crops 1 1.270 0.515 0.948 0.582 *** 13.0% specialist horticulture 2 0.424 0.302 0.719 0.565 *** 87.2% specialist permanent crops 3 0.700 0.503 0.833 0.513 2.0% specialist grazing livestock 4 0.574 0.378 0.730 0.457 *** 21.1% specialist granivore 5 0.817 0.515 1.450 0.648 ** 25.7% mixed cropping 6 0.664 0.448 0.990 0.588 *** 31.2% mixed livestock and mixed crops-livestock 7 0.857 0.429 0.954 0.593 *** 38.1% farm size classes (esu classes) small (classes 1, 2 and 3) 1.198 0.531 1.160 0.585 *** 10.1% medium (classes 4, 5 and 6) 0.593 0.424 0.740 0.509 *** 20.1% large (classes 7 and 8) 0.983 0.438 0.768 0.549 ** 25.4% productivity classes (fi per unit of labour) low 1.454 0.665 1.422 0.701 *** 5.4% low-medium 0.649 0.465 0.802 0.524 ** 12.5% medium-high 0.590 0.380 0.602 0.438 *** 15.1% high 0.452 0.354 0.627 0.485 *** 36.8% total sample 0.786 0.449 0.863 0.531 *** 18.1% 2003-2007 2008-2012 types of farming (tf) ^ significant at 1% (***), 5% (**) and 10% (*) according to wilcoxon rank sum tests. source: own elaborations on a constant sample of the whole italian fadn farms for the period 2003 2012. table 7. variability levels of income over the 6 sub-periods. mean and median of v1 calculated on the whole sample. 2003 2007 2004 2008 2005 2009 2006 2010 2007 2011 2008 2012 mean 0.786 0.786 0.830 0.857 0.892 0.863 median 0.449 0.505 0.539 0.559 0.571 0.531 source: own elaborations on a constant sample of the whole italian fadn farms for the period 20032012. 76 s. severini, a. tantari, g. di tommaso grazing livestock farms than in other types of farming. on the contrary, the analysis has confirmed that income variability is higher in small than in large farms. farms with relatively lower productivity levels are affected by a relatively larger income variability. fifth, results suggest that income variability has increased over the considered decade. all these results have policy implications. the relevant aggregation bias and the differences within farms groups show that the recently introduced income stabilisation tool correctly refers to the income of individual farms (i.e. does not rely on more aggregated data) for assessing income evolutions and the indemnities to be paid. the high and increasing level of income variability supports the idea that the introduction of risk management tools within the cap toolbox could be justified. however, it is worth noting that income risk can be also coped by, for example, accumulating enough cash reserves or by integrating farm income with off-farm income. the differences among farm groups suggest that there is scope for targeting income stabilising policies on those farm types where income is more unstable and that are less able to manage income risk. empirical results suggest that low productivity farms are likely those with the most pressing needs. these have been found to have a relatively high level of income variability and are very likely more vulnerable than farms with higher productivity levels even because are less able to accumulate large cash reserves (mishra et al., 2002). unfortunately, the analysis suffers from at least three main limitations that should be overcome in future analyses. first, the selected sub-sample cannot be considered representative of the whole farm population because it is not randomly selected. furthermore, the results cannot be reported to the farm population based on representative weights. therefore, sample statistics can differ from the population parameters. however, because the distribution of the farms within the sub-sample is very similar to the distribution of farms within the whole sample in terms of several dimensions, it is possible to say that the considered subsample does not provide an incomplete representation of the italian farming sector. the second limitation is that, as previous studies on the same topic, it does not investigate the causes of the assessed income variability. thus it seems important to develop future research to disentangle the role of different income components including cap direct payments. the third limitation is that, as in most of the analysis developed so far in the eu, the analysis accounts only for farm business income and this arises three main problems. first, a high variability of farm income does not necessarily imply an high farm household income variability provided that off-farm incomes have been found to stabilize the income of farm households (mishra and sandretto, 2002). second, farmers can manage income instability by accumulating cash reserves to be used in low income years. third, cash flow variability can be a different but promising way to look at the risk farmers are facing. these last three issues suggest that future analyses should enlarge the focus beyond what has been done in this paper by considering off-farm incomes (when data will become available), the availability and use of cash reserves and by comparing the variability of income with that of cash flow indicators. references agrosynergie (2011). evaluation of income effects of direct support. final report. bruxelles, may. 77the instability of farm income cafiero, c., capitanio, f., cioffi, a. 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(2005). spatial yield risk across region, crop and aggregation method. canadian journal of agricultural economics 53(1): 103-115. severini, s. and tantari, a. (2013). the effect of the eu farm payments policy and its recent reform on farm income inequality. journal of policy modeling 35(2): 212-227. shapiro, s.s. and wilk, m.b. (1965). an analysis of variance test for normality (complete samples). biometrika 52(3-4): 591-611. tangermann, s. (2011). risk management in agriculture and the future of the eu’s common agricultural policy. ictsd paper, 34, geneva, june. vrolijk, h.c.j., poppe k.j. (2008). income variability and income crisis in the european union. in: meuwissem, m.p.m., van asseldonk, m.a.p.m. and huirne, r.b.m. (eds), income stabilisation in european agriculture. wageningen academic publishers, wageningen, the netherlands, pp. 33-54. 79the instability of farm income vrolijk, h.c.j., de bont, c.j.a.m., van der veen, h.b. and poppe, k.j. (2009). variability of farm incomes, prices and yields in the european union. report 2009-005. lei wageningen ur, the hague. appendix table a1. comparison of the distributions of the farms in the considered sub-sample and in the whole fadn sample among geographical areas and altimetry zones. similari ty^ n. of farms % n. of farms % geographical areas: 90% center 343 14% 2098 19% islands 171 7% 1172 10% south 626 26% 2903 26% northwest 765 32% 2423 22% northest 499 21% 2593 23% whole country 2404 100% 11189 100% altimetry zones: 98% hilly 1131 47% 5072 45% mountain 478 20% 2326 21% plan 795 33% 3791 34% whole country 2404 100% 11189 100% sub-sample whole sample (2012) ^ finger and kreinin (1979) similarity index. 100% complete similarity. source: own elaboration on italian fadn data. 80 s. severini, a. tantari, g. di tommaso table a2. comparison of the distributions of the farms in the sub-sample and in the whole fadn sample among types of farming and economic size. n. of farms % n. of farms % types of farming (tf) code 93% specialist field crops 1 572 24% 3007 27% specialist horticulture 2 276 11% 824 7% specialist permanent crops 3 715 30% 3073 27% specialist grazing livestock 4 493 21% 2504 22% specialist granivore 5 84 3% 524 5% mixed cropping 6 161 7% 691 6% mixed livestock and mixed crops-livestock 7 103 4% 566 5% economic size (esu classes) 98% small (classes 1, 2 and 3) 699 29% 3100 28% medium (classes 4, 5, 6) 1595 66% 7311 65% large (classes 7 and 8) 110 5% 778 7% whole farms 2404 100% 11189 100% sub-sample whole sample (2012) similari ty^ ^ finger and kreinin (1979) similarity index. 100% complete similarity. source: own elaboration on italian fadn data. table a3. evolution of farm income level by types of farming in the analysed period (2003=100). 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 types of farming (tf): tf specialist field crops 1 100 101 102 99 131 138 124 115 115 113 specialist horticulture 2 100 97 96 99 100 108 103 101 95 73 specialist permanent crops 3 100 90 94 92 99 123 99 133 113 113 specialist grazing livestock 4 100 87 90 109 107 114 104 109 112 105 specialist granivore 5 100 137 104 79 74 66 88 76 56 74 mixed cropping 6 100 96 91 98 140 114 123 140 145 128 mixed livestock and mixed crops-livestock 7 100 93 115 112 154 127 131 113 99 117 total sample 100 99 97 98 106 112 105 110 103 101 farm income level (2003=100) source: own elaboration on italian fadn data. 81the instability of farm income figure a1. evolution of agricultural product deflated price indexes in the years 2003 2012 (2005=100). 100 105 110 115 120 125 130 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 all crops crop prod.s but vegetables livestock and livestock prod.s all agric. products source: italian national institute of statistics (istat); www.istat.it. figure a2. evolution of agricultural input deflated price indexes in the period 2003 2012 (2005=100). 95 100 105 110 115 120 125 130 135 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 intermediate consumptions feed investments general index source: italian national institute of statistics (istat); www.istat.it. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(1): 29-47, 2013 transformation for sustainable agriculture: what role for the second pillar of cap? janet dwyer1 professor of rural policy, university of gloucestershire abstract. eu agriculture and rural areas face significant medium-term challenges arising from existing and ‘new’ sources; as acknowledged in the eu2020 vision. the european commission has placed emphasis upon innovation as a key element in achieving transformation, in the coming decade. findings from a recent study for the european parliament highlight the potential role of pillar 2 rural development programmes as vehicles for enabling innovation. key roles include supporting knowledge exchange; collaboration; and research-into-practice linkages. effective knowledge exchange (ke) is a critical element, but innovation in ke itself is often needed. collaboration can be valuable in fostering cross-sectoral linkages, and communities of learning show innovation potential. integration of measures in tailored packages appears to increase the scope for innovation. equally important, there is a vital need for innovation in policy design and delivery, to enable a cost-effective transformation of agriculture and rural areas. the cap proposals 2014-2020 make a positive contribution to better promote innovation through pillar 2, but there is room for improvement. models for policy innovation adapted from experience in commercial organisations are suggested as worthy of further research. keywords. rural development programmes, innovation, knowledge exchange, policy design and delivery jel codes. q18 1. the context – challenges facing eu agriculture and rural areas it can be argued that europe’s greatest rural challenge in the years ahead will be to provide a wider, more varied and more complex set of goods and services than has been encouraged over the past 50 years, in a way which can be sustained in the face of increasing pressures from exogenous factors. the realisation that we have probably passed ‘peak oil’ – meaning that global oil exploitation has passed its peak levels of extraction – is set to change significantly europe’s reliance upon fossil fuels, going forward. petro-chemicals today provide most of europe’s energy; also some of the key inputs to agriculture (most notably, chemical fertilisers); and materials used in food supply chains. the eu will need to reduce its reliance upon 1 corresponding author: jdwyer@glos.ac.uk. 30 j. dwyer non-renewable energy, not just because it will become more expensive to obtain, but also because its use contributes to global warming. thus renewable fuel and energy production seem likely to become a significant and lasting feature of the european countryside, and there may also be a sustained growth in the use of rural resources for construction, textiles, packaging and other non-food consumables. at the same time, the characteristics and capacities of rural europe will change. the espon-climate study (2011) sought to assess eu regions’ vulnerability to climate change over the next century. it predicted that in the north, higher rainfall and more coastal surges will increase the risks of regular and severe flooding around lowlying deltas and coastlines. in the south, the key challenges will be water scarcity and extreme summer temperatures, with significant consequences for rural resource use. less water will be available, and rainfall will come in shorter and more severe episodes, placing emphasis upon the need for rainwater harvesting, recycling and conservation. people in towns and cities will demand more energy-intensive air-conditioning, as well as more water, during the summer months: all of this has implications for rural resource use. the demifer study (2010) suggests that population in the eu will continue to grow slowly but unevenly across the territory, with decreases likely in many new member states, finland and north-west spain, whilst populations expand in the south-central mediterranean and around the coastal areas of northwest europe. this pattern seems likely to exacerbate demands upon rural resources in certain poorer regions such as western portugal and ireland; to increase water demand all around the mediterranean coastline; and to expose many more people to the risks of flooding in north-western regions. the eu 2020 vision (european commission, 2010) identifies that europe must become more competitive, in the context of a liberalising global trading regime. the underlying aim is to give the eu a qualitative advantage over other countries and continents which produce goods and services at lower cost. with significant growth in asia and south america, shifts in demand and supply are likely to displace the eu from its current position as the largest market for food in the world. global food demand is predicted to increase substantially in the next 50 years. food production in europe remains important for eu food security and perhaps also for export, but it must be achieved in ways which accommodate other needs. ecological science over the past 30-40 years has greatly increased human understanding of how living and non-living elements within the environment work together in complex ecosystems. soil health and ecology and the viability of freshwater habitats are key ingredients. yet these assets are declining, despite more than 30 years of policy action (millennium ecosystem assessment, 2005). agriculture and rural resource management must become much more supportive, and less destructive, of basic ecosystem services, in future. economic circumstances create new challenges. the eu is in a period of economic stagnation and/or recession, negatively affecting growth and quality of life in many member states (ms). whilst the demand for basic resources like food, energy and water is not predicted to decline, a contraction in the availability of public support is likely, as governments implement austerity plans and public services are down-sized or privatised. the two largest spending elements in the eu budget are the cap, and regional development policy. a sustained reduction in the eu budgets for both these elements can be expected. these are some of the main challenges facing rural europe and its agriculture, in the 31transformation for sustainable agriculture decades ahead. their message for current policy and planning, is that ‘business as usual’ will not be a sufficient response. within the next 20 years, the ms need to achieve a ‘step change’ in agri-food systems and in the careful planning and management of rural resources through policy, in order to sustain their capacity and provide for european society. europe’s standing committee on agricultural research (scar) refers to the need to “enable agriculture to cope with a range of complex and interlinked challenges, such as rapidly increasing globalisation, climate change and unsustainable consumption of natural resources” in its report “agricultural knowledge and innovation systems in transition” (march 2012). 2. transformation and the role of innovation in agriculture and rural development in this context, innovation has emerged as an important concept for eu policy. innovation simply means doing things, or seeing things, in a new way. and ‘new’ must be a relative concept, in this analysis. innovation embraces not only situations where someone invents and exploits something which has never before been produced, but also situations where someone introduces a different approach which might not be unique, but which represents a novel application or new context for that approach. so, innovation can be more or less radical, but what defines it is its relative novelty and, by implication, its potential for positive development within a particular context. the european commission has written much about innovation in recent years (hermans et al., 2010). innovation is seen as playing a critical role in achieving the eu2020 strategy goals, and greater emphasis on innovation has been placed within the legislative proposals for rural development policy under the cap. historically, innovation in the agricultural sector has tended to focus upon cost reduction and/or new product development. in respect of rural development, innovation has been promoted in leader and other community initiatives, as well as in research. in general terms, it is possible to identify at least two dimensions to innovation: 1. technological progress, where commercial farmers, food processors or other rural businesses adopt new technologies or practices where innovation is embodied in these new practices as developed and promoted by research firms and/or extension agents. farmers or entrepreneurs ‘innovate’ by being early adopters of such practices. in some cases, innovation comes direct from businesses themselves (e.g. as among farmers practising integrated crop management, experimenting to reduce inputs without compromising yields); 2. more fundamental innovation, where the whole land and/or business management system changes to incorporate new modes of behaviour – for example, communitysupported agriculture or social farming; or integrating food production with leisure, energy generation, retail or tourism; or new forms of vertical integration or short supply chains (e.g. for pharmaceuticals or novel crops). it is commonly assumed that scientific and technological research promotes innovation. but simply generating new knowledge through this process won’t necessarily lead to innovation in practice, in agriculture or rural development. analyses provided in the context of the “international assessment of agricultural knowledge, science and technology 32 j. dwyer for development (iaastd)” (mcintyre et al., 2009) suggest that the further evolution of agricultural innovation needs to go beyond simple technological and technical questions to innovate in scales of thinking and action, in addressing the challenges of global food security and climate change. what are the conditions needed for innovation? innovation at the level of individual businesses, regions or product sectors often depends upon entrepreneurial confidence and the acquisition of skills and information which can be applied to generate improved performance or increased resilience. at a practical level, ingredients for successful innovation include good information and understanding; the means to effect change on the ground; and the ability to be recognised and/ or rewarded for the positive results of changes. in recent work on farmer behaviour, these have been termed farmers’ ‘willingness to change’; ‘capacity to change’; and ‘engagement’ with the wider socio-political agenda (dwyer et al,. 2007). at the territorial scale, innovation is particularly linked to the ability to undertake problem-solving activities through the bringing together of actors and/or expertise from different territorial and policy contexts, to gain new insights and wider perspectives, enriching actions (knickel et al., 2009). social capital, particularly ‘bridging’ or ‘linking’ social capital which enables groups to source ideas and knowledge from outside their immediate circle, can be an important factor (copus et al., 2011). in these contexts, therefore, how best can policies promote innovation? as one official from the commission recently emphasised (dg agri, pers comm), you cannot force people to innovate. however, you can attempt to create a climate in which innovation is favoured, and policies can play an important role in this. drawing together experience with cap and rural development funding to date, as reported and assessed in local evaluations, policy reports and interviews with selected experts across the eu, a recent assessment for the european parliament (dwyer et al., 2012) highlighted the following as critical factors. knowledge exchange (ke) as innovation, and innovation in ke it has long been recognised that knowledge exchange is critical for innovation and transformation. a body of work in respect of agricultural innovation indicates that bringing science closer to practitioners can be a positive step. so, scientists may work closely together with farmers to monitor how changes in practice affect key environmental variables like soil condition and water quality, for example (whatmore et al., 2010). and farmers may be able to help scientists to identify where best to focus their research attention, through interaction and discussion. in some situations, farmers can themselves be involved in ‘doing’ research: analysing and testing different options and learning from the results (as noted in respect of ‘minimum-tillage’ developments in england – dwyer et al., 2007). researchers at wageningen have promoted the benefits of ‘communities of practice’ and ‘co-learning’, based upon work in the netherlands and elsewhere, in which scientific knowledge and endeavour partner with commercial land management so that both sides learn from each other (rőling and wagemakers, 1998). effectively, these approaches see 33transformation for sustainable agriculture innovation as a social process, involving networks and interactions as well as technical and managerial change at farm level (hermans et al., 2010). considering good practice within the leader community initiative across the eu (e.g. lukesch, 2003), also similar approaches under the regionen aktiv programme in germany (peter, 2006), and the italian experience of integrated territorial programmes (pit); it seems that cross-sectoral collaboration in planning and strategy development often stimulates innovation. bringing together different strategic interests can enable change both in how individual businesses operate, and in how they work together to generate new, positive economic, social and environmental outcomes (dwyer et al., 2012; mantino, 2011). the key to effective innovation here appears to be the capacity to pool knowledge from different spheres of activity, and to use this knowledge to identify new linkages and new ways to achieve joint goals through collaboration. the production of social capital can be an important result of bringing people together, and this in turn generates more tangible results in the form of integrated projects with multiple benefits (ibid). as dargan and shucksmith note, in a paper discussing leader experiences of innovation (2008): in reflecting on the practical experience of rural development, innovation has been understood in terms of social innovation (to encourage local linkage and collective learning cultures) and cultural innovation (to improve the rural milieu) rather than in the sense of science policy and technological innovation which dominates national policy discourses. collaboration up and down the food chain can also bring valuable innovation to agriculture and rural development. there is positive experience in this respect from ‘filière’ initiatives in germany, france and italy where public agencies have either directly engaged in, or have encouraged with funding, the formation of food supply chain partnerships which bring producers, processors, distributors and/or retailers together to plan and implement new systems, creating new products or new ways of working which increase cost-effectiveness and competitive success (morgan et al., 2006; dwyer et al., 2004). support for both vertical and horizontal collaboration can be effective, in this context. experience with integrated planning and programme delivery in the southern regions of italy has shown how such policy frameworks and funding can foster innovation, when established over a sustained period of time (dwyer et al., 2012). challenges and barriers to effective ke, and the role of policy knowledge exchange through training and advice to farmers is a long-established element in eu farm policy. as well as promoting improved productivity, using advice to raise awareness about the natural environment and offering training in environmental protection can be important ways to encourage more sustainable resource management (dwyer et al., 2007; mills et al., 2009; lobley and bullock, 2013). but reviewing experience with training and advisory provision to farmers across the eu, it is clear that not all actions have been successful, in this regard (ade, 2009). dwyer et al. (2007) found that farmers’ willingness and capacity to change are critical to ensure that they can make use of new knowledge but too often, training and advisory support to farms is not designed and delivered in ways which will foster a willingness to change. 34 j. dwyer in some cases, training and advice are ineffective because they are insensitive to the real barriers to change faced by the farmers at whom they are directed (dwyer et al.,2007; jacobson et al., 2003). so, for example, a course or an advisory booklet about reducing diffuse pollution might promote the principle of matching input applications to crop needs through the growing season, but if few farmers know how to get their soils tested or know the nutrient content of the manures that they spread, they cannot implement this good practice effectively. courses for dairy farmers which aim to increase their production efficiency through closer monitoring, analysis and management, may fail because factors such as indebtedness to the banks, or lack of time for planning and reflection, effectively ‘lock’ producers into their high-input, high-output systems. a significant body of research (reviewed in dwyer et al., 2007) has concluded that effective training or advice should be designed to emphasise its tangible benefits for beneficiaries – whether these be savings in costs or time, or reduced risks of falling foul of regulatory requirements (e.g. posthumus and morris, 2010). and good trainers spend time and effort to understand the situations of target groups, to ensure that their courses and events take these into account and that their advice can be readily acted upon, once received. this may require courses to be hosted on farms themselves, in the evenings or at less busy times of the day, to make it easy for people to attend. the language needs to be direct and accessible, with frequent recourse to the direct demonstration of efficacy, wherever possible. sometimes it can help if trainers are themselves farmers, fostering ‘trust in the messenger’ in order to encourage reception of the message (dwyer et al., 2007).such kinds of well-targeted training can raise awareness and understanding of environmental and other challenges, as well as helping to provide farmers and other rural actors with the confidence and entrepreneurial skills to take action to address these challenges (mills et al., 2013). it can take many forms: from the simplest interactions such as visits for groups of farmers to farms in other regions (e.g. within a leader exchange project: dwyer et al, 2010a), to more sustained and complex courses in rural business management (e.g. bus in germany: langosch, 2011) which may take several years to complete. in summary, drawing upon evidence from research, policy can encourage the kinds of innovation needed for agriculture and rural development particularly through: • funding and designing more appropriate types of knowledge exchange, training and advice for farmers and other rural actors, to raise awareness and increase their capacity to act; • funding new partnerships, in communities of learning, between research and practice; • supporting collaboration to promote innovation in production, processing and marketing and consumer education – bringing together different sectors and groups with complementary knowledge and skills, and linking producers to others in the supply chain. 3. assessing existing pillar 2 policies for agricultural transformation pillar 2 as a useful toolkit – considering the measures in respect of the common agricultural policy, the most directly relevant policy instruments for promoting innovation are found within the second pillar for rural devel35transformation for sustainable agriculture opment. reviewing experience to date with these policies, dwyer et al. (2012) concluded that the goals and instruments of the european agricultural fund for rural development (eafrd) have proven ability to promote innovation and transformation in agriculture, when used well at the local level. giving evidence to a uk parliamentary inquiry, the european commission noted that at least 15 measures within the eafrd are relevant to the process of fostering innovation (house of lords, 2011). here, the main measures of relevance are discussed briefly. pillar 2 aids for training both within and beyond the farm gate have apparently been effective, along with aids for adding value and increasing consumer awareness, which have been used to respond and to help increase the demand for products from more sustainable farming systems – for instance, in the case of organic action plans in germany, wine production in spain, and initiatives to promote regional quality products, in poland (all cited in dwyer et al., 2012). aids for farm investment and agricultural infrastructure have, in some cases, been used to help shift farming practices away from more damaging or wasteful forms of production and towards greater resource efficiency – for example, in the management of water and soils, or in climate change mitigation strategies. in south-west england, these measures have been offered along with training and demonstration aids within ‘soils for profit’ and ‘south west agricultural resource management’ projects, which help farmers to understand and implement actions to improve soil structure and reduce erosion and leaching, interacting closely with scientists (natural england, 2013). in ireland, investment aids for livestock farms to improve manure processing facilities are seen as an important ingredient in climate change mitigation, which represents an element of innovation in the irish rural development programme (rdp) (holman, 2010). in some cases, agri-environment measures (aem) have been used to facilitate change in farming systems towards more resilient styles of production, better able to cope with future climate-related stress (ec, 2009). examples of innovation with aem include co-operative approaches which can deliver landscape-scale impacts. in the netherlands, local organisations of farmers and non-farmers (technical and environmental specialists, local community interests) work in close collaboration with each other and local, regional and national agencies to integrate nature management into farming practices. introduced in 1992, there are over 100 cooperatives and by 2004 these embraced ten per cent of all farmers and 40 per cent of agricultural land (franks and mcgloin, 2007). also, innovation is seen in the piloting of outcome-based approaches to aem, for example in germany, with meadow preservation schemes in baden-württemberg and lower saxony; and collective approaches on common land, where individual farm-level agreements are difficult to secure (jones, 2011). among the measures in axis 3, funding for village renewal, basic services, and tourism and craft development have been used to help stimulate new patterns of rural development which offer a more diverse and robust mix of income sources in territories where agriculture is in decline. in some cases, this has been a characteristic of leader actions (particularly under leader ii and leader + programmes, 1994-2006); whilst in others it has been stimulated through other local institutions (knickel and krőger, 2006; shucksmith, 2009). 36 j. dwyer the innovation potential of packages of measures, designed to suit the territory analysis of existing rdps in respect of single measures or axes risks ignoring one of the most significant instances of good practice in supporting innovation. this is the approach of combining measures within or between axes, into packages designed to stimulate change in a co-ordinated way, in micro-regions and/or particular sub-sectors of agriculture. territorial approaches were among the first experiments in integrated project design and implementation using different measures or funds. many offer a combination of measures in order to promote local areas as attractive venues for tourists, with a range of high-quality providers of food products, places to stay and to eat, and leisure opportunities. because these require many years to develop as a fully integrated “offer”, most date from earlier programme periods (knickel et al., 2009; von münchhausen et al., 2010). but several current rdps (2007-2013) have been innovative in adopting a territorial approach, or strengthening the importance of this approach (e.g. portugal, ireland, some italian and french regions). there is growing interest in designing and experimenting with territorial approaches in rdps, in different ms and regions. these approaches frequently involve protected areas, regional and national parks, as they already pursue environmental, economic and social goals with a local strategy (mantino, 2011). persistent weaknesses in practice despite these positive examples, the achievements of pillar 2 programmes are variable across the eu and research evidence suggests that conservative, or narrow and piecemeal application of the measures in many countries has not achieved any significant transformation in agriculture or rural areas. this has been a particular criticism of new member states (erjavec, 2012), but it may also apply within the eu-15 (dwyer et al., 2008, 2010b, 2010c). studies conclude that the effective use of pillar 2 relies heavily upon good policy design and delivery, which should take into account local specificities and develop a coherent vision of the way forward for a territory or a sector, before aids are disbursed. it is also important to apply an approach which looks at the menu of rd measures as a bundle of complementary tools that often need to be applied together, in order to achieve desired outcomes (dwyer et al., 2012). to support innovation it is important for policy to enable risk-taking, both by individual beneficiaries and within the policy administration, in respect of the facilitation, appraisal and approval of projects. in many parts of europe today, these conditions do not apply. measures are specified largely by reference to standard conditions; calls are issued separately for different measures at different times; there is little accompanying advice or technical guidance on how to put together strong proposals for funding; and the selection of successful applications is made more on the basis of simple administrative criteria (e.g. deadline achieved, application completed correctly?) or lowest-risk funding criteria (e.g. match funding secured? business plan professionally prepared? partnerships for collaborative bids largely within/heavily controlled by the public sector?), than on strategic consideration of the real or lasting value of likely outcomes (mantino et al., 2010; dwyer et al., 2010a). these styles of operation inevitably reduce the realised achievements of programmes. there is evidence that the way in which eu financial controls and audit are interpreted at the 37transformation for sustainable agriculture level of managing authorities frequently works against support for innovation, as fear of disallowance or claw-back of funds encourages risk-averse behaviour which is unwilling to invest in previously untried and untested project ideas, however well their cases may be prepared (e.g. dwyer et al, 2008; maye et al, 2010; dwyer et al, 2011). considering the potential of pillar 2 to promote more entrepreneurial investment in innovative business ideas and novel approaches to resource efficiency, evidence suggests that training and advisory measures are under-utilised in favour of spending on investments and annual compensatory aids to beneficiaries (dwyer et al., 2012). there are a variety of reasons for this, including: reluctance and/or lack of appreciation of the value of training by farmers; as well as institutional unwillingness (in managing authorities, paying agencies and delivery bodies) to invest in advice and training due to perceived difficulties in demonstrating impacts, and in effective monitoring and control; as well as a lack of appropriate targeting and promotion. a greater emphasis at the eu level upon the importance of using information, promotion, demonstration and increased learning by exchange and networking, to encourage more positive experiences, may be necessary. the absence of effective national public-private infrastructure and investment for innovation and knowledge transfer in rural and land-based sectors, which has further deteriorated since the beginning of the financial and economic crisis, is an important factor in the relative lack of innovation in eu rural development. in the new member states, knowledge formation at the national level is weak. although public extension services exist, they are reportedly more engaged in assisting farmers to access public funds than in contributing to learning and new ideas (erjavec, 2012). summarising the performance of the farm advisory system required under pillar 1 of the cap, hermans et al. (2010) note that: ‘a problem with the implementation of the fas in many countries has been that the fas was seen as a control mechanism to force farmers into compliance. the effectiveness of the fas was therefore still limited with only a few farmers seeking advice’. to a large extent, therefore, it can be concluded that poor performance of rdps in respect of innovation for agricultural and rural transformation results from inadequate capacity, resourcing or attention being developed among those charged with programme design or delivery in the ms. however, there are ways in which the eu can develop its overarching policy framework and its supporting guidance, to help improve the situation. direct policy incentives could be used to encourage innovation: for instance, offering earlier or more generous release of the performance reserve for rd funding where ms can provide evidence of policies enabling innovation and transformation. higher rates of cofinancing might stimulate preferential use of key measures in this role, and stronger promotion of their benefits through the eu rd network. 4. assessing the proposed new eafrd for 2014-2020 the commission’s draft regulations for the eafrd beyond 2013 contain several important changes which should be positive, in fostering innovation and transformation in agriculture and rural areas. there is a clear strengthening of the elements within rdps which promote more strategic and co-ordinated use of measures to reflect local circumstances, and match local opportunities, more effectively than before. key changes to pillar 2 can be summarised as follows. 38 j. dwyer • the former four-axis structure and the minimum spending thresholds per axis have been removed, in favour of a structure focused around six strategic goals. the scope and purpose of all existing measures are retained but consolidated into fewer, more flexible instruments, and there is an obligation upon ms to use whichever measures they choose, to deliver against the strategic goals (i.e. no pre-determined link between measures and goals). the new goals are: knowledge transfer; improved competitiveness; innovation in products and processing; protection of biodiversity and landscapes; more efficient and sustainable use of water, energy and low-carbon technologies; and tackling rural poverty and social exclusion through local economic development and improved rural viability. this more strategic and flexible framework should increase the opportunities to combine instruments at a local level in order to create an effective climate for innovation. • there is explicit support for rural development programmes made up of a number of focused ‘sub-programmes’, tackling specific themes or challenges which may qualify for higher co-funding rates from the eafrd. this could encourage more filière or territorially-integrated actions. • a new approach is added for ‘european innovation partnerships’ (eip), which build links between research and land-based sectors, to tackle rural challenges. in addition, a new measure for collaboration significantly increases the types of collective and partnership-based planning and delivery that can be assisted. for example, it can involve partnerships between farmers, local authorities and environmental experts to manage agri-environment-climate actions; and partnerships to develop added-value or innovative products and supply arrangements. these changes should strengthen the potential to foster effective knowledge exchange and build social capital in ways that stimulate innovation. • there is a reinvigorated approach to leader which reinstates its relative independence from the wider measures’ architecture, requires all eafrd programmes to spend at least 5% of their total funds on it and promotes funding from all eu funds, as appropriate. there are also new measures to promote capacity-building in leader-type delivery approaches. both these changes appear to liberate the leader approach from the heavy bureaucracy which has held back its ability to innovate in the 2007-13 period (strahl et al., 2010; maye et al., 2010). the basic architecture of the proposed rural development policy is more similar to that which has formerly characterised erdf and esf funding programmes, in giving more choice to ms about which measures they wish to use, in which combinations, and for which aims. all these changes should facilitate innovation in the choice of actions at local level, compared to the current situation. looking in more detail at the new measures and considering the points made about innovation in the first section of this paper, the six strategic objectives for rd chime well with needs and opportunities identified as relevant to innovation. three are particularly pertinent. 1. fostering knowledge transfer in agriculture and forestry, focused on promoting human capital and smart networking; fostering innovation and the knowledge base; and strengthening links between the sectors and research and development. actions under 39transformation for sustainable agriculture this priority include ‘cooperation between the agriculture, food and forestry sectors and other actors and the creation of clusters and networks’; ‘the establishment and use of advisory services….[and] enabling farmers, forest holders and smes to access advisory services in order to improve economic and environmental performance’; and ‘strengthening the links between agriculture and forestry and research and innovation through setting up operation groups….[as] part of the european innovation partnership for agricultural productivity and sustainability’ (swd (2012) 61final, p. 4) (european commission, 2012).this priority could be used to build upon initiatives that have already developed in several ms under the current programming period, to help to increase the skills and knowledge of farmers in respect of more efficient water use, soil conservation, protection of water quality and increased productivity. knowledge transfer and networking can also be a key part of securing shorter supply chains which offer greater returns to the primary producer. 2. enhancing competitiveness of all types of agriculture and enhancing farm viability, with a focus on restructuring for farms facing major structural problems, with a low degree of market participation, and farms in need of agricultural diversification; also facilitating generational renewal in the sector. the main measures that might enable innovation in pursuit of this goal are different kinds of investment aid (private or public, but mainly productive). encouraging farm business start-ups among the younger generation can enable new or more diverse types of farming. 3. promoting food chain organisation and risk management in agriculture: integrating primary producers into the food chain through quality schemes, promotion in local markets and short supply chains, producer groups and inter-sectoral organisations. there are important opportunities for using this priority to innovate, making closer links with major food users in hotels and catering, as well as with processors and food retailers in order to identify the best opportunities. the types of investments that might be promoted could include equipment and collective structures to enhance or support supply chains which preserve product quality, guarantee origin and authenticity and improve traceability. overall, therefore, the new eafrd framework appears more attuned to the circumstances in which innovation is likely to occur than its predecessor, and it contains new instruments and enhanced flexibility which should enable novel rd approaches, where local conditions favour this. however, a good framework on its own is insufficient to ensure transformative and innovatory rural development at the local level; this depends upon creative and enthusiastic engagement by managing authorities with the innovation agenda. in respect of mechanisms to explicitly require a priority for innovative projects and initiatives within programmes, the draft regulation offers relatively little new. in that sense, it seems likely that path-dependency, the audit culture and innate conservatism within managing authorities will act to reduce innovation on the ground in response to rural development funding. to coin a metaphor – an effective plumber needs more than a good toolkit, in order to perform effectively. where pillar 2 fails to stimulate innovation, it is perhaps more because of a lack of skill and creativity in programme design and delivery, than of insufficiency in the eafrd. 40 j. dwyer 5. the new cap as a whole – under-playing the value of pillar 2? assessing the proposed cap reform package as a whole, it appears insufficiently oriented towards achieving agricultural and rural transformation through innovation, because of the significant emphasis upon retaining the majority of resources in pillar 1 decoupled farm payments, and in redistributing these aids between territories with no explicit consideration of their likely impact upon farming and wider rural development need. the cap pillar 1 instruments are discussed elsewhere in this edition (see matthews, 2013), so will not be analysed further, here. nonetheless, given the evident shift in the reforms towards a more holistic vision for both pillars of cap, in which their roles have become less distinct and it is only really their operational characteristics which separate them, one can ask why it is felt necessary to retain the existing and significant resource (im)balance between pillars, rather than shifting more towards pillar 2 measures which have greater potential for promoting innovation and transformation. at present, whilst several new member states have funding allocations for pillar 2 programmes which are equal to, or larger than, those for pillar 1, most eu-15 countries spend less than 30% of their total eu cap funds on rural development under pillar 2. for as long as this imbalance continues, it is likely to result in an inadequate emphasis upon innovation, within the cap (dwyer et al., 2012). to improve the proposals it would be advisable to strengthen pillar, ensuring it receives a higher proportion of total cap funds overall but particularly within the eu-15 member states. it would also be beneficial for the policy to adopt a balanced set of objective criteria reflecting the economic, social and environmental goals of the cap, to be used to determine budgetary allocations between member states. 6. a need for innovation and transformation in policy itself to increase the likelihood of pillar 2 stimulating greater innovation in rural development, there would be value in promoting more effective and imaginative design and delivery of rural development programmes, in all the ms. this could be achieved through enhanced networking; more incentives for innovation beyond the single instrument of the european innovation partnership; and renewed institutional and capacity-building efforts to address widespread risk aversion among public administrations, which prevents them from using these programmes in an enabling way. this final point deserves more detailed consideration. beyond the assessment of how well policy supports innovation or transformation ‘on the ground’, it is important to recognise that some of the greatest obstacles to cap performance in respect of innovation lie within the policy institutions themselves. too often, policy makers seem to assume that if they want something to happen in rural areas, they just have to allocate some funding to that particular goal and issue a call for applications. beneficiaries will come forward, money will be spent and things will change. but a catalogue of evaluation experience from the past 20 years in europe shows clearly that this is frequently an insufficient response. it can lead to significant wasted resources, as well as undesirable outcomes (see prazan et al., 2010; kirwan and maye, 2010, dwyer et al., 2010a). rdp success can be undermined by a panoply of reasons, not 41transformation for sustainable agriculture least including adverse effects and drivers which may be created or exacerbated by interactions with other parts of the policy framework (e.g. regional funding – see mantino et al., 2010). sometimes, instruments work in perverse ways. evaluation of risk management measures as policy tools, for example, suggests that they may have negative impacts upon innovation because they discourage farmers from feeling a need to do anything differently (effectively, the risk management tool encourages complacency, which can in turn increase vulnerability – see mantino’s case study in dwyer et al., 2012). some critics of pillar 1 payments claim that they have a similar ‘dampening’ effect upon the incentive to innovate in agricultural production or sustainability (e.g. swinbank, 2012, group of agricultural economists, 2009). a lack of institutional capability is also a widespread issue: as seyfang and smith (2007) note: ‘a further challenge is policy-makers’ risk aversion. innovation is an experimental process, and an important aspect of this is openness to learning from failure. the policy culture is insufficiently mature to identify this as a positive process.’ innovation in policy is a significant need for the future, across rural europe. as future governments must try harder to ensure that they use scarce resources to good effect, more emphasis should be placed on effective design and delivery of policy by skilled and experienced policy makers, so programmes can achieve more, more sustainably and to a higher standard, despite their likely reduced scale. this is a significant challenge – in effect, there is a need to improve policies’ cost-effectiveness in a context where the policy agenda is significantly more complex. seeking to develop new approaches at different stages within the policy cycle (bassi et al., 2012) may be a critical element in achieving rural sustainability and resilience. the many stages of the policy cycle offer scope for innovation – at initial conception, in design, in delivery, and in monitoring and evaluation, so that we can more effectively learn from and improve policy performance at all levels, from the eu to the local. just as change on the ground is often best fostered by ‘communities of learning’; the same is true in respect of good policy-making. we need to become smarter at making policy which can be tailored to local situations, open to recognising and working with situations of risk, and meeting global needs without costing a great deal more in bureaucracy and administration. the eu networking approach, formalised within the enrd and the eenrd at present, and soon to be extended with the eip network, marks one attempt to build institutional learning processes into cap policy-making at the level of practitioners and delivery agents. at the other end of the scale, the cmef was the commission’s instrument intended to require a more rigorous and reflective process of policy making and enhancement at national and eu levels. however, neither of these has yet devoted a great deal of time or effort to understanding policy processes and considering how to improve cost-effectiveness. the potential value of so doing was particularly emphasised in the rudi (rural development impacts) study (schiller et al., 2010). looking for parallels elsewhere, recent developments within the corporate sector seek to address challenges of improved resource allocation and process-management, under situations of increasing complexity in customer demand. in the business literature on organisational change and transformation within industrial processes, bpr (business process re-engineering – usgao, 1997), and ‘lean systems’ methods claim to offer ways of reorganising and improving the efficiency and effectiveness of complex management tasks. whilst each approach has specific detailed methods, their common underlying theoretical 42 j. dwyer perspective is interesting, because it echoes points made earlier in this paper, in respect of rural development policy. bpr approaches any ‘business process’ with the same analytical and diagnostic technique, in order to build an understanding of its pros, cons and scope for improvement. this essentially means talking to the different people involved in the ‘chain’ of delivery, in order to understand how the process is understood and dealt with, and to seek evidence and opinions about the scope to ‘do things differently and better’. once all views are collected, the analyst builds a series of process diagrams and considers how elements might be reconfigured or recombined in ways which remove or minimise the problems identified and offer potential to enhance performance. not only does the analysis generate ideas for different delivery models; but the task of speaking to all the actors and engaging them in considering this issue has a knock-on benefit of clarifying and improving corporate commitment among staff. lean systems has gradually replaced bpr as the dominant ‘new approach’ to seeking transformation and beneficial change in corporate organisational management. it shares the bpr interest in reviewing business operations from the varied perspectives of all those, from senior to very junior employees, engaged in the process. but its style of operation and its outcomes appear more radical than those of bpr, relying more on empowerment and ownership, in the analysis and redesign stages. a recent review of the application of ‘lean systems’ approaches in public policy in the uk, where it has been used by local authorities seeking increased efficiencies in public service delivery, concluded the following. by redesigning their systems, the authorities were able to realise that the boundaries of the systems which they were operating stretched further than the domain in which their service had control. each case study had examples where other agencies had received benefits from the improvements from redesign. cumulatively, this evidence suggests that the benefits that can be achieved from systems thinking interventions can be even greater than expected when seen at the level of higher system interactions. (zokaie et al., 2010) interestingly, the report identified one specific benefit of this approach as its abiiity to recognise, and incorporate within the analysis, impacts and interactions ‘beyond the domain’ of the main focus of review. it is a classic weakness of much current policy evaluation that it tends only to focus upon specific policy instruments or initiatives, rather than take account of ‘beyond-domain’ influences upon local-level performance (see schiller et al., 2010, and dwyer et al., 2010a). both these models could offer useful applications to policy-makers working in agriculture and rural development. it is an area which is ripe for experiments, in the sense of ‘action research’, bringing together research experts with policy practitioners and strategists. placing new emphasis upon research and development in cost-effective rural policy design and implementation could easily be justified by the scale of existing inefficiencies and inertia in cap policy, and thus the potential gains from more effective organisation and management, in future. 7. conclusions rural development policy under the cap must seek to encourage significant innovation and transformation of europe’s agriculture and rural areas in order to meet future 43transformation for sustainable agriculture challenges. pillar 2 policies already include elements which can be used to do this; but the balance of evidence suggests that such use is not widespread, to date. achievements are held back by barriers at the level of individual businesses and in respect of awareness, understanding and the confidence to act. at the same time, weaknesses in policy design and delivery frequently constrain the potential of rdps to tackle these barriers effectively. the proposed new pillar 2 framework incorporates a number of improvements in respect of promoting innovation and transformation; with more flexible and integrated approaches and a new emphasis upon some of the key elements, such as knowledge exchange and collaboration. however, a better toolkit does not on its own guarantee success. persistent weaknesses in the implementation and design of policies will limit their ability to promote innovation in practice. more emphasis is therefore needed to improve the capacity and capability of rdp actors and institutions in effective policy formulation and delivery, if the framework is to be used to good effect. this requires additional efforts by the commission, also within the various eu networks for exchange of good practice and collaborative learning, and via the wise deployment of technical assistance funding within the member states and regions. scientific research can also play a valuable role, here. this paper has particularly highlighted the potential for ‘new communities of learning’ where scientific evaluators work with policy makers to better understand organisational and institutional processes within policy, perhaps borrowing ideas from the commercial world, to enhance the enabling power of pillar 2. references ade, in collaboration with adas, agrotec and evaluators.eu (2009). evaluation of the implementation of the farm advisory system. final report – evaluation part. european commission. at: http://ec.europa.eu/agriculture/eval/reports/fas/report_eval_ en.pdf bassi, s., mazza, l., ten brink, p., medarova, k., gantioler, s., polakova, j., lutchman, i., fedrigo-fazio, d., hjerp, p., baroni, l. and portale, e. 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(2010), lean and systems thinking in the public sector in wales. lean enterprise research centre report to the welsh audit office. june 2010. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(2): 113-130, 2013 transfer of single farm payment entitlements to farm successors: impact on structural change and rental prices in switzerland gabriele mack1, anke möhring, ali ferjani, albert zimmermann, stefan mann agroscope reckenholz-tänikon (art), socioeconomics research group, 8356 ettenhausen, switzerland abstract. this paper analyses the impact of tradable and non-tradable single farm payment (sfp) entitlements for farm successors on structural change and the lease market. using the example of swiss agriculture, the effects on rental-price trends and farm-exit rates are investigated. an ex-ante normative impact analysis is performed with the agent-based agricultural-sector model swissland, which simulates structural change processes and income trends in swiss agriculture over a period of up to 15 years. a land market implemented at municipality level simulates the plot-by-plot leasing of land to surrounding neighbouring agents that is common in switzerland. allocation of plots to tenants as well as lease pricing is modelled taking into account the farm-specific land rents. the results show that personalised sfp entitlements which could not be transferred to a farm successor not only cause an intensification of structural change, but would also thus lead to a substantial reduction in rental prices. sfp entitlements which were successfully transferred to farm successors have only a slight impact on structural change and the rental prices of arable land. only for grassland in the mountain region does a stronger shift result in a significant reduction in rental prices. keywords. rental market, agent-based modelling, agricultural sector model, structural change, single farm payments, farm succession. jel codes. q150; q120 1. introduction the introduction of single farm payments (sfp) is an integral part of the agricultural policy reform planned by the swiss government from 2014 onwards. before the reform, direct payments in switzerland were primarily based on agricultural area and livestock units. now, after the introduction of the reform, payments based on livestock units have been substantially reduced, and are disbursed exclusively for animal-friendly housing systems. single area payments (sap) and sfp will be the most important payment schemes from 2014 onwards. to ensure that agricultural policy and the direct-payment system are 1 corresponding author: gabriele.mack@agroscope.admin.ch. 114 g. mack, a. möhring, a. ferjani, a. zimmermann, s mann as effective and efficient as possible, concrete aims that bear close scrutiny are set out in the federal council’s message on agricultural policy for 2014-17 (ap 14-17). sap are spent to ensure the provision of adequate supplies of high-quality food and to preserve the natural heritage, the environment and biodiversity. the introduction of sfp in 2014 was completely new in the almost-20-year history of the swiss direct-payment system. entitlements for sfp are farm-specific, and the payments amount to the total support received by the farm in the period prior to the reform in order to ensure a socially acceptable level of income after the reform. during the consultation period which marks the beginning of every policy-reform process in switzerland, policy-makers intended to couple sfp entitlements to the cultivation of the farm and to the person managing the farm in the first year after the reform, while the transfer of sfp entitlements from this person to his farm successor was not planned. the aim of fully decoupling sfp from production factors was to increase land mobility and to lower rental prices in switzerland (blw, 2011). the consultation period resulted in the policy decision to enable the transfer of sfp entitlements to farm successors (bundesrat, 2012). it was also decided to shift sfp to sap within a period of eight years. a large number of studies have examined the distributional effects of a shift from sap to sfp in the eu from both a static and a dynamic perspective (happe and balmann, 2002; kilian and salhofer, 2008; kilian et al., 2012; ciaian and kancs, 2012). the dynamic perspective takes into account the fact that subsidy payments may induce structural changes in the economy, such as productivity growth and exit/entry of farms. these structural changes may in turn affect the distributional effects of subsidies. happe and balmann (2002) have shown for the hohenlohe region in baden-württemberg that more frequent farm exits and falling rental prices are especially to be expected when farm payments are completely decoupled – i.e. when they impose no obligations on the recipients and allow for the possibility of continuing to receive the direct payments even in the case of farm exit. ciaian et al. (2008) have demonstrated theoretically that in the case of inelastic land supply, area payments may be capitalised into land values, benefiting landowners instead of farmers. based on a partial equilibrium model, ciaian and kancs (2012) have shown that the capitalisation of sap into land rents is increasing. by applying a general equilibrium model, ciaian et al. (2008) discovered that from a static perspective, i.e. without considering structural change, the sfp only benefits farmers. in a dynamic world, effects depend upon the nature of structural change, as well as on the tradability of entitlements. the aim of this paper is to analyse the impact of transferring sfp entitlements to farm successors on the latters’ farm-take over decisions, on structural change, and on rental prices in switzerland. the paper then goes on to present the impact on structural change and rental prices of a shift from sfp to sap. in this study, a normative impact analysis is performed with the agent-based agricultural sector model swissland, which simulates the structural-change processes and income trends in swiss agriculture over a maximum period of 15 years. the study describes in detail the municipal scale of swissland, and the modelling of both the rental-market decisions taking place at the municipal level, and of rental pricing. it focuses primarily on the methods for specifying the fadn-based agents in terms of geo-referenced attributes in the initial year, as well as illustrating the modelling of farm-succession decisions. 115transfer of single farm payment entitlements to farm successors chapter 2 gives an overview of the agent-based sector model swissland, introduces the database, and describes the spatial structures and modelling of the rental market. chapter 3 outlines the model scenarios and describes the results. chapter 4 contains a discussion of the results. 2. methods 2.1 overview of the agent-based sector model swissland the agent-based sector model swissland simulates structural-change processes and income trends for swiss agriculture. it also explicitly models farm-takeover decisions by young successors, and the transfer of sfp entitlements to these successors. the agent population of the sector model is based on the 3,400 fadn farms which provide their bookkeeping data to agroscope reckenholz-tänikon art’s farm accountancy data network. these fadn farms represent a sample of the approximately 55,000 swiss farms operating in 2006-2008. geographically, the 3,400 fadn farms are spread throughout switzerland. because, however, a spatially realistic local structure is necessary for simulating land trade among agents, a spatial reference on the municipal scale was implemented in the sector model. figure 1 gives an overview of the data sources, as well as specifying the agents’ attributes in the initial year and the agent’s behaviour. the selection of all 3,400 fadn farms as agents ensures detailed individual farm records for modelling the agents’ production resources. a study by moehring et al. (2010) describes the agents’ attributes in great detail in terms of production resources and biographical attributes. in moehring et al. (2011), detailed explanations are given for the modelling of production and investment decisions based on individual-farm optimisation models, as well as for the modelling of farm exit and handover decisions. in zimmermann et al., 2011 model results are shown. because only about 0.2% of the entire utilised agricultural area in switzerland changes owner each year (giuliani, 2002), whilst the majority of the land provided by exiting farms is rented, land-purchase decisions are not modelled in swissland. 2.2 defining neighbourly relationships for fadn-based agents the 3,400 fadn-based agents are located throughout switzerland, and do not usually have a neighbourly relationship with each other. to model land trade among these fadn-based agents, a spatially realistic municipality structure including neighbourhood patterns among farm locations was implemented in the sector model. the municipality structure is based on seven existing swiss municipalities. the seven reference municipalities were chosen from among switzerland’s 2,765 municipalities in a two-step procedure. firstly, a municipal typology was created on the basis of size (utilised agricultural area or uaa), difference in altitude (between the lowest and highest point above sea level of the uaa), and distribution of the farmland over different altitude levels within a municipality. these attributes were selected because they determine the accessibility and the driving time between the farm locations and the plots of a municipality. the horizontal and vertical distances between the farm locations and the plots were estimated for switzerland’s 116 g. mack, a. möhring, a. ferjani, a. zimmermann, s mann 2,765 municipalities. on this basis, five municipality groups were selected to which all of switzerland’s 2,765 municipalities were assigned (table 1). secondly, taking the representativeness of farm type and size into account, at least one genuinely existing municipalfigure 1. schematic model overview of the agent-based sector model swissland.   fadn data and survey data at farm scale geo-referenced spatial data at municipal scale specification of the agents' attributes for the inital year t production resources biographical attributes model procedures for the years t,t+1,t+n farm optimisation of each agent selection of exiting agents and agents with farm sucessor allocation of the plots to the neighbourhood agents geo-referenced attributes specification of the agents' behaviour production and investment decisions pmp-based optimisation model farm succession decision specification of rules based on empirical results lease decisions for land plots specification of rules based on microeconomic theory data sources selection of plots for lease on municipal level agricultural-policy data and price forecasts farm optimisation of neighbourhood agents with plot for lease update of the agents' attributes for the next year t+1 production resources biographical attributes geo-referenced attributes source: own illustration. 117transfer of single farm payment entitlements to farm successors ity per group was chosen, and specific geo-referenced data (topology of the plots cultivated on each farm, location of the farm buildings) were determined for each farm in the municipality. data were processed in a gis in order to generate information on distances from farm buildings to plots, as well as on neighbourhoods, plot size, and form of cultivation. the main selection criterion for the reference municipalities was the availability of geo-referenced data for all farm locations and fields within the municipality (table 2). the farms located in the reference municipalities served as a source of information for the description of the fadn-based agents in terms of spatial and topographic characteristics. table 1. description of the municipality groups. municipality group no. of municipalities per group average uaa (ha) standard deviation uaa (ha) average difference in altitude (m) standard deviation of difference in altitude (m) 1 1,016 316 301 90 54 2 571 411 273 231 132 3 480 614 627 372 131 4 350 1,125 928 1,421 389 5 334 1,223 1,565 1,381 377 uaa: utilised agricultural area. source: own calculations. table 2. features of the reference municipalities. name of reference municipality municipality group uaa (ha) per reference municipality no. of plots per reference municipality difference in altitude (m) no. of farms per reference municipality total no. of reference municipalities in the model oberembrach 1 591 394 202 31 9 illnaueffretikon 2 1,158 735 197 54 7 vechigen 3 1,467 799 396 102 7 trimmis 4 479 995 1,961 49 14 alpnach 5 922 729 1,551 78 7 engelberg 5 722 483 1,823 58 8 giswil 5 1,181 1,076 1,727 109 7 source: own calculations. to model land trade among the fadn-based agents, each agent was assigned to a matching farm location in a reference municipality (figure 2). the principal criteria for the assignment of fadn-based agents to farm locations of the reference municipalities were matching attributes which were present in both datasets, 118 g. mack, a. möhring, a. ferjani, a. zimmermann, s mann particularly farm area (ha uaa, ha grassland, ha arable land), altitude (m.a.s.l.) and the zones to which they belonged (lowland zone to mountain zone 4). because the number of fadn-based agents was significantly higher than the number of farm locations in the reference municipalities, our first step was to duplicate the reference municipalities which were underrepresented in terms of these allocation features. this was done by minimising the sum of the squared deviations between the attributes of the fadn-based agents and the farms locations of the reference municipalities. because over half of all fadn-based agents had farm locations in a reference municipality of group 5, taking one reference municipality for group 5 into account would require approx. 20 duplications per municipality. in order to limit the number of duplications per municipality, it was decided to take three genuinely existing municipalities into account for group 5. the duplication procedure led to the total number of 59 swissland municipalities (last column of table 2). an advantage of this method is that it allows the topology and the distances between plots to be modelled highly realistically, even though it is difficult to ensure the representativeness of all features (topography, uaa per municipality, farm type, zone). the 3,400 fadn-based agents were assigned to the actual farms of the swissland municipalities by minimising the sum of the squared deviations of these attributes between the fadnbased agents and the actual farm in each case. the allocation of agents to the 59 municipalities could in turn be formulated as an optimisation problem. the binary solution varifigure 2. assignment of fadn-based agents to farms in reference municipalities to model land trade. reference municipalities with genuine farm locations and genuine land trade fadn-based agents throughout switzerland: no land trade among fadn-based agents assignment of fadn-based agents to farm locations in the reference municipalities to model land trade, taking into account the neighbourhood relationships of these agents 119transfer of single farm payment entitlements to farm successors able of such a system would, however, be a matrix in the ‘no. of agents  *  no. of farms in the reference municipalities’ dimension, which would overtax the available solution capacities. assignment is therefore via a gradual loop formulation in which each agent is successively assigned to the most suitable farm in the reference municipalities in each case that is not yet taken. the most suitable farm in each case is the one with the smallest deviations in its attributes. in the event of unequal size representations of the attributes, this process could result in no suitable actual farm in the reference municipalities still being available for the last agents to be allocated. because of this, the attributes of both the agents and the farms of the reference municipalities are transformed beforehand into rank values. in this way, and because it leaves several farms of the reference municipalities still available to the last agent to be allocated, an adequate distribution result is achieved. because the most important criterion for the assignment of the fadn-based agents to the farms of the swissland municipalities was the matching of the attribute ‘farm area’, differences in farm size between the agents and the farms of the reference municipalities were very small. where minor differences existed, reference-municipality farm plots were uniformly scaled up or down so that farm area corresponded exactly to that of the relevant fadn-based agents. in addition, several other fadn-based agent attributes (e.g. percentage of arable land; percentage of slopes and steep land) were transferred to the allocated plot structures. consequently, all fadn-based agents are determined by spatial characteristics (farmyard coordinates; number of plots with meadows and arable land; coordinates of the plots and their field-farmyard distance), and ultimately possess ‘virtual’ neighbouring agents whose plots border on other agents. 2.3 modelling farm-takeover decisions in switzerland, farming exit is shaped primarily by the farm manager’s life cycle. normally, once the farm manager turns 65 and starts receiving his state pension – coinciding with the lapse of entitlement to direct payments – the farm either closes down and the land is put up for lease, or the farm’s production resources (i.e. land and capital resources) are transferred in their entirety to a family successor (meier et al., 2009). under the present framework conditions, farms are only very seldom given up before pensionable age is reached (rossier and wyss, 2006). furthermore, fewer than 10% of swiss farm managers are older than the statutory retirement age of 65. in an empirical study, rossier and wyss (2006) discovered that in switzerland, farm-succession decisions are significantly influenced by number of sons in the family, location of the farm (lowland, hill or mountain region), size and type of the farm, receipt of direct payments, and farm income. in the agent-based model swissland, these findings on the determinants of farm transfer are implemented in a two-stage decision-making process. in a first step, agents with sons determine their potential successors. if an agent has no son, the farmland is put up for lease. in a second step, an income criterion for the takeover decision by the ‘successive agent’, to the effect that the attainable household income of the farm must be higher than an exogenously determined minimum household income, is implemented in the model. this minimum income is based on an average reference income for the second and third sector in switzerland. takeover of the farm’s production resources (land and 120 g. mack, a. möhring, a. ferjani, a. zimmermann, s mann capital resources) by the ‘successive agent’ occurs only when this income criterion is met. farm succession and farm exit are therefore determined both by the rate at which agents reach pensionable age, and by the percentage of successful takeovers, which is in turn dependent on income trends. in the subsequent years of the farm succession, land-leasing options and investment options in animalhousing systems are modelled. 2.4 underlying data for modelling the land market the individual farm records (2006-2008 three-year average) of 3,400 currently operating fadn family farms provided a detailed factual basis for modelling the agents’ production resources (land use, livestock, family and non-family labour, financial values), production costs, prices and direct payments. land-ownership data was used to define the total area of both owned and rented land (table 3). table 3. area of owned and rented farmland in the plain, hill and mountain regions. plain region hill region mountain region fadn farms no. 1,420 980 994 area mean standard deviation mean standard deviation mean standard deviation total land ha 20.06 10.96 17.24 10.24 17.92 11.36 owned land ha 11.30 7.02 10.57 6.90 10.80 8.57 rented land ha 8.76 9.59 6.66 8.80 6.90 9.74 source: calculated on the basis of agroscope reckenholz-tänikon art’s farm accountancy data network (fadn) data pool (art, 2006-2008). in the absence of a nationwide swiss statistical survey on grassland and arable-land rental prices, the fadn sample of specialised arable and grassland farms was used to provide a rough estimate of the lease prices for grassland and arable land in the plain, hill and mountain regions of switzerland (table 4). the compulsory upper limits of the lease prices depend on the productive value of the land, determined on a regular basis by the swiss cantonal authorities. 2.5 modelling land exchange and lease pricing a precondition for land trade among agents is a neighbourly relationship. because such a relationship derives from farm locations within a municipality, land-trade modelling is limited to agents whose farm locations are in the same municipality. swissland models a plot-by-plot land lease from ‘exiting agents’ to the remaining agents in the immediate vicinity. the ‘exiting agents’ are those having no farm successor to whom 121transfer of single farm payment entitlements to farm successors they can hand over, or those whose potential successor decides against taking over the farm on economic grounds (see section 2.3). the agents in the near vicinity of an exiting agent form the group of agents interested in the latter’s plots. decisions to allocate land to the neighbouring agents as well as lease pricing are modelled as a plot-by-plot bidding process. the initial lease price asked by an exiting agent is based on the average regional values of the fadn farms for arable land and grassland in the base year (see table 4). because these regional averages are close to the compulsory upper limits for rental prices that are measured against the productive value of the land, these values are also taken as upper limits for regional lease prices. an agent’s lease decision for a plot depends on its income growth. as the supply of plots rises, other production resources such as labour generally exert a limiting effect, causing the plot-based economic benefit to decline. to calculate the increase in income of all neighbouring agents involved in the bidding process, each of these agents is optimised with the new plot. the neighbouring agent receives the plot which generates the highest profit at the upper limit of the lease price. if, however, the upper limit of the lease price is higher than the increase in income of all agents in the near vicinity, the bidding process is repeated, taking other agents in the wider vicinity into account. where the upper limit of the lease price is also too high for agents in the wider vicinity, it is assumed that the exiting agent will reduce the lease price in steps and that the bidding process will recommence. should the situation arise where the lease price is greater than zero and no neighbouring agent is able to generate a profit for a plot, the plot becomes fallow land. provided that a neighbouring agent benefits from leasing only when the lease price is zero, it is assumed that the exiting agent leaves the neighbouring agent the plot before it becomes fallow land. empirical studies conducted in two regions of germany (strom, 1998) show that the number of farms involved in the lease market is limited, ranging between one and five farms. based on the findings of strohm (1998), it was assumed that the five nearest neighbours were involved in the bidding process. only in the event that no agent could be found were three further neighbours considered. these restrictions on the number of bidding agents in the same municipality limit the number of optimisation runs to an acceptable range. a stepwise 20% reduction in lease prices was also stipulated in order to limit the number of optimisation runs. table 4. 2006-2008 three-year-average lease prices (chf/ha) for arable land and grassland in the swiss plain, hill and mountain regions. region plain hill mountain arable land average for 2006-20081) 745 690 527 standard deviation1) 301 327 135 grassland average for 2006-20081) 629 516 420 standard deviation1) 190 215 230 calculated on the basis of the sample of specialised arable and grassland farms from agroscope reckenholz-tänikon art’s farm accountancy data network (fadn) data pool (art, 2006-2008). 122 g. mack, a. möhring, a. ferjani, a. zimmermann, s mann 3. scenario definitions and results the first scenario assumes that sfp are transferable from the exiting agent to the successor agent in the subsequent years of the reform. this scenario is hereinafter referred to as ‘transferable sfp’. the second scenario implies that sfp could not be transferred to a successor agent after the reform. this is thought to negatively influence the successor agent’s decision regarding takeover, as well as to enhance structural change. scenario 2 is hereinafter referred to as ‘non-transferable sfp’. in both scenarios, all output and input prices except those for land are constant over time. to investigate the effects of a partial shift from sfp to sap, the sfp were reduced and shifted to sap on a percentage basis, whilst sap-based ecological payments and lu-based payments for animal-friendly housing systems remained steady in order to safeguard the swiss government’s environmental objectives. in both scenarios, four versions of allocation of direct payments to sap and sfp are considered. version 1 is characterised by high sap in combination with low sfp, version 4 by low sap in combination with high sfp. versions 2 and 3 lie in between. figure 3 shows the average level of the single area payments per hectare for the four versions. since the higher the altitudinal belt, the greater the hardship allowances paid, area payments increase from the plain to the mountain region. relatively speaking, sap decrease from version 1 to 4 by up to 40% (figure 4). viewed in absolute terms, the reduction is around chf 700 per hectare. figure 3. sap per hectare (including ecological direct payments and cultural-landscape payments) 0 500 1000 1500 2000 2500 3000 d1 d2 d3 d4 d1 d2 d3 d4 d1 d2 d3 d4 c h f pe r h a ‘direct payment’ version plain regionhill regionmountain region sources: own calculations. 123transfer of single farm payment entitlements to farm successors figure 4. ratio of sap to sfp averaged over all agents. ,0 10,000 20,000 30,000 40,000 50,000 60,000 d1 d2 d3 d4 c h f pe r f ar m ‘direct payment’ version sfp sap sources: own calculations. the transferability of sfp entitlements to successive agents substantially affects the number of exiting agents and growing agents (figure 5). the number of exiting agents increases from 11% (‘transferable sfp’ scenario) to 17% when sfp entitlements are nontransferable. since the number of exiting agents also determines the supply of land put up for lease, the number of growing agents also increases, from around 46% to between 51 and 59%. the number of non-growing agents decreases from between 44 and 40% (‘transferable sfp) to between 36 and 23% (‘non-transferable sfp) (figure 5). the modelling of a plot-by-plot leasing procedure from exiting agents to growing agents ensures that one exiting agent’s plots are rented on average by four agents (figure 5). this closely matches leasing patterns in swiss agriculture (lauber, 2006). figure 6 shows that the number of exiting agents is disproportionately high in the ‘smallest farm size’ category (< 10 ha) for both scenarios, whilst the number of non-growing agents is over-represented in this category. by contrast, the number of growing agents is disproportionately high in the ‘middle farm size’ categories, and is decreasing in the ‘largest farm size’ category. this means that the ‘middle farm size’ categories experience the highest economic gain from leasing land, whilst in the ‘smallest’ and ‘largest’ categories the economic benefit is lower, due to limited production capacities. the percentage of agents taking over their predecessor’s farm is also higher in ‘larger farm size’ categories (fig. 7). 124 g. mack, a. möhring, a. ferjani, a. zimmermann, s mann figure 5. share of growing, non-growing and exiting agents in the transferable and non-transferable sfp scenarios. 44% 43% 42% 40% 36% 32% 26% 23% 46% 47% 48% 49% 51% 54% 58% 59% 11% 11% 11% 11% 13% 14% 16% 17% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% d1 d2 d3 d4 d1 d2 d3 d4 no of agents in % non-growing agents growing agents exiting agents non-transferable sfptransferable sfp sources: own calculations. figure 6. share of growing, non-growing and exiting agents in different farm-size categories (d1). 55% 40% 45% 54% 66% 51% 33% 38% 42% 59% 24% 49% 51% 43% 32% 28% 54% 54% 53% 38% 21% 11% 4% 2% 2% 21% 13% 8% 5% 3% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% <10 ha 10-20 ha 20-30 ha 30-40 ha >40 ha <10 ha 10-20 ha 20-30 ha 30-40 ha >40 ha no of agents in % non-growing agents growing agents exiting agents non-transferable sfptransferable sfp sources: own calculations. transferability of sfp from the exiting agents to their successor agents yields an average farm-exit rate of around 1.5% in the period under consideration (figure 8). this more or less corresponds to the farm-exit rate observed in switzerland from 2000 to 2010. a gradual reduction in sap (direct-payment versions d1-d4) does not affect the 125transfer of single farm payment entitlements to farm successors figure 7. percentage of exiting agents and agents taking over farm from predecessors in different farm-size categories (d1). 7% 18% 23% 20% 21% 8% 15% 19% 18% 20% 72% 71% 73% 77% 77% 72% 71% 73% 77% 78% 21% 11% 4% 2% 2% 21% 13% 8% 5% 3% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% <10 ha 10-20 ha 20-30 ha 30-40 ha >40 ha <10 ha 10-20 ha 20-30 ha 30-40 ha >40 ha no of agents in % agents taking over farm other agents exiting agents non-transferable sfptransferable sfp sources: own calculations. figure 8. average farm-exit rates for the whole of switzerland in the transferable and non-transferable sfp scenarios. 0 0.5 1 1.5 2 2.5 d1 d2 d3 d4 fa rm -e xi t r at e in % p er y ea r ‘direct payment’ version transferable sfp non-transferable sfp source: own calculations. 126 g. mack, a. möhring, a. ferjani, a. zimmermann, s mann agents’ exit rate if the sfp are completely transferable (cf. figure 8). by contrast, in the ‘non-transferable sfp’ scenario, successors receive no sfp. this causes a corresponding drop in their income, which in turn brings about a rise in the exit rate from version 1 to 4 (figure 8). figures 9 and 10 show the effects of transferable sfp entitlements on the lease prices of recently leased arable and grassland plots. both figures give the average values of the last three forecast years as a percentage of the lease prices of the base years 20062008. figure 9 shows that the lease prices for arable land in the ‘transferable sfp’ scenario change only slightly. only if the sap are reduced by up to chf 700 per ha (version 4) do the lease prices fall by around 7%. this clearly demonstrates that with a relatively moderate farm-exit rate of 1.5%, a shift from sap to sfp has only a limited influence on lease prices. if, on the other hand, the supply of leased land increases – as in the ‘nontransferable sfp’ scenario – a somewhat sharper fall in lease prices for all recently leased arable plots of up to 12% on average is to be expected. lease prices for grassland, especially in the mountain region, are falling by up to 30% (figure 10). accelerated structural change and an increase in the supply of land are the main causes of significantly lower lease prices. figure 9. lease prices for arable land (plain region): average value of all newly leased plots in the last 3 forecast years (100% corresponds to the lease-price level in the initial years 2006-2008). 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% d1 d2 d3 d4 d1 d2 d3 d4 ‘direct payment’ version non-transferable sfptransferable sfp source: own calculations. 127transfer of single farm payment entitlements to farm successors 4. discussion and conclusion the agent population of the sector model is based on the 3,400 fadn farms. geographically, the 3,400 fadn farms are spread throughout switzerland. because, however, a spatially realistic local structure is necessary for simulating land trade among agents, a spatial reference on the municipal scale was implemented in the sector model. the geo-referenced local data describe the location of all farmyards and their cultivated plots of land, with the actual plot sizes as well as the distance and altitude differences between the farmyard and plots constituting the underlying data for the simulation of the land market in swissland. in this respect, the sector model differs fundamentally from other agent-based models that divide the space into raster cells of equal size (balmann, 2000; happe, 2004; lobianco and esposti, 2010; van der straeten et al., 2010). in total 59 municipalities were introduced in swissland in order to model neighbourly relationships among the fadnbased agents. these municipalities were derived from seven duplicated genuine reference municipalities. neighbouring-agent type, number of plots per agent, and plot sizes – all of which influence the leasing decisions of the agents and their changes in farm size – are determined by the models’ spatial references, as defined by the selected reference municipalities. model validations with further reference municipalities are therefore necessary in order to estimate the impact of the selected reference municipalities on the model results. figure 10. lease prices for grassland (mountain region): average value of all newly leased plots in the last 3 forecast years (100% corresponds to the lease-price level in 2006-2008). 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% d1 d2 d3 d4 d1 d2 d3 d4 ‘direct payment’ version non-transferable sfptransferable sfp source: own calculations. 128 g. mack, a. möhring, a. ferjani, a. zimmermann, s mann for the modelling of structural change in agent-based models farm-takeover criteria specified by numerical constraints are necessary (lauber, 2006). although driving forces of structural change are known for switzerland from rossier et al. (2006) specified constraints are not available. for that reason an exogenously determined minimum household income for farm exits was introduced in the model. this assumption influences the number of exiting farms and their farms size distribution substantially. for that reason further studies on driving forces of farm-takeover decisions are necessary. the modelling of the land market with the agent-based sector model swissland simulates the plot-by-plot leasing of land to the surrounding neighbour agents that is customary in switzerland. the bidding process is restricted to eight neighbour agents within the same reference municipality. based on the findings of strohm (1998), it was assumed that only five nearest neighbours were involved in the bidding process. only in the event that no agent could be found were three further neighbours considered. according to berger (2001) it was also assumed that plots which could not find a tenant are reoffered in subsequent years on the land market. the lease price is modelled iteratively, with the current lease price being assumed as an upper limit. if there is no tenant for this value, the lease price is lowered incrementally. all these model assumptions ensure that plots finding no tenant farmer and becoming fallow land are not overestimated by the model (lauber, 2006). the allocation of plots to agents is performed according to purely economic criteria – not always the case in real-life scenarios in switzerland, where leasing decisions are often made based on personal relationships (lauber, 2006; strohm, 1998). this leads to an overestimation of economic driving forces of land allocation processes and results in the economic advantage of leasing and farm growth being overestimated rather than underestimated. on the other hand the modelling of the plot-by-plot leasing of land to neighbouring agents is common practice in switzerland. in addition the modelling of real plot sizes, and the differences in distance and altitude between the farmyard and the plots enables the simulation of farm-size distributions changing fairly smoothly in the context of structural change as the results show. the results show that sfp which are tradable to farm successors increase the pace of structural change by very little, and have only a slight effect on lease prices. as long as a delayed structural change of the current order of magnitude of about 1.5% per annum prevails in switzerland, only minimal effects on lease prices are to be expected, especially for arable land. it is only for grassland in the mountain region that a fairly intensive shift to single farm payments causes a significant reduction in the lease prices. moreover, calculations confirm that intensification of structural change through non-tradable sfp would lead to a significant reduction in lease prices, meaning that non-tradable sfp entitlements would be an effective policy instrument for enhancing land mobility and structural change. the results of this normative study confirm the findings of previous studies. references art (agroscope reckenholz-tänikon research station art), 2006 to 2008. grundlagenbericht 2006 to 2008. agroscope reckenholz-tänikon research station art, ettenhausen. 129transfer of single farm payment entitlements to farm successors balmann, a. 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(2010). the regional multi-agent simulator (regmas): an open-source spatially explicit model to assess the impact of agricultural policies. computers and electronics in agriculture 72: 14-26. meier, b., g. giuliani and flury c. (2009). flächentransfers und agrarstrukturentwicklung bis 2007. agrarforschung 16(5): 152-157. möhring, a., zimmermann, a., mack, g., mann, s., ferjani, a. and gennaio, m.-p. (2010). multidisziplinäre agentendefinitionen für optimierungsmodelle. schriften der gesellschaft für wirtschaftsund sozialwissenschaften des landbaus e.v. 45: 329-340. 130 g. mack, a. möhring, a. ferjani, a. zimmermann, s mann möhring, a., mack, g., zimmermann, a., gennaio, m.-p., mann, s. and ferjani, a.(2011). modellierung von hofübernahme und –aufgabeentscheidungen in agentenbasierten modellen. ysa 2011, 163-188. rossier, r. and b. wyss, (2006). determinanten der hofnachfolge. agrarforschung 13(4): 144-149. strom., r. (1998). verlaufsformen der faktormobilität im agrarstrukturwandel ländlicher regionen. eine empirische studie am beispiel von betriebsaufgaben in den kreisen emsland und werra-meissner. dissertation, university of göttingen. wissenschaftsverlag vauk. kiel. van der straeten, b., buysse, j., nolte, s., lauwers, l., claeys, d. and van huylenbroeck, g. (2010). a multi-agent simulation model for spatial optimisation of manure allocation. journal of environmental planning and management 53(8): 1011-1030. zimmermann, a., möhring, a., mack, g., mann, s., ferjani, a. and gennaio, m.-p. (2011). die auswirkungen eines weiterentwickelten direktzahlungssystems. modellberechnungen mit silas und swissland. art-bericht 744. agroscope reckenholztänikon research station art, tänikon, ch-8356 ettenhausen. bio-based and applied economics 3(3): 249-269, 2014 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-14681 structures and dynamics of transnational cooperation networks: evidence based on local action groups in the veneto region, italy elena pisani*, laura burighel dipartimento territorio e sistemi agro-forestali (tesaf), università di padova, italy abstract. the paper assesses the structures and dynamics of transnational cooperation projects promoted by local action groups (lags) in different periods (from leader ii to leader axis) using social network analysis (sna) in a specific case study: the veneto region in italy. the classical indexes of sna have been critically examined, and the paper also presents innovative indexes that can capture the peculiarity of transnational cooperation: disaggregated densities of the network and transnational centrality of the node. these indexes are useful in order to quantify how transnational a network actually is, and to measure the power-information that each actor (lag) can acquire through its transnational contacts. the methodology can become a tool for managing authorities to implement new forms of evaluation of transnational cooperation of lags. keywords. rural, transnational cooperation, leader, social network analysis, evaluation. jel codes. o22, o18 1. introduction since the 1950s rural areas have been extensively analyzed and evaluated using a sectorial approach that allows the comparison with certain urban standards and, consequently, deprives them of their peculiar characteristics (bell et al., 2010; sotte, 2006). in the last 30 years a new narrative has emerged considering each rural territory as a unique environment, with a local combination of social, economic and institutional factors (saraceno, 2013). the rural space is now conceived as a multifunctional territory and the diversities of rural areas are reckoned as potential economic opportunities, complementary to urban ones (sivini, 2006; leon, 2005). the importance of a territorial approach to rural development has been officially recognized by the european commission (ec 1997, 1996, 1988) and the concept has been applied through the leader (liaison entre actions de développement de l’économie rurale) initiative, which proposes an ‘area-based’, ‘bottom-up’ and ‘multi-sectorial’ approach (ec no 1698/2005 and ec no 1305/2013). according to its prin* corresponding author: elena.pisani@unipd.it. 250 e. pisani, l. burighel ciples, leader tries to promote each territory preserving and fostering its specificities and diversities, which are considered as relevant economic opportunities (saraceno, 2013) but with a new socio-institutional method that distinguishes leader from other classical rural development programmes and projects. with this method, local actors should be inserted in a process of territorial regeneration achieved through innovative local governance, structured on a ‘new social order’ (papadopoulou et al., 2011). in order to highlight strengths and weaknesses when passing from ambitious theoretical premises to real applications, a critical and systematic evaluation of the financed initiatives are required. this study contributes to the literature on the evaluation of rural development with specific reference to the eu-funded leader projects, proposing a quantitative evaluation approach based on classical and new indexes of social network analysis (sna) applied to transnational cooperation (tnc) projects promoted and implemented by local action groups (lags). the results, referred to the veneto region and evidencing the evolution of the networks in different programming periods (leader ii, leader + and leader axis), could be used by the managing authority to assess lag performance and informative efficiency in implementing these initiatives. the application is essentially descriptive and does not attempt to exhaustively explain the dynamics behind the projects’ ties; it is a pilot study that could be integrated in future researches by questionnaires and interviews assessing the quality of the initiatives. the paper opens with a brief theoretical overview of the neo-endogenous rural development approach and then focuses on the main features of the transnational cooperation projects financed by leader. despite the innovative lines, critical elements have appeared in the implementation, mainly related to regulatory and administrative frameworks, setting up of partnerships, project design and effective cooperation during implementation (enrd, 2014). all these elements evidence the necessity for a detailed monitoring and evaluation process (follow-up phase), with core elements that refer to ‘project’s outputs in social, economical, and environmental terms; method of implementation and partnership performance; and future prospects or mainstreamed outcomes’ (enrd, 2012: 32). the evaluation method – sna – is then briefly presented. sna has already been applied to the investigation of internal network structures and network dynamics of lags (marquardt et al., 2012; nardone et al., 2010; franceschetti, 2009), but not yet to a detailed examination of the transnational networks promoted by the lags. the proposed methodology, applied to the case study of tnc projects of veneto lags for different leader programming periods, applies to specific measures and results. these show a reduction in the size and density of the network that, at first sight, could be interpreted as a worsening performance of tnc projects. in practice, the data attest to a reshaping of the transnational, national and regional compositions, suggesting increased efficiency in the information flows within the network of tnc projects and a better selection of cooperating lags. the paper concludes with an overview of the case study results and some suggestions for possible future research, evidencing the applicability of the method to different regional situations. 2. transnational cooperation in neo-endogenous rural development the present state of discussion on rurality is the result of an evolution in the approaches to rural development, passing from an ‘exogenous’ to an ‘endogenous’ 251structures and dynamics of transnational cooperation networks approach, till reaching the contemporary ‘neo-endogenous’ approach (shucksmith, 2009). the first was influenced by keynesian and neo-liberal economic theories that, even if starting from diverging viewpoints, led to a sectorial top-down approach, and conceived the development of rural areas as a consequence of the development process initiated in urban areas (lowe, 2006; lowe et al., 1995). the endogenous perspective enhanced the local human and environmental differences, and balanced the economic, social and environmental factors using a combined locally-based approach (gkartzois and scott, 2013), progressively integrated in a new mode of governance to coordinate the actors at different levels. as a consequence, various local actors have to promote local development, within a network composed not only of horizontal but also vertical relations (shucksmith, 2009). the locally-based approach retained from the previous endogenous perspective the consideration of rural areas as places with unique characteristics and resources that necessitate flexible and specific paths for development (lysgard and cruickshank, 2013); it also gives continuity to some elements of the exogenous approach, as it draws attention to connections with extra-local territories (shucksmith, 2009; vitale, 2006; leon, 2005). the post-modern economy is ‘informational, global and networked’ (castells, 2011), therefore local and supra-local actors are also interconnected within a complex global network. these connections are developed in a bi-dimensional way (vertical and horizontal) within an economic sector and intersectorially to gain access to new economic opportunities (murdoch, 2000; ray, 1998). the rural interconnections can be graphically represented by a ‘rural web’; this continuously reshapes its power relations and development opportunities (esparcia, 2014). moreover, the capacity to develop relations represents one of the possibilities for rural areas to renew their image and become more attractive and, consequently, stimulate the urban demand for rural products and services, thus increasing their economic performances (ploeg, 2006; vitale, 2006). the increased importance of rural development is acknowledged within the eu common agricultural policy (cap)1 (european commission, 2011; oostindie et al., 2010). in particular, the leader approach provided an opportunity for the european institutions to implement a neo-endogenous approach that could deal with rural areas from a multidimensional perspective (shucksmith, 2009; vitale, 2006; murdoch, 2000; storey, 1999). the leader approach considers the diversities of the territories as the starting point for development programmes in such a way that the specific economic, social, environmental and institutional conditions become the basis for a territorial route to integrated economic development (wellbrock et al., 2013;). thus, the capacity of any territory to be integrated in the globalized economy only partly relies on sub-national social, cultural and institutional forms of support; it is through the enhancement of a local network in parallel to the supra-local network that multi-level governance is strengthened (tola, 2010; depoele and ebru, 2006). local action groups, the local public-private partnerships implementing the leader approach, are characterized by the following elements: empowerment and social capital (casieri et al., 2010; nardone et al., 2010; storey, 1999), local governance (mackenwalsh and curtin, 2013, secco et al., 2010; shucksmith, 2009) and local service provision (lukesch, 2007; gaudio and zumpano, 2006). lags promote all these elements by means 1 the contribution of cap to market support decreased from 74% in 1992 to 10% in 2009. contemporarily, the expenditure for rural development rose from 8% to 20% and the contribution to direct payments rose from 18% to 70% (ec, 2011). 252 e. pisani, l. burighel of cooperation, structured on the network and its dynamics that provide the opportunity to acquire resources and innovate (esparcia, 2014, moseley, 2003). as with the ‘rural web’, the network is formed by relations developed on horizontal and vertical levels and, referring to the horizontal relations, it is possible to distinguish between local horizontal ties, among local partners that form the lag, and extra-local horizontal ties. different forms of cooperation can be activated to strengthen these extra-local ties: article 63 of the council regulation no 1698/2005, dealing with cooperation within leader, distinguishes between interterritorial and transnational cooperation. the former refers to cooperation of territories within the member state, the latter to cooperation of territories among member states and possibly extra-eu countries. lags can also implement and facilitate other forms of cooperation related to the other three axes, but in this study the focus is on transnational cooperation2. according to ray (2006), the transnational perspective on european rural development acknowledges the ‘big transformation’ of the last 30 years. in fact, transnational cooperation has the potential to intensify knowledge exchange and expand the pooling of expertise of individual and collective actors that are fundamental to obtain new viewpoints in the solution of problems and, consequently, innovation (dwyer, 2013). local territories have specific knowledge and information that are part of their competitive advantage (saraceno, 2013) and transnational cooperation can contribute to knowledge sharing among different european territories (saxena et al., 2007; ray, 2001). moreover, ray (2001) specifies three rationales that motivate lags participation in tnc projects. the first is ‘to take advantage of similarity’, as the project stems from commonalities, related to natural resources, cultural heritage or services provided. the second is ‘to take advantage of complementarity’, combining different resources or places for a continuous action, according to a strategic alliance of co-opetition where conflicting and shared interests are combined to create a fruitful relationship (pasquinelli, 2013; bengtsson and kock, 2000). these two rationales are fundamental for a learning process where common and different knowledge is shared and elaborated by the local actors to their reciprocal advantage, enhancing a european added value (mariussen and virkkala, 2013). moreover the flows of innovative knowledge among the network actors are fundamental in order to promote smart development, based on social innovation (erdn, 2013; dax et al., 2013). the third rationale is ‘to reach critical mass’, for example using international contacts to increase the size of local markets and the number of end consumers, thus final beneficiaries (ray, 2001). tnc projects create the possibility for integration in the european system, keeping pace with international economic structures (enrd, 2012). lags can thus be compared to network organizations with ‘repetitive exchanges among semi-autonomous organizations that rely on trust and embedded social relationships to protect transactions and reduce their costs’ (borgatti and foster, 2003: 995). transnational and inter-territorial cooperation is a way to enlarge lags networks in order to become integrated in the supra-local system, and take advantage of the creation of shared capital for some common actions. indeed, lags have a public-private nature and should try to create economic and social benefits for their territory (council regulation (ec) no 1698/2005, art. 61, 2 it is not easy to take a census of transnational projects at european level during the leader axis period, since the enrd projects database is updated voluntarily and could result as incomplete. the eu rural review – leader and cooperation (2012) presents 209 projects approved and notified to the european commission. 253structures and dynamics of transnational cooperation networks 62; regulation (eu) no 1305/2013). the network activity is fundamental to produce these impacts, as pointed out by various authors (aral and alstyne, 2007; borgatti and foster 2003; burt, 2002; granovetter, 1973). based on the above discussion, it appears necessary to identify some tools that can assess the results not only concerning policy standards but also networks and governance. sna can be a useful tool for the structural evaluation of this activity, especially if adequately integrated by a qualitative assessment capable of explaining the context and dynamics of the network (midmore et al., 2010). more specifically, the added value of sna corresponds to a quantitative assessment of the entire network (i.e. relations, information and knowledge flows), not limiting the analysis to the performance of single lags. 3. methodology transnational cooperation projects are an opportunity for lags to exchange fruitful information, contextual expertise and local knowledge, thus enhancing the opportunities for innovation and economic benefits. these projects create a social and institutional grid of direct and indirect relations, thus sna is the most appropriate tool to quantitatively and graphically describe the network structure and the power distribution within it (hanneman and riddle, 2005; borgatti and foster, 2003). actors are parts of various and overlapping networks that influence their behaviour and norms, so an analysis of their relations can depict the social and relation dimensions of the economic activity (wellman, 1988). different authors have applied sna to the analysis of lags structures and relations, but the focus has mainly been on the network composing the lags, while here it is on the network created by the lags during tnc projects. some of the classical sna indexes have been adopted and these are summarized in table 1. in the proposed evaluation approach of tnc projects, it is also important to identify specific indexes able to capture the peculiar features of the transnational cooperation. analysing the network of tnc projects implemented by the lags (that is a regional ego-network3), different kinds of nodes have to be considered: transnational, national and regional. these are related according to the squared matrix in table 2. in order to assess the peculiarities of tnc project networks , the densities of each type of relation are summarised in table 3. these decomposed indexes are based on the classical idea of density (ranging between 0 and 1) as a proportion of all ties actually present in the network compared to those that could potentially be (borgatti and everett, 1997), but here the formula is applied on specific types of relations. it is now possible to analyze how much the projects invest in the activation of transnational, interterritorial or regional ties according to the potential opportunities they have. these indexes indicate the composition of the network density according to equation (1). 3 an ego-network is a network composed of a specific actor (ego) and the actors to which ego is connected (alters); all of them are connected by ties (everett and borgatti, 2005). in this case study the ego corresponds to a group actor (the veneto lags implementing tnc projects), and the alters are the partners of veneto lags involved in tnc projects (national and transnational lags). 254 e. pisani, l. burighel d tot n n n d p d p d p d p d p d p n n    1 2 * * * * * *   1 2 n rr rr nn nn tt tt rn rn rt rt nt nt( ) ( ) ( )= − = + + + + + − (1) where tot n( ) is the total number of ties present in the network and n is the total of nodes in the network, drr is the regional density, prr is the regional ties potential, dnn is the national density, pnn is the national ties potential, dtt is the transnational density, ptt is the transnational ties potential, drn is the regional-national density, prn is the regionalnational ties potential, dnt is the national-transnational density, pnt is the national transnational ties potential, drt is the regional-transnational density, prt is the regional transnational ties potential. to have a clearer idea of the composition of the network ties and to facilitate the comparison over time, the proportion of effective types of ties is also calculated, according to the formula in table 4. this calculation obtains the percentage of the different types of relations within the network. table 1. some classical social network analysis indexes. size (n) number of nodes in the network. degree (d(n)) number of relations that involve the specific node. density (dn) proportion of all ties that are present in the network compared to those that could be present. it corresponds to: , where tot(n) is the total number of ties present in the network. geodesic distance number of ties of the shortest path linking two nodes. diameter the larger geodesic distance of a network. component maximal subgraph in which a path exists from every node to every other. degree centrality normalized number of edges incident upon a node, corresponding to: d n n   1 ( ) − closeness centrality normalized geodesic distance of a node from all the other nodes in the network. betweenness centrality number of geodetic paths that pass through a given node, indicating the role of connector of one actor for the others. centralization normalized distribution of degree centrality among all the nodes in the network. eigenvector centrality weighted degree measure in which the centrality of a node is proportional to the sum of centralities of the nodes it is adjacent to. intended as a measure of node importance in a network based on its connections. source: borgatti and everett (1997). table 2. classification of nodes and their relations in a tnc project. regional lags national lags transnational lags regional lags rr rn tr national lags nn tn transnational lags tt source: own elaboration. only one-dimensional relations are considered. 255structures and dynamics of transnational cooperation networks table 3. density for specific types of relations. regional density drr proportion of ties among regional nodes that are present in the network (rr(n)) compared to all the ties that could be present among regional nodes prr where p r r 1 2rr ( )= − and r is the number of regional nodes in the network. rr n prr ( ) national density dnn proportion of ties among other national nodes that are present in the network compared to all the ties that could be present among other national nodes pnn where p na na 1 2nn ( )= − and na is the number of national nodes in the network. nn n pnn ( ) transnational density dtt proportion of ties among transnational nodes that are present in the network compared to all the ties that could be present among transnational nodes ptt where p t t 1 2tt ( )= − and t is the number of transnational nodes in the network. tt n ptt ( ) regionalnational density drn proportion of ties among regional nodes and national nodes that are present in the network compared to all the ties that could be present among regional and national nodes prn where p r na*rn = rn n prn ( ) nationaltransnational density dnt proportion of ties among national nodes and transnational nodes that are present in the network compared to all the ties that could be present among national and transnational nodes pnt where p na t*nt = nt n pnt ( ) regionaltransnational density drt proportion of ties among regional nodes and transnational nodes that are present in the network compared to all the ties that could be present among regional and transnational nodes prt where p r t*rt = rt n prt ( ) source: own elaboration. table 4. proportion of specific types of relations. regional/total proportion of ties among regional nodes that are present in the network and all the ties present in the network. rr n tot n  ( ) ( ) national/total proportion of ties among other national nodes that are present in the network and all the ties present in the network. nn n tot n  ( ) ( ) transnational/total proportion of ties among transnational nodes that are present in the network and all the ties present in the network. tt n tot n  ( ) ( ) regional–national/ total proportion of ties among regional and national nodes that are present in the network and all the ties present in the network. rn n tot n  ( ) ( ) nationaltransnational/total proportion of ties among national and transnational nodes that are present in the network and all the ties present in the network. nt n tot n  ( ) ( ) regionaltransnational/total proportion of ties among regional and transnational nodes that are present in the network and all the ties present in the network. rt n tot n  ( ) ( ) source: own elaboration. 256 e. pisani, l. burighel the transnational dimension can also be analyzed through transnational centrality (tc) in relation to the veneto lags, which traces the formula of degree centrality, defined as the normalized number of ties in a node (borgatti and everett, 1997), but is based on transnational edges, calculating the total number of transnational relations in the specific node: t t n n     1c ( )= − (2) where t(n) is the number of transnational relations in the node and n is the number of nodes in the network. these indexes can be useful to understand the transnational structure of a network. in particular, through the decomposed indexes of density and the proportion of different types of relations over the effective edges it is possible to measure the composition and transnational component of a network. on the other side transnational centrality helps to understand the relevance given by a node to transnational relations, especially if compared to degree centrality. 4. empirical application the sna and the new indexes presented were applied in a specific case study: the tnc projects implemented by the lags of the veneto region in different programming periods. 4.1 case study in veneto, local action groups cover 65% of regional municipalities and 38% of the population (veneto region, 2010). for the period 2007-2013, veneto lags have implemented the highest number of tnc projects in italy. compared to the national level, veneto is also the region where there is the highest public and private financial contribution to tnc projects implemented during the leader axis period. despite these positive results, the regional data show discrepancies among planned, approved and effectively implemented projects (see figure 1), probably due to procedural and administrative difficulties (veneto region, 2010). the number of projects implemented passed from 7 in leader ii to 8 in leader + and 7 in leader axis, so it was interesting to analyze the evolution of the network of projects over time. the following analysis is based on secondary data collected by the italian rural network (rrn) for different programming periods. in particular, the data referred to leader ii and leader + periods have been extracted from the final tnc inventory (zanetti, 2009; zumpano, 2001), while the data of the leader axis period represent the state-ofplay with regard to implementation of tnc projects (rrn)4. to guarantee the consistency and comparison of information no other types of territorial cooperation were considered and the focus is on transnational cooperation of lags within leader. the collected 4 italian rural network database: http://89.119.249.9:8080/birt/procoopleader/index.jsp. accessed 4 april 2014. 257structures and dynamics of transnational cooperation networks data have been processed through the gephi5 open source software, which enables the mathematical and graphical elaboration. the results have to be considered with caution because of the changing of lags members and territory from leader ii to leader axis, and the mutating lags priorities and political focuses. besides the assessment of network structure and dynamics, the best and worst lag performances are evidenced. this could represent a possible limit of the present analysis and a more detailed focus on qualitative issues is required in order to correctly understand the results. 4.2 analysis of the structure of tnc projects network in veneto, leader ii, leader + and leader axis programming periods the analysis considers the network of veneto lags involved in tnc projects as a whole, in order to understand the potential and real possibilities for information and knowledge exchange and the power distribution within the network. the network referring to the leader ii period is presented in figure 2. the values of the most interesting sna indexes are presented in table 5, with reference to the leader ii period. during leader ii the network is composed of 39 nodes clustered in 3 components. one of these is formed by two nodes, another by three nodes, while the third is composed by the majority of nodes (34), including the other nationals. the network density is 53.3% and is related to the high number of effective relations compared to the theoretical ones. the disaggregated indexes show the highest values for the density of ties among national nodes (83.6%) and among regional and other national nodes (75.4%). the proportion between the various types of relations and the total possible show a prevalence of ties among national and regional-national nodes (32.7%), only 5.3% are among regional nodes. 5 https://gephi.org/. figure 1. comparison between planned, approved and effectively implemented tnc projects by lag in veneto region, leader axis programming period. 0 1 2 3 4 5 alto bellunese prealpi e dolomiti patavino bassa padovana polesine delta del po polesine adige marca trevigiana terre di marca venezia orientale antico dogado montagna vicentina terra berica baldo lessinia pianura veronese planned projects approved projects effective projects source: own elaboration based on projects data from italian rural network at http://89.119.249.9: 8080/birt/procoopleader/index.jsp (accessed 2014), www.gal.veneto.it (accessed 2013), veneto region (2010). 258 e. pisani, l. burighel figure 2. transnational cooperation network of veneto lags, leader ii. source: own elaboration based on projects data from zumpano (2001). to facilitate the visual representation, the black dots indicate veneto lags, dark grey transnational partners and light grey other italian partners. table 5. indexes of the transnational cooperation network of veneto lags, leader ii. classical sna indexes new sna indexes of tnc projects sna index value sna index value sna index % size 39 regional density 0.583 regional/total 5.3 connected components 3 national density 0.836 national/total 36.2 average degree 20.256 transnational density 0.073 transnational/total 1.0 centralization 0.009 regional-national density 0.754 regional–national/total 32.7 density 0.533 national-transnational density 0.282 national-transnational/total 14.9 regional-transnational density 0.394 regional-transnational/total 9.9 source: own elaboration based on projects data from zumpano (2001). table 6 shows that the lag with the highest degree centrality is “sinistra piave” (0.868)6, while lag “alto bellunese” has the lowest (0.026). the values of transnational centrality confirm these findings: the lag “sinistra piave” appears to be the most active in 6 the lag “sinistra piave” is now part of lag “alta marca trevigiana” 259structures and dynamics of transnational cooperation networks terms of capacity to acquire new information and knowledge from transnational partners (0.211). also considering the betweenness centrality, the “sinistra piave” presents the highest value (90.222), meaning that this node functions as a relevant connector for many other lags and it can share information, expertise and knowledge with many other partners. the network of tnc projects evolves during the leader + programming period and it is presented in figure 3, with the overall values of the network given in table 7. during leader + the size of the network partially decreases to 32 nodes (from the 39 of the previous programming period), grouped in 4 components (two of these are composed of only two nodes, one is of 8 and one of 20). the density strongly decreases to 19.2% (the value was 53.3% in the previous period). the disaggregated densities also decrease, even if at a much lower level than before. during this period, the majority of relations are among transnational nodes (31.6%) and only 1% of the effective ties are among regional nodes. thus, it seems that in this period the veneto lags are mostly inserted in a network of transnational projects, mainly implemented by transnational table 6. indexes of the transnational cooperation network of veneto lags, by node, leader ii. gal degree transnational degree degree centrality closeness centrality betweenness centrality transnational centrality alto bellunese 1 1 0.026 1.000 0.000 0.026 prealpi e dolomiti bellunesi e feltrine 28 4 0.737 1.152 1.556 0.105 cargar montagna 31 6 0.816 1.061 28.222 0.158 sinistra piave 33 8 0.868 1.000 90.222 0.211 destra piave 31 6 0.816 1.061 28.222 0.158 baldo lessinia 28 4 0.737 1.152 1.556 0.105 colli berici 28 4 0.737 1.152 1.556 0.105 patavino 28 4 0.737 1.152 1.556 0.105 venezia orientale 2 2 0.053 1.000 0.000 0.053 source: own elaboration based on projects data from zumpano (2001). table 7. indexes of the transnational cooperation network of veneto lags, leader +. classical sna indexes new sna indexes of tnc projects sna index value sna index value sna index % size 32 regional density 0.067 regional/total 1.1 connected components 4 national density 0.444 national/total 16.8 average degree 5.938 transnational density 0.221 transnational/total 31.6 centralization 0.005 regional-national density 0.185 regional–national/total 10.5 density 0.192 national-transnational density 0.124 national-transnational/total 20.0 regional-transnational density 0.186 regional-transnational/total 20.0 source: own elaboration based on projects data from zanetti (2009). 260 e. pisani, l. burighel nodes and the opportunity for new knowledge exchange and cooperation among regional lags is strongly reduced. the lag with the highest degree centrality is “prealpi e dolomiti” (0.323), but in this case the lag with highest degree centrality and the highest betweenness centrality does not correspond to the lag with highest transnational centrality, which is lag “alto bellunese” (0.226). the role of information brokers in the network is played by lag “prealpi e dolomiti” and lag “patavino”, since their betweenness centrality values are the highest (84 and 90) and they are part of the largest component. to complete the analysis, the whole network referring to the leader axis period is presented in figure 4. as for the other programming periods, a summary of the most significant indexes is presented in table 9. during leader axis the size of the network further decreases to 28 nodes, grouped in 3 components, one including 5 actors, another with 11 and the third with 12. however, the density slightly increases (20.4%) with a reshaping of disaggregated densities. the national density again has the highest value (57.4%), while the lowest value refers to transnational density. considering the proportion of effective types of relations over the effective relations of the network, the regional/total proportion is the highest (22.1%). as shown in table 10, the node with the highest degree centrality is “polesine adige” (0.370) and that with the highest betweenness centrality is “montagna vicentina” (30). the nodes with the highest transnational centrality are “alto bellunese” (0.111) and “polesine adige” (0.111). considering transnational and betweenness centrality together, lag “alto bellunese” represents a key actor and information broker for the whole network, thanks to the international ties from its involvement in three figure 3. transnational cooperation network of veneto lags, leader +. source: own elaboration based on projects data from zanetti (2009). 261structures and dynamics of transnational cooperation networks different tnc projects and its betweenness power. it also shows a certain evolution and stability in its transnational relations, with an increasing number of projects and partners over time. table 8. indexes of the transnational cooperation network of veneto lags, by node, leader +. gal degree transnational degree degree centrality closeness centrality betweenness centrality transnational centrality alto bellunese 7 7 0.226 1.000 6 0.226 baldo lessinia 1 1 0.032 1.000 0 0.032 polesine delta del po 8 6 0.258 2.474 0 0.194 prealpi dolomiti 10 2 0.323 1.895 84 0.065 gal delle aree rurali di la spezia 3 1 0.097 2.263 0 0.032 gal patavino 4 2 0.129 1.789 90 0.065 venezia orientale 1 1 0.032 1.000 0 0.032 source: own elaboration based on projects data from zanetti (2009). figure 4. transnational cooperation network of veneto lags, leader axis. source: own elaboration based on projects data from italian rural network at http://89.119.249.9: 8080/birt/procoopleader/index.jsp (accessed 4 april 2014). 262 e. pisani, l. burighel 4.3 analysis of the dynamics of tnc projects network in veneto, leader ii, leader + and leader axis programming periods the size and density of the networks evolved over time, as shown by the graphs in figure 5. the network size decreased from 39 during leader ii, to 32 during leader table 9. indexes of the transnational cooperation network of veneto lags, leader axis. classical sna indexes new sna indexes of tnc projects sna index value sna index value sna index % size 28 regional density 0.327 regional/total 23.4 connected components 3 national density 0.571 national/total 20.8 average degree 5.5 transnational density 0.056 transnational/ total 2.6 centralization 0.007 regional -national density 0.091 regional–national/ total 10.4 density 0.204 national-transnational density 0.222 national-transnational/ total 20.8 regional-transnational density 0.172 regional-transnational/ total 22.1 source: own elaboration based on projects data from italian rural network at http://89.119.249.9: 8080/birt/procoopleader/index.jsp (accessed 4 april 2014). table 10. indexes of the transnational cooperation network of veneto lags, by node, leader axis. lag degree transnational degree degree centrality closeness centrality betweenness centrality transnational centrality alto bellunese 5 3 0.185 1.545 26.000 0.111 prealpi dolomiti 2 1 0.074 2.364 0.000 0.037 gal montagna vicentina 8 2 0.296 1.273 30.000 0.074 gal patavino 6 1 0.222 1.727 0.000 0.037 gal bassa padovana 6 1 0.222 1.727 0.000 0.037 gal terra berica 6 1 0.222 1.727 0.000 0.037 gal antico dogado 6 1 0.222 1.727 0.000 0.037 gal pianura veronese 6 1 0.222 1.727 0.000 0.037 gal baldo lessinia 4 2 0.148 1.000 0.000 0.074 polesine delta del po 2 1 0.074 1.800 0.000 0.037 polesine adige 10 3 0.370 1.000 16.000 0.111 source: own elaboration based on projects data from italian rural network at http://89.119.249.9: 8080/birt/procoopleader/index.jsp (accessed 4 april 2014). 263structures and dynamics of transnational cooperation networks + to 28 nodes during leader axis. this could signify a progressive decreasing interest in transnational projects, possibly due to bureaucratic limits, but it could also mean that lags implement tnc projects only if able to respect the complex administrative procedures established for this type of cooperation and if the project is effectively relevant for them. the network density also decreased with a negative peak during the leader + (0.533; 0.192; 0.204). according to burt’s theory of structural holes, a dense network can have limited efficiency, because the cost of connection is not compensated by the value of the information shared, which could be indirectly gained through another tie (burt, 1992). this would mean that the case studied has a positive trend in relation to the efficiency of the information flow, since the density decreased. this trend is also confirmed figure 5. evolution of tnc leader network in veneto. source: own elaboration. figure 6. comparison between the trend of regional-transnational density and trend of the proportion of regional-transnational ties. 0 5 10 15 20 25 30 35 40 45 leader ii leader + leader axis drt rt(n)/tot(n) source: own elaboration. 264 e. pisani, l. burighel by the regional-transnational density and by the proportion of effective regional-transnational ties. as shown in figure 6, the density of regional-transnational ties decreased; this means that each veneto lag chose to cooperate with differentiated transnational partners. according to burt, this is an optimization of the access to information and knowledge that will be more diversified and less redundant. furthermore, the proportion of transnational ties increased, meaning that the investment in transnational relations increased, despite the reduction of their density. the analysis of the evolution of disaggregated density also shows that most potential relations were implemented within national nodes. at the same time, the trend of regionalregional density is negative, with a strongly negative peak during leader + (0.583, 0.067, 0.327). however, this cannot have a clear interpretation, because it can be supposed that other forms of lags cooperation are taking place within the regional territory because opportunities are easier compared to transnational projects. furthermore, the proportion of relations among regional nodes, increased during the different programming periods. the relations among national nodes were prevalent during leader ii and leader axis periods while during leader + most of the relations were among transnational nodes. the actors more central in terms of transnational ties changed in the different periods. during leader ii “sinistra piave” registers the highest transnational centrality and lag “alto bellunese” has the lowest value. nevertheless, during leader + “alto bellunese” shows the highest value for the same index, confirming a positive trend during leader axis. this attests to its positive evolution in terms of number of projects presented and implemented and in partners’ continuity, which probably indicates good experience and stability. even if it is possible to identify a correspondence between the values of degree centrality and those of transnational centrality during leader ii, the following periods do not show a clear correspondence. thus, the nodes with more connections within the network do not necessarily implement the highest number of transnational relations. 5. conclusions the discussion presented in this paper proposes an innovative approach for the evaluation of transnational projects implemented by local action groups. in order to clarify the importance that these projects can have for lags, an initial explanation has been given of the advantages of cooperation: the improvement of competitiveness, the pooling of expertise and know-how, the promotion of innovation by sharing best practices and new ideas, and the enhancement of territorial identity (esparcia, 2014; dwyer, 2013; ray, 2006, 2001; pasquinelli, 2013). transnational cooperation projects can increase the opportunities for their partners to take advantage of ‘similarity’ and ‘complementarity’, thus of co-opetition (pasquinelli, 2013; ray, 2001). during the implementation of these projects, lags compose a network that facilitates the sharing of knowledge and information in a form of social learning at both local and extra-local level. these elements are part of the advantages of extra-local relations among territories that, together with vertical and local horizontal relations, guarantee a neo-endogenous approach to rural development as proposed by leader. the tnc network has been analysed through social network analysis, in order to have a complete overview of the projects not only in terms of quantitative data, but also considering the direct and indirect relations that they can produce; to visualize 265structures and dynamics of transnational cooperation networks the evolution of the network the analysis also included past programming periods. the proposed sna indicators are useful to evaluate the structure and performance of transnational cooperation networks and can be used by the managing authority at regional level in order to understand the informative efficiency of the financed initiative. the case study presented shows a very dynamic network, which evolved in terms of size and density, and also with respect to the internal composition of the actors and their relations. this is reflected in new possibilities for information flow and for the access to new knowledge. the analysis of the structure and dynamics of the tnc projects network in veneto suggests a positive evolution of the efficiency of information transmission, since the proportion of transnational partners increased but the regional-transnational density decreased. these elements attest to the importance of the new indexes, which capture critical features that the classical indexes of sna are not able to assess. the analysis focused only on the case study of transnational cooperation projects implemented by lags within the leader measure “cooperation”. a deeper analysis could also consider other forms of territorial cooperation implemented by lags (such as cross-border, interregional or interterritorial cooperation), because other forms of cooperation could give the same advantages highlighted for tnc in leader. cooperation plays a central role in the creation of a network among rural groups in different territories and nations, but it represents an interesting opportunity for the integration of economic actors operating in more than one territory and economic sector, as in the case of agrofood chains (mantino, 2014). this suggests that the proposed methodology could also be applied to this field of research, especially given that article 35 of regulation 1305/2013 covers this type of cooperation. the use of sna indicators for the evaluation of transnational cooperation is a relatively simple system based on secondary data that could be applied in all italian and european lags in order to understand different trends and changes in transnational cooperation in diverse regions and countries. a deeper analysis could be useful, in association with a qualitative study to better interpret the evaluation results, considering different variables of particular interest such as: (i) the lag context and present and past tnc experience; (ii) identification of the different ways to become involved in tnc projects; (iii) the effectiveness of the tools for partners search; (iv) the usefulness of preparatory and joint actions; (v) the lessons learnt and tnc best practices at european level (vi) identification of the factors facilitating participation in tnc (vii) the perceptions on results achieved and value added of the project. the combination of qualitative and quantitative methods in the evaluation process can more fully express the social and institutional learning dimensions of transnational cooperation in leader as suggested by high et al. (2007). furthermore, the investigation could be useful in order to identify the best qualitative and quantitative network characteristics necessary to determine a long-term impact of the cooperation, also considering the central role that this is going to acquire in the 20142020 programming period. acknowledgments an earlier version of this paper was presented at the 3rd conference of the associazio ne italiana di economia agraria e applicata (aieaa), 25-27 june 2014 at alghero, italy. 266 e. pisani, l. burighel references aral, s. and alstyne, m. van (2007). network structure & information advantage. in: proceedings of the academy of management conference, philadelphia, pa: 1-36. bell, m., lloyd, s. and vatovec, c. (2010). activating the countryside: rural power, the power of the rural and the making of rural politics. sociologia ruralis 50(3): 205224. bengtsson, m. and kock, s. 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(2009). i progetti di cooperazione transnazionale in leader+. inea in http://dspace.inea.it/handle/inea/752. accessed 3 december 2013. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(3): 301-321, 2013 an integrated pmp model to assess the development of agro-energy crops and the effect on water requirements michele donati1,*, diego bodini2, filippo arfini2, annalisa zezza3 1 department of biosciences, university of parma, italy 2 department of economics, university of parma, italy 3 national institute of agricultural economics, italy abstract. this paper presents an integrated model for the economic and environmental assessment of the use of natural resources when new activities (i.e. biomass crops for energy production) are introduced into the farm production plan. the methodology is based on the integration of positive mathematical programming (pmp) with the aquacrop model developed by fao. pmp represents farmer decision processes and evaluates how farms react to the biomass-sorghum activity option at different price levels. aquacrop evaluates the relationship between water needs and biomass production and assesses the effect of the land allocation on water requirements at regional level. the integration of these two models assists global policy evaluation at regional level as it makes it possible to identify the economic threshold for biomass crops, the change in land allocation and total water requirement. the model can help policy makers to evaluate the impacts of variations in crop profitability and market innovations on farm profitability, land use and water consumption and the sustainability of the market scenario. keywords. agroenergy economics, positive mathematical programming, water demand, aquacrop jel codes. c61, q42, q24, q25, q51 1. introduction farmer interest in agro-energy production is mainly due to the high level of financial subsidies for renewable energy production. european strategy against climate change indicates agriculture as one of the main sectors that should contribute through specific actions to achieve the objectives established for 2020 (european commission, 2010). the 20142020 cap reform also states that european agriculture can play a major role in mitigating global warming through new entrepreneurial vision and the substitution of fossil energy with renewable energy (european commission, 2009). agro-energies are thus considered as an instrument to enhance energy security, reduce greenhouse gas emissions and raise farm income (petersen, 2008). agricultural biomass and agronomic management are the main resources to generate environmentally virtuous processes on farms. * corresponding author: michele.donati@unipr.it. 302 m. donati, d. bodini, f. arfini, a. zezza despite the theoretical advantages for european society, the threats as well as the benefits of agro-energy need to be carefully evaluated. on one hand, agricultural biomass used for producing fuel, heat and electricity has favourable effects on the environment, but on the other hand, it can determine end-consumption competition and excessive pressure on natural resources. biofuel gives rise to concern about the sustainability of the non-food supply chains. rising demand for biofuel has led to widespread exploitation of arable land for growing first and second generation biofuel crops. according to the internal biofuel strategy, 10% of total transport fuel should be biofuels (biodiesel or bioethanol) in european union (eu) by 2020 (european commission, 2006). if this is considered in terms of available land surface, the risk of agricultural land overexploitation is real (zezza, 2008). another example of how renewable energies can contribute to a sustainable society is biogas. biogas in the po valley, in italy, is very profitable as a result of large public subsidies and very short payback time, and is expanding rapidly. most of the biogas plants maximize economic performance using biomass that in part originates from waste such as slurry and agroindustrial wastes, and often from specialized crops, in particular maize. about 200 ha of dedicated crops are used in a biogas plant of 1mw. this type of agroenergy farms present a low level of crop diversification, which brings the risk of creating monoculture requiring intensive use of pesticide. they also use large quantities of water for irrigation for the entire plant growing cycle. overexploiting resources means reducing agricultural commodities for food and feeding purposes but also jeopardizing the equilibrium of the ecosystem. soil depletion, biodiversity reduction, aquifer pollution and water scarcity are important concerns which need to be taken into consideration alongside global warming. paradoxically, it sometimes appears that it is necessary to sacrifice the environment in order to save it (doornbosch and steenblick, 2007). to produce food and fibre, eu agriculture takes about 50% of the available fresh water, while at global level this percentage is 70% (oecd, 2010). the rapid growth of agro-energy poses the need for management of irrigation water in order to avoid excessive pressure on the resource and water crisis in drought periods (oecd, 2010). increasing demand for water from other economic sectors and society worsens the problem of water scarcity. in line with the eu water framework directive (wfd) (directive n. 2000/60/ ec), member states will have to apply administrative and economic tools for saving irrigated water and controlling the use of water for agriculture. the objective of the wfd is threefold: protect water resources, optimize water use and sustain agricultural productivity. as a limited resource, water should be managed through appropriate economic instruments to drive user behaviour to improve efficiency in water distribution and application. wfd encourages the use of a set of economic instruments, including the tariff system and the market for water entitlements to rationalize water allocation, which might assign an economic value to water. without an explicit value, users pursuing private interests will use water as a free access resource compromising its sustainable exploitation (hardin, 1968). but if water is perceived as a scarce resource and an economic good, farmers have to compare alternative crops to identify those maximising farm income and minimizing the cost of the water consumption (garcía-vila and fereres, 2012; bazzani et al., 2007; bazzani et al., 2004; ward and michelsen, 2002). the problem becomes particularly important in mediterranean regions and in drought periods (howell, 2001). 303pmp to assess agroenergy impact on water consumption agricultural biomass production for agro-energy supply chain is an alternative that farmers are increasingly considering in their decision making, but according to the type of crop it has a big effect on water availability. in the case of sorghum for second generation bioethanol production, water consumption for irrigation is not high, but in other cases, like maize for biogas, where maize is intensively cultivated, it can be much higher. evaluating the impact of agro-energy production on land and water use can support policy makers in planning intervention to optimize the use of scarce resources. local water management authorities in particular need to be made aware of likely farm allocation decisions and the potential water consumption of agro-energy crop development. this information supports water distribution planning and implementation of tools to regulate water for irrigation. to support policy and management decisions, quantitative models based on mathematical programming (mp) are usually implemented. the literature shows that mp can be used for farm management and policy assessment. mp models generate an optimization process of an objective function subject to a set of constraints and can be implemented following normative and positive approaches. the normative approach can be considered the “classical” mp tool for farm management. the characteristics of normative mp models are related to the level of knowledge about the farmer technological set, prices and costs and farmer assumptions in a suboptimal condition. the model requires a large amount of technical and economic data. a normative mp model will typically identify optimal production level and the optimal use of inputs to maximize revenue or minimize costs. these models have a prescriptive character, they indicate what the decision maker ought to do in order to optimise his objective and they do not reproduce what he is actually doing. normative models are useful for their capacity to predict the use of inputs. in the case of water, demand can be calculated for single farms or for groups of farms. for groups of farms, models can reproduce water supply nodes in order to reorganize the network and water allocation more efficiently (harou et al., 2009; bartolini et al., 2007; rosengrant et al., 2000; garcía-villa and fereres, 2012). positive mp models, or positive mathematical programming (pmp) (howitt, 1995; paris and howitt, 1998; heckelei et al., 2012) are used on the other hand for policy assessment when the size of inputs and variable costs are not precisely known. in a context of poor information, they can predict farm use under the hypothesis that the observed production level is considered optimal by the entrepreneur. variation in output market price, or in specific coupled (or decoupled) payments, leads to a new optimal production level. the main feature of positive mp models is to calibrate, for a given farm, the observed production level and to estimate a non linear cost function that reproduces the cost of the inputs. the technological matrix defines the productivity level for the observed crops and activities. pmp typically identifies the cost that economic agents are willing to pay in order to be optimal at the observed level. these models can be developed for a single farm or a group of homogeneous farms belonging to a large region (arfini and donati, 2011). while normative mp models can describe in great detail the technological level and predict the use of inputs, they meet difficulties assessing the impacts of new market and policy scenarios when many farms in a region are considered. this is because of the difficulty in collecting and differentiating technological information among farms. by contrast, positive mp models can easily estimate the cost of technology and thus assess the 304 m. donati, d. bodini, f. arfini, a. zezza impact of new market and policy scenarios for a large group of farms. such farms usually belong to large samples such as fadn which do not collect micro information related to the input use of each farm activity. for pmp models, the drawback is the lack of information related to the physical use of inputs. there are two possible strategies in order to provide information on input use: i) consider in pmp models the use of specific inputs (helming and peerlings, 2003), although there is the problem of considering different technological sets and input-output relationships for many farms; ii) integrate pmp models with other methodologies that consider more specifically input-output relationships for some specific inputs. water as an input can be introduced into mp models (including pmp) in various ways: i) water is considered as a constraint with fixed water requirement coefficients (graveline and mérel, 2012; cortignani and severini, 2009; medellin-azuara et al., 2010); ii) water is included within the production function linking crop yields to water application (garcía-villa and fereres, 2012; graveline and mérel, 2012) through the introduction of biophysical information including local environment characteristics (garcía-villa and fereres, 2012; cortignani and severini, 2009). in particular, a model estimating the relationship between yields and water use has recently been developed by fao: aquacrop. aquacrop (steduto et al., 2009) permits evaluation and simulation of yield responses with regard to water application for a group of crops. it takes account of specific information on climate conditions, soil characteristics and irrigation management. pmp has shown to be very efficient for policy analysis purposes (heckelei et al., 2012) and particularly for assessing the introduction of a new crop in the production pattern of a group of farms (arfini and donati, 2013). the present analysis was conducted considering new activities as “latent” information that the entrepreneur can use for maximizing the objective function. pmp provides results using small amounts of information on the latent crop (yield and market price) without a detailed description of the technological set for all the inputs. it was used to evaluate the farmer’s ability to choose agricultural activities not observed in the base year (röhm and dabbert, 2003; blanco et al., 2008; arfini and donati, 2013) this paper presents an integrated framework of models answering research questions on assessment of impact of a new activity (e.g. agro-energy crops) on the agricultural production plan of a region. the assessment considers key issues for policy makers: land use, supply variations, territorial specialization, economic impact and environmental implications of the change in water use by farmers. the framework integrates micro-based pmp and the aquacrop model. more specifically the goal is to assess the effects of the introduction of sorghum for biomass production on land and water allocation in the province of parma, in emilia romagna region in northern italy. the model simulates different market scenarios for sorghum in order to evaluate the level of sustainability and policy implications at different market prices. the paper consists of five sections. the first discusses pmp methodology using latent crop information. the second presents the aquacrop model, adapted to the characteristics of the area under investigation and the purpose of this research. the third section describes the integration of the models and their structure. the fourth presents results of simulations using aquacrop. the fifth section presents the main results and their implications for policy. in the conclusion, opportunities for future research are discussed. 305pmp to assess agroenergy impact on water consumption 2. pmp model with latent information pmp appeared in the world of mp quite recently thanks to the pioneering studies of howitt (1995) and paris and arfini (1995). its impact on agricultural economists was important in generating a wide field of literature that has led over time to more sophisticated modelling. various elements have influenced the development of pmp models over time: research objectives, characteristics of available information, number of farms in the sample, number of farms that are represented by the models, level of representativeness of the data at regional level, method of calibration, method of estimating the non linear cost function as well as theoretical assumptions underpinning the models. two distinct strands can be seen in the literature: the first considers pmp as a calibration method (howitt, 1995; heckelei and wolff, 2003), while the second considers pmp as a method of estimating variable costs (arfini and paris, 1995; paris and howitt, 1998; paris and arfini, 2000; paris, 2012). this paper follows the approach proposed by paris (2012), where pmp assesses the impacts of market scenarios and agricultural policy by estimating the variable cost function associated with the use of inputs. as regards the pmp models that consider latent information, the background hypothesis is based on the assumption that farmers have knowledge of a set of information regarding production activities, which is larger than the set of information that can be observed from the production plan. some of this farmer information comes from their previous experience, some from the experience of their neighbours or from advice on new crops given by experts (agronomists). the economic and technological information regarding activities that are perceived as more costly or more risky will not be used in the production plan, but will remain latent until it becomes economically useful. the decision to change the production plan or technological choices is usually motivated by variables such as risk aversion, level of technical knowledge, availability of capital, family structure, age of the farmer, the presence of support agencies in the territory, etc. all these variables affect the farmer’s decision process and lead to the selection of a certain combination of crops. why does a farmer produce soft wheat and alfalfa and not, for instance, tomato and sugarbeet, which are produced on other farms in the area? the farmer could potentially insert tomato and sugar beet into his production plan, but he does not currently produce them because they are not profitable for his farm. until they become profitable, information is “latent” in the sense that it is known by the farmer but not used. economic information related to farm crops identified as “latent” can however be introduced into a pmp model in two different ways: 1. as “latent technology”, when a given crop is adopted only by a group of farms in the sample: each farm belonging to the same sample is cross-linked with the others through a common set of cost function parameters and shares the same technology. because there is self-selection, a given crop existing in the production pattern of farm “a” can be adopted by farm “b” if there is economic advantage; 2. as “latent crop”, when the information is related to a given crop that does not exist in the farm production plan for any farms belonging to the sample. of course, in the real world farmers are not alone. they are in an environment characterized by different production decisions deriving from different production possibili306 m. donati, d. bodini, f. arfini, a. zezza ties, among which the farmer selects what he assumes to be the best solution. the main driving force that leads farmers to select one production plan and not others is the cost function associated with each activity. the total cost function is the economic measure of the available technology and all the other factors. the total cost includes all variable costs perceived by the farmer for each activity. some of these costs are clearly registered by bookkeeping and identified as explicit costs, while others are only perceived by entrepreneur and are implicit costs of the decision. farmer behaviour can be formally reproduced by a production plan where the observed activities are accompanied by latent activities. we present the pmp model, articulated in three phases (paris and howitt, 1998), including non-observed activities, and consider, for sake of simplicity, only one farm with two sets of products: activities realized r (for r=1,2,…,r) and latent activities l (for l=1,2,…,l). we assume that xr is the vector of the realized output quantities and x l the vector of latent output quantities. the observed quantity levels for realized and latent activity are known and correspond to xr and x l respectively. farm activity is subject to limiting factors i (for i=1,2,…,i) and the upper bound values for each factor are included in vector b farm technology is provided by the coefficients of the i by r matrix ar for the realized activities, and of the i by l matrix a l for the latent activities. given this information, we can develop the first pmp phase through a problem that maximizes the gross margin (gm) as follows: = − + − ≥ ≥ gm p c x p c xmax ( ) ' ( ) 'r r r l l lx x0, 0r l (1) where pr and pl represent the vectors of realized output prices and latent output prices respectively, while, cr and cl the vectors of the exogenous specific costs for the realized and latent activities. the objective function (1) is maximized with respect to the non-negative variables xr and x l and is subject to the following constraints: + ≤a x a x b y( )r r l l (2) ε λ≤ +x x ( )r r r (3) ε λ≤ +x x ( )l l l (4) constraint (2) identifies the relationship between the total demand of input to produce xr and x l (left hand side) and the total input supply (right hand side). the shadow prices of the binding farm resources b are represented by the vector y. constraints (3) and (4) are the pmp calibrating constraints, while λr λl are the dual values of the realized and latent activities. λr represents the hidden costs, i.e. the implicit marginal costs of the realized activities, and λl the implicit marginal cost associated with the latent activities. 307pmp to assess agroenergy impact on water consumption in the definition of the problem (1)-(4), the latent crop is added from the first phase, assigning it a very low production level x l close to zero, while the data related to the prices and specific costs are taken from market and must guarantee a condition of positive marginal profit. yields are assumed by experts and by literature. this first pmp setting provides dual information for realized and latent crops to be used in the second phase, where a non-linear function is estimated. we choose the following quadratic cost function             x x q x x 1 2 r l rl r l (5) where matrix q is symmetric positive semidefinite and includes parameters to be estimated by properly methods. in this work, the parameter estimation is carried out adopting the maximum entropy approach (paris and howitt, 1998) considering the following relationship: λ λ         +         =         c c x x qr l r l r l rl (6) where the explicit and implicit costs recovered in the previous phase should be equal to the marginal cost derived from the quadratic total cost function (5). in analytical terms: λ λ λ λ λ λ                             +                             =                                                         � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � c c c c c c x x x x x x q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q r r rr l l ll r r rr l l ll r r rr l l ll r r r r r rr r l r l r ll r r r r r rr r l r l r ll rrr rrr rrrr r l rrl rrll l r l r l rr l l l l l ll l r l r l rr l l l l l ll llr llr llrr lll lll llll 1 2 1 2 1 2 1 2 1 2 1 2 1 1 1 2 1 1 1 1 2 1 2 1 2 2 2 2 1 2 2 2 1 2 1 1 2 1 1 1 2 1 1 1 1 2 1 2 1 2 2 2 2 1 2 2 2 1 2 1 2 (7) as shown in equation (7), the parameters of the q matrix provide the information about the substitution and complementarity relationships among activities and, thus, between realized and latent activities (arfini and donati, 2013; paris and howitt, 1998). the new non linear cost function estimated by using the maximum entropy technique is used in the third phase of pmp to calibrate the observed situation without the calibrating constraints: 308 m. donati, d. bodini, f. arfini, a. zezza + −             + ≤ ≥ ≥ s t p x p x x x q x x a x a x b max 1 2 . . r r l l r l rl l l r r l l x x ' 0, 0 ' r l (8) at this stage, all the information about latent activities is incorporated in a model that can be applied to evaluate policy and market scenarios. the model output shows the change in resource allocation (e.g. land), the marginal value of resources (dual values) and the dynamics in output levels as well as other important economic information on revenue, subsidy, total variable cost and gross margin variations. 3. an overview of aquacrop in an economic system characterized by a limited availability of resources, models that can indicate a better allocation of inputs and resources are a key to making optimal decisions. evaluating the use of sensitive inputs for the environment, like water, becomes more complex if carried out at regional level. specific environmental characteristic of an area (e.g. rainfall, soil characteristics, temperature, etc.) have to be associated with the technical capability of farmers and with crop profitability. moreover, yields are the simplest expression of crop productivity, but a direct relation between yield and input use is hard to identify especially if the information is not directly registered in a bookkeeping system. researchers have thus developed models able to create a link between yields and input use on the basis of information collected over time or by experimental methods. one of the models which can best simulate water-limited attainable yield is aquacrop developed by fao (steduto et al., 2009). the model provides and predicts the crop yield according to water needs and different irrigation methods. fao methodology considers the use of empirical production functions to evaluate crop yield response to water (doorenbos and kassam, 1979). the central feature is the following equation, relating yield to water used in the irrigation process: −    = −    y y y k et et et x a x y x a x (9) yx ya are the maximum and actual yields, etx and eta are the maximum and actual evapotranspiration, and ky the proportionality factor between relative yield loss and relative reduction in evapotranspiration. the problem of water scarcity led to a different theorization of equation (9), which now separates field crops from tree crops. in particular, for field crops, fao researchers proposed re-elaborating the equation in order to plan, manage and simulate different water management scenarios. so on the basis of work by doorenbos and kassam (1979), aquacrop evolved by: i) dividing the et into crop transpiration tr and soil evaporation (e); ii) developing a model relating to canopy growth and senescence in order to estimate 309pmp to assess agroenergy impact on water consumption tr and its separation from e; iii) considering the final yield y as a function of final biomass b and harvest index hi and iv) considering and separating the effects of water stress into four components: canopy growth, canopy senescence, tr and hi. the central equation of the new aquacrop is thus: ∑= ×b wp tr (10) where tr is the crop transpiration (in mm) and wp is the water productivity parameter (kg of biomass per m2 and per mm of cumulated water transpired over the time period in which the biomass is produced). equation (10) implies a shift from the seasonal or longterm evaluation used by doorenbos and kassam (1979) to a daily time relationship that is closer to the time scale of crop responses to water deficits. aquacrop considers three important aspects: soil, crop and atmosphere. soil is considered by determining the level of fertility that can affect crop development as well as water balance. crops and plants are measured and simulated using data relating to growth, development, and yield processes. atmosphere is reproduced considering thermal regime, rainfall, evaporative demand, and carbon dioxide concentration. in order to better simulate reality, a wide range of parameters such as the irrigation system, water productivity, crop adjustments to stress can be modified and reflect on the final yield. 4. model architecture the model is divided into three parts or modules. each module is devoted to a specific task and interfaced with the others providing the input information. figure 1 presents a scheme where the different phases of the model are specified and linked. the first module provides information about the characteristics of the farms belonging to the sample and information related to the water use. in a diversified territorial context where different agronomic areas are embedded, it becomes important to cover different farm types and to truthfully represent land use and productivity levels. there is thus a compromise between minimizing the amount of data entered into the model and the need to provide enough details of individual farmer behavior and technologies and production decisions at farm level. the use of two databases providing complementary farm information is very useful: iacs (integrated administration and control system) database provides information on land use of each farm, while fadn (farm accountancy data network) database provides economic2 and technical information regarding farm type. the integration of iacs with fadn makes it possible to measure the exact dimension of agricultural production systems, gross marketable output, subsidies distributed and the amount of variable costs attributable to each process in areas smaller than nuts3 (arfini et al., 2005). the integration of fadn and iacs databases with aquacrop into a single database gives a complete dataset of land use, technical and economic parameters for production processes and water use of all the farms included in the area considered by the model. the 2 fadn farm accountancy data network, provides information for italy at farm level on the explicit variable cost (accounting cost) per activity. 310 m. donati, d. bodini, f. arfini, a. zezza aggregation is performed at macro-farm level, i.e. farms grouped by size and farm specialization and by each agricultural area (a homogeneous altitude area belonging to the same province). more precisely, in each province three altitude levels, seven classes of size (0-10 ha, 10-20 ha, 20-30 ha, 30-50 ha, 50-100 ha, 100-300 ha, > 300 ha) and three economic sectors (arable crops, fruit and vegetables, and animal production) were considered. figure 1. pmp model architecture. aquacrop simulations iacs datafadn data pmp model constraints land constraints agronomic constraints land allocation farm income dynamics water allocation input pmp model output water constraints cap constraints scenarios cap and market prices agroenergy crops the second module consists of the pmp optimization model, which estimates variable costs for all activities and assesses the impact of policy and market scenarios. there are two calibration phases: the first is obtained by n linear programming models (one for each macro-farm) adopting the calibration constraints, and the second calibration is achieved by using the non-linear cost function estimated in the second pmp phase. the second calibration allows to verify that the model reproduces the observed land allocation without calibrating constraints. once verified, the model is ready to be implemented for the simulation phase of all sets of constraints: land, agronomic, water and policy constraints (cap). the simulation phase considers at baseline level (year 2012) the main first pillar cap measures, like decoupling and payment modulation. this part of the model can support modification in cap mechanisms, such as the transition from historical to regionalized decoupling, as well as market price variation. the third module addresses the results of the simulation. the results are stored in specific output files readable by statistical and spreadsheet software by adopting gdx routines (gams, 2012). these routines generate an organized output comprising calibrating checks, land and water allocation, and farm economic variable dynamics. 311pmp to assess agroenergy impact on water consumption 5. aquacrop simulations with regard to the research question of this paper, aquacrop makes it possible to calculate the amount of water that each crop requires in relation to the level of observed yield. one of its key features concerns the climate characteristics of the area. the model requires information on daily maximum and minimum air temperatures, daily rainfall, daily evaporative demand of the atmosphere expressed as reference evapotranspiration, and the mean annual carbon dioxide concentration in the bulk atmosphere. the climatic parameters adopted were collected from the meteorological measurements recorded in the emilia-romagna region, from 1 january 2002 to 31 december 2009. for the annual carbon dioxide value, we adopted the co2 concentration in the mauna loa observatory in hawaii, a monitoring centre used by aquacrop. the reference value of evapotranspiration was calculated using fao criteria (allen et al., 1998) and parameters were determined using the fao’s model eto-calculator (raes et al., 2009). in aquacrop, the crop system is represented by five different linked and interrelated modules concerning: phenology, aerial canopy, rooting depth, biomass production and harvestable yield. each different crop grows and develops over its cycle by expanding its canopy and its rooting system at the same time. table 1 shows crop growing characteristics used in the evaluation of water requirement. the six crops are the arable crops for which aquacrop provides information for simulations on response of yield to irrigation water use. table 1. crop growing characteristics adopted in the simulation process. crop sowing date harvest date plant density (plant ha-1) maize 01-apr 10 august 95 x 103 silage maize 01-apr 10 august 85 x 103 sorghum 13 may 11 september 200 x 103 sugar beet 10-apr 29 august 100 x 103 soybean 1 may 7 september 350 x 103 tomato 01-apr 19 july 35 x 103 the characteristics of soil (hydraulic conductivity, water content, field capacity, permanent wilting point) adopted in the study follow the standard profile suggested by the fao model. this means that soil characteristics within the area are not differentiated. aquacrop also has an irrigation management component which makes it possible to operate on soil fertility and on the type of irrigation system. standard model values were used for soil fertility, while the period and the method of irrigation were differentiated according to crop for the irrigation system (table 2). as noted above, the study focused on the interdependence between water requirement and biomass in order to analyze how an increase in applied irrigation water (aiw) influences the production in terms of biomass (fresh yields). the aquacrop model was used to generate non-linear yield response functions to aiw, able to highlight the production trend of the yield level in relation to aiw. the simulations were carried out starting with a rain-field crop, and increasing aiw by season, 312 m. donati, d. bodini, f. arfini, a. zezza applying constant increments of irrigation water (different for each type of crop), in order to reach the maximum level of biomass. table 3 reports an example of how results were organized to generate non-linear yield response functions for sugarbeet. table 2. irrigation management hypothesized for each crop considered in simulation procedure. crop sowing date harvest date irrigation treatments method maize 01-apr 10 august 4 irr.schedule sprinkler silagemaize 01-apr 10 august 4 irr.schedule sprinkler sorghum 13 may 11 september 2 irr.schedule sprinkler sugarbeet 10-apr 29 august 8 irr.schedule sprinkler soybean 1 may 7 september 3 irr.schedule sprinkler tomato 01-apr 19 july 16 irr.schedule drip table 3. production level according to different aiw sugarbeet (t/ha). year applied irrigation water (mm) 0 50 100 150 200 250 300 350 400 450 500 550 2002 35.1 39.7 43.6 44.6 44.5 44.0 43.5 42.7 42.1 41.6 41.2 41.0 2003 4.2 5.2 9.7 17.1 24.8 32.4 36.5 40.1 41.8 42.8 43.0 42.2 2004 4.6 5.3 13.3 27.4 33.1 38.2 42.5 43.6 44.2 42.7 41.6 41.4 2005 14.5 18.4 27.2 33.1 38.0 41.7 44.2 45.0 44.4 42.2 41.6 41.5 2006 3.6 4.8 9.0 16.7 28.3 36.1 41.4 44.4 45.1 44.8 43.5 42.3 2007 18.7 20.9 24.2 29.4 34.1 38.0 40.6 42.3 43.4 44.2 44.2 43.4 2008 26.2 27.7 30.8 34.4 36.9 39.2 40.7 41.5 42.0 42.2 42.0 41.7 2009 4.9 7.1 13.0 20.7 28.4 34.6 39.2 41.0 42.0 42.0 42.2 42.2 from the data in table 3 and according to the methodology proposed by garcía-vila and fereres (2012), three non-linear crop-water production functions related to the 20th, the 50th and the 80th percentile were identified. the percentile values made it possible to obtain a non-linear function (polynomial), that reports the contribution of irrigation to the production of fresh yield. table 4 shows the values of fresh yield obtained for the different levels of aiw according to a distribution over three percentiles for sugarbeet. table 4. percentiles values from simulation for sugarbeet (t/ha). percentile applied irrigation water (mm) 0 50 100 150 200 250 300 350 400 450 500 550 20th 4.0 5.2 9.5 17.0 27.6 34.2 38.7 40.8 41.9 41.9 41.5 41.3 50th 9.7 12.8 18.8 28.4 33.6 38.1 41.0 42.5 42.7 42.5 42.1 41.9 80th 27.9 30.1 33.4 36.4 39.3 42.1 43.6 44.5 44.6 44.3 43.7 42.5 313pmp to assess agroenergy impact on water consumption the results obtained for each crop were represented by polynomial functions in order to better describe the evolution of fresh yield production in relation to the applied irrigation water, i.e. the yield-aiw functions. in figure 2, the vertical axis of each chart shows the level of fresh yield produced (t/ha), and the horizontal axis shows the different levels of applied irrigation water. the results demonstrate that the polynomial functions fit the simulations responses provided by aquacrop with a high level of r2. figure 2. simulated biomass production for 8 years in response to different levels of applied irrigation water (aiw): (a) sugar beet; (b) silage maize; (c) maize; (d) sorghum; (e) soybean; (f ) tomato. 1   (a) (b) (c) (d) (e) (f) 2    3   314 m. donati, d. bodini, f. arfini, a. zezza 6. impact assessment 6.1 land allocation the integrated model was developed in the agricultural context of a lowland area of emilia romagna (northern italy). for the purposes of clarity and brevity, the impact assessment presents the results of a homogenous area, the province of parma. the latent crop introduced in the simulation phase was sorghum for bioethanol production. we assume that this variety of sorghum is currently not present in the regional production plan. the information concerning average yields, price and specific production cost for sorghum was taken from an experimental study promoted by the emilia romagna region aiming to evaluate the possibility of building a regional supply chain for bioethanol. the results presented were developed in the framework of the “health check” cap reform. the market simulations consist of increasing the price of sorghum by 1 €/t in 200 steps, to reach the maximum price of 200 €/t. the results identify the economic threshold for sorghum, that is the starting price from which sorghum for biomass can be inserted into the production plan of farms in the province of parma. figure 3 shows the total area that would be grown with sorghum in the region at different level of sorghum price. the graph presents a curve that starts to increase from a level of 58 €/t, the profitable threshold for the crop. the price dynamics causes a big increase in sorghum acreage. the rotational constraints and the complementary and substitution relationships within the cost matrix prevent farm surfaces from becoming specialized in a single crop. the graphical representation of the simulation shows the different production levels with regard to different price levels, so that it is possible to identify the price to pay to producers in order to obtain a certain quantity of raw material. so, for example, if the supply chain needs 20.000 ha of sorghum, the price that should be paid to farms is more or less 108 €/t. the simulation also shows variation in the relative incidence of sorghum compared to other crops in the regional production plan. figure 4 highlights that the increase in the incidence of sorghum on the regional production plan is due to a big fall in the incidence of fodder crops and wheat. it is important to note that in this study, the prices of other crops are assumed to be constant throughout, but likely market price modifications would produce a change in profitability across the model, with different impacts on sorghum production responses. 6.2 water consumption the simulations carried out by the integrated model capture the relationships between yields and the applied irrigation water. for the six crops in table 1, this relationship was estimated by using aquacrop as previously described, while for the other crops information has been taken from the literature. for sugarbeet, maize, silage cereals, tomato, soya and sorghum, yields are non-linearly related to aiw, while for the other irrigated crops (e.g. alfalfa) an exogenous fixed relationship was assumed. the polynomial regressions estimated for the six crops are used in the aquacrop model to identify the quantity of irrigated water each farm needs to obtain the observed yield. polynomial functions from the 50th percentile were used. the difference in terms of aiw among farms is reflected in 315pmp to assess agroenergy impact on water consumption the observed yield. to adapt the yield-aiw functions to the observed information, a correction factor was applied as follows: = ⋅yield f w v( )n j n j n j j, , , (11) figure 3. evolution of the sorghum hectares due to price variation (parma province). 0 20 40 60 80 100 120 140 160 180 200 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000 (ha) (€ /t ) figure 4. evolution of the specific incidence of each crop on the regional production plan (parma province). 0 10 20 30 40 50 60 70 80 90 1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190 199 €/ton    -­‐  sorghum in c.  % wheat maize other  cereals fodder  crops sorghum others 316 m. donati, d. bodini, f. arfini, a. zezza where the yield for each irrigated crop j (for j=1,2,…,j) and each farm n (for n=1,2,…,n) is derived from the non linear function obtained by aquacrop simulations multiplied by a weighting value v j calculated as the ratio between the average observed yield and the average yield provided by aquacrop simulations. wn j, the variable related to aiw to be estimated for each farm and crop. this correction makes it possible to retain the shape of the yield-aiw function and model feasibility. in fact, the difference between observed and experimental yield data can be unavoidable (dillon and hardtacker, 1993; cortignani and severini, 2009). table 5 presents the aiw estimated levels for the six crops simulated in aquacrop. from experimental tests, sorghum yield is assumed to be 23 t/ha corresponding to 121.1 mm of aiw. table 5. estimated aiw per crop in parma province. area altitude farm type class of size (ha) no. of farms sugarbeet silage cereals maize tomato soya sorghum aiw (mm) parma plain arable crops 0-10 1233 158.8 28.3 159.8 86.9 121.6 121.1 parma plain arable crops 10-20 452 185.4 234.5 169.4 131.4 121.1 parma plain arable crops 20-30 175 118.8 8.9 171.7 240.7 151.6 121.1 parma plain arable crops 30-50 129 220.4 32.1 21.2 211.2 168.0 121.1 parma plain arable crops 50-100 97 271.9 418.9 63.7 151.3 407.8 121.1 parma plain arable crops 100-300 20 51.4 145.2 234.5 240.7 121.1 parma plain arable crops > 300 1 30.1 121.1 parma plain horticulture 0-10 10 158.6 86.9 121.1 parma plain horticulture 10-20 6 185.4 169.4 121.1 parma plain horticulture 20-30 8 118.8 172.1 240.7 121.1 parma plain horticulture 30-50 14 220.4 21.2 211.1 121.1 parma plain horticulture 50-100 10 271.9 63.7 151.3 121.1 parma plain dairy 0-10 90 160.0 211.1 121.1 parma plain dairy 10-20 127 185.5 630.5 234.5 160.1 121.1 parma plain dairy 20-30 121 118.8 8.9 171.7 240.7 121.1 parma plain dairy 30-50 180 220.4 32.1 21.2 196.3 121.1 parma plain dairy 50-100 97 271.9 418.9 63.7 164.9 121.1 parma plain dairy 100-300 28 51.4 145.2 234.5 211.1 121.1 parma plain dairy > 300 6 13.8 418.9 234.5     121.1 parma plain all all 2804 174.6 169.3 234.5 196.4 154.7 121.1 for five crops out of six, the aiw changes according to the farm, revealing a direct relationship with the specific crop yield. but for sorghum the aiw level is the same for all farms in relation to the supposed uniform yield in the area. the relationship yield-aiw was used to evaluate the impact of the potential introduction of sorghum for biomass production in terms of water demand. price rises may persuade farmers to convert cereal land and grassland to sorghum with a negative effect on 317pmp to assess agroenergy impact on water consumption the water consumption. in particular, the process of substitution of a non-irrigated crop, like wheat, with an irrigated crop, like sorghum, generates an increase in total irrigated water for the province of parma. more specifically, when a price of 70 €/t for sorghum is applied, about 30% of the wheat disappears in favour of sorghum. this shift produces an increase of the total irrigated water of 2.5%, which corresponds to an increase of 1.5 million m3 compared to before sorghum was introduced (see figure 5). figure 6 shows the trend in consumption of water for irrigation. up to a price of 61 €/t irrigated water consumption declines by 0.5%, due to a process of substitution between sorghum and grassland with high aiw. this substitution is interrupted by the demand for forage from dairy farms, represented by a specific constraint in the model. figure 6. dynamics in water consumption with sorghum cultivation (province of parma). 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 57 61 65 69 73 77 81 85 89 93 97 10 1 10 5 10 9 11 3 11 7 12 1 12 5 12 9 13 3 13 7 14 1 14 5 14 9 15 3 15 7 16 1 16 5 16 9 17 3 17 7 18 1 18 5 18 9 19 3 19 7 price  scenarios  -­‐  €/t. .0 00  m 3 figure 5. irrigated water allocation. 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 57 62 67 72 77 82 87 92 97 10 2 10 7 11 2 11 7 12 2 12 7 13 2 13 7 14 2 14 7 15 2 15 7 16 2 16 7 17 2 17 7 18 2 18 7 19 2 19 7 price  scenarios  -­‐  €/t. .0 00  m 3 sorghum grassland soya silage  maize tomato maize sugarbeet 318 m. donati, d. bodini, f. arfini, a. zezza the model captures the impact of farm production decisions on the allocation of water, but it does not capture efficiency in the use of irrigated water. an implicit assumption of the model is that farmers in the province take the production decisions considering that water is available, and water allocation is a consequence of their decision. from this point of view, the calibrated aiw represents the willingness to use water for each crop. this implies that there are no direct constraints on the total available water per farm and, therefore that trade of water entitlements cannot be evaluated in this model. 7. conclusions this paper uses positive mathematical programming model to evaluate the effects of agro-energy crop cultivation on land and water allocation. the agro-energy crop is sorghum for the production of second generation bioethanol. the model simulates the introduction of sorghum as a new crop in the farm production plan in the lowlands of parma, an agrarian region in northern italy, and evaluates crop economic threshold and the change in farm crop set and irrigated water use. the model considers latent crop information in each farm production plan, so that in the simulation phase, new crops, such as sorghum for biomass production, are considered a new option with regard to the crops already activated in the observed situation (arfini and donati, 2013). the second pmp phase estimates non-linear cost functions where information about the latent crop is included. the main constraint of the model is related to the total farm land. water appears in the third phase through the yield-aiw function which defines the relationship between crop yields and the quantity of aiw. the yield-aiw function is estimated by simulating the yield response of the crop in relation to different aiw quantities; simulations were carried out by implementing aquacrop (steduto et al., 2009) for six crops: sugarbeet, maize, silage cereals, tomato, soya and sorghum. the simulation results revealed the capacity of the model to evaluate the productive potential of sorghum for biomass in the region and the consequences on the regional production plan. the scenarios developed to estimate the sensitivity of the sorghum in relation to a variation in its market price made it possible to identify the price level necessary for providing a sufficient quantity of raw material to feed a second generation bioethanol supply chain. the increase in sorghum price leads to a reduction in wheat and fodder crops; maize is also affected by a process of substitution. the pmp model makes it possible to evaluate the impact of sorghum cultivation on water consumption for irrigation. the results show that increasing land use for biomass sorghum in the lowlands of parma province, a dairy and horticultural area, entails an increase in the total irrigated water quantity due to a decline in non-irrigated crops, like wheat. the information about water consumption will be useful for policy makers in preventing excessive demand for water in areas where the risk of drought might generate non-efficient water allocation. furthermore, the model can evaluate the impact of the cap on irrigation water needs as well as land use. the 2014-2020 cap reform, which emphasized environmental objectives in agricultural policies, could also be evaluated in order to predict effects on water resources using a similar approach. the model does however present certain limitations. the aquacrop simulation was run on the crops included in the fao model dataset. this is because yield-aiw functions for fodder crops, such as alfalfa and permanent meadows, have not been estimated. 319pmp to assess agroenergy impact on water consumption furthermore, the pmp model presented in this study does not consider an explicit water constraint, and it is thus not possible to simulate reductions in water availability and identify the dual value associated with this resource. optimizing the model to include this information would provide a value for levels of water scarcity in certain groups of farms or areas, and would be useful in identifying an appropriate price. finally, a water constraint in the model would make it possible to simulate the water entitlement market and put in place economic tools to make the use of water for irrigation more efficient. acknowledgments an earlier version of this paper was presented at the 2nd aieaa conference “between crisis and development: which role for the bio-economy”, 6-7 june, 2013, parma, italy. the present work was carried out in the framework of the project biosea “optimization for bioenergy supply chains for an economic and an environmental sustainability” financed by the italian ministry of agriculture and forestry (www.biosea.dista. unibo.it). we thank the azienda agraria sperimentale “stuard” of parma, italy for providing experimental data on sorghum h133 for biomass. the authors wish to thank the two anonymous referees and the editor for their helpful suggestions and constructing comments on an earlier version of the paper. references allen, r.g., pereira, l.s., raes, d., and 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(2008). bioenergie:quali opportunità per l’agricoltura italiana, collana studi e ricerche inea, edizioni scientifiche italiane, napoli. bio-based and applied economics 4(1): 55-75, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-14196 italian agri-food exports in the international arena anna carbone1, roberto henke2,*, alberto f. pozzolo3 1 università della tuscia 2 italian national institute of agricultural economics – inea 3 università del molise date of submission: march 21st, 2014 abstract. the aim of the paper is to highlight the positioning of the so-called made in italy agro-food exports in foreign markets considering global world tendencies as well as country specific trends. besides, we aim at disentangling the role of product quality from price competition as a driver of competitive advantages. to these ends, the work combines two different methodologies, applied in three steps. first, we estimate the elasticities of italian exports, with respect to world imports, italian export prices and the competitors’ prices. second, an index of the sophistication of exports’ flows that captures the role of quality in global competition is calculated. third, the estimated elasticities are compared with the changes in the sophistication levels. results allow for product-specific trends to stem out from the overall picture. exports’ performance varies according to the type of product and to the degree of market completion. although, the made in italy aggregate seems overall competitive, the analysis pinpoints some drawbacks in the positioning of some products in the world arena. focus on wine and on olive oil, two major italian exporting sectors, helps in understanding the potential of the joint methodology adopted. keywords. exports’ elasticities, exports’ sophistication, made in italy, world demand jel codes. q17, f14 1. introduction world agri-food markets are increasingly competitive. competitive strategies are based on a wide range of attributes that segment the market and smooth price competition. nevertheless, given the emergence of new competitors with different production cost levels, price competition is altogether relevant and not only in world bulk markets. the paper seeks at highlighting the position of the italian agro-food exports in international markets given major global tendencies; trends related to the importing countries; as well as, those specific of italy as an exporter. besides, one more goal is to disentangle * corresponding author: henke@inea.it. 56 a. carbone, r. henke, a.f. pozzolo the role of product quality from that of price competition as drivers of the observed competitive advantages. in order to meet these ends the paper suggests a three-step methodology that helps to combine information on the trends developing in the world markets with those related to a single exporter. first, we estimate the elasticities of exports to: i) world demand; ii) prices of exports; and iii) prices applied by main competitors. these highlight the capacity of exports to adjust to market trends and to react to competitive pressures. second, the level of so-called “sophistication” (lall et al., 2006) of exports in the world markets is assessed. the “sophistication” concept, as expressed by the prody index, encompasses all product’s and process’s quality attributes and is related to its capability to reward inputs, as well as to the kind of competition prevailing on the market for that very product. last, the estimated long-run elasticities and the changes in the level of “sophistication” of each product over the observed time span are put together in a simple graphical analysis. the analysis considers 30 export sectors of the italian agro-food balance commonly referred to as the agro-food made in italy. the observed time span is the period 1996/972010/11. it is important to underline that the level of aggregation chosen for the analysis is intermediate, and quite different from the usual trade analysis. in fact, on the one hand, trade analysis in applied industrial economics is usually based on more aggregated data and seeks at comparing performances across macro sectors. on the other hand, agribusiness marketing analysts look at trade focusing on a single product (or on small bunches of similar/substitute products), thus going deeper at the search for drivers of competitiveness. while these two approaches have both evident and well known advantages, it is the authors’ opinion that the meso-level used for the present research is a useful complement to the previous ones because it allows for a sound comparison among many products within the same sector. our results show that the positioning of italian exports greatly varies according to the type of product and to the degree of market completion. in some cases, italian exports contrast increasing world competition by increasing quality levels (i.e. their sophistication content); this happens when producers are capable to enhance the quality and the origin of their products. in other cases, when quality cannot be increased and/or costs can be lowered via higher level of efficiency at firm or at chain level, price competition is chosen, by keeping average unit values at lower levels than those of the competitors. all considered, in many cases, in spite of a growing world competition, these products seem successful in defending, and sometimes even to increase, their world market shares. the rest of the paper is organized as follows: section 2 presents some descriptive data on mii exports; section 3 describes the methodology; section 4 presents the main results, including a couple of detailed examples on olive oil and wine: two italian strategic exporting sectors. section 5 concludes. 2. agri-food made in italy exports: a short description moving from trade data available in the un comtrade databank at 6 digit level (hs6), we aggregated the over 700 items referred to agri-food into 95 items from which we 57italian agri-food exports in the international arena selected the 30 ones included in the agri-food made in italy1. the trend from 1996/97 to 2010/11, of the share of the agri-food made in italy is reported in figure 1 where 5 biennial averages represent the whole time span the aggregate includes a mix of fresh produce (vegetables, tomatoes, grapes and the cluster apples, kiwis and pears) and processed food (all the other items, as shown in table 1). actually, the specific features of mii can stem both from the nature of the agricultural produce – this is especially the case for fresh vegetables and fruit – and/or from traditional processing techniques. mii agri-food products are therefore connected to the country, no matter if processed or not, or if made with imported raw material. 1 total exports are referred to 122 countries (world). values are at current values in us dollars. table 1. agri-food made in italy exports: shares and variations. shares on total made in italy share var. (diff.) export var.% rca rca var. % 2010/2011 2010-11/1996-97 2010-11/1996-97 2010-11 2010-11/1996-97 wines < 2 lts 16,4 2,4 195,0 11,3 75,8 dry pasta 6,5 -1,3 108,3 20,8 12,5 sauces and other condiments 6,4 2,2 282,0 1,5 60,7 canned tomatoes 6,2 0,0 149,9 13,5 18,8 apples, kiwi and pears 5,5 -0,1 148,1 6,8 63,1 other cheese 5,3 0,3 168,1 5,0 110,9 bakery products 4,6 0,8 206,3 3,1 10,1 virgin olive oil 4,6 0,4 178,2 28,4 147,7 fresh vegetables 4,3 -1,2 97,6 2,1 -24,3 chocolate products 4,1 0,5 186,4 3,0 70,4 processed coffee 3,8 1,7 355,4 6,2 -45,4 grapes 2,9 -1,4 67,0 11,6 17,7 fresh pasta 2,8 0,2 171,4 8,1 0,9 sparkling wine 2,5 0,4 192,6 18,7 23,0 fruits juices 2,5 -0,5 108,4 2,5 5,4 confectionary products 2,2 -0,6 95,1 0,9 -30,2 prepared vegetables 2,1 0,0 152,5 1,2 13,5 processed rice 2,1 -1,1 66,0 2,2 -81,7 fresh cheese 2,0 1,4 678,9 9,0 244,4 meat cut 2,0 0,4 210,8 4,4 43,2 mineral water 1,9 0,9 354,2 3,1 78,6 prepared fruit 1,9 -1,6 37,8 1,7 -60,7 wines > 2 lts 1,8 -1,5 39,0 5,4 -24,8 ice creams 1,2 0,3 221,6 4,1 15,3 non-virgin olive oil 1,1 -1,9 -8,6 9,7 -47,7 fresh tomatoes 1,0 -0,2 116,8 1,2 -18,6 grated cheese 0,9 0,2 234,9 12,3 47,9 vermouth 0,8 -0,5 49,9 15,2 26,3 blue cheese 0,5 -0,2 74,0 9,6 73,3 mixed olive oil 0,3 -0,1 70,5 9,0 -37,8 total made in italy 100,0 151,0 mil on total agri-food exp. 71,2 67,9 * variation of total italian agri-food exports source: our elaborations on un-comtrade data. 58 a. carbone, r. henke, a.f. pozzolo the common feature of these products is in that they are well reputed abroad due to their italian origin and recall the generally appreciated italian diet and life-style. their net trade balance is mostly positive even if there are few exceptions (one of the most noticeable is olive oil whose net trade balance is negative). altogether, these products are the core of italian exports as shown by the large share of the total agri-food exports they represent: 71% of the total in 2010/11, slightly increasing compared to 1996/97 (table 1 and figure 1). in the observed period of time, bottled wine (i.e. wine in bottles with less than two litres, in tables and figures is: wine< 2 lts) shows by far the largest share of the export values (16.4% in 2010/11, 13.9% in 1996/97). in 2010/11 wines are followed, at a distance, by dry pasta (6.5%), sauces and other condiments (6.4%) and canned tomatoes (6.2%). of these products, sauces and other condiments show the largest increase in the share, as they were featuring a value of 4.2% in 1996/97. actually, all the other major items represent quite a stable share of the total export values over the period observed, as a result of export trends basically aligned and with a limited expansion over time, especially when compared to minor flows which are relatively more dynamic. looking at the fresh component of the agri-food made in italy exports, data show a sort of steadiness of the shares (as in the case of apples, kiwifruits and pears) if not a cerfigure 1. trend of the share of the agri-food made in italy on the total agri-food italian exports. 59italian agri-food exports in the international arena tain degree of reduction (fresh vegetables and grapes), so that their position in the ranking of the 30 mii products becomes relatively low. looking at the export dynamics, the variation of the export values at current prices are all positive, with the only exception of the non-virgin olive oil. in the same table 1 we considered also the balassa index (also referred to as the revealed comparative advantage – rca). the index compares the share of a country’s export flow for a specific product (sector) with the share hold by the same product (sector) in world’s total exports. the index formula is as follows: =rcai j xi j xj xi w xw , , , where xi,j is the exports of the item i of the country j; xj the total agri-food exports of the country j; xi,w is the world exports of the good i and e xw the world agri-food exports. the index is greater than 1 for all the agri-food made in italy items, indicating specialisation of the country for these items (except confectionery products for which italy does not show a revealed competitive advantage, with a value of balassa index of 0.9). the range of values is wide as it spans from 1.2 for prepared vegetables to 28.4 for virgin olive oil. the lower values (around 1-2) are relative to fresh and prepared vegetables as well as prepared fruits and fruit juices; while for products such as dry pasta, canned tomatoes, grated cheese, virgin olive oil, grapes and wine < 2 lts, sparkling wines and vermouth, italy shows higher level of revealed competitive advantage (index values above 10). the variations of rca show the trends in the specialization pattern of the country relative to world specialization. index variations are mixed; with some products that show negative values while other facing a positive one, with varying intensity. particularly high rates of increase in specialisation are recorded for many cheese categories, and for virgin olive oil, chocolate products, sauces and other condiments, mineral water and wine < 2 lts. on the contrary, for some products such specialisation significantly decreases by time: fresh tomatoes and fresh vegetables, processed coffee and rice, non-virgin and mixed olive oils, confectionery products, prepared fruit and wine > 2 lts. for all these products, italy is de-specialising due to a wider presence of the old competitors or due to the entry of new ones on the international arena,. another interesting point is that overall the distance covered by the agri-food mii exports has increased, with larger flows of exports going to further countries such as china, and canada. 3. methodology econometric analyses of export demand elasticities, specific both at country and at product level, have long tradition in economics, going back at least to adler (1946) and horner (1952). more recent examples include, among others, the estimates of shortand long-run elasticities of exports and imports for the g7 countries in hooper et al. (2000) 60 a. carbone, r. henke, a.f. pozzolo and those of us import and export at the sector level by mann and pluck (2007).2 a vast strand of literature has also focused on country level imports and exports of agricultural products, in particular commodities.3 overall, the estimates of export demand elasticities obtained in literature are quite heterogeneous, depending on the goods, the countries and the time periods under scrutiny. however, a common view in the most recent literature is the importance of conducting analyses at a narrow sector level, to reduce the impact of changes in product quality. for this reason, our estimates are based on a detailed data set where italian exports are represented by 95 food and agricultural products (30 of which are gathered in the socalled made in italy) sold to 48 among the largest trade partners4. the econometric specification follows mann and plück (2007), where the annual growth rate of each product exports on each customer country is a function of two distinct sets of variables, respectively catching the short and the long run reactions: δln(exportijt) = β0 + β1 δln(exportijt-1) + β2 δln(importijt) + β3 δln(importijt-1) + + β4 δln(auvexpijt) ++ β5 ln(δauvimpijt) + β6 ln(exportijt-1) + + β7 ln(importijt-1) + β8 ln(auvexpijt-1) + β9 ln(auvimpijt-1) + + β10 yt + α ij + ε ijt (1) where: δx is the annual variation of the generic variable x; ln(exportijt) is the logarithm of the italian exports of product i towards country j in year t; ln(auvexpijt) is the logarithm of the average unit values (auv) of italian exports of product i toward country j in year t; ln(importijt) is the logarithm of the total imports of the product i from the country j in year t; ln(auvimpijt) is the logarithm of the auv of the imports of product i from the country j in year t; yt is a dummy variable per each year t; α ij is a dummy variable per each product i and country j; εijt is a zero-mean error term. the estimated coefficients can be interpreted as follows: • β1 measures the inertia of italian exports; • β2 is the instant elasticity of italian exports to the import demand; • β2 + β3 is the short-run elasticity of italian exports to the import demand; • β4 is the short-run elasticity of the italian exports to the export auv; • β5 is the short-run elasticity of the italian exports to the import auv; • -β7/β6 is the lung-run elasticity of the italian exports to the import demand; • -β8/β6 is the long-run elasticity of the italian exports to the export auv; • -β9/β6 is the long-run elasticity of the italian exports to the import auv. 2 see also sawyer and sprinkle (1996), for a survey of the previous literature. 3 see for example devadoss et al. (1988) and, more recently, reimer et al. (2012). 4 the selected countries, are: albania, arab emirates, australia, austria, belgium, brazil, bulgaria, canada, chile, china, croatia, cyprus, czech republic, denmark, finland, france, germany, greece, hong kong (sar), hungary, india, ireland, israel, japan, korea, rep., kuwait, latvia, lithuania, luxembourg, malta, norway, poland, portugal, romania, russia, saudi arabia, slovak republic, slovenia, south africa, spain, sweden, switzerland, thailand, tunisia, turkey, ukraine, united kingdom, united states. in 2010-2011, these cover more than 90% of the agro-food made in italy trade (de filippis, 2012) and have been selected out of 122 trade partner countries, based on the share of italian foreign trade they cover, provided that yearly detailed data were available for the whole time span under analysis. 61italian agri-food exports in the international arena the model is estimated using the procedure first suggested by arellano and bond (1991) for dynamic panels, using the stata routine of david roodman (2009). although mann and plück (2007) prefer to use a fixed effect estimator, we follow this more rigorous and efficient procedure that avoids introducing the bias caused by the presence of the time-invariant component in the error term that is by definition correlated with the lagged dependent variable. we use the two-steps, system estimator with orthogonal deviations. moreover, our specification search suggests to use lags 5 to 7 of δln(exportijt-1) and of ln(exportijt-1) used as instruments, that guarantees passing the hansen test for overidentifying restrictions.5 in addition to estimating standard export demand functions, we looked at the mii exports watching at their “sophistication”. this is defined as the content of a good in terms of technology, packaging, branding, other aspects of quality, as well as scale economies and any other factor affecting the value of the product (lall et al., 2006). the basic idea is that the more sophisticated the items produced and exported the higher the income earned. thus, the sophistication content of a product can be indirectly measured by the per-capita gdps of the exporting countries through the prody index (lall et al., 2006; rodrik, 2006; hausmann et al., 2007). following the literature, the prody index associated to each good (or set of goods) i is defined as the sum of the per-capita gdp of all the countries j exporting that good, where each country’s gdp is weighted by a measure of the trade specialisation of the country in that item, expressed by balassa’s index of revealed comparative advantages – rca, normalized by the sum of rca of all exporting countries. formally: ∑=prody s gdppci j ij j (2) where si,j is the weighting factor given by   j ji ji ji rca rca s , , , the index produces a ranking of values that is interpreted as a relative measure of the content of attributes that better remunerate inputs. more in details, products with a high index are sold by richer countries, that are supposedly better able to focus on quality attributes and on market imperfections to reduce the impact of sheer price competition. furthermore, the evolution of the prody index reflects changes in the sophistication level of each product. here the time trend is caught by comparing the 2010-11 values with those at the beginning of the observed period, 1996-97. from the formula above, it is clear that its variation over time can be explained by two different effects. first, it can 5 in unreported regressions, available from the authors upon request, we have verified that our results are basically unchanged using a fixed effect estimator. 62 a. carbone, r. henke, a.f. pozzolo change according to variation in gdp per capita of the exporting countries. second, it may reflect changes in countries’ export specialization patterns. though relatively new, the prody index has already been applied to the analysis of chineese exports (rodrick, 2007), portoguese exports (lebre de freitas and salvado, 2009), italian total exports (di maio e tamagni, 2008) and italian agri-food exports (carbone and henke, 2012). in this paper, we apply the prody index to agri-food products that are defined also by the quality level within each category. following minondo (2007), for each product we considered two levels of quality according to the median world-level value of the export auv and then apply the usual formula. formally: ∑∑ =prody rca rca gdpiq j i jq j i jq j , , were q indicates the different level of quality of the exports (high and low), and all other expressions are as defined above. the index is built using the same export dataset as in other parts of the research and per capita gdp as released by world bank (development indicators series) measured in purchasing power parity (ppp) at constant 2005 values. combining the analysis of the elasticities with that one of the sophistication, we can obtain a broader picture of the global and country specific determinants of competitiveness for mii agri-food exports. more in detail, the proposed approach allows on one side, to look at the kind of competition that characterizes world markets for a given product and, on the other side, to detect to what extent italian exports are able to adjust to trends in both costumers’ demand and competitors supply, focusing on markets where price competition is less intense and exports are, hence, more rewarding. 4. empirical findings 4.1 elasticities of italian exports of mii agri-food products measures of the short-run and long-run elasticities of italian exports with respect to the evolution of world demand allow a better understanding of the strength and weaknesses of the country agri-food international specialization. a high elasticity with respect to world demand, for example, witnesses the ability to single out the fastest growing markets. on the contrary, it clearly implies a higher vulnerability during recessions. the elasticity with respect to relative prices is also a crucial characteristic of exports. indeed, when exporters enjoy some degree of market power, the total value of aggregate exports is less affected by changes in italian export auvs or in the auvs of the imports of our clients from our competitors. table 2 presents the summary statistics of the data used in the regressions, showing a high degree of variability. in particular, although the mean and median values of the annual rates of growth of exports and auvs by country of destination, year and product are relatively small, the standard errors and the minimum and maximum values point to significant heterogeneities, that justify our econometric analysis. 63italian agri-food exports in the international arena table 3 reports the results of the estimation of the econometric model described in section 3 on a sample of yearly data within 15 years (1996-2011) on exports to our 48 major trade partners. panel (1) reports the results of the estimates on the whole sample. all coefficients have the expected sign and are statistically significant at the 1% level, providing strong support to our empirical specification. in particular, the negative coefficient of the lagged dependent variable suggests the presence of an error correction mechanism in the dynamics of exports. columns (2) to (5) present the results of the estimates on different sub-samples, distinguishing between mii and other products and between exports towards high income and lower income countries, according the world bank classification. these respectively accounts for about 94% and increasing over the period, and about 6% and descreasing. the distinction between the two groups of clients is relevant as high income countries are the major buyers of quality products. in all specifications, the coefficients have the expected sign, although in some cases they are not statistically significant. interestingly, the estimates obtaiened from the different sub-samples are rather similar. a neater interpertation of the economic meaning of these coefficients can nonetheless be gained whatching at the short and long-run elasticities with respect to world demand and prices that can be obtained as a function of the estimated coefficients. table 4 presents such values. the estimated instant elasticity to import demand is 0.37. this means that, on average, a 10% increase in the total value of agri-food imports of our trade partners determines immediately a 3.7% increase in the value of italian exports. this reaction is larger for mii agri-food exports (0.40) than for other agri-food products (0.34), although the difference is not statistically significant. the expansion of our exports in reaction to an increase in demand is higher for high income countries (0.39) than for low income countries (0.32), but then again, the difference is not statistically significant. the estimates of the shortrun elasticity – that measures the total effect after two years – show the capacity to better adjust to changes in demand as time goes by. the average coefficient raises to 5.2%, and the adjustment remains significantly higher for mii agri-food products (0.54 vs 0.45)and for high income countries (0.49 vs 0.35), although in none of the cases the difference is statistically significant at the standard confidence levels. table 2. summary statistics of regression data. variable mean median standard deviation minimum maximum import – rate of growth 0.12 0.10 1.05 -10.37 13.10 export – rate of growth 0.10 0.09 0.61 -12.77 10.14 auv of exports – rate of growth 0.03 0.03 0.60 -9.55 9.06 auv of imports – rate of growth 0.04 0.03 0.47 -10.12 14.86 exports – natural logarithm 12.96 13.19 2.76 0.00 20.79 imports – natural logarithm 16.46 16.73 2.60 0.00 24.00 auv of exports – natural logarithm 0.91 0.92 1.22 -4.52 12.54 auv of imports natural logarithm 0.56 0.57 1.28 -7.94 15.80 64 a. carbone, r. henke, a.f. pozzolo the value of -0.11 of the short-run elasticity with respect to export auvs implies that a 10% rise of italy’s export prices determines a reduction in total revenues of 1.1%, a small value also in comparison to similar analyses for other sectors (e.g., hooper et al., 2000). this means that italian exports enjoy a relatively stable demand even in situations of increasing prices. in the case of non mii agri-food produtcs, this elasticity is slightly smaller (0.08 as opposed to 0.11), however, the difference is not statistically significant. accordingly, the coefficient is larger in the case of exports to low income countries (1.3% as opposed to 0.09% for high income countries), although also in this case the difference is not statistically significant. as expected, exports to low income countries are therefore more sensitive to price competiton. table 3. regression results. variable total (1) made in italy (2) others (3) high income countries (4) low income countries (5) coeff. s.e. sig. coeff. s.e. sig. coeff. s.e. sig. coeff. s.e. sig. coeff. s.e. sig. δln(exportijt-1) -0.346 *** -0.235 ** -0.305 *** -0.194 -0.214 * 0.084 0.105 0.117 0.147 0.123 δln(importijt) 0.366 *** 0.399 *** 0.335 *** 0.394 *** 0.326 *** 0.020 0.032 0.028 0.030 0.029 δln(importijt-1) 0.158 *** 0.139 *** 0.111 *** 0.096 * 0.123 *** 0.030 0.038 0.045 0.056 0.042 δln(auvexpijt) -0.107 *** -0.085 -0.106 *** -0.092 *** -0.135 *** 0.021 0.055 0.026 0.031 0.039 δln(auvimpijt) 0.097 *** 0.097 *** 0.086 *** 0.091 *** 0.144 *** 0.020 0.032 0.027 0.027 0.038 ln(exportijt-1) -0.117 *** -0.126 *** -0.133 *** -0.145 *** -0.045 0.028 0.049 0.046 0.048 0.041 ln(importijt-1) 0.074 *** 0.094 *** 0.081 *** 0.097 *** 0.027 0.018 0.037 0.029 0.033 0.020 ln(auvexpijt-1) -0.105 *** -0.057 -0.114 *** -0.123 -0.051 * 0.022 0.044 0.030 0.038 0.031 ln(auvimpijt-1) 0.088 *** 0.068 * 0.078 *** 0.111 * 0.030 0.023 0.040 0.028 0.038 0.031 no. observations 38,496 15,202 23,294 29,259 7,743 hansen test (p-value) 0.05 0.18 0.05 0.08 0.04 the dependent variable is the rate of growth of exports. estimates are conducted using the two-steps, orthogonal, system estimator of the xtabond2 stata routine by david roodman (2009), that follows the arellano and bond (1991) gmm methodology; lags 5 to 7 of δln(exportijt-1) and of ln(exportijt-1) are used as instruments; standard errors are robust to heteroskedasticity; *** indicates significance at the 1% level, ** at the 5% level and * at the 10% level. 65italian agri-food exports in the international arena in the case of the elasticity with respect to average auvs of imports of the trade partners, the average short-run elasticity is relatively low (0.09), implying that in the shortrun italian exporters are relativley shed from price competition coming from their foreign competitors. looking at the disaggregated coefficients, we see that the results are consistent with the previous findings, with mii products ready to get advantage of competitors’ price increases and with low income countries more sensitive to prices. consistent with economic theory, long-run elasticities are higher than instant and short-run elasticities. table 4 shows that a 10% increase in world import agri-food products determines a raise in italian exports of 6.3%. a high value, although smaller than unity, indicating that italian shares are declining in the long-run. as expected, the elasticity in the case of mii agri-food products is higher than the one of the other products, and in this case the difference between the two values is statistically significant at the 1% level. once again, this is evidence of the ability of mii to better follow world demand. a similar result applies to long-run elasticity to import avus when separating high income countries and low income countries. the former value being well above the second with statistically significant difference. long-run elasticities with respect to italy’s export auvs are higher for low income countries than for high income countries, while the opposite is true for the average import auvs of trade partners. elasticities to export’s and import’s auvs are also lower for mii agri-food products. the elasticities presented so far are average values. however, we are interested in differences across products, depending on their capacity to match consumers’ needs and table 4. elasticity of italian exports of agrifood products. elasticities total made in italy (a) others (b) difference (a) vs. (b) low income countries (c) high income countries (d) difference (c) vs. (d) β2 instant to import demand 0.37 *** 0.40 *** 0.34 *** 2.26 0.32 *** 0.39 *** 3.83 ** β2 + β3 short-run to import demand 0.52 *** 0.54 *** 0.45 *** 1.34 0.45 *** 0.49 *** 0.14 β4 short-run to export auv -0.11 *** -0.08 -0.11 *** 0.12 -0.13 *** -0.09 *** 1.50 β5 short-run to import auv 0.09 *** 0.10 *** 0.09 *** 0.07 0.14 *** 0.09 *** 0.68 -β7/β6 long-run to import demand 0.63 *** 0.74 *** 0.60 *** 12.11 *** 0.61 *** 0.67 *** 1.21 -β8/β6 long-run to export auv -0.89 *** -0.45 ** -0.85 *** 0.08 -1.13 ** -0.84 *** 0.10 -β9/β6 long-run to import auv 0.75 *** 0.54 *** 0.59 *** 3.18 * 0.68 0.77 *** 0.81 the colums difference reports the values of the chi-squared test for differences between the coefficients estimated in the two samples; *** indicates significance at the 1% level, ** at the 5% level and * at the 10% level. source: our elaborations on un-comtrade data. 66 a. carbone, r. henke, a.f. pozzolo the degree of market power gained in different sectors and countries. to gauge a sense of these differences, we have estimated the econometric model of section 3 separately for each mii agri-food product. figure 2 presents the long-run elasticities of italian exports of each mii agri-food product with respect to: i) the total import of that same product by our 48 major trade partners; ii) the export auvs; iii) auvs of imports of the same product by our main trade partners6. 6 in the case of few products, econometric estimates did not provide statistically and economically significant results; for this reason we have decided to drop them from this and the following figures; results of regressions for each product are available from the authors upon request. table 5. prody index for the made in italy agri-food products. products ($) ranking ($) ranking ($) % herborinated cheese lq* 27,759 1 47,196 1 19,437 70.0 grated cheese 19,988 16 40,636 2 20,649 103.3 processed coffee 19,481 17 34,534 3 15,053 77.3 fresh cheese 26,754 2 34,209 4 7,455 27.9 fresh pasta 20,548 14 33,422 5 12,874 62.7 other cheese 26,742 3 30,669 6 3,927 14.7 sparkling wine lq** 10,732 29 27,592 7 16,860 157.1 chocolate products 24,133 5 27,254 8 3,121 12.9 confectionery products 21,070 10 27,497 9 6,427 30.5 bakery products 24,897 4 27,216 10 2,320 9.3 meat cuts 20,086 15 26,350 11 6,263 31.2 sauces and other condiments 21,808 9 25,873 12 4,065 18.6 ice creams 23,136 7 24,994 13 1,858 8.0 virgin olive oil 19,314 18 24,045 14 4,731 24.5 apples, kiwi and pears 23,520 6 22,906 15 -614 -2.6 fruit juice 14,859 22 21,479 16 6,620 44.6 vermouth lq** 18,636 19 20,118 17 1,483 8.0 fresh tomatoes 22,971 8 19,409 18 -3,563 -15.5 fresh vegetables 17,284 20 18,358 19 1,074 6.2 mixed olive oil lq* 10,203 30 17,782 20 7,579 74.3 canned tomatoes lq** 15,013 21 16,818 21 1,805 12.0 non virgin olive oil lq* 20,921 12 16,802 22 -4,119 -19.7 prepared vegetables lq** 13,666 24 16,583 23 2,917 21.3 wine <2lt lq* 20,606 13 15,827 24 -4,780 -23.2 mineral water lq** 13,202 26 15,810 25 2,608 19.8 dry pasta lq* 13,938 23 14,201 26 263 1.9 wine>2 lt lq** 10,915 28 13,346 27 2,431 22.3 prepared fruit lq** 12,406 27 11,555 28 -851 -6.9 grapes lq** 13,395 25 10,237 29 -3,159 -23.6 processed rice 21,028 11 6,065 30 -14,963 -71.2 variation2010-111996-97 source: our elaborations on un-comtrade and world bank data. *low quality products at 2010-11. ** low quality products both at 1996-97 and at 2010-11. 67italian agri-food exports in the international arena with respect to the ability of mii exports to satisfy an increase in foreign demand, data show that the majority of the products are capable to take advantage from an increase in clients demand. with the exception of blue cheese (that shows a negative value, probably due to some export dynamics that are not adequatly captured by the econometric specification), all other products show elasticities ranging from slightly below 0.5 for sauces and other condiments to values above unity for ice creams, mixed olive oil and nonvirgin olive oil, fruit juices, vermouth, sparkling wine and wine in large bottles. in these sectors, exporters are therefore able to successfully exploit the long-run dynamics of foreign demand. the smallest values are, instead, those of processed rice, virgin olive oil and of processed coffee. in the long-run, many items show negative elasticities to exports auvs, consistent with the hypothesis that market power of exporting firms becomes weaker over time, because buyers can change their consumption habits and other exporting countries may adopt more aggressive pricing strategies. products that suffer most from a long-run increase in their export price are virgin olive oil, processed rice and dry pasta, followed by all cheeses, but grated ones. nevertheless, four products show a statistically and economically significant positive elasticity: mixed olive oil, non-virgin olive oil, canned tomatoes and vermouth. in the whole, this measure shows much more mixed patterns across products compared with the previous one. finally, also the long-run elasticities of italian mii agri-food exports, with respect to competitor’s auvs, show quite different values depending on the product considered. figure 2. long-run elasticity of italian exports. 68 a. carbone, r. henke, a.f. pozzolo for many products, the estimated elasticity is positive and larger than unity, indicating a strong ability of italian exporters to take advantage from any increase in the price of competitors and, conversely, the risk of loosing market shares if they reduce their prices. in the long-run, the elasticites are particularly high for non-virgin olive oil, prepared vegetables, cheeses, excluding blue cheese, and ice creams. although less common, negative values are recorded for: processed coffee, fresh fruit and vegetables (excluding grapes), mixed olive oil, vermouth and sparkling wines. last, it is worth to pinpoint that further unreported results show that, although the relationship between the long-run elasticities of italy’s export auvs and import auvs from our competitors is, as expected, negative and statistically significant (since a rise in export auvs has the same effect of increasing relative prices as a reduction of our competitiors’ auvs), it is not particularly strong. similarly weak is the relationship between the shortand long-run elasticities to export and import avus. finally, the long-run elasticity of exports with respect to import and export avus shows no correlation with the incidence of each product on total exports at constant prices. in other words, contrary to what one might expect, the elasticities with respect to avus are not higher in absolute value for the products that represent a significant share of our mii agri-food exports. in a nutshell, our results show that although italian exporters of mii agri-food products enjoy some degree of market power, price competition remains a relevant issue, suggesting the existence of a significant trade-off between strategies based on quality, brand reputation and so forth, on the one side, and strategies basically focused on prices, on the other side. 4.2. the sophistication level of the made in italy exports as underlined in the methodological section, the prody index is a measure of the sophistication level of an exported good. table 3 shows, for agri-food mii exports, the values of the index as well as the rankings built upon these values and the absolute and percentage variations of the index in the period under study. focusing on the prody values at 2010-11, it is easy to see that the range covered is quite wide, spanning from a maximum of about 47,000 usd for blue cheese, to a minimum of about 6,000 usd for processed rice. although mii products embrace almost the entire range of prody values, it is important to underline that mii exports are predominantly located in the upper half of the distribution (>15,000 usd). in other words, this means that mii mostly includes agri-food items that are quite sophisticated relative to other agri-food exports. the market segments in which these products are competing are diverse but, overall, high quality and highly differentiated; conversely, there are also some products for which price competition is relatively more important. it is interesting to highlight that at the top of the ranking there are highly processed products such as cheese, bakery, sparkling wines, chocolate products, confectionery, processed coffee, and others: all products for which branding, packaging and market segmentation are all cues of competition. on the contrary, at the bottom of the ranking there are less processed, simpler products such as preparation of fruit and vegetables, fresh fruit, canned tomatoes, olive oils, wine, processed rice, for which sophistication seems to be a less important key to compete in the world markets. 69italian agri-food exports in the international arena fully consistent with the observed sophistication ranking, at the bottom of this distribution there are many products for which the italian exports are classified as low quality according to their auv (see section 3). these are, overall, 8 products with auvs below the world median for the entire period (prepared vegetables, canned tomatoes, grapes and prepared fruits, and also mineral water wine> 2 lt., sparkling wine and vermouth); plus 5 products whose auvs were above the world median at the beginning of the period and fell under this value at the end of the period (dry pasta, wine< 2 lt., non-virgin olive oil and mixed olive oil, and blue cheese). the tendency of italian exports to reach world markets at low prices for those products that compete on low sophisticated market segments indicates that italy is somehow catching up the kind of competition that characterize more the market for these products. looking at the variations of the prody index, the first evidence is that there is a majority of positive signs (23 products) while for 7 products the level of sophistication reduced over the period. among the latter there are: processed rice, grapes and tomatoes, processed fruits, non-virgin olive oil and wine in bottles with less than 2 litres. due to this reduction, these products fall in the lower part of the distribution where the role of lower income countries is increasing and, thus, price competition is more intense and remuneration of inputs tends to be lower. on the other side, the products that met the major increase of the prody index climbed many positions on the sophistication ranking and are ready to engage competition on quality attributes that better reward inputs. 4.3. export elasticities and changes in prody index the very different values of the elasticity of italian mii agri-food exports to world demand are due to different factors, such as the degree of substitutability among similar goods in the food consumption basket, the quality of our products, constraints on the supply side and the market strategies of producers, the market power of exporting companies. these features may also drive changes over time in the prody indeces. indeed, both elasticities and sophistication deal somehow with the nature and intensity of competition: the first refers to the country’s exports, while the second refers to overall world exports. it is then interesting to merge the two measures in order to obtain a unified view of the impact of world markets trends for mii goods and of italy’s position in those markets. this is done comparing the values of the long-run elasticities with the rate of change of the prody index (figures 3-5). at first sight, there seems not to be a simple relationship between these two variables: the coefficient in a cross-section regression is not statistically significant on all the three cases considered. however, dividing each figure into four quadrants, depending on whether the prody index has grown or decreased over the sample period and on whether each elasticity is greater or smaller than unity in the case of the elasticity of imports to world demand – or grater or smaller than zero in the case of elasticity to auv –, we obtain a more nuanced picture where italian exports performance is compared to global tendencies. as we have already mentioned earlier, an increase of the prody index indicates that competition in world markets increasingly relies on quality and product/process attributes, and less on price. clearly, a reduction indicates the opposite trend, where price competition becomes more pressing. 70 a. carbone, r. henke, a.f. pozzolo figure 3 shows that most of the products that have registered an increase of their prody index show a long-run elasticity of exports with respect to world demand that is smaller than one (e.g., processed coffee, grated cheese, but also confectionery and chocolate products, fresh pasta, meat cuts and virgin olive oil). this means that, while world demand for these products is increasingly sophisticated and therfore should allow higher price/ cost margins, italian exports are not fully responsive to these trends and, hence, italian firms are unable to fully benefit from the opportunities arising in international markets. of course, an elasticity lower than one implies at the same time that italian exporters are better shielded from the negative consequences of contractions of world demand. for other products (i.e., mixed olive oil, ice creams, sparkling wines and wine in large bottles, fruit juice and canned tomatoes) the increase in the degree of sophistication is associated instead with a high elasticity with respect to the demand for imports. in these cases, italian exporters are able to exploit the opportunities that come from foreign markets, although this clearly implies that they are more severly affected during downturns. coming to the elasticity of demand wih respect to changes in the auvs of exports (figure 4), this is negative for the majority of mii agri-food exports, showing that italian exporter have narrow margins to increase their prices. in other words, despite the high reputation commonly tributed to mii, many of these products are definitely price sensitive. however, it is worth to pinpoint that there is a group of items whose demand is less price sensitive even in the long run, among these mixed olive oil, canned tomatoes, vermouth, confectionery, fresh pasta and meat cuts. the fact that most of the products that show a rise in their prody index also have an elasticity of exports with respect to the auvs of imports from our competitors that is larger than zero (figure 5) confirms that, even in the higher market segments, price competition is relevant in the long term and that italian goods not only suffer from other countries’ competition but it is also able to take advantage from competitors’ lack of price competitiveness. finally, some products show very perculiar market trends. canned tomatoes, for example, have registered a strong increase of the prody index and at the same time show a high long-run elasticity with respect to export auvs, a trend that is consistent with a progressive switch towards higher quality and, at the same time, a good export performance. on the contrary, in the case of processd rice, the reduction of the prody index associated with a low elasticity with respect to both the foreign demand and auvs suggests that the overall performance is likely to depend on a qualitative mismatch between the italian product attribute specification and the world demand major trends. 4.4 further insights on two important italian export sectors: wine and olive oil in this subsection we focus on two sectors, the olive oil and the wine sectors. these are of major importance for italian agro-food exports and help in getting a sound idea of the useful insights that can be derived from the joint methodology proposed in the paper. in our dataset the olive oil sector is represented by three lines of exports: virgin olive oil, non-virgin olive oil and mixed olive oil. among these, the first features higher intrinsic quality and is more rooted in the place of production. the analysis shows that: competition on international markets for this product is increasingly based on sophis71italian agri-food exports in the international arena figure 3. changes in the prody index and long-run elasticity of italian exports to world demand. sparkling wine, processed rice, fresh tomatoes and blue cheese have been removed from the gaph as they are outliers figure 4. changes in the prody index and long-run elasticity of italian exports to export auvs. sparkling wine, processed rice, fresh tomatoes and blue cheese have been removed from the gaph as they are outliers 72 a. carbone, r. henke, a.f. pozzolo tication; italy acts in the higher layer of the market (auv is high) where it has high and increasing revealed competitive advantages. besides, long-run elasticities show that the italian product is not well able to benefit entirely from the positive trends in world demand and, also, that it is affected from the concurrence of other countries both when its own exports price increases and when facing changes in the competitors’ prices. thus, virgin olive oil well represents the general trends pinpointed for the whole mii. on the contrary, non-virgin and mixed olive oils occupy less sophisticated market segments, with the former facing a decline of the sophistication index in the time span observed, while the latter records an improvement. also the positioning of italian export flows in these two sectors is different from what we have seen for virgin oil: in this case auv has shifted below the world median and the rca are smaller and declining even if the value of the long-run elasticity of exports to world demand is higher than what we have found for the virgin olive oil. we interpret this as a consequence of the more stringent conditions on the supply side for virgin olive oil than for the other two product types. also different is the behaviour of exports with respect to auvs: for both, direct price elasticity is positive, indicating the capability to face price increase with a smaller reduction of sales. this is especially true for mixed olive oil where branding is increasingly important; as it is also witnessed by the mentioned improvement in the sophistication ranking. the same line of interpretation may be applied to the negative values of cross-price elasticity for the mixed olive oil for which strong market segmentation seems to reduce substitutability. summing up, the analysis made it clear that these items represent three well distinct segments in the world’s market both from the consumers’ perspective and the supply side and also that italy plays different roles and shows different performances in each of them. figura 5. changes in the prody index and long-run elasticity of italian exports to import auvs. sparkling wine, processed rice and fresh have been remouved from the gaph as they are outliers 73italian agri-food exports in the international arena another example of the potential of the joint methodology when looking at trade dynamics is provided by the wine sector. also in this case the data set distinguishes three different export flows: wine in small bottles (< 2lt), wine large in bottles (>2 lt) and sparkling wines, the first being by far the most important for italian agri-food trade balance (16.4% of mii exports) with italy showing high rcas for the three of them. the three sectors are connected in different respects at the production level via reputational links, scope economies and, in many cases, by joint production. world markets for these products seem to be influenced by different determinants in the observed time span, with competition driven by higher and increasing sophistication in the sparkling wine market; a prevalence of price competition for wine in large bottles and a shift from more to less sophisticated markets for wine in small bottles. in such a complex global arena italy is a major player both for the quantity sold and the quality of its products. actually, the low level of the auvs of its exports in these cases is to be regarded as the mere consequence of its very high shares of world supply. more significant here are the elasticity values that confirm the different behaviours of the three products. indeed, as expected to some extent, wine in large bottles, that is a rather bulk production, shows the highest value of demand elasticities as a consequence of looser constraints on the production side that allow for easier adjustments to expansion of clients’ demand. the values of price elasticities, both direct (positive) and cross (negative), for sparkling wine seems to indicate the behaviour of a luxury good signalling a higher status and for which consumers are willing to pay more. this is just the opposite of what holds for the other two wine categories. 5. conclusions the paper assesses the export performance of the so-called made in italy agro-food products. our results show that world markets for mii products are characterized by high quality and sophisticated attributes and that these increased over the observed time span. these markets seem to remunerate better mii exports, which seem to be also growing at a constant pace, allowing italy to be a leading global competitor in world supply, as it is also confirmed by its high revealed comparative advantages. the general picture stemming out from the elasticity analysis seems also good, with mii exports that are able to at least partially follow world demand and enjoying quite stable share even in presence of rising prices and increased competition by competitors. nonetheless, it has been also highlighted that these exports show only a slightly better performance on more rewarding markets of high income countries with respect to nonmii exports. furthermore, some specific weaknesses are depicted by the product specific analysis. actually, for some products, quality is still too low to be able to provide strong competitive advantages; while for others, in spite of the high quality of our exports, italy is unable to defend its world market shares. among the 30 sectors that form the mii, 8 have an auv below the world median and another 5 switched from the upper half of the auv distribution to the lower half. furthermore, the small values of the long-run elasticities of exports to world demand indicates that italian agri-food exports are so far on a declining trend as these are not able to fully catch up with the expanding demand, even though the mii products do better. in addition to that, both direct and cross price elasticities show that even in these partially 74 a. carbone, r. henke, a.f. pozzolo competitive markets where product quality is key, price competition is still important and affects the competitive dynamic although with varying intensity. all in all, the methodology suggested, that combines the traditional elasticity analysis with the newer “sophistication” approach, has been able to capture product specific behaviours and relevant discrepancies between the tendencies on world markets and the country trends. this is clearly shown by the cases of canned tomatoes and processed rice. where the strong increase of the prody index and the high long-run elasticity with respect to export auvs, for the first indicate a progressive switch towards higher quality and, a good performance of italian export. on the contrary, the reduction of the prody index associated with a low elasticity with respect to both the foreign demand and auvs, in the case of processd rice, suggests the italian product is not able to adjust the world demand major trends. the in-depth line of reasoning on the different market positioning and exports performance for wine and olive oil exports provided an example on the potential of the combined methodology proposed that allows for comparisons that are revealing of trends that cannot emerge neither from more aggregate analysis nor from analysis that focuses only on one product at a time. acknowledgments we gratefully acknowledge coldiretti and gruppo 2013 for providing financial aid for carrying out the study on which this article is based.we also wish to thank fabrizio de filippis, coordinator of gruppo 2013, and the anonimous referees of the review for their comments to a previous version of the text. references adler, j.h. 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(1996). the demand for imports and exports in the us: a survey. journal of economics and finance 20: 147-178. bio-based and applied economics 4(3): 235-259, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-16368 the evaluation of ecosystem services production: an application in the province of ferrara parthena chatzinikolaou1,*, davide viaggi1, meri raggi2 1 department of agricultural sciences, university of bologna, italy 2 department of statistical sciences, university of bologna, italy date of submission: june 30th, 2015, accepted november 4th, 2015 abstract. this paper presents an evaluation of the provision of ecosystem services (es). the analysis is based on the design of a framework suitable to be translated into a multi-criteria evaluation process, followed by empirical testing. it focuses on the different categories of es and applies a set of non-overlapping indicators available from existing statistical sources. the framework is applied in a traditional cultural landscape, the province of ferrara, situated in the emilia-romagna region of italy. to develop an applicable framework, we have chosen a set of es indicators from the millennium ecosystem assessment. according to the results and based on the indicators used in each category, the provision of cultural and provisioning services is high in all of the municipalities, while there is greater diversity in the provision of regulating and supporting services. a key challenge in our analysis was related to the lack of information on the actual provision of es at the municipality level, which led to a significant use of proxy indicators. use of improved datasets, explicit consideration of policy scenarios and accounting for local priorities about es provision have been identified as the most relevant avenues for future research in this area. keywords. ecosystem services, evaluation, indicators, multi-criteria analysis, classification jel codes. q57, c38 1. introduction the ecosystem concept describes the interrelationships between living organisms and the non-living environment. “an ecosystem is a dynamic complex of plant, animal, and microorganism communities and the non-living environment interacting as a functional unit” (mea, 2005). there is a full range of ecosystems, from natural forests, to ecosystems managed and modified by humans, such as agricultural land. ecosystems provide a variety of benefits to people that are divided into market and non-market ecosystem goods or ecosystem services (es) and classified in multiple ways. * corresponding author: parth.chatzinikolao2@unibo.it 236 p. chatzinikolaou, d. viaggi, m. raggi the millennium ecosystem assessment framework (mea, 2003) identified four categories of es: (1) provisioning services, such as food and water; (2) regulating services, such as flood and disease control; (3) supporting services, such as nutrient cycling; and (4) cultural services, such as spiritual, recreational, and cultural benefits. following the economics of ecosystems and biodiversity (teeb, 2010a), es are the direct and indirect contributions of ecosystems to human well-beings and are also categorized into four types: provisioning, regulating, habitat and cultural services. a new classification of es is currently under development at the international level by the common international classification of ecosystem services (cices, 2013). according to cices, there are three types of services: provisioning, regulation (maintenance), and cultural services. the concept of es is being integrated into current biodiversity policies at the global and european levels (ec, 2009; perrings et al., 2011). the eu has adopted an ambitious strategy to halt the loss of biodiversity and es by 2020 (ec, 2010b, 2011). there are 6 main targets, and 20 actions to help europe reach its goal. target 2 focuses on maintaining and enhancing es and restoring degraded ecosystems by incorporating green infrastructure into spatial planning. improved ways and methods for es quantification, mapping and assessment are needed to investigate the number and quality of es produced by individual ecosystems and to increase the ability to feed such knowledge into policy design (teeb, 2010b). while provisioning es can often be directly quantified thanks to the availability of primary data, for other ecosystem services the collection of such information is often impossible (maes et al., 2015). thus, for most regulating, supporting, and cultural services, researchers must rely on proxies for their quantification. as a result, altogether, data on quantifiable es remain limited and only a small number of indicators are being used for those that cannot be measured directly (feld et al., 2010, 2009; layke et al., 2012). reviews of indicators used for es are now available from the literature and contribute to developing reliable indicators for modelling, as well as for bridging current data gaps (cowling et al., 2008; egoh et al., 2012)few studies are embedded in a social process designed to ensure effective management of ecosystem services. most research has focused only on biophysical and valuation assessments of putative services. as a mission-oriented discipline, ecosystem service research should be user-inspired and user-useful, which will require that researchers respond to stakeholder needs from the outset and collaborate with them in strategy development and implementation. here we provide a pragmatic operational model for achieving the safeguarding of ecosystem services. the model comprises three phases: assessment, planning, and management. outcomes of social, biophysical, and valuation assessments are used to identify opportunities and constraints for implementation. the latter then are transformed into user-friendly products to identify, with stakeholders, strategic objectives for implementation (the planning phase. several studies have assessed changes in land use and their connection with the provision of es (carreño et al., 2012; fontana et al., 2013; silvert, 2000). in many cases, their output includes environmental and land use information that are connected to landscape features, although few yield a direct assessment of changes in es provision (burkhard et al., 2012; swetnam et al., 2011). according to de groot et al. (2010), es approaches and es valuation efforts have changed the terms of discussion on nature conservation, natural resource management, and other areas of public policy. these efforts have strengthened 237the evaluation of ecosystem services production both public and private sector development strategies and improved environmental outcomes (de groot, 2006; de groot et al., 2002). the multidimensional logic of es seems highly consistent with approaches based on multi-criteria decision analysis (mcda). mcda is a general framework for supporting complex decision-making situations with multiple and often conflicting objectives. regarding es evaluation, mcda methods have been applied as decision support systems that integrate economic and noneconomic values (newton et al., 2012), used as approaches for cost-benefit analysis (wegner and pascual, 2011), or as a methodological framework for addressing value dimensions related to es (mendoza and prabhu, 2003). oikonomou et al. (2011) proposed a conceptual framework that combines ecosystem function analysis, multi-criteria evaluation and social research methodologies for introducing an ecosystem function-based planning and management approach. ananda and herath (2009) provided a review of research contributions on forest management and planning. the objective of this paper is to evaluate the provision of ecosystem services focusing on the different categories of es and applying a set of non-overlapping indicators available from secondary data sources. the approach used is based on the outranking method preference ranking organisation methods for enrichment evaluations (promethee). to evaluate the provision of ecosystem services, we used a traditional cultural landscape, the province of ferrara, situated in the emilia-romagna region and a set of es from the millennium ecosystem assessment framework as criteria for the evaluation. the area consists of 26 municipalities, comprising the urban centre of ferrara and adjoining agricultural lands within the ancient and vast po river delta. the area is characterised by historicalcultural locations, the surrounding landscape and protected areas of natural importance. in the present study, along with the application of promethee ii, a comparison and ranking of the 26 municipalities of the province is performed, based on the selected es indicators. different studies have used ranking approaches as a tool to evaluate es. at times those tools are used as part of a larger es assessment process that can involve simultaneously identifying es and drivers of change, as well as ranking the most important services (lópez-marrero and hermansen-báez, 2011; shelton, 2001). the model assumes that the criteria are equally important, i.e. they did not use any weighing approach to reflect the relative preferences of decision makers. the structure of this paper is as follows: the following section describes the promethee multi-criteria analysis framework; in the third section (application), the case study and the selected es indicators are introduced. in the fourth section, we present the results of the application of the promethee method for the evaluation of the provision of the es in the province of ferrara. in the discussion sections, the key challenges as well as difficulties and limitations in our analysis are summarized, followed by a final concluding section. 2. methodology among the various mcda methods and the different software applications available, we used promethee ii, which applies the outranking method and provides a complete ranking of a discrete set of possible alternatives, from the best to the worst, using the concept of net flow (brans and mareschal, 2005; brans and vincke, 1985). it is well adapt238 p. chatzinikolaou, d. viaggi, m. raggi ed to problems where a finite number of alternatives are to be ranked whilst considering several and sometimes-conflicting criteria (brans et al., 1998). in addition, the mathematical model is relatively easy to understand and is capable of determining preferences among multiple decisions (vinodh and jeya girubha, 2012). promethee, unlike other outranking methods, does not aggregate good scores on some criteria and bad scores on others (it is non-compensatory), uses less pairwise comparisons, does not have the artificial limitation of rigid scoring systems (e.g. the use of a 9-point scale for evaluation) and allows more flexibility in the determination of the weights (albadvi, 2007). a considerable number of successful applications has been treated by the promethee methodology in various fields such as banking, manpower planning, water resources, investments, medicine, chemistry, health care, tourism, and dynamic management (andreopoulou et al., 2011, 2009; behzadian et al., 2010, 2013; olson, 2001; olson et al., 1998). wolfslehner et al. (2011) based on a promethee ii algorithm, calculated relative sustainability impact rating. moreover, madlenera et al. (2007) used the promethee method to compare and rank different energy scenarios according to 16 economic, social, environmental, and technological criteria. regarding es evaluation and assessment, segura et al. (2015) applied a promethee-based method to obtain new composite indicators for provisioning, maintenance and ‘direct to citizen services’. fontana et al. (2013) have also used promethee to compare land use alternatives considering es as criteria. queiruga et al. (2008) applied promethee to rank spanish municipalities according to their appropriateness for the installation of waste electrical and electronic equipment recycling plants. moreover, vaillancourt and waaub (2004) used promethee to rank regions in order to allocate greenhouse gas emission rights. chatzinikolaou et al. (2013) applied promethee for the comparison and ranking of eu rural areas based on social sustainability indicators. hermans et al. (2007) used promethee to evaluate river management alternatives and elicit preferences to rank and compare individual and group preferences. promethee has also been used in environmental management for ranking and selecting environmental projects (yan et al., 2007) and environmental impact assessments for ranking waste management alternatives and air quality/emission problems (huang and wang, 2014). for the implementation of the method the following procedure is recommended. 2.1 problem definition the procedure proposed by brans et al. (1986) starts by considering the multi-criteria problem (1): max {f1(a),…fk(a),\ a ∈ k} (1) where k is a finite set of actions and fi,i = 1,…k, are k criteria to be maximized. the promethee methods include two phases (roy, 1991): • the construction of an outranking relation on k, • the exploitation of this relation in order to provide an answer to (1). in the first phase, a valued outranking relation based on a generalization of the notion of criterion is considered: a preference index representing the preferences of the alterna239the evaluation of ecosystem services production tives is defined. the exploitation of the outranking relation is realised by considering a positive and a negative flow for each action. 2.2 identification of alternatives the procedure is carried out by choosing among different elements to be examined and assessed using a set of criteria. these elements are called actions or alternatives. in the present study, the “alternatives” to be examined and evaluated are the 26 municipalities of the province of ferrara. in this sense, the concept of an alternative is used to identify different objects to compare rather than reciprocally excluding courses of action. 2.3 defining a set of criteria. the criteria represent the tools that enable alternatives to be compared from a specific point of view. the alternatives are compared pairwise under each criterion. two alternatives a and b, can express an outright preference, a weak preference or indifference. in the present study, criteria are represented by a set of es indicators, which are presented in the next section. 2.4 evaluation matrix once the set of criteria and the alternatives have been selected, then the payoff matrix is built. this matrix tabulates, for each criterion–alternative pair, the quantitative and qualitative measures of the effect produced by that alternative with respect to that criterion. 2.5 determining the multi-criteria preference index the preference structure of promethee is based on pairwise comparisons. the preference index, for each pair of alternatives a, b ∈ k (where k is the set of alternatives) ranges between 0 and 1. the higher it is (closer to 1), the higher the strength of the preference for a over b. when the pairs of alternatives a and b are compared, the outcome of the comparison is expressed as follows: • p(a,b) = 0 means indifference between a and b, or no preference of a over b; • p(a,b) ~ 0 means a weak preference of a over b; • p(a,b) ~ 0 means a strong preference of a over b; • p(a,b) = 1 means a strict preference of a over b; h(d) is an increasing function of the difference d between the performances of alternatives a and b on each criterion and d is the deviation between the evaluations of two alternatives on each criterion (2) (vincke, 1992). h d( ) = p a,b( ) ,d ≥ 0, p b,a( ) ,d ≤ 0. ⎧ ⎨ ⎪ ⎩⎪ (2) 240 p. chatzinikolaou, d. viaggi, m. raggi 2.6 weighting once the preference function pi (i= 1,2,3,…n represent the criteria) is defined, the weights of each criterion must be determined. the weights π represent the relative importance of the criteria used, if all criteria are equally important then the value assigned to each of them will be identical (hermans and erickson, 2007). the multi-criteria indicator of preference π(a,b) which is a weighted mean of the preference functions p(a,b) with weights πi for each criterion, express the superiority of the alternative a against alternative b after all of the criteria are tested. the values of π(a,b) are calculated using the following equation (brans and mareschal, 2005) (3): ∑ ∑ π π ( ) ( ) π = = = a b p a b , , i k i i i k i 1 1 (3) in the present study, the shape of the h(d) function selected is the gaussian form (koutroumanidis et al., 2002) (4): h(d) = 1 exp{-d2/2σ}2 (4) where d is the difference among the alternatives a and b [d = f(a) = f(b)] and σ is the standard deviation of all differences d and for each criterion. the model simulates 50 scenarios of weights and on each scenario of weights ten scenarios on the standard deviation of s distribution of gauss. the ten scenarios σ oscillate from 0.25 s until 2.5 s with step 0.25 s, where s the standard deviation of all differences d for each criterion. the model formulates 500 different net flows for each alternative and calculate the medium value (mareschal and brans, 1991). 2.7 ranking the alternatives the traditionally non-compensatory models include some for which the preferences are aggregated by means of outranking relations. the ranking of alternatives under promethee uses the positive flow (5), which indicates the preference of the alternative a above all others, the negative flow (6) that indicates the preference of all of the alternatives compared with the alternative a, and the net outranking flow (7), which is the balance (difference) between the positive and the negative flows. φ+(a) = σb∈k π(a,b) (5) φ-(a) = σb∈k π(b,a) (6) φ(a) = φ+(a) φ-(a) (7) 241the evaluation of ecosystem services production 3. application in the province of ferrara 3.1 case study the study area is the province of ferrara, located on the eastern side of the emiliaromagna region. it is composed of 26 municipalities covering an area of 2,632 km2 and a total population of about 359,000 (table 1). extending to the po river delta, the province offers sceneries of rare charm and contains important natura2000 sites: the river po delta of the only true delta in italy and contains a complex national wetlands system. the natura 2000 site’s important coastal habitats and water bird species are under threat from the eutrophication of the lagoon waters, due to the accumulation of underwater vegetation. the regional park of the po delta is part of a system of the protected areas in the region. the park is divided into six “stations” around the southern area of the po delta, which are characterised and differentiated by particular environmental and landscape features. all of the areas are characterised by a wonderful natural environment which have led to the development of human activities such as fishing, agriculture, tradition, culture and art. agriculture and trade are the most important sectors in the area, followed by building and industry. the main environmentrelated activities are connected to habitat restoration and conservation, species protection habitat, management of selected critical areas and the elaboration of development plans (marino et al., 2014). the rural development plan (rdp) of the emilia-romagna region has proposed different measures that contribute to the preservation of landscapes and focus on the delivery of ecosystem services. specific rdp amendments include reinforced efforts contributing to water management, restructuring of the dairy sector, improved broadband internet infrastructure in rural areas, biodiversity, climate change mitigation and adaptation. furthermore, thanks to this policy action the park is improving agriculture in a positive and sustainable manner, e.g. organic production. since reclaimed lands have replaced the wetlands, agriculture has replaced the typical landscape elements (marshes, pine woods) with large extensions of embankments and water channels (viaggi et al., 2014). table 1. municipalities in the province of ferrara code territory population x1 argenta 22,087 x2 berra 5,088 x3 bondeno 14,864 x4 cento 35,444 x5 codigoro 12,337 x6 comacchio 22,428 x7 copparo 16,943 x8 ferrara 131,842 x9 formignana 2,802 x10 goro 3,879 x11 jolanda di savoia 3,016 x12 lagosanto 4,978 x13 masi torello 2,344 x14 massa fiscaglia 3,543 x15 mesola 7,092 x16 migliarino 3,670 x17 migliaro 2,225 x18 mirabello 3,420 x19 ostellato 6,462 x20 poggio renatico 9,770 x21 portomaggiore 12,190 x22 ro 3,380 x23 sant’agostino 7,052 x24 tresigallo 4,553 x25 vigarano mainarda 7,491 x26 voghiera 3,823 source: istat and own elaboration 242 p. chatzinikolaou, d. viaggi, m. raggi 3.2 selection of ecosystem service indicators identifying consistent, quantifiable and comparable indicators supports the development of models and evaluation of es. determining what to measure and what method to use is directly related to the availability of data and the type of indicator. however, mainstreaming es concepts more broadly will require information designed for policymakers, including data, decision support tools, and “indicators” – information that condenses complexity to a manageable level and informs decisions and actions (bossel, 2002). although global indicators provide an overview that allows for a regional or national scale analysis, in many cases there is limited information available. the demand for ecosystem services is increasing in many european countries, yet there is still a scarcity of data on values at regional scale (gatto et al., 2013). as a result, proxy indicators are often used as surrogates. proxy methods are especially used for cultural services, as these services are difficult to directly measure and model. yet there are limitations to their use. several reviews have tried to assess and summarize the use of indicators to provide information (feld et al., 2009; layke et al., 2012; van zanten et al., 2014). moreover, egoh et. al. (2007) provided an extensive literature review of studies, excluding sub-global assessments, and identifying es indicators. the selected es indicators in the present study are those that are considered to give sufficient information on the benefits that people derive from an ecosystem (de groot et al., 2012) among those available in the regional databases (i.e. publicly available for the entire emilia-romagna region). this was partly done on purpose to assess the usability of secondary data to assess the provision of es at the municipality level. the data obtained from statistics usable as proxies of es provision in the area were provided by the national institute of statistics (istat), other statistical databases (eurostat; faostat) and regional sources (e-r; pr ferrara). provisioning and cultural services have the greatest number of indicators compared to other services. land cover proved to be an important indicator for all four categories of services. land cover data typically contains land use, such as agricultural land, vegetation types, and forest. the selected es indicators are presented in table 2, divided according the different categories of es. (see appendix for more detailed information). 3.3 provisioning services among the studies that evaluate provisioning services, food provision receives the most attention. indicators used for food production include agricultural production (potential) measured in hectares of land, livestock numbers or vegetation suitability for fodder production and grain yield (fezzi et al., 2014; palacios-agundez et al., 2015; pohle et al., 2013). other provisioning services directly linked to human well-beings are crop production, capture fisheries, and livestock production (pohle et al., 2013). in the present study, the number of agricultural holdings, the utilised agricultural area and the area of arable land have also been used as indicators to measure food provision. regarding raw materials, the indicator used is the wooded area. another service is water provision. it is important to note that water provision or supply is not the same as water regulation. the latter is the process through which clean water becomes available, whilst water provision or supply is water that 243the evaluation of ecosystem services production table 2. selected ecosystem services indicators ecosystem service category (mea) ecosystem service group ecosystem service indicators code indicator source provisioning food provision k1 number of agricultural holdings eurostat -2012 food provision k2 utilised agricultural area eurostat-2012 food provision k3 arable land faostat -2010 water provision k4 irrigated area istat-2010 water provision k5 irrigated area surface water (natural and artificial basins, lakes, rivers or waterflows) istat-2010 water provision k6 irrigated area underground water istat-2010 raw materials k7 wooded area istat-2010 regulating regulation of water k8 volume of irrigation water istat-2010 regulation of water k9 volume of irrigation water surface water (natural and artificial basins, lakes, rivers or water flows) istat-2010 regulation of water k10 aqueduct, irrigation and restoration consortiums istat-2010 supporting biological control k11 organic agricultural area istat-2010 production quality k12 agricultural area of pdo and/or pgi farms istat-2010 cultural recreation and tourism k13 visitors arrivals pr ferrara -2010 recreation and tourism k14 italian visitors, arrivals pr ferrara -2010 recreation and tourism k15 foreign visitors, arrivals pr ferrara -2010 aesthetic enjoyment k16 collective accommodation establishments e-r -2010 aesthetic enjoyment k17 hotels and similar establishments e-r -2010 aesthetic enjoyment k18 holiday and other short-stay accommodation, camping grounds, recreational vehicle parks and trailer parks e-r -2010 recreation and tourism k19 number of active enterprises (total) e-r -2010 recreation and tourism k20 number of active enterprises in agriculture (crop and animal production, support activities to agriculture and post-harvest crop activities, forestry and logging, fishing and aquaculture ) e-r -2010 recreation and tourism k21 number of active enterprises in accommodation and food services activities e-r -2010 recreation and tourism k22 number of farms with other gainful activities (agritourism, recreational and social activities, initial processing of agricultural products, renewable energy production, wood processing) e-r -2010 source: μεα and own elaboration. 244 p. chatzinikolaou, d. viaggi, m. raggi is already available for use. a number of previous ecosystem service studies have used water production, i.e. the volume of water produced by area, as an es or as a surrogate for an es. water provision is measured through different indicators that include surface or ground water availability (fan and shibata, 2014; karabulut et al., 2015). in the present study, the indicators for water provision are related to the irrigated area, by distinguishing surface water use (natural, artificial basins, lakes, rivers or waterflows) from underground water. 3.4 regulating services generally, there is a lower number of indicators for regulating services as they are not directly consumed or physically perceived by people. the majority of studies that evaluate regulating services has evaluated in particular climate and water regulation (larondelle et al., 2014; pan et al., 2014). climate regulation services mainly relate to the regulation of greenhouse gases; therefore, the indicators for climate regulation included carbon storage, carbon sequestration, and greenhouse gas regulation. another common regulating service that is mapped is water flow regulation (simonit and perrings, 2011; stürck et al., 2015). indicators used for mapping water flow regulation are nutrient retention and land cover (boyanova et al., 2014; schmalz et al., 2015). the total benefit to people from water supply is a function of both the quantity and quality. however, due to the lack of suitable municipality scale data on water quality for quantifying the service, proxies are used as an estimation of the benefit (egoh et al., 2008; müller and burkhard, 2012). in the present study, the indicators used for water regulation are: a) the volume of irrigation watersurface water (natural and artificial basins, lakes, rivers or waterflows); and b) underground, aqueduct, irrigation and restoration consortiums. 3.5 supporting services this category of es, according to the conceptual framework of the common international classification of ecosystem services (cices), is categorized under regulating and maintenance services. the few indicators that have been identified relate to species and habitat. the comparatively lower numbers of indicators for supporting services could be attributed to the lack of information available on these services (barbier, 2007, 2013). the identification of indicators for services such as life cycle maintenance and maintenance of genetic diversity are rather generic and it is hence difficult to find suitable indicators (balvanera et al., 2006; swinton et al., 2007). the most common examples include indicators for primary production, production quality and controls and nutrient cycling (benayas et al., 2009; crafford and hassan, 2013). in the present study, the indicator used for biological control is the organic agricultural area and the area of protected designation of origin (pdo farms) and the area of protected geographical indications (pgi farms) are applied for production quality. 3.6 cultural services cultural services are non-material benefits that include recreation, spiritual and aesthetic value. identifying an indicator that represents these challenges, and that is spatially 245the evaluation of ecosystem services production represented, is fundamental for the measurement of the capacity of ecosystems to generate human benefits. schaich et al. (2010) proposed an alternative approach to fill the knowledge gaps in cultural services that links es research with cultural landscape research. this approach is based on the development of a well-being index based on indicators and metrics derived from existing measures of well-being. groups of indicators described by suites of metrics are commonly aggregated to evaluate components of well-being. these indicators represent social cohesion, education, health, leisure time, safety and security (guhn et al., 2012; huntington, 2000). the majority of these indicators describe the quantity and quality of ecosystems, economic drivers, and social inputs. however, these types of measures are not directly used in quantifying the delivery of es. the individual indicators are usually used to develop composite measures and are based on quantitative values, such as generally recognised qualitative assessments (smith et al., 2013). the most common indicators for cultural services include recreation and ecotourism, which can be directly measured through a number count of visitors (milcu et al., 2013). visitor information can also be obtained from national statistics or from park inventories. in the present study, we used the number of foreign or italian visitors. indicators used for recreational activities vary among studies, from accommodation suitability and summer cottages, deer hunting and fishing to natural areas and forested area for recreational purposes (naidoo et al., 2011). indicators include scenic sites, water bodies or forests as well as visitor numbers and accessibility to natural areas. in the present study, with respect to recreational activities, we used the active enterprises in agriculture, the active enterprises in accommodation and food service activities and the farms with other gainful activities, such as agritourism, recreational and social activities. although these indicators are relatively easy to quantify, indicators for aesthetic and spiritual activities are still in the early stages of development and those that exist are difficult to quantify and compare between countries or regions (eagles, 2002). in addition, in the present study, we used the collective accommodation establishments, hotels and similar establishments, holiday and other short-stay accommodations, campgrounds, recreational vehicle parks and trailer parks as proxy indicators for aesthetic services. 3.7 application of promethee ii the initial stage is the evaluation matrix, which presents the performance of each alternative in relation to each criterion. in our analysis, the alternatives are the 26 municipalities of the province of ferrara (x1-x26, table 1) and the criteria are the 22 es indicators (k1-k22, table 2). the performance of each alternative in relation to each criterion is presented in table a1 and the evaluation matrix is presented in table a2 (see appendix). using the data contained in the evaluation matrix, the alternatives are compared pairwise with respect to each criterion. the second stage involves the exploration of the outranking relation. the results are expressed by the preference functions, which are calculated for each pair of options. in the present study, the model assumes that the criteria are equally important and simulates different scenarios for weighing accordingly. in the final stage, two alternatives (a,b) are compared with each other and each one is assigned two values of flows. the positive flow expresses the total superiority of the alternative against all of the other alternatives for all of the criteria. the negative flow 246 p. chatzinikolaou, d. viaggi, m. raggi expresses the total superiority of all of the other alternatives against alternative for all of the criteria. φ(x) the net flow of each alternative (the difference between the positive and the negative flow) and is used to obtain the final evaluation. 4. results table 3 presents the evaluation of the study areas, as obtained from the net flows. according to the value of the net flow, the 26 municipalities are divided into 5 groups. the first group of municipalities, characterised by high positive net flows, consists of: comacchio, goro, argenta and jolanda di savoia, all located in the western area of the province. comacchio and argenta have the highest values in the indicators that represent cultural services, such as foreign visitors, hotels and similar establishments, the number of active enterprises providing accommodation and food service activities and the number of farms with other gainful recreational activities. goro has the highest rate in the number of active enterprises in agriculture (crop and animal production, support activities to agriculture) and the highest number of farms with other gainful agricultural activities. moreover, jolanda di savoia has the highest rate in the agriculture area of pdo and/o pgi farms. these features are indeed connected to key features of the area. since a large part of the territory is within the po delta park, it contains important natura2000 sites. visits to the area increase considerably during the summer months. during this period, demand for beaches, areas of high naturalistic value and historical sites has resulted in the development of receptive structures, such as rental houses, hotels, camping areas, beaches with restaurants, etc. summer tourism is also an important market for horticultural farms (most of which are close to the seaside). the second group of municipalities, with a positive but lower net flow, are migliaro, codigoro, vigarano mainarda and bondeno. migliaro and codigoro, located in the western area of the province, have high rates in the indicators that represent cultural services, such as italian visitors, holiday and short-stay accommodation, camping grounds, recreational vehicle parks and trailer parks. migliaro also has the highest rate in organic agricultural area. moreover, bondeno table 3. classification of the 26 municipalities municipality net flow (φ) 1 comacchio 2,888194373 2 goro 2,543589598 3 argenta 1,997682356 4 jolanda di savoia 1,190854183 5 migliaro 0,720865791 6 codigoro 0,709070084 7 vigarano mainarda 0,694387495 8 bondeno 0,614876652 9 massa fiscaglia 0,402104543 10 portomaggiore 0,257389617 11 mesola 0,194863948 12 poggio renatico 0,146803521 13 cento 0,008314139 14 ro -0,14634547 15 sant’agostino -0,21655112 16 migliarino -0,27198083 17 ostellato -0,28124392 18 lagosanto -0,30769265 19 mirabello -0,68414923 20 masi torello -1,00385534 21 ferrara -1,14179801 22 voghiera -1,26554807 23 formignana -1,32908587 24 copparo -1,34379219 25 tresigallo -2,09068952 26 berra -2,28626409 source: own elaboration 247the evaluation of ecosystem services production and vigarano mainarda, located in the eastern area of the province, have the highest rate in the irrigated area from natural and artificial basins. the third group, with positive net flows around 0, consists of massa fiscaglia, portomaggiore, mesola, poggio renatico and cento. small negative flows around 0 distinguish the fourth group including ro, sant’agostino, migliarino, ostellato lagosanto, and mirabello. these groups of municipalities are in the middle of this evaluation, since the rates are neither extremely high nor particularly low. municipalities with negative net flows have low rates in more than one ecosystem system indicator, like agricultural farms with other gainful activities such as agritourism, recreational and social activities, initial processing of agricultural products or renewable energy production and the agricultural area of pdo and/or pgi farms. these results are connected to key features of the area, since the main recreational activities in the area are related to habitat restoration and conservation and species protection habitat (especially birds) while other agricultural activities are seen negatively, mainly because of the negative effect on water quality. the fifth and last group of municipalities, located in the central area of the province, (masi torello, ferrara, voghiera, formignana, copparo, tresigallo and berra) has negative net flows below -1. berra has no organic agricultural area, hotels or similar accommodation services. tresigallo has no wooded area. formignana has no hotels or similar establishments. other indicators with low rates are agricultural farms with other gainful activities such as agritourism, recreational and social activities, initial processing of agricultural products or renewable energy production and the agricultural area of pdo and/or pgi farms. there is the potential to modify/improve the landscape through different projects. for example, some such initiatives include: the evaluation of the economic impact of climate change on agriculture, conservation of natural areas, valorisation of local products, restoration of ecological areas as tourist attractions, restoration of forested areas, and the greening of farms to restore the traditional landscape). 5. discussion due to its explorative nature, this study is subject to several weaknesses and a number of options for improvement. the main issue concerns the number of gaps in the es metrics and indicators available at the regional level, with respect to the number and quality of indicators needed to reflect the es approach in a comprehensive way. the most important challenge in our analysis was, accordingly, the lack of information with respect to the provision of es at the regional level. the indicators available for most es are not fully satisfactory in their ability to evaluate the quality and quantity of benefits provided. the number of es indicators in each category varies significantly due to the different data availability and reliability. this prevented us from achieving a thorough understanding of the behaviour of individual services. in addition, due to data paucity, it was not possible to consider the interactions between specific services. another limitation in the application of promethee is that it did not use any weighing approach to reflect the relative preferences of potential decision makers or society. according to macharis et al. (2004), the model assumed that the criteria were equally important and simulated different scenarios for appropriate weighing. 248 p. chatzinikolaou, d. viaggi, m. raggi moreover, we did not consider alternative scenarios of es production. in this respect, there is potential for further research as the model could be used to simulate alternative scenarios based on post-2013 measures that can affect the supply or demand of es. in that case, the model could involve stakeholder preferences with respect to the services to be provided and the indicators and criteria to assess the services. for the valuation of es, identification of relevant stakeholders is a critical issue (hein et al., 2006). in almost all steps of the valuation procedure, stakeholder involvement is essential to determine main policy and management objectives and to identify the main relevant services and assess their values. this is an aspect that could be strengthened in further research. 6. conclusions the objective of this paper is to evaluate the provision of ecosystem services in a traditional cultural landscape in the province of ferrara. it is mostly an explorative paper intended to verify the possibility of using available secondary data at the municipality level to comparatively assess es provision. the case study area is characterised by historicalcultural sites, agricultural areas and protected areas of natural importance. from the final outranking, we can observe that the provision of ecosystem services varies greatly from one municipality to the next. regarding the various es categories, all of the municipalities offer a significant number of provisioning and cultural services, mainly connected to recreational opportunities; on the contrary, there is greater diversity in the provision of regulating and supporting services. this evaluation can support the characterisation of agricultural lands in terms of the provision of multiple es. this exercise also contributes to the discussion surrounding the public goods provided by agriculture and efforts toward a better use of resources and can ultimately improve the spatial targeting of policy measures. a key challenge of ecosystem management is determining how to manage multiple es across landscapes. enhancing important provisioning es, such as food and timber, often leads to trade-offs between regulating and cultural es, such as nutrient cycling, flood protection, and tourism. in terms of further research, the model can also be used to simulate alternative scenarios, based on future agricultural policies that may affect the supply or demand of es (ec, 2010b). alternative scenarios could be built based on the provisions of the new programming period affecting landscape structure and behaviours related to es. the objectives of the cap 2014-2020 are oriented towards the sustainable management of natural resources and climate action (e.g. ‘greening’ in the first pillar) (viaggi, 2015). in particular, post-2013 measures include agri-environmental payments to improve es, mechanisms that can affect landscape management, such as water and nature conservation measures, and other mechanisms promoting demand for es, such as rural tourism. this should also provide an opportunity for more focused evaluations that address the data gaps and indicator limitations observed in this study. references albadvi, a. 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(2014). european agricultural landscapes, common agricultural policy and ecosystem services: a review. agron. sustain. dev. 34: 309-325. 255the evaluation of ecosystem services production appendix table a1. ecosystem services provision. n um ber of agricultural holdings u tilised agricultural area a rable land irrigated area irrigated area surface w ater (natural and artificial basins, lakes, rivers or w aterflow s) irrigated area underground w ater w ooded area argenta 777 23104,96 21202,5 7897 650,83 69,83 317,2 berra 241 5005,19 4662,83 1692 422,35 37,98 38,17 bondeno 587 12818,7 12156,22 2864 1588,8 61 22,7 cento 459 4965,41 4561,23 503 256,98 32,85 4,34 codigoro 327 10891,06 10769,79 6685 343,19 22,6 75,71 comacchio 293 10033,64 9694,82 6406 1260,9 44,07 114,77 copparo 677 11631,09 10465,28 2402 404,45 40,68 33,63 ferrara 1604 27874,6 22799,17 7433 1744,5 591,27 86,82 formignana 103 1720,67 1470,55 382 78,18 7,5 2,09 goro 24 638,48 635,48 174 22 0 3 jolanda di savoia 199 8230,48 7991,19 3200 53,8 12 23,13 lagosanto 68 2124,74 1981 1468 59,16 0 17,73 masi torello 98 1527,95 1316,11 349 7,2 0 16,64 massa fiscaglia 102 3042,2 3000,49 1017 57,06 13,6 1,77 mesola 282 4698,31 4592,52 3375 32,58 0 11,7 migliarino 92 2831,47 2382,05 1121 55,52 0 5 migliaro 52 3111,55 3073,59 264 10,85 0 15,24 mirabello 43 1293,97 1196,75 70,7 11,6 23,06 0 ostellato 349 11857,18 11206,6 5738 490,46 61,99 8,69 poggio renatico 244 5894,04 5233,23 1423 538,62 121,05 15,26 portomaggiore 324 10036,12 9166,19 2901 254,48 70,35 59,04 ro 163 2756,83 2590,52 709 5,9 37,34 20,12 sant’agostino 168 2404,4 2134,56 414 196,09 23 0 tresigallo 80 1436,99 1240,67 359 52,41 8,31 0 vigarano mainarda 177 3182,31 2538,07 638 353 200,76 9,54 voghiera 214 3763,29 2814,05 1301 348,72 20,62 11,61 256 p. chatzinikolaou, d. viaggi, m. raggi table a1. ecosystem services provision (continued). volum e of irrigation w ater volum e of irrigation w ater surface w ater (natural and artificial basins, lakes, rivers or w aterflow s) volum e of irrigation w ater underground w ater in or near the farm aqueduct, irrigation and restoration consortium o rganic agricultural area a gricultural area of pd o and/or pg i farm s v isitors a rrivals argenta 22219871,85 1842392,49 18277528,04 6542 3169 5409 berra 8888067,05 1295545,48 3634800,82 0 164 91 bondeno 8721393,28 4753715,27 880400,95 100 563 898 cento 1425067,73 785070,95 108623,73 18,3 56,5 11696 codigoro 43698065,67 1058176,82 40023575,57 1389 426 3985 comacchio 18585945,8 3681841,57 13115943,65 1140 651 455142 copparo 10260339,76 1248347,74 3813132,4 143 451 4889 ferrara 22737104,04 6201258,36 6654913,52 2535 440 175549 formignana 1257376,39 290371,02 529924,59 5,2 18,7 88 goro 514795,61 264000 236735,34 0 0 465 jolanda di savoia 28055933,9 138237,82 27293092,73 25,6 3802 56 lagosanto 4133759,17 141047,59 2738931,48 0 16,3 358 masi torello 1019254,47 23917,94 83147,47 20,4 129 124 massa fiscaglia 3653570,09 476962,64 2930395,31 552 0 88 mesola 8472806,43 60708,87 7768578,41 29,5 528 2944 migliarino 3917812,87 94048,48 3562210,88 1126 88,7 1025 migliaro 943095,47 28136,22 914959,25 2187 0 88 mirabello 198455,23 35615,66 100623,67 0 0 88 ostellato 18812898,76 1416033,01 16451586,06 435 28,1 5668 poggio renatico 3957393,46 1420865,38 2017315,88 6,2 93,5 271 portomaggiore 8556809,49 843795,16 6507314,36 316 246 3328 ro 2460678,75 21073,95 667629,1 29,8 11,9 97 sant’agostino 1241949,89 557231,38 572812,61 18,1 14,3 792 tresigallo 1326476,31 139231,6 171284,57 108 85,3 1066 vigarano mainarda 1858061,51 995519,79 228545,16 13,9 41,9 2471 voghiera 4077942,32 866873,9 161755,34 1,62 863 258 257the evaluation of ecosystem services production table a1. ecosystem services provision (continued). italian v isitors, a rrivals foreighners v isitors, a rrivals c ollective accom m odation establishm ents h otels and sim ilar establishm ents h oliday and other short-stay accom m odation, cam ping grounds n um ber of active enterprises (total) n um ber of active enterprises in agriculture (crop and anim al production, n um ber of active enterprises in accom odation and food n um ber of farm s w ith other gainful activities (agritourism , recreational and social activities) argenta 4579 830 25 5 20 1347 16 89 80 berra 79 12 0 0 0 260 9 13 14 bondeno 735 163 9 2 7 748 16 50 19 cento 9101 2595 16 7 9 2154 17 131 15 codigoro 3244 741 14 5 9 837 56 60 22 comacchio 365022 90120 107 27 80 2545 289 393 22 copparo 4152 737 10 3 7 975 7 69 27 ferrara 126404 49145 172 34 138 10860 30 697 64 formignana 78 10 1 0 1 139 3 8 6 goro 442 23 8 2 6 1197 1009 21 5 jolanda di savoia 56 0 3 0 3 130 6 13 11 lagosanto 303 55 3 1 2 343 25 19 4 masi torello 114 10 5 0 5 152 0 10 6 massa fiscaglia 78 10 1 0 1 194 7 15 3 mesola 2542 402 10 4 6 604 163 34 35 migliarino 929 96 7 0 7 266 1 20 9 migliaro 78 10 2 0 2 116 1 5 2 mirabello 78 10 1 0 1 185 2 11 4 ostellato 4788 880 10 2 8 363 10 27 16 poggio renatico 223 48 7 1 6 488 5 27 8 portomaggiore 2969 359 10 1 9 759 6 55 30 ro 93 4 4 0 4 161 4 14 12 sant’agostino 633 159 4 3 1 386 1 28 5 tresigallo 807 259 3 2 1 268 2 18 4 vigarano mainarda 1758 713 7 3 4 390 4 28 7 voghiera 206 52 3 0 3 273 5 15 13 258 p. chatzinikolaou, d. viaggi, m. raggi table a2. evaluation matrix k1 k2 k3 k4 k5 k6 k7 k8 k9 k10 k11 x1 10,03% 91,20% 91,77% 12,99% 8,24% 0,88% 1,37% 9,62% 8,29% 82,26% 28,31% x2 3,11% 90,02% 93,16% 2,78% 24,95% 2,24% 0,76% 3,85% 14,58% 40,90% 0,0% x3 7,58% 92,72% 94,83% 4,71% 55,48% 2,13% 0,18% 3,78% 54,51% 10,09% 0,78% x4 5,92% 91,54% 91,86% 0,83% 51,07% 6,53% 0,09% 0,62% 55,09% 7,62% 0,37% x5 4,22% 91,36% 98,89% 11,00% 5,13% 0,34% 0,70% 18,92% 2,42% 91,59% 12,76% x6 3,78% 91,09% 96,62% 10,54% 19,68% 0,69% 1,14% 8,05% 19,81% 70,57% 11,36% x7 8,74% 91,06% 89,98% 3,95% 16,84% 1,69% 0,29% 4,44% 12,17% 37,16% 1,23% x8 20,70% 91,12% 81,79% 12,23% 23,47% 7,96% 0,31% 9,84% 27,27% 29,27% 9,09% x9 1,33% 92,18% 85,46% 0,63% 20,48% 1,96% 0,12% 0,54% 23,09% 42,15% 0,30% x10 0,31% 92,06% 99,53% 0,29% 12,63% 0,0% 0,47% 0,22% 51,28% 45,99% 0,0% x11 2,57% 90,57% 97,09% 5,26% 1,68% 0,37% 0,28% 12,15% 0,49% 97,28% 0,31% x12 0,88% 92,51% 93,23% 2,42% 4,03% 0,0% 0,83% 1,79% 3,41% 66,26% 0,0% x13 1,27% 92,73% 86,14% 0,57% 2,07% 0,0% 1,09% 0,44% 2,35% 8,16% 1,33% x14 1,32% 94,61% 98,63% 1,67% 5,61% 1,34% 0,06% 1,58% 13,05% 80,21% 18,15% x15 3,64% 88,17% 97,75% 5,55% 0,97% 0,0% 0,25% 3,67% 0,72% 91,69% 0,63% x16 1,19% 90,54% 84,13% 1,84% 4,95% 0,0% 0,18% 1,70% 2,40% 90,92% 39,77% x17 0,67% 92,68% 98,78% 0,43% 4,11% 0,0% 0,49% 0,41% 2,98% 97,02% 70,27% x18 0,56% 86,05% 92,49% 0,12% 16,42% 32,64% 0,0% 0,09% 17,95% 50,70% 0,0% x19 4,50% 93,54% 94,51% 9,44% 8,55% 1,08% 0,07% 8,14% 7,53% 87,45% 3,67% x20 3,15% 92,84% 88,79% 2,34% 37,85% 8,51% 0,26% 1,71% 35,90% 50,98% 0,11% x21 4,18% 92,09% 91,33% 4,77% 8,77% 2,42% 0,59% 3,70% 9,86% 76,05% 3,15% x22 2,10% 92,93% 93,97% 1,17% 0,83% 5,27% 0,73% 1,07% 0,86% 27,13% 1,08% x23 2,17% 90,23% 88,78% 0,68% 47,32% 5,55% 0,0% 0,54% 44,87% 46,12% 0,75% x24 1,03% 90,48% 86,34% 0,59% 14,58% 2,31% 0,0% 0,57% 10,50% 12,91% 7,49% x25 2,28% 90,62% 89,76% 1,05% 55,31% 31,46% 0,30% 0,80% 53,58% 12,30% 0,44% x26 2,76% 92,05% 84,78% 12,99% 26,81% 1,59% 0,31% 9,62% 21,26% 3,97% 0,04% 259the evaluation of ecosystem services production table a2. evaluation matrix (continued). k12 k13 k14 k15 k16 k17 k18 k19 k20 k21 k22 x1 13,72% 86,14% 84,66% 15,34% 91,20% 20,0% 80,0% 91,77% 1,19% 6,61% 10,30% x2 3,28% 98,63% 86,81% 13,19% 90,02% 0,0% 0,0% 93,16% 3,46% 5,0% 5,81% x3 4,39% 97,75% 81,85% 18,15% 92,72% 22,22% 77,78% 94,83% 2,14% 6,68% 3,24% x4 1,14% 84,13% 77,81% 22,19% 91,54% 43,75% 56,25% 91,86% 0,79% 6,08% 3,27% x5 3,91% 98,78% 81,41% 18,59% 91,36% 35,71% 64,29% 98,89% 6,69% 7,17% 6,73% x6 6,49% 92,49% 80,20% 19,80% 91,09% 25,23% 74,77% 96,62% 11,36% 15,44% 7,51% x7 3,88% 94,51% 84,93% 15,07% 91,06% 30,0% 70,0% 89,98% 0,72% 7,08% 3,99% x8 1,58% 88,79% 72,0% 28,0% 91,12% 19,77% 80,23% 81,79% 0,28% 6,42% 3,99% x9 1,09% 91,33% 88,64% 11,36% 92,18% 0,0% 100% 85,46% 2,16% 5,76% 5,83% x10 0,0% 93,97% 95,05% 4,95% 92,06% 25,0% 75,0% 99,53% 84,29% 1,75% 20,83% x11 46,19% 88,78% 100% 0,0% 90,57% 0,0% 100% 97,09% 4,62% 10,0% 5,53% x12 0,76% 86,34% 84,64% 15,36% 92,51% 33,33% 66,67% 93,23% 7,29% 5,54% 5,88% x13 8,43% 89,76% 91,94% 8,06% 92,73% 0,0% 100% 86,14% 0,9% 6,58% 6,12% x14 0,0% 84,78% 88,64% 11,36% 94,61% 0,0% 100% 98,63% 3,61% 7,73% 2,94% x15 11,23% 91,20% 86,35% 13,65% 88,17% 40,0% 60,0% 97,75% 26,99% 5,63% 12,41% x16 3,13% 90,02% 90,63% 9,37% 90,54% 0,0% 100% 84,13% 0,38% 7,52% 9,78% x17 0,0% 92,72% 88,64% 11,36% 92,68% 0,0% 100% 98,78% 0,86% 4,31% 3,85% x18 0,0% 91,54% 88,64% 11,36% 86,05% 0,0% 100% 92,49% 1,08% 5,95% 9,30% x19 0,24% 91,36% 84,47% 15,53% 93,54% 20,0% 80,0% 94,51% 2,75% 7,44% 4,58% x20 1,59% 91,09% 82,29% 17,71% 92,84% 14,29% 85,71% 88,79% 1,02% 5,53% 3,28% x21 2,45% 91,06% 89,21% 10,79% 92,09% 10,0% 90,0% 91,33% 0,79% 7,25% 9,26% x22 0,43% 91,12% 95,88% 4,12% 92,93% 0,0% 100% 93,97% 2,48% 8,70% 7,36% x23 0,60% 92,18% 79,92% 20,08% 90,23% 75,0% 25,0% 88,78% 0,66% 7,25% 2,98% x24 5,93% 92,06% 75,70% 24,30% 90,48% 66,67% 33,33% 86,34% 0,75% 6,72% 5,0% x25 1,32% 90,57% 71,15% 28,85% 90,62% 42,86% 57,14% 89,76% 1,03% 7,18% 3,95% x26 22,94% 92,51% 79,84% 20,16% 92,05% 0,0% 100% 84,78% 1,83% 5,49% 6,07% bio-based and applied economics 4(2): 125-147, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-14776 farmer’s motivation to adopt sustainable agricultural practices davide menozzi1, martina fioravanzi2, michele donati2,* 1 department of food science, university of parma, via kennedy 6, 43125 parma (italy) 2 department of biosciences, university of parma, via kennedy 6, 43125 parma (italy) date of submission: august 11th, 2014 abstract. the 2014-2020 common agricultural policy (cap) reform defines new rules for farmers including maintenance of the ecological focus area (efa). sustainability is also a requirement to meet consumer expectations and a competitive advantage for firms. this paper aims to evaluate the farmers’ intention to implement sustainable practices related to the efa measure and to the private sustainability schemes proposed by the food industry. the theory of planned behaviour (tpb) was applied on a sample of durum wheat producers to analyse intentions 1) to maintain 7% of the arable land as an efa, and 2) to implement the private sustainability scheme. structural equation modelling was applied to test for the relative importance of intention determinants. the farmers’ attitude and past behaviour positively affect intentions to implement the efa, while perceived behavioural control and attitudes predict intentions to adopt the private sustainability scheme. these results suggest possible interventions that public authorities and supply chain leaders might implement to stimulate farmers’ sustainable behaviours. keywords. common agricultural policy, durum wheat, theory of planned behaviour, sustainability, ecological focus area. jel codes. q18, d22, q01 1. introduction the design of alternative, sustainable agricultural systems and technologies is largely debated in the literature and rapidly evolving. the environmental objectives have become increasingly integrated into the eu’s common agricultural policy (cap), and the new 2014-2020 reform reinforces eco-friendly agricultural practices by introducing new commitments for beneficiaries. the cap green direct payment (greening), accounting for 30% of the national direct payment envelope, rewards farmers for respecting three actions: crop diversification, maintenance of permanent grassland and the ecological focus area * corresponding author: michele.donati@unipr.it. mailto:michele.donati@unipr.it 126 d. menozzi, m. fioravanzi, m. donati (efa). as defined by the regulation (eu) n. 1307/2013, the greening is a new mandatory component of the cap which compensates for the possible profit losses incurred by farmers for provisioning environmental public goods and services to the wider public, such as landscapes, farmland biodiversity, etc. (european commission, 2013)1. crop diversification aims to avoid the monoculture practice and establishes that farms with more than 10 ha of arable land will need to cultivate a least two crops, while on farms with more than 30 ha farmers will need to cultivate at least three crops. the ratio between the permanent grassland and the total agricultural area (at national, regional, sub-regional or farm level) must not fall by more than 5% of a reference ratio to be established in 2015. the efa action forces farms with more than 15 hectares of arable land to maintain at least 5% of the arable land (likely 7% after 2017) to an area with particular environmental characteristics, such as strip and buffer areas, environmental set-aside and nitrogenous fixing crops. because of its high compliance costs, the new cap mechanism will likely affect farmers’ decisions (input allocation) and the economic results of farms (schulz et al., 2014; solazzo et al., 2014). besides the cap, sustainability is becoming an important requirement to meet consumer expectations and, thus, a competitive advantage for firms that can guarantee the monitoring of the processes’ environmental performances. in several cases the cooperation between farmers and food industries to meet retailer and consumer needs has produced effective private (voluntary) sustainability schemes able to achieve a more sustainable supply chain and to create value for the stakeholders (hamprecht et al., 2005; muradian and pelupessy, 2005). examples are the processed tomato district in northern italy and the sustainable agricultural initiative (sai, 2013). in this context, farmers’ acceptance to participate in these schemes needs to be considered. although private sustainability schemes may contribute to improve the farm profitability, some barriers can prevent the participation of farmers. there are significant transaction costs in implementing sustainability schemes (falconer, 2000), such as those necessary to get additional information on procedures and profitability of similar experiences; in this context, internal or external barriers may exist, like farmers’ risk aversion, market and policy uncertainty, cultural resistance, high farm fixed investments, and lack of long-run entrepreneurial vision. all these factors drive farmers’ behaviour and potentially reduce their willingness to adopt eco-friendly agricultural practices. while the effect of cap payments on farmers’ behaviours has been widely studied in the economic literature, the agri-environmental measures have been less investigated. in particular, the second pillar agri-environmental actions have been evaluated to understand the responsiveness of farmers and their effectiveness at the territorial level (primdahl et al., 2010; godard et al., 2008; buysse et al., 2007). attempts to predict the impact of agri-environmental measures and related payments on farmers behaviour and decisions have been developed mainly applying mathematical programming techniques (arfini and donati, 2013; louhichi et al., 2010; janssen et al., 2010; galko and jayet, 2011; buysse et al., 2007; röhm and dabbert, 2003) and other econometric models, such as stochastic production frontier (reinhard et al., 1999), random parameter logit (espinosa-godet et 1 according to the regulation (eu) n. 1306/2013, failure to respect the greening component should lead to penalties up to the 125% of the greening payment. 127farmer’s motivation to adopt sustainable agricultural practices al., 2010), nonparametric regression (kleinhanß et al., 2007), and dual approach in production theory (bonnieux et al., 1998). despite the extensive use of quantitative methodologies to study the adoption of agri-environmental measures, behavioural approaches such as attitude-based methods, have also been used to predict the farm response to new environmental policy design. in particular, the theory of planned behaviour (tpb) (ajzen, 1991), which focuses on the assessment of behavioural intentions determinants, has been widely applied to understand and predict the likely behaviour of farmers regarding environmental protection actions. the tpb suggests that the likelihood of a particular behaviour can be predicted by the individual’s intention to perform that behaviour, capturing the motivational factors that influence behaviour. according to the tpb, behaviour is guided by the favourable or unfavourable evaluation of the behaviour (attitude towards the behaviour), perceived social pressure (subjective norms, sn) and perceived ability to perform the behaviour (perceived behavioural control, pbc). in general, the more favourable the attitude and subjective norm, and the greater the perceived control, the stronger the intention to perform a given behaviour should be (ajzen, 1991). although widely applied in the analysis of consumer’s behaviour (see, e.g., menozzi and mora, 2012; menozzi et al., 2015), the tpb has been successfully used to predict entrepreneurial behaviour, such as starting a business (kautonen et al., 2013), or to test the determinants of farmers’ strategic behaviour (bergevoet et al., 2004), providing more predictive power than personality traits or demographic characteristics. there have been a large number of tpb studies addressing environmental and sustainability-related behaviours in agriculture, such as farmers’ conservationrelated behaviour (beedell and rehman, 2000; yazdanpanah et al., 2014), improved grassland management (martínez-garcía et al., 2013), climate information use (sharifzadeh et al., 2012), adoption of soil erosion control practices (wauters et al., 2010), adoption of environmentally-oriented behaviour (power et al., 2013), participation in sustainability programs (corbett, 2002), and in other sustainable agricultural practices (e.g., fielding et al., 2008). all of these tpb applications attempted to identify the driving factors that lead producers to adopt a given decision. the results are important for policy makers and food chain actors who consider the cause-effect linkage between policies and producer behaviour to develop the most appropriate strategy and intervention to stimulate farmers’ sustainable behaviour (beedell and rehman, 2000). this paper adds to this stream of literature by evaluating the farmers’ intention to adopt sustainable agricultural practices. in particular, we have studied the intention 1) to maintain at least 7% of the arable land as efas and 2) to implement at farm level a private sustainability scheme as proposed by the food industry. in relation to the 2014-2020 cap reform, we decided to concentrate the study only on the efa actions because of its high estimated compliance cost (solazzo et al., 2014). the analysis focused on durum wheat producers in italy. this mediterranean production represents the raw material for one of the most important italian food chain: the pasta’s food chain. in 2013, the pasta industry showed an annual turnover of 4.5 billion € equal to 3.5% of the italian food industry turnover. almost the entire quantity of semolina used in italy, obtained from 1.2 billion hectares cultivated with durum wheat, is addressed to the pasta industry (ismea, 2014). moreover, this sector has shown to be particularly sensitive to cap changes (cisilino et al., 2012), and its intensive production in some parts of italy constitutes an environmental 128 d. menozzi, m. fioravanzi, m. donati issue to consider. understanding the farmers’ perception towards greening cap practices and private approaches to sustainability can contribute to improve the cooperation among the food chain operators and a better integration of the public and private sustainable measures. the following section describes the theoretical framework and its application to durum wheat farmers defining the hypotheses to be tested. based on this theory a working model was developed to evaluate the farmers’ intention to adopt sustainable agricultural practices. the data collection and analytical procedures to test the hypothesis are outlined in the next section and the empirical results are presented. discussion and conclusions make up the final section of the paper. 2. the theoretical framework the econometric models generally applied to study farmers’ adoption of eco-friendly agricultural practices employ a range of determinants such as farm and farmer characteristics, institutional setting, individual perceptions related to the economic environment, etc. hansson et al. (2012) noticed that psycho-social models have recently been used in the field of behavioural economics and have been shown to explain economic behaviour and to increase the relevance of economic models. this paper extends this research by introducing psychosocial constructs to explain farmers’ intention to adopt environmental sound practices (i.e. maintenance of at least 7% of the arable land as efas and implementation of a private sustainability scheme) by applying ajzen’s (1991) theory of planned behaviour (tpb). originating from social psychology, the tpb considers the individual’s intention to perform a given behaviour a central factor in performing the behaviour. intentions are assumed to capture the motivational factors that influence a behaviour, and depend on beliefs that link the given behaviour to certain outcomes (attitudes) and on the perceived social pressure to perform the behaviour (subjective norms). intentions are expected to influence behavioural performance to the extent that the person has actual control over the behaviour (pbc). in the agri-environmental context, the tpb thus contributes to our understanding of the emergence of farmers’ behaviour and determinants prior to any observable action, which has notable implications for agricultural policy and food industry strategy (kautonen et al., 2013), for example if the objective is to promote sustainable management of natural resources by fostering a culture of sustainability among farmers (matthews, 2013). indeed, from a methodological perspective, tpb is an appropriate theoretical framework providing a parsimonious model for understanding farmers’ beliefs and motivations, and how information can influence behaviour (fielding et al., 2008). attempts to promote sustainable agricultural systems, e.g., by private sustainability schemes, will require an understanding of how behavioural change can be influenced. in this context, questions regarding farmers’ environmental sustainability behaviours are increasingly tackled by using the tpb for its ability in addressing complex behaviours depicting the mechanisms that lead people to be supportive of such ecological practices (yazdanpanah et al., 2014). the role of individual attitudes, social pressure and control over the behaviour can be evaluated and assessed to understand what encourages farmers to accept or reject agri-environmental practices as part of their farm management activities. 129farmer’s motivation to adopt sustainable agricultural practices prior applications of the tpb to the agri-environmental context suggest that attitude, subjective norms and pbc explain from 23% to 72% of the variance in intention (corbett, 2002; sharifzadeh et al., 2012; fielding et al., 2008; wauters et al., 2010; yazdanpanah et al., 2014). the farmers’ intention to adopt environmental sound practices was found to be positively affected by their personal attitudes towards the behaviour (e.g., fielding et al., 2008; wauters et al., 2010). secondly, it has been argued that farmers’ behaviour is not fully under volitional control (sharifzadeh et al., 2012), whilst is strongly influenced by external stakeholders such as producers’ organizations, food industries, public authorities, etc. thus, pbc and subjective norms become valuable theoretical constructs. therefore, in this study we suggest that: h1a: a favourable attitude would significantly predict farmers’ intention to adopt sustainable agricultural practices. h1b: subjective norms would significantly predict farmers’ intention to adopt sustainable agricultural practices. h1c: pbc would significantly predict farmers’ intention to adopt sustainable agricultural practices. although the success of the tpb in predicting behaviour has been proved (armitage and conner, 2001), it has been argued that for some behaviour and contexts the inclusion of other variables may increase the model’s predictive power (menozzi et al., 2015). it is reasonable to assume that the farmers with greater environmental awareness and who feel moral responsibilities toward environmental behaviours could be expected to have more positive attitudes towards the adoption of sustainable agricultural practices (beedell and rehman, 2000; corbett, 2002; fielding et al., 2008). therefore, a measure of moral obligation, defined as an individual’s perception of the moral correctness or incorrectness of performing a behaviour (ajzen, 1991), has been added to the model suggesting the following hypothesis: h2: moral obligations have a positive and significant effect on farmers’ attitudes towards sustainable agricultural practices. several studies have also suggested that past behaviour may be an important predictor of future behaviour (armitage and conner, 2001). fielding et al. (2008) argued that past efforts in ecological management practices, comprising a set of behaviours that require substantial outlay of time and capital, are likely to have a substantial impact on future intentions. consistent with their argument and findings, a variable measuring past behaviour was also included in the model relative to efas and expected to be a positive predictor of intentions. thus, we propose the following: h3: farmers that have already an ecological area on their agricultural holdings would intend to maintain the efas also in the future. consistent with similar studies (see, e.g., kautonen et al., 2013), the model specification includes also other variables related to the structure of agricultural holdings (e.g., farmer age, % of rented agricultural land, % of durum wheat acreage, etc.), in order to monitor their effect on behavioural intentions. therefore the contributions of this paper are twofold. first, it provides an understanding of the determinants of durum wheat farmers’ adoption of sustainable agricultural practices (efas and private sustainability schemes), in the context of the forthcoming 2014-2020 cap reform and the possible initiatives proposed by the food industry. sec130 d. menozzi, m. fioravanzi, m. donati ondly, the relationships between tpb predictors and intention to adopt environmental sound practices will be addressed, justified, and empirically tested using structural equation modelling (sem) technique. 3. data and methodology a survey on a sample of durum wheat producers in italy, involved in the pasta’s supply chain, allowed to analyse two behaviours related to sustainable agricultural practices: 1) the maintenance of at least 7% of the arable land as efas, and 2) the implementation at farm level of a private sustainability scheme including the adoption of eco-friendly farming practices. for this latter behaviour, the questionnaire suggested as an example few possible actions that farmers could have implemented within the private sustainability scheme, such as pesticides and chemical fertilizers reduction, renewable energy provision, integrated agriculture, etc. the questionnaire specified that the farmers would have to agree with other stakeholders (i.e. the food industry) their commitments to the ecofriendly farming practices included in the scheme. the specific mechanisms of the private sustainability scheme and related economic incentives were not investigated. the aim was to assess the intention to adopt at farm level the new environmental strategies developed within the pasta supply chain, once they become available. since at the time of the survey neither the 2014-2020 cap reform nor the private sustainability schemes were implemented by the italian durum wheat producers, the study focused on intention to behave as a proxy for actual behaviour (lobb et al., 2007). however, explorative research including extensive literature searches and a focus group was conducted prior to questionnaire design in an attempt to minimise the differences in observed and real responses. a sem technique was used to test for the relative importance of determinants in the two considered behavioural intentions. 3.1 data collection a survey was conducted from june to july 2013 on a sample set of farmers involved in the pasta supply chain producing durum wheat in italy. in particular, all of the contacted farmers signed contracts with the world’s largest pasta producer. contract farming establishes the technical and agronomic criteria for growing and delivering durum wheat with a specified quality as well as price. most of these farms belong to producers’ organisations (pos), which represent the main interface between farmers and the industry. through the pasta industry and pos, we have identified 211 durum wheat producers that were formally involved in the supply chain of the world’s largest pasta industry, distributed uniformly in the three italian geographical areas, i.e., north, centre and south. as shown in figure 1, this survey was conducted in different steps, starting with the organisation of a preliminary focus group of 6 participants (4 farmers, 1 food industry representative and 1 agronomist) to identify the main issues perceived by durum wheat producers regarding the new cap reform (fioravanzi, 2013). the focus group identified the relevant behaviours related to cap reform and agro-environmental practices to be tested with statistical analysis. then, a questionnaire based on the tpb constructs 131farmer’s motivation to adopt sustainable agricultural practices was defined and sent to the farmers by regular and electronic mail. in the beginning of the questionnaire, a request of participation was emphasised with the explanation of the study’s aim and the instructions to fulfil the questionnaire to prepare and commit the farmers to the survey. farmers could fill in the questionnaire in three ways: on-line by a specific webpage, by phone through direct interview, and by paper questionnaire to return via regular mail. figure 1. analysis design. questionnaire  building   focus  group identifying  relevant   behaviours questionnaire   formulation questionnaire  submission  and  data   collection preparation   questionnaire   webpage request  of  survey   participation  send  by   regular  mail  and  email on-­‐line by  phone data  quality  check  and   processing statistical  analysis by  paper   support   identifying  target  farms northern  farms central  farms southern  farms a total of 73 questionnaires were completed, 16 by paper and 57 on-line questionnaire; no farmers decided to reply by phone interview. after two incomplete questionnaires were removed, the final sample consisted of 71 respondents. as shown in table 1, the sample set was almost equally distributed between the three geographical areas. most of the farms of the sample are specialised in field crops, particularly cereal production, while 63% of the farms belonged to pos. the farms that were surveyed in central italy were larger, in terms of utilised agricultural area (uaa), than were those in the northern and southern regions. as shown in table 1, their size is much larger than the average size of farms specialised in field crops in italy (i.e. 13 ha). the percentage of durum wheat surface on total uaa is on average 43% for the sample, with higher values 132 d. menozzi, m. fioravanzi, m. donati in southern and central regions (respectively 64% and 45%), and a lower value in northern italy (20%). this data is higher than the italian average (29%), and the average for the three macro-areas (north 6%, centre 35% and south 53%). on average, 40% of the uaa of the surveyed holdings was rented, with a greater incidence in central and northern italy (respectively 58% and 36%) as compared to southern regions (20%). this figure is slightly higher than the italian average data for farms with durum wheat production. approximately 35% of the total revenue is represented by the single farm (decoupled) payment, demonstrating the high level of dependence of these farms on public subsidies. the introduction of more balanced cap payments could strongly affect farms’ revenues and, consequently, investments. the high percentage of durum wheat revenue and cultivated surfaces of the total reflects the high degree of specialisation, particularly in the surveyed farms of central and southern italy. in this sample, the farmer’s average age is approximately 50 years; this figure is lower than the average age of italian farmers producing durum wheat (62 years). the average distance of farms from the mill is 125 km, with higher values in the southern regions (172 km), and a lower distance in the central italy (88 km). it can be concluded that the surveyed farms are larger, more specialized in the production of durum wheat, managed by younger farmers then the national population. these differences are likely to be influenced by the sampling criteria, i.e. farms with contractual agreements with the food industry; indeed, vertically integrated farms are more likely to be larger and managed by younger and trained managers than non-integrated ones (deininger and byerlee, 2012). table 1. description of the main characteristics of the sample set and italian farms producing durum wheat. description sample italy north centre south total north centre south total no. of farms 21 29 21 71 14,718 31,818 156,254 202,790 specialised in cereals 17 22 20 59 n.a. n.a. n.a. n.a. belonging to the producers’ organisation 16 16 13 45 n.a. n.a. n.a. n.a. utilised agricultural area per farm (uaa, average ha) 114.6 212.6 71.3 141.8 13.4 a 14.5 a 11.5 a 12.8 a durum wheat surface per farm (average ha) 23.1 94.8 45.3 80.4 8.0 9.9 6.3 7.0 % durum wheat surface on total uaa 20.2 44.6 63.6 43.0 5.5 a 34.9 a 52.5 a 28.9 a % rented agricultural land on total uaa 36.1 58.3 19.7 40.4 44.3 24.7 21.7 31.4 % single farm payment of total revenue 29.1 31.1 46.8 35.1 n.a. n.a. n.a. n.a. % durum wheat revenue of the total revenue 18.8 45.0 59.2 41.5 n.a. n.a. n.a. n.a. farmer age (average) 50.6 47.1 53.3 50.1 62.9 a 62.4 a 58.5 a 61.5 a distance from milling plant (km) 129.1 87.6 172.2 124.9 n.a. n.a. n.a. n.a. a data referred to farms specialised in field crops. source: own elaboration on data of the 2010 agricultural census. 133farmer’s motivation to adopt sustainable agricultural practices 3.2 model measures the questionnaire was defined considering a) ajzen’s conceptual and methodological guidelines for constructing a tpb questionnaire (ajzen, 1991; 2006), b) previous findings on similar topics (beedell and rehman, 2000; corbett, 2002; fielding et al., 2008; wauters et al., 2010; hansson et al., 2012), and c) the preliminary focus group (fioravanzi, 2013). after having explained in detail the 2014-2020 cap reform in terms of greening commitments and the possibility to adopt private environmental sustainability schemes, two behaviours were surveyed: 1) the maintenance of at least 7% of the arable land with particular environmental characteristics (ecological focused area, efa), and 2) the implementation at farm level of the private sustainability scheme including the adoption of eco-friendly farming practices like fertilizers and pesticides reduction, green energy, integrated agriculture, etc. the questionnaire explained that the farmers, that voluntarily had decided to participate in the scheme, would have agreed with other stakeholders (i.e. the food industry) their commitments to the included environmental sustainability practices. the participants received a questionnaire containing items measuring the model variables across these two behaviours: attitudes, subjective norms, pbc, moral obligation and behavioural intentions. all of the items were scored on a 7-point likert scale (1 = “strongly disagree”, 7 = “strongly agree”); the questionnaire items related to model variables are listed in table 3 and table 4 with the resulted means and standard deviations. regarding the first behaviour analysed, i.e. maintenance of efas, attitudes were measured using four questionnaire items (e.g., “maintaining at least 7% of the arable land as an efa is negative – positive for the environment”), the subjective norms were measured by means of eight items (e.g., “the mills and the food industry expect me to maintain at least 7% of the arable land as an efa”), three items measured the pbc (e.g., “whether i maintain at least 7% of the arable land as an efa is a decision that depends entirely on me”) and two items measured behavioural intentions (e.g., “i intend to maintain at least 7% of the arable land as an efa”). a measure of perceived moral obligation (beedell and rehman, 2000; corbett, 2002; fielding et al., 2008) was added to the tpb model, including two items in the questionnaire (e.g., “i believe that maintaining at least 7% of the arable land as an efa is fair for future generations”). a variable measuring past behaviour was also modelled to consider farmers that have already efa features on their arable land. the respective single-item measure in the survey questionnaire was: “my farm is already maintaining part of its arable land as an efa”. concerning the second behaviour, i.e. the adoption of the private sustainability scheme, a total of four questionnaire items measure attitude in this survey (e.g., “implementing sustainability schemes will improve the environmental quality”). subjective norms were measured by six questionnaire items (e.g., “the public authorities expect me to implement sustainability schemes”), while four questionnaire items assessed the pbc (e.g., “my skills and knowledge allow me to implement sustainability schemes”). two questionnaire items measured behavioural intentions (e.g., “i intend to implement sustainability schemes”). finally, moral obligation was measured by two questionnaire items (e.g., “i believe that implementing sustainability schemes is a commitment to society”). the questionnaire also included items forming variables related to the structure of agricultural holdings (e.g., farm size, farm location, crop cultivation, etc.), and other 134 d. menozzi, m. fioravanzi, m. donati socio-economic aspects (e.g., % durum wheat revenues of the total farm revenues), in order to monitor their effect on behavioural intentions. 3.3 data analysis we tested the hypothesis specified in section 2 by applying an extended version of the tpb model, as defined by ajzen (1991), where intention is determined by attitudes, subjective norms and pbc, farm characteristics and other socio-economic aspects, and where attitudes are influenced by farmers’ moral obligations. a structural equation model (sem) technique was employed on the data that were collected to test for the relative importance of intention determinants in the two considered behaviours. in contrast to other techniques, like multiple regression, sem determines the specifications of the model structure with both latent and observed variables. the latent variables are abstract phenomena that cannot be directly measured by the researcher; latent variables are formed by observed variables (in this case the questionnaire items) that are hypothesised to measure them. the extent to which each questionnaire item is measuring the same psychological construct (e.g., attitudes) is assessed by confirmatory factor analysis, cfa (byrne, 2010). cfa, often referred to as the measurement model, is used when the researcher has some knowledge of the underlying latent variable structure or wishes to evaluate a priori hypotheses driven by theory. in other words, the measurement model (cfa) depicts the links between the latent variables and their observed measures. the internal consistency of the latent variables has been assessed by cronbach’s alpha coefficient. the relationships between the latent variables, identified as the structural model, are usually formulated by linear regression equations, graphically expressed by so-called path diagrams using arrows (see figure 2 and figure 3). sem deals not only with a single simple or multiple linear regression, but with a system of regression equations allowing more complex modelling (nachtigall et al., 2003; mulaik, 2009). using sem it is possible to examine the influence of several variables on several other variables, according to a specified model. in sem exogenous latent variables (i.e. independent variables) “cause” fluctuations in the values of other latent variables in the model (byrne, 2010). in the case studied, subjective norms, pbc, moral obligation and other background variables, such as farmers’ age, are examples of such external factors. endogenous latent variables (i.e. dependent variables) are influenced by the exogenous variables in the model either directly or indirectly, i.e. mediated by other (endogenous) variables. fluctuation in the values of endogenous variables is explained by the model (byrne, 2010). thus, the whole tpb can be tested in relation to the dataset in one analysis (hankins et al., 2000)2. the use of different goodness-of-fit indices is generally recommended to test how well the observed data fit the model. the model fit was assessed with chi-square normalised by the degrees of freedom (χ2/df), comparative fix index (cfi) and root mean square error of approximation (rmsea). the coefficient of determination r-square was used to measure the explained variance of the endogenous variable (i.e., intention). the models were estimated using maximum likelihood procedures. to make sure that the overall fit was not inflated because of the small sample size relative to the degrees of freedom of the model, 2 information on the mathematical foundations of structural equation models can be found in mulaik (2009). 135farmer’s motivation to adopt sustainable agricultural practices we performed a model-based bootstrapping simulation (yuan and hayashi, 2003; byrne, 2010). bootstrapping methods are re-sampling simulations with repetition from the initial collected sample (byrne, 2010). bootstrapping is widely used with path modelling and sems, as these models usually are associated with many degrees of freedom and therefore require a larger sample size than the collected sample (dentoni et al., 2012). in this study, a model-based bootstrapping simulation increasing the sample up to one thousand repetitions leaves the overall fit of the model still acceptable on the basis of the chi-square, rmsea and cfi. 4. results 4.1 farmers’ intention to maintain part of the arable land as an efa farmers reported a moderately low level of knowledge regarding the overall new cap reform. table 2 shows that farmers believe that the new reform will moderately reduce the land value and farm labour. the beliefs of the modifications of the input use (labour included) indicate that the durum wheat producers expect to reduce rather than increase the level of input used in response to the cap reform. moreover, given a supposed reduction in the level of subsidies and farm margins, the respondents have indicated a significant land value reduction in response to the new cap. on the other hand, farmers don’t believe that the cap reform will significantly affect the durum wheat acreage and the fallow areas. table 2. the perceived effects of the new cap. description mean (sd) p value 3 self-reported level of knowledge regarding the new cap 1 3.62 (1.60) 0.049 how do you believe that the cap reform will affect the durum wheat acreage? 2 3.85 (1.13) 0.252 how do you believe that the cap reform will affect the input use? 2 3.70 (1.26) 0.050 how do you believe that the cap reform will affect farm labour? 2 3.52 (1.21) 0.001 how do you believe that the cap reform will affect the fallow areas? 2 4.23 (1.46) 0.196 how do you believe that the cap reform will affect the land value? 2 3.49 (1.31) 0.002 source: own elaboration. 1 scale: 1 (“worst”) – 4 (“moderate”) – 7 (“excellent”). 2 scale: 1 (“strong reduction”) – 4 (“no variation”) – 7 (“strong increase”). 3 one-sample t-test on value 4 (“moderate” or “no variation”). then, we have investigated the intention to maintain at least 7% of the arable land as an efa (behaviour 1), which is considered the most costly greening measure included in the cap reform (matthews, 2013). farmers have expressed a low intention to adopt the new agro-environmental measure (items scores lower than 2.60), even though they believe that the efa is “positive” for the environment (table 3). in this case, farmers are called to adopt practices foreseen by a regulatory public body that might be perceived as an intrusion in the farmer’s decision process. the attitude towards the behaviour is generally nega136 d. menozzi, m. fioravanzi, m. donati table 3. variables and questionnaire items of behaviour 1 “ecological focus area”, cronbach’s alpha, means and standard deviations (sd). variables questionnaire items mean sd p value 2 intention (alpha = 0.95) i intend to maintain at least 7% of the arable land as an efa 1 2.59 2.00 0.000 i’m sure that i will maintain at least 7% of the arable land as an efa 1 2.39 1.96 0.000 attitude (alpha = 0.81) maintaining at least 7% of the arable land as an efa is bad (1) – good (7) 3.79 2.06 0.391 maintaining at least 7% of the arable land as an efa is unrealistic (1) – realistic (7) 3.45 1.67 0.007 maintaining at least 7% of the arable land as an efa is unprofitable (1) – profitable (7) 2.41 1.29 0.000 maintaining at least 7% of the arable land as an efa is negative (1) – positive (7) for the environment 4.97 2.04 0.000 subjective norms (alpha = 0.89) other farmers expect me to maintain at least 7% of the arable land as an efa 1 2.89 1.74 0.000 my family expects me to maintain at least 7% of the arable land as an efa 1 3.63 2.02 0.130 the mills and the food industries expect me to maintain at least 7% of the arable land as an efa 1 3.68 1.86 0.146 the public authorities expect me to maintain at least 7% of the arable land as an efa1 4.87 1.83 0.000 the cooperatives and pos expect me to maintain at least 7% of the arable land as an efa1 3.69 1.78 0.146 the agronomists expect me to maintain at least 7% of the arable land as an efa 1 3.54 1.76 0.029 other durum wheat producers will maintain at least 7% of the arable land as an efa1 3.25 1.65 0.000 consumers (society) expect me to maintain at least 7% of the arable land as an efa1 4.39 1.98 0.098 pbc (alpha = 0.75) i think that maintaining at least 7% of the arable land as an efa is possible 1 3.77 2.11 0.372 my skills and knowledge allow me to maintain at least 7% of the arable land as an efa 1 3.37 2.09 0.013 whether i maintain at least 7% of the arable land as an efa is a decision that depends entirely on me 1 4.45 2.20 0.088 moral obligation (alpha = 0.94) i believe that maintaining at least 7% of the arable land as an efa is fair for future generations 1 4.24 2.01 0.319 i believe that maintaining at least 7% of the arable land as an efa is a commitment to society 1 4.10 1.99 0.678 past behaviour my farm is already maintaining part of its arable land as an efa 1 3.68 2.69 0.313 source: own elaboration. 1 scale: 1 (“strongly disagree”) – 7 (“strongly agree”). 2 one-sample t-test on intermediate value 4. 137farmer’s motivation to adopt sustainable agricultural practices tive; although durum wheat producers believe that they would provide public goods by maintaining at least 7% of the arable land as an efa (i.e., is “positive” for the environment, score 4.97), they also note that this measure could have negative consequences on farm profitability (score 2.41) and be unrealistic (score 3.45). this result is not contradictory, while suggesting that the farmers’ greatest concern is the supposed economic losses from the reduction of productive arable land and not the uncertainty of the positive externality generated. in fact, the general statement “bad – good” yielded 3.79, not significantly different than the median value 4, indicating that farmers perceive both positive and negative consequences. according to the subjective norm items, farmers perceive that especially public authorities and, to a lesser extent, consumers/society expect and would approve their decision to adopt the greening efa measure. from the farmers’ point of view, the public authorities (e.g., the eu and regions) maintain the role of agricultural policy makers and controllers, while consumers represent the end-users of their environmental services provision. the items measuring the moral obligation, however, show different opinions since the mean values, not significantly different than 4 (intermediate level), and the relatively high standard deviations indicate that while some respondents believe in the relevance of the efa for future generations and society, others are not convinced. the scores of the other subjective norm items indicate that farmers perceive that agronomists and other producers would not expect them to perform the behaviour. the other mean scores of the subjective norms, not significantly different than 4, may suggest that farmers might require more participation by external subjects in making their efa decision, such as industries or pos, who may give suggestions on how implement (interpret) the efa measure and provide technical advice. the pbc items confirm that farmers believe to a lesser extent that their skills and knowledge allow them to maintain at least 7% of the arable land as an efa. nevertheless, farmers claim that this decision would be made autonomously. the cronbach’s alpha coefficient values showed a good internal reliability of the constructs. figure 2 shows the results of the structural equation model predicting the intention to maintain at least 7% of the arable land as an efa. the overall goodness-of-fit of the illustrated model, as measured by the fit indices, indicated a good fit to the data. the results show that attitude, subjective norms, pbc, moral obligation and other farms characteristics (i.e., the relative importance of the single farm payment, the relative importance of the durum wheat surface and revenue) accounted for 55% of the variance in the intention to maintain at least 7% of the arable land as an efa (figure 2). this confirms that the hypothesised antecedents account for a significant amount of the variance in intentions. hypothesis h1a suggests that a favourable attitude would predict farmers’ intention to adopt sustainable agricultural practices. the farmers’ attitude towards the behaviour, i.e., the positive or negative personal evaluation of maintaining the arable land as an efa, is indeed the main determinant of the intention (β=0.87, p<0.05), supporting h1a. the other tpb variables are not significant predictor of behavioural intentions; these findings are in contrast to h1b and h1c which suggest, respectively, that subjective norms and pbc would significantly predict farmers’ intention to adopt environmental sound practices. the past behaviour is a significant positive predictor of intentions (γ=0.21, p<0.05). this finding confirms h3 predicting that farmers that have already an ecological area on their agricultural holdings would intend to maintain the efas also in the future. 138 d. menozzi, m. fioravanzi, m. donati the percentage of the durum wheat acreage positively affects the intentions to implement the efa (γ=0.27, p<0.10). the perceived moral obligation, i.e., the personal normative considerations felt by farmers with respect to future generations and society, strongly affects attitude (γ=0.88, p<0.01). hypothesis h2, which predicts that moral obligations have a positive effect on farmers’ attitudes towards sustainable farming, is thus supported. this result suggests that, rather than directly influencing intentions, the farmers who felt a self-generated personal moral obligation had more positive personal attitudes towards the behaviour, which significantly affects the intention to maintain at least 7% of the arable land as an efa. the percentage of the durum wheat surface and the percentage of the durum wheat income are positively correlated (φ=0.62, p<0.01). hence, the moral obligation construct and the other tpb variables are all positively correlated. 4.2 farmers’ intention to implement a private sustainability scheme the analysis of the participation in private sustainability schemes suggests a moderately positive willingness to manage farm activities to achieve environmental goals (table 4). this result is probably due to the voluntary nature of the proposed eco-friendly scheme which is viewed as a flexible entrepreneurial tool for fostering the environmental effort of farmers in a market key. it is likely that the energy production from agricultural biomass indicated as part of a sustainability scheme to be agreed with the food industry, has more contributed to define a clearer connection of the farmer’s environmental efforts with the market, than the cap greening did. however, the attitude of durum wheat producers, based on behavioural belief, personal perception, knowledge and experience, points out that the sustainability scheme is good for environment and human wellbeing, although it will not affect farm’s profitability. indeed, farmers moderately disagree that it would improve farm income (score 3.49), and neither agree or disagree that it would figure 2. structural equation model results, behaviour 1 “ecological focus area”: r-squared, standardised coefficients, correlations and standard errors (in parenthesis). intention attitude subjective norm pbc % durum wheat acreage % rented agricultural land % durum wheat income 0.87 (0.56)** 0.03 (0.30) 0.10 (0.88) -0.16 (0.15) 0.12 (0.09) 0.27 (0.14)* r2 = 0.55 0.68 (0.13)*** 0.62 (0.10)*** -0.21 (0.09)** moral obligation 0.88 (0.06)*** 0.73 (0.12)*** 0.83 (0.10)*** past behaviour 0.21 (0.10)** signif. codes: *** = p < 0.01; ** = p < 0.05; * = p < 0.10. model fit: χ2/df = 1.387; cfi = 0.922; rmsea = 0.074. 139farmer’s motivation to adopt sustainable agricultural practices increase durum wheat price (score 3.97). this uncertainty may have affected the individual evaluation. farmers are normally risk averse agents (hennessy, 1998) and a change in farm production can engender concerns for future farm profitability. the high score for table 4. variables and questionnaire items of behaviour 2 “sustainability scheme”, cronbach’s alpha, means and standard deviations (sd). variables questionnaire items mean sd p value 3 intention (alpha = 0.94) i intend to implement sustainability schemes 1 4.58 1.91 0.013 i’m sure that i will implement sustainability schemes 1 4.17 1.90 0.457 attitude (alpha = 0.83) implementing sustainability schemes will improve the environmental quality 2 5.08 1.85 0.000 implementing sustainability schemes will improve the life quality 2 4.77 1.81 0.001 implementing sustainability schemes will increase farm income, if associated with a certification 2 3.49 1.79 0.020 implementing sustainability schemes will increase the durum wheat price, if associated with a certification 2 3.97 1.91 0.902 subjective norms (alpha = 0.92) my family expects me to implement sustainability schemes 1 4.52 1.84 0.020 the mills and the food industries expect me to implement sustainability schemes 1 4.59 1.80 0.007 the public authorities expect me to implement sustainability schemes 1 5.04 1.73 0.000 the environmental associations expect me to implement sustainability schemes 1 5.39 1.78 0.000 the cooperatives and pos expect me to implement sustainability schemes 1 4.46 1.68 0.023 consumers (society) expect me to implement sustainability schemes 1 5.01 1.66 0.000 pbc (alpha = 0.80) my skills and knowledge allow me to implement sustainability schemes 1 4.61 1.81 0.006 whether i implement sustainability schemes is a decision that depends entirely on me1 5.10 1.88 0.000 i think that implementing sustainability schemes is possible 1 4.97 1.51 0.000 the new technologies could encourage me to implement sustainability schemes 1 5.04 1.44 0.000 moral obligation (alpha = 0.90) i believe that implementing sustainability schemes is fair for future generations 1 5.30 1.65 0.000 i believe that implementing sustainability schemes is a commitment to society 1 4.83 1.79 0.000 source: own elaboration. 1 scale: 1 (“strongly disagree”) – 7 (“strongly agree”). 2 scale: 1 (“extremely unlikely”) – 7 (“extremely likely”). 3 one-sample t-test on intermediate value 4. 140 d. menozzi, m. fioravanzi, m. donati items related to subjective norms indicates that farmers agreed that environmental associations, public authorities, consumers and society in general expect them to implement private sustainability schemes. hence, farmers’ beliefs concerning family, industries, cooperatives and pos expectations about the implementation of eco-friendly practices are also not negligible. the personal skills and the technology can positively contribute to adopt the sustainability schemes; similarly, the autonomy in decision making is a key factor of the pbc. however, the information collected cannot clarify the farmer’s willingness to cooperate with other actors, other farmers and industries, which might reduce the level of autonomy but improve the results. the cooperation along the supply chain represents an important straightness for making sustainability actions effective and for achieving economic and environmental objectives (ilbery and maye, 2005). figure 3 shows the results of the structural equation estimation concerning the intention to implement at farm level the private sustainability scheme as proposed by the food industry. the estimated model shows good fit with the data. the tpb model accounted for 81% of the variance in the intention to implement the private sustainability scheme. the pbc positively affects intentions (γ=0.63; p<0.01). this result confirms h1c, which suggests that pbc would predict farmers’ intention to adopt sustainable agricultural practices. in other words, the individual perceived capacity to face difficulties in performing the behaviour is the main determinant of intentions. the pbc construct is mainly defined by the role of knowledge and the real possibility to implement at farm level the private sustainability scheme. behavioural attitude also play a major role, although to a lesser extent than the pbc, as antecedent of intention (β=0.36; p<0.05). this result supports h1a suggesting that a favourable attitude would predict farmers’ intention to adopt sustainable figure 3. structural equation model results, behaviour 2 “sustainability scheme”: r-squared, standardised coefficients, correlations and standard errors (in parenthesis). signif. codes: *** = p < 0.01; ** = p < 0.05; * = p < 0.10. model fit: χ2/df = 1.276; cfi = 0.948; rmsea = 0.063. 141farmer’s motivation to adopt sustainable agricultural practices farming practices. consistent with h2, predicting that moral obligations have a positive effect on farmers’ attitudes towards sustainable agricultural practices, farmers’ attitude is positively influenced by their moral considerations for future generations and society (γ=0.74; p<0.01). the subjective norm is even not a significant predictor of intention, contrasting with h1b. the incidence of durum wheat acreage on total uaa is another influent driver of behavioural intentions (γ=0.17; p<0.05). this means that the wider the share of durum wheat area, the greater is the intention to join private sustainability schemes, that could result in a lower demand for fertilizers, in the production of renewable energies and/or in the conversion to ecological practices (e.g., integrated and organic farming). 5. discussion and conclusions the results show that attitude, subjective norms, pbc and other farms characteristics accounted for 55% and 81% of the variance in the intention, respectively, to maintain at least 7% of the arable land as an efa and to implement at farm level the private sustainability scheme. these results are satisfactory because a meta-analysis of 185 independent studies in a wide number of domains found that attitude, subjective norms and pbc, on average, accounted for 39% of the variance in intention (armitage and conner, 2001). hence, past applications to the agri-environmental context suggest that tpb variables explain from 23% to 72% of the variance in intention (corbett, 2002; sharifzadeh et al., 2012; fielding et al., 2008; wauters et al., 2010; yazdanpanah et al., 2014). the particular novelty of this paper is in evaluating the italian durum wheat farmers’ intention to adopt sustainable agricultural practices related to the 2014-2020 cap reform design (the efa) and to the private schemes as proposed by the food industry. the efa, although being evaluated as a positive initiative for enhancing the public good provision, is perceived by farmers as a costly measure that can depress the farm economic performance. farmers evaluate the efforts that are required by the greening not properly compensated by the economic transfer (approximately 100 €/ha). this study shows that the farmers’ attitude is the main determinant that positively affects the intention to implement the efa. thus, the awareness that farm investment in efa can protect and improve rural environmental quality is the key element that may support the farmers’ decision to dedicate at least 7% of the arable land to areas with particular environmental features. in past tpb research related to agri-environmental practices, farmers’ attitude has consistently emerged as an important predictor of intentions in different domains, including the adoption of soil erosion control practices (wauters et al., 2010), riparian zone management (fielding et al., 2008), water conservation activities (yazdanpanah et al., 2014) and climate information use (sharifzadeh et al., 2012). as suggested by other authors, a measure of moral obligation may contribute to an independent effect in the prediction of behavioural intentions for certain forms of social behaviour (sparks et al., 1995; beedell and rehman, 2000). in this study, however, the measure of moral obligation did not prove to be a significant direct predictor of intention. perceived moral obligation may be less important in situations in which behaviour is compulsory (de lauwere et al., 2012), as for the commitment to an efa. nevertheless, in this study, the farmers who felt a self-generated personal moral obligation had more positive personal attitudes, which significantly affected the intention to dedicate at least 7% of the arable land to an efa. the farm’s level of spe142 d. menozzi, m. fioravanzi, m. donati cialisation can explain the relationship between the percentage of durum wheat acreage and the intention to maintain arable land as an efa, with the supposed better knowledge of the cap reform and the related criteria of exclusion (matthews, 2013). hence, several studies have argued that repetition of past behaviours can influence current behaviour (see, e.g., corbett, 2002). in this study, past actions taken to maintain natural elements that are required by the efa (e.g., strip and buffer areas, environmental set-aside, etc.) have been strong indicators of farmers’ intention to behave in the future. meaningful past behaviour could include past participation in public-sponsored programs, as well as past actions taken independently to care for the agricultural area, such as planting vegetation not intended for livestock feed (corbett, 2002). the results show a moderate intention to adopt private sustainability schemes for providing positive externalities to society, but preferably if under farmers’ control. attitudes and pbc, including the capacity to manage with own knowledge the sustainability scheme, are the intention’s main drivers. the significant effect of pbc, confirmed by other studies (see, e.g., fielding et al., 2008), may indicate the presence of inhibiting factors or the absence of necessary skills or resources to perform the behaviour. the relevance of collective actions for adopting environmental practices in durum wheat production resulted negligible: the subjective norm, that evaluates the perceived pressure of industries, cooperatives and pos for introducing private schemes, is not significant and cannot be considered as a driving factor. this can mask a lack of coordination between durum wheat producers and other actors along the supply chain in managing the environmental strategies. this situation is highlighted in other studies aiming to identify the level of synergy for integrating environmental sustainability practices into the food chain (ilbery and maye, 2005; renting et al., 2003; falconer and saunders, 2002; falconer, 2000). in particular, farmers seem to perceive that pos are uniquely finalized to sell the product on the market at best possible economic conditions, rather than contributing to redesign the food chain in a more innovative and sustainable way. the cross-comparison of the two analysed behaviours shows both differences and similarities. the intention to maintain the efa land is negative, with average scores of the related questionnaire items significantly below the intermediate value, while the intention to implement the private sustainability scheme is moderately positive. this confirms that the efa requirement is likely considered as an unavoidable costly restriction, whose economic effects are unequally distributed along the chain. on the other hand, the private sustainable scheme seems to be perceived as a possible strategy for improving farmers’ profitability and to catch opportunities within a common effort throughout the pasta supply chain. attitude is a significant predictor of intention in both cases, indicating that as long as durum wheat producers favourably evaluate the sustainable farming practices, they will have higher intention to implement them, and vice versa. the pbc items for the private sustainability scheme yielded significantly higher values than the efas3, indicating that farmers perceive a greater level of knowledge and skills in implementing the private sustainable scheme then in maintaining the efas. in both cases, the subjective norms is not statistically significant in predicting intentions, suggesting that the farmers’ perception of social pressure (e.g., what the food industry, pos, agronomists, public authorities, 3 the mean score of the pbc items for the private scheme is 4.93, while for the efa is 3.86 (p<0.001). 143farmer’s motivation to adopt sustainable agricultural practices etc., expect them to do about the behaviour) and descriptive norm (i.e., how other farmers would behave) doesn’t affect their intention to implement the sustainable practices. this may also suggest that the pasta supply chain and other key stakeholders (e.g., public authorities) should improve the involvement of farmers in exchanging information and sharing the benefits of implementing the eco-friendly practices. the tpb may provide suggestions for possible interventions aiming to stimulate the behaviour (ajzen, 1991). in particular, the analysis clearly indicates the need for a better understanding of farmers regarding the new cap tools. although the questionnaire provided farmers with some specific information regarding the cap reform, we believe that most of the farmers’ concerns towards the greening are due to an incomplete understanding of the new policy instrument. thus, efforts to improve not only the farmers’ knowledge of the greening agricultural payments per se, but also their awareness of the rationale for greening payments, including the new role that the society requires of agriculture, is a central issue that must be addressed by both the policy makers and the food chain operators. although this would require many efforts in terms of time and money, public training programs enabling farmers to acquire the necessary complete understanding of the new policy design are highly recommended. given the low intention of durum wheat farmers to implement the efa measure, a peripheral route of communication using implicit persuasion techniques, which is recommended when farmers are less motivated to perform the desired behaviour, may be more appropriate (jansen et al., 2010; de lauwere et al., 2012). for the adoption of private sustainability schemes, it is evident the need of a greater involvement of farmers in the environmental strategy of the food chain through an enhancement of the strategic role of the pos. as suggested by the analysis, the relationships between pos and food industry should be strengthened to give response to environmental issues creating economic conditions to compensate for transaction costs, and to provide technical support for farmers to improve their skills and knowledge to implement sustainability schemes. farms are not isolated entities but rather participate through their cooperatives and pos in enhancing the competitiveness path of each food chain. this research also shows that the success of a private sustainability scheme will be limited unless the supply chain leaders succeed in shaping more positive farmer attitudes towards ecological practices. indeed, efforts to solve technical difficulties when adopting eco-friendly farming practices are likely to have little effect when farmers’ attitudes remain negative. given a moderately positive intention to implement private sustainability schemes, supply chain leaders could effectively involve durum wheat producers with a more traditional argument-based communication campaign (e.g., instruction cards, checklists and software, etc.). however, tailored communication, taking different farmers’ attitudes into account, is also recommended (jansen et al., 2010). we acknowledge that the limited number of respondents and the length of the questionnaire are the primary limitations in the current study. moreover, our analysis has only modelled self-reported behavioural intention. the triangulation of these results with onfield observations may provide further consistent results. it is also possible that the questionnaire formulation, providing examples of private schemes mainly based on integrated farming, reduction of fertilizers and pesticides and agro-energies, might have limited the spectrum of alternatives to individual farm environmental strategies. nevertheless, this study, originally designed in the context of the forthcoming 2014-2020 cap reform, pro144 d. menozzi, m. fioravanzi, m. donati vides a comprehensive picture of the main determinants that public authorities and food chain operators must address to improve the italian durum wheat producers’ adoption of the new cap’s greening practices and private sustainable schemes. aknowledgments the authors gratefully acknowledge the assistance of dr. davide ampollini in data collection, dr. cesare ronchi for 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(2003). bootstrap approach to inference and power analysis based on three test statistics for covariance structure models. british journal of mathematical and statistical psychology 56(1): 93-110. http://www.saiplatform.org issn xxxx-xxxx (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(1): 1-2, 2012 presidential address this is the first issue of the journal bio-based and applied economics (bae), promoted by the newly funded associazione italiana di economia agraria ed applicata (italian association of agricultural and applied economics). the reasons for the foundation of a new association are rooted in the rapid change over time of the purpose and the methods of scientific research in the field of agriculture and food. the idea of launching a new journal is deeply embedded in the awareness of this change. accordingly, the journal will seek to contribute to scientific debate in the most cutting edge and innovative areas covered by the disciplines and themes addressed by the association, with a close attention to the developing policy debate and clearly focusing on the international dimension of this debate. it is not by chance that the new journal is launched almost at the same time as the recent communication on the bioeconomy released by the eu commission on “innovating for sustainable growth: a bioeconomy for europe”. the discussion on the bioeconomy, now developing exponentially worldwide, allows to make more explicit new challenges and to foster the debate on the sustainable use of biological resources, as well as to explore new dimensions of development and rural economies. bae provides a forum for the presentation and discussion of applied research in the field of bio-based sectors and related policies, informing evidence-based decision-making and policy-making. it intends to provide a scholarly source of theoretical and applied studies while remaining widely accessible for non-researchers. bae seeks applied contributions on the economics of bio-based industries, such as agriculture, forestry, fishery and food, dealing with any related disciplines, such as resource and environmental economics, consumer studies, regional economics, innovation and development economics. besides well-established fields of research related to these sectors, bae aims in particular to explore cross-sectoral, recent and emerging themes characterizing the integrated management of biological resources, bio-based industries and sustainable development of rural areas. special attention will also be paid to the linkages between local and international dimensions. this first issue provides a set of review papers on selected ‘hot’ issues in and around the field of the bio-based economy, and economics. far from being an exhaustive set of papers that shed light on a systematic view of the broad field addressed by the journal, 2 presidential address they are rather teasers that we hope will provide opportunities for thought-provoking discussion and stimulating research. we sincerely hope this initiative will contribute to boost the debate on the bio-based sectors and will help to move attention from the policy discussion to vanguard advances in the related multiple disciplinary fields. prof. giovanni cannata president associazione italiana di economia agraria e applicata bio-based and applied economics 4(1): 77-100, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-14996 the welfare cost of maize price volatility in malawi maria sassi department of economics and management, university of pavia, v. s. felice 5, 27100 pavia date of submission: september 22nd, 2014 abstract. this paper investigates conditional and unconditional maize price volatility in malawi at the country and local-economy market levels and related welfare costs. the empirical analysis applies an arch/garch approach that uses monthly data from january 1991 to march 2013, and the welfare cost is estimated via the lucas formula. the study findings underline the importance of the domestic factors in explaining maize price volatility and of seasonality in affecting the unconditional variance and welfare cost. keywords. maize price volatility, welfare cost, local-economy market, malawi jel code. q11, q12, q13 1. introduction using an arch/garch approach, this paper investigates maize price volatility in malawi at the country level and with reference to nine local-economy markets using monthly data over the time period of january 1991 to march 2013. this information is collected to estimate related welfare costs via the lucas formula. approximately 80 percent of the malawian population relies on maize production for income and food consumption. dominating the country’s maize sector, 96 percent of the total cropland is occupied by smallholder and resource-poor farmers who practice rain-fed methods in typically harsh climates (feed the future, 2013). maize is cultivated for subsistence needs, and only 20 percent of the total production is marketed by one-third of the country’s smallholder farmers (fuentes, 2013). however, the main harvest of the year does not provide an adequate food supply over several seasons, particularly during the lean season. for this reason, a large proportion of the population depends on the local economy or national market food purchases when stocks are depleted. moreover, market dependence on maize has increased over time with pressure from populations whose growth is exceeding the pace of household production (elis and manda, 2012; sahley et al. 2005). thus, the analysis of maize price volatility provided in this paper can support informed policy strategies aimed at addressing the negative effects that these processes * corresponding author: msassi@eco.unipv.it. 78 maria sassi have on welfare trends for a significant portion of the malawian population that lives in chronic poverty and food insecurity. in a recent paper, minot (2014), analysing the trends of food price volatility in africa, noted that, in the wake of the global food crisis of 2007-2008, there was an unprecedented interest by international organisations and governments in high and volatile prices in the international cereal markets and very few empirical investigations addressed this issue with reference to the african context. explanations for developing countries were based on the ideal maximum pass-through assumption, an hypothesis that tends to divert attention away from domestic factors, such as production and consumption shocks, which are often more influential in affecting local market volatility (rapsomanikis, 2009). the possible influence of these factors may, for instance, be reflected in the fact that reported staple food price volatility levels in the global market are lower than those of african countries (minot, 2011). moreover, the existing literature shows that the maximum passthrough hypothesis is not valid for all cases (conforti, 2004; quiroz and soto, 1995). the global price corresponds to the futures market price, which is not equivalent to the price at which the majority of households and farmers buy and sell cereals in africa. rather, households and farmers trade based on local market prices that are denominated in local currencies that reflect local market conditions (gilbert, 2011). it is also important to note that transportation costs, stabilisation policies, and the variety of cereals traded on international and african markets may limit or even fully protect local markets from the world market price pass-through mechanism. for these reasons, studies on international cereal markets must include investigations of volatility at the national and local market levels. such examinations are necessary for constructing responsive policies aimed at mitigating the negative effects of cereal price volatility and its consequent reduction in welfare losses in particular (aizeman and pinto, 2005; deaton, 1999). an extensive body of literature indicates that unforeseen price variations following endogenous or exogenous shocks in world cereal markets can lead to sudden and major social and economic consequences for individuals, households, and farmers while also impacting economic growth, inequality, and balance-of-trade trends in developing countries (see, for example, prakash, 2011). recent studies also show that these negative impacts are not typically offset by good economic times, and as a consequence, negative effects are likely to have a permanent effect (aizeman and pinto, 2005). this impact is exacerbated by the fact that numerous developing countries are not currently implementing adequate mechanisms designed for reducing or managing risks for producers and consumers (balcome, 2011). the aforementioned study by minot (2014) includes malawi in its sample of analysed african countries and investigates maize price volatility. however, the study does not consider three critical aspects that are relevant to developing accurate conceptualisations of volatility. first, the adopted measures of price volatility do not control for seasonality and trends. second, the issue of data quality is not discussed. third, the study’s focus on aggregate average volatility, standard deviation and unconditional variance, does not account for intra-year and inter-month variability in the data series. these aspects must be considered to develop an adequate understanding of maize price volatility in malawi, and the present study will incorporate these aspects using the approach outlined below. 79the welfare cost of maize price volatility in malawi in this paper, volatility is measured from unpredictable components of price variability. for this reason, the maize price series has been de-trended and seasonally adjusted. the control for seasonality is especially important as it acts as a predictable indicator of cereal prices. in malawi, a major maize price increase occurs during the lean season before the maize harvest from january to march, whereas a major reduction in prices occurs after the harvest from april to june, when most households sell their maize yields (sassi, 2012; 2014). the quality of maize price data for malawi should also be adequately discussed. for example, the number of local-economy markets utilised for the computation of average prices at the country level has improved over time, leading to possible bias in long-term price investigations. this limitation justifies our decision to integrate our analysis at the country level with the investigation of the nine local-economy markets. moreover, following suggestions from the study by minot (2014), we examine a longer study period to produce more robust results. finally, our analysis is based on the conditional and unconditional variance value that is estimated using the arch/garch approach. the existing literature generally focuses on aggregate average values of volatility while providing very few indications regarding whether distributions are leptokurtic, i.e. on the possible “heavy” nature of the distribution tails and, hence, the amount of variability in the data they capture. this study overcomes this limitation focusing on monthly dynamics of volatility and allowing a deeper understanding of the effect of domestic factors on the unpredictable variability of maize price. to this end, our analysis begins with an investigation of the pass-through mechanism through examinations of integration between the malawian maize market and international and south african markets. the south african market is included because 60 to 70 percent of cereal imports into malawi originate either formally or informally from southern african countries, namely mozambique, zimbabwe, zambia and tanzania (zant, 2005). moreover, the existing literature focusing on the recent food price spike excludes short-term effects, focusing only on long-term relationships with south african maize prices. we have accounted for this aspect by considering a longer time period, and we provide a more robust analysis of the influence of domestic, regional, and international factors that affect maize price volatility in malawi. the study also compares volatility in international and malawian maize prices for this same reason. the paper is structured as follows. section 2 addresses the issue of data quality. section 3 illustrates the adopted empirical strategy, which is outlined in three steps. first, we present the approach selected for determining maize price transmission. we then discuss the procedure followed to compute the unpredictable component of maize price. finally, we describe the arch/garch approach adopted to estimate unconditional and conditional volatility as well as the lucas formula, which is used to calculate the welfare cost of volatility. this same structure is applied for the presentation and discussion of results provided in section 4. section 5 presents the conclusions. 2. data our empirical investigation on volatility in malawi is based on the price of maize in malawi as well as in international and south african markets. 80 maria sassi for the study of malawian maize prices, we consulted the monthly retail white maize prices in kwacha (mwk) per kilogram provided by the famine early warning system network (fewsnet) national representative in malawi as well as the malawian ministry of agriculture and food security. our definition of white maize includes local, composite, and hybrid varieties. in malawi, there are three maize markets, the local-economy, farm-level, and national markets. in our empirical investigation, we consider the local-economy market. as illustrated by mapila et al. (2013), a local-economy market is defined as a trading centre for a specific rural area that consists of villages and communities. at this market level, maize is sold by producers directly to consumers, or by large traders stationed at the reference central market, and by roving traders who buy from producers and sell to large traders at the same reference trading centre. the local-economy market does not account for maize traded in villages or communities, i.e., in the farm-level market, where prices are discussed in terms of the farm-gate price. this market also excludes maize sold to the malawian agricultural development marketing corporation (admarc) at national market prices established by this government-controlled institution. the quality of our dataset reflects the evolution of the methodology adopted by the ministry of agriculture and food security for the collection of food crop prices at the local-economy market scale. the current agricultural price data system was established in 1988 as an agricultural marketing and estate development initiatives and funded by the world bank until 1995, when responsibility for the system was transferred to the agroeconomic survey of the ministry of agriculture and food security with the financial participation of various donors. over time, data collection procedures were improved with the support of the initiative for development and equity in african agriculture (idea malawi, 2005). in 2005, efforts to refine this system were accelerated in response to unusual variations in prices between markets and time and with the discovery of missing information in the official data. the retail price survey for crops currently covers 80 local-economy markets of its original 30, 72 of which are used to calculate monthly data (government of malawi, 2003). data are collected on a weekly basis at the local-economy market level and aggregated according to the simple monthly average at the market and national levels. over the time period analysed in this paper, the number of local-economy markets with at least one maize price observation per year used for country-level, averageprice calculations by the ministry of agriculture and food security has increased from an average of 17 from 1991 to 1995 to an average of 67 from 2009 to 2013 (figure 1). this fact represents a weakness in our long-term price investigation as it may lead to biased conclusions. malawi is characterised by poor maize market integration across livelihood zones and regions (world food programme, 2010; mapila et al., 2013). in fact, maize price levels and their degree of variability are affected by numerous factors such as harvest conditions, climatic circumstances, transportation costs, commercial openness, and household welfare, all of which vary, often significantly, across the country (malawi vulnerability assessment committee, 2005). in this circumstance, data cannot be comparable over time. the price series also exhibit a number of missing monthly values that have largely been collected in the field, with the support of local key informants, and remaining values have been estimated using the classic method of mean substitution. 81the welfare cost of maize price volatility in malawi for the local-economy market-level analysis, we selected market datasets that had missing values for no more than two consecutive months per year, and missing values were treated as previously described. as a consequence, only nine local-economy markets were considered. however, these markets account for various districts and regions, as illustrated in table 1. table 1. local-economy markets analysed by district and region. local-economy market district region chitipa chitipa northern karonga karonga rumphi rumphi mzuzu mzimba nkhotakota nkhotakota central mitundu lilongwe chimbiya dedza lizulu ntcheu nchalo chikwawa southern for the analysis of the pass-through mechanism, we used the international price in us dollars (us$) for us no. 2 yellow maize, f.o.b. gulf of mexico provided by the world bank and the white maize spot price of south african future exchange (safex) in south african rand (zar), f.o.b. johannesburg. figure 1. number of maize markets with at least one observation made each year (1991-2013). 0   10   20   30   40   50   60   70   80   19 91   19 92   19 93   19 94   19 95   19 96   19 97   19 98   19 99   20 00   20 01   20 02   20 03   20 04   20 05   20 06   20 07   20 08   20 09   20 10   20 11   20 12   20 13 *   n um be r  o f  m ar ke ts   * data are for the period of january to march 82 maria sassi for the purposes of our investigation, we also used the consumer price index (cpi) provided by the imf for the us and south africa in addition to the same index variable provided by the malawian national statistical office and reserve bank of malawi for malawi. finally, the exchange rates for mwk to us$ and zar were provided by the reserve bank of malawi. 3. empirical strategy 3.1 approach to maize price transmission following baffes and gardner (2003), gilbert (2011), and minot (2011), we adopted a vector error correction (vec) model to analyse the transmission of maize price changes from the international and south african markets to the malawian market. this approach allowed us to examine the nature of this relationship over time and, as a consequence, to examine the extent of the pass-through (listorti, esposti, 2012). more precisely, we examined the long-run equilibrium between the international and south african maize price and the malawian maize price, the short-run dynamics and adjustment to the log-run price relationship, and the flow of price information from international and south african maize markets to the malawian maize market (rapsomanikis, 2009). the vec model was adopted because our variables were non-stationary, i(1), and cointegrated (engle and granger, 1987). first, we tested the international, south african, and malawian maize price series for the presence of a unit-root using the augmented dickey-fuller (adf) with the number of lagged variables used to determine the residuals of serial correlation based on the schwarz information criterion (sbic) with 15 maximum lags. the results of the adf test were compared with those of the phillips-perron (1988) non-parametric unit-root test (see, for example, moledina et al., 2004), which is supported by asymptotic theory and which therefore performs better for large samples (mahadeva and robinson, 2004). moreover, this test has two main advantages over the adf test. the test is robust for general forms of heteroskedasticity in the error term and does not require any lag length specification for the test regression. we then detected whether linear combinations of international or south african and malawian maize prices were stationary by conducting the johansen (1991, 1995) test. as previously mentioned, due to the presence of i(1) and cointegrated series, we estimated a vec model specified as follows: δpt m =α +θ pt−1 m − β pt−1 w( )+δδpt−1w + ρδpt−1 m + ε t w = international, south african (1) where δ is the difference operator; pm is the natural logarithm of the real maize price in malawi expressed in mwk and converted to us$ and zar according to the specification of w; pw is the natural logarithm of the real market maize price in us$ for the international price and in zar for the south african price; α is a constant; θ is the error correction coefficient; β is the cointegration factor expressing the long-run elasticity of domestic prices with respect to international prices; δ is the short-run elasticity of the domestic maize price relative to the global or south african maize price; ρ is the autoregressive term; and εt is the error term. 83the welfare cost of maize price volatility in malawi as suggested by minot (2011), the nominal malawian maize price in us$ and the international price of maize in us$ were converted to real terms using the us consumer price index. the same procedure was adopted for testing pass-through values from the south african to malawian maize market. in this case, we used the south african consumer price index to compute the real price series. 3.2 unpredictable components of price variability following moledina et al. (2004), we focused on the stochastic component of the price process as an appropriate measure of volatility. for this reason, the malawian and global maize price time series were seasonally adjusted and de-trended. we applied this procedure to both nominal and real prices, with the latter obtained by deflating the nominal price by the consumer price index. the results are compared based on these two price typologies due to concerns raised in the literature (see, for example, peterson and tomek, 2000) that problems of biased estimates can occur when series are deflated. such biases include potential changes to the time series process and the generation of spurious cycles that do not reflect original data. as the results for the variables for real and nominal prices were virtually identical, following what suggested by the literature (see, for example, sarris, 2000; moledina et al., 2004; huchet-bourdon, 2011), this paper only presents empirical findings based on deflated prices. as was previously underlined, predictable and seasonal movements around the trend are of peculiar importance to the analysis of cereal prices and, more specifically, to the analysis of maize prices in malawi. for this reason, the time series were seasonally adjusted using an x-12-arima procedure (findley et al., 1998) based on multiplicative adjustments. in other words, the price time series is a multiplicative function of the trendcycle, seasonal and random components. according to the statistical tests, such as the akaike’s information criterion (aic), this decomposition method provided a better fit to the additive model, showing that seasonal effects fluctuate proportionately with the trend. this result is confirmed by empirical literature that makes reference to the same methodology for calculating the composition of malawian maize prices (cornia et al., 2012; sassi, 2012, 2014) the hodrick-prescot (1997) filter was adopted to extract the long-term trend component from the price series. this method minimises series variance in the smoothed area of the series. the smoothness of the trend estimate depends on a penalty parameter. the higher is the value of this parameter, the smoother the resulting trend will be. in our analysis, the value of the penalty parameter was assumed to be equal to 14,400, following the original hodrick-prescot value for monthly data (quantitative micro software, 2007). the natural logarithm for the seasonally adjusted price series divided by the trend component represents the price time series used to analyse maize price volatility, i.e. the deviation of the observed maize price from its trend. a de-trended time series was adopted because the global bai-perron l breaks vs. none stability test (bai and perron, 2003) for the regression coefficients of the seasonally adjusted series over its trend, highlighted the existence of multiple structural breaks in all considered price series. as suggested by sarris (2000), by de-trending the time series we avoided misrepresenting structural breaks in the trend as increases in series variance. 84 maria sassi 3.3. measuring price volatility and its welfare cost this paper estimates conditional volatility as a measure of price volatility using an arch/garch approach. this particular model typology requires the use of a stationary time series. for this reason, we first tested our seasonally adjusted and de-trended price series in natural logarithm to determine the presence of a unit-root. according to the existing literature, when the unit-root-hypothesis is rejected, i.e., the mean and autocovariance are not time-dependent, the series remains in levels. on the contrary, the first-difference series must be adopted. however, the unit root test has low power in the presence of a small sample and structural breaks in the series (sarris, 2000). for this reason, we tested the existence of structural breaks by performing the global bai-perron l breaks vs. none stability test, which confirmed multiple structural breaks in the series. thus, following sarris (2000) and dhen (2000), we decided to use the first-difference series as a measure of price volatility. as the second step of our investigation, we tested for serial correlation using the following model: δyt = αt + εt (3) where y is the natural logarithm of the de-trended and seasonally adjusted price. all variables are in natural logarithm. we investigated the possibility that the residuals from our regression may be correlated with their own lagged values using the breusch-godfrey test. when a serial correlation was found, we determined the order of the autoregressive integrated moving average (arima) process and adjusted equation (3) by including autoregressive (ar(p)= p ∑θ p yt− p and moving average (ma(q)= q ∑ρq ε t−q terms as follows: δyt =α t + p ∑θ p yt− p + q ∑ρq ε t−q + ε t (4) model (3) is the “null hypothesis model” which is tested against the complete and alternative model (4) which includes also ar and ma parts. the abovementioned breusch-godfrey test, combined with the aic and schwarz criterion (sbic), informed the selection of ar(p) and ma(q) terms. after correcting for serial correlation, we tested for arch terms, i.e., autoregressive conditional heteroskedasticity in the residuals, by performing the arch lagrange multiplier (lm) test. because all of the time series presented an arch term, we estimated an arch-type model for all of the series. more precisely, we initially made reference to a garch(1,1) model because previous studies, specifically those focusing on the analysis of financial time series, have favoured its performance over that of other models (hansen and lunde, 2011). 85the welfare cost of maize price volatility in malawi in our garch(1,1) model, equation (4) was used as the mean equation, whereas the equation for the conditional variance (σ2) was σ 2 =ω +αε t−1 2 + βσ t−1 2 (4.a) where ω is a constant α and β are parameters, ε t−1 2 is the previous month’s residual volatility (the arch term given by the square residual lag of equation 4), and σ t−1 2 denotes the last month forecast variance—the so-called garch term. four types of error distribution have been verified: the normal gaussian distribution, the student’s t-distribution, the generalised error distribution, and the generalised error distribution with a fixed parameter. to determine the most appropriate model, we performed three tests: the q-statistics test for detecting the absence of serial correlations in the mean equation; the jarque-brera test for verifying normal distributions among the residuals; and the arch lm test for proving the absence of residual arch effects. when the results of the abovementioned tests indicated the garch(1,1) model as not appropriate to describe the investigated phenomena we estimated other variance models (arch, tarch, egarch, and parch) selecting the appropriate according to the aforementioned tests. the model estimated in each case is indicated in the tables presenting results in section 4. as previously indicated, the trend was not included as an exogenous variable in the estimated arch-type models contrary to what has generally been done in empirical investigations reported in the literature on volatility (see, for example, sarris, 2000). in fact, we decided to de-trend the time series. to detect possible efficiency losses in our two-step approach for the definition of the de-trended and seasonally adjusted price time series, we tested our garch models using: a seasonally adjusted price time series and including the hodrickprescot filter and a linear trend as regressor alternatively; and the original price time series with the hodrick-prescot filter or linear trend and a seasonal factor as regressors. the aic and sbic tests indicated that our choice did not compromise the efficiency of our estimate. we monitored the conditional variance process using the estimated variance of returns. moreover, as equation (4.a) satisfied the non-negative constraints (0 ≤ α and 0 ≤ β) and stationarity condition (α + β < 1) which ensures that the process has finite variance (hamilton, 1994), we calculated the unconditional variance of ε as follows: σ 2 = ω 1−α − β (5) the conditional and unconditional variance of maize price in malawi at the country and local-economy market levels were both adopted to assess the volatility welfare cost according to the lucas formula (lucas, 1987). lucas constructed an agent model in which the utility function (u) of a single consumer over an infinite horizon in the case of absence of volatility is u v = t=0 ∞ ∑β t aegt( )1−γ 1− γ (6) 86 maria sassi where a is the mean level of consumption at time t, g is its rate of growth, γ is the degree of risk aversion, and β is the discount factor. in the presence of volatility, consumption in each period includes a stochastic stream such that equation (6) becomes u v = t=0 ∞ ∑β t aegt e−0.5σ 2 ε t( )1−γ 1− γ (7) where σ2 the natural logarithm of consumption variance, describes the amount of risk present and ε is a random variable whose natural logarithm is normally distributed with a mean of zero and a variance of σ2. the welfare cost of volatility (λ) is represented by the level of utility calculated via (6) and (7), where λ is chosen such that the consumer is indifferent to the deterministic stream and risk stream adjusted by compensation (lucas, 2003, p.4). the result, when solving for λ is the lucas formula, which follows the mathematical notation λ ≅ 0.5 γσ 2 (8) according to lucas (2003), the compensation parameter depends, naturally enough, on the amount of risk present (σ2) i.e. the conditional and unconditional variance estimated by our garch models, and the consumer’s aversion to the determined risk(γ). concerning this latter parameter, we referred to varying degrees of risk aversion that are commonly observed in the literature, over a range of one to four in magnitude, and thereby adopted the highest value (prakash, 2011). 4. results and discussion 4.1. market integration the results for price transmission are illustrated in table 2. these results confirm that from january 1991 to march 2013, maize prices in malawi only show a statistically significant, long-term relationship with the safex maize price and are consistent with the results reported in the literature, such as the findings of rapsomanikis (2009) for the 1998-2008 period. as maize is an imported commodity, β, the cointegration factor, is positive and less than one. according to the estimated parameter, approximately 70 percent of the proportional change in south african maize prices is transferred to the malawian price in the long-run, with an error correction coefficient of nearly 12 percent. short-term effects between international or safex maize prices and maize prices in malawi are found to be statistically insignificant over the time period investigated. thus, in malawi, the short-run maize price movements are primarily affected by domestic market conditions. moreover, the statistically significant autoregressive term indicates that past shocks in the domestic market play an important role in determining future maize price trajectories. 87the welfare cost of maize price volatility in malawi the studies that focus on malawi attribute this result to government price interventions and active government involvement in the maize market operations. as underlined by jayne et al. (2006, 2008, 2010), the admarc, which is controlled by the government, can purchase and sell maize, set private sector price bands, and control imports and exports with licenses and duties. this board operates not only in response to economic conditions but often acts according to political circumstances. moreover, malawi possesses a strategic grain reserve that is operated by a state agency, the national food reserve agency (nfra). ellis and manda (2012), focusing on the 2000s, provide a detailed discussion of the failure of the malawian government to stabilise maize markets, of the role of the admarc and nfra in exacerbating maize price variability levels, and of variability trends resulting from seasonal and climatic shocks in particular. the effect of government presence on volatility is first demonstrated by the fact that our analysis finds that unconditional variance for the unpredictable maize price component of malawi is greater than that of international maize price volatility (table 3). in both the analysed cases, the estimated constant term (ω) and garch parameters (α and β) are strongly statistically significant. moreover, the garch process is meanreverting (α+β<1). table 2. maize price pass-through mechanism (january 1991-march 2013). δpt m = 0.0017 (0.0081) − 0.0283 (0.0124) pt−1 m − 1.3826 (0.6354) pt−1 w ⎛ ⎝ ⎜ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟ ⎟ − 0.05936δ (0.1375) pt−1 w + 0.4560 (0.05589) δpt−1 m + ε t m = malawi w = international standard ols regression statistics adj. r-squared  0.1951 sum sq. resids 4.4961 s.e. equation 0.1312 f-statistic 22.3285 log likelihood 164.1200 aic -1.2084 sbic -1.1544 mean dependent 0.0024 s.d. dependent 0.1462 summary statistics for the var system determinant resid. covariance 5.39e-05 determinant resid covariance 5.23e-05 log likelihood  554.1317 aic -4.1067 sbic -3.9715 δpt m = −0.0012 (0.0112) − 0.1156 (0.0279) pt−1 m − 0.6953 (0.0279) pt−1 w ⎛ ⎝ ⎜ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟ ⎟ − 0.0519 (0.1276) δpt−1 w + 0.4765 (0.0754) δpt−1 m + ε t m = malawi w = safex standard ols regression statistics adj. r-squared 0.2829 sum sq. resids  2.1300 s.e. equation 0.1284 f-statistic 18.3648 log likelihood  86.2061 aic -1.2361 sbic -1.1492 mean dependent -0.0036 s.d. dependent 0.1517 summary statistics for the var system determinant resid. covariance  0.0001 determinant resid covariance 0.0001 log likelihood  223.9019 aic -3.2165 sbic -2.9992 (...) p-value 88 maria sassi our empirical findings also show that the unpredictable component of the international maize price is more sensitive to external shocks during the volatility phase: the arch parameter is found to be greater than the garch value. conversely, in malawi, the higher estimated garch parameter found with respect to the arch value indicates that volatility during the previous period has a stronger effect on the development of volatility. 4.2 the garch process the limited effectiveness of policy interventions to respond to volatility-inducing crisis events (sahley et al., 2005) becomes increasingly apparent through an analysis of the conditional variance of the unpredictable component of malawian maize prices (figure 2). according to our findings, the distribution of this variable is leptokurtic, with the highest values coinciding with crisis episodes related to climatic events, namely in 1992, 1994, 2002-2003, 2005-2006, and 2012. this result is fairly predictable. rain-fed agriculture is the dominant farming system applied in malawi, and thus, maize production is highly vulnerable to climatic shocks, which have resulted in acute food shortages and food insecurity over the last two decades (sahley et al., 2005). with maize being the chief dietary staple and with very limited alternative sources of dietary energy available, the elasticity of maize demand is low, and thus, any variation in the volume of maize production significantly affects price fluctuations (manda, 2010). hence, increases in maize prices resulting from a production shortage generate higher levels of volatility. however, during the 2000s in particular, the impact of these events was largely fuelled by strategic grain reserve mismanagement, the admarc interventions based on poor crop estimates, and more recent failures in fertiliser policy (chirwa, 2009; jayne et al. 2010). with constraints limiting both supply and demand, the introduced market restrictions appear to have further accentuated fluctuations in price volatility. considering the yearly average conditional volatility by month over the investigated time period, it can be argued that maize price volatility is also driven by factors other than weather events and government policies. table 3. garch estimate of maize price volatility for the international and malawian markets (january 1991-march 2013). international market malawian market ω 0.0012 [0.0067] 0.0029 [0.0177] α (arch term) 0.4332 [0.0004] 0.2425 [0.0307] β (garch term) 0.2917 [0.0224] 0.4209 [0.0423] arima process (2,2) (0,1) garch (1,1) (1,1) garch distribution normal normal unconditional variance 0.0041 0.0094 [...] p-value 89the welfare cost of maize price volatility in malawi figure 3 shows that, on average, the conditional variance of the unpredictable component of maize price reaches its highest levels in february, may, june, and september in relation to variations in maize stocks. this seasonal component of volatility further limits food access for poor households. our findings show that in february, maize prices reached a peak. this period marks the end of the lean season when the majority of poor households have depleted maize grain stocks produced in the previous season and when most farmers have already sold their maize yields (cornia et al., 2012; sassi, 2012; 2014). these abnormally high maize prices during a time of intensified population dependence on the local-economy market for maize demand is an incentive for large-scale wholesalers to release maize stocks accumulated throughout the year (jayne et al., 2010). in february, market activity intensifies, resulting in relatively higher levels of volatility. at the start of the main harvest period, which lasts from april to july, maize is readily available, and the majority of poor farmers sell their maize production early on in the marketing season, often in a desperate effort to repay debts incurred during the previous farming season and to meet short-term cash needs (jayne et al., 2010). as a consequence of this excess supply, maize prices decline, reaching their lowest and most volatile levels in march. during this period, distressed sellers become price takers, and because the admarc is active only in the latter part of the season (june), there is no floor price. moreover, wholesalers and traders compete to acquire as much maize as possible before the adcmrc sets the floor price (jayne et al. 2010, manda, 2010). as illustrated by mapila et al. (2013), private traders start to buy maize at the beginning of the harfigure 2. conditional variance of the unpredictable component of maize price in malawi by month (january 1991-march 2013). .004 .008 .012 .016 .020 .024 .028 .032 92 94 96 98 00 02 04 06 08 10 12 c on di tio na l v ar ia nc e 90 maria sassi vest period, whereas the admarc typically does this at a later date, primarily because the organisation first waits until the official selling and purchasing price are announced. for this reason, to secure income, poor smallholder farmers often prefer or are forced to sell their maize yields to private traders at lower prices than those established by the admarc. under the pressures induced by these dynamics, the maize market becomes more volatile. as was reported by jayne et al. (2010), when the admarc enters the market, the parastatal agency competes with private traders to acquire maize. as a consequence, maize prices tend to rise rapidly. however, volatility reduced because the admarc price band limits the floor and ceiling of price fluctuations, and by this time, traders have already purchased the majority of maize supplies needed. as a result, the majority of poor households begin to run out of food stocks from their own production by september (sassi, 2012). maize demand intensifies and there is an expansion in maize sales with the approach of the next farming season. during this period, maize prices and maize price volatility increase. 4.3 the welfare cost of volatility the high level of maize price volatility estimated through our empirical investigation corresponds with a relevant welfare cost for smallholder farmers that increase during specific periods of the year due to the estimated seasonal component of the maize price volatility. assuming the highest level of consumer’s price risk aversion (γ=4) as suggested by the literature for poor smallholder farmers in malawi (mac brey msusa, 2007), over the figure 3. yearly average maize price and conditional variance of the unpredictable component of maize price in malawi by month and the maize seasonal calendar. 1   2    3   91the welfare cost of maize price volatility in malawi analysed time period, the lucas formula indicates that the welfare cost of the estimated maize price volatility constitutes an average of 1.7 percent of 1 percent of average monthly consumption, with a maximum value of 6.3 percent of 1 percent of average monthly consumption. because the assumption of complete markets of the lucas formula cannot be confirmed in the case of malawi, the estimated welfare cost of volatility is likely to be higher. following lucas (2003), this cost should be considered negligible for a mature economy in light of the implementation cost of policies aimed at eliminating fluctuations. however, on this point it must be reminded that the welfare cost measured by this paper is not related to aggregate consumption as in lucas, but to the consumption of a subsistence staple food. thus, its burden on the food security of poor households in malawi may be relevant, particularly for distressed sellers. the increase in maize price volatility during the high-price period further compromises the ability of poor households to purchase food. in addition, poor households must endure the consequences of high maize price volatility during the low-price period when they are forced to sell maize with reductions in expected real incomes, which act as disincentives to investment in farming activity. the maize price volatility dynamics estimated through our study also accentuate the low-income and food-insecure status of poor households because the most common method for coping with shocks in the country involves limiting food portion sizes and the frequency of meals (world food programme, 2010). the poorest households may also suffer from irreversible impacts on human capital and future production and income flows because another typical response strategy to shocks involves selling livestock and assets at low prices, seeking employment in the informal market, and migrating to urban centres (malawi vulnerability assessment committee, 2005). furthermore, surplus maize producers, that do not have stock capacities, suffer due to the seasonal component of volatility. 4.4 volatility in local-economy markets the aforementioned considerations become even more critical at the local-economy market level, in which the unconditional volatility of the stationary garch process, with the estimated parameters being statistically significant (table 4), is considerably higher than the value at the country level (figure 4). in addition, over the analysed time period, the intensity of volatility varies across the local-economy markets of the same region (figure 5). mapila et al. (2013) emphasise the key role of the admarc price in maize price formation at the local market level in combination with the minor effects of spatially varying factors. in contrast, our analysis suggests that volatility appears to be more heavily influenced by factors that reflect specific conditions of local-economy markets (figure 6 and 7). such factors include maize supply and demand features such as the existence of maize imports from bordering countries, food aid provisions, the development of the informal market, diversification, road networks and transportation costs, weather conditions, wealth levels, soil quality, and population density. for example, local informants suggested that maize price volatility in rumphi is attributable to frequent droughts. this possibility is consistent with the results of the estimated garch(1,0) model, in which the arch term is found to strongly affect volatil92 maria sassi table 4. garch estimate of maize price volatility in malawian local-economy markets (january 1991-march 2013). deflated ω α (arch term) β (garch term) arima process garch garch distribution chitipa 0.0077 [0.0029] 0.3354 [0.0031] 0.3666 [0.0133] (1,1) (1,1) normal karonga 0.0030 [0.0050] 0.1706 [0.0008] 0.7324 [0.0000] (1,1) (1,1) normal rumphi 0.0089 [0.0000] 0.8320 [0.0000] (1,1) (1,0) normal mzuzu 0.0059 [0.0026] 0.1546 [0.0034] 0.5545 [0.0000] (1,2) (1,1) normal nkhotakota 0.0080 [0.0096] 0.2515 [0.0114] 0.3929 [0.0321] (2,2) (1,1) student’s t mitundu 0.0262 [0.0000] 0.1654 [0.0300] (1,1) (1,0) normal chimbiya 0.0160 [0.0000] 0.2032 [0.0455] (0,1) (1,0) normal lizulu 0.0059 [0.0016] 0.3549 [0.0005] 0.4379 [0.0001] (1,1) (1,1) normal nchalo 0.0111 [0.0001] 0.6245 [0.0000] 0.2006 [0.0434] (1,1) (1,1) normal [...] p-value figure 4. unconditional variance of the unpredictable component of maize price for the malawian local-economy markets and country-average value (january 1991-march 2013). 1    2   0,026   0,031   0,053   0,02   0,023   0,031   0,02   0,028   0,063   0   0,01   0,02   0,03   0,04   0,05   0,06   0,07   u nc on di ti on al  v ar ia nc e   malawi=  0.009   93the welfare cost of maize price volatility in malawi fi gu re 5 . c on di tio na l v ar ia nc e of t he u np re di ct ab le c om po ne nt o f m ai ze p ric e fo r m al aw ia n lo ca l-e co no m y m ar ke ts b y m on th ( ja nu ar y 19 91 -m ar ch 20 13 ). 1   2   3   .0 0 .0 2 .0 4 .0 6 .0 8 .1 0 .1 2 92 94 96 98 00 02 04 06 08 10 12 c hi tip a conditional variance .0 0 .0 2 .0 4 .0 6 .0 8 .1 0 .1 2 .1 4 92 94 96 98 00 02 04 06 08 10 12 k ar on ga conditional variance .0.1.2.3.4.5.6 92 94 96 98 00 02 04 06 08 10 12 r um ph i conditional variaice .0 1 .0 2 .0 3 .0 4 .0 5 .0 6 .0 7 .0 8 92 94 96 98 00 02 04 06 08 10 12 m zu zu conditional variance .0 0 .0 2 .0 4 .0 6 .0 8 .1 0 .1 2 92 94 96 98 00 02 04 06 08 10 12 n kh ot ak ot a conditional variance .0 0 .0 4 .0 8 .1 2 .1 6 .2 0 .2 4 92 94 96 98 00 02 04 06 08 10 12 m itu nd u conditional variance .0 1 .0 2 .0 3 .0 4 .0 5 .0 6 .0 7 .0 8 .0 9 .1 0 9 2 9 4 9 6 9 8 0 0 0 2 0 4 0 6 0 8 1 0 1 2 c hi m iy a conditional variance .0 0 .0 4 .0 8 .1 2 .1 6 .2 0 .2 4 9 2 9 4 9 6 9 8 0 0 0 2 0 4 0 6 0 8 1 0 1 2 li zu lu conditional variance .0 0 .0 5 .1 0 .1 5 .2 0 .2 5 .3 0 .3 5 .4 0 92 94 96 98 00 02 04 06 08 10 12 n ch al o conditional variance 94 maria sassi ity dynamics. this aspect may also partly explain why the highest levels of volatility were reached in the months of may and august, when the dramatic need to sell a poor a short maize production increased the price response of the market, resulting in an intensification of volatility. in the local-economy market of chitipa the monthly average maize price volatility is relatively low during the lean season, likely due to the region’s location adjacent to the figure 6. yearly average conditional variance of the unpredictable component of maize price in the local-economy markets of northern malawian region by month (january 1991-march 2013). figure 7. yearly average conditional variance of the unpredictable component of maize price in the local-economy markets of northern and southern malawian regions by month (january 1991-march 2013). 95the welfare cost of maize price volatility in malawi major maize surplus area of tanzania (minot, 2011), which in turn causes the area to be affected by cross-border trade: imports can mitigate the price market response to domestic factors. according to our results, the highest level of unconditional volatility found in the local-economy market of nchalo, which is located in southern malawi, seems to be dependent on shocks: the arch term in the garch(1,1) model is higher than the garch term. this local-economy market is located in a densely populated area that is prone to flooding and in a region where the dominant farming system is characterised by households with the smallest plots of land in the country. our analysis indicates that not only the level but also the time dynamics of maize price volatility varies across the local-economy markets. as discussed by jayne et al. (2010), this latter aspect may be determined by the fact that market activity varies seasonally and regionally. maize is marketed at earlier periods in areas dominated by poor smallholder farmers who specialise in maize production or who wish to purchase chemical fertilisers. these farmers effectively wish to use income from other activities or livelihood strategies during the lean season. a different strategy is adopted by poor smallholder farmers who combine tobacco production with maize production. these farmers sell tobacco first while selling maize later in the season to capitalise on higher prices. in turn, maize markets in these areas are activated later on in the marketing season. as a consequence of the estimated intensity of conditional maize price volatility, the welfare costs calculated at the local economy-market level are found to be dramatically higher than those at the country level (table 5). table 5. summary of the welfare cost of conditional price volatility by local-economy market (january 1991-march 2013). local-economy market mean minimum maximum std. dev. no. observations chitipa 0.0487 0.0195 0.2223 0.0327 265 karonga 0.0558 0.0125 0.2617 0.0397 265 rumphi 0.0521 0.0177 1.0097 0.0945 265 mzuzu 0.0387 0.0269 0.1490 0.0163 265 nkhotakota 0.0456 0.0266 0.2011 0.0271 264 mitundu 0.0627 0.0523 0.4727 0.0320 265 chimbiya 0.0401 0.0320 0.1974 0.0175 266 lizulu 0.0528 0.0219 0.4485 0.0484 265 nchalo 0.0725 0.0279 0.7705 0.0841 265 moreover, as demonstrated in figures 6 and 7, the seasonal component of maize price volatility is also confirmed at the local-economy market level. despite single-market specificities, maize price volatility and thus welfare cost reach a peak when the majority of poor households depend on markets as buyers only, net buyers, or distressed sellers. 96 maria sassi 5. conclusions several interesting findings emerge from our empirical investigation. first, the study confirms the findings of the existing literature focusing on more restricted time periods that argue that maize price volatility in malawi is primarily dependent on domestic factors rather than on international market shocks. the malawian maize market is integrated with the south african market but only with respect to long-run trends. as a consequence, to reduce maize price volatility levels, which in malawi are higher than those of the international market, a primary focus on domestic policies is needed. one of the most relevant findings of the country-level analysis relates to the effect of seasonality on unconditional volatility: maize price volatility levels are higher during certain periods of the year. this trend is confirmed at the local-economy market level despite varying spatial factors that determine different volatility dynamics. these findings underline the need to improve or establish an effective storage, marketing, and trade structure for inter-seasonal and spatial arbitrage. moreover, this finding partly confirms the storage model. according to the model’s prescriptions, increases in price and volatility are positively correlated: price increases tend to deplete stocks and increase volatility (williams and wright 1991). in our study, maize price volatility levels also rise when the price of this staple food declines due to the poor stock capacity of poor smallholder farmers and their often desperate need to sell produce. moreover, the seasonal dynamics of maize price volatility reflects the complex maize marketing system established in malawi and the composite production and consumption strategies put in place by poor smallholder farmers and their households in a poor and highly food-insecure country. in fact, the analysis suggests that the conditions in farm-level and national maize markets affect price volatility at the local-economy market level. poor households depend on the local-economy market for purchasing and selling maize when the farm-level market cannot satisfy their food and income needs. the farm-level market remains undiversified on both the demand and supply side in addition to being heavily exposed and vulnerable to climatic shocks due to the presence of a rain-fed-dominated farming system. trade business remains poorly developed and, due to high transportation costs, operative over short distances (zant, 2005). due to the existence of a single major harvest and poor stock capacity, the majority of poor households, assuming their combined role of consumers and producers, depend on the local-economy market during the same periods of the year (jayne et al. 2010). according to our analysis, maize price volatility levels increase with the intensification of negotiations between poor smallholder farmers and private traders and declines when the admarc activates the national market. this finding suggests the importance of enhancing poor farmer productivity and marketing knowledge in an effort to limit maize price volatility. minot (2014) presents a number of possible explanations for his findings regarding the fact that african countries with large state agencies attempting to stabilise food prices generally show higher volatility levels than those with little or no stabilisation efforts. our empirical investigation of the conditional variance allows us to better address this issue. our results suggest that maize price volatility on the maize local-economy market in malawi intensifies with an increase in competition among private actors before the admarc 97the welfare cost of maize price volatility in malawi enters the market. in particular, private traders during the post-harvest period strengthen their activity to avoid price band limitations and competition from this state agency. it should also be noted that volatility is partly affected by competition within the private sector in the local-economy market, which is severely constrained by government trade controls and, more generally, by a dysfunctional policy environment (sahely et al., 2005). moreover, due to the presence of a diet composition and agricultural production structure that are both dominated by maize, maize demand and supply dynamics are not affected by the price of substitutes and complements. for this reason, the recent discussion on clarifying the role of the admarc in private-sector participation (jayne et al., 2010) should be enlarged and be also focused on the need to diversify the maize economy as a means for poor households to manage risks of maize price volatility. with respect to the seasonal aspect of volatility, according to our results, volatility intensity reaches its highest levels during the adverse price conditions for poor households when they are sellers and buyers hurting their food security because a more limited food access. in contrast to the conclusions of certain authors (waug, 1994; oi, 1961; massel 1969), such households only suffer from the welfare costs of volatility. for this reason, our findings support the body of literature (see, for example, pallage and robe, 2003) that perceives smoothing price volatility gains as being significantly high particularly during maize price volatility seasonal peaks in this land-locked country, which exhibits widespread food insecurity and poverty and a lack of major natural resources other than lake malawi, thus making populations dependent on maize production for household welfare, economic growth, and employment. according to our analysis, a new food price problem faces policymakers in malawi. the classical policy dilemma examines strategies for keeping prices low enough to ensure low-income consumers’ access to food while also keeping prices high enough to incentivise farm production (jayne et al., 2010). a new consideration involves devising strategies for reducing volatility levels when maize prices reach their peak and lowest values. this problem, which is related to the classical dilemma, is of specific importance for the improvement of food security in malawi. 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(2005). food import risk in malawi: simulating a hedging scheme for malawi food imports using historical data. commodity and trade policy research working paper no. 13. rome: fao. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 3(1): 83-91, 2014 doi: 10.13128/bae-14189 observing and analysing the bioeconomy in the eu – adapting data and tools to new questions and challenges robert m’barek*, george philippidis, cornelia suta, cristina vinyes, arnaldo caivano, emanuele ferrari, tevecia ronzon, ana sanjuan lopez, fabien santini european commission, joint research centre (jrc), institute for prospective and technological studies (ipts) 1 abstract. the concept of ‘bioeconomy’ is receiving increased attention in policy and business circles. the european commission (ec) has initiated the bioeconomy strategy which is a signal of intent that the eu seeks to meet the challenge of reconciling responsible-resource usage respecting sustainability criteria, with wealth-generation. to this aim, the ec’s joint research centre (jrc) has been entrusted to implement a bioeconomy information systems observatory within which the objective is to develop an ongoing coherent picture of the activities of this sector, whilst developing forward-looking tools of analysis to help respond to the aforementioned challenge. this paper provides a discussion on the research activities which are currently under development at the jrc. whilst the scale of ambition of the bioeconomy observatory is significant, it is recognised that much of the research conducted so-far remains workin-progress and is therefore only a starting point to fully capturing the nuances of this diverse and complex sector. keywords. bioeconomy, european union, social accounting matrix, cge, databases. jel codes. q1, q2 1. introduction as a reflection of the importance of the ‘bioeconomy’ in european union (eu) policy circles, in late 2012 the european commissioner for research and innovation led an initiative known as the ‘bioeconomy strategy’, co-signed by the commissioners for agriculture and rural development, environment, maritime affairs, and industry and entrepreneurship. within this strategy, the ec defines the bioeconomy as, “the production of renewable biological resources and the conversion of these resources and waste streams into value added products, such as food, feed, bio-based products and bioenergy” (ec, 2012, p. 3). indeed, as a potentially important source of sustainable growth within the eu, the european commission (ec) definition of ‘bioeconomy’ encompasses all kinds of biomass use whilst respecting three sustainability goals (economic, social, and environmental). * corresponding author: robert.m’barek@ec.europa.eu. http://dx.doi.org/10.13128/bae-14189 84 r. m’barek et alii this holistic approach represents an opportunity for the eu to simultaneously reconcile responsible-resource usage and wealth-generation. on the other hand, existing european and global policies do not always provide a set of coherent objectives to be pursued at the same time. to better understand these conflicts of interest, one must first have a clear picture of the status quo. according to the latest available official numbers for 2009, the bioeconomy in the eu represents a market estimated to be worth over eur 2 trillion, providing 20 million jobs and accounting for 9% of the total employment (ec, 2012, p. 5). a more recent study (carus, 2012a) estimates a turnover of eur 2.8 trillion and approximately 29 million jobs for the year 2012. nevertheless, both sources only provide broad estimates which are highly uncertain. the bioeconomy strategy aims at focusing the eu’s common efforts in the right direction to “help europe to live within its limits. the sustainable production and exploitation of biological resources will allow the production of more from less, including from waste. the bioeconomy will also contribute to limiting the negative impacts on the environment, reduce the heavy dependency on fossil resources, mitigate climate change and move europe towards a post-petroleum society” (ec, 2014a). yet, the definition of right direction is open to interpretation. accordingly, under the auspices of the bioeconomy strategy, a european bioeconomy panel has been set up to support interactions between different policy areas, sectors and stakeholders, in order to get a global overview of the different components and dimensions of the bioeconomy. the bioeconomy panel brings together, in one group, people with different perspectives and areas of expertise (ec, 2014b). in addition, an expert group for bio-based products has been created to assist the commission in the preparation of legislation or in policy definition in particular in the framework of the reviewed industrial policy (bio-based news, 2014). furthermore, the eu research and innovation programme from 2014 to 2020, known as horizon 2020, also includes research relevant to this sector (ec, 2014c). in addition to the eu strategy, several eu member states have also designed their own national bioeconomy strategies, which are linked in particular through the standing committee for agricultural research (scar) to the european commission. on a separate front, the bioeconomy action plan also foresees the establishment of a bioeconomy information systems observatory “to regularly assess the progress and impact of the bioeconomy and develop forward-looking and modelling tools.” (ec, 2014d) the european commission’s joint research centre (jrc), entrusted to implement the bioeconomy observatory, has proposed three main pillars which aim to: i) monitor investments in research, innovation and skills; ii) reinforce policy interaction and stakeholder engagement; and iii) enhance the knowledge of markets and competitiveness. in this short paper, the focus is on the last of these pillars, which aims at the description, quantification and analysis of the development of sectors in the bioeconomy from a socioeconomic point of view. more specifically, this paper provides some insights into ongoing research activities within bioeconomy observatory of the jrc related to data and economic assessment tools implemented in-house under the auspices of the integrated agroeconomic modelling platform (imap) (m’barek et al., 2012). the paper is therefore organized as follows. section 2 describes the jrc state-of-theart data storing, comparing and visualisation tool for agriculture, markets and models, 85observing and analysing the bioeconomy in the eu known as datam. section 3 deals with the description of bio-based sectors in each of the eu member states (ms), based on the application of a detailed set of social accounting matrices (sam). section 4 introduces a global simulation model for forward-looking scenario analysis. section 5 concludes. 2. databases and visualisation of the bioeconomy since 2010, the jrc’s in-house data management tool, known as datam, has been employed to streamline data related tasks when researching agricultural markets. more specifically, it has been used as a source for economic modelling purposes, an easy to use data validation tool when cross checking between different databases, and as a way of analysing results. using one interface only, users can rapidly access the main agricultural and trade databases (as provided by eurostat, faostat, fapri, usda, oecd and others) as well as the in-house model databases. the tool therefore addresses different needs, ranging from data collection and data checks to advanced reporting with the possibility to export data. the overall accessibility to datam is extended through a web-based version, which was inaugurated in january 20142. more information about this tool can be found in hélaine et al. (2013). the lack of data for measuring bioeconomy is one of the main challenges of the bioeconomy observatory. currently, relevant indicators can be found in varied databases from different providers. datam’s contribution consists of providing access to different existing datasets collected both from publicly available sources, such as eurostat and fao, and non-public and/or ad-hoc data and that can be used by researchers, policy makers and general public to monitor bioeconomy figures in europe. to some extent, datam already includes data relevant to certain areas of the bioeconomy, (e.g., agriculture, food, energy). for example currently available data for agriculture refers to production, land area, prices, agricultural trade, population and employment. moreover, crops are more than just a source of food and feed (animal or human consumption), but also a main provider of biomass. as one of the data sources already available in datam, the fao food balance sheet database provides, among others, valuable information for the bioeconomy as it also contains non-feed and non-food uses of crops. in the future, the aim is to incorporate a comprehensive picture in relation to the other main sectors providing biomass: forestry, fisheries and aquaculture, industries other than food, biotechnology-based or not, and waste sectors. since the objective of the bioeconomy observatory is to measure bioeconomic activity in the european union, the main public data provider used for this purpose will be eurostat. biomass is an important product of the bioeconomy which, until recently, has only received limited attention from statistical data providers. very few databases include comprehensive data on biomass supply (such as production, market value); among those, two could be mentioned: eurostat material flow accounts and seri global material flow database. to partly fill in the gap with respect to agriculture, the jrc in collaboration with the nova institute is working on a database with estimates of biomass supply (fresh matter and dry matter). using coefficients found in the literature, biomass dry mat2 accessible at http://www.datamweb.com/ 86 r. m’barek et alii ter is extracted from harvested agricultural biomass (fresh matter). in addition, thanks to the use of recovery coefficients, this dataset responds to the need of measuring used and unused crop biomass at the eu level. unused biomass can then be used as an indicator of the scope of biomass available for recycling or fertilising activities. further work will be needed for other sources of biomass. there are also significant data gaps regarding the total biomass flows (use of biomass supplied), in particular concerning trade aspects, different uses between food, feed, fuel and other industrial purposes, and waste. the bioeconomy observatory will seek to combine information from various sources and datasets (such as, for example, industrial production and structural business statistics) to portray as comprehensive a picture as possible of the eu biomass balance sheet. 3. profiling the bioeconomy using sam multipliers in the context of different projects, the agrilife unit at jrc developed ‘social accounting matrices for eu27 with a disaggregated agricultural sector’ (agrosam) (mueller et al., 2009) for each of the member states. a description of the database building process and an example of its use in the examination of the bioeconomy for spain can be found in cardenete et al. (2012). in their paper, the authors employ multiplier analysis to examine the economic structure and relative influence of bio-based sectors relative to the rest of the economy. following this line of research, traditional multiplier analysis is also employed as a vehicle to better understand the role of the bioeconomy across all eu27 member states. forward linkage (fl) and backward linkage (bl) are relative measures of supplierbuyer relationships within the economy under conditions of leontief technologies. more specifically, the fl measures the relative importance of sector x as a supplier to the remaining industries in the economy whereas the bl measures sector x’s relative importance as a demander of goods from remaining sectors. thus, if sector x has a bl or fl greater than 1, then 1€ of intermediate input (bl) or output change (fl) in that sector generates an above-average level (i.e., greater than €1) of wealth measured against the remaining sectors. those sectors which encompass fl and bl greater than 1 are identified as key or strategically important sectors. as an initial step, it was deemed necessary to improve the relevance of the study by updating the agrosam tables for all of the eu27 members from the year 2000 to 2007.3 the choice of update year is motivated by the availability of eurostat supply and use tables (sut) for almost all of the eu27 regions. thus, the non-agrofood accounts in the updated agrosam (i.e., industry costs, commodity supplies, exports, imports, final demands, investment demands, margins and net taxes) are taken directly from eu27 sut tables. the agri-food sector accounts are updated to 2007 subject to additional secondary data targets from eurostat (2013), whilst a further set of macroeconomic aggregate demand targets for each eu27 member state (eurostat, 2013) are also enforced subject to sam balancing restrictions. 3 given the data intensive nature of input-output tables, there is typically a time-lag in their construction. this problem is further exacerbated when one is attempting to find a consistent year across 27 eu member state. 87observing and analysing the bioeconomy in the eu as noted above, the desired application for these data is the calculation of multipliers for 45 designated bio-based sectors.4 to aid the process of comparison and profiling of the bioeconomic sectors across member states, a series of statistical tests are envisaged. thus, employing a hierarchical cluster analysis, it is possible to identify regional clusters of eu members employing bl and fl variables as segmenting variables. in this way, the original 27 member states are reduced to a smaller number of regional clusters, with a similar identifiable set of bio-based structures. to identify which bioeconomic sectors are structurally heterogeneous across clusters, one-way anova tests can be implemented to focus on comparing the differences in the bl mean multiplier by sector and the fl mean multiplier by sector, across the clusters, whilst further identification of key sectors (fl and bl greater than 1) will pinpoint strategically significant sectors. 4. simulating macro-economic impacts of biobased technologies in the eu to promote and monitor the development of the eu bioeconomy, the european commission launched a complementary project to the bioeconomy observatory, the systems analysis tools framework for the eu bio-based economy strategy project (satbbe5). the sat-bbe project aims at developing an analysis tool for monitoring the evolution of the biobased economy based on both quantitative and qualitative analytical models and tools. there are already several models and tools that can be, and have been, used to evaluate certain aspects of the bioeconomy. for example, plevin et al. (2013), wicke et al. (2011), edwards et al. (2010), laborde (2011) and kim et al (2011) analyse the impacts of first-generation biofuels on indirect land use change (iluc). the first three aforementioned papers review existing models and highlight the modelling challenges when modelling iluc. laborde (2011) presents a detailed study using a customised computable general equilibrium (cge) model linking trade liberalization, iluc, biofuels and greenhouse gas (ghg) emissions. employing a statistical analysis, kim et al. (2011) detect evidence for iluc that might have been catalysed by united states biofuel production. other studies (ciaian and kancs, 2011; rathmann et al., 2010) focus more on food security and land use competition between food production and biofuels with the use of statistical, time-series analytical mechanisms. while timilsina et al., (2011) employ a cge for the same purpose, linking oil price, biofuel and food supply. lastly, tyner (2010) and banse et al. (2008) have focused on the link between biofuel policies and global agricultural markets, the former with descriptive analytical tools and the latter with the use of a customised cge model. in the context of the bioeconomy observatory, a collaboration involving the jrc is currently underway which employs a customized recursive dynamic global cge model 4 only those sectors where a clear bioeconomic input is identifiable are chosen (based on an examination of the intermediate input structure in the supply and use tables of the eu27. other (aggregate) sectors such as chemicals, textiles etc., where a bioeconomic sector is present, were discarded owing to a large non bioeconomic component in the output of the sector, whilst a lack of information was available to disaggregate the bioeconomic from the non-bioeconomic component across all 27 eu members. 5 http://www3.lei.wur.nl/satbbe/project.aspx. 88 r. m’barek et alii known as the magnet model (modular applied general equilibrium tool)6. this model has already been applied to analyse the behaviour of the bio-based sectors and their inter-linkages with the rest of the economy (van meijl et al. 2012). one specific aim of the current study is to understand the role of second generation biofuel technologies capturing both the direct and indirect economic impacts of this bio-based sector. from a data perspective, magnet principally relies on the well-known gtap database which describes bilateral trade patterns, production, consumption and intermediate use of commodities and services. the current release of the gtap database (narayanan et al., 2012) includes dual reference years (2004 and 2007) as well as coverage of 129 regions and 57 gtap commodities. the input-output tables of several countries, especially oecd members, were adjusted in the gtap 8 version, to match 2004 and 2007 agricultural production statistics by sector. these are particularly important adjustments for the bioeconomy, given the large percentage that agricultural products contribute to the final costs of bio-energy products. within the sat-bbe project, approximately 30 of the 57 sectors are classified as bio-based or potentially bioeconomy sectors, while some sectors include activities that are partially bio-based, such as food processing, wood and paper industries. in terms of modelling, magnet builds on the standard gtap cge template by including non-standard modules that can be activated depending on the relevance of the policy question at hand. thus, additional modules are activated to incorporate endogenous land supply and land transformation between sectors, whilst a specific module encompassing the activities of first generation biofuels and its interaction with (inter alia) agricultural sectors, is also included. in addition, magnet characterises fertilizer as a separate activity, considering the impact of this sector on crop yields and thereby on land use change. as noted above, the jrc will be looking to further extend the work to analyse the potential role of second generation biofuels, biochemicals and/or bio-electricity. the supply of biomass from woody or grassy crops and from residues and waste are not considered yet, but subject to data constraints, are important possible candidates for further research. 5. conclusions before starting any analysis related to bioeconomy and partially incoherent objectives this concept brings about, a clear picture of available data and methodologies is needed. to this end, the european commission established a bioeconomy observatory. within this framework, the jrc is extending its web-based data management tool (datam) to provide access to different datasets that can be used by researchers, policy makers and the general public to monitor in particular the primary sectors of the bioeconomy in europe. an analytical and economic way to better understand the role of the bioeconomy across all eu27 (28) member states is performed through multiplier analysis. updating and employing the ‘social accounting matrices for eu27 with a disaggregated agricultural sector’ (agrosam), developed at jrc, regional clusters of eu members, different bioeconomic structures and strategically significant sectors can be identified. indicating significant and key sectors is already an important evidence for potential investments to 6 http://www.wageningenur.nl/en/expertise-services/research-institutes/lei/research-areas/international-policy. htm. 89observing and analysing the bioeconomy in the eu foster growth and/or create jobs. however, constituting new markets, and at least parts of the bioeconomy are novel markets, as well as incentivizing investments, need a stable and long-term policy framework. capturing the influence of policies and other drivers in a coherent framework can be achieved with the so-called general equilibrium models. within the bioeconomy observatory and in collaboration with other research institutions, the magnet model (modular applied general equilibrium tool) is used to analyse the behaviour of the bio-based sectors and their inter-linkages with the rest of the economy and europe’s trade relationship with third countries. providing a consistent forward-looking economic framework, for instance the potential role of second generation biofuels, biochemicals and/or bio-electricity under specific macro-economic and policy assumptions, is quantified. as an approach to steer biomass to its most beneficial uses for the economy, society, and the environment, the “cascading principle” (ep, 2013; p. 6) could become the guiding paradigm. as defined by the german federal environment agency, this framework provides a, “...strategy for using raw materials or the products made from them in chronologically sequential steps as long, often and efficiently as possible for materials and only to recover energy from them at the end of the product life cycle. it is based on the use of so-called ‘cascades of use’ that flow from higher levels of the value chain down to lower levels, increasing the productivity of the raw material” (carus, 2012b, p. 2). this definition places the use of biomass for energy generation at the end of the cascade, which at present is not the case, and therefore would not provide a level playing field for all actors (ec 2011) and equal access to – sustainable biomass. beyond these criteria, it is ultimately up to the society (and policy makers) to decide on the use of biomass. choices are to be made. to analyse the cascading use, a particular challenge from a modelling perspective is to identify and disaggregate those bioeconomic components of existing industry classifications to form new subsectors. in this way, a more detailed quantitative approximation of the importance of individual bioeconomy sectors on upstream suppliers and downstream markets is plausible, which more closely captures the concept of the cascading use. acknowledgment: the project benefited from support granted by the european union’s seventh framework programme under grant agreement no 341300 (bioeconomy information systems observatory project). authors acknowledge valuable comments by hans van meijl, alejandro cardenete, edward smeets, andrzej tabeau, maria del carmen delgado and damien plan. the views expressed in this paper are the sole responsibility of the authors and do not necessarily reflect the views of the european commission. references banse, m., v. meijl h., tabeau a. and wojter g. 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(2011). indirect land use change: review of existing models and strategies for mitigation. biofuels 3(1): 87-100. bio-based and applied economics 4(1): 1-16, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-14715 effects of switching between production systems in dairy farming antonio alvarez1, carlos arias2 1 university of oviedo and oviedo efficiency group 2 university of león and oviedo efficiency group date of submission: july 17th, 2014 abstract. the increasing intensification of dairy farming in europe has sparked an interest in studying the economic consequences of this process. however, empirically classifying farms as extensive or intensive is not a straightforward task. in recent papers, latent class models (lcm) have been used to avoid an ad-hoc split of the sample into intensive and extensive dairy farms. a limitation of current specifications of lcm is that they do not allow farms to switch between different productive systems over time. this feature of the model is at odds with the process of intensification of the european dairy industry in recent decades. we allow for changes of production system over time by estimating a single lcm model but splitting the original panel into two periods, and find that the probability of using the intensive technology increases over time. our estimation proposal opens up the possibility of studying the effects of intensification not only across farms but also over time. keywords. dairy farms, intensification, latent class model, panel data. jel codes. c23, d24, q12 1. introduction the number of dairy farms in the european union has fallen dramatically in recent decades and continues to decline. given that farm output remains roughly at quota level, over the same period the average size of dairy herds has increased steadily. at the same time, genetic and management improvements in dairy cattle have permitted large increases in milk production per cow. these structural changes have provided the basis for the propagation of intensive systems of production in the dairy sector. in fact, intensification is often mentioned as a feature of structural change, reinforcing the interest in the analysis of the impact of intensification on productivity and efficiency. the relationship between structural change and intensification has an interesting aspect in our empirical example since institutional and geographical reasons make it difficult to grow by accruing more land. * corresponding author: carlos.arias@unileon.es. mailto:carlos.arias@unileon.es 2 a. alvarez, c. arias some of the farms are small, with fragmented land and located in hilly areas (del corral et al., 2011). obviously, the cessation of these farms does not increase the land available for the farms that stay in production and that are mainly located in flatter coastal areas. extensive dairy farming, on the other hand, consists of producing milk using mainly on-farm produced forage with low stocking rates. fostering production using extensive systems has often been an explicit goal of agricultural policy, justified by factors such as environmental soundness, improved animal welfare, or use of abundant land in some areas. in contrast to this, many dairy farms in europe have gone in the opposite direction, adopting more intensive production systems. despite the importance of the intensification process and the intense policy debates it has generated, few papers have studied the intensification of dairy farming using economic analysis. most articles have adopted a technical perspective, describing the physical changes of the process (e.g., simpson and conrad, 1993), while very few have analyzed the economic consequences of this process (some exceptions are alvarez et al., 2008, 2010, and nehring et al., 2011). from an empirical point of view, the coexistence of both extensive and intensive farms implies that there are two different technologies in the sector. this runs contrary to the assumption of a common technology for all farms which is the most frequent in the production literature. however, there is also awareness among researchers of the estimation bias that arises if such an assumption is unrealistic. for this reason, several approaches have been followed in dairy sector studies in order to account for the likely existence of different technologies. the most basic one is to drop a number of farms from the sample on the grounds that they may operate under a different technology (tauer and belbase, 1987). a second approach is to split the sample into several groups based on some observable farm characteristics. for example, hoch (1962) divided the sample into two groups based on the location of farms, while newman and matthews (2006) considered two different technologies depending on the number of outputs each farm produces. classifying farms as intensive or extensive is not as straightforward as it might appear. for example, nehring et al. (2011) used the number of cows per hectare in order to split the sample. however, the stocking rate partition does not fully describe the production system as other aspects such as the productivity of cows or the share of concentrates in the feed ration could also be taken into account. in this paper we avoid an ex-ante classification of farms as extensive or intensive by estimating a latent class model (lcm). this model assumes that several unknown technologies (classes) have generated the sample, and allows for the estimation of the parameters of the different technologies plus the probability that each observation has been generated by a specific technology. to the best of our knowledge, the starting point of previous models in the literature is a set-up and estimation proposal that assumes that the probability of each observation belonging to a class (i.e. using an intensive or extensive technology) is constant over time. this implies that changes during the period of analysis, no matter how dramatic, do not lead to a farm being labeled as belonging to a different class (use of a different technology). such an assumption becomes increasingly untenable as the number of observed periods gets larger. the objective of the present paper is twofold. first, we wish to determine whether the intensification process that has been taking place in recent decades has come to an end or whether dairy farms are still switching from extensive to more intensive produc3effects of switching between production systems in dairy farming tion systems. for this purpose, we make a simple methodological proposal to circumvent the assumption of class probability being constant over time. second, we are interested in analyzing the effects of intensification on farms’ efficiency. in particular, for given inputs we would like to know whether intensive farms have the potential to produce more output than extensive farms, and if so, the degree to which intensive farms achieve such potential. in order to fulfill these two objectives, in the empirical section of the paper we use a panel of dairy farms in northern spain to illustrate the feasibility of estimating a lcm with time-varying probabilities. our objectives are firstly to measure the changes over time in the probability of belonging to a class (technology) and secondly to analyze the effects on production potential and efficiency of dairy farm intensification1. unlike previous studies, our methodological proposal allows us to look specifically at the farms that might have tilted towards a more intensive production system in the period of analysis. the organization of the paper is the following. in the next section we describe the model. in section 3, we present the data and the empirical model. in section 4 we show the econometric estimation of the latent class models. in section 5 there is a discussion of the empirical results. the paper ends with some conclusions. 2. the model we use a lcm to analyze the extent of intensification in our dataset. the initial step is to check whether the lcm identifies different technologies and if those technologies represent different degrees of intensification. the starting point is a log-linear stochastic production frontier (orea and kumbhakar, 2004) such as: ( )= + −y f x v uln ln | | |it it j it j it j (1) where x is a vector of inputs, y a single output, v a symmetric random disturbance, and u a one-sided random disturbance that measures technical inefficiency. subscript i (i=1,… ,n) denotes firms, t (t=1,…,t) denotes time, and subscript j (j=1,…,j) indicates a technology in a finite set. the vertical bar means that there is a different production function (different parameters) for each class j. we assume that, conditional on each class, the random disturbances v and u follow a normal and a truncated normal random distribution respectively. in a latent class model we need to consider three likelihood functions. the first is the likelihood function of a firm i at time t belonging to class j: ( )= θlf g y x, ,ijt it it j (2) where θj represents the set of parameters of technology (class) j, and g denotes the likelihood function of a production frontier (kumbhakar and lovell, 2000). the second is the likelihood function of a firm i conditional on class j, obtained as the product of the likeli1 the present paper is related with a strand of literature linking technological choices with efficiency. as an example, kompas and nhu che (2006) studied the effect of different technologies on the efficiency of dairy farms by including in the inefficiency model a set of variables reflecting technological choices. 4 a. alvarez, c. arias hood functions in each period. ∏ ∏ ( )= = θ = = lf lf g y x, ,ij ijt t t it it j t t 1 1 (3) finally, the unconditional likelihood function of firm i is calculated averaging the likelihood conditional on each class using the prior probabilities of class membership pij as weights: ∑= = lf lf pi ij ij j j 1 (4) prior probabilities can be interpreted as the probabilities attached to class j membership (greene, 2005). these prior probabilities can be parameterized using a multinomial logit model: ∑ δ δ ( ) ( ) = = p z z exp exp ij j i j i j j 1 (5) where zi is a vector of “separating variables” and δj a vector of parameters to be estimated. the “separating variables” are related to the adoption of a technology and, as a result, can be used as explanatory variables of the prior probability of using that technology. after estimation of the model in (1) by maximum likelihood a “posterior probability” can be computed as: ∑ = = lf p lf p prij ij ij ij ij j j 1 (6) as equation (6) shows, the prior probability of class membership for each farm is weighted by the empirical likelihood that the farm belongs to that class. this implies that the ability of each technology to explain the observed production of a farm is incorporated in the calculation of the posterior probability. in a sense, the estimates obtained with the parsimonious parametric model of the prior probability are complemented with information on individual fit to provide a more accurate evaluation of the probability of class membership. in fact, the posterior probability is considered the best estimate of class membership (greene, 2005), and as such, the value of this probability is the criterion that we will use for determining whether a farm is using a particular technology. as mentioned above, a subtle feature of the lcm for panel data is that prior probabilities are modeled as time invariant. in practical terms, time-invariant probabilities amount 5effects of switching between production systems in dairy farming to assuming that changes in farms over time do not affect the classification of a farm as intensive or extensive. however, we expect some farms to change the use of inputs during the period of analysis in ways that suggest tilting towards intensive farming. our aim, therefore, is to circumvent the assumption of time-invariant probabilities in the empirical analysis. for that purpose, we estimate a single lcm model for the whole period of analysis but split the observations for each farm into two periods, where the first period roughly corresponds to (t=1,…,t/2) and the second period to (t=t/2+1,...,t). in this approach, farm i is considered to be a different farm in each of the two periods. as a result, the probability of class membership is constant for a given farm during each of the two periods but can change from the first period to the second. alvarez and del corral (2010) report time-varying probabilities of class membership in the conventional panel lcm through a slight modification of equation (6). they use as weighting factor of prior probabilities the likelihood function of each observation in each year (lfijt in equation 2) instead of the likelihood function of each farm (lfij in equation 3). this computational choice allows them to report time variation of a feature of the model, latent class probability, assumed to be constant over time for estimation purposes. therefore, the time variation of probability reported is akin to variation around a constant mean. instead, our estimation proposal avoids such problems by allowing class probability to change from the first sub period to the second while being constant in each sub period. in principle, it is possible to divide the original sample in three or more sub periods. however, the time length of the panel shortens accordingly, causing econometric and numerical problems. in fact, the limit of this increasingly finer division in sub periods would be to estimate a pooled model. this amounts to treating the dataset as a cross section of farms instead of taking into account the existence of a panel. in this case, in each observation farm i is treated as a different farm, thereby allowing probabilities of class membership to vary freely over time. the downside is that we disregard the information provided by observing the same farm in several periods and that the computed probabilities of class membership are not necessarily parsimonious. indeed, it would be possible to observe some farms moving repeatedly from one class to the other. all in all, this approach seems prone to numerical problems and to difficulties in interpreting the results. in fact, in the empirical application that we propose the lcm with pooled data failed to converge. 3. data and empirical model the data used in the empirical analysis consist of a balanced panel of 128 spanish dairy farms observed over the 12 year period from 1999 to 2010. in general, the units in the sample are small to medium-sized family farms. in table 1, we show the evolution of the average value of a set of variables related to intensification such as milk, number of cows, land, stocking rate, milk yield per cow, milk per hectare and purchased concentrate per cow. table 1 shows that, on average, farm output has grown during the sample period (64%) through an increase in the number of cows (44%) but much less so in land (16%). the stocking rate (23%), milk yield per cow (13%), milk per hectare (41%) and purchased concentrate per cow (6%) all grow in the period analyzed. in summary, these descriptive statistics depict a process of farm growth with intensification. 6 a. alvarez, c. arias table 1. evolution of intensification features (sample means). year milk (l) cows land (ha) cows per hectare milk per cow (l) milk per hectare (l) purchased concentrate per cow (kg) 1999 233250 33 18 1,97 6823 13657 3291 2000 253326 35 18 2,02 7005 14365 3266 2001 279077 37 18 2,17 7200 15896 3370 2002 312127 40 18 2,35 7419 17783 3434 2003 313470 41 18 2,38 7292 17675 3451 2004 332461 43 18 2,41 7501 18420 3465 2005 348243 43 19 2,42 7729 19027 3483 2006 361358 44 19 2,41 7877 19323 3470 2007 360131 45 19 2,41 7841 19089 3554 2008 362113 45 20 2,39 7661 18765 3557 2009 363012 46 20 2,40 7591 18912 3537 2010 383172 47 21 2,43 7699 19304 3489 % increase 1999-2010 64% 44% 16% 23% 13% 41% 6% the empirical specification of the production function is translog. we have chosen a flexible functional form in order to avoid imposing unnecessary a priori restrictions on the technologies to be estimated. the empirical counterpart of equation (1) is the following translog production frontier: ∑ ∑∑ ∑β β β γ= + + + + − = == = y x x x td v uln | | ln 1 2 | ln ln | | |it j k j k kit kl j kit lk lit m j m m it j it j0 1 5 1 5 1 5 2 12 (7) the dependent variable (y) is the production of milk (liters). we have considered only one output since these farms are highly specialized (more than 90% of farm income comes from dairy sales). five inputs are included: (x1) number of cows, (x2) purchased feed (kilograms), (x3) ‘farm expenses’ (includes expenditure on inputs used to produce forage crops, namely seeds, sprays, fertilizers, fuel, and machinery depreciation), (x4) ‘animal expenses’, such as veterinary, medicines, milking and other expenses, and (x5) land. all monetary variables are expressed in constant euros of 2004. additionally, 11 time dummy variables (tdm=1 if t=m, tdm=0 otherwise ) were introduced to control for factors that affect all farms in the same way in a particular year but which vary over time, such as weather (the excluded period is 1999). prior to estimation each input was divided by its geometric mean. in this way, the first order coefficients of the translog production function βk j can be interpreted as output elasticities evaluated at the geometric mean of the inputs. the prior probabilities of class membership are assumed to be a function of two “separating variables”: the natural logarithm of the stocking rate (cows per hectare) and 7effects of switching between production systems in dairy farming the natural logarithm of purchased concentrate feed per cow. since prior probabilities are modeled as time-invariant in each sub period, the separating variables are averaged over time within each period. 4. econometric estimation in this section, we report the main results of the estimation of two latent class models by maximum likelihood: a) panel model, i.e. using panel data and time-invariant class membership probabilities. b) split-panel model, i.e., estimating a single lcm model but allowing the probabilities of class membership to differ over time. this is achieved by splitting the sample into two periods where the probability of class membership is constant within each period but can change for the ‘same’ farm from the first period to the second. in other words, the estimation proceeds by treating each farm in the second period as a different farm. in both models two latent classes were found.2 as mentioned above, we make the prior probability of belonging to a latent class a function of two variables: ‘cows per hectare of land’ and ‘purchased concentrate per cow’. since these variables measure the degree of intensification of a dairy operation, we have labeled as “intensive” the latent class which shows positive estimates of the coefficients of both ‘separating’ variables in the prior probability equation. the estimates of the prior probability function of the intensive class for both models are shown in table 2. table 2. prior probability equation for the intensive class. panel model split-panel model constant -11.906* -13.112** ln(cows/land) 1.7919** .8632* ln(concentrate/cows) 1.3510* 1.5513** *, **, significantly different from zero at 0.05 and 0.01 significance levels respectively standard errors reported in tables a1 and a2 in the appendix next, the farms were classified as intensive using the highest (greater than 0.5) estimated posterior probabilities (equation 6) since these provide the best estimates of class membership for an individual (greene, 2005). in table 3 we show descriptive statistics of the two groups (intensive and extensive) for the two models estimated. the descriptive statistics of each group roughly agree with the labels we gave to the classes based on the effects of intensification variables on the probability of being in each 2 we also tried to fit a model with three classes but it did not converge. 8 a. alvarez, c. arias class. as expected, intensive farms have larger values of key variables such as milk per cow, milk per hectare and feed per cow. intensive farms are also larger in terms of milk production but are rather similar to extensive farms in terms of land. in our view, the explanation for this result is that marginal increases of land are unlikely to be an option for farmers due to the fact that most abandonments take place in less favoured (mountainous) areas while remaining farms are mainly located in the coastal plain, so that the land available after some farms shut down cannot be used by the remaining farms. for this reason, farmers who wish to increase production need to use more feed per cow and in some cases buy more productive cows, thereby becoming more intensive. in table 4 we report the output elasticities for the two groups evaluated at the geometric mean of the sample. the differences in the elasticities across groups can be seen as evidence of different technological characteristics. the complete set of estimated parameters of the production functions are reported in tables a1 and a2 in the appendix. all elasticities, with one exception, are significantly different from zero at conventional levels of significance. despite the different assumptions behind the two models, the table 3. characteristics of the estimated production classes (sample means). panel model split-panel model intensive extensive intensive extensive observations 804 732 798 738 milk (l) 365442 280884 371525 274994 cows 42.7 40.3 43.2 39.9 land (ha) 18.0 19.8 18.3 19.6 cows per hectare 2.45 2.16 2.4 2.1 milk per cow (l) 8129 6745 8202 6677 milk per hectare (l) 20329 14779 20428 14718 purchased concentrate per cow (kg) 3533 3352 3579 3304 table 4. output elasticities evaluated at the geometric mean of the sample. panel model split-panel model intensive extensive intensive extensive cows .7176** .4626** .7491** .4553** feed .2969** .3623** .2737** .3685** farm expenses .0405** .0718** .0508 ** .0767** animal expenses .0296** .0964** .0206 * .0789** land .0368** .0339** .0214 .0295* *, **, significantly different from zero at 0.05 and 0.01significance level, respectively 9effects of switching between production systems in dairy farming output elasticities evaluated at the sample geometric mean are similar between them. the similarity between the estimates and farm classifications obtained suggests that the differences between the two models (time varying versus constant latent class probability) are a significant but moderate feature of the sample. on the other hand, it is interesting to note that there are wide differences between the parameters of the two latent classes (within each model). for example, the output elasticity with respect to cows is almost twice as large in the intensive group as it is in the extensive group. on the other hand, the output elasticity of feed is always larger in the extensive group. these different elasticities imply large differences in marginal productivity of inputs across technologies (extensive or intensive), especially for cows and feed. 5. empirical analysis of intensification and efficiency in this section we use the results of the estimation of the lcm to analyze a set of issues with important policy implications. first, we are interested in studying the evolution of the intensification process over time. second, we want to analyze the differences in technical efficiency between intensive and extensive farms. 5.1. evolution of intensification over time we want to check if the probability of adopting the intensive technology increases over time. we should note that this analysis is only possible in the split-panel model proposed in the present paper and not in the conventional panel lcm. for this purpose, we regress the posterior probabilities of being in the intensive class against individual dummies (fixed effects) and a binary variable (dt) that takes the value zero for the first half of the period analyzed and one for the second half. this is an unconditional analysis over time and is clearly different from the conditional analysis of prior probabilities that could be achieved by including a time trend in equation (5). in the conditional analysis, prior probabilities can change over time, keeping input use and separating variables constant. in the unconditional analysis performed here, the posterior probability varies over time due to changes in the use of key inputs such as feed, cows or land. the equation to be estimated is the following: = + +a b d wpr | | |ijt i j j t it j (8) where j denotes the latent class and w is a random disturbance assumed to follow a normal distribution with zero mean and constant variance. in each class, we have estimated the posterior probability for each individual (i=1,…,128) and for each period (t=1,..,12). expression (8) represents a general proposal with j different classes. in this case, there would only be j-1 free equations since the dependent variable, the posterior probability, adds up to one. as we are considering only two latent classes (intensive and extensive) in our setting, we only have one relevant equation. we choose to estimate the equation corresponding to the posterior probability of the intensive class. 10 a. alvarez, c. arias table 5 shows the estimated coefficient of the time binary variable (b|j) for the posterior probability of intensive class in the split-panel model. this coefficient is positive and significantly different from zero, indicating that the probability of being classified as an intensive farm is larger in the second period. we interpret this result as evidence of “intensification” of dairy production in our sample over the period analyzed. in our view, the average change over time of the probability of belonging to the intensive class provides evidence of farms in the sample tilting towards an intensive production system. additionally, the change in class probability between the two periods allows for some descriptive analysis of the subset of farms that have switched production system in the conventional sense of crossing the threshold defined by a class probability of 0.5. in particular, 13 extensive farms became intensive in the second period, while 8 farms switched from intensive to extensive. in table 6 we show the characteristics of the farms that changed to a different production system. the farms that become intensive in the second period reflect the typical transformation pattern: increase in the stocking rate and feed per cow, resulting in higher milk per cow and per hectare. on the other hand, the farms that switch from the intensive to the extensive class keep the stocking rate unchanged but reduce the amount of feed per cow, lowering milk per cow and per hectare. we would like to stress two features of this exercise: first, its descriptive and exploratory nature since the 0.5 probability cut-off point that defines intensive or extensive farms is arbitrary; second, the intensification measured by the increase in the probability of being in a latent class is an average effect compatible with farms moving in both directions. table 5. analysis of the evolution over time of posterior probabilities. coefficient (b|intensive) standard error t-statistic .0363 .0099 3.28 fixed effects not shown table 6. characteristics of the farms that switch production system over time. from extensive to intensive from intensive to extensive period 1 extensive period 2 intensive period 1 intensive period 2 extensive farms 13 13 8 8 milk (l) 279777 383108 269831 301237 cows 37.2 42.9 35.3 42.5 land (ha) 16.5 16.6 14.8 17.7 cows per hectare 2.1 2.4 2.4 2.4 milk per cow (l) 7061 8381 7456 7004 milk per hectare (l) 15796 21242 18246 17191 feed per cow (kg) 3483 3824 3307 3204 11effects of switching between production systems in dairy farming 5.2. the effect of intensification on dairy farm efficiency in this section, we want to explore the effect of intensification on production efficiency. two questions are addressed. first, are intensive farms more efficient than extensive farms? second, which technology is more productive (i.e., does one of the two frontiers lie above the other one)? the level of technical efficiency can be calculated as the ratio between current output and potential output, as defined by the technological frontier. an output-oriented index of technical efficiency can be computed as: ( )= −te u| exp |it j it j (9) subscript j in equation (9) indicates that the technical efficiency index can be calculated with respect to each of the latent class frontiers (orea and kumbhakar, 2004). in our case, we can thus consider two different frontiers. table 7 shows the average technical efficiency for the two technologies. table 7. average efficiency of the estimated classes (intensive/extensive). panel model split-panel model intensive frontier extensive frontier intensive frontier extensive frontier full sample .92 .92 .91 .93 intensive farms .94 .96 .94 .96 extensive farms .88 .89 .87 .89 as can be seen, for the full sample the average level of technical efficiency is quite similar both between models (panel vs. split-panel) and between latent technologies (intensive frontier vs. extensive frontier). however, if we consider the two groups of farms separately, a very interesting result is found: intensive farms have a higher level of technical efficiency in all the four frontiers. additionally, the average technical efficiency is higher in the extensive frontier than in the intensive one. this last result seems to indicate that the latent frontier of the intensive group dominates the other, that is, for any given set of inputs it is possible to produce more output with the intensive technology. we try to shed some light on this issue by calculating the difference in frontier output between the two frontiers using the actual inputs of the farms: = −d y yln ˆ ln ˆit it i it e (10) where yln ˆit i yln ˆit e is the (log of) frontier output of farm i at time t evaluated at the intensive (extensive) technology. table 8 shows the average value of the differences between the frontiers (dit) calculated using the actual inputs of the farms for the full sample as well as for the two classes. 12 a. alvarez, c. arias as the differences between the frontiers are calculated using natural logs, dit can be interpreted approximately as the percentage difference of potential output between both frontiers. the intensive frontier is, on average, 9.6% above the extensive frontier in the panel model, while this difference is 10.8% in the split-panel model. the relationship between technical efficiency and intensification can be explored further by analyzing the correlation between the estimated indexes of efficiency and the probability of being in the intensive class. as technical efficiency is time-varying and so is the probability in the split-panel model, we have included a time dummy as a control variable in the regression. by doing so, we avoid confounding the effect of the change of probability over time with the effect of time. table 8. average difference between the frontier output of the two technologies. panel model split-panel model full sample .0962 .1080 intensive farms .0883 .0988 extensive farms .1049 .1180 table 9. relationship between technical efficiency and intensification. panel model split-panel model ols ols ols with farm dummies intensive frontier extensive frontier intensive frontier extensive frontier intensive frontier extensive frontier probability of intensive class 0.0607** 0.0658** 0.0786** 0.0752** 0.0545** 0.0439** time dummy -0.0010** 0.0009** -0.0014** 0.0007* -0.0013** 0.0008** *, **, significantly different from zero at 0.05 and 0.01significance level, respectively in table 9, we show the results of regressing the level of technical efficiency against the probability of being in the intensive class and a time dummy that takes the value 1 for the second period. we show the results for both models (panel and split-panel) and for both frontiers (intensive and extensive). in the split-panel model we perform two different estimations: standard ols and ols with farm dummy variables. the inclusion of individual effects is not possible in the conventional panel model because the probability of class membership is constant over time. we find common patterns of results for both models and estimators. the probability of being in the intensive class increases the level of technical efficiency with respect to both frontiers in the two models and for both estimation methods. the coefficient of 13effects of switching between production systems in dairy farming the time dummy indicates that the index of technical efficiency estimated using the extensive frontier increases over time while the index of technical efficiency estimated using the intensive frontier decreases over time. this result is probably due to different patterns across frontiers of the yearly shifts of the production frontier measured by the coefficients of the time dummy variables. the time dummy coefficients of the intensive frontier (tables a1 and a2 in the appendix) show a clear upward trend at the beginning of the period followed by a fall in the final years. the upward shift of the production frontier over time is compatible with decreasing technical efficiency if the movements of the frontier are due to productive improvements of a subset of leading farms while other farms do not move immediately towards the shifted frontier. on the other hand, the extensive frontier features smaller and erratic shifts over time. in our view, it is not surprising to observe farms approaching, on average, a production frontier with no sudden upward shifts. additionally, the split-panel model provides a subtle result: the probability of being in the intensive class changes across farms and increases over time (on average). in other words, there are two sources of variation. the coefficient of the probability using plain ols is estimated using both sources of variation. however, the coefficient of the probability using ols with farm dummy variables is estimated using only the over time variability. the results show that the estimates of the coefficient of probability are 0.0545 (intensive) and 0.0439 (extensive) when using only changes over time, while the same estimates increase to 0.0786 (intensive) and 0.0752 (extensive) when using both cross-section and time variation of the posterior probability. in summary, it seems that the bulk of the change in efficiency caused by changes in the probability of being in the intensive class can be attributed to changes over time of this probability. 4. conclusions the assumption of time-invariant prior probability of a latent class can be circumvented by estimating a single lcm but splitting the original panel into two or more periods. this proposal allows for the estimation of the technology of different production systems without an assumption that becomes increasingly untenable as the period of years analyzed increases. on the positive side, our proposal is simple and straightforward. on the negative side, it is not part of a formal model. as a result, there is no clear rationale for choosing the number of periods in which the original panel should be split. by splitting the original panel into two periods, we find in our empirical application that the probability of being in the “intensive dairy” class increases over time. this result can be interpreted as evidence of dairy farming intensification over the sample period. we find differences in technical efficiency if we split the sample in terms of the production system using the posterior probability of each latent class. more precisely, the average technical efficiency is higher for farms that belong to the intensive latent class. additionally, the intensive frontier dominates the extensive frontier, indicating that the intensive technology is more productive that the extensive one. this result can be seen as an economic rationale for the observed trend towards the intensification of dairy farms. what are the policy implications of these findings? given that the intensive technology is more productive and that intensive farms are more efficient, i.e. produce closer to their frontier than extensive farms, it seems that the trend towards intensification will 14 a. alvarez, c. arias continue in the near future. this trend can be seen as a problem if the environmental consequences of intensification are taken into account. the new reform of the common agricultural policy may also affect farmers’ technology choices. on the one hand, the phasing out of milk quotas may result in higher production. in this sense, intensive farms will find it easier to boost production since they do not depend heavily on forage. at the same time, productivity and efficiency gains associated with intensification can be a plus on a more competitive environment. on the other hand, the new direct payment scheme which will move towards a uniform payment per hectare may lower the incentives to adopt intensive systems. references alvarez, a., del corral, j., solis, d. and pérez, j.a. (2008). does intensification help to improve the economic efficiency of dairy farms? journal of dairy science 91(9): 3693-3698. alvarez, a. and del corral, j. (2010). identifying different technologies using a latent class model. extensive versus intensive dairy farms. european review of agricultural economics 37(2): 231-250. del corral, j., perez, j.a. and roibas, d. (2011). the impact of land fragmentation on milk production. journal of dairy science 94(1): 517-525. greene, w. (2005). reconsidering heterogeneity in panel data estimators of the stochastic frontier model. journal of econometrics 126(2): 269-303. hoch, i. (1962). estimation of production function parameters combining time-series and cross-section data. econometrica 30(1): 34-53. kompas, t. and nhu che, t. (2006). technology choice and efficiency on australian dairy farms. australian journal of agricultural and resource economics 50(1): 65-83. kumbhakar, s.c. and lovell, c.a.k. (2000). stochastic frontier analysis. cambridge: cambridge university press. newman, c. and matthews, a. (2006). the productivity performance of irish dairy farms 1984-2000: a multiple output distance function approach. journal of productivity analysis 26(2): 191-205. nehring, r., sauer, j. gillespie, j. and hallahan, c. (2011). intensive versus extensive dairy production systems: dairy states in the eastern and midwestern u.s. and key pasture countries the e.u.: determining the competitive edge. selected paper prepared for presentation at the southern agricultural economics association annual meeting, corpus christi, tx. orea, l. and kumbhakar, s.c. (2004). efficiency measurement using a latent class stochastic frontier model. empirical economics 29(1): 169-183. simpson, j. r. and conrad, j.h. (1993). intensification of cattle production systems in central america: why and when. journal of dairy science 76(6): 1744-1752. tauer, l. and belbase, k. p. (1987). technical efficiency of new york dairy farms. northeastern journal of agricultural and resource economics 16(1): 10-16. 15effects of switching between production systems in dairy farming appendix table a.1. estimation of the panel model. variable class 1 class 2 coefficient std. error coefficient std. error constant 12.5724 .0158 12.542 .0152 cows (lnx1) .7176 .0213 .4626 .0241 feed (lnx2) .2969 .0137 .3623 .0152 crop (lnx3) .0405 .0067 .0718 .0080 animal (lnx4) .0296 .0091 .0964 .0110 land (lnx5) .0368 .0104 .0339 .0125 (lnx1)2 .2030 .1445 -.0854 .1096 (lnx2)2 .0257 .0486 -.1737 .0612 (lnx3)2 .0575 .0152 .0523 .0149 (lnx4)2 -.0731 .0247 .0069 .0320 (lnx5)2 .2857 .0398 -.0624 .0542 lnx1lnx2 -.3277 .0641 .0663 .0732 lnx1lnx3 -.0999 .0395 .0103 .0301 lnx1lnx4 .3738 .0532 -.0394 .0561 lnx1lnx5 -.2055 .0584 -.0263 .0657 lnx2lnx3 .1055 .0238 -.0608 .0219 lnx2lnx4 -.0960 .0321 .0827 .0369 lnx2lnx5 .1288 .0405 -.0160 .0413 lnx3lnx4 -.0307 .0149 .0025 .0164 lnx3lnx5 -.0426 .0196 .0196 .0231 lnx4lnx5 -.0619 .0275 .0138 .0282 td00 .0206 .0129 .0072 .0172 td01 .0366 .0131 -.0046 .0173 td02 .0358 .0130 .0243 .0175 td03 .0313 .0131 -.0181 .0173 td04 .0519 .0131 .0038 .0171 td05 .0707 .0132 .0359 .0172 td06 .0952 .0132 .0366 .0173 td07 .0839 .0132 .0378 .0175 td08 .0529 .0134 -.0156 .0175 td09 .0559 .0134 -.0275 .0177 td10 .0661 .0139 .0032 .0172 sigma .0909 .0078 .1537 .0064 lambda 1.0506 .3484 2.7329 .3866 prior probability equation constant -11.906 5.2881 ln(cows/land) 1.7919 .6078 ln(concentrate/ cows) 1.3510 .6600 16 a. alvarez, c. arias table a.2. estimation of the split-panel model. variable class 1 class 2 coefficient std. error coefficient std. error constant 12.5754 .0135 12.536 .0146 cows (lnx1) .7491 .0203 .4553 .0211 feed (lnx2) .2737 .0148 .3685 .0149 crop (lnx3) .0508 .0068 .0767 .0078 animal (lnx4) .0206 .0093 .0789 .0118 land (lnx5) .0214 .0115 .0295 .0136 (lnx1)2 .2943 .1448 -.0405 .1092 (lnx2)2 .0121 .0460 -.0964 .0645 (lnx3)2 .0732 .0147 .0410 .0134 (lnx4)2 -.0530 .0263 -.0447 .0331 (lnx5)2 .2786 .0400 .0407 .0609 lnx1lnx2 -.3067 .0613 -.0549 .0693 lnx1lnx3 -.1158 .0375 -.0217 .0316 lnx1lnx4 .2953 .0530 .0704 .0591 lnx1lnx5 -.2457 .0563 .0135 .0596 lnx2lnx3 .0825 .0226 -.0131 .0217 lnx2lnx4 -.0526 .0331 .0418 .0396 lnx2lnx5 .1130 .0410 -.0562 .0452 lnx3lnx4 -.0275 .0151 .0003 .0160 lnx3lnx5 -.0263 .0199 -.0053 .0224 lnx4lnx5 -.0350 .0274 .0306 .0289 td00 .0201 .0126 -.0013 .0162 td01 .0376 .0129 -.0108 .0174 td02 .0351 .0133 .0238 .0188 td03 .0283 .0130 -.0151 .0166 td04 .0471 .0132 .0099 .0165 td05 .0660 .0128 .0408 .0167 td06 .0917 .0130 .0457 .0167 td07 .0790 .0129 .0463 .0172 td08 .0531 .0131 -.0143 .0171 td09 .0563 .0131 -.0292 .0172 td10 .0697 .0134 .0007 .0170 sigma .0883 .0067 .1497 .0060 lambda 1.1750 .3155 3.0344 .4630 prior probability equation constant -13.112 4.2444 ln(cows/land) .8632 .3943 ln(concentrate/ cows) 1.5513 .5247 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 3(2): 119-135, 2014 doi: 10.13128/bae-14477 the contribution of different off-farm income sources and government payments to regional income inequality among farm households in italy simone severini*, antonella tantari1 università degli studi della tuscia. dep. dafne, via s. c. de lellis, snc – 01100 viterbo (italy) abstract. this paper investigates the contribution of different off-farm income sources and common agricultural policy direct payments on income inequality among farm households. the analysis uses the gini coefficient concept and its decomposition on the whole sample of farm accountancy data network individual farms of italy in 2011. a marginal increase in either off-farm incomes or direct payments reduces income concentration. this result could feed the current debate regarding the application of the new cap in italy. deciding on a narrow definition of “active farmer” or not using the redistributive payment could increase dp and fhi concentration. of the five considered off-farm income sources, only pensions reduce income concentration. therefore, policies reducing the level of pensions will increase income inequality. finally, if rural development policies have also to reduce income inequality, these should be aimed at increasing job opportunities for additional family members. keywords. farm household income, income concentration, disaggregation of the gini coefficient, off-farm income, cap direct payments. jel codes. q12, d31, q18. 1. introduction raising farm income and changing its distribution are still among the main goals of government intervention in the farm sector, even if new perspectives regarding the “farm problem” have been developed (gardner, 1992). studies on the evolution of income of italian farms show an increasing trend of the level of farm net value added per unit of labor during the past decades (henke and salvioni, 2010) and a decreasing gap among income levels of farm and non-farm families (rocchi et al., 2012). however, the large heterogeneity in asset positions across italian farm households suggests to also focus on the income distribution within the farm population (i.e. income inequality) as it has been done in other countries (gardner, 1968; de janvry and saudolet, 2001). furthermore, due to the ever increasing contribution of off-farm incomes (ofi), it seems more appropriate to move the attention from farm income (fi) to farm household income (fhi). * corresponding author: severini@unitus.it. full research article 120 s. severini, a. tantari the analysis focuses on the concentration of farm household income among farm families in italy and is developed on the whole sample of fadn individual farms in italy for the last available year (2011). this large sample also allows its stratification according to different macro-regions and zones within italy. the analysis, developed by calculating the gini concentration coefficient2 and its decomposition by income sources, is aimed at assessing the concentration of fhi and the role played by the considered income components with regard to it. more specifically, the objectives are to assess: 1) how and how differently dp contribute to the concentration of both farm income (fi) and farm household income (fhi); 2) whether ofi as a whole would bring about a reduction in fhi concentration; 3) sign and extent of the effect of the different ofi sources on fhi concentration. furthermore, by comparing the results referring to different macro-regions and zones of italy, it is possible to show whether the obtained results vary according to the physical, structural and economic environments in which farm families live. the originality of this paper is that it extends the analysis from the fi to the whole fhi considering different off-farm income sources as well as cap direct payments (dp) and uses a large farm-level dataset. this allows to discuss the issue of which income concept should be the reference for the measurement of the contribution of dp to income distribution, to compare and to quantify the contribution of single off-farm income sources to farm household income inequality and, finally, to consider the possible role played by some other policies (including pension, rural development and regional policies) affecting the possibility that farm families have to earn ofi. the next section provides a brief review of the literature on income concentration within farm families and few elements motivating the analysis in the italian case. section 3 presents data and methodology, while section 4 presents and discusses the obtained results. the final section provides some policy considerations and identifies possible future research developments. 2. literature review on the income inequality among farm households a large part of the literature on income distribution deals with the contribution of agricultural policies on income distribution and variability because these are concerned with income support and distribution as well as with other environmental, sustainability and rural development goals. this topic has been explored by several studies conducted in the us (ahearn et al., 1985; gardner, 1969; mishra and sandretto, 2002; mishra and elosta, 2005; mishra et al., 2006; mishra et al., 2009; mishra et al., 2010), in europe (allanson, 2008; el benni and finger, 2013; keeney, 2000; schmid et al., 2006) and in a crosscountry comparison including canada (moreddu, 2011). this topic is very relevant also in italy because farm households strongly differ in asset positions. for example, italian farms have very different size: the last agricultural census (2010) show that farms smaller than 5 ha of utilized agricultural area (uaa) are around 73% of the italian farms but have less 2 using the gini coefficient, this paper refers to relative inequality in contrast with absolute inequality such as in allanson (2008). 121contribution of different income sources to regional inequality than 15% of the whole uaa. on the contrary, farms with more than 100 ha of uaa are around 1% of the farm population but have around 26% of the whole uaa. most of the analyses on income concentration have found that government payments decrease income inequality (ahearn et al., 1985; el benni and finger, 2013; keeney, 2000; mishra et al., 2002; mishra et al., 2009; moreddu, 2011; severini and tantari, 2013a and 2013b) even if some other studies reached the opposite conclusion (allanson, 2008; schmid et al., 2006). only a limited number of these analyses decompose the gini coefficients to analyze the contribution of each income source on income inequality (el benni and finger, 2013; keeney, 2000; mishra et al., 2009; severini and tantari, 2013a and 2013b) and few of these account for differences in the farm population within the considered countries (el benni and finger, 2013; mishra et al., 2009). the role of cap policies in changing income distribution is currently under scrutiny in italy because the italian government is deciding on how to apply the recently reformed dp and rural development policies. in particular, the definition of the requirements needed to qualify for being beneficiaries of dp (i.e. “active farmer”) and the possibility to introduce a redistributive payment for the first 30 ha of land of each farm3 could strongly influence farm income distribution among italian farms. the increased importance of ofi in generating farm household income has been documented in many countries including the us and switzerland, making this income component a relevant share of fhi (el-osta et al., 1995; el benni and finger, 2013, mishra et al., 2009) and influencing farm organization and performances (lien et al., 2010; pfeiffer et al., 2009). this stimulated a growing literature on the effect of ofi on fhi both in developing and in developed countries (de janvry and saudolet, 2001; ahearn et al., 1985; boinsvert and ranney, 1990; el-osta et al., 1995; findeis and reddy, 1987; mishra et al., 2009). empirical analysis on this specific topic in europe is more limited and, with the noticeable exceptions of el benni and finger (2013), hill (1999) and allanson and rocchi (2008), most of the papers focus on the concentration of only farm income. as shown by hill (1999), this seems a limitation because it does not consider the multiplicity of sources of income farm households are relying upon (de janvry and saudolet, 2001). this is also true in italy given that the relative importance of ofi can be very high especially in those families managing small farms provided that off-farm labor participation in italian farm households is negatively correlated with farm size (corsi and salvioni, 2012). thus, taking into account ofi allows to analyze how dp affect fhi concentration and to consider also nonagricultural policies affecting ofi (boisver and ranney, 1990; findeis and reddy, 1987). this paper, building on the previously described literature, looks at the following research topics. first, it compares the contribution of dp on fi concentration with that on fhi concentration. indeed, different results are expected not just because the relative importance of dp decreases when moving from fi to fhi, but also because of the effect of ofi on the distribution of fhi among farm households. second, off-farm incomes as a whole reduce income concentration (de janvry and saudolet, 2001; ahearn et al., 1985; boinsvert and ranney, 1990; el-osta et al., 1995; findeis and reddy, 1987; mishra et al., 2010; mishra et al., 2009; el benni and finger, 3 respectively articles 9 and 41 of reg. (eu) no 1307/2013 of the european parliament and of the council of 17 december 2013. official journal of the european union, l 347/608, 20.12.2013. 122 s. severini, a. tantari 2013). however, we test whether this is also true in italy and, if this is the case, whether this effect is more or less strong than in other countries. third, this paper investigates the contribution of the different single sources of offfarm income. the results of this analysis could feed the policy debate provided that some governmental programs, including rural development policies, could be used to increase the importance of some of these income components. finally, because of the differences across territories in terms of availability of off-farm income opportunities, structure and level of income from farming, and the relative importance of dp in the generation of income, the analysis is developed not just on the whole italian sample, but also separately for farm households belonging to different regions and zones. this allows us to assess whether policies should be tailored differently in the different cases. 3. data and methods 3.1. data the analysis is based on all individual farms belonging to the italian farm accountancy data network (fadn)4. this is a sample of 9,722 units in 2011 corresponding to a weighted sample of 728,440 families (table 1)5. the whole sample has been stratified by altimetry zones and macro-regions. the former are hilly, mountain and plain zones while the five macro-regions are north-west, north-east, center, south, and main islands. the decomposition of family income has been performed for every subsample. the sampled farms are mainly located in hilly zones (4,579 families) while in plain and mountain zones there is a smaller distribution of farm families (3,077 and 2,066 families, respectively). the distribution by macro-regions highlights that a large share of the sample is located in the south and north-east of italy. the analysis focuses on farm household income (fhi) that consists in two components: income from farming (or farm income) (fi) and off-farm income (ofi). the former is made of revenues from farming activities and cap direct payments minus costs for intermediate consumptions and external factors (european commission, 2010b). in the analysis, fi is divided into two components: market income (mi) and direct payments (dp), with mi = fi dp. it is important to note that, as it is the case of many eu member states and other countries, mi is negative in some farms (european commission, 2010a; mishra, 2009) causing also around 6.3% of the farms having negative fi levels (table 1). the italian fadn provides data regarding the relative importance of ofi coming from the following five sources: wages, income from independent activities, pensions, income from capital and a residual group of off-farm income sources. first of all, the fhi is decomposed into three categories (mi, dp and ofi) in order to assess their impact on 4 corporate farms are excluded from the analysis. fadn is managed by the european commission (european commission, 2010 b). the european commission relies on national liaison agencies. in italy, it is the istituto nazionale di economia agraria (inea) of rome. 5 it is important to recall that fadn does not include farms below a given size. this is not the case of other surveys such as, for example, the agricultural business survey (rea). thus, families managing very small business are not considered in the analysis. 123contribution of different income sources to regional inequality the whole fhi. subsequently, the decomposition has been expanded further to consider each one of the five ofi components previously described. 3.2. gini decomposition by income source gini decomposition by income source has been developed following pyatt et al. (1980): g= rk *gk *sk k=1 k ∑ (1) in which: rk (the gini correlation) denotes the ratio of the covariance between the income component yk and the rank of total income y and the covariance between yk and its own rank, with observations ordered with respect to total income and income from the k-th source, respectively: rk = cov yk ,f y( )( ) cov yk ,f yk( )( ) −1≤rk≤1, where: rk = cov yk ,f y( )( ) cov yk ,f yk( )( ) −1≤rk≤1 (2) table 1. sample size and average income level (total and by income sources) in the whole sample and in the sub-samples. year 2011.   number of families (n) weighted number of families (nw) households with negative fi (%) farm household income (fhi) (euro/ house-hold) market income (mi) (euro/ house-hold) direct payments (dp) (euro/ house-hold) off farm incomes (ofi) (euro/ house-hold) whole sample 9,722 728,440 6.3 35,548 17,790 9,946 7,812 altimetry zones               plain 3,077 239,090 7.4 38,619 19,362 12,246 7,011 hill 4,579 370,522 5.1 32,216 16,127 8,910 7,179 mountain 2,066 118,828 7.4 38,358 19,136 8,816 10,406 macro-regions               north-west 1,983 87,256 10.7 36,022 19,667 12,753 3,603 north-east 2,069 137,925 7.3 42,184 22,155 8,180 11,849 center 1,794 104,622 5.1 35,080 16,322 11,451 7,308 south 2,826 272,255 3.9 32,111 14,886 8,708 8,517 islands 1,050 126,383 4.9 31,622 15,968 8,887 6,767 note: fhi = mi + dp + ofi; fi = mi + dp. source: own elaboration on italian fadn sample. 124 s. severini, a. tantari inspection of the latter equation suggests that rk = 1 only if f (yk) = f (y), implying that farm families have the same ranking with respect to the k-th income component as they have with respect to total income (see pyatt et al., 1980). for example, if the rk for dp is close to 1, this means that households having relatively higher income levels also receive relatively higher levels of dp. gk denotes the gini coefficient for the k-th income component. sk denotes the income share of the k-th income source (i.e. share of yk relative to y). the product between rk and gk gives the concentration coefficient of the k-th income source (ck). it measures how income from each source is transferred across a population that is ranked with respect to the level of total income each member of the population received. equation (1) means that each income component influences income concentration according to how important that source of income is (sk), and to how it is distributed among the sample (gk), as well as according to the level of the “gini correlation” between this income component and the rank of total income (rk) (stark et al., 1986). pyatt et al. (1980) and lerman and yitzhaki (1985) developed a measure that partitions the overall inequality of a particular distribution into contributing components. this measure, in the case of income, accounts for the ‘proportional contribution to inequality’ of the k-th income source: pk = (rk * gk * sk)/g (3) in order to evaluate the relative contribution of a single income component to income inequality, lerman and yitzhaki (1985) derived the following measure of the gini coefficient rate of change with respect to the mean of k-th income component: dg dµk = 1 µ * ck −g( ) (4) in which µk is the mean value of the k-th income component. from this it is possible to derive the elasticity of the gini coefficient for each income component as follows: ηk = µk g * dg dµk = 1 g * µk µ * ck –g( )⎡ ⎣ ⎢ ⎤ ⎦ ⎥ (5) this allows the measurement of the contribution of a one percent change of a single income source on the income concentration, assuming that the internal ratio between the total income distribution and the mean of the income source remained undisturbed (el benni and finger, 2013, p. 641). as noted by keeney (2000) and mishra et al. (2009), with a substantial incidence of negative incomes, g(y) may become overstated, perhaps causing values greater than 1. however, the decomposition procedure previously described remains applicable as long as the average value of all income sources is positive for the entire sample (pyatt et al., 1980; findeis and reddy, 1987). therefore, because the average income for each income source is ηk = µk g * dg dµk = 1 g * µk µ * ck –g( )⎡ ⎣ ⎢ ⎤ ⎦ ⎥ 125contribution of different income sources to regional inequality always positive in the whole sample and in the sub-samples, it has been possible to use this procedure for our dataset. furthermore, because the focus of this analysis is to decompose farm household income and to analyze the contribution of ofi and dp on income concentration, it did not seem fundamental to calculated adjusted gini coefficients. 4. results of the empirical analysis 4.1. level and composition of farm household income average farm household income is higher in the north-east and north-west and in families located in plain zones. the same distribution occurs for market income: in the north-east mi is around 22,000 euros. as already noticed, 6.3% of the considered families have negative farm income indicating that the amount of direct payments (dp) received is not big enough to compensate for negative mi. these families are mainly located in the north-west (table 1). the average level of dp is around 10,000 euro per farm: in plain zones and in the north-west this value is higher because these farms used to produce commodities that have received a strong policy support. in the whole sample, offfarm incomes (ofi) are around 7,800 euro per family, but this level is higher in mountain zones and in the north-east. the distribution of the main sources of income is not homogeneous in the different geographical areas: in fact, the relative share of ofi is very high in the south (around 47%), while it is lower in the north-west (around 13%) where market incomes generate more than half of the fhi (table 1). stratification by altimetry zones shows that ofi contributes more to the generation of farm household income in hilly areas (around 43% of fhi) while mi and dp are relatively more important in families located in the plains (table 1). 4.2. concentration of farm household income in the whole sample fhi is not very concentrated, showing a gini coefficient of around 0.53, lower than fi concentration. indeed, the relative importance of ofi in reducing total income concentration should be stressed. the relative shares as well as the relative concentration coefficients of the different sources of income strongly differ (table 2). mi is very concentrated (gini coefficient of 0.987), also because of the presence of negative values in the sample, while dp and ofi are less concentrated. while the gini coefficients of dp and ofi are quite similar, the degree of correlation with the rank of total income (r) is higher for ofi, meaning that this source of income is more important for high income families. the relative importance of ofi in the formation of total income is bigger than that of dp but their relative contribution to total inequality, as measured by the gini elasticity, is quite similar. both these sources of income contribute to the reduction of total income inequality, even if the magnitude of this effect is low. because of its high degree of concentration, mi increase total income inequality. the opposite is true for dp. indeed, the proportional contribution of dp to fi inequality is larger than the proportional contribution of dp to fhi concentration. this is mainly due 126 s. severini, a. tantari to the declining share of dp moving from fi to fhi. however, this is also due to the fact that dp are less correlated with fhi. as it will be discussed in paragraph 4.4, the different sources of ofi contribute differently to fhi inequality. 4.3. concentration of farm household income in the altimetry zones and macro-regions the analysis shows that the results for the considered subsamples are very similar to those obtained from the whole sample (table 3) with very few exceptions. thus, the following text refers only to these few exceptions. the gini coefficient for farm household income is only slightly higher in families located in plain zones than in the other altimetry zones. mi and dp are relatively more important in the plain zones. the gini coefficient for mi is slightly higher in the plain zones where it becomes bigger than one, due to the presence of cases with negative mi6. finally, elasticity values for ofi are relatively smaller in the hilly zones and larger in the plain zones than in the whole sample. the analysis performed on geographical areas gives very similar results as those obtained on the whole sample. ofi reduce fhi inequality in all macro-regions except for the south (table 4). the contribution of each source of income to fhi inequality is comparable with what has been observed for the whole sample. however, the gini elasticity of dp in the northwest is bigger than in the rest of the sample: this is mainly due to the relatively high share of dp in this area. 6 negative mi have also been reported in previous studies on farm income concentration such as, for example, findeis and reddy (1987), keeney (2000) and mishra et al. (2009). table 2. gini decomposition of farm household income and farm income in the whole sample. year 2011.         share (%) gini coefficient correlation coefficient concentration coefficient proportional contribution to inequality elasticity (%) s g r c p η market income mi 37.5 0.987 0.731 0.721 0.509 0.134 direct payments dp 21.2 0.713 0.496 0.353 0.141 -0.071 off-farm income ofi 41.3 0.660 0.681 0.450 0.350 -0.063 farm household income fhi 100.0 0.531 1.000 0.531 1.000 0.000 market income mi 63.8 0.987 0.882 0.870 0.777 0.139 direct payments dp 36.2 0.713 0.617 0.440 0.223 -0.139 farm income fi 100.0 0.714 1.000 0.714 1.000 0.000 note: fhi = mi + dp + ofi; fi = mi + dp. source: own elaboration on italian fadn sample. 127contribution of different income sources to regional inequality 4.4. the contribution of different sources of off-farm incomes on total household inequality the decomposition of the 5 ofi sources allows the assessment of their specific contribution to fhi concentration. pensions generate the biggest share of ofi in all subgroups of families, accounting for around 17.6% of the fhi, even if wages account for a similar share. the other sources of ofi only account for a limited portion of farm household income. among the different ofi sources, pensions are the least concentrated and have the lowest gini correlation: this means that a relative increase in pensions’ share could lead to a bigger decrease of fhi concentration if compared with other sources of off-farm income (gini elasticity of -0.077) (table 5). among the analyzed sources of ofi, pensions seem to be the most effective in reducing fhi inequality, mainly because of their higher share and their lower degree of concentration. among the other sources of ofi, wages and, to a lesser extent, incomes from independent work have a not negligible proportional contribution to fhi inequality (table 5). both sources have gini coefficients and correlations higher than the pensions. this is why these income sources tend to increase fhi inequality and to partially counterbalance the contribution of pensions. this is true in the whole sample and in all examined subsamples (see table a.1 in the appendix). the elasticity value of ofi (i.e. the effect of a unitary increase of the share of ofi) in the italian farm families is lower than the values estimated, for example, by mishra et table 3. gini decomposition of farm household income in the altimetry zones of italy. year 2011.     share (%) gini coefficient correlation coefficient concentration coefficient proportional contribution to inequality elasticity (%) s g r c p η hill             market income 36.5 0.924 0.696 0.643 0.463 0.098 direct payments 20.3 0.701 0.474 0.332 0.133 -0.070 off-farm income 43.2 0.659 0.717 0.473 0.404 -0.029 farm household income 100.0 0.506 1.000 0.506 1.000 0.000 mountain             market income 37.8 0.997 0.773 0.771 0.540 0.162 direct payments 20.4 0.673 0.406 0.273 0.103 -0.101 off-farm income 41.8 0.674 0.683 0.460 0.357 -0.062 farm household income 100.0 0.540 1.000 0.540 1.000 0.000 plain             market income 38.6 1.055 0.749 0.789 0.548 0.162 direct payments 22.9 0.740 0.551 0.408 0.168 -0.061 off-farm income 38.5 0.655 0.625 0.409 0.284 -0.102 farm household income 100.0 0.556 1.000 0.556 1.000 0.000 source: own elaboration on italian fadn sample. 128 s. severini, a. tantari al. (2009) in the us but in line with most of the values estimated by el benni and finger (2013) in switzerland. the elasticity values of ofi in the italian farm families seem limited because some sources of ofi do not decrease fhi concentration. the relative importance of ofi in italian families is lower than the share of ofi estimated for the us but bigger than the relative importance of ofi in switzerland agriculture (mishra et al., 2009; el benni and finger, 2013). income from pensions remains the only ofi source that strongly reduce income inequality7. 7 this is particularly true in the north-east, in which the contribution of this source of income is above the average value (table a.2 in the appendix). in the north-west all ofi sources reduce fhi inequality, with pensions table 4. gini decomposition of farm household income in the macro-regions of italy. year 2011.     share (%) gini coefficient correlation coefficient concentration coefficient proportional contribution to inequality elasticity (%) s g r c p η north-west             market income 51.9 1.352 0.872 1.179 0.748 0.229 direct payments 34.7 0.734 0.610 0.448 0.190 -0.157 off-farm income 13.4 0.845 0.447 0.378 0.062 -0.072 farm household income 100.0 0.818 1.000 0.818 1.000 0.000 north-east             market income 38.5 0.937 0.776 0.727 0.545 0.159 direct payments 16.0 0.733 0.493 0.361 0.112 -0.048 off-farm income 45.5 0.599 0.648 0.388 0.343 -0.112 farm household income 100.0 0.514 1.000 0.514 1.000 0.000 center             market income 37.6 1.009 0.679 0.685 0.486 0.111 direct payments 22.5 0.729 0.508 0.370 0.157 -0.068 off-farm income 40.0 0.679 0.695 0.472 0.357 -0.043 farm household income 100.0 0.529 1.000 0.529 1.000 0.000 south             market income 32.1 0.858 0.591 0.507 0.368 0.047 direct payments 20.6 0.678 0.463 0.313 0.146 -0.060 off-farm income 47.3 0.623 0.729 0.454 0.486 0.013 farm household income 100.0 0.442 1.000 0.442 1.000 0.000 islands             market income 38.3 0.896 0.750 0.673 0.498 0.115 direct payments 19.9 0.709 0.480 0.341 0.131 -0.068 off-farm income 41.8 0.646 0.712 0.459 0.371 -0.047 farm household income 100.0 0.517 1.000 0.517 1.000 0.000 source: own elaboration on italian fadn sample. 129contribution of different income sources to regional inequality results of the analysis performed on the considered regions and zones in which the whole sample has been divided do confirm the main findings previously described for the italian whole sample (see appendix). the different off-farm income sources are not equally effective in decreasing income inequality: only pensions could be useful to decrease fhi inequality, while the other sources of ofi have a negligible impact on fhi inequality or, as in the case of wages and income from independent work, contribute to increase it. 5. conclusions the main result of the analysis is that the concentration of farm household income (fhi) is not very high and lower than that of only farm income (fi). this arises two policy relevant questions provided that the inequality of income distribution among the farm population is also used to justify policy intervention in rural areas. the first is whether it is correct to focus our attention only on fi as it is often the case in the cap policy debate. the results of the analysis suggest that, if the interest is on the wellbeing of farm families, it seems more appropriate to consider fhi because narrowing the analysis only on fi allows just a partial approach to cope with this issue. the second question refers to whether fhi is so concentrated to require an intervention to reduce its concentration. the provided evidences on the level of fhi concentration contributing the most. in all three altimetry zones, pensions decrease fhi inequality to a similar extent and the other sources of off-farm income have only a limited contribution on farm household income concentration. pensions are particularly effective in reducing fhi concentration in plain areas in which, due to their higher contribution to fhi and lower concentration, they have an elasticity above the average in absolute values (table a.2). table 5. gini decomposition of farm household income including all different sources of off-farm income. whole italian sample. year 2011.     share (%) gini coefficient correlation coefficient concentration coefficient proportional contribution to inequality elasticity (%) s g r c p η market income 37.5 0.987 0.731 0.721 0.509 0.134 direct payments 21.2 0.713 0.496 0.353 0.141 -0.071 off-farm incomes (ofi):             pensions 17.6 0.735 0.409 0.301 0.100 -0.077 wages 16.5 0.872 0.650 0.567 0.176 0.011 independent work 5.8 0.952 0.605 0.576 0.063 0.005 capital 1.2 0.987 0.497 0.491 0.011 -0.001 other off-farm income 0.3 0.996 0.166 0.165 0.001 -0.002 farm household income 100.0 0.531 1.000 0.531 1.000 0.000 source: own elaboration on italian fadn sample. 130 s. severini, a. tantari can be used by policy makers to decide whether income distribution policies specifically focused on farm households are really needed given that general policies, such as tax and welfare policies, are already available also to pursue income distribution goals. however, it is important to consider that future evolutions of cap can change this situation. the analysis has shown that reducing the level of dp and concentrating them on a smaller number of beneficiaries may result in an increase of fhi concentration. on the contrary, the increasing role ofi are playing in generating fhi is expected to have the opposite effect. in the case reducing fhi concentration is perceived as a policy relevant goal, it is important to identify the most appropriate policy tools to reach it. the analysis has provided insights regarding the use of agricultural as well as other policies. as dp are concerned, the analysis has confirmed the findings of previous studies: dp play an important role in reducing income concentration of italian farm households and this is the case also when the whole fhi is considered. this is because dp are relatively more important for those farms generating limited levels of market income (i.e. farm income net of dp). these results can feed the current debate regarding the application of the new dp policy measures in italy taking into account also their potential implications in terms of income distribution. on the one hand, applying a “narrow” definition of “active farmers” could strongly reduce the number of beneficiaries of dp and increase dp concentration. on the other hand, the redistributive payment for the first 30 ha could reduce dp concentration because it moves part of the overall amount of dp from large to small farms. both measures can have an effect on household income concentration even if the extent of such effect is reduced in those households where the relative importance of ofi is high. fhi inequality can be affected also by a large set of not agricultural policies by means of their impact on ofi. the results of the analysis allow for considerations regarding pension, rural development and regional policies. pensions received by members of farm households have been found to reduce fhi concentration. therefore, policy makers should be aware that any change in welfare policies causing a reduction of the level of the pensions earned by farm families will increase fhi concentration. rural development and regional policies can affect the possibility of farm family members to work off-farm and to generate off-farm incomes. however, the analysis has shown that an increase of wages or incomes from independent activities does not result in a decrease of fhi concentration. therefore, rural development and regional policy measures increasing only the level of income of the current beneficiaries of these sources of income are not expected to decrease income inequality. this suggests that, if policy makers want these policies also to decrease fhi concentration, these should be specifically aimed at enlarging the number of family members earning these sources of income. this issue could be taken into consideration in the new rural development programs that italian regions are currently designing. finally, it is important to underline some methodological issues. first of all, the developed large individual farm dataset reporting both farm and off-farm incomes could provide the basis for further research such as, for example, on the degree of pluriactivity of italian farm households (severini et al., 2014) or on the variability of farm income and farm household income. however, the analysis presented in this paper is subject to some limitations apart from those that are common to the studies based on the decomposition 131contribution of different income sources to regional inequality of the gini coefficient (lerman and yitzhaki, 1985) and the difficulties in measuring fhi (hill, 1999). the empirical analysis has been developed on a single year and this neither allow to assess the stability of the obtained results over time nor to investigate the dynamic of the investigated phenomena accounting for policy or market induced responses. thus, future researches should analyze more years to cope with these issues. furthermore, it seems useful in future research to compare the income disparity within farm families with that within non-farm families because this can show whether the income disparity within farm households is higher than that within non-farm families. this is an important issue because this can help in answering whether it exists a justification for policies specifically aimed at decreasing income inequality in farm households. acknowledgments we would like to thank the participants of the research project “analisi delle dinamiche evolutive del reddito in agricoltura attraverso l’utilizzo della banca dati rica” funded by the italian ministry of agriculture (mipaaf) and coordinated by the istituto nazionale di economia agraria (inea) 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(2007). agricultural and trade policy reform and inequality: the distributive effects of direct payments to german farmers under the eu’s new common agricultural policy. humboldt university berlin, department agricultural economics. working paper nr. 79/2007. 134 s. severini, a. tantari appendix. results for the macro-regions and altimetry zones of italy. table a.1. gini decomposition of farm household income. data regarding only the sources of offfarm income in the macro-regions of italy. year 2011.   share (%) gini coefficient correlation coefficient concentration coefficient proportional contribution to inequality elasticity (%) north-west             pensions 7.9 0.858 0.315 0.270 0.026 -0.053 wages 4.0 0.969 0.557 0.540 0.026 -0.014 independent work 0.9 0.993 0.629 0.624 0.007 -0.002 capital 0.5 0.996 0.534 0.532 0.003 -0.002 other off-farm income 0.2 0.997 -0.078 -0.078 0.000 -0.002 north-east             pensions 21.1 0.635 0.308 0.195 0.080 -0.131 wages 18.3 0.855 0.659 0.564 0.200 0.018 independent work 5.3 0.952 0.563 0.535 0.055 0.002 capital 0.7 0.994 0.537 0.534 0.007 0.000 other off-farm income 0.1 0.998 0.240 0.240 0.001 -0.001 center             pensions 18.5 0.704 0.381 0.269 0.094 -0.091 wages 12.6 0.915 0.702 0.642 0.153 0.027 independent work 6.8 0.958 0.714 0.684 0.087 0.020 capital 1.9 0.985 0.609 0.599 0.022 0.003 other off-farm income 0.2 0.996 0.137 0.136 0.001 -0.001 south             pensions 18.5 0.744 0.450 0.335 0.140 -0.045 wages 21.4 0.829 0.641 0.531 0.256 0.043 independent work 6.2 0.946 0.594 0.562 0.079 0.017 capital 1.1 0.984 0.400 0.393 0.010 -0.001 other off-farm income 0.2 0.997 0.349 0.348 0.002 0.000 islands             pensions 16.5 0.752 0.438 0.329 0.105 -0.060 wages 14.7 0.871 0.642 0.559 0.159 0.012 independent work 7.9 0.926 0.596 0.552 0.085 0.005 capital 1.9 0.976 0.544 0.531 0.019 0.001 other off-farm income 0.8 0.986 0.208 0.205 0.003 -0.005 source: own elaboration on italian fadn sample. 135contribution of different income sources to regional inequality table a.2. gini decomposition of farm household income. data regarding only the sources of offfarm income in the altimetry zones of italy. year 2011.     share (%) gini coefficient correlation coefficient concentration coefficient proportional contribution to inequality elasticity (%) hill             pensions 17.8 0.737 0.443 0.326 0.115 -0.063 wages 16.6 0.871 0.655 0.571 0.188 0.021 independent work 7.2 0.943 0.628 0.593 0.085 0.012 capital 1.4 0.985 0.561 0.553 0.015 0.001 other off-farm income 0.2 0.998 0.450 0.449 0.002 0.000 mountain             pensions 14.2 0.767 0.377 0.289 0.076 -0.066 wages 21.1 0.854 0.660 0.563 0.220 0.009 independent work 4.6 0.963 0.581 0.559 0.047 0.002 capital 1.3 0.986 0.524 0.516 0.013 -0.001 other off-farm income 0.7 0.988 0.041 0.040 0.001 -0.006 plain             pensions 19.1 0.713 0.370 0.263 0.090 -0.100 wages 14.1 0.881 0.641 0.565 0.144 0.002 independent work 4.4 0.959 0.599 0.575 0.046 0.002 capital 0.7 0.990 0.318 0.315 0.004 -0.003 other off-farm income 0.2 0.995 0.010 0.010 0.000 -0.002 source: own elaboration on italian fadn sample. bio-based and applied economics 4(2): 165-178, 2015 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-13597 technical efficiency in brazilian citrus production felippe clemente*, viviani silva lírio, marília fernanda maciel gomes federal university of viçosa, minas gerais, brazil date of submission: november 17th, 2013 abstract. the purpose of this study was to analyze the technical efficiency of citrus producing properties in sao paulo state, in 2009 and 2010. for this, producers were interviewed; non–parametric data envelopment analysis approach was applied to calculate levels of technical efficiency, and an econometric approach was applied to establish technical efficiency determinants. the results showed that a great part of sao paulo citrus producing properties works inefficiently and the variables that mostly contribute to increase efficiency are “producer schooling “ and “experience as rural producer”. keywords. technical efficiency, citrus sector, dea. jel codes. q12, c25 1. introduction orange culture is present in all brazilian states in quite different production standards. at the same time, orange production is an activity concentrated in part of the country, as 96% of production comes from only six states, with distinction for sao paulo which is responsible for 78% of the production (agrianual, 2010). in the context of brazilian agro-business the citrus sector stands out, comprising over 24 thousand rural properties and directly employing 11.2% of the agricultural workforce of sao paulo state and 2.2% of brazil. in 2010, brazil was the main orange producer in the world with 31% of production, followed by the united states and european union with 16% and 11% respectively. in external sales the occupied post is equivalent: the country is the main exporter of concentrated frozen orange juice with nearly 80% of the international market. added to this information is the fact that since 1994 orange juice exports have settled around 1.1 and 1.4 million tons, producing more than a billion dollars in foreign exchange (neves, 2005). regarding the orange production in sao paulo state, despite its relative importance, it is noted that production is not uniformly distributed amongst citrus producers; there * corresponding author: felippe.clemente@ufv.br. mailto:felippe.clemente@ufv.br 166 f. clemente, v.s. lírio, m.f. maciel gomes is a discrepancy between the number of producers and the quantity produced. according to clemente (2010), the orange production structure in the interior of são paulo state is characterized by “many produce little” and “a few produce much”. producers of up to 100 hectares correspond to 48% of the total number of producers, but they respond for only 17.5% of the production. in another extreme, producers with over 300 hectares correspond to only 17% of the total number of producers, but respond for 43.3% of the são paulo production. from this information it is believed that there is room to investigate the existence of possible inefficiency in orange production in sao paulo. in fact, the efficiency concept is relative and differs from efficacy and yield. efficacy is linked only to what is produced, without considering the resources used for production. yield is established by the ratio between what was produced and what was spent in production. whereas efficiency compares what was produced, given the resources available, with what might have been produced with the same resources, so that, if the productive unit is very far from this parameter, it can be considered inefficient. there are two ways by which a technically inefficient unit can become efficient: the first one is by reducing inputs, maintaining constant production; the second is increasing production, maintaining constant inputs (mello et al., 2005). in applied terms, efficiency analysis of productive units is important both for strategic aims (comparison between productive units), and for planning (evaluation of results of use of different combination factors) and for taking decision (such as improving current performance by analysis of distance between current production and potential production). in this context, the purpose of the present study is to analyze the technical efficiency of citrus producing properties in sao paulo state, based on information corresponding to years 2009 and 2010, phase in which interviews were carried out with citrus producers so as to obtain information regarding orange production in each property. for that, the present study is structured in four sections, besides this introduction. in section two, theoretical referential system of the study is presented and in section three is the analytical referential system. the main results obtained and the study conclusions are presented in sections five and six. 2. evolution of brazilian citrus sector the orange crop is widespread in brazil, being cultivated in almost all states. at the same time, the orange is a spatially concentrated culture: around 96% of production comes from only six states, especially são paulo, which accounts for about 78% of production (agrianual, 2010). next are bahia, sergipe, minas gerais, paraná and rio grande do sul, all with less than 7% of the national production (table 1). the southeast region has become the largest development center of the sector due to the exceptional fruit quality and favorable climate. in the 20s, the establishment of experimental stations in são paulo and conducting research for citrus improvement enabled the provision of good quality fruit for the domestic market and export to argentina, uruguay, england and other european countries. according to reis (2001), the citrus industry in brazil took off from the stage called “conservative modernization” of agriculture, between the years 1965 to 1979, which had as its main features, among others, subsidized rural credit, export incentives 167technical efficiency in brazilian citrus production and tax exemptions. in addition, large fluctuations in the production of the united states, brazil’s main international competitor in the marketing of citrus, due to frequent frosts opened space for the placement of domestic production of fruit “in natura” and the frozen concentrated orange juice abroad which enabled the growth of acreage and processing industries orange (maia, 1996). however, unlike other cultures strongly encouraged in this period, the citrus sector continued on a strong expansion also during the 1980s, when the international financial crisis caused depressive effects on the economy, reducing the supply of agricultural credit and reducing subsidies to the sector. in the 1990s, the growth process of the country continued to occur and currently brazil is the main producer of orange and also the main exporter of frozen concentrated orange (slcc) juice, with about 80% market share. table 1. brazilian citrus production – 2008. states production % sp 353,228,235 78.4 ba 27,374,902 6.1 se 18,923,284 4.2 mg 14,311,863 3.2 pr 12,681,373 2.8 rs 8,217,108 1.8 brazil 450,850,956 100 the development of the brazilian citrus industry began in the late 1950s. in 1959, it was installed the first juice concentrate factory in brazil, mining beverage company. in 1961, a company of sao paulo, paulista citrosuco, sent to the united states the first 1,000 tons of concentrated juice. however, the major impetus for the development of the brazilian citrus industry was the frost that hit the orchards of florida in 1962, reaching the destroy of 13 million adult trees. this frosting turned out to be a milestone for the brazilian industry (abecitrus, 2007). there was a shortage of raw material to supply the u.s. domestic market and european markets and the brazil came to occupy these markets, accelerating the development of orange processing industry. with the development of the processing industry of juice, there have been significant changes in the citrus sector. firstly, the production structure has changed considerably. according margarido (1998), industry growth stimulated the concentration of production in medium and large orange-producing properties, with a high rate of employment. moreover, the consumption profile of the fruit changed with the possibility of processing and export. while in 1972 about 69% of brazilian production was intended for the domestic market of fresh fruit and only 2% for export, at the end of the 80s, with the development of the industry, about 37% of the production was intended market “in natura” and more than 60% of brazilian production was aimed at processing industry (coelho, 1996). currently, according to neves (2005), 82% of fruit harvested are processed, leaving only 18% to the market in natura. thus, the majority of brazilian orange production is intended for the juice industry, which is concentrated in são paulo. the citrus sector involves 168 f. clemente, v.s. lírio, m.f. maciel gomes more than 24,000 farms and directly employs about 11.2% of the agricultural labor force in the state of são paulo and 2.2% in brazil. exports of orange juice remains, since 1994, between 1.1 and 1.4 million tons, generating more than $ 1 billion of foreign exchange. (neves, 2005). 3. theoretical background according to lovell (1993), in micro-economic analyses the production function is normally represented as a relation between y the produced quantity of the good and a set of x1, x2,…, xn which identify the quantities of several factors used, observing the most efficient production process. in other words, it is “a technical relation which associates to each endowment of production factors the maximum quantity of product obtained through the use of these factors” (barbosa, 1985). in this extent, the question of efficiency gains important outlines. for sato (1975), the aggregation of production functions and their subsequent econometric estimate aiming at generating a macro production function, not taking into account differences in productive efficiency, yield biased results. such results, when employed by agents responsible for the productive process, may compromise the efficient allocation of resources, which, most times, are scarce and expensive. in other words, comparing different production units, errors may occur, should the analysis be based only on the mean production function estimate. this happens because there are differences in the use of production factors, which produce different levels of technical efficiency in production. thus, in order to correctly appreciate the production function associated to a certain region or estate, it is necessary to eliminate the existing inefficiencies in each production unit, or to consider them appropriately in the intended analyses. given that, it is necessary to estimate a frontier production function that characterizes the best technology (best practice), from which comparisons can be made between production units in terms of production efficiency and structure of production technology. figure 1 illustrates difference between a mean production function estimated by least squares and a frontier production function. it can be observed that in the mean function, while minimizing the deviation squares, there are points above and below the function. in the frontier function, all points are situated on or below it. points on the frontier refer to efficient units. similarly, points below the frontier present some type of inefficiency (färe et al., 1994). once this is done, it is possible to estimate the production function, which will best express the relations between inputs and product with no inefficiency. 4. methodology 4.1 data envelopment analysis in a productive structure, the maximum quantities of products that can be obtained, given the inputs used, determine the production frontier (lins and meza, 2000). orange production, as well as other agricultural activities, involves very variable production systems, making decision taking on the best allocation of resources a very complex matter. 169technical efficiency in brazilian citrus production data envelopment analysis (dea) (charnes et al., 1978) use dmu’s concept and is a non-parametric technique based on mathematical planning to analyze the relative efficiency of production units. in the literature related to dea models, a production unit is considered a decision making unit (dmu), since these models provide measures to evaluate the relative efficiency of decision making units. according to ferreira (2005), the fundamental difference between the dea approach and parametric analysis, such as the stochastic frontier, is in fact the first to be non-parametric estimating a deterministic frontier, and the second is parametric, based on stochastic function. a limitation to the use of the parametric approach to measure efficiency stems from the fact that it can present estimation problems, some of them are highlighted in maietta (2002). thus, the non-parametric approach that uses mathematical programming, like dea, seems more appropriate. another considerable advantage of dea in relation to parameter estimation is the individual identification of each producer in the issue efficiency, which is possible through the efficiency scores generated by the operation of the model. these characteristics make the method a potentiality to explain with greater property and little interference from reviewers, the complexities inherent in real conditions (ferreira, 2005). despite the advantages presented, this methodology also has disadvantages, among which stands out the sensitivity to the presence of outliers and the inclusion or exclusion of one or more units in the set of observations, the number of variables considered in the analysis, the impossibility of statistically test the results and also to disregard the presence of random factors and measurement errors, so that the whole distance to the border is considered due to inefficiency (bonaccorsi and daraio, 2004). alternatives to dea methodologies were developed in order to advance in studies on efficiency. in this sense, aigner, lovell and schmidt (1977) proposed a specific production function for “cross-section” data, in which deviations in relation to the production function could be due to productive inefficiency and random effects. this function is called stochastic production frontier (sfa). the sfa model has advantages, with little sensitivity to the problems of measurement errors, the estimation of confidence intervals for the coefficients of efficiency to take a figure 1. representation of production function 170 f. clemente, v.s. lírio, m.f. maciel gomes chance on returns to scale and the major disadvantages such as the fact can suffer from the same problems of traditional regression analysis, the limitations related to the omission of variables, possible autocorrelation of errors, heteroscedasticity and endogeneity (bonaccorsi and daraio, 2004). thus, notes-that there is no consensus in the literature justifying the choice of the dea or stochastic frontier, as both have advantages and disadvantages. the choice of dea model to this work was due mainly to the size of the database analysis. to incorporate the multi-product and multi-input production nature, charnes et al. (1994) proposes the dea technique for analysis of different units, regarding relative efficiency. the distance1 function is employed to incorporate the multi-product and multi-input nature in the productivity and efficiency analysis, without the necessity of specifying behavioral goals of decision makers. according to bravo-ureta and pinheiro (1993), the convenient form of describing the multi-product characteristic of production is by production technology, defined by set s, represented in equation (1): s={(x, y) : x can produce y} (1) which is defined by the set of all input and product vectors (x, y) so that x can produce y in which x is a non-negative input vector (k x 1) and y a non-negative product vector (m x 1) the set of production technologies can equivalently be defined by the set of production possibilities p(x), which represents the set of all product vectors y, that can be produced by the input vector x in other words, px={y : x can produce y} (2) the distance function with product orientation, according to coelli et al. (2005), can be defined by the set of products p(x) as ( ) ( )= ∅ ∅ ∈d x y y p x, min :  0 (3) ( ){ }( ) ( ) ( )= ∅ ∅ ∈ − d x y y p x, máx :    0 1 (4) in which, ∅ in expression (3) is: the inverse of the factor by which the production of all output quantities could be increased while still remaining within the feasible production possibility set for the given input level (coelli, prasada rao and battese, 1998). the distance function d0(x, y) might have values lower or equal to 1, if the product vector y is an element of the set of possibility of production p(x); if it is equal to 1, (x, y) it will be on the technological frontier; thus, production will be technically efficient. 1 it can be defined as input orientation and product orientation. input orientation characterizes production technology by proportional minimization (contraction) of input vector, given a product vector. product orientation characterizes production technology by proportional maximization of product vector, given an input vector. 171technical efficiency in brazilian citrus production the product-oriented dea model with assumption of non-constant returns to scale tries to maximize the proportional increase at the product level, maintaining the quantity of inputs fixed. in accordance with charnes et al. (1994), it can be algebraically represented as: φ( ) = θ λ − + −d x y max, s s0 1 , , , (5) subject to: φ λ− + =+y y s 0i λ− + + =−x x s 0i λ ≤n1' 1 λ ≥ 0 ≥+s 0 ≥−s 0 in which yi is a vector (m x 1) of product quantities of the i-th dmu; xi is a vector (k x 1) of input quantities of the i-th dmu; y is a matrix (n x m) of ndmus products; x is a matrix (n x k) of n dmus inputs; λ is a weight vector (n x 1); n1 is a vector (n x 1) of number ones; s+ is a vector of floats related to products; sis a vector of floats related to inputs; and ϕ is a scalar that has vectors equal to or higher than 1 and indicates dmus efficiency score, in other words, a value equal to 1 indicates technical efficiency of the i-th dmu, in relation to the rest, while a value higher than 1 shows the presence of relative technical inefficiency. the problem presented in (5) is solved n times – once for each dmu, and, as a result, presents values of ϕ and λ, ϕ the dmu efficiency score under analysis λ supplies the peers (efficient dmus that serve as reference for the i-th inefficient dmu). 4.2 the second stage although the use of tobit models as “second stage”, to explain efficiency indices from dea estimation of frontiers, has gained popularity in the 1990s and 2000, most recently mcdonald (2009) showed that its use may be inappropriate, and that, in such applications, the estimator maximum likelihood (ml) is generally inconsistent, unlike the ordinary least squares estimator (ols). as a consequence, it was estimated a regression using the method ols. at this point, one should try to concentrate on information referring to the properties characteristics (size and number of employees) and producer characteristics (age, schooling and experience as producer). it is expected that these variables positively impact on the fact of properties being efficient. therefore the following equation was estimated, based on primary data obtained with a sample of citrus producers in sao paulo state: β β β β β β ε= + + + + + +y i e te t nfi i i i i i i1 2 3 4 5 6 (6) in which: yi = efficiency scores obtained by data envelopment analysis. consequently, each dmu has a positive efficiency coefficient, limited to the interval 0 to 1; 172 f. clemente, v.s. lírio, m.f. maciel gomes ii = producer age (in years); ei = producer schooling (in years); tei = as rural producer (in years); ti = property size (in hectares); nfi = number of employees in properties; εi = term. the equation estimate (6) allows inferences to be made for the whole population without quality loss. it is expected that all of these variables impacting positively on the efficiency of the property. age and time as farmer demonstrate the skill from the branch of citrus. the education variable indicates the level of expertise of the grower and the variable size of the transformed property in the production of oranges implemented. 4.3 study area in order to investigate the level of production of the orange growers and the quantity of inputs used, interviews with orange producers of são paulo were previously selected for application of a structured questionnaire were conducted from december 2009 to february, 2010. growers of eleven cities in the state of são paulo, a region that has the largest orange production in the country were interviewed. the state of são paulo had, in 2006, approximately 6300 active growers, concentrating 81% of the national production of oranges. neves (2005) shows that citrus regions in são paulo are divided into north and northeast, central, south and new south (figure 2). the first comprises the region trough and barretos, são josé do rio preto and votuporanga and catanduva region, accounting for 45% of production in the state. second, participating region of araraquara and matão, itápolis and taquaritinga, representing 30% of production. in the south, part of the region of limeira, avare/botucatu and itapetininga. have the new south comprises of bauru, itapetininga. these last two regions concentrate 25% of production (tavares, 2006). when the sample structure, aimed to select the regions where most growers are concentrated orange production in the state of são paulo. the calculation of sample2 size, with a confidence interval and tolerance of sampling error of 10%, resulted in 67 questionnaires. for stratification of the sample, the participation of the regions in orange production in the state of são paulo and then the production of the main regions in 2003 were used. thus, we selected 30 questionnaires for the north and northeast, 20 questionnaires for the central region and 17 questionnaires to the south/new south. because regions of barretos, catanduva and sao jose do rio preto is located in the north and northeast, 17 questionnaires for the region of barretos, 6 to 7 and catanduva region to the region of são josé do rio preto were applied. for the center, the 20 questionnaires were administered in the araraquara region and for south/south new 10 questionnaires were administered in the region of limeira, in the region of jau 4 and 3 in the region of bauru. 2 sample size: = − + n z p q n d n z p q . . . [ .( 1) . . ] 2 2 2 (grenee, 1993). 173technical efficiency in brazilian citrus production figure 2. são paulo regions. 4.4 source of data the mean capital cost of properties in the interior of sao paulo state and the mean price of orange sold for industry were collected at the institute of agricultural economy (iea). aiming at investigating the characteristics of farmers and properties, an exploratory survey was carried out with 67 orange producers in eleven cities of sao paulo state in 2010. 5. results and discussion 5.1 descriptive characteristics of sample structured questionnaires were applied to 67 orange producers in sao paulo state. the questionnaires aimed at investigating the characteristics of citrus producers and at collecting information about the property, such as size and number of employees. among the main characteristics, it was noticed that, in total, 34% of interviewed producers were aged between 23 and 50 years and 66% were over 50 years, which indicates the prevalence of older producers. in addition, it was observed that 33% of producers had studied up to 5 years and 51% had studied over 10 years. regarding the experience as rural producer, the results show that 26% of producers were up to 20 years in the activity, 24% were in activity between 20 and 30 years and 50% have been producing over 30 years, showing the prevalence of citrus producers with a wide experience in the orange production. from the analysis of property size (figure 3), presence of “small“ and “average size” properties stand up (up to 100 hectares), with 48%. for 81% of producers, the main source of income is agriculture and 55% obtain an annual gross income of over r$ 100.000 with orange (figure 4). 174 f. clemente, v.s. lírio, m.f. maciel gomes figure 3. property size. 48%   34%   15%   3%   from  1  to  100  hectares   from  101  to  300  hectares   from  301  to  600  hectares   over  600  hectares   figure 4. annual gross income with orange. 3%   28%   14%   19%   15%   21%   up  to  r$  10,000   from  r$  10,001  to  r$  50,000   from  r$  50,001  to  r$  100,000    from  r$  100,001  to  r$   200,000   from  r$  200,001  to  r$  500,000   over  r$  500,000   as for the labor profile, it is noticed that 84% of properties have contracted labor and 16% count exclusively on family labor. though the use of family labor is a characteristic of small properties, in this type of property the use of constant and paid labor has been found. regarding the number of constant employees, it was verified that the average of workers hired per property is approximately 6. however, there is a large disparity of labor between farms, since there are properties with up to 75 employees and properties with none. this is also shown by the high standard deviation value (11.75). in summary, the results show differences in the productive characteristics of citrus properties in sao paulo state, particularly regarding schooling, gross income and experience as rural producer. these divergences may imply in certain orange production inefficiency in the region. 175technical efficiency in brazilian citrus production 5.2 analysis of technical efficiency technical efficiency compares what was produced, given the available resources, with what might have been produced with the same resources. therefore, data envelopment analysis was used to verify the orange production efficiency in properties. the variables employed in the efficiency model for sample as a whole, along with descriptive statistics are presented in table 2. table 2. descriptive statistics of variables employed in producer efficiency model, 2010. variable mean standard deviation maximum minimum orange production (40.8kg box) 34,920.59 39,444.87 136,263.00 738.37 property size (in ha) 190.66 287.95 2,100.00 12.00 number of employees 6.13 11.75 75.00 0.00 capital cost (in r$) 714,063.98 1,049,592.85 7,578,522.00 46,695.12 producer age (in years) 55.21 13.11 79.00 23.00 producer schooling (in years) 9.93 5.43 17.00 1.00 experience as producer (in years) 31.52 14.29 63.00 5.00 these variables reflect property characteristics (production, size, employees and capital cost) and producer characteristics (age, schooling and experience as producer). a relative difference in magnitude is evident in units that constitute the sample, particularly the high standard deviation resulting from relative dispersal of data around the mean, which declines central tendency inferences table 3 shows statistical summary of technical efficiency calculation of units that constitute the sample. by the technical efficiency score means, it is possible to visualize the efficiency level in properties. individualized scores allow more specific notes on each productive unit, indicating inefficiency in resources, as well as pointing to dmus that serve as model. this observation is important to analyze the real situation of each property in detriment to group performance. table 3. technical efficiency scores of citrus properties. variables efficient units mean standard deviation maximum minimum technical efficiency 13.43% 0.79 0.21 1.00 0.25 the results demonstrate that citrus properties of sao paulo present significant technical inefficiency level. the technical efficiency mean was 0.79, which suggests the possibility of production increase, considering the same proportion of inputs currently used, taking as reference the product-oriented model. while analyzing the producing regions of sao paulo state, 176 f. clemente, v.s. lírio, m.f. maciel gomes it can be observed that 67% of efficient properties are in the north and northeast of the state. this occurs due to the fact that these regions are the oldest in orange production in the country, which enabled producers to gain greater knowledge on the best combination of inputs. regarding the most inefficient properties, 71.4% are located in the south and southeast regions of the state. to compare the inefficiency level of productive units based on the technical efficiency mean score, an indicator was created and defined by ferrier and porter (1991), which follows: 1 score −1⎛ ⎝⎜ ⎞ ⎠⎟ x 100 (14) thus, as this analysis is output-oriented, on average inefficient firms could  produce 26.6% more output, given their inputs. aiming at verifying the efficiency determinants in citrus producing properties in sao paulo state, the ols econometric model was used. the results of the model can be observed in table 4. for analyze the possible correlation between “producer age” and “experience as rural producer”, we made other estimations in ii and iii. table 4. factors associated to technical efficiency in citrus producing properties in sao paulo state – ols model. variable i ii iii property size 0.00028ns 0.00037ns 0.00017ns number of employees -0.010 ns -0.0121** -0.0076ns producer age 0.0063 ns 0.0099** producer schooling 0.0145** 0.0128* 0.0136* experience as rural producer 0.0048ns 0.0087*** constant 1.391*** 1.422*** 1.148*** r2 22.85 20.50 19.46 * 10% of significance; ** 5% of significance; *** 1% of significance ns no significance. we could observe in estimation i that only the variable “producer schooling” is significant at 5%. to the estimation iii the variable “experience as rural producer” is a positive and highly significant determinant of efficiency, however the impact of “producer schooling” on technical efficiency is much stronger. 6. conclusion this study analyzed the efficiency of citrus producing properties in sao paulo state from 2009 to 2010, using the non-parametric data envelopment analysis approach to calculate technical efficiency levels. in addition, the ols econometric model was used to find the technical efficiency determinants of citrus producers. 177technical efficiency in brazilian citrus production the results confirmed the hypothesis that citrus producing properties are inefficient. in other words, given that we have an output-orientation in this analysis, the farmers do not obtain the maximum output from their resources. the reduction of these inefficiencies could increase the production in the entire sector. the variables that mostly contribute to increase efficiency are “producer schooling” and “experience as rural producer”. references aigner, d., lovell, k. and schimidt, p. (1977). formulation and estimation of stochastic frontier production function models. journal of econometrics 6(1): 21-37. abecitrus (2007). associação brasileira dos exportadores de cítricos. informativo abecitrus. ribeirão preto. agrianual 2010. (2010). anuário estatístico da agricultura brasileira. são paulo: argos, inc. bonaccorsi, a. and daraio, c. (2004). econometric approaches to the analysis of productivity of r&d systems. production functions and production frontiers. in: moed, glanzel, shmoch (eds.), handbook on quantitative science and technology research, kluwer academic press. bravo-ureta, b.e. and pinheiro, a.e. (1993). efficiency analysis of developing country agriculture: a review of the frontier function literature, agricultural and research economic review 22: 88-101. clemente, f. (2010). viabilidade de implantação de contratos futuros de suco de laranja concentrado congelado no brasil. monograph, department of rural economics, federal university of viçosa, viçosa, mg, brazil. charnes, a., cooper, w.w. and rhodes, e. (1978). measuring the efficiency of decision making units. european journal of operational research 2: 429-444. charnes, a., cooper, w. w., lewin, a. y. and seiford, l. m. (1994). data envelopment analysis: theory, methodology and application. dordrecht: kluwer academic. coelho, f.c. (1996). análise do mecanismo de transmissão de preços de laranja e soja entre os mercados externo e interno. msc dissertation, department of rural economics, federal university of viçosa, viçosa, mg, brazil. coelli, t., o’donnell, c.j. and battese, g.e. (2005). an introduction to efficiency and productivity analysis. springer. ferreira, m.a.m. (2005). eficiência técnica e de escala de cooperativas e sociedade de capital na indústria de laticínios do brasil. phd dissertation, department of rural economics, federal university of viçosa, viçosa, mg, brazil. gomes, a.p. and baptista, a.j.m. s. (2009). função de produção de fronteira e tomada de decisão na agropecuária. sober(2009), belo horizonte. lins, m.p.e. and meza, l.a. (2000). análise envoltória de dados a perspectiva de integração no ambiente de apoio à decisão. rio de janeiro: coppe/ufrj, inc. lovell, c.a.k. (1993). production frontiers and productive efficiency. in: fried h., lovell, c.a.k., schimdt, s. (eds), the measurement of productive efficiency: techniques and applications. london: oxford university press. maia, m.l. (1996). citricultura paulista: evolução, estrutura e acordo de preços. são paulo: iea, inc. 178 f. clemente, v.s. lírio, m.f. maciel gomes maietta, o.w. (2002). the effect of the normalisation of the shadow price vector on the cost function estimation, economics letters 77(3): 381-385. margarido, m.a. (1998). transmissão de preços internacionais de suco de laranja para preços ao nível de produtor de laranja no estado de são paulo. são paulo: iea, inc. mello, j.c.b.s., meza, l.a., gomes, e.g. and neto, l.b. (2005). curso de análise de envoltória de dados. sbpo(2005), gramado, rs. mcdonald, j. (2009). using least squares and tobit in second stage dea efficiency analyses. european journal of operational research 197: 792-798. neves, m.f. (2005). estratégias para a laranja no brasil. são paulo: atlas s.a., inc. reis, b.s., lirio, v.s. (2001). negociações internacionais e propriedade intelectual no agronegócio. ufv: der, inc. sato, k. (1975). production functions and aggregation. amsterdam: north-holland publishing company. tavares, m.f.f. (2006). o mercado futuro de suco de laranja concentrado e congelado: um enfoque analítico. phd dissertation, department of economics, federal university of rio grande do sul, porto alegre, rs, brazil. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(3): 209-211, 2013 between crisis and development: which role for the bioeconomy (and bio-economists) donato romano president associazione italiana di economia agraria e applicata (aieaa) the metaphor of the economic cycle, that is the natural fluctuation of the economy between periods of expansion and contraction, is as old as modern economic thought and dates back at least to early nineteen century when authors as sismondi (1819) provided the first systematic exposition of economic crises. however, the recent years – featuring first the commodity price spikes, followed by the great recession and a very slow and highly uneven recovery in different regions of the world – have marked a dramatic change in the way lay people as well as economists look at recent developments in the economy. acknowledging that “this time is different” (reinhart and rogoff, 2009) and we are perhaps entering a “new normality” (krugman, 2013) of “secular stagnation” (summers, 2013) are the ideas the current debate is focusing upon. why is it so? one reason, probably the most important one, is the length of the global economic crisis and its apparent inertia to any effort to get rid of it. the other one is the very long period of macroeconomic (and price) stability – the so called “great moderation” – preceding the 2007-08 economic turmoil. we do not know when and how the current economic conditions will be overcome, but what we do know is that they are changing profoundly our lives and have already had significant welfare and distributional effects all over the world. this is very likely to permanently redesign the loci of economic activity at global, regional and country level as well as at sectoral level within a given economy. the reshaping of the economic landscape brings about a series of consequences for the bio-economy as well as bio-economists, linked to the double meaning of crisis, which is at the same time a threat and an opportunity, as always in economic history. indeed, the global economic crisis has changed the relationships between bio-based activities and economic development. on one side, the more traditional food and agricultural production has been suffering, because of the drop in the final and intermediate demand of agrofood products. on the other side bio-economic activities act as social safety nets, creating employment opportunities, reducing vulnerability of the poor and delivering a multiplicity of environmental services. at the same time, the recent crisis-induced changes have reinforced an already ongoing process of “renaissance” of agricultural economics (sexton, 2013) and, at a broader level, the blossoming of bio-economics. indeed, after a couple of decades of complacency among policy makers and the public at large that we had solved the food problem, the recent commodity price spikes as well as the global economic crisis brought back at the 210 donato romano top of the policy agenda the role of agricultural economics and policy, and the role of agricultural economists as researchers and key policy advisors to decision makers. to what extent, where and how the crisis has changed those relationships and what role bio-based activities can play in the economic recovery towards a sustainable development path were the overall objectives of the second aieaa conference held in parma (italy) on 6-7 june 2013. the conference gathered more than seventy papers addressing an array of research and policy relevant questions such as: is agriculture playing a buffer role against the global crisis? how the crisis has changed the pro-poor features of agricultural growth? what are the impacts of crisis on poverty and food insecurity? what are the impacts of crisis on rural-urban disparities and horizontal and vertical inequality? what are the impacts of crisis on migration and remittance flows? what strategies have been implemented by households, firms and farms to cope with the shocks? what is the role that bio-economy activities can play in recovering and future economic development strategies? the bae editor invited some of the speakers to revise and submit their own papers for publication in this bae special issue. this is not meant to be an exhaustive account of all the topics debated in parma1; nevertheless it represents a significant essay of the wide range of topics discuss at the conference, covering issues such as food commodity price volatility and institutional arrangements to manage risk in commodity markets, structural and economic dynamics in italian farming sector, the spatial allocation of eu rural development funds, and the impact in terms of water requirements of agro-energy corps. in particular, sarris makes a thorough assessment of the state of the art on the food price volatility and food security conundrum. specifically, the author defines what food price volatility is about and shows that the major risks for a food importing developing country involve not only large and unpredictable price variations but also trade finance as well as import contract enforcement. finally, the author suggests how to design institutions and policies to assist developing countries better cope with the risks of commodity market volatility. the purpose of the paper by revoredo-giha and zuppiroli is twofold: analyze whether futures markets are still useful for hedging (analyzing the european wheat futures markets and the chicago board of trade’s wheat contracts as a comparison), and testing whether the increasing presence of speculation has made futures markets divorced from physical markets. the results show that hedging with futures markets is still an effective option for reducing price risk, particularly in short term hedges. salvioni et al. investigate the structural change and economic dynamics of farms pursuing diversification strategies (i.e. pursuing multifunctionality through non-farming activities) vs. differentiation strategies (i.e. adopting product differentiation through a quality certification) in italy. the most important outcome is that only diversified farms show a significant improvement in labor productivity expressed as income per worker, while the labor productivity performances of differentiated farms are not that good. camaioni et al. try to assess the coherence of eu rural development fund allocation with the real characteristics of eu rural space. in doing so, that authors go beyond the usual dichotomous definitions and approaches, proposing a composite and comprehensive 1 the interested reader can download all conference papers at http://ageconsearch.umn.edu/handle/149623. paper presentations are also downloadable at aieaa website (www.aieaa.org). 211between crisis and development: which role for the bio-economy (and bio-economists) measure of rurality and peripherality, the so-called peripherurality indicator. using this indicator the eu rural development policy appears less “rural” than stated in the political intentions. in relative terms (per unit of land and, above all, of labour), urban and central regions tend to be more supported than more rural and peripheral ones. finally, donati et al. present an integrated model for the economic and environmental assessment of natural resources use when new activities (i.e. bioenergy crops) are introduced into the farm production plan. the model integrates a standard positive mathematical programming model with the aquacrop model developed by fao. the results of the simulations show that this model can help policy makers in assessing the impacts of changes in farm production plans on farm profitability, land use and water consumption and the sustainability of new market/policy scenarios, such as the 2014 cap reform which emphasizes environmental objectives in agricultural policy. references camaioni, b., esposti r., lo bianco a., pagliacci, f. and sotte, f. (2013). how rural is the eu rdp? an analysis through spatial fund allocation. bio-based and applied economics 2(3): 277-300. donati, m., bodini, d., arfini, f. and zezza, a. (2013). an integrated pmp model to assess the development of agro-energy crops and the effect on water requirements. bio-based and applied economics 2(3): 301-321. krugman, p.r. (2013). “a permanent slump?” the york times, november 13, 2103. accessed at http://www.nytimes.com/2013/11/18/opinion/krugman-a-permanentslump.html?_r=0 on december 3, 2013. reinhart, c.m. and rogoff, k.s. (2009). this time is different: eight centuries of financial folly. princeton university press. princeton. revoredo-giha, c. and zuppiroli, m. (2013). commodity futures markets: are they an effective price risk management tool for the european wheat supply chain? biobased and applied economics 2(3): 237-256. salvioni, c., ascione, e. and henke, r.(2013). structural and economic dynamics in diversified italian farms. bio-based and applied economics 2(3): 257-276. sarris, a. (2013). food commodity price volatility and food insecurity. bio-based and applied economics 2(3): 213-236. sexton, r. (2013). “the renaissance of agricultural economics”. the president’s column, the exchange, newsletter of the aaae, may-june 2013. accessed at http://www. aaea.org/publications/the-exchange/newsletter-archives/mayjune-2013/presidentscolumn on december 3, 2013. sismondi, j.c.l.s. de (1819). nouveaux principes d’économie politique ou de la richesse dans ses rapports avec la population. chez delaunay, treuttel et wurtz, paris. summers, l.h. (2013). speech at fourteenth jacques polak annual research conference “crises: yesterday and today”. international monetary fund, washington d.c., november 8, 2013. accessed at http://larrysummers.com/video/ on december 3, 2013. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(2): 173-189, 2013 income analysis in south american domestic camelid farms francesco ansaloni1,* , francesco pyszny1, rodolfo marquina2, álvaro claros liendo3, álvaro claros goitia3, josé luís quispe huanca3, japhet zapana pineda4 1 school of environmental sciences, university of camerino, italy 2 desco centro de estudios y promocion del desarollo, ngo, arequipa, perù 3 proreca programa regional de camélidos, la paz, bolivia 4 acra asociacion de cooperacion rural en africa y américa latina, ngo, la paz, bolivia abstract. this paper analyses the production costs and income of eight groups of farms: five private farms and three belonging to the andean rural community. these farms are located in peru and bolivia and breed alpacas and llama for both meat and fibre. the research is based on case studies. each case study includes several farms, grouped according to similar characteristics: available resources; breeding techniques and geographical location. a farm economic data analysis was undertaken by determining economic budget income. statistics and data from 2003 were analysed to determine farm resources and farm production costs, per animal head and net farm income per labour unit and livestock head. this paper is relevant as regards economic data for production systems which are more often analysed for sociological and cultural aspects and less often for economic data and identification of real productive economic data which are not generally market driven. keywords. south american domestic camelids, farm management analysis, meat production, peru, bolivia. jel codes. q12 1. introduction and objectives from an environmental sustainability point of view, the peruvian and bolivian altiplano is extremely fragile. the altiplano rises to heights from 3,800 to 5,000 metres above sea level and extends over 180,000 square kilometres. the plateau is a unique eco-region shared by peru and bolivia where the main pastoral farms of south america are located. the main problems of the area are its geographical isolation from rural communities, over-grazing, soil erosion, deforestation, underdevelopment of human resources and low farm productivity. alpacas and llamas are fundamental to the livelihood of a large part of the altiplano. the importance of these animals is evidenced by the fact that the vicuña, the wild ances* corresponding author: francesco.ansaloni@unicam.it. 174 f. ansaloni, f. pyszny, r. marquina, a. claros l., a. claros g., j.l. quispe h., j. zapana p. tor of the alpaca, is portrayed on the peruvian flag while the llama is represented on the bolivian flag. many studies have been dedicated to the analysis of various aspects of the altiplano: ecosystem and farming; livestock genetics; farming and breeding systems to rural development and social conditions. there are very few economic analytical studies of camelid breeding farms (westreicher et al., 2006); this number is even more limited when compared to available analyses of other animal species in other countries. when potential livestock production within countries is analysed, the particular aspects and contribution to farm economic sustainability and their market prospects have neither been assessed nor recognised. the key objective of this paper is an economic budget income and production cost analysis of eight groups of llama and alpaca camelid breeding farms in the altiplano: five private enterprises and three belonging to the andean rural community. these farms, located in peru and bolivia, breed camelids for both meat and fibre. furthermore, we try to make recommendations for farm improvement in the future. the initial thrust of this project responds to needs expressed by camelid breeders and some of their organisations to increase income levels. the overall aims of the research, however, were motivated by the necessity to improve breeding and meat processing techniques and to inform various organisations in order to stimulate the domestic camelid sector. the aim, as above, is relevant as regards economic data for production systems which are more often analysed for sociological and cultural aspects and less often for economic data and identification of real productive economic data which are not generally market driven. 2. camelid farming in south america alpacas and llamas, south american domestic camelids (sadc), are the only practical and productive activity in the altiplano (fernández-baca, 2005: 11; ipacperu, 2012). the camelidae family is a small group of mammalian animals. two members of the old world camels live in africa and asia (the arabian and the bactrian) and four members of the new world camels live in south america: two domestic alpaca and llama (scla, 2012) and two protected species – guanacos and vicuñas, which remain in the wild (fernández-baca, 2005: 16; westreicher et al., 2006: 2). currently there are 2,863,333 alpaca and 3,227,412 llama in latin america (pachao, 2005; cfr. fernández-baca, 2005: 14; fairfield, 2006: 31; petrie, 1995). there are 3,128,000 alpaca and 3,315,000 llama worldwide (fao, 2000 cited by bonanni, 2004: 87). ninety-six percent of the south american alpaca and llama are in peru and bolivia. the remaining 4% is divided among argentina chile, ecuador and colombia. more than 80% of alpacas and the entire population of llamas belong to small farmers and peasant communities with very limited resources, located in isolated areas of the andes without access to basic services such as health care and education (fernándezbaca, 2005: 7). the remaining 20% of alpaca are distributed between medium sized farmers and communal enterprises. according to research by inei-cenagro, 60% of alpaca and 76% of llamas in peru are found in farms with less than 50 hectares (fernández-baca, 2005: 20). farms under 3 hectares of land have 32% of alpacas and 46% of llamas. this 175income analysis in south american domestic camelid farms situation creates high animal population per land unit, and overgrazing resulting in land erosion and insufficient feed supply. several differences in farm size, organisational level and technical skills can be observed in alpaca and llama breeding farms. according to fernadez-baca (2005: 23) there are three categories of breeders: 1) andean rural community (comunidades campesinas) and minifondi (small farms); 2) small and medium sized producers; and 3) associated companies. rural communities in peru (ayllu) and minifondi possess 80% of alpaca farms and almost all of llama farms. communal ownership of land is usually found in rural communities while livestock belongs to individuals or single families (fernándezbaca, 2005: 21). furthermore among these farms the ayni system of reciprocity is widespread (shepherd, 2005: 38). this is a system of time differentiated exchange of labour, seed, coca and the like which works to bond the andean peoples together (distaso and ciervo, 2006; cepes, 2009). distribution of seed was governed by informal and flexible networks based on reciprocity between families and communities, the ayllu. for farm jobs that require several individuals, a family may ask relatives and neighbours to collaborate. payment is generally made by returning the favour. in general, farm breeder communal organisations group together several family units ranging from 25 to 100 or more families; the number of livestock heads depends on the number of families in the community. families are related to one another at various levels of kinship. traditionally and within the livestock farms, families share grazing areas according to local norms; this is true especially for crop rotation and times when land lies fallow (castañeda, 2011). the property system introduced under spanish colonialism, originally despoiled traditional andean kinship-based rural communities of their collective livelihood. ayllu territorial control was limited almost exclusively to remote herding communities whose pastoral economies had little appeal for the landed oligarchy (healy, 2004: 28). smallholders constitute the majority of farmers and comprise over 80% of the altiplano farm population practicing a highly diversified agriculture. an average, alpaquero families (alpaca herders) own 80 animals; families with fewer than 100 animals represent 80% to 90% of all producers. however, a small proportion of families owns 400 or more animals and has much greater economic opportunities. alpaqueros with fewer than 100 animals are engaged in subsistence production. a herd size of less than 100 animals is considered too small to maintain good genetic quality, unless reproducers are regularly exchanged with other herds. in some very poor regions, families may own as few as 20 animals (fairfield, 2006: 32). over 70% of privately owned andean farms in peru are less than five hectares in size. the largest land holdings are the property of corporate communities, such as the numerous peasant communities (comunidades campesinas) and peasant groups (grupos campesinos). in 1991 it was estimated that 5,500 of these communities still existed. in 1990, these official forms of common entitlement, as opposed to individual private ownership, accounted for over 60 percent of pasture lands, much of which lies in the punas of the southern andes (vera, 2006). in bolivia in 2001, about 2.7 million people were living on small farms, which constitute 87% of all agricultural units. these small farms cover only 14% of the country’s arable land. forty six percent of these small farmers live in the valleys, 37% live in the altiplano, 176 f. ansaloni, f. pyszny, r. marquina, a. claros l., a. claros g., j.l. quispe h., j. zapana p. and 17% live in the lowlands. small farms in the altiplano and the valleys are defined as those spanning 3.5 or fewer hectares, while small farms in the lowlands refer to those with less than 50 hectares (fairfield, 2004: 3). small and medium peruvian producers own from 10-12% of the alpaca population in farms with 500-2,000 or more heads of livestock with above average production levels. these farmers have an enterprising spirit, carry out animal checks, use modern technology, are informed and market oriented (fernández-baca, 2005: 21) and reach above average production levels. it has been estimated that associated farm enterprises in peru own about 8% of the total alpaca livestock in farms with several thousand heads. various institutions work with sadc. these include regional and state herder and producer associations, peasant worker confederations, government institutions (minag, 2012; conacs; conveagro), along with research centres, universities and ngos (bebbington, 1996, 1997a and 1997b; bebbington and thiele, 1993). despite there being numerous organisations, their political and overall influence is weak (fairfield, 2004: 23-24). in general, there is a lack of co-ordination between and among public institutions. in the agricultural sector, for example, numerous different agencies handle projects that should either be closely coordinated or handled by a single institution. principal sadc products and services include: fibres for clothing and other textiles, fresh meat or salted charqui (jerky) and chalona, transporting individuals and goods such as hides, manure pellets for fuel and fertilizers. peruvian alpaca fibre is a major export for high quality products. llama fibre on the other hand is not as highly prized and is destined for internal use (fernández-baca, 2005: 15). peruvian alpaca fibre producers are among the very poorest members of peruvian society. the alpaqueros are poorly organised and geographically spread out over the area (papaioannou, 2005). intermediaries capture much of the value in the production chain, leaving alpaqueros with very low prices for their fibre. it has been estimated that 85% of alpaca fibre production goes to the industrial market – mainly the export market; 15% is destined for skilled craftsmen and personal use (fernández-baca, 2005: 34). in 2004, 3,200 tonnes of alpaca fibre was produced in peru (conacs, 2005 cited by fernández-baca, 2005: 33). statistics for llama fibre are not reliable. it is estimated that 40 percent of llama fibre is destined for processing by skilled craftsmen or industry; the remaining 60% is for personal use. in 2001, according to the peruvian ministry of economy and finance, the total production was 800 tonnes (fernández-baca, 2005: 35-36). for sadc breeders meat is an important product, both nutritionally and from an income point of view and is on a par with fibre production (fernández-baca, 2005: 20 and 39; fairfield, 2006: 43). in peruvian urban areas camelid meat consumption is quite low while in rural localities and among poorer individuals it is rather widespread (fernández-baca, 2005: 41). among the factors with a positive influence on sadc meat consumption are its nutritional value, the high level of environmental sustainability of the breeding method and the positive consumer perception of the product due to the uncontaminated landscape of the andean plateau production areas. the main problems limiting market potential for camelid meat involve sanitation issues and strong prejudices on the demand side. urban consumers tend to identify this food with the poverty of the campesinos. in fact, as income 177income analysis in south american domestic camelid farms grows, urban consumers prefer other kinds of meat, such as beef (bonanni, 2004: 44; sammells, 1995; sammells & markowitz, 2004). camelid meat is largely traded in informal markets. significant quantities of meat travel from the highlands to markets as far as lima (peru), but there is no cold chain in transport (fairfield, 2006: 43). uncertified llama meat was marketed in quasi-clandestine fashion often camouflaged as sausages sold as hotdogs on the street corners of downtown la paz (bolivia); its unauthorised possession is punisheable by municipal fines (healy, 2004: 31). the absence of municipal slaughterhouses equipped for llamas in bolivian cities in the altiplano, such as la paz and oruro, penalised indigenous camelid herders as well as poor urban consumers by preventing fresh llama meat from entering the marketplace in sanitary conditions (healy, 2004: 31). a potential international market for alpaca meat exists but it is a far and distant goal. sanitary conditions are still inadequate, and strict restrictions have been imposed by western countries on meat imports (fairfield, 2006: 45). 3. data collection and methodology a case study approach was used and the methodology adopted to analyse economic data of some groups of breeding farms (case studies) in bolivia and peru was the calculation of the economic budget (kay et al., 2004; olson, 2004). farm net income was calculated by subtracting the variable and fixed costs from farm gross income. farm net income incorporates remuneration from the following production variables: rented land, farm capital interest, farm labour and profit. production costs are the individual farm’s total variable and fixed costs; these costs have been calculated per animal. net income has also been calculated per labour unit and per animal head, subdividing total net income by the corresponding number of workers and animal heads. although this is not an innovative methodology, the overall lack of accounting data, absence of market driven policy on some farms in the research area and the scarcity of economic studies on camelid breeding farms, ensure that the application of original data is still of interest. the choice of this research method arises from the need to collect, elaborate and compare detailed data from various productive and cultural situations in a uniform manner; to the capacity of farm groups to reflect the various production systems in a specific territory; to constraints due to limited resources for the survey. each group is made up of several farms grouped by similar available resources, homogeneous breeding techniques and geographical location. farm groups were selected to represent the specific local area where they are located; for this reason they do not reflect state statistical averages in the countries studied. the methodological starting point was the selection of farms in each group. each individual farm was required to meet the following criteria to be included in a group: 1) available resources: quantities per farm system; 2) highly specialised production: the largest share of farm income comes from the product being studied; 3) production technique: per farm system; 4) profitability: the farm is able to remain in the market; 5) entrepreneur (breeder): market oriented, relatively young, open to innovation and collaboration in the research project. 178 f. ansaloni, f. pyszny, r. marquina, a. claros l., a. claros g., j.l. quispe h., j. zapana p. the criteria for selecting the farms for the survey were established by determining first the farm reference group (type) to be investigated. the farm reference model is used to illustrate the farm production system and most common income levels achieved in the environmental, professional and institutional context of the territories under discussion. in general, the farm system should reflect the wealth of the majority of the people employed in a significant number of farms specialised in the production of the goods studied and located in a determined territory. in this way, case studies (with each farm group representing a case study) are determined by regional camelid experts taking into consideration: 1) agro-ecology and location of the farm, 2) herd size; and 3) production systems that make the most significant contributions to regional production. the first step in the construction of the farm system is the identification of the particular territory and/or the geographical area devoted to sadc production: the geographical area with the highest density of animal population. the definition of the most convenient dimension of the farm system productive resources can be deduced either by statistical analysis of the farm’s reality or empirically. the analysis of the regional statistic distribution of the farms for classes of economic dimension (average turnover) makes it possible to identify the most common classes and their average size. the choice of this method is conditioned by the availability of reliable statistical data. the most frequent empirical investigation in the farm system consists in the information offered by experienced witnesses. the advantage obtained by operating empirically lies in eliminating the cases of scarce importance. the risk could consist in devoting greater importance to more visible productive realities although with lower statistical incidence in the context. a farm group consists of a minimum of 5-7 farms and reflects a farm system representing the local productivity area (agribenchmark, 2012; deblitz et al., 2002; garcia and gomez, 2006). technical and economic data for each farm group are the average values of the farms that belong to that group. a preliminary test interview was carried out on two farms before beginning the actual research. this was done to ensure that the questionnaire was fit for purpose to generate meaningful data and to reduce interview time. this preliminary test was carried out by the authors of this paper in collaboration with local technicians. data reliability was ensured by way of a budget analysis presented by these two test farms. in conclusion, we have identified eight farm groups, five of them being private enterprises and three rural community farms. data was collected from 53 peruvian farms (pe) and 16 bolivian farms (bol) located in the altiplano and specialising in llama and alpaca breeding. three peruvian farm groups are family managed and located in the south of the country (arequipa region).they include small, medium and large farms: the size of the farm system is determined by herd size (llama, alpacas and sheep) and available land area. the two private bolivian farm groups are in the south-central region and western region of the country. the three andean rural community farm groups are located in the southcentral and southern regions of bolivia (table 1). the reference year used for six of the farm groups was 2003. the biennial period 2003/04 was used for bol curahuara and bol oruro turco; values for these two latter farms have been averaged over the two-year period. data collection was initially carried out using a standard questionnaire drafted in english and translated into spanish. the questionnaire was identical for all farms. the 179income analysis in south american domestic camelid farms questionnaires have been edited by the authors who personally, or in collaboration with some local technicians known to farm personnel have visited the breeding farms, interviewed the persons in charge and collected the technical and economic farm data. farm data collection was carried out using structured interviews with farmers during repeated visits to their farms by the authors of this paper or technicians trained by the authors. the difficulties encountered during data collection were due to the complexity of this activity caused mainly by limited or non-existent farm accounting data; a further complication was the fact that many farms, especially those in rural bolivia, are not market oriented; the need to travel hundreds of kilometres to reach farms that are isolated from populated centres rendered the task time consuming and difficult. the number of farms in each farm group range from a minimum of three to a maximum of twenty-eight. although the small number of farms in the bol quetena, bol coroma and the bol pozo cavado group is not highly representative they have been included because they are not private farms but rather rural community farms with total surface areas of about 4,000, 3,000 and less than 1,000 hectares respectively. a labour unit (lu) has been defined as 2,700 work hours per year, 300 work days per year, nine hours per day. these numbers reflect a situation where available labour exceeds demand both as regards the market and the individual family farm needs. defining labour units in terms of hours facilitates comparisons among the farms studied. in some farms, various jobs are carried out by several individuals only for some months of the year and on some days actual work is limited to a few hours on family farms. the total number of nine work hours is the average effective minimum number of work hours per day. 4. results and discussion 4.1 farm resources the main farm resources are grazing pastures and livestock followed by farm equipment and machinery. the first resource is represented by the quantity of bofedal land available per head. bofedales are naturally irrigated peat pasture grazing land capable of producing large quantities of forage. wetlands of the high andean mountains are exceptional ecosystems due to their hydrological characteristics in a surrounding arid environment (klima 2012). in general and above all there are very few bofedales or wetlands on farms and especially in rural communities. in all farms camelid breeding is carried out along with sheep rearing. the use of grazing land depends on the total number of livestock, camelid and sheep, per hectare. the minimum number observed in rural communities is 0.14 heads per hectare and in one community, where only wetlands were taken into consideration, there were none. the largest total number of livestock per land surface, on the other hand, was noted in a group of peruvian farms: 0.70 for small farms, 1.07 medium farms and 1.61 for large farms. in other private bolivian farms the average number was 0.42 livestock heads per hectare. the composition of farm capital reported in the farm groups has been shown to depend almost exclusively on livestock patrimony. the relationship between the number of llama and alpaca heads varies greatly (table 2). the alpaca is the most common species noted in the peruvian farms, whereas in bolivia it is the llama. finally, in the bolivian 180 f. ansaloni, f. pyszny, r. marquina, a. claros l., a. claros g., j.l. quispe h., j. zapana p. rural communities only the llama is present. the average financial amount for case studies, calculated by multiplying the number of llama, alpaca and ovine heads by their annual average market value surveyed in the territory where the farms are situated, varies from a minimum of usd 4,500 to a maximum of just under usd 38,000. the equipment and machinery capital includes only some health and sanitation tools (syringes for vaccination equipment), sheep shears and in some instances, motorcycles or trucks. agricultural machinery is nonexistent. in the peruvian case studies and for the bol quetena andean rural community 3% of company capital was allocated to equipment and machinery whereas in the other cases it was less than 1%. the value of these production factors corresponds to their actual value when wear and tear is taken into consideration. the infrastructure available to camelid breeders is, in general, quite limited as is the farm area allocated for: butchering, administering anti-parasite baths, animal rest as well as pens, and rudimentary fenced areas built with any materials available. the deficiency of electric energy determines the impossibility to realize a cold chain (bonanni 2004: 86). in all of the farm groups there is a complete lack of farm product reinvestment (stored forage, seeds etc.). the reduced number of labour units (lu), at a maximum of 4.2 lu for peruvian farm groups, highlights the characteristics of family farms. there is also a wide variety in the total number of animals per lu which varies from about 29 animals to over 160 animals (table 3). table 1. average values for groups of farms (2003 and biennial 2003/2004 for bol curahuara and bol oruro). farm groups country, department, region no. of farms in the group land (ha) livestock (heads number) no. of labour unitstotal bofedal -wetlands llama and alpacas sheep private family farms pe small peru, arequipa, arequipa 9 176 28 70 53 4.2 pe medium peru, arequipa, arequipa 28 232 47 191 57 4.2 pe large peru, arequipa, arequipa 6 427 120 583 106 4.2 bol curahuara de carangas bolivia, oruro, curahuara de carangas 5 522 164 249 26 2.9 bol oruro turco bolivia, oruro, turco 5 789 25 226 25 3.0 andean rural community bol quetena bolivia, potosì, sud lipez 3 3055 50 313 17 2.9 bol coroma bolivia, potosì, sud oeste (south west) 3 910 48 191 28 1.8 bol pozo cavado bolivia, potosì, altopiano sur 3 3834 0 190 79 4.0 181income analysis in south american domestic camelid farms table 2. farm stock capital value (usd)* (2003 and biennial 2003/2004 for bol curahuara and bol oruro). farm groups livestock equipment and machinery total amount % amount % amount % private family farms pe small 5794 97.8 129 2.2 5923 100.0 pe medium 13228 97.3 357 2.7 13585 100.0 pe large 37961 97.2 1086 2.8 39047 100.0 bol curahuara 11208 100.0 0 0.0 11208 100.0 bol oruro turco 12401 99.2 103 0.8 12504 100.0 andean rural community bol quetena 16084 96.9 508 3.1 16591 100.0 bol coroma 4491 99.6 19 0.4 4510 100.0 bol pozo cavado 7483 99.7 24 0.3 7507 100.0 *1.00 us dollar equals 3.479 peru nuevo sol (pen), year 2003; 1.00 us dollar equals 7.85 bolivian bol., year 2003 and 2004. table 3. animal heads (2003 and biennial 2003/2004 for bol curahuara and bol oruro). farm groups llama and alpaca heads number per lu total livestock heads number per lu private family farms pe small 16.7 29.3 pe medium 45.4 59.0 pe large 138.8 164.0 bol curahuara 85.9 94.8 bol oruro turco 75.3 83.7 andean rural community bol quetena 107.9 113.8 bol coroma 106.1 121.7 bol pozo cavado 47.5 67.3 4.2 farm proceeds in general the main portion of income depends on the result of the economic budget for llama and alpaca breeding and sales of other products of animal origin (sheep, cuts of meat, charqui, chalona, fibre and leather). the result of the economic budget for llama and alpaca breeding takes into consideration the following variations: (±)growth of the livestock during the year (comparison between initial and final inventory of the live capital); (+)sales of live heads during the production year; (+)personal meat consumption (farm product not sold), i.e. fresh and dried meat, maize, potatoes, quinoa, etc. consumed by the 182 f. ansaloni, f. pyszny, r. marquina, a. claros l., a. claros g., j.l. quispe h., j. zapana p. herder and his family (-)purchasing of heads during the year. other farm proceeds includes sales from: vegetable products (vegetable crop sales, including maize, potatoes, quinoa, etc.) and vegetable family consumption (chuño, or dehydrated products or even freeze dried mountain potatoes or other tubers, and quinoa); other products/services (rural tourism, house building and handcrafted articles such as knitted items, textile products and and work activity, also extra-agricultural, done in other farms; personal consumption of sheep heads, fibre, leather, charqui and chalona. farm gross income per head (total farm proceeds of the sale of the farm products divided by the average number of animal heads present in the farm groups) varies from 19.07 usds to 6.03 usds (fig. 1). the high farm proceeds in the pe small herd might be due to intensive family work in other activities, for example the sale of handmade textile products and meat processing. on the other hand, those with the lowest proceeds, namely the rural communities, have a production system based on local product and service exchange which is not open to business markets. results show that farm proceeds derive in large part either directly or indirectly from animal stock capital. in particular, and excluding the bol coroma, proceeds from llamas and alpaca ‘breeding economic budget’ and from other sales of animal origin products (‘sheep sale, meat cuts, charqui, chalona, fibre and leather’) account for a minimum of 73% of total farm group proceeds to a maximum of 93%. the bol coroma is different from the other farm groups because farms engage in highly differentiated activities. farm proceeds from ‘other products/services’ represents from two to seven per-cent, with the exception of bol coroma which exceeds 40%. bol coroma does shearing and slaughtering, bol curahuara operates in rural tourism while bol oruro works in the building trade-house construction. there were only two farm groups with vegetable production. finally, personal consumption of heads of sheep, fibre, leather, charqui, chalona, for the breeder and his family is common in every farm group and varies from a minimum of 3% to a maximum of around 17% of total farm proceeds . 4.3 production costs we can distinguish between variable and fixed costs; the former indicates costs that change with production levels, for example part-time labour, transport, sanitary costs to maintain healthy livestock (medicinal, sanitary consultations), etc. fixed costs are the production means that remain constant regardless of production levels, for example, wages, taxes, mortgage costs, maintenance and building insurance, depreciation of machinery and instruments, bank interest, etc. finally, fixed costs include land costs and farm capital interest. land costs refer to costs for land use; for example if the land is owned, the cost can be equivalent to the cost of local rents. farm capital interest is the cost sustained to invest money in stock and to advance capital. the production cost per animal per year ranges from usd 9.40 to a low of usd 3.00. variable costs range from usd 0.20 per head to a maximum of usd 4.40 per head (fig. 2) and are lower in the bolivian farm groups. in fact, the rural bolivian communities, which use grazing lands only for animal feeding do not purchase production equipment and have no variable wage costs (cfr. distaso and ciervo 2006). 183income analysis in south american domestic camelid farms fixed costs range from a low of usd 0.40 per head to a high of usd 2.90 per head. land costs have been taken into consideration only for the peruvian farm groups. bolivian rural communities have no market for land. in peru, the amount of average rent contracts for land is known and it is therefore on this basis that land costs for production have been estimated. land costs range from a low of usd 0.20 per head to a high of usd 0.40 per head. interest on overall farm capital is in proportion to return on stock capital (animals, equipment and machinery), advanced capital or loans necessary to cover at least a part of costs to purchase production equipment to begin farm production. interest varies from usd 1.90 per animal head to usd 4.7 per head. the high incidence of interest costs in bolivia is due to the following: 1) high value of animals; in the bol oruro turco, for example, the value of the 257 animals (llama, alpacas, sheep) accounts for 99% of company capital; 2) extended period needed for capital advances (on average 12 months); and 3) inflation – at the time of this study inflation in peru was 4.52% while in bolivia it was as high as 8.34%. figure 1. farm proceeds per animal head per year (usd) (2003 and biennial 2003/2004 for bol curahuara and bol oruro). 0 2 4 6 8 10 12 14 16 18 20 bol oruro turco pe small pe large bol curahuara pe medium bol quetena (rc) bol coroma (rc) bol pozocavado (rc) u s $ self consumption of heads sheep, fiber, leather, charqui, chaloma rural tourism, house building, handicraft activity sheep sale, meat cuts, charqui, chalona, fibre, leather result economic budget llamas alpacas breeding vegetable products 184 f. ansaloni, f. pyszny, r. marquina, a. claros l., a. claros g., j.l. quispe h., j. zapana p. figure 2. production cost per animal head per year (usd) (2003 and biennial 2003/2004 for bol curahuara and bol oruro). 0 1 2 3 4 5 6 7 8 9 10 bol oruro turco pe small pe large bol curahuara pe medium bol quetena (rc) bol coroma (rc) bol pozocavado (rc) u s $ variable cost fixed cost land cost farm capital interest 4.4 farm net income the variability of net income per labour unit (lu) and per animal head in farm groups highlights the difficulty in arriving to a general conclusion. the positive results of net income, both for lu and per head, show that all of the farm groups’ income exceeded fixed and variable costs. the highest level of total farm income was 7,606 usd while the lowest was as little as 834 usd (table 4). in peru, although the total number of lus does not noticeably change among farm groups, it would seem that the total farm income is proportional to the number of raised heads. in the private bolivian farms, the bol oruro higher income (4,323 usd) in comparison to that of bol curahaura (2,840 usd) seems justified, with an equal number of lus, by a greater sale of “other products of animal origin”. in the rural communities, the bol coroma with a higher level of income seems to depend on the presence of a smaller 185income analysis in south american domestic camelid farms number of working units and on lower costs per head. farms in the pe large reported the highest net income per lu at usd 1,811.00, while bol pozo cavado reported the lowest net income at usd 279.00. for bolivian private farms, net income ranged from usd 1,441.00 per lu to a low of usd 979.00. this seems to indicate that since the work hours are the same, the difference is due to greater sales of other farm products rather than from animals. the highest net income per lu in the rural communities was usd 805.00 and this seems to be the result of a lower number of lu and lower costs per animal head. it is interesting to compare data from the existing literature in the field. according to fairfield, whose information is based on data collected from interviews with experts, an average altiplano family earns about usd 200-300 per year (2004: 4-5). in the poorest regions of northern potosí, family incomes are as low as usd 70 per year. the highest incomes for small producers in the altiplano reach around usd 2,000-3,000 per year. healy describes how llama breeders in the southern bolivian province of pacajes saw their incomes increase from about usd 996 to usd 1,752 per year in 1995 as a result of the services provided by aigacaa, the herders association of the high andes and some assistance from universities and ngos (2001: 194 cited by fairfield 2004: 4-5). fairfield’s income estimates for a typical family with about 80 animals range from usd 345 to usd 800 per year (2004). on the low end, a conacs representative estimates income from fibre at a mere usd 122 per year, with a total annual income of only usd 345 from all sources, including fibre, meat, and sale of live animals (2006). some studies, however, have concluded that small alpaca herders receive about half of their income from fibre and about half from meat. net income per animal head per year ranges from usd 17.20 for the bol oruro farm to usd 2.50 for bol quetena. among the most important causes that contribute to explain the variability of farm net income, are the low farm productive specialization and limited market orientation. as regards meat production techniques, for example, we have noted the following less than pertable 4. farm net income per year (usd) (2003 and biennial 2003/2004 for bol curahuara and bol oruro). farm groups total farm net income usd farm net income per lu usd farm net income per animal head usd private family farms pe small 1642 391 13,3 pe medium 2025 482 8,2 pe large 7606 1811 11,0 bol curahuara 2840 979 10,3 bol oruro turco 4323 1441 17,2 rural community bol quetena 834 287 2,5 bol coroma 1449 805 6,6 bol pozo cavado 1116 279 4,1 186 f. ansaloni, f. pyszny, r. marquina, a. claros l., a. claros g., j.l. quispe h., j. zapana p. fect production techniques: i) animal stock is the main, if not exclusive, farm capital. farms breed several types of animals. llama is present on all farms but is never the only animal as llamas are raised along with alpacas or sheep; ii) high variation in age at which the animal is slaughtered. as reported by condori et al. (2001) the best age for slaughtering is between 16 and 18 months for alpacas and 19–21 months for llamas, while in reality slaughtering takes place when animals are over 30 months old. the main reason for this is not due to market type but depends on the family’s need for money to cover private expenses. in order to accomplish this, the best heads are saved to produce quality textile fibre for eventual sale and to meet personal family needs. consequently, those animals not suitable to produce high quality fibre are slaughtered. farmers do not consider specialised animal breeding for meat to be an important factor to raise income; and iii) farms engage in producing a wide range of raw materials and processed products, including selling livestock, fresh meat, llama and alpaca charqui and lamb cured meats along with textile fibre and skins. 5. conclusions from the point of view of farm breeding techniques and economic data we have identified the farm production systems of some groups of breeding farms in bolivia and peru by applying empirical criteria and identical methodology. among production systems in general, the level of intensive farming seems to depend above all on the size of the herd, which in turn depends on the ability of the farm to produce animal feed, the amount of land available, particularly naturally irrigated peat pasture grazing land, and the type of farm, namely whether it is a private or rural community farm. for example, the number of llama and alpaca head does not exceed 0.20 per hectare in rural communities; in particular the bol pozo cavado, with its 3,834 hectares, barely reaches 0.04 head per hectare. on the other hand, and as far as private farms are considered, there are higher numbers ranging from a minimum of 0.28 heads per hectare (bol oruro) to a maximum of 1.36 (pe large). furthermore, the number of heads raised per labour unit increases from small private family farms to larger farms. in the pe small family farm, the number of llama and alpaca heads per labour unit is scarcely more than 16.66, while in other farm groups there are on average 46.48 heads per pe medium sized private family farms and in the bol pozo cavado rural community, 80.59 on the bolivian oruro and curahuara private farm groups and 117.61 on the bol coroma, bol quetena and pe large private family farms. from the point of view of farm net income, all farm production systems identified even with different farm net income results, located in the difficult geographical regions and from the point of view of fodder production and in spite of the fact that they are not generally market driven are still sustainable. farm net income incorporates remuneration from the following production variables: rented land, farm capital interest, farm labour and profit. economic sustainability is an important factor necessary to channel these production systems towards an improved market approach. factors that impact greatly on farm net income include the size of productive areas and the level of production specialisation. both in peru and in bolivia private market orientated farms have herds that are larger than average. on the other hand, low income farms are for the most part in rural communities with herd numbers below average and 187income analysis in south american domestic camelid farms located at a noteworthy distance from the market. from the point of view of a market economy, the fact that these rural communities have persisted should not be the only factor to be considered. their production system is unique and characterised, in particular, by reciprocity of services and collective land ownership. in this sense, their vulnerability depends for the most part on how they face future social changes and in particular the impact on community equilibrium should they become more market orientated. this study, due to the lack of farm organisation that made technical and accounting data collection difficult, in no way represents an exhaustive study of the economic situation of the andean camelid breeding farms. this study is limited in fact, to a small sample of farms over a one or two year period. it is a small contribution that can be extended to other situations to overcome difficulties associated with technical and economic data collections. it also stimulates the application of this method to determine net income per farm group. based on these research results, the authors have made recommendations for future farm improvement. these recommendations include reducing farm production costs by improving technical management such as introducing proper slaughtering procedures and implementing budgeting techniques to include systematic collection of accounting data. to increase income, farms could implement the following strategies: improve genetic selection for both alpaca and llama to be used for fibre processing; and increase the sale of live heads and fibre, add other sellable products with higher added value including handmade textile products, meat processing and rural tourism. finally, and to ensure that the farms increase their market share, they should set up professional training programmes and encourage breeders to join breeding associations with an eye to producing greater added value in rural areas. although this research project is a joint effort, francesco ansaloni is responsible for sections 1, 3 and 5; francesco pyszny for sections 2 and 4. the other authors are responsible for farm budget data collection, advising farms on data analysis methods and interpretation of results. acknowledgements funding for this research was provided by the european union (fifth european community framework programme for research, technological development and demonstration (1998-2002) – inco dev programme – ica4-ct-2002-10014) through the decama (sustainable development of camelid products and services defined as market oriented in the andean region project.) the authors wish to thank paola bonanno and ioannis papaioannou for their assistance and encouragement. references agribenchmark (2012). what we do> methods> data, http://www.agribenchmark.org/ . bebbington, a. 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(2006). review of the literature on pastoral economics and marketing: south america, report prepared for the world initiative for sustainable pastoralism (wisp). iucn (the world conservation union). earo, argentina. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(2): 151-172, 2013 post-2013 eu common agricultural policy: predictive models of land use change severino romano, mario cozzi1, paolo giglio, giovanna catullo technical-economic department for the management of agricultural and forest land, faculty of agriculture, university of basilicata, italy abstract. this article presents a multi-temporal uncertainty-based method that incorporates a statistical regression model with a view to establishing the risk (probability) of land cover changes as a function of a set of environmental and socio-economic driving factors. the morphologic, climatic and socio-economic variables were examined using an artificial neural network (ann) model and the multi-layer perceptron (mlp). following the analysis, maps indicating the suitability to future changes were generated on the basis of observed transitions. from these maps two possible land use scenarios were built, applying the markov chain principle. the region of basilicata, in southern italy, was selected for the analysis. the results highlight: a) a good inclination to change towards specialised crop systems, provided there is sufficient water supply; b) that some cropping patterns are not suitable for changes, partly because they are found in a context with severe limitations for alternative uses. keywords. neural networks, multi-layer perceptron (mlp), common agricultural policy, rural development. jel codes. c45, q58 1. introduction agriculture and forestry play a crucial role in the production of environmental public goods, such as landscapes, agricultural lands, biodiversity, climate stability, and for their ability to prevent natural disasters, such as floods, drought and fires. on the other hand, many agricultural practices may have an environmental impact, thus causing soil degradation, water pollution as well as the destruction of natural habitats and biodiversity loss. this was reported in 2010 in the ec communication “cap towards 2020”, com (2010) 672/5 (european commission, 2010). the ec declaration emphasises that the role of agriculture and forestry is extremely important for the climate and the environment, both locally and globally. changes in land cover ensue from interacting processes which act at different scales in space and time and impact on human and physical environments (munroe and müller, 2007; schneeberger et al., 2007). at the same time, those processes are driven by biophysi1 corresponding author: mario.cozzi@unibas.it. 152 s. romano, m. cozzi, p. giglio, g. catullo cal and socio-economic variables (driving forces), which shape landscape patterns and determine their spatial organisation (van doorn and bakker, 2007; serra et al., 2008). this article aims seeks to analyse the spatial and temporal dynamics of land use, and the mechanisms leading to changes by using multi-temporal variables and socio-economic indicators. understanding the above relationships is extremely important for enabling scientists, landscape managers and policy makers to design conservation/promotion strategies aimed at preserving the unique features of landscapes (kates et al., 2001). studies dealing with land use changes often make reference to research on global changes (dai et al., 2005; turner, 1990; turner et al., 1994). such models have been developed to assess the interactions between driving factors and land use changes, with a view to predicting variations in space and time (pin lin et al., 2011). over the last few years, several studies have highlighted different approaches, as classified by agraval et al. (2002) and verbug et al. (2004). such classifications include stochastic models of optimization, dynamic models of simulation and empirical models (li and yeh, 2002; verburg et al., 2002; dai et al., 2005; castella et al., 2007; dendoncker et al., 2007). in many cases, empirical models can correctly simulate the spatial processes of land use changes, although they are less reliable when they are confronted with human behaviour as the main factor affecting the changes in land use (irwin and geoghegan, 2001). this is not because empirical models do not take into account economic factors; on the contrary, they often include variables that catch economic effects (irwin and geoghegan, 2001). there are variables used in agriculture, such as the distance to roads, slope and agricultural gdp that help understand economic impacts. in addition to the empirical component, hybrid models also include simulation models that are designed to foresee all changes that are likely to occur in given scenarios. an example is provided by markov chains, the aim of which is to simulate changes, as a function of explanatory variables. the innovative aspect of this work consists in the real possibility to correlate spatial and temporal variations of land use to environmental and socio-economic variables and assess, at the same time, their possible effects on land use. in fact, the primary sector has been largely influenced by the past cap measures, notably those related to the direct payment system. a significant example is the set-aside measures: it has been proven that they have resulted in expanding uncultivated areas, with the subsequent increase in the risk of erosion and land abandonment (boellstorff et al., 2005). moreover, the subsequent measures, such as the single farm payment and the midterm review of the cap, have produced direct impacts on the primary sector, causing, in particular, a decrease in the value of the agricultural landscapes (riccioli et al., 2007). in addition, it has been demonstrated recently that reducing direct payments would result in the reduction of arable lands in favour of areas intended for pastures and natural grasslands (sieber et al., 2013). changes in land use result from complex interactions between physical, but also social, economic and environmental, factors (versterby and heimlich, 1991; dale et al., 1993; houghton, 1994; pijanowski et al., 2002; erfu d., 2005). this means that the knowledge and understanding of territorial dynamics can help foresee the future trends of change. to do that, we need a modelling method that takes into consideration several variables and adjusts them over time to build reliable change scenarios (chen et al., 2010). 153post-2013 eu common agricultural policy a typical approach to land use change modelling is based on the understanding of the cause-and-effect relationship between some variables and historic changes. such relations will be the cognitive layer needed for the implementation of an analytical model to make future predictions of transition/change in land use. to this purpose, it can be useful to use neural networks (artificial neural network) and multivariate analyses for assessing the potential future transitions of land use. through the simulation of a deductive logical path, neural networks constitute excellent models of space-time simulations. one of the most important classes of unidirectional feed-forward ann with supervised training is the multi-layer perceptron (werbos, 1974; rumelhart et al., 1986). this procedure is able not only to assess the degree of relationship between the cause and effect variables of past phenomena, but can also simulate future scenarios of potential changes. two possible scenarios are taken into account in our simulation: the baseline scenario describes a stationary trend of incentives projected into the future, while the future cap scenario simulates the effects induced by the next agricultural incentive system provided for by the 2014-2020 cap reform. the ann-mlp model has several advantages, including its non-linear modelling ability and the possibility to be spatialised. the results obtained represent a cognitive support and a valuable tool for decision-making intended to respond by way of targeted actions to the new economic and environmental challenges of the future. 2. methodology 2.1 artificial neural network model an ann can be defined as an information/mathematical calculation model based on biological neural networks. the model includes several information interconnections, made up of artificial neurons, appropriately linked by connections2. neurons receive and then elaborate some input stimuli, which are mathematically represented by weights. the result of such elaborations is called activation value and the neuron is activated when the result reaches a given threshold. early stage neurons are connected to late stage neurons so as to form a neural network. a network is normally made up of three stages. in the first stage we have inputs (i): this layer has the function to deal with inputs in order to adjust them to the requests of neurons; the second layer is the hidden one (h) and deals with the real elaboration, and can also be made of several levels of neurons. the third layer is the output (o) and deals with gathering the results together and adjusting them to the requests of the following block of the neural network. as compared to other predictive techniques, anns have the advantage of describing the existing relations between input and output variables, without previous knowledge of the links between the variables themselves. moreover, they are able to identify the interactions and the nonlinear responses existing between the considered variables (batchelor et al., 1997). application examples of anns have been carried out to quantify land use changes (nemmour et al., 2006) for risk analysis (kanungo et al., 2006) and for predicting environmental dynamics (villa et al., 2007; follador, 2008). 2 connections determine the information flow between the units. they can be unidirectional when information is transferred in one way, bidirectional when information is transferred in both ways. 154 s. romano, m. cozzi, p. giglio, g. catullo there are many different types of ann. their main differences are represented by the applied function, the accepted values and the learning algorithm. for the present work, the selected ann model is the one with a supervised learning algorithm based on backpropagation (rumelhart et al., 1986). this is an iterative gradient algorithm designed to minimize measurement errors between the real output of the neural network and the desired output. in the case under study, we have used an ann model based on the use of the multilayer perceptron (mlp). mlp is a recurring multilevel neural network with a feedback configuration. such a neural structure is made up of three layers: an input layer (that in our case is represented by the variables involved in land use changes), one or more hidden layers and an output layer (represented by land use changes). the first layer (input) is represented by ith neurons; each of them is associated with a variable x involved in land use change. each variable is in turn associated with a weight w, generating the signal, which will be sent to the neuron in the following layer: netj = σixi•wi,j (1) where netj is the signal received by neuron j, xi is the variable and wi,j is the weight related to the input layer i and the hidden layer j. then, the signal netj is submitted to jth neurons of the hidden layer. such a layer is activated only if it reaches a given pre-established threshold. it may be calculated using a sigmoid function: ϕ j = 1 1+e−net j (2) the sigmoidal-type activation function produces an output ranging from 0 to 1; hence the response of the network can be interpreted as a changing probability. this study predicts 5,000 interactions with an initial activation value of 0.1, as indicated by eastman (2006). from the hidden layer, if activated, the signal will be transferred to the following layer (output), made up of lth neurons, whose values represent the transition probabilities. pl = σjwj,lφj (3) where pl is the transition probability of lth neuron of the output layer; wj,l is the weight related to the hidden layer and the output layer; j is the activation function of jth neuron of the hidden layer. the algorithm used for the generation of the output is a back-propagation algorithm. this type of algorithm was chosen since it can be applied to nonlinear functions, just like the cause-effect relationship considered in the analysis of land use changes. it is a supervised learning algorithm by which the output estimated by the network is compared with a known or desired output (i.e. the actual changes in land use occurred in the period under examination). the purpose of this comparison is to obtain an estimated output, which is as close as possible to the desired output. the difference between the two outputs produces an error used to correct the weights. 155post-2013 eu common agricultural policy in this study, the error is quantified using the standard deviation; the training set is repeated until the error function is reduced to an acceptable level el = σl(outl − pl ) 2 (4) where el is the error of lth neurons of the output layer; outl is the output value of lth neuron of the output layer; pl is the estimated output of lth neuron of the output layer. the tested variables can be expressed in terms of presence/absence, or by a gradient, which measures the variation along well-defined sets of time, such as topographic data, i.e. slopes and exposures, and climate data, i.e. rainfall and temperature. sometimes there is no linear relationship between the two types of variables, whereas non-linear mathematical relations may occur, so it is necessary to carry out statistical regression assessments. the output layer has 2 neurons that correspond to 2 possible states: 1 = transition, 2 = permanence. the result of the transition is a raster (risk) map containing values ranging from zero (no likelihood of change) to 1.0 (maximum likelihood of change). 2.2 scenario analysis upon obtaining the risk maps, we built some possible future land use change scenarios by means of the markov chain methodology. a markov chain is a dynamic process made up of a finite number of states and some known probabilities in discrete sets of time (logofet and lesnaya, 2000; yemshanov and perera, 2002). an existing discrete state ut can be used to predict an existing discrete state ut+1 multiplied by a transition probability matrix pt, corresponding to the current set of time t: ut+1 = ut • pt (5) thus, the transition probability pi,j (that is from state i to state j) generally derives from a transition sample, which occurs in a set of time. the assessment of maximum likelihood of transition probability (anderson and goodman, 1957) is given by: pi , j ,t =nij / nij j=1 n ∑ (6) where nij is the number of transitions from state i to state j. once the potential transitions at a given time have been obtained, it is necessary to localize them in space. one of the most widely used and tested methods is the multiobjective analysis, the main function of which is to determine the set of all efficient solutions, which allows for the allocation of land across multiple use classes. if we observe the ith pixel which passes from the land use u’ to u (xiu→u’), and the transition potential of the ith pixel pi u→u’, and considering the surface demand for the 156 s. romano, m. cozzi, p. giglio, g. catullo land use u (su), the allocation of changes is calculated in compliance with the principle of the function maximization, by using whole numbers according to the following equation: max xu '→u •pl u '→u k ,u∑ (7) we use the principle of function maximization since in the final phase of the analysis it is necessary to place the ith pixel, which passes from the use u’ to u, where it is most likely to occur. if we do not apply this principle, the potential transitions calculated up to this point would not have the right space location but would be randomly distributed in the territory under examination. in the case study carried out, we used a simulator, the land change modeller, which was positively tested in a survey on the most important land change prediction models (bibby and sheperd, 2000). this simulator, operating in a gis environment, enables taking into account any constraints (presence of protected areas, ope-legis constraints, etc.) and present and future incentives/disincentives (environmental and socio-economic parameters, etc.), through the creation of suitable maps that have a considerable impact on potential transition and change in land use (figure 1). figure 1. applied simulation model. 157post-2013 eu common agricultural policy 2.3 study area and characterization of land use basilicata is a southern italian region the geographical position of which is marginal compared to the main driving centres of italian economic life. the region covers a territory of 9,992 square kilometres. despite its high diversity, it has a mediterranean climate, characterised by hot and dry summers, cold rainy winters, with continental characteristics in the hinterland. the clear orographic diversification between the western and eastern parts of the region corresponds to a clear differentiation in climate between the territories of the two provinces. rainfall is lower in the east, whereas the eastern-most part of the region usually records values ranging from 500 to 600 mm/year, which is typical of semi-arid or arid climates. according to the specific morphologic and climate conditions, land use distribution is quite heterogeneous, ranging from extensive agricultural systems and natural areas, mostly in the western area of the region, to more specialised agricultural systems in the hilly and flat lands of the eastern part of the region. such a distribution has been accepted in the national planning instruments (national strategic plan for rural development, 2009) that classify the region as a totally rural territory (less than 150 inhab./km2), in which the following areas may be distinguished: b: flat area deemed to be a “rural area with specialised intensive agriculture” this area is located on the ionian side of basilicata region; it accounts for 8% of the regional surface area and includes six municipalities. it is characterised by flat land and access to water resources. its agriculture is specialised, intensive and profitable. in fact, on an agricultural area accounting for 9.4% of the regional utilised agricultural land, the value added of the primary sector in this area is 25% of the value added of the regional primary sector (basilicata rural development plan 2007-2013). d: hilly and mountainous “rural areas with severe limitations for development” (92% regional surface; 125 municipalities). within the macro-area d the following districts may be distinguished: • d1: areas with more advanced farming models. this district covers 39% of the regional surface area and includes 60 municipalities. its land area is mostly hilly with alternating plains. the agricultural activities in this district basically include arable crops and pastures, with specialised crop production in flat areas, specialised tree crops that account for 12% of the district utilised agricultural area (basilicata rural development plan 2007-2013). • d2: the hinterland of hilly and mountainous areas. this district is located in the central area of basilicata region; it accounts for 53% of the regional surface area and includes 65 municipalities. it encompasses mostly mountainous lands with large woodland and pasture areas. specialised crop systems are practiced only on 5% of the district utilised agricultural area (basilicata rural development plan 2007-2013), due largely to the elevation and slope of the area that is unfavourable to those crops. 2.4 multivariate analysis of potential future transitions once the most significant changes were identified, the multivariate analysis of potential future transitions was applied by examining a set of possible causes for changes. 158 s. romano, m. cozzi, p. giglio, g. catullo the variables taken into account in land use changes can have a different level of correlation depending on the changes that have already occurred. moreover, as reported by irwin and geoghegan (2001), the empirical models of land use change include the explanatory variables acquired from different sources and calculated in a gis. in accordance with the literature (bernetti et al., 2010; lombardo et al., 2005; pijanowski et al., 2002), physical (distances, land type, slopes, altitude) and socio-economic variables (population, gross domestic product) are taken into account. in order to highlight a statistical correlation between the cause (accessibility, climate, geomorphologic, and socio-economic data) and the subsequent change, we used cramer’s test v3 (cramer, 1999). this test was useful for the selection of the most significant variables to be taken into account for change. the choice of test v as a correlation measurement is due to the data structure in the raster matrix, which does not show the same number of lines and columns. this method represents a symmetric index of association that takes values ranging from 0 to 1, extremes included. its value is 0 only if there is independence between the characters, while it is 1 if there is a perfect connection, namely at least one of the two characters perfectly depends on the other. cramer’s v gives non-significant information if it is referred to continuous characters; its objective is to supply indications on the level of non-structured association between characters, especially qualitative and/or nominal ones. in the present study the applied v coefficient is >0.15, since beyond such a value there is good intensity of dependence between the variable and the considered change (eastman 2006). the choice of the variables, reported in table 1, is based on the literature (bernetti et al., 2010; lombardo et al., 2005; pijanowski et al., 2002) and is statistically confirmed by cramer’s test v. each variable was included in the model as a raster datum; in particular the first group of variables includes information concerning the accessibility, defined as the easiness of reaching a specified point within the area under analysis. moreover, among the three accessibility variables we have considered the distance from current soil cover, assuming that bordering areas between two different covers may have a higher transition probability. the second group of variables refers to morphology, where the associated information layers express a different level of influence on land use. they were included as variables in the model since they help calculate several operational limitations, by restricting land uses and the level of mechanization. the third group reports climate data. the environmental variables have been considered as being important in the analysis, as their values affect the crop choices for different areas. lastly, the fourth group includes different socio-economic variables concerning the primary sector. the value reported in the information layers is obtained from the following equation: 3 the test is used to assess the correlation level between the variables considered. v is calculated from the standard deviation, according to the following function: v = sqrt(χ2 / (n (k 1))) where χ2 is the standard deviation, and k is the lowest number of rows and columns in the matrices of the raster map. 159post-2013 eu common agricultural policy table 1. tested variables. factor variable description accessibility road distance (m) urban land cover distance (m) distance from current soil cover (m) binary maps (presence/absence) on which the distance was calculated. geomorphologic data digital elevation model* (mamsl) slope* (%) layers reporting the altitude and grade of slope of the area (100mx100m resolution). climate data total precipitation* (mm) mean temperature* (°c) layers reporting total yearly rainfall, and average annual temperatures of the area; both calculated as average values. socio-economic data gdp in agriculture (€) agricultural employment (%) change of bred cattle (%) change of sheep cattle (%) all variables refer to the municipality unit and are calculated as the percent variation recorded in the reference period. * variable used in the creation of change suitability maps for the “future cap” scenario”. final value initial value initial value ×100 (8) where the final and the initial values represent, respectively, the variables’ values at the end and at the beginning of the reference period of the analysis. the land use maps, considered in different time frames, refer to corine (co-ordination of information on the environment, heymann, 1994) land cover (clc) database. the complete nomenclature includes 44 classes organised in 3 levels; in the specific case it has been reclassified into 14 land use classes (table 2) indicating also the extent and percentage of regional surface in each class. as for the agricultural sector, cereals are mainly cultivated in hilly regions, while fruit and vegetables are almost exclusively concentrated in the flat and irrigated area. pastures are instead spaced out by cereals in hilly areas and are associated with husbandry. the analysis of the agricultural and rural context highlighted the widespread presence of agricultural and forestry activities, which may have beneficial effects on land management, protecting the environment, and enable processes of enhancement of endogenous resources (de vivo and d’oronzio, 2007). in analysing the land cover corresponding to the two time frames, according to the scheme reported in table 2, there are 196 combinations (14 classes t x 14 classes t+1). among these combinations we highlighted the ones that have a surface larger than 500 ha (0.5% regional surface). table 3 shows the transitions drawn from the comparison between clc1990 and clc2000. the testing and training set represent, respectively, the number of pixels on which the network performances are verified and the number of pixels on which the network is “trained”. the accuracy indicates the level of precision recorded at the end of the iterations. its value is not constant across land uses , as it depends on the dimension of the testing set and on the number of variables involved in the change process. 160 s. romano, m. cozzi, p. giglio, g. catullo table 2. land use classes considered in the analysis. land use classes 3rd level clc extension (ha) regional surface % urban areas 111-142 14314 1.43% arable land 211-213 369882 37.05% permanent crops 221-223 39002 3.91% pastures 231 12626 1.26% complex cultivation patterns 241-242 92546 9.27% land principally occupied by agriculture, with significant areas of natural vegetation 243-244 56317 5.64% broad-leaved forest 311 264556 26.50% coniferous forest 312 9634 0.96% mixed forest 313 13718 1.37% natural grasslands 321 40555 4.06% moors and heathland 322 17739 1.78% transitional woodland-shrub 324 42628 4.27% open spaces with little or no vegetation 331-334 20044 2.01% wetlands 411-523 4826 0.48% source: corine data elaboration, 2006. the variables reported in table 1 have been used for building transition potential maps, or suitability maps for the “baseline” scenario. as in the case of suitability maps for the “future cap scenario” (see figure 1), they were designed by considering the same geomorphologic and climate variables in all of the transitions considered (table 3), with an average level of accuracy of 75%. we have generated a suitability map of each considered transition, reporting the most significant ones in figure 2. the maps indicate the suitability of a given land area or territory to undergo a transition, and provide an indication of locations susceptible to change in the future. we have reported the two most significant maps, in terms of potential land use changes: the former shows the potential abandonment of scarcely productive arable areas, while the latter indicates the potential degradation of forests, above all in hard-to-reach areas. 2.5 scenario building and simulation the scenario analysis supplies a strategic planning method aimed at supporting decision-makers in making flexible long-term plans. it is based on the development and assessment of a future series of structurally different but plausible scenarios, which include the main uncertainties of the given context (wack, 1985). based on transition potential maps, or suitability maps (figure 2), we designed land use maps for 2050, by applying the markov chain (eastman and toledano, 2000). to this end, we opted to perform the analysis on a broad time horizon, because rural development measures do not produce “immediate” effects. 161post-2013 eu common agricultural policy table 3. matrix of potential future transitions (baseline scenario). transition description variables testing and training set (number of pixels) accuracy from to annual crops associated with permanent crops arable land poorly productive associated crops converted in arable land digital elevation model (dem, mamsl) slope (%) mean temperature (°c) total annual precipitation (mm/y) distance from arable land (m) gdp from agriculture (€/y) agricultural employment (%) 169 79.84% land principally occupied by agriculture, with significant areas of natural vegetation arable land local increase in arable land, with cultivation in shrub lands dem (mamsl) slope (%) mean temperature (°c) total annual precipitation (mm/y) distance from arable land (m) gdp from agriculture (€/y) change of cattle farms (%) change of sheep and goat farms (%) 354 79.55% annual crops associated with permanent crops fruit trees and berry plantations areas which have specialised in fruit plantations dem (mamsl) slope % mean temperature (°c) total annual precipitation (mm/y) distance from orchards (m) gdp from agriculture (€/y) agricultural employment (%) 287 95.47% land principally occupied by agriculture, with significant areas of natural vegetation annual crops associated with permanent crops increase of arable lands and permanent crops, with the transformation of shrub lands dem (mamsl) slope (%) mean temperature (°c) total annual precipitation (mm/y) gdp from agriculture (€/y) agricultural employment (%) change of sheep and goat farms(%) 307 89.77% arable land land principally occupied by agriculture, with significant areas of natural vegetation scarcely productive arable lands, left to natural spontaneous vegetation dem (mamsl) slope (%) mean temperature (°c) total annual precipitation (mm/y) gdp from agriculture (€/y) agricultural employment (%) 327 87.18% land principally occupied by agriculture, with significant areas of natural vegetation broad-leaved forest scarcely productive arable lands, partially covered with shrubs, left to natural woodland dem (mamsl) slope (%) mean temperature (°c) total annual precipitation (mm/y) distance from broad-leaved forest (m) change of cattle farmers (%) change of sheep and goat farmers (%) 154 79.74 162 s. romano, m. cozzi, p. giglio, g. catullo figure 2. examples of suitability maps. from annual crops to land principally occupied by agriculture, with significant areas of natural vegetation from broad-leaved forest to sparsely vegetated areas this was a stochastic process where the transition probabilities (table 4) were used in a matrix (pt). starting from the analysis of the changes that occurred in the time interval 1990-2000 and using the probability matrix pt, it is possible to implement a forecast for 2050 (ut+1). table 4. stochastic matrix. status i (t+1) st at us j (t) p11 p12 … … p1n p21 p22 … … p2n … … … … … … pn1 pn2 … … pnn where n is the number of discrete statuses of markov chain, and pij the transition probabilities (included between 0 and 1) from status j to status i in the time interval between t and t+1 (coquillard and hill, 1997). the matrix obtained describes a system that changes by time-discrete increases, where the sum of the fractions along a line of the matrix is equal to one; the diagonal, instead, gathers the number of pixels which do not undergo a transition between the initial (t) and the final (t+1) date. some authors (schwartz 1991, roxburgh 2009) suggest the creation of just a small number of sufficiently distinct scenarios usually two to four -, to demonstrate more low transition probability high transition probability 163post-2013 eu common agricultural policy clearly the existing differences. in the present study two scenario analyses were proposed. scenarios were constructed by long-term simulations (2050) : • “baseline” scenario, based on current socio-economic trends; • “future cap” scenario to highlight the effects of the next cap reform 2014-2020. the two scenarios have been distinguished on the basis of the possible application of post-2013 measures. thus the “baseline” scenario that simulates the persistence of current eu policy, with no hypotheses of future interventions, was solely created on the basis of the current socio-economic trends, determined by the cap rules in force. therefore, it is a strongly “deterministic” scenario because it forecasts that such trends will continue in the future. on the contrary, the “future cap” scenario that simulates the implementation of new post-2013 measures may not be affected by the current socio-economic trends. thus the transition probabilities included in the markov matrix are only determined by geomorphologic and climate variables; this allows the identification of the effects of the post-2013 measures, simulated by incentive/disincentive maps, that modify the transition probabilities of the matrix. the scenarios obtained do not predict the future situation per se, but are rather a tool to improve the understanding of the possible long-term consequences of present and future trends of incentives/disincentives in the agricultural and agro-environmental sector. accordingly, we chose a long-term projection, without allowing for intermediate stages which could divert attention from the focus of the analysis and which would provide partial results or a poor differentiation between the scenarios. within the agricultural policies we find plenty of driving forces which can result in meaningful future projections; however, building scenarios that include all of the components would ultimately make the analysis and assessment phase too confusing. therefore, we opted to choose some specific measures relative to both the new direct payment system and the new priorities of rural development. the scenario was built by adopting raster maps of constraints/incentives. constraints values equal to 0 indicate an absolute constraints, and values equal to 1 indicate areas free to evolve . for incentives, values lower than 1 act as disincentives, whereas values above 1 act as incentives. following this approach, three ‘new’ cap measures have been “translated” into three raster maps, two being connected with the new direct payment system and the other concerning the new rural development plan, differentiated on the basis of different effects that measures would have on the area, according to the land use. the aim of these information layers is to modify the transition probabilities reported in the markov matrix, in order to orient the change processes. in other words, a layer of incentives corresponds to an increase in the transition probability in the direction it translates, while a layer of constraints corresponds to a decrease in the transition probability in the direction indicated by the layer itself. the first raster map of cap measures (2014-2020) was built by considering a significant innovation in the direct payment system, as money allocation to fruit and vegetable crops and vineyards was not previously allowed, except for tomatoes, citrus orchards and processed fruit. based on these remarks we have created an information layer of incentives for the irrigated areas, which could potentially host fruit and vegetable crops but 164 s. romano, m. cozzi, p. giglio, g. catullo which are now widely used for extensive cereal cultivation, since there is no incentive to transform them into more complex cropping patterns. the second information layer was created by modifying the new direct payment system and simulating the compliance with greening measures. this results in a constraint map that does not actually allow for the transformation of the meadow and permanent pasture areas into arable lands. finally, the third variable we introduced is once again an incentive, spatially differentiated on the basis of land use. such an information layer was built on the new priorities of the rural development policy, putting emphasis on the contents of priorities 4 and 5 of the new rural development policy, namely “protecting and improving ecosystems depending on agriculture” and “transition towards a low carbon economy”, respectively. such priorities include a large number of measures ranging from environmental sustainability to forestation. these measures have been thus translated into an information layer with different incentive levels. more specifically, this layer provides incentives to permanent wooded areas, mixed and broad-leaved woods, areas presently occupied by shrubs and evolving woods, to turn them into permanent woods. incentives are also foreseen, to a lesser extent, to meadow and permanent pasture areas, which were already stimulated in the previous information layer, while the other land uses are left free to evolve, with no particular constraints, or incentives. this information layer is extremely important for the reference land, characterised by large areas directly concerned by the measures simulated by the layer. as previously indicated, the measures associated with rural development may produce results only in the medium-long term, therefore the effects of raster maps were simulated until 2050 so as to be able to assess their impact, notably on forestry. 3. results figures 3 and 5 show the localization of the changes recorded in the individual areas highlighted in the comparison (cross-tabulation) between the present land cover and the cover foreseen for 2050, respectively for the baseline and the future cap scenarios. figures 4 and 6 show the land use changes as percent distribution of the area type to which they were recorded in 2010. in line with what has already been emphasised in the previous paragraph, the areas intended for pasture are expected to further decline (-16.50%) in the future, as a possible consequence of a progressive decrease in the number of raised heads. in the remaining areas we do not notice any particular change compared to the current state, except for a slight increase in wooded areas. the decline in pasture areas can be explained by the lack of a policy specially targeted to protect those lands. the moderate increase in woodland areas may, instead, be linked to the abandonment of marginal areas and the subsequent transformation of the same areas that, if left uncultivated, would evolve towards natural environments. the observation in figure 3 highlights how almost all transitions concerning arable land are localized in the area characterised by a hilly topography, with difficult access to water resources. on the contrary, the transitions concerning the forestry sector are mostly recorded in area d2, where most of the regional woodlands are located. 165post-2013 eu common agricultural policy figure 3. land use change map: “baseline” scenario. 1 2 3 in this area we record a 10.5% increase of woodland. the limited transitions involving the fruit and vegetable sector are localized in the area b that is characterised by a flat trend and the possibility to irrigate. in this area the fruit and vegetable area increases by 10.15% (figure 4). observing the map of “future cap” scenario shown in figure 5, there are no new land conversions into arable crops, and the loss of wooded areas is prevented. this is the result of the pasture protection policy, simulated through the interaction of direct payments and rural development measures (first and third information layers). the analysis points out that natural grasslands and pasture areas declined compared to the baseline scenario, evolving towards wild woodlands. at the same time, there are more specialised crops as well as better infrastructures in the areas featured by favourable geomorphologic and climate conditions. the increase in crop specialisation is related to the second information layer applied, 166 s. romano, m. cozzi, p. giglio, g. catullo that simulates an incentive for the irrigated areas that could be intensively farmed. the incentive system applied led to an increase in crop specialisation mostly reserved to area b. this is an area characterised by better access to water resources and more efficient and modern infrastructures. figure 6 shows that the transitions concerning the fruit and vegetable sector are mostly concentrated in area b, where specialised agriculture is practised. within this scenario the fruit and vegetable land is shown to increase by 25.7% that is significantly higher than 10.15% observed in the baseline scenario. the other transitions concerning wood and pasture covers are distributed rather unevenly between the areas d1 and d2. the comparison of the two change maps shows how the adoption of targeted cap measures may play a role in the evolution of land use, in particular for the preservation of some natural environments that would be exposed to the risk of degradation and abandonment in the absence of appropriate measures aimed at recovering their protective function. the simulation of eu measures through the incentive/disincentive layers leads to the permanence of agricultural activities in areas d1 and d2 and the reduction of agricultural land abandonment, compared to an evolution that would not be guided by targeted measures. figure 4. distribution of “baseline” scenario transitions (in brackets the areas that shifted from use u to u’). 167post-2013 eu common agricultural policy therefore, it is worth highlighting how the area covered by sparse vegetation, which is increasing in the baseline scenario, would decrease as a result of the adoption of the measures predicted in the “future cap scenario”, particularly following the application of priorities 4 and 5 of the rural development policy. in particular, we can observe that when shifting from the “baseline” scenario to the “future cap” scenario, arable land decreases while wooded areas, as well as pastures and grasslands, increase. these two different evolutions are associated with the interaction between the first and third information layers applied in the “future cap” scenario. the concurrent use of these two layers has, on one hand, hampered the conversion of pastures into arable lands (that occurred in the baseline scenario) and, on the other, by encouraging wooded areas, it resulted in the increase of natural areas and the decline of marginal land abandonment observed in the baseline scenario. figure 5. land use change map: “future cap” scenario. 1 2 3 168 s. romano, m. cozzi, p. giglio, g. catullo figure 6. distribution of “future cap” scenario transitions. 4. conclusions in the model applied we have planned three post-2013 macro-interventions related to both the new system of direct payments and the new rural development priorities. in particular, we have envisaged incentive measures for the fruit and vegetable sector, programs targeted to limit the increase in annual (arable) crops, instruments aimed at improving the maintenance of wooded areas, and forestation actions. using these models in the regional context has revealed the different levels of reactiveness of the basilicata territory to the driving forces leading to change. in particular, the model underscored the low transition potential of areas d1 and d2, characterised by a geomorphologic system that has severe limitations and difficult access to water resources. in fact, such areas are characterised by vast rain-fed arable lands. moreover, due to incentives to more specialised crops, they are poorly susceptible to change. on the other hand, we have observed a high transition potential in specialised fruit and vegetable cropped areas where geomorphologic and climate conditions are susceptible to change, notably where water resources are easily accessible. in these areas we have observed that the implementation of specific measures could actually lead to turn extensive crops into specialised fruit and vegetable production resulting in higher income per surface unit. 169post-2013 eu common agricultural policy moreover, the “future cap” scenario showed the positive influence of the incentive layer for shrubs and woodlands, mainly found in areas d1 and d2 as well as in the regions characterised by hillsides at risk of erosion. thus, the safeguard of forest cover reduces the risk of hydrogeological instability. the analysis of results confirms that the applied approach can be a valuable tool for studying the prediction of future land change scenarios and understanding the impacts of current policy strategies, which include actions involving land use in general and agronomic practices in particular. its main advantage lies in the possibility to gather a large number of variables in the model that affect, to a varying extent, the evolution of land use change. the strength of such an approach lies in the possibility of formulating ex-ante assessment models of local development policies, on the basis of the results obtained. the reliability of the model is closely connected to the availability of the spatial data involved in the change processes; hence, it is better to have a wide basis of geo-referenced variables to emphasise the positive effects of some policies and mitigate the possible undesired consequences. the limitations of the applied model are above all the quality and the level of spatial detail of input variables. for improving the accuracy of the analysis it would be useful to consider a higher number of variables involved in land use changes, maybe by means of discrete choice models (choice experiment) that can effectively describe behaviours, thus getting closer to understanding the actual evolutionary dynamics. future developments of the model would require the use of dynamic climate variables in order to assess the effects of changes more accurately and identify the strengths and weaknesses of agriculture and forestry, which are playing an increasingly important role in the dynamics of climatic and environmental 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(2002). a spatially explicit stochastic model to simulate boreal forest cover transitions: general structure and properties. ecological modelling 150: 189-209. bio-based and applied economics 3(3): 229-247, 2014 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-15087 assessment and governance of ecosystem services for improving management effectiveness of natura 2000 sites davide marino1, pierluca gaglioppa2, uta schirpke3,4, rossella guadagno1, angelo marucci1, margherita palmieri1, davide pellegrino1*, natalia gusmerotti5 1 university consortium for socioeconomic and environmental research (cursa), rome, italy 2 regione lazio, rome, italy 3 institute for alpine environment, eurac research, bolzano, italy 4 institute of ecology, university of innsbruck, innsbruck, austria 5 scuola superiore sant’anna, pisa, italy abstract. the natura 2000 network is the cornerstone of the eu biodiversity strategy aimed at halting the loss of biodiversity and ecosystem services. yet in many eu member states the level of development and execution of management plans and conservation measures of natura 2000 sites is often very low due to scarce financial resources; for this reason management effectiveness is rarely achieved. this paper presents initial insights from the life+ mgn project and highlights the costs and benefits associated with 2 out of 21 natura 2000 study sites in italy in order to present a new governance approach relying on the qualitative and quantitative valuation of ecosystem services (es). preliminary results suggest that the quantification of costs and benefits related to the natura 2000 network is crucial for reaching natura 2000 conservation objectives and measuring management effectiveness. keywords. natura 2000, ecosystem services, pes, governance, local communities. jel codes. q570 1. introduction 1.1 costs and benefits associated with the natura 2000 network the eu’s biodiversity conservation policy framework follows eu environmental action programmes, as well as international initiatives such as the convention on biological diversity (cbd) and the bern convention. the main legal umbrella for the protection of nature and biodiversity in the eu consists of the habitats directive (92/43/eec) and the birds directive (79/409/eec), under which the european natura 2000 network of protected areas was established. the main purpose of the natura 2000 network is to * corresponding author: d.pellegrino@cursa.it. 230 d. marino et al. ensure the long-term protection of europe’s most valuable and threatened species and habitats. according to the european natura 2000 barometer, the natura 2000 network currently includes 5,315 special protection area (spa) sites encompassing 593,486 km2, and 22,529 sites of community importance (sci) (719,015 km2) covering around 18% of the eu land area (european commission, 2011; hoyos et al. 2012). eu member states are responsible for the management of natura 2000 sites through the implementation of conservation measures and the development of specific management plans. although the latter are not mandatory, they are a major instrument for reaching conservation goals and clearly define allowed and forbidden activities, roles and responsibilities of authorities and other stakeholders potentially involved in managing natura 2000 sites (kruk et al., 2010). on the basis of the principle of subsidiarity, member states are responsible for determining management costs, but the habitats directive article 8 also allows for european community co-financing, where needed. however, one of the main challenges for member states, and particularly for the sites’ management authorities, remains the lack of sufficient financial resources for the complete implementation of management plans or other measures. this is a threat to species and habitat conservation goals. according to gantioler et al. (2010), the overall cost for implementing natura 2000 in the eu-27 is estimated at €5.8 billion per year. the current amount of funding available to support the network is not clear, even though the annual eu budget for natura 2000 is estimated at around €550-1,150 million (kettunen et al., 2011). however, while putting a monetary figure on the cost of implementing these plans is an essential prerequisite for ensuring sufficient economic resources for their management, establishing the economic benefits of natura table 1. funds available for financing natura 2000 during period 2014-2020. eu funding instruments proposed budget 2014-2020 (€) european commission regulation european financial instrument for the environment (life) € 3.2 billion (of which €2,713.5 million for subprogramme for environment) com(2011) 874 final european fund for regional development (erdf) european territorial cooperation under erdf € 183.3 billion com (2011) 614 final european territorial cooperation (etc) under erdf € 11.7 billion com (2011) 614 final european social fund (esf) € 84 billion com(2011) 500 final european agricultural fund for rural development (eafrd) €435.6 billion for common agricultural policy €101 billion for rural development com(2011) 627 final european maritime and fisheries fund (emff) €7,535 billion com(2011) 804 final framework programme for research and innovation (horizon 2020) €80 billion com(2011) 500 final source: own elaboration by european commission regulation 231assessment and governance of ecosystem services 2000 helps to determine its social desirability, as well as increasing awareness about the importance of natura 2000 for human well being (hoyos et al., 2012). in this context, primary economic valuation studies can be considered a promising evaluation instrument for natura 2000, as they can contribute to managing the network by explicitly acknowledging relevant socio-economic implications (rojas-briales, 2000; halahan, 2002; ten brink et al., 2002) particularly in a regional context (getzner and jungmeier, 2002). specific actions of the eu biodiversity strategy include securing adequate financing for the conservation measures required for natura 2000 sites at both the eu and national/regional level. to date, most eu co-funding for natura 2000 has been made available by integrating biodiversity goals into various existing eu funds or instruments. table 1 shows the eu funds available for financing natura 2000 during next programming period 2014-2020. in particular, only the life fund provides dedicated support to biodiversity and natura 2000, while other eu funding instruments primarily contribute to eu goals on rural, regional, infrastructural, social and scientific development. the integrated cofinancing model continues to form the basis for eu funding of natura 2000 in the next programming period 2014-2020, supporting strategic goals to further embed the implementation of the eu biodiversity strategy into other relevant policy sectors and their financing instruments and, at a practical level, linking biodiversity goals with a broader management of land and natural resources (kettunen et al., 2014). 1.2 governance of ecosystem services many studies have demonstrated the role of biodiversity in supporting the provision of ecosystem services (ma, 2005; díaz et al., 2006; harrison et al., 2014; byrnes et al., 2014). moreover, our understanding of the linkages between biodiversity and ecosystem services and the possible effects of biodiversity loss on the delivery of ecosystem services is increasing (schulze and mooney, 1993; loreau et al., 2002; balvanera et al., 2006; cardinale et al., 2006). benefits from ecosystems are, however, rarely taken into account by politicians, private companies and other important decision makers. in this regard, the recognition and demonstration of the wider socio-economic benefits of natura 2000 should be an important tool for influencing stakeholder attitudes, attracting new funding, informing land-use decisions, and integrating protected areas into regional development planning (european commission, 2013). furthermore, the value of these benefits mostly exceed the management costs associated with natura 2000 and have been estimated at around €200-€300 billion per year (european commission, 2013). the acknowledgement of the value of es increases not only the social acceptance and attainment of conservation objectives, but their economic valuation also raises new arguments in favour of biodiversity conservation (cimon-morin et al., 2013). the integration of ecosystem services arguments into management plans and strategies for protected areas is becoming a pillar of public policies aimed at environmental protection (garcía-mora and montes, 2011; harrison et al., 2014). moreover, policy makers are committed to identify adequate policy tools to manage the natural environment within and outside protected areas. in order to protect biodiversity and ecosystems (bes), guarantying the provisions of their services, the teeb studies identified three clusters of tools that policy makers could implement (teeb, 2011): 232 d. marino et al. • providing information, for instance, by reforming national accounting systems and integrating bes values into policy assessments; • setting incentives, for instance, by rewarding benefits through payments and markets, reforming harmful subsides and addressing losses through regulation and pricing; • regulating use, for instance, by creating protected areas and investing in green infrastructure. accordingly, it is necessary to define and implement a wide range of governance and management tools, referring to the environmental policy mixes, including both the command and control approach and market based instruments (ackerman and steward, 1985; freeman, 1997). the latter are instruments that provide incentives for undertaking particular actions (oecd, 2004; oecd, 2008), such as price-based instruments (taxes and charges); liability instruments; subsidies; market creation measures and the assignment of well-defined property rights and other instruments, such as environmental agreements (ea) for biodiversity conservation. ea consist of legal frameworks for contracts between landowners and other parties, where the landowner voluntarily commits himself/herself to refrain from using land (conservation contracts) or to carry out activities that conserve or promote biodiversity (management contracts) in a specific area. the other party (either a private or a public participant) makes a financial payment in return that can take different forms, such as money transfers, tax exemptions or reductions (subsidies), or a credit (for instance, in the case of carbon market). in this framework, particular attention should be paid to payments for ecosystem services (pes), defined as voluntary transactions where a well-defined es (or land-use likely to secure that service) is ‘bought’ by at least one es buyer from at least one es provider, if and only if the es provider secures es provisions (conditionality) (wunder, 2005; teeb, 2011). 1.3 aim of the paper in italy, the management authorities natura 2000 sites can adopt a management plans or integrate conservation measures into other planning instruments such as sectorial or territorial plans to achieve a site’s conservation goals. yet the integration of regulatory instruments (general regulatory measures, specific administrative measures or contracts between public and private stakeholders) is not always clear for the management authorities of sites and this can affect achieving conservation goals and management effectiveness. thus, innovative management tools are needed and, in our opinion, the acknowledgement of the value of biodiversity and ecosystem services provided by sites is a prerequisite for better defining and implementing conservation strategies. in this context the aim of this paper is to assess and compare es and management costs related to natura 2000 sites in italy, according to the methodology elaborated at the european level (gantioler et al., 2010), in order to highlight the benefits and costs associated with conservation actions and stimulate discussion regarding new instruments for effective management. despite several limitations, our analysis allows to define a new governance approach aimed at improving management effectiveness through the valorisation of es provided by italian sites. 233assessment and governance of ecosystem services 2. italian case studies our analysis started from initial insights from the life+ making good natura (mgn) project involving 21 italian agro-forest natura 2000 sites. for each site, habitat cover and land use were analysed, the site’s management instruments were examined and socio-economic data were gathered through questionnaires for site management authorities. subsequently, meetings with local public and private stakeholders were organised to assess their perceptions and identify the most important es. after the local meetings, the main es were selected on the basis of the socio-economic and environmental characteristics of the sites, considering critical issues and opportunities for the development of the territory. after a brief description of the above-mentioned cognitive steps of the project (analysis of site management instruments, questionnaires for management authorities and stakeholder meetings), we presented the results of 3 es assessments in 2 out of 21 natura 2000 study sites and compared these values with costs for conservation measures. the two sites presented in this paper (table 2) are “bagni di masino e pizzo badile” (it2040019) in the forest of lombardy val masino (lombardy region) and “monte della stella” (it8050025) in cilento and vallo di diano e alburni national park (campania region). table 2. natura 2000 study sites. type code name region bioregion extent [km²] sci it8050025 monte della stella campania mediterranean 11.8 sci it2040019 bagni di masino pizzo badile lombardy alpine 27.6 source: schirpke et al., 2013a,b; marino et al., 2014 the sci “bagni di masino pizzo badile” coincides with the forest of lombardy val masino, in the western branch of the valley. different types of vegetation were identified: deciduous and coniferous forests in the basal part and on the upper side of the valleys; timber forests , especially in the valley and in the most accessible areas and mountain pastures to the upper limit of vegetation. this area exhibits the qualities of the classic alpine glacial cirques, rugged granite peaks, glacial deposits and accumulations of debris slopes, that are appreciated by mountain enthusiasts. the considerable wealth of the environment is accompanied by the abundance of species belonging to the alpine fauna, including: chamois, deer, ibex, marmots and eagles. the sci “monte della stella” is a typical mountainous-hilly site. it is largely covered by forests consisting mainly of chestnut trees; the lower altitudes include holm-oak woods, often mixed with downy oak and flowering ash. in the sci, there are also thermo-mediterranean shrubs and mountain grasslands and xeric mediterranean shrubs. with regard to the avifauna, the total species known to date are 119, of which 22 are listed in annex i of the birds directive and 57 are nesting on the site. the site presents degradation associated with inappropriate forest management and the abandonment of pastures and/or overgrazing, as well as the presence of pylons on the peaks of mount stella. a dense network 234 d. marino et al. of trails guarantees access to the site. in addition, the prey rescue centre of sessa cilento serves as an environmental education centre. 3. methodology 3.1 management instrument analysis the habitats directive has a crucial role for natura 2000 site conservation and management. according to the habitats directive article 6, member states establish necessary conservation measures including, in as need be, appropriate management plans specifically designed for natura 2000 sites or integrated into other development plans, and appropriate statutory, administrative or contractual measures which correspond to the ecological requirements of the natural habitat types in annex i and the species in annex ii present on the sites. in particular, the term “conservation measures” refers to “a series of measures required to maintain or restore the natural habitats and the populations of species of wild fauna and flora at a favourable conservation status”. in our study, we analysed two site management instruments with regard to all measures and tools developed for and implemented in the site areas. in particular, with the help of site management authorities we collected and examined all available documentation (specific management plans and conservation measures) for both sites. this first review helped us to identify differences in management approaches between the sites and to highlight conservation objectives for each habitat and/or species and related management and environmental issues involving the provision of es. 3.2 questionnaires submitted to local management authorities in order to acquire other specific information on the natura 2000 study sites, we analysed the questionnaire sent by email to both site management authorities (monte della stella e bagni di masino-pizzo badile). the main objective of the questionnaire was to collect information related to the environmental and managerial context of each natura 2000 study site in order to provide a functional cognitive framework for the es analysis and evaluation in the study sites. the questionnaire included both closed and open questions and assistance was provided to interviewees in compiling their answers. according to gaglioppa et al., 2013 the main difficulty with the design of the questionnaire was related to the different characteristics and responsibilities of local partners (national parks, interregional parks, regional administrative authorities, regional forest management bodies, etc.), and consequently the different management approaches of each natura 2000 site (direct, mediated by regions or by provinces). therefore, it was decided to structure the questionnaire in such a way as to have different levels and sectors for the different types of information sought, in order to gather an initial general set of data with the collaboration of management authorities and other local administrative bodies and stakeholders. the questionnaire was divided into five sections including both closed and open format questions: 1. general information: information identifying pilot site and interviewee; 235assessment and governance of ecosystem services 2. description of the site: a synthetic description of the site from an ecological, administrative and managerial point of view (connection with protected areas, state of maintenance of habitats, fauna and flora, river basin description, state of surface and groundwater, cartographic and gis data, different authorities involved in managing the site and their interaction also with citizens, land planning instruments for the site, management plans and conservation measures and regulatory frameworks for natura 2000 sites, local communities’ civic uses and rights of common, scientific publications and research on the site); 3. economic-financial resources: information about the site’s economic and financial resources (management authorities’ budgets, the sites’ annual institutional financing, site management outflows over the last 5 years, administration costs, management and conservation measures, human resources, participation in projects and other measures for improving maintenance of habitat and species; 4. economic, environmental and social aspects: qualitative information on some environmental, economic and social aspects such as change of land cover and landscape in recent years and the relationship between this change and site creation, the state of conservation of the habitats, present forest and agricultural activities within the site and other economic issues, difficulties and threats to the maintenance of protected habitats due to social-economic activities, stakeholders involved directly and indirectly in managing the site, rdp (rural development programme) measures to promote organic farming and financing of natura 2000 network, land maintenance and environment conservation contracts); 5. ecosystem services (es): information on main es provided by the site on the basis of management authorities’ in-depth knowledge of natura 2000 sites; stakeholders directly or indirectly involved in the management of these es; fauna species threatened by habitat fragmentation, fundraising activities, self-financing and pes or peslike schemes implemented (wunder, 2005; pettenella et al., 2012). 3.3 stakeholder meetings along with the questionnaires submitted to site management authorities (gaglioppa et al., 2013), integrated by way of a cartographic analysis of the study sites’ habitat and land uses (schirpke et al., 2013b), another useful source of information for defining main es for each study site were meetings with institutional and private stakeholders during the preliminary project phase. indeed, habitats and land use-based preliminary es analyses (see chapter 2.5) identified some differences with respect to the relative qualitative values among es; while the analysis based on the questionnaires mainly reflected the management authorities’ point of view without fully taking into account local institutions and community perceptions and needs. meetings with institutional and private stakeholders were organised at each natura 2000 pilot site and generally involved municipalities, county administrations, park management authorities, farmers, hunters, fishermen, ngos, volunteers, as well as agronomists, biologists, environmentalists, hotel and restaurant managers, local associations, environmental guides, tour operators, and local residents. during these events project actions and objectives were presented and the main es and environmental issues were 236 d. marino et al. debated in order to highlight various specific aspects (local peculiarities, the local community’s perception of the site, rivalries and oppositions between different stakeholders etc.) and to make informed choices with regard to the main es to which innovative financing schemes could be targeted (marino et al., 2014). 3.4 management costs analysis the management cost analysis for the bagni di masino e pizzo badile (it2040019) and monte della stella (it8050025) study sites was based on the eu methodology presented in the report “costs and socio-economic benefits associated with the natura 2000 network” elaborated by the institute for european environmental policy (ieep) (gantioler et al., 2010; figure 1). the costs of natura 2000 can be classified into (gantioler et al., 2010): 1. one-off management costs: • costs for the finalisation of sites, such as costs for scientific studies, administration, consultation, etc.; figure 1. structure of natura 2000 costs. source: gantioler et al., 2010 237assessment and governance of ecosystem services • costs for management planning, i.e. one-off costs for preparing management plans, establishing management bodies, consultations, etc. 2. investment costs: • cost of land purchase; • one-off payments of compensation for development rights; • infrastructure costs for the improvement/restoration of habitat and species; • other infrastructure costs contributing to conservation, e.g. for public; • access, interpretation works, observatories and kiosks, etc.. 3. costs for management planning (unlike the costs for management plans): • running costs of management bodies; • costs for review of management plans; • costs for public communication. 4. habitat management and monitoring costs: • conservation management measures– maintenance and improvement of habitats’ favourable conservation status; • conservation management measures– maintenance and improvement of species’ favourable conservation status; • implementation of management schemes and agreements with owners and managers of land or water for following certain prescriptions; • provision of services; compensation for rights foregone and loss of income; developing acceptability ‘liaison’ with neighbours; • monitoring; • maintenance of infrastructure for public access, interpretation work, observatories and kiosks, etc.; • risk management (fire prevention and control, flooding etc.); • surveillance of the sites. the dotted lines (figure 1) indicate that two categories are covered both under “one off costs” and “recurrent costs”, namely “management planning” and “compensation”. their inclusion under one or the other heading depends on the frequency of the payment (gantioler et al., 2010). financial resources identified in monte della stella’s management plan are related to the regional operational programmes (rop) and the rural development plans (rdp) structural funds 2007-2013 rop campania and, in particular, axis i “environmental sustainability and attractiveness of culture and tourism”, axis ii “improving the environment and the countryside” and axis iii “quality of life in rural areas and diversification of the rural economy”. bagni di masino e pizzo badile’s management plan was prepared within preparatory project life03nat/it/000139 “reticnet: 5 sci for the conservation of wetlands and priority habitat”, funded by the european union. 3.5 assessment of ecosystem services values the es assessment included three different services: wild food (provisioning service), erosion regulation (regulating service) and the recreational value (cultural service). this selection of es was carried out on the basis of the management instrument analysis, the 238 d. marino et al. questionnaire to management authorities, the stakeholder meetings and cartographic data analysis for both study sites. according to similar studies (burkhard et al., 2012; bastian, 2013), es were first valued qualitatively by assigning an ordinal score of es provision (3-high, 2-medium, 1-low, 0-not significant) to natura 2000 habitats and corine land cover classes. the scores were obtained by expert knowledge and account for specific ecological functions, potential distance of es demand1, and intrinsic biodiversity (further details in schirpke et al., 2013b). for each study site, an area-weighted mean value was calculated for the selected es based on the cartography after attributing an ordinal score to each habitat and land cover class. furthermore, this qualitative es valuation was integrated by way of additional qualitative or quantitative data, where possible. in the following, we provide a short description of the applied method: • wild food (mushrooms): the productivity of mushrooms is particularly variable and depends on local conditions such as climate, vegetation, and soil, as well as disturbance, e.g. harvest activities or timber removal. as no data on collected quantities were available, the annual mean production was estimated at 1.5 3 kg mushrooms per ha forest (croitoru and gatto, 2001; goio, 2006). the production area was delimitated by including forest land cover classes and excluding areas above 2000 m a.s.l. and slopes over 80%. the monetary value was estimated based on the mean market value of 22.50 €/kg (de marchi and scolozzi, 2012). • erosion regulation: as forest has a protective role (scrinzi et al., 2006), the area with an elevated erosion and landslide risk, obtained from the inventory of landslide phenomena in italy (iffi) (apat, 2007), was identified and the percentage of the area without forest in respect to the area covered by forest was calculated to quantify the contribution of the forest to the avoided erosion. in this case the es valuation did not include an economic valuation. • recreational value: the qualitative valuation based on the land cover was integrated with information including a list of the possible recreational activities. for the bagni di masino e pizzo badile (it2040019) study site, data from two automatic counting stations were available. the tourism development of the intersecting municipalities was measured by the bed capacity obtained from statistical data (istat, 2011). the bed capacity was then used for an economic valuation of the recreational value by calculating the mean accommodation value based on the mean overnight cost and the degree of utilization (trademark italia, 2013), as mean overnight costs can be considered as a measure of the recreational economic value and the people spending nights in hotels are usually tourists. moreover, potential day-trippers were identified and quantified up to 1.5 hours driving from the study areas (schirpke et al., 2013c), but could not be included in the monetary valuation due to the lack of data, i.e. number and origin of visitors that is necessary to quantify travel costs. 1 an es only exists if there is a beneficiary (boyd and banzhaf, 2007). 239assessment and governance of ecosystem services 4. results 4.1 management approaches from the management instruments analysis we found that both the monte della stella (it8050025) and bagni di masino e pizzo badile (it2040019) sites have a specific management plan2 the main objective of which is to ensure the maintenance of habitats and species of community interest present on the site area according to the habitats and birds directives (table 3). however, it is worth noting that these management plans differ according to the specific local context. the bagni di masino e pizzo badile (it2040019) management plan was adopted in order to define a milestone for the natura 2000 network implementation in a large and complex area including valtellina and valchiavenna. accordingly, this plan was defined to connect different elements of the environmental network (such as pian di spagna-lago di mezzola natural reserve and other four mountain sics) and to define the main guidelines for their integration. its general objective is to make human activity development more sustainable and to reduce their direct or indirect impact on species and habitats. the plan also defines primary management provisions to follow outside the site area. 2 piano di gestione sic it 8050025 monte stella dd a.g.c.5 n. 2 del 21/02/2012; piano di gestione sic it2040019 bagni di masino pizzo badile pizzo del ferro. table 3. management instrument identified for each study site. type code natura 2000 site management authority management instrument general objective sic it8050025 monte della stella campania region management plan ensuring the restoration or maintenance of natural habitats and species of community interest (habitats directive annex i and annex ii and birds directive annex i) at a favourable conservation status. this general objective includes: ecological sustainability objectives, (habitats and species conservation); socio-economic sustainability objectives aimed at promoting a functional socio-economic development for reaching biodiversity conservation objectives. sic it2040019 bagni di masino e pizzo badile lombardy region management plan ensuring the restoration or maintenance of natural habitats and species of community interest and guaranteeing the maintenance and/or restoration of ecological balances through proper management actions. source: own elaboration 240 d. marino et al. since monte della stella (it8050025) is already part of an ecological network along with cilento, vallo di diano e alburni national park and other natura 2000 sites, the main objective of its management plan is to ensure ecological connectivity between these areas along with the maintenance of specific habitats and species. in bagni di masino e pizzo badile the status of habitats is excellent (97%) and species conservation is mostly good (66%), whereas in monte della stella habitat and species conservation is generally good (100% of habitats and 64% of species). 4.2 management costs on the basis of eu methodology (gantioler et al., 2010) we estimated current management costs for both study sites (bagni di masino e pizzo badile and monte della stella). however we could not estimate the costs for all types of activity because many items are not reported in the sites’ management plans. for example, for both study sites, the running costs of the management bodies were not available as they do not have their own management bodies, and are rather managed by other authorities. data extrapolated from bagni di masino e pizzo badile and monte della stella’s management plans revealed a total cost estimation of €3,132,000 and €497,000 respectively for a period of around 5 years (average length of a management plan). of this amount more than 90% of the costs are dedicated to habitat management and monitoring costs for both study sites. since the use of average costs provides more comparable indicators, an average cost per hectare was estimated during the process of total cost assessment (table 4). 4.3 results of the assessment of ecosystem service values for the qualitative valuation, an area-weighted mean es value was calculated for the selected es for both study sites (table 5, figure 2). while for bagni di masino e pizzo badile (it2040019) the es values are generally low due to the presence of large areas without vegetation, monte della stella (it8050025), which is mainly covered by forest, has high es values. regarding the single es, the following results were obtained: • wild food (mushrooms): almost 95% (1,126 ha) of the total area of monte della stella (it8050025) can be considered as suitable for mushrooms, producing a total of between 1,689 to 3,379 kg/year. the potential total economic value was estimated between 38,000 and 76,000 euro/year. • erosion regulation: a total of 1,021 ha (37%) of the bagni di masino e pizzo badile (it2040019) study site has an elevated erosion and landslide risk of which 178 ha (17%) are covered by forest. in addition, alpine grasslands contribute to the stabilization of the soil as indicated in figure 2a). • recreational value: the alpine landscape of the bagni di masino e pizzo badile (it2040019) study site, with its rich flora and fauna, has an elevated aesthetic value and offers many possibilities for hiking, climbing, and excursions. a campground is located just before the entrance of the sic,. a hotel with thermal spa (hotel relais bagni masino terme & spa), is an important point of attraction, and is located on-site at bagni masino (1,132 s.l.). furthermore, two mountain huts offer accommodation 241assessment and governance of ecosystem services during the short summer period and are linked to the rome path, the most famous hiking trail in the alps. two automatic counting stations installed in the study area indicate between 36 and 435 visitors daily all the year round. the sic is located within two municipalities with several accommodation facilities available with a capacity of 1,707 beds and an economic value of 61,650 euro for the year 2013. up to 1.5 million potential day-trippers reach the study site with 1.5 hours driving or less. table 4. monte della stella and bagni di masino e pizzo badile sites’ recurrent costs. recurrent costs monte della stella (it80500025) €/ha bagni di masino e pizzo badile (it20400019) €/ha € % € % c os ts fo r m an ag em en t pl an ni ng running costs of management bodies 0 0 0 0 costs for review of management plans 190,000 6 0 0 costs for public communication. 100,000 3 30,000 6 subtotal 290,000 9 245.76 30,000 6 10,88 h ab ita t m an ag em en t a nd m on ito rin g co st s conservation management measures– maintenance and improvement of habitats favourable conservation status 1,528,000 49 85,000 17 conservation management measures– maintenance and improvement of species favourable conservation status 0 0 30,000 6 implementation of management schemes and agreements with owners and managers of land or water for following certain prescriptions 300,000 10 5,000 1 provision of services; compensation for rights foregone and loss of income; developing acceptability ‘liaison’ with neighbours 0 0 0 0 monitoring 344,000 11 80,000 16 maintenance of infrastructure for public access, interpretation work, observatories and kiosks etc. 590,000 19 242,000 49 risk management (fire prevention and control, flooding etc.) 80,000 3 25,000 5 surveillance of the sites 0 0 0 0 subtotal 2,842,000 91 2,408.47 467,000 94 169.39   total cost 3,132,000 100 497,000 100 source: own elaboration based on study sites’ management plans 242 d. marino et al. monte della stella (it8050025) has a dense trail network offering nature itineraries and excursions to historic sites. for example, an ancient fort was situated at the summit of monte stella, also referred to as ‘sacred mountain’. nowadays, it hosts an old chapel and a radar station. the seven municipalities intersecting the sic offer various accommodation facilities with a total of 2,143 beds and an economic value amounting to ca. 72,600 euro for the year 2013. moreover, the study area has huge potential for day-trippers, given that almost 4 million people live within a 1.5 hour drive from the study area. table 5. area-weighted mean value obtained from qualitative es valuation (3-high, 2-medium, 1-low, 0-not significant) based on two different maps. study area es qualitative es value corine land cover map natura 2000 habitat map bagni di masino e pizzo badile (it2040019) erosion regulation 1.25 1.29 recreational value 1.48 1.92 monte della stella (it8050025) wild food (mushrooms) 2.82 2.79 recreational value 2.91 2.80 5. discussion and conclusions the objective of this paper was to attempt an initial quantification of the value of the es and management costs for two italian natura 2000 sites. due to the explorative nature of this study it should be noted that the preliminary results discussed here require further development and that some weaknesses were faced during the analysis of the data. additional socio-economic data is needed to valuing certain ecosystem services and more detailed information about management costs is also required. to date, it has not been possible to estimate the monetary value of erosion regulation services for the bagni di masino e pizzo badile (it2040019) site due to the lack of data. for the same reason, we also experienced difficulties in estimating various cost items, such as “one off costs”. accordingly, in the analysis we have only included “recurrent costs” for those management plan measures aimed at ecological and socio-economic sustainability. the results show a higher financial investment level for the monte della stella (it2040019) site, likely due to the greater availability of eu funding, in particular through rop-rdp campania funds. from our analysis, it was noted that 49% of costs are dedicated to maintaining (or improving) habitat conditions that are, at present, generally in a “good state of conservation”. the assessed es show both high qualitative values, although the economic value is relatively low with respect to management costs. besides the two es (wild food and recreational value) included in this study there are many other important ones such as water provision, climate regulation, and erosion control. furthermore, our results indicate that, given the good conditions for recreational activities, the study area has great potential for day-trippers. with regard to the bagni di masino pizzo badile (it2040019) site, the share of costs related to habitat conservation is around 17% and 98% of habitats are in an excel243assessment and governance of ecosystem services lent state of conservation. in this case, costs for maintaining facilities for tourists (49%) are high, which is in keeping with the identification of “recreational value” as one of the most important es for the site area, and confirmed by the qualitative and quantitative es assessment. due to limited data availability, its economic valuation indicates only a small part of the total value and should be integrated with expenses for transportation, food, recreational equipment, etc. moreover, for this area, the number of potential day-trippers figure 2. es value (3-high, 2-medium, 1-low, 0-not significant) based on spatial land cover information for a) erosion regulation and b) recreational value of bagni di masino e pizzo badile (it2040019), c) wild food and d) recreational value of monte della stella (it8050025). where available, further qualitative information was included as indicated on the map legends. a) erosion regulation (it2040019) b) recreational value (it2040019) c) wild food (it8050025) d) recreational value (it8050025) 244 d. marino et al. greatly exceeds the accommodation opportunities available in the area and should be considered as an important potential financial resource. an initial comparison between annual economic benefits related to the three (2 for monte della stella and 1 for bagni di masino) es assessed (see par. 3.3) and annual management costs (table 4) shows an average benefit-cost ratio of around 50%. however, due to the current limited data availability, in this paper we have only considered a small number of es, without taking into account the monetary value of erosion regulation services that could increase the benefit-cost ratio, and have limited the analysis to its qualitative (non-monetary) importance for bagni di masino. hence, we suppose that when all es are evaluated, the benefit-cost ratio of overall benefits will likely exceed costs, as was the case in previous assessments (gantioler et al., 2010; ten brink, 2011). the results of this paper show that information about management costs is often incomplete. however, we argue that the quantification of costs relating to the natura 2000 networks is crucial for a systematic approach to environmental accountability that measures and evaluates the management effectiveness of natura 2000 sites whilst redefining sites’ conservation strategies. in a context of stagnant and uncertain funding for biodiversity conservation, the need to define governance and management tools, such as pes or pes-like schemes, should offer a considerable potential to raise new funds for biodiversity or to use existing funding more efficiently. it is also necessary, however, to pay attention to their design, and to ensure both their fit within specific socio-economic contexts and their capacity to modify rule-making structures. these two aspects are fundamental when seeking both effectiveness and social acceptability (muradian and rival, 2012). governance of ecosystem services is characteristically multi-layered and entails a complex structure involving a multiplicity of actors and many interrelations between the ‘local’ and the ‘global’. solving such problems normally requires moving from thinking in terms of single, ideal managerial approaches (e.g. command-and-control, markets or community-based management) to combining governance structures, scales and tools (muradian and rival, 2012). acknowledgments an earlier version of this paper was presented at the 3rd conference of the associazio ne italiana di economia agraria e applicata (aieaa), 25-27 june 2014 at alghero, italy. this work was supported by the european union through the project life+ “making good natura” (life11 env/it/000168). references ackerman, b.a. and stewart, r.b. 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(2011) estimating the overall economic value of the benefits provided by the natura 2000 network. final synthesis report to the european commission, dg environment on contract env.b.2/ser/2008/0038. institute for european environmental policy/ghk/ecologic. brussels. trademark italia (2013). il trend alberghiero 2013. http://www.trademarkitalia.com. accessed 13 may 2014. wunder s (2005). payments for environmental services: some nuts and bolts. cifor occasional paper no. 42. http://www.cifor.cgiar.org/publications/pdf_files/occpapers/op-42.pdf. accessed 30 october 2013. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 3(1): 45-61, 2014 doi: 10.13128/bae-12713 an expost economic assessment of the intervention against highly pathogenic avian influenza in nigeria mohamadou l. fadiga*, iheanacho okike, bernard bett1 international livestock research institute, box 320 icrisat bamako mali abstract. this study assesses the intervention against avian influenza in nigeria. it applied a simple compartmental model to define endemic and burn-out scenarios for the risk of spread of hpai in nigeria. it followed with the derivation of low and high mortality risks associated to each scenario. the estimated risk parameters were subsequently used to stochastically simulate the trajectory of the disease, had no intervention been carried out. overall, the intervention costs us$ 41 million, which was yearly disbursed in various amounts over the 2006-2010 period. the key output variables (incremental net benefit, disease cost, and benefit cost ratio) were estimated for each randomly drawn risk parameter. with a 12% annual discount rate, the results show that the intervention was economically justified under the endemic scenario with high mortality risk. on average, incremental benefit under this scenario amounted to us$ 63.7 million, incremental net benefit to us$27.2 million, and benefit cost ratio estimated to 1.75. keywords. avian influenza, infection control, biological risk management, damage function, incremental net benefit jel codes. q02, q18, q14 1. introduction nigeria has a poultry industry with about 160 million birds estimated at us$ 250 million (fdlpcs, 2007).the industry contributes up to 10% to the country’s agricultural gross domestic product and accounts for 36% of the country’s total protein intake. the overall sector attracts investment and yields a net worth of us$ 1.7 billion a year (frn, 2007). the commercial poultry sector represents 15% of the total poultry population and is of significant economic importance to the country and the west africa region because of its contribution to employment, food security and livelihoods. however, the sector is still hampered by difficulties linked to obsolete infrastructure for animal health, poor disease surveillance strategy, and weak diagnosis and control systems. these factors put nigeria at a high risk of introduction and spread of trans-boundary animal diseases such as the highly pathogenic avian influenza (hpai) subtype h5n1 as confirmed by the report of the technical committee of experts on the prevention and (eventual) control of hpai h5n1 in nigeria (fdlpcs, 2005). the necessity of this committee’s work was triggered * corresponding author: m.fadiga@cgiar.org. http://dx.doi.org/10.13128/bae-12713 46 m.l. fadiga, i. okike, b. bett by the enormous and unprecedented social and economic impacts of avian influenza in asia where over 200 million domestic poultry had either died or been destroyed with 175 people having contracted the infection, of which, 93 had died as a direct result of hpai infection between 2003 and 2005 (world bank, 2006). the federal government of nigeria (fgn) had developed emergency preparedness plans for dealing with any incursion of the disease into nigeria (fdlpcs, 2006) because of the economic significance of poultry farming and the potential for the outbreak of becoming a pandemic with incalculable consequences. however, the first wave of outbreaks, which started in february 2006 lasted for 21 months and created panic among the populace. there was significant unease among scientists and policy makers as little was known about the disease although the role of migratory bird species of the anseriformes and charadriiformes as natural reservoirs for the disease had been confirmed (stallknecht and shane, 1988). keeler, berghaus, and stallknecht (2012) also established that the persistence of virus on surface water depends on the temperature (ambient), degree of salinity (hypothesized), and ph (neutral to slightly basic). however, the case of nigeria is complicated by common practices such as illegal poaching and trades of wild birds for food and medicines and maintaining captive wild birds next to domesticated poultry, which are major factors contributing to the spread of the disease (teru et al., 2012). the fact that 60% of all poultry produced in nigeria originate from village extensive and backyard intensive production system referred to as “sector 4” according to fao classification of poultry production systems (fdlpcs, 2007) is also a compounding factor. “sector 4” is the main supplier of birds to retailers, consumers, and live bird markets notwithstanding the limited, if at any, application of biosecurity measures (muteia et al., 2011). it was feared that this situation combined with a notable weak infrastructure for animal health, disease surveillance, diagnosis and control across sub-sahara africa, the effects of hpai escaping the boundaries of nigeria would be disastrous for the food security and livelihoods of millions of people (gueye, 2007) and catastrophic for the continent if it evolved as an influenza pandemic. such a dire scenario was possible given the virus’ high propensity for random mutations and capacity to change in antigenicity, infectivity, and virulence (holmes, 2010; pfeiffer et al., 2011). the picture painted above was seriously taken and urgently addressed by the nigerian government. it provided the near-perfect impetus for policy makers to decide to intervene based on the precautionary principle. in the immediate aftermath of the initial outbreaks, the worst of the fears became increasingly plausible. the disease spread rapidly to 97 local government areas in 25 states and the federal capital territory. four hundred thousand birds were culled in the first two months; egg and chicken sales declined by 80% within two weeks following the announcement of hpai outbreaks (oie, 2007). there was a near total boycott of poultry products in the country with all the associated negative effects of a zoonotic and trans-boundary disease on the industry (tiongco, 2009; beach et al., 2008; akinwumi et al., 2010; rich and wanyioke, 2010). at the west african regional level and in quick turns, niger, cameroon, benin, ghana, and côte d’ivoire, all confirmed outbreaks of the disease (oie, 2011). these events galvanized the international community into action with nigeria receiving significant in-kind aid materials such as disinfectants, protective gears, vehicles and equipment (perry et al., 2011). on its part, the fgn began receiving funds from the world bank through a loan contracted to support its effort to minimize the threats posed by h5n1, prepare against 47assessment of intervention against avian influenza influenza pandemic, and prevent further spread of hpai to other parts of the country under the nigeria avian influenza control project (naicp) (world bank, 2006). the naicp had a total of four components: animal health (budget us$ 29.2 million), human health (us$ 18.25 million), social mobilization and strategic communication (us$ 4 million), and implementation support/monitoring and evaluation (us$ 6.8 million). the allocation of funds across these activities let transpire an understanding by the fgn for a need to move beyond health issues to successfully deal with the disease. using the case of vietnam, herington (2010) argued for the necessity to account for economic, political, and social factors in addition to the epidemiological factors in order to design the most objective and effective approach to deal with disease outbreaks. it is through this process known as securitization that a country could evoke the argument of national security interest to be able to undertake actions often not consistent with the narrow interest of market agents and politicians at the local level (herington, 2010). international perception about the disease may not be enough to undertake actions such as culling birds, destroying eggs, and quarantining poultry farms according to herington (2010). drawing from the cases of severe acute respiratory syndrome (sars) and avian influenza in china, wishnick (2010) warned that securitization is not a panacea and advocated for a more balanced approach that enlists the public as a partner for the government to be successful in implementing its plans. this approach is broadly consistent with the actions taken by the fgn to quell hpai. in the entire event, nigeria suffered two major waves of outbreaks in 2006 and 2007 with a small and final episode in july 2008 (figure 1). in all, there were 362 outbreaks that led to one human case fatality and the destruction of 1.3 million infected birds for which figure 1. distribution of total outbreaks in nigeria by months for 2006-2010 0   25   50   75   100   125   jan ua ry   fe bru ary   ma rch   ap ril   ma y   jun e   jul y   au gu st   se pte mb er   oc tob er   no ve mb er   de ce mb er   number  of   outbreaks   month   2006   2007   2008   2009   2010   48 m.l. fadiga, i. okike, b. bett $5.4 million was paid in compensation to 3,037 affected poultry farms/farmers (world bank, 2010). the naicp was implemented from april 2006 to may 2011 during which time us$ 41 million were disbursed by the world bank to the project. as the project was ending the world bank commissioned the international livestock research institute (ilri) to conduct an independent impact assessment of the project to determine the degree to which the project outputs have contributed to the achievement of the project development objectives. the objectives of this study are threefold: (1) develop counterfactual scenarios to measure the extent to which the intervention had minimized losses; (2) assess the economic justification of the intervention; and (3) determine the threshold composite risk level necessary to justify the intervention. in this article, we first outlined a theoretical framework of disease control to understand the economic foundation of disease risk management and mitigation. second, we presented an assessment of hpai risk based on observed data to derive the scenarios that were considered, followed by a presentation of the data, and a stochastic analysis of hpai risk. we proceeded with the economic assessment of the intervention against hpai followed by a final section on the concluding remarks. 2. theoretical framework disease outbreaks induce cost due to production losses, untimely slaughters, and reduced productivity causing welfare losses at all levels of the supply chain (wolf, 2005). these losses could be mitigated through strategically targeted interventions. theoretically, these targeted interventions seek to minimize losses in expected damage from disease outbreaks. accurate assessments of damages caused by animal diseases require integrating epidemic and economic models (paarlberg et al., 2005; pritchett and johnson, 2005). rich and winter-nelson (2007) applied an integrated epidemiological and economic model to study the spatial and temporal impacts of foot-and-mouth outbreak in the southern cone of south america. egbendewe-mondzozo et al. (2013) applied similar approach with stochastic transmission rate for an ex ante assessment of using vaccination as an option to control avian influenza in the u.s. under this framework, the expected damage function could be calculated at the country level. it is proxied by the expected total (tc) a sum of expected direct (dc) indirect (ic), and could be formally defined as follows: tc (ϕ) = dc (ϕ) +ic (ϕ) (1) the specification of the direct cost component broadens the deterministic approach proposed by bennett (2003). it contains a component ϕ to indicate the vector of intervention that links expected total cost of the disease, overall risk, and size of the intervention used to reduce risk. equation (1) is a function of the probability, p(ϕ) of a bird being affected. this probability is a composite risk parameter derived as a product of risk of spread, s(ϕ), and mortality risk, m(ϕ). it is a function of the intervention cost, r(ϕ). both the composite risk parameter and the cost of intervention are function of the vector of ϕ that includes surveillance, culling, carcass disposal, cleaning, compensation, and edu49assessment of intervention against avian influenza cation, among other things. under this specification, the optimal composite risk, p φ( )(ϕ) under which the intervention would make economic sense could be numerically derived. at that point, the marginal cost of an additional unit of intervention equals its marginal benefit and the net social welfare gain of the intervention to its incremental net benefit. the direct cost refers to the monetary values of physical losses as a result of mortality associated with hpai calculated by applying the respective unitary prices to each category of physical losses. these physical losses include chicken death and egg losses (using the share of laying hens out of the dead chickens) as a result of the disease and the applied control strategies. the expected chicken death l(ϕ) the basis of the expected physical losses is calculated by multiplying the composite risk estimate and the population of z and can be formally expressed as follows: l φ( )= p φ( )*z p φ( )= s φ( )*m φ( ) ⎧ ⎨ ⎪ ⎩⎪ (2) the expected egg loss is found by multiplying annual production per layer by the share of layers out of the expected total dead chickens. the indirect cost involves the ripple effects such as effects of price shocks on supply chain actors, spill-over effects such as effects on the tourism sector, and long term macroeconomic effects of the disease (oie, 2007). while the direct cost could be easily assessed with a partial budgeting approach, the indirect cost proves more complicated to determine. there is a general agreement based on the existing body of research on hpai that the indirect cost of hpai dwarfs the direct cost (oie, 2007; diao et al., 2009). there is also an agreement that their measurements require extensive data that may be difficult to obtain, especially in developing countries (oie, 2007). hence, we utilised a ratio that puts the indirect cost at 1.24 times the direct cost derived from estimated direct and indirect costs of hpai in nigeria in an ex ante analysis that used a computable general equilibrium (diao et al., 2009). the incremental benefit represents the cost saving accrued to nigeria as a result of the intervention. it is the difference between what would have been the total cost of hpai to nigeria had no action been taken and what it was under the intervention. the incremental cost corresponds to the world bank’s yearly disbursements between 2006 and 2010. the incremental net benefit is obtained by subtracting the incremental cost from the incremental benefit. 3. risk assessment and scenario derivation the estimation of economic impacts of hpai outbreak in nigeria is based on losses from mortality/culling incurred in the course of the outbreak. the magnitude of these losses is assumed to depend on the risk of disease spreading between states and the risk of a bird dying from the disease following exposure in the affected states (figure 2). there are at least two levels of aggregation between the state and the bird that should have been considered (i.e., local government area and village) to minimize making erroneous inferences when using aggregated data (state level) to address issues at local level (local government and village level). this could not be done because the outbreak dataset 50 m.l. fadiga, i. okike, b. bett used had more reliable information at higher (state) than lower (village) levels. a composite estimate of the risk of a bird being affected could be obtained by combining the two measures of risk shown in figure 2 and outlined in the theoretical framework. given the high uncertainty associated with the hpai risk estimates defined above, two risk scenarios (i.e., the best and the worst case scenarios) that are expected to enclose the plausible risk levels are provided for each risk estimate. the risk of introduction of the disease is not considered in this analysis because the focus is on an outbreak that had already occurred. though multiple introductions of the virus might have occurred over the three-year period when the outbreak was active, these introductions are not considered as being independent events since they happened at a period when hpai epidemic was active in many parts of the world. the risk of spread of hpai in nigeria was analyzed using a simple compartmental susceptible-infectious (si) model assuming that all newly infected states were infected by indirect contact with infectious states during the same wave of the epidemic. this is a system of differential equations that calculates the effects of a disease in a population by using preceding disease parameters known as transition rates (rich and winter-nelson, 2007). two scenarios were considered: (i) the outbreak burns out due to a reduction in the number of susceptible states, and (ii) the outbreak becomes endemic after a short peak. the assumption made for the first scenario is that re-stocking is done 90 days after culling and adequate biosecurity measures are put in place that will protect a large proportion of the newly introduced birds from getting exposed to the virus. for the second scenario, it is assumed that restocking is done routinely after 90 days but inadequate biosecurity measures are implemented. in this case, the replacement stock has an equal chance of being exposed to the disease as the indigenous population. the model assumes that all newly infected states (c) were infected by indirect contact with infectious states (i) during the same wave of the epidemic. the contact between states could be associated with purchases, movements of infected birds, or transfers of infectious material (e.g. feaces) via fomites from infected to uninfected villages. let s be the number of susceptible states per day, i the number of infectious states per day, and n the total number of states, the number of newly infected states c is given βsi/n. the parameter β the transmission coefficient of the disease. it represents the average rate at which an infected state infects susceptible state in a day in a population consisting of susceptible states. the β therefore derived nc/si (ward et al., 2009) for each day (t) of the epidemic. this model ignores spatial transmission dynamics as it assumes that states had similar epidemiological characteristics (defined by hpai risk factors, contact patterns and recovery rates) and were equally at risk of infection at the start of the epidemic. the outbreak dataset (naicp, undated) was, hence, aggregated at the state level in figure 2. components of the risk of occurrence of hpai outbreak in nigeria. proportion of states affected given that the country is infected (risk of spread) probability that a bird will be affected given that a state is infected (mortality risk) 51assessment of intervention against avian influenza order to obtain one record/state/phase of the outbreak. the first phase occurred between january and august 2006 while the second occurred between november 2006 and november 2007. the duration of α was assumed to be equal to the duration between the dates when the outbreak was reported and when depopulation was done at the state level (bett et al., 2014). the transmission coefficients and the duration of infectiousness were estimated using the outbreak data set. the transmission coefficient was estimated β = 0.02 and the mean duration of infectiousness α = 49 days while the incubation period of the disease, was assumed to be γ = 3 days. henning et al. (2009) reported that incubation period could be anywhere between 2 and 14 days. the transmission rate, mean duration of infectiousness, incubation period, number of days, say ρ, required before restocking after culling, and number of states were used to solve the system of differential equations (3) and (4) for the number of susceptible, infected, incubating, and resolved cases, r, at each day (t). the prevalence at each day (t) is derived as i/n and the average prevalence over time represents the risk of spread of the disease between states. the estimates of the risk of spread over a 1 year period are 0.13 and 0.27 for the burn-out and endemic scenarios, respectively. s c i r s c i r 1 0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1 1 t t t t t t t t 1 1 1 1 β ρ β γ γ α α ρ               = − − − −               ×               − − − − (3) s c i r s c i r 1 0 0 0 1 1 0 0 0 1 1 1 0 0 0 1 1 t t t t t t t t 1 1 1 1 β β γ γ α α               = − − −               ×               − − − − (4) the initial conditions are s0 = n, n = 37, c0 = 0, and r0 = 0. the parameters α, γ, β, and ρ remains as previously defined. figure 3 provides an illustration of the evolution of hpai prevalence under these two scenarios. the risk of infection (or mortality risk, as the case fatality rate is 100%) was derived using the mean proportions of poultry that died (combining case fatalities and culled birds) out of the total population at risk in 2006 and 2007 for the states that had population at risk data. the mean proportions obtained were 2% and 1% for 2006 and 2007, respectively. overall, given the high uncertainty associated with the hpai risk estimates defined above, two risk scenarios (i.e., the best and the worst case scenarios) that are expected to enclose the plausible risk levels are provided for each risk estimate. 4. data consideration this study utilises both factual data and projected data under the counterfactual scenarios. the factual data include outbreak numbers, number of affected states, number of 52 m.l. fadiga, i. okike, b. bett dead chickens, number of culled chickens, compensation cost, and cost of culling and disposal per bird were compiled by naicp and available on the project’s website at www. aicpnigeria.org. the production and price data are from fao (fao, 2010). table 1 illustrates the data used in the study. projected mortalities under the counterfactual scenarios are derived using the disease risk parameters as described in table 2. about 85% of the flocks that was affected by hpai in nigeria were layers (fasina et al., 2007). the rest was mainly comprised of broilers and village chickens. the expected physical losses in egg and broiler and their expected monetary values were calculated using these parameters accordingly with the composite risk estimate (i.e., product of risk of spread and mortality risk) and the population at risk. 5. stochastic analysis outbreak data at state level were used to simulate an empirical distribution of the mortality risk. the risk of spread was also simulated using a truncated normal distribution generated from the previously described average risk spread estimates. both the risk of spread and the risk of a bird becoming infected were assumed to evolve stochastically over the studied period. the simulation was conducted using simetar, an excel add-in software that randomly draws from the distribution of the risks of spread and the mortality risk to iteratively solve for the key output variables. richardson et al. (2004) provide detailed information about the algorithm. figures4 and 5 illustrate the distribution of risk of spread and risk of infection, respectively. the stochastic averages of risk of spread were 0.2746 and 0.1663 under the endemicity and burn-out scenarios, respectively. the stochastic averages of infection risk were 0.0088 and 0.0174for the low and high mortality risk scenarios, respectively. all key output variables (disease cost, incremental benefit, incremental net benefit, and benefit cost ratio) were stochastically determined. the resulting outputs were a set of figure 3. risk of spread of a hpai outbreak in nigeria. 0%   10%   20%   30%   40%   50%   0   100   200   300   400   day   prevalence   endemic  state   unstable  epidemic   53assessment of intervention against avian influenza table 1. data sources. names value sources disease parameters number of outbreaks 320 naicp (undated) number of states affected 25 naicp (undated) mean incubation period (days) 3 assumed. see henning, pfeiffer, and vu (2009) mean duration of infectiousness (days) 49 assumed. see bett et al. (2014) at village level case fatality rate 100 who (2011) transmission rate 0.02 calculated from outbreak data mortality risk 0.01 and 0.02 calculated from outbreak data risk of spread 0.13 and 0.27 calculated from outbreak data population affected number of susceptible varies year-to-year calculated number of infected varies year-to-year calculated number of dead see table 3 naicp (undated) for factual data and derived for counterfactuals number of culled see table 3 naicp (undated) for factual data and derived for counterfactuals production parameters chicken losses vary year-to-year calculated based on epidemiological and production parameters egg losses vary year-to-year calculated based on the share of layers (85%) among affected bird (fasina, 2007) and epidemiological and production parameters price of chicken varies year to year. us$ 2.76/head on average fao(2010) price of egg varies year to year. us$ 1.8/kg on average fao (2010) compliance and compensation compensation cost us$ 1.96/head oie (2007) cost of culling and disposal per bird us $ 1.00 oie (2007) control cost per bird us $ 0.38 oie (2007) other parameter ratio indirect to direct cost 1.24 derived from diao et al. (2009) 54 m.l. fadiga, i. okike, b. bett 500 possible solutions from which parameter of central tendencies and distribution were derived and presented. 6. disease cost and economic assessment of intervention table 3 presents a comparison between observed and expected birds’ mortalities from culling and the disease itself under the four previously outlined scenarios. these estimates were based on the stochastic averages of the spread and infection risk parameters. the results show that the additional number of birds that would have been saved by the intervention between 2008 and 2010 under the burn-out scenario would be 46,960and 93,794 for the low and high mortality paths, not significant enough to warrant the investment from a financial standpoint. hence, the analysis mainly focused on the two scenarios of endemicity. the descriptive statistics on the key output variables in table 4 indicate that had the intervention not been carried out, the average cost of hpai to the nigerian economy over the 5-year period (2006-2010) would have amounted to us$ 144.97 million under the high mortality path. studies such as fasina (2008) estimated the total cost of hpai at us$ 244 million for a mild case scenario whereby 10% of the commercial flock would be affected and us$690 for a severe scenario. you and diao (2007) estimated the direct cost at us$ 250 million under a worst case scenario that involves the disease spreading along the two major flyways and between us$ 48 and us$ 52 million for a best case scenario where the disease would be confined to a single flyway. these studies deal with ex ante analysis of hpai. the distribution of the potential cost of the disease was also evaluated. as indicated in table 4, there was 30% chance that the economic damage caused by hpai would be above us$ 173.61 million under the most disastrous scenario and 90% chance that it would be greater than us$ 53.36 million. the incremental benefit of the intervention over the five-year period would amount to us$ 63.7 million. the incremental net benefit is obtained by subtracting the bank disbursements from the net benefit, which yielded us$ 27.22 million. this amount is the net gain of the intervention. the results in table 4 indicate that there is 40% chance that the intervention would not lead to positive incremental benefit. the distribution of the generated incremental net benefit of the intervention ranged from a minimum of -us$65.10 million to a maximum of us$702.33 million. the derived incremental benefit and the cost of the intervention are used to calculate the benefit cost ratio of the project. the average benefit cost table 2. composite risk estimates for the various risk scenarios considered. scenario risk estimate composite estimatespread mortality spread mortality burn-out low 0.13 0.01 0.0013 burn-out high 0.13 0.02 0.0026 endemic low 0.27 0.01 0.0027 endemic high 0.27 0.02 0.0054 55assessment of intervention against avian influenza ratio amounts to 1.75 (table 4). it indicates for the endemic scenario with high mortality path, the intervention would have made economic sense. however, there is no certainty to the economic justification, as it is determined by the magnitude of the risk of bird infection in affected states. in any case, as illustrated in table 4, there is more than 50% chance that the investment would be economically justified under the endemic scenario with high mortality path. furthermore, there is 15% chance that the benefit cost ratio would be above 3.86, 10% chance that it would be above 5.36, and 5% chance that it would reach at least 8.79. results of the economic justification of the project should be interpreted with care. for instance, the diagnostic capability of the nigerian veterinary public health service has improved under the intervention, the diagnostic laboratories have been upgraded, and the morale of veterinarians in public sector has been boosted figure 4. simulated risk of spread of hpai to other states. 0%   3%   6%   9%   12%   15%   18%   frequency   0.01   0.07   0.13   0.19   0.25   0.31   0.37   0.43   risk  of  spread   figure 5. simulated risk of bird infection in affected states. 0%   12%   24%   36%   48%   60%   frequency   0.00   0.06   0.12   0.18   0.23   0.29   0.35   0.41   mortality  risk   56 m.l. fadiga, i. okike, b. bett as a result of their performance during the outbreaks. currently these veterinarians are better trained and equipped to deal with hpai and other diseases than they were before the outbreaks (perry et al., 2011). these are referred to as preventive spillover benefits by gramig and wolf (2007). these benefits are difficult to quantify in a cost benefit analysis framework although documented in the assessment of the intervention, which call for more caution in the interpretation of the results. a breakeven analysis was conducted to find the minimum hpai risks (of spread and of infection) that would justify the investment. various combinations of risk of spread and risk of bird infection led to the threshold benefit cost ratio, as the two parameters simultaneously determine losses. notwithstanding, our findings indicate that a composite risk estimate at 0.006 would be necessary to cause economic damage high enough to justify the intervention. this would correspond to an infection risk of 0.022 under the endemic scenario, considering the disease is already present in the country. at breakeven risk level, the disease would have caused economic damages amounting to us$ 118 million over the five-year period. table 3. observed and projected mortalities under the counterfactual scenarios (thousand birds).   2006 2007 2008 2009 2010 factual(a)   died 612 135 2 0 0 culled 405 360 3 0 0   total 1,017 496 5 0 0 counterfactual scenarios(b)   burn-out low died 612 135 46 23 11   culled 405 77 37 19 9   total 1,017 212 83 42 21       burn-out high died 612 184 90 45 22   culled 405 151 74 37 18   total 1,017 335 163 82 41       endemic low died 612 155 151 151 151   culled 405 127 124 124 124   total 1,017 282 274 274 274       endemic high died 612 304 296 296 296   culled 405 250 243 243 243   total 1,017 554 540 540 540 notes: (a) monthly distribution of the observed mortalities is presented in appendix 1. (b) expected mortalities are obtained by multiplying population at risk with the risk of spread and the mortality risk. the number of culled birds is derived from the susceptible birds that have not died. the expected egg loss is found by multiplying annual production per layer by the share of layers out of the expected total dead birds (see table 1 for more information). 57assessment of intervention against avian influenza 7. conclusion outbreak data in nigeria were used to simulate the risk of spread of hpai to states in nigeria and the risk of bird infection. these risk parameters were applied to assess the potential cost of hpai to nigeria under four scenarios (burn-out with low mortality, burn-out with high mortality, endemic with low mortality, and endemic with high mortality) had the intervention not been carried out. our findings indicate that for the two burn-out scenarios, the number of birds that would have been saved would not have been enough to warrant the investment of us$ 41 million. this conclusion, for the burn-out scenarios, is purely financial as it discounts the possibility of loss of human lives and the application of precautionary principle informed by the poor state of infrastructure and known weaknesses of the veterinary service in nigeria at the time of the outbreak. from a global view point, the potential evolution of the disease to a pandemic could have been enough to warrant such an investment given concerns within the international community on the high likelihood of the disease becoming endemic in nigeria. nevertheless, the analyses show that under the scenario where the disease became endemic with high mortality risk, the investment would generate incremental net benefit significant enough to justify the investment. in reality, the nigeria hpai outbreaks claimed more than 1.3 million birds and could not be classified under any of the two table 4. descriptive statistics and percentile distribution of the effects of intervention against hpai on key output variables over 2006-2010 under the high mortality path. percentiles risk of spread risk of infection disease cost incremental benefit incremental net benefit benefit cost ratio stochastic average 0.27 0.02 144.97 63.70 27.22 1.75 standard deviation 0.06 0.02 115.99 115.99 115.99 3.18 5% 0.18 0.00 52.94 -28.33 -64.81 0.00 10% 0.20 0.00 53.36 -27.91 -64.39 0.00 30% 0.24 0.00 55.14 -26.14 -62.62 0.00 40% 0.26 0.00 64.40 -16.87 -53.35 0.00 50% 0.27 0.02 124.20 42.93 6.45 1.18 60% 0.29 0.02 150.53 69.26 32.78 1.90 70% 0.31 0.02 173.61 92.34 55.86 2.53 75% 0.31 0.03 190.12 108.85 72.37 2.98 80% 0.32 0.03 201.70 120.43 83.95 3.30 85% 0.33 0.03 222.25 140.98 104.50 3.86 90% 0.35 0.04 276.87 195.60 159.12 5.36 95% 0.37 0.07 401.91 320.64 284.16 8.79 note: the results presented in each column should be interpreted separately. they are based on the 500 possible solutions. the risk parameters and the benefit cost ratio are unitless while cost of inaction, incremental benefit, and incremental net benefit are in us$ million over five year period with 2006 as base year. a 12% discount rate was applied. the rate is consistent with that currently applied by the central bank of nigeria (cbn) (cbn, 2012). the stochastic averages of yearly values of key output variables are presented in appendix 2. 58 m.l. fadiga, i. okike, b. bett burn-out scenarios explored in this paper. similarly, the outbreaks did not persist with high mortality as the last one occurred in july 2008. if the intervention is perceived to have helped averting the endemic and high mortality scenario, then the benefit would have by far exceeded the cost, hence the investment would be highly justified. there also are multiple positive externalities that are difficult to account in the calculations of the benefit of the intervention. for example, the investment helped nigeria improve its health service delivery infrastructure and strengthened its public health and veterinary services capacity in biosecurity protocols and communications to deal with disease outbreaks of significant magnitude. so, the lessons learnt from this intervention, including the induced behavioral changes within the populace and the acquired knowledge of what to do when confronted with similar situations in the future, are incalculable. from the financial, economic and welfare standpoints also outlined in this paper, an overall conclusion could be reached that the intervention was useful. references akinwumi, j., okike, i. and rich, k. m. 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(2005). producer livestock disease management incentives and decisions. international journal of agribusiness management review 8(1): 46-61. you, l. and diao, x. (2007). assessing the potential impact of avian influenza on poultry in west africa – a spatial equilibrium model analysis. journal of agricultural economics 58(2): 348-367. appendix 1. monthly distribution of dead and culled chickens, 2006-2010. month year   total 2006 2007 2008 dead culled dead culled dead culled january 63,166 92,692 35,582 107,915 0 0 299,355 february 59,087 100,539 7,456 47,592 0 0 214,674 march 3,616 14,920 2,754 9,944 0 0 31,234 april 329,549 44,128 168 462 0 0 374,307 may 3,443 10,946 734 1,350 0 0 16,473 june 18,108 8,626 8,735 42,736 0 0 78,205 july 7,409 13,910 23,970 54,179 1,913 2,587 103,968 august 47,703 3,968 30,189 62,178 0 0 144,038 september 3,004 4,000 3,134 10,736 0 0 20,874 october 1,402 2,486 573 1,821 0 0 6,282 november 58,528 82,600 8,439 18,296 0 0 167,863 december 17,314 25,988 4,606 3,030 0 0 50,938 total 612,329 404,803 126,340 360,239 1,913 2,587 1,508,211 appendix 2. yearly discounted stochastic averages of key output variables of the intervention against avian influenza in nigeria (in us$ million). key output variables years total 2006 2007 2008 2009 2010 incremental cost 20.00 5.32 7.31 3.34 0.49 36.48 incremental benefit -3.55 0.28 24.29 22.32 20.34 63.70 incremental net benefit -23.55 -5.03 16.98 18.98 19.85 27.22 note: the figures are stochastic averages of discounted yearly key output variables (except for incremental cost). a 12% discount rate was applied with 2006 as base year. bio-based and applied economics 6(3): 259-278, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-23340 immigrant workforce and labour productivity in italian agriculture: a farm-level analysis edoardo baldoni, silvia coderoni*, roberto esposti department of economics and social sciences, università politecnica delle marche, ancona, italy date of submission: 2017 1st, august; accepted 2017 25th, december abstract. the objective of this paper is to detect stylized facts and put forward testable hypotheses on the presence and role of immigrant workforce in italian agriculture. this research focuses on professional agriculture as represented by the italian fadn over the period 2008-2015. descriptive statistics show that immigrants are an important component of the workforce employed in professional agriculture over this period, even with wide disparities between regions, sectors and classes of economic size. immigrants are concentrated in larger and more productive farms and their presence is positively correlated with farm’s labour productivity (lp). to understand whether they are more productive, or they are just occupied by more productive farms, the relationship between lp and their contribution to agricultural production, in terms of annual working units (awu), is modelled at the farm level, by assuming alternative model specifications. results emphasize that, in many cases, statistically significant relationships between the contribution of immigrants and farm-level lp can result from model misspecifications. accounting for farms’ heterogeneity can greatly influence the dimension of this link. moreover, when assuming persistence of lp with a dynamic specification, this relationship disappears. keywords. immigrant workforce, fadn sample, labour productivity, dynamic panel models. jel codes. q12, j24, j61. 1. introduction according to official statistics, in italy, in 2015, immigrants represented almost 10% of the total workforce and were mainly employed in the services sector (66%), followed by the manufacturing sector with 29% (mainly construction) and, finally by the agricultural sector (6%) (istat, 2016). a strand of scientific literature supports the idea that immigrants contribute to economic growth because they provide relatively cheap workforce especially in those cyclical or seasonal sectors strongly based on cost competition, *corresponding author: s.coderoni@staff.univpm.it 260 edoardo baldoni, silvia coderoni, roberto esposti such as construction or agriculture (somerville and sumption, 2009a and 2009b). moreover, against a background of declining employment in agriculture, they play a crucial role in meeting seasonal labour demand of the sector because they represent a highly mobile workforce (hanson and bell, 2007). however, as immigrant workers frequently replace native ones in less skilled jobs, they often appear to be less productive. this also motivates why in non-scientific literature and in the media immigrant workforce is often regarded just as unskilled and cheap labour. nonetheless, in sectors like agriculture where skillness is not necessarily linked to human capital accumulation (i.e., education) but mostly to experience and traditional knowledge, it can be rather the case that immigrant workers are more productive and that they can increase the productivity of other factors of production. the main objective of the present work is to assess the contribution of immigrant to market-oriented italian farms’ production and productivity. in particular, the attention is on the empirical relationship between the presence of immigrants and farm-level labour productivity. while this empirical assessment in not completely new (see section 2 for a brief review of the recent literature in this respect) the main interest here is in performing such analysis not with aggregate (national or regional) sectoral data but on the basis of farm-level (micro) data. a balanced panel of farms allows detecting the high heterogeneity occurring within italian agriculture in terms of presence and performance of immigrant workforce. to pursue this research objective, we use here the italian farm accountancy data network (fadn) sample, which includes information on professional and market-oriented farms and excludes all those farming practices that do not exceed a minimum economic size. this fadn panel is extracted over years 2008-2015 (section 3) and the presence, the distribution and the main features of immigrant workers across farm typologies, farm size and geographic location is firstly investigated (section 4). then, the relationship between farm-level labour productivity and the presence of immigrant workforce is estimated adopting alternative panel model specifications and the respective estimators (section 5). section 6 concludes. 2. labour productivity and migration: overview of the literature between 1960 and 2010, the proportion of foreign-born in the population of highincome oecd countries has increased from less than 5% to about 11%, and the proportion of immigrants originating from developing countries has grown from 1.5% to 8.0%. although on average this workforce can be regarded as low-educated, an increasing proportion of these migrants has a higher education level. therefore, it should not surprise that, on the one hand, the effects of immigration on the economy and, above all, on the labour market of the rich receiving countries have been widely investigated. on the other hand, within these countries immigration and its labour market effects have become a major political issue (brunello et al., 2017; docquier and machado, 2017). though still lagging behind countries such as the us, canada and australia, this interest has particularly increased in those countries where immigration, and its implications, are relatively more recent. this wide literature largely agrees on the fact that immigration flows increase employment, raise total output and per capita income of natives, but it also redistribute income 261immigrant workforce and labour productivity in italian agriculture: a farm-level analysis among factors of production (lalonde and topel, 1997; devadoss and luckstead, 2008; clemens, 2013). in fact, immigration has important effects because it increases the relative supply of some types of workers, changing factor proportions and relative prices. therefore, while the overall impact is expected to be positive, this is not necessarily true for all the components of an economy, i.e., all groups of workers and all sectors. eventually, most of the policy debate about immigration relates to its effects on income distribution. this redistribution effect has mostly to do with the quality of this labour force (education and skillness) but also with the activities and sectors where it is eventually employed.1 on the one hand, low skilled immigration increases the supply of low-skilled labour mostly concentrated in low-productivity sectors. one major consequence of this is that immigrant workforce induces a higher supply of low skills thus reducing the wages and employment probabilities of low skilled natives especially in low-productivity sectors. therefore, immigration tends to be regarded as a major source of unfair competition and potentially social dumping (rye and andrzejewska, 2010), on native low-skills workers and this seems particularly critical in the case of unauthorized or irregular immigrant workers (edwards and ortega, 2017). on the other hand, however, when low and high skills are complements in production, immigration increases the productivity and wages of high skilled workers: the returns to education raise and natives have stronger incentives to acquire additional schooling (brunello et al., 2017). this may justify why countries that receive migrants regularly (us and several other migrant-receiving countries such as canada, australia, and new zealand) have in place skills-based admission procedures (stark et al., 2017). all the arguments above about the impact of immigrant workforce on the domestic labour market and economy eventually calls back the impact of migrant workforce on aggregate, sectoral and firm-level productivity and, consequently, on respective wages (anderson et al., 2006). nonetheless, the results in the empirical literature concerning the existence of productivity differentials between migrants and native workers are mixed and controversial.2 at the aggregate level, a negative relationship between immigrant workforce and labour productivity is very likely to emerge whenever this workforce (due either to lower quality or to any other reason) concentrates in low-productivity sectors. agriculture, construction and some service sectors are typically among these. but if we remove this composition effect and concentrate on the impact of immigrant workers on labour productivity at the sector or firm-level, the evidence is much less clear and, more importantly, the underlying assumption that these workers are of lower quality (skills and education) may be seriously questioned. if we limit the attention to agriculture, i.e. one of the most relevant and studied sector in this respect, recent evidence suggests that the immigration inflow seems to have generated a productivity slowdown within the agricultural sectors of countries where the phenomenon is much more recent. this does not occur in countries that have traditionally seen substantial immigration like us and uk (kangasniemi et al. 2012). this difference can be attributed, again, to the different migrant labour quality in the uk. 1 this effect may also occur because of internal migration especially from rural to urban areas (combes et al., 2017). 2 for example, peri et al. (2015) and albarrán et al. (2017) present evidence of the positive impact of stem (scientists, technology professionals, engineers, and mathematicians) immigrant workers on total factor productivity in us. 262 edoardo baldoni, silvia coderoni, roberto esposti despite these country differences, however, evidence in favour of a positive effect of migration on agricultural productivity seems to prevail. bove and elia (2017) point out that such positive impact depends on the fact that immigrant workers carry a new range of skills and perspectives, which stimulate technological innovation and fuel entrepreneurship and this contribution seems to be more relevant in developing countries. for instance, klocker et al. (2018) show that migrant workers’ knowledge represents a key resource for climate change adaptation in agricultural production. this role played by immigrants in agriculture as environment builders, bringing expertise encouraging productivity improvements also within the wider rural economy, is confirmed by other studies and in other contexts like australia (hanson and bell, 2007), greece (kasimis et al. 2003; kasimis and papadopoulos 2005; labrianidis and sykas, 2009) and spain itself (gómez–tello and nicolini, 2017). another relevant aspect pointed out within this strand of empirical studies is that in cyclical or seasonal sectors, such as agriculture, immigrants may contribute to production performance as they represent a highly mobile workforce thus playing a crucial role in meeting seasonal labour demand despite the declining employment of natives (hanson and bell, 2007; somerville and sumption, 2009a and 2009b). in this respect, siudek and zawojska (2016) analyse migrant agricultural workers from poland to the uk finding that immigrant workers are very relevant for old member states’ agricultures, since the natives are less likely to accept low wages and bad working conditions and not always meet the employers’ demands in terms of work motivation and mobility. an often disregarded aspect dealing with the immigrants’ productivity in agriculture is that such contribution may largely differ within the sector due to the wide heterogeneity across farms especially in terms of size and production specialization. those agricultural specializations, such as horticulture and fruits production, that rely heavily on unskilled and cheap labour to meet their seasonal demand, could greatly benefit from the presence of an abundant immigrant workforce (wells, 1996). on the other hand, livestock production, that requires specialized workers along production phases similar to those of the manufacturing sector could be a less suitable activity for unskilled immigrants while, on the contrary, may take advantage from immigrants with long-term experience in animal breeding (huffman and evenson, 2001). analysing california vegetable production, devadoss and luckstead (2008) show that immigrant workers positively influence productivity of the other factors of production, i.e., native skilled workers, material input, and capital. within this recent literature, contributions on the italian agriculture case are rare. this seems surprising considering that italy, like spain, is one of the affluent countries where intense immigration is a relatively new phenomenon and, also for this reason, the majority of immigrants originate from developing countries and are relatively low skilled. moreover, italian agriculture is very heterogeneous and shows strong geographical specificity. therefore, immigrant workers tend to concentrate in specific farm typologies and areas. according to ievoli and macrì (2008) and macrì et al. (2017) the presence of immigrants in italian agriculture is higher in two specific and quite different contexts: areas characterised by high seasonality of labour demand (mostly fruit and vegetable productions in the southern part of the country);3 areas experiencing a lack of permanent labour 3 coderoni et al. (2015) also stresses the presence, in these specific contexts, of a significant and growing amount of irregular and over-exploited immigrant workers. 263immigrant workforce and labour productivity in italian agriculture: a farm-level analysis supply in specific agricultural activities (mostly the intensive livestock productions in northern italy). as a consequence, italian agriculture seems to be an interesting case to investigate whether the relationship between immigrant workers and labour productivity occurs because of the specific quality of this workforce or because of a pure intrasectoral composition effect, that is, immigrants concentrate in farms with higher (or lower) labour productivity. such kind of assessment evidently requires farm-level data and the italian case is particularly suitable in this respect thanks to the availability of the fadn sample allowing the extraction of a pretty numerous and heterogeneous balanced panel of farms. 3. the fadn sample official statistics might be inaccurate in representing the real immigrant workforce in italian agriculture (coderoni et al., 2015).4 indeed, due to the presence of undeclared and seasonal workers, the number of immigrant workforce employed in agriculture could be largely underestimated.5 additionally, these data do not involve only the italian professional agriculture, that is, the real market-oriented farms, but all the farms operating on the italian territory, regardless of their professional nature. the well-known problem concerning “real” labour data collection cannot be easily overcome with any data surveyed. instead the issue of assessing only the market oriented farms can be addressed by referring to proper surveys. for this reason, we used the farm accountancy data network (fadn) dataset, which includes only professional farms intended as an entrepreneurial market-oriented activity. according to the italian fadn, on average the sample covers 97% of standard output (so), 95% of utilized agricultural area (uaa), 92% of annual working units (awu) and 91% of lu at national level.6 the reference population from which the fadn sample is drawn includes only those farms with an economic size (es) of more than a certain threshold that changes over the years (4,800 euro of yearly standard gross margin until 2013 and more than 8,000 since 2014). in this respect, the fadn sample is only representative of a sub-population of italian farms that can be here referred to as professional or commercial farms (sotte, 2006). data used to describe the relevance of immigrant workforce in italian agricultural professional farms in the first part of the analysis refer the full unweighted italian fadn sample observed from 2008 to 2015. this sample consists of 24,950 farms, each recorded up to eight years; the total number of observations is thus 87,351. the share of farms by specialization and classes of economic size is presented in the table 1.a. farms are grouped into three categories: small farms (with a so less than 25,000 euros), medium farms (with a so between 25,000 and 100,000 euros) and large farms (with a so higher than 100,000 euros). 4 the un migrant workers’ convention (article 2.1) defines a migrant worker as “a person who is to be engaged, is engaged or has been engaged in a remunerated activity in a state of which he or she is not a national”, irrespective of his/her migratory legal status (un, 1990). though in the eu policy context, mobility refers to movements within the eu, while migration – to movements between eu and non-eu countries, in this paper we will use the term immigrant worker to refer to both eu (other than italians) and not-eu workers. 5 see among others fondazione ismu (2017) and amnesty international (2012) for data on the estimation of the presence of irregular immigrant workforce in italian agriculture. 6 http://www.rica.inea.it/public/it/presentazione.php. 264 edoardo baldoni, silvia coderoni, roberto esposti instead, to study the relationship between farms’ productivity and the presence of immigrants in the second part of the research, only the 2008-2015 fadn constant sample has been used, which consists of 2,233 farms that sum up to 17,856 observations (table 1.b). the use of the balanced panel has the advantage of preventing issues that might be caused by the random nature of the sample, i.e., farms entering and exiting the market. however, it might decrease the representativeness of results. in this particular case, the constant sample includes a larger share of medium-sized farms and a smaller share of smaller farms with respect to the full unweighted sample. however, these aspects do not represent a major problem for the analysis proposed (see section 5 for further details). table 1. numbers and shares of observations in the fadn sample 2008-2015 by typology and size: a) full italian sample and b) constant sample. a) italian fadn b) italian fadn constant sample nr. observations % n. observations % type of farming dairy 8,566 9.80 1,999 11.20 cereals 10,528 12.10 2,371 13.28 grazing livestock 11,101 12.70 1,904 10.66 fruits 11,605 13.30 3,020 16.91 granivores 3,456 4.00 383 2.14 mixed 7,477 8.60 1,613 9.03 olives 3,637 4.20 401 2.25 horticulture 10,440 12.00 2,183 12.23 arable crops 10,317 11.80 1,721 9.64 wine 10,224 11.70 2,261 12.66 economic size large 30,112 34.50 5,774 32.34 medium 37,010 42.40 8,843 49.52 small 20,229 23.20 3,239 18.14 total 87,351 100.00 17,856 100.00 4. immigrant workforce in italian agriculture thorough fadn data the fadn dataset gives interesting insights on the presence of immigrant workforce in italian agriculture highlighting their relevant contribution to italian agricultural production (table 2). in the sample analysed, total immigrant workforce in 2015 is of 4,684 units, which represent 22.5% of total salaried workforce in the sample. these shares are quite similar to what macrì et al. (2017) find analysing the sub-sample of farms that employ salaried workers in the italian agricultural census. according to the authors, immigrant workforces in 2010 is 25% of agricultural workforce (233,055 units on a total employment of 938,103). 265immigrant workforce and labour productivity in italian agriculture: a farm-level analysis since 2008, the share of immigrants on total occupation is increased on average by 33%. seasonal immigrants, as expected, represent 89% of total immigrant workforce and this share is almost stable over the period analysed. indeed, the share of seasonal immigrants on total seasonal workers, which is 22% in 2015, is increasing over time (by an average 31%), denoting a growing relevance of this type of flexible workers for italian seasonal agricultural activities. in terms of awu, absolute figures are rather different from the numbers of immigrants, in particular they are lowered by the presence of not fully employed workforce. however, shares of immigrants’ awu are quite similar. as regard the qualification of these workers, information provided by fadn reveal that unskilled workers (both permanent and unseasonal) represent around the 95% of total immigrants in 2015. this evidence somehow confirms the idea that immigrant work in italy is generally unskilled (brunello et al., 2017). for what concerns country of origin, the majority of immigrants comes from a single european country, romania. in 2015, they account for 37% of the total immigrant workforce in italy. asia and africa7 are respectively the second and third most important areas of origin of immigrants. workers from slovakia and czech republic exhibited the fastest growth rates, though their shares are small (table 3). as regards the country of origin, thus, we could distinguish also for immigrant agricultural workers, the same characteristics underlined by rye and andrzejewska (2010) of a southern european model of migration, with heterogeneity of immigrants’ nationalities and related differentiation of cultural origins. besides aggregated figures, analysing the relevance of immigrants by disaggregating data at different levels can provide more interesting insights on their actual relevance. firstly, looking at percentages of immigrants on total workers by farm specialization and economic size, as an average value of the overall sample (table 4), some well-known patterns seem to emerge. indeed, not all labour-intensive sectors show a high presence of immigrants.8 the concentration of immigrants is higher in the dairy sector (41%), horticulture and grazing livestock sector (30%), fruit production (27%) and arable crops production (25%), while for other specializations, such as olives and cereals, it is less important. again, immigrants are mostly seasonal workers, with higher shares in fruits production, horticulture and livestock. in the farm typologies where they are most occupied, they represent an important share of the total seasonal workforce. table 4 shows also data on the presence of immigrants in relation to the economic size of farms. results are quite clear: at national level, medium and large farms have a higher presence of immigrants. on average, they almost have three times the concentration of small farms. the share of immigrants on employed workforce is 8% in small farms, 22% in medium-sized farms and 23% in large ones. in terms of awu, proportions do not change significantly, apart from the lower share of seasonal immigrants over total immigrants, for all the categories analysed. sig7 the dataset does not contain the information necessary to disaggregate further locations of origins for these two groups. 8 the labour-intensive sectors in the sample are those with a labour factor share higher than 40% (horticulture, wine, cereals and mixed crops and livestock), those with more than 50% (arable crops), and those with more than 60% (fruits and olives). data on factor shares are available upon request. 266 edoardo baldoni, silvia coderoni, roberto esposti table 2. numbers and shares of immigrants and working units of immigrants by year. italian fadn sample 2008-2015. year numbers immigrant workforce (n) employees (n) immigrant/ employees (%) immigrant seasonal/total seasonal (%) immigrant seasonal/tot immigrants (%) 2008 4,778 28,429 16.81 16.84 89.07 2009 6,119 27,191 22.50 23.32 92.04 2010 5,500 29,856 18.42 18.28 89.73 2011 5,777 28,293 20.42 20.54 91.34 2012 6,152 29,106 21.14 21.01 89.91 2013 7,213 27,370 26.35 26.66 90.97 2014 6,983 23,700 29.46 29.79 89.85 2015 4,684 20,823 22.49 22.06 87.66 ∆%15/08 -1.97 -26.75 33.84 31.00 -1.59 year awu immigrant workforce (n) employees (n) immigrant/ employees (%) immigrant seasonal/total seasonal (%) immigrant seasonal/tot immigrants (%) 2008 1,391 9,610 14.47 15.30 73.14 2009 1,570 8,473 18.53 21.01 75.73 2010 1,540 8,426 18.28 19.57 74.20 2011 1,648 8,402 19.61 21.50 77.70 2012 1,821 8,936 20.38 21.27 74.63 2013 2,199 8,861 24.81 27.07 78.11 2014 2,133 8,015 26.61 28.80 77.10 2015 1,453 6,748 21.53 21.42 70.86 ∆%15/09 4.46 -29.78 48.76 40.03 -3.12 table 3. nationality of major groups of immigrants per year and average variation (2008/2015). year africa albania asia poland czech republic romania slovakia total 2008 715 1,217 273 544 89 1,230 164 4,232 2009 649 1,438 433 812 312 1,541 300 5,485 2010 885 1,224 693 453 127 1,425 108 4,915 2011 690 1,151 707 600 53 1,643 325 5,169 2012 940 747 1148 447 60 1,779 318 5,439 2013 1152 864 1459 385 112 2,231 441 6,644 2014 1149 473 1423 353 89 2,292 677 6,456 2015 764 234 488 232 175 1,527 711 4,131 % 2015 18.49 5.66 11.81 5.62 4.24 36.96 17.21 100.00 average growth (%) 4.30 -16.84 20.28 -6.35 44.14 5.16 44.99 1.85 267immigrant workforce and labour productivity in italian agriculture: a farm-level analysis nificant differences can however be hidden by aggregate data. table 5 shows the shares of immigrants by type of contract and region. when disentangling data at sub-national level, the question of reliability of the information provided becomes much more relevant. two issues regarding data must be underlined here: first, the problem of fadn data representativeness becomes more important when disaggregating data; secondly, it is likely that data in some regions are influenced by the higher presence of not regularly employed workforce (ievoli and macrì, 2008, coderoni et al., 2015), thus their presence can be underestimated in regions where these workers are not declared (neither for statistical purposes), especially in seasonal activities (mac, 2013). even with the issue of data reliability, disentangling figures by regions still gives some interesting information on the distribution of immigrant workforce in the italian agriculture. figure 1 maps the average share of immigrants by region as obtained with the fadn dataset over the period 2008-2015. regions differ quite remarkably in terms of their concentration of immigrant workforce on regularly employed workers. against a national average value of 22%, trentino alto adige, liguria, campania and valle d’aosta show percentages higher than 50% (70% for valle d’aosta) of immigrant workforce on total salaried workforce, while apulia, emilia romagna and calabria have a value equal or less than 5%. about the type of contract, in some regions (piemonte, sicilia, valle d’aosta, liguria, abruzzo, puglia, calabria, lazio and lombardia) mostly of the south more than 90% of these workers are seasonal. the importance of seasonal immigrant workers in the southern regions reflects the specific agricultural specializations of these regions. in fact, table 4. shares of immigrants by farm specialization and economic size. italian fadn sample 20082015. type of farming numbers awu immigrant/ employees immigrant seasonal/tot. seas. immigrant seasonal/tot. imm. immigrant/ employees immigrant seasonal/tot. seas. immigrant seasonal/tot. imm. dairy 41.20 42.22 82.39 36.38 40.93 61.57 cereals 9.83 9.37 76.01 8.03 9.48 51.34 grazing livestock 29.77 28.47 67.21 27.16 26.63 49.17 fruits 26.70 27.16 97.14 21.43 22.95 89.64 granivores 10.77 7.73 49.15 11.75 7.37 28.48 mixed crops and livestock 14.10 13.66 86.24 14.46 14.63 66.23 olive 4.10 3.69 87.97 3.91 2.83 65.97 horticulture 29.74 30.34 92.42 27.05 29.22 86.12 arable crops 25.15 25.84 95.79 22.38 25.65 88.64 wine 10.32 10.35 89.29 10.23 11.35 70.70 economic size large 23.18 23.68 89.12 20.42 22.29 74.37 medium 21.51 21.06 93.33 21.99 22.27 80.62 small 8.27 7.82 91.43 10.12 10.06 77.02 italy 21.98 22.09 90.19 20.39 21.92 75.46 268 edoardo baldoni, silvia coderoni, roberto esposti with few exceptions, the share of immigrants in the total seasonal workforce is larger in the south. looking at awu, the picture is quite similar even if shares are lower. 5. immigrant workforce and farm level labour productivity 5.1 correlation coefficients at farm level given that immigrant workers are a relevant part of italian professional agriculture’s workforce, and that they represent the bulk of the workers in some regions and farm types that are the more productive ones (like bigger farms), it is essential to understand whether their contribution is associated or not with higher levels of productivity. to analyse the possible relation between the incidence of immigrant work and farm’s productivity, we figure 1. share of immigrants by region. italian fadn sample 2008-2015. 269immigrant workforce and labour productivity in italian agriculture: a farm-level analysis have used a measure of partial productivity, i.e. labour productivity (lp), defined as lpit = nvait/awuit where, for any i-th generic farm and year t, nva is the net value added and awu are the annual working units at the farm level. this indicator has been calculated for the entire sample. statistical relationships between farms’ productivity and the contribution of immigrant workers, in terms of the share of immigrant awu over the total awu, have been inspected by means of a correlation analysis. table 6 shows correlation coefficients for the total observations in the sample between the share of immigrant awu and lp for regions, farm typologies and economic size. data reveal a generalized positive relation between the share of immigrant awu and labour productivity at both farm typology and regional level. regions with higher correlation coefficients are trentino and campania and, consistently, farm typologies are fruits production and horticulture, which are very relevant in these regions. indeed, it could be argued that the “real relationship” between economic performance and the share of immigrants is better captured at the level of types of farming rather than at geographical level. the magnitude of correlation coefficients for the different classes of economic size are lower and a slightly negative coefficient is found for table 5. shares of immigrants by region and type of contract. italian fadn sample 2008-2015. region number awu immigrant/ employees immigrant seasonal/tot. seas. immigrant seasonal/tot. imm. immigrant/ employees immigrant seasonal/tot. seas. immigrant seasonal/tot. imm. abruzzi 27.07 25.81 81.23 35.30 39.05 70.60 apulia 5.16 5.04 97.09 7.63 7.13 88.25 basilicata 12.92 12.59 95.76 15.33 14.76 91.51 bolzano 57.27 58.70 99.85 47.56 52.67 99.45 calabria 0.57 0.57 100.00 0.48 0.49 100.00 campania 52.38 53.88 99.19 33.86 37.37 95.99 e.romagna 1.63 1.61 92.65 1.80 1.85 82.74 f.v.giulia 26.07 27.38 89.81 22.20 29.27 72.30 lazio 42.44 46.39 77.46 38.17 46.44 68.72 liguria 51.49 50.43 91.77 58.39 58.52 90.98 lombardy 44.70 50.48 61.91 37.01 64.38 43.36 marche 8.26 7.73 76.80 7.87 9.30 62.79 molise 8.28 7.52 86.20 10.25 7.80 60.62 piedmont 44.89 48.29 81.86 37.28 49.77 55.68 sardinia 6.74 6.02 69.39 7.83 5.61 30.30 sicily 8.52 8.80 97.43 13.29 14.64 96.39 toscany 19.00 21.82 77.57 16.05 21.88 56.27 trento 73.43 73.98 99.27 70.21 75.61 94.69 umbria 12.68 12.77 75.87 12.97 15.38 59.57 v.d’aosta 69.55 70.55 68.08 72.60 81.50 58.94 veneto 15.61 17.07 83.19 14.19 19.03 66.86 270 edoardo baldoni, silvia coderoni, roberto esposti larger farms. this could hint at a possible spurious correlation due to size effects between the share of immigrants and other farms characteristics. this generalised positive relationship between lp and the share of immigrant awu is an aspect that requires deeper investigation. though small, this link exists and signals that more productive italian farms are associated with higher presence of immigrant work. correlation, of course, does not imply causality and the direction of the possible link between the two indicators needs to be tested. 5.2 the empirical model to more properly assess the possible nexus between lp and immigrant workforce at farm level, a panel data analysis has been performed. as already clarified, data used for this part of the analysis refer to the constant fadn italian sample of n=2,233 farms observed over the years from 2008 to 2015. the use of the balanced sample can decrease the representativeness of results, however, for the purposes of our analysis this can be a minor problem, as less represented farms in the constant sample (i.e. small farms) have, table 6. correlation coefficients between the share of immigrants awu and lp at farm level. italian fadn sample 2008-2015. region correlation coefficient t-value types of farming correlation coefficient t-value trentino 0.332 16.505 fruits 0.130 14.126 campania 0.216 14.640 horticulture 0.125 12.823 liguria 0.185 11.706 dairy 0.073 6.736 lombardy 0.141 10.006 mixed 0.071 6.176 tuscany 0.131 9.459 grazing livestock 0.066 7.019 sardinia 0.135 8.595 olives 0.055 3.33 alto adige 0.172 8.423 granivores 0.049 2.863 basilicata 0.130 7.471 cereals 0.038 3.913 emilia romagna 0.078 6.585 arable_crops 0.035 3.594 umbria 0.103 6.270 wine 0.012 1.189 friuli venezia giulia 0.097 6.249       abruzzi 0.090 5.623       valle d’aosta 0.110 4.711       veneto 0.060 4.500       marche 0.045 2.774       molise 0.054 2.761       sicily 0.038 2.620 economic size corr. coefficient t-value lazio 0.034 2.171 small 0.061 8.650 apulia 0.020 1.345 medium 0.020 3.890 piedmont 0.010 0.896 large -0.014 -2.462 calabria 0.004 0.258 italy 0.049 14.516 271immigrant workforce and labour productivity in italian agriculture: a farm-level analysis on average, a lower concentration of immigrant workers. a major advantage, however, is that using a balanced sample minimizes the possible attrition due to farms entering and exiting the agricultural sector (and the fadn sample). in particular, working with a constant sample of farms eliminates the risk that the actual farm-level productivity performance, its evolution and its relationship with immigrant workforce is confused with change in the composition of the sample and, thus, by the different productivity and presence of immigrants of the entering farms with respect to the existing ones. to model the relationship, we assume that the farm’s share of awu immigrant workers (on the total workforce) influences farm’s lp. the argument underlying this link is that immigrants have been found to be a highly relevant workforce for some regions, farm typologies and sizes and their contribution is correlated with farm lp. this relationship is specified with the following dynamic polynomial form: lpit = α + η1lpit-1 + η2lpit-2 + βiit + ∑aρaiit,a + γaltit +μageit + ∑fωftit,f + ∑mδmsit,m + ∑kφkdt,k + ∑rτrrit,r + εit (1) where i indicates the generic i-th farm ("iîn) and t the year ("tî2008-2015); lp is the labour productivity; alt is the log of the average elevation of the farm; age is the age of the farm holder; i the share of immigrant awu on total farm awu while ia is the share of immigrant awu per type of farm activity (i.e., a= livestock, cultivation, generic/not specified); sm the economic size expressed by 3 dummies (namely, m=small, medium and large); tf is the farm specialization typology (i.e., f=arable crops, cereals, dairy, etc.); dt are time dummies; rr regional dummies (where r indicates the region); ε is the usual spherical disturbance; α,η1,η2,β,ρa,μ,γ,ωf,δm,φk,τr is the set of unknown parameters to be estimated. equation (1) is estimated following a sequence of steps in order to elicit the role of exclusion/inclusion of variables in determining the observed linkage between labour productivity and the presence of immigrants of interest here (i.e., parameters β or ρa). a static model is first estimated (η1,η2 =0) also disregarding the different activities where immigrant workers are employed within the farm (β ≠ 0 and ρa = 0, "a) and the heterogeneity among farms both in terms of specialization (ωf = 0, "f) and size (δm = 0, "m) (model 1). then, farm heterogeneity is admitted (ωf ≠ 0, "f; δm ≠ 0, "m) (model 2), and the activities where immigrants are employed are detailed (β = 0 and ρa ≠ 0, "a) (model 3).9 finally, a dynamic specification is adopted (η1,η2 ≠ 0) in order to take the typical time dependence (i.e., serial correlation) of agricultural labour productivity into account (esposti, 2012 and 2014). the dynamic model is estimated through the same sequence of specifications of the static case (models 4 to 6).10 therefore, all the estimated models assume that farm lp is a function of the share of immigrants’ awu, the altitude of the farm location, the age of the farm’s owner and regional and time dummies controlling for spatial and time dependence of the farm-level lp. as panel models 1 to 3 are static, they can be treated as conventional pooled models 9 in this latter case, farm typology dummies are not included (ωf = 0, "f) as they overlap with information about immigrants’ activity. 10 two lags of the dependent variable lp are included in this dynamic model as this ar(2) specification turns out to be the best fitting lag order. 272 edoardo baldoni, silvia coderoni, roberto esposti and estimated via ordinary least squares (ols).11 on the contrary, dynamic panel models 4 to 6 are estimated via generalized method-of-moments (gmm).12   5.3 results and discussion results of the static models are reported in table 7. besides other control variables13, in all these models the existence of a positive contribution of immigrant workforce to farms’ lp, seems to be confirmed by the high value of the statistically significant parameters associated with i. in model (1), this coefficient is at first very high. however, when introducing farms’ heterogeneity this effect seems to weaken considerably; besides, when controlling by immigrants’ activities, this positive effect appears to be linked only to croprelated activities. indeed, fruits and horticulture farm types have shown the highest correlation coefficients between the immigrants’ work and lp at the farm level. however, when assuming persistence of lp, results change substantially (table 8). in the simplified model (4), migrant workforce still plays an important role in explaining the lp performance, as it may capture all the other characteristics of the farms that are not accounted for (e.g. as immigrants are mostly occupied in biggest and more productive farms, without controlling for farm size, can make emerge a spurious relationship). in fact, when introducing farms’ heterogeneity, the link between i and lp disappears, both the in the aggregate and disaggregate specifications. these results would suggest that in the case of italian agriculture the relationship between productivity and immigrant workforce essentially is a composition effect, that is, it depends on the fact that these workers concentrate in more productive farms in terms of size and specialization. the main consequence of this, is that many empirical studies assessing this relationship in agriculture may suffer from a severe misspecification problem whenever the farm heterogeneity in this respect is not properly taken into account. in such cases, the positive contribution of immigrant workforce to the farms’ labour productivity would just be the result of an improper specification of the relationship. introducing a more complex specification this relationship eventually disappears. 6. concluding remarks and policy implications this research analyses the presence of immigrant workforce in italian agriculture by exploiting farm-level data and proposes an evaluation of their role in explaining farm level labour productivity using panel data econometrics. immigrants emerge as a relevant component of italian agriculture, representing 22.5% of the employed workforce in 2015. there are wide disparities in the share of immigrants between regions, sectors and classes of economic size, that underline quite well known territorial patterns of seasonal and permanent migration. more seasonal or labour-intensive farm typologies (namely cultivations 11 the pooled model is here preferred to a fixed-effect specification as the dummies included among regressors in (1) already take most of farm heterogeneity into account. 12 the estimator chosen is system gmm for its asymptotic properties (arellano, 2003). 13 all the models confirm the importance of the altitude of the farm, the age of the farm owner, the year and the region in which the farm operates. 273immigrant workforce and labour productivity in italian agriculture: a farm-level analysis table 7. ols estimation of models 1 to 3 (standard error in parenthesis). coefficient model 1 model 2 model 3 α 50.598* 31.480* 33.981* (1.083) (1.193) (1.038) β 24.447* 6.696* (1.661) (1.556) γ -1.715* -0.914* -1.156* (0.182) (0.179) (0.168) μ -0.234* -0.097* -0.104* (0.015) (0.014) (0.014) ρ_livestock 2.276 (1.950) ρ_cultivation 13.043* (2.714) ρ_generic -3.099 (0.695) ω_arable 3.178* (0.802) ω_cereals 8.724* (0.761) ω_dairy 1.212 (0.818) ω_fruits 0.826 (0.745) ω_granivores 0.179 (1.342) ω_grazing -0.328 (0.806) ω_horticulture -1.080 (0.878) ω_olives 10.261* (1.328) ω_wine -5.050* (0.766) δ_large 19.989* 19.432* (0.416) (0.409) δ_small -10.917* -9.305* (0.495) (0.493) regiona yes yes yes yeara yes yes yes aestimates of the regional and time dummy coefficients are available upon request. *statistically significant at 5% level. 274 edoardo baldoni, silvia coderoni, roberto esposti table 8. gmm estimation of models 4 to 6 (standard error in parenthesis). coefficient model 4 model 5 model 6 η1 0.387* 0.410* 0.398* (0.028) (0.024) (0.025) η2 0.097* 0.120* 0.116* (0.025) (0.022) (0.022) β 8.844* -0.199 (2.086) (1.784) γ -0.667* -0.530* -0.604* (0.299) (0.253) (0.259) μ 0.024 -0.085* -0.003 (0.057) (0.036) (0.050) ρ_livestock -5.031 (7.144) ρ_cultivation 1.904 (2.498) ρ_generic -2.384 (2.618) ω_arable 1.049 (0.780) ω_cereals 5.034* (0.901) ω_dairy 0.192 (0.889) ω_fruits 0.468 (0.739) ω_granivores -1.918 (2.151) ω_grazing -0.953 (0.770) ω_horticulture 0.142 (1.050) ω_olives 2.310 (3.554) ω_wine -2.472* (0.766) δ_large 9.678* 9.060* (0.939) (0.970) δ_small -5.380* -5.542* (0.605) (0.579) regiona yes yes yes yeara yes yes yes aestimates of the regional and time dummy coefficients are available upon request. *statistically significant at 5% level. 275immigrant workforce and labour productivity in italian agriculture: a farm-level analysis and livestock breeding) seem to attract the bulk of the available immigrant workforce, while their concentration is less relevant in other sectors. the geographic distribution is quite uneven and is strictly linked to farm typology. the positive correlation between share of immigrants and labour productivity at the farm level, which is robust across all farm sizes and typologies, seems to indicate these two measures go by some means together. however, when analysing this relationship with more sophisticated model specifications, results do not confirm a clear link between the two measures. when a static modelling framework is adopted, results point to a positive relationship between immigrants’ work and farms’ labour productivity. when introducing the assumption of temporal persistency of lp and controlling for farms’ heterogeneity, this effect seems to disappear. indeed, it can be also argued that another source of misspecification arises from the choice of indicators. in fact, lp might not be a proper indicator of farm’s productivity because it does not account for all factors of production. thus, further extensions of the present analysis include to improve the measure of farms’ productivity with an indicator of total factor productivity that better represents the economic performance of farms. even if results do not point to a positive relationship between immigrant workforce and lp, this research confirms, using farm-level data, that immigrants are a relevant and growing component of the italian agriculture. they are concentrated in large and medium-sized farms and their presence is associated with higher level of lp. moreover, their contracts are largely (90%) seasonal ones. given this picture, it is clear why (not only) italian agriculture needs a legislative framework more adapted to facilitating the hiring these workers to complete several of the critical farm operations. indeed, the issue of agricultural seasonal immigrant workers, in italy, is not exclusively a legislative issue, as many of these workers are – in some regions more than in others – irregular ones. in these cases, legislation must be at first enforced, to improve the workers deprecable conditions, even to avoid regular (both domestic and immigrant) workers occupation to be displaced by this “social dumping”. however, for the legally and regularly occupied immigrant workforce, the legislative framework is part of the problem. the european union has a specific directive promoting the use of selection and recruitment procedures of immigrant workforce directly in the areas of origin. the current legislative framework, is represented by the eu directive 2014/36/ue “on the conditions of entry and stay of third-country nationals for employment purposes as seasonal workers”. this is, in principle, consistent with european migration policy and the needs of the agricultural production. however, for obvious reasons, excludes european workers that are, in the italian case, the bulk of immigrant workers. a more coordinated vision of the subject could help governing the phenomenon and facilitate the regular and reciprocally fruitful employment of these workers to complete several of the critical farm operations. to this respect, studies on this field can provide evidence and suggestions for policy making. acknowledgments authors are listed in alphabetic order. authorship may be attributed as follows: section 5 to edoardo baldoni; sections 3, 4 and 6 to silvia coderoni; section 1 and 2 to roberto esposti. 276 edoardo baldoni, silvia coderoni, roberto esposti references albarrán, p., carrasco, r. and ruiz-castillo, j. 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(2006). imprese e non-imprese nell’agricoltura italiana. politica agricola internazionale 1: 13-30. 278 edoardo baldoni, silvia coderoni, roberto esposti un (1990). international convention on the protection of the rights of all migrant workers and members of their families. adopted by general assembly resolution 45/158 of 18 december 1990. wells, m. (1996). strawberry fields. politics, class, and work in california agriculture. cornell university press, itacha. rural-urban migration and implications for rural production alan de brauw migrants to rural areas as a social movement: insights from italy giorgio osti immigrant workforce and labour productivity in italian agriculture: a farm-level analysis edoardo baldoni, silvia coderoni*, roberto esposti economic and social impact of grape growing in northeastern brazil linda arata1,*, sofia hauschild2, paolo sckokai1 is the question of the “active farmer” a false problem? maria rosaria pupo d’andrea*, simona romeo lironcurti issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 3(2): 93-117, 2014 doi: 10.13128/bae-12944 agricultural and oil commodities: price transmission and market integration between us and italy franco rosa1,*, michela vasciaveo1, robert d. weaver2 1 department of food science, economic unit, university of udine, italy 2 department of agricultural economics, sociology and education, penn state university, usa abstract. purpose of this article it to get some evidences of market interaction between united states and italy using the time series analysis of spot prices spanning from january 1999 to may 2012 for crude oil and three ag-commodities: wheat, corn and soybean. these crops have been selected for their relevance in ag-commodity exchanges between us and italy markets. the integration between us and italy agricultural markets is hypothesized for the consistent volume of crop traded between these two countries while the price transmission is related to the leading price signals of the cbt (chicago board of trade). the integration between oil and ag-commodity markets is suggested both by the large use of energy intensive inputs, (fertilizer, seed, machinery) in production of these ag-commodities, and their use in biofuel production. the results suggest: a) for us market the evidence of market integration between crude oil and us ag-commodities; b) for italy the integration with us ag-commodity markets and less evidence of integration with the oil market. these results are valuable information both for the agents and policy makers contributing to improve the information accuracy to predict the price movements used by marketing operators for their strategies and policy makers to set up policies to re-establish conditions of market efficiency and allocate these ag-commodities in alternative market channels. keywords. agricultural commodity prices, time series analysis, cointegration, price transmission, market integration. jel codes. c22, q11, q13 1. introduction since 2006, the biofuel market in the united states has established a link between the prices of crude oil and grains such as corn characterized by the co-evolution of ag-commodity prices (abbott et al., 2008). the massive production of energy, mainly liquid fuels, from agricultural commodities has continued to strengthen these links between agricultural and energy markets and defined a dominant feature of current conditions in the agricultural sector. a resulting trend has been noted as a stronger dependency between crude oil, gasoline, and ag-commodity prices (tyner and taheripour, 2008). brazil pro* corresponding author: franco.rosa@uniud.it. assess overall price transmission full research article 94 f. rosa, m. vasciaveo, r.d. weaver vides a useful example of a well-integrated, long-term agro-energy market with the oil and cane-bioenergy (ethanol and electricity) market integration to define an energy market in which oil and sugar cane prices exhibit strong comovement. there are other countries where these price links have become increasingly strong: the prices of wood pellets and, to a lesser extent, the wood chips in austria have been following with a growing degree of correlation the prices for heating fuel in 2006 and 2007 (schmidhuber, 2007). prices of oil and agricultural commodities sharply rose in 2007, peaking in the second half of this year for some products and in the first half of 2008 for others. the causes of price spikes during 2007-2008 include some which were exogenous to the agriculture (macroeconomic), growth of food demand by the bric countries, and speculation on the oil prices and other factors (piot-lepetit and m’barek, 2011; oecd, 2008). others causes are due to physiological changes in market conditions from period to period, natural shocks such as weather, pests or regulatory restrictions in domestic markets (fao et al., 2011). these events raise new questions concerning ag-commodity price movement. first, have new trends been established for ag-commodity prices? second, to what extent have recent price shock been temporary or permanent? third, how have these shocks and possibly persistent trends affected comovement of commodity prices? fourth, how has the nature of unanticipated shocks changed? price transmission depends on the market efficiency and it may have limited by a number of causes, at least in the short-run. for example, these conditions may have included supply availability to the final consumer, demand circumscribed by bottlenecks in the distribution, logistic problems in transportation, blending systems (e10), spatial arbitrage, and political constraints like the border measures and subsidies affecting the exchanges. thus, the purpose of this paper is to contrinbute to the understanding of the transmission of oil prices to agricultural markets. we expect that our results will contribute to better understand the nexus of agricultural and energy markets and its consequences for trading relations between us and italy. we choose to study italy and us as settngs that provide the basis for a test of the hypothesis of market integration and price transmission (yang and leatham, 1999). during the 20092010 commercial campaign, italy imported 60% of soft wheat, 87% of soybeans and 20% of maize from usa (associazione nazionale cerealisti, 2011). the paper is organized as follows: section 2 reviews relevant literature; section 3 presents the time series methodology used; section 4 describes the price series used and provides a preliminary descriptive analysis of price correlations; section 5 presents results; and final section 6 provides conclusions and suggestions for future research. 2. literature review market efficiency exists if price pass through between markets is complete such that they differ only by the transaction costs (ardeni, 1989). rational expectations and competitive storage theory supports the hypothesis that commodity stocks, expected prices and hauling costs are keydrives of commodity prices in equilibrium. importantly, shortages can induce substantial price shocks, helmberger and weaver (1977) and helmberger et al.(1982). it is widely acknowledged that the increased use of central commodity exchanges affects the extent and speed of transmission to market levels in response to leading price signals (rapsomanikis et al., 2006). deaton and laroque (1991) have used 95agricultural and oil commodities: price transmission and market integration the storage model to show that prices are not normally distributed, because the stockholding behaviour by risk-averse agents generates an autoregressive pattern which is much stronger than what can be explained by the storage activity of risk-neutral agents. this market behaviour induces shocks in supply and demand that are correlated over time. on the supply side, this correlation is also induced by correlated shocks while on the demand side persistence in demand for working stocks induces intertemporal correlations. however, prices jumps may be induced by speculative demand by producers in response to anticipated stock-outs, see helmberger and weaver (1982). the decline of the dollar value (trostle, 2008) and the speculation stemming from increased futures market volume are further factors contributing to recent agricultural commodity price movement (robles et al., 2009). further, supply side factors include relatively lower growth in agricultural production and yield, increases in energy prices that have induced increased farm production costs (tyner and taheripour, 2008; sumner, 2009; von braun et al., 2008), and climatic events (trostle, 2008). the indirect price transmission through energy feed stock substitutes (e.g. sugar) led to increased demand for land and other limited resources diverting them from other agricultural crops, reducing their supply and driving up their prices (schmidhuber, 2007). the growth of the biofuels production is an important driver of recent corn and oilseed demand growth (gilbert, 2010; zhang et al., 2010; ciaian and d’artis, 2011a,b). biofuel policies, encouraging farmers to produce feed-stocks for biofuel, have increased the dependency between agricultural and energy prices (yu et al. 2006; campiche et al., 2007; zhang and reed 2008; gilbert, 2010; gohin and chantret, 2010; nazlioglu, 2011). policy has also conditioned price transmission as trade restriction, import tariffs, export subsidies or taxes, and macroeconomic exchange rate policies have impacted the efficiency of arbitrage by insulating the domestic markets and hindering the transmission of price signals (sarris, 2013). esposti and listorti (2013) investigate the role of the trade policy by analyzing the agricultural price transmission in presence of bubbles, using italian and international weekly spot (cash) price over the years 2006–2010. they observe that the bubble has had only a slight impact on the price spreads and that the temporary trade policy measures, when effective, have limited this impact. these interventions are responsible for excess demand or supply schedules of domestic commodity markets possibly generating asymmetric price responses with nonlinear price adjustment (quiroz and soto, 1996; sharma, 2002, rapsomanikis et al., 2006, 2011; harri et al., 2009; gutierrez et al., 2013). the market integration between oil and ag-commodities has been explored using econometrically estimated demand and supply models based on partial or computable general equilibrium models (lapan and moschini, 2012; de gorter and just, 2010; hertel et al., 2010). these models incorporate calibrated price elasticities and long-run assuptions to simulate dependence of agricultural commodity prices to oil shock prices. an alternative approach used to explore market efficiency is time series analysis to test market integration, price transmission, cointegration, asymmetric response, and causal nexus among markets (2011; ciaian, 2011b; gilbert, 2010; gohin et al., 2010; goodwin, 1992; granger and lee, 1989; harri et al., 2009; minot, 2011; rapsomanikis et al., 2006; sarris, 2013; zhang et al., 2008). here, we use time series analysis to test the hypothesis of market integration between us and italy and in a broader sense to verify the efficiency of agricultural markets for some ag-commodities selected for their importance in trade 96 f. rosa, m. vasciaveo, r.d. weaver (tomek and myers, 1993; rosa, 1999; nazlioglu, 2011; rosa and vasciaveo, 2012; esposti and listorti, 2013; gutierrez et al., 2013). whether agricultural and food commodity prices are unjustifiably volatile and unrelated to the market fundamentals has been extensively considered by balcombe (2009, 2013) and gilbert (2010). persistence of these effects on prices has been considered by serra and gil (2012), algieri (2012), chatellier (2011), balcombe (2009), listorti and esposti (2012), rosa and vasciaveo (2012). in presence of excess volatility beyond that which can be accounted for by changes in market fundamentals, the prices may be driven by fad or speculative bubbles, and commodity prices may become inefficient signals for resource allocation (gilbert and morgan, 2010; balcombe and fraser, 2013). timevarying volatility of commodity price series leads to autocorrelation patterns in the conditional variance of price innovations where the variance is conditional on an information set available at the time forecasts are being formed. engle (1982) has termed this conditional heteroscedasticity and developed the autoregressive conditional changes in economic fundamentals. time-varying volatility in commodity prices has the same general effect on statistical inference as any other form of heteroscedasticity causing a loss of efficiency and estimated standard errors may be biased (engle, 1982). excess kurtosis causes also problems whenever inference requires a particular distributional assumption on the disturbance terms. although the normal distribution is typically chosen, the actual distribution of commodity prices appears to have fatter tails than the normal. this can be a particular problem in maximum likelihood estimation of commodity market models. (myers, 1992). 3. methodology our analysis uses cointegration and vector error correction models to explore spatial market relationships and price transmission (rapsomanikis et al., 2006). the analysis is performed in the following three steps: 1) we determine whether univariate price series are nonstationary or i(1) (if both price series are not i(1), they cannot be cointegrated); 2) if they are both stationary or i(0), we examine their dynamic interrelationship (leucci et al., 2013) with the vector autoregressive (var) model; 3) if the series are both i(1), the null hypothesis that they are not cointegrated is tested with the johansen procedure; 4) and if the results suggest evidence of long-run relationship between variables, we estimate vector error correction models (vecm). the scheme of this approach is reported in figure 1. 3.1. unit root test the first step of the analysis is to test for stationarity and whether each series is integrated with the same order. we employ several unit root tests to consider robustness of our inference including: augmented dickey-fuller (1979) [adf], phillips-perron [pp] (1988) and kwaitkowski-phillips-schmidt-shin [kpss] (1992). these tests (except for kpss) examine a null hypothesis of a unit root against the alternative of i(0) stationarity. stationarity is the null hypothesis for kpss. if the adf statistic has a negative sign, as the absolute value increases the level of confidence for rejection of the hypothesis of unit root 97agricultural and oil commodities: price transmission and market integration is increased. the usefulness of adf is limited in the presence of an explosive root (balcombe and fraser 2013). the adf test follows from µ β α ε= + + + +∑− − = y t y c yt t i t i t i k 1 1 (1) where μ is the constant, β is the coefficient on the time trend, k is the lag order of the autoregressive process, dyt-i is the lagged difference of y whose magnitude is measured by c and ε is the error. the unit root test is carried out under the null hypothesis α = 0 against the alternative hypothesis of α < 0; nonstationarity is rejected when α is significantly different from 1. a common problem with conventional unit root tests is that they do not allow for any break in the data generation process. if a structural break is hypothesized, the conventional adf test is biased toward the acceptance of the null resulting in a dramatic loss of power. further, the adf allows for higher-order autoregressive processes including lags of the order k that have to be pre-determined. assuming the break time to be exogenous, perron (1989) suggests that the power to reject a unit root decreases when the stationary alternative is true and the structural break is ignored. following perron’s characterization of the form of structural break, zivot and andrews (za, 1992) formulate three different characterizations of the trend break: i) model a, “the crash model”, allows the break in the intercept; ii) model b, “the changing growth model”, allows for a one-time change in the slope of the trend function with the two segments joined at the break point; and iii) model c, “the mixed model”, combines simultaneously the one-time changes in the level figure 1. scheme of the price analysis to test the market integration and price transmission conditions.                   assess overall price transmission reject accept if not the same johansen test h0: no cointegration specify and estimate vecm; assess dynamic, speed of adjustment if both i(0) if both i(1) test for the order of integration (adf, pp, kpss, za) no cointegration estimate var on levels estimate var on differences source: own elaboration of the scheme proposed by rapsomanikis et al. (2006). 98 f. rosa, m. vasciaveo, r.d. weaver with the slope of the trend function of the series2. the aim of this procedure is to sequentially examine evidence of breakpoint candidates and select the one that gives most weight to the trend stationary alternative. hence, to test for a unit root against the alternative of a one-time structural break, zivot and andrews propose the following regression equations (derived from equation 1) corresponding to the three cases noted above: µ β α γ ε= + + + + +∑− − = y t y du c yt t t i t i t i k 1 1 (model a) µ β α θ ε= + + + + +∑− − = y t y dt c yt t t i t i t i k 1 1 (model b) µ β α θ γ ε= + + + + + +∑− − = y t y dt du c yt t t t i t i t i k 1 1 (model c) where dut is an indicator dummy variable for a mean shift occurring at each possible break-date while dtt is the corresponding trend shift variable. the za unit root test is an endogenous structural break test with unknown timing in the individual series that uses the full sample and different dummy variables for each plausible break date. the break time is selected where the t-statistic from the adf test of unit root is at a minimum (most negative), then a break date is chosen where the evidence is least favourable for the unit root null. the null hypothesis is that the series is integrated without an exogenous structural break against the alternative that the series can be represented by a trend-stationary process with only one break point occurring at some unknown time. the za test is a variation of pp’s original test with the endogenous implementation of structural breaks in the analysis: the date of the break is determined with the t-statistics test of the unit root, with respect to the criteria of minimum values. the za test regards every point as a potential break-date and runs a regression for every possible break-date sequentially. 3.2. cointegration analysis: the johansen test cointegration analysis examines whether two series are linked to form an equilibrium relationship. the intuition of cointegration is that two price series cannot evolve in opposite directions for very long time if they are cointegrated. this condition is examined by estimation of the static regression between i(1) variables: µ α ε= + +y xt t t (2) 2 for the three models, zivot and andrews estimate the testing equation by allowing the break to take place beginning successively in the second, third, fourth, and so on, observation, up to observation t l, where t stands for the total sample size used in the estimation and l are the lags. the alternative specifications are estimated by ols, and the length of the lag (k) for the difference terms is determined by starting at k = 8, and working backwards until significant values are identified. the estimate of the breakpoint is that particular observation corresponding to the minimum t-value for the one period lagged term, for each model a, b, and c. in order to test the unit root hypothesis, this minimum t-value is compared with a set of asymptotic critical values from the work of zivot and andrews (1992). 99agricultural and oil commodities: price transmission and market integration where xt is a vector of independent variables. the system is cointegrated if the errors εt are i(0). in this case, equation (2) may be interpreted as a long-run equilibrium condition of the process y(t). the johansen cointegration test uses the vector autoregressive (var) model with k lags assuming the variables are i (1) written in error-correction form (johansen, 1995). to determine the presence of cointegration between variables, the lag length (k) is determined with the schwarz bayesian criterion (sbc or bic test, schwarz, 1978) and then the cointegration rank (r) is estimated. 3.3. gregory hansen test gregory and hansen (1996) propose cointegration tests which are an extension of the zivot and andrew (1992) unit root tests to incorporate a single structural break in the underlying cointegrating relationship. the gh test extends the adf*, zt * and zα * type tests designed to test the null of no cointegration against the alternative of cointegration in presence of a single structural break. these authors consider three variations of equation (2) that includes dummies for the structural change: model c: level shift: µ θ α ε= + + +y du xt t t t (3a) model c/t: level shift with trend µ θ β α ε= + + + +y du t xt t t t (3b) model c/s: regime shift (intercept and slope coefficients change) µ θ α α ε= + + + +y du x du xt t t t t t1 2 (3c) where t is time subscript, ε is an error term and du is a dummy variable. model c entails a level shift in the equilibrium relationship, model c/t adds a trend component to the previous model whilst model c/s deals with the regime shift by adding a change in the slope coefficients. the structural change is endogenously determined by the smallest value (the largest negative value) of the cointegration test statistics across all possible break points. 4. data and descriptive analysis to perform the empirical analysis, we use weekly spot prices3 of three ag-commodities and the oil prices for the period spanning from 1999 to 2012; this frequency has been used to capture more accurately the price movements and linkages (nazlioglu, 2011). soft wheat, maize and soybeans are selected for their importance in ag-commodity trade between us and italy: wheat is highly energy intensive and is a key product for human nutrition while corn and soybean are the most important ag-commodities for animal feeding and biofuel. table 1 reports the list of variables used in the analysis. 3 spot prices are used because most of the transactions in italy are made in these markets. for more details see rosa and vasciaveo (2012) 100 f. rosa, m. vasciaveo, r.d. weaver table 1. description of ag-commodity price series. variable description source italian corn price (cit) weekly average of spot prices in €/ton of national hybrid corn-market at the origin (cit) datima provided by ismea1 soybean price (sit) weekly average of spot prices in €/ton of soybeans with 14% of moisture--market at the origin (sit) datima provided by ismea wheat price (wit) weekly average of spot prices in €/ton of good mercantile wheat--market at the origin (wit) datima provided by ismea us corn price (cus) weekly average of spot prices converted in €/ton of us yellow no. 2 corn at the gulf of mexico (cus) fao international commodity price database soybean price (sus) weekly average of spot prices converted in €/ ton of us no. 1 yellow soybean at the gulf of mexico (sus) fao international commodity price database wheat price (wus) weekly average of spot prices converted in €/ ton of us no. 2 soft red winter wheat at the gulf of mexico (wus) fao international commodity price database oil price oil weekly spot prices of brent crude oil converted in €/barrel (oil) us energy information administration (eia, 2012) datima is a collection of statistical databases including italian agricultural market data and foreign trade; ismea is the italian agri-food market institute to be comparable, the us agricultural and oil commodities price series quoted in $ are converted into euro currency, using the official $/€ exchange rate4 and converted to natural logarithms. visual inspection of the price series reported in figure 2 suggests a nonlinear trend component exists for each of the series. figure 2 also suggests a relatively steady price period existed during 2005, followed by wider fluctuations to the end of 2008, and wider fluctuations in the final stage for all commodities prices. the wider oil price variability does not seem to affect the fluctuation of the ag-commodity prices. these observations motivate the need to examine the existence of structured breaks that define sub-samples to examine better the effect of volatility. 4.1. testing for presence of bubbles figure 2 also suggests the possible presence of bubbles. during the period 2006-10, sub-periods of explosive price are apparent as also noted by huchet-bourdon (2011). bubbles have been noted as occurring in 2006 when levels of agricultural and food prices increased sharply followed by a collapse, as well as between 2006 and 2008, 2008-2010 and more recently in autumn 2012. (phillips, p.c.b., shi s., yu j., 2012). a number of 4 available at http://www.statistics.dnb.nl/index.cgi?lang=uk&todo=koersen 101agricultural and oil commodities: price transmission and market integration tests have been used to identify the sharp increases and declines in prices also known as explosive bubbles. we followed phillips, shi and yu (2012), psy hereafter, who developed a method to test for explosive behavior and date the origin and collapse of bubbles. this method is used to check for presence of multiple bubbles of the pcb5 type in a sample data. (phillips, shi and yu, 2012; gilbert and morgan, 2010). we apply the more recent generalized sup-augmented dickey-fuller (gsadf) test proposed by psy, for explosive bubbles with variable windows widths in the recursive regression: α β φ ε= + + ∑ +− = −y r r r r y r r y, ,t t i t t1 2 1 2 1 1 1 2 1 eτ ~ n (0,s2) (4) here the null hypothesis of nonstationarity (h0: br1,r2 = 0 ) is tested against the alternative hypothesis h1: br1,r2 > 0 which implies explosive behaviour. our results reveal evidence of bubble behaviour for wheat, rice soybean oil and rapeseed oil price series during the first month of 2008. beyond fundamentals, the gsdaf test does not provide sufficient evidence to infer whether these bubbles are the result of a trend and may persist in the agcommodity market. we have tested with the psy test the series used for this analysis and results are reported in table 2 for different length of time series and window widths. the analysis provides insights to price behaviour during the examined periods and their consequences for the analysis. the results of table 2 do not support the hypothesis that bubbles occurred during the sample period with an exception for sit with window width 0.1, however for window width of 0.4 the values are substanitially below the critical threshold at 99 and 95% critical values. the period 2006:1-2008:52 is also examined for bubbles as past literature reports more evidence of price volatility during this period (gil5 pcb is the acronym for price collapsing speculative bubbles that are nonlinear processes (evans,1991 explosive during the phase of bubble eruption, but they may be stationary over the whole sample period. figure 2. index of current prices of some agri commodities and oil prices  source: own elaborations. cit, wit, sit, cus, wus, sus: €/ton; for oil: €/barrel; jan 04, 2002= 100. 102 f. rosa, m. vasciaveo, r.d. weaver table 2. results of the gsadf recursive test with one lag. series lag window nr of observations test statistics gsadf critical values 99 95 90 cus 1 0.1 168 1.1787 2.9822 2.2381 2.0277 cus 1 0.4 168 1.1788 1.9957 1.4001 1.1322 cit 1 0.1 168 2.0310 2.9822 2.2381 2.0277 cit 1 0.4 168 -0.0909 1.9957 1.4001 1.1322 sit 1 0.1 168 3.5970 2.9822 2.2328 2.0278 sit 1 0.4 168 0.2132 1.9975 1.4001 1.1322 sus 1 0.1 168 1.8955 2.9822 2.2328 2.0278 sus 1 0.4 168 -0.1245 1.9957 1.4002 1.1392 wit 1 0.1 168 2.1124 2.9822 2.2328 2.0278 wit 1 0.4 168 -0.5607 1.9957 1.4001 1.1392 wus 1 0.1 168 0.9183 2.9822 2.2328 2.0277 wus 1 0.4 168 0.0573 1.9957 1.4002 1.1392 oil 1 0.1 168 1.2566 2.9822 2.2328 2.0278 oil 1 0.4 168 0.1011 1.9976 1.4002 1.1392 time series 1999:1-2012:52. shaded values are above the critical values. table 3. results of gsadf recursive test with one lag. series lag window nr of observations test statistics gsadf critical values 99 95 90 cus 1 0.1 156 2.5989 3.0681 2.2972 1.9947 cus 1 0.4 156 2.5989 2.0938 1.4633 1.1652 cit 1 0.1 156 2.1584 3.0681 2.2972 1.9947 cit 1 0.4 156 3.4991 2.1114 1.4575 1.1524 sit 1 0.1 156 2.3889 3.1413 2.2660 1.9882 sit 1 0.4 156 2.3889 2.0939 1.4633 1.1652 sus 1 0.1 156 1.9957 3.1413 2.2660 1.9882 sus 1 0.4 156 2.1787 3.0682 2.2972 1.9947 wit 1 0.1 156 4.7593 3.1413 2.2660 1.9882 wit 1 0.4 156 4.7593 2.0939 1.4633 1.1652 wus 1 0.1 156 3.1313 3.1413 2.2660 1.9882 wus 1 0.4 156 2.1584 2.0938 1.4633 1.1652 oil 1 0.1 156 3.0871 3.1413 2.2660 1.9882 oil 1 0.4 156 3.0871 3.0682 2.2972 1.9947 time series 2006:1-2008:52. shaded values are above the critical values. 103agricultural and oil commodities: price transmission and market integration bert, 2010, rosa and vasciaveo, 2012). results are reported in table 3. for the series cus, cit, sit the test values are above the critical values, using the windows width 0.4 but below critical values using the windows width 0.1; a possible explanation is that the smaller window width includes price values less volatile compared to the larger window. for wheat, the results are above the critical values for both window widths. these results are more difficult to explain because in contrast with wus and other ag-commodity in italy (areal et al., 2013), the 2007-2008 us wheat market experienced reduced stock levels. reduced production levels, in conjunction with very low carryover stocks, resulted in an extremely tight global market and is likely to have affected the expectations of market operators in italy. another possible explanation is the interaction between spot and future markets. given an high share of wheat open interest held by noncommercial traders in an already tight market, the demand for long-term wheat future contracts may have affected spot prices and generated bubbles due to strengthening inventory demand. the tests performed by areal et al. (2013) have revealed weak presence of multiple bubbles in the food prices finding that when present, bubbles have been quite short, continuing between two and fourth months before collapsing. 4.2. stationary and structural break tests we next consider the order of integration and testing the stationary condition6 with the unit root test for levels and first differences. a number of tests are used with results reported in table 4. we find all variables are integrated of first order i(1)7 table 5 reports the results of the zivot-andrews test with one break. minimum za statistics for the levels of the variables reject the hypothesis of structured breaks implying the evidence of the unit root tests may be accepted with the exception of oil and sus. allowing for the identified breaks and a deterministic trend for these products, the null hypothesis of unit root process is rejected. the test is performed in three versions reported in previous section 3. a structural break is found in the us soybeans series, the estimated date is july 2004 (week 29) fitted with drift (model a) and a change in the trend slope and drift (model c). the oil series is stationary with a break in october 2008 (week 40) and change in trend slope and drift. a possible explanation of the 2004 structural break is the massive growth in biofuel production in the us starting with 2004. the successive 2008 break in the oil series corresponds to the oil price peak. the other price series are found to be i(1) confirming the results of traditional unit root tests. while sus and oil price series are stationary in model c, this condition is not so evident in model a (only for oil) or in model b; for this reason it is conservatively assumed that all the variables are integrated of order one i(1). 4.3. preliminary evidence of comovement among italian and us ag-commodity prices the graphic evidence of comovement in levels for the historical price series (figure 2) suggests strong comovement with a moderate deviations from cyclical long-run move6 this condition implies that the mean, variance and autocorrelation of the series do not change over time; 7 the differences of the alternative tests used in this analysis are not contradictory about stationary condition. 104 f. rosa, m. vasciaveo, r.d. weaver ments, non-linear trend components, and wider range fluctuation in latest periods. oil price patterns could have affected the agricultural markets during the period (1999-2012) and changed the price dynamics generated by market fundamentals (headey and fan, table 4. unit root test results. levels first differences adf pp kpss adf pp kpss intercept cus -0.31 -1.28 1.93* -13.84* -33.22* 0.14 sus -1.42 -1.35 2.20* -30.21* -30.99* 0.08 wus -2.23 -2.15 1.79* -28.81* -28.75* 0.03 cit -1.77 -2.08 1.18* -16.35* -16.41* 0.05 sit -1.28 -1.02 2.22* -14.74* -21.10* 0.06 wit -1.48 -1.77 1.03* -18.51* -19.42* 0.06 oil -1.23 -1.17 2.48* -21.42* -21.42* 0.04 trend & intercept cus -1.59 -2.91 0.44* -13.88* -33.66* 0.05 sus -3.06 -3.08 0.36* -30.20* -31.02* 0.03 wus -3.36 -3.29^ 0.12^ -28.80* -28.73* 0.02 cit -2.47 -2.76 0.12^ -16.35* -16.37* 0.03 sit -2.73 -2.46 0.17° -14.73* -21.09* 0.04 wit -2.07 -2.36 0.16° -18.50* -19.41* 0.04 oil -2.78 -2.71 0.14^ -21.41* -21.41* 0.04 schwarz information criterion to determine the optimal lags for adf test; the bandwidth for pp and kpss tests is selected with newey-west using bartlett kernel (by default). */°/^ denote statistical significance at 1, 5 and 10% respectively.   table 5. zivot andrews one break test. model a change in drift model b change in trend model c change in drift and trend critical value 1% 5% 10% model a -5.34 -4.80 -4.58 model b -4.93 -4.42 -4.11 model c -5.57 -5.08 -4.82 the asymptotic critical value for zivot and andrews (1992) test at different levels of significance cus -3.63 -3.10 -3.42 sus -4.98** (2004: w29) -4.15 -5.48*** (2004: w29) wus -3.78 -3.50 -3.87 cit -3.20 -2.83 -3.82 sit -4.03 -3.02 -4.03 wit -3.18 -2.81 -3.26 oil -3.27 -3.30 -5.16** (2008: w40) ***/** denote statistical significance at 1% and 5% respectively; break date in brackets. 105agricultural and oil commodities: price transmission and market integration 2008; oecd, 2008). the price correlation between two variables, here given by the prices of ag-commodities is measured linearly with the pearson correlation coefficient (r). given nonstationarity of the underlying series of price levels, we examine correlation of stationary first differences.8 the correlation coefficients between the oil price and each ag-commodity price are computed over the whole sample and for subsamples with critical 5% values; the results are reported in table 6. for the entire period (1999-2012), we find the pearson correlation in price differences are small with values that vary in the range between 0.06 (dwitdsus) and 0.37 (dcusdwus) with two correlations below the critical 5% value: dsus-dcit and dsus-dwit. these results are consistent with the hypothesis of innovations in prices commove, though such comovement is small in magnitude. we also find that the innovations in italian ag-commodity prices are not influenced by those of the oil prices, though we find evidence of comovement in innovations in oil and us ag-commodity prices. for the subsample period 1999-2004, we find the pearson correlation values vary in the range between -0.03 (dwus and dcit) and 0.53 (dwus-dcus). however, compared to the full sample period we find more coefficients (9) are below the critical value indicating no comovement. across countries, we find evidence of comovement between dwit and dcit and the us ag commodity prices, as well as with oil prices. for italian prices, we find that only for the (dcit, dwit) pair can we reject the hypothesis of correlation. for the period 2004-2008, the estimated pearson correlation values vary in the range between -0.02 (dcus-dwit) and 0.40 (dsus-dcus). critical values indicate there are seven correlation coefficients below the critical value, in each case for pairs of italian and us prices (dcit and dwit with us ag-commodity prices). for the period 2008-2012, we find the pearson correlation values vary in the range between 0.11 (dsus and dwit) and 0.40 (dwit-dcit; dwus-dcus; doil-dsit). with respect to evidence of comovement across oil prices and commodity prices for entire period and sub-periods we find evidence of weak comovement of oil and ag-commodity prices that is weakest during the sub-period 1999-2004 and stronger in the later sub-periods; correlation is smaller with each of the italian ag commodities compared to us. the analysis suggests that for sub-periods energy and agricultural market price comovement seems to become stronger in more recent periods. the general conclusion is that the us ag commodity price innovations appear to be more correlated with those of oil prices while little evidence of such correlation is found between italian ag commodity price innovations and oil price innovations. 4.4 market integration the cointegration analysis is used to examine the comovements between oil and agricommodity prices (johansen and juselius, 1990). some of the series checked for unit roots are found to be stationary with a breaking trend, then the johansen and gregoryhansen (gh) tests are used to check for the presence of cointegration for all pairwise price series that accounts for a break in the cointegration relationship. 8 in a bivariate time series characterized by nonstationarity, correlation of in nonstationary levels is meaningless as by definition the series are not generated by population data generation processes that are invariant with respect to time. in the absence of a population counterpart, correlation would result in spurious inference (johansen, 1989). 106 f. rosa, m. vasciaveo, r.d. weaver table 6. pearson correlation coefficients for first difference series of ag-commodity prices. series 99/01/08 12/05/25; two tail critical value 5% = 0,0742*; n = 699 d_cit d_wit d_sit d_cus d_wus d_sus d_oil 1.0000 0.2597 0.2037 0.1153 0.1420 0.0706 0.1130 d_cit 1.0000 0.1544 0.0827 0.1907 0.0648 0.0751 d_wit 1.0000 0.2329 0.1603 0.2513 0.2096 d_sit 1.0000 0.3655 0.2150 0.2135 d_cus 1.0000 0.2186 0.2149 d_wus 1.0000 0.2151 d_sus 1.0000 d_oil series 99/01/08 04/07/16; two tail critical value 5% = 0,1154*; n = 289 d_cit d_wit d_sit d_cus d_wus d_sus d_oil 1.0000 -0.0451 0.2196 -0.0258 -0.0333 0.0346 -0.0831 d_cit 1.0000 0.1664 0.0463 0.0921 0.0380 0.0411 d_wit 1.0000 0.1564 0.1312 0.1860 0.0508 d_sit 1.0000 0.5333 0.3176 0.1840 d_cus 1.0000 0.2001 0.2180 d_wus 1.0000 0.1676 d_sus 1.0000 d_oil series 04/07/23 08/10/03; two tail critical value 5% = 0,1323*; n = 220. d_cit d_wit d_sit d_cus d_wus d_sus d_oil 1.0000 0.4456 0.1257 -0.0064 0.1300 0.0769 0.1330 d_cit 1.0000 0.0302 -0.0227 0.1644 0.0577 -0.0529 d_wit 1.0000 0.2964 0.1082 0.2573 0.1080 d_sit 1.0000 0.3518 0.3994 0.2014 d_cus 1.0000 0.3007 0.1356 d_wus 1.0000 0.2280 d_sus 1.0000 d_oil series 08/10/10 12/05/25; two tail critical value 5% = 0,1424*; n = 190. d_cit d_wit d_sit d_cus d_wus d_sus d_oil 1.0000 0.3850 0.2688 0.2402 0.3013 0.1207 0.2538 d_cit 1.0000 0.2399 0.1363 0.2978 0.1076 0.1949 d_wit 1.0000 0.2550 0.2356 0.3585 0.3905 d_sit 1.0000 0.3943 0.1662 0.2519 d_cus 1.0000 0.1970 0.3013 d_wus 1.0000 0.2893 d_sus 1.0000 d_oil * shaded values are below the critical values. source: author computation. 107agricultural and oil commodities: price transmission and market integration table 7. johansen trace test for cointegration in price level: 1999:w1-2012:w21. cus sus wus cit sit wit cus sus 18.87 wus 27.79** 26.44** cit 26.60** 27.15** 30.74** sit 12.74 25.91** 32.52*** 29.56** wit 23.93* 29.33** 42.17*** 31.24*** 28.69** oil 15.36 17.73 21.16 23.11 16.38 19.50 the critical values are 31.15, 25.87 and 23.34 for 1%, 5% and 10% respectively (mackinnon-haugmichelis, 1999, critical values). ***, ** and *denote statistical significance at 1%, 5% and 10% level of significance, respectively. null is no cointegration. the results of the trace test reported on table 7 indicate that all ag-commodity prices are pair wise cointegrated with the exception of sus and cus and sit and cus. these results suggest that italian and us agricultural markets are integrated while there is not statistical evidence that oil market affects the ag-commodity markets in us or in italy. an absence of cointegration is found for cus and sus. this is consistent with a dominance of ethanol demand for corn that drives a wedge between the two markets. table 8 reports the results of the cointegration test for the first sub-period and shows the null hypothesis of no cointegration is rejected for us corn and wheat with italian commodity prices. us soybeans is cointegrated with the italian ag-commodities, confirming the results obtained during the entire period of observation and is also cointegrated with the oil prices; us corn is cointegrated with us soybean and wheat. the italian ag-commodity markets appear to be cointegrated with the us soybean market, and a significant cointegration exists between it soybeans and oil prices. table 8. johansen trace test for cointegration: period 1999:w1-2004:w29. cus sus wus cit sit wit cus sus 26.10** wus 30.80** 25.95** cit 21.47 34.33*** 20.40 sit 17.32 39.44*** 15.48 18.18 wit 17.24 34.24*** 17.60 23.18 17.75 oil 21.37 30.75** 18.89 22.44 24.10* 17.03 ***, ** and *denote statistical significance at 1%, 5% and 10% level of significance, respectively. the results reported in table 9 for the sub-period (2004-2008) suggest a different market condition: italian corn, soybeans and wheat prices are cointegrated with their corresponding italian prices though not in all cases with us prices and never with the oil 108 f. rosa, m. vasciaveo, r.d. weaver prices. the pairwise cointegrations between soybean and corn in the italian markets suggest also that the price movements of these two major ag-commodity markets are moving together. the first observation inherent in this period is that us and italian ag-commodity prices move together and us commodity prices are not in general cointegrated with italian ag-commodity prices. we also find an absence of cointegration between us commodity price pairs. table 9. johansen trace test for cointegration: period 2004:w30-2008:w40. cus sus wus cit sit wit cus sus 15.60 wus 19.70 12.93 cit 21.70* 23.18 12.69 sit 13.71 24.74* 34.77*** 31.06** wit 23.95* 24.37* 30.09 16.53 30.44** oil 15.32 19.03 12.66 10.85 15.24 17.36 ***, ** and *denote statistical significance at 1%, 5% and 10% level of significance, respectively. for the last sub-period presented in table 10, the situation is consistently different. the null hypothesis of absence of cointegration with oil is rejected for all products except cit. these findings are consistent with the increasing use of ag-commodities in biofuel production that has generated more interdependence between oil and ag-commodity markets. we also find us prices to be cointegrated except between soy and wheat. us corn is cointegrated with italian corn and with wheat while italian soy appears not to be cointegrated with us corn prices. for each product, us and italian markets appear cointegrated. no evidence of cointegration across italian product prices is found. table 10. johansen trace test for cointegration: 2008:w41-2012:w21. cus sus wus cit sit wit cus sus 27.75** wus 29.53** 16.70 cit 24.50* 9.51 14.86 sit 18.05 47.26*** 11.59 9.20 wit 42.80*** 19.27 62.59*** 13.35 9.62 oil 28.80** 36.68*** 36.22*** 20.46 32.39*** 35.91*** ***, ** and *denote statistical significance at 1%, 5% and 10% level of significance, respectively. 109agricultural and oil commodities: price transmission and market integration these results are supported by the observations of other authors. campiche et al. (2007) found that while there is no evidence of cointegration among the variables for the period 2003-2005, corn and soybean prices are cointegrated with crude oil prices in the next period 2006-2007. harri et al. (2009) found robust evidence of cointegration between crude oil and corn, soybeans starting in april 2006. nazlioglu (2011) examined the cointegration between oil and three key ag-commodity prices and found evidence of corn and soybean price cointegration with the oil prices during the period 2008-2010. structural break timing has been determined a priori in the previous papers. here, we examine evidence of breaks within the context of our cointegration models, using gregory-hansen tests based on equations (3a – 3c) that allow for identification of structural break for the entire period 1999-2012. results are reported in table 11. table 11. g-h cointegration test with one structural break9 for us ag-commodities and oil: 1999:w1-2012:w21. cus-oil sus-oil wus-oil adf* c -3.45 -4.21 -4.23 c/t -3.84 -5.38** (2004: w34) -4.26 c/s -4.06 -4.94* (2008: w10) -4.65 zt * c -4.69** (2010: w19) -4.44* (2007: w39) -3.81 c/t -5.52*** (2004: w22) -5.72*** (2004: w33) -3.85 c/s -5.72*** (2004: w37) -5.20** (2007: w39) -4.03 zα * c -42.20** (2010: w19) -40.26** (2007: w39) -28.61 c/t -56.96** (2004: w22) -61.15*** (2004: w33) -28.91 c/s -62.39*** (2004: w37) -52.52** (2007: w39) -31.49 ***/**/* statistical significance at 1%, 5% and 10% level of significance, respectively; break dates in brackets. for oil and cus price, the adf* test did not reject the null hypothesis of no cointegration with model in the versions c, c/t and c/s whereas zt * and zα * type test results suggest the rejection of the null for each of the three models. significance of structural breaks were found for may and september 2004 (week 22 and 37) and may 2010 (week 19). for soybeans and oil, the three tests do not support the rejection of the null hypothesis of cointegration and structural break evidence is found for august 2004 (week 33); besides, zt * and zα * fail to reject the null in the regime shift model with a break in july 2007 (week 39). for the long-run relationship between wheat and brent prices, no evidence was found 9 model c: level shift, model c/t: level shift with trend, model c/s: regime shift. null hypothesis: no cointegration. for adf* and zt * tests, critical values in model c are: -5.13 at 1%, -4.61 at 5% and -4.34 at 10%; in model c/t:-5.45 at 1%, -4.99 at 5% and -4.72 at 10%; in model c/s: -5.47 at 1%, -4.95 at 5% and -4.68 at 10%. critical values for zα * test are -50.07, 40.48, -36.19 respectively at 1, 5 and 10% in model c; -57.28, -47.96 and –43.22 at 1, 5 and 10% in model c/t; -57.17, -47.04 and -41.85 at 1, 5 and 10% in model c/s. the optimal lag length for adf* test was selected by akaike information criterion. 110 f. rosa, m. vasciaveo, r.d. weaver for cointegration; a possible explanation is the wheat prices are less dependent on energy prices because only a limited quantity is used in the ethanol production. table 12. g-h cointegration test with one structural break for it ag-commodities and oil. cit-oil sit-oil wit-oil adf* c -3.77 -4.11 -3.75 c/t -4.01 -4.13 -3.84 c/s -4.27 -4.20 -4.27 zt* c -3.58 -3.69 -3.36 c/t -3.58 -3.71 -3.33 c/s -3.73 -3.84 -3.59 zα* c -24.72 -27.95 -21.93 c/t -24.63 -28.21 -22.15 c/s -28.14 -29.42 -25.67 ***/**/* statistical significance at 1%, 5% and 10% level of significance, respectively; break dates in brackets. the results of gregory hansen tests reported in table 12 do not in any case support the inference of cointegration between the crude oil and the italian ag-commodity prices. the results of table 13 suggest the cointegration between the italy and us ag-commodity markets. these findings are consistent with those obtained by running the cointegration test without structural breaks. results appear to be more robust for wheat and soybean commodities (confirmed by all the three tests). for corn, evidence of cointegration follows only from the zt * and zα * tests. table 13. cointegration test with one structural break between italian and us ag-commodities. cit-cus sit-sus wit-wus adf* c -3.89 -4.83** (2010: w19) -5.29** (2001: w34) c/t -4.21 -4.96* (2010: w19) -5.26** (2001: w14) c/s -4.43 -6.11*** (2008: w29) -5.56*** (2004: w28) zt * c -4.81** (2008: w31) -7.11*** (2010: w19) -5.70*** (2001: w19) c/t -5.10** (2003: w27) -7.04*** (2010: w19) -5.71*** (2001: w19) c/s -4.86** (2008: w26) -7.63*** (2010: w19) -5.98*** (2004: w29) zα * c -45.21** (2008: w31) -90.15*** (2010: w19) -61.46*** (2001: w19) c/t -50.52** (2003: w27) -88.94*** (2010: w19) -61.49*** (2001: w19) c/s -46.00* (2008: w26) -103.52*** (2010: w19) -67.48*** (2004: w29) ***/**/* denote statistical significance at 1%, 5% and 10% level of significance, respectively. break dates in brackets. 111agricultural and oil commodities: price transmission and market integration 5. price transmission market imperfections may interfere with the price adjustment process in many ways: asymmetric response, speed adjustment, biased information, decisions of storage and inventory holding, policy intervention and others (granger et al., 1989). market conditions determine price transmission. if the condition of market efficiency holds, the price change in one market is instantaneously and completely transmitted to the related market and the price difference will reflect only the transfer cost (fama, 1970; goodwin, 1992). the cointegration condition of long-run equilibrium requires that the integrated pair-wise series comove together. price transmission is tested with a cointegration error correction model (rapsomanikis et al., 2006; minot, 2011). the hypothesis of price transmission from us (here assumed to be the leading market) versus italy (domestic market) is empirically justified by the large volume of unidirectional commodity flow that has created a strong dependency of italy on us exports of these ag-commodities: more than the 90% of the entire volume of italy’s ag-commodity trade is with us. the price transmission analysis is performed by using the vector error correction model vecm in the following general form: α π γ ε= + + ∑ +− = −p p pt t kk q t k t1 1 (5) where pt is a n x 1 vector of n price variables; δ is the difference operator, δpt = pt – pt-1; εt is a n x 1 vector of error terms; α is a n x 1 vector of estimated parameters that describe the trend component; π is a n x n matrix of estimated parameters for the long-term relationship and the error correction adjustment; and γk is a set of n x n matrices of estimated parameters for the short-run relationship between prices, one for each of q lags of the model. the vecm provides a basis for evaluation of relationships across cointegrated series given that cointegration implies that the two prices move closely in the long-run, though in the short-term the two series could drift apart. this approach is appropriate if the following two conditions are held: i) all variables are nonstationary and integrated of order one i(1), following a random walk; ii) the variables are cointegrated in a linear combination that satisfies the stationary condition. the cointegration equation is: pd = α + β pw + ε10 (6) pd and pw are the prices representative of two spatially separated markets integrated of the same order and the error term ε is stationary, β, the cointegrating vector is the price response of dominated market to price changes of the leading market in the long-run. since prices are expressed in logarithms, β is the long-run elasticity of the domestic price with respect to the us price or the long-run elasticity of price transmission. the expected value for imported commodities is a β value ranging between 0 < β < 1; for β = 0 the 10 this equation is comparable to eq (2) of the previous section (goodwin, 1992). 112 f. rosa, m. vasciaveo, r.d. weaver us market has influence on the italian markets, for β=1 the price change in us market is entirely passed to the italian market (ravallion, 1986); considering the lagged effect i.e. β = 0.5 the 50% of the change in us price will be transmitted to the italian price in the long-run (minot, 2011). the regression equation form for vecm model is: α θ β δ ρ ε( )= + − + + +− − − −p p p p pt d t d t w t w t d t1 1 1 1 (7) where pt d is the natural logarithm of the italian (domestic) price of corn, soybeans and wheat respectively, pt w is the natural logarithm of the us (world) price of the same italian commodities, α, θ, β, δ, and ρ are parameters to be estimated and εt is the error term, the expression in parenthesis (pd t-1βpw t-1) is the deviation from the long-run equilibrium. the following two terms measure the short term impact of the lagged increments (δ) of the natural logarithm of international and domestic prices (conforti, 2004). the error correction coefficient (θ) measures the speed of adjustment, expected to fluctuate in the range between -1<θ<0. if the lagged error correction term (the term in parentheses) is positive, the domestic price is too high given the long-term relationship, then the negative value of θ “corrects” the error by making it more likely that the δpt d is negative. the larger θ is in absolute value (closer to 1), the more quickly the domestic price (pd) will adjust to the value consistent with its long-run relationship to the world price (pw). the coefficient of change in the world price (δ) is the short-run elasticity of the italian price relative to the us price and represents the percentage adjustment of domestic price one period after a one percent shock in international price. the expected value is 0<δ<β (minot, 2011). the coefficient of the lagged change in the domestic price (ρ) is the autoregressive term, indicating the change of the italian price caused by the change of the corresponding price in the next period, the expected value ranges between -1<ρ<1. table 14 reports the results for the transmission of us prices to the corresponding italian prices. the unit root tests reported above suggest that each domestic price is nonstationary, and the johansen cointegration test is used to test for a long-run relationship between the italian and the us prices. the results suggest that all the domestic prices have a long-run relationship with the us prices for the corresponding commodity. the long-run elasticity of price transmission is statistically significant for all the commodities and very high for soybeans (0.96) and wheat (0.74) meaning that a high percent change of the us price is passed through to the italian price in the long-run. the speed of adjustment coefficient (θ) is negative as expected for sit and wit and statistically significant at 1% level while for corn there is a slightly positive θ. the value of short run adjustment coefficient (δ) is in the expected range but is not significant for all pairs of commodities. the auto-regressive term is statistically significant for all the variables and is higher for corn. summarizing the results obtained from the transmission model, for each commodity we find the long-run relationship values (β coefficient) are larger than those that indicate short-run transmission (δ). an important role is performed by the autoregressive term meaning that for the corn market in italy, the 42% of the change of corn price in period t, is transmitted to period t+1; for the soybean market the value of lr adjustment increases to 0.96 and the short run autoregressive value declines to 0.013; for the wheat market, the lr adjustment value is 0.74 and the autoregressive value is 0.026. 113agricultural and oil commodities: price transmission and market integration 6. conclusion a number of studies have presented results that support the hypothesis of integration among the ag-commodity markets; more recently many researchers have demonstrated the growing interaction between oil and the ag-commodity price, and more difficulties in predicting the price changes of ag-commodities. since the price comovements are becoming increasingly complex, this research has been dedicated to test the hypothesis of market integration and price transmission between us and italy for oil and some relevant ag-commodities. our results offer traders and policy makers more reliable information to improve their decisions in market trade and policy formation. time series analysis has been used for testing the initial hypothesis that the oil price is an exogenous signal driving the ag-commodity prices. this is intuitively justified by the large amount of energy inputs used for the ag-commodity production (e.g. fuel, fertilizer, seed, machinery) and the growing quantity of ag-commodities used for biofuel production. the link between us and italy ag-commodity markets is grounded on the large volume of ag-commodities flowing from us to italy and the recognized leadership of us prices settled at the cbt. however, price transmission is a more complex phenomenon embedding the comovement, completeness, speed of adjustment asymmetries, in a contest of rapid market change (engle and granger, 1987; johansen and juselius, 1990). in short our results highlight the importance of identifying sample breaks in time series. as clearly illustrated in table 9 and 10, results and inferences are not robust across sample periods. intuitively, this highlights the need to empirically determine the time location of structural breaks. by definition, the presence of such a break implies a change in the underlying data generating mechanism and, therefore, a change in parameter values and perhaps functional form of any relationships. based on identified structural breaks, we find cointegration to vary across sample periods for both cointegration between us and italian markets within us or italian domestic settings. these results also highlight the sample dependence of structural estimates, an intuitive statement that is often overlooked when results are compared across papers using different sample periods. table 14. transmission of the us food prices (world) versus the italian food prices (domestic). commodity unit root in italian prices adf pp kpss za long-run relationship johansen test error correction model long-run adjustment β speed of adjustment θ short-run adjustment δ autoregressive term ρ cit yes yes 0.569* 0.002 0.015 0.416* sit yes yes 0.963* -0.038* 0.013 0.202* wit yes yes 0.738* -0.017* 0.026 0.296* * statistically significant at 1% level. 114 f. rosa, m. vasciaveo, r.d. weaver references abbott, p., hurt, c., and tyner, w. 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(1992). further evidence on the great crash, the oil price shock and the unit root hypothesis. journal of business and economic statistics 10: 251-270. bio-based and applied economics 3(3): 285-294, 2014 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-14767 short communication global governance of biofuels: a case for public-private governance? farhad mukhtarov1, patricia osseweijer2, robin pierce3 1 ada university, ahmadbey aghaoglu street, 11, baku az1009, azerbaijan 2 delft university of technology, julianalaan 67, 2628bc delft, the netherlands 3 harvard law school, 23 everett st., cambridge, ma 02138, usa abstract. with this paper, we examine the current state of global governance of biofuels and assess the potential regulatory and non-regulatory mechanisms for such governance. we ask two questions: a) what are the current efforts and initiatives towards the global governance of biofuels; and b) which form of global governance is more likely to emerge in the field of bioenergy. we come to the conclusion that institution building through private governance and non-state actor partnerships can offer a viable and effective means of governance. however, the primacy of partnerships and networks in global governance also means a number of pitfalls to avoid, especially with regard to legitimacy and inclusion. drawing lessons from the fields of forest and marine policy, we close with a number of policy recommendations for better private initiatives and partnerships for global governance of biofuels. keywords. private governance, biofuels, partnerships, legitimacy. jel code. f5 1. introduction: the rise of biofuels a bio-economy is a new buzzword in the global public policy community. a bioeconomy can be broadly defined as a socio-economic system where biomass is used for the production of materials previously derived from fossil fuels. one of the major tenets of a bio-economy is the production and use of bioenergy, which have gained global prominence in the first decade of the new millennium (fao, 2008). the first decade of the 20th century has seen the six-fold increase in the production volumes of biofuels from 18 billion litres to 110 billion litres (unep, 2009). according to the world energy outlook (2013), this trend will grow and the use of biofuels will triple by 2035. it would then represent eight per cent of all road-transport fuel consumed (ibid). despite the fact that oil prices have been high in the last decade, the price of bioethanol and biodiesel mostly remain higher than conventional petroleum (rathenau institute, 2011). this explains why bio-energy requires support from nation-states through man* corresponding author: fmukhtarov@gmail.com. 286 f. mukhtarov, p. osseweijer, r. pierce dates of blending, production and subsidies to the industry (bailis and baka, 2011). as a result, government programmes have been launched in support of biofuels (jenkins, 2008; kircher, 2012), and international public-private partnerships, academic and practitioner conferences, workshops and university curricula are established worldwide (e.g. levidow et al., 2013). by framing biofuels as a solution to the looming climate change, greater energy security and new economic opportunities in agriculture, prominent global actors such as the european union (eu), the european commission (ec), the governments of the us, uk, brazil, india support some form of a transition to a bio-economy (rathenau institute, 2011). the political-economic system around biofuels is characterised by global production, consumption and movement of capital and demands a global form of governance. relatively few studies to date have examined the extent of governance of biofuels, and those which have, emphasize the prevalence of national level regulation and the ad hoc and discordant state of global initiatives (e.g. bastos lima and gupta, 2013; mccormick et al., 2012; bailis and baka, 2011). scholars increasingly realize that some form of global governance of biofuels is necessary (e.g. scarlat and dalemand, 2011; bastos lima and gupta, 2013). for example, bastos lima and gupta (2013) made a case for global ‘non-governance’ of biofuels and called attention to more research in this field. given the policy importance of the bio-economy transition as pushed by the european commission, the us and the bric (brazil, russian, india, and china) countries, and the controversy which biofuels spurred in terms of their sustainability, global governance indeed needs more policy and research attention. with this policy commentary, we examine the current state of global governance of biofuels and assess the potential forms for governance to emerge. we ask two questions: a) what are the current efforts and initiatives towards the global governance of biofuels; and b) which form of global governance needs to be pursued and how. section 2 discusses various types of global governance mechanisms from the international relations literature; section 3 reviews the existent global governance mechanisms in the area of biofuels. section 4 makes the case for institution building through multi-stakeholder partnerships and makes policy suggestions on strengthening it based on parallels with the areas of water and forest resources. finally, section 5 discusses the policy implications and conclusions of the commentary. 2. the global environmental governance the concept of global governance was introduced in the field of international relations a few decades ago, and has since emerged as a burgeoning academic and policy field (rosenau, 1992; pattberg, 2005; biermann and pattberg, 2008). the rise of this concept is linked to the process of globalization and the realization that many policy issues cross state boundaries and need to be tackled transnationally. global governance allows us to move beyond the traditional dichotomies of national versus international and public versus private governance (pattberg, 2005), and emphasizes the non-state actors and networks in managing public policy issues as opposed to government hierarchies (rosenau, 1992). from the outset, global governance has been conceptualized in the form of international regimes (conca, 2006; biermann et al. 2009). international regimes are more than 287global governance of biofuels formal international agreements; these are the set of norms and rules, which guide the behaviour of actors and provide stability to a certain arena of international relations without immediate legislative enforcement (conca, 2006). regimes exist in many areas of transnational environmental governance; examples include the montreal protocol for the protection of the ozone layer, the kyoto protocol of the united nations framework commission for the climate change (unfccc), and the basel convention for control of transboundary movements of hazardous wastes and their disposal (undp, 2003). at the same time, the policy areas where efforts to build an international regime have failed also abound, as with freshwater resources, forests, and soil management (conca, 2006). in view of this, some authors have argued that global governance should be viewed broader than only international regimes and include institution-building through academic and policy conferences, global assessments, reports, and public-private partnerships and global networks (e.g. biermann et al., 2009; pattberg 2005; gupta 2009; mukhtarov, 2007). a notable form of global institution building takes place through the spread of certain norms, practices and models across political jurisdictions (mukhtarov, 2014). in such areas as global water governance and innovation management, researchers have studies the mechanisms and outcomes of such spread of institutions as a form of global governance (dobbin et al., 2007; mukhtarov, 2014; mukhtarov and gerlak, 2013). the third form of governance in addition to regimes and institution building is the international market. market-based instruments are common in establishing certain forms of governance, as with the greenhouse gas emissions and the biodiversity offsets and payments for ecosystem services (e.g. bouma et al., 2012). however, letting international market decide on bioenergy issues is questionable due to the market externalities this may trigger, such as land-grabs and that of associated resources, such as water (mehta et al., 2012); the hikes in food prices and concerns with food security and social justice (rathenau institute, 2011). the market governance of biofuels is the current status quo, and regulated by only national level legislation, which is arguably insufficient for the environmental protection. in the words of the brundtland commission, “the earth is one but the world is not”, and for an effective environmental protection, the whole life cycle of biofuels has to be regulated globally (the world commission on the environment, 1987: 27, cited in conca, 2006: 10). below we assess the current initiatives around biofuels against other two forms of global governance. 3. global initiatives to govern biofuels the latest reports of international organizations, such as fao, suggest that the policies for biofuel subsidies need to be reconsidered given the uncertainties around their impact on climate change, land use change in producer countries and effects on the environment at large (fao, 2008). recent research has further raised questions on whether large-scale production of biofuels contributes to the social and environmental objectives set out by the millennium development goals (bailis and baka, 2011: 828). some critics of biofuels have claimed that the food price hikes of 2008 are directly linked with the increased global biofuel production (rathenau institute, 2011). furthermore, the public and academic debates around biofuels are polarized as to what constitutes a bio-based future (levidow et al., 2013). one divergent view of such future is the agro-industrial vision of the bio-based 288 f. mukhtarov, p. osseweijer, r. pierce economy where large-scale biofuel production is encouraged; and another is the agro-ecological vision, where biofuels are produced at a local scale (levidow et al., 2013; schmid et al., 2012). putting global initiatives in place where views are polarised is a policy challenge. according to bastos lima (2009), the international efforts to govern biofuels can take three forms: through the reports and studies on biofuels ordered and conducted by multinational organizations such as the un, fao and unep; through networks, forums and partnerships to promote the bio-fuels, such as the bio-energy associations and the be-basic research consortium; and through the roundtables and networks whose primary goal is to establish international certification and sustainability standards for biofuels. all of these forms rely on non-state actors and their initiatives (bailis and baka, 2011). among those non-state actors, three groups of actors can be distinguished: the private sector actors such as the world bioenergy association (wba), and the global bioenergy partnership (gbp); environmental groups, such as the friends of the earth (foe), world wide fund for nature (wwf); and scientific networks, such as the nuffield council on bioethics (nuff) and the scientific committee on problems of the environment (scope). the non-state initiatives mostly converged around the issue of environmental social certification of biofuels as products and production processes (bailis and baka, 2011). examples of global initiatives include the roundtable on sustainable palm oil, the roundtable on sustainable biofuels, bonsucro, and the roundtable on responsible soy, as well as the roundtable on sustainable biofuels (rsb), and better sugar cane (bsc) (scarlat and dalemand, 2011). these initiatives represent the form of global private governance, which have been created by the agri-business and aim to establish the international standards for biofuel trade (bastos lima and gupta, 2013; mccormick et al., 2012). such an upsurge in the private initiatives can be explained by the lack of inter-state agreements on biofuels. the recent un efforts to create a regime on biofuels, such as the high-level conference on world food security in 2008, have not led anywhere (e.g. bastos lima and gupta, 2013). such “deficit” of global governance is alarming (fao, 2008; bastos lima and gupta, 2013), and scholars have called for governments to link the biofuel negotiations to international negotiations on climate change and wto (scarlat and dalemand, 2011), or to international water or energy negotiations (bastos-lima and gupta, 2009). at the same time, the formation of an interstate regime is formidable given that the authority of states has been eroded in the last few decades, the knowledge and perceptions about the impacts of biofuels are highly contested and uncertain; and the production, trade and consumption of biofuels happen at a global level making multi-party government negotiations extremely challenging (conca, 2006). the failures of regime building efforts in other areas, such as freshwater and forests, indicate the new reality where private forms of global governance gain importance and need to be fostered. the private governance of biofuels, however, presents certain challenges, such as the legitimacy of certification schemes and rules set by roundtables and led by private actors (bernstein and cashore, 2007; bailis and baka, 2011). in addition, critics claimed that science, and often economics and land use planning, become the most important sources of legitimacy in such type of global governance surpassing the questions of ethics and possible value conflicts (mukhtarov and gerlak, 2014). there is a lack of accountability of voluntary initiatives and partnerships and there are concerns about the extent to which social and environmental movements and actors from the global south participate (bastos lima 289global governance of biofuels and gupta, 2013; mukhtarov 2007). having studied two such certification schemes, namely, the roundtable on sustainable palm oil (rspo) and the cramer commission criteria, partzsch (2009) claimed that these initiatives failed to include actors from developing countries, and lack mechanisms of control and accountability. awareness raising and capacity building programmes at national and local scales are necessary to strengthen the legitimacy of the voluntary schemes at large. in a separate analysis of legitimacy of rspo, schouten and glasbergen (2011) suggested that legitimacy could be approached from multiple dimensions, such as legality, moral justifications, and consent or acceptance. they claimed that rspo is facing the tension between building internal legitimacy with its diverse actors whose opinions need to be taken seriously in compromised decisions, and external legitimacy with the influential environmental and social movements and ngos. while rspo has faired relatively well in terms of internal legitimacy, it has not been able to ensure acceptance of influential external actors (shouten and glasbergen, 2011: 1898). they suggested that the legitimacy of rspo and private governance institutions more generally need to be studied ‘bottom-up’ in order to understand how legitimate they are at the local level where the production processes take place. from this brief literature analysis, we can conclude that the current forms of global governance in biofuels converge on non-binding institution-building led by the industry in partnership with states, universities and, to a lesser extent, civil society actors. there is no international regime for biofuels and it is unlikely to emerge given the complexity of negotiations. at the same time, the prevalence of non-state institution building raises questions about the legitimacy, accountability and equal representation of various actors in such fora. the task of a researcher and policy-maker engaged in this area is thus to explore how private forms of governance of biofuels may be strengthened through multistakeholder partnerships. 4. private governance: lessons from forestry and marine policy the challenges faced by the voluntary schemes of certification of biofuels are typical problems of private global governance. there is an emerging stream of literature, which analyses the role of private actors in global governance and the mechanisms to encourage their legitimacy, accountability and transparency (e.g. pattberg, 2005, 2007). lessons can be drawn from other areas of governance, such as forest management, freshwater resources and marine policy to help build or improve the existent partnerships and initiatives in biofuels. successful examples of private governance initiatives include the forest stewardship council (pattberg, 2005), the programme for the endorsement of forestry certification (pefc), and the marine stewardship council (msc) (kalfagianni and pattberg, 2013). a valuable lesson comes from the field of freshwater governance, where the efforts to build an international regime have failed at the 1992 earth summit and other high-level international meeting including within the united nations (conca, 2006; gupta, 2009). a more successful path of governance has been taken by the growing number and diversity of discourses and social movements as driving forces of an emerging world order in the field of freshwater resources (gupta 2009; mukhtarov and gerlak, 2013). conca (2006: 7) 290 f. mukhtarov, p. osseweijer, r. pierce referred to this as “a plethora of institutional forms that do in fact constitute the global governance of these problems”. similarly, the global governance of biofuels is more likely to succeed promoting diverse and seemingly discordant non-state actor based institutionbuilding initiatives and discourses rather than targeting an inter-state agreement (shubert and gupta, 2013; conca, 2006). this may eventually culminate in a formation of an international regime, which will build upon the strengthened institutions and state and nonstate actors. in such a manner, we treat the efforts of regime-formation and institution building as complementary, but place more emphasis on institution building as a more promising avenue to create a lasting system of rules. another lesson comes from the field of forest governance and is the success of forest stewardship council (fsc), a standard-setting partnership of private actors, environmental groups and experts, which brought together former adversaries. fsc in addition to its standard-setting function also facilitates institution building at the global level, brokers knowledge and norms and provides an arena for actors to learn and share experiences (pattberg, 2005). the success of fsc has encouraged the development of marine stewardship council (msc), which is now an established and successful organization (pattberg, 2006). furthermore, the structure and organization of fsc may provide lessons for private initiatives in biofuels. the tripartite system of organization within fsc is an interesting model in which business, social and environmental interests are equally represented in the general assembly, which in turn, elects the board of directors, an executive branch of fsc. the participation of actors from the south and north is equal and therefore contributes to the external and internal legitimacy of fsc. this in turn enhances the reputation of the council as impartial and contributes to consumer confidence in purchasing the products with an fsc label (pattberg, 2005). a further lesson from the fsc experience is that it has multiple functions in the world of forest governance and is not limited to sustainability certification. in addition, it sets rules of what is considered as sustainable forestry and the ‘regulatory rules’ on the style of communication, conflict resolution and suchlike. furthermore, fsc has been instrumental in brokering knowledge and norms more broadly and to provide the necessary environment for learning in various types of networks it facilitates and is part of (pattberg, 2005). more lessons for private biofuel governance may be drawn from the area of marine policy. in their study of private governance of marine ecosystems, kalfagianni and pattberg (2013) found that msc and the aquaculture stewardship council (asc) differ in rule-setting work in a way that more stringent standards promoted by msc resulted in less adoption of these standards and are mostly limited to the rich north, whereas the asc standards, being more relaxed, are more widely spread. the same relationship exists in forestry, where the fsc standard being stricter than that of programme for the endorsement of forest certification (pefc), has attracted less membership and is not as widely used (kalfagianni and pattberg, 2013). thus, the less stringent rules may be more conducive to membership in such organizations. drawing attention to the example of globalgap, the european based sustainable agriculture certification initiative which also benchmarks the national versions of gap from outside europe, such as mexicogap and newzealandgap, kalfagianni and pattberg (2013: 131) further suggested that standardsetting may also happen at the national level with later acceptance at the global level. this allows for taking a stepwise approach to global governance as an emerging cumulative form of national level regulations and voluntary standard setting. 291global governance of biofuels 5. conclusions in the introduction to this paper, we have posed two questions: a) what are the current efforts and initiatives towards the global governance of biofuels; and b) which form of global governance is more likely to be successful? concerning the first question, we observed that there is a diversity of initiatives at both national and international levels. however, the current drive for biofuels happens through the mandates and incentives of nation-state actors; and those international efforts are in the form of voluntary partnerships and mostly in the area of sustainability reporting, which has to do with the jurisdictional constraints in that legislation in one nation neither applies nor is enforceable in another. secondly, we believe that private forms of governance, largely, through partnerships with governments, is the future centrepiece of the biofuel governance. pursuing global governance through institutions means fostering multi-stakeholder partnerships between multinational corporations, states, research institutions, environmental and social movements as well as representatives of local and regional governments and individual stakeholders. the sustainability standards will have to be pushed and adopted by the agri-business in close cooperation with the states, social movements and environmental groups and other actors. the task of the international community, more specifically, the politicians, business leaders and researchers, is therefore to ensure this in a manner that allows all involved actors to speak out and be heard in decision-making. however, private and non-state governance also have pitfalls. the issue of legitimacy of partnerships led by the private sector, and that of their accountability and transparency need to be discussed in academic and practitioner circles and studied further. another important issue is inclusion of less privileged actors in partnerships and giving them voice in decision-making process in order to enhance the democratic decision-making, the external legitimacy and reputation of these partnerships. without legitimacy, neither the partnerships, nor the sustainability criteria or certification schemes, which they develop will be accepted by the major actors involved in global biofuel policy. looking at other areas of global environmental governance, such as forests, freshwater resources and marine ecosystems can be useful for fostering such legitimacy and acceptance. surely, global governance of forest and water resources is different from that of biofuels. water and forest resources are primary resources, whereas biofuels are derived from biomass, which in turn comes from forests, agriculture and requires water. nevertheless, we believe that successful examples of private and multi-stakeholder governance from forest and marine governance may provide models for sustainable biofuels, including certification and standard setting. further research exploring theses comparisons is a promising area to pursue in order to foster multi-stakeholder partnerships and institutional forms of governance of biofuels at the global level. acknowledgements we thank be-basic research consortium for funding for this research and two anonymous reviewers for their comments. 292 f. mukhtarov, p. osseweijer, r. pierce references bailis, r., and baka, j. 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(2005). business unusual: facilitating united nations reform through partnerships. global public policy institute. accessed 30 march 2007. available from www.gppi.net. world energy outlook factsheets (2013). how will global energy markets evolve to 2035? available from http://www.worldenergyoutlook.org/media/weowebsite/factsheets/ weo2013_factsheets.pdf issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(3): 297-312, 2012 product differentiation and brand competition in the italian breakfast cereal market: a distance metric approach paolo sckokai1, alessandro varacca dipartimento di economia agroalimentare, facoltà di agraria, università cattolica del sacro cuore, piacenza, italy abstract. this article employs a nation-wide sample of supermarket scanner data to study product and brand competition in the italian breakfast cereal market. a modified almost ideal demand system (aids), that includes distance metrics (dms) as proposed by pinkse, slade and brett (2002), is estimated to study demand responses, substitution patterns, own-price and cross-price elasticities. estimation results provide evidence of some degree of brand loyalty, while consumers do not seem loyal to the product type. elasticity estimates point out the presence of patterns of substitution within products sharing the same brand and similar nutritional characteristics. keywords. distance metric, almost ideal demand system, breakfast cereals, differentiated products jel-codes. q11, d12, l11. 1. introduction although it’s not as developed as in the united states and in the rest of europe, the market of breakfast cereals has been expanding over the last ten years also in italy, showing an upward trend in market penetration, volume and value sales. in particular, in the years 2004-07 we observed a sharp increase in households’ expenditure in breakfast cereals, even though this positive trend has flattened in late 2008-09, probably because of the ongoing economic crisis, and begun to fall late 2009-10. the market for breakfast cereals is characterized by a relevant concentration: the concentration ratio (cr4) is almost 80% and the first two players hold 75% of the market shares. overall, in 2007 kellogg’s accounted for a share of 49.9% in volume sales considering the entire market, followed by nestlé which accounted for 25%. because of its presence in the muesli business only, cameo had a relatively low market share (4%) when considered within the overall market. the same applies also to barilla: the italian brand accounted for 1.4% of the entire market, since it is present only in the segments of simple and fortified cereals. in terms of volume sales across different market segments, kellogg’s was clearly dominating the market with a 45% share in muesli cereals, 46% in fortified cereals and 54% in 1 corresponding author: paolo.sckokai@unicatt.it. 298 p. sckokai and a. varacca simple cereals. the following player was nestlé, accounting for 38% of volume sales in fortified cereals and 20% in simple cereals. cameo was the second player in the muesli business after kellogg’s, with a volume sales share of 32%, after being the segment-leader for years. although it was the second player in the muesli business, cameo did not play any significant role in other segments. after launching its new product line “gran cereale”, in 2006 barilla had just re-engaged the competition in fortified and simple cereals after the withdrawal of “mulino bianco armonie di cereali”, thus its share was still below 2% in both businesses, but increasing over time. given this strong concentration and the strong reputation of the leading brands, manufacturers wishing to set up new businesses in this market incur in high entry barriers when trying to build their (new) reputation and when trying to set up new brands; the result is that consumers can choose among a range of products supplied by a limited number of firms. although the italian market is still lagging behind the european and american markets in terms of per capita consumption, most operators argue that outlooks are encouraging (even for new manufacturers) thanks to the likely evolution of demand in terms of new target consumers (mainly women and children). in fact, innovation plays a strategic role, since a relevant number of new products is systematically launched on the market every year by the two biggest players. in this context of extreme concentration and relevant product innovation activity, we aim to study this market from two points of view: the role that brand loyalty plays on consumers’ choices as well as consumers’ behavior when faced with (new) different products. in line with the methodology proposed by pinkse, slade and brett (2002) and applied by bonanno (2013), our aim of investigating consumers’ attitude towards different breakfast cereals is carried out through a modified almost ideal demand system (aids) that accounts for products’ qualitative attributes. both continuous and discrete characteristics are employed to compute distance metrics, which are included in the model as interaction terms among cross-product prices. the dm approach was developed to address the challenges of differentiated products in demand applications. it was first proposed by pinkse, slade and brett (2002) which developed the dm technique to overcome the dimensionality limitation of neoclassical demand models. recent examples of studies based on dm are pinkse and slade (2004), rojas and peterson (2008), pofahl and richards (2009) and bonanno (2013). the insight of this approach is that each product can be viewed as a unique combination of characteristics and that substitution patterns among products might be the result of the proximity between these characteristics. in line with the lancaster’s approach to demand theory (lancaster, 1966), such characteristics can be thought as spatial attributes where different products can be positioned along, according to their own peculiarities; this means that any differentiated product can be considered as a combination of characteristics in a multidimensional space and substitution patterns are spatially determined. the products attributes can be both continuous and discrete. in demand estimation, the dm method is applied by defining cross-price coefficients as functions of different distance measures between products. besides accounting for spatial distance in products’ characteristics, this methodology also allows us to address one of the aids model weaknesses, namely the large number of cross-price coefficients to be estimated when the model accounts for a sizable number of products. 299brand competition in breakfast cereals 2. the model the demand for breakfast cereals in italy is modeled following the linear approximated–almost ideal demand system developed by deaton and muellbauer (1980): w b p x plog log ( / )jrt jrt k j jkrt krt jrt rt rt jrt 1 ∑α β ε= + + + = . (1) the subscript r denotes the regional markets we’re dealing with (r = 1,...r) while t denotes the time period (t = time = 1,...t). the system is made up of j equations, where j=1,…j is the number of products in each market r, and in each time period t; these equations are linked each other by the expenditure term xrt and by the properties of the demand functions. the total level of expenditure for the j products is xrt, defined as j ∑pjrt*qjrt . the product j’s sales share (the share of expenditure allocated to product j) in market r, at time t is wjrt, defined as p q x ( * ) /jrt jrt rt . prt is a log-linear analogue of the laspeyeres price index defined as j ∑log pjrt*wjrt where w jrt are fixed budget shares, and it is employed to normalize the total expenditure xrt. in principle, j-1 equation and j(j-1)/2 cross price coefficients can be estimated by imposing to the system the restrictions implied by the properties of the demand functions2. for large j, it might become impractical to estimate a large system of equations, and this is likely to be the case when analyzing brand-level data. the adoption of the distance metrics approach will reduce the number of cross-price parameters bjkrt to be estimated through the definition of a new subset of metrics related to the cross price coefficients. in this application of the distance metrics method (which will be referred to as dmla/aids), let z j c and z j d be product j’s attributes, measured in continuous space (calories, fat content, etc…) and in discrete space (brand, flavour, etc…), respectively. let jk cδ and jk dδ be measures of closeness between products j and k, function of continuous and discrete attributes, respectively. the continuous measure of closeness, namely the continuous distance metrics ( jk cδ ), is defined adopting the inverse measure of the euclidean distance in product space between j and k. the euclidean distance indicates how distant two products are in the attribute space, given their characteristics: if two products were different, the magnitude of this indicator would get larger. mathematically, this distance between j and k is the square root of the sum of the squared differences between continuous attributes zl c belonging to product j and k: z zed .jk l jl c kl c 2 ∑( )= − (2) 2 the restrictions are the following: (1) adding-up: a b 1; 0; 0 j j jrt j j jkrt j j jrt 1 1 1 ∑ ∑ ∑β= = = = = = ; (2) homogeneity of degree 0: b 0 k j jkrt 1 ∑ = = ; (3) symmetry: bjkrt = bkjrt; ∀ jk 300 p. sckokai and a. varacca the continuous dm ( jk cδ ) is then specified as the reciprocal of the euclidean distance: ed 1 1 2*jk c jk δ = + (3). this measure varies between 0 and 1, and gets closer to 1 the more similar are the products in terms of characteristics, since the value of ed is closer to 0. this provides a continuously defined indicator of the proximity of two products within the defined attribute space.3 discrete dms do measure the competitive effect of attributes that are not measurable through continuous characteristics. mathematically, discrete dms are represented by dummy variables whose value is 1 whenever product j and k share the same qualitative status or level for a discrete attribute d: if z z if z z 1 0 0 0 jk d jl d kl d jl d kl d δ = − = − ≠ (4) for food products, examples of discrete attributes might be: brand, category, presence of a given characteristics (i.e. functional vs. nonfunctional,...)4 given the closeness measures jk cδ and jk dδ , the cross-price parameter term of the la/ aids is reformulated as follows: k=1 j ∑bjkrtlog pkrt = bjjrtlog pjrt + λ j k≠ j j ∑δ jk c log pkrt + d ∑ϕ j d k≠ j j ∑δ jk dlog pkrt . (5) replacing the cross price parameter (2) in the la/aids equation (1) we obtain: wjrt =α jrt + bjjrtlog pjrt + λ j k≠ j j ∑δ jk c log pkrt + d ∑ϕ j d k≠ j j ∑δ jk dlog pkrt + β jrtlog (xrt / prt )+ ε jt , (6) which gives bj1 = λ jδ j1 c + d ∑ϕ j dδ j1 d ,…, bjm = λ jδ jm c + d ∑ϕ j dδ jm d , where ϕ j dand λ j are param3 the use of bilateral indexes in a multilateral context (more than two different products) can bring to some problems related to the intransitivity of such indexes (rao et al., 2002). in principle, with n products, n(n-1)/2 bilateral indexes can be obtained. however, the index obtained comparing two products’ characteristics may be different from the indirect index obtained by comparing the same two products with a third product that is part of the analysed set. this problem was addressed by hill (1997) and diewert (2005) through the proposal of several multilateral formulas. further research may refine the model introducing such formulas in the dms calculations. 4 for example, taking d1 = brand, a value of jk d1δ = 0 implies that the two products are made by different manufacturers. 301brand competition in breakfast cereals eters to be estimated. since (zjl – zkl)2 = (zkl – zjl)2 then jk cδ = kj cδ and symmetry can be imposed to the λj parameters across equations (i.e., for each product j). furthermore, symmetry can also be imposed for ϕ ds because there is no difference between jk dδ and 1kj dδ = (if d=brand and 1jk dδ = , then also 1kj dδ = ). therefore this implies that bjk = bkj across the j equations. given symmetry, in principle j-1 equations can be estimated with only two cross price parameters. to further reduce the dimensionality of the estimation one may assume ownprice and expenditure coefficients to be constant across equations, thereby reducing the estimation to a single equation. because of the restrictiveness of this assumption, in this work we assume that these coefficients, together with the intercept, are functions of subsets of product characteristics and market parameters: z jrt m m jm0 ∑α α α= + α (7) b b zjjrt s s js b 0 ∑γ= + zjrt h h jh0 ∑β β β= + β the choice of these shifters is arbitrary and results may not be invariant to their choice. however, when using dms, such choice can be justified based on the available data and on the objectives of the study (rojas and peterson, 2008; pofahl and richards, 2009; bonanno, 2013). for example, in this study, the discrete shifters of the cross-price and expenditure parameters (z z ,j b j β ) are those that allow to identify a specific product category, for which we wish to estimate a demand elasticity, while the shifters of the intercept (z j a) are those identifying market specificities of each product (i.e. regional/seasonal demand features). thus, the final specification of the la/aids model becomes5: wjrt =α0 + m ∑αmz jm α + log pjrt γ 0 + s ∑γ sz js b⎛ ⎝⎜ ⎞ ⎠⎟ + λ k≠ j j ∑δ jk c log pkrt + d ∑ϕ d k≠ j j ∑δ jk dlog pkrt + β0 + h ∑βhz jh β⎛ ⎝⎜ ⎞ ⎠⎟ log xrt / prt( )+ ε jrt (8) wjrt =α0 + m ∑αmz jm α + log pjrt γ 0 + s ∑γ sz js b⎛ ⎝⎜ ⎞ ⎠⎟ + λ k≠ j j ∑δ jk c log pkrt + d ∑ϕ d k≠ j j ∑δ jk dlog pkrt + β0 + h ∑βhz jh β⎛ ⎝⎜ ⎞ ⎠⎟ log xrt / prt( )+ ε jrt 3. data the database employed for estimating the model has been obtained from a symphonyiri census® scanner dataset including forty-eight monthly observations of breakfast 5 one problem arising from the inclusion of distance metrics into the model has to do with the imposition of the standard demand theory restrictions: with the new interaction terms in equation (8), the standard parametric restrictions of homogeneity and adding up cannot be imposed in a straightforward way. further research may refine the model in order to solve this problem. 302 p. sckokai and a. varacca cereals sales for the period january 2004 – december 2007. sales are recorded in hyper and supermarkets located in seventeen italian iri regions covering most of the national territory. each of the forty-eight monthly observations reports product-specific data for: volume sales; value sales; unit sales; percentage of store selling (indicating the share of stores where at least one unit of a particular product was sold); weighted distribution (indicating the share of annual sales represented by the stores where at least one unit of a particular product was sold); average number of items per store (the average number of barcodes available for that product in the stores selling the product; namely, it is a measure of the depth of the product’s distribution); volume of sales under any type of price promotion; value of sales under any type of price promotion; units sold under any type of price promotion. the products chosen for this analysis belong to firms operating nationally with a value of expenditure share in the national market of at least 0.5%. since the database accounted for some small producers typically bound to regional markets, data were reduced by filtering for these producers, in order to obtain a nationally representative market of italy with 8,160 observation: 10 product combination identified by vendor (barilla, cameo, kellogg’s, nestle and private label) and segment (muesli, fortified and simple) observed across 48 months (from january 2004 to december 2007) and 17 regions (liguria, lombardia, trentino alto adige, friuli venezia giulia, veneto, emilia romagna, toscana, lazio, umbria, sardegna, marche, puglia, campania, sicilia, valle d’aosta+piemonte, abruzzo+molise, basilicata+calabria)6. these 8,160 observations built up the database from which the share values wjrt were calculated. euclidean distances and continuous dms were computed from hand collected information on: calories (kcal/100g); proteins (g/100g); carbohydrates, total and simple (g/100g); fats, total and saturated (g/100g); fiber (g/100g); calcium (mg/100g); sodium (g/100g); iron (mg/100g) (the matrix of continuous distance metrics is reported in appendix 1). such measures were included in the database together with the dummy variables representing discrete dms. finally, prices for each of the ten products were computed dividing value sales by the corresponding volume sales. table 1 presents descriptive statistics of the data for the 10 products included in our analysis; products’ characteristics are included together with average prices, expenditure shares and the promotion share in value terms per item. muesli products are the fattest and most caloric ones while private labels are the cheapest products on the market. despite their high price, kellogg’s fortified products are the ones showing the second largest expenditure share after kellogg’s simple, that are also the most merchandised ones. overall kellogg’s holds more than 50% of the market in breakfast cereals, followed by nestlé and private labels. 4. model specification in estimating the la/aids model we need to account for the issue of endogeneity, since the model may not account for factors affecting consumer’s behaviour (wjt) which 6 iri regions are defined consistently with the administrative borders of the italian regions except for “piemonte and val d’aosta”, “basilicata and calabria” and “abruzzo and molise”. 303brand competition in breakfast cereals ta bl e 1. d es cr ip tiv e st at is tic s of th e te n pr od uc ts (n =8 16 0) br an d ty pe c al or ie s (k ca l/1 00 g) pr ot ei ns (g /1 00 g) to ta l c ar bo hy dr at es (g /1 00 g) si m pl e su ga rs (g /1 00 g) to ta l f at s (g /1 00 g) sa tu ra te d fa ts (g /1 00 g) fi be r (g /1 00 g) pr ic e (€ /k g) pr om ot io n sh ar e (% ) ex p. s ha re (% ) ba ril la fo rt ifi ed 40 5. 00 9. 50 70 .4 0 22 .0 0 8. 00 5. 10 6. 50 7. 03 8. 03 0. 58 c am eo m ue sli 39 6. 00 8. 85 59 .9 0 23 .2 0 12 .6 5 3. 80 8. 50 6. 34 18 .8 0 4. 32 ke llo gg ’s fo rt ifi ed 38 8. 17 9. 33 79 .3 3 30 .1 7 3. 00 1. 42 3. 08 8. 30 19 .4 0 26 .7 7 ke llo gg ’s m ue sli 48 2. 00 8. 00 60 .0 0 19 .0 0 22 .0 0 11 .0 0 6. 00 7. 67 9. 17 3. 09 ke llo gg ’s si m pl e 37 9. 00 8. 35 83 .2 2 17 .7 3 1. 15 0. 28 2. 13 6. 48 22 .8 6 28 .0 2 n es tlé fo rt ifi ed 38 2. 80 7. 56 76 .5 2 31 .0 4 4. 32 1. 92 5. 56 7. 15 16 .7 2 17 .5 1 n es tlé si m pl e 36 7. 00 7. 60 77 .1 0 23 .3 0 1. 85 0. 85 5. 70 6. 65 17 .7 0 10 .8 6 pr iv at e la be l fo rt ifi ed 38 5. 50 8. 15 77 .6 1 26 .0 4 3. 94 2. 34 4. 70 4. 77 14 .3 3 2. 90 pr iv at e la be l m ue sli 42 8. 50 7. 63 62 .3 2 24 .7 0 15 .0 0 6. 93 6. 82 4. 61 8. 25 1. 21 pr iv at e la be l si m pl e 37 4. 64 8. 18 81 .4 5 10 .8 9 1. 05 0. 29 4. 29 4. 15 20 .0 3 4. 42 304 p. sckokai and a. varacca are related to suppliers/retailers’ price setting choices. considering that we are actually dealing with differentiated products, retail prices may not be considered fully exogenous. in fact, in an oligopolistic market, prices are likely to be determined by strategic pricing rules of firms, incorporating both supply and demand characteristics of those products. whenever these pricing rules involve some unobserved demand characteristics, assuming prices as exogenous would lead to biased and inconsistent parameter estimates. the same problem arises with the expenditure variable. instruments are therefore employed to deal with the endogeneity of prices and expenditure. instrumental variable estimates were obtained following dhar, chavas and gould (2003): ten first-stage regressions for price were specified as: p us mrch prd itps irt i i irt i irt i irt i irt0 1 2 3 4θ θ θ θ θ= + + + + (9) i j k j j, ,∑= + ≠ … while the expenditure first stage regression was specified as: x ttr d inc inc , rt rt r r rt rt rt 1 1 2 2∑η φ φ= + + + = (10) where: usirt = unit sales; adopting this variable means accounting for package-related cost variations if we assume that large package sized products are likely to sell a smaller number of units with respect to small package sized products; mrchirt = merchandised units sold/unit sales; this variable measures the amount of product sold through any merchandising and ought to capture the cost of selling a brand. prdirt = price reduction = 100 * price merchandised product – price not_merchandised product price not_merchandised product ; the price-reduction variable is thought to capture costs associated with this kind of merchandising (note that this price reduction must be communicated to consumers for this variable to be relevant); itpsirt = average number of items per store selling; for a given product in a particular geographical area, this measure is the average number of barcodes available for that product per store selling that product. this variable is able to capture the depth of the product’s presence on the shelves and is adopted as an indicator of market power. ttrt = linear time trend, capturing any time-specific unobservable effect of consumers’ expenditure; 305brand competition in breakfast cereals drt = regional dummy variables able to capture variation in expenditure across regions; incrt= yearly household expenditures on all consumer goods differentiated by region, as a proxy of household income. fitted values for prices and expenditure obtained by estimating (9) and (10) were used to replace the corresponding actual values in model (8).with regard to model (8), we have to choose appropriate shifters for the intercept, own-price and expenditure coefficients. we adopt brand and type discrete shifters both for the own-price parameter ( z js b ) and the expenditure parameter ( z jh β ), in order to derive proper elasticity measures. in fact z js b and z jh β identify different products through 2 sets of dummy variables: the former identifies the brand (namely: barilla, cameo, nestlé, kellogg’s and private label), the latter identifies the type (namely: fortified, muesli and simple). the own-price parameters are also shifted by one physical product’s characteristics: average calories kcal( ). this particular products’ characteristic was adopted because of its property to describe synthetically multiple nutritional features of the products we study (sugar content, fats and fiber); this should avoid the risk of multicollinearity. thus the price and expenditure shifters in (8) are defined as: z brand type kcal s s js b n n jn b p p jp b l∑ ∑ ∑γ γ γ γ= + + (11) z brand type h h jh n n jn p p jp∑ ∑ ∑β β β= +β β β (12) the intercept term is shifted by two sets of discrete variables z jm a , one set of monthly dummies accounting for seasonal patterns of consumption and one set of regional dummies accounting for regional differences in food consumption habits. thus: z region month m m jm g g jg m m jm∑ ∑ ∑α α α= +α α α model (8) then becomes: wjrt =α0 + g ∑α gregionjg α + m ∑αmmonthjm α + log p̂ jrt (γ 0 + n ∑γ nbrand jn b + p ∑γ ptypejp b + γ l kcal)+ λ k≠ j j ∑δ jk c log p̂krt + d ∑ϕ d k≠ j j ∑δ jk dlog p̂krt + (β0 + n ∑βnbrand jn β + p ∑β ptypejp β ) log ( x̂ rt / prt )+ ε jrt wjrt =α0 + g ∑α gregionjg α + m ∑αmmonthjm α + log p̂ jrt (γ 0 + n ∑γ nbrand jn b + p ∑γ ptypejp b + γ l kcal)+ λ k≠ j j ∑δ jk c log p̂krt + d ∑ϕ d k≠ j j ∑δ jk dlog p̂krt + (β0 + n ∑βnbrand jn β + p ∑β ptypejp β ) log ( x̂ rt / prt )+ ε jrt in this model, d ∑ϕ d can be considered as indicators of loyalty. in fact, assuming d = brand, ϕ br would be a measure of the cross-price effect of j with respect to other products 306 p. sckokai and a. varacca sharing the same brand (if j and k share the same brand, 1jk brδ = and log p k j j jk br krt∑δ ≠ will account only for those products k whose 1jk brδ = ). in fact, if ϕ br > 0 consumers are likely to respond to an increase in price of the products sharing the same brand by switching to an alternative item produced by the same manufacturer; thus, consumers are brand loyal. on the other hand, if ϕ br < 0 , consumers are likely to respond to an increase in price of the products sharing the same brand by switching to an alternative manufacturer; thus, consumers are not brand loyal. since jk cδ represents a measure of how distant two products j and k are in the attribute space, two products with similar characteristics will show a higher value for jk cδ as compared to couples showing different attribute sets. thus, any term log p* jk c krtδ will influence more the demand response if j and k are close to each other. this means that log p ˆ k j j jk c krt∑δ ≠ can be interpreted as a weighted average of cross-prices, where their weights jk cδ are their distance from j. in this context the continuous closeness measure, λ, is a measure of the impact of all products similar to j on its expenditure share: for λ > 0 consumers tend to respond to any increase in similar products’ prices by switching to items with similar nutritional profiles, while for λ < 0 consumers tend to respond by switching to items with different nutritional profiles. 5. estimation and empirical results parameter estimates of the first-stage regressions for prices and expenditure are omitted for brevity7. estimation of model (13) was carried out through least squares estimation in tsp 5.0. results showed that most parameters associated with the monthly dummies were not statistically significant; for this reason, a second restricted model was estimated excluding such dummies. the fit of the two models was then compared through a likelihood ratio test which showed that the null hypothesis of all monthly dummy parameters being equal to zero could not be rejected8. thus, we have chosen the restricted model as final specification of our demand system. parameter estimates of the restricted model are reported in table 2. in terms of explanatory power, the model shows a very good r-squared (0.95), which is a remarkable result for a model of this type. 5.1 estimated coefficients the regional shifters of the intercept highlight differences in regional food habits: α0 identifies basilicata+calabria (region 17) which does not statistically differ from liguria (α31), friuli venezia-giulia (α34), marche (α311), puglia (α312), abruzzo+molise (α316). dif7 such results are available from the authors upon request. 8 the chi-square of the likelihood ratio test was 11.013 with 11 degrees of freedom, with a corresponding p-value of 0.442. 307brand competition in breakfast cereals ferent patterns appear in other regions, consistently with estimated parameters for the corresponding regional dummies. the own-price shifters related to “type” (γ0 = simple, γ11 = fortified, γ12 = muesli), “brand” (γ0 = private label, γ21 = barilla, γ22 = cameo, γ23 = kellogg’s, γ24 = nestle) and caloric content (γ34) are statistically significant at the 1% level and positive, except for that associated with caloric content. table 2. least squares estimated parameters variables parameter parameter estimate p-value intercept basilicata+calabria/month12 α0 0.1971 0.000 intercept liguria α31 -0.0009 0.538 intercept lombardia α32 0.0110 0.000 intercept trentino alto adige α33 -0.0037 0.033 intercept friuli venezia-giulia α34 -0.0011 0.458 intercept veneto α35 0.0065 0.000 intercept emilia romagna α36 0.0061 0.001 intercept toscana α37 0.0058 0.001 intercept lazio α38 0.0080 0.000 intercept umbria α39 -0.0050 0.005 intercept sardegna α310 -0.0035 0.023 intercept marche α311 -0.0014 0.345 intercept puglia α312 0.0006 0.662 intercept campania α313 0.0038 0.017 intercept sicilia α314 0.0042 0.009 intercept valle d’aosta+piemonte α315 0.0053 0.001 intercept abruzzo+molise α316 -0.0012 0.413 own-price simple cereals/private label γ0 0.4963 .000 own-price fortified cereals γ11 0.1299 .000 own-price muesli cereals γ12 0.2416 .000 own-price barilla γ21 0.2600 .000 own-price cameo γ22 0.0981 .000 own-price kellogg’s γ23 0.1236 .000 own-price nestlé γ24 0.2370 .000 own-price*caloric content γ34 -0.0022 .000 closeness continuous attributes λ 0.4059 .000 closeness brand ϕbr 0.0629 .000 closeness type ϕt -0.0384 .000 expenditure simple cereals/private label β0 0.0047 .000 expenditure fortified β11 -0.0165 .000 expenditure muesli β12 -0.0085 .000 expenditure barilla β21 0.0036 .001 expenditure cameo β22 -0.0070 .000 expenditure kellogg’s β23 0.0057 .000 expenditure nestlé β24 -0.0149 .000 308 p. sckokai and a. varacca any positive sign of the own-price coefficient would reduce the negative impact of the own-price on the corresponding quantity, making the demand function more inelastic and consumers less price sensitive9. therefore, given the significance and the positive sign of all γs in our estimation, the consumers’ price sensitiveness for brands like barilla, cameo, kellogg’s and nestlé and for types like muesli and fortified is lower than for the reference category (private labels for simple cereals). nevertheless this effect is mitigated by the caloric content of the product, which increases the price sensitivity due to its negative sign. the positive sign of ϕ br (.0647) suggests that any increase in price for a products k sharing the same brand with j induces a switch in consumption to products of the same manufacturer. the market is therefore characterized by a certain level of brand loyalty. on the contrary, the negative sign of ϕt (-.037504) implies that consumers are likely to switch to other types of cereals as a response to a price increase. the last set of parameters to be discussed are expenditure shifters, both related to “brand” (β21 = barilla, β22 = cameo, β23 = kellogg’s, β24 = nestle) and to “type” (β11 = fortified, β12 = muesli). in particular, negative and significant coefficients for the interaction between expenditure and “type” discrete attributes indicate that an increase in the total purchases of breakfast cereals leads to a smaller share of fortified and muesli cereals. differently, an increase in expenditure for breakfast cereals leads to a larger share for simple cereals. the same intuition applies also for “brand” discrete shifter: results show that an increase in expenditure for cereals leads to larger (smaller) shares allocated to barilla, kellogg and private label (cameo and nestle). 5.2 own-price and cross-price elasticities estimated marshallian own-price and cross-price elasticities are obtained using the restricted model’s estimated parameters and applying the following formulas: ∑ ∑ ∑ ∑ ∑ ∑ ∑ η γ γ γ γ β β β λδ δ β β β = + + + − + + − = + − + + ≠ β β β β z z z w z z if j k w z z w w if j k 1 jk n n jn b p p jp b l jl b jrt n n jn p p jp jk c d d jk d jrt n n jn p p jp krt jrt 0 o 0 (14) where wjrt ( wkrt ) is the average of product j’s (k’s) expenditure share. own-price and cross-price elasticities, together with their standard errors, are reported in table 3. all elas9 this is consistent with the structure of the slutsky term for estimating price responses in the aids model (moro, 2004): s x p z w w z lnx lnp jj rt jrt s s js b jrt jrt h h jh rt rt2 0 2 0 2 ∑ ∑γ γ β β ( ) ( ) ( )= + + − + + −β if j=k sjk = xrt p jrt pkrt λ + d ∑ϕ d⎛ ⎝⎜ ⎞ ⎠⎟ +wjrtwkrt + β0 + h ∑βhz jh β⎛ ⎝⎜ ⎞ ⎠⎟ β0 + h ∑βhz jh β⎛ ⎝⎜ ⎞ ⎠⎟ lnxrt − lnprt( )⎡ ⎣ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ if j≠k 309brand competition in breakfast cereals ticities but two (namely, “barilla fortified” own price elasticity and “private label fortified” cross-price elasticity with respect to“kellogg’s fortified”) are statistically significant at 5% or lower. own-price elasticities are negative as expected and their magnitude is the largest in products whose price sensitivity is expected to be higher (private labels). furthermore, muesli products appear to have larger own-price elasticities with the exception of cameo’s. note that the absolute size of these own-price elasticities is in line with those estimated with the dm approach by pofhal and richards (2009) and bonanno (2013) on similar datasets. different patterns of positive and negative cross-price elasticities arise and some rationales can be provided to support these findings. first, cross-price elasticities for products of the same brand but of different type are positive, thus showing a substitution relationship. the rationale for these results can be related to our findings concerning the presence of brand loyalty and the absence of type loyalty. since products of the same brand are perceived as substitutes, any change in prices for kellogg’s muesli or kellogg’s simple may lead consumers to switch to kellogg’s fortified. the same reasoning applies also to other brands or to private labels, which show the same pattern for cross-price elasticities. the magnitudes of these cross-price elasticities are higher for private labels and kellogg’s muesli, while they are lower for nestlé; thus, the incidence of cross price changes on purchasing behaviour is more relevant in the first case. second, cross-price elasticities for products of the same type but different brand are negative, thus showing a complementarity relationship. however, their magnitudes are rather low; thus, their effect on purchasing behaviour is likely to be rather limited. moreover, consistently with the value of λ, the above within-brand substitution is likely to be oriented towards products with similar nutritional characteristics. thus, consumers will tend to switch from fortified to simple (and vice versa) rather than to muesli, whose caloric content is much higher. however, motivating these findings for those brands with just one product type (cameo and barilla) is not straightforward. one possible explanation may be related to the small value of their expenditure share, that makes demand very sensitive to small changes in prices. 6. concluding remarks in our analysis of the italian market for breakfast cereals, the modified aids model including distance metrics has proven to be a good method for estimating demand effects of differentiated products. besides reducing the number of cross-price parameters from forty-five to two, thus increasing the number of degrees of freedom and reducing the burden of estimating a large number of parameters, the model allow us to obtain excellent results in terms of significance of the relevant parameters. most cross-price, own-price and expenditure parameters are all significant at the 1% level and the r-squared of the model is 0.95, indicating the large explanatory power of the variables explaining the expenditure shares. price elasticities are also significant, and most of the times they are consistent with other results obtained from the estimation. 310 p. sckokai and a. varacca ta bl e 3. m ar sh al lia n el as tic iti es a nd s ta nd ar d er ro rs e st im at ed a t t he m ea n po in t o f t he s am pl e ba ril la fo rt ifi ed c am eo m ue sli ke llo gg ’s fo rt ifi ed ke llo gg ’s m ue sli ke llo gg ’s si m pl e n es tlé fo rt ifi ed n es tlé si m pl e pl fo rt ifi ed pl m ue sli pl si m pl e ba ril la f or tifi ed -0 .9 35 2. 24 3 -4 .4 92 0. 46 3 1. 45 3 -4 .8 16 0. 98 8 -4 .8 13 1. 27 7 0. 99 7 (1 .3 35 ) (0 .0 91 ) (0 .0 88 ) (0 .0 19 ) (0 .0 80 ) (0 .0 68 ) (0 .0 43 ) (0 .0 62 ) (0 .0 52 ) (0 .0 40 ) c am eo m ue sli 0. 30 8 -1 .6 90 0. 25 3 -0 .6 97 0. 21 3 0. 23 3 0. 15 7 0. 20 8 -0 .7 47 0. 14 3 (0 .0 13 ) (0 .1 20 ) (0 .0 12 ) (0 .0 08 ) (0 .0 11 ) (0 .0 10 ) (0 .0 06 ) (0 .0 08 ) (0 .0 06 ) (0 .0 06 ) ke llo gg ’s fo rt ifi ed -0 .1 09 0. 03 1 -1 .3 64 0. 24 3 0. 28 6 -0 .0 38 0. 03 5 -0 .0 21 0. 01 6 0. 03 2 (0 .0 01 ) (0 .0 01 ) (0 .0 27 ) (0 .0 00 ) (0 .0 02 ) (0 .0 05 ) (0 .0 02 ) (0 .0 05 (0 .0 01 ) (0 .0 01 ) ke llo gg ’s m ue sli 0. 08 1 -1 .1 64 2. 07 2 -7 .2 02 2. 06 5 0. 05 2 0. 04 8 0. 06 3 -1 .1 18 0. 05 5 (0 .0 03 ) (0 .0 04 ) (0 .0 12 ) (0 .2 94 ) (0 .0 12 ) (0 .0 08 ) (0 .0 05 ) (0 .0 03 ) (0 .0 05 ) (0 .0 03 ) ke llo gg ’s si m pl e 0. 02 3 0. 02 0 0. 25 7 0. 22 9 -1 .7 52 0. 03 7 -0 .0 94 0. 05 3 0. 01 2 -0 .0 59 (0 .0 01 ) (0 .0 01 ) (0 .0 02 ) (0 .0 00 ) (0 .0 24 ) (0 .0 02 ) (0 .0 02 ) (0 .0 02 ) (0 .0 00 ) (0 .0 03 ) n es tlé f or tifi ed -0 .1 74 0. 05 3 -0 .0 24 0. 01 6 0. 11 2 -0 .8 25 0. 43 9 -0 .0 34 0. 02 5 0. 05 7 (0 .0 02 ) (0 .0 02 ) (0 .0 07 ) (0 .0 00 ) (0 .0 04 ) (0 .0 41 ) (0 .0 03 ) (0 .0 07 ) (0 .0 01 ) (0 .0 02 ) n es tlé s im pl e 0. 04 7 0. 05 6 0. 10 6 0. 01 9 -0 .2 07 0. 69 6 -1 .6 28 0. 09 9 0. 03 0 -0 .2 31 (0 .0 02 ) (0 .0 02 ) (0 .0 04 ) (0 .0 01 ) (0 .0 05 ) (0 .0 04 ) (0 .0 63 ) (0 .0 04 ) (0 .0 01 ) (0 .0 05 ) pl f or tifi ed -1 .0 04 0. 31 5 -0 .0 93 0. 08 2 0. 64 0 -0 .2 20 0. 40 2 -8 .4 30 2. 30 7 2. 52 5 (0 .0 13 ) (0 .0 13 ) (0 .0 50 ) (0 .0 03 ) (0 .0 26 ) (0 .0 45 ) (0 .0 16 ) (0 .2 84 ) (0 .0 06 ) (0 .0 15 ) pl m ue sli 0. 63 6 -2 .6 77 0. 44 6 -2 .8 79 0. 38 5 0. 39 1 0. 29 2 5. 59 0 -1 7. 57 4 5. 51 7 (0 .0 26 ) (0 .0 21 ) (0 .0 32 ) (0 .0 13 ) (0 .0 33 ) (0 .0 23 ) (0 .0 16 ) (0 .0 15 ) (0 .8 46 ) (0 .0 12 ) pl s im pl e 0. 12 7 0. 12 4 0. 16 0 0. 03 7 -0 .3 92 0. 17 9 -0 .5 89 1. 64 5 1. 49 1 -8 .2 93 (0 .0 05 ) (0 .0 05 ) (0 .0 09 ) (0 .0 02 ) (0 .0 20 ) (0 .0 08 ) (0 .0 11 ) (0 .0 09 ) (0 .0 03 ) (0 .1 74 ) 311brand competition in breakfast cereals our findings indicate that, globally, consumers tend to be brand loyal but not type loyal, so that they respond to price changes by switching to products of the same brand, but with similar nutritional profiles. furthermore, private labels are confirmed to be the most price-sensitive products, while the products supplied by the biggest players on the market are the less-price sensitive ones. although being the produce with the smaller market share, barilla grancereale shows the lowest own-price elasticity, which probably indicates that it is perceived as a niche product. despite the relatively large range of products available in the market, resulting from innovation proposed by market leaders, the strong concentration of the market makes very difficult for consumers to take any advantage from the competition among national brands (nb). this intuition is supported by our findings, since the price sensitiveness of nb cereals products is rather low. in this context, private labels represent an alternative and offer an increasing range of cheaper products; on the other hand, their reliance on low prices makes private labels demand extremely price sensitive. besides the strong implications these findings have on the demand side, the supply side deserves to be taken into account as well. as we previously mentioned, the biggest players on the market (namely kellogg’s and nestlé) show an important product innovation rate: new products are yearly launched on the market, and some of them disappear rather quickly. consistently with our findings concerning the absence of consumers’ loyalty to the product type and their tendency to switch to products with similar nutritional attributes, it would be interesting to carry out a cost-benefit analysis for innovating manufacturers. product innovation in a broad sense will undoubtedly help to keep consumers loyal to the brand; however one should propose new lines in line with this consumer profile. for example, a consumer may be happier to switch from simple cereals to fortified cereals rather than to more caloric ones (muesli). from a methodological point of view, the model can certainly be further improved. the choice of the dm shifters is rather arbitrary and may strongly affect the results; moreover, the issue of multicollinearity is very likely to arise. further difficulties could arise when dealing with spatial attributes other than the nutritional ones; in such cases, defining a unique unit of measurement and choosing the correct dimensionality could represent a significant hurdle. finally, if one adopts the dm approach, the imposition of the standard demand theory restrictions is no longer straightforward. thus, further research may try to overcome these problems through an appropriate reformulation of the model. acknowledgements an earlier version of this paper was presented at the 1st aieaa conference ‘towards a sustainable bio-economy: economic issues and policy challenges’. 4-5 june, 2012, trento, italy. references bonanno a. 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(2004). analisi della domanda, teoria e metodi. milano: franco angeli. pinkse, j., slade, m. and brett, c. (2002). spatial price competition: a semi-parametric approach. econometrica 70: 1111-1155. pinkse, j. and slade, m. (2004). mergers brand competition, and the price of a pint. european economic review 48, 617-643. pofahl, g.m. and richards, t.j. (2009). valuation of new products in attribute space. american journal of agricultural economics 91(2): 402-415. rao, d.s.p., o’donnell, c.j. and ball v.e. (2002). transitive multilateral comparison of agricultural output, input and productivity: a nonparametric approach. in: ball, v.e. and norton g.w. (eds), agricultural productivity: measurement and sources of growth, kluwer academic publishers, 85-116. rojas, c. and peterson, e.b. (2008). demand for differentiated products: price and advertising evidence from u.s. beer market. international journal of industrial organization 26(1): 288-307. appendix table a1. continuous distance metrics barilla fortified cameo muesli kellogg’s fortified kellogg’s muesli kellogg’s simple nestlé fortified nestlé simple private label fortified private label muesli private label simple barilla fortified 1.0000 0.0326 0.0223 0.0063 0.0160 0.0194 0.0125 0.0224 0.0188 0.0140 cameo muesli 0.0326 1.0000 0.0197 0.0057 0.0152 0.0201 0.0138 0.0214 0.0150 0.0140 kellogg’s fortified 0.0223 0.0197 1.0000 0.0050 0.0300 0.0668 0.0216 0.0803 0.0107 0.0206 kellogg’s muesli 0.0063 0.0057 0.0050 1.0000 0.0046 0.0048 0.0042 0.0050 0.0091 0.0044 kellogg’s simple 0.0160 0.0152 0.0300 0.0046 1.0000 0.0300 0.0321 0.0378 0.0088 0.0551 nestlé fortified 0.0194 0.0201 0.0668 0.0048 0.0300 1.0000 0.0273 0.0781 0.0100 0.0216 nestlé simple 0.0125 0.0138 0.0216 0.0042 0.0321 0.0273 1.0000 0.0257 0.0076 0.0316 private label fortified 0.0224 0.0214 0.0803 0.0050 0.0378 0.0781 0.0257 1.0000 0.0105 0.0252 private label muesli 0.0188 0.0150 0.0107 0.0091 0.0088 0.0100 0.0076 0.0105 1.0000 0.0082 private label simple 0.0140 0.0140 0.0206 0.0044 0.0551 0.0216 0.0316 0.0252 0.0082 1.0000 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(2): 199-212, 2012 the determinants of farmer’s intended behaviour towards the adoption of energy crops in southern spain: an application of the classification tree-method giacomo giannoccaro1, julio berbel department of agricultural economics, university of cordoba, spain abstract. despite growing interest in biomass over the last number of years, bio-energy derived from biomass currently contributes to a very small share of the total spanish energy market. how individual farmers choose to respond to the opportunities presented by these relatively novel crops has still received scarce attention. in this paper, farmers’ intentions towards the adoption of energy crops are analyzed. a survey of 201 farmhouseholds in southern spain is explored using a non-parametric approach based on classification tree algorithms. the main outcome of this analysis is that off-farm labour factor affects the adoption of energy crops on farm, together with farm specializations, size of owned land and farmer’s education. while the study confirms the relevance of the main determinants available from the literature, need for further research is emphasised. keywords. farmer behaviour, energy crops, classification tree, adoption of innovation jel-codes. d22, q00, q491. 1. introduction and objective the use of biomass as an energy source has undergone a revival in industrial societies during the last 15 years. with the strong growth in human populations worldwide, global energy consumption is beginning to exhaust conventional fossil energy resources. in addition, the release of co2 from the burning of fossil energy has led to global climate change. increased use of biomass for energy is therefore considered a potential solution as it offers moderate to significant greenhouse gas (ghg) savings compared with the use of fossil energy. indeed, biomass could contribute to rural development through job creation and improved competitiveness in rural areas (fischer et al., 2005). for this purpose, bio-energy is being promoted through the most recent eu directive (2009/28/ec) as well as national policy (renewable energies plan 2005-2010, spain). despite growing interest in biomass in recent years, bio-energy derived from biomass currently contributes to a very small share of the total spanish energy market. indeed, approximately 77% of the primary energy used in spain in 2010 was fossil fuel 1 corresponding author: es2gigig@uco.es. 200 g. giannoccaro and j. berbel based. only 11.3% of total spanish primary energy production came from renewable sources. of these, biomass accounted for a mere 3.8%, far from the established objective (idae, 2010). through the renewable energies plan 2005-2010, spain has fixed the objective of covering 29.67% of the total renewable energy production from biomass. of the three main biomass sources (agriculture, forest and waste), agriculture biomass production is generally considered to have the greatest energy potential (eea, 2006). agricultural biomass includes biomass produced directly from agricultural activities, such as cereal grains, sugar crops, oilseeds and other arable crops, as well as farm forestry in short rotation (e.g. willow and poplar). at the same time agricultural biomass includes crop residues such as straw, and livestock wastes, for instance manure and animals fats. nevertheless, for biomass to play a significant role in the world’s energy future, dedicated energy crops are essential (evans et al., 2010). energy crops on farmland can produce biomass from fast-growing species in high densities and can be collected in short cycles. many studies have been conducted in several areas of spain, on the assessment (technical and environmental) of bio-energy production by means of energy crops, but in the assessment analysis only the off-farm chain has been considered. biomass potential has often been evaluated on the basis of agronomic and climatic conditions (gómez et al., 2011), in terms of the global feasibility of a bio-energy system (gasol et al., 2009; martínez-lozano et al., 2009), as well as environmental issues (butnar et al., 2010; sevigne et al., 2011). yet, none of the studies conducted to date have looked at the farm economics, preferring to focus on the agronomic and technical feasibility of these crops. generally, there is a lack of research on the farm economic issues and little is known about farmers’ attitudes towards the adoption of bio-energy crops. growing energy crops is a non-traditional land use option (i.e. crop farming) which could be considered as innovation (villamil et al., 2008). energy crops face competition from other, arguably more standard uses of farmland, and if not seen as profitable to individual farmers, they will not be grown. farmers’ decisions are therefore a key constraint to potential supply. for instance, sherrington et al. (2008), analysed barriers to adopting new cropping systems (i.e. dedicated energy crops) at the farm gate level in uk. the authors found several barriers to widespread adoption, such as financial returns, and the fact that competing activities were much more rewarding in particular, wheat due to the increasing price a few years before. in addition, farmers need trusted information to make decisions (sherrington et al., 2008), somewhat through differentiated channels (villamil et al., 2008). the authors found that, in the areas studied, farmers need reliable information about technical and agronomic aspects of cultivation, as well as economic returns and contract agreements to produce energy crops. in general, farmers’ attitudes and intentions towards the adoption of energy crops on their farmland have still received scarce attention. to the best knowledge of the authors, there is not a single study focusing on farmers’ attitudes towards the adoption of energy crops in spain. in this context, this paper seeks to analyse farmers’ intentions towards the adoption of energy crops in spain. the research aims to explore farmers’ attitudes towards energy crops assuming that the other external driver factors remain constant. it should be stressed that in the geographic area under consideration energy crops are still not cultivated, therefore adoption of energy crops is seen as product innovation. in addition, all 201an application of the classification tree-method consequences in terms of land and water use, as well as changes in farming practices are outside the scope of this paper. the analysis is based on the stated preferences theory and relies on a sample of 201 farm-households in andalusia (southern spain) carried out in 2009. a non-parametric approach based on classification tree algorithms is used to identify the main socio-economic determinants of farmers’ intentions towards the adoption of energy crops. the remainder of the paper is organised as follows: in section 2 the area study and sample descriptions are provided, followed in section 3 by the methodology. section 4 illustrates the results, and finally concluding remarks are provided. 2. materials 2.1 area description andalusia is the most populous and the second largest, in terms of land area, of the seventeen spanish autonomous communities. figure 1 reports a map of spain. the agricultural utilized area amounts to 4,974,173 ha accounting for 57% of total surface. in 2009 gdp from the agricultural sector was around 6%, with an employment rate of 7% (department of agriculture and fisheries, 2010). the main climatic constraint for agricultural activities is water shortage. the rainfall pattern is typically mediterranean, with wet winters and, hot and dry summer seasons. the average annual precipitation is 560 mm, but drought periods are quite common. as a result, irrigation is the most important economic factor. while only 25% of figure 1. map of case study 202 g. giannoccaro and j. berbel the total cultivated area is irrigated land, more than 60% of total agricultural gdp comes from irrigated crops. with respect to farm size, there is the classical dualism between the number of farmers and farm size. the majority of farmers (60%) cover a very small portion of farmland (7.5%). concerning the cropping pattern, arable land accounts for 32% of farmland. a rain fed system consisting of winter cereals and sunflower prevails. in other tilled areas where water is available, cotton and sugar beet are commonly grown. however, due to the last cap (common agricultural policy) reform in 2006 the prevalence of both crops has decreased considerably. from 2005 to 2009 they have seen a decrease of 44%. on the other hand, permanent crops are quite extensive (33%) with olive grove systems being the most important. citrus, fruit and grapes are also cultivated. in addition, there are permanent meadows called ‘dehesa’ for pig rearing that cover 26% of total utilized area. finally, fresh cut crops (i.e. irrigated horticulture) and other secondary field crops cover a small percentage (department of agriculture and fisheries, 2010). the region also includes a protected zone with 27% of total area belonging to the natura 2000 red, which is the largest area of this kind in the european union. 2.2 farm-household sample in the spring of 2009, farm-households across 3 main provinces of andalusia (jaen, córdoba and seville, accounting for 57% of farmland and 52% of farm-households for the andalusia region) were surveyed by way of a questionnaire and a dataset of 201 interviews was collected. data was collected through face to face interviews. the questionnaire was divided into the following sections: a) information about the household; b) information about the farm; and c) planned behaviour about a number of issues, including towards energy activity. the survey questionnaire was developed in order to analyze farmers’ intentions towards the adoption of energy crops with the rest of the external driver factors being constant. the horizon fixed was 2020 and the scenario (next ten years) was defined assuming as constant circumstances with regard to prices, employment opportunities and other conditions (e.g. water availability) would remain stable at january 2009 levels. moreover, it was assumed that the cap would continue as it is currently planned, particularly with regard to the single farm scheme (sfs), rural development policy (rdp), and other instruments such as milk quotas and crosscompliance. all of these factors and existing differences from farm to farm were also considered stable. the objectives of the survey were: a) to understand the farmers’ plans with respect to energy crops; and b) what factors explain differences in farmers’ intentions. care was taken to gain broad representation of the farming community age, farm size, and type of crop specialization. the main features and representativeness of the data sample are reported in table 1. the main farm specialization covered by the sample was specialist olive groves accounting for 30% of surveyed farmers. otherwise, the group of arable crops reached 45%, special 203an application of the classification tree-method ist cop crops covered 19%, general field crops accounted for 27%; and other permanent crops and, mixed livestock with others crops, represented 10 and 13% respectively. the representativeness of sample is fair with prevalence being for farmers specialized in olive grove systems and other permanent crops. however, it should be stressed that arable crop farmers are overvalued mainly with general field crops meanwhile class of farm size above 50 ha is overrated. as a whole the farmers sampled manage approximately 20 000 ha. finally, the average farmer age in the survey is 54 years, with 56 years being the average in the study area. table 1. comparison between area study and sample total study area sample total surface (ha) % % plain 2361900 57.19% 13267.4 66.41% hill and mountain 1767600 42.80% 6711 33.59% total 4129500 100% 19978.4 100% farm specialization farm cop* 25630 16.47% 38 18.91% general field crops 26420 22.05% 54 26.87% olive grove 71655 46.06% 61 30.35% other permanent 25830 16.60% 21 10.45% livestock and field crops 23785 15.29% 27 13.43% total 155570 100% 201 100% farm classified by class of size farm 0 5 115259 62.55% 42 20.90% 5-20 45753 24.83% 57 28.36% 20-50 12243 6.64% 48 23.88% > 50 11009 5.97% 54 26.87% total 184264 100% 201 100% ha 0 5 209413 7.40% 90.9 0.45% 5-20 243893 8.61% 707.5 3.54% 20-50 350319 12.37% 1598.5 8.00% > 50 1845788 65.19% 17581.5 88.00% total 2831240 100% 19978.4 100% livestock number of unit cattle 324873 10.52% 1715 11.72% sheep and goats 1645406 53.27% 7797 53.29% pigs 1118260 36.21% 5120 34.99% total 3088539 100% 14632 100% farmer’s age (mean of years) 56 54 *cereals, oil seed, and protein. 204 g. giannoccaro and j. berbel 3. methodology the methodology used is a classification tree method, aimed at classifying farmers according to their attitudes (adopt vs reject) in order to identify and profile potential energy crop growers. firstly, discussion on the nature and elicitation of stated intentions is set, then the methodology used in the scope of paper as well as the variables considered as determinants are reported. the use of stated reactions as a good indicator of actual behaviour is a debated issue in the literature. this approach was chosen given that at the time of the study reliable information about the farmer adoption of energy crops was not yet available. indeed, when the questionnaire was set, energy crops were still inexistent in the area (aae, 2008). consequently, ex-post econometric regression in order to underline an adoption pattern of energy crops was discarded in favour of an ex-ante analysis based on the stated responses. in this context, according to attitude theory, and empirical data, behavioural intention is a better predictor of behaviour than any other measures (ajzen, 1991; viaggi et al., 2011a). the information about stated adoption intentions was collected through a closed question formulated as follows: within the next ten years, will there be any energy crops on your farm? the options were ‘yes’ or ‘no’; in addition, farmers’ responses that were not clearly stated (i.e. they did not answer and, they did not know what they would do) were also collected. in addition, it should be stressed that this analysis concerns adoption intentions and, consequently, we are not able to discuss the level of adoption, or in other words what surface area will be devoted to energy crops. finally, the question did not include an explicit reference to a comparison between different crops, farming practices (e.g. rotation, irrigation, fertilization) and other relevant aspects of energy crops farming. these explicit references were considered complicated and unpredictable at the time of surveying given the lack of energy crops in the area. in the following analysis only the stated answer concerning reactions in terms of onfarm adoption is considered as a dependent variable. the dependent variable derived from the question concerning the energy cropping described above, and used in this paper, is quantified as i = [0,1]. value 1 is assigned if the answer to the option of the question was ‘yes’, and 0 if ‘no’. other unclear responses were discarded. indeed, of the 201 farm-households interviews, 154 observations had a valid value. for the purpose of research, the methodology applied is a non-parametric method based on classification tree algorithms. tree-based methods split the sample step by step into smaller and smaller groups according to a mathematical condition. there are several variants of tree-based methods with different splitting criteria (i.e. algorithm). for example, the oldest tree classification algorithm, the chaid (chi-square automatic interaction detector) technique uses a χ2 test to decide which group to split (kass, 1980). however, the algorithm hints a misappropriation in using continuous variables. the cart (classification and regression trees) is used here. it was firstly proposed by breiman et al. (1984). the cart algorithm uses both continuous and categorical attributes for building the decision tree. the splitting measure in selecting the splitting attribute is gini index. usually, it is claimed that cart is most suitable for forecasting, while chaid is better for data analysis. in addition, cart algorithm gives a room to manage 205an application of the classification tree-method missing value. anyanwu and shiva (2009) found that the cart algorithm is largely used as a decision tree technique with high classification and prediction accuracy. for these reasons the cart algorithm is chosen. by using the cart algorithm the tree is obtained in two phases: firstly the tree is built using an algorithm that recursively divides the sample in smaller sub-samples as the tree grows. the procedure takes into account all available variables from the sample and checks if there is any statistically significant difference within the pair with respect to the target variable (in our case the adoption of crop energy). this procedure may result in a too complex tree achieved by growing an overly large tree. then the second phase goes namely the pruning procedure, where ‘unreliable’ branches are pruned in order to minimize over-fitting (anyanwu and shiva, 2009). the pruning technique we used follows the post-pruning approach as the one used in system cart (breiman et al., 1984). the process results in a tree-like structure of groups, also called nodes, in which each node has two child nodes. terminal nodes, also called branches of the tree, define the classification of subjects. in this case, the classification tree was built by splitting each node until its child nodes contained less than six observations. we made this choice based on the size of sample and, essentially taking into account the shortage of adopters. a minimum of 20% of adopters in a branch of the tree was seen as the most convenient splitting result. tree classification has been commonly used in medicine (witbrodt and kaskutas, 2005), veterinary science (nagy et al., 2010) and agricultural economics (viaggi et al., 2011b). the variables considered as determinants are all those derived from the questionnaire. the questionnaire was designed following a review of the literature on farms adopting innovation, even if specific literature concerning energy crops is still scarce. in particular, several farmer characteristics emerge related to an adoption attitude. for instance it is assumed that the younger the farmers, the more likely they are to adopt innovations early in their respective life cycles (rogers, 1995). formal education level is also recognized among farmers’ human capital linked with the adoption of innovation (e.g. fernandezcornejo et al., 1994; breustedt et al., 2008). it is assumed here that better and more educated farmers will increase adoption. on the other hand, it is well documented that farm structural features (e.g. farm size, land ownership, farm specialization) have a strong influence on the farmers’ adoption process (e.g. cutforth et al., 2001; breustedt et al., 2008; villamil et al., 2008; keelen et al., 2009). in addition, it has been claimed that off-farm jobs may be related to the farmer’s attitudes towards new activities. the flexibility of the farmer’s scheduling as well as the complexity of new crops may have a significant bearing on the decision to adopt new farming pattern (hipple and duffy, 2002; fernandez-cornejo et al., 2005; keelen et al., 2009). the full list of variables used, and the way each variable was measured, is shown in table 2. only 21% of the farm-households interviewed stated the intention to adopt energy crops on farm, which is the dependent variable chosen. the farm characteristic variables are related to current farm size in terms of owned land and land rented-in. renting plays a major role in land availability, particularly for annual crops and livestock; about a half of farms rent-in some land. farming specialization covers the main agricultural crop systems across the study area, namely specialized 206 g. giannoccaro and j. berbel cop farm (i.e. winter cereal, sunflower and leguminous crops usually cropped in annual rotation), other general arable crops labelled field crops, olive grove systems, other permanent crops which cover citrus, orchard fruit and vineyards, and finally livestock systems. the latter category covers both specialist livestock and livestock farming with field crops. there are also the farm features related to geographic characteristics, such as altitude. table 2. list of variables used as determinants code variable description coding mean s.d. fa rm fe at ur es land owned total land owned (ha) 78.29 269.34 land rent in (dummy) land rent-in 0 = no, 1 = yes 0.45 0.49 off-farm job (dummy) off-farm labour by household members including farm head 0 = no, 1 = yes 0.60 0.49 specialization main farm specialisation cop field crops olive grove other permanent livestock systems 16.3% 26.8% 34.6% 10.5% 11.8% _ altitude (dummy) location of the farm with respect to the altitude 0=plain, 1=hill/mountain 0.24 0.43 fa rm er ’s fe at ur es age age of farm head (years) age 52.02 13.01 education education level of farm head elementary school, primary school, high school, master, degree, ph.d. 50% 2.6% 24.7% 12.3% 9.1% 1.3% _ extension service (dummy) farmer assisted by an extension service 0 = no, 1 = yes 0.92 0.14 farmer union (dummy) membership of farmer union 0 = no, 1 = yes 0.41 0.49 share gross revenue share of farm income from agricultural activity over total household income (%) less than 10% 10-29% 30-49% 50-69% 70-89% more than 89% 21.5% 21.5% 9% 10.4% 10.4% 27% _ note: 154 observations (only valid answers). indicators connected to off-farm jobs by a household member reports a mean of 0.60. the remaining variables concern the age of the farm owner, his/her education level, the use of extension services and membership in a farm union. finally, there is the share of farm income with respect to the total household income accounting for six levels, ranging from less than 10% to higher than 89%. 207an application of the classification tree-method 4. results figure 2 shows the variables selected by the cart algorithm. the first ramification point represents the first determinant of adoption selected by the procedure. it tells us figure 2. classification tree for the adoption of energy crops on-farm 208 g. giannoccaro and j. berbel that the prevalence of adoption behaviour is higher amongst farmers who do not have offfarm jobs compared to those who do. although only 21.4% of the farms would adopt the energy crops in the group, this share is highly differentiated between farmers who have off-farm jobs and those who do not. while the right route represents 39.6% of surveyed farmers (61 out of 154 observations), of these 24 would adopt new crops on farm by 2020, which in turn covers the largest share of stated adoption behaviour. in fact, this first ramification accounts for 24 out of 33 farmers who would adopt energy crops. in this route the next ramification point, that is, the next predictive factor found by the procedure, was farm specialization. while specialized cop and olive grove farms demonstrate the smallest share of willingness to adopt (only 4 out of 24 farmers), specializations such as general field crops, other permanent crops and, livestock with other crops, account for the majority of adopters. indeed, node 5 accounts for 60.6% (20 over 33) of those who would adopt energy crops in the next ten years. at this point of the tree, node 6 is also a branch of this tree ramification. by contrast, node 5 was additionally split with the size of farm land being the selecting factor. land owned emerges as a relevant factor in the farmer’s decision to adopt energy crops. node 10, where farms with larger land sizes were selected, accounts for 42.4% (14 over 33) of total adopters. although this node covers only 18.2% (28 respondents) of the total sample it constitutes half of those who would adopt energy crops. on the other hand, in node 9, the grouping of smaller farms, there are 6 adopters out of 33 (18%).this route tree ramification follows with two additional nodes, namely node 11 and node 12. these nodes are branches of this tree ramification, which in turn means that non additional ramifications are possible considering all the available variables. once again, the splitting variable is the size of land owned. likewise, the larger the farm, the higher the number of adopters. as a whole, node 12 is the branch node with the higher number of adopters. almost 40% of all farmers who would adopt energy crops (13 over 33) are at this terminal node. let us turn now to the left side of tree ramification, where farmers who have an off-farm job were further divided by the algorithm according to education level. as a result, the sub-group of farmers who have an off-farm job was split into two nodes, respectively node 3 for those who have less than a high school education and node 4 for those who held a higher level of education. in this regard, the findings show that amongst farmers who have an off-farm job, those with a higher level of formal education seem to be more amenable to adopt energy crops on farm. indeed, 6 out of 9 in this ramification fell into node 4. while node 3 is a branch of tree, node 4 was split into two additional nodes, namely node 7 and node 8. the splitting variable was farm specialization. basically, in this ramification the level of farmer education and farm specializations, such as field crops and cop, are the most important features related to the farmers’ adoption. with respect to the overall sample, the results show that only 33 of the 154 farmers interviewed, namely 21.4%, are willing to adopt energy crops on farm. most of these fall into node 2, with off-farm jobs being the discriminating variable. terminal nodes performed by the tree classification method also show that the largest sub-group is node 3, where the majority of rejections are covered with the farmer’s education being the selecting variable. the cart algorithm also performs a ranking of importance for each independent variable of the tree. farm specialization, farm size, off-farm job and farmer’s education were respectively ordered from major to minor importance. 209an application of the classification tree-method the performance of the classification tree was rather good and in line with the experience of other authors (viaggi et al., 2011b; nagy et al., 2010) with about 84% of the choices correctly predicted. indeed, 88.4% of farmer’s rejection behaviour and 69.7% of adoption was correctly classified. 5. concluding remarks the main outcome of this analysis is that farm features such as the off-farm labour factor together with specializations and the size of land owned affect the adoption of energy crops on farm. in addition, personal features such as farmer education levels are also relevant. a large number of southern spanish farmers have jobs off-the-farm. farming activities and practices that create scheduling conflicts between on-farm management and off-farm employment discourage adoption of alternatives. this aspect of compatibility is discussed in the literature. likewise, formal education level is also recognized among farmers’ human capital linked with the adoption of innovation. these findings are in line with the literature on innovation adoption. at the same time, energy crops face competition from other arguably more standard crops in the study area, such as olive grove systems therefore these specialized farms do not seem to perceive energy crops to be as attractive as other specializations. the result of this attitude is that most of the farms in the study area would not adopt energy crops. on the other hand, specializations such as field crops appear to be relevant in the study area. among the field crops cultivated in the area, cotton and sugar beet are most common. it should be stressed that by mean of the last common agricultural policy reform started in 2006 both crops have been constrained by national entitlements. this means that each eu member can produce a maximum area of these crops at a subsided price. as a result, those farmers that were obliged to reduce the amount of farmland devoted to these crops might be more willing to adopt energy crops. similar results have been obtained regarding french farmers who have retreated from sugar beet production and who would be more likely to participate in miscanthus activity (bocquého et al., 2011). finally, farm land size was significant in the tree classification. generally, the adoption of energy crops would be more likely on larger farms. contrary to expectations, factors such as farmer age do not appear here. it should be emphasised that farmer age and education level are strictly correlated with the younger farmers being those who reach higher education levels. in addition, since the sample here is very small it could well be biased. however, according to the results, only the education factor is significant. according to the findings further research should be carried out taking into account for instance the age of assets and the actual available liquidity of farm-households. moreover, farmer’s expectation about market price and job opportunity could well be related to the adoption of energy crops. these latter factors could be relevant in the adoption process, affecting the profitability of food and fibre crops as a whole, and obviously, energy crops. moreover, in times of market price volatility, energy crops might also be considered to be a risk reducing crop through diversification. more insights are also needed with respect to the influence of the common agricultural policy reforms. indeed, incentives for energy crops, as well as other changes in the scheme of support, should be addressed. this aspect is also related to idle/marginal 210 g. giannoccaro and j. berbel lands and the change in the crop-mix that could arise as a result of policy amendments. energy crops could be very interesting alternatives on marginal lands, as campbell et al. (2008) emphasize. this research aimed to explore farmer attitudes and responses towards a new cropping activity, namely energy crops, in a study area that lacked existing examples. the results should be considered as preliminary findings. other aspects related to energy crops, such as potential social (food competition) and environmental (water and land use) threats (evans et al., 2010), need to be analysed. the latter could be relevant in the study area (i.e. water availability) dealing with farm choices. acknowledgment we acknowledge funding from the european commission, 7th framework programme through the project cap-ire (assessing the multiple impacts of the common agricultural policies (cap) on rural economies, <www.cap-ire.eu>. the views expressed here do not reflect those of the eu. the authors thank the anonymous referees and the editor for insightful comments on an earlier draft of this article. references aae (2008). situación de la biomasa en andalucía. agencia andaluza de energía, sevilla, <www.agenciaandaluzadelaenergia.es>, accessed 15 june 2008. ajzen, i. 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(2005). does diagnosis matter? differential effects of 12-step participation and social networks on abstinence. american journal of drug alcohol abuse, 31 (4): 685-707. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 3(2): 137-157, 2014 doi: 10.13128/bae-12994 explaining determinants of the on-farm diversification: empirical evidence from tuscany region fabio bartolini1, maria andreoli, gianluca brunori department of agriculture, food environment, university of pisa abstract. on-farm diversification towards multifunctional activities is perceived as central in the common agricultural policy (cap) reform and in the horizon 2020 strategies, because it strengthens territorial and social cohesion of rural areas. while from a “macro” point of view relations between farm-household diversification and rural economies are central in the process of multi-functionality and in the provision of public goods through agricultural activities, from a “micro” point of view onfarm diversification activities can represent a relevant share of farm income. agricultural economics and rural sociology have developed models aiming to explain the determinants of on-farm diversification thus providing a set of variables potentially influencing on-farm diversification. the paper applies a count model to explain the number of on-farm diversification activities that are implemented by farms in tuscany. since the high number of agricultural holdings that do not apply any diversification activity, we propose a two-step model where, firstly a simulation of adoption of diversified strategy as binary variable is considered and secondly, a model analysing the determinants of diversification intensity among the farms that have decided to diversify is implemented. results confirm that location near main touristic areas and vicinity to urban markets are important determinants of on-farm diversification intensity. results highlight a positive contribution of the pillar 2 agricultural policies both in determining the diffusion of on-farm diversification activities and in influencing the intensity of adoption, while high per hectare single farm payments have a negative influence on diversification intensity. keywords. on-farm diversification, multi-functionality, determinants, tuscany. jel codes. q18, q10 1. introduction on-farm diversification towards new activities is seen as central in the common agricultural policy (cap) reform and in the horizon 2020 strategies, since it strengthens the territorial and social cohesion of rural areas (european commission, 2010). in fact, in developed economies several available strategies provide means to differentiate income, while providing additional services and fulfilling functions that have a public 1 corresponding author: fabio.bartolini@unipi.it. full research article 138 f. bartolini, m. andreoli, g. brunori utility. thus, the increase of farm income through the allocation of household labour to on-farm diversified activities represents one of the main strategies to pursue viability of rural areas. consequently, pluriactivity and diversification have brought about a greater integration and interdependency between farm households and rural economies. the relevance of diversification strategies is growing in rural economies. according to knickel and renting (2000), the relations between farm-household diversification and rural economies are central in the process of multi-functionality and in the provision of public goods through agricultural activities. in fact, based on 2007 agricultural resource management survey (arms) data, vogel (2012) estimates that the contribution of on-farm diversification activities on the total value of us agricultural production is about 40%. other authors (see e.g. carter, 1999) have reported positive effects of diversification activities diffusion on rural development. the british department of environment, food and rural affairs (defra, 2012), estimates that since 2006, the share of uk farmer’s income coming from diversification activities has continuously increased, while irpet (2013), affirms that the share of farm income coming from diversification activity in tuscany region has increased by 20% during the last fifteen years. in 2010, according to henke and povellato (2012) about 5% of italian farmers stated having diversified activities that accounted for 10 million of full time equivalents, i.e. 4% of the total labour force in agriculture. after the world war ii, labour saving technologies have brought about a dualistic development among farms, which have either pursued concentration and specialisation in agriculture production or diversification strategies (see e.g. mcnamara and weiss 2005). large amounts of literature deal with understanding the phenomena of farm diversification and pluriactivity, looking at the contribution of farm, farmer, and household characteristics (hansson et al., 2013) as well as location and space in explaining farm household behaviours (lange et al., 2013). in this paper, determinants and motivations of on-farm income diversification towards activities other than crop and animal production are investigated. the paper addresses the activity classified as “agricultural secondary activities” by eurostat statistics on agriculture, forestry and fishery (see, e.g. eurostat, 2013) and the italian 2010 agricultural census. the determinants of diversification strategies have been studied at micro level, focusing, on one hand, on the adoption of a specific activity (see amanor-boadu 2013) and of categories of activities (e.g. vik and mcelwee, 2011) and, on the other hand, on the adoption intensity by using indices measuring the degree of diversification (e.g. barbieri and mahoney 2009). this paper applies a count data model with the aim to explain the determinants of diversification intensity among farms in tuscany. the level of intensity is measured as a count of available diversification activities. furthermore, zero-inflated count models are applied in order to get an insight on the determinants of adoption of a diversification strategy by using a two step-model, where firstly, a simulation of adoption of a diversification strategy as a binary variable (adoption versus non-adoption) is considered and secondly, the determinants of intensity of on-farm diversification are analysed. the empirical analysis, which used micro-data collected from agricultural census 2010 and from the tuscany agency for agricultural payments (artea), has highlighted heterogeneities among tuscany farms in relation to explanatory variables between determinants of adoption of a diversification strategy and determinants of the intensity of adoption. 139explaining determinants of the on-farm diversification the paper contains five sections each focusing on one topic. in section 2 a review of literature is presented. section 3 focuses on the theoretical model discussing the methodology underlying the econometric approach adopted. section 4 gives a presentation of the data that has been used. the results and conclusions are given respectively in section 5 and 6. 2. adoption of on-farm diversification strategy the theoretical basis of the diversification strategy is rooted on a farm-household model, where a generic farm household chooses to allocate household labour between on-farm or off-farm activities, with the aim to increase income and to ensure a selected consumption level, while meantime ensuring a sufficient level of utility derived by the time allocated to leisure (singh et al., 1986). agricultural economics literature has investigated diversification adoption/diffusion under several perspectives focusing on the identification and definition of a diversification strategy and clarifying how this definition differs from the one of pluriactivity. following the classification proposed by oecd, (2009) and by salvioni et al., (2013), pluriactivity is a consequence of off-farm allocation of household labour while we have diversification when household labour is allocated on-farm. authors highlight that diversification can be pursued by choosing one of the following pathways, a) agricultural output diversification (e.g. crops diversification), b) product diversification (e.g. organic products, protected denomination of origin products) and, c) non-agricultural output diversification (e.g. tourism activities). as salvioni et al. (2013) point out the last two strategies can be grouped into a general category named on-farm income diversification. on-farm income diversification strategy has been largely studied in agricultural economics. since the seminal work of johnson (1967), agricultural economics has identified the increase in returns of productive factors or the reduction of the risk of agricultural activities as main reasons to diversify farm activities. andersson et al. (2003), applying a dynamic portfolio model, found that for risk adverse farmers a lower economic return was compensated by a reduction of the risk, when on-farm diversification is a risk-reduction activity. in this sense the differentiation of portfolio activities might reduce the exposure to several sources of uncertainty that affect farms, i.e. weather, pests and diseases, price and polices related to agricultural production, market and trade uncertainties. thus, on-farm income diversification allows increasing utility for risk adverse farmers by reducing risk exposure due to specialisation (see mcnamara and weiss 2005). mishra et al., (2010) and (2013), have found that on-farm diversification and off-farm labour allocation are less risky activities compared with agricultural production due to lower exposure to financial and physical risks (see bowman and zilberman, 2013). barbieri and mahoney (2009) applying principal component analysis found that in addition to risk and uncertainty reduction (which remains the main goal of diversification) other five goals affect farmers’ behaviour towards on-farm income diversification. these goals are the following, 1) retaining and expanding markets, 2) enhancing financial condition of the farm household, 3) individual aspiration and pursuit of personal interest/hobbies, 4) increasing revenues by means of additional income sources and 5) maintaining family connections keeping household labour on farm. vik and mcelwee (2011) applying a multinomial model to the adoption of diversification categories, found that motivations differ according with the categories of diversification activities. they point 140 f. bartolini, m. andreoli, g. brunori out that the search for additional income sources is the main motivation to diversify into tourism or other agricultural secondary activities while resource scarcity and availability as well as the desire to meet people are motivations to diversify into green care or social activities. mcelwee and bosworth (2010) individuated farm and household structures affecting the adoption of a diversification strategy and found that larger farms with young and high-educated male farmers are those that diversify. furthermore they point out that farmers using internet on a regular basis show a higher probability to diversify while farmers with established business relations and involved in networking show lower statistical differences between those who diversify and those who do not diversify. other studies have shown how space and location influence the decision making towards on-farm income diversification strategies. the reason that increases the propensity to diversify is the demand and supply of diversified activities due to differences in the attractiveness of landscape, amenities and distance from urban areas and from main markets (zasada, 2011). recently, lange et al. (2013) studied the spatial effect of rural attractiveness in explaining the diffusion of on-farm diversification activities, when this strategy aims to maintain farm viability. mishra et al. (2010) have found that location in peri-urban areas influences the expectations regarding off-farm labour earnings and consequently, determines a higher allocation of household labour to off-farm activities due to the lower profitability of on-farm diversification strategies. both pillar 1 and 2 payments affect the propensity to diversify production. agricultural economics literature has highlighted that cap strongly affects farm structures and types of production, and consequently directs farmers’ behaviour towards on-farm diversification activities (ilbery et al., 2006). based on literature, income support payments affect the overall profitability of the entire agricultural sector and consequently the propensity to invest/innovate within the sector, as well as the propensity to diversify towards activities such as energy production or high value added production (bartolini and viaggi 2012). furthermore, pillar 1 payments reduce the propensity to exit from the agricultural sector by increasing the returns of on-farm activities and consequently reducing the willingness to allocate off-farm factors of production. bowman and zilberman (2013) have pointed out that the mechanism of payments based on eligible crops designed to produce commodities determine an increase in crop specialisation rather than in diversification. on the contrary, pillar 2 payments affect positively on-farm diversification adoption in two ways, firstly via the co-funding mechanism, which reduces investment costs especially for first (competitiveness) and third axes (improving the quality of life in rural areas) payments (bartolini and viaggi, 2013) and secondly via the effects of payments for ecosystems services on rural viabilities and on the maintenance of valuable amenities within rural areas (zanten et al., 2013). 3. methodology 3.1 theoretical model this paper analyses the determinants of intensity of income diversification towards on-farm activities. despite a large amount of literature, only a few empirical papers have studied the issue of diversification intensity. barbieri and mahoney (2009) deal with diver141explaining determinants of the on-farm diversification sification intensity by using a diversification index based on the number of activities carried out on the farm. in this paper we use directly the count of diversified activities adopted that are included in a set of available diversification strategies. such a count can be seen as a proxy of on-farm income diversification intensity. the portfolio of feasible onfarm income diversification strategies is created on the base of the agricultural secondary activities surveyed by the italian 2010 agricultural census, which are mainly related to activities other than agricultural commodities production but that are connected with it. these activities belong to the following groups: a) agri-tourism, b) recreational and social activities, c) educational farms, d) processing farm products, e) aquaculture, f) contract work, g) processing of feed and services for breeding , h) forestry activities; i) renewable energy production; j) handicraft; k) “other” category. as aforementioned, risk attitude and farm income stabilisation are used in economic literature to explain farm diversification strategies. this paragraph presents a theoretical formulation of the optimal intensity of on-farm income diversification, expressed as count of diversified on-farm activities. several authors have stressed the simultaneity between decisions about diversification and household labour allocation between on-farm and offfarm activities. the paper adapts the model developed by mcnamara and weiss (2005) to the choice of diversification. the optimal diversification intensity could be modelled by the allocation of household labour li between a portfolio of n activities ∑ + ≤ = l l li i n 1 0 where l0 measures the amount of household labour allocated off-farm and l is the total household labour endowment. assuming that a generic farm output/service i is produced by the production function2 fi(li) and that farmer faces only uncertainty in output/service price, so that for a generic activity/service i pi is the expected economic return of this diversifying activity, and considering two generic activities i and j whose variance and covariance are σ σ=ii k 2 and σ ρσ=ij k 2 ∀ ≠ =i j n 1,..., with ρ as correlation coefficient such as ρ− ≤ ≤1 1 the expected profit for a generic farm household can be formalised as π ( )( ) = ∑ + − = e p f l wl cni i i i n o 1 that when substituting li = l – l0 yields = −    + −e npf l l n wl cno 0 in w the expectation wage of off-farm labour allocation and c the fixed costs involved in undertaking a generic activity (investment costs, learning costs and transaction costs), with l0 ≥ 0. the equation yields the optimal farm strategy as a function of choice of optimal count of diversified activities. assuming risk aversion of farmers and a utility function that follows a negative exponential distribution π( ) = − −y e1 ra with a constant absolute risk aversion ra and a normal distribution of possible outcomes, it is possible to approximate the decision problem to a function of the first two moments (freund, 1956), such as 2 for simplicity, we assume equal production function among the portfolio of activities; productive factors and policy are omitted in the formal presentation of the model. 142 f. bartolini, m. andreoli, g. brunori π π π( )= −ce e rv( ) ( ) 0.5 a where ce(π) is the certain equivalent; e(π) is the expected profit; v(π) is the variance. as pointed out by robinson and berry (1987) the variance of the decision problem equals to ρ σ( ) ( )= − + −     v l l n n 1 1 k0 2 2 and then ce(π) equals to π ρ σ( )( ) ( )= −    + − − − + −     ce npf l l n wl cn r l l n n2 1 1o a k0 0 2 2 maximising ce with respect to the decision count of diversified activities yields: ρ σ( ) ( ) = −    − − − − =ce n pf l l n c r l l n ' 1 2 0o a k0 2 2 2 that in a case of linear production function yields: σ ρ( ) ( )= − −    n l l r c * 2 1o k a 1/2 the optimal count of diversification activities increases with a high-risk aversion coefficient ra and with uncertainty in the diversified outcome, and consequently diversification determines a gain due to risk reduction strategies. this gain, as suggested by mcnamara and weiss (2005), is reduced by the amount of learning and transaction costs c needed to implement a new diversification strategy. thus, the farmers’ choices are based on risk attitude, which measures their preferences as regards the set of alternatives and the variance of the expected profit this latter being determined by the expected return of the alternatives and by the productivity in the use of farm household’s resources. albeit purely income motivations might be at the base of farmers’ choices and notwithstanding several papers point to that, nevertheless no explicit economic model has been presented so far; hence, this paper tries to explore empirically whether variables related to both motivations are affecting the intensity of diversification. 3.2 econometrics specification the intensity of the on-farm income diversification, measured as the count of activities adopted among a set of feasible portfolio, is the dependent variable. many economet143explaining determinants of the on-farm diversification ric models such as regression or quantile regressions are used in agricultural economics to estimate the degree or intensity of income diversification (mcnamara and weiss, 2005). those models have been used to explain motivations and determinants by using, as a proxy of diversification intensity, a diversification index obtained either by the count of activities or by a weighted sum of portfolio of activities. mcelwee and bosworth (2010) have applied a categorical data model in explaining determinants of diversification, simulated as a binary variable or as a categorical variable. in this paper, applying a zero inflated count model we estimate diversification intensity via a two-step process, where in the first step the discrete decision about whether to diversify, or not, is explained, while the second step is used to explain determinants of diversification intensity. the use of a zero-inflated count model allows us to treat data with excess of zero value (lambert, 1992; green, 2003) by separating the decision process in these two steps. the simulation of diversification adoption as a two-step process provides a better representation of farmers’ behaviour. the first step allows coping with the boundary in pursuing at least one diversification alternative while the second step analyses the behaviour of farmers who have decided to diversify and who have access to the implementation of several alternatives (amanor-boadu, 2013). application of zero inflated count data are quite common in agricultural economics, e.g. isgin et al. (2008) estimate the factors affecting the intensity of implementation of technological elements in ohio farms, while uematsu and mishra (2011) estimate the determinants affecting the total number of direct marketing strategies adopted by farmers, and bartolini et al., (2011) study the cap impacts on the intensity of innovation. formally, the count of on-farm diversification activities is a function of a set of independent variables xi so ln(λi) = α0 + β’ xi where λi is means of the on-farm differentiation activities, α0 the constant term and β’ is the coefficient of the set of explanatory variables. to analyse the variables, two distributions are considered, namely poisson and negative binomial models (paxton et al., 2011). let υi be the observed event of count data, the parameter β’ depends on the value of explanatory variables; consequently, it is possible to write: e(υi | xi) = λi = exp(β’ xi) i = 1,…n the probability density function for poisson model is λ( ) ( )= = λ− p y x f y e y .pr | !i i i y i i i the poisson specification assumes that the first two moments are equal e[(υ)] = λ v[(υ)] = λ to take into account overdispersion, a more flexible negative binomial regression (nbr) model has been applied. the density function for the negative binomial model is 144 f. bartolini, m. andreoli, g. brunori γ α γ α α α µ µ α µ ( ) ( )( ) ( )= = + +     +     α− − − − − − nb y x f y y y .pr | !i i i y1 1 1 1 1 i 1 where γ is the gamma distribution function. with α = 0 a negative binomial model is equal to a poisson model. in this paper, we compare the results of a zero-inflated poisson (zip) model with those of a zero-inflated negative binomial (zinb) model. the mechanism underlying the models is related to how zero is generated, since zero value could be originated by two different regimes: a) the first one, where the outcome is always zero (the model explains the determinants affecting the non-adoption of a diversification strategy,) and b) the second one, using poisson (or negative binomial) distribution to explain the outcome produced by a non-negative integer value (green 2003). zero inflated approaches estimate determinants by combining two steps models. the first model is a logit model analysing the discrete choice about the decision whether to diversify or not (first regime). the second model is a poisson or negative binomial model generating a prediction of the count of the diversification activities (second regime). the main interest in applying such type of model is that the results of zero-inflated models return a correction to the estimation by separating the determinants of the count of diversified activities from the determinants of observed zero value that represents the non-adoption of a diversification strategy. in fact, the main assumption of the model is that two separate sets of covariates affect the decision to adopt diversification strategies. following mullahy (1986) and lambert (1992), it is possible to describe the choice as: υi = 0 with ωit υi ~ poisson(αit) probability 1 ωit (in case of poisson model) υi ~ nbr(αit) with probability 1 ωit (in case of negative binominal regression model) the probability of the zero positive outcomes can be expressed as: pr[υi = 0] = {ωi + (1 ωi)g(0) pr[υi = k] = (1 ωit) + (1 ωit)g(k), k=1, 2, 3... , where g(•) depends on the type of model considering the negative binomial probability function, as mentioned above. 4. data used in this paper, we have used micro-data from the 2010 italian agricultural census relating to on-farm differentiation activities undertaken by each farm and a set of variables that describe characteristics of farms, farmers and households. the census database has been merged with data relating to farm location, territorial description of the area, and with data on the amount of single farm payments (sfp) received by farmers and their participation to some rdp measures. this latter information have been taken from artea (the tuscany regional agency for agricultural payments) database. the depend145explaining determinants of the on-farm diversification ent variable represents the count of alternative diversification strategies applied at farm level. in the census questionnaire, the adoption of on-farm diversification activities is analysed by using a list of 17 alternatives that we have classified into 11 categories taking into account their degree of similarity, e.g. putting together contract work provided to other agricultural or non-agricultural enterprises; handcraft and wood processing, etc. these categories include rural-tourism, recreation and social activities, contract work, renewable energy production (different from energy crops production), handicrafts, processing farm products, educational farm activities, aquaculture, production of feed and services for breeding and forestry. table 1 shows the frequency of farms that have the considered activities in tuscany. table 1. tuscany – absolute (#) and relative (%) frequency of implementation of on-farm diversification activities. diversification categories farms (#) (%) agri-tourism 3,487 4.80 contract work 1,375 1.89 processing farm products 1,314 1.81 production of feed and services for breeding 1,004 1.38 forestry activities 891 1.23 handicraft activities 360 0.50 recreational and social activities 244 0.34 educational farms 204 0.28 renewable energy production 230 0.32 aquaculture 25 0.03 other activities 388 0.53 source: data from istat, 6th italian agriculture census (our processing). diversification activities are quite heterogeneous in terms of needed work and skills, provided services and as a source of income. among the several groups of on-farm diversification activities, agri-tourism has the highest frequency among farmers in tuscany. this activity counts ca. 3500 farmers, i.e. almost 5% of all farmers. contract works and on farm processing of farm products come as second and third in importance as diversification activities. both of these activities involve more than 1300 farmers in the region, i.e. more than 1.80% of all farmers. supply of services for livestock and forestry activity count ca. 1000 farmers each, i.e. around 1.3% of all farmers, while social and educational activities have a frequency of 0.5% or below. intensity of on-farm diversification is measured, for each farm, by the count of implemented diversification activities. table 2 presents the number of farms in tuscany according to their diversification activities count. this is the dependent variable in the econometric model and it measures the diversification intensity at farm level. 146 f. bartolini, m. andreoli, g. brunori table 2. tuscany – absolute (#) and relative (%) frequency of farms according to their count of onfarm differentiation activities. diversification intensity (# of activity farms (#) (%) 0 65,747 90.45 1 5,124 7.05 2 1,309 1.80 3 336 0.46 4 107 0.15 5 44 0.06 6 12 0.02 7 4 0.01 8 3 0.00 source: data from istat, 6th italian agriculture census (our processing). the data illustrate that a vast majority (above 90%) of farmers in tuscany have not applied any diversification strategy. furthermore, data show that with respect to ca. 70,000 farms, some 5,000 (7.05%), implemented one diversified activity and ca. 1,300 farms (1.8%) implemented two diversified activities. the number of farms progressively reduces with the increase of the count of on-farm diversification activities and only some sixty farms apply five differentiation activities or more. the data used contain an excess of zero observations and consequently, zero-inflated models have been used in order to correct the estimation, when having such a large number of zero value observations (see methodology section). as mentioned earlier the dependent variable is the count of the adopted diversified alternatives. as pointed out in theoretical model section, the determinants of diversification focus mainly on two dimensions: risk aversion and income (e.g. increasing profitable use of farm households’ resources). therefore, we have identified explanatory variables belonging to the following five categories, which can be related with those two dimensions: a) geography/location, b) farmer, c) household, d) farm characteristics, and e) policy. descriptive statistics of the selected explanatory variables are presented in annex 1. the first category includes geographical variables such as altitude and regional development programme (rdp) zoning. location in urban or rural areas and the altitude are expected to be relevant as determinants of diversification patterns due to the priority mechanism defining eligibility to the measure 311, promoting diversification in rural areas, and as determinants of change in the demand of diversification related to services (zasada 2011). according to literature, farm location affects expectations regarding offfarm wage and consequently differences in location modify the preferences of household labour allocation (mcnamara and weiss 2005). thus, location variables are more related with the income dimension due to the effects in changing farm households labour revenue expectation between off-farm and on-farm activities and in changing the demand for diversified services provided by the farmers (zasada, 2011; lange et al., 2013) 147explaining determinants of the on-farm diversification agricultural economics research has highlighted the influence of farmer and household characteristics (i.e. our second and third categories) on the risk attitude towards alternative farm strategies. however, while vik and mcelwee (2011) show that male educated young farmers have a higher probability to diversify, other studies show opposite effects due to the influence of the same characteristics on attitude and ability to obtain credit (see bowman and zilbermann 2013). the fourth category of explanatory variables includes farm characteristics that relate to farm structure, farm specialisation, and production technology. according to previous research, farm structure and farming systems influence the level of risk exposure while economies of scale on credit access influence the attitude towards farm specialisation (chavas et al., 2001; mishra et al., 2004; bowman and zilbermann 2013). the fifth category includes variables relating to payments received via agricultural policies. these variables may affect the decision to diversify in several ways, e.g. by preserving rural amenities, by producing changes on the productivity of farm structures or on the profitability and on the timing of adoption of new investments. therefore, policy category affects mainly income dimension, due to the change of relative profitability between output diversification and on-farm income diversification. 5. results and discussion as above mentioned the zero-inflated count model estimates the determinants of diversification intensity in two steps, firstly by identifying variables that affect the probability to observe a zero value and secondly by identifying variables affecting on-farm diversification intensity. results of both poisson and zero-inflated negative binomial models are shown in tables 3 and 4. table 3 presents results in terms of determinants of the adoption of at least one diversification alternative versus non-adoption, while table 4 shows the determinants of diversification intensity. the coefficients of the zero-inflated models can be interpreted in the same way than in standard binary choice models; therefore, coefficients describe the probability to observe a zero value of the count variable, i.e. non-diversification. thus, a significant positive coefficient (table 3) means a high likelihood to observe a zero value (non-diversification), while a significant negative coefficient has opposite meaning. vice versa, coefficients of table 4 describe changes in the expected count of the farmers adopting on-farm differentiation activities. consequently, a positive value of the count outcome indicates that the variable determines an increase in expected outcome of the model and consequently has a positive effect on intensity of diversification, while a significant negative coefficient means that the variable reduces the expected count of diversification intensity. both tables show a comparison between zero-inflated poisson (model 1) and zeroinflated negative binomial (model 2) models. due to joint estimation of the determinants between zero-inflated and count outcome, the selection of estimation affects model results. however, both models (zero-inflated poisson and zero-inflated negative binomial) have positively passed the vuong test. this test compares respectively zero inflated poisson versus standard poisson model and zero-inflated negative binomial versus negative binomial model. results suggest that, due to excess of zero values, zero-inflated poisson and 148 f. bartolini, m. andreoli, g. brunori zero-inflated negative binomial provide a better fit compared to standard count models3. the results of logit models show that geographical, farm, farmer, household and policy variables affect the probability to observe at least one diversification activity. farmers’ location in urban areas as identified for rdp purposes by inhabitant density is more likely to determine adoption of diversification strategies, while in locations in remote rural areas with development problems (defined by rpd zoning, as rural area where income are lower compared with other regions), one is less likely to observe diversified strategies; such 3 vuong test for model 1 zero-inflated poisson versus standard poisson has obtained a score of 25.25 and significance at 0.01, while voung test for model 2 zero-inflated negative binomial versus negative binomial model has shown a score of 24.08 and significance at 0.01. table 3. determinants of the adoption of a diversification strategy, results of full zero-inflated poisson (zip) and zero-inflated negative binomial (zinb) models (logit). variable (description) variable (code) zero inflated outcome (logit) zip (model 1) zinb (model 2) location in urban areas (dummy) poli_urb -0.053 * -0.0364 * location in rural areas with developing problems (dummy) rur_probsv 0.0807 0.1415 * fourth uaa percentile – very small (dummy) uaa_vl -0.5551 *** 0.222 first uaa percentile – very large (dummy) uaa_vs 0.8244 *** 0.5076 *** amount of uaa (natural logarithm) uaa1_ha -0.0119 *** -0.08732 *** farms with rented land (dummy) uaarent_d -0.7674 *** -0.7663 *** farm specialization in horticulture (dummy) spec_horticolture 0.8047 *** 0.5235 *** farm specialization in permanent crops (dummy) spec_permanent 0.361 *** 0.4114 *** organic farming (dummy) d_bio -1.3439 *** -1.6665 *** use of internet for farm activity (dummy) inform_d -2.3899 *** -3.5944 *** square of farmers’ age age2 0.0018 *** 0.0155 *** farmers younger than 40 years (dummy) d_young -0.1712 * -0.3036 *** education lower than secondary school (dummy) edu_low 0.2701 *** 0.1771 *** use of paid labour (dummy) cond_salecon -1.1772 *** -1.1356 *** participation at rdp first-axis measures (dummy) part_axis1 -0.7710 *** -1.1975 *** participation at rdp agri-environmental schemes (dummy) part_axis2env -0.3083 *** -0.2685 *** participation at rdp forestry measures (dummy) part_axis2for -0.3102 -1.9128 * participation at rdp measure 311 diversification (dummy) part_311 -2.1450 *** -3.1291 *** sfp payments per ha (1000 €) sfpr_ha_uaa -0.1138 * -0.2051 ** constant cons 1.6122 *** 1.644 *** number of observations 72686 72686 *** significant at 0.01; ** significant at 0.05; *significant at 0.1; not significant variables have been omitted. 149explaining determinants of the on-farm diversification variable is significant only for zero-inflated negative binomial model. our results confirm the hypothesis proposed by lange et al. (2013) and zasada (2011) about the demanddriven effects of diversification, due to closeness to potentially high demand for services provided by farm diversification. results emphasise the effects of farmer’s characteristics as a barrier against diversification adoption. in fact, as outlined by mcnamara and weiss (2005), age, education and attitude have a prominent role in explaining diversification due to their influence on risk aversion attitude, wealth and reduction of working load over time. in particular, young and high-educated farmers are more likely to diversify activities according to the life-cycle hypothesis in relation to the decision to allocate resources table 4. determinants of diversification intensity, results of full zero-inflated poisson and zero-inflated negative binomial models (count variable). variable (description) variable (code) count outcome zip (model 1) zinb (model 2) location in urban areas (dummy) poli_urb -0.2164 *** -0.1966 *** location in intensive agricultural rural areas (dummy) rur_int -0.1119 * -0.1097 location in rural areas with developing problems (dummy) rur_probsv 0.4664 *** 0.4095 *** first uaa percentile – very small (dummy) uaa_vs -0.1545 ** -0.1119 * amount of uaa (natural logarithm) uaa1_ha 0.0006 *** 0.0003 ** farms with rented land (dummy) uaarent_d 0.0851 *** 0.005 farm specialization in arable crops (dummy) spec_arable -0.1719 *** -0.1705 *** farm specialization in permanent crops (dummy) spec_permanent -0.0293 -0.0782 ** farmers older than 65 years (dummy) d_old -0.1397 ** -0.1795 ** farmers younger than 40 years (dummy) d_young -0.173 *** -0.1492 *** agricultural education (dummy) edu_agr 0.1026 ** 0.0944 ** education lower than secondary school (dummy) edu_low -0.256 *** -0.1735 *** household lives on the farm (dummy) live_on 0.219 *** 0.1983 *** farm that use mainly household labour (dummy) cond_coltdir -0.2106 *** -0.1458 *** participation at rdp first axis measures (dummy) part_axis1 0.1531 *** 0.0923 ** participation at rdp agri-environmental schemes (dummy) part_axis2env 0.3481 *** 0.2771 *** participation to rdp measure 311 – diversification (dummy) part_311 0.4867 *** 0.3721 *** sfp payments per year (1000 €) sfpr_year -0.0039 *** -0.0051 *** constant constant -0.7281 *** -0.5664 *** ln alpha -0.932 *** alpha     0.1914 *** number of observations 72686 72686 *** significant at 0.01; ** significant at 0.05; *significant at 0.1; not significant variables have been omitted. 150 f. bartolini, m. andreoli, g. brunori between on-farm and off-farm activities (mishra et al., 2010). results show that farmers who use internet in their business have a higher probability to diversify due to the possibility to reach spatially distant markets and to a higher probability to be involved in networks (mcelwee and bosworth 2010). as pointed out by literature, farm structure strongly affects the likelihood of adopting diversification. according to our results farm specialisation, farm size and production system have a significant effect on adoption of diversification. there is no consensus in literature regarding the effect of farm size on diversification. in fact, while from one hand economies of scale push farms to become more specialised in agricultural production and consequently to diversify less (mcnamara and weiss 2005), on the other hand, the decrease in marginal return of specialisation determines a higher probability of allocating labour to diversified activities due to higher marginal value when allocating an additional household labour unit to diversified activities with respect the specialisation (robinson and barry, 1987). our results seem to confirm the second hypothesis showing that for very small farms scarce land endowment represents a barrier to adopt any diversification strategy. results show that farm specialisation, due to market structure and investment specificity, determine a higher level of risk exposure and consequently risk adverse farmers react by increasing diversification (mishra et al., 2010). however, farm specialisations in horticulture and permanent crops show a low probability of diversification because of the specific market structure (vertical integration). in fact, this kind of market structure reduces price related risk and consequently determines higher investments and a more labour intensive production. according to research results, organic farming is more likely to be diversified because of synergies between diversification strategies (mcelwee and bosworth, 2010). both cap pillars (pillar 1 and pillar 2) influence the probability to observe the implementation of at least one diversification activity. as expected, participation to rdp measures affects positively the probability to observe a diversification strategy. these results confirm the expectations that when participating to modernisation measures or agri-environmental schemes farmers renew and rethink their entire production system. single farm payments (sfp) show same effects, in fact, increasing the amount of received sfp the probability to observe a diversification strategy on-farm increase. the determinants of diversification intensity model are presented in table 4. table 4 shows model results for farms whose dependent variable value is different from zero (farms with at least one implemented diversification activity). positive coefficients mean an increase in the expected count of the dependent variable, while negative coefficients reduce the expected count of diversification intensity. as mentioned in the methodology section, zero-inflated poisson and zero-inflated negative binomial models give different results due to the form of the used distribution function. as explained in the methodology part the main difference arises from the inclusion of α in the zero-inflated negative binomial model. positive and significant observed value of α suggests the best fit for zero-inflated negative binomial compared to zero-inflated poisson. model results show that farm location has a strong effect to the expected count of diversification intensity. vice versa, in the case of location in areas with development problems, due to lower offfarm opportunities compared to other areas, farmers who try to reduce risk exposure are pushed to increase their income diversification through on-farm activities (mishra et al., 2014). 151explaining determinants of the on-farm diversification results highlight that farm characteristics such as size and degree of specialisation affect diversification intensity. farms with a large amount of uaa (utilized agricultural area) and renting of land show an expected count increase towards diversification intensity, confirming the effect of decreasing marginal return of land in case of specialised activities (mcnamara and weiss, 2005). vice versa, farms specialised in arable crops and farms specialised in permanent crops show a reduction in the expected count of diversification intensity due, in the latter case, to a lower flexibility of farm production, although this variable is significant only for zero-inflated negative binomial model. age and education are variables that strongly affect the expected outcome of diversification intensity. results show that both young and old farmers have low expected diversification intensity, and consequently, our work confirms the findings of mcnamara and weiss (2005) regarding the non-linearity of age effects on farm diversification intensity. they pointed out that young farmers are less risk adverse and show a lower propensity towards diversifying activities, while older farmers tend to reduce the amount of workload due to life cycle expectations. according to our study, families living on farm have a higher expected count of diversification. however, when the majority of on-farm labour is satisfied using household labour there are significant negative effects on diversification intensity, while variables related to policy context affect positively the expected outcome of diversification intensity. the results of zip and zinb models stress the positive effects of rdp measures on diversification intensity. results show that farmers are developing new business plans that focus more on the integration of farm income via diversification activities rather than just co-funding agro-tourism or production of renewable energy (the only available diversification activities eligible for measure 311). this allows us to consider participation at rdp measures as a driver to rethink the entire farm production system. participation to any measure belonging to rdp second, environmental, axis positively affects the expected count of diversification intensity due to the improvement of provision of environmental quality, or the breeding or management of endangered species. at the same time, according to the research result the amount of sfp received reduces the diversification intensity due to the mechanism of promotion of specialisation in the production of commodities. 6. conclusion in this paper, determinants of diversification activities are analysed, using the italian census of agriculture and artea (tuscany regional agency for agricultural payments) micro-data. the paper develops an econometric model that explains determinants firstly, of the discrete choice of adoption or non-adoption of a diversification activity and secondly, of the intensity of diversification, measured as a count of adopted alternative diversification categories. a relevant share (ca. 7%) of farmers in tuscany has diversified their farm’s activities mainly by implementing rural tourism, contract work or farm products processing activities. there is a large amount of literature explaining the determinants of adoption of categories of diversified activities, where categories are grouped according to provided services or the amount of efforts and of investments required. these models return interesting results, but in our case, synergies in adoptions and farm strategy show not-mutually exclusive alternatives. this represents a violation of independence of irrelevant alternatives and consequently does not make possible the application of multinomial 152 f. bartolini, m. andreoli, g. brunori models. in this sense, the paper’s main novelty is represented by the choice to treat diversification as a count variable when trying to explain diversification intensity as a strategy that may be used to reduce on-farm risk exposure. model results show that farm, farmer, household and geographical characteristics strongly influence the attitude towards on-farm diversification activity. in particular, results confirm that diversification activity requires skills, competence and endowment of productive factors that represent a barrier to the adoption for many farms. results confirm previous literature findings in representing diversification strategy as a way to increase household income using on-farm resources and to reduce farmhousehold risk exposure. results show a picture, where diversification is one of the strategies used by farms that a) are viable from an economic point of view for their structural characteristics, b) improve their income by expanding their size (rented land), c) search new ways for increasing farm income, through farm modernization, the research of new markets (organic farming) and the use of tools needed to compete on a global market (internet). on the other hand, the group of farms, which are located in areas with development problems where involvement on agricultural activities is diminishing, are highly dependent on sfp and the activities that are carried out are mainly horticulture crops, olives and vineyards, likely using subcontractor services and/or family labour. in this case, it seems that farmers have a low interest in investing, modernising or improving farm capacity in order to provide an income other than the one coming from pillar 1 subsidies. results confirm that location and geographical variables determine changes in the observed diversification activity. in particular, relations between demand for services provided by diversification (e.g. tourism, handicraft, or contract work) and expectation regarding income sources represented by off-farm activities are determinant for diversification adoption and intensity (lange et al., 2013). results show that these variables are relevant especially in urban areas (which are mainly in plain areas) and in marginal areas, such as rural areas with development problems. in fact, these areas have opposite direction as regards diversification adoption or diversification intensity. urban and peri-urban areas show a low probability to observe diversification adoption and intensity of diversification. location on rural areas with development problems represents a barrier to the adoption of diversification activities. however, at the same time in many areas diversification represents the main opportunity for income creation and risk reduction, due to scarcity or absence of other opportunities for off-farm labour and to the necessity to overcome territorial constraints, in the case of the farmers who internalise by providing social services. consequently, farms, located in these areas, which have adopted a diversification strategy, show a high intensity of diversification. results confirm the effects of cap in driving on-farm diversification intensity. both pillar 1 and 2 payments affect the attitude to diversify, although in opposite directions. pillar 1 payments positively affect the decision to adopt diversification strategy by ensuring liquidity to invest on agriculture (bartolini and viaggi, 2012) and by contrasting exit from farming activities (or off-farm household labour allocation) due to an increase of the overall agricultural sector profitability (raggi et al. 2013). furthermore, rdp measures (pillar 2) promote diversification activity in several ways, such as, a) co-funding investments on diversification (third axis) or on technology provision, such as new 153explaining determinants of the on-farm diversification machinery or new energy plants (first axis), and b) by promoting a sustainable agricultural production (organic production), maintaining and preserving landscape elements and biodiversity. in this paper, when trying to identify determinants of on-farm diversification as a framework of risk reducing behaviour, we have made an assumption that simplifies the definition of diversification intensity by counting the diversification activities provided by the italian agricultural census data. this assumption influences the dependent variable that focuses on the count of activities rather than on income sources, and therefore determines an application of portfolio model based purely on a proxy of intensity. nevertheless, the use of census data for investigating diversification strategy is quite common in agricultural economics literature (see e.g. mcnamara and weiss 2010; mishra et al., 2010) even if in the census a set of relevant economic data (both on-farm and offfarm), networking and social capital dimension are missing. therefore, future works need to be directed to the inclusion of those dimensions, that may have significant effects on the intensity of diversification, and that need ad hoc surveys due to the lack of official data. in the same way, the lack of data does not allow studying the trade-off between diversification and pluriactivity (allocation of household labour and investing in offfarm activities) that together with specialisation represent the main strategy of farmers in reducing risk exposure. thus, further improvements through the identification of alternative farm strategies by understanding dynamics of off-farm labour pattern in other sectors would represent a viable strategy to understand risk aversion effects on agriculture. acknowledgements an earlier version of this paper was presented during the 2nd 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(2011). multifunctional peri-urban agriculture-a review of societal demands and the provision of goods and services by farming. land use policy, 28: 639-648. annex 1. descriptive statistics of data used category variable description observation mean standard deviation min max geographical plain location in plain areas 72686 0.1206 0.3257 0 1 hill location in hill areas 72686 0.6984 0.4589 0 1 mount location in mountain areas 72686 0.1809 0.3850 0 1 poli_urb location in urban areas 72686 0.1737 0.3789 0 1 rur_int location in rural areas with intensive agriculture 72686 0.1008 0.3011 0 1 rur_trans location in rural areas in transition 72686 0.3408 0.4740 0 1 rur_decl location in rural areas in declining 72686 0.2413 0.4279 0 1 rur_probsv location in rural areas with developing problems 72686 0.1430 0.3501 0 1 farm household live_on famers’ household livees on tha farm 72686 0.8402 0.3663 0 1 selfcons self consumption of agricultural products (more than 50 % of productions) 72686 0.5026 0.4999 0 1 farm d_bio organic production 72686 0.0325 0.1775 0 1 uaa_l small farm size 72686 0.2456 0.4304 0 1 uaa_s medium-small farm size 72686 0.2344 0.4236 0 1 uaa_vl medium-large farm size 72686 0.2489 0.4323 0 1 uaa_vs large farm size 72686 0.2711 0.4445 0 1 uaa_1n logarithm of usable agricultural areas 72686 1037.81 3508.67 0 2292.1 uaarent_d rented-in 72686 0.1552 0.3621 0 1 spec_ livestock farm system specialised in livestock production 72686 0.0554 0.2289 0 1 spec_arable farm system specialised in arable production 72686 0.1738 0.3790 0 1 spec_ permanent farm system specialised in permanent crops 72686 0.5871 0.4923 0 1 spec_ horticulture farm system specialised in vegetable crops 72686 0.0447 0.2068 0 1 cond_ coltdir direct conduction by farmer 72686 0.9561 0.2047 0 1 cond_ salecon conduction using paid labour 72686 0.0378 0.1908 0 1 cond_oth other conduction 72686 0.0059 0.0770 0 1 farmer d_young farmers young than 40 years old 72686 0.1042 0.3056 0 1 157explaining determinants of the on-farm diversification category variable description observation mean standard deviation min max d_old farmers old than 65 years old 72686 0.4138 0.4925 0 1 age2 square of age 72686 3851.43 1740.82 256 9801 inform_d use of internet for farming activities by the farmer 72686 0.0590 0.2351 0 1 edu_agr farmer with agricultural education 72686 0.0388 0.1932 0 1 edu_high farmer with education higher than secondary school 72686 0.3302 0.4703 0 1 edu_low farmer with education lower than secondary school 72686 0.6697 0.4703 0 1 policy part_axis1 participation in at least one measure of first rdp axis 72686 0.0270 0.1622 0 1 part_ axis2env participation in at least one measure of second rdp axis (environmental measures) 72686 0.1038 0.3050 0 1 part_ axis2for participation in at least one measure of second rdp axis (forestry measures) 72686 0.0013 0.0359 0 1 rdp_311 participation in measure 311 of rpd 72686 0.0021 0.0461 0 1 sfp_year amount of single farm payments received per years 72686 1411.35 5758.9 0 426822 sfp_ha amount of single farm payments received per hectare 72686 152.33 1670.14 0 41130 bio-based and applied economics 3(3): 187-204, 2014 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-15017 review article biotechnologies and agrifood strategies: opportunities, threats and economic implications justus wesseler wageningen university, hollandsweg 1, 6706kn wageningen, the netherlands abstract. the production of food employs different kinds of biotechnologies, some of which are more controversial than others. the public and the agrifood sector have developed a number of responses to address their concerns about biotechnologies considered controversial. in this contribution the different strategies chosen by the agrifood sector in the eu in response to the introduction of new biotechnologies in the agrifood complex will be discussed. the contribution concentrates on the example of the introduction of genetically engineered crops and the strategic responses by the eu food industry, namely food processors and food retailers. the contribution concludes with an outlook on the future of the eu agrifood complex. keywords. biotechnology, regulation, technical change. jel codes. q1, l5, l66 1. introduction food processing without biology is nearly impossible. the processing of primary food products such as grains to higher quality products via fermentation uses enzymes. this is one of man’s oldest food processing technologies and has been claimed to be the origin for domesticating grains (katz and voigt, 1986). man’s knowledge about food and food processing has improved and today we have a wide range of biotechnologies available to help us to cultivate and process agricultural products. in particular advances in molecular biology have generated new biotechnologies and will continue to do so. these new technologies offer opportunities for the agrifood sector and society at large, allowing for the reduction of agriculture’s environmental footprint; for increasing food supply, and the diversity of food products available (bennett et al., 2013). while a wide range of opportunities for using biotechnologies are available, views about their benefits differ substantially. the recent debates surrounding the introduction of vitamin a enriched rice (golden rice) (wesseler and zilberman, 2014) and the approval of the genetically engineered (ge) maize 1507 for cultivation in the european * corresponding author: justus.wesseler@wur.nl. 188 j. wesseler union (eu) (rabesandratana, 2014) serve as examples.1 the differences in views are often correlated with the distribution of the benefits and costs of the technology (graff et al., 2009) that are unevenly distributed between different sectors of our economies and between economies (smyth et al., 2014). insect resistant crops reduce pesticide use to the disadvantage of the agriculture chemical industry, but benefit the companies holding the property rights on the new seed products (bennett et al., 2013). countries with a less restrictive regulatory system in agriculture food production might benefit more from biotechnologies increasing their comparative advantage (qaim, 2009). simultaneously, food markets become more differentiated in a response to demands for special food items such as “gm-free” (gm = genetically modified) dairy products providing new opportunities for the agrifood sector. in parallel to the development of new biotechnologies, the regulatory environment has changed. concerns about environmental and health impacts of existing and new technologies have become an important part of regulations (wesseler and kalaitzandonakes, 2011). the regulatory environment in europe has also been influenced by the establishment of the eu and its enlargement (smart et al., 2014). further, different stakeholder groups try to influence regulations on gmos depending on their individual gains, losses and beliefs (graff et al., 2009). in this contribution the different strategies chosen by the agrifood sector in the eu in response to the introduction of new biotechnologies in the agrifood complex will be discussed. the contribution concentrates on the example of the introduction of genetically engineered crops and the strategic responses by the eu food industry. the contribution unfolds as follows. first, a brief eu history on ge food and the regulatory regime will be provided followed by a discussion and assessment of the gm-free response strategy responses of the eu food industry. the assessment mainly concentrates on food processors and food retailers and concludes with an outlook on the future of the eu agrifood complex. 2. eu history on genetically engineered products ge food product developments until 1999 the development of the recombinant dna technology in the early 1970s was the start of modern biotechnology (tramper and zhu, 2011) (see table 1). the bayh-dole act of 1980 in the united states (us), which provided universities and other forms of organisations with the right to exploit patents that had been obtained with public funding, has been seen as key for innovations in modern biotechnology (stevens, 2004). some of the first successful products using rdna technology were a vaccine for swine diarrhoea in 1982 by the dutch company intervet and the production of human insulin for diabetics from ge bacteria by the us company eli lilly. since 1984, the dutch company gist-bor1 for convenience, applications of modern biotechnology in agriculture will be abbreviated with ge for genetically engineered. the term “genetically modified organism” or gmo will be used when referring to eu policies. the differentiation is relevant, as according to the definition of a gmo by the european union, strictly (scientific) speaking they would not exist, except one rejects evolution and follows a “creationist onthology” and hence the term is a political construct (herring, 2008). 189biotechnologies and agrifood strategies table 1. important events in the eu history of modern biotechnology in agriculture. year event 1973 development of rdna technology 1980 bayh-dole act, providing intellectual property right to organisation and individuals from inventions with public funding in the us. 1982 vaccine against swine diarrhoea and production of human insulin (us) by means of rdna technology 1986 first cases of bovine spongiform encephalopathy (bse) in cattle in the uk reported starting the “mad-cow-disease” crisis in the eu. 1986 oecd publication on “recombinant dna safety considerations”, so called “blue book”, setting international standards for safety assessments. 1990 hermann the bull, the first genetically engineered bovine, was born. female off-springs of hermann the bull would produce milk with a high content of lactoferrin to be used to strengthen the immune system of humans. product developed by pharming group n.v., the netherlands 1991 report about hiv contaminated blood samples knowingly be distributed in france published and together with the handling of the “mad cow disease” undermining public trust in regulatory health safety systems in the eu. 1995 flavr savr tomato introduced by calgene (us) but withdrawn in 1999. 1996 dolly, a cloned sheep was born. 1998 first ge crop approved for cultivation in the eu (mon810) 1999 study on mortality effects of pollen from genetically engineered plants on larvae of monarch butterflies published in may 1999 in nature. 1999 environmental council of the eu calls for a temporary ban of approvals of gmos (“quasi moratorium”) in july 1999. 1999 apad pusztai claims negative effects of gm technology on the biology of rats in august 1999. 2000 starlink case: traces of starlink corn, not approved for human consumption were found in food products (taco shells) in the us. 2000 friends of the earth europe launches an eu wide campaign “calling for a halt to the gmo pollution of food and the environment”. 2001 eu directive 2001/18 on the deliberate release of gmos into the environment published. includes the safeguard clause. 2002 european food safety authority established. tasks among others the environmental and food safety assessment of genetically modified organisms (gmos). 2003 regulation 1830/2003 on traceability and labelling of gmos published. introduces the 0.9% threshold level for labelling. 2003 recommendations by the european commission on guidelines for the development of national strategies and best practices to ensure the coexistence of genetically modified crops with conventional and organic farming. 2009 study on the effect of bt maize on the two-spot ladybird published and used as an argument by the german and french government to ban the cultivation of mon810. 2009 lisbon treaty enters into force on december 1, 2009. among others some changes in the approval process of gmos including explicit deadlines for different steps. 2011 regulation (ec) no 1331/2008 of the european parliament and of the council establishing a common authorisation procedure for food additives, food enzymes and food flavourings 2011 regulation on low-level-presence of unapproved events establishing a 0.01% threshold for feed. zero tolerance level for unapproved events for food remains published in june 2011. 2011 judgement on the content of gm pollen in honey by the european court of justice starting a debate on how to measure gmo content in food in september 2011. 2012 study published by seralini et al. claiming toxic health effects of herbicide resistant maize as well as glyphosate. used by the government of france to invoke the safeguard clause. 2013 ttip negotiations launched. gmo approval policy in the two regions important part of the agenda. 190 j. wesseler cades (now dsm) started to insert the bovine chymosin gene in yeast cells, which allows for cultivating the yeast in large fermenters to be used for cheese production. in the late 1980s, the technology was adopted by cheese producers in switzerland, followed respectively by producers in the netherlands, germany, and france, in 1992, 1997, and 1998. parallel, applications for enzymes produced from ge bacteria for bakery products have been introduced (tramper and zhu, 2011). the first ge food product, the flavsavr tomato by calgene, was introduced in 1996 in the uk. the flavrsavr tomato has been an interesting case: it was developed by calgene and introduced to the uk under a licensing agreement by zeneca in 1996, and was removed from the market in 1999. the tomato paste derived from this tomato was labelled and sold by safeway’s and sainsbury, and initially even outsold alternative tomato paste brands. sales drastically declined in 1998, and in 1999 both supermarket chains delisted the product. the problem started according to bruening and lyons (2000) with the broadcasting by dr. arpad pusztai in 1999 about his claim that genetic engineering may have effects on the biology of rats, which resulted in demand declining. safeway and sainsbury not only removed this tomato from their shelves, but also declared that they would refrain from selling any ge food in their stores including animal products derived from animals fed with gm feed (ibid). the first transgenic maize crop was introduced in the us in 1995, followed by ge cotton, soybeans, oil seed rape, and corn (smart et al., 2012 ). the first ge maize approved for cultivation in the european union was the event mon810 by monsanto. cultivation first started in 1998 in france and spain, a year later portugal followed, and germany followed in 2000 (see table 2). while mon810 was approved under the regulations for novel food, regulations changed in the early 2000’s after a temporary ban on approvals, the so called “quasi moratorium”. france and portugal implemented a temporary ban in the early 2000’s (brookes, 2007). france and germany did ban cultivation of mon810 from 2007 and 2008 onwards respectively. the “quasi moratorium” and policy developments since in june 1999 at the meeting of the environmental council five member states, namely denmark, france, greece, italy and luxembourg declared they would block new approvals of genetically modified organism (gmos) until the european commission proposed additional legislation governing their introduction (eu environmental council, 1999). those five member states asked for establishing a more transparent framework for the approval of gmos including a risk assessment that considers explicitly the specificities of european ecosystems, a monitoring scheme and a positive labelling policy. those member states saw this as being important steps to restore public and market confidence; otherwise, “they will take steps to have any new authorisations for growing and placing on the market suspended.” (ibid).2 similarly, austria, belgium, finland, germany, the netherlands, spain, and sweden asked for a thorough risk assessment of gmos and in particular intended “not to author2 in particular many people lost their confidence in government regulators since the mid 1980’s because of the bse scandal, hiv contaminated blood products in france, and other such incidents. 191biotechnologies and agrifood strategies ise the placing on the market of any gmos until it is demonstrated that there is no adverse effect on the environment and human health.” (ibid). since then, the approval process for gmos in the eu differentiates between risk assessment and risk management. technical risk assessment is performed by the european food safety authority (efsa), while risk management, a political decision, involves standing committees, the commission, and the council of ministers (wesseler and kalaitzandonakes, 2011). while prior to the lisbon treaty the council of ministers was involved in the approval process, since the treaty this has been replaced by the appeal committee. with the adoption of the lisbon treaty the approval process has become more strict with respect to deadlines that need to be met. further, with the implementation of directive 2001/18, the procedures for the approval process for gmos in the eu have been revised. regulations addressing monitoring, traceability, and labelling followed (commission of the european communities 2003a,b,c). important to notice, is that in the eu a differentiation is made between approval for release into the environment and for placing on the market (wesseler and kalaitzandonakes, 2011). while several gmos have approval for placing on the market, only five events3 have received approval for cultivation so far: three maize, one potato and three carnation events (gmo-compass, 2014), while 3 genetically engineered crops are produced by introducing dna coding for beneficial traits into the germplasm of crop varieties. each time a transformation happened is referred to as an event and plant breeders select those that are of interest. the derived selected transformation event is defined by an abbreviation such as mon810.ta bl e 2. c ul tiv at io n of b t m ai ze in th e eu ro pe an u ni on (h a) . 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 sp ai n 22 ,0 00 30 ,0 00 20 ,0 00 25 ,0 00 25 ,0 00 32 ,0 00 58 ,0 00 53 ,2 25 53 ,6 67 75 ,1 48 79 ,2 69 76 ,0 57 76 57 5 97 32 5 11 63 06 fr an ce 2, 00 0 <2 ,0 00 <5 00 0 0 0 0 49 2 5, 00 0 21 ,1 47 0 0 0 0 0 c ze ch r ep ub lic 0 0 0 0 0 0 0 15 0 1, 29 0 5, 00 0 8, 38 0 6, 48 0 48 68 50 90 30 80 po rt ug al 0 1, 00 0 0 0 0 0 0 75 0 1, 25 0 4, 50 0 4, 85 1 5, 09 4 55 00 77 23 92 78 g er m an y 0 0 <5 00 <5 00 <5 00 <5 00 <5 00 34 2 94 7 2, 68 5 3, 17 1 0 0 0 0 sl ov ak ia 0 0 0 0 0 0 0 0 30 90 0 1. 90 0 87 5 17 40 76 1 0 ro m an ia 0 0 0 0 0 0 0 ? ? 35 0 7, 14 6 3, 34 4 82 3 58 8 21 7 po la nd 0 0 0 0 0 0 0 0 10 0 32 0 3, 00 0 3, 00 0 35 00 39 00 40 00 to ta l 24 ,0 00 >3 1, 00 0 >2 0, 00 0 >2 5, 00 0 >2 5, 00 0 >3 2, 00 0 >5 8, 00 0 54 ,9 59 62 ,2 84 11 0, 05 0 10 7, 71 7 94 ,7 50 91 19 3 11 53 86 13 30 70 so ur ce s: b ro ok es , 2 00 7; d em on t a nd t ol le ns , 2 00 4; g m o c om pa ss , 2 01 4. n ot es : ? , c ul tiv at ed b ut a re a no t kn ow n. z er os in di ca te n o co m m er ci al c ul tiv at io n, b ut fi el d tr ia ls p os si bl e. in t he e ar ly y ea rs t he a re a cu lti va te d w ith b t m ai ze in th e eu h as n ot b ee n w el l d oc um en te d. 192 j. wesseler only one maize event, mon810, is currently cultivated as a field crop. for events that have not received approval, a zero tolerance level applies, with exceptions under certain conditions for feed products where a 0.1 per cent level applies (commission of the european communities, 2011). table 3 shows the mandatory labelling requirements for gm food and feed according to regulation 1830/2003. what is noteworthy in the context of the strategic responses of the food sector are the exemptions for the labelling of enzymes derived from genetically modified bacteria and of animal products derived from animals fed with gm feed, while food products derived from gm crops such as soybean oil (derived from gm soybeans) or sugar (derived from gm sugar beets) must be labelled as such. an important part of the directive 2001/18 is the safeguard clause under article 23, which provides member states with the opportunity to ban cultivation in their territory but only under certain conditions: “where a member state, as a result of new or additional information made available since the date of the consent and affecting the environmental risk assessment or reassessment of existing information on the basis of new or additional scientific knowledge, has detailed grounds for considering that a gmo as or in a product which has been properly notified and has received written consent under this directive constitutes a risk to human health or the environment, that member state may provisionally restrict or prohibit the use and/or sale of that gmo as or in a product on its territory.” (commission of the european communities, 2001, p. l 106/13). several member states have invoked the safeguard clause to ban the cultivation of mon810, including, among others, france in 2007, germany in 2009, greece in 2006, and hungary in 2005. efsa has dismissed their arguments as they have failed in providing new scientific reasons for justifying a national ban. the council, who had to decide on the validity of this claim, has not voted in favour, asking the respective members states to remove the national ban (wesseler and kalaitzandonakes, 2011). in 2003 the european commission also published recommendations “on guidelines for the development of national strategies and best practices to ensure the coexistence of genetitable 3. labeling requirements for gmos in the eu. gm product example labeling requirement gm plants, seeds, and food maize, maize seed, cotton seed, soybean sprouts, tomato yes food produced from gmos maize flour, soybean oil, rape seed oil yes food additive/flavouring produced from gmos highly filtered lecithin extracted from gm soybeans yes gm feed maize yes feed produced from a gmo corn gluten feed, soybean meal yes feed additive produced from a gmo vitamin b2 yes food from animals fed on gm feed eggs, meat, milk no food produced with the help of a gm enzyme bakery products produced with the help of amylase no source: modified from commission of the european communities (2003a). 193biotechnologies and agrifood strategies cally modified crops with conventional and organic farming”, which “should provide a list of general principles and elements for the development of national strategies and best practices for coexistence.” (commission of the european communities, 2003). as these guidelines are only recommendations member states not necessarily have to implement specific guidelines for the cultivation of gmos. national coexistence regulations and their impacts on adoption of gm crops are quite diverse. while spain uses existing regulations to govern the production of gm crops, other countries, such as bulgaria, use coexistence regulations which effectively ban gm crop production. many member states have implemented legal rules and regulations governing the cultivation of gmos that in most cases increase the burden of famers that like to cultivate gm crops and in particular making it more difficult for smaller farms (beckmann et al., 2011; 2010; 2006; groeneveld et al., 2013). the approval process of gmos for cultivation for several reasons prevented the approval of gmos for cultivation since 2001. a proposal introduced by commission president barroso at the end of 2009 attempted to circumvent the rules of the qualified majority by shifting the authority of cultivation approval to the national level. this proposal was rejected by a number of member states. legal issues were invoked, including compliance with wto rules and the single european market principle (eesc, 2010). in the same spirit of creating regulatory flexibility, the commission of the european communities has prepared another proposal that would allow member states to declare gmo-free areas for different reasons but in line with the principle of the single european market. the discussion came to a halt but has gained new momentum since january 2014. by the end of 2014 an agreement between the european parliament and the ec has been reached on the possibility for “member states to restrict or prohibit the cultivation of genetically modified organisms (gmos) in their territory” and is expected to be in place in the first half of 2015 (keating, 2014). the year 2011 was another important year affecting the economics of ge crops in the eu. in march of 2011 a new regulation on the authorization procedure for food additives, food enzymes and food flavourings has been introduced that may also effect the use of enzymes produced by genetically engineered bacteria for use in food products. they may need approval ex-post and decision making bodies such as the standing and appeal committees involved may decide similar as they did in the past on other gmos. in june 2011 the council approved a 0.1% tolerance level for unauthorized gm feed imports to the eu and to maintain a zero-tolerance level for unauthorized gm food imports, which has not been considered a change (wesseler and kalaitzandonakes, 2011), while the decision by the european court of justice in september 2011 has a stronger disruptive effect on food markets. the decision (europäischer gerichtshof, 2014) confirms that honey containing pollen of unapproved events cannot be sold in the eu. this practically ends all field trials and hence renders applications for cultivation of gmos in the eu impossible which require testing in the field under european conditions as part of the approval process as preventing pollen from those trials to appear in honey is almost impossible. yet, the eu is not the only region requiring the approval of gmos. almost all other countries in one or the other way regulate the cultivation and import of gmos (ebata et al., 2013; falck-zepeda et al., 2013). as approval processes differ, between countries the approval of gmos shows asynchronicity, which can result in substantial disruptions in international trade (fao, 2014). the appearance of the cdc triffid event in imports of 194 j. wesseler flax seed from canada, an event that has never been commercialised in canada; and the appearance of a rice event from research field trials by bayer in commercial rice fields in the us are two examples. the starlink case shows that split approvals, approval for use as a feed but not as a food, can cause major market disruptions (carter and smith, 2007). the international trade disruption caused by asynchronicity in the approval process has been become one of the important issues of the transatlantic trade and investment partnership (ttip) negotiations between the eu and the us launched in 2013. substantial welfare gains are in particular expected by reducing regulatory barriers to trade (felbermayr et al., 2013). societal responses in the eu the introduction of the first products using rdna technology, including the production of insulin and lactoferrin, met with resistance from different lobby groups (tramper and zhu, 2011). still today, the pharma industry in europe is concerned about the impact of anti-gmo lobby group activities on pharmaceutical products based on rdna technology (lim, 2014; tramper and zhu, 2011). similarly, from the onset food products derived from gm crops have received strong opposition by anti-gmo lobby groups in europe, while in the beginning greenpeace united kingdom (uk) supported monsanto’s development of a biodegradable credit card from ge bacteria (nottingham, 2002). despite the alliance in the case of the credit card with monsanto, greenpeace uk stated: “we will continue to campaign against the use of genetically engineered foodstuffs” (daily news, 1997). interestingly, the use of enzymes produced by ge bacteria for cheese and bakery products industry has not been a major issue by opponents of the rdna technology. additional problems for the food industry arose with the introduction of the aforementioned favrsavr tomato (bruening and lyons, 2000), and continued with the cultivation of ge food crops such as maize, oilseed rape, and soybeans. several field trials as well as farmer cultivation of gmos in the eu have been destroyed. as a result to the opposition on gmos, the number of field trials in the eu since the late 1990s has been reduced substantially (europabio, 2011). notifications for environmental releases decreased from 264 in 1997 to 51 in 2012 (jrc, 2014), and farmers are hesitant to cultivate gm crops because of social pressure (punt, 2013), despite them wanting to cultivate the crop (skevas et al., 2012; 2010; venus et al., 2011). strategies chosen by food processors and retailers strategies chosen by food processors and retailers can be grouped in four major responses: continue with business as usual; adopt a gm-free labelling strategy; adopt a gm-free but not a labelling strategy; or openly not adopt a gm-free strategy (gaugitsch et al., 2012; venus et al., 2012). some retailers have linked their gm-free policy with their sustainability strategy (vigani and olper, 2014). voluntary gm-free standards by retailers increased in the mid of the first decade in the 21st century. major german retailers such as lidl (schwarz group), aldi, rewe, and the edeka group introduced gm-free standards. in italy, barilla and coop introduced 195biotechnologies and agrifood strategies gm-free standards, while in the uk tesco, sainsbury, and other uk retailers abolished their gm-free standards in 2013 and 2014. overall, the degree of gm policies in the eu differs by retailer and by country (greenpeace 2013, 2005). table 4 lists the largest food retailers in the eu selected among the 250 largest retailers in the world (deloitte, 2014) and whether or not they have a sustainability strategy, an explicit gmo-free policy and if they have a gmo-free policy if this has been linked with the company’s sustainability strategy based on their annual report of 2013. what can be observed is that all 37 retailers had a sustainability strategy in 2013. fourteen (about 38%) had an explicit gmo-free policy for their home brand and 12 out of the 14 had their gmo-free policy as part of the sustainability strategy in 2013. those 12 retailers have a share of 32% on the retail revenue. on average, eu food retailers with an explicit gmo-free policy as part of the sustainability strategy included in table 4 have lower retail revenues, 29,676 mio. usd in comparison to the overall average of 30,425 mio. usd, indicating those are the slightly smaller retailers among the top food retailers in europe. food processors also developed gm-free product lines. according to a year-2000 survey by friends of the earth “companies that said that they currently source all their ingredients from gmo-free crops for the food and drink they sell in europe, include pepsi cola, coca cola, heinz, mars, danone, kellogs, campbell foods, cadbury schweppes and kraft/jacobs/suchard.” (friends of the earth, 2000). some companies have mixed strategies such as friesland-campina, which sells gm-free labelled dairy products (landliebe brand) as well as non-labelled ones. in germany, several dairy processing companies developed gm-free dairy products such as bauer and zott. meat processors in germany and other countries developed gmfree meat products too. barilla, danone, nestle, and unilever all have gm-free labelled product lines. barilla is one of the larger food processors that have a clear gm-free strategy. according to the company’s web-site: “barilla has therefore decided to play it safe and refrain from the use of genetically modified ingredients, guaranteeing not to use gmo ingredients for all its products. this choice, which stems from our manufacturing strategy, is unrelated to any ideological commitments.” while a number of retailers and food producers entered the gm-free supply of food products, retailers in the uk have deliberately chosen to exit the market. tesco, among others, has mentioned that the additional costs for gm-free product lines cannot be covered, which is an indication that consumers are unwilling to pay for the additional costs of gm-free products. the costs for gm-free products are an important argument for food processors and retailers is also supported by the market for gm-free products in germany. prior to the introduction of modified labelling criteria in 2008 followed by an increase in “gm-free” labeled food products, stricter requirements for gm-free production lines had been requested (transgen, 2014). also, mcdonalds germany announced that it will not require its suppliers of chicken meat to feed their chickens gm-free feed (topagrar, 2014). here, the argument again had been the high costs this sort of policy would incur. 3. economics of gm-free standards the recent shift to gm-free food products by several food processors and retailers has implications along the value chain. first, retailers and food processors have to ensure that 196 j. wesseler ta bl e 4. g m o p ol ic y as p ar t o f r et ai le r s tr at eg y. c ou nt ry n o. o f f oo d re ta ile rs in to p 25 0 re ta ile rs 20 11 re ta il re ve nu e (u sd m ill io n) c ou nt rie s pr es en t su st ai na bi lit y st ra te gy g m o -f re e po lic y g m o -f re e po lic y as p ar t of su st ai na bi lit y st ra te gy o rg an ic o w n br an d 20 11 re ta il re ve nu e w ith g m o -fe e po lic y (u sd m ill io n) sh ar e of re ta il re ve nu e g m o fr ee p ol ic y au st ria 1 12 49 8 8 1 1 1 1 12 49 8 10 0% be lg iu m 3 50 23 2 20 3 0 0 3 0 0% fi nl an d 2 21 66 0 13 2 2 2 2 21 66 0 10 0% fr an ce 7 32 94 74 15 3 7 2 2 7 16 77 98 51 % g er m an y 8 37 32 63 10 6 8 3 2 5 10 49 28 28 % ita ly 3 36 45 5 4 3 1 1 3 15 27 9 42 % n et he rla nd s 3 60 88 9 19 3 1 1 3 89 50 15 % po rt ug al 1 57 37 10 1 1 1 1 57 37 10 0% sp ai n 2 30 69 3 3 2 0 0 0 0 0% sw ed en 2 19 26 0 6 2 2 2 2 19 26 0 10 0% u k 5 18 55 62 24 5 1 0 4 0 0% to ta l 37 1, 12 5, 72 3 n. a. 37 14 12 31 35 61 10 31 .6 3% so ur ce : c om pa ny a nn ua l r ep or ts o f 2 01 3, d el oi tt e, 2 01 4. n ot e: n .a . – n ot a pp lic ab le d ue to p os si bl e do ub le c ou nt in g of c ou nt rie s. 197biotechnologies and agrifood strategies ingredients are gm-free; and farmers producing the raw commodity have to comply with the standards. the impact is mainly on livestock farms as arable farms in the eu do not produce gm crops, except for gm maize, which is almost exclusively used as animal feed. livestock farmers have to ensure that the feeds they purchase are gm-free. this causes a problem mainly for farmers sourcing soybean-based protein feed as soybeans are imported mainly from argentina and brazil where more than 90% are gm. as an example, more than 70% of germany’s raw protein is imported, of which more than 76% is derived from soybeans of which more than 96% are imports from non-eu countries cultivating gm soybeans such as argentina, brazil, canada, and the us (ulmer, 2012). some farmer organizations in collaboration with politicians and other stakeholders in the eu also see the trend towards gm-free labelling as a crop production opportunity. since 2008, an increase in the area allocated to soybean production in the eu can be observed, which mainly took place (according to the most recent data up to 2012) in eastern europe. also, protein substitutes such as oilseed rape in the eu benefit from the demand for gmfree feed. austria, together with bavaria, started a danube soya initiative to increase gmfree soybean production in the danube region (http://www.donausoja.org/). however, the gm-free strategy in the eu depends on the availability on non-gm protein sources outside of the eu. in may 2013, a group of retailers signed the brussels soy declaration to signal to brazilian producers that there is a continuous demand for gm-free soybeans. pro terra, the gm-free soybean producers association in brazil (abrange), announced they would have no problems providing the european market with gm-free soybeans in 2014 (gyton, 2014). nevertheless, gm-free soybeans demand a higher market price. the price premium – about 10% – has been relatively stable between 2008 and 2013 in the eu (felhoelter, 2013) as well as the japanese market (foster, 2010). for maintaining gm-free standards, contracts between the parties involved are used increasing the cost for maintaining the standard. extra costs include the development of contracts, monitoring and enforcement of the contracts, as well as higher farm level production costs of the gm-free commodity. these extra costs need to be recovered, and in the end, part of them (not all) has to be shouldered by final consumers. higher production standards such as a gm-free strategy involve sunk setup costs including: facilities exclusively processing gm-free commodities, uncertain returns on the market, and the possibility of liability for cases when product standards are not met (venus et al, 2012). economic theory tells us that the incentives for adopting a gm-free food products strategy are higher for smaller retailers and food processors than for larger ones (venus et al., 2012; weaver and wesseler, 2004), primarily, as the latter face relatively higher ex-post liability costs in the case of fraud or mislabelling. ex-ante irreversible costs with respect to coexistence at farm level and segregation are also important cost factors (beckmann et al., 2010), which also depend on the specific regulations with respect to coexistence (beckmann et al., 2014; beckmann and wesseler, 2007). there is the risk for food processors and retailers that not only the specific product, but also other own brands and even the whole chain will be affected. this also discourages the adoption of a higher standard if retailers and food processors are present in several countries and trade between the countries, as well as if the flow of information between the different countries works well (vigani and olper, 2014). 198 j. wesseler but processors and retailers might also adopt different strategies if they are present in several markets. this will be easier for retailers than for food processors as retailers will have it easier to source domestic products to cater differences in consumer preferences. the market size can also increase the adoption of a higher standard if the markets are similar (vigani and olper, 2014) such as those in germany, austria, and southern tyrol with a similar history, cultural background, institutions, and language. independent of the countries being present food processors and in particular those producing several products using one input (such as in the dairy sector) will observe an increase in costs not only for the gm-free product line such as milk, but also for other product lines that use the same raw product such as cheese and yoghurt. higher market prices for the gm-free products have to cover not only the additional cost of gm-free product line, but also the additional costs of the other product lines to avoid overall negative effects on company profits (venus et al., 2011). the market power of retailers on the demand side versus food producers, farmers, and food processors on the supply side, may also have an impact on the strategic choices. a higher market power on the demand side encourages vertical product differentiation (von schlippenbach and teichmann, 2012), while the same may hold for the supply side depending on its market power, monitoring and enforcement costs, and the size of the vertically differentiated markets (hamilton and zilberman, 2006) and also effects whether or not producers will ask for mandatory or voluntary labelling to vertically differentiate food products (anania and nistico, 2004). nevertheless, producers may not have the same interest with respect to the labelling standards being used (menapace and moschini, 2014). a look at german dairy processors shows that a great number of small rather than larger processors indeed use a gm-free labelling strategy (venus and wesseler, 2012). some larger retailers use a gm-free strategy for their own brands, but do not label, such as lidl and aldi. this strategy protects the user against complaints from anti-gm food product lobby groups and possible law suits in the event that internal standards are not met, and hence reduces ex-post liability costs. nevertheless, those companies face the costs of maintaining their internal standards. another group of retailers and food processors such as metro or the müller group do not implement a dedicated gm-free policy for their products. metro and the müller group state that they trust the eu’s food safety system and consider gm food products as being safe. another important factor for food processors and retailers is the pressure environmental lobby groups exert. the two major environmental lobby groups, greenpeace and friends of the earth, are particularly visible in france, germany, the netherlands, and the uk (friends of the earth, 2013; greenpeace, 2013; 2005) and have affected company strategies as mentioned earlier. adopting a gm-free standard increases horizontal product differentiation to the benefits of the retailers (scatasta et al., 2007) and hence it is not surprising that retailers embrace gm-free labelling strategies, while food processors that shoulder the extra costs are more careful. 4. discussion and conclusion the gm-free standards for own brands in the late 1990s by uk retailers seems mainly to have been a response to pressure from lobby groups. other retailers in the eu followed 199biotechnologies and agrifood strategies in the early 2000’s. the uk retailers are also the first ones to abandon the gm-free strategy, and according them, because of costs. interestingly, many food processors and retailers have a corporate sustainability strategy with strong commitments for environmental sustainability. the gm-free strategy contradicts the corporate sustainability strategy considering the contribution to sustainability generated by gm crops in general (bennett et al., 2014; barrows et al., 2013; brooks and barfoot, 2014; wesseler et al., 2011) and for europe in particular (groeneveld et al., 2011; wesseler et al., 2007; demont et al., 2004). surprisingly, this argument has not yet been picked-up by the food industry in europe as an argument in favour of gm crops. environmental lobby groups dismiss the aforementioned benefits; and their views and their power seems to be more important. their arguments seem to be more convincing for the general public than the arguments of those supporting gmos, which may become a problem for the food industry. some food processors and retailers may start to use the environmental argument for leaving a gm-free strategy and generate pressure on those in the food industry that have a pronounced gm-free policy, which is often linked to a sustainability strategy . a challenge for the eu food industry will be the costs of a gm-free strategy. the experience of the uk retailers shows that those additional costs are difficult to recover. abandoning a gm-free strategy, or not having one, reduces costs for those retailers and increases their comparative advantage as long as consumers do not differentiate in their purchasing behaviour. while surveys about consumer willingness-to-pay indicate a price premium for gm-free food products, the revealed preferences (marks et al., 2003) and more differentiated willingness-to-pay studies (e.g. kikulwe et al., 2011) tell a different story. this will, in particular, affect the gm-free product lines of food processors and retailers’ gm-free own brands. the larger the share such products have on overall revenues, the larger the potential exposure of those companies to economic sustainability. but food processors and retailers that are globally active will have better possibilities than those active just in one country. nevertheless, there is a niche market for gm-free products and this provides opportunities for smaller food processors and retailers. from an international perspective the eu gm policy increases the costs of its food products and reduces the international competitiveness of its food industry. the asynchronicity in approval processes in combination with a zero tolerance policy for unapproved events generates a disadvantage for the european food industry. but it is not the zero tolerance policy as such, this applies to other countries such as canada and the us as well, but it is the asynchronicity that generates problems as most new events are developed by companies located in the us and first approved there before receiving approval in the eu. this asynchronicity provides a strategic advantage for the food industry in those countries where the gm event has been approved over those countries where it has not. the agriculture commodity traders in those countries do not face the threat of rejected cargos due to the presence of unapproved events in shipments, while this is different in the country where the event has not yet been approved. this is an advantage that walmart, for e.g., with a stronger presence in the us has over, for e.g., carrefour, edeka, or tesco. farmers in the eu may receive some short-term gains from the strategic response of the food industry. the demand for gm-free soybeans benefits soybean farmers in the eu, as well as oilseed rape producers, which serves as a substitute for soybean based protein 200 j. wesseler feeds. also, those who produce for the gm-free market may benefit if the price mark-up overcompensates for the extra costs. this has not yet been observed and it remains a zero sum game for farmers, at least in the dairy sector (venus and wesseler, 2012). many of the issues discussed in this paper are based on anecdotal evidence. this, to a certain extent, can be justified as the number of observations for the specific cases discussed is small. a systematic empirical investigation is urgently needed to further substantiate the arguments being made on moving into and out of the gm-free standards as the economic implications of strategies chosen are substantial from an economic, social, and environmental perspective. with more than 25 years of observations at hand time series analysis should allow us to empirically test those arguments. acknowledgments an earlier version of this paper was presented at the 3rd conference of the associazio ne italiana di economia agraria e applicata (aieaa), 25-27 june 2014 at alghero, italy. references anania, g. and nistico, r. 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(2014). the economic power of the golden rice opposition. environment and development economics. doi:10.1017/s1355770x1300065x. bio-based and applied economics 5(1): 83-98, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-16492 entry deterring effects of contractual relations in the dairy processing sector yvonne zavelberg*, thomas heckelei, christine wieck chair of economic and agricultural policy, university of bonn date of submission: 2015 18th, august; accepted 2016 28th, february abstract. the european commission has launched the so-called “milk package” in october 2012 that allows member states to require compulsory written contracts between milk producers and investor-owned processors. we argue that compulsory contracts have anticompetitive effects when they are exclusive in the sense that they comprise the obligation to supply to the contractor only. the objective of this paper is to set up a game theoretic model to analyze imperfect competition on the raw milk market that may result from entry deterring effects of exclusive contracts between dairy producers and processors. building on the antitrust literature, the model incorporates the specific characteristics of the milk market and considers the risk attitude of milk producers and uncertainty of a rival dairy’s market entry. under certain combinations of probability of the rival’s market entry and risk aversion of the producer, an incumbent can deter market entry by offering an exclusive contract. keywords. entry deterrence, imperfect competition, buyer power, exclusive contracts, dairy processing jel codes. l13, l14, l41 1. introduction in the eu-27, milk is one of the most important agricultural goods, representing about 13% of the total turnover of the european food and beverage industry (eda, 2013). in the past years, structural changes on both producer and processor side, innovations in milk production and the decision of the eu commission to abolish the quota regime changed the milk market. the increasing concentration of dairy processing1 facilities raise concerns regarding buyer market power of dairy processors (bka, 2009, 2012). addressing producers’ position on the market, the european commission launched the so-called 1 own calculation based on data from the agrarmarkt informationsstelle (ami) 2014, a german institution that collects data of agricultural entities. * corresponding author: yvonne.zavelberg@ilr.uni-bonn.de 84 y. zavelberg, t. heckelei, c. wieck “milk package” in october 2012 which aims at strengthening producers’ market position by improving their bargaining power and the transparency of the market. the “milk package” sets criteria for the formation of producer organizations and specifies rules for the regulation of pdo/pgi cheese supply. further, it suggests member states to introduce compulsory written contracts between milk producers and investor-owned processors. due to their specific ownership structure, cooperatives are exempted from this policy. as contracts used in recent years were often not well specified, the recommendation is that contractual agreements should not only become compulsory but also contain a minimum standard of specified criteria (e.g. details on price, volume and duration of contract). currently, 12 member states have introduced compulsory contracts (bulgaria, croatia, cyprus, france, hungary, italy, latvia, lithuania, portugal, romania, slovakia and spain) with a minimum contract durations of 6 month in most states, 1 year in spain and even 5 years in france. other member states have not introduced compulsory contracts but agreed on codes of good practice between producers and processors (belgium, united kingdom). in germany, contracts between farmers and investor-owned dairies are usually negotiated by producer organizations. these contracts usually contain details on quality, price parameters and specify the length of the contracts. in addition to these criteria, contracts shall be more precise about the contracted milk volume in the future. (ec, 2014) in the sector inquiry of the german milk market the question arose how long-term contracts in combination with the obligation to supply the whole production quantity to processors affect competition (bka, 2012). the concern is that by tying up milk producers through long-term contracts without appropriate cancellation periods, strong or dominant processors may use their market power to deter competition or entry to the market for raw milk. combined with the producer’s obligation to supply the full production quantity, this could lead to an abuse of a dominant position, which is prohibited by law (article 102 of the treaty for the functioning of the european union (tfeu)) (bka, 2012, section 106). according to the sector inquiry, 45% of the contracts between private dairies and milk producers have durations longer than two years. furthermore, 85% of german producers delivering milk to private dairy processing facilities are obliged to supply their entire production and the processing facility is likewise obliged to accept the whole amount (bka, 2009). in 2012, 60% of milk is processed in cooperatives. however, in terms of number of processing facilities, only approximately 40% of the 137 active processing facilities in 2012 were cooperatives, the remaining ones are investor owned firms (ami, 2014).2 the frequent use of contracts on the milk market may be explained by their ability to reduce price risks and to secure delivery quantities for dairy producers and input quantities for processors. however, they may also let processors exercise buyer market power by binding dairy producers and reducing delivery flexibility, which may even lead to entry deterrence of other dairy processors. we argue that compulsory contracts have anticompetitive effects as they are exclusive given the obligation to deliver to the contractor only. 2 concerning the eu, the importance of investor-owned companies is even more pronounced: among the top ten of europe’s largest dairy processors in terms of turnover, six firms are organized as investor-owned dairies and four as cooperatives. among the top three only investor-owned dairies can be found (nestlé, danone, lactalis) (miv, 2012). even though investor-owned dairies process only about 36% of european raw milk, this highlights the importance of investor-owned firms in the european dairy market. 85entry deterring effects of contractual relations in the dairy processing sector this is usually the case between milk producer and processor (bka, 2009) and holds likewise for investor-owned and cooperative dairies. anticompetitive effects of exclusive contracts have been scarcely studied under the specific characteristics of agricultural markets (exception e.g.: xia and sexton (2004) for the u.s. cattle industry), and only in the context of the antitrust literature that focusing on seller market power (e.g. segal and whinston, 2000; rasmusen et al., 1991; aghion and bolton, 1978). the analysis of seller market power is however of limited relevance on agricultural markets. although they are often assumed to be perfectly competitive, the structure of agricultural markets is more precisely characterized by a low concentration of producers and a high concentration of processors and retailers (sexton, 2013; mccorriston, 2002; rogers, 2001; rogers and sexton, 1994). therefore, the analysis of buyer market power is central (sexton, 2013; macdonald et al., 2004). however, oligopsony competition or monopsony behavior in an input market are rarely treated in the agricultural economic literature (exceptions are sexton, 2013; mérel, 2011; crespi et al., 2012; sexton, 2013; graubner et al., 2011; alvarez et al., 2000). given the high adoption rate of compulsory contracts and in light of the concern of the german sector inquiry about resulting anticompetitive effects on the milk market, the aim of this paper is to analyse if exclusive contracts between dairy processor and producer restrict competition on the raw milk market.3 this paper goes beyond the existing literature (1) providing a game theoretic analysis of the competitive effects of exclusive contractual relations based on the antitrust literature but in the framework of a monopsonistic market structure and (2) motivating the signing of an exclusive contract with the uncertainty of rival’s entry and risk aversion of the signer in contrast to former models of exclusive contracts. competitive effects of exclusive contracts are modelled between an incumbent investor-owned dairy processor on the milk market and one representative raw milk producer. we assume that a rival (investor-owned) dairy processor with lower marginal production costs threatens to enter the market. a short-term equilibrium in which the incumbent dairy offers an exclusive contract to the producer in order to deter the rival dairy’s market entry is analyzed. after the producer decided whether to accept the contract with the incumbent, the rival decides upon entry. by incorporating uncertainty of rival’s entry and producer’s risk attitude, we show that exclusive contracts can indeed be used to deter entry of a rival processor into a downstream market when the upstream producer is risk averse. most raw milk producers nowadays are highly specialized farms where the main income source results from milk production (eu commission, 2014). not a lot is known about the real “level” of risk aversion among dairy producers, but some evidence exists that dairy farmers are generally risk averse (loughrey et al., 2014; melhim and shumway, 2011). this may lead farmers to sign exclusive contracts with a dairy to reduce the income risk related to their main production activity. the paper is organized as follows: the next section gives an overview on the relevant literature. section 3 presents the game theoretic model. in a baseline model, an exclusive contract between dairy producer and processor is analyzed without incorporating uncer3 germany, as the largest milk producer in europe, stays in the focus of our analysis, motivated by the sector inquiry of the german milk market conducted by the german national competition authority (bka, 2012). however, the analysis may also be relevant for other member states with a similar market structure. 86 y. zavelberg, t. heckelei, c. wieck tainty about the rival’s entry and producer’s risk attitude, resulting in failure to deter entry. in a next step, we show how the inclusion of risk attitude and uncertainty may allow to deter rival’s entry. subsequently, a numerical example underlines the theoretical results. section 4 discusses the model while section 5 concludes. 2. literature review the analysis of contractual relations in the dairy processing sector requires the consideration of the contract design and the competitive effects. empirical studies find that producers strongly favor a redesign of raw milk contracts in terms of contract length and cancellation periods (steffen et al., 2009; schlecht et al., 2013). further, releasing producers of their supply obligation and allowing them to sell to more than one dairy processor is seen as an improvement in terms of both producers’ flexibility and bargaining position (steffen et al., 2009; schlecht et al., 2013; bka, 2012; schaper et al., 2008). concerning the competitive effects of contracts, entry deterring effects of exclusive contracts are analyzed in the antitrust literature focusing on seller market power (e.g. aghion and bolton, 1987; bork, 1978; rasmusen et al., 1991; segal and whinston, 2000). roger and sexton (1994) and sexton (2013) emphasize the importance of oligopsony power in agricultural markets. however, there is little work on exclusionary effects of contracts in the context of the specific oligopsonistic structure between agricultural producers and food processors. macdonald et al. (2004) and vavra (2009) analyze the use of contracts in agricultural markets and discuss the possibility to deter entry of buyers into local markets. to our knowledge, none of the existing studies explicitly models risk behavior of producers and its effects on entry deterrence of exclusive contracts (although innes and sexton (1994) discuss at least the implication). the models used in the antitrust literature for the analysis of seller market power are usually designed in the following way: an incumbent seller contracts a buyer who is usually a consumer with an exclusive supply contract. the contract specifies a compensation for the buyer to accept the contract and to not purchase from the incumbent’s rival, which leads to entry deterrence in the upstream market. the “chicago school” view (director and levi, 1956; posner, 1976; bork, 1978) criticizes the entry deterring effects of contracts and argues that an incumbent confronted with buyers preferring entry of a rival due to increased competition and potentially better prices, would have to pay more for the rival’s exclusion than to be gained from it. the reason is that the incumbent has to compensate buyers for the additional consumer surplus they would have gained in case of entry, which they lose by signing the contract. it has been shown that entry deterrence is not profitable in this case as the lost consumer surplus is higher than the monopoly profit in case of entry deterrence. therefore, the chicago school explained the observable use of exclusive contracts with efficiency reasons rather than anticompetitive behaviour (director and levi, 1956; posner, 1976; bork, 1978). since the 1980s, economists have developed game theoretic models that analyze anticompetitive effects of exclusive contracts. aghion and bolton (1987) developed a model where exclusive contracts are used to extract some of the surplus a potential rival would gain in case of market entry. they analyze the optimal contract length and differentiate between symmetric and asymmetric information about the probability of the rival’s entry 87entry deterring effects of contractual relations in the dairy processing sector and their impacts on entry deterrence. furthermore, the entrant endures fixed costs for entry. they find that entry deterrence leads to a lower economic welfare. later, rasmusen et al. (1991) used buyer’s lack of information to explain the existence of exclusive contracts and their entry deterring effects. if a buyer expects other buyers to sign an exclusive contract, he will also sign the contract without considering the overall economic effect, which leads to entry deterrence and a lower welfare. segal and whinston (2000) reconsidered rasmusen et al.’s (1991) model and showed that market entry is profitable when the rival can sell his product to a minimum number of buyers to cover fixed costs. if buyers sign exclusive contracts, it is difficult for the entrant to get the minimum scale needed and thus entry is deterred. segal and whinston (2000) show that when the incumbent makes discriminatory offers to the buyers, the externalities present between buyers lead to a profitable exclusion of rivals. these analyses explain the signing of exclusive contracts with market disorganization (rasmusen et al., 1991; segal and whinston, 2000) or complex contract terms (aghion and bolton, 1978), even though the signer would be better off without contracts. fumagalli and motta (2006) point out that the above mentioned models assume that buyers are final consumers whereas typically exclusive agreements are rather signed amongst producers or producers and processors or wholesalers. they consider the case where buyers procure a good from an upstream firm that is either from an incumbent producer or a rival producer and then sell it in a final market. in the case of buyers being final consumers, the demand and the payoff of a buyer depend only on the price of the good. but when buyers compete in a downstream market, their market share, the input price and the rival buyer’s price are relevant for demand and affect the possibility of entry deterrence. in recent years, a separate strand of literature emerged where raw milk pricing behavior and the implications for competition are analysed in a spatial market setting (alvarez et al., 2000; huck et al., 2006; graubner et al., 2011). in our analysis, the spatial dimension is not explicitly considered. 3. the model 3.1 general model assumptions on the upstream market one representative4 dairy producer takes the price for raw milk w as given. the (inverse) supply function for raw milk is defined as an inelastic function with w = x2 defined for x > 0 implying that the producer is able to extend production at increasing marginal cost in the short to medium term. dairies accept the entire production quantity x of the producer and cannot choose the quantity they would like to procure. therefore, we assume that processors compete in prices for raw milk à la bertrand. on the intermediate stage of the market, an incumbent dairy (dairy a) is procuring the milk quantity xa from the representative milk producer (producer p). a rival dairy (dairy b) with lower marginal production costs than the incumbent, cb < ca, threatens to enter the market. to enter the market, the rival dairy has to consider fixed costs f. we assume that f is too large for the entrant to offer a compen4 we do not consider a specific number of dairy producers and as we assume that producers take the price for raw milk is given, we just speak about a producer in the following. 88 y. zavelberg, t. heckelei, c. wieck sation for signing an exclusive contract.5 we abstain from incorporating spatial characteristics of the market and assume that the raw milk price offered to the producer is independent of the transport costs or distance between producer and processor. regarding the final dairy product q, we assume a processing relation of x = q for both dairies. if dairy b entered, both processors would be competitors on the market for raw milk and compete in milk prices. due to bertrand price competition, the producer delivers milk to the highest bidder. in order to deter rival dairy b’s entry, the incumbent dairy a can offer an exclusive contract to the producer. the exclusive contract comprises a compensation θa for selling all the produced milk to the incumbent and not to the rival. in case of a signed contract, the fact that the whole amount of raw milk is delivered to the incumbent dairy deters entry as the potential entrant can only procure milk from a free producer. if entry is successfully deterred, monopsony prices and profits are realized. the marketing of the final dairy product is not restricted to a regional market but can be sold on the national or even on the world market, which allows the assumption of a competitive downstream market. hence, dairies take the output price p as given. a short-term equilibrium in which the incumbent dairy offers an exclusive contract to the producer in order to deter the rival dairy’s market entry is analyzed. the timing of the game is as follows: at stage one, the incumbent dairy  a can offer an exclusive contract that specifies a compensation and an exclusive delivery obligation for the whole production amount. the producer decides whether to accept the contract. at stage two, the rival dairy b decides upon entry. at stage 3, active processors set prices. first of all, a basic model demonstrates the effects of exclusive contracts in a framework with a risk neutral producer and certainty of rival’s entry in absence of an exclusive contract. then, these restrictions are relaxed and producer’s risk attitude and uncertainty of rival’s entry are incorporated in the model. 3.2 the basic model in order to discuss the implications of exclusive contracts we analyze two scenarios. in scenario 1, a basic monopsony model structure without contracts is constructed. here, only dairy a and the producer are active on the market. scenario 2 analyzes market entry of dairy b. let us assume that ci < 1, ci < p and wis < p where subindices i = a, b represent the market actor and s=1,2 the scenario. in scenario 1, the monopsony scenario, dairy a maximizes its profit over the price for raw milk offered to the producer. the raw milk price that maximizes dairy a’s profit is given by wa1 and leads to a profit of πa1 the corresponding profit for the producer is denoted by πp1 (see table 1). in scenario 2, the case of dairy b’s market entry, dairies compete à la bertrand. the highest price dairy b can offer is = −w p cb b whereas dairy a’s highest price is = −w p ca a since cb < ca, processor b is able to offer a higher price for raw milk, >w wb a2 2 in case of market entry, dairy b will offer a slightly higher price than dairy a, 5 it would become more difficult for the incumbent to deter a rival’s entry if we would remove the assumption that rival’s fixed costs of entry are too high to also offer a compensation for an exclusive contract. however, the assumption is justified as the entry into a new market involves high entry costs. 89entry deterring effects of contractual relations in the dairy processing sector wb2 = p ca + ε with ε > 0. consequently, the producer will sell to the rival and dairy a will loose its market share resulting in a positive profit πb2 for dairy b and a zero profit for dairy a (see table 1). in this setup, there exists no equilibrium in which both dairies are active on the market. however, we assume that dairy a will not exit the market but is still present in the region with its production facility. in this case, dairy b’s market entry will not result in another monopsony situation, as dairy b has to keep its pricing strategy to prevent dairy a from re-entering the market. table 1. comparison of scenario 1 and 2. sc. price for raw milk raw milk quantity profit dairy a 1 w p c a a 1 3 = x p c a a 1 1 2 3 = ( ) / π a1 = 2 3 3 ( p− ca) 3/2 2 = −w p ca a2 xa2 = 0 πa2 = 0 dairy b 1 wb1 = 0 xb1 = 0 πb1 = 0 2 wb2 = p ca + ε xb2 = (p ca + ε)1/2 πa2 = (ca cb + ε) (p ca + ε)1/2 f producer 1 w p c a a 1 3 = x p c a a 1 1 2 3 = ( ) / π p1 = 2 9 3 ( p− ca) 3/2 2 wb2 = p ca + ε xb2 = (p ca + ε)1/2 π p2 = 2 3 ( p− ca +ε) 3/2 the comparison of the two scenarios demonstrates the incentive for dairy a to deter dairy b’s market entry. in case of market entry, dairy a achieves a zero profit, whereas the profit in the monopsony case, πa1, is positive. the producer, on the other hand, is better off in case of dairy b’s market entry as πp2 > πp1. without taking producer’s risk aversion and uncertainty of rival’s entry into account, the compensation that dairy  a needs to offer to the producer for an exclusive contract must compensate for the producer’s surplus lost when accepting the contract. this is the difference between the profits in the two scenarios, θp ≥ πp2 πp1, which is equal to θp ≥ 2 3 ( p− ca +ε) 3/2 − 2 9 3 ( p− ca) 3/2 (1) the maximum compensation dairy a is willing to offer is θa ≥ πa2 πa1 which leads to θa ≤ 2 3 3 ( p− ca) 3/2 (2) comparing (1) with (2) we observe that the compensation the producer requires is higher than the one dairy a is able to offer, i.e. θp > θa. therefore, offering an exclusive 90 y. zavelberg, t. heckelei, c. wieck contract is not beneficial for dairy a in this setup. consequently, if a lower cost producing dairy b enters the market, dairy a is not able to keep its raw milk source, as the compensation dairy a is able to offer does not offset the higher price dairy b is able to pay. 3.3 risk attitude and uncertainty of entry in order to incorporate producer’s risk attitude, producer’s utility function is defined as u = π p r where the exponent r determines the risk attitude of the producer. if r > 1 the utility function implies a risk loving producer, if r = 1 risk neutrality and if 0 < r < 1 absolute risk aversion. exogenous determinants lead dairy b to enter the market. depending on dairy a’s assumptions on the probability of dairy b’s market entry, dairy a offers an exclusive contract to the milk producer. the probability of entry is denoted by k such that 1 k is the probability of no entry, both for the case of no contract. if successful, the signing of the exclusive contract deters entry of dairy b and thus, probability of entry is zero. whether the offering of an exclusive contract leads to entry deterrence depends on the compensation that dairy  a can pay, which depends on the entry probability of the rival and producer’s risk attitude. is the compensation high enough for the producer to accept, the contract will be signed and entry of the rival is deterred. the market is in a monopsony situation with prices and quantities being as in scenario 1 of the basic model. if the contract with dairy a is not accepted, the producer will sell the entire production quantity to dairy b. the compensation the producer requires for signing a contract with dairy a depends on the payoff required for not staying free on the market. this compensation is the difference between the certainty equivalent (cep) and the payoff under contract (πp1); θp risk =cep −π p1which is equal to θp risk = k π p1( )r + 1− k( ) π p2( )r⎡ ⎣⎢ ⎤ ⎦⎥ 1/r −π p1 (3) the highest compensation that dairy a is able to offer under uncertainty is equal to θa risk = π a1 − kπ a2 + (1− k)π a1⎡⎣ ⎤⎦ (4) for simplicity we define the margin of dairy a as p ca = m and assume that ε = 0. then, inserting the findings from table 1 yields θp risk (ρ,r,m) = k 2 3 m3/2 ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ r + (1− k) 2 9 3 m3/2 ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ r⎡ ⎣ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ 1/r − 2 9 3 m3/2 (5) θa risk = k 2 3 3 m3/2 ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ (6) rival’s entry can be deterred if θp risk ≤θa risk ((5) ≤ (6)). hence, dairy a can offer a compensation that induces the producer to sign the contract and thus deters entry if θa risk −θp risk ≥ 0 91entry deterring effects of contractual relations in the dairy processing sector to better understand under which conditions this is valid, rearranging leads to m3r /2 k 2 3 3 + 2 9 3 ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ r − k 2 3 ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ r − (1− k) 2 9 3 ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ r⎡ ⎣ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ≥ 0 (7) whether this inequality holds depends on the values of k, r and m. the margin m = p ca is by definition positive. therefore, entry can only be deterred if the term in brackets in equation is larger than zero, which depends on the variables k and r. figure 1. effective entry deterrence depending on k and r figure 1 shows levels of k and r that lead to a positive term in equation (7) i.e. a situation where entry deterrence is possible. this is valid for all combinations of r and k that lie on the curve and underneath the curve in figure 1. a highly risk averse producer would even accept the contract when the entry probability is high enabling dairy a to maintain the monopsony situation on the market. if, on the other hand, the entry probability is relatively high and risk aversion only moderate dairy a has no possibility to deter entry. contrary to the basic model, it is now possible to deter rival’s entry for certain levels of producer’s risk aversion and the probability of rival’s entry. if the market entry is deterred, the market is in a monopsony situation, resulting in prices and profits of the basic scenario 1 and providing the incentive for dairy a to sign an exclusive contract with 92 y. zavelberg, t. heckelei, c. wieck the producer. if deterrence is possible, then the level of compensation dairy a has to pay to maintain the monopsony position increases with the probability of market entry by dairy b and decreases with the increasing risk aversion of the farmer. 3.4 numerical example using a numerical example roughly reflecting the current situation on the german dairy market, we assume that the marginal costs of the rival are cb = 0.18 ct/kg and the marginal costs of the incumbent are 20% higher, ca = 0.22 ct/kg. the downstream price p for one unit of a (not further specified) dairy product is assumed to be p = 0.48 ct/kg6. basic model based on these numbers, the price that dairy a offers in scenario 1 of the basic model is equal to 9 ct/kg (see table 2). this is a rather low price for raw milk, which results from our crude assumptions and the monopsonistic market structure. table 2. numerical example of the basic model. sc. price for raw milk demanded raw milk profit dairy a 1 wa1 = 0.09 xa1 = 0.29 πa1 = 0.0510 2 wa1 = 0 xa1 = 0 πa1 = 0 dairy b 1 wb1 = 0 xb1 = 0 πb1 = 0 2 wb2 = 0.27 xb2 = 0.52 πb2 = 0.0156 producer 1 wa1 = 0.09 xa1 = 0.29 πa1 = 0.0170 2 wb1 = 0.27 xb1 = 0.52 πb1 = 0.0935 for scenario 2 of the basic model, the highest price dairy  a is able to offer when dairy b enters the market equals w p ca a= = 0 26. and dairy b’s highest price is given by w p cb b= = 0 30. if dairy a has not contracted the producer and rival b enters the market, dairy b is able to outbid dairy a by offering a slightly higher price for raw milk, say wb = 0.27, given bertrand competition. then, dairy a has a profit of zero and dairy b πb. the producer’s expected payoff is given by πp (see table 2). comparing the two scenarios shows that the bertrand price competition leads to a higher price in scenario 2 compared to scenario 1 and a higher quantity of raw milk. this results in more than a fivefold producer’s profit. the example demonstrates dairy a’s incentive to deter rival b’s market entry due to the higher profit that can be achieved in the monopsonistic case. from the producer’s perspective, it would be better if the rival processor enters the market, as this results in higher competition for raw milk and thus in a higher price. for dairy a, holding the monopsony position on the market can only be achieved with an exclusive contract that obliges the producer to deliver the full production of raw milk. 6 data derived from a cost figure provided by the german dairy association(miv, 2011). 93entry deterring effects of contractual relations in the dairy processing sector for the producer to accept, the contract must enclose a compensation for not being able to negotiate/contract with dairy b. therefore, the compensation must at least contain the difference between producer’s profit in scenario 2 and 1. consequently, the compensation must be θp ≥ 0.0765. dairy  a’s profit in scenario 1 is πa = 0.051 and zero in scenario 2, therefore the highest compensation dairy a is able to offer equals θa ≤ 0.051. this compensation is not high enough for the producer to accept, therefore market entry of dairy b will take place. without taking risk aversion into consideration, dairy a cannot deter market entry of dairy b. dairy b will enter the market and bertrand competition for raw milk occurs. risk attitude and uncertainty of entry the compensation that dairy  a is able to offer depends on the expected entry probability of the rival. producer’s required compensation also depends on the entry probability and further on the risk attitude. consequently, both affect the possibility to deter the rival’s entry. the relationship of affordable and required compensations for entry deterrence depending on entry probability and the risk attitude is presented in figure 2. the bold line represents the compensation dairy a is able to offer (θa line). the thin lines represent the compensation that the producer requires under a given level of risk attitude (θp lines). generally, the figure shows that given our numeric assumptions and if risk aversion of the producer is not lower than r = 0.1, entry cannot be deterred if the entry probability is higher than k = 0.552. all compensation lines of the producer (θp lines) lie above dairy a’s compensation line (θa line) after this point. if k = 0.552 and r = 0.1 then θp risk =θa risk = 0.02815 . therefore, if r = 0.1, an exclusive contract and an entry probfigure 2. development of compensations depending on probability. 94 y. zavelberg, t. heckelei, c. wieck ability of k ≤ 0.552 lead to an effectively deterred entry. addressing the risk attitude of the producer, rival’s entry can only be deterred if r ≤ 0.6 and if the entry probability is low enough respectively. starting from a risk behavior of r > 0.6, entry cannot be deterred (all θp lines lie above the θa line there). for a risk averse producer with r = 0.6 the entry probability would need to be very low (k < 0.018) to effectively deter rival’s entry with an exclusive contract. with increasing risk aversion of the producer and with decreasing entry probability, the required compensation of the producer is decreasing. however, at the same time, the compensation that dairy a is able to offer decreases with decreasing entry probability. this shows that under our assumptions regarding marginal costs of production and processing, there are certain ranges of interaction between entry probability and risk attitude where the incumbent dairy a can use an exclusive contract to deter rival dairy b’s entry. 4. discussion even though the concentration of dairy processors is increasing, the entry of rivals into an incumbents market area is still relevant. in the dairy concentration process, processing quantities are continuously increasing which leads to larger market areas (ami, 2014). hence, a rival’s entry can also be interpreted as an existing dairy who wants to increase its market area. certainly, the above presented model covers a complex market structure and therefore relies on abstract assumptions. the complexity of the market presents itself in the different relations along the supply chain. on the one hand, consedering the relation between producers and dairies, the model does not take into account the possible existence of producer organizations. these might exert bargaining power in contrast to the model assumption of the producer being a price taker. on the other hand, regarding the relation between dairies and the downstream market, the model lacks to cover the possible existence of buyer power of downstream firms. however, in order to focus the analysis on the relation between the producer and the dairy, perfect competition on the downstream market was assumed. nevertheless, both assumptions might be worth to relax in future studies. as cooperatives are exempted from the policy of compulsory and exclusive written contracts, our analysis focused on investor-owned dairies. however, the theory of exclusive contracts can also be applied to cooperatives. the literature provides three possible profit maximizing objectives for cooperatives (royer and matthey, 1999). first, cooperatives act like investor-owned firms, they maximize profit and afterwards split profit between members. second, cooperatives maximize total member welfare by maximizing profit over quantities. however, this is not applicable to the milk market due to the obligation to supply the entire production amount to the same dairy. third, cooperatives maximize the price paid to their members and generate a zero profit. therefore, our theory can be applied to cooperatives if we assume that the cooperative maximizes its profit like an investor-owned firm. then, the compensation corresponds to the shared profit of a cooperative. the model does also not change for the entrant, who can then either be an investor-owned dairy or a cooperative. if we stick to the assumption that the fixed costs of market entry are too high for the entrant to offer a 95entry deterring effects of contractual relations in the dairy processing sector compensation, the theory can completely be translated to the case of cooperatives that maximize profits like investor-owned firms. 5. conclusion in this article, we analyzed entry deterring effects of exclusive contracts in an oligopsonistic market. the model is based on the framework of studies analyzing exclusive contracts in the literature (e.g. segal and whinston, 2000; rasmusen et al., 1991; aghion and bolton, 1978, fumagalli and motta, 2006). in contrast to these models on exclusive contracts, we incorporate risk aversion of the producer and uncertainty of rival’s entry. in our model, we assume increasing marginal cost of the raw milk producer and an exogenous downstream market price. the rival’s entry can effectively be deterred under certain combinations of the rival’s entry probability and producer’s level of risk aversion. increasing farmer’s risk aversion reduces the compensation the producer requires to sign an exclusive contract. this implies that the producer forgoes uncertain higher prices in a competitive market environment for the compensation paid. generally, the producer is better off in terms of profit in case of market entry of the rival dairy. only for rather high values of producer risk aversion and low entry probability of the rival, the incumbent dairy can use an exclusive contract to deter market entry. according to empirical studies, the majority of producers prefer a short-term contract period up to two years (schlecht et al., 2013) and the possibility to change the processor on a short notice. in addition, short cancellation periods and extraordinary termination clauses are preferred by the majority of producers which is perceived as a strong bargaining instrument for a better milk price (steffen et al., 2009). these requests are reflected by our model, which shows that the possibility to change the processor is beneficial for producer as he can achieve a higher milk price when the rival dairy processor enters the market. long-term contracts combined with the obligation to supply and long cancellation periods reduce competition on the raw milk market. to assure decision flexibility for farmers regarding their contractual relationship and to improve their ability to change processors in case of unsatisfactory raw milk pricing, we conclude that contracts should have appropriate cancellation periods. from the perspective of the dairy processor there is always an incentive to keep the monopsonistic position on the market. this occurs because market entry of the rival results in market foreclosure for the incumbent as the producer will offer all production to the rival who can pay the better price. the market entry of the rival does not lead to another monopsony as we assume that the incumbent is still active with its processing facilities and wants to regain its market 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(2009). role, usage and motivation for contracting in agriculture. oecd food, agriculture and fisheries working paper, no. 16, oecd publishing. xia, t. and r.j. sexton (2004). the competitive implications of top-of-the market and related contract pricing clauses. american journal of agricultural economics 86(1): 124-138. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(3): 213-236, 2013 food commodity price volatility and food insecurity alexander sarris*1 university of athens, greece abstract. the paper first reviews several issues relevant to global food commodity market volatility as it pertains to food security, and food importing developing countries, and then discusses international and national policies and measures to prevent or manage this volatility and related risks. it is shown that market volatility relates to unpredictability of market fundamentals, and price spikes occur when unpredictability increases excessively. the food security risks faced by food import developing countries are discussed and it is highlighted that the major risks involve not only large and unpredictable price variations but also trade finance as well as import contract enforcement. the problem of identifying a price spike is analyzed and it is seen that, despite difficulties in commodity modeling, there are empirical techniques that allow the assessment of the probabilities of price spikes, and could facilitate the triggering of responses. suggestions are made concerning institutions and policies to assist developing countries better cope with the risks of commodity market volatility. keywords. agricultural price volatility, developing countries, food insecurity. jel codes. q11, q17, q18 1. introduction the period since 2006 has seen considerable instability in global agricultural markets. between september 2006 and february 2008, world agricultural commodity prices rose by an average of 70 percent in nominal dollar terms, with prices in some products rising by much more than that. the strongest price rises were observed in wheat, maize, rice, and dairy products. prices fell sharply in the second half of 2008, although in almost all cases they remained above the levels of the period just before the sharp increase in prices started. in 2010 sharp price rises of food commodity prices were observed again, and by early 2011, the fao food commodity price index was again at the level reached at the peak of the price spike of 2008. in 2011 and 2012 prices fell again and then rose again considerably in early 2013. in other words within the past six years many food commodity prices increased very sharply, subsequently declined equally sharply, and then again increased rapidly to reach the earlier peaks. such rather unprecedented volatility in world prices creates much uncertainty for all market participants, and makes both short and longer term planning very difficult. * department of economics, university of athens, greece, 8 pesmazoglou street, athens, 10559, greece. email alekosar@otenet.gr, asarris@econ.uoa.gr. tel no. +30 6944 291796. 214 a. sarris the sudden and unpredictable increases in many internationally traded food commodity prices in late 2007 and early 2008 caught all market participants, especially governments of net food importing developing countries, by surprise and led to many short term policy reactions that may have exacerbated the negative impacts of the price rises. given that several such interventions were in many cases inadequate or inappropriate, many governments, think tanks, and individual analysts called for improved international mechanisms to prevent and/or manage sudden food price rises. similar calls for improved disciplines of markets were made during almost all previous food market price bursts, but were largely abandoned after the spikes passed, largely because they were deemed difficult to implement. the purpose of this paper is first to review several issues relevant to global food commodity market volatility as it pertains to food security, and then to discuss international and national policies and measures to prevent or manage this volatility and related risks, mainly, albeit not exclusively, from the perspective of food commodity dependent developing countries. staple food commodity price volatility, and in particular sudden and unpredictable price spikes, creates considerable food security concerns, especially among those, individuals or countries, who are staple food dependent and net buyers. these concerns range from possible inability to afford increased costs of basic food consumption requirements, to concerns about adequate supplies, irrespective of price. such concerns can lead to reactions that may worsen subsequent instability. for instance excessive concerns about adequate supplies of staple food in exporting countries’ domestic markets may induce governments to take measures to curtail or ban exports, thus inducing further shortages in world markets and higher international prices. the latter in turn may induce permanent shifts in production and/or consumption of the staple in net importing countries, with the result that while subsequent global supplies may increase, import demands may decline permanently altering the fundamentals of a market. the recent food market spike and volatility occurred in the midst of another important longer term development, that highlights additional developing country concerns. over the last two decades there has been a shift of developing countries from the position of net agricultural exporters up to the early 1990’s to that of net agricultural importers (fao, 2006). growing dependence on food commodity imports implies growing vulnerability to external commodity shocks. projections to 2030 and 2050 indicate a deepening of this trend (ibid.), which is due to the projected decline in the exports of traditional agricultural products, such as tropical beverages and bananas, combined with a projected large and growing deficit of basic foods, such as cereals, meat, dairy products, and oil crops. since 1990, the food import bills of least developed countries (ldcs) have not only increased in size, but also in importance, as they constituted more than 50 percent of the total merchandise exports in all years. in contrast, the food import bills of other developing countries (odcs) have been stable or declined as shares of their merchandise exports. these trends were reinforced during the 2007-8 food crisis (prakash, 2011). the above suggests that the problem of managing the risks of food imports has increased in importance, and is already a major issue for several ldcs and low-income food deficit countries (lifdcs)2. the major problem of lifdcs is not only price or quan2 lifdcs are a fao classification. the latest list of may 2012 includes 62 countries. the list of ldcs is one used by the united nations (un) and as of 2012 includes 49 countries. almost all ldcs are also included in the 215food commodity price volatility and food insecurity tity variations per se, but rather major unforeseen and undesirable departures from expectations, that can come about because of unanticipated food import needs due to unforeseen adverse domestic production developments, as well as adverse global price moves. in other words, unpredictability is the major issue. this is also the gist of the argument of dehn (2000a) and cavalcanti, et. al. (2011) who argued that the negative impacts on growth of commodity dependent economies come from unanticipated or unpredictable shocks, rather than from ex-post commodity instability per se. apart from the problem of unpredictability of food import bills for lifdcs, another problem that surfaced during the recent food price spike was the one of reliability of import supplies. several net food importing developing countries (nfidcs) that could afford the cost of higher food import bills, such as some of the middle income oil exporting countries and small island states, during the 2007-8 period faced problems of not only unreliable import supplies but also the likelihood of unavailability of sufficient food import quantities to cover their domestic food consumption needs. this raises a different problem for these countries, namely the one of assurance of import supplies. several of these countries, e.g. those surrounding the arab peninsula and the persian gulf, have unfavorable domestic production conditions and rely on imports for a substantial share of their domestic consumption, as indicated in table 1. unavailability of supplies creates large food security concerns for these countries. the rest of the paper proceeds as follows. in the next section the issue of market volatility and its importance are examined. section 3 considers the issue of the types of risks faced by food importing developing countries. in section 4 the issue of how to lifdc list. the list of nfidcs is a world trade organization (wto) group, which as of 2012 includes all 49 ldcs and another 31 higher income developing countries, for a total of 80 countries. table 1. cereal import dependence 2007-9 (number of countries with percentage share of imports to total domestic supply in given range). 0-10 10-20 20-50 50-75 75-100 total no of countries in group hic 5 3 6 22 36 ldc 16 6 12 9 6 49 lic 18 6 16 8 1 49 mic 16 6 28 14 20 84 oil exporters 3 1 6 1 4 15 sids 1 4 6 31 42 total no of countries 58 20 69 44 84 275 hic-high income countries, mic-middle income countries, lic-low income countries,(world bank definitions), sids=small island developing states, ldc-least developed countries (un definitions). some countries in the ldc, oil exporter and sids groups are included in the other categories as well. source. author’s computations from fao data. 216 a. sarris prevent or lower the occurrence of market volatility and crises is considered. the final section concludes. 2. what is market volatility and why it matters market volatility normally refers to variations of market prices from period to period. as such it is an ex-post concept, in the sense that everyone can observe the market variations. however, what matters for both market participants as well as policy makers are not the market price variations per se, but their unpredictability, and the risks they create. uncertainty of the variable x, when looked at from some period before its realization, is basically a summary measure of the unpredictable elements in the process determining x, that are likely to occur between the time of the prediction and the time of realization of the variable x. for instance if a producer is contemplating producing a crop, he/ she may know the basic process (the model) that determines the yield and the price of the commodity, but he also knows that there are elements of this process, such as rainfall and future price, that cannot possibly be predicted say one year ahead. these unpredictable elements are what create the uncertainty about the outcome of his action to produce the crop. uncertainty then depends on how far into the future one is interested in the variable of interest. in the sequel uncertainty and upredictability are used interchangeably as they refer to the same concept. risk, in turn is generated by uncertainty. in other words risk is generated by actions whose outcomes are subject to unpredictability. in the case of the producer, he knows that production of a crop is uncertain. as long as he does not produce the crop he is not at risk. if, however, he decides to produce it, he places himself at risk, as the outcome of the crop affects his income and welfare. thus it is unpredictability that defines uncertainty, and it is the actions that have uncertain outcomes that create the attendant risks. in the face of uncertain outcomes and prices, agricultural producers, for instance, tend to reduce the risks facing them, by diversification, namely by producing a less uncertain mixture of products. prices normally fluctuate in commodity markets in response to new and continuously changing information about the state of the markets. similarly the underlying uncertainty about future events gives rise to expectations about future market outcomes, such as prices, and different degrees of confidence about these expectations. hence at any point in time one can talk about the underlying uncertainty of the market about a future outcome. the level of information and the actions of the various market participants based on this information determine the probability distribution of expectations as well as actual market outcomes. it is normal in commodity markets that actual prices vary from period to period, and also that expectations of market outcomes, such as prices, also vary. volatility is normally associated with two concepts. the first is variability of the observed prices, and as such it is a concept that can be readily quantified ex-post through some a measure based on observable market prices. the second concept is that of unpredictability, and this, at any one time, refers to the conditional probability distribution of some subsequent market outcome, given current information. such a concept cannot be readily and objectively quantified, as there is no corresponding market variable. it can only be inferred from observed market variables through some appropriate model. 217food commodity price volatility and food insecurity the principal concern of market participants and policy makers alike is not large ex-post variations in observed prices per se, but large shifts in the degree of unpredictability or uncertainty of subsequent prices. such large shifts normally also cause large changes in observed market prices and are associated with what has been termed “excess volatility” (shiller, 1981, prakash, 2011), a rather elusive concept referring to variations of prices outside what maybe inferred or predicted on the basis of expectations of rational efficient markets. a very popular measure of ex-post or realized or historical market volatility, used extensively in finance, is the annualized historic volatility, computed as the standard deviation of the logarithmic returns of prices over a given period of time multiplied by the square root of the frequency of observations. € v =σ t , where € σ = (ri − µ)2 i=1 n ∑ /(n −1) , and € rt = ln(pt ) − ln(pt−1) (1) in the above rt is the logarithmic return of price, p is the (detrended) price of the commodity, n is the number of observations, µ is the average of the logarithmic returns, and t is the frequency of the observations on a yearly basis (252 if daily3, 12 if monthly, etc.). unpredictability in turn is not easily measured as indicated above. one relatively objective measure of unpredictability is “implied volatility”, which is a measure of the market estimate of the ex-ante or conditional variance of subsequent price, based on current observations of values of options on futures prices in organized exchanges, and using the black-scholes model for the computations. estimates based on the two concepts may point in different directions, depending on data. for instance illustrations in prakash (2011) indicate estimates of realized volatilities of cereals, based on observed spot prices in major international markets, such as gulf (as compiled by fao), which exhibit mild upward trends, while estimates of implied volatilities of the same cereal prices, as inferred from option prices in the major exchange trading these derivative instruments, namely chicago mercantile exchange (cme), exhibit strongly upward trends. this suggests that there maybe different determinants of the ex-post and the ex-ante volatilities of food commodities depending on the market where prices are measured. unfortunately there are not many organized commodity options markets, and hence implied volatilities cannot be estimated from readily observed option prices for most commodities. however, there are other ways to measure unpredictability. a popular measure is an estimate of the conditional variance of future price, based on a time series model of the price. models of prices that allow direct estimation of such conditional variances are the class of generalized autoregressive conditional heteroscedastic time series models (garch), introduced by bollerslev (1986). the detrimental effects of uncertainty or unpredictability on both private agents, as well as governments are not hard to understand, and have been the object of both discus3 252 refers to the number of trading days within a year 218 a. sarris sion as well as research for a long time. for instance, keynes (1942) argued that commodity price fluctuations led to unnecessary waste of resources, and, by creating fluctuations in export earnings, had a detrimental effect on investment in new productive capacity, and tended to perpetuate a cycle of dependence on commodities, what we may call in modern growth terminology a “commodity development trap”. the above discussion implies that mere ex-post variability of outcomes does not constitute uncertainty, which is inherently an ex-ante conept. this issue of uncertainty versus mere ex-post variability is important in the discussion of this paper, as compensatory schemes like stabex, as well as the imf’s commodity compensatory financing facility (cff) have adopted a notion of uncertainty that is related to the mere ex-post variability or fluctuations of outcomes such as export earnings or import costs, rather than to their predictability. more recently, there have been efforts to construct indices that correspond more closely to the theoretical notion of uncertainty, namely the notion of unpredictability. dehn (2000b), constructed an index of price instability that distinguishes between negative and positive shocks, and finds, as expected theoretically, that negative commodity price shocks have a significant negative effect on overall economic growth. this was the first study to establish a strong negative empirical link between negative unanticipated shocks and overall economic growth. recently cavalcanti et. al. (2011) also estimated that negative terms of trade shocks (which include high food import costs) have stronger negative growth impacts than positive terms of trade shocks for developing countries. that unpredictability rather than instability is the main problem in agricultural production is one of the oldest, but apparently forgotten or not appreciated, issues in agricultural economics. in fact one of the earliest classic works in agricultural economics considered exactly the issue of agricultural price unpredictability and the benefits of establishing forward prices for producers (johnson, 1947). by establishing forward prices for agricultural producers, one basically eliminates one of the most troublesome and potentially damaging sources of income unpredictability, and makes producers able to plan better their activities. establishing predictability in agriculture has been one of the earliest institutional developments of the modern era in developed countries. in fact the modern us agricultural marketing system realised very early the benefits of a market based system of forward prices, and through the simple system of warehouse receipts, emerged one of the most sophisticated and useful marketing institutions in modern agriculture, namely the institution of futures markets. it is not perhaps coincidental that futures markets developed independently in several countries and long time ago. in more recent years, the development and globalisation of financial markets has led to the proliferation of many other risk management commodity related instruments, notably options, and weather related insurance contracts. while in some developed countries the marketing system response to unpredictability has been the establishment of sophisticated forward markets, in most other countries, both developed and developing, the response of producers, and through their pressure of governments, has been the institution of fixed or minimum price marketing arrangements. the major problem, however, of most such schemes is not that they are in principle wrong, but that they have most often been transformed to price support or taxation instruments that have veered off their purpose of providing forward signals and minimum prices based on proper predictions. 219food commodity price volatility and food insecurity it, therefore, appears that a major issue in post adjustment agriculture in most developing countries, with respect to market volatility, is how to establish some forward pricing or insurance system for agricultural producers and governments without distorting the markets. once such forward mechanisms can be established then one can talk about systems of insurance or systems of compensation. considerable literature has been devoted to understanding the costs of market volatility. prakash (2011) offers a thorough survey. while some literature (lucas, 2003) suggested that the cost of market volatility is quite small in developed countries with efficient capital markets, other literature, that took into account credit constraints and imperfect transmission from international to domestic markets, showed that the cost of market volatility can be substantial for low income developing countries exposed to commodity shocks (guillaumont, et. al., 1999, prasad and crucini, 2000, subervie, 2008, rapsomanikis and sarris, 2008, bellemare, et. al. 2010). 3. volatility risks faced by food importers policies for the effective management of price booms or general market volatility depend on the proper identification and assessment of the risks facing each country. these differ by country, and involve the identification of the parts of a country’ s economy and inhabitants that are vulnerable to food commodity market shocks, as well as the types of market uncertainties which affect these agents. in other words one must outline a “risk profile” of the country to food commodity shocks. in the sequel risks that depend on upheavals in international food markets are discussed. proper response to a food commodity shock differ depending on whether the shock affecting the country is transitory or permanent. factors to consider are the following: (i) does the price shock have its origins in factors external to the country, such as world markets, or in domestic production supply imbalances in the markets concerned? (ii) how transitory are the factors that have led to the price shock? (iii) what is the level of uncertainty concerning the factors that may influence the future course of prices? the answers to these questions are not easy, and there may be legitimate differences of opinion among analysts concerning such assessments. the second issue concerns the possible impacts of the price shock on the country’s economy and its citizens. the impact of increasing prices on the wider economy is determined by a number of structural characteristics, such as the structure of production and food consumption, and the types and income-consumption profiles of households. any adopted policy measure should not try to protect or benefit one vulnerable group by damaging the benefits to another poor constituency. in this context, it is important to ascertain the extent to which price signals are transmitted to the domestic markets, the identification of vulnerable population groups that can be targeted for support, as well as the agricultural sector’s ability to respond to increasing prices. the third issue that is imperative before a country adopts specific policy measures is to ascertain and be clear about the objective of the policy. too often policy measures are adopted with a very narrow objective, and may end up affecting negatively other areas of equally important domestic concern. also if the objective is known and generally agreed upon, then any policy measure can be judged against others that may offer similar ben220 a. sarris efits, but with smaller side effects or negative secondary consequences. finally, if there are more than one policy objectives, it may well be that a combination of measures is necessary to simultaneously achieve all of them. the reactions to the recent price boom, suggest that policy reactions to the food price surge have been prompt, with governments in many developing countries initiating a number of short-run measures, such as reductions in import tariffs and export restrictions, in order to harness the increase in food prices and to protect consumers and vulnerable population groups. other countries have resorted to food inventory management in order to stabilize domestic prices. a range of interventions have also been implemented to mitigate the adverse impacts on vulnerable households, such as targeted subsidized food sales (rapsomanikis, 2009). demeke, et. al. (2011) made a review of policies adopted in response to the recent food price spike and they indicate that the responses of developing countries to the food security crisis appear to have been in contrast to the policy orientation most of them had pursued over the last decades as a result of the implementation of the washington consensus supported by the bretton woods institutions. this period had been characterized by an increased reliance on the market – both domestic and international – on the ground that this reliance would increase efficiency of resources allocation, and by taking world prices as a reference for measuring economic efficiency. the availability of cheap food on the international market was one of the factors that contributed to reduced investment and support to agriculture by developing countries (and their development partners), which is generally put forward as one of the reasons for the recent crisis. this increased reliance on markets was also concomitant to a progressive withdrawal of the state from the food and agriculture sector, on the ground that the private sector was more efficient from an economic point of view. the crisis has shown some drawbacks of this approach. countries depending on the world market have seen their food import bills surge, while their purchasing capacity decreased, particularly in the case of those countries that also had to face higher energy import prices. this situation was further aggravated when some important export countries, under intense domestic political pressure, applied export taxes or bans in order to protect their consumers and isolate their prices from world prices. as a result, several countries changed their approach through measures ranging from policies to isolate domestic prices from world prices; moving from food security based strategies to food self sufficiency based strategies; by trying to acquire land abroad for securing food and fodder procurement; by trying to engage in regional trade agreements or; by interfering with the private markets through price controls, anti-hoarding laws, government intervention in output and input markets, etc. before one discusses any mechanism to manage food import risks it is important to ascertain the types of risks that are relevant to food importers. food imports take place under a variety of institutional arrangements in developing countries. a study by fao (fao, 2003) contains an extensive discussion of the state of food import trade by developing countries. it notes that while in some lifdcs state institutions still play a very important role in the exports and imports of some basic foods, food imports have been mostly privatized in recent years, although with some exceptions, and in some countries, state agencies operate alongside with private importers. 221food commodity price volatility and food insecurity a public sector food importer, namely a manager of a food importing or a relevant food regulatory agency each year faces the problem of determining the requirements that the country will have to satisfy the various domestic policy objectives. such objectives may include domestic price stability, satisfaction of minimum amount of supplies, demands to keep prices at high levels to satisfy farmers, or low to satisfy consumers and many others relevant to various aspects of domestic welfare. the problem of the manager of the food agency is four-fold. first there needs to be a good estimate of the requirements, which is not easy given uncertainties in estimations of domestic production and demand. secondly, the public sector food agency manager, must decide how to fulfill them, namely through imports, or by reductions in publicly held stocks, if stock holding is part of the agency’s activities. a related problem is the risk of non-fulfillment of the estimated requirements which may cause domestic social problems and food insecurity. the third problem of such an agent is how to minimize the overall cost of fulfilling these requirements, given uncertainties in international prices and international freight rates, and to manage the risks of unanticipated cost overruns. for instance, if the agency imports more than is needed, as estimated by ex-post assessment of the domestic market situation, then the excess imports will have to be stored or re-exported and these entail costs. finally, but not least, and related to the overall cost of fulfilling the requirements, the agent must finance the transaction, either through own resources, or through a variety of financing mechanisms. in many countries the state has withdrawn from domestic food markets, and it is private agents who make decisions on imports. the problem, however, of private agents, is not much different or easier than that of public agents. a private importer must assess with a significant time lag, the domestic production situation, as well as the potential demand just like a public agent, and must plan to order import supplies so as to make a profit by selling in the domestic market. clearly the private importer faces risks similar to those of the public agent, as far as unpredictability of domestic production, international prices, and domestic demand are concerned, and in addition faces an added risk, namely that of unpredictable government policies that may change the conditions faced when the product must be sold domestically. during the recent food price crisis, surveys by fao documented the adoption of many short-term policies in response to high global staple food prices, which must have created considerable added risks for private sector agents. furthermore, the private agent maybe more credit and finance constrained than the public agent. in fact the study by fao (2003) indicated that the most important problem of private traders in lifdcs is the availability of import trade finance. the main external uncertainty facing food importers is international price variability and hence unpredictability. once the level of imports needed is determined, there are two additional risks faced by import agents, apart from the price risk. the first is the financing risk, namely the possibility that import finance may not be obtainable from domestic of international sources. this is the risk identified as most crucial by the fao (2003) study for agents in lifdcs. the second risk is counterparty performance risk, namely the risk that a counterparty in an import purchase contact will default and fail to deliver. this latter risk is one that came to the fore during the recent price spike, and is can be due to both commercial and non-commercial factors. commercial factors may include the inability for the supplier to secure the staple grain at the amount and prices contracted because of sudden adverse 222 a. sarris movements in prices. non-commercial factors includes things such as export bans, natural disasters or civil strife, in the sourcing country that may render it impossible to export an agreed upon amount of the staple. market and price variability in agricultural commodities is a fact of everyday life, and most countries and agents have adapted to this reality. the issue, however, which is of concern is “excessive volatility”. conceptually excessive volatility in a commodity should refer to unpredictable movements of price outside some bounds that are deemed to occur infrequently and are deemed to be undesirable. how can one define these bounds? a useful approach is to refer to the concept of risk layering which is well known in the field of risk management and insurance (see for instance world bank, 2005). the idea applied in this context is to start by considering the probability distribution of prices or price changes. this could be a distribution based on historical observations. then one could try to split the range of all possible prices into three intervals defined by a floor and a ceiling price level pf and pc. the choice of these upper and lower bounds could be made with the idea that markets would fail for prices above or below these bounds, and that occurrence of prices outside the bounds would be infrequent. this is what maybe termed the “market failure” risk layer. prices in some range around the mean could be considered to define a “retention layer”, namely price variations that can be handled by agents without any additional measures or risk management instruments. the remaining intermediate price ranges could be termed the “market insurance” risk layer, and the idea is that within these price ranges, there is a variety of market based risk management instruments that can be used to manage market risk. the range outside the minimum and the maximum bounds could then be considered as the “market failure level”, and excessive volatility could be defined as cases when prices fell in that range. figure 1 illustrates the concepts, and figure 2 indicates how excessive market volatility could be measured with actual price series data. clearly defining the relevant bounds is not straightforward, as it is not clear what level of prices constitutes market failure. the notion of frequency maybe more applicable, but even then to agree on the frequency at which prices could be considered to be outside “normal” levels is not straightforward. neverhteless, it is the principle that is illustrated here. 4. policies to manage market volatility and price spikes ηow can individual countries and the international community manage excessive market volatility? there are basically two ways in which individual countries can manage their domestic food markets in the face of excessive international market volatility. one involves trade actions, and the other involves public stockholding. if countries or other agents can be assured their commodity supplies through trade, then they would need to carry lower levels of security stocks. hence trade can be an important substitute for carrying costly physical inventories. trade, however, can be impeded by a variety of problems. policies aimed at facilitating commodity trade, may therefore obviate the need for policies to carry costly security or emergency physical stocks, both nationally and internationally. in the recent as well as previous food crises, there were three major trade facilitation related problems that caused governments to examine carrying larger security stocks. the first concerned unexpected and uncoordinated export bans by key exporters, which tend 223food commodity price volatility and food insecurity to increase international prices. the second was the unavailability of import financing for several lower income food importing countries, and the third was the uncertainty about international contract enforcement in a time of rising prices. the sequel discusses proposals to deal with these problems. figure 1. “risk layers” of market prices.   figure 2. defining price spikes.   224 a. sarris can export bans be prevented? export bans are very disruptive to international markets, as they disturb established trade flows and cause significant losses to traditional trading partners of the countries that import from those imposing export bans. as export bans are a trade measure, the appropriate international forum to discus this is the world trade organization (wto). currently export bans are not forbidden by the wto agreement, as the concern of wto members in the past was with low prices and hence import restriction measures, rather than high prices, which are reinforced by export bans. it would cost little to implement such an agreement among wto members, once they agreed to it, and it would involve a small change in existing wto rules. this, however, is not assured, as some members may not want to abandon the flexibility to control their domestic commodity markets via such an instrument. clearly the developed countries would have a large role to play in revising the wto rules in this direction. a fund for the establishment of an internationally coordinated “global financial food reserve” (or gffr) of basic food commodities the only sure way to avoid excessive market upheavals is to have some amounts of previously accumulated stocks, but every proposal along these lines runs up against coordination and financing problems. the idea of the proposal here is to combine the best parts of the two proposals on reserves that have been discussed considerably, namely the establishment of a coordinated global physical reserve and a virtual reserve aimed at calming futures market speculation. the idea is to have a market based global safety net which would create physical or financial resources in times of price spikes. the major problem with all proposals that have been proposed and deal with market volatility is that they purport to try to prevent the occurrence of a price spike. this, however, is very difficult to accomplish within a globalized market system, and may need very large and uncertain amounts of financial resources, that rightly makes donors uneasy and unwilling to consider. however, if the major objective of a system to deal with market volatility is to prevent the weakest members of the international community from paying the price for an upheaval, which for the most part is not their fault, then one could consider a limited and much cheaper safety net system to ensure support only for those countries. the proposal made here would be an agreement by a group of a few important world grain market participants that would include members of the g8+5 as well as major grain exporters and other donors, to commit funds that could be utilized to hold specified amounts of publicly owned long positions in organized exchanges. in other words the proposal calls for the establishment of an international publicly held “global commodity fund” specifically targeted to basic foods. given low margin requirements, this fund could assure, with relatively modest financial resources, control over a considerable amount of physical reserves. the idea is that a certain amount of financial resources would be used to initially buy an amount of long futures contracts in one or more basic grains. these contracts would be held and rolled over, when the time of expiration comes, and in addition there would be some additional funds to cover potential margin calls in the course of holding the long positions. this could then constitute a “virtual commodity reserve”, but 225food commodity price volatility and food insecurity in its concept it is very different from what has been proposed before by von braun and torrero (2009), and von braun et. al. (2009), as the fund would consist of committed long positions, and would basically act as dormant physical reserve. the fund’s positions would be rolled over from period to period, much like the commercial commodity funds do. the fund’s positions would be dormant and passive when markets are operating in normal conditions. hence its resources would not be used for any “stabilization operations”, albeit, they maybe used to cover margin calls in periods when prices fall below the acquisition price. however, when markets go into an unusual spike, which could be signaled by either the breeching of some prespecified price upper ceiling, the fund would have the option to either take physical delivery, so as to utilize the physical stocks for prespecified purposes, or to sell off the long positions. in either case the fund would command at a time of a price spike either physical stocks or financial profits from its long positions, if liquidated under market spike conditions. these physical stocks or profits could be utilized to promote a global safety net to assist most affected poor countries in obtaining food commodity imports at lower than spiking market prices. in other words the fund and the stocks it could support would not be utilized for market or price stabilization but rather for supporting assistance to needy countries in times of global price spikes. given that the fund’s purpose would not be to stabilize markets, but rather to assure market weak participants that their excess food import costs would be covered, the gffr could be restricted in size to what is estimated as needed for additional or extraordinary assistance to needy food importing countries in times of a food crisis. the cost of such a reserve would be modest. for instance between 2006 and 2008 the total cereal import bill of ldcs increased by roughly 20 percent or about 4 billion us$. if 10 percent of that could have been considered as extraordinary cost of vulnerable poor countries that would be compensated by developed countries as extraordinary aid under some global safety net, then this would amount to 400 million us$. this is much smaller than the funds that were committed by developed countries in support of developing countries in the context of the global food crisis. if the fund before the crisis was of a size of 100 million us$, and it was all invested in cereal stocks via long future positions, then at 5 percent margin it would have commanded physical amounts, worth about 2 billion us$. the profits from a 20 percent increase in prices during the spike (and the actual increase during a spike would have been much larger than this) would then have been around 400 million us$, which would have allowed the fund to compensate some low income developing countries for the extraordinary costs of the import bills. needless to say that these calculations are very quick and simple but are intended to give an order of magnitude to the amounts involved. the gffr would act as a global market based safety net. as its major market operation would be to roll over positions in each period if needed, it would not interfere in the normal functioning of the commodity markets. the allocation of the proceeds or the profits of the gffr from any price spike to needy developing countries could be a separate process, that would entail allocation according to some prespecified development criteria. food import financing and a dedicated food import financing facility (fiff) a major problem facing least developed countries (ldcs) and some net food importing developing countries (nfidcs) is financing for both private and parastatal entities of 226 a. sarris food imports, especially during periods of excess commercial imports. the financing constraint arises from the imposition, by both international private financial institutions and domestic banks that finance international food trade transactions, of credit (or exposure) limits for specific countries or clients within countries. these limits can easily be reached during periods of needs for excess imports, or periods of high prices, thus constraining the capacity to procure finance for food imports and as a result, food import capacity. to this end a fiff was proposed in 2005 to the wto by fao and unctad and elaborated further by sarris (2009), to overcome this problem. the purpose of a food import financing facility (fiff) would be to provide financing to importing agents/traders of ldcs and nfidcs to meet the cost of excess food import bills. the fiff is not intended to replace existing financing means and structures; rather it is meant to complement established financing sources of food imports when needed. the financing will be provided to food importing agents. it will follow the already established financing systems through central and commercial banks, which usually finance commercial food imports using such instruments as letters of credit (lcs). the extra contribution of the fiff would be to provide guarantees to these financial institutions so that they can increase their exposure to the importing countries. it will do so by inducing the exporters’ banks to accept the lcs of importing countries in hard currency amounts larger than their credit ceilings for these countries. a key aspect of the fiff is that it will not finance the whole food import bill of a country, but only the excess part induced by a food crisis. in this way “co-responsibility” will be established, so that only real and likely unforeseen needs will be financed, and the cost of excess financing will be kept at a low level. the basic feature of the proposed fiff is to provide the required finance at a very short notice, and exactly when needed, once the rules of operation are agreed upon in advance. thus, the delays common to past ex-post insurance or compensation schemes that rely on ex-post evaluation of “damages” can be avoided. the proposed fiff will operate in real time. its financial strength would be based on guarantees provided to the fiff by a number of countries or international financial institutions. the costs of a fiff would be minimal through risk pooling for a large number of countries and food products, and low operational costs owing to its risk management activities. the principal risk for the fiff is that the guarantees that it provides will be called to finance non-repayments. this risk could be managed actively. as the facility would not set out to disturb the normal functioning of international food trade, there is a “non-zero” risk that the local or central banks cannot be reimbursed by their local food importing clients. this would primarily be the concern of the domestic and central banks of each country, and not the fiff. nevertheless, lack of reimbursement by the ultimate beneficiaries of the finance may lead commercial banks to default on their obligations (or delay repayment) to the fiff. the fiff would benefit from guarantees from a number of countries. ideally, this would include a number of oecd countries, which would enable the fiff to borrow at aaa terms, when needed. but any group of countries could provide guarantees; the risk rating of the fiff is then likely to be that of the best-rated among these countries. a food import financing facility has existed in the imf since 1981 under the compensatory financing facility (the imf cff). the objective of that was not food import 227food commodity price volatility and food insecurity financing, but rather compensatory financing to countries facing balance of payments problems, and hence could not import food. despite its availability it has been utilized very little, largely owning to the conditionalities imposed on borrowers by the imf. the proposed fiff would be different from the cff in the sense that it would provide guarantees for normal food import finance, and would act in a much more timely fashion, namely before the undesirable event, rather than after. while the fiff envisioned in the current proposals is an international initiative, it could operate also as a policy of major food exporters, such as the eu, canada and others. the us already operates a system very similar to this under its gsm-102 program of the commodity credit corporation. the eu does not have a system of this type, despite the fact that many major agricultural commodity exporting firms and financial institutions operate in the eu. a drawback of the fiff, as mentioned by gilbert and tabova (2011), would be the fact that potential donors would have to count the guarantees provided to the fiff as part of their public debt, even though the guarantees may not be exercised, something that may not be easy for some donors. to this end it is helpful to make rough estimates of the types of amounts of guarantees needed. sarris (2009) made some empirical estimates for the yearly guarantee needs that ldcs and lifdcs would require under such a system and given the data for years up until 2007. the computations suggest that average yearly fiff guarantee financing for ldcs would be in the vicinity of 200-430 million us$, while the financing needs in an exceptional year may reach as much as 2,400 million us$. to put these figures in perspective the average yearly ldc commercial food import bill for all foods between 2000 and 2007 was 10.7 billion us$. hence the fiff average annual financing and hence guarantee needs would constitute about 2-4 percent of yearly ldc combined commercial food imports. in a year of exceptional needs, the value of fiff guarantee financing needed could rise to as much as 23 percent of the total ldc food import bill. if all lifdcs were to be covered by the fiff, then the annual guarantee financing needed would be in the range of 960-1937 million us$, and this constitutes around 1.83.7 percent of the average lifdc food import bill for the period 2000-2007. in an exceptional year the maximum financing needed could rise to as much as 10 billion us$, which would be about 19 percent of the total lifdc average food import bill of the same period. the above amounts are very small compared to the debt levels of the major donors, which, for instance for the us currently stands at around 14 trillion us$, for france to 2 trillion us$, for germany to near 2 trillion us$, etc. the g7 group of most developed countries currently has a level of public debt in the neighborhood of 20 trillion us$. a system to guarantee food import contracts a problem that is acute during food crises is counterparty performance risk, namely the risk of reneging on a delivery contract, faced by many food importers. in other words, the problem in this case is not so much unpredictability of food import costs, or high food import prices, or financing, but rather assurance that supplies will be delivered. this does not only pertain to short term contracts but also longer term contracts. the basic reason for non-performance of international staple food import contracts is adverse price movements or adverse financial events that prevent a food exporter or trader to fulfill an 228 a. sarris import contract. there seems to be no contract enforcement mechanism in international staple food grain transactions. contracts in organized commodity exchanges are enforced because there is a clearing house which is responsible for making sure that all transactions are executed. similarly contracts within one national legal jurisdiction can be enforced as there is a legal system to ensure contract enforcement, albeit a court based legal enforcement system is quite slow. most international contracts are very similar to over the counter (otc) contracts in the sense that is it only the financial and reputation status of the two parties that instills confidence in contract enforcement. there is no mechanism for international contract enforcement, and whatever juridical procedures exist are slow, uncertain, and costly, and cannot deal with the immediate risk of contract cancellation. the basic missing institution is an international contract together with an international clearing house type of arrangement similar to the clearing houses that are integral parts of the organized commodity exchanges, which ensure that all contracts are executed. the key question is whether an international contract along with a clearing type of mechanism can be envisioned to ensure the performance of staple food type of import contracts. a proposal for an international grain contract has been made by berg (2011b), while sarris (2009) proposed the institution of an international grain clearing arrangement (igca). these are complementary proposals, as they aim at the same objective namely global contract enforcement. the objective of an igca would be to guarantee or insure performance of grain import trade contracts (short, medium and long term) between countries or private entities based in different countries. a major function of a commodity exchange clearing house, apart from the settlement of the financial contracts, which amount to the bulk of settlements, is to ensure that physical delivery can take place, if needed. this is for instance one of the functions of the chicago mercantile exchange (formerly the chicago board of trade), and to ensure this a variety of rules and regulations with respect to delivery obligations are adopted by the exchange and the clearing house. in most organized exchanges physical delivery is a very small portion of all transactions, but if a trader insists on delivery then this must be arranged by the exchange. many exchanges have arrangements with warehouses so that physical deliveries can be made against a futures contract, and there are severe penalties for anyone with an open contract who either does not fulfill the financial terms or does not deliver a physical commodity on it. it is these properties that would need to be emulated by an envisioned international contract and a igca, in order to it to be viable as a guarantee institution in international staple food transactions. a global contract, according to berg, (2011) rather than tracking prices in one geographical region, would track “cheapest to deliver” commodities, by designating delivery points in several places in the world. the traders who could deliver on such a contract would be those with relatively low prices. there are precedents to this type of global contract, namely the global sugar futures contracts of the intercontinental exchange and the euronext liffe. in these cases the ports able to provide the cheapest sugar are the first to deliver against the contract. this provides a global signaling system of both price and regional availabilities of sugar ready to export. given that the contracts are provided through organized international exchanges, the delivery on a given contract is guaranteed through the clearing houses of the relevant exchange. 229food commodity price volatility and food insecurity the only potential drawback is the logistical difficulty of having the supplies delivered in some part of the world, which maybe unknown at the time of contracting, and different from the location of the desired place of delivery. however, it would not be difficult to envision that transport services would be readily available in all major delivery points. if a global contract is not instituted by an international exchange then the next best way to implement something on an international scale resembling the functions of an international contract and the clearing house of existing organized exchanges would be to link existing or envisioned commodity exchanges, with their respective clearing houses, or to have international exchanges list contracts with several international points of delivery. in other words, it maybe appropriate to think of how parts of contracts bought in on one exchange could be guaranteed not only by the clearing house of the exchange in question but by clearing houses of other linked exchanges. the problem is that delivery at a recognized warehouse, e.g. near chicago where the cme delivery locations are, may not be what the importer wants, and may need to incur considerable cost to transport those amounts to his desired import location. hence what would be desirable is to have the possibility of taking delivery of the same amount of grain but at a location much closer to the importer’s desired destination. one way to do this would be to establish links between various commodity exchanges around the world, so that the price difference between grain stocks in different locations would be equal to the relevant cost of transport and other transactions charges. the igca could be envisioned as a branch of the linked commodity exchanges which would in essence consist of some parts of the underlying clearing houses of the exchanges. the igca would try to guarantee that physical supplies around the world at various exchanges are available to execute the international contracts in its member exchanges. this could be done, for instance, if part of the financial reserves of the clearing houses that are members of the igca could be transformed into a physical reserve, via for instance holding warehouse receipts in various reliable locations around the world. the advantage of transforming part of the financial reserves into physical reserves would be two fold. first, the value of the underlying reserves would fluctuate with the price of the underlying commodity. this is like marking the underlying assets to market. this would obviate the need by contracting parties to post additional margins in case the price of the commodity increases suddenly. second, and this is perhaps a major positive aspect, if some of the financial reserves of the igca were to be transformed into warehouse receipts, the physical execution of the underlying contracts, and not only their financial settlement, could be guaranteed. the commitments in futures or warehouse receipts of the igca could be liquidated once the actual deliveries on the relevant contract were executed. the liquidation of the physical positions or futures holdings of the igca would provide the funds to return to the contracting parties their posted insurance margins. in fact, since the liquidation of the igca margins would result in a variable amount as prices fluctuate on the underlying warehouse receipts or futures contracts, the restitution to the contracting parties of their initial margins would be variable and close to a fixed share (minus some transactions cost) of the underlying transaction value. hence the true cost to the two parties to an international contract would be the interest foregone or paid for the posted good faith margin. given all the other transactions costs in an international staple food import contract this may not be too high. 230 a. sarris the igca would guarantee the execution of contracts by pooling the resources of several exchange related clearing houses. this would ensure that there would be liquidity in terms of physical reserves to honor individual contracts in case of non-performance by a participant. in fact, the major underlying benefit of the igca would be that by investing a small part of its reserves into physical warehouse receipts or deliverable futures contracts, it would create a global physical commodity reserve stock that could be utilized to execute international staple food contracts in case of non-performance of the exporting party to a transaction. the major difference, however, of such a stock and stocks envisioned in previous discussions on global price stabilization would be that this reserve stock would be used only to make the market work, namely ensure physical delivery and not to change the fundamentals of the market, as most of the other stock holding ideas envision. in the words, the stocks held in the form of warehouse receipts or other physically executable contracts, would perform the function normally done by so-called pipeline stocks, which are held by various market participants to ensure that there is uninterrupted performance of the normal market functions of the agent. their function would not be to stabilize or speculate, but simply to ensure liquidity in the market, much as the financial reserves of the commodity clearing houses ensure liquidity to execute all underlying financial contracts. the necessity for an international arrangement to have such stocks is that there is no such physical liquidity mechanism internationally. in other words one of the main functions of the igca would be to ensure global physical grain liquidity. the igca could spread the risk of non-performance or country problems by holding its commodity reserves in several geographic locations, as well as several organized exchanges. a major risk of such a igca would be that a sovereign country in whose territory, the warehouses of the underlying stocks in which the igca has invested are physically located, could impose export restrictions or bans that may make the physical release of stocks impossible. here, however, is where appropriate export related disciplines could be formulated in the context of the world trade organization (wto), or another regional arrangement, to prevent exactly this type of phenomenon, as discussed above. also if major ifis, such as the world bank, the imf, and other ifis are financiers of such a igca, then the type of sovereign type of default could be guaranteed by these ifis, perhaps in the same manner they provide sovereign guarantees and insurance for other investment projects. in other words, default on any of the contracts insured with the igca would entail default with the ifis behind it, and this may make it harder to default. on the downside, the relevant ifis may be required to devote part of their sovereign guarantee capacity to this. another major risk of the igca maybe the possibility of default by a party. this does not have to be only a supplier (in case for instance of increased prices), but could also be the buyer (in case of suddenly decreased prices), who may not be interested in a contract at some prices that may now be considered too high. in such a case the seller would be losing a portion of the value of the contract due to the decrease in price. given that the igca would be an extended arrangement among viable commodity clearing houses, it could compensate the seller by the difference in the original and current value of the contract insured through the relevant exchange or clearing house. an essential element then of the proposed igca is the internationalization and linkage of commodity exchanges. this implies that the additional performance guarantees that 231food commodity price volatility and food insecurity are envisioned here can be obtained if two conditions exist. first appropriate exchanges must exist in different geographic locations around the world. such locations should most likely be near the major production areas for the commodity in question. second most importers of the food commodity would hedge their subsequent purchases in such exchanges. this can become part of most food importers trading practices, and it probably is already a practice by many importers. the existence of more exchanges would probably reduce the basis risks and hence make trade more efficient. clearly this idea needs more thinking and analysis as there are many details that need to be elaborated. this could be done by a group of knowledgeable market analysts, but if implemented it could go some way to instill more confidence in global food commodity markets. market based approaches to managing market volatility the idea of this approach is to utilize existing market instruments to anticipate food price spikes and insure against their adverse consequences. the major way to do this is via futures and options contracts or similar “over the counter” (otc) instruments. the problem to deal with is whether the use of organized or otc futures and options markets can reduce the unpredictability of the food import bill, and at what cost. consider an agent who needs to plan imports of some basic food and desires to protect himself against a price spike. by buying a futures contract or a call option contract (namely the right to purchase at a future date an amount of the commodity at a prespecified strike price), the agent hedges the risk of a price spike, by locking in a maximum price for the subsequent transaction. when the subsequent transaction in the cash market is executed, the agent can lift the hedge by executing and opposite transaction in the futures or option market (namely sell the futures contract or exercise the option contract if prices have moved above the strike price), so as to counteract any price variation that was not anticipated at the time of planning4. while, on average this type of hedge will not make or lose money, there will be a significant reduction in the conditional volatility of both price and subsequent purchases. the major advantage to the hedger is that the subsequent price for the transaction is known much better than if the agent waited until the time the supplies need to be ordered. in other words predictability is enhanced. sarris, et. al. (2011) as well as dana, et. al. (2006) have examined in detail cases of food importers using futures and options in organized markets and have shown that indeed there are substantial reductions in upredictability. a drawback of using these types of instruments in a developing country context is that credit requirements arising from the need to manage on a daily basis the exchange margin calls (in the case of futures), may run up against credit constraints. another drawback is that if the futures market moves in an opposite direction from the one that the hedge anticipated, the agent (which could be a government agency) may have to lose money, which may be unacceptable to the financing authorities. call options lessen these problems as they basically act as price insurance, by allowing an agent to lock a maxi4 the hedge will be affected by “basis risk”, namely the imperfect correlation between the border price of the country where the agent operates, and the price at the exchange where the hedge is placed. 232 a. sarris mum price for subsequent imports. the cost is that on average the reduction in unpredictability is smaller than when futures are utilized (sarris, et. al. 2011). on the other hand options are more flexible and with known ex-ante costs. they are also less costly than physical stocks. compensatory finance systems these systems arose in the 1970s and 1980s from the need to assist developing commodity exporting countries to deal with sudden drops in export commodity prices. the main ones that have been instituted are the imf compensatory financing facility (cff), and the european union’s stabex, which was replaced by the flex. the imf’s cff (for more extensive recent discussions see gilbert and tabova, 2011, and konandreas, 2011) was created in 1963 and the cereal import element was added in 1981, following the food crisis of 1973-75. its primary purpose was to help imf members cope with temporary export shortfalls and high cereal import costs which create balance of payments problems. imf arrangements and conditionalities applied to such borrowing. the main benefit to the countries that used it was an additional imf window. however, while the trigger for disbursements was tied to commodity prices, the schedule for repayments was not tied to export recovery or import cost declines. this tended to undermine its unique function. strict eligibility requirements and costly financial terms led to it not being used very much by countries, and it was officially abolished in 2009. a smaller imf scheme named the “exogenous shock facility” (esf) was established in 2006 to provide quick and easy access to concessional financing for low income countries facing exogenous shocks such as food commodity price spikes, natural disasters, or other exogenous crises. conditionalities under this scheme are restricted to measures needed to adjust to the shock. the system is currently active. the eu’s stabex was active between 1975 and 2000 as part of the conventions signed between the eu and its former colonies in the asian caribbean and pacific (the acp countries), many of which were dependent of commodities for the bulk of their external income. the idea was to compensate the governments of the acp countries, on a grant basis, for export income shortfalls due to variations in export prices or export quantities. the funds were given, ex-post to the governments, which used them during early periods in a flexible way as balance of payments support, while later they were targeted mostly to the sector affected by the shock. the compensation was given for earnings shortfalls in individual commodities rather than a group of commodities. there were several shortcomings of the stabex, such as delays in fund disbursements that tended to making them procyclical rather than countercyclical, its tendency to not stabilize export earnings, and others, that led the eu to replace the scheme in 2000 by the fluctuations of exports (flex) scheme. the flex had many of the principles of the stabex, but was designed for faster disbursement, and triggers based on overall export income losses rather that on commodity specific losses. the basic problem of all compensatory finance schemes is that they are of necessity backward looking, and hence slowly disbursing. this does not help with smoothing of the export income fluctuations. food import bill variations have not been part of the stabex or flex schemes, albeit the balance of payments and other impacts maybe similar. 233food commodity price volatility and food insecurity if, however, they were to be made part of the existing compensatory finance schemes they would be plagued by similar problems as the existing instruments. they have been viewed by most analysts as additional development assistance tools, rather than commodity risk management schemes. safety nets the idea of a food related safety net is to have a system whereby sudden erosion of the capacity of food insecure households or countries to maintain food consumption, can be dealt with by rapid access to financial resources and food commodities targeted to those most vulnerable to food price spikes. several developing countries have such quick reaction programs, and international assistance could help the affected countries keep the cost of such programs reasonably low in times of crisis. an example of such a global safety net program is the world bank’s global food crisis response program (gfcrp) that became operational in 2008. the program aims to reduce the negative impact of high food prices on the poor, help countries in the design of policies to mitigate the adverse impacts of volatile food prices, and support food producers to enhance productivity and reduce vulnerability to future crises. the gfcrp envisages safety nets in the form of funds to provide cheap food to targeted poor, and financing and technical assistance to increase agricultural supply. its major advantage is that it is quick disbursing. as of mid-march 2013 , the gfcrp had financed operations amounting to 1.56 billion us$ in projects. as of july 2012, world bank emergency response is channeled through the international development association (ida) crisis response window, and the recently approved immediate response mechanism. the facility depends considerably on donor support, which has been substantial. the main issue with such programs is their sustainability in the future. the gffr proposed above could be a way to enhance sustainability in a cost effective way. 5. concluding remarks the problem of food market volatility and intermittent crises and price spikes, does not seem likely to go away in the future, and in fact appears likely to become more acute. the most vulnerable countries are those who normally have little part in creating the food crisis. if growth opportunities of these countries are not to be stalled by occasional food crises, the international community must provide appropriate systems to prevent or manage the spikes. the paper has reviewed several facets of the global food market volatility problem, and the proposals that have been made to deal with it, and has made proposals for what maybe deemed as most cost effective and appropriate policy measures. the first major conclusion is that the major problem that creates undue market volatility and price spikes is excessive unpredictability of the market. when the degree of unpredictability or uncertainty about the market outcomes becomes large, market agents (both public and private) tend to overreact to underlying information, and take destabilizing actions to hedge possible information gaps. in such cases the markets tend to fail, and prices tend to spike. it is these market outcomes, which are rather infrequent, that need to be prevented or controlled. 234 a. sarris it was seen that food price spikes are possible to define and monitor. hence, it seems that there can be an empirical base on the basis of which the international community can base action. it appears that there are several ways to manage (rather than prevent) market volatility and spikes for the benefit of low-income food importing countries, and there have been several proposals along these lines. the paper has reviewed all these proposals and made some new ones. the ones that seem most cost effective and least distorting of international markets are those that are market based. among those, utilizing existing systems of commodity risk management, such as futures and options is the easiest, and could be enhanced by the support of new exchanges in developing countries as well as technical assistance on how to exploit the various instruments available. a new proposal for a new system of a global financial food reserve (gffr) was made, in the form of a fund to finance long positions or food commodities in organized exchanges. such a fund could constitute a dormant virtual physical reserve that could generate physical and financial resources in times of a spike, so as to benefit highly negatively affected developing countries. in other words the gffr would be a market based global safety net. apart from the gffr, the proposal for a food import financing facility (fiff) was also deemed cost effective and an appropriate mechanism to ensure the continuous flow of food imports in times of a spike. it was seen that there are ways to guarantee the performance of international food import contracts, through the promotion of standardized international food contracts in existing international commodity exchanges or the linking of existing exchanges and their clearing houses, through an international grain clearing arrangement (igca). these could be explored further with the collaboration of existing exchanges. the final set of measures that could be taken involve global safety nets. the gffr proposed in the paper is one form of such a global safety net, and a physical emergency reserve to smooth out flows of food aid is another. however, others in the form of permanent funds or technical assistance to help needy countries maintain their local food safety nets can also be envisioned. in summary it appears that there are quite a few cost effective and non-distorting measures and options to lower the probability of food price spikes, and help food importing low-income developing countries to manage the attendant risks. given that food security is of paramount concern to all counties, especially those that are at low levels of food intake, it appears that the international community has a major role to play in ensuring global food security in a world of growing uncertainty. acknowledgements an earlier version of this paper was presented at the 2nd aieaa conference “between crisis and development: which role for the bio-economy”, 6-7 june, 2013, parma, italy. references bellemare, m.f., c.b. barrett, and d.r. just (2010). the welfare impacts of commodity price fluctuations: evidence from rural ethiopia, mpra paper 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(2009). eliminating drastic food price spikes – a three pronged approach for reserves. international food policy research institute, note for discussion. world bank (2005). managing agricultural production risk: innovations in developing countries. agriculture and rural development department. report no 32727-glb. washington dc. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(1): 1-27, 2013 greening agricultural payments in the eu’s common agricultural policy alan matthews1 trinity college dublin, ireland abstract. environmental objectives have become increasingly integrated into the eu’s common agricultural policy (cap) since the mid-1980s. integration has been pursued through the attachment of environmental conditions to the receipt of direct payments in pillar 1 (cross compliance) and the use of voluntary agri-environment measures in pillar 2. in formulating its proposals for the revision of the cap post-2013, the commission opted to pursue further integration largely through pillar 1 through the introduction of a ‘green’ payment for farmers following a specified set of mandatory farm practices. the legislative process was not concluded in february 2013, but enough is known of the positions of the council and the european parliament to indicate that the level of greening ambition in this cap reform will be very limited. some explanations for the apparent failure to significantly reshape the cap to tackle the problems faced by the natural environment are reviewed. it is suggested that, far from being complementary, cross compliance and voluntary agri-environment measures are competing approaches to further greening of the cap. advocates of a greater focus on environmental objectives need to choose between these approaches. keywords. cap post-2013, greening, cross compliance, pillar 1 jel codes. q01, q18, q24 1. introduction agriculture and forestry have a key role in producing environmental public goods such as landscapes, farmland biodiversity, and greater resilience to natural disasters such as flooding, drought and fire. however, many farming practices also put pressure on the environment, leading to soil erosion, water shortages and pollution, and loss of wildlife habitats and biodiversity. agriculture is also required to contribute to the eu’s climate and energy agenda by reducing ghg emissions, improving energy efficiency, increasing biomass and renewable energy production, and protecting and sequestering carbon in soils. at the same time, agricultural production conditions will be increasingly affected by ongoing climate change. helping to mitigate and adapt to climate change has become a major new chal1 alan matthews is professor emeritus of european agricultural policy at trinity college dublin, ireland. helpful comments from an anonymous referee are gratefully acknowledged. e-mail: alan.matthews@tdc.ie 2 a. matthews lenge for the agricultural sector. managing scarce resources more effectively and increasing resource efficiency in agriculture in terms of external chemical inputs, water and energy use, land use and waste generation is also one of the goals under the flagship initiative a resource-efficient europe under the europe 2020 strategy (cec, 2011a). there has been progress in limiting agriculture’s negative impacts on the environment as well as encouraging more environmentally-friendly agricultural practices on a proportion of european farmland (eea, 2010). emissions of nitrogen and phosphorous into waterways as well as greenhouse gases have been falling. however, successive investigations of the state of the european environment show that we are not yet in a sustainable position. the eu has set ambitious targets for further environmental improvement in connection with water, soils, air, climate and biodiversity (cec, 2011c, annex 2a; eea, 2010). the improvements that have taken place occurred during a period in which growth in eu agricultural output marked time. now, the market context for agricultural production has changed. projections of global food demand and prices suggest there will be strong incentives to increase production in the coming decade (oecd/fao, 2012). in the context of europe’s ongoing economic crisis, policy-makers in many eu countries are targeting increased food production and agri-food exports as a potential growth sector to help lead economic recovery. at the same time, biofuel and renewable energy targets add further to the demand for agricultural resources within the eu, increasing the competition for land with nature (burrell, 2010; deiagra, 2008). this is the context in which the european commission published its proposals for new regulations for the eu’s common agricultural policy (cap) in october 2011 (cec, 2011c). sustainable management of natural resources and climate action is one of the three objectives of the cap post-2013 (cec, 2010). this objective is addressed in the commission’s proposal through a mandatory ‘green’ component of direct payments supporting environmental measures applicable across the whole of the eu territory; through changes in cross compliance; and through more strategic targeting in pillar 2, with the environment and climate change as guiding considerations. the commission proposed to allocate 30% of each country’s national ceiling for direct payments as a green payment to farmers who would be required to follow a number of ‘agricultural practices beneficial for the climate and the environment’. the requirements include ecological focus areas (efas), crop diversification and the maintenance of existing areas of permanent grassland at farm level. an important consideration was that greening should not threaten the viability of the farming sector nor unduly complicate the management of the policy (cec, 2011c, annex 2, p. 6). the commission’s stated goal was to improve the balance between different policy objectives through more targeted measures which would imply greater spending efficiency and greater focus on the eu value added (cec, 2010). both the council (council of the european union, 2011) and the european parliament (european parliament, 2011) initially supported the further greening of the cap towards 2020. this paper assesses the commission’s proposal in the light of the history of efforts to integrate environmental objectives into the cap. there have been essentially two approaches (oecd, 2011).2 one approach has been to add the observance of environmen2 the environmental impact of agriculture has also been addressed through environmental legislation, see jack (2009). 3greening agricultural payments in the eu’s common agricultural policy tal standards and practices as a condition for eligibility to receive direct payments in pillar 1 of the cap. the other approach has been to remunerate farmers who voluntarily enrol in agri-environment measures (aems) for the extra costs involved in managing their land to produce additional environmental public goods in pillar 2 of the cap.3 while these two approaches have proceeded in parallel, expenditure on pillar 1 direct payments remains far more significant than expenditure on environmental measures in pillar 2. nonetheless, there appeared to be a logic in the cap reform process whereby resources would be gradually transferred from direct payments in pillar 1 to the more targeted measures in pillar 2. moving from broad-brush, undifferentiated policies which provide support to all farmers and all land indiscriminately to more targeted measures was the policy approach recommended by oecd agriculture ministers in their 1998 communique and spelled out in the oecd report a positive reform agenda (oecd, 2002). the rationale for targeting is that it leads to reduced transfers, greater policy efficiency with fewer leakages to unintended beneficiaries, and reduced distortions, albeit with the downside of higher transaction costs. following the oecd advice would mean gradually reducing the importance of undifferentiated support in pillar 1 of the cap but increasing targeted payments through aems in pillar 2 where there was evidence of under-supply of environmental public goods. this had been the direction of cap reform between 1992 and 2008, even if the extent and pace of the shift in cap funds left advocates of more radical reform dissatisfied (buckwell, 1997). however, this was not the approach taken by the commission in its proposals for further greening of the cap post-2013. instead, the proposals envisage adding further conditions to the receipt of direct payments in pillar 1. the commission argued that greening pillar 1 payments was the more appropriate choice because the voluntary approach using aems in pillar 2 was unlikely to cover a significant part of the community land area where the environmental pressures due to agriculture were greatest. there was also increasing resistance to a further transfer of funds to pillar 2 both from farm organisations (representing the main beneficiaries of pillar 1 payments) and member states (which are required to finance the non-co-financed share of pillar 2 payments). in february 2013, the commission’s proposals are still in the legislative process and the ultimate outcome is not known. however, the reaction to the proposals in the agricultural council, in the european parliament’s agriculture committee (comagri) and among member states and interest groups was generally critical. environmental groups criticised them for not being ambitious enough. farmers’ groups criticised them for imposing higher costs and taking land out of production when the global need is to produce more food. national administrations were unhappy because of the greater complexity they added in the administration of direct payments. it looks like greening will survive as a concept, but with very limited impact and limited environmental significance – an outcome described by environmental groups as ‘green-washing’ (birdlife and others 2011). in this paper, we explore the commission’s rationale for greening pillar 1 payments, the implications of this approach and the political reactions in the light of previous efforts 3 the two pillars of the cap were introduced in the 1999 agenda 2000 cap reform. pillar 1 funds market price and income support on an annual basis and is fully financed from the eu budget. pillar 2 funds one-off and multi-annual rural development measures on a programmed basis, and is co-financed by the eu budget with member states. 4 a. matthews to green the cap. section 2 describes the gradual integration of environmental instruments into the cap, focusing particularly on pillar 1.4 section 3 briefly evaluates the experience to date with the different cap measures to target environmental objectives. section 4 describes the commission’s recent proposals for the green payment in pillar 1 and the reactions to this. section 5 concludes with some reflections on the reasons for the likely legislative outcome and its implications for those seeking to further green the cap to better meet the major environmental challenges facing agriculture in the coming decade. 2. adapting direct payments to environmental objectives the process of integrating environmental objectives into the cap has been slow and arduous stretching back over the past 25 years. the high levels of market price support that characterised the cap in the 1970s and 1980s were an important contributory factor to the process of intensification which has been the main cause of environmental damage in agriculture. the gradual reduction of price support and its replacement, first, by coupled payments and, subsequently, by decoupled payments was expected to contribute to alleviating some of these environmental pressures. this reform of price and markets policy was accompanied by specific environmental initiatives within the cap. within pillar 1, the introduction of direct payments allowed conditions to be attached to the receipt of aid with the aim of promoting environmental objectives. at the same time, pillar 2 has funded agri-environment measures which have remunerated farmers for practices which provide additional environmental benefits. this section briefly reviews the main milestones in integrating environmental objectives into the cap to date (see buller, 2002 for an earlier review). attempts were made to limit budget expenditure on eu agricultural policy from the early 1980s with the introduction of milk quotas, followed by a series of budget stabiliser mechanisms in the second half of the 1980s. these measures failed to address the fundamental disequilibria in agricultural markets. the reform process began with the macsharry (1992) and agenda 2000 (1999) reforms which compensated farmers for reductions in guaranteed intervention prices through granting coupled direct payments limited to predetermined quantities (hectares in the case of major crops, animal numbers in the case of livestock). the agenda 2000 reform formalised the distinction between market price and income support policies (financed through pillar 1) and rural development policies (financed through pillar 2). the mid-term review (mtr) (2003) and health check (2008) reforms transformed the majority of direct payments into decoupled payments. reforms to a number of commodity regimes (sugar, wine, tobacco, cotton, rice, olive oil) during this period also lowered support prices and provided compensation to farmers in the form of increased direct payments. following the implementation of the health check reforms from 2010, guaranteed support prices now operate at safety net levels and support to farming takes the form of largely decoupled payments through pillar 1 and more targeted interventions through pillar 2. at the same time, the negative environmental costs of growth both in the wider economy and in agriculture were increasingly recognised. the first recognition of environmen4 a companion article reviews the history of agri-environment measures in pillar 2 (dwyer, 2013). 5greening agricultural payments in the eu’s common agricultural policy tal issues in the cap was the introduction of the measure on mountain and hill farming in certain less favoured areas (lfas) in 1975 (directive 75/268/ec). the objective of this measure was to ensure the continuation of agricultural production thereby maintaining a minimum population level and conserving the countryside. it addressed a very specific need to avert the depopulation of marginal farming areas but otherwise did not represent a break-through in terms of integrating environmental objectives into the cap. the commission first drew attention to the need for agricultural policy to take more account of environmental issues, both as regards the control of harmful practices, and the promotion of practices friendly to the environment, in its green paper on perspectives for the cap in 1985 (cec, 1985). the paper recognised that the changes in farming practices in previous decades which had been important in increasing agricultural output had also contributed to the loss of biodiversity, the destruction of valuable ecosystems such as wetlands, and had increased risks of ground and surface water pollution. it identified the importance of setting the environmental reference level or baseline. where the negative consequences of agricultural production justified extending regulatory controls designed to avoid deterioration of the environment, the ‘polluter pays’ principle should apply, and it would not be normal for farmers to expect to be compensated by the public authorities for the introduction of such rules. at the same time, the paper recognised that ‘at least as important as the ‘passive’ protection of the environment is a policy designed to promote farming practices which conserve the rural environment and protect specific sites’ (p. 51). in these cases where agriculture contributes to the conservation of the rural environment and thus produces a public good, society should provide the financial resources to permit farmers to fulfil this task. the paper noted that an added attraction of promoting such practices was that generally they were less intensive (and thereby less productive) and could help to contain the unwanted growth of agricultural production. in the same year, the first agri-environment measure was introduced with article 19 of regulation (eec) 797/85 which allowed member states to make payments to farmers who followed practices compatible with the environment in environmentally sensitive areas. however, only two countries, germany and the uk, made use of this voluntary scheme (cec, 1988). the commission further spelled out its thinking in a 1988 communication which put emphasis particularly on the problems caused by large-scale intensive livestock rearing and intensive crop production in zones at risk of pollution of surface and ground waters by nitrates (‘vulnerable zones’) (cec, 1998), prefiguring the introduction of the nitrates directive in 1991. also in 1988, a five-year voluntary set aside scheme was introduced as a mechanism to control surpluses in arable production (regulation (eec) 1272/88). these ideas formed part of the background to the 1992 macsharry cap reform (cec, 1991). the main thrust of this reform was to reduce the level of guaranteed prices for arable crops and livestock, while compensating farmers through coupled direct payments (area aids in the case of arable crops, headage payments in the case of livestock). to control the increase in budget costs, maximum areas for arable aid and ceilings for the numbers of animals supported by headage payments were set for each country. set aside was made compulsory (for larger farms) as a condition to apply for compensatory payments. the reform included specific instruments (‘accompanying measures’) to encourage less intensive production, both to reduce market surpluses and to alleviate environmen6 a. matthews tal pressures. these included an early retirement scheme, an afforestation scheme and an agri-environment scheme which was mandatory for member states to introduce. member states were obliged to apply ‘appropriate environmental conditions’ to the management of compulsory set aside in arable cropping, and were allowed to introduce environmental conditions on the direct payments offered as headage subsidies for beef cattle and sheep. however, the uk was one of the few to apply conditions to livestock subsidies (alliance environment, 2007). the coming into force of the single european act (1987) added a title on the environment to the european treaties and, for the first time, gave a legal basis for eu environmental policies. the growth in environmental awareness led to the introduction of a raft of environmental legislation affecting agricultural practices. among the more important were the nitrates directive (1991), the pesticides regulation (1991), the habitats directive (1992) and the water framework directive (2000). following the rio summit on sustainable development in 1992, the amsterdam treaty (1997) made sustainable development one of the community’s tasks and clarified the basis for environmental policy. the cardiff process, which required the various formations of the eu council of ministers to develop comprehensive strategies to integrate environmental concerns within their respective areas of activity, began in 1998. the agricultural council’s response was embodied in the agenda 2000 reform (cec, 1999), which thus can lay claim to being the real start to the process of integrating environmental objectives into agricultural policy. under the ‘horizontal measures’ regulation establishing common rules for direct support schemes under the cap, member states were required from 1 january 2000 to apply environmental measures they considered appropriate in view of the land used and the production concerned using one of three options (regulation (ec) 1259/1999). first, implementation of appropriate agri-environment measures applied under rural development programmes could be sufficient. second, member states could make direct payments conditional on observance of generally applicable mandatory environmental requirements. third, member states could attach specific environmental conditions to the grant of payments under a market regime where the environmental situation requires additional efforts. member states could use the proceeds from withholding payments in cases of non-compliance with environmental measures or from reducing payments to larger farms (modulation) to fund increased expenditure on environmental measures in pillar 2. in practice, member states were slow to use the new opportunities. few countries introduced new environmental standards through cross compliance and only two member states chose significantly to expand expenditure on agri-environment and other accompanying measures through the use of modulation (baldock, dwyer, and sumpsi vinas, 2002). separately, under the rural development regulation (regulation (ec) 1257/1999), farmers in receipt of the less favoured areas allowance and participating in aems were required to observe ‘usual good farming practice’, while those granted one-off aids (investment, young farmers, processing and marketing) were required to observe ‘minimum standards regarding the environment’. payments of compensatory allowances under the lfa directive were changed to an area basis to reduce the tendency to overstock resulting from the headage payment system. usual good farming practice was defined as the standard of farming which a reasonable farmer would follow in the region concerned (regulation (ec) 1750/1999). member states were required to set out verifiable standards in their 7greening agricultural payments in the eu’s common agricultural policy rural development plans which, in any case, should entail compliance with general mandatory environmental requirements. these requirements for pillar 2 payments were the precursor for cross compliance in pillar 1 in the mtr reform in 2003. with the implementation of the macsharry and agenda 2000 reforms, eu farm support was characterised by a mixture of market price support and production-limited direct payments. this regime was threatened by the opening of agricultural trade liberalisation negotiations in 1999 in the wto following the completion of the uruguay round agreement on agriculture. the eu along with a number of other countries developed the concept of multifunctionality to try to defend these payments in the forthcoming negotiations, building on the commitment (in article 20(c) of the agreement) that in negotiating the continuation of the agricultural policy reform process ‘non-trade concerns’ would be taken into account (norwegian ministry of agriculture, 2000). multifunctionality asserts that agricultural production produces a range of valued outputs including not only food but also environmental protection, landscape preservation, rural vitality and other public goods. importantly, these multifunctional outputs are jointly produced so that a decline in production would also lead to a reduction in these accompanying services. to maintain the public goods provided by agriculture it was thus necessary to continue to be able to use coupled payments. the multifunctionality concept gave rise to a significant research agenda, not least at the oecd (oecd, 2001, 2003) which identified the degree of jointness in production as a key determinant in policy choice. jointness was found to vary significantly depending on which non-commodity output was in question. it was rarely or never found to exist in fixed proportions, but to vary by farm, region, or production system. with the decoupling of direct payments in the mtr the eu’s payments moved into the green box in wto terminology and were no longer threatened by reduction commitments. as a result, the eu lost interest in promoting multifunctionality although it still survives in the arguments for coupled payments for suckler cow and sheep production in marginal farming areas to prevent land abandonment. while the mtr is known principally for the decoupling of direct payments, it also strengthened the integration of environmental objectives into the cap (regulation (ec) 1782/2003). cross compliance which was introduced as a voluntary option for member states in the agenda 2000 reform became mandatory with the 2003 reform. as of january 2005, for farmers to receive the single farm payment, they had to comply with 19 statutory management requirements (smrs) – five of which are environmental – and with a number of standards to ensure the ‘good agricultural and environmental condition’ (gaec) of agricultural land. the smrs are based on pre-existing eu directives and regulations, while gaec was a new requirement and consisted of 11 standards relating to soil erosion, soil organic matter, soil structure and a minimum level of maintenance of the land. this approach was extended from january 2007 with respect to the whole holding to beneficiaries receiving aid with regard to eight measures under ‘axis 2’ of the second pillar of the cap (art. 51 of regulation (ec) 1698/2005) (alliance environment, 2007). the reform also established the obligation to maintain the ratio of permanent pasture to the total agricultural area at either the national or regional level in view of its positive environmental effect. compulsory set aside was continued, and the environmental benefits connected with the measure were highlighted. a new article 69 was introduced which allowed member states to retain up to 10% of the previously coupled payments in spe8 a. matthews cific sectors (arable crops, beef and sheep) to support specific types of farming which are important for the environment or for improving the quality and marketing of agricultural products. also important was the transfer of funds from the first to the second pillar of the cap through a progressive reduction of direct payments to be used for the funding of rural development measures (modulation). furthermore, it became compulsory for member states to allocate 25% of their pillar 2 funding in the 2007-2013 programming period to axis 2 measures covering land management and agri-environment measures. the health check agreement in 2008 was primarily concerned with completing the mtr reforms by moving to decouple most of the remaining coupled payments, agreeing the abolition of milk quotas, as well as transferring additional funds from pillar 1 to pillar 2 through modulation (regulation (ec) 73/2009). coupled payments at the current level or below were only permitted for the suckler cow and sheepmeat and goatmeat sectors where maintaining a minimum level of agricultural production may be necessary for the agricultural economies in certain regions and, in particular, where farmers cannot have recourse to other economic alternatives. the possibility to make payments to specific sectors up to 10% of direct payment national ceilings was continued, but the menu of options was expanded under the now-relabelled article 68. uses can now include protecting the environment, improving the quality and marketing of products (as previously permitted under article 69) or for animal welfare support; payments for disadvantages faced by specific sectors (dairy, beef, sheep and goats, and rice) in economically vulnerable or environmentally sensitive areas as well as for economically vulnerable types of farming; top-ups to existing entitlements in areas where land abandonment is a threat; support for risk assurance in the form of contributions to crop insurance premia; and contributions to mutual funds for animal and plant diseases. schemes to protect the environment should be designed in the same way as aems in pillar 2; they must build on the cross compliance baseline and the payment can only cover the additional costs actually incurred and income foregone. this particular measure clearly blurs the supposedly distinctive characteristics of measures funded by pillar 1 and pillar 2 as it allows 100% eu financing for multi-annual environmental measures undertaken voluntarily by farmers. from a slow start, some member states have started to use article 68 for environmental objectives, including schemes for supporting permanent grassland under low-intensity use (e.g. in denmark), and for local high nature value farming support schemes (in the burren, ireland), while france has moved its prime à l’herbe grassland support scheme out of pillar 2 and is now funding it under article 68 of pillar 1. cross compliance standards were adjusted and simplified in the health check. two new issues were added to gaec standards in relation to the ‘protection and management of water’ and to ‘protect water against pollution and run-off and manage the use of water’, with associated new related standards. a more contentious element was the division of gaec standards into those that are mandatory and those that are optional. this was seen by some environmental organisations as weakening the environmental baseline as well as opening up the possibility of an unequal mandatory baseline across europe (ieep, 2008b). also significant was the abolition of compulsory set aside from 2009 (after agreement to set the set aside rate at 0% in 2008) in response to tight world market supplies. the loss of the environmental benefits of set aside (in place for arable farmers since 1994) was a significant negative outcome of the health check reform. an optional standard to provide for the establishment 9greening agricultural payments in the eu’s common agricultural policy and/or retention of habitats was introduced specifically to offset the loss of environmental benefits from the abolition of set aside. a potentially important limitation on the use of farming standards by member states was the addition of a prohibition that ‘member states shall not define minimum requirements which are not foreseen in that framework’. the commission had identified four new ‘challenges’ – climate change, renewable energies, water management and biodiversity, later extended to include innovation in these environmental areas and accompanying measures for dairy – which it wanted to address in pillar 2. it proposed to further modulate resources from pillar 1 to finance additional activities to address these challenges. the modulation elements of the commission’s proposal were among the more contentious and were significantly weakened in the final agreement. as an additional inducement, the co-financing rate for the modulated funds was reduced to 25% and to as low as 10% in convergence regions. member states were required to amend their rural development programmes to show how they planned to use these new resources to address these challenges (ieep, 2008b). 3. assessment of cap environmental measures this section briefly evaluates the experiences to date with the different cap instruments to target environmental objectives. the other major influence on the environmental impact of agricultural production is the body of environmental legislation introduced since the 1990s, but we do not discuss this further in this paper. decoupling the move from market price support to direct payments was itself an important contribution to reversing some of the pressures for intensification and environmental damage attributed to the early cap. support to eu agriculture declined from 39% of farm receipts in the mid-1980s to 34% in 2002-2004, and to 20% in 2009-2011 (as measured by the oecd producer support estimate). more important, almost all (98%) of this support in eu15 was output and input linked in the mid-1980s, falling to 70% in 2002-2004 and 52% in 2009-2011 (oecd, 2013). while it is important to avoid attributing causation to correlation, it is striking how the upward increase in eu agricultural output came to a halt at the same time as the implementation of the macsharry reform and has stagnated since that time (figure 1). as agricultural productivity has continued to increase during this period, this implies reduced use of inputs and not only land. decoupling leads to less intensive production because it reduces the effective market price received by producers. a profit-maximising producer would use inputs, such as fertilisers or chemicals, up to the point where the expected marginal return from using an additional unit of input equals its marginal cost. lowering the effective market price lowers the expected marginal return, and thus reduces the optimal input usage. overall production was expected to fall as a result of decoupling (relative to the counterfactual) and to be produced under more extensive (less input-intensive) conditions. although the theoretical conclusion is clear, empirical evidence to support a causal link between decoupling and lower input use is harder to find. ex-post studies examine the 10 a. matthews trend in input use pre-and post decoupling. for example, lmc international (2005), using fadn farm survey data, found that fertiliser use had declined in a number of countries following the macsharry reform (which decoupled arable payments from yield), but there were several exceptions to this finding. they concluded that the fadn data were broadly consistent with the hypothesis that there has been a general reduction in the intensity of cereal farming, but the evidence was neither clear-cut nor particularly strong. one reason is that decoupling also encouraged a reshuffling in the portfolio of crops grown and different crops have different input requirements. in italy, for example, vollaro (2010) found that the expansion of profitable crops like vegetables, flowers and vineyards, along with the receipt of the single payment, increased expenditure on fertilizers and crop protection inputs. other studies using simulation modelling methodologies have found that decoupling reduced the incentive for intensification and use of inputs but that impacts on pollution risk are fairly insignificant (brady, 2011). this is because pollution is influenced primarily by crop-specific characteristics given the geophysical characteristics of a region, and the balance between crop and livestock output, rather than the level of production per se. the environmental impact of direct payments can be strengthened by attaching conditions for eligibility (cross compliance rules are discussed in the next section). in the case figure 1. eu-27 production and land use changes, 1961-2009 source: faostat note: the figures are for the 27 current member states over the whole period. the trend is influenced by the fall in agricultural output in the new member states following their transition to market economies after 1989 but as their share of eu27 output is only around 15% the trend also accurately reflects the evolution in the old member states 11greening agricultural payments in the eu’s common agricultural policy of coupled payments, setting maximum areas or maximum numbers of animals for aid as under the macsharry reform limits the scope for intensification. stocking rate restrictions were another mechanism used to discourage intensification. for the basic beef premium introduced in the macsharry reform, payments were only made on animals up to 2 livestock units (lu)/ha. for the additional extensification premium, a maximum level of 1.4 lu/ha was established, calculated on the basis of the total number of adult bovine animals and sheep and goats. set aside originally introduced for supply control purposes had important environmental benefits especially where land was left fallow (the obligation could also be fulfilled by cultivating non-food crops on set aside land). the scheme was obligatory for larger (commercial) arable crop producers. set aside could be either rotational or non-rotational, with different environmental benefits. farmers could volunteer to set aside a larger share of their land than that stipulated in the regulations. the proportion of the arable crop area that commercial producers had to put into set aside varied from year to year, between 5% and 17.5%, and was reduced to 0% for the 2008 harvest before the scheme was abolished in the following year. over the 2000-2006 period, set aside land covered on average around 6 million ha (around 4 million ha being compulsory set aside), mainly concentrated in the eu-15 and representing around 8% of total arable land. around 800-900,000 ha of set aside land was cultivated with non-food crops (areté consulting, 2008). re-introducing fallow land into arable rotations delivered a range of environmental benefits (ieep, 2008a). the benefits depended on a variety of factors, such as the specific environmental and climatic conditions of the areas concerned by the measure, the type of set aside (fallow, rotational, seeded etc.), the features of the green cover on set aside land, and the land management practices applied but were generally assessed as positive, particularly for water consumption, nitrogen losses, biodiversity, ghg emissions and energy consumption (areté consulting 2008). however, where set aside land was used for the cultivation of energy crops, its environmental impacts generally are not dissimilar to those of the main alternative conventional agricultural systems. the main environmental threat foreseen from decoupling was that it could lead to the abandonment of farming in marginal farming areas where the environmental services depend on farming activities taking place. in the cap itself, the first compensatory allowances for less favoured areas (lfas) were introduced in 1975 to ensure the continuation of farming in areas where natural handicaps caused lower agricultural productivity and farming was becoming vulnerable. in marginal agricultural regions, decoupling risks having a negative impact on biodiversity and landscape mosaic because of the homogenisation of land use that results from land being taken out of production. model simulations show that voluntary aems and national support can act to buffer the full potential impacts of decoupling on landscape values in these regions. also the gaec standards to maintain a minimum level of farming activity help to mitigate the expected effects of the reform (brady et al., 2009). respect for environmental standards and cross compliance it is only when farmers are in receipt of direct payments that requiring them to observe particular management practices beneficial to the environment through cross 12 a. matthews compliance becomes possible; market price support benefits all producers regardless of their production practices. the steady evolution of environmental conditionality, from voluntary to mandatory, and from pillar 2 to pillar 1, was described in the previous section. the standards are derived either from eu legislation (smrs) or form part of a member state’s definition of good agricultural and environmental condition (gaec). furthermore, member states have to ensure that the ratio between arable and grassland does not decrease more than 10% to the detriment of grassland at regional level, compared to the year 2003 (new member states have a different base year). the current gaec framework following the health check which distinguished between compulsory and optional standards is set out in table 1. table 1. eu framework of issues and standards for good agricultural and environmental condition issue compulsory standards optional standards soil erosion: protect soil through appropriate measures minimum soil cover retain terraces minimum land management reflecting site-specific conditions soil organic matter: maintain soil organic matter levels through appropriate practices arable stubble management standards for crop rotations soil structure: maintain soil structure through appropriate measures appropriate machinery use minimum level of maintenance: ensure a minimum level of maintenance and avoid the deterioration of habitats retention of landscape features, including, where appropriate, hedges, ponds, ditches trees in line, in group or isolated and field margins minimum livestock stocking rates or/and appropriate regimes establishment and/or retention of habitats avoiding the encroachment of unwanted vegetation on agricultural land prohibition of the grubbing up of olive trees protection of permanent pastures maintenance of olive groves and vines in good vegetative condition protection and management of water: protect water against pollution and run-off, and manage the use of water establishment of buffer strips along water courses (implemented by 2012) where use of water for irrigation is subject to authorisation, compliance with authorisation procedures note: standards shown in italics were added in 2009 source: annex iii of regulation (ec) 73/2009 13greening agricultural payments in the eu’s common agricultural policy the eu regulation on cross compliance leaves many details of design and implementation to the discretion of individual member states and its regions. eu legislation, e.g. the nitrates directive, does not apply directly to individual farmers but rather to the member state. farm-specific requirements have to be transposed into national legislation by the member states which thus determine what specific management requirements will apply to farmers. farmers’ obligations to ensure gaec are often based on or adapted from previously existing standards of ‘good farming practice’. national gaec standards may have previously been implemented in national legislation (often for specific areas such as protected areas or nitrate vulnerable zones) or may have been non-statutory (e.g. promoted by the advisory services). as for smrs, there are differences in how member states have interpreted and implemented the gaec standards (alliance environment, 2007). member states are also required to establish an inspection and enforcement system, with reduction or withdrawal of direct payments for those farmers who do not comply with the smr and gaec standards. pillar 2 agri-environment measures aems in pillar 2 have the dual role of ‘supporting the sustainable development of rural areas and in responding to society’s increasing demand for environmental services’ (regulation (ec) 1698/2005). aems are designed and implemented by member states or regions as part of their rural development programmes (rdps). it is currently the only measure that all member states/regions must include in their rdps. the measure functions by supporting voluntary commitments (beyond a baseline set by cross compliance) undertaken for a period of five years or longer by farmers and other land managers who enter a management agreement that requires specific environmentally-friendly standards to be met. commitments can cover the following activities: organic farming, integrated production, other extensification of farming systems (i.e. fertilisers and pesticides reduction, extensification of livestock); diversification of crop rotations; reduction of irrigation; action to conserve soil; management of landscape and pastures; actions to maintain habitats favourable for biodiversity; genetic resources; and other targeted actions which for example include the use of integrated environmental planning. to ensure wto compatibility payments are based on costs incurred and income foregone, with the possibility of paying for transaction costs in addition. other environmental measures in pillar 2 include payments related to natura 2000 areas, the water framework directive, natural handicap areas, forests and environmental investments. also measures that support training and the diffusion of knowledge and information, as well as support to the setting-up and use of advisory services, play an important role in improving knowledge of farmers and foresters on environmental matters and in the uptake of more environment-friendly management practices. in 2009, the agricultural area under aems amounted to nearly 38.5 million ha and represented 20.9% of the uaa in the eu-27 (dg agri, 2011). use of aems varies widely between member states. this share was significantly higher in the eu-15 (25.2% or 33.5 million ha) than in the eu-12 (9.7% or 5 million ha). some countries (luxembourg, finland, sweden and austria) had more than two-thirds of their uaa enrolled in agrienvironmental commitments, but in 8 other countries (portugal, cyprus, malta, romania, 14 a. matthews lithuania, the netherlands, poland, bulgaria) this share was below 10%. within countries, schemes tend to be more attractive to farmers who are already farming in a less-intensive way, and it has been difficult to attract more intensive farmers and farmers in more intensively-farmed regions to participate. enrolment also fluctuates in response to the rhythms of programming and budget cycles. aem design and therefore effectiveness in terms of delivery varies widely. design is partly a trade-off between environmental effectiveness and administrative complexity and cost. effectiveness is also influenced by the relative importance of farm income versus environmental objectives in the design of the scheme. the degree to which these measures have led to an improvement in the environmental performance of agriculture is a matter of continued debate. the environmental impacts of pillar 2 measures are monitored through mid-term and ex post evaluations of past and current rdps and data on indicators within the common monitoring and evaluation framework. however, the level of detail of the evidence provided varies considerably between member states, making eu wide assessments problematic (eca, 2011; oecd, 2011). one recent literature review concluded that they have had mixed success depending on the schemes and indicators under investigation (uthes et al., 2011). there is some evidence that aems reverse negative trends in bird monitoring data, particularly in diversified, small-scale landscapes. studies in intensively farmed regions usually reported less successful results and concluded that much more and different conservation efforts are needed. a recent review of aem implementation by the european court of auditors drew attention to the need for more monitoring and recommended for the next programming period that aem expenditure should be more precisely targeted; that there should be a clear distinction between simple and more demanding agri-environment sub-measures; and that member states should be more pro-active in managing agrienvironment payments (eca, 2011). conclusions on pillar 1 payments and environmental objectives the eu has followed two approaches to integrating environmental objectives into the cap. the first, within pillar 1, is to add respect for environmental standards as a condition to receive direct payments and to couple some payments to specific types of farming which are important for protection and enhancement of the environment. the second, within pillar 2, is to remunerate farmers who voluntarily agree to implement practices beneficial for the environment beyond the baseline level. however, the relative funding levels for these two approaches is very different and, despite a widespread perception, there has been no shift to a greater emphasis on aems within pillar 2 over time (table 2). comparisons are made difficult because direct payments are annual payments where changes from year to year reflect policy decisions, mainly the decision to phase in direct payments to the new member states after their accession in 2004 and 2007 respectively. pillar 2 payments show a different rhythm as they are linked to programming periods and payments reflect issues to do with policy implementation as much as policy change. payments made fell in 2007, the first year of the new programming period, because of the time taken for approval of new programmes and to enter into contracts with farmers and others to spend the money. within pillar 2, aems are less affected by this disruption because pay15greening agricultural payments in the eu’s common agricultural policy ments continue to be made to farmers who enrolled in aems in the previous period and because aems are among the first rdp measures that are implemented at the beginning of a programming period. thus, we observe the relative importance of pillar 2 expenditure increasing over time, but within pillar 2 the relative importance of aem expenditure is decreasing year on year in the current programming period. with two years to go, the 2007-2011 annual averages may provide a reasonable guide to the final outcome. within the rural development budget, there is a strong environmental focus. according to the rdps submitted by member states, 45% of the eafrd funding for the 20072013 period (some €43 billion) has been allocated to axis 2 measures (‘improving the environment and the countryside’). around half of this funding, €22 billion, will be spent on agri-environment measures; €472 million will be spent on natura 2000 measures on farm land; and €111 million on natura 2000 measures on forestry land (cec, 2011c, annex 2). actual expenditure figures show that, if anything, axis 2 measures have been even more important to date and that aem expenditure has maintained its projected share of around 50% (table 2). however, the relative importance of pillar 1 and axis 2 payments has not changed in the current programming period. indeed, based on expenditure figures to date, the share of aem expenditures has declined compared to the previous programming period both with respect to pillar 1 and pillar 2 payments, even if the absolute amounts, in nominal terms, show an increase. table 2. relative importance of expenditure on direct payments in pillar 1 and environmental payments in pillar 2, € million and percent chapter 2000-06 average 2007 2008 2009 2010 2011 2007-11 average € million direct aids 29,861.3 37,045.8 37,568.6 39,113.9 39,675.7 40,178.0 38,716.4 total pillar 2 4,705.6 2,517.4 6,064.5 8,204.3 10,677.0 12,175.0 7,927.6 axis 2 measures 3,456.3 2,054.3 4,546.5 4,740.7 5,437.2 5,834.5 4,522.6 aems in axis 2 2,053.9 1,204.0 2,312.0 2,547.5 2,897.4 3,077.0 2,407.6 share pillar 2 (1) 13.6% 6.4% 13.9% 17.3% 21.2% 23.3% 17.0% share axis 2 (1) 10.4% 5.3% 10.8% 10.8% 12.1% 12.7% 10.5% share aems (1) 6.4% 3.1% 5.8% 6.1% 6.8% 7.1% 5.9% share axis 2 (2) 73.5% 81.6% 75.0% 57.8% 50.9% 47.9% 57.0% share aems (2) 43.6% 47.8% 38.1% 31.1% 27.1% 25.3% 30.4% notes: two measures of agri-environment expenditure are shown in this table. aem expenditure refers only to expenditure on agri-environment measures, while all axis 2 measures include natural handicap payments to farmers in disadvantaged areas, natura 2000 payments, and afforestation payments as well as aem payments. annual expenditure is from q4 of the previous year to q3 of the year shown. it represents payment claims declared by member states. the 2000-2006 figures may not be fully comparable due to methodological differences between the two programming periods. shares labelled (1) are the ratio of the chapter heading to the sum of direct payments and the chapter heading. shares labelled (2) are the ratio of the chapter heading to total pillar 2 expenditure. sources: 2000-2006 figures dg agriculture rural development in the european union 2007; 2007-2011 figures are from dg agriculture financial reports for the eagf and the eafrd for the respective years. 16 a. matthews the continued importance of pillar 1 payments in delivering environmental benefits shows how entrenched is the support for these payments – this was evident in the watering-down of the commission’s modulation proposals in the 2008 health check (ieep 2008b). this is mainly because of the importance of direct payments in providing income support to eu farmers. but an influential secondary narrative defends direct payments as the basis for the delivery of public goods through agriculture.5 lumbroso and garvey (2013) point to the paradox that, from a strong argument for radical reform, the public goods argument has become a legitimation tool for existing measures and a by-product of income support. indeed, it is only because it is linked to direct payments that cross compliance retains its effectiveness. at the same time, the new legitimacy conferred by cross compliance hampers the reallocation of funds in favour of more targeted rural development measures. we return to this paradox in the conclusion. this narrative also sheds light on what is considered to be the appropriate environmental reference level. even if cross compliance is the baseline for pillar 2 voluntary aems, the argument that direct payments in part compensate farmers for the high environmental standards required by cross compliance indicates that, in practice, the environmental reference level is lower, potentially as low as the smrs which are the legal mandatory eu requirements. in practice, in some countries, some gaec standards would also be backed by national legislation which raises the environmental reference threshold. this point is overlooked by proponents of more radical cap reform. not only would more radical reform imply the gradual elimination of most untargeted pillar 1 direct payments in favour of aems in pillar 2, but it would also require securing the environmental benefits currently due to gaec standards. whether this would be done by transferring the gaec standards to broad-based entry-level aems (implying that society continues to bear the costs of meeting these standards) or by transforming the gaec standards into legislation (thus placing the onus of meeting the standards on farmers) will reflect society’s view of the appropriate allocation of property rights in the environment, taking into account the impact on agriculture’s competitiveness. 4. commission’s post-2013 greening proposals promoting sustainable management of natural resources and climate action is one of the three stated objectives of the cap post-2013. in pursuing this objective, the commis5 according to the commission: ‘decoupled direct payments provide today basic income support and support for basic public goods desired by european society.’ (cec 2010, 4). this is further developed in the impact assessment as follows: ‘without basic income support, the less competitive farmers who very often manage marginal land and land in remote areas in an extensive manner, thereby helping to maintain areas of high natural value, may cease their agriculture activity because they no longer make a sustainable income; moreover, gaec that are part of the baseline for agri-environment measures no longer apply to land that does not receive direct payments.’ (cec 2011a, annex 2). according to copa-cogeca: ‘‘direct payments under pillar 1 enable eu farmers to provide a series of public benefits as a result of their farming activity which are valued by society but are not currently rewarded by the market and, in many cases, will never be.’ (copa cogeca, 2010, p 13). the agricultural council underlined: ‘that direct income support to eu farmers currently contributes to ensuring a fair standard of living for the agricultural community; it also enhances the provision of public goods and services by farmers for which the market does not pay, and broadly agrees that this support has proven its worth and will remain an essential element in the cap towards 2020, notably in the context of the additional costs producers face in meeting the eu’s high environmental and animal welfare standards.’ (council of the european union 2011). 17greening agricultural payments in the eu’s common agricultural policy sion had the choice to emphasise either greening pillar 1 (by raising the cross compliance threshold or otherwise adding environmental conditions to part or all of direct payments) or to further modulate funds from pillar 1 to pillar 2 with a view to expanding the scale of aems. the policy advice to the commission, from the oecd and other sources, would be to favour the transfer of funds to more targeted measures in pillar 2. in fact, the commission chose a variant of the former approach in proposing a ‘green’ payment in pillar 1 to farmers following a mandatory set of farm practices contributing to the environment. however, it now looks as if the legislature (council and parliament) will water down this proposal to such an extent that the additional benefits for the environment will be very minimal. in this section, the commission’s proposal is described and the reactions to it are examined with a view to understanding the reasons for the apparent failure of the commission’s strategy. the commission began its reflections of the cap post-2013 in its november 2010 communication which outlined three potential directions for the cap which it called the adjustment, integration and refocus scenarios, respectively (cec, 2010). this communication contained for the first time the proposal to introduce a top-up payment in pillar 1 as part of a greening strategy. specifically, the communication proposed: enhancement of environmental performance of the cap through a mandatory ‘greening’ component of direct payments by supporting environmental measures applicable across the whole of the eu territory. priority should be given to actions addressing both climate and environment policy goals. these could take the form of simple, generalised, non-contractual and annual environmental actions that go beyond cross compliance and are linked to agriculture (e.g. permanent pasture, green cover, crop rotation and ecological set aside). in addition, the possibility of including the requirements of current natura 2000 areas and enhancing certain elements of gaec standards should be analysed. (italics added) the communication attributed the idea of restructuring pillar 1 payments to the european parliament. however, the parliament’s resolution in july 2010 (based on the lyon report) called for the vast bulk of agricultural land to be covered by agri-environment measures and for additional incentives for improved environmental management to be delivered through an enlarged pillar 2 budget (european parliament, 2010). it mentions the idea of a top-up payment in pillar 1 but in the context of multi-annual contracts linked to carbon reduction/sequestration and biomass products.6 in its resolution responding to the communication (based on the dess report), the parliament accepted that ‘natural resource protection should be more closely linked to the granting of direct payments and calls, therefore, for the introduction, through a greening component, of an eu-wide incentivisation scheme with the objective of ensuring farm sustainability and long-term food security through effective management of scarce resources (water, energy, soil) while reducing production costs in the long term by reducing input use’ (european parliament, 2011). 6 the resolution reads (paragraph 71): ‘believes that an eu-funded top-up payment should be made available to farmers through simple multiannual contracts rewarding them for reducing their carbon emissions per unit of production and/or increasing their sequestration of carbon in the soil through sustainable production methods and through the production of biomass that can be used in the production of long-lasting agro-materials;’ (european parliament, 2010). 18 a. matthews it specified that ‘further greening should be pursued across member states by means of a priority catalogue of area-based and/or farm-level measures that are 100% eufinanced; considers that any recipient of these particular payments must implement a certain number of greening measures, which should build on existing structures, chosen from a national or a regional list established by the member state on the basis of a broader eu list, which is applicable to all types of farming; considers that examples of such measures could include: support for low carbon emissions and measures to limit or capture ghg emissions; support for low energy consumption  and energy efficiency; buffer strips, field margins, presence of hedges, etc.; permanent pastures; precision farming techniques; crop rotation and crop diversity; feed efficiency plans’. these ideas prefigure the flexibility options put forward in the debate on the commission’s legislative proposals following their publication. the commission’s intentions were elaborated in its proposal for the next multi-annual financial framework in july 2011 which called for 30% of direct support to be made conditional on ‘greening’ to ensure that the cap helps the eu to deliver on its environmental and climate action objectives, beyond the cross compliance requirements of current legislation (cec, 2011d). in its legal proposals presented to the european council and the european parliament setting out proposed changes to the common agricultural policy (cap) for the post-2013 period on 12 october 2011, the greening requirements were specified to include ecological focus areas (efas), crop diversification and the maintenance of existing areas of permanent pasture at farm level. participants in the proposed small farmers’ scheme are exempt and organic farmers would automatically receive the greening payment (cec 2011c). other greening elements included in the draft regulations include changes to gaec standards, a revamping of pillar 2 aems and a more important role for the farm advisory service in facilitating innovations to deliver climate change and environmental objectives. the changes to the gaec standards were driven in part by a simplification agenda and results in a new framework arranged into four thematic areas and nine issues (cec, 2011e, annex ii). certain articles from the birds and habitats directives were removed from the smr requirements. member states are required to develop new gaec standards for maintaining soil organic matter and protecting wetland and carbon rich soils. the compulsory gaec on ‘avoiding the encroachment of unwanted vegetation on agricultural land’ has been removed. although this was seen as a way of avoiding the abandonment of agricultural land, it was also criticised as driving the removal of habitat in several member states. the optional gaec standards for ‘appropriate machinery use to maintain soil structure’, minimum livestock stocking rates and/or appropriate regimes and ‘establishment and/or retention of habitats’ have also been removed. requirements related to the water framework directive and sustainable use of pesticides directive would become part of cross compliance once implemented by all member states. participants in the small farm scheme would be exempted from cross compliance requirements. the restriction that member states shall not define minimum standards which are not established in the relevant annex is continued. despite the potential significance of some of these changes for environmental management, the real novelty of the commission’s proposals was its attempt to define and fund mandatory green standards applicable across the eu which could be administered as a pillar 1 direct payment. 19greening agricultural payments in the eu’s common agricultural policy in its impact assessment of the proposals in the 2010 communication, the commission asked the question whether it would not be simpler to use part of pillar i funding for complying with environmental measures within rural development policy instead? ‘seen from the perspective of providing choice for the farmers, it would seem preferable to envisage measures with payment levels differentiated by measures according to cost incurred and income forgone, as well as to give more discretion to member states for their design so as to tailor them as much as possible to specific situations’ (cec, 2011c, annex 2, p. 14). its objection to this approach was that it would give too much discretion to member states and farmers. even in a best case scenario, it would not link the greening requirements to pillar i payments and it would not cover the entire eu territory. this would be partly because of insufficient budget resources (comparing existing premia in aems with the future payment levels for the greening component) as well as the varied uptake of agri-environment across member states. the commission saw particular problems for climate change objectives as it would leave open the possibility for only a part of the farm to adopt climate friendly practices while the rest of the farm continues to be operated with potentially detrimental methods undermining the global result. the commission also considered and rejected the option to include the greening requirements as part of gaec standards. ‘to make the greening effective, the measures in the greening component should be compulsory for the farmer, the discretion left to the member state limited, and sanctions effective. if greening is effectively a requirement in the direct payments system, then wouldn’t it be simpler to work instead on enhancing cross compliance?’ (cec, 2011c, annex 2, p. 13). it responded to this question as follows: ‘although this line of reasoning is put forth arguably on simplification grounds, it hides the complexities inherent in member states defining and administering gaec tailored to regional specificities. as the experience with the optional gaec on crop rotation has shown, this approach would not necessarily ensure that the entire eu territory is effectively greened. at the same time, it would meet with considerable resistance from farmers as it would be framed as a requirement rather than an incentive, and arguably do away with the political visibility of greening direct payments that is one of the main drivers of this reform’ (cec, 2011c, annex 2, p. 13). these passages point to the concerns the commission had when formulating its greening proposal. it wanted a universal set of measures which would apply to all farms, it wanted to avoid giving member states discretion, it wanted farmers to see this as an incentive rather than an imposition, but most particularly, it wanted greening to be associated with pillar 1 payments in order to promote their legitimacy and to provide an additional justification for maintaining the pillar 1 budget of the cap. the commission’s proposals gave rise to a lively and mostly critical debate (hart and little, 2012; house of commons, 2012; matthews, 2012, 2013). in february 2013, the legislative process has not yet been concluded. but enough is known of the positions of the main players to suggest that the outcome will be much less ambitious than what the commission proposed, which itself was strongly criticised by environmental ngos as an inadequate response to the stressed state of europe’s natural environment (birdlife et al., 2011). the following resumé of the state of play is based on the european council conclusions on the next mff at its february 2013 meeting (european council, 2013); the summary by the cyprus presidency of the discussions in the agricultural council in december 2012 20 a. matthews (council of the european union, 2012), and the amendments to the commission’s legislative proposals adopted by comagri in january 2013 (european parliament, 2013). virtually all the amendments to the legislative drafts indicate a considerable weakening of the commission’s proposals. 1. the conditions attached to the three greening measures proposed by the commission (crop diversification, ecological focus areas, maintaining permanent pasture) will be relaxed or eliminated, for example, by raising the minimum farm size threshold where the measures apply or extending the types of land uses that count towards efas. for example, the european council particularly specified that “the requirement to have an ecological focus area (efa) on each agricultural holding will be implemented in ways that do not require the land in question to be taken out of production and that avoids unjustified losses in the income of farmers” (european council, 2013, p. 27). 2. greening will effectively be made voluntary by limiting the penalty for non-compliance to the loss of the green payment excluding the possibility of also reducing the basic payment as proposed by the commission. this is despite the commission’s insistence that mandatory participation in the green payment is essential if the measures are to be effective. 3. additional ‘equivalent’ greening measures will be introduced in the name of flexibility. although flexibility in the implementation of environmental measures is often positive, it leaves open the possibility that the equivalent measures selected may have even less impact on the environment than what was proposed by the commission. 4. farmers will be permitted to qualify for the green payment in pillar 1 provided they show they are already managing land in an environmentally-responsible way (‘green by definition’), for example, through enrolment in a pillar 2 aem or in an environmental certification scheme. the problem with these exceptions is that there is clearly no environmental additionality. there is also the risk that farmers might be paid twice (‘double funding’) for the same practices both in pillar 1 and pillar 2. 5. it is probable that the commission’s proposals on gaec standards will be weakened. the elimination of the inclusion of the water framework directive and the sustainable use of pesticide directive as part of cross compliance once the obligations relevant to farmers have been identified has been recommended by comagri and is likely to be supported in council. 6. there will be less money for aems in the rural development pillar. not only has the funding for the pillar 2 budget been reduced in the european council’s conclusions on the next mff, but flexibility will be given to member states to shift a proportion of their pillar 2 budgets to pillar 1 which could further reduce the funds available for rural development. rural development programmes are given new tasks, notably income stabilisation and risk management, which could potentially crowd out spending on aems. the commission had proposed that member states should maintain a minimum spend (25%) of their pillar 2 budgets on agri-environment and climate measures but only in the preamble to the draft rural development regulation and not in in the regulation itself. here there is a difference between the two legislative bodies, with the council favouring the commission’s proposal while comagri has proposed to make this a mandatory requirement in the regulation. 21greening agricultural payments in the eu’s common agricultural policy it must be stressed again that these are predictions based on negotiations in progress in the two legislative bodies and the final outcome could be different. however, the commission’s proposals look likely to be seriously emasculated when they eventually emerge from the legislative process. certainly, neither of the two institutions is pushing for a more ambitious greening agenda. we conclude that the additional environmental benefits likely to materialise as a result of adopting the new regulations for the cap post-2013 will be very minimal, certainly in the context of the budget resources justified by this objective. 5. final reflections on greening the cap through pillar 1 the integration of environmental objectives into the cap has until now progressed along two tracks: attaching environmental conditions to pillar 1 payments and supporting voluntary agri-environment measures in pillar 2. looking at the history of cap reform, the commission might have proposed a redistribution of cap resources in favour of pillar 2 and voluntary aems. such a shift would also be in line with oecd recommendations to favour targeted and transparent measures as part of agricultural policy reform. the commission’s proposals for the cap post-2013 instead proposed to designate 30% of each country’s direct payments national ceiling as a ‘green’ payment conditional on following a set of practices beneficial for climate and the environment. however, we concluded that the eventual legislative outcome is unlikely to lead to major environmental improvements. in this concluding section, we reflect on this apparent failure of the commission’s greening strategy and the reasons for it. a mixture of strategic, technical and political economy factors appear to have played a role. first, farm organisations, as the main beneficiaries of direct payments under pillar 1, are naturally its strongest defenders. direct payments represented on average 29% of agricultural income in the eu in the period 2007-2009 (with total subsidies coming close to 40% of agricultural income) (dg agri, 2012). greening would add to the costs of production although the commission’s calculations suggested that the overall impact would be slight (cec, 2011c, annex 2d). it projected an average decrease in overall farm income per worker of between 1.4% and 3.2%. livestock farms would be more adversely affected because of higher feed costs, while arable farms might even expect to gain because the higher market margin (due the higher market prices caused by the slight reduction in supply) would be sufficient to outweigh the costs of greening. this calculation assumes that farmers would continue to receive the same level of direct payments even in the absence of greening. if greening were the quid pro quo for preventing a cut in the direct payments envelope by anything more than 1-3% income reduction calculated above as the cost of greening, then arguably farmers are better off under the commission’s proposals. second, the commission’s attempt to establish this quid pro quo and to link greening to the size of the cap budget was never credible. it put forward the green payment in pillar 1 as a way to enhance the legitimacy of direct payments and to defend its proposal to maintain a constant cap budget in nominal terms in the next mff. the promise to green the cap may have been necessary to gain the support of the college of commissioners to propose the continuation of cap funding in the commission’s mff proposal. the difficulty was that, once the proposal was made, there was no credible threat to reduce direct payments if ambitious greening measures were not adopted. the two legislative bod22 a. matthews ies worked on the assumption that the budget allocation was exogenous (not necessarily given but not something which would be influenced up or down by decisions taken on greening). there was thus no counterweight to the incentives for agricultural ministers to minimise the additional ‘burdens’ that greening imposes on farmers. while the european council conclusions on the next mff endorsed the commission’s proposal to use 30% of direct payment ceilings for the green payment, this was not linked to any specific level of greening ambition; indeed, the european council called for ‘a clearly defined flexibility for the member states relating to the choice of equivalent greening measures’. by proposing greening as a way of legitimising the existing flow of untargeted pillar 1 payments to farmers, the commission framed the issue in a way that it was bound to lose. third, the farm organisations had a new card which they played to maximum advantage, namely, food security. during the ‘reform period’ 1992 to 2008, agricultural policy reform and the integration of environmental objectives into agricultural policy were mutually supportive. decoupling discouraged the use of off-farm inputs, while encouraging more extensive agricultural production helped to limit the budgetary cost of overproduction during this period when eu market prices were still above world market levels. but since the 2007-2008 price spike and the growing realisation of the fragility of global food supplies, more emphasis is now put on the necessity for europe to contribute to increased food production in the name of ‘food security’. this argument is used particularly against the proposal to designate 7% of arable land as efas (which, given the existence of trees, hedgerows, field margins and awkward corners on many farms which count towards efas implies leaving around 3-4% of cultivated land fallow). it explains the european council’s decision that efas should be implemented in ways that do not require land to be taken out of production. yet only a few years ago arable farmers had to set aside up to 10-15% of their arable land in order to be eligible for direct payments. the change in the market environment explains the different perceptions of the burden of fallowing land in the two situations. fourth, member state governments were unenthusiastic about the commission’s proposal. they have no appetite to pursue further greening through pillar 2 because of the requirement to co-finance this expenditure. but they are concerned about greening in pillar 1 because of the additional administrative complexity it implies, which flies in the face of the continuing demand from member states for simplification. member states have therefore pushed hard for flexibility and the recognition of alternative practices as being equivalent to the commission’s greening proposals. they have also supported extending automatic eligibility for the green payment (‘green by definition’) to other groups of farmers, e.g. those enrolled in aems, for the same reason. in this way, member state interests have also contributed to the hollowing-out of the commission’s greening proposal. fifth, although the european parliament was broadly in favour of some further greening of the cap, its preferred approach was to advocate further reliance on voluntary aems in pillar 2. it never embraced the commission’s idea of a mandatory green payment in pillar 1 in return for higher environmental standards (a form of super cross compliance). instead, it has sought to effectively connect pillar 2-type aem measures to the commission’s pillar 1 green payment through offering a wider ‘menu’ approach to the practices which would determine eligibility for the payment. while many of these individual measures are worthy and desirable, it is hard to see how they belong to the broad23greening agricultural payments in the eu’s common agricultural policy brush payments in pillar 1. by pursuing this approach instead of a more principled position of transferring funds to pillar 2, the parliament has also helped to undermine the commission’s proposal. sixth, a lack of confidence in the environmental effectiveness of the measures proposed made them difficult to defend. requiring every farmer throughout the eu to follow exactly the same management prescriptions, regardless of the ecological context, environmental pressures, or opportunity costs, is a highly inefficient policy approach. environmental ngos pointed out that requiring individual farms to maintain existing levels of permanent pasture would not necessarily help to protect species-rich semi-extensive grasslands and grasslands of high nature value. crop diversification was seen as a secondbest alternative to crop rotation. while the environmental potential of ecological focus areas was more widely recognised, particularly for bidioversity, questions were raised as to whether science supports setting aside individual parts of every farm regardless of its conservation value, or whether a more targeted approach might not be more effective (godfray 2012). the absence of management prescriptions also reduces their likely environmental value. as the european court of auditors pointed out: ‘… the regulation does not specify the concrete objectives, which should be achieved by the farming community in that domain, nor does it explain the impact which is expected from implementing such measures. the absence of such justification raises the questions as to the claimed aim that the policy is results oriented’ (eca, 2012, p. 40). the apparent failure of the commission’s greening strategy points to a more fundamental dilemma for those seeking to orient the cap more towards environmental objectives. during past reforms of the cap, greening pillar 1 payments through cross compliance and promoting voluntary aems in pillar 2 were seen as complementary strategies to green the cap. in fact, it appears they are increasingly competitive, at least as long as pillar 1 payments are primarily intended as income support. increasing the budget for voluntary aems in pillar 2 can only occur by transferring resources from pillar 1. but the effectiveness of cross compliance in pillar 1 depends on the level of direct payments. strengthening voluntary aems in pillar 2 can only occur at the expense of weakening the sanctions for cross compliance in pillar 1, and vice versa. in future, those seeking to orient the cap more towards environmental objectives may need to choose between one approach or the other. targeted agri-environment payments linked to the provision of identifiable and specified environmental public goods are a cost-effective way to achieve environmental benefits. however, if further greening of the cap were pursued through targeted aems in pillar 2, there is a risk that the environmental benefits achieved through cross compliance could be lost. these are mainly the gaec standards which go beyond the environmental baseline set by legislation and incorporated in statutory management requirements. currently, gaec standards do not apply to farmers who opt out of or otherwise do not receive direct payments. it seems necessary that, to be effective, legal force should be given to these codes of good farming practice. this suggests a need to revisit where european society wants to draw the ‘environmental baseline’ or reference level which distinguishes between those obligations which farmers are expected to carry as part of the normal practice of farming (‘polluter pays principle’) and those obligations which society accepts go beyond normal good farming 24 a. matthews practice and where farmers should be remunerated for the additional costs and income foregone in achieving them (‘provider gets principle’). it is often assumed that this is currently given by cross compliance (both statutory management requirements and gaec standards). however, the strong political support for the view that direct payments are, in part, a recognition of the costs that society asks farmers to bear through cross compliance implicitly undermines the ‘polluter pays principle’. if farmers who do not receive direct payments are not expected to observe the cross compliance standards, then these do not form the environmental baseline. whether or not this should be the case deserves wider 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(2011). report on analytical framework – conceptual model, data sources, and implications for spatial econometric modelling. work package no. 3, spard – spatial analysis of rural development measures, fp7 contract no. 244944. vollaro, m. (2010). the impact of the single farm payments on the expenditure on fertilizers and crop protection inputs: a comparative study of the italian agriculture. in: 120th seminar, september 2-4, 2010, chania, crete. http://ageconsearch.umn.edu/ bitstream/109428/2/vollaro.pdf. issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 2(1): 91-111, 2013 exploring the characteristics of innovation alliances of dutch biotechnology smes and their policy implications philipp j.p. garbade1, s.w.f. (onno) omta2,*, frances t.j.m. fortuin1 1 food valley organization, postbus 294, 6700 ag wageningen, the netherlands 2 wageningen university, p.o. box 8130, 6700 ew wageningen, the netherlands abstract. policy makers are becoming increasingly aware of the fact that r&d intensive smes play a pivotal role in providing sustainable economic growth by maintaining a high rate of innovation. to compensate for their financial vulnerability, these smes increasingly conduct innovation in alliances. this paper aims to explore the impact of different alliance characteristics on the performance of dutch biotechnology smes. the conceptual model was tested using a sample of 18 biotech smes reporting about 40 alliances. the main findings indicate that alliance performance is positively related to the level of complementarity, the cognitive distance and tacit knowledge transfer by the human resources exchanges. policy makers are recommended to support innovation alliances by providing the infrastructure in which alliances can flourish, e.g. through stimulating the foundation of cluster organizations that can function as innovation brokers. these cluster organizations can provide network formation, demand articulation, internationalization and innovation process support to their member companies and can act as a go-between among alliance partners. as part of the innovation process support activities, they can organize special workshops for biotech smes on how to successfully behave in an innovation alliance. keywords. knowledge transfer, human resources exchange, biotechnology alliances jel codes. o32 1. introduction policy makers are becoming increasingly aware of the fact that r&d intensive smes play a pivotal role in providing sustainable economic growth by maintaining high innovation rates. to compensate for their financial vulnerability, these smes increasingly conduct innovation in alliances. a good understanding of the mechanisms that govern such innovation alliances is therefore a prerequisite for an effective innovation policy. this paper aims at contributing to our understanding of the dynamics of innovation alliances by exploring the impact of different alliance characteristics on the performance of dutch biotechnology smes. an r&d intensive biotech sme is often the product of an innovative idea; an ad-hoc creation triggered by the presence of a “star scientist” (zucker * corresponding author: onno.omta@wur.nl. 92 p.j.p. garbade, s.w.f. (onno) omta, f.t.j.m. fortuin and darby, 1997), following a high-risk strategy that involves cutting edge science (pisano, 2006). hall and bagchi-sen (2002) found in their sample of 74 biotech companies in canada (95% smes) that 28% of the companies originated from universities and 15% were industrial spin-offs, whereas more than 50% was founded as independent ventures (hall and bagchi-sen, 2002). small biotechnology companies are frequently facing resources constraints (majewski, 1998). considerable capital investments are needed to pay for specialized staff and equipment to develop new products and processes and to successfully introduce them to the market. this problem is even more pressing because biotechnology companies, especially those related to the health sector, face long time horizons until first revenues begin to pour in, which often makes them not profitable for a long period of time (denis, 2004). bagchi-sen (2002) mentions the following barriers to innovation: lack of skilled managers or researchers, a lack of physical facilities for research or manufacture, as well as a lack of marketing or distribution channels. biotechnology firms may suffer from resource constraints, but at the same time they generally have a lower bureaucratic burden. they are usually considered to be more flexible and therefore better innovators. unique competencies, a low level of hierarchy and a high internal flexibility (nooteboom, 1994, pisano, 2006) make up for their lower financial power (argyres and liebeskind, 2002). a general overview of the advantages and disadvantages of smes, not limited to the biotech sector, is provided in the literature review of ale-ebrahim et al. (2010), while khilij et al. (2006) conducted exploratory research specifically on the challenges of biotech smes. mangematin et al. (2003) found in their study of 60 french biotech smes, an average growth rate of 30% in turnover and 13% in staff. still, only a small number of biotech firms grow into reasonably large companies, especially those companies that have learned to engage in collaboration projects with a heterogeneous set of partners (powell et al., 2005). the forming of alliances as an effective tool to overcome the problems of limited resources was also found in a study of rothaermel and deeds (2004) on 325 health related biotechnology companies. strategic alliances constitute a powerful strategy for biotech firms to overcome their capital constraints (khilji et al., 2006) and are found to accelerate innovation (terziovski and morgan, 2006). alliances allow access to missing competences and material resources and thus enhance the innovative potential and firm performance (hall and bagchi-sen, 2002, 2007). however, khilji et al. (2006) indicate that not enough is known on how to successfully manage a strategic alliance. pisano (2006) criticizes that business models, organizational strategies and approaches from other high-tech sectors have been used while not taking into account the special characteristics of the biotech sector, and that organizational and institutional innovations are needed to unlock the potential of biotechnology (pisano, 2006:202). the high level of innovativeness of the biotech sector, combined with the high level of alliance formation makes it an ideal sector to study the critical success factors for collaborative ‘open’ (chesborough, 2006) innovation. to date, a number of empirical studies have been carried out to study alliance collaboration in this sector. they focus on the company performance related to the alliance portfolio (see baum et al., 2000; george et al., 2001), the network composition and dynamics (gay and dousset, 2005), or the alliance duration in an uncertain environment (pangarkar, 2003). the alliance collaboration process concerning knowledge management (i.e. nooteboom et al., 2007, standing et al., 2008), 93exploring the characteristics of innovation alliances alliance capabilities (heimeriks and duysters, 2007) and governance (phene and tallman, 2012) are also the focus of more recent studies. however, a literature review (not only on biotech alliances) done by comi and eppler (2009), is still highlighted a lack of research on alliance management, especially in startup biotech companies. the objective of the present paper is to fill this gap by studying the alliance collaboration process to explore the attributes of both successful and less successful innovation alliances among biotech smes. this will allow the improvement of policy support focusing on alliance collaboration process. to this end, different types of alliances, that were set up to carry out joint open innovation projects (in the remainder of this paper termed ‘innovation alliances’), are investigated and their effect on the alliance potential, alliance execution and the alliance performance is mapped. the remainder of this paper is organized as follows: section 2 contains the theoretical framework. in this section the discussion of the resource based view (rbv) will lead to the conceptual model in which the concepts relevant for this research and their relationships are identified. at the beginning of section 3, research methodology, the operationalization of the constructs is presented. section 3 also discusses the methods of data collection and data analysis. section 4, results, starts with the baseline description of the participating companies and their alliances. next, the results are analyzed using partial least squares, while comparing the theoretically expected results with the empirically model. in section 5, conclusions and discussion, the main conclusions are drawn, and suggestions for further research are given and recommendations for practitioners and policy makers are provided. 2. theoretical framework in management literature it has been argued that strategic technology partnering can induce the effective use of heterogenic resources (hagedoorn, 1993; powell et al., 1996; ahuja, 2000; rowley et al. 2000; rosenkopf & almeida, 2003). a better understanding of this phenomenon is achieved by application of the rbv (barney, 1991, alegre et al., 2011). rbv is based on two fundamental assumptions: companies in an industry do not all possess the same resources which provides resource heterogeneity, whereas the partial immobility of these resources preserves this state of disequilibrium (barney, 1991). following the line of thought of the rbv, an alliance is a tool to (partly) overcome the problem of the immobility of resources by creating a new entity with a unique set of resources. nooteboom et al., (2007) claims that while the antecedents of resource heterogeneity and the consequences for the firm’s innovation performance have been studied, the direct effects on the innovation process have largely been ignored. nooteboom et al. fills this gap by shedding light on the causal factors underlying the inter-firm learning process, especially with regard to the cognitive distance between firms. our study aims at taking nooteboom et al.’s important conceptual idea a step further, by looking at alliance formation and execution from a process perspective. we do this by modeling the collaborative open innovation process for which the alliance is set up: from the potential of the alliance for the participating companies via the alliance execution to the final alliance performance. 94 p.j.p. garbade, s.w.f. (onno) omta, f.t.j.m. fortuin 2.1 conceptual model the model developed for the present study conceptualizes the alliance as a collaborative entity created by two or more companies in order to innovate. several factors that are expected to play a role in the innovation alliance collaboration process have been identified. these factors and their assumed relationships are presented in the conceptual model in figure 1. the different constructs are discussed in more detail in the following sections. figure 1: conceptual model 1 2 level of complementarity alliance importance cognitive distance alliance compliance knowledge transfer governance mechanisms alliance synergy exploration and exploitation performance alliance potential alliance execution alliance performance 2.2 alliance potential in the phase preceding the actual start of an alliance, potential partners have to be identified. each potential partner has not only its own distinct set of material and immaterial resources, but also a different expectation of what it might gain from the alliance. the decisions made in the selection process set the stage for the alliance, and determine the alliance potential. we identified the following factors as determinants for the alliance potential: level of complementarity, cognitive distance and alliance importance. 2.2.1 level of complementarity and cognitive distance each potential partner has its own distinct set of material and immaterial resources. the decision has to be made whether to search for partners with similar or complimentary resources. by combining similar resources, realizing economies of scale and scope can be expected (ansoff, 1965; montgomery, 1985), whereas an alliance with a partner that has complementary resources synergy effects might be obtained (harrison et al., 2001). according to rbv, alliances can be considered as tools to create new unique sets 95exploring the characteristics of innovation alliances of resources that enable the partnering firms to (partly) overcome the immobility problem of certain resources. we therefore assume that the potential of an alliance will be positively influenced by the level of complementarity of the resources that are brought into the alliance by the partnering companies and the extent to which these are exchanged. complementary resources deliver learning opportunities and allow the creation of new capabilities (harrison et al., 2001). besides its importance in acquisitions harrison et al. (2001) proved complementary resources to play a major positive role in strategic alliances. chesbrough (2006) also stresses the importance of complementary resources for open innovation projects. so, with the choice of partners, the focal company determines how far its material and immaterial resources will be complimented by those of the partner(s). however, the level of complementarity alone is not enough. selecting partners with complementary resources only makes sense if the partners are able to understand each other’s knowledge contribution. indeed, park and russo (1996) found that joint ventures that used complementary resources failed more often than what was expected. an explanation for this finding might be found in the work of cohen and levinthal (1990), who point at the importance of absorptive capacity; defined as a company’s capability to recognize the value of new information, its ability to assimilate it and to apply it to commercial ends. absorptive capacity is dependent on information redundancy or as nonaka (1994: 29) states: ‘ only individuals sharing overlapping information can sense what the others are trying to articulate’ . it is therefore expected that when the research domains of the partners and the range and methods used (termed ‘ cognitive distance’ by nooteboom, 2000) are too far apart, the level of absorptive capacity will decrease and with this the synergy potential of the alliance. indeed nooteboom et al. (2007) found that there is an optimum cognitive distance in alliances. 2.2.2 alliance importance next to the distinct material and immaterial resources that partners bring into an alliance, their intentions and motivation to turn the alliance into a success are expected to play an important role. the more partners who expect to benefit from the alliance, the more they are likely to be motivated and willing to invest in it. this factor is termed ‘alliance importance’. it is expected that a high level of importance attached to the alliance will positively correlate with alliance performance. 2.3 alliance execution during the alliance execution phase knowledge is transferred and utilized to develop new products and processes. the following factors were identified as essential elements to characterize the quality of project execution: knowledge transfer, technology mapping, outsourcing, governance mechanisms and alliance compliance. 2.3.1 knowledge transfer according to nooteboom (2000, p. 70) knowledge transfer between the alliance partners in all its complexity is the core of every innovation alliance. knowledge transfer deals with the challenge of two or more companies acting on a chosen task as one entity, as 96 p.j.p. garbade, s.w.f. (onno) omta, f.t.j.m. fortuin a sense making system (weick, 1995; choo, 1998). nonaka (1994) identifies two forms of knowledge: explicit and tacit knowledge, and four ways in which knowledge can be transferred: from explicit to explicit, from explicit to tacit, from tacit to explicit, and from tacit to tacit. where explicit knowledge is stored in codified form and can be transferred through documents, tacit knowledge requires human interaction to transfer i.e. through shared experience (nonaka, 1994). nonaka and van krogh (2009) therefore recommend human resource exchange as an effective way to transfer knowledge because it implies a flow of information comprising of both tacit and explicit knowledge. it allows for all four forms of knowledge transfer by creating a mutual understanding including social practices and enables the transformation of resources from both alliance partners into something new. also mutual support in terms of management, coaching and training is assumed to lead to knowledge transfer between the alliance partners. 2.3.2 alliance compliance low alliance compliance is a factor that can crush the expectations resulting from the alliance potential assessment. issues like trust and cooperation within high tech alliances are found to be positively related to human resource exchange practices (collins and smith, 2006). the level to which partners adhere to the agreements made before the actual start of the innovation alliance (termed ‘alliance compliance’) is expected to play an important role by creating the trust and spirit of cooperation necessary for the smooth execution of the collaborative open innovation project. if there is no compliance because of mistrust due to opportunism, missing coordination of company actions knowledge transfer might be lowered. in a company collaboration context this also means that the process of transforming tacit into explicit knowledge slows down or even stops. 2.3.3 governance mechanisms nooteboom (2000) provides a literature review on the problems in governing knowledge transfer. he lists notion of “hostages”, redistribution of ownership of specific investments, balance of mutual dependence, and reputation mechanisms as possible governance mechanisms to deal with these problems. in this paper, we focus on technology mapping, with mutual dependence, and outsourcing of certain innovation activities which encompasses the (re) distribution of ownership. the notions of hostages and reputation mechanism were not included in the present paper, since no trustworthy quantitative data could be collected, due to socially desired answers. here an observatory approach is suggested for further research. 2.3.4 technology mapping intellectual property (ip) is important in alliances in which the partners work closely together to reach certain innovation aims and objectives. ip management is connected to terms like, ip valuation, ip licensing, ip preparation for sale, detection of infringements, and use of ip intermediate markets (chesbrough, 2006). to secure the ownership of ip after a completed discovery is a big issue and is becoming even more challenging in the world of open innovation, where “technologies flow across the boundary of the firm” (per97exploring the characteristics of innovation alliances haps multiple times) and where “obtaining the ability to practise a technology without incurring an infringement action by another firm is more challenging because the full history of the technology development is well known” (chesbrough, 2006, p. 67). patents are often used to protect knowledge from being stolen, provide a possibility to legally own it and make it tradable. patents also indicate the value as a network partner as shown by the research of smith-doerr et al. (1999) on biotechnology firms. patents reduce the risk of infringement but only if all of the knowledge used in the technology application is included in that patent, or possibly in several patents. so to prevent infringements patent mapping is unavoidable. patent mapping checks for all of the granted claims of a patent that is owned by the company and looks also at possible claims that could arise from other patent holders (chesbrough, 2006). this might lead to efforts to obtain possession of patents that are holding key positions in the innovation process of the company or the alliance. in order to reduce the risk of exploring without being able to exploit, one should think of starting patent mapping already early in the innovation process. this reduces the risk of being left with a discovery at the end of the innovation process that cannot be exploited. in an innovation alliance there is also the possibility that the alliance partners look at each other’s patents in order to investigate their potential. in a study about canadian biotechnology start-ups, baum et al. (2000) found alliances that provide access to more diverse information and capabilities per alliance … will prove most beneficial to startups. so resources exchange in the form of ip may be enhanced by letting the alliance partner having a closer look at the patents in store or at technologies with no patents or no patents granted yet. this is also the reason that we prefer to use the term technology mapping in this paper. we extend the meaning beyond the ip protection aspect and focus also on using it as an alliance internal communication tool, and consequently, also as a governance mechanism to transfer knowledge. by mapping the different technologies used in an alliance in a shared document, explicit knowledge as well as redundancies are created. this will help to understand each other’s knowledge domains (e.g. nooteboom, 2000) and since tacit knowledge is turned into explicit knowledge, alliance coordination is simplified. 2.3.5 outsourcing complementary resources possess the potential to enhance synergy. in cases where knowledge is easy to transfer this process is straight forward. however, in cases where the complexity of matching complementarity resources is high, this could lead to hold up (nooteboom, 2000). the way to synergy creation might then go via direct outsourcing of activities to the alliance partner without much direct contact lowering coordination costs. however outsourcing also demands knowledge transfer in order to determine which activities are to be outsourced and in what way the outsourcing process will be set up. 2.4 alliance performance in industry, performance can be assessed at the innovation process level (innovative performance) and at the industrial outcome level (industrial performance, omta and de leeuw, 1997). since the research process takes place within an alliance we are looking at 98 p.j.p. garbade, s.w.f. (onno) omta, f.t.j.m. fortuin the innovative performance at the alliance level. alliance performance in our paper therefore focuses on the output resulting from the collaboration. next to the direct results in terms of new products and processes, this could be new contacts, a better reputation within a network, or a new line of thinking. all of these outcomes may lead to a higher potential of future alliances with current or other partners and therefore demands a dynamic model. however, for the present paper, the choice was made to look at the alliance at one point in time. therefore the output focus lies on the synergy created and in how far the alliance resulted in new knowledge (inventions) and new products and processes (innovations). 2.4.1 alliance synergy synergy describes a situation where the final outcome of a system is bigger than the sum of its parts. this can be found in an alliance in the form of new knowledge that surmounts the knowledge input that was brought into the alliance from both alliance sides as well as to new processes and technologies resulting from the alliance. 2.4.2 exploration and exploitation performance alliance performance covers exploitation performance and exploration performance, where exploitation is concerned with the refinement and extension of existing technologies (lavie and rosenkopf, 2006) and exploration is rooted in the extensive search for potential new knowledge (march, 1991). the theory leads to the following general hypothesis: innovation alliances that show a higher level of complementarity and overcome cognitive distance with intense knowledge transfer lead to the creation of synergy and ultimately to a higher level of innovation performance. 3. research methods for this study a sample was composed of firms active in the dutch biotechnology sector. eighteen smes participated in the study, reporting about 40 alliances. for the empirical test of the model a two-step approach was chosen. firstly the constructs used in the model were operationalized. then the respondents were given the survey to answer the indicator questions on a likert scale of 1 (“not at all”) to 7 (“to a very large extent”). the detailed items used to measure each construct are listed in table 1. partial least squares (pls) software ( ringle et al., 2005) was used to model the alliance collaboration process and to test the main hypothesis. pls delivers construct scores, i.e. proxies of the constructs, which are measured by one or several indicators (henseler et al., 2009:283). pls is a causal modeling approach, developed by wold in 1975 and applicable in strategic management research (hulland, 1999). pls is similar to regression, but simultaneously models the structural path (i.e. theoretical relationship among constructs) and the measurement path (i.e. relationship between a construct and its indicators, chin et al., 2003, p. 25). the procedure enables the modelling of constructs and gives more accurate estimates of interaction effects between constructs, as it takes the measuring errors in the underlying indicators into account. pls shows the significant effects of the different constructs on each other, while every construct itself is reflected by its indicators (measures). with the help of pls (a series of 99exploring the characteristics of innovation alliances ordinary least squares) the constructs are estimated as linear combinations of its measures, by maximizing the explained variance for the indicators and the constructs. as a result the construct is not only maximally correlated with its own set of indicators, but also with the other constructs, according to the structure of the pls model (chin et al., 2003). although table 1. operationalization of constructs level construct convergent validity cross loading indicator questions (likert scales from 1 to 7 are employed) a lli an ce p ot en tia l level of complementarity 0.94 0.95 0.94 to what extent are there complementary resources in this alliance. the exchange of resources in this alliance is important. cognitive distance 0.89 0.85 0.87 0.86 to what extent does the expertise your company posses differ from your alliance partner? to what extent does the research field your company operates in differ from your alliance partner? to what extent are the patents you posses located in a different research field? alliance importance number of staff of your company involved in the alliance a lli an ce e xe cu tio n knowledge transfer 0.81 0.73 0.81 0.78 this alliance partner supports in management coaching and training. this alliance partner exchanges human resources with you. the exchange of human resources in this alliance is important. alliance compliance 0.85 0.91 0.81 in this alliance opportunism occurred to be a problem. in this alliance coordination occurred to be a problem. (both rescaled to 7= ” not at all “down to 1= ” extremely high amount of ”) governance mechanisms outsourcing 0.83 0.90 0.79 activities outsourced to the partner due to restrictions of company apparatus. activities outsourced to the partner due to restrictions of company skills. technology mapping extent of technology mapping used in this alliance. a lli an ce p er fo rm an ce alliance synergy please give the synergy created due to this alliance. exploration performance due to this cooperation new knowledge was generated. exploitation performance 0.83 0.91 0.78 due to this alliance products were developed, that were new to the market. due to this alliance production processes were created or significantly improved. 100 p.j.p. garbade, s.w.f. (onno) omta, f.t.j.m. fortuin partial least squares (pls) can be used for theory confirmation, it can also be used to suggest where relationships might or might not exist and to suggest propositions for later testing (chin and newsted, 1999:313). marcoulides and saunders (2006) warn researchers not to use pls as a “silver bullet” while hair et al. (2011) specify under which conditions pls is indeed a silver bullet. for the decision to apply pls, the scaling, the number of cases and distribution of the data had to be taken into consideration. in contrast to lisrel, pls can deal with small sample sizes as small as 20, depending on the complexity of the model and the size of the effects to be detected (chin and newsted, 1999), and doesn’t require a normal distribution of the data (chin et al., 2003). with 40 alliances, in the present study the necessary condition that the number of cases at least exceeds the number of indicators (haenlein and kaplan, 2004) was reached. the significances of the interaction effects uncovered with pls were tested with bootstrapping. bootstrapping is a cross-validation method. it is a resampling procedure, which yields the same number of cases as in the original sample. as the bootstrapping is based on trial and error it gives slightly different results every time it is used for the same model, which differ even more from each other, the smaller the number of resamples. the number of resamples was chosen to be 1000 exceeding the 200 indicated as minimum by chatelin et al. (2002). the kolmogorov smirnov z test was used for finding differences between pharmaceutical related and agrifood related alliances and the kruskal wallis exact test was used to identify mean differences between the alliances stated by different companies of different size and location categories. 4. results 4.1 baseline description the hypothesis was tested analyzing 40 alliances of 18 smes in the dutch biotechnology sector. of the companies that answered the questionnaire, 6 have business activities in diagnostics, 6 in therapeutics, 3 in food and neutraceuticals and 6 in plants and seeds biotechnology. the majority of the smes are product oriented, with products such as biomarkers for cancer treatment, but also microbiological products related to food safety and breeding. fourteen smes are located in different clusters or cluster like set-ups, such as incubator centers, university campuses or business parks (see table 2), while 4 companies are not connected to a company agglomeration. the cluster or cluster like set-ups are spread all over the netherlands2. they are frequently headed by a coordinating organiza2 the leiden bioscience park (www.leidenbiosciencepark.nl) around leiden university includes more than 70 member companies, the science park amsterdam (www.scienceparkamsterdam.nl) around the university of amsterdam and the ‘vrije’ university of amsterdam consists of around 70 companies, and science park utrecht (www.utrechtsciencepark.nl) around the university of utrecht, more than 60 companies. in the north of the netherlands we find seed valley: (www.seedvalley.nl) a cluster of 22 breeding companies, located at one of the world’s largest plant breeding areas. the food valley cluster, around wageningen university, is coordinated by food valley organization (www.foodvalley.nl) and has more than 100 member companies. about 30 km to the south is the core of the healthvalley cluster (www.health-valley.nl), which is located around radboud university nijmegen, with around 100 member companies. in eindhoven, located in the south east of the netherlands, lies the high-tech campus of the university eindhoven (www.tue.nl) with 108 companies located on it. further to the east on the german border we find the biopartner center maastricht (www.bpcm.nl) located on the health campus of the university of maastricht with 22 companies. 101exploring the characteristics of innovation alliances tion, and financed by membership fees of the participating companies and/or public money. these coordinating organizations provide network formation, demand articulation, internationalization and innovation process support (omta and fortuin, 2011). they do so by organizing consortium meetings and annual conferences, providing matchmaking opportunities for member companies (network formation support), issuing (web-based) innovation alerts and providing information about marketing trends (demand articulation support), representing the member companies at international fairs and organizing business missions (internationalization support), and stimulating facility sharing and organizing of special workshops to enhance innovation management (innovation process support). the smes organized in science parks or university campuses indicated possessing the highest alliances performance levels. smes not related to bioscience parks or clusters described fewer complementary resources, fewer synergistic effects, and used outsourcing to a lesser extent. furthermore, they allocated a smaller level of relative importance to resource exchange, and especially to human resources exchange. their alliance performance levels were lower compared to the other alliances in our study, although they indicated they faced opportunism problems to a lesser extent. table 2. demographics of smes (n=18) and their alliances (n=40) in the present study. sme alliances bioscience park, university campus* 7 16 clusters** 7 15 not related to bioscience parks or clusters 4 9 18 40 * leiden bioscience park, utrecht science park, amsterdam science park, maastricht biopartner center, eindhoven tu ** food valley, health valley, seed valley eleven companies employ 2 to10 full time employees, while six companies employ 10 to 30 employees, and one company even has 113 employees. mangematin et al. (2003) found differences in their study on 60 french biotech smes between the slightly smaller smes (around 10 employees) with small innovation projects targeting niche markets and the slightly bigger smes (around 30 employees), which focused on radical innovation projects and grew faster than the first type (mangematin et al., 2003). also in our study we found a number of differences related to the size of the company. the companies with 10 to 30 employees indicated a higher number of staff involved per alliance, older alliances, used technology mapping to a lesser extent and stated—probably because of the more radical nature of the technology involved—a lower alliance performance level, compared to the smaller firms and the very big firm. the 40 alliances which are the focus of our analysis are split into 26 health-related alliances in 12 companies, and 14 agrifood related alliances between 6 companies. the most important alliance partners were knowledge institutions (in 40% of the cases), big pharmaceutical and chemical trusts (in 15%), other biotech smes (in 18%), and non-bio102 p.j.p. garbade, s.w.f. (onno) omta, f.t.j.m. fortuin tech firms, such as manufacturers (in the 27% of the cases). the sample is dominated by research consortiums and strategic alliances but also includes two informal partnerships. as primary reasons for strating the alliance, increasing innovation and the use of complementary technologies was mentioned most often, followed by the increased access to funding and markets. thirty of the 40 alliances involved 1 to 3 persons from the companies that answered the questionnaire. in 8 alliances, 3 to 10 persons were involved and in two alliances the number of employees involved was not given. in 30% of the alliances more than 2 companies were involved. the ages of the alliances were equally distributed from 1 to 7 years. there were some differences between the groups of health-related and agrifood-related alliances that were able to be uncovered by using the kolmogorovsmirnov z test. among the health-related alliances, more technology was licensed out to the alliance partner(s), and a smaller cognitive distance characterized these alliances. the agrifood related alliances scored higher when it came to complementary resources and also with regard to the number of new products or processes that were created and improved in the alliance. 4.2 the measurement model hulland (1999) suggested a general methodology for applying pls on management issues. first, the reliability and validity of the measurement model has to be assessed before the structural model can be examined and path coefficients interpreted (hulland 1999: 198). the measurement model consists of the constructs and the indicators connected to them. its reliability and validity is assured by verifying individual item reliability, the convergent validity of measures associated with the individual constructs, and the discriminant validity. to measure individual item reliability, the cross-loadings between the indicators and the constructs were checked. every indicator should, in relation to its construct, have a cross loading higher than 0.7, and indicators to which constructs are not connected should never receive high cross-loadings than those to which they are (hulland, 1999). the cross-loadings of the indicators generated here fulfill these requirements (table 1). to assess the convergent validity of the measurement model a choice can be made between cronbach’s alpha and the composite reliability, as developed by fornell and larcker (1981). nunnally (1978) suggests 0.7 as a benchmark and according to hulland (1999) it can be used as a cut-off point for both measures. as cronbach’s alpha tends to underestimate the internal consistency in pls path models (hensler et al., 2009), the composite reliability was used to measure the convergent validity of the constructs in this paper. these were all above 0.7 (table 1). the traditional methodological complement to convergent validity is discriminant validity, which represents the extent to which measures of a construct differ from measures of other constructs in the same model (hulland 1999:199). by making use of the variance the construct shares with its indicators, compared to the variance it shares with the other constructs, the discriminant validity can be assessed by using the ave (i.e., the average variance shared between a construct and its measures). the square root of the ave should be higher than the construct correlations. table 3 shows that this requirement is also met. 103exploring the characteristics of innovation alliances table 3. construct correlations   1 2 3 4 5 6 7 8 9 10 sqrt ave ave 1 alliance compliance 1 0.86 0.74 2 alliance importance -0.20 1 1 1 3 cognitive distance -0.03 -0.11 1 0.86 0.74 4 exploitation performance -0.21 0 -0.11 1 0.84 0.71 5 exploration performance -0.13 0.19 0.28 0.25 1 1 1 6 knowledge transfer 0.15 0.37 0.26 -0.18 0.56 1 0.77 0.59 7 level of complementarity -0.36 0.1 0.07 0.23 0.48 0.29 1 0.94 0.89 8 outsourcing -0.13 0.22 -0.12 0.31 0.38 0.41 0.22 1 0.85 0.72 9 alliance synergy -0.23 -0.14 0.06 0.48 0.56 0.02 0.68 0.34 1 1 1 10 technology mapping -0.32 0.07 -0.53 0.51 -0.08 -0.37 0.19 0.14 0.29 1 1 1 sqrt ave 0.86 1 0.86 0.84 1 0.77 0.94 0.85 1 1   4.3 the structural model to what extent the path coefficient can be trusted depends on the significance level, verified by the t-values (huber et al., 2007) that are generated with the bootstrapping procedure. for our model all path coefficients are significant at least at an α = 0.05 level. the significance of estimated coefficients in the structural model can be seen in the t-values of table 4. the extent to which the endogenous constructs are explained by the exogenous constructs in the model can be determined on the basis of the r² values (displayed below in the text along with the equations derived from the model), wherein r² values of 0.67, 0.33, and 0.19 (with regard to pls path models) are seen as substantial, moderate, and weak, respectively (chin 1998). because the model was specified based on our hypotheses before the data were collected, the sample data were used to test the hypothesis only, and not to determine the structure of the model itself. consequently there was no need for any further model validation (kumar 2010). the following equations can be derived from the model: knowledge transfer[r²=0.29] = 0.31*cognitive distance + 0.46*alliance importance + e1 alliance synergy[r²=0.50] = 0.64*level of complementarity + 0.20* outsourcing + e2 technology mapping[r²=0.41] = -0.46*cognitive distance -0.28*alliance compliance -0.21*knowledge transfer + e3 alliance compliance[r²=0.13] = 0.36*level of complementarity + e4 104 p.j.p. garbade, s.w.f. (onno) omta, f.t.j.m. fortuin table 4. significance of the estimated coefficients in the structural model   original predicted path coefficients sample mean (bootstrap) standard deviation (stdev) t-values alliance compliance -> knowledge transfer 0,23 0,21 0,11 2,16* alliance compliance -> technology mapping -0,28 -0,3 0,13 2,18* alliance importance -> knowledge transfer 0,46 0,45 0,15 3,14** cognitive distance -> knowledge transfer 0,31 0,33 0,12 2,49** cognitive distance -> technology mapping -0,46 -0,46 0,15 3,15** knowledge transfer -> exploration performance 0,54 0,55 0,1 5,43** knowledge transfer -> outsourcing 0,41 0,43 0,14 2,89** knowledge transfer -> technology mapping -0,21 -0,19 0,12 1,78* level of complementarity -> alliance compliance -0,36 -0,38 0,1 3,5** level of complementarity -> alliance synergy 0,64 0,63 0,11 5,97** outsourcing -> alliance synergy 0,2 0,21 0,12 1,75* alliance synergy -> exploitation performance 0,37 0,36 0,15 2,47** alliance synergy -> exploration performance 0,55 0,55 0,08 6,72** technology mapping -> exploitation performance 0,4 0,42 0,11 3,71** *= significant with a likelihood of mistake of 5 percent one tailed **= significant with a likelihood of mistake of 1 percent one tailed figure 2. pls model for testing hypotheses with path coefficients 1 2 cognitive distance alliance potential alliance execution technology mapping outsourcing -0.36 0.64 0.46 0.31 -0.21 0.41 0.40 0.20 0.55 0.37 exploration performance exploitation performance level of complementarity alliance importance knowledge transfer alliance synergy -0.28 0.23 alliance compliance -0.46 0.54 alliance performance 105exploring the characteristics of innovation alliances outsourcing[r²=0.17] = 0.41*knowledge transfer + e5 exploration performance[r²=0.61] = 0.54* knowledge transfer + 0.55*alliance synergy + e7 exploitation performance[r²=0.38]= 0.40*technology mapping + 0.37*alliance synergy + e8 the hypothesis, innovation alliances that show a higher level of complementarity and overcome cognitive distance with intense knowledge transfer lead to the creation of synergy and ultimately to a higher level of innovation performance, holds true as the significant path coefficients in the smart pls model indicate. the central role of knowledge transfer predicated upon human resource exchange becomes visible through the model. the positive path coefficient from cognitive distance leading to knowledge transfer and from knowledge transfer directly to exploration performance underscores that human resource exchange is the primary way of exchanging and converting both explicit and tacit knowledge. what is further interesting to note is the significantly negative path coefficient from the level of complementarity to alliance compliance, which seems counterintuitive at first glance. however, if it is assumed that the value of the complementary resources of each alliance partner is not equally spread among the alliance partners and if unexpected high returns might turn up, one of the partners might decide to harvest them alone, leading to opportunism and a lower alliance compliance. this explanation is supported by the fact that a moderately negative correlation of -0.30* was found from synergy created in the alliance to no problems of opportunism in the alliance. other negative path coefficients are found from alliance compliance to technology mapping, from cognitive distance to technology mapping, and from knowledge transfer to technology mapping. this effectively translates into: in cases of low alliance compliance, a small cognitive distance and a low level of knowledge transfer, a higher use of technology mapping is also found. to explain the interdependence of these constructs, we can elaborate the story which begins from a higher level of complementarity and results in a lower level of alliance compliance. to avoid opportunistic behavior, there is a clear need to put the technological contribution from each partner on paper, using technology mapping in order to create a common shared vision of this. the same holds in case of a smaller cognitive distance where it is even more likely that the partners doubt who is contributing what, and where concerns over becoming deprived of similar ip is higher than in alliances with a higher cognitive distance, i.e., where the knowledge/ip contribution is clearer from the beginning. this conclusion was given further support by an interview with an r&d manager of a high-tech sme who indicated that technology and patent mapping was used only in interaction case of alliance partners with close cognitive distance. in cases of a low level of knowledge transfer the higher level of technology mapping can instead therefore be seen as a communication tool. the positive link between knowledge transfer and outsourcing suggests that there is a need to transfer knowledge first in order to determine an innovation locus based on the core competencies and facilities of the companies. however, no significant positive link was found from technology mapping to outsourcing, which underlines the finding that the potential of technology mapping as an alliance communication tool is often not exploited to full extent. when it comes to alliance performance, 106 p.j.p. garbade, s.w.f. (onno) omta, f.t.j.m. fortuin the direct link from knowledge transfer to exploration performance stands in contrast to exploitation performance, where knowledge transfer—via outsourcing—leads to alliance synergy, or—via technology mapping—to exploitation performance. 5. conclusions and discussion the objective of the present study was to explore the attributes of alliances in the biotech industry in order to derive recommendations for how to improve policy support for smes in the open innovation process. it was hypothesized that innovation alliances that show a higher level of complementarity and deal with cognitive distance by intensive knowledge transfer would show greater synergy creation, and ultimately a higher level of innovation performance. our empirical findings in the dutch biotech sector support this hypothesis. our research also supports earlier findings that alliances allow for access to complementary resources (ireland and hitt, 1999) and that alliance companies often get close enough to each other in order to acquire tacit knowledge (lane and lubatkin, 1998). furthermore it was shown that, depending on the alliance potential, a differentiated alliance execution process employing different governance mechanisms is required to achieve synergy and, in the end, a higher level of exploration and/or exploitation performance. as hypothesized, both explicit and tacit knowledge transfer were demonstrated to be of central importance in the alliance execution phase. the level of knowledge exchange also determined which governance mechanisms were selected for the alliance. knowledge transfer has to be based on the exchange of human resources and an open information flow in order to allow the exchange and conversion of tacit knowledge. we therefore conclude that mechanisms to enhance knowledge transfer, such as human resources exchange, deserve special attention in innovation alliances. the level of knowledge transfer was found to suffer from a lack of clarity about the division of the alliance outcomes among the partners. this indicates that, first of all, it is of crucial importance to make good contractual arrangements prior to alliance execution (tepic, 2012). a collaboration support tool, such as technology mapping, can then be used to create a common view on ip division among the partners and prevent the problem described by shan (1990), i.e., both parties claiming ownership of the generated alliance output. up front clarity about future ip division is also of crucial importance in order to secure investors. shan and song (1997) found that the number of patents acquired was one of the principal triggers for obtaining foreign equity investors. in the agricultural biotechnology sector there are fewer alliances and partnerships relative to the health biotech sector (see bagchi-sen et al., 2011). bagchi-sen et al. (2011) have explained this finding on the basis of the structure of the agrifood biotech industry, in which few big companies state a permanent threat of take-over that prevents smaller biotech firms from entering into alliances with them; as one of the ceos of an agricultural biotech sme in our sample stated, she would rather cooperate with other small companies, research institutes and universities, or with a client, but not with the big competitors next door. such a situation calls for a further stimulation of the open innovation process itself, by creating the trust necessary to enter into cooperations by providing clarity on the ip situation not only when entering the alliance but also during the alliance process itself. we therefore recommend policy makers to support innovation alliances by provid107exploring the characteristics of innovation alliances ing an infrastructure in which alliances can flourish. one way of doing this is by stimulating the formation of cluster organizations that can function as innovation brokers that provide network formation-, demand articulation-, internationalizationand innovation process-support to their member companies (see also the baseline description in omta and fortuin, 2011); such organizations could also act as a go-between among alliance partners (nooteboom, 1999a,b). as part of the innovation process support activities, they can organize special workshops for biotech smes on how to behave in an innovation alliance successfully. in such workshops open innovation support tools, such as contractual arrangements and technology and ip mapping can be presented. enhanced use of these collaborative support tools may also reduce the fear among many biotech smes of stepping into an alliance, because a lack of clarity regarding the future ownership of upcoming results often destroys the necessary compliance level needed to bring the collaboration successfully to a close. acknowledgments the authors would like to thank the companies that contributed to the research. we are also grateful for the suggestions of two anonymous reviewers. part of the study received funding from the european union seventh framework programme (fp7/20072013) under grant agreement n° 245301 netgrow“enhancing the innovativeness of food smes through the management of strategic network behavior and network learning performance”. references ahuja, g. 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(1996). star scientists and institutional transformation: patterns of invention and innovation in the formation of the biotechnology industry. proceedings of the national academy of sciences 93: 12709-12716. bio-based and applied economics 8(1): 3-19, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8144 how did farmers act? ex-post validation of linear and positive mathematical programming approaches for farmlevel models implemented in an agent-based agricultural sector model gabriele mack*, ali ferjani, anke möhring, albert von ow, stefan mann agroscope, socioeconomics research group, 8356 ettenhausen, switzerland abstract. this study evaluates linear programming (lp) and positive mathematical programming (pmp) approaches for 3,400 farm-level models implemented in the swissland agent-based agricultural sector model. to overcome limitations of pmp regarding the modelling of investment decisions, we further investigated whether the forecasting performance of farm-level models could be improved by applying lp to animal production activities only, where investment in new sectors plays a major role, while applying pmp to crop production activities. the database used is the swiss farm accountancy data network. ex-post evaluation was performed for the period from 2005 to 2012, with the 2003-2005 three-year average as a base year. we found that pmp applied to crop production activities improves the forecasting performance of farm-level models compared to lp. combining pmp for crop production activities with lp for modelling investment decisions in new livestock sectors improves the forecasting performance compared to pmp for both crop and animal production activities, especially in the medium and long term. for short-term forecasts, pmp for all production activities and pmp combined with lp for animal production activities produce similar results. keywords. agent-based sector model, farm-level model, linear programming, positive mathematical programming, ex-post validation. jel codes. c61, q18, q19. 1. introduction agricultural policy models apply either linear programming (lp) or positive mathematical programming (pmp) approaches to analyse the impact of policy changes. the main advantages of pmp models over conventional lp models are that they guarantee exact calibration to the base year and avoid predicting overspecialisation without adding weakly justified constraints to the model formulation (kanellopoulos et al., 2010). further *corresponding author: gabriele.mack@agroscope.admin.ch 4 g. mack et alii advantages of pmp models are that they do not require large datasets and can be viewed as a bridge between econometric models, with substantial data requirements, and more limited lp models (heckelei and britz, 2005; howitt et al., 2012). studies evaluating the practice of pmp more than 15 years after howitt published the first paper on this subject in 1995 show that pmp has become very popular in aggregated policy-decision support models (garnache et al., 2015; heckelei et al., 2012). the popularity of pmp is underscored by the fact that the majority of both european and non-european aggregated sector models1 have used it for the calibration of crop and animal production since 2000. however, pmp is much less popular in farm-level models. one reason for the limited use of pmp in this context is that farm-level models generally only take into account the activities observed during the reference period, even though new policies and market conditions allow farmers to undertake new production activities. to date, only a few farmlevel models have used pmp to calibrate the crop activities of arable farms (iglesias et al., 2008; kanellopoulos et al., 2010) or both animal and crop production activities of dairyfarm models (buysse et al., 2007, louhichi et al., 2010). iglesias et al. (2008) extended the pmp approach by incorporating new irrigation technologies for crop production activities in farm-level models using pmp. farm-level models implemented in agent-based models, which use mathematical programming methods to determine the production decisions of the farm agents (happe, 2004; röder and kantelhardt, 2009; lobianco and esposti, 2010; schreinemachers et al., 2011), also prefer an lp approach over pmp. to our knowledge, there have been, to this point, no farm-level models implemented in agent-based models which use pmp. the aim of this study is to assess the best mathematical programming approach for farm-level models implemented in the swissland2 agent-based agricultural sector model on an empirical basis, i.e. going beyond theoretical considerations. we analysed the forecasting performance of a linear optimisation approach compared to a pmp approach. because there is no single pmp approach in practice, but several different mathematical versions of pmp which all influence the forecasting performance of farm-level models, this study reviewed the most frequently used approaches for application in single farm models. to overcome limitations of the pmp approach regarding the modelling of investment decisions, we further investigated whether the forecasting performance of farmlevel models could be improved by applying lp only to those production activities where investment in new sectors plays a major role. this is why we also validated an approach which combines pmp for crop production activities and lp for animal production activities. the ex-post evaluation was carried out for the period from 2005 to 2012, with the 2003-2005 three-year average as a base year. over this period, swiss agricultural policy changed decisively, particularly for milk and meat production. to cite an example, swit1 examples of pmp-based, aggregated models representing either farm-type groups or whole regions are the german farmis model (offermann et al., 2005), the italian fipim model (arfini et al., 2011), the spanish promapa model (júdez et al., 2008), the european capri-farm model (gocht and britz, 2011), the swiss silas model (mann et al., 2003), the german-austrian glowa-danubia decision-support system model (winter, 2005), the european capri-reg model (britz and witzke, 2014), the dutch dram model (helming, 2005), the usda reap model (johansson et al., 2007), the california swap model (howitt et al., 2012) and the new zealand model nzfarm (daigneault et al., 2014). 2 swissland’ is the german acronym for ‘structural change information system switzerland. 5how did farmers act? zerland concluded a free-trade agreement for cheese with the eu in 2007. the same year saw the country’s gradual withdrawal from the milk quota system (flury et al., 2005), as well as the introduction of direct payments for dairy cows. section 2 of this paper gives a brief overview of an lp approach for single farm optimisation models and describes the most relevant pmp versions considered for the evaluation. section 3 gives an overview of the swissland agent-based sector model and describes the different pmp and lp modelling options tested for the 3,400 single farm models implemented in the swissland model for the ex-post period from 2005 to 2012. by drawing a comparison with the historical pathway, section 4 illustrates the forecasting performance of the single farm models at the farm and sectoral scales, and section 5 provides conclusions as to how pmp and lp could be used in farm-based modelling. 2. overview of lp and pmp approaches mathematical programming has been used in agricultural economics for more than fifty years. mathematical programming starts from a decision rule of the decision maker, which determines the levels of the different variables when aiming to optimise the objective set by the decision maker (hazell and norton, 1986). mathematical programming applied to farm models maximises the farm profit. max z =∑ipixi – cixi (1a) subject to: ∑iawixi ≤ bw and xi ≥ 0 (1b) in equation 1a, parameter z denotes the farm profit to be maximised, p is the vector of product prices, c is the vector of variable costs, x is the vector of production levels and i is the index for the production activities. the optimal solution must fulfil the constraints in equation 1b, where bw is the available quantity of resource endowments w, and a is the demand of resource endowments of one unit of x. mathematical programming models assuming constant marginal costs in the objective function became generally known as lp models. a main disadvantage of lp models is a tendency to overspecialise in crop production (howitt, 1995). this was the main reason why howitt (1995) developed models based on the pmp technique. howitt et al. (2012; 245) describe pmp as a ‘deductive approach to simulating the effects of policy changes on cropping patterns at the extensive and intensive margins. the term positive implies the use of observed data as part of the model calibration process’. pmp models use information contained in shadow values of an lp model which is bound to observed activity levels by calibration constraints (step 1). based on these shadow values, a non-linear objective function is specified such that observed activity levels are reproduced by the optimal solution of the new programming problem without bounds (step 2). many pmp-models use a quadratic, decreasing marginal gross margin function (equation 2) that assumes increasing marginal costs in the objective function, whilst returns to scale remain constant. this functional form was proposed by howitt (1995) because of increasing variable costs per unit of production due to inadequate machinery 6 g. mack et alii and management capacity, and due to decreasing yields related to land heterogeneity. max z = ∑ipixi – dixi – 1/2 xiqiixi (2) q revenue xii ii i = ∗ 1 ρ * * (3) di = ci – λi – qiixi* (4) in equation 2, parameter di denotes the vector of the linear term for crop and animal production activity i of the quadratic objective function, whilst qii denotes the symmetric, positive (semi-) definite matrix of the quadratic cost term. most pmp models estimate the matrix coefficients qii and di of the quadratic cost terms based on exogenous supply elasticities ρii from the literature, according to equation 3. in equation 3, the parameter revenue* denotes the observed revenues from product sales in the base year and parameter xi* denotes the production levels of the base year. to determine the coefficients di and qii (equations 3 and 4), the shadow values λi of the calibration constraints for both marginal and preferential activities need to be recovered from the primal lp model described in equation 1. in ‘standard’ pmp the cost functions are estimated for each production activity xi separately, whilst röhm et al. (2003) consider the elasticity of substitution among interrelated crops. because, in ‘standard’ pmp, increasing marginal costs are only assumed for preferential activities whilst constant costs are applied for marginal activities, pmp has often been criticised for its arbitrary assumptions (howitt et al., 2012; kanellopoulos et al., 2010). thus, two pmp versions (howitt et al., 2012) have been developed to overcome these limitations. the first pmp version, the ‘extended pmp variant’, was published by kanellopoulos et al. (2010). it solves this problem by estimating a q matrix for either marginal or preferential activities by using exogenous land rents β in the linear objective function for the available area y according to equation 5: max z =∑ipixi – cixi – β * y (5) another variant of pmp was proposed by paris and howitt (1998). this variant estimates the resource and calibration constraint shadow values based on maximum entropy (me). 3. methods and database 3.1 overview of the agent-based sector model swissland the agent-based swissland model depicts 3,400 farms from the swiss farm accountancy data network [fadn] data pool as realistically as possible in terms of their operational and cost structures, as well as their social behaviour, as a representative sample of the estimated 50,000 family farms in switzerland. the key objects of the model are agents representing fadn farms. for each farm, we model production and investment decisions, farm takeover and farm exit decisions, as well as lease decisions for land plots and inter7how did farmers act? action among agents on the land market (table 1, categorised according to an [2012]). table 1 also lists the various data sources and the methods we use for modelling the decision-making of the agents. for the modelling of lease decisions, a spatial structure of representative reference municipalities was implemented in the model (mack et al., 2013). this allows the farms to interact on the land market. these interactions are only possible within the lease regions and with (constructed) neighbouring agents, however. a lease algorithm enables the plotby-plot allocation of exiting farms’ land to the remaining farms operating in the immediate vicinity. a plot-by-plot bidding process models which neighbouring agent receives the freed-up land and at what lease price. the neighbouring agent achieving the highest expected increase in income with the lease of the plot receives the lease plot. exiting farms are those where the farm manager is not passing on the farm to a successor, or where the potential successor decides against farm takeover on economic grounds. two income parameters, (1) household income per farm and (2) agricultural income per total labour input, were selected to model farm takeover decisions. income criteria to model farm exits and farm entries were derived from the regional income levels in the previous period from 2005 to 2012. a detailed description of the different modules of the swissland model can be found in möhring et al. (2016). because this paper focuses on the modelling of production and investment decisions, we present this issue in detail in section 3.2. the model simulates a forecast period of up to thirty calendar years, corresponding more or less to a generational cycle of the farming family. the adaptive reactions of the individual agents and their behaviour when interacting with other agents are depicted in annual steps. table 1. behavioural and decision submodels included in the swissland agent-based sector model and data collection sources. submodels behaviour data collection decision model sa m pl e su rv ey (f a d n ) sa m pl e su rv ey (r ep re se nt at iv e) c en su s d at a g is d at a ba ye sia n ne tw or k m ic ro ec on om ic h eu ris tic r ul eba se d sp ac e th eo ry -b as ed in st itu tio nba se d pr ef er en ce -b as ed h yp ot he tic al r ul es agent rational decision module production decisions x x farm manager’s life cycle farm takeover, farm exit x x x land market lease decisions for land plots x x x x x x growth and investment investment decisions x x strategy for shifts in labour input x x x x x 8 g. mack et alii swissland calculates sectoral output indicators via an extrapolation algorithm. zimmermann et al. (2015) have compared various extrapolation alternatives for the model. product quantities and prices, land-use and labour trends, income trends according to the economic accounts for agriculture, sectoral input and output factors for calculating environmental impacts, and key structural figures, such as number of farms, size and type of farm or number of farms changing their farming system, are all sectoral output indicators. 3.2 options for modelling production and investment decisions rational agent behaviour is taken as an important basic assumption of the model. hence, each agent maximises its annual household income for each time period t (equation 6). in keeping with the theory of adaptive expectations, the agents (a) make their production decisions based on price (p) and yield (ε) expectations from the previous year (t-1) for the various animal (l) and crop production (g) activities. prices and yields were estimated for each agent on an individual-farm basis using the fadn data for the base year, with the observed price trends and average annual yield changes (∆) resulting from 2000 to 2012 being stipulated exogenously for each time period. household income results from the sale of agricultural products originating from land use (land g) and livestock farming (animal l), from off-farm work (offfarm o), and from the proceeds of direct payments (payment d) less the means-of-production costs (costfunction). the level of direct payments corresponds to the year-specific, production-dependent and production-independent approaches in each case, in accordance with current agricultural-policy provisions. because this study tests various linear and pmp-based quadratic cost functions for crop and animal production activities, the cost functions are described in detail in the equations 7-12 below. max incomea,t = ∑gpa,g * ∆pt-1,g * εa,g * ∆εt-1,g * landa,t,g + ∑lpa,l * ∆pt-1,l * εa,l * ∆εt-1,l * animala,t,l + ∑opa,o * ∆pt-1,o * offfarma,t,o + ∑dpd,a * ∆pt,d * paymenta,t,d – ostfunctiona,t subject to ∑gωa,g,w * landa,t,g ≤ areaa,t ∑lωa,l,w * animala,t,l ≤ placesa,t ∑fωa,f,w * laboura,t,f * landa,t,g + laboura,t,f * animala,t,l ≤ labourcapa,t (6) the resource endowment (ω) of a farm consists of the available area (area), animal places on the farm (places), other capacities limiting animal and crop production (e.g. sugar beet quota, milk quota up to 2007, provisions on the receipt of direct payments), and labour force (labourcap). the use of individual-farm fadn data ensures that various factors influencing the objective-function and production-coefficient matrix are automatically taken into account, 9how did farmers act? allowing the depiction of numerous management options that are typical for switzerland. the cost and output parameters of the production activities are therefore heterogeneous and influence the agents’ decision-making scope. five different options for modelling animal and crop production decisions were analysed in this study (table 2). option 1 determines both crop and animal production decisions based on linear cost functions for 17 crops and 8 animal production activities according to equation 7: max incomea,t = revenuea,t – ∑lcl,a * ∆ct-1,l * animala,t,l – ∑gcg,a * ∆ct-1,g * landa,t,g (7) option 1 does not calibrate the production activities to base-year levels. it takes into account the uptake of crop production activities which were not observed in the base year, but which occur in the farm’s historic crop mix. for animal production activities, it considers the adoption of new production sectors. for modelling new production activities, which were not observed in the base-year, missing information must be added with the help of average values for other farms, or extrapolated using standard data. for all agent activities occurring in the production programme of the forecast years rather than in the base year, the yield and price coefficients are estimated with the aid of a random distribution based on the means and standard deviations of the values for all agents from the same region and farm type (see möhring et al. [2016]). options 2a and 2b apply linear cost functions for animal production activities only, while pmp-based quadratic cost functions are used to determine crop production decisions (equation 8). these options consider only crop production activities which were observed in the base year, whereas, for animal production activities, investment activities in new production sectors are taken into account. max incomea,t = revenuea,t – ∑gcg,a * ∆ct-1,g * landa,t,g – ∑gda,g * landa,t,g – 0.5 ∑gqa,g * land2 a,t,g – ∑lcl,a * ∆ct-1,l * animala,t,l (8) option 2a estimates the matrix coefficients q of the non-linear cost term based on base-year revenues (revenue*) and base-year crop production levels (land*), and uses supply elasticities equal to one owing to the lack of empirical data (equation 9). q revenue landg a g a g a , * * , , = (9) for those production activities where the output is used on the farm itself, is calculated based on linear costs and shadow values according to the german farm type model farmis (schader, 2009): qg,a = (cg,a + λg,a) / land*g,a (10) the linear term d of the quadratic cost function is calculated according to equation 11. dg,a = λg,a – qg,a land*g,a (11) 10 g. mack et alii option 2b estimates the matrix coefficients of the quadratic cost functions for crop production activities on the basis of maximum entropy. the maximum entropy technique in combination with the pmp calibration allows us to recover a quadratic activity variable cost function accommodating complementarity and substitution relations between activities. to estimate the parameter vector dg,a and the matrix qg,a of the variable cost support points for the parameters were defined. as a starting point, the linear parameters dg,a could be centred around the observed accounting cost per unit of the activity. for example, the two unknown parameters are specified as an additive function of a number of support points. we could choose five support points zd (d1,..d5) and zq (zq1,.. zq5) for parameter dg,a and the matrix qg,a. the entropy problem is maximised using support-points consisting of a zd vector and a zq matrix. because no cross cost effects are expected between crop and animal activities, the linear vector d of the quadratic activity cost function is partitioned into one vector which includes the crop activities and a second vector which includes the animal activities. similarly, the quadratic matrix q is partitioned into one matrix which includes the crop activities and a second matrix which includes the animal activities. both pmp approaches guarantee exact calibration of supply decisions at farm and aggregated levels, taking into account the trade of factors among farms. nevertheless, different approaches can produce different results when used to predict the future behaviour of the farmer. options 2a and 2b combine the advantages of both pmp and lp modelling, with pmp calibrating crop production activities to observed base-year levels taking into account the pedoclimatic conditions of the individual farms, and lp enabling modelling of the adoption of new animal production sectors. in all models with a linear cost function in animal husbandry, agents can invest in new barns, allowing them to expand their herd size considerably even within a specific time period, provided that all other necessary resources are available in sufficient quantity. moreover, switching to new production activities is easily possible in the animal husbandry sector. in order to avoid an objective function with an integer formulation, however, individual barn construction variants (previously selected and evaluated according to plausibility) are tested iteratively with the aid of the loop process for each agent entitled to investment. here, the annual external costs of the entire building (depreciation, repair, insurance and interest) are taken into account, irrespective of whether the barn can be fully utilised. if the agent is entitled to receive investment credits or investment aid, these lower the interest charges. ultimately, the variant with the highest positive objective-function value is implemented. in the following year, all animal places resulting from the investment in the barn are available to the farmer. in this case, further use of the old barn is ruled out. investment activities in new animal sectors are taken into account when a farm successor takes over from his predecessor. only for older agents it was assumed that investment was primarily in the animal sectors pursued to date. options 3a and 3b test pmp-based quadratic production-cost functions for both animal and crop production activities (equation 12): max incomea,t = revenuea,t – ∑gcg,a * ∆ct-1,g * landa,t,g – ∑gda,g * landa,t,g – 0.5 ∑gqa,g * land2 a,t,g – ∑lcl,a * ∆ct-1,l * animala,t,l – ∑lda,l * animala,t,l – 0.5 ∑lqa,l * animal2 a,t,l (12) 11how did farmers act? because investments in new barns radically alter the cost structure, the pmp-based cost function completely changes the function values derived in the base year. since no methods were previously available to estimate the change in the pmp-based cost functions derived from the base year, a continuous model approach in which the agents continuously expand their barns by individual animal places was chosen for options 3a and 3b. table 2. modelling options for determining production and investment decisions in the farm-level models of the swissland agent-based sector model. option no name cost function for crop production activities cost function for animal production activities pmp calibration method estimate of matrix coefficients of quadratic cost function investments 1 linear linear linear investment activities for new buildings 2a linear-quadrevenues pmp-based quadratic linear extended revenues investment activities for new buildings 2b linear-quadentropy pmp-based quadratic linear extended maximum entropy investment activities for new buildings 3a quadrevenues pmp-based quadratic pmp-based quadratic extended revenues continuous investment costs for buildings 3b quadentropy pmp-based quadratic pmp-based quadratic extended maximum entropy continuous investment costs for buildings pmp: positive mathematical programming 3.3 assessing forecasting performance in this study, we assess the forecasting performance of the options based on the average forecasting error (afe) measuring the difference between forecasted and historical parameters at the farm and sectoral scales. the farm-scale parameters assess the forecasting performance only of those agents who remained in the sample for the entire simulation period (2005 to 2012). in contrast, sectoral parameters represent changes in the total swiss farm population over the period from 2005 to 2012 and take into account the farm sample changes due to farm exits and entries. therefore, the simulation results from all agents were extrapolated to the sectoral scale based on zimmermann et al. (2015). at the farm scale, the afe measures the percentage difference between historical and forecasted average production levels for each activity. the weighted average forecasting error (wafe) of crops aggregates the afe of all crops based on average production share in the fadn farm sample. the wafe is calculated analogously for animals. finally, the total weighted average annual forecasting error (twafe) aggregates the wafe for crops 12 g. mack et alii and animals equally. at the farm scale, average crop and animal production levels from all fadn farms over a period of three years represent historical parameters. at the sectoral scale, we calculate the production changes from 2003-2005 and 20102012 in percent. the forecasting error measures the deviation from historical values. at sectoral scale, historical values are based on production changes in the total swiss farm population over this period. 4. results the swissland results were obtained for each specification rule of the cost function. table 3 presents the historical average production levels of the corresponding fadnfarms and the afe for crop and animal production activities in the short and long term. linear cost functions for both crop and animal production activities (option 1) lead at farm scale to the wafe of almost 50% for crops and to the twafe for both animal and crop production in both time periods (table 3). the results in table 3 also show that crop activities supported by direct payments, such as extensive grassland, fallow land, oilseed rape, soya and sunflower, are highly overestimated in the linear version (option 1), whilst pmp for crop production activities significantly reduces the afe in both time periods. in the short term, the approaches with quadratic production costs for crop activities and linear production costs for animal activities (options 2a and 2b) show, on average, the same wafe as options 3a and 3b with quadratic production costs for both animal and crop production activities. however, in the long term, options 2a and 2b show better forecasting performance than options 3a and 3b. the forecasting performance of options 2a and 2b improves, in particular, for the livestock categories of cattle, dairy cows, suckler cows, horses and hens, which showed above-average production increases from 2005 to 2012 due to investment activities. furthermore, the afe of fodder and grassland activities decreases in options 2a and 2b because these activities are highly influenced by the cattle production level. only for marginal animal activities, such as sheep and goats, which are underrepresented in the swiss fadn farm sample, is the afe higher in the linear version than in the pmp variants. for crop activities as a whole, the entropy versions and the revenue versions lead to similar results in the short and long term. the results also show that both pmp variants (based on revenues or entropy) do not influence forecasting performance where pmp is combined with lp. where pmp is used for both production categories, the entropy method leads to slightly better forecasting performance in the long term. table 4 shows that all model options using pmp (options 2a to 3b) reproduce the observed farm exits in the long term much better than the linear version (option 1), which significantly underestimates farm exits. because high farm income reduces the probability of a farm exit, these results indicate that the linear version (option 1) significantly overestimates farm specialisation and farm income. comparing the extrapolated production changes of all agents with the historical production changes in the agricultural sector shows that the options with linear cost functions for animals (options 2a and 2b) lead to better results in the long term, particularly in the sectors where the highest production increases were previously observed, such as suckler cows, hens, horses, goats and poultry. in these animal sectors, above-average investments in new housing, which overcompensate for the reduced production owing to farm exits, were observed in the past. 13how did farmers act? table 3. shortand long-term results at farm scale: historical crop and animal production levels of all swiss fadn farms and forecasting errors of the modelling options. historical parameters average production levels of all fadn farms average forecasting error of modelling options [afe in %] 2003 2005 2006 2008 2010 2012 no 1 linear§ no 2a linearquadrevenues‡ no 2b linearquadentropy† no 3a quad revenues¶ no 3b quad entropy¤ base year s l s l s l s l s l s l unit (ha) (ha) (ha) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) bread grain 1.39 1.40 1.46 50 52 10 13 8 12 8 10 6 10 feed grain 1.07 1.13 0.92 84 80 14 5 14 6 12 12 9 12 grain maize 0.22 0.20 0.21 28 21 8 2 8 2 4 6 11 4 silage maize 0.88 0.91 1.00 9 17 7 3 7 3 2 6 3 6 sugar beet 0.30 0.32 0.32 11 12 3 4 3 4 2 3 3 3 potatoes 0.30 0.27 0.25 299 326 2 4 2 5 13 8 3 9 oilseed rape 0.21 0.23 0.30 237 162 14 34 14 33 12 33 12 32 sunflower 0.04 0.05 0.04 321 444 11 15 11 15 15 15 11 15 legumes 0.07 0.08 0.05 130 236 19 18 18 19 12 24 15 24 vegetables 0.09 0.10 0.11 237 224 13 16 13 16 11 15 12 15 fallow land 0.04 0.05 0.03 73 148 13 25 15 23 2 29 14 24 temporary grassland 2.86 2.92 3.34 10 22 5 8 3 10 1 11 0 13 extensive grassland 1.30 1.30 1.31 68 64 1 1 3 5 20 1 3 5 less-intens. grassland 0.69 0.68 0.65 8 13 16 21 17 23 2 25 18 24 intensive grassland 8.66 8.79 8.92 9 11 1 2 0 2 14 3 1 2 extensive pastures 0.21 0.25 0.25 20 21 11 13 7 9 3 16 8 9 intensive pastures 1.77 1.78 1.69 2 3 2 3 5 0 3 2 6 1 unit (lu) (lu) (lu) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) livestock (total) 26.98 27.65 29.83 10 13 5 5 4 6 5 11 5 13 cattle (total) 21.60 22.07 24.00 13 16 7 8 6 8 7 14 8 14 dairy cows 14.80 14.97 16.21 11 15 6 6 3 6 4 11 4 11 suckler cows 1.28 1.57 1.86 15 5 1 2 7 5 22 34 22 35 horses 0.19 0.22 0.20 16 2 1 11 10 2 6 27 4 34 sheep 0.21 0.22 0.22 14 33 29 33 14 30 4 7 3 8 goats 0.05 0.05 0.06 8 6 11 10 7 11 5 23 2 23 sows 3.98 4.14 4.08 5 7 9 10 7 10 6 9 5 7 fattening pigs 2.52 2.61 2.67 7 1 2 4 8 3 14 13 14 2 hens 0.34 0.35 0.59 1 25 19 18 0 21 1 40 3 39 poultry 0.60 0.60 0.67 1 11 13 23 6 12 0 10 0 12 14 g. mack et alii the results show that modelling investment decisions in new animal capacities based on linear cost functions (options 2a and 2b) leads to better results than using continuous investment activities combined with quadratic cost functions (options 3a and 3b). the results also show that pmp used for crop production activities underestimates production increases which are above-average (such as rapeseed, sugar beet, field vegetables etc.). these results are caused by two characteristics of pmp. on the one hand, the farm-level models only take into account the activities observed during the 2005 reference period, so the adoption of new crop production activities in subsequent years could not be taken into account. on the other hand, the quadratic cost functions prevent overspecialisation and above-average production increases for single activities. we can only assess the performance of the model based on its forecasting capacity. 5. conclusions this ex-post validation at farm scale clearly shows that, in the short term, supply curve specifications based on pmp only or on pmp combined with lp for selected historical parameters average production levels of all fadn farms average forecasting error of modelling options [afe in %] 2003 2005 2006 2008 2010 2012 no 1 linear§ no 2a linearquadrevenues‡ no 2b linearquadentropy† no 3a quad revenues¶ no 3b quad entropy¤ base year s l s l s l s l s l s l weighted average forecasting error [wafe in %] crop production       50 55 4 5 4 5 4 7 4 7 animal production     10 14 6 10 6 10 7 13 7 11 total weighted average forecasting error [twafe in %] average       30 34 5 8 5 8 5 10 5 9 s = short term; l = long term; lu: livestock unit; fadn: swiss farm accountancy data network data pool; § linear = linear cost functions for crop and animal production activities; ‡ linear-quad-revenues = linear cost functions for animal production activities and pmp-based quadratic cost functions for crop production activities. estimate of pmp coefficients based on revenues; † linear-quad-entropy = linear cost functions for animal production activities and pmp-based quadratic cost functions for crop production activities. estimate of pmp coefficients based on maximum entropy; ¶ quad-revenues = pmp-based quadratic cost functions for animal and crop production activities. estimate of pmp coefficients based on revenues; ¤ quad-entropy = pmp-based quadratic cost functions for animal and crop production activities. estimate of pmp coefficients based on maximum entropy. 15how did farmers act? table 4. long-term results at sectoral scale: historical sectoral production changes from base year 2003/05 to 2010/12 and deviation of model results from historical sectoral changes (+/%) of the modelling options. unit observed sectoral change from 2003/05 2010/12 no 1 linear§ no 2a linearquadrevenues‡ no 2b linearquadentropy† no 3a quadrevenues¶ no 3b quadentropy¤ historical change (+/-%) deviation from historical sectoral change (+/-%) of the modelling options farm exits total farms  qty. -11% 5% 1% 0% 2% 3% valley region  qty. -12% 5% 4% 2% 4% 6% hill region  qty. -9% 4% -3% -4% 0% -1% mountain region  qty. -10% 3% 1% 1% 1% 1% farm size < 20 ha  qty. -18% 0% -3% 1% -1% 6% farm size 20-30 ha  qty. +4 8% 8% -4% 6% -2% farm size > 30 ha  qty. +15% 9% -4% -13% -4% -19% crop production bread grain ha  -4% -17% -11% -14% -1% -3% fodder crop ha -17% -36% -2% -5% 12% 9% potatoes  ha -17% 28% -7% -7% -1% 1% rapeseed  ha 35% -52% -48% -50% -41% -43% sunflower  ha -32% 23% 12% 12% 18% 15% field vegetables  ha 11% 173% -14% -17% -9% -10% silage maize  ha 12% 2% -6% -5% -14% -6% sugar beet  ha 6% -23% -12% -12% -11% -7% open arable land  ha -6% 8% -6% -8% 0% 1% temporary ley  ha 9% 2% -3% -7% -15% -13% total arable area  ha -2% 5% -4% -7% -4% -3% permanent grassland  ha -2% 5% 2% -2% 3% -2% total utilised agricultural area  ha -2% 5% 0% -3% 1% -2% total livestock  lu 3% 1% -3% -5% -9% -11% dairy cows  lu -6% 4% 2% 1% -3% -2% suckler cows  lu 55% -6% -17% -20% -60% -60% pigs  lu -3% -1% -6% -6% -3% -34% fattening calves  lu -13% 2% 1% 2% 12% 24% fattening bulls  lu -6% 14% 5% 2% 2% 2% cattle total  lu 2% 0% -4% -4% -11% -9% sheep  lu -1% -19% -21% -21% -5% -3% goats  lu 25% 78% 78% 78% -39% -42% horses  lu 13% 93% 81% -11% 88% 122% broilers  lu 31% 18% 13% 8% -38% -40% hens  lu 19% 10% 5% 2% -20% -20% 16 g. mack et alii production activities significantly improve the forecasting performance of an agentbased model compared with specifications based on lp only. for short-term forecasts, where investment decisions do not play a major role, pmp for all production activities and pmp combined with lp produce similar results. for long-term forecasts, the results at farm scale and at sectoral scale show that combining lp for animal production activities with pmp for crop production activities leads to the best forecasting performance. the combined approach could mitigate some limitations of pmp which are relevant mainly in the medium and long term, such as the adoption of new production activities, while still exploiting the advantages of pmp in order to avoid overspecialised model results. this study confirms also the finding of buysse et al. (2007) that, in sectors where new production activities are expected to be adopted owing to market and policy changes (i.e. switching from direct payments towards market support or opening borders of an isolated country), the lp approach could represent an appropriate solution, in particular in long-term forecasts, whereas, in the case of minor policy changes or in the short term (i.e. slight modifications of direct payments or tariffs), pmp could improve the forecasting results. the underlying reason for this might lie in the fact that farmers have to take both gradual and binary decisions. in animal production, either a new house will be built or it will not. after a radical reform of agricultural policy, the farming business will be continued or not. our results have shown that, for such binary decisions, lp is effective. for situations where price fluctuations suggest an increase in potatoes at the expense of a farmer’s wheat acreage, pmp is a more suitable instrument. the results show that supply curve specifications based on the extended variant of pmp and that revenues and specifications based on pmp and maximum entropy lead unit observed sectoral change from 2003/05 2010/12 no 1 linear§ no 2a linearquadrevenues‡ no 2b linearquadentropy† no 3a quadrevenues¶ no 3b quadentropy¤ historical change (+/-%) deviation from historical sectoral change (+/-%) of the modelling options average of absolute deviation from historical sectoral change (%) all attributes 20% 12% 10% 13% 16% lu: livestock unit; § linear = linear cost functions for crop and animal production activities; ‡ linear-quad-revenues = linear cost functions for animal production activities and pmp-based quadratic cost functions for crop production activities. estimate of pmp coefficients based on revenues; † linear-quad-entropy = linear cost functions for animal production activities and pmp-based quadratic cost functions for crop production activities. estimate of pmp coefficients based on maximum entropy; ¶ quad-revenues = pmp-based quadratic cost functions for animal and crop production activities. estimate of pmp coefficients based on revenues; ¤ quad-entropy = pmp-based quadratic cost functions for animal and crop production activities. estimate of pmp coefficients based on maximum entropy. 17how did farmers act? to similar results. the results support other studies by gocht (2005) and winter (2005), both of whom discovered that the different pmp versions led to similar model results. although all tested approaches lead to deviations in the actual observable trends, we may conclude that pmp for crop production activities combined with lp for animal production activities is preferable to full pmp when assessing the forecasting performance of sectoral production changes in the medium or long term. at the same time, this paper shows that, in general, an ex-post validation makes a valuable contribution to improving the accuracy of the model, but can also make a theoretical contribution to the methods used. on the other hand, this example demonstrates that pmp and lp approaches have their strengths and weaknesses in individual areas. for this reason, the methodological considerations for improving mathematical programming should be continued. this will not only improve the predictive accuracy of the model results, but, just as importantly, it will also have positive consequences for the acceptance of model simulations for use in policy advice. 6. references an, l. 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(2015). pathways to truth: comparing different upscaling options for an agent-based sector model. journal of artificial societies and social simulation 18(4): 11. the future of bio-based and applied economics daniele moro1, fabio gaetano santeramo2, davide viaggi3 how did farmers act? ex-post validation of linear and positive mathematical programming approaches for farm-level models implemented in an agent-based agricultural sector model gabriele mack*, ali ferjani, anke möhring, albert von ow, stefan mann the impact of assistance on poverty and food security in a fragile and protracted-crisis context: the case of west bank and gaza strip donato romanoa, gianluca stefania,*, benedetto rocchia, ciro fiorilloa assessing price sensitivity of forest recreational tourists in a mountain destination gianluca grilli1,2 estimating a dual value function as a meta-model of a detailed dynamic mathematical programming model claudia seidel, wolfgang britz bio-based and applied economics 9(3): 263-282, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-7758 not my cup of coffee: farmers’ preferences for coffee variety traits – lessons for crop breeding in the age of climate change1 abrha megos meressa, ståle navrud* school of economics and business, norwegian university of life sciences, p.o. box 5003, n-1432 ås, norway abstract. the advent of biotechnology and conservation of genetic resources hold promise to improve traits to meet the challenges to coffee growing from climate change. developing new varieties by integrating traits in high demand by farmers could greatly increase farmers’ adoption of new varieties. this study aims to inform breeding priority setting by examining farmers’ preferences for coffee traits. a discrete choice experiment was applied to smallholder farmers in northern ethiopia to map their willingness-to-pay for improvements in four coffee traits: i) yield, ii) weather tolerance, iii) disease resistance, and iv) the maturity period. the traits are important to the farmers in their choice of coffee varieties. they prefer weather tolerant and disease resistant varieties; implying that they prefer yield stability over high yielding and early maturing varieties. education level, access to irrigation and farmers’ experience in coffee farming explain the preference heterogeneity across farmers. these results suggest that breeding programs should give priority to yield stability in order to increase farmers’ adoption of new varieties, and secure in situ preservation of these traits. thus, ex situ conservation programs are needed for early maturing and high yielding varieties, which farmers do not give priority to maintain in their own fields. this would improve climate resilience of coffee farming, and at the same time conserve the arabica coffee genetic heritage of ethiopia. keywords. coffee, traits, crop breeding, climate change, discrete choice experiment, willingness-to-pay. jel codes. q18, q51, q55, q57. 1 we would like to acknowledge funding from the norhed project through capacity building for climate smart natural resource management and policy (clinsrap), a collaboration project between the norwegian university of life sciences (nmbu) and mekelle university, ethiopia. we would also like to thank two anonymous referees for very detailed and constructive comments, which greatly helped us improve the paper. any mistakes that remain are, of course, the sole responsibility of the authors. *corresponding author. e-mail: stale.navrud@nmbu.no editor: meri raggi. 264 abrha megos meressa, ståle navrud 1. introduction coffee is grown by 20-25 million families in more than 80 tropical and subtropical countries (bacon, 2005; vega et al., 2003). two main coffee species are grown; arabica coffee (coffea arabica) and robusta coffee (coffea canephora), with the former accounting for more than half of the world coffee production. meeting the growing demand for coffee while safeguarding the genetic biodiversity of coffee is, however, a great challenge for policy makers. the advent of biotechnology and conservation of genetic resources hold promise to improve phenotypes of high economic importance and bring socially desirable outcomes. ethiopia is one of the world’s largest coffee producing countries and known to harbor a wide range of coffee genetic diversity in a diverse array of coffee farming systems. there are more than 5,000 varieties of arabica coffee in the country (labouisse et al., 2008, tsegaye et al., 2014), and they can still be found growing wild or semi-wild in the undergrowth of tropical highland forests. ethiopian foreign exchange earnings largely depend on coffee export. there are four main coffee farming practices in ethiopia: i) forest coffee, accounting for 8-10 % of the production, ii) semi-forest coffee (30-35 %), iii) garden coffee (50-57 %) and iv) plantations (5 %)(kufa, 2012). thus, 95 % of the total coffee produced can be attributed to smallholder farmers. the productivity of forest coffee and semi-forest coffee farming is about 200-500 kg per hectare, which is lower than the national average productivity (600 -700 kg per hectare). the coffee species in the forests and farms vary in productivity per hectare, appearance and internal genetic structure (lópez-gartner et al., 2009). the vast genetic variability in coffea arabica genotypes of ethiopia provides opportunities for creating coffee varieties, through selection and hybridization, with good yield performance, distinct quality characters, and resistance to major diseases. the few common pests and coffee diseases include coffee berry disease (cbd) (colletotrichum kahawae), coffee root-knot nematode (meloidogyne spp.) and coffee rust (muller et al., 2009; dubale & teketay, 2000). the threat of cbd remains prevalent in coffee growing regions despite research efforts and policy interventions encouraging planting of disease resistant coffee varieties and fungicide spraying. pest and disease resistant cultivars yield economic benefits because they reduce yield losses and pesticide costs of coffee growers (hein & gatzweiler, 2006). previous studies and policies on annual crops narrowly focus on evaluating the benefits of high yielding varieties, but farmers’ adoption of these improved varieties is low (e.g., dalton, 2004; shiferaw et al., 2014; zeng et al., 2014). in addition, evidence from multi-attribute crop studies in developing countries show that farmers exhibit higher preferences for drought tolerant than high yielding crops (asrat et al., 2010; kassie et al., 2017). however, these studies examine farmers’ preferences for crops such as teff (eragrostis abyssinica) and maize. in contrast, coffee is arguably more robust to weather shocks than annual crops, but the practice of coffee farming is more challenging because of longlasting effects of farming decision, less opportunities for inter-annual agronomic adjustments, as well as the ecological importance of preserving genetic diversity. farmers focus on their private economic benefits, and select and cultivate coffee varieties based on the benefits they obtain and/or expect to obtain from a particular trait 265not my cup of coffee: farmers’ preferences for coffee variety traits (hein & gatzweiler, 2006). however, farmers’ emphasis on adoption of high yield coffee varieties could erode the genetic diversity of coffee in the forests and the semi-forest coffee farms. fluctuating market price of coffee, coffee diseases, increased frequency of extreme weather events, and substitute cash crops like khat (catha edulis) can also reduce the genetic diversity of coffee. in coping with the environmental stressors, farmers’ selection of coffee varieties to cultivate and maintain on their farm along with natural processes over generations of cultivation shapes the genetic structure of coffee (baidu-forson et al., 1997; smale et al., 2001). farmers’ interest in increasing yield per hectare, reducing yield loss or shortening the waiting period to start harvesting a normal yield might motivate their decisions to cultivate new varieties and maintain them in their fields. climate change is threatening global coffee yields as changing temperatures and rainfall patterns affect plant growth. the changing climate may also be leaving coffee plants more vulnerable to diseases. thus, in the age of climate change it is important to conserve the genetic diversity in arabica coffee in countries like ethiopia, as this genetic pool is likely to improve the possibilities for adapting coffee growing to future climates and secure the livelihood of smallholder coffee farmers in developing countries (fao 2015).. this paper aims at increasing our understanding of ethiopian smallholder farmers’ preferences for arabica coffee traits. this knowledge can be used to construct breeding programs for coffee varieties farmers are likely to adopt, and thus conserve in-situ. for example, if farmers have strong preferences for high yield traits, they are more likely to maintain such varieties in their farmed fields. however, the farmers would then be less likely to cultivate or maintain other coffee varieties with lower yields, but with drought tolerance and other traits that could critically affect the future ability of coffee to adapt to climate change. in order to preserve these traits, ex-situ conservation efforts would be needed to supplement on the farm (in situ) conservation. while previous studies of ethiopian smallholder farmers have examined trait preferences for annual crops like teff and sorghum (asrat et al., 2010), and found environmental adaptability and yield stability to be important, very little is known about the trait preferences of farmers for perennials like coffee. this paper seeks to answer the following three research questions: 1) which traits of arabica coffee varieties do smallholder farmers prefer to cultivate? 2) are there trait preference variations among the farmers? 3) which sociodemographic factors explain the variations in farmers’ preferences for coffee traits? we employ a discrete choice experiment (dce) to elicit farmers´ preferences and willingness-to-pay (wtp) for improvements in the following traits of arabica coffee: i) yield per hectare, ii) weather tolerance, iii) diseases resistance, and iv) the maturity period. we also explore the preference heterogeneity among the smallholder farmers, and the sources of heterogeneity. the latter is found to be important for designing targeted communication programs, differentiated product offerings, and for identifying market segments and market niches (allenby & rossi, 1999). thus, the results from this study can be used in the dissemination and adoption of improved coffee varieties. 266 abrha megos meressa, ståle navrud 2. method and data 2.1 description of the study area the study area is the raya alamata and raya azebo districts of the regional state of tigray in northern ethiopia. the study area is located about 600 km north of addis ababa, the capital of ethiopia and 180 km south of mekelle, the capital of the regional state of tigray with about 4 million inhabitants. most people in this rural area base their livelihood on rain-fed agriculture. the study area includes most of the raya valley, which is one of the focal areas for agricultural expansion with its fertile soils and high agricultural potential. the ethiopian ministry of water resources initiated a hydrogeological study in the raya valley in 2008 aiming to encourage farmers to adopt new technologies to improve productivity and ensure food security in the region (ayenew et al., 2013). the study area, like the other regions in ethiopia, has seen frequent variability in the weather; i.e. fewer normal years and more frequent droughts and flooding (siam & eltahir, 2017). higher rainfall variability in the region has become a challenge for agriculture and environmental conservation as farmers have not adopted technologies that could mitigate crop yield losses. agriculture, being the main source of livelihood activity, involves a mixture of food and cash crop production. the main crops grown are maize, sorghum and teff; but also coffee and khat are found. fruits are also grown as cash crops in the lowland areas. although annual rainfall is moderate, ranging from 450 to 600 mm, the availability of farmland and fertile clay loam soils makes the area well suited to crop production. since 2001/02, the regional government has made unsuccessfully efforts to get khat producers to convert to coffee production. the regional government has banned transportation, selling and buying of khat in the regional markets during the coronavirus pandemic state of emergency, and is planning to introduce new lasting laws to permanently prohibit the use and marketing of khat. one of the tentative measures proposed is to provide subsidies and other incentives to farmers that convert from khat to coffee farming. thus. understanding farmers’ preferences for coffee traits, and factors explaining potential preference heterogeneity among these farmers, could help us understand how effective alternative measures would be and improve their design. 2.2 design of survey, choice experiment and attributes 2.2.1 survey instrument discrete choice experiments (dces) enable us to study goods and attributes for which no market exists (hanley et al 2001). we use dce to evaluate farmers’ preferences for the various traits of coffee varieties, as the other stated preference technique of contingent valuation is not able to value each individual trait. the dce approach is based on a combination of lancaster’s household production theory (lancaster, 1966), and mcfadden’s random utility theory (mcfadden, 1973). lancaster’s household production theory states that the total utility of a good is derived from the characteristics or attributes of the good (lancaster, 1966); while the random utility maximization (rum) model is used 267not my cup of coffee: farmers’ preferences for coffee variety traits for analyzing discrete choices, based on the assumption of utility maximizing behavior of individuals (mcfadden, 1973). in dces, individuals are asked to make repeated hypothetical choices among alternatives in choice sets where the pre-specified levels of the different attributes vary. the final survey instrument was designed in a stepwise process; including discussions with key informants and experts from mekelle university, focus group discussions with the farmers; and a series of pretests of the survey instrument prior to the final survey; see table 1. we conducted pre-test surveys in april and may 2016 in four villages in the study area. in the first exploratory survey, we used a structured questionnaire, and carried out face-to-face interviews with informed village community members and local agriculture and development extension agents in the study area. the focus group discussants (n= 20, in five groups, each with four participants) and informant interviewees were used to determine the coffee attributes that were most important to them and to the community. in a pre-test survey we tested the questionnaire on a broad range of respondents in order to reflect the variation we expected to see in the final survey sample and checked whether respondents understood the questionnaire. we kept refining and clarifying the attributes and their levels using reports and opinions from discussants to make them easier for the respondents to understand. using information from the pre-testing, focus group discussions, key informants, model farmers and extension workers in the study area as well as discussions with experts, we selected five coffee attributes to define new coffee variety alternatives. the questionnaire was translated into the local language (tigrigna), and a pre-test face-to-face survey was conducted in may 2016. 36 farmers from the study area who were table 1. description of process of developing the discrete choice experiment (dce) survey. stage research activity period description purpose 1. literature review and semistructured interviews with key stakeholders in the area march-april 2016 identification of coffee attributes, and farming practices in the case study area identify relevant attributes to include in the dce, and sociodemographic and other factors explaining farmers´ choices 2. focus groups (5 groups; each with four discussants; n=20), used both to explore and to pretest a tentative version of the dce april-may 2016 assess farmers’ perception towards the coffee attributes and climate change identify and refine relevant attributes to include in the dce exercise, and questions to map factors affecting respondents´ choices 3 pre-test survey (n =36 face-to-face interviews) may 2016 test survey instrument and follow-up questions about the attributes and the credibility of the valuation scenarios/ choice cards check whether the choice cards and questions are found to be realistic, acceptable and understandable to the respondents 4. final survey (n = 358 face-to-face interviews) may-august 2016 assess preferences of the local people towards different coffee attributes conduct the dce exercise with the selected coffee attributes 268 abrha megos meressa, ståle navrud engaged in farming activities (not only coffee production) at the time of the survey were randomly selected for the pre-test. during the pretest of the dce, the choice sets included “quality” and “marketability” attributes, and each choice set had three alternatives and an opt-out option (i.e. none of the alternatives). each alternative was characterized by five attributes. in the pretest, the respondents reported the choice sets to be too complex. therefore, we changed each choice set in the final survey to include only two new alternatives and the opt-out option, where the alternatives included four non-monetary coffee attributes and a cost attribute. previous studies have shown that the use of labeled alternatives in dce has a significant effect on individual choices, and could reduce respondents’ attention to the actual attributes and make them look only at the labels of the alternatives (jin, jiang, liu, & klampfl, 2017). since the goal of this study is to examine preferences for coffee traits, the choice sets comprised the unlabeled alternatives: “alternative a” and “alternative b”; and the opt-out alternative “neither alternative a nor alternative b”, having no additional cost. the final survey was conducted from may to august 2016 by seven experienced interviewers who were trained for three days in survey techniques. they conducted face-toface interviews of a random sample of 358 heads of farming households in the study area. during the interview, interviewers started by explaining the proposed breeding program and possible improvements in the coffee traits/attributes in order to help respondents to prepare for the choice cards. after addressing questions from the respondents, if any, the interviewers proceeded to the dce. afterwards, information about the sociodemographic characteristics of respondents were collected. 2.2.2 design of attributes the procedure in the final selection of attributes and definition of attribute levels is based on a review of previous studies (asrat et al., 2010; wale & yalew, 2007), and examination of opinions expressed in the carefully crafted focus group discussions that include experienced and model farmers, ordinary farmers (mainly coffee breeders) and agricultural researchers as well as extension workers in the area. the experts on crop breeding and agricultural researchers have hands-on experience and practical knowledge about which coffee attributes are important. similarly, the discussants reported that they considered the attributes as important for their selection of a particular coffee variety. the additional payment to fund the breeding program to improve the coffee attributes is presented as an extra cost of the seedlings for that particular coffee plant and is included along with the coffee attributes. thus, the attributes included in the choice sets are: i) yield, ii) weather tolerance, iii) disease resistance, iv) maturity period, and v) extra cost of the seedling. table 2 provides a description of the attributes and their levels. yield refers to the increase in average productivity of a coffee variety in quintal (1 quintal (q) = 100 kg) per hectare. the improvement in yield has been emphasized by policy makers and development practitioners aiming at increasing farmers´ income and ensuring food security. the yield attribute has three levels: no change (the current yield per ha), and 1/4th (one fourth) and 1/3rd (one third) increase in productivity. the current yield per ha varies across different production systems and the coffee varieties. the average productivity in quintals per hectare (q/ha) is 2-3 for forest coffee, 4-5 in semi-forest coffee, 7-8 for garden coffee and 9 for plantation coffee; and the national average is 6-7 q/ha. the productiv269not my cup of coffee: farmers’ preferences for coffee variety traits ity for selected varieties and hybrid varieties is in the range of 6-17 q/ha and 15-24 q/ha, respectively. increased yield per hectare raises household income and is expected to have a positive effect on farmers´ willingness-to-pay (wtp) for seedlings of a coffee variety. weather tolerant and disease resistant traits are associated with the performance of the coffee variety in terms of giving a stable yield. weather tolerance refers to the capacity of the coffee variety to withstand drought and frost, and to give a stable yield year after year. this attribute has three levels: no change (meaning little drought or frost tolerant), drought tolerant, and drought and frost tolerant. disease resistance refers to the resilience and resistance of the coffee variety to diseases and pest infections when there is neither drought nor frost and it gives a stable yield year after year. the disease resistance attribute has three levels: no change (meaning little disease resistant), resistance only to common diseases, and high resistance to common and uncommon diseases. increased weather tolerance and disease resistance are expected to increase farmers´ wtp for coffee traits. maturity period refers to the duration of time (in years) the coffee plant need to fully develop and start giving a normal yield. the maturity period attribute has two levels: five years and three years. an increase in the maturity period of the coffee is expected to have a negative effect on people’s wellbeing and their preferences for the coffee variety. the cost attribute is defined as extra costs per seedling. the average cost of a coffee seedling in the area at the time of the survey was approximately etb 5-7. 2.2.3 experimental design this study employs an orthogonal main effect experimental design (omed) to combine attribute levels and create choice sets. in creating the choice sets, we used the r softtable 2. attributes and attribute levels, including the “no change” levels of the opt-out option, used in the discrete choice experiment. attribute description attribute levels yield increased average productivity in terms of yield per hectare of a particular coffee variety no change*, 1/4th increase, 1/3rd increase weather tolerance whether the coffee variety is tolerant to drought and frost and gives stable yield in the face of such weather stress factors. no change*, drought only tolerant, drought and frost tolerant disease resistance whether the coffee variety gives stable yield despite the occurrences of coffee diseases or pest infections in scenarios of no drought and/or no cold weather. no change*, moderate disease resistant, strong disease resistant maturity period the time (in years) the coffee variety needs before giving its first normal yield. no change*, 3 years, 5 years extra cost per seedling the additional payment, in ethiopian birr (etb), an individual farmer is expected to pay per seedling 0, 7, 15, 20, 25 etb notes: # etb = ethiopian birr; at the ppp conversion factor on 31 december 2016, 1 usd=8.68 etb. * “no change” in the opt-out option correspond to a maturity period of approximately 7 years for the traditional coffee varieties. no change to weather tolerance and diseases resistance traits are associated with a little drought and frost tolerance and a little disease resistance, respectively. the opt out traits/attribute levels are not included in constructing the hypothetical choice sets. 270 abrha megos meressa, ståle navrud ware version 3.3.2 and adopted the code by aizaki (2012) to execute the experimental design and randomly assign the choice sets into two blocks. the experimental design creates 16 choice sets, and the two blocks include 8 choice sets each. figure 1 shows a choice set as it was presented in a choice card to the respondents. the choice tasks put respondents in a hypothetical setting, offering them choice sets comprising two new alternative coffee varieties (presented as “alternative a” and “alternative b”), and an opt-out option (“neither alternative a nor b”). the two new coffee varieties come at an extra cost of the seedling in order to cover the costs of developing a new variety. the opt-out option has no extra cost of the seedlings as the farmers will then have the traditional coffee variety. the alternatives in the choice sets differ in one or more of the attribute levels. the respondents are randomly assigned to the two blocks, and asked to choose his or her most preferred alternative in a sequence of eight choice sets. the respondents are subjected to only eight choice sets each, with the aim of attaining a balance between fatigue and learning (caussade et al., 2005). similar to meyerhoff and liebe (2009), this study imposed restrictions to avoid unrealistic choice tasks by making the new alternatives have at least one higher attribute level than the opt-out alternative. this avoids new alternatives having inferior values to the opt-out option, but they can have higher extra costs. however, dominant choices created from the experimental design were also presented to the respondents as the removal of irrational or inferior preferences from the choice experiments could affect statistical efficiency (lancsar & louviere, 2006). besides, the presence of new alternatives with higher/ lower non-monetary attribute levels but less/equal cost (dominant/dominated alternatives) than other alternatives could help to examine whether respondents pay enough attention to and understand the choice task. further, having generic alternatives such as “alternative a” and “alternative b” can make respondents focus on the attributes/traits rather than the labels we could have put on the alternatives/ coffee varieties. figure 1. example of a choice card as it appeared in the questionnaire in the final survey. the “neither a nor b” alternative to the right is the opt-out option. which of the following coffee varieties do you prefer? alternative a and alternative b would entail a cost to your household, while no payment would be required for the “neither” option alternative a alternative b neither alternative a nor alternative b: i prefer none of the new varieties yield 1/4th increase 1/3rd increase weather tolerance drought and frost drought disease resistance disease resistant disease resistant maturity duration 3 years 5 years cost per seedling etb 5 etb 20 i would prefer: alternative a _____ alternative b ____ neither ____ note: etb = ethiopian birr; 1 usd=8.68 etb in terms of purchase power parity (ppp) corrected exchange rate on december 31st, 2016. 271not my cup of coffee: farmers’ preferences for coffee variety traits 2.3 sample characteristics in the final survey we interviewed 358 farmers residing in the rural areas of raya alamata and raya azebo districts of tigray in northern ethiopia. we applied proportional sampling to give larger quota to districts and villages with larger population and vice versa, and systematic random sampling to select farmers from household head name lists in subdistrict offices. according to the most recent ethiopian central statistical agency census report (csa, 2007), the total number of households in raya alamata and raya azebo was 20,532 and 32,360, respectively. accordingly, the proportion of sampled household heads from the two districts was 60 percent from raya azebo and 40 percent from raya alamata. the sociodemographic characteristics of the farmers are presented in table 3. 2.4 model specification and estimation the conditional logit model is commonly used to analyze consumer choice behavior based on random utility theory (mcfadden, 1974). conditional logit assumes the idiosyncratic errors to be independently and identically distributed (iid) extreme values, and the tastes for observed attributes to be homogeneous. evidence shows that individuals exhibit significant heterogeneity in preferences for goods and services (see alberini & ščasný, 2013; allenby & rossi, 1999; birol, karousakis, & koundouri, 2006). mixed logit (mixl) models relax the independence of irrelevant alternative (iia) assumption of the more restrictive closed-form discrete choice models and allows for heterogeneity of preferences for observed attributes (hensher & greene, 2003; mcfadden & train, 2000). in this model, utility u is assumed to be latent, but observed only with the choice y of alternative j (0, 1, 2) by individual i (i=1, … 358) in choice set t (t=1,2, … 8). a utility function given a choice set t with j alternatives for individual i can be written as; uijt=βixijt+εijt table 3. description of sociodemographic variables used to explain the variations in farmers’ preferences for the selected coffee traits. variable mean median std. dev. definition age 43.2 40 13.6 age of the household head; in years family size 5.6 6 2 total number of family members in the household (including the respondent) education 1.8 0 3 education level of household head; in years market 60 60 49.9 the distance to the main market from home; walking time in minutes farm size 2.9 3 1.9 the area of the farmed land the farmer owns; in timad (1 hectare= 4 timad) irrigable land 0.44 0.5 whether the farmer owns irrigable land; 0=no; 1=yes experience 0.28 0.47 whether the farmer has ever managed a coffee farm (now or before); 0=no; 1=yes 272 abrha megos meressa, ståle navrud where xijt is a vector of observed explanatory variables including coffee attributes and sociodemographic characteristics, βi is a vector of conformable parameters (unknown utility weights) the individual assigns to these variables; and εijt is a random term that does not depend on underlying parameters or observed data, with zero mean and iid over alternatives. the utility weight (βi) for a given attribute is given as; βi=β+δ’ivij where β is a vector of mean attribute utility weights in the population, δ is a diagonal matrix which contains the standard deviation (σ) of the distribution of the individual taste parameters (βi) around the mean taste parameter (β), and vij is the individual specific heterogeneity with mean equal to 0 and standard deviation of 1. the mixl model permits random parameters to vary over individuals, and not observation, in order to measure interpersonal heterogeneity. the vector xijt, can include 0/1 terms to allow for alternative specific constant (asc), where asc takes the value 1 for “alternative a” and “alternative b” and 0 for the opt-out option. asc accounts for the systematic differences in choice patterns between the alternatives. behaviorally speaking, the asc parameter reflects the average effect of various components such as endowment effect, status quo bias, omission bias, and the impacts of complexity such as fatigue effects and other unobserved attributes (boxall et al, 2009; meyerhoff & liebe, 2009). the inclusion of an opt-out option can also reflect actual behavioral phenomena by avoiding forced demand, and hence improves the reliability of the welfare measures (boxall et al., 2009; veldwijk et al., 2014). we set the parameters on yield, weather tolerance, disease resistance and maturity period attributes as random and with normal distribution, and the parameter on the cost attribute is set as fixed. a positive sign for significant coefficients of the attributes in the econometric estimation indicates a positive effect of the increase in the respective attribute on farmers’ preferences, whereas a negative sign indicates a negative effect of the attribute on their preferences. statistically significant coefficients on the attributes also enable the calculation of wtp for a change in the attribute. in a utility function linear in its parameters, the marginal wtp equals the negative ratio of the respective coefficient of non-monetary attribute and the coefficient of the monetary attribute (hensher & greene, 2011). the wtp estimates presented in table 4 refer to a marginal, one level change in the attributes. the attributes levels included in this model are presented in table 2, and the sociodemographic variables are defined in table 3. the coefficients in mixl models are estimated with a simulated maximum likelihood estimation technique. this study used the gmnl-package by (sarrias & daziano, 2017) in r software version 3.3.2 to estimate the coefficients on alternative attributes and sociodemographic variables. since the sociodemographic variables do not vary across choices/ observations, their interaction with asc are included to test whether they explain the observed taste variations across farmers or are random parameters across individuals. akike information criteria (aic), bayesian information criteria (bic) and likelihood ratio tests are used to compare the goodness of fit of the model and select the model with superior goodness of fit compared to other models. the inclusion of the sociodemographic variables in the mixl model is used to uncover the factors explaining farmers’ preference heterogeneity. 273not my cup of coffee: farmers’ preferences for coffee variety traits 3. results and discussions standard multinomial logit (mnl) models were estimated first, before proceeding to mixl models. table 4 presents the results. other models such as scaled-multinomial logit model and generalized multinomial logit model were also estimated; see appendix a-1. the results from the mixl models show superior fit to the data in this study. in the mixl estimation, we set the coefficients on the attributes yield, weather tolerance, disease resistant and maturity duration to be random parameters with normal distribution, while the coefficient on the cost of seedlings is fixed in order to use it to compute wtp estimates. the maturity duration and cost of seedlings attributes are continuous variables; while the yield, weather tolerance and disease resistance attributes are categorical. the coefficient on asc is significant and positive, implying that farmers prefer the new alternative varieties at some additional cost to the existing varieties that come at no additional cost. less than two percent of the respondents chose the opt-out option, but none of these respondents protested the proposed coffee variety development program and the changes in traits/attributes. although the interviewers were trained to avoid experimenter demand effects (zizzo, 2010), i.e. the respondent trying to please the interviewer by saying what they assumes the interviewer would like to hear, we cannot rule out that this effect might have contributed to the low opt-out percentage. asc captures the average effect of all relevant factors that are not included in the model. thus, farmers´ choice of new improved varieties over the traditional ones seem to be motivated not only by coping with frequent weather changes and occurrence of coffee diseases, but also by the desire for high yield and early maturing traits. results from the mixl model show that the estimated coefficients on yield, weather tolerance, disease resistance and maturity duration are all statistically significant. this implies that any developments in the specified coffee traits have significant effects on farmers’ preferences for coffee varieties. the parameter on the yield attribute is interpreted in relation to an increase in productivity per hectare or an increase in farm income resulting from cultivating a coffee variety. the weather tolerance trait enhances resilience against drought and frost, while the disease resistance trait increases resilience against coffee diseases and pest infections occurring under “no drought” and “no frost” weather conditions. thus, the coefficients on disease resistant and weather tolerant traits can be interpreted as farmers´ preferences for yield stability or resilience to risk of yield loss, and hence is also as an indicator of farmers` risk preferences. the parameter for the maturity period attribute reflects the time preference of farmers. the signs of the coefficients for all attributes/traits are consistent with standard economic theory as farmers prefer increased weather tolerance, higher disease resistance, and higher yield per hectare, but reduced duration of the maturity period and lower extra cost per seedling. the significant and positive coefficient for the yield attribute implies that farmers prefer high yield coffee varieties to low yield coffee varieties, holding all other things constant. this implies that improvement in productivity per hectare of a coffee variety increases the farmer’s preference for this variety. previous dce studies of annual crops (asrat et al., 2010; kassie et al., 2017) showed farmers to have similar positive preferences for the yield improvement attribute. 274 abrha megos meressa, ståle navrud weather tolerant and disease resistant attributes are associated with the ability of a coffee variety to withstand environmental stressors and to give stable yield. the estimated coefficients for these two attributes are consistently significant and positive. this could imply that farmers are willing to pay more for seedlings with these traits and are thus willing to give up part of their income in order to ensure stable yield. a dce by asrat et al. (2010) assessing the trait preferences of ethiopian farmers for sorghum and teff crop table 4. results of the mnl model and mixl models without (mixl1) and with (mixl2) sociodemographic determinants of preferences heterogeneity. mnl model mixl1 model mixl2 model asc 4.621*** 8.750*** 6.825*** (0.220) (0.572) (0.600) yield high 0.754*** 1.078*** 0.838*** (0.065) (0.117) (0.231) weather tolerant 0.970*** 1.292*** 1.453*** (0.067) (0.135) (0.284) disease resistant 0.929*** 1.425*** 2.713*** (0.061) (0.131) (0.521) maturity duration -0.452*** -0.548*** -0.665*** (0.034) (0.071) (0.129) cost of seedling -0.044*** -0.058*** -0.065*** (0.005) (0.006) (0.009) yield high. experience 0.028* (0.012) weather tolerant. irrigation -0.001* (0.001) disease resistant. education -0.018* (0.009) disease resistant. age -0.063 (0.045) maturity duration. education 0.051** (0.018) maturity duration. market -0.005 (0.004) maturity duration. experience -0.001* (0.001) n 2860 2860 1869 log-likelihood -1765.161 -1594.251 -1131.047 bic (bic/n) 3578.073 (1.251) 3315.839 (1.159) 2435.356 (1.303) aic (aic/n) 3542.321 (1.239) 3220.502 (1.126) 2308.094 (1.235) note: standard error in parentheses. ***, ** and * denote significant at the 1, 5 and 10 % level; respectively. 275not my cup of coffee: farmers’ preferences for coffee variety traits varieties showed that  farmers are willingly forego some income or yield to obtain a more stable and environmentally adaptable crop variety. the coefficient on the maturity period is significant and negative, indicating that farmers prefer early maturing coffee varieties over those coffee varieties that take longer to start giving normal yield. similarly, experimental evidence on rice traits in western africa showed farmers to be willing to pay for early maturing traits (dalton, 2004) note, however, that both asrat et al. (2010) and dalton (2004) looked at annual crops, while coffee is a perennial crop. policy makers often stress the importance of high yield varieties to meet the growing demand for food, but adoption of high yielding variety technologies is low. our study shows that farmers are willing to pay more for improving traits associated with yield stability, such as weather tolerant and diseases resistant traits, than for increasing the yield per hectare or early maturity. the magnitude of the coefficients corresponds to the importance the farmers put on the traits. in a related study, kassie et al. (2017) examined farmers’ preferences for drought tolerant maize in rural zimbabwe, and found that farmers are willing to pay five times more for a variety with a drought tolerance trait than for a variety providing an additional ton of yield per hectare. this implies that farmers are willing to forgo an increase in yield per hectare to get a stable yield on the farm. the subsistence nature of agriculture and escalated poverty in the area might restrain them from adopting a high yield cash crop variety technology with some risk and keep farmers trapped with a low yield and low cost variety technology. table 4 also reports the coefficients of sociodemographic factors that can explain preference heterogeneity among the farmers. heterogeneity around the mean of the taste parameters is consistently apparent with respect to yield, weather tolerance, diseases resistance, and maturity duration traits. therefore, we included age, education, experience with coffee farming, access to irrigation and distance to market in order to assess the observed sources of variation and to identify factors responsible for the heterogeneity. note that the models in table 4 are not directly comparable in the conventional model fit criteria of log likelihood, akaike information criterion (aic) and bayesian information criterion (bic); as the number of observations in the model with the sociodemographic factors (mixl2) is much smaller than in the models without these variables. although bic divided by number of observations (bic/n) is higher in mixl2, this is not the case for the aic/n. thus, we cannot conclude that the inclusion of these sociodemographic factors increases the model fit. we focus on the estimates from the mixl model since the results demonstrate the presence of preference heterogeneity among the farmers. education, access to irrigation, and experience of the farmer in coffee farming were found to be the factors that explain variation around the average level of taste preference for the traits. about 28% of the respondents reported having some experience in coffee farming activities, which explains preference variations for high yield and early maturing traits. considering the high yield trait, farmers with experience in coffee farming exhibit higher preferences for improvements of yield per hectare than farmers without experience. some farmers in the study area are replacing low yield coffee varieties with improved coffee varieties, while others are shifting towards cultivation of other more lucrative cash crops such as khat. farmers with relatively high levels of literacy are found to have lower preferences for disease resistant traits. this finding coincides with gächter et al (2007) that found increased level of education to decrease loss aversion. on the other hand, farmers with better access 276 abrha megos meressa, ståle navrud to irrigation reveal lower preferences for weather tolerant coffee traits than the farmers who have no access to irrigation. this is as expected as farmers’ lack of access to irrigation could increase their vulnerability to drought, and thus their risk aversion. the coefficient on the maturity duration attribute is negative. a negative significant coefficient on maturity duration indicates that an increase in maturity duration of the coffee variety reduces farmers’ preferences for that particular variety. farmers’ years of education reduces the negative effect of increasing maturity duration of late-maturing coffee varieties, whereas coffee farming experience increases the negative effect of increasing maturity duration. the could be explained by farmers´ private discount rate to increase with age and decrease with educational level and literacy, as observed by (kirby et al., 2002). these days, almost the entire coffee farming area in the study area has been turned into production of khat and other cash crops. thus, farmers with coffee farming experience tend to be older, and older farmers could have higher private discount rates and thus prefer early maturing traits. in dce analysis, the coefficients in themselves have no direct economic interpretation, but the negative ratio of the coefficients of the attribute to the cost coefficient give the marginal wtp estimate for the changes in the attributes (hensher & greene, 2003). positive and negative marginal wtp estimates reflect utility and disutility of the attribute, respectively. the wtp for a change in an attribute level combined with the increment in the attribute level, leaves the deterministic part of the respondent’s utility for a profile unchanged (fiebig et al 2010) table 5 presents the marginal wtp of the four coffee traits. observing the marginal wtp estimates (deferring the heterogeneity, i.e. the mixl2 model), the farmers are willing to pay more for frost and drought tolerance as well as disease resistance traits, compared to increased yield. the premium is 2-3 times the amount they are willing to pay for a 1/3rd increase in the yield of 1 quintal/ha (1 quintal = 100 kg). this compares well with a similar study of farmers’ preference for maize traits in zimbabwe. kassie et al. (2017) showed that the value farmers attach to drought tolerance is about five times higher than the wtp they attach to changing a variety. our results also reflect the difficulties in making inter-annual adjustment in coffee farming practices. these results can explain the prevailing low adoption of high yield varieties by farmers in ethiopia (wale & yalew, 2007). the coefficient on the maturity period is significant and negative, which implies that an early maturity trait is more preferred to a late maturing trait. the negative sign implies table 5. marginal wtp; in ethiopian birr (etb) (1 usd=8.68 etb in terms of purchase power parity (ppp) corrected exchange rate on december 31st 2016). attributes wtp estimates from the mixl1 model wtp estimates from the mixl2 model asc 150 105 yield, high 18 13 weather tolerant 22 22 disease resistant 24 42 maturity period -9 -10 277not my cup of coffee: farmers’ preferences for coffee variety traits that farmers are willing to give up part of their income or yield to shorten the waiting period for the full development of the coffee plant and to start harvesting normal yield. in other words, farmers have disutility from a delay in the time it takes for the coffee seedling to give normal yield. the significant and positive coefficient on asc implies that other unobservable systematic factors also increase farmers’ preferences for new alternative coffee variety over traditional varieties. to summarize, the wtp results confirms that farmers prefer stable yield varieties (i.e. high disease resistant and weather tolerant traits) to high yield varieties or early maturing varieties, holding all other things constant. 4. conclusion understanding farmers’ preferences for coffee traits can help develop policies and breeding programs for new varieties that integrate traits in demand by the farmers, and thus increase farmers’ adoption of new varieties. using a discrete choice experiment, this paper examines farmers’ preferences for increased yield, weather tolerance in terms of adaptation to drought and frost, disease resistance, and early maturing traits of arabica coffee. the results show that farmers are willing to cultivate and pay more for weather tolerant and disease resistant coffee varieties than high yielding and early maturing ones. this indicates that farmers prefer improvements in yield stability traits to traits that maximize yields. thus, crop-breeding programs aiming for larger uptake of new coffee varieties among farmers in order to increase coffee production should primarily develop weather tolerant and disease resistant varieties and combine them with high yield and early maturing traits. the trait preferences of smallholder farmers also have implication for in-situ versus ex-situ conservation of coffee genetic diversity in ethiopia. smallholder farmers with no experience in coffee farming will not cultivate and maintain coffee varieties in their fields if yields are unstable, as they prefer the yield stability traits of weather tolerance and disease resistance. thus, the uptake of varieties with high yield and early maturing traits will be low among farmers in regions without a history of coffee growing. ex-situ conservation programs should therefore give priority to coffee varieties with these and other traits that are less preferred by farmers in order to preserve the full genetic heritage ethiopian coffee. although farmers prefer stable yield to high yield traits, the mixed logit model results show heterogeneity in farmers’ preferences for the coffee traits. farmers with coffee farming experience exhibited higher preferences for high yielding and early maturing coffee traits than those that had no experience in coffee farming. in contrast, farmers with more years of education prefer maturing traits and disease resistant traits less than those with little education. further, farmers with access to irrigable farmland exhibit lower preferences for weather tolerant traits. this implies that tailoring the improved coffee varieties to the preferences of these different groups of farmers would enhance farmers’ adoption of the new varieties. this could make a significant contribution to improving coffee farmers’ adaptation and resilience to climate change. future research is needed in order to test whether our findings on smallholder farmers’ preferences can be generalized to other coffee growing regions in ethiopia and around the world. such new stated preference surveys should be based on best practice guidelines; 278 abrha megos meressa, ståle navrud see johnston et al (2017). preferably, similar surveys should be carried out at the same time in different regions in order to better understand what measures are needed for coffee farmers to adapt to climate change impacts. references aizaki, h. 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(2010). experimenter demand effects in economic experiments. experimental economics, 13(1):75–98. appendices table a-1. results from multinomial logit (mnl), scaled multinomial logit (s-mnll, mixed logit model with correlated alternatives (mixl), mixed logit model without correlation (mixl_u) and generalized multinomial logit (g-mnl) models. mnl s-mnl mixl_u mixl g-mnl asc 4.621*** 25.530 8.512*** 8.392*** 9.636*** (0.220) (16.563) (0.559) (0.512) (0.813) yield high 0.754*** 1.907** 1.067*** 1.041*** 1.198*** (0.065) (0.701) (0.108) (0.113) (0.143) weather tolerant 0.970*** 2.309* 1.421*** 1.252*** 1.342*** (0.067) (0.965) (0.125) (0.122) (0.145) disease resistant 0.929*** 2.092** 1.366*** 1.388*** 1.631*** (0.061) (0.717) (0.119) (0.127) (0.173) maturity duration -0.452*** -1.406* -0.734*** -0.493*** -0.593*** (0.034) (0.595) (0.069) (0.063) (0.065) cost seedling -0.044*** -0.112* -0.064*** -0.056*** -0.065*** (0.005) (0.046) (0.006) (0.006) (0.007) tau 1.410*** 0.477*** (0.323) (0.091) gamma -0.648 (0.354) n 2860 2860 2860 2860 2860 log-likelihood -1765.161 -1751.089 -1632.741 -1596.428 -1577.198 bic 3578.073 3557.888 3345.067 3320.192 3297.651 aic 3542.321 3516.178 3285.482 3224.855 3190.396 notes: ***, ** and * denotes significant at the 1, 5 and 10 % level; respectively. standard error in parentheses. 282 abrha megos meressa, ståle navrud table a-2. standard deviations of the random parameters from mixed logit model results. estimate std. error z-value pr(>|z|) yield high 1.0931 0.1985 5.51 3.7e-08 *** weather tolerant 1.3818 0.1906 7.25 4.2e-13 *** disease resistant 1.3674 0.2494 5.48 4.2e-08 *** maturity duration 0.6452 0.0964 6.69 2.2e-11 *** note: ***, ** and * denotes significant at the 1, 5 and 10 % level; respectively. figure a-1. distribution of the individuals’ conditional mean for the parameters of yield, weather tolerant, diseases resistant and maturity duration. the grey area displays the proportion of individual with positive conditional mean. a) kernel density for yield improvement b) kernel density for weather tolerant c) kernel density for disease resistant d) kernel density for maturity duration investigating determinants of choice and predicting market shares of renewable-based heating systems under alternative policy scenarios cristiano franceschinis, mara thiene multi-country stated preferences choice analysis for fresh tomatoes maria de salvo1,*, riccardo scarpa2,3,4, roberta capitello2, diego begalli2 “not my cup of coffee”. farmers’ preferences for coffee variety traits. lessons for crop breeding in the age of climate change abrha megos meressa, ståle navrud* does the place of residence affect land use preferences? evidence from a choice experiment in germany julian sagebiel1,*, klaus glenk2, jürgen meyerhoff3 the use of latent variable models in policy: a road fraught with peril? danny campbell*, erlend dancke sandorf bio-based and applied economics 5(3): 325-332, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-18510 short communication “gmo” maize and public health – a case of schumpeterian policy vs. free market in the eu giovanni tagliabue independent researcher, italy date of submission: 2016 3rd, july; accepted 2016 25th, october abstract.eu lawmakers have long refused the cultivation of “genetically modified organisms”. an example of this struggle is the revision of the accepted level of contaminants in maize: rather than admitting that bt maize is safer than “non-gmo” varieties, and therefore european farmers should be allowed not only to import it, but also to produce it, politicians have raised the threshold of the poisonous fumonisins that may be legally present in food and feed. this decision is an example of a “schumpeterian” approach to policy, where public choices are not inspired by a science-based mindset, but are substantially dictated by a calculus of consent; economic/commercial protectionism has also been considered as a motivation. while scholars must continue to explain that every policy decision should have a basis in sound science, no way out of the “gmo” imbroglio seems to be foreseeable, as long as politicians stick to the schumpeterian iron law. keywords. gmo maize, fumonisins, eu biotech regulation, schumpeterian policy, free market jel codes. k32, q18 1. background: the incoherent “gmo” policy in the eu for more than a quarter of a century (1990s to today), the approach of the european union to so-called “genetically modified organisms” has been steered by a steadfast rejection. such ongoing approach led to disruption of the international market: in the years around the turn of the millennium, the block on commercialization in the eu of certain genetically engineered agricultural products, appealing to the alleged inherent riskiness of “gmos”, triggered recourse to the wto by countries which claimed that their exports were unjustly discriminated. the eu lost the dispute (world trade organization, 2006; bernauer and aerni, 2008) and authorized the import of quite a few recombinant corresponding author: giovanni.tagliabue@uniedi.com 326 giovanni tagliabue dna crops and vegetables, but did not stop prohibiting the cultivation of them1 – a sort of de facto compromise. therefore, a clear double standard is currently applied in “gmo” eu politics: on the one hand, we see the persistent refusal to allow the cultivation of such cultivars; on the other, there is a regular, huge stream of importation, above all “gm” soybeans and corn as animal feed, accounting for several million tons annually. european farmers are not allowed to grow ge [genetically engineered] crops, even if they are identical to imported cultivars; apparently against all logic, numerous products are “safe to eat, but only if imported”! (masip et al., 2013, p. 319) the paradox by which the cultivation of “gmos” is substantially banned in europe, while enormous quantities of recombinant dna cereals and legumes are imported to be used as feedstuff, can be explained (tagliabue, 2016b): cultivation of them is prohibited in order not to harm the old-fangled products of eu farmers (graff et al., 2014, p. 13-14), to gain the political and electoral consensus of “organic” food producers (masip et al. 2013, p. 319), to protect the interests of the traditional herbicide/pesticide chemical industry (zilberman et al., 2015, p. 215) and to appease the “anti-gmo” brigade; it is necessary to import them to allow animal breeders to work. europeans must hope that there are no significant drops in the availability of “gmo” animal feed for import, or very serious economic problems would occur, as the european commission itself warns (european commission, 2007a). the costs of such schizophrenic rules are shown by a particularly bizarre example: “extraordinarily, in romania before they joined the eu, gm soybeans were extensively grown and exported to europe. since they joined the eu, romania is now forbidden to grow gm soy as it is not authorized for cultivation in europe. instead, the eu pays farmers in brazil, argentina and us to grow gm soy, and provides subsidies to romania from regional funds.” (baulcombe et al., 2014, p. 35) the path of special regulation for “gmos” took the form of two directives in 1990; the one regarding agricultural products is 90/220 (european community, 1990), whose approach was broadly reiterated, a decade on, in directive 2001/18 (european union, 2001), regarding “deliberate release into the environment” (i.e. cultivation); a partial change was introduced by directive 2015/412 (european union, 2015), but its significance is outside the scope of this article. the method of systematic obstructionism has worked. indeed, the eu has approved the cultivation of just one recombinant dna variety, bt corn mon810 (european commission 2013), which has not stopped various countries constantly blocking it with bureaucratic hurdles, or even (illegally) banning it. for example, the eu court of justice condemned france twice: 1. court of justice, case c-419/03 of 15 july 2004, commission of the european communities against french republic, oj c 275 of 15 november 2003, where the court of justice held that france had infringed community law by failing to transpose directive 2001/18/ec. 2. court of justice, case c-121/07 of 9 december 2008, commission of the european communities v french republic, oj c 95, 28 april 2007, in which france 1 the eu’s official list of authorized “gmos” is not so short: 58 items were imported until recently, plus 19 cleared on 24 april 2015 (http://europa.eu/rapid/press-release_ip-15-4843_en.htm, accessed 8 august 2015), and some 40 requests are still pending; but for all the cultivars – except maize mon810 –use (importation) is allowed for “marketing of food and feed and derived products”, “with the exception of cultivation”: http:// ec.europa.eu/food/dyna/gm_register/index_en.cfm (accessed 8 august 2015). 327“gmo” maize and public health was condemned for failing to comply with the previous judgment (mereu, 2012). various national governments have imposed this constant opposition by appealing to the only legal instrument apparently available until 2015, the “safeguard clause” (european union, 2001, art. 23), by which an eu member state can refuse a “gmo” only when there are well-grounded reasons which are scientifically proven by adequate studies regarding the negative impact of the product on the environment and/or on human health. the european food safety authority is responsible for assessing the grounds claimed by governments; more than once, it has declared as invalid dossiers which this or that country has presented (for mon810, see european food safety authority 2009. the ban by the german and french governments is discussed in ricroch et al., 2010). the efsa’s outcomes are in line with the current consensus: “the main conclusion to be drawn from the efforts of more than 130 research projects, covering a period of more than 25 years of research, and involving more than 500 independent research groups, is that biotechnology, and in particular gmos, are not per se more risky than e.g. conventional plant breeding technologies.” (european commission, 2010a) however, since the opinion of the efsa, even if it is required by law, does not green light products when unjustified requests to block them are rejected (unlike the situation, for example, with similar american agencies), in many cases the «safeguarding» countries have preferred to risk an infraction procedure – which in any case the european commission, for political and diplomatic reasons, is very slow and reluctant to implement – rather than give “gmos” their due go-ahead. to be clear, the commission itself declared that the “anti-gmo” manoeuvres of certain eu countries are inappropriate: “the fact that member states have currently no margin of appreciation on cultivation of authorised gmos has led in several cases some member states to vote on the basis of non-scientific grounds. some of them have also invoked the available safeguard clauses, or used the special notification procedures of the treaty under the internal market, as ways to prohibit the cultivation of gmos at national level.” (european commission, 2010b) such instrumental use of a clause that was designed for other purposes has been blamed again by the same european commission, which underlines that no negative data have emerged regarding any genetically modified product previously authorized: “no member state which had adopted a so-called “safeguard clause” had ever been in a position to put forward new evidence.” (european commission, 2015) it is therefore worth noting that, regarding “gmo(s)”, there has always been a cleavage between the “executive” approach of the commission and the more “political” eu bodies, first of all the parliament – the actual decision-maker which passed the directives. 2. a dubious decision we will now look at a terribly toxic phenomenon: we will see how the inflexible “antigmo” stance of europe’s politicians can inspire regulatory approaches that explicitly increase some small but significant risks for public health. fumonisins2 are powerful mycotoxins, i.e. a highly poisonous product from microscopic fungi: only discovered in the late 20th century, their carcinogenic effect has been 2 http://en.wikipedia.org/wiki/fumonisin_b1 (accessed 8 august 2015). 328 giovanni tagliabue confirmed in horses, pigs, rats and in humans; ingesting such moulds – among other possible pathological consequences – can generate neural malformations in the foetus, increasing the probability of the child being born with spina bifida. the ecological mechanism by which such substances become a real danger is easy to understand: a pestilent butterfly feeds on corn, deposits faeces where fungi of the fusarium genus abound, especially in the small cavities of the grains that have not been completely consumed. whatever and whoever feeds on the contaminated corn can suffer serious consequences; worse still, the toxic substances can pass into the milk produced by mammals who have digested them. externally applied pesticides have a limited impact, because it is difficult for them to reach the well-hidden target; moreover, the epidemiological incidence is much higher in poor countries, where the cereal is consumed in abundance and where, at the same time, the price of insecticides and fungicides can be prohibitive for farmers. bt corn substantially reduces the infestation, for one very simple reason: many of the insects which start to feed on it do not live long enough to generate the holes in which the fungi can take root. (kaplan, 2000; kershen, 2006; ostry et al., 2010; pazzi et al., 2006). as can be imagined, in many nations healthcare provisions establish clear limits to the acceptable levels of fumonisins in corn destined for human and animal consumption, and impose strict controls. the quantity of toxic substances present in maize varies significantly, depending in part on the climate (it is relatively higher in hotter countries) and above all on seasonal weather trends (higher temperatures encourage the proliferation of the insects that accompany moulds). europe established contamination limits in 2001, then in 2005 (european commission, 2005) and then again in 2006 (european commission, 2006), to come into force on 1 october 2007. it was a wise decision, because incidents are possible: “in the uk in september 2003, the analysis of 30 samples of maize products in supermarkets led to the removal of ten of them because of excessively high levels of fumonisin content. the contaminated samples with the highest fumonisin contents were those labelled ‘organic’” (heldt, 2010, p. 25). but here we must insert a disturbing tale. corn crops in recent years, in particular in italy and france, show a level of fumonisins which makes it impossible to use most of the product for human and animal consumption: the consequent obligation to send hundreds of tons to be destroyed or, in the best-case scenario, to produce energy, is a source of serious damage for agricultural firms; for this reason politicians in italy and france would have liked greater flexibility on the thresholds of the contaminants (for italy, see camera dei deputati, 2007). as a result of the italo-french pressure, the eu food chain and animal health committee unanimously recommended raising the tolerance levels for fumonisins (european commission, 2007b); the related regulation with looser limits was approved in extremis, two days before the coming into force of the law it was amending (european commission, 2007c). probably this decision does not entail a significant risk for consumers, because the new levels should still be low compared to the threshold for real toxicity, but what we want to stress here is the rationale of the political decision, which can be summarised as follows. 1) there are thresholds for tolerance to certain natural poisons, established on scientific bases. 2) in some seasons, it is found that an agricultural product exceeds these thresholds. 3) instead of banning the consumption of the illegal foodstuff, which would entail significant economic losses, let’s raise the allowed toxicity limits: in other words, we choose what seems to be the lesser evil. 329“gmo” maize and public health but let’s go back for a moment to point 2. the presence of unacceptable levels of moulds is not a law of nature; a cultivar which is almost immune to those pests which attacks other varieties exists, it is indeed the only “gmo” authorized for cultivation on the old continent (bt mon810 corn). the only field trial which has been carried out in italy, by experts from a public organisation, showed that such cultivar was much less subject to the deadly phenomenon; a row even erupted over the late dissemination of the data: the malicious think, probably rightly, that if the results had been unfavourable to “gmos”, they would have been published immediately and with a lot of fanfare in the media (nature, 2007). in this specific case, in order not to encourage the use of a “gmo”, which moreover is theoretically authorized, the choice is made to adjust the legal limits of the higher toxicity, which is frequent in the traditional product. yet, european rulers must have known that the cereal whose cultivation they are blocking is imported in huge quantities for use as animal feed. 3. a case of “schumpeterian” policy why did eu office-holders refuse to embrace a science-based approach in this policy decision, and rather opted to adjust the legally admitted levels of food poison? rational observers must be very puzzled, if they are not aware that public choices are often dictated by a different kind of logic: politicians always proclaim their approach to be inspired by the search for common good, but a much less idealistic reading was proposed decades ago by joseph schumpeter, when he argued that, in a democracy, any political or administrative action is a mere corollary of the opportunistic estimates which every law-maker adopts. “the democratic method produces legislation and administration as by-products of the struggle for political office” (schumpeter, 1942, p. 286). it is impossible to escape the clear impression that such a disposition is applicable in our case, and maybe most “normal” politics falls into the narrow definition highlighted by the great economic-political thinker. another quotation may reinforce understanding of the mindset which leads to such apparently illogical policy decisions: “politically speaking, the man is still in the nursery who has not absorbed, so as never to forget, the saying attributed to one of the most successful politicians that ever lived: «what businessmen do not understand is that exactly as they are dealing in oil so i am dealing in votes».” (schumpeter, 1942, p. 286) crude but truthful realism, which explains policy outcomes substantially dictated by a calculus of consent: most probably, in our case eu politicians reckoned that an adjustment of the admitted threshold of maize contaminants would have cost them less than the possible outrage deriving from encouraging “gmo” cultivation. an exquisite example of political expediency. we could also call it a para-machiavellian approach: if the end is to conquer and/or maintain power, and in democracy this means anticipating the probable reaction of public opinion (read: voters), it is easy to link a means to an end: avoiding the complaints from the “anti-gmo” brigade was worth a decision which sets science aside, while at the same time those affected by the consequences (consumers, farmers interested in better seeds) were not expected to protest too much. they did not. 330 giovanni tagliabue 4. by way of conclusion we must distinguish two aspects, i.e. the unscientific approach too frequently used by politicians when they make public choices and the possible ways to correct it. i think that realpolitik, as explained by schumpeter, really helps to understand the hidden motivations of apparently illogical decisions. changing this attitude is a very different story. scientists – both life scientists and social scientists, including agricultural economists – should continue in their efforts: explaining to the public that the “gmo” pseudo-category is a major blunder, that an actual scientific consensus on the subject exists (tagliabue, 2016a) and should be a basis for evidence-based regulation. as for politicians, pleas for a change of policy on “gmo” will probably continue to fall on deaf ears, because schumpeter’s iron law seems to be insuperable. schumpeter does not offer a way to coax or nudge lawmakers into choosing a science-based mindset – neither can the author of this little article, in its limited remit, do so. to imagine a possible development, let’s go back to the beginning of this article: since the european “gmo” policy has already been condemned as a violation of free market, i.e. rules which are voluntarily adopted by the eu, a forced change may come from pressures at the wto level. the 2003-2006 dispute has been settled with argentina and canada, not yet with the usa (wto, 2010). new challenges are to be expected by eu states from other wto members (punt and wesseler 2015, p. 167). if the old continent lawmakers cannot guarantee a coherence between a binding free market framework and certain decisions they have adopted, external forces may drive the eu towards a more consistent agriculture regulation. references baulcombe, d., dunwell, j., jones, j., pickett, j. and puigdomenech, p. 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(2016a). the necessary “gmo” denialism and scientific consensus. journal of science communication 15(04), 1-11. http://jcom.sissa.it/archive/15/04/ jcom_1504_2016_y01. accessed 8 august 2015. tagliabue, g. (2016b). european incoherence on “gmo” cultivation vs. importation. nature biotechnology 34(7): 694-695. world trade organization (2006). reports out on biotech disputes. www.wto.org/english/ news_e/news06_e/291r_e.htm. accessed 8 august 2015. world trade organization (2010). dispute ds291 european communities – measures affecting the approval and marketing of biotech products, up-to-date at 24 february 2010. www.wto.org/english/tratop_e/dispu_e/cases_e/ds291_e.htm zilberman, d., graff, g., hochman, g. and kaplan, s. (2015). the political economy of biotechnology. german journal of agricultural economics 64(4): 212-223. bio-based and applied economics 5(3): 267-285, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-18527 the circular economy and agriculture: new opportunities for re-using phosphorus as fertilizer michele vollaro, francesco galioto, davide viaggi university of bologna/department of agricultural sciences, agricultural economics, bologna, italy date of submission: 2016 24th, august; accepted 2017 2nd, february abstract. the increasing demand of phosphorus (p) worldwide is posing important challenges on the market stability of fertilizers. extracting more p would not guarantee high p quality and low prices. globally, only the european commission, in a recent document about the circular economy strategy, has begun to address the challenge of the dependence on phosphate rock. based on a simple circular economy theoretical framework, this paper proposes an impact analysis of the use of recycled p as a substitute of chemical p fertilizers. two new technologies applied to retrofit existing wastewater treatment plants (wwtp) are considered: moving-bed bio-reactors and struvite crystallization modules. the analyses indicate that the introduction of these technologies prove to be economically sustainable for specific levels of inhabitant equivalent (ie) and that the profitability of struvite, as a substitute of chemical p, increases with increasing levels of p fertilizer prices and for increasing sizes of wwtps. keywords. circular economy, agriculture, phosphorus, fertilizer jel codes. o13, o33, q15, q52, q56, r11 1. introduction agriculture is the main user of phosphorus (p) as fertilizer (p2o5), with a share of about 90% of total phosphate rock (pr) consumption (cordell et al., 2009; brunner, 2010; van vuuren et al., 2010; ec, 2013). this datum would not provoke interest if p reserves were infinite and p extraction costs were marginally decreasing. the actual consumption dynamics of p, however, indicate that p reserves are not sufficient to meet the growing p demand worldwide, even at lower prices (scholz et al., 2013), and that improving p extraction or investing in searching new reserves might yield higher costs and lower quality of p fertilizers (van vuuren et al., 2010). the consequences of such dynamics will negatively affect food security worldwide, especially in developing countries where the consumption of p fertilizers would likely prove to be economically unsustainable. on the other hand, the corresponding author: michele.vollaro@unibo.it 268 m. vollaro, f. galioto, d. viaggi increased use of low-quality p fertilizers would have reduced effectiveness on crop productivity and would be responsible for increased water pollution, higher risks of eutrophication and wider contamination of heavy (radioactive) metals (von horn et al., 2009). although the increasing awareness about the likely impacts of p dynamics has led decision-makers to devise measures aimed at optimizing p-fertilizers use and reducing water pollution (neset et al., 2012), there is a perception that it may be impossible to significantly reduce dependence on pr as a source of p-fertilizers, as demonstrated by the absence of an international agenda addressing the issues of food security and environmental change (cordell et al., 2009). beyond the theoretical/empirical interest of the scientific community and some actions at country level (cordell et al., 2009), only the european commission (ec) seems to show a concrete interest in facing the issue from a wider political perspective by adopting a circular economy strategy (ec, 2015), in which the willingness to reduce the dependency on pr is demonstrated by the adoption of an action plan addressing, inter alia, the need to improve the recovery rate of secondary raw materials from wastes. based on the recent policy evolutions of the eu, and with the intention of contributing to the development of a new institutional setting inspired by a circular economy approach, the paper proposes first a general theoretical explanation of the circular economy concept as applied to phosphorus resources followed by a preliminary analysis to estimate the cost-effectiveness of two innovative technologies for the recovery of p from wastewater treatment plants (wwtp), based on moving-bed biofilm-reactor (mbbr) and struvite crystallization modules (scm), respectively. as a follow up step, the paper provides a territorial economic impact analysis of the use of struvite as a substitute of chemical p fertilizers by assuming that phosphorus recovery is an economically feasible process as long as environmental benefits1 are considered (hernandez-sancho et al., 2010). the chosen territory is the entire administrative area of the emilia-romagna region, for which data on fertilizer use and on urban wwtps are readily available. the paper continues in the next section with a brief presentation of the policy context and a literature review on economic analyses of p recovery. a theoretical framework introducing the concept of circular economy applied to the case of p-fertilizers is presented in the third section. methodology and data are presented in the fourth section, followed by the presentation of results, a discussion and conclusions. 2. background 2.1 policy context the awareness of the essential nature of phosphorus for life has increased significantly in recent years because the uncertainty in future availability, quality and especially price stability of p (and p fertilizers) is matched with population growth, the overhanging food security and especially the impacts of chemical p fertilizers on water resources. interest 1 environmental benefits are what, in economic terms, are known as positive externalities, i.e., benefits that occur when the actions of firms and individuals have an effect on people other than themselves without economic compensation. 269the circular economy and agriculture has, therefore, grown in facing the issue from a policy perspective, at least in the european union (eu), with the adoption of a circular economy package based on the recovery and reuse of waste materials with the twofold objective of reducing the production of primary materials and the impact on the environment. however, given the scarce interest at the international level in the evolution of p, the action of the eu toward promoting the circularity of the economic systems represents an enormous step forward for setting the future policy agenda according to a sounder economic, social and environmental sustainability perspective. the new european policy course, launched in 2014 with the europe 2020 strategy, has set the ground for driving the eu toward a circular economy framework. major policy weight, indeed, has been attributed to the capacity of member states (ms) to reduce the pressures and impacts of economic and social activities on natural resources, especially soil and water resources. this choice represents the ‘natural’ continuum – and in certain aspects the completion – of a fragmented policy path, initiated decades ago for facing single issues, but which evolved with the emanation of several directives concerning environmental protection (or the limitation of environmental pollution), such as the urban waste water treatment directive 91/271/eec (uwwtd), the nitrates directive 91/676/eec, the water framework directive 2000/60/ce (wfd), the fertilizers regulation 2003/2003/ec, the waste framework directive 2008/98/ec. as stated by the ec (2015), a new paradigm for enhancing the competitiveness of the eu economy without impairing the environmental and natural resources is only possible by “closing the loop” and starting a transition process towards a more circular economy. indeed, this statement introduces the circular economy plan adopted in 2015 by the ec with the specific objectives of reducing eu dependence upon the production and import of raw materials, highly subject to both scarcity of resources and price variability. the intention to drive an economic system towards the circularity of materials (especially secondary raw materials) largely rely upon the innovation capacity of the eu, based on the vast existing technological know-how, the enhancement of research potential (through the horizon 2020 programme) and the financial support for knowledge-based investments through dedicated structural funds. within the broad framework of the action plan for the circular economy, a section is dedicated to recovering and recycling materials, aiming at generating a new lifecycle for wastes and new trading opportunities for secondary raw materials. phosphorus is included in this section as a secondary raw material recovered from sewage and food wastes and recycled through organic and other typologies of fertilizers (e.g. struvite). however, the route for full implementation of recycled p must pass through a further and parallel process for setting the production and trade standards of secondary raw materials. in fact, in 2015, the ec received a working/position document from the fertilizer working group (2014), in which the opportunities for fostering market access for recycled phosphorus is proposed as a valid alternative to the current and future perspective of the dependence of the eu on chemical p fertilizers. this document, de facto, proposes the modification of regulation 2003/2003 on fertilizers in order to allow for the commercialization of recycled p. the opportunity to recover and recycle phosphorus has emerged as a complementary action of the previous eu policies on the reduction of pollution loads in water resources, specifically the uwwtd and wfd, which open to the possibility of reusing treated wastewater. in particular, the uwwtd states that all generated wastewater agglomerations of size between 2,000 and 10,000 inhabitant equivalent (ie) must set up collection and treatment 270 m. vollaro, f. galioto, d. viaggi systems by december 2005. therefore, one of the main challenges for european authorities in the achievement of the good ecological status of water bodies, as stated in the wfd, is to implement the appropriate treatment of wastewater, even in small agglomerations. this objective is of crucial importance, especially in some territories where urbanisation is characterised by extensive and less populated areas and where investments in sludge networks and advanced treatment plants are relatively costly and financially unsustainable. this is the case, for example, in emilia-romagna, where there are about 1,900 treatment plants (89% of total across the region) serving less than 2,000 each. out of 1,900, about 73% (1,400) involves only the first treatment stage (imhoff tanks) able to retain, at the exit, only the 10% of the introduced p charges. indeed, such conditions, which are common across europe, ought to convince the ec about the need to identify additional sensitive areas, and their related catchments, characterised by punctual and critical pollution loads. this, in turn, would imply the need to upgrade the treatment facilities dedicated to a significant quantity of discharges and the development of new facilities in the near future. in this context, it is crucial to identify the most feasible technologies, from an integrative point of view, to be tackled through new wastewater management projects, designed according to each specific wastewater scenario2. 2.2 literature review the fact that about 90% of global p production (extraction from pr) is used in agriculture as fertilizer has generated noteworthy interest with respect to the dependence of life on pr such to question the long-run sustainability of the resource. indeed, although pr are generated through natural cycles, in terms of social and environmental sustainability, p is considered a finite resource due to the disparity between the rates of natural generation and human use. since most of the p absorbed by plants and animals could be returned to agriculture, for example through crop residues, manure and wwtp effluents, p can be considered as a renewable resource, provided that it can be recovered and returned to agriculture as a convenient and non-polluting input. however, in order to meet the growing global demand for food, agricultural production has become more specialised and – for many commodities – globalised, implying the unlikely return of the p extracted to the soil of production. another important cornerstone of the sustainability of p production is the fact that it is essential for life. indeed, the growing global demand for food has given rise to increased demand for p fertilizers, globally. the main – and relatively less costly– source of p fertilizers is the phosphate rocks (pr), the known reserves of which are located mostly in morocco, the united states and china. extracting p fertilizers (mostly p2o5) from pr (and distributing it worldwide) is cheaper than returning p to soil and waiting for it to be available to plants. therefore, from an economic perspective, p needs to be treated as a non-renewable resource. this perspective is strengthened by the fact that p has no substitutes in nature and there is no way to obtain it by synthesis. 2 by wastewater scenario we intend the combination of pollution sensitiveness of the areas (which depends upon the catchments location) and the typology and charge of pollutants. 271the circular economy and agriculture the main implication deducible from these considerations, together with the diffuse distribution of relatively small treatment plants unable to retain phosphorus, is that the most practical alternative to non-renewable p would be to invest in technologies capable of recovering ‘used’ p and to make it available for use. in such a perspective, p recovery is an economically feasible process as long as environmental benefits are taken into consideration (hernandez-sancho et al., 2011). taking as an example the data on the balance of p charges (ratio between p in entry and exit) in emilia-romagna (arpa, 2012), the level of effectiveness for p retention in wwtps increases with the level of treatment3. in particular, for the first stage the retention level is 10%, whilst for second and third is about 57%and 85%, respectively. however, the total ie served by each typology represents 2%, 11% and 87%, respectively, indicating that most p is still lost in lowand medium-sized treatment plants. from these data, it can be deduced that innovations for the recovery of p and improving water treatments are mostly needed to retrofit the lowand medium-sized plants, in order to raise the effectiveness of p recovery and to lower the investment and operational costs. in fact, one of the promising technologies4 for the recovery of p from wwtp, as well as from other types of effluents, is represented by a reactor to add as a module into existing treatment plants, and capable of precipitating p into struvite5, a mineral composed of p, n and mg (booker et al., 1999; laridi et al., 2005). struvite can be used as a slow release fertilizer at high application rates without the danger of damaging plant roots (bridger et al., 1962; lunt et al., 1964). likewise, because struvite is insoluble in natural water, eutrophication problems and infiltration in groundwater are prevented, hence representing another advantage in its use as fertilizer. therefore, phosphorus recovery as struvite can be seen as a basic process for achieving sustainable development. the other valid alternative for recovering p from wwtp is represented by an innovative technology based on the concept of microbiological accumulation and digestion of waste p, capable of being accumulated as an organic compost. such technology is implemented by the means of a moving-bed biofilm reactor6, inoculated with specific micro-bacterial material, on which the effluent is left to stream (mcquarrie et al., 2011; barwal et al., 2014). 3 the available (and traditional) technology organizes the wastewater process in three subsequent levels: primary, allowing the sedimentation of large and solid waste (imhoff tanks); secondary, providing biological oxidation and a low level of microbial disinfection; tertiary or advanced, with filtration and a high level disinfection (mostly using advanced technologies such as uv ray). the higher the level the less is the risk for human health in using treated wastewater. according to the standard set by environment protection agency (epa) in the usa, wastewater from primary depuration must not be used; the second level allows for superficial uses such as furrow irrigation for orchards and industrial crops not intended for human and animal consumption, for recharging non-potable water-tables and the preservation of humid habitats and minimum vital flows of rivers; the tertiary level allows for irrigation of crops intended for human consumption, the recharge of water bodies for bathing (or more generally for recreational uses) and the recharge of potable water-tables (unep, 2011). 4 promising in terms of potential large diffusion. 5 the presence of struvite as a mineral precipitate into the wwtp has been discovered because it usually accumulates into the pipes creating obstacles to the flow of the treated water. in order to keep the plants working in an efficient way, the pipes need to be continuously cleaned from struvite, implying additional maintenance and operating costs. 6 mbbr is composed by a bio-plastic film, inoculated with specific microorganisms capable of catching and retaining selected materials. the films become carriers once inoculated. such carriers work inside a tank and they are set in movement when the wastewater flows inside the tank. in the last a stage the whole system becomes a reactor (a system in which a chemical-physical reaction occurs). 272 m. vollaro, f. galioto, d. viaggi the costs of recovering phosphate during wastewater treatment can be calculated at 2€ per kg p as a minimum and may total more than 8€ per kg p under specific conditions (dockhorn, 2007; dockhorn, 2009 schaumm, 2007). pr7 in the united states is sold at between $35 and $50 per tonne (us geological survey) depending on purity. these values show that there are no economic incentives for implementing p recovery technologies in the wastewater sector since it is cheaper for the fertilizer industry to continue using pr as feedstock. however, it is important to remember that the recovery of p from wastewater involves noteworthy environmental benefits because it prevents eutrophication in the receiving environment, and increases the availability of a non-renewable resource. in this light, when the economic feasibility of projects with environmental effects is analysed, beyond the internal impacts, environmental benefits should also be considered (hernandez et al. 2006). it follows that the economic performance of new technologies potentially improving environmental conditions should be evaluated in terms of their pollutant abatement capacity and total benefits, including the related monetized environmental benefits. 3. theoretical insights surrounding the circular economy concept applied to phosphorus resources as previously stated, p is an essential building block of life. indeed, it is an irreplaceable element of modern agriculture, as there is no substitute for its use either as animal feed or as fertiliser (ec, 2013). the reserves of pr in the european union (eu) are very limited and not sufficient to meet the internal demand of p, especially fertilizers. hence, given that the eu is a net importer of p, the prospective growth in eu domestic fertilizer demand would determine, beyond import dependency, a resulting rise in the extraction costs of p from pr. moreover, the current use of p, especially as chemical fertilizers, is partly inefficient as the share not taken up by plants leaks out causing water pollution and eutrophication. such externalities constitute the increasing costs to be borne by society. therefore, the prospective increase in the demand and externalities of chemical p-fertilizer use would imply the likely increase in private and social costs. this scenario provides for incentives for the exploitation of alternative sources of p, such as the one presented in this paper, and the related theoretical implications concerning the circular economy framework. a possible interpretation of the depicted scenario is offered in figure 1 where the demand for p is satisfied by two alternative sources of supply, characterised by two different technologies: p extracted from pr, namely technology a, and p extracted from secondary raw materials (recycled from treated wastewater), namely technology b. it is assumed that the marginal costs required to supply p through technology a increases with increasing demand, while the costs to bear with technology b are independent on the quantity demanded. panel 1 of figure 1 describes a scenario in which the current demand for p is satisfied exclusively by technology a, as the supply costs for technology b are relatively higher. on the other hand, panel 2 of figure 1 describes a scenario where the prospective higher 7 high quality pr have a title of about 30% in p2o5. 273the circular economy and agriculture demand for p is satisfied by both technology a and b. in the context of the latter scenario, p would be supplied by technology a up to a given amount of resource use, say q(a), while the residual amount of the resource, required to satisfy the entire demand, say q(b), would be supplied through technology b. it follows that level q(a) could be interpreted as the threshold value below which the resource is supplied solely through traditional technologies and above which the resource is supplied also through new technologies, while q* remains the point identifying the optimal level of overall phosphorous use. scenario 2 shows two interesting features. first, by decreasing the cost of technology b, we reach a solution in which the social optimum is given by a combination of the two technologies rather than the substitution of one with the other. we can expect that the combination will be more in favour of technology b, the more its cost is reduced. second, by reducing the cost of b, the overall amount used increases. this could be interpreted as a sort of rebound effect and should be considered carefully if an environmental effect is included in the model (which is not the aim of this paper). the uptake of alternative technologies, therefore, seeks to modify the equilibrium points of resource usage, q*, and to induce related changes in private and social welfare. indeed, welfare changes are determined by the cost loss brought about by the transition from the traditional to the alternative technology for the supply of phosphorus. it can be deduced that the supply of recycled p is feasible when it contributes to an increase in the welfare of society. figure 1. welfare impacts caused by the implementation of a circular economy strategy for phosphorus resources. resource use (q) m ar gi na l b en efi ts a nd m ar gi na l c os ts ( €) supply with technology b q* demand supply with technology a resource use (q) m ar gi na l b en efi ts a nd m ar gi na l c os ts ( €) supply with technology b q(a) q* demand supply with technology a q(b) – social optimum q(a) – resource consumption with technology a q(b) – resource consumption with technology b w – welfare gain obtained by combining supply technologies 1 2 w 274 m. vollaro, f. galioto, d. viaggi however, if a scenario of invariance of p-fertilizer demand is considered, a gradual increase in recycled p-fertilizer use might occur as well because of a reduction in the costs required to supply p with alternative technologies or by an increase in the supply costs of traditional technologies. all of these conditions might be plausible to explain the coexistence of alternative technologies, but is not yet sufficient to guarantee the transition to a full circular economy strategy (sole use of recycled resources) because of lock-in effects (zeppini et al., 2014). namely, a technology that is dominant in a particular application domain, could be resistant to competing alternatives even if the latter can be considered socially desirable. scholars attribute this phenomenon mainly to the fact that the introduction of new technologies might not reach the critical mass of adopters causing the transition (bikhchandani et al., 1992; arthur, 1989) or a critical price (solomon, 2000). 4. methodology cost effectiveness analysis (cea) and cost benefit analysis (cba) are the two main methods adopted for carrying out economic assessments. cea usually compares monetary costs and physical benefits (i.e., nutrient recovery). cba, for its part, compares monetarily valued costs and benefits (i.e. nutrient recovery and the shadow prices of water quality improvement). cea avoids the controversial monetization of intangible assets, such as the environment, and is usually designed for the comparative assessment of alternative measures, rather than for a clear-cut judgment on the feasibility of a project or a policy. cba is designed to assess the viability of the intervention, as it requires an estimation of costs and of both tangible and intangible benefits. this approach could also be adopted to deal with the theoretical background previously described as it allows for an assessment of the impacts brought about through the transition from traditional supply technologies to recycling. however, in the present study we assume that recycling is preferable to non-recycling, so the problem we face is to compare the two most recent wastewater treatment technologies. although the two innovative technologies are related to distinct phases of the wastewater treatment process and they can be combined together to upgrade current wwtps, it is assumed that due to budget constraints the regulator seeks to identify the most cost effective option. in the following section, a cost effectiveness (ce) analysis has been implemented to compare the two alternative systems as regards the p abatement capacity, which, for the considered technologies, is equivalent to the recovery capacity8: ( ) ( ) ïþ ï ý ü ïî ï í ì = j j i i ji a xnc a xncz ;max , (1) 8 recovery capacity is not the same as recycle capacity, however, both the considered technologies enable recycling. the step of recovery is limited to the capacity of the technology of retaining the p that otherwise would have been left in the effluent, while the recycling is the further step in which the recovered p is made available for being traded. as for the technology producing struvite, both steps coincide, while for mbbr the sludge obtained from the digestion might be subject to specific processes before being traded. 275the circular economy and agriculture where z is the recovery capacity, x is the size of the treatment plant in terms of ie, nc(x) is the net cost obtained by the difference between costs c(x) and benefits b(x) and a is the level of p abatement for the two alternative options, i and j. 5. data the methodology has been implemented by simulating the uptake of the described technologies in the emilia-romagna region. the territory of emilia-romagna can be considered as a unique case study given the availability of reliable data, provided by arpa (the regional agency for environmental protection) on fertilizer use as well as on the wastewater sector. fertilizer prices have been extracted by the chamber of commerce of modena (a province of emilia-romagna) where imported fertilizers are traded throughout the emilia-romagna region. table 1. evolution of fertilizer consumption in the emilia-romagna region. fertilizers (000 t) period nitrate phosphatic potassic compost organic <2007 227 48 9 123 38 average 2007-2011 178 39 8 104 52 >2011 219 25 7 91,5 64 ∆% total 2004-2011 -15.11% -61.40% -37.50% -33.85% 50.00% ∆% excluding crises pre07-post11 -3.67% -47.55% -19.23% -25.81% 68.42% ∆% per ha 2004-2011 3.19% -55.32% -52.00% source: own elaboration on arpa data (elaborated from istat). table 1 shows the evolution of fertilizer consumption in emilia-romagna during the period 2004-2013. given the occurrence of the global economic crises, which has resulted in significant price volatility and a price peak between 2007 and 2011 (table 2 and figure 1), the series has been truncated in two sub-periods in order to assess the evolution by isolating the crises period. chemical fertilizers have recorded a gradual reduction in consumption during the considered decade. in particular, by excluding the crises period in which the reduction was largely induced by the price peak, it can be noted that nitrate fertilizers, for which substitutability makes it an elastic product, the reduction reached about 4%, while it has been more accentuated for the phosphate with a fall of about 48%, indicating a very low elasticity. on the contrary, organic fertilizers, having a function more related to an amendment than a fertilizer, have experienced a consumption increase of about 70%. such data indicate an actual change in the regional agricultural sector toward a major use (attitude) of substitutes for replacing chemical products. table 2 and figure 2 clearly show that, excluding the crises period of 2008-2011, the price for chemical p in emilia-romagna region has steadily increased, from 230 €/t in january 2007 to about 500 €/t in october 2015, an increase of about 100%. nitrates fer276 m. vollaro, f. galioto, d. viaggi tilizers, for their part, recorded a positive increase in prices up until november 2011 and maintained a steady pace thereafter at an average level of 350 €/t. the high price volatility recorded during the period 2007-2011, with a peak in 2008 at 870 €/t, has yielded an average reduction in chemical p fertilizer consumption of about 19%. the estimated elasticity of demand of about -0.19 indicates that chemical p fertilizer is inelastic with respect to price variations. table 2. evolution of fertilizer prices in the emilia-romagna region. nitrates (can) phosphatic (tsp) <jun 2007 209 237 average >jul2007-2010 280 463 >2010 334 474 ∆% 2007-post 2011 59.74% 100.56% source: own elaboration on arpa data (elaborated from istat) figure 2. evolution of n and p chemical fertilizer prices in the emilia-romagna region. 100 200 300 400 500 600 700 800 900 €/t phosphatic (tsp) nitrates (can) source: own elaboration on cciaa modena. such evidence is coherent with the typology of a product unique to chemical p fertilizers obtained from a non-renewable (fossil) resource and exchanged in markets characterised by both monopolistic competition and natural monopoly forms (few states selling similar goods). as mentioned in the previous paragraphs, the potential substitution of chemical p fertilizer could come from the recycling of p present in sludge and wwtp effluents. in order to evaluate the recovery potential, an overview of the wwtp system in emilia277the circular economy and agriculture romagna region is presented. the emilia-romagna region, in the last decade, has considerably invested in the improvement of the efficiency and effectiveness of wwtps and related networks. data collected biannually by arpa since 2005 indicate the realisation of economies of scale due to specific investments in infrastructure, for modernization, new urban sludge networks and connections to treatment plants, which have augmented the treatment capacity and increased the effectiveness of the plants with the resulting improvement to the efficiency of the treatment system. further steps forward have been realised by improving the nutrient recovery capacity and the return of the muds into the economic system, especially to agriculture. such basic condition results to be highly favourable for more important and longer-run engagements in investments for developing new technologies, to be applied, in particular, to nutrient recovery. table 3 presents the situation highlighted by arpa in 2012 with regard to wwtps in the emilia-romagna region by typology (expressing the current level of technology) and the classes of urban conglomerations served. during the period 2005-2012, as concerns systems greater than 2,000 ie, reductions in the number of agglomerates, in the number of plants (from 245 in 2005 to 222 in 2012) and industrial ie have been recorded, with improvements in the efficiency and reduction in environmental pressures. table 3. wwtps in the emilia-romagna region per typology and conglomeration classes in 2012. urban conglomeration classes number of plants (per typology) project potential (per typology) (ie) i (n°) ii (n°) iii (n°) tot (n°) i (ie) ii (ie) iii (ie) tot (ie) 0 – 1,999 1,377 469 31 1,877 174,515 311,940 47,045 533,500 2,000 10,000 0 65 75 140 0 397,515 531,620 929,135 10,001 15,000 0 2 21 23 0 18,500 457,700 476,200 15,001 – 100,000 0 2 37 39 0 96,000 1,684,400 1,780,400 >100,000 0 0 20 20 0 0 4,681,333 4,681,333 total 1,377 538 184 2,099 174,515 823,955 7,402,098 8,400,568 source: arpa emilia-romagna. note: typology i stays for primary, ii for secondary and iii for tertiary (or advanced). indeed, since 2005, there has been a noteworthy reduction in the pollution loads at the exit stage of the wwtp. in particular, recent data shown in table 4 point to an improvement in abatement capacity9 from 2009 to 2012 of about 4.2% and 2.6% for secondary and advanced wwtps, respectively, a stagnation for primary plants and a reduction for plants equipped with den and dep technologies. improvements in the reuse of muds resulting from the treatment systems have been recorded since 2007. as shown in table 5, the reuse of muds in agriculture has increased by 40%, contributing to the substitution of costly chemical fertilizers and a reduction in dumping. 9 abatement capacity is the relative difference between pollutants loads entry and exit. 278 m. vollaro, f. galioto, d. viaggi ta bl e 4. 2 00 920 12 d iff er en ce s in p lo ad s pe r w w tp ty po lo gy in th e em ili aro m ag na r eg io n. pl an ts (n °) fl ow (m 3 /y )/ 10 3 pr oj ec te d ie (i e) tr ea te d ie (i e) n itr at es lo ad s ph os ph at ic lo ad s a ba te m en t ca pa ci ty en tr y (t/ y) ex it (t/ y) en tr y (t /y ) ex it (t /y ) pr im ar y (i ) 1, 37 7 7, 78 2 17 4, 51 5 85 ,2 86 34 2. 4 29 1. 1 49 .8 44 .8 10 .0 % se co nd ar y (i i) 53 8 44 ,6 94 82 3, 95 5 53 5, 74 8 2, 01 8. 7 63 7. 7 24 7. 6 10 5. 3 57 .5 % a dv an ce d (i ii ) 18 4 38 6, 58 5 7, 40 2, 09 8 4, 40 2, 50 9 17 ,6 86 .8 4, 06 0. 5 2, 38 5. 2 35 6. 4 85 .1 % ∆% 2 00 920 12 i -6 .3 9% -1 7. 95 % -1 6. 35 % -1 7. 52 % -1 7. 53 % -1 7. 51 % -1 7. 55 % -1 7. 50 % -0 .6 % ii 4. 67 % -3 .0 2% 6. 30 % 8. 29 % 15 .8 4% -1 3. 00 % 7. 79 % 2. 13 % 4. 3% ii i 3. 95 % -1 0. 47 % 1. 37 % 1. 09 % 7. 73 % -1 1. 00 % 8. 18 % -5 .2 9% 2. 6% d en itr ifi c (d en ) 80 29 ,7 75 46 3, 54 0 32 4, 39 9 1, 17 9. 8 30 2. 6 15 3. 9 58 .6 61 .9 % d ep ho sp ha t ( d ep ) 9 6, 48 0 13 7, 80 0 10 9, 27 8 25 6. 9 89 .8 27 .7 8. 5 69 .3 % d en + d ep 95 35 0, 33 0 6, 80 0, 75 8 3, 96 8, 83 2 16 ,2 50 .0 3, 66 8. 1 2, 20 3. 6 28 9. 3 86 .9 % ∆% 2 00 920 12 d en 9. 59 % -5 .4 3% 4. 76 % 10 .9 2% -2 .4 1% -6 .7 8% -1 0. 31 % -4 .7 2% -3 .5 % d ep -1 0. 00 % -8 8. 33 % 27 7. 02 % -8 4. 01 % -8 8. 31 % -9 0. 99 % -9 0. 07 % -8 4. 93 % -1 3. 1% d en +d ep 1. 06 % 1. 61 % 16 .5 6% 17 .4 5% 24 .8 8% 13 .1 8% 25 .6 1% 11 .9 1% 1. 9% to ta l 2, 09 9 43 9, 06 1 8, 40 0, 56 8 5, 02 3, 54 3 20 ,0 47 .9 4, 98 9. 3 2, 68 2. 6 50 6. 5 so ur ce : a rp a e m ili aro m ag na . n ot e: d en a nd d ep re fe r t o tr ea tm en t p la nt s eq ui pp ed w ith s pe ci fic te ch no lo gi es a do pt ed fo r t he im pr ov em en t o f t he n a nd p re co ve ry . 279the circular economy and agriculture table 5. quantity of reused and disposed muds per typology in the emilia-romagna region. reuse disposal total agriculture other reuse dumping incineration other   2007 8,309 9,514 33,550 8,043 8,379 67,794 2009 8,766 14,874 25,817 7,840 0 57,297 2012 11,860 19,555 14,751 10,071 0 56,237 ∆ % 40.51% 67.51% -72.82% 25.87% -100.00% -20.17% source: arpa emilia-romagna. note: values in tonne/yr of dry substance. the most important aspect emerging from the presentation of the data in tables 4 and 5 is the potential for achieving the abatement of p in primary plants and the large room for improvement in the abatement of secondary and advanced plants by employing the proposed new technologies. in support for such statement, a simulation of the possible market prices for muds is realized on the basis of the relative (to the p title10) prices of chemical p fertilizer, conditional on a simple analysis of market opportunities for the development of fertilizer products based on muds, such as soil improver and compost. table 6. simulation of market price for wwtp muds relative to the title in p and n and market opportunity muds from wwtps (2007-2012) fertilizers regional market – since 2007 abatement n from 65 to 75%   chem fertilizers prices: n raised by 60% p from 68 to 81%     p raised by 100%   fertilizers consumption: n (chem) stable reuse (agriculture and compost) from 30 to 56%     p (chem) reduced by 50%       organic raised by 70% value of p 0,5 – 0,6 €/kg   value of p (chem – p2o5) 1,1 €/kg value of n 0,6 – 0,7 €/kg   value of n (chem) 0,7 €/kg source: own elaboration on arpa emilia-romagna and cciaa modena data. note: chem stays for chemical. indeed, as illustrated in table 6, while chemical p consumption declined during the period 2007-2012, organic fertilizers and the reuse of muds in agriculture (as compost or soil improver) increased in response to the price rise of n and p. in particular, it is important to highlight that the increase in reuse of muds occurred in concomitance to the improvement in the p abatement capacity of muds, which implies a higher level of p returned to the land. from all such information it would be reasonable to deduce that there is evidence of a substitution of muds and organic fertilizers for chemical p ferti10 the title of p is the physical content of phosphorus in the chemical compound, expressed in percent terms. 280 m. vollaro, f. galioto, d. viaggi lizers. on the basis of such a deduction, a simulation of the value of p contained in the muds would provide for a reference for the comparative assessment of alternative p sources obtained by innovative technologies. supposing that, in terms of nutrient availability and chemical title, mud is a full substitute for p and n chemical fertilizers, the simulation results, shown in table 6, indicate that p components of muds present a half relative value with respect to chemical fertilizers, while the value of n does not vary. this simulation, therefore, provides preliminary information regarding the relative advantage in using p from muds as a possible substitute of chemical p. a second consideration is related to the absence of heavy metals in muds, such as uranium and cadmium, which are instead present in chemical p extracted from pr and which, inevitably, remain in soil and leach into water. 6. results the results of the investment and costs analysis are depicted in figure 3. the adoption of mbbr technology (red line) does not significantly alter the maintenance and operating (m&o) costs up to the size of 10,000 ie (for technical reasons11), while beyond such a level the costs follow an exponential increase. on the other hand, the results show that the costs for the scm (blue line) marginally decrease as the size of wwpts increase (in terms of ie). figure 3. economic impact trends of two alternative treatment processes with increasing size of the treatment plant: absolute (a) and average (b) values. source: own elaboration. more specifically, figure 3 shows the trend of the cost-effectiveness indicators per increasing size of the treatment plants for both innovative technologies. comparing both innovative technologies, mbbr and scm, panel a and b show the estimated evolution of the costs-pollutant abatement ratio for increasing size of the wwtp. in particular, the 11 the technical efficiency of mbbr depends on the flow of wastewater. in particular, mbbr modules can efficiently treat wastewater up to a predetermined flow. this implies that higher flows need to be split and treated by more than on module of mbbr. mbbr technologies do not show increasing returns to scale. 281the circular economy and agriculture abatement in panel a is measured in per cent levels, regardless of the wwtp size, while in panel b it is measured in per cent referred to ie. the graphs reveal the existence of a threshold for a size of about 10,000 ie, below which mbbr proves to be more cost-effective and above which the scm is more cost-effective. in the emilia-romagna region, as exposed in table 3, about 70% of the treatment plants are smaller than the estimated threshold, but do not reach the 5% of the wastewater treated in the region. this consideration, on one hand, implies the wider opportunity offered by the new technology to retrofit primary stage (small) plants at relatively low costs, improving p abatement and reducing pollution loads on water resources. on the other hand, however, the scarcity of technical information does not allow us to elaborate a more comprehensive cost-benefit analysis on the use of mbbr. for the scm, instead, a preliminary cost-benefit analysis has been elaborated on the basis of both the presented technical data at the regional level and the technical data of the scm, for a plant size of 120,000 ie. figure 4 indicates the relative costs and benefits for the employment (retrofit) of a scm based on a secondary depuration stage wwtp (about 55% abatement of p). the annual benefits are computed by accounting for the reduction of operative costs, mainly due to lower maintenance and operations, the relative reduced mud production and the revenue from the sale of struvite as fertilizer. the costs, instead, take into account the annualized capital costs, the specific factors needed to separate the struvite and the electricity. the balance returns positive, especially for technical reasons due to the fact that the struvite is a direct residue of the treatment procedure that obstructs the tubes and, hence, needs to be removed (a cost for the depuration procedure). figure 4. costs and benefits of the use of a scm in a 120,000 ie plant. 0 20.000 40.000 60.000 80.000 100.000 120.000 140.000 benefits (€) fertilizers (struvite) selling reduction of operative costs for recovering pumps and tubes reduction of reagent centrifugation for discaling reduction of polimeries used for discaling reduciton of produced muds due to production of struvite reduciton of produced muds due to reduction of reagents for smell abatement reduction of reagents for smell abatement 0 10.000 20.000 30.000 40.000 50.000 60.000 costs (€) annual capital cost struvite reagents (mgcl) electricity source: own elaboration. 282 m. vollaro, f. galioto, d. viaggi based on the cost and benefit estimations for struvite production and by allowing for a range of possible prices for p fertilizer, a market-based cost-benefit analysis is provided in figure 5. as shown, a net convenience in investing in a scm retrofit for large-sized wwtps might be obtained for prices of p fertilizers greater than the 150-200 €/t range. over such range, the profitability should linearly increase for higher values of p prices. this simulation, together with the results of the cea, implies non-linear profitability of installation of scms for increasing wwtp size. figure 5. market-based cost-benefit analysis for the employment of a scm in a 120,000 ie plant. 0 20.000 40.000 60.000 80.000 100.000 120.000 140.000 0 100 200 300 400 500 600 v al ue (€ ) sale price of p fertilizer (€/t) benefits costs source: own elaboration. with regard to the adoption of scm, a concise representation of the results could be expressed in a twofold perspective: the production costs (transformation value) of struvite diminish for increasing sizes of wwtps (in terms of ie) and the relative profitability of installing (retrofitting) a scm, given by the substitution value of the struvite, increases for increasing prices of chemical p-fertilizers. having estimated the economic value of the struvite on the basis of the value of current p fertilizer title, the results of the profitability analysis indicate a substitutability rate of 1.2 (in terms of p title), which would be sufficient to cover about 2% of the regional territory. the environmental impacts of mbbr and scm adoption can be identified in the significant reduction in p-nutrient leaching in water bodies, leading to a consistent reduction of environmental damage. these considerations, however, make it possible to infer that, from an environmental perspective, the implementation of the chemical p removal processes is better off for the scm than for the mbbr processes. 7. discussion and conclusions the analytical approach and related results have shown that the implementation of innovative technologies applied to wwtps (in particular for the introduction of scm) 283the circular economy and agriculture have the potential to improve pollution abatement and to reduce abatement costs. furthermore, the results indicate that the production of an alternative and sustainable source of p fertilizer, namely struvite, show decreasing marginal costs for increasing size of the wwtp (in terms of ie). this outcome supports the outlined theoretical framework of circular economy according to which the alternative supply of recycled p-fertilizers would become profitable and, hence, represent an environmentally and economically sustainable substitute to chemical p-fertilizers, inducing a virtuous cycle of consumption and production of fertilizers. although the approach is limited to specific conditions (p recovery from wwtps only and from one region), the paper is able to demonstrate that, for an outlook of increasing food demand and the need for enhancing food security while preserving/ improving environmental conditions, the investments in (social, economic and environmental) sustainable technologies might stimulate confidence in devising measures/policies that respond to a circular economy paradigm. improvements for this line of research might be possible by widening the scope of the analysis by including animal manure as raw material for p recovery and by considering a larger geographical territory. another factor that limits the analytical reach of the paper is the lack of specific technical information concerning the functionality of the mbbr technology, which has prevented a specific cost-benefit analysis. the main message this article intends to convey is that economic approaches, especially within the framework of the circular economy, that are employed to evaluate environmentally beneficial innovative alternatives, should play a greater role in contributing to satisfy environmental targets, in an economically sustainable manner, through the identification of the most promising innovations. this can be instrumental in determining a rational and efficient level of pollution/abatement in a watershed, which is considered to be a very important policy objective (ancev et al., 2006). according to ancev et al. (2006), indeed, the efficient target should not be identified as a single number for any given watershed. rather, its value will be dependent on the abatement options available, and on the policies targeted at incentivizing the adoption of those options. as an expected consequence, it can be stated that instead of imposing exogenous environmental targets, policy makers ought to consider the costs of environmental damages and the abatement options available to the polluters. the targets can be set in such a way that the total costs i.e., the sum of abatement and damage costs are minimized (ancev et al., 2006). acknowledgments this paper has been elaborated within the project “set up of protocols for the interdisciplinary assessment of technologies for the improvement of water cycle” funded by the basic research academy fund (farb) of the university of bologna. the authors wish to convey special gratitude to prof. claudio ciavatta, prof. claudio marzadori, dr. marco grigatti, dr. luigi sciubba, dr. cesare accinelli and dr. mariangela mencarelli of the university of bologna for having provided the technical data on the mbbr and scr technologies. 284 m. vollaro, f. galioto, d. viaggi references ancev, t., stoecker, a.l., storm, d.e. and white, m.j. 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(2014). thresholds models of technological transitions. environmental innovation and societal transitions 11: 54-70. websites us geological survey: http://minerals.usgs.gov/minerals/pubs/commodity/phosphate_ rock/ arpa (istat): http://dati.istat.it/index.aspx?datasetcode=dccv_impdep cciaa modena: http://www.borsamercimodena.it/listino.asp?dat=30/05/2016&tip=1&no megr=concimi+chimici,+anticrittogamici,+antiparassitari&idgr=18&anno=201 6&set=22 fertilizers working group (2014): 5a com position paper_final.pdf; (the files can be found under the tab “additional information”) bio-based and applied economics 8(1): 1-2, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-8143 editorial the future of bio-based and applied economics daniele moro1, fabio gaetano santeramo2, davide viaggi3 1 università cattolica del sacro cuore, piacenza, italy 2 università degli studi di foggia, italy 3 università di bologna, italy bio-based and applied economics, the official journal of aieaa (associazione italiana di economia agraria ed applicata), was founded in 2011 and published the first issue at the beginning of 2012. bae is now a well-established international journal, currently indexed in several scientific databases, including isi-web of science and scopus. its citation performances have been ever growing and are expected to grow further during the next years. years from 2012 to 2019 have been characterised by major changes in the profession and in the editorial practices. the choice to give the journal a thematic focus on one of most innovative aspects of the profession (the emerging bioeconomy), as well as encouraging contribution from all the most traditional areas, has been a distinguishing appreciated choice. several other new topics have meanwhile emerged in the economic literature, such as ecosystem services, climate change, digitalisation and the circular economy, accompanied by new approaches to study complex human, firms and markets behaviour. the academic context has changed even more dramatically. publishing has become more competitive and provides continuous stimuli to operate in new and more effective ways. the open access approach (chosen by bae since the beginning) is becoming the new normal for publications; authors are expecting quick reactions and timely decisions; linkages with social media and diffusion of the papers published has become a key strategic feature. bae has always tried to keep pace with changes in the surrounding environment, but 2019 will represent a major milestone in this direction. this editorial is aimed at presenting the state-of-art of bio-based and applied economics (bae) and the main changes that have led to a major reorganization of the journal. bae continues to be a free open-access on-line journal promoted by aieaa, and to welcome contributions on the economics of bio-based industries at large. the journal is open to topics related to agriculture, forestry, fishery and food, and is also open to submissions of related disciplines, such as resource and environmental economics, consumer studies, regional economics, innovation and development economics. in order to face the challenges listed above, and to deal with a fast-increasing number of submissions, coupled with a higher quality of submitted manuscripts, bae has gone through a deep reorganization of the board. the team is now composed by two editors in chief, responsible of the overall management of the journal, one managing editor and three associate editors that oversee the peer-review process. the editorial team also 2 d. moro, f.g. santeramo, d. viaggi includes an editorial assistant that helps managing submission, proofs, and dissemination of information related to bae through media and social networks. apart from the renewal of the editorial structure, bae has been interested by a major transition to a new online platform, which provides improved services for authors and readers of the journal. the new platform is designed to be more user-friendly and to increase the visibility of manuscripts hosted in bae. the editorial strategy has been improved as well. the new board is working to ensure a faster process from submission to publication. first, the board has started a more explicit policy on suggested reviewers: each author is asked to suggest a list of potential reviewers (that do not have conflicts of interests) that are likely to be willing to review the submission. second, the eic and the ae are encouraged to complete a timely review process by following a protocol designed to having a first (editorial) decision within fifteen days and, for papers sent out for revision, a first round completed in three months. a third change is the inclusion of junior reviewers, a new strategy that is expected to have good impact on the process. in addition, the board has started again to publish a very limited number of invited papers, authored by emerging or widely recognized experts. the rationale of publishing a limited number of invited papers is to guarantee space to host articles on topics that are considered of particular interest for the readers of bae. the board will also renew the tradition of open calls for special issues that will now be managed by guest editors, in charge of proposing the theme of the special issue and of managing the entire review process, under the constant supervision of the eic. the journal will encourage proposals for review articles that are likely to provide a valuable synthesis on the state of art on selected topics. last but not least, bae has now social media profiles (e.g. facebook, linkedin and twitter) that help communicating news and events related to the journal. we believe these changes will provide a strong input to the growth of bae. the future of our profession will be certainly characterised by even faster changes in the topics, in the scale of analysis and in the methods able to match the emerging new problems. the dialogue with society will also become more important as well as the ability to valorise the role of research in a world characterised by a high amount of information, but difficulties with interpretation and growing complexity of processes leading to action. this will affect the whole policy of aieaa, looking at the perspective role of scientific associations as key actors in a context of worldwide transformations. and, of course of bae, being one of the flagship initiatives of the association. in turn, the ability of bae to be an active actor in detecting and promoting scientific debate on such new issues, as well as taking up the challenges and the opportunities, and anticipating (or even leading) transitions, will be key for the future of the journal. in such a dynamic context, the changes listed above are for sure not definitive, rather a key step for enabling bae to ensure timely and proactive adaptations to the future. further changes are expected already in 2020, with a partial renewal of the editorial board as well as with initiatives boosting the connection with the associates and with the scientific community. the future of bio-based and applied economics daniele moro1, fabio gaetano santeramo2, davide viaggi3 how did farmers act? ex-post validation of linear and positive mathematical programming approaches for farm-level models implemented in an agent-based agricultural sector model gabriele mack*, ali ferjani, anke möhring, albert von ow, stefan mann the impact of assistance on poverty and food security in a fragile and protracted-crisis context: the case of west bank and gaza strip donato romanoa, gianluca stefania,*, benedetto rocchia, ciro fiorilloa assessing price sensitivity of forest recreational tourists in a mountain destination gianluca grilli1,2 estimating a dual value function as a meta-model of a detailed dynamic mathematical programming model claudia seidel, wolfgang britz bio-based and applied economics 6(2): 159-182, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-19162 once part-timer always part-timer? causes for persistence in off farm work state of farmers alessandro corsi1,*, cristina salvioni2 1 department of economics and statistics “cognetti de martiis”, università di torino, italy 2 department of economics, università di chieti-pescara, italy date of submission: 2016 13th, october; accepted 2017 23th, may abstract. off-farm labour participation is an important way in which farm households adapt their labour resources to farm labour needs, and is often viewed as an income integration and an insurance against risk. nevertheless, it has also been questioned as a step for exiting agriculture. it is therefore important to assess whether or not it is a permanent status and which are its determinants. most papers on this issue are based on cross-sectional analyses and thus disregard the problem of persistence in the state. using a 5-wave panel of italian family farms we estimate different dynamic nonlinear panel data models of the determinants of off-farm labour participation. we allow for two sources of persistence: unobserved heterogeneity and state dependence, and in addition we control for the initial conditions problem. we find a strong persistence in the state and our findings show that, when taking all these features into account, the present work state is almost totally explained by the previous state and by idiosyncratic characteristics. the variables concerning the farm and the farmer’s characteristics, typically found to be relevant in cross-sectional analyses, are not significant in the dynamic setting. the reasons for the inconsistency between our results and those of cross-sectional studies are discussed, and an interpretation of how the determinants influence the off-farm labour participation is presented. the distinction between true state dependence and individual heterogeneity has important policy implications that are discussed. keywords. off-farm work, farm household, state dependence, panel data, cap reform. jel codes. j220, j430, q120. *corresponding author: alessandro.corsi@unito.it 160 a. corsi, c. salvioni 1. introduction agriculture in most western countries is characterized by an overwhelming share of family farms, which means that farm operation and farm household are tightly linked1. in particular, the labour allocation of household members between farm and off-farm labour is an important choice for the household. indeed, the off-farm labour participation of farmers is a structural feature of both developed and developing economies, and off-farm income is an increasing part of farm household income (oecd, 2003; eurostat, 2002). it is part of the secular trend in declining agricultural employment, as productivity in agriculture increases and, due to the finite nature of land, no additional productive and employment basis can be created in the sector (except for intensification). combining onand off-farm work, at the individual and/or at the household level (pluriactivity), may indeed be an efficient use of the labour resources of households. taking into account income opportunities stemming from the farm and from alternative employments allows the growth and stabilization of household income. nevertheless, part-time farming has also been considered for its influence on the prospects of farms. a discussed related issue is whether it favours the survival of family farms or it is a step towards the exit from farming, with contrasting results. for instance, weiss (1999) found a negative impact of part-time farming on farm survival, while breustedt and glauben (2007) found the opposite. these papers are part of a stream of literature dealing with the changes in agricultural labour. this stream is theoretically based on todaro’s (1969) and harris and todaro’s (1970) model of labour migration, extended by barkley (1990), and uses aggregated data (e.g. breustedt and glauben, 2007; d’antoni et al., 2012; petrick and zier, 2011, 2012; olper et al., 2014). their focus is on the net exits from farming, and they do not deal with the contemporary presence of on-and off-farm work. our focus is rather on the process of combining on-and off-farm labour and in our perspective it is important to ascertain whether off-farm labour participation is a permanent status. one important feature in this respect is the flexibility of off-farm work status, i.e., to what extent farmers can and do enter and exit an off-farm work status, and for which reasons. hence, our theoretical reference is the literature on farm household models. starting from the seminal paper by huffman (1980), labour choices in farm households have been widely analysed, initially considering the off-farm labour participation of farm operators (sumner, 1982) and subsequently considering the joint decision-making of off-farm labour participation of husbands and wives (e.g. huffman and lange, 1989; tokle and huffman, 1991; lass and gempesaw, 1992), in addition to including the use of waged labour (benjamin et al., 1996; blanc et al., 2006) and the onand off-farm labour participation of operators, spouses and other household members (kimhi, 1994, 2004; benjamin and kimhi, 2006; bjørnsen and biørn, 2010; corsi and salvioni, 2012; biørn and bjørnsen, 2015). most of the research has concerned the u.s., but several analyses are also devoted to the determinants of off-farm labour participation in the eu (corsi, 1994; benjamin et al., 1996; weiss, 1997; woldehanna et al., 2000; juvančič and erjavec, 2005; benjamin and kimhi, 2006; salvioni et al., 2008; bjørnsen and biørn, 2010; corsi and salvioni, 2012; biørn and bjørnsen 2015). 1 the high supervision costs of hired labour, due to the technical features of agricultural production, have been noted to explain this situation: by contrast, family labour does not require supervision because family members are involved in the income that it provides (pollack, 1985). 161causes for persistence in off farm work state of farmers a common feature of this stream of research, with a few exceptions, is that the analyses are based on cross-sectional samples2. this approach nevertheless prevents analysing the dynamic nature of off-farm labour participation choices and disregards the persistence of the phenomenon. persistence means that individuals that have experienced the event under study in the past are more likely to experience it in the future than those who have not experienced it in the past. however, the reason for persistence can be true state dependence and/or heterogeneity (heckman, 1981a). true state dependence exists when the previous state modifies the attitudes, constraints or parameters so that the probability of the present state is affected by the previous state. true state dependence has a truly behavioural origin. for instance, when taking on an off-farm job implies sunk search costs, this creates a difference between the choice of taking on a new off-farm job and the choice of continuing keeping an off-farm job. or, if an off-farm job is taken on, there might be costs related to the adaptation of the farm operation to the new situation (e.g., higher mechanization or a change in the type of farming or in the farming intensity to cope with lower family labour input), costs that are not incurred if the farmer already has an off-farm job. obviously, the same holds for the change in opposite direction, i.e., from off-farm occupation to no off-farm occupation. this makes the continuation of the offfarm state more likely than its change. heterogeneity represents the unobservable time-invariant idiosyncratic characteristics that affect the choices across time periods. while some time-invariant variables influencing the choice can be observed and hence controlled for, some are not measured or are inherently unobservable, such as, for instance, a farmer’s taste or aversion for off-farm work. such variables create a permanent push towards a specific state, so that the previous state apparently affects the future one. persistence and its reason are relevant for policy implications. weiss (1997) notes that “asymmetric adjustment behaviour may cause serious problems for designing an appropriate policy to encourage (or impede) a specific form of agricultural production such as part-time farming”, in particular because policies cannot be easily reversed. by contrast, as noted by corsi and findeis (2000), if heterogeneity is the principal source of state persistence, then more traditional instruments can be used. persistence has actually been initially analysed in two waves panel data (gould and saupe, 1989; weiss, 1997; corsi and findeis, 2000; ahituv and kimhi, 2002; juvančič and erjavec, 2005) and, more recently, in long span studies (bjørnsen and biørn, 2010 and 2015) accounting for both state dependence and heterogeneity. however, in the case of state dependence it is important to control for both the observed and unobserved determinants of initial participation status. it has been shown (arulampalam et al., 2000) that even if the model controls for unobserved heterogeneity, in order to disentangle the effect of state dependence from unobserved heterogeneity, the initial conditions need to be modelled instead of being assumed as exogenously given, since they may be correlated with the unobservables. not accounting for the initial conditions may lead to biased estimates of the effects of true state dependence and individual heterogeneity which, in turn, has important policy implications. 2 even the most recent research addressing issues related to off-farm labour participation like, e.g., the interrelationship between off-farm work and agro-tourism (khanal and mishra, 2014), or between off‐farm labour market decisions and agricultural shocks (mathenge and tschirley, 2015) use cross-sectional data. 162 a. corsi, c. salvioni this paper analyses the off-farm labour participation with a dynamic panel data model3 allowing for both heterogeneity and true state dependence, and handling the initial conditions problem. since our estimation is performed on a panel sample of italian farms for the 2003-2007 period and in this period a major reform of the common agricultural policy (cap) occurred, this study also accounts for the relevant changes4. this paper is structured as follows. section 2 presents the theoretical framework and the econometric strategy. in section 3, the data on which the analysis is based are presented. the estimation results are presented and discussed in section 4, both from the econometric and the factual perspective. the paper is concluded by some final considerations. 2. theoretical model and econometric strategy 2.1 cross-sectional modelling the theoretical model typically employed to analyse farmers’ off-farm labour choices is the well-known farm-household model (nakajima, 1986; singh et al., 1986; huffmann, 1991), which can be adapted according to corsi (2007 and 2008) to take into account the cap reform. the model assumes that the farmer maximizes utility over consumption and leisure, under constraints of income and time. the income constraint comprises both farm income and off-farm wages. the kuhn-tucker maximization conditions yield the following first-order conditions: ∂(p+s)q /∂f ≤ μ/λ (1) w ≤ μ/λ (2) where q is the quantity of good produced by the farm; p is its price; s is the coupled payment per unit of output; f is time spent working on the farm; w is the off-farm market wage; and μ and λ are the marginal utilities of leisure and income, respectively. in a pluriactive farm, equations (1) and (2) hold as equalities: that is, the marginal value product of farm labour (inclusive of the coupled support) is equal to the market wage and to the leisure-income marginal rate of substitution (mrs)5. 3 all panel data model are dynamic in that they take into account the time series dimension of the sample. however, functions that specifically model the effect of lagged dependent variables are usually referred to as dynamic panel data models. 4 the 2003 reform (the so-called fischler reform) has been the most important step in the process of change in the cap from trade and market distorting measures to more neutral interventions, leading to a more decoupled, and hence less market distorting, support (oecd, 2004). its most important measure was replacing the semicoupled subsidies (i.e., subsidies per hectare of specific crops and per animal head) with the fully decoupled single farm payment scheme (sfp), providing income support to farmers regardless of their current production decisions. in italy the decoupling was immediately implemented in the first possible year, 2005. furthermore, disregarding the possibility of introducing forms of regional distribution for the direct subsidies, italy decided to adopt the so-called historical criterion: farmers were granted their sfp according to an entitlement based on the semi-coupled subsidies that they received in the 2000-2002 reference period. 5 using this model, the impact of a direct payment has long been established (el-osta et al., 2004; ahearn et al., 2006; corsi, 2007 and 2008; hennessy and rehman, 2008): a decoupled direct payment is tantamount to an increase in non-labour income. hence, for a farmer participating in off-farm work, a decoupled direct payment 163causes for persistence in off farm work state of farmers the reduced form typically employed in analyses of off-farm labour participation starts by defining the reservation wage w*, obtained by setting the off-farm labour supply to zero and solving for w. the reservation wage is compared to the market wage w, which is a function of personal characteristics and the conditions of the local labour market. therefore, participation (indicated by a dichotomous variable y) occurs if w>w*, which means that prob(y=1) = prob(w>w*). from the theoretical model, w* is a function of the price of agricultural products, variables affecting on-farm labour productivity, and taste shifters. the market wage is a function of personal characteristics that influence human capital and labour market characteristics. typically, the reduced-form equations are probit or logit equations that estimate the influence of these variables on the probability of (w-w*) being greater than zero. four categories of explanatory variables are typically included: individual6, household, farm and local market characteristics. 2.2 dynamic modelling in a dynamic setting, expected utility and expected income streams have to be considered. moreover, the costs for shifting from one work condition to the other are to be taken into account. to a large extent, these costs are sunk costs, such as, for instance, job search costs and costs for adapting the farm to the new work status and the implied labour needs. call c1 the costs for shifting from non-participation to participation and c2 the cost for the reverse change; t the time-horizon for work incomes; r the discount rate; wt the wage that the farmer could earn by working off the farm in time t; f1t and f2t the farm income that the farmer could earn when working and not working off the farm, respectively, in time t. the probability of participation in year t for a farmer not already participating is (for simplicity, we consider utility as only stemming from income; non-pecuniary benefits from different jobs can be easily incorporated): prob yt =1| yt−1 =0( )= prob e t=1 t ∑ wt + f1t − f2t( ) 1 1+r( )t −c1 ⎡ ⎣ ⎢ ⎤ ⎦ ⎥>0 ⎧ ⎨ ⎪ ⎩⎪ ⎫ ⎬ ⎪ ⎭⎪ (3) and the probability of continuing not participating is: prob yt =0| yt−1 =0( )= prob e t=1 t ∑ wt + f1t − f2t( ) 1 1+r( )t −c1 ⎡ ⎣ ⎢ ⎤ ⎦ ⎥≤0 ⎧ ⎨ ⎪ ⎩⎪ ⎫ ⎬ ⎪ ⎭⎪ (4) will decrease off-farm work. however, if production and labour allocation decisions are not separate, then it is unclear which type of work – on-farm or off-farm – will be reduced. by contrast, a decrease in coupled support has both a wealth and a substitution effect. the decrease in the marginal value product of family farm labour induces a reduction of on-farm work. simultaneously, the decrease in income decreases the mrs, which means that the farmer consumes less leisure. hence, the overall result is an increase in off-farm work. given that the cap reform is a combination of an income payment and a decrease in the average revenue, the two effects operate in opposite directions. 6 observable personal characteristics are taken as proxies for individual idiosyncratic preference shifters; some attempt has been undertaken to capture non-pecuniary benefits of farm work through personal statements (howley et al., 2012). 164 a. corsi, c. salvioni the probability of not participating in time t for a farmer who participates in time t-1 is: prob yt =0| yt−1 =1( )= prob e t=1 t ∑ f2t −wt − f1t( ) 1 1+r( )t −c2 ⎡ ⎣ ⎢ ⎤ ⎦ ⎥>0 ⎧ ⎨ ⎪ ⎩⎪ ⎫ ⎬ ⎪ ⎭⎪ (5) or: prob yt =0| yt−1 =1( )= prob e t=1 t ∑ wt + f1t − f2t( ) 1 1+r( )t +c2 ⎡ ⎣ ⎢ ⎤ ⎦ ⎥<0 ⎧ ⎨ ⎪ ⎩⎪ ⎫ ⎬ ⎪ ⎭⎪ (6) hence, the probability of continuing to participate for a farmer already participating is: prob yt =1| yt−1 =1( )= prob e t=1 t ∑ wt + f1t − f2t( ) 1 1+r( )t +c2 ⎡ ⎣ ⎢ ⎤ ⎦ ⎥≥0 ⎧ ⎨ ⎪ ⎩⎪ ⎫ ⎬ ⎪ ⎭⎪ (7) from a comparison of (3) to (7), it can be concluded that, for every strictly positive c1 or c2, prob(yt=1|yt-1=1) > prob(yt=1|yt-1=0). hence, costs for shifting from one condition to another create a state dependence, and not including them among the explanatory variables would lead to an omitted variable bias. since potential shifting costs are not observable, the issue can be addressed by including the past off-farm work state as an explanatory variable in the participation equation as a proxy for the costs for shifting from one condition to another. a further reason for observed persistence in the labour state can be heterogeneity, i.e., farmers’ or farms’ unobservable idiosyncratic characteristics that make participation more or less likely; thus, when observing panel data, heterogeneity translates into an apparent dependence of the present state on the previous state (spurious state dependence). 2.3 econometric model unlike most previous studies, we exploit the panel nature of our data to analyse the determinants of off-farm labour participation, controlling for both true state dependence, created by shifting costs, and heterogeneity, and handling the initial conditions problem. the probability that a farm operator will work off the farm is estimated by applying a dynamic non-linear (probit) random effects model that accounts for both unobserved heterogeneity and true state dependence. the non-linear dynamic random effect model we start from is: prob(yit=1) = φ(β0xit + γyit-1 + ui + εit) (8) where yit is a dummy variable equal to 1 if the farm operator i participates in off-farm work in year t (t=1,…,t), and 0 otherwise, so that yit-1 is used to test the effect of true state dependence; φ is the normal cumulative density function; x is a vector of observable 165causes for persistence in off farm work state of farmers time-variant and time-invariant explanatory variables; β0 and γ are parameters to be estimated; ui is an individual idiosyncratic term that represents time-constant unobservable individual characteristics, i.e., heterogeneity; and εit is a random component, uncorrelated across individuals and years. the standard random effects model assumes that the unobserved individual-specific components ui are uncorrelated with the observed explanatory variables. in the real world, this assumption may not hold. a further issue that may result in biased estimates of the parameter of the lagged variable, and hence of the magnitude of state dependence, is the initial conditions problem – whether yi1 is independent of ui. the problem is caused by the presence in the equation of both the past value of the dependent variable and of an unobserved heterogeneity term and the correlation between them. the treatment of initial conditions is crucial because misspecification will result in an inflated parameter of the lagged dependent variable that measures the magnitude of the cost of shifting employment. different estimators that cope with these issues for the nonlinear dynamic panel data model have been proposed: heckman (1981), wooldridge (2005) and orme (2001). it has been shown (arulampalam and stewart, 2009) on the basis of simulation experiments that none of the three estimators dominates the other two in all cases. in most cases, all three estimators display a satisfactory performance, except when the number of time periods is very small. we estimated all three models to test the robustness of our results, but for the sake of brevity, we present here only wooldridge’s (2001) model. the results of the other models are largely consistent with the results that we present and are reported in the appendix. in wooldridge’s approach, the individual term is assumed to be a function of the initial condition and the means of the explanatory variables7: ui = α0 + α1yi1 + α2μi+νi (9) where yi1 is the state in the first observed year; μi is a vector of the means of the explanatory variables over the entire observed period; and νi is a random individual component, uncorrelated across individuals. the overall model is therefore: prob(yit=1) = φ(α0 +β0xit + γyit-1 + α1yi1 + α2μi+νi +εit) (10) the model is estimated as a random effects probit by a maximum likelihood method. 3. data this study relies on data collected by the italian farm accountancy data network (fadn) survey. the survey started to be conducted on a statistically representative basis 7 to reduce the number of parameters to be estimated, we adopt the popular version of wooldridge’s (2005) solution to the initial conditions problem, and we use the within means of time-varying explanatory variables calculated over the 4 years following the initial one. skrondal and rabe-hesketh (2014) showed that for periods larger than 3 this model does not produce significant bias. 166 a. corsi, c. salvioni in 2003. the sample is stratified according to criteria of geographical region, economic size (european size units -esu) and type of farm (tf)8. the field of observation is the total of commercial farms, that is, farms with an economic size greater than 4 esu (4,800 euro)9. in this study, we employ a 5-wave balanced panel of 3,294 farms for which data were collected in all years from 2003 to 2007. we only kept family farms10. the models are estimated over the 2004-2007 period because the year 2003 is used to define the initial condition. table 1 presents the descriptive statistics for the dependent and explanatory variables over the 5 years11. the dependent variable is a dummy variable that indicates whether the farm operator works off the farm. the share of farms operators who have an off-farm job is 6.6 percent over the entire 2003-2007 period. it increases from 6.3 percent in 2003 to 7.3 percent in 2007, with a drop to 6 percent in 2004. these percentages are much lower than the participation in off-farm work revealed by the usda’s agricultural resource management survey data (fifty-two percent of farm operators) in the us (fernandez-cornejo, 2007) and by the farm structure survey (thirty-six percent of eu-27 family farm managers) in the eu (european commission, 2008). the lower participation rate recorded in the italian fadn is partly because the fadn refers to professional, larger farms that typically participate less in off-farm employment and partly because pluriactivity is less widespread in the southern countries of the eu (european commission, 2008). following previous research, we use four categories of explanatory variables to specify the model for the off-farm participation decision: individual, household, farm and local market characteristics. individual attributes include age, age squared and gender of the operator; it was not possible to control for the effect of education because this information is not collected by the survey. previous research with cross-sectional data typically has found a positive effect of age on participation but with a curvilinear pattern (shown by the age squared variable), reaching a peak and then declining. the household non-labour incomes, i.e., capital income and pensions, are used to explore the existence of a wealth effect. non-labour income (capital and pension income), is –admittedly, poorly – measured by dummy variables. larger values are theoretically expected to have a negative effect on off-farm participation. unfortunately, we can only measure the presence of these types of income with dummy variables, and we have no information on the number and characteristics of other household members. farm characteristics include farm size (in hectares); farm location (mountain, hills and plains as base category); total debts in thousand euro; degree of mechanization (horse 8 fadn defines a farm as specialized in a tf if the standard gross margin (sgm) for the particular production covers more than 2/3 of the total sgm. sgms are obtained on the basis of the farm area (number of heads) and a crop (livestock) and area specific standardised gross margin. 9 small farms are more interested in pluriactivity. though 4 esu (4,800 euro) is a small size, the exclusion of farms below this threshold leads to underestimate the importance of the phenomenon. the results should therefore be interpreted as concerning the observation field of fadn. 10 farms that were not characterized as sole ownership or private partnership were not considered in this analysis. 11 ours is an artificial panel data set obtained from a series of cross-sectional surveys. hence, the weights provided by the fadn, which are based on standard gross margin, standard output, and costs, are no more valid for inference to the general population. therefore, we decided to apply no weight to the observations in our sample. the generalization of our results to the general population should therefore be performed with some caution. 167causes for persistence in off farm work state of farmers power per hectare of used land); working capital in thousand euro; specialization in the production of labour intensive, seasonal and all-year-round types of farming; the presence of direct selling; and dummies for organic farming and agro-tourism. farm size is typically expected to decrease off-farm participation because larger farms are usually more profitable, which makes off-farm labour comparatively less attractive. mountain and hill table 1. descriptive statistics of the variables (2004-7 unless otherwise stated).   mean std.dev. share of off-farm labour participants 2003-2007 0.07 0.07 share of off-farm labour participants 2003 0.06 0.01 share of off-farm labour participants 2004 0.06 0.01 share of off-farm labour participants 2005 0.07 0.01 share of off-farm labour participants 2006 0.07 0.01 share of off-farm labour participants 2007 0.07 0.02 personal characteristics operator’s age 54.19 13.60 operator’s age squared 3121.84 1513.44 operator’s gender (1=m, 0=f) 0.83 0.38 farm characteristics uaa (ha) 33.70 69.74 share of land in property (%) 0.67 0.39 working capital (1000 euro) 128.04 357.75 total debts (1000 euro) 11.87 106.42 hp/uaa 19.03 44.27 types of farming labour intensive all year round (0/1) 0.36 0.48 types of farming labour intensive seasonally (0/1) 0.33 0.47 mountain (0/1) 0.19 0.39 hills (0/1) 0.45 0.50 plains (0/1) 0.37 0.48 direct sales (0/1) 0.21 0.41 organic farming (0/1) 0.04 0.19 agro-tourism (0/1) 0.03 0.16 household characteristics pension income (0/1) 0.23 0.42 capital income (0/1) 0.01 0.11 cap reform coupled payments (1000 euro) 3.48 33.79 single farm payment (1000 euro) 7.84 33.21 economic environment agricultural to total employment (%) 6.35 4.03 va per inhabitant (1000 euro) 21.72 5.23 168 a. corsi, c. salvioni farms are typically characterized by low returns. accordingly, the farm location in mountains and hills should be expected to positively affect the off-farm participation of farm household members attempting to increase total household income with alternative offfarm incomes. nevertheless, these areas typically also provide less employment opportunities, which would have the opposite effect. the higher the debt is, the higher the interest that farmers have to pay to lenders. this situation might provide an incentive to farm households to work off-farm to find new sources of income to pay back the debt. there are no clear theoretical expectations regarding the sign of the coefficient of mechanization. the use of machines could reduce the labour hours required on farms and therefore increase the probability of off-farm labour participation. by contrast, a high investment in machinery could be a sign of the deep commitment of the household in farm activities and, more importantly, could raise farm income, thus discouraging off-farm labour participation. high levels of working capital are expected to increase productivity and to lower off-farm labour attractiveness. off-farm work is also expected to be discouraged in those cases in which farms are specialized in all-year-round labour intensive and, to a lesser extent, in seasonal labour intensive types of faming. the use of organic farming is anticipated to reduce the likelihood of off-farm work, given the higher labour requirements of these farming systems as compared to conventional farming. finally, direct selling, in addition to offering agro-tourism services, is labour intensive and should reduce off-farm activities12. the variables that describe the local labour market and the external economic environment are at the provincial level (provinces are administrative bodies that correspond to the nuts-3 level of eurostat). all data are drawn from national accounting data published by istat, i.e., the national official statistical agency. the agricultural to total employment ratio is introduced to account for employment opportunities outside agriculture13. value added per inhabitant is an overall measure of the degree of economic development of the area where the farm is located and is also a proxy for the opportunity cost of agricultural labour. these two variables are expected to positively influence the probability of off-farm participation because the higher these ratios, the more job opportunities are available. the variable called single farm payments (sfp) is equal to zero before the cap reform implementation in 2005, and is equal to the relevant payment in the following years. hence, it is an indicator of both the policy structural change and of the intensity of the intervention. given that sfp is allocated based on the historical production mix, it can be considered as exogenous (indeed, decoupling aims at making public support exogenous to production choices). the amount of the sfp is farm-specific, and its impact is a pure 12 some of these variables (in particular, the share of rented land, the mechanization variable, and the semicoupled payments, presented below) may be suspected of being endogenous because, in principle, the off-farm labour choice can influence them. we tested the use of lagged values of these explanatory variables to overcome the possible issue, but the results were almost identical to those presented here. the results are available from the authors upon request. 13 it would have been desirable to introduce other important variables concerning local labour markets, the activity rate and the employment and unemployment rates. unfortunately, in 2004 istat changed the methodology of the labour force surveys; thus, the series from 2004 onward is not comparable to that of previous years. for this reason, we could not add these variables. regardless, if these variables are introduced in the models for the 20042007 period, they are never significant. 169causes for persistence in off farm work state of farmers wealth effect. prior to the introduction of the sfp, eligible farms received partially coupled payments (per hectare or per animal head) represented by the relevant variable. the overall effect of these latter payments is ambiguous because they may have both an income and a substitution effect. it is important to note that all the above expected effects of the explanatory variables refer to the static farm household model. hence, they are what the theory predicts when changing state implies no cost and no change in farm setting. 4. results 4.1 econometric results table 2 shows the results of the estimates. in addition to the results of wooldridge’s model, we report the estimation of a static pooled probit model that assumes no heterogeneity and no state dependence. this estimation is tantamount to having cross-sectional estimates, which means that, in a sense, it is a benchmark for “traditional” cross-sectional analyses of off-farm labour participation. both models are estimated for the 2004-2007 period to allow comparisons. we also report the estimates of the marginal effects of the variables on the probability of off-farm labour participation. as usual, they are estimated at the mean values of the continuous variables and at the median value for the dichotomous variables. column [1] gives the standard pooled probit estimates. actually, the results are largely similar to those typically found in cross-sectional analyses. the probability that the farm operator participates in off-farm work is found to be affected significantly by the idiosyncratic characteristics of the farmer (age, sex) and of her household (the presence of pensions and of capital income), whereas no statistically significant influence is found for the socio-economic conditions at the provincial level. regarding the farm characteristics, higher mechanization rates, higher percentages of owned land, the farm being located in mountainous areas and making use of direct selling, and a higher single farm payment increase the probability of working off-farm. by contrast, the use of organic farming techniques, higher percentages of working capital and being specialized in the production of all-yearround labor-intensive agricultural products significantly decrease the probability of the operator working off-farm. all these results are commonly found in cross-sectional studies. column [2] gives the estimates of wooldridge’s model, which accounts for the effect of yt-1, i.e., the past off-farm labour state (state dependence), and controls for the initial conditions and heterogeneity14. the past state parameter is highly significant15. the cor14 we also estimated two further models. the first model (dynamic pooled probit) accounted for state dependence but did not address heterogeneity or the issue of the initial conditions. the parameter related to the past state was strongly significant and large. in addition, this model, compared to the pooled sample model, presented a dramatic increase in the log-likelihood, and a likelihood ratio test strongly rejected the restriction implied by the static pooled sample. important changes also occurred even in the covariates, because the parameters of all individual characteristics and of several farm characteristics were no more statistically significant.. a further model (dynamic random effects) added the state in the initial year and allowed individual heterogeneity. these results are presented in the appendix. 15 as a robustness check, the past state parameter has been compared to the estimates of the other models. to compare the parameters of random effects models, they must be rescaled by multiplying them by 1 ρ− where 170 a. corsi, c. salvioni table 2. estimates of the models of off-farm labour participation. [1] pooled [2] wooldridge coeff. std.err. marg. eff. coeff. std.err. marg. eff. y(t-1) 1.234*** 0.127 0.048 y(0) 2.724*** 0.293 0.107 personal characteristics     operator’s age 0.011 0.010 0.029 0.040 0.001 operator’s age squared -0.000*** 0.000 0.000 0.000 0.000 operator’s gender (1= male) 0.258*** 0.050 0.045 0.321 0.002 farm characteristics     uaa (ha) -0.001 0.000 -0.000 -0.004 0.006 0.000 share of land in property (%) 0.370*** 0.051 0.040 0.140 0.350 0.006 working capital (1000 euro) -0.001*** 0.000 -0.000 0.000 0.001 0.000 total debts (1000 euro) 0.000 0.000 0.000 0.000 0.002 0.000 hp/uaa 0.001** 0.000 0.000 0.001 0.005 0.000 tf labor int. all year round (0,1) -0.380*** 0.048 -0.038 0.129 0.239 0.005 tf labor int. seasonally (0,1) -0.091** 0.043 -0.010 0.258 0.223 0.010 mountain (0,1) 0.107** 0.050 0.012 0.276** 0.137 0.011 hills (0,1) 0.027 0.042 0.003 0.140 0.121 0.006 direct sales (0,1) 0.175*** 0.043 0.021 -0.019 0.133 -0.001 organic farming (0,1) -0.296*** 0.101 -0.026 -0.135 0.401 -0.005 agritourism (0,1) -0.094 0.116 -0.009 -0.906 0.623 -0.036 household characteristics     pension income (0,1) -0.271*** 0.049 -0.026 -1.384*** 0.127 -0.054 capital income (0,1) 0.641*** 0.129 0.111 0.695** 0.336 0.027 cap reform     coupled payments (1000 euro) -0.002 0.002 0.000 -0.001 0.005 0.000 single farm payment (1000 euro) 0.002*** 0.001 0.000 0.002 0.002 0.000 economic environment     ag. to total employment (%) 0.001 0.006 0.000 -0.005 0.071 -0.000 va per inhabitant (1000 euro) 0.000 0.005 0.000 0.085** 0.039 0.003 2004-2007 averages1     pension income (0,1) 1.205*** 0.197 0.047 tf labor int. all year round (0,1) -0.607** 0.273 0.014 constant -1.660*** 0.290 -2.839*** 0.776 rho 0.573*** 0.053 log likelihood -3030.73 -1509.54 chi-squared (df) 438.25*** (21) 3480.62*** (41) 1 only significant four-year averages variables are reported. *, **, *** = significant at the 10%, 5%, 1%, respectively. 171causes for persistence in off farm work state of farmers relation coefficient, which measures the effect of unobserved heterogeneity, is 0.573 and is also highly significant. hence, the results suggest that both true state dependence and heterogeneity are important in determining persistence. heterogeneity represents individual unobservable factors, but its effect is symmetrical, as is the case with the other covariates, and the issue of heterogeneity is basically an econometric issue, implying biased estimation if not properly controlled. by contrast, true state dependence creates an asymmetry in farmers’ behaviour and is an economic issue that stems from true changes in the farm setting. the existence of true state dependence is not simply an econometric issue; rather, it is a behavioural issue. furthermore, the means of the explanatory variables, i.e., the parameters involved in the correction of the potential bias due to the initial conditions, are also significant. this finding suggests that coping with the initial conditions issue is important to avoid biased estimates16. to measure the effect of the past off-farm labour state on present participation, in addition to the marginal effect (me) shown in table 2, we calculated the average partial effect (ape). the former is an estimate of the change in probability of the outcome due to a unit change of the relevant dummy variable, which is evaluated at the mean values of the independent variables (or at their median, in the case of dummy variables) and can be interpreted as the effect of the relevant variable for a “representative” farm. the latter is an average of the difference between the probability of the outcome when the past state is set to one and when it is set to zero for each farm at the actual values of the variables. the results of the mes and apes are 0.048 and 0.058. that is, the probability that the present state is participation is approximately 5 percent higher if the past state was participation. this finding provides a measure of the part of persistence that is due to heterogeneity. in the dynamic model (not shown here), not accounting for heterogeneity or for the initial state, the percentages are much higher (0.175 for ape, 0.686 for me). the reason is that, in the dynamic model, the past state variable also absorbs the effect of the unobservable idiosyncratic characteristics17. hence, the comparison with the model accounting for heterogeneity suggests that an appreciable part of persistence is due to heterogeneity. although our results provide strong evidence that there is indeed persistence in offfarm labour participation and that both heterogeneity and state dependence determine it, it is difficult to appreciate how much of the persistence is due to either. however, the above-mentioned considerations would suggest that heterogeneity could be of greater importance than true state dependence. this notion contrasts with the results of biørn and bjørnsen (2015), who actually find very low levels of covariance among the statespecific random effects that account for heterogeneity. this can be due to the fact that they do not control for the initial conditions problem, which may induce an overestimate of true state dependence (heckmann, 1981b). from their study, it is nevertheless unclear ρ is the constant cross-period error correlation (arulampalam, 2009). after rescaling, the past state parameters for the heckman, orme, and wooldridge models are of remarkably similar magnitude (0.874, 0.763 and 0.806, respectively). 16 the past state parameter estimated in the dynamic random effects model, including the past state and the initial state but not controlling for the initial state bias, is 0.704 compared to 0.773 in wooldridge’s model (rescaled values), thus suggesting that not controlling that bias would cause the effect of the past state to be underestimated. 17 vice versa, excluding the past state variable would cause the effect of the other variables to be overestimated; see ahituv and kimhi (2002). 172 a. corsi, c. salvioni what the relative weight of true state dependence is, although they also find evidence of persistence. in summary, from the econometric perspective, the main conclusions are that crosssectional pooled estimation is rejected vs. models that account for heterogeneity and state dependency, and that correcting for the initial state is also crucial. 4.2 effects of the variables from the factual perspective, after introducing state dependence and heterogeneity and controlling for the initial state conditions, none of the idiosyncratic farmer observable characteristics, usually found to be significant in cross-sectional models, is found to have statistically significant effects on the probability that the operator works off the farm (see section 4.3 for a discussion of these differences in results). the two variables related to household income (pensions and capital income) are strongly significant. if the household has any capital income, the probability of off-farm labour participation is significantly increased, albeit in a limited measure. at the mean values of the variables, the probability is increased by 2.8 percent. this finding is not consistent with our expectations and with the results of previous studies (mishra and goodwin, 1997; mishra and goodwin, 1998; mishra and holthausen, 2002), according to which farm household wealth acts as a substitute for off-farm work. this effect is interpreted as a lower need for extra work income because wealth helps these farms to smooth consumption when income falls. a possible interpretation of the positive result is that off-farm employment may be induced by high off-farm wages that, in turn, provide more financial assets that yield capital incomes18. if a household member has a retirement or invalidity pension income, then the probability that the farm operator has an off-farm job, as expected, significantly decreases. in this case, the marginal effect is -5.4 percent. a possible interpretation is that, for poor farm households, off-farm income that stems from pensions of other members decreases the income needs, thus raising the off-farm reservation wage of the operator. alternatively, the old age of the operator and of the other members of the family make them less interested in off farm work. finally, the presence of elders and individuals with a disability in the household increases the time that is devoted to caring activities, thus discouraging off-farm work (salvioni et al., 2008). a farm’s being located in mountain areas has a significant and, as expected, positive effect. farms in those areas are typically less profitable, which might induce more pluriactivity. the value added per inhabitant is significant and, as expected, positive. this result confirms our expectations about larger probabilities of off-farm work in more economically developed areas i.e., where more job opportunities are available and the alternative income provided by off farm jobs is higher. all-year-round labour-intensive types of 18 remark nevertheless that if this were the case, a problem of endogeneity would result (we are grateful to a referee for drawing our attention to this point). though, applying to non-linear models, such as the one we estimate, the 2sls method usually employed in linear models leads to “forbidden regression” (angrist and pischke, 2009). forbidden regressions produce consistent estimates only under very restrictive assumptions that rarely hold in practice (see for instance wooldridge, (2010), pp. 265-268). the risk of producing inconsistent estimates further increases when the endogeneity problem is referred to a binary explanatory variable such as capital income in our model. 173causes for persistence in off farm work state of farmers farming, the share of land in property and the amount of working capital are not significant in wooldridge’s model19. neither the variable of coupled payments (before the cap reform) nor the variable of the single farm payment (after the reform) exhibits significant effects. again, this finding makes a difference in respect to the pooled model, for which the sfp variable is strongly significant. regardless, the sign is negative for semi-coupled payments and positive for sfp. the former sign would be consistent with theoretical predictions and would suggest that the substitution effect of coupled payments dominates the wealth effect. by contrast, the positive sign of the sfp would contradict the theoretical expectations, at least if production choices were separable from consumption and family labour allocation choices (if not, there are no a priori predictions). the non-significance of decoupled payments is not consistent with the results of many previous parametric us studies (dewbre and mishra, 2002; el-hosta et al., 2004; goodwin and mishra, 2004; ahearn et al., 2006; serra et al., 2005), which have found – indeed, very small20 – effects of decoupled payments on farm and off-farm labour and on total work hours, but they support more recent results (pandit et al., 2013), which find that neither direct nor indirect government payments have any impact on the off-farm labour supply of farm operators. also olper et al. (2014) find for the eu that the evidence of a negative effect of decoupled payments on agricultural outmigration is weak in a dynamic setting, and in no model by petrick and zier (2012) do direct payments have any effect on agricultural employment. the cap reform explicitly attempted to substitute decoupled for coupled payments without substantially changing the total expenditure. hence, even from a theoretical perspective, its effect is ambiguous (corsi, 2007 and 2008), and its weak effects on off-farm participation, if any, are not surprising. 4.3 discussion in this paragraph, we discuss the meaning and the implication of persistence in terms of farmers’ behaviour and of policy implications, by comparing cross-sectional and dynamic panel analyses. most likely the most striking result – also robust across all estimated models – is that, after introducing state dependence and heterogeneity and controlling for the initial state conditions, in none of these dynamic models do the idiosyncratic farmer observable characteristics have statistically significant effects on the probability that the operator works off the farm. additionally, many of the farm characteristics found to be significant in cross-sectional models are no more significant in this dynamic setting. although more research is needed to generalize our results, our findings are at odds with an entire stream of literature based on cross-sectional samples (including some of our previous studies), where personal, farm and labour market characteristics are typically found to be important determinants. we conclude that past experience has a genuine behavioural effect in the sense that otherwise identical individuals who did not experience participation would behave differently from those who did experience it.  in other 19 their coefficients are nevertheless significant in both the heckman and orme specifications. 20 for instance, the coefficients estimated by dewbre and mishra (2002), although statistically significant, indicate that a thousand-dollar payment decreases the total work by approximately or less than one hour per year. regarding off-farm work, the largest estimate is that by ahearn et al. (2006), who estimate that a thousand-dollar payment decreases the probability of off-farm labour participation by 0.4-0.5 percent. 174 a. corsi, c. salvioni words, we find structural state dependence. we also find that unobservable idiosyncratic characteristics do have an influence on off-farm labour choice and most likely have a stronger effect, but we find scarce evidence of an effect of observable farm and personal characteristics. importantly, this difference emerges when estimating different models from the same data and comparing the dynamic model accounting for heterogeneity and state dependence vs. the pooled model equivalent to a cross-sectional setting. thus, the problem is how to explain the relevance of the personal and farm characteristics found in cross-sectional studies. this problem calls into question the interpretation given to the results of cross-sectional studies, that are typically interpreted in causal terms, implying, e.g., that an older age or a smaller farm size makes farmers more likely to take on an off-farm job. tentatively, we conjecture that the relationship between personal and farm characteristics, such as age and farm size, found in models in which past experience is not taken into account could be interpreted in the following manner: i) some unobservable characteristics, of both the farm and the farmer, exert a permanent push in the direction of a particular state. for example, low soil fertility (hence, unobservable lower returns from the farm) or a farmer’s preference for non-agricultural work, may create a tendency to look for an off-farm job. ii) some observable characteristics of the farm and the farmer (such as small farm size or education) also create a tendency to take on an off-farm job. iii) these two groups of variables influence the choice whether to take an off-farm job the first time it is made, which is nevertheless contingent on the availability of sufficiently remunerative job opportunities and on a sufficiently low cost of changing status. iv) the influence of the age variable found in cross-sectional studies is therefore actually because an older age means that, for those who look for an off-farm job, more years have passed, which implies that more opportunities of finding such a job may have occurred. v) once the choice is made, the off-farm work status tends to remain the same because some farm setting is changed after this choice, because the unobservable and observable characteristics remain at work, and because of the costs implied by changing status again. hence, once a status is chosen, the subsequent situation is dominated by the previous situation; thus, the influence of the observable variables apparently vanishes. in this sense, one might think of an “off-farm job trap” and of a “full-time on-farm job trap”. this interpretation of the discrepancy between the cross-sectional and the dynamic models in terms of “status traps” should nevertheless be made with some caution, given that our panel is not very long, even if it has been proven that 4 years are enough to produce valid and reliable results (arulampalam and stewart, 2009). anyway, we feel that our results deserve attention on a possible misinterpretation of the results of cross-section models. the effect of personal and farm characteristics are at risk of being severely exaggerated if the econometrician fails to recognise the presence of persistence. hence, our results and their interpretation also have policy implications. if pluriactivity is considered by policy makers to be desirable, e.g., because it helps maintain the agricultural activities in a rural area and thus prevents depopulation, then appropriate policies have to be chosen to induce farmers to engage in off-farm activities in the area and to choose not to quit farming. our results suggest that the unobservable specific characteristics of farms and farmers are prevailing in determining persistence. hence, the levers on which to operate are those that permanently change the individual propensity to work off the farm, e.g., focusing on education and development of personal skills. our results 175causes for persistence in off farm work state of farmers show that also true state dependence determines persistence. hence, along with policies affecting individual characteristics, also policies facilitating farmers’ access to the off-farm labour market could be appropriate. given true state dependence, an initial effort is necessary to induce the off-farm labour participation of farmers, but after this initial treatment, incentives are less necessary to sustain the desired behaviour over time. finally, our results suggest that both decoupled and coupled payments are not significant variables in explaining the off-farm labour participation of italian farmers. this is consistent with recent us studies (pandit et al., 2013), while the related literature on the effects of farm subsidies on labour outflow from agriculture gives mixed results. for instance, a negative effect of pillar i subsidies on net labour outflow from agriculture had been found by olper et al. (2014) using panel data at the aggregate level, and similar effects are found for subsidies by breustedt and glauben (2007), though other papers do not find significant effects (glauben et al., 2006; pietrick and zier, 2011 and 2012). remark nevertheless that these results apply to quitting agriculture, not to combining onand off-farm labour. the implication of our results is that policymakers should not increase government spending in the form of agricultural subsidies to reduce unemployment. 5. summary and conclusions in this paper, we examine the issue of the off-farm labour participation of farm operators. we estimate different models that allow both heterogeneity and true state dependence, and that cope with the initial conditions problem, using a panel sample of italian farms. we find a strong persistence in the state, which is mainly explained by unobserved heterogeneity. the past labour state is also an important determinant of the off-farm labour participation choices of italian farmers. these results imply that policies aimed at fostering off farm employment should focus on interventions that permanently change the individual propensity to work off the farm, e.g., focusing on education and development of personal skills. variables typically found to be significant determinants of these choices, and also appearing as such in pooled sample estimations, are no more significant when true state dependence and heterogeneity are controlled for. we also find that the effects of the change in cap, and more generally of coupled and decoupled payments, are weak and generally insignificant. to some extent, our results are at odds with the usual interpretation given to the results of cross-sectional studies concerning the effect of farm and operator characteristics. in general, our findings point to the conclusion that persistence effects are at work, because the choice of a work state modifies the preferences or the constraints that the farm operator faces (e.g., because changing status may imply sunk costs), or because an operator’s unobservable preference toward off-farm work can be met contingent on local job opportunities, and the cumulative probability of finding such a job increases along with time. the discrepancy between our results and those of the traditional cross-sectional studies calls for more research to clarify these issues. promising research lines could include modelling the changes of labour state rather than the states per se, in the line undertaken by biørn 176 a. corsi, c. salvioni and bjørnsen (2015), but coping with the issue of the initial values, or using job search models and modelling the permanence in the states. this task is left for further research. references ahearn, m.c., el-osta, h.s. and dewbre, j. 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(2000). off-farm work decisions on dutch cash crop farms and the 1992 and agenda 2000 cap reforms. agricultural economics 22: 163-171. wooldridge, j.m. (2005). simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity. journal of applied econometrics 20: 39–54. wooldridge, j.m. (2010). econometric analysis of cross section and panel data, cambridge, massachusetts, the mit press. appendix we present here the different models that we estimated for dealing with the problems of true state dependence heterogeneity, and the initial conditions issue. in the following yit is a dummy variable equal to 1 if the farm operator i participates in off-farm work in year t (t=1,…,t), else 0; x is a vector of observable time-variant and time-invariant explanatory variables; β0 and γ are parameters to be estimated; φ is the normal cumulative density function; ui is an individual idiosyncratic term representing time-constant unobservable individual characteristics, i.e., heterogeneity; and εit is a random component, uncorrelated across individuals and years. the benchmark is the pooled model, equivalent to a cross-sectional model, since it assumes that past states have no effect and that all time-invariant variables are included: 180 a. corsi, c. salvioni prob(yit=1) = φ(β0xit + εit) with corr(εit, εis) = 0 ∀ s≠t (pooled model) a second model accounts for state dependence, but does not deal with heterogeneity nor with the issue of the initial conditions. prob(yit=1) = φ(β0xit + γyit-1 + εit) (dynamic pooled model) the third one adds to the past state the state in the initial year (y1) among the explanatory variables and allows for individual heterogeneity (ui), modelled as a random effect. prob(yit=1) = φ(β0xit + γyit-1 + ιy1 + ui + εit) (dynamic random effects model) the heckman approach to the initial conditions problem involves assuming the following specification of the initial value of the dependent variable: yi1 = zi1π + ηi where zi1 is a vector of exogenous variables including xi1 and other instrumental variables (for example pre-sample variables) and ηi is assumed to be correlated with ui, but uncorrelated with εit for t ≥ 2. then ηi can be written as ηi = θui + εi1 (θ > 0), with ui and εi1 independent of one another. it is also assumed that εit and ui are normally distributed with variance 1 and σu, respectively. the function for the initial time period is therefore specified as yi1 = zi1π + θui + εi1 and this equation is estimated jointly with the one of the remaining time periods. the output is therefore an estimate of the initial year equation, and of the following years equation, including the correlation term. since the initial conditions problem is due to the correlation between the yit-1 regressor and the individual heterogeneity term, orme’s approach is to insert a correction term in the equation. he substitutes ui with another unobservable term uncorrelated with the initial observation, under the assumption that ui and εit follow a bivariate normal distribution. this term: hi = e(ui|yi1) = (2yi2-1)συϕ(ζ’zi /σu)/φ[(2yi2-1) ζ‘zi /σu] is the generalized error term from a probit estimated for the initial year, where z are the covariates, ζ the relevant parameters, ϕ and φ the normal density and distribution functions. the generalized error terms are inserted as regressors in a random effect probit model for the remaining years, so that the overall model is: prob(yit=1) = φ(α0 +β0xit + γyit-1 + τhi +νi +εit) (orme’s model) the following table presents the results of all the above models, also including for comparison wooldridge’s model. 181causes for persistence in off farm work state of farmers ta bl e a 1. e st im at es o f t he m od el s of o fffa rm la bo r p ar tic ip at io n. [1 ] po ol ed [2 ] d yn am ic p oo le d [3 ] d yn am ic r e [4 ] h ec km an [5 ] o rm e [6 ] w oo ld rid ge c oe ff. st d. er r. c oe ff. st d. er r. c oe ff. st d. er r. c oe ff. st d. er r. c oe ff. st d. er r. c oe ff. st d. er r. y( t1) 2. 65 0* ** 0. 05 5 1. 14 3* ** 0. 12 1 1. 41 7* ** 0. 11 5 1. 22 3* ** 0. 11 8 1. 23 4* ** 0. 12 7 y( 1) 2. 95 3* ** 0. 30 2 0. 14 2 2. 72 4* ** 0. 29 3 pe rs on al ch ar ac te ri sti cs             o pe ra to r’s a ge 0. 01 1 0. 01 0 -0 .0 11 0. 01 2 -0 .0 31 0. 02 2 -0 .0 15 0. 02 2 -0 .0 19 0. 02 1 0. 02 9 0. 04 0 o pe ra to r’s a ge sq ua re d -0 .0 00 ** * 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 o pe ra to r’s g en de r ( 1= m al e) 0. 25 8* ** 0. 05 0 0. 09 1 0. 06 5 0. 09 2 0. 13 0 0. 16 4 0. 12 3 0. 20 3 0. 12 8 0. 04 5 0. 32 1 fa rm ch ar ac te ri sti cs             u a a (h a) -0 .0 01 0. 00 0 -0 .0 01 0. 00 1 -0 .0 02 0. 00 2 -0 .0 01 0. 00 1 -0 .0 02 0. 00 1 -0 .0 04 0. 00 6 sh ar e of la nd in p ro pe rt y (% ) 0. 37 0* ** 0. 05 1 0. 26 7* ** 0. 06 7 0. 41 0* ** 0. 13 7 0. 51 5* ** 0. 12 8 0. 39 0* ** 0. 13 4 0. 14 0 0. 35 0 w or ki ng c ap ita l ( 10 00 e ur o) -0 .0 01 ** * 0. 00 0 -0 .0 00 * 0. 00 0 0. 00 0 0. 00 0 -0 .0 01 ** * 0. 00 0 -0 .0 00 ** 0. 00 0 0. 00 0 0. 00 1 to ta l d eb ts (1 00 0 eu ro ) 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 0 0. 00 2 h p/ u a a 0. 00 1* * 0. 00 0 0. 00 0 0. 00 0 0. 00 1 0. 00 1 0. 00 1 0. 00 0 0. 00 1 0. 00 1 0. 00 1 0. 00 5 tf la bo r i nt . a ll ye ar ro un d (0 /1 ) -0 .3 80 ** * 0. 04 8 -0 .2 55 ** * 0. 06 4 -0 .3 92 ** * 0. 12 3 -0 .2 98 ** * 0. 10 9 -0 .4 96 ** * 0. 12 1 0. 12 9 0. 23 9 tf la bo r i nt . s ea so na lly (0 /1 ) -0 .0 91 ** 0. 04 3 -0 .0 32 0. 05 7 0. 04 2 0. 10 9 0. 04 7 0. 09 8 -0 .0 11 0. 10 7 0. 25 8 0. 22 3 m ou nt ai n (0 /1 ) 0. 10 7* * 0. 05 0 0. 13 9* * 0. 06 6 0. 27 2* 0. 14 3 0. 20 2 0. 13 9 0. 27 2* * 0. 13 8 0. 27 6* * 0. 13 7 h ill s ( 0/ 1) 0. 02 7 0. 04 2 0. 04 8 0. 05 7 0. 18 1 0. 12 4 0. 12 4 0. 10 9 0. 13 5 0. 12 1 0. 14 0 0. 12 1 d ire ct sa le s ( 0/ 1) 0. 17 5* ** 0. 04 3 0. 10 9* 0. 05 7 0. 09 6 0. 09 7 0. 13 2 0. 09 0 0. 15 6 0. 09 6 -0 .0 19 0. 13 3 o rg an ic fa rm in g (0 /1 ) -0 .2 96 ** * 0. 10 1 -0 .1 91 0. 13 3 -0 .2 26 0. 25 5 -0 .1 72 0. 21 7 -0 .2 54 0. 25 0 -0 .1 35 0. 40 1 a gr oto ur ism (0 /1 ) -0 .0 94 0. 11 6 -0 .2 08 0. 15 6 -0 .3 56 0. 28 8 -0 .1 42 0. 27 2 -0 .3 20 0. 27 9 -0 .9 06 0. 62 3 ta bl e a 1. (c on tin ue d) . [1 ] po ol ed [2 ] d yn am ic p oo le d [3 ] d yn am ic r e [4 ] h ec km an [5 ] o rm e [6 ] w oo ld rid ge c oe ff. st d. er r. c oe ff. st d. er r. c oe ff. st d. er r. c oe ff. st d. er r. c oe ff. st d. er r. c oe ff. st d. er r. h ou se ho ld ch ar ac te ri sti cs             pe ns io n in co m e (0 /1 ) -0 .2 71 ** * 0. 04 9 -0 .3 62 ** * 0. 06 7 -0 .7 81 ** * 0. 10 2 -0 .7 71 ** * 0. 10 8 -0 .8 18 ** * 0. 10 2 -1 .3 84 ** * 0. 12 7 c ap ita l i nc om e (0 /1 ) 0. 64 1* ** 0. 12 9 0. 53 6* ** 0. 16 6 0. 90 3* ** 0. 21 9 0. 73 5* ** 0. 25 9 0. 86 8* ** 0. 20 9 0. 69 5* * 0. 33 6 ca p re fo rm             c ou pl ed p ay m en ts (1 00 0 eu ro ) -0 .0 02 0. 00 2 -0 .0 02 0. 00 2 -0 .0 02 0. 00 6 -0 .0 01 0. 00 3 -0 .0 02 0. 00 5 -0 .0 01 0. 00 5 si ng le f ar m p ay m en t ( 10 00 e ur o) 0. 00 2* ** 0. 00 1 0. 00 1* 0. 00 1 0. 00 2 0. 00 2 0. 00 2* 0. 00 1 0. 00 2 0. 00 2 0. 00 2 0. 00 2 ec on om ic e nv iro nm en t             a g. to to ta l e m pl oy m en t ( % ) 0. 00 1 0. 00 6 0. 00 7 0. 00 8 0. 02 1 0. 01 6 0. 00 9 0. 01 5 0. 01 7 0. 01 6 -0 .0 05 0. 07 1 va p er in ha bi ta nt (1 00 0 eu ro ) 0. 00 0 0. 00 5 0. 00 9 0. 00 6 0. 02 7* * 0. 01 3 0. 01 3 0. 01 2 0. 01 8 0. 01 2 0. 08 5* * 0. 03 9 20 04 -2 00 7 av er ag es 1             pe ns io n in co m e (0 /1 ) 1. 20 5* ** 0. 19 7 tf la bo r i nt . a ll ye ar ro un d (0 /1 ) -0 .6 07 ** 0. 27 3 c on st an t -1 .6 60 ** * 0. 29 0 -1 .8 71 ** * 0. 37 3 -3 .0 00 ** * 0. 72 6 -2 .7 73 0. 66 1 -2 .7 59 ** * 0. 70 7 -2 .8 39 ** * 0. 77 6 rh o 0. 62 0* ** 0. 04 8 0. 61 9* ** 0. 03 8 0. 60 2* ** 0. 04 9 0. 57 3* ** 0. 05 3 h (o rm e) 1. 36 7* **   th et a (h ec km an ) 1. 25 5* ** 0. 14 7   lo glik el ih oo d -3 03 0. 73 -1 64 0. 47 -1 53 7. 42 22 69 .1 8 -1 54 5. 95 -1 50 9. 54 c hi -s qu ar ed (d f) 43 8. 25 ** * (2 1) 32 18 .7 63 ** * (2 2) 33 34 .8 7* ** (2 4) 40 0. 17 ** * (2 2) 16 64 .3 8* ** (2 5) 34 80 .6 2* ** (4 1) n ot e: s ig ni fic an ce le ve ls a re d en ot ed b y on e as te ris k (* ) a t t he 1 0% le ve l, tw o as te ris ks (* *) a t t he 5 % le ve l, th re e as te ris ks (* ** ) a t t he 1 % le ve l. 1 o nl y si gn ifi ca nt fo ur -y ea r a ve ra ge s va ria bl es a re re po rt ed . bio-based and applied economics 5(3): 217-235, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-18766 quality, market mechanisms and regulation in the food chain stéphan marette umr économie publique, inra, université paris-saclay, f-78850 grignon, france date of submission: 2016 24th, august; accepted 2017 2nd, february abstract. this paper focuses on questions related to the regulation of quality in the food chain. the paper first recalls the new challenges related to quality in the food chain, with an emphasis on the issue of sustainability. this first part underlines that the numerous dimensions related to sustainability make the regulation necessary but difficult. then the paper introduces a partial equilibrium model calibrated with empirical data, for helping evaluate which regulatory instrument should be socially preferred ex ante on a case-by-case basis. an application to the milk market focuses on linseed for feeding dairy cows, which reduces methane emissions and increases the omega-3 content of milk. simulations compare the impact of the exclusive use of one label signaling omega-3, versus the impact of a minimum-quality standard imposing linseed in the diet of all dairy cows. both instruments would have a positive impact on consumers’ surpluses and some producers’ profits. this application shows that details about the influence of regulatory tools on surpluses may be given on a case-by-case basis. keywords. market regulation, quality, sustainability, experiment, welfare estimation jel codes. d82, c91 1. introduction there are still many open questions when designing public policy for improving food quality (unnevehr et al., 2010). food-borne diseases, pesticides use, various pollutions coming from agriculture, nutrient deficiencies, malnutrition, obesity, animal welfare (…) are among the numerous pathways in which food impacts consumers’ welfare, public health and environment. the choice of regulatory instruments for improving quality is difficult, since the regulator should choose among many instruments, such as mandatory norms and standards, labels and information programs, taxes and/or subsidies depending on products quality (hahn and tetlock, 2008). these regulatory questions are particularly acute for the sustainability of the food sector, since many public debates now turn to questions related to the long-term viability and the public impact of the food sector (fao, 2010). corresponding author: marette@agroparistech.fr 218 stéphan marette the paper focuses on questions related to quality/sustainability regulation in the food chain. the paper first recalls the new challenges related to quality in the food chain, with an emphasis on sustainability. it underlines that the numerous dimensions related to sustainability make the regulation necessary but difficult. the paper also underscores that the theory cannot directly conclude which instrument should be socially preferred. in such a context, estimations of willingness-to-pay (wtp) and econometric estimations are useful for complementing the theoretical analysis and evaluating policies ex ante, on case-by-case basis. in particular, calibrated models with empirical estimations try to combine different sources of data to understand and predict possible responses to policy changes, aiming at improving quality and sustainability of products. we introduce a partial equilibrium model calibrated with empirical data, for helping evaluate which regulatory instrument should be socially preferred ex ante on a case-by-case basis. an application to the milk market focuses on linseed for feeding dairy cows, which reduces methane emissions and increases the omega-3 content of milk. by mixing up an environmental dimension and a heath dimension, linseed for feeding dairy cows directly pertain to the sustainability of the milk sector. the calibrated model uses willingness to pay for milk elicited in a lab. indeed, lab experiments provide many details about the way participants receive information, since the organizer precisely controls messages revealed to participants. from elicited wtp, simulations were conducted for estimating variations in surpluses related to different policy. more precisely, simulations compare the impact of the exclusive use of one label signaling omega-3, versus the impact of a minimum-quality standard (mqs) imposing linseed in the diet of all dairy cows. the integration of estimated marginal cost in profit functions of supply chains leads to endogenous market prices of products, following the choice of one regulatory instrument. we show that both instruments would have a positive impact on consumers’ surpluses and some producers’ profits. eventually, we insisted on the limits of these simulations. this paper adds to the literature by focusing on some overlooked questions resulting from regulation. the application with milk products shows that interesting details, like the impact of regulatory tools on sustainability, may be tackled for helping public debates. this papers differs from general equilibrium models like fapri (see fapri, 2016) or globiom (see globiom, 2016), in which regulatory tools for improving quality are not studied with such a level of precision. this paper also contributes to the literature on welfare estimates with wtp elicited in the lab. in particular, we expand the choice of regulatory instruments with a label policy and a mqs, which may increase the role of experimental data. we also detail the endogenous prices competition following the choice of a regulatory instrument. this differs from previous contributions by lusk et al. (2005), lusk and marette (2010), marette et al. (2008 and 2011), rousu et al. (2007) and rousu and lusk (2009), only focusing on the impact of information and only using fixed market prices. this paper is organised as follows. the next section first recalls the new challenges related to quality in the food chain, with an emphasis on sustainability. after presenting the motivations of regulation, we show that an empirical application helps evaluate policies ex ante, since economic theory alone cannot conclude which regulatory instrument should be preferred. an empirical application quantifies the welfare impacts of the exclusive use of one label signaling omega-3, and one minimum-quality standard imposing linseed in the diet of all dairy cows. the final section concludes. 219quality, market mechanisms and regulation in the food chain 2. new challenges linked to the sustainability of the food sector quality of foods encompasses many dimensions. in the food sector, the concept of sustainability has recently gained momentum, and this term can be seen as a “holistic” concept, gathering all the private and public dimensions related to the quality, defined in a broad sense (see krystallis et al., 2012). the sustainability questions have recently emerged on the agenda of firms, non-governmental organizations (ngos) and governments. sustainability mixes up many dimensions such as security of the supply of food, health, nutrition, safety, affordability, organoleptic characteristics, strong food industry in terms of jobs and growth, durability of the farm sector with “reasonable” land use, animal welfare, relative naturalness of the production, and obviously many environmental characteristics such as climate change, quality of air, soil and water, biodiversity, absence of dangerous residues (tobler et al., 2011). passionate debates around these topics suggest a complex balance between private and public interventions for reaching sustainability, integrating all these dimensions. the three following examples underline the complex links between different dimensions including environmental, public health and social dimensions. 2.1 farmed tropical shrimps the environmental impact of farmed shrimp production is particularly acute, since the expansion of farmed tropical shrimps implies many problems (see debaere, 2010). in particular, natural-coastal habitat has been destroyed to create ponds for shrimp production. shrimp farming has destroyed mangroves areas in some asian countries. these mangroves are particular vital for wildlife protection and also serve as a natural barrier against storms. the supply of water to shrimp farms have contaminated some coastal-land areas with salt water. eventually, the high concentration of shrimps in ponds leads to serious pollutions, with possible outbreaks of disease such as salmonella for shrimps and ultimately for consumers (wwf, 2014). producers use antibiotics for thwarting pollution and foodborne diseases, which led to health regulations with bans of some dangerous antibiotics (disdier and marette, 2010). one solution for improving the sustainability of this process consists in developing organic shrimps that reduce both concentration in ponds and use of antibiotics, even if this organic process significantly increases the cost of products (disdier and marette, 2012). for other goods like avocado and quinoa, the booming demand also entails many problems regarding the land use by farmers and the management of natural resources (may, 2016). 2.2 the palm oil controversy palm oil is increasingly used in many products sold in supermarkets (friends of the earth, 2005). its production, mainly in malaysia and indonesia, has become a sensitive topic, with ngos intensive campaigns against its use (e.g., greenpeace, 2007). researches have highlighted environmental damage related to palm oil production, as the destruction of rainforests in southeast asia and their replacement by gigantic palm groves, with numerous detrimental consequences for biodiversity, endangered species, such as oran220 stéphan marette gutans, and greenhouse gas emissions. the health impact of palm oil, which has a high concentration of saturated fat compared to alternative oils, is another issue generally overlooked by ngos and the media. eventually, the issue of land use also matters when focusing on the production of alternative oleaginous crops (e.g., groundnut, cotton, sunflower, soy or rapeseed oil). indeed, to supply the same amount of oil, one would need to plant 5 to 10 times as much land with other oleaginous plants compared to palm groves (economist, 2010). this makes the palm oil relatively efficient in terms of yields per hectare compared to other oleaginous crops (disdier et al., 2013). consumers are faced with difficulties to consider all these previous issues. 2.3 linseed for ruminants because of enteric fermentation, ruminants are emitting a lot of methane that is one important greenhouse gas (fao, 2012). as the methane lifespan is only twelve years, reducing the related emissions would have a rapid effect on the atmosphere (fao, 2012). adding oilseed lipid supplements from linseed, rapeseed, soybeans, or sunflower to the diet of dairy cows significantly reduces the production of methane. linseed appears as the most efficient supplement leading to configurations with a 20% or 30% reduction in methane emissions (nguyen et al., 2012). beyond the reduction of emissions, linseed in dairy cows diet also leads to milk with a higher content in omega-3 polyunsaturated fatty acids protecting the cardiovascular system for consumers (glasser et al., 2008). eventually, growing more linseed for supplementing milk offers other advantages regarding the land use. in particular, linseed is tailored to crop rotations as a break crop for cereal production (chan, 1996).1 despite these advantages coming from linseed, a vast majority of bottles sold in the world offer milk from cows fed a diet without linseed, namely with corn or other fodders (marette and millet, 2014). 2.4 sustainability and market mechanisms these three previous examples lead to important remarks regarding the link between market mechanisms and regulation. first, with many food productions, environmental and public health characteristics are interdependent and cannot be considered separately. second, as environmental characteristics are related to nutrition characteristics, there is a complexity in trade-offs related to the production for reaching a satisfactory level of sustainability. third, consumers cannot take into account all criteria and trade-offs linked to sustainability, which limits their ability to influence the market for providing incentives towards sustainable products. fourth, supply chains are not always organized for promoting new sustainable practices. this question of supply chains particularly matters for food multinationals, since being publicly perceived as “unsustainable” companies could lead to financial losses and/or a negative reputation. fifth, because of all previous points, market failures and absence of incentives to provide all public/private characteristics lead to suboptimal provision of quality/sustainability by sellers of a supply chain. sixth, regulation 1 because of cow feeding in pasture, organic milk also reaches a higher level of omega-3 compared regular milk coming from cows mainly fed a diet with corn in barns. 221quality, market mechanisms and regulation in the food chain is necessary, but the numerous dimensions related to sustainability make this regulation extremely difficult and potentially doomed to failures. these previous points lead to the following question: is a relevant and coherent regulation possible with so many dimensions at stake for defining sustainability? as it is almost impossible to reply to such a broad question, the rest of this paper will try to offer some arguments for “streamlining” the debate. the next section will search in the economic and theoretical contributions for understanding the tools that could be selected, in order to improve quality and sustainability. 3. the regulatory tools for improving quality and the economic analysis market failures and the absence of incentives to provide public/private goods lead to sub-optimal choices of quality/sustainability by a supply chain. regulation is then required to guarantee that products provide satisfying attributes for consumers. however, there is no certainty that regulation improves agents’ situations, because of costs and distortions coming from regulatory instruments. despite the abundance of theoretical works, no single instrument is clearly superior for improving welfare and guiding policy choices (goulder and parry, 2008). we may distinguish three categories of regulation, for which advantages and distortions can be underlined. (1) the norms and mqs impose on producers a minimum level of quality/safety and they can take many forms, such as obligations to achieve a particular result, like pesticide residues in products, or specifications on processes, like some crop rotations bringing natural sources of nitrogen rather than chemical fertilizers. a mqs also concerns the authorization procedures for new products such as genetically modified organisms or meat from cloned animals. by guaranteeing a minimum level of quality/sustainability to consumers, standards make trade easier (disdier and marette, 2010). however, mqs have the drawback of reducing both the diversity of products, by eliminating poor qualities, and the competition, by limiting entry to the market for some firms (ronnen, 1991, and marette, 2007). (2) information and labelling policies are more favourable to product diversity because they allow the presence of various qualities bought by consumers under full knowledge of food characteristics (bonroy and constantatos, 2015). labels are sometimes compulsory, as in the case of informative messages on nutrition. these information policies aim at guaranteeing the consumers’ freedom of choice. the main limit of labels comes from the imperfect recall by consumers and their possible confusion between good and bad characteristics related to products, as soon as the information given is technical or complex. furthermore, a tendency toward the proliferation of labels is observed, with in particular, the multiplication of claims on health, environment and/or sustainability, which may limit the impact of labels for helping consumers (marette, 2010). (3) the mechanism of taxation and/or subsidization is a regulatory alternative based on a price impact on the consumers’ choices, since prices are affected by a tax/subsidy per unit sold (marette et al., 2008). if taxes aim at limiting the purchases of dangerous or unsustainable products, the revenue that they generate creates a tax resource available for subsidising sustainable products or other actions such as information campaigns. such a process results in double profit, called double dividend. but the low elasticity of food 222 stéphan marette demand in relation to prices limits the impact of price variations induced by tax/subsidies on the quantities consumed. as shown by hahn and tetlock (2008), goulder and parry (2008) and disdier and marette (2012), economic analyses do not give any definitive conclusions on the optimal use of these tools. the combination of tools often limits some of the drawbacks presented above, even if the unnecessary redundancy of tools is a burden for the society. it is then important to examine the regulatory costs linked to each type of tools, which may affect the supply chain’s competitiveness as well as its long-term viability, in particular for small and medium-sized firms. regarding the supply chain and the contractual relationships, there is a lack of analysis studying the share of regulatory costs inside the supply chain and the consequences on entry/exit. among the exceptions, bonroy and lemarié (2012) theoretically study the downstream labeling and the upstream price competition, and they underline the complexity of contractual links among producers of a supply chain. regarding producers, the industrial organization particularly raises the issue of the link between competition and regulation. for firms, the regulatory requirements usually lead to an increase in variable costs and so-called sunk (non-recoverable) costs. variable costs directly depend on production and are passed on to consumers via prices. conversely, once the investment has been made, sunk costs like the purchase of specific equipment for refrigeration, or staff training expenses do not directly depend on production. they do not directly affect prices but influence the competition structure when the producers’ margins do not cover these sunk costs. shaked and sutton (1987) and sutton (1991) underscored the importance of endogenous sunk costs on concentration. counterintuitively, they showed that concentration increases as market size increases, which is particularly the case with the process of trade liberalization or the emergence of a monetary union. if quality, r&d and information are produced at a fixed/sunk cost, a firm by selecting a relatively high level of quality can potentially drive competitors with lower-quality products out of a market. existing producers may choose not to pass on the fixed/sunk cost to consumers via prices, thus eliminating potential rivals. regulation may reinforce the firms’ concentration via its impact on sunk costs presented in the previous paragraph. very few works have raised questions about the possibility of preventing the reduction in competition induced by regulation in the presence of endogenous sunk cost. crespi and marette (2009) show that the generic advertising policies of the type “eat fruit and vegetables” allow the advertising costs to be shared among producers, and competition to be maintained (the same mechanism applies to pooled r&d expenses). by pooling the sunk cost of generic advertising among producers, generic advertising counteracts the phenomenon inherent to the reduction in competition linked to quality, safety or advertising expenses when these are sunk. industry-funded check-off programs of generic advertising affect firms’ strategies and can be procompetitive. the check-off ’s crowding-out effect reduces the ability of a firm to use its private expenditures to avoid a rival’s market access. even if an empirical measure is difficult, this effect of maintaining (or destroying) a competitive structure must be taken into account by models, when a regulatory choice is decided. there are a few theoretical works to help the public decision-maker when faced with a particular food question. the theory cannot directly conclude which instrument should be socially preferred. as shown by roosen and marette (2011) and disdier and marette 223quality, market mechanisms and regulation in the food chain (2012), empirical works are useful for complementing the theoretical analysis and evaluating policies ex ante, on case-by-case basis. 4. an empirical application: the regulatory tools and the welfare analysis integrating experimental results this section provides one example of an applied welfare estimate, even if other types of calibrated models are possible (see roosen & marette, 2011).2 this section analyses the integration of individual estimates of willingness-to-pay (wtp), using a partial equilibrium approach to provide a welfare analysis for choosing regulatory instruments on a caseby-case basis. experiments are useful for quantifying consumers’ reaction to information about attributes and regulation, leading to quantitative welfare analysis. this application to the milk market studies linseed for feeding dairy cows, which reduces methane emissions and increases the omega-3 content of milk, as explained in the subsection 2.3 above. we now turn to a brief description of the protocol of the experiment (see also marette, 2014). 4.1 the experiment we conducted an experiment in dijon, burgundy, france, in multiple one-hour sessions in june 2012. the sample consisted of 114 people aged between 18 and 69 years and representative for age and socio-economic status of the population in dijon. our experiment focused on bottles of milk. we offered 3 liters sold by the brand candia and called grandlait and 3 liters sold by the supermarket brand carrefour since these supermarkets are located in dijon. the choice of the grandlait candia is motivated by its use of linseed for feeding cows leading to more omega-3 compared to conventional milk, as the carrefour one. a label filière nutrition, oméga-3 naturels posted on the bottle grandlait candia indicates the high-content in omega-3. the size of this label filière nutrition, oméga-3 naturels posted on the bottle is relatively small (length: 2 cm, width: 1.5cm). two sentences linked to this label mention an improved diet for cows leading to more omega-3. three other labels of a similar size were posted on this bottle. these labels indicating specific characteristics were entitled saveur de l’année 2012, agriconfiance and la route du lait. the size of these labels was also relatively small (length: 2 cm, width: 1.5cm). to elicit participants’ wtp, our experiment used the bdm procedure (for becker, degroot and marschak, 1964). further information was revealed to participants, and a willingness to pay (wtp) was elicited after each message for both types of milk. the question was: “what is the maximum price you are willing to pay for the following bottles of milk?” the question was repeated for each product. the initial explanations made clear that one of the elicited wtps would be randomly selected at the end of this experiment 2 roosen and marette (2011) compared the approach of this paper with an alternative approach based on a calibrated model combining elasticities of demand obtained from times-series econometrics and average wtp value obtained from the experiment. rousu et al. (2014) introduce a new approach with a hicksian surplus measure that couples variations in wtp coming from information under a non-hypothetical experimental auctions, with time-series revealed preference demand estimates. 224 stéphan marette for determining whether participants will have to buy milk (i.e., performance-based financial incentives). the bdm procedure implemented at the end of the experiment works as follows.3 if the selected wtp is smaller than the randomly drawn price, the participant will receive the €15 indemnity without purchase. if the wtp is higher than the price, the compensation is equal to €15 less the price randomly drawn, and the participant receives the milk bottles (3 liters). the purchasing price was randomly selected among “prices” uniformly distributed between €0.1 and €5 with an increment of 10 cents. in the explanations, we did not reveal the distribution of prices between €0.1 and €5 and no participant asked any question about this range of prices. however, with the candy bar example given in the initial instruction, we insisted on the fact that, with the bdm procedure, the dominant strategy for a participant really consists in reporting her/his “true” wtp. the timing of the experiment was the following. the session started with the initial instructions to explain the bdm mechanism. they received general instructions and signed a consent form. based on different types of information revealed to participants, several rounds of wtp elicitation with the bdm procedure were successively carried out. in the initial round (round #0) of the experiment, only the carrefour milk from cows fed a diet without linseed was made available to the participants, since milk without linseed is dominating the french market (with a market share in volume equal to 95%). participants had two minutes for observing the bottle. we simply informed participants that 3 bottles of 1 liter were sold between €2 and €2.5 in supermarkets in burgundy. no more price information was given in the subsequent rounds. consequently, round #0 only elicited participants’ wtp for 3 liters of milk from cows fed a diet without linseed. the grandlait candia milk from cows fed with linseed was introduced before round#1 with two additional minutes for observing the new bottle. participants observed this new bottle with the posted labels, but we did not insist on these labels in our talk. then this round #1 elicits wtp for both products, namely for 3 liters of carrefour milk and 3 liters of candia milk from linseed fed cows. in round #2, a simple message was revealed. the experiment was conducted in two treatments, varying the order of messages provided to two subgroups of participants. 54 participants first received messages on omega-3 (and after, other messages on the environment), while 57 participants first received messages on the environment and the methane emissions (and after, other messages on the omega 3). as information on omega-3 is at the core of this section, we restrict our attention to the 54 participants who first receive the message on the omega-3 without any other previous messages. regarding round#2, the following additional message was communicated to the subgroup of 54 participants as following: “one liter of milk grandlait candia has 3 times more omega-3 than a liter of milk carrefour. the presence of omega-3 in milk grandlait candia comes from feeding cows with linseed.” new wtp were elicited for both products. other rounds eliciting wtp are not disclosed in this paper. our analysis will focus on two bids (wtp1) for each product before receiving the message on the omega-3 content, and two bids (wtp2) for each product after receiving this message. 3 with a candy bar example given with the initial instructions, we carefully explained that a purchasing price will be drawn at random at the end of the experiment, and purchasing choices will be enforced by comparing this purchasing price to one of the wtp randomly selected (namely, by following the bdm procedure). 225quality, market mechanisms and regulation in the food chain at the end of the experiment, participants completed an exit questionnaire on different issues, including their judgments on the labels posted on the bottle grandlait candia with a high content in omega 3. bottles were still on the participants’ tables, which allowed them to recheck the labels if they wanted. the experiment concluded by randomly selecting one type of milk (carrefour or grandlait candia) and one of the elicited wtp, which were used to determine whether participants had to purchase the bottles. 4.2 impact of the simple message on wtp and perception of participants regarding the labels the omega-3 message is valued by some consumers and leads to significant change in wtp. for each product, a wilcoxon test for comparing paired samples confirms a significant difference at 1% between wtp in rounds #2 and #1. after the revelation of the message, the average wtp for the milk grandlait candia significantly increases, while the average wtp for the conventional milk carrefour significantly decreases (see also marette, 2014). table 1. participants’ opinion about the labels from the exit questionnaire. labels posted on the bottle grandlait candia % of participants noticing the label at round #1 a % of participants considering the label as useful b nutrition for a sector, omega-3 25.4 % 94.1 % flavor of the year 2012 76.4 % 50.9 % agri-trust 41.1 % 78.4 % road of milk 27.4 % 23.5 % note: table 1 summarizes the replies from 51 participants, since 3 participants did not reply to these questions. a the exact question was: “when we gave you the bottle grandlait candia in round #1, did you notice the following labels?” b the exact question was: “on the bottle grandlait candia, do you think that the following labels are useful?” we did not ask additional questions about which characteristic could be deemed as useful. at the end of sessions, the exit questionnaire asked several questions about the labels. table 1 details questions on the four labels posted on the bottle grandlait candia with a high content in omega 3. table 1 clearly indicates that some labels were not noticed at the time the bottle was given to participants (in round #1). the first column shows that the label nutrition for a sector, omega-3 has the lowest rate of “recognition”, namely 25.4 % of participants. table 1 also shows that participants favor this label nutrition for a sector, omega-3 indicating a high omega-3 content, deemed as useful by 94.1% of participants (second column). the 25.4 % of participants who initially noticed this label nutrition for a sector, omega-3 suggests that some significant improvements for signaling the high omega-3 content are possible, via regulatory interventions that are now studied. 226 stéphan marette 4.3 simulations regarding the exclusive use of one label signaling the omega-3 content or the mqs before studying the impact of two regulatory tools, we first introduce the baseline scenario without regulation. with the following methodology, (1) consumers’ choices are inferred from participants’ wtp elicited in the experiment, and (2) the market equilibrium is not an experimental finding but rather an induced outcome coming from the model. 4.3.1 baseline scenario in the baseline scenario representing the market situation in france at the time of the experiment, four labels were printed on the bottle grandlait candia. for this baseline scenario, the round#1 is the closest one to the market situation. for the surplus determination, it is assumed that a participant purchases a good if her/his wtp in round#1 for a good is higher than the price observed on average in the supermarkets in france, namely if wtp p1ii i ii, > for 3 liters of the product ii={conv,omeg} (see rousu et al., 2007). the indexes conv and omeg are respectively related to the conventional milk carrefour and the grandlait with a high content in omega-3. at the time of the experiment, the observed average prices for 3 liters were =p €2.1conv and =p €2.9omeg , and these average market prices are fixed in the baseline scenario. a participant i chooses the product that generates the highest utility, and thus the surplus for the baseline scenario is: = − −cs wtp p wtp pmax{ 1 , 1 ,0}a i conv i conv omeg i omeg, , . (1) in round#1, participants simply observed products with a limited knowledge. the lack of information leads to decisions the participants could subsequently regret if information was revealed. if the information on omega-3 was revealed as in round#2 with wtp2ii i, , some participants would not buy the product, start to purchase a product or change the products they were purchasing. for a participant i, the effect of ignorance was linked to the absence of information about the omega-3 is = −d j wtp wtp[ 2 1 ]ii i ii i ii i ii i, , , , , where jii,i is an indicator variable taking the value of 1 if, at round#1, participant i is predicted to have chosen the product ii at the market price pii , with >wtp p1ii i ii, (and 0 otherwise). this effect of ignorance is added to the surplus given by (1) leading to the complete surplus: = − − + − + − cs wtp p wtp p j wtp wtp j wtp wtp max{ 1 , 1 ,0} [ 2 1 ] [ 2 1 ] b i conv i conv omeg i omeg conv i conv i conv i omeg i omeg i omeg i , , , , , , , , . (2) with our simple specification, the different actors in the supply chain (farmers, processors, retailers etc.) are grouped into a single production stage representing the supply of a given product. for each product, the supply chain’s profit depends on the number of participants choosing the conventional milk, which is the case for consumers with a net surplus, ω1 i > 0 , where ω1 i =wtp1conv ,i − pconv −max{wtp1omeg ,i − pomeg ,0} , and the number participants choosing the milk with omega-3, with a net surplus δ1 i > 0 , where δ1 i =wtp1omeg ,i − pomeg −max{wtp1conv ,i − pconv ,0} . we also consider the total marginal cost over all the supply chain equal to cconv for 3 liters of conventional milk and to comeg 227quality, market mechanisms and regulation in the food chain or 3 liters of milk with omega-3. the estimated profit for each supply chain over the 54 participants is: π1,conv = (pconv − cconv ) k ω1 i( ) i=1 i=54 ∑ π1,omeg = (pomeg − comeg ) k δ1 i( ) i=1 i=54 ∑ , (3) with k(z) equal to 1 if z>0 and 0 otherwise. the marginal cost are given by bonnet and bouamra-mechemache (2016) who computed both retailers’ and manufacturers’ margins. the total marginal cost estimates for retailers and manufacturers are equal to €0.47 per liter for conventional fluid milk (and cconv = €1.41 for 3 liters of conventional milk), and €0.55 per liter for organic milk (and comeg = €1.65 for 3 liters). we assume that the marginal cost for the grandlait candia is equal to comeg, since this production process of production is very close to the organic-milk process. the welfare taken into account by the regulator is given by the sum of consumers’ surpluses and profits given in (2) and (3). we now turn to the impact of each regulatory tool. 4.3.2 exclusive label under this first scenario, we assume that the regulator (1) uses generic advertising for informing all consumers about the label nutrition for a sector, omega-3 (with precise explanations) and/or (2) provide enough incentives to the producers/supply chain for only developing and promoting the label nutrition for a sector, omega-3. the cost of the generic information is incurred by the regulator and not detailed in this analysis. only this label nutrition for a sector, omega-3 indicating a high-content in omega 3 would be printed on the front of the bottle grandlait candia and/or recognized by consumers because of the generic advertising. this label could even be posted with a greater size compared to the actual size. it is assumed that all producers of conventional milk continue producing conventional milk.4 this new marketing strategy would be very close to the message revealed in round #2 to the subgroup of 54 participants. wtps elicited after the short message on omega-3 can be used as a credible approximation of consumers’ reaction to a single nutrition for a sector, omega-3 label. the market prices pconv and pomeg are now endogenous. the impact of this exclusive label is measured by considering the wtp following the message released at round#2. because the message is revealed to all participants, the effect of ignorance disappears, and the complete surplus is: = − −cs wtp p wtp pmax{ 2 , 2 ,0}c i conv i conv omeg i new, , . (4) compared to the baseline scenario with the complete surplus given by (2), the average relative variation in surplus following the introduction of the exclusive label is equal to 4 the other labels could also be withdrawn for promoting the clarity of this label nutrition for a sector, omega-3, which would make clear the presence of omega-3 because consumers focus on one logo. 228 stéphan marette δcs = [ i=1 i=54 ∑ csc i −csb i ] csb i i=1 i=54 ∑ . for each product, the supply chain’s profit depends on the number of participants choosing the conventional milk which is the case with a gain ω2 i > 0, w he re ω2 i =wtp2conv ,i − pconv −max{wtp2omeg ,i − pomeg ,0} , and t he nu m ber of participants choosing the milk with omega-3, with a gain δ1 i > 0 , where δ2 i =wtp2omeg ,i − pomeg −max{wtp2conv ,i − pconv ,0} . the profit for each supply chain is π 2,conv = (pconv − cconv ) k ω2 i( ) i=1 i=54 ∑ π 2,omeg = (pomeg − comeg ) k δ2 i( ) i=1 i=54 ∑ (5) with k(z) equal to 1 if z>0 and 0 otherwise. each supply chain maximizes its profit by considering the price of the other supply chains as given. the optimal prices pconv * and pomeg * are given by a groping process, since all participants’ wtp are discrete values. we assume that the groping process started with a first choice of pomeg 1 ≠ pomeg with pomeg 1 maximizing π 2,omeg given by (5). for a price pomeg 1 , the other supply chain selects a price pconv 1 maximizing π 2,conv * given by (5). for this price pconv 1 , the other supply chain adjusts its price by selecting pomeg 2 that maximizes the profit π 2,omeg (…). when no individual deviation is beneficial, the process stops with pconv * and pomeg * leading to π 2,conv * and π 2,omeg * . the average variations of profits following the introduction of the exclusive label are δπconv = π 2,conv * −π1,conv( ) π1,conv and δπomeg = π 2,omeg * −π1,omeg( ) π1,omeg . 4.3.3 mqs imposing linseed in the diet of all dairy cows a quality standard would impose linseed intake in the diet of all dairy cows in all farms. the simulations take into account the following key factors. the mqs is imposed without any additional information revealed to consumers via generic advertising. the regulator puts resources in the management of the certification, rather than in generic advertising. the existing certification process filière nutrition, bleublanc-coeur could be extended to all farmers (blanc bleu coeur association, 2012).5 even if the “new conventional” products could show the label signaling the omega-3, very few consumers would notice it, as shown in the first column of table 1, and, for many consumers, the value of this label would be likely depreciated because all producers would use it. even if no additional information is revealed, the mqs modifies the effect of ignorance accounted for in the complete surplus. the standard increases the complete surplus of “ignorant” consumers, because of the improvement in the quality of the conventional product. a new wtp for the “conventional” product with linseed is included in the effect of ignorance. 5 in essence, this mandatory linseed supplementation is close to the mandate for ethanol and biodiesel that are blended with gasoline for automobiles in many countries. 229quality, market mechanisms and regulation in the food chain the mqs is costly for producers since linseed is more expensive than other ingredients of the conventional diet of cows. this standard leads to an increase of the marginal cost cconv of bottles of milk from cows actually fed without linseed and turning to linseed because of the mandatory regulation. we assume that the standard leads to either a 5% increase of the total marginal cost, with the new marginal cost equal to 1.05×cconv, or to a 10% increase of the marginal cost, with the new marginal cost equal to 1.1×cconv.6 this instrument changes the nature of the whole conventional milk that becomes a new product with linseed. in this case, the wtp premium ( −wtp wtp2 1omeg i omeg i, , ) observed for milk with linseed after the release of information on the impact of linseed can be applied to the conventional milk. however, a statistically significant difference was observed in round#1 between wtp1omeg i, and wtp1conv i, . this small difference is such that δ = =e wtp e wtp[ 1 ] / [ 1 ]conv omeg 0.89. it means that, in stage #1, the milk candia from cows fed with linseed differs from the conventional milk because of the taste, the brand, and other characteristics indicated by different labels. even if the premium −wtp wtp2 1omeg i omeg i, , linked to the message on the impact of linseed is applied to the conventional milk enriched with linseed because of the standard, we correct the new wtp of the new conventional product by the parameter δ . it means that the wtp difference between both products is the same in stages #1 and #2. thus the wtp for the “new conventional” milk coming from cows fed a diet with linseed is equal to δ= ×wtp wtp2 2conv with lin i omeg i_ _ , , . as the mqs does not reveal any information, participants take their decisions as in round#1 and as described with equation (1), except that prices are endogenous with pconv and pomeg replacing pconv and pomeg . the new value δ= ×wtp wtp2 2conv with lin i omeg i_ _ , , is accounted for in the effect of ignorance, −j wtp wtp[ 2 1 ]conv i conv with lin i conv i, _ _ , , , where jconv i, is an indicator variable taking the value of 1 if participant j is predicted to have chosen the conventional product at the new market price. under a mqs, participants have no additional information compared to the baseline scenario, which leads to choices equivalent to the ones of equation (1). however, the effect of ignorance changes with the mqs compared to the baseline scenario. for a participant i, the complete surplus integrating the effect of ignorance with a mqs is: = − − + − + − cs wtp p wtp p j wtp wtp j wtp wtp max{ 1 , 1 ,0} [ 2 1 ] [ 2 1 ] d i conv i conv omeg i omeg conv i conv with lin i conv i omeg i omeg i omeg i , , , _ _ , , , , , . (6) for each product, the supply chain’s profit depends on the number of participants choosing the conventional milk which is the case with a net surplus ω3 i > 0 , where ω3 i =wtp1conv ,i − pconv −max{wtp1omeg ,i − pomeg ,0} , and the number of participants choosing the milk with omega-3, with a net surplus δ3 i > 0 , where δ3 i =wtp1omeg ,i − pomeg −max{wtp1conv ,i − pconv ,0} . compared to the profits indicated by 6 we found an extra-cost of € 0.016 per liter of milk linked to 0.6 kg of linseed per cow and per day and including the certification cost (see table “lait” p.17 in the blanc-bleu coeur association, 2012). as we offered 3 liters, the extra marginal cost is € 0.05 for bottles of conventional milk impacted by the mqs, which corresponds to an increase of 3.5% compared to the marginal cost cconv = €1.41. this increase of 3.5% is a lower bound of the possible increase, since the linseed price would increase if the linseed use was mandatory. 230 stéphan marette equation (3), the total marginal cost over the entire supply chain increases of 5% or 10%, leading to a new marginal cost equal to 1.05×cconv or 1.1×cconv for 3 liters of new conventional milk with linseed. by only reporting the case with 1.1×cconv, the estimated profit for each supply chain over the 54 participants is: π 3,conv = (pconv −1.1× cconv ) k ω3 i( ) i=1 i=54 ∑ π 3,omeg = (pomeg − comeg ) k δ3 i( ) i=1 i=54 ∑ , (7) the optimal prices pconv * and pomeg * are given by the groping process similar to the one detailed after the equation (5). the variations of surpluses and profits are given by formulas similar to the ones related to the exclusive label, namely δcs, δπconv and δπomeg, presented above. the variations of surpluses and profits were estimated on an excel spreadsheet. table 2, shows the impact of the exclusive label given in the second column, and the impact of the mqs, given in the third and fourth columns for two possible changes in the marginal cost of the conventional milk, 1.05×cconv and 1.1×cconv. table 2 shows significant economic benefits coming from both regulatory instruments, with the welfare variations at the bottom of table. with a regulator who maximizes the welfare, the choice of the instrument leading to the highest welfare depends on the mqs marginal cost. when the increase of the marginal cost is relatively low, 1.05×cconv in the third column, the welfare variation with the exclusive label is lower than the one with the mqs, despite the reduction of products diversity coming from the mqs. in this case it is socially optimal to select the mqs, leading to a quality increase of the conventional milk with a very low impact on both marginal cost and profit of the supply chain with the new conventional milk. conversely, when the increase of the marginal cost is relatively high, 1.1×cconv in the fourth column, the welfare variation with the exclusive label is higher than the one with the mqs, implying a reduction of products diversity. the comparison between instruments in table 2 reveals important differences in gains among participants. the exclusive label provide a high variation in benefits to the supply chain offering milk with linseed (+40%), since this supply chain may charge a higher price based on wtp positively influenced by the exclusive label. this relative high-price increase (+17.2%) of the high-quality milk with linseed explains the relatively low increase in the consumers’ surplus (+12.7%). because of this relative high price for the milk with linseed, some consumers/participants switch to the conventional milk, which explains the positive profit variation for the supply chain of conventional milk despite the price decrease of conventional milk. compared to the exclusive label, the mqs tends to relatively favor consumers compared to producers. as consumers do not change their demand with the mqs, the changes in prices are small, which limits the gains for producers.7 even if no additional infor7 the optimal prices are the same for both configurations of marginal costs related to mqs, since the wtp determining the demand in (7) are discrete values. 231quality, market mechanisms and regulation in the food chain mation is revealed, the mqs modifies the effect of ignorance in the complete surplus given by (6), with the wtp increase for the new conventional product with linseed. the standard increases the complete surplus of “ignorant” consumers because of the quality improvement of the product. 4.4 limitations of the methodology and extensions because of flaws linked to lab experiments, and also because profits and surpluses of table 2 are inferred with simulations, there is no definitive conclusion. the reader should keep in mind that the results come from lab experiments and questionnaires that are criticized by some economists. because of possible biases coming from lab experiments, the results of this section only provide suggestions that could help debates and future research. moreover, many simplifications were made with the model represented by equations (1) to (7). several extensions are necessary for confirming the results of table 2. first, the supply chain can/should be taken into account in this partial equilibrium model. by using real purchase data it is possible to develop a structural econometric model of demand and supply that takes into account the relationships between manufacturers and retailers (see bonnet and bouamra-mechemache, 2016). this structural econometric model allows to distinguish between processors and retailers margins, which is promising for understanding vertical relationships. for instance, the different actors and margins in the supply chain could be integrated in an alternative calibrated model, combining elasticities of demand obtained from time-series econometrics and average wtp obtained from the experiment (see roosen and marette, 2011). second, sunk costs could/should be integrated by considering profits over a whole population. in other words, we could extrapolate the profits coming from (3), (5) and (7) and linked to the 54 participants to the overall population of france by considering the average yearly consumption. this would lead to yearly gross-profits from which sunk costs related to quality effort could be subtracted. boland et al. (2014) provide a precious methodology for evaluating sunk costs and values of assets in the industry. the evaluated sunk table 2. relative variations (%) in profit and surplus coming from the exclusive label or the mqs. exclusive label mqs with 1.05×cconv mqs with 1.1×cconv price variation of milk without linseed 4.7% 4.7% 4.7% price variation of milk with linseed and label(s) 17.2 % 3.4 % 3.4 % profit variation for supply chain offering conventional milk, initially without linseed 1.1 % 0.7% 14.2% profit variation for supply chain offering milk with linseed 40 % 8 % 8 % consumers’ surplus variation 12.7 % 37.6 % 37.6 % welfare variation1 10.3 % 15.8% 9.4 % 1the welfare is given by the sum of consumers’ surplus and profits. 232 stéphan marette costs related to the milk quality could be integrated in the previous profits estimate. eventually, the regulator should also take into account all administrative/regulatory costs overlooked in this section, but essential for getting a complete costs-benefits analysis. third, the analysis should consider some configurations for which the regulation targets several goods and/or several characteristics related to goods. wtp for a good/ characteristic may vary depending on whether it is evaluated on its own, or as part of a “broad basket” of goods/characteristic, which ultimately raises the question of the stability of wtp.8 kahneman and knetsch (1992) underlined the sub-additivity effect that occurs, when the estimated wtp for the improvement of one characteristic plus the estimated wtp for another characteristic is greater than the “common wtp”, when participants are asked to value the two characteristics together. an interesting extension would consist in diversifying the contexts in which wtp for one and/or several characteristics are estimated, for understanding the sensitivity of wtp elicitations. 5. conclusion despite shortcomings, we have been able to shed lights on one important question related to quality and sustainability regulation. the application to the milk market focused on linseed for feeding dairy cows, which reduces methane emissions and increases the omega-3 content of milk. simulations compared the impact of the exclusive use of one label signaling omega-3, versus the impact of a minimum-quality standard imposing linseed in the diet of all dairy cows. both instruments would have a positive impact on consumers’ surpluses and some producers’ profits. the integration of experimental results in calibrated models helps to assess ex ante the impacts of regulatory measures, that is to say, before the effective implementation of food, environmental or health policies. the experimentation results are a basis to anticipate consumers’ reactions and so the calibrated models help to anticipate the price adjustments on markets and provide useful results to the public decision-maker. applied welfare estimations are useful for evaluating policies ex ante (or ex post) on a case-by-case basis and helping public debates. these methods can be applied to costs-benefits analyses showing the consequences of the various public choices. acknowledgements this paper was presented the 5th conference of the italian association of agricultural and applied economics (aieaa), held in bologna, june 16-17, 2018. the speech given at this conference and the present paper are both dedicated to the memory of giovanni anania. i thank stefano boccaletti and participants at the conference for their comments. the research leading to these results received funding from the european union’s h2020 8 the stability of wtp is questionable even when only one product is considered. marette et al. (2017) show that the wtp for a given type of product are particularly sensitive to the order of different mechanisms and to the period of the experiment. conversely, for a product, the variations of wtp coming from explanatory messages about private or public attributes are particularly stable over the order of different mechanisms and the period of experiments. in other words, as marginal wtp for characteristics are stable, they can be credibly integrated in costs-benefits analyses. 233quality, market mechanisms and regulation in the food chain programme under grant agreement number 633692 (susfans: sustainable food and nutrition security through evidence based eu agro-food policy, details on http://www.susfans.org/). the author only is responsible for any omissions or deficiencies. references becker, g.m., degroot, m.h. and marschak, j. 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(world wildlife fund). farmed shrimp. available at http://www.worldwildlife.org/industries/farmed-shrimp, washington, dc, usa. bio-based and applied economics 6(2): 139-157, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-18007 non-livestock value chains. lateral thinking for the securing of the sahelian livestock economies abdrahmane wane1,*, ibra touré2, aliou diouf mballo3, cheikh ibrahima nokho4, aminata konaté ndiaye5 1 research economist cirad-selmet research unit-ppzs joint appointment position at ilri-pil programme, ilri campus, mara house building, po box 30709, nairobi, kenya 2 research geographer cirad-selmet research unit-ppzs – hosted at cilss – 03 bp 7049, ouagadougou, burkina faso 3 statistician economist, fao rome, italy, viale del terme di caracalla, 00153 rome, italy (previously trainee engineer at cirad-ppzs, dakar, senegal) 4 statistician economist, currently being recruited by the world bank washington dc, usa (previously trainee engineer at cirad-ppzs, dakar, senegal) 5 rural economist, currently being recruited by the usaid – feed the future senegal “naatal mbay” program (previously trainee economist at cirad-ppzs, dakar, senegal) date of submission: 2016 10th, february; accepted 2017 9th, may abstract. in a rapid rural appraisal conducted in 2012 in the senegalese sahel, agropastoralists of thiel expressed their need for technical and scientific support in peanut value chain development. value chain analysis assessed the performance of the stakeholders. multiple correspondence analysis clarified power relationships among them. social network analysis facilitated the understanding of social and technical relationships inside the particular node of agropastoralists. results show that the peanut crop is both a source of cash flow (marketing) and a pillar of food (basic consumption) and feed (by-products) security. this paper also highlights a lack of convenient economic environments, mutual assistance, capacity transfer and knowledge sharing on the best agricultural practices among agropastoralists, despite their weak production performance. agropastoralists have no influence in the peanut value chain and are dependent on decisions from other actors. technical support and knowledge sharing appear to be key for agropastoralists to control and adopt agricultural innovations. keywords. diversification, value-chain, social network, sahel. jel codes. d12, d13, q02. *corresponding author: awane@cirad.fr 140 a. wane et al. 1. introduction in the sub-saharan agricultural sector, diversification consists of a gradual movement away from subsistence food crops toward additional market-oriented crops (parthasarathy et al., 2008). it has long been accepted that rural producers adopt diversification economic activities in situations with high levels of uncertainty and in the absence of contingency markets (berhanu et al., 1996) to manage both predictable and unpredictable flows of income (robinson and barry, 1987) and to secure rural livelihood systems (niehof, 2004; wane et al., 2009a). farmer motivations to diversify are also specifically addressed through research activities on diversification, risk and income (bezu et al., 2012; baird and gray, 2014); food insecurity mitigation (michler and josephson, 2015); climate change coping strategies (di falco and veronesi, 2013a; 2013b; phillipo et al., 2015); and/ or violent conflicts (paul et al., 2015). similarly, sahelian agropastoralists must diversify their economic activities as they encounter global challenges—including climate change— as well as economic, social and political crises (ickowicz et al., 2012). annually, sahelian pastoral households also deal with extreme seasonal variability, leading to spatiotemporal and asymmetrical distributions of resources. they mainly use various forms of mobility recognised as sustainable strategies of herd management. due to the threat of acute damage to pastoral productive assets, the environment and individual livelihoods by the aforementioned constraints, sahelian households use livestock markets to balance short-term consumption needs and long-term herd building strategies to meet future consumption needs (wane et al., 2009a; fadiga, 2013). thus, they primarily attempt to secure their livestock production systems (tran, 2011), as well as their socioeconomic and cultural heritage. in addition, diversification of income and food sources remains a prevalent ex-ante uncertainty-reduced management strategy to secure the living conditions of sahelian agropastoralists. on the other hand, ex-post alternative mechanisms are used to smooth consumption, but they remain random, like the mechanisms of most peasant households in developing countries (valvidia et al., 1996). however, in the sahel, diversification strategies in the crop sector continue to be treated as secondary modes of production. in addition, gap-efficiency analysis along commodity value chains remains very weak. the diversification of agropastoral product value chains in the sahel is similar to the notion of the a-system, which was initially highlighted by ruben et al., (2007). the a-system is characterised by traditional production systems with multiple small producers and intermediaries, local value chains, weak market information systems, longer transportation distance and time and complex distribution networks. accordingly, based on the concept of productivity measured as the economic output of a combination of production factors – including labour, capital and other factors – this paper emphasises the importance of the term “other factors” in the sahelian contexts by analysing their complex relationships and the induced incentives that exist in agropastoral value chains. the main objective of this paper is to better describe this complexity, identify bottlenecks and highlight alternative interventions that significantly impact the position of agropastoralists in non-livestock value chains. by doing this, we also address the risk and uncertainty management decisions of the sahelian agropastoralists by using social network and multiple correspondence analysis approaches in an unprecedented way. an 141non-livestock value chains unexpected finding is the predominance of strong social relationships, with a tiny technical knowledge network among actors. this paper begins with an overview of value chain analysis. section 1 focuses on the methodological challenge of quantitatively assessing the performance of different stakeholders. it then provides a justification for the use of social network analysis to better understand relationships between agropastoralists and to explore new ways to improve the agropastoralists’ position in non-livestock value chains. section 2 describes a case study in senegalese agropastoral areas, where producers clearly expressed the desire to be technically and scientifically supported in peanut value chain improvement. section 3 develops a discussion on the main findings and shows how groups of individuals differ based on influential and non-influential factors. finally, a conclusion outlines alternative interventions that could support agropastoralists in adopting innovations and best practices. 2. area of study, conceptual framework, method description and sampling strategy after a presentation of the area of study, the peanut value chain is mapped, followed by a description of the conceptual framework, the methodological approach and the household sampling strategy for primary data collection. 2.1 area of study value chain analysis increasingly requires a minimum amount of statistical data. in the sahel, data collection requirements were broadly highlighted by a large study from the world initiative for sustainable pastoralism (hatfield and davies, 2006). moving towards quantifying value chain analysis is highly recommended (rich et al., 2011) to better evaluate the performance and impact of innovative interventions, although network complexity is already qualitatively characterised in livestock systems (riisgaard et al., 2010). our study was conducted in 2013 in the agropastoral area of thiel, located in the south of the senegalese sahel on the edge of the peanut basin (figure 1). this main peanut cultivation area extends along the north-south strip of 220 km and the east-west strip of 200 km. it represents a third of the farmland and is a source of employment for many households in the region. in 2012, peanut production in senegal declined significantly. in this context, the agropastoralists of thiel produced 270 tons of peanuts, averaging 1.9 tons per encampment. this production remains too marginal compared to national peanut production. in terms of scientific research, there is an increasing consensus concerning the need to provide statistical evidence to better understand complex agropastoral production systems and livelihoods, even though the mobility of actors, informal relationships and nonmarket drivers do not facilitate such an orientation. in senegal, the main agropastoral and pastoral production system is concentrated in the ferlo region, which is 67,610 km² and makes up approximately a third of the national territory (touré et al., 2003). with a surface area of 1,031.46 km2, the agropastoral unit of thiel is located in the silvopasture reserve of ferlo, which is approximately 60 km southeast of dahra, the largest livestock market in senegal. its closeness to the senegal 142 a. wane et al. groundnut basin and the advancement of the agricultural front strengthen the land tenure competition between farmers and breeders. however, crop-livestock integration is increasingly observed in the rural community of thiel. thus, during our investigations in 2013, figure 1. the agropastoral rural community of thiel. source: glcn-fao, 2005, touré 2014. 143non-livestock value chains 92 percent of household heads identified themselves as agropastoralists, and 1 percent of respondents reported exclusively practicing agriculture, whereas in surveys conducted in 2006–2007, only 48.1 percent of household heads identified themselves as agropastoralists (wane et al., 2009a). crop-livestock integration facilitates cross-fertilization because crop residues feed animals, which in turn contribute to soil fertility through organic matter transfer. the ethnic composition in the rural community of thiel is diverse and includes three major groups: the fulani, the seereer and the wolofs. 2.2 conceptual framework: peanut value chain mapping the “value chain” concept is similar to the notions of productive chains, the french “filières,” marketing chains, supply chains and distribution chains (webber and labaste, 2009). basically, the value chain is the full range of value-adding activities required to bring a product or service through the different phases of production, including procurement of raw materials and other inputs, assembly, physical transformation, acquisition of required services (such as transport or cooling) and ultimate response to consumer demand (kaplinsky and morris, 2002). farmers and agropastoralists are the first link in the peanut value chain in thiel (figure 2). another important link in this chain are the public and private support unit firms that provide inputs and buy back the peanuts produced. traditionally, government authorities sell subsidised seed to producers, then collection points are set up at harvest time to buy peanuts in exchange for vouchers redeemable in the future. other actors in the value chain include women—usually the wives of household heads—who produce oil, peanut paste and food concentrate from peanuts that are purchased or harvested. in 30 percent of the encampments in thiel, women perform this processing activity. another important value chain node is the traders, who buy large amounts of peanuts from the producers for resale in markets in touba, the flagship city located 100 km from thiel and the economic peanut hub in senegal. much of the production is sold in thiel. traders process the peanut seeds to remove mould and pests before reselling. transactions on peanuts also involve a large group of players that are very active, such as middlemen who buy and shell peanuts. these traders are often based in touba, and they also sell seeds, as well as the derivative products used for animal feed and energy products. these traders also constitute an important link in the value chain and offer support services to producers trying to sell to touba. usually, they have trucks that can carry 40 peanut bags from 30 to 50 kg, for a transportation cost of 1,000 xof1 per bag. note the existence of two terminal markets that shorten the marketing chain of peanuts: the local market of the rural community, where most collectors and processors sell shelled peanuts, oil and peanut butter, and the assembly market of touba, where the middlemen trade only unshelled peanuts. 1 xof represents the iso currency code for the west african cfa franc. in june 30, 2012: 1 xof = 0.0019305381 usd. 144 a. wane et al. 2.3 method description the limitations of the traditional value chain approach are most notable in agropastoral value chain analysis. such analysis is supposed to assist in both the invention of mechanisms of data collection adapted to the characteristics of pastoral activities (mobility, informal activities, self-centred economy and self-bounded rationality) and the intelligent use of tools, such as social network analysis. challenges within the social network can be identified by analysing vulnerabilities within the network, thus helping to move to a more resilient system (alary et al., 2016). the role of networks as facilitators of the coordination and distribution of productive resources could be analysed by using social network analysis as a comprehensive framework for investigating network phenomena through primary data collection. for the agropastoralists of thiel, the main hypothesis that should be tested is whether there are strong links among social relationships, technical cooperation and knowledge sharing. the two most important parameters in social network analysis are the individual factors called “nodes” or “vertices” and their links or relationships, also called “edges” or “ties.” thus, a social network r(n,g) is defined by a group of nodes: n = 1,2,…,n and real-valued nxn matrix g. each line i and each column j of this matrix represents an individual node, and their intersection gij indicates whether there is a relationship between them; gij takes a binary value according to the existence (1) or absence (0) of a link. in addition, there is an important notion of distance that characterises the position of one node in relation to another. this distance between two nodes – also called the geodesic – determines relevant indicators, such as the density measures (which are easily calculable) and the centrality measures (which are slightly more complex to determine) (gomez et al., 2013). whereas density measures highlight the connection level of the network by comparing the present links and those possible if all nodes were linked, centrality measures determine what makes one node more prominent than another (gomez et al., 2013). figure 2. the peanut value chain mapping of thiel. fu nc tio ns a ct iv iti es a ct or s input supply production collection processing wholesale & retailing consumption seed supply fertiliser supply storage growing harvesting drying peanut shelling peanut collection peanut transportation and selling peanut peanut paste oil feed animal wholesaling retailing consumption public authorities and enterprises farmers farmers public authorities and enterprises hauliers middlemen public enterprises processing women wholesalers and retailers consumers source: authors’ elaboration. 145non-livestock value chains centrality refers to the importance and the influence of a node in terms of the numbers of links that the node has. centrality indicators can be elaborated to reflect the multidimensional character of this notion: the degree centrality considers which important nodes have many relationships or links (edges) with others (wasserman and faust, 1994). the degree centrality is equal to the number of neighbours that a node has. in the case of an oriented social network, one distinguishes between in-degree and out-degree measures. in-degree indicates the number of links that nodes receive and is interpreted as a prestige indicator, and out-degree indicates the number of relationships from the node and represents the influence of the node. dividing by n 1 should standardize these indicators. the betweenness centrality considers that the importance of a node depends on the frequency with which it serves as an intermediate between two nodes. therefore, a vertex has power if it stands on the geodesic of two actors. however, this power is lowered if the geodesic of these two nodes is not unique; in this case, there is another canal from which the information can pass. formally, the importance of a node i is measured by dividing the number of times that i stands on the geodesic between two nodes j and k by the number of geodesics of these two nodes. thus, the betweenness of a node is calculated as i p kj p kjk j i i , ∑ ( ) ( ) = ≠ (1) with pi(kj) being the number of geodesics between k and j that pass by i and p(kj) being the total number of geodesics between k and j. the standard version of this indicator is i p kj p kj n ns k j i i 2 3 2 , 2 ∑ ( ) ( ) = − + ≠ (2) the closeness centrality measures the distance between one node and other nodes of the social network (beauchamp, 1965). it is equal to the inverse of the average geodesic between this node and the other. the closer a node is to other nodes, the shorter the average distance between one node and the others. thus, the formula for the closeness centrality of a node i is p d n ij ij 1 1 ∑ = − (3) with dij equaling the number of links in the geodesic between i and j2. 2 the analysis could be technically extended to consider bi-dimensional cost-distance communication aspects between two nodes and to select from detailed information about non-dominated vectors (gomez et al., 2013). however, this is outside the scope of this exploratory paper. 146 a. wane et al. table 1. procedures for determining the level of influence of value-chain actors. dimension variables questions asked modalities setting power of the price qa35 how are input prices fixed? regardless of him, by consensus, by himself, by market prices qa37 are you able to influence input prices? yes, no qa41 how output prices are fixed? regardless of him, by consensus, by himself, by market prices qa43 are you able to influence output prices? yes, no dependence on market qa38 do you have to buy inputs even when prices are high? yes, no qa44 do you have to sell outputs even when prices are low? yes, no qa49 do you have alternative possibilities to sell even if your major clients don’t buy? yes, no asymmetric information qa47 are you able to control the market? yes, no qa48 do you know the final destination of your product? yes, no sources: authors’ elaboration. relationships between peanut value chain actors should be further characterised by the analysis of the influence of each of them (i.e., the power relationships). in this paper, this influence is studied through three criteria: dependence on markets, information asymmetry and pricing power. the power relationships in the value chain will be located through simultaneous examination of these three criteria and a multiple correspondence analysis (mca). the first approach is traditional; it only allows the identification of groups of influential actors. it does not give details about the individuals that influence or the level of their power. thus, the concepts of distance and dispersion between two groups of individuals, the distance between two modalities, the distance between a modality and the centre of gravity, and barycentric relationships between groups of individuals and modalities allow an initial overview of the degree of influence of each stakeholder. however, this does not necessarily serve to identify power disparities inside each group of stakeholders. the second approach provides an indicator that calculates the influence of particularly influential individuals as follows: i q w ki q q q j j j q ij q 1 1 ∑∑= = = ∈ (4) with wj q being the weight accorded to the modality j of the question q and kij being the 147non-livestock value chains answer given to the question by the individual i. the indicator of influence is the sum of the modality weights declared by individuals. following this, to classify individuals according to their degree of influence, we developed a threshold of influence calculated as follows: s = max(inon-influent*mnon-influent)+min(iinfluent*minfluent) (5) with mj being the weight of the class i. 2.4 sampling strategy the livelihoods of ferlo’s herders are so specific that it is useful to specify the contents given to the investigation units. the pastoral family does not limit itself to the parents and their direct descendants. there are four types of residency units in the sahel: (1) villages (in fulani: nokuu), or places where physical presence is important; (2) pastoral household activities, which allow us to identify the concessions (guuré in plural, wuro in singular) that are large units of residence; (3) encampments (gallédji in plural, gallé in singular) that are socio-economic units of people (with blood ties or not) who totally or partially combine their resources to foster collective well-being; and (4) households (poye in plural, foyré in singular) composed of relational atomic units of people tied by blood or by marriage (wane at el., 2009b). for the fieldwork, we focused on the borehole of thiel, which is a structuring or keystone element of pastoral and agropastoral activity in this area. the first phase of the fieldwork involved updating an existing database built in 2006–2007 with a census of pastoral and agropastoral encampments dependent on this borehole (wane et al., 2009). this complete database of 163 encampments provides basic information, such as the names and surnames of the encampment head, his ethnicity, the geographical coordinates, and herd composition by species. this updated database facilitates the definition of a representative sampling of the socio-demographic diversity of the thiel site. in 2013, our investigation covered 120 encampments, which from a statistical point of view should represent a margin of error of 4.61 percent and a confidence level of 95 percent. from this initial investigation, we found that only 80 encampments among the 120 encampments (67 percent) produce peanuts. of these 80 encampments, only 22 have women as owners of peanut processors. we interviewed the household heads of these 80 encampments, as well as 22 women who were peanut processors. investigations in these 120 encampments will allow us to identify the real position of producers (in terms of influence) from thiel in the peanut value chain, calculate the added value created at each node, analyse the interactions between the peanut value chain actors, and identify bottlenecks in order to provide alternative interventions to improve the value chain. to achieve these goals, we conducted interviews with other key value chain actors: three big collectors, three important middlemen and nine haulers. 148 a. wane et al. 3. position of peanut producers in the value chain: solid social relationships but weak technical and economic interconnections using descriptive statistics, we first present some overall features of peanut activity at the agropastoral site of thiel. we then highlight what seems to be the main difficulty for the producers: technical cooperation and knowledge sharing are seriously limited. finally, we describe the asymmetry in the distribution of power among the stakeholders of the value chain. 3.1 overall features of peanut activity in 2012, the production of peanuts in thiel was carried out at four sites in the southwest of the rural community near agricultural areas (thiel seerere, moola, darou-nahimnguer and siilat). nearly 95 percent of agropastoralists create value from their primary crop production; the remaining 5 percent produce crops exclusively for self-consumption. however, the overall value is still unevenly distributed among producers (gini index: 0.65). only a small number of agropastoralists manage to capture significant value creation, and 90 percent of them receive half of the total value added along the value chain. almost all agropastoralists (95 percent) create value by selling at markets, and they generate 162 xof or each kilogram of peanut product, which represents 29 percent of the total added value per kilogram obtained (figure 3). the middlemen of touba contribute more by providing 177 xof (32 percent) towards the formation of the overall added value, processing women contribute 146 xof (27 percent) per kilogram of peanut processed to oil, and collectors contribute 63 xof (12 percent). figure 3. contribution of each group of actors to the formation of the overall value. collectors 12% processing women 27% agropastoralists 29% middlemen 32% sources: authors’ elaboration. 149non-livestock value chains the dominance of middlemen in the formation of the added value is explained by their monopoly position in the market. they are price-makers and thus have the power to set the purchase price at which they buy peanuts from growers and the price at which they sell. they are in high demand, and agropastoralists use them to sell their products. the middlemen are in theory able to make an option on the total production. in the process of peanut production, the producers of thiel use peanuts as seed and fertilizer. an important proportion of the peanuts used for seed comes from purchases made by public authorities or middlemen (89 percent), but they also come from stocks after harvest. bearing in mind the lack of flexibility in government administrative procedures, seed obtained from middlemen is more available and accessible for 58 percent of agropastoralists. in addition, 44 percent of producers are supplied from the public support units, and a similar proportion uses stock; however, the ability to use stock depends on the quality and quantity of the previous crop year. few farmers (1 percent) declared getting seeds from their neighbourhoods. groundnut cultivation in thiel is largely dependent on the volumes of seed and to a lesser extent on the quantities of fertilizer used, as well as the amount of labour and livestock available. the villages, which get large quantities of seed, experience better performance. a number of agropastoralists (30 percent) only use fertilizers and labour to cultivate, whereas a very large proportion (80 percent) use cattle to till the land for growing peanuts. the latter situation is not surprising because the area is dominated by agropastoral activities. seed prices vary depending on the suppliers. the average price fixed by the government is 225 xof/kg. however, the average price from other suppliers is 433 xof/kg and, at times, the price reaches 800 xof/kg. fluctuations in current prices fixed by middlemen depend on the quality of the previous crop year and prices of inventories and influence the supply of crop seeds from agropastoralists, as well as from those who are engaged in farming. the main uncertainty of the government’s supplies derives from the uncertainty of their ability to provide sufficient quantities and quality at the right moment. although the prices offered by the government are relatively stable due to a form of implicit subsidy, there are some logistical constraints in the distribution channel. sahelian agropastoralists react to fluctuating prices by combining various supply sources, including the government, the middlemen and/or their own stocks. 3.2 social network analysis of the peanut value chain the matrix of social networks (figure 4), which represents the positions of the different players in the value chain, shows that the bigger the square, the more important the requests from actors to any one given actor. middlemen from touba (in yellow) are the most requested actors and have a dominant role in the peanut value chain. it seems that all the 120 investigated farmers sell to one of the three middlemen, who are in an oligopsony situation. considering past interactions, most farmers seem to be conservatively attached to at least one of these three middlemen. in fact, there is perhaps hardly any support for or transfer of technical and cultural practices among the agropastoralists themselves (figure 5). only agropastoralists from 150 a. wane et al. thiel seerere (in red) and moola (in green) share knowledge on their agricultural practices. the others do not help each other; they almost all consider agricultural production as secondary to livestock activities. cooperation in disseminating knowledge, understanding of practices and cultivation techniques could help minimise input quantities and costs in light of current conditions and help reduce post-harvest losses. figure 4. relationships among actors in the peanut value chain of thiel. source: authors’ elaboration. figure 5. capabilities transmission among producers. source: authors’ elaboration. 151non-livestock value chains however, overall in the rural community of thiel, there are very few technical and economic links. this was discovered by measuring the density of the value chain, which accounted for only 3 percent of the overall potential links. social links are also very important as revealed by almost all interviewees. 3.3 influential power of peanut value chain actors to thoroughly represent the influence of each actor in the value chain, we use a graph (figure 6, here) that is supported by questions summarised in table 1. thus, the first axis summarises all the information contained in the data. indeed, from the left to the right of this axis, we pass from actors that have modalities related to influential individuals to actors that have modalities for non-influential individuals. therefore, using the first axis ordering consistency (faoc-i) criterion, the first axis is sufficient to characterise the actors according to their level of influence. there are two types of individuals: those attracted by modalities that characterise an influential individual, and those attracted by modalities of the variables specific to a noninfluential individual (refer to table 1 for the variables). the individuals who are on the figure 6. influence levels of the different nodes of the peanut value chain. source: authors’ elaboration. 152 a. wane et al. side of negative abscissa participate in pricing the inputs; they are not obliged to buy if the price of inputs is high (i.e., they do not depend on the market). these individuals fix or contribute to fixing the sale price, and they are not obliged to sell if the price is low. they master the market and know the final destination of the product. these characteristics are typical for an influential individual in a value chain. however, the non-influential individuals must buy even if the price of the inputs is high. the sale prices of their products are fixed, and they must sell even if the price is low. finally, they do not master the market or know the final destination of their products. they are real price takers, and their modalities are those of individuals who have little influence within the value chain. the main actors of the value chain are all in very different environments. the middlemen of touba act within a vast market, with many agents coming from several cities of senegal or from abroad (morocco, vietnam, the ukraine and china). the agropastoralists also deal with the middlemen of touba if they cannot sell in the local market of thiel. the processing women and the collectors, for whom intermediary activities are not a priority, are in the same position. therefore, the middlemen of touba have a larger market share and are the dominant actors in the value chain. they even have equipment for post-harvest crop processing, such as machines for hulling and the capacity to recruit personnel. on the contrary, agropastoralists and processing women have little capital with which to purchase equipment and machines or to hire competent personnel. the middlemen of touba, the collectors and some processing women have the modalities characteristic of an influential individual. this is explained by the fact that touba is considered the heart of the peanut market in senegal. the middlemen completely master the market and buy the crops for the prices they have set, even if they consider the prices fixed by other middlemen. the collectors can buy the products supplied by agropastoralists with more competitive prices because they expect that some of the agropastoralists must sell to meet their daily consumption needs. therefore, the balance of power gives no choice to agropastoralists because they have low production volumes as well as weak financial and economical capacity, although some producers can be influential. influential producers use their stock to sow and sell it only if they perceive favourable prices. they seem to be more informed than the others. the position of the processing women is mixed. those who are on the positive side of the abscissa are not influential; others are on the negative side and are influential. the collectors, the middlemen of touba, and to a lesser extent the processing women, are the most influential members of the peanut value chain of thiel. on the other hand, agropastoralists and haulers remain vulnerable. the indicator built on the multiple correspondence analysis (mca), which appears more precise in the identification of influential actors, supports this result. therefore, 80 percent of the processing women are influential within the value chain. only 22 percent of haulers are in this position. the producers are the most vulnerable in the market: 80 percent do not have any influence in the value chain. capital is also very important in the development of a value chain: it reinforces the production capacity of agropastoralists through the purchasing of seeds, materials and fertilizer, and it helps middlemen practice product management and motivates them to buy more peanuts from the producers. access to capital assists the processing women in 153non-livestock value chains acquiring high-quality process materials, which are also more adaptable and better adhere to health and safety specifications. in the rural community of thiel, the agropastoralists do not have access to formal agricultural credit. only 29 percent of the peanut producers turn to financial credit. among these agropastoralists, 77 percent were somewhat or completely dissatisfied with the quality of financial services, which are expensive and require prohibitive repayment conditions. most agropastoralists practice self-financing to buy seeds and fertilizer and/or to recruit people. the processing women are in the same position because the decision to borrow from financial operators depends on the position of the encampment chief and his creditworthiness. the collectors also claim that inadequate capital is a barrier. according to them, one of the principal ways to improve the peanut value chain is through access to credit. 4. population-specific discussion in the background of relatively high risks and uncertainties that characterises the sahelian livestock production systems (d’alessandro et al., 2015), agropastoralists of thiel also depend on peanut production to secure their livelihoods. the peanut crop development in thiel largely arises from its closeness to the senegal groundnut basin that influences agropastoralists’ engagement in these agricultural practices and production. the agropastoral area of thiel is inhabited mostly by three categories of populations: the seereer, the wolofs and the fulani. each of these categories is characterised by specific cultural practices that influence production systems. functionally, peanut production helps to distinguish between two groups. on the one hand, the group composed of both the seereer and the wolofs is keener to practice agriculture for cultural and economic reasons, although livestock remains a form of insurance and patrimony. on the other hand, the group exclusively composed of fulani is mainly oriented towards animal production and practices agriculture as a secondary or diversification activity. for all these categories of population, the peanut crop is used as is and/or processed into cooking oil by women agropastoralists for self-consumption and marketing, whereas crop residues are destined for animal feed. in this overall context, which is also characterised by relatively weak production volumes, market fundamentals are not the main drivers for peanut crop production, given its multiple uses. there are some similarities with herd management in extensive and pastoral systems, in which the objective function of producers is a composite utility function that balances their short-term consumption needs and long-term herd building strategy (fadiga, 2013). for these reasons, they participate in market(s) in an opportunistic way (wane et al., 2010). however, the peanut crop remains key for household livelihoods and provides them with food, animal feed and a cash crop. in this regard, the peanut value chain analysis displays the overall context shaping the actions of the fulani agropastoralists, who are mostly hindered and characterised by a lack of incentives to perform adequately or at least at the levels reached by others ethnic groups, such as the seereer. for the fulani, the low standard of living and acute need for cash could lead them to sell their weak production even without adequate and expected market prices. this sit154 a. wane et al. uation is exacerbated by the poor supply of financial services (for instance, in the area, there is only one financial structure, and it provides predatory short-term loans with very high interest rates). in addition, infrastructure is weak, with all-weather poor rural roads driving up transportation costs. furthermore, the strong dependency on rainfed agriculture constitutes an aggravating factor in a context of climate change. in such a situation, instead of trying to sell to the middlemen who usually propose higher prices, agropastoralists end up limiting the area in which they sell their production. in addition, the lack of incentive to engage in such a demanding journey to reach the main market forces agropastoralists to rely heavily on some collectors present in the local market. finally, their lack of knowledge regarding peanut production in comparison to the other group (the seerere and wolofs), as well as the frailty of technical cooperation, does not facilitate the emergence of an enabling environment for producers, who remain the weaker node of the peanut value chain. 5. conclusion with options to move forward in these conditions, improving the peanut value chain in thiel requires taking action to boost the agropastoralists’ position—locally and eventually globally—to refine the economic environment. 5.1 at the producer level one of the main points highlighted in this case study is the weakness of the economic, technical and organizational cooperation between producers, despite their claim to be developing strong social relationships. thus, the capacity building of the producers should be more focused on pragmatic goals. for instance, it could be decided to gradually increase the level of technical cooperation and the sharing of knowledge in agricultural practices. in this case, it could be interesting to inclusively build from 3 percent of the overall potential economic and technical cooperation towards more significant levels. 5.2 at the value chain level public policies could help by providing a better integration of heterogeneous knowledge (including local scales) and by orienting agricultural research toward development outcomes. due to the economic dominance of livestock activities in this semi-arid area and the importance of food in household budgets, the main option is to intensify croplivestock activities to obtain viable and sustainable production systems (mcintire et al., 1992). this implies the increased use of external inputs, adaptation of agricultural innovations to local conditions, and the provision of incentives for smallholder farmers in order to strengthen their production capacity, build trust along the value chains and create a favourable business environment. one concrete way to attempt this inclusive objective could be to ensure that the actors of the value chain benefit from moving towards the establishment of genuine inter-professional actions through a technical, organizational, managerial and management innova155non-livestock value chains tion platform. this platform, through which all stakeholders will be represented, should be a framework that considers different expectations, identifies the main constraints within the value chain, and allows the co-construction of innovations and the facilitation of their appropriation, which should include collective sharing of best practices in production and management. the innovation platform members will be notably responsible for the (i) development of rules for the micro sector (participation requirements, definition of floor prices to maintain economic viability, reference prices, and technical specifications); (ii) the validation of quality conventions for inputs and final products; (iii) the capacity building of stakeholders through technical and technological training programs to increase agricultural yields, as well as post-production training in terms of management and organization (business plans, networking, central purchasing units, etc.) to improve agricultural productivity; (iv) the development of credit and insurance systems adapted to crop-livestock systems and (v) the implementation of market information systems to reduce information and position asymmetries. in terms of policies, public authorities should consolidate existing producer organizations and networking across permanent frameworks for consultation, exchanges, collaboration and learning. they should design sustainable intensification options, minimise losses and wastes in peanut value chains and systematise impact assessments of major solutions through a gender lens, given that the impact of gender on these issues should be considered as improvement strategies are developed. finally, this study stresses the role and importance of social networks, which could be more finely analysed to design improvements for the training and organization of producers. therefore, combining the traditional value chain approach with social network analysis is crucial to move forward towards resilient systems. 6. acknowledgements the authors would like to thank the coraf/wecard (the west and central african council for agricultural research and development) and the ausaid (the australian agency for international development) for commissioning and supporting this study as part of a regional research project (code n°: nrm/6/cf/ausaid/2011-14/). many people have generously contributed to the study by providing documentation and ideas, including researchers from cirad-selmet and the group of scientific interest “ppzs” (pole on pastoralism and drylands). references alary, v., messad, s., daoud, i., aboul-naga, a., osman, m.a., bonnet, p., and tourrand, 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(2009). building competitiveness in africa’s agriculture – a guide to value chain concepts and applications, washington d.c., world bank, 204 pp. bio-based and applied economics 6(2): 183-208, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-18518 quality of life and territorial imbalances. a focus on italian inner and rural areas paola bertolini, francesco pagliacci* department of economics “marco biagi”, università di modena e reggio emilia, italy and capp (centro di analisi delle politiche pubbliche), italy date of submission: 2016 28th, june; accepted 2017 8th, march abstract. the italian national strategy for inner areas stresses the importance of improving socio-economic conditions of people as the only way to reverse negative demographic trends in those areas. in this respect, improving quality of life (qol) may represent a key driver. this work provides a statistical tool to measure existing gaps in qol levels across italian nuts 3 regions, by focusing on inner areas. being qol a multidimensional concept, a composite indicator is computed following a noncompensatory approach: the qol mazziotta-pareto index. firstly, we consider the variability of this indicator across italy, with respect to the presence of inner areas. this analysis breaks down the supposed negative relationship between qol and presence of inner areas, which the paper proves to be mostly overlapping with rural ones, by controlling for sub-national structural divides. secondly, spatial aspects make the picture more complex. neighbourhood affects qol at local level and through global, and local indicators of spatial autocorrelation, groups of nuts 3 regions sharing similar qol levels with their neighbours, are detected. from a policy perspective, locked-in paths among neighbouring regions can influence the effectiveness of place-based policies. keywords. inner areas, rural areas, quality of life, spatial effects. jel codes. o18, r00, r10, r11. 1. introduction across european countries, geographical differences in terms of economic and social development may also affect quality of life (qol). qol is similar to the multidimensional concept of wellbeing, being a function of people’s life circumstances (mea, 2005). thus, it does not comprise just economic aspects (e.g., meeting people’s basic material needs): it may also refer to social networks, people’s health, their sense of worth and the sustainability of the environment on which they depend (cagliero et al., 2011; costanza et al., 2008; *corresponding author: francesco.pagliacci@unimore.it 184 p. bertolini, f. pagliacci petrosillo et al., 2013). at eu level, qol shows wide territorial imbalances, for instance among urban and rural areas (eurofound 2014). even the eu rural development policy has traditionally stressed the importance of the quality of life in rural areas. nevertheless, when focusing on qol territorial imbalances, the urban-rural divide is just part of the story. even the concept of ‘inner areas’, which has been introduced by the italian government, may play a role (barca et al., 2014). the idea behind this concept is rather simple: as stressed by christaller (1933), cities and larger towns have always provided population with essential services (e.g. education, health, mobility). according to the model of economic growth that had occurred in italy since the end of world war ii, those urban hubs have been attracting more and more people also because of the variety of services they could offer (barca et al., 2014). conversely, minor municipalities and other remote areas have started lagging behind. suffering from geographical (and economic) remoteness and being affected by negative demographic trends, they have experienced a steady deprivation of essential services, which, in turn, has made the population decrease faster. these trends have led to negative effects such as: population abandonment and reduction of economic activities, disaggregation of the fabric of society, increasing costs in terms of land management. despite the surge of counter-urbanization processes since the 1980s (dematteis, 1986; oecd, 2009), most of rural and remote areas still suffer from these drawbacks (bertolini et al., 2008; copus et al., 2015), whose costs are paid by the country as a whole (barca et al., 2014). thus, besides traditional north-south socio-economic divides, other kinds of spatially divergent dynamics still affect italy, suggesting the existence of local core-periphery patterns. to tackle this situation, the italian government launched a specific national strategy for inner areas, firstly aimed at defining them. according to it, inner areas are those municipalities that, being located at some considerable distance from major urban poles, suffer from a limited provision of essential services. thus, such a definition essentially refers to a spatial (hence, more conventional) theoretical framework for inner areas, although a wide part of the european literature now points out ‘aspatial’ models of peripherality (copus, 2001; kuhn, 2015; bock, 2016; noguera and copus, 2016). conversely, inner areas as defined by the national strategy (namely, on a spatial basis) tend to share rural and agricultural traits (barca et al., 2014). since its launch, this strategy has fuelled the attention of italian policymakers towards the need for improving social and economic conditions of people living in inner areas, as the only way to reverse negative demographic trends. assuring a good performance of the local labour market, creating new forms of employment, and enhancing qol levels represent the only way to cut emigration from inner areas and to attract new people and households (barca et al., 2014). given these important policy implications, this paper provides some simple statistical tools for policy analysis, and in particular for assessing and measuring existing gaps in qol levels across italy, with a specific attention to rural and inner areas. as qol is a multidimensional concept, its measurement poses three major methodological issues (oecd, 2008), which this paper explicitly tackles: i) defining the most appropriate territorial level of analysis, according to available data; ii) returning a composite and comprehensive qol indicator, whose variability across italy can be eventually assessed; iii) properly stressing the role of spatial spillovers in influencing such a variability. 185quality of life and territorial imbalances the rest of the paper is organised as follows. section 2 introduces the concept of inner areas, as defined by the italian national strategy for inner areas. section 3 tackles the main measurement issues linked with a spatial definition of inner areas, suggesting some ways to assess the importance of inner areas at nuts 3 level. section 4 provides a synthetic indicator of qol, discussing main methodological approaches and returning main results. main relationships between inner areas and qol are also described. section 5 focuses on spatial issues, by introducing into the analysis the role of spatial neighbourhoods. section 6 concludes the paper. 2. the italian national strategy for inner areas in 2014, the italian government launched the national strategy for inner areas as a way to promote innovative projects within remote municipalities. in this framework, remoteness is assessed in terms of lack of essential services, considered as constituents of the eu ‘citizenship’ (barca et al., 2014). focusing on the provision of services, the strategy defines inner areas on the basis of their geographical distance from those centres (i.e. large cities) providing services. thus, rather than an ‘aspatial’ definition of inner peripheries, a spatial approach is mostly adopted. referring to service provision as a key element to classify the territory is not a completely new approach: it was introduced in 2008 by the eu dg regio, with the goal of better classifying rural areas in comparison to the official oecd classification, which had been mainly based on population density (dijkstra and poelman, 2008). dg regio combined the oecd population criterion with an indicator of distance and it considered the driving time (namely 45 minutes) to reach a city of at least 50.000 inhabitants, as a main centre of services. in contrast to the dg regio classification, the methodology suggested by the italian national strategy does not consider rural conditions or number of inhabitants of cities: it only focuses on the effective availability of services at municipal level, defining inner areas in terms of their spatial remoteness. in particular, inner areas deserve political attention – and a national strategy – for many reasons. firstly, they hide wide potentials, holding an important environmental (e.g., water, forests, natural and human landscapes), cultural (historic settlements, small and rural museums, skills centres) and agricultural heritage (barca et al., 2014). secondly, they still represent a key part of italy (60% of the total land area and 25% of italian population). in order to improve their socio-economic conditions, the national strategy moves from inner areas’ main problems. in fact, most of them have been facing a steady process of marginalisation, followed by a degradation in the provision of essential services (health, education and mobility). therefore, they are expected to increase their own marginalisation, boosting national social costs in terms of hydro-geological instability, degradation of both cultural and landscape heritage, decay and soil consumption (barca et al., 2014). as a way to reverse these trends, both enhancing qol and improving labour market performances represent effective policy tools (barca et al., 2014). in particular, the strategy moves from the idea that, despite common sense, some inner municipalities have been able to implement good practices, over time. thus, if inner 186 p. bertolini, f. pagliacci areas’ economic marginalisation does not represent an unavoidable process, the strategy just aims to spread the knowledge about those best practices, trying to replicate them across italy (barca et al., 2014). in most cases, interventions involve the promotion (and preservation) of local environment and local cultural resources. to this respect, the definition of inner areas as suggested by the strategy could overlap with the identification of rural regions, which experience a significant lack in essential services provision as well. nevertheless, both inner and rural areas may also share some potential strengths. for instance, they both have plenty of area-specific agricultural productions, which originate from tight connections between the territory and local skills. inner areas are home for many typical productions (pdos and pgis), prompting local food industry1 (barca et al., 2014). given the existence of such a potential, studies on rural development have often highlighted the emergence of positive tendencies (such as the increase in rural tourism and the diffusion of agriculture multifunctionality), which may prompt the development of both rural and inner areas (hoggart et al., 1995; paniagua 2012), overcoming traditional urban-rural economic divides (pagliacci, 2017). besides a radical change in its theoretical perspective, even the implementation of this strategy is innovative, as each region is forced to select a limited number of pilot programme areas2, to promote territorial safeguarding, valorisation of natural and cultural assets (namely sustainable tourism), agricultural activities, renewable energy and energy saving, handicraft and local knowledge. as underlined above, the enhancement of qol at local level sits at the heart of the national strategy for inner areas, their socio-economic development being the main aim of the strategy. in other words, qol emerges as an important target of this strategy. actually, the enhancement of qol is crucial to promote local development, involving both economic growth and a greater social inclusion. as already mentioned, the ultimate objective – and guiding light – of the strategy is reversing population trends in inner and remote areas. a reversal in demographic dynamics is acknowledged as a key factor to limit social costs linked to socio-economic marginalisation, hydrogeological instability and degradation of both human and environmental capital. therefore, qol levels cannot be ignored: actually, they represent key drivers in people’s settlement choices. it follows that assessing qol divides between urban poles and inner areas represents a key issue, especially in helping policy makers in fine-tuning their own policies (barca et al., 2012). furthermore, qol divides matter even within inner areas, which now show polymorphic traits, having followed differentiated trajectories of development for decades (barca et al., 2014). thus, assessing different needs across different areas as well as different geographic patterns may represent a great improvement to the strategy itself. 1 foodstuffs represent cultural assets as they refer to local identities. furthermore, new types of employment may originate, thanks to major changes in agro-food activities and in the distribution process, which may also show positive effects on the environment (barca et al., 2014). indeed, common agricultural policy stresses cross-compliance as a key point (matthews, 2013). 2 although following nationally shared criteria, regions are in charge of identifying the neediest areas, according to a well-defined selective approach (barca et al., 2014). 187quality of life and territorial imbalances 3. how “inner” are nuts 3 regions? methodological and measurement issues 3.1 share of inner areas at nuts 3 level the national strategy provides a detailed and innovative methodology to classify italian municipalities. while mapping and zoning have always represented challenging tasks for policy makers, barca et al. (2012) suggest that any place-based policy would take great advantage from more accurate indicators of existing territorial differences. here, the identification of inner areas moves from the polycentric structure of italy, where just some main cities provide services to other municipalities that gravitate around them, each of them with its own level of spatial remoteness. three main theoretical assumptions drive this way of mapping inner areas (barca et al., 2014): • the network of differentiated urban centres provides the whole range of essential services, generating catchment areas according to a gravitational models (christaller, 1933); • other minor municipalities’ degree of spatial remoteness from this network may hinder social inclusion as well as qol levels (inner areas in a spatial perspective); • inner areas are not homogeneous and, in fact, they are becoming more diverse with regard to their own socio-economic and territorial development (sotte et al., 2012; barca et al., 2014; copus et al. 2015). for decades, they have followed different evolutions, according to both their natural/geographical characteristics and their relative proximity/remoteness to urban areas. in other words, different time-space evolutionary patterns have occurred among inner areas. from a methodological perspective, identification of inner areas is a two-step procedure. firstly, italian municipalities acting as service providers are defined as those municipalities (or groups of neighbouring municipalities) being able to provide simultaneously: i) the full range of secondary education; ii) at least one major emergency care hospital; ii) at least one medium railway station, with an average degree of uptake for regional services and some long-distance journeys3. accordingly, both urban poles and inter-municipal poles are defined as those cities (or groups of contiguous cities) that provide the whole set of these services4. then, all remaining municipalities are classified into four different typologies (outlying areas; intermediate areas; peripheral areas and ultra-peripheral areas), according to spatial accessibility. number of minutes taken to get from each municipality to the nearest urban pole is considered to compute each band (less than 20 minutes, less than 40 minutes, less than 75 minutes, more than 75 minutes) (barca et al., 2014). as stressed, such a classification moves from a spatial definition of inner areas, considering no other socio-economic weaknesses but remoteness. eventually, moving from this six-typology classification, a broader definition of inner areas is provided by just putting together intermediate, peripheral and ultra-peripheral areas (barca et al., 2014). given the purposes of this work, here we refer to this broader definition of inner areas. nevertheless, some methodological drawbacks occur. a first issue deals with the territorial level of the analysis. inner areas are defined at municipality level, but no reliable 3 barca et al. (2014) provide further details on the characteristics of services under consideration. 4 all nuts 3-level capital municipalities are considered as urban poles, even when they do not provide all the aforementioned set of essential services (barca et al., 2014). 188 p. bertolini, f. pagliacci qol indicators are available at such a territorially disaggregated level. at the maximum, any analysis can refer to nuts 3 level (i.e., 110 observations). thus, municipal data have to be converted into nuts 3 level data5. to return robust results, the relevance of inner areas within each nuts 3 region is computed according to three alternative indicators. firstly, number of municipalities is considered. given the i-th nuts 3 region and its n municipalities, the inner-municipality indicator (ii) is defined as follows: i m ni jj n 1∑ = = (1) where j is one of the n municipalities in the nuts 3 region i and the generic element mj can take two different values: mj = 1, when j is classified as either intermediate or peripheral or ultra-peripheral; mj = 0, otherwise. alternatively, both population and land area are considered. as in (1), given the i-th italian nuts 3 region and its n municipalities, the inner-population indicator (ipi) and the inner-area indicator (iai) are defined as follows: ip m p p ( ) i j jj n jj n 1 1 ∑ ∑ = = = (2) ia m a a ( ) i j jj n jj n 1 1 ∑ ∑ = = = (3) where j is one of the n municipalities in the nuts 3 region i, pj is its population and aj is its land area. as in (1), the generic element mj can take two values (either 0 or 1). each indicator may range from 0 to 1: 0 stands for the absence of inner area; 1 stands for the absence of non-inner areas. figure 1 returns the values of each of the three indicators at nuts 3 level. while ii and iai show similar patterns, when focusing on population, the share of inner areas at nuts 3 level is generally lower. just in a few southern nuts 3 regions, the share of population living in inner municipalities is above 50%. a sharp north-south divide also emerges when looking at average values at regional level (table 1). among italian nuts 2 regions, liguria, piedmont and lombardy share the lowest shares of population living in inner municipalities (less than 12%). on the opposite side, in three southern regions (i.e., basilicata, molise and calabria) more than 55% of their population lives in inner areas. thus, such a north-south divide should be always taken into account in the rest of the analysis. 5 the authors are aware that such a transformation may results into concrete limitations for the analysis. in some cases, nuts 3 regions might be internally heterogeneous. thus, a focus on lau 2 territorial units would be much more appropriate for this kind of analysis, if data about qol were available. 189quality of life and territorial imbalances figure 1. inner areas, share out of the total by nuts 3 region: number of inner municipalities (left); inner population (centre); inner land area (right). 1 source: authors’ elaboration table 1. inner areas, share out of the total by region. regions inner municipalities (ii) inner population (ipi) inner land area (iai) north-west piedmont 38.06% 11.70% 46.29% aosta valley 59.46% 30.50% 71.60% lombardy 33.03% 10.69% 45.95% liguria 43.83% 8.89% 50.52% north-east trentino-alto adige 76.28% 44.93% 81.24% veneto 33.05% 18.72% 38.06% friuli-venezia giulia 39.45% 13.77% 53.79% emilia-romagna 41.95% 13.11% 42.84% centre tuscany 44.25% 13.10% 51.30% umbria 61.96% 25.31% 48.51% the marches 44.35% 14.77% 42.73% latium 76.72% 28.06% 64.62% south abruzzo 75.41% 37.05% 70.96% molise 80.15% 61.11% 83.37% campania 49.00% 14.70% 63.19% apulia 54.26% 26.05% 44.92% basilicata 96.18% 74.65% 92.32% calabria 79.95% 55.21% 81.10% the islands sicily 74.62% 41.34% 73.36% sardinia 84.35% 52.27% 84.54% italy 51.72% 22.43% 59.77% source: authors’ elaboration. 190 p. bertolini, f. pagliacci 3.2 inner areas and other indicators of rurality as already stressed, inner areas are expected to share important rural traits (barca et al., 2014). having computed nuts 3 level indicators, we can compare them with alternative indexes of rurality: eurostat urban-rural typologies (eurostat, 2010); the pri (peripherurality indicator) (camaioni et al., 2013); the fri (fuzzy rurality indicator) (pagliacci, 2017). each indicator is built on an alternative methodology, all of them referring to the whole eu-27. eurostat (2010) defines urban-rural typologies according to population density and controlling for the presence of large cities. such a single indicator is eventually collapsed into a discrete ordinal variable, returning three urban-rural typologies: predominantly urban (pu), intermediate (ir) and predominantly rural (pr) regions. thus, it is too rough to capture increasing rural areas’ polymorphism (camaioni et al., 2013). camaioni et al. (2013) compute the pri, following a multidimensional approach. they apply a conventional principal component analysis to a 24-variable dataset (covering sociodemographic features, economic structure, land use, remoteness). then, an ideal urban benchmark (i.e., a region being extremely urban in europe) is identified and statistical distances between any other eu region and this benchmark are computed (camaioni et al., 2013). so, for each region, the pri returns jointly the extent of rurality and peripherality. eventually, the fri stresses the concept of urban-rural continuum. it applies fuzzy logic to six input variables (covering role of agriculture, population density and landscape/ use of land) and it returns a final output (i.e., the fri), which ranges from 0 to 1, where 0 stands for completely urban; 1 stands for completely rural (pagliacci, 2017). the statistical relationship between indicators of inner areas and indicators of rurality can be assessed by means of pearson correlation coefficients. table 2 returns the correlation between ii, ipi, iai respectively and the aforementioned three indicators of rurality computed for italian nuts 3 regions6. in any specification, correlations are positive and statistically significant, also thanks to the spatial definition of inner areas adopted by the strategy. coefficients are larger for the fri than for the pri, although the latter also assesses nuts 3 regions remoteness (thus, a spatial concept, similar to the one referring to inner areas). similar findings emerge when looking at the presence of inner municipalities among different eurostat urban-rural typologies. point-biserial correlation between each dummy variable and the presence of inner areas is consistent with expectations: correlation is positive for pr regions (inner areas’ share is larger in pr regions than in nonpr ones), and it is negative for both pu and ir ones. when comparing average shares of inner areas among three typologies, similar evidence is returned: one-way anova (analysis of variance) tests whether average values are statistically different or not.7 tests show statistically significant differences in any specification. as a strong relationship between rural and inner areas emerges, the national strategy for inner areas implicitly refers to rural areas, as well. 6 here, just 107 observations are considered, as neither pri nor fri values are available for monza and brianza, fermo, barletta-andria-trani. actually, those nuts 3 regions were just instituted in 2004. 7 preliminarily, levene’s test is computed. it tests the null hypothesis that groups’ variances are equal. if they are, simple f test for the equality of means in a one-way anova is performed; otherwise, welch (1951) method is adopted. 191quality of life and territorial imbalances table 2. relationships between inner indicators and indicators of rurality (pri, fri, urban-rural typology) (p-values in parenthesis). ii ipi iai pearson correlation coefficients: pri (camaioni et al., 2013) 0.522* 0.560* 0.487* (0.000) (0.000) (0.000) fri (pagliacci, 2017) 0.657* 0.601* 0.638*   (0.000) (0.000) (0.000) point-biserial correlation: urban-rural typology: pr regions 0.471* 0.538* 0.421* (0.000) (0.000) (0.000) ir regions -0.242* -0.269* -0.248* (0.012) (0.005) (0.010) pu regions -0.291* -0.341* -0.218* (0.002) (0.000) (0.024) avg. comparison: avg. pr regions 0.689 0.461 0.677 avg. ir regions 0.459 0.224 0.478 avg. pu regions 0.343 0.112 0.407 levene’s test 0.182 6.608* 0.318 (0.834) (0.002) (0.728) one-way anova  17.919* 31.324* 12.871* (0.000) (0.000) (0.000) * statistically significant at the 5% level. source: authors’ elaboration. 4. qol as a multidimensional concept 4.1 the mazziotta-pareto index as a multidimensional concept (mea, 2005), qol includes both economic aspects and social-relational ones (cagliero et al., 2011; costanza et al., 2008; petrosillo et al., 2013). thus, measuring qol is harder than measuring the presence of inner areas: it requires the challenging construction of a composite and multidimensional index (oecd, 2008; mazziotta and pareto, 2014). in the case of qol, both ‘objective’ and ‘subjective’ aspects matter. the former dimension refers to physical and health status, personal income, local standards of living (malkina-pykh and pykh, 2008; petrosillo et al., 2013). the latter focuses on individuals’ subjective experience of their lives (land, 1996) as well as psychological responses (e.g., life and job satisfaction and personal happiness). although the european foundation for the improvement of living and working conditions follows a subjective approach in carrying out surveys on the level of quality of life across europe (e.g., eurofound, 2014), here 192 p. bertolini, f. pagliacci no subjective measures of qol are included, as assessing them is rather difficult. actually, no sociologic surveys or investigations (shin and johnson, 1978) are available at nuts 3 level. therefore, this analysis just focuses on objective qol indicators. according to this perspective, a wide literature has already discussed the main drivers of qol at sub-national level. in particular, urban-rural divides have been widely investigated (see for instance cagliero et al., 2011; florida et al., 2013; shucksmith et al., 2009; sørensen, 2014). in italy, the most cited qol indicator, available at nuts 3 level, is provided by the financial newspaper “il sole 24 ore”. every year, it returns a qol indicator based on 36 single variables, grouped into six different thematic areas (economic wealth, business activities and employment, services and environment, population, crime, leisure). despite its large popularity, this indicator suffers from some drawbacks. firstly, it assumes perfect substitutability among original variables (i.e., a good performance in a thematic area may compensate a bad performance in another one). secondly, different standard deviations among each variable may affect the outcome8 (mazziotta and pareto, 2010a; 2010b; 2016). lastly, the set of original variables changes every year: this makes impossible to assess time comparisons. to tackle these drawbacks, an alternative indicator is suggested here: the mazziottapareto index (mpi), a well consolidated indicator to assess qol at local level. the mpi is a non-linear composite index, which transforms individual variables into a standardized indicator. it sums original data up, using arithmetic mean but adjusting it by a ‘penalty’ coefficient, which is related to the variability observed for each unit (mazziotta and pareto, 2016). accordingly, those observations showing unbalanced values of the initial variables are penalised, according to a non-compensatory perspective (mazziotta and pareto, 2010a; 2016). in particular, here we adopt the following methodology to compute a qol mpi. firstly, original variables standardisation occurs. let’s consider the original matrix x, whose generic element is xij. it has n rows (observations) and m columns (variables), which are grouped into p thematic areas. from x, a standardised matrix z is computed (mazziotta and pareto, 2010a), whose generic element zij is alternatively defined as follows: z x m s 100 10ij ij x x j j = + − (4) z x m s 100 10ij ij x x j j = − − (5) where: m x nx ij i n 1 j ∑ = = and s x m n ( ) x ij i n x j 1 2 j ∑ = − = in particular, (4) is applied to those indicators that are concordant in sign with the qol mpi; otherwise, (5) is applied. accordingly, p sub-indicators of qol are computed, 8 this distortion comes from the fact that the synthetic indicator is computed through distances from a benchmark (i.e. the best performing nuts 3 region). 193quality of life and territorial imbalances each of them referring to a thematic area. given h thematic areas, each of them comprising k variables, the h-th sub-indicator of qol is given by: z z k ih i k h j j k , ( 1) 1 ∑ = − + = (6) the p sub-indicators zih are then grouped together and a qol mpi is returned as: mpi m s cvi z z zi i i = − (7) where: m z pz ih h p 1 i ∑ = = s z m p ( ) z ih h p z 1 2 j i∑ = − = cv s mz z z i i i = the s cvz zi i product represents the most innovative aspect of this approach. it penalises those units showing unbalanced values of the p thematic sub-indicators (mazziotta and pareto, 2016). in addition, due to the standardisation provided by (4) or (5), each indicator’s mean is 100 and each standard deviation is 10 (mazziotta and pareto 2010; aiello and attanasio, 2004). here, this methodology is applied to a set of 28 original variables, retrieved for each italian nuts 3 region. they refer to seven different thematic areas linked to qol: • wealth & economic competitiveness (3 indicators), • services (3 indicators), • labour market (5 indicators), • neighbourhood safety (3 indicators), • population (7 indicators), • leisure (2 indicators), • environment & energy (5 indicators). thematic areas partially overlap with the ones provided by “il sole 24 ore”. nevertheless, original variables are open data published by the opencoesione (oc) dataset: the fact that the source of data is istat in most cases assures full comparability of results across time. 9 (table 3). 4.2 qol and its sub-indicators: main territorial patterns seven sub-indicators of qol are returned. each sub-indicator shows standardised values. figure 2 shows the values of each sub-indicator across italian nuts 3 regions. wealth and economic competitiveness show a strong north-south divide, confirming larger qol 9 replicability of the analysis over time is a key issue. indeed, changing the set of variables under study may dramatically affect final outcomes. 194 p. bertolini, f. pagliacci table 3. list of input variables, by thematic area. variable definition effect on qol year source economic wealth & competitiveness per capita gva (€) gross value added (current prices) per inhabitants, all sectors + 2013 istat per capita export (€) exports per inhabitants + 2014 istat (oc) per capita patents patents registered to the european patent office, per million inhabitants + 2011 istat on eurostat data (oc) provision of services diffusion of pre-school services % of municipalities out of the total adopting pre-school services (e.g. nursery schools) + 2012 istat (oc) children 0-3 attending day care and pre-school % of young children (aged 0-3 years) who use day care facilities and other pre-school services + 2012 health emigration ratio share of the out-migration in hospital in other regions out of total hospital admissions 2013 labour market employment rate employed persons (aged 15-64) over the number of people 15-64 (%) + 2014 istat (oc) elderly people employment rate employed persons (aged 55-64) over the number of people 55-64 (%) + 2014 youth unemployment rate unemployed persons (aged 15-24) over the number of persons 15-24 in the labour force (%) 2014 unemployment rate unemployed persons (aged 15+) over the number of persons (aged 15+) in the labour force (%) 2014 gender differences differences in % points between male and female employment rates 2014 neighbourhood safety istat on ministero interno, dipartimento pubblica sicurezza data (oc) rate of thefts number of recorded thefts per a thousand inhabitants 2013 rate of robberies number of recorded robberies per a thousand inhabitants 2013 rate of homicides number of recorded intentional homicides per 100 thousand inhabitants 2013 population population density inhabitants per km2 2014 istat old-age dependency ratio ratio of older dependents (people aged 65+) to the working-age population (15-64) 2014 ageing index number of persons aged 65+ per hundred persons under age 15 2014 195quality of life and territorial imbalances in the north of the country. throughout southern regions and the islands, just ragusa and cagliari show local values which are close to the national average. provision of services is at a maximum across emilia-romagna and tuscany, due to a long-lasting attention to these political items (bripi et al., 2011; giordano and tommasino, 2011). conversely, education and health services show poor performances across the south (e.g., molise, basilicata and calabria) and in lazio. similarly, labour market performance is poor in southern nuts 3 regions, whereas the best performances occur across the socalled third italy (bagnasco, 1977 and 1988), namely in the north-east and alongside the adriatic. neighbourhood safety shows a less sharp north-south divide. best performances variable definition effect on qol year source internal net migration rate difference of immigrants and emigrants within the country in a year, divided per 1000 inhabitants + 2014 external net migration rate difference of immigrants and emigrants (from/to abroad) in a year, divided per 1000 inhabitants + 2014 istat life expectancy at birth, males number of years a new-born male infant would live (assuming no changes in patterns of mortality throughout its life) + 2014 life expectancy at birth, females number of years a new-born female infant would (assuming no changes in patterns of mortality throughout its life) + 2014 leisure live theatre and live music performances tickets sold to live theatre and live music performances, per 100 inhabitants + 2007 istat on siae data (oc) tourists number of overnight stays spent by national and foreign tourists in tourist accommodations, per inhabitant + 2013 istat (oc) environment and energy water use efficiency % of water distributed to customers out of the total volume introduced into the municipality water network + 2008 istat (oc) waste recycling share of municipal waste recycled out of total solid waste (%) + 2014 istat on ispra data (oc) renewable energy % of gwh renewable energy to total energy production in gwh + 2010 air quality monitoring network number of control stations of the air quality monitoring network, per 100 thousands inhabitants + 2012 istat open coesione discontinuity of electricity supply number of long-lasting interruptions in electricity supply (average number per single customer) 2014 istat on autorità energia elettrica, gas, sistema idrico data (oc) source: author’s elaboration. 196 p. bertolini, f. pagliacci are observed across mountain areas (the alps and the apennines), while metropolitan and urban nuts 3 regions show poorer performances. population sub-indicator shows a good performance across emilia-romagna and trentino-alto adige. nevertheless, southern regions do not lag behind northern ones, despite a lower presence of foreign people. leisure activities show a scattered pattern across italy, with urban areas and many northern figure 2. sub-indicators of qol and qol mpi, by nuts 3 region. economic wealth & competitiveness provision of services labour market neighbourhood safety population leisure environment & energy qol mpi source: authors’ elaboration. 197quality of life and territorial imbalances and central italian regions performing above the average. lastly, when considering environment and energy, local performance is good across north-east nuts 3 regions as well as in the aosta valley. in the south, sicily and calabria show bad performances, whereas other inner nuts 3 perform generally better. moving from these sub-indicators, a comprehensive qol mpi is computed, by penalising those nuts 3 regions that show more unbalanced performances. figure 2 also returns main results for qol mpi: most of northern nuts 3 regions share above-the-average levels of qol mpi, while southern ones generally lag behind. rather than returning a ranking of nuts 3 regions (which may change over time), the following sections aim to analyse existing correlations between inner areas and qol levels. furthermore, it is possible to notice that results would not have changed much, if we had not considered the penalty coefficient s cvz zi i indeed, qol mpi and the average mean of the seven indicators for each sub-thematic area are actually highly correlated. nevertheless, the adopted procedure, although being more complex, seems to be more robust from a theoretical perspective. 4.3 qol and inner areas: main relationships the analysis of pearson correlation coefficients makes possible the preliminary assessment of the main relationship between qol levels and the presence of inner areas at nuts 3 level (table 4). at national level, qol dimensions are negatively correlated to the presence of inner areas, with the only exception of neighbourhood safety, which shows a positive relation with the presence of inner areas. a first – hence, preliminary – overlook of these results would suggest that italian inner areas generally suffer from low levels of qol: thus, the launch of a national strategy targeted to them is definitely good news. furthermore, as shown in section 3, given the aforementioned relationship between the presence of both inner and rural areas, same results are expected to hold even with respect to the rural part of the country. nevertheless, same data may hide some more complex patterns, which could contrast this general and first overlook. firstly, different patterns may arise at subnational level. on average, italian southern regions tend to show a larger presence of inner areas than northern ones (section 3). this could affect overall results in terms of qol mpi, as well. thus, it is useful to disentangle previous results by macro-groups of regions. for sake of simplicity, here we refer to the classification provided in table 1: table 5 shows pearson correlation coefficients per sub-indicator and per group of regions. when disentangling by group of regions, differences between urban poles and inner areas seem disappearing. in particular, the negative relationship between inner areas and qol no longer hold. in fact, just a few sub-indicators appear to be statistically related to qol at a sub-national level: • north-west: a positive relation between the sub-indicator neighbourhood safety and the presence of inner areas occurs. actually, the presence of large and unsafe metropolitan areas plays a role. • north-east: service provision is negatively tied to the presence of inner areas at nuts 3 level, when considering total population. nevertheless, both ‘population’ and ‘envi198 p. bertolini, f. pagliacci ronment and energy’ are positively related to the presence of inner areas, as well as the qol mpi. • centre: a negative relation between qol and the presence of inner areas affects many sub-indicators of qol (e.g. economic wealth, service provision, labour market, environment and energy). the only sub-indicator that is positively related to the presence of inner areas is neighbourhood safety. • south: a negative relationship emerges when considering service provision and inner areas; on the contrary, safety is positively associated with a larger presence of inner areas. • the islands: relationships between qol and presence of inner areas are never significant. here, data confirm inner areas’ polymorphism: when controlling per single macro-region, strikingly different results emerge. in the north-east, inner areas do not lag behind urban poles when referring to qol mpi, whereas opposite findings occurs when focusing on central nuts 3 regions. thus, these findings seem supporting the choice made by the national strategy about the implementation of a place-based policy, in accordance with regional governments: such a strategy seems to be more appropriate when dealing with specific problems, which may occur locally. table 4. pearson correlation coefficients between inner areas indicators and indicators of qol (p-values in parenthesis).   ii ipi iai economic wealth & competitiveness -0.504* -0.534* -0.443* (0.000) (0.000) (0.000) provision of services -0.523* -0.518* -0.478* (0.000) (0.000) (0.000) labour market -0.405* -0.465* -0.352* (0.000) (0.000) (0.000) neighbourhood safety 0.310* 0.350* 0.314* (0.001) (0.000) (0.001) population -0.112 -0.200* -0.071 (0.245) (0.036) (0.459) leisure -0.213* -0.298* -0.193* (0.025) (0.002) (0.043) environment & energy -0.370* -0.388* -0.294* (0.000) (0.000) (0.002) qol mpi -0.420* -0.470* -0.357*   (0.000) (0.000) (0.000) * statistically significant at the 5% level. source: authors’ elaboration. 199quality of life and territorial imbalances 5. the role of the neighbouring space regional patterns are just part of the story: actually, spatial effects can be modelled in a more accurate way. italian nuts 3 regions show a narrow extension: on average, their surtable 5. pearson correlation coefficients between inner areas indicators and indicators of qol by macro-regions (p-values in parenthesis). wealth & competitiveness services labour market neighbourhood safety population leisure environment & energy qol mpi north-west ii -0.388 -0.237 0.088 0.493* -0.186 -0.016 0.062 -0.018 (0.056) (0.254) (0.676) (0.012) (0.373) (0.938) (0.768) (0.930) ipi -0.218 -0.114 0.177 0.551* -0.082 -0.098 0.274 0.170 (0.295) (0.588) (0.398) (0.004) (0.697) (0.640) (0.185) (0.418) iai -0.362 -0.145 0.04 0.464* -0.100 0.021 0.074 0.048 (0.075) (0.488) (0.850) (0.019) (0.633) (0.922) (0.726) (0.820) north-east ii 0.212 -0.363 0.267 0.083 0.491* 0.181 0.587* 0.430* (0.344) (0.097) (0.230) (0.713) (0.020) (0.421) (0.004) (0.046) ipi 0.017 -0.487* 0.115 0.407 0.317 0.148 0.428* 0.334 (0.941) (0.022) (0.609) (0.060) (0.151) (0.510) (0.047) (0.129) iai 0.175 -0.378 0.239 0.177 0.452* 0.156 0.597* 0.423* (0.437) (0.083) (0.284) (0.432) (0.035) (0.489) (0.003) (0.050) centre ii -0.663* -0.630* -0.504* 0.295 -0.451* -0.193 -0.428* -0.623* (0.001) (0.002) (0.017) (0.182) (0.035) (0.390) (0.047) (0.002) ipi -0.697* -0.703* -0.538 0.496* -0.421 -0.376 -0.454* -0.672* (0.000) (0.000) (0.010) (0.019) (0.051) (0.084) (0.034) (0.001) iai -0.549* -0.533* -0.421 0.279 -0.324 -0.294 -0.238 -0.531* (0.008) (0.011) (0.051) (0.208) (0.142) (0.184) (0.287) (0.011) south ii 0.082 -0.529* 0.306 0.642* 0.194 -0.251 0.083 0.177 (0.704) (0.008) (0.146) (0.001) (0.363) (0.236) (0.700) (0.408) ipi -0.021 -0.584* 0.179 0.670* 0.071 -0.380 0.047 0.045 (0.923) (0.003) (0.402) (0.000) (0.740) (0.067) (0.826) (0.836) iai 0.070 -0.573* 0.358 0.682* 0.207 -0.331 0.158 0.199 (0.745) (0.003) (0.086) (0.000) (0.333) (0.114) (0.462) (0.352) the islands ii 0.091 0.229 0.476 0.264 -0.068 0.12 0.034 0.333 (0.729) (0.376) (0.053) (0.306) (0.794) (0.646) (0.896) (0.192) ipi -0.107 0.138 0.310 0.219 -0.120 -0.07 0.171 0.180 (0.684) (0.597) (0.225) (0.398) (0.646) (0.792) (0.513) (0.488) iai 0.222 0.221 0.421 0.080 -0.090 0.327 -0.059 0.279 (0.391) (0.394) (0.093) (0.759) (0.731) (0.200) (0.821) (0.279) * statistically significant at the 5% level. source: authors’ elaboration. 200 p. bertolini, f. pagliacci face is 2,745 km2, i.e. a square whose side is just 52 kilometre. thus, people are used to live, work and spend part of their own leisure time across neighbouring nuts 3 regions, and it could be misleading to consider qol at nuts 3 level by just focusing on the relationships between it and socio-economic features in the same nuts 3 region. in fact, space matters (tobler, 1970), at least in two ways. firstly, qol may show spatially clustered patterns, given the fact that neighbouring nuts 3 regions tend to share similar qol levels. secondly, structural characteristics of neighbouring nuts 3 regions (e.g., the presence of either urban poles or inner areas among them) may also affect qol levels, having an impact on people’s everyday life10. to this respect, these characteristics matter and should be considered separately. 5.1 spatial autocorrelation: qol across neighbouring nuts 3 regions the simplest way to assess qol differentials across neighbouring observations is represented by the analysis of global and local indicators of spatial autocorrelation. according to the first law of geography (tobler, 1970), patterns of spatial association are formally assessed by means of the degree of dependency among observations within a given geographic space (anselin, 1988 and 1995). global moran’s i statistics tests for the presence of spatial dependence. it is a synthetic measure of global spatial autocorrelation, computed as follows (moran, 1950; cliff and ord, 1981): i n w w y y y y y y i j n, , ijj n i n ij i jj n i n ii n 11 11 2 1∑∑ ∑∑ ∑ ( )( ) ( ) = − − − ∀ ∈ == == = (8) where yi and yj are observations of a given variable in locations i and j, and wij is the generic element of a (n x n) row-standardized spatial weights matrix (w) defined as follows: w w w ij ij ijj n * * 1∑ = = (9) the generic element wij * in (9) can take two alternative values: w 1ij * = if i ≠ j and j ∈ n(i) w 0ij * = if i = j or i ≠ j and j ∉ n(i) where n(i) is the set of neighbours of the i-th region. n(i), thus w, can be identified in several alternative ways. literature has emphasized the fact there is no univocal preferable specification of w (anselin, 1988). despite alternative suitable weight matrices (e.g. those based on the nearest neighbours), here w is a first-order queen contiguity matrix. thus, two regions are considered as neighbours only if they share a common boundary or vertex (anselin, 1988). on average, each observation shows 4.45 neighbouring regions11. 10 pagliacci (2014) suggested this idea in a preliminary analysis on qol patterns across urban and rural italian nuts 3 regions. nonetheless, that work simply considered the rough indicator returned by “il sole 24 ore”. 11 most of italian nuts3 regions show either 4 or 5 neighbours. nevertheless, the least connected nuts 3 region has just 1 neighbour, whereas the most connected one has 9 neighbours. 201quality of life and territorial imbalances this row-standardized spatial weights matrix (w) allows computing global moran’s i statistic (thus their degree of spatial dependency) on both the qol mpi and other subindicators of qol. global approaches do not allow the detection of specific regional structures of spatial autocorrelation (i.e., either spatial cluster or spatial outliers): to do that, local approaches are also considered. a local indicator of spatial association – lisa (anselin 1995; anselin et al., 1996) is similar to the global moran’s i statistic, but it is region-specific. it tests the hypothesis of random distribution by comparing values in specific locations and values in their neighbourhood (as defined by w). local moran’s statistics returns the distribution of local spatial clusters, which are groups of neighbouring locations showing significant lisa values. at a given significance level, such as 1%, it is possible to detect five alternative cases (anselin, 1995): i) hot spots (locations with high values and similar neighbours); ii) cold spots (locations with low values and similar neighbours); iii) spatial outliers (locations with high values but with low-value neighbours); iv) spatial outliers (locations with low values but with high-value neighbours); v) locations with no significant local autocorrelation. table 6 returns the values for both the global and the local moran’s i statistics, computed for both sub-indicators of qol and the qol mpi itself. a positive spatial autocorrelation occurs for all indicators but neighbourhood safety. the question thus becomes whether this general tendency to clustering yields to some given spatial clusters or not. table 6. global moran’s i statistics (p-value in parenthesis) and local moran’s i statistics (number of nuts 3 regions within each typology).   global moran’s i local moran’s i (lisa) moran’s i hot spots (i) cold spots (ii) spatial outliers (iii & iv) no local autocorrelation (v) wealth 0.678* 14 11 0 85 (0.000) services 0.763* 16 13 0 81 (0.000) labour market 0.809* 6 23 0 81 (0.000) neighbourhood safety 0.057 0 3 0 107 (0.152) population 0.289* 7 6 0 97 (0.000) leisure 0.215* 5 0 0 105 (0.000) environment 0.630* 7 14 0 89 (0.000) mpi 0.802* 10 20 0 80 (0.000) * statistically significant at the 5% level. source: authors’ elaboration. 202 p. bertolini, f. pagliacci the analysis on the lisa values returns straightforward results (table 6). in no cases, spatial outliers occur (confirming the sharp tendency to a positive spatial autocorrelation of observed values). in particular, neighbourhood safety and leisure are characterised by a fewer numbers of both hot and cold spots, whereas economic wealth, service provision and environment are much more clustered in space. referring to the qol mpi, 10 nuts 3 regions are classified as hot spots, thus they benefit from large qol levels even across their neighbourhood. conversely, in 20 cases, low qol levels are reinforced by bad performances even across neighbouring nuts 3 regions. for the sake of simplicity, figure 3 maps the spatial clusters occurring when considering the comprehensive qol mpi. hot spots are mostly located across north-eastern and central italy. conversely, cold spots cover most of southern regions, from campania and apulia to calabria and sicily. in particular, the presence of neighbouring nuts 3 regions sharing similar qol mpi low values may reinforce their lags compared to northern italy. 5.2 neighbouring inner areas and neighbouring urban poles: an opposite effect in analysing spatial effects among neighbouring nuts 3 regions, also the presence of either neighbouring inner areas or neighbouring larger urban poles may play an addifigure 3. hot and cold spots – qol mpi. source: authors’ elaboration. 203quality of life and territorial imbalances tional role in explaining differences in qol levels across the country. to assess it, we refer to the same spatial weights matrix (w) shown in section 5.1, in order to return the spatial lags of the aforementioned indicators of inner areas (ii, ipi, iai): wi w i i j n,i ij ij n i n 11∑∑= ∀ ∈ == (10) wip w ip i j n,i ij ij n i n 11∑∑= ∀ ∈ == (11) wia w ia i j n,i ij ij n i n 11∑∑= ∀ ∈ == (12) where wij is always defined as in (9). table 7 returns pearson’s correlation coefficients between qol indicators and wii, wipi, wiai, at nuts 3 level. overall national data may hide same north-south divides already pointed out, while data disentangled by group of regions provide more insightful findings. in the north-west, no indicators of qol are correlated with the spatially-lagged share of inner areas. in the north-east, both the population sub-indicator and the environment-energy one are positively linked to the presence of inner areas in neighbouring nuts 3 regions. also, the qol mpi as a whole shows a positive correlation with inner areas across the neighbourhood. on the contrary, across central regions, most relationships are negative. as observed in advance, even the share of inner areas across the neighbourhood shows a negative correlation with economic wealth, service provision, environment and energy. thus, in this group of regions, the presence of neighbouring inner areas plays a detrimental effect on qol. therefore, this divide seems increasing qol differentials as well. in southern regions, the presence of urban poles in the neighbourhood seems to have a positive effect just on the provision of services. same relationship is perfectly reversed in the islands, where the share of inner areas in the neighbourhood plays a positive effect also on labour market performances, environment and energy and the qol mpi as a whole. moreover, with the only exception of nuts 3 regions in the centre, the share of inner areas in the neighbourhood is positively related to qol. thus, if inner areas do not show high levels qol, their presence in the neighbouring space surely plays a more positive role, suggesting the existence of positive spatial spillovers. 6. conclusions through the improvement of both social and economic conditions of people living in inner areas, the italian national strategy for inner areas ambitiously aims to reverse negative demographic trends, which still affect most of them. to this respect, improving qol represents a key issue (barca et al., 2014) for both inner and rural areas. indeed, the paper has singled out that in italy they largely overlap. nevertheless, this analysis has partially broken up the negative relationship between presence of inner/rural areas and local qol levels. such a result is suggested by the analysis of both the qol mazziotta-pareto index, a 204 p. bertolini, f. pagliacci table 7. pearson correlation coefficients between spatially-lagged indicators of inner areas and indicators of qol, by macro-region (p-values in parenthesis) wealth & competitiveness services labour market neighbourhood safety population leisure environment & energy qol mpi italy wii -0.629* -0.569* -0.583* 0.136 -0.155 -0.222* -0.524* -0.575* (0.000) (0.000) (0.000) (0.157) (0.105) (0.020) (0.000) (0.000) wipi -0.615* -0.546* -0.637* 0.155 -0.150 -0.234* -0.552* -0.583* (0.000) (0.000) (0.000) (0.106) (0.117) (0.014) (0.000) (0.000) wiai -0.604* -0.537* -0.562* 0.167 -0.216* -0.195* -0.494* -0.546* (0.000) (0.000) (0.000) (0.079) (0.024) (0.041) (0.000) (0.000) north-west wii -0.403* -0.122 -0.356 0.221 -0.379 0.024 -0.800 -0.246 (0.046) (0.560) (0.091) (0.288) (0.062) (0.910) (0.704) (0.235) wipi -0.070 0.252 -0.274 0.192 0.121 -0.101 0.027 0.140 (0.739) (0.224) (0.184) (0.357) (0.565) (0.629) (0.898) (0.503) wiai -0.367 -0.057 -0.262 0.296 -0.378 -0.008 -0.014 -0.157 (0.071) (0.787) (0.207) (0.150) (0.063) (0.968) (0.946) (0.453) north-east wii 0.192 -0.403 0.191 0.028 0.599* 0.358 0.570* 0.496* (0.391) (0.063) (0.395) (0.902) (0.003) (0.102) (0.006) (0.019) wipi 0.161 -0.510* 0.190 0.353 0.385 0.387 0.514* 0.534* (0.475) (0.015) (0.397) (0.107) (0.077) (0.075) (0.014) (0.010) wiai 0.176 -0.406 0.270 0.109 0.479* 0.510* 0.526* 0.580* (0.433) (0.061) (0.224) (0.630) (0.024) (0.015) (0.012) (0.005) centre wii -0.448* -0.581* -0.411 0.293 -0.367 -0.138 -0.524* -0.526* (0.036) (0.005) (0.057) (0.186) (0.092) (0.539) (0.012) (0.012) wipi -0.436* -0.491* -0.373 0.155 -0.347 -0.024 -0.523* -0.480* (0.043) (0.020) (0.087) (0.491) (0.114) (0.917) (0.012) (0.024) wiai -0.439* -0.553* -0.460* 0.249 -0.446* -0.127 -0.605* -0.561* (0.041) (0.008) (0.031) (0.265) (0.037) (0.574) (0.003) (0.007) south wii 0.059 -0.482* 0.268 0.210 0.132 0.028 -0.097 -0.008 (0.786) (0.017) (0.205) (0.325) (0.538) (0.897) (0.651) (0.972) wipi -0.173 -0.533* -0.001 0.243 0.213 -0.174 -0.152 -0.177 (0.418) (0.007) (0.997) (0.253) (0.317) (0.415) (0.477) (0.408) wiai 0.075 -0.436* 0.252 0.120 0.002 0.030 -0.032 -0.041 (0.726) (0.033) (0.234) (0.578) (0.993) (0.889) (0.883) (0.850) the islands wii 0.068 0.500* 0.694* 0.331 0.125 0.448 0.707* 0.703* (0.795) (0.041) (0.002) (0.195) (0.632) (0.071) (0.002) (0.002) wipi 0.394 0.570* 0.587* 0.274 0.194 0.451 0.465 0.688* (0.118) (0.017) (0.013) (0.287) (0.455) (0.069) (0.060) (0.002) wiai 0.126 0.500* 0.644* 0.633* -0.017 0.311 0.654* 0.770* (0.631) (0.041) (0.005) (0.006) (0.948) (0.224) (0.004) (0.000) * statistically significant at the 5% level source: authors’ elaboration 205quality of life and territorial imbalances composite and comprehensive indicator of qol, computed at nuts 3 level, and different sub-indicators of qol (e.g. neighbourhood safety, labour market, leisure). results suggest that, when controlling for sub-national structural divides, the expected negative relationships between inner/rural areas and qol is softened. for instance, when just focusing on north-eastern regions, a larger share of inner areas at nuts 3 level is associated to higher level of qol. furthermore, even neighbourhood safety (a key driver of qol) is generally larger in more inner/rural nuts 3 regions than in urban ones. accordingly, it is hard to find conclusive results about the relationship between inner areas and qol because of at least two major issues: the way inner areas are measured and the existence of spatial aspects, which make the picture even more complex. referring to the former issue, the computation of three indicators that aim to assess the importance of inner areas according to three different perspectives (i.e. number of municipalities, total population, and land area) represents an important advancement in this field of study. indeed, each of them might be suitable for analysing specific dimensions of qol, for instance, iai seems to be particularly suitable for considering “environment and energy” aspects, whereas ipi can be linked to the provision of services to population. accordingly, also policy implications are expected to differ, as opposite political domains might be interested in assessing the importance of inner areas at nuts 3 level in different ways. the latter issue refers to spatial aspects. people may spend significant parts of their lives out of their own nuts 3 region. therefore, even the neighbouring space is expected to matter in qol. here, main results support this idea. both qol sub-indicators and qol mpi show a positive spatial autocorrelation and it is possible to detect groups of regions whose neighbours share similar qol levels. it follows that even the local development may be influenced by neighbouring regions’ development, as both positive and negative spatial spillovers can affect place-based policies and their effectiveness. the same holds true when considering the presence of inner areas among neighbouring regions: for instance, this work proves that being located close to a nuts 3 region with a higher share of inner areas could have positive effects on qol, especially in the north-east and in the south. thus, inner areas’ diversity clearly emerges. indeed, some of them show more socioeconomic potential, even with respect to qol drivers. such a finding has important policy implications, even with respect to the national strategy for inner areas. the top-down approach, carried out by the italian central government, is crucial when setting policy targets. nevertheless, it is even more important to maintain the decision-making process partially decentralised, in order to identify the most appropriate policy tools to target the neediest areas to be targeted. besides these considerations, this paper points out the effectiveness of the innovative approach chosen by the national strategy for inner areas, which highlights territorial imbalances in terms of people’s needs rather than territorial features. indeed, just the provision of essential services to the population is seen as the main engine for local development, now and in the future. such an approach would allow both scholars and policymakers to go beyond traditional urban-rural divides, which in fact are mostly considered by eu policies (such as the rural development policy). although providing partially overlapping results, a focus on inner areas seems stressing inter-sectoral policies as the best answer to overcome territorial divides and to cope with population changes, both in italy and in the eu. 206 p. bertolini, f. pagliacci acknowledgements authorship may be 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(1951). on the comparison of several mean values: an alternative approach. biometrika 38: 330-336. bio-based and applied economics 6(2): 209-227, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-16395 analysing the impact of targeted bio-ethanol blending ratio in turkey selim çağatay1,*, celal taşdoğan2, reyhan özeş1 1 akdeniz university, department of economics, 07058, antalya, turkey 2 gazi university, ankara, turkey date of submission: 2015 7th, july; accepted 2017 16th, may abstract. in turkey, a legal requirement of blending bio-ethanol with conventional fuels has been imposed, and this is likely to increase in the future. the blending target policy is multi-dimensional as it has direct and indirect impacts on agricultural and factor markets, trade and budget deficit, income distribution, food security and on environment. in this study, policy analyses are carried out to investigate whether the blending target is feasible and sustainable in terms of the economic structure of turkey. analyses were carried out by employing an agricultural bilateral trade model and agriculture focused social accounting matrix. findings suggest that target rate can be feasible and harmless on food security, if the extra required supply is provided through tariff reduction particularly on imports of wheat and maize rather than promoting their production through price premiums. for achieving sustainability of the target blending rate, new supportive policies have to be implemented to create alternative job opportunities in the rural areas and/or to shift farmers for alternative crops. keywords. bio-ethanol, agricultural trade model, social accounting matrix, price multiplier. jel codes. c61, c67, q11, q18, q42. 1. introduction in order to reduce oil dependence that adversely affects national economies especially in oil importers, recently, use of bio-fuel is encouraged especially in transportation sector in many countries. while transportation sector’s share in the global fuel use is 30 percent, of this, 99 percent is covered by fossil fuels and it is known that approximately 21 percent of the global emission is sourced by fossil fuels (rajagopal and zilberman, 2007). therefore, reduction of the use of fossil fuel in the transportation sector is thought to make some contribution to the solution of environmental problems on global scale as well as reducing the dependence of those countries that have energy deficit. additionally, because *corresponding author: selimcagatay@akdeniz.edu.tr 210 s. çağatay et al. bio-fuels are mainly produced from agricultural products, their possibility to create an increase in agricultural revenues, their potential to create new employment opportunities and their provision of efficiency of use similar to that of fossil fuels lead to the expectation that the use of bio-fuel shall become more widespread in the future (rajagopal and zilberman 2007). nevertheless, one should never forget the possible negative impact of rising bio-fuel use on food security especially in countries which have limited agricultural production and especially if big agricultural producers increasingly shift their production to provide input for energy sector rather than food industry. this would obviously put upward pressure on food prices and might create deteriorating impacts on budgets of low income and poor people. a vast empirical literature that analyzes the impacts of bio-fuel use from various angles and in various countries has been accumulated since the beginning of 2000s. fonseca et al. (2010), timilsina et al. (2010), demirbaş (2009), banse et al. (2008) and birur et al. (2008) provide a comprehensive review particularly of the applied ones and at the same time inform the researchers about alternative methodologies used in these studies. some studies also investigated the pros and cons of bio-fuel use from the sustainability point of view such as diaz-chavez (2011), janssen and rutz (2011), ravindranath et al. (2011) and walter et al. (2011). however the literature with regard to turkey is quite limited, although the importance of transportation industry, energy demand, fossil fuel use and food security in turkey is not different from that of most of the other countries. one of the limited numbers of empirical studies carried out on the impacts of bio-fuel production and consumption in turkey is that of hatunoglu (2010). this study has searched for the potential effects of mandatory blending rate applied on bio-diesel use on the agricultural sector. in the analysis, it has been found out that in cases of the application of 2 and 5 percent mandatory blending, respectively 300 and 750 million litres of bio-diesel should be produced, considering the existing gasoline and diesel oil consumption. the study claims that the degree of sufficiency of the plants with oilseeds is rather low in turkey, that external dependence on these products continues, that the mandatory blending rate may not be possible in agricultural terms and that danger of food safety shall be faced. considering the developments in the world and turkish bio-energy markets çağatay et al. (2012) has intended to establish alternative bio-energy policy proposals for turkey. two empirical models have been used in the study. the first one is a multi-country, multiproduct partial equilibrium agricultural net trade model which statically and comparatively measures the effects of foreign trade policies on the domestic and world markets; and the second one is the turkish agricultural policy analysis model which is a multi-crop econometric model with a focus on the turkish agricultural sector. in the empirical part, under the assumption that turkey has not changed its existing crop varieties to a great extent, it is determined that bio-fuel (bio-diesel and bio-ethanol) production shall require more sunflower and sugar beet supply for which the former should be satisfied by rising imports and the latter by rising domestic production. in addition, soybeans and rapeseed are found as alternative crops to provide bio-diesel however their production should be promoted by government support in order to be used for bio-diesel production. based on the methods used in the above very limited literature on turkey, the methodology employed in this study is more comprehensive as it partially allows simulating 211analysing the impact of targeted bio-ethanol blending ratio in turkey on macroeconomic variables such as income distribution and policy cost. in addition, the empirical framework provides information regarding the changes in bilateral trade. when one considers the matter from turkey’s standpoint, it is observed that approximately 70 percent of the energy demand in turkey is satisfied through imports (tmmob, 2012). it may be said for turkey that dependence on foreign energy sources in a country which continuously faces external deficit, bears serious risks for a sustainable growth. lowering external dependence on energy and activating renewable energy sources to be able to reduce emission percentages have been an issue significantly discussed in turkey (www.eie.gov.tr). there are three crops that might be used in the production of bio-ethanol; wheat, maize, sugar beet. the current production is realised by provision of price premiums while protecting them with high import tariffs. therefore, one side of the issue is the policy front where turkey hardly keeps her world trade organization (wto) commitments. in addition, the budget burden and/or burden on tax payers and consumers should not be forgotten. on the other side of the issue, there are alternative uses of these raw materials such as feed and food industry. when turkey’s self-sufficiency statistics are considered we may conclude that to sustain the food security in the country the required bio-ethanol raw material demand should be achieved through extra supply rather than shifting some raw materials from food/feed use to energy production. apparently this extra supply would either require extra agricultural land, for which the country has reached the boundary of its fertile land; otherwise this extra would be imported. while the former would necessitate provision of more premiums (keeping in mind budget burden, wto constraint), the latter would necessitate lower tariffs (keeping in mind reduced tariff revenues, negative impact on the current trade balance deficit); the impacts of domestic agricultural markets are also another side to be dealt with. last but not the least, shifting from fossil fuel to bio-ethanol would obviously create positive impacts on environment as well. recently, targets have been identified for mandatory bio-fuel blending ratios in order to reduce external dependence on energy and bio-ethanol blended fuel sales have started as of 2013. accordingly, the energy market regulatory authority (emra) has targeted the minimum ethanol content, made from domestic agricultural products, of gasoline to be 2 percent for 2013 and 3 percent for 2014 through modifications made in the technical regulatory communication related to the types of gasoline and diesel oil in turkey (emra 2009). additionally, just to see the impacts in the extreme case that could become a target in the medium to long run, we assumed the ethanol content of gasoline to reach 10% in 2020. based on the importance of bio-ethanol use in the world and in turkey, this study aims at analysing effects of the imposed bio-ethanol blending rates by the emra on the agricultural products’ markets, household income, factor markets and policy costs. depending on the findings, the main aim is actually to discuss the sustainability and feasibility of this recent bio-ethanol blending rate target. the analyses are carried out in two stages. at the first stage, effects of the bio-ethanol raw material demand created by the mandatory blending rates on the agricultural products market are investigated. at the second stage, impacts of changes in agricultural markets on household income and factor markets are discussed. the next section explains the empirical methods used to carry out the analyses. section three provides empirical findings and relevant discussion. finally the research concludes in section four. 212 s. çağatay et al. 2. the modelling framework the impact analysis in our study necessitates both a decomposition at product level and a modelling framework which shall reveal the interaction between sectors/markets and their distributional impacts. as the impact analysis has both partial and general equilibrium characteristics, the study has been designed in two stages. at the first stage, various scenarios are simulated in order to calculate the impacts the bio-ethanol blending targets in turkey have on wheat, maize and sugar beet markets by using the mediterranean world agricultural trade model (mwatm). at the second stage price multipliers from agriculture focused social accounting matrix (sam) are used to measure the effects on household income and factor markets. to link both empirical methodologies, in other words, to analyse the impacts of partial equilibrium findings on macro accounts, empirical outputs provided by simulations of mwatm are used as inputs to start simulations with sam1. 2.1 the mediterranean world agricultural trade model2 mwatm is a multi-commodity, multi-country, agriculture-focused partial equilibrium trade model utilized specifically to model bilateral trade. the base year of the modelling platform is 2008 and it simulates till 2020. in this dynamic framework, each year is solved to reach equilibrium by using the current year’s levels of exogenous variables and equilibrium findings of the previous year. thus, a connection is established between consecutive time frames. mwatm falls into the class of the “price-equilibrium” models. newton’s global algorithm (kehoe, 1991; wooldridge, 2002) is used to solve the price set which shall equalize excess supply and demand occurring in the country/product markets in the world market. products are assumed to be heterogeneous between countries and therefore the platform individually models imports and exports between two countries rather than modelling the net trade on a country/product basis. in other words, domestic and imported products are differentiated and modelled by the armington method (armington, 1969). 1 actually, mwatm is a sequential dynamic model which provides empirical solutions until year 2020 and on year-by-year basis. the sam employed is a static one. however, we would not see this as a major problem in terms of modelling because the inputs used in static sam for year 2020 simulations are obtained through dynamic simulations solved for 2020 in mwatm. in mwatm cobb-douglas type supply functions are used whereas the sam has leontief production function. however, we ignored this fact because we only use prices derived in mwatm as inputs in sam. 2 mwatm finds its roots in ltem (lincoln university trade and environment model) which is founded on swopsim (static world policy simulation model) and vorsim (vernon oley roningen simulation model) modelling frameworks. mwatm is directly derived from ltem. the country composition of the mwatm is expanded to include bric countries and “trade modelling” part is modified from net trade to armington type. the base year of the model was upgraded to 2008 (from 2004) and the simulation period was extended to 2020 (from 2012). for more technical details about ltem and mwatm see çağatay et al. (2013), (2012); saunders et al. (2008), (2006); saunders and çağatay (2003); çağatay and saunders (2003a; 2003b). 213analysing the impact of targeted bio-ethanol blending ratio in turkey mwatm includes 25 agricultural products, and of this number, 10 belong to the livestock sector and 15 to crop products3. in the platform, 12 countries4 including turkey, 3 country groups and the rest of the world are endogenously modelled. equations used to connect each country/region to each other and to world market have a standard form. within this standard form only substitute product properties in the agricultural sector may create a difference with respect to the country. the theoretical underpinnings of the model are of an ad hoc nature (colman, 1983). coefficients used in the equations are synthetically specified and are taken from relevant literature, obeying to symmetry and homogeneity conditions5. in general, there are 36 equations, of which 35 are behavioural and one is identity for each country/product. therefore, the whole platform has 13,500 equations. the system in which there are 35 endogenous variables per country/product is simultaneously solved by finding an equilibrium price for each pair of bilateral-country foreign trade in an optimization algorithm. behavioural equations represent trade prices, domestic supply, imports, domestic demand for food, demand for animal feed and demand by processing industry for country pairs. the identity equation solves for exports6. 2.2 price multiplier analysis: social accounting matrix in order to analyse the distributional impacts of policy changes regarding the agricultural sector, the input-output table and the sam that employs it are modified to better focus on the agricultural sector. first of all the year 2002 i-o table (the latest when the paper was written) was updated to year 20087 (base year of the mwatm). then, by using shares in production value the agriculture sector was re-specified to endogenously include wheat, maize, cotton, sugar beet, sunflower and soya. the rest of the agriculture sector consisted of other cereals and annual crops, vegetables, fruits, livestock, agricultural services, forestry, fisheries and 13 agri-food industries including beverages and tobacco. the rest of the economy was grouped under 7 manufacturing, construction, and 3 services industries. in the factor markets, labour use was reclassified according to skills based on education level as unqualified, less qualified and qualified. the same shares of qualifications are used in each agricultural production activity. similarly only for bio-fuel inputs (cereals, maize, sugar beet, soya, sunflower), land was included in 4 different scales based 3 wheat, maize, rice, other cereals, sugar, cotton, sunflower, sunflower flour, sunflower oil, soybeans, soyaoil, soya flour, other oilseeds, other oils, other flour, raw milk, liquid milk, butter, cheese, milk powder, beef, pork meat, sheep meat, poultry, eggs. 4 argentina, australia, blacksea economic cooperation countries (group), brazil, canada, china, european union, indonesia, india, mediterranean countries (group), mexico, new zealand, russia, turkey, usa, rest of the world. 5 see çağatay et al. (2013), (2012); saunders et al. (2008), (2006); saunders and çağatay (2003); çağatay and saunders (2003a; 2003b). 6 as the armington approach is used to differentiate imports and domestic products, bilateral imports are endogenously determined and hence in most of the country/products exports are “closing variable” solved as a residual of the difference between total supply and domestic demand. 7 the 2002 i-o table was updated to 2008 in two steps. first macroeocomic balances and aggregates in 2008 were installed in sam. then, by keeping the technology matrix of 2002 constant (in other words by keeping the intermediate demand constant), the elements of final demand and value added in the i-o matrix were adjusted proportionally to become equal to those in the sam. 214 s. çağatay et al. on size as less than 2 ha, between 2-5 ha, between 5-10 ha and more than 10 ha. finally, in the sam, based on household budget survey, both urban and rural households were grouped according to their status in the job as unemployed, regularly paid labour, labour on daily payment, employer, self-employed and unpaid family labour (previously used in taşdoğan et al., 2010; bhutto and çağatay, 2010). in the sam multiplier models, income and expenditure elasticities are assumed to have unit elasticity. relaxation of this assumption leads us to flexible prices and derivation of price multipliers in flexible price sam is presented in roland-host and sancho (1995). decomposition of the sam income multipliers into transfer, open loop and closed loop effects are presented in stone (1985). in the flexible price model first, endogenous accounts (production activities, goods, production factors and households) and second, exogenous accounts (public, capital and rest of the world accounts) are identified and ordered in accordance with the desired/required policy shocks. then price indices that represent endogenous accounts are replaced by the raw sums in the last column of the sam. finally, effects of an exogenous price shock on the economic system (defined in the endogenous accounts) are simulated through price multiplier analysis to derive the new set of prices that equalize industrial demand and supply (defourny and thorbecke, 1984). derivations of price multipliers are shown in pyatt and round (1979) and rolandhost and sancho (1995). a matrix aij 8 is created by dividing the endogenous accounts (tij) -defined as activities, input demand, factor use and household incomecolumn-cells contained in the sam by the column-sums (yj) corresponding to them (a t yij ij j= -1). a new set of prices is explained as a function of output vector (x) and value of endogenous accounts (p a p xi ij i i= + ). to solve price equation leontief inverse is introduced to the equation (p a xi ij i= -( )1 1 ). ( )1 1-aij is defined as the price transmission matrix and it is used in the derivation of different multiplier effects of a change in exogenous accounts on the endogenous accounts. these multiplier effects include the transfer effect representing the net multiplier effect of a transfer to the exogenous accounts; the open-loop effect revealing the cross effects between different accounts; the closed-loop effect which calculates the last round effects and return back to account where the simulation has started. once the base year of mwatm is updated to the same year with the sam, feedstock equilibrium prices derived from simulation of mwatm were used to create percentage changes in the related feedstock cells of the last “price” column of the sam (çağatay et al., 2013). then the price multipliers were calculated to derive distributional impacts. 3. policy scenarios some presumptions and pre-calculations were made before running the scenarios (table 1). first, gasoline and road fuel consumption forecasts for 2013-2020 period were made by using the past consumption trends in turkey. then, by considering the bio-ethanol blending targets set by the emra, the bio-ethanol equivalent of this forecasted road fuel consumption (over 2013-2020) were calculated. in the next step, corresponding agricultural raw material equivalents were calculated separately for wheat, maize and sugar beet. this calculation is done in order to compare findings when extra bio-ethanol demand 8 this is a 38x38 inter-industry (technology) matrix with i representing outputs and j representing inputs. 215analysing the impact of targeted bio-ethanol blending ratio in turkey is compensated by one feedstock. the emra’s blending rates for bio-ethanol in 2016 were assumed to be valid until 2019 and the extreme target case of 10% was assumed to be valid in turkey in 2020, in order to respond to the need for an extreme point calculation. an expectation is that the bio-ethanol blending rates which suddenly emerge in the market may increase the demand for relevant agricultural (food) raw materials and this might increase their prices due to temporary excess demand. the question here is whether or not this assumption is valid for each food-raw material and if so, how much the price change will be. the current production of bio-ethanol raw materials in turkey is higher than the required extra quantity by the mandatory blending rates. in this case, new bioethanol demand in the market is not expected and assumed to create a huge effect on the market prices. however, the use of the current supply of bio-ethanol raw materials (domestic production and imports of either of wheat, maize, sugar beet) in the production of extra bio-ethanol might create a fall in availability of feed, oil, etc. therefore, during simulations, the model is not allowed to disturb the current consumption9 patterns of bio-ethanol raw materials. this setting might affect the empirical findings, however it is set to maintain the food security. therefore the extra raw material should be obtained either through planting on new agricultural land or through extra imports, or both. in other words, such recognition prevents the present condition of food security from getting worse. whether the extra supply shall be achieved at home or from abroad, or in other words, whether this will be achieved through a relaxation in the border policies or through a rise in the domestic incentive policies is an important problem and to see their impacts two policy instruments are used in the analyses: import tariffs and price premiums. 9 in the model only consumption of bio-fuel feedstock is fixed, not the consumption of other products. table 1. required bio-ethanol and agricultural raw product equivalents. road fuel consumptiona (million lt.) blending targetb agricultural equivalentc, d (%) (million lt.) wheat (000 t) maize (000 t) sugar beet (000 t) 2013 2,506 2 50 147 125 456 2014 2,408 3 72 212 181 657 2015 2,309 3 69 204 173 630 2016 2,210 3 66 195 166 603 2017 2,112 3 63 186 158 576 2018 2,013 3 60 178 151 549 2019 1,915 3 57 169 144 522 2020 1,816 10 182 534 454 1,651 a: values over the 2013-2020 period were estimated by using the relevant data before 2013. b: blending rate in 2017, 2018 and 2019 were assumed to be same with the rate in 2016. c: production volumes represent the values in the case the bio-ethanol demand is satisfied with only one agricultural raw material at each time. d: conversion coefficients from agricultural production to bio-ethanol: each ton of wheat, maize and sugar beet is equal to 340, 400 and 110 litres of bio-ethanol respectively (ertaş, 2010). 216 s. çağatay et al. to compensate for the extra bio-ethanol demand presented in column 4 of table 1, more than one scenario could have been simulated involving various policy and feedstock mixtures. however, it was decided to stick to two main scenarios, and two criteria were used to give the final decision on scenarios. first, we have checked the self-sufficiency ratios of wheat, maize and sugar beet in turkey and found that these ratios are very close to each other, and all are about 90%; so we decided to compensate the extra bio-ethanol demand equally from the three products. secondly; the main question raised by policy makers was whether it was possible and feasible economically and in terms of food security to meet the extra bio-ethanol demand by sole domestic production and if not what would be the outcome if all the extra demand was imported. therefore, we have not simulated policies’ combinations and instead the decision was between increasing price premiums (to promote production using deficiency payment instrument within the limits allowed by wto) and reducing import tariffs10. from the above perspective only two different scenarios were run. first, to meet the required supply domestically, simultaneous price premiums were introduced on wheat, maize and sugar beet; second, the extra supply was met by simultaneous tariff reductions on imports of wheat, maize and sugar. the current rates of tariffs, price premiums and changes made in the scenarios are summarized in table 2. table 3 presents extra supply amount of wheat, maize and sugar beet to compensate for the policy driven bio-ethanol demand. in the first part of the simulations supply, demand, price and foreign trade effects of the scenarios within the agricultural sector are derived by employing the mwatm model. then empirical findings from the first part are used as inputs to derive the price multipliers in sam, to calculate the impacts on household income and factor markets. in the sam it is not technically possible to simultaneously model the premium increase and the tariff reduction applied on the same product. in other words, it is not possible in the sam to decompose the effects of the shocks in question by policy instruments and only net effects are derived. therefore, effects of the premium and tariff changes in the products are separately modelled in the sam11 and as the sam represents a static accounting framework, price multipliers are calculated for 2013 and 2020 separately. the modelling platform includes a multi sectorial part and a multi-commodity, multicountry part which make it possible to provide numerous empirical findings just after one scenario. however, trying to give more results would have the risk of moving away from the focus and missing the main messages. therefore we decided just to focus on what policy makers were curious about, that is, on findings regarding rural areas, specifically agricultural sector and budget burden. 10 knowing exactly domestic/international price elasticity of supply/imports we refrain from providing outcomes of sensitivity analyses due to limited space and to already existing plenty of empirical findings to present. nevertheless, we would like to mention two aspects that are quite influential on the empirical findings and create the main difference with regard to findings for grains (wheat and maize) and sugar beet. sugar beet is more effective/productive in terms of bio-ethanol yield. however, response to tariff and price change is lower compared to wheat and maize, resulting in higher policy cost in comparison to grains. 11 transformation of tariff reductions to the sam accounts are presented in annex. 217analysing the impact of targeted bio-ethanol blending ratio in turkey table 2. policy assumptions. current policy-% scenario 1 scenario 2 wheat maize sugar beet wheat maize sugar beet wheat maize sugar beet premium 2013 5.9 6.3 0.0 6.2 7.0 4.0 no change 2014 5.9 6.3 0.0 6.2 7.0 5.0 2020 5.9 6.3 0.0 7.0 10.0 11.0 import tariff       2013 130.0 125.0 135.0 no change 110.0 100.0 80.0 2014 130.0 125.0 135.0 110.0 100.0 80.0 2020 130.0 125.0 135.0 110.0 100.0 80.0 table 3. required supply. blending rate-% blending amount (mil lt.) in both scenarios required extra amount either through domestic production or imports (000 ton) wheat maize sugar beet 2013 2 50 49 42 20 2014 3 72 71 60 29 2020 10 182 178 151 73 *: required bio-ethanol is assumed to be equally supplied by wheat, maize and sugar beet. 4. empirical findings table a1 presents the findings from the simulation of bilateral trade model mwatm. due to the rise in price premiums (1st scenario) an increase both in producer prices and production amounts compared to the base scenario is experienced for the whole period. the increase in production ranges between 0.5%-3% and therefore we may say that these findings are quite feasible considering turkey’s agricultural land. there is a slight fall in imports of wheat and maize and no change in imports of sugar beet. this fall in imports is also expected as the extra domestic production compensates for the domestic demand. there is almost no change in exports of these products which shows that the rise in supply is used in domestic market to satisfy the rising demand. in table a2 the change in bilateral imports are presented. turkey mainly imports wheat from the european union and russia. the fall in imports from each market is about 0.5%. maize is mainly imported from argentina, canada and usa and again there is a fall of about 0.5% from each market. total imports of sugar beet are negligible and there is no change. tariff reductions (2nd scenario) are expected to create a fall in domestic prices (due to increase in imports and total supply) as they represent the difference between world and 218 s. çağatay et al. domestic prices. to convert tariff reductions and incorporate them in sam we referred to sigwele (2007: 231-232)12. as the producer prices are not changed exogenously only slight falls occur in producer prices, and the resulting changes in production, feed demand and export amounts are quite small through the period (table a1). a major change is expected in imports and the increase in imports ranges between 2%-3.2%, 3%-8.5% and 32%100% in wheat, maize and sugar markets, respectively. although the percentages in sugar market are quite high in absolute terms, their absolute value is lower than that of wheat and maize. the rise in wheat imports from the two main markets the european union and russia is about 3% each (table a2). maize imports rise between 3%-10% from argentina and usa while they fall about 1% from canada. although lower in absolute terms, imports from the european union also rise about 7%. sugar is imported mainly from brazil and it shows an increase of about 30%-100%. the budget burden of the two alternative scenarios is given in table 4. the cost of rising the price premium is larger than the lost tariff revenue and it is observed that in the second scenario gains in terms of imported supply for per unit tariff reduction are greater compared to domestic supply increase for per unit premium increase. one should be careful about interpreting import rise in sugar beet. although the percentage is high it is quite small in absolute value (table a2). table 4. budget burden. scenario 1 scenario 2 budget burden (000 $) change in production -% budget burden (000 $) change in imports -% wheat maize sugar beet wheat maize sugar beet 2013 216,583 1.26 1.31 0.79 64,678 2.13 3.05 32.5 2014 274,235 1.49 1.50 1.06 78,318 2.12 4.12 50.0 2020 546,511 2.07 2.95 2.22 144,883 3.26 8.50 100.0 distributional impacts of scenarios are calculated by using price multipliers derived from agriculture focused sam and findings are given through tables 5, 6, 7 and a3. while tables 5 and a3 provide empirical findings regarding the changes in agricultural land, table 6 presents the effects due to the change in production and finally table 7 shows income effects due to changes in labour income. as sam is a static platform we run simulations in years 2013 and 2020, and present the results for rural areas only13. 12 conversion of tariff reductions to reflect their impact on price vector in the sam: tm in pd = pw(1 + tm) gives us the tariffs as the difference between domestic and world price, pd and pw. domestic and imported products are assumed to be homogenous and pw is assumed to be 1. hence world price is derived as pw = pd/(1 + tm) and change in domestic price due to a tariff change is given as in δpd = 1 1+tm ⎛ ⎝ ⎜ ⎞ ⎠ ⎟−1 . 13 in general a significant change in technology matrix in the i-o is not expected in short-term. therefore, an income and/or price multiplier analysis through the use of sam is not appropriate and we prefer to derive longterm simulation effects due to the fact inter-industry relations are adopted from i-o matrix. 219analysing the impact of targeted bio-ethanol blending ratio in turkey table 5. income effect sourced by agricultural lands-transfer effect (million tl)*. rural 2013 2020 scenario 1 scenario 2 scenario 1 scenario 2 wheat maize sugar beet wheat maize sugar beet wheat maize sugar beet wheat maize sugar beet land payments < 21 da 0,08 0,34 1,58 -0,79 -2,41 0,40 1,38 4,36 -1,19 -4,13 land payments < 51 da 0,17 0,74 3,41 -1,71 -5,17 0,85 2,96 9,36 -2,56 -8,87 land payments < 101 da 0,06 0,28 1,27 -0,64 -1,93 0,32 1,10 3,49 -0,95 -3,30 land payments > 100 da 0,01 0,03 0,16 -0,08 -0,24 0,04 0,14 0,44 -0,12 -0,41 * annual average exchange rate (tl/usd) is 1.90 and it is assumed to stay constant till 2020. after the increase in price premiums (1st scenario), in both years the main production increase is experienced in sugar beet followed by maize and wheat (table 6). these findings are consistent with those regarding the change in agricultural lands, given in table 5. it is observed that majority of the farms sown for wheat, maize and sugar beet are between 2 ha and 5 ha, followed by farm sizes smaller than 2 ha and then sizes between 5 ha and 10 ha. therefore the main income rise occurs for the farmers producing wheat, maize and sugar beet who own farm areas between 2 ha and 5 ha. in table 6 we also observe the distribution of agricultural income created by the production rise. for all products income mainly goes to low qualified labour while the share accruing to unqualified labour is very small. the remaining part goes to qualified labour force. after the fall in tariffs (2nd scenario), in both years we expect a fall in domestic prices due to two reasons. first, rising imports would create a rise in excess supply and this might put downward pressure on domestic prices. however, this might not be the case if the import rise is just enough to compensate for the rising bio-ethanol demand. secondly, tariffs maintain the difference between word and domestic prices and lowering tariffs might also lower domestic prices as well. rising imports causes a fall in domestic production yielding a further fall in incomes (table 5). the income loss mostly accrues to maize producers and to farmers whose area is in the range of 20%-50% ha. this is followed by the farmers whose area is less than 2 ha. the distribution of income fall among household classes is provided in table a3. in both years, among the income groups, the farmers work for their own account and wage/salary earners loose the most, followed by unemployed. as expected the majority of losers plant area between 2-5 ha. in table 6 we also observe the change in incomes of various labour classes due to the fall in production. for all products income loss mainly accrues to low qualified labour and the share of unqualified labour is very small, while the rest accrues to qualified labour force as in the 1st scenario. table a3 and 7 summarizes the distributional impacts among household groups after the increase in land use and labour demand respectively. as explained before in scenarios 1/2 main income gain/loss is experienced by farmers whose production area is between 2 ha and 5 ha and among these households the ones who work on their own account and the group work on wages get the majority of the income gain/loss. these two groups 220 s. çağatay et al. are followed by unemployed workers. labour income is also distributed in the same way in both years. given the fact that majority of agricultural production is done on areas between 2-5 ha (tuik, 2004) and about 40% and 23% of agricultural income in rural areas accrues to the ones who work on his/her own account and to paid labour force respectively (tuik, 2011), we think it is quite likely that an increase/decrease in agricultural income should affect more small land owners/farmers and the mentioned income groups. 5. conclusion and policy implications bio-ethanol blending target was introduced mostly to cope with the rising gas emissions and energy bill in turkey; however the area of influence of this target is multidimensional. hence, discussing the sustainability and feasibility of this target requires a deeper look into all dimensions. in turkey the agricultural sector has been supported and subsidized in significant amounts for the last 60 years and beginning in 2000s main policy instruments used to support the sector have shifted towards more decoupled policies aligned with the impositable 6. payments to labour force sourced by production increase-open loop effect (million tl)*. rural 2013 2020 scenario 1 scenario 2 scenario 1 scenario 2 wheat maize sugar beet wheat maize sugar beet wheat maize sugar beet wheat maize sugar beet unqualified 0,00 0,01 0,04 -0,02 -0,05 0,01 0,03 0,11 -0,02 -0,09 low qualified 0,03 0,13 0,72 -0,29 -0,92 0,14 0,53 1,99 -0,43 -1,58 qualified 0,02 0,07 0,40 -0,16 -0,52 0,08 0,30 1,11 -0,24 -0,90 total 0.05 0.21 1.16 -0.47 -1.49 0.23 0.86 3.21 -0.69 -2.57 * annual average exchange rate (tl/usd) is 1.90 and it is assumed to stay constant till 2020. table 7. household income effect sourced by labour income-open loop effect (million tl)*. rural 2013 2020 scenario 1 scenario 2 scenario 1 scenario 2 unemployed 0,09 -0,13 0,28 -0,21 wages/salaries 0,16 -0,22 0,49 -0,37 daily paid 0,03 -0,04 0,09 -0,06 employer 0,03 -0,04 0,09 -0,07 own account 0,21 -0,29 0,64 -0,49 unpaid family workers 0,00 -0,00 0,01 -0,01 * annual average exchange rate (tl/usd) is 1.90 and it is assumed to stay constant till 2020. 221analysing the impact of targeted bio-ethanol blending ratio in turkey tions of the wto agreements. however, the financial burden of this support especially on government budget has been always questioned and criticized by policy makers and sometimes by academics. another long lasting problem for turkish economy is the growing overall trade deficit and recently the agricultural sector contributed increasingly to this deficit in spite of the wide range of products grown on large agricultural lands in turkey. moreover, rising rural unemployment and dominance of small scale producers are also important factors in turkey yielding fluctuations in rural income and low agricultural productivity. last but not the least rising imported energy demand and cost, and rising difficulty in achieving food security are also problems highly related to the overall economy and population growth. the bio-ethanol blending targets planned to decrease the co2 emissions particularly sourced by transportation sector in turkey seem to be influential without any doubt. in the last 25 years, the average share of transportation in overall co2 emissions is about 17% and more than 90% belongs to land transportation14. when the average annual road fuel consumption is considered, it is observed that 17% of total emissions is caused by the use of more than 2 million liters of road fuel. therefore, both policy scenarios create at least a decrease between 2% and 3% in transportation based co2 emissions. however, a more significant decrease (about 8%) requires a more rigid target such as the one in the extreme target case (10% in 2020). this shift to bio-ethanol also creates a fall in road fuel imports between 300.000-500.000 thousand tons (when the target is set between 2%-3%) which rises almost to 1.5 million tons with the rising blending rate up to 10%15. in terms of the impact on import bill and trade deficit, this shift might cause a fall between 1.1%7% in total cost of road fuel imports and a fall of about 30% in trade deficit16. when it comes to the cost of the blending target, significance of suggesting different policy scenarios and/or policy instruments is seen. if price premiums are used as the main policy instrument, the additional cost changes between 1.5%-3.5% of total agricultural support depending on the target blending rate. in addition extra premiums create an additional 0.02%-0.07% rise on the share of total agricultural support in government central budget outlays (demirdöğen et al., 2012). however if import tariff reductions are used as policy instrument, agricultural support and its share in government budget do not change but instead a decrease in tariff revenues is experienced between 1/3-1/4 of the extra premium payments in the first scenario depending on the target blending rates. apparently, the reduction of tariff rate is less costly to the government but deteriorates agricultural and overall trade deficit. the rise in sugar beet imports is ignorable in both scenarios, but the increase in imports of wheat and maize especially in second scenario creates a rise between 2%-15% in cereals trade deficit, depending on the blending rate. nevertheless, because the simulations do not allow for current consumption pattern to change we do not expect a significant change in food security in the country. the other variable that the two scenarios affect in opposite direction is the agricultural income created through the use of both land and labour. while there is an increase in transfers to producers with the rise in price premiums, there is negative transfer after the tariff reduction due to the rising imports and total supply. therefore, unless there is 14 http://www.tuik.gov.tr/prehaberbultenleri.do?id=10829. 15 http://www.tuik.gov.tr/pretablo.do?alt_id=1046. 16 http://www.tuik.gov.tr/pretablo.do?alt_id=1046. 222 s. çağatay et al. a policy precaution in place, the second scenario might create an excess capacity in rural areas both in the form of unemployed labour and unused fertile land. however, the first scenario has the opposite impact both on agricultural land use and rural labour force. the trade-off is between a relatively higher transfer from government budget to agricultural producers in the first scenario, and a lower transfer both from rural households and government to importers of agricultural products, in the second. to conclude, based on the current domestic production capacity in wheat, maize and sugar beet, decreasing the imported energy bill and co2 emissions through bioethanol blending ratio as policy instrument seems to be feasible. however, to make it sustainable, first the blending rates should be reached by reducing import tariffs rather than providing price premiums and second new policies should be in place to promote alternative job opportunities in the rural areas. otherwise with the reduction in tariffs there will be an excess of land and labour in rural areas indicating an inefficient economic situation. for example, social security can be provided for a certain period to those who become unemployed and farmers can be moved to produce alternative crops, and/or development of agriculture related processing industries could be promoted. in fact shifting this excess labour to alternative job opportunities might increase productivity in the agricultural sector. while tariff reductions are not contradictory to the wto impositions, food security would not deteriorate from these reductions. in addition, it is possible that first small scale producers would exit the market due to rising imports. in any case putting a sole blending rate target would not solve problems automatically. because the issue is multi-dimensional a fundamental policy package that deals with all dimensions is needed. 6. acknowledgements this research was funded by agricultural 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(2002). econometric analysis of cross section and panel data, mit press. annex table a.1. producer price ($/ton), production, feed demand, total imports, total exports (quantities are in 000 tons). base scenario scenario 1 scenario 2 2013 2014 2015 2016 2020 2013 2014 2015 2016 2020 2013 2014 2015 2016 2020 ppwh 404 398 393 388 371 410 406 401 397 384 403 398 393 388 371 ppmz 307 303 299 295 281 317 314 310 307 302 307 303 299 295 281 ppsu 481 481 481 481 481 501 506 506 506 534 481 482 482 482 482 qpwh 19,275 19,578 19,908 20,243 21,559 19,519 19,871 20,221 20,572 22,005 19,283 19,606 19,917 20,251 21,559 qpmz 5,021 5,154 5,288 5,422 5,964 5,087 5,231 5,372 5,512 6,139 5,021 5,154 5,292 5,425 5,960 qpsu 2,858 2,890 2,923 2,956 3,087 2,880 2,921 2,955 2,988 3,156 2,858 2,890 2,923 2,956 3,087 qfwh 982 1,005 1,030 1,054 1,146 986 1,011 1,035 1,060 1,154 983 1,008 1,031 1,055 1,149 qfmz 2,684 2,750 2,820 2,889 3,145 2,680 2,747 2,816 2,883 3,144 2,689 2,762 2,826 2,895 3,167 qmwh 4,036 4,196 4,355 4,516 5,159 4,024 4,181 4,339 4,497 5,135 4,122 4,285 4,448 4,612 5,327 qmmz 1,443 1,504 1,564 1,625 1,870 1,430 1,488 1,547 1,606 1,846 1,487 1,566 1,628 1,692 2,029 qmsu 59 60 60 61 64 59 60 60 61 64 78 90 91 92 128 qxwh 13 13 14 14 16 13 13 14 14 15 13 13 14 14 16 qxmz 19 19 20 21 23 19 19 20 20 23 19 19 20 21 23 qxsu 7 7 7 8 9 7 7 7 8 9 7 7 7 8 9 wh: wheat; mz: maize; p su: sugar beet; p: producer price; qp: production; qf: feed demand; qm: total imports; qx: total exports. 226 s. çağatay et al. table a.2. bilateral imports (000 tons). base scenario scenario 1 scenario 2 2013 2014 2015 2016 2020 2013 2014 2015 2016 2020 2013 2014 2015 2016 2020 qccanwh 197 205 212 220 252 196 204 212 219 250 196 204 212 219 250 qcchnwh 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 qceurwh 1,015 1,055 1,095 1,136 1,297 1,012 1,051 1,091 1,131 1,291 1,038 1,079 1,120 1,161 1,342 qcruswh 1,884 1,958 2,033 2,108 2,408 1,878 1,951 2,025 2,099 2,396 1,927 2,003 2,079 2,156 2,492 qcusawh 31 32 33 35 40 31 32 33 35 39 31 32 33 35 39 qcrowwh 910 946 982 1,018 1,163 907 942 978 1,013 1,157 930 967 1,004 1,041 1,203 qcargmz 417 434 452 470 540 413 430 447 464 533 432 455 473 492 593 qccanmz 149 155 162 168 193 148 154 160 166 191 148 154 160 166 190 qceurmz 43 45 47 49 56 43 45 47 48 56 45 47 49 51 62 qcrusmz 13 13 14 14 16 13 13 14 14 16 13 13 14 14 16 qcusamz 505 526 547 568 654 500 520 541 562 646 523 551 573 595 718 qcrowmz 316 329 342 356 409 313 326 339 352 404 327 345 358 373 449 qcbrasu 44 45 45 46 48 44 45 45 46 48 62 72 73 74 106 qceursu 5 5 5 5 5 5 5 5 5 5 7 8 8 8 11 qcindsu 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 qcrowsu 7 7 8 8 8 7 7 8 8 8 7 7 7 8 8 arg: argentina; bra: brazil; can: canada; chn: china; eur: european union; ind: india; row: rest of the world; rus: russia; usa: united states of america 227analysing the impact of targeted bio-ethanol blending ratio in turkey table a3. household income effect sourced by agricultural lands-open loop effect (million tl). rural 2013 scenario 1 scenario 2 lands < 2.1ha lands < 5.1ha lands < 10.1ha lands > 10.0ha lands < 2.1ha lands < 5.1ha lands < 10.1ha lands > 10.0ha unemployed 0,10 0,21 0,08 0,01 -0,15 -0,33 -0,12 -0,02 wages/salaries 0,17 0,36 0,13 0,02 -0,27 -0,58 -0,21 -0,03 daily paid 0,03 0,06 0,02 0,00 -0,05 -0,10 -0,04 -0,00 employer 0,03 0,07 0,03 0,00 -0,05 -0,11 -0,04 -0,01 own account 0,22 0,48 0,18 0,02 -0,35 -0,76 -0,28 -0,04 unpaid family workers 0,00 0,01 0,00 0,00 -0,01 -0,01 -0,00 -0,00 rural 2020 scenario 1 scenario 2 lands < 2.1ha lands < 5.1ha lands < 10.1ha lands > 10.0ha lands < 2.1ha lands < 5.1ha lands < 10.1ha lands > 10.0ha unemployed 0,10 0,21 0,08 0,01 -0,25 -0,55 -0,20 -0,03 wages/salaries 0,17 0,36 0,13 0,02 -0,44 -0,96 -0,36 -0,04 daily paid 0,03 0,06 0,02 0,00 -0,08 -0,17 -0,06 -0,01 employer 0,03 0,07 0,03 0,00 -0,09 -0,19 -0,07 -0,01 own account 0,22 0,48 0,18 0,02 -0,59 -1,26 -0,47 -0,06 unpaid family workers 0,00 0,01 0,00 0,00 -0,01 -0,02 -0,01 -0,00 bio-based and applied economics 7(1): 19-38, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-24046 sam multipliers and subsystems: structural analysis of the basilicata’s agri-food sector mauro viccaro1,*, benedetto rocchi2, mario cozzi1, severino romano1 1 school of agricultural, forestry, food and environmental sciences, university of basilicata, potenza, italy. e-mail: mauro.viccaro@unibas.it; mario.cozzi@unibas.it; severino.romano@unibas.it 2 department of economics and management, university of florence, firenze, italy. e-mail: benedetto.rocchi@unifi.it date of submission: 2017 14th, february; accepted 2017 24th, november abstract. local agri-food products are conceived as a form of cultural capital, representing potentially fruitful resources for rural development. italy and its regions offer a rich and diverse agricultural and food heritage that has led to the creation of numerous quality agri-food systems. despite their ability to absorb disturbances and maintain their functions, it is important to develop economic models targeted to analyse the relationships among the components of food systems, in order to identify their strengths and weaknesses and drive the implementation of sectoral policies. in view of the new rural development programme (2014-2020), the aim of this work is to analyse the structure of the basilicata’s agri-food system using a multi-sector model based on a two-region sam, specifically developed for basilicata, an italian region characterised by a highly specialised agri-food sector. results show that the availability of a highly disaggregate multi-sector model of the regional economy may be a valuable supporting tool to design regional policies for innovation and for the development of rural areas, laying the foundation for further analysis. keywords. rural development, agri-food systems, multi-sector model, sam multipliers, subsystem approach. jel codes. e16, r15. 1. introduction agri-food is considered as one of the most crucial sectors for economic growth, as it is the main source of livelihood that makes growth possible (dethier and effenberger, 2012; schultz, 1964). over the last few years, different studies have emphasised the contribution that agri-food can make for rural development, on the regional scale (ilbery and kneafsey, 2000; kneafsey et al., 2001; marsden et al., 2000; murdoch et al., 2000; parrott et al., 2002; tregear et al., 2007). *corresponding author: mauro.viccaro@unibas.it 20 mauro viccaro et alii local agri-food products are conceived as a form of cultural capital; according to the principles of the endogenous development theory (ray, 1998; terluin, 2003), they represent potentially fruitful resources for development, as they can incorporate and add value to many local resources with features that are peculiar to a specific area (brunori and rossi, 2000; marsden et al., 2000). this awareness, combined with the growing consumers’ demand for healthy and safe food, has induced producers to explore new ways of doing business, through initiatives that take over the idea that localised agri-food systems could provide not only economic but also social and environmental benefits, thereby combining marketing with political, socioeconomic and cultural activities that improve collective well-being (volpentesta and ammirato, 2012). in basilicata region (southern italy), agri-food makes a significant contribution to the regional economy in terms of output, employment and exports. the sector contributed €771 million in value added to the basilicata economy in 2014 (istat, 2016a). while its contribution to the regional economy is small relative to other sectors, the basilicata agri-food sector is of strategic importance for the sustainable development of rural communities. the spatial distribution of the production activities, as well as the presence of potential food and wine tourism products with geographical designations (bencivenga et al., 2016), provide it with a key role in sustaining remote rural areas through the generation of income and jobs. different elements show the presence of a valuable “quality” potential in the regional agri-food sector that may be exploited to increase the competitiveness of the regional economy. in the near future, the new 2014-2020 programming period of rural development programme (rdp) will play a major role in enhancing innovation for agriculture, forestry and food industry providing financial support to business choices directed to improve economic and environmental performances, and promoting the organization of competitive food chains. the design and the implementation of regional policy, however, should be increasingly oriented by relevant knowledge on the structure of economy. there are different approaches applied in the analysis of agribusiness1. cook and chadded (2000) describe their evolution linked to the agribusiness, agri-food system and sub-sector (filière) concepts. however, it is important to point out that any strategy of sectoral development should be based on top-down multi-sector approaches, which take into account the dynamics of the agri-food production activities within the wider regional economic system. by the mid-1950s, davis and goldberg (1976) developed the “agribusiness” concept valuating the extent and amount of agricultural and industrial relationships, by the use of the leonetief input-output (i-o) model. since davis and goldberg’s work, the input1 according to david and goldberg’s definition (davis and goldberg, 1976), agribusiness includes three components: 1. the farm supplies aggregate including all intermediate consumptions of agriculture (backwards linkages of agriculture); 2. the farming aggregate composed by all operations of crop cultivation and livestock breeding at the farm level; 3. the processing and distribution aggregate composed by all operations of storage, processing and marketing of agricultural products both for food and non food uses. the processing component can be divide into two further components, the fiber processing and the food processing. this further decomposition of agribusiness allows to separate two subsystems: 1. the agri-food block, including a part of the farming aggregate (agricultural products for food production) and the food processing and marketing component ;  2. the agriindustry block composed by the remaining part of the farming aggregate (non food products) and fiber processing and marketing component. 21sam multipliers and subsystems output analysis has largely been used to study the structure of an economy both at the regional and national level. with reference to italy, among others, chang thing fa (1981) and chang ting fa et al. (2013) use a triangulation method of i-o tables to analyse the structural change of the agribusiness at italian and european level, respectively. belletti (1992), using an i-o subsystem approach, analyses the agri-food sector in more detail studying the delocalization of the agri-food supply chain in tuscany region. however, there are some limitations in using leotief input-output approach. in the leotief model, “…the omission of the general equilibrium links relating output to factorial income and final consumption may be of critical relevance both in aggregate terms (lost gross output) and in the rank ordering of sectors (hierarchy shifting)” (cardenete and sancho, 2006: 322). these limitations can be overcome by extending the conventional i-o methodology in a social accounting matrix (sam) (miller and blair, 2009; rocchi et al., 2015; viccaro et al., 2015). in addition to the inter-industry transactions specific to input-output tables, a sam include balanced accounts for factors, institutions (such as producers, consumers, government) and foreign sectors, closing the cycle of the income distribution and spending. there are numerous examples of the use of sam models in the context of agricultural and food analysis. caskie et al. (1999) use a regional sam to analyse the impact (in term of output, income and employment) of a reduction in the final demand in beef on northern ireland’s economy. psaltopoulos et al. (2006) evaluate the impact of cap measures on rural development in archanes area (crete, greece) using an inter-regional sam. the possibility to decompose through multiregional model the multiplier effects in interregional and intraregional effects is helpful to understand in depth the differences in economy structure among regions, in this case between rural and urban areas. other examples of sam-based studies are reported in rocchi (2009), vega et al. (2014), cardenete et al. (2014) e more recently campoy-muñoz et al. (2017). the cited work by cardenete and colleagues shows the effectiveness of multiplier analysis by using sam models in order to avoid the “missing linkages” typical of the leontief models. based on that, the objective of this work is to analyse the structure of basilicata’s agrifood sector using a multi-sector linear model based on a two-region sam, specially developed for basilicata. through the sam multiplier matrix (miller and blair, 2009), we will evaluate the contribution of each production sector in the agri-food sector to the regional economy and after, following the sub-system approach proposed by momigliano and siniscalco (1982), we will analyse the shares of the production sectors represented in the model that are directly and indirectly committed to satisfy the final demand towards different categories of food. in the light of results of the analysis, a set of policy implication will be discussed. in the following section the potential development linked to the “quality” of agri-food production in basilicata is shortly discussed. section 3 presents the sam and describes the model used in the analysis. the main results are provided and discussed in section 4 while some final remarks are proposed to reader in section 5. 2. the “quality” potential of the agri-food sector in basilicata basilicata’s agri-food sector plays a major role in the regional economy, due to the significant weight of employment in agriculture and the wide range of typical and quality 22 mauro viccaro et alii agri-food products (eight products with a pdo/pgi), besides the number of farms and research bodies involved in the sector. the share of the agriculture’s contribution to the total value added is definitely the highest in italy (5.4% against about 2% at the national level) (istat, 2016a). considering basilicata’s entrepreneurial community, the agriculture has the highest number of firms operating in the region (table 1), which reflects, however, the small average size of agricultural holdings. as to the food industry, its contribution to the overall value added is lower compared to agriculture (2.3%) achieving, however, one of the highest levels on the national scale: this is due to the fact that a large part of the sector still focuses on the production and trade of low value added agri-food products. the importance of the food industry within the regional economy can be clearly seen when considering the manufacturing sector only: food industry ranks just after the automotive sector, both in terms of value added produced (20% vs. 35%) and number of labour units employed (just over 3,200 against about 4,500) (istat, 2016a); food industry is the first in terms of number of operating firms (table 2), which are mainly small and medium enterprises. the analysis of foreign trade enables an outline of the structure and trends of basilicata’s agri-food sector. the most interesting aspect is the reduction in imports recorded just after the economic crisis of 2007 till now, both for the agricultural products (-18%) and for food and beverages (-39%), combined with a significant increase in exports, equal to +24.5% for agriculture and +49% for the food sector (istat, 2016b). basilicata’s food exports in 2015 were above eur 36 million, mostly represented by bakery products accounting for 55% of exports (about eur 20 million), followed by vegetable fats and oils, mainly olive oil (14%) and beverages (10%), basically related to wine exports. another important factor is the trend of food export observed over the last few years. compared to the trend of the other manufacturing sectors (figure 1), food is the only industry that has not been affected by the negative impact of the economic crisis of 2007, but rather continued to grow. table 1. number and % of basilicata’s operating firms by economic sectors (2015). sector n° % agriculture 17,500 34% construction 6,161 12% other services 11,987 23% trade 12,428 24% manufacturing 3,818 7% total 51,894 100% source: own calculations on istat data (istat, 2016a). table 2. number and % of basilicata’s manufacturing firms (2015). sector n° % food and beverage 895 23% textiles, wearing, leather and related products 300 8% wood and of products of wood 553 14% chemicals and plastic products 112 3% non-metallic mineral products 308 8% basic metals and metal products 778 20% machinery and electrical equipment 266 7% motor vehicles, trailers and semi-trailers 39 1% others manufacturing 567 15% total 3,818 100% source: own calculations on istat data (istat, 2016a). 23sam multipliers and subsystems these positive results seem to have been affected by the quality upgrading of exported products and the subsequent strengthening of those factors, such as certified quality, innovation (organic products) and originality, which constitute the established strengths of image abroad. a recent study on the commercial performance of regional agri-food sectors in italy between 1991 and 2012 confirms the good performance of basilicata in foreign trade, mainly related to the level of specialization of its productions (platania et al., 2015). evidence of the main regional specializations, in economic but also cultural and social terms, derives from some experiences of specialized territorial clusters with a broad production base, such as the distretto agroindustriale del vulture (6,489 businesses covering 15 municipalities), the distretto agroalimentare di qualità del metapontino (7,430 businesses covering 12 municipalities), which have been operating since 2004, and the most recent rural districts (since 2010), including the distretto rurale di pollino-lagonegrese (27 municipalities) and the distretto delle colline e delle montagne materane (19 municipalities). these clustering systems, involving the largest part of the region, focus on quality specialized productions (wine, olive oil, mineral waters, dairy products, pork meat processing, fresh pasta, bakery products, fruit and vegetables and cereal production, preserved food, honey) and their promotion through tourism activities. considering the potential integration that agri-food production activities have with the tourism sector, tourist flows can be an asset in the forthcoming years to promote the development of basilicata agri-food sector, especially with reference to the opportunities offered by “matera 2019”, event, when the town of “sassi” will be european capital of culture. based on a direct interview to the agribusiness operators working in basilicata’s agrifood districts, even though the event has not yet generated any impact on the sector, the figure 1. trend of export of the main sectors of basilicata’s manufacturing industry1 (2005-2015) � �� �� �� �� ��� ��� ��� ��� ��� ��� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� � ��� �� �� � ����������� ����� � ������� ��������������������� ���������������� ����������� ������������� �������������������� �������� ������������� ���������� �������� �������� � ��������������� �­������� source: own calculations on istat data (istat, 2016b). 1 the graph does not show the automotive sector, due to a purely graphical reason, given the significant amount of exports (over 2.2 billion of euro in 2015). even this sector, however, shows a drop just after 2007, with a gradual recovery starting only from 2014. 24 mauro viccaro et alii shared perception of operators is that it can offer good development opportunities in the forthcoming years. a further aspect of interest that emerged from the interviews is the great attention the agribusiness operators attach to research, development, training and innovation in the sector. indeed, based on the analysis conducted by the european commission on the european regions’ innovation capacity in 2014, basilicata was classified as “moderate innovator” (european commission, 2016). investments in innovation in order to improve the competitiveness of the sector (contò et al., 2009) may be supported by the research institutions present in the region that are already operating in that direction. other notable initiatives include the spin-offs coming out of research, recognized by the university of basilicata since 2012 in the following areas: environment, new agri-food products (donkey milk) and innovative services. finally, the new 2014-2020 programming period of rural development programme (rdp) represents an opportunity to increase the competitiveness of the regional economy, providing financial support to business choices directed to improve economic and environmental performances, and promoting the organization of competitive agri-food supply chains. in this context, the structural analysis of the basilicata’s agri-food sector presented in the following paragraphs will provide a set results with interesting policy implications. 3. materials and methods 3.1 a two-region sam model in the present study, the structure of the regional agri-food sector has been analysed using a two-region sam model (basilicata vs. rest of italy) with a detailed disaggregation of accounts for agriculture and food industry production activities (see section 3.3 below). the sam (miller and blair, 2009) is a two-entry matrix recording the flows occurring between all actors of an economic system, in a given place and for a given time period (usually one year). each row/column pair represents respectively the inflows and the outflows of a given account, so that by definition the matrix is balanced (the row totals must equal the column totals). a sam may be considered as an expansion or a generalization of a leontief input-output table. while in the latter, emphasis is laid on the production system, in the sam the perspective is larger. the simultaneous representation of the accounts for production activities, production factors, institutions (households, firms, and public administration), capital formation and exchanges with the rest of the world makes it possible to follow the formation of value-added and its distribution and redistribution in the form of income to the institutions. sams are crucial databases for many quantitative models (e.g. sam linear models and computable general equilibrium models). beside their statistical content, sams are a useful tool to evaluate policy interventions both at the national and regional level. through the solution of a linear model (establishing an appropriate “closure rule”: miller and blair, 2009), it is possible to analyse the structural features of the economy and calculate the impact that exogenous changes on single components have on the whole economic system. in our study, the closure rule considers as exogenous the accounts for national government, capital formation and the rest of the world’ so that the resulting sam multi25sam multipliers and subsystems pliers take thus the value of leontevian-keynesian multipliers. let us consider the matrix of sam coefficients of a single region “r” (miller and blair, 2009: 515): [1] where a is the matrix of inter-industry technical coefficients, c is the matrix of endogenous final expenditure coefficients, v is the matrix of endogenous value-added factors shares, y is the matrix of endogenous coefficients distributing income to institutions and h is the matrix of endogenous coefficients for income re-distribution among institutions. the structure of the matrix of sam direct coefficients of our two-region model, as in any two-region i-o model, is (miller and blair, 2009: 77-80): [2] where the blocks along the main diagonal account for flows within the two regions (b = basilicata and i = rest of italy) while the blocks along the other diagonal represent the (commodity and financial) flows between the two regions. by solving the linear system x = sx + f (where x is the vector of totals of endogenous accounts and f is the vector of exogenous account flows) for x, we have: x = (i s)-1f [3] where m = (i s)-1 is the matrix of sam multipliers. each coefficient quantifies the total increase for each account i deriving from a unit exogenous shock on the account j. note that since the sam-based model endogenizes transactions that not included in the input-output interindustry models, the sam multipliers will generally result larger than the input-output ones. the advantage of using a two-region disaggregation of accounts lies in the possibility of considering the rest of italy as being endogenous to the model; this makes it possible to breakdown impacts on basilicata’s economy calculating not only the total but also the intraand interregional impacts (spillovers and feedbacks). if the calculated matrix of multipliers m enables the estimate of the total impact, the breakdown of the matrix of accounting coefficients s into intraregional and interregional elements enables to calculate the following2: intraregional effects: [4] 2 for details of the multiplier decomposition for multi-region model see the chapter “decompositions in an interregional context” (miller and blair, 2009: 286-288). 26 mauro viccaro et alii where ; interregional spillover effects: mspill = i + s* [5] where ; interregional feedback effects: mfeed = [i (s*)2]-1 [6] 3.2 the sub-system approach starting from the concept of vertically integrated sector (pasinetti, 1973), the structural analysis of basilicata’s agri-food sector has been integrated by the sub-system i-o approach that makes it possible to study an individual sector, or group of sectors, that is considered a subsystem which interacts with the rest of the productive system (belletti, 1992; llop and tol, 2013; momigliano and siniscalco, 1982; montresor and marzetti, 2010). in particular, we use the approach proposed by momigliano and siniscalco (1982), extending belletti’s work (belletti, 1992) in a two-regional model. the input-output approach is based on the representation of the interdependencies existing between different economic sectors. in fact, the level of activation of different production processes in the sectors of the economy depends not only on the final demand directed to them, but indirectly, via the circular flow of the economy, on the final demand directed towards all sectors. in the subsystem approach the production system is divided into blocks, constituted by the shares of the production sectors represented in the original matrix that are directly and indirectly committed to satisfy the final demand towards different categories of goods. let be a the matrix of accounting coefficients representing only the interdependencies existing among production sectors (industries) in the economy. the re-classification of economic quantities from the production sectors to the different “blocks” of the economy working to meet the final demand towards different sectors, may be carried out using the following “b operator” (momigliano and siniscalco, 1982: 155): [7] where the symbol ^ indicates diagonalisation. a generic element bij of the matrix b is the share of activity of the i sector triggered by the final demand directed to the j sector. thus the sum of all rows of matrix b is 1, since the level of activation of different production sectors is completely covered by the production required by the final demand towards the whole production system. any economic quantity may be reclassified from sectors to “blocks” by multiplication, using the operator b. for instance, if l is the vector of employment in different sectors, the matrix l: [8] 27sam multipliers and subsystems subdivides the labour employed in different sectors among different blocks (“subsystems”) of the economy. through matrix l it will be possible not only to assess the relative importance of different subsystems in terms of employment but also to characterise the composition of different subsystems in terms of “shares” of the original sectors. the same operation may be carried out using the vector of value added of different sectors. the reclassification by subsystems is based only on the matrix of coefficients representing the interdependencies existing between different production sectors (the a submatrix in the right side of equation 1), implicitly using a leontevian-type multiplier that does not consider feedbacks through consumption as in the sam multiplier decomposition proposed in the previous section. however, the application of the sub-system approach to a two-region model, like that used in this study, makes it possible to extend the analysis to the participation of each region’s sectors in the fulfilment of the demand addressed to the production sectors of the other region. 3.3 a social accounting matrix for the agri-food system analysis the sam used in this study is a two-region (basilicata vs. rest of italy) matrix referring to 2011, produced in collaboration with the regional institute for economic planning of tuscany (irpet, florence) with most recent statistical records available. the structure of the matrix includes a total of 347 accounts, concerning 51 production activities, 64 goods and 3 production factors (employment and self-employment, and capital), 3 types of institutions (households, businesses, public administration) in the two regions. the household sector is subdivided by income deciles into ten groups, whereas the public administration is distinguished as local and central. there are of course also the accounts entitled to the capital formation and to real and financial flows with the rest of the world. in order to analyse the structure of basilicata’s agri-food sector, the accounts concerning agriculture and the food industry have been broken down in some detail for both basilicata and the rest of italy, using the matrix of inter-industry technical coefficients derived from the national supply-use table produced by the dipartimento di scienze per l’economia e l’impresa of the university of florence for the year 2009 (rocchi et al., 2016). by combining it with the official statistics made available by istat for the year 2011 (value added, employment, import, export), the accounts concerning the food industry activities have been broken down into ten sub-sectors and relative commodities: 1. meat 2. fish 3. olive oil 4. vegetable oils, sugar, pasta 5. vegetables and fruits 6. dairy products 7. cereals 8. animal feed 9. wine 10. water and other beverage the agricultural sector, conversely, has been subdivided using the data of rica (rete di informazione contabile agricola) (crea, 2016) as well as the available data of 28 mauro viccaro et alii fadn (farm accountancy data network) (european commission, 2016b). by combining the two databases, the agricultural sector has been initially broken down into 8 groups of businesses by type of farming for the rest of italy, and into 5 production activities for basilicata. in order to ensure a greater consistency of the analysis, the italian agriculture has been subsequently regrouped in the following 5 subsectors: 1. cereal grains 2. horticulture 3. permanent crops 4. livestock 5. mixed agricultural commodities are grouped under the heading of agricultural products while the final demand is represented by consumption functions (bundle of commodities classified according to the coicop classification). discrepancies between row and column totals of accounts after disaggregation were reconciled balancing the table according the stone-camperhown-meade approach (round, 2003). 4. results 4.1 the structure of basilicata’s agri-food sector based on the matrix of multipliers according to the sam, in 2011 the output value of the fifteen sectors of basilicata’s agri-food system amounted to about eur 1.2 billion, 7.9% of the regional total value. the agri-food share increases when considering the value added (8.3%) and, above all, employment (16.6%). high values are due, as previously mentioned, to the importance of agriculture, which is a typically a labour intensive sector, within basilicata’s economy. table 3 compares the output multipliers of seven macro-sectors making up basilicata’s production system, as it is represented in the sam. the first two rows indicate the increase in final output required to satisfy 1 eur of additional demand addressed to each macro-sector. since basilicata is a region framed within a national economy, a significant share of activation is transmitted outside its regional boundaries. for example, a one-million additional demand addressed to basilitable 3. output multipliers in basilicata’s economy macro-sectors. agriculture other primary activities food industry other manufacturing constructions trade and services public administration bas’ output 2.813 2.745 3.017 2.922 3.027 2.829 2.803 roi’ output 0.934 0.819 1.243 1.156 1.015 0.931 0.848 roi/bas* (%) 34.0% 32.0% 38.1% 37.5% 33.4% 33.8% 32.2% labour** 64.3 0.8 8.7 5.7 19.3 18.4 26.8 *bas: basilicata; roi: rest of italy. **labour unit for millions of euro. 29sam multipliers and subsystems cata’s agriculture generates a 2.8 million increase in the output produced by basilicata’s economy (mostly in the agricultural sector but also in all other sectors), and nearly one million euros in the rest of the italian economy. as a whole, the share of the output multiplier operating outside regional boundaries is about 30 to 40%. significantly, agriculture also shows the highest employment multiplier, whereas the food industry shows a multiplier value that is basically in line (although slightly above) the average of the other manufacturing activities. the analysis of multipliers is detailed in table 4 that proposes data referred to the fifteen sectors in which agri-food has been broken down. the table shows the results of the regional breakdown of multipliers. the three columns to the right of the total multiplier break down the multiplier effect (that is the output growth generated in addition to the initial stimulus) into three components: the regional effect, i.e. the additional output generated by interdependencies (among industries and through final consumption) within the region; the interregional spillover, which is the impact transmitted outside the regional boundaries and generating an increase in the activity in different sectors in the rest of italy; and the interregional feedback, i.e. the additional increase in the regional output resulting from the output growth in the rest of italy. the share of each subsector within the two components of agri-food system (agriculture and food industry) is shown in the second column. it can be noted that most of the regional agricultural output is produced by the farms “specialised” in arable crops and animal husbandry, and by the farms classified as “mixed”. to assess these data we should table 4. regional breakdown of output multipliers in the agri-food sectors. macrosectors’ output (%) total multiplier regional effects interregional spillover interregional feedback roi/bas* (%) agriculture cereal grains 13.2% 2.814 0.866 0.944 0.004 109.01% horticulture 0.0% 2.473 0.705 0.765 0.003 108.51% permanent crops 6.5% 3.060 1.048 1.008 0.003 96.18% livestock 67.1% 2.641 0.744 0.893 0.004 120.03% mixed 13.2% 3.068 0.963 1.101 0.004 114.33% food industry meat 6.0% 3.268 1.048 1.214 0.006 115.84% fish 0.7% 2.774 0.864 0.907 0.003 104.98% olive oil 6.0% 3.438 1.207 1.226 0.006 101.57% vegetable oils, sugar, pasta 41.6% 3.154 0.967 1.182 0.005 122.23% vegetables and fruits 5.9% 3.268 1.066 1.197 0.006 112.29% dairy products 17.4% 3.277 0.987 1.284 0.006 130.09% cereals 3.2% 3.331 1.115 1.210 0.006 108.52% animal feed 1.1% 3.327 1.018 1.303 0.006 128.00% wine 5.7% 3.311 1.038 1.268 0.005 122.16% water and other beverage 12.5% 3.481 1.029 1.447 0.005 140.62% *bas: basilicata; roi: rest of italy. 30 mauro viccaro et alii consider that the type of farming (based on which the agricultural sector is broken down) classifies farms (typically multi-product firms) based on the prevalence of certain production processes. a farm is classified as “specialised” in a given process if the latter represents at least two thirds of the output value. hence the output produced in the farms classified as “livestock farms” is not consisting solely of livestock products but includes also a significant share of other products. similarly the output produced by the other groups of farms consists for its part of a basket of goods. the output multipliers tend to be higher in the food industry than in the agricultural activities. in the first case the initial impact determined by the final demand addressed towards the sectors generates almost always a three times larger total increase of the output produced: in the case of olive oil and of the beverage industry, the overall growth of output is about three and half times the initial stimulus. this is a quite typical structural difference, because in the agricultural activities a lower ratio between intermediate consumption and output results in a lower impact on the activities supplying inputs. considering the regional breakdown of multipliers, since basilicata is a small regional economy open to the rest of the italian production system, it is not surprising that the “return” feedback towards basilicata’s production activities is negligible. conversely, spillovers towards the rest of italy are significant, thereby certifying how basilicata’s production activities depend on imports from the rest of italy. spillovers tend to be higher for industrial activities. the last column of the table sums up these results showing the percentage ratio between the share of the multiplier effect remaining in the region (regional effect plus feedback) and the spillover component. only in the case of farms specialised in permanent crops, the additional growth of the output that remains within basilicata is higher than that “transmitted” to the rest of the italian economy. the industrial activities most open to the rest of italy are the dairy and the beverage industries. these are two important sectors within basilicata’s food industry, accounting for about 30% of the output value: especially in the case of dairy industry, this figure could suggest interesting spaces for an additional integration with the regional agriculture. on the other hand, the olive oil industry shows a higher integration with the regional production system, with an internal multiplier effect that is equal to the produced spillovers: although this is a minor sector in terms of output value, it proves that the commitment to quality can have positive impacts on the rest of the regional economy. 4.2 sub-system analysis the structural analysis of basilicata’s agri-food system can be enhanced through indepth studies using the results of the subsystem-analysis. figures 2 and 3 show the contribution of each subsector of basilicata’s agri-food sector to four different subsystems of the italian economy (basilicata and rest of italy) satisfying certain “blocks” of final demand. since the four blocks represent the total final demand in the sam, the total of the per cent values of each row is always 100. comparing the data classified as “sector” in the two figures, it is possible to see how industrial activities tend to meet the sector-specific demand more than agricultural activities (with the only exception of those of the farms specialised in horticulture that represent, however, a negligible component of basilicata’s agriculture). the participation in the subsystems associated with the demand of the other sectors of basilicata’s agri-food 31sam multipliers and subsystems industry is quite variable in the case of the regional food industry, ranging from 15% of the activity of grains and starch products to 2.2% of the meat-processing industry. the value is more homogeneous in the case of agricultural sectors, which commit, on average, around 11% of their activity to the participation in other subsystems. the datum shows the degree of integration among the components of the agri-food system and could be an interesting indicator of possible areas for innovation to promote its competitiveness. considering the participation in subsystems oriented to the final demand of the other regions (rest of italy), the higher percentage in agricultural subsectors may be assessed as figure 2. share of basilicata’s agricultural sectors to the various “blocks” of the production area. 0% 20% 40% 60% 80% 100% cereal grains horticulture permanent crops livestock mixed sector other agrifood sectors other sectors rest of italy figure 3. share of basilicata’s food sectors to the various “blocks” of the production area. 0% 20% 40% 60% 80% 100% meat fish olive oil vegetable oils, sugar, pasta vegetables and fruits dairy products cereals animal feed wine water and other beverage sector other agrifood sectors other sectors rest of italy 32 mauro viccaro et alii an interesting opportunity for the economy in so far as it expresses the capacity of “attracting” (either directly or indirectly) a higher share of final demand towards the regional production system. however, since basilicata is a small economy open to the rest of the national economy, it would be important to assess the stability of this participation to the subsystems of the rest of the italian economy. much depends on the upgrading of agricultural products directed towards the rest of the national production system: in the case of commodities without a specific quality differentiation (as may be the case of cereal production), they would suffer pressures from regional (or international) potential competitors. tables 5 and 6 show the composition of the subsystems satisfying the final regional demand (broken down into macro-sectors of activity). the composition is expressed both in terms of employment and value added produced. the analysis of composition is repeated using two different breakdowns of the final demand, in order to fully exploit both sectoral and regional breakdown of the model. interdependencies between agriculture and food industry obviously appear especially in the subsystem concerning the food industry, which includes basilicata’s agricultural activities accounting for 26.9% of employment and 11.6% of the value added produced. to satisfy the demand of food industry products, however, the relevant subsystem also activates the agriculture of the other regions (12.5% of subsystem employment and 9% of the value added), in addition to a far more important component in services’ activities. this is again an indicator of interesting areas of integration that might be boosted up in the regional system. the participation of other sectors of basilicata’s economy in the food industry subsystem could probably be increased, resulting lower than that in other subsystems of regional manufacturing. the same analysis of tables 5 and 6 is proposed again in table 7, except that subsystems are referred to the final demand oriented towards the single sectors of basilicata’s table 5. composition of subsystems in terms of employment: macro-sectors. subsystems final regional demand agriculture food industry other manufacturing and constructions trade and services public administration regional agriculture 92.3% 26.9% 0.4% 1.1% 0.2% regional food industry 0.2% 30.6% 0.1% 0.4% 0.1% rest of italy’ agriculture 1.8% 12.5% 0.9% 1.1% 0.2% other manufacturing and constructions 2.1% 7.2% 67.9% 5.1% 4.7% trade and services 3.2% 21.3% 28.9% 90.9% 9.8% public administration 0.4% 1.5% 2.0% 1.4% 85.1% regional agri-food sector 92.5% 57.5% 0.4% 1.5% 0.2% rest of regional economy 2.7% 13.9% 71.3% 85.4% 94.0% rest of italian economy 4.8% 28.6% 28.3% 13.1% 5.8% labour (lu x 1,000) 15.2 10.7 56.6 55.1 67.1 33sam multipliers and subsystems agri-food production activities. for the sake of convenience, the presentation of data has been transposed, with the subsystem components into columns (totals of rows equal to 100). table 7 shows the subsystem composition in terms of regional location of activities. table 6. composition of subsystems in terms of value-added: macro-sectors. subsystems final regional demand agriculture food industry other manufacturing and constructions trade and services public administration regional agriculture 79.6% 11.6% 0.1% 0.3% 0.1% regional food industry 0.5% 36.5% 0.1% 0.3% 0.1% rest of italy’ agriculture 3.3% 9.0% 6.0% 1.6% 0.9% other manufacturing and constructions 6.4% 11.6% 63.6% 5.3% 5.6% trade and services 9.7% 30.0% 29.0% 91.6% 12.1% public administration 0.5% 1.3% 1.2% 0.8% 81.3% regional agri-food sector 80.1% 48.0% 0.2% 0.6% 0.1% rest of regional economy 7.4% 18.0% 67.1% 84.3% 91.3% rest of italian economy 12.5% 34.0% 32.7% 15.1% 8.6% value-added (m€) 320 456 3 347 3 450 3 334 table 7. subsystem composition: sectors of basilicata’s agri-food system. regional subsystems employment value-added regional agri-food sector rest of regional economy rest of italian economy regional agri-food sector rest of regional economy rest of italian economy cereal grains 97.0% 1.1% 1.9% 73.2% 9.9% 16.9% horticulture 100.0% 0.0% 0.0% 96.2% 0.9% 2.9% woody 96.4% 2.3% 1.3% 57.5% 24.5% 18.0% livestock 83.5% 4.9% 11.6% 85.2% 4.8% 10.0% mixed 82.2% 6.4% 11.4% 68.1% 11.6% 20.2% meat 62.7% 10.2% 27.1% 51.6% 14.9% 33.5% fish 54.9% 17.9% 27.2% 48.4% 18.6% 33.0% olive oil 56.2% 15.7% 28.1% 32.3% 26.9% 40.8% vegetable oils, sugar, pasta 58.6% 15.3% 26.1% 52.1% 18.1% 29.9% vegetables and fruits 61.6% 11.0% 27.4% 48.9% 16.5% 34.6% dairy products 60.6% 9.2% 30.2% 52.4% 12.1% 35.5% cereals 64.3% 8.8% 26.8% 50.5% 14.5% 34.9% animal feed 55.0% 12.1% 32.9% 44.6% 16.1% 39.2% wine 53.7% 16.8% 29.5% 39.3% 22.6% 38.1% water and other beverage 42.2% 20.7% 37.1%   34.3% 23.7% 42.0% 34 mauro viccaro et alii except for the case of the few farms classified as specialized in horticultural production and showing a sub-system virtually “self-contained” at the regional level (notably in terms of employment generated both directly and indirectly), activities involving animal husbandry (both specialized and mixed) are, among the agricultural ones, those with the highest participation of non-regional components to the satisfaction of final demand. in the case of food industry products, the level of participation in the subsystems by nonregional production activities is quite homogenous, especially in terms of employment. the comparison of the “parallel” subsystems referred to the final demand in the two regions (basilicata and rest of italy) can provide relevant additional indications. table 8 shows the case of the two regional subsystems devoted to fulfill the final demand for accommodation and food services. these are production activities that might benefit from a regionally-based integration with the agri-food sector, in particular with a view to qualitative differentiation and promotion of typical regional products. the level of participation of both regional agriculture and food industry is lower in the case of the basilicata’s subsystem as compared with “the average” of the rest of italy. this means that the final demand towards basilicata’s activities supplying restaurant and accommodation services is less able to activate production and employment within the regional borders than those operating in the rest of italy. such results suggest the existence of an unexploited space for the integration of tourism activities with the local agri-food system. 5. conclusions this study has proposed a structural analysis of basilicata’s agri-food system, based on a two-region sam model, appropriately broken down. the objective of the analysis was to make available helpful information for defining sectoral regional policies, associated with the implementation of the new programming period of rural development policies. the 2014-2020 rural development programme actually offers a major opportunity to increase the regional system competitiveness, by providing public funds equal to eur 680 million. agri-food is an important component of basilicata’s economy, not only in terms of value produced and employment created, but especially for its quality production and local production systems in which all steps of supply chains (agricultural, industrial, and table 8. final demand composition of the restaurant and accommodation services. subsystems employment value-added basilicata rest of italy basilicata rest of italy regional agriculture 3.0% 7.3% 1.4% 3.7% regional food industry 1.7% 2.8% 2.3% 3.4% other primary activities 3.8% 0.3% 3.7% 0.4% other manufacturing and constructions 4.4% 3.3% 8.2% 5.2% trade and services 86.3% 85.6% 83.5% 86.4% public administration 0.8% 0.7% 0.8% 0.9% 35sam multipliers and subsystems marketing steps) find their coordination. the structural analysis of interdependencies between the various components of the agri-food system, via the two-region model, has highlighted important areas of further integration that could drive innovation processes. to enhance the positive impact of agri-food production activities on the regional economic development, two basic strategies may be followed. the first consists in attracting increasing shares of non-regional demand towards basilicata’s products. in this respect, the growth of exports in challenging times, like the recent ones, indicates that first steps in that direction have been made. but the scope for improvement and strengthening in this broad area is still large, encompassing the trade with the other italian regions. basilicata’s agri-food system (in particular its agricultural component) invests a relevant part of its activities for the direct or indirect fulfillment of the final agri-food demand of the other regions. this is a segment of activity requiring a specific strategy to consolidate the comparative advantages to base them mostly on unique features of the regional system, including the quality of the environment, the specificities of the varieties produced, and the knowledge of the context related to production traditions. if the participation in the market of agricultural commodities (like in the case of cereals for the pasta industry) is an important business segment in basilicata’s agri-food system, it can and must be made stable by innovation processes aimed at increasing product qualitative differentiation. the second strategy could be described as the strengthening of interdependencies within the regional production system aimed at increasing the share of the multiplier effect remaining within the regional economic system. the subsystem analysis has demonstrated that there are large areas for increasing integration within regional food chains, in particular between agricultural production and industrial processing. in this sense, rural development policies, especially the measures aimed to promote coordinated actions at the district scale, may be a good basis for creating local supply chains and tighter links among regional production activities. this process, however, should again be driven by qualitative differentiation. if, on the one hand, “shorter” food chains can increase the regional multiplier effect through an enhanced integration between agriculture and food industry, they could also represent an important factor to upgrade (and hence add value to) production, with the possibility of increasing “downstream” integration with other regional sectors. the analysis has shown that in basilicata the integration with food and restaurant and accommodation services is lower than in other italian regions. but there also is much room for intensifying the interdependencies of the regional agri-food system with elements of the public administration (such as, for instance, public providing activities in school canteens or in hospitals). these market opportunities would be useful to improve final consumers’ awareness of regional production peculiarities and might have long-term additional effects on the growth of demand addressed towards the regional production system. results show that the availability of a highly disaggregate multi-sector model of the regional economy is a valuable tool in supporting the design of regional policies for innovation and for the development of rural areas. the structural analysis described in this paper could be further extended at the level of each single chain, with the characterization of the main forward and backward linkages and the interaction with the rest of the national economy. 36 mauro viccaro et alii aknowledgments the paper presents part of the results of the mauro viccaro’s phd thesis on “sambased models for the impact analysis of regional policies on growth, poverty and inequality” developed within the research project “models for the analysis of the development processes at regional scale: the social accounting matrices (sam) as a tool for the analysis of the effects of regional policies on growth, poverty and inequality’ funded by shell italia e&p references belletti, g. 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(2013). alternative agrifood networks in a regional area: a case study. international journal of computer integrated manufacturing 26(1-2): 55-66. bio-based and applied economics 8(2): 101-132, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8927 consumers’ rationality and home-grown values for healthy and environmentally sustainable food simone cerroni1,2,3,*, verity watson4, jennie i. macdiarmid5 1 department of economics and management, university of trento, trento 38122, italy 2 c3a centre, university of trento, trento 38010, italy 3 institute of global food security, school of biological sciences, queen’s university belfast, belfast bt9 7bl, united kingdom 4 health economics research unit, school of medicine and nutrition, university of aberdeen, polwarth building, foresterhill, aberdeen ab25 2zd, united kingdom 5 the rowett institute, school of medicine and nutrition, university of aberdeen, ashgrove rd w, aberdeen ab25 2zd, united kingdom abstract. consumers’ food choices often deviate from rationality. this paper explores whether deviations from rationality impact home-grown values elicited using either bidor choice-based value elicitation techniques. the paper focuses on second-price vickrey auctions and discrete choice experiments, which are widely used to value innovative private goods and the welfare benefits of policy interventions. the paper reports the results of an experiment that combines induced value and home-grown value elicitation procedures. home-grown values are elicited for a public food policy. the experiment has two treatments that differ in the elicitation technique: secondprice vickrey auction and discrete choice experiment. for each technique, inducedvalue elicitation procedures are used to measure subjects’ deviations from rationality. deviations from rationality are more likely in the second-price vickrey auction. subjects who behave irrationally have higher home-grown values than rational subjects in the second-price vickrey auction. the impact of deviations from rationality is weaker in the discrete choice experiment. keywords. home-grown value, induced value, rationality, experimental auction, discrete choice experiment. jel codes. c91, d12, q18, q51. 1. introduction second-price vickrey auctions (spvas) (vickrey, 1961) and discrete choice experiments (dces) (lancaster, 1966) are widely used to determine the demand for innovative multi-attribute goods in marketing research and estimate welfare benefits of new agri-food, environmental, health and transportation policy interventions in public pol*corresponding author: simone.cerroni@unitn.it 102 simone cerroni, verity watson, jennie i. macdiarmid icy research. such value elicitation techniques are based on standard economic theory’s assumptions. one of the most stringent assumption is that economic agents behave rationally and always make decisions that maximize a given utility function (becker 1962; simon 1986). empirical evidence from disciplines, such as psychology, suggests that economic behavior often deviates from this definition of rationality (e.g., camerer 1995; camerer 1999). this is a problem in non-market valuation because departure from rational behavior “undercuts […] the non-market valuation methods used to evaluate private choice and public policies […]” (cherry et al., 2007, scarpa et al., 2007; burton et al., 2009). this paper contributes to this literature in several ways. the main aim of this paper is to empirically test whether respondents deviate from rational choice behavior and whether deviations from rationality have an impact on respondents’ home-grown values (hgvs) elicited via spva and dce. hgvs are genuinely formed by people without any direct interference from researchers about the value of the good under study (rutsröm, 1998). our empirical application focuses on consumers’ evaluations of an informationbased public policy (i.e., labelling-based intervention) aiming to shift consumers choices towards healthier and more environmentally sustainable food products. more specifically, hgvs for healthier and more environmentally sustainable versions of a ready meal (i.e., beef-based lasagne) are elicited using spva and dce. in this paper, individual deviations from rationality are investigated using induced value (iv) elicitation procedures. the value is said induced because the experimenter provides subjects with the value of the fictitious good under study during the experiment (smith, 1976). irrationality (or rationality) is measured investigating subjects’ deviations from the payoff maximizing strategy in iv settings. rational subjects are those who consistently make demand revealing choices (dce) or submit demand revealing bids (spva) in the iv experiments. subjects who behave irrationally are those who fail to do so. the effect of departure from rational behavior on hgvs is explored within treatment. second, this paper aims to test if deviations from rational behavior and the impact of such deviation on elicited hgvs depend on the nature of the elicitation mechanism: bid or choice-based (spva or dce). the framing of bidand choice-based elicitation mechanism are different and this may have an impact on behavior in iv and hgv settings (lusk and schroeder, 2006; gracia et al., 2011). third, this paper aims to test if underbidding and overbidding in the iv setting is related to bidding behavior in the hgv setting. in particular, we investigate whether underbidding and overbidding behavior in the iv setting spills over to the hgv setting. for example, subjects who tend to underbid in the iv setting bid lower than others in the hgv setting. there is empirical evidence that rationality spills over from different settings, more specifically from market-like contexts to non-market ones (cherry et al., 2003; cherry and shogren, 2007). here, we aim to test whether underbidding or overbidding are behavioral phenomena that are linked more to the specific individual than the type of task. finally, in this paper, we develop and estimate a behavioral model to identify main determinants of subjects’ rationality in iv settings. to the best of our knowledge, we are not aware of other studies performing this analysis in the literature. by using the same dataset used in cerroni et al. (2019), this paper generates new insight into the link between rationality, bidding and choice behavior. the study provides 103consumers’ rationality and home-grown values for healthy and environmentally sustainable food new evidence on whether rationality is affected by the use of bidand choice-based value elicitation mechanisms and whether potential deviations from rationality have an impact on hgvs across mechanisms. this evidence can generate new knowledge regarding the reliability and accuracy of hgvs elicited via spva and dce and have important implications for businesses and policy makers who need reliable evidence in order to predict people’s behavior and allows making cost-effective decisions (kassas et al., 2018; ortega et al., 2018). 2. background 2.1 healthier and more environmentally sustainable food choices consumers’ food choices contribute to the high prevalence of diet-related diseases and climate change (e.g., tilman and clark, 2014). a shift towards more sustainable diets is needed to reduce the cost that obesity and climate change are having on the economy (e.g., bryngelsson et al., 2016; santini et al., 2017). sustainable diets are very complex and were defined as: “those diets with low environmental impacts which contribute to food and nutrition security and to healthy life for present and future generations. sustainable diets are protective and respectful of biodiversity and ecosystems, culturally acceptable, accessible, economically fair and affordable; nutritionally adequate, safe and healthy; while optimizing natural and human resources.” (fao, 2012). a relatively substantial amount of research has focused on identifying the main traits of sustainable diets from a nutrition and environmental point of view (e.g., macdiarmid et al. 2012). however, few studies have investigated consumers’ acceptability of proposed sustainable diets (e.g., macdiarmid et al., 2016). the present study contributes to this literature investigating acceptability of sustainable diets by exploring consumers’ trade-offs between two food attributes, namely healthiness and carbon footprint. the vast majority of research generally focused on one attribute or the other (e.g., drichoutis et al., 2006; belcombe et al., 2010; caputo et al., 2013; akaichi et al., 2017; castellari et al., 2019), but failed to investigate whether and to what extent consumers compromise between healthiness and environmental sustainability of food products when they make purchasing decisions (a noticeable exception is koistinen et al. 2013). the understanding of such tradeoffs is important to design information-based policy intervention aiming to promote the uptake of sustainable diets. 2.2 home-grown values elicited via spva and dce spva and dce are widely used to elicit hgvs for innovative food products and estimate net benefits of new public policies. elicitation procedures used in spva and dce are very different (lusk and schroeder, 2006; gracia et al., 2011). in spva, subjects are asked to bid for a series of goods. the bidder who submit the highest bid buys a good, which is randomly selected at the end of the experiment, at a price equal to the second highest bid for that good. in dce, subjects are asked to make repeated purchasing choices in a series of choice scenarios that generally present a couple of goods and an opt-out alternative. subjects buy the good that they have chosen (if any) in one 104 simone cerroni, verity watson, jennie i. macdiarmid choice scenario that is selected at random. they pay the price that is associated to the chosen good.1 economic theory predicts that hgvs elicited for the same good should be equal across methods when a proper incentive scheme is used (i.e., isomorphism). however, empirical evidence does not support this prediction. lusk and schroeder (2006) showed that wtp estimates elicited via spva are lower than those elicited via dce. grebitus et al. (2013) suggested that personality traits partially explain this difference.2 cerroni et al. (2019) found that this difference is due to value-formation and value-elicitation issues. subjects form their preferences differently across mechanisms and the spva is less empirically demand revealing than dce. differences in value formation may be driven by the fact that spva and dce expose subjects to very different valuation environments and framings (lusk and schroeder, 2006; gracia et al., 2011). while, in dce, subjects are asked to make private purchasing choices and each subject’s outcome is independent from others’ decisions, in spva, subjects are asked to place bid in a competitive environment and each subject’s outcome depends on others’ bidding behavior. while, in dce, the price of goods is provided in the choice scenarios and represents only one additional attribute of the presented goods, in spva, subjects are asked to formulate the price that they are willing to pay for the auctioned good without having any reference. 2.3 rationality in spva and dce standard economic theory suggests that spva and dce are theoretically demand revealing (incentive compatible) under a proper monetary incentive scheme. value elicitation issues (or empirical demand revelation) can be tested by using iv experimental procedures (smith, 1976). experimental evidence shows that subjects often deviate from rational behavior in iv experiments. in spva, the weakly dominant strategy is to bid the iv associated to the fictitious good under valuation. empirical evidence suggests that bidding behavior often deviates from the weakly dominant strategy in spva (e.g., kaegel et al., 1987; kaegel and levine, 1993; shogren et al., 2001; lusk and shogren, 2007; drichoutis et al., 2015). overbidding is the most common form of departure from rationality (e.g., kaegel et al., 1987; georganas et al., 2017), however a number of studies reported underbidding (e.g., shogren et al. 2001; hong and nishimura, 2003; noussair et al., 2004). subjects deviate from rational behavior for two reasons. first, they fail to understand the incentives for truthful value revelation. kagel, harstad and levine (1987) and, more recently, ausubel (2004) argued that subjects find spvas difficult to understand. li (2017) differentiated “obviously strategy-proof ” and “not obviously strategyproof ” elicitation mechanisms. a mechanism is obviously strategy-proof when the best 1 we acknowledge that dce has been mostly used in hypothetical settings. in this paper, we only focus on research using dce in incentivised and non-hypothetical settings. 2 other studies showed that isomorphism is not satisfied when hg preferences are elicited using other institutions. for example, rutström (1995) compared english auction, vickrey auction and the becker-degrootmarschak mechanism (bdm); gracia et al. (2011) compared random nth auction and dce; lusk et al. (2004) compared spva, english auction, random nth auctions and bdm; akaichi et al. (2013) compared choice-based dce and ranking-based dce. 105consumers’ rationality and home-grown values for healthy and environmentally sustainable food outcome that subjects can obtain by deviating from the dominant strategy is never superior to the worst outcome they can obtain by sticking to the dominant strategy. spva is not obviously strategy-proof and therefore becomes cognitively demanding for subjects (lee et al., 2017). second, spva is not necessarily incentive compatible when subjects behave accordingly to some non-standard expected utility theories (horowitz, 2006). for example, reference-dependent preference models such as those formulated by kȍszegi and rabin (2006). demand revelation in dces has received less scrutiny. nevertheless, deviations from the dominant strategy, which is choosing the payoff maximizing alternative in each choice scenario, seems to be less systematic (collins and vossler, 2009; luchini and watson, 2014; bazzani et al., 2018). collins and vossler’s (2009) found a high level of demand revelation in referenda-style dces. however, luchini and watson (2014) provided less encouraging results in a dce for a private good. bazzani et al. (2018) showed that demand revelation at individual level depends on assumptions made about the distribution of estimated marginal willingness to pay (wtp). recently, cerroni et al. (2019) found that dces are more empirically demand revealing than spvas and showed that value-elicitation issues contribute to differences in hgvs elicited via the two mechanisms in their artefactual field experiment (harrison and list, 2004) that combines hgv and iv procedures. 3. material and methods 3.1 empirical application the paper focuses on hgvs for a new food policy that aims to inform consumers about the healthiness (measured in terms of saturated fat content) and environmental sustainability (measured in terms of carbon footprint) of food products. this information is delivered using a traffic light system (tls) related to food’s carbon footprint, where red stands for high, amber for average, and green for low carbon footprint. this tls is presented alongside a standard tls indicating the healthiness of food products: where red stands for unhealthy, amber for average, and green for healthy food (department of health, 2016).3 the experimental product is a popular ready meal in the uk: frozen beef lasagne. during the experiment, subjects are presented with nine different lasagne that vary in terms of healthiness (3 levels) and carbon footprint (3 levels). these parameters are varied across lasagne by changing the proportions of the traditional lasagne’s ingredients (e.g. beef, pasta, sauce, cheese, etc.). all lasagne have similar appearance and portion size (400 grams). recipes were developed by nutritionists, lasagne were pre-cooked by professional cooks and kept frozen at the rowett institute (university of aberdeen). the experiment was conducted at the scottish experimental economics laboratory (seel, university of aberdeen). 3 more information on how the three different levels of healthiness and carbon footprint were generated is provided in the online supplementary appendix a. 106 simone cerroni, verity watson, jennie i. macdiarmid 3.2 recruitment and sample characterization the pool of sample subjects is the same included in the study by cerroni et al. (2019) and consists of 128 consumers recruited from the general population of aberdeen and surroundings (scotland, uk). subjects were recruited using a variety of methods, including posters and flyers distributed in the city (e.g. university campus, community centers, local workplaces, retail outlets, community events) as well as snowball sampling. this means that we have a non-probability sample. an information sheet describing the study was sent to people who responded to the adverts. they were told that the aim of the study was to understand the decisions people make when choosing food (in this case a beef lasagne) and they would have the chance to buy one of the lasagne based on the choices they made in the experiment. subjects aged 18 or older were recruited. the average age was 36 years, the minimum and maximum age were 19 and 70 respectively. the sample consisted of 64% females and the average annual income was approximately £38,000. subjects were given a show-up fee of £10 for participating to the study. those who purchased food paid in cash and left the experiment with £10 minus the price they paid. subjects who purchased the food were given a cooling bag to keep the food frozen during the remaining part of the day. the study received ethical approval from the rowett institute ethics committee at the university of aberdeen. 3.3 experimental design the experimental design consists of two treatment groups, one for each value elicitation mechanism: spva or dce. in each treatment, both ivs and hgvs are elicited. hgvs were elicited for the multi-attribute lasagne described above. the spva treatment consists of 63 subjects, the dce treatment of 65. subjects who signed up for the study were randomly assigned to treatments. subjects were asked to complete a number of tasks in the following order: a warm-up questionnaire on self-reported level of hunger and satiety, iv task, hgv task and a questionnaire on consumption habits and socio-economic status. to avoid biases such as the earning effect, subjects were informed about earning (or losses) from the iv task at the very end of the experiment. in total, eight sessions were conducted between january 2015 and september 2017, eight for the spva and five for the dce. four of the spva sessions hosted eight subjects, two sessions hosted nine subjects, one session hosted seven subjects and the remaining session hosted six subjects. two of the dce sessions hosted nine subjects, the remaining three sessions hosted ten, eighteen and nineteen subjects. sessions took place either at 1.30pm or 5.30pm to control for possible time and hunger effects. 3.3.1 spva in the induced value setting in the iv setting, each subject participates in nine spvas for nine different tokens (see the supplementary online appendix b). each token is associated with a different iv, 107consumers’ rationality and home-grown values for healthy and environmentally sustainable food which ranges from £1.00 to £5.00 in £0.50 increments.4 subjects are informed that their profit depends on their bids for one specific token, called the binding token. the binding token is randomly draw at the end of the experiment. the highest bidder buys the binding token at a price, which is equal to the second highest bid. the profit made by the highest bidder is the difference between the iv associated to the binding token and the buying price. if the profit is positive, this is paid in addition to the show-up fee at the end of the experiment. if the market price is higher than the iv, the subject incurs a loss that is subtracted from the show-up fee. standard economic theory suggests that the weakly dominant strategy is to place a bid equal to the iv of the token. subjects who constantly follow the weakly dominant strategy are considered rational. the others’ behavior departs from rationality. all steps faced by subjects during the experiment are reported in figure 1. figure 1. all steps faced by subjects during the experiment. 3.3.2 spva in the home-grown value setting in the hgv setting, each subject bids for the nine different lasagne (all possible combinations of lasagne's healthiness and carbon footprint levels) (see the supplementary online appendix b). the order in which lasagne were presented was randomized across subjects to minimize order learning and fatigue effects. subjects can purchase only one lasagne, the binding lasagne. they were informed that the binding lasagne is randomly draw at the end of the study. as standard in spva, the highest bidder buys the binding 4 each subject faces the whole range of induced values, but the order of induced values varied across subjects. 108 simone cerroni, verity watson, jennie i. macdiarmid lasagne at a price, which is equal to the second highest bid. this amount of money is subtracted from the show-up fee. all steps faced by subjects during the experiment are reported in figure 1. 3.3.3 dce in the induced value setting in the iv setting, each subject faces nine choice sets that are generated using a fractional factorial design (choicemetrics 2012) (see the supplementary online appendix b for an example). each choice set contains two tokens plus an opt-out alternative. tokens are described using two attributes: the market price and the iv. the market prices and the iv range from £1.00 to £5.00 in £0.50 increments. subjects are informed that their profit depends on the option they chose in the binding choice set. the binding choice set is randomly drawn at the end of the experiment. the profit is the difference between the iv and the market price associated to the chosen token in the binding choice set. if the profit is positive, this is paid in addition to the show-up fee at the end of the experiment. if the market price is higher than the iv, the subject incurs a loss that is subtracted from the initial show-up fee. the order of choice sets was randomized across subjects. standard economic theory suggests that subjects should always choose the alternative that maximizes their payoff. subjects who constantly follow this strategy are considered rational. the others’ behavior departs from rationality. this experimental design differs from previous studies (collins and vossler, 2009; luchini and watson, 2014; bazzani et al., 2018) where tokens with multiple attributes (i.e., color and shape) were used and marginal ivs were associated with attribute levels. while in previous studies, subjects are asked to compute the final iv of tokens mathematically, in this experiment, subjects are provided with that. this typology of design was chosen because it mirrors the design of a standard spva conducted in an iv setting. in the iv spva literature, subjects are not asked to compute the ivs of tokens, instead, they are directly provided with these.5 all steps faced by subjects during the experiment are reported in figure 1. 3.3.4 dce in the home-grown value setting in the hgv setting, each subject is presented with nine choice sets created by using a d-efficient design (choicemetrics, 2012) (see the supplementary online appendix b for an example).6,7 each choice set contains two lasagne and an opt-out alternative. lasagne are described by three attributes: healthiness, carbon footprint and market price. healthiness and carbon footprint can be green, amber or red (3 levels per attribute). the market price ranges from £1.00 to £5.00 in £0.50 increments. the order of choice sets was randomized across subjects. subjects are informed that they buy the selected option in the binding 5 an alternative design would involve the provision of tokens with multiple attributes (i.e., colour and shape) and marginal ivs associated with attribute levels. subjects would be asked to mathematically compute the ivs for each token and place their bids. this design will make the spva mirroring the dce as designed by collins and vossler (2009) and luchini and watson (2014). 6 priors were estimated using data collected from a pilot study with 10 subjects. 7 data from the additional nine choice sets that are presented to subjects after being provided with additional information on saturated fat and carbon footprint are not included in our analyses to avoid confounding. 109consumers’ rationality and home-grown values for healthy and environmentally sustainable food choice set. the binding choice set is randomly selected at the end of the experiment. if they chose a lasagne, they buy the lasagne at the corresponding price. this amount of money is deducted from the show-up fee. if they selected the opt-out alternative, they do not purchase the lasagne. all steps faced by subjects during the experiment are reported in figure 1. 4. testable hypotheses, model specifications, results and discussion 4.1 deviations from rationality and home-grown values elicited via the spva 4.1.1 overview of deviations from rationality subjects’ bidding behavior and deviations from rationality in the iv spva are reported in table 1a and 1b. subjects are considered rational if and only if they submit only demand revealing bids in the iv task, meaning that 9 demand revealing bids (out of 9) are submitted. bids are demand revealing, if and only if, these are equal to ivs. in fact, the payoff maximizing strategy is to submit bids that are equivalent to tokens’ ivs. subjects who fail to submit only demand revealing bids deviate from rational behavior. in our sample, we have 14 rational subjects (22.22%) and 49 subjects (77.80%) who deviate from rationality (table 1a). it is interesting to note that there are no subjects who submit 7 or 8 (out of 9) demand revealing bids. this may indicate that subjects do not make random mistakes, they simply understand the experimental procedure (when they submit 9 demand revealing bids out of 9) or not (when they submit 6 or less demand revealing bids out 9). overall, these results seem to suggest that subjects do not easily identify the payoff maximizing strategy of spva as already argued by kagel et al. (1987), ausubel (2004) and li (2017). among subjects who deviate from rationality, we have 22 (34.92%) who constantly underbid (9 underbids out of 9 bids) and only 2 (3.17%) who constantly overbid (9 overbids out of 9 bids). a subject underbids (overbids) when submits a bid that is lower (higher) than the associated iv. the remaining sample has a mixed behavior (25 subjects, 39.68%). in the “mixed behavior” category we have: i) those who underbid and overbid (5 subjects, 20.00%), ii) those who underbid and submit demand revealing bids (7 subjects, 28.00%), iii) those who overbid and submit demand revealing bids (6 subjects, 24.00%) and iv) those who underbid, overbid and submit demand revealing bids (7 subjects, 28.00%) (table 1b). despite the bulk of research reports overbidding (e.g, kaegel et al., 1987; georganas et al., 2017), there are a number of empirical studies that provide evidence for underbidding (e.g., shogren et al., 2001; noussair et al., 2004). previous research has conjectured that overbidding arises when subjects understand that high bids increase the probability of winning, but fail to realize that high bids may generate negative payoffs (georganas et al., 2017). our subjects seem to overestimate the additional cost of overbidding on the final payoff. 4.1.2 testable hypotheses and model specifications the influence of departures from rational behavior on hgvs for lasagne is explored by estimating model 1 using a feasible generalized least-square regression with correction 110 simone cerroni, verity watson, jennie i. macdiarmid for heteroscedasticity. this model tests whether hgvs differ between subjects who consistently submit demand-revealing bids in the iv spva (i.e., subjects who behave rationally) and the others (i.e., subjects whose behavior deviates from rationality).8 main statistics of all variables used in model 1 are described in table 2.9 model 1 takes the functional form in equation 1: bid_hgi,q = α + βhea_a hea_ai,q + βhea_g hea_gi,q + βcf_a cf_ai,q + βcf_g cf_gi,q + βhea_a_irr hea_ai,q * irri,q + βhea_g_irr hea_gi,q * irri,q + βcf_a_irr cf_ai,q * irri,q + βcf_g_irr cf_gi,q * irri,q + εi,q (1) the dependent variable (bid_hgi,q) is each subject i’s bids for lasagne q≠1 (bid_ hgi,q≠1) minus subject i’s bid for the lasagne, which is red in healthiness and carbon footprint, bid_hgi,q=1. therefore, bid_hgi,q = bid_hgi,q≠1 bid_hgi,q=1. the coefficients βhea_a and βhea_g indicate the average marginal willingness to pay (mwtp) for lasagne that are amber (hea_ai,q) and green (hea_gi,q) in healthiness, respectively. the coefficient βcf_a and βcf_g denote the average mwtps for lasagne that are 8 this estimation procedure was used because we tested and rejected normality and homoscedasticity conducting a shapiro-wilk test and a log-likelihood ratio-test, respectively. a random-effect model for panel data was not used because less efficient. 9 detailed summary statistics of marginal bids for each lasagne type are provided in tables c1 in the online supplementary appendix c. table 1a. categorization of subjects’ bidding behaviora. consistent rational behaviorb consistent underbiddingc consistent overbiddingd mixed behaviore 14 (22.22%) 22 (34.92%) 2 (3.17%) 25 (39.68%) table 1b. categorization of subjects’ bidding behavior within the mixed behavior categorya. underbidding and overbiddingf underbidding and rational behaviorg overbidding and rational behaviorh underbidding, overbidding and rational behaviori 5 (20.00%) 7 (28.00%) 6 (24.00%) 7(28.00%) a number of subjects per category. b consistent rational behavior = 9 demand revealing bids out of 9 submitted bids. c consistent underbidding behavior = 9 underbids out of 9 submitted bids. d consistent overbidding behavior = 9 overbids out of 9 submitted bids. e mixed behavior = all the other subjects. f underbidding and overbidding = subjects who underbid and overbid. g underbidding and rational behavior = subjects who underbid and submit demand revealing bids. h overbidding and rational behavior = subjects who underbid, overbid and submit demand revealing bids. i underbidding, overbidding and rational behavior = subjects who overbid and submit demand revealing bids. 111consumers’ rationality and home-grown values for healthy and environmentally sustainable food amber (cf_ai,q) and green (cf_gi,q) in carbon footprint, respectively. these mwtps are estimated with respect to red levels of healthiness and carbon footprint, respectively. the variable irr is equal to 1 if subject i fails to submit only demand revealing bids in the iv task, meaning that less than 9 demand revealing bids (out of 9) are submitted. hence, the variable irr is equal to 1 if subject i behaves irrationally. the coefficient βhea_a_irr, βhea_g_irr, βcf_a_irr and βcf_g_irr measure the difference in mwtps for healthy and environmental sustainable lasagne between subjects who behave irrationally (those who fail to submit only demand revealing bids in the iv task) and rationally (those who submit only demand revealing bids in the iv task). 4.1.3 results and discussion results from the estimation of model 1 are reported in table 3. the positive and statistically significant coefficients βhea_a_irr (0.255, p<0.05), βhea_g_irr (0.546, p<0.01), βcf_a_irr (0.279, p<0.05) and βcf_g_irr (0.520, p<0.01) indicate that subjects who deviates from rational behavior have higher mwtps for lasagne's attributes than rational ones. a wald test rejects the null hypothesis that coefficients βhea_a_irr, βhea_g_irr, βcf_a_irr, βcf_g_ irr are jointly equal to zero (100.130, p<0.01).10,11 if we are willing to assume that bids 10 other models were estimated to test the consistency of our results. these models incorporate the rate of submitted non-demand revealing (irrational) bids. estimation results are provided in the online supplementary appendix d. 11 as model 1 is estimated using feasible generalized least squares (fgls), r2 is not an appropriate indicator of explanatory power. here, we report the wald χ2 which is equal to 282.88 and is significant level at p<0.01. we also estimated model 1 using the iterated gls estimator (igls), which allows estimating the log-likelihood. table 2. summary statistics of variables included in the spva-related models. variable description obs. mean st.dev. min max bid_hg marginal bid for healthy and low carbon footprint lasagnea 504 0.794 1.447 -4.000 5.000 hea_r = 1 if health is red = 0 otherwise 504 0.250 0.433 0.000 1.000 hea_a = 1 if health is amber = 0 otherwise 504 0.375 0.485 0.000 1.000 hea_g = 1 if health is green = 0 otherwise 504 0.375 0.485 0.000 1.000 cf_r = 1 if carbon footprint is red = 0 otherwise 504 0.250 0.433 0.000 1.000 cf_a = 1 if carbon footprint is amber = 0 otherwise 504 0.375 0.485 0.000 1.000 cf_g = 1 carbon footprint is green = 0 otherwise 504 0.375 0.485 0.000 1.000 irr = 1 if subject behaves irrationally = 0 otherwise 504 0.778 0.416 0.000 1.000 und = 1 if subject consistently underbids = 0 otherwise 504 0.349 0.477 0.000 1.000 a a marginal bid is the difference between any lasagne other than a red in health and red in carbon footprint (in £) and the bid for a red in health and red in carbon footprint lasagne. 112 simone cerroni, verity watson, jennie i. macdiarmid submitted by rational subject are most accurate, these results suggest that failure to submit demand revealing bids in the iv setting generate upwardly biased hgv estimates. this assumption appears to be reasonable, if we consider that irrational subjects are those who failed to consistently identify the payoff maximizing strategy in the iv setting. deviations from rationality can therefore have an important impact on the evaluation of innovative food products and welfare benefits produced by new agri-food policies. 4.2 underbidding and home-grown values elicited via spva 4.2.1 testable hypotheses and model specifications model 2 is estimated to investigate whether underbidding in the iv setting spills over to the hgv setting. model 2 is equivalent to model 1, except for the addition of the interaction variable irr_und = irr * und. the variable und denotes subjects who constantly underbid (9 underbids out of 9 bids) and hence the interaction variable irr_und denotes those subjects who consistently underbid among those categorized as irrational. a subject underbids when submits a bid that is lower than the associated iv. the subjects who constantly underbid are 22 (34.92%) (table 1a). we refrain to investigate whether overbidding spills over from the iv to the hgv setting because only 2 subjects (3.17%) in our sample constantly overbid (9 overbids out of 9 bids) in the iv task (table 1a). model 2 is estimated using a feasible generalized least-square regression with correction for heteroscedasticity and inform on whether subjects who constantly underbid in the iv task have lower hgvs for lasagne’s attributes than the other subjects whose behavior deviates from rationality. others are those who constantly overbid and those who have a mixed behavior. model 2 takes the form below (equation 2): bid_hgi,q = α + βhea_a hea_ai,q + βhea_g hea_gi,q + βcf_a cf_ai,q + βcf_g cf_ gi,q + βhea_a_irr hea_ai,q * irri,q + βhea_g_irr hea_gi,q * irri,q + βcf_a_irr cf_ai,q * irri,q + βcf_g_irr cf_gi,q * irri,q + βhea_a_irr_und hea_ai,q * irr_undi,q + βhea_g_ the latter is equal to – 676.632. table 3. generalized leastsquare regression models with correction for heteroscedasticity for spva data. dep. var: bid_hg coefficients model 1 βhea_a 0.710*** (0.096) βhea_g 1.254*** (0.0961) βcf_a 0.578*** (0.0961) βcf_g 0.873*** (0.0961) βhea_a_irr 0.255** (0.126) βhea_g_irr 0.546*** (0.126) βcf_a_irr 0.279** (0.126) βcf_g_irr 0.520*** (0.126) α -0.298*** (0.099) wald test b: χ2 100.130*** obs. 504 subjects 63 note: ***p<0.01; **p<0.05; *p<0.10 a standard errors in parentheses b h0: βfat_a_irr=βfat_g_irr=βcf_a_ irr=βcf_g_irr = 0 113consumers’ rationality and home-grown values for healthy and environmentally sustainable food irr_und hea_gi,q * irr_undi,q + βcf_a_irr_und cf_ai,q * irr_undi,q + βcf_g_irr_und cf_gi,q * irr_undi,q + εi,q (2) 4.2.2 results and discussion results from the estimation of model 2 are shown in table 4 and suggest that underbidding spills over from the iv to the hgv task. subjects who consistently underbid in the iv setting have lower hgvs than the other subjects who behave irrationally. the coefficients βhea_a_irr_und, βcf_a_irr_und and βcf_g_irr_und are not statistically significant. however, the coefficient βhea_g_irr_und is negative and statistically significant (-0.361, p<0.05). a wald test rejects the hypothesis that all these coefficients are jointly equal to zero (11.940, p<0.05).12,13 these results are consistent with previous finding by cherry et al. (2003) and cherry and shogren, (2007) and indicate that underbidding may be an intrinsic individual-specific behavior that does not depend on the type of task (iv or hgv). further research is needed to investigate further this intriguing hypothesis. 4.3 deviations from rationality and home-grown values elicited via the dce 4.3.1 overview of deviations from rationality subjects are considered rational when they submit only demand revealing choices (9 out of 9 choices) in the iv dce task. a choice is demand revealing when it maximizes the subjects’ payoff that subjects can obtain in the choice set. in other words, when it maximizes the difference between the iv and the market price. deviations from rational choice behavior occur when subjects fail to submit only demand revealing choices. in our sample, 40 subjects out of 65 (61.50%) deviate from rational choice behavior, while 25 subjects (38.50%) are rational. similar to the spva, we found that no subjects submit 7 or 8 demand revealing choices which may indicate that subjects do not make random mistakes. 4.3.2 testable hypotheses and model specification we estimate random-parameter logit models in wtp space to test whether hgvs elicited from subjects who behave irrationally in the iv dce task differ from those elicited from rational subjects. models in wtp space reduce possible biases due to the confounding of variation in scale and wtp (train and weeks, 2005). some studies have shown that models in wtp space fit data better than those in preference space (e.g., scarpa et al., 2008) in model 3, the indirect utility function is specified as in equation 3: 12 other models were estimated which incorporate the rate of underbidding and exclude those subjects who constantly overbids (just two) from the analyses, considering them as outliers. results are provided in the online supplementary appendix e. 13 models 2 is estimated using feasible generalized least squares (fgls) and r2 is not an appropriate indicator of explanatory power. here, we report the wald χ2 which is equal to 297.140 and is significant level at p<0.01. we also estimated model 2 using the iterated gls estimator, which allows estimating the log-likelihood. the latter is equal to – 660.525. 114 simone cerroni, verity watson, jennie i. macdiarmid vi,j,k = -λipri,j,k + (λi + ωi)xi,k,j (3) in equation 3, λi = αi /μi, where αi indicates subjects’ preferences for the price of lasagne pri,j,k and μi is the scale parameter. the coefficient vector ωi = θi /αi is the ratio of the vector of coefficients θi that are associated to the vector of non-price attributes xi,j,k and the coefficient αi. the vector ωi indicates the mwtps associated to the vector of non-price attributes xi,j,k. the coefficient ωopt-out is an alternative specific constant related to the opt-out alternative. the coefficients ωhea_a,i and ωhea_g,i denote mwtps for lasagne that are amber (hea_ai,j,k) and green (hea_gi,j,k) in the health dimension, respectively. the coefficients ωcf_a,i and ωcf_g,i indicate mwtps for lasagne that are amber (cf_ai,j,k) and green (cf_gi,j,k) in carbon footprint, respectively. these mwtps are estimated with respect to red levels of healthiness and carbon footprint, respectively. to account for unobserved heterogeneity, we assume that the coefficients ωhea_a, ωhea_g, ωcf_a and ωcf_g are normally distributed, while the αi is lognormally distributed with means and standard deviations to be estimated. the variable irr is equal to 1 if subject i behaves irrationally in the iv dce task, meaning that she/he fails to submit only demand revealing choices (9 out of 9 choices). the coefficients ωhea_a_irr, ωhea_g_irr, ωcf_a_irr and ωcf_g_irr inform on whether mwtps differ between subjects whose behavior deviates from rationality in the iv task and the others (i.e., rational). models 3 is estimated by using methods of maximum simulated likelihood relying on 1,000 halton draws (train, 2009). summary statistics of variables used in model 3 are presented in table 5. 4.3.3 results and discussion results from estimation of model 3 are reported in table 6. we find that coefficients ωhea_a_irr and ωhea_g_irr are not statistically significant. the coefficient ωcf_a_irr is positive and statistically significant (0.433, p<0.05), which suggests that subjects who behave irrationally (in the iv dce task) are willing to pay more than others (i.e., rational subjects) for lasagne that are amber in carbon footprint. in contrast, ωcf_g_irr (-0.317, p<0.01) is negative and statistically table 4. generalized leastsquare regression models with correction for heteroscedasticity for spva data. dep. var: bid_hg coefficients model 2 βhea_a 0.457*** (0.116) βhea_g 0.710*** (0.116) βcf_a 0.301*** (0.116) βcf_g 0.355*** (0.116) βhea_a_irr 0.186 (0.142) βhea_g_irr 0.704*** (0.142) βcf_a_irr 0.245* (0.142) βcf_g_irr 0.582*** (0.142) βhea_a_irr_und 0.141 (0.150) βhea_g_irr_und -0.361** (0.150) βcf_a_irr_und 0.0654 (0.150) βcf_g_irr_und -0.155 (0.150) α -0.300*** (0.0980) wald test b: χ2 98.330*** wald test c: χ2 11.940** obs. 504 subjects 63 note: ***p<0.01; **p<0.05; *p<0.10 a standard errors in parentheses b h0: βfat_a_irr =βfat_g_irr =βcf_a_irr =βcf_g_irr= 0 c h0: βfat_a_irr_und =βfat_g_irr_und =βcf_a_irr_und =βcf_g_irr_und = 0 115consumers’ rationality and home-grown values for healthy and environmentally sustainable food significant which indicates that subjects who behave irrationally (in the iv dce task) are willing to pay less than others (i.e., rational subjects) for lasagne that are green in carbon footprint. a wald test rejects the null hypothesis that coefficients βhea_a_irr, ωhea_g_irr, ωcf_a_irr , ωcf_g_irr are jointly equal to zero (9.570, p<0.05). overall, these results show that deviations from rationality in the iv task affect estimated hgvs far less in the dce than in the spva treatment group.14 such results may be related to the fact that dce does not require any strategic interaction among subjects participating to the experiment and expose subjects to decision tasks that resemble “real-life” purchasing situations. these factors may lower the impact that deviations from rationality investigated using iv procedures have on hgvs elicited for lasagne. 4.4 determinants of irrational bidding and choice behavior a behavioral model aiming to capture variables explaining irrational bidding and choice behavior is developed (model 4). data from the spva and dce treatment groups are pooled. the dependent variables irr is a binary variable, indicating if subjects’ bidding or choice behavior deviates from rationality in the iv settings. we included only independent variables that potentially affect the probability of submitting/making demand 14 to test the consistency of estimation results, an alternative model was estimated. in this model, we incorporate the rate of non-demand revealing choices made per subjects. this variable indicates the rate of irrationality. estimation results are provided in tables f2 and f3 of the supplementary online appendix f. table 5. summary statistics of variables included in the dce model. variable description obs. mean st.dev. min max ch_hg = 1 if alternative a is selected = 0 otherwise 585 1.099 0.800 0.000 2.000 hea_ra = 1 if health is red in alternative a and b = 0 otherwise 585 0.333 0.472 0.000 1.000 hea_a = 1 if health is amber in alternative a and b = 0 otherwise 585 0.333 0.472 0.000 1.000 hea_g = 1 if health is green in alternative a and b = 0 otherwise 585 0.333 0.472 0.000 1.000 cf_ra = 1 if carbon footprint is red in alternative a and b = 0 otherwise 585 0.333 0.472 0.000 1.000 cf_a = 1 if carbon footprint is amber in alternative a and b = 0 otherwise 585 0.333 0.472 0.000 1.000 cf_g = 1 if carbon footprint is green in alternative a and b = 0 otherwise 585 0.333 0.472 0.000 1.000 prb price of alternative a and b 585 3.000 1.292 1.000 5 irr = 1 if subjects behave irrationally = 0 otherwise 585 0.615 0.486 0.000 1.000 a health and environmental sustainability are not defined in the not-buy alternative (c). b price ranges from £1 to £5, it is =0 for the not-buy alternative (c). 116 simone cerroni, verity watson, jennie i. macdiarmid revealing bids/choices. these are: dce which indicates whether the subject belong to the dce treatment or not; time which indicates whether the subjects participated to the 13.30 or 18.30 session; hungry which indicates the self-reported level of hunger of subjects at the beginning of the experiment (from a minimum of 1 to a maximum of 7), female which indicate if the subject is female or not; age indicating each subject’s age; income which indicates each subjects’ annual net income. summary statistics of variables incorporated in our behavioral models are provided in table 7. the estimation results of model 4 are presented in table 8. we find that the coefficient βdce is negative and statistically significant (-2.282; p<0.01) which indicates that irrational behavior is more likely in the spva than in the dce. we also find that subjects’ hunger level (βhungry) has a negative and statistical significant (-0.260, p<0.10) effect on being irrational. this might indicate that subjects who were hungrier paid more attention to the tasks as they knew lasagne were at stakes during the experiment.15 5. conclusions second-price vickey auctions and discrete choice experiments are widely used to evaluate welfare benefits of new food policies that are not implemented yet. these evaluations are often used in benefit-cost analysis to decide whether to operationalize food policies or not. therefore, it is important to explore the reliability and robustness of evaluations that are conducted using these value elicitation techniques. this paper contributes to this literature by testing if subjects behave rationally when exposed to these value-elicitation procedures and if deviations from rational choice behavior affect policy evaluation. psychologists and behavioral economists have challenged the main underlying assumption of neoclassical economics: economic agents always behave rationally to maximize utility. simon’s notions of sat15 an alternative model in which the dependent variable is the rate of irrational bids/choices submitted is estimated. results are provided in the online supplementary appendix g. table 6. wtp-space multinomial logit models for dce dataa,b. dep. var.: choice coefficients model 3 ωopt-out 2.332*** (0.417) ωhea_a,mean 0.497*** (0.143) ωhea_g.mean 1.583*** (0.152) ωcf_a,mean 0.691*** (0.145) ωcf_g,mean 1.772*** (0.164) ωhea_a,sd 1.051*** (0.0981) ωhea_g.sd 1.115*** (0.121) ωcf_a,sd 0.547*** (0.0589) ωcf_g,sd 1.341*** (0.0954) ωhea_a_irr -0.193 (0.223) ωhea_g_irr -0.590 (0.414) ωcf_a_irr 0.433** (0.174) ωcf_g_irr -0.317*** (0.190) λmean -0.393 (0.286) λsd 2.018*** (0.463) wald test c: χ2 9.570** log-likelihood -433.913 obs. 1,755 subjects 65 note: ***p<0.01; **p<0.05; *p<0.10 a standard errors in parentheses b 1,000 halton draws c h0:ωhea_a_irr =ωhea_g_irr =ωcf_a_ irr =ωcf_g_irr = 0 117consumers’ rationality and home-grown values for healthy and environmentally sustainable food isficing and bounded rationality are classic examples (1955; 1986). kahneman and tversky have based part of their research on economic decision making on the idea that two types of cognitive processes exist, the well-known systems 1 and 2. the former is characterized by speed, intuition, associations, heuristics and emotions. the latter by slowness, reasoning, rules, logic and self-control. it is possible to argue that system 2 is dominated by rationality, while system 1 does not. this paper explores the impact of deviations from rationality on the evaluation of new public policies interventions and focuses on an information-based food policy which aims to promote consumption of healthy and environmentally sustainable food products. these are two of the pillars of the notion of sustainable diets. specifically, this study investigates the impact of deviations from rationality on consumers’ home-grown values for ready meals (i.e., frozen lasagne) that are labelled using nutritional and carbon footprint labels. home-grown values are elicited via bid(i.e. second-price vickey auctions) and choicebased methods (i.e. discrete choice experiments). deviations from rationality are explored using induced value procedures. our results suggest that deviations from rationality are more likely to occur in second-price vickey auctions than discrete choice experiments: 77.78% of the sample deviates from rational behavior in second-price vickey auctions, only the 61.50% of the sample in discrete choice experiments. this result suggests that choice-based valtable 7. summary statistics of variables included in the behavioral model. variable description obs. mean st.dev. min max irr = 1 if subjects behave irrationally = 0 otherwise 128 0.719 0.451 0.000 1.000 dce = 1 dce treatment = 0 otherwise 128 0.508 0.502 0.000 1.000 time = 1 if lunch session = 0 otherwise 128 0.516 0.502 0.000 1.000 hungry reported level of hunger from 1 (not hungry at all) to 5 (extremely hungry) 128 4.102 1.502 1.000 6.000 female = 1 female = 0 otherwise 128 0.637 0.482 0.000 1.000 age age in years 128 36.466 13.616 19.000 70.000 inc yearly net income in £ 128 38,578.740 29,334.850 5,000.000 150,000.000 table 8. behavioral binary logit modela. model 6 dep. var.: dm coefficients βdce -2.282*** (0.509) βtime 0.310 (0.442) βhungry -0.260* (0.153) βfemale 0.334 (0.475) βage 0.017 (0.016) βincome 1.08e-05 (1.12e-05) α 2.069* (1.125) log-likelihood -61.935 obs. 128 subjects 128 note: ***p<0.01; **p<0.05; *p<0.10 a standard errors in parentheses 118 simone cerroni, verity watson, jennie i. macdiarmid ue elicitation techniques, such as discrete choice experiments, induce rationality more than bid-based methods, such as second-price vickey auctions. this result seems to support li’s (2017) argument that second-price vickey auction is not an obviously strategyproof technique and hence identification of the payoff maximizing strategy is not obvious. which method predict choice behavior better in real settings remains an open question. the impact of irrationality on home-grown values in second-price vickey auctions is rather substantial and systematic. subjects whose behavior deviates from rationality have higher home-grown values for lasagne than rational ones. also, our results indicate that underbidding spills over from induced-value to home-grown value settings, meaning that subjects who consistently underbid in the induced-value setting, tend to submit lower bids than the others in the home-grown setting. this is a very intriguing result, indicating that underbidding may be an intrinsic individual-specific behavior. future research could explore cognitive processes or personal traits driving this phenomenon. on the other hand, deviations from rationality do not seem to follow a clear pattern and barely affect home-grown values elicited via discrete choice experiments. these results may be due to the fact that subjects are exposed to rather different valuations environments and framings in the second-price vickery auctions and discrete choice experiments. for example, subjects may perceive the second-price vickrey auction as a competitive institution and they may tend to adopt a strategic bidding behavior which is consistently used in both induced value and home-grown value settings. in contrast, in the discrete choice experiments, subjects make individual choices that do not generally depend on other consumers’ decisions. hence, strategic behavior is very limited in discrete choice experiments and this may explain why deviations from rationality in induced value setting have little impact on elicited home-grown values. additionally, in second-price vickrey auctions, subjects are asked to form their own home-grown values for different food products, while, in discrete choice experiments, subjects are asked to make choices among food products and market prices are given to subjects in each choice set. the former is a rather unusual situation for a consumer, while the latter is very familiar. hence, it is reasonable to argue that irrationality may play a more substantial role in home-grown values elicited via second-price vickery auctions than discrete choice experiments. overall, we conclude that home-grown values elicited via discrete choice experiments are rather robust. these results may be significant for policy makers who wish to use findings from second-price vickrey auctions and discrete choice experiments in ex ante benefit-cost analyses of new policy interventions. 6. ackowledgements we thank christian reynolds and dimitrios kalentakis for their help in organising sessions and running the experiment at the scottish experimental economics laboratory (seel) at university of aberdeen. we also thank sylvia stephen for her help in creating the recipes for the 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(2009). mindless eating and healthy heuristics for the irrational. american economic review 99(2): 165-179. 123consumers’ rationality and home-grown values for healthy and environmentally sustainable food appendix a healthiness was based on the amount of saturated fat in the lasagne. the criteria for the saturated fat content of the different lasagne was based on the uk food standard agency guidance; green ≤1.5g/100g, amber >1.5 to ≤5.0g/100g, red >5.0g/100g (fsa 2013). a second tls was used for the carbon footprint. the carbon footprint was the sum of ghge (kgco2e) for each ingredient in the lasagne (ghge data published by audsley et al. (2009)). the system boundaries for these data are from primary production to the point of the regional distribution centre. this does not include food processing, retail, household use and waste but these would be similar for all the lasagne as only the ingredients varied. there are no standardised guidelines for labelling ghge for foods therefore the three levels were set by the researchers; green ≤0.26 kgco2e/100g, amber >0.26 to <0.4 kgco2e/100g, red ≥0.4 kgco2e/100g. the range of meat content between the lasagne was similar to commercially pre-prepared lasagne at the time of the study (7% to 20% meat). references audsley, e., brander, m., chatterton, j., murphy-bokern, d., webster, c., williams, a., 2009. how low can we go? an assessment of greenhouse gas emissions from the uk food system and the scope for reduction by 2050. report for the wwf and food climate research network. 124 simone cerroni, verity watson, jennie i. macdiarmid appendix b induced value spva home-grown value spva induced value dce home-grown value dce 125consumers’ rationality and home-grown values for healthy and environmentally sustainable food appendix c table c1. summary statistics of marginal bids in the spva treatmenta. variable description obs. mean st.dev. min max bid_hghea_r_cf_a marginal bid for red health and amber carbon footprint lasagne 63 0.237 0.707 -3.000 1.500 bid_hghea_r_cf_g marginal bid for red health and green carbon footprint lasagne 63 0.240 0.981 -4.000 1.950 bid_hghea_a_cf_r marginal bid for amber health and red carbon footprint lasagne 63 0.321 1.025 -4.000 2.000 bid_hghea_a_cf_a marginal bid for amber health and amber carbon footprint lasagne 63 0.773 1.407 -3.500 3.350 bid_hghea_a_cf_g marginal bid for amber health and green carbon footprint lasagne 63 0.764 1.459 -4.000 3.500 bid_hghea_g_cf_r marginal bid for green health and red carbon footprint lasagne 63 1.086 1.508 -3.500 4.000 bid_hghea_g_cf_a marginal bid for green health and amber carbon footprint lasagne 63 1.325 1.581 -4.000 4.500 bid_hghea_g_cf_g marginal bid for green health and green carbon footprint lasagne 63 1.606 1.922 -4.000 5.000 a a marginal bid is the difference between any lasagne other than a red in health and red in environmental sustainable lasagne (in £) and the bid for a red in health and red in environmental sustainable lasagne. 126 simone cerroni, verity watson, jennie i. macdiarmid appendix d in model 1a, we replace the variable irr with irr_freq. the latter indicates the percentage of non-demand revealing bids submitted in the iv setting by each subject. main summary statistics of the variable irr_freq is reported in table d1. results from the estimation of model 1a indicate similar to model 1, but weaker effects (table d2). while, the coefficients βhea_a_irr_freq and βcf_a_irr_freq are not statistically significant, the coefficient βhea_g_irr_freq and βcf_g_irr_freq are positive and significant (0.422, p<0.01 and 0.338, p<0.05). this suggests that mwtp for healthiest and low carbon footprint lasagne (i.e., green) increases when the rate of irrational iv bids increases (i.e., the degree of irrational behavior). a wald test rejects the null hypothesis that coefficients βhea_a_irr_freq , βhea_g_irr_freq, βcf_a_irr_freq, βcf_g_irr_freq are jointly equal to zero (37.800, p<0.01). table d1. summary statistics of variables included in the spva-related models. variable description obs. mean st.dev. min max irr_frq rate of non-demand revealing bids per subject 504 0.681 0.394 0.000 1.000 table d2. generalised least-square regression models with correction for heteroscedasticity for spva data. dep. var: bid_hg coefficients model 1a βhea_a 0.525*** (0.126) βhea_g 0.833*** (0.126) βcf_a 0.383*** (0.126) βcf_g 0.483*** (0.126) βhea_a_irr_freq 0.196 (0.146) βhea_g_irr_freq 0.422*** (0.146) βcf_a_irr_freq 0.175 (0.146) βcf_g_irr_freq 0.338** (0.146) α -0.297*** (0.105) wald test c: χ2 37.800*** obs. 504 subjects 63 note: ***p<0.01; **p<0.05; *p<0.10 a standard errors in parentheses b h0: βfat_a_irr_freq =βfat_g_irr_freq =βcf_a_irr_freq =βcf_g_irr_freq = 0 127consumers’ rationality and home-grown values for healthy and environmentally sustainable food appendix e three variations of model 2 are estimates: i) model 2a: we estimate model 2 while excluding from the sample the two subjects who constantly overbid in the iv task. these are considered as outliers. ii) model 2b: we specify the variable und as percentage of underbids (per subject) in the iv setting. this variable measures the rate of underbidding. the main statistics for this variable are provided in table e1. iii) variation 2 (model 2c): we estimate model 2b while excluding from the sample the two subjects who constantly overbid in the iv task. table e1. summary statistics of variables included in the spva-related models. variable description obs. mean st.dev. min max und_freq percentage of underbidding per subject 504 0.681 0.394 0.000 1.000 128 simone cerroni, verity watson, jennie i. macdiarmid results from the estimation of models 2a, 2b and 2c are provided in table e2. results are consistent across specifications. the coefficient βhea_g_irr_und is always negative and statistically significant. we always reject the null that coefficients βhea_a_irr_und, βhea_g_irr_ und, βcf_a_irr_und and βcf_g_irr_und are jointly equal to zero. table e2. generalised least-square regression models with correction for heteroscedasticity for spva data. dep. var: bid_hg coefficients model 2a model 2b model 2b βhea_a 0.451*** 0.547*** 0.531*** (0.116) (0.124) (0.124) βhea_g 0.703*** 0.703*** 0.786*** (0.116) (0.116) (0.124) βcf_a 0.294** 0.294** 0.373*** (0.116) (0.116) (0.124) βcf_g 0.348*** 0.348*** 0.456*** (0.116) (0.116) (0.124) βhea_a_irr 0.211 0.211 0.127 (0.143) (0.143) (0.184) βhea_g_irr 0.730*** 0.730*** 0.763*** (0.143) (0.143) (0.184) βcf_a_irr 0.267* 0.267* 0.135 (0.143) (0.143) (0.184) βcf_g_irr 0.594*** 0.594*** 0.420** (0.143) (0.143) (0.184) βhea_a_irr_und 0.114 0.114 0.116 (0.151) (0.151) (0.174) βhea_g_irr_und -0.388** -0.388** -0.507*** (0.151) (0.151) (0.174) βcf_a_irr_und 0.0435 0.0435 0.0936 (0.151) (0.151) (0.174) βcf_g_irr_und -0.166 -0.166 -0.102 (0.151) (0.151) (0.174) α -0.290*** -0.290*** -0.285*** (0.0984) (0.0984) (0.104) wald test b: χ2 105.360*** 105.360*** 46.230*** wald test c: χ2 13.18** 13.18** 13.490*** obs. 488 488 488 subjects 61 61 61 note: *p<0.01; **p<0.05; ***p<0.10 a standard errors in parentheses b h0:βhea_a_irr =βhea_g_irr =βcf_a_irr =βcf_g_irr=0 c h0: βhea_a_irr_und =βhea_g_irr_und=βcf_a_irr_und =βcf_g_irr_und =0 129consumers’ rationality and home-grown values for healthy and environmentally sustainable food appendix f detailed summary statistics of the choice variable (ch_hg) are provided in table f1 below. table f1. summary statistics of dce choices. variable description obs. mean st.dev. min max ch_hga = 1 if alternative a is selected = 0 otherwise 585 0.275 0.446 0 1 ch_hgb = 1 if alternative b is selected = 0 otherwise 585 0.350 0.477 0 1 ch_hgc = 1 if alternative c is selected = 0 otherwise 585 0.374 0.485 0 1 model 3a replaces the variable irr in model 3 with irr_freq. this variable indicates the rate of irrational choice made by each subject. main statistics of this variable are presented in table f2. results from the estimation of model 3a are reported in table f3. none of the coefficients βhea_a_dm_freq, βhea_g_dm_freq, βcf_a_dm_freq and βcf_g_dm_freq is statistically significant and a wald test fails to rejects the hypothesis that these coefficients are jointly equal to zero (0.840). these results indicates that the rate of irrationality does not affect hgv elicited via dce. table f2. summary statistics of variables included in the dce model. variable description obs. mean st.dev. min max dm_freq percentage of non-demand revealing choices 585 0.376 0.252 0.000 1.000 130 simone cerroni, verity watson, jennie i. macdiarmid table f3. wtp-space multinomial logit models for dce dataa,b. dep. var.: choice coefficients model 3a ωopt-out 1.884*** (0.416) ωhea_a,mean 0.233 (0.277) ωhea_g,mean 1.209*** (0.194) ωcf_a,mean 0.626*** (0.225) ωcf_g,mean 1.526*** (0.192) ωhea_a,sd 0.969*** (0.0796) ωhea_g,sd 1.548*** (0.114) ωcf_a,sd 0.0816* (0.0450) ωcf_g,sd 1.416*** (0.105) ωhea_a_irr_freq -0.333 (0.414) ωhea_g_irr_freq 0.204 (0.298) ωcf_a_irr_freq 0.262 (0.353) ωcf_g_irr_freq -0.052 (0.305) λmean -0.505 (0.319) λsd 1.963*** (0.350) wald test d: χ2 0.840 log-likelihood -431.878 obs. 1,755 subjects 65 note: *p<0.01; **p<0.05; ***p<0.10 a standard errors in parentheses b 1,000 halton draws c h0:ωhea_a_irr_freq =ωhea_g_irr _freq=ωcf_a_irr_freq =ωcf_g_irr_freq = 0 131consumers’ rationality and home-grown values for healthy and environmentally sustainable food appendix g in model 4a, the dependent variable is irr_freq which indicates the rate of irrational bids/choices submitted per subject. summary statistics for this variable are presented in table g1. table g1. summary statistics of variables included in the behavioral model. variable description obs. mean st.dev. min max dm_freq percentage of non-demand revealing observations 128 0.588 0.345 0.000 1.000 results from the estimation of model 4a suggests that the rate of irrationality is higher in the spva treatment as compared to the dce treatment (table g2). the coefficient βdce is negative and statistically significant (-1.731; p<0.01). we find that females (βfem) are more likely to act irrationally (0.492, p<0.10). interestingly, the coefficient βincome is positive and statistical significant (1.05e-05, p<0.10). this may suggest that monetary payoffs in the iv tasks were not high enough to incentivise higher income subjects. table g2. behavioral binary logit modela. generalized linear model dep. var.: dm_freq coefficients βdce -1.731*** (0.260) βtime 0.318 (0.243) βhungry -0.048 (0.076) βfemale 0.492* (0.279) βage 0.02 (0.00827) βincome 1.05e-05* (6.31e-06) α 0.203 (0.578) log-likelihood -60.347 obs. 128 subjects 128 note: ***p<0.01; **p<0.05; *p<0.10 a standard errors in parentheses consumers’ rationality and home-grown values for healthy and environmentally sustainable food simone cerroni1,2,3,*, verity watson4, jennie i. macdiarmid5 the wellbeing of smallholder coffee farmers in the mount elgon region: a quantitative analysis of a rural community in eastern uganda anna lina bartl agricultural sector performance, institutional framework and food security in nigeria romanus osabohien1,3,*, evans osabuohien1,3, precious ohalete2,3 innovation adoption and farm profitability: what role for research and information sources? michele vollaro1,*, meri raggi2, davide viaggi1 determinants of farm households’ willingness to accept (wta) compensation for conservation technologies in northern ghana evelyn delali ahiale1,*, kelvin balcombe2, chittur srinivasan2 bio-based and applied economics 7(1): 39-58, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-24047 measuring the complexity of complying with phytosanitary standard: the case of french and chilean fresh apples federica demaria1,2, pasquale lubello2,*, sophie drogué2 1 crea (consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria), via po n. 14, 00198, rome, italy 2 umr moisa, cirad, ciheam-iamm, inra, montpellier supagro, univ montpellier, montpellier, france date of submission: 2017 13th, march; accepted 2018 1st, february abstract. nowadays, complying with technical, sanitary and phytosanitary (sps) regulations and standards is becoming more and more demanding due to their proliferation and increasing complexity. consequently, increasing requirements in plant health protection and food safety can lead to a loss of competitiveness in countries that are major exporters of fresh products, causing a redistribution of the market shares in certain sectors. exporters complying with regulatory standards benefit from better market access and avoid boarder rejection or product downgrading but incur additional costs due to additional procedures and paperwork. this is the case for french apples producers which are losing competitiveness compared to the chilean ones on foreign markets. this situation can be partially explained by the difficulties of french exporters to comply with international sps requirements. the aim of this article is first to make a compilation of phytosanitary requirements facing french and chilean exporters of fresh apples, then to propose a score (hereafter phytosanitary score) which allows to assess the degree of complexity of these sps requirements. this score is interesting as it synthesizes qualitative information in a metric which can be easily used in quantitative analysis. the results show that even if france and chile are rather close in terms of sps requirements, chilean apples exporters are more capable to comply with foreign sps requisites than the french ones. keywords. cost of compliance, scoring, apples, sanitary and phytosanitary regulations. jel codes. c51, i18, q18. 1. introduction the literature on sanitary and technical regulations has shown that if regulations and standards are market facilitators by decreasing asymmetries, they also hamper trade (swinnen and vandemoortele, 2011; marette and beghin, 2010). the effects that sps regu*corresponding author: pasquale.lubello@supagro.fr 40 federica demaria, pasquale lubello, sophie drogué lations have on the economy depend on how they impact consumers, domestic producers and foreign competitors (swinnen and vandemoortele, 2009). the cost of production and marketing will increase with the increasing complexity of the regulations abroad. in the importing country, compliance with a regulation involves a cost to foreign suppliers, which acts like a trade tax, resulting in a deadweight loss as well as transfers from consumers to producers (beghin and bureau, 2002). on a specific market, foreign producers are impacted by the sps requirements depending on their relative differences in the marginal cost of the regulation, thus on their relative efficiency to comply with importers’ standards. this may affect countries that were major exporters, causing a redistribution of the market shares in certain sectors. it is the case for french apples exporters who compete now with newcomers as china which were not even producers 10 years ago. international trade of apples (and more generally of fruits and vegetables), requires that products intended for marketing come with a phytosanitary certificate (pc) which certifies that they are properly inspected, pest-free, and comply with national and international phytosanitary regulations. however, regulatory constraints and requirements in the importing countries may differ substantially from those in the country of departure. this asymmetry directly impacts the phytosanitary risk management and therefore the costs of compliance. usually, to deliver the pc for fresh apples, countries require either a cold treatment and/or fumigation with methyl bromide (aphis usda, 2014 calvin and krissof, 1998). the former, even if simple to apply, can become quite complicated because the required temperature for cold treatment may vary from one destination to another. moreover, if the majority of countries agree on a pre-shipment cold treatment, others require it during transportation or even at the port of arrival complicating the procedure. but the cold treatment is one among many requirements and paperwork an apple exporter faces before selling its products abroad. even if a producer is able to comply with all these measures, a possible refusal of the apples still remains if at the port of arrival, a further inspection proves that something went wrong during the transportation or if the regulations have changed meanwhile. rejections of apples occurred between the us and japan in 2002, the us and taiwan, australia and new zealand in 2007 (wto, 2010) also between france and vietnam in 2012 (france agrimer, 2015). these examples illustrate that quantifying costs of compliance is not an easy task due to the proliferation of technical and sanitary regulations and standards and to their increasing complexity. moreover, whereas models for policy analysis often require quantitative data, these regulations are often not quantitative. for qualitative standards, like labelling, no numerical values can be directly used. further, these qualitative policies affect different components of costs of production and marketing and cannot be easily aggregated into a single price equivalent. evaluating the protectionist component of these numerous qualitative policies into a protectionist score is likely to remain a challenge (li and beghin, 2014). several authors worked on the issue of introducing qualitative policy instruments in quantitative analysis by producing different synthetic indicators. among others we can quote works on technological positions (jaffe, 1986), regulations on genetically modified organisms (vigani et al., 2011) or varieties of grapes and wines (anderson, 2010). more recently, ferro et al. (2015), li and beghin (2014), winchester et al. (2012) or drogué and demaria (2012) also built synthetic metrics to compare bilateral regulations on maximum residual level of food contaminants. 41measuring the complexity of complying with phytosanitary standard in this article we build a phytosanitary score that allows approximating the relative complexity of phytosanitary requirements in the marketing of fresh apples. we compiled the sanitary and phytosanitary regulations french and chilean apples exporters must comply with on their main markets of destination. these two countries have been chosen for two main reasons. first, at international level, in comparison with chile, french producers are losing market share, which could be explained by their difficulties to comply with international phytosanitary regulations. the second reason lies in the characteristics of the countries themselves. france is a traditional provider of apples with a long history of production and consumption, while chile is a more recent producer export-oriented, and, being located in the southern hemisphere; apples in chile are produced off-season. the indicator presented henceforth can be seen as a proxy for higher compliance cost born by exporting countries when shipping their apples abroad. this kind of indicator can be used in econometric models to evaluate the impact of non-tariff barriers on trade. at the same time, supply chain operators can also use it as synthetic information on the complexity of phytosanitary requirements in importing countries. in order to compute our indicator, we first identified all the components of apples phytosanitary requirements chile and france must comply with by destination (number of inspections, number of treatments and location of treatment, signature of an agreement between countries, etc.). then, each component is graded with an increasing value according to its degree of complexity; finally we sum them up in a normalized score. results show that the scores for france and chile are rather close, but suggest that overall france suffers from more stringent foreign regulations and chile is able to reach more easily any destination markets thanks to a better geographical position and phytosanitary situation. the originality of this work is a deeper understanding on sanitary and phytosanitary requirements that french and chilean apples producers necessarily face if they decide to gain foreign markets’ share, and more particularly the design of a tool that allows to grade and to translate regulatory data into a single score useful for quantitative analysis. the paper is organized as follows: section 2 is an overview of the international market of apples and the recent redistribution of market shares between countries in this sector. section 3 is devoted to the presentation of the data on phytosanitary requirements. section 4 presents the building of the score. section 5 is devoted to the sample and the numerical results. section 6 concludes. 2. the international market of apples compared with other markets of agricultural commodities, such as sugar, coffee or bananas, the apple world market can be broadly considered as residual: in 1961, only 9.5% of the world fresh production was traded on international markets and reached 11% fifty years later (2012). the main reason is that, historically, traditional producing countries (essentially western countries) were also the main consumers. from the 90s, an evolution took place in the global geography of production and consumption, leading to evolving trade flows. the description of these changes is therefore important to understand the main opportunities and obstacles encountered by the major exporting countries (like france or chile). 42 federica demaria, pasquale lubello, sophie drogué according to the faostat database, apples are nowadays the second most produced and consumed fruit in the world after bananas and before oranges and grapes. its production evolved greatly during the last 50 years, from 17 million tons in 1961 to more than 76 million tons in 2012 (+300%). this apparently linear development, hides some recent and deep changes in the geography of production. first and foremost, there is the spectacular increase, from the beginning of the 90s, of the chinese production (figure 1). from just 1% of the world production in 1961, it represents today half the global output in the world (48% in 2012)1. in general, during this same period there is a globalisation of apple production and traditional producers (as france or italy) have lost market shares in relative and absolute value, to the benefit of china and emerging countries (figure 2). on the demand side, we observe the same evolution: in countries of traditional consumption, with high incomes, saturated food demand, and with stronger health and environmental concerns, apples suffer the competition of other fruits, including exotic ones (figure 3). in contrast, population growth recorded in emerging economies, combined with higher average incomes and the dissemination of national and international education policies promoting fruit consumption2 explain the increase of their respective demand for fruits, especially apple, one of the easiest to store (figure 3). finally, if the geographical area of apple production and consumption has greatly expanded in the last 20 years, the new consumer countries are not necessarily the producing ones. therefore, and except for china, which is largely able to meet its own domestic 1 what explains this phenomenon is the liberalization process of the chinese market implemented by deng xiaoping (murphy et al., 1992). his reforms have allowed the chinese farmers to sell their excess production on the free market, leaving the market price system drive the allocation of productive investments. 2 who: http://www.who.int/mediacentre/news/releases/2003/pr1/en/. figure 1. world apple market: production (with and without china). source: faostat. 43measuring the complexity of complying with phytosanitary standard demand, the increasing consumption of apples in developing countries (like india, indonesia or brazil for example) represents a new opportunity for all exporters. among the major producing and exporting countries in 2012, table 1 differentiates those for which the domestic market remains a priority (such as china) from those for which external demand represents a major challenge. in the latter category, chile, france figure 2. world apple market: production shares selected countries. source: faostat. figure 3. world apple market: per-capita apple consumption – selected countries. source: faostat. 44 federica demaria, pasquale lubello, sophie drogué and italy3, represent about 30% of apple’s worldwide exports. if in the following study, we limit the comparative analysis to france and chile, thus excluding italy, several reasons justify our choice. first, to avoid duplication effect: france and italy have similar characteristics in terms of seasonality, produced varieties, production conditions and supplied export markets. second, the lack of data, especially regulatory data (bilateral phytosanitary agreements), for italy, does not allow us to add this country to the comparative analysis. therefore, we focus on the comparison between france and chile. these two countries differ not only in terms of geographical location, seasonality, climate characteristics or supplied markets4. they also face contrasting trends in exports. french apples exports are falling in the last 20 years, while they are increasing in chile (figure 4). these trends can partly be explained by the differences in importers’ sps requirements. 3. data description of phytosanitary requirements in the apple sector diseases and pest invasions vary greatly with place and time affecting the risk management and the protection of trees. the main pests damaging apples and apples orchards are: insects (codling moth, fire blight, sawfly insects, tortricid, aphids, and fruit tree spider mites), fungal diseases (apple scab venturia inaequalis and powdery mildew podosphaera leucotricha) and viral diseases. viral diseases have been less damaging since plants carried a certificate which guarantees against the presence of the mycoplasma-like organism (mlo) disease, the apple mosaic or the bitter pit disease (affecting the fruit). in order to mitigate the phytosanitary risk, regulators impose that crop products intended for marketing are accompanied by a phytosanitary certificate, defined above. 3 we could add to this short list, new zealand, a strongly export-oriented country. however, it does not represent a sufficient volume of exportations to be mentioned among the major players of the apple world market. 4 according to the detailed trade matrices published by faostat, france exports about 75% to eu countries and 11% to asian countries (in particular middle east). contrariwise, chilean exports are more diversified: half of its exports concern the americas (especially canada and usa), 23% come to asia and 23% to europe. table 1. world apple market: production and export shares – selected countries. country national production on world production (%) national net exports on world exports (%) national net exports on national production (%) italy 3.2 11.3 38.9 chile 2.1 9.6 50.4 china 47.3 9.1 2.1 usa 5.6 8.2 16.0 france 2.4 7.1 31.7 iran 2.4 1.3 5.7 turkey 3.5 1.0 3.0 india 3.8 -1.8 -7.0 source: faostat, 2012. 45measuring the complexity of complying with phytosanitary standard these regulatory constraints and associated additional treatment operations impact the sps risk management and increase, the costs of production and marketing. however, even if a producer is able to comply with all these measures, some possible rejection/refusal of products may still happen if at the port of arrival further inspection prove the presence of a pest. rejections of apples occurred between the usa and japan in 2002, the usa and taiwan, australia and new zealand in 2007 (wto, 2010) and in 2012 vietnam stopped apples coming from france and re-negotiated a bilateral sps agreement (france agrimer, 2015). to illustrate the complex nature of pest risk management in the framework of international apple trade, let’s take the example of cold treatment. cold treatment is a common practice to fight main apple pests (especially ceratitis capitata), which in some cases, must be associated to fumigation (aphis usda, 2014). the cold treatment requires that fruits must be stored at a constant temperature between 0° and 4° for a period of 14 to 21 days to prevent contamination of products by harmful organisms. even if simple to implement, the cold treatment may become quite complicated because in case of a random interruption, the procedure must start again from the very beginning. an interruption is more likely to occur during shipment because temperature sensors cannot be verified easily and the common practice is that of cold treatment in transit5. moreover, doubts about the presence of pests or harmful organism in a given area may rise the alert level with consequent tightening of controls. this happened, with vietnam, which denied market access to its trading partners between 2013 and 2015 in order to modify the phytosanitary regulations. in this context analysing sps regulations imposes a case by case analysis. therefore, for the countries under scrutiny (france and chile) we retrieved information from vari5 source: eppo, url: https://www.eppo.int/quarantine/data_sheets/insects/certca_ds.pdf figure 4. france and chile apple exports (1993-2013). source: faostat. 46 federica demaria, pasquale lubello, sophie drogué ous sources. the first and main sources of information are the websites of the national food safety authorities managed by the respective ministries of agriculture (exp@don for france and the servicio agricola y ganadero (sag) / department of agriculture and livestock for chile). however, in some cases information was missing, thus we also consulted the world integrated trade solution (wits) maintained by the world bank, the world trade organisation (wto) dataset and finally the international plant protection convention (ippc). all this information was crossed-checked with experts from the sral (service regional de l’alimentation / french food regional service). the analysis of all the information at our disposal allowed us to identify an exhaustive list of the many requirements apples exporters face6. these requirements are of two types: (i) operational as the cold treatment or fumigation: in this case the requirements from the animal and plant health inspection service of the united states department of agriculture (aphis/usda) are the leading reference in many countries; (ii) administrative, taking the form of inspections or of declarations and can vary a lot according to bilateral agreements between the countries of origin and destination. we identify 9 requirements, called “dimensions” and described in the box a2 in appendix. to each dimension of the phytosanitary regulation we assigned a grade increasing with the complexity of implementation. the lowest grade is 0 (no constraints). then, 1 when the regulation requires a form of monitoring easy to apply; a value equal to 2 or 3 when fulfilling the requirements is complex and finally the maximum value in case of a ban. for instance, the grade for the cold treatment ranges between 0 and 3. it takes a value equal to 0 if any cold treatment is required; a value equal to 1 if the cold treatment is applied in transit, a value of 2 when the regulation requires a cold treatment at the port of arrival and a value of 3 for ban. we assume that any kind of activity is more difficult or more expensive to implement in the country of destination than during the shipment or pre-shipment. indeed (i) the absence of national operators in the foreign countries, (ii) the difficulties related to the use of different languages or different standards or (iii) the potential higher cost of the cold treatment activities in the foreign countries makes the procedure more difficult. the ban is not difficult to implement but it prevents all imports from the banned country; this is the reason why we consider the ban equivalent to assigning the highest grade to each dimension. table 2 displays the grades by dimension. as we can see from table 2, the number and the values of each restriction vary from country to country depending on the underlying domestic regulation. each phytosanitary requirement is controlled and certified by the representative safety authority: the sral in france, the sag in chile. they perform the required inspections and deliver the phytosanitary certificates. once this evaluation has been made, in the next section we synthesize all the components into one metric which gives the relative “phytosanitary distance” between the exporter (i.e. france or chile) and their importers. 4. building a phytosanitary score in order to assess the complexity of the overall sps regulations imposed to french and chilean apple exporters we built a phytosanitary score (hereafter ps). follow6 the analysis was carried out between 2014 and 2016. during this period, no major changes took place in trade relations, except for the negotiation of a new bilateral protocol between france and vietnam. 47measuring the complexity of complying with phytosanitary standard ing ferro et al. (2015), ps is designed as the sum of the grade obtained by each phytosanitary constraint (dimension) imposed by the importing country to the exporting one. we then normalized it in order to obtain a value ranging between 0 and 1 and further imposed convexity as in li and beghin (2014). in our analysis we consider that ps measures the relative severity of the phytosanitary constraints imposed by the importing country. table 2. dimensions and grades of the phytosanitary requirements and underlying regulations. dimension values underlying regulations territorial restriction / qo restriction 0 (no restriction) 1 (yes restriction) 2 (ban) bilateral agreements: between france and china, indonesia, sri lanka, taiwan, thailand, vietnam, usa between chile and china, india, taiwan, thailand, usa, mexico. in the other cases, the information comes from: exp@don database (for france) sag database (for chile) wits database (by world bank) food safety authority of importing countries (website) agreement 0 (no agreement needed) 1 (agreement on pre-listing) 2 (agreement on yearly check) 3 (ban) import permission 0 (no ip needed) 1 (the ip has been negotiated) 2 (the ip has not been negotiated) 3 (ban) phytosanitary certificate 0 (no pc) 1 (the pc has been negotiated) 2 (the pc is under negotiation) 3 (the pc is non official) 4 (ban) pre-inspection 0 (no pre-inspection) 1 (pre-inspection is required) 2 (ban) pre-clearance 0 (no pre-clearance) 1 (pre-clearance is required) 2 (ban) pre-cold treatment/fumigation 0 (no treatment needed) 1 (treatment needed) 2 (ban) cold treatment 0 (no cold treatment) 1 (in transit cold treatment) 2 (at arrival cold treatment) 3 (ban) inspection at arrival 0 (no inspection at arrival) 1 (inspection at arrival) 2 (ban) total requirements 24 (maximum requirements) 48 federica demaria, pasquale lubello, sophie drogué subscript i denotes the exporting country and j importing country (here i is equal to france or chile), phytoijn is the grade of the requirement imposed by country j to country i in the dimension n; maxphyton is the highest grade in the dimension n; minphyton is the lowest grade in the dimension n. the ps indicator ranges between 1 (in the absence of any specific requirements) and e ≈ 2.72 which corresponds to the case of a ban, the greater the score the more difficult to comply with all the dimensions of the country of destination’s sps regulation. the advantage of introducing the convexity in the standard is that it imposes more weight on more demanding requirements suggesting that it is more difficult to reach higher standards and thus that the marginal cost of compliance is increasing. we are particularly interested in verifying the relationships between trade and ps that is to say between trade and the phytosanitary requirement (phyto). our intuition being that the two variables are negatively correlated. 5. sample and results crossing data on french and chilean apple exports during the period 1986-2013 with the sanitary regulations, we have been able to select a sample of 82 countries (over 146 destinations in 2013) for france, and a sample of 51 countries (over more than 100 destinations in 2013) for chile (see the complete list of countries in table a1 in appendix). for the selected countries there is a positive flow of apples from france and chile over the period and information on phytosanitary regulations is available. we exclude from our samples, countries with zero trade flows except when those countries imposed a ban on french or chilean apples. the countries in the sample represent, for both exporters and for the entire period, 99% of their exports of apples on average. our sample can be disaggregated in 3 sub-groups. the first one gathers european countries which apply similar phytosanitary regulations (directive 2000/29 ce, european commission, 2000). in this common phytosanitary area, french apples move freely without control or particular certificates, while chilean apples need a simple inspection at arrival. the second sub-group gathers 52 extra-european destinations for which french and chilean apples must be accompanied by a pc or by a specific phytosanitary document or both. the third group is constituted by countries which banned imports of apples from france or chile (indonesia, japan, south africa, south korea and tunisia). table a1 in appendix reports the values of the scores for all countries importing french or/and chilean apples. it shows in the first column the selected countries importing apples from france; in the second column the values of ps; and in the third column the average trade in volume. columns 4 to 7 display the same information for chile. this score is able to capture the degree of complexity of the regulation. in order to test the relationship between trade and the score we proceed by simple correlation analysis. in figure 5 and 6, we can appreciate the position of both exporting countries in comparison to their own trading partners. it is interesting to note that the distribution of the phytosanitary score (ps) seem comparable in the two graphs: the group of european countries is always on the left of the distribution, while the group essentially composed by asian countries is, in both cases, on the right. this illustrates that european countries apply relatively looser regulatory restrictions compared to asian countries, regardless 49measuring the complexity of complying with phytosanitary standard of the source of exports. however, while it is obvious that france belongs to the group of european countries (as importer, figure 6), it is also important to note that chile as importer, belongs to the group of countries applying more complex regulations (as china, indonesia, taiwan, thailand, or the usa). the box plot in figure 7 shows the distribution of ps by region. in this figure the higher the boxes the more demanding the phytosanitary requirements between france or chile and their clients. first, we can observe that both exporters face similar average level of complexity by region. however, france is almost always facing a higher degree of variability accordfigure 5. ps country mapping (france). figure 6. ps country mapping (chile). 50 federica demaria, pasquale lubello, sophie drogué ing to the destination. this variability is at its maximum within the asian countries. more generally, the variability increases with the level of complexity. it is also interesting to underline the results obtained for african destinations: while the phytosanitary requirements are strongly homogeneous vis à vis chile, they are very heterogeneous for france. the next figures from 8 to 11 present the relationship between the importers’ complexity of phytosanitary constraints and exports. in order to reduce the high trade variability, we aggregate trade volumes by countries sharing similar or identical phytosanitary scores. in the case of france, we are able to distinguish 6 ranges. conversely, for chile we only have 5 ranges, because of the strong requirements’ homogeneity. figure 8 suggests that for france, the level of trade is, as expected, inversely related to the level of complexity in the sanitary requirements of its partner, and reaches zero in the case of a ban (maximum restriction). figure 9 shows that this result is globally confirmed, even when we eliminate extreme values, such as eu (no restriction) and bans (full restriction). however, results are quite different for chile. as figure 6 shows, the phytosanitary constraints imposed to chile by its trade partners are particularly homogenous (except for a few countries on the right side of the distribution). this strong homogeneity of the score does not allow us to discriminate between several ranges and therefore correctly test the correlation between trade flow and score value. therefore, although figures 10 and 11 show a negative and clear correlation between trade and the complexity in phytosanitary regulations (as for france), the results seem more difficult to interpret. in order to support our argument, we try to provide further analytical details about this topic. if we look at the trade between chile and north-american countries, we can see that, while the volume of apples from chile to the usa is important (103,000 tons on average between 2008 and 2013), this is not the case for mexico (8,000 tons on the same period). the reason has to be found in the stronger demand of mexican regulations. yet, although the usa and chile are located in the same continent (and thus closer in distance), chile exports more with the eu (347,000 tons in average between 2008 and 2013) figure 7. ps distribution by region (average and standard deviation). 51measuring the complexity of complying with phytosanitary standard than with the usa. we suggest that the cause can also be attributed to the stringency of the us regulations in comparison with those of the eu. another explanation could be found in the existence of a trade agreement between the two countries under scrutiny and their trade partners. table a1 in appendix shows the existence or absence of a trade agreement. the information suggests that for france the geographical proximity and the existence of a trade agreement often overlap and the link figure 8. ps value by range and volume of french apple export (2007-2013). figure 9. ps value by range and volume of french apple export (2007-2013) without eu countries. 52 federica demaria, pasquale lubello, sophie drogué between the existence of the agreement and the level of trade cannot be clearly traced. moreover, even if the eu (and therefore france) has signed a trade agreement with south africa, south korea and tunisia, french apples are still banned from these countries for phytosanitary reasons. for chile, it is slightly different. there is no particular overlapping between the existence of a trade agreement and proximity. but there is also no clear link between the absence of a trade agreement and the absence of trade. chile exports more apples to colombia, ecuador or peru where no agreement has been signed compared to brazil with figure 10. ps value by range and average volume of chile apple export (2007-2013). figure 11. ps value by range and average volume of chile apples export (2007-2013) without usa. 53measuring the complexity of complying with phytosanitary standard which an agreement has been signed. the same is true when the importer is farer: chile is able to export high volumes even without the existence of a trade agreement; it is the case with india, russia, saudi arabia, taiwan or the arab emirates (see table a1). 6. conclusion for a long time, france has taken the world leadership in the apple international markets. but the french competitiveness is short of breath. french exporters point at the increasing complexity of the phytosanitary rules governing fresh fruits trade, especially in asia and the usa. on the other side, chile, a growing stakeholder in the apple sector has seen its exports increase regardless of the destination. even if chile benefit from its off-season supply with respect to its main destinations (usa, europe, china), it seems generally less sensitive to the phytosanitary restrictions. using a synthetic measure, we studied the link between the level of french and chilean apples exports and the complexity of the phytosanitary requirements imposed by importing countries. analysing the regulations for more than 130 destinations (84 importing countries for france and 51 for chile), we were able to draw several conclusions. first, we observe that no significant difference between phytosanitary restrictions imposed to france and chile by destinations exists; therefore, the distributions of ps in figures 5 and 6 are rather similar for both exporting countries. second, there is no clear link between the existence or absence of a trade agreement between the two countries and their trade partners and their capacity to penetrate a specific market. third, we have yet underlined that the french and chilean positions inside the ps distributions is not the same. france belongs to the eu which is less demanding in terms of phytosanitary regulations, while chile belongs to the group of countries applying more complex phytosanitary regulations (as china, indonesia, taiwan, thailand or the usa). therefore, this difference in the relative phytosanitary positions of france and chile with respect to phytosanitary restrictions abroad, allows us to better explain why chile resists better to more demanding destinations in terms of phytosanitary regulations than france (see figures 7 to 11). french exporters suffer higher costs in complying with phytosanitary rules, especially when they are imposed by the most dynamic importing countries (as asian countries). for instance, french producers must make a greater effort in pest risk management in comparison to the chilean producers, when they want to export apples free from the mediterranean fly to china or taiwan. as emerging economies increase their consumption of fruits, with the increase in their per capita income, a new demand appears, especially in asian countries, opening opportunities for apple growers and exporters. however even if chile and france face regulations from asian countries (especially china, taiwan or india), its geographical location, the off-season nature of its production and its natural phytosanitary conditions (mediterranean fly free area) give the former an advantage in terms of capacity of compliance. in the chilean case, as their phytosanitary restrictions are very close to those imposed by asian countries or usa, it acts as a “common regulatory language”. it reduces asymmetries in pest risk management and facilitates 54 federica demaria, pasquale lubello, sophie drogué trade. thus, it is possible to understand why chilean exports to taiwan or usa coexist with high score value: once the constraints overcome, due to a learning effect or similarities in natural phytosanitary conditions, trade can unlock its potential. in the french case, phytosanitary restrictions imposed by asian countries or usa are the translation of really different natural and phytosanitary conditions. then the regulations imposed to france by third countries act as real barriers with high costs of compliance (and learning). these results, despite apparently opposed for france and chile, are both consistent with the economic literature on international trade and non-tariff barriers, and suggest once more that sanitary and technical regulations can facilitate as well as hamper trade causing redistribution in the market shares. aknowledgments this research has been financially supported by the french agency of research (anr) under the project n° anr-13-alid-004 sustain’apple. we want to thank the two anonymous reviewers that helped us improved our article. references anderson, k. 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(2012). the impact of regulatory heterogeneity on agri-food trade. the world economy 35 (special issue): 973-993. world trade organisation (wto) (2010). panel report out on apple dispute. retrieved from http://www.wto.org/english/news_e/news10_e/367r_e.htm. accessed march 2014. appendix table a1. ps by country of 20 selected destinations of france and chile apples. france chile country ps value trade average 2007-2013 existence of a trade agreement country ps value trade average 2007-2013 existence of a fta algeria 1.16 63,437 yes algeria 1.18 601 no angola 1.24 46 no bahrain 1.14 1,307 no australia 1.22 14 no belgium 1.18 5,756 yes austria 1.07 614 yes bolivia 1.18 14,974 no bahrain 1.24 365 no brazil 1.15 13,400 yes bangladesh 1.28 261 no canada 1.18 15,548 yes belgium 1.07 37,748 yes china 1.47 12,467 yes brazil 1.21 2,049 no colombia 1.15 68,894 no bulgaria 1.07 16 yes costa rica 1.14 7,810 yes canada 1.15 422 no cyprus 1.18 535 yes china 1.47 638 no denmark 1.18 1,952 yes colombia 1.28 965 no domin. republic 1.25 1,705 no costa rica 1.24 93 no ecuador 1.07 44,748 no cote d’ivoire 1.24 727 no egypt 1.18 5,378 no czech republic 1.07 449 no el salvador 1.14 4,841 yes denmark 1.07 12,580 yes finland 1.18 1,162 yes djibouti 1.00 24 no france 1.18 9,452 yes ecuador 1.20 57 no georgia 1.18 327 no egypt 1.08 1,619 yes germany 1.18 12,456 yes 56 federica demaria, pasquale lubello, sophie drogué france chile country ps value trade average 2007-2013 existence of a trade agreement country ps value trade average 2007-2013 existence of a fta equat. guinea 1.30 31 no greece 1.14 5,028 yes estonia 1.07 318 yes guatemala 1.18 5,403 yes ethiopia 1.24 0.1 no honduras 1.22 3,243 no finland 1.07 11,745 yes hong kong 1.18 8,716 no germany 1.07 56,902 yes india 1.47 20,605 no greece 1.07 96 yes ireland 1.18 2,530 yes guinea 1.20 254 no italy 1.18 10,135 yes honduras 1.24 0.1 no japan 2.72 0 no hong kong 1.00 1,968 no jordan 1.30 797 no hungary 1.07 54 yes kuwait 1.18 3,740 no iceland 1.07 65 yes latvia 1.18 360 yes india 1.19 339 no libya 1.14 2,568 no indonesia 2.72 1,126 no malta 1.18 495 yes iran 1.24 1,576 no mexico 1.51 8,053 yes ireland 1.07 20,274 yes netherlands 1.18 63,406 yes israel 1.46 1,021 yes norway 1.18 3,990 yes italy 1.07 3,952 yes oman 1.14 1,814 no jordan 1.28 213 yes panama 1.19 1991 no kazakhstan 1.18 103 yes peru 1.22 38,402 no kenya 1.28 199 no portugal 1.18 2,978 yes kuwait 1.20 2,226 no qatar 1.18 1,193 no latvia 1.07 72 yes russia 1.18 38,062 no libya 1.20 2,992 no saudi arabia 1.18 49,620 no lithuania 1.07 718 yes south korea 2.72 0 yes luxembourg 1.07 1,092 yes spain 1.18 21,593 yes malaysia 1.11 4,885 no sweden 1.18 5,573 yes maldives 1.00 276 no taiwan 1.47 41,995 no malta 1.07 7 yes turkey 1.18 2,266 yes mauritania 1.20 657 no uae 1.18 26,322 no mayotte 1.07 438 yes united kingdom 1.18 31,919 yes morocco 1.10 914 yes usa 1.51 103,697 yes n. caledonia 1.03 163 yes venezuela 1.18 28,416 no netherlands 1.07 66,287 yes nigeria 1.36 3 no norway 1.07 2,279 yes oman 1.24 2,631 no poland 1.07 644 yes portugal 1.07 25,520 yes romania 1.07 78 yes russia 1.21 26,118 yes saudi arabia 1.12 16,631 no 57measuring the complexity of complying with phytosanitary standard france chile country ps value trade average 2007-2013 existence of a trade agreement country ps value trade average 2007-2013 existence of a fta seychelles 1.20 56 no singapore 1.09 3,770 no slovenia 1.07 36 yes south africa 2.72 40 yes south korea 2.72 0 yes spain 1.07 101,845 yes sri lanka 1.28 49 no sudan 1.24 479 no sweden 1.07 9,104 yes switzerland 1.07 643 yes taiwan 1.47 287 no thailand 1.45 3,375 no togo 1.20 200 no tunisia 2.72 18 yes turkey 1.28 204 yes united arab emirates 1.10 16,033 no uganda 1.17 14 no united kingdom 1.07 132,141 yes uruguay 1.22 24 no usa 1.52 25 no venezuela 1.22 200 no vietnam 1.38 120 no 58 federica demaria, pasquale lubello, sophie drogué box a1. sps requirements description. 1. ban and territorial restriction. the ban forbids all exports of a product towards a third country. the ban may be justified either because of the presence of a quarantine organism in the country of origin but in the country of destination, as it is the case in tunisia or in south africa for french apples. furthermore, countries of destination can temporary refuse imports as in the case of the apples from usa in japan and from france in vietnam (see above). territorial restriction/quarantine organism restriction: the importing country can impose to its providers that goods crossing its borders originate only from specific parts of the country of origin where quarantine organisms are absent or under control. for instance, france has negotiated a protocol with indonesia which makes sure that only apples from the region «pays de la loire» can be exported. china and taiwan impose similar restrictions to chile. area restriction is then an actual trade restriction. 2. accreditation: is a more advanced form of territorial restriction. for instance, china or taiwan establishes a precise list of orchards, of storage and packing facilities, of exporters with the domestic sanitary authorities. the list of accredited organisms can be defined in different ways. in the simplest case it is the local authority (in france the sral) which compiles the list of producers complying with phytosanitary requisites and the importer only needs to approve or not the list. or, the importing country may decide to approve the list after the inspection of the producing units by its own inspectors. the frequency of inspections may vary according to what has been agreed upon by both parties. 3. the import permit (ip): this document is required by few countries imposing additional/reinforced inspections of goods. for instance, israel phytosanitary authorities require that 2% of the total french exports are examined by local authorities (sral). similar requests are addressed to chile by countries like honduras or bolivia. in both cases, it is a more demanding control compared to the one usually performed by national sanitary authorities to deliver the pc. it is for this reason that the results of ip’s inspections are quoted in the pc in the box “additional documents”. 4. the phytosanitary certificate (pc): in the simplest case (as it is the case for france vs. norway), the pc is obtained after a visual inspection by the sral of apples to be exported. thus, issuing the pc is equivalent to an inspection. in more complex cases, the pc must mention also all the additional inspections required by the importing country and certified by the sral (origin of the products, agreement, import permit, cold treatment etc.) 5. pre-inspection (or internal inspection): is an additional inspection required by a few countries among which usa and taiwan. it is also qualified as double internal inspection because it must be implemented by the storage/packing employees before and during the packing operations. this double checking must be validated by the national safety authority. 6. pre-clearance is an additional pre-shipment inspection required by the usa. the pre-clearance procedure must be performed by the aphis/usda inspectors and aphis/usda trained domestic inspectors (from the sral). moreover, the volumes of the sample intended for inspection are defined by the aphis/usda regulation and are larger than those usually required by the sral (it is the reason why the presence of the sral is necessary during the samples’ inspection). however, though we have to consider here the preclearance as a simple additional inspection, negotiations between usa and italy or new zealand show that pre-clearance is a heavier system of export control (2 or 3 inspections) which can coincide in the french case with a mix of pre-inspections and cold treatment. 7. the pre-cooling/fumigation: in case of the presence of the mediterranean fly in the producing country, some importers require that the exporter prove that before the loading of apples in the refrigerated container, the merchandise has already reached the temperature recommended by the regulation (pre-cold treatment) or has been subjected to fumigation (with methyl bromide). in this case the exporter requests the national safety authority to certify the apples have been subject to fumigation or pre-cooling during the storage and they have reached the temperature needed to start the cold treatment. 8. the cold treatment requires that fruits must be stored at a constant temperature between 0° and 4° for a period of 14 to 21 days to prevent contamination of products by harmful organisms. for all destinations requiring the cold treatment during the transit, the sral is requested to inspect and certify all the stages of loading in the refrigerated containers and the position of the sensors. the sral certifies the first stage of the process. 9. inspection at arrival: it is a final and additional (or unique) inspection performed by representatives of the local phytosanitary authority, which sets the volumes of the samples to be inspected. bio-based and applied economics 6(2): 115-118, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-21212 editorial aieaa discussion paper on the cap after 2020 the diversity of agricultural systems in the european union challenges policy makers asking for a renewed capacity to take into account the needs and the aspirations of all actors of the european countryside: farmers, consumers, institutions and territories. the future pac should carefully look at what can be considered today the “value added” that a european framework can produce for agricultural policy. transforming national and local specificities into shared resources may refresh the role of the cap as a fundamental laboratory for the european integration, 60 years after the signature of the rome treaties. as a scientific society deeply committed to the process of knowledge creation supporting the design, implementation and evaluation of the cap, the italian association of agricultural and applied economics (aieaa) wants to contribute to the reform process in the perspective of a dynamic and diversified countryside, where different forms of agriculture can contribute to the flourishing of european society. this discussion paper was prepared in spring 2017 by a working group composed by aieaa members who joined the public consultation on “modernising and simplifying the common agricultural policy (cap)” promoted by the european commission. the aim of the paper is twofold. it allows our association to contribute to the public consultation and it provides a starting point to feed the debate on the future of the cap. promoting a thorough reflection on the design and the development of a rural and agricultural policy, in connection with needs and expectations of italian and european citizens, is part of the aieaa mission. the hope is that this discussion paper may foster a wide and fruitful debate within both our scientific community and the european society. issues at stake in the current cap the functions that european people assign to agriculture are performed to a different extent and with a varying balance among different actors and territories. large and competitive farms ensure the security of the food supply chain and manage a major share of the rural space. however, even though this productive model represents the largest part of output and income produced by agriculture, other models of small or alternative agriculture exist. they provide both commodities and public/social goods, often in marginal and fragile areas, as in urban and peri-urban agriculture, in the growing sector of social farming, in forms of integration oriented to solidarity between farmers and consumers or in forms oriented to the sustainability of rural economies. such a variety of farming models are likely to follow different personal motivations and economic incentives. in front of this variety of european “agricultures”, the current framework of the cap 116 editorial mainly refers to an entrepreneurial-based and market-oriented model of farming, unsuitable to fit other forms of agricultural production and to adapt to marginal and disadvantaged rural areas. the european society faces a further decline of these forms of agriculture with the risk of losing much more in terms of public goods than in terms of private goods. as, for example, in case of the local economies and social cohesion in marginal areas, the conservation of valuable element of landscape linked to no longer profitable forms of cultivation, the countryside stewardship in rural areas. the current cap is a policy framework providing a number of objectives and instruments. not always there are clear evidences of the matching between actual outputs of the policies (involved beneficiaries, supplied financial resources, affected agricultural land, etc.) and expected impacts. this entails problems of overlapping of tools, conflicts among instruments and objectives, which create, in turn, problems of effectiveness and efficiency. the reasons of such a complex structure have to be found in the pressure of countries, stakeholders and the commission itself, each of which pursuing its own objectives. this translated into the use of multi-objective intervention tools (e.g. agricultural income support constrained to the reduction of environmental impacts) that, according to available empirical evidence, do not always work well. direct payments are the main policy tool within the current cap. they were introduced in order to move towards a less distortive support of production and trade, better targeted and more evenly distributed among farms and among territories. the main justification for the current european system of direct payments was tied to the provision of public goods that are better produced at the supra-national level. the last cap reform 2014-2020, however, shaped a complex system of payments to support agricultural income as well as to remunerate specific behaviours or specific status of farmers. this produced a poor consistency between the goals of support pursued (competitiveness of european agriculture, support of agricultural income and provision of public goods) and the instruments applied (direct payments linked to the hectares of owned land). in particular: • the increased income variability, due to the joint effect of market price volatility and climate change trends, limits farmers’ ability to plan investments. although direct payments tend to increase investment propensity, they represent a rent when product market prices are high, but are insufficient to ensure a fair income in case of low prices. • instruments proved to take scarcely into account the territorial differences: the same rules apply for the mountains and the plains, urban and rural areas, different climate areas. the measures devoted to less favoured areas, in particular for agriculture in mountain areas, showed to be ineffective and remained almost unapplied. an insufficient endowment of financial resources, together with a lack of coordination with measures activated under the second pillar of the cap, mostly explain their poor performance. • the green payment, which once again does not take into account the structural diversity of farms, showed to be scarcely effective in addressing the environmental issues, translating into a small environmental effect. • the rules followed in determining individual support do not respond to a fair distribution of the financial resources among farms. small farms are substantially not 117editorial considered inside the cap and they are strongly discriminated with respect to large and intensive farms. further, the policy does not take into account the total income of agricultural households, reducing the effectiveness of targeting of income support. • the mix of income support, incentives and complex regulations make transaction costs in the sector very high. a large share of financial resources ends up in the hands of intermediate agents that successfully lobby to maintain the status quo. • compliance rules and bureaucratic commitments that are sustainable for a large and structured farm, easily becomes a complex and burdensome red tape for small farming. • the land-ownership linked system of direct payments affects land prices reducing land access opportunities for younger farmers. this in turn reduces the effectiveness of policy fostering innovation in agriculture. recommendations for the reform the future cap should match agriculture and food-related objectives with higher level eu and country objectives in an increasing complex, rapidly changing and globalised environment. in this context, a major issue should be the need for a better coordination between the cap and closely related policies, such as those for food &health, the promotion of bioeconomy, the generation and diffusion of innovation, policy for environment and climate change and those affording social issues (e.g. migration, youth condition). innovation will be a key topic for the future eu strategy and should be taken in higher consideration within the future cap. this may include not only measures encouraging innovation adoption by individual farms, as well as by integrated supply chains, but also improving the organization of the agricultural knowledge innovation system. social innovation as well as technological innovation should be considered. entry into the sector (especially by young farmers and new players) and investments should be better connected to innovation actions within the future cap. a better harmonization between cap and national policy should be pursued. a fair minimum standard of living for farmers and other rural people should be assured by fiscal and social policy at the national level. the cap should rather consider factors affecting agricultural income that cannot be addressed neither at the individual nor at the regional or national level, such as price variability and increased environmental risks, and provide a common ground for food safety, food and nutrition security within the common european market, and trade policy. a critical issue is to ensure coordination and critical mass to the adopted measures. in this respect, incentives should be increasingly provided to communities or collective initiatives rather than to individual farmers, switching from a “cap of farmers” to a “cap of territories”. this approach could increase the resilience of rural systems in providing public along with private goods, as the former are often local and their provision requires coordination among the agents from a given area. a territorial approach, involving different subjects on common projects and goals, should characterize the future policy framework. a simplified cap should be more and more based on a contractual approach in favour of whoever is able to achieve the objectives set (certain, measurable and based on a 118 editorial national strategy). market or local community-based provision of public goods should be pursued whenever feasible also with new approaches to regulation. the conservation of the “diversity” of rural and agricultural systems should be included among the cap goals. this would include a clearer vision of diversity among systems and within each system, in which a relevant issue asking for diversified actions is the different role of professional market-oriented farms and other types of farm. the forms of support must be increasingly differentiated according to farm types. professional farms are in charge of producing food and other agricultural commodities in a competitive and sustainable way; for these farms the cap must mainly provide measures to stabilize agricultural income and employment, and encourage investment in innovation and aggregation. conversely, the public function of alternative forms of farming strengthening rural society and protecting the environment should be recognized within the cap and encouraged within territorial projects. the cap should increase its flexibility in quickly addressing the changes in the international economic and social environment. more freedom to adapt the common policy to national, regional and local conditions should be ensured along with an adaptive approach to administrative procedures, with the aim of enhancing the effectiveness of measures and reducing the red tape costs, especially in the rural development policy. at the same time a stronger link to environmental impacts that are more easily measurable at the territory level (with incentives for those governments able to reach the targets) should guarantee a fair commitment of the national, regional and local governments on sustainability agriculture labour issues and social objectives should be strengthened in the cap (migrations, remote areas, entry of new farmers) and regulation in labour and land use should be improved in order to achieve better sustainability. among the cap goals also the maintenance of employment in remote and internal rural areas should be included. a relevant issue not well addressed by the current cap concerns the organization of farmers and the low bargaining power of farmers in the supply chain. strengthening the coordination of the supply chains will need further efforts. the empowerment of farmers within the supply chain should be pursued together with an increased involvement of consumers as active players in the agri-food system. a cap of territories may support the link between healthy food diet programs and local agri-food chain strategies, or may introduce incentives and training opportunities addressed to consumers for improving the demand for agricultural products with eu certification (e.g. pdo, pgi, organic products) and the sustainability of consumer behaviour. at the same time, chain coordination is a key driver to competitiveness on international markets; in this respect it should be prioritised, promoting consistent actions of market penetration, protection of origin and innovation. bio-based and applied economics 6(2): 119-137, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-19112 the productivity and environment nexus with farm-level data. the case of carbon footprint in lombardy fadn farms edoardo baldoni, silvia coderoni*, roberto esposti department of economics and social sciences, università politecnica delle marche, ancona italy date of submission: 2016 28th, september; accepted 2017 13th, june abstract. this paper aims to assess whether and to what extent environmental and productivity affect each other within heterogeneous farms. the analysis concerns the sample of fadn lombardy farms observed from 2008 to 2013. using the fadn information on production structures and activities, a productivity index (total factor productivity tfp) and an environmental indicator (emission intensity ei) are properly reconstructed at the farm level. the nexus between tfp and ei is then investigated by admitting heterogenous behaviour across farm sizes and specializations. results show that the relationship between tfp and ei is not univocal and suggest that the mitigation of ghg emission can be based on the diffusion of the best practices adopted by high-productivity farms of different size and specialization. keywords. total factor productivity, ghg emissions, fadn, farm-level indicators. jel codes. o13, q12, d24. 1. introduction the main challenge faced by the european agriculture in the early 21st century is how to increase production in order to respond to the significant growth in global food demand while preserving natural resources and the environment. however, assessing to what extent eu agriculture is really moving along this innovative path of, at once, higher productivity and higher environmental sustainability (i.e., better economic and environmental performances), remains a complex methodological challenge. productivity gains are typically measured as total factor productivity (tfp) growth (oecd, 2001a; european commission, 2014), but tfp measures do not account for nonmarketable inputs and outputs. this could lead to a systematic bias in productivity calculations and incorrect policy conclusions as non-marketable goods are important compo*corresponding author: s.coderoni@staff.univpm.it 120 e. baldoni et al. nents of the contribution agriculture gives to overall social welfare (oecd, 2010). nonetheless, some of these environmental effects used or produced by agricultural activities can be precisely measured by appropriate environmental indicators in order to accompany the tfp and provide a more comprehensive representation of the agricultural sector’s performance. this is the case of the agricultural greenhouse gases (ghg) emissions. as the long-run relationship between agricultural tfp and ghg has relevant policy implications, it has been already investigated in the case of italian regions using aggregate (i.e., macro) data (coderoni and esposti, 2013 and 2014). however, the analysis at the micro level remains, to our knowledge, unexplored. such a farm-level viewpoint could provide a more insightful perspective on the production implications of agricultural ghg mitigation. in fact, both in tfp and ghg emission calculation at the macro level, significant aggregation bias can occur eventually obscuring the strongly heterogeneous farm-level relationship between economic and environmental performances. whether and by how much productivity and environmental performance affect each other, and to what extent this influence varies across different farms is an empirical issue. this paper aims to answer these questions using fadn data. the proposed approach firstly computes farm-level tfp and ghg emission measures. secondly, the nexus between the two is assessed econometrically. the investigation is performed on the balanced panel of lombardy fadn farms observed over the period 2008-2013. the rest of the paper is structured as follows. section 2 introduces the topic and overviews the relevant recent empirical literature in this respect. section 3 illustrates the fadn panel dataset and the methodology adopted to reconstruct the tfp index and the emission intensity (ei) at the farm-level. section 4 presents the results of the estimated farm-level relationship between tfp and ei. section 5 discusses the main policy implications of these results and concludes. 2. the productivity and environment nexus: the micro-level evidence the need for a new impulse to productivity growth in western modern agriculture is one of the motivation that led the european union (eu) to launch the european innovation partnership for agricultural productivity and sustainability (eip-agri) in 2012 (european commission, 2012). eip aims to build a bridge between science and the practical application of innovative approaches, with the purpose of addressing the most fundamental challenge faced by european agriculture in the early 21st century: satisfying the expected growth in global food demand, while conserving natural resources and the environment (european commission, 2012). tfp measures productivity as the ratio between an index of total commodity output (crop and livestock products) and an index of total inputs used in production (i.e.: land, labour, capital, and materials). hence, an increasing tfp implies that more output is being produced from a given bundle of agricultural resources (fuglie, 2012 and 2015). however, a major drawback of the conventional tfp measurement is that it only accounts for those inputs and outputs for which there are observable market transactions, while non-marketable resources or outputs are not considered. among these non-marketable goods, agricultural production involves, on the input side, the use of natural resources and, on the output side, the generation of environmental impacts. disregarding non-marketable goods 121the productivity and environment nexus with farm-level data in agricultural tfp estimation may induce systematic biases in productivity calculations and, thus, incorrect policy implications (oecd, 2014). moreover, according to fuglie et al. (2016), the appropriate metric for sustainable agriculture should have the property of spatial and temporal variance. in other words, it should be defined at that “natural” scale at which ecosystem processes are affected by agricultural production. as a matter of fact, many environmental factors are highly scale dependent, thus they affect productivity differently depending on the scale of measurement (fuglie et al., 2016). if a too aggregate scale is followed (e.g. the national level), we incur the potential risk of concealing relevant regional or local differences, thus failing in detecting those specific conditions where agriculture is actually unsustainable from an environmental perspective. more importantly, this aggregate scale is not able to assess how farm-level behaviour and choices affect productivity (tfp) and environmental (ei) performances. for instance, rather than being the consequence of a generalized technological improvement, the aggregate tfp growth can be the result of a number of farms entering and exiting agriculture or moving from one type of farming or specialization to another. therefore, working with micro data allows to better detect the nexus between productivity and environmental performances highlighting how these performances vary across space. firm heterogeneity is an essential aspect when this kind of performances is investigated regardless of the sector. however, this aspect is even more critical in the case of agriculture and, in particular, within the italian agricultural sector where large farm heterogeneity and very different productivity levels are observed (esposti, 2011). recent empirical literature on agricultural productivity growth has focused on farmlevel analysis (kimura and sauer, 2015; sheng et al., 2015). nonetheless, empirical studies on the nexus between agricultural productivity and environmental performances using farm-level data are rare and only focus on specific farm typologies (serra et al., 2014). in this respect, more evidence has been reported outside the agricultural sector. cui et al. (2016) analyse productivity, export and environmental performance in us manufacturing firms and find that more productive and export-oriented facilities also show a significantly lower emission intensity. a similar negative relationship between production and environmental performance is found by batrakova and daves (2012) and forslid et al. (2014). other recent studies, however, suggest that the firm-level relationship between emissions intensity and productivity may be more complex (see, for instance, barrows and ollivier, 2014, in the case of indian manufacturing firms). the most relevant evidence emerging from this empirical literature, however, is not a productivity-emission relationship of a general validity, but rather how strongly this relationship may differ across sectors and firms’ typologies. consequently, the main advantage of micro data consists in better capturing such heterogeneity. this seems particularly true in the case of agricultural ghg emissions. as noticed by coderoni and esposti (2014), the dynamic of agricultural ghg emissions depends on two fundamental effects: the scale effect that makes the emission always growing with the size of the farm, and the production technology effect, that may either reduce or increase the emissions. this latter effect is the combination of different forces: technological change, strictu sensu, and the change of agricultural output composition. both forces influence, at the same time, agricultural ghg emissions and productivity and, therefore, the long-term relationship between the 122 e. baldoni et al. two. working with micro data is thus helpful to distinguish the role of the production scale (farm size) and of production technology on these emission performances. 3. farm-level performances 3.1 the fadn sample in the present work, the reconstruction of the farm-level tfp and ei measures is performed on a balanced panel of 345 fadn farms of one of the largest italian region (lombardy) observed over the period 2008-2013. it is worth reminding that the fadn sample is not fully representative of the whole national agriculture. the reference population from which the fadn sample is drawn excludes a significant (in terms of numerosity) amount of italian farms, those with an economic size (es) of less than 4,800 euro of yearly standard gross margin. in this respect, the fadn sample is only representative of a sub-population of italian farms that can be here refereed as professional or commercial farms (sotte, 2006). the choice is here made to limit the analysis to lombardy not only for the relevance of this regional agricultural sector in terms of production and ghg emission. more importantly, this region represents a good compromise between maintaining geographical homogeneity, particularly avoiding the huge differences of farming conditions in the north and in the south of the country, and preserving large heterogeneity across farm typologies. lombardy’s agriculture presents farms operating in mountainous and flat areas, extensive and intensive production processes, very different production specializations also in terms of ghg emissions (e.g. rice and dairy farms are widely represented within this sample). the use of these micro data to compute tfp and ei performances is the main novelty of the present study but it also implies several empirical challenges. 3.2 the farm-level tfp index in the present study, we derive the farm-level tfp measure using the index number approach (oecd, 2001a; fuglie, 2015). this approach is here preferred to other methodologies because it is relatively simple and reproducible and can be used to create comparisons across farms over time (baldoni, 2017). according to this methodology, the tfp index is computed as the ratio between a transitive output index and a transitive input index. transitive output and input indices are obtained using the minimum spanning tree method proposed by hill (2004). the fisher formula is used to create bilateral comparisons. see annex 1 for further details on this tfp calculation. table 1 reports the summary statistics of the computed farm-level tfp indices by specialization and economic size. these statistics clearly highlight the heterogeneity of the productivity performance within any group of farms. in terms of specialization, the farmlevel tfp index shows a higher median in dairy farms followed by rice and wine farms. farms specialized in arable crops, horticulture, mixed crops and livestock, and grazing livestock show highly dispersed tfp levels around the median values. largely heterogeneous productivity performance is observed also in terms of es, though there seems to 123the productivity and environment nexus with farm-level data be a positive relation between size and tfp index. larger farms are those with a higher median tfp value followed by medium-sized and small-sized farms. 3.3 the farm-level ei index the environmental performance of agricultural production is multidimensional as farming activity involves several environmental goods and generates diverse positive and negative externalities. within the latter, the environmental impact of agriculture includes among others: soil erosion, chemical residuals, nutrient leaching and ghg emissions. however, the interest here is only on farm-level ghg emissions. sustainability will be thus intended, henceforth, in a restricted sense, with exclusive reference to the farm-level environmental performance and, in particular, to the specific aspect of ghg emissions. the focus on ghg emission depends on both technological and policy arguments. from a policy perspective, climate change mitigation has become one of the most important and most controversial objectives of the international political agenda (gerber et al., 2013). in the eu, in particular, the climate policy sets ambitious mitigation targets also for agriculture (european commission, 2011 and 2012) and the recent common agricultural policy (cap) reforms were expected to put forward instruments and incentives to reach these targets (european council, 2014). from a technological point of view, farm-level ghg emissions actually summarize a whole set of production choices with environmental implications (e.g. use of fossil fuel and fertilizers, livestock breeding and land use changes). moreover, measuring these emissions with a unique aggregate indicator (see annex 2), we can easily express how much an table 1. summary statistics of the computed tfp index by farm specialization and economic size. tfp min median max type of farming: dairy 0.035 0.554 4.693 rice 0.062 0.455 3.967 wine 0.023 0.205 1.339 arable crops 0.022 0.204 2.993 mixed crops and livestock 0.035 0.201 4.222 cereals 0.009 0.175 1.420 fruits 0.014 0.164 1.365 grazing livestock 0.015 0.154 1.707 horticulture 0.002 0.136 4.32 granivores 0.007 0.095 2.067 economic size (es): large 0.007 0.562 4.693 medium 0.014 0.310 4.222 small 0.002 0.124 1.250 124 e. baldoni et al. individual farm contributes to global warming regardless its locations. the fact that ghg emissions are less scale (and territorial) dependent than other environmental indicators (e.g. eutrophication and erosion of soils can have different impact depending on the location) (oecd, 2001b) facilitates the comparison of environmental performances across heterogeneous farms. taking only one environmental aspect into account could provide an incomplete representation of the farms’ environmental performance particularly when there could be trade-off between ghg emissions and other environmental indicators (buratti et al., 2017; laurent et al., 2012). therefore, the ghg emission performance is not assumed here as a comprehensive indicator of the whole environmental impact of farming, but only of the contribution of the farm to global warming. at both the european and global level, the main concern in this respect is how to reduce or limit agricultural ghg emissions without affecting productivity, i.e. without increasing costs or decreasing output. studying the joint ghg and productivity performances can thus be particularly informative. to reconstruct the farm-level ghg emission, we have adapted the intergovernmental panel on climate change (ipcc) methodology (ipcc, 2006) using activity data connected to agricultural production. ipcc standards represent well-established international criteria and protocols, which can be used also to achieve a proper farm-level indicator of ghg emissions (dick et al., 2008; coderoni and bonati, 2013). methane (ch4), nitrous oxide (n2o) and carbon dioxide (co2) emissions are estimated from the following source categories: livestock production, soils, land use, fuel and fertilizers. these different farm-level ghg emissions are then summarised into a unique indicator here called, for the sake of simplicity, the farm carbon footprint (cf). see annex 2 for a more detailed description of the methodology used. in applying the ipcc methodology, the main novelty of the present work with respect to previous studies (coderoni et al., 2013; coderoni and esposti, 2015) consists in the adoption of a farm-specific emission factor (ef), that varies according to farm characteristics or management practices (i.e. more or less intensive management of livestock). due to the limited data availability, this has been possible only for emissions from enteric fermentation of two animal categories (bovine and sheep).1 nonetheless, this emission source is the most relevant at national level as it accounts for 45.6% of total national ghg emissions in 2013 (ispra, 2015). table 2 reports minimum and maximum values of emission factors calculated with this farm-specific methodology. data show large differences with respect to national values. this reflects the importance of the farm specific factors affecting the ef, that vary across farm typologies and sizes.2 table 3 reports the consequent farm-level average cf expressed in tonnes of co2e (see annex 2) and distinguished among its five emission categories. 1 with respect to the other livestock categories, default ef have been used for enteric fermentation of swine, whose contribution is in fact negligible, while in the case of poultry emissions from enteric fermentation are null. 2 among these farm-specific factors we can mention the average age and weight of animals, quantities of milk produced, presence of grazing animals etc. the large variation between minimum and maximum value per livestock category reflects different sizes of the animals included in broad categories (i.e. cattle category includes even lambs). 125the productivity and environment nexus with farm-level data table 2. minimum and maximum values of the ef calculated at the farm-level for cattle and sheep. (kg ch4 head-1 year-1). livestock category: national values 2008 2009 2010 2011 2012 2013 min max min max min max min max min max min max cattle–male 47.5 2.00 90.4 2.00 86.9 2.00 68.5 2.00 68.50 2.00 68.5 2.0 72.3 cattle–dairy 134.2 60.6 199 51.9 214 56.7 284 57.1 182 57.8 181 54.3 174 cattle-female 47.5 2.00 69.6 2.00 75.9 2.00 68.3 2.00 65.1 2.00 39.1 2.00 43.7 sheep (>1 year) 8.0 4.60 14.3 1.60 13.6 4.60 13.2 4.60 10.3 1.60 16.7 2.30 16.7 sheep (<1 year) 8.0 1.60 10.1 1.60 9.20 1.60 10.5 3.40 9.20 3.40 17.7 1.60 17.7 table 3. farm-level cf distinguished into the five macro emission categories (avg. ton co2e per farm).   2008 2009 2010 2011 2012 2013 % median yearly variation cf livestock 343 367 347 355 346 363 -0.31 cf soils 50.8 51.9 54.7 56.6 51.3 46.3 -0.64 cf fertilizers 30.3 26.1 30.3 29.7 33.0 31.1 -1.52 cf energy 37.7 41.7 34.3 38.5 42.8 39.9 0.01 cf land usea -6.09 -6.50 -6.12 -6.60 -6.46 -6.28 -4.25 cf total 269 282 271 276 274 272 -1.03 a negative sign indicates that there is a removal of emissions due to carbon sequestration. some regularities clearly emerge. values are higher than other studies on cf at the farm level using fadn data (coderoni and esposti, 2015), reflecting a change in the methodology and an increase in emission sources analysed (e.g. urea application, pasture, manure distributed in fields, etc.). the cf associated to livestock largely represents the most important emission source. soils fertilizers and energy follow. nonetheless, the value of cf associated to energy also deserves attention as this source is often disregarded in the empirical studies on agricultural ghg emissions (coderoni and esposti, 2014). in fact, the ipcc methodology attributes it to the energy sector rather than to agriculture. the cf associated to land use (carbon sequestration) is almost irrelevant compared to all other categories. it is worth reminding, however, that here this source only considers agricultural land use since, as detailed in annex 2, most forestry-related activities are not included due to the lack of appropriate and complete information in the fadn dataset. for most categories, slightly declining emissions are observed, with the only exception of energy. this evidence seems to confirm the reduction of overall ghg emission observed within the italian agriculture in the same period (-5.04%) (ispra, 2015). in order to relate emission performance to the scale-independent tfp measure, the cf has been divided for the farm standard output (so), obtaining the ghg emission intensity (ei), i.e. the level of ghg emitted to produce a unit (€) of so. evidently, the scale effect always makes emissions grow with the size of the farm, but here the interest 126 e. baldoni et al. is in assessing whether scale matters in relative terms, i.e. the larger the farm, the higher (lower) the tfp and/or the ei. table 4 reports descriptive statistics of the evolution of ei over time and across farm typologies and sizes.3 it emerges that variability is significantly reduced when a size-dependent indicator is used. nonetheless, physical size still matters: the greater the farm’s utilized agricultural area (uaa), the larger its ei. however, as also confirmed by the negative correlation coefficient, this evidence is not as much clear in terms of es especially because larger farms show sharper decline over time. among agricultural specializations, rice producing farms have the highest ei.4 activities associated to livestock show high ei, as well, but a much sharper declining trend. with the exception of arable crops, all specializations show a declining ei over time. this decline is very relevant for wine and fruit producers whose performances, in fact, are 3 it is worth noticing that the remarkable variation of the ei observed in some cases (years and specializations) is not the consequence of a major change in the ghg emissions but it rather depends on the large variation of the denominator (the so) due to the intense price variations occurred during the period of observation. 4 rice cultivation is relevant in lombardy (32 farms in the balanced fadn panel) and farm size is particularly high, with medium to big farms and 60 ha of average rice uaa. table 4. 2008-2013 evolution of the farm-level emission intensity across different farm typologies (kg co2e/€). 2008 2009 2010 2011 2012 2013 % median yearly variation economic size (es):               small 2.070 2.272 1.159 1.132 1.330 1.145 -6.6 medium 2.434 2.263 1.562 1.567 1.630 1.610 -5.1 big 2.906 2.906 1.479 1.562 1.563 1.446 -5.0 correlation coefficient es-ei -0.082 -0.051 -0.089 -0.080 -0.098 -0.090   physical size (uaa):               uaa < 10 ha 1.649 2.066 0.927 0.904 0.892 0.852 -13.9 uaa 10-50 ha 2.571 2.411 1.420 1.422 1.572 1.430 -4.5 uaa > 50 ha 3.337 3.087 2.193 2.336 2.422 2.397 -2.1 correlation coefficient uaa-ei 0.204 0.112 0.346 0.231 0.343 0.374   type of farming: rice 5.555 5.705 4.257 4.517 4.512 4.168 -1.4 dairy 4.096 3.952 1.832 1.789 1.828 1.826 -4.6 grazing livestocka 3.382 3.034 1.688 1.663 1.866 1.826 -4.1 mixed crop and livestock 2.379 2.381 0.899 0.864 1.059 0.824 -9.3 cereals 1.303 1.504 1.096 1.142 1.291 1.167 -2.3 arable crops 1.094 0.905 0.919 1.056 1.375 1.154 1.9 granivores 0.851 0.909 0.379 0.390 0.317 0.319 -6.7 horticulture 0.466 0.644 0.211 0.369 0.309 0.359 -1.9 fruits 0.293 0.299 0.248 0.077 0.158 0.104 -61.7 wine 0.206 0.418 0.134 0.082 0.167 0.304 -67.1 a grazing livestock contains bovine, sheep and goats. 127the productivity and environment nexus with farm-level data strongly affected by few farms with very large variations in fertilizers and land use ghg emissions. juxtaposing tables 1 and 4 some evidence in favour of a relationship between the two performances seems to emerge. farm size (smaller farms show lower productivity, but also lower ei) and farm specialization (intensive livestock farms often show high productivity and higher ei) matter for both tfp and ei. nonetheless, a more appropriate statistical assessment is needed to conclude whether, to what extent and in which direction, such a nexus between tfp and ei actually exists. 4. farm-level nexus between tfp and ei the micro level assessment of the relationships occurring between tfp and ei can be very informative about the existence of synergies between productivity growth and ghg mitigation, i.e. the so-called win-win mitigation strategies (unfccc, 2008). looking at the correlation coefficient between the two performance variables (see annex 3), it would emerge that such a positive synergy does not occur since a positive correlation between productivity and emission intensity is observed. at the same time, however, results also indicate that the nexus between ei and tfp is largely heterogeneous across farms. to more properly assess this nexus, here we assume that the farm ei influences its farm tfp. the argument underlying this influence is that the ei can be considered as a sort of proxy or a determinant of the farm technological level. this relationship is specified with the following polynomial functional form (quadratic), also including variables expressing the farm size: tfp ei ei d s s ei s eiln * *  it it it k k t k m m it m m m it m it m m it m it it 2 , , , , 2∑ ∑ ∑ ∑α β γ δ θ π ε( ) = + + + + + + + (1) where i indicates the generic i-th farm and t the generic t-th year. in (1) tfp is the farmlevel tfp, ei the farm-level emission intensity, dt are time dummies, s are dummy variables expressing whether the i-th farm is small, medium or large), ε is the usual spherical disturbance. α, β, γ, φk, δm, θm, are unknown parameters to be estimated. (1) is a conventional linear regression model and can be properly estimated via ols estimation. results are reported in table 5. the existence of a nexus between ei and tfp seems to be confirmed by statistically significant parameters associated with ei and ei2. however, this nexus differs across farms depending on their es. in particular, it is weaker for smaller farms. this relationship, however, is not only dependent on farm size but it is also non-linear and non-monotonic. this emerges clearly if we plot the estimated functional relationship relating tfp to ei. this is done by replacing in (1) the observed independent variables (ei and the dummies) and the respective estimated parameters, and then computing the consequent tfp. the estimated relationship takes an inverted-u shape (figure 1). this occurs for all farm sizes but it’s more evident for medium and large farms. this means that a win-win combination of productivity and sustainability (in terms of ei) is feasible. the inverted-u shape curve suggests that better productivity performances can be still obtained with lower ei. all the points on the left side of the curve’s inversion point (s) thus represent a bench128 e. baldoni et al. mark in terms of environmental sustainability for those that are on the right side. this result is not new in the agricultural sector where farm structures and management techniques are various and complex and, as several international studies on the subject suggest (unfccc, 2008), there is no one-size-fits-all solution to ghg emission mitigation. 5. policy implications and concluding remarks achieving higher productivity levels while preserving environmental resources is a major challenge for the european agricultural sector in the coming decades. this work table 5. ols estimation of model (1) (standard error in parenthesis). coefficient estimate a -1.886 * (0.090) b -0.009 (0.064) g 0.079 (0.065) φ_2009 -0.009 (0.064) φ_2010 0.079 (0.065) φ_2011 -0.043 (0.065) φ_2012 -0.074 (0.065) φ_2013 -0.094 (0.065) δ (medium size) 0.412* (0.097) δ (small size) -0.238* (0.091) θ (medium size) -0.679* (0.087) θ (small size) -0.904* (0.072) π (medium size) 0.079* (0.015) π (small size) 0.107* (0.012) r2: 0.367 observations: 2070 (345 farms, 6 years) * statistically significant at 5% level 129the productivity and environment nexus with farm-level data aims to analyse the relationship between ghg emissions and productivity at the farm level. this micro level of analysis, represents the main originality of the study. it seems to be the most appropriate scale to assess the nexus between productivity and emission performance, as it better captures the possible heterogeneity of this nexus across different farm typologies, that would have been missed with aggregated analysis. results confirm the large heterogeneity of farm performance, supporting the need of farm-level approaches. the analysis here presented states that a nexus between ei and tfp actually exists, but it is not univocal. it differs across farm sizes and, within a given size, it is not monotonic. thus, high-productivity and low-emission farms can coexist with farms showing both high tfp and high ei. if this evidence were confirmed for other regions, or at the national scale, it would have critical policy implications. in fact, several studies concerning the linkage between sustainability and productivity with micro data (see section 2) would indicate that the highest productivity firms are also the most sustainable in terms of environmental performance. this would occur because best technologies imply both higher productivity and lower emissions. these findings would induce the policy recommendation that fostering productivity is also going to increase, in turn, environmental sustainability at aggregate level. results here presented, however, give a more complex picture. it is confirmed that there is no inevitable dualism between productivity and sustainability. at the same time, there are farms with high productivity that also show poor emission performances. thus, in these cases, raising productivity might not lead to greater sustainability. an appropriate policy for agricultural ghg emissions mitigation should then stimulate the diffusions of best practices that combine high tfp with low ei. previous studies always suggested even for the italian livestock sector (coderoni et al., 2015), the possibility of introducing mitigation techniques that are able to reduce emissions with very low or even negative costs (i.e. savings). these mitigation actions reveal that there are more efficient ways to produce the same output. this kind of actions can be very important in figure 1. the tfp and ei nexus for large, medium and small farms. 130 e. baldoni et al. reaching climate change mitigation targets, without affecting farm productivity and, therefore, income. a key implication of this result is that some mitigation measures may be somehow selffinancing, thus sustainable in economic terms. whenever farms experience the combined productivity and environmental effects of these measures, the respective policy support can be gradually decreased and even eliminated. in particular, agri-environment climate measures in the rural development policy seem to be suitable to spread best practices in the mitigation approach, e.g. promoting instruments that represent incentives to the farms to adopt climate friendly techniques. under this win-win result, however, this support can be progressively reduced as these techniques will spontaneously spread across farms. results obtained in this study are interesting also from another perspective. as the eip-agri views productivity and environmental sustainability as a unique major objective for the eu agriculture of next decades, it would be particularly helpful to have a unique indicator of these joint performances. this can be achieved with an environmentally-adjusted tfp (eatfp), also known as total resource productivity (trp) (fuglie et al., 2016), which relies on the concept of joint production (use) of marketable and nonmarketable outputs (inputs). this indicator is relevant also in an international policy perspective as the oecd (2014) includes it among the key indicators for monitoring progress towards green growth in agriculture. the present work represents thus just an initial step in the direction of such a joint indicator of economic and environmental performance of the farm level. in this respect, however, results are relevant and encouraging. they suggest that the farm-by-farm correction of tfp with a ei indicator, could be not univocal, i.e. not invariant with the farm structure. in particular, this correction would be more important for smaller than larger farms since, for the same ei, the latter usually show a lower tfp. on the possible extension of the present analysis towards the calculation of a tfp adjusted for the farm ei, future research is expected to provide significant steps forward. 6. acknowledgements authors would like to thank marina vitullo and eleonora di cristofaro (ispra) for the ghg emission reconstruction and antonio giampaolo, davide longhitano and antonella bodini for the fadn dataset. authors are listed in alphabetic order. authorship may be attributed as follows: sections 1, 3.2 and annex 1 to edoardo baldoni; sections 2, 3.3, 5 and annex 2 to silvia coderoni, section 3.1, 4 and annex 3 to roberto esposti. references baldoni, e. 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(2006). imprese e non-imprese nell’agricoltura italiana. politica agricola internazionale, 1: 13-30. unfccc‐united nation framework convention on climate change (2008). challenges and opportunities for mitigation in the agricultural sector. technical paper fccc/ tp/2008/8. bonn. annex 1. methodology for the tfp calculation productivity measures are here derived using the index number approach. index numbers are a very useful tool widely used in the literature on productivity analysis because they are relatively simple to compute and possess a number of desirable properties (for example, formulas can be derived from microeconomic theory under certain assumptions). however, an important issue arises when using these formulas in cross-sectional or panel comparisons. the use of binary indices to compare each possible pair in the dataset yields a matrix of binary comparisons that might not satisfy the property of transitivity (rao et al., 2002), i.e., a direct comparison between two farms might not be equal to the indirect comparisons of the two through a third one. this property is extremely important because it ensures the internal consistency and the uniqueness of results (hill, 2004). to address the issue of transitivity in this analysis the minimum spanning tree method proposed by hill (1999) has been used. this method is based on chaining a sequence of bilateral comparisons. chaining is typically applied when making chronological comparisons because it exploits the natural ordering of chronological observation. however, in a cross-sectional or panel data settings such a natural ordering does not exists and needs to be identified in order to construct the chain. here we followed hill (1999 and 2004) who suggested to identify the ordering by selecting the most reliable among all possible bilateral comparisons. the author also suggested the use of the paasche-laspeyres spread (pls) to quantify the reliability of comparisons across farms. the pls is a distance function that is zero in the case the vectors of quantities or prices of two farms are proportional. the spread will be small whenever the production structures of two farms are similar, i.e. in the case two farms supply similar productions or set similar prices. after the minimum spanning tree is identified, the fisher index is used to chain bilateral comparisons and derive transitive output and input indexes. productivity measures are then obtained using the hickmoorsteen approach defined as a ratio of an output quantity index on an input quantity index (coelli et al., 2005). 134 e. baldoni et al. the output index is created using the information on the 137 crop and livestock products of the lombardy fadn panel sample here used. the italian fadn dataset contains information on the respective quantity produced and total values. prices are obtained by deflating total values by the quantity produced. all values are recorded in current value and converted into 2008 constant value by using eurostat agricultural price indexes. the input index aggregates the following factors of production: labour, fertilizers, pesticides, external services, water, energy, seeds, feeding stuff, capital, land, reuses and other costs. with respect to labour, the italian fadn dataset records information on hours worked, and salary for all workers (occasional workers, fixed-term contract workers, and permanentcontract workers) except family labour as salary for family workers is not recorded. thus, the annual salary for any family worker is obtained by dividing the farm’s annual net income by the number of its family workers. capital assets considered are machinery, buildings, plantations, and livestock. information on their value and expected life-length are contained in the italian fadn dataset and are used to construct an index of capital services. using the fisher formula, the index of capital services is obtained by aggregating information on the productive stock of each asset weighted by its corresponding user cost. to obtain the productive stock and user costs, a hyperbolic efficiency-loss function is assumed and the average annual yield of italian government bonds with 10-year maturity between 2003 and 2013 is used as exogenous rate of return to capital (rizzi and pierani, 2006). annex 2. methodology for the ei calculation according to the ipcc methodology, the sector “agriculture” produces emissions mainly of two non-co2 greenhouse gases: methane (ch4) and nitrous oxide (n2o), from seven different categories (relevant in italian ghg inventory): enteric fermentation, manure management, agricultural soils, field burning of agricultural residues, liming and urea application. emissions of carbon dioxide (co2) (from the use of machinery, buildings, agricultural operations and transport of agricultural products) are accounted in the sector “energy” and emission and removals of co2 from agricultural soils and biomasses are estimated in the sector “land use, land use change and forestry” (lulucf). as the farm in fact produces emissions from all these three ipcc categories (agriculture, lulucf and energy), the approach here adopted accounts for ghg emissions from all sources with a crosscutting method that combines what ipcc estimates separately. ipcc methodology is based on a linear relationship between activity data and emission factors. the methodology here used basically follows coderoni and esposti (2015), that have applied the methodology described in coderoni and bonati (2013) and coderoni et al. (2013). however, some changes have been made in order to better harmonize the data available in the fadn recent surveys with the most recent ipcc guidelines (2006). table a.1 details the fadn data used to compute emissions from the different sources. emission factors are alternatively default (ipcc, 2006), country specific (ispra, 2015) or farm specific. this latter case represents one of the major novelties of the present approach and occurs only in the case of enteric fermentation for cattle and sheep, because of specific parameter availability. to express all these emissions in a unique unit of measure, i.e., total co2 equivalent (co2e), any different ghg is multiplied by its global warming potential (gwp). the 135the productivity and environment nexus with farm-level data conversion factors updated over time by the ipcc are used. currently, italy uses gwps in accordance with ipcc fourth assessment report, i.e. 25 for ch4 and 298 for n2o (ispra, 2015). ghg emissions expressed in co2e represent what we define here as the farm carbon footprint (cf). ghg emission values are aggregated in different ways to enable more detailed analysis at farm and production level. the main aggregates obtained are the cf for five macro categories of emissions. table a.1 shows how all the emission sources considered are grouped into the respective cf categories. table a.1. summary of ghg emission sources considered and respective fadn activity data used. emission sources cf category fadn data n2o manure management cf livestock animal numbers ch4 manure management cf livestock animal numbers ch4 enteric fermentation cf livestock animal numbers, milk production, pasture, % birth, animal average weight ch4 rice cultivation cf crops rice area (uaa) n2o agricultural soils: various -use of synthetic fertilisers cf fertilizers n quantities or fertilisers expenditure -animal manure cf crops manure reuse -histosols cf crops crop area (uaa) -crop residues cf crops crop area (uaa) or crop yield -atmospheric deposition cf fertilizers/cf crops n quantities or fertilisers expenditure. and animal numbers -leaching and run-off cf fertilizers/cf crops n quantities or fertilisers expenditure and animal numbers co2 urea cf fertilizers urea quantities co2 energy cf fuel fuel expenditure or quantities co2 forest land cf land use uaa co2 cropland cf land use uaa co2 grasslands cf land use uaa as the fadn survey is not designed to collect all the information needed for the estimation of farm-level ghg emission, some assumptions have been made to overcome the information gap to compute the farm-level cf. in this respect, an important improvement of the cf calculation compared to previous studies (coderoni and esposti, 2015) concerns the “cf fertilizers”. both direct and indirect emission (due to nitrogen leaching and run-off) are accounted for, starting from data on nitrogen (n) content in the fertilizers applied. as quantities of n purchased are not a compulsory information to be provided to fadn survey, an indirect methodology has been used to compute n applied by farms for which these data are missed. in these cases, as suggested by coderoni and esposti (2015), data on fertilizers expenditures have been used. moreover, the “cf fertilizers” contains also nitrogen input to soils from manure application, and emissions from urea application. the former has been obtained using farm data on manure reuse, and the latter has been 136 e. baldoni et al. estimated applying a default ef (0.20 t c/t urea) (ipcc, 2006) to the quantities of urea distributed as provided by fadn survey. the “cf fuel” has been estimated using alternatively the quantities of fuel purchased and total fuel expenditure at farm level. data on expenditure have been divided by the price of agricultural gasoline observed over time and across different italian provinces (available online) adjusted for the eurostat index price of the means of agricultural production (input/motor fuels). this datum has been used to correct figures on quantities of fuel purchased that are not compulsory in the fadn survey. this allows computing the year-by-year farm-level use of fuel and, thus, the consequent cf applying the respective ef taken from ispra estimates (ispra, 2015). for what concerns rice emission and emissions from land use, the approach adopted is the same of coderoni and esposti (2015). for what concerns rice emissions, the fadn survey does not allow to distinguish between single and multiple aeration cultivation method, which highly influence ch4 emissions. thus, multiple aeration ef is applied, as it is the most widespread cultivation technique. the “cf land use” has been estimated adopting implied emission factors (ief) (ispra, 2015) and multiplying them by the uaa of the respective land use. land use changes have not been considered, if not as a consequence of reduced (or increased) uaa. following ispra (2015), the change in biomass has been estimated only for perennial crops. since the ief obtained with this approach for perennial wood crops would have been negative (thus, represent a source of emissions), for the value of this carbon stocks at maturity a different ief has been used in order to take into account that perennial crops give a higher contribution than annual crops in carbon sequestration. this approach considers a positive value for perennial wood crops using, in the absence of country specific values, an average value of 10 t c/ha (for carbon stock at maturity) considering a cycle of 20 years (ispra 2015 and 2016). annex 3. correlation between farm-level tfp and ei an initial and intuitive evidence about the nexus between tfp and ei at farm level is provided by the simple (pearson) correlation coefficient between the two indicators (table a.2). correlation is significant and positive (0.20) when all farm typologies are considered. thus, it seems that a positive relationship between productivity and emission intensity exists. if we consider individual farm typologies, however, some different performances emerge. first of all, not all farm typologies matter. when statistically significant, correlation is positive for livestock, excluding dairy, and mixed crop and livestock farms. in these cases it is confirmed that the more productive farms are also those with higher ei. on the contrary, the correlation for crops and cereal specializations are negative, therefore more productive farms are also the less polluting ones. this different result would suggest that the nexus between ei and tfp is actually more complex than what appears in the aggregate data that actually hide the large heterogeneity among farm performances. 137the productivity and environment nexus with farm-level data table a.2. correlation between the farm-level tfp and ei across different farm specializations. type of farming: tfp-ei correlation coefficient n. of farms granivores 0.236** 123 grazing livestock 0.227** 172 mixed crop and livestock 0.180* 98 dairy 0.050 563 horticulture -0.026 70 rice -0.074 165 fruits -0.104 129 wine -0.111 111 cereals -0.130** 511 arable crops -0.155* 128 total 0.201** 2070 *,** statistically significant at 10% and 5% respectively bio-based and applied economics 9(1): 1-24, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8416 the role of trust and perceived barriers on farmer’s intention to adopt risk management tools elisa giampietri1, xiaohua yu2, samuele trestini3,* 1 department of land, environment, agriculture and forestry, university of padova. viale dell’università 16, 35020 legnaro (padova), italy. e-mail: elisa.giampietri@unipd.it 2 department of agricultural economics and rural development, georg-august university of goettingen, platz der göttinger sieben 5, 37073 goettingen, germany. e-mail: xyu@gwdg.de 3 department of land, environment, agriculture and forestry, university of padova. viale dell’università 16, 35020 legnaro (padova), italy. e-mail: samuele.trestini@unipd.it abstract. this paper adds to the ongoing debate about low farmers’ uptake of risk management (rm) tools subsidised by the common agricultural policy (cap). in particular, the research pioneers the investigation of whether and how trust towards the relevant intermediaries and the perceived barriers to adopting may influence farmers’ intention to adopt the insurance and to participate in mutual funds (mf) and in the income stabilisation tool (ist). in the light of the current cap reform, as a novel contribution this paper also questions the efficiency of the new operating rules established by the omnibus regulation. the research proposes a conceptual framework to simultaneously assess these underinvestigated factors and several other determinants of the intention to adopt (e.g. risk attitude). data were gleaned from direct interviews among 105 italian farmers and analysed through structural equation modeling. the results confirm the positive role of trust in influencing the intention to adopt the insurance, which is notoriously affected by problems of information asymmetry. similarly, trust is a key element in influencing the intention to participate in the ist, which is a collective instrument based on solidarity and mutuality indeed. moreover, the higher the perceived barriers to adopting, the lower the intention to participate in a mutual fund, for which therefore further informative initiatives (e.g. on benefits from the adoption and the ease of use) are required. interestingly, the results show a positive impact of the new cap policy changes on the intention to both take out the insurance and participate in the ist, thus opening up to positive prospects for the eu risk management strategy post-2020. to conclude, this study paves the way for new research avenues in the field of farmers’ adoption of subsidised rm tools. keywords. insurance, mutual fund, income stabilisation tool, trust, structural equation modeling. jel codes. d81, g22, q18. *corresponding author. editor: fabio gaetano santeramo. authors’ contributions: samuele trestini (st) and elisa giampietri (eg) conceived and designed the research idea; eg collected data and analysed them with support from xiaohua yu (xy); all authors discussed the results; eg wrote, reviewed and edited the manuscript under the supervision of st. 2 elisa giampietri, xiaohua yu, samuele trestini 1. introduction risk is embedded in the agricultural production, leading to adverse outcomes as yield and income losses for farmers (komarek et al., 2020). particularly, nowadays agricultural risk sources can be mainly identified in the increased severity and frequency of extreme weather conditions, pests and diseases that strike farm yields, and in the global phenomenon of price volatility that determines growing pressures on farmers’ income (ec, 2017a). to cope with multiple risks, in the european union (eu) farmers can resort to adopting subsidised risk management (rm) tools. accordingly, the common agricultural policy (cap) has recently emphasized the role of these tools (meuwissen et al., 2018): in addition to supporting insurances and mutual funds (mf) to cover yield losses, it has introduced the so-called income stabilisation tool (ist) to cope with income drops (el benni et al., 2016). as opposite to the most part of the other member states, italy allocated a specific budget for each of these tools for the period 2014-2020. however, despite the pervading exposure to risks for farmers (trestini et al., 2017a) and the advantages that these instruments provide to farms (enjolras et al., 2014; severini et al., 2019a), in italy the participation rate in subsidised insurance schemes is currently below what policy makers hope for, and the uptake is not homogeneous (coletta et al., 2018). as opposite, hitherto only several private mfs existed at national level, while both the subsidised mutual fund and the ist did not take up; however, it is worth noting that new initiatives (i.e. four mfs and three ists) will be available soon in italy (these are currently requiring the approval). nowadays, there exists a policy interest in understanding how to enlarge the adoption of subsidised tools among the potential beneficiaries in italy. in line with this, nowadays the understanding of farmers’ decision-making process when choosing their preferred risk management tools represents a significant issue for many stakeholders (i.e. academics and researchers, private insurance companies, policy makers, etc.) (cao et al., 2019; meraner and finger, 2019). in particular, as regards the eu rm toolkit this may be useful to provide new insights for reversing the low demand and thus enhancing the efficiency of the rm policy at eu level. a burgeoning effort was given to studying the determinants of crop insurance uptake over the last years. as broadly demonstrated (goodwin, 1993; mishra et al., 2005; enjolras and sentis, 2011), moral hazard and adverse selection represent two major reasons to explain the poor development of insurance market, also justifying the policy intervention through public subsidies by the governments. in recent years the literature extensively discussed the role of several factors affecting farmers’ demand for agricultural insurance: first of all, farmer’s risk attitude and risk perception (hellerstein et al., 2013; menapace et al., 2012 and 2016; van winsen et al., 2016); the adoption of self-coping strategies (enjolras and sentis, 2011); off-farm income and direct payments (finger and lehmann, 2012); expected indemnity from the insurance (liesivaara and myyrä, 2017); prior indemnification (wąs and kobus, 2018); previous experience with farm losses and the level of farm’s debts (wąs and kobus, 2018); direct and indirect experience with the insurance (santeramo, 2018); finally, farm and farmer’s characteristics (ogurtsov et al., 2009; farrin et al., 2016; santeramo et al., 2016). further to the above, in 2017 castañeda-vera and garrido drew attention on farmers’ willingness to adopt as a relevant factor to investigate. furthermore, many authors (see e.g. 3the role of trust and perceived barriers on farmer’s intention marr et al., 2016) called for the necessity not to overlook the effect of behavioural indicators, alongside the most commonly investigated neoclassical determinants (i.e. risk aversion). for instance, this supports the importance of studying the intention to adopt (i.e. antecedent of the decision makers’ behaviour); in addition, opportunities exist to further knowledge in this area, e.g. by exploring the role of other potential determinants that are still underinvestigated. hence, a serious reflection follows: are other not yet explored factors reducing the interest of eu farmers in adopting these tools? further, it is worth noting that both mfs and the ist received only limited empirical attention both in terms of demand and research, thus representing a relevant focus of investigation to address nowadays. given the above, as a novel contribution this paper aims at investigating whether and how trust towards the relevant intermediaries and the perceived barriers to adopting may influence the intention to adopt the subsidised insurance, and also to participate in the mutual fund and the new ist (these two forms of mutual funds are separately investigated in this work). finally, in the light of the current cap reform, this analysis questions the efficiency of the new rm toolkit’s operating rules provided by the omnibus regulation as follows: do these policy changes affect the intention to adopt? the paper is structured as follows: paragraph 2.1 includes a description of the agricultural risk management at eu level, while the literature and conceptual framework with the hypotheses underlying the analysis are developed in paragraph 2.2; next, data collection, the questionnaire and the methodology are described in paragraph 3.1, 3.2 and 3.3, respectively; moreover, the empirical results are presented and discussed in paragraph 4; finally, the paper concludes with paragraph 5. 2. background 2.1 the eu agricultural risk management strategy in italy, the participation in subsidised rm instruments dates back to 1970, with the creation of the national solidarity fund (law n. 364), then reformed in 2004 (legislative decree n. 102). in particular, the recourse to the insurance tool recorded a long history, also by reason of premium subsidies to farmers (up to 80%). with the health check reform1, in 2009 european reserves were added to national resources, in order to support (up to 65%) the insurance (i.e. the premium) and the mutual fund (i.e. administrative expenses for the setting up) covering for losses caused by adverse climatic events, animal or plant diseases, pest infestations, or environmental incidents. within the eu borders, the policy debate on supporting rm in agriculture has progressively evolved over the last decade: the most recent demonstration comes from both the last cap 2014-2020 reform2 and further its middle-term revision known as omnibus regulation3. in particular, the reform in 2013 has introduced the new ist in the form of a mutual fund to support farmers facing a severe income drop (el benni et al., 2016; castañeda-vera and garrido, 2017; trestini et al., 2018a; cordier and santeramo, 2019; severini et al., 2019b). in 2017, the 1 regulation (ec) n. 73/2009. 2 regulation (eu) n. 1305/2013. 3 regulation (eu) n. 2393/2017. 4 elisa giampietri, xiaohua yu, samuele trestini omnibus regulation has introduced new operating rules: for instance, the increase of the support rate to 70% for each tool and the introduction of sectoral ists with a threshold for compensation lowered at 20% (from 30%). finally, the more recent proposal for the cap post 20204 confirms the possibility for a financial contribution to the aforementioned rm toolkit under national strategic plans. turning to the market of rm tools, the italian agricultural insurance sector grew rapidly over the last 15 years. the most recent data (ismea, 2018) depict this as highly concentrated in terms of products and characterized by a strong imbalance between the north (that concentrates up to 81% of the insured value and 86% of the insured areas), the central italy (10% and 8%, respectively) and the south (9% and 6%, respectively). nevertheless, nowadays the participation rates to subsidised insurance in italy are still below those desired, although the recent history shows a substantial level of public intervention (with a budget of 1,4 billion euro for the period 2014-2020) dedicated to the insurance market, and an ascertained high level of income losses for the italian farms (trestini et al., 2017b). furthermore, it is worth noting that both subsidised mfs and the ist do not yet exist in italy at the moment, even if 97 million euro have been budgeted for each of these tools over the 2014-2020 period. to this purpose, the major difficulties recurrently encountered are related to pre-implementation issues (e.g. design of sectorial or multi-sectorial funds, initial capital stock, organisation) (trestini et al., 2018b), to the lack of a dedicated legislation (actually, with the official approval of specific national legislative decrees, improvements have been recently made on this), and to questions on benefits and limits from the farmers’ side (ec, 2017b). moreover, a major constrain to the development of the ist was represented by the difficulty to correctly and objectively assess farmers’ income losses, due to the current lack of a formal accountancy in the farm sector in italy; however, this has recently been overcome with the introduction of an index-based costing method that opens up new development opportunities for this instrument. as opposite, several private mf initiatives exist in the north of italy (i.e. in trentino province and veneto and friuli venezia giulia regions): these run without subsidies and are promoted by the defence consortia, i.e. producers’ associations based on consolidated mutual agreements and established reciprocity between members, that are historically rooted in those areas. to conclude, it is noteworthy that there are no available observational data on subsidised mfs and the ist to the present time. 2.2 literature and conceptual framework research on this topic has been extensively rooted in the standard expected utility theory: as refers the insurance tool, we know that the expected utility maximizing farmer’s choice to subscribe the contract must be greater from profit with insurance than from profit without it (goodwin, 1993). however, many authors (e.g. kahneman and tversky, 1979) raised an objection to its predictive power of decision-making under risk. based on this, the present study considers several determinants and investigates their simultaneous effect on farmers’ intention, hereafter referred as int, to adopt the subsidised rm tools. this is to satisfy the necessity for a reference frame that is most likely that in which farm4 com (2018) 392 final. 5the role of trust and perceived barriers on farmer’s intention ers behave under uncertainty. indeed, to our knowledge the use of consolidated frameworks studying the combined action of several factors contemporaneously (as actually happens in a decision-making process) is rare in the literature on rm tools’ adoption and the most part of the studies focuses on one strategy or instrument, and very few exceptions to this exist, e.g. van winsen et al. (2016) and meraner and finger (2019). inspired by the study by van winsen et al. (2016) that explores the role of risk perception and risk attitude as determinants of farmer intended behaviour, in this research we propose three different conceptual models: other things equal, the first (model 1) regards the intention to adopt the insurance whereas, as a novel contribution, the second model (model 2) refers to the mutual fund and the third model (model 3) to the new ist. furthermore, this study focuses on the intention to behave (i.e. the intention to adopt each instrument) as a proxy for actual behaviour (i.e. adoption) (lobb et al., 2007), due to the fact that no forms of subsidised mfs and ist operate in italy to date, as opposite to the insurance. in the literature on farmers’ adoption of rm tools, especially those subsidised by the cap, hitherto scarce attention has been paid to the role of trust, with very little exceptions: e.g. cole et al. (2012) argued that farmers’ mistrust in the insurance market can represent a friction to the uptake. grebitus et al. (2015, p. 85) argued that “the role of trust is considered to be of particular importance where information is sparse, hard to assess or complex; in these situations, trust can substitute for full knowledge”. accordingly, pascucci et al. (2011) highlighted how trust is a relevant factor to efficiently cope with problems of asymmetric information, that notoriously lower the insurance demand due to two major problems as adverse selection (i.e. the tendency of riskier farmers to purchase the insurance) and moral hazard (i.e. the tendency of insured farmers to adopt a riskier behaviour). hence, here we refer to trust as the farmer’s belief in the reliability of relevant intermediaries involved in those settings characterized by imperfect or asymmetric information (i.e. a situation where one actor has greater information than the other actor), as the relationship between principal and agent (jensen and meckling, 1976; eisenhardt, 1989). with regards to the rm tools, farmers are likely to show limited knowledge and a reduced ability to perfectly evaluate if both the insurance contract and the membership rules of mutual funds are adequate for the own interest; at the same time, they tend to assume opportunistic behaviour (e.g. moral hazard). for example, in the insurance market farmers do not always show complete trust that they will receive the payout from the insurance company in return for the premium paid to subscribe the contract, and this can inhibit the contract purchase. therefore, the intermediaries (e.g. insurance providers, local agents, etc.) play a key role in this respect: they gain and retain trust from farmers and, based on this, they match the farmer and the insurer (cummins and doherty, 2006) and encourage farmers’ participation in the insurance program (ye et al., 2017). to summarise, it is reasonable to assume that trust can represent a solution for those situations that are inherently characterized by increasing complexity (see the insurance contract), uncertainty and reciprocal lack of knowledge (this characterizes the insurance, by nature), scarce experience, and the necessity for membership control (as for mutual funds, where members derive utility from a good conduct of all members and a good exercise of the instrument5). as regards 5 this is especially true for mutual funds that, according to a recent ministerial decree (n. 10158/2016), in italy can be created and managed by cooperatives, consortia, producers’ organizations, farm associations, etc. mutual funds are voluntary alliances among members who formalize an agreement related to duties and rights, membership rules, etc. 6 elisa giampietri, xiaohua yu, samuele trestini the mutual funds, a recent document of the european commission (2017b) reported that farmer’s reluctance to trust these collective instruments represents a principal ambiguity that justifies the failure to create mutual funds in italy. in fact, since the fund implies the creation of a financial reserve by the annual contribution by all the members, the potential beneficiaries may question the level of solidarity and mutuality between who benefits and who loses within the fund, and raise questions as who is paying for whom. as opposite, the same document highlighted that the high level of trust between members is conducive to the good operation of those mutual funds run by the defence consortia (see e.g. in trento province in italy), but little empirical evidence exists on this nonetheless. to conclude, since uncertainty is inherent in the choice of rm tools (as farmers may not fully understand the instruments or may have harbour doubts about the behaviour of intermediaries), it is reasonable to suppose that trust represents a catalyst for the adoption of these instruments. following this, we test this hypothesis: h1: trust significantly affects the intention (i.e. the intention to adopt the insurance or to participate in a mutual fund or in the ist). similarly, evidence into the role of farmers’ perceived barriers on farmers’ adoption are limited, with the exception of a recent paper by ye et al. (2017) on crop insurance, thus stimulating our interest in this field. indeed, farmers often know little about the benefits of using rm tools primarily because they receive little education about the instruments. as opposite to this, the literature shows that farmers who are better-informed on the operating rules of the insurance contract and its benefits, thus showing lower perceived barriers, are more willing to purchase the coverage (santeramo, 2019). in line with this, it is worth investigating the role of farmers’ perceived barriers to adopting (that here serve as proxy for the lack of understanding), and we reasonably expect a negative role on the intention for all the investigated tools. based on this, we test the following hypothesis: h2: perceived barriers to adopting significantly affect the intention. nowadays, a further important but still unanswered question is the extent to which policy interventions actually influence farmers’ choice to adopt the cap’s rm toolkit. to this purpose, another innovative element of this study is the investigation of the effect of the changed operation rules established by the agricultural package of the new omnibus regulation as potential drivers of the intention. in our opinion, this may provide interesting insights on cap’s effectiveness to encourage the adoption of subsidised tools. as alluded to in the introduction, the core contribution of this paper is represented by the pioneering investigation of the role of trust and perceived barriers. in addition to these, the conceptual model that we propose considers some other determinants of the intention to adopt the three subsidised tools: their role on farmers’ insurance uptake has already been found to be relevant by the literature, as opposite to their role on the intention to participate in a mf and in the ist that is still unclear at the moment, to the best of our knowledge. inspired by the extant literature on insurance, we investigate the role of past adoption of rm tools on the intention. to this purpose, in line with some other authors (see e.g. 7the role of trust and perceived barriers on farmer’s intention enjolras and sentis, 2011; cole et al., 2014), santeramo (2019) found that farmers who experienced the insurance tool in the past are more likely to buy it further, with respect to uninsured farmers. moreover, as the previous experience in using rm tools can change farmers’ perception of these instruments (ye et al., 2017), we also test the influence of past adoption on perceived barriers; similarly, we analyse the effect of previous adoption also on trust, for an explorative purpose. furthermore, this study tests whether farmers’ attitude towards risk has power in explaining their intention to purchase the insurance tool or to participate in mfs or the ist. indeed, risk attitude influences many decisions in a farm management context (vollmer et al., 2017): it follows that its understanding is essential to explain and predict farmers’ risk behaviour (i.e. how they act upon risk) and any related policy implications (van winsen et al., 2016; iyer et al., 2020). against this background, we consider the individual risk attitude (namely, the individual orientation towards taking risks) as a fundamental determinant of farmer’s int. based on the standard expected utility theory and thus assuming farmers’ rational behaviour, we expect that more risk averse individuals are more likely to insure (cao et al., 2019). in addition, several authors (enjolras and sentis, 2011; lefebvre et al., 2014) emphasized that farmers facing a higher risk exposure (e.g. a greater frequency of insurable risks) are expected to insure, being the demand positively related to past risky occurrences. thus, we test the role of the perceived risk frequency at farm level (namely, their perceived exposure to risks), assuming that it positively affects int. also, this study investigates the impact of the perceived risk control on int for an explorative purpose, inspired by the literature: coherently with other authors that they cited, wauters et al. (2014) recalled the link existing between people’s behaviour and their degree of control over something. however, no study has yet experimentally explored this link. as intuition suggests, we can assume that farmers with a lower perceived control over risks may be more willing to adopt rm tools. in addition, farmers can adopt several self-coping strategies for coping with risks in order to minimise their losses (bowman and zilberman, 2013; meraner and finger, 2019): these includes (but are not limited to) production contracts (i.e. contracts that ensure that the product will be bought at a set price), diversification and investments for new farm structures and new technologies. to this purpose, marr et al. (2016) stated that the higher is the variety of risk mitigation strategies and the lower the demand for insurance is. hence, our model combines self-coping strategies and int in a unique framework to better fit the real context of farmers’ risk behaviour. finally, we also take into account some individual indicators as gender, age and the level of education, analysing their effect on both the intention to adopt and the attitude towards risk. in particular, the literature suggests that elder farmers, i.e. more experienced, and the better educated ones are expected to be insurance users (sherrick et al., 2004), probably because they can better understand the insurance product (ye et al., 2017) or because they can assess risks more precisely (el benni et al., 2016). as regards risk attitude, franken et al. (2017, p. 42) argued that “risk attitudes have been shown to vary systematically with socioeconomic and individual characteristics, such as age, education, gender”. in particular, van winsen et al. (2016) showed that age has a positive relation with risk aversion, while education can have both a negative and a positive effect. 8 elisa giampietri, xiaohua yu, samuele trestini summarising the above discussion, we also test the hypotheses included in table 1 and figure 1. it is worth highlighting that, among the investigated variables, trust, perceived barriers, perceived risk frequency and risk control, self-coping strategies, past adoption and the effect of cap changes are not observable by scholars, whereas the attitude towards risk is not observable neither by farmers nor by researchers. table 1. hypotheses on relations among variables. relation sign* h1: trust significantly affects the intention (i.e. the intention to adopt each subsidised rm tool) (+/-) h2: perceived barriers to adopting significantly affect the intention (+/-) h3: perceived risk frequency significantly affects the intention + h4: perceived risk control significantly affects the intention (+/-) h5: risk attitude significantly affects the intention + h6: past adoption of rm tools (whatever) significantly affects the intention + h7: policy change provided by the omnibus regulation significantly affects the intention (+/-) h8: self-coping strategies (past_strat1; past_strat2; past_strat3) significantly affect the intention h9: past adoption significantly affects the trust (+/-) h10: past adoption significantly affects the perceived barriers (+/-) * the sign here reported represents the expected positive (+) or negative (-) influence, as evidenced by the existing literature (related to the insurance tool); however, there is also a possible double effect (+/-) and the reason is twofold: i) because the effect has not yet been investigated by the existing literature or ii) because the literature reports both a positive and a negative effect. 3. data and method 3.1 data collection from december 2017 to march 2018 a survey collection was conducted among 127 italian farmers in veneto6 region through direct interviews. respondents who freely accepted to answer the questionnaire were the participants of some training courses organized by a farmers’ association. consistent with wauters et al. (2014), it was indeed a purposive sampling, as the authors needed informed respondents who, based on their farming experience and understanding of rm tools, could provide reliable answers (flick, 2006). the data collection recovered 105 fully completed questionnaires representing the final sample7. a structured questionnaire, pre-tested on a small sample (n = 15), was designed based on the existing literature on this topic and on a preliminary survey8 previously conducted among 23 italian farmers. in the final questionnaire, farmers were pro6 veneto region is the first in terms of value of crop-hail insurance coverage (with over 1.4 billion euros) (https://www.statista.com/statistics/818978/value-of-crop-hail-insurance-coverage-by-region-in-italy/). 7 this sample size is in line with similar studies (see iyer et al., 2020). 8 some open-ended questions asked for: major sources of income and production risks occurring at farm level; most important barriers preventing farmers’ adoption of subsidised rm tools; main self-coping strategies employed to manage risk at farm level. 9the role of trust and perceived barriers on farmer’s intention vided with a short description of the rm tools subsidised by the cap (i.e. insurance, mfs, ist) to ensure a full understanding. 3.2 questionnaire the questionnaire was divided into four sections investigating: i) the intention; 2) the antecedents of the intention; 3) risk attitude; 4) farm’s and farmer’s characteristics and past strategies to cope with risks at farm level. in particular, in the first one, it was asked to self-assess the individual intention to adopt a subsidised agricultural insurance (int_ ins) or to participate in a mf (int_mf) or in the ist (int_ist): more in depth, the average value from three items (5-point scales) was transformed into a dummy (1 if the value was greater than 3, 0 otherwise) to measure each type of intention9. furthermore, 9 as regards the intention to adopt the insurance tool, the agreement with the following items was asked: “next year, i will consider the adoption of the subsidised agricultural insurance to face yield risk”, “for the next year, i plan to adopt the agricultural insurance to face yield risk”, and “next year, i will adopt the agricultural insurfigure 1. conceptual path model with hypotheses. * intention refers to: insurance adoption (int_ins) in model 1; participation in a mutual fund (int_mf) in model 2; participation in the ist (int_ist) in model 3. the figure does not represent the standard graphical representation of sem: indeed, measured variables (i.e. those determining latent variables, namely indicators) are not shown. in the figure there are two types of unobservable variables as antecedents of the intention: one measured through the lottery task (i.e. risk attitude shown as an oval) and some measured through the indicators within the survey (i.e. trust, perceived barriers, perceived risk frequency, and perceived risk control shown as ovals). finally, past adoption, policy change, selfcoping strategies and farmers’ characteristics are observed variables shown as squares. 10 elisa giampietri, xiaohua yu, samuele trestini the second section of the questionnaire included: several statements to elucidate all the latent variables that cannot be directly measured; a binary yes or no question asking for the past adoption of rm tools (at least once) during the previous five years (past adoption); a five-point psychometric scale (1 = not at all important; 5 = very important) to measure the subjective relevance of the new rules for indemnification provided by the omnibus regulation in order to further adopt the three subsidised tools (policy change). as regards latent variables, for each item respondents were asked to score their agreement on several five-point likert scales. for instance, participants were asked to selfassess their trust (trust) by scoring their agreement with three statements on a likert scale from 1 (strongly disagree) to 5 (strongly agree); these statements were based on hartmann et al. (2015), with adjustments. furthermore, three items were used to elucidate the barriers for each tool (perceived barriers), ranging from 1 (not at all a barrier) to 5 (a very important barrier). finally, with regards to risk frequency (perceived risk frequency; five items) and risk control (perceived risk control; five items) farmers were asked to score the likelihood (1 = very unlikely; 5 = very likely) of six different risk sources identified through the above mentioned preliminary survey (i.e. storm, hail, ice, heavy rain, other negative weather conditions, plant diseases) and the degree of control (1 = no control; 5 = very much control) they exerted on them at farm level, respectively. particularly, the items related to risk frequency derived from wauters et al. (2014) with adjustments. the third section of the questionnaire included a lottery task to measure farmers’ risk attitude (risk attitude) (menapace et al., 2012; vollmer et al., 2017; iyer et al., 2020). we used a lottery choice task inspired by eckel and grossman (2008) and assumed constant relative risk aversion (crra) for which the utility is defined as u(x)= x(1-r)/(1-r). in order to measure their subjective preferences for taking risks, respondents were asked to imagine to have 28€ and to gamble over this sure amount: they were asked to select, among six different gambles, the one they wished to play10. with the exception of the first gamble showing a sure outcome (28€) in both cases, every other gamble involved a 50% chance of receiving a low payoff and a 50% chance of a high payoff (expressed in €) as an outcome; gambles from 2 to 6 presented risky outcomes where the expected payoff and risk linearly increased. this method, derived from charness et al. (2013), represents a simple way of eliciting risk aversion: in particular, risk averse respondents choose gamble 1-4, whereas those who choose gamble 5 and gamble 6 are risk neutrals and risk seekers, respectively. following menapace et al. (2012), we considered crra lower bound for the analysis. finally, the last section of the questionnaire investigated farmer’s and farm’s characteristics (i.e. gender, age, education, average farm revenue, utilised agricultural area) and the previous adoption of self-coping strategies as diversification (past_ ance to face yield risk” (composite reliability: 0.88). in relation to the intention to participate in a mutual fund we used: “next year, i will consider the participation in a mutual fund to face yield risk”, “for the next year, i plan to become a member of a mutual fund to face yield risk”, and “next year, i will be a member of a mutual fund to face yield risk” (composite reliability: 0.88). finally, with regards to the intention to participate in the ist we used: “i will consider the participation in the ist to face income risk”, “i plan to become a member of a ist to face income risk”, and “i will be a member of the ist to face income risk” (composite reliability: 0.88). 10 we chose this easily comprehensible lottery task derived from dave et al. (2010) as it is simple, easy to explain and implement, while retaining a reasonable range of risky choices, and it is totally understandable by the respondents. 11the role of trust and perceived barriers on farmer’s intention strat1), production contracts (past_strat2), investments for new farm’s structures and new technologies (past_strat3). 3.3 methodology the analysis applied a structural equation model (sem) that deals with a system of regression equations. indeed, this multivariate analysis consists of a set of linear equations that simultaneously estimate two or more hypothesized causal relationships between several variables (bollen, 1989): by including them in a single model, sem traces the structure of the decision-making process. in sem models, variables can be both exogenous (independent) and endogenous (dependent), both observed and latent variables (namely, unobservable variables that require two or more measured indicators) as perceptions, selfreported behaviour, or personality traits; moreover, in some cases a variable can be both a predictor and a dependent variable at the same time, whereas the relationship can be direct or indirect. within sem it is possible to distinguish both structured models (that represent the relationship between latent variables) and measurement models (that represent the relationship between the latent variable and its observable indicators). in the model, the parameters to estimate are the regression coefficients, the variances and the covariances of the independent variables. as above mentioned, the popularity of this technique derives from the possibility to concurrently test different impacts among variables (i.e. multiple and simultaneous testing), as opposite to ordinary regression analysis (schreiber et al., 2006); another main advantage is the capability to handle latent variables, which can be both dependent variables and predictors, while controlling for farm’s and farmers’ characteristics. however, an adequate (i.e. large) sample size is required11; moreover, only identified models can be estimated. the interested reader may want to read ullman (2006, p. 40) for a more extensive description and a more extended model statistical specification. following ullman (2006), sem can be expressed as follows: η = bη + γξ + ζ (1) where η is a vector of endogenous variables, b is a matrix of coefficients between endogenous variables, γ is a matrix of regression coefficients denoting the effect of exogenous variables on endogenous variables, ξ is a vector of exogenous variables, and ζ is a vector of the measurement errors. although widely tested in many different contexts, this approach has been only recently proposed in the field of study on farmer’s risk behaviour and the work of pennings and leuthold (2000) represents a pioneering example. more recent applications to risk behaviour analysis are the study by van winsen et al. (2016) and the study by franken et al. (2017): this latter analyses the impact of farm socio-economic and farmer individual characteristics on risk attitude. against this background, our paper represents an innovative attempt to use a sem in explaining the potential relationships of several factors with farmers’ intention to adopt risk management strategies. the descriptive analysis was performed using spss version 24, whereas sem was performed using amos package. in sem models, 11 to overcome this limit, it is worth noting that bentler and yuan (1999) developed test statistics for small sample sizes. 12 elisa giampietri, xiaohua yu, samuele trestini the goodness-of-fit statistics assess the model-data matching; to do this, we used the following indexes: the ratio between χ2 and the degrees of freedom (cmin/df), the comparative fit index (cfi), and the root mean square error of approximation (rmsea). 4. empirical results and discussion as shown in table 2, the average age of respondents is 40 years and the majority of the sample are men (72%), with an upper secondary school level of education (63%) and an table 2. sample descriptive statistics. categories description n. obs % mean s.d. gender (sex) (0) female 29 27.6 (1) male 76 72.4     age (age) n. years     40.12 13.55 education (education) (1) primary school 3 2.9 (2) secondary school 14 13.3 (3) upper secondary school 66 62.9 (4) university degree 22 21.0     average farm revenue (revenue) (gross income from farming/year) (1) less than 50,000€ 62 59.0 (2) 50,000€ 100,000€ 28 26.7 (3) 100,000€ 250,000€ 11 10.5 (4) more than 250,000€ 4 3.8     utilised agricultural area (uaa) n. hectares   14.25 17.04 how relevant are the changes to rm policy provided by the omnibus regulation, in order to adopt risk management tools in your farm? (policy change) (1) not at all important 6 5.7 (2) scarcely important 6 5.7 (3) neutral 52 49.5 (4) sufficiently important 27 25.7 (5) very important 14 13.3     intention to adopt the agricultural insurance (int_ins) (0) no 47 44.8 (1) yes 58 55.2     intention to participate in a mutual fund (int_ mf) (0) no 57 54.3 (1) yes 48 45.7     intention to participate in the ist (int_ist) (0) no 50 47.6 (1) yes 55 52.4     previous adoption of rm tools at farm level (past 5 years) (past adoption) (0) no 73 69.5 (1) yes 32 30.5 adoption of diversification (past_strat1) (0) no 92 87.6 (1) yes 13 12.4 adoption of production contracts (past_strat2) (0) no 98 93.3 (1) yes 7 6.7 previous investments for new farm structures and new technologies (past_strat3) (0) no 101 96.2 (1) yes 4 3.8     13the role of trust and perceived barriers on farmer’s intention average farm revenue lower than 50,000€ per year (59%). the average utilized agricultural area of farms is 14 hectares and these are heterogeneous in terms of production orientation: permanent crops’ production represents the majority of the sample (50%), followed by livestock (28%), arable crops and horticulture (23%), and only a minority are mixed farms. moreover, up to 70% of the respondents declares no previous adoption of rm tools at farm level. finally, on average respondents show a positive intention to adopt subsidised agricultural insurance schemes (55%) and to participate in the ist (52%) in the near future (i.e. the next year), as opposite to mfs (46%). interestingly, 36% show a positive intention to both subscribe the insurance and to participate in a mutual fund, 38% to both subscribe the insurance and to participate in the ist, 37% to participate in both a mf and the ist, and finally 29% show a positive intention with regard to the three tools. as shown in table 3, all the items present mean values above the scale mean, with the exception of perceived risk control, as expected. hence, the majority of farmers perceive a high risk frequency and considerable barriers to the adoption of subsidised rm tools, are endowed with a scarce control over adverse weather events striking their farm and display a high trust towards the intermediaries. cronbach’s α scores are higher than 0.75 for each considered latent variable, denoting an adequate internal consistency. moreover, the standardized regression weights of the items are significant at 1% level and show values ranging from 0.320 to 0.916. as expected and consistent with the literature (iyer et al., 2020), table 4 shows that our farmers’ sample mainly consists of risk averse subjects (84.8%) who chose gamble 1, 2, 3 and 4, whereas only 6.7% are risk neutral and 8.6% are risk seekers. goodness-of-fit indexes of the estimated models are acceptable, with a root mean square error of approximation (rmsea) of 0.05 (model 1) and 0.06 (model 2 and 3), a comparative fit index (cfi) of 0.9 and cmin/df always lower than 2 in each model. hence, our results demonstrate the usefulness of sem in exploring the relationships of intention and other decision-making attributes with regard to risk management behaviour, consistent with van winsen et al. (2016). furthermore, the variance of farmers’ intention, risk attitude, perceived barriers and trust is explained in the measure of 25%, 15%, 6% and 5% in the first model, respectively; whereas in the measure of 27%, 15%, 1% and 5% in the second model. to conclude, the third model explains up to 25% the intention, 15% the risk attitude and 4% the trust, whereas it does not explain the barriers at all. interestingly, the results (table 5) show a positive effect of trust on the intention in model 1 and 3 (h1 βtrust = 0.22 and 0.24 respectively, significant at 5% level), showing that a greater individual trust increases the intention to adopt the insurance and to participate in the ist. consistently with this, karlan et al. (2014) argued that the more farmers are confident the payout will be properly made by the insurance company and the greater their demand for insurance is. the evidence that trust tends to increase the intention to participate in the ist let us assume that this personality trait might be considered as a substitute for farmers’ need to understand this new instrument (that is currently unfamiliar to them), at least during the setting-up: the greater the amount of trust, the lower the perceived uncertainty linked to these tools (operating rules, management, etc.); however, this deserves further investigations. consequentially, if reinforced (by the bodies responsible for its management, e.g. defence consortia), we can assume that trust might overcome farmers’ original reticence about participating in the ist and foster its progressive 14 elisa giampietri, xiaohua yu, samuele trestini table 3. latent variables. measure item code mean s.d. std. factor loading trusta (cronbach’s α = 0.86) i perceive the intermediaries who support me for the adoption of the agricultural insurance to be reliable trust1 3.17 0.87 0.73*** i am confident that the intermediaries which i refer to for the adoption of agricultural insurance take care of my interest trust2 3.05 1.01 0.86*** i trust in the intermediaries who support me for the adoption of agricultural insurance trust3 2.85 0.89 0.86*** perceived barriers to insurance adoptingb (α = 0.79) i have a scarce perception of the benefits of agricultural insurance’s adoption ins_barr1 3.31 1.17 0.73*** there is low transparency in the mechanisms of agricultural insurance ins_barr2 3.53 1.07 0.78*** i think that the management of agricultural insurance tool is difficult at farm level ins_barr3 3.02 1.05 0.74*** perceived barriers to participating in a mutual fundb (α = 0.78) i have a scarce perception of the benefits of my participation in a mf mf_barr1 3.54 0.94 0.89*** there is low transparency in the mechanisms of mfs mf_barr2 3.45 0.96 0.87*** i think that my participation in a mf is difficult to manage at farm level mf_barr3 3.17 0.86 0.48*** perceived barriers to participating in the istb (α = 0.80) i have a scarce perception of the benefits of my participation in the ist ist_barr1 3.53 0.93 0.92*** there is low transparency in the mechanisms of the ist ist_barr2 3.50 0.85 0.83*** i think that my participation in the ist is difficult to manage at farm level ist_barr3 3.15 0.83 0.57*** perceived risk frequencyc (α = 0.80) storm freq1 3.56 1.11 0.59*** hail freq2 4.20 0.88 0.68*** ice freq3 3.79 1.00 0.68*** heavy rain freq4 3.47 1.15 0.59*** other negative weather conditions freq5 3.36 0.96 0.66*** plant diseases freq6 3.85 0.96 0.52*** perceived risk controld (α = 0.84) storm cont1 2.04 1.22 0.74*** hail cont2 2.19 1.39 0.84*** ice cont3 2.08 1.22 0.82*** heavy rain cont4 2.09 1.23 0.73*** other negative weather conditions cont5 2.35 1.03 0.56*** plant diseases cont6 3.10 1.22 0.32*** *** significant at 1% level. a5-pt likert scale (1=strongly disagree; 5=strongly agree); b5-pt likert scale (1=not a barrier; 5=very important barrier); c5-pt likert scale (1=very unlikely; 5=very likely); d5-pt likert scale (1=no control; 5=very much control). 15the role of trust and perceived barriers on farmer’s intention development. also, the mutual nature of the ist considers the risk sharing among farmers, thus the need to support and cover other members’ losses (meuwissen et al., 2013): for that reason, farmers need to feel assured and a deep trust can play a crucial role for this. interestingly, trust increases if the individual has formerly made use of subsidised rm tools (h9 βpast adoption = 0.21 at 5% level), suggesting that the previous experience somehow positively drives farmers to be more confident. this result somehow considers the importance of the quality (positive / negative) of past experience which, to the best of our knowledge, has not yet been considered by the extant literature (see e.g. enjolras and sentis, 2011; santeramo, 2019) that focused on investigating direct or indirect experience, or distinguishing between long or recent experience over time: indeed, increased trust is necessarily linked to a positive past experience. so far, the literature highlighted how several bureaucratic and administrative hurdles, as for instance the difficulty in monitoring the historical farm income, constrain the development and demand of mfs and the ist (cordier and santeramo, 2019). to this purpose, our results reveal that the individual perceived barriers also matter: in fact, the higher is the perceived existence of barriers to adopting and the lower is the intention to participate in a mf (h2 βperceived barriers = -0.20 at 5% level); as opposite, no significant effect emerges in model 1 and 3. this foreshadows the hypothesis that our respondents would make use of this instrument if they were provided with practical knowledge about it. in this regard, the competent authorities eligible for setting up and managing mfs in accordance with the national law could play a key role in providing farmers with adequate information (e.g. benefits and transparency in the functioning mechanism), and in reassuring them about the streamlined management rules at farm level, in order to encourage the participation. as regards the perceived frequency of risk occurring at farm level, we can appreciate a positive effect on the intention to both adopt the insurance (βperceived risk frequency = 0.19 at 10% level) and participate in a mutual fund (h3 βperceived risk frequency = 0.21 at 5% level), as expected. this is consistent with meraner and finger (2019) who argue that more risk literate farmers are more likely to resort to off-farm measures as insurance contracts. in table 4. gamble task experiment and crra measure of risk aversion and share of farmers choosing each gamble. gamble low payoff (50%) high payoff (50%) expected payoff riska crra rangesb farmers (%) 1 28 € 28 € 28 € 0 r>7 11.4% 2 24 € 36 € 30 € 6 1.2<r<7 18.1% 3 20 € 44 € 32 € 12 0.8<r<1.2 34.3% 4 16 € 52 € 34 € 18 0.5<r<0.8 21.0% 5 12 € 60 € 36 € 24 0.1<r<0.5 6.7% 6 2 € 70 € 36 € 34 0.09<r<0.1 8.6% a the risk is calculated as the standard deviation of the expected payoff. b crra ranges are calculated as the range of r in the function u(x)= x(1−r)/(1−r) for which the subject chooses each gamble assuming constant relative risk aversion utility. 16 elisa giampietri, xiaohua yu, samuele trestini ta bl e 5. s ta nd ar di ze d re gr es si on w ei gh ts fo r m od el 1 , 2 a nd 3 .     m od el 1 (i n t_ in s) m od el 2 (i n t_ m f) m od el 3 (i n t_ is t) in t ra ba r tr u in t ra ba r tr u in t ra ba r tr u β β β β β β β β β β β β tr u 0. 22 4* *   -0 .0 39   0. 23 9* * ba r -0 .1 51   -0 .1 97 **   0. 02 3 rf 0. 19 3*   0. 21 0* *   0. 12 7 rc -0 .0 65   0. 15 6   0. 01 6 ra 0. 19 7* *   -0 .0 12   -0 .0 28 pa st 0. 10 7 -0 .2 34 ** 0. 21 2* * -0 .0 74 -0 .0 32 0. 21 2* * -0 .2 25 ** 0. 00 6 0. 20 8* * po 0. 10 4   0. 26 1* **   0. 27 1* ** st ra 1 0. 10 0   0. 02 0   0. 00 8 st ra 2 -0 .0 36   0. 03 1   0. 14 9* st ra 3 0. 18 7* *   0. 15 5*   0. 13 0 ed u -0 .0 38 0. 26 7* **   0. 19 2* * 0. 26 7* **   0. 11 5 0. 26 7* ** se x 0. 09 8 0. 09 1   0. 13 8 0. 09 1   0. 14 6* 0. 09 1 a g e -0 .1 04 0. 27 1* **   0. 09 7 0. 27 1* **   -0 .0 22 0. 27 1* ** r2 0. 25 0. 15 0. 06 0. 05 0. 27 0. 15 0. 01 0. 05 0. 25 0. 15 0. 00 0 .0 4   χ2 = 4 36 .9 81 ; d f = 3 39 ; χ2 = 4 49 .7 36 ; d f = 3 39 ; χ2 = 4 49 .6 75 ; d f = 3 39 ; p < 0. 00 0 p < 0. 00 0 p < 0. 00 0   c m in /d f = 1. 29 ; c fi = 0 .8 7; t li = 0 .8 6; rm se a = 0 .0 5 c m in /d f = 1. 33 ; c fi = 0 .8 6; t li = 0 .8 4; rm se a = 0 .0 6 c m in /d f = 1. 33 ; c fi = 0 .8 6; t li = 0 .8 5; rm se a = 0 .0 6 si gn ifi ca nt le ve ls a re u nd er th e co effi ci en t: ** * si gn ifi ca nt a t 1 % le ve l; ** s ig ni fic an t a t 5 % ; * s ig ni fic an t a t 1 0% . ra = r is k at tit ud e; b a r = pe rc ei ve d ba rr ie rs ; t ru = t ru st ; r f = pe rc ei ve d ris k fr eq ue nc y; r c = pe rc ei ve d ris k co nt ro l; pa st = p as t ad op tio n; p o = p ol ic y ch an ge ; e d u = e du ca tio n; s ex = g en de r; ag e = ag e; s tr a 1 = pa st _s tr at 1; s tr a 2 = pa st _s tr at 2; s tr a 3 = pa st _s tr at 3. 17the role of trust and perceived barriers on farmer’s intention this study, farmers that more often face some major risks (e.g. storm, hail, ice, heavy rain, other negative weather conditions, plant diseases) are more likely to use those instruments that are specific to cope with yield losses, indeed. as opposite, no significant effect emerges with respect to the ist, which aims at facing income losses instead. as regards the perceived risk control (h4), our findings do not show significant effect in every model. also, the results show that the individuals who are less willing to take risks (namely, more risk averse) are more likely to subscribe an insurance contract (h5 βrisk attitude = 0.20 at 5% level), being consistent with the more recent literature on crop insurance uptake in italy (santeramo, 2019), while contrasting some other authors (hellerstein et al., 2013; van winsen et al., 2016). conversely, we find no significant effect of risk attitude on the participation in both mf and ist. surprisingly, we found that the most important contributor of farmers’ intention to participate in both mfs and to the newly established ist is represented by the changed operating rules provided by the agricultural part of the omnibus regulation (h7 βpolicy change = 0.26 in model 2 and = 0.27 in model 3, both at 1% level). when adequately informed about the existence of advantageous conditions for the adoption, farmers show a positive intention to make use of these tools. hence, this reinforces the importance of information (santeramo, 2019). not surprisingly, the results show a significant and negative effect of past adoption on perceived barriers in model 1 (h10 βpast adoption = -0.23 at 5% level): this is to indicate that having a previous experience with a subsidised rm tool facilitates the understanding (ye et al., 2017), thus reducing the reluctance to adopt the insurance in the future (santeramo, 2019). as opposite, no significant results emerge in model 2 and 3. furthermore, we find no significant effect of past adoption on the intention to subscribe the insurance and to participate in mfs, contrary to enjolras and sentis (2011) and santeramo (2019). as opposite, in model 3 we find a significantly negative effect on the intention to participate in the ist (h6 βpast adoption = -0.23 at 5% level): this may suggest that the farmers in our sample who previously experienced tools other than the ist, in other words unsubsidised mfs’ initiatives or subsidised insurance, are less inclined to experiment with this innovative tool. regarding the hypothesis h8, the results show that the individuals who already apply some risk reduction actions (i.e. self-coping strategies) as investments for new structures and technologies are more likely to adopt the insurance (βpast_strat3 = 0.19 at 5% level) and to participate in a mf (βpast_strat3 = 0.16 at 10% level). on the other hand, farmers in our sample who already use production contracts to secure their income show a higher intention to participate in the ist (βpast_strat2 = 0.15 at 10% level), as expected: indeed, this latter is designed to satisfy farmers’ growing request to protect their income from losses at farm level. a similar finding is shown by lefebvre et al. (2014) on insurance adoption in bulgaria. also, findings reveal that the intention to participate in a mf is higher for farmers with a higher education (βeducation = 0.19 at 5% level), whereas men are more likely to participate in the ist, compared to women (βsex = 0.15 at 10% level). the effect of education in relation to the insurance found no significant evidence, coherently with menapace et al. (2016) and van winsen et al. (2016), as well as the ist. a second line of findings shows that elder and higher educated individuals are more risk averse (β = 0.27 at 1% level for both education and age), thus corroborating the findings of previous research (see e.g. harrison et al., 2007; nielsen et al., 2013; van winsen et al., 2016). 18 elisa giampietri, xiaohua yu, samuele trestini 5. conclusion in the wake of the contingent debate on farmers’ adoption of subsidised risk management tools, this contribution contrasts three conceptual models to test the simultaneous effect of some major interrelated factors on farmer’s propensity to adopt rm tools, as pertains to a real decision-making process: namely, subsidised insurance and, for the first time, mutual funds and the new ist. instead of relying on secondary data as the most part of the existing literature in europe, this study presents the results from a field investigation: this allowed to collect relevant determinants as trust and perceived barriers, that the existing literature on eu risk management in agriculture has not experimentally addressed so far. it is worth to note that the investigation of trust and barriers represents a novelty, similarly to the inclusion of the new ist and the adoption of a sem approach. moreover, it is worth highlighting that this represents an ex-ante analysis which does not consider farmers’ behaviour after the practical introduction of the ist in the agricultural sector. as the scarce attention to trust mainly inspired this study, the most intriguing result is represented by its positive influence on the intention to both subscribe an insurance contract and to participate in the ist. this confirms the key role of this personality trait in decision-making under uncertainty, and suggests that trust probably works as a substitute for knowledge as pertains to the insurance, while it can overcome the lack of experience for the new ist, whose functioning mechanisms and rules are still unfamiliar to farmers. even if we do not focus neither on the nature of trust, nor the context in which it arises, we can suppose that the subsidised rm tools’ adoption may be incentivized in the future by building trust nonetheless. indeed, trust may be essential for the demand of the insurance (e.g. between the farmer and the insurance sale agent), that is notoriously affected by information asymmetry, and the ist especially. in fact, this latter represents a fund that creates a financial reserve through the annual contributions by all members and that compensates only farmers who lose beyond a certain threshold: it follows that farmers can hesitate to participate in such collective tools, compared e.g. with individual instruments. to this purpose, it is recommendable to build strong interpersonal relationships, also confirmed over time, within whichever body designated for the ist’s management (e.g. farmers’ cooperatives or organizations or defence consortia). accordingly, this may represent a trust-making strategy useful to guarantee farmers’ positive expectations regarding the other members’ behaviour, and thus to attract more beneficiaries just fading their initial reticence. the evidence that trust can play a role let us assume that this represents a promising area of research regarding the agricultural risk management, deserving further research to analyse its determinants and to understand how to increase it, in order to provide practical policy recommendations. generally speaking, we can only assume that several strategies implemented by both the insurance companies and the mutual fund’s managers or defence consortia might positively affect farmers’ trust by decreasing the uncertainty linked to rm tools. among these, establishing reputation, increasing transparency on losses and indemnities’ monitoring, and promoting a greater comprehensibility of contracts’ conditions or the operation and membership rules (as regards the new mutual funds), and about the advantages (both in terms of risk coping and affordability) for farmers to adopt rm instruments. furthermore, we found that trust is positively affected by previous adoption, thus evoking the importance of personal satisfaction from past experi19the role of trust and perceived barriers on farmer’s intention ence (e.g. with previous compensations or from the participation in a mutual fund); this confirms the definition of trust by mutti (1998), that is “an expectation born from experiences deemed positive by the individual, developed under conditions of uncertainty”. in addition, our results show an indirect effect of previous experience with rm tools on the intention to adopt (at least for the insurance), mediated by trust. based on this, we can assume that efforts should be made to promote the initial adoption of rm instruments (e.g. encouraging farmers to use these tools through information campaigns or incentives as the reduction of the participation fee for the first year) in order to increase trust and, as a consequence, to positively impact the intention to adopt rm tools further. moreover, this study confirms that the changed rules recently established by the omnibus regulation positively influence the intention to participate in a mutual fund and the ist. on the other hand, we can suppose that the insurance decision is not sensitive to these policy changes probably because of its greater understanding among farmers, as it boasts a long-standing tradition in italy, compared to the other instruments that are less known. since our results show that these recent policy changes are perceived as relevant and suitable by the beneficiaries, it seems increasingly important to bridge the gap between the current policy efforts in implementing specific measures to encourage farmers’ adoption of subsidised rm tools and the lack of knowledge among the potential beneficiaries; indeed, this represents a friction to enlarge the audience of farmers. thus, we merely conclude that a greater information about the operating rules is advocated among farmers, as a greater support to the advisory systems that mainly drive initiatives to increase the knowledge among farmers. in line with this, another interesting evidence comes from the negative effect of perceived barriers on the intention to participate in a mutual fund in our sample: this highlights the necessity of spreading the knowledge about this tool among the potential beneficiaries, as they reasonably have difficulties in evaluating the benefits properly and in understanding the operation of the instrument in depth without an advice. although this paper presents many innovative cues on the heterogeneous literature on rm tools’ adoption, some limitations exist. firstly, the hypothetical nature of the gambles and the absence of a context specification for the measure of risk attitude. to this purpose, despite many authors may criticize this, we remind that many others (e.g. rommel et al., 2019) argue that adding context does not necessarily improve the ability to predict real-world decision-making. nevertheless, due to the fact that risk attitude is not central in our study, this may not necessarily represent a strong limitation at the moment. secondly, the non-representativeness of the italian population prevents our results from being generalizable: for this reason, we cannot discuss policy implications at this stage. in line with this, we highlight that the overarching objective of this research is to provide new evidence on the potential role of factors not yet explicitly explored before, and therefore to pave the way for new research avenues in the field of farmers’ adoption of subsidised rm tools: accordingly, if supported by a wider and more representative sample, further research would reasonably lead to relevant policy implications, e.g. informing policy makers to devise and plan more adequate strategies and initiatives to foster farmers’ adoption. moreover, the analysis does not consider several relationships and factors that the literature found to be significant determinants, due to the necessity to keep the models as parsimonious as possible; 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(2017). farmers’ crop insurance perception and participation decisions: empirical evidence from hunan, china. journal of risk research 20(5): 664-677. the role of trust and perceived barriers on farmer’s intention to adopt risk management tools elisa giampietri1, xiaohua yu2, samuele trestini3,* drivers and barriers of process innovation in the eu manufacturing food processing industry: exploring the role of energy policies federica demaria, annalisa zezza step-by-step development of a model simulating returns on farm from investments: the example of hazelnut plantation in italy alisa spiegel1,*, simone severini2, wolfgang britz3, attilio coletta2 the role of group-time treatment effect heterogeneity in long standing european agricultural policies. an application to the european geographical indication policy leonardo cei1, gianluca stefani2, edi defrancesco1 the impact of food price shocks on poverty and vulnerability of urban households in iran ghasem layani1, mohammad bakhshoodeh1, mona aghabeygi2*, yaprak kurstal3, davide viaggi3 bio-based and applied economics 5(2): 135-151, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-17155 geographical indications, food safety, and sustainability: conflicts and synergies david a. wirth boston college law school, newton, massachusetts, usa date of submission: 2015 6th, october; accepted 2016 28th, july abstract. this paper examines the legal and policy relationships amongst international standards for gis, food safety requirements, and voluntary claims related to a food’s attributes. the paper addresses those relationships within the context of international trade agreements protecting gis, such as the 1994 trips agreement, the eu-canada comprehensive economic and trade agreement (ceta), and the chapter on intellectual property and geographical indications in the transatlantic trade and investment partnership (ttip) currently under negotiation. trade agreements also discipline food safety measures and non-gi indications of quality or safety such as “organic” and “gmo-free.” accordingly, the paper also considers the extent to which international trade agreements such as the wto agreements on the application of sanitary and phytosanitary standards (sps agreement) and technical barriers to trade (tbt) might interact with the analysis. keywords. geographical indications, transatlantic trade and investment partnership (ttip), trips, world trade organisation, sanitary and phytosanitary standards jel codes. k32, k33, q18 1. introduction reports over the past few years of widespread contamination in french wines,1 and prosecution of organic wine producers from bourgogne for failure to use pesticides,2 have 1 “des pesticides dans les vins (pesticides in wines),” que choisir (october 2013) (reporting on study of french wines conducted by excell enology laboratory); “lab tests on french wines find pesticide residue in every bottle,” food safety news, 20 september 2013 (reporting that even organic wine contained pesticide residues), http://www.foodsafetynews.com/2013/09/lab-tests-on-french-wines-find-pesticide-residue-in-every-bottle/#.vrbgefzf8ru. accessed 2 march 2016. 2 “french organic winegrower fined for refusing to spray grapes with pesticide,” the guardian, 7 april 2014, http://www.theguardian.com/world/2014/apr/07/french-organic-winegrower-fined-refusing-spray-grapes-pesticorresponding author: david.wirth@bc.edu 136 david a. wirth highlighted the previously under-appreciated potential for contrary messages or even outright conflicts amongst various regulatory schemes for foodstuffs. these include: • geographical indications (gis); • substantive food safety standards; and • non-gi label indications of quality, safety, or sustainability such as “organic,” “gmofree,” and “sustainably produced.” these tensions have always been latent in the multiplicity of arrangements for communicating information about foodstuffs. to that end, this paper: • identifies the varying purposes of these schemes; • categorizes the various sources of policy and law that apply to them; and • compares their treatment in various contexts, including the transatlantic trade and investment partnership (ttip) and other free trade agreements. the paper consequently examines the potential for conflict or reinforcement amongst gis, food safety standards, and other claims of quality or safety. these claims of food safety and quality share a common feature in that they are transmitted to consumers through labels on the packaging of the product itself, and consequently are representations by the manufacturer or processor as to the identity, safety, or quality of that product. they differ, however, in their underlying policy aims. and they vary greatly in the sources of international law and policy that govern their treatment, including but not limited to international trade. amongst other things, the paper will address those relationships within the context of international trade agreements protecting gis, such as the 1994 trips agreement, the eu-canada comprehensive economic and trade agreement (ceta), and the chapter on intellectual property and geographical indications in the transatlantic trade and investment partnership (ttip), currently under negotiation. trade agreements also discipline food safety measures and, potentially, non-gi indications of quality or safety such as “organic” and “gmo-free.” consequently, the paper examines the extent to which free trade agreements such as the wto agreements on the application of sanitary and phytosanitary standards (sps agreement) and technical barriers to trade (tbt agreement) might interact with the analysis. the paper first identifies sources of law and policy related to each of these three categories of indications of food safety and quality. then the paper examines the relationship amongst these three categories of attributes from the point of view of the variety of sources of law and policy governing them. last, the paper makes recommendations for reconciling these disparate sources of law and policy, as well as policy goals, in the context of free trade agreements such as ttip. the paper examines these questions from a structural and institutional, as contrasted with an empirical, point of view. cide. accessed 2 march 2016. “pesticides in french wine,” new york times, 2 jan. 2014 (noting that “[o]rganic wine producers in the burgundy region of france are facing prosecution for refusing to use pesticides.”), http:// www.nytimes.com/2014/01/03/opinion/pesticides-in-french-wine.html. accessed 4 october 4 2015. 137geographical indications, food safety, and sustainability: conflicts and synergies 2. international protections for geographic indications as is well-known, designations of origin and geographical indications (collectively “gis”) have been protected through legislation adopted at the european union level since 1992 (european council, 1992), as amended through 2012 (european parliament and council, 2012). at present, any number of wines, beers, spirits, cheeses, and processed meats are protected at the european level, including cognac, sherry, roquefort and parmigiano reggiano cheeses, and teruel and parma hams (hughes, 2006). according to the eu, “[t]he protection of geographical indications matters economically and culturally. they can create value for local communities through products that are deeply rooted in tradition, culture and geography. they support rural development and promote new job opportunities in production, processing and other related services (european commission, 2013).” conversely, “geographical names with commercial value are exposed to misuse and counterfeiting. the abuse of geographical indications limits access to certain markets and undermines consumer loyalty. fraudulent use of geographical indications hurts both producers and consumers.” an alternative view is that protections for gis by countries in which they originate operate as trade barriers. a letter from 50 u.s. senators, expressing concern for american agricultural interests, summarizes this position: in country after country, the eu has been using its free trade agreements (ftas) to persuade its trading partners to impose barriers to u.s. exports under the guise of protection for its geographical indications… reportedly, the eu now seeks to more directly impair u.s. competition by imposing restrictions on the use of common food names through ttip. in the states that we represent, many small or medium-sized family owned farms and firms could have their business unfairly restricted by the eu’s push to use geographical indications as a barrier to dairy trade and competition. as we begin to engage in ttip negotiations that are ultimately intended to bring about a better economic climate on both sides of the atlantic by lowering barriers to trade, we strongly oppose the eu’s gratuitous use of gis as a protectionist measure.3 trade disputes over non-tariff barriers frequently arise from differences in national regulatory approaches (wirth, 2013), as in the protection of gis. the eu and the united states protect names of origin in different ways. eu law protects “geographical indications,” whilst u.s. law allows producers to protect these names as trademarks. nonetheless, many eu gis are not protected in the united states, and may not be registrable as trademarks because of their widespread generic use. this means that products can be sold in the united states which use gis protected in europe, but which were not produced in that region. for example, although the gi “parmigiano reggiano” is registered under the eu system, “parmesan” cheese produced in the united states is regularly sold there under that name.4 according to one view, this situa3 letter from 50 u.s. senators to tom vilsack, secretary of agriculture and michael froman, united states trade representative (11 march 2014). http://www.donnelly.senate.gov/download/usda-ustr-cheese-letter. accessed 2 march 2016. 4 larry olmstead, most parmesan cheeses in america are fake, here’s why, forbes, 19 november 2012 (noting that kraft was prohibited from selling its parmesan cheese on european markets). http://www.forbes.com/ 138 david a. wirth tion may mislead consumers in the united states. according to the other, the exclusionary use of a term such as “parmesan” is a protectionist measure that disadvantages potential competitors, to the detriment of consumers as well. although other organizations such as the world intellectual property organization (wipo) have established international legal standards with respect to gis (lisbon agreement, 1958), the most important legal context for addressing gis has been trade agreements, chief amongst them the world trade organization (wto) agreement on trade-related aspects of international property rights (trips) (wto, 1994a). with the collapse of the wto’s doha agenda, the trips agreement is the sole potentially global trade authority establishing rules for gis, and pending the adoption of the ttip, the only one governing eu-u.s. trade. the theory of the trips agreement is unique amongst wto agreements, in that it establishes affirmative obligations for members to enact identified legal protections for intellectual property, such as patents, trademarks, and copyrights. this approach in effect reifies intellectual property, such as creative products like motion pictures, by creating goods that can be identified as such in international trade. other provisions in trade agreements are typically “negative,” in that they constrain governmental behavior. the trips agreement treats gis as a form of intellectual property requiring affirmative governmental protection and mutual recognition in international trade. article 22 applies to all goods, and provides a standard level of protection. article 23 provides enhanced protection to gis related to wines and spirits. article 24 provides for exceptions, as for products where the indication of origin has become generic, or has already been registered as a trademark. the doha mandate identified two agenda items, both of which would have resulted in enhanced protections for gis: (1) the creation of a multilateral register for wines and spirits; and  (2) extension of the higher level of protection  found in article 23 beyond wines and spirits to such products as cheeses and dried meats, both of which are now covered only by the generic provisions of article 22.5 gi protections in the eu-canada comprehensive economic and trade agreement (ceta) provide a higher level of protection to a wider array of products, including cheeses and dried meats, than does trips. as of this writing the entire negotiating text of the ttip is not public, and the eu has released only a portion of its negotiating text addressing customs enforcement of gis (european commission 2015a). based on their previously-stated objectives, is nonetheless probably reasonable to assume that in the ttip negotiations the eu is seeking further protections for gis and that the united states is likely to oppose that agenda. according to the official eu fact sheet on the subject, “we want the us to improve its system in several important ways. these include: protecting an agreed list of eu gis, with rules to stop other producers misusing them; [and] enforcing those rules effectively (european commission, 2015b).” sites/larryolmsted/2012/11/19/the-dark-side-of-parmesan-cheese-what-you-dont-know-might-hurt-you/. accessed 4 october 2015. 5 even before the doha agenda, article 23.4 of the trips agreement called for the trips council to establish multilateral system of notification and registration of geographical indications. 139geographical indications, food safety, and sustainability: conflicts and synergies 3. international standards for food safety international efforts to address food safety generally fall into one of two categories: (1) affirmative (“positive”) efforts to establish minimum standards; and (2) the establishment of trade-based (“negative”) disciplines designed to prevent abuse. 3.1 harmonized international food safety standards in contrast to intellectual property rights, including gis, affirmative international undertakings to assure food safety are generally non-binding or hortatory in nature. one such effort, codex alimentarius, has been undertaken in the context of existing multilateral, government-to-government negotiations, representing the highest level of governmental involvement. another major forum, the international organization for standardization is something of a hybrid, with mixed business and governmental inputs, the relative proportions of which vary from country to country. others are purely private voluntary standards, ranging in coverage from potentially global consortia to individual firms such as importers and retailers. 3.1.1 codex alimentarius codex alimentarius is an international standard-setting body whose members are states. the codex alimentarius commission was created in 1962 as a joint undertaking of the un food and agriculture organisation (fao) and the world health organisation (who). the commission, membership in which is open to all fao and who member states and now numbers 188 (including the eu), has a dual function: “protecting the health of the [sic] consumers and ensuring fair practices in the food trade (codex alimentarius, 2015).” to this end, the commission is specifically charged with adopting advisory multilateral “good practice” standards on such matters as the composition of food products, food additives, labelling, food processing techniques, and inspection of foodstuffs and processing facilities. in the food safety area, as of 2006, in the form of nonbinding standards, guidelines, and codes of practice, codex had evaluated 218 pesticides, establishing 2,930 maximum residue limitations, and published 1,112 food additive provisions for 292 substances (codex alimentarius, 2006). codex standards are primarily intended as hortatory guidance to governments in the establishment of their own national regulations. although it performs a variety of functions and reaches a range of constituencies, codex tends to be an institutional setting to which developing countries with less rather than more regulatory infrastructure can look in establishing national requirements. more specifically, developing country agricultural exporters, typically small and without much political leverage, can also look to codex for assistance in creating export markets. 3.1.2 iso 22000 the international organization for standardization (iso), created immediately after world war ii with headquarters in geneva, is an international federation of standardizing bodies from 162 countries. iso is not an intergovernmental organization, such as 140 david a. wirth the united nations, constituted by multilateral agreement whose members are states represented by governmental authorities. although the iso member from some countries is a governmental entity, iso is primarily a forum for coordinating standardizing efforts by private business. the u.s. member of iso is the american national standards institute (ansi), a private entity. for the united states, the primary, although not sole, participants in iso processes are representatives of private industry. iso’s principal work product consists of voluntary standards adopted by consensus. in contrast to some of the output of intergovernmental organizations, iso standards are strictly hortatory and are not binding under international law. iso standards are both adopted by and addressed to private parties. iso has adopted its 22000 series of standards on food safety, which establish procedurally-oriented requirements for food safety management systems based on haccp (hazard analysis and critical control points) principles (iso, 2005 & 2014). iso 22000 series standards address traceability in the feed and food chain and specific prerequisites for food manufacturing, catering, farming, and food packaging manufacturing (iso, 2007, 2009, 2011, 2013a, 2013b). a distinctive feature of iso 22000 is that it is “auditable” or subject to verification by appropriately accredited private, thirdparty auditors or certifiers (iso/ts, 2013c). iso’s 17000 series also establishes standards for “conformity assessment,” which includes such activities as testing and inspection (iso/ iec 2015, 2013, 2004a, 2004b). 3.1.3 global food safety initiative the global food safety initiative (gfsi) is an industry-initiated effort commenced in 2000 after a number of food safety scares, organized as a belgian non-profit. the original members were eight major retailers: carrefour, tesco, ica, metro, migros, ahold, walmart and delhaize. others that have since joined include cargill, coca cola, starbucks, and mcdonald’s, although the latter has subsequently withdrawn. originally european in its focus, gfsi subsequently became increasingly accepted in the united states, to the point that it is now arguably the principal scheme for coordinated industry action on voluntary food safety standards in north america. gfsi responded to a concern that many of the larger food suppliers and retailers were simultaneously complying with, and being audited by reference to, a variety of private voluntary food safety standards. instead of attempting to harmonize these standards or “schemes,” gfsi “benchmarks” existing food safety schemes. “benchmarking” is “the method by which a food safety scheme is compared to defined [gfsi] requirements . . . to determine equivalence (gfsi, 2013).” currently there are four benchmarked (i.e., determined by gfsi to be equivalent) private voluntary schemes: (1) british retail consortium (brc) global standard for food safety; (2) dutch haccp; (3) international featured standards (ifs) for food; and (4) safe quality food (sqf) standards. gfsi has a defined internal governance system addressing the roles of participating industry members. one of gfsi’s principal missions is to reduce costs by minimizing duplication of audits, as suggested by its slogan “once certified, accepted everywhere.” gfsi does not itself perform any audits or certifications, but it has adopted and enhanced the requirements of iso standards as a model for audits of gfsi standards. 141geographical indications, food safety, and sustainability: conflicts and synergies 3.1.4 other voluntary food safety standards in addition to the four gfsi-benchmarked schemes, there are a variety of other sets of private, voluntary food safety standards. one important one is global gap, roughly an analogue of gfsi, although with many more members, both producers/suppliers and retailers. additionally, various industry sectors have undertaken to establish private voluntary standards. for example the american spice trade association has developed its own guidance. there has recently been concern voiced about the capacity of private voluntary standards established by industry consortia to operate as trade barriers, in effect constricting market access for agricultural exporters in developing countries.6 3.2 trade-based disciplines on food safety standards the treatment of food safety in international trade agreements is designed to discipline otherwise non-discriminatory measures to prevent their abuse as non-tariff barriers to trade. the negotiation of the uruguay round of trade agreements in gatt coincided with a long-running conflict between the united states and the eu over the eu’s prohibition of the sale of meat and meat products treated for growth promotion with any of three synthetic or three natural hormones. the united states, where use of the same hormones is permitted for these purposes, for more than a decade objected to the eu hormone ban as a non-tariff barrier to trade unsupported by scientific evidence. because of concerns such as these, a new agreement on the application of sanitary and phytosanitary standards (sps agreement) (wto, 1994b) was adopted as part of the package of instruments creating the world trade organization (wto). the sps agreement governs measures applied to protect the life or health of humans, animals, or plants from pests, disease-causing organisms, additives, contaminants, and toxins. consequently, the agreement disciplines or governs both food safety measures and agricultural quarantines. an sps measure that is based on international standards, where they exist – in the case of food safety standards, as identified in the codex alimentarius – is entitled to a presumption of validity. if an international standard does not meet a member’s nationally-determined “appropriate level of protection,” and the member’s food safety measure is therefore more rigorous than the international standard, then the measure must be justified by reference to a series of science-based tests. such a measure must be supported by “a scientific justification.” a challenged measure must be “based on scientific principles,” must not be “maintained without sufficient scientific evidence,” and the regulatory process leading to the measure must “take into account available scientific evidence.” a central feature of the sps agreement, is a requirement for a risk assessment, and the principal operative test in the agreement is the need for the measure to be “based on” that risk assessment. the sps agreement consequently codifies requirements for an approach to regulation roughly commensurate with a risk assessment/risk management duality, which confines scientific questions 6 wto doc. g/sps/r/62 paras 132-146 (27 may 27, 2011) (report of wto committee on sanitary and phytosanitary measures expressing concern about private and commercial standards for food). see also members take first steps on private standards in food safety, animal-plant health (wto news, 30 & 31 march 2011), http://www.wto.org/english/news_e/news11_e/sps_30mar11_e.htm. accessed 2 march 2016. 142 david a. wirth to the risk assessment stage. the choice and design of a measure designed to prevent or ameliorate actual or potential harms are frequently identified as attributes of “risk management.” not surprisingly, the first dispute under the uruguay round sps agreement was initiated by the united states and canada against the eu over hormone-treated beef (wto, 1998 & 2008), which became one of the longest-running disputes in wto history because of the eu’s refusal to remove the measure. another important dispute concerned the eu’s treatment of genetically modified foods and crops (wto, 2006). the north american free trade agreement (nafta, 1992) and other regional and bilateral trade agreements to which the united states is party (cafta, 2004) contain provisions on sanitary and phytosanitary measures, as does the eu-canada comprehensive economic and trade agreement (ceta, 2014). the ttip negotiations include a portion on sanitary and phytosanitary measures. according to the eu’s official fact sheet (european commission, 2015c) and its proposed negotiating text (european commission, 2015d), the eu does not appear to anticipate new substantive disciplines for food safety measures. indeed, the fact sheet states that standards for food safety, gmos, and animal welfare will not be relaxed. the eu proposal seems to be more focused on streamlining the process for entry of eu agricultural exports into the u.s., particularly as the united states implements new food safety legislation (u.s. congress, 2010). the u.s. for its part appears to be seeking enhanced substantive disciplines on food safety measures (ustr, 2014). 4. other international standards for labeling of food in contrast to gis and food safety standards, many of which have been in place for some time, foodstuffs are now voluntarily labeled for any number of other attributes, including: • organically produced; • sustainably produced; • natural or all-natural; • gmo-free; • antibiotic-free; • hormone-free or no hormones added; • free-range or cage-free; • grass-fed or pasture-raised; and • humane raised and/or handled. some consumers also seem to be particularly interested in purchasing locallyproduced food, lending greater importance to non-gi indications of the locality of origin. such claims or labels are specific to foodstuffs, but there are many others for non-food articles in commerce, including those awarded by private voluntary certifying organizations. examples include fair trade coffee and sustainably harvested timber. although the eu has mandatory standards for animal welfare (european council, 1998), there are no comparable, comprehensive governmentally-established requirements for the treatment of farm animals during their lifetimes in the united states. 143geographical indications, food safety, and sustainability: conflicts and synergies 4.1 codex alimentarius harmonized standards for food labelling because of the proliferation in the range of claims that have been made with respect to an attribute of a foodstuff or the process by which it has been produced, the coverage of international standards is less than complete. moreover, the source of labels can vary greatly, from unilateral claims that are basically advertising to governmentally-mandated requirements. although not directly applicable to food, iso has developed a generally-accepted approach to categorizing labels. unilateral claims made by manufactures are known as “type ii” labels (iso, 2000). “type i” labels address governmentallyor privatelyestablished schemes that include a single mark (iso, 1999a), such as the eu ecolabel for identifying products and services that have a reduced environmental impact throughout their life cycle or the u.s. energy star logo for identifying energy-efficient personal computers. both of these schemes are voluntary, with the standards for awarding the label established by a governmental authority. type iii labels transmit disaggregated quantified information in a manner similar to the identification of fat, carbohydrates, and protein on nutrition labels in the united states and other countries (iso 1999b). although labeling for many of the quality attributes identified above is regulated at the national or supranational (eu) level, the efforts at more extensive international harmonization have been few (czarnezki et al., 2015) and are largely confined to codex alimentarius. the codex commission has established guidelines for a limited number of defined categories of labeling including: • nutrition labeling (codex alimentarius, 1985); • organically produced foods (codex alimentarius, 1999); • gmos (codex alimentarius, 2011). because of codex’s government-to-government multilateral character and the structure of the instruments, codex guidelines, although non-binding, in effect replace unregulated type ii (unilateral) claims with governmentally-established standards. the codex guidelines may recommend mandatory labeling, as in the case of nutrition labeling, or voluntary labeling to governmentally-established requirements, as in the case of organically produced foods and gmos. the codex guidelines for organically produced foods are very detailed and address such issues as restrictions on use of pesticides and synthetic fertilizers, absence of gmos, prohibition of subtherapeutic doses of antibiotics and hormones for preventative purposes, and mandatory use of organic feed. the codex guidelines for labelling of gmos, which were under consideration for two decades, have been particularly contentious because of opposition by the united states, which eventually agreed to guidelines recommending a voluntary as opposed to mandatory requirement. 4.2 trade-based disciplines on food labeling all these claims relate to some attribute of the foodstuff concerned, and are typically contained in a label. the uruguay round agreement on technical barriers to trade (wto, 1994c), which in many respects is similar in structure to the uruguay round sps 144 david a. wirth agreement, governs a wide variety of regulatory requirements that have environmental or public health implications, including specifications for consumer products and children’s toys, appliance efficiency criteria, and vehicle fuel efficiency standards. the tbt agreement specifically covers “packaging, marking or labelling requirements.” similar in structure to the wto sps agreement, the tbt agreement requires the utilization of “relevant international standards” where they exist in promulgating governmentally–mandated regulatory requirements. “standards,” as that term is used in the agreement, includes voluntary guidelines adopted by an “international standardizing body,” a term which encompasses both codex alimentarius and iso. governmental regulations that conform to the standards adopted by such an international standardizing body are entitled to a rebuttable presumption of validity. to justify a departure from international standards, presumably because they are insufficiently rigorous, a wto member would have to demonstrate that a harmonized international standard “would be an ineffective or inappropriate means for the fulfilment of the legitimate objectives pursued.” the wto jurisprudence interpreting the tbt agreement suggests that the threshold for justifying a departure from an international standard is high. one of the first major disputes decided by the wto appellate body concerned the labelling of sardines by the eu (wto, 2002), which is the subject of a codex alimentarius standard. although the wto appellate body established that the burden is on the challenging party to demonstrate that the international standard is both effective and appropriate, the conclusion that the codex standard met this test turned upon the eu’s failure to establish facts related to consumers’ perceptions as to the meaning of the term “sardine.” the appellate body has since addressed several disputes concerning labeling requirements established by the united states. as in the sardines dispute against the eu, u.s. labeling schemes for meat (wto, 2012a) and tuna (wto, 2012b) were found to be inconsistent with the tbt agreement, although for failure to satisfy the national treatment requirement in the agreement and not because of departures from international standards. the appellate body’s report, reversing the panel’s findings on this issue, clarified and narrowed the scope of a “relevant international standard” within the meaning of article 2.4. in particular, an international standardizing body must be open to the relevant body of every country. the appellate body also interpreted key terms in the tbt agreement relating to international standards as used in article 2.4: “international;” “recognized activities in standardization;” and “body” as used in the term “international standardizing body.” thanks to the structure of the tbt agreement, those national regulatory requirements that are not based on the output, when it exists, of such a body are therefore particularly vulnerable to challenge as unnecessary obstacles to international trade. and the sorts of governmental requirements that are most likely to create impediments to international trade are those that are more rigorous than the international requirements, which may well be the product of a least-common-denominator consensus. in a setting such as iso, those standards originate from an industry-dominated forum. the result is that, through a trade agreement, the requirements of non-binding, hortatory guidance intended to establish minimum requirements in the form of good practice standards – a floor – are transformed by the tbt agreement into a legally binding 145geographical indications, food safety, and sustainability: conflicts and synergies outer limit of rigor—a ceiling. conversely, those national measures that conform to international standards are effectively insulated from challenge. within the tbt agreement, international standards may operate either as a sword – a negative screen used to challenge a domestic regulatory action, as in ec-sardines – or a shield – an internationally agreed reference point that bolsters the legitimacy of a national measure (wirth, 2009). as with sanitary and phytosanitary standards, the north american free trade agreement (nafta, 1992) and other regional and bilateral trade agreements to which the united states is party (dr-cafta, 2004) contain provisions disciplining technical barriers to trade, as does ceta (ceta, 2014). the ttip negotiations include a portion on technical barriers. according to the eu’s official fact sheet (european commission, 2015e), it is seeking to: • be able to use international standards (such as those agreed in the international organization for standardization-iso) to make it easier to export to the us; such standards are widely used in the eu and around the world; • eliminate or at least reduce unnecessarily duplicative or burdensome procedures for checking products; • ensure easy access to information on regulations and standards that apply to goods in the us and the eu; • improve cooperation between eu and us standardisation bodies when they draw up new standards; this will help reduce differences and they might even be able to agree on common standards; • get more transparency in the us system on standards. the eu’s proposed negotiating text (european commission, 2015f) would incorporate the wto tbt agreement by reference. specifically with respect to labeling, after reiterating the requirements of article 2.2 of the wto tbt agreement, the eu would add a new discipline: compulsory marking requirements, while continuing to provide the necessary information to the user or consumer as well as to public authorities regarding compliance of products with specific requirements, should be limited as far as possible to what is essential and to what is the least trade restrictive to achieve the legitimate objective pursued. the u.s. for its part is seeking: to increase transparency and openness in the development of standards and technical regulations, ensure that u.s. bodies are permitted to test and certify products sold in europe, promote eu recognition of international standards used to support global trade by u.s. exporters and producers, and establish an ongoing mechanism to discuss tbt concerns (ustr 2014). 5. conclusion as an aid in comparing and analyzing these three categories of regulatory provisions relating to food, it is useful clearly to summarize the sources of international requirements (table 1). 146 david a. wirth table 1. comparison of international legal standards for gis, food safety standards, and non-gi claims of food quality. international protections for national measures affirmative (positive) harmonization trade-based (negative) disciplines gis trips ttip? no need – international protection for nationallyestablished gis in trips none food safety standards not applicable codex (non-binding) iso (non-governmental, non-binding) private certifying organizations wto sps agreement ttip sps chapter non-gi labeling of quality, sustainability, humane treatment, etc. not applicable codex (non-binding), but coverage very limited wto tbt agreement ttip tbt chapter table 1 is very revealing in highlighting one of the singular attributes of free trade agreements, namely their asymmetric character. by and large, free trade agreements contain no minimum standards of performance in the fields of food safety, food quality, or in most other areas of social and regulatory policy. intellectual property is an important exception, in which unilateral, national measures are given international protection. consequently, gis are structurally favored, as they fall within the ip regime. returning to the examples at the beginning of this paper, the laboratory tests conducted on french wines detected   residues of an insecticide (bromopropylate) and a fungicide (carbendazim) prohibited in france. but according to an eu parliamentary question,7 there are no maximum residue limitations for pesticides in wine, in contrast to those for food and other beverages. then there is m. giboulot, who produces high-quality organic or “bio-dynamic” red and white wines from 35 acres of vines in burgundy under the appellations “côte de beaune” and “haute côte de nuits.” he refused to spray his grapes with pesticides to fight flavescence dorée, a bacterial disease spread by the leaf hopper, scaphoideus titanus. although his conviction was overturned, there is a serious question about the consistency of the strict requirements of gis, which are protected at the international level, and organic standards, which are subject at the international level only to non-binding affirmative harmonization in codex. if either of these considerations – minimal food safety standards for residues of prohibited substances or internationally-agreed requirements for the designation “organic” – were to be folded into the relevant gis, the international situation would be entirely 7 http://www.europarl.europa.eu/sides/getdoc.do?type=wq&reference=e-2013-011147&language=hu. accessed 2 march 2016. 147geographical indications, food safety, and sustainability: conflicts and synergies different. in those situations the relevant standards, food safety or organic as the case may be, would be mandated as part of the gi requirements and then affirmatively protected through the trips agreement. one could even imagine utilizing the theory of gis for the benefit of food safety, quality, sustainability and the environment. for instance, an “appellation nature contrôlée” could be created for the promotion of organic wines.8 in any event, this analysis demonstrates that the long-held view that free trade agreements like ttip are incompatible with affirmative obligations with respect to food quality, food safety, the environment, and sustainability is simply incorrect (wirth, 1992). gis receive affirmative benefits in trade agreements, and at the absolutely highest level in the form of a monopoly. at the end of the day, gis are simply a protected indication of quality – albeit presumably high quality – and at an even more rudimentary level fundamentally the identification of the place of origin of a particular foodstuff. but as demonstrated in this paper, the high level of protection accorded gis is unrelated to food safety or many other attributes that might be labeled as of concern to consumers, such as organic production methods or gmo-free content. at the same time, safety and other attributes are subjected to negative requirements designed to protect abuses, while gis receive affirmative protection. as a matter of principle, it is difficult to justify the highly disparate treatment of these indications of quality and safety. although gis are treated for trade purposes as having a proprietary component – hence the treatment as intellectual property – this is purely a legal construct designed to assure protections for particular interests and discrete social policy goals. at present, trade agreements are deployed in exactly the opposite direction with respect to all other domestically-established standards for food. the negative disciplines in the sps and tbt agreement exert downward pressure on excessively rigorous standards, which are entirely analogous to the demanding requirements set by the protected regime of gis. it would be entirely consistent to extend such an approach to regulation designed to address other attributes of food safety and quality, and those regulations could even be incorporated into the existing regime of geographical indications treated as intellectual property. acknowledgements this paper was supported by a generous grant from the dr. thomas f. carney ’47 gift fund. the author gratefully acknowledges helpful comments and advice from sherry xin chen, justin hughes, and joan shear. the responsibility for all views expressed in this paper is nonetheless the author’s own. portions of this paper are based on the author’s previously published writings. 8 a blog by this very name exists to promote the concept. http://appellationnaturecontrolee.com/. accessed 2 march 2016. 148 david a. wirth references world trade organization declarations and agreements world trade organization (2001). doha ministerial declaration. wto doc. wt/ min(01)/dec/1. world trade organization (1994a). agreement on trade-related aspects of intellectual property rights. 1869 u.n.t.s. 299. world trade organization (1994b). agreement on the application of sanitary and phytosanitary measures. 1867 u.n.t.s. 493. world trade organization (1994c). agreement on technical barriers to trade. 1868 u.n.t.s. 120. other international agreements lisbon agreement for the protection of appellations of origin and their international registration (1958). as revised at stockholm on 14 july 1967,  and as amended on 28 september 1979, 923 u.n.t.s. 189, and 21 may 2015, http://www.wipo.int/edocs/ mdocs/geoind/en/li_dc/li_dc_19.pdf (geneva act of the lisbon agreement on appellations of origin and geographical indications). accessed 2 march 2016. north american free trade agreement (1992). 32 i.l.m. 296 (1993). dominican republic-central america-united states free trade agreement (2004). https://ustr.gov/trade-agreements/free-trade-agreements/cafta-dr-dominicanrepublic-central-america-fta/final-text. accessed 2 march 2016. eu-canada comprehensive economic and trade agreement (2014). http://trade. ec.europa.eu/doclib/docs/2014/september/tradoc_152806.pdf. accessed 2 march 2016. codex alimentarius documents statutes of the codex alimentarius commission (2015). procedural manual. ftp://ftp.fao. org/codex/publications/procmanuals/manual_23e.pdf. accessed 2 march 2016. codex alimentarius (2011). compilation of codex texts relevant to the labeling 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(l 221) 23-27.  european council (1992). regulation (eec) no. 2081/92 on the protection of geographical indications and designations of origin for agricultural products and foodstuffs, 1992 o.j. eur. comm. (no. l 208) 1-8. european parliament and council (2012). regulation (eu) no 1151/2012 on quality schemes for agricultural products and foodstuffs, 2012 o.j. eur. comm. (no. l 343) 1-29. u.s. congress (2010). food safety modernization act, pub. l. no. 111-353, 124 stat. 3885. wto panel and appellate body dispute settlement reports world trade organization (2012a). united states certain country of origin labelling (cool) requirements, wto doc wt/ds384/ab/r, wt/ds386/ab/r, ab-2012-3. world trade organization (2012b). united states measures concerning the importation, marketing and sale of tuna and tuna products, wto doc wt/ds381/ab/r, ab-2012-2. 150 david a. wirth world trade organization (2008). united states continued suspension of obligations in the ec-hormones dispute, wt/ds320/ab/r. world trade organization (2006). european communities measures affecting the approval and marketing of biotech products, wt/ds291/r, wt/ds292/r, wt/ dr293/r. world trade organization (2002). european communities trade description of sardines, wt/ds231/ab/r. world trade organization (1998). european communities measures concerning meat and meat products, wt/ds26/ab/r, wt/ds48/ab/r. other authorities american spice trade association (2011). clean, safe spices: guidance from the american spice trade association. http://www.astaspice.org/food-safety/clean-safespices-guidance-document/. accessed 2 march 2016. czarnezki, j.j., homan, a. and jeans, m. (2015). creating order amidst food eco-label chaos. duke environmental law & policy forum 25(2): 281-311. european commission (2013). geographical-indications, http://ec.europa.eu/trade/policy/accessing-markets/intellectual-property/geographical-indications/. accessed 2 march 2016. european commission (2015a). textual proposal: customs enforcement of intellectual property rights. http://trade.ec.europa.eu/doclib/docs/2015/july/tradoc_153672.pdf. accessed 2 march 2016. european commission (2015b). intellectual property rights (ipr) and geographical indications (gis) in ttip, http://trade.ec.europa.eu/doclib/docs/2015/january/tradoc_153020.7%20ipr,%20gis%202.pdf. accessed 2 march 2016. european commission (2015c). food safety and animal and plant health in ttip. http:// trade.ec.europa.eu/doclib/docs/2015/january/tradoc_153004.3%20food%20safety,%20a+p%20health%20(sps).pdf. accessed 2 march 2016. european commission (2015d). textual proposal: sanitary and phytosanitary measures. http://trade.ec.europa.eu/doclib/docs/2015/january/tradoc_153026.pdf. accessed 2 march 2016. european commission (2015e). technical barriers to trade (tbts) in ttip. http://trade. ec.europa.eu/doclib/docs/2015/january/tradoc_153003.2%20tbts.pdf. accessed 2 march 2016. european commission (2015f). textual proposal: technical barriers to trade (tbt). http://trade.ec.europa.eu/doclib/docs/2015/january/tradoc_153025.pdf. accessed 2 march 2016. global food safety initiative (2013). gfsi guidance document, version 6.3. http://www. mygfsi.com/images/mygfsi/gfsifiles/gfsi_guidance/gfsi_guidance_document.pdf. accessed 2 march 2016. hughes, j. (2006). champagne, feta, and bourbon the spirited debate about geographical indications. hastings law journal 58(2): 299-386. office of the united states trade representative (2014). u.s. objectives, u.s. benefits in the transatlantic trade and investment partnership: a detailed view. https://ustr. 151geographical indications, food safety, and sustainability: conflicts and synergies gov/about-us/policy-offices/press-office/press-releases/2014/march/us-objectivesus-benefits-in-the-ttip-a-detailed-view. accessed 2 march 2016. wirth, d.a. (2013). the world trade organization dispute over genetically modified organisms: the precautionary principle meets international trade law. vermont law review 37(4): 1153-1188. wirth, d.a. (2009). the international organization for standardization: private voluntary standards as swords and shields. boston college environmental affairs law review 36(1): 79-102. wirth, d.a. (1992). the international trade regime and the municipal law of federal states: how close a fit? washington and lee law review 49(4) 1389-1401. bio-based and applied economics 5(3): 287-324, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-20270 review article the political economy of trade-related regulatory policy: environment and global value chain alessandro olper department of economics, management and quantitative methods university of milano and centre for institutions and economic performance licos (ku leuven). date of submission: 2016 10th, october; accepted 2017 3rd, february abstract. this paper proposes a selective review of the political and economics literature on non-tariff measures (ntms), with special emphasis on the so-called behindthe-border measures, such as environmental and food standards. we focus on both theory and existing empirical evidence. the complex issue of international trading rules over domestic regulatory standards, as well as the emerging role of outsourcing and global value chains, is also considered. keywords. endogenous policy, environmental and food standards, heterogeneous firms, global value chains jel codes. f12, f13, d72, q18, q56 1. introduction in the last decades, international markets have been characterized by two interconnected phenomena. on the one hand, the world economy has experienced a never reached level of integration through an impressive growth of international trade and capital flows. this has been largely the result of trade and financial liberalization within the gatt-wto system (see caliendo et al., 2015), as well as of the ict (information and communication technology) revolution that shaped a profound reorganization of the world economy. on the other hand, a second phenomenon – the subject of this paper – has been the global proliferation of government regulatory activities mainly related to process and product standards, such as technical, quality and environmental regulations.1 trade econ1 a parallel trend is related to the explosion of private and voluntary standards set by business groups and large retailers finalized, among other things, to better coordinate modern global value chain (see beghin et al., 2015; swinnen et al., 2015). however, because this paper refers on the political economy of regulatory protection, the focus will be mainly on public standards. among others, see mccluskey and winfree (2009) and vandemoortele and deconinck (2015) on the interaction between private and public standards, also from a political economy perspective. corresponding author: alessandro.olper@unimi.it 288 alessandro olper omists have called this trend “regulatory protection”, referring to the myriad of cost-raising, behind-the-border measures, that keep substantially inhibiting trade (baldwin, 2000; staiger, 2012). according to the unctad, these policy measures are “non-tariff measures (ntms) other than ordinary customs tariffs, that can potentially have economic effects on international trade in goods, changing quantities traded, prices or both”. ntms are de jure regulations, prima facie imposed to protect consumers from unhealthy or low quality products and the environment from environmental damages. these measures should be the firstor second-best instruments to achieve public policy objectives, such as market failures, information asymmetries and imperfect competition (wto, 2012). however, de facto they often induce an increase in trade costs acting as conventional barriers to trade (wto, 2012; orefice, 2015; beghin et al., 2015a).2 in what follows, i propose a review of the literature on the political economy of trade and regulatory standards, considering both conceptual models and the empirical evidence, with special emphasis on environmental and food standards. these specific regulatory measures represent just a sub-set of ntms. however, in the last decades they became largely the most important and diffuse type of regulatory standards affecting trade in the agri-food sector. the key objective is not just to systematically survey the current literature. indeed, given the importance and the complexity of the field, this issue has been already surveyed from different points of view by several recent contributions, including baldwin (2000), oates and portney (2003), sturm (2003), wto (2012), beghin et al. (2015a) and swinnen et al. (2015). i focus instead on what i believe are some of the key contributions of the political economy of trade policy as extended to product and environmental regulations. in doing that, i give emphasis also to the implications stemming from considering trade models with firm heterogeneity, the emerging issue of international trading rules over standards and the role of global value chain. the paper is organized as follows. after a short overview on the recent diffusion of ntms (section 2), i introduce the basic logic behind the political economy approach (section 3). then, i discuss shortly the efficiency, distributional and trade consequences of standards (section 4). next, i present the leading approaches to endogenous trade policy formation and their extensions to quality and environmental standards (section 5). finally, i discuss the problem of negotiating international trading rules over ntms, also in the context of modern global value chains (section 6). the conclusions discuss the implications in considering regulatory policies as endogenous, suggesting future research avenues. 2. stylized facts about ntms in this section i consider the definition of non-tariff measures and some emerging evidence about their diffusion and their trade effects. 2.1 definition and quantification of standards as the term suggests, ntms are policy measures other than tariffs that can affect trade flows. they can usefully be divided into three categories (staiger, 2012). 2 note, however, that by providing more information about a given good, or by improving the characteristics of the good itself, several ntms may be also a catalyst for trade (see, e.g., disdier et al., 2008; maertens and swinnen, 2009; olper et al., 2014). 289the political economy of trade-related regulatory policy a first category of ntms includes those imposed on imports, including import quotas, import prohibitions, import licensing, customs procedures and administration fees. a second category is made up by those imposed on exports, such as export taxes, export subsidies, export quotas, and export prohibitions. these first two categories encompass ntms that are applied at the border, either to imports or to exports. a third category of ntms, the focus of this review, includes those imposed internally in the domestic economy, also called behind-the-border measures. this category embraces domestic legislation covering an increasing numbers of product characteristics, such as technical, safety, environmental, health, nutrition, and labor standards, as well as internal taxes and domestic subsidies.3 in what follows, the main focus is on behind-the-border standards, and in particular on sanitary and phytosanitary (sps) standards and technical barriers to trade (tbt), disciplined in the wto by the respective sps and tbt agreements. within behind-the-border measures a classical distinction is between product standards and process standards. product standards have to do with the characteristics of goods, in particular with respect to aspects such as quality and safety, e.g. the maximum content of methyl mercury in fish. process standards instead apply to the condition under which goods are produced, packaged, or refined, e.g. dairy product without hormones. a related distinction is between vertical and horizontal standards. vertical standards involve regulations that can readily be characterized by being more or less stringent. as an example, the eu versus us legislation on growth hormone in beef or on aflatoxin, with the first being more stricter than the second. this kind of legislation is more complex because it exerts emotion and a strong media attention as it is identified as protecting local consumers from low-quality imports (baldwin, 2000). horizontal standards, however, are often more common especially in non-food manufacturing goods. in this case, a typical example is the difference in electric plugs around the world. note that, when two countries have such horizontal standards, the differences tend to create reciprocal trade barriers. to date, the most comprehensive information of different ntms, comes from the countries’ notifications at the wto, currently collected in the i-tip database. although still problematic, the wto data have the advantage to offer a comprehensive picture across countries, over time and also among different types of ntms.4 moreover, as recently shown by the wto 2012 report on ntms (wto, 2012), other databases, such as the unctad-trains or the firm-level survey collections of the world bank, broadly confirm the ntms patterns of the wto data. as far as december 2015, the sps and tbt measures notified to the wto were 11,174 and 18,279, respectively. this number is very high compared to all others (border) ntms, equal to about 5,500. currently, sps and tbt measures represent approximately 3 note moreover that, any standard needs a peculiar testing procedure to check the extent to which products are in compliance with the standard. the existence of asymmetry in this testing procedure could create further trade costs and tensions in trade relations, as it happened between the ue and japan in the 1990s (baldwin, 2000). 4 as explained by the wto i-tip database, the difference between notifications vs. measures in force is problematic because the same measure can be notified through several notifications, or several measures can be notified in only one notification. moreover, another lack comes from the fact that very rarely countries notify the withdraw of the measures, and moreover members have not the obligation to notify all measures imposed. hence, it is not so clear if the number of the sps and tbt measures notified to the wto are inflated or underestimated, although the former hypothesis appears more likely. 290 alessandro olper 85% of all the ntms notified to the wto. this is the trend resulting over the last two decades during which border ntms have significantly decreased, while sps and tbt measures have strongly increased (figure 1). the growing importance of sps and tbt standards is confirmed by other data sources. for example, considering the unctadtrains data, in 1994 about 55% of all ntms were classified as sps or tbt, a number that increased up to 85% in 2004 (beghin, 2006). similarly, for the us, data from 2014 ntms reveal a share of sps and tbt measures higher than 85% (pacca and olper, 2016). starting from 2005, the growth of ntms does not show a clear trend, although in the aftermath of the financial crisis they started increasing again. yet, in the last period, sps figure 1. sps and tbt notifications 1995-2010 (# of notifying countries and measures). 0 10 20 30 40 50 60 0 200 400 600 800 1000 1200 1400 1600 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 (a) sps numer of measures (left axis) numer of notifying countries (right axis) 0 10 20 30 40 50 60 70 80 90 100 0 200 400 600 800 1000 1200 1400 1600 1800 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 (b) tbt numer of measures (left axis) numer of notifying countries (right axis) source: wto (2012), based on wto i-tip database. 291the political economy of trade-related regulatory policy and tbt measures are still growing in their importance (wto, 2012). a significant confirmation of the sps/tbt intensification comes from the inspection of the so called “specific trade concerns” (stc). these are formal complaints raised by wto members to the sps and tbt committee on particular trade inhibiting standards. the stc concerning both sps and tbt measures have increased significantly from 1995 to 2010 (figures 2 and 3). such trend has been confirmed in the last years. sps and tbt measures are mainly raised by developed countries, even if some important emerging economies have been particularly active in the recent period (e.g. china, figure 2. # sps specific trade concerns (stc) “maintaining” and “raising” countries as a share of the total number of countries by the level of development. 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1995-2000 2000-2005 2005-2010 (a) sps (maintaining) developed developing 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1995-2000 2000-2005 2005-2010 (b) sps (raising) developed developing source: wto (2012), based on wto stc database. 292 alessandro olper india and brazil). considering again stc, developed countries do not only maintain the largest number of stc in place, but they are also the most active in raising complaints against sps/tbt measures (figures 2 and 3). this pattern appears to be in line with the broad picture coming from the world bank surveys, where implementation issues related to standards are the most important source of concerns for exporters from developing countries toward developed ones. figure 3. # tbt specific trade concerns (stc) “maintaining” and “raising” countries as a share of the total number of countries by the level of development. 0 0,1 0,2 0,3 0,4 0,5 0,6 1995-2000 2000-2005 2005-2010 (c) tbt (maintaining) developed developing 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1995-2000 2000-2005 2005-2010 (d) tbt (raising) developed developing source: wto (2012), based on wto stc database. 293the political economy of trade-related regulatory policy 2.2 trade effect of ntms the key question concerning standards is not just their (likely) trade effects, but their overall welfare effects. yet, as a matter of fact, the large part of the empirical literature over ntms and standards has focused its attention mainly on the more simplistic issue of the trade effect.5 from this point of view, the extent to which the large diffusion of ntms, and in particular of sps/tbt measures, negatively affects trade flows, is not out of contention (see wto (2012) for a recent survey). while many studies showed that, at least on average, ntms significantly reduce trade flows (see disdier et al., 2008; olper and raimondi, 2008; vigani et al., 2012), sps measures, and more often tbt standards, could be trade and quality enhancing, especially when harmonized and/or voluntary technical standards are considered (see moenius, 2004; shepherd, 2007; olper et al., 2014). to rationalize the large and contrasting literature on the trade effect of standards, li and beghin (2012) have recently proposed a meta-analysis of the trade effects of sps and tbt measures. main results showed that: international trade in agricultural and food products tends to be more impeded by these barriers in comparison to trade in manufacturing; sps regulations affect negatively trade in agriculture and food products coming from developing countries toward rich countries, and less between rich countries; finally, the trade effects of other technical regulations are more uncertain. the most recent empirical literature on the trade effects of ntms started to shed new light on two important questions. first, their effect on the probability to export considering firm level trade. second, the old but still unresolved issue of the interaction between tariffs and ntms. the extent to which sps/tbt standards, other than the volume of trade, also affect the number of firms entering in the export market – the extensive trade margin – has been recently addressed by fontagnè et al. (2015). by matching france custom trade data with sps specific trade concerns raised by the eu against its trading partners, these authors find three important results. firstly, the probability of firm exiting from the export market is higher in sps-imposing foreign markets. secondly, the value exported by each firm is decreasing in the presence of sps, but instead the export unit values (a proxy for price) are increasing. hence, compliance with new sps standards may require additional fixed costs to adapt the production process. finally and interestingly, these negative effects of sps on the intensive and extensive trade margins are significantly attenuated when large firms are considered. all these stylized facts are consistent with the sps-induced redistribution of market shares from small toward large firms, predicted by firms heterogeneity trade models (abel-koch, 2013). we will come back on this point shortly. a second important issue is the relation between standards and tariffs (figures 4 and 5). globalization and the diffusion of standards are clearly two interconnected phenomena. however, understanding whether custom tariffs and standards, and more in general ntms, are substitutes or complements is a challenge. the existing empirical evidence, although scarce, seems to support more the substitution hypothesis. however, there is 5 see disdier and marette (2010) for one of the few contributions that investigated simultaneously the trade and welfare effects of sps standards. 294 alessandro olper figure 4. sps trade concerns and applied tariff level 1996-2010. -3 2 7 12 0 10 0 20 0 30 0 199619971998199920002001200220032004200520062007200820092010 ta rif f ( in p er ce nt ag e) hs -4 co de s ta rg et ed b y st cs o n sp s (b y at le as t o ne co un tr y) numer of hs-4 targeted by sps tariff on agricultural products source: orefice (2015), based on wto stc database and trains (for applied tariff). figure 5. tbt trade concerns and applied tariff level 1996-2010. 0 2 4 6 8 10 0 20 0 40 0 60 0 80 0 199619971998199920002001200220032004200520062007200820092010 ta rif f ( in p er ce nt ag e) h s4 co de s ta rg et ed b y st cs o n tb t (b y at le as t o ne co un tr y) numer of hs-4 targeted by tbt tariff on manufacturing products source: orefice (2015), based on wto stc database and trains (for applied tariff). 295the political economy of trade-related regulatory policy also evidence of complementarity in the literature (see ray, 1981; goldberg and pavcnik, 2005). the work by kee et al. (2009) perhaps represents the most compelling evidence on the substitutability between tariff and the ad-valorem equivalent (ave) of ntms. using cross-sectional data for 91 countries, they find support to the substitutability hypothesis. yet, given the conceptual difficulty and the huge data problems in transforming the ntm trade effects in their respective ave (beghin et al., 2015b)6, cross-sectional evidence cannot be taken as definitive answer. beverelli et al. (2014) make an important contribution by exploiting the cross-country and time-series dimension of sps/tbt specific trade concerns. interestingly, considering these specific measures, they find a strong confirmation of the substitutability hypothesis, meaning that sps and tbt standards are raised in response to a tariff cut, and this is especially true in developed countries where the cost of meeting the standards (for home firms) is relatively lower.7 instead, their findings support a complementarity relationship between tariffs and tbt in developing countries, while not for sps, where the substitution effect prevails also in developing countries. all these results have implications for the political economy of standards. finally, an important point is to recognize that the mere fact that sps/tbt standards restrict trade and competition is by no means an argument for their removal (swinnen et al., 2015). good governance requires regulation to protect environment, health, safety and well-being of citizens, animals, plants as well as to facilitate market transactions and global value chains. however, as many evidence and trade disputes have already showed, the problem with standards is the difficulty to know whether a particular regulation serves the public or the protectionists’ interest and indeed, both motives are often combined in a single sps/tbt (baldwin, 2000). 3. a general political economy framework figure 6 presents a stylized view of the democratic policymaking. starting from the box and arrows on the right in the figure, voters and interest groups in society have conflicting preferences over trade policy. political institutions – i.e. electoral rules, forms of government and decision rules – aggregate these preferences into specific political outcomes. this result translates into ad hoc political incentives, transforming economic and political interests in economic policy decisions, such as trade policies, by interacting with private interests and decision rules. moving to the box and arrows on the left of figure 6, trade policies interact with markets influencing the prices of different goods, and factors’ return in different industries (and/or firms). these market outcomes will affect individual preferences over trade policy depending on the sources of individual income. the economic (left) and political (right) boxes of the figure are occasionally called the “demand side” and the “supply side” of the political market, respectively (see rodrik, 1995). 6 beghin et al. (2015b) showed that the ntm ave estimates of kee et al. (2009), being based on the (assumed) restriction that ntms can affect only negatively trade flows, may be distorted upward. by modelling the (possible) externalities addressed by ntms, they confirmed this statement quantitatively. 7 see also the contribution of orefice (2015), who studied the relationship between stc and tariff variation from the point of view of the exporting country raising sps/tbt specific trade concerns. 296 alessandro olper until recently, models have treated analytically mainly the left box and arrows of the figure, considering the right box largely as implicit components. yet, in the last decade economists have started to look seriously also to the right box – traditionally a domain of political scientists – by developing political economic models of different democratic (and autocratic) institutions, with the aim of formally addressing how different institutional structures affect political outcomes and, in turn, economic policy and growth.8 given the complexity of the issue, a satisfactory treatment of all these factors is hard, in the sense that a complete political economic model would be intractable. hence, apart few notable exceptions,9 political economy models used to study the formation of traderelated regulatory policy leaves implicit some of the right-hand side elements of the structure summarized above. every political economy model of trade-related policy starts from the economic domain. this is important because the interaction between trade policy, markets and economic outcomes (left box) is at the heart of coalitions formation over government policy. the simplest approach, still used nowadays, is the one of a small open economy based on the ricardo-viner (specific-factors) or on the heckscher-ohlin trade model.10 8 comprehensive reviews of these comparative political economy studies can be find in persson and tabellini (2000) and acemoglu and robinson (2006). empirical evidence of the effect of political institutions on policy outcomes is reported in persson and tabellini (2003) for macroeconomic policy, fredriksson and millimet (2004) for environmental policy and olper and raimondi (2013) for agricultural and food policy. 9 considering trade policy, models of grossman and helpman (2005) and zissimoss (2014) are examples going in that direction. the first considers the formation of trade policy in a democracy comparing majoritarian versus proportional electoral rules. the latter develop a political economic model of trade policy under autocracy and democracy, by merging the acemoglu and robinson (2006) model of democracy with the heckscher-ohlin trade model. 10 in the ricardian model, everybody gains from trade and loses from trade protection, since there is a single factor of production (labor). hence, no clear domestic coalition will emerge having a real interest to depart from figure 6. the democratic policymaking process. “demand side” trade policy outcomes costitutional rules political outcomes economic outcomes markets voters & interest groups preferences “supply side” source: adapted from persson and tabellini (2003). 297the political economy of trade-related regulatory policy these neoclassic approaches have the advantage to predict stark distributional consequences of trade policy and thus to clear-cut predictions about who gains and who loses from protection, pointing to coalitions formation that have conflicting interests on trade policy (hillman, 1989).11 as it is well known, within this perfectly-competitive small open economy framework, whatever the (neo-classical) trade model is chosen, free-trade is the optimal trade policy for the welfare maximizer government. in this framework, any deviation from freetrade should be labelled as “protectionist”, and is the result of a self-interested government who cares about its own welfare such as (re)-election and uses trade policy to transfer resources to special interests with that purpose (hillman, 1989). 4. modelling the economic effect of standards the basic framework outlined above, although useful, does not work clearly in the presence of regulatory standards introduced to address consumption (or production) externalities. this is because domestic regulations, such as food safety and environmental standards, by adding technological constraints on how goods can be produced (e.g. genetic modified technology, or the use of hormon in cattle breeding), have complex effects on both the trade patterns and the firms’ competititive environment, other than on consumer welfare. for this reason, understanding the extent to which a regolatory standard can be labeled as “protectionist”, is often a complex issue driven by the nature of the standard itself and the (trade) model assumptions. before moving to an open economy, it is useful to address shortly the main effects of a standard in a closed economy. consider a product standard that address a consumption externality, as studied by swinnen et al. (2015) and many others. the introduction of this standard has both efficiency (welfare) and equity effects. in particular, social welfare increases when consumers gain exceeds producers implementation costs, and vice-versa. however, standards always induce rent distribution between consumers and producers, as well as among producers when firms differ in their implementation costs. thus, due to the distributional effects of standards, different groups in the society have different interests towards them, raising political economy problems. 4.1 the economic effect of standards in an open economy one of the first theoretical contributions on the economic effect of product standards is proposed by fischer and serra (2000), who studied the impact of a minimum quality standard on home and foreign firms, modelling a duopoly in a homogeneous goods setfree trade. yet, things change considering neo-ricardian models with firms heterogeneity (see eaton and kortum, 2002; arkolakis, 2010). 11 within the ricardo-viner model the consequence of trade policy can be clearly traced knowing the individuals’ factors ownership in the import-competing and export sectors. a tariff in the import-competing sector leads to an increase in the return of the specific factor, while reducing the return of the specific factor used in the export sectors. in the heckscher-ohlin model, the effect of a tariff works along factors (not sectors) lines, i.e. the stolper-samuelson theorem. an import tariff increases the return to the scarce factor and reduce the return to the abundant factor. 298 alessandro olper ting, under the small country assumption. the modeled standard is reflected in a change in the level of a negative consumption externality, along the line of copeland and taylor (1995). the authors’ main conclusion is that, in the presence of a consumption externality, e.g. pollution, the standard chosen by the domestic social planner is always protectionist. this is because, on the one hand, rents can be transferred from the foreign to the home producer due to the increase in market power of the domestic firm, and on the other hand, part of the cost in reducing the (domestic) externality is absorbed by the foreign producer. these result motivate the authors’ definition of a protectionist standard as “one that is higher than the standard the social planner would choose if all firms were domestic.” (fischer and serra, 2000).12 marette and beghin (2010) question this conclusion. specifically, within a similar framework, they analyze the introduction of a domestic standard to address a consumption externality, allowing heterogeneity of foreign and domestic firms in the cost of addressing the standard. in this setting, they argue that the welfare maximizing (domestic) standard chosen by the social planner can be anti-protectionist i.e. lower than the standard which maximizes international welfare when foreign producers are much more efficient than domestic producers at addressing the externality. this conclusion is interesting because, at least within this model structure, it has consequences for the practical estimation of the ad-valorem equivalent of a ntm, that would also be sensitive to the heterogeneity of costs structure in meeting the standards of the domestic and foreign firms. the two papers mentioned above refer to a particular type of ntm, namely a standard that accounts for a consumption externality. hence, the natural question is on whether similar conclusions can be drawn by comparing different types of ntms. marette (2014), extends the previous analysis highlighting the complexity in characterizing protectionism – in the fisher and serra (2000) sense – in relation to different ntms and producers’ costs structure. in particular, marette (2014) analyzed two extreme cases where a standard can affect only variable costs or, alternatively, fixed costs, and where the home government, alternatively, could adopt a mandatory label to inform consumers.13 under these assumptions, he showed that when the standard affects only variable costs, the introduction of a mandatory label can be protectionist. differently, when the standard impacts only fixedcosts, the standard can be protectionist. these and other similar results and conclusions are mainly obtained in models with homogeneous goods and price-taker competitive firms (see swinnen, 2015 for an extensive survey). yet, because ntms systematically affect fixed firm’ costs other than variable costs, even a simplified model should conceptually require imperfect competition, increasing returns and differentiated goods (baldwin, 2000). adding these more realistic features to the analysis, however, significantly increases the complexity and the source of ambiguity of the ntms effect. this is deeply documented with several examples by baldwin (2000), which represents, to date, one of the most interesting survey on the trade effect of ntms. 12 essaji (2010) extends the fisher and serra’s model analyzing the simultaneous impact of trade liberalization on ntms and showing that one cannot make an unequivocal connection between falling tariffs (or subsidies) and rising standards. ultimately, this will depend on the government objective function, namely on the weight government attaches to different interests (see section 5). 13 note, in marette (2014) because the introduction of the standard removes the externality, then it is alternative to the mandatory label. 299the political economy of trade-related regulatory policy furthermore, whenever ntms involve fixed costs, it would be appropriate to analyze them within firms heterogeneity trade model. in fact, as summarized in section 2.2, this is strongly supported by a growing empirical evidence showing that, although ntms can sometimes affect positively the volume of trade, they systematically negatively affect the probability that a firm exports to a certain market destination (see wto, 2012; fontagnè et al., 2015). moreover, considering firms’ heterogeneity, is particularly important from a political economy perspective, because it adds a new dimension to the possible coalition formation in favor or against ntms. in models with firms heterogeneity, indeed, conflicts of interests over trade policy, traditionally based on the cross-sectoral or cross-country dimension, are broken-down over the within-sector dimension depending on firms’ characteristics, such as size and productivity. interestingly, this is consistent with the observation that larger firms, as measured by sales, are systematically more active in lobby activities both in the us (bombardini, 2008) and in the eu (bernhagen and mitchell, 2009; belloc, 2015). for all these reasons the next section pays some attention to this more realistic setting. 4.2 effects of standards when goods and firms are heterogeneous when firms are heterogeneous and the standard involves also fixed costs, the latter affects differently home (and foreign) firms’ profitability, forcing the least efficient firms to exit. to see this, let’s consider the model of abel-koch (2013) who studied the economic effects of a standard in the melitz (2003) model.14 on the supply side, firms operate in a monopolistically competitive market with increasing returns to scale, using only one input, labor. each firm produces one variety, with marginal costs ci. trade between home and foreign is subject to both variable (iceberg) and fixed trade costs, τ and f, respectively. given firms’ heterogeneity, the higher the firm marginal costs, the lower its sales on the domestic market. hence, there exists a cutoff level of marginal costs, ch, defined by the zero profit condition, πh(ch) = 0, such that only home firms with ci ≤ ch will be active on their domestic market. similarly, for foreign firms with ci ≤ cf.15 next, let’s consider a standard which increases the fixed costs of accessing the home market by a factor s > 1, such as food safety and health standards, and assume also that it has not beneficial effects on consumer welfare. such standard has an anti-competitive effect because it forces the least efficient home producers and foreign exporters to exit from the home market. this is because by reducing the respective cutoff level, ch (and cf), it will increase the minimum firm’s efficiency needed to operate with non-negative profit in the domestic market for both home and foreign firms. 14 in melitz (2003) consumers have identical preferences over a composite (numéraire) good and a continuum of varieties of a differentiated good, with constant elasticity of substitution (ces). this specification ensures that a variation of the number of product varieties affects directly the ideal price index, and thus welfare, i.e. dixitstigliz (1977) “love of variety” preferences. 15 the model assumes that the distribution of marginal costs, c , follow a pareto distribution of marginal productivity 1/c, characterized by the shape (k) and the scale parameter ( a ). the shape parameter is a measure of the dispersion of firms efficiency, e.g. if kh < kf, it means that in home the ratio of very efficient firms to rather inefficient firms is higher than in foreign. instead, the scale parameter defines the (positive) lower bound of the distribution. 300 alessandro olper as a result, the ntm induces a reduction in home market competition, creating a gain in the market share of the most efficient firms (at the expense of the least efficient home and foreign firms) more than compensating the increase in their fixed costs (due to scale economies). this profit-shifting effect for the home firms active in the domestic market is summarized in figure 7. a similar picture applying to foreign firms exporting in the home market. with this model, the implementation of anti-competitive regulations can never be a social optimum because the potentially positive effect of the standard s on the aggregate profits of home firms is always dominated by its negative effect on consumer surplus, due to the loss of the home available varieties.16 clearly, this conclusion could be reversed under the condition such that the standard is implemented to reduce a consumption externality, whenever the increase in the consumers’ surplus attributable to the externality reduction, more than compensate the decrease in the number of home available varieties. 4.3 summary and implications from the above discussion we can derive some important considerations useful for the introduction of the political economy of standards. first, there is a huge economic 16 the introduction of a ntm can also induce an across countries profit-shifting when industries in the two countries are characterized by differences in the shape parameter. indeed, when kh < kf, the ratio of very efficient firms to rather inefficient firms and hence the ratio of winners to losers from the introduction of ntms is higher in home than in foreign, implying that in the aggregate, profits are shifted from foreign to home firms. figure 7. introduction of a standard (s > 1) on the profits of home firms. 𝑐𝑐𝐻𝐻(𝑠𝑠 > 1) 𝑐𝑐𝐻𝐻(𝑠𝑠 = 1) �̅�𝑐𝐻𝐻 𝑐𝑐 𝜋𝜋𝐻𝐻𝐻𝐻(𝑐𝑐) exit losers winners profit shifting from least efficient to most efficient home firms source: adapted from abel-kock (2013). 301the political economy of trade-related regulatory policy uncertainty over the true economic effects of a standard, because results are sensitive to the researcher’s modelling assumptions. this may raise problems in characterizing univocally the conflict of interests over ntms, that will depend on specific market conditions, heterogeneity of the costs structure and type of standard under investigation. hence, generalizations are always difficult. second, as shown by many wto disputes on ntms, there is a genuine uncertainty about the safety, health and environmental benefits of many regulations. this is because, frequently, the scientific consensus can rarely offer a conclusive answer to the effects generated by certain product or technology, especially in the initial phase of the introduction of a new technology (sturm, 2006). third, many modelling exercises often abstract from the precise characterization of the ntm, and thus considerable uncertainty will persist about the level of the welfare maximizing standard.17 this uncertainty opens the door to interest groups and politicians opportunistic behaviors. in fact, politicians, who are electorally accountable for their policy choice, may prefer to implement the least efficient product standard to protect a domestic industry, as the uncertainty on the optimal level of standard translates into a lower electoral penalty for his policy choice. 5. political economy models models of endogenous trade policy start from an economic model as the ones summarized above, and relax the assumption of the (exogenous) welfare maximizer social planner (rodrik, 1995). this is done, firstly by assuming that the objective function maximized by the government gives different preferences to certain distributional outcomes and, secondly, by assuming that voters and lobbying groups are able to transmit their particular preferences to shape the government’s behavior. political economy models can be firstly distinguished in voting models, where the interaction is between unorganized voters and politicians, and lobbying models, where the interaction is among organized interest groups and politicians. the first typology assumes that political parties compete only for votes, with a framework based on some variations of the median voter model.18 the models presented by mayer (1984) and list and sturm (2006) are two examples with applications to trade policy and environmental regulation, respectively. the majority of models used to study both trade policy formation and the effect of environmental and food standards can be classified into interest groups or lobbying models, and have their roots in the olson (1962) logic of collective action, the stigler (1971) theory of economic regulation, and the becker (1983) model of competition among pres17 here the problem is similar to the pigovian tax and the measurability problem of the externality. for example, baumol (1972) argued that it is extraordinarily difficult to measure the social costs of any externality, especially because many costs are psychological and individual in nature. see also the discussion in vaughn (1980), on the relevance of the subjective costs. 18 the most important variations of the basic median voter model are models that assume probabilistic voting behavior (coughlin, 1992), where voters’ intentions are uncertain, and political agency models, which stress the importance of (voters) imperfect information. for an in-depth treatment of the different voting models, see persson and tabellini (2000). 302 alessandro olper sure groups. these models differ by the degree of micro-foundation in the interaction between lobbies and politicians, and by the extent to which they consider an explicit role for voters, elections and/or social welfare within the government objective function. there are different variations, surveyed by rodrik (1995) and helpman (1995). a further distinction is related to the motives of lobby groups, i.e. the electoral motive approach and the influence motive approach (grossman and helpman, 1994 and 1996). the first argues that lobbies wish to promote the candidate that reflects their preferences on a policy issue before upcoming elections. the second argues that lobbies aim at influencing the policy choice of an incumbent politician. currently, for several reasons discussed below, the leading approaches are still based on the menu-auction framework firstly proposed in the “protection for sale” model of grossman and helpman (1994), from now on the gh model. we start by summarizing the main intuition of the gh model, discussing its relevance for empirical analyses, and the most recent extensions to study the endogenous formation of ntms. next, we summarize other recent prominent approaches based on electoral competition. 5.1 the protection for sale framework grossman and helpman (1994) proposed a menu-auction model to study trade policy formation in the context of active pressure groups.19 the key model assumption is that the influence motives of pressure groups are at the heart of campaign contributions. the underlying economic framework is the one of the specific-factor model in a small open economy. interest groups move first, offering politicians campaign contributions linked to their policy preferences, with the objective to maximize the group members’ economic return. next, politicians decide on their policy stances, after knowing how campaign contributions are linked to their selected policies. finally, the government will set the policy vector, t, that maximizes the objective function g(t). this is represented by a weighted sum of lobby group contributions, c, and the wellbeing of the population, w, g(t) = ϕw(t) + c(t), where ϕ is the weight the government places on the voters welfare relative to lobby contributions. gh showed that, at the non-cooperative nash equilibrium, trade policy is selected to maximize the joint surplus of all the parts involved. hence, setting πj(t) as the welfare of the specific-factor owners, the equilibrium trade policy is obtained by maximizing a weighted social-welfare function:20 ∑φ( ) ( ) ( )= + π ∈ t w t t ω j l j . (1) equation (1) says that, in equilibrium, “truthful” contributions schedules by the interest groups induce the government to behave as if it were maximizing a social-welfare 19 the model of peltzman (1976) and the derived political-support function approach of hillman (1982), can be interpreted as reduce forms of the pfs model. see helpman (1995) for an indebt discussion of these models and their link with the gh model, and its extension to electoral competition and international trade negotiations. 20 to solve the equilibrium lobbying game, gh rely on the bernheim and whinston (1986) subgame-perfect non-cooperative nash equilibrium. these authors showed that the set of a lobby’s best responses to any combination of contribution schedules offered by all other lobbies always includes a “truthful” contribution schedule. 303the political economy of trade-related regulatory policy function that weights different members of the society differently, with individual represented by an interest group receiving a weight of 1 + ϕ, while those not represented receiving the smaller weight ϕ (grossman and helpman, 1994, 841). the basic gh model has been firstly derived for trade taxes (tariffs and export subsidies), but the same approach can be applied to other policy instruments as well, such as environmental policy (fredriksson, 1997), import quota (facchini et al., 2006), and food standards (swinnen and vandemoortele, 2008). other important extensions implied the use of monopolistic competition, instead of the specific-factor model, and firms’ heterogeneity (bombardini, 2008; abel-koch, 2013). the most important reason of the gh model success,21 other than its simplicity and elegance, is its micro-foundation for lobbying. indeed, being built on sound economic principles, the model allows for a structural estimation of the theory and the underlying structural parameters, and in particular the relative weight, ϕ. 22 goldberg and maggi (1999) and gawande and bandyopadhyay (2000) tested for the first time the gh model in the context of the us industry protection, using non-tariff measures as protection variable.23 their results strongly support the model predictions, namely that us industry protection structure is increasing in the (inverse) of import penetration ratio, but only for organized industries. yet, the result of these and almost all the applications of the gh model is that the estimated structural parameter ϕ, the weight government attaches to social welfare, is much higher than the one attached to campaign contributions. this means that welfare carries a strong weight in government’s payoff, a conclusion that it is at odd with the model name “protection for sale”.24 5.2 protection for sale and environmental standards one of the first applications of the gh model to the determination of domestic regulations has been developed in the domain of environmental policy.25 fredriksson (1997) modelled a production emission proportional to output. emissions induce disutility to an organized subset of the population defined as “environmentalists” who lobby for reducing 21 there are also criticisms to the gh model structure, and the possibility of testing it empirically. see in particularly rausser et al. (2011) and ederington and minier (2008), respectively. 22 the model predicts that protection in organized industries is growing in the level of (inverse) import penetration, and is decreasing in the value of import demand elasticity (ramsey rule). at the empirical level the main challenge is to estimate a lobby equation using campaign contributions and the import penetration equation, noting that both are simultaneously determined with the protection equation (see goldberg and maggi, 1999, for details). this last point, together with the low disposability of data on campaign contributions, represents the main difficulty in estimating the gh model. 23 the use of ntms instead of tariffs, the standard approach in the majority of the empirical test of the gh model, is the result of the following consideration. while tariffs are decided cooperatively within the gatt/ wto negotiations, ntms are largely decided unilaterally and, as such, this is more consistent with the noncooperative nash equilibrium of the gh model. 24 note, this does not mean that the us government is a pure welfare maximizer. recently, some authors suggested that one reason can be related to firms’ heterogeneity (e.g. bombardini, 2008), namely the fact that within the same industry firms lobby in opposite direction depending on their export (or import) status. however, to date we do not have a sound empirical test of this hypothesis. 25 note, several papers on the political economy of environmental policy, strictly speaking, focused on green taxes and subsidies more than on environmental standards per se. 304 alessandro olper them. as in the standard gh model the specific factor owners called “industrialists” are organized and lobbying the government, while the consumers are unorganized. in this setting, the government chooses an optimal environmental tax rate, t#, aiming at maximizing lobby contributions from the two organized interest groups “environmentalists” (ce) and “industrialists” (ci) conditional to overall welfare w: φ φ( ) ( ) ( ) ( )= + +t c t c t w tω e e i i , (2) where ϕe and ϕi are the relative weights that government attaches to the environmental and industrial lobby groups, respectively. the welfare of the environmental and industrial groups depends on the share of total pollution tax revenue and labor income allocated to the respective lobby groups, the aggregate disutility from pollution of the environmental lobby group, and the aggregated profit of the industrial group. the main result of the paper shows that the political equilibrium environmental tax rate, t#, tends to be different from the (optimal) pigouvian tax, t*, depending on the size of the two lobbies, their political contributions, and the relative weight the government attaches to social welfare. schleich (1999) adds an important extension to the above framework through the use of a trade tariff (or subsidy) within the available government policy instrument set, to study the interaction between green and trade policies. main results show that, with a production externality, only the environmental subsidy will be implemented at the equilibrium because the government, being sensitive to social welfare, has an incentive to implement the most efficient policy that will internalize the externality, in order to maximize campaign contributions from lobbies.26 yet and quite paradoxically, environmental quality could be higher in a situation where only trade policy is available, because the additional distortion induced by the trade policy will damp the government’s income redistribution. see also aidt (1998) and schleich and orden (2000) for similar results considering polluting inputs and a large country, respectively. an important question studied within this strand of literature is the extent to which environmental tax or subsidy are affected by exogenous trade liberalization episodes. fredriksson (1999) extended his lobby model by including an abatement technology and a tariff on the imported pollution good that is exogenously given. key results show that trade liberalization has ambiguous effects on the environmental policy, mainly because tariff elimination reduces output in the pollution sector. hence, environmental quality could increase or decrease after trade liberalization when political economy motives are taken into account. a further extension by eliste and fredriksson (2002) focuses on a situation where the government can use an environmental tax and a production subsidy for the pollution sector. the authors analyzed the effects of an exogenous increase in the green tax showing how this event endogenously increases the production subsidy affecting both the level of output and trade flows. interesting, the authors test their predictions running cross-country regressions on agricultural sectoral data. main results confirm key model propositions, showing that more stringent environmental standards in agriculture are associated with larger direct transfers to farmers. 26 under consumption externality, the same logic implies that the equilibrium government green policy will be a consumption tax on the polluting good, and a trade tax necessary for distributing income from unorganized to organized interest groups. 305the political economy of trade-related regulatory policy lai (2005 and 2007) and kawahara (2014) represent further refinement of the interaction between trade liberalization and environmental standards in a gh model setting. interestingly, kawahara (2014) following mitra (1999) considered the case where interest groups are endogenously given. in particular, they showed that, under certain conditions, unilateral trade liberalization in a large country importing a pollution good, might raise (endogenously) inefficient environmental standards in another (small) exporting country. similarly, fredriksson and matschke (2016) extend the gh model to the federal system case (e.g. the us), showing that trade liberalization leads to a decline in pollution taxes, regardless whether these taxes are set at federal (centralized) or local (decentralized) level, and increases welfare.27 from an empirical point of view results are mixed, in the sense that more open economies affect differently emissions of several pollutants, but tend to reduce so2 in many countries (see frankel and rose, 2005). an interesting application is the one by ederington and minier (2003), who studied the extent to which environmental policy represents a secondary trade barrier. in line with the endogenous trade policy literature and in particular with the important contribution of trefler (1993), these authors accounts for the inherent endogeneity problems in studying the impact of the stringency of environmental standards on trade flows. main results show that import-competing industries are under regulated in the environmental area, but also that lower tariff rates are associated with more stringent standards, i.e. government uses environmental regulation as a secondary means in providing protection to domestic import-competing industries.28 to date, however, a formal test of the gh model in the domain of environmental standards, that exploits information on lobby campaign contributions, does not exist yet. one possible reason is that, when secondary policy issues such as environmental policy are considered, electoral incentives matter more than lobby contributions, as argued by list and sturm (2006). we will come back on this paper and this important hypothesis later. 5.3 protection for sale and food quality standards as it is well known, agriculture and food industry represent, by far, sectors where the diffusion of ntms, and in particular sps/tbt standards, is more pervasive. one of the first applications of the gh model to study food (quality) standards is the work by swinnen and vandemoortele (2008 and 2011).29 these authors introduced two main changes into the standard gh model. first, the modelled standard, s, addresses a consumer externality, e.g. it guarantees a minimum quality level or safety features to a credence good and, as such, it increases consumers’ welfare. second, differently from gh, one key 27 other applications studied how free trade impacts the burden sharing of environmental policies between producers and consumers, and the implication of the stringency of environmental standards (see, e.g., gulati, 2008). 28 by combining data on environmental regulation at the country level with data on pollution intensity at the industry level, broner et al. (2012) showed that countries with laxer environmental policy have a comparative advantage in pollution industries. this represents one of the few robust empirical evidence supporting the pollution-haven hypothesis, namely the idea that because the stringency of regulation varies across countries (and sectors), this affects the location of polluting industries in countries with more laxer environmental regulation, e.g. in developing countries. 29 these and subsequent papers by the same authors are summarized and extended in swinnen et al. (2015). 306 alessandro olper assumption is that consumers, not only producers, are organized into an interest group lobbying the government through campaign contributions.30 on the production side, the structure is similar to gh and the standard is assumed to affect only the firms’ variable costs, as in marette and beghin (2010) and many others. in this setting, defining social welfare w(s) as the sum of producers profit πp and consumers surplus πc, the government objective function ω(s) can be written as φ φ( ) ( ) ( ) ( )= + +s c s c s w s ω c c p p , (3) where cc and cp are the lobby contributions of consumers and producers, and ϕc and ϕp represent the relative weight government attach to consumers and producers interests, respectively. following gh, the government will set the political optimal standard, s#, by maximizing consumer and producer group’ contributions, conditional to social welfare. under these assumptions, the authors find several interesting results. first, in contrast with the socially optimal tariff in a small open economy, where t* = 0, the socially optimal standard may be strictly positive, s* > 0. in fact, although this could lead to a trade reduction effect, it can improve domestic welfare when the quality standard induced increase in consumer welfare more than compensates the producer implementation costs. then, whether the standard can be defined as “protectionist” or not strictly depends by its definition. if we focus on the home country national welfare (the so-called domestic-efficiency argument) then the standard is not protectionist. differently, if we focus on the world welfare, as fischer and serra (2000) did, then the social optimal standard may be protectionist.31 moreover, the authors highlight two key dimensions for analyzing the political optimal standards in an open economy: first, the issue of overor under-standardization; second, whether the standard is protectionist or not, namely if the standard results in higher domestic producer profits at the expense of domestic consumers. when ϕp > ϕc, the public standard is always protectionist, although it can result in both over(s# > s*) or under (s# < s*) standardization. over-standardization happens when producers’ profits rise with a higher standard ∂π ∂ >s( / 0)p at s*. by contrast, under-standardization occurs when the producer profits decrease with the standard (∂π ∂ <s/ 0p ) at s*, implying the protectionist optimal standard to be lower than the social optimum. differently, when φ φ>c p , government weights more consumers interest in setting its optimal standard. in this case the result will be reversed, namely over-(under) standardization will occur when the consumer welfare is increasing (decreasing) with the standard ∂π ∂ s( / 0)c at s*. what is interesting from this results is that, although the politically public standard s# will be sub-optimal, it will never be protectionist, ceteris paribus. 30 the assumption that consumers are politically organized and, therefore, they make campaign contributions, is mainly the result of the observation that in some countries, e.g. european ones, consumers can be organized into interest groups, also through political parties representing their interests (see swinnen et al., 2015). the problem with the empirical implementation of this framework is that very rarely, even in the us, consumer groups make direct campaign contributions. however, the informational lobby approach used by belloc (2015) may be a promising strategy to overcome this issue. 31 this line of reasoning has been proposed in the trade literature by baldwin (1970), who argues that a measure could be defined protectionist if it lowers real global income. this “cosmopolitan-efficiency case”, using the word of bhagwati (1988), is particular relevant to issues such as the design of international trade regimes, i.e. the wto. on this point, see also the discussion in beghin et al. (2015). 307the political economy of trade-related regulatory policy the above discussion highlights the conceptual difficulty behind the analysis of trade effects of standards in general, and in particular when political motives are taken into consideration. under this framework, the classification of ntms as protectionist or not is a priori difficult and uncertain, as such standards should be analyzed carefully and case-by-case. this conclusion appears in line with the inherent difficulty to find a solution to many international trade disputes over food and environmental standards within the wto. from an empirical point of view, no paper to date has tested explicitly the predictions outlined above on the determinants of quality standards using information on both producers and consumers lobby activities.32 pacca and olper (2016) tested the gh model on us manufacturing sectors using as policy variable ntms from unctad-trains related to 2014. interesting, from an inspection of the different us ntms used in that study, one rapidly concludes that more than 85% of them are now sps and tbt measures. their results confirms the gh model predictions, irrespective to the fact that, today, the us protection structure and import penetration are totally different than 25 years ago. yet, the supposed role of the consumer lobby activity is not still considered in this empirical application due to the intrinsic difficulty of measuring it. moreover, to test seriously the extension of the gh model to ntms, one has also to recognize the externality component of the ntms, e.g. along the line of beghin et al. (2015b). yet, this raises further conceptual and empirical issues to the correct specification of the gh model. belloc (2015) presents one of the few applications to the european union of a lobby model to explain the formation of ntms. since data on lobby contributions are not available for europe, she relies on informational lobby, namely information on the participation of national and international business organization in the european commission consultations on trade issues. merging this original information with ntms at tariff line level from 1999 to 2007, she was able to exploit the panel structure of the dataset, showing that participation in consultation meetings increases the probability to find ntms at the industry level. from a theoretical point of view, the author extended a lobbying model of trade policy formation in the spirit of the gh model, to informational lobby. empirical evidence on the economic and political determinants of food standards are provide by li et al. (2014). in particular, they investigated the determinants of the maximum residue limits (mrls) on pesticides and veterinary drugs, showing that mrls are stricter in countries with high income and larger population, and in sectors with comparative disadvantage. interesting, they also found that mrls and import tariffs are policy substitutes. finally, vigani and olper (2013) studied the determinants of gmo standards across 60 developed and developing countries. their main findings showed that the stringency of gmo standards are growing in the country comparative disadvantage in agriculture, in the size of the rural population, in the parallel restriction in environmental regulation, and so on. they also found a strong (non-linear) effect of the share of private media outlets on the stringency of gmo regulations. this result supports the view that, when 32 in the domain of agricultural and food trade policy, gawande and hoeckman (2006) and lopez (2008), tested the basic gh model on us data. main results show that campaign contributions from industry lobbies are central in explaining the cross-industry variations in the protection structure of both ntms and tariffs. 308 alessandro olper ntms address consumers’ sensitive issues, the way the different media outlets inform them becomes a key element of the political economy of standards.33 5.4 protection for sale and standards when firms are heterogeneous to date, only few papers have studied the political economy of a public standard within a firms’ heterogeneity model. abel-koch (2013), building on bombardini (2008), extended the basic gh framework relaxing the assumption of identical firms. in line with the empirical evidence on lobby behavior (bombardini, 2008), it is assumed that only the largest and the most efficient firms will lobby together for ntms, as they gain the most from their introduction (see section 3). in the differentiated good sector all firms with marginal costs c ∈ (0, cl] are organized into a single lobby l, with the upper bound of the marginal cost of these firms lower than the cut-off for selling in the home market ch (cl < ch). instead, all firms with marginal costs c > cl do not join the lobby.34 the welfare of this lobby is then the joint welfare of its member, π π( ) ( )= +s swl h l e l , with the last term indicating the profit of home exporting firms ( π e l ), that is clearly unaffected by the (home) standard s. the equilibrium trade policy is obtained by maximizing a weighted social-welfare function, φ π( ) ( ) ( )= +s w s sω h l , in which organized home firms are weighted 1 + ϕ, while nonorganized (small) firms and consumers are only weighted ϕ. from this maximization process the government sets the political optimal standard, s#. the model assumes that the standard, s, does not address any consumption externality, but represents a “pure” non-tariff measure to trade, like several ntms do. under this assumption, the social planner optimal standard will be s* = 1, namely no standard into the home market. finally, the standard increases the fixed costs of accessing the home market for both the home and foreign firms, by a factor s ∈ [1, ∞). suppose now that the lobby’s marginal gain in profits, and thus the home government’s marginal gain in political contributions, is higher than the weighted marginal loss in social welfare from introducing a ntm, namely π( ) ( )∂ ∂ + ∂ ∂ > w s s s s 0hd l# # at s* = 1. then, home government has an incentive to deviate from the socially optimum, setting s# > 1. the equilibrium level of the optimal government standard s# resulting from this lobby game is a function of several parameters related to the standard’s induced fixed and variable costs on foreign and home firms, the distribution of firms’ marginal costs in the two countries, and the weight the home government puts on social welfare. interestingly, it can be shown that the political optimal standard is decreasing in the import penetration ratio at home, a result that mimics the baseline gh model. this is because, when foreign firms become more competitive relative to home firms, due to a reduction in trade costs or an increase in their productivity, the import penetration ratio will increase and the profit-shifting effect of the standard on home firms will become weaker, reducing the incentive to lobby for the standard. 33 vigani et al. (2012) in studying the trade effects of gmo standards showed that when gmo regulations are treated as endogenous in the trade equation, their trade reduction effect increases substantially. 34 the basic model assumes that there is only one lobby (sector). 309the political economy of trade-related regulatory policy this result is important since it suggests that liberalizing trade and fostering competition from abroad will lower the equilibrium level of the optimal standard, by reducing the gains from the standard of the (large) home firms. as a consequence, the model predicts a positive relationship between tariffs and ntms, that within this framework are thus complements, rather than substitutes (abel-koch, 2013). an important extension is referred to the interaction between home and foreign governments. here, the result depends on how the two governments interact, non-cooperatively or cooperatively. in the first case the result is identical to the unilateral trade policy determination summarized above. instead, when the ntms are set cooperatively in a trade negotiation and countries are symmetric (i.e. same shape parameter), the equilibrium level of the standard which restricts market access for small firms will be higher than in the non-cooperative case. this is due to the fact that also the lobby of large foreign firms has a “voice” on the home government objective function, when policies are set in an international trade agreement (see grossman and helpman, 1995).35 finally, when it is assumed that ntms address a consumption externality, the consumer loss from the variety reduction effect induced by the optimal standard will be compensated by consumer gains of addressing the externality. yet and interesting, the overall results remain unchanged, and the only difference is that even in the absence of lobbying, it may be beneficial to introduce this kind of ntm, at least when the positive welfare effect on consumer health outweighs the negative effect on product varieties. to date, no empirical application tested these predictions using as dependent variable behind-the-border measures. however, bombardini (2008) exploiting the same dataset of gawande and bandyopadhyay (2000), showed that us industries characterized by higher firm size dispersion obtain a higher level of protection because they are more active in lobbying. 5.5 the role of elections and political competition the first attempt to study environmental policy with a voting model is made by congleton (1992), who applied the median voter approach contrasting the policy selection under autocracy and democracy. the simple intuition is that authoritarians tend to prefer a lower environmental standard with respect to the median voter, because decision makers in democracies have a smaller marginal cost for pollution control than authoritarians do. overall, the author finds cross-country support to this prediction. however, more recently fredriksson et al. (2005) qualified both theoretically and empirically this result, showing that what matters is not democracy per se but the interaction between voters participation and political competition, other than environmental pressure groups. mcausland (2003) uses a median voter model to explain environmental policy in a small open economy with two sectors a clean and a polluting one considering also the role of inequality and trade in affecting the behavior of heterogeneous voters. 35 when countries are not symmetric, the shape parameter of the productivity distribution differs. suppose that firm size is more dispersed in home than in foreign, then the standard implemented in home reduces the aggregate profits from exporting of foreign firms, and its social welfare, as showed in section 3. interesting, in a non-cooperative setting this will lead to over-standardization from a global welfare point of view. instead, in a cooperative setting this negative externality is taken into account and leads to under-standardization. 310 alessandro olper contrary to the conventional view, when the economy is closed to international trade, richer voters prefer a weaker environmental policy than poor voters. yet, opening to trade affects the political optimal policy, since in this case the price of goods is less affected by the change in the environmental policy, and therefore changes the voters’ preferences towards it. sturm (2006) modelled an environmental standard using a political agency model where the government (the agent) searches political accountability from voters (the principal). the author studied how the standard raised by the home (importer) country, can be challenged as “green protectionism” by the foreign (exporter) country. home politicians have an informational advantage in evaluating the probability that foreign exported goods cause health or environmental damage, creating an intrinsic divergence between the home and the foreign government. main results show that there exists a political equilibrium in which the importing country applies a more stringent standard than the exporting country, a situation that can be due to both too lax standards in the exporting country or too stringent standards in the importing country. list and sturm (2006) apply the political agency model above to study how politicians decide on both a frontline policy issue and a secondary policy issue, i.e. environmental policy. the key prediction of the model is that the incumbent government manipulates the secondary (environmental) policy to attract voters. using us states and panel data econometrics the authors find that us states environmental expenditures are determined by electoral incentives and the degree of electoral competition. this result is in contrast with the popular view that secondary policies are largely determined by lobbying. one limit of the sturm (2006) approach is that it does not consider lobbying as a determinant of environmental policy, disregarding a key element of the policy making. the first attempt to consider simultaneously electoral incentives and lobbying contributions is due to besley and coate (2001), who combine the citizen-candidate model of representative democracy with the gh menu-action model of lobbying. the model is based on a three-stage game.36 one of the most interesting result from this modelling framework is that lobbying may not matter at all for policy outcomes when there is electoral competition, subject to certain conditions, i.e. when the public good policy is continuous. more in general, the authors conclude that both lobbying and electoral competition should be considered to understand the policy game, as well as the nature of the policy. an interesting model that consider lobbying and electoral incentives together is the one of yu (2005), who adds the relevant concept of direct and indirect lobbying, with the latter referring to the lobbying effort to send message to citizens for influencing their preferred policy. yu (2005) applied the model to environmental policy.37 in general, the model predicts complementarity between direct (money contribution) and indirect (messages) lobbying. however, it also showed that, under certain condition, e.g. when public persuasion of environmentalists is substantially stronger than for industrialists, a substitution relationship between indirect and direct lobbying comes out from the model. this is 36 in the first stage, citizens decide whether to run for office; in the second, utility maximizing voters express their electoral preferences; in the third stage, the candidate will select the political optimal policy. 37 see jaeck et al. (2015) for a recent application of the yu (2005) model logic of indirect lobbying to the case of sustainability standards, related to biofuel in the eu and us. 311the political economy of trade-related regulatory policy consistent with the observed behavior of us green groups where the effort in indirect lobbying tends to overcome the direct ones.38 finally, when electoral competition is taken into account, how this competition translates to policy outcomes depends also on electoral rules and institutions (persson and tabellini, 2000). starting from this consideration, fredriksson and millimet (2004) studied the formation of environmental standards in different electoral systems contrasting majoritarian versus proportional electoral rules. under majoritarian rule with single-member districts, a party needs to receive only 50% of the vote in 50% of the districts to win an election. this implies that political parties may focus on a subset of the population rather than maximizing aggregate welfare (persson and tabellini, 2000), making majoritarian systems more grounded in local interests (milesi-ferretti et al., 2002), and/or inducing a district majoritarian bias in public policy (grossman and helpman, 2005). in contrast, a party needs 50% of the national vote to win under a proportional system. consistently with this intuition, fredriksson and millimet (2004) show that indeed governments set stricter environmental policies under proportional electoral systems, as opposed to the majoritarian one.39 6. the role of international agreements and global value chain this section discusses the problem of international trading rules over ntms shortly. the economics literature on this topic is quite complex, and in recent years experienced a new revival motivated by the doha round crisis, the proliferation of new form of preferential trade agreements (i.e. deep ptas) and, notably, the increasing importance of trade in intermediated goods, outsourcing and the role of global value chain. 6.1 international trading rules over ntms as shown above, both domestic interest groups and/or opportunistic politicians behavior may influence the formation of protectionism public standards. this often happens as a by-product of a trade liberalization process.40 in theory, this government regulatory capture could be prevented by negotiating international agreements not only on trade policy (e.g. tariffs), but also on environmental and food standards (copeland, 1990; maggi and rodriguez-clare, 1998; wto, 2012; swinnen et al., 2015). yet, given the complexity of the effects of many regulatory standards, there is an intrinsic difficulty in setting and applying international rules. moreover, this difficult is also the consequence of the incomplete contract nature of these rules, as by definition 38 interestingly, recent evidence on the lobby’s behavior in the us highlighted the existence of an inverted u-shaped relationship between money contributions and workers/voters controlled by the lobby (see bombardini and trebbi, 2011). in other words, when an interest group controls many voters, it need less money to reach the same lobby’s result. 39 interestingly, heller and holahan (2013) by contrasting electoral rules with the data of the comparative manifesto project on party positions, showed that proportional rules significantly increase the probability that the political parties position is pro-environment. 40 this is because trade liberalization, by inducing the elimination of tariffs, left governments without the firstbest policy instrument to exploit term-of-trade effects (see staiger, 2012, for a discussion of this point). 312 alessandro olper they cannot specify standards for products that may arise in the future (battigalli and maggi, 2003; sturm, 2006). all this precludes the possibility to write efficient international agreements over standards. there are several and diverse approaches to standards in international agreements (wto, 2012). the so called “shallow integration” agreements leave substantial autonomy to national governments in setting standards. “non-discrimination” and the related “national treatment” in the gatt agreement (articles i and iii, respectively) are examples of this approach. by contrast, “deep integration” agreements on domestic policy regulation aim not only at coordinating border protection, but also at addressing more complex coordination problems. the principle of “harmonization” is an example of deep integration. considering the current wto rules, it is not always clear if they should be interpreted as a shallow or deeper integration agreement. this is because, on the one end, the sps and tbt agreements incorporate also harmonization through the use of international standards, and include obligations that are additional to the gatt non-discrimination rules such as, for instance, the need to ensure that standard requirements are not unnecessarily trade restrictive. on the other hand, when a ntm is inconsistent with the nondiscrimination obligations of gatt articles i and iii, it eventually may be justified under one of the general exceptions of gatt article xx.41 more in general, choosing which kind of approach works better to solve the countries’ coordination problem over ntms depends on several factors and also on the level of the externality that the ntm is intended to address. for instance, costinot (2008) focusing on product standards, highlighted that that mutual recognition, in case of local negative consumption externalities, may induce to set too lax standards because governments do not account for externalities generated by their export on foreign markets. by contrast, national treatment has the opposite effect, i.e. too stricter standards, since the government does not take foreign’ compliance costs into account. the political economy literature identifies two key issues that a trade agreement might solve (bagwell and staiger, 2010). first, governments may view trade agreements as helping them avoid beggar-my-neighbor policies – in particular term-of-trade effects – that are unilaterally attractive but mutually inefficient (bagwell and staiger, 1999).42 a second reason is related to the commitment value of international agreements, namely the idea that, ex-post, trade agreements render the government less prone to the pressure of special interest groups (maggi and rodriguez-clare, 1998). one of the first formal analyses of international trading rules over standards is the bagwell and staiger (2001) work, developed within the gh model and stressing the term-of-trade motives. focusing specifically on the trade effects of domestic standards, and so ruling out “global common” issues, they argued that current wto rules – i.e. reciprocity and non-discrimination – with small changes, are already equipped to 41 exceptions of the gatt art. xx relevant for ntms should assure that “nothing in this agreement shall be construed to prevent the adoption or enforcement by any member of measures” necessary to protect human, animal or plant life or health, or relating to the conservation of exhaustible natural resources. 42 see broda et al. (2008) and ludema and mayda (2013), for interesting empirical evidence in support of the role of country market power (term-of-trade effect) in determining the tariffs’ level, for both non-wto and wto countries, respectively. 313the political economy of trade-related regulatory policy address the raising issue of the proliferation of domestic standards. accordingly, negotiations over tariffs alone, coupled with an effective “market access preservation rule” that prevents governments from subsequently manipulating their domestic policy choices to undercut the market access implications of their tariff commitments, can bring governments to the efficiency frontier (bagwell and staiger, 2001).43 yet, recently, antràs and staiger (2012a) showed that when international prices are determined as a result of bilateral bargaining – e.g. between the domestic purchaser and the foreign supplier – the above result is overturned, namely one need for deep integration where direct negotiations occur over both tariffs and behind-the-border policies. we will return shortly on this issue. limào and tovar (2011), focused, instead, on the commitment value of international agreements. these authors investigated theoretically and empirically the interaction between government commitment over tariffs and the subsequent use of (less efficient) ntms. the paper extends the basic gh model to the government choice over ntms, modelling the political value of commitment over tariffs in international trade agreements as it improves the bargaining position of a weak government relative to domestic interest groups. the authors tested the model predictions on turkey considering tariffs cap introduced within the wto and the pta with the eu. main results showed that tariff commitments in trade agreements increase the likelihood and restrictiveness of ntms but not enough to offset the original tariff reductions,44 broadly confirming that (domestic) bargaining motive is an important source of the political value of commitment in international agreements. yet, there are other interpretations on the recent diffusion of ntms, especially when developed countries are considered. for example, bagwell and staiger (2013), argued that the proliferation of ntms particularly in developed countries, at a certain degree, could be the result of what they call “globalization fatigue”. with this term, they refer to the fact that the increase in ntms as substitutes to tariffs could be seen as a second-best policy to leave room for negotiations with developing countries. indeed, developed countries’ tariffs have been lowered too much to represent a good “bargaining chip” toward developing countries. the evidence by beverelli et al. (2014) discussed in section 2 is consistent with this interpretation. 6.2 global value chain and trade policy governments’ incentive to cooperate on international agreements over standards could be also the result of the effect of ntms on firms’ fixed costs for entering foreign markets, as summarized in the firms heterogeneity model discussed in section 4.2. in this setting, ntms could determine the extent of competition. ntms that affect fixed costs, besides acting like a tariff, and thus affecting international terms-of-trade, would have an additional effect on market entry decisions in the foreign country. moreover, ntms, by 43 note however that, in the growing situations where international externalities are not pecuniary – such as global warming – this focus on the term-of-trade motive looses importance. 44 limào and tovar (2011) also emphasized that, although in the majority of the investigated situations the government justified the introduction of the new ntms using consumer and/or environmental concerns, the selected ntms never really account for these externalities. 314 alessandro olper imposing fixed costs, will induce trade concentration in larger and more efficient firms (wto, 2012; abel-koch, 2013).45 the proliferation of global chains coordinated by large players increases the international interdependency and may provide a rational for a deep cooperation on ntms (and tariffs) within trade agreement (wto, 2012). in fact, other than spillovers associated to the term-of-trade effect, the break-up of the production process across different countries creates new forms of cross-border spillovers (staiger, 2012; antràs and staiger, 2012b; blanchard, 2014). one of the salient characteristics of global value chain is represented by the surge of trade in (processed) intermediate goods. this is the result of different phenomena, such as the strong reduction of the costs of international transactions – due to both declining trade costs and ict – and the rising role of global players and foreign direct investmentsfdis (see baldwin and lopez-gonzalez, 2014). intermediate input purchases tend to be associated with significant lock-in effects for both buyers and sellers. this is because intermediate input varieties are often customized to the needs of the buyers, incorporating a growing amount of relationship specific investments, which may be hard to recoup when transacting with alternative parties. moreover, offshoring often involves the costly search for suitable foreign suppliers or foreign buyers, which makes separations costly and thereby provides another source of lock-in (antràs and staiger, 2012b). these global value chain linkages alter the conventional calculus of trade policy (blanchard et al., 2016). introducing import tariffs hurt those upstream domestic firms that supply inputs to foreign producers, as tariffs reduce the value of foreign goods, and so the revenue accruing to domestic input suppliers. these linkages reduce the governments’ incentives to impose a tariff. similarly, when domestic firms use foreign value added in production, a part of the gains due to an import tariff translates back through the supply chain to foreign input suppliers (blanchard et al., 2016). the extent to which the current institutional framework, originally drafted for a world trade in final goods, can address this new forms of interdependency, appears to be an open and difficult question (wto, 2012; blanchard, 2014). antrà and staiger (2012b) reported an interesting empirical evidence in support of the ideas that actual wto rules do not work well in presence of global value chain interactions. specifically, for a sample of 16 countries that joined the wto after its creation in 1995, they showed that tariff concessions were markedly greater in sectors with low levels of input customization than in sectors with high levels of input customization (figure 8).46 conceptual and empirical works on the role played by global value chains in affecting government’ incentives over trade policy is still in its infancy. yet, important contributions already exist. for example, orefice and rocha (2014) provide one of the first empirical evidences showing that there exists a two-way link between deep ptas and the share in trade attributable to vertical specialization. in particular, the authors showed that sign45 see alfaro et al. (2015) for a recent model of global value chains where heterogeneous firms decide the boundary along the value chain. see baldwin and lopez-gonzales (2015) for an overview of recent global supply chain stylized facts. 46 antrà and staiger (2012) measured the sectoral level of inputs customization as the share of an industry’s inputs not traded in organized exchanges (see nunn, 2007). 315the political economy of trade-related regulatory policy ing deeper agreements increases trade in production networks between member countries by almost 12 percentage points on average. in addition, the impact of deep integration is more significant for industries that by their nature require higher levels of regulation. blanchard and matschke (2015) combine firm-level data on us foreign affiliate activity with detailed measures of us trade policy to study the interlink of offshoring and ptas. in line with the theoretical expectation they showed positive correlation among us trade preferences and offshoring activity, and the size of the economic effect is important.47 blanchard et al. (2016), introduced cross-border supply chain linkages into the standard terms-of-trade model of trade policy formation based on the gh framework. they used this model to study how government objectives over final good tariffs depend on the nationality of the value-added content embodied in home and foreign final goods. the key prediction of the model is that the surge of global value chain trade is reshaping the political incentive over trade policy, by erasing distinction between final goods made at home versus made abroad. theoretically, blanchard et al. (2016) add two important contributions. first, they showed that, considering the value added content of trade in the production process changes the mapping from prices to income, altering government incentives over trade policy. secondly, by incorporating this new incentives into a political economy model, they reach several new results. for example, the domestic content embodied in foreign final goods dampens a country’s incentive to manipulate its terms-of-trade. interesting, 47 for example, they estimated that a 10% increase in us foreign affiliate exports to the us is associated with a 4% increase in the rate of preferential duty-free access. figure 8. percent deviation from concession by tercile of input customization measure. -30 -25 -20 -15 -10 -5 0 5 10 15 20 low input customization medium input customization high input customization pe rc en t de vi at io n fro m m ea n co nc es io n tercile of nunn's (2007) input customization measure source: antras and staiger (2012b). 316 alessandro olper blanchard et al. (2016) using bilateral applied tariffs, temporary trade barriers (i.e. antidumping and countervailing duties) and value-added contents of trade, find strong support to the model predictions. finally, gawande et al. (2015) investigated the trade policy response to the 2008 crisis in seven large emerging countries. the main aim of the paper was the attempt to understand the extent to which the country participation in global value chains represented an important economic factor in driving the trade policy response to the crisis. as it is well known, the effect of border protection in presence of trade in intermediate goods is significantly amplified simply because “goods” crossed the country borders several times (hummels and yi, 2001). interestingly, although they find heterogeneity in the country-level results, overall they support the notion that the position of domestic and foreign exporters in the global supply chain exerted offsetting forces in many countries. in particular, the demand for cheap inputs by downstream users, and the demand for a country’s exports by vertically specialized producers in partner countries, exerted countervailing pressure against protectionist pressure from domestic lobbies. hence, the main message of this paper’s results is that today the nature of trade produces powerful incentives against protectionism, which goes well behind the standard term-of-trade motives. 7. concluding remarks agricultural economists have been traditionally aware that the key determinants of agricultural and food policy are largely political in nature, and this also applies to environmental and food standards, as it clearly emerges from this survey. there is a long and important literature that documents this awareness, taking the peculiarities of ntms into account and raising new theoretical and empirical challenges. as briefly discussed in section 2, building a sound model on the economic effects of environmental and food quality standards is difficult. for the same reason, modelling them as endogenous response to the political process should have higher priority in our research agenda. there are several areas where further progress is needed. first, political economy models in general, and in particular when applied to the formation of regulatory standards, need to consider simultaneously both electoral incentives and lobbying behavior (and their possible interaction). some preliminary effort has been done from a theoretical point of view (e.g. yu, 2005; swinnen et al., 2015). yet, empirical tests of the model predictions are rare, and often too simplistic for understanding the complex interaction between voters, lobbying and political interests. in particular, models of environmental and quality standards are waiting for sound empirical tests. here, the difficulty is to consider more seriously the exact role and behavior of different interests (e.g. “green” versus “industrialist” groups), as well as whether and how consumers’ and voters’ interests really matter. second, the underlying modelling structure to study the political economy of standards should be the one of monopolistic competition with firms’ heterogeneity. this is not only because this modelling framework accounts for more realistic features of the effects of standards, but because it adds a new important dimension in the coalition formation in favor or against ntms. indeed, with firms’ heterogeneity the traditional cross-sectoral conflict over trade policy is broken-down over the within-sector dimension depend317the political economy of trade-related regulatory policy ing on firms’ characteristics. the extension of the gh model to firms heterogeneity could perhaps contribute to solve some empirical inconsistency of the model, as suggested by chang and willmann (2014). yet, a downside of this approach is the need of detailed lobby and industry data at firm level to test the model predictions properly. third, the design of international rules over standards requires a better understanding of the complex coordination problem between countries and firms. however, the policy implications for the effect of environmental and food standards from a model that includes more realistic details of the current globalization waves, i.e. the structure and linkages of modern gvcs, have not been developed yet.48 this perhaps represents one of the most compelling challenges for our profession, given the analytical complexity of this literature and the necessity to adapt actual modelling tools to the peculiarity of agri-food value chains. indeed, this emerging literature, briefly summarized in section 6, has been largely developed with specific manufacturing industries in mind, such as chemicals and electronics, where vertical specialization and global outsourcing are a fact, and focusing mainly on trade policy (tariffs). although also the agri-food sector experienced an increase in vertical specialization, some features of this industry are still different and peculiar. for example, the dependence of the agricultural process on natural resources (land, water, and climatic conditions), the perishable nature of many food products, and differences in international transactions and contracts between players, raise further complexities calling for a careful adaptation of the current modelling tools. all that, clearly, could result in policy implications for regulatory standards that go in different directions with respect to what is emerging with reference to the manufacturing industry. acknowledgment: the author thanks susan senior nello, the editor, and a referee of the journal for several constructive comments on an early draft, as well as lucia pacca and chiara falco for critically reading the manuscript. references abel-koch, j. 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(2014). a theory of trade policy under dictatorship and democratization, discussion papers 1403, exeter university, department of economics. bio-based and applied economics 9(1): 85-107, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8464 the role of group-time treatment effect heterogeneity in long standing european agricultural policies. an application to the european geographical indication policy leonardo cei1,*, gianluca stefani2, edi defrancesco1 1 university of padova (italy) 2 university of florence (italy) abstract. in recent years, the european union is stressing the importance of monitoring and evaluating its policies, among which the common agricultural policy plays an important role. policy evaluation, in order to provide reliable results on which to take important legislative decisions, should rely on robust methodological tools. a recent strand of literature casts some doubts about the reliability of the two-way fixed effect estimator when the effect of a treatment is heterogeneous across groups of units or over time. this estimator is widely used in agricultural economics to estimate the effect of policies where effect heterogeneity may be at stake. using the european geographical indication (gi) policy, we compared the two-way fixed effects estimator with a novel non-parametric estimator that accounts for the issues created by effect heterogeneity. the results show that the two estimators, consistently with the concerns expressed by the technical literature, may lead to different estimates of the policy effect. this suggests that treatment effect heterogeneity is likely a concern when assessing the impact of gi-type policies. therefore, the use of the standard estimator may lead to misleading conclusions and, as a result, to inappropriate policy actions. keywords. treatment heterogeneity; geographical indications; impact assessment; two-way fixed effects; policy evaluation jel codes. q18, q56. 1. introduction in recent years, the european union (eu) is stressing the need to move toward an ever more evidenced-based policy making. despite the renewed attention it is attracting nowadays, evidence-based policy making is not a new concept. the discussion about the need to use empirical evidence to understand how policies work and to identify their results was already in place in the 1990s (e.g., oecd, 1994; pawson and tilley, 1997). *corresponding author. e-mail: leonardo.cei@phd.unipd.it editor: fabio gaetano santeramo. 86 leonardo cei, gianluca stefani, edi defrancesco sanderson (2002) claims that two kinds of evidences are required to improve the governmental action. on the one hand, it is necessary to understand whether the policy action is effective. on the other hand, acquiring knowledge about how a certain policy works is of fundamental importance. in the language of yin (2013), this corresponds to answer, respectively, a “what” and a “why” question. especially the former aspect plays an important role in the current eu common agricultural policy (cap), where the legislator stresses the importance of a constant monitoring and evaluation of its measures, also providing indicators and methodological guidelines, as well as some ex-ante evaluations on quantitative goals. on the verge of the new cap programming period (2021-2027), the policy course that aims at providing evidences about the effectiveness of the policies and measures of the cap is confirmed and stressed. the new cap regulation proposal states that “the current common monitoring and evaluation framework (cmef) and the current monitoring system of direct payments and rural development would be used as a basis for monitoring and assessing policy performance, but they will have to be streamlined and further developed” (european commission, 2018: pg. 9). the rising interest in evidence-based policy making, however, requires proper tools to collect evidences, analyze them and interpret the results. in this respect, a useful reservoir of approaches, methods and techniques to be used in the evaluation process is represented by quasi-experimental approaches. adopting an ex-post perspective (i.e., after the policy has been implemented), the main goal of quasi-experiments is to identify the effect that a certain policy, program or treatment produces on some indicator that measures the policy objectives. basically, this requires to clearly identify the causal relationship between the treatment and the outcome, in order to isolate the effect of the policy from the role played by other confounding factors (khandker et al., 2009). the identification of this causal link, however, constitutes the major effort in real socio-economic contexts. different policy settings have different pitfalls that hinder the correct identification of the causal effect. to overcome these issues, researchers came up, over the years, with strategies and techniques tailored to specific policy settings. to cite some examples, regression adjustment and matching are ways to account for the effect of observable covariates; instrumental variables and difference-in-differences (did) can get rid of the influence of unobservable factors (cerulli, 2015); the regression discontinuity design is well suited in contexts where the administration of the treatment is based upon certain thresholds. as a result, before starting an impact analysis, the researcher should pay attention to the policy he/she aims at evaluating and to the setting where the policy is implemented. the ideal policy setting for impact analysis involves a binary treatment that is administered to one group of individuals, while another group can be used as a control. the two groups can be observed at a single point in time or over a couple of periods. however, some policies are characterized by more complex settings, as is the case of several eu agricultural policies. this is especially the case of long-standing policies, where the participation is voluntary, the enrollment in the treatment not simultaneous, and individuals can be observed for multiple time periods. the policy we refer to in our article, the geographical indication (gi) policy, is an example of this situation. provided that their farm is located in the area of origin of a gi product, farmers do not have any obligation about whether or when to join the specific gi system. 87the role of group-time treatment effect heterogeneity policy settings where the treatment administration is based on voluntariness and is not simultaneous can be included in the category that is referred to as event study designs (borusyak and jaravel, 2017) or staggered adoption designs (athey and imbens, 2018). an important aspect in event study designs is that the effect of the treatment might not be constant across groups of individuals or time periods, a condition that is referred to as group-time treatment effect heterogeneity. the standard econometric model that has been used so far to deal with this kind of policy frameworks is the two-way fixed effects (twfe), a panel fixed effect estimator with group (or individual) and time effects. de chaisemartin and d’haultfoeuille (2019) noted that the twfe was used in 20% of the empirical articles published on the american economic review between 2010 and 2012. this tool is used in agricultural economics as well, where is exploited to study a variety of topics. dawson (2005), for example, used a twfe regression to measure the contribution of agricultural exports in less developed countries, finding a positive effect of agricultural exports on economic growth. lien and hardaker (2001), in a study on norwegian farmers, showed that, in the choice of the optimal farm plans, subsidy schemes, market conditions and available labor have more importance than the farmer’s risk attitude. in the context of the gi policy, raimondi et al. (2019) investigated the effect of these quality labels on trade, highlighting that the gi policy promotes the export of agri-food products and has positive effects on export prices, while it has weak negative effects on imports. despite the wide use of the twfe, however, a recent bunch of literature questions the validity of this estimator when estimating the impact of the treatment in presence of group-time effect heterogeneity, claiming that it does not provide easy-to-interpret estimates (goodman-bacon, 2018; athey and imbens, 2018; imai and kim, 2019) and, more important, that this estimator can produce, in some cases, biased results (de chaisemartin and d’haultfoeuille, 2019; borusyak and jaravel, 2017; abraham and sun, 2018). given the practical relevance of impact analysis, biased results are a serious concern, especially when institutions stress the link between policy making and empirical evidence, as in the european case. moreover, the european agricultural context is quite rich in policies that have an event study structure, such as the gi policy, the organic certification, or the rural development programs. some studies tried to investigate the effects of these policies. torres et al. (2016) compared, over a 25 years period, the performance of organic and conventional citrus farms in spain using profitability indicators to evaluate farms investments. nordin (2014) and nordin and manevska-tasevska (2013) assessed the impact of the grassland support on agricultural employment in sweden at the municipality and farm level, respectively. within the gi context, cei et al. (2018a) and raimondi et al. (2018) estimated the impact of gis at the regional level, respectively, on agricultural value added in italy and on agricultural value added and employment in italy, france and spain. to our knowledge, however, so far, no study explored the relevance of grouptime treatment effect heterogeneity in measuring the impact of this kind of policies and measures in europe. in light of this, the objective of this paper is to understand whether group-time treatment effect heterogeneity is a concern when estimating the effects on the agricultural value added of the gi policy, an eu agricultural policy characterized by both voluntariness, not-simultaneity of the treatment and persistence of the treatment over time. this is done comparing the results of the standard twfe estimator with the results obtained using a novel estimator proposed by callaway and sant’anna (2018) that spe88 leonardo cei, gianluca stefani, edi defrancesco cifically accounts for the presence of group-time treatment effect heterogeneity. ideally, if group-time treatment effect heterogeneity is not an issue in the studied context, the results of the two estimators should coincide. understanding the relevance of group-time treatment effect heterogeneity would help in identifying the best strategies to correctly assess the impact of this kind of eu policies. in the next section, we provide a brief overview of the gi policy in the eu and of the economic effects of this policy on the rural economy, and we review the technical literature addressing the issue of group-time effect heterogeneity in impact analysis. here, we also present the novel non-parametric estimator that we will use as a comparison for the twfe estimator. the third section describes the data and methods we used in the analysis, while in the fourth section we present our results, that will be discussed in a critical way in the fifth section. we end the article drawing some conclusion and highlighting the relevant research and policy implications of our work. 2. policy and technical background 2.1 geographical indications in europe and their economic impact geographical indications (gis) are defined as “indications which identify a good as originating in the territory of a member, or a region or locality in that territory, where a given quality, reputation or other characteristic of the good is essentially attributable to its geographical origin” (wto, 1994, article 22). in europe, geographical indications were given a common legal framework in 1992, but some countries (especially mediterranean ones) already had in place, by that time, national provisions regulating gis. according to the european definition of gis, the quality of a gi product directly stems from specific and unique characteristics of the area where the product is produced, i.e. from the terroir. the gi policy regulates two types of gis, the protected designation of origin (pdo) and the protected geographical indication (pgi), but the link between the product quality and the terroir is stronger for the pdo, whose entire production process must take place in the delimited area of origin, while the pgi just requires that at least one of the production steps takes place in the area of origin. the distinctive sign of the eu gi policy is that, in contrast to what happens in other countries, where the protection of gis is mainly based on trademarks, pdo and pgi are public-owned signs. farmers are thus free to join gi schemes, provided they are located within the area of origin and they comply with the rules contained in the product specification. the strong link between gi products and the territories from which they originate is reflected in the objectives of the policy. reg.(eu) no 1151/2012, that currently regulates the european gi system, places a considerable importance on the value adding function of the gi certification, claiming that this legislative tool is able to improve the income of local farmers. in turn, this would reflect in positive effects for the local economy and rural development. the idea that gis can positively affect the economy of the area where their production takes place relies on several economic foundations. first, gis are widely recognized to be market instruments that reduce the information gap between producers and consumers (marette et al., 1999; josling, 2006; anania and nisticò, 2004). providing additional infor89the role of group-time treatment effect heterogeneity mation to consumers is expected to raise their willingness to pay for the product. if this added value manages to be transferred up the supply chain, it will turn into an economic benefit for producers. another function fulfilled by the gi certification is to act as a substitute for producer’s reputation (menapace and moschini, 2012), which, according to shapiro (1983), needs time to be built, but eventually grants a price premium on the market. finally, gi-type certifications are able to create a rent for a limited number of producers because of the excluding mechanisms that operate in this kind of systems (moran, 1993; perrier-cornet, 1990; josling, 2006; thiedig and sylvander, 2000) as a consequence of area restrictions, yield limits, or both (landi and stefani, 2015; hayes et al., 2004). the value-creation function of gis and quality schemes in general is supported by several studies that approach the problem from a theoretical and modeling perspective (anania and nisticò, 2004; menapace and moschini, 2014; moschini et al., 2008; zago and pick, 2004). on the other hand, however, empirical studies offer a more controversial scenario. consumers usually attach a greater value to gis, despite the occurrence of positive label effects is heterogeneous across gi products (see deselnicu et al. (2013), leufkens (2018) and santeramo and lamonaca (2020) for some meta-analysis of studies on gis and regional products, garavaglia and mariani (2017), menapace et al. (2011) and de-magistris and gracia (2016) for specific studies) however, some difficulties are identified for that value to be transferred to agricultural producers (cei et al., 2018b). with respect to proper impact evaluation analysis, to our knowledge, to date, only two studies have addressed the topic from this perspective. cei et al. (2018a), found a positive impact of the gi protection on regional agricultural value added in italy while raimondi et al. (2018) estimated a positive impact of gis on regional employment in france, italy and spain, and a positive effect on labor productivity in spain. 2.2 group-time treatment effect heterogeneity in impact evaluation the gi policy allows farmers to voluntarily start the production of a gi product provided they are located in the area of origin and they comply with the gi product specification. moreover, the policy has been continuously in place for more than 25 years, so that its activity has been observed for several periods. these characteristics create suitable conditions for the presence of group-time treatment effect heterogeneity. while treatment effect heterogeneity is defined as “the degree to which different treatments have differential causal effects on each unit” (imai and ratkovic, 2013), in line with the relevant literature (see, for example, athey and imbens (2018), borusyak and jaravel (2017), abraham and sun (2018)), group-time treatment effect heterogeneity arises when the effect of the treatment varies across groups of individuals (group heterogeneity), over time (time heterogeneity), or both. in this respect, group-time treatment effect heterogeneity can be considered a specific case of the general treatment effect heterogeneity, where the effect varies not at an individual level, but at the level of groups of individuals (e.g., groups that receive the treatment in the same year, groups for which the effect is estimated in a certain year). three types of group-time heterogeneity can be distinguished according to callaway and sant’anna (2018). the first type, which they refer to as selective treatment timing, is the pure group heterogeneity case, where the effect of the treatment depends on when an individual is treated for the first time (groups are made of individuals who receive the first 90 leonardo cei, gianluca stefani, edi defrancesco treatment at the same time). time heterogeneity is decomposed into a dynamic treatment effect and a calendar treatment effect. the former considers the possibility that the effect of the treatment may depend on the amount of time an individual has been exposed to the treatment. the latter lets the treatment effect vary according to the moment (period) when the effect is measured. in contexts where group-time treatment effect heterogeneity can show up, researchers usually exploit a standard parametric way to measure the average treatment effect on the treated (att), the two-way fixed effects model. the twfe model, whose specification is reported in (1), is a modification of the classical fixed effects regression. yit = αi + δt + βdit + θxit + εit (1) in (1), i and t are the individual/group and year subscripts, y is the outcome, x is a set of covariates that account for possible confounders, and ε denotes the error term. αi and δi are, respectively, the unit/group and time fixed effects. β, the coefficient associated to the treatment variable dit, is the estimator for the att. the twfe is a regression-based did estimator (abadie, 2005) and as such is able to get rid of the selection bias introduced not only by observable factors (which can be directly included in the set of covariates x), but also by unobservable factors, provided that these factors are constant over time. this characteristic makes the classical fixed effects the perfect parametric counterpart of the did method in the basic impact analysis setting where two groups (treated and controls) are observed over two periods (before and after the treatment). similarly, working with multiple groups and multiple periods, the twfe is expected to provide an average estimate of the treatment effect. this average estimate is the result of the aggregation of the various group-time atts, i.e. the atts for each group of individuals measured in a specific time period1. the aggregation of the group-time atts, however, involves a not linear weights structure, which makes the interpretation of the β coefficient not straightforward (imai and kim, 2019; athey and imbens, 2018; goodman-bacon, 2018). more importantly, in contexts characterized by group-time treatment effect heterogeneity, some of the group-time atts can receive negative weights when aggregated into the twfe estimator (abraham and sun, 2018; borusyak and jaravel, 2017; de chaisemartin and d’haultfoeuille, 2019). negative weights are a potential risk not only for the interpretation, but also for the reliability of the estimator, since they alter the sign of some atts that form the aggregated estimate and thus introduce a bias. to face this issue, several authors suggested some novel estimators, either parametric (imai and kim, 2019) or non-parametric (de chaisemartin and d’haultfoeuille, 2019; callaway and sant’anna, 2018), that do not involve negative weights. in our study, we use the one suggested by callaway and sant’anna (2018) (hereinafter referred to as the csa estimator) and we compare its results with the estimates obtained using the twfe. the csa estimator computes the att for each group of treated units (g) in each time period (t). treated units are those observations that receives the treatment at some point in time during the observation period and they are assumed to not withdraw from the treatment 1 the group-time atts are not actually estimated by the twfe, but some authors offer several decompositions of the twfe estimate in terms of group-time atts (imai and kim, 2019; athey and imbens, 2018; goodman-bacon, 2018). 91the role of group-time treatment effect heterogeneity once they received it. each group g of treated units is composed of individuals that are treated for the first time in period g (i.e., they are not treated at t < g). controls are the units that never receive the treatment. the authors provide two versions of the estimator, one for balanced panel data, reported in (2), and one for repeated cross sections. (2) in (2), gg is a group binary indicator that identifies individuals first treated at time g, c is a binary variable identifying control units, y is the outcome variable, and is the generalized propensity score2, estimated on a set of covariates x, that estimates the probability of a certain unit to be first treated at time g. the idea behind the estimator resembles the one in lemma 3.1 in abadie (2005) for the classical two groups-two periods setting. basically, control units are weighted down when they have characteristics that are uncommon in the treated group, and weighted up when their characteristics are frequent in the treated group. this mechanism guarantees the balancing of the covariates between the treated (g) and the control group (abadie, 2005; callaway and sant’anna, 2018). basically, the csa strategy computes, for each (g,t) pair, a did estimate weighting control units on the basis of a propensity score measure. the propensity score is estimated for each (g,t) sample, i.e. using all control units and those treated units that form group g. it is important to note that the att can be estimated even for pre-treatment periods, i.e. with g > t. because the treatment is supposed to not affect the outcome before it is administered, the analysis of the pre-treatment atts allows to verify that the conditional parallel trends assumption (i.e., the trends of the outcome variable in the treated and control groups are parallel conditional on x for all g and t) holds. the parallel trend assumption is common in did settings, where we assume that the change in the outcome variable would have been the same in the treated and control group had the treatment not been administered. in a setting with multiple groups and multiple periods, it is required that the parallel trends assumption holds for all g ≤ t. this assumption is fundamentally untestable (callaway and sant’anna, 2018), but once we extend it to cover also pre-treatment periods it can be tested looking at the significance of the pre-treatment att estimates. the means through which the csa strategy addresses the group-time treatment effect heterogeneity issue are the avoidance of making “functional form assumptions about the evolution of potential outcomes” (callaway and sant’anna, 2018, p.9) and the devising of several summary measures that avoid the drawback of negative weights. the main summary measures suggested in callaway and sant’anna (2018) are reported in table 1. the first measure (simple weighted average) is a simple average where each att(g,t) is weighted by the number of treated observations in the respective (g,t) subsample. the selective treatment timing, the dynamic treatment effects and the calendar treatment effects meas2 this definition is provided in callaway and sant’anna (2018), despite the term “generalized propensity score” is used with different meanings in the literature. in rosenbaum and rubin (1984), it refers to a form of the propensity score that accounts for missing data in the covariates, while hirano and imbens (2004) use the same term to indicate a propensity score that also accounts for cases when the treatment is not a binary variable. 92 leonardo cei, gianluca stefani, edi defrancesco ure the three types of heterogeneity that we mentioned in the first part of this subsection, where the effect is thought to vary according either to the group, to the length of exposure to the treatment, or to the moment when the effect is estimated, respectively. the last summary measure, selective + dynamic, is a combination of selective treatment timing and dynamic treatment effects. each of these summary measures has two levels of aggregation. the first level indicates the att within each group (g), number of periods after the treatment (e), or period (t). the second level measure is an average of the first level measures. as we can see from table 1, to obtain these measures, the group-time atts are weighted on the basis of the size of the samples of interest (which vary according to the different summary measures). in this way, weights are assured to be always positive and meaningful, thus avoiding the occurrence of any bias or difficulty in their interpretation. to better clarify the meaning of the summary measures, we propose a simple practical example. in figure 1, we report hypothetical atts for three groups of individuals. one group (bold line) is first treated at period 1 (g = 1), the second group (dashed line) at period 2 (g = 2) and the last group (dotted line) is treated for the first time at period 3 (g = 3). according to the formulas in table 1, these atts are aggregated into the first level summary measures, which are reported in figure 2. the selective treatment timing (“selective” pane of figure 2) highlights that the average effect is larger for the group that receive the table 1. summary parameters of the att proposed by callaway and sant’anna (2018). summary parameter first level second level weighted average1 selective treatment timing dynamic treatment effects2 calendar time effects selective + dynamic3 1. the weighted average parameter had a single level of aggregation. the term k assures the normalization of weights, and is equal to . 2. e represents the number of periods (years) after a group g of units receive the first treatment. 3. e’ is a specific number of periods, selected by the researcher, after a group g of units receives the first treatment. δgt(e,e’) abbreviates the logic function . 93the role of group-time treatment effect heterogeneity first treatment in the second period. the dynamic treatment effect (“dynamic” pane) shows that, on average, the longer individuals stays in the treatment, the higher the treatment effect. finally, the calendar treatment effect (“calendar” pane) suggests that the effect measured in the last periods (t = 3 and t = 4) is higher. for this simple example, all this information were easily retrievable from figure 1, but the summary measures gain importance when the number of groups and periods gets large. a final major contribution of callaway and sant’anna (2018) is the derivation of the respective asymptotic theory for both the att(g,t) estimators and the summary parameters. specifically, they derived both a consistent estimator of the variance and a specific bootstrap procedure. the suggested bootstrap procedure in particular has some advantages over traditional bootstrap. it avoids the re-estimation of the propensity score in each draw; it includes, in each iteration, observations from each group; and it allows to compute confidence bands simultaneously valid in g an t. 3. data and methods 3.1 data sources and samples in our study we used two data sources: the italian farm accountancy data network (fadn) and the eu eambrosia database3. the fadn data we worked with cover a nine years period, from 2008 to 2016. fadn is an unbalanced panel collecting farm-level data using a stratified sample design common to all eu member states4. the database reports 3 the eambrosia database is accessible at: https://ec.europa.eu/info/food-farming-fisheries/food-safety-andquality/certification/quality-labels/geographical-indications-register/ 4 the fadn field of observation consists of commercial farms, which are defined according to country-specific economic size thresholds (see reg.(ec) 1242/2008). for italy, the threshold is set to 4000 euros until 2014 figure 1. hypothetical group-time atts: each line identifies a group of treated units that receive the treatment in a specific time period. figure 2. first-level summary measures for the hypothetical group-time atts reported in figure 1. 94 leonardo cei, gianluca stefani, edi defrancesco data on farm structure, the farmer and workforce characteristics, the production process and several economic indicators. a specific section of the database reports whether a farm is involved in gi production and details which crop (or animal type, in case of livestock production) is under pdo and/or pgi certification. eambrosia (formerly door), is an european database where all the registered gi products are listed. for each product, several information is reported, including the product specification. to identify the gi case studies on which to perform the analysis, we crossed the information from the two databases. specifically, we know, from fadn, whether a farm is involved in the gi production, to which crop/animal type the certification refers, and where the farm is located. rearranging the information from product specifications, we know which gis can be produced in the area where the farm is located. using this information, we selected two cases, based on: i) no overlap between gis of the same product category in the same area; and ii) presence of control farms (i.e., farms producing the same product without the certification) in the gi area. considering also the need for sufficiently large sample sizes, we selected two gis: mela val di non pdo (apple) and riviera ligure pdo (extra-virgin olive oil). as mentioned in the previous section, a form of the csa estimator for unbalanced panel has not been provided yet, thus we needed to balance the samples to conduct our analysis. since fadn data cover a nine-years period, we created several balanced panels selecting different time spans and dropping units that were not observed in all the years included in the selected span. this balancing procedure will affect our results, because we are dropping treated units. however, as we discuss in the fifth section, this is not a concern for our purpose of comparing the two estimators. the balancing provides a data structure that complies with all the assumptions required by callaway and sant’anna (2018) to implement their technique. 3.2 impact analysis in each sample, the treatment variable, giit, is the binary indicator showing whether a farm i produces the gi product in year t. treated units are those farms for which giit = 1 in at least one year t, that is, farms that at some point in time certify their production as a gi5. in line with the csa assumptions, once a farm adopts the certification, it is not supand to 8000 euros afterward. the stratification is based on three levels: geographical location (european nuts2 regions), economic size, and type of farming. further details can be found at https://ec.europa.eu/agriculture/ rica/index.cfm. 5 it could be the case that some farms produce two versions of the same product certifying a part of the production and commercializing the remaining share without the gi sign. in these cases, the structure of the fadn dataset does not allow to distinguish between the two kinds of production. in the analysis, whenever a farm is reported to use the gi certification for a certain product, is considered to produce “only” gi-certified product. therefore, farms that possibly has a “mixed” production (gi and non-gi) for the same crop are always considered as treated. it must be noted that this issue is probably more relevant for apple farms than for olive oil farms. in the mela val di non pdo origin area the production of non-certifiable varieties is possible and common, while olive varieties grown in the riviera ligure pdo area are quite exclusively the ones admitted by the product specification. 95the role of group-time treatment effect heterogeneity posed to withdraw from the gi scheme, i.e., the treatment is irreversible6. on the other hand, a farm is included in the control group if it is never treated, i.e. giit = 0 in every year t. control units are selected only among farms located in the same region (nuts2 level for riviera ligure pdo and nuts3 level for mela val di non pdo), and producing the same product of treated farms (e.g., apple farms without the certification for the mela val di non pdo sample). this allows us to perform our analysis in a sufficiently homogeneous socio-economic and legislative setting. to measure whether the gi certification is actually able to increase the added value of the crop to which it applies, the crop gross margin per hectare is used as the outcome variable. the use of this variable has several advantages for our aim. in contrast to farm-level economic indicators, crop-level indicators are not affected by the economic performance of other processes or by the organization of the farm as a whole, and this allows to isolate the effect of the certification7. in addition, the crop gross margin indicator is defined as the difference between the total crop production and total variable costs. measuring the certification impact on the crop gross margin thus allows to consider the effects of the certification both on the crop revenues (e.g., increased prices) and on the variable costs associated to that specific crop (e.g., inputs and certification costs). in turn, this definition of crop gross margin does not account for other eu subsidies that farms might benefit8. the exclusion of other subsidies from the indicator is important to isolate the effect of the gi certification from the possible effects of other cap measures connected to product quality (e.g., second pillar measures). another possible option would have been to use farm prices to measure the effect of the certification, thus focusing on the expected ability of gis to increase these prices, supposing that this is the main effect of the certification. however, it must be noted that the certification usually entails additional costs (e.g., the certification cost to be allowed to use the gi sign). even if one assumes that those additional costs have just a minor importance with respect to the possible effects on farm prices, disregarding the cost side would inevitably lead to a bias in the estimation of the ability of the gi certification to generate an additional value. with respect to the outcome variable, we decided to focus on relative performance improvements rather than on absolute ones. for this reason, since in the did setting results are not scale invariant (lechner, 2010), we use the crop gross margin per hectare in the logarithmic form. the analysis proceeded creating two completely balanced panels, one for each sample. in each sample the effect was first estimated using the twfe and then implementing 6 in the original samples, few farms exit the certification scheme. in these cases, we dropped, before creating the balanced panels, the observations of receding farms from the year when the certification is removed onward. similarly to the reduction in farms due to the balancing, we deem this is not an issue for our purpose of comparing the two estimators. 7 had the objective of the study been to measure the effect of the certification on farm profitability, the crop gross margin would have been a poor choice because it does not allow to attribute to the gi process the costs of factors shared between different farm processes (e.g., labor and capital). this indicator does in fact include the remuneration of these factors. however, the inclusion of these remunerations is exactly what one seeks in estimating the effects on the value added of the gi-certified crop, as in our case. 8 in the fadn database, subsidies are included in the computation of farm-level indicators, such as farm gross margin, farm net value added or farm net income. 96 leonardo cei, gianluca stefani, edi defrancesco the csa procedure. initially, for each sample, we performed basic analysis using models without covariates. in a second stage, we included some independent variables to consider also the role of other factors that may confound the relationship between treatment and outcome. the identification of these factors was based both on previous studies investigating the determinants of gi adoption (van de pol, 2017; marongiu and cesaro, 2018; niedermayr, kapfer and kantelhardt, 2016) as well as on our knowledge of gi systems. we reported these factors in table 2 (first column), where the type of each variable and their summary statistics are also shown. in the last two columns of table 2, we specified how, in the two methods of analysis that we compared (twfe and csa), we controlled for each factor. the unobservable factor (individual characteristics of the farmer) is automatically controlled for by the did structure of the two estimators (estimator structure in columns 4 and 5 of table 2), under the assumption that farmer’s characteristics do not change over time (at least in the period considered in the analysis). the structure of the estimators accounts for the less favored area (lfa) variable and for the year of observation as well. the location of a farm in a less favored area does not change over time and the did framework differences out its effect. on the other hand, the year of observation is controlled by the time fixed effects in the twfe estimator and by the within-year propensity score estimation in the csa estimator. we controlled for the other observable factors in three different ways. most of them are included in the twfe equation as covariates and in the propensity score equation of the csa estimator (covariate and propensity score in columns 4 and 5 of table 2, respectively). on direct selling and organic a sort of direct matching is performed (direct matching in table 2). because, in the two samples, none or very few treated farms adopt organic farming or directly sell their products, we dropped organic and/or direct selling farms from both the treated and control groups (this procedure explains the absence of sample variation for these variables in table 2). dropping organic and direct selling farms is like directly matching farms on a specific value (i.e., zero) of these variables. this strategy, therefore, allows to control for these factors without including them among the regressors of the twfe model or in the csa propensity score equation9. finally, the definition of the control group (control group in table 2) allows to control for the type of gi product variable, because control units are selected among farms that produce the same type of product of treated farms. 4. results the analysis were conducted on the two samples (mela val di non pdo and riviera ligure pdo) for different time spans, first using basic models without covariates and then adding independent variables10. the first one is the mela val di non pdo sample in a seven years period (from 2008 to 2014). in this sample, 15 farms join the certification system in 2009 and 13 farms enter the gi scheme in 2010. the control group consists of 9 it should be noted that, in this way, only specific farm types are compared (i.e., non-organic and nondirect selling), which makes the results of the analysis not extendable to organic or direct selling farms. again, the objective of our analysis makes this issue irrelevant. 10 the whole analysis was performed using the statistical software r. callaway and sant’anna (2018) provide a specific r command to implement their methodology. 97the role of group-time treatment effect heterogeneity table 2. factors to be controlled for in the models. factor type summary statistics1 method (twfe) method (csa)mela val di non pdo riviera ligure pdo age of the farmer continuous [min;max] mean st.dev [min,max] mean st.dev covariate propensity score farm located in a less favored area binary [1.00;1.00] 1.00 0.00 [0.00;1.00] 0.71 0.45 estimator structure estimator structure farm performing direct selling binary [0.00;0.00] 0.00 0.00 [0.00;0.00] 0.00 0.00 direct matching direct matching farm producing other gi products binary [0.00;1.00] 0.15 0.35 [0.00;1.00] 0.16 0.37 covariate propensity score farm with organic production binary [0.00;0.00] 0.00 0.00 [0.00;0.00] 0.00 0.00 direct matching direct matching farm utilized agricultural area continuous [0.42;40.85] 5.56 4.73 [0.25;14.62] 1.94 1.70 covariate propensity score individual characteristics of the farmer not observable estimator structure estimator structure labor intensity continuous [0.08;4.04] 0.37 0.26 [0.07;3.87] 0.84 0.58 covariate propensity score education of the farmer: none binary [0.00;0.00] 0.00 0.00 [0.00;1.00] 0.02 0.14 covariate propensity score education of the farmer: primary binary [0.00;1.00] 0.09 0.29 [0.00;1.00] 0.13 0.34 covariate propensity score education of the farmer: lower secondary binary [0.00;1.00] 0.42 0.49 [0.00;1.00] 0.41 0.49 covariate propensity score education of the farmer: upper secondary binary [0.00;1.00] 0.42 0.49 [0.00;1.00] 0.42 0.49 covariate propensity score education of the farmer: university binary [0.00;1.00] 0.07 0.25 [0.00;1.00] 0.01 0.10 covariate propensity score type of gi product categorical control group control group year of observation categorical estimator structure estimator structure 1summary statistics were omitted, in addition to the not observable variable, for the type of product, because it is unique in the samples (either apple or olive oil) and for the year of observation, because the balanced structure of the panels that makes each level (year) equally represented. 98 leonardo cei, gianluca stefani, edi defrancesco 17 farms that never use the gi certification in the observed period. farms in the riviera ligure pdo sample are observed continuously for 5 years (from 2008 to 2012). only one treated group is present (farms that start to certify in 2010), which consists of 17 units. the control sample is larger, including 91 farms. the results of the impact analysis performed using the twfe for the basic models (without covariates) and for the models with independent variables are reported in table 3. in two of the four models, the gi certification has no statistically significant effect on the outcome variable. however, the gi certification has a negative impact on the crop gross margin per hectare in the mela val di non model without covariates, while the gi effect is positive for riviera ligure olive oil when independent variables are included. in both cases, the parameters associated to the treatment variable are statistically significant at the usual 5% level. in table 4, we report the csa group-time att estimates, which are also displayed graphically in figures 36, along with their 95% confidence intervals. according to the csa estimator definition, groups refer to individuals that receive the treatment (i.e., adopt the gi certification) for the first time in year g. on the other hand, the year column in table 4 indicates the time at which the effect is estimated. in figures 3-6 the estimates in red (post = 0 in the figures boxes) refer to pre-treatment atts and can be used to validate the extended parallel trend assumption. in all samples, the pre-treatment atts do not statistically differ from zero, therefore the assumption is not rejected. the standard errors were computed using the csa bootstrap procedure. referring to the same level of table 3. twfe results for mela val di non pdo and riviera ligure pdo. variable mela val di non pdo riviera ligure pdo basic covariates basic covariates gi -0.35** (0.09) 0.02 (0.10) 0.15 (0.13) 0.31** (0.14) uaa 0.00 (0.02) -0.12 (0.13) age -0.11** (0.02) -0.01 (0.01) education (primary) 2.64** (0.65) 1.39 (0.161) education (lower secondary) 1.59 (1.10) education (upper secondary or university) 0.00 (0.56) other gi 0.21 (0.18) -0.23* (0.13) labor/ha 0.17 (0.14) 0.24* (0.15) note: asterisk (*) and double asterisks (**) denote group-time atts significant at 10% and 5% respectively. 99the role of group-time treatment effect heterogeneity statistical significance (5%), we note that all samples are characterized by few significant estimates, while the majority of the group-time atts are not statistically significant. the differences between the basic models and the models where covariates were included are minor. the effect of the certification, in the mela val di non case, is positive and statistically significant at the 5% level, for the group first treated in 2009, in 2012 and 2014 (only in 2014 when covariates are considered). conversely, in the same sample, the effect is negative, for the group first treated in 2010, in 2011. in the other sample, the only significant estimate (att(2010,2010)) shows a positive sign. table 4. csa group-time results for mela val di non pdo (without covariates) and riviera ligure pdo (with covariates). mela val di non pdo riviera ligure pdo group1 year att (basic) att (covariates) group1 year att (basic) att (covariates) 2009 2009 -0.17* (0.09) -0.29* (0.17) 2010 2009 0.15 (0.18) 0.37 (0.20) 2009 2010 -0.10 (0.11) -0.15 (0.16) 2010 2010 0.33** (0.17) 0.37** (0.19) 2009 2011 -0.21 (0.16) -0.33 (0.24) 2010 2011 0.06 (0.19) 0.07 (0.20) 2009 2012 0.49** (0.17) 0.78* (0.46) 2010 2012 -0.09 (0.16) -0.08 (0.18) 2009 2013 0.07 (0.12) 0.28 (0.26) 2009 2014 0.54** (0.25) 1.23** (0.44) 2010 2009 -0.17 (0.12) -0.17 (0.12) 2010 2010 -0.20* (0.12) -0.23 (0.17) 2010 2011 -0.66** (0.21) -0.69** (0.20) 2010 2012 -0.01 (0.21) -0.20 (0.25) 2010 2013 -0.21 (0.21) -0.31 (0.20) 2010 2014 -0.25 (0.35) -0.59* (0.32) 1the column group identifies farmers that enter the gi scheme in a specific year g. in the mela val di non sample some farmers adopt the certification in 2009 and others in 2010. conversely, all farmers in the riviera ligure pdo sample start certifying in 2010, therefore only one group is present. note: asterisk (*) and double asterisks (**) denote group-time atts significant at 10% and 5% respectively. 100 leonardo cei, gianluca stefani, edi defrancesco finally, in table 5, we report the csa summary measures. similarly to what observed for the group-time atts, with the exception of the selective treatment timing for the mela val di non sample, the significance levels of the basic models estimate are similar to those of the models where covariates are considered. because of the presence of only one group of treated units in the riviera ligure pdo sample, all the summary measures for this sample converge to the weighted average. the weighted average is the csa counterpart of the twfe impact estimate, and therefore the one in which we are most interested in for the comparison of the two estimators. in all samples this summary measure is not statistically different from zero. while this results are in line with the twfe estimates for two models (mela val di non pdo covariates model and riviera ligure pdo basic model), for the other two models the evidence is in contrast to what obtained from the twfe estimation. with respect to the other parameters, that can be estimated only in the mela val di non pdo sample, the first-level measures that are statistically significant are usually dynamic or calendar effects but, for the covariates model, selective timing too. we must consider that both dynamic and calendar measures are obtained aggregating two grouptime atts. in this way, each att has a considerable power in shaping the aggregated measure. with respect to the second-level of aggregation measures, none of them are stafigure 3. group-time atts estimates (basic model) – mela val di non pdo sample. figure 4. group-time atts estimates (covariates model) – mela val di non pdo sample. figure 5. group-time atts estimates (basic model) – riviera ligure pdo sample. figure 6. group-time atts estimates (covariates model) – riviera ligure pdo sample. 101the role of group-time treatment effect heterogeneity tistically significant, indicating that there is no trend of the effect due to selective, dynamic or calendar effects. 5. discussion the results of our analysis show that, in a european agricultural policy framework characterized by event study characteristics, the twfe, the parametric technique that has been commonly used in literature to estimate the att in these contexts, might provide different estimates than a novel non-parametric estimator that accounts for treattable 5. csa summary measures for mela val di non pdo and riviera ligure pdo. mela val di non pdo1 weighted average selective treatment timing dynamic treatment effects calendar time effects summary measure basic covariates summary measure basic covariates summary measure basic covariates summary measure basic covariates θ -0.05 (0.13) -0.02 (0.14) θs(2009) 0.10 (0.09) 0.25** (0.18) θd(1) -0.18** (0.08) -0.26** (0.12) θc(2009) -0.17 (0.11) -0.29 (0.18) θs(2010) -0.27 (0.18) -0.40** (0.16) θd(2) -0.36** (0.14) -0.40** (0.13) θc(2010) -0.15* (0.09) -0.16 (0.11) θs -0.07 (0.13) -0.05 (0.14) θd(3) -0.12 (0.13) -0.27 (0.19) θc(2011) -0.42** (0.14) -0.50** (0.17) θd(4) 0.16 (0.15) 0.28 (0.23) θc(2012) 0.25 (0.17) -0.33 (0.28) θd(5) -0.08 (0.16) -0.12 (0.21) θc(2013) -0.06 (0.13) 0.01 (0.19) θd(6) 0.54* (0.27) 1.23** (0.47) θc(2014) 0.17 (0.27) 0.39 (0.30) θd -0.01 (0.11) 0.08 (0.19) θc -0.06 (0.10) -0.04 (0.16) riviera ligure pdo weighted average selective treatment timing dynamic treatment timing calendar time effects summary measure basic covariates summary measure basic covariates summary measure basic covariates summary measure basic covariates θ 0.16 (0.13) -0.03 (0.21) 1 for the definition of each summary measure reported in this table refer to table 1. note: asterisk (*) and double asterisks (**) denote group-time atts significant at 10% and 5% respectively. 102 leonardo cei, gianluca stefani, edi defrancesco ment effect heterogeneity. the main concern we observed is not the discrepancy in the magnitude of the estimated effects, which could be traced back to a cumbersome interpretation of the twfe estimate (imai and kim, 2019; athey and imbens, 2018; goodman-bacon, 2018). rather, in some samples, there is a substantial difference in the significance levels of the two estimates. technical literature warns about the possibility that this eventuality may occur in contexts characterized by a differed administration of the treatment and by the continuation of the treatment over multiple periods, and attributes this fact to the possible occurrence of negative weights in the construction of the twfe estimate (abraham and sun, 2018; borusyak and jaravel, 2017; de chaisemartin and d’haultfoeuille, 2019) when treatment effect heterogeneity is at stake. in two of our samples, evidences of group-time effect heterogeneity emerged. in the mela val di non samples, the aggregate measures show that the effect varies over time. we must use caution in relying heavily on these measures, because of the few number of groups in the sample. therefore, despite we found some hints of time heterogeneity, it is difficult to clearly attribute it to dynamic rather than to calendar effects. a stronger evidence of the presence of effect heterogeneity is provided by the single group-time att estimates, whose variability is observed in all samples used in the analysis. the presence of time heterogeneity is reliable given the structure of the gi policy. especially under a calendar point of view, the economic effect of this policy may well depend on factors that varies over time (e.g. prices, level of production, demand). this variability might translate into an inter-annual effect variability. in light of this, the issue of negative weights pointed out by the literature, which may cause the twfe estimator to be biased, may be relevant when estimating economic impacts in the gi context. since the csa estimator is specifically built to address the issue of negative weights when aggregating the single grouptime atts, our results cast doubts about the reliability of the twfe estimates in this policy context. it should be noted, however, that our results are valid just for our scale of analysis, i.e. the farm level, and should not be extended to contexts where the analysis is performed at different scales or with a continuous treatment variable. for example, among the studies we referred to in the introduction, raimondi et al. (2019) study the trade effects of the number of gis in a given product line using decomposed bilateral trade flows at the hs 6-digit level as their units of analysis. in cases like this the csa estimator simply cannot be computed in the current specification. in addition, a characterization of calendar and group effects when the treatment is continuous has not been developed yet. a possible limitation of our study derives from the fact that we dropped several units from the samples we used in the analysis. this was done to balance the panels as well as to perform direct matching on some covariates. on the one hand, dropping observations increases the variance of the estimates (caliendo and kopeinig, 2008; faries et al., 2020). on the other hand, similarly to what happens when trimming observations that lay out of the common support in matching studies, the reference population change (yang et al., 2016). especially the latter issue would be a relevant concern if the aim of the study was to provide a rigorous impact assessment of the gi policy, because results would not be externally valid. however, since our aim is to compare the two estimators using a real policy setting, these concerns are not relevant. 103the role of group-time treatment effect heterogeneity 6. conclusions in this article, we explored the relevance and the possible effects of group-time treatment effect heterogeneity in impact analysis in the context of the european gi policy. as highlighted by a recent strand of literature, this kind of heterogeneity creates some problems for the estimation of the impact with traditional techniques. in line with these concerns, we observed that the standard parametric way to estimate the effect of the certification, the twoway fixed effects regression, provides different results from a recently developed estimator that accounts explicitly for group-time effect heterogeneity and its negative effects on the estimate unbiasedness. while these results are in line with the evidences reported in technical literature, our study represents, to our knowledge, a first application of a method that takes into account group-time treatment effect heterogeneity in the european agricultural economics context. moreover, our results showed that this kind of heterogeneity might have important practical implications when measuring the impact of policies where the treatment is administered on a voluntary basis and whose effect may depend on factors that changes over time. in the case that we analyzed, i.e. the gi policy, the estimation through the twoway fixed effects estimator not only masks the underlying time effect heterogeneity, but also fails in providing an unbiased estimate of the average gi effect. therefore, estimating the effect of this policy through the twfe might provide biased results and wrong conclusions. notably, time, but also group effects are particularly relevant for agricultural productions which are dependent on weather vagaries and related biotic factors which in turn impact on market equilibrium and resulting prices. since we worked on logarithms of gross margin, wide changes in the level of prices for the baseline (i.e the conventional) product are likely to impact on the percentage premium of the gi counterpart. this issue is particularly relevant because of the tendency of agricultural economists, so far, to estimate the impacts of this type of policies through the classical twfe estimator. it is important to note that the use of the standard parametric estimator does not automatically lead to biased results, because both the presence and the extent of the bias depend on the structure of the weights associated to the single group-time atts. however, using an estimator that does not consider the possibility that the effect of these policies may vary over time and/or across groups of units can lead to misleading conclusions. this is particularly important whenever the outcome of a policy is affected by market conditions, in which case calendar effects are likely to arise. the focus of our study was the european gi certification system, but several other policies can be found in the european agricultural body of legislation where treatment effect heterogeneity may be at stake, such as the rural development programs and their measures that are developed in each cap programming period, or other types of voluntary certifications (e.g., organic certification). in addition, in the eu political context, the assessment of the policies’ performances, especially in the agricultural sector, is acquiring a leading position, which makes the concern of group-time treatment effect heterogeneity even more pressing. the evidence-based policy making course, undertaken in the current cap programming period and confirmed and strengthened for the next one, needs, in fact, reliable evidence to show out its usefulness in building new policies or in improving old ones. the methodological accuracy of impact 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(2004). labeling policies in food markets: private incentives, public intervention, and welfare effects. journal of agricultural and resource economics 29(1):150–165. the role of trust and perceived barriers on farmer’s intention to adopt risk management tools elisa giampietri1, xiaohua yu2, samuele trestini3,* drivers and barriers of process innovation in the eu manufacturing food processing industry: exploring the role of energy policies federica demaria, annalisa zezza step-by-step development of a model simulating returns on farm from investments: the example of hazelnut plantation in italy alisa spiegel1,*, simone severini2, wolfgang britz3, attilio coletta2 the role of group-time treatment effect heterogeneity in long standing european agricultural policies. an application to the european geographical indication policy leonardo cei1, gianluca stefani2, edi defrancesco1 the impact of food price shocks on poverty and vulnerability of urban households in iran ghasem layani1, mohammad bakhshoodeh1, mona aghabeygi2*, yaprak kurstal3, davide viaggi3 bio-based and applied economics 9(2): 155-170, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8340 benefits for the local society attached to rural landscape: an analysis of residents’ perception of ecosystem services stefano targetti1,*, meri raggi2, davide viaggi1 1 department of agricultural and food sciences, university of bologna 2 department of statistical sciences, university of bologna abstract. ecosystem services are the benefits for society deriving from ecosystems. the perception of ecosystem services by local residents is relevant to understand the extent to which such services contribute to society and regional development. the objective of this study is to assess the perception of ecosystem services associated to rural landscape by local residents and to use them to respond to two main questions: are residents able to attribute flows of services from specific landscape elements to the different socioeconomic sectors? are such perceptions affected by the different landscape features of the area of residency (e.g. rural vs. urban dwellers)? the analysis is carried out using data from a survey (n=295) in a rural area located in north italy (po delta lowlands, province of ferrara). the results show that the urban population has a rather generic and positive consideration of ecosystem services associated to rural landscape elements and that perception is largely related to their recreational experience. the rural population has a more complex understanding of services and is more prone to acknowledge disservices associated to specific elements and/or specific socioeconomic sectors. such differences are likely connected to a more direct experience and to the different spatial scales that affect the perception of ecosystem services. the results indicate that cultural services such as recreation and actions linked to the promotion of the territory are commonly acknowledged. on the other hand, initiatives to enhance the awareness of less visible services (e.g. regulation services) would be useful for improving the valorization of specific landscape elements. keywords. sociocultural valuation, ecosystem disservices, social preference, values, emilia-romagna, agriculture. jel codes. q26, q57. 1. introduction the extent to which landscape and its management impact on socio-economic benefits has been investigated in several studies, following a wide range of approaches (e.g. *corresponding author. e-mail: stefano.targetti@unibo.it editor: francesco vanni. 156 stefano targetti, meri raggi, davide viaggi courtney et al. 2013; schaller et al. 2018). evidence from several case studies supports the idea that landscapes play a relevant role on regional economic and social development and that rural landscape is a resource for the different sectors of the rural economy. for instance, impacts on local economies that can be directly or indirectly related to landscape can be summarized as: opportunities for employment (dissart and vollet, 2011), population growth and socio-cultural benefits (enrd et al., 2010), tourism and recreation (vanslembrouck et al., 2005; vandermeulen et al., 2011), added-value for local products and estates, and attraction of investments and businesses (cooper, hart, and baldock 2009). nevertheless, disentangling the processes affecting the pathways between landscape and local economy is challenging in particular when pubic goods are included in the assessment (schaller et al., 2018). for instance, cooper, hart, and baldock (2009) reviewing the provision of public goods by agriculture reported several case studies with positive impacts of landscape on regional economies, but also underlined that the economic quantification of such impacts was a remarkable challenge. in a recent study carried out in finland, tienhaara et al., (2020) confirm a high consideration of landscape-related benefits by the society. nevertheless, they reported a significant gap between citizens’ willingness to pay and farmers’ willingness to accept for such benefits. such evidence supports the need of a more comprehensive evaluation of the impacts of landscape on local economies and a better understanding of what factors influence people perceptions of these impacts (fieldsend, 2011). in this context, the ecosystem services (es) approach (mea, 2005) provides an appropriate framework of anaysis that focuses on the broad range of socio-economic benefits linking ecosystems and the rural economy (hein et al., 2006). recently, van zanten et al., (2014a) adapted the es cascade (haines-young and potschin, 2010) to connect agricultural landscapes with regional competitiveness and support the assessment of the complex range of benefits for society linked to agro-ecosystems. in the context of es evaluation, three main methodological streams can be identified: ecological approaches focusing on the biophysical processes involved in service provision, economic valuations and sociocultural evaluations (de groot et al., 2002; ruiz-frau et al., 2018). the latter is rooted in the research stream focused on the assessment of people perceptions to assess values and trade-offs between different bundles of ecosystem services and/or landscape elements (martín-lópez et al., 2012). as such, the sociocultural approach entails a wide range of processes of value attribution that relate to intrinsic and relational values as well as mental, social, and health well-being (chan et al., 2016; kumar and kumar, 2008). a common approach to study people perception is based on collecting information on the perception of ecosystem or landscape services from different groups of stakeholders or local residents through different techniques such as participatory methods (e.g. brown and raymond, 2014), or statistical surveys (e.g. martín-lópez et al., 2012). the aim of these studies is generally to find which services are more demanded, the relevant spatial scale of analysis and determinants of values, and the trade-offs between different services and stakeholder groups. a range of works in different rural areas highlights some general trends or drivers of landscape perception from local dwellers. for instance, a general negative perception towards changes in traditional landscapes is very often reported (van zanten et al., 2014b). a relevant heterogeneity is also common in rural societies and spatial scales are considered as one of the most relevant aspect influencing such differences in landscape 157benefits for the local society attached to rural landscape perception (tempesta, 2010). indeed, the mismatch between the biophysical scale of service provision and the institutional scales of benefit perception greatly influence people values and their interaction with the environment (hein et al., 2006). therefore, several studies have focused on different determinants of the attribution of benefits and the links between awareness and both the use of the landscape and the acknowledgment of ecosystem services. in general, it is underlined that: i) the same landscape can be perceived differently by different observers according to their interests and feelings and ii) these differences affect people attitude towards landscape. therefore, evidence reported in literature is consistent with a bi-directional relationship between humans and landscape: on the one hand, landscape affects people values and on the other hand, values affect attitudes and intrinsic motivation of residents towards the environment (eigenbrod, 2016). even though it is commonly acknowledged that non-tangible or less visible es are perceived by people (bell, 2001), the assessment of es perception and its usefulness for the evaluation of landscape effects on regional economies is still in its infancy. less studied issues regards for instance i) the capacity of people to acknowledge the different flows of services to the different sectors of the local economy, and ii) the different perception of services and disservices of residents of areas featuring different landscape features (adams et al., 2003; zhang et al., 2007). in this study, we present an analysis carried-out in a rural coastal region in north italy (po delta lowlands, emilia-romagna). the objective is to assess the different perception of benefits associated to rural landscape in different groups of residents. the goal of the analysis is to respond to two main questions: are residents able to identify different flows of services from landscape elements to the economic sectors? are there gradients of benefit perception related to the different landscape features of the area of residency (e.g. rural vs. urban dwellers)? our work builds on a phone-questionnaire aimed at exploring the relations between the residents’ perception of the benefits and disservices flow from specific landscape elements to agriculture, tourism and residents. in our approach, we employ the definition of landscape as a territory ‘perceived by people’ (council of europe, 2000). therefore, the survey is concerned with the assessment of benefits from biophysical elements of the landscape (e.g. wetlands) and also from less-tangible elements directly related to rural landscape and its character (e.g. “wine roads” or “food festivals”). in that interpretation, landscape entails both “physically” determined elements and socio-cultural aspects that together drive and characterize the territory and its peculiarities (eigenbrod, 2016). such an approach is supported by recent literature that considers social values and perception studies complementary to the analyses focused on economic and ecological criteria (de groot et al., 2002). indeed, the relationship between people and ecosystems and therefore the generation of es, includes intangible aspects linked to ‘relational values’, sense of place and belonging to a community (chan et al., 2016; diaz et al., 2015). the remainder of the paper is organized as follows. section 2 provides the description of the po delta area, the statistical sampling, the questionnaire and the methodological approach aimed at analyzing the database. sections 3 presents the results of the data analysis showing the relations between perception of benefits and the variables describing the respondents’ zone of residency. section 4 discusses the results related to the differences between urban and rural people, the different scale of perception of es and ecosystem dis158 stefano targetti, meri raggi, davide viaggi services and the limitations of the study. section 5 concludes highlighting the most salient issues and providing policy implications related to the study. 2. methods 2.1 description of the case study area. the case study area (csa) is in the po river delta (ferrara province, emilia romagna administrative region, north italy; table 1; figure 1). the area is predominantly plain with intensive agricultural activities, an urbanized coastal area and the relevant presence of landscape elements dominated by water (overall 153 km2 of the csa features water elements such as wetlands, ponds and water channels). population is slightly decreasing in the inner part of the area (-6%) whereas the trend is opposite in the urban centers on the coast (+7% between 1980 and 2000; data: national institute of statistics [istat]). 55% of the csa is under agricultural management with rice as a typical product of the area (namely the pgi: “riso del delta del po”). agriculture has traditionally an important impact on the local economy, but farm structure is rapidly changing: in the decade 20002010, almost 1/3 (28%, istat, 2010) of farms has ceased activity, whereas utilized agricultural area has been stable (-1%, istat, 2010). that trend of farm concentration is similar to other parts of the eu (piorr, 2003). on the contrary, the tourism sector has developed significantly (mainly on the seaside) since the last decades of the 20th century. a peculiarity of the csa is the historical impact of reclamation activities that transformed a wetland-dominated landscape in an agriculture-dominated area (wetlands area is currently c.a. 25% of the original). around 30% of the csa is currently included in the po delta natural park and the whole area is part of the unesco site “ferrara, city of the renaissance, and its po delta”. the main criticalities of the csa are connected to water regulatable 1. general features of the case study area (data: national institute of statistics). area (km2) 957 altitude (m a.s.l.) (–3, +8) topography plain protected areas/total area (%) 29 uaa/total area (%) 55 main agricultural systems cereals, horticulture industrial crops population (inhabitants) 67,988 population density (inhabitants/km2) 71 population trend (% last ten years) –6 (average; +7 in the coastal strip) employed population/total population (%) 49 jobs in tertiary sector/total jobs (%) 47 jobs in industry/total jobs (%) 35 jobs in agriculture and forestry/total jobs (%) 18 159benefits for the local society attached to rural landscape tion (part of the csa is under the sea level) and the growing anthropic impact on the coastal area. in particular, issues related to agricultural activities and the related pollution is relevant also for the tourism (eutrophication of the adriatic sea), whereas the concentration of human settlements on the coast and the summer season tourism has significant effects on availability of water resources for agricultural production and the salinization of groundwater. 2.2 survey description and data analysis. in 2013, a phone survey was carried out in the csa. the survey (295 questionnaires) targeted local residents of the ten municipalities of the csa that were aggregated in three zones according to the main landscape characteristics: • comacchio (comacchio municipality) located by the coast is the main urban center (c.a. one third of the population of the csa lives in comacchio) with relevant tourism activities and historical heritage features; • po delta (codigoro, goro, mesola municipalities) located in the delta where the river po dominates the landscape. • rural wetlands (lagosanto, jolanda di savoia, ostellato, migliarino, migliaro, massa fiscaglia municipalities) located in the hinterlands and with a rural-dominated ‘landscape where rice paddy fields and protected areas such as wetlands characterize the territory; the three zones of residency, together with gender and age classes were employed as stratification levels in the survey (table 2). the questionnaire aimed at collecting information about the perception of benefits from a list of elements typical of the csa including tangible components of the landscape (e.g. wetlands) and other less tangible elements that were strictly connected to the characfigure 1. location of the case study area: po river delta, ferrara province, emilia-romagna. 160 stefano targetti, meri raggi, davide viaggi terisation and promotion of the rural territory (e.g. pgis and pdos, wine and typical food roads, etc.). according to the information collected during a local focus group (composed by 15 representatives of relevant local stakeholder groups such as agriculture and tourism associations, local government and land planning agencies, the po delta natural park, researchers, and the president of the local action group) carried in 2012, the list of elements selected for the survey included nine items that together were considered to contribute to the overall perception of typical landscape: “water channels” (channels and ponds), “waterfowls” (flamingos being the most typical wader in the csa), “wetlands” (wetlands and natural areas), “rice paddy fields” (paddy fields and related fauna), “protected areas”, “bicycle paths”, “wine roads” (wine and typical food roads), “local food festivals”, and “local food products” (local pgis and pdos). the interviewees were asked to state their perception of the benefits flow from the landscape elements to specific sectors of the local economy (agriculture and tourism) and to residents. in particular, the respondent was asked to state for each of the three socio-economic sectors if the element represented a benefit, a disservice or if it was indifferent1. the questionnaire also included a self-assessment question to characterize the respondents’ place of living: as the most typical landscape feature of the csa was related to water, the interviewee was asked to specify if his dwelling area was characterized by water-related elements, rural elements (but not water), or if he/she was living in or close to a urban center. additionally, the job sector of the respondent was recorded to test for potential effects on benefit perceptions related to employment in the specific sectors included in the survey (agriculture and tourism sectors). the respondents’ perception of benefits was categorised as homogenous if the same perception (benefit, disservice or indifference) was attributed to agriculture, tourism and residents, or heterogeneous if the interviewee was able to acknowledge a differentiated perception (i.e. benefit for one sector and disservices or indifference for the others). a multiple correspondence analysis (husson et al., 2020) was employed to assess the relationships between the categorical variables (perception of landscape element benefits, zone of residency and place of living). the variable scores on the axes of the multiple correspondence analysis were also analysed through hierarchical cluster analysis (kaufman and rous1 benefits and disservices were translated from the italian “vantaggio” and “svantaggio” respectively. table 2. demographic features of the csa (istat, 2013) and sample description according to the three stratification levels: residency area, gender and age class. response rate of the survey was 41%. area inhabitants (>18 years) of the csa share of inhabitants per area gender age class (years) ff mm 18 30 30 50 50 – 70 istat, 2013 comacchio 19,485 32% 51% 49% 13% 36% 51% po delta 20,635 34% 52% 48% 12% 28% 61% rural wetlands 21,016 34% 52% 48% 11% 33% 56% sample comacchio 35% 48% 52% 22% 47% 31% po delta 29% 55% 45% 20% 44% 36% rural wetlands 36% 51% 49% 23% 46% 31% 161benefits for the local society attached to rural landscape seeuw, 1990) for the identification of the associations between the variable categories. the perception differences were further analysed with cross tabulation to test whether significant differences were linked with general features of the dwelling area (i.e. coast vs. rural wetlands vs. delta) or to more micro-scale proximity to specific landscape elements (i.e. water vs. urban vs. rural elements). to this aim, the chi-squared test was performed to evaluate the frequency of heterogeneous perceptions attached to the landscape elements and their correlation with the variables “place of living” and “zone of residence”. data analysis was performed with the r statistical software (r core team, 2018). 3. results in general, the largest part of the sample (82%) considered the different landscape elements or initiatives of local promotion linked to the territory as a benefit for at least one socio-economic sector (agriculture and/or tourism and/or residents). the perception of benefits was homogenous in 62% of cases (i.e. the attribution of benefit, disservice or indifference from a specific landscape element did not differ between the three socio-economic sectors), whereas a heterogeneous perception was outlined in the remaining 38% of cases. the most positive elements were those linked with the promotion and characterization of the territory. in particular, “local food festivals” and “local food products” were considered on average the most positive elements (between 92% and 96% of the sample attributed benefits from these elements to agriculture, tourism and residents). “local products” was also perceived as the most positive for the agricultural sector (96%), whereas the highest perception of benefits for tourism and residents (97% and 96% respectively) was attributed to “bicycle paths”. on the other hand, “rice paddy fields” were the element with the lowest perception of benefits (53% on average of acknowledged benefits) and in particular the least positive element of the landscape for tourism and residents (48% and 41% of acknowledged benefits respectively). the results of the multiple correspondence analysis (figure 2 and appendix a) show the variable categories linked to a heterogeneous perception (e.g. benefit for one sector and disservices or indifference for the others or vice versa) grouped on the positive side of axis 1. the categories linked to no differences between sectors concerning the perception of benefits are clustered on the negative side of axis 1. the second axis of the multiple correspondence analysis indicates a gradient between the perception towards elements related to initiatives of local landscape promotion and variables linked to more tangible elements of the landscape like “wetlands”, “waterfowls” and “bicycle paths”. the multiple correspondence analysis also shows a relation between the category “comacchio” in the variable “zone of residence” and the category “urban” in the variable “place of living” and the categories linked to a homogenous perception of benefits for all the sectors (left-hand side of fig. 2) and particularly to the variables of local promotion. on the contrary, the categories linked to a differentiated perception of benefits for agriculture, tourism and residents are more related with the categories “po delta” and “rural wetlands” and the categories “water” and “agriculture”. figure 2 shows a close relation between living closer to waterrelated elements (category “water”) and a higher perception of differentiated benefits from landscape elements such as “wetlands”, “waterfowl” and “rice paddy fields”. similarly, living close to an agricultural area (category “agriculture”) is linked to a more differentiated 162 stefano targetti, meri raggi, davide viaggi perception of benefits from “wine roads” and elements of landscape promotion like “local products” and “local food festivals”. the presence of these associations between variables in the dataset is confirmed by the hierarchical cluster analysis (figure 3) performed on the scores of the first five axes (overall, 54% of variance explained by the five axes of the multiple correspondence analysis). the cluster analysis clearly shows the presence of two separate groups in the dataset: a sub-group highlighting a differentiated perception of benefits between the economic sectors and a sub-group with a more positive perception towards the landscape elements. the relations between “place of living” and “zone of residence” and the landscape elements evidenced in the multiple correspondence analysis are tested through the chi squared test (table 3, cfr. appendix for further details and pearson residuals). heterogeneous perceptions of benefits are significantly different in the three zones of residence for the elements “water channels” and “protected areas”. similarly, living close to specific landscape elements outlines significant differences for “water channels” and “protected areas” but also for “wetlands” and “local food festivals”. in particular, living close to water elements and to rural areas is significantly related with a heterogeneous benefit perception of benefits for the different socio-economic sectors, whereas living in urban areas and in the municipality of comacchio is related with a lower frequency of perceiving differentiated benefits for agriculture, tourism and residents. the job sector of the respondent does not record significant effects on the benefit perception (appendix c). the only exception figure 2. biplot of the multiple correspondence analysis showing the relation between residents’ perception of benefits from the landscape elements and the variables “place of living” and “zone of residence”. landscape variable categories identifying a heterogenous perception are reported in red; categories linked to homogenous perception are reported in black. in blue are reported the categories for the variable “place of living” (close to agricultural areas, water-related elements, urbanized area) and in green the categories for the variable “zone of residence” (comacchio, rural wetlands, po delta). cfr. to table 3 for the acronyms of the variable categories. 163benefits for the local society attached to rural landscape regards wetlands that are more frequently considered a disservices for the agricultural sector by the respondents working in the agro-food sector (with p < 0.05). the influence of living close to specific landscape elements is further described in figures 4 and 5. on the one hand, cases living in urban centers have a higher frequency of perceiving benefits from the landscape and the perception of benefits is less differentiated between the different economic sectors. on the other hand, living close to water or rural elements has an impact on the perception of benefits from water-related landscape. more specifically, cases living close to rural elements have a higher perception of disservices from water channels and waterfowl and generally a higher perception of benfigure 3. hierarchical cluster analysis performed on the first 5 axes of the multiple correspondence analysis (overall 54% of variance explained). the dendrogram shows the similarity between the variable categories (acronyms are presented in figure 2). labels identifying a heterogeneous perception of benefits between agricultural and tourism sectors, and residents are reported in red; categories linked to a homogenous perception are reported in black. table 3. relation between place of living, zone of residence and the frequency of differentiated perceptions of benefits from the landscape elements for agriculture, tourism and residents in the sample. chi-square test and p-values (* = < 0.05; **= < 0.01; ns= not significant) of differences (cfr. appendix b for pearson residuals). place of living zone of residence water channels * x-squared = 7.0743, p-value = 0.0291 * x-squared = 7.2596, p-value = 0.02652 waterfowl ns x-squared = 0.38567, p-value = 0.8246 ns x-squared = 1.3773, p-value = 0.5022 wetlands ** x-squared = 10.89, p-value = 0.004318 ns x-squared = 0.91837, p-value = 0.6318 rice paddy fields ns x-squared = 1.2321, p-value = 0.5401 ns x-squared = 2.7338, p-value = 0.2549 protected areas ≈* x-squared = 5.4052, p-value = 0.06703 * x-squared = 6.6451, p-value = 0.03606 bicycle paths ns x-squared = 0.28116, p-value = 0.8689 ns x-squared = 1.0128, p-value = 0.6027 wine roads ns x-squared = 0.91085, p-value = 0.6342 ns x-squared = 3.8925, p-value = 0.1428 local food festivals ≈* x-squared = 4.98, p-value = 0.08291 ns x-squared = 1.9833, p-value = 0.371 local products ns x-squared = 2.1278, p-value = 0.3451 ns x-squared = 1.0804, p-value = 0.5826 164 stefano targetti, meri raggi, davide viaggi figure 4. perceived benefits from water channels and waterfowls, and wetlands and protected areas. results are presented as gap (%) from total average for cases living close to urban areas, water elements, and rural elements (but not water elements). figure 5. perceived benefits from rice paddy fields and local promotion initiatives. results are presented as gap (%) from total average for cases living close to urban areas, water elements and rural elements. 165benefits for the local society attached to rural landscape efits from elements such as wetlands and protected areas. on the contrary, cases living by water elements have a higher perception of benefits in particular for tourism and residents from elements such as water channels and waterfowl and a higher perception of disservices from wetlands and protected areas. cases linked to water elements have also a slightly higher perception of benefits from paddy fields in comparison to the average, but a lower tendency to consider the local promotion initiatives as a benefit in particular for the agricultural and tourism sectors. 4. discussion the survey outlines that a large share of the respondents associate ecosystem services to specific local landscape elements. moreover, the majority of the sample does not perceive differences in the flow of benefits to the different sectors of the local society. however, a relevant portion of the population (almost 40%) shows a more nuanced awareness concerning the capacity of the territory to deliver benefits to residents, tourism or agriculture. such perception also outlines contrasts in some cases. for instance, rice paddy fields are very often considered as a benefit for the agricultural sector only and a disservice for residents and tourism activities. on the contrary, most of the population acknowledges that the elements linked to the promotion and characterization of the territory are positive. as expected, such elements are perceived as the most advantageous for tourism and for residents. even though many of the considered elements of local promotion were clearly linked to food production, the perception of benefits for agriculture is rather low. that result may be linked to the peculiarity of the csa where multifunctional forms of agricultural production are less developed than in other areas. a further element of interpretation concerns a diffused perception in the csa of agriculture as an artificial activity linked to reclamation and not as part of the authentic traditions of the region. our evidence supports the presence of differences between urban-dominated areas and rural areas. namely, rural dwellers evidenced a more articulated perception of the territory, whereas urban people had the tendency to attribute a more positive meaning to the landscape. an explanation could be that rural people have the tendency to weigh services with disservices from specific landscape elements and are more able to discern a differentiated attribution of benefits between the different economic sectors. also, micro-scale effects were relevant for the perception of disservices: closeness to water elements increased the perception of disservices from swamp-related areas such as wetlands and protected areas (indeed the areas of the natural park are strictly connected to wetlands), whereas in more agriculture-related areas the perception towards waterfowl and water channels was less positive. a potential explanation of that evidence may relate to the different awareness of rural people about the role and the functions of the territory. for instance, living close to specific elements increases the perception of disservices from these elements (e.g. mosquitos, fog, etc. in the case of wetlands). on the other hand, people living in urban areas may attribute a higher value to the recreational function and cultural meanings attached to specific landscape elements, whereas the perception of disservices may be less important. the impacts of micro-scale effects that is evidenced in this work could entail the need to consider with more attention the attitudes of the portion of the population living in rural areas or in more direct relation with specific elements of the landscape. the micro-scale effect on 166 stefano targetti, meri raggi, davide viaggi the perception of landscape elements was however not confirmed in the case of rice paddy fields. indeed, the generalized low perception of benefits from those areas was not linked to spatial effects. such result is likely related with the less positive perception of paddy fields in the urban population. the scarce association of es to those elements of the territory can be related to three main factors related to cultural and regulation services: i) recreational activities that can be attached to paddy fields are limited in comparison to the other landscape features included in the survey, ii) traditional elements of the territory are perceived more positively by people (van zanten et al., 2014) and rice paddy fields are more linked to the reclamation activities carried out in the csa and iii) awareness of regulation services such as the potential of paddy fields in protecting the territory from flood events is often inadequate in local populations (adams et al., 2003). the results point to considerable differences in comparison to other studies on es perception. for instance, muhamad et al., (2014) report a direct relation between es perception and proximity to the ecosystem elements providing the services. the analysis carried out in our csa seems to indicate, though, that people living close to specific elements of the landscape ponder disservices and services. that points to a different spatial scale between es and ecosystem disservices: while es perception covers a wider spatial scale, the perception of disservices is more localized. on the other hand, that result could be interpreted according to a common finding concerning the relation between people and the environment. indeed, a consistent body of literature (e.g. brody et al., 2004) outlines a higher knowledge of people in relation to their proximity to specific landscape elements. in our csa, living closer to specific landscape elements was confirmed to be related with the capacity to attribute services or disservices to specific socioeconomic sectors and thus to a higher knowledge. however, further research would be required to disentangle the causeeffect mechanisms between perception of disservices, spatial scales and awareness of es. various limitations apply to this study. the specificities of the case study limit to some extent the potential for generalization of the results and the nature of the elaborations carried out which remain rather explorative and descriptive. nonetheless, this work suggests the need of in-depth analyses focusing more on the perception of disservices. even though the qualification of benefits and disservices was carried out using rather simple scales and constructs that do not allow more precise quantifications of the relationships among variables, our results support the idea of peculiar attitudes of rural residents driven by disservices rather than by services. this might also be driven by a better knowledge of the related ecosystem services, that tend to suggest that benefits are something “given” because are part of normal rural life, while disservices are more evident as they provide disutility either related to agricultural production or to quality of life. this asymmetry certainly deserves further investigation. 5. conclusions in this study, we analyzed residents’ perception of es associated with rural landscape in a csa featuring relevant anthropic pressure and historical heritage features. the objectives were to assess whether residents were able to identify different flows of services from landscape elements to the different economic sectors and whether such a perception was mediated by different landscape features of the area of residency. 167benefits for the local society attached to rural landscape the work confirms the complex relation between landscape elements, awareness and perception of people that is reported in a range of other studies. in our work, we found that living closer to specific elements have a significant impact on the perception of services and also on the capacity to discern between benefit for residents, agriculture and tourism. the results also corroborate the idea that urban population has a rather generic understanding of ecosystem services produced by landscape elements and tends to see them in a rather indistinct way, largely related to their recreational experience. rural population has a much more complex understanding of benefits and disservices, likely connected to direct experience and/or knowledge of the investigated landscape elements. that effect is probably associated to the different perception scale between services such as recreation (perceived at a wider range) and disservices (perceived more in proximity of specific landscape elements). our results attain to the specificity of the csa, but they support the idea that the different scale of perception between services and disservices is a topic that deserves further research. in particular, regional assessments (including monetary evaluation such as the willingness to pay) should consider with more attention the role of disservices and the spatial heterogeneity of people perception that can entail micro-scale effects. these results can also support a better design of policies related to landscape valorization. the results clearly hint at the usefulness of different communication strategies to inform residents about landscape, building on their different experience. also, levers for value creation maybe different and relate to valorization of different landscape elements depending on the target beneficiary/user. an aspect of our results concerns the rather negative perception of rice paddy fields that is not related to proximity to specific elements. even though rice is a feature of the territory and a traditional product, the residents’ perception in the csa is the least positive. that evidence is in contrast with the general positive results for traditional rural elements that are reported in the literature. such a result is likely related to the low multifunctional value attached to paddy fields but also to the historical background of the csa where agriculture is more connected to the reclamation of the territory and less to the traditions of the region. this however may hint at further reflections about the discrepancy between historically relevant features and the ability to actually valorize them, as well as among the different understanding of these historical features between residents and non-residents. clearly, where these discrepancies do exist, it can be a key priority issue to address in actions for landscape valorization. acknowledgements this work was funded by the eu 7th fp for research, technological development and demonstration under grant agreement n° 289578 (claim project, www.claimproject. eu). this work does not necessarily reflect the view of the eu and in no way anticipates the commission’s future policy. authors’ contribution: st performed data analysis, interpretation of results and the bibliographic research. mr designed the survey and revised the data analysis. dv coordinated 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accepted 2016 15th, november abstract. a growing number of people in high income countries, also from the segments of population once considered secure, seek food assistance. diverse food aid initiatives and practices are developed by a range of actors to tackle food poverty; alongside traditional difficulties, new challenges emerge from welfare expenditure cuts, the reorganization of eu funds for the most deprived (fead) and from the spreading of surplus food recovery practices by private companies. based on a preliminary analysis on food assistance practices in tuscany (italy), it emerged that operators involved in food assistance activities are reflecting upon future developments: how is food assistance re-thinking its role to deal with the challenges posed by the current context of change? this work adopts a participatory scenario approach to examine pathways that can be considered robust under uncertainties in the planning context of food assistance. we combine the strengths of back-casted planning, which develops desirable pathways for the future, and explorative scenarios that describe plausible future contexts. results comprise the definition of shared priority themes and plans tested across a set of downscaled scenarios. the methodology provides a promising learning tool to engage with stakeholders and foster a creative future oriented thinking approach to food assistance system’s vulnerability and resilience. keywords. food security, food poverty, scenario analysis, strategic planning, high income countries jel codes. q18, r58, i3 1. introduction in recent years severe crisis, unemployment, immigration and political instability are challenging food and nutrition security worldwide. food and nutrition security (fns) is comprised of availability, access, utilization and stability dimensions (fao, 2001). increascorresponding author: francesca.galli@for.unipi.it 238 francesca galli et al. ingly in high income countries, vulnerable socio economic groups struggle with (food) poverty and health, not primarily as a matter of availability of food but of inadequate income and poverty (riches and silvasti, 2014). in this context of change, a growing number of people seek food assistance (caraher and cavicchi, 2014; lambie-mumford and dowler, 2015). in europe in 2014, 122 million people (24.4%) were at risk of poverty or social exclusion and among these 55 million (9.6%) were not able to afford at least one protein meal every two days (meat, fish, chicken, or vegetarian equivalent) (eurostat, 2015). italy ranks eighth (14.2%) after eastern european countries, with values above the average. the variations before and after the economic crisis (since 2007 to 2013, see figure 1) show that the highest increase of food poverty was recorded in italy: in just six years, people who did not afford a protein meal increased by 129.0%, followed by the uk, (+ 117.5%), and greece (+ 112.3%). figure 1. inability to afford a protein meal every two days in eu countries, comparison 2007-2013 (percentage variation). source: eurostat, 2015. the increase of those requiring food aid is tied to an economic emergency and families in need can be pushed to save on food to meet “less flexible” expenditure items such as electricity, gas and rent (tait, 2015; dowler 2003). before the crisis people were sharply divided between those who were food secure and those who were extremely insecure (i.e., chronic poverty). however, due to the complexity of vulnerability pathways, there has been a gradual intensification of poverty gradients and there is no longer such a clear division: individuals are distributed among poor, temporarily poor, vulnerable and non-poor, and the conditions can quickly worsen or improve (maino et al., 2016, our translation). a temporary relief and support to households living a phase of discomfort comes from a wide range of actors and structures engaged in food assistance activities in different coun239exploring scenario guided pathways for food assistance in tuscany tries. in italy, according to the european court of auditors, seven charities were appointed by the state and operated at national level, 249 organizations operated at regional level and 14.973 charitable organizations operated locally for the distribution of 2 million and 300 thousand final beneficiaries (european court of auditors, 2009). the extensiveness and local specificity of food aid initiatives makes it extremely hard to achieve an exhaustive picture. more widely, several authors raise the concern on the lack of (official) data collection through systematic food insecurity and food charity monitoring (silvasti and karjalainen, 2014; lambie-mumford et al., 2014; perez de armino, 2014; pffeifer et al., 2011), while unofficial data collection by charitable organizations, aimed at keeping track of activities and performances, is by definition partial, therefore incoherent and sometimes unreliable. food aid consists in a diversity of practices tailored to different recipients’ needs including distribution of food parcels, soup kitchens, social restaurants and others. these services are directed to a specific profile of users and rely on a specific supply of food and financial resources. in fact, food assistance practices entail several actors and resource flows that are, formally and informally, interconnected into a delicate “system”. charitable food donations are often combined with the collection and redistribution of ‘surplus’ food – safe food that, for various reasons, is not sold through regular market channels (garrone et al., 2014). food assistance also relies on public support, deriving from the european (i.e., currently, the european funds for the most deprived, fead), national, regional and municipal administrative levels. the continuous relationship between welfare and third sector actors highlights what poppendieck (1998) called the “persistent dilemma” of such initiatives, namely the “deeply felt tension” between responding to immediate hunger with charitable food assistance, the preoccupations associated with the (lack of) supply and tackling the factors underlying the growing demand for these services (tarasuk et al., 2014). several actors, that strive to respond to the emergency faced by the most vulnerable groups of the population, are at the same time under pressure to reframe food assistance in a “right to food” perspective (dowler and o’connor, 2012). elmes et al. (2016) consider the critical role that food bank leaders play in “sense-making” around the ethical and justice dimensions of hunger and food-related illnesses in the united states, contributing to understanding how some food bank leaders in the united states have been adopting a variety of innovative, sustainable and just approaches to food banking, that try to address the root causes of growing levels of hunger in the united states. among italian regions, tuscany has for years been a fairly wealthy region, with per capita income above the national average. however, the crisis that began in late 2007 has threatened this advantage mainly because it brings out some structural weaknesses1. after several years of increase, in line with the general trend in italy, poverty in tuscany seems to be stabilized, as indicated by relative poverty index. in 2014, relative poor have been 5.1% of resident population, 1.6 percentage points less than the previous year. this makes tuscany the fifth less relatively poor region in italy (after trentino, lombardy, emilia romagna and veneto regions). in absolute values, the percentage indicates that 191 thousand residents live below the relative poverty line. 1 in particular, the crisis in the manufacturing sector, the low investments in innovation (with the exclusion of the fashion industry and other technological fields), and the decrease in consumption deriving from the decrease in employment due to industrial restructuring (tomei and caterino, 2013). 240 francesca galli et al. the growth of poverty and social exclusion and the multiplication of possible sliding paths to poverty, as well as those social segments thought to be at low risk only until very recently, raise the need to revise and tailor appropriate and specific policies that are able to deal with this whole series of critical issues through specialized services. based on a preliminary analysis of the main practices adopted by food assistance operators in tuscany (italy), it emerges that food assistance is a highly hybridized system as it assembles components of the food system, civil society organizations, voluntary workers, public social services, consumers (brunori et al., 2016). the degree of government involvement, funding, regulatory controls, voluntary sector participation and reliance on surplus and donations from food chain actors is highly variable and context specific. the initiatives that arise in order to supply surplus food for redistribution are not always coordinated or, sometimes, even competing with each other. they rely upon the interaction between voluntary actors – acting within religious and non religious organizations – that have their own specific history, professional profile and cultural references, but nonetheless collaborate to pursue fns in tuscany, in different ways and different contexts. more consolidated practices, such as food parcels, complement with other emergent ones, such as “emporia of solidarity” (i.e., “shops” where recipients directly “purchase” food through income-based electronic cards), where the charitable aim is coupled with the attempt to reduce stigmatization, increase empowerment and support nutritional choices2. tomei and caterino (2013) made a survey on the services and tuscan structures that deal with combating the phenomenon of food poverty. 75 (out of approximately 150) structures indicate the existence of a more or less formal but consolidated relationship between public social services and non-profit organizations (religious and non religious), as well as procedures through which social services officers signal and ask individuals to seek help from charitable organizations. among the 68 cases out of 75 surveyed, the organizations assert that they routinely handle cases of people reported by the social services of the local municipalities. this assumption of responsibility is not surprising with reference to the associations that conduct their activities in a structured way (i.e., within an ‘institutionalized’ framework), although it is striking that such a link is stated by associations that conduct their activities on an occasional basis. the delicate balance between actors, resources and responsibilities makes the food assistance system vulnerable to increasing demands, changing need and decreasing resources. the elaboration of strategies towards future fns represents a relevant goal to be accomplished. by confronting with leading actors of caritas3, the food bank4, tuscany 2 for example, in emporia, prior to releasing the cards, recipients are encouraged to take part to organized classes in which support is provided in relation to nutritional choices, healthy life styles and family budget management. 3 caritas is the pastoral body of the italian episcopal conference to promote charity. caritas’ main features are: advocacy, widespread presence on the territory and direct contact with recipients; it relies mostly on voluntary resources, both human and material. 4 food aid in italy entails first and second level entities. a second level entity in the chain, acting according to a warehouse model, is the food bank (fondazione banco alimentare onlus, fbao). this is a non-profit organization that for over 25 years has been engaged in the daily recovery of surplus edible food aimed at food poverty, through a dense network of relationships that has allowed to save and redistribute food, otherwise destined for destruction. the regional food banks are 21 territorial organizations that refer to the food bank (fbao) (see santini and cavicchi, 2014). 241exploring scenario guided pathways for food assistance in tuscany regional administration and others, it emerged that these actors are re-thinking their role to address changing needs: private companies are increasingly involved in food assistance operations and adjust their activities and strategies accordingly; public institutions rethink the boundaries between charitable assistance, welfare system and market-based food system. this paper reports on the results of a participatory process, developed around two workshops, involving key players of the food assistance system in tuscany. as a main interlocutor, we addressed caritas, which is now reconsidering its role in contributing to food poverty mitigation by setting up a territorial “alliance for food”, a desirable goal which has been thought of in abstract terms but has not be reflected in concrete yet. by adopting a combination of scenario approaches – namely, explorative scenarios describing plausible future contexts and normative pathways that explore the feasibility of transformative change in different scenarios – we ultimately aim at answering the following research questions: how are actors re-thinking their roles in relation to changing needs? does the scenario-guided planning method enable participants in engaging in new themes, identifying shared priorities and conceptualizing new partnerships that have not been discussed before? we tested the combined methodology with the main actors of the food assistance system in tuscany (italy) in order to address the challenges and pressures of the current context of change. the paper is organized as follows: the next section presents the methodology adopted, by placing it within relevant literature and explains how it was applied. section 3 presents main results and section 4 provides a discussion and concludes. 2. methods 2.1 methodology background the methods we used – explorative scenarios combined with visioning and backcasting – can together be identified as ‘foresight’. a foresight exercise can be defined as “any process focused on building medium to long term futures aimed at influencing present day decisions and mobilizing actions” (gavigan et al., 2001). foresight methodology is increasingly used to orient policy making around food systems: vervoort et al. (2015) show that scenario studies carried out at european level on the food system have increased five-fold between 2002 and 2014, and that an average of five food-related foresight studies are conducted or ongoing in the eu per year. many foresight exercises are participatory, seeking to engage relevant stakeholders, more often conducted in a consultative fashion, and can be developed through qualitative, quantitative or mixed approaches (mallampalli et al., 2016). moreover, foresight can be done for different objectives, of which policy development/formulation is only one of the possibilities. bourgeois (2012) identified three 3 categories of objectives: content (producing new knowledge), process (connecting people, changing ideas) and impact (policy development). foresight is not a tool to predict the most probable future but the main purpose is to “explore” the future in order to guide, or inform, current decision-making processes (fahey, l., 1998: 18). we use scenarios and visioning/back-casting as tools to explore futures for food assistance (vervoort et al, 2014). scenario methodology, based in complex systems research, seeks to recognize and explore uncertainty and complexity in decision-makers’ contexts 242 francesca galli et al. (van der sluijs, 2005; kok et al., 2006). a range of approaches is available to develop different types of scenarios (van notten, 2003). planners must be aware of their decision contexts and how these contexts can evolve, both as a results of external factors and internally through attempts at transformational change. the work presented in this paper combines the strengths of two approaches. together with a diverse group of participants, we developed explorative scenarios that describe food systems contexts on the one hand, and normative “transition pathways” on the other hand, combining them to explore the feasibility of transformative change in the different scenario contexts. making up one part of our approach, explorative scenarios are defined as “multiple plausible futures described in words, numbers and/or images” (van notten et al., 2003). in multi-stakeholder contexts, exploratory scenarios can engage multiple legitimate perspectives involved in framing and addressing challenges such as food security and sustainability (reilly and willenbockel, 2010). however, while explorative scenarios offer diverse contexts for decision-making, they do not represent plans and, by themselves, they provide no direction for action. because of this, outside of specific contexts like the military and the private sector, many scenario processes, especially those led by academics, have been limited in their potential for impact because scenarios are often created but not used to help consider different actions and strategies (wilkinson and eidinow, 2008). however, explorative scenarios can be used to test and inform the feasibility of plans. this is done by cross-examining a plan or policy across different scenarios, each posing their own challenges and opportunities. if a plan is considered to be feasible under a wide range of challenging futures, it can be considered robust. in this project, we therefore combined explorative scenarios with the development of normative strategies through “back-casting”. in a back-casting process, participants start with a vision of a desirable future, and then work backward in time from that vision to identify each step needed to lead to that vision (kok et al., 2011; robinson et al., 2011). this approach has the benefit of allowing for the creation of actionable, proactive futures. by exploring the feasibility of normative transition pathways in the context of different explorative scenarios, we allow for a conscious focus on the changing interactions between actors’ agency and their contexts (vervoort et al., 2014). our approach also adds a cross-level dynamic, in that the explorative scenarios used are a local interpretation of pre-existing european-level scenarios (see brzezina et al., 2016) that provide wider socioeconomic contexts; conversely, the back-casted transition pathways are combined with case studies across europe to contribute to the conceptualization of transition pathways for the future of the european food system5. this combined approach of using explorative scenarios to test back-casted transition pathways is particularly suitable to the case of food assistance for several reasons. first of all, the food and nutrition security challenges that food assistance responds to are contingent upon changing and uncertain socio-economic contexts. explorative scenarios offer distinct and diverse accounts, co-created by local participants, of how future contexts could develop and change the challenges and opportunities of food assistance. secondly, because robust food and nutrition security strategies are needed in the face of this future uncertainty, the back-casting of strategies has the potential to provide food 5 this part of the research is still on-going and will be completed by end of 2017. 243exploring scenario guided pathways for food assistance in tuscany assistance actors with a format in which they can look beyond present limitations and start with their desired long-term objectives, which can then be tested against scenarios to make them more robust. the methodology includes a preliminary phase, consisting of in-depth, semi-structured interviews to food assistance operators, on-site visits and primary data gathering, aimed at identifying current and historical context of practices, actors, resources and skills employed and vulnerable groups addressed. then, a participatory scenario approach was developed within two workshops. the first step of the first workshop focused on creating a draft of local food assistance strategies through formulating a vision for this future and then back-casting planning steps towards the present. in the second step of the first workshop, the set of pre-existing european food system scenarios was down-scaled to the level of food assistance in tuscany to provide explorative scenario contexts. local scenarios were created by examining what the local situation would look like in the context of each european scenario, with attention to key variables that affect the goals of the focal project in the future. the second workshop focused on using the explorative scenario contexts for testing the desirable future visions and multiple pathways toward these visions created in the first workshop, to investigate and challenge the feasibility of these concrete plans for the future of the case, aiming to facilitate new ideas in the process. 2.2 structure workshops based on the preliminary study, we selected 20 participants. participants included representatives of tuscany regional administration, members of the department on citizenship’ rights and social cohesion (youth policy, family and sports, and the regional observatory for the promotion of social citizenship rights). we had the participation of the directors of 10 diocesan caritas tuscany, the coordinators of the emporia in prato and pisa, the manager of the fund-raising of the food bank tuscany, the director of members section of unicoop florence (i.e., retailer), in addition to academic experts on the topic of food poverty, food and agricultural systems. participants’ affiliations are listed in annex 2 and 3. the first workshop was held on the 1st of february 2016, while the second on the 3rd of may, 2016, both in florence at the headquarters of the theological faculty of central italy. the same participants were invited in the two workshops, although on the second one some guests dropped at the last moment, while new participants from the regional administration asked to take part. various contextual reasons contributed to a lower number of participants but still, key senior participants were present, for a total of 12 participants overall. the workshops consisted of several building blocks, which are now briefly described. figure 2 summarizes the workflow of the two workshops. the first three steps were developed in the first workshop and the last three steps in the second workshop. 2.2.1 visioning participants were asked to answer the following question: “what are the elements of a desirable future to ensure access to healthy and nutritious food for everybody in tuscany? and, specifically, “what it is the ideal future for food assistance in tuscany?”. 2030 was chosen as a suitable time horizon for the realization of change, with reference to caritas 244 francesca galli et al. activities. this visioning exercise developed in two steps: 1) brainstorming and 2) clustering of the elements of the vision. participants were invited to reflect in pairs (5 minutes speed-meets repeated three times) on the features of a desirable future for food assistance in tuscany. the post-its were then grouped (collectively) into macro-areas, that constitute the vision. once macro-themes were identified, each participant had 8 stickers to vote for the most important ones: participants voted based on their preference (no explicit rules were given). in order to steer the engagement of stakeholders in the elaboration of the themes within the vision, we asked them to visually represent the elements of each theme, by developing a “rich picture” exercise6. 2.2.2 back-casted planning back-casting is a systematic process for working backwards from a desirable future to identify the steps required that connect the future to the present (kok et al., 2011; robinson et al., 2011; vervoort et al., 2014). at each step we asked the question “if we want to attain [current step] what would we need to do/have in place for that to be possible?”. this 6 the rich picture is a method from soft systems methodology (smm). here, simple drawings and sketches are used to illuminate systemic relationships that are not so easily captured in narrative form. figure 2. building blocks of the two workshops 245exploring scenario guided pathways for food assistance in tuscany question is asked over and over again until the present situation is reached. these steps can then be implemented from the present successively to achieve the desired future. 2.2.3 downscaling european scenarios and causal mapping the goal was to create a clear image of the local scenario at the end of the chosen time horizon (2030) starting from four given scenarios, previously developed by a range of european food system actors and researchers to represent plausible european scenario for the food system (see brzezina et al., 2015). the emphasis was on the introduction of scenarios and their adaptation to make them coherent to the specific context of the case. this meant that a new, local story was invented, where the european scenarios were used as an inspiration and to provide a wider european context. in practice, this step involved immersing the participants in the european scenarios and engaging in an open, imaginative conversation about what the scenario could mean for their decision context. each group discussed individual views and developed a coherent image of the scenario end state, which is developed in further detail through the following activities. the participants in each scenario group discussed what the scenario meant for a list of key elements, to ensure that relevance of the scenario for the decision context of the initiative. the outcome was a narrative description of the scenario end state. participants within their scenario groups were asked to explore the chains of cause and effect amongst the discussed aspects. causal mapping was also used to elaborate the scenarios (coyle, 1996). participants drew arrows between concepts and assigned a plus (+) or minus (-) to the arrow. a (+) indicates a positive effect of increase in one element on the level of the other, for example “an increase in the number of food recipients results in an increase in social inequality”. a (-) indicates an inverse relationship, for example “an increase in the ageing of population results in a decrease in the number of volunteers”. consensus on adding concepts, drawing arrows and giving a sign to the arrows was reached by discussion within scenario groups. the graph-based nature and relative visual simplicity encourages the use of this approach by stakeholders with different backgrounds. 2.2.4 scenario-based review of plans in the second workshop, each scenario group reconvened and used the content of the previous workshop, based on the digitized local scenarios and accompanying materials, such as insights from the causal map and the drawings representing the main features of scenarios. a short round of conversation happened to make sure everyone, including any newcomers to the process, understood the scenario, and still considered it plausible. missing elements were written down on post-its and collected. then, after receiving all the back-casted plans from the first workshop, for every aspect of each plan, the group asked: “is this action/strategy/etc. possible in this scenario, or not? if not, what could be recommended (concretely) to make the plan more feasible in this scenario?”. 2.2.5 plans across scenarios: the matrix during a plenary discussion, each scenario group presented the comments and adaptations made to each plan in order to fit in each scenario. scenario groups prepared their 246 francesca galli et al. comments on what they thought were the main strengths and weaknesses of the plan in their scenario and what their main recommendations would be to make the plan work better in their scenario. these comments were reported on a table organized per plan (horizontal) and per scenario (vertical), as an additional way of capturing the discussion. 2.2.6 review of plans the last step is dedicated to plans’ groups, discussing how to integrate into the plans the comments received by each scenario group. the discussion aimed at identifying which of the scenario-based comments and recommendations occurred across all of the different scenarios and therefore highlighted key strengths, weaknesses and potential improvements to make the plans work better regardless of the scenario (i.e., essentially making them more robust). moreover, scenario-specific recommendations were identified to determine whether it was worth considering as an option to make the plan more flexible in case a certain scenario occurs. 3. results 3.1 identification of the desired vision and elaboration of back-casted plans an overall ideal vision on how stakeholders would imagine food assistance in 2030 in tuscany has been outlined based on the participants’ suggestions. the vision was articulated in macro-themes, which have been prioritized based on voting7. above all, the protagonists of the food assistance system believe that governance is a priority to focus on. in fact, one of the main features of the current food assistance network is the fragmentation of the actors and activities on territory: this is a strength, in terms of flexibility and adaptability to the context, but also one of the main vulnerabilities, whereas rules and actors change also for reasons not linked to the system. the second theme in order of importance is education. a major concern of the food assistance actors is to flank practices, that deal with resolving contingent emergencies, with training and education on issues of food security and nutrition, aimed at all stakeholders in the system. training and education processes should address, first of all, those covering a role of educators and trainers, both internal to the food assistance system (for example, the volunteers and the third sector) or beyond (for example, retail or agribusinesses). the third macro-theme is the definition of a “person-oriented approach”, which is crosscutting. it refers to the ability of the food assistance system to identify, understand and respond more effectively to the needs in relation to the individual conditions, in a flexible and adaptable manner. this also refers to a system that involves the recipients, in a perspective that goes beyond the logic of assistance. the participants split into three groups, one per macro-theme, and through backcasting (i.e., working backwards, from the desirable future to the present, identifying all the steps and actions needed, striving to overcome the limitations and constraints of the 7 the following themes were elaborated and ranked: rights (13 points); governance (23 points) and networks (16 points) – these two themes were joined; person-centred approach (17 points); education (25 points); monitoring (12) – this was considered as a cross cutting issue; food waste (11 points); food quality (11 points). 247exploring scenario guided pathways for food assistance in tuscany present) developed three plans (see annex 4 for detail on each plan), summarized in the following paragraphs. the plans are presented based on the draft elaborated and further refined in the following steps of the process. 3.1.1 a plan for governance and network towards fns in order to address the concern on improving the governance of food assistance, the creation of a coordination table is one of the main instruments proposed, along with the participatory definition of rules and criteria for monitoring and evaluation of the food and nutrition security situation in tuscany. this implies the clarification and definition of rules and multi-level responsibilities (at european, national and regional level). the plan for governance and network consist of two main goals: i) development of an integrated and coordinated network for fns and ii) development of a fns policy according to a prevention approach. the integrated network for fns starts from the creation of a promoters’ group, as a first step of the process. the promoters’ group should be active on a regional level, in charge of the direction of actions, responsible for brokering and raising awareness among regional and local actors. it should also identify local institutional actors to be involved in the coordination of fns in tuscany, addressing among others social health districts. the promoters’ group engages with municipalities and third sector actors in network building activities. based on the network built and the knowledge exchanged, an ad-hoc regional committee on fns is established. a fundamental task to be accomplished by the committee is the activation of monitoring activities of food insecurity on the territory (i.e., a regional observatory on fns). within the promoters’ group stands the third sector network, which is made in charge of involving actors of the supply chain (producers and retailers). inside this network, the third sector organizations develop a self reflection on inner functioning, in order to find common aims and synergic solutions (e.g. on food drives, volunteer pooling, university training/ stage, vouchers, etc.) and develop fundraising actions. the committee works to develop incentives for small and medium enterprises (smes) and retailers in order to encourage corporate social responsibility and donations (e.g. tax relief measures) and, at the same time, steers public authorities to develop tendering processes that award projects of food recovery in public canteens. universities and retailers should also be involved in this process. in addition, the committee lobbies at the european level to ensure continuity in fead support funding. ii) the second main goal is to develop a regional plan for fns in tuscany. the development of a fns policy and action plan, adopting a prevention approach relates primarily to the creation of a dedicated board for the coordination of actions towards fns within the tuscany regional departments. the networking process (described in the first step) should be antecedent to this task (i.e., the initial phase of dialogue should be started with local actors, involving tuscany region departments, local health districts and the committee). it was clarified that the establishment of the committee represents an instrumental objective, functional to achieving the second main objective and not an end per se. the governance model must include higher levels beyond the municipality. governance should take into account homogeneous territorial levels, also beyond institutional borders, 248 francesca galli et al. in order to understand and interpret local specificities8. this approach could also allow to redefine roles between public and private actors. further suggestions were made by stakeholders in relation to “governance and network”: for example setting very short term goals, such as organizing a meeting/seminar with the main actors of the food assistance system, or building a mailing list, or an online platform for sharing experiences (e.g., videos) among food assistance actors. 3.1.2 a plan for education towards fns beyond contingent practices and emergency responses to food need, equal attention should be given to developing education paths to achieve fns. education relates to stimulating openness towards societal problems, voluntary action and gift, together with a food culture. education processes should be planned to address, first of all, those who have a role as educators and trainers, both internal to the food assistance system (e.g., volunteers and third sector) and the food system in general (e.g., retailers or food processors). the plan elaborated for education for fns in tuscany develops around three main goals, which are interconnected and reinforcing one another: i) increasing awareness on available resources and production processes; ii) educating to cultural change towards healthier lifestyles; iii) achieving coordination and sharing of information on relevant themes. i) awareness on available resources. a key issue concerns the definition of a role for private food system actors (i.e., retailers and food producers) who recognize their social responsibility and represent an asset and a strength to aim for quality and healthy food. to this aim, it is necessary to work on increasing awareness on the cost savings linked to surplus recovery and the reduction of waste and the possibilities for reinvestment. the private food system actors should be involved in awareness raising activities (and the extent will depend on the scenario), for example by adjusting new promotion strategies to discourage consumers from buying excess food with respect to their needs. the monitoring activity and the quantification of indicators on food surplus, waste and (hopefully) increased efficiency plays a key role, also to facilitate communication on the overall convenience at all levels (economic, social, environmental). another point on resources was made with regard to the development of relations between local producers and retailers, adapting their supplying strategies to promote local chains (see galli et al. 2016 on the meaning of local food chains), or through “civic food projects” that link restaurants and producers in a local network based on the use of local products. instrumental to the mentioned objectives is enhancing project skills and planning as a specific competence of food assistance actors, that can open new avenues to food recovery. this concerns training to project design and planning, exploiting public-private synergies and activating food assistance actors. this objective also links to education/training for cultural change: third sector should work through projects to encourage donations: develop targeted gift in place of surplus recovery. ii) educating to healthier life styles and cultural change. a key issue concerns the “education of educators”, that means those who have an educational responsibility must be 8 the governance approach adopted by the civil protection in italy mentioned as a best practice, in which the third sector has an explicit and recognized role. 249exploring scenario guided pathways for food assistance in tuscany trained on the specificities of food and nutritional aspect: for example, school teachers and programs should include awareness raising on food, health and environment, by discussing the right to food into civic education programs. education processes must address institutional, food system and food assistance actors. the third sector – specialized on food themes – could provide a support to those who deliver education (e.g., alternating schooling and working), together with higher education and university system. a specific focus was dedicated to religious communities, priests and religion teachers who are responsible for educating parishioners, mostly young citizens. a wider form of communication can address the wider public by organizing debates in public meetings, encourage the use of social media, promote spaces for aggregation and collective activities (e.g., food classes). iii) sharing information on food and nutrition themes. a cross-cutting objective to the mentioned above is the sharing of information among relevant actors. stakeholders have proposed the setting of a board for education on fns at regional level, able to coordinate actions and pursue coherent communication. this eventually may lead to the elaboration of a charter on shared principles among all stakeholders of the education system (social actors, media, ...). for example what is the meaning of right to food? what does it imply? for different people it may mean different things and a shared meaning should be reflected upon. 3.1.3 a “person centred approach” towards fns stakeholder agree that the food assistance system should become able to involve recipients, in a perspective that goes beyond the assistance logic. the food assistance system should be able to identify, understand and respond to specific needs (also in relation to wider conditions), according to a person centred approach. the approach has been declined by the actors in five sub-objectives: i) tailoring assistance practices to receivers needs (e.g. as identifying ways to tailor help to individual, as it happens in emporia of solidarity). food should represent an instrument towards more social inclusion. this objective considers going beyond a more traditional food aid approach and setting up a direction for the recognition of the right to food: recipients should be reactivated through dedicated programs, based on reciprocity (i.e., recipients return in relation to the aid received). an underlying question to be tackled is: who is this “central” person” and what does she/he needs. examples of concrete ways in which to turn this approach into practice are placing food aid within the individual social support path. this will require the involvement of mayors, health services and other institutional actors, in order to cover multiple territorial levels, although caritas and ngos can be the leading actors. ii) identifying multiple and integrated responses to food poverty. this can be achieved through a more efficient and creative recovery of surplus, the siticibo project9 is one example. 9 siticibo, started in milan in 2003 with the good samaritan law (enacted in italy as law n. 155, 16th of july 2003) aims at the recovery and redistribution of surplus food from canteens and catering services (hotels, corporate, hospitals and schools canteens, etc.) as well as big retailers (rovati and pesenti, 2015). food recovered through this channel consists primarily of fresh and ready to eat meals from catering services, while bread, fruits and desserts are often recovered from school canteens. since highly perishable foods are handled, in order to ensure food safety, modes of collection are described in very strict and scrupulous procedures, agreed with donors, and volunteers are properly trained on haccp. essential material requirements to carry out this practice are facilities for heat removal (for the donors) and refrigerated vans, key components of the fleet vehicles. 250 francesca galli et al. iii) effectively identifying food needs (e.g., involving categories of “key witnesses” such as paediatricians, teachers, priests, etc.). integrated responses implies an effective identification of needs and public authorities support, by tuscany region, would be desirable. in order to identify people’s needs, the involvement of “witnesses of poverty” was suggested: paediatrician, family doctors, school teachers, priests, health and social services’ operators and pharmacies are the first figures to be trained on how to recognize food poverty situations and intervene. to be able to monitor needs, the setting up of an “observatory on fns” would represent a fundamental step. the activation of social professions (such as the “frontier operator”) should be explored and valued. mapping opportunities as well as problems of interventions should be supported by the use of it networks, that can contribute to streamline food recovery activities (e.g., involving retailers, producers, collective catering) and consolidate alternative responses to food poverty (e.g., social farming). vi) promoting networking opportunities among citizens within neighbourhoods (e.g., leveraging on suitable spaces available in the various districts). a person centred approach should address the community in which the individual lives. strengthening a sense of community is both an instrument and a desirable goal per se. municipalities represent a key partner of civil society and third sector organizations in contributing to safe and active neighborhoods. green areas and urban spaces, suitable equipment, cleaning and safety of public spaces are local administration responsibilities. the municipality, together with csos and active citizens, should identify and recover available spaces; establish community centres aiming at developing initiatives around food related themes (e.g., urban gardens) and involve schools in these activities and initiatives; organize local fairs, street food occasions to include migrant communities, and neighborhood dinners. vi) enhancing nutritional value and quality of food. this objective refers to the food currently distributed through food aid. incentivizing the recovery of food surplus and the reduction of food waste, as well as the simplification of rules on products’ expiration dates and the alignment of national legislation all over the territory are key themes that should be regulated by law. this process should be led by agriculture and health ministries, and lobbying activities by ngos should have a supportive role to raise awareness on the difficulties met by food assistance operators on a daily basis. 3.2 downscaling of european scenarios to the local context the other outcome consists in the development of four future scenarios for food assistance in tuscany in 2030. these have been elaborated by participants by downscaling four european scenarios, previously elaborated at the european level (see brzezina et al. 2015 for detail). the downscaling process consisted in addressing the question: “what does each (eu) scenario mean for food assistance in tuscany?”. although the four scenarios were developed around a wide set of relevant variables (i.e., economic up or downturn, immigration flows, urban rural relations, public health, availability of food surplus, availability of volunteer workers, degree of government involvement), two key variables across the four scenarios can be identified as being most relevant to food assistance and can be used to simultaneously compare them (see annex 1 for detailed information on the content of each scenario). the first is way of intervention by government, which may 251exploring scenario guided pathways for food assistance in tuscany entail the adoption of an emergency approach (i.e., the state responds from time to time to social emergencies, when they arise) or a strategic approach (i.e., the state anticipates social emergencies by adopting a proactive approach). the second variable relates to the openness of society towards societal problems, such as immigration (i.e., civil society demonstrates an open or a closed attitude). figure 3 shows the locally adapted scenarios. “tuscany in 3d” (top-right). the right to food enters fully into the political debate: food assistance is conceived as a strategic task that allows to tackle bigger problems and needs. public authorities develop a strategic approach to achieve closer collaboration between all players in the food system. citizens are willing to contribute with voluntary work. the role of civil society associations is viewed by government as a resource for survival and functioning of the welfare system. “it could be better” (bottom-right). the pressure on the national health care system – due to rising incommunicable diseases derived from years of poor diet – brings a reduction to public expenditure on social services. a reactive public management approach and poor coordination between services prevail. social actors must find a way to cope with the increased (food) poverty. “solidarity in half ” (top-left). italian government adopts a high budget but targeted welfare strategy, by supporting eligible citizens with minimum incomes, exacerbating the figure 3. local downscaled scenarios based on i) way of intervention by the government and ii) attitude of society towards societal problems. 252 francesca galli et al. differences with the most vulnerable groups. market and redistributive policies ensure fns to all eligible citizens. civil society is very closed and uninterested to social problems. “do i want to go to live in the countryside?” (bottom-left). the government decides budget cuts on social measures, considering these not as a priority. food assistance support is limited to transferring european resources to social parties. the food assistance actors must intercept surplus of small producers and retailers, which are most resilient in the regional context, but this has become more complicated. society is very closed, therefore human resources, i.e. volunteers, are also scarce. 3.3 scenario based review of plans the ultimate aim of this work was to obtain a final version of the plans enriched by the additions, revisions and comments made during the sessions of the second workshop. this was done during the scenario-based review of plans and a last plenary session, during which key recommendations and priorities were indicated by the stakeholders on each plan previously discussed. the table in annex 5 provides detail on the main strengths and weaknesses of each plan across the scenarios and suggestions for improvement of the plans. we can distinguish two levels of elaboration of the plans: revisions and additions to the plans which are valid across all scenarios and revisions and comments which are scenario specific, therefore suitable in case of contingent events happening in different scenarios. in tables 1, 2 and 3, contingent suggestions specific to each of the four scenarios are reported. some general remarks: in the governance and network plan, as well as in the other plans, the initiative by tuscany region is strongly called for. however, the leading role of the tuscany region is not plausible under all the scenarios: this introduces the possibility for other actors/networks to gain a leading role in this process. another general remark applies to all the scenarios: in order to achieve these sub-objectives, creating opportunity of exchange between actors will be necessary. tuscany region leadership would be desirable and, in order to mitigate a scenario of increasing lack of public support, sharp short-term and bottom-up actions by civil society and third sector should be put in place and should gradually involve other institutional and private actors. 4. discussion and concluding remarks the present work has dealt with the elaboration of a strategy for food and nutrition security in tuscany. this has been done by addressing the main stakeholders of the food assistance system, with a primary involvement of caritas but also other key actors, such as the food bank, the regional administration and retailers representatives. the process we have followed is more valuable to stakeholders if it is clear that it is tailored to their aims and improves strategic planning to achieve shared goals in an uncertain future. therefore, the preliminary interviews and meetings were necessary in order to understand what the needs of the organizations were. our work was aimed at supporting the food assistance network in tuscany, with caritas as leading actor, to address and develop the “alliance for food”, a vision which was suggested by stakeholders, although only conceptualized on an abstract level. during the preliminary research and the two workshops organized, the 253exploring scenario guided pathways for food assistance in tuscany “alliance for food” was declined into key themes and fine-tuned into draft strategies, that were not discussed collectively before. a challenging work is still ahead, but this starting point has set the base for further collaborations and developments. this paper started by asking if scenario-guided planning can be a suitable tool to support relevant stakeholders willing to engage in a process of change, and what the combination of methods (i.e., explorative and normative) enables in terms of elaboration of new themes and blind spots and identification of shared priorities in the process of change. table 1. contingency plan for “governance and network”. strategic role of the government the government retreats civil society is open · favorable conditions and relationships: the objectives could be merged into one in order to save time and resources; · promotion of social responsibility for public administration; · creation of opportunities for cross coordination; · promotion of the committee by the region. · take the opportunity to recover food from public canteens: due to the health issue in this scenario, hospital food will be abundant; · as a remedy to some level of conflict, effort to dialogue and pressure by caritas to have more public education/training; · strong reliance on eu funds. civil society is closed · initiative must come from civil society organizations instead of institutional actors; · an initial emergency phase guided by cso is followed by a regime phase, where institutions take the lead. · fns should be included into a social integration policy. · the problem is building a network in a less dense environment: the most relevant intermediate entities should be identified. · consider bank foundations for fundraising campaigns. table 2. contingency plan for “education”. strategic role of the government the government retreats civil society is open · most comments have been included into the main plan · turning to local resources may be a problem if large processors and retailers dominate. in this case municipalities should play a role in promoting local products and territory. · if the state does not spend resources for prevention and education, it is important to identify who are the alternative actors in charge (churches or other actors with a mission on education) civil society is closed · awareness raising campaigns by the third sector to sensitize private actors (retailers and producers) to a “gift” culture. · trainers in charge of education must recognize the need to promote social inclusion while maintaining identity: those who have been integrated into society are a resource · there is need for a cultural change in volunteers: to dedicate to self production and organize gardens, and educating people. · moving beyond the collection of food from others. · leveraging on fund raising and targeted projects are another option in this scenario (e.g. breakfast for kids). 254 francesca galli et al. our first reflection is that the tool provided to stakeholders the opportunity to address uncertainty of future context in a systematic way. during back-casting, participants tried to work backwards from the desirable future to the present, identifying all the steps and actions needed, overcoming the limitations and constraints of the present. this turned out to be a challenging task, because of the difficulties not only in imagining long term ideal goals, but also connecting these long-term goals to concrete actions, that should take place in the medium and short-term. many of these operators spend most of their time facing immediate, daily necessities, which hamper their capacity to have a broader look on structural problems and potential opportunities and make long term plans. in practice this turns, for example, into different kinds of services provided and the lack of a basic, homogeneous level of assistance, as indicated by previous literature (tomei and caterino, 2013). therefore, gathering all these people together in order to engage constructively in a joint discussion on planning fns in tuscany could be considered as a first step towards the alliance for food that had not been considered much as a concrete objective, at least not by all the people involved. in relation to future oriented thinking, a key point concerns the boundary between actors’ sphere of influence and the given scenario context. it is important to realise that this boundary between actors’ sphere of influence and their larger contexts is not fixed or fully exogenous. for instance, changes in policy may normally be considered as part of the decision context for local food initiatives which they will simply have to adapt to. downscaling the scenario in the local context requires dealing with the delicate balance table 3. contingency plan for “person centred approach”. strategic role of the government the government retreats civil society is open · act in order to anticipate objectives which are achieved in this scenario; · enjoying institutional support, parallel action on the plan for fns; · set dietary guidelines for food provision. · diversification of responses: food recovery along social farming strategies, in order to compensate the lack of social policies; · witnesses of food poverty: doctors, pediatricians, etc. could be overwhelmed by the emergency on diseases and health. therefore operators of civil society must be trained. civil society is closed · lay the ground for advocacy work by encouraging social research and sharing studies on social justice at all levels; · awareness-raising campaigns targeting civil society, as well as institutions at local and national level; · move towards education and social inclusion and allocate resources for these tasks. · in a context of scarce resources , could tuscany region act as a broker, at least supporting the network? concentrate strengths on network development; · role of social health districts (sds) could be the most appropriate level for the coordination of actors. however, a strong leadership is deemed necessary to counteract a closed society. this is also valid for witnesses of food poverty; · lobbying for fead resources; · encourage self-production; · work on specific projects, such as breakfast for children. 255exploring scenario guided pathways for food assistance in tuscany between exogenous events in relation to strategic actions: to what extent can stakeholders impact on the scenario and change it? the distinction depends on the public roles of the actors, but also on their perceptions of their own (potential) roles. therefore, this process intended to allow for a conscious focus on actors’ agency potential: implicit in the method is questioning the supposed limitations on agency that participants have in the scenarios10. another crucial aspect in our study was the heterogeneous composition of the group of stakeholders invited to the workshop. caritas represented the main partner, in this case, but it also involved a broader network of stakeholders who have their own critical perspectives and aims. such a “hybrid user environment” – a middle point on a spectrum between a “one client” case and a fully dispersed case – is a specific feature of our case study: it poses a challenge in terms of “appropriation” of the results (i.e., the application of the plan for the achievement of focused impacts becomes inevitably harder) and requires to find a balance between the particular objectives of the main partner and other stakeholders. at the same time, it allows stakeholders to meet with leading partners such as caritas in an inclusive planning process, in a shared space where relevant collaborations and potential synergies can be explored. beyond being appreciated by participants, this hybrid composition allowed them to take a step back while looking at their own plans and to adopt an external vision on the strategies. the discussions took place among a broader range of stakeholders, that would not be involved in a single organizational planning process if conducted only by an actor such as caritas alone. this is particularly relevant for food assistance in tuscany, as this reveals to be a system de facto but not in explicit terms, in which actors otherwise meet and exchange to tackle daily needs but lack a strategic approach to food assistance (at least on a regional level)11. furthermore, the hybridity of the approach is also a useful frame for caritas itself, as it is a highly fragmented organization, where each diocese (there are 17 in tuscany) is quite independent from all the others. another point relates to the downscaling of scenarios on the european food system, that were built by considering a range of eight variables with different states (see brzezina et al., 2016). the adaptation to the local context in relation to the characteristics of food assistance shifted the focus on case study specific variables: the coordinates around government approach and openness of civil society, in the first place, but also other key issues, such as availability of food surplus, volunteers, vulnerable groups and food assistance overall demand. two final remarks: first, the process was initially designed to be developed in four days. we had to shrink into two days for organizational reasons, in order to fit into stakeholders agendas. this inevitably impacted on the degree of elaboration and completeness of downscaled scenarios and planning. second, it is early to make a final 10 moreover within the transmango research process the participation of local cases, and upscaling to the eu level in the final parts of the project, means that their ideas and recommendations could have some impacts at the eu level (which means that eu policy now falls within their sphere of influence to some degree). 11 co-designing of plans across scenarios has not only supported the elaboration and testing of planned actions, but has favoured exchanges between different organizations on ongoing mechanisms, strategies and actions (especially during working groups and lunch time side talks. for example, in relation to nutrition security, it was raised that shortage of fresh fruits and vegetables can be a problem for some food assistance practices (e.g, such as emporia). it emerged instead that there is a large availability of fresh fruits and vegetables in other regions (e.g., in emilia romagna due to the impacts of the russian embargo, or in southern parts of tuscany). it was clarified that it is mainly a matter of logistics and connections between the different actors of the food system. 256 francesca galli et al. statement on the actual feasibility of the plans drafted, let alone their implementation: this needs to be verified through careful monitoring in the next year time, to allow researchers to check on actual implementation, although the first short-term steps have already been set by including the results on the plans in next caritas annual report for tuscany region. acknowledgments the research leading to these results has received funding from the european union’s seventh framework programme (fp7/2007-2013) under grant agreement n°613532 (theme kbbe.2013.2.5-01). the contents of this publication are the sole responsibility of the implementing partner of the project transmango (www.transmango.eu) and can in no way be taken to reflect the views of the european union. the transmango project aims to investigate and empower innovative sustainable food practices across europe. by interacting with decision makers at different levels, the overall aim of the project is to explore how innovative practices could lead to local and european transition pathways towards sustainable food and nutrition security (fns). in transmango, a number of diverse local cases have been selected as relevant practices that can contribute to sustainable fns. in order to support these initiatives in thinking about and taking action toward these transitions we focus on developing transition pathways and scenarios at the level of specific practices. following the local case study workshops, the transition pathways developed within each of the country case studies will be scaled up to european level in the context of 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(2014). challenges to scenario-guided adaptive action on food security under climate change. global environmental change 28: 383-394. vervoort j., helfgott a., lord s., arteaga l., mmachukwu o., curtis j., torres p., ramadhani d.c., mangalagiu d. (2015). analysis of foresight methods in european food futures and effects on european policies at national and eu levels. deliverable d3.1, transmango: eu kbbe.2013.2.5-01 grant agreement no: 613532., 31.10.2015. available at: http://www.transmango.eu/userfiles/project%20reports/d3.1%20foresight%20and%20policy.pdf wilkinson, a. and eidinow, e. (2008). evolving practices in environmental scenarios: a new scenario typology. environmental research letters 3(4): 045017. 259exploring scenario guided pathways for food assistance in tuscany annex 1. summary of local scenarios for food assistance in tuscany in 2030. solidarity in half italian government adopts a restrictive welfare strategy, by supporting “eligible” citizens with minimum incomes, exacerbating the differences with the most vulnerable groups. civil society is very closed and uninterested to social problems. the narrative of the scenario comprises the following key points: • the political environment is becoming more and more closed and racism and xenophobia are widespread. with the slowdown of the crisis and the economic upturn, the resident population improves living conditions and expects better food and environmental quality. • public authorities adopt a strategy of restricting welfare to italian citizens (eg. introduction of minimum income), exacerbating the differences with the most vulnerable groups. • the food system is oriented towards quality production and there is a tighter supply chain coordination. larger companies develop social responsibility projects mainly in the environmental field. • there is growing public attention to the environment, both at european and national levels, restrictive public measures are adopted for environmental protection and sustainable agriculture. the agricultural system is geared towards the recovery of land for agricultural purposes. the overall production is falling in terms of quantity and increases in value. the food prices are very high; due to greater efficiency in the food system, surpluses and waste along the chain are minimized. • civil society is very closed and uninterested to social problems. do i want to go to live in the countryside? the government decides budget cuts on social measures, considering these not as a priority. food assistance support is limited to transferring european resources to social parties. the food assistance actors must intercept surplus of small producers and retailers, which are most resilient in the regional context, but this has become more complicated. society is very closed, therefore human resources, ie volunteers are also scarce. the narrative of the scenario comprises the following key points: • the economy is stagnating. the cost of living in the cities becomes unaffordable for most citizens, who move to rural areas but especially in the peri-urban areas, where poverty and vulnerable groups are concentrated. the greater poverty also leads to a divergence between the dietary habits of the poor, which worsen, and those of the rich, that enhance and sustain the demand for high quality products. • the food scandals undermine consumer confidence in the largest agro-food industry and retailing. the small and medium enterprises reveals to be the most flexible, resilient to the crisis and able to better respond to an increasing attention to the relationship between diet and health and between power supply and local identity. tuscany leverages its local industry tradition and supports small and medium enterprises. the local product is represented as the alternative to a healthy and sustainable globalization of food “taste”. • large retailers are trying to adapt to the new situation in an articulated manner: a part of them meets the demand for local products and high quality, and another pushes on lowering prices and standardization. • public opinion is very sensitive to health, safety and the environment, but not very sensitive to societal problems. the government, faced with cuts in spending on social measures, doesn’t consider food assistance as a priority. they merely manage european resources. • intercepting surplus of small producers and retailers has become more complicated for food assistance actors. moreover there is a lack of volunteers. tuscany in 3d: gifts, rights, duties the “right to food” enters fully into the political debate: food assistance is conceived as a strategic task that allows to tackle bigger problems and needs. public authorities develop a strategic approach to achieve closer collaboration between all players in the food system. citizens are willing to contribute with voluntary work. the role of civil society associations is viewed by government as a resource for survival and functioning of the welfare system. the narrative of the scenario comprises the following key points: • the crisis escalates. inequality and social conflict are increasing. migratory waves exert strong pressure on food assistance systems. public health is deteriorating because of inadequate eating habits. 260 francesca galli et al. • the food system is concentrated in a few large companies. they reduce the surplus because they become more efficient and therefore greater attention is put to avoid waste. to justify itself, businesses engage in social responsibility projects. growing public pressure on big companies to help the solutions. • the welfare state is in crisis. people are seeking new answers and customized to emerging needs. the right to food enters fully into the political debate: food assistance is conceived as a strategic task that allows you to tackle larger problems and needs. public authorities develop a strategic approach that aims to achieve closer collaboration between all players in the food system. • an increasing number of citizens are willing to contribute with voluntary work. the role of civil society associations and is viewed by government as a resource for survival and functioning of the welfare system. it could be better the pressure on the national health care system – due to rising incommunicable diseases derived from years of poor diet – brings a reduction to public expenditure on social services. a reactive public management approach and poor coordination between services prevail. social actors must find a way to cope with the increased (food) poverty. the narrative of the scenario comprises the following key points: • the crisis persists: the middle class impoverishes, the need of assistance, including food, increases. social conflict has become worse in part because of the stronger migration flows. the deterioration of lifestyles generates a deterioration of food styles and this has impacts on health. • the food system is concentrated in the hands of a few large industries who invest in technological development and product innovation (eg. new proteins and quasi-meat). they reduce the surplus because there is more efficiency and therefore greater attention to waste. to justify itself, businesses engage in social responsibility projects. • welfare spending is further compressed, also challenged by the pressure on the national health care system because of diseases related to years of poor diet. • public resources to manage food poverty are increasingly scarce. at the state and regional government levels a management approach continues to prevail, together with the emergency containment and poor coordination between policies, instruments and practices. social actors are having to cope with the increased demand for social services, and in particular food assistance. annex 2. participants workshop 1. it could be better solidarity in half do i want to go to live in the countryside? tuscany in 3d university of macerata, expert university of pisa, expert university of florence, expert university of pisa, expert health district pisa coop retailer florence food bank caritas tuscany caritas lucca regional observatory on poverty caritas pisa caritas pistoia emporia coordinator from prato caritas siena caritas arezzo tuscany region caritas firenze tuscany region tuscany region caritas siena 261exploring scenario guided pathways for food assistance in tuscany annex 3. participants workshop 2. it could be better solidarity in half do i want to go to live in the countryside? tuscany in 3d tuscany region university of pisa, expert university of florence, expert university of pisa, expert caritas lucca regional observatory on poverty food bank caritas tuscany caritas firenze tuscany region caritas pisa caritas pistoia annex 4. backcasted plans. 1. governance and networks sub objective actions (2016→2030) 1. integration and coordination of food assistance activities(2030) 1.1: creation of a “promoters group” active on a regional level, in charge of the direction of actions, responsible for brokering among regional and local actors. this “promoters group” works towards raising awareness of regional stakeholders. 1.2 a: it identifies local institutional actors to be involved in the coordination of fns in tuscany 1.2 b: promoters group address social health districts, which must coordinate and interact. 1.3: the promoters group engages with municipalities and “third sector” actors in network building activities. 1.4.a: based on the network built and the knowledge exchanged, the creation of an ad-hoc regional committee on fns is established. 1.4.b: the third sector network is made in charge within the promoters group to involve actors of the supply chain (producers and retailers) and stimulate a debate on food and nutrition security. 1.5.a: the committee activates a monitoring of food insecurity on the territory, and supports project development. 1.5.b: the third sector develops a self reflection on its inner functioning. they try to find common aims and synergic solutions (example on food drives, volunteer pooling, university training/stage, voucher…) and develop fundraising actions. 1.6 a: the committee elaborates incentives for smes and retailers to encourage csr and donations, tax relief measures. universities and retailers can also be involved. 1.6 b: the committee puts pressure on public authorities to develop tendering process that award points based on the recovery of food in public canteens (needs regulation, green public procurement that is also social). 1.7: the committee lobbies at the european level to ensure fead continuity planning. 2. developing a food and nutrition security action plan (a prevention approach) 2.1: creation of a regional board for the coordination of actions towards food security (same committee as above). actor: tuscany region department 2.2: confronting with local actors (see first column). actors involved: tuscany region dept + regional committee + local health district. providing support to innovative projects existing on the territory, by tuscany region, rdp resources, municipalities, in interaction with bank foundations, universities 2.3: developing a regional plan for fns in tuscany. actor: tuscany region department dedicated to social policies 262 francesca galli et al. 2. education sub objective actions (2016→2030) 1 increase awareness on resources available and production processes 1.1.a retailers favor food surplus recovery 1.2.a emphasize the cost reduction and the possible reinvestment 1.3.a change promotion strategies by retailers (do not encourage buying beyond effective needs) 1.4.a indicators on food waste and increase efficiency in resource use. 1.5.a make explicit and communicate overall convenience ( not only economic advantage ) at all levels 1.1.b gdo increases sale of local products 1.2.b gdo supply with local producers: alliance with gdo 1.3.b promotion of territory and local productions 1.1.c enhance project skills and planning as a specific competence 1.2.c educating the human resources to project design and programming to improve project planning capacity 1.3.c the food assistance actors promote collaboration in order to exploit public-private synergies 1.4.c the food assistance actors activate fundraising strategies 2 cultural change, lifestyles 2.1.a work on training priests and religion teachers 2.2.a educate parishioners. educational training agencies packages 2.1.b training teachers 2.2.b laboratories and trainings in schools 2.1.c create and animate debates in public meetings, encourage the use of social media, promote spaces for aggregation and collective activities (example, food classes) 3 coordination 3.1 sharing of information among relevant actors 3.2.a board on education 3.2.b board on food and nutrition security 3.3.a charter of shared principles among all stakeholders of the education system (social actors, media ...) 3.3.b civic food project: join together restaurants and producers in a local network, focusing on local productions 3. person’s centered approach sub objective actions (2016→2030) 1 finding multiple and integrated responses to the food poverty 1.1 create opportunities for exchange between actors. the region should be the leading facilitator 1.2 map opportunities. the region facilitating the process 1.3 use of it technology to create networks for food recovery. gdo, collective catering and producers of food. 1.4 evaluate the available amount of food. ex. recovery and redistribution of surplus food. 1.5 involvement of local producers networks 2 effective identification of needs 2.1 create opportunities for exchange between actors. the region should be the leading facilitator 2.2 identify the “witnesses” of food poverty: pediatrician, school teachers, priests, health and social services and pharmacies 2.3 creation of an observatory on food and nutrition in security needs, coordinated by social services (regional level) 2.4 training of “witnesses” on how to recognize food poverty needs 2.5 monitoring needs of people 263exploring scenario guided pathways for food assistance in tuscany 3 safe and active neighborhoods 3.1 create opportunities for exchange between actors. this should be led by neighborhoods 3.2 involvement of schools to develop food culture and social relations. the municipality is in charge. 3.3 identify and recover available neighborhood spaces for interaction. the neighborhood and municipality should interact on this action 3.4.a create community centers aiming at developing initiatives around food related themes. interaction between municipality and neighborhood. 3.4.b municipalities allow neighborhoods to use available green spaces (municipal regulations). predisposition of equipment , cleaning , checking safety conditions (ex. children playground). the neighborhood creates food production spaces, (such as urban gardens). 3.5 organize local fairs, street food occasions to include migrant communities, neighborhood dinners. organized with the help of caritas and third sector actors. 4 recipients as protagonists 4.1 create opportunities for exchange between actors. the region should be the leading facilitator 4.2 set up a direction for the recognition of the right to food. mayors, health services … (cover multiple territorial levels) 4.3 place the food aid within the individual social support path 4.4 decrease and gradual substitution of food parcels with emporia (i.e. social markets) establishment. caritas and ngos should be leading actors. 5 food quality 5.1 create opportunities for exchange between actors. the region should be the leading facilitator 5.2 approve the law to promote food recovery and reduce waste 5.3 simplify legislation and on product expiration dates 5.4 alignment of national legislation on the territories these actions should be led by agriculture and health ministries. lobbying by ngos. 264 francesca galli et al. a nn ex 5 . p la ns a cr os s sc en ar io s: s tr en gt hs a nd w ea kn es se s. g ov er na nc e an d ne tw or k tu sc an y 3d it co ul d be b et te r so lid ar ity in h al f d o i w an t t o go to li ve in th e co un tr ys id e? · in th is sc en ar io th e ta rg et s fi xe d fo r 20 30 a re a ch ie ve d, h ow ev er , t o ge t th er e yo u ne ed to st ar t i m m ed ia te ly an d fil l t he g ap s i de nt ifi ed a nd th e de la ys . · th e pr io rit y to a ch ie ve th is sc en ar io is on e st ab lis hi ng a g ov er na nc e sy st em : a p ac t b et w ee n al l t he a ct or s th at a re p ar t o f t he fo od a ss ist an ce sy st em is th e fir st a im , ( a pa ct fo r in te gr at ed p ol ic ie s o n fn s) . · in th is sc en ar io w e ca n th in k of tw o po ss ib ili tie s. th e fir st is “s tr on ge r “ : i n th e ab se nc e of a pr oa ct iv e st at e, so ci et y be co m es se lf or ga ni ze d , o cc up ie s t he la nd , d oe s no t r ec og ni ze th e in st itu tio ns , e ve n op po se s t he in st itu tio ns . th is ra ise s a pr ob le m o f r ep re se nt at iv en es s o f th es e ac to rs . · th e se co nd h yp ot he sis so fte r is th at c iv il so ci et y re or ga ni ze s i ts el f tr yi ng to m ed ia te b et w ee n th e de m an ds o f a ll, to tr y to re co ve r a di al og ue w ith th e in st itu tio ns . i n th is ca se it is n ec es sa ry , b et w ee n no w a nd 20 30 , t o fin d su ita bl e “s pa ce s” w he re th er e ar e re pr es en ta tiv es a ct or s t ha t un de rt ak e a di al og ue a ro un d sh ar ed ob je ct iv es . · in th is sc en ar io th er e is no po ss ib ili ty o f e xp en di tu re : p re ss in g th e pu bl ic a ct or o n no t r et re at in g fr om it s c oo rd in at in g ro le is th e pr io rit y. · g iv en th e sc ar ci ty o f r es ou rc es , eu ro pe an fu nd s t ha t a re a va ila bl e m us t b e us ed w el l. · th is sc en ar io is c ha ra ct er iz ed b y a te ch no cr at ic g ov er nm en t: a ce nt ra l in st itu tio n w hi ch d ec id es fo r a ll in di vi du al s w ho h av e ci tiz en sh ip (e .g ., fo od se cu rit y of th e ci tiz en s is ac hi ev ed , f or e xa m pl e vi a th e in tr od uc tio n of a m in im um w ag e) . · m ar gi na liz ed p eo pl e re pr es en t a ri sk an d a vu ln er ab ili ty : u nd er st an di ng th e po te nt ia l h az ar d lin ke d to m ar gi na liz ed p eo pl e co ul d br ea k th ro ug h th e sy m bo lic (a nd m at er ia l?) w al ls of so ci et y an d le t t he in st itu tio ns d em on st ra te a pr og re ss iv e op en ne ss to w ar ds ex te nd ed ri gh ts . · w hi le th e or ig in al v er sio n of th e pl an h ad a ss ig ne d th e le ad in g ro le to pu bl ic (l oc al ) a ct or s, in th is sc en ar io th er e sh ou ld b e a ro le re ve rs al . th ird se ct or sh ou ld a ct a s a tr ig ge r f or th e cr ea tio n of a n et w or k of a ct or s, in o rd er to d ra w th e at te nt io n of pu bl ic in st itu tio ns o n th e on go in g em er ge nc y an d to in vo lv e th em to co lla bo ra te a nd c ode sig n fu rt he r br oa de r g oa ls. · th er e is ne ed fo r p er va siv e an d effi ci en t co m m un ic at io n flo w s a nd in fo rm at io n. c iv il so ci et y ai m s f or th e rig ht to fo od a s a n en tr y po in t to re di sc us s a nd w id en so ci al ri gh ts an d ci tiz en sh ip . · o ur sc en ar io is c ha ra ct er iz ed b y a w ill in gn es s o f t he p ub lic a ct or to de le ga te · th er e is no c on fli ct b et w ee n so ci al pr iv at e an d pu bl ic . · h er e a pr ev en tiv e ap pr oa ch sh ou ld be d ev el op ed to a nt ic ip at e ne ga tiv e tr en ds . eff or ts sh ou ld b e pu t i n cr ea tin g a ne tw or k w he re th e pu bl ic co or di na te s a nd e xp er im en ts w ith in no va tiv e pr oj ec ts in vo lv in g pr iv at e re so ur ce s . th is ca n al so h el p to so lv e th e la ck o f a bi lit y of th e fo od as sis ta nc e ac to rs t o at tr ac t r es ou rc es . th es e ca te go rie s o f s ta ke ho ld er s sh ou ld b e in cl ud ed w ith in th e co m m itt ee s ( e.g ., po te nt ia l l en de rs a s ba nk in g fo un da tio ns ) . 265exploring scenario guided pathways for food assistance in tuscany ed uc at io n · th e su bs ta nt ia l g oa ls ar e th re e: ac tin g on th e ch an ge o f l ife st yl es , aw ar en es s o f r es ou rc es , an d th e rig ht to fo od . · tw o in st ru m en ta l g oa ls, w hi ch a re th e co or di na tio n an d ed uc at io n of e du ca to rs . e du ca tin g to b et te r lif es ty le s s ta rt in g fr om sc ho ol ed uc at io n an d pr iv at e en tit ie s, su ch as th e m as s d ist rib ut io n. · re so ur ce s : u ni ve rs ity c ou rs es a re no t v er y ke en a nd p riv at e en tit ie s ar e th e m ai n re so ur ce m an ag er s. th e rig ht to fo od is re la te d to th e po lit ic al d im en sio n. m ed ia a nd so ci al m ed ia , bu t a lso c om m itt ee s at d ist ric t a nd n at io na l l ev el s , a nd g a s ca n ra ise a w ar en es s o n th es e iss ue s. · so ci al m ed ia a nd p ol iti ca l c am pa ig ns ac t a s “ m ul tip lie rs n et w or ks ” ar ou nd th e th em e of th e rig ht to fo od . · in re la tio n to re so ur ce s t he re a re tw o ke y ac to rs : o n on e sid e th e in du st ry m an uf ac tu re rs a nd “r es po ns ib le an d aw ar e” c om pa ni es , w hi ch a re no ne th el es s f ra gm en te d; o n th e ot he r l ar ge re ta ile rs . · th e pr ot ag on ist is th e th ird se ct or , w ho sh ou ld p us h fo r m in im um ac ce pt ab le le ve ls in te rm s o f ch ar ac te ris tic s o f q ua lit y an d w ho le so m en es s o f n ew p ro du ct io ns . m or eo ve r i t s ho ul d su pp or t s m al l pr od uc er s a nd o th er n ew w ay s o f in te rc ep tin g fo od s. · in re la tio ns to c ha ng in g lif es ty le s: ho w c an w e fin an ce e du ca tio n pr oj ec ts re la te d to sc ho ol if th e pu bl ic d oe s n ot h av e a st ra te gy a nd re tr ie ve s? a ga in , t he ro le o f c iv il so ci et y an d or ga ni za tio ns ! m an y ac tio ns a nd re sp on sib ili tie s a re a bu rd en fo r c iv il so ci et y as a ct iv e pa rt ic ip an ts . · in te rm s o f c oo rd in at in g co m m un ic at io n. it sh ou ld b e re -d efi ne d at w hi ch le ve l t hi s w ou ld ha pp en : w id er a nd h om og en eo us te rr ito rie s , a s i n th e di st ric ts sh ou ld b e id en tifi ed (o th er th an ad m in ist ra tiv e di st ric ts ). · ed uc at io n pl ay s a k ey ro le in h el pi ng in fo rm at io n flo w s a nd c oo rd in at io n (“ ce nt ra lit y of th e pe rs on ”, w ho is th is pe rs on ?) . · in th is sc en ar io it is n ec es sa ry to w or k on th e id en tifi ca tio n of n ee ds . · w e ar e in a sc en ar io w ith li ttl e or z er o w as te to b e re co ve re d, th er ef or e ed uc at io n pl ay s a k ey ro le to ra ise a w ar en es s, bo th to w ar ds th e co m m un ity a nd to w ar ds th e re ta ile rs . · t ar ge te d gi ft to n ee ds sh ou ld b e bo os te d. · d ev el op c ar e pa th w ay s: c ar ita s en co ur ag es e du ca tio n pa th w ay s t ha t al lo w to in cl ud e w ith ou t lo sin g ow n id en tit y . · ed uc at io n pl ay s a p re ve nt io n ro le ag ai ns t c lo sin g up o f s oc ie ty . · e du ca tio n in cl ud es tr ai ni ng o f op er at or s a nd in st itu tio ns . i n th is sc en ar io tr ai ni ng a nd su pp or t t o se lf pr od uc tio n sh ou ld b e ta rg et ed . · n ee d of re th in ki ng th e su pp ly o f w ha t n ow c om es fo r f re e (s ur pl us fo od ). 266 francesca galli et al. pe rs on ’s ce nt er ed a pp ro ac h · th is sc en ar io p ro vi de s a ra th er po sit iv e sit ua tio n. it is n ec es sa ry to a nt ic ip at e so m e ob je ct iv es a nd di st in gu ish s ub st an ce fr om m et ho d an d pr oc ed ur e. · w e ha ve a ss ig ne d a di ffe re nt p rio rit y to su bob je ct iv es . r ec ip ie nt s a s pr ot ag on ist s b ec om es th e nu m be r 1 pr io rit y, w he re o ne o f t he fi rs t ac tio ns id en tifi ed is to g o to w ar ds re pl ac in g pa rc el s w ith e m po ria w he re p os sib le . th e se co nd ob je ct iv e is “m ul tip le re sp on se s to p ov er ty ”, th e th ird is “q ua lit y of fo od ” an d th e fo ur th is “s af e ne ig hb or ho od s”. · a c ro ss cu tti ng o bj ec tiv e is th e “i de nt ifi ca tio n of th e ne ed s o f t he te rr ito ry ” w hi ch m us t b e de al t w ith m uc h in a dv an ce in c om pa ris on to th e ot he rs . th is is be ca us e, in o rd er to a do pt a st ra te gy it is n ec es sa ry to kn ow a nd m ap o pp or tu ni tie s a nd pr ob le m s i n th e fir st p la ce . · th e co ns ol id at io n of th e ne tw or ks an d re la tio ns hi p w ith re ta ile rs a re a ne ce ss ar y co ns eq ue nc e of th e id en tifi ca tio n of n ee ds . s af et y an d nu tr iti on a re tw o fu nd am en ta l pi lla rs . · in “s af e an d ac tiv e ne ig hb or ho od s”, th e ro le o f p ar ish es to st ee r t he aw ar en es s a ro un d ne ed s o f s oc ie ty is em ph as iz ed . · in th is sc en ar io th e re gi on a nd th e in st itu tio ns a re in th e ba ck st ag e, w hi le th e ac to rs o f s el for ga ni ze d ci vi l s oc ie ty a re in th e fo re gr ou nd . · th is gi ve s a (d iff er en t) pr io rit y to th e ob je ct iv es : i n th e fir st p la ce , a ct in g to cr ea te sa fe a nd a ct iv e ne ig hb or ho od s by st ee rin g co m m un ity a ct io ns , s uc h as u rb an v eg et ab le g ar de ns . · m on ito rin g ne ed s o n th e te rr ito ry an d al so d ea l w ith e du ca tio n ac tiv iti es . a ga in , w ith th e re tr ea t o f pu bl ic a ct or s m an y of th e ac tio ns co m e th ro ug h th e ci vi l s oc ie ty , th at is b ei ng re or ga ni ze d . a ll re sp on sib ili tie s f ro m in st itu tio na l pu bl ic e nt iti es a re n ow fa ce d by c iv il so ci et y , a s w el l a s d iv er sifi ca tio n of a ct iv iti es . th e lo bb y ac tiv ity to w ar ds p ol ic y m ak er s a lso b ec om es a pr io rit y . · d ea lin g w ith th e ce nt ra lit y of th e pe rs on is c om pl ex , w ith in th is sc en ar io , b ec au se o f t he “ in vi sib le s”. w ho is th e “c en tr al p er so n” ? th e in vi sib le s a re a m as s o f p eo pl e in ne ed . · w e ha ve d ist in gu ish ed tw o st ep s: m an ag in g th e em er ge nc y an d ru nn in g th e re gi m e. d ur in g th e em er ge nc y w e se e a ro le fo r t he th ird se ct or , t ha t l ob bi es in st itu tio ns w ith th e ai m o f b rin gi ng th e at te nt io n on fo od ri gh t t o in st itu tio na l l ev el s. c ar ita s m ov es re so ur ce s o n th e as sis ta nc e of th e in vi sib le s ( th e “e xi st en tia l p er ip he rie s” ). · in th e re gi m e it is ex pe ct ed th at th er e w ill b e th e re fr am in g of ci tiz en sh ip . c ar ita s t he re fo re , i s a pr om ot er o f s oc ia l i nc lu sio n de di ca tin g re so ur ce s a nd in fr as tr uc tu re a nd p ro m ot es a ct iv e ci tiz en sh ip o f n ew in cl ud ed p eo pl e. · m on ito rin g of th e ne ed s i s r el ev an t bo th in th e em er ge nc y an d re gi m e . th e “b or de r o pe ra to r” is a k ey fi gu re to g ra sp th e ne ed s o f t he te rr ito ry an d ac ts a s a n in te rm ed ia ry b et w ee n th e tw o “w or ld s” (i .e. , v isi bl e an d in vi sib le ). · th e cr iti ca l a sp ec t i n th is sc en ar io is lin ke d to th e ab se nc e of th e st at e an d a vo lu nt ar y se ct or w ith fe w re so ur ce s. · in th e ba ck gr ou nd th e pu bl ic a ct or do es n ot in te rv en e in th e sc en ar io . lo bb yi ng a nd sp ec ifi c tr ai ni ng w hi ch ad dr es se s p ol ic y m ak er s i s n ec es sa ry : th e pu bl ic a ct or c an no t f ai l t o ac t a s a fa ci lit at or o f t he n et w or k. · ke y ro le o f s oc ia l s er vi ce s b ut w ith a di ffe re nt lo gi c, n ot tr an sf er rin g re so ur ce s b ut h el pi ng to d ev el op sk ill s , a bi lit ie s , e tc . · re ce iv er s a s p ro ta go ni st s: se lfpr od uc tio n pa th w ay s , fo rm s o f ci rc ul ar e co no m y an d tr ad e. · re co ve ry o f s ur pl us : r et ai le rs a lso ch an ge th ei r a pp ro ac h, b y re th in ki ng in in no va tiv e w ay s t he a va ila bl e su rp lu s f oo d sy st em re co ve ry . f or ex am pl e th ey e xp er im en t s pe ci fic pr oj ec ts li nk ed to g ro up s w ith sp ec ia l n ee ds (e .g ., ch ild re n) . bio-based and applied economics 6(1): 1-17, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-20567 a systematic approach to understanding and quantifying the eu’s bioeconomy tévécia ronzon1*, stephan piotrowski2, robert m’barek1, michael carus2 1 european commission, joint research centre (jrc), directorate for sustainable resources, economics of agriculture, seville, spain 2 nova-institut gmbh, hürth, germany date of submission: 2016 24th, october; accepted 2017 21st, january abstract. in 2014, approximately 18.6 million people in the european union (eu) were employed in the bioeconomy, generating annual turnover of around eur 2.2 trillion. and over the period 2008-2014, almost all sectors of the bioeconomy in the eu experienced labour productivity gains (in terms of turnover per person employed). agriculture and the manufacture of food, beverages and tobacco accounted for three quarters of the jobs and two thirds of the turnover of the european bioeconomy, while, among different sectors, the highest levels of labour productivity were achieved in the manufacture of bio-based chemicals, pharmaceuticals, plastics and rubber, as well as the production of bioelectricity. this eu bioeconomy overview has been compiled after estimating (using comext codes) the bio-based content of hundreds of products produced and manufactured in the bioeconomy sectors. using official statistics, such quantification is easy to replicate and update. it also allows us to highlight similarities and diversities in national bioeconomy patterns within the eu, and to discuss how analysis can support the development of bioeconomy strategies in eu member states. keywords. bioeconomy, europe, jobs, turnover, statistics jel codes. q57 1. introduction ‘the europe 2020 strategy calls for a bioeconomy as a key element for smart and green growth in europe’ (european commission, 2012). monitoring the bioeconomy, a strategic sector in the european union (eu), presents several challenges (m’barek et al., 2014). in particular, it should address the complex task of dealing with a multisectorial and fast-evolving sector (e.g. the emerging bio-based industries). the european commission (ec) defines the bioeconomy as encompassing ‘the production of renewable biological resources and the conversion of these resources and waste streams into value added *corresponding author: tevecia.ronzon@ec.europa.eu 2 t. ronzon et al. products, such as food, feed, bio-based products and bioenergy’; the bioeconomy is operated by ‘the sectors of agriculture, forestry, fisheries, food and pulp and paper production, as well as parts of chemical, biotechnological and energy industries’. the document presenting the eu’s bioeconomy strategy also highlights the social and economic importance of the bioeconomy sectors, which ‘are worth eur  2 trillion in annual turnover and account for more than 22 million jobs and approximately 9 % of the workforce’ (ec, 2012). based on the ec’s definition, the present study defines a methodology for the quantification of the two aforementioned bioeconomy indicators: turnover and number of persons employed (see section 2). designed to provide bioeconomy monitoring indicators, the methodology has to cope with specific constraints, which are to be transparent and replicable, while providing updatable time series data harmonised across the 28 member states of the european union (eu-28). hence, for the sake of transparency and replicability, this methodology relies on official statistics as a data source, addressing the major challenge of estimating the bio-based part (as opposed to the fossil-based part) of mixed sectors or products. the results of the study are presented in section 3, illustrating the sectorial performances and trends of the european bioeconomy (section 3.1) and the diversity of the bioeconomies at eu member state level (section 3.2). in section 4, we discuss the caveats of the approach and we propose some steps forwards. finally, section 5 concludes illustrating how the indicators could be used to support the development of bioeconomic strategies in eu member states. 2. methodology 2.1 defining the scope of the bioeconomy as a first step, we propose a match between the official definition of the bioeconomy given in ec communication com(2012) 60 and the latest european classification of activity sectors, i.e. the second revision of the ‘statistical classification of economic activities in the european community’ (nace rev. 2) (eurostat, 2008). defining the bioeconomy as encompassing the production and manufacture of biomass, 16 nace sectors can be considered to belong, fully or partially, to the bioeconomy. • the production of biomass is covered by section a of nace rev. 2, comprising the agricultural (a01), forestry (a02) and fishing (a03) sectors. • the manufacture of biomass is the result of 12 downstream activity sectors listed in section c. six of these exclusively use biomass as a feedstock, in the manufacture of food products (c10), beverages (c11), tobacco products (c12), leather and leather products (c15), wood and products of wood and cork (16) and paper and paper products (c17). the other six make use either of biomass feedstock or of carbon fossil-based feedstock. since official statistics do not distinguish the manufacture of biomass from the manufacture of other kinds of feedstock, it is necessary to estimate their ‘bio-based share’ (see section 2.3). those six sectors are the manufacture of textiles (c13), of wearing apparel (c14), chemicals and chemical products (c20), basic pharmaceutical products 3a systematic approach to understanding and quantifying the eu’s bioeconomy and pharmaceutical preparations (c21), rubber and plastic products (c22) and furniture (c31). • finally, section d of nace rev. 2 comprises the production of electricity (d3511), from which the production of bio-based electricity is estimated. the nace rev. 2 divisions presented in table 1 (two-digit) are broken down at nace rev. 2 classes (four-digit), providing a more detailed description of the activity sectors constituting the bioeconomy. furthermore, tables of convergence have been established to link nace sectors with other classifications by product (e.g. the classification of products by activity (cpa) (ec, 2008) and the combined nomenclature (cn) used by eurostat in trade statistics (ec, 2015a)). hence, the nace-based definition proposed here is also compatible with a product-based definition of the bioeconomy, and with the use of other indicators measured at product level (e.g. the trade of bio-based products). 2.2 data sources relying on eurostat data as a basis for calculation of jobs and turnover in the european bioeconomy is justified by the fact that eurostat data already comply with our criteria of being regularly updated, available as time series and harmonised across member states. in particular, the structural business statistics from eurostat report on the two indicators put forward in com(2012) 60 – number of people employed and turnover – for the manufacturing sectors (12 sectors out of the 16 bioeconomic sectors listed in sub-section 2.1) and the production of electricity. the structural business statistics are complemented in this study by other data sources reporting on the primary sectors (i.e. the biomass-producing sectors). employment data are retrieved from eurostat’s labour force surveys (lfsa_egan22d for the agricultural sector and for_emp_lfs for the forestry sector) and economic accounts (aact_eaa01 for the agricultural sector and for_eco_cp for the forestry sector). since there are no economic accounts for the fishing sector (a03) among eurostat databases, we used the annual reports of the scientific, technical and economic committee for fisheries (stecf) as an alternative source. fishing related data are released by the stecf in two different documents: (i) aquaculture data are compiled in the report on ‘the economic performance of the eu aquaculture sector’ (stecf, 2014) while (ii) landings data are released in the ‘annual economic report on the eu fishing fleet’ (stecf, 2016). the data sources and indicators serving as a basis for calculation in this study are listed in table 1 (see also eurostat (2016)). 2.3 estimating the bio-based share by sector of the bioeconomy as mentioned in section 2.1, nine bioeconomic sectors out of the 16 constituting the bioeconomy are fully bio-based, either because they produce biomass (sectors a01, a02 and a03) or because they exclusively use biomass as a feedstock (sectors c10, c11, c12, c15, c16 and c17). the remaining seven transform biomass among other feedstock (c13, c14, c20, c21, c22, c31 and c3511). quantifying their contribution to the bioeconomy entails estimating the extent to which they are bio-based (i.e. their bio-based share). 4 t. ronzon et al. table 1. activity sectors covered in this study, with data sources and indicators used. sector nace code data source (code) indicator used label (code) agriculture a01 eurostat – labour force survey (lfsa_ egan22d) eurostat economic accounts for agriculture (aact_eaa01) employment (-) agricultural goods output (14000), production value at basic prices (prod_ bp) forestry a02 eurostat forestry employment (for_emp_ lfs) eurostat forestry economic accounts (for_ eco_cp) employed persons (emp) output (p1_tot) fisheries a03 stecf 2014 stecf 2016 employees (-) turnover (tur) total employed (totjob) landings income (totlandinc) manufacture of… …food products …beverages …tobacco products …textiles* …wearing apparel* …leather and leather products …wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials …furniture* …paper and paper products …chemicals and chemical products (excl. liquid biofuels)* …basic pharmaceutical products and pharmaceutical preparations* …rubber and plastic products* …other organic basic chemicals* (o.w. bioethanol) …other chemical products* n.e.c (o.w. biodiesel) c10 c11 c12 c13 c14 c15 c16 c31 c17 c20 c21 c22 c2014 c2059 eurostat structural business statistic (sbs_ na_ind_r2) turnover (v12110) number of persons employed (v16110) production of electricity* d3511 eurostat structural business statistic (sbs_ na_ind_r2) turnover (v12110) number of persons employed (v16110) *partly bio-based sectors. a bio-based share has been applied to the original data to estimate the contribution of this sector to the bioeconomy. data on sectorial bio-based shares (e.g. the bio-based share of the manufacture of chemicals and chemical products, nace c20) are currently unavailable, and are extreme5a systematic approach to understanding and quantifying the eu’s bioeconomy ly difficult to determine. we propose to infer them from the relative value of bio-based products manufactured by a sector, relative to the total value generated by this sector: ∑ ∑ = = = bbs bbs x turnover turnover i k l j n j j k l j n j k l , , 1 , , 1 , , (1a) where: • bbsi,k,l is the bio-based share of sector i (nace rev. 2), in eu member state k and for year l; • bbsj is the bio-based share of product j, given that sector i manufactures j = n products. bio-based shares vary from 0 for products that do not embed biomass (e.g. prodcom code 20.12.23.30, synthetic organic tanning substances) to 1 for those that are made entirely of biomass (e.g. prodcom code 20.12.22.50, tanning extracts of vegetable origin); • turnoverj,k,l is the turnover of product j, in eu member state k and for year l. before attributing a bio-based share to bio-based products, this approach requires all products manufactured by a given sector to be listed (i.e. n products manufactured by sector i). the more disaggregated the list, the easier it is for experts to estimate the proportion of any particular product that is bio-based. for this reason, we used the combined nomenclature (cn, 2015), which is the most detailed product list in use in european official statistics. correspondence tables allowed us to match the bioeconomic sectors defined in the nace rev. 2 classification with the corresponding products in the cn 2015 nomenclature1. for this study, the bio-based shares of the bio-based products listed in the cn (eight-digit) nomenclature have been determined by around 15 experts from various european countries, who were interviewed by the nova-institute between april 2015 and summer 2016. the experts came from various sectors of the bio-based economy, from companies and industrial associations including the chemicals industry (drop-ins, biotechnology, oleochemistry, organic acids, surfactants, paints, etc.) and the wood industry. other shares were estimated by experts from the nova-institute. the cn nomenclature is used in the eurostat-comext database, which reports only on trade indicators. hence, we applied the products’ bio-based shares (bbsj) to export data by value, in order to calculate sectorial bio-based share (bbsi). indeed, we consider that exports represent the domestic product mix better than imports do. in summary, sectorial bio-based shares (bbsi,k,l; see equation 1a) were approximated as follows: ∑ ∑ = = = bbs bbs x export export i k l j n j j k l j n j k l , , 1 , , 1 , , (1b) 1 in reality, the correspondence is not direct. a first correspondence was obtained between the nace rev. 2 classification of activities (four-digit level) and the prodcom list of products (eight-digit level), before using the correspondence table prodom 2015-cn 2015. note that the prodcom metadata warns: ‘prodcom statistics relate to products (not to activities) and are therefore not strictly comparable with activity-based statistics such as structural business statistics’. 6 t. ronzon et al. where exportj,k,l is the value of the exports of product j, by eu member state k and for year l. 2.4 estimates of the number of persons employed, turnover and location quotient for eu member state k and for year l, the number of people employed in sector i and the turnover of sector i are calculated as: =number of people employed bbs . number of people employedi k l i k l i k l, , , , , , (2) and = xturnover bbs turnoveri k l i k l i k l, , , , , , (3) note that, from a methodological point of view, the calculation of turnover per person in partly bio-based sectors reflects the performance of the sector as a whole (i.e. including the non-bio-based part): = x x turnover per person employed bbs turnover bbs number of people employedi k l i k l i k l i k l i k l , , , , , , , , , , = turnover number of people employed i k l i k l , , , , thus, in place of the turnover per person of a bio-based sector, we have reported the turnover per person employed in mixed sectors (bio-based and non-bio-based parts) in section 3, as the only point of reference we could obtain for those sectors. in addition, as proposed by golden et al. (2015) for the usa, the location quotient of the bioeconomy was estimated at member state and eu-28 level. the location quotient is the indicator usually used to measure how ‘concentrated’ a sector is in a member state compared with the european union overall, i.e. the share of member states’ employment in the bioeconomy (or in a given sector of the bioeconomy) divided by the eu employment share in the bioeconomy (or in the same given sector): =lq % people employed % people employedi k l i k l i eu l , , , , , 28, (4) where: • lq i,k,l is the location quotient of sector i (nace rev. 2), in eu member state k and for year l; • % people employedi,k,l is the proportion of people employed in sector i (the bioeconomy or a nace rev. 2 sector), in eu member state k and for year l; and • and % people employed i,eu-28,l is the proportion of people employed in sector i (the bioeconomy or a nace rev. 2 sector), in the eu-28 and for year l. 7a systematic approach to understanding and quantifying the eu’s bioeconomy if lqi,k,l    >  1, the proportion of people employed in sector i of eu member state k during year l is higher than the proportion of people employed in sector i in the eu-28 during year l. the labour force of eu member state k is then considered to be more concentrated in sector i than on average in the eu-28. 2.5 data transformation and update the methodology presented above has been integrated into the datam management tool. in particular, this tool allowed us to deal with the complexity of estimating the biobased shares for hundreds of comext products, and to apply them to data obtained from seven datasets updated at various points during the year. the resulting database will be made public online at https://datam.jrc.ec.europa.eu/ datam/mashup/bioeconomics/index.html, and updated automatically several times a year. 3. results 3.1 main features of the eu bioeconomy the bioeconomy employed approximately 18.6 million people in the eu-28 in 2014, generating turnover of around eur 2.2 trillion. between 2008 and 2014, employment in the european bioeconomy contracted, with the loss of nearly 2 million people employed. agriculture and the manufacture of food, beverages and tobacco constituted three quarters of the jobs and two thirds of the turnover of the european bioeconomy. these two sectors are the two main providers of bioeconomy jobs in europe, employing, respectively, 51% and 24% of the persons employed in the european bioeconomy in 2014 (see figure 1). the ongoing restructuring of the agricultural sector led to the loss of 1.2 million of persons employed in the eu-28 between 2008 and 2014. hence, it is the main driver of employment trends in the european bioeconomy. during the same period, employment in the manufacture of wood products and wooden furniture and the manufacture of bio-based textiles also contracted, with the loss of 680,000 of persons employed. this is a significant figure, and contrasts with the modest contribution made by these sectors to total bioeconomy employment (respectively 9% and 5.3% of the total number of persons employed in the eu-28 bioeconomy in 2014). the food, beverages and tobacco manufacturing sector also lost nearly 200,000 jobs. emerging sectors, such as the manufacture of bio-based chemicals (including liquid biofuels), pharmaceuticals, plastics and rubber, employed nearly 18,000 additional persons in 2014 compared with 2008. in contrast, the turnover of the european bioeconomy increased by nearly eur 140 billion between 2008 and 2014. the manufacture of food, beverages and tobacco was the main contributor to the eu-28 bioeconomy turnover in 2014 (51%) (see figure 1). its turnover increased by eur 98 billion over the 2008-2014 period. it is followed by agriculture (17% of the eu-28 bioeconomy turnover), which experienced a turnover increase of eur 26 billion. gains are observed in all sectors of the bioeconomy except the manufacture of wood products and wooden furniture (–eur 20 billion over the period) and the manufacture of bio-based textiles (–eur 6.5 billion). 8 t. ronzon et al. figure 1. persons employed and turnover generated in eu-28 bioeconomy sectors (2014). 0 1000 2000 3000 4000 5000 000 7000 8000 0 100 200 300 400 500 600 700 800 production of bio-based electricity manufacture of liquid biofuels manufacture of bio-based chemicals, pharmaceuticals, plastics and rubber (excl. … manufacture of paper and paper products manufacture of wood products and wooden furniture manufacture of bio-based textiles manufacture of food, beverage and tobacco fishing forestry agriculture 1000 persons employed (dark colours) billion euros of turnover (light colours) 900 1,000 9000 10000 11000 1,100 the biomass-producing sectors, i.e. agriculture, forestry and fisheries, tended to be the most labour-intensive sectors of the european bioeconomy; this was particularly the case with agriculture and the fishing sectors, which employed more than 20 persons per million euros of turnover (see figure 2). forestry, the manufacture of bio-based textiles and the manufacture of wood products and wooden furniture were close to the eu average (i.e. 8.3 persons employed per million euros of turnover). the manufacture of food, beverages and tobacco and the manufacture of paper and paper products employed half this average (per million of person employed), while the production of bio-electricity and the manufacture of chemicals (including liquid biofuels), pharmaceuticals and rubber and bio-plastic products employed fewer than 3 persons per million euros of turnover. a decreasing number of persons employed and an increasing turnover resulted in labour productivity gains in the european bioeconomy during the seven-year period 2008-2014 (in terms of persons employed per turnover). interestingly, in absolute numbers, highest gains were obtained in the manufacture of chemicals (including biofuels), pharmaceuticals and plastics, in the manufacture of paper and paper products and in the manufacture of food, beverages and tobacco. however, the highest growth of turnover per person employed was registered in the most labour-intensive sectors. these were, in decreasing order, forestry, agriculture and the manufacture of bio-based textiles. 9a systematic approach to understanding and quantifying the eu’s bioeconomy figure 2. number of persons employed per million euros of turnover in the bioeconomy sectors. agriculture forestry fishing food, beverage and tobacco textile wood products and wood furniture paper and paper products chemicals, pharmaceuticals and plastics and rubber (excl. biofuels) liquid biofuels electricity 5 10 15 20 25 persons employed per million euros of turnover 3.2 diversity of bioeconomies across eu member states the european bioeconomy shows very heterogeneous sectorial contribution at member state level. bioeconomy sectors have developed differently according to member state biomass endowment or member states access to biomass (e.g. commercial harbours), and also according to prior sectorial development (e.g. maturity of bio-based manufacturing sectors). in this paper, we use two differentiation criteria to portray the diversity of national bioeconomies within the eu (see figures 3 and 4): (i) the degree of concentration of the bioeconomy labour force in biomass producing sectors versus that in (partly) biomass manufacturing sectors; (ii) the average amount of turnover generated by a person employed in the bioeconomy (i.e. turnover per person employed, which is an indicator of labour productivity). according to these two criteria, three main types of bioeconomy can be identified: • group a: below eu average labour productivity in the bioeconomy and above eu average employment share in biomass-producing sectors; • group b: below eu average labour productivity in the bioeconomy and above eu average employment share in (partly) biomass manufacturing sectors; • group c: above eu average labour productivity in the bioeconomy and above eu average employment share in (partly) biomass manufacturing sectors. 10 t. ronzon et al. figure 3. the three main bioeconomy patterns in eu member states. dotted lines represent the eu average (location quotient of the biomass-producing sectors in green and turnover per person employed in red) 3.2.1 group a: below eu average turnover per person employed in the bioeconomy and above eu average employment share in biomass-producing sectors this group comprises bulgaria, croatia, greece, latvia, lithuania, poland, portugal, romania, and slovenia. in all bioeconomy sectors of these countries, the turnover per person employed is lower than the eu average, with only a few exceptions (the fishing sector in lithuania and the manufacture of paper and paper products in portugal). additionally, their domestic labour forces are more concentrated in biomassproducing sectors (i.e. agriculture, forestry and the fishing sector) than in the other eu member states. this is reflected in a location quotient of biomass-producing sectors greater than 1 (see figure 3). a closer look at the composition of their bioeconomy labour forces highlights the importance of the agricultural labour force (from 37% of the people employed in the bioeconomy in latvia to 83% in romania). it also reveals that in all these countries, a lower proportion of people than the eu average is employed in the manufacture of food, beverages and tobacco, in the manufacture of paper and paper products and in the manufacture of bio-based chemicals, pharmaceuticals, plastics and rubber. 11a systematic approach to understanding and quantifying the eu’s bioeconomy beyond these common features, the nine member states in group a show different specialisations of their bioeconomy labour force in the three biomass-producing sectors. as already stated, agriculture is the largest employment sector by far in these nine member states, but it is particularly developed in romania, greece, poland and slovenia (≥ 65% of bioeconomy labour force). the fishing labour force is also more developed than the eu average in greece, portugal and croatia, as is the forestry sector in latvia, bulgaria, lithuania, and croatia. taking advantage of their forestry resources, latvia and lithuania also show a high concentration of their bioeconomy labour force in the manufacture of wood and wooden furniture. figure 4. geographical distribution of member states belonging to groups a, b and c. note: group a in green, group b in purple and group c in red. 12 t. ronzon et al. 3.2.2 group b: below eu average labour productivity in the bioeconomy and above eu average employment share in (partly) biomass manufacture sectors this group comprises hungary, estonia, cyprus, malta, the czech republic and slovakia. the bioeconomy in group b shows a level of turnover per person intermediate between groups a and c (see figure 3). sectorial turnovers per person are of the same magnitude as those in group a, with the exception of agriculture, which shows higher levels. in contrast to group a, but similarly to group c, the national labour force of group b member states is more concentrated in (partly) biomass manufacturing sectors than on average in the eu2. although agriculture remains the foremost bioeconomy employment sector, the proportion of bioeconomy labour force employed in agriculture is much lower than in group a. this probably reflects a more advanced restructuring process in agriculture than in group a, also resulting in a higher turnover per person employed. it is noteworthy that, after agriculture, group b member states show a concentration of their bioeconomy labour force higher than the eu average in either forestry (slovakia, estonia, the czech republic and hungary) or the fishing sector (malta, cyprus and estonia). member states with a concentration of their biomass-producing sectors in forestry also have a high proportion of their bioeconomy labour force working in the manufacture of wood and wooden furniture (with the exception of hungary). estonia in particular shows the highest proportion among all eu member states (32%). slovakia and the czech republic employ a higher proportion than the eu average in the manufacture of wood and wooden furniture (more than 15%, compared with 7% on average in the eu-28) and in the manufacture of paper and paper products. additionally, the manufacture of biobased textiles employs a higher share than the eu average in slovakia, the czech republic, hungary and estonia. the same is true of the manufacture of bio-based chemicals, pharmaceuticals, plastics and rubber in the czech republic, slovakia, malta and hungary. 3.2.3 group c: above eu average labour productivity in the bioeconomy and above eu average employment share in (partly) biomass manufacture sectors this group includes austria, belgium, denmark, finland, france, germany, ireland, italy, luxembourg, the netherlands, spain, sweden and the united kingdom. the turnover per person employed in the bioeconomy is above the eu average in these countries, which is the result of higher turnover per person employed, in agriculture and all the (partly) biomass manufacturing sectors, than in other eu member states. in addition, their biomass-producing sectors employ a lower proportion of the labour force than the average in the eu. this is mainly because agriculture accounts for only very small proportion of total employment (less than 4%). ireland and austria are the exceptions, with contribution of agriculture to the total labour force still at the eu average level or higher. there are also additional specificities in the composition of group c’s bioeconomy labour force, which is less concentrated in the manufacture of bio-based textiles than on average in the eu (except in italy, where this sector employs 15% of the bioeconomy labour force). in contrast, the manufacture of paper and paper products concentrates a 2 location quotient of the biomass manufacturing sectors higher than one, which is the equivalent of a location quotient of biomass-producing sectors of less than 1 (as shown in figure 3). 13a systematic approach to understanding and quantifying the eu’s bioeconomy higher proportion of the bioeconomy labour force than in the member states of groups a and b (except in ireland). the proportion of the labour force employed in the manufacture of bio-based chemicals, pharmaceuticals, plastics and rubber is of the same order of magnitude as in group b, but is larger than in group a. those sectors, together with the manufacture of food, beverages and tobacco, are also those displaying the largest labour productivity gap (in terms of turnover per person employed) with groups a and b. belgium, the netherlands, denmark, ireland, sweden and finland have reached very high levels of bioeconomy turnover per person compared with other group c member states. such high levels stem from exceptional performance in one or several bioeconomic sectors. for instance, the highest level of sectorial turnover is reached in the finnish manufacture of paper and paper products, which generated eur 854 0003 of turnover per person employed in 2014. ireland, the netherlands and belgium also rank joint first in terms of turnover per person employed in the manufacture of bio-based chemicals, pharmaceuticals, plastics and rubber (around eur 500,000 of turnover per person employed or higher). similarly, the highest levels of turnover per person employed in the manufacture of food, beverages and tobacco were achieved in ireland, the netherlands, belgium and denmark (more than eur  400,000 of turnover per person employed). the forestry sector achieved more than eur 350,000 of turnover per person in sweden and ireland. luxembourg, finland and sweden reported the highest levels of turnover per person employed in the manufacture of wood and wooden furniture (> eur 270,000 per person employed), denmark and belgium the highest levels in the fishing sector (> eur 230,000 of turnover per person employed), and belgium, denmark and the netherlands the highest values in the manufacture of biobased textiles (> eur 220,000 of turnover per person employed). finally, denmark, belgium and the netherlands show the highest turnover per person employed in agriculture. 4. evaluating the approach the aim of the present paper and database4 is to provide a systematic and transparent system for analysing key indicators of the bioeconomy in europe. the use of official statistics (mainly eurostat) as a data source offers publicly available and consolidated time series, which are harmonised across eu member states. the estimation of bio-based shares at eight-digit cn codes for partly bio-based manufactured products gives insights into the performance of emerging, non-traditional bio-based sectors. the integration of different data sources and calculations into an efficient data management tool (datam) allows for fast data updating as soon as the original data sources are renewed. finally, the use of advanced visualisation software renders the data more accessible, easier to understand and more attractive to the user. throughout the paper, the reader has been informed about specific assumptions taken and/or caveats regarding the chosen approach. in particular, we would like to stress the following points. 3 the data source, eurostat structural business statistics, points out a break in time series. nevertheless, even before the break, the turnover per person employed in the manufacture of paper and paper products in finland reached eur  590,000 per person, which remains the highest level reached in that sector among eu member states. 4 database available at https://datam.jrc.ec.europa.eu/datam/mashup/bioeconomics/index.html. 14 t. ronzon et al. the proposed indicators, jobs and turnover, are compiled in accordance with current policy and analytical needs. in particular, turnover as an economic indicator has advantages and disadvantages. on the one hand, it is a measure of overall economic dynamics of a particular sector, as it includes all totals invoiced during the reference period. on the other hand, it might lead to some double counting and does not provide the real value added of that sector. apart from the number of jobs, we also propose the calculation of the location quotient as a relative measure indicating the concentration of countries in a specific bioeconomy sector. the aggregate numbers for the location quotient are particularly high in some countries because of the enormous numbers of jobs in the agricultural sector. therefore, when using aggregates, the reader should be aware of the important weight of this ‘traditional’ sector in transition, which could disguise the expanding nature of smaller, emergent sectors. an important effort has been made to determine sectorial bio-based shares, e.g. the bio-based share of the manufacture of chemicals and chemical products (nace c20). we propose to infer them from the relative value of bio-based products manufactured by a sector relative to the total value generated by this sector. over time, we expect these numbers to be refined by experts in the field. based on turnover and jobs, we also calculate derived indicators, such as turnover per person. because of the methodological limitation mentioned in section 2.4, the calculation of turnover per person in partly bio-based sectors reflects the performance of the sector as a whole (i.e. including their non-bio-based part). to further analyse the state and potential of the bioeconomy, additional indicators are under development. we propose to complement ‘turnover’ with ‘value added at factor costs’ derived from eurostat structural business statistics, in order to capture more precisely the contribution of individual sectors to the overall economy (without double counting). other indicators are being prepared, derived mainly from eurostat-prodcom (e.g. volume and value of bio-based production, including bio-based chemicals and plastics), or from eurostat structural business statistics (the number of enterprises, investments, wages and salaries). the integration of similar indicators in model-based forward-looking analysis (e.g. van meijl et al., 2016; philippidis et al., 2016) is an important step to link past developments with future pathways. 5. conclusions the results presented in section 3 are one piece of a broader research program aimed at supporting the development of bioeconomy strategies in the eu in the context of the bioeconomy strategy review and of the circular economy package5 (ec, 2015b). supported by data on biomass availability and additional information on bioeconomy companies and plants, a more detailed analysis could be undertaken, with the aim of identifying opportunities for the development of member states’ bioeconomies. 5 the commission will examine the contribution of its 2012 a bioeconomy strategy to the circular economy and consider updating it if necessary (ec, 2015b pp17). 15a systematic approach to understanding and quantifying the eu’s bioeconomy on the basis of the three broad clusters of national bioeconomies proposed in this paper, one could differentiate policy targets related to the strengthening of (partly) manufacturing sectors versus biomass-producing sectors, and the improvement of labour productivity in general. this would have to take into account biomass endowment and previous sectorial developments (infrastructures). several member states have already proposed strategies (german bioeconomy council, 2015); in this article we name only a few. countries with sufficient biomass endowment and well-developed primary sectors have certainly many opportunities to develop downstream value chains. for example, thanks to finland’s abundant resources, its forestry industry is the core element of the finnish (group c) bioeconomy strategy (finnish ministry of employment and the economy, 2014). key elements are timber, diversification of wood products and bioenergy including wood-based transports, but also biotechnologies for health and pharmaceutical applications. the high turnover per person and location quotient in the specific sectors highlights finland’s exemplary role among northern european countries. examples of member states where potential of value chains in the blue economy is partly untapped are portugal (group a) and ireland (group c). each equipped with important marine resources, they have defined national strategies to develop the blue economy, planning the development of aquaculture, blue energy and blue biotechnologies for the manufacture of pharmaceutical, medical and cosmetic products (irish department for agriculture, 2012; government of portugal, 2013). obviously, previous sectorial developments and industrial clusters often provide the basis to foster the transition from traditional, originally non-bio-based sectors to a higher bio-based share. for example the long-standing experience of the netherlands (group c) in the biotechnology, chemicals and agri-food sectors, combined with excellent logistics and biomass supplies in harbours, opens many options for establishing biorefineries (ser, 2010; dgbi-pdbbe, 2013). several countries in groups a and b, starting from a much lower labour productivity level but endowed with abundant primary production and a sound manufacturing base, could add value through bio-based methods of production. the use of the existing indicators for broad analysis of individual member states has to be accompanied by more in-depth investigation at sector level and including the breakdown of information on regional (nuts2) level. the presented indicators and database are further steps in the provision of more information on the european bioeconomy by the joint research centre of the european commission6 and its research partners. the availability and easy access of the full dataset at https://datam.jrc.ec.europa.eu/datam/mashup/bioeconomics/index.html is in accordance with the open data policy and should trigger feedback from all stakeholders, with the overall aim of improving the existing methodologies and datasets. acknowledgements this paper was part of the bioeconomy information system and observatory (biso) project, a fp7 project coordinated by the joint research centre (jrc) of the european 6 the establishment of a bioeconomy knowledge centre is planned for 2017. 16 t. ronzon et al. commission (https://biobs.jrc.ec.europa.eu/). the authors wish to thank fabien santini (european commission-dg agri) for his support during its time at jrc, lara dammer (nova-institut) for her input on methodological choices, as well as arnaldo caivano (jrc) and alexander lovchev (prognoz) for it support. disclaimer the views expressed are those solely of the authors and should not in any circumstances be regarded as stating an official position of the european commission. 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(2015). an economic impact analysis of the us biobased products industry – a report to the congress of the united states of america. joint publication of the duke centre for sustainability & commerce and the supply chain resource cooperative at north carolina state university. government of portugal (2013). national ocean strategy 2013-2020: council of ministers resolution no.12/2014. irish department for agriculture, food and the marine. (2012). harnessing our ocean wealth – an integrated marine plan for ireland. dublin, irish government. m’barek, r., philippidis, g.and ferrari, e. (2014). observing and analysing the bioeconomy in the eu-adapting data and tools to new questions and challenges. bio-based and applied economics 3(1): 83-91. 17a systematic approach to understanding and quantifying the eu’s bioeconomy philippidis, g., m’barek, r. and ferrari, e. (2016). is ‘bio-based’ activity a panacea for sustainable competitive growth? energies 9: 806. ser (2010). more chemistry between green and growth: the opportunities and dilemmas of a bio-based economy (2010/05 e). the hague, the social and economic council of the netherlands. stecf (2014). the economic performance of the eu aquaculture sector (stecf 14-18). eur 27033 en, jrc 93169. luxembourg, publications office. stecf (2016). the 2016 annual economic report on the eu fishing fleet (stecf 16-11). italy, ec-jrc. van meijl, j., i. tsiropoulos, h. bartelings, m. van den broek, r. hoefnagels, m.g.a. van leeuwen, e.m.w. smeets, a.a. tabeau and a. faaij (2016). macroeconomic outlook of sustainable energy and biorenewables innovations (mev ii). the netherlands, lei wageningen ur. a systematic approach to understanding and quantifying the eu’s bioeconomy tévécia ronzon1*, stephan piotrowski2, robert m’barek1, michael carus2 cost function and positive mathematical programming quirino paris what if meat consumption would decrease more than expected in the high-income countries? fabien santini*, tevecia ronzon, ignacio perez dominguez, sergio rene araujo enciso, ilaria proietti a stakeholder engagement approach for identifying future research directions in the evaluation of current and emerging applications of gmos davide menozzi1*, kaloyan kostov2, giovanni sogari1, salvatore arpaia3, daniela moyankova2, cristina mora1 a spatial analysis of terrain features and farming styles in a disadvantaged area of tuscany (mugello): implications for the evaluation and the design of cap payments laura fastelli1*, chiara landi2, massimo rovai1, maria andreoli1 bio-based and applied economics 8(2): 179-210, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8930 innovation adoption and farm profitability: what role for research and information sources? michele vollaro1,*, meri raggi2, davide viaggi1 1 university of bologna/department of agricultural sciences, agricultural economics, bologna, italy 2 university of bologna/department of statistical sciences, bologna, italy abstract. the paper analyses the determinants of farmers’ adoption of innovations and studies the effect of the source of information and the connection with agricultural research on the contribution of innovation to farm performance. the paper uses primary data collected ad hoc in the province of bologna (emilia-romagna, italy) and analyses it through an econometric analysis. the results indicate that structural factors and farm specialisation still play a relevant role in innovation adoption. connection to scientific research triggers significant improvements in terms of value-added and quality of production but does not affect other profitability-related parameters. the results confirm the need for policy to better consider the role of intermediate actors between research and the farmer as well as to better clarify the final performance strategy in order to set the policy instruments right. the paper also highlights the need for further research about farms’ proactivity in searching for and selecting information during the process of innovation adoption and competitive advantages in terms of profitability components. keywords. innovation adoption, information sources, research-innovation link, farm profitability. jel codes. d83, o14, o31, o33. 1. introduction the interest in studying the process of innovation adoption and impact, both from theoretical and empirical perspectives, is motivated by the key role of innovation in fostering agricultural competitiveness and socio-economic growth (ramos-sandoval et al., 2018; sauer et al., 2019). in fact, a noteworthy share of the literature to date has focused primarily on understanding the patterns of innovation diffusion, rather than adoption. in recent decades, several studies have started to broaden the research perspectives on agricultural innovation by introducing frameworks and models aimed at understanding the process of innovation adoption in agriculture (gadhim and pannell, 1999; diederen et al., 2002). the early approaches can be roughly classified into those mostly focusing on eco*corresponding author: michele.vollaro@unibo.it 180 michele vollaro, meri raggi, davide viaggi nomic interpretations and those taking a more sociological perspective (marra et al., 2003). economists have argued that adoption and diffusion of innovation is motivated by changes in economic factors, such as prices, production efficiency, risk attitude and utility, whilst sociologists have, for their part, highlighted the major role of the adopter’s characteristics and the social environment in which the adoption process occurs. although from different perspectives, both approaches have converged in identifying in the learning behaviour of individuals one of the most important factors in the innovation adoption process which, in turn, characterises the diffusion pattern (rate of adoption) (ruttan, 1996). micro-level studies concerning the adoption and diffusion of innovations has progressed over time by testing new models explaining adoption and new patterns of diffusion, characterised by the inclusion of (farm level) information (uncertainty and risk) and time (diffusion as a sequence of adoptions) factors. while new insights have been identified with respect to the theoretical evolution, the empirical results provide an increasingly varied range of explanations for adoption (ghadim and pannell, 1999), including the recent attention to the innovation behaviour of farmers (läpple et al., 2015; sauer et al., 2019). at the aggregate level, the evolution of theories and practices concerning the concept of innovation have moved from a linear model of knowledge transfer from public research to the farm (röling, 1990), to the so-called agricultural knowledge system (aks), to an even more complex and dynamic innovation process, in which different actors (including public and private stakeholders beyond the research, education and consulting/extension sectors) cooperate in a network, referred to as agricultural knowledge and (information) innovation system (akis) (esposti, 2012; scar, 2012; ramos-sandoval et al, 2019). the akis concept supports the idea that the development and realisation of innovations are not limited to pre-defined and unidirectional processes (path-dependence, demand-pull or technology-push), as in the case of aks, but rather fed by a multitude of processes characterised by a continuous interaction between stakeholders within a network. such a paradigm although, on the one hand, makes the study of approaches to innovation adoption more complex, on the other hand it broadens the research perspectives by allowing for the inclusion of latent or hidden elements in modelling innovation adoption in agriculture, such as multiple information channels, for which the contribution of literature is still limited, and the role of research, recently highlighted by several european policy initiatives such as the european innovation partnership (eip) and the (related) innovation operation groups (iog). an especially relevant gap in the literature concerns the link between upstream connections with research as a source of information and innovation performance on the farm, in a context characterised by the growing role of the farmer in combining information and new technologies in designing farm-level innovations. this paper seeks to contribute to this literature through a farm-level study on the impacts of scientific research in agriculture (sra) on the economic performance of farms taking into account the intermediary steps of innovation adoption. the paper relies mainly on primary farm-level data, collected through direct interviews with farmers using an ad hoc survey questionnaire, with the broad aim of collecting data suitable for analysing the determinants of farmers’ adoption of innovations, the effect of innovation on farm economic outcomes, in terms of different components of profitability of the introduced innovation, and link these back to the role of research in innovation development. 181innovation adoption and farm profitability: what role for research and information sources? the contribution of the paper is mainly on empirical grounds, using insights and variables from a wide range of literature. however, our study is inspired by concepts mainly derived from two seminal theoretical frameworks, namely induced technical change by hayami and ruttan (1985) and the evolutionary model by nelson and winter (1982). we apply a demand-driven approach, as proposed by walker et al. (2010), which, together with the recall technique strategy, allows us to set an impact pathway, tracing back the determinants of the effects of successful innovation adoption on economic performance. in this paper, the use of the term innovation is intended as new to the farm/farmer and not as new to the market (mairesse and mohnen, 2010). the main novelty of the paper is the attempt to clarify whether a higher farm performance might be linked to the fact that the adopted innovation is rooted in scientific research. in particular, we investigate the extent to which, and how, the fact that an innovation is known to derive from scientific research affects, beyond the adoption decision, the economic performance of the farm. the origin of innovation from scientific research is identified through collected data about prior-knowledge of farmers. performance is also measured based on farmers’ statements regarding the gains realised from the adoption of the innovation, in terms of reduced costs, increased production, higher value-added and higher product quality. we investigate the effects of information on the adoption decision processes in two steps. after having presented the survey results in terms of descriptive statistics, we first analyse which factors and processes influence farmers’ decisions to adopt, or not to adopt, new technologies; then, as concerns the innovators (the farmers who introduced an innovation), we investigate whether the origin of innovation from scientific research yielded effects on profitability at farm level. the paper continues with a literature review in section 2. the methodology is outlined in section 3, followed by the presentation of the case study area (province of bologna, emilia-romagna) in section 4. section 5 illustrates the results, followed by a discussion in section 6 and concluding remarks in section 7. 2. literature review early studies on innovation adoption at farm level focused mostly on disentangling the innovation adoption process through a micro-economic approach (cochrane, 1958; hayami and ruttan, 1985; thirtle, 1985), by relying on the basic assumption of profit maximisation as the main economic driver for adoption (sunding and zilberman, 2001). on the other side, recent studies on innovation in agriculture, although relying on the same framework, focus more on the variety of different elements determining the adoption, as well as the diffusion processes. indeed, hall (2012) sketches how the modern innovation adoption process goes largely beyond the (public) function of introducing technology to farmers, as exogenously intended by cochrane (1958) and hayami and ruttan (1985), conceiving the innovation in agriculture as a system in which partnership, alliance and network actors work together to develop and spread innovation. a fundamental role in this system is acknowledged to be played by producers and users of knowledge, but the issue of who and how such links are created is still very much under scrutiny. indeed, the multiplicity of underlying dynamics characterising the links between the actors of the 182 michele vollaro, meri raggi, davide viaggi agricultural innovation system might be at the basis of the discordant findings of studies on innovation adoption. in fact, recent studies on the topic have addressed this issue and are evolving towards the definition and role of knowledge and/or innovation brokers within the akis (klerkx et al., 2009; ramos-sandoval et al, 2019). as regards the adoption of innovation in agriculture, in fact, different studies report varied results with regard to the relative importance of different determinants of adoption (ghadim and pannell, 1999), such as education, credit constraints, land size and others (feder and umali, 1993). one reason for such discordant results can be attributed to the difficulty in relating empirical information, model hypotheses and the conceptual/ theoretical framework in which innovation adoption in agriculture is modelled (lindner, 1987; besley and case, 1993). in fact, the evolution of the theoretical framework progressed towards the inclusion of informational attributes (koundouri et al., 2006; walder et al., 2019) and learning behaviour (ramos-sandoval et al., 2018) into the models hence making it possible to envisage innovation adoption as a dynamic process. information has played a major role in modelling the uncertainty concerning adoption decisions as well as farmers’ risk attitudes and risk aversion behaviour in the face of uncertainty. indeed, in a context of incomplete information, the degree of risk perception is assumed to be affected by learning, as learning can reduce the uncertainty concerning the innovation adoption (especially the downside production risk) (marra et al., 2003; koundouri et al., 2006). time, especially in connection to learning, is another important factor characterising the speed and rate of aggregate adoption and, hence, diffusion (sunding and zilberman, 2001). other additional factors beyond profitability, such as environmental and social sustainability concerns, potentially determining innovation adoption have been explored as well (walder et al., 2019). in relation to the above, diffusion itself has been subject to different interpretations. in economic terms it can still be interpreted as depending mostly on the perceived short-run profitability of the innovation (levins and cochrane, 1996; diederen et al., 2002). however, from a more sociological perspective, innovation diffusion also depends on the spread of information and is negatively related to the distance from the propagation point (rogers, 1983). improvements in human capital through learning affect positively the adoption rate and diffusion of innovation. based on this concept and starting from the evolutionary model of nelson and winter (1982), a stream of research advanced up to the adaptation of the technology acceptance model (tam), proposed by davis (1989), to the farming sector (flett et al., 2004; folorunso et al., 2008; rezaei-moghaddam and salehi, 2010). through tam, innovation adoption is explained as a process that depends on the perceived usefulness and perceived ease of use of the technology which, in turn, affects the acceptance (and the adoption) of the innovation. this theoretical framework belongs mainly to the psychological perspectives of the topic and attributes more importance to the individual beliefs and perceptions underlying the learning behaviour involved in the adoption process. a noteworthy gap in the literature concerns investigating whether and how the origin of innovation, and in particular research, may be related to the economic performance of innovation adopted by the farms1. two aspects can be distinguished: a) one is the “objec1 this topic has been recently explored by hockmann et al. (2018) in the food processing sector, evaluating the impact of internal r&d activities on the economic performance of multinational corporations. 183innovation adoption and farm profitability: what role for research and information sources? tive” origin of innovation; and b) the second is the knowledge of the origin. this distinction and the attribution of innovation to specific events or projects is often difficult due to the fact that multiple players and activities may contribute to its development, including the farmers themselves. as demonstrated in the literature, knowledge about innovation, improved through learning, plays a central role in the adoption process (marra et al., 2003). this holds especially in agriculture where the relatively high costs of internal r&d activities do not allow for the easy and affordable development of innovations within the farm (sunding and zilberman, 2001; diederen et al., 2003). for a large part of the literature, the positive role of knowledge by the farmer in the process of innovation adoption is referred to (or limited to) the adoption of available innovations and limited to the features of innovative technologies/solutions, disregarding its origin. in particular, learning (ability) is mostly considered to be a skill that makes the farmer (the innovator) able to reduce the downside risk of the innovation adoption and to improve the performance of the innovation through a process of adaptation to his/her farm’s peculiar characteristics. this approach implicitly assumes that the learning behaviour is considered to be detached from the path leading from research to innovation. instead, here we assume that knowledge about the innovation development process matters in terms of improved adoption processes and economic performance of the farm. this view is consistent with theoretical frameworks and empirical evidence that highlight how the cognitive elements of the innovator, such as his/her educational background (lin, 1991; reimers and klasen, 2013), attainments and experience (foster and rosenzweig, 1995), affect positively both adoption and performance of innovations (sauer et al., 2019), though (knowledge about) origin is not generally explicitly addressed. moreover, this hypothesis easily accommodates the theoretical framework pertaining to akis, according to which farmers interact with articulated networks of actors in the innovation research, development and adoption processes and may hence be aware of, or participate in, the research stages of innovation development or in the further stages of knowledge dissemination (scar, 2012). in this paper, we consider both the knowledge of research generating the innovation and the sources of information about the innovation as potential factors affecting the economic performance of innovation adoption. 3. methodology 3.1 overall approach the analysis proposed in this paper seeks to link information, research and farm-level performance by analysing the declared effects of innovation introduction with respect to farm structural factors, farmers’ characteristics and elements related to the process of innovation adoption, such as the sources of information, specifically the origin from research. the main objective of our study (besides explaining innovation adoption) is to evaluate whether the effects of adopted innovation on farm profitability is affected by the origin of the adopted innovation, in particular how innovation originating from research can affect various aspects of farm performance in different ways. the paper is based on survey data, provided from farmers’ responses to questions. this will require some qualifications, which are provided in the discussion section. 184 michele vollaro, meri raggi, davide viaggi as mentioned, the paper does not refer to one specific theoretical framework. however, the set of explanatory variables draws mainly from the analysed literature, which is grounded upon the induced technical change theory by hayami and ruttan (1985), for which innovation adoption is responsive to both economic conjuncture and technical evolution brought about by r&d, and the evolutionary model (nelson and winter, 1982), according to which farmers put effort into searching for better techniques and the selection of successful innovations (local searches for innovations, imitation of the practices of others and satisficing economic behaviour). these theoretical frameworks are integrated with insights drawn from the most recent literature on the akis framework and innovation adoption, especially considering linkages with non-farm actors, different sources of information and personal attitudes towards adoption. the theoretical development of the topic involves further aspects of the process in order to better qualify innovation adoption and diffusion, such as diffusion in terms of imitation of adoption, timing of adoption, endogenous and exogenous factors affecting adoption, elements characterising heterogeneity of farmers, etc. in order to address the evolving theoretical framework and to adapt to available data, a variety of methodological approaches have been used in the literature. sunding and zilbermann (2001), in reviewing the innovation process in agriculture, argued that the analytical methodologies mostly suited to evaluate the process of technology/innovation adoption are the binary or the limited dependent variable approaches. this opinion hinges upon the fact that innovation adoption is regarded as a discrete choice and, as such, represented by the means of threshold models. alternative and more articulated approaches have been employed over time, e.g. ghadim and pannell (1999) adopted time-series methodologies, diederen et al. (2002, 2003) used nested and ordered logit models, dimara and skuras (2003) tested the application of partial observability models, while koundouri et al (2006) applied a two-stage binary choice model. given that the objectives of the present study mainly pertain to the evaluation of the effects of different elements of the innovation adoption process on both the adoption choice itself and the consequences of the adoption in terms of positive economic performance, we use econometric techniques belonging to the class of limited dependent variable models on cross-section data derived from an original survey. 3.2 methodological approach a two-stage conceptual framework is employed for modelling the analysis. the first stage concerns farmers’ choice to adopt an innovation and the second concerns the profitability of the adopted innovation. the underlying process is composed by a participation stage and an outcome stage, where the outcome depends on the participation: the first stage is about the choice to adopt or not and, conditional on this first decision, the second is about the economic performance of the adopted innovation. an expected utility maximization framework is used to examine farmers’ choice to adopt, including the sequential adoptions as well. assuming that farmers are profit oriented and that their expected utility depends on the level of profit earned, the objective function of the farmers will be to maximize expected utility through maximizing expected profits (posed that utility is monotonically increasing in expected profit). it follows that a higher profit implies a higher expected utility for farmers. 185innovation adoption and farm profitability: what role for research and information sources? thus, for the ith farmer: ui=u(πi(ii,xi,si)), where ui is expected utility of farmer i, πi is expected profit of farmer i, ii is the innovation adopted (that guaranteed the highest performance) by farmer i, xi is vector of determinants of adoption of farmer i that impact expected profits of production, and si is a vector of other factors affecting the ability of farmer i of generating profit. according to lynes et al. (2016), the choice of adopting an innovation occurs if the expected utility ui, expressed in terms of expected profit from the adoption of ii, is greater than the expected utility of no adoption, namely no ii. assuming that the choice of ii depends on xi and si as well, ii(xi,si) and by simplifying the notation, so that ui is stated as a function of ii, the following condition applies: ui(ii)>ui(no ii), such that ui(ii)–ui(no ii)= ∆(ui)>0. expected higher profits, i.e. the outcome, is dependent on the choice of adopting, i.e. the participation. the outcome stage can be identified according to two different specifications. on one hand, the outcome of adopting an innovation, as suggested by cochrane (1958) and levins et al. (1996), can be intended as a continuous choice or a sequence of adoptions, namely more than one adoption, in order to guarantee, according to the technology treadmill, the competitiveness and the profitability of the farm. on the other hand, the outcome stage can be meant as the profitability consequent to the adoption of a specific innovation, namely the realized economic performance resulting from the introduction of the innovation into the farm. in both cases, it is assumed that farmers who choose to adopt knows that the outcome is affected by adoption determinants, such as structural factor (farm size, specialization, mechanization, market), subjective characteristics of the farmer (education, experience, off-farm income, business motivation, entrepreneurial attitude), but they also know that, to maximize the profitability, innovations need to be introduced after a learning process has been made and after that other elements have been scrutinized and evaluated accurately, such as the ability of self-developing the innovation, trial and error, the sources of information from others and links with r&d. expected higher profit can, therefore, be considered as an indirect function of both the determinants, the farmers’ subjective characteristics and the learning process leading to the adoption of a specific innovation. the stage two can be represented as follows: πi[ii(xi,si)]>0, for which >0, >0 and, in turn, >0, while is ambiguous. 3.3 econometric modelling strategy the econometric modelling strategy proceeds in two main steps: first, we provide an analysis of the adoption choice; then we proceed by explaining the performance and connecting it to the source of information. in order to avoid potential confusion across analyses and models, the first group is called adoption models, while the second is referred to as performance models. the analytical models chosen to analyse such variables belong to the class of limited dependent variable models. in the general case, the choice to adopt, namely the adoption model, is observed as a binary action, representing the underlying outcome of the utility maximization: if yai=1 means that ∆(ui)>0, while in the opposite case yai=0. that is, yai=1 when farmer i chooses to adopt the innovation, and yai=0 otherwise. determinants of (xi) 186 michele vollaro, meri raggi, davide viaggi and other factors (si) are assumed to linearly affect the adoption decision related to the farmers’ choice to adopt. let zai(za1,…,zak) be the set of both the determinants of (xi) and the other factors (si) affecting the adoption choice, aai(aa1,…,aak) be a vector of parameters and εi be a mean zero iid error term. then, the adoption choice can be modelled as: . the choice variable is simply the record of the adoptions, recorded as a single choice (in the case of one innovation) and as a sequence of choices (in the case of more than one innovation). this part of the analysis was carried out by evaluating the determinants of both the propensity to innovate and the number of innovations introduced, by employing a probit and a poisson model, respectively. in addition, a double-hurdle model has been used. this type of model has the advantage of making it possible to analyse the number of adoptions (single or repeated) that are conditional on the analysis of the choice to innovate (participation), which potentially follows a different data generating process (or, rather, that may be affected by different explanatory variables). the additional contribution of the double-hurdle regression is the capacity to clearly separate the factors mainly affecting the choice from those mostly affecting the adoption. the determinants include the technical and commercial characteristics of the farms and the subjective, socio-demographic characteristics of the farmers. other factors include the motivations of farmers to innovate, the knowledge of the adopted innovation prior to its adoption, the sources of information that farmers consulted, including the origin of innovation from scientific research, as well as whether farmers developed the innovation by themselves. following the same rationale, the profitability induced by the adopted innovation, namely the performance model, is observed as a binary outcome as well: if ybi=1 means that >0, while in the opposite case ybi=0. that is, ybi=1 when the adopted innovation yielded an improvement in profitability and ybi=0 otherwise. even in this case, determinants of (xi) and other factors (si) are assumed to linearly affect the improvement in profitability. let zbi(zb1,…,zbk) be the set of both the determinants of (xi) and the other factors (si) affecting the profitability (they do not need to be the same employed in step one), αbi(αb1,…,αbk) be a vector of parameters and ξi be a mean zero iid error term. then, the profitability can be modelled as: . the determinants are the same as the previous analytical model, while other factors include the motivations of farmers to innovate, the knowledge of the adopted innovation prior to its adoption, the sources of information that farmers consulted, including the origin of innovation from scientific research, as well as whether farmers developed the innovation by themselves. profitability is the measure of the realized gains, based on farmers’ declarations, resulting from the introduction of the innovation, in terms of cost reduction, production increase, value-added increase and quality increase. the first three have been collected in per cent terms, while the last in ordinal categorical terms (not at all, low, high, very high). however, they have been transformed in binary variables in order to evaluate solely the presence (not the magnitude) of the declared (positive) effects of the introduced innovation. however, given the use of the recall technique, these variables could suffer from approximation due to difficulties in providing precise estimates of the actual amount (mairesse and mohnen, 2010). such potential measurement errors could lead to biases in estimates and inefficient statistical conclusions and, in turn, render the use of the tobit 187innovation adoption and farm profitability: what role for research and information sources? model ineffective. despite this, these data provide for (i) important quantitative information, when used for explorative descriptive statistics and for comparative exercises, and (ii) qualitative information, when opportunely transformed into binary or categorical variables, to be used in econometric models for inferential purposes. indeed, the hypothesis of experiencing better performance if the innovator knows that the adopted innovation is derived from research could be reformulated in terms of positive (or non-null) performances. this implies the cost of losing the magnitude of the effect (marginal effect) but, at the same time, the benefit of at least keeping the presence of the effect (propensity of experiencing a positive outcome). such a perspective makes it possible to approach the analysis by considering the measured performance in terms of latent continuous variables and, in turn, by employing a probit and a heckit model, with the aim, respectively, of analysing the propensity of obtaining positive performances, with regard to the innovators, and of accounting for the possible presence of sample selection bias. in fact, the presence of positive performance outcomes due to the research-innovation link might depend upon the self-selection process of those farmers who decided to innovate because of higher expected gains. each model has been applied separately to each of the four performance variables, using the same set of explanatory variables. the analysis on economic performance has the same specification of the probit adoption regression with the inclusion of the other factors, namely the variables accounting for knowledge of the research-innovation link and source of information (hereafter “information variables”). specifically, the research-innovation link is the variable expressing whether the farmer is informed that the innovation originated from research, while source of information indicates whether the farmer knew about the innovation from external sources or developed the innovation by himself. age of innovation, for its part, is a measure of time distance between the year of introduction and 2015 (maximum 20 years) and is a proxy of farmers’ experience using such innovation (fine-tuning of innovation usage) as well as for the innovation to fully express its effects in terms of economic performance. the dependent variables used in the probit performance models are cost reduction, production increase, value-added increase and quality increment, all expressed as binary variables. 3.4 survey: sampling procedure and questionnaire a survey strategy was adopted because of the absence of datasets on innovation adoption processes and/or the existence of datasets characterised by noteworthy margins of non-representativeness and of collection/transcription errors, such as the ones operated by regional administrations to evaluate measures of the rural development plans (rdp) or the regional level fadn data. the survey strategy represents an appropriate research tool for this work because, like similar research works, this type of approach is preferred for anticipatory/forecast purposes and for studying elements and factors that are much more difficult to identify, such as the innovation adoption process (besley and case, 1993). the sampling plan, aimed at collecting complete information from at least 300 farms in the province of bologna, randomly picked from a sequential selection of about 1000 farms, constrained to be representative of both the agricultural specialization (type of farming) and the altitude level. 188 michele vollaro, meri raggi, davide viaggi the data have been collected by the way of an ad hoc questionnaire, first checked through direct interviews, further adapted to be used by telephone and finally carried out by telephone interviews (of approximately 15 minutes in length). the survey was designed to collect information about the farm, information about the farmer, specific elements pertaining to the innovation adoption process realised by the farmer and, in sequence, the relative effects on farms’ economic performance from the adopted innovation. the questionnaire is structured in six sections: • the introduction presents the aims of the survey and the project it relates to (eu fp7 project impresa); • the first section includes questions about farm structure: production specialisation and ancillary activities; land, labour, machines, technological plants; • the second section deals with the adoption process, including the choice of innovating, the number and types of innovations introduced, the motivation for, and for not, innovating; • the third section concerns one introduced innovation, namely the most important innovation (in terms of profitability), the sources of information and the link with research; • the fourth section addresses the financial aspects of innovation adoption, in particular whether the innovators benefited from supports from the common agricultural policy (cap) and the amount of total investments; • the fifth section deals with the effects of the adopted innovation in terms of economic performance: perceived changes in costs (efficiency gains), in production (output gains), in value-added products and (higher) product quality; • the sixth section includes questions about future behaviour of the farmers and expectations/sentiments with respect to the cap; • the last seventh and final section includes questions about the socio-demographic characteristics of the farm and the farmer’s family. the first and the last sections of the questionnaire aim to collect, respectively, structural (objective) data about the farms and socio-demographic (subjective) data about the farmers, focusing on those elements considered in the literature as “classic” determinants of innovation adoption, such as specialisation, size, mechanization, altitude, farm income, education, and experience. the second section inquiries into the process of innovation adoption by first exploring (eliciting) the opinion of the farmer about the existence of important innovations (in terms of profitability) in his specialisation sector in the last 20 years. the subsequent information regards the types of innovation introduced on the farm in the last 20 years, as well as the choice of not introducing any particular innovation, specifying innovation with regard to products, production factors and process innovations. crossing these two types of information makes it possible to clearly frame the individual choice context in which the adoption process has been developed. in this section, the farmer indicates which of the introduced innovations is, in his/her view, the most important in terms of profitability. the third section focuses solely on the most important innovation indicated by the farmer and deals mainly with the motivations underlying the adoption. this section has been built on the basis of the induced innovation adoption (iia) by hayami and ruttan (1985) and the evolutionary model (em) by nelson and win189innovation adoption and farm profitability: what role for research and information sources? ter (1982). with regard to the iia, farmers were asked whether the choice of innovating was determined, inter alia, also by a reaction to changes in products’ and factors’ prices or by the intention to anticipate the evolution of the markets of both products and production factors. further, the condition of being early adopters or laggards has been investigated by asking farmers for how long the introduced innovation was already commonly used. as regards the em, farmers were questioned about the origin of the introduced innovations, with the aim of exploring the connections between the farmer and the other actors involved in the akis, including the research sector. in primis, a distinction was made between farmers who stated to have created/developed the innovation by themselves (self-innovators) and those who declared to have learned of the innovation from external sources. in this way, for the latter, the information channels can be explored in more detail by referring to a menu of possible sources. the external sources are split into three groups: institutional, market and acquaintances. the institutional group includes sources related to the sphere of agricultural research and extension, such as universities, research centres and other private and public entities (i.e. regional administrations, local authorities, r&d from firms, training etc.); the market group refers to the sources of information from producers, retailers and commercial agents; whereas the acquaintances group involves as a source of information the network of people surrounding each farmer, such as relatives, neighbours and others. this section seeks to highlight the role of information and research, namely the elements representing the potential contribution to further understanding the innovation adoption process as well as the relative weight of agricultural research to the farm-level effects of innovation adoption. the key element meant to establish a connection between external sources and effectiveness of the adopted innovation is the investigation of the research-innovation link that is whether the farmers know about the research behind the development of the adopted innovation. the fifth section is dedicated to the declared effects of the introduced innovation in terms of changes in economic performance, combination of inputs and leisure time. information on the effects on economic performance of the introduced innovation were collected by breaking down the profitability into four elements: cost reduction, production increase, value-added increase and quality increase. the importance of these variables within the context of the innovation adoption process is found in their potential to reveal the mechanism allowing the adopted innovation to contribute to the farms’ overall economic performance (profit). the sixth section investigates the future intentions of the farmers regarding the continuation of the agricultural activity and the adoption of further innovations in the next five years. further inquiries are posed in order to record the opinions of farmers about the relationship between innovation and agricultural policy, as well as the role of innovation for the improvement of competitiveness in agriculture. the last section concludes the questionnaire by inquiring into the future of the farm and of the farmers and eliciting opinions about innovation and the cap. the data collected in this section are used for supporting the evaluation of, and better interpreting, some farmers’ choices, such as the motivations for not innovating. most of the data collected have been recorded as binary or categorical variables, whilst data related to farm size, labour, introduced innovations and others, have been recorded as continuous variables. exceptions are represented by the information related to farms’ economic performance, which has been surveyed according to four variables, 190 michele vollaro, meri raggi, davide viaggi namely cost reduction, production increase and value-added increase, collected in per cent terms, and quality increase, recorded according to an ordinal categorical variable (not at all, low, high, very high). 4. case study area, data collection process and descriptive statistics 4.1 case study area the agricultural sector in emilia-romagna is one of the most advanced and productive in italy, due to the favourable geographical and climatic conditions (the southern part of the territory is mountainous, whilst the northern part belongs to the po valley, which is a very fertile zone), and the presence of highly specialised enterprises. emilia-romagna is particularly active in the production of cereals (wheat and maize), fruit and livestock (mainly bovines, pigs and poultry) (fanfani and pieri, 2016). the province of bologna is located in the central part of the region, is agriculturally varied and composed of plains, hilly and mountain areas. according to the last agricultural census carried out in 2010 by the italian institute of statistics (istat), the province of bologna accounts for about 10,800 agricultural units over an uaa2 of about 173,000 ha. as shown in table 1, the agricultural sector is mainly based on arable crops, involving about 7,000 farms and about 141,000 ha of uaa. arable crop farming is mainly composed of farms growing cereals (about 4,000) and forage (about 2,000), whose uaa shares are 53% and 27%, respectively. the average size of farms producing cereals and forage crops is 12 and 10 ha, respectively, and more than half of them are located in plain areas. the second major type of farming in the province is livestock and related activities, involving about 800 cattle-holding farms with 33,000 heads as well as 150 swine-breeding farms and 75,000 heads. the largest livestock farms are based in plain areas. regarding fruit cultivation, about 2,700 farms grow orchards over an uaa of about 16,000 ha. 2 uaa stands for ‘utilised agricultural area’. table 1. agricultural census data per specialization (type of farming) and altitude level. specialization plain hill mountain total   cattle farms (milk, beef, ovine-caprine and mixed) 295 454 369 1118 10% cereal crops (wheat, maize, oats, barley) 3177 633 187 3997 37% other arable crops (open field, horticultural, mixed and grain pulses crops) 1284 849 608 2741 25% fruit (orchards, olives and grapes) 1529 1082 90 2701 25% non-classifiable 65 109 28 202 2% total 6350 3127 1282 10759   59% 29% 12%     source: our elaboration on istat data. 191innovation adoption and farm profitability: what role for research and information sources? 4.2 sample data and selected descriptive statistics the sample, represented in table 2, includes 178 farms located in the plains (59%), 87 in hilly (29%) and 35 in mountain (12%) areas. according to the principal specialisation, the sample is composed of 20 livestock farms, 116 cereal farms, 69 ‘other arable’ crop farms, 88 fruit farms (including olives, grapes and 11 nurseries), and 7 non-classifiable farms. cereal crop results are the most frequent specialisation with about 39% of the total farms, followed by fruit farms (about 26%), arable crop farms (22%) and cattle farms (7%). given that it is a direct result of the sampling procedure, the sample can be considered to be representative of the province of bologna. table 2. sample units per specialisation (type of farming) and altitude level. specialisation plain hill mountain total   cattle farms (milk, beef, pork and mixed) 5 7 8 20 7% cereal crops (wheat, maize, oats, barley) 86 21 9 116 39% other arable crops (open field, horticultural, mixed and grain pulses crops) 33 21 15 69 23% fruit (orchards, olives and grapes) 49 36 3 88 29% non-classifiable 5 2   7 2% total 178 87 35 300     59% 29% 12%     source: our elaboration of primary data collected. the sample accounts for about 8,000 ha of uaa, of which about 5,000 in ownership. the larger share of the land is that of cereal crop farms with about 36% of total land, followed by cattle farms (27%), (arable) crop farms (18%) and fruit farms (15%). the descriptive statistics of the collected data are presented in table 1a (see annex), while a wider presentation of the statistics of the sections from second to fifth is illustrated in the results section. 5. results 5.1 descriptive results altogether, 121 out of 300 farmers adopted at least one innovation in the last twenty years (about 40%) (the precise question was “in the last 20 years, what kind of product or process innovations have been introduced on your farm?”). this question was posed after asking the farmers about the existence of important innovations in agriculture (“do you believe that in the last 20 years there have been very important innovations in your main field of specialisation (measured in terms of income)?”). almost 47% (140 out of 300) of respondents replied positively. table 3 provides an illustration of these results by crossing the answers to these two questions. 192 michele vollaro, meri raggi, davide viaggi table 3. cross-tabulation of innovation introduction and consideration of important innovations in the last 20 years. introduction of at least one innovation in the last 20 years total no yes important innovations in the last 20 years no 125 35 160 yes 54 86 140 total 179 121 300 source: own elaboration on collected data. the consistent replies on the diagonal combinations (no-no and yes-yes) are somehow intuitive, while a less straightforward reasoning may emerge from an analysis of the off-diagonal cross-answers. we discuss these four options, in turn, by also attaching some descriptive statistics of the farmers/farms belonging to each combination. the 125 no-no answers (roughly 42% of the sample) are composed of 50% cereal, 26% other arable crop and 15% orchard growers. with respect to the total of each specialisation, cereal growers represent 54% (63 out of 116), other arable crop, 49% (33 out of 67) and the orchard growers, 25% (19 out of 77). these 125 respondents are mainly small farms with low agricultural income. in fact, on average, 83% of them operate on less than 20 hectares and 76% of them have an income from agricultural activities that accounts for less than 30% of family income. such conditions are consistent with the declared reasons for ‘no adoption’, mainly related to high costs. the 54 yes-no answers indicate no adoption in spite of the existence of important innovations in the sector of specialisation. these 54 farms are composed of 41% cereal, 24% other arable crops and 30% orchard growers. with respect to the total of each specialisation, cereal growers represent 19% (22 out of 116), other arable crop, 19% (13 out of 67) and the orchard growers, 21% (16 out of 77). this group also is composed of small farms with low agricultural income, but the frequency of these types of farms is slightly lower than in the previous group. in fact, on average, 72% of them operate on less than 20 hectares and 71% receive an income from agricultural activities that is less than 30% of family income. in this case also, such conditions seem to be consistent with the declared reasons for ‘no adoption’, mainly related to high costs, the expectation of soon retiring from farming (cereal) and maintaining production traditions (orchard). the 35 no-yes replies indicate adoption despite the declaration that there have been no important innovations in the sector of specialisation. these 35 farms are composed of 17% livestock farms, 43% cereal producers, 9% of other arable crops and 26% of orchard growers. with respect to the total of each specialisation, livestock farms represent 30% (6 out of 22), cereal growers, 13% (15 out of 116), other arable crops, 4% (3 out of 67) and the orchard growers, 12% (9 out of 77). this group of innovators is characterised by the fact that they operate on larger farms with higher agricultural incomes. in fact, on average, only 47% of them operate on less than 20 hectares and 49% receive an income from agricultural activities that is less than 30% of family income. these figures clearly differ from those of the previous two groups and the declared motivations for having introduced 193innovation adoption and farm profitability: what role for research and information sources? at least one innovation (with positive effects on profitability) mostly refer to reducing costs and increasing production. both groups, and in particular the first one, are consistent with innovations that are more linked to late adoption of existing solutions motivated by economies of scale, rather than by strong innovation behaviour. in the last group, the 86 yes-yes replies consist of 12% of livestock farms, 19% of cereal producers, 21% of other arable crops and 38% of orchard growers. with respect to the total of each specialisation, livestock farms represent 45% (10 out of 22), cereal growers 14% (16 out of 116), other arable crops 28% (18 out of 67) and the orchard growers represent 43% (33 out of 77). this other group of innovators operates on farms with sizes similar to the ones of the previous group, but with higher agricultural income. in fact, on average, 43% of them operate on less than 20 hectares and only 27% of them obtain an income from agricultural activities that is less than 30% of family income. similarly to the previous group, this last group also indicates as the main motivations for adopting at least one innovation (with positive effects on profitability) cost reduction and production increases, with the addition of other motivations pertaining to the improvement of labour conditions, such as reducing fatigue and improving the safety of workers. this profile, which is particularly consistent with the orchard specialisation, denotes farmers who are focusing on agricultural production on well-structured farms and who are open to an understanding of the outside markets’ trends as well as who are highly focused on innovation. on the other hand, about three-fifths of the interviewees (179 farmers) decided not to innovate due to the economic and managerial hurdles that reduce the capacity of farmers to obtain new technology and adopt innovations3. we asked these farmers to motivate their decision not to adopt innovation by choosing among two categories of responses: obstacles and intentional choice. among the obstacles, we proposed high costs, bureaucracy and risks, while for intentional choice we asked about ethical reasons, the intention to quit the business, negative past experiences and the desire to maintain traditional production processes. eighty-four (84) out of 179 replies deemed the excessive costs of adopting innovations to be the main hurdle, while 16 and 18 answers indicated their intention to quit the business soon and to keep maintain production traditions, among the intentional choice group, respectively. therefore, the sample revealed that the main reason for not having adopted innovations in the last 20 years was the excessive cost, highlighting economic barriers and the lack of managerial skills for gaining access to new technology. however, since for 33 out of 51 (65%) other reasons were expressed by cereal farms, we deduce that for about two-thirds of respondents such choice is due to a disinterest in innovation given that they possess less than 20 hectares, no weeding and harvesting machines, and therefore opt for the services of other companies. this is an important point considering the recent structural trends as it points at a dichotomy between larger professional farms, for which innovation remains important, and small farms that keep land tenure but farm via contracts, for which innovation is rather carried out or adopted by contractors themselves, i.e. outside the farm. for the remaining third, we equally deduce that they have not been interested in adopting innovations, but unlike the pre3 detailed descriptions of such data have been omitted in order to save text. these are, however, available from the authors upon request. 194 michele vollaro, meri raggi, davide viaggi vious farmers, because the technology they possess is considered to still be effective and hence does not need to be replaced or upgraded. the number of innovations introduced in the last 20 years is more than 200 for 121 innovators (an average rate of about 2 innovations per farmer). the distribution of adoptions, shown in table 4, reveals that mechanical innovations are the most frequently adopted ones (32%), followed by energy-water saving (21%), diversification (15%) and biological, agricultural and informatics (about 8% each). the distribution of type of innovations changes if considering the unique (most important) innovation that, according to farmers, yielded the highest impact on profitability. in fact, the shares of mechanical (42%) and energy-water saving (25%) innovations increases, while the others decreased slightly. as for motivations, the adoption of these types of innovations is mainly motivated by the need to reduce costs, to increase production and to face new climatic challenges affecting the availability of natural resources, such as water. as concerns the timing of introduction, about 65% of the mechanical innovations were introduced during the 2010-2015 period, while about 66% of the energy-water saving technologies were adopted in the 2005-2015 period. the adoption timing of the other types of innovation is smoothly spread across the considered time span (1995-2015). the main reasons motivating the adoption of the (one) most important innovation are concentrated in cost reduction (66 or 35%) and production increase (56 or 30%) (122 table 4. number of innovations introduced in the last 20 years and selection of the most important in terms of profitability. type of adopted innovations all adoptions share of adoptions on total unique adoption considered mostimportant in terms of impact on profitability share of important innovations on total biological-genetic 18 8.5% 8 7.5% diversification or manufacturing 32 15.0% 15 14.0% agricultural-zootechnic 18 8.5% 7 6.5% mechanical-automation 68 31.9% 45 42.1% informatics 17 8.0% 2 1.9% energy-water saving (irrigation plants, solar panels, biogas) 44 20.7% 27 25.2% marketing strategies (quality systems, production protocols) 5 2.3% 2 1.9% operational (cooperatives, associations, logistics) 2 0.9% 0 other 9 4.2% 1 0.9% total adoptions 213 100% 107 100% does not know 14 source: own elaboration on collected data. 195innovation adoption and farm profitability: what role for research and information sources? replies out of 187)4. however, out of these 122 replies, 31 prove to be jointly chosen by the same farmer, indicating an important synergy between the two aspects in contributing to the increase of profitability5. other motivations, collected in open format, result in general profitability improvement, without any reference to specific motivation, and reduction of worker fatigue. the main motivations for cost reduction and production increases are more frequent for cereals (25%), fruit (19%) and grape farms (16%). in particular, by looking at the (one) most important innovations, mechanical-automation and energy-water saving proves to be the most frequent with 32 and 20 replies out of 66 for cost reduction and 19 and 10 out of 56 for increasing production, respectively. beyond the motivations underlying the choice of the selected innovations, the survey investigated the selection and the adoption processes operated by the farmers. indeed, farmers were asked whether they designed and/or developed the (adopted) innovation by themselves or obtained the information regarding the introduced innovations from external sources (and from whom the farmer was informed about the existence of such innovation). in this respect, farmers who declare to have designed and/or developed the innovation by themselves are denominated “self-developers” and are considered to be the(ir) internal source of information as opposed to the other innovators who declared to have learned about the innovation from an external source of information. the data about the sources of information, shown in table 5, indicate self-developed innovation in the first column and the list of proposed external sources. self-development of innovation has been declared by 31% of innovators, with prevalence for cereal, fruit and nursery farms. it follows that the remaining 69% learned about the innovation from external sources and, in particular, mostly from sources other than public institutions and unions/farmer associations. indeed, 37% of the innovators declared to have acquired information about the innovation they decided to introduce from consultants, courses, local and visits to farms abroad. the second largest share is the 17% represented by the sources of information from people belonging to the sphere of personal relationships of the farmers such as friends, relatives and neighbours. unions and sectorial associations cover 10% of the external sources of information and the relative frequency appears to be uniformly distributed across specialisations. only a residual share of about 2% represents the public institutions devoted to research and development in agriculture as the external sources of information. such a result highlights the importance of intermediation between research and farmers. as a follow up question, farmers were asked to declare their knowledge of the maker/ producer of the innovation. by excluding self-developers, this inquiry reveals that most innovators (about two-thirds), who learnt about the existence of the introduced innovation from external sources, were also aware of who developed the innovation. this might indicate that farmers engage in a careful decision-making process before adopting the innovation or at least show a good level of awareness about its background. qualitative 4 the number of replies is greater than the number of adopters as the inquiry was devised as a multiple-choice question. 5 the link was not explicitly asked, but, in the explicit list, we included the reduction of risks and the diversification of the activity in order to evaluate the motivations directly related to profitability. very few replies were collected. 196 michele vollaro, meri raggi, davide viaggi ta bl e 5. s ou rc es o f i nf or m at io n fo r i nn ov at io n ad op te d pe r s pe ci al iz at io n. sp ec ia liz at io n se lf de ve lo pe d ex te rn al so ur ce o f i nf or m at io n n o re pl y to ta l in st itu tio ns (u ni ve rs ity , r eg io n, pr ov in ce , m in ist ry ) u ni on s, as so ci at io ns a cq ua in ta nc es , fr ie nd s, re la tiv es , ne ig hb ou rs o th er so ur ce s (c on su lta nt s, re fr es he r c ou rs es / tr ai ni ng s, vi sit in g… ) m ilk -b ee f c at tle be ef c at tle 1 1 2 4 m ilk c at tle 3 2 2 2 9 m ix ed c at tle , m ai nl y pa st er n 1 1 o vi ne -c ap rin e an d pa st er n ca ttl e 1 1 c er ea l c ro ps (w he at , m ai ze , o at s, ba rle y) 12 3 6 10 31 o pe n fie ld c ro ps 2 1 1 4 7 m ix ed c ro ps h or tic ul tu ra l c ro ps 2 2 2 3 1 10 h ig h pr ot ei n cr op s ( gr ai n pu lse s) 2 1 3 c om bi na tio n of c ro ps a nd c at tle 1 1 fr ui t 9 1 2 4 9 25 o liv es 1 1 g ra pe s 3 1 2 10 16 n ur se ry 5 1 2 8 n on -c la ss ifi ab le 1 1 1 3 to ta l 38 2 12 20 45 4 12 1 31 .4 % 1. 7% 9. 9% 16 .5 % 37 .2 % 3. 3% so ur ce : o w n el ab or at io n on c ol le ct ed d at a. 197innovation adoption and farm profitability: what role for research and information sources? additions during the interview revealed indeed that farmers rely upon trusted external sources of information and acquaintance with the producers6,7. overall, the sample reveals that the majority of farmers either strictly rely on their own ability to develop an innovation or, on one’s own initiative, search for information and cues from others’ experience in order to make the best innovation choice and to meet their profit expectations. in order to explore the connection between innovation adoption at farm level and research, farmers were asked to state whether they knew that the innovation they adopted originated from a specific agricultural research. this question was addressed only to those farmers that previously declared to have learned of the innovation through an external source of information. we excluded self-innovators from this question because we suppose that they engage in a process for introducing innovation that is mainly based on the self-development of their own ideas, which is completely different from the process followed by the other interviewed innovators. hence, this question was asked to 83 innovators. fifty-three respondents (about 64%) stated that they knew that the innovation was derived from specific research in agriculture. in particular, 29 out of these 53 (about 55%) concern mechanical innovations, mainly related to cereal, grape and fruit farms. the stated effects on economic performance are reported in figure 1. cost reduction, production increase, and value-added increase are measured in per cent increase, while quality increase is measured through four categorical levels (not at all, low, high and very high) of increase due to the introduction of the innovation. the number of observations of these variables does not correspond to the numerousness of the innovators’ sub-sample (121), because not all respondents provided a reply to each of the four questions. zero answers correspond to the actual observation of the performance by the farmer, while a missing reply might be justified by the lack of expectation, detection or perception of any impact on that specific component of profitability (in fact many farmers stated to not know the specific performance). since the answers were not mutually exclusive, respondents had the choice to indicate more than one positive effect and potentially all of the four asked. cost reduction (a), production increase (b), value-added increase (c) show a noteworthy frequency of zeros; this was expected since it is unlikely that one innovation might yield positive profitability outcomes on all of the four considered components at the same time. the effect on cost presents a concentration of positive outcomes within the range of 10-60% cost reduction (with the highest share on the lower boundary of the interval and no case recorded between 40% and 50%), while production and valueadded are more frequently within the 10-40% interval of increase. production increase also shows a fairly high frequency around the 50-60% range. as far as quality increase is concerned, it is observed that about 60% of the replies indicate an improvement in profitability due to high and very high quality increases, while only about 25% show no quality increases at all. 6 for some types of innovation, such as mechanical ones, farmers have a better knowledge of the major brands/ producers because of the presence, in the emilia-romagna region, of a large number of mechanical manufacturers that have been operating there since the beginning of the last century. farmers in the province of bologna possess a deep knowledge of the evolution of mechanical technologies and mechanical manufacturing, which provides them with a sufficient ability to develop their own mechanical innovations. 7 detailed results are available from the authors upon request. 198 michele vollaro, meri raggi, davide viaggi 5.2 econometric analysis according with the methodology illustrated in section 3, the results obtained from the econometric analyses are reported in two groups: the first pertains to the adoption of innovation (adoption models) and the second concerns the linkage between adopted innovation and performance (performance models). the results of the poisson and probit adoption models, shown in table 6, indicate which factors are most important in determining respectively the number of innovations and the choice of (propensity to) introducing an innovation. the ability of both models to analyse the survey data is quite good, as indicated by the wald χ2 statistics. the results from both models indicate that the propensity to innovate, in particular to adopt more than one innovation, is highly determined by the ecofigure 1. frequency distribution of cost reduction (a), production increase (b), value-added increase (c) and quality increase (d). a (63) b (71) c (75) d (121) 1 source: own elaboration on collected data; number of observations in parentheses. 199innovation adoption and farm profitability: what role for research and information sources? nomic size and other structural characteristics of the farm, as well as by some individual and behavioural characteristics of the respondents. the positive role of the share of rented, over total, land may be connected to both the structural characteristics of the farm, likely qualified by a rent-based expansion, and to the overall size in terms of land area. the number of tractors is positively and significantly correlated to the number of innovations (but not to the decision to innovate) and shows that multiple innovations are more likely on large and capital-intensive farms. the positive and significant coefficient of the share of agricultural income shows a higher propensity to innovate on more professionally farms focused on agricultural activity. on the contrary, a higher number of family labourers and the juridical status of individual farms indicate that small farms are less inclined to adopt innovation (these are also correlated to the specialisation given the remarkable share of small cereal farms). as concerns individual and behavioural features, instead, we can observe that more educated farmers and those declaring that, in the last 20 years, important innovations in terms of profitability have been released show a higher propensity to innovate and, in particular, to adopt more than one innovation. in order to further support these first results, and to better explain the process, a twostep model has been applied by employing a double-hurdle regression8. the results are shown in table 7. 8 thanks to an anonymous referee for the suggestion of including a two-step model. table 6. poisson and probit adoption models. characteristics number of introduced innovations (poisson) introduction of innovation (0-1) (probit) coefficient marginal effect coefficient marginal effect innovation important innovations (last 20 yrs.) 0.91*** 0.66*** 0.78*** 0.21*** farm share of rented over total land 0.73*** 0.53*** 1.00*** 0.26*** number of tractors 0.05*** 0.03*** 0.03 0.01 livestock specialisation 0.22 0.16 0.42 0.11 cereal specialisation -0.54*** -0.39*** -0.59*** -0.16*** socio-economic education > than mid-school 0.64*** 0.47*** 0.57*** 0.15*** family income from agric<30% -0.58*** -0.42*** -0.53*** -0.14*** number of family labour -0.16** -0.11** -0.11 -0.03 individual farm -0.38* -0.28* -0.59*** -0.16*** constant -0.70** observations 244 244 wald χ2 146*** 80*** aic 478.6 248.4 bic 513.6 283.4 note: robust standard errors; * p < 0.10, ** p < 0.05, *** p < 0.01. 200 michele vollaro, meri raggi, davide viaggi table 7. double-hurdle model. characteristics number of introduced innovations outcome (quantity) equation farm number of tractors 0.09** livestock specialisation 1.15** cereal specialisation -0.64* fruit specialization, including grape and olives 0.29 socio-economic specialised ag education -0.47* family income from ag <30% -0.63** family workers per ha 1.00* constant 1.21** choice (participation) equation innovation important innovations in last 20 yrs 1.01*** farm location: plain=1; hill=2; mountain=3 -0.22** total land 0.01*** socio-economic education superior than middle school 0.75*** family workers per ha -0.65** σq 1.86*** σqσp -1.69*** observations 245 note: robust standard errors; * p < 0.10, ** p < 0.05, *** p < 0.01; σq is the estimated value of the standard deviation of the error term of the quantity equation; σqσp is the estimated value of the covariance between the error terms of the quantity equation and the participation equation. the results obtained through the double-hurdle model confirm those from the poisson and the probit models. in addition, they indicate that the choice of innovating depends highly upon location, especially in the plains and hills. larger farms and higher education contribute to improve the probability of adoption. the consideration of important innovations in the last 20 years notably affects adoption, but it does not contribute to explain the number of adoptions. moreover, what seems to determine increases in the number (quantity) of adoptions are factors related to the type of farming (and relative physical and economic size of the farm). in fact, larger farms with higher agricultural income, such as livestock farms, or farms with higher family labour and higher mechanisation (number of tractors) are more prone to adopt more than one innovation. the core part of the analysis concerns the explanation of the economic performance of the adopted innovation, specifically in relation to its origin from research and in connection to the source of information. the results from the probit performance model concerning each of the four components of the farm’s profitability are shown in table 8. given the application of the performance models to each measure of performance, the number of observations for each group of regression is reduced with respect to the entire sample. 201innovation adoption and farm profitability: what role for research and information sources? table 8. probit performance models. characteristics economic performance cost reduction [yes=1; no=0] production increment [yes=1; no=0] value added increment [yes=1; no=0] quality increment [very high, high=1; otherwise=0] innovation research-innovation link 0.21 0.77* 0.76* 0.83** source of innovation [external=1; self =0] -0.93 -1.22* -1.59** -0.87** age of innovation -0.01 0.04 0.06** 0.05* important innovations (last 20 years) -0.73 -1.13** 0.05 -0.07 farm cereal specialisation 0.65 0.53 -0.67 -0.05 share of rented land over total land -0.42 -0.27 -0.42 0.18 socioeconomic individual farm [yes=1; no=0] -0.20 0.11 0.67* 0.10 family income from ag <30% -0.91* -1.72*** -1.37*** -0.32 education > than mid-school 0.34 -0.11 0.52 -0.28 constant 1.98** 1.88** 0.25 0.41 observations 50 56 62 88 pseudo r2 0.176 0.245 0.317 0.115 wald χ2 12.1 14.9* 30.4*** 11.7 aic 71.7 76.6 78.7 123.9 bic 90.8 96.9 100.0 148.7 note: robust standard errors; * p < 0.10, ** p < 0.05, *** p < 0.01. the probit performance models applied on cost reduction and quality improvement proved to have a scarce capacity to explain the likelihood of obtaining positive performances. in the first model (cost reduction) only one regressor out of nine is significant and the sample is relatively small, while in the last model only the group of information variables contributes to explaining the variability in quality improvement. on the contrary, the probit performance model proved to perform better when applied on production and value-added increment. in fact, for the latter models, the results show significant contributions in both groups of variables. from all significant results, a common pattern can be identified in the positive contribution of innovation originating from research, but also in the negative effect of external information on the likelihood of obtaining a positive economic performance. these results indicate that farmers who knew the innovation from external sources have lower chances to obtain positive economic performance, especially in terms of valueadded and production, with respect to self-innovators. on the other hand, the positive contribution of research on economic performance is more pronounced in terms of quality. 202 michele vollaro, meri raggi, davide viaggi however, although the probit performance analysis provides interesting results, its specification might be affected, beyond the reduced number of observations, by selection bias in that only farmers who expect higher economic performance, on the basis of the information they possess, might decide to effectively adopt the innovation. in order to evaluate such a hypothesis, a heckit model, specifically a probit model with sample selection, is run by formally dividing the variables into two groups, namely the selection (adoption) and outcome (performance) variables. the heckit models indicate the presence of a self-selection process of innovation introduction related only to positive expected gains in value-added, as indicated by the significance of ρ, while the other model specifications indicate that both processes are essentially independent9 (table 9). the results indicate that the heckit models appear to be more appropriate in explaining the effects of the information variables on the economic performance. indeed, these models, on one hand, confirm the results related to research and source of information from the previous probit performance models, and, on the other hand, report the same results as the introduction models, with the exception of the variable number of tractors. 6. discussion in this paper we investigate the determinants of innovation adoption and the relationship between origin of innovation and economic performance at farm level. in the sample considered there is a noteworthy share of farmers who are actively innovating, which is partly explained by the long-time horizon taken into account. most frequent innovations are in the field of mechanical innovations and innovation aimed at water-energy saving. this is consistent with the fact that mechanisation is a widespread need across farm specialisations, on the one hand, and with the current need to save resources in a context characterised by climate change; the latter issue is potentially emphasised by the location of the study area in a mediterranean region. multiple innovations are frequent among innovators, which may be explained by both the existence of connections among innovations (innovation packages) and the tendency of most active farm(er)s to innovate continuously (läpple et al., 2015). the results from the adoption models, mainly testing the adoption determinants, are largely consistent with the findings in literature in terms of structural characteristics of the farms, such as farm size, mechanization, labour and production type, and subjective characteristics of the farmers, such as farmer education, experience and off-farm income10. the main novelty arises from the consideration of the judgement of farmers regarding the existence of important innovations in their field of specialisation, which helps to distinguish between cases in which the innovation choice by the farm results from the need of keeping up with a general technology shifts (i.e. replacing obsolescence), aligned to the technological treadmill, from cases in which innovation is more a choice tuned to the specific production and marketing needs of the farm. it also helps to understand the differ9 indeed, results were verified by running a probit regression on the performance variables by solely employing the information variables. the results confirm the ones obtained in the output equation of the heckit model. 10 the consistency of our results has been compared to the following literature: feder and slade, 1984; lin, 2001; daberkow et al., 2003; diederen et al., 2003; dimara et al., 2003; kounduri et al., 2006; cavallo et al., 2014, läpple et al., 2015; ramos-sandoval et al., 2018; sauer et al., 2019. 203innovation adoption and farm profitability: what role for research and information sources? ent profiles of the non-innovators, namely those for whom no-innovation is linked to the absence of innovation in the sector in contrast to those foregoing innovation for personal or farm reasons, in spite of the progresses of innovations in the sector. the second group of models, namely the performance models, represent, in our knowledge, the first attempt to evaluate the existence of a relationship between research and farm performance, also taking into account farmer intermediation. the first results support the hypothesis of a differential impact of innovations originating from research, which increase profitability by positively affecting value-added and quality improvetable 9. probit performance model with sample selection. characteristics economic performance cost reduction [yes=1; no=0] production increment [yes=1; no=0] value added increment [yes=1; no=0] quality increment [very high, high=1; otherwise=0] outcome equation (o) innovation research-innovation link 0.31 0.53 0.58* 0.79** source of innovation [external=1; self =0] -1.20* -1.16** -1.16*** -0.91** age of innovation -0.01 0.03 0.04 0.05* constant 1.36** 0.98** 0.98** 0.30 selection equation (s) innovation important innovations (last 20 years) 0.41* 0.48* 0.48** 0.84*** farm breeder specialisation 0.52 -0.11 -0.20 0.56 cereal specialisation -0.47** -0.32 -0.64*** -0.52** share of rented over total land 1.15*** 0.76** 0.99*** 1.03*** number of tractors 0.04 0.05 0.05 0.02 socioeconomic education > than mid-school 0.44* 0.49** 0.53*** 0.52** family income from ag <30% -0.22 -0.62*** -0.44** -0.55*** family labour -0.26** -0.29*** -0.22** -0.09 individual farm [yes=1; no=0] -1.13*** -0.75*** -0.77*** -0.71*** arctan(ρ)† 0.05 -0.27 -1.13* -0.14 observations 241 243 240 232 uncensored obs 50 56 62 88 aic 272.5 301.6 308.7 344.3 bic 321.3 350.5 357.4 392.5 wald χ2 (o) 3.76 4.55 8.42** 10.2** note: robust standard errors; t statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01; † arctan(ρ) indicates the correlation coefficient between output and selection equations. 204 michele vollaro, meri raggi, davide viaggi ments. on the contrary innovations originating from research do not appear connected to improvements in productivity or cost reduction. although this paper contributes to evidence on the role of research and information sources in improving farms’ economic performance, it is also affected by some limitations that may affect the robustness and the generalisation potential of the results. first, the sample is rather small, in particular for the adopters’ subsample, in particular considering the heterogeneity brought about by the large coverage of different farm specialisations. this may have contributed to the low significance of some of the models and some difficulty in estimation. this has also made potential additional explanatory variables difficult to use. second, the case study relies on a specific province in italy, which, while benefiting from an internal heterogeneity (in terms of farm specialisation and altitude), still represents a specific context in terms of general ecological and legal conditions (including specific priorities e.g. for investment). a third limitation concerns the way the data were collected. due to a lack of better information availability (e.g. from accounting data) and resource limitations, most of the variables are based on statements made by farmers. this is a sensible topic, in particular with respect to the estimation of the impact of innovation on profitability parameters, which also implies a request for a difficult judgement on the part of the farmers, and of the origin of innovation, especially with respect to research, that incorporates a mix of actual information about the origin and level of documentation by the farmers. the origin of innovations and knowledge about it, in turn, relate to each other and are almost impossible to distinguish in the way in which the survey was run. based on other questions and statements by farmers on their own level of information, we can interpret this information mostly as revealing the true origin of innovation, however there is certainly some level of (unmeasurable) approximation. fourth, and connected to the above, using stated information coupled with resource constraints implied the need to collect this information in a simplified way (e.g. using qualitative or dichotomous variables) and, in some cases, to use classes in the data treatment in order to account for “perceptive discontinuities” (such as round numbers in per cent statements). this, however, implies some further difficulty in the estimation and interpretation of the models. these limitations, associated with the promising results achieved, highlight relevance and provide more precise hypotheses for further investigation on this issue. this would require, however, a larger sample, wider territorial coverage and would benefit from linkages to structural and performance data not available for this study. an important message arising from the paper, in spite of the limitations, is that the role of farmers is crucial for innovation development and that farmers who are willing to innovate are engaged in a continuous learning process which includes, beyond the practical knowledge of the available innovations, the knowledge and awareness of the process leading from research to the realisation of the innovation as well. this evidence supports the paradigmatic change of the innovation process from aks towards the akis and multi-actor concepts (scar, 2012), by providing additional insight into the proactive role of farmers in the management of external information coming from different sources, including research, and of own-knowledge within the innovation adoption pro205innovation adoption and farm profitability: what role for research and information sources? cess (klerkx et al., 2009; läpple et al., 2015). such proactivity might represent a relative competitive advantage for the improvement of farm performance and a key feature of entrepreneurship. however, its ‘anatomy’ would need to be better analysed in future studies, with the collection of more specific information about on-farm processes leading to innovation adoption or implementation on the farm. 7. conclusions the results of this paper show the importance of innovation for a large share of farms, considering a substantial time frame of 20 years. most frequent innovations are in the field of mechanical innovations and innovation aimed at water-energy saving. multiple innovations are frequent among innovators. classical factors, such as proxies related to farm size, remain the most suited variables to explain the adoption of innovations, while motivations for innovation adoption are largely related to the combination of cost reduction and production increases. the process of innovation development and adoption follows two main pathways: self-development by farmers and development by mostly private companies. agricultural research is generally known to be in the background, but rarely seems to lead directly to technology development and even less to adoption. this may also be connected to the prevailing technologies that are considered to be relevant in the area (mechanisation and water/energy saving), which require important steps in terms of ‘engineerisation’ of knowledge and fine tuning in local conditions (including machinery set-up and feedback from users). in either case, the mediation between research and farmers has an important industry component or, in any case, involves different layers of actors. the (knowledge of) existence of research activities in developing the innovation seems to be associated to better performance only for the specific but important cases of improving the value-added and of achieving very high-quality production. this suggests that scientific research can have a specific role in terms of different performanceimproving strategies, and, in particular, that it can contribute comparatively more to quality, while self-development or industry-led technology adaptation can have a better role in cost reduction. these results also yield relevant insights in terms of research policy. in particular, when promoting multi-actor approaches, innovation policies should better consider different regional/sector objectives in terms of quality, productivity or cost reduction, and related to this, more explicitly evaluate the potentially different roles of private and public research and innovation players. in addition, while it can be expected that economic incentives linked to factor and product prices mainly affect cost reduction through selfinnovation, a stronger role has anyway to be attributed to direct research and innovation incentives if quality objectives are to be pursued. in spite of its limitations, the study hints at the need to further explore the co-existence and interplay among different innovations, different innovation pathways and different innovation impacts. moreover, the interaction between awareness of technology development pathways and actual technology performance at farm level is an issue that was only partially untangled in this paper and one that is undoubtedly worthy of further investigation. 206 michele vollaro, meri raggi, davide viaggi 8. acknowledgement this study was conducted in the framework of the “impresa” project, which received funding from the european community’s seventh framework programme under the ga 609448. we would like to thank pedro andres garzon delvaux and pavel ciaian for the review of a previous version of the paper. the content of this study does not reflect the official opinion of the european union. 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(2010). impact assessment of policy-oriented international agricultural research: evidence and insights from case studies. world development 38(10): 1453-1461. annex obs mean std. dev. min max structural data zootechnics specialisation 300 0.08 0.26 0 1 fruit specialisation, including grape and olives 300 0.26 0.44 0 1 cereal specialisation 300 0.40 0.49 0 1 protein crop specialisation 300 0.06 0.23 0 1 arable crop specialisation, including horticultural crops 300 0.62 0.49 0 1 presence of ancillary activity: yes=1; no=0 300 0.26 0.44 0 1 209innovation adoption and farm profitability: what role for research and information sources? obs mean std. dev. min max sale contracts 300 0.33 0.47 0 1 share of rented land over total land 300 0.20 0.30 0 1 own land 300 17.03 27.82 0 300 rented land 300 9.72 25.11 0 200 total land 300 26.75 45.67 0 500 number of tractors 300 3.43 2.83 0 20 number of operational machines 300 3.15 2.32 0 9 demographic data individual farm: yes=1; no=0 300 0.80 0.40 0 1 family farm: yes=1; no=0 300 0.96 0.20 0 1 family labour 285 1.89 1.09 0 7 family labour full time 285 1.35 0.89 0 6 family labour part time 285 0.54 0.86 0 4 education inferior than medium school =1 300 0.22 0.41 0 1 education superior than elementary school =1 300 0.73 0.45 0 1 education superior than high schoo l=1 300 0.09 0.29 0 1 specialized ag education =1 300 0.46 0.50 0 1 family income from ag <30% =1 142 0.55 0.50 0 1 family income from ag <50% =1 176 0.69 0.46 0 1 considerations important innovations in last 20 years: yes=1; no=0 300 0.47 0.50 0 1 continue farming in 5 years: yes=3; maybe yes=2; maybe no=1; no=0 277 2.36 0.79 0 3 introduce innovation in next 5 years: yes=1; no=0 176 0.35 0.48 0 1 innovation important for competitiveness: not at all=0; little=1; enough=2; much=3 272 2.42 0.72 0 3 cap help innovation adoption: not at all=0; little=1; enough=2; much=3 248 1.57 1.00 0 3 cap necessary for supporting agriculture: not at all=0; little=1; enough=2; much=3 267 2.19 0.97 0 3 description of data for non-innovators (reasons for not innovating) no introduction = 1 300 0.60 0.49 0 1 no introduction for high costs = 1 179 0.47 0.50 0 1 no introduction for ethical reasons = 1 179 0.01 0.11 0 1 no introduction for too bureaucracy = 1 179 0.05 0.22 0 1 no introduction for high risks = 1 179 0.06 0.23 0 1 no introduction for quitting activity soon = 1 179 0.09 0.29 0 1 no introduction for negative past experiences = 1 179 0.02 0.13 0 1 no introduction for keeping traditions = 1 179 0.10 0.30 0 1 no introduction for other reasons = 1 179 0.28 0.45 0 1 description of data for the subsample of innovators number of introduced innovations 300 0.71 1.16 0 8 introduction of innovation: yes=1; no=0 300 0.40 0.49 0 1 210 michele vollaro, meri raggi, davide viaggi obs mean std. dev. min max year of introduction of the innovation 109 2007 6.01 1995 2015 age of innovation wrt to introduction 109 8.03 6.01 0 20 intro for reducing risks = 1 121 0.11 0.31 0 1 intro for diversifying ag activity = 1 121 0.14 0.35 0 1 intro for reducing costs = 1 121 0.55 0.50 0 1 intro for increasing production = 1 121 0.46 0.50 0 1 other reasons (most increasing profitability and reducing labour) 121 0.27 0.45 0 1 reaction to increase in input prices 121 0.49 0.50 0 1 reaction to reduction in output prices 121 0.52 0.50 0 1 anticipate inputs markets trend 121 0.36 0.48 0 1 anticipate outputs markets trend 121 0.37 0.49 0 1 external help from private or seller 120 0.37 0.48 0 1 external help from public institutions 120 0.01 0.09 0 1 no external financial support for introducing innovation 120 0.56 0.50 0 1 level of self-financing: 0=less than 5.000; 3=more than 50.000 91 1.85 1.10 0 3 type of innovations biological and genetic innovations 121 0.07 0.25 0 1 agronomical and zoological innovations 121 0.06 0.23 0 1 mechanical innovations 121 0.37 0.49 0 1 informatics innovations 121 0.02 0.13 0 1 energy and water saving innovations 121 0.22 0.42 0 1 diversification innovation 121 0.12 0.33 0 1 market strategies innovations 121 0.02 0.13 0 1 information about origin of innovation source of information about innovation: external=1; self produced=0 121 0.69 0.47 0 1 knowledge of innovation origin from research 121 0.44 0.50 0 1 effects of introduced innovation on economic performance all effects: presence of (positive) effect=1; otherwise=0 121 0.87 0.34 0 1 cost reduction in % 63 17.81 20.94 0 90 cost reduction: yes=1; no=0 63 0.71 0.46 0 1 production increment in % 71 16.17 24.02 0 100 production increment: yes=1; no=0 71 0.65 0.48 0 1 value added increment in % 75 11.20 18.56 0 100 value added increment: yes=1; no=0 75 0.52 0.50 0 1 quality increment >0: very high, high and low=1; nothing=0 121 0.80 0.40 0 1 quality increment >1: very high, high=1; otherwise=0 121 0.60 0.49 0 1 quality increment: not at all=0; little=1; enough=2; much=3 110 1.64 1.04 0 3 consumers’ rationality and home-grown values for healthy and environmentally sustainable food simone cerroni1,2,3,*, verity watson4, jennie i. macdiarmid5 the wellbeing of smallholder coffee farmers in the mount elgon region: a quantitative analysis of a rural community in eastern uganda anna lina bartl agricultural sector performance, institutional framework and food security in nigeria romanus osabohien1,3,*, evans osabuohien1,3, precious ohalete2,3 innovation adoption and farm profitability: what role for research and information sources? michele vollaro1,*, meri raggi2, davide viaggi1 determinants of farm households’ willingness to accept (wta) compensation for conservation technologies in northern ghana evelyn delali ahiale1,*, kelvin balcombe2, chittur srinivasan2 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-20479 bio-based and applied economics 5(2): 213, 2016 it is with great honor that i have taken the position of chief editor for bio-based and applied economics. in only five years since its foundation bae, the official journal of the italian association of agricultural and applied economics (aieaa), has regularly published three issues per year and has been indexed in several bibliographic databases, including the emerging sources citation index (esci) of web of science and the scopus database. i wish to take this opportunity to acknowledge the great job made by the previous chief editor, davide viaggi, and his enthusiasm and dedication to the journal. many thanks also to the previous team of associate editors for their contribution in establishing the current standard of bae, and particularly to alessandro corsi and gianluca stefani, who left the board. according to its focus and scope, bae provides a forum for presentation and discussion of applied research in the field of bio-based sectors and related policies, informing evidence-based decision-making and policy-making. in his farewell editorial, davide viaggi stressed a number of challenges, relating to the fact that “bioeconomy economics and policy is still far from being a well-established discipline”; he also concluded that “the economics of the bioeconomy needs most likely to develop not as an independent research area, but rather in close connection with the more traditional areas of agriculture and food economics”. since the beginning, bae has been pursuing this scope, welcoming contributions from several fields and disciplines, such as resource and environmental economics, consumer studies, regional economics, innovation and development economics, production economics, stimulating cross-fertilization among research fields, pointing-out connections and driving towards a tentative definition of a research field for the bioeconomy. much more needs to be done, and i will work to maintain and consolidate this peculiar role of bae, in shaping a new research area. the new team of associate editors is highly qualified to this scope: pavel ciaian, ro berto esposti and simone severini have joined ornella wanda maietta and francesco mantino. i am truly confident that with their help and expertise in different research fields, and the support and contribution of the staff from firenze university press, we will be able to steadily increase the high quality standard of our published articles and even aim to further achievements and diffusion among scholars. daniele moro bio-based and applied economics 8(2): 161-178, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8929 agricultural sector performance, institutional framework and food security in nigeria romanus osabohien1,3,*, evans osabuohien1,3, precious ohalete2,3 1 department of economics and development studies, covenant university, ota, nigeria 2 department of economics and development studies, alex ekueme federal university, ndufe alike, ebonyi, nigeria 3 centre for economic policy and development research (cepder), covenant university, ota, nigeria abstract. this study examines how the performance of the agricultural sector can be enhanced in the long-run through institutional framework thereby ensuring food security in nigeria. it employs the ardl (autoregressive distributed lag) with data from the central bank of nigeria (cbn) statistical bulletin, food and agriculture organisation (fao), world development indicators (wdi), and world governance indicators (wdi). food security is used as the dependent variable proxied by the number of the people undernourished under the stability dimension; agricultural sector performance and institutional framework as the independent variables, while population is a control variable. two agricultural variables (agriculture production and agriculture credit) are employed with six variables of institutional framework. the findings show that in the log-run, agriculture production and agriculture credit (agriculture variables) will increase food security by reducing the number of people undernourished by 2% and 18%, respectively. in terms of institutional framework; political stability and absence of violence and rule of law increase food security by reducing undernourishment by approximately 69% and 29%, respectively; control of corruption and voice and accountability tends to reduce food security by increasing the number of the people undernourished by 74%, 51% and 63% respectively. therefore, the study concludes by recommending, among others, that the nigerian institutional framework should be improved (especially the control of corruption) in addressing the challenges in the implementation of food security programmes and ensuring timely distribution of food resources. keywords. agriculture, food security, governance, institutions. jel codes. g38; h1, o43. 1. introduction this study explores the nexus between agricultural sector performance, institutional framework and food security in nigeria. it engages autoregressive distributed lag (ardl) as the econometric technique in examining the long-run relationship among the selected variables. the results show that in the long-run, agricultural performance con*corresponding author: romanus.osabohien@covenantuniversity.edu.ng 162 romanus osabohien, evans osabuohien, precious ohalete tribute to food security by reducing the number of the people who are undernourished (ejikeme, ojiako, and ezeh, 2017; ibe, alozie and, iwueke, 2017). this is germane as ensuring food security is an important factor for human survival (babatunde, omotesho and sholotan, 2007; omonona and agoi, 2007; dias, juliana, giller, and ittersum, 2017; waldron 2017). extant studies have presented the subject of food security from a number of perspectives: government’s involvement, climate change and the need for availability of food and related resources for human consumption (ike, jacpbs and kelly, 2017; osabohien, osabuohien, and urhie, 2018; osabohien, ufua, moses and osabuohien, 2020). the role of institutional framework in ensuring food security cuts across all tiers of government: federal, state and local (osabohien et al., 2018). the different mandates in each tier and territories produce a continuous state of change in the expectations and roles of government (dollery et al., 2003). a great deal of responsibility is of essence at the local level as it interacts directly to the population. at some point the revenue has decreased (bell, 2007) while local government human service responsibilities have increased, mostly at local government level, the impact of such challenges is often evidenced (agranoff, 2014). policy deduction occasionally springs out from the local strata which in turn is been reshaped at the federal level. there exists a strong relationship with specific agricultural policies and the evolution of food security in different regions, which are structured by international relations, changing conditions in urbanized areas and local societal factors (koning, 2017). the effectiveness and/or economic output are no measuring factors on the multiple level problems but it demands for fitness in the government levels for a comprehensive solution (batley and larbi, 2004). provision of access to food for the population is also not properly eradicated as this challenge is been combated on a regular basis. combining biophysical, geographical, political and societal factors appears to be a location outcome with respect to food security (huisman et al., 2016; sheahan and barrett, 2014) the institutionalization of society provides a vital insight in the government proceedings. on a broader view, understanding how innovation, food processing, agricultural development, and access to food get shape is considered as the importance of institutions (acemoglu et al., 2012; booth et al., 2015; frankema, 2014; rodrik et al., 2004; ruttan and hayami, 1984). in general terms, institutions can be defined as “systems of established and prevalent social rules that structure social interactions” (hodgson, 2006). the interaction of human in its environment might not solely be captured by institution but there exists an agreement that institutions are important determinants of the trajectories of socio-ecological systems (young, 2002). despite the effort of successive government administrations in nigeria, non-governmental organizations (ngos), and the international agencies, the challenge of achieving food security has remained a herculean task (ufua, 2015; abdulrahman mani, oladimeji, abdulazeez, and ibrahim, 2017; osabohien, osabuohien, and urhie, 2018; osabohien et al., 2020). however, while the government has made some efforts through various budgetary allocations, supports from international agencies, and so on (androsova et al., 2016; lynam, beintema, roseboom and badiane; osabohien, matthew, aderounmu and olawande, 2019), the instrumentality of accountability, government effectiveness in equitable distribution and preservation of food resources, which could provide relevant support in ensuring food security tends to have been inadvertently neglected in the literature. nev163agricultural sector performance, institutional framework and food security in nigeria ertheless, with a stable population growth, the possibility of eradicating hunger by 2050 becomes questionable in nigeria (fao, 2017). this forms one of the motivations for this study that focuses on the need for developing agriculture for sufficient food production and institutional framework food even distribution. the foregoing is essential, as the challenge of distribution along the relevant value chain has resulted in the scarcity of certain food resources. hence, the poor and lower class of the society are usually excluded through hiked prices occasioned by increased cost along the value chain. this points out the need for strong value chain and distribution of food resources in terms of food management in the interest of citizenry (ufua et al., 2018). institutional framework in the context of this study promotes the use of records and data for planning food security issues, with due attention given to all stakeholders who are either involved or affected in the planning and implementation of food security programs (haddad, hawkes, achadi, ahuja, bendech, bhatia and fanzo 2015; olurankinse and oloruntoba, 2017). this could be achieved through the practice of meaningful engagement with the stakeholders at each stage of the implementation of food security programs (ufua et al. 2018). this would result in mutual understanding between the stakeholders and the interveners that may undertake the task of designing the right food distribution strategy and facilitate a conflict free platform to execute the task of accountable food distribution (womack and jones, 2003; ufua et al. 2015; osabohien, afolabi and godwin, 2018). the study is structured as follows: the next session presents the literature review, followed by the adopted methodology, next is the presentation of result and the last session is conclusion, which includes managerial implications, and suggestions for further research. 2. empirical literature it has been predicted that food demand will increase in the coming years, especially nigeria with high population and to control this food demand, strategies for efficient and effective supply of food to all households in nigeria needs to be put in place to mitigate food shortage. this can be done through innovation like warehouse and other storage facilities, among others (osabohien et al., 2018). populations spread of countries in west african sub-region during the period under review; nigeria, which is the focus of this study has a high population growth rate. this has not been reflected on food production and security practice in nigeria. instead, the growth in national population has resulted in a further complexity in terms of availability of food that meets the demands of the population density, especially in urban areas where food production is minimal and the demand is high (ojo, 2004; echebiri and edaba, 2008; jhingan, 2003). it is widely believed in literature that increase in production generates more food capable of reducing food shortage and the exclusion of the poor as a result of hunger as experienced in france and england (fogel, 2004). the improvement in supply of food for both countries showed efficient production of food systems. in terms of food production, nigeria as the most populated country in africa with over 190 million people lags behind other west african countries as its food production observed to be lower (fao, 2017). in this regard, more attention is needed to boost food production, food preservation and distribution, which could form a notable base for projecting the economy to better performance 164 romanus osabohien, evans osabuohien, precious ohalete in the future. furthermore, it has been noted in mali that, food production (especially food crops) has conventionally formed the bedrock for the pursuit of food security agenda (sidibe et al., 2018). this idea has been a long position of giving main concern of successive governments since mali’s gained political independence in 1960. structural responses to food insecurity in mali have mainly consisted of strategic reforms to enable the nation enhance agricultural production for the attainment of food security (bélières et al., 2008). in rethinking the strategies for sustainable development in ensuring food security in nigeria, the potentials of agriculture can be enhanced through institutional frameworks, effective governance, accountability and regulatory quality. from the empirical study of osabohien, osabuohien, and urhie (2018) employing the autoregressive distributed lag (ardl) technique in examining the role of institutional framework on food security, pointed out that institutional framework in nigeria exerts a negative effect on food security, due to weak institutional quality in nigeria. according to osabohien et al., (2018), the nigerian agricultural sector remains an important sector of the economy, owning to the fact that the sector employs approximately 75% of the total workforce, especially in the rural communities where most of the farmers earn their livelihood. the study of munene, swartling and thomalla (2018) employed the adaptive governance approach which pointed out that strategies to achieve sustainable development needs to be redirected. this would be more effective through the implementation of the framework requiring non-traditional management and governance approaches for a substantial reduction of food waste. it was noted that adaptive governance (ag) has been known to be the medium to change the link between development and disaster risk, with potentially far-reaching implications for policy and practice to ensure food security. similarly, osabuohien et al., (2018) used the qualitative method with focus group discussion to examine how local institutions contribute to food (rice) production in ogun state, nigeria where it was pointed out that local institutions play a key role in food production. in a study conducted by herbel, crowley and ourabah (2012), it was shown that achieving food security and the enhancement of dietary level is at the heart of the sustainable development goals (sdgs). in line with that, sidibé, totin, thompson-hall, traoré, traoré, and olabisi (2018) noted that achieving food security can be done through the enforcement of rules and laws designed at the national level which remains one of the central institutional mechanisms for efficient multi-scale governance in most countries. according to termeera, drimieb, ingram, pereirad, whitting (2018), policymakers are increasingly enlightened on the food security perspective, which has over the years reflected poorly in terms of institutional framework. thus, this paper fills this gap by addressing the question as to what forms of institutional framework is more appropriate to govern food systems in a more holistic way to achieve sustainable development goals (sdgs) of the united nations by the year 2030 and agenda 2063 of the african union. in africa, food security in relatively is high on the policy agenda of governmental authorities all over the globe (candel, 2014). food and agricultural organization-fao (2011) report, ‘food security governance’ relates to the ‘formal and informal’ rules and processes through which interests are expressed, and decisions which are germane to food security in a country are prepared, implemented and enforced on behalf of members of society. from the findings of rodrik (2010), osabuohien et al., (2018) and osabohien et al., (2018), to achieve food security, there is a need for equal opportunity in resource alloca165agricultural sector performance, institutional framework and food security in nigeria tion and the delivery of services; coherent and coordinated policies, institutions, and actions. this means that the challenge for policymakers interested in addressing the key policy issues are to redesign strategies that allow countries to have a stable and affordable food supply that is distributed as household food insecurity continues to be widespread with strong inequities across and within countries governance and strategies. given the economic situation in some critical parts of the country, for example; the north-east (scribner, 2017; ajayi and adenegan, 2018), where starvation has been prevalent due to insurgency of boko haram, the use of the right approach to addressing the national challenge of food insecurity, based on a platform of accountability, have remained a maximum requirement for achieving the right results of this subject area. thus, from the fallouts in the literature, this study addresses the gaps in knowledge and takes up the debate to a new level with respect to the issues of food security and agriculture and institutional framework in nigeria. 3. methodological approach of the study the food system concept is poorly reflected in institutional terms at local, national, and international levels (osabohien et al., 2018; fresco, 2009; kennedy and liljeblad, 2016; osabohien et al., 2020). handling problems associated with food insecurity requires a more holistic approach in terms of institutions to fully address it. to achieve the objective of the study, the autoregressive distribution lag (ardl) econometric approach to cointegration is applied in examining the log-run relationship between agricultural sector performance, institutional framework and food security in nigeria. the study engaged time series data sourced from the statistical bulletin of the central bank of nigeria (cbn), world governance indicators (wgi), world development indicators (wdi) of the world bank, and food and agricultural organization (fao). the study adopted the malthusian theory of population growth model (malthus, 1798) as recently explained in agarwal (2019); thus, the implicit function of the model is specified in equation (1). foodsec = f(agricvar, insvar, pop) (1) in equation (1) foodsec means food security, used as the dependent variable; agricvar means agriculture variables (two agriculture variables; agricultural production and agricultural credit) were employed, insvar represents institutional variables employed in the study; six major institutional variables were included in the model which are: voice and accountability, political stability and absence of violence, control of corruption, rule of law, government effectiveness and regulatory quality. pop means population, which was used as a control variable in the model. the variables are incorporated in a comprehensive model as shown in equation (2). foodsec = f(agricva2, instvar6, pop) (2) from the model, 2 represents the tow agricultural variables included, 6 represents the six institutional variables included in the model. the explicit form of the model is specified as shown in equation (3) 166 romanus osabohien, evans osabuohien, precious ohalete foodsec = α0 + α1 agricpro + α2 agriccredit + α3 va + α4 psav + α5 coc + α6 rol + α7 ge +α8 rq+ α9 pop + µ (3) where foodsec represents food security (stability component) proxied by the number of people undernourished, agricpro represents agricultural production, agriccredit represents agricultural credit; va represents voice and accountability, psav represents political stability and absence of violence, coc represents control of corruption, rol represents rule of law, ge represented government effectiveness, rq represents regulatory quality, pop represents population and µ represents the stochastic term. insight of the ardl model is drawn from the empirical work of osabohien et al (2018). the reason for the use of ardl approach compared to other econometric techniques like the johansen cointegration approach is built on the assumption that time series data trend in difference order of stationarity. hence, other approaches to cointegration becomes inefficient in handling this situation. the ardl model is specified in equation (4) the ardl model is presented in equation (4), while the error correction model is presented in equation (5) showing the mechanism and the adjustment speed which presents the extent to which the system adjust to equilibrium when disturbed by exogenous shocks. from equation (5), where: δ is the change in operator and ecmt-1 denotes error correction term. γ represents the speed of adjustment from the short-run to the long-run equilibrium (osabohien et al., 2018). the hypothis is stated that: h0: α0 = α1 = α2 = α3 = α4 = α5 = α6 = α7 = α8 = α9 (there is no long-run relationship) h1: α0 ≠ α1 ≠ α2 ≠ α3 ≠ α4 ≠ α5 ≠ α6 ≠ α7 ≠ α8 ≠ α9 (there is a long-run relationship) 167agricultural sector performance, institutional framework and food security in nigeria the a priori expectation of the study is that: agricultural performance and institutional framework increase food security by reducing the number of the people undernourished, while population contributes to food insecurity. this can be demonstrated mathematically as: α0 > 0, α1 > 0, α2 > 0, α3 > 0, α4 > 0, α5 > 0, α6 > 0, α7 > 0, α8 > 0, α9 < 0, implying that the coefficient of the explanatory variables are expected to be positively related to food security (negatively related to the number of the people undernourished), except population. irrespective of the overall progress in reducing food insecurity across the world, nigeria remains one of the countries with the highest number of undernourished people (fao, 2011). some countries have shown progress in terms of food security in recent years, this progress occurred in most countries in europe, eastern and south eastern asia, as well as countries in latin america, while nigeria showed little progress as the country lags behind even among other african countries. food security can be referred to the state where all people, at all times, have physical, social, and economic access to adequate, safe and nourishing food which meets their dietary needs and food preferences for an active and healthy life (fao, 2015). basically, there are four major dimensions of food security, which are availability, accessibility, utilisation, and stability; each of the four dimensions has its own unique component as a measure of food security (pangaribowo, gerber, and maximo, 2013; osabohien et al., 2018). though, the four dimensions of food security are highly important, in this study, given the peculiarity of the economy of our study, we considered mainly the stability aspect. the main reason for focusing on stability is because it addresses the stability of the other three dimensions over time.  individuals will not be considered food secure until they feel so and they do not feel food secure until there is stability of availability, accessibility and proper utilization condition (bajagai, 2019). another major reason for the use of the number of the people undernourished as proxy for food security is because the world is in a nutrition crisis. out of 667 million children under the age of five, scholars have shown that approximately159 million are undernourished (adams, 2017) and household living in poverty suffer to purchase nutritious foods for themselves and other members of the household. most times, these households of which most of them are farmers are constrained by limited access to sufficient agricultural inputs materials like seeds and fertilizers, making it difficult to cultivate the crops that could feed their families. moreover, undernourishment and poverty exist in vicious cycle – children who are undernourished face intellectual deficiency, are less likely to do well in school, and therefore less likely to be productive as adults. as a result, they either struggle to earn enough income in adulthood to purchase nutritious foods or they do not have the productive capacity to grow the food needed to feed their households (adams, 2017). instability of market price of staple food and inadequate risk baring capacity of the people in the case of adverse condition (e.g. natural disaster and adverse weather conditions), political instability is the major factor affecting stability of the dimensions of food security, which we have considered in this study. the dependent variable, food stability is proxied by the number of people who are undernourished. two main independent variables (agricultural performance and institutional framework) with population as the control variable proxied by growth rate of population are engaged in the analysis. 168 romanus osabohien, evans osabuohien, precious ohalete the study builds on the malthusian theory of population as recently explained in agarwal (2019). this is because according to malthus theory, the population grows exponentially while food production grows arithmetically doubling in each generation; in this wise, while food production is likely to increase in arithmetic progression, population is capable of increasing in geometric progression (agarwal, 2019; malthus, 1798). this situation of arithmetic food growth with simultaneous geometric human population growth predicts a future when people would have no resources to survive. this means many people will have to chase the few available food, in turn, leading to food insecurity. the data, table 1. data sources, measurement of variables and summary statistics. variable identifier data source measurement mean standard deviation minimum maximum food security foodsec fao number of people undernourished (% of total population) 10.8 1.4 8.8 14.3 agriculture agricpro cbn total volume of agriculture production (units) 3707.3 4405.70 38.4 14709.1 agriccredit cbn credit to agricultural sector (million naira) 3827678 4325308 80845.8 1.3 population pop wdi total number of people 1.2 3.1 8.4 1.9 institutional framework va1 wgi institutional qualities -0.7 0.3 -1.6 -0.5 psav2 -1.92 0.20 -2.19 -1.52 coc3 17.10 10.13 0.70 28.00 rol4 1.14 0.19 0.72 1.43 ge5 -0.4 0.81 3 7 re6 -0.8 0.2 0.1 1.5 note: fao: food and agricultural organization; cbn: central bank of nigeria; wgi: world governance indicators; wdi: world development indicators. 1 voice and accountability reflects perceptions of the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media (wgi, 2019). 2 political stability and absence of violence/terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism (wgi, 2019). 3 control of corruption reflects perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence (wgi, 2019). 4 rule of law reflects perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests (gwi, 2019). 5 government effectiveness reflects perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies (wgi, 2019). 6 regulatory quality reflects perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development (wgi, 2019). 169agricultural sector performance, institutional framework and food security in nigeria sources, and measurement of the variables for the study are presented in table 1. 4. results the results obtained from the ardl approach is presented in this section. 4.1 unit root test to conduct the ardl effectively, the unit root test for stationarity was conducted to determine the integrating order of the selected variables. this is considered as a necessary step in order to validate the assumption that none of the variables should be stationary at second differenced (that is, i [2]). this assumption is aimed at preventing the issue of ‘spurious result. insight of the ardl methodology was drawn from the empirical work of osabohien et al. (2018) and ouattara et al. (2006). ouattara et al. (2006) has it that f-statistic that pesaran (2007) presented seems ineffective when differentiated at order two [i (2)], since the method is based on the premise that variables either co-integrated at order zero [i (0)] or co-integrated at order one [i (1)]. therefore, engaging a unit root tests in the ardl approach to cointegration is to ensure that none of the variables is integrated of order 2 as presented in table 2. in table 2, variables exhibit different levels of stationarity, regulatory quality, political stability and absence of violence, control of corruption, agricultural credit and population are stationary at first difference, while others are stationary at levels. the result from the ardl econometric analysis is presented in table 3 the result obtained from the ardl for both the short-run and long-run dynamics are shown in table 3. the short-run result showed that: 1% change in agricultural productable 2. unit root test for stationary. variables adf t-statistic @ levels cv @ 5% adf t-statistic @ 1stdifference cv @ 5% integration order remark number of people undernourished -1.82 -1.95 -2.75 -1.96 i(1) stationary agricultural production -17.10 -1.95 i(0) stationary agricultural credit -2.90 -3.21 -4.67 -3.69 i(1) stationary population -1.81 -0.92 -4.83 -1.95 i(1) stationary voice and accountability -2.18 -1.95 i(0) stationary political stability/absence of violence -2.31 -2.87 -5.31 -3.82 i(1) stationary control of corruption -1.52 -1.89 -4.41 -3.67 i(1) stationary rule of law -3.91 -3.71 i(0) stationary government effectiveness -3.82 -3.00 i(0) stationary regulatory quality -0.62 -2.99 -5.79 -2.90 i(1) stationary note: adf means augmented dickey-fuller, cv means critical value. source: authors’ using stata 13. 170 romanus osabohien, evans osabuohien, precious ohalete ta bl e 3. a rd l re su lt. a gr ic ul tu ra l pr od uc tio n a gr ic ul tu re cr ed it po pu la tio n vo ic e & ac co un ta bi lit y po lit ic al st ab ili ty c on tr ol o f c or ru pt io n ru le o f l aw g ov er nm en t eff ec tiv en es s re gu la to ry q ua lit y lo ng -r un r el at io ns hi p -0 .0 2 (0 .0 0) [0 .0 1b ] [0 .0 1* *] -0 .1 8 (0 .1 0) [0 .0 9b ] 0. 74 (0 .1 9) [0 .0 8b ] 0. 51 (0 .6 8) [0 .0 1 b ] -0 .6 9 (0 .4 0) [0 .0 0 a ] 0. 63 (0 .0 8) [0 .4 68 ] -0 .2 9 (0 .0 5) [0 .0 5 c ] 0 .0 8 (0 .0 02 ) [0 .0 01 a ] 0 .2 4 (0 .0 6) [0 .0 8 c ] sh or t r un r el at io ns hi p l1 d l2 d l1 d l2 d l1 d l2 d l1 d l2 d l1 d l2 d l1 d l2 d l1 d l2 d l1 d l2 d l1 d l2 d -0 .0 5 (0 .0 0) [0 .0 0a ] [0 .0 0* ] -0 .0 3 (0 .1 7) [0 .0 1b ] -0 .0 6 (0 .3 5) [0 .0 2b ] [0 .0 2* *] -0 .2 0 (0 .8 0) [0 .0 0a ] 0. 11 (0 .4 9) [0 .1 91 ] 0 .2 00 (0 .6 3) [0 .1 9] -0 .0 7 (0 .3 3) [0 .5 5] -0 .0 6 (0 .1 4) [0 .1 3] -0 .2 1 (0 .4 0) [0 .0 4b ] -0 .0 9 (0 .4 2) [0 .0 2 b ] 0. 03 (0 .0 2) [0 .1 1] 0. 03 (0 .0 2) [0 .0 0 a ] -0 .5 8 (0 .5 6) [0 .0 b ] -0 .6 7 (0 .8 1) [0 .3 2] -0 .0 5 (0 .0 2) [0 .0 0a ] -0 .0 8 (0 .0 9) [0 .0 0a ] -0 .0 9 (0 .8 5) [0 .9 1] -0 .2 1 (0 .9 2) [0 .6 2] n ot e: t he s ta nd ar d er ro r a nd th e pr ob ab ili ty v al ue s ar e in p ar en th es is () a nd [] re sp ec tiv el y. a , b , c m ea ns th at v ar ia bl es a re s ta tic al ly s ig ni fic an t a t 1 % , 5 % an d 10 % re sp ec tiv el y, w hi le l d s ho w s th at v ar ia bl es a re la gg ed a nd d iff er en ce d. d ep en de nt v ar ia bl e is fo od s ec ur ity p ro xi ed b y th e nu m be r o f p eo pl e un de rn ou ris he d. so ur ce : a ut ho rs ’ u si ng s ta ta 1 3. 171agricultural sector performance, institutional framework and food security in nigeria tion all things being equal, leads to approximately 2% decrease in the number of people undernourished. this is done by increasing the availability of food as posited in pangaribowo et al. (2013). similarly, increase credit to agriculture helps to increase production that in turn leads to food security by reducing the number of people undernourished by 18%, similar to the findings of osabohien et al. (2018). given the weak control in the level of corruption, increase population, voice and accountability, which are positively related to the number of people who are undernourished meaning that change in these variables increase the number of the people undernourished by 63%, 74%, and 51% respectively, which is similar to the findings of osabohien et al. (2018). the long-run result showed that 1% changed in the first and second lag of agricultural production reduces the number of people who are undernourished by approximately 5% and 3% respectively, meaning that; increase agricultural production contribute to food security by diminishing the number the people who are undernourished. similarly, 1% change in first and second lag of agricultural credit also contribute to the reduction of the number of the people who are undernourished and contribute to food security by approximately 20% and 11%, respectively. population both the first and second lag increased the number of the people undernourished this is akin to malthusian population theory, increase in population increases undernourishment, the reason for this high increase is because of low food production and many people chase little available food produced. the findings of this study in similar to the study of sidibé et al. (2018). sidebe et al. (2018) argued that enforcement of effective rules and laws contribute to food security. in this study, accountability, government effectiveness, regulatory quality, political stability and absence of violence and rule of law in the long-run increase food security by reducing the number of the people who are undernourished these variables in the long-run these variables increase food security in the first lag 6%, 9% and 58% respectively, while in the second lag 21%, 3% and 67% respectively, but corruption and population reduce food security by 3% and 11% respectively. in summary, the general socio-economic and political conditions affect directly affect food security. the major causes, as outlined in the social, economic, and political context, imply that macroeconomic stability; economic growth and its distribution, public expenditure, and governance as well as quality of institutions are among the crucial factors affecting nutritional level (pangaribowo et al. 2013). in line with adams (2017) there is a strong relationship between undernourishment and infection. while undernourishment can cause increased vulnerability to infection, infection also contributes to undernourishment reinforcing a vicious cycle. the consequences of undernourishment include weight loss, damage to mucus membranes surrounding vital organs, impaired growth and development in children, and lowered immunity. this makes it easier for children to become infected by various pathogens. once infected, nutritional status is further worsen, which, in turn, causes reduced dietary intake. chronic exposure to pathogens from living in contaminated conditions can worsen health outcomes and damage the intestine, impairing long-term nutrient absorption. as a result, even if an individual were consuming enough food with the correct nutrients, the body would not be able to use and process those nutrients (adams, 2017). in a study by adams (2017) measuring the costs of hunger in rwanda, it has been estimated that in 2012 there were an additional 280,385 clinical episodes as a result of childhood undernourishment of those, 47,064 were directly resulting from diarrhoea, fever, respiratory 172 romanus osabohien, evans osabuohien, precious ohalete ta bl e 4. e st im at es fr om e rr or c or re ct io n m ec ha ni sm . re gr es so rs re gr es sa nd d _n pu d _ ag ric pr o d _a cg sf d _ po p d _v a d _p sa v d _c c d _r la w d _g e d _r q ec te rm -0 .0 24 5 a -0 .3 13 7 a -0 .0 35 1 a -0 51 08 7 b -0 .0 95 1 c -0 .0 03 8 -0 .5 56 1 a -0 .0 48 1 a -0 .1 20 1 a -0 .3 21 (0 .0 00 ) (0 .0 02 ) (0 .0 04 ) (0 .0 35 ) (0 .5 45 ) (0 .1 42 ) (0 .0 00 ) (0 .0 00 ) (0 .0 00 ) (0 .5 99 ) np u( ld ) 0. 92 16 a -5 86 .9 92 6 -0 .3 54 1 a -2 12 82 .7 3 0. 17 09 0. 11 57 73 -9 .5 02 4 a -0 .5 99 a -0 .5 99 a -0 .2 13 ag ric pr o( ld ) (0 .0 00 ) (0 .1 79 ) (0 .0 00 ) (0 .3 15 ) (0 .5 38 ) ( 0 .4 41 ) (0 .0 00 ) 0. 00 0 0. 00 0 (0 .2 00 ) -0 .0 00 48 -0 .0 34 5 -1 11 7. 83 b -9 .1 98 3 0. 02 02 a 0. 00 17 c -0 .0 11 6 a -0 .0 03 a 2. 20 8 3. 10 8 (0 .0 34 ) (0 .8 56 ) (0 .0 22 9) (0 .6 60 ) (0 .0 43 0) (0 .0 60 ) (0 .0 00 ) (0 .0 32 ) (0 .5 44 ) (0 .6 7) ac gs f ( ld ) 4 .0 80 9 a 0. 01 21 6 c -0 .0 55 1 0. 00 31 -2 .1 50 8 -4 .0 40 8 b -2 .4 90 9 a 2. 20 8 2. 20 8 2. 20 8 a (0 .0 00 ) (0 .0 20 ) (0 .8 09 ) (0 .3 95 ) (0 .7 53 ) (0 .0 25 ) (0 .0 01 ) 0. 54 4 0. 54 4 (0 .0 00 ) po pu la tio n (l d ) -2 .8 00 7 a 0. 09 24 * 1. 60 65 c 1. 04 6 a * -1 .9 40 7 1. 41 07 0. 01 21 a 3. 42 1 a 4. 02 7 a 1. 04 3 a (0 .0 00 0) (0 .0 00 ) (0 .0 68 ) (0 .0 00 ) (0 .4 56 ) (0 .1 13 ) (0 .0 00 ) (0 .0 01 ) (0 .0 01 ) (0 .0 01 ) va (l d ) -0 .0 63 87 a (0 .0 00 ) -3 6. 72 33 2 a (0 .0 00 ) -2 93 06 0. 8 b (0 .0 24 4) -9 66 5. 71 5 (0 .8 67 ) -0 .2 57 39 * (0 .0 08 ) -0 .0 46 4 (0 .5 77 ) 4. 75 68 (0 .0 03 ) 0 .6 68 5 a (0 .0 00 ) -.1 80 11 1 (0 .7 05 ) -.1 80 11 1 (0 .7 05 ) ps av (l d ) -0 .5 86 7 b (0 .0 97 ) 23 1. 20 85 a (0 .0 00 ) 32 .8 65 6 b (0 .0 23 ) 25 76 5. 93 b (0 .0 42 7) -0 .1 91 9 (0 .5 76 ) -0 .6 71 3 a (0 .0 04 ) 0. 44 08 4 (0 .8 41 ) 0. 20 47 (0 .3 65 ) -.1 80 11 1 (0 .7 05 ) -.1 80 11 1 (0 .7 05 ) c c (l d ) -0 .0 02 7 a (0 .0 00 ) -4 5. 12 74 a (0 .0 02 ) 24 78 3. 82 (0 .3 50 ) 13 97 .0 85 (0 .3 86 ) -0 .0 89 0 (0 .4 59 ) -2 .0 17 3 (0 .1 28 ) -2 .3 72 (0 .2 60 ) 0. 92 65 (0 .2 58 ) -.1 80 11 1 (0 .7 05 ) -.1 80 11 1 (0 .7 05 ) rla w (l d ) -0 .7 85 5b (0 .0 47 ) 70 9. 43 12 b (0 .0 31 ) -1 57 1. 81 9 a (0 .0 00 ) -6 16 3. 00 4 (0 .8 65 ) -.1 80 11 1 (0 .7 05 ) 0. 02 00 (0 .9 38 ) 6. 75 78 a (0 .0 00 ) 1. 10 67 a (0 .0 00 ) -0 .2 31 1 (0 .7 05 ) -0 .1 11 a (0 .0 05 ) g e( ld ) -0 .7 85 5b (0 .0 47 ) 0. 00 27 a (0 .0 00 ) 0. 00 27 a (0 .0 00 ) 0. 00 27 a (0 .0 00 ) 0. 00 27 a (0 .0 00 ) 0. 00 27 a (0 .0 00 ) -0 .4 52 11 (0 .4 05 ) -2 .0 16 7 (0 .7 05 ) -0 .4 92 a (0 .0 00 ) -0 .9 75 (0 .4 05 ) rq (l d ) -0 .7 85 5b (0 .0 47 ) 0. 00 27 a (0 .0 00 ) 0. 00 27 a (0 .0 00 ) 0. 00 27 a (0 .0 00 ) 0. 00 27 a (0 .0 00 ) 0. 00 27 a (0 .0 00 ) -0 .8 21 3 (0 .7 05 ) -9 .0 11 1 (0 .7 05 ) -0 .8 11 (0 .7 05 ) -5 .0 11 1 (0 .7 05 ) a dj . r -s q 0. 88 20 0. 98 04 0. 60 62 0. 74 80 0. 83 1 0. 90 95 0. 79 73 0. 69 5 0. 75 0 0. 62 0 a ic : 5 7. 97 31 7 h q ic : 5 9. 21 47 6 sb ic : 6 2. 14 86 5 n ot e: a , b , c m ea ns t ha t va ria bl es a re s ta tic al ly s ig ni fic an t at 1 % , 5 % a nd 1 0% r es pe ct iv el y. d is d iff er en ce o pe ra to r, w hi le l d s ho w s th at v ar ia bl es w er e la gg ed a nd d iff er en ce d ba se d on a ka ik e in fo rm at io n cr ite rio n (a ic ), h an na nq ui nn in fo rm at io n cr ite rio n (h q ic ) a nd s ch w ar z’s b ay es ia n in fo rm at io n cr ite rio n (s bi c) so ur ce : t he a ut ho rs ’ 173agricultural sector performance, institutional framework and food security in nigeria infection, and anaemia – all conditions correlated with the adverse impact of undernourishment to ensure the long-run estimates are not spurious and the system adjusted properly to equilibrium, the error correction mechanism as presented in table 4 was employed because, time series regression model is based on the behavioural assumption that two or more time series exhibit an equilibrium relationship that determines both short-run and long-run behaviour for the correction of error. the error correction relates to the fact that last period deviation from long-run deviation influences the short-run dynamics of the dependent variable. the result (-0.0245) from the error correction mechanism showed that the system adjust by approximately 2.5% to equilibrium. the error correction model is that each variable acts dependent (regressand) and independent (regressor). 5. conclusion and recommendations the role of institutional framework in ensuring food security is multifaceted, as it is subjective to collective factors operating at diverse tiers of the social-ecological model. these factors comprise the accessibility of a sufficient food supply and access to food from the federal to the state and local government levels. access to the food supply is in turn mainly influenced by agricultural production; this means that; the higher the production, the more people gain access to food. at the macro-level, food access is driven by factors such as food prices, job opportunities, minimum wages, and social protection policies. therefore, this study has made contribution by explores the importance of food security in nigeria, considering agriculture and institutions as key variables. in other words, the population of nigeria is not equating with the productivity, which in turn has a high negative significant effect on the state of undernourished people. the study found that it is a worthwhile practice for nigeria to pursue food stability as this can form a background to channel the national economy to address the challenges of food. thus, food security can be controlled with a high impact intervention from the government with an indelible intention of reducing corruption at a minimal rate. this could be done through an aggressive support initiative and other pragmatic actions to engage stakeholders to embark on effective food production and distribution that meet household demands. in order to meet households food demand, agricultural incentives should be granted to farmers to increase food production, this is evident from the result obtained in the study which shows that in the long-run increase in agricultural production reduces the number of undernourishment by 5% and 3%, agricultural credit enhances food production base thereby reducing undernourishment by 20% and 11%, respectively. the need to address the issue of food insecurity in nigeria demands a strong institutional framework, which could help demarcate the current situation in its entirety, highlighting the key areas affected, and encourage the advancement of relevant methods that can resolve the issue. this would create a platform of food supply resilience aimed at keeping the developed approach on a rapid response to emerging food security challenges. fresh fruits and vegetables can be made available to the respondent communities without giving lots of dependency on vehicles and increasing the avenue of learning on healthy food options and opportunity to own and grow food (hobsoons bay city council, 2009a: city of darebin 2010). under-development of agriculture, among other factors points to 174 romanus osabohien, evans osabuohien, precious ohalete the fact that food security would pose the challenge of low per capita productivity, especially in food production, which is relevant to food security. in nigeria, uneven distribution of food probably reflects in price instability, which effects vulnerable households’ ability to make long-term adjustments to their resource constraints. it is necessary to understand the nature of fluctuations in a food system that can aid researchers and policymakers on the strategies to be employed in enhancing the food systems in nigeria. institutional framework is also required to address gender imbalance because social and economic inequalities between men and women also stand in the way of balanced nutrition. more often than not, undernourishment disproportionality affects women. in households vulnerable to food insecurity, women are shown to be at greater risk of undernourishment than men. undernourishment in mothers, especially those who are pregnant or breastfeeding can create a cycle of deficiency that increases the likelihood of a low birth weight child and childhood undernourishment additionally, lack of decision-making power around family planning means that women have less ability to harmonize childbirth and breastfeeding schedules, which has direct implications for nutritional status this menace of food insecurity, especially in nigeria could also be traceable to the inherent crises by herdsmen and boko haram insurgency in the northern parts of the country as the violence between the fulani herdsmen and farmers have become one of nigeria’s most constant security challenges and have left thousands of people displaced and dead in recent years (vanguard, january 11, 2018). crisis in these locations (especially benue that is referred to as the ‘food basket of the nation’ and other high agricultural states) have adversely affected food production and supply, because when there is crisis in these locations, there would be a further challenge on food security which would in turn result to the challenge of food shortage in supply to the various parts of the country like lagos where demands are high, leading to higher prices and scarcity. there could also be wastage of scarce food resources with the emergence of a crisis that could prevent distribution. boko haram insurgency has been an ungodly act that has greatly affected the country’s level of food security. maiduguri, which has been the capital city of borno state, have had food insecurity treats since the outbreak of boko haram conflict. food items supplied from the north such as beans, yam, carrots, beef, potatoes, groundnuts, and vegetables have been affected by the crises emanating from the northern part of the country. utilization of food is of importance for the well been of human development, which is been affected by the crises northern part of nigeria. 6. acknowledgement the initial version of this paper was presented at the first faculty of management and social sciences (fmss) international conference, alex ukwueme federal university, ndufu alike, nigeria, 25-27th june, 2019. comments from the conference panelists are highly appreciated. also, comments from the editor (s) and two anonymous reviewers, which helped in improving the paper, are acknowledged. the views expressed in this article are those of the authors. 175agricultural sector performance, institutional framework and food security in nigeria 7. references abdulrahman, s., mani, j.r., oladimeji, y.u., abdulazeez, r.o. and ibrahim, l.a. 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(2002). the institutional dimension of environmental change: fit, interplay, and scale. mit press, cambridge, massachusetts, usa, accessed from https://mitpress.mit.edu/books/institutional-dimensions-environmental-change. consumers’ rationality and home-grown values for healthy and environmentally sustainable food simone cerroni1,2,3,*, verity watson4, jennie i. macdiarmid5 the wellbeing of smallholder coffee farmers in the mount elgon region: a quantitative analysis of a rural community in eastern uganda anna lina bartl agricultural sector performance, institutional framework and food security in nigeria romanus osabohien1,3,*, evans osabuohien1,3, precious ohalete2,3 innovation adoption and farm profitability: what role for research and information sources? michele vollaro1,*, meri raggi2, davide viaggi1 determinants of farm households’ willingness to accept (wta) compensation for conservation technologies in northern ghana evelyn delali ahiale1,*, kelvin balcombe2, chittur srinivasan2 bio-based and applied economics 9(2): 171-200, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8287 assessing preferences for rural landscapes: an attribute based choice modelling approach cathal o’donoghue1, stephen hynes1, paul kilgarriff2, mary ryan3,*, andreas tsakiridis3 1 national university of ireland, galway 2 luxembourg institute for socio economic research 3 teagasc, the irish agriculture and food development authority abstract. this study adopts a choice modelling framework to disentangle individual preferences for rural landscape attributes based on the viewing of photographs of the irish countryside. using ordered logit and standard panel and pooled regression models, societal preferences are quantified for rural landscape attributes, grouped into natural, agricultural and human-built non-agricultural categories. the preferences of 430 individuals towards 50 rural landscape photographs are analysed. the results show positive preferences for landscapes with natural attributes such as cliffs, mountainous features, water and native trees, as well as preferences for neat/managed agricultural landscapes and traditional human-built features such as stone walls and planted hedgerows. the study shows negative preferences for features such as flooding, unmanaged landscapes, industrial turf cutting and mechanised features such as wind turbines. there is significant preference heterogeneity observed across the sample particularity across the urban-rural residency divide. it is argued that analysing preferences for specific attributes of landscapes rather than preferences for individual landscape photographs allows for further applications particularly in the area of simulation. keywords. rural landscapes, choice modelling, ordered logit, attribute preference heterogeneity. jel codes. q18, q24, q57. 1. introduction agriculture is a multifunctional, natural resource based sector that takes place predominately in rural areas. it provides private goods like the ‘5 fs’: food, feed, fuel, fibre and forest (kern, 2002), generating income for farm families and contributing to the aesthetic character of human-ecological systems. these landscapes also support the delivery of other public goods such as recreation and cultural heritage (kantelhardt et al., 2015) *corresponding author. e-mail: mary.ryan@teagasc.ie editor: francesco vanni. 172 cathal o’donoghue, stephen hynes, paul kilgarriff, mary ryan, andreas tsakiridis and ecosystem services (es) relating to greenhouse gas emissions, water quality and biodiversity (vanni, 2014; van zanten et al., 2014; oecd, 2015; kantelhardt, 2006). these benefits, supplied by a sustainable agricultural sector, are reflected at eu policy level with increasing levels of funding dedicated to protecting rural landscapes and providing additional public goods from farming. as landscape values are often perceived as public goods, in the sense that they are non-excludable and non-rival in consumption, markets cannot place a price on landscape features and quality of landscape services (hanley et al., 2009), nor can they guarantee their adequate provision (schaller et al., 2018; villanueva et al., 2015; rodríguez-entrena et al., 2017). thus, where there is a market failure, there is a case that governments should implement measures to ensure an adequate provision. to do that however knowledge is required in terms of the preferences of society for alternative landscape types and features. a wide range of studies, using different methodologies, have attempted to examine rural landscape preferences and values in order to guide policy and better target expenditure to the most ‘valued’ landscapes. there are a number of studies that use expert judgement to assess the aesthetic quality of landscapes (frank et al., 2013; hermes et al., 2018). however, the perception of value may vary with perspective. for example, land owners and agricultural scientists may place a higher value on landscape attributes that involve the delivery of provisioning of ecosystem services, while members of the general public may subjectively place a higher value on cultural ecosystem services such as the aesthetics and recreational opportunities (lothian, 1999). thus expert opinion may not reflect what is of value personally to individuals or the wider population (tveit, 2009). elsewhere, kirillova et al. (2014) and plieninger et al. (2013) perform a qualitative assessment of the cultural importance of landscapes, while willingness to pay (wtp) is assessed by hynes et al. (2011; van berkel and verburg, (2014); rodríguez-entrena et al., (2017); dupras et al., (2018); bernués et al., (2019) and huber and finger, (2019). the publics’ stated preferences for landscapes and their features have also been surveyed (howley, 2011; howley et al., 2012; schirpke et al., 2016, santos-martín et al., 2019). stated preference surveys often measure landscapes in a holistic way focusing on concepts or characteristics reflected in the landscape (ives and kendal, 2013; tveit et al., 2006). many landscape preference studies also employ non-monetary techniques where landscapes are assessed through rankings of a number of photographs, or monetary techniques to estimate direct and indirect use values (e.g. forest fibres) and/or non-use values (e.g. biodiversity, wilderness, spiritual) for preserving landscapes (garcía-llorente et al., 2012). assessments based on cognitive attributes, such as landscape coherence, mystery, safety, and naturalness, provide a holistic assessment of a visual entity through its single components, rather than defining or focusing on specific physical landscape attributes, such as tree density or presence of hedges (tagliafierro et al., 2013; van zanten et al., 2014). hynes and campbell (2011) analysed the most appropriate economic valuation methodologies for agri-environment policies. they concluded that a holistic valuation approach should be used where the objective is the valuation of the landscape as a whole, whereas an attribute-based approach is appropriate if the objective is to understand preferences for individual components, which may allow for extrapolation using other gis datasets in policy evaluation. choice experiments have been utilised to assess the preference for individual characteristics (hynes and campbell, 2011; rodríguez-entrena et al., 2017; dupras et al., 173assessing preferences for rural landscapes 2018). although they present monetary measures of the willingness to pay for landscape attributes, there is a limit to how many attributes can be considered, albeit some papers (such as bernués et al., 2019) have an extensive array of choice attributes. thus, it may be difficult to apply a choice experiment methodology to assess the preferences for a wide variety of landscape characteristics. garcía-llorente et al. (2012) used photographs within the contingent valuation method to examine preferences for alternative landscape types. follow-up expert opinion was employed to relate the observed willingness to pay for ecosystem services connected to the different landscapes in the photographs. two studies of particular relevance to this research are howley (2011) and schirpke et al. (2016). howley (2011) assessed the effect of personal, geographic and environmental value orientations on landscape preferences. they did not however examine how the landscape attributes themselves could influence preferences or whether the potential effects could vary across survey respondents according to their personal, socio-demographic and geographic characteristics. schirpke et al. (2016) similarly examined attitudes in relation to landscape images by assembling specific landscape attributes using viewsheds from a digital elevation model. although schirpke et al. (2016) consider the relationship between socio-economic characteristics and holistic image-based landscape attributes (as does howley, 2011), their study does not consider the differential preference for specific landscape preferences across socio-demographic characteristics. this paper aims to contribute to the literature of landscape preference valuation by (a) investigating whether individuals’ characteristics interact with landscape attributes, and (b) how these interactions may ultimately affect public preferences for landscapes. the paper used data from howley’s (2011) analysis and builds on schirpke et al. (2016)’s approach by applying expert judgement as opposed to a combination of gis-based and observational attributes to each of the photos. the literature is extended by utilising an attribute choice framework to disentangle individual preferences for a holistic image of a landscape photograph into preferences for specific attributes of that landscape. the approach adopted in this paper facilitates the creation of a formalised model of landscape preferences based on the component attributes. the study uses ireland’s rural landscapes as a case study. the irish rural landscape has, and still is undergoing considerable change. agriculture remains the largest rural land use with the irish agri-food sector accounting for over half of the country’s exports and almost 10% of the economy and employment (teagasc, 2017). in many predominantly rural countries like ireland, landscape images provide a visible representation of how the world sees the country and advertising campaigns such as ireland’s ‘origin green’ are used to promote global agri-food exports. as rural based sectors and the public goods they provide are heavily influenced by public policy, societal preferences in relation to rural areas are important. landscape aesthetics, as one of the most visual and understandable public goods, is as a result, one of the most important drivers of support for the delivery of additional rural public goods. the next section of this paper presents a review of models of landscape preference as a basis for model development. section 3 then describes the data used in the analysis. the methodology is reviewed in section 4 while section 5 presents results and discussion. finally, policy relevant conclusions are provided in section 6. 174 cathal o’donoghue, stephen hynes, paul kilgarriff, mary ryan, andreas tsakiridis 2. models of landscape preferences increasingly, policy is focusing on the role of landscapes in the provision of es, with landscape aesthetics being consistently included as an example of cultural es. many of these es relate to the structure and composition of the landscape (tscharntke et al., 2005; zhang et al., 2007; van berkel and verburg, 2014; van oudenhoven et al., 2012). a variety of ecological/landscape indicators have been used to estimate the relationship between landscape characteristics and the potential for supply of es (kienast et al., 2009; burkhard et al., 2010; van berkel and verburg, 2014), whilst integrative analytical approaches and models have been developed to assess trade-offs between es and economic decisions (vidal-legaz et al., 2013). studies have also assessed the socio-cultural values of ecosystem services delivered by different landscape types (hynes and campbell, 2011; martín-lópez et al. 2012). while the value of the agricultural provisioning function of landscapes can be quantified using farm activity data, the quantification of the aesthetic value of landscapes remains a challenge. there are however studies that focus on particular cultural services that can be attributed to visual landscape characteristics, rather than the totality of potential es. such landscape preference studies use landscape photos to represent different types of landscapes (see for example campbell et al., 2006; rambonilaza and dacharybernard, 2007; moran et al., 2007; hynes and campbell, 2011). while the use of interviews with photo-elicitation and ranking enables researchers to identify landscape preferences and propose reasons underlying them, there are some criticisms of the reliability of evaluating aesthetic preference using photos. bias in stated preferences may arise due to photo quality, light, weather, photo composition, and the number of photos presented (van berkel and verburg, 2014, gill et al., 2015). however, empirical results from numerous studies support the use of landscape images and other visual approaches combined with questionnaires, as a reliable method for the public evaluation of landscapes (svobodova et al., 2012; häfner et al., 2018). 2.1 landscape attributes the concept of utilising landscape photographs as a proxy for landscape characteristics is commonplace in the literature (kaltenborn and bjerke, 2002; arriaza et al., 2004). while a photographic image does not represent the actuality of the experience of being in a landscape, there is a substantial literature that supports their use (häfner et al., 2018). according to dramstad et al. (2006), preferences based on well-selected colour photographs of landscapes are similar to those made in the field. in this study, landscapes are decomposed into their individual attributes to examine the personal preferences for these attributes. in a meta-analysis, van zanten et al. (2014) created a typology of landscape attributes consisting of two levels. at the first level there are four attribute groups: human influence on agricultural landscapes, land cover attributes, landscape elements and biophysical features. the second level decomposes level one attributes into their various components, e.g. farm system, level of fragmentation, mountains etc. landscape scenes used in preference studies need to account for these different types of attributes. it is also important to distinguish the intensity of the various attributes. häfner et al. (2018) found there was a higher preference for point attributes such as 175assessing preferences for rural landscapes individual trees, as opposed to lines of trees or hedgerows, with a higher frequency preferred. the attributes extracted from landscape scenes for this analysis are also in line with those of de ayala et al. (2012). they list the common attributes in landscape level discrete choice experiment studies as vegetation (e.g. trees, hedgerows), rural aspects (grassland, farm buildings), wildlife, water, cultural heritage (monuments, traditional farming), boundaries (stone walls and fences) and recreation (walking trails, fishing). 2.2 judgements landscape has been described as the intersection between physical attributes of a place and individuals’ perceptions of that place (hanley et al., 2009). studies examining landscape values may use either expert judgement (objectivist approach), where the focus is on characterizing the landscape as an object, or personal preferences in the form of a survey (subjectivist approach), where the focus is on viewers’ experiences of the landscape (lothian, 1999; tveit et al., 2006). the objective approach considers landscape quality as an intrinsic attribute of the landscape, and requires an implicit understanding of human preferences for landscape. the subjectivist approach considers landscape quality as a human construct based on the interpretation of what is perceived as landscape through individuals’ memories, associations and imagination. in the subjectivist approach, landscapes provide a means of understanding preferences of landscape viewers (lothian, 1999). within the field of landscape aesthetics, evolutionary theories and cultural preference theories have been developed to explain landscape perception and identify the factors and mechanisms that shape human preferences towards landscapes (häfner et al., 2018). when using personal preferences, the context in which the survey is collected is important. studies that are context specific make upscaling of results difficult (van zanten et al., 2014). studies should therefore control for local context such as attitudes, location and demographics of the respondents. education, for example, has been found to positively influence landscape preferences (häfner et al., 2018). however, in an assessment of landscape aesthetics, frank et al. (2013) found few differences in the preference values across three different categories of respondents: the general population, experts and stakeholders. the location in which a respondent lives can also influence their preferences. metaanalysis results show that urban residents have a higher preference for forest and natural landscapes (van zanten et al., 2014). the landscape value of an area also includes the value placed on it by tourists and those not living in an area. kirillova et al. (2014) examined the aesthetic judgement of tourists using semi-structured interviews and disaggregated their judgements into a total of nine dimensions. zoderer et al. (2016) found that tourists’ perceptions of landscape value vary with the land-use type and their socio-economic characteristics. in summary, some of the spatial, methodological and attribute choices in recent studies are presented in table 1. 3. methodological framework a range of indicators is required to comprehensively describe landscapes. the european landscape convention (elc, 2000) for example integrates biophysical, cul176 cathal o’donoghue, stephen hynes, paul kilgarriff, mary ryan, andreas tsakiridis tural, social, and visual attributes of landscapes. in order to incorporate this integrated view and to combine public and expert opinion, sowinska-swierkosz and chmielewski (2016) developed a methodological framework to identify landscape quality objectives (lqos) which include gis analysis, quality assessments, social survey and expert value judgements. this study also combines expert and public viewpoints in developing a model that links the visual attributes of landscapes (as defined by agricultural scientists) with individuals’ landscape preferences, socio-demographic data and gis analysis. the main benefit of using such a modelling approach is the ability to rank a landscape, using personal preferences derived from a survey but without the need to conduct surveys in every location. similar to the use of value transfer approaches this means that the parameters of the preference model can be used to estimate rank orderings of landscapes without the need for further primary surveys providing time and monetary savings to both researcher and policy maker (hynes et al. 2018). in creating a formalised model of landscape preferences, it is first necessary to define the characteristics or attributes of landscapes. in doing so, choices are made (discussed previously), between broad holistic descriptions and more discrete, generalisable and quantifiable attributes of the landscape. the objective of this study to estimate a landscape preference model that is generalisable in an irish context, thus a model of quantifiable landscape attributes is developed (equation 1) where: max u = ∑i βi × li (1) table 1. choice of landscape attribute in recent studies. paper country general scene or attributes scale (local or national) expert or survey häfner et al. (2018) germany attributes local stated preference survey (n=200) hermes et al. (2018) germany. 100m x 100m scene national expert vidal-legaz et al. (2013)spain. no spatial component scene local stated preference survey (n=226) van der jagt et al. (2014)scotland scene local preference matrix survey (n=100) zoderer et al. (2016) italy scene local stated preference survey (n=659) frank et al. (2013) germany scene local survey consisting of laymen and experts (n=153) bernués et al. (2019) multiple countries (spain, norway, italy) attributes country regional/ provincial stated preference survey (n=1,044) dupras et al. (2018) canada (three regions; saint-jacque, repentigny, and montréal) attributes country regional survey consisting of laymen (n=250) 177assessing preferences for rural landscapes such that in maximising landscape preferences, or in economic terms utility from the landscape, u, a series of parameters βi are estimated that indicate a level of preference for individual attribute li. as a social science analysis, we are interested not only in the landscape attributes that are preferred but also preference heterogeneity across individuals or across groups of attributes , with personal characteristics and attitudes z. max uj = ∑i βi × li × zj (2) individuals’ preference heterogeneity can be decomposed into different components. beyond standard demographic characteristics in describing different groups, attitudinal factors are important (swanwick, 2009). appleton (1975) argues that individual preferences for landscapes depend upon the relationship between an individual and their environment, their experiences of the landscape, where individuals live and how they experience the landscape, while howley (2011) finds heterogeneity in landscape preferences due to both demography and environmental orientations. the model should therefore account for the different drivers of preference variability (equation 3): max uj = ∑i βi × li × zj (demograhics,attitudes,location) (3) in order to understand the structure of individuals’ preferences for landscape attributes, survey respondents were first asked to rank preferences for individual photographs on a 6-point likert scale from (1) ‘not very highly’ to (6) ‘very highly’. while the ranking variable is potentially continuous over the range 1 to 6, discrete values were used for convenience. treating the ranking as an underlying continuous variable, an ordinary least squares (ols) model, of the form: yi = β’xi + εi (4) can be used for individual i, where yi * is the dependent variable reflecting landscape preferences and xi the explanatory variables and εi the error term. as an alternative modelling strategy the dependent variable can also be treated as discrete and the ranking is ordinal, an ordered logit model is employed (greene, 2004): yi * = β’xi + εi (5) for individual i, where yi * is the underlying latent variable reflecting landscape preferences and xi the explanatory variables and εi the error term. where there are six preference values 1,…,6, the following is the observed value of the dependent variable: y = 1 if 0 < yi * < μ1 (6)y = 2 if μ1 < yi * < μ2 y = 6 if μ5 < yi * < μ6 178 cathal o’donoghue, stephen hynes, paul kilgarriff, mary ryan, andreas tsakiridis where y is the preference value for the landscape image and μ, the vector of unknown threshold parameters that is estimated with the β vector. since the dependent variable is an ordered, qualitative variable, we estimate the relationship between y and x with an ordinal response model assuming a logistic distribution. however, as respondents were asked to rank their preference level, the difference between ranking variables has a meaning and is consistent between values. given that the difference between values has a meaning, utilising the ordered logit loses information in the estimation. thus even though the survey respondents use discrete values in their judgement, a continuous framework is also employed to model preferences. 3.1 landscape attributes in classifying landscape attributes, we move from preferences for individual photographs to preferences for a number of specific attributes . these include agricultural attributes, natural attributes, human-built non-agricultural attributes, topography and other attributes. given the nature of the data, where there are repeated values for each survey respondent for each of the 30 attributes selected, we employ a fixed effect panel data ordered logit model (greene, 2001, 2004), which has been widely used for attitudinal studies (fairlie et al., 2014), for the panel data continuous dependent variable: yij = β’zij + ui + εij (7) and for the panel data ordered logit (equation 8): yij * = β’zij + ui + εij (8) where zij represents the landscape characteristics’ specific attributes, ui represents the individual fixed effect and where the panel data variance component σ2 u is also estimated. 3.2 preference heterogeneity we move from person-specific preferences (xi) in the cross-sectional ordered logit model to landscape attributes (zij) in the panel data model. interaction terms (taste-shifters) between the personal and the landscape attributes are incorporated in equation 9 so that the influence of personal characteristics on preferences can be examined: xzij = xi × zij (9) to produce the following model: yij * = β’zij + β1’xzij + ui + εij (10) however, given that there are many landscape characteristic attributes, we combine the attributes into three aggregate characteristics representing natural, agricultural and human-built (non-agricultural) attributes: 179assessing preferences for rural landscapes (11) 4. data to assess the preferences of the public in relation to landscape attributes, a nationally representative survey1 of 430 individuals aged 15+ was conducted in ireland in 2010 (howley, 2011). the survey contained a number of components including: • personal information and demographic characteristics • preferences and attitudes to agriculture, the environment and natural resources • landscape characteristics this demographic and environmental information is later interacted with the respondents’ locations to generate ‘taste-shifters’. the initial parts of the survey also elicited responses in relation to the respondent’s environmental attitudes and orientations. respondents were then asked to indicate their preferences (from 1 not very highly ranked, to 6 – highly ranked) at an aesthetic level, for a range of photographs of rural landscapes. respondents were asked to make full use of the ranking scale and to give the highest ranking to their most preferred landscapes. 4.1 landscape preferences to ascertain landscape preferences, 50 photographs of rural landscapes with a variety of different characteristics were presented to survey respondents. the photos used were selected from a database of 1,000 photos from the national agricultural development authority. they were selected in collaboration with colleagues to attempt to be representative of rural settings, incorporating extensive farming landscapes along with intensive farming landscapes. as the process of selecting images to represent the range of landscapes is relatively arbitrary, it is possible that a different set of photos would produce different outcomes. in order to improve reliability, photos were selected that had similar weather and light conditions. to ensure a representative sample, the survey was collected at different times of the day over the summer months. tables 2 and 3 respectively report the six most preferred and the six least preferred landscapes. the most obvious conclusion is that there is a higher preference for water and coastal features in the landscape. similarly, the presence of animals or heritage features is important. on the other hand, the least preferred landscapes contain human-built features such as motorways or wind turbines and also contain disorder such as flooding or unmanaged scrub and grassland or contain harvested peat bogs. in the data annex, we report the preferences for all photographs. beyond the six most preferred, the next cohort of photos represents well-managed pastoral agriculture scenes and broadleaf forests/trees. those photos ranked just above the least preferred landscapes, represent intensive cereal and horticultural farming on the one hand, as well as marginal scrubland, along with conifer forest. 1 quota sampling and survey validation are reported in howley (2011). 180 cathal o’donoghue, stephen hynes, paul kilgarriff, mary ryan, andreas tsakiridis table 2. most preferred landscapes (photo numbers correspond to ranks in table a1-data annex). 1. coastal image of sea and headland 2. aerial photo of a river estuary 3. coastal cliffs 4. lake in rural setting 5. horses in field 6. large tree next to castle ruin in field table 3. least preferred landscapes (photo numbers correspond to ranks in table a1). 50. flooded farmland 49. new motorway cutting through landscape 48. scrubland next to woodland 47. barren hillside with wind turbine 46. landscape of industrial bogland 45. trees and scrubland with blue horizon 181assessing preferences for rural landscapes 4.2 landscape attributes this study took a relatively simple approach to classifying attributes, attempting to score the significant presence of an attribute, rather than trying to grade the photo for the degree of importance of a particular attribute. thus the presence of an attribute that was immediately visible on a quick inspection was scored as 1, as it was felt that these reflect the dominant attributes of an image. if an attribute was not immediately visible on a quick inspection, the attribute was scored as 0. thus while each photograph has a specific rating of 1-6, we have added additional dummy attributes or explanatory variables for each photo. in the dataset, it is expressed as a separate line for every attribute, with 1 for the presence of the attribute and a 0 otherwise. it thus appears as a panel, with personal characteristics invariant over the panel and landscape attributes varying over the panel. table 4 describes the share of ratings from ‘not very highly’ (1) to ‘very highly’ (6) for these landscape attributes based on the original landscape rankings. ranking these attributes on the basis of where they appear in landscapes with ‘very highly’ ranked preferences, we note the higher preferences for the attributes lakes, cliffs, horses, water, monuments, hedgerows and connemara-type landscape which can be collectively described as ‘landscape descriptions’2. the next highly ranked attributes can be described as ‘pastoral agriculture’ attributes such as livestock and pasture. at the other end of the preference scale, anthropogenic features such as wind turbines, fencing and problems like flooding and rough grazing landscapes (including gorse) have the lowest preference rankings. 4.3 environmental attitudes to gain a deeper understanding of how environmental attitudes might influence landscape preferences, the survey instrument included questions relating to preferences for landscapes as a provider of es (in addition to its aesthetic or intrinsic value), or as a provider of food and fibre, and questions relating to negative attitudes towards the environment in general. the resulting environmental attitudes were aggregated using factor analysis as described by howley (2011), resulting in three underlying factors that accounted for 61% of the underlying variation in responses to the attitudinal statements, namely ‘multifunctionalist’, ‘productivist’ and ‘environmental apathy’. these factors are used in the models as explanatory variables. 4.4 spatial heterogeneity given the heterogeneity of landscapes, spatial heterogeneity of preferences for attributes may exist. previous approaches to account for this used distance decay, where wtp is a function of distance between residence and the site being valued (hanley et al., 2003) or where area-based approaches improve basic distance decay using a radial analysis to model wtp as a function of both distance and quantity of the es (granado-díaz et al., 2020). the distance decay function may also be impacted by the presence of substitute environmental attributes (jørgensen et al., 2013). use and non-use values are also impact2 connemara is a remote, scenic, rugged landscape in the west of ireland. 182 cathal o’donoghue, stephen hynes, paul kilgarriff, mary ryan, andreas tsakiridis ed by distance (jørgensen et al., 2013). for option value related reasons, non-users may prefer an improvement in local landscapes (hanley et al., 2003). we also attempt to capture some of the spatial heterogeneity of preferences by using an urban-rural classification based on the respondent’s location. summary statistics for a variety of taste shifters are presented in table 5. these are categorised in terms of city, town and rural dwellers and include characteristics of individtable 4. landscape attribute summary statistics showing shares of preference rankings from not very highly (1) to very highly (6). attribute 1 2 3 4 5 6 lakes 0.04 0.05 0.09 0.16 0.21 0.46 cliffs 0.07 0.06 0.07 0.13 0.22 0.44 horses 0.1 0.03 0.09 0.17 0.2 0.41 water 0.06 0.08 0.13 0.15 0.23 0.36 monuments 0.09 0.08 0.11 0.18 0.24 0.3 hedgerows 0.07 0.07 0.15 0.19 0.24 0.28 connemara-type landscape 0.04 0.19 0.16 0.17 0.19 0.25 pasture 0.1 0.09 0.15 0.2 0.22 0.24 sloping 0.11 0.12 0.16 0.18 0.21 0.23 stonewalls 0.11 0.1 0.15 0.21 0.22 0.22 cattle 0.13 0.1 0.15 0.2 0.21 0.21 mountains 0.16 0.17 0.16 0.15 0.16 0.2 neat agricultural landscape 0.1 0.13 0.16 0.2 0.21 0.2 sheep 0.11 0.11 0.16 0.21 0.22 0.2 green 0.11 0.13 0.17 0.19 0.2 0.2 blue sky 0.15 0.17 0.17 0.17 0.17 0.18 bog (peatland) 0.15 0.16 0.16 0.17 0.17 0.18 sunny 0.14 0.17 0.17 0.18 0.17 0.17 native trees 0.18 0.16 0.16 0.17 0.17 0.17 old buildings 0.1 0.14 0.17 0.21 0.21 0.17 flowers 0.11 0.17 0.19 0.21 0.19 0.14 flat 0.16 0.19 0.18 0.17 0.16 0.14 cars and machinery 0.22 0.2 0.17 0.15 0.14 0.13 crops 0.13 0.17 0.19 0.21 0.18 0.12 turf 0.18 0.23 0.19 0.16 0.1 0.12 brown 0.19 0.21 0.18 0.16 0.14 0.12 yellow 0.16 0.16 0.2 0.2 0.17 0.12 unmanaged landscape 0.19 0.21 0.2 0.16 0.13 0.12 conifer trees 0.17 0.18 0.21 0.18 0.15 0.11 other buildings 0.18 0.2 0.2 0.17 0.15 0.1 gorse 0.26 0.21 0.18 0.14 0.12 0.09 fencing 0.4 0.21 0.12 0.09 0.09 0.09 turbine 0.21 0.21 0.19 0.18 0.13 0.08 flooding 0.58 0.27 0.1 0.03 0.01 0.01 183assessing preferences for rural landscapes ual respondents, along with their environmental attitudes, illustrating the degree to which preferences vary depending on where respondents live. specifically, social and demographic information includes respondent’s age range as a continuous variable with values of 1 (under 30) to 4 (60+), with dummy variables indicating respondents’ education level and whether they have a child. two social groups were created; the first includes manual workers and unemployed individuals, whereas professional and managerial workers were classified in the second social class (high social class). in addition, respondents or family members who are involved in farming were compared with those without a farming background. similarly, dummy variables were created to control for the importance of landscape in choosing where to live, the level of respondents’ satisfaction with respect to the area in which they live, the quality of surrounding landscape, and their concern about the environment and conservation. 5. results the results of the models of landscape attribute preferences are considered separately for the ordinal logit and the continuous dependent variable panel and pooled ols models. the influence of personal characteristics on preferences, using taste-shifters (interaction terms) between the personal characteristics and landscape types are also presented and discussed. in table 6, the coefficients for the landscape attributes are reported in terms of natural, agricultural and human-built features, as well as other general attributes such as colour and unmanaged landscapes. although there are many variables, the ols specification table 5. summary statistics of personal characteristics and environmental preferences used as taste shifters. personal characteristic city town rural total has a child (p) 0.365 0.352 0.424 0.379 aged under 30 0.256 0.246 0.250 0.251 aged 30-50 0.410 0.423 0.394 0.409 aged 50-60 0.103 0.092 0.152 0.114 aged 60+ 0.231 0.239 0.205 0.226 university educated 0.442 0.254 0.242 0.319 believes landscape is important in choosing where to live 0.186 0.268 0.424 0.286 satisfied with area in which they live 0.147 0.113 0.106 0.123 believes surrounding landscape is of high quality 0.487 0.599 0.689 0.586 higher social class 0.763 0.634 0.606 0.672 farming background 0.231 0.394 0.614 0.402 care about conservation 0.301 0.359 0.432 0.360 concerned about the environment 0.186 0.324 0.242 0.249 factor loading: multifunctionalist -0.114 0.128 -0.002 0.000 factor loading: environmental apathy -0.036 0.114 -0.081 0.000 factor loading: productivist -0.173 0.073 0.126 0.000 184 cathal o’donoghue, stephen hynes, paul kilgarriff, mary ryan, andreas tsakiridis table 6. coefficients of panel and pooled ordered logit model and panel and pooled ols models for landscape attributes. panel ordered logit model pooled ordered logit model panel ols model pooled ols model explanatory variables beta sd beta sd beta sd beta sd natural landscape characteristics                 connemara type landscape 1.397* 0.419 1.454* 0.118 1.005* 0.073 0.98* 0.075 lakes 0.988* 0.531 1.004* 0.149 0.67* 0.096 0.662* 0.094 cliffs 1.033* 0.294 1.015* 0.083 -0.039 0.035 0.592* 0.052 water 0.628* 0.202 0.632* 0.056 0.393* 0.041 0.383* 0.036 flowers 0.37* 0.213 0.383* 0.058 0.214* 0.045 0.258* 0.038 bogland 0.35* 0.016 0.349* 0.016 0.189* 0.01 0.213* 0.01 sloping 0.193 0.182 0.165* 0.05 0.117* 0.033 0.11* 0.032 native trees 0.034 0.138 0.061 0.038 0.066* 0.03 0.057* 0.025 mountains -0.188 0.26 -0.136* 0.072 -0.095* 0.049 -0.097* 0.046 flat -0.389* 0.153 -0.254* 0.051 -0.113* 0.027 -0.138* 0.033 conifer trees -0.547* 0.314 -0.435* 0.087 -0.262* 0.064 -0.25* 0.057 gorse -0.806* 0.269 -0.639* 0.08 -0.34* 0.047 -0.365* 0.052 flooding -2.409* 0.482 -2.375* 0.134 -1.681* 0.086 -1.708* 0.085 agricultural landscape characteristics                 horses 0.533 0.467 0.6* 0.132 0.26* 0.084 0.26* 0.082 neat agricultural landscape 0.424* 0.232 0.362* 0.067 0.167* 0.048 0.198* 0.043 pasture 0.184 0.176 0.166* 0.049 0.115 0.194 0.109* 0.032 crops -0.375 0.274 -0.368* 0.093 -0.249 0.298 -0.246* 0.06 cut-silage -1.245* 0.591 -1.145* 0.169 -0.755 0.65 -0.688* 0.11 human landscape characteristics                 monuments 0.947* 0.294 0.874* 0.096 0.618* 0.321 0.568* 0.061 hedgerows 0.347 0.302 0.291* 0.095 0.297 0.329 0.257* 0.061 stonewalls 0.117 0.309 0.109 0.095 0.123 0.338 0.123* 0.062 old buildings 0.026 0.3 0.036 0.094 0.073 0.328 0.082 0.06 turf -0.122 0.397 -0.33* 0.125 -0.099 0.434 -0.26* 0.079 turbine -0.271 0.326 -0.429* 0.105 -0.151 0.355 -0.284* 0.068 other buildings -0.167 0.212 -0.395* 0.075 -0.1 0.228 -0.273* 0.048 cars and machinery -0.382* 0.225 -0.509* 0.074 -0.285 0.244 -0.393* 0.047 other landscape characteristics                 yellow 1.019* 0.388 0.905* 0.107 0.646 0.428 0.564* 0.069 green 0.129 0.155 0.138* 0.042 0.091 0.171 0.092* 0.027 unmanaged landscape 0.072 0.257 -0.051 0.074 0.035 0.283 -0.062 0.048 185assessing preferences for rural landscapes is satisfactory from a multi-collinearity perspective, as the vif (variance inflation factor) for all values is less than 10 (kassie et al., 2008). in comparing the models, it is evident that virtually all of the coefficients are within the significance limits of the panel data ordered logit model, so that the models do not in general have substantial differences in their coefficients. we note however that the confidence intervals are wider for the panel data ordered logit than for the pooled version of the model or for the panel and pooled ols specifications, reflecting perhaps that we utilise less of the information in the panel ordered logit model estimation than the in the pooled version or continuous dependent variable ols models. unsurprisingly the breusch-pagan lagrangian multiplier finds the fixed effects insignificant. therefore, we focus on the ols pooled model for the discussion and for the introduction of the taste shifter interactions. overall, the pseudo r2 is 24%, representing relatively large unexplained heterogeneity of landscape preferences. of the natural attributes, the connemara type landscape, which represents a remote rugged mountainous area, has the highest positive coefficient. this is followed by preferences for cliffs, lakes and water as landscape attributes. landscapes with flowers, native trees, bog (peat), sloping land and native trees have the next highest coefficients. landscapes with flooding have the lowest coefficient of the natural landscapes. the mountain landscape has an unexpected sign, but it shares considerable information with the connemara type landscape. in relation to the agricultural landscape attributes, the presence of horses has the greatest positive significance, followed by neat agricultural land and pasture, whilst crops and cut-silage have negative coefficients. in relation to human-built landscape attributes, the presence of monuments has the highest positive and significant coefficient. indeed, it has the second highest coefficient overall. human-built landscape attributes associpanel ordered logit model pooled ordered logit model panel ols model pooled ols model brown -0.368* 0.204 -0.325* 0.056 -0.261 0.226 -0.238* 0.036 constant         3.686 0.276 3.674 0.061 cut point 1 -2.623 0.258 -2.548 0.102 cut point 2 -1.435 0.256 -1.378 0.096 cut point 3 -0.202 0.256 -0.175 0.095 cut point 4 1.105 0.256 1.102 0.095 cut point 5 2.531 0.256 2.508 0.096 sigma squared (u)         0.358       sigma squared (e)         1.149   1.168   rho         0.089                   0.223   pseudo r2     0.079           within         0.080       between         0.866       overall         0.240       n 20600.000   20600.000   20600.000   20600.000   number of groups 50.000       50.000       186 cathal o’donoghue, stephen hynes, paul kilgarriff, mary ryan, andreas tsakiridis ated with farming such as hedgerows and stone walls have the next highest coefficient, followed by old buildings. meanwhile negative preferences are observed on average for industrial or mechanised objects or activities such as wind turbines, cars and industrial turf-cutting. also, yellow and green colours (conditional on other attributes) are positive while unmanaged rural landscapes have a negative coefficient. this preference for managed agricultural landscapes highlights the frequent mismatch between aesthetic preferences and ecological diversity (gobster et al., 2007). interestingly, amongst the least preferred landscapes are unmanaged (potentially biodiversity-rich) landscapes, perhaps reflecting evolutionary processes that favour landscapes that have a greater possibility of providing food and shelter. 5.1 taste shifters we interact personal characteristics and attitudes with preferences for natural, human built and agricultural attributes to form taste shifters. interaction terms between the personal characteristics and landscape types allow us to examine the influence of personal characteristics on preferences and are a means of controlling for observed heterogeneity in preferences within the model. in interacting personal characteristics and landscape characteristics, we group characteristics into natural, human and agricultural characteristics, thus reducing the degrees of freedom. reflecting the fact that attributes have both positive and negative signs in table 8, we break up the groups into positive and negative coefficients. we combine 15 personal characteristics with six different types of landscape attribute. given that there are 90 combinations of these variables with potentially overlapping information and multi-collinearity, we use a principal component analysis (pca) to reduce the dimensionality, and present the detailed results in table a2 data annex. although there are 25 factors with an eigenvalue of more than 1, accounting for 75% of information, on the grounds of parsimony, we select only those with an eigenvalue of 2 or higher (hair et al., 2010). to aid the interpretation of these, we employed a method known as component rotation (bechtold and abdulai, 2014). this method was used to distinguish between components and to facilitate the interpretation of components (see table a3 data annex for detail on rotated components). the widely applied varimax rotation (abdi and williams, 2010) was also employed. table 7 presents the interpretation of the principal components and the coefficients of the pooled ols model interacted with the taste shifters, referencing both the socio-economic characteristics and the landscape attribute group associated with the principal component. for half of these principal components, a single socio-economic characteristic was found to be dominant combined with four landscape attribute groups, positive natural, positive agricultural, positive human and negative natural, highlighting a coherent association with different landscape attribute types. taste shifters capture preference heterogeneity relative to observed characteristics. for example, pc1 corresponds to a negative coefficient on agricultural landscapes for high social class (professional and managerial workers) city dwellers. a positive coefficient on this component suggests a less negative preference for crops and cut-silage than other groups. pc2 refers to the preferences of town dwellers for human and agricultural characteristics that have a positive coefficient. here, a positive coefficient indicates a higher preference for these attributes than the general population. pc3 relates to preferences for natural attributes, 187assessing preferences for rural landscapes where higher social classes, older respondents and those living in towns have lower than average preferences for these attributes. there is a similar impact on human attributes (pc4) with a negative score for higher-educated city dwellers or those with children. city dwellers in pc5 have lower than average preferences for human and agricultural attributes, while for pc6, town dwellers have higher preferences for both positive and negative agricultural attributes and more negative human attributes than average. in pc8, those with environmental concerns and landscape views have a lower preference for negative human aspects. the remaining principal components all relate to individual socio-economic characteristics interacted with the four sets of attributes highlighted above. those that place a high ranking on the importance of landscape in choosing where to live have higher landscape preferences than average, while those that are concerned about the environment or with multi-functional attitudes have lower preferences. in summary, grouping the landscape attributes into natural, agricultural (including human built) and non-agricultural human-built attributes, the results show positive associations with natural attributes such as cliffs, mountainous landscapes, landscapes with water and native trees, neat/managed agricultural landscapes and traditional human-built features such as stone walls and planted hedgerows. the results, as expected, show negative associations with events such as flooding, unmanaged landscapes, industrial turf cutting and mechanised features. table 7. coefficients of pooled ols model interacted with taste shifter principal components. explanatory variables landscape characteristics interactions interpretation landscape attributes coefficient standard error pc1 high social class city dwellers na 0.038 0.005*** pc2 town dwellers ph pa 0.019 0.007** pc3 older, town dwellers and higher social class nn -0.031 0.008*** pc4 higher educated city dwellers with children nh -0.042 0.006** pc5 city dwellers ph pa -0.015 0.006*** pc6 town dwellers pa, nh, na 0.015 0.006*** pc7 satisfaction of area pn, ph, pa, nn, -0.030 0.004*** pc8 environmentally concerned nh -0.035 0.006*** pc9 importance of landscape in choosing where to live pn, ph, pa, nn, 0.040 0.005*** pc10 farming background pn, ph, pa, nn, 0.005 0.005 pc11 multi-functional agriculture pn, ph, pa, nn, -0.048 0.005*** pc12 concerned about the environment pn, ph, pa, nn, -0.015 0.006** note: pn – natural attributes (positive sign); ph – human attributes (positive sign) ; pa – agricultural attributes (positive sign); nn – natural attributes (negative sign); nh – human attributes (negative sign); na – agricultural attributes (negative sign). 188 cathal o’donoghue, stephen hynes, paul kilgarriff, mary ryan, andreas tsakiridis there is significant preference heterogeneity however with different groups favouring or disfavouring different attributes. an urban-rural classification used to capture the spatial heterogeneity of preferences (based on the respondents’ locations) showed that those living in urban areas feel they have a lower quality of surrounding landscape compared to rural areas. unsurprisingly those that have chosen to live in a rural landscape place the highest value on this type of landscape, while farmers have the highest preference for agricultural landscape attributes. urban dwellers are more indifferent towards natural and farming landscapes. underlying eco-centric attitudes are also important drivers. 6. conclusions this study adopted an attribute choice framework to disentangle individual preferences for a holistic image of landscape photographs into preferences for specific attributes of that landscape, and subsequently used these attributes in landscape preference models to relate societal preferences to quantifiable landscape attributes. the study further investigated whether individuals’ characteristics interact with landscape attributes and how these interactions ultimately affect public preferences for landscapes. this paper adopts a middle-ground approach between the methods found in the literature for landscape preference modelling. on the one hand, it is ambitious in relation to the range of landscape attributes as in the case of schirpke et al. (2016) or bernués et al. (2019), but is less ambitious in focusing on preference attributes rather than willingness to pay, as in the stated preference valuation literature. it also extends the work of schirpke et al. (2016) by considering the preference heterogeneity for specific landscape attributes. although unobserved heterogeneity is not considered in this study, the variety of observed heterogeneity incorporated may be more useful for policy and from a simulation modelling perspective. ultimately, the model results highlight differences in how people with different attitudes and characteristics rank landscape features. the impact of taste shifters on various groups illustrates the heterogeneity in rankings. as noted by hynes et al. (2011) the attribute based approach to landscape preferences allows the researcher to examine the general trade-offs which society is willing to make between different attributes of the countryside. on the other hand, modelling landscape preferences based on landscape photos, such as in howley’s (2011) study, is useful if the researcher is interested in understanding preferences for the wider landscape. the approach adopted here is particularly useful where one is interested in the utility gained or lost through a policy that may cause only incremental changes in the landscape or impact on only a small number of attributes. interacting personal characteristics as taste shifters can help us to understand local preferences if the characteristics of the local population differ. the analysis does have the limitation of not being able to identify local preferences in terms of sense of place or relational value. qualitative studies or localised surveys are needed to understand these more nuanced perspectives (pérez-ramírez et al., 2019; vannier et al., 2019; wartmann and purves, 2018). moving from a holistic view of landscapes to analysing preferences for specific attributes of landscapes allows for further applications particularly in the area of simulation. being able to assess preferences for an individual attribute makes it possible to extrapolate the preference ranking of a landscape in an area that has not been ranked directly. it is 189assessing preferences for rural landscapes important to note however that the method adopted in this paper is based on the assumption that the sum of the singular landscape element’s preference scores equates to the preference ranking of the landscape as a whole. that simplifies the way in which humans value the environment and should be considered as a limitation of our study. as such, the method is more appropriate when there are only a limited number of attributes to be considered in a given landscape. human-built landscape characteristics such as stone walls and hedgerows are found to be positively associated with the preference rankings of photos in this study. thus, future land-use changes and landscape development plans should promote the aesthetic role of stone walls and hedgerows and prioritise their conservation. similarly, the recognition of the high aesthetic value that the public places on well-managed/neat agricultural landscapes provides policy justification to incentivise farmers to maintain these public goods in future agri-environmental schemes. the results presented in this paper provide evidence of the preferences of a diverse range of individuals across a number of characteristics that should be of assistance to policy makers attempting to maximise the benefit for society from rural landscapes. the model developed here provides information for decision-makers to examine whether a proposed policy change involving one or more landscape attributes will have a positive or negative impact across the population, while also allowing for more targeted policy formation by disaggregating the population into different preference cohorts. the approach adopted in this paper facilitates the creation of a formalised model of landscape preferences based on the component attributes. decomposing complete landscape images into quantifiable attributes is a common feature of preference studies and can help bridge the gap between the gis literature and landscape analysis. the latter typically takes quantifiable landscape attributes from gis datasets to create typologies of different types of landscapes. meanwhile the former assesses societal preferences for holistic images. our methodology can further allow for the application of societal preferences to quantifiable datasets of landscape attributes, rather than using expert judgement as is currently the case. the approach developed in this study therefore, has implications for planners for landscape character assessments (lca) that often utilise a broad expert knowledge approach to developing lca maps, which may under/over estimate the value of various landscape attributes. future work will apply this methodology in a gis landscape database to re-assess lcas from a societal rather than an expert point of view. future work should also test for the existence of spatial dependence and use spatial regression methods to examine spatial heterogeneity in more detail. acknowledgement the authors acknowledge research funding received from department of agriculture, food and the marine (farm-ecos project ref 15/s/619), teagasc farm management & rural development department and the eu interreg atlantic area programme 2014– 2020 (eapa_261/2016 alice). 190 cathal o’donoghue, stephen hynes, paul kilgarriff, mary ryan, andreas tsakiridis references abdi, h., williams, l.j., 2010. principal component analysis. wiley interdisciplinary reviews computational statistics 2: 433–459. appleton, j. 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(2016). identifying and mapping the tourists’ perception of cultural ecosystem services: a case study from an alpine region. land use policy 56: 251–261. 195assessing preferences for rural landscapes data annex: assessing population landscape characteristic preferences using disaggregated attributes for rural landscapes table a1. ranking of photos by survey participants. rank photo description 1 coastal image of sea and headland 2 aerial photo of a river estuary 3 coastal cliffs 4 lake in rural setting 5 horses in field 6 large tree next to castle ruin in field 7 rolling hills, with conifers and well-kept fields 8 copper beech tree in parkland 9 sandy beach 10 stream flowing through deciduous forest 11 patchwork quilt of fields and river 12 the rock of cashel historic monument 13 rich farmland and hillside in background 14 remote hillside, with trees 15 large rock in field on hillside 16 field of sheep in lowland good grass and stone walls 17 hillside of bluebells and deciduous trees 18 remote (connemara) mountainous landscape 19 traditional farm building 20 forest track in deciduous trees 21 stonewalls with neat field of sheep 22 stonewall with cows in field and trees on hillside 23 dairy cows in field 24 large field after silage cut 25 stonewalls with neat field of oil seed rape 26 sheep in front of traditional farmhouse 27 statue of harpist in rural village 28 hilly woodland and trees 29 large field of cereal crops 30 wildflower in field of ferns 31 hillside of conifer trees 32 trees and field of rushes 33 mature forest 34 rows of horticulture crops in field 35 neat rows of cereal crops 36 rocky mountain with extensive agriculture 37 tillage field after harvest with blue sky 38 mechanical cutting of turf from bog 39 hillside of conifer 196 cathal o’donoghue, stephen hynes, paul kilgarriff, mary ryan, andreas tsakiridis rank photo description 40 reeds and scrubland 41 marginal land with trees in background 42 large horticulture field 43 barren bogland 44 heather in bogland 45 trees and scrubland with blue horizon 46 landscape of industrial bogland 47 barren hillside with wind turbine 48 scrubland next to woodland 49 new motorway cutting through landscape 50 flooded farmland table a2. principal component analysis. principal component eigenvalue cumulative proportion of variance p. component 1 8.79458 0.0977 p. component 2 6.75435 0.1728 p. component 3 5.62239 0.2352 p. component 4 4.78296 0.2884 p. component 5 4.50848 0.3385 p. component 6 3.63383 0.3789 p. component 7 3.32083 0.4157 p. component 8 2.9566 0.4486 p. component 9 2.75087 0.4792 p. component 10 2.55781 0.5076 p. component 11 2.43572 0.5346 p. component 12 2.2716 0.5599 p. component 13 1.72736 0.5791 p. component 14 1.71575 0.5981 p. component 15 1.64843 0.6165 p. component 16 1.49928 0.6331 p. component 17 1.44856 0.6492 p. component 18 1.39257 0.6647 p. component 19 1.34819 0.6797 p. component 20 1.27425 0.6938 p. component 21 1.15625 0.7067 p. component 22 1.09155 0.7188 p. component 23 1.08972 0.7309 p. component 24 1.07077 0.7428 p. component 25 1.03101 0.7543 197assessing preferences for rural landscapes ta bl e a 3. r ot at ed c om po ne nt s (o rt ho go na l v ar im ax ) w ith lo ad in g < 0. 3. c or re la tio ns ty pe c om p1 c om p2 c om p3 c om p4 c om p5 c om p6 c om p7 c om p8 c om p9 c om p1 0 c om p1 1 c om p1 2 en vi ro nm en ta l a pa th y pn m ul tif un ct io na l pn 0. 43 72 pr od uc tiv ist pn c hi ld pn a ge pn u ni ve rs ity e du ca te d pn so ci al c la ss pn c ity pn to w n pn fa rm in g ba ck gr ou nd pn 0. 52 74 im po rt an ce o f l an ds ca pe in c ho os in g w he re to li ve pn 0. 52 21 sa tis fa ct io n of a re a pn 0. 51 58 q ua lit y of s ur ro un di ng l an ds ca pe pn c on ce rn ed a bo ut th e en vi ro nm en t pn 0. 54 44 c ar e ab ou t c on se rv at io n pn en vi ro nm en ta l a pa th y ph m ul tif un ct io na l ph 0. 54 15 pr od uc tiv ist ph c hi ld ph a ge ph u ni ve rs ity e du ca te d ph so ci al c la ss ph c ity ph 0. 53 63 to w n ph 0. 53 23 fa rm in g ba ck gr ou nd ph 0. 34 01 im po rt an ce o f l an ds ca pe in c ho os in g w he re to li ve ph 0. 35 49 198 cathal o’donoghue, stephen hynes, paul kilgarriff, mary ryan, andreas tsakiridis c or re la tio ns ty pe c om p1 c om p2 c om p3 c om p4 c om p5 c om p6 c om p7 c om p8 c om p9 c om p1 0 c om p1 1 c om p1 2 sa tis fa ct io n of a re a ph 0. 38 08 q ua lit y of s ur ro un di ng l an ds ca pe ph c on ce rn ed a bo ut th e en vi ro nm en t ph 0. 34 69 c ar e ab ou t c on se rv at io n ph en vi ro nm en ta l a pa th y pa m ul tif un ct io na l pa 0. 43 15 pr od uc tiv ist pa c hi ld pa a ge pa u ni ve rs ity e du ca te d pa so ci al c la ss pa c ity pa 0. 35 94 to w n pa 0. 33 58 0. 31 34 fa rm in g ba ck gr ou nd pa 0. 48 66 im po rt an ce o f l an ds ca pe in c ho os in g w he re to li ve pa 0. 49 83 sa tis fa ct io n of a re a pa 0. 49 35 q ua lit y of s ur ro un di ng l an ds ca pe pa c on ce rn ed a bo ut th e en vi ro nm en t pa 0. 49 43 c ar e ab ou t c on se rv at io n pa en vi ro nm en ta l a pa th y nn m ul tif un ct io na l nn 0. 44 12 pr od uc tiv ist nn c hi ld nn a ge nn 0. 44 26 u ni ve rs ity e du ca te d nn so ci al c la ss nn 0. 33 84 c ity nn to w n nn 0. 37 78 199assessing preferences for rural landscapes c or re la tio ns ty pe c om p1 c om p2 c om p3 c om p4 c om p5 c om p6 c om p7 c om p8 c om p9 c om p1 0 c om p1 1 c om p1 2 fa rm in g ba ck gr ou nd nn 0. 37 69 im po rt an ce o f l an ds ca pe in c ho os in g w he re to li ve nn 0. 39 79 sa tis fa ct io n of a re a nn 0. 41 21 q ua lit y of s ur ro un di ng l an ds ca pe nn 0. 34 42 c on ce rn ed a bo ut th e en vi ro nm en t nn 0. 40 1 c ar e ab ou t c on se rv at io n nn en vi ro nm en ta l a pa th y nh -0 .3 38 1 m ul tif un ct io na l nh 0. 37 pr od uc tiv ist nh c hi ld nh 0. 30 44 a ge nh 0. 38 86 u ni ve rs ity e du ca te d nh 0. 30 16 so ci al c la ss nh 0. 40 32 c ity nh 0. 46 38 to w n nh 0. 34 59 fa rm in g ba ck gr ou nd nh im po rt an ce o f l an ds ca pe in c ho os in g w he re to li ve nh 0. 41 22 sa tis fa ct io n of a re a nh q ua lit y of s ur ro un di ng l an ds ca pe nh c on ce rn ed a bo ut th e en vi ro nm en t nh 0. 43 73 c ar e ab ou t c on se rv at io n nh 0. 39 08 en vi ro nm en ta l a pa th y na m ul tif un ct io na l na pr od uc tiv ist na c hi ld na a ge na u ni ve rs ity e du ca te d na 200 cathal o’donoghue, stephen hynes, paul kilgarriff, mary ryan, andreas tsakiridis c or re la tio ns ty pe c om p1 c om p2 c om p3 c om p4 c om p5 c om p6 c om p7 c om p8 c om p9 c om p1 0 c om p1 1 c om p1 2 so ci al c la ss na 0. 30 04 c ity na 0. 48 51 to w n na 0. 56 fa rm in g ba ck gr ou nd na im po rt an ce o f l an ds ca pe in c ho os in g w he re to li ve na sa tis fa ct io n of a re a na q ua lit y of s ur ro un di ng l an ds ca pe na c on ce rn ed a bo ut th e en vi ro nm en t na 0. 30 44 c ar e ab ou t c on se rv at io n na n ot e: p n – na tu ra l a tt rib ut es ( po si tiv e si gn ); ph – h um an a tt rib ut es ( po si tiv e si gn ) ; p a – ag ric ul tu ra l a tt rib ut es ( po si tiv e si gn ); nn – n at ur al a tt rib ut es (n eg at iv e si gn ); nh – h um an a tt rib ut es (n eg at iv e si gn ); na – a gr ic ul tu ra l a tt rib ut es (n eg at iv e si gn ); economics of culture and food in evolving agri-food systems and rural areas severino romano1, francesco vanni2, davide viaggi3 on the relevance of the region-of-origin in consumers studies fabio gaetano santeramo1, emilia lamonaca1,*, domenico carlucci2, biagia de devitiis1, antonio seccia1, rosaria viscecchia1, gianluca nardone1 benefits for the local society attached to rural landscape: an analysis of residents’ perception of ecosystem services stefano targetti1, meri raggi2, davide viaggi1 assessing preferences for rural landscapes: an attribute based choice modelling approach cathal o’donoghue1, stephen hynes1, paul kilgarriff2, mary ryan3, andreas tsakiridis3 exploring governance mechanisms, collaborative processes and main challenges in short food supply chains: the case of turkey yaprak kurtsal, emel karakaya ayalp, davide viaggi bio-based and applied economics 5(2): 199-214, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-18043 short communication attribute in di ca to r u ni t lo w b en ch m ar k h ig h be nc hm ar k re fe re nc es fo r c re at io n an d be nc hm ar ks d at a g lo ba l d at a lo ca l sc or e g lo ba l (% ) sc or e lo ca l (% ) food wastage u se o f b io ga s p la nt s no /y es no ye s ri se no no 0 0 u se o f b yp ro du ct s fr om th e fo od in du st ry as a ni m al fe ed (% o f fa rm er s) % o f f ar m er s 0% 10 0% be re tta e t a l. 20 13 6 20 17 .6 20 18 m ilk lo ss o n fa rm % 10 % 0% sa fa (e 5 .3 .4 ) be re tta e t a l. 20 13 1 1. 5 90 85 m ilk lo ss a t p ro ce ss in g% 0. 5% 0% sa fa (e 5 .3 .4 ) be re tta e t a l. 20 13 0. 5 0. 2 0 60 traceability tr ac ea bi lit y up st re am of th e su pp ly c ha in av er ag e of c at eg or ie s fo r a ll fa rm er s ca te go rie s: no 0% / pa rt ia lly 50 % /y es 10 0% sa fa (c 3 .3 .2 ) 20 67 .6 20 68 tr ac ea bi lit y do w ns tr ea m o f t he su pp ly c ha in no /y es ca te go rie s: no 0% / pa rt ia lly 50 % /y es 10 0% sa fa (c 3 .3 .2 ) 10 0 66 .7 10 0 67 animal welfare pr op or tio n of pa rt ic ip at io n in ou td oo r g ra zi ng pr og ra m % o f p ar tic ip at io n (f ro m a ll fa rm er s) 0% 75 % ex pe rt c . n ot z 69 10 0 92 10 0 li fe sp an o f t he d ai ry co w s ye ar s 5 ye ar s 8 ye ar s ex pe rt c . n ot z 4. 5 5 0 0 pr op or tio n of pa rt ic ip at io n in lo os e ho us in g pr og ra m % o f p ar tic ip at io n (f ro m a ll fa rm er s) 0% 30 % sa fa (e 6 .2 .1 ) ex pe rt c . n ot z 23 70 .6 77 10 0 pr op or tio n of a ni m al s tr ea te d by a nt ib io tic s in a y ea r % tr ea te d co w s 90 % 30 % sa fa (e 6 .1 .2 ) ex pe rt c . n ot z 17 .5 35 .8 10 0 90 tr an sp or ta tio n du ra tio n to th e sla ug ht er ho us e m in ut es 12 0 m in ut es 30 m in ut es sa fa (e 6 .2 .3 ) ex pe rt c . n ot z 46 .3 42 .9 82 86 1 fe de ra l o ffi ce fo r a gr ic ul tu re (f o ag ). (2 01 4) . m ar kt be ric ht m ilc h. k on su m m ilc h : m ar kt an te ils ge w in n fü r d is co un te r. be rn . 2 h oo p, d ., an d sc hm id , d . ( 20 13 ). g ru nd la ge nb er ic ht 2 01 2. e tt en ha us en . 3 n em ec ek , t ., vo n ri ch th of en , j ., d ub oi s, g ., ca st a, p ., ch ar le s, r ., an d pa hl , h . ( 20 08 ). en vi ro nm en ta l i m pa ct s of in tr od uc in g gr ai n le gu m es in to e ur op ea n cr op r ot at io ns . eu ro pe an jo ur na l o f a gr on om y 28 : 3 80 -3 93 . d oi :1 0. 10 16 /j. ej a. 20 07 .1 1. 00 4 4 g re nz , j . e t a l. (2 00 9) . r is e – a m et ho d fo r a ss es si ng th e su st ai na bi lit y of a gr ic ul tu ra l p ro du ct io n at . r ur al d ev el op m en t n ew s 1: 5 -9 . 5 ja co bs en , s .e . e t a l. (2 01 3) . f ee di ng th e w or ld : g en et ic al ly m od ifi ed c ro ps v er su s ag ric ul tu ra l b io di ve rs ity . a gr on om y fo r s us ta in ab le d ev el op m en t 3 3: 6 51 -6 62 . 6 be re tt a, c . e t a l. (2 01 3) . q ua nt ify in g fo od lo ss es a nd th e po te nt ia l f or re du ct io n in s w itz er la nd . w as te m an ag em en t 3 3( 3) : 7 64 -7 73 . on perishability and vertical price transmission: empirical evidences from italy fabio gaetano santeramo1,*, stephan von cramon-taubadel2 1 department of agricultural, food and environmental sciences, università di foggia, italy 2 department of agricultural economics and rural development, georg-august-universität göttingen, germany date of submission: 2016 2nd, february; accepted 2016 19th, july abstract. studies on the causes for asymmetries in vertical price transmission date back to decades ago, but the attention of theorists and empirical economists is still vivid. in particular the role of perishability is not fully defined. we investigate the vertical price transmission for a heterogeneous group of fruits and vegetables that differ for their degree of perishability. the error correction model we estimate allows to conclude that asymmetries in vertical price transmission tend to vanish for perishable products. keywords. asymmetries, avecm, fruits and vegetables, perishability, vertical price transmission jel code. q11, q13, c32, d40 1. introduction the interest in price transmission, and the number of studies focused on these topics, have rapidly increased during last decades (e.g.griffith and piggott, 1994; benson et al., 2008; santeramo, 2010; cioffi et al., 2011; santeramo and cioffi, 2012a; santeramo and cioffi, 2012b; abdelradi and serra, 2015; kinnucan and zhang, 2015; santeramo, 2015; garcia-german et al., 2016): the implications they have on agricultural markets, industrial strategies, producer and consumer welfare are strong. studies on vertical price transmission (vpt) have preeminently addressed four topics (vavra and goodwin, 2005): the magnitude of price shocks transmission along the supply chain, the speed of transmission, the nature of price transmission in term of symmetry and asymmetries, and the direction of transmission (i.e. whether a shock is transmitted upwards or downwards). asymmetries in vpt may be due to imperfect competition (i.e. market power), adjustment costs, inventory management, political interventions, or asymmetric information (meyer and von cra*corresponding author: fabiogaetano.santeramo@gmail.com 200 f.g. santeramo, s. von cramon-taubadel mon-taubadel, 2004). a vast majority of studies (and scholars) have analyzed the effects of imperfect competition on vpt (e.g. mccorriston et al., 1998; mccorriston et al., 2001; bunte and peerlings, 2003; lloyd et al., 2006; tekgüç, 2013; assefa et al., 2014); the other possible explanations for asymmetries remain quite underinvestigated (few exceptions are saghaian, 2007; abbassi et al., 2012; santeramo, 2015). we depart from previous studies by focusing on the role of adjustments costs and in particular on the role of perishability on vpt. we use monthly prices of ten products that differ for their degree of perishability. apart from reviewing the current knowledge on vpt our main contribution is to provide empirical evidence on how perishability and asymmetries are related. 2. what causes asymmetries in vertical price transmission? asymmetric vpt (avpt) has been motivated in several ways: market power, adjustment costs, inventory management, government interventions, asymmetric information, perishability. mccorriston et al. (1998, 2001) and lloyd et al. (2006) link market power and imperfect vpt. bailey and brorsen (1989) point out that there is not an a priori explanation on whether market power leads to positive or negative asymmetry. a vast majority of authors (e.g. boyd and brorsen, 1988; karrenbrock, 1991; appel, 1992; griffith and piggott, 1994; mohanty et al., 1995) suggest that market power can lead to asymmetric transmission, most predicting a positive asymmetric price transmission1. peltzman (2000) shows that positive asymmetric price transmission is detected in both concentrated and atomistic markets2, while tappata (2009) derives a model of asymmetric price transmission in highly competitive markets. another major explanation for asymmetric price transmission (avpt) is provided by asymmetric adjustment costs3 arising when firms change the quantities and/or prices of inputs and/or outputs. bailey and brorsen (1989) and peltzman (2000) argue that positive avpt is consistent with the easiness for firms facing output reduction to disemploy inputs rather than to recruit new inputs in order to increase output. on the contrary, ward (1982) suggests that avpt is plausible in markets of perishable products in that retailers might hesitate to raise prices for fear of reduced sales leading to spoilage. heien (1980) argues that changing prices is less of a problem for perishable products as their prices are more dynamics. inventory management determines how firms adjust to exogenous shocks and thus may lead to avpt (balke et al., 1998). blinder (1982) argues that inventory management leads to positive avpt: in periods of low demand firms will adjust the quantity produced and increase inventory rather than decrease output prices, increasing prices during periods of high demand (reagan and weitzman, 1982). 1 according to meyer and von cramon-taubadel (2004), positive price transmission mean that prices react more to price rises than to price falls. 2 the results by peltzman (2000) on positive asymmetric price transmission are confirmed by several applied studies in agricultural sectors: pork (abdulai, 2002; gervais, 2011); vegetables (brooker et al., 1997); fruits (pick et al., 1990), among others. 3 the adjustment costs are defined as costs associated with changing retail prices and subsequently adapting retail logistics, wholesale costs and sales (e.g. advertisement and relabeling costs, storage and volume discounts, etc.). 201on perishability and vertical price transmission: empirical evidences from italy gardner (1975) explains the asymmetries in farm-to-retail price dynamics focusing on the role of government interventions to support producer prices. kinnucan and forker (1987), and serra and goodwin (2003) provide some evidence for diary products in support of gardner’s thesis. kinnucan and forker (1987) and von cramon-taubadel (1998) predict a stronger impact of retail-level demand shifts than of farm-level supply shifts on the farm-retail price spread. according to kinnucan and forker (1987) the different impacts imply avpt, while von cramon-taubadel (1998) underlines that only if one type of shift is predominantly positive or negative avpt will arise. bailey and brorsen (1989) conclude on the role of asymmetric information in determining avpt and point out that asymmetries in price series data can result from a distorted price reporting process. as for perishability, contradictory theories have been proposed. ward (1982) suggests that in perishable goods markets price decreases are likely to be fully passed on to the retail and producer level sectors while price increases are partially transmitted. girapunthong et al. (2003) confirm ward’s theory for fresh tomatoes markets: wholesale prices react more to falling producer prices than to rising producer prices. heien (1980) argues that changing prices is less of a problem for perishable products than it is for those with a long shelf life. sexton et al. (2003) suggest that price rises are faster transmitted than price falls which can be avoided by retailers able to exert market power on wholesalers. the empirical literature provides mixed results (table 1). 3. perishability and vertical price transmission in order to understand the role played by perishability on avpt we proceed in two steps. first (lhs of equation 1) we ask ourselves if price changes at different levels of the supply chain of perishable products (e.g. producer δp1 and wholesaler prices δp2, or wholesaler δp1 and retailer prices δp2 etc.) react differently to positive δp+ 1 and negative δp− 1 price changes. second (rhs of equation 1), we observe how the degree of perishability (i.e. the expected losses for spoilage) is related with avpt: 0 ≠ e δp2 δp+ 1 − δp2 δp− 1 ⎡ ⎣ ⎢ ⎤ ⎦ ⎥ = f per.( ) (perishability and avpt) (1) we do not have a priori expectations: heien (1980) argues that changing prices is less of a problem for perishable products than it is for those with a long shelf life, because for the latter changing prices incurs higher time costs and losses of goodwill; on the contrary ward (1982) hypothesizes that retailers selling perishable goods might be reluctant to raise prices in line with an increase in farm-level prices given the risk that they will be left with unsold spoiled product. we have extracted monthly prices (at wholesale level, and representative of national prices) for 29 products from the ismea osservatorio prezzi ortofrutta database: 14 fresh vegetables (artichokes, carrots, cauliflowers, onions, green beans, fennel, radishes, lettuces, eggplants, potatoes, peppers, tomatoes, spinaches and zucchinis), and 15 fresh 202 f.g. santeramo, s. von cramon-taubadel ta bl e 1. m aj or fi nd in gs in a pp lie d an al ys es o f v er tic al p ric e tr an sm is si on in p er is ha bl e m ar ke ts au th or jo ur na l ye ar pr od uc t fr eq ue nc y re su lts a gu ia r & s an ta na a gr ib us in es s 20 02 to m at oe s m on th ly po sit iv e a sy m m et ry o ni on s “ sy m m et ry ba ku cs , e t a l. st ud ie s i n a gr ic ul tu ra l e co no m ic s 20 07 po ta to es m on th ly sy m m et ry c ar ro ts “ sy m m et ry pa rs le y “ sy m m et ry to m at oe s “ po sit iv e a sy m m et ry pe pp er s “ sy m m et ry be rn ar d & w ill et t jo ur na l o f a gr ic ul tu ra l a nd a pp lie d ec on om ic s 19 96 br oi le r m on th ly n eg at iv e a sy m m et ry be rn ar d & w ill et t ap pl ie d ec on om ic s l et te rs 19 98 br oi le r w ee kl y sy m m et ry br oi le r m on th ly po sit iv e a sy m m et ry br oo ke r e t a l. jo ur na l o f f oo d d ist rib ut io n re se ar ch 19 97 pe pp er s w ee kl y po sit iv e a sy m m et ry h as sa n & si m io ni éc on om ie r ur al e 20 04 to m at oe s w ee kl y n eg at iv e a sy m m et ry c hi co ry “ n eg at iv e a sy m m et ry g ira pu nt ho ng e t a l. jo ur na l o f f oo d d ist rib ut io n re se ar ch 20 03 to m at oe s m on th ly a sy m m et ry b h as sa n & si m io ni éc on om ie r ur al e 20 04 c hi co ry w ee kl y sy m m et ry to m at oe s “ sy m m et ry h ei en a m er ic an jo ur na l o f a gr ic ul tu ra l e co no m ic s 19 80 po ta to es m on th ly po sit iv e a sy m m et ry ap pl es “ sy m m et ry o ra ng es “ n eg at iv e a sy m m et ry le ttu ce “ sy m m et ry to m at oe s “ sy m m et ry ku ip er & la ns in k a gr ib us in es s 20 13 br oi le r m on th ly po sit iv e a sy m m et ry ap pl es m on th ly sy m m et ry c ar ro ts “ sy m m et ry po ta to es “ sy m m et ry pi ck e t a l. a gr ib us in es s 19 90 le m on s w ee kl y po sit iv e a sy m m et ry c o ra ng es “ po sit iv e a sy m m et ry c po w er s a gr ib us in es s 19 95 le ttu ce w ee kl y po sit iv e a sy m m et ry 203on perishability and vertical price transmission: empirical evidences from italy au th or jo ur na l ye ar pr od uc t fr eq ue nc y re su lts sc he rt z w ill et e t a l. a gr ib us in es s 19 97 ap pl es m on th ly po sit iv e a sy m m et ry w ar d a m er ic an jo ur na l o f a gr ic ul tu ra l e co no m ic s 19 82 c ar ro ts m on th ly sy m m et ry c el er y “ n eg at iv e a sy m m et ry c ab ba ge “ n eg at iv e a sy m m et ry cu cu m be rs “ sy m m et ry pe pp er s “ n eg at iv e a sy m m et ry po ta to es “ n eg at iv e a sy m m et ry to m at oe s “ n eg at iv e a sy m m et ry w or th ec on om ic r es ea rc h se rv ic e 19 99 c ar ro ts m on th ly po sit iv e a sy m m et ry c el er y “ sy m m et ry le ttu ce “ sy m m et ry o ni on s “ sy m m et ry po ta to es “ sy m m et ry to m at oe s “ po sit iv e a sy m m et ry a re su lts o n sy m m et ry , p os iti ve a nd n eg at iv e as ym m et ry d ep en d on ti m e fr eq ue nc y. b p os iti ve a sy m m et ry a m on g w ho le sa le r a nd re ta ile r p ric es ; n eg at iv e as ym m et ry a m on g w ho le sa le r a nd p ro du ce r p ric es c h ow ev er , o ve r t im e pr ic e ch an ge s ap pe ar to b e sy m m et ric . 204 f.g. santeramo, s. von cramon-taubadel fruits (kiwis, apricots, watermelons, oranges, cherries, clementines, strawberries, tangerines, lemons, apples, melons, pears, peaches and nectarines, plums and table grapes)4. we observe prices at three stages of the supply chain origin, wholesale, and retail – from 2001 to 2011: producer prices are collected on more than thirty collection points, representative markets for volume of production and geographical position; wholesaler prices are collected by fedagromercati on the main wholesaler markets; retail prices are based on sales from surveys on domestic purchases of italian families. we selected products in order to include heterogeneous products according to their perishability, avoiding price series with discontinuities and several missing values5. the final dataset consists of three low perishable vegetables carrots, potatoes and peppers –four medium perishable vegetables – tomatoes, cauliflowers, radishes, eggplants6, and three low perishable fruits lemons, apples and pears. in line with several scholars (griffith and piggott, 1994; powers, 1995; brooker et al., 1997; and worth, 1999; girapunthong et al., 2003; sexton et al., 2003), we assume that producer prices lead wholesale prices, and wholesale prices lead retail prices. we estimated an unrestricted error correction model (von cramon-taubadel, 1998; peltzman, 2000) which allows to capture asymmetries, and long-run and short-run adjustments, and to control for seasonality: δpt i =γ0 +γtt +γ1δpt−1 i +γ2δpt−1 j +α+ectt−1 + +α−ectt−1 − +εt (2) and ectt−1 = pt−1 i −β0 −β1pt−1 j (3) δpt i = pt i −pt−1 i the apexes i and j represent the supply chain level (origin, wholesale or retail), ect the error correction term, t = 11, …, 12 controls for α+ and αadjustment coefficients) are statistically different the price transmission is asymmetric. we test for unit-roots using augmented dickey-fuller (dickey and fuller, 1981), philips-perron (perron, 1988), and zivot-andrews (zivot and andrews, 1992) tests. all series are stationary in level or in their first difference (table 2). the estimates of the error correction models (tables 3 and 4) suggest that prices tend to correct their dynamics and converge towards the equilibrium. the test for asymmetries (table 5) is in 17 out of 40 (43%) cases in favor of avpt. however, asymmetries are found in 16 out of 24 (67%) cases for “low perishable” vegetables and for fruits and only in 1 out of 16 (6%) cases for “medium perishable” vegetables. our evidence favors several theories and empirical studies: peltzman (2000) observes weaker evidence of avpt for perishable products; ward (1982) argues that sellers of perishable goods might be reluctant to raise prices in line with an increase in farm-level prices given the risk that they will be left with unsold spoiled product; serra 4 some of these products are characterized by different market cycles and seasonality in production and consumption, therefore prices cannot be observed throughout the entire year. 5 in order to avoid bias due to missing values we restricted the analysis to time series for which missing values represent less than 5% of the total sample. the series have been interpolated in order to obtain continuous series. 6 our classification of fruits and vegetables according to their perishability relies on a report from the usda (2009). we consider medium perishable the vegetables incurring in average losses for spoilage during transportation larger than the 10% of the traded volume, and low perishable those for which spoilage is lower. 205on perishability and vertical price transmission: empirical evidences from italy ta bl e 2. u ni t r oo t t es ts (p -v al ue s fo r a d f, pp te st s an d te st s ta tis tic s fo r z a te st ). lo w pe ris ha bl ev eg et ab le s m ed iu m p er ish ab le ve ge ta bl es fr ui ts c ar ro ts pe pp er s po ta to es c au lifl ow er eg gp la nt s to m at oe s ra di sh es ap pl es le m on s pe ar s pr od uc er a d f <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 0. 01 6 <0 .0 1 <0 .0 1 pp <0 .0 1 <0 .0 1 0 .0 47 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 0. 04 4 <0 .0 1 <0 .0 1 za -7 .8 5 -7 .5 4 -4 .1 7 -6 .9 7 -7 .5 1 -7 .7 6 -6 .6 3 -4 .1 0 -5 .9 2 -5 .8 8 w ho le sa le r a d f <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 0. 04 8 <0 .0 1 0. 01 4 pp <0 .0 1 <0 .0 1 0 .0 13 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 0. 16 4 0. 01 5 0. 07 6 za -7 .3 3 -7 .4 7 -4 .5 4 -6 .8 8 -7 .7 2 -7 .4 7 -7 .1 4 -3 .9 3 -5 .2 4 -4 .3 0 re ta ile r a d f <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 0. 01 1 pp <0 .0 1 <0 .0 1 0 .4 71 <0 .0 1 <0 .0 1 <0 .0 1 <0 .0 1 0. 01 6 <0 .0 1 0. 04 7 za -5 .1 0 -7 .9 2 -4 .1 4 -7 .4 6 -8 .0 1 -7 .3 4 -7 .1 4 -4 .9 1 -5 .1 9 -5 .0 9 th e nu ll hy po th es is f or t he a d f an d pp t es ts i s un it ro ot . t he n ul l hy po th es is f or t he z a t es t is s ta tio na rit y. t he n um be r of l ag s is s ug ge st ed b y in fo rm at io n cr ite ria . za c rit ic al v al ue s 1% = 5 .5 7 , 5 % = -5 .0 8 , 1 0% = -4 .8 2. 206 f.g. santeramo, s. von cramon-taubadel ta bl e 3. e st im at ed e cm m od el fo r p ro du ce r a nd w ho le sa le r p ric es . lo w pe ris ha bl ev eg et ab le s m ed iu m p er ish ab le ve ge ta bl es fr ui ts c ar ro ts pe pp er s po ta to es c au lifl ow er eg gp la nt s to m at oe s ra di sh es ap pl es le m on s pe ar s pw = f( pp ) γ 0 0. 05 1 0. 11 4 0. 03 9 0. 02 9 -0 .1 87 -0 .0 44 0. 17 4 0. 05 8 0. 05 6 0. 04 7 (0 .0 15 ) (0 .0 66 ) (0 .0 12 ) (0 .0 48 ) (0 .0 79 ) (0 .0 77 ) (0 .1 37 ) (0 .0 21 ) (0 .0 28 ) (0 .0 24 ) γ t -0 .0 06 -0 .0 09 -0 .0 04 -0 .0 05 0. 03 1 0. 01 1 -0 .0 31 -0 .0 04 -0 .0 04 -0 .0 02 (0 .0 02 ) (0 .0 09 ) (0 .0 01 ) (0 .0 06 ) (0 .0 10 ) (0 .0 09 ) (0 .0 15 ) (0 .0 03 ) (0 .0 04 ) (0 .0 03 ) γ 1 0. 22 5 0. 15 1 -0 .0 18 0. 51 5 0. 07 5 0. 39 3 0. 02 7 0. 11 3 0. 14 1 0. 02 6 (0 .0 90 ) (0 .1 24 ) (0 .1 07 ) (0 .1 70 ) (0 .1 40 ) (0 .1 73 ) (0 .1 41 ) (0 .0 98 ) (0 .1 24 ) (0 .1 26 ) γ 2 -0 .0 22 0. 22 0 0. 38 1 -0 .7 21 0. 03 3 -0 .2 72 0. 11 1 -0 .0 38 0. 69 4 0. 09 4 (0 .0 70 ) (0 .2 20 ) (0 .1 33 ) (0 .2 45 ) (0 .1 86 ) (0 .1 86 ) (0 .2 08 ) (0 .1 64 ) (0 .3 41 ) (0 .1 68 ) α+ -0 .5 24 -0 .5 30 -0 .5 16 -0 .9 35 -0 .1 77 -0 .4 49 -0 .2 76 -0 .7 95 -0 .4 97 -0 .4 91 (0 .1 60 ) (0 .1 44 ) (0 .1 38 ) (0 .3 02 ) (0 .1 73 ) (0 .3 14 ) (0 .2 02 ) (0 .1 83 ) (0 .1 62 ) (0 .1 52 ) α-0 .3 25 -0 .1 61 -0 .1 23 -0 .7 35 -0 .1 42 -0 .2 04 -0 .4 67 0. 06 9 0. 10 8 0. 09 2 (0 .2 05 ) (0 .2 58 ) (0 .2 04 ) (0 .3 46 ) (0 .2 04 ) (0 .3 57 ) (0 .3 34 ) (0 .1 98 ) (0 .2 75 ) (0 .1 69 ) pp = f( pw ) γ 0 0. 06 6 0. 03 1 0. 02 7 -0 .0 27 -0 .1 96 -0 .1 06 0. 15 4 0. 00 2 0. 00 8 0. 00 7 (0 .0 21 ) (0 .0 43 ) (0 .0 12 ) (0 .0 35 ) (0 .0 59 ) (0 .0 70 ) (0 .0 96 ) (0 .0 13 ) (0 .0 09 ) (0 .0 19 ) γ t -0 .0 07 -0 .0 04 -0 .0 01 -0 .0 02 0. 02 9 0. 01 5 -0 .0 21 0. 00 0 0. 00 0 0. 00 2 (0 .0 03 ) (0 .0 06 ) (0 .0 01 ) (0 .0 04 ) (0 .0 07 ) (0 .0 08 ) (0 .0 11 ) (0 .0 02 ) (0 .0 01 ) (0 .0 02 ) γ 1 0. 04 4 0. 00 2 0. 31 3 -0 .4 30 -0 .0 36 -0 .3 34 -0 .0 28 0. 37 9 0. 13 6 0. 39 8 (0 .0 96 ) (0 .1 45 ) (0 .1 35 ) (0 .1 47 ) (0 .1 40 ) (0 .1 70 ) (0 .1 45 ) (0 .1 00 ) (0 .1 10 ) (0 .1 32 ) γ 2 -0 .2 38 0. 06 3 -0 .2 46 0. 26 7 0. 02 6 0. 31 5 0. 06 7 -0 .0 46 0. 09 7 -0 .1 80 (0 .1 24 ) (0 .0 82 ) (0 .1 09 ) (0 .0 97 ) (0 .1 05 ) (0 .1 58 ) (0 .0 99 ) (0 .0 60 ) (0 .0 40 ) (0 .0 99 ) α+ -0 .0 87 -0 .1 15 -0 .4 57 0. 41 5 0. 15 6 0. 33 9 0. 00 8 0. 10 8 -0 .0 26 -0 .1 72 (0 .2 21 ) (0 .0 95 ) (0 .1 40 ) (0 .3 65 ) (0 .1 30 ) (0 .2 87 ) (0 .1 41 ) (0 .1 12 ) (0 .0 52 ) (0 .1 19 ) α0. 75 2 -0 .1 01 0. 33 6 -0 .8 12 0. 08 8 0. 22 2 0. 12 7 0. 02 9 0. 24 0 0. 11 2 (0 .2 84 ) (0 .1 70 ) (0 .2 07 ) (0 .6 15 ) (0 .1 53 ) (0 .3 27 ) (0 .2 34 ) (0 .1 21 ) (0 .0 89 ) (0 .1 33 ) o bs . 12 4 12 4 12 4 12 4 12 4 12 4 12 4 12 4 12 4 12 4 st an da rd e rr or s in p ar en th es is . pp an d pw s ta nd fo r p ro du ce r a nd w ho le sa le r p ric e re sp ec tiv el y. 207on perishability and vertical price transmission: empirical evidences from italy ta bl e 4. e st im at ed e cm m od el fo r w ho le sa le r a nd re ta ile r p ric es . lo w pe ris ha bl ev eg et ab le s m ed iu m p er ish ab le ve ge ta bl es fr ui ts c ar ro ts pe pp er s po ta to es c au lifl ow er eg gp la nt s to m at oe s ra di sh es ap pl es le m on s pe ar s pr = f( pw ) γ 0 0. 06 8 0. 09 7 0. 10 5 0. 08 7 -0 .1 82 -0 .0 06 0. 16 6 0. 06 4 0. 05 7 0. 07 8 (0 .0 22 ) (0 .0 79 ) (0 .0 25 ) (0 .0 49 ) (0 .0 64 ) (0 .0 63 ) (0 .1 06 ) (0 .0 25 ) (0 .0 30 ) (0 .0 36 ) γ t -0 .0 07 -0 .0 01 -0 .0 07 -0 .0 12 0. 03 1 0. 01 2 -0 .0 27 -0 .0 03 -0 .0 03 -0 .0 06 (0 .0 02 ) (0 .0 09 ) (0 .0 03 ) (0 .0 06 ) (0 .0 09 ) (0 .0 08 ) (0 .0 13 ) (0 .0 03 ) (0 .0 04 ) (0 .0 04 ) γ 1 0. 12 8 0. 09 0 -0 .2 30 -0 .0 37 0. 13 5 0. 23 1 0. 52 1 0. 17 6 0. 03 1 0. 27 2 (0 .1 28 ) (0 .1 58 ) (0 .0 89 ) (0 .1 54 ) (0 .1 80 ) (0 .1 93 ) (0 .1 55 ) (0 .1 27 ) (0 .1 42 ) (0 .1 66 ) γ 2 -0 .0 82 0. 22 5 0. 25 9 0. 10 6 0. 02 6 -0 .0 18 -0 .3 65 -0 .0 85 0. 16 8 -0 .2 21 (0 .1 53 ) (0 .1 61 ) (0 .1 74 ) (0 .1 38 ) (0 .1 73 ) (0 .1 87 ) (0 .1 29 ) (0 .1 65 ) (0 .1 34 ) (0 .2 21 ) α+ -0 .6 17 -0 .6 02 -0 .8 67 -0 .4 67 -0 .3 33 -0 .7 31 -0 .4 79 -1 .2 21 -0 .3 70 -0 .6 59 (0 .2 97 ) (0 .2 81 ) (0 .1 48 ) (0 .3 41 ) (0 .2 93 ) (0 .3 28 ) (0 .2 23 ) (0 .1 69 ) (0 .2 30 ) (0 .2 27 ) α0. 21 9 0. 26 9 0. 34 6 -0 .2 16 -0 .2 96 0. 06 0 -0 .6 16 0. 04 7 0. 22 1 0. 01 2 (0 .3 18 ) (0 .2 64 ) (0 .1 85 ) (0 .2 78 ) (0 .3 70 ) (0 .3 16 ) (0 .2 56 ) (0 .1 99 ) (0 .1 68 ) (0 .2 87 ) pw = f( pr ) γ 0 0. 02 6 0. 05 3 0. 03 0 0. 07 1 -0 .2 57 -0 .0 52 0. 07 6 0. 06 1 0. 05 7 0. 04 8 (0 .0 19 ) (0 .0 71 ) (0 .0 15 ) (0 .0 54 ) (0 .0 67 ) (0 .0 68 ) (0 .1 29 ) (0 .0 24 ) (0 .0 33 ) (0 .0 29 ) γ t -0 .0 04 0. 00 3 -0 .0 03 -0 .0 04 0. 03 9 0. 01 6 -0 .0 17 -0 .0 05 -0 .0 05 -0 .0 02 (0 .0 02 ) (0 .0 08 ) (0 .0 02 ) (0 .0 07 ) (0 .0 10 ) (0 .0 09 ) (0 .0 16 ) (0 .0 03 ) (0 .0 04 ) (0 .0 03 ) γ 1 0. 15 6 0. 09 2 0. 23 8 0. 02 6 -0 .1 75 0. 10 5 -0 .3 26 0. 27 4 0. 05 7 0. 02 1 (0 .1 32 ) (0 .1 44 ) (0 .1 00 ) (0 .1 52 ) (0 .1 81 ) (0 .2 02 ) (0 .1 56 ) (0 .1 55 ) (0 .1 48 ) (0 .1 80 ) γ 2 -0 .0 54 0. 17 7 0. 04 3 -0 .0 10 0. 28 1 0. 09 4 0. 50 6 -0 .1 55 0. 21 2 0. 06 0 (0 .1 10 ) (0 .1 41 ) (0 .0 51 ) (0 .1 69 ) (0 .1 88 ) (0 .2 08 ) (0 .1 88 ) (0 .1 19 ) (0 .1 57 ) (0 .1 36 ) α+ 0. 33 8 -0 .0 56 -0 .0 42 -0 .3 20 0. 18 3 -0 .1 83 0. 10 1 -0 .5 14 0. 01 3 -0 .3 04 (0 .2 57 ) (0 .2 51 ) (0 .0 85 ) (0 .3 74 ) (0 .3 05 ) (0 .3 53 ) (0 .2 70 ) (0 .1 59 ) (0 .2 54 ) (0 .1 86 ) α-0 .1 68 0. 74 0 0. 11 3 0. 66 9 0. 05 2 0. 44 1 -0 .1 86 0. 23 7 0. 42 9 0. 24 0 (0 .2 75 ) (0 .2 36 ) (0 .1 06 ) (0 .3 05 ) (0 .3 86 ) (0 .3 40 ) (0 .3 10 ) (0 .1 87 ) (0 .1 86 ) (0 .2 35 ) o bs . 12 4 12 4 12 4 12 4 12 4 12 4 12 4 12 4 12 4 12 4 st an da rd e rr or s in p ar en th es is . p w an d pr s ta nd fo r w ho le sa le r a nd re ta ile r p ric e re sp ec tiv el y. 208 f.g. santeramo, s. von cramon-taubadel and goodwin (2003) find asymmetric price transmission in the diary sector while no evidences of asymmetric price transmission along the supply chain of perishable diary products; kim and ward (2013, p. 234) state that “prices higher in the vertical system respond quicker to rising than falling prices, again, except for the most perishables.” 4. concluding remarks asymmetries in vpt may be due to imperfect competition, adjustment costs, inventory management, political interventions, or asymmetric information. evidences and theories on the effects of perishability on vertical price transmission are mixed. we examined how the degrees of asymmetries in vpt and perishability are related. our evidences suggest that vpt is asymmetric for products not affected by large losses for spoilage (e.g. fruits and low perishable vegetables), and tends to be symmetric for more perishable products. our results are consistent with numerous studies (ward, 1982; peltzman, 2000; serra and goodwin, 2003) and in contrast with the results of a meta-analysis conducted by kim and ward(2013, p 234), who state that “the perishables are where the most dramatic differences are seen, where falling farm prices are transmitted far faster than rising farm prices. much of this has to be due to perishability, where rising prices in a highly perishable good can lessen volume sales among goods that have a very short shelf life”. based on our findings, several policy considerations may be expressed, and in particular it may be inferred on the level at which market crises should be administered in f&vs markets (santeramo et al., 2014), or on the efficacy of trade policies (seccia et al., 2009; cioffi et al., 2011; santeramo and cioffi, 2012; dal bianco et al., 2016). deepening on these issues is beyond the scope of the present short note, and is left to future research. we acknowledge that our findings rely on one time frequency (monthly data), however by adopting monthly data our analysis is directly comparable with the vast majority of empirical studies on price transmission. 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(1992). further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. journal of business and economic statistics 10: 251-270. stampato da logo s.r.l. borgoricco (pd) bio-based and applied economics 9(2): 137-154, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8337 on the relevance of the region-of-origin in consumers studies fabio gaetano santeramo1, emilia lamonaca1,*, domenico carlucci2, biagia de devitiis1, antonio seccia1, rosaria viscecchia1, gianluca nardone1 1 university of foggia (italy) 2 university of bari (italy) abstract. the existing literature on the consumers’ attitude toward region-of-origin (roo) provides numerous and varying evidence on the relative importance of this extrinsic attribute as compared to other product characteristics. the article aims at characterising the heterogeneity in the relative importance of roo. we systematically review the literature on roo and build an ad hoc indicator to measure the relative importance of roo as compared to other attributes of agri-food products under investigation. we then explain, through a meta-analytical approach, how the relative importance of roo varies according to factors related to publication process, methodological issues, and characteristics of articles. findings reveal the limited influence of publication process and methodological issues on the relative importance of roo. in contrast, we find a strong effect of characteristics of articles, with the relative importance of roo being highly dependent on products and origins under investigation. the results also highlight that roo is an effective differentiation instrument in the agri-food markets only if supported by geographical indication labels. managerial implications are also provided. keywords. agri-food, consumer, meta-analysis, region-of-origin, systematic review. jel codes. q13, p46, m31. 1. introduction regional imagery is increasingly being recognised as having a commercial value for agri-food products. it provides a subjective source of quality differentiation (henchion and mcintyre, 2000; marcoz et al., 2016). in fact, even though countries operate within an increasingly globalised context, the indication of the region-of-origin (roo) of agri-food products still appears to be a relevant cue for both consumers and producers or marketers (pucci et al., 2017). the roo of agri-food products still matters when examining consum*corresponding author. e-mail: emilia.lamonaca@unifg.it editor: davide viaggi. 138 fabio gaetano santeramo et al. ers’ product evaluations and buying behaviour (chamorro et al., 2015). for producers and marketers, roo allows them to charge prices above marginal cost, thus achieving market power. by using a regional indication, producers and marketers are able to exploit existing associations consumers have with roo and provide their product with an image (bruwer et al., 2012). indeed, the strategic advantage of regional branding is that an agri-food product can be differentiated on the basis of geographic origin, an unique attribute difficult to reproduce and presumed to be a quality cue for the product (van ittersum et al., 2007; chan and marafa, 2013). the existing literature on the consumers’ attitude towards roo provides numerous and varying evidences on the relative importance of this extrinsic attribute as compared to other product characteristics. the roo effect has been analysed, among others, by henchion and mcintyre (2000), who concluded that roo is an important consideration for two out of three irish consumers when deciding to buy quality products and that products from rural areas are generally perceived to be of high quality. in addition, stefani et al. (2006) showed that the narrower and more precisely defined the roo, the higher the quality expectation of consumers supporting the role of origin as a quality cue. empirical evidence shows that roo effect on product evaluation is product-specific and varies depending on the characteristics of consumers. in particular, consumers from different countries tend to perceive roo in a different manner and their knowledge influences the impact of roo on their behaviour (perrouty et al., 2006). in this regard, engelbrecht et al. (2014) demonstrated that roo of wine plays a secondary role in influencing consumers when faced with a purchasing decision on its own, while dekhili and d’hauteville (2009) found that the image of roo has a specific influence on consumers’ selection behaviour for olive oil although with differences between consumers from different countries. similarly, dekhili et al. (2011) showed that french consumers tend to choose olive oil based on official signals, while tunisian consumers mainly use roo and sensory cues. differences across consumers emerges also at the regional level, as in aranda et al. (2015) who found that spanish consumers tend to value la mancha region less than rioja region in choosing wine. their findings suggest different level of importance for roo, relative to other products’ attribute under investigation. overall, roo has an effect if consumers perceive substantial differences between regions in terms of their product-origin associations (marcoz et al., 2016). literature on roo is vast and fragmented, so we aim at characterising the heterogeneity in the relative importance of roo. on the basis of a systematic review of the literature on roo, we have built an ad hoc indicator to measure the relative importance of roo as compared to other attributes of agri-food products under investigation. we have then explained, through a meta-analytical approach, how the relative importance of roo varies according to specific factors related to publication process, methodological issues, and characteristics of articles. the meta-analysis is based on a sample of 27 papers, which differ by products and origins under investigation, and type of methodological framework. we have also expanded the study by santeramo and lamonaca (2020), who evaluated geographical label in consumers’ decision-making process, by proposing a quantile regression analysis. the quantile regression allows us a better representation of the heterogeneity in the index measuring the relative importance of roo. indeed, the quantile regression estimator is robust, which means that the influence of outlying observations is bound. 139on the relevance of the region-of-origin in consumers studies our analysis would identify patterns in heterogeneous results in the vast body of research that examines the regional branding construct and the various effects of roo on consumer buying behaviour (e.g. atkin et al., 2017; pucci et al., 2017). the success of regional branding strategies and of regulations protecting regional products largely depends on consumers’ evaluation of roo that informs them on the authenticity of those products (van ittersum et al., 2007). our contribution is to provide a finer granular overview of the roo effects as relates to consumer product evaluations. a better understanding of consumers’ evaluation of roo may benefit producers and marketers in designing differentiation strategies that support the competitiveness of regional products more effectively. furthermore, it may facilitate policymakers in developing roo-based communication strategies and policies on the protection of regional products aimed at supporting rural economies, especially disadvantaged areas. the reminder of the article is as follows. the next section describes the protocol adopted for the systematic review of literature on consumers’ evaluation of roo, as well as the quantitative methods used to examine determinants of heterogeneity in the relative importance of roo across studies. the results, presented in section 3, describe the contribution of publication process, methodological issues and characteristics of studies in explaining heterogeneity in the index measuring the relative importance of roo as compared to other attributes of the product under investigation. the last section concludes with implications for the food industry and policymakers. 2. materials and methods 2.1 systematic review and sample description we systematically reviewed the literature based on roo following the preferred reporting items for systematic reviews and meta-analyses (prisma) protocol (moher et al., 2009; shamseer et al., 2015). our bibliographic research took place in july-september 2018 and focused on articles published in scopus, including articles up to 20181. in order to be included in the quantitative synthesis (i.e. meta-analysis), papers had to meet two general criteria. first, they had to deal with consumers’ attitude, or preference, or intention to buy or willingness to pay for roo of agri-food products. second, papers had to provide a comparison between roo and other products’ attributes. further inclusion criteria allowed us to select only peer-reviewed published studies, supposed to be validated knowledge with a potentially greater impact in the field, and papers in english, the foremost language used to spread scientific knowledge. we identified an initial set of articles (n = 947) which contained all possible combinations of roo-based, consumer-related, and sector-specific keywords in their title, abstract or keywords. in particular, we ran separate searches in scopus using the following strings: [“place brand” or “region-of-origin” or “umbrella brand”] and [“attitude” or “attribute” or “behaviour” or “choice” or “consumer” or “consumption” or “preference” or “segmentation” or “willingness to pay”] and [“agri food” or “food”]. after removing duplicates (n = 680), two independent researchers screened 267 studies and selected eli1 we do not set time limits in the bibliographic search. 140 fabio gaetano santeramo et al. gible articles on the basis of information contained in the full text (n = 60). we excluded 17 full text articles with reasons2. the steps of the systematic review are synthesised in a prisma flow diagram (figure 1). the final sample includes 43 articles, of which 27 (listed in table 1) included in the quantitative synthesis for a total of 194 observations (articles may include more than one observation). the vast majority of articles in the sample are published in peer-reviewed journals of high-medium prestige (48% in q1, 37% in q2); 37% of articles falls into the subject areas of agricultural and biological sciences, 33% into business, management and accounting, 26% into economics, econometrics and finance. the first study was published in 2001 (van der lans et al., 2001), however the interest for the topic has grown progressively overtime. indeed, about two thirds of articles (74%) were published after 2010, demon2 a general review of place branding literature was excluded due to the lack of evidence on consumers’ attitude, or preference, or intention to buy or willingness to pay for roo of agri-food products. other articles were excluded due to their focus, i.e. city brand not agri-food specific, private brands, physicochemical composition parameter and sensory attributes. further articles not in english (3 out of 17) and not peer-reviewed published (8 out of 17) were excluded. figure 1. prisma flow diagram. source: elaboration on moher et al. (2009). 141on the relevance of the region-of-origin in consumers studies strating the dynamic character of the literature on roo (figure 2). more than half of the papers share at least one of co-authors, and the author with the most articles on the topic is s. mueller loose (author of 3 articles). 2.2 a measure of the relative importance of roo we identified 47 types of attributes within our sample, other than roo. we ideally classified them in three categories (table 2): i) intrinsic attributes (13 types) which directly describe products; ii) extrinsic attributes (14 types) which indirectly characterise products (dekhili and d’hauteville, 2009); iii) additional attributes (20 types) which refer to the level of product knowledge and involvement (arancibia et al., 2015). the most frequent attributes in our sample are two extrinsic attributes, namely price and packaging, followed by two intrinsic attributes, namely type and variety. in order to measure the relative importance of roo as compared to other attributes of a product under investigation, we built an index specific for each i-th observation (i.e. estimate) within the j-th reviewed study. the index ( ) is equal to the ratio between the sum of the relative importance of each k-th attribute ( ) and the number of attributes other than roo (ki,j): (1) where , the relative importance of the k-th attribute compared to roo, may assume the following values: figure 2. evolution of the literature on region-of-origin. 0 5 10 15 20 25 30 0 1 2 3 4 5 cu m ul at iv e nu m be r ye ar -b yye ar n um be r year-by-year cumulative 142 fabio gaetano santeramo et al. (2) the index, , measures the relative importance of roo with respect to other generic attributes of a product, and ranges between -1 and 1: the higher the index, the table 1. list of articles included in the meta-analysis. reference journal journal rank journal subject area arancibia et al. (2015) agricultural economics review q2 abs atkin et al. (2017) wine economics and policy q1 eef bernabéu et al. (2012) british food journal q1 bma bruwer et al. (2012) journal of foodservice business research q3 abs bryła (2015) appetite q1 nursing dekhili and d’hauteville (2009) food quality and preferences q1 abs dekhili et al. (2011) food quality and preferences q1 abs fernandes-ferreira-madureira et al. (2013)international journal of wine business research q2 bma grebitus et al. (2018) agribusiness q2 eef hollebeek et al. (2007) food quality and preferences q1 abs johnson and bruwer (2007) international journal of wine business research q3 bma marcoz et al. (2016) international journal of tourism research q1 bma mccutcheon et al. (2009) international journal of wine business research q2 bma mtimet et al. (2013) journal of international food and agribusiness marketing q2 bma mueller loose and szolnoki (2012) food quality and preferences q1 abs mueller loose et al. (2013) food quality and preferences q1 abs mueller loose and szolnoki (2010) food quality and preferences q1 abs nunes et al. (2016) wine economics and policy q1 eef perrouty et al. (2006) agribusiness q2 eef rahnama and fadei (2017) journal of food products marketing q2 bma resano-ezcaray et al. (2012) food policy q1 eef robertson et al. (2018) journal of wine research q3 abs sanjuán-lópez et al. (2009) spanish journal of agricultural research q3 abs scarpa et al. (2015) agribusiness q2 eef schnettler et al. (2018) british food journal q1 bma sutanonpaiboon and atkin (2012) journal of food products marketing q2 bma van der lans et al. (2001) european review of agricultural economics q2 eef notes: the rank, provided by the scimago journal & country rank (sjr), refers to the date of publication for the corresponding sjr subject area. abbreviations are agricultural and biological sciences (abs), business, management and accounting (bma), economics, econometrics and finance (eef). 143on the relevance of the region-of-origin in consumers studies greater the relative importance of roo as compared to other attributes. the index is distributed with mean 0.34 and standard deviation 0.64, however the relative importance of roo tends to vary according to structural and methodological differences across studies. 2.3 meta-analytical approach and data description we adopt a meta-regression approach to investigate the determinants of heterogeneity in the relative importance of roo as compared to other attributes of products under investigation. we regress the index measuring the relative importance of roo ( ) on its accuracy (i.e. sample size3) and on a set of and -type moderator variables: (3) 3 stanley et al. (2008) suggest to use degrees of freedom (or sample size) as a measure of the accuracy of the variable under investigation. other meta-analyses on the issue follow the same approach (e.g. deselnicup et al., 2013). table 2. relative frequencies (rf) of intrinsic, extrinsic, and additional attributes in the sample. intrinsic rf extrinsic rf additional rf type 0.53 price 1.00 distribution channel 0.39 variety 0.45 packaging 0.62 frequency of choice 0.28 appearance 0.26 brand name 0.39 accompaniment 0.23 alcohol content 0.25 appellation 0.33 concerns for environment 0.21 colour 0.23 medal 0.32 recommendation by others 0.16 vintage 0.21 label information 0.21 concerns for health 0.07 taste 0.20 producer 0.20 preparation format 0.07 sensorial characteristics 0.15 production process 0.14 availability 0.06 serving temperature 0.13 organic label 0.13 advertising 0.05 hedonic liking 0.10 country-of-origin 0.10 consumption for specials 0.04 product quality 0.09 retailer 0.09 nostalgia 0.04 expiry date 0.03 informed liking 0.06 concerns for animal welfare 0.03 smell 0.03 concerns for safety 0.03 curiosity 0.03 state 0.02 fashion of consumption 0.03 knowledge level 0.03 loyalty 0.03 pleasure of consumption 0.03 touristic issues 0.03 traceability 0.03 uniqueness 0.03 notes: the relative frequencies are computed on a total of 194 observations. 144 fabio gaetano santeramo et al. where the accuracy of the index (ni,j) models and corrects publication selection bias4; χr is a vector of r regressors thought to affect the magnitude of the publication selection bias5; ψs is a vector of s regressors, related to relevant characteristics of a study, that influence the magnitude of the index and explain its systematic variation across the observations (i) of the reviewed studies (j); γr and δs are coefficients which reflect the biasing effect of publication selection and of study’s characteristics; εi,j is an independently and identically distributed error term. the vector of x-type moderator variables includes information related to the publication process and methodological issues (table 3). it controls, through a dummy variable, for the presence of more than one article published by the same author (42% of observations). in order to account for the prestige of the journal, specific dummies control for articles published in q1 (51% of observations), q2 (baseline), and q3 (6% of observations) journals, according to the rank provided by the scimago journal & country rank at the date of publication. the dynamic character of the literature on the issue is accounted for using a dummy that discriminates between articles published before and after 20106, whereas a numerical variable controls for the cumulative number of articles published overtime. as for methodological issues, dummies control for methods and reference variables adopted to assess the relative importance of roo. in our sample, we observe articles based on best-worst scaling analyses (6% of observations), choice models (8% of observations), conjoint analyses (19% of observations), focus groups (1% of observations), hedonic price models (9% of observations), latent classes analyses (12% of observations), descriptive statistics (baseline). the reference variables mostly used are percentages in terms of importance of attributes (41% of observations), average importance of attributes (40% of observations), estimated willingness to pay (3% of observations), beta (baseline). the set of ψ-type moderator variables includes dummies related to specific characteristics of studies, to account for heterogeneity in the relative importance of roo (table 3). it controls for specific product category such as olive oil (13% of observations), wine (58% of observations), other products (baseline), and origin such as argentina (13% of observations), australia (14% of observations), chile (2% of observations), new zealand (5% of observations), tunisia (4% of observations), united states (9% of observations), other countries (baseline). lastly, a dummy identifies paper that associate a certified label, such as pdo or pgi, to roo (25% of observations). in order to correct for heteroskedasticity and to obtain efficient estimates, we normalised all but one elements (i.e. xr 7) of the equation (3) by the accuracy of the index, ni,j. 4 publication selection may distort evidence from literature, undermining the external validity of inferences and implications (santeramo and lamonaca, 2021). biases from publication selection may occur if certain results are more likely to be published (e.g. statistical significant results, estimated coefficients of certain sign or magnitude) (stanley, 2005). 5 the x-type moderator variables allow us to capture the wide dimension of selection bias, which is a complex socio-economic phenomenon that goes beyond the mere publication selection (stanley et al., 2008). 6 the year 2010 is the median year of the articles in the sample. in addition, 72% of observations are included in articles post 2010. 7 the rationale of the exclusion is that χ-type moderator variables may influence the likelihood of acceptance for publication, but should not be informative on the index. 145on the relevance of the region-of-origin in consumers studies after the normalisation, the intercept and slope coefficients are reversed from the equation (3). the new intercept (α0) is a test for publication selection bias and the new slope (α) is a test for the average value beyond the publication selection bias (stanley et al., 2008). if statistically significant, α0 suggests the existence of publication selection bias, and α allows to conclude on the accuracy of the index (santeramo and shabnam, 2015). we used probit specifications to assess how determinants of heterogeneity in the relative importance of roo influence the likelihoods of observing lower or higher values of the index . these likelihoods are captured by two dependent variables defined as dummies, that distinguish between cases in which roo tend to be less or more important for consumers as compared to other attributes of the product under investigation. the likelihood of observing lower values of the index equals to 1 for negative observations of , and zero otherwise. the likelihood of observing higher values of the index equals to 1 for positive observations of , and zero otherwise. observations of equal to zero, indicating that roo is important as much as a generic attribute of the product under investigation, serve as baseline. table 3. list and description of moderator variables. moderator variable type of variable mean std. dev. obs. x-type moderator variables sample size numerical 372.89 373.47 159 authorship dummy 0.42 0.50 194 q1 (journal prestige) dummy 0.51 0.50 194 q3 (journal prestige) dummy 0.06 0.24 194 post-2010 dummy 0.72 0.45 194 number of paper (cumulative) numerical 15.41 7.07 194 best-worst scaling analysis (method) dummy 0.06 0.24 194 choice model (method) dummy 0.08 0.27 194 conjoint analysis (method) dummy 0.19 0.39 194 focus group (method) dummy 0.01 0.07 194 hedonic price model (method) dummy 0.09 0.29 194 latent classes analysis (method) dummy 0.12 0.33 194 % (reference variable) dummy 0.41 0.49 194 avg. (reference variable) dummy 0.40 0.49 194 wtp (reference variable) dummy 0.03 0.16 194 ψ-type moderator variables certified origin dummy 0.25 0.43 194 argentina dummy 0.13 0.34 194 australia dummy 0.14 0.35 194 chile dummy 0.02 0.14 194 new zealand dummy 0.05 0.22 194 tunisia dummy 0.04 0.20 194 united states dummy 0.09 0.28 194 olive oil dummy 0.13 0.34 194 wine dummy 0.58 0.50 194 146 fabio gaetano santeramo et al. the model in equation (3) is also estimated in a quantile regression fashion. these models allow us to identify factors determining more or less importance of roo (observations of within 50th percentile), less importance of roo (observations of within 25th percentile), more importance of roo (observations of within 75th percentile). the quantile regression allows us to particularise the dependency of the index on determinant of heterogeneity in the relative importance of roo for every quantile and, thus, it can be tailored to the extremes by conditioning on lower quantiles. in addition, the quantile regression estimator is robust, which means that the influence of outlying observations is bound. these properties lead to a better representation of the heterogeneity in the index . 3. results and discussion the results of the probit and quantile regression models are presented in table 4. the analysis of the constant term (α0) and slope coefficient (α) allows us to detect potential publication selection bias. looking at the results from probit models, we find that publication selection bias is more likely to occur for higher values of the index, measuring the relative importance of roo as compared to other product’s attributes under investigation; in contrast, lower values of the index are less likely to be affected by publication selection bias. however, the results of the quantile regression reveal that the constant term is not significantly different from zero at any conventional level, suggesting that the publication selection bias is not a major issue in our sample. we conclude that publication selection does not distort evidence from the literature on roo, or undermine the external validity of inferences and implications on the relative importance of roo (stanley, 2005). this implies that all the heterogeneity we observe in the index depends on publication process, methodological issues, and characteristics of studies. given the absence of publication selection bias, values of the index within the 50th and 75th percentiles tend to be more accurate with the estimated coefficients being positive and significant at 1% level. reflecting on the publication process, the likelihood of having roo more important increases for studies co-authored by experienced scholars (column a); in addition, the higher the values of the index (75th percentile), the greater the importance of roo (column e). as for journal prestige, the quantile regression results show that the relative importance of roo decreases in articles published in q3 journals. this is true in particular for higher values of the index (within 50th and 75th percentile). a comparable result is found in probit models, where the coefficients estimated for articles published in q1 and q3 journals are negative and significant in the specifications in column (b). the probability of having roo more important decreases for studies published in medium-high prestigious journals. however, the probability of having roo less important increases for studies published in journals with lower prestige (q3, for which the estimated coefficient in the specification in column (a) is positive and significant). it is worth noting that, in our sample, the vast majority of articles published in q3 journals are wine-based studies (e.g. johnson and bruwer, 2007; bruwer et al., 2012; robertson et al., 2018) that tends to be negatively correlated with the relative importance of roo (negative and significant coefficient reported in column e). overall, the relative importance of roo tends to decrease with the prestige of the journal in which articles are published. similarly, santeramo and 147on the relevance of the region-of-origin in consumers studies lamonaca (2019) argue that the authorship and the prestige of the publication outlet help in explaining the variability in studies’ outcome. if articles are published after 2010, roo less important is less likely to be observed; the opposite is true for roo more important. this evidence support the idea that the importance of roo is likely to increase when the background on the issue is based on a wider set of empirical evidence; indeed, evidences available on the issue show an increasing trend overtime (cfr. figure 2) and the vast majority of them, in our sample, are observed in articles published after 2010. as for methodological issues, both methods or reference variables used in our sample tend to have a limited influence in determining the relative importance of roo. the few exception are best-worst scaling and latent classes analyses. the former tends to be associated with higher relative importance of roo (positive and significant coefficient reported in column e); the latter tends to provide evidence of lower likelihood of roo more important (negative and significant coefficient reported in column b). our results differ from deselnicup et al. (2013), who find a positive influence of conjoint analysis and hedonic price model on the price premium for origin-based labels (not statistically significant in our specifications). the result is not surprising. in fact, estimation techniques may influence the estimated willingness to pay (wtp), whereas latent characteristics of specific sub-sample within the population under investigation are likely to affect the relative importance of roo. the characteristics of studies have a varying contribution on the relative importance of roo. the relative importance of roo decreases in country-specific studies (columns c-e), in particular for australia and new zealand for which the probability of having roo less important increases (column a) and the probability of having roo more important decreases (column b). as suggested in dekhili et al. (2011), nationality or culture appears to influence consumers’ perceptions of the importance of roo as compared to other product’s attributes. similarly, perrouty et al. (2006) show that consumers from different countries tend to perceive roo in a different manner and their knowledge influences the impact of roo on their behaviour. verbeke et al. (2012) suggest the existence of substantial differences in consumers’ awareness of geographical origin between countries with versus countries without a tradition of geographical indications in their agrifood quality policies. for instance, such awareness tends to be higher in countries with a strong tradition of using these quality schemes, e.g. southern (italy and spain) and western (france) europe. we also find product-specific differences; the relative importance of roo decreases in the analysis of olive oil (negative and significant coefficients in columns c-e), for which the probability of having roo more important is decreasing (negative and significant coefficient in column b). in fact, consumers tend to judge olive oil more for intrinsic than for extrinsic attributes (dekhili and d’hauteville, 2009; dekhili et al., 2011). lastly, the relative importance of roo increases in studies where roo is certified by an origin-based label. our result is in line with findings from deselnicup et al. (2013), who provide evidence of greater price premiums for product with certified origin than one using a non-regulated regional name. the interest in the origin of foods is a strong direct and indirect driver of consumers’ use of labels (verbeke et al., 2012). adding regional certification labels (e.g. protected designation of origin –dop–, protected geographical indication –pgi–, american viticultural area–ava–) allows to strengthen regional branding, in particular in the case of lesser known regions (bruwer and johnson, 148 fabio gaetano santeramo et al. table 4. probit model estimation: analysis of publication selection bias. variables probit estimates quantile regression estimates negative index (a) positive index (b) 25th percentile (c) 50th percentile (d) 75th percentile (e) constant (α0) -1.291* 2.215*** 0.001 -0.001 -0.001 (0.729) (0.841) (0.004) (0.00325) (0.002) bias (α0) 6.324 -21.900 -0.106 0.573*** 0.591*** (50.360) (50.200) (0.207) (0.173) (0.087) authorship -0.845 1.892** 0.001 0.002 0.002** (0.597) (0.762) (0.003) (0.002) (0.001) q1 (journal prestige) 0.637 -1.061** -0.001 -0.001 -0.001 (0.577) (0.511) (0.003) (0.002) (0.001) q3 (journal prestige) 1.268* -1.451* -0.001 -0.012*** -0.004*** (0.705) (0.873) (0.003) (0.003) (0.001) post-2010 -2.627*** 4.590*** 0.004 0.004 0.002 (0.836) (1.094) (0.004) (0.003) (0.002) cumulative 0.146** -0.235*** -0.0003 -0.0001 -0.00004 (0.058) (0.073) (0.0003) (0.0002) (0.0001) best-worst (method) omitted omitted 0.003 0.001 0.004** (0.004) (0.003) (0.002) choice (method) -0.798 -0.439 -0.0001 0.001 0.001 (0.652) (0.660) (0.003) (0.003) (0.001) conjoint (method) 0.348 -0.358 -0.001 -0.001 0.001 (0.501) (0.503) (0.003) (0.002) (0.001) focus group (method) omitted omitted 0.003 0.003 0.003 (0.008) (0.007) (0.004) hedonic price (method) 0.192 -1.163 -0.001 -0.0003 -0.001 (0.722) (0.765) (0.003) (0.003) (0.001) latent class (method) 0.816 -2.021*** -0.001 -0.002 -0.0002 (0.689) (0.655) (0.003) (0.003) (0.001) % (reference variable) -0.479 0.254 0.002 0.001 0.001 (0.361) (0.332) (0.002) (0.002) (0.001) avg. (reference variable) -0.662 -0.598 0.002 0.001 0.001 (0.567) (0.650) (0.003) (0.003) (0.001) wtp (reference variable) 0.748 omitted -0.001 -0.001 -0.003* (0.498) (0.004) (0.004) (0.002) certified origin -0.327 0.330 0.440** 0.431*** 0.409*** (0.289) (0.376) (0.187) (0.156) (0.079) argentina 4.470 -4.236 -0.447 -0.106 0.250 (3.187) (3.338) (1.469) (1.228) (0.618) australia 2.616*** -3.562*** -0.749*** -0.689*** -0.609*** (0.730) (1.001) (0.253) (0.212) (0.107) chile omitted 1.642* 0.702* -0.004 -0.123 (0.929) (0.396) (0.331) (0.167) 149on the relevance of the region-of-origin in consumers studies 2010). similarly, van der lans et al. (2001) find that the roo cue and pdo label influence regional product preferences through perceived quality. in a nutshell, the relative importance of roo is highly dependent on structural characteristics of studies and, to a lower extent, on issues related to the publication process and methodological issues. the paper however is not exempt from limitations. the evaluation of the relative importance of roo for consumers, through a meta-analytical approach, is based on information retrieved from literature, thus, it is highly dependent on the quality of each article. although the comprehensive analysis of heterogeneity in the relative importance of roo should minimise the biasing effect due to the quality of each article, further studies on the issue should consider to applying a quality assessment tool of articles included in the quantitative synthesis (e.g. cox et al., 2016). 4. conclusions and implications the existing literature on the consumers’ attitude toward region-of-origin (roo) provides numerous and varying evidences on the relative importance of this extrinsic attribute as compared to other product characteristics. in order to characterise the heterogeneity in the relative importance of roo, we systematically reviewed a large number of variables probit estimates quantile regression estimates negative index (a) positive index (b) 25th percentile (c) 50th percentile (d) 75th percentile (e) new zealand 3.037** -4.326*** -0.762** -1.310*** -1.331*** (1.253) (1.291) (0.342) (0.286) (0.144) tunisia 2.164 omitted 0.960 0.154 -0.093 (2.074) (0.695) (0.581) (0.292) united states 5.237 -11.470** -0.915 -0.914 -0.043 (5.005) (4.921) (1.912) (1.598) (0.805) olive oil 0.887 -1.733** -0.950*** -0.825*** -0.379** (0.612) (0.772) (0.359) (0.300) (0.151) wine -0.323 0.823 -0.132 -0.066 -0.216** (0.524) (0.577) (0.242) (0.202) (0.102) observations 142 137 159 159 159 notes: probit and quantile regression estimates of model in equation (3). the dependent variable is a dummy equal to 1 for negative observations of the index in specification in column (a), a dummy equal to 1 for positive observations of the index in specification in column (b), the index in specifications in columns (c), (d) and (e). coefficients estimated for -type moderator variables, related to study characteristics, have been scaled by a factor of 102 in specifications in columns (a) and (b). the index is -0.001 in 25th percentile, 0.001 in 50th percentile, 0.003 in 75th percentile. omitted variables in probit models due to a perfect prediction of failure for observations different from zero. *** significant at the 1 percent level. ** significant at the 5 percent level. * significant at the 10 percent level. 150 fabio gaetano santeramo et al. studies on the issue and provided a quantitative synthesis of empirical evidences on the consumers’ perception of roo. we explained the differences in the relative importance of roo with several control factors related to publication process, methodological issues, and characteristics of articles. the meta-regression results allowed us to conclude on the limited influence of publication process and methodological issues on the relative importance of roo. in contrast, we found a strong effect of characteristics of articles, with the relative importance of roo being highly dependent on products and origins under investigation. we can also conclude that roo is an effective differentiation tool in the agri-food markets only if supported by geographical indication (gi) labels, such as protected designation of origin (pdo), protected geographical indication (pgi), american viticultural area (ava). for instance, it is well-known the higher propensity of consumers in attributing a great importance to gi labels for agri-food products; consumers benefit from gi schemes that certify quality at different geographical levels (van ittersum et al., 2007; verbeke et al., 2012). in this regard, it is worth of mention the positive relation between roo and gi; the addition of regional information on a product label increases consumer confidence in the quality of that product (bruwer and jhonson, 2010). overall, our study suggests that protecting and marketing agri-food products with regional certification labels, such as pdo or pgi, may be beneficial for producers and marketers. they should fine-tune the differentiation of agri-food products through roo, particularly when roo have a positive reputation (santeramo et al., 2020a, b). consider as a representative example the tuscan experience, characterised by a strong regional image (stefani et al., 2006; bryła, 2015). it is therefore critical for policymakers to develop focused communication strategies towards consumers in order to convey attractive information about roo that, as suggested in verbeke et al. (2012), stimulates their interest in the origin of foods and builds favourable perceptions about quality and distinctiveness of products with roo labels. for instance, italian consumers stated that the label of origin “produced in puglia” is considered the preferred attribute for mozzarella cheese due to the high reputation of this region for mozzarella production (viscecchia et al., 2019). the effectiveness of communication strategies should be enhanced by targeting different messages to different target markets (van ittersum et al., 2007; marcoz et al., 2016) and by a new concept of label in terms of contents and communication channels (corallo et al., 2019). indeed, our analysis revealed that the importance of roo for consumers tends to vary according to products and countries involved. hence, communication efforts should stimulate consumers’ interest in roo, especially for wine and in countries without a strong tradition of geographical indications in their agri-food quality policies. furthermore, policymakers should consider the benefits of a collaborative marketing program for regional products. indeed, while many regional products are already under regional certification labels, many more remain out of the protection of an incisive regional logo. in this regard, examples of best practices come from rural development programmes 2014-2020 implemented in the eu member states, where the measure 03 “quality schemes for agricultural products and foodstuffs” allows local policymakers to support regional agri-food products in order to improve competitiveness of producers, create value added for agri-food products of high quality, promote regional products at the local, national and international level. the measure 03 also compensate producers for costs arising from specific management activities 151on the relevance of the region-of-origin in consumers studies required to adhere to quality schemes. similar policy approaches would benefit consumers, who obtain information on the authenticity of regional products, producers, who enhance competitiveness in marketing regional products, and overall rural economies, in particular disadvantaged areas. as suggested in atkin et al. 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davide viaggi bio-based and applied economics 9(3): 225-240, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-7768 investigating determinants of choice and predicting market shares of renewable-based heating systems under alternative policy scenarios cristiano franceschinis*, mara thiene university of padova, italy abstract. fostering the uptake of heating technologies based on renewable resources is an important part of the eu energy policy. yet, despite e!orts to promote their diffusion, heating systems based on fossil fuels are still predominant. in order to better tailor energy policies to citizens preferences, it is crucial to collect accurate information on their determinants of heating choices. at this purpose, we adopted a choice experiment and a latent class model to analyze preferences of householders in the veneto region (north-east italy) for di!erent heating systems and their key features. we focused on three devices based on biomass and three on fossil fuels, and accounted for technical, economic and environmental characteristics of such systems. model estimates highlight the presence of substantial preference heterogeneity among the population, which can be partially explained by citizens socio-demographics. we also use model outputs to simulate market shares for heating systems under alternative policy scenarios. results provide interesting suggestions to inform the design of policies aimed at fostering the adoption of biomass-based heating systems. keywords. ambient heating systems choice, latent class model, market shares, willingness to pay. jel codes. c01, q42, q47. 1. introduction developing a strategy to increase the sustainability of the heating sector is a priority for the european union, in order to reduce energy imports and dependency and meet the greenhouse gas emission target established under the paris agreement. currently, heating and cooling account for half of the eu energy consumption and 75% of the fuel used in this sector comes from non-renewable resources (european commission, 2016). to tackle such issues, the 2030 climate and energy policy framework adopted by the european council in 2014 includes three key targets for 2030: i) a 40% reduction of green*corresponding author. e-mail: cristiano.franceschinis@unipd.it editor: meri raggi. 226 cristiano franceschinis, mara thiene house gas (ghg) emissions compared to 1990 levels, ii) a 27% share of renewable energy in gross "nal energy demand and iii) a 27% increase in energy e#ciency (european council, 2014). $e targets for renewables and energy e#ciency were revised upwards in 2018 at 32% and 32.5% respectively. member states are obliged to adopt integrated national climate and energy plans (necps) for the period 2021-2030 to de"ne how they plan to achieve such goals. member states submitted their dra% plans in 2018 and "nal plans must be submitted by the end of 2019. italy, in its dra% plan, set the targets of a 33% reduction of ghg emissions compared to 2005 levels and a 30% share of renewable energy on "nal consumption to be achieved by 2030 (italian government, 2018). in 2017, the values for the two targets were 18% and 17% respectively. $us, as emphasized in the eu country report 2019 (european commission, 2019), further e!orts are needed to ensure the achievement of 2030 objectives. among the speci"c targets set by the plan, there is an annual increase of 1.3% of renewables share in the sector of residential heating and cooling. to achieve such target – among other measures – the plan aims to promote an active role by citizens on the energy demand market and the uptake of micro-generation technologies based on renewables. as such, installation of renewable based residential heating systems in new buildings and replacement of fossil fuel technologies in existing ones plays a crucial role in the energy system transition. to entice the active participation of citizens, it is important to collect information on their heating preferences, in order to retrieve determinants of heating choices. information about heating preferences can be collected via choice experiment, an increasingly popular method for stated preferences analysis. for example, rommel and sagebiel (2017) investigated preferences of german homeowners for micro-cogeneration units for residential use. $eir results suggest how householders have a strong interest in adopting such technologies, with willingness to pay (wtp) values ranging from 11.000 to 23.000 euros. features of micro-cogeneration products, as well as socio-demographics characteristics of houseowners, were found to substantially a!ect their wtp. scarpa and willis (2010) investigated wtp for the adoption of di!erent renewable micro-generation technologies in the uk. speci"cally, they focused on solar photovoltaic, micro-wind, solar thermal, heat pumps, biomass boilers and pellet stoves. $eir results suggest that householders are willing to adopt such technologies, but for most of them wtp values do not cover capital cost. a similar study was carried out by su et al. (2018) in lithuania. authors found householders to prefer solar energy-based technologies over the other renewable based ones. claudy et al. (2010) also estimated consumers’ wtp for di!erent microgeneration technologies, namely micro wind turbines, wood pellet boilers, solar panels and solar water heaters. $e study showed how wtps vary substantially among di!erent technologies and how consumers attitudes and beliefs about the technologies signi"cantly in&uence their wtps. rouvinen and matero (2013) focused on preferences towards di!erent types of heating systems (based on fuel used) and examined the role of system features on householders’ choices in finland. investment cost was identi"ed as the most impactful attribute on householders’ decisions, but non-monetary attributes played a signi"cant role as well. results also provided evidence of preference heterogeneity, partially linked to individuals’ characteristics. similarly, michelsen and madlener (2012) analyzed the in&uence of sensitivity to di!erent heating systems’ attributes on homeowners’ adoption decision. $eir "ndings suggest that importance attached to di!erent attributes a!ects technological features choice: for example, people focused on energy saving are 227market shares of renewable-based heating systems more likely to adopt condensing boilers with thermal support, while consumers attaching a strong value to use of renewables prefer pellet-"red boilers. furthermore, they found socio-demographics and spatial factors to a!ect preferences. ruokamo (2016) explored homeowners’ attitudes towards innovative hybrid home heating systems, described in terms of fuel used and key features, such as costs, comfort of use and environmental impact. $e author found that such technologies are generally well accepted by houseowners and that their preferences are strongly a!ected by socio-demographic characteristics. yoon et al. (2015) compared householders’ wtp for district heating and individual heating. while they found citizens to be generally willing to pay more for district heating, substantial di!erences emerged when accounting for preference heterogeneity: consumers with higher income and education were found to prefer district heating, while those more concerned about costs were willing to pay more for the individual one. in this paper we present the results of a choice experiment aimed at eliciting householders’ preferences towards di!erent heating systems in the veneto region (italy). speci"cally, we focus on six di!erent heating systems, three based on renewables (chip wood, "rewood and wood pellet) and three on fossil fuels (methane, oil and lpg). veneto is a fairly populated region (almost "ve million residents) characterized by air pollution mainly related to high road tra#c in all main cities and by the presence of large industrial districts in several sectors, such as tanning, cement production and furniture manufacturing. when accounting speci"cally for carbon dioxide emission, the residential impact is substantial as well, around the 20% of total emissions (arpav, 2015). to decrease the negative impact of residential sector on the production of greenhouse gases, since 2014 the regional authority supports the purchase of biomass-based heating systems by annually allocating "nancial subsidies (up to '1,600 for stoves and '5,000 for boilers). such policy, however, seems to only marginally meet the expectations of the population, in terms of fostering the adoption of such technologies. for example, in 2018 only 29 citizens applied for the funding and 25 requests were approved, for a total of around '55.000, out of '500,000 allocated policy budget. in 2019 the number of requests was higher (76, of which 66 approved) but again most of the policy budget was not used (around '120,000 allocated out of a budget of '500,000)1. $us, a better understanding of underlying factors motivating householders to stick with a fossil fuel system or to switch to a renewable one, is of crucial to reach the goals of the energy transformation process in the region. building on the evidence provided by the literature on how preferences towards different heating systems are highly heterogeneous and on how socio-demographics play an important role in such variability, we adopt a latent class approach and use socio-demographic characteristics of respondents to predict probability to belong to di!erent classes. $is approach allows to: i) identify di!erent segments of the population according to sociodemographic characteristics; ii) explore how preferences towards di!erent heating systems and their features vary across segments. we then use the estimates of such model to predict market shares for alternative heating systems within two policy scenarios, considering/based on a reduction of: i) investment costs for biomass fueled heating systems; ii) operating costs for biomass-based technologies. both simulated scenarios are in line with the policies implemented by the veneto region, the idea being that our empirical results may become useful 1 data retrieved from https://www.regione.veneto.it/web/ambiente-e-territorio/rottamazione-stufe-bando-2019. 228 cristiano franceschinis, mara thiene to better tailor the features of such policies to the population of the region. reduction of investment cost is a commonly adopted policy to foster use of renewable based technologies, such as the subsidies provided by the veneto region. reduction of operating costs is a possible e!ect of targeted policies as well (e.g. subsidies on fuel purchase). $e objective of our study is twofold: on one hand to investigate how socio-demographic characteristics in&uence citizens choice of heating systems, in order to gain insights on the determinants of adoption of renewable based technologies; on the other, to identify which, among a set of possible policy interventions, can be more e!ective in terms of fostering the di!usion of such technologies among the population. $e remainder of the paper is organized as follows: section 2 describes data collection (sampling procedure, survey design and administration); section 3 formally describes the econometric approach; section 4 reports the results of our study and section 5 draws its conclusions. 2. data collection and survey $is section reports a succinct description of sampling procedure and survey. further details can be found in franceschinis et al. 2016, 2017. data were collected with the support of a market research "rm via a web-based survey addressed to a sample of householders of the veneto region. we used a random sample of householders, strati"ed on the main socio-demographics (age, education, gender, place of residence). a total of 1,557 questionnaires were collected, out of which 1,451 were complete and used for the analysis. $e questionnaire was structured in "ve sections. $e "rst focused on heating system and energy resources currently used by respondents. $e following section included the choice experiment, which is described in detail below. $e third section included follow-up questions about the choices made in the previous section. $e fourth section presented attitudinal questions related to respondents’ psychological traits. $e last collected socio-demographic information. $e choice experiment involved a hypothetical scenario in which respondents were asked to select the heating system they would adopt if they had to renovate their current one among a set of alternative options. $e heating systems presented to respondents were six, three based on biomass ("rewood, chip wood and wood pellet) and three on fossil fuels (methane, lpg and oil). each alternative system was described in terms of six attributes: i) investment cost, ii) investment duration, iii) annual operating cost, iv) co2 emissions, v) "ne particle emissions and vi) required own work. $e respective levels were system-speci"c and are reported in table 1. investment cost is the cost for purchasing and installing the heating system. possible public incentives were not accounted for in de"ning the levels of the attribute. investment duration refers to the lifespan of the heating system, from purchase to dismantling. operating costs include fuel price, maintenance costs and electricity costs for those systems that need it to work. co2 emissions and "ne particles emissions refer to the quantity of co2 and "ne particles released by the fuel combustion processes. to facilitate the evaluation of co2 emissions levels, respondents were informed that 1,000 kg of co2 corresponds to the emissions from driving 6,000 km in a new generation car. to illustrate "ne particles health impacts, respondents were informed that “it has been estimated that if annual "ne particle emissions for one house are 2,000 g, then 229market shares of renewable-based heating systems the total emissions of 10,000 similar houses cause one premature death per year”. finally, required own work refers to the time required to ensure the faultless operation of the heating system (e.g., cleaning and handling fuel loads). $e choice of attributes and their levels was based on earlier studies and on feedback from experts. $e annual operating cost and co2 and "ne particle emissions were computed using as reference the energy consumption of an average detached house with a living area of 120 m2. $e experimental design adopted in the study was an e#cient availability design (rose et al., 2013), according to which only three alternatives were shown in each choice task. $e combination of levels that appeared in each scenario was de"ned with three different sub designs, namely near orthogonal, d-e#cient (scarpa and rose, 2008; rose and bliemer, 2009) and serial designs. for the latter, an orthogonal design was used for the "rst respondent. a%er the choice sequence was completed, a multinomial logit model was estimated in the background and statistically signi"cant parameters were used as priors to generate an e#cient design. $is process continued a%er each respondent and priors were continuously updated to generate a gradually more e#cient design. overall, the design generated 60 choice scenarios blocked in six groups, so that each respondent faced 10 of them. $e sample was split so to have the same number of respondents assigned to the three di!erent sub designs. an example of choice scenario is reported in table 2. 3. econometric approach in our study we estimated a latent class model to investigate variation of tastes towards heating systems types and their features among the householders of the veneto table 1. choice experiment attributes and levels. attributes firewood chip wood wood pellet methane oil lp gas investment cost (') 9,500; 11,000; 12,500 11,500; 13,000; 14,500 13,000; 15,000; 17,000 4,000 4,800; 5,600 4,500; 5,500; 6,500 4,000; 5,000; 6,000 investment duration (y) 15; 17; 19 17; 20; 23 16; 19; 22 16; 18; 20 16; 18; 20 14; 17; 20 operating cost ('/y) 1,200; 2,000; 2,800 2,000; 2,800; 3,600 2,500; 3,750; 5,000 4,000; 5,500; 7,000 6,000; 8,000; 10,000 9,000; 12,500; 16,000 co2 emissions (kg/y) 150; 225; 300 300; 375; 450 375; 450; 525 3,000; 3,750; 4,500 3,900; 4,575; 5,250 3,525; 4,125; 4,725 fine particle emissions (g/y) 4,500; 6,000; 7,500 2,250; 3,750; 5,250 750; 1,500; 2,250 15; 30; 45 150; 450; 750 15; 30; 45 required own work (h/m) 5; 10; 15 1; 2; 3 1; 2; 3 0.5; 1; 1.5 0.5; 1; 1.5 230 cristiano franceschinis, mara thiene region. $e model is based on the random utility $eory (luce, 1959; mcfadden, 1974), according to which a respondent n facing a set of j mutually exclusive alternatives has utility ui for alternative i as a function of attributes xk, so that: uni=!xni+"ni (1) where "ni is the unobserved error assumed to be i.i.d. extreme value type i. to account for heterogeneity in sensitivity to attributes xk, we adopted a latent class model. such model assumes the existence of c classes of respondents, where c is exogenously de"ned by the analyst, based on information criteria indexes. preference vary across classes but are homogeneous within them. as the classes are latent, an equation explaining the probabilistic assignment of individual n into class c needs to be de"ned. using a logit formulation for the class allocation model, with zn being a vector of socioeconomic variables and (c a vector of estimated coe#cients, the probability that individual n belongs to segment c is given by (bhat, 1997): (2) speci"cally, the variables we used in z vector are: i) age, ii) education, iii) income, iv) currently owning a biomass-based heating system. $en, the probability that individual n chooses alternative i, conditional on belonging to class c, takes the logit form (hensher and greene, 2003): (3) where xi represents the vector of attributes associated with each alternative and )nc the vector of estimated coe#cients for class c. $e estimated parameters of the latent class model were used to simulate the market shares of di!erent heating systems under di!erent policy scenarios. speci"cally, the scenarios involve reductions of investment cost (ranging from none to 50% reduction) and operational costs (same levels as previous case) for biomass-based heating systems. we table 2. example of choice scenario. attributes wood pellet lp gas firewood investment duration (years) 19 20 19 fine particles emissions (g/year) 2,250 15 7,500 co2 emission (kg/year) 375 3,525 150 required own work (hours/month) 1 1 15 investment cost (') 17,000 5,000 12,500 operative cost (') 3,750 9,000 1,200 your choice ○ ○ ○ 231market shares of renewable-based heating systems computed choice probabilities in each scenario with the logit formula described in equation 3, by including in it estimated coe#cients )nc and by varying the levels of investment and operational costs according to the reduction scenarios. 4. results $is section reports the results of our study. in the "rst part of the section the estimates of the latent class model are presented, while the second part focuses on the policy scenarios. 4.1 lc model estimates $e "rst step of our modelling approach involves the identi"cation of the optimal number of classes. as suggested by the literature (hurvich and tsai, 1989; nylund et al., 2007), we referred to the aic and bic information criteria, which both favour a speci"cation with 4 classes (table 3). class membership probabilities are 23% for class 1, 36% for class 2, 16% for class 3 and 25% for class 4 (table 4). results of latent class model with four classes are reported in table 22. $e table also reports wtp values for heating systems features, which were computed with respect to the investment cost. to class 1 are more likely to belong older individuals with low income and education, who currently do not own a biomass-based heating system. such class exhibits a strong preference towards methane-fuelled technologies with seemingly no interest in biomassbased ones. as it concerns the attributes, it can be noticed how members of this class are very sensitive to installation and operational costs, which is consistent with their feature of individuals with low income. $is class seems also sensitive to technical features of heating systems, and it shows a preference for systems with a long duration (wtp value of '0.38 for each additional year of duration) and which require low amount of time for maintenance ('0.27 to avoid an hour of work per month). emissions, instead, do not seem to a!ect choices of members of this class. moving to class 2, to this class are more likely to belong younger individuals with high education and income who currently do not possess a biomass-based technology. 2 a part of these results was included in the report “veneto 100% rinnovabile: fotogra"a e prospettive” by the interdepartmental centre giorgio levi cases for energy economics and technology (university of padova), available at http://levicases.unipd.it/wp-content/uploads/2019/11/relazione-"nale.pdf table 3. information criteria for alternative model speci!cations. number of classes number of parameters ll aic bic 1 (mnl) 11 -15,713 31,448 31,506 2 28 -15,081 30,218 30,367 3 44 -15,017 30,122 30,356 4 60 -14,894 29,908 30,227 5 76 -14,886 29,924 30,328 232 cristiano franceschinis, mara thiene as it concerns preferences towards di!erent types of heating system, it can be noticed how members of such class show a strong interest in biomass fuelled system, especially those based on wood pellet. $is suggests that such class has a strong potential in terms of switching from a fossil fuel-based system to a biomass one. $is seems corroborated by the high sensitivity to carbon dioxide and "ne particles emissions of members of this class, which are those willing to pay the most to avoid them among all classes ('0.73/kg/ year for co2 and '0.28/g/year for "ne particles). at the opposite, in this class there seems to be no concern about technical features of heating systems. to class 3, instead, are more likely to belong older individuals who currently own biomass-based heating system. as in the previous class, there seems to be a strong interest in biomass technologies, but in this case, "rewood is the preferred fuel. as it concerns heating systems features, members of this class seem to strongly appreciate technologies with long investment duration ('1.58 for each additional year, largest value among all classes) and low emissions of carbon dioxide ('0.36 to avoid a kilogram per year). finally, members of class 4 (the baseline class) seem to be interested mainly in methane-based systems and in those fuelled by wood pellet. $ey also have a strong aversion to oil-based technologies. as it concerns systems’ features, they seem interested in low maintable 4. lc model results. class size class 1 23% class 2 36% class 3 16% class 4 25% estimate wtp estimate wtp estimate wtp estimate wtp class membership function intercept 0.16 0.24 -0.11 -0.07 age 0.31 -0.31 0.12 degree -0.22 0.43 -0.23 income -0.15 0.32 0.02 owning a biomass fuelled system -0.22 -0.11 0.19 heating system features investment cost -0.41 -0.89 -0.18 -0.49 operational cost -0.38 -0.95 -0.31 -0.46 investment duration 0.16 0.38 0.34 0.38 0.28 1.58 0.17 0.35 required own work -0.11 -0.27 -0.24 -0.27 0.09 0.50 -1.41 -2.87 co2 emissions -0.01 -0.03 -0.73 -0.82 -0.06 -0.36 -0.02 -0.04 fine particle emissions 0.04 0.10 -0.28 -0.31 -0.05 -0.25 0.01 -0.01 heating system type firewood 2.00 4.99 1.89 1.55 chip wood -3.96 1.94 0.99 1.21 wood pellet 0.42 10.88 0.46 4.20 methane 4.81 7.65 0.19 6.29 oil -0.39 2.14 -0.04 -1.26 note: coe"cients statistically signi!cant at 95% in bold. wtp values were computed with respect to the investment cost. 233market shares of renewable-based heating systems tenance requirements and to a lesser degree to avoiding emissions, with low wtp values ('0.04/kg/year to avoid co2 and '0.01/g/year to avoid "ne particles). 4.2 market shares for di!erent heating systems in alternative policy scenarios in this sub-section we report results of market shares simulations for di!erent heating systems in two sets of policy scenarios: i) reduction of investment cost for biomass fuelled heating systems, speci"cally none, 10%, 20%, 30%, 40% and 50%; ii) reduction of operating costs for biomass based technologies (same range as above). in the "rst part of the sub-section (4.2.1) we present the average market shares weighted by class size for different heating systems; in the second (4.2.2) we report the shares for biomass technologies within each class. 4.2.1 average market shares in the investment cost reduction scenarios table 5 and figure 1 illustrate weighted average market shares for di!erent heating systems under the investment cost reduction scenario. in the baseline scenario, i.e. no investment cost reduction, most of the population would choose a methane heating system to replace the current one (64.40%), followed by wood pellet (12.61%), lpg (8.82%), "rewood (7.35%), oil (5.39%) and chip wood (1.23%). moving to the 10% reduction scenario, it can be noticed how shares for biomass fuelled technologies slightly increase (around 1% for each system). a 20% reduction seems to trigger a stronger response, with an increase of around 3% for wood pellet and 1.5% for the other biomass-based systems compared to the 10% reduction scenario. in the 30% reduction scenario the share for biomass-based systems further increases, in particular for wood pellet technologies, with a share of around 21% compared to the 12.61% of the baseline scenario. $e fourth scenario (40% reduction), instead, does not show substantial di!erences compared to the previous one. finally, in the last scenario (50% reduction) around 30% of citizens would choose a wood pellet "red system, around the 17% a "rewood one and around the 5% a chip wood one. overall, in such scenario, nearly half of the population would choose to adopt a biotable 5. average market shares under the investment cost reduction scenarios. investment cost reduction heating system none (baseline) 10% 20% 30% 40% 50% chip wood 1.23 1.83 2.69 3.21 4.55 5.49 firewood 7.35 7.90 10.32 13.86 14.93 16.59 wood pellet 12.61 13.94 15.21 21.36 23.79 26.91 biomass total 21.19 23.67 28.22 38.43 43.27 49.00 methane 64.60 63.66 62.49 55.91 52.32 47.33 oil 5.39 4.84 3.53 2.31 1.70 1.21 lpg 8.82 7.84 5.76 3.36 2.71 2.46 total 100.00 100.00 100.00 100.00 100.00 100.00 234 cristiano franceschinis, mara thiene mass-based system. $e overall increases of the share of biomass-based systems compared to the baseline scenario are: 2.5%, 7%, 17.2%, 22% and 28% for the alternative investment cost reductions. 4.2.2 average market shares in the operational costs reduction scenarios table 6 and figure 2 report the estimated average shares under the alternative operational cost reduction scenarios. firstly, it is of interest to notice how reducing operational costs seems to have a stronger e!ect in terms of increasing biomass systems market shares compared to the reduction of investment cost. $is seems true in each scenario (i.e. for each magnitude of the reduction) and is particularly evident in the 50% reduction scenario, under which the overall biomass technologies share is around 54% for operational costs reduction and 49% for investment cost reduction. in terms of fostering di!usion of biomass "red systems, this seems to suggest how policies aimed at decreasing operational costs for citizens may be more e!ective than those providing a reduction of the investment cost. by looking more closely at the operational costs reduction scenarios, it can be noticed that, similarly to the previous scenario, a 10% reduction does not lead to a substantial increase in the shares of biomass technologies (between 1% and 2% increase for each system). a 20% reduction has an only slightly stronger e!ect, with an increase of about the 3% for biomass systems shares compared to the previous scenario. a similar relative increase is also shown for the 30% and 40% reduction scenarios. finally, in the last scenario there is a substantial increase in the shares for biomass technologies, especially as it concerns wood pellet, which would be chosen by around the 30% of the population. overall, it seems that a reduction of the operational costs would strongly favour the dif0 10 20 30 40 50 60 70 80 90 100 none 10% 20% 30% 40% 50% m ar ke t s ha re (% ) investment cost reduction chip wood fire wood wood pellet methane oil lpg figure 1. average market shares under the investment cost reduction scenarios. 235market shares of renewable-based heating systems fusion of wood pellet systems and only to a lesser degree the di!usion of other biomassbased systems. $is may be due to higher operational costs of pellet "red heating systems, compared to other biomass ones. 4.2.3 market shares in the investment cost reduction scenarios within each class in this section we move from the population-level picture to a class-speci"c analysis, to explore how preference heterogeneity in&uences the di!usion of biomass heating systems. speci"cally, we report and discuss probabilities to choose a biomass-based technology as replacement of the current one within each class in di!erent policy scenarios. table 6. average market shares under the operational costs reduction scenarios. operational costs reduction heating system none (baseline) 10% 20% 30% 40% 50% chip wood 1.23 2.11 3.37 3.88 4.91 6.11 firewood 7.35 8.65 11.21 14.65 15.32 17.71 wood pellet 12.61 14.99 18.07 23.16 25.89 29.94 biomass total 21.19 25.75 32.65 41.69 46.12 53.76 methane 64.60 63.19 60.66 54.08 51.80 45.19 oil 5.39 4.65 2.60 1.71 0.98 0.46 lpg 8.82 6.41 4.09 2.52 1.10 0.59 total 100.00 100.00 100.00 100.00 100.00 100.00 figure 2. average market shares for under the operational costs reduction scenarios. 0 10 20 30 40 50 60 70 80 90 100 none 10% 20% 30% 40% 50% m ar ke t s ha re (% ) operational costs reduction chip wood fire wood wood pellet methane oil lpg 236 cristiano franceschinis, mara thiene starting from class 1, table 7 and figure 3 show how in class 1 the market share for biomass devices in the baseline scenario is extremely low (2.60%). such value is consistent with the pro"le illustrated in section 4.1, which highlighted how members of this class are characterized by little interest in biomass technologies and absence of sensitivity to carbon emissions. moving to the cost reduction scenarios, their e!ect on adoption probability seems limited. only in the 50% reduction scenario there seems to be a substantial increase in the biomass share (8% increase compared to the 40% reduction scenario). it seems that a very strong incentive is needed to foster di!usion of renewable based systems in this class. moving to class 2, the share for biomass devices in the baseline scenario equals 32.11%. such results – together with the lc estimates – suggest that this class includes individuals who currently own a fossil fuel "red heating system and around one third of them would switch to a biomass fuelled one, even with no cost reduction. $is seems to corroborate the potential of this class in terms of increasing the di!usion of renewtable 7. class-speci!c biomass systems market shares under the investment cost reduction scenarios. class investment cost reduction none (baseline) 10% 20% 30% 40% 50% class 1 2.60 3.39 5.91 8.66 12.11 19.99 class 2 32.11 36.11 40.18 44.18 48.12 50.08 class 3 61.16 64.12 73.18 78.81 82.18 88.88 class 4 19.98 23.11 27.61 32.81 38.11 45.18 figure 3. class-speci!c biomass systems market shares under the investment cost reduction scenarios. 0 10 20 30 40 50 60 70 80 90 100 none 10% 20% 30% 40% 50% bi om as s m ar ke t s ha re (% ) investment cost reduction class 1 class 2 class 3 class 4 237market shares of renewable-based heating systems able based technologies across the population. as for the previous class, the e!ect of the investment cost reduction seems limited. in this case, however, the low e!ect may be linked to the high income of its members, that could make them less sensitive to costs. class 3 exhibits the highest biomass system adoption probability in the baseline scenario (61.6%). overall, this class seems characterized by individuals that currently use a biomass system and show a high probability of choosing one of the same kind as replacement. importantly, this class seems to be strongly a!ected by cost reduction, with an around 28% increase of the biomass devices share between the baseline and the 50% reduction scenario. $is might be due to the low income of members of this class. finally, biomass systems share in class 4 equals 19.89% in the baseline scenario and 45.18% in the 50% reduction one, thereby suggesting a high sensitivity to investment cost reduction. 4.2.4 market shares in the operational costs reduction scenarios within each class table 8 and figure 4 report market shares within each class in the operational costs reduction scenarios. by comparing results with those reported in the previous section, it is interesting to notice how class 2 and 3 are a!ected more strongly by operational costs reduction, while classes 1 and 4 are a!ected more by investment cost reduction. $is seems to be related to di!erent sensitivity to the two costs highlighted in section 4.1: classes 2 and 3 are more sensitive to operational costs, and as such reducing it has a stronger e!ect in such classes, while the opposite is true for classes 1 and 4. for example, in class 1, at a 50% reduction the share is around 15% for operational costs and 20% for investment cost. at the opposite, in class 3 the share in 5% higher in the case of operational cost reduction. 5. discussion and conclusions in the light of the importance of increasing the sustainability of the residential heating sector, it is crucial to inform energy policies with an accurate knowledge of the determinants of citizens heating choices. to this purpose, we designed a choice experiment aimed at investigating preferences towards heating systems and their features among the citizens of the veneto region. we analysed choice data by means of a latent class model and we used the estimates to forecast market shares for di!erent heating systems under alternative policies scenarios. table 8. class-speci!c biomass systems market shares under the operational costs reduction scenarios. class operational costs reduction none (baseline) 10% 20% 30% 40% 50% class 1 2.60 3.11 4.88 7.91 10.12 15.18 class 2 32.11 37.14 42.24 46.11 49.88 53.11 class 3 61.16 66.89 76.11 81.56 86.88 92.21 class 4 19.98 22.41 26.22 30.33 34.16 40.11 238 cristiano franceschinis, mara thiene $e results of our study suggest how householders’ preferences towards di!erent heating systems and their features are strongly heterogeneous and how such variability can be partially explained by householders socio-demographic characteristics. such "ndings support those of previous studies of the energy literature (e.g. yoon et al., 2015, ruokamo, 2016). importantly, our estimates highlight the presence of population segments which seem to have a strong potential in terms of switching from a fossil fuel system to a renewable-based one. to this segment are more likely to belong individuals who already own a biomassbased heating systems and young individuals with high income and education level. at the opposite, our results highlight the existence of segments of the population with low interest towards the adoption of renewable-based technologies. such segments are characterized by individuals with low income and education who currently own a fossil fuel system. $e simulations of policy scenarios allowed us to retrieve some important information about the e#ciency of di!erent policy measures in terms of fostering the di!usion on biomass technologies. overall, we found that measures aimed at reducing operational costs for householders may induce a broader uptake of biomass appliances compared to those which target investment cost, even if the opposite is true in some segments of the population. $is is particularly important in the context of the veneto region, where subsidies for investment cost are currently in place and they seem to be only partially successful in nudging citizens towards the adoption of biomass-based appliances. we also found that only a large reduction of costs (i.e. 40% or 50% reduction) has a substantial e!ect on the increase of biomass systems shares, in classes with low interest in such technologies. $is suggests that current incentives provided by authorities may not be enough to entice such segments of the population to switch from a fossil fuel to a biomass-based technology. figure 4. class-speci!c biomass systems market shares under the operational costs reduction scenarios. 0 10 20 30 40 50 60 70 80 90 100 none 10% 20% 30% 40% 50% bi om as s m ar ke t s ha re (% ) operational costs reduction class 1 class 2 class 3 class 4 239market shares of renewable-based heating systems acknowledgments $e research was funded by interdepartmental centre giorgio levi cases for energy economics and technology (university of padova), “sustainability of introduction of pellet based heating systems in a mountain area”. references arpav, 2015. inventario regionale delle emissioni in atmosfera. bhat, c.r. 1997. an endogenous segmentation mode choice model with an application to intercity travel. transportation science, 31(1):34–48. claudy, m.c., michelsen, c., o’driscoll, a., mullen, m.r. 2010. consumer awareness in the adoption of microgeneration technologies: an empirical investigation in the republic of ireland. renewable & sustainable energy reviews, 14(7):2154-2160. european commission. 2016. an eu strategy on heating and cooling. https://eur-lex.europa.eu/legal-content/en/txt/pdf/?uri=celex:52016dc0051&from= en 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energy policy, 86:7. investigating determinants of choice and predicting market shares of renewable-based heating systems under alternative policy scenarios cristiano franceschinis, mara thiene multi-country stated preferences choice analysis for fresh tomatoes maria de salvo1,*, riccardo scarpa2,3,4, roberta capitello2, diego begalli2 “not my cup of coffee”. farmers’ preferences for coffee variety traits. lessons for crop breeding in the age of climate change abrha megos meressa, ståle navrud* does the place of residence affect land use preferences? evidence from a choice experiment in germany julian sagebiel1,*, klaus glenk2, jürgen meyerhoff3 the use of latent variable models in policy: a road fraught with peril? danny campbell*, erlend dancke sandorf bio-based and applied economics 8(2): 133-159, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8928 the wellbeing of smallholder coffee farmers in the mount elgon region: a quantitative analysis of a rural community in eastern uganda anna lina bartl agricultural economics and rural development, university of göttingen, germany abstract. for many smallholder farmers in the mount elgon region of uganda, arabica coffee cultivation is the major income-generating activity. although it is widely known that smallholder coffee farmers often live under conditions that barely assure their survival, research to date has failed to examine the composition and distribution of wellbeing within this group. in the present study, a composite indicator of wellbeing is created using information collected from interviews with 431 coffee-cultivating households to investigate wellbeing in the mount elgon region of uganda. from results of an explorative principal components analysis, the factors of trust, security, housing, and landholding, covering a total of ten indicators, provided a comprehensive measure of wellbeing, explaining 81.20% of the total variance. the results show substantial differences in wellbeing within the sub-counties of bulegeni, simu, and namisuni, and even greater differences between these sub-counties. these differences are explained primarily by the physical wellbeing factors of housing and landholding. efforts to improve the quality of housing, particularly in namisuni and bulegeni, for instance, by providing improved access to financial services, construction loans, or subsidized prices for bricks and other construction materials, as well as official land registration in all three sub-counties could improve the wellbeing of households in this area. keywords. composite indicator, mount elgon, smallholder coffee farmers, uganda, wellbeing. jel codes. i31, q12, r21. 1. introduction producing more than 38 thousand tons of arabica coffee (in 2017/18), uganda is among the most important arabica coffee producers in the world (ucda 2018). with around 1.3 million rural households (hh) engaged in coffee production, uganda’s coffee sector has high socioeconomic importance for the country (ubos 2010). in most coffee cultivation areas, smallholder coffee farmers barely live above the subsistence level. in corresponding author: anna.bartl@uni-goettingen.de 134 anna lina bartl uganda, extreme poverty affects more than 33% of the country’s 39 million people, among them a large number of smallholder coffee farmers (bmz 2016). however, only a few research projects have investigated the wellbeing of coffee farmers to date. most of the research to date dealing with the wellbeing of coffee farmers has measured the impacts of participation in specialty markets or cooperatives and focused on how certification programs affect specific aspects of coffee farmers’ wellbeing (e.g., ahmed and mesfin 2017, bacon et al. 2005, ruben and fort 2012). even recent studies on coffee producers’ wellbeing refer to concepts of wellbeing that have been challenged or developed further, or equate wellbeing with welfare. ahmed and mesfin (2017), for example, use the equivalent of consumption per adult as a wellbeing indicator. the analysis of a single dimension of wellbeing such as income or expenditure has been criticized by authors in other research fields including decancq and lugo (2012), who investigated inequality of wellbeing in russia. although some researchers have used questionnaires containing direct questions about farmers’ wellbeing to assess the impacts thereof (e.g., frank et al. 2011), these studies do not clarify how farmers themselves understand wellbeing. other authors have used related terms, such as “quality of life” (see bacon et al., 2005), in their research on the impacts of participation in certification programs among coffee farmers in nicaragua. however, results like those of bacon et al. (2005) show that most coffee farmers (74% of the nicaraguans surveyed) perceive their quality of life as independent of whether they are part of conventional or alternative trade networks, because “sales to alternative markets is not enough to offset the many other conditions that influence the quality of one’s life” (bacon 2005). estoque et al. (2018) claim that wellbeing is a prerequisite for quality of life. as these diverse findings reflect, wellbeing is complex, usages of wellbeing and related terms differ widely, and research is still needed on the structure of wellbeing among hhs engaged in coffee farming. a better understanding of wellbeing will not only enable comparison between individuals within a given area or between groups of different coffee cultivation areas, but also provide the basis for better evaluation of certification programs or policy measures. the high importance of wellbeing is widely recognized in other fields, and the research on wellbeing has been growing worldwide in recent decades (e.g., suh et al. 1996, kahnemann 1999, allen 2001, decancq and lugo 2012, keyes et al. 2002, beaumont 2011, seligman, 2011, dodge et al. 2012). disciplines including psychology, medicine, economics, and sociology have proposed different instruments for the measurement of wellbeing. one of the more recent and broadly applicable definitions is the one proposed by dodge et al. (2012), who define wellbeing as “the balance point between an individual’s resource pool and the challenges faced” in terms of physical, social, and psychological components of wellbeing. in other words: stable wellbeing exists “when individuals have the psychological, social and physical resources they need to meet a particular psychological, social and/or physical challenge” (dodge et al. 2012). hendry and kloep (2002) developed this concept further. the so-called lifespan model, incorporates the idea that solving challenges successfully leads to development in the individual and/or environment, whereas failing to solve challenges impedes the solution of future challenges. their model also assumes that success in meeting challenges depends on the resource pool individuals have. they conclude that research on wellbeing is not only crucial to adequately measure development; wellbeing is indeed also the prerequisite for development. 135the wellbeing of smallholder coffee farmers in the mount elgon region adding the assumptions of the lifespan model to the theory of subjective wellbeing proposed by headey and waring (1992), who cite external forces as the precondition for change in the wellbeing balance, one could assume that external forces could lead to a positive or negative development in the wellbeing of individuals and groups. humans could be faced with more challenging situations, for instance, in the environment. considering the estimated decrease in the climatic suitability of most ugandans’ arabica coffee cultivation areas, climate change could have a major impact on coffee farming (damatta et al. 2012, jassogne et al. 2012). coffee farmers are already facing heightened environmental problems such as a higher occurrence of pests and diseases (undp 2012) and greater uncertainties regarding temperature and irrigation. changing weather patterns are also expected to reduce coffee quantity and quality (e.g., jassogne et al. 2012, undp 2012, läderach et al. 2012). this will lead to lower income from coffee sales, which would also have a long-term impact on the resources’ farmers have to devote to other aspects of their wellbeing. considering the aforementioned difficulties, the uganda coffee development authority (ucda) developed a program to counteract the challenges coffee farmers are facing. they state the ambitious aim of quadrupling uganda’s coffee production by 2040 by stabilizing coffee farmers’ resources through measures such as workshops on coffee management and distribution of free coffee seedlings (ucda 2019). the present paper investigates the composition of wellbeing of hhs engaged in coffee farming based on data from 431 quantitative interviews. this investigation requires a definition of wellbeing that takes the coffee farmers’ point of view into account as the basis for policy recommendations that will be able to improve coffee farmers’ and their families’ wellbeing. using the definition of wellbeing formulated by dodge et al. (2012), this paper proposes a composite indicator (ci) of wellbeing based on material wealth (physical component), the fulfillment of social needs (social component), and the fulfillment of basic psychological needs (psychological component), to enable the measurement of wellbeing in one of the three most important arabica coffee cultivation areas of uganda. along with a better understanding of what wellbeing means to farmers, this paper uses a well-established ci for wellbeing and meaningful wellbeing indicators to test the hypotheses that (1) wellbeing is not equally distributed within and between sub-counties in the mount elgon region, and that (2) the physical wellbeing component shows a more substantial impact than the social and psychological components on the constitution of wellbeing among these hhs. both assumptions were formulated based on the observation during previous field visits that the group under investigation is economically vulnerable and based on previous data showing high differences in income from coffee-selling activities. the results of this study show dependencies between different indicators of wellbeing and identify the impact levels of the various indicators. as such, they provide an important idea of how the living conditions of coffee-farming hhs are developing and are of high practical relevance for policy makers. in the materials and methods section that follows this introduction, i provide information on the area in which the study was conducted, the sample and data collection, and the methodological background for the construction of the ci, and briefly explain the framework of the data analysis. in the results section, i present descriptive statistics on the wellbeing indicators and the construction and composition of the ci formula, and also provide insights into wellbeing on a factor level and an overview of the wellbeing distribu136 anna lina bartl tion in this research area. in the final sections, i discuss potential policy implications of the findings for improving wellbeing and methodological limitations of the study. 2. materials and methods 2.1 study area the study was conducted on the western slopes of the mount elgon region, one of the three main arabica coffee-producing regions in uganda (knutsdatter formo and padegimas 2012). for many farmers in the mount elgon region, arabica coffee cultivation is the main source of income. in this region, arabica varieties as bugisu local, sl14, sl28, and kp423 are usually intercropped with bananas, beans, peas, ground nuts, vegetables, and shade trees like avocado and mango. it is estimated that 90% of coffee cultivation takes place on plots of less than 3 hectares (chiputwa et al. 2015). data collection for this study took place in the bulambuli district, which extends over about 809 km², reaches elevations of up to 1526 meters above sea level, and is divided into two counties, elgon and bulambuli county (nphc 2014). surveys were administered in elgon county because 60.5% of its hhs were engaged in coffee farming, whereas coffee farmers in bulambuli county only represented 2.2% of existing hhs (nphc 2014). in elgon county, the three sub-counties of bulegeni, simu, and namisuni were chosen (fig. 1). figure 1. map of (b) south uganda and (a) details of bulambuli district with bulambuli county (grey) and elgon county (white) with the sub-counties bulegeni (blue), simu (turquoise), and namisuni (orange). 137the wellbeing of smallholder coffee farmers in the mount elgon region for data collection, 460 coffee-cultivating hhs were randomly selected and visited. the only prerequisite for participation in the study was that farmers were willing and that their hh was engaged in coffee cultivation activities. of these 460 hh, 29 did not provide (sufficient) data for different reasons: hh heads were located but not available for an interview even on the third attempt, hh heads had died or were ill, or another person representing the hh head was unable to provide reliable answers. this left a final data set with completed questionnaires from 431 hhs (table 1). 2.2 sample description comparing the sample distribution with the average hh characteristics for the area in which the study was conducted, slight deviations in socio-demographic characteristics can be seen (table 2). however, statistics on the area’s population were either collected in 2012 (see nphc 2014) or, for those from the most recent reliable source, only refer to the mount elgon region as a whole (unhs 2018). taking the high fertility rates in uganda into account (5.4 children per woman in 2016), these deviations in sample characteristics can generally be accepted due to the broad similarity in socio-demographic trends (supre 2018). as production of cash crops like coffee is usually male-dominated in rural areas of sub-saharan africa, and only coffee farmers were included into the sample group, female-headed hhs are clearly underrepresented (e.g., bolwig 2012, doss 2002).1 however, the data set can be considered largely representative for this research area, except for the small percentage of female-headed hhs and the larger number of people per hh in this study than in the statistics. discrepancies in the data, particularly for the gender of the hh head, could not be excluded in the interpretation of the results. 2.3 data collection data were collected as part of the project “potential improvements for the income situation of smallholder coffee farmers in mount elgon, uganda” developed and implemented by the georg-august university of göttingen, germany, and the national agricultural research organization (naro) of uganda. the theoretical selection of relevant dimensions for the present study and context was conducted based on a literature review (decancq and lugo 2012, dodge et al. 2012, among others) and on data from hh surveys implemented in the mount elgon region in 2015. the resulting framework was discussed with the local research team, consisting of five research assistants who had grown up in coffee-cultivating hhs in the area, in 2017. based on that, questionnaire pre-tests were developed and implemented in the area to 1 only 20.7% of the female hh heads are married. the rest of the female hh heads are single (20.7%), divorced (10.3%), or widowed (48.3%), whereas only 1.5% of the male hh heads are widowed. table 1. number of hhs participating in the study. sub-county participants hh survey bulegeni 156 (36.2%) simu 90 (20.9%) namisuni 185 (42.9%) total 431 (100%) 138 anna lina bartl evaluate the feasibility of the survey and to test the content and construct validity and reliability. after some revisions, the survey was finally successfully implemented in 431 coffee-cultivating hhs in the period from july to december 2018. the final survey of the project comprised seven sections; (i) hh demographics, (ii) farm management system, (iii) access to information and extension material, (iv) general hh living conditions, (v) expected yield and income, (vi) community relations, and (vii) shortages and shocks experienced so far. within five of these sections (ii-vi), a set of subdimensions comprising a total of 44 variable dimensions was developed to measure the wellbeing level of the hhs (table 3). because of different levels of english proficiency in the population, the five local assistants (four male and one female) were trained to conduct an average number of five interviews per day in the local language lugisu. for the time spent to complete the questionnaire (50 minutes on average), each farmer received compensation in the form of bookkeeping and small business management materials. after data cleaning, 431 interviews remained for data analysis. table 2. sample characteristics. quantitative data set bulegeni simu namisuni total research area number of hhs n= 156 n= 90 n= 185 n=431 21,244 1 gender of hh head male 94.2% 95.6% 93.5% 94.2% 81.4% 1 female 5.8% 4.4% 6.5% 5.8% 18.6% 1 age of hh head <18 0.0% 0.0% 0.0% 0.0% 1.0% 1 18-30 7.1% 11.1% 16.1% 11.7% 25.9% 1 31-59 60.3% 62.2% 65.0% 62.7% 53.9% 1 >60 32.7% 26.7% 18.9% 25.6% 19.2% 1 highest level of education for head of hh illiterate 3.9% 4.4% 2.2% 3.3 % 9.3% 2 primary school 45.8% 41.1% 59.7% 50.7% 58.7% 2, 3 high school 44.4% 47.8% 34.3% 40.8% 27.8% 2, 4 college 3.9% 3.3% 2.8% 3.3% 8.2% 2, 5 university 2.0% 3.3 % 1.1% 1.9% people per hh md/sd 6.31/2.338 6.41/2.238 5.21/2.170 5.86/2.312 4.638/0.135 2 coffee production is the main source of income 83.2% 93.3% 88.6% 87.7% 83.0% major economic activity is crop farming 2 1data for elgon county from nphc 2014. 2data for elgon region from unhs 2018. 3sum from category: some primary and completed primary for the whole hh. 4sum from category some secondary and completed secondary for the whole hh. 5post-secondary and above for the whole hh. 139the wellbeing of smallholder coffee farmers in the mount elgon region 2.4 methodical background for the construction of the ci for wellbeing as pointed out in the introduction section, a ci was constructed for wellbeing based on hh material wealth (physical component), the fulfillment of social needs (social component), and the fulfillment of basic psychological needs (psychological component). in contrast to dodge et al. (2012), the present paper does not investigate wellbeing at the individual level but at the hh level. the hh reflects a social construct, which leads to a high level of overlap in content between social and psychological indicators in the data set presented here (see table 3). for the indicators that reflect the level of trust, for instance, a clear classification into either social or psychological categories cannot be made. mistrust could reflect instability within the community, but it could also stem from fears of opportunistic behavior or from other psychological discomfort, especially when considering the economic vulnerability and dependency of the farmers in our sample group. therefore, the social and psychological wellbeing indicators were merged into a “social-psychological” component. to examine the fulfillment of social-psychological needs, only indicators dealing with the individual-level emotions and social interactions of farmers have been selected for the construction of the ci. all social-psychological wellbeing variables were measured with a five-point likert scale ranging from 1 (not at all) to 5 (very much; the highest subjective wellbeing for that item), except for hh participation in meetings, workshops, and training over the last 12 months, which was measured with a binary survey question and represented as a population percentage. for the physical component, only variables that focus table 3. structure of survey sections relevant to wellbeing. section of the survey dimensions (ii) farm management system area of land for agricultural activity in general, area of land for coffee cultivation, ownership of land, intercropping with other products, livestock, membership in farmer organizations, certification of coffee, farm management practices (iii) access to information and extension material main sources and interest in information on farm management, participation in meetings, workshops (iv) general hh living conditions characteristics of the main house; toilet, wall material, floor material, roofing, source of lighting, cooking, source, distance and mode of treating drinking water, distance to village market, doctor/hospital, and school (v) expected yield and income, access to productive capital/credit yield, prices for coffee cherries and parchment coffee, expected income from coffee selling, other sources of income, labor input coffee production, loans, farm equipment, consumer durables, cellphone, bicycle, motorbike (vi) community relations safe from violence and crime, safe from economic disasters, level of happiness, most people can be trusted, most government officials can be trusted, local government considers concerns voiced by you, most people are willing to help, collaboration with other farmers, heterogeneity within the village, frequency of getting together with others 140 anna lina bartl on the measurable status of material wealth have been included in the analysis: indicators measuring the value of hh belongings or productive hh activities. the scales used here describe an objectively measurable condition, ranked by the status of wealth on the hh level, expressed in some cases by the number of items belonging to the hh, hectares of land, or construction materials for housing. material for housing was ranked from reflecting low wealth (non-permanent materials like mud/soil for walls, earth for floors, and grass/banana leaves for roofing), mid-wealth status (semi-permanent materials like plaster for walls, wood for floors, and sheet metal for roofing) to high wealth (permanent materials like bricks for walls, cement for floors, and tiles for roofing) within the community2. variables used for the construction of the ci were measured using different units and scales. to enable comparison within and between individual indicators and different scales, and to preserve the empirical distribution of the data, the indicators were standardized by computing z-scores (santeramo 2015). for each individual indicator , the average and the standard deviation were calculated. a similar dispersion across indicators emerges when implementing into the normalization formula: to explore whether the theoretically developed indicators of wellbeing are statistically well-balanced and whether the indicators are suitable for the underlying data structure, a principal component analysis (pca) was performed. factors that meet the prerequisites of having eigenvalues larger than one and of individually contributing more than 10% to total explained variance are included in the ci for wellbeing. the square of factor loadings represents the proportion of total unit variance of the ci of wellbeing explained by the factor (jrcec 2008). referring to santeramo (2015), equal weighing does not only represent the weak assumption that all variables have the same importance; it may also induce double-counting bias because a higher number of variables in a subgroup leads to a higher weight of that subgroup. there are many weighing approaches preventing the conclusion that dimensions have similar importance, among them the pca, which relies on data variability and variable correlation (jrcec 2008, nicoletti et al. 2000, santeramo 2015). for the pca-based approach of nicoletti et al. (2000), the variance explained by the factor after varimax rotation could be used to calculate the weight of each factor if correlations between indicators are found. variance explained by the factor weight of the factor for the ci of wellbeing (wq) = total variance of the four factors the z-standardized scores used for the pca were regressed for each factor, and the ci of wellbeing was calculated for each interviewed hh. 2.5 data analysis to compute the ci of wellbeing, i followed the methodological approaches described in the handbook on constructing composite indicators’ of the joint research centre of the 2 the scales were developed based on estimated values of construction material quality within the community resulting from previous qualitative interviews. 141the wellbeing of smallholder coffee farmers in the mount elgon region european commission to best fit the constitution of the data at hand (jrcec 2008). spss version 25 was used for tasks including to perform the required pca for the construction of the ci of wellbeing. pearson’s correlation coefficient was used to check for correlations between individual indicators using the z-scores of the items. to assess whether sub-county had an influence on the indicators, factors, or the ci of wellbeing, a one-factor anova was performed. 3. results 3.1 descriptive statistics of indicators after testing all previously mentioned variables, only 19 wellbeing indicators were able to provide specific, measurable, accessible, relevant, and timely (smart) information and fulfill the aforementioned selection criteria for wellbeing (fao, 2013). table 4 provides the categorization into components of wellbeing and descriptive statistics for absolute (not standardized) values of those indicators. the social-psychological indicators with the highest scores (up to 4.56) are represented by the willingness to help and intensity of collaboration with other farmers. whereas the level of happiness and local government considers farmers’ concerns show means of approximately 3.7, trust in government officials shows a lower mean, and trust in most people represents the lowest level of satisfaction at 3.10. all social-psychological indicators show different means in different sub-counties. the lowest means for all trust-related indicators is in bulegeni sub-county. namisuni shows the highest mean for the indicators local government considers farmers’ concerns and trust in government officials. whereas simu subcounty has the highest mean for trust in most people, it is also represented by the highest percentage (68.89%) of hhs that have participated in meetings, workshops, and training during the last 12 months. the individual indicators for the physical component of wellbeing show a very high percentage of hhs (84.4% to 97.8%) with floor and wall materials consisting of earth (floors) and mud/soil (walls) that indicate the lowest level of welfare. in consequence, percentages for indicators showing mid-to high-valued housing materials are low (0.0% to 15.6%), which could also explain the presence of extreme values. hh access to belongings ranges from farm equipment, which nearly all hhs (97.45%) have access to, to consumer durables (mainly radio) (79.58%), cellphone (70.30%), bicycle (ranging from 37.78% to 7.03%) all the way down to motorbikes, which are only present in 8.35% of hhs. only namisuni is an exception, with a higher percentage of access to motorbikes than bicycles. for landholding for agricultural activities in general, the mean for the sample shows 0.95 ha, whereas the hh use on average 0.5 ha of their land for coffee cultivation. in table 4, the wide range of landholding in simu reveals differences in access to land within the sub-county. however, there is also evidence of a general trend of differences in all physical indicators (except from the indicator roofing material) between the sub-counties: simu shows a higher percentage of higher values than other sub-counties. bulegeni also shows a much lower wealth status than other sub-countries, directly followed by namisuni. 142 anna lina bartl table 4. descriptive statistics for the 19 wellbeing indicators. indicator bulegeni (n= 156) simu (n= 90) namisuni (n= 185) total (n=431) extreme values1 social-psychological wellbeing component hh members participated in meetings, workshops, and training (last 12 months) 62.18% 68.89% 60.54% 62.88% 0 trust in most people mean 3.08 3.17 3.09 3.10 0 sd 1.56 1.50 1.54 1.53 trust in government officials mean 3.26 3.36 3.44 3.36 0 sd 1.57 1.34 1.44 1.47 local government considers the farmers’ concerns mean 3.61 3.71 3.77 3.70 0 sd 1.38 1.38 1.30 1.35 willingness to help mean 4.56 4.04 4.06 4.24 0 sd 1.09 1.52 1.49 1.39 intensity of collaboration with other farmers mean 4.06 3.97 4.31 4.15 0 sd 1.49 1.42 1.49 1.35 economically secure mean 3.08 3.72 3.72 3.49 0 sd 1.63 1.39 1.42 1.52 safe from violence and crime mean 3.87 3.27 3.32 3.51 0 sd 1.16 1.60 1.69 1.52 level of happiness mean 3.85 3.77 3.68 3.76 0 sd 1.20 1.45 1.40 1.34 physical wellbeing component farm equipment belongs to hh 98.08% 98.89% 96.22% 97.45% 0 consumer durables belong to hh 78.85% 86.67% 76.76% 79.58% 0 cellphone belongs to hh 71.79% 81.11% 63.78% 70.30% 0 bicycle belongs to hh 26.92% 37.78% 7.03% 20.65% 0 motorbike belongs to hh 5.13% 13.33% 8.65% 8.35% 0 wall material mud/soil 93.6% 84.4% 96.2% 92.8% 31 plaster 3.2% 3.3% 1.6% 2.6% brick 3.2% 12.2% 2.2% 4.6% floor material earth 92.9% 84.4% 97.8% 93.3% 31 wood 0.6% 0.0% 0.0% 0.2% cement 6.4% 15.6% 2.2% 6.5% roofing material grass/banana leaves 1.92% 0% 1.08% 1.16% 0 sheet metal 79.49% 93.33% 93.51% 88.40% tile 18.59% 6.67% 5.41% 10.44% land used for coffee cultivation (ha) mean 0.46 0.77 0.39 0.50 26 sd 0.48 0.99 0.42 0.62 range 3.03 5.26 2.83 5.26 min. 0.00 0.00 0.00 0.00 max. 2.83 5.26 2.83 5.26 143the wellbeing of smallholder coffee farmers in the mount elgon region 3.2 pca for the ci of wellbeing a pca was applied to these 19 variables. the best result of the pca (shown in fig. 2) was found for a four-factor solution that can explain 81.20% of the total variance (kaisermeyer-olkin measure (kmo) = 0.681, bartlett’s test of sphericity sig. =0.000) by including ten of the previously derived indicators. the factor trust consists of the indicators trust in most people, trust in government officials, local government considers the farmers’ concerns (fig. 2). the factor security consists of the indicators economically secure, safe from violence and crime, and the level of happiness. the connection between happiness and security can be explained by citing one of the interviewed farmers: “[…] well, to me, happiness is the state of being content with all the prevailing circumstances in life.” for the physical component of wellbeing, the factor housing consists of walls and floors, whereas the factor landholding includes land for agricultural activity and land for coffee cultivation. testing the combination of the variables of the four-factor solution for reliability, the cronbach’s coefficient alpha (c-α) for the total internal consistency shows a value of 0.741, which is acceptable (field 2009). consequently, for the development of the ci of wellbeing, only the variables for the resulting factors trust, security, housing, and landholding are investigated further. factor 1 (trust) explains 31.868%, factor 2 (security) explains 24.568 %, factor 3 (housing) explains 13.959%, and factor 4 (landholding) explains 10.805% of the total variance. as a last step, the relationships between individual indicators are investigated and depicted in table 5 in order to inspect whether correlations between indicators are present and to calculate the weight for each factor using the results of the pca, as suggested by nicoletti et al. (2000). correlation results yield a strong positive relationship between trust in most people and trust in government officials (corr=0.718**), between local government considers the farmers’ concerns and trust in most people (corr=0.587**), and between trust in government officials and local government considers the farmers’ concerns (corr=0.736**). for the indicators of the factor security, the positive relationship is not as strong. there is a positive relationship between wall and floor materials (corr=0.932**) and between land used for agricultural activity and land used for coffee cultivation (corr=0.480). in addition, all physiindicator bulegeni (n= 156) simu (n= 90) namisuni (n= 185) total (n=431) extreme values1 land used for agricultural activity (ha) mean 0.91 1.32 0.80 0.95 24 sd 0.81 1.29 0.61 0.88 range 5.97 7.99 2.95 8.04 min. 0.10 0.10 0.05 0.05 max. 6.07 8.09 3.00 8.09 1to ensure that all levels of wellbeing are included in the data analysis, these extreme values were not excluded. 144 anna lina bartl cal indicators have positive relationships with each other. pearson’s correlation indicates a relationship between economically secure and (a) land for coffee cultivation (corr=0.129**), (b) land used for agricultural activity (corr=-0.160**). similar relationships between the level of happiness and the indicators of landholding are visible. furthermore, all indicators of the factor trust show a highly significant (p≤0.01) positive correlation with the indicators of security and the landholding indicator land used for coffee cultivation. the perception of being safe from violence and crime correlates negatively with land used for agricultural activity (corr=-0.257**). relationships between indicators of different factors can also be found, but their correlation is not strong (corr<0.5). however, the correlations found between the individual indicators are strong enough to enable the calculation of weights of each factor by dividing the percentage of variance explained by the factor after varimax rotation by the total variance of all factors (jrcec 2008). the results of the pca, relevant for the construction of the ci of wellbeing, are shown in table 6. previously indicated results from the pca lead to the following formula for the ci of wellbeing: figure 2. summarizing the components of wellbeing, the indicators investigated, and the results ofthe principal component analysis (pca). extraction method: principal component analysis; rotation method: varimax with kaiser normalization, rotation converged in 5 iterations. ** highly significant p=0.01. 145the wellbeing of smallholder coffee farmers in the mount elgon region ta bl e 5. p ea rs on ’s co rr el at io n w ith z -s co re s of th e te n si ng le in di ca to rs . fa ct or in di ca to r 1 2 3 4 5 6 7 8 9 10 tr us t 1. tr us t i n m os t p eo pl e 1 0. 71 8** 0. 58 7** 0. 15 2** 0. 24 2** 0. 21 1** n. s. n. s. 0. 13 7** n. s. 2. tr us t i n go ve rn m en t o ffi ci al s 0. 71 8** 1 0. 73 6** 0. 23 6** 0. 30 9** 0. 28 8** n. s. n. s. 0. 14 6** n. s. 3. lo ca l g ov er m . c on sid er s t he fa rm er s’ co nc er ns 0. 58 7** 0. 73 6** 1 0. 36 3** 0. 36 4** 0. 38 9** n. s. n. s. 0. 16 3** n. s. se cu rit y 4. ec on om ic al ly se cu re 0. 15 2** 0. 23 6** 0. 36 3** 1 0. 40 2** 0. 58 9** n. s. n. s. 0. 12 9** -0 .1 60 ** 5. sa fe fr om v io le nc e an d cr im e 0. 24 2** 0. 30 9** 0. 36 4** 0. 40 2** 1 0. 55 9** n. s. n. s. n. s. -0 .2 57 ** 6. le ve l o f h ap pi ne ss 0. 21 1** 0. 28 8** 0. 38 9** 0. 58 9** 0. 55 9** 1 n. s. n. s. 0. 11 7* -0 .2 44 ** h ou sin g 7. w al l n. s. n. s. n. s. n. s. n. s. n. s. 1 0. 93 2** 0. 33 8** 0. 10 1* 8. fl oo r n. s. n. s. n. s. n. s. n. s. n. s. 0. 93 2** 1 0. 36 0** 0. 10 2* la nd ho ld in g 9. la nd u se d fo r c off ee c ul tiv . 0. 13 7** 0. 14 6** 0. 16 3** 0. 12 9** n. s. 0. 11 7* 0. 33 8** 0. 36 0** 1 0. 48 0* * 10 .l an d us ed fo r a gr ic ul tu ra l a ct iv ity n. s. n. s. n. s. -0 .1 60 ** -0 .2 57 ** -0 .2 44 ** 0. 10 1* 0. 10 2* 0. 48 0* * 1 *s ig ni fic an ce fo r t w ota ile d co rr el at io n is p ≤0 .0 5, * *s ig ni fic an ce fo r t w ota ile d co rr el at io n is p ≤0 .0 1, c or re la tio ns o f 0 .5 a nd a bo ve a re s ho w n in b ol d. ta bl e 6. v ar ia nc e ex pl ai ne d by e ac h fa ct or a nd w ei gh t o f e ac h fa ct or . fa ct or % o f v ar ia nc e cu m ul at iv e % % o f v ar ia nc e aft er va rim ax ro ta tio n w ei gh t o f e ac h fa ct or tr us t 31 .8 68 31 .8 68 23 .4 51 0. 28 88 se cu rit y 24 .5 68 56 .4 36 21 .4 92 0. 26 47 h ou sin g 13 .9 59 70 .3 95 19 .5 67 0. 24 10 la nd ho ld in g 10 .8 05 81 .2 00 16 .6 89 0. 20 55 ex tr ac tio n m et ho d: p rin ci pa l c om po ne nt a na ly si s. w ei gh t o f e ac h fa ct or = % o f v ar ia nc e ex pl ai ne d th e fa ct or a ft er v ar im ax ro ta tio n/ to ta l v ar ia nc e. 146 anna lina bartl ci of wellbeing = (wtrust * trust) +(wsecurity * security) + (whousing * housing) + (wlandholding * landholding) implementing the weights of each factor into the formula for the ci of wellbeing, the final ci formula is: ci of wellbeing = (0.2888 * trust) + (0.2647 * security) + (0.2410 * housing) + (0.2055 * landholding) 3.2.1 influence of sub-county on wellbeing to test the first hypothesis, the first step is to conduct an investigation at the indicator level. the results of the one-factor anova (shown in table 7) confirm the hypothesis of an influence of the sub-county on the perception of being economically secure, and on all indicators of the physical components of wellbeing with p=0.000***. assumptions are also confirmed by the one-factor anova, with p=0.001*** for the influence of sub-county on being safe from violence and crime. for the other social-psychological indicators, no significant influence of sub-county could be found. however, these results should be interpreted carefully for safe from violence and crime (p=0.000***), economically secure (p=0.000***), level of happiness (p=0.000***), and trust in local government officials (p=0.011*) and for all physical indicators (p=0.000***) because levene’s test is undesirably significant, which means that homogeneity of variance cannot be assumed. in addition, the requirement for normally distributed data is not met according to the kolmogorov-smirnov (ks) test (p = 0.000***). however, to fully test the first hypothesis, i also examined to what extent this regional influence is also given for the factors. the results of the one-factor anova (shown in table 8) show a significant influence of sub-county on housing (p=0.004**) and on landholding (p=0.000***). the influence of sub-county on trust (p=0.858) and security (p=0.988) is not significant. however, due to the significance of levene’s test for housing (p=0.000***), landholding (p=0.000***) and security (p=0.043*), these results should be interpreted carefully. in the following section, i investigate factors after standardisation (z-score transformation). using the previously specified formula for wellbeing, the wellbeing index shows a mean of 0.000 for the total group. negative values for the ci show a lower wellbeing compared to the rest of the sample group. the greater the positive figure, the better the wellbeing relative to the mean of wellbeing index for all hhs. to better illustrate how the wellbeing distribution differs by sub-county, boxplots are provided in fig. 3. regarding the wellbeing distribution between sub-counties, it can be seen that simu has the highest mean (0.158), followed by bulegeni (-0.026) and namisuni (-0.055). the range indicates differences within the sub-counties: namisuni has the lowest range (2.238), followed by bulegeni (2.742), whereas simu has the highest range (3.178) for the ci of wellbeing between hhs. the last step is now to have a look at whether this relation is also evident for the overall wellbeing construct. based on the anova (table 9), the influence of sub-counties on the wellbeing index is highly significant (p = 0.003**), but again, levene’s test is significant (p=0.006**), which points to the need for careful interpretation of this result. 147the wellbeing of smallholder coffee farmers in the mount elgon region ta bl e 7. o ne -fa ct or a n o va fo r t he in flu en ce o f s ub -c ou nt y on th e in di ca to rs o f t he s oc ia l-p sy ch ol og ic al a nd p hy si ca l w el lb ei ng fa ct or s. fa ct or in di ca to r so ur ce pa rt ia l s s df m s f p( >f ) tr us t tr us t i n m os t p eo pl e be tw ee n gr ou ps 0. 47 5 2 0. 23 8 0. 10 1 0. 90 4 w ith in g ro up s 10 11 .0 33 42 8 2. 36 2 to ta l 10 11 .5 08 43 0 tr us t i n go ve rn m en t o ffi ci al s be tw ee n gr ou ps 2. 95 5 2 1. 47 7 0. 68 4 0. 50 5 w ith in g ro up s 92 4. 02 0 42 8 2. 15 9 to ta l 92 6. 97 4 43 0 lo ca l g ov er nm en t c on sid er s t he fa rm er s’ co nc er ns be tw ee n gr ou ps 2. 28 9 2 1. 14 4 0. 63 1 0. 53 3 w ith in g ro up s 77 6. 10 1 42 8 1. 81 3 to ta l 77 8. 39 0 43 0 se cu rit y ec on om ic al ly se cu re be tw ee n gr ou ps 41 .1 64 2 20 .5 82 9. 22 9 0. 00 0* ** w ith in g ro up s 95 4. 51 6 42 8 2. 23 0 to ta l 99 5. 68 0 43 0 sa fe fr om v io le nc e an d cr im e be tw ee n gr ou ps 32 .5 02 2 16 .2 51 7. 26 6 0. 00 1* ** w ith in g ro up s 95 7. 22 0 42 8 2. 23 6 to ta l 98 9. 72 2 43 0 le ve l o f h ap pi ne ss be tw ee n gr ou ps 2. 47 2 2 1. 23 6 0. 68 8 0. 50 3 w ith in g ro up s 76 8. 94 8 42 8 1. 79 7 to ta l 77 1. 42 0 43 0 h ou sin g w al l be tw ee n gr ou ps 3. 00 6 2 1. 50 3 7. 84 9 0. 00 0* ** w ith in g ro up s 81 .9 59 42 8 0. 19 1 to ta l 84 .9 65 43 0 fl oo r be tw ee n gr ou ps 4. 34 6 2 2. 17 3 9. 19 7 0. 00 0* ** w ith in g ro up s 10 1. 11 6 42 8 0. 23 6 to ta l 10 5. 46 2 43 0 la nd ho ld in g to ta l h ec ta re s o f l an d us ed fo r c off ee c ul tiv at io n be tw ee n gr ou ps 9. 10 9 2 4. 55 5 12 .4 48 0. 00 0* ** w ith in g ro up s 15 5. 86 8 42 6 0. 36 6 to ta l 16 4. 97 8 42 8 to ta l h ec ta re s o f l an d us ed fo r a gr ic ul tu ra l a ct iv ity be tw ee n gr ou ps 16 .6 14 2 8. 30 7 11 .1 46 0. 00 0* ** w ith in g ro up s 31 8. 99 3 42 8 0. 74 5 to ta l 33 5. 60 6 43 0 148 anna lina bartl table 8. one-factor anova for the influence of sub-county on the factors trust, security, housing, and landholding. factor source partial ss df ms f p(>f) trust between groups 0.307 2 0.153 0.153 0.858 within groups 427.693 426 1.004 totai 428.000 428 securit y between groups 0.023 2 0.012 0.012 0.988 within groups 427.977 426 1.005 totai 428.000 428 housing between groups 11.080 2 5.540 5.661 0.004** within groups 416.920 426 0.979 totai 428.000 428 landholding between groups 22.228 2 11.114 11.668 0.000*** within groups 405.772 426 0.953 totai 428.000 428 number of observations = 431. figure 3. bloxplots depicting the ci of wellbeing by sub-county. table 9. one-factor anova for the influence of sub-county on wellbeing index. source partial ss df ms f p(>f) between groups 2.923 2 1.462 5.891 0.003** within groups 105.696 426 0.248 total 108.619 428 149the wellbeing of smallholder coffee farmers in the mount elgon region ta bl e 10 . d es cr ip tiv e st at is tic s fo r t he fo ur w el lb ei ng fa ct or s (a ft er z -s co re tr an sf or m at io n) . c om po ne nt fa ct or su bco un ty n v al id m ea n m ed ia n sd va ria nc e ra ng e m in m ax so ci al -p sy ch ol og ic al tr us t bu le ge ni 15 6 -0 .0 26 0. 35 0 1. 07 1 1. 14 8 3. 71 8 -2 .1 89 1. 52 8 si m u 90 -0 .0 19 0. 21 9 0. 95 3 0. 90 9 3. 46 2 -1 .8 28 1. 63 4 n am isu ni 18 5 0. 03 1 0. 31 1 0. 96 4 0. 93 0 3. 81 0 -2 .1 82 1. 62 8 to ta l 43 1 0. 00 0 0. 28 5 1. 00 0 1. 00 0 3. 82 3 -2 .1 89 1. 63 4 se cu rit y bu le ge ni 15 6 0. 00 6 0. 14 4 0. 90 4 0. 81 7 3. 65 3 -2 .0 77 1. 57 6 si m u 90 0. 00 7 0. 38 0 1. 03 6 1. 07 4 3. 67 3 -1 .9 36 1. 73 7 n am isu ni 18 5 -0 .0 08 0. 28 9 1. 06 2 1. 12 7 3. 82 8 -2 .2 49 1. 57 9 to ta l 43 1 0. 00 0 0. 27 3 1. 00 0 1. 00 0 3. 98 7 -2 .2 49 1. 73 7 ph ys ic al h ou sin g bu le ge ni 15 6 -0 .0 26 -0 .2 10 0. 91 1 0. 83 1 5. 13 2 -0 .8 85 4. 24 7 si m u 90 0. 30 0 -0 .2 36 1. 49 0 2. 22 1 5. 95 3 -1 .5 23 4. 43 0 n am isu ni 18 5 -0 .1 25 -0 .2 21 0. 70 8 0. 50 1 5. 46 7 -0 .9 01 4. 56 6 to ta l 43 1 0. 00 0 -0 .2 21 1. 00 0 1. 00 0 6. 08 9 -1 .5 24 4. 56 6 la nd ho ld in g bu le ge ni 15 6 -0 .0 69 -0 .3 32 0. 83 9 0. 70 3 6. 17 0 -1 .2 30 4. 94 0 si m u 90 0. 43 58 0. 10 2 1. 56 3 2. 44 2 8. 62 6 -1 .6 32 6. 99 3 n am isu ni 18 5 -0 .1 54 -0 .3 50 0. 66 3 0. 43 9 5. 15 4 -1 .6 36 3. 51 8 to ta l 43 1 0. 00 0 -0 .2 70 1. 00 0 1. 00 0 8. 62 9 -1 .6 36 6. 99 3 150 anna lina bartl 3.2.2 comparison of physical and social-psychological factors to compare physical and social-psychological influences in a second step, i investigated z-standardized factors. following the descriptive results shown in table 10, the total range and the ranges per sub-county for z-scores of the social-psychological factors trust and security are < 4. the ranges of the z-scores of the factors housing and landholding of the physical component of wellbeing show greater (5.132 to 8.629) differences between minimum and maximum values of z-scores in total and for individual subcounties. the physical component of wellbeing thus indicates greater variation and also a higher diversity in the percentage impact on wellbeing compared to the social-psychological component. however, figure 4 gives a more detailed explanation for the impacts of the individual factors on wellbeing by sub-county. it shows the means for all factors and the ci of wellbeing for the individual sub-counties and reveals that landholding has the strongest impact on wellbeing in all individual sub-counties, although this impact is negative (negative scores correspond to values less than the mean) in the sub-counties of bulegeni and namisuni. the means of the social-psychological factors trust and security are clearly smaller for all individual sub-counties, except for bulegeni, where trust causes lower means for wellbeing of the hhs. with regard to the mean of total wellbeing, the results in fig. 4 show that in simu, the wellbeing score is the highest, followed by bulegeni and namisuni. figure 4. means for the factors trust, security, housing, and landholding, and the ci of wellbeing for the sub-counties bulegeni, namisuni, and simu (after z-score transformation). 151the wellbeing of smallholder coffee farmers in the mount elgon region 4. discussion the findings presented here suggest that wellbeing can be explained mainly by the four factors trust, security, landholding, and housing quality, containing in total ten indicators divided into physical and social-psychological components that have different impacts on the wellbeing of individual hhs in the research area. in addition, an influence of the sub-county on wellbeing was found that can be explained primarily by the significant differences found for the physical factors housing and landholding. the physical conditions of the hhs show that only a few have homes with plaster or brick walls, or wood or cement floors. this finding confirms those of the nphc (2014), where only 6.6% of the interviewed hhs responded that their dwellings were constructed with permanent floor materials and 6.9% with permanent wall materials. the total mean for our sample group for land used for agricultural activity is 0.95 hectares, whereas the total mean for land used for coffee cultivation is about half that (0.5 ha). other studies in the mount elgon region of uganda found that the majority of their sample group had less than one hectare of land (e.g., mugagga 2011). for both land used for agricultural activity in general and land used for coffee cultivation, the ranges of values for the area are up to ten times higher than the mean, which indicates wide disparity with regard to landholding in the community, especially in simu. the smaller means for the social-psychological factors could also be explained by the transformation into z-scores: indicators with extreme values, such as those for landholding, have a greater effect on the ci, because indicators are converted to a common scale with a mean of zero and standard deviation of one. here, extreme values were not excluded, because differences in hectares of land could not be ignored in cases where the main economic activity is farming. nevertheless, it is widely accepted that landholdings are the major factor for hhs depending on agriculture. even though the entire social-psychological component and also the individual factors trust and security do not significantly differ between sub-counties (quality of results from anova were confirmed by levene’s and ks tests), the results still show a significant influence of the sub-county on the single indicators economically secure and safe from violence and crime. the means for the final calculated ci of wellbeing indicate the highest wellbeing for the sub-county of simu, followed by bulegeni and namisuni. the same order is found for the range of wellbeing within the sub-counties. to conclude, the results show differences in wellbeing within, and even greater differences in wellbeing between sub-counties, confirming the hypothesis stated at the outset. although there has been no direct research on wellbeing in the area under investigation here, findings reported by the nphc (2014) also indicate differing levels of wealth in different sub-counties: for instance, the percentage of 6-12-year-old children not attending school is 17.6% in bulegeni, 13.6% in namisuni, and 12.9% in simu. in addition, the percentage of 18-30-year-olds who are not in school and not working ranges widely, from the lowest in bulegeni (8.7%), followed by simu (12.5%), to the highest in namisuni (27.1%). looking at the percentage of people eating less than two times a day, simu has the highest rate at 9.3%, followed by namisuni at 7.2% and bulegeni at 5.3% (nphc 2014). in contrast to the results of the present paper, the findings of the nphc (2014) do not clearly indicate distinct trends for the individual sub-counties. 152 anna lina bartl the sub-counties investigated in this study do not border each other. looking at the map of the sub-counties, it becomes clear that namisuni and bulegeni are closer to each other than to simu. the geographical distances between the sub-counties correspond to similarities in the results of the wellbeing index, with sub-counties that are geographically closer showing more similar results. further research should investigate whether geographic location really matters for wellbeing and whether there are other reasons that could explain the differences in physical wellbeing in different sub-counties. a possible explanation for the higher welfare in simu could be the better access to roads, which enable faster and safer transport to the next town and could also lead to economic advantages. another possible explanation could be better ecological conditions. it can be assumed that the presence of sisiyi falls in simu could provide a more constant water source for crop cultivation or lessen the impact of droughts. this could lead to higher income from coffee selling or lower expenditures for food that has to be purchased in addition to self-sufficiency agriculture. proving this assumption would require further investigation of the water sources in bulegeni and namisuni. also, the housing quality parameter could explain the differences in wellbeing, because in namisuni and bulengeni, soils are too poor to make bricks, and transportation costs for bricks in both of these sub-counties with lower wellbeing far exceed the cost of the bricks themselves, whereas in simu, the conditions for building a permanent house do not entail such high transaction costs. there could, however, be several other reasons for better physical wellbeing in simu that should be included in further analyses. it might also be interesting to find reasons explaining the higher trust levels and the lower security perceptions in namisuni than in the other sub-counties. nonetheless, several impacts of the data-driven development of the ci of wellbeing should be considered. here, we interviewed the hh heads, who in our sample group are mainly men. considering that based on their role within the hh, women are more likely to consider the wellbeing of the entire family, there might be differences in the indicators impacting wellbeing. a female perspective could be somewhat more representative of the wellbeing of the entire hh and might also consider more health or educational indicators, such as those found in the women’s capabilities index for malawi developed by greco (2018). due to the widespread gendered division of labor in uganda and the corresponding differences in men’s and women’s responsibilities for coffee-related tasks (bantebya et al. 2014), it might be difficult to collect high-quality data from the hh heads’ wives on questions about the economic or security level of the hh, realms that traditionally are the husband’s responsibility. this issue does not have a major impact on the comparability within our sample group, but the higher number of male-headed hhs interviewed for this study compared to the research area reduces the representativeness of the results for the entire research area. nevertheless, the construction of a ci has the advantage of measuring wellbeing indirectly. indirectly answered questions can lead to a lower impact of social desirability of the answers given by the farmers. it can further prevent low response quality due to different understandings of what complex terms like wellbeing mean. however, there are many different ways to construct a ci, starting with the definition of the term wellbeing, contentrelated selection criteria for indicators, the statistical analysis of reliability of indicators, all the way to the choice of a tool for measuring the weight of influencing factors. even 153the wellbeing of smallholder coffee farmers in the mount elgon region if many indicators were involved here, indicators such as the hh head’s health status or coffee productivity might be considered for further data collection. in the future, social and psychological components could be differentiated in more detail to provide an even better picture of what wellbeing means for the hhs investigated. each individual step in constructing the ci influences how well the ci reflects wellbeing. nevertheless, even the best choices in each step would lead to a loss in information due to the merging of single indicators. however, the weighting of the factors was also calculated by the variance of the pca and resulted in higher weighting for trust and security than for landholding and housing. using results from (male) expert interviews would have led to higher weighting for landholding because “land comes first for farmers”3, but for reasons of objectivity, the results of the pca were used. the precondition for the calculation of weights based on results of the pca was that the indicators identified as relevant correlate, which was given after pearson’s correlation. along with the measurement of suitability to use the factor weighing approach of the pca developed by nicoletti et al. (2000), correlations between indicators also provide deeper insights into the data set, which i briefly discuss in the following. from the positive relationship between the perception economically secure and land used for coffee cultivation, it can be assumed that an increase in land used for coffee cultivation is associated with higher income. the positive relationship between the level of happiness and the area for coffee cultivation may be explained by a higher level of business activities and greater freedom to spend money for the cultivation of cash crops. food crops grown by farmers for their own consumption could improve the nutritional status of the hh. sometimes leftovers from subsistence agriculture are sold at local markets, which yields small amounts of cash income. however, this cash income is not sufficient to cover the costs of families’ basic needs such as health care, education, and shelter. one should keep in mind that coffee is only harvested once a year and coffee prices and coffee yields differ from season to season depending on weather and world market prices for coffee. it cannot be assumed that farmers are willing to switch the total area used for subsistence agriculture to coffee cultivation due to significant changes in market prices for the already low prices they get per kilo of coffee. sometimes prices do not even cover the production costs (sayer 2002). if, in such cases, farmers would only cultivate coffee and not engage in any subsistence agriculture, a reduction in wellbeing would likely be the result. in addition, landholding as such usually cannot be increased in this region, while access to land often decreases substantially from one generation to the next due to the high fertility rates and the division of inherited land among siblings (mugagga 2011). this issue will become even more critical if fertility rates remain high and if inherited land continues to be split from one generation to the next (supre 2018). if farmers want to increase the area for coffee cultivation, they will have to do so on the land they currently own. otherwise, land dispossession could lead to even higher negative impacts on the wellbeing of farming hhs. previous results from liebig et al. (2016) also indicate that some of the plots in the same districts “showed no or a very low coffee productivity as a consequence of old 3 in addition to the quantitative interviews presented here, qualitative interviews and expert interviews were conducted by the research team. 154 anna lina bartl coffee bushes or inappropriate management practices”. improving farm management practices could therefore also help the ucda to reach the goal of quadrupling uganda’s coffee production (see ucda 2019) by improving farmers’ resource situation and enabling them to increase their coffee productivity. research on the basic conditions for this could not only help to increase coffee productivity; it could also prevent or slow down the reduction in coffee production as the suitable land for arabica coffee cultivation in uganda declines due to climate change (jassogne et al. 2012). furthermore, results show that farmers’ belief that their concerns are taken into consideration by the local government is stronger than their trust in government officials. therefore, it can be assumed that the trust in institutions is higher than the trust in most people the farmers work with directly. the level of happiness also increases with higher values for trust in most people, trust in government officials, and consideration of farmers’ concerns by the local government (and the other way around). the same correlation is visible for the relationship between all other indicators of security and the indicators of the factor trust. research on the individuals who act as middlemen for coffee sellers (baffes 2006) has shown that they are known to engage in unfair and exploitative business practices. this could explain, for instance, the positive relationship between trust in most people and the perception of being economically secure. mosley and verschoor (2005) confirmed the latter correlation in their study investigating trust levels in sironko and bufumbo, districts close to bulambuli investigated here, and found that trust increases with the wealth status of a hh. however, the high knowledge and information gap in the research area could also have an impact on trust. according to the study by sseguya et al. (2012), which was implemented in southeast uganda, information networks among farmers, extension workers, local governments, and the private sector are very uncommon and lead to a high information gap on the part of the farmers, depending on which sources of information a farmer has access to. the positive relationship between the perception of being safe from violence and crime and (a) trust in government officials and the high positive values for (b) local government considers the farmers’ concerns is consistent with the assumption that farmers who have trust in institutions feel more protected. the results of the data set presented here are only suitable to provide a static specification of wellbeing at the time of data collection. to measure dynamic changes in wellbeing over time, further data collection could enable repeated evaluations and could also include medical or nutritional status or additional aspects of housing quality to increase the number of potential physical indicators. in addition, detailed investigation of factors influencing wellbeing (e.g., income, education, number of children) and of the relationship between perceived deficiencies and wellbeing should be a focus of further research. 5. conclusions the main aim of this paper was to understand how coffee farmers in the mount elgon region of uganda perceive their wellbeing. the ci for wellbeing and the wellbeing indicators served as suitable instruments to test the hypotheses that (1) wellbeing in the investigated research area is not equally distributed within and between sub-counties and that (2) the physical wellbeing component causes a lower wellbeing level of hhs than social 155the wellbeing of smallholder coffee farmers in the mount elgon region and psychological indicators. the findings from these hypotheses regarding the composition of wellbeing and the dependencies between wellbeing indicators provide a sound basis for policy recommendations. the selection of nineteen potential indicators for the resulting physical and socialpsychological components of wellbeing was made according to statistical relevance. the results of the explorative pca show that trust, security, housing, and landholding, containing in total ten of the previously selected indicators, provide the most comprehensive composite picture of wellbeing, explaining 81.20% of the total variance. the weight of each factor within the ci of wellbeing—which was calculated by the percentage of variance explained by the factor after varimax rotation divided by the total variance of all factors—was ranked from trust, showing the highest weight, to security and housing, all the way to landholding with the lowest weight. nevertheless, housing and landholding provided the highest z-scores in both directions, negative and positive, and were thus identified as the highest-impact factor for all sub-counties, even after the lowest weighting within the ci formula. due to the high negative values for landholding in bulegeni and namisuni, the hypothesis that physical wellbeing causes lower wellbeing constitutions of hhs than social-psychological wellbeing can be confirmed. for simu, a sub-county with greater access to land, the factor landholding also has a high impact on wellbeing, but in a positive way. the main finding for the physical indicators is the overall low level of wellbeing. the final ci for wellbeing shows differences in wellbeing between and also within sub-counties, which confirms the previously mentioned first hypothesis. the dependencies between indicators and different impact levels of indicators of wellbeing within the ci of wellbeing provide the basis for several policy recommendations. because land area is a variable that cannot be increased in the area under investigation here, the only recommendation that can be made would be to mandate official registration of land to prevent potential land-grabbing motivated by the nutrient-rich volcanic soils in the mount elgon region (undp 2012). official land registration should attempt to establish equal land rights between husbands and wives. at the moment, it is common that the man holds the land rights (even though this is not official). in cases of the male hh head’s death, his male relatives inherit those rights. the loss of land often increases the economic vulnerability of the remaining hh members. this is an important issue, considering that about the half of the female-headed hhs of the hhs presented here are widowed. the level of happiness and the perception of being economically secure could be improved by a higher percentage of land used for coffee cultivation. due to the previously mentioned drawbacks for coffee farmers when increasing the percentage of land for coffee cultivation, this cannot be generally recommended. instead, the results point to policy recommendations that farmers should be trained in methods to improve their currently low coffee productivity to increase their income from coffee production. another recommendation to increase income from coffee selling would be to implement standardized processes for coffee-selling activities. contracts between buyers and sellers, improved access to information about the coffee market to reduce the information gap between buyers and coffee farmers, reliable weighing scales, or even a statutory minimum price could be explored as approaches to increase income from coffee selling and mitigate issues of mistrust. 156 anna lina bartl the currently low housing quality of the majority of hhs is also having an impact on the wellbeing of the coffee-farming hhs. here, development activities could focus on improving housing quality, for instance, by improving access to financial services, providing construction loans, or offering subsidized prices for bricks and other construction materials. this might be an effective approach to improve the level of wealth in the area under investigation and could improve wellbeing levels, especially in namisuni and bulegeni. the results presented here could further be used to investigate the success of existing development approaches in the bulambuli district. to conclude, the results presented here suggest that in the future, the already low wellbeing of the hhs in this area will decrease further with each subsequent generation due to existing land inheritance structures and the steadily decreasing suitability of land for arabica coffee cultivation as a result of changing weather conditions. policy and market-related activities should be implemented to help the coffee farmers in the mount elgon region by enabling them to improve their resource levels and cope with the growing challenges (dodge et al. 2012) in order to maintain—or better, increase—their current levels of wellbeing. 6. acknowledgments the author would like to thank the whole team of naro for logistical support. i am also very grateful to all local field assistants for their reliable assistance and to all of the 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list of abbreviations ci composite indicator hh household jrcec joint research centre, european commission ks kolmogorov-smirnov naro national agricultural research organization pca principal component analysis ubos uganda bureau of statistics ucda uganda coffee development authority unhs uganda national household survey consumers’ rationality and home-grown values for healthy and environmentally sustainable food simone cerroni1,2,3,*, verity watson4, jennie i. macdiarmid5 the wellbeing of smallholder coffee farmers in the mount elgon region: a quantitative analysis of a rural community in eastern uganda anna lina bartl agricultural sector performance, institutional framework and food security in nigeria romanus osabohien1,3,*, evans osabuohien1,3, precious ohalete2,3 innovation adoption and farm profitability: what role for research and information sources? michele vollaro1,*, meri raggi2, davide viaggi1 determinants of farm households’ willingness to accept (wta) compensation for conservation technologies in northern ghana evelyn delali ahiale1,*, kelvin balcombe2, chittur srinivasan2 bio-based and applied economics 6(1): 57-79, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-18535 a stakeholder engagement approach for identifying future research directions in the evaluation of current and emerging applications of gmos davide menozzi1*, kaloyan kostov2, giovanni sogari1, salvatore arpaia3, daniela moyankova2, cristina mora1 1 department of food and drug, university of parma, parma 43125, italy 2 abi – agrobioinstitute, sofia 1164, bulgaria 3 enea – national agency for new technologies, energy and sustainable economic development, rotondella (mt) 75026, italy date of submission: 2016 30th, june; accepted 2017 3rd, february abstract. the yield of several commodity crops is provided in large part by genetically modified crops in north and south america. however, reservations exist in europe due to possible negative effects on human health or environment. this paper aims to analyse the current research priorities identified in eu countries and to engage european stakeholders into the formulation of future common research needs regarding the effects of the possible adoption of commercially available and forthcoming genetically modified organisms (gmos) in the areas of socio-economics, human and animal health, and environment. additionally, it aims to identify the requirements for sharing available research capacities and existing infrastructures. first a mapping exercise of existing research activities in europe was performed. a questionnaire was developed on a webbased platform and submitted to national focal points to collect information from eu member states. information was collected from 320 research projects conducted in the last 10 years in europe. to refine results of the surveys, twenty invited experts and stakeholders from the public funding agencies of different eu member states participated in an international workshop. this paper reports the main findings of these activities. keywords. genetically modified organisms (gmos), socio-economic, human and animal health, environment, workshop jel codes. q16, q55, o30 1. introduction scientific and technological progress in agriculture has resulted in innovations that have contributed to increase production and productivity. genetically modified (gm) *corresponding author: davide.menozzi@unipr.it 58 d. menozzi crops have shown an extremely rapid adoption rate in many areas of the world. about 12 percent (179.7 million of 1.5 billion hectares) of global cropland was invested with gm crops in 2015 (james, 2015). maize area summed up to 53.7 million hectares in 2015 and gm soybean was cultivated over 92 million hectares during the same cropping season. herbicide tolerance (ht) crops occupy 100 million ha, insect resistant (ir) 26 million ha and crops expressing stacked ht and ir traits were planted on 45 million ha. however at the same time, in different world areas genetically modified organisms (gmos) have experienced a transnational opposition from different interests groups (herring, 2008). opposition to transgenic crops has often argued the lack of sufficient scientific data demonstrating that gm crops are harmless to humans and to the environment (rausser et al., 2015; yang and chen, 2016). although these uncertainties about food and feed products derived from plant breeding is not confined to transgenic plants (herring, 2008) and that, beyond transgenic plants, alternative methods are being applied to obtain new crop varieties (parisi et al., 2016), gmos are often questioned in regards of the uncertainty of their possible risks. schurman and munro (2010) describe how these concerns gained consensus in a network of stakeholders, including consumer, environmental, and social-justice organizations. the european union has endorsed the precautionary principle and therefore in its risk assessment a central role is sought in addressing and dealing with these uncertainties. the eu regulations on gmos constitute a salient issue of risk governance given the politically high visibility of the topic (drott et al., 2013). the eu regulatory framework on gmos includes rules on authorization conditions, traceability, labelling, segregation, co-existence, which are established by the european commission based on the risk assessment procedures conducted by the european food safety authority (efsa) which provides independent scientific advice on this topic (drott et al., 2013). accordingly, scientific research promoted by the european commission so far has also been framed considering the (potential) positive and negative effects of gmos. despite the global opposition, transgenic crops have spread rapidly in the agribusiness, and the number of gm events at the commercial cultivation, precommercial or regulatory stages has more than doubled between 2008 and 2014 (parisi et al., 2016). the uneven adoption rate of gm crops is still evident. us continues to be the lead country with 70.9 million hectares (ca 40% of global) with about 90% adoption for the principal crops: maize, soybean and cotton (james, 2015). while gm plants currently available feature a limited set of different traits, there are several crops with novel traits in the regulatory pipeline and at late stages of research and development (r&d) (e.g., resistance to viruses and pests, tolerance to drought, modified chemical composition, enhanced nutritional content, etc.) (national academy of science, engineering and medicine, 2016). cultivation in the eu has remained limited to bt-maize that in 2013 has been cultivated in almost 150,000 hectares mainly in spain (137,000 ha), followed by portugal, the czech republic, and romania and slovakia (european commission, 2015). however, five new gm events for cultivation are currently being examined by eu commission for a possible imminent approval. at the same time, the number of experimental field release trials has seen a continuous decline over the last years (gómez-galera et al., 2012). it is notable that although a plethora of research projects have been conducted resulting in scientific publications which examine the impacts of gm crops on the receiving environments, on animal and human health, and on the functioning of farms, markets and 59stakeholder engagement: identifying future directions for gmo research rural communities, the technology is still controversial on a number of levels. there is a large body of scientific evidence suggesting that, although there are still reasons for concern and associated risks which must be carefully assessed (e.g., crop failures, price increases, seed market monopolisation and farmers’ dependency on a few technology providers, coexistence with non-gm crops, negative impacts on non-target organisms, and resistance development in target pest populations, etc.), when managed and used appropriately gm crops may provide notable benefits (e.g., reduced use of pesticides, implementation of notill agriculture which sequesters carbon and builds up exhausted soils, increased harvests, revenues and profits for farmers, reduced mycotoxin content in harvested maize, etc.) (baram and bourrierm, 2011; graef et al., 2012; mora et al., 2012; jacobsen et al., 2013; devos et al., 2014; mannion and morse, 2012). at the same time, there is also a growing body of completed and on-going scientific programmes specifically dedicated to the assessment of the potential socio-economic, environmental and health effects of the use of gm crops within both europe and globally (european commission, 2010). given the number of research initiatives, it is important to focus the available resources for research on the most critical gaps in our knowledge, so that more informed regulatory and policy decisions can be made in the future. this means, at the eu level, to significantly enhance the alignment of the research programmes of the individual member states, identifying knowledge gaps and capacity building needs, in order to avoid duplication of work in these areas, to leverage complementarities, and to enhance coordination between scientists from all over europe. this should be done improving the engagement of stakeholders (e.g., industry, farming organisations, civil society organisations – cso, non-governmental organisations – ngos, eu and national competent authorities, funding organisations, academia, etc.) in the shaping of future research agendas and programmes, in order to make these research programmes more meaningful to the end-users of the scientific results, and to increase legitimisation of research trajectories and ownership (ross, 2007; noteborn and van duijne, 2011; graef et al., 2012). the involvement of stakeholders in the identification of risks and concerns is believed to have a key role in the process of technology evaluation. the use of gmos is given as an example for contested innovation in the eu, which failed to take into consideration the ethical concerns, uncertainties and risks at an early stage of the technology development (van den hoven, 2013). although the use of gm crops in agriculture remains greatly questioned in the eu, new varieties have been developed around the world, which may find their way into the eu market in the near future (parisi et al., 2016). genomic technologies have substantially improved since the appearance of first cultivated gm crops, so that individual plant’s genome can be sequenced and analysed, genotyping methods have improved in throughput and cost efficiency; thus, it is likely that additional traits can be introduced into cultivated plants with increased efficiency and reduce costs associated with breeding. such developments imply the need to steer the public research policy to invest its resources in better correspondence to the social concerns related to genetic technologies. this paper is part of the presto gmo era-net project1 aiming at creating and successfully implementing an era-net (european research area network)2 that will coor1 presto gmo era-net (preparatory steps towards a gmo research era-net), eu fp7, grant agreement n. 612739. see the website: http://www.presto-gmo-era-net.eu. 2 the objective of the era-net scheme is to step up the cooperation and coordination of research activities 60 d. menozzi dinate research activities carried out at national or regional level in the member states and the mutual opening of national and regional research programmes on the effects of gmos in the areas of socio-economics, human and animal health, and the environment (rauschen et al., 2015). in particular, this paper aims to identify knowledge gaps and future research needs on the effects of gmos based on the analysis of the research priorities and a dialogue with the stakeholders. we present the results of a mapping exercise of existing research activities on the effects of gmos in europe, and the main outcomes of an international workshop with relevant experts and stakeholders, european institutions, and csos held in milan in november 2014. 2. material and methods 2.1 mapping of existing research activities the first step was to provide an overview of existing research activities and knowledge regarding the socio-economic, health, and environmental effects of gmos in europe. this was performed by an up-to-date mapping of national research programmes, projects, infrastructures, activities, research groups and capacities in the eu and internationally. the following gmo assessment databases or datasets were used in mapping existing research activities: scar-collaborative working group “gmo risk research” (scar-cwg, 2012), the biosaferes database3, and the european commission’s compendium summarizing the results of 50 gmo research projects, co-funded by the ec and conducted in the period 2001-2010 (european commission, 2010). the data were integrated and updated with a questionnaire developed on a web-based platform (the cadima4 database) and submitted to national focal points to collect information from member states (moyankova and kostov, 2015). through the questionnaire it was possible to collect details describing recent and ongoing projects examining the gmo effects, such as the thematic area of the research, the sources of funding and the type of organizations that carried out the research. the questionnaire was designed in a specialized section of the cadima database for collection of data about the recent and ongoing gmo assessment projects. the projects were characterized by one reviewer according to several categories including: a. type of funding (government, eu funding, industry, other); b. type of project leading organization (research/academy, individual, private company, other); c. regional level of the project consortium (national, international eu, international beyond eu); d. type of organizations in the project consortium (industry, research/academy, governcarried out at national or regional level in the member states and associated states through the networking of research activities conducted at national or regional level, and the mutual opening of national and regional research programmes. 3 the biosaferes database is a worldwide, web-based, free and public-access database of past and current research projects in gmo biosafety, is improving communication within the scientific community, and thus clearly facilitates development of more and better worldwide collaborative research ventures in this field by encouraging synergy. available at: http://biosaferes.icgeb.org. 4 central access database for impact assessment of crop genetic improvement technologies, see the website: http://www.cadima.info. 61stakeholder engagement: identifying future directions for gmo research ment, other, or mix of them); e. type of gmo analysed in the project (gm plant, gm animal, gm micro-organisms); f. main topic of gmo impact assessment (environment, animal health, human health, technology/society, other); g. sub-topics of impact assessment (e.g., for environment: soil, water, air, biodiversity, plant pest and diseases, geochemical variables, landscape structure, target effects, nontarget effects, other). 2.2 the stakeholder engagement approach starting from the results of the mapping exercise, the objective of the international workshop held in milan on november 24th, 2014, was to use a transparent and structured approach for recommending a list of transnational research needs regarding the effects of gmos in the areas of socio-economics, human and animal health, and environment, as well as requirements for sharing of available research capacities and existing infrastructures. the focus of the workshop was on gm crops or other applications (e.g., animals, micro-organisms, etc.) on the marketplace or near to be commercialized, not necessarily in the eu, but that may have effects in the eu. applications intentionally released into the environment and/or used immediately in feed and food applications were considered. a stakeholder engagement protocol was agreed with project partners to clarify workshop aims, activities, and research question development (menozzi et al., 2014). the stakeholder involvement process began with the generation of a “potential stakeholder database”. the experts were specifically selected based on their career, successfully achievements and long-standing expertise in the field of gmos related to scientific, economic, social and policies aspects. in addition, a broadest group of stakeholders in different fields were added in the database, including representative leaders of farmer’s organizations, public authorities and agencies, eu research institutions, private companies and other relevant stakeholders. a preliminary list was sent to the project partners and integrated with their suggestions. then, the experts and stakeholders were contacted following a step-by-step criteria in order to have a right balance between the three scientific areas, as well as a fair representation of the member states. a registration before the deadline was required in order to define the number of participants, their role and activities, to guarantee the right balance of the attendees. forty-five stakeholders were invited to represent a right balance of expertise between the three areas (i.e. socio-economics, human and animal health, and environment), organizational perspective (academic, member state and eu agency, cso communities) and geographical areas. the international workshop activities, conducted by three facilitators of the university of parma, were divided into two sections. the morning session was dedicated to share and discuss preliminary results of the project with the participants, including the results of the mapping of existing research activities. in the afternoon session the participants were divided into three working groups, based on the area of expertise or interest, to identify the relevant transnational research needs. the three working groups used a structured multi-stage approach, consisting in six steps. steps from 1 to 5 aimed at populating the list of transnational research needs (figure 1), while step 6 consisted in the identification of capacity/infrastructure needs to cover those research needs. 62 d. menozzi figure 1. flow chart for identifying a list of future research needs by the stakeholders. stage 1. a questionnaire was sent two weeks before the workshop to all the experts and stakeholders to identify the main research questions across gm species/traits and effects. based on their replies to the questionnaire, a structured dataset of initial research questions was populated. stage 2. participants in each working group were encouraged to submit modifications to existing research questions or add new ones, given the evidence provided by the mapping of existing research activities. the initial dataset of research questions was then established. stage 3. each working group reviewed the initial dataset of research questions on its area of expertise discussing whether an existing solution to the research need exists and was available. if this was the case, the use of research outcomes already undertaken was recommended. stage 4. if the research need was not investigated so far, or if the results were not already available or applicable, the experts considered if it was on the european agenda or not. if yes, an eu funded project was more appropriate and therefore recommended. stage 5. if the research need was not on the current eu agenda, the experts checked if it could be defined as a transnational interest. if yes, then a programme funded transnationally by the era-net was a likely solution (transnational research need); otherwise, if it was only on a national agenda, then a national project or programme was suggested. stage 6. once the list of transnational research needs was populated, the moderator asked participants which capacity/infrastructure needs were available in order to cover those transnational research needs. a trained facilitator conducted the discussion in each working group. potential disagreements were discussed, and eventually reported as such. all inputs from the working groups were presented in the plenary session, including the areas of persistent disagreements. 63stakeholder engagement: identifying future directions for gmo research 3. results: existing research activities on gmos in europe information about 320 projects on existing research activities regarding the techno and socio-economic, health, and environmental effects of gmos in europe were collected through the mapping exercise (moyankova and kostov, 2015). the data included the type of organizations leading the projects, the funding source, the topics of the gmo assessment projects (human health, animal health, environment, technological/social), and the studied gmo species. unfortunately, it was not possible to collect consistent information about the projects’ budget and timing. this may have introduced a bias since it was not possible to evaluate the projects’ relative importance and achieved results. nevertheless, the number and type of projects alone provides a valid mapping of current research activities to be used as a starting point for the purposes of the study. the surveyed gmo projects in europe have started between 1989 and 2010. most of the projects (85%) were led by research or academy organizations such as universities, institutes or research centers. a relatively small portion of the projects were led by government organization and private companies, accounting for only 9% and 5% respectively. most of the projects were carried out at national level (198 projects). international collaboration was predominantly among european countries (80 projects), while only 27 projects included countries outside europe. a number of 15 projects did not provide this information. the gmo projects were led by institutions in different european countries. when considering the number of gmo projects per million capita inhabitants, austria is on leader position with 6.0 projects per million capita, followed by denmark (3.1), norway (2.8), finland (2.3), ireland (1.4), hungary (1.2) and belgium (1.1). the other countries, i.e. france, germany, greece, italy, netherlands, spain, sweden, switzerland and united kingdom, have less than 1 project per million capita. the eu is the only funding source for projects leaded by greek and irish organizations, while other organizations based in hungary, sweden and switzerland had only projects funded by national sources (figure 2). seven of the project leader countries are funded mainly by governments, namely uk, spain, norway, italy, germany, finland and austria. the eu was the major funding source for project leaders based in the netherlands, denmark, france and belgium. not surprisingly, projects were mostly focusing on gm plants (196 projects, 68% of the total, figure 3). gm micro-organisms and gm animals were analysed only in 20 (6%) and 10 (3%) projects, respectively. a few projects (7) were dealing with gm plants and micro-organisms at the same time. many projects did not specify the type of gmo that was analysed (n=65, 20%). the interaction of gmo with the environment was investigated in more than a half of the projects (52%, figure 3). one third of the projects (33%) were dealing with the developments of new methods, tools for detection in and analyses of food and feed, methods for risk assessments, new technique, etc. the effect of gmos on human and animal health is a topic of interest in 10% and 4% of the projects, respectively. many projects covered several topics at the same time. the main subjects (environment, human health, animal health and technology/society) showed the same distribution when crossed by type of studied gmos (figure 3). biodiversity preservation is the predominant sub-topic (58%) in the projects studying the interaction of gmos with the environment (table 1). the effect on non-target species was analysed in 25% of the projects. other sub-categories were objects of only few 64 d. menozzi projects. among the different types of gmos, the gm crops are the most investigated for their effect on the environment (table 1). the technological and socio-economic aspects of the gmos were analysed in 120 projects. development of new methods for gmo detection and technological innovation were predominantly studied in 76% of these projects. among the socio-economic issues, the economic efficiency was studied the most, followed by consumer demand and food security (table 1). many projects covering this figure 2. number of gmo projects by funding source (eu, government, industry and other funding source) in europe per country (n = 305). 0% 20% 40% 60% 80% 100% united kingdom switzerland sweden spain norway netherlands italy ireland hungary greece germany france finland denmark belgium austria eu funding government industry other source: own elaboration. figure 3. type of analysed gmos and main topics (n = 320*). 0 50 100 150 200 250 300 gm animals gm microorganisms gm crops gm crops and microorganisms not specified number of projects technology / society environment human health animal health source: own elaboration. * note: many projects covered more than one topic. 65stakeholder engagement: identifying future directions for gmo research topic did not specify the type of gmo and this hampered the data analysis. fewer projects treated the effects of gmos on human health (table 1). food safety and allergenicy were the most explored sub-topics in particular for gm crops. the effect of gmos on animal health was explored in 15 projects, where feed safety was the only subject analysed. gm crops interaction with human and animal health was the most studied topic, whilst gm animals impact was analysed in only one project. considering the country of the leading organization, switzerland and spain had projects that only dealt with the effects of gmos on the environment. this subject was the most studied in 7 project leader countries, namely uk, sweden, ireland, france, finland, denmark and austria. organizations from belgium, italy and norway were leading mainly projects about technology and social effect of gmos. gmos safety for human health is relatively more studied in 2 countries, i.e. hungary and greece. the less studied subject was the effect of gm feed on animal’s health. only 5 countries were leading few projects about this topic, the most relevant of which was hungary. table 1. health, environment and socio-economics subtopics of the gmo assessment projects in europe (n=320*). gm crops microorganisms gm animals gm crops and microorganisms not specified total food safety 17 1 1 4 23 feed safety 13 2 15 allergenicity 5 5 toxicity 3 3 therapeutic use 1 1 2 nutritional value 2 2 total health 41 2 1 0 6 50 biodiversity 67 11 2 4 14 98 non-target effects 39 2 1 42 soil 8 2 1 1 12 target effects 6 3 9 plant pest and diseases 6 6 other effects 2 2 total environment 128 13 5 7 16 169 economic efficiency 11 1 2 14 consumer demand 1 1 9 11 food security 2 2 other effects 2 2 total socio-economics 14 0 0 2 13 29 innovative technology 53 7 4 1 18 83 technical application 5 3 8 total technology 56 7 4 1 21 91 total 241 22 10 10 55 339 source: own elaboration. * note: many projects covered more than one topic. 66 d. menozzi 4. results: stakeholders view on the future research needs 4.1 workshop participants of the total 45 experts contacted for participating in the international workshop, a final number of 20 individuals participated in the activities (table 2); powers et al. (2014) suggested that a number between 20 and 25 gives a right balance between the diversity of technical and sector perspectives and fluid working relationships. table 2. workshop contracted persons and participants per category. category contacted persons participants participation rate expert environment 11 7 64% expert socio-economics 4 4 100% expert health 7 2 29% stakeholders 14 2 14% european institutions 4 1 25% funding bodies 5 4 80% total 45 20 44% the experts studying the environmental effects of gmos were the most represented, followed by the socio-economic experts. the participants were 44% of total the number of contacted persons. although a fair gender balance was taken into consideration in contacting the experts, males were more represented than women in the final group of participants (75% males). the weakest participation was found among the stakeholders (14%), whilst the highest rate was found among the socio-economic experts (100%), then the environmental (64%) and finally among the health (29%). the participants originated from ten european countries (italy 3, germany 3, the netherlands 2, austria 2, uk 2, swiss 1, spain 1, denmark 1, bulgaria 1, romania 1); three stakeholders represented european organizations/institutions (i.e., copa/cogeca, the public research & regulation initiative – prri –, and the european food safety authority – efsa). the career stage of the participants was quite homogeneous, since most of them were in a senior position, leaders of research units or project leaders. as observed by schneider and gill (2016) having both young and senior researchers in the same group could discourage some participants to express their views, leading seniors to control the discussion with their point of view. 4.2 initial dataset of research questions the initial dataset of research questions to consider in developing research needs, as resulted from stages 1) and 2) (see section 2.2), have been categorised across the subject areas (i.e. socio-economics, human and animal health, and environment), and species/ traits (i.e. gm crops, insects, animals, micro-organisms and all gmos). thus, defined categories of questions were aligned with the results from the mapping exercise to identify 67stakeholder engagement: identifying future directions for gmo research potential gaps in the research that has been done so far, and the future research directions considered relevant by the stakeholders. table 3 provides a synthetic representation of the initial research needs considered by the experts in their deeper analysis. a list of 48 research questions was developed by the experts in the socio-economic area. the identified projects in the mapping of current research activities (see table 1, section 3) are related mainly to the economic efficiency and consumer demand. similarly, the stakeholders consider important the general economic effects, such as the costs and the profitability, but also more specific subjects like the economics of segregation/co-existence, legislative framework, consumers perception and attitudes, macro-economic, yields, and other effects (table 3). most of them were considered for all gmos and gm crops in general, or insect resistant (ir) crops and herbicide tolerant/herbicide resistant (ht/ hr) crops. for instance, the experts considered the necessity to include ‘non-pecuniary’ benefits in the analysis of costs savings related to ht and ir crops (e.g., off-farm income, management time saving, labour flexibility, equipment cost savings, better standability, etc.), to improve the methodology to analyse the segregation rules of gmos (e.g., the welfare effects of labelling and segregation policy), and to evaluate the economic effects of relatively less widespread varieties, such as virus-resistant cassava and golden rice. only one research question was identified by the workshop participants for species other than crops: the assessment of the socio-economic impact of gm animals. the large portion of research questions in the initial dataset defined by the stakeholder are specific to different types of gm crops which already exist on the market or are under development. gm plants and crops are also subjects of many projects identified during the mapping exercise (table 1). since new gm crops and traits are under development outside the eu, and potentially access to the common market in the next future, the experts and stakeholders have suggested to concentrate research efforts on vegetable species. the european research carried out so far in the area of human and animal health is primary related to the safety of gm food and feed (table 1). during stages 1) and 2) a number of 25 research needs were identified in the area of health (table 3), divided into the main effects food and feed safety, nutritional value, allergenicity, toxicity and other. for instance, the experts considered the need to assess the positive health effects of the reduced fumonisin content in ir crops. it was argued that gm crop unintentionally produce high amount of toxic substances, e.g. acetylated aspartic acid, and that animal will be the main users being exposed to such substances. therefore, research is needed to focus on developing and validating a test protocol in livestock animals. most of these research needs were considered for all gmos, while none where identified for insects and other animals. this probably because it was stressed that the focus was on gmos on the market or near to be commercialized. these results indicate no substantial differences in the research carried out so far and the future research needs in the area of human and animal health as the questions related to the toxicity, allerginicity and food safety are still central for the novel applications of gmos (parisi et al., 2016). a number of 47 research needs were identify by the experts to cover the area of environment (table 3). most of the projects carried out so far on the environmental impact of gmos are dealing with biodiversity and non-target effects (table 1). questions related to the possible effects to biodiversity of the emerging crop varieties and traits are still of interest according the stakeholders for different kinds of gmos. quality of soil and water 68 d. menozzi ta bl e 3. in iti al re se ar ch n ee ds in th e th re e ar ea s: h ea lth , e nv iro nm en t a nd s oc io -e co no m ic s. a ll g m o s g en er al cr op s ir c ro ps h t/ h r cr op s g m c ro ps w ith st ac ke d tr ai ts rn a iba se d pl an ts o th er pl an ts in se ct s o th er an im al s m ic ro or ga ni sm s to ta l fo od sa fe ty 2 2 2 6 n ut rit io na l v al ue 1 1 3 5 a lle rg in ic ity 3 1 4 to xi ci ty 2 1 3 fe ed sa fe ty 2 1 3 o th er e ffe ct s 4 4 to ta l h ea lth 10 2 3 0 0 4 3 0 0 3 25 bi od iv er sit y 2 3 2 4 2 1 14 so il 2 1 1 2 6 w at er 2 1 2 5 pl an t p es t a nd d ise as es 2 1 1 1 5 a ir 1 1 1 3 ec os ys te m se rv ic es 2 2 c lim at e ch an ge 1 1 o th er e ffe ct s 2 5 4 11 to ta l e nv ir on m en t 11 1 7 4 4 5 9 0 0 6 47 ec on om ic e ffe ct s i n ge ne ra l 1 3 2 2 2 1 11 c os ts 2 1 2 1 6 pr ofi ta bi lit y 1 1 2 1 5 se gr eg at io n/ co ex ist en ce 2 2 4 le gi sla tiv e fr am ew or k 2 2 4 c on su m er s a nd so ci et y 1 1 1 1 4 m ac ro -e co no m ic e ffe ct s 1 1 1 3 yi el ds 1 1 2 o th er e ffe ct s 4 2 2 1 9 to ta l s oc io -e co no m ic s 12 12 10 6 3 0 4 0 1 0 48 so ur ce : o w n el ab or at io n. 69stakeholder engagement: identifying future directions for gmo research are also pointed as important followed by the effects to plant pest and diseases, air, ecosystem services, climate change, and other effects. most of the research needs were identified for all gmos, ir crops or other plants. for instance, stakeholders claimed to develop field methods to monitor soil process intensity and changes, to implement an application of environmental dna to detect changes in biodiversity, and to evaluate the potential effects on non-targeted organisms (ntos) and the possible protein interactions or synergistic effects of stacked events expressing cry and vip proteins. no research need was identified for gm insects or other animals. the initial dataset of research questions was provided to the workshop participant as a starting point, to support thinking about potential research needs that were outside their specific expertise area. the lists supplemented explicit encouragement to participants to think broadly about potential research areas. the activities of the three working groups have followed the structured multi-stage approach described in figure 1. each working group was arranged of 5/6 experts, one moderator and one note-taker, chosen among the project partners. the participants in each working group reached a relatively small list of research needs (from 14 to 18), also by individual questions into groups of questions that were similar or closely related. consolidating individual questions into broader research areas was encouraged when they had similar implications for an assessment or subsequent risk management decision. the complete list of research needs is reported in menozzi et al. (2014). 4.3 socio-economic research needs the “socio-economic” working group identified several categories of research needs. according to the experts efforts are needed to develop a methodological framework for assessing the socio-economic effects of gmos. this framework should be used to inform policy development. socio-economic considerations are already included in the regulatory frameworks on gmos of some countries (binimelis and myhr, 2016). however, it is necessary to work to develop a robust framework and methodology, including criteria, indicators, etc., capturing socio-economic considerations in biosafety decision-making. garcia-yì et al. (2014) identified six topics, i.e. the farm-level economic impacts, the economics of co-existence, the economics of segregation at the supply chain level, the consumers’ acceptance of gmos, the environmental economics impact and the impacts of gmos on food security. thus, although there is a high interest in the implementation of socioeconomic considerations in biosafety regulations (binimelis and myhr, 2016), research is needed to establish a robust framework on the socio-economic impacts of gmos, and methodology covering data gathering, assessment and decision making. from the supply chain point of view, park et al. (2011) have estimated the revenue forgone by eu farmers due to on-going limited use of ir and ht crops. similarly, the effects of the eu regulation on gmos on eu competitiveness and on innovative research developed at the eu level should be assessed, as well as the welfare effects across different groups in society (e.g., farmers, consumers, etc.) in the context of different policy settings (e.g., labelling). for instance, the eu co-existence measures affect farmers differently across eu member states according to the isolation distances (between gm and non-gm crops) required by different countries and for different species (ramessar et al., 2010). an eco70 d. menozzi nomic evaluation of the welfare distribution of flexible co-existence regulations may assist the adoption of proportionate measures (devos et al., 2014). the stakeholders claimed that the effects of gmos along the whole supply chain should be investigated further. this point goes beyond the common cost-benefit analysis; it considers how the structure of supply chains is affected by innovation, how the efficiency is impacted, how the horizontal/ vertical relationships could change, what are the implication on labour market, etc. up to now, research mostly concentrates on how costs and benefits are distributed along the supply chain (garcia-yì et al., 2014). few projects have already dealt with supply chain impact, on structure and performance, of co-existence and segregation measures (e.g., the eu fp6 projects sigmea and co-extra; see also ghozzi et al., 2016); however, a more deep analysis of supply chain effects (e.g., on structure and relationships) is missing. consumers’ attitude towards the use of new techniques in food production (e.g., new breeding techniques, nanotechnology – gmos) needs to be investigated further. in fact, although most of what can be known by questioning on a hypothetical base has already been investigated (dannenberg, 2009), research is missing on consumers’ acceptance studies using real settings. further research efforts are needed to explore on the economic evaluation of the effects (positive and negative) of gmos on the environment, using a multidisciplinary approach (garcia-yì et al., 2014). in this context, a comparative analysis of gm, organic and conventional crops in terms of environmental, social and economic sustainability, should be elaborated. at farm level, research usually evaluated the main economic effects of gm crops, such as yield, costs, gross margin (european commission, 2011). more research is needed to study the economic implications of more efficient gm varieties, like second generation gmos (e.g., nitrogen-efficient gm wheat), e.g., assessing how these gmos move the yield frontier, how improvements in yield efficiency affect the economic performance of farmers, etc. this research need is likely a transnational one, since not all the countries could have the same interest. moreover, other socio-economic impacts are scarcely documented, such as the indirect effects arising from the gm crops management (e.g., how gm applications affect farm management planning, cropping system, crop rotation, etc.). research should also study the differences between intensive and extensive margin effects (bennett et al., 2013), the stability of new gm crops yields (e.g. draught resistant) on a midand longterm basis, and the economic performance of ht crops (areal et al., 2012). finally, the socio-economic group noted the need to develop systematic reviews and meta-analyses to consolidate existing knowledge, and to improve the communication of available evidence. in terms of communication, research is also needed to better understand the key elements in stakeholders’ communication and interaction. 4.4 human and animal health research needs the “human and animal health” working group distinguished research needs across all types of gm species. major consideration related to the gm crops intended to be used as food and feed is whether they are safe for consumption, which should be evaluated under the eu risk assessment frame. while there is substantial amount of experimental data (e.g., feeding studies with laboratory and livestock animals) for the varieties which already exist on the market (flachowsky et al., 2012; snell et al., 2012; ricroch 71stakeholder engagement: identifying future directions for gmo research et al., 2014), a specific food safety concerns could emerge with the development of new types of gm crops such as plants combining several modifications (stacks) and gm plants with deliberately modified nutritional properties (halford et al., 2014; ramon et al., 2014). specific health related questions were pointed by the stakeholders; for instance, the group agreed that research is needed to explore toxicity effects of multiple bt proteins in in-vitro systems, the potential hypo-allergenicity of gm crops, and the definition of the minimal required inclusion level of plant-expressed phytase for efficient phosphorus utilization of animals. traceability and post-marker monitoring are also among the stockholders concerns; there is also a considerable lack of data on the traceability of specific gm crops, on verification of consumption and/or potential health impacts of gm food ingredients (e.g., gm crops with enhanced fatty acids), as well as on toxic substances produced by gm plants used in feed production (i.e. acetylated aspartic acid). further, the group noted that little knowledge is available on the health effects of producing pharmaceuticals by the use of gm plants. while substantial progress has been made in the development of gm plants for molecular farming, still the scale remains relatively small, mainly performed in laboratory or contained conditions. the major challenge is the legislative frame and the adaptation of the risk assessment principles to this plant biotechnology applications (sparrow et al., 2013). the group agreed that a common feature of risk assessment of potential protein toxicity is needed (bioinformatics). a specific issue was raised for myco-toxins in bt maize; research is needed to assess whether reduced fumonisin content can be found, as well as their potential and real benefits. although there is a large body of knowledge available, there is a need to develop a systematic analysis of the data collected by the regulatory bodies. gm plants producing rnai molecules represent a biotechnological development which seems close to the eu market; therefore, experts have raised concerns over the potential health implications due to the technological differences with the first generation of gm plants. steps towards the identification and evaluation of the specific human and animal health risk that may come with the new generations of gmos and the adaptation of the regulations and risk assessment guidelines have already been taken by the scientists, agencies and regulatory bodies (petrick et al., 2013; ramon et al., 2014). the “health” working group considered a number of research needs related to rnai based plants, for instance more information is needed on survival and uptake in humans and animals, and post-market monitoring. although it’s a corporate responsibility to deal with the commercial production and marketing, the eu authorities must provide the appropriate methodology and tools for their monitoring along the whole supply chain. an example for this is the database on food consumption of the eu member states that is being collated by the efsa. the same is needed for feed ingredients. allergenicity is commonly considered for humans, whereas there is only limited knowledge available on the impact on farm animals. this type of investigation should also explore possible links with post-market monitoring. finally, there is still uncertainty about the potential for horizontal gene transfer of genetically modified micro-organisms (gmms) and viral dna. although this concern has been investigated and discussed in the past (dröge et al., 1998; keese, 2008), stakeholders pointed that better methods need to be developed to assess the presence and diffusion of recombinant dna and cells. 72 d. menozzi 4.5 environmental research needs the “environment” working group determined that the research needs can be prioritised according to the criterion of “ecosystem services” provision (tscharntke et al., 2005). such an approach would involve the monitoring of cultivated land (on-crop area), but also of the space between crops in a landscape (off-crop area), and analyse how these two different kinds of areas influence each other in terms of ecological functionality. in this respect, the need for comparative study of different integrated pest management (ipm) systems used in the eu member states was highlighted, as well as the need to assess the role that gmos (plants and insects) might play in such ipm systems (hokkanen, 2015). moreover, efforts should be directed to study the efficacy of gmos in different gm events which hold promises of relevant economic and environmental benefits, such as blight resistance potato (haverkort et al., 2016), and to a deeper understanding of the development dynamics of insecticidal protein resistance mechanisms in target insects (e.g., corn borers, etc.). additionally, regulating and supporting services (pollination, pest control, soil fertility maintenance, etc.) were considered. the experts concluded that goals for the protection against undesirable effects have to be assessed at the landscape level. this type of monitoring, however, requires instruments to study the possible effects of different stressors, including gmos, on key species and ecosystem services (e.g., on bees and wild pollinators). system interfaces (i.e. land and water) were also defined as important points to explore further, as well as the change of dynamics in the system over time. more information on species assemblages before introduction of gm crops, to define appropriate baseline indicators, is needed for plants, arthropods and micro-organisms (e.g., soil indicators) (van capelle et al., 2016). further, the protection of cultural services was discussed (e.g., how people perceive agriculture, recreation, psychological benefits from contact with nature, etc.). research is needed to study biodiversity in protected areas from different perspectives, in particular to qualify what type and level of biodiversity that society would like to maintain locally. cropping practices (i.e. weed control efficiency) may have indirect effects on nearby valued areas. for instance, the elimination of weeds by farmers on their fields has also an impact on the trophic level in terms of available resources for sap feeders, pollinators, natural enemies, etc., that may in turn affect the functional biodiversity of neighbouring areas and need to be investigated further (bürger et al., 2015). in general, there is also a need to study the people’s perception of different agricultural systems, and this should ideally involve both the natural and the social sciences (multi-disciplinary research). finally, the group included research needs not strictly related to ecological services. there is strong support for looking more into automated and harmonized methods for general surveillance, for studying non-target effects of new modes of action (e.g., rnai) (lundgren and duan, 2013), and for new traits and breeding techniques on the environment. there are still knowledge gaps about the traceability and environmental fate of gmms, and to develop bioinformatic tools for studying their evolution in the system. finally, there is also limited knowledge about the effects of gm arthropods on the environment, and this is also an area characterized by quickly progressing of genomic technologies for possible applications in agricultural as well as in human diseases prevention projects. 73stakeholder engagement: identifying future directions for gmo research 5. results: requirements for sharing capacities and infrastructures the main requirements for sharing existing (national) capacities and infrastructures were identified during the working groups’ activities. the “human and animal health” working group found that in various countries there is a high level of expertise available for studying the hypo-allergenicity of gm crops which needs to be shared. harmonization and joint initiatives are possible for sharing experiences on the traceability of specific gm crops. since applications for rnai-expressing crops have been mostly developed outside the eu, and limited expertise is available at the eu level, the group concluded that a transnational organization of capacities would put the eu in a position to overcome this deficit in the future. as a lot of research has been done on peptide (e.g., cytotoxic peptides, food peptides) and their physiological effects (e.g., dairy research, antibiotics), the group defined the need to integrate these research capacities available across certain eu countries and sectors, for the purpose of assessing potential protein toxicity as another important study area. for the assessment of allergenicity in farm animals there are probably only limited capacities available at the eu level, and future research would also benefit from transnational organization of resources. a high level of expertise for gmms and viral dna horizontal gene transfer was found in various eu countries; again, the relative research needs are invited to be organized transnationally to increase the overall efficiency. the “environment” working group defined requirements for sharing controlled experimental field sites throughout europe, to allow gmo field testing on representative environments of the various european settings. the fields could also be used to avoid several regulatory constrains which make it difficult for public research in europe to study the effects of gmos. the group discussed the necessity to have meso-cosm facilities for soilbased experiments. the group concluded that calls for multi-/inter-disciplinary actions and projects should invite applications that combine different technologies/methods of scientific enquiry. finally, it was felt that the gm regulatory, testing and monitoring methods should be harmonised, as much as possible, with other, similar methods and approaches in related areas (e.g., pesticide registration, international work on the valuing and monitoring of ecosystem services, etc.). the “socio-economic” working group discussed the need to develop protocols and guidelines for conducting socio-economic impact assessments, which would ensure basic compatibility of results, without sacrificing the flexibility of approaches in the process. similarly to the “environment” group, they also highlighted the need to share field trials, and to develop more field studies for assessing yields, costs, and other economic aspects of the use of gmos. in addition, the need to develop multidisciplinary tasks capable of taking qualitative research (e.g., socio-psychology, behavioural economics, etc.) into account, was included in the list of priorities. finally, the group concluded that researchers’ capacities should be shared, via training and staff exchange programs, thus developing ways to facilitate future collaboration among researchers from different countries (e.g., sharing capacities, phd programmes, etc.). 6. conclusions this paper aims to promote a critical debate among relevant stakeholders and policy makers by identifying future research directions in the evaluation of environmental, health 74 d. menozzi and socio-economic effects of current and emerging applications of gmos. the outcome of the international workshop, which was the follow up of a mapping exercise of existing research activities in europe, is a list of perceived transnational research needs and consequent requirements for sharing existing capacities and infrastructures; it therefore represents more than a generalized description of research trends. the list of research needs will allow those developing research plans to focus more deeply on one or a few themes that were deemed more relevant by the scientific community as well as the european risk managers’ network. this process may provide a basis to develop a prioritization from the perspective of the diverse group of stakeholders, which is to be developed in a next step of the project. the results of the presto gmo era-net project formed the basis for a jointly prepared strategic plan and roadmap for the implementation of the era-net that will coordinate transnational research on the effects of gmos (rauschen et al., 2015). as pointed by twardowski and małyska (2015), the slow progress in the eu decision making process forced several biotech companies to move r&d and applied activities to other regions, thus reducing personnel in the eu and transferring existing know-how outside the eu. moreover, this prevented the commercialization of innovative gmos, possibly resulting in competitive disadvantages for european farmers (park et al., 2011). future directions of socio-economic research on the effects of gmos should primarily consider the development of a methodological framework for analysing the socio-economic effects of gmos. an assessment of the welfare effects across different groups in society may assist the definition of more informed policies. the research questions related to the effects of newly emerging gmos to human and animal health raised by the stakeholders during the workshop were general in many occasions or rather case specific in others. while some of them might be found relevant for the risk assessment to different extend, others are related to traceability and post-market monitoring, and some could appear to be within the scope of the risk assessment frameworks to be developed for the new gmos, such as rnai plants. the environmental research needs identified were prioritised according to the criterion of “ecosystem services”, involving the monitoring of on-crop and off-crop land area, and how these two different kinds of areas influence each other in terms of ecological functionality. the emerging applications, as well as the tendency of gm developers to combine different traits to produce new commercial varieties (the so-called “commercial stacks”) (parisi et al., 2016), pose relevant questions to researchers, risk assessors and policy makers on how to adapt the eu regulatory framework considering the environmental and health-related issues, as well as the socio-economic dimensions (e.g., international trade) when making decisions on gmos. a key factor in implementing this process will be the confidence that those planning and funding research have in the method used (schneider and gill, 2016). the activities described in this paper has explicitly taken into account the wider views of a diversity of stakeholders and end-users (i.e. industry, farming organisations, csos, ngos, eu and national competent authorities, funding organisations, academia). this intended to encourage participation of different scientific communities (scientists from all over europe) in the future joint transnational calls managed through the era-net, to enhance collaboration between actors (to leverage complementarities) and to increase the accountability of research trajectories and outcomes (create an internationally recognizable critical mass). the decision to consider a single group of 20 participants allowed balancing 75stakeholder engagement: identifying future directions for gmo research the greater diversity of technical and sector perspectives, and the facilitation of more fluid working involvement from all participants. breakout groups allowed the experts to elaborate on a greater number of details surrounding the interfaces of their disciplines with others, and this can thoroughly inform the scientific community in planning and promoting interdisciplinary and transdisciplinary research (powers, 2014). viewed from the perspective of group dynamics, the entire process aimed to achieve a common understanding of the research gaps and needs in the evaluation of current and emerging applications of gmos able to design future research directions. 7. funding this work was supported by the european commission contract 612739, presto gmo era-net (preparatory steps towards a gmo research era-net), funded by the eu fp7 programme. the authors declare no competing financial interests. 8. acknowledgements the authors wish to thank the presto gmo era-net project partners for their inputs into the research, in particular the project coordinator dr. stefan rauschen. the authors gratefully acknowledge the experts and stakeholders who participated in the international workshop for their active contribution to the identification of future research directions in the evaluation of emerging applications of gmos. the authors thank also the 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(2016). governing gmos in the usa: science, law and public health. journal of the science of food and agriculture 96: 1851-1855. a systematic approach to understanding and quantifying the eu’s bioeconomy tévécia ronzon1*, stephan piotrowski2, robert m’barek1, michael carus2 cost function and positive mathematical programming quirino paris what if meat consumption would decrease more than expected in the high-income countries? fabien santini*, tevecia ronzon, ignacio perez dominguez, sergio rene araujo enciso, ilaria proietti a stakeholder engagement approach for identifying future research directions in the evaluation of current and emerging applications of gmos davide menozzi1*, kaloyan kostov2, giovanni sogari1, salvatore arpaia3, daniela moyankova2, cristina mora1 a spatial analysis of terrain features and farming styles in a disadvantaged area of tuscany (mugello): implications for the evaluation and the design of cap payments laura fastelli1*, chiara landi2, massimo rovai1, maria andreoli1 bio-based and applied economics 6(3): 229-242, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-23338 reviews articles rural-urban migration and implications for rural production alan de brauw1 international food policy research institute, washington d.c., usa date of submission: 2017 21st, september; accepted 2017 14th, december abstract. rural to urban migration has always been an inherent part of the economic development process, but its impacts are poorly understood, and are often feared by governments, which has led to policies that either attempt to explicitly or implicitly hinder migration. a major concern is that rural-urban migration can threaten food security, through reductions in agricultural production. in this paper, i examine the recent literature on migration and agriculture, which takes the challenge of statistically identifying impacts of migration seriously. i begin by discussing rural-urban productivity gaps and implications for policy, following through to impacts on agricultural production and rural investment. keywords. migration, rural development, agricultural production, rural investment. jel codes. j61, o13, q12, r23. rural to urban migration is part and parcel of the economic development process. as economies develop, people move from working solely in agriculture into the manufacturing and service sectors. this movement takes place through literally millions of decisions made by individuals and households to begin to move away from farms held by households and into either other, more productive rural areas or urban areas. a substantial literature in development economics shows that migration is, on average, quite beneficial to those who migrate (e.g. young, 2013). moreover, not all movement is rural-urban within countries; migrants also gain when they move from one urban area to another (e.g. bryan and morten, 2017). so voluntary migration plays an important role in economic development. however, the implications of migrants are quite contentious. due to negative perceptions of migration, policies often either explicitly or implicitly attempt to hinder migration. under the guise of maintaining food security, some governments have used explicit barriers to 1 alan de brauw is a senior research fellow, international food policy research institute, 2033 k st nw, washington, dc 20006 usa, e-mail: a.debrauw@cgiar.org. i want to specifically thank colleagues who have helped me think about migration over the years, including kate ambler, michael clemens, ed taylor, and many others. i also thank participants at the 2017 aieaa meeting in piacenza for comments that improved this paper. 230 alan de brauw movement, akin to china’s hukou system that has been gradually relaxed since the late 1980s (e.g. mallee, 1995). such restrictions may lead over time to the misallocation of resources within the economy either geographically (jalan and ravallion, 2002) or across sectors (adamopolous et al., 2017). given food and nutrition security concerns of governments, it is important to quantify the effects of internal migration on agricultural production and productivity, if any exist. this paper describes the drivers of rural-urban migration and the developing evidence base related to implications of migration for the rural economy of developing countries. the first section presents an illustrative model to motivate potential implications of migration for rural areas, and discusses its implications. the second section describes evidence related to the rural-urban wage gap, and the third section reviews the literature on effects of migration on agriculture and rural investments, with a careful eye towards papers that provide more carefully argued identification strategies. the final section concludes with policy implications. 1. theory: implications of migration for agriculture and rural investment to understand the potential impacts that migration can have on rural areas as migrants leave, a simple theoretical framework can provide useful insights. migration is inherently a dynamic process, so the illustrative model presented here uses a two period framework. consider a household that is deciding whether or not to send out a migrant. the household is assumed to initially have some capital (k), a labor endowment (l), and a fixed amount of land ( ), and produces agricultural goods according to a production function . if a migrant is sent out, then they may send back remittances (r), which can either be consumed or invested in period two. the household makes two choices; the first is whether or not to send out a migrant in period one, and the second is how much of any remittances to invest in further production in period two. for simplicity, the household is not assumed to save between period one and two, and further it optimizes the utility of consumption across the two periods using the following objective function: (1) the expectations operators suggest that agricultural production, the capital investments, and remittances in period 2 are uncertain. according to the model, the household trades off potential labor (and production) in period 1 for either increased consumption through remittances in year two, or increased production through investment. based on the household level objective function, migration would only occur if the gain in utility in period 2 exceeds any loss from period 1. as a result, theoretically rural-urban migration can have multiple effects in the short term on agriculture or agricultural production. first, migration can potentially cause a lost labor effect; if the individual migrating worked on the farm prior to migrating, household production might suffer if that individual’s labor cannot be replaced either by other family labor, hired labor, or potentially by capital services. slightly extending the model, households might also substitute out of labor intensive crops into less labor intensive 231rural-urban migration and implications for rural production crops, or they might rent out some of their land to other households if property rights over that land are strong enough. second, migrants might send back remittances which allow households to make investments in the family farm. such investments could include inputs such as fertilizer, herbicides, or pesticides, all of which could increase productivity. so at first glance migration could have indeterminate effects on agricultural production or productivity, at least beyond the immediate effects. further, in the short term there are potentially more subtle effects on agricultural production. first, one might perceive that by sending away migrants, households are signaling that they do not need all of their land. if land rights are not fully secure, then migration might lead to higher land expropriation or at least weaker property rights over land. second, decision making about agriculture within the household may change. if the migrant played a role in making those decisions, depending upon the duration of migration spells and communication channels to the migration, someone else within the household might take over deciding what crops to grow, the use of inputs or techniques, how much product to sell, and to whom to sell crops. third, migration may interact with agricultural risk in complex ways. migration is inherently risky, but the correlation between migrant income and agricultural income is likely much lower than the correlation between local off-farm wage labor and agricultural income, since weather risks are spatially correlated. in the longer term, investments that households make in the rural economy may also be affected by migration. three types of investments are particularly important—investments in the nutrition of young children, in education of older children, or in various forms of investment, whether housing, durable goods, or production. investments in the nutrition of young children may come either through remittances allowing the consumption of more nutritious foods, or through the removal of one mouth for households to feed. other investments would take place through remittances or migrants bringing home income, and would depend upon the perceived highest return. for example, the household might perceive long-term investments in education would have the highest return; alternatively, households might perceive high returns (in terms of comfort) to improving housing stock or purchasing durables. productive investments are risky but if expected returns (less any risk premiums) were high enough, migration could stimulate productive investments as well. in sum, theoretically rural-urban migration may have several different impacts on agriculture or agricultural households, which can either be quite direct or indirect. these impacts differ due to context specificity, but there are clear average gains to migration at the individual level. from a whole economy perspective, migration is also beneficial, as it allows economies to reap returns to locally increasing returns to scale from agglomeration (krugman, 1991). therefore, it is important to understand some of the ways that policy makers can embrace migration and find ways to mitigate costs associated with migration; some of these costs are clearly borne publicly while many of the benefits accrue to individuals or businesses. 2. migration, selection, and the productivity gap the labor shift out of agriculture into manufacturing and services has long been thought of as essential to the economic development process (e.g. lewis, 1954), and the 232 alan de brauw share of labor in the agricultural sector has a strong, negative correlation with gdp per capita on a country basis (figure 1). a recent literature both demonstrates that a large gap exists between returns to labor within and outside of agriculture (e.g. gollin et al., 2014), and a debate has surfaced on whether this shift causally relates to the shift from agricultural to non-agricultural work, or relates to worker selection into the non-agricultural sector. if the former explains the gap, then implicit or explicit barriers must exist that constrain individuals from moving out of agriculture. as a result, a policy response to improve welfare would be to reduce such barriers, which might include property rights over land (e.g. jacoby, li, and rozelle, 2002), or access to formal insurance (munshi and rosenzweig, 2016). on the other hand, if the whole gap is due to selection on individual characteristics, then policies to help improve human capital to improve returns to labor both within and outside agriculture should be emphasized. lagakos and waugh (2013) theoretically demonstrate that worker sorting can generate a substantial productivity gap due to the presence of a subsistence food constraint in a roy model. from a macroeconomic perspective, several authors have documented the gap between returns to labor in and outside of agriculture. in studying cross-country productivity differences, restuccia et al. (2008) find that that the agricultural productivity gap is 3.2—in othfigure 1. correlation between gdp per capita and the share of the workforce in agriculture, by country, 2015 alb arg arm ausaut aze bel bgr bih blr blz bmu bol bra brb brn btn can ceb che chl chn civ col cri cub cyp cze deu dnk dom dza eap easeca ecs ecu egy emu esp est eth euu finfragbr geo gmb grc gtm hic hnd hrvhun ibd idn irl irn isl isr ita jam jpn kaz kgz kor lac lca lcn lka lte ltu lux lva mac mar mda mdg mdv mea mex mkd mlt mna mne mng mus mys nac nam nld nor nzloed pan per phl pol prt pry pse pst qat rou rus rwa sau slv srb sur svk svn swe syc tea tec tha tla tmn tto tun tur tza uga ukr umc ury usa ven vnm wsmxkx zaf zmb zwe 4 6 8 10 12 lo g, g d p pe r c ap ita 0 20 40 60 80 share of workforce in agriculture source: world development indicators (2016). 233rural-urban migration and implications for rural production er words, that productivity is higher outside of agriculture by a factor of 3.2. gollin et al. (2014) combine national accounts with cross-sectional data from microeconomic surveys, and find that by controlling for differences in hours worked and human capital per worker between the two sectors, the average agricultural productivity gap falls to 2.1. from a microeconomic perspective, several papers have also documented a wage gap between laborers in agriculture. using household fixed effects, beegle et al. (2011) find an average wage gap of 36 percentage points between migrants and non-migrants from kagera, tanzania. moreover, they find that as migrants have moved farther, they are better off. consistent with this idea, bryan and morten (2017) build a model of migration with migration costs as well as potential benefits to agglomeration, and find it consistent with costly movement in indonesia. they suggest that eliminating moving costs could increase output by an average of 20 percent, by improving selection of workers into specific markets. two other micro studies suggest there may not be gains to reducing migration constraints. first, young (2013) argues that the entire gap can be explained by selection on education, through the study of demographic and health surveys in 65 countries. however, his study uses proxy variables for consumption, including household asset ownership, adult education levels, and child health. perhaps more convincingly, hicks et al. (2017) use two long panels in very different countries (indonesia and kenya) and show that the productivity gap can fully be explained by a complete set of individual and year fixed effects. however, due to the nature of their analysis any gains are measured only among individuals with productivity measured in both sectors; in other words, they cannot rule out the possibility that moving costs or other frictions may constrain individuals who could not migrate, and would have higher productivity in the non-agricultural sector. the discussion above has neglected the fact that returns to labor in agriculture are highly variable over the year. during specific points in the agricultural calendar, returns to labor may be quite high in agriculture, and could even exceed returns to labor outside of agriculture. an example would be during the harvest of perishable crops for which capital is not available or is too costly. on the other hand, at times returns to labor in agriculture may be close to zero, which can lead to seasonal migration. however, seasonal migration does not always occur on its own. in a randomized intervention, bryan et al. (2014) induce seasonal migration during the hunger season in bangladesh, when agricultural tasks are minimal, and find that consumption increases by 30 to 35 percent among households of those who were induced to migrate, and further seasonal migration remains 8-10 percentage points higher in years after the intervention among those who were induced to migrate. in sum, there is clear evidence in the literature that there is a labor productivity gap between agriculture and non-agriculture. it also seems clear that some of that gap is due to the selectivity of migrants out of agriculture a large portion of that gap can be accounted for by selectivity. however, there is also strong evidence that migration is constrained either in general or at the very least seasonally. such constraints include potential access to land (jacoby et al., 2002) and other informal rural institutions. certain institutions, such as caste-based informal insurance networks in india, may further hinder migration by reducing access to such insurance (munshi and rosenzweig, 2016). moreover, due to agglomeration urban areas are likely to continue to grow economically more rapidly, implying that even if selective sorting drove all migration, the equilibrium is constantly shifting at the margin. 234 alan de brauw 3. effects of migration on agriculture and rural livelihoods a major challenge in conducting research on the impacts of migration in general is that a number of endogenous and difficult to observe factors affect the migration process. migrants are likely to be different from non-migrants in both observable and unobservable ways. the opportunities that potential migrants observe outside the village are also not likely to be observable in advance of migration. moreover, if we consider migration from a household decision making perspective, households must both choose whether or not to send out a migrant, as well as which individual or individuals should migrate. as a result, understanding the implications of migration for sending households and areas in general is challenging empirically. the above discussion implies that in order to be able to credibly measure the impacts of migration on rural livelihoods, one needs either a randomized experiment (e.g. bryan et al., 2014), a natural experiment that causes variation in the opportunity to migrate, or a credible instrumental variable that plausibly causes differences in the opportunity to migrate but does not affect household production. for the latter strategy, it is worth noting that such instruments are both probably best linked to a policy change, and represent an estimate akin to a local average treatment effect (angrist and krueger, 2001). in other words, impacts estimated with instrumental variables represent impacts on those most likely to be affected by the instrument, rather than the population at large. given the challenge in identifying impacts of migration, in some cases below evidence from studies of international migration will be included as they have strong identification strategies. 3.1 impacts of migration on agricultural production as discussed in the introduction, there are several competing effects that migration can have on smallholder agricultural production. relatively recent loosening of restrictions on rural mobility in both india and china have substantially increased the volume of internal migrants in the world (deshingkar, 2006). much of the resulting literature focuses on internal migration in china, though there are examples from india, bangladesh, and vietnam as well. in china, there is suggestive evidence that migration shifts production on the margin from more labor to capital intensive techniques. rozelle et al. (1999) and taylor et al. (2003) find that maize yields and agricultural income in northeast china, respectively, are reduced with migration, but increase with remittances. however, they instrument migration with a community network variable and remittances with a community remittance norms variable; both are likely influenced by long term factors that may also affect agricultural production practices and incomes. giles (2006) uses panel data and weather shocks to instrument for village level migration, and finds that as more village migration leads to lower variance in household agricultural income. perhaps the best evidence on the implications for internal migration for agriculture in china is indirect. de brauw et al. (2013), in studying agricultural labor changes resulting from migration, show that in the china health and nutrition survey panel labor inputs fall substantially between 1993 and 2009 (figure 2), both in terms of the share of households doing any farmwork and the number of hours reported conducting farmwork. 235rural-urban migration and implications for rural production nonetheless, according to national statistics, during the same period the value of agricultural production rose by 297 percent in real terms, while cereal yields increased by 19.5 percent. meanwhile, the power (measured in kilowatts) of agricultural equipment being used increased by 175 percent over the same period (china statistical yearbook, 2010). whereas these statistics do not explain how production has been changing at the household level, they clearly show that rapidly increasing internal migration has not negatively affected production in the aggregate, and are highly suggestive that capital has begun to replace labor in chinese agriculture. outside of china, there is further evidence that internal migration does not have much of an effect on overall production. quisumbing and mcniven (2010) study a panel from mindanao, in the philippines, and find no evidence that either internal or international migration has much effect on overall agricultural production. similarly, de brauw (2010) finds evidence of a shift from labor intensive crops (specifically, rice) to land intensive crops in vietnam among vietnamese households participating in seasonal migration. further, there is some emphasis in the literature on understanding the relationship between migration and agricultural technology adoption. for example, in studying data figure 2. hours of farmwork and share of households farming, by survey round, rural areas of china, china health and nutrition survey 0 10 20 30 40 50 60 70 80 90 100 0 500 1000 1500 2000 2500 3000 1993 1997 2000 2004 2006 2009 sh ar e of h ou se ho ld s fa rm in g to ta l h ou rs o f f ar m in g (h jo us eh ol d) hours of farmwork share of households farming 236 alan de brauw from bangladesh mendola (2008) finds a negative correlation between internal (either permanent or seasonal) migration and high yielding variety (hyv) adoption, but a positive correlation between international migration and hyv adoption. however, she uses both household and village level network variables as instruments, either of which could be correlated with agricultural outcomes that might be affected by network participation. foster and rosenzweig (2008) look, in fact, for an opposite correlation; they find that households with initially higher yields for hyvs are less likely to send out migrants in following years. the discussion of impacts on household labor returns to this paper. finally, it is worth mentioning two papers that use exogenous variation to identify the impacts of international migration on a large set of outcomes, including agricultural income. specifically, in studying migration from pacific islands to new zealand made possible through lotteries, gibson et al. (2011a) find no effect on agricultural income caused by migration from tonga, and gibson et al. (2013) find a positive effect on agricultural income caused by international migration from effect from samoa, though it trails off as migrants have been gone for a longer period of time. in sum, there is scant evidence that migration has a negative impact on agricultural production or income among those left behind. instead, households appear to adjust either by shifting to more land intensive crops at the margin, or by substituting capital for labor, and consequently the value of agricultural production does not appear to change. to shed more light on the types of changes before discussing whether migration affects household investments, we explore whether it affects either household or village level labor allocations. given that migration is often gender specific, it can further change bargaining within households that migrants leave, with implications for agricultural decision making. chen (2006) uses the chns to show mild evidence of non-cooperative behavior within households when fathers leave; specifically, mothers spend less time on chores and labor within the household enterprise (agriculture). using later rounds of the chns, mu and van de walle (2011) find that women do more farmwork after migrants leave. de brauw et al. (2013) find that the increasing out-migration of males in china during the 2000s do not affect overall agricultural productivity using several data sources. much of the further evidence in the literature relates to international migration. mendola and carletto (2009) find that having a migrant abroad from albania increases the supply of unpaid work among women, presumably including agriculture. but antman (2015) finds an increase in decision making power among households that male mexican migrants left for the united states; she finds that resources shift towards girls from boys, and women exhibit more decision making power. in sum, recent evidence suggests that as internal migration progresses, it leads to specialization on the farm. wage impacts do not appear to be zero as predicted by lewis (1954); rather, direct and indirect evidence from south asia suggests that as labor is withdrawn from rural markets wages do increase. perhaps most important from a policy perspective, migration has complicated interactions with insurance, as sending out a migrant acts as a substitute for formal insurance. and migration can affect gender relations within households; to the extent that there is a feminization of decision making as a consequence of migration, women may make more decisions about crops to grow, sell, or related to investment decisions, which can affect the way crops are sold on input and output markets. 237rural-urban migration and implications for rural production 3.2 impacts of migration on household investments migration is a dynamic process, and perhaps not surprisingly it therefore can also affect the types of investments that rural households make. assuming that migrants send back remittances, some portion of those remittances may be consumed or saved for investment. if household decision makers can estimate the returns to various types of investment, one might broadly consider three categories of investments. first, households might decide to invest in productive activities within the village. second, they might decide to invest in durables or improving their housing stock. such investments might be preferred to productive investments, because the return in terms of consumption is stable, and returns to productive investments might be expected to be lower or quite variable. third, households could decide to invest in their children, either through ensuring that younger children eat better or older children stay in school longer. the latter effect might be tempered as children get older, however, and become candidates for migration (glewwe and jacoby, 2004). there is little evidence that rural-urban migration leads to productive investments in rural areas. de brauw and giles (2018) find that increased village level migration leads to increased productive investment levels, but among richer households, not among poorer ones. since migration in their sample is more likely to emanate from poorer households, this finding is likely due to general equilibrium effects. in some contexts, remittance rates may be too low to generate enough capital for investment; de brauw et al. (2014) for example compute remittance rates among internal migrants from several african countries, and find they are below 50 percent. there is more strong evidence from the literature on international migration; a potential rationale is that international migration may lead to larger remittances. related to migration from mexico to the united states, woodruff and zenteno (2007) find that households in villages with larger long term migrant networks exhibit higher investment in microenterprises. yang (2008) uses exchange rate shocks during the asian financial crisis to statistically identify remittances back to the philippines, and finds that they lead to increased levels of self-employment and entry into new types of entrepreneurship. on the other hand, gibson et al. (2011a) actually find negative impacts on investments in agriculture and livestock among households in tonga that migrants left for new zealand; it could be that households sold livestock to finance the initial migration and did not replace them. there is substantial anecdotal evidence that migrant remittances are often invested in either durables or improved housing. housing both might serve to make the household better off and to make the household more attractive for the migrant to return to (e.g. yang, 2011). however, the evidence is largely not causal but shows interesting correlations; a challenge is that one needs both panel data that can demonstrate the timing of investments, and one has a plausible identification strategy. an example related to rural-urban migration is de brauw and giles (2018), who use plausibly exogenous variation in the timing of id distribution to show that in china low income households make investments in housing when exposed to more migration. from the international perspective, perhaps the most interesting correlation in the literature is found by osili (2004), who uses a unique sample of nigerian migrants in the united states matched to households in nigeria to demonstrate housing investments among those households as well. 238 alan de brauw somewhat more tractable to measure, at least in terms of timing, is the relationship between migration and investments in the nutrition of young children (children under 5). a positive nutritional status among children under 5 years old has been shown to lead to improved outcomes later in life, including higher wages (maluccio et al., 2011). remittances are not the only mechanism by which nutrition investments might occur; increased women’s decision making power within households is also correlated with better nutritional outcomes among young children (ruel and alderman, 2013), though there might be negative impacts if less time is spent caring for children as a consequence of migration. as such, understanding the correlation between migration and the nutritional status of young children is an empirical question. the main source of evidence on internal migration and young child nutrition is again china; mu and de brauw (2015) use a panel within the chns and an interaction between wage growth in capital cities and the initial migrant network size to show parental migration improves the weight of children for their age, but not their height. internationally, carletto et al. (2011) use difference-in-difference methods to find a positive correlation between migration from guatemala to the united states and child height. in both papers, the effect of migration is measured as a net effect, rather than being caused by either income or changes in women’s decision making power. in contrast, gibson et al. (2011b) find a causal negative impact of migration on child height in tonga. there, one would assume that the time allocation channel discussed above dominates the income and women’s decision making power channels. among older children, households may use migrant remittances to make investments in education, as additional schooling is positively correlated with higher wages. however, from a policy perspective in some countries school attendance among younger children is nearing universal, so there is potentially little chance of impact at young ages in such countries. moreover, as children get older, there is a potential tradeoff between schooling and finding work as a migrant. so as with other questions related to migration, the net effect of migration on school enrollment is an empirical question. perhaps not surprisingly, there is credible evidence in the literature related to findings in both directions, and as with some of the other topics covered in this paper, some of the best evidence relates to international migration. specifically, yang (2008) finds that remittances lead to increased school enrollment in the philippines; theorahides (forthcoming) extends yang’s analysis by aggregating individual level data on destinations from specific localities to instrument for demand shocks, and similarly finds a school enrollment increase of 3.5 percent with spillovers to non-migrants. on the other hand, mckenzie and rapoport (2011) use historical migration rates by state as an instrument, and find that migration reduces school enrollment among 12 to 18 year old boys and 16 to 18 year old girls. two recent papers demonstrate that not all impacts of migration on education are positive. de brauw and giles (2017) show that as migrant opportunity increases within villages from a four province sample in china, high school enrollment decreases. during most of the period they study, completion of middle school is mandatory, so the first major schooling decision individuals make is whether to continue with school or not after mandatory schooling is completed. similarly, pan (2017) uses a change in the hukou policy related to children with urban registered fathers and rural registered mothers reduces high school enrollment among affected children at the margin. 239rural-urban migration and implications for rural production to summarize, migration can affect rural investment through remittances. these investments can have important implications for rural productivity if they are made in productive activities or in children; the effects of housing or durables investments are not likely to change the rural returns to labor in the longer run. to enhance the ability to make productive investments, it is important to be able to remit—in some african countries, remittance levels are low, potentially due to high transaction costs of remitting. if returns to productive investment are high enough in expectation, then rural returns to labor should increase, potentially changing the evolution of the rural-urban wage gap. 4. realizing the development potential of migration the previous two sections have demonstrated that first, in most of the world a ruralurban labor productivity gap exists, and urban laborers obtain roughly twice the return to their labor that rural laborers do. even if this gap is largely due to selection rather than migration restrictions, the presence of local agglomeration effects and locally increasing returns to scale suggest that so long as economies are growing migration will continue from rural areas to urban areas. moreover, the rural economy appears to adapt quickly to these changes; impacts of migration on agricultural production in much of the developing world seem minimal. migration can lead to important investments in microenterprises, nutrition, or schooling, but all differ by context. from a policy perspective, internal migration would seem to be a phenomenon that can help foster economic growth by improving the allocation of labor across sectors. as a result, it seems important to consider migration as policies are developed. while migration might make the provision of public goods such as schools and health clinics a moving target, these are less costly to build and maintain in urban areas than in rural areas on a per capita basis, as population densities are higher. but migration should also be considered when designing other policies, such as social protection. an excellent example is the example of the nrega workfare program in india, which imbert and papp (2017) show was not as effective at growing the rural economy as it would have been had it not reduced seasonal migration. perhaps more effective would be policies that enhance linkages back to rural areas, such as information and communications technologies. ensuring that financial regulations allow for the growth of mobile money services, for example, can help facilitate remittances back to rural areas, also dampening any potential negative effects on agricultural production. moreover, ensuring that land rights are not threatened or perceived to be threatened when migrants leave can help provide informal insurance to migrants, potentially allowing more people to choose where they want to live. 5. references adamapolous, t., brandt, l., leight, j. and restuccia, d. 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(2007). migration and microenterprises in mexico. journal of development economics 82(2): 509-528. yang, d. (2008). international migration, remittances, and household investment: evidence from philippine migrants’ exchange rate shocks. economic journal 118, no. 528: 591–63. yang, d. (2011). migrant remittances. journal of economic perspectives 25(3): 129-152. young, a. (2013). inequality, the rural-urban gap, and migration. quarterly journal of economics 128: 1727-1785. rural-urban migration and implications for rural production alan de brauw migrants to rural areas as a social movement: insights from italy giorgio osti immigrant workforce and labour productivity in italian agriculture: a farm-level analysis edoardo baldoni, silvia coderoni*, roberto esposti economic and social impact of grape growing in northeastern brazil linda arata1,*, sofia hauschild2, paolo sckokai1 is the question of the “active farmer” a false problem? maria rosaria pupo d’andrea*, simona romeo lironcurti issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae bio-based and applied economics 1(1): 81-108, 2012 horizontal price transmission in agricultural markets: fundamental concepts and open empirical issues giulia listorti1,* and roberto esposti2 1 federal office for agriculture (foag), berne, switzerland 2 università politecnica delle marche, ancona, italy abstract. following the dramatic changes experienced by the prices of agricultural commodities in 2007-2008, the analysis of horizontal price transmission mechanisms in agricultural markets has attracted renewed interest. in particular, this has led to the emergence of new challenges for the empirical analysis. how to model the increasing volatility and non linear behaviour of prices, to assess the impact of the policy responses to market turbulence, and how to account for the increasing interconnections between agricultural and non-agricultural commodity markets are amongst the most investigated issues. building on a common analytical framework, this paper discusses and reviews the most recent methodological developments and empirical contributions in the field. keywords. price transmission, cointegration, vecm, volatility, market policies jel classification. q110, c320 1. introduction in recent years, the empirical research on agricultural price transmission has gathered considerable attention. interest in this topic unquestionably increased after the so-called «food crisis» of 2007-2008 in which international agricultural markets were shocked by increased volatility, i.e., a rapid rise and fall of the so-called price bubbles as well as a possible change in the long-term downward trend of agricultural prices (european commission, 2008; irwin and good, 2009). such a dramatic change in the price behaviour clearly brought about a number of crucial research questions which are currently being investigated. in this work, we focus on horizontal price transmission, that is, the transmission of price shocks both across different places and commodities. the objective of this short note is to present some recent developments and open issues of the literature on this topic, all building upon a basic and well-established analytical framework, and to point out future possible research developments. three aspects, in particular, are attracting increasing attention. * corresponding author: giulia.listorti@blw.admin.ch. 82 g. listorti and r. esposti the first key issue concerns the development of appropriate econometric models for the quantitative analysis of price transmission during periods of price exuberance and, more specifically, for the econometric treatment of non-linearities and volatility. secondly, agricultural markets are characterized by a high degree of policy intervention. during the food crisis, the dramatic rise in agricultural price volatility led many governments to adopt or strengthen specific policy measures (tangermann, 2011). these interventions, however, raise serious doubts about their actual direct, indirect and unintended effects across agricultural markets, as the impression is that they boosted rather than mitigated market turbulence. therefore, how to properly model the impact of policy intervention on price transmission, especially in the light of these recent developments, has become a major challenge for the empirical analysis. a third major issue has emerged in recent years which concerns the increasing and complex interconnections between agricultural markets and other commodity and financial markets. a growing body of literature has been focusing on the relationship between food, feed and fuel prices; links between energy (e.g., oil) and agricultural prices; and interconnections between spot and future prices. despite the different underlying theoretical motivations, this empirical literature converged toward a common econometric approach. therefore, it is now possible to refer to horizontal price transmission in agricultural markets and to the related empirical literature, considering all these instances as a unique study. this paper is structured as follows. the key concepts underlying price transmission analysis are described in section 2: the fundamental definitions (section 2.1), the time series properties of agricultural prices (section 2.2), the basic framework for price transmission analysis (section 2.3) with a focus on cointegration models (section 2.4) are presented. then, the most recent literature on agricultural price transmission is reviewed in section 3 by focusing on the three crucial aspects characterizing the current scientific debate on this topic: non linearities (section 3.1), time-varying price volatility (section 3.2), and impact of policy measures (section 3.3). some final considerations and a summary classification of this recent literature conclude our study in section 4. 2. fundamental concepts and methods for the study of price transmission while vertical price transmission refers to price linkages along a given supply chain, with horizontal price transmission we mean the linkage occurring among different markets at the same position in the supply chain. the notion of horizontal price transmission usually refers to price linkages across market places (spatial price transmission). lato sensu, however, it can also concern the transmission across different agricultural commodities (cross-commodity price transmission) (esposti and listorti, 2011), from non-agricultural to agricultural commodities (notably, from energy/oil prices to agricultural prices) (serra et al., 2008; hassouneh et al., 2011), and across different purchase contracts for the same commodity (typically, from futures to spot markets and vice versa; baldi et al., 2011). as detailed below, the key underlying theoretical explanation of spatial price transmission is the spatial arbitrage and the consequent law of one price (lop). on the contrary, for cross-commodity price transmission, the co-movement of prices is mostly driven by the substitutability and complementarity relations among the products (saadi, 2011), 83horizontal price transmission in agricultural markets while transmission from non-agricultural to agricultural commodities is prevalently due to the underlying production technology and cost structure, but also due to the complex drivers (expectations, speculative behaviour, etc.) of financial markets which also underlies the linkage between spot and futures prices1. however, even though the background theory differs, the empirical framework and the econometric implications of these different cases of horizontal price transmission are the same. as our attention in the present paper is on the common methodological issues rather than on different theoretical explanations, we will review empirical applications in all these cases2. in the sections that follow, the general methodological framework for the analysis of horizontal price transmission is presented: the fundamental definitions (section 2.1), the time series properties of the prices (section 2.2), the basic model of price transmission (section 2.3) and, finally, the cointegration approach (section 2.4). 2.1 the basic definitions if we limit the notion of horizontal price transmission to the co-movement of prices of a given product in different locations (spatial price transmission), the spatial arbitrage condition is the key theoretical concept. it implies that the difference between prices in different market places will never exceed transaction costs3, otherwise the profiting opportunities would be immediately exploited by arbitrageurs. the consequence of spatial arbitrage is the law of one price (lop), as already derived by marshall (1890; see also fackler and goodwin, 2001): in markets linked by trade and arbitrage, homogeneous goods will have a unique price, when expressed in the same currency, net of transaction costs. two other familiar theoretical concepts complement those of spatial arbitrage and the lop. in this context, market efficiency indicates the capacity of markets to minimize costs when they match supply and demand. in a competitive market with perfect information, arbitrage will ensure that price differentials will reflect all marketing costs. the concept of market integration refers rather to the tradability of products between spatially distinct markets, irrespective of the presence or absence of spatial market equilibrium and efficiency (barrett and li, 2002; thompson et al., 2002)4. 1 though there may be significantly different interpretations on the drivers of price interdependence, in our survey we will indifferently consider studies working with spot and futures prices. 2 in fact, also in the case of vertical price transmission, the methodological issues and solutions are analogous to those of horizontal price transmission. however, theoretical and policy implications are much different. for instance, due to market power and supply contracts, the asymmetries in price transmission assume crucial importance. therefore, given space limitations and with only few exceptions (kuiper and bunte, 2011; rezitis et al., 2009; rezitis and stavropoulos, 2011), all the empirical literature on vertical price transmission is ignored in this paper. 3 in this paper, in analogy with marshall (1890, p. 325) (see also fackler and goodwin, 2001, p. 977), the term «transaction costs» refers to all costs necessary to transfer commodities between two different locations, thus including transportation costs. 4 though the concept of market integration finds its sound theoretical justification in the takayama and judge price and allocation model (barrett, 2001), it has been used in the literature quite loosely to generally indicate the degree of co-movement shown by prices across spatially separated markets (goodwin and piggott, 2001). actually, other mechanisms, such as information flows, might indeed explain price transmission rather than physical trade flows. therefore, price transmission might occur in the absence of trade (segmented equilibrium) as well as trade might take place in the absence of price transmission (imperfect market integration). 84 g. listorti and r. esposti most empirical works in this field essentially aim at assessing whether the lop holds true. as a matter of fact, it is well recognized that the universal validity of this ‘law’ can be easily questioned, as its assumptions are quite restrictive and unlikely to hold in practice. the lop is a static concept while, in reality, economic processes are dynamic and may show temporary deviations from equilibria. assuming that prices are always in equilibrium is not realistic. indeed, temporary arbitrage opportunities (disequilibrium) might co-exist with long-run equilibrium conditions. moreover, it is clear that many factors can prevent or slow down price convergence (see miljkovic, 1999; conforti, 2004). notably, transaction costs are relevant in agriculture if compared to the unit value of the commodities considered (fackler and goodwin, 2001; barrett, 2001). prices might still not move together if transaction costs are large and volatile or might move together only when their difference is high enough, with respect to transaction costs, to make arbitrage convenient5. in addition to conventional transaction costs, other factors may prevent the validity of the lop: domestic and border regulation policies, market power, product heterogeneity and perishability, exchange rate risks, imperfect flow of information and expectations are some of the factors that interfere with spatial arbitrage, and then with price transmission (miljkovic, 1999; graubner et al., 2011; rezitis and stavropoulos, 2010; santeramo and cioffi, 2010). as these sources of deviations from the lop are often unobservable, in many empirical models they are not explicitly considered and are therefore implicitly captured by disturbance terms. this leads to three major consequences for the empirical analysis. first of all, the assumptions on disturbances imply strong assumptions on how these drivers behave. secondly, the estimated parameters sum up the combined effect of a whole set of factors affecting price transmission, not only the lop. thirdly, all the knowledge and information about these drivers are helpful in finding the appropriate empirical specification and interpretation of the estimation results (fackler and goodwin, 2001). 2.2 time series properties of the prices a fundamental characteristic of a price series is the persistence of its shocks as indicated by its autocorrelation coefficients. if equal to 1, shocks will never vanish over time and the series is said to contain a «unit root» (or integrated of order 1, i(1), since it needs to be differentiated to become stationary, i(0)). as a matter of fact, empirical tests often find evidence of unit roots, then non stationary price behaviour, in the time series of commodity prices6. it is well known, however, that the outcome of unit root tests can be influenced by a number of factors, like data frequency and alternative test specifications (wang and tomek, 2007). furthermore, if not properly taken into account, the presence of structural breaks reduces the ability to reject a false unit root null hypothesis. accordingly, vari5 as transactions costs are usually associated to the real movement of commodities between markets, myers and jayne (2012) and stephens et al. (2012) investigate the possibility that price transmission between spatially distinct markets might vary during periods with and without physical trade flows and might depend on the size of trade flows. 6 for a recent contribution on the theoretical and empirical properties of the agricultural price series, see stigler (2011). 85horizontal price transmission in agricultural markets ous unit root tests have been developed to allow for structural breaks in the time series (amongst others, perron and vogelsang, 1992; clemente et al., 1998; zivot and andrews, 1992; see glynn et al. 2007 for a review). besides structural breaks, in some cases agricultural price series appear to be neither i(0) nor i(1) but rather i(d) processes with 0 < d < 1 (fractional integration). originally proposed by granger and joyeux (1980), the idea of fractional integration implies that, although not behaving as random walks, the price series keep the memory of a given shock for a long period, and this may also generate non-linear patterns quite close to chaotic processes. as emphasized by wei and leuthold (1998) and mohanty et al. (1998) (see also stigler, 2011), this is often the case for agricultural prices (mostly, in fact, future prices). conventional unit-root tests may fail in assessing whether price series are i(0) or i(1) while, in fact, they are i(d) with 0 < d < 1. thus, the presence of such a long memory within the price series has to be tested following appropriate approaches, as that proposed by geweke and porter-hudak (1983) and modified by phillips (1999a,b). therefore, in a standard time series analysis, non-stationary variables are usually assumed to be either first-order integrated, i(1), or second-order integrated, i(2), or fractionally integrated (engsted, 2006). however, the temporary explosive patterns of prices observed in recent years represent a true problem for the analysis. a price series showing explosive behavior is not necessarily an i(2) series. indeed, i(2) series would imply a permanent exuberance of prices while, on the contrary, the observed patterns inflate and deflate within a relatively limited period of time («temporary collapsing bubbles») (diba and grossman, 1988). in other words, price bubbles induce a temporary explosive root in price series in addition to a unit root. if this additional root is not appropriately considered, conventional testing may fail to detect the real underlying stochastic process (evans, 1991). recent works by phillips and magdalinos (2009), philips et al. (2009) and phillips and yu, 2009 (see also gutierrez, 2010) have provided an appropriate framework for assessing the presence of an explosive root within processes that would be otherwise ruled as i(1)7. these sequential tests not only assess on a period-by-period basis the nonstationarity of the price series against an explosive alternative but they are also able to date the beginning and the end of price exuberance («the bubble»). 2.3 the basic empirical framework for price transmission analysis once the time series properties of the agricultural prices have been investigated, price interactions can be properly analysed. in this respect, and also concerning the econometric techniques put forward in empirical applications, fackler and goodwin (2001) identify simple regression and correlation analysis as the oldest approach. the basic representation of a price transmission equation is the following: p1t = β0 +β1p2t +β2tt + εt (1) 7 other modelling frameworks can actually admit transitory deviations from a regular i(0) or i(1) process. koenker and xiao (2006), for instance, show that quantile autoregression models (qar) may allow temporary unitroot tendencies or even explosive behavior, while maintaining stationarity in the long run. 86 g. listorti and r. esposti where p1t and p2t are the prices in locations 1 and 2 at time t, respectively. t represents transaction costs and εt is conventional disturbance. markets 1 and 2 are taken to be perfectly integrated if β1 = β2 =1 and β0 = 0 . these models can be also evaluated in logarithmic form, i.e. p1t = β0 +β1p2t +β2τ t + εt (2) the underlying equation in levels being p1t = e β0p2t β1tt β2eεt (3) where p = log p, τ = log t and β1 is the elasticity of price transmission. for spatial price transmission, β1=1 reflects the validity of the lop, while for transmission across commodities β1 is expected to be close to 1/-1 under perfect substitutability/complementarity. as t (or τ) can be hardly observed, this term and the parameter β2 are actually skipped, and β0≠0 roughly captures all factors contributing to price differentials8. regression model (1), or (2), however, raises two major conceptual and practical concerns. first of all, markets 1 and 2 being interdependent, p2t cannot be assumed exogenous with respect to p1t. in other words, all prices are endogenous. secondly, as mentioned before, any adjustment toward an equilibrium between markets and prices (as expressed by the lop) takes time, and temporary deviations from this equilibrium can be observed. dynamic regression models have thus gained increasing attention because they allow to represent both contemporaneous and lagged price linkages and take price endogeneity into account. the basic version of these dynamic specifications is the vector autoregression (var) model9: pt = cipt i i=1 k + dxt + t (4) where pt is the (n×1; n is the number of price series considered in the analysis) vector of prices (either in levels or logarithms) at time t (thus, pt-i indicates the vector of prices at time t-i, i being the generic time lag from 1 to k), xt is a (m×1) vector of m possible exogenous factors with the associated (n×m) parameter matrix d. the ck are the (n×n) matrices of coefficients of the k-th lagged prices, and εt is a (n×1) vector of disturbances expressing the unobservable serially independent market shocks. a common template embedding all dynamic regression models is provided by fackler and goodwin (2001). granger causality, the so-called ravallion (1986) market integration criteria (based on a radial structure with central and satellites markets) as well as the analyses of impulse response functions (irfs) and cointegration analysis (see next paragraph) can all be interpreted as empirical tools to analyse price transmission within this basic framework. 8 the strong underlying assumption is that all factors possibly contributing to price differentials, but not explicitly taken into account among regressors, are either constant (if a specification like (1) is used) or a constant proportion of prices (if a specification like (2) is used). 9 the var model (4) actually represents the reduced-form specification of the linkages across prices. in order to make the structural relationships (that is, the contemporaneous linkages) explicit, the model has to be rewritten in a structural form (svar): a0pt = aipt−i i=1 k ∑ +bxt +ut . 87horizontal price transmission in agricultural markets the validity of such basic framework lies on the fact that it analyzes price transmission without any available information but prices. other empirical models have been proposed, as variants or alternatives with respect to (4), whenever more information on transaction costs, trade flows, and agents’ expectations are made available (barrett and li, 2002). this is the case of the switching regime models (sexton et al., 1991; baulch, 1997) or of rational expectations models (goodwin et al., 1990). however, the availability of long high-frequency (weekly or daily) price series, on the one hand, improves the capacity of capturing the dynamics of arbitrage processes; on the other hand, it prevents the use of variables other than observed market prices. for these practical reasons, reduced models like (4) have become the most prevalent framework for the empirical analysis of agricultural price transmission. 2.4 cointegration models since the seminal work of ardeni (1989), the concept of cointegration has demonstrated an intuitive appeal for the study of price transmission mechanisms within dynamic regression models. as price series are often nonstationary, as discussed before, cointegration models are ideal to represent how non-stationary variables are linked by a stationary long run relationship (which is, in fact, the main interest of price transmission analysis), though they can diverge from it in the short run. cointegration models thus allow to disentangle short and long run dynamics in price interdependence. their empirical specification takes the form of vector error correction models (vecm) that can be intended as the natural development of model specification (4) under such circumstances. a standard vector error correction model (vecm) can be written as follows (engle and granger, 1987): δpt = αβ 'pt−1 + γiδpt−ii=1 k−1∑ + ε t (5) where pt is the (n×1) vector of prices at time t; α (n×r) is the loading matrix which contains the adjustments parameters toward the equilibrium (the ‘speed’ of price transmission; prakash 1999 cited in conforti 2004); β (n×r) is the cointegration matrix containing the r long-run relationships (cointegrating vectors) expressing the ‘degree’ of price transmission (when prices p are expressed in logs, the coefficients of β can be read as price transmission elasticities); γi (n×n) are matrixes containing coefficients expressing the short-run responses to price shocks; εt is a conventional (n×1) vector of zero-mean, unitvariance, and independent and identically distributed disturbances. the rank of π = αβ’ allows to determine the presence of cointegration (i.e., of a long run relationship) amongst the variables: if rank(π) = 0, the variables are not cointegrated, and the model becomes equivalent to a var in the first differences, δp. in such case, the conclusion would be that there is no long run relationship among prices, and their interdependence is limited to short-run responses to shocks. if rank(π) = n, the variables are stationary, and the model is equivalent to a var in levels like (4)10. if 0 < rank(π) = r < n, 10 though not distinguishing between long-run and short-run price relationships, a var specification of the price transmission equations still admits all the modelling variants presented for the vecm case in section 3. 88 g. listorti and r. esposti the variables are cointegrated11. therefore, for nonstationary series, cointegration has been considered to be a sufficient condition for integrated markets. in a system with n prices, the number of cointegration relations (r) can be also considered as an index of the degree of integration of the respective markets. in the empirical literature on agricultural price transmission of the last twenty years, the vecm approach has become dominant (goodwin and fackler 2001; miljkovic, 1999; listorti, 2009) also because several improvements of the basic specification (5) have been progressively introduced. for instance, threshold cointegration models (balke and fomby, 1997) and asymmetric cointegration models (ghoshray, 2002; meyer and von craumontaubadel, 2004) allow for a more sophisticated representation of how prices respond to shocks in the short run and adjust to their long run equilibrium. these developments will be extensively discussed in the next section. despite this intuitive appeal and its success in price transmission and market integration analysis, the use of cointegration techniques also presents some shortcomings (barrett, 1996; miljkovic, 1999). in particular, given that the long run relation expresses the lop, the fundamental assumption underlying the vecm (5) is that price spreads β’pt-1 (therefore, all components which account for these spreads) are constant, if prices are in levels (or, in the case of prices expressed in logarithms, are a constant proportion of prices). more generally, cointegration is not a necessary condition for markets to be efficiently integrated, since transaction costs (as well as other elements contributing to price spreads) could vary over time and may themselves be nonstationary processes. also in this respect, some possible developments will be discussed in the next section. nonetheless, cointegration analyses should always be accompanied by a careful exploration of the economic characteristics of the markets under study. 3. recent literature and open issues in all variants discussed in the previous section (var models in case of i(0) price series, vecm models when the series are cointegrated, and d-differences var models whenever the i(d) series are not cointegrated), the dynamic regression models constitute the predominant framework for the empirical analysis of agricultural price transmission. however, in its conventional specifications, this modelling approach may fail in providing an adequate representation of the often complex price co-movements. to better capture the observed behaviour of prices, recent research on horizontal price transmission expanded this framework in three major directions: price transmission and non-linearities; price transmission and changing volatility; price transmission and policy intervention. table 1 displays a selection of the most recent contributions with regard to the three directions of research12. for instance, santeramo and cioffi (2010, 2012) and cioffi et al. (2011) present several applications to agricultural markets of the threshold var (tvar) model. 11 hassouneh et al. (2012: 22) review the proper specifications of the price transmission equations depending on the outcome of the unit-root and cointegration testing procedures. 12 stigler (2011) provides a good survey on the main empirical issues raised by the recent price rally on agricultural markets. many of these issues are related to those under discussion here, although our attention is more on the methodological developments put forward by the recent literature rather than on the actual identification of the drivers of the market turmoil in the past few years. 89horizontal price transmission in agricultural markets 3.1 price transmission and non-linearities the general framework of price transmission presented in section 2 is based on the assumption that the prices under study can be represented as linear i(d)13 (mostly i(1)) autoregressive integrated moving average (arima) processes. under such circumstance, a preliminary condition for price transmission analysis is assessing whether these prices show the same order of integration. the knowledge of this common order of integration allows analyzing price transmission by identifying linear short-term and/or long-term relationships. however, actual price movements may seriously question this assumption. 13 d indicates the order of integration. table 1. tentative mapping of the recent contributions on horizontal agricultural price transmission primary focus secondary focus non-linearities volatility policies non-linearities goodwin and piggott (2001) mainardi (2001) sephton (2003) balcombe et al. (2007) balcombe and rapsomanikis (2008) götz et al. (2008) serra et al. (2008) ihle and amikuzuno (2009) ihle et al. (2009) ubilava and holt (2009) amikuzuno (2010) santeramo and cioffi (2010) baldi et al. (2011) brosig et al. (2011) greb et al. (2011) hassouneh et al. (2011) liu (2011) myers and jayne (2011) natanelov et al., (2011) stephens et al. (2012) listorti (2007, 2009) götz et al. (2010) cioffi et al. (2011) djuric et al. (2011) ihle et al. (2011) santeramo and cioffi (2012) volatility rezitis and stavropoulos (2011) busse et al. (2010) hernandez et al. (2011) serra (2011) rapsomanikis (2011) rapsomanikis and mugera (2011) serra et al. (2011) policies dawson et al. (2006) dawson and sanjuan (2006) serra et al. (2006) barassi and ghoshray (2007) esposti and listorti (2011) hernández-villafuerte (2011) rezitis and stavropoulos (2010) thompson et al. (2002) mohanty and langley (2003) verga and zuppiroli (2003) barassi and ghoshray (2007) 90 g. listorti and r. esposti in other words, real observations may suggest that the data generating process is rather a non-linear function of the lagged prices (harvey and leybourne, 2007). in such cases, differentiation does not restore stationarity, and price transmission cannot be analysed as a linear stationary combination of prices. in agricultural markets, such deviations from the conventional linear cointegration framework should not come as a surprise. the price «bubble» observed in 2007-2008 confirmed that these series can hardly be represented by i(d) processes. therefore, the recent literature has increasingly expressed the need for an extension of the basic framework, particularly taking advantage of the developments of applied econometrics coping with the combination of nonstationarity and nonlinearity. more specifically, in the case of agricultural prices, two lines of research can be identified. the first concentrates on nonlinearities found in the individual price series and on the consequences on price transmission. in particular, the attention is on price series showing a temporary explosive root in addition to a unit root: indeed, for these series, no univocal order of integration may be found14. engsted (2006) and nielsen (2010) show that, even if one series shows a temporary explosive root, the cointegration model (and the vecm specification) remains an «ideal framework» for analyzing the linkage between variables that have a common stochastic trend (they are cointegrated), but in which one of the series also has an explosive root. under this circumstance, specification (5) should be written in a form that admits two structural relations. the first contains the usual cointegrating parameters (the linear combination of prices that is i(0)); the second contains the co-explosive parameters (the linear combination of prices that is not i(0) but not explosive) (engested, 2006): δ1δ ρpt = α1β1 'δ ρpt−1 +α ρβρ 'δ1pt−1+ γiδ1δ ρpt−i i=1 k−2 ∑ + ε t (6) where pt is the (n×1) vector of prices at time t; δ ρ = 1− ρl( ) and ρ is the explosive (ρ>1) root. α1 is the conventional cointegration vector15 while βρ contains the co-explosive parameters. all other parameter matrices (α1, αρ, γi) can be interpreted according to equation (5). it is worth noticing that, if the interest rests prevalently in the usual cointegrating (i.e., long run) relationship between the prices (and/or we have not a reliable estimate of ρ), the standard johansen (1995) estimation procedure holds its validity, and we may simply proceed to estimate (5) (see esposti and listorti, 2011) for an application to agricultural prices). however, if we were also interested in investigating the price relationships 14 as mentioned in section 2.2, also fractional integration could generate temporarily non-linear patterns. the presence of long memory in price series can be represented with an arfima (autoregressive fractionally integrated moving average) model (geweke and porter-hudak, 1983; phillips 1999a,b). an application of this approach to agricultural markets can be found in wei and leuthold (1998). wang and garcia (2011) present an extension of this modelling framework to the analysis of time-varying volatility within a garch representation. nonetheless, this approach has found quite limited interest in the analysis of the transmission of agricultural prices and seems less insightful with respect to the recent turbulence of agricultural markets (esposti and listorti, 2010). therefore, it will not be considered in the present review anymore. 15 it can be demonstrated that β1 corresponds to the β of specification (5) (nielsen, 2010). 91horizontal price transmission in agricultural markets within the (co)explosive period, we should firstly find out the value of ρ>1 and then estimate (6) to obtain an estimate of βρ 16. the second direction of research on linearity concentrates on the nonlinearity of the relations among price series (the vecm representation). this is, in fact, what is commonly intended as the «non-linearity problem» within price transmission literature. this issue has already received much attention also because it took advantage of the continuous developments in the concepts and tools of nonlinear cointegration over the last fifteen years (dufrénot and mignon, 2002). three different empirical strategies to include nonlinearity within the conventional vecm can be identified17. an easy and intuitive way to account for nonlinearity is to assume that a regime change intervenes at a certain point in time. it consists in a change in the relationship among prices and is deterministic, that is, induced by some observed exogenous factor (for instance, a new policy, a new regulation, a new technology, etc.). this kind of regime change is introduced in the vecm in the form of a structural break within the cointegration relationship. johansen et al. (2000) generalize the standard johansen cointegration framework by admitting up to two predetermined breaks in the cointegration space, and propose a model where breaks in the deterministic terms are allowed at known points in time. johansen et al. (2000) propose to divide the sample in q periods, separated by the occurrence of structural breaks, where j denotes each period. the general vecm becomes: δ1pt = α β µ ⎡ ⎣ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ' pt−1 te t−1 ⎡ ⎣ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ + γe t + γ iδ1pt−i i=1 k−1 ∑ + k i,j j=2 q ∑ d j,t+k−i i=1 k ∑ + θmwm,t m=1 d ∑ + ε t (7) where pt is the (n×1) vector of prices at time t; k is the lag length of the underlying var. β contains the usual long run coefficients in the cointegrating vector and µ = µ1t µ2t ... µqt ⎡ ⎣ ⎤ ⎦ ' is the vector containing the long run drift parameters of the q periods. et is a vector of q dummy variables that take the value 1, i.e.,ejt = 1, if the observation belongs to the jth period (j = 1, …, q), and 0 otherwise; that is, et = e1t e2t ... eqt ⎡ ⎣ ⎤ ⎦ ' . α includes the adjustment coefficients. dt is an impulse dummy (with its lagged values) that equals unity if the observation t is the ith of the jth period, and is included to allow the conditional likelihood function to be derived given the initial values in each period. wt are the intervention dummies (up to d) included to obtain well-behaving residuals. the short run parameters are included in matrices γ (2 x q), γ (2 x 2), k (2 x 1) for each j and i, and θ (2 x 2). εt are assumed to be i.i.d. with zero mean and symmetric and positive definite variance, ω. the cointegration hypothesis 16 once an explosive root is found with an appropriate testing procedure (phillips and magdalinos, 2009; philips et al., 2009; phillips and yu, 2009) and then estimated (engested, 2006), the analysis of price transmission can be carried through this adaptation of the conventional vecm framework. 17 an extensive survey on how to cope with nonlinearities within price transmission analysis can be found in ihle (2009); see also hassouneh et al. (2012). 92 g. listorti and r. esposti is formulated by testing the rank of π = α β µ ⎡ ⎣ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ' ; its asymptotic distribution cannot be generalized as it depends on the number of non-stationary relations, on the location of breakpoints and on the trend specification (johansen et al., 2000). in this approach, the regime changes (the breaks) must be known ex-ante, though appropriate tests can be run in this respect (listorti, 2009). dawson et al. (2006), dawson and sanjuan (2006), listorti (2009) and esposti and listorti (2011) present applications of this structural break approach to the analysis of agricultural price transmission, each with possible variants or adaptations to specific contexts18. a second and more sophisticated approach has received a great deal of attention especially in the last five years19. it collects a set of alternative variants under the common label of regime-dependent or state-dependent vecm models. the underlying assumption is that price series behave as autoregressive processes whose parameters are not constant but change under different regimes, or, in other words, under different values of the prices. this leads to a non-linear representation of the individual data generating process and, possibly, to a non-linear relationship among cointegrated prices. such representation is particularly appealing in the analysis of price interdependence as the presence of transaction costs make arbitrage (thus, the lop) occur only (or differently) when the price differential exceeds a given threshold. the general case of this non-linear vecm (teräsvirta, 1994), is the smooth-transition vecm (stvecm): δpt = α1β 'pt−1 + γi 1pt−ii=1 k−1∑( ) 1−g(st ,γ ,c)( )+ α2β 'pt−1 + γi 2pt−ii=1 k−1∑( )g(st ,γ ,c)+ ε t (8) where superscripts 1,2 indicate the two regimes in which observed prices can be. (8) evidently represents a combination of two vecms which, eventually, implies a non-linear representation of the linkage between prices. while the long run relationship (β) remains the same, all the other parameters expressing the adjustment and short-run dynamics differ across the regimes. in fact, the basic justification behind this approach is to capture the role of transaction costs in regime switching under the assumption that transaction costs actually concern the adjustment and short-run dynamics, not the underlying lr relationship. g(st) is the so-called transition function; its value ranges between 0 and 1 and represents the extent to which the price relationship lies in the two regimes. st is the transition variable which usually takes the form of some lagged price values or lagged residuals from the error correction relationship, while c and γ are just two parameters expressing the thresholds between the two regimes and the speed of transition from one regime to another, respectively. the logistic and exponential specifications of g(st) are the ones that are most frequently adopted in the literature20. 18 an alternative approach is proposed by barassi and ghoshray (2007) that assume that the structural break affects the cointegration rank r rather than the cointegrating relationship: the break generates (or eliminates) the cointegrating relationship among the prices. 19 see hassouneh et al. (2012). 20 ubilava and holt (2009), for instance, adopts an exponential stvecm to analyse price transmission across vegetable oil world markets. 93horizontal price transmission in agricultural markets with respect to the structural break approach, this second stream of literature has the advantage that the regime change is not entered as an exogenous instantaneous shifter, and the movement across the regimes depends on price data themselves. thus, it seems more suited to analyse nonlinear price transmission during period of market instability and whenever the timing and the causes of this change in regime are hardly identifiable. though, in principle, (8) can take many different forms, two specific cases on nonlinear agricultural price transmission have become prevalent in the empirical literature21. in the first one, it is γ = 0. given st, the threshold variable c establishes whether the price linkage is in regime 1 or 2. this is the popular threshold cointegration framework (already mentioned in section 2.4); a threshold vecm (tvecm) specification (ihle, 2009) can be even generalised to more than two regimes, provided that the sum of the value of the transition functions in any regime remains = 1. an early application to agricultural markets comparing a stvecm and a tvecm can be found in mainardi (2001). recent applications of the tvecm approach to agricultural price transmission, just to mention a few, are goodwin and piggott (2001), sephton (2003), serra et al. (2006), ihle and cramon-taubadel (2008), serra et al. (2008), amikuzuno (2010), brosig et al. (2011), greb et al. (2011), rezitis and stavropoulos (2011). in the second case, the actual regime depends on a probabilistic process behaving like a markov chain. the chain determines regime switching; the transition probabilities from one regime to another are established by a time-invariant transition matrix. this version of (8) is the so-called markov-switching vecm (msvecm) specification. among others, recent applications to agricultural price transmission are ihle and cramon-taubadel (2008), and ihle et al. (2009), djuric et al. (2011). out of these two modelling approaches many variants have been proposed (natanelov et al., 2011). analysing in details all these variants as well as their pros and cons is well beyond the scope of this article22. it is worth emphasizing, however, that the common feature of all these tvecm and msvecm approaches is that they all concentrate on the nonlinearity of price transmission (i.e., on the regime change) in the adjustment (error correction) and short-run parameters, leaving the long run equilibrium unchanged (non-regime dependent). for this reason, such models can be called nonlinear error correction models to distinguish them from those where the cointegrating relationship may also be nonlinear (nonlinear cointegration models; see below). therefore, these approaches reflect the idea that nonlinearities in price movements mostly occur during periods of market turbulence or instability and represent temporary changes in how prices respond to deviations from 21 for instance, either symmetric or asymmetric tvecm can be specified (liu, 2011) as well as exogenous or endogenous thresholds (the so-called self-extracting tvecm; ihle, 2009). moreover, in this context, though not necessarily after a formal derivation from (8), other specifications have been proposed to model the adjustment processes and the short-run dynamics. the main purpose of these approaches is to minimize the ex ante restrictions imposed on data with respect to these dynamics. nonlinear parametric (sephton, 2003) and nonparametric specifications (serra et al. 2006; hassouneh et al., 2011) have been used, the latter receiving increasing attention by analysts in this field. 22 note that a sort of structural break approach could also be obtained as a special case of (8). if γ = 0 and the change in regime does not depend on a transition variable, st, but only on an exogenous threshold, c, we obtain a specification where, however, an exogenous break only operates in the adjustment and short-run dynamics, not in the long run (cointegrating) relationship. in this sense, such a solution cannot be regarded, strictly speaking, as a real «structural» break model. 94 g. listorti and r. esposti long run equilibria. this somehow implicit choice of introducing nonlinearities only in the short-run components of the vecm not only prevents the identification (and interpretation) of problems of possibly multiple long-run equilibria, but also expresses the assumption of an underlying theoretical long-run relationship whose validity holds regardless of ‘disturbing’ variables, such as policy interventions, temporary market turbulence, etc. this is evidently appealing in the case of the lop. it must be noticed, however, that some of these ‘disturbing’ variables affecting the short-run dynamics may, in fact, limit spatial arbitrage (thus, the lop itself), as in the case of trade and market policy measures. the rapid development of these nonlinear correction models raised several issues on their estimation and interpretation. on the one hand, the stvecm (and the tvecm variant, in particular) gives the analyst a great flexibility in adapting the model specification to the observed price data. on the other hand, however, it remains true that these models depend on several subjective aspects (how many regimes, which transition functions and/ or which thresholds) and generate ad hoc specifications, where transitions to new regimes or thresholds are determined by the specific price series under study, and results are sometimes hardly replicable, generalizable and even interpretable. a further concern is the appropriate estimation of these nonlinear specifications. classical estimation procedures are based on the nonlinear least squares (nls) or maximum likelihood (ml) estimator, but the application of such estimation procedures to these generalized models may generate results that lack robustness and consistency with theory and expectations. in recent years, bayesian techniques have been often suggested to avoid some of these problems (balcombe et al., 2007; balcombe and rapsomanikis, 2008; greb et al., 2011). a third empirical strategy to cope with nonlinear price transmission responds to the need of consistently admitting nonlinearities and changes in regime in both long run relationships and in adjustment and short run dynamics. for the sake of simplicity, we can call this approach nonlinear cointegration models. in fact, we can group under this category a pretty heterogeneous set of approaches whose common feature is to admit both kinds of nonlinearity. it is worth noticing that an easy way to impose nonlinear short and long run relationships is to specify a vecm, like (5), not in the levels of prices but in some nonlinear transformation of them. cointegration would represents the stationary relationship occurring not among price levels but rather among nonlinear functions of prices. these functions may take the form of n-order polynomials, for instance, though imposing these specifications ex ante may be arbitrary and have a poor theoretical justification. this simple solution to introduce nonlinearities is more frequent in empirical literature than usually acknowledged. in particular, very often the vecm of price transmission is expressed in the logarithm of prices rather than in price levels (listorti, 2009; esposti and listorti, 2011). as mentioned before, this transformation not only facilitates the interpretation of results (estimated parameters can be directly interpreted as elasticities) but it often provides a larger goodness of fit in the estimation stage. still, this transformation implicitly imposes nonlinearities both in the long-run equilibrium and in the adjustment and short-run dynamics. in this third stream of empirical works on agricultural price transmission, however, we actually include those relatively few applications in which a regime switch is admitted in both the short run and in the long-run parts of the vecm. in doing this, as in the structural break case, these works relax the already mentioned limiting assumption 95horizontal price transmission in agricultural markets underlying the conventional vecm in price transmission analysis, namely, that long run price spreads (β’pt-1) and, consequently, all their determinants are time invariant. in particular, götz et al. (2008) propose an approach where not only the short-run adjustment process towards equilibrium is non-linear, as in threshold vecm and markov switching vecm frameworks, but also the long-run equilibrium relationship can display thresholdtype nonlinearity. in listorti (2009), both the adjustment parameters and the cointegrating relationship are assumed to vary according to regime changes that enter the model as structural breaks. stephens et al. (2012) apply grrr (generalized reduced rank regression) techniques to estimate a vecm model admitting regime-dependent long-run and short-run coefficients23. this kind of generalized regime-dependent modelling framework, however, still needs to be developed not only in the estimation stage but also in achieving a consistent regime-switching representation of the different parts of the vecm in addition to providing a sound theoretical justification24. 3.2 price transmission and time-varying volatility the problem of volatility is somehow related to non linearities, and especially to market ‘bubbles’ and instability. volatility is a key concept in the analysis of financial markets: it expresses the standard deviation of the logarithmic returns of a given financial instrument. the major interest in the concept of volatility within price transmission analysis lies in the fact that periods of exuberance can be generated by a temporary increase in volatility (volatility clustering) rather than by a temporary or permanent change in price formation and transmission mechanisms. in fact, one possible reconciliation of conventional i(1) series with the nonlinearities implied by «price bubbles» can be found in a sharp and temporary increase in volatility. in recent years, in particular, the turmoil observed in many agricultural markets has increased the attention of researchers and policy makers on the increasing volatility of prices (balcombe, 2011; european commission, 2011; fao, 2011; hernandez et al., 2011; huchet-bourdon, 2011; prakash, 2011) and on the need for explicitly modelling the change of volatility over time (time-varying volatility) in price transmission analysis25. the modelling issue is, in fact, twofold: how do we include time-varying volatility in price 23 another application to agricultural markets of a modelling framework where both short-run and long-run parameters may be regime-dependent can be found in by myers and jayne (2011). they actually present a singleequation approach which is, however, analogous to a conventional vecm specification. 24 a mention has to be made to a quite different line of research recently emerged in the empirical literature on nonlinearities in agricultural price transmission. as imposing ex ante some forms of these nonlinearities may be arbitrary and hardly confirmed by the data, an alternative approach is to maintain the conventional linear vecm representation and then use the method of local projections to compute nonlinear impulse response functions. kuiper and bunte (2011) analyse price transmission along the meat supply chain by applying the jorda’s method (jorda, 2005) to compute nonlinear responses without the need to specify and estimate an underlying nonlinear dynamic system. 25 here, the interest in the volatility of agricultural prices concentrates on its empirical implications for price transmission analysis. a detailed discussion on its theoretical explanations as well as on its micro and macro implications is beyond the scope of this paper (see stigler, 2011, for a valuable review on the topic). moreover, in the present review of the literature, we only consider studies where volatility (garch effects) is admitted within price transmission models (vecm) while we disregard those empirical works concentrating only on the analysis of the volatility in agricultural price series (piot-lepetit and m’barek, 2011; busse et al., 2011). 96 g. listorti and r. esposti transmission models26? how is the change in volatility itself transmitted across markets (volatility spillovers or contagion)? regarding the first aspect, it is natural to analyse volatility by looking at the variance of the error term of the stochastic process generating the price series under observation. the typical tool of such analysis is the specification of garch (generalized autoregressive conditional heteroskedasticity) effects, that is, the specification of a price generating stochastic process whose error term follows itself a stochastic (arma) process. as far as the second aspect is concerned, it is worth noticing that in multivariate stochastic processes, as in price transmission models, these garch effects can also arise across individual series, consequently allowing the time-varying variance of one series to affect that of another series. these are also called multiple garch (mgarch) effects (bollerslev, 1990) and are appropriate tools to analyse volatility spillovers. more generally, admitting mgarch effects in price transmission analysis means to make the difference between market interdependence and contagion explicit. interdependence identifies the permanent «normal (or tranquil) times» linkage across prices, while contagion indicates the temporary increase of this interdependence after a significant shock, that is, in «turbulent times» (bukug et al., 2003). in a context of a remarkable change in price volatility, therefore, the key issue in modelling price transmission is how to take into account these two different situations (forbes and rigobon, 2002; bacchiocchi and bevilacqua, 2009). although the empirical literature ranges over a broader set of methodologies (dungey et al., 2004), mgarch models are one of the currently prevalent methodological solutions to model, at once, market interdependence and contagion during market crises. the flexibility of the mgarch specification and its capacity to give a parsimonious representation of the formation and transmission of time-varying volatility across markets explains the increasing interest in this kind of models. in principle, admitting a mgarch effect within the basic price transmission modelling framework (vecm) is relatively straightforward. this vecm-mgarch model is specified as follows: δpt = αβ 'pt−1 + γiδpt−ii=1 k−1∑ + ε t ε t =ht 1 2 νt (9) where, in addition to the usual notation (see equation 5) νt is an (n×1) vector of zero-mean, unit-variance, and independent and identically distributed disturbances27, while ht 1/2 is the cholesky factor of the time-varying (n×n) conditional covariance matrix ht. this latter is the matrix generalization of univariate garch models and expresses how the current shocks and current volatility of a given price depend on past shocks and past volatility of the same price as well as those of other prices (volatility spillovers). the specification of ht is the key issue underlying the specification and estimation of the vecm-mgarch models (bauwens et al., 2006). the easiest solution is the conditional correlation mgarch (cc26 actually, many studies, especially applications to financial markets, also focus on how volatility affects asset returns (smith, 2009). in agricultural markets, this kind of application may be of interest in the case of futures prices. 27 multivariate normality is usually assumed. 97horizontal price transmission in agricultural markets mgarch) where the conditional covariance matrix has a simple structure as it is decomposed into a matrix of conditional correlations, rt, and a diagonal matrix of conditional variances, dt, of the error terms εt : ht =dt 1 2rtdt 1 2 . in order to facilitate the estimation of such specification, bollerslev (1990) originally proposed a parameterisation of rt that assumes time invariance (constant conditional correlation mgarch, or ccc-mgarch). such specification, however, is not particularly helpful in price transmission analysis as it strongly restricts the capacity of the model to take into account time-varying volatility and volatility spillovers. empirical applications of the vecm-mgarch model to agricultural price transmission thus adopt time-variant parameterisation of rt. it is the case of the dynamic conditional correlation mgarch (dcc-mgarch)28 and of the bekk-mgarch specifications. the latter has been originally proposed by engle and kroner (1995) and specifies the conditional covariance matrix as follows: ht =c 'c+a 'ε t−1 'ε t−1a+b 'ht−1b where a, b and c are (n×n) matrices containing time-invariant parameters to be estimated. c is a lower triangular matrix, a models the influence of past market shocks on current price volatility, while b models the influence of past volatility on current volatility. therefore, with a limited amount of time-invariant parameters, this specification provides a highly flexible representation of how volatility varies and is transmitted. serra (2011) presents an application to agricultural price transmission of this vecmbekk-mgarch model. the two parts of the model (the vecm modelling the price conditional mean and the bekk-mgarch modelling the conditional heteroscedasticity) are estimated jointly using the standard maximum likelihood procedures. several other vecmmgarch approaches to agricultural price transmission have been proposed. all can be considered variants of the approach depicted above. the focus is particularly on possible parametric misspecifications of the conditional covariance. nonlinear specifications have thus been proposed as in the case of the exponential garch (egarch) model (bukug et al., 2003) that also allows for an asymmetric representation of the impact of positive and negative innovations on conditional variances function. serra (2011) proposes a semiparametric variant of the vecm-bekk-mgarch model while ifpri (2011) adopts a combination of both linear and nonlinear specifications of univariate garch models. further possible specifications of the approach admit more complex nonlinearities like in the case of regime-dependent garch structures (the switching regime, swgarch, the smooth transition, stgarch, and the threshold, tgarch, garch models) (bacchiocchi and bevilacqua, 2009). in other cases, modifications are introduced to relax the assumption of multivariate normality of the disturbance terms (copulagarch models) (lee and long, 2009). these latter developments, that can enrich the representation of how volatility varies over time and transmits across markets, are currently less explored in the agricultural price transmission analysis. 3.3 price transmission and policy interventions a final major field of research on horizontal price transmission is the analysis of the role played by policy measures. whatever the underlying theoretical justification be, sev28 applications of the dcc-mgarch specification within vecm approaches to agricultural price transmission can be found in rapsomanikis (2011) and rapsomanikis and mugera (2011). 98 g. listorti and r. esposti eral policy instruments may affect price formation and the relationships among prices generating regime-switching and nonlinearities in price linkages, as well as volatility clustering and volatility spillovers. this is particularly evident in the case of spatial (especially cross-country) price transmission. though not always fully understood (stigler, 2011), it is evident that border and domestic policies (notably, price stabilisation policies) can have a strong influence on cross-country price transmission. in particular, variable levies, export subsidies, non tariff barriers, tariff rate quotas, and prohibitive tariffs are expected to prevent prices from convergence, whereas ad valorem and fixed tariffs should affect price spreads behaving as proportional or fixed transaction costs. in all cases, variations in the adoption of such instruments interfere with spatial arbitrage (that is, the lop) and, thus, affect price and volatility transmission. as a matter of fact, policy factors remained at the margin of the literature on price transmission in agricultural markets (fackler and goodwin, 2001) at least until the recent price crisis. this created a major interest in researchers on the eventual effect of changes in trade policies (introduction of export taxes, reduction of import duties, etc.). while analysing the key-forces underlying an unprecedented price rally, the role of policy factors has become an area that deserves increasing attention, also leading to a constant monitoring of the individual (country-level) policy measures in place29, and to a careful assessment of their intended and unintended consequences on price transmission and volatility (tangermann, 2011). the basic question is: how can policy variables enter the above-mentioned price transmission modelling framework? in general terms, introducing policy variables within the vecm is relatively straightforward from a strictly methodological point of view. from an economic perspective, however, whether this representation is really consistent is more questionable. in particular, one may wonder whether the policy variables affect the short-run (adjustment) or the long-run (equilibrium) relationships or both; whether they affect the price expected value or its volatility, or both; whether they can be treated as exogenous variables or are, in fact, endogenous, that is, they depend on price movements themselves30. on the one hand, the increasingly sophisticated empirical specifications and econometric procedures mentioned above augment the toolbox one can use to include policy variables within the adopted models. on the other hand, however, these more sophisticated approaches often raise questions about the proper way to account for these variables in the analysis. interestingly, despite the recent strong interest of policy makers and public institutions in this respect, the empirical studies explicitly analysing the role of policy in agricultural price transmission remain relatively few compared to the great amount of applications that focus on methodological developments31. 29 see, for instance, <http://www.fao.org/giews/english/index.htm>. 30 listorti (2007, 2009) discusses how to derive empirical models from a theoretical framework that consistently considers how domestic and border policies affect the co-movement of commodity prices in international markets. 31 we restrict our attention to studies where policy variables are considered within a vecm framework. thompson et al. (2002) in some way represent a borderline case as the impact of the 1993 cap reform on wheat price convergence across eu is analysed within a surecm approach where price interdependence is expressed through the correlation across error terms of individual price equations. 99horizontal price transmission in agricultural markets the prevailing solution to enter policy variables within price transmission analysis remains to interpret the policy regime change as a structural break. once the breaks have been identified, a straightforward way of taking them into account in the estimating procedure is to split the sample according to the structural breaks (barassi and ghoshray, 2007; mohanty and langley, 2003; verga and zuppiroli, 2003), or to introduce specific dummy variables which allow the transmission parameters to vary according to the various policy regimes (dawson et al., 2006; dawson and sanjuan, 2006). in this latter case, following (7), policy variables enter the vecm representation as exogenous structural breaks. even in this apparently straightforward case, however, several practical issues remain. first of all, the location of the structural break points may not be so trivial. in some cases, the policy change intervenes in a well-identified point in time as in mohanty and langley (2003), verga and zuppiroli (2003), listorti (2007, 2009), esposti and listorti (2011). for instance, a structural break approach is adopted by ihle et al. (2011) to assess the impact of the different national implementation of the 2003 cap reform on market integration. the application covers the 2003-2009 period. it concerns the market of young calves, and it also includes, among possible structural breaks, the eruption of the blue tongue disease in 2006. in other cases, dating the structural break may require appropriate unit root or cointegration tests, like in dawson et al. (2006), dawson and sanjuan (2006), barassi and ghoshray (2007). esposti and listorti (2011) adopt a modified unit root test to date the beginning and the end of the 2007-2008 «price bubble». this structural break is then combined with the policy intervention decided to cope with price exuberance, that is, the temporary suspension of eu import tariff on cereals. the very assumption of an exogenous change in policy regime can be questionable, as in some circumstances this change depends on price movements, therefore it is endogenous (this can be the case of some domestic market measures or some border policies). more in general, as underlined by esposti and listorti (2011), the economic interpretation of how policy instruments affect price transmission may not be trivial and may significantly vary moving from (7) to the more sophisticated specifications in (6), (8) and (9). in these latter cases, at least in principle, policy variables may also have an impact on the co-explosive relationship, in (6), on the short-run and adjustment nonlinear dynamics by affecting regime-switching (either movements between the regimes and behaviour within each regime) in (8), on volatility clustering and transmission by entering the mgarch part of (9). the role of trade policies, and in particular of the entry price scheme, is investigated in cioffi et al. (2011) and santeramo and cioffi (2012) in the case of fruit and vegetables within a threshold model (in the tvar specification). in these applications the threshold is, respectively, exogenously set or endogenously determined, to assess whether prices behave differently according to the functioning of the eu entry price system for fruits and vegetables. among the most recent applications, also the msvecm approach seems to be a viable solution to include policy interventions. we can mention djuric et al. (2011) who analyse how the export restrictions implemented by the serbian government during the 2007-2008 price crisis affected international price transmission. this analysis is carried out within an msvecm where the policy change enters the model by determining the regime switch. the same kind of approach is also used by götz et al. (2010) to assess the impact of the temporary export controls introduced in russia and the ukraine on wheat price transmission during the 2007-2008 global food crisis. as in djuric et al. (2011), the 100 g. listorti and r. esposti results emerging from this msvecm approach suggest that the export ban or restrictions increased rather than disciplined market instability. rezitis et al. (2009) uses an analogous msvecm framework to analyse the impact of the 2003 cap reform on vertical price transmission within the greek lamb market. beside these few recent applications, we may acknowledge that the potentials of the current methodological developments on nonlinearities and time-variant volatility in price transmission analysis are still underexploited to assess the role of policy interventions. these few works clearly demonstrate, on the one hand, how far these approaches can take the insight on policy impacts. on the other hand, the way they enter policy measures within the price transmission framework generally remains ad hoc. a critical discussion on possible alternative solutions as well as a comparison of respective results could provide a more robust evidence in this respect. 4. mapping the recent literature: some summary considerations it seems helpful to conclude this survey on the broad and often very technical recent literature on agricultural price transmission by attempting a general and more detached view on the directions this literature is taking and on the consequent perspectives and challenges. table 1 tries to offer this wider perspective by mapping the recent contributions with respect to the three major fields of research discussed in the previous section32. as a matter of fact, most of the recent empirical literature on agricultural price transmission actually concentrates on one of these three major topics or on a combination of them. the picture provided in table 1 suggests some general considerations on the developments achieved as well as on the open issues. first of all, it can be noticed that, although the recent turbulence in agricultural markets demonstrates that these three aspects always co-exist, empirical works taking into account, at the same time, nonlinearities, time-varying volatility and volatility spillovers, and changes in policy regime are still lacking. many empirical works acknowledge the need for more comprehensive approaches and thus cope with two of these issues but, in fact, the novel contribution is usually focused on one of them (the primary focus). the impression is that the major attention is more on purely methodological issues rather than on understanding the real drivers of price comovements and interdependence. this impression is reinforced by the bias of the recent empirical works toward some fashionable methodological solutions, often brought to the spotlight by the successful work of some research groups. in particular, much attention is paid to tvecm, msvec and mgarch models. the prevalence of these approaches, however, does not have to necessarily be intended as a demonstration of their supremacy with respect to alternative methodological solutions. it is clear that the recent evolution of agricultural markets suggests that nowadays a careful analysis of agricultural price transmission and of the respective role of market and trade policies cannot ignore possible nonlinearities, time-varying volatility and volatility spillovers. however, this literature has still to achieve an agreement on which approach 32 due the notable amount of studies provided on this topic in the recent years, table 1 cannot be exhaustive and limits the analysis to the last decade focusing on the most recent contributions (particularly those coping with the 2007-2008 price crisis), and on those introducing some novelty in the methodological toolbox. 101horizontal price transmission in agricultural markets can be generally preferred; on the contrary, the choice of the methodology still remains mostly driven by specific conditions (e.g., available data) and objectives of the study. in more general terms, this emerges as the most significant limit of this recent literature and, consequently, of the present review: it pays a lot of attention to often sophisticated methodological aspects but often disregards, or leaves in the background, several relevant practical issues. these latter issues are critical to find the most suitable and intelligible ways to include policy measures within the existing modelling frameworks and, therefore, to make these empirical studies really able to inform the debate on how policy reforms may «pass-through» across markets (mostly, across national borders) via price transmission (brooks and melyukhina, 2005). in particular, the methodological developments have gone much further compared to the improvements in data availability and quality. the implication is that practitioners now have a wide and robust toolkit to study agricultural price transmission but appropriate data are often lacking. reliable high-frequency agricultural price data remain rare and few research contributions seem to really provide steps forward in this direction. this lack of effort and attention on the availability and quality of data has two major consequences. first of all, researchers may be tempted to apply the abovementioned powerful toolkit to inappropriate data. for instance, amikuzuno (2010) shows how low frequency (e.g., monthly) data might not capture the dynamics of the arbitrage processes thus providing imprecise estimates and misleading inferences about price transmission mechanisms. in other applications, futures rather than spot price data are used without paying much attention to the appropriateness of the methodological framework, and of the underlying theoretical justification, for this kind of data. the second consequence is the strong concentration of empirical applications on a limited group of agricultural commodities and sectors. many studies focus on cereal markets, while several others focus on meat and vegetable oil markets; a significant amount of studies concern the oil-biotehanol-corn or oil-biodiesel-oilseeds price linkage. the bias toward these agricultural commodities can be motivated by the lack of appropriate data for other agricultural products. in particular, the key assumption implied by these studies on horizontal price transmission is that products have a substantially homogenous quality (or time invariant quality differentials) across space. these requirements are quite restrictive and are met only by few agricultural commodities: those on which applications can be found. in many other cases (fruit and vegetables, wine, cheese, just to provide some examples) the suitability of these approaches can be strongly questioned unless data taking into account product quality are available. therefore, future research on agricultural price transmission is expected to continue to produce further improvements «vertically», by incessant refinements of the methodological toolkit, but also to make some progresses «horizontally», by improving the availability and the quality of data thus extending the application to a wider set of agricultural markets. acknowledgements the views expressed in this article are the sole responsibility of the authors and do not reflect, in any way, the position of the foag. authorship may be attributed as follows: sections 1 and 2 to listorti; 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(1992). further evidence on the great crash, the oil price shock, and the unit root hypothesis. journal of business and economic statistics 10 (3): 251-261. _goback _goback issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-20085 bio-based and applied economics 5(2): 99, 2016 after almost five years from its foundation and four and a half years of active publishing, the bio-based and applied economics (bae) journal has seen a change in its editorial team. i have stepped down as editor-in-chief and have been replaced by daniele moro. alessandro corsi and gianluca stefani have also decided to leave the team. pavel ciaian, roberto esposti and simone severini have, for their part, joined ornella wanda maietta and francesco mantino as associate editors. bae has, from the beginning, been one of the cornerstones of the newly founded italian association of agricultural and applied economics. launching a journal from scratch as a new association has been, as expected, a huge challenge. however, vision and good work have yielded more than satisfactory achievements. in slightly more than four years, bae has regularly published ten issues (three issues per year) with a total of 66 papers. over the past years, bae has been indexed in a number of major catalogues; in the autumn 2015 it was included in both the emerging sources citation index (esci) of web of science and in the directory of open access journals (doaj). in june 2016 it was accepted for inclusion in scopus. on behalf of the other past editors i wish to thank the aieaa, the members of the international editorial board, and the authors, reviewers and readers. this would not have been possible without their assiduous suggestions, support and contributions. we also wish to thank firenze university press for their guidance in the difficult waters of scientific publishing and indexing. over these years the interest in the scope of the journal has grown steadily. the bioeconomy is becoming a reality and is much better known than it was in 2012. the literature is also gradually taking shape. yet, the use of the term is still accompanied by a good deal of question marks in terms of scope, definitions and avenues for research (see editorial in this issue). agriculture, food and resource issues are attracting growing attention as well. in parallel, the logic of research evaluation is putting a lot of pressure on researchers, who are increasingly selective in choosing their target journals and publication strategies. in short, there are a number of challenges ahead, above all that of matching intellectual ambitions in shaping a new research area with the need for recognition and short-term impact. bae is, i believe, in an excellent position to face these challenges and, even more so, to use them as opportunities to boost the contribution of our scientific community to the welfare and development of society. finally, and again together with past editors, i would like to wish the new editorial team the same exciting and enriching professional and personal growth that we experienced over the past four years. davide viaggi bio-based and applied economics 6(3): 295-313, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-18519 is the question of the “active farmer” a false problem? maria rosaria pupo d’andrea*, simona romeo lironcurti crea research centre for agricultural policies and bioeconomy, italy date of submission: 2016 24th, june; accepted 2017 20th, september abstract. the “active farmer” issue has gained attention in the last cap reforms with the increasing attention to the decoupling of the support. the 2014-2020 cap reform has ignited the debate among stakeholders on “who” is actually entitled to receive direct payments. the analysis carried out highlights the heterogeneity in the national implementation of the rules on “active farmer” and the importance of the national legislation of some member states in limiting the access to direct payments, regardless of eu rules. the article points out how the complexity of the rules on “active farmer” arises from the unresolved question on the nature of direct payments. the new rules do not satisfy neither who wants to grant direct payments only to “genuine” farmers nor who wants to grant support to those who, in the spirit of the new approach of direct payments, manages the agricultural land, regardless of the main activity carried out. keywords. active farmer, first pillar, direct payments. jel codes. q18. 1. introduction the issue of the “active farmers” has gained attention over the last common agricultural policy (cap) reforms when the shifting of the support from production to land owners and, consequently, the shifting of the burden of the support from consumers to taxpayers has disclosed the entity of the financial resources granted to the agricultural sector and the need to improve the use of public funds. the decoupling of support provided for by the new cap aims at giving farmers the flexibility to choose the products to be farmed on the basis of market signals (market orientation) or even not to produce. the changes of the policy from a price support to direct payments, made the support more visible and in need to be justified. according to the european commission (2010 and 2011a), the legitimation of support comes from the fact that it is conditional on strengthening the role of agriculture in enhancing the competitiveness of the sector, the environmental protection and the development of rural areas. however, the recognition of the multifunctional role of agriculture in producing not only *corresponding author: mrosaria.pupodandrea@crea.gov.it 296 maria rosaria pupo d’andrea, simona romeo lironcurti primary products, but, also public goods has resulted in the arising of the “active farmer” issue in the attempt to identify “genuine” farmers eligible for payments. the issue is moreover complicated by the fact that the cap reforms that have followed, did not clarify the nature of direct payments, considered as an income support (to producers), but granted to all who maintain the land in good condition independently from the existence of any production. starting from the analysis of the choices made by the member states under the cap reform 2014-2020, our purpose is trying to answer to a number of questions. first of all we will verify whether these choices tend towards a real limitation on the number of beneficiaries or if they are more formal than substantial. another issue is to ascertain the presence of similarities or differences in the choices of the member states in order to understand if a common ground on which european union (eu) could introduce mandatory rules in the next cap reform (beyond 2020) exists. moreover, looking at the decisions made, we will try to understand how coherent is the figure of the “active farmer” with the logic behind the last cap reform, where an important role is played by the support to the production of public goods and services. in fact, in the member states where the rules are actually binding, the risk is that the people who run the farm, but for whom agriculture is not the main activity, are prevented from receiving support from the cap, thus excluding a large part of people who operate in the spirit of the new functions of agriculture. finally, we will end by highlighting the two conflicting approaches on “active farmer” arising from the different visions of the cap and we will propose two different ways to resolve the dilemma concerning the debate on “active farmer”. the following section provides a summary of the debate developed around the “active farmer” figure and a detailed description of the framework envisaged in the regulation eu 1307/2013. in section 3, the choices made by the member states are analyzed in order to understand the aim of the decisions and the existence of differences/similarities. in section 4, two case studies are presented in order to highlight how the apparent restrictiveness/lack of selectivity represented, respectively, by the large/low number of constraints applied is not confirmed when the actual national implementation is taken into account. finally, some conclusions from the main results of the analysis are drawn and the future development of the rules are discussed on the “active farmer” in an attempt to reconcile the two alternative visions of the “active farmer” role: the “productivistic” active farmer vision, aiming to income support, and the “multifunctional” vision, aiming to remunerate the provision of public goods. 2. the “active farmer” in the cap 2.1 the debate on “active farmer” the debate on “active farmer” was raised for the first time by the european court of auditors in the annual report on the implementation of the budget concerning the financial year 2006 (european court of auditors, 2006). in this report, the system to establish entitlements in the 10 member states which introduced the single payment scheme 297is the question of the “active farmer” a false problem? (sps) in 2005 has been examined1. the court highlighted how, especially with the introduction of the regional model, railway companies, horse riding/breeding clubs and golf/ leisure clubs and city council benefited of the sps (european court of auditors, 2006, p. 103). following the court indications, the first attempt in limiting the beneficiaries of the direct payments of the first pillar of the cap dates back to the 2008 reform, the so-called health check of the cap. this limitation was included in the overall rules regarding the “minimum requirements for receiving direct payments” (regulation ec 73/2009, article 28). under this framework, member states had the possibility to ensure that no direct payments were granted to a natural or legal person whose agricultural activities formed only an insignificant part of its overall economic activities, or whose principal business or company objects did not consist of exercising an agricultural activity. with the exception of the netherlands, no member state made use of these former rules to define the “active farmers” (matthews, 2012). the question of the active farmer was raised again in two special reports (european court of auditors, 2011 and 2012b), where it was highlighted how the lack of precision in rules defining farmers and agricultural activity in order to receive the decoupled payment meant that the support was received by “persons or entities having no or only marginal agricultural activity” (european court of auditors, 2011, p. 19). moreover, in the case of new member states applying single area payment scheme (saps), decoupled payments were received also by “public entities managing state land and not otherwise involved in farming” (european court of auditors, 2012b, p. 7). taking into account the fact that no member states made use of the option provided by the health check to exclude non active farmers from benefitting of direct payments, the court, once again, identified a number of beneficiaries whose principal business is other than agricultural activities (european court of auditors, 2011, p. 24). the main difficulty in dealing with the issue of the “active farmer” is that it is closely related with the definition of decoupled support, as envisaged by the commitments undertaken by the eu under the uruguay round of the gatt (general agreement on tariffs and trade). in 1993, at the end of negotiation on agricultural trade liberalization, eu and the other contracting parties agreed commitments in the area of internal support providing for a 20% reduction of support distortive of production and trade (i.e. all forms of more or less coupled support). domestic support with no, or minimal, distortive effect on production and trade (i.e. decoupled direct payment to producers which is not linked to production decisions) was exempted from commitments of reduction and included in the so-called “green box” category. the discussion on the “active farmer” has been further developed in the last cap reform 2014-2020, with the introduction of the new system of direct payments. the strengthening of the decoupling, on one side, and the reduction in financial resources for the cap, both at member state and farm level, on the other side, have fed the debate among stakeholders on “who” is actually entitled to receive cap support, in order to prevent the payments from being granted to farmers for doing nothing2. as matthews (2012) 1 the member states were: austria, belgium (flanders), germany (bavaria), italy, denmark, ireland, luxembourg, portugal, sweden and united kingdom (england, scotland and northern ireland). 2 as result of the decisions taken in the 2014-2020 multiannual financial framework (mff), the financial resources for direct payments and market measures (the first pillar of the cap) was reduced by 12.9% in real 298 maria rosaria pupo d’andrea, simona romeo lironcurti states “the dilemma at the heart of decoupled direct payments is that if farming activity is no longer required for eligibility, how to ensure that payments go only to ‘farmers’ as conventionally understood?”. the rules on “active farmer” later undertaken under the 2014-2020 cap reform represent a more determined attempt to regulate, under a general eu framework, the issue of the competition between potential beneficiaries on scarce resources (the cap budget). the initial proposal of the european commission established that people for whom (a) the annual amount of direct payments was less than 5% of the total receipts they obtained from non-agricultural activities or (b) their agricultural areas were mainly areas naturally kept in a state suitable for grazing or cultivation and they did not carry out on those areas the minimum activity established by member state (european commission, 2011b) were excluded from the benefit of the payments. the proposal was considered by the court of auditors (2012a) as a first step towards the right direction. however, the court pointed out the difficulties in implementing and monitoring these criteria. in the following debate, also jambor (2012) and matthews (2012) underlined the increasing in the bureaucracy complexity arising from the need to access to information on the non-agricultural receipts of landholders and costs associated to the calculation of the percentage of direct payments on non-agricultural receipts for each farm. jambor, moreover, stressed the risk to exclude from the payments those farms that manage land, providing for public goods without produce for food, in the absence of a clear definition of what is intended for “non-agricultural activity”. the author has liquidated the question of “active farmer” as a cosmetic operation, created to divert attention from the ineffectiveness of the direct payments instrument. on the same wavelength were bureau and mahé (2015) who questioned the legitimacy of farmers as the only suppliers of environmental services on agricultural lands as a result of the system of direct payments introduced (p. 97). they also asserted that the weakness and complexity of the “active farmer” measures are the result of the choice to point on entitlements and cross-compliance instead of focusing on contractual payments in providing environmental services “in which compensation and cost of services could be made more equal” (p. 98). the authors, moreover, emphasized the potential nullification of the effects of the rules on “active farmers” because of the derogations at member states disposal in reducing their strictness . on the effectiveness of the “active farmers” rules also anania and pupo d’andrea (2015) were doubtful as they assert that “the decision to restrict the set of the beneficiaries of direct payments to ‘active farmers’ only will also be likely to have no tangible results” (p. 82). 2.2 the “active farmer” in the 2014-2020 cap reform the 2014-2020 cap reform has introduced a new and more complex system of direct payments, moving further away from historical farm-related payments towards regional payments more evenly distributed in terms of per hectare support across farms (anania terms. moreover a “external convergence” mechanism was agreed providing for a redistribution of the financial allocation between member states in order to achieve a more equal average direct payment per hectare at national level. under the 2014-2020 cap reform, instead, a mechanism of “internal convergence” was included in order to reduce the differences in the per hectare payment per farm within member state or in each region of a member state (henke et al., 2015). 299is the question of the “active farmer” a false problem? and pupo d’andrea, 2015, p. 52). the new system has introduced a more selective form of support, replacing the single payment of the fischler reform with a set of new payments each of which devoted to remunerate specific behaviours or specific status of the farmer. the basic payment, aiming at ensuring the income support, is the main component of this new set of payments; in fact, all other payments (the so-called green payment, the payment for young farmers, the redistributive payment, the payment for areas with natural constraints and the small farms scheme) are restricted only to farmers entitled to receive the basic payment3. however, only “active farmers” receive support under the basic payment scheme, i.e. in order to receive the most selective and targeted support the farmer must be recognized as “active farmer”. in order to limit the rent position resulting from the fact that beneficiaries of direct payments had no longer to produce in order to receive them, the 2014-2020 cap reform established more detailed rules for the identification of who is entitled to receive direct payments. actually, article 9 of regulation eu 1307/2013 specifies who is not “active farmer”, giving member states great flexibility in making criteria more restrictive (or in some cases loosening the constraints imposed by the eu) under a common framework of reference. most likely, the reason why it is used the definition of “active farmer” in a negative connotation (who is not an “active farmer”) is related to the fact that in this way the burden of proof is on those who are defined not active, that have to prove the opposite, i.e. being active. on the contrary, if all farmers were required to prove that they are active, this would increase administrative burdens and bureaucratic costs to them and to the member state. article 9 provides a number of different kind of provisions regarding the rules for being considered “non-active farmer” and the derogations available. in principle, no direct payment shall be granted to a person (natural or legal): 1. whose agricultural areas are mainly areas naturally kept in a state suitable for grazing or cultivation and who does not carry out on those areas the minimum activity as defined by the member state (article 9(1))4. this provision regards only member states that declared to have self-maintained area5; 2. who operates in airports, railway services, waterworks, real estate services, permanent sport and recreational grounds (the so-called “black list”, or negative list, of people non-active by definition) (article 9(2)). in order to make this provision more restrictive, member state may decide to integrate the “black list” with other non-agricultural business. member states may decide later to withdraw any national addition but they cannot reduce the list contained in the article 9; another provision (the third) concerns the possibility for a member state to exclude from the benefit of direct payments, the natural or legal persons whose agricultural activity is insignificant compared to their overall economic activities (article 9(3)(a)), or whose principal activity or company objects does not consist of exercising an agricultural activity (article 9(3)(b)) (hereinafter referred to as “professional and economic requirements”). 3 for more details on the new system of direct payments see anania and pupo d’andrea, 2015. 4 in accordance with article 4(2)(b) of the regulation eu 1307/2013. this provision applies when the agricultural area naturally kept in a state suitable for grazing or cultivation represents over 50% of the agricultural area declared by a farmer. 5 these member states are belgium (flanders), germany, france, italy, cyprus, romania, slovakia, united kingdom (scotland and wales). 300 maria rosaria pupo d’andrea, simona romeo lironcurti however, the persons above considered as “non-active farmers” according to the second and the third provisions can receive direct payments (and thus, to be considered as “active farmers”) if they are able to prove the importance of the agricultural activity carried out in terms of ‘magnitude’ of direct payments received or in terms of non marginality of the agricultural activity. in particular, the above persons are considered “active farmers” if they demonstrate that: • the annual amount of the direct payments is at least 5% of the annual total receipts they obtained from non-agricultural activities in the most recent fiscal year6 (article 9(2)(a)); or • their agricultural activities are not insignificant7 (article 9(2)(b)); or • their principal business or company objective consists of exercising an agricultural activity (article 9(2)(c))8. the rationale of article 9(2) as compared to article 9(3) is that, people who are on the negative list may qualify for direct payments if they provide evidence about the importance of their economic activity in the form required by the member states, in accordance to the provisions contained in the third paragraph of article 9(2) (from 9(2) (a) to 9(2)(c)). however, the same people may still be excluded from direct payments if they do not respect professional and economic requirements eventually applied by member states in accordance with article 9(3). finally, people not qualified as “active farmers”, as they are included in the “black list” or because of the marginality of their agricultural activities, are entitled to receive direct payments, and, therefore, to be considered “active farmers”, if the direct payments received in the previous year were not exceeding 5,000 euro (article 9(4)). member states have the possibility to lower this threshold (also differentiating between regions) making the definition of “active farmer” even more restrictive (henke et al., 2015, p. 43). people who do not carry out a minimum activity on their agricultural lands remain “non-active farmers” even though they receive an amount of direct payment not higher than the threshold set (table 2). the rules for identifying the beneficiaries of the direct payments are also applied to the beneficiaries of a number of measures under the rural development policy of the cap: payments to farmers in mountain areas or in other areas facing natural constraints, support to young farmers, support to new farmers participating to union or national quality scheme, payments to farmers for the converting to, or maintaining, organic farming, support to farmers who adopt high standards of animal welfare, support under risk management measures (regulation eu 1305/2013). 6 receipts from non-agricultural activity are considered all the receipts except those obtained from agricultural activity, those arising from processing of agricultural products and the support received under the first and second pillar of the cap (regulation ec 639/2014, article 11). 7 an agricultural activity is considered not insignificant if the related receipts represent at least one third of the total receipts. member states may lower this threshold, thus loosening the constraint, without allowing a marginal activity to receive direct payments. member states may establish alternative criteria (regulation ec 639/2014, article 13). 8 the agricultural activity is considered to be the principal business or company object of a legal person if recorded as a principal business or company object in the official business register or any equivalent official evidence. in case of natural person equivalent evidence shall be required (regulation ec 639/2014, article 13). 301is the question of the “active farmer” a false problem? table 1. “active farmer”: provisions and derogations. derogations to be af naf no minimum activity carried out on area naturally kept article 9(1)1 being into the negative list article 9(2) not meet the (optional) additional national criteria article 9(3) dp ≥ 5% of total non-agricultural receipts article 9(2)(a) naf af af agricultural activity not insignificant article 9(2)(b) naf af af agricultural activity is the principal business or company objective article 9(2)(c) naf af af dp not higher than 5,000 euro (or below) article 9(4) naf af af naf = non active farmer af = active farmer 1 in member states where the provision of article 9(1) is applicable. table 2. option at member state disposal in order to make more or less restrictive the rules on “active farmer”.   negative list article 9(2) agricultural activity not insignificant article 9(2)(b) professional and economic requirements article 9(3)a-b different threshold from 5,000 euro article 9(4) make more restrictive widen the list by adding other activities add additional national requirements lower the threshold below 5,000 euro make less restrictive lower the ratio between agricultural receipts and total receipts less than one third     2.3 some reflections on the “active farmer” rules under the 2014-2020 cap reform we can read the legislation concerning the “active farmer” through two well defined perspectives: on one hand, the rules governing the use of agricultural land, as the basis for accessing to the cap measures; on the other side, the rules concerning the farmer who exercises an agricultural activity, mainly identified by the possession of professional and economic requirements. 302 maria rosaria pupo d’andrea, simona romeo lironcurti analyzing in depth the rules on active farmer it emerges, as albisinni points out (2012, p. 26), that in the new definition of agricultural activity9 the emphasis is on the (“productivistic”) side of the activity in terms of its ability to perform breeding or cultivation, compared to the ‘old’ (“multifunctional”) definition10 included in regulation ec 73/2009 where for agricultural activity was intended also the “maintaining the land in good agricultural and environmental condition” (article 2(c)). these two definitions contain the ambiguity of the rules on active farmer: the shift to a more “productivistic” vision of the agricultural activity is an attempt to identify farmers towards which to address payments in order to support the income; at the same time, decoupling allows farmers to receive support a part from the production. moreover, the rules on “active farmer” require beneficiaries of certain measures of rural development policy to meet professional and economic requirements or land use, introducing the obligation to comply with a predetermined subjective model, thus moving away from the traditional approach of granting payments to those who operate in accordance with the objectives set (albisinni, 2012, p. 27). farmers involved in organic farming or in maintaining the land in mountainous areas or areas with natural constraints exert an important environmental function, that is independent of the main business of those who practice them. in this regard, it seems that the need to identify “active farmers”, in response to requests from european court of auditors, has been used by national stakeholders and eu key actors for concentrating the financial resources available for rural development on agriculture. the setting of the threshold of 5,000 euro (or lower), below which farmers are considered active by definition, it should ensure that small farmers and part-time farmers, with a significant off-farm income but a valuable activity in the provision of public goods, are not excluded from receiving direct payments (krzyzanowski, 2013). exclusion may occur if a member state has made use of the possibility provided for in article 9(3) to introduce additional professional and economic requirements that must be met by farmers; as we will see in section 4, these rules have been applied only by 4 member states. when dealing with the rules for demonstrating to be an “active farmer” (for those who are above the threshold), it is introduced an element of great uncertainty and differentiation between member states where the status of “active farmer” derives from external elements (the total non-agricultural receipts to which compare the amount of direct payments or the total income to which compare the agricultural income) rather than on what is done in a farm. moreover, it escapes the link between the percentage of direct payments and the total non-agricultural receipts and the meaning of the percentage chosen (d’oultremont, 2011, p. 4). in this regard we have to consider that, the amount of direct payments, in the light of the progressive move toward a flat rate payment, will be increasingly influenced by the amount of land owned rather than the agricultural activity carried out on this land (albisinni, 2012, p. 28-29). 9 article 4, regulation eu 1307/2013, defines as agricultural activity: i) the production, rearing or growing of agricultural products, ii) the maintaining an agricultural area in a state suitable for grazing or cultivation without preparatory action going beyond usual agricultural methods and machineries, iii) the carrying out a minimum activity, defined by member states, on agricultural areas naturally kept in a state suitable for grazing or cultivation. 10 the two definitions, “productivistic” and “multifunctional” vision of the agricultural activity, are taken from erjavec, lovec, erjavec (2015) containing an interesting analysis of the 2014-2020 cap reform that focuses on three reading keys emerged during the negotiations. 303is the question of the “active farmer” a false problem? looking the threshold of direct payment below which a person is an “active farmer” by definition, it is immediately evident that by doing so the legislator does not select the “genuine” farmers, but simply chooses to support small farms defined as such in financial terms (the amount of direct payments received). however, as the literature highlights, it is evident that small farms in terms of direct payments received not necessarily correspond to small farms from the physical point of view (in terms of labour force input or hectares of agricultural area) or small farms in terms of economic size (european commission, 2011c; forgacs, 2015). thus, the aim of the threshold is related only to the need to reduce the excessive administrative burdens for farms receiving small amount of direct payments and for national governments, resulting from the enormous complexity of the article 9. 3. the implementation of the “active farmer” in eu member states starting from the choices made by member states on the implementation of the rules on “active farmer” under the 2014-2020 cap reform, in this section we will provide a picture of how this flexibility has been used and whether there is evidence of similarities in the behaviours or if differences prevail. in table 3, we have summarized the choices made by the member states and communicated to the dg agri through the isamm form11. other information were collected in the first semester of 2015 under a study carried out for the european parliament (henke et al., 2015), then compared with the most recent information. the regulatory framework defined by the member states in relation to the figure of the “active farmer” is analysed taking into account four variables. in table 3, the first column pinpoints the countries that have areas “naturally kept in a state suitable for grazing or cultivation” and that, consequently, have defined the minimum agricultural activity that farmers have to respect in order to be considered as “active farmers”. it is worth recalling that, in this case, the minimum activity to be carried out by farmer is a compulsory and non-derogatory requirement to access direct payments, even in the case of farmer who are below the threshold identified according to article 9(4). the second column indicates the countries that have chosen to expand the black list of non-agricultural business. the third column identifies the countries that have chosen to introduce additional professional and economic requirements in order to be considered as “active farmers”. the information in the fourth column point out the countries that have chosen to lower the threshold below 5,000 euro (the last column shows the threshold chosen). farmers who have received direct payments below the threshold are excluded from the restrictions imposed by the provisions on the negative list and, where appropriate, by the ones on additional professional and economic requirements. however, the threshold does not exempt farmers to perform the minimum activity on areas naturally kept in a state suitable for grazing or cultivation if these areas were defined by the member state. in the table, a tick “v” indicates that a member state has used the available flexibility in a scale of possibilities from 0 to 4, where 4 indicates the implementation of the all con11 ‘information system for agricultural market management and monitoring’. the implementation of the rules on “active farmer” is contained in form 2. 304 maria rosaria pupo d’andrea, simona romeo lironcurti straints available and zero indicates that no constraint has been implemented. only italy has chosen the implementation of all the options available and seems to qualify itself for a high degree of selectivity with respect to the access to direct payments. thirteen countries have opted instead for applying the regulation without defining national adaptations. the rest of member states has chosen to use the flexibility allowed, applying from 1 to 3 options. nine member states have defined the minimum activity to be carried out on agricultural areas naturally kept in a state suitable for grazing or cultivation. these activities are greatly differentiated among member states, in terms of minimum activities requested, time span within which these activities must be carried out and possibilities, for local authorities, to provide deviations from the general rules. information about the national choices are very fragmentary. however, among the minimum activities are signalled: grazing with the maintaining of minimum stocking density in the case of permanent grassland12, mowing (with destruction or removal of the grass), avoiding deforestation, controlling the spread of unwanted vegetation, avoiding (dense) shrubs, renewing of the grass (dg agri, 2015b). eight member states have decided to extend the black list in order to exclude from the cap support the activities that are not considered agricultural business.13 twelve countries opted for the lowering of the threshold below 5,000 euro, up to 0 euro in belgium-flanders and 1 euro in the netherlands. in the cases of france (200 euro) and luxembourg (100 euro) the threshold has been set at the same level of the minimum requirement to receive direct payments (article 10, regulation eu 1307/2013). in the case of spain and austria the threshold is the same of that for the access to the small farmers scheme. only four countries (greece, the netherlands, italy and spain) provided additional exclusions adding professional and economic requirements that a farmer must comply in order to be considered “active”.14 in these member states, farmers being above the threshold have to prove to be “active”. the percentage of farmers involved ranges from little less than 20% of total farmers in greece to 100% in the netherlands, showing a different administrative burden for national administrations (elaboration on data european commission, 2016c). the variability of this percentage is due to the threshold chosen (1 euro in the netherlands, 5,000 euro in greece) and the structural characteristics of the agri12 for example, in scotland, the minimum agricultural activity for farmers in regions two and three is to undertake an average level of stocking of 0.05 livestock units per hectare for 183 days a year. as an alternative to minimum stocking levels, farmers can carry out an annual environmental assessment across the holding. in italy, the minimum activity is represented by the grazing that must take place for at least 60 days a year with a minimum load of 0.2 livestock unit per hectare. alternatively, the farmer may prove to have carried out at least one mowing a year or other operation aimed at improving pasture. 13 for example they have been excluded enterprises involved in mining activity (germany), forest management (estonia and romania), banking, brokerage business and insurance (italy), construction firms (romania), prisons (romania), national, regional or municipal administrations (bulgaria, italy, malta, the netherland and romania). for further details on the activities added to the black list in the member states concerned see european commission, 2016a. 14 greece and the netherlands exclude natural or legal persons whose agricultural activity represents only an insignificant part of their overall economic activity (article 9(3)(a)) while spain excludes persons whose principal activity or company objects does not consist of exercising an agricultural activity (article 9(3)(b)). italy has chosen to apply both the type of exclusion. 305is the question of the “active farmer” a false problem? table 3. the choices of the member states under the legislative framework on the “active farmer”1. definition of minimum activity on areas naturally kept widening of negative list additional professional and economic requirements different thershold from 5,000 euro threshold chosen in derogation to 9(4) article 9(1) article 9(2) article 9(3)a-b article 9(4) euro detail items belgium-wallonia ѵ 1 350 belgium-flanders ѵ ѵ 2 0 bulgaria ѵ ѵ 2 3,000 czech republic 0 denmark 0 germany ѵ ѵ 2 estonia ѵ 1 greece ѵ (a) 1 spain ѵ (b) ѵ 2 1,250 france ѵ ѵ 2 200 ireland 0 italy ѵ ѵ ѵ (a),(b) ѵ 4 1,250 5,0002 cyprus ѵ 1 latvia 0 lithuania ѵ 1 500 luxembourg ѵ 1 100 hungary 0 malta ѵ ѵ 2 250 netherlands ѵ ѵ (a) ѵ 3 1 austria ѵ 1 1,250 poland 0 portugal 0 romania ѵ ѵ 2 slovenia 0 slovakia ѵ ѵ 1 2,000 finland 0 sweden 0 uk-scotland ѵ ѵ 2 uk-wales ѵ 1 uk-england 0 uk-northern ireland 0 croatia 0 eu member states number of contraints implemented pointer positive choice 1 in the table, the percentages of the beneficiaries over the threshold are calculated on the beneficiaries of direct payments in the financial year 2015 (european commission, 2016c). where the stratification does not coincide with the choice of the country, we have done a mathematical proportion (for example for the choice of a threshold equal to 250 euro made by malta, we have subtracted to the cumulative descending share of beneficiaries (in that case <0 i.e. 100%), the product between the percentage of the next stratification (i.e. 0-500) and 2.5 / 5 (i.e. half of range 0-500, the choice of malta of 500 euro). 2 the threshold is fixed to 5,000 euro if more than 50% of the agricultural surface is located in disadvantaged or mountain areas; 1,250 euro for the other farmers. source: own elaboration on dg agri isamm data. 306 maria rosaria pupo d’andrea, simona romeo lironcurti cultural system of each countries. most of the other member states decided to not apply the additional requirements because of the additional administrative burden (dg agri, 2015a). it is worth noting that three of the four countries that have opted to add professional and economic requirements have also chosen to lower the threshold, making it even harder for “non genuine” farmers to access direct payments, and two of these three countries have opted to widen the negative list, so reducing the number of people allowed to receive direct payments. moreover, in some member states the implementation of the cap reform occurred at regional level, so we have 2 different schemes in belgium (one for wallonia and one for flanders) and 4 different schemes in the united kingdom (england, northern ireland, scotland and wales). as regard the criteria chosen to prove the importance and relevance of the agricultural activity the picture is very heterogeneous among countries. to prove that the agricultural activity is not insignificant (article 9(2)(b)), in 19 countries the agricultural income should represent at least one third of the total income; only finland has lowered this ratio to 5%; 9 countries opted for alternative criteria (e.g. number of eligible hectares above a certain threshold). in order to demonstrate that the principal business consists of exercising an agricultural activity, 11 countries have opted for the registration in an official business register, 4 countries opted for equivalent evidence, 6 countries for alternative criteria, 5 countries opted for a combination of criteria (dg agri, 2015a; european commission, 2016a). 4. the understanding of the weakness about the active farmer’s rules: some case studies in this section we focus our attention on two selected member states placed on opposite sides of the spectrum in terms of restrictions implemented. the aim is to highlight that the greater or lesser selectivity represented by the number of constraints implemented (cfr. table 3) doesn’t always correspond, respectively, to a greater or lesser selectivity when we take into account the implementation of national legislation. in other words, in this section we will demonstrate that a high score, in terms of number of restrictions implemented, does not necessarily correspond to a high selectivity and, conversely, a low score is not always equivalent to a low selectivity. for this purpose we consider the czech republic, which has not implemented any national adaptation, so as to appear non-selective (the options implemented, in table 3, are equal to 0) and, on the other side, italy, who has decided for a strong implementation of the possibilities offered by the cap reform (the options implemented are 4), so as to appear highly selective. 4.1 the czech republic case in the czech republic, stakeholders (ngos, associations and trade unions) were strongly involved in the decision-making process. as a result of this process it has been decided not to extend the negative list. a person or a group of persons which fall under the scope of article 9(2) (the black list) can be regarded as an active farmer if they prove 307is the question of the “active farmer” a false problem? that the total receipts obtained from agricultural activities represent at least one third of the total receipts obtained in the most recent fiscal year and if these information are confirmed by an auditor´s report. moreover, czech republic decided not to implement any additional professional and economic requirement (article 9(3)). the threshold below which a farmer is considered active by definition is maintained at 5,000 euro. in this way, the czech republic seems to dispense the small farmers (the 16% of holdings with an economic size of farm lower than 4,000 euro and an average business size of 20 hectares) from the burden of an excessive bureaucracy, preserving their role of maintaining vitality in rural areas. looking at the decisions taken, the czech republic would seem “not selective at all” (henke et al., 2018). however, the perspective changes if one takes into account the national legislation (coll. 50/2015) requiring that the recipient of direct payments has to be an “agricultural entrepreneur” within the meaning of statute no 252/1997 coll. on agriculture. according to this law, it is considered agricultural entrepreneur any natural or legal person that carries out an agricultural production, understood as crop production, livestock production and the processing and selling of agricultural production. this is a much more restrictive definition than that provided for in article 4 of the regulation 1307/2013, where for “agricultural activity” is considered also the maintaining an agricultural area in a state suitable for grazing or cultivation or the carrying out a minimum activity on agricultural areas naturally kept in a state suitable for grazing or cultivation (see note 9). the issue of the national requirement of “agricultural entrepreneur” has been raised by the association of private farming of the czech republic. then it has been the object of a written question of the european parliament to the european commission (e-003886-16), as czech national legislation appears to be in conflict with european legislation on direct payments, resulting in a reduction in the number of aid applications. the commission has stated that member states may add supplementary criteria if those rules are not contrary to union law. in the specific case, there are no elements that show a conflict between eu legislation and czech law. this does not alter the fact that the more restrictive definition of “agricultural entrepreneur” in czech legislation, as compared to the broader definition of “agricultural activity” in eu legislation, makes the czech republic much more selective than it does at first reading. despite the fact that the country has decided not to implement any restriction, the need to be an “agricultural entrepreneur” could be a significant factor in excluding a large portion of farmers (mainly small farmers) from access to direct payments. 4.2 the italian case on the opposite side we find italy, which has decided to apply all the available restrictions: a) widening the negative list to banks, brokers and insurance, and to the public administrations; b) adding professional and economic requirements in order to access to direct payments (the registration into the national social security (inps) as direct farmer, agricultural entrepreneur; or the possession of an active vat number for agricultural activity); c) lowering the threshold to being considered active farmer by definition to 1,250 euro, except for farms located in disadvantaged or mountain areas for which the threshold is maintained to 5,000 euro; d) setting minimum activity on area naturally kept in a state 308 maria rosaria pupo d’andrea, simona romeo lironcurti suitable for grazing or cultivation. looking at the implementation of the rules on active farmer, italy appears to be as “highly selective” (henke et al., 2018). the debate that characterized the implementation of these rules showed two conflicting positions. from one side, those who were favorable to a strong selection of beneficiaries of the cap, in order to focus support in the hands of those who carry out agricultural activity as their main business; on the other side, those in favor of maintaining a large number of beneficiaries in order to take into account the environmental function carried out. a compromise was reached between the two positions, that makes italy the country with the largest number of restrictions implemented, but with a small number of farmers potentially excluded from direct payments. in fact, a high percentage of beneficiaries is considered active farmer by definition because they receive an amount of direct payments less than the threshold fixed (at least 64% of beneficiaries receive less than 1,250 euro; however, this figure is underestimated because it does not take into account of farms located in less favored areas for which the threshold is set at 5,000 euro). moreover, for those who perceive an amount of direct payments above the threshold, the additional requirements are not very strict and they are such to include almost all the current beneficiaries of the direct payments of the cap (frascarelli, 2014a). as regard the additional economic requirements, in italy there are many beneficiaries of direct payments who have not a vat number, but most of them fall in the “threshold of non-compliance” (farmers with a turnover of less than 7,000 euro are exempt from any obligation to declare and accounting). those who are above the threshold may require the opening of the agricultural vat number and must exercise a productive activity oriented towards the market, because in italy is not allowed to have a vat number “non-active”. finally, in the case of subjects included in the black list, such as banking or insurance, the exclusion from the benefits of direct payments is mitigated by the possibility of considering “active farmers” the companies they participate. in italy there are important agricultural companies controlled by banks and insurance companies that do not manage their agricultural activities directly but through specific subsidiary agricultural companies that don’t fall in the black list (frascarelli, 2014b). looking at the decisions taken by all the member states, what emerges is a considerable bureaucratic complication, even if a country has decided not to apply any restriction. in fact, legislation concerned is difficult to understand. at the same time, the procedures implemented internally by each country in order to check the provisions on active farmer are very different and, as a consequence, different is the bureaucratic burden on national authorities and farmers. the final result is a great uncertainty about the effectiveness of the provisions in limiting and selecting cap beneficiaries and canalizing support towards the farmers with prevailing agricultural activity. in fact the usefulness of revising the legislation is already being discussed under the omnibus regulation (com(2016)605). 5. discussion and conclusions the analysis carried out in the previous sections has highlighted a picture of high heterogeneity among member states, in terms of measures implemented, of goals pursued and farmers affected. in terms of options implemented, only two member states, italy and the netherlands, have adapted all (or almost all) the eu rules to their national constraints in order to limit 309is the question of the “active farmer” a false problem? the access to direct payments. on the other side of the spectrum, thirteen member states have applied article 9 without any national requirement. the other member states have applied from 1 to 3 options. analyzing in depth the choices made by selected member states it is evident that the high number of options implemented does not correspond to a greater selectivity of beneficiaries. in the case of italy, for example, that has implemented all the available options, the national application of the rules on “active farmer” did not guarantee a real limitation in the access to direct payments. this is both for the high share of farmers lying below the threshold for being considered active by definition and for the ‘non strictness’ of the additional requirements. the national implementation of the rules, moreover, is proving to be very complex, with a considerable bureaucratic burden in charge of farmers and national agency of payments and delays in payments. conversely, the czech republic, where the article 9 has been applied without national adaptations, is selective for the access to direct payments on the basis of national legislation. the rules implemented in the member states vary across eu also in the case of the minimum activity on the areas naturally kept in a state suitable for grazing or cultivation, which is the only constraint that would prevent the access to direct payments, without the possibility of derogations, in case of non compliance. at the moment, after the first year of implementation, it is not known whether this complexity is compensated by a real limitation on the number of beneficiaries of the direct payments of the cap. nor the member state where the rules have been implemented in the most effective way. what is clear is the different function assigned to direct payments. in some countries, the choice was to qualify for direct payments who contributes to the vitality of rural areas not excluding a priori persons whose agricultural activity is marginal. in other countries the choices go in the direction of supporting those engaged in agricultural activity in the strict sense. in this latter case, the high costs associated to a strict implementation of the rules can generate effects if these rules translate in a selection of the beneficiaries in order to support the income of producers. the final result is a distorted treatment of the farmers according to the national choices: some activities are considered not-agricultural business in some countries but they are considered agricultural business in others; in some countries the farmers must satisfy additional requirements, not requested in others; the threshold below which a farmer is active by definition is very different among countries. taking into account the heterogeneous distribution of direct payments between member states and the great diversity among member states in terms of practices and agricultural activities and in terms of legal and fiscal framework, it seems difficult to find a field on which to build common rules to define the “active farmers” and, at the same time, for planning the policies aimed to achieve adequate selectivity to access the cap support. unfortunately, there is no information on how many potential beneficiaries were excluded due to the rules on active farmer in the first year of implementation. however, the high administrative costs in respect to the benefit of excluding a very limited number of non-active farmers were recognized by the commission which, for this reason, has proposed, as part of the omnibus regulation (com(2016)605), to give member states the possibility to make optional the application of the rules on “active farmer”, starting from 2018. 310 maria rosaria pupo d’andrea, simona romeo lironcurti as we have seen, the complexity of the “active farmer” rules arises from the unresolved issue on the nature of direct payments. in fact, the cap reforms which have followed after the 1992 reform (the so-called macsharry reform which first introduced the concept of decoupled payments) changed the tools to support the farmers but the philosophy itself of support remained unchanged15. despite the move from a single payment scheme to a more articulated system of payments, even in 2014-2020 cap reform the better targeted and more selective payments are received, if and only if, a farmer receives the basic payment, i.e. the payment aimed to income support, and in turns, if and only if, she/he is an “active farmer”. so, the “active farmer” rules struggle between the “productivist” and “multifunctional” souls of the cap. the “productivistic” vision of the cap would circumscribe the benefits of support only to those who produce food and/or fiber, being engaged in an agricultural activity in the strict sense. only to those farmers an income support has to be guaranteed. the “multifunctional” soul would provide support to the land managers, independently from the existence of the food/fiber production function. in this sense the direct payments are seen as a remuneration for the provision of public goods (candel et al., 2014). the “productivistic” active farmer vision, making production mandatory, contrasts with the green box constraints under the wto as well as the market orientation towards which the decoupled support aims. the “multifunctional” active farmer vision, on the opposite, suffers from the fact that the payment is irrevocably tied to income support of the farmer and not to the environmental function of land managers in allowing, for example, biodiversity. the conciliation of the two souls of the cap has generated the rules contained in the article 9 which do not meet neither who would rule more strictly on “active farmer” in order to keep the payment in the hands (or the pockets) of the “genuine” farmers, nor who fears that the rules applied could exclude from the benefit of payments, potential beneficiaries who only manage the land (kloranis and vlahos, 2012, p. 10). for instance, the reform broadens the application of the rules on “active farmer” to organic farming or to farmers in mountainous or other areas with natural constraint. this, in member states where additional criteria have been added could, potentially, prevent those who do not meet the requirements of the economic or professional importance of the agricultural activity from receiving support (hart and baldock, 2011, p. 21). the issue is the inconsistency of the direct payments system: only those who receive direct payments need support for the environmental functions and land preservation they carry out? being equal functions, the person who is not a farmer and receives no direct payments is therefore not entitled to receive support? the environmental issue must necessarily be limited to the cap? in our opinion, there are two ways, opposed, to solve this issue. the first is to broaden the definition of “active farmer” to encompass all those who manage the land, regardless of the production function, including also those who are now excluded (those in the black list) and those for whom farming is marginal or insignificant, 15 the analysis of erjavec and erjavec (2015) shows that due to the path dependency in the 2014-2020 cap reform the measures were only gradually altered and the attention towards “environment” and “greening”, proclaimed in all discourses of the eu key actors, was not proportionally integrated into measures and in the budget distribution. 311is the question of the “active farmer” a false problem? provided that the support is related to the cost or loss of income resulting from land management, which must be sustainable both in environmental and social terms. this would mean not consider the status of the beneficiaries but what they do. in the second way, it makes sense to narrow the definition of “active farmer” only to those who produce food and fiber in a sustainable way from the economic and environmental point of view, with a support truly commensurate to farm income (savings should be better targeted to farms “needy”). this would require to completely separate the two functions and allocate to the cap, in the strict sense, only part of the current budget, allocating the rest to environmental payments as described above. this could cause problems of compatibility with the wto, depending on how this farm income support is designed. however, taking into account the total ams (aggregate measurement of support) at eu disposal (72,378 million euro for marketing year 2012/13)16 there should be no problems to meet commitments also in the case where eu decided to go back towards fully or partially coupled payments to production. in the last reform, eu has decided to allow member states to devote a high percentage of their financial resources to coupled support. the “new” tool differs from the coupled payment to production of the “old” cap (the one prior the macsharry reform) in the fact that the support is linked to the factors of production (land or head of livestock) rather than to production itself. moreover, its purpose is to create an incentive to maintain current levels of production and must not lead to increased production. the real question is whether it is appropriate for the eu to turn back from its decisions on decoupling, strengthening coupled payments only in order to respond to the problem of the “active farmer”. the future of the rules on “active farmer” is not obvious. the complexity of the national implementation, as well as the lack of a unique path by the member states or of an easily interpretable behaviour, it raises serious doubts about the possibility that in the next cap reform these rules can be made, another time, mandatory and even more restrictive, reducing the flexibility available to countries. however there is no doubt that the future of the “active farmers” issue depends on the future of the direct payments. references albisinni, f. (2012). il cantiere agricolo. report presented at conference soggetti e discipline in agricoltura dopo le riforme europee organized by centro studi di diritto agrario. cosenza. 18 may 2012. available online at http://www.cesdaa.it/downloads/ il_cantiere_agricolo.pdf. anania, g. and pupo d’andrea, m.r. (2015). the 2013 reform of the common agricultural policy. in swinnen, j. (ed), the political economy of the 2014-2020 common agricultural policy: an imperfect storm. ceps paperback, centre for european policy studies, brussels, 33-86. bureau, j. c. and mahé, l.p. (2015). was the cap reform a success?. in swinnen, j. (ed), the political economy of the 2014-2020 common agricultural policy: an imperfect storm. ceps paperback, centre for european policy studies, brussels, 87-135. candel, j.j.l., breeman, g.e., stiller, s.j. and termeer, j.a.m. (2014). disentangling the 16 eu notification to wto g/ag/n/eu/26, 2 november 2015. 312 maria rosaria pupo d’andrea, simona romeo lironcurti consensus frame of food security: the case of the eu common agricultural policy reform debate. food policy 44: 47-58. dg agri (2015a). workshop on the active farmer’s provisions, 27/10/2015. dg agri (2015b), exchange of experience of implementation and control on agricultural activity and permanent grassland elp. expert group meeting, 9/12/2015. d’oultremont, c. (2011). the cap post-2013: more equitable, green and market-oriented?. european policy brief: 5. available online at http://www.egmontinstitute.be/publication_article/the-cap-post-2013-more-equitable-green-and-market-oriented/. erjavec, k. and erjavec, e. (2015). ‘greening the cap’ – just a fashionable justification? a discourse analysis of the 2014–2020 cap reform documents. food policy 51: 53-62. erjavec, e., lovec, m. and erjavec, k. (2015). from ‘greening’ to ‘greenwash’ : drivers and discourses of the cap 2020 ‘reform’. in swinnen, j. (ed), the political economy of the 2014-2020 common agricultural policy: an imperfect storm. ceps paperback, centre for european policy studies, brussels, 215-244. european commission (2010). communication from the commission to the european parliament, the council, the european economic and social committee and the committee of the regions. the cap towards 2020: meeting the food, natural resources and territorial challenges of the future. com(2010) 672 final. brussels, 18.11.2010 european commission (2011a). commission staff working paper. impact assessment. common agricultural policy towards 2020. sec(2011) 1153 final/2. brussels. 20.10.2011. european commission (2011b). proposal for a regulation of the european parliament and of the council establishing rules for direct payments to farmers under support schemes within the framework of the common agricultural policy. com(2011) 625 final/2. brussels. 19.10.2011. european commission (2011c). what is a small farm?. eu agricultural economic briefs. n.2, july. european commission (2016a). direct payments 2015-2020. decisions taken by member states. state of play as at june 2016. information note. european commission (2016b). direct payments. eligibility for direct payments of the common agricultural policy. september 2016. european commission (2016c). report on the distribution of direct aids to agricultural producers (financial year 2015). ref. ares(2016 2016)6181665 28/10/2016. european court of auditors (2006). annual report on the implementation of the budget concerning the financial year 2006, together with the institutions’ replies. european court of auditors (2011). single payment scheme (sps): issues to be addressed to improve its sound financial management. special report n. 5. european court of auditors (2012a). opinion no 1/2012 on certain proposals for regulations relating to the common agricultural policy for the period 2014-2020. european court of auditors (2012b). the effectiveness of the single area payment scheme as a transitional system for supporting farmers in the new member states. special report n. 16. forgacs, c. (2015). development of small farms in eu 10 (ceecs) between 2005–2010. in raupelienė, a. (ed), proceedings of the 7th international scientific conference 313is the question of the “active farmer” a false problem? rural development 2015, 1-7. available online at http://conf.rd.asu.lt/index.php/rd/ article/view/196/134. frascarelli, a. (2014a). identikit dell’agricoltore attivo. terra e vita 26: 8-10. frascarelli, a. (2014b). l’agricoltore attivo, la soglia minima e la regressività. in de filippis, f. (ed), la pac 2014-2020. le decisioni dell’ue e le scelte nazionali, gruppo 2013, edizioni tellus, rome, 41-61. hart, k. and baldock, d. (2011). greening the cap: delivering environmental outcomes through pillar one. institute for european environmental policy (ieep). available online at http://www.ieep.eu/assets/831/greening_pillar_1_ieep_thinkpiece_-_ final.pdf. henke, r., benos, t., de filippis, f., giua, m., pierangeli, f. and pupo d’andrea, m.r. (2018). the new common agricultural policy: how do member states respond to flexibility?. journal of common market studies 56(2): 403–419 (doi: 10.1111/ jcms.12607). henke, r., pupo d’andrea, m.r., benos, t., castellotti, t., pierangeli, f., romeo lironcurti, s., de filippis, f., giua, m., rosatelli, l., resl, t. and heinschink, k. (2015). implementation of the first pillar of the cap 2014-2020 in the eu member states, study for the european parliament, directorate-general for internal policies, policy department b: structural and cohesion policies. ip/b/agri/ic/2014_45. jambor, a. (2012). on the complexity of defining active farmers. cap reform.eu. available online at http://capreform.eu/on-the-complexity-of-defining-active-farmers/. kloranis, s. and vlahos, g. (2012). the politics of cap: the case of greece. aua working paper series no. 2012-5, agricultural university of athens, department of agricultural economics. krzyzanowski, j.t. (2013). small, young, active – some new elements of the cap reform. problems of world agriculture 13(28): 105-114. matthews, a. (2012). understanding the ‘active farmer’ debate. cap reform.eu. available online at http://capreform.eu/understanding-the-active-farmer-debate. rural-urban migration and implications for rural production alan de brauw migrants to rural areas as a social movement: insights from italy giorgio osti immigrant workforce and labour productivity in italian agriculture: a farm-level analysis edoardo baldoni, silvia coderoni*, roberto esposti economic and social impact of grape growing in northeastern brazil linda arata1,*, sofia hauschild2, paolo sckokai1 is the question of the “active farmer” a false problem? maria rosaria pupo d’andrea*, simona romeo lironcurti bio-based and applied economics 5(2): 131-133, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-17141 introduction sustainability of gi production systems in the framework of the ttip negotiations filippo arfini1, maria cecilia mancini1, mario veneziani1, michele donati2 1 department of economics, università degli studi di parma, via j. kennedy 6, 43125 parma, italy 2 department of bio-sciences, università degli studi di parma, parco area delle scienze 11/a, 43124 parma, italy a vast body of literature has appeared (barham and sylvander, 2011) and a number of seminars of the european association of agricultural economists (eaae) have focussed on the “issue of gis” by highlighting the fundamental characteristics of gi products(arfini and mora, 1998; sylvander et al., 2000; arfini et al., 2012). two elements identify and characterise gi products: the complexity and multifaceted nature of the concept of quality and the multifunctional role of gi systems. the quality of gi products derives from the close dependence on natural and anthropic local resources, the history of the territory of production, the cultural heritage and their own reputation. the reputation of a gi product has developed over time and consumers identify it with the concept of typicity (casabianca and touzard, 2009). the latter is an intrinsic part of the gi quality concept and is perceived by consumers as not reproducible. the multifunctional role of gi systems highlights the necessity of considering different “dimensions” of gi products at the same time. it also helps us to recognize that the gi system is not niche (sylvander and baraham, 2011) but a wider system which is part of an overall economy (allaire et al., 2011). describing the numerous dimensions of gi products entails adopting a multiplicity of approaches to overcome the limits of methodologies used in traditional marketing analysis of value chains. this multiplicity is required to evaluate aspects impacting on quality such as the natural, productive, recreational and cultural aspects of the territory and the multifunctional role of gi systems which shape the rural and local development path, system coordination, agricultural and commercial policy dimensions and the protection of intellectual property rights related to the use of the geographical name. in international trade, these aspects become even more problematic because of the difficulty of safeguarding and protecting gi systems, which synthesise them into the geographical name. furthermore, the two international organisations, the world trade organisation (wto), with its trade-related aspects of intellectual property rights (trips) agreement, and the world intellectual property right organization (wipo), with its lisbon agreement, that regulate the international trade of food products, have adopted different definitions and protection regimes in relation to gi products (addor and grazioli, 2002): • the trips agreement (articles 22.1 and 22.4) reflects a compromise between countries that have different levels of “sensitivity” to gi products. the compromise gives 132 f. arfini, m. cecilia mancini, m. veneziani, m. donati “weak” protection for food items and “strong” protection for wines and spirits. in fact, art. 22 gives the burden of proof of usurpation to the party reporting usurpation. on the other hand, art. 23 concerning wines and spirits establishes protection ex-officio without placing any burden of proof on the party who reports it; • the lisbon agreement regulates the international register of the designations of origin and offers strong protection for all gi products in countries signing the agreement. these, however, are fewer than the wto trips signatory countries. the distinctions brought about by the trips agreements have generated a trade war (josling, 2006) among wto countries. the trade disputes concerning gis exported from the sui generis to the trademark area are not yet resolved in spite of the multilateral negotiations of the wto agreements. nowadays, bilateral negotiations on trade rules between the eu and the usa have been taking place. the ttip – “transatlantic trade and investment partnership” have the objective of laying down rules and solving outstanding trade conflicts, including the one on gis between the usa and the eu in order to allow stable trade relations. all these gi related issues have been discussion topics in the 145th eaae seminar (parma, italy, april 2015) “intellectual property rights for geographical indications: what is at stake in the ttip?”. this section includes a selection of three papers presented at the 145th eaae seminar. the first one, presented by wirth, examines the legal and policy relationships amongst international standards for gis, food safety requirements and voluntary claims related to a food’s attributes within the context of international trade agreements protecting gis, such as the 1994 trips agreement, the eu-canada comprehensive economic and trade agreement (ceta) and the chapter on intellectual property and geographical indications in the ttip currently under negotiation. the second contribution analyses the role of innovation applied to gi products. mancini and consiglieri state that innovation can make gi products competitive in the logic of the global market provided that information asymmetry between producers and consumers is filled. the third contribution by schmitt et al. measures the sustainability of value chains and it is aimed to provide a scientific methodology to objectively assess the real benefits and drawbacks of local versus global value chains. contributions presented at the 145th eaae agree on the fact that the “gi issue” is not limited to the protection of the geographical name but it also has qualitative and socioeconomic implications. therefore, discussions about ttip also need to examine the use of gis as a rural development tool and their production model as an example of sustainability. certainly, it is time to cease the “war of terroir” in the interest of the environment and society worldwide, as well as consumers and producers. in this spirit, the debate on gis in the ttip negotiations could be an arena for fruitful discussion, and an opportunity to recognise what is really at stake with the gi issue. references addor, f. and grazioli, a. (2002). geographical indications beyond wines and spirits – a roadmap for a better protection for geographical indications in the wto trips agreement. the journal of world intellectual property 5: 865-897. 133sustainability of gi production systems in the framework of the ttip negotiations allaire, g., casabianca, f. and thevenot-motted, e. (2011). geographical origin a complex feature of agri-food products. in: barham, e. and sylvander, b. (eds.), labels of origin for food: local development, global recognition. wallingford: cab international. arfini, f. and mora, c. (eds.) (1998). typical and traditional products: rural effect and agroindustrial problems. parma: parma university press. arfini, f., mancini, m.c. and donati, m. (eds.) (2012). local agri-food systems in a global world: market, social and environmental challenges. cambridge: cambridge scholars publishing. barham, e. and sylvander, b. (eds.) (2011). labels of origin for food: local development, global recognition. wallingford: cab international. casabianca, f. and touzard, j.m. (2009). le projet proddig: promotion du developpement durable par les indications geographiques. paris: agence nationale de la recherche, retrieved from <http://www.corte. inra.fr/lrde2/images/files/ proddig%20rapport%20scientifique.pdf> sylvander, b. and barham, e. (2011). introduction. in: barham, e. and sylvander, b. (eds.), labels of origin for food: local development, global recognition. wallingford: cab international. sylvander, b., barjolle, d.b. and arfini, f. (eds.) (2000). the socioeconomics of origin labeled products in agro-food supply chains: spatial, institutional and co-ordination aspects. france: inra. josling, t. (2006). the war on terroir: geographical indications as a transatlantic trade conflict. journal of agricultural economics 57: 337-363. bio-based and applied economics 5(2): 175-198, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-17140 sustainability comparison of a local and a global milk value chains in switzerland emilia schmitt1,2, dominique barjolle1,2,*, anaëlle tanquerey-cado2, gianluca brunori3 1 sustainable agroecosystems group, swiss federal institute of technology zürich eth, 8092 zürich, switzerland 2 research institute for organic agriculture (fibl), frick ch-5070, switzerland 3 department of agriculture, food and environment, università di pisa, 56124 pisa, italy date of submission: 2015 30th, september; accepted accepted 2016 3rd, august abstract. local food generally has a positive image, supported among consumers by the perception of reduced negative impacts on the environment and other dimensions. however, a critical analysis of local food chains’ performance in comparison with more global ones is still needed to objectively assess the real benefits and drawbacks of local and global food chains. a careful analysis needs to be conducted to compare the sustainability performance of local food value chains with global ones. in this paper, the methodology of selecting a set of attributes and indicators of performance to compare the multi-dimensional performance of a local with a global food chain is presented. a specific selection of attributes of performance around five sustainability dimensions (economic, social, environmental, health and ethical) is used to measure and evaluate two swiss milk chains’ performances and compare the local chain with the global one. keywords. local, global, attributes, sustainability, indicators, milk jel codes. q56, q57 1. introduction currently, there is an increasing consumers’ interest about the impact of food products on the environment, on their health or on social aspects. consumers’ demand for “local” food has increased significantly as a consequence of their willingness to purchase quality products and to support local economy and its farms (adams and salois, 2010; king et al., 2010). however, a critical analysis of local food chains’ performance in comparison with more global ones is still needed to objectively assess the real benefits and drawbacks of local and global food chains. in the last years several authors have stressed the need to set up metrics, such as indicators, to assess the sustainability of food systems (ericksen, 2007; van der vorst, 2006). in *corresponding author: barjolle@ethz.ch 176 e. schmitt et al. their article, pretty et al. (2010) even express the question: “how can we develop agreed metrics to monitor progress towards sustainability in different agricultural systems that are appropriate for, and acceptable to, different agro-ecological, social, economic and political contexts?”, which means that such systems of attributes of performance should also be transposable to other countries and contexts, in addition to being objective, holistic and multidimensional (born and purcell, 2006). for the purpose of this paper, we take the conceptual framework proposed by neven (2014), which proposes that a sustainable food value chain can be conceptualized as “the full range of farms and firms and their successive coordinated value-adding activities that produce particular raw agricultural materials and transform them into particular food products that are sold to final consumers and disposed of after use, in a manner that is profitable throughout, has broad-based benefits for society, and does not permanently deplete natural resources”. in this study, the sustainability impact is assessed on two milk value chains in order to compare a local chain with a more global one. actually, a clear distinction between the local and the global remains very unclear because there is no strict definition of local food (edwards-jones, 2010). in this study, we have considered the six criteria listed in brunori et al. (2016) to select the case studies: (i) spatial configuration, (ii) product identity, (iii) physical distance, (iv) size of operations, (v) governance, and (vi) technologies. the local and global cases should be as opposite as possible in a maximum number of criteria. the two case studies in the fresh milk sector that are compared through the sustainability assessment are further described in chapter 3. 2. methodology: sustainability assessment for food value chains several methods for assessing sustainability already exist, such as life cycle assessment (lca) that focuses on the environmental impacts of a defined product all along the production chain, or the response-inducing sustainability evaluation (rise), focusing at a farm or firm level of assessment. however, these methods usually do not include a multidimensional assessment operated at the scale of the entire food value chain (from input suppliers to consumers). the inclusive approach of sustainability assessment (whole supply chain and all aspects of sustainability) is currently rarely conducted as sustainability is often reduced to its environmental aspect or the assessment remains at the farm level (schader et al., 2014). therefore, there is a need to develop a methodological framework to assess the performance of food value chains as a whole, in a way that allows the comparison of all dimensions of sustainability between different chains. the method used in this study has thus this goal of evaluating the sustainability performance of food value chains and comparing a more local chain and a more global chain in the milk sector. the methodology proposed by the sustainability assessment of food and agriculture systems (safa) guidelines from the fao (2013) was the starting point for the elaboration of our methodological framework and proposes 4 main steps (mapping, contextualization, selecting indicators, reporting) that were adapted as follows. these steps are also explained in brunori et al. (2016). 1. mapping: this step mainly focuses on the scope and definition of the system boundaries, in terms of spatial definition and identification of entities. in this case, it is 177sustainability comparison of a local and a global milk value chains in switzerland important that the compared value chains encompass the same entities and comparable scopes. these are defined and represented in chapter 3. 2. contextualization: as suggested in the safa guidelines, information should be gathered on all aspects of the value chains under study and the surrounding context. knowledge about aspects such as the flows within the chain, interactions between actors, prices, geographical situation of the sector and national physical and socioeconomic contexts, will be crucial to select the relevant indicators (as described in the third step below) and benchmarks. this is in order to grasp what can be the influence of the context on the performance of the value chains. for this reason, additional information has been collected in relation to the context surrounding the cases by what can be called descriptive indicators or ‘descriptors’. they concern agricultural policies, tax and subsidies’ systems, food regulations or natural conditions being used to describe and further define the chain and its context, helping in the later comparison. these descriptors also concern the data for the criteria of local-global distinction. 3. selecting indicators: for the goal of comparison of a local and a global value chain, a list of indicators was developed from different sources and not only from the safa listing. instead of using safa themes, own themes, (what have been designated as “attributes”) were used. attributes are aggregations of a wide range of sustainability criteria for assessment, identified through a media analysis exercise and a delphi survey conducted in both countries (schmitt et al., 2014; kirwan et al., 2014), as described in brunori et al. (2016). sources were selected for their reference to how the performance of food value chains is viewed in the public, scientific, market and policy spheres and most frequent themes were identified through software of qualitative data analysis (kirwan et al., 2014; schmitt et al., 2014). these themes were refined into attributes of performance through a participatory process in which twelve key actors of the food sector were interviewed (schmitt et al., 2014). because the sustainability assessment should be holistic and multidimensional (ostrom, 2009), experts from socio-economical to natural sciences and stakeholders from all stages of the food supply chains were consulted to define attributes and afterwards benchmarks. actors were asked to rank the proposed attributes in order of importance and to change or complete the terms used. the final list of 12 attributes was selected through this iterative process and is shown in table 1. each attribute is thereafter assessed with two or more indicators, which contain specific questions addressed to obtain performance scores. the selection of the indicators was made according to feasibility, data availability and relevance, three criteria often quoted in the literature on sustainability assessment (bockstaller et al., 2009). feasibility means that indicators can realistically be measured in quantitative and qualitative terms and scored in relation to a benchmark. according to fao (2013, p.216), benchmarks are “values or qualitative descriptions of activities, used as the basis by which the performance of an enterprise is evaluated within an indicator domain to facilitate a rating of sustainability performance. regional and/or sectorial averages, as well as defined average (standard) and best practice values can be used as benchmarks”. indicators were adapted from existing lists of indicators (safa, rise, etc.) as these lists also give insights about how such indicators have been measured and what benchmarks can be applied. further indica178 e. schmitt et al. tors have been created according to the case and consulted stakeholders. data availability means that certain indicators were suppressed after checking existing databases and possibilities to gather sufficient representative primary data. relevance means that the selected indicators are relevant for the purpose of comparing local with global, and that means that indicators with a probable difference between the local and global chains were prioritized. the selection of indicators is however specifically adapted to a swiss context and concerns a dairy sector. table 1 shows all indicators by attributes and the questions used for data collection. the benchmarks applied for the assessment, the specific units as well as references are listed in the extended table in annex. 4. reporting: this last step includes the data analysis and its visual representation and discussion. data can be qualitative or quantitative, primary or secondary, and have been collected through semi-structured interviews, online questionnaires and secondary sources (details in table 2). after entry of all data into a database (excel sheets), the performance was calculated for both chains based on the average measures on all the actors of the supply chain’s step concerning each indicator. a score on a percentage scale was calculated for each indicator according to the benchmarks of lower and higher performance. the process of scoring the indicators’ performance is presented in figure 1 with the example of the indicator “producers’ income”. it is a continuous indicator for which the performance is evaluated on a continuous scale between pre-defined values of what could be the highest performance (higher benchmark) and what can be considered as the lowest acceptable performance (lower benchmark). the performance is then calculated with a cross-multiplication as on figure 1 and as of schmitt et al. (2014). the benchmarks are either available from standardized indicators (fao, 2013) or can be adjusted according to context justification (step 1) and experts’ consultation. for example, a veterinary scientist was consulted regarding animal welfare indicators, in addition to consulting swiss statistics on farm animal treatment and programs. most sources consulted to establish the benchmarks are from institutions of the agricultural sector, as the benchmarks need to be in the same relevant context as the data. this limits the use of peer-reviewed literature in the definition of the benchmarks. for example, comparing income with some worldwide standard would not make sense, as incomes in switzerland are usually much higher than in other countries. references included the swiss annual agricultural reporting (federal office for agriculture (foag), 2013), the milk sector statistics (union suisse des paysans, 2012), and reports (federal office for agriculture (foag), 2014) or websites of institutions and organizations in this sector1. benchmarks regarding practices were established following the safa indicators (fao, 2013) or by simulating the worst case and best case scenarios like for the ghg emissions. the references used to define indicators and benchmarks are listed in the table in the annex and the safa indicators are specified with their code (e.g. e 5.1.3). as it can be seen in the table in the annex, some indicators do not have values as benchmarks, but rather a yes/no (e.g. “differentiation of the product”), which 1 swissmilk.ch; www.sbv-usp.ch; blw.admin.ch; etc 179sustainability comparison of a local and a global milk value chains in switzerland table 1. attributes and indicators for the sustainability assessment. attributes dimensions indicator question value creation and distribution economic differentiation of the product is the product clearly differentiated in order to increase its value? producers’ income what is the price obtained by primary producers? share of producers’ price on sale price what is the share of producers’ price on the sale price? social capital social, economic, ethical cooperative or association of producers in place do producers form cooperatives or associations to defend their interest? interprofessional association or negotiation platform is there an inter-professional association or a platform for actors of the chain to meet and negotiate? working conditions social, economic average wage paid to farm employees what is the salary paid to employees on farm? average annual income of farmers what is the average annual income? (agricultural familial net income incl. direct payments) eco-efficiencyenvironmental, economic production per lifespan of dairy cows how long do you keep the dairy cows before slaughter? what is the average milk production per cow per year? packaging material used what type of packaging is used for the milk (multiple choice cf. categories)? climate change potential environmental, economic transport greenhouse gas emissions what transport means do you use to deliver your product? what is the distance of delivery? production greenhouse gas emissions how much ghg is emitted on the farmproduction stage? biodiversity environmental, health ecological compensation area what is the percentage of the ecological compensation surfaces in relation to the total agricultural area? crop rotations how many crop rotations do you undertake on average per field? locally adapted/resistant/ endangered crop varieties do you use locally adapted/resistant/ endangered crop varieties? (according to pro specia rara) area free of pesticide use on what percentage of your total cropland area is no pesticides applied? gmo-free feed (certified) in the supply chain is the animal feed gmo free (labelled/certified) and do you renounce on the plantation of gmo crops? breeding degree of the livestock what breeds compose your dairy herd? 180 e. schmitt et al. attributes dimensions indicator question soil preservation environmental, health growing of legumes in proportion of cropland surface on what percentage of your cropland do you regularly grow legumes? percentage of organic fertilizers in the total fertilizer application what is the percentage of organic fertilizers in the total fertilizer application? (mineral and organic) food quality & food safety  health, ethical concentrated feed used per kg milk how much concentrated feed do you give to your cows per year? percentage of roughage in the animal feed what is the percentage of roughage in the daily feed ration? food safety standards from suppliers does the food chain actor have food safety insurance from the participants preceding them in the chain? transparency ethical, health proportion of information available to farmers which information is available to farmers (tick from: final price, type of product, place) sufficient and clear information available for consumers what is the information available to consumers on packaging? information made publicly available what information do you make freely available (online)? food wastageethical, environmental use of biogas plants is the farmyard manure and organic waste further processed in biogas plants? use of byproducts from the food industry as animal feed (% of farmers) are byproducts from the food industry used as animal feed? milk loss on farm what percentage of milk is lost (not incl. converted as by-product)? milk loss at processing what percentage of milk is lost at processing stage? traceability ethical, economic, health traceability upstream of the supply chain is it possible to retrace the whole supply chain of the purchased products (incl. feed, package, etc)? traceability downstream of the supply chain are the produced food products clearly marked so that the buyer can completely retrace them to their source? animal welfare ethical proportion of participation in outdoor grazing program do you take part in the project regular outings? life span of the dairy cows what is the average age of the cow at slaughter? proportion of participation in loose housing program are the animals loose in the stable? (according to bts program) proportion of animals treated by antibiotics in a year what proportion of dairy cows is treated with antibiotics on average per year? transportation duration to the slaughterhouse what is the average transportation time to the slaughterhouse? 181sustainability comparison of a local and a global milk value chains in switzerland indicates that the indicator is qualitative and is not evaluated on a continuous numerical scale. rather, the fact to fulfil the criteria as a whole is considered as the maximum performance. in this case, the performance does not vary but is either 100 or 0%. in some other cases (e.g., “packaging material used”), the indicator is also qualitative but there are other stages of performance between “yes” and “no” and the categories for each percentage of performance are then given in the annex. the last step consisted in analysing the differences of performance in each indicator between the local and the global chain. 3. the swiss milk case study in switzerland, two specific supply chains have respectively been chosen as “global” and “local” examples for comparison according to the six criteria of brunori table 2. overview of the informing stakeholders and data collection procedures. chain actor data collection method local milk value chain cooperative interview 1.5 hour farmers online survey sent to 53 farmers on a total of 75 farmers (17 answers) retailer 1 interview 1.5 hour retailer 2 e-mail and telephone questionnaire processor 1 e-mail questionnaire input provider interview 1.5 hour global milk value chain farmers written questionnaire (5 answers) secondary data processor interview 1.5 hour retailer interview 1.5 hour (in common with interview local chain) input provider interview 1.5 hour (common with local chain) figure 1. benchmarking system of indicators with the example of the indicator “producers’ income”. 182 e. schmitt et al. et al. (2016). the two cases are described in this chapter and table 3 summarizes their characteristics in the six criteria, although the sixth criterion does not show a difference. the global supply chain is represented by a generic milk distributed all over the country by the supermarket owning the brand, thereafter named “global milk”. the supply chain is composed of the steps presented in figure 2. these steps of the supply chain also limit the scope of the assessment by the indicators of sustainability. the milk may come from at least 2,000 dairy farmers. however, the processor uses “industry milk” for a whole segment of products from yogurts to desserts and so it was not possible to know in detail which quantities of milk are used for the global milk and from exactly how many producers it comes. the company processing and packaging the milk is also active at the international level, exporting specific products, but not the fresh milk. the company processes 265 millions kg milk per year but the exact part of fresh milk is not known, though it has been evaluated as around 11% during an interview. the supplying dairy farmers are located in three regions of switzerland: the north-west around basel, the north-east around st-gallen, both assembling milk through collecting centres, and some more independent dairy farmers in the south-west range of jura. these farmers are members of “milk centres” that are responsible for collecting and bulging the milk before delivery to the processor. their governance is however rather weak and the price of industry milk has been falling constantly in the last few years. thus the dairy farmers in this segment often have to produce a large quantity at a low production cost. they are mostly located in the low land and farm intensively with the type and quantity of input allowed within the swiss agricultural legislation. the processing and packaging take place in south switzerland and the milk is distributed all over the country. the distance can be evaluated as a minimum of 200 km between collecting centre and supermarket, and up to 500 km or more travelled within switzerland. fig. 2 shows the estimated average distances (according to interviews and road distances on google.maps) between some steps of the supply chain. in addition, a substantial distance is covered by inputs used as feed for the dairy farmers. they for example use soymeal feed from brazil in the mix fed to dairy cows. although the supply chain is mostly represented at the swiss national level, it is the most “global” fresh milk product available to swiss consumers and which can be contrasted in their purchasing decisions with the local milk described below. the local supply chain on the other hand is represented by fresh milk sold as “pasture milk”, which is based on local resources and sold only in two defined regions by the same supermarket chain (which is divided in autonomous regions). the chain concerns a limited but increasing number of dairy farmers: 57 in the region aare and 18 in the region lucerne. in total the chain concerns approximately 13 to 15 million litres per year. one collector truck picks up the milk from the producers and one manufacturer packages it in each region and then delivers it to the distributing centre of the region. the total distance from farm to supermarket is evaluated between 40 and 100 km. in contrary to the global chain, dairy farmers are restricted in the use of imported feed and soymeal is specifically banned in this special regional milk chain. they have to follow a system of points attributed for good practices and if they do not obtain enough points they can be excluded from the supply chain (ip suisse, 2015). however, some imported cereals like maize, might still be used (mostly from europe) but the exact provenance is hard to 183sustainability comparison of a local and a global milk value chains in switzerland monitor and highly variable. the next objective of the initiators of this product is then to also control the use of cereals for feed. concerning the social criteria of distinction between local and global, the main difference is that the local milk was an initiative from a farmers’ association, thus united and represented by this cooperative defending their interests and also deciding on the code of practice. the local actors thus have a higher control on the governance of the value chain. the local milk is also clearly differentiated as a local product as it is sold under a label for regional products. 4. results the data collected and the scores of performance of both supply chains are presented in table 4. of the 36 indicators, 20 obtained a better score in the local chain (56%), 7 figure 2. supply chains of the two milk case studies (global above, local below). >1000km 20km 195km 165km 24km >100km 100km (round trip for all farms) 54km 24km global and national inputs 2000 dairy farmers 2 milk collecting center 1 processor 10 distribution centers supermarkets global and national inputs 75 dairy farmers 2 collectorsprocessors 2 distribution centers supermarkets 184 e. schmitt et al. were equal and 9 were better in the global chain. these differences can also be seen in figure 3. on this chart the performance of the global chain has been artificially set to the middle of the scale (50%) and the performance of the local chain normalized to this score and limited between 0 and 100%. it can thus be seen in which indicators the local chain performs two times better or just slightly better, or worse than the global chain. we have set the global chain as reference because it is a conventional supermarket supply chain and the local chain is corresponding more to an alternative. on this radar, it is quite clear that the local chain is situated more at the outsides of the radar, thus showing higher performances. it is especially clear for the attributes transparency, soil preservation and food quality and safety. the local milk performs better for 6 attributes composed each by 2 to 4 indicators. it performs better in multiple dimensions like in the economic and social dimensions (value creation and distribution), in the environmental and health dimensions (climate change potential, biodiversity, soil preservation), and in the ethical and health dimensions (food quality and safety, and transparency). in the economic dimension, which is concerned by the attribute “value creation and distribution”, there are three indicators. the indicator “differentiation of the product” is a yes/no indicator concerning the clear promotion of the product with ecological and/or provenance aspects. the answer is yes for the local chain and no for the global chain, thus explaining the total difference in the score. for the two other indicators, it seems that milk producers in the local chain obtain a slightly higher price on the milk, even though the performance is really low for both chains (12 and 9%). but in proportion to the price of the final product in the supermarket local farmers get a lower share than in the global chain (local farmers get 60 cents out of chf 1.55 (39%) and global farmers get 59.3 cents out of chf 1.43 (43%) for a litre of milk). the increase of the retail price of the local milk is thus translated in a higher margin for the retailer. table 3. description of the case studies along the criteria of local-global distinction (brunori et al., 2016). criteria global milk local milk spatial configuration widely spread production, 2 main collectors, 1 packaging hub and national consumption two separate regions with their own concentrated producers, common collecting and packaging and regional consumption product identity generic product (supermarket brand) differentiated with a label of regional origin and ecological quality physical distance from 200 to 500 km or more (main supply chain). global inputs 40 to 100 km (main supply chain) controlled inputs (continental) size of operations the biggest national enterprise in this sector, transforming 265 million kg milk per year (incl. other dairy products) two regional dairies, overall production of 13 to 15 million litres per year governance farmers weakly organized around regional collecting centers, the processor/retailer detains the decision power on price, processing, etc initiative of farmers organized in an association who manages a book of requirements and negotiate prices technologies most modern and automated technologies most modern and automated technologies 185sustainability comparison of a local and a global milk value chains in switzerland table 4. data for indicators and performance scores. indicator unit data score (%) global local global local differentiation of the product no/yes no yes 0 100 producers’ income ct/ kg milk 59.3 60.6 9 12 share of producers’ price on sale price % 42.7 39.1 14 0 cooperative or association of producers in place no/yes yes yes 100 100 inter-professional association or negotiation platform no/yes yes yes 100 100 average wage paid to farm employees chf 3200 3250 1 3 average annual income of farmers chf 54 927 51 471 45 30 production per lifespan of dairy cows kg milk per lifespan 28 135 38 233 9 38 packaging material used categories packaging from certified ecological production packaging from certified ecological production 40 40 transport greenhouse gas emissions co2eq./km 51.3 19.8 0 59 production greenhouse gas emissions kg co2eq./kg milk ecm 1.1 1.5 69 53 ecological compensation area % of total agricultural area 11.8 13.3 41 52 crop rotations number of crop rotations 3 5.7 0 68 locally adapted/resistant/endangered crop varieties no/yes no no 0 0 area free of pesticide use % of crop land 27.9 33.5 28 34 gmo-free feed (certified) in the supply chain no/yes 0 87.5 0 88 breeding degree of the livestock average of categories for all farmers 50 22 50 22 growing of legumes in proportion of cropland surface % of the total crop land 0 10.4 0 100 percentage of organic fertilizers in the total fertilizer application % of total fertilizers used 71.4 69.7 71 70 concentrated feed used per kg milk g concentrated feed / kg milk produced 130.4 90.3 25 52 percentage of roughage in the animal feed % of total feed 70 77.3 33 58 food safety standards from suppliers no/yes yes yes 100 100 proportion of information available to farmers average of categories for all farmers 0 42.4 0 42 sufficient and clear information available for consumers categories (%) 40 80 40 80 186 e. schmitt et al. the social dimension concerns two attributes and four indicators. the two indicators of the attribute “social capital” do not show any difference as both chains perform with 100%. in both cases cooperatives and inter-professional organisations are present to support farmers in the defence of their interests and to offer space for negotiations. concerning the attribute “working conditions”, farm employees are paid a little better in the local chain although the difference in performance is minimal (2%) and both are extremely low (1 and 3%). when looking at the annual income of dairy farmers in comparison with the national average in this sector, the ones in the global chain obtain a performance 15% higher. in summary, both chains obtain their equal share of indicators performing better in the social dimension. the environmental dimension contains more attributes and indicators: four attributes measured by 12 indicators, but all of them are also relevant to other dimensions (cfr. table 1). the eco-efficiency is considered both environmentally because the production of more with less is responsible in terms of resource use and planetary boundaries (pretty, 2013) and economically because it can obviously reduce production costs. the first indicator in this attribute looks at the production per cow on their entire lifespan. cows in the local chain live in average a half-year longer and also were reported to produce more per year so the local chain has a better performance. the second indicator concerns the material used for packaging: the most ecological and economical would be to have no indicator unit data score (%) global local global local information made publicly available categories (%) 70 100 67 100 use of biogas plants no/yes no no 0 0 use of byproducts from the food industry as animal feed % of farmers 20 17.6 20 18 milk loss on farm % 1 1.5 90 85 milk loss at processing % 0.5 0.2 0 60 traceability upstream of the supply chain average of categories for all farmers 20 67.6 20 68 traceability downstream of the supply chain no/yes 100 66.7 100 67 proportion of participation in outdoor grazing program % of participation (from all farmers) 69 100 92 100 life span of the dairy cows years 4.5 5 0 0 proportion of participation in loose housing program % of participation (from all farmers) 23 70.6 77 100 proportion of animals treated by antibiotics in a year % treated cows 17.5 35.8 100 90 transportation duration to the slaughterhouse minutes 46.3 42.9 82 86 187sustainability comparison of a local and a global milk value chains in switzerland packaging at all (re-used bottles) but actually both milks are packaged in similar paperbricks, with however a label of ecological paper production (the fsc label). it is however not recyclable or reusable in both cases and both chains obtain a score of 40%. regarding climate change impacts, the local chain performs better on limiting emissions from transport because of the much shorter distance travelled in the local chain. these scores were calculated from data about transport means and distance and using a life-cycle assessment database that gives coefficients of ghg emissions for transport means. for the second indicator “production greenhouse gas emissions”, no direct measurement of ghg emissions on farms was possible and a secondary source was used. sutter et al. (2013) compare two systems very similar to ours in switzerland. as the local system produces less milk on the same area because of grass-based feed, more ghg, especially methane, are emitted at the production stage (sutter et al., 2013). the biodiversity attribute contains six indicators and the global chain performs better in only one of them. a certain percentage of the farming surfaces must be set aside (noncultivated): this is a requirement for being eligible to certain direct payments and that’s why all farmers comply with this indicator. interestingly, farmers in the local chain still had larger “compensation surfaces” (13.3% on average against 11.8%). the diversity of crops in the rotation is also much higher with an average of 7 crops for local farmers and only 3 for the global chain. farmers in neither chains use locally adapted or rare varieties (according to the pro specie rara catalogue (2016)) and both perform 0% for this indicator. the use of pesticides is done on larger surfaces among farmers of the global chain although the difference is small (performance 28% vs. 34% in the local chain). the use of gmo is figure 3. performance of the local chain compared to the global chain. 1. differentiation of the product 2. producers' income 3. share of producers' price on sale price 4. cooperative or association of producers in place 5. inter-professional association or negotiation platform 6. average wage paid to farm employees 7. average annual income of farmers 8. production per lifespan of dairy cows 9. packaging material used 10. transport greenhouse gas emissions 11. production greenhouse gas emissions 12. ecological compensation area 13. crop rotations 14. locally adapted/resistant/endangered crop varieties 15. area free of pesticide use 16. gmo-free feed (certified) in the supply chain 17. breeding degree of the livestock 18. growing of legumes in proportion of cropland surface 19. percentage of organic fertilizers in the total fertilizer application 20. concentrated feed used per kg milk 21. percentage of roughage in the animal feed 22. food safety standards from suppliers 23. proportion of information available to farmers 24. sufficient and clear information available for consumers 25. information made publicly available (online) 26. use of biogas plants 27. use of byproducts from the food industry as animal feed 28. milk loss on farm 29. milk loss at processing 30. traceability upstream of the supply chain 31. traceability downstream of the supply chain 32. proportion of participation in outdoor grazing program 33. life span of the dairy cows 34. proportion of participation in loose housing program 35. proportion of animals treated by antibiotics in a year 36. transportation duration to the slaughterhouse 188 e. schmitt et al. controversial regarding sustainability and the swiss legislation is one of the strictest in their restriction but still allows some amount in animal feed. farmers in the global chain do not renounce to it and do not use certified gmo-free feed but 87.5% of farmers in the local chain do. regarding traditional species conservation on farms, neither chains had many traditional dairy cows and most tend to have high-producing breeds like red holstein. concerning the attribute soil preservation, the local chain’s farmers use much more legumes in their crop cultures, which give them the advantage in the first indicator. concerning the proportion of organic fertilization, both chains have surprisingly very close scores (71 and 70%). the attribute “food quality and food safety” covers the health dimension of the assessment. the two first indicators are linked to the fat quality found in the milk and in both cases the local chain performs better as the feeding of cows relies much more on fodder rather than concentrates. as a consequence the content of fatty acids in the milk, especially the omega3 to omega6 ratio, is healthier (thomet et al., 2011). there is no difference in terms of safety standards followed by both chains (both 100% performance in the last indicator of this attribute). the four last attributes are linked to the ethical dimensions but also to the social or environmental dimensions. for all indicators of “transparency” the local chain performs around 40% better. these indicators were constructed with categories of information that should be available to farmers, consumers or the public about the product, its production and the enterprises in the supply chain. for the attribute “food wastage”, neither of the chains’ farmers use a biogas plant (first indicator). concerning the use of industry by-products as feed, farmers of the global chain seem slightly better, as well as in avoiding milk losses. at processing, the local processors seem better in avoiding milk losses during packaging. in terms of traceability, the global chain has a better performance concerning the monitoring of traceability downstream of the supply chain (marking products which are sold) but a worse performance for traceability upstream of the supply chain (ability to know the origin of all components). the last attribute “animal welfare” contains five indicators and the local chain presents higher performances in three of them. farmers of the local chain participate in more animal-welfare voluntary programs and thus perform better in “proportion of participation in outdoor grazing program” and “proportion of participation in loose housing program”. they also perform better in the last indicator “transportation duration to the slaughterhouse” probably due to their general geographical position closer to a major slaughterhouse in basel. both chains have cows who do not live for many years (4.5 and 5 years), so both get a null performance according to the benchmarks with a minimum at 5 years, but the local chain still performs a little bit better. concerning animal health, farmers in the global chain seem to give fewer antibiotics and thus perform better in the indicator “proportion of animals treated by antibiotics in a year”. 5. discussion the analysis of several chosen indicators shows a clear distinction between the global and local milk that reflects the difference concerning geographical flows, governance, 189sustainability comparison of a local and a global milk value chains in switzerland production systems and logistics. the local milk clearly performs better in terms of number of indicators. this difference in performances can be explained in part by important factors that influence the performance score of several indicators, and were mentioned by most actors as relevant. these factors were the strategies in the choice of animal feed and the differentiation of the product. the animal feeding strategy for example influences the whole organization of farms by changing the possibilities of crop rotations, productivity per hectare and per cow, ghg emissions, animal welfare, fat quality in the milk, and also greatly influences the impact on biodiversity abroad where concentrated feed is produced. the differentiation of the product is a whole different marketing strategy that triggers a different supply chain arrangement and the sharing of information. it thus influences transparency, relation among actors, communication with the consumers and price. in contrast, the standardization of the product that is a strategy more typical of global products leads to a decrease of precise information available to consumers and of transparency as well as traceability (for consumers and farmers), but on the other side, it can help to decrease the production costs, reduce waste and deal better with consumption variations over the year. however, the inclusion of social and environmental externalities might balance this. moreover, the local milk chain was still at the beginning of the initiative at time of data collection and is still expected to improve its performance. for the moment, some of the local milk sometimes has to be de-classified and is then mixed with other milks into generic brands. when this happens, a part of the added value due to the differentiation of the product is lost. however, all in all, the efforts of this initiative to promote localness and ecological values around the local milk are shown to contribute to sustainability through our indicators. in comparison with other studies, binder et al. (2012) realized a sustainability assessment of the swiss milk sector in general, which would correspond to our global case study. their indicators are constructed differently and the data are used in a too different way to allow direct comparison with our indicators. however, it is worth to underline that both studies identify similar themes of sustainability like biodiversity, social capital, ghg emissions, hourly wages, etc. applicable to the same stages of the value chain. furthermore, both studies identify similar critical issues and trade-offs, for example that the increase of the biodiversity in switzerland (by increasing conservation surfaces) might impact biodiversity in brazil through the production of concentrated feed and the deforestation linked to the cultivation of the corresponding soya and maize (binder et al. 2012). this was however not the case in our local chain, as local farmers have larger conservation areas while feeding less concentrated feed at the same time. interestingly, their study followed the same methodological process for the selection of indicators and benchmarking, which they call upper and lower boundaries of the sustainability range. a first important result in this study consist as well in the nature of the attributes. in switzerland, milk production is seen by various stakeholders as being important from all points of views (multi-dimensional), but the choice of the attributes themselves reflect the sensitivity that is peculiar to that country and sector. as in binder et al. (2012), social topics like fairness of remuneration of farmers, social capital and working conditions, but as well environmental issues like climate change, biodiversity, soil, food waste and more ethi190 e. schmitt et al. cal and health concerns like food quality, transparency along food chain and animal welfare, are topics that strongly came forward. it is also necessary to acknowledge that the choice of the examined “critical issues” of sustainability of the milk value chain is very hard to maintain objectivity, as the selection of the attributes integrates the stakeholders directly. it then becomes inevitably context-dependent, as they tend to give importance to what is relevant in their daily activities. the final selection is thus linked to that specific cultural and biophysical context. the validity of the specific analysis framework and subsequent results is thus limited to a certain sector and to a certain country. in contrary to pretty et al. (2010)’s hope for a universal tool, we rather think that such indicator tools have to be context-related. therefore, the selection of the attributes and indicators really needs to be done in a participatory way and in connection with that context in order to be relevant (van passel and meul, 2012; binder et al., 2010; bossel, 1999). a participatory process moreover has the advantage to avoid misinterpretations of the issues and results, which is often the case in sustainability assessments, as noted by gasparatos and scolobig (2012). the choice of the key stakeholders is therefore crucial and the researcher has to be aware that the final list of indicators could change the results one way or another (schader et al., 2014). the benchmarking of each indicator is also a crucial phase. it already requires a holistic vision of what the limits in performance of the chain are and could be in the most sustainable and most unsustainable cases and a good knowledge of the context. the stakeholders interviewed during the attributes’ selection phase often emphasized the economic aspects as being the most crucial because without a substantial profit nothing can be done. this leads to the issue of weighting the indicators according to their importance. we have chosen not to dedicate this study to the weighing and further averaging of the indicators because the detailed results and multidimensionality should not be lost. as schader et al. (2014) wrote, there is often a trade-off between the precision of data that researchers can collect and the multi-dimensionality of an evaluation; we then tried to overcome this challenge by downsizing the amount of attributes according to their relevance in the specific context of the dairy sector, while keeping some precise indicators. however the results show that some indicators could have been set aside as they do not show any difference between the local and global chains, such as social capital. collecting data proved to be difficult for the very first (input provision) and last steps (distribution and consumption) of the supply chains in the study conducted. indeed, some agricultural inputs are often imported through market channels that are hard to entirely trace and the sustainability of their production is even harder to assess. the end of the supply chain, with the biggest companies and sometimes the exportation of products, is also hard to be completely captured as stakeholders are harder to anonymise and fear more for the use of their confidential data. these two ends of the value chains are thus a sort of darker zones that deserve more attention in future sustainability assessments. 6. conclusion as seen in this study developing a set of attributes of performance to compare local and global food value chains, the process of selecting the appropriate indicators and benchmarks are crucial. an in-depth exploration of the context and the participation of stakeholders in an iterative process were thus required to define the attributes and focus on the most rel191sustainability comparison of a local and a global milk value chains in switzerland evant ones. the use of numerous interviews and the wide sources to contextualize and assess each chain’s performance gives to the followed methodology great insight on each chain’s critical issues and on the most relevant attributes to assess. however, at the indicator level, more work should be carried out to weigh them for aggregation; such a process could however be very time-consuming and reduce the transparency of the results and the objectiveness of the assessments. nevertheless, the assessment succeeded in remaining multidimensional and in finding the critical issues that differentiate the local and global chains in their sustainability. the two major advantages of the studied “more local” chain in terms of sustainability are its marketing strategy to differentiate the product in terms of provenance and ecological label. this induces a more coordinated governance among producers and with the retailer. it also prompted a reflexion on the production’s book of requirement and ecological practices on farms. a major impact on sustainability comes from the animal feeding strategy as a grass-based diet influences the rotation of cultures (soil preservation), the biodiversity in switzerland and brazil, the time animals spend outside (animal welfare), and even the nutritional quality of the milk fatty acids. however, the global chain might have the advantage to emit less ghg emissions per kilo milk produced but an lca from input production to consumption would be more adequate to evaluate this specific question. the global chain also might be more efficient in terms of production costs as farmers in the global chain showed higher annual wages, but the local initiative is still at its beginning. references adams, d.c. and salois, m.j. 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(2012). multilevel and multi-user sustainability assessment of farming systems. environmental impact assessment review 32: 170–180. a n n ex attribute in di ca to r u ni t lo w b en ch m ar k h ig h be nc hm ar k re fe re nc es fo r c re at io n an d be nc hm ar ks d at a g lo ba l d at a lo ca l sc or e g lo ba l (% ) sc or e lo ca l (% ) value creation and distribution d iff er en tia tio n of th e pr od uc t no /y es no ye s sa fa (c 3 .3 .1 ) no ye s 0 10 0 pr od uc er s’ in co m e ct / k g m ilk 55 c t/k g 15 6 ct /k g sa fa (c 1 .4 .1 ) u sp 2 01 2 59 .3 60 .6 9 12 sh ar e of p ro du ce rs ’ pr ic e on sa le p ric e % 40 % 60 % u sp 2 01 2, f o a g 20 14 1 42 .7 39 .1 14 0 social capital c oo pe ra tiv e or as so ci at io n of pr od uc er s i n pl ac e no /y es no ye s sa fa (s 2 .2 .1 ) ye s ye s 10 0 10 0 in te rp ro fe ss io na l as so ci at io n or ne go tia tio n pl at fo rm no /y es no ye s sa fa (s 2 .2 .1 ) ye s ye s 10 0 10 0 working conditions av er ag e w ag e pa id to fa rm e m pl oy ee s c h f/ m on th 31 70 c h f/ m on th 61 25 c h f/ m on th sa fa (c 1 .4 .1 ) u sp 2 01 2 32 00 32 50 1 3 a av er ag e an nu al in co m e of fa rm er s c h f/ ye ar 44 ’7 72 c h f/ ye ar 67 ’1 58 c h f/ ye ar h oo p & sc hm id 2 , a gr os co pe 2 01 3, ex pe rt o . s ch m id 54  9 27 51  4 71 45 30 a n n ex attribute in di ca to r u ni t lo w b en ch m ar k h ig h be nc hm ar k re fe re nc es fo r c re at io n an d be nc hm ar ks d at a g lo ba l d at a lo ca l sc or e g lo ba l (% ) sc or e lo ca l (% ) value creation and distribution d iff er en tia tio n of th e pr od uc t no /y es no ye s sa fa (c 3 .3 .1 ) no ye s 0 10 0 pr od uc er s’ in co m e ct / k g m ilk 55 c t/k g 15 6 ct /k g sa fa (c 1 .4 .1 ) u sp 2 01 2 59 .3 60 .6 9 12 sh ar e of p ro du ce rs ’ pr ic e on sa le p ric e % 40 % 60 % u sp 2 01 2, f o a g 20 14 1 42 .7 39 .1 14 0 social capital c oo pe ra tiv e or as so ci at io n of pr od uc er s i n pl ac e no /y es no ye s sa fa (s 2 .2 .1 ) ye s ye s 10 0 10 0 in te rp ro fe ss io na l as so ci at io n or ne go tia tio n pl at fo rm no /y es no ye s sa fa (s 2 .2 .1 ) ye s ye s 10 0 10 0 working conditions av er ag e w ag e pa id to fa rm e m pl oy ee s c h f/ m on th 31 70 c h f/ m on th 61 25 c h f/ m on th sa fa (c 1 .4 .1 ) u sp 2 01 2 32 00 32 50 1 3 a av er ag e an nu al in co m e of fa rm er s c h f/ ye ar 44 ’7 72 c h f/ ye ar 67 ’1 58 c h f/ ye ar h oo p & sc hm id 2 , a gr os co pe 2 01 3, ex pe rt o . s ch m id 54  9 27 51  4 71 45 30 attribute in di ca to r u ni t lo w b en ch m ar k h ig h be nc hm ar k re fe re nc es fo r c re at io n an d be nc hm ar ks d at a g lo ba l d at a lo ca l sc or e g lo ba l (% ) sc or e lo ca l (% ) eco-efficiency pr od uc tio n pe r lif es pa n of d ai ry c ow s kg m ilk p er li fe sp an 25 ’0 00 k g m ilk 60 ’0 00 k g m ilk ex pe rt c . n ot z 28  1 35 38  2 33 9 38 pa ck ag in g m at er ia l us ed ca te go rie s c at eg or ie s: co nv en tio na l p ac ka ge 0 % / r ec yc la bl e pa ck ag in g 2 0% / pa ck ag in g fr om ce rt ifi ed e co lo gi ca l m at er ia l 4 0% / pa ck ag in g fr om re cy cl ed m at er ia l 60 % /p ac ka gi ng fr om re cy cl ed c er tifi ed ec ol og ic al m at er ia l 80 % / no p ac ka gi ng or re us ed p ac ka ge s 1 00 % sa fa (e 5 .1 .3 ) pa ck ag in g fr om ce rt ifi ed e co lo gi ca l pr od uc tio n pa ck ag in g fr om ce rt ifi ed ec ol og ic al pr od uc tio n 40 40 climate change potential tr an sp or t g re en ho us e ga s em iss io ns c o 2e q. /k m 48 .4 c o 2 e q. /k m 0 c o 2 eq ./k m sa fa (e 1 .1 .2 ) be nc hm ar ks c al cu la te d fr om th eo re tic al w or se -c as e 51 .3 19 .8 0 59 pr od uc tio n g re en ho us e ga s em iss io ns kg c o 2e q. /k g m ilk ec m 2. 5 kg c o 2e q. /k g m ilk e c m (e ne rg y co rr ec te d m ilk ) 0. 5 kg c o 2e q. /k g m ilk ec m sa fa (e 1 .1 .2 ). n em ec ek e t a l. 20 08 3 1. 1 1. 5 69 53 attribute in di ca to r u ni t lo w b en ch m ar k h ig h be nc hm ar k re fe re nc es fo r c re at io n an d be nc hm ar ks d at a g lo ba l d at a lo ca l sc or e g lo ba l (% ) sc or e lo ca l (% ) biodiversity ec ol og ic al co m pe ns at io n ar ea % o f t ot al a gr ic ul tu ra l ar ea 6% 20 % sa fa (e 3 .2 .2 ), ex pe rt a . f lie ss ba ch 11 .8 13 .3 41 52 c ro p ro ta tio ns nu m be r o f c ro p ro ta tio ns 3 cr op s 7 cr op s sa fa (e 4 .2 .4 ). ri se 4 ex pe rt a . f lie ss ba ch 3 5. 7 0 68 lo ca lly a da pt ed / re sis ta nt /e nd an ge re d cr op v ar ie tie s no /y es no ye s sa fa (e 4 .3 .3 ) pr o sp ec ie r ar a 20 16 no no 0 0 a re a fr ee o f p es tic id e us e % o f c ro p la nd 0% 10 0% sa fa (e 4 .1 .2 ) r is e ex pe rt a . f lie ss ba ch 27 .9 33 .5 28 34 g m o -f re e fe ed (c er tifi ed ) i n th e su pp ly c ha in no /y es no ye s ja co bs en e t a l. 20 13 5 0 87 .5 0 88 br ee di ng d eg re e of th e liv es to ck av er ag e of c at eg or ie s fo r a ll fa rm er s ca te go rie s: hi gh ly b re d sp ec y fo r i nt en siv e pr od uc tio n -0 % / m ed iu m b re d sp ec y 50 % / t ra di tio na l s pe cy 1 00 % sa fa (e 4 .2 .2 ), ex pe rt c . n ot z 50 22 50 22 soil preservation g ro w in g of le gu m es in p ro po rt io n of cr op la nd su rf ac e % o f t he to ta l c ro p la nd 0% 5% ex pe rt a . f lie ss ba ch 0 10 .4 0 10 0 pe rc en ta ge o f o rg an ic fe rt ili ze rs in th e to ta l fe rt ili ze r a pp lic at io n % o f t ot al fe rt ili ze rs us ed 0% 10 0% sa fa (e 3 .1 .1 ) ex pe rt a . f lie ss ba ch 71 .4 69 .7 71 70 food quality & food safety c on ce nt ra te d fe ed us ed p er k g m ilk g co nc en tr at ed fe ed / kg m ilk p ro du ce d 16 8 g 18 g ip s ui ss e 20 15 13 0. 4 90 .3 25 52 pe rc en ta ge o f r ou gh ag e in th e an im al fe ed % o f t ot al fe ed 60 % 90 % sa fa (e 1 .1 .3 ) ex pe rt c n ot z 70 77 .3 33 58 fo od sa fe ty st an da rd s fr om su pp lie rs no /y es no ye s sa fa (c 3 .1 .3 – 3 .2 .1 ) ye s ye s 10 0 10 0 attribute in di ca to r u ni t lo w b en ch m ar k h ig h be nc hm ar k re fe re nc es fo r c re at io n an d be nc hm ar ks d at a g lo ba l d at a lo ca l sc or e g lo ba l (% ) sc or e lo ca l (% ) biodiversity ec ol og ic al co m pe ns at io n ar ea % o f t ot al a gr ic ul tu ra l ar ea 6% 20 % sa fa (e 3 .2 .2 ), ex pe rt a . f lie ss ba ch 11 .8 13 .3 41 52 c ro p ro ta tio ns nu m be r o f c ro p ro ta tio ns 3 cr op s 7 cr op s sa fa (e 4 .2 .4 ). ri se 4 ex pe rt a . f lie ss ba ch 3 5. 7 0 68 lo ca lly a da pt ed / re sis ta nt /e nd an ge re d cr op v ar ie tie s no /y es no ye s sa fa (e 4 .3 .3 ) pr o sp ec ie r ar a 20 16 no no 0 0 a re a fr ee o f p es tic id e us e % o f c ro p la nd 0% 10 0% sa fa (e 4 .1 .2 ) r is e ex pe rt a . f lie ss ba ch 27 .9 33 .5 28 34 g m o -f re e fe ed (c er tifi ed ) i n th e su pp ly c ha in no /y es no ye s ja co bs en e t a l. 20 13 5 0 87 .5 0 88 br ee di ng d eg re e of th e liv es to ck av er ag e of c at eg or ie s fo r a ll fa rm er s ca te go rie s: hi gh ly b re d sp ec y fo r i nt en siv e pr od uc tio n -0 % / m ed iu m b re d sp ec y 50 % / t ra di tio na l s pe cy 1 00 % sa fa (e 4 .2 .2 ), ex pe rt c . n ot z 50 22 50 22 soil preservation g ro w in g of le gu m es in p ro po rt io n of cr op la nd su rf ac e % o f t he to ta l c ro p la nd 0% 5% ex pe rt a . f lie ss ba ch 0 10 .4 0 10 0 pe rc en ta ge o f o rg an ic fe rt ili ze rs in th e to ta l fe rt ili ze r a pp lic at io n % o f t ot al fe rt ili ze rs us ed 0% 10 0% sa fa (e 3 .1 .1 ) ex pe rt a . f lie ss ba ch 71 .4 69 .7 71 70 food quality & food safety c on ce nt ra te d fe ed us ed p er k g m ilk g co nc en tr at ed fe ed / kg m ilk p ro du ce d 16 8 g 18 g ip s ui ss e 20 15 13 0. 4 90 .3 25 52 pe rc en ta ge o f r ou gh ag e in th e an im al fe ed % o f t ot al fe ed 60 % 90 % sa fa (e 1 .1 .3 ) ex pe rt c n ot z 70 77 .3 33 58 fo od sa fe ty st an da rd s fr om su pp lie rs no /y es no ye s sa fa (c 3 .1 .3 – 3 .2 .1 ) ye s ye s 10 0 10 0 attribute in di ca to r u ni t lo w b en ch m ar k h ig h be nc hm ar k re fe re nc es fo r c re at io n an d be nc hm ar ks d at a g lo ba l d at a lo ca l sc or e g lo ba l (% ) sc or e lo ca l (% ) transparency pr op or tio n of in fo rm at io n av ai la bl e to fa rm er s av er ag e of c at eg or ie s fo r a ll fa rm er s % o f t he fo llo w in g ca te go rie s: fin al p ric e, ty pe o f p ro du ct , p la ce of sa le sa fa (g 2 .3 .1 ) 0 42 .4 0 42 su ffi ci en t a nd c le ar in fo rm at io n av ai la bl e fo r c on su m er s ca te go rie s ( % ) ca te go rie s e ac h w or th 2 0% : l eg al ly re qu ire d in fo rm at io n/ ad di tio na l i nf or m at io n on n ut rit io n / ad di tio na l i nf or m at io n on a ni m al h us ba nd ry co nd iti on s / a dd iti on al in fo rm at io n of in gr ed ie nt s’ so ur ce s / o th er a dd iti on al in fo rm at io n sa fa (c 3 .3 .1 ) 40 80 40 80 in fo rm at io n m ad e pu bl ic ly a va ila bl e ca te go rie s ( % ) c at eg or ie s e ac h w or th 2 0% : l eg al ly re qu ire d in fo rm at io n/ in fo rm at io n on en te rp ris es ’ s tr uc tu re / in fo rm at io n on st an da rd s an d pr oc es se s/ a nn ua l fi na nc ia l re po rt in g/ c or po ra te re sp on sib ili ty re po rt / d at a fr ee ly a va ila bl e sa fa (g 2 .3 .1 ) 70 10 0 67 10 0 attribute in di ca to r u ni t lo w b en ch m ar k h ig h be nc hm ar k re fe re nc es fo r c re at io n an d be nc hm ar ks d at a g lo ba l d at a lo ca l sc or e g lo ba l (% ) sc or e lo ca l (% ) food wastage u se o f b io ga s p la nt s no /y es no ye s ri se no no 0 0 u se o f b yp ro du ct s fr om th e fo od in du st ry as a ni m al fe ed (% o f fa rm er s) % o f f ar m er s 0% 10 0% be re tta e t a l. 20 13 6 20 17 .6 20 18 m ilk lo ss o n fa rm % 10 % 0% sa fa (e 5 .3 .4 ) be re tta e t a l. 20 13 1 1. 5 90 85 m ilk lo ss a t p ro ce ss in g% 0. 5% 0% sa fa (e 5 .3 .4 ) be re tta e t a l. 20 13 0. 5 0. 2 0 60 traceability tr ac ea bi lit y up st re am of th e su pp ly c ha in av er ag e of c at eg or ie s fo r a ll fa rm er s ca te go rie s: no 0% / pa rt ia lly 50 % /y es 10 0% sa fa (c 3 .3 .2 ) 20 67 .6 20 68 tr ac ea bi lit y do w ns tr ea m o f t he su pp ly c ha in no /y es ca te go rie s: no 0% / pa rt ia lly 50 % /y es 10 0% sa fa (c 3 .3 .2 ) 10 0 66 .7 10 0 67 animal welfare pr op or tio n of pa rt ic ip at io n in ou td oo r g ra zi ng pr og ra m % o f p ar tic ip at io n (f ro m a ll fa rm er s) 0% 75 % ex pe rt c . n ot z 69 10 0 92 10 0 li fe sp an o f t he d ai ry co w s ye ar s 5 ye ar s 8 ye ar s ex pe rt c . n ot z 4. 5 5 0 0 pr op or tio n of pa rt ic ip at io n in lo os e ho us in g pr og ra m % o f p ar tic ip at io n (f ro m a ll fa rm er s) 0% 30 % sa fa (e 6 .2 .1 ) ex pe rt c . n ot z 23 70 .6 77 10 0 pr op or tio n of a ni m al s tr ea te d by a nt ib io tic s in a y ea r % tr ea te d co w s 90 % 30 % sa fa (e 6 .1 .2 ) ex pe rt c . n ot z 17 .5 35 .8 10 0 90 tr an sp or ta tio n du ra tio n to th e sla ug ht er ho us e m in ut es 12 0 m in ut es 30 m in ut es sa fa (e 6 .2 .3 ) ex pe rt c . n ot z 46 .3 42 .9 82 86 1 fe de ra l o ffi ce fo r a gr ic ul tu re (f o ag ). (2 01 4) . m ar kt be ric ht m ilc h. k on su m m ilc h : m ar kt an te ils ge w in n fü r d is co un te r. be rn . 2 h oo p, d ., an d sc hm id , d . ( 20 13 ). g ru nd la ge nb er ic ht 2 01 2. e tt en ha us en . 3 n em ec ek , t ., vo n ri ch th of en , j ., d ub oi s, g ., ca st a, p ., ch ar le s, r ., an d pa hl , h . ( 20 08 ). en vi ro nm en ta l i m pa ct s of in tr od uc in g gr ai n le gu m es in to e ur op ea n cr op r ot at io ns . eu ro pe an jo ur na l o f a gr on om y 28 : 3 80 -3 93 . d oi :1 0. 10 16 /j. ej a. 20 07 .1 1. 00 4 4 g re nz , j . e t a l. (2 00 9) . r is e – a m et ho d fo r a ss es si ng th e su st ai na bi lit y of a gr ic ul tu ra l p ro du ct io n at . r ur al d ev el op m en t n ew s 1: 5 -9 . 5 ja co bs en , s .e . e t a l. (2 01 3) . f ee di ng th e w or ld : g en et ic al ly m od ifi ed c ro ps v er su s ag ric ul tu ra l b io di ve rs ity . a gr on om y fo r s us ta in ab le d ev el op m en t 3 3: 6 51 -6 62 . 6 be re tt a, c . e t a l. (2 01 3) . q ua nt ify in g fo od lo ss es a nd th e po te nt ia l f or re du ct io n in s w itz er la nd . w as te m an ag em en t 3 3( 3) : 7 64 -7 73 . bio-based and applied economics 8(1): 63-74, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8146 assessing price sensitivity of forest recreational tourists in a mountain destination gianluca grilli1,2 1 economic and social research institute, sir john rogerson’s quay, d02 dublin, ireland 2 trinity college dublin, dublin, ireland abstract. despite the large use of the travel cost method as estimation technique for the demand for forest recreation, information on price elasticity is only seldom reported. in this way, it is hard to understand if a large consumer surplus could be reflected in income opportunities for the local populations, because it is unknown whether the number of annual trips will decrease as a consequence of price changes. this is particularly relevant in remote rural areas, where few other opportunities for additional earnings are available. this contribution attempts to fill this gap, estimating price elasticities for two different specifications of the cost for travel; a first specification includes cost for travel only, while the second comprise on-site expenditures (such as food and accommodation). data were collected by means of a questionnaire survey administrated to a sample of local visitors and analysed with a poisson model. results suggest that visitors have different sensitivities to distance travelled and to expenses locally sustained, the first being more elastic. keywords. travel cost, forest recreation, price elasticity, carpathians, rural development. jel codes. c21, d61, q26. 1. 1. introduction a community-based destination may obtain several benefits from the development of an integrated tourism strategy, including the increase in work places, stimulus to local entrepreneurship and income generation (hearne and salinas, 2002). the development of nature-based forms of tourism may represent an effective strategy to balance the social, economic and environmental spheres of the sustainability (bhuiyan et al., 2016). to understand the strengths and the potentialities of the territory as a tourist destination, decision makers should be aware of the benefits that people obtain from the local resources (faccioli, 2011; tempesta and thiene, 2000). a typical technique used to evaluate recreational benefits is the travel cost model (tcm), which estimates consumer surplus (cs) per trip as a measure of the individual recreational benefit. cs represents corresponding author: gianluca.grilli@esri.ie 64 g. grilli the difference between what the individual actually pays for the trip and the maximum amount he/she was willing to pay for the same trip. a large cs suggests that the foresttourism sector (e.g. local hotels and restaurants) and forest managers could increase prices and obtain higher remuneration, because the willingness to pay of the tourists is (on average) larger than the current cost (hanley and barbier, 2009). however, this information is not enough to design effective policies, because it does not consider the sensitivity of tourists to price changes, i.e. the price elasticity. when tourists are price-sensitive, higher prices could result in a decrease of the number of annual trips (or shorter trips), with no benefits for the local population (levin and milgrom, 2004). in the literature there are plenty of contributions dealing with the estimation of cs for forest recreation but elasticity is rarely estimated, so that the margin for additional earnings is uncertain. despite recreational benefits have been broadly studied, information on price elasticity for forest-based recreation is rare in the literature. to the best of my knowledge, the paper by simões et al. (2013), which illustrates a case study in portugal, is the only recent contribution providing the estimation of price elasticity. while this study is interesting for mediterranean forests, results could be hardly generalized to other areas, for example mountain forests and northern european forests, because they are different in terms of tourists profile and tree species composition. in this paper, i expand the study of price elasticity for forest-based recreation, using a mountain area as case study and two different specifications of the cost for travel. in the first specification, the travel cost depends only on the distance travelled, while in the second all the self-reported costs sustained for the trip are included. in this way, it is possible to distinguish between sensitivity to distance travelled and to expenses inside the location. elasticity informs about how price could be used to increase revenues from a single tourist without the risk to decrease the total number of visitors. therefore, this study is useful not only to raise the question on the importance of elasticity as a policy measure to consider but also for managers and local entrepreneurs to develop an effective management of the destination. the study area is the beskid zywiecki range, a mountainous area in the southern poland, located in the silesian voivodeship. the area is in the carpathians, the highest mountain chain of the central europe, comprehending poland, ukraine, romania, slovakia and the czech republic. understanding the tourists’ demand, its elasticity and the benefits that people obtain from visiting the beskid may contribute in raising the awareness of the role that tourism may play for local development, stimulating an integrated tourism strategy (mirani and farahani 2015). 2. materials and methods 2.1 the study area beskid is the traditional name that it is used to identify some portions of the carpathian mountains. the beskid zywiecki range is a territory of about 60.000 ha of the silesian region (southern poland) composed by three forest districts: jeleśnia, ujsoły and węgierska górka (49º23’42”–49º38’54”n; 18º58’29”–19º27’16”e). the area includes 31,000 ha of landscape park, out of which around 30,000 are included in the natura 2000 net65assessing price sensitivity of forest recreational tourists in a mountain destination work, and the babia gora biosphere reserve is included in the unesco natural heritage list. beskid zywiecki has a vast forested territory, forests represent the main natural ecosystem and tourists use to visit the area for nature-based activities. the main tourists´ activities are trekking, sightseeing and sport practising but surrounding villages include other attractions such as churches and castles (i.e. żywiec castle and sucha beskidzka). 2.2 data a questionnaire survey was implemented to collect the necessary information for the tcm. questionnaires were hand-delivered in some strategic places within the destination (hotels, restaurant and main places of interest) in summertime with the help of local workers and forest managers and collected after one month. the sample is unlikely to be perfectly random, because completing the questionnaire is potentially subject to selection bias. the outcome could be described as convenience sampling, which is a limitation that must be considered when interpreting the results. nonetheless, data does provide policyrelevant information and insights on the local forest use. the questionnaire is part of a broader research and it was divided in three section: section a contained questions about tourist characteristics, which was used to collect data for the tcm and for general features of the tourists. section b was designed in order to investigate people´s preferences about a series of environmental issues, including mixed forests and ecosystem services. section c cellected socio-economic characteristics and it was included at the end of the questionnaire, in order to reduce fatigue effects in compiling the most important questions. the present paper discusses the results of section a, interacted with socio-demographic variables obtained in section c. in order to collect data for the tcm, people were asked to state their place of origin and the distance from the destination. the questionnaire included also questions on the main holiday motivations. the number of collected questionnaires was 145, out of which 142 were compiled enough to allow the application of the tcm. the size of our sample is small but it is comparable to other studies, as travel cost model estimation is less data demanding compared to stated preference surveys (champ et al., 2003). as an example, curtis (2002) surveyed a sample of 118 anglers for a travel cost estimation of salmon angling for the whole ireland. englin et al. (1997) used a sample of 120 respondents for the estimation of the recreational benefits of four american states (new york, vermont, maine and new hampshire). table 1 shows the descriptive statistics of the sample. surveyed tourists were 55% females and 45% males. respondents were mostly below 50 years old with a relatively high education, in fact, more than 50% of the sample had at least a bachelor degree. despite the high level of education, which is usually connected with an income higher than the average, most of the people declared a low-income. this apparent odd result may be due to the fact that most of the people are young, so they are still student or at their first job experience, as the age structure of the sample shows. the mean travel cost for reaching the destination was assessed to be 40.8 pln, while the average daily expenditure for additional goods and services (i.e. meals, accommodation) 128.5 pln. the average number of night overstay derived from the sample has been proved to be 5.5 per trip. through the questionnaire, it was possible to collect information regarding the main holiday motivation of the tourists visiting beskid zywiecki. the questionnaire 66 g. grilli contained a list of six typical holiday motivation in mountain areas (kozak, 2002) with the possibility to add other options. two people indicated working as a motivation for their overstay, so they were excluded from the sample. each respondent could mark more than one motivation. table 2 shows that the most cited activity is walking in the mountains (59.3 % of the sample), followed by ecotourism and visiting relatives. this result may indicate that the main source of recreation is nature, in particular forests, which are the main natural element, with its biodiversity. 2.3 the travel cost method the tcm is an evaluation technique, frequently used to value the recreational benefit of particular site (herath and kennedy, 2004; hill et al., 2014), proposed by harold hotelling for the first time in 1947 (h. hotelling, 1949) and then refined by clawson and knetsch (clawson m. and knetsch j. l., 1966). the method assumes that the costs sustained by visitors for visiting the site may approximate the value of their recreational experience (willis and garrod, 1991). another basic idea of the method is that people are travel cost-sensitive, meaning that the higher is the cost (and the longer is the distable 1. socio-demographic characteristics of the respondents. category profile n % mean median st. dev. min max income (pln) 0 -1500 1500-2500 2500-3500 3500-4500 4500-5500 5500-6500 6500+ 39 42 20 19 6 3 11 29.9 30 14.3 13.6 4.3 2.1 7.9 2.74 2 1.77 1 7 age 0 30 30-40 40-50 50-60 60+ 47 29 33 22 9 33.6 20.7 23.6 15.7 6.4 2.38 2 1.32 1 5 education primary high sc. bachelor master phd 9 60 21 43 7 6.4 42.9 15 30.7 5.0 2.85 3 1.08 1 5 gender male female 63 77 45.0 55.0 0.55 1 0.5 0 1 household 140 3.40 3 1.75 0 10 table 2. holiday motivation declared by respondents. holiday motivation frequency % visiting relatives 30 21.4 museums 7 5 walking 83 59.3 sport practising 15 10.7 ecotourism 40 13.57 sightseeing 19 5.7 67assessing price sensitivity of forest recreational tourists in a mountain destination tance travelled) and the smaller is the number of trips they make. the demand function is integrated with socio-economic characteristics and sometimes with environmental and site-specific considerations. the resulting demand curve models the number of trips to the recreational site as a function of the cost sustained for the travel and other characteristics: yi = f[(tci,ii,hi(di,vi,si)] where yi is the number of trips of the individual i, tci is the cost that the individual i per round-trip, ii is the individual income while hi is a vector of visitor-specific characteristics. hi may include information about alternative sites (si), study site (vi) and sociodemographic characteristics (di). the dependent variables i used in this paper are (1) the number of trips done in the last year and (2) the number of trips in the last 5 years. these take only non-negative values, so count data models are the most common approaches for the analysis (hellerstein, 1991), in particular the poisson and negative binomial (nb) regressions. the theoretical framework for the use of the poisson model for modelling recreational demand was provided by hellerstein and mandelsohn (hellerstein and mendelsohn, 1993). the authors state that the choice whether visiting or not a site can be described with a binomial distribution, converging to a poisson as the number of trips increase. the poisson distribution for the number of trips y is pr[y=y] = e y y !  y= 1,2,….n where µ is the rate parameter. the poisson distribution can be used in regression by explicating the relation between the mean parameter µ and the vector of x regressors. the usual approach is to use an exponential mean parametrization: µi=exp(x’β) i= 1,2...,n where x is the matrix of regressors and β the coefficients. the poisson regression is estimated through the maximum likelihood method, as all generalized linear models. the poisson model is equi-dispersed, meaning that the mean is equal to the variance. in many cases data are over-dispersed, i.e. the variance is larger than the mean. when data are over-dispersed and the sample is truncated the poisson model returns inconsistent estimates and a nb model should be used, as it adds an extra parameter controlling for overdispersion. the presence of overdispersion was tested with a log-likelihood ratio test that failed to reject the hypothesis of over-dispersion returning a non-significant p-value. the suitability of the poisson model for this case was also enforced when a nb model was tested, as the α parameter was not significant. for this reason, the following analyses continued with a poisson model. when data are collected on-site, there are two other characteristics of the sample that should be considered, truncation and endogenous stratification, for which both poisson and nb models can be corrected (shaw, 1988). truncation occurs because people with zero trips are not surveyed. endogenous stratification is instead related to the higher probability of sampling frequent visitors compared to tourists with only few trips in the 68 g. grilli timeframe. englin and shonkwiler (1995) showed that a poisson model can be corrected for both truncation and endogenous stratification simply replacing the response variable y with y-1. the model was all estimated using stata 12 (statacorp 2011). after the estimation of the econometric model, cs and elasticities can be derived. the cs per trip is estimated with the following formula: cs tc 1 where βtc is the parameter associated with the travel cost variable. elasticity of the demand to the cost of travel (ep) is computed in this way: e x x xp tc tc tc tc μ where xtc is the travel cost variable and μ the mean of the distribution. table 3 describes more in details the variables considered, together with the description and the expected effects. the fuel cost per round-trip was estimated by asking respondents the travelled distance from their starting point to the place where the interview took place. then the travel distance (in km) was multiplied per a cost per km of 0.4 pln, which is the average cost per km available in the official statistics. the number of days spent in the destination and socio-economic variables, including gender, education, occupation, income, education and number of people in the household represent the other covariates and were also collected through the questionnaires (section c). table 3. list of the explanatory variables used in the travel cost. variable code description expected effect* tc pln/trip (fuel) cost per round-trip tc_complete pln/trip average cost of one day including food, accommodation and other expenses n_days integer number average number of days per each trip income classes from 1 to 7 1 represent the poorest class, 7 the reachest + gender 0 1 male female age 1 0 older than 60 otherwise +/education classes from 1 to 6 1 is elementary education, 6 is for phd holders + household integer number number of people in the household + employed 1 0 full-employed otherwise * expected relationship between the explanatory variables and the number of individual trips. 69assessing price sensitivity of forest recreational tourists in a mountain destination 3. results and discussions the tcm results are summarized in table 4 and table 5, showing the econometric model and the welfare analysis, respectively. the cost of travelling towards the destination has a negative sign as expected and it is highly significant (p value lower that 0.001) in all the specified models, indicating that the number of visits decrease as the distance (and related cost) increase. the coefficient for tc_expense is also negative. the number of days of each trip has a negative sign suggesting that people making longer trips have fewer annual visits. age is also negatively connected with the likelihood of visiting the zywiec area, so young people contribute more to tourism and recreational activities. the income variable has a positive coefficient, therefore annual visits increases with higher incomes. income shows a very high significance (1% confidence level), which is not common in tcm studies (martínez-espiñeira and amoako-tuffour, 2008). the gender variable has a negative sign; since the male tourists were coded as 0 and females as table 4. results of the different poisson. poisson tc -0.0213*** (0.00205) tc_complete -0.000460** (0.000192) n_days -0.0164** (0.00769) age60more -0.250** (0.119) gender -0.542*** (0.0929) employed -0.266*** (0.101) education 0.161*** (0.0519) household 0.0630** (0.0299) income 0.0610*** (0.0131) constant 1.517*** (0.197) observations 142 aic 974.3 bic 1003.8 ll -477.13 standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01 70 g. grilli 1, the coefficient states that males are more likely to visit the beskid zywiecki range. people in full employment are less likely to visit the study area, maybe because of less availability of time. personal education is another important variable for describing tourism in the beskid zywiecki range, it has a positive and significant coefficient. tourists seem to be more willing to visit as their education increase. finally, the household variable has positive relationship with the number of visits, suggesting that larger household are more likely to visit. a possible explanation for this result could be that the beskid is a destination for families with children. we now move to the conventional welfare and policy measure, i.e. cs and elasticity, that are calculated from the coefficients of the cost variables. it is important to remember that, in order to extrapolate the welfare measures from truncated models, it has to be assumed that non-visitors have the same demand function as the visitors (hellerstein, 1991). welfare measures are summarized in 5. the polish currency (plz) was converted into € using an average exchange rate of 4.50 plz per euro for 2014 (i.e. when the survey was undertaken). the cs per visit using only the cost of travel (labelled ‘tc’ in table 5) is what is typically shown in tcm studies and it is estimated to be 10€ per visit. this result is comparable to other studies. for example, grilli et al. ( 2014) investigated recreation in mountain areas through a meta-analysis of studies, achieving a mean value of about 11 € per visit and an upper bound of 112€ per visit. the value is also lower than the one found by getzner in the tatra mountains (getzner, 2010), which represent the most important destination within the carpathians and therefore with a higher recreational potential. the cs per year is calculated multiplying the cs per one visit by the average number of trips of the sample, which is 4.6 per year. calculating cs using the total expenses sustained in the destination is less common in the forest recreation literature while it is more popular in the study of consumptive activities, such as fishing or hunting. this study assessed a cs per visit of about 480€ per day, which is comparable to that of fishing (curtis, 2002; curtis and breen, 2017) and lower than natural park tourism in the united states (martínez-espiñeira and amoako-tuffour, 2008). in addition to cs, what is interesting to notice is the elasticity of the demand. the demand appears to be inelastic in the first model (-0.12), suggesting that the number of visit is expected to make only minor variations when the cost for travel (mainly related to fuel) changes. at a practical level an increase of 10% of the average cost for travel would cause an average decrease of 0.05 trips per year. if the cost for travel doubles the number of annual trips decreases by only 0.55 (one trip less every two years). table 5. marginal consumer surplus and elasticity derived from the different models. model tc tc_complete cs per visit (plz) 47 2173 cs per year (plz) 216 9996 cs per visit (€) 10 480 cs per year (€) 46 2208 elasticity -.12 -.90 71assessing price sensitivity of forest recreational tourists in a mountain destination when in-situ expenses are also considered in the computation of the travel cost, the estimated elasticity becomes -.90. according to the conventional definition the demand is still considered inelastic but it is closer to one, which is the conventional threshold for the price elasticity of the demand to become elastic. this means that a 10% increase in the average cost of the trip causes a decrease of trips of 0.8, almost one per year. in the remote case that the average cost of travel doubles, people would do 4 trip less per year, i.e. they would not visit anymore. 3.1 implications although information derived from a convenience sampling should be read with care, this study provides useful information to policy-makers. mountain villages all over the world are facing problems connected with depopulation and the necessity to assure sources of income for the inhabitants. valorising the local natural resources for tourism may be an effective strategy to allow additional income generation. local communities might obtain larger profit from tourism (in terms of expenditures locally sustained for food, accommodation, technical equipment etc…) either increasing the number of annual visitors or increasing average prices. with respect to the first option, the close silesian district is one of the most populated areas in poland and represents an interesting basin of potential visitors, which could be reached with more intense marketing activities (vogt et al., 2018). the recent literature on tourism planning suggests that tourism development is perceived positively by local communities (coccossis, 2017; muresan et al., 2016) but raising the number of tourists is likely to increase relevant environmental impacts (lake et al., 2017; mccombes et al., 2015), therefore visitor management is fundamental to preserve the environment (gios and clauser, 2009). the second option to increase local incomes is to raise local prices. the high cs suggests that visitors would be willing to pay more than current amounts for a single visit because they obtain a large benefit from visiting beskid zywiecki. on the other hand, the elastic demand indicates that the number of annual trips could be lower if prices will be too high. therefore, the net effect of raising prices will be uncertain. such evidences suggest that there is not a unique strategy to develop the territory and decision makers should obtain as much information as possible to undertake an effective planning. 4. conclusions forest recreation is a valuable activity and the economic relevance should be carefully monitored. in this paper an investigation of recreational values of mountain forests was presented using a case study located in the polish carpathians, with a focus on price elasticity because this policy measure is not often considered. a travel cost model based on the poisson regression has been estimated using two different cost variables, the first capturing only the cost of travel and the second including also the cost for food and accommodation incurred on site. the estimated consumer surplus of 480€ suggested that there is space for local operators to increase prices and revenues, however the estimated price elasticity of -.90 suggests that visitors are sensitive to local expenditures and therefore local prices 72 g. grilli should be fixed with care, because they may cause a decrease in the number of annual visitors. there is a trade-off between the number of visitors and the expenditures they sustain in the territory, therefore local managers wishing to obtain higher revenues can hardly increase both and should carefully evaluate their preferred management strategy. being aware that a single case study is not enough to draw general conclusions, this study would like to raise the issue and encourage other researchers to further investigate price sensitivity in future recreational studies. 5. acknowledgements the present paper has been realized with the financial contribution of the european cost action eumixfor fp1206 (http://www.mixedforests.eu/). the author wishes to acknowledge the local forest districts, for the help in delivering the questionnaires, in particular to jaroslav jonkisz. the author wishes also to personally thank prof. jerzy lesinski, for his help in translating the questionnaire and for his useful suggestions in writing the manuscript. 6. references bhuiyan, m.a.h., siwar, c., and ismail, s.m. 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(1991). an individual travel-cost methof of evaluating forest recreation. journal of agricultural economics 1: 33–42. the future of bio-based and applied economics daniele moro1, fabio gaetano santeramo2, davide viaggi3 how did farmers act? ex-post validation of linear and positive mathematical programming approaches for farm-level models implemented in an agent-based agricultural sector model gabriele mack*, ali ferjani, anke möhring, albert von ow, stefan mann the impact of assistance on poverty and food security in a fragile and protracted-crisis context: the case of west bank and gaza strip donato romanoa, gianluca stefania,*, benedetto rocchia, ciro fiorilloa assessing price sensitivity of forest recreational tourists in a mountain destination gianluca grilli1,2 estimating a dual value function as a meta-model of a detailed dynamic mathematical programming model claudia seidel, wolfgang britz bio-based and applied economics 7(1): 87-98, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-24049 short communications sanitary and phytosanitary measures in the context of the cptpp agreement sofía boza faculty of agricultural sciences and institute of international studies, university of chile, santiago, chile. date of submission: 2017 16th, march; accepted 2018 15th, march abstract. the comprehensive and progressive agreement for trans-pacific partnership (cptpp) is a notorious example of the proliferation of so-called mega trade agreements. the countries constituting its signatory parties include five hundred million inhabitants and almost fifteen percent of the global gross domestic product. the objective of this paper is to analyze the role of sanitary and phytosanitary (sps) provisions within the cptpp regarding international food trade. three sections are presented: (i) food production, imports and exports among cptpp countries, (ii) the content of the sps cptpp chapter regarding the text of the wto-sps agreement and (iii) concluding remarks. it stands out among the results that there are significant differences in agricultural production capabilities between cptpp parties, which should be addressed in order to achieve the desired integration. keywords. mega trade agreements, agricultural trade, food safety, sanitary and phytosanitary measures, trans pacific partnership. jel codes. f13, f15, q17. 1. introduction the comprehensive and progressive agreement for trans-pacific partnership (cptpp) is a notorious example of the proliferation of so-called mega trade agreements. it was signed as trans pacific partnership agreement (tpp) initially on february 2016 by 12 pacific basin countries: australia, brunei darussalam, canada, chile, japan, malaysia, mexico, new zealand, peru, singapore, united states and vietnam, which altogether comprise almost eight hundred million inhabitants and 40% of the global gdp. tpp partners had two years after signing to ratify the agreement. in january 2017, the president of the united states, donald trump, withdrew the country from the agreement on his first day in office. after a few months of impasse, the rest of the tpp members decided to go ahead without the united states, signing the new version of the agreement in march 2018. the *corresponding author: sofiaboza@u.uchile.cl 88 sofía boza cptpp will come into effect 60 days after at least six of the signatory countries have ratified it. one of the chapters in the new cptpp that did not change at all from the tpp version is the chapter on sanitary and phytosanitary measures (sps). these technical, nontariff measures, which aim to protect food safety as well as animal and plant health, have been characterized in recent decades by their increased visibility, with various effects on agricultural trade flows. the objective of this paper is to analyze the sps provisions within the cptpp, considering the already existing regulatory framework under the agreement on sanitary and phytosanitary measures of the world trade organization (wto) and the agricultural sector profile of signatory countries. our research represents a relevant contribution to the discussion on the possible implications of cptpp for the agricultural sector, as the literature on the trans pacific partnership has been focused so far on its repercussions in general terms (unctad, 2016). 2. agricultural production and trade among cptpp members the cptpp partners are quite diverse regarding size, contribution to gdp and productivity of their agricultural sector. some countries, such as mexico, peru and vietnam, have labor intensive agriculture with low productivity per worker. australia, canada and japan, on the other hand, have low participation in agriculture in terms of total employment but remarkable productivity. these differences are mainly due to technological development and the ability to add value to the products. in fact, in peru and vietnam, small-scale family farming and even subsistence agriculture are still common, but the fast economic growth of both economies and the lack of profitability of family farming are table 1. cptpp partners’ general data on agricultural production (2014). au str ali a br un ei ca na da ch ile ja pa n m ala ys ia m ex ico n ew z ea lan d pe ru vi et na m total population (millions) 23.6 0.4 35.5 17.8 127 30.2 123.8 4.6 30.8 92.5 rural population (millions) 2.4 0.1 6.8 1.8 8.8 7.6 26 0.6 6.7 62 area harvested (millions ha) 36 0 66 4 12 100 61 1 11 49 area equipped for irrigation (1000 ha) 2,550 1 870 1,110 2,469 380 6,500 722 2,580 4,600 employment in agriculture (%) 3.3 2.4 10.3 3.7 12.6 13.4 6.6 25.8 47.4 agricultural value added per worker (constant us$) 52,701 83,868 6,638 50,720 10,127 4,416 28,677 1,949 489 food production value (200406 millions us$) 25,035 50 27,181 8,424 17,730 14,311 35,142 10,334 9,145 27,498 agriculture, value added (% gdp) 3 1 2 3 1 9 3 7 7 18 source: prepared by the author based on fao (2015). 89sanitary and phytosanitary measures in cptpp agreement motivating land abandonment. in vietnam, 56% of rural youth express a desire to migrate to big cities for work (the ahn and minh chanh, 2015). the contribution of agricultural products to total trade also differs considerably among cptpp partners. in some asian economies such as brunei, japan and singapore, the participation of the agricultural sector in exports is negligible (less than 2%). for new zealand, however, it represents more than half of total exports. for most cptpp partners, such as australia, chile, malaysia, peru and vietnam, the contribution of agriculture to exports is ten to fifteen percent. vietnam, in spite of the low agricultural productivity, is now the world’s leading coffee exporter. with respect to agricultural products as a percentage of total imports, the differences are not as significant among countries, with only a 10% gap between the minimum and the maximum. if we consider the size of each market, however, the situation changes. global imports of agricultural products to japan, canada and mexico alone constitute 58% of total cptpp agricultural imports. with regard to intra cptpp agricultural trade, the situation is even more dramatic. canada, japan and mexico concentrate 72% of imports of agricultural products from cptpp countries. 3. analysis of the cptpp sps chapter considering the wto sps agreement the cptpp agreement declares that one of its objectives is to reinforce and build on the sps agreement (art. 7.2.b). from its overture, however, it shows some substantial differences with the wto-sps agreement with regards to its approach to sps issues. the cptpp agreement stresses the importance of preserving compatibility between sps measures and trade. in fact, it establishes within its objectives the protection of human, animal figure 1. contribution of agricultural and non-agricultural products to total exports (%, 2005-2015). source: prepared by the author based on wits. 90 sofía boza and plant life or health, as the sps agreement does, but while facilitating and expanding trade (art. 7.2.a). the wto-sps agreement merely states that sps measures must not constitute a disguised restriction on international trade. figure 2. contribution of agricultural and non-agricultural products to total imports (%, 2005-2015). source: prepared by the author based on wits. figure 3. destination markets of intra cptpp agricultural exports (2014). source: prepared by the author based on wits. 91sanitary and phytosanitary measures in cptpp agreement one of the strategies posed in the cptpp agreement to reduce the potential impacts of sps in trade is to strengthen communication, consultation and cooperation between the parties (art. 7.2.c). for this, as well as for the general supervision of the parties’ implementation of the provisions in the chapter, the cptpp agreement establishes its own committee for sps measures. the main functions of this committee are to: i) act as a forum for the parties on sps matters, ii) identify and develop cooperation projects on sps within the parties and iii) share issues and positions for the meetings at the wto committee on sps measures and at the three international standard-setting organizations recognized by the wto-sps agreement, including codex alimentarius commission, the world organization for animal health and the international plant protection convention. with regards to the third function, suppan (2015) suggests that, despite these consultations being voluntary, it would be difficult for the related authorities to ignore them if the country’s representatives want to give the impression that they are enhancing cooperation. increased communication between cptpp parties also relies on transparency provisions. a particularity of the cptpp agreement is that it highlights the importance not only of sharing information between the parties, but also with “interested persons”, giving both the opportunity to comment on their proposed sanitary and phytosanitary measures (art. 7.13.1). this suggests that the cptpp agreement seeks to facilitate the inclusion of parties’ private sectors in the conception of sps measures; which would be consistent with the large amount of interest and support that food industry representatives gave to tpp sps chapter negotiations in the us (johnson, 2014). in fact, in the us, a high level of participation already exists among companies, public opinion and interest groups in the development of the country’s sps measures (ustr, n.d.). furthering the goal of a discussion of sps measures beyond the institutional level, another addition to the provisions of the wto-sps agreement is that cptpp parties shall make available to the public, by electronic means in an official journal or on a website, the proposed sanitary or phytosanitary measure (…) the legal basis for the measure, and the written comments or a summary of the written comments that the party has received (art. 7.13.5). electronic publication is also mandatory for the final version of the sps. these requirements are also recommended by the wto. in the 2008 document “recommended procedures for implementing the transparency obligations of the sps agreement” (g/ sps/7/rev.3) the wto encourages the submission of an electronic version of the draft regulation along with the traditional notification format. the cptpp goes further, making electronic communication mandatory and expanding its scope. another particular provision of the transparency within the cptpp sps chapter is that it enhances the communication between parties beyond the notification of sps measures, through their competent authorities and contact points. the information that parties exchange is related to: i) detected sps risks of exports from the other party’s territory, ii) relevant changes in the sanitary or phytosanitary situation in all or part of the exporting party which may affect the existing trade, iii) research progress possibly impacting sps regulation and iv) significant changes in the party’s food safety and pest and disease management policies, as well as related practices that have the potential to affect trade. the wto sps agreement also enhances members’ communication through national enquiry points. however, the cptpp focuses this communication on the triggers for the generation of sps measures, seeming to push for the cptpp parties to anticipate complex, pos92 sofía boza sible, forthcoming scenarios related to sps regulation and procedures of the other parties in order to adapt and avoid negative impacts on trade flows. in fact, the cptpp agreement gives relevance not only to the process through which measures are communicated, but also to their conception. like the wto-sps agreement, preference is given to adherence to international standards. however, when a party decides to develop its own sps measures, different from the international ones, the cptpp agreement establishes that they must be based on documented and objective scientific evidence (art. 7.9.2). the use of the adjectives “documented and objective” instead of “available”, as in the wto-sps agreement, means that the precautionary principle (e.g., as in sps art. 5.7), by which the existence of a possible risk has to be considered, is undermined (labonté et al., 2016). another particularity in the cptpp agreement on the topic of transparency is that parties give to other parties, but also interested persons, the possibility to comment on their risk analysis. this is another sign of the aim to facilitate the inclusion of interested groups in sps measures development. the problem is that, given the technical complexity of risk analysis, it is possible that only resourceful counterparts and their operators or interested groups will be able to make informed comments. for the cptpp parties, this could amplify the current gap in regulatory performance on sps that already exists between wto developed and developing countries due to their different scientific capabilities (boza and muñoz, 2017). in this sense, the cptpp sps chapter assumes that every party, and in this case also their interested groups, had a similar infrastructure “for doing science”, which is not factual whatsoever (strether, 2015). the approach that the cptpp sps chapter adopts for the cooperation between parties is quite different from the technical assistance and special and differential treatment provisions of the wto-sps agreement, materialized, for example, in the standards and trade development facility. the cptpp focuses on cooperation in terms of facilitating trade and exchanging information, but is not very specific on what comprises technical assistance. in fact, it establishes that the objective of cooperation in sps is eliminating unnecessary obstacles to trade between the parties (art. 7.15.2). an important means to facilitate trade is the recognition of equivalence of other parties’ sps measures. in this sense, the cptpp agreement goes further than the wto-sps agreement (sps-art. 4), as it establishes that, beyond the specific measures, the parties shall apply equivalence to a group of measures or on a systems-wide basis (art. 7.8.1). the recognition of equivalence starts at the request of the exporting country, which is followed by an assessment carried out by the importing country. this evaluation has to be based on available knowledge, information and relevant experience, as well as the regulatory competence of the exporting party (art. 7.8.5). the last criterion, “regulatory competence”, can be especially challenging, as it is difficult to quantify, and can lead to different interpretations. a measure, group of measures or systems wide basis is considered equivalent when it achieves the same level of protection as the importing party’s measure; or has the same effect in achieving the objective as the importing party’s measure (art. 7.8.6). these requirements are more specific than those in the wto-sps agreement, which considers a measure equivalent if it guarantees an “appropriate” sanitary or phytosanitary protection level for the importer. an additional way to facilitate trade is “regionalization”, a principle in the wto-sps agreement and also explicitly recognized in the cptpp sps chapter. the cptpp pro93sanitary and phytosanitary measures in cptpp agreement cess declaring pest or disease-free areas, and areas of low pest or disease prevalence is very similar to the one specified for the equivalence assessment. it has to be requested by the exporting country, evaluated by the importing country and maintain a continuous exchange of information during the procedure. in this case the cptpp agreement is quite similar to the wto-sps agreement and wto-sps committee guidelines. as already mentioned, transparency is one of the principles that the cptpp sps chapter tries to enhance the most. to that end, another novelty proposed in the cptpp is the auditing of the competent authorities and inspection bodies by the other parties. that procedure is not mentioned in the wto sps agreement. the objective of audits is to determine an exporting party’s ability to provide required assurances and meet the sanitary and phytosanitary measures of the importing party (art. 7.10.1). before the audit starts, both the auditing and the audited parties will discuss the objectives, scopes, requirements to be assessed, and the procedures to assess them. the audit process does not imply a moratorium on the establishment of new sps measures. the results of the audit will be known by the audited party, which can make comments that have to be considered by the auditing party in the preparation of the final report of the conclusions of the process. meanwhile, the information generated during the auditing procedures will not be released to the general public. the costs of the audit will be borne by the auditing party, unless both parties decide otherwise. the cptpp agreement allows the auditing party to take decisions or actions considering the results of the audits. however, those decisions have to be based on objective evidence and data that can be verified, taking into account the auditing party’s knowledge of, relevant experience with, and confidence in, the audited party (art. 7.10.6). it is important to consider that the generation of “objective evidence and data” requires an adequate level of technical capabilities that are specialized in sps issues. as we have already mentioned, the costs of the process are assumed by the auditing party. it is therefore reasonable to wonder whether this mechanism will be used much more frequently by cptpp parties with the lowest specialized human resource constraints. additional interesting innovations in the cptpp sps chapter are related to the procedures for the inspection of imports. first, if required, parties have to exchange complete information about the character, frequency and criteria of their inspections. the cptpp agreement establishes that parties can adjust the frequency of inspections considering past experience, as well as “actions or discussions” under the agreement. according to the cptpp agreement, if a party decides to refuse the import of a good from another party, it has to notify the importer or its agent; the exporter; the manufacturer; or the exporting party at the very least (art. 7.13.6). that notification has to be communicated no later than seven days after the date of the decision (art. 7.13.7) containing the reasons for the refusal, the legal basis and the situation of the rejected goods. one important thing to note is that, according to this provision, the party refusing the shipment is not obligated to communicate its decision to the party from which it proceeds, but only to the producer or to the trader. again, the cptpp is encouraging the role of the private sector. it allows the affected party to request a review of the decision, providing any relevant information during the process. that review process has received some criticism that considers it to be a mechanism that allows a sort of “state-to-state” or “business-to-state” dispute (food and water watch, 2015). 94 sofía boza the cptpp sps chapter also establishes parallel mechanisms to those under the wto sps agreement. for example, the cooperative technical consultations (ctc) can be used by a party whenever there is an sps matter that can potentially affect its trade and cannot be solved administrative or bilaterally. the ctc process is initiated when the concerned party presents its request in writing and the responding party acknowledges receipt. both parties have to meet within 30 days and attempt to resolve the matter within 180 days. the documents generated during the ctc remain confidential, unless the parties agree otherwise. this concealment follows an aim of protecting confidential business information, but neglects that the sps objective, i.e. protection of public, animal and plant life and health, is of a collective nature (suppan, 2015). when parties are not able to arrive at a solution within the ctc, the concerned party can use the cptpp dispute settlement procedure, one of the most significant novelties of the agreement. this dispute settlement will begin operating progressively: for disputes related to equivalence principle, audits or import checks the procedure will be available one year after the agreement comes into effect for the responding party; for disputes related to science and risk analysis, two years later. there are specific provisions on equivalence and risk analysis that the cptpp explicitly excludes from the dispute settlement. in the first case, that parties have to recognize the equivalence of an sps when it has the same effect in achieving the objective as the importing party’s measure (art. 7.8.6.b.). the second is the already mentioned article 7.9.2., according to which parties have to assure that their sps measures follow international standards, guidelines or recommendations or, if not, that they are based on documented and objective scientific evidence that is rationally related to the measures. these exceptions within the scope of disputes are not present in the wto. the details of how the cptpp dispute settlement will operate are described in article 28 of the agreement. one of the most interesting features is that the deadlines for each stage of the dispute process are specified and are quite constraining. for example, once the panel has been fully established, it has 150 days to deliver a preliminary report on the case and another additional 30 days to present the final report to the disputing parties. if these terms were followed and are not regarded as a hopeful declaration of intentions, they would be much shorter than what is common for the wto dispute settlement. that seems to be one of the main motivations for the establishment of the cptpp dispute settlement. in any case, the cptpp allows for the concurrent use of both the wto and cptpp dispute settlements. finally, indirectly related with future sps disputes under the cptpp is the inclusion of the “trade of products of modern biotechnology” in the national treatment and market access for goods chapter. this means that any controversy between cptpp parties related to biotech food products, genetically modified organisms (gmo’s) included, will be primarily approached via the cptpp dispute settlement considering principles of market access, rather than those of sanitary or phytosanitary protection. this is a completely different scenario than the 2003 wto dispute on the european union moratorium on the import of biotech products. in that case, the panel decision and aspects of the procedure were exclusively based on the wto-sps agreement. 95sanitary and phytosanitary measures in cptpp agreement 4. concluding remarks the tpp agreement is a paradigmatic example of a mega trade agreement given the high proportion of the world economy that is included. the economic weight and level of development of cptpp partners is heterogeneous, however. if we focus on the agricultural sector, we can also see important differences in both production and trade patterns; for instance, the level of productivity, which is expected to be largely related to technical capabilities. the sps-cptpp chapter has a stated goal of higher integration between partners. however, all of the above differences can make achieving this difficult. particularly, dissimilarities in technical capabilities must be taken into account, as they are essential in the context of sps. thus, although the chapter provides equal rights for all members, the power to properly exercise some of those rights seems very unequal. an important consequence of this is that the developed partners might override the rest. the tpp sps chapter also encourages the participation of companies in the discussion related to partners’ sps measures. although this can be very positive because companies are directly related to compliance with sps, those in the most developed economies might have higher technical and human capabilities. it is therefore strongly recommended to consider mechanisms for technical assistance in sps issues among the countries in the cptpp, which are not limited to information exchange only, but also balance capabilities to a greater extent. acknowledgement research for this paper was funded by the swiss state secretariat for economic affairs under the seco/wti academic cooperation project, based at the world trade institute of the university of bern, switzerland. the author would like to express her gratitude to dr. christian häberli from world trade institute for his valuable suggestions. references boza, s. and fernandez, f. (2016). world trade organization members’ participation in mechanisms under the sanitary and phytosanitary agreement. international journal of trade and global markets 9(3): 212-227. boza, s. and muñoz, j. (2017). factors underlying sanitary and phytosanitary regulation for food and agricultural imports notified by wto members. the journal of international trade & economic development 26(6): 712-723. fao (2015). fao statistical pocketbook. world food and agriculture. rome, italy: fao. food and water watch (2015). the tpp attack on commonsense food safety standards. http://www.safsf.org/wp-content/uploads/2016/03/fww-tpp-food-safety-analysis. pdf. accessed 13 june 2016. johnson, r. (2014). sanitary and phytosanitary (sps) and related non-tariff barriers to agricultural trade. washington dc: congressional research services. labonté, r., schram, a. and ruckert, a. (2016). the trans-pacific partnership: is it everything we feared for health?. international journal of health policy and management 5(8): 487-496. 96 sofía boza strether, l. (2015). tpp’s orwellian definition of “science” in its sanitary and phytosanitary (sps) chapter. http://www.nakedcapitalism.com/2015/11/tpps-orwellian-definition-of-science-in-its-sanitary-and-phytosanitary-sps-chapter.html. accessed 22 june 2016. suppan, s. (2015). the tpp sps chapter: not a “model for the rest of the world”. institute for agriculture and trade policy. the ahn, d. and minh chanh, n. d. (2015). family farming and farmland policy in vietnam: current situation and perspective. selected paper prepared for presentation at the fftc-mardi international seminar on cultivating the young generation of farmers with farmland policy implications, serdang, selangor, malaysia, may 25-29. unctad (2016). exploring new trade frontiers: viewing the trans-pacific partnership agreement through an agriculture lens. geneve, switzerland: unctad. ustr. tpp made in america. sanitary and phytosanitary measures. https://medium.com/ the-trans-pacific-partnership/sanitary-and-phytosanitary-measures-139878f69771#. rsm791exe. accessed 21 june 2016. vol 18, no 1 (2018) table of contents article book review reseña de la monografía economía y comercialización de los aceites de oliva. factores y perspectivas para el liderazgo español del mercado global samir milli 183-186 universitat politècnica de valència e-issn: 2174-7350 issn: 1578-0732 https://doi.org/10.4995/earn assessing food retail competitors with a multi-criteria gis-based method norat roig-tierno, amparo baviera-puig, juan buitrago-vera, carmen escriba-perez 5-22 the performance of spanish wine exports in international markets francesc j. cervera ferrer, raúl compés lópez 23-48 impact of trade and international transport on environmental quality, a study in latin american and caribbean countries christian martín garcía 49-78 choosing not to choose: a meta-analysis of status quo effects in environmental valuations using choice experiments jesus barreiro-hurle, maria espinosa-goded, jose miguel martinez-paz, angel perni 79-109 product-country image and crises in the spanish horticultural sector: classification and impact on the market m. mar serrano-arcos, juan carlos pérez-mesa, raquel sánchez-fernández 111-133 spatial analysis of agricultural land prices baldomero segura, inmaculada marqués pérez 135-159 is the place of purchase important in shaping consumers’ preferences towards olive oil? héctor corbeto-fabón, zein kallas, josé m. gil 161-182 bio-based and applied economics 7(3): 233-247, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-7677 corporate r&d and the performance of food-processing firms: evidence from europe, japan and north america heinrich hockmann1, pedro andres garzon delvaux2,*, peter voigt3, pavel ciaian2, sergio gomez y paloma2 1 leibniz institute of agricultural development in central and eastern europe (iamo), germany 2 joint research centre (jrc), european commission, spain 3 dg ecfin, european commission, belgium date of submission: xxxx xxth, xxxxxxxxxx; accepted xxxx xxrd, xxxxxxxx abstract. this paper investigates the impact of corporate research and development (r&d) on firm performance in the food-processing industry. we apply data envelopment analysis (dea) with two step bootstrapping using a corporate data for 307 food-processing firms from the eu, us, canada and japan for the period 1991-2009. the estimates suggest that r&d has a positive effect on the firms’ performance, with marginal gains decreasing in the r&d level as well as the performance differences are detected across regions and food sectors. r&d investments in food processing can deliver productivity gains, beyond the high-tech sectors generally favoured by innovation policy. keywords. corporate r&d, dea, double bootstrapping, food-processing industry. jel codes. o30, l66. 1. introduction both the theoretical and empirical literature established that r&d is critical for firm productivity growth. for example, the empirical literature has found that between 1% and 25% of variance in the actual productivity across firms can be explained by differences in r&d investment (hall et al., 2010). however, there is considerably less agreement on the size of the r&d impact on the firm’s productivity (e.g. the size of marginal impact, diminishing vs. increasing returns to r&d). existing analysis of the implications of r&d mainly focus on knowledge-intensive businesses; there are less studies covering r&d and innovation in lowmedium-tech sectors such as food-processing. the literature is highly scattered in the field of agro-food sector ranging from conceptual analysis, system-oriented approach analysis (e.g. jongen and meulenberg, 2005; oecd, 2012, 2013) to public r&d in agro-food sector (alston, *corresponding author: pedro.garzon-delvaux@ec.europa.eu 234 heinrich hockmann et alii 2010). analyses on public r&d and its impact on primary agriculture production are more numerous given that the relevant data is more accessible. conversely, much less effort has poured into the private r&d even though it probably represents the largest share of the overall sector’s r&d (e.g. 59% in japan, 51% in us according to alston et al. 2010). furthermore, the firm level studies seldom focus on specific aspects of r&d (e.g. adoption, product variety). most are case studies with a limited regional or sectorial coverage (e.g. one country, part of the sector). broader quantitative analyses are limited by data measurement and availability constraints. the food-industry is usually considered to be a medium to low r&d intensity sector representing around 0.27 % of the total output in the eu agro-food industry (fooddrinkeurope, 2015) compared to other sectors such as the automobile (5.5%) or pharmaceutical (13.1%) industries (hernández et al. 2015). this is understood, among others, to be related to the fact that the agro-food sector is dominated by smes which do little research, many innovations are often derived from other input sectors and thus are incorporated in machinery, packaging and other manufacturing supplies (e.g. menrad, 2004) as well as many food-products are rather easy to imitate with significant r&d spillovers which reduces firms’ incentive to invest in r&d (gopinath & vasavada, 1999). although this general patterns may hold, the agro-food industry shows a high heterogeneity in the r&d intensity (avermaete et al., 2003; winger and wall, 2006; feigl and menrad, 2008; capitanio et al., 2010). there is a strong geographic heterogeneity in the level of private r&d. heterogeneity is also present in the type of innovation among firms: process, product, or organisational innovation. finally, it is important to mention that firms also differ whether they invest in r&d externally or internally. the objective of this paper is to contribute to this literature by providing empirical evidence on the impact of private (corporate) r&d on productivity of food-processing firms. more specifically, we analyse the size of firm inefficiency and explore the determinants of the inefficiency against the frontier production function using a unique corporate data set of food-processing firms from the eu, us, canada and japan for the period 19912009. to derive productivity parameters, we apply data envelopment analysis (dea) with two step bootstrapping which allows us to correct the bias in (in)efficiency and generate unbiased estimates for (in)efficiencies. 2. methodology to estimate the impact of private r&d on firm productivity we adopt a two-step approach. first, we use dea to estimate firm performance (inefficiencies). second, we run regression to explain the determinants of firm inefficiencies on a set of explanatory variables including private r&d. different approaches have been applied in the literature to identify production frontiers using both parametric and non-parametric methods. here we adopt a non-parametric approach dea with two step bootstrapping (simar and wilson, 2007). the advantage of dea is that it does not require imposing assumption on the functional form of the frontier, there are no restrictions regarding the number of parameters required, it is relatively easy to deal with a whole range of inputs and outputs, and inputs and outputs can have very different units. however, in general, some limitations remain in terms of con235corporate r&d and the performance of food-processing firms sidering time series, sensitiveness to outliers, demanding to incorporate (nonparametric) statistical inference, etc. methodologically, however, the assumption of a common frontier across countries and sectors is a sensitive issue potentially leading to biased results (koop et al., 2000; limam and miller, 2004; orea and kumbhakar, 2004). this paper avoids assuming a common technology across sectors by estimating at industry-specific technology level. a frontier production function, in general, defines the maximum output achievable, given the current production technology and available inputs. we estimate dea model in the formulation of output distance function: δ̂ i = δ i(x,y |t ) = max δ > 0 |δyi ≤ yλ, xi ≤ xλ, i'λ = 1{ } (1) where δi is inefficiency parameter of firm i, yi is output; δyi is maximum output achievable (frontier), xi and x are inputs; λ are weights used to construct the virtual producer (frontier). the main idea of dea is to find virtual firm (combination of other firms) capable of producing more output for the given inputs. in the second stage, the inefficiency parameters are regressed on a set of explanatory variables, zi, to estimate the determinants of inefficiency: δ i = ziβ + ε í ≥1 (2) where β are parameters to be estiamted and εi is an independent and identically distributed (i.i.d.) error term. for estimation, δ i has to be replaced by δ̂ i (the estimated efficiency scores from the first stage): δ̂ i = ziβ +ξí ≥1 (3) usually a tobit regression is applied to estimate the parameters of β. this procedure become necessary because the error term ei is truncated and not symmetrically distributed with mean zero. examples of the z variables – and as such also used in this study – are r&d intensity, capital intensity, time, country dummies (capturing different institutional settings), etc. simar and wilson (2007) point to several problems with this approach and advocate for the use of a truncated regression, instead. the δ̂ i are serially correlated in an unknown way since each δ̂ i depends on all observation in t. thus the δi are not independent of each other which induces biased estimates in the second step since the usual assumption regarding the error term does not hold. moreover, since xi and yi are correlated with zi (otherwise the second step would make no sense), zi is correlated with ξi. the correlation disappears asymptotically, however, at a very slow rate. as a solution to this bias they suggest a two-step bootstrap algorithm (simar and wilson, 2007). first, we correct the bias in (in)efficiency (in dea). second, we get unbiased 236 heinrich hockmann et alii estimates for (in)efficiencies (in the truncated regression). that is, the bootstrap allows to bias-adjust coefficient estimates and also for calculating proper confidence intervals for the statistical inference. bootstrapping tends to affect the structure of the data, potentially generating other forms of bias through an ‘over-manipulation’ of the data. a possible alternative is to develop an instrumental variable to control for the bias. however, this alternative was not seen as operational taking in consideration the available data. 3. data and variables considering strengths and limitations of several potential sources of data,1 standard & poor’s (s&p) compustat data set (s&p, 2014) was favoured which contains data at firm level collected from companies’ audited annual/quarterly reports. the selection process of firms from the available population of companies entailed several steps. the first consisted in retrieving firms classified as belonging to agriculture (industry code: 0xxx) as well as those to the food-industry (industry code: 2xxx); covering the period 1991-2009. data had to cover revenue, sales, net income, capital and r&d expenditures (if any); number of employees and/or wage sum, industry code, and region/ country (i.e. info on the location of the company’s headquarter/where it is registered). however, as most companies from agriculture did not report r&d expenditures, they were dropped from the final sample. labour input is critical when considering firm performance. in the case of missing ‘number of employees’ but available labour expenditures, the number of employees was approximated by using average wage levels taken from international labour organisation (ilo) and for values of labour costs vice versa. the dataset does not allow distinguishing whether r&d was conducted domestically or abroad. all companies’ r&d expenditure was assigned to the country where the company is registered. the dea approach applied in this paper is sensitive to outliers. moreover, presuming a common production frontier for companies across countries implicitly assumes that all companies have access to the same technology and produce under virtually the same technological restrictions. hence, reducing the sample to a sub-sample comprising of rather homogeneous countries/companies appeared advisable in order to ensure widely unbiased empirical results. outlier observations, however, still need to be excluded from the sample. after carrying out a final outlier check (checking for consistency and order of magnitude across observations as well as along the time series) some further firms/observations had to be dropped. thus, outliers were excluded based on the results of grubbs’ tests centred on the sectoral average growth rates of firms’ r&d stock intensity (k/revenue) over 1 for instance, the amadeus database may contain sufficient cross-section and time series firm level data, but provides information on r&d (if at all) only for very recent years. the presumed emergence of the food-processing sector as medium-tech, evolving from formerly low-tech, could not be investigated accordingly based on such data. another possible source of data could be the eu industrial r&d scoreboard (released by ec joint research centre). this database comprises of fully consolidated firm level data of top r&d investors in europe and elsewhere (year of last audited report + 3 years back in time). however, among the listed companies, there are too few belonging to the food-industry. 237corporate r&d and the performance of food-processing firms the investigated period.2 moreover, some further observations were dropped for reasons related to the computation of the r&d and capital stocks. in accordance with the literature (see hulten, 1991; jorgenson, 1990; hall and mairesse, 1995; bönte, 2003; parisi et al., 2006), stock indicators (rather than flows) were used as impact variables. it is thus implicitly assumed that a firm’s productivity is affected rather by the cumulated stocks of capital and r&d expenditures and not only by current or lagged flows.3 accordingly, our main impact variable is a firm’s r&d stock (k) and the second impact variable is ‘capital expenditures’ (c) captured as capital stocks. by considering the per capita values of these variables (i.e. per number of employees), it allows us both to standardise the data and to eliminate firms’ size effects (see, for example, crépon et al. 1998). in this framework, knowledge (r&d) and physical capital stocks were computed using the perpetual inventory method based on the following formulas: kt0 = r&dt0 gs,c(k )+δ (4) kt = kt−1 ⋅(1−δ )+ r&dt with t = 1991, … , 2009 (5) ct0 = it0 gs,c(c)+φ j and (6) ct = ct−1(1−φ)+ it (7) where r&d is r&d expenditure and i is gross investment (capital expenditure). the oecd anberd and the oecd stan databases were used to provide growth rates g(k) and g(c) for k and c, respectively. we computed the compounded average rates of change in r&d and fixed capital expenditures in the food-processing sector and per country (c). for some european countries the mentioned databases did not report or allowed calculating specific growth rates for r&dand capital-stocks. the corresponding european averages were assumed in these cases instead. for the us, canada, and japan, however, the growth rates were taken from the literature.4 in general, different depreciation rates (δ) and (ϕ) for k and c should be assumed depending on whether the industry is high-, medium-high, medium-low/low-r&d intensity. in fact, more technologically-advanced sectors are characterised (on average) by short2 grubbs’ test – also known as maximum normalised residual test – assumes normality (which is a desirable property anyway). accordingly, we ran normality tests on the relevant variables (assumption was never rejected). 3 using cumulated r&d and capital stocks – as in the previous relevant literature – overcomes a potential endogeneity problem which can arise if flows are used. 4 for capital growth from oecd (capital services, total; mean percentage change 1985-2009; see: http://stats. oecd.org/index.aspx) and for r&d growth rates the average over the period 1980-1998 was taken from (http:// www.ulb.ac.be/cours/solvay/vanpottelsberghe/resources/dgbvp_oes.pdf 238 heinrich hockmann et alii er product life cycles and by a faster technological progress which together accelerates the obsolescence of the current knowledge and physical capital. in this light, ortega-arquiles et al. (2009) suggested sectoral depreciation rates of 20%, 15% and 12% to the knowledge capital and 8%, 6% and 4% to the physical capital respectively for the high, medium-high-, and medium-low/low-tech sectors, with the latter (δ=12%, ϕ=4%) to be applied here to the foodprocessing industry. these are similar to the 15% and 6% commonly used in the literature (musgrave, 1986; nadiri and prucha, 1996; pakes and schankerman, 1986; hall, 2007). all variables in monetary units were transformed into 2007 euro using the end of year exchange rate. in cases where no direct exchange rate to euro was provided by compustat, for a certain year, the corresponding currency was transferred into usd first and then into euro. after processing the data, the sample used in this paper consists of 307 companies (2948 observations) for the period 1991-2009 registered in either of the following country groups: eu (557 observations), north america (usa and canada, 1,050 observations), and japan (1,341 observations), as shown in table 1. europe is less represented than japanese and north-american counterparts. there is no information on japanese firms prior to 1999 and most regions are less represented for this period. however, the period starting in 2000 is more balanced, including for europe. to control for this data structure we use a dummy variables in our estimations to distinguish these two periods. as shown in table 1, there is observed significant heterogeneity among the 307 firms. the mean number of employees varies between 2211 in japan to 15293 in the eu. nevtable 1 descriptive statistics of main variables variable mean std. dev. min max firms obs. total sample 307 2948 revenue 2308.3 5192.3 0.4 51514 cogs-costs 1443.5 3295.6 0.4 47137 r&d expenditure 89.7 451.7 0 7290.3 capital expend. 1286.4 2996.3 0 25846 employees 10610 31443 2 486000 eu companies 85 557 revenue 2705.8 6602.6 0.4 51514 cogs-costs 1561.2 3323.9 0.4 22873 r&d expenditure 175.6 926.9 0 7290.31 capital expend. 1768.9 4020.4 0 25846 employees 15292.7 36441.3 2 269000 us & canada 79 1050 revenue 3684.8 6607 1.7 50659 cogs-costs 2309.5 4578.3 1 47137 r&d expenditure 72.5 266.4 0 2476 capital expend. 1839.9 3584.2 0 24759 employees 18054 43375 2 486000 japan* 143 1341 revenue 1065.3 1983.5 5 15913 cogs-costs 716.6 1330.7 2 9785.7 r&d expenditure 67.5 181.5 0 1642.2 capital expend. 652.6 1497.7 0 13127 employees 2211 4203 16 36554 *(1999-2009 period only) table 2 sample composition observations per subsector subsector codes no. observations beverages, including alcohol 2080-2087 561 mixed/generalist 2000 490 prepared foods 2090-2099 491 meat and poultry packing 2010-2015 272 sugar and confectionery 2060-2068 252 canned fruits and vegetables 2030-2038 225 grain 2040-2048 226 bakery 2050-2053 197 dairy 2020-2026 18 oils 2070-2079 116 total 2948 239corporate r&d and the performance of food-processing firms ertheless, in each macro region, apparently, there are also a number of small and even micro-companies. it has to be stressed that the final sample gathers rather large companies, inherent with stock listed company data. this entails that results cannot be easily generalised as rather small private companies operating in the food-processing sector are not captured, but should be considered pertinent to large firms which, in fact, are inclined to be more active in terms of r&d. also, this kind of “pick the winner” effect might be particularly severe in medium and low-tech sectors (like food-processing), where the overall company population tends to be dominated by smaller firms which scarcely engage in r&d investment (becker and pain, 2002). the sample mean of r&d-intensity (r&d/sales) is above 1% in all macro-regions with the eu reporting the highest rate (~6%). this would allow classifying the companies/ sector as medium-tech (even medium-high), according to the commonly applied classification (hatzichronoglou, 1997). considering the median r&d-intensity rather than the mean, the r&d/sales ratios do not change significantly in magnitude in europe and the us/can, but they drop below 1% in japan. however, in the eu and the us/can only a few firms perform r&d at all (but those which do, however, have significant spending), while in japan most companies are engaged in r&d activities but modestly at individual level. in general, the companies active in the food-processing sector in the eu and in the us/ can seem to be fairly similar: eu companies are, in average, a little smaller in terms of revenue (sales) and number of employees but have almost exactly the same ratio of net income/ revenue as those from us/can and also comparable figures in terms of spending on r&d and capital (including their accumulated stocks). in contrast, japanese firms appear smaller and less profitable, more inclined to do corporate r&d, but, in average, at a lower financial (table 1). these differences between macro regions need to be taken in consideration when interpreting the estimated results and performing cross-country comparisons. in terms of sub-sector representation, observations from beverages companies are the most present followed by mixed-activity or generalist food-processing firm and prepared foods, accounting for 53% of the total sample. the remaining subsectors account individually between 4% and 9% the dairy sub-sector which is marginally present in the sample (table 2). table 1 descriptive statistics of main variables variable mean std. dev. min max firms obs. total sample 307 2948 revenue 2308.3 5192.3 0.4 51514 cogs-costs 1443.5 3295.6 0.4 47137 r&d expenditure 89.7 451.7 0 7290.3 capital expend. 1286.4 2996.3 0 25846 employees 10610 31443 2 486000 eu companies 85 557 revenue 2705.8 6602.6 0.4 51514 cogs-costs 1561.2 3323.9 0.4 22873 r&d expenditure 175.6 926.9 0 7290.31 capital expend. 1768.9 4020.4 0 25846 employees 15292.7 36441.3 2 269000 us & canada 79 1050 revenue 3684.8 6607 1.7 50659 cogs-costs 2309.5 4578.3 1 47137 r&d expenditure 72.5 266.4 0 2476 capital expend. 1839.9 3584.2 0 24759 employees 18054 43375 2 486000 japan* 143 1341 revenue 1065.3 1983.5 5 15913 cogs-costs 716.6 1330.7 2 9785.7 r&d expenditure 67.5 181.5 0 1642.2 capital expend. 652.6 1497.7 0 13127 employees 2211 4203 16 36554 *(1999-2009 period only) table 2 sample composition observations per subsector subsector codes no. observations beverages, including alcohol 2080-2087 561 mixed/generalist 2000 490 prepared foods 2090-2099 491 meat and poultry packing 2010-2015 272 sugar and confectionery 2060-2068 252 canned fruits and vegetables 2030-2038 225 grain 2040-2048 226 bakery 2050-2053 197 dairy 2020-2026 18 oils 2070-2079 116 total 2948 240 heinrich hockmann et alii 4. results 4.1 the size of inefficiency an output-oriented efficiency model (variable returns to scale-vrs) was run with a simple specification made of one output and three inputs. inputs consist of capital stock (c), labour (number of employees, e) and total cost of goods sold (cosd). the output was measured as the value of total revenues assumed to be total food related sales, although firms may have sales revenue from other lines of activity and streams of income such as asset management (fuglie et al., 2011). the distribution of efficiency scores by frequency is displayed in figure 1. in general, the figure shows that the inefficiency distribution is skewed to the left indicating that most of the companies operate relatively close to their frontier (panels b and c). very high inefficiencies could only be found for a few companies. moreover, panel (a) shows an estimate of the bias of the inefficiency estimate. the distribution reveals that the bias is considerable. thus conducting an analysis without bootstrapping would have led to largely biased estimated parameters in the second step. panel (b) gives an example of the inefficiencies calculated with the adjusted technology t*. finally, panel (c) give the unbiased estimator (distribution) of the inefficiency. 4.2 the determinants of inefficiency the basic hypothesis of the second stage is that r&d has a positive impact on firm performance. in general, the determinants of inefficiency will be captured by the knowledge base of a company which depends on (a) on own r&d and (b) knowledge created elsewhere (universities, research institutes, companies) and diffuses to the public domain. the main objective of this paper is to capture the effect of the first type of knowledge. as a result, we include the variable own (private) r&d expenditure of companies figure 1. illustration of inefficiency estimates and estimated bias, frequencies. (a) (b) (c) inefficiency units inefficiency units inefficiency units biâs(δ̂ i ) = bias(δ̂ i )+ vi δ̂ i * = δ (x i ,yi |t*) ˆ̂δ i = δ̂ i − biâs(δ̂ i ) 241corporate r&d and the performance of food-processing firms (without distinguishing whether it is internal or external r&d) in the set of explanatory variables (z) considered in the second stage estimations. usually the information is available when the companies are required to publish their investments. although it can be safely assumed that large companies in all countries have some r&d, however, they have no spontaneous incentive to report it since this would reveal information about the firm’s strategy and threaten the firm’s competitive position. this lack of data may bias the result. however, no information on r&d is less severe than expected. given the basic hypotheses, the impact of r&d on performance might be less significant since firms which do not report but conduct research should be more efficient than expected. regarding the knowledge created elsewhere (technological opportunities), firm r&d impacts not only the revenues directly but in addition also affects the technological opportunities of the firm. the firm’s technological opportunities consist of two parts: the knowledge external to the sector (universities, public research institutes) and the existing knowledge at the competitors which diffuses to some extent into the public domain (cohen and levinthal, 1989). the degree of openness depends on the institutional regulations regarding the protection of firm specific knowledge but also from the type of technology. the use of public knowledge depends on the absorption potential. this absorption depends on the height of the r&d expenditure as well the characteristics of the scientific and technological foundations. in addition it is determined by the ease how this knowledge can be absorbed. in order to account for differences in the knowledge and research infrastructure we consider regional dummy variables in the estimation. we expect that the us and japan have a favourable knowledge base to conduct r&d and this knowledge base also finds its expression in better firm performance. some indication of this can be seen table 1 which shows that japan and the us have the highest research expenditures related to outputs. the same effect can be expected for the old eu member states (“eu15”). similar to japan and the us, they belong to the group of countries with a highly developed research infrastructure. given the structural difficulties of eu new member states (“nms”) from eastern europe in particular related with their past history of planned economy, the research systems in these countries are likely less developed thus attaining lower productivity levels. the reference region for these regional dummy variables is canada. note that, some studies find that canada reports lower performance of food-processing firms than their peers from other developed countries such as us (chan-kang et al. 1999; in fuglie et al. 2011) to further control for the knowledge and research infrastructure beyond the regional dummies, the contemporaneous general public r&d investments per capita is also introduced in the regression (gerd of government sector, euros equivalent, 2007 constant prices). the time lags and dynamic effects (e.g. see andersen & song, 2013) are not controlled for in the analysis, given that the availability of data in the sample for different years varies strongly across firms and regions. however, to account for the differences in the sample structure over time, dummy variables are used for the 1990s period and the period 20042009 with the 2000-2004 period serving as reference. 242 heinrich hockmann et alii ta bl e 3. t ru nc at ed re gr es si on e st im at es o f t he d et er m in an ts o f e ffi ci en cy . in de pe nd en t v ar ia bl es 1a (b ia se d) 1a 1b 2a 2b 3a 3b 4a 4b 5a 5b c on st an t 2, 28 80 5 ,7 32 1* 5, 74 37 * 4, 99 21 * 5, 02 46 * 5, 91 01 * 5, 77 05 * 5, 70 44 * 5, 83 31 * 5, 98 44 * 6, 07 73 * r& d , p er pe tu al in ve nt or y -0 ,8 24 3 -0 ,7 93 1* -1 ,0 60 6* -0 ,8 68 4* -1 ,1 70 3* -0 ,6 63 9* -0 ,9 15 7* -1 0, 76 39 * -1 0, 89 21 * -0 ,9 76 7* -1 ,1 72 9* (r & d , p er pe tu al in ve nt or y) ² 0, 04 47 * 0, 05 32 * 0, 03 86 * 0, 11 76 * 0, 08 10 * g er d , g ov . s ec to r/ c ap ita -0 ,0 02 3 -0 ,0 02 6* -0 ,0 02 8* -0 ,0 04 0* -0 ,0 04 1* -0 ,0 06 2* -0 ,0 05 4* -0 ,0 05 2* -0 ,0 05 7* -0 ,0 06 3* -0 ,0 06 6* ja pa n -0 ,7 09 8 -0 ,9 46 9* -0 ,9 24 8* -1 ,2 18 1* -1 ,1 46 5* -1 ,1 87 8* -1 ,2 03 3* -1 ,2 02 0* -1 ,2 32 8* u sa -0 ,7 37 8 -1 ,0 09 0* -1 ,0 08 2* -1 ,0 15 7* -1 ,0 20 4* -1 ,0 54 2* -1 ,0 57 9* -1 ,1 01 3* -1 ,1 26 3* eu 12 , n m s 0, 96 66 1, 65 72 * 1, 64 79 * 1, 48 37 * 1, 51 42 * 1, 46 66 * 1, 48 20 * 1, 52 67 * 1, 63 51 * eu 15 -0 ,2 25 6 -0 ,2 82 3* -0 ,3 05 2 -0 ,5 03 5* -0 ,4 02 2* -0 ,4 32 1* -0 ,3 46 5* -0 ,4 83 2* -0 ,5 17 3* 19 90 s d um . 0, 11 87 0, 19 38 * 0, 18 52 * 0, 27 08 * 0, 28 27 * 0, 09 04 0, 10 29 0, 09 04 0, 07 68 0, 08 61 0, 07 90 a fte r 2 00 4 du m . 0, 23 99 0, 28 15 * 0, 27 63 * 0, 38 97 * 0, 39 90 * 0, 16 97 * 0, 18 43 * 0, 17 81 * 0, 18 08 * 0, 19 50 * 0, 19 65 * d ai ry 0, 30 69 * 0, 32 73 * -0 ,0 63 6 -0 ,0 55 6 -0 ,0 90 1 -0 ,1 27 0 0, 18 76 0, 20 57 c an ne d 0, 11 05 0, 10 26 0, 29 52 * 0, 25 45 * 0, 25 93 * 0, 23 13 * 0, 25 00 * 0, 22 80 be ve ra ge s -0 ,5 80 4* -0 ,5 82 8* -0 ,7 58 5* -0 ,7 19 0* -0 ,7 06 9* -0 ,7 59 8* -0 ,7 69 8* -0 ,7 93 6* g en er al 0, 17 60 0, 15 83 0, 15 51 0, 14 61 0, 16 76 * 0, 12 92 0, 11 08 0, 18 26 m ea ts 0, 31 46 * 0, 28 54 * 0, 42 65 * 0, 38 32 * 0, 40 36 * 0, 34 87 * 0, 62 28 * 0, 62 48 * o ils -0 ,5 02 0* -0 ,5 39 6* -0 ,3 13 0* -0 ,3 25 1* -0 ,2 88 7* -0 ,3 19 8* -0 ,0 02 2 -0 ,0 19 9 ba ke ry 0, 30 42 * 0, 29 58 * 0, 46 44 * 0, 41 24 * 0, 39 15 * 0, 35 27 * 0, 46 86 * 0, 46 96 * pr ep ar ed fo od s 0, 38 93 * 0, 39 19 * 0, 42 36 * 0, 40 49 * 0, 38 70 * 0, 37 86 * 0, 34 61 * 0, 36 03 * su ga r -0 ,1 58 7 -0 ,1 69 6 -0 ,0 46 6 -0 ,0 95 2 -0 ,0 75 8 -0 ,0 79 2 0, 01 55 -0 ,0 03 0 ja pa n x r& d 10 ,2 00 2* 9, 98 97 * u sa x r & d 10 ,1 01 5* 9, 83 61 * (e u 12 , n m s) x r & d -0 ,2 31 7 -0 ,5 67 5 eu 15 x r & d 9, 99 56 * 8, 70 63 * d ai ry x r & d -1 ,5 26 5* -1 ,5 85 1* c an ne d x r& d -0 ,1 39 0 -0 ,0 88 3 be ve ra ge s x r & d 0, 21 83 0, 19 48 g en er al x r & d 0, 18 30 -0 ,4 62 4 m ea ts x r & d -9 ,0 02 5* -9 ,1 33 2* o ils x r & d -4 ,4 68 9* -4 ,5 71 5* ba ke ry x r & d -0 ,8 57 3 -0 ,8 75 4 pr ep ar ed fo od s x r & d 0, 56 44 * 0, 47 38 * su ga r x r & d -1 ,2 18 6* -1 ,1 68 2* n m s: e u n ew m em be r s ta te s. * in di ca te s st at . s ig ni fic an ce a t 5 % . s ou rc e: o w n ca lc ul at io ns o n r v2 .1 4 w ith f ea r pa ck ag e. 243corporate r&d and the performance of food-processing firms the estimated results of the second stage pooled truncated regression are reported in table 3. we have estimated several alternative and complementary model specifications to avoid potential collinearity between explanatory variables. model 1a starts with a simple specification of the estimated equation which includes private r&d (perpetual inventory), public r&d (gerd/per capita), time dummies, and regional dummies (us, japan, eu, etc.) with canada serving as the reference country. for comparison purposes, we also report the results obtained with the biased estimators for the first model (1a biased). the remaining models are only presented with their unbiased estimators. the extended first model (1b) also considers squared value of private r&d with the aim to capture the change in marginal gains from additional investment in private r&d. the second set of models (2a, 2b) considers sectoral dummies instead of regional dummies with firms specialised in grain processing being used as the reference sub-group. model 2b expands 2a with adding squared value of private r&d. the third set of models (3a and 3b) add both regional and sectoral dummies in the estimated equation. again, model 3b expands 3a with adding squared value of private r&d. the remaining model sets (4 and 5) consider interaction variables between private r&d and regional and sectoral dummy variables, alongside the variables considered in the first three model sets, in order to capture whether the impact of private r&d vary across regions or sectorial circumstances, respectively. that is, the fourth set of models (4a, 4b) includes interaction variables between private r&d and regional dummies, while the fifth set of models (5a, 5b) interacts private r&d and sectoral dummies. the estimates largely confirm the hypothesis that private r&d has a positive effect on performance (i.e. it reduces inefficiency) of the food-processing firms (table 3). however, the variable controlling for marginal gain of additional investment does systematically capture decreasing marginal returns of r&d investments on performance at firm level. public r&d has also statistically significant contribution to performance, in line with country specific studies such as for the spanish food sector by acosta et al (2015). however, the relationship is complex as hinted by maietta et al (2017) whose analysis of the r&d sector in europe over the 2007-2009 period suggest a displacement effect on intra-muros (internal) r&d by government r&d. these results are consistent across all estimated models. private r&d investing seems to more positively affect performance in canada (the reference country) than in the usa, japan or eu15 countries (4a and 4b). the estimated coefficient for new eu member states is not significant in both models where the interaction variables between private r&d and regional dummies are considered (i.e. 4a and 4b). these results suggest that additional r&d investment in canada and nms would produce greater firm efficiency gains than in in the usa, japan or eu15. with regards to sub-sectorial sensitivity to r&d investment on firm performance (5a and 5b), some sub-sectors (dairy, meat processing, oils and sugar) seem to be more responsive to r&d investment and statistically significant compared to the reference sector (grain). in contrast, processed food sectors are less sensitive to r&d investment, while the remaining sub-sectors were found to be statistically insignificant relative to the reference sector. the performance of food-processing firms during the period after 2004 is significantly lower compared to the 1990s and especially compared to the reference period (20002004). in terms of regional variation of firm performance, the estimates suggest that japa244 heinrich hockmann et alii nese, us, and eu15 firms are more efficient that canadian firms which corroborates with previous studies comparing us and canadian firms (chan-kang et al. 1999, fuglie et al. 2011). the food-processing firms from the nms tend to underperform the canadian peers, and hence the firms from other countries. firm operating as generalist of the food-processing sector tend not to indicate a statistically significant difference with the reference group (grains). in most models, this is also the case for dairy and sugar-related firms, while for oil and canned producers the results are mixed in terms of statistical significance. however, firms specialised in meats, bakery and prepared foods tend to be less efficient than those involved in grains; these resulte are statistically significant across all models. 5. conclusions this paper confirms the hypothesis that r&d investment influences firm performance: food-processing firms which invest in r&d tend to be closer to the efficiency frontier compared to those that do not invest in r&d (i.e. private r&d has a negative effect on inefficiency). estimates of this paper also point to decreasing marginal returns in reducing (increasing) inefficiency (efficiency) by private r&d as well as that that the general public r&d has a positive effect on efficiency of food-processing firms. when looking at the drivers of firm performance, country/region dummies do capture differences and similarities in knowledge systems and nature of the sector. similarities can be detected in the us and japanese contexts. further, as expected, less favourable eastern european (nms) context is indentified in the estimated results as compared to the performance of firms from old eu member states. however, the results suggest that gains from additional investment in r&d could be greater in nms than old eu member states or the us. the findings of this paper have to be considered, however, with some caution on the account of the data limitations. the persistent lack of reporting r&d in certain countries in the eu may create biases in the estimated effects. further, the sample contains rather larger firms from the food-processing industry (a key factor determining r&d, as illustrated by acosta et al (2015) for the spanish food sector), while small firms are underrepresented. this data limitation does not allow to fully extrapolate the results obtained in this paper to the whole food-processing industry. overall, the results of this paper show that r&d in food-processing industry is associated with higher firm performance. at the same time, the sample used in this paper includes medium-high-tech (and larger) food-processing firms, questioning the generally held view on the sector as being rather low-tech. by prioritising high-tech sectors, emerging technologies, knowledge-based services, etc., the current backbone of the european economy, mainly constituted by industries that are often rather mediumand even lowtech, tend to be somewhat marginalised from the policy attention perspective (hanse and winther, 2011). results of this paper show that growth opportunities could also be expected and encouraged from this type of non-high-tech innovative sectors. further, the results of this paper suggest heterogeneity in r&d effects across eu member states, hence innovation policies may have different implications across eu regions. 245corporate r&d and the performance of food-processing firms 6. acknowledgements the analytical framework and analyses of r&d effects on the performance of foodprocessing firms presented in this paper are based on garzón delvaux et al. (2018) developed within the impresa project. we would like to thank our impresa consortium partners for their comments received during the development of the project. special thanks go to davide viaggi and michele vollaro for the review of a previous version of the paper. also, our thanks go to the following jrc colleagues: mark boden, andrea conte and pietro moncada paternò castello, for having first described and then provided access to the compustat database relevant to our analysis. we would like to express our appreciation to our former colleagues marianne lefebvre and sébastien mary who initiated impresa at jrc. finally, we would like to acknowledge the funding received for this project from the european union’s seventh framework programme for research, technological development and demonstration under the grant agreement no 609448. 7. disclaimer the authors are solely responsible for the content of the paper. the views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the european commission. 8. references acosta, m., coronado, d., & romero, c. 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(2007). estimation and inference in two-stage, semi-parametric models of production processes. journal of econometrics 136(1): 31-64. s&p (2014).compustat. http://marketintelligence.spglobal.com/about-us/about-us.html (now, s&p global market intelligence). winger r, wall g (2006). food product innovation: a background paper. agricultural and food engineering working document 2. fao (ed.), rome. positive mathematical programming and risk analysis quirino paris the hedonic contents of italian super premium extra-virgin olive oils luca cacchiarelli1,*, anna carbone2, tiziana laureti1, alessandro sorrentino1 corporate r&d and the performance of food-processing firms: evidence from europe, japan and north america heinrich hockmann1, pedro andres garzon delvaux2,*, peter voigt3, pavel ciaian2, sergio gomez y paloma2 can menu labeling affect away-from-home-dietary choices? elena castellari1,*, stéphan marette2, daniele moro3, paolo sckokai1 a preliminary test on risk and ambiguity attitudes, and time preferences in decisions under uncertainty: towards a better explanation of participation in crop insurance schemes attilio coletta1, elisa giampietri2, fabio gaetano santeramo3,*, simone severini1, samuele trestini2 bio-based and applied economics 7(1): 59-86, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-24048 the italian microbrewing experience: features and perspectives matteo fastigi1,†,*, elena viganò2,†, roberto esposti3,† 1 department of economics and social sciences, università politecnica delle marche 2 department of economics, society, politics, university of urbino. e-mail: elena.vigano@uniurb.it 3 department of economics and social sciences, università politecnica delle marche. e-mail: r.esposti@univpm.it † these authors contributed equally to this work. date of submission: 2016 15th, september; accepted 2017 9th, october abstract. the so-called italian craft beer revolution is a new phenomenon characterised by a rapidly growing number of microbreweries and popularity of their products. the evolution of the italian craft beer sector has interesting potentialities in terms of local/rural development. the analysis is based on available statistics as well as on a survey carried out in may 2014 which discloses features, motivations and expectations of the craft beer producers. together with the risk of overproduction due to the high number of recent entries, the creation of local supply chains (from barley cultivation to its transformation into malt) is emerging as a possible evolution of the sector, thanks to the advent of a new typology of microbrewery, the agricultural brewery. keywords. microbreweries, agricultural beer, barley-to-beer supply chain. jel codes. l11, l66, q13. 1. introduction in italy, craft beer production is a recent and original phenomenon which is not only growing at a fast pace and being appreciated by consumers, but is also outperforming many other sectors of the domestic food and beverage industry, right in the middle of an adverse economic scenario. the rise of this phenomenon has been strongly influenced by the so-called us craft beer revolution, which was the grass-roots answer to a highly concentrated beer industry run by few ‘giant brewers’, as well as to the standardisation and homogenisation of the product (tremblay and tremblay, 2005). started in california in the early 1970s, this “revolution” has led to the rediscovery of old, tastier and more flavourful beers, as well as to a great increase in the number of us producers. in the last decades, this “revolution” has crossed the us borders, spread across europe (cabras and *corresponding author: m.fastigi@univpm.it 60 matteo fastigi, elena viganò, roberto esposti bamforth, 2016; danson et al., 2015; esposti et al., 2017) and, partly, also in australia (argent, 2018), asia (tsang and li, 2016) and latin america (toro-gonzales, 2015). eventually, the craft beer revolution reached italy in the mid-nineties and its growth has become very intense in the last ten years. the italian experience is peculiar for two reasons. first of all, italy is a traditionally wine-producing and consuming country with an almost complete lack of beer culture and tradition (except in a few areas of the former lombardo-venetian kingdom). despite this, both craft brewers’ number and popularity have been growing steadily, thus making it interesting to investigate what factors may have influenced their diffusion and success, privileging small-scale producers and generating new modes of consumption. secondly, in the italian experience, a further innovation has occurred in the last few years, which consists in the advent of a new and somehow unique typology of production units, the agricultural breweries. this new typology has emerged as a major part of the intense recent growth, opening new perspectives in terms of economic, social and environmental sustainability – mostly due to the creation of local supply chains, also in peripheral territories that normally have fewer development prospects. while there is a wide literature referring to the wine sector (in which italy has always stood out for its high-quality productions and widespread consumption), studies covering the italian brewing sector are mainly descriptive or focusing on specific aspects1, showing that more thorough analyses are needed in order to understand the astonishing development of craft beer productions and the adoption of new brewing business models. in this context, the aim of this paper is to investigate the main features of the italian craft brewing experience and the increasing role of agricultural breweries especially with regards to longer-term sustainability. paragraph 2 provides a theoretical framework in order to understand how this phenomenon has become so popular in italy while, in paragraph 3, economic data concerning the evolution of the beer (and craft beer) industry are discussed for the us, europe, and italy. paragraph 4 presents an empirical investigation on the sector dynamics and, in particular, a survey focused on the specific features and role of agricultural breweries. paragraph 5 draws some concluding remarks. 2. conceptual framework although the italian craft brewing sector is still considered a small economic niche, it can be legitimately regarded as an example of broader transformations within food production and consumption spheres. favourable dynamics of the market2, particular local/ territorial features as well as the state’s intervention may have contributed to its success. simultaneously, as has already happened for other mature industries3, the beer industry has been experiencing a significant restructuring process: although the beer market 1 see cannatelli and pedrini (2012), fastigi (2015), fastigi et al. (2015), garavaglia (2015), ravelli and pedrini (2015), francioni (2016) and menghini (2016). 2 such as, i.e., the diffusion of new lifestyles, more politically and ecologically oriented, which have been fostering increasing attention towards locally grown food and artisanal forms of production (brunori, 2007; cavanaugh, 2007; goodman et al., 2012; grasseni, 2013; paxson, 2013). 3 as in the case of newspapers (carroll, 1985), wine production (swaminathan, 1995; 2001), investment banks (park and podolny, 2000), etc. 61the italian microbrewing experience: features and perspectives is notoriously oligopolistic, in the past few years a considerable number of new artisanal beer producers have made their appearance. a few theoretical backgrounds may be helpful to understand the reasons behind the emergence and development of craft breweries. within the social sciences, the transformations of production/consumption systems have been analysed in different disciplinary contexts and with different theoretical and methodological approaches. a first reading is provided by the italian economic and sociological literature and its interpretation of local development and industrial districts, such as the idea that the italian industrialisation process, particularly in the so-called third italy, was based on localised systems of small and medium enterprises in semi-peripheral areas (becattini, 1979; bagnasco, 1988; blim, 1990; trigilia, 2005). the “local”, seen as a socio-cultural and institutional milieu, can condition economic agents’ behaviour, either creating new opportunities or imposing restrictions upon the extension of the market (granovetter, 1985 and 2005; magatti and borghi, 2002). therefore, a particular milieu can either turn into localised advantages (i.e., in terms of relatively lower costs, as in the case of large availability of a critical production factor, or higher productivity, due to better knowledge and skills) or, conversely, into localised disadvantages, which often take the form of congestion effects (such as an higher density of economic activities operating in the same area and in the same market, which intensify the competition for getting the best local production factors or the highest share of local consumers) (esposti et al., 2017). this concept is deeply linked to a central theoretical interpretation of local development, namely the idea that economic actions are embedded in social relations which, in turn, condition economic behaviours and impose restrictions upon the extension of the market. more in general, though, the changes that have occurred over the last decades are coherent with the postmodern society, characterised by a transition from the fordist large-scale production to an outsourced/service-based economy, with a more flexible way of production and the co-existence of more differentiated goods in order to meet the rapidly changing and increasingly heterogeneous consumers’ tastes (antonelli et al., 2015). also, in the agri-food sector different production and distribution systems have progressively emerged with a focus on quality in food practices (goodman, 2003). this phenomenon has led to several experiences, i.e. those proposed by the slow food association, that have spread rapidly from italy to europe and then to the rest of the world4 (antonelli and viganò, 2017). what is more, on the fringe of global/industrial supply chains, the socalled alternative food networks (afns) are creating a more direct relationship between farmers and consumers and offering, at the same time, ideas for local development that is socially, environmentally and economically sustainable (marsden et al., 2000; goodman, 2002; norberg-hodge et al., 2002; sonnino and marsden, 2006; brunori et al., 2012; goodman et al., 2012; torquati et al., 2016). the afns can determine several positive effects, including an interaction between urban and rural areas, the preservation of local knowledge, traditions and local food products, as well as reducing the negative impact of transport, storing and packaging. furthermore, afns allow farmers and small food producers to differentiate their products, giving the possibility to define new development 4 in particular, to secure distinctive foods – in terms of ‘taste quality’ and linkage to a specific territory – facing extinction (ark of taste project), or aimed at protecting biodiversity, such as the slow food presidia (slow food foundation for biodiversity). 62 matteo fastigi, elena viganò, roberto esposti strategies for small and medium-sized farms and increasing their survival probability (van der ploeg et al., 2000; watts et al., 2005; winter, 2003; coley et al., 2009; cleveland et al., 2015). the afns vary widely in terms of organisational procedures5, motivations, targets, development strategies and especially in how the relationships between producers and consumers are established. undoubtedly, a crucial and original aspect of these networks is the consumer’s behaviour, which is increasingly pro-active: an increasing number of consumers, in fact, has been questioning the unsustainability of the conventional/industrial agri-food system and its process of de-localisation, supporting (or actively participating in) the process of re-localisation. consumption, in fact, is not only aimed at satisfying functional needs, but it is increasingly being used to strengthen social relationships as well as to exhibit political and ethical beliefs. food has a strong link-value, so the focus on ‘quality’ shows a strong tendency to re-embed food in social networks as well as a counteraction to the mcdonaldization of society (ritzer, 1993) therefore favouring the “food from somewhere” instead of the “food from nowhere” (mcmichael, 2009). the consumers’ increasing interest for quality and craftsmanship results in different emerging behavioural styles. for example, an interesting profile of the postmodern consumer is the one known as the “craft consumer” (campbell, 2005), who exhibits a propensity to participate in the production process – a tendency that is gradually becoming more widespread in developed societies. the roots of this trend can be found in the “antisystem” and “anti-alienation” components including a form of consumer opposition to marketing pressure (rullani and fabris, 2007). however, this explanation has become less relevant as it has been replaced by a form of consumption that is more similar to a creative act. the value of manual labour has risen dramatically (weiss, 2012; paxson, 2013; cavanaugh and shankar, 2014) as more and more people, find in food and in its preparation both the possibility to learn certain artisanal and manual skills – which are often alien to modern forms of work – and a way of creating and strengthening social relationships. indeed, all this opens new market spaces to small firms aiming at the quality of their output, as well as to new forms of entrepreneurship, such as those that transform a passion (i.e. homebrewing) into a remunerative and job-creating economic activity (de solier, 2013). in general, it seems that cultural transformations, together with the use of consumption as a means of social distinction (bourdieu, 1984), are generating new economic opportunities and offer new choices for satisfying desires and – increasingly educated – tastes of many consumers (scarpellini, 2011). not surprisingly, some craft beer lovers seem to show a sense of elitism which is translated into preferences for beers that are neither highly publicised nor sold too far from their production site6 (schnell and reese, 2003). as a matter of fact, demand can increasingly be seen as a way to express one’s identity and personal lifestyle more than just the satisfaction of one’s needs (blaiech et al., 5 according to different types of producer-consumer relations and/or to the degree of “connectedness” to the act of food production, afns can be classified in four sub-groups: producers as consumers, producer-consumer partnerships and direct sell initiatives (short supply chain), specialist retails (venn et al., 2006). 6 for the craft beers, it should be noted that several studies have shown consumers’ preferences to be greatly influenced by their values, over and above their objective taste propensity; in some blind taste tests, many of these discerning consumers were unable to recognise their favorite products or the possible presence of contaminants in beer (see garavaglia, 2010). 63the italian microbrewing experience: features and perspectives 2013). modern consumers are less snobbish and more culturally multifaceted than in the past, where the status rank relied on a few highbrow genres of culture, while nowadays “high status is signalled by selectively drawing on multiple cultural forms from across the cultural hierarchy” (johnston and baumann, 2010: p. 35). in fact, consumers’ increasing interest for food quality and craft productions, other than showing a certain level of cultural capital, makes them decisive in the success of the craft beer sector and in creating new patterns of production, exchange and consumption. these theoretical considerations and the social and economic transformations they aim at interpreting are relevant for a proper understanding of the italian craft beer revolution: however, there are other sectoral and specific aspects that actually matter and that have to be carefully considered as well. 3. the international beer market scenario 3.1 global trends beer is, without any doubt, the most popular alcoholic drink internationally: both in terms of volume and value, world beer consumption is higher than any other alcoholic drink, such as wine and spirits (colen and swinnen, 2011). despite a slight decrease in 2014 and in 2015, the world’s beer production had increased for three decades (kirin beer university report, 2015; 2016), with the threshold of 2 billion hectolitres close to being surpassed for the first time in history. asia and latin america count together around 50% of the global beer market now, and china has been the world’s largest beer-producing country since 2002 (the barth report, 2004) (table 1). in 2015, the first four world’s largest brewing companies (anheuser-busch inbev, sabmiller, heineken and carlsberg) were all headquartered in western europe (belgium, the uk, the netherlands and denmark respectively), despite the fact that the centre of the beer market has been shifting consistently from europe towards other geographical areas. the share of world beer production of these four major brewing companies rose from 39.7% in 2004 to 47% in 2015 (the barth report, 2005; 2016). to name just a few examples, the belgian inbev7 purchased the american brewing company anheuserbusch for $52 billion in 2008 to form the industrial giant belgium-based ab inbev (howard, 2014) which, in turn, completed in late 2015 the acquisition of its closest rival, sabmiller, for over $100 billion, creating the first “truly global brewer” (bray, 2015). despite mega-brewers attempts to enter the craft beer market (see below), ab inbev’s strategy might also be interpreted as a way to compensate losses in traditional markets (like the united states) with the penetration into (relatively) new markets (such as china) with huge growth potential and where craft beers are not yet popular (shadbolt, 2015). in fact, it is worth noticing that the craft beer revolution is mainly occurring in those traditional beer-drinking geographical areas (europe and north america) whose level of beer production and/or share of beer consumption over total alcohol consumption has significantly decreased in the past years. the us is the country where, in the 1970s, the craft 7 resulting from the merger, in 2004, between the belgian interbrew with the brazilian ambev, for $11.5 billion (howard, 2014). 64 matteo fastigi, elena viganò, roberto esposti beer movement started (tremblay and tremblay, 2005) and where the craft beer sector still registers by far the best performance in the world. according to the brewers association8 (2016a; 2016b), in 2016 the us craft beer share was 12.3% of the us beer market. it is a remarkable result, also considering that the craft beer sales volume grew by 6.2% in the same year while the overall beer market remained stable. in 2016, the number of craft breweries in the us was 5,234 (on a total number of 5,301). this is a substantial number if considered that only a few dozen breweries were operating in 1983 when the smallest number was reached in 150 years (ronnenberg, 1998; watson, 2015). beside the us pioneering experience, however, it must be acknowledged that an international convergence in alcohol consumption patterns is gradually happening. in emerging countries with lower income per capita (such as, i.e., china and russia) the share of beer consumption has been growing steadily. on the contrary, in traditional european “beer-drinking” countries (such as austria, belgium, czech republic, germany, ireland and the uk) per-capita beer consumption has decreased in favour of wine and/or spirits, while the opposite has occurred in “wine-drinking” (such as italy, spain, france) and “spirit-drinking” countries (such as poland). in the past 5 years solely (between 2010 and 2015), in the 28 european member states the number of active breweries went from 4,035 to 7,397 (the brewers of europe, 2016) and, as the former president of the brewers of europe (demetrio carceller) acknowledged, “«almost 100 per cent» of the new entrants are microbreweries producing speciality beers and mirroring the craft trend that has shaken up the us beer industry”, with the result that the artisanal brewers are taking market share off industrial ones (daneshkhu, 2014). 8 the brewers association is the us craft industry body, promoting and protecting american craft brewers. table 1. beer production by country (1,000 hl; 1961, 2000, 2015). 1961 2000 2015 ranking china 500 220,000 471,572 1 usa 111,505 232,500 223,513 2 brazil 8,000 82,600 138,575 3 germany 76,266 110,429 95,623 4 ussr/russia 26,000* 54,900 78,200 5 mexico 8,303 57,812 74,500 6 japan 12,431 70,998 53,800 7 vietnam n.a. 7,430 46,700 8 united kingdom 45,374 55,279 44,054 9 poland 7,064 24,000 39,800 10 france 18,154 18,926 20,520 17 belgium 13,850 14,733 18,250 22 italy 3,081 12,575 15,397 28 * 1961 production refers to the whole former soviet union; 2000 and 2015 data refer to the russian federation. source: elaboration on the barth report (1962; 2002; 2016). 65the italian microbrewing experience: features and perspectives following the craft beers success, many big brewing companies have started either to produce premium beers as well (carroll and swaminathan, 1992; 2000; swaminathan, 1998; carroll et al., 2002; hannan, 2005; garavaglia, 2010) or to directly purchase craft breweries. anheuser-busch (wholly owned subsidiary of the belgian ab-inbev) dominates the us. beer market with a 45% market share, even though this share has continuously declined over the past years (trefis team, 2017). from developing their in-house craft beer brand shock top to acquiring american craft brands, anheuser-busch has looked to penetrating the craft beer market. despite the “threat of loss of customers due to the tie-up of their favourite local craft beer brand with a corporate giant” being real, on the other hand the increased reach and distribution channels could add new customers (trefis team, 2015). but this phenomenon has not been confined to the united states (allyn, 2016): in 2015, two very important london-based craft breweries, such as meantime and camden town, were bought by sabmiller and ab inbev respectively (turco, 2016a). and the same trend is now also concerning italy: in fact, the first case of an industrial brewing company – ab inbev – buying an italian craft beer producer – birra del borgo, one of the most popular and innovative italian craft breweries – dates back to 2016 (montagnoli, 2016; turco, 2016b). a final consideration is needed regarding the malting barley supply chain. in 2015, the european malting capacity was around 42% of the global malting capacity (euromalt, 2017a), and the barley suitable for producing malt (which must be of high quality and able to germinate evenly and rapidly) was mainly produced in france (12.5 million tons), germany (11.6 million tons), uk (7.3 million tons), and spain (6.4 million tons)9 (euromalt, 2017b). 3.2 the italian beer landscape it is worth emphasising that a universally recognised definition for craft beer in italy did not exist until 2016. in italy, the craft beer movement started in the mid-1990s, mostly in the central and northern regions. this growth was fostered by some legislative and institutional innovations. in particular, in 1995 the legislative decree no. 504 introduced some simplifications and innovations into the complex bureaucratic procedures concerning beer production, and this explains why 1996 is usually considered the initial year of the italian craft brewing sector. the new legal definition of artisanal beer (disegno di legge s 1328-b, article 35), approved by the italian parliament in 2016, defines it as beer produced by small, independent breweries that does not undergo pasteurisation or microfiltration during its production. a small independent brewery is defined as one that is legally and economically independent of any other brewery, that uses equipment physically distinct from any other brewery10, that does not operate under an operating license 9 the total eu production of malting barley, in 2015, was 61.11 million tons (euromalt, 2017b). 10 the requirement that an artisanal brewery use only its own equipment seems to exclude contract brewing from this definition, although its application has yet to hit the ground. while it may lead to a decline in brewing in this manner, it may also lead to more simple changes in marketing, as those who practice it may choose to dispense with the use of “artisanal” in their labels and other promotional materials. as beer firms are the most popular type of microbrewery adopted by new craft brewers, it will be interesting to see how and if this legal definition shapes the italian craft brewing landscape (fastigi and cavanaugh, 2017). 66 matteo fastigi, elena viganò, roberto esposti of any other company, and whose production does not exceed 200,000 hectolitres per year. at the moment, very few italian craft breweries produce more than 10,000 hectolitres per year while all the industrial ones (except for hausbrandt group and menabrea) have a much larger brewing capacity, from 616,000 hectolitres of birra forst to 5.2 million hectolitres of heineken italia (assobirra, 2016; data refers to the year 2015)11. the first italian microbreweries had a very small productive capacity and their beers distinguished from industrial ones because they were neither pasteurised nor filtered. compared to other european countries, in italy the lack of tradition left room to creativity and experimentalism: this creativity, combined with the italian artisan ability, soon made italian craft beers more and more respected and popular among beer experts, both in italy and abroad and many of them are now recognised worldwide especially for their original tastes and styles. this increasing credibility of the italian craft beer players is also reflected in the takeover, in 2012, of the thomas hardy’s ale – a famed historic british beer brand – by brew invest, a subsidiary of the vecchiato brothers’ interbrau, one of the most important specialty beers distributors in italy as well as owner of the agricultural craft brewery birra antoniana. the evolution of the italian craft brewing sector is impressive and its extraordinary growth has been concentrated largely in the last 10 years. in 2015, the italian craft beer sector produced 390,000 hectolitres (with a growth of 22% with respect to 2014) and made up 2.1% of the national beer production12. despite the lack of beer tradition in italy, craft beers are now much more than a marginal component of the national beer offer. it is, rather, a very dynamic portion of the industry which is successfully capturing the evolution of consumers’ tastes and behaviours, that tend to penalise industrial and homogenised productions in favour of more differentiated and creative beers. on the other hand, however, this rapid growth also raises serious questions about the long-term sustainability of this sector in italy: in fact, this intense growth will likely slow down in the future, not only reducing the number of new entries but also negatively affecting the performance of the incumbents – eventually pushing some of them out of the market. furthermore, a dip in craft beers prices could be expected as approaching its maturity phase. finally, the lack of beer-tradition in italy has obliged the vast majority of national small producers to import raw materials from abroad (from regions with a longer and stronger beer tradition), with the consequence that local food supply chains are still often not involved in the creation of added value. according to the current regulation, italian microbreweries can be divided into four categories: 1) craft brewery, the most common type, which owns a production facility and sells its beer mainly off-site; 2) brew pub, which has a production facility as well but distributes its beer mainly on-site (i.e., in its pub/restaurant); 3) beer firm, a firm that rents beer brewing equipment and space from other breweries to brew their own beer. the 11 paying attention solely to the production capacity, in the us the brewers association stated that a craft brewery can produce up to 6 million barrels of beer per year (little more than 7 million hectolitres) whereas, in italy, the association unionbirrai (cultural association which promotes craft beer culture in italy) as well as other authors (cannatelli and pedrini, 2012; ravelli and pedrini, 2015) – before the introduction of the legal definition of artisanal beer – used not to consider breweries as microbreweries if their production exceeded 10,000 hectolitres per year. 12 elaboration on assobirra (2016). 67the italian microbrewing experience: features and perspectives fourth category, agricultural brewery, was included in 2010 following the approval of the ministerial decree no. 212. this typology is, to all intents and purposes, an agricultural firm which can therefore benefit from certain advantages with respect to other non-agricultural brewers, such as a more advantageous tax treatment and the possibility to benefit from european funds for rural development. to keep this status, agro-brewers must produce at least 51% of the raw materials used in their brewing process, as well as become members of a consortium, which malts the grains conferred by the members13. this latter typology represents a major novelty within the italian craft brewing movement. on the one hand, according to the farmer’s perspectives, it offers an important opportunity of production diversification for the agricultural firm. on the other hand, and more importantly, agricultural brewing may be the key link to local supply chains, opening the possibility of growing and malting barley locally as more than 80% of barley cultivation in italy is currently used for feeding livestock (fontana et al., 2005). this shows an unexploited space for barley cultivation intended for beer production, largely insufficient at the moment. apart from the recent opening of the “consorzio italiano di produttori dell’orzo e della birra” (called cobi), a micro malt house in the marches region that malts barley conferred by its members, the production of malting barley has always been localised in the southern part of italy where, in fact, the only two industrial malt houses are based. however, following the boom of the italian craft brewing sector in the last decade, the creation of regional supply chains, as cobi did, will add value both to final products and to raw materials (fastigi et al., 2015). of major interest here is the emergence of agricultural breweries as the most dynamic and promising typology, representing also a hope for the long-term sustainability of the sector, on multiple levels (fastigi, 2015). in economic terms, agricultural breweries are much more marketand business-oriented than the majority of very small, family-based and often amateur traditional microbreweries. from the social and environmental points of view, instead, they are expected to be more sustainable because, by italian regulations, the bulk of the raw materials (in particular the production of barley and its transformation into malt) must come from the agricultural brewery itself thus implying a much shorter (local) supply chain and positive spillovers for the territory in terms of creation of knowledge and new satellite economic activities. 4. empirical analysis the main objective of the present paper is to provide some empirical evidence on the evolution of italian craft brewing sector with particular attention to issues concerning its long-term sustainability and the role of agricultural breweries in this respect. such empirical analysis is here pursued through a twofold approach. first of all, a descriptive but detailed analysis of the firms’ dynamics within the sector is carried out in order to identify the emergence in the last few years of some tendencies that may indicate risks and opportunities in terms of long-term sustainability. on the one hand, the increase of turnover may signal some initial problems while, on the other hand, the emergence of agricultural breweries can be interpreted as a positive evolution. the geographical characterisation of 13 this is the usual case, but there are also very few brewers that malt their cereals by themselves. 68 matteo fastigi, elena viganò, roberto esposti these processes may be relevant, and are also investigated, as it may indicate a stronger local dimension of these native activities. such descriptive analysis, however, does not take into account many relevant aspects concerning the recent evolution of the sector and its perspectives in terms of socio-economic sustainability. motivations and expectations as well as specific characteristics of these firms and producers are of major relevance to detect the real entrepreneurial dimension of the phenomenon, its business and market orientation as well as its strategic choices. in particular, it is of primary interest here, given the hypotheses put forward above concerning the possible role of agricultural breweries in order to investigate the peculiarities of these firms and whether their emergence may represent a significant step of the whole sector towards a higher economic sustainability. this kind of investigation is herein performed though an online survey administered to all the microbreweries which were active by the end of may 2014 (fastigi, 2015). finally, an ordered logit model is estimated in order to empirically assess the determinants of the different expectations about the future evolution of the sector and, in particular, the role of agricultural breweries in this respect. the data for these elaborations were collected from the web portal microbirrifici.org, the most accurate (online) database with regards to microbreweries in italy. 4.1 a descriptive analysis of the recent italian craft brewing dynamics the emergence of the craft brewing sector within the italian beer industry is analysed in the present paper through a descriptive analysis of market dynamics14. table 2 shows the striking upward trend in the italian craft beer sector, with a large number of new small craft producers entering the market in the last two decades. in 2015, there were 920 active craft breweries in italy. this is the result of 1,077 firms entering the market in the 1996-2015 period while 157 left it over the same period. therefore, the number of italian microbreweries has been increasing year after year demonstrating a rising growth rate but some new phenomena have also emerged in recent years. first of all, together with an intense entry rate, the last 4 years have also been characterised by a significant number of exits signalling that a kind of turnover process has also begun. secondly, the sector has recently experienced an increasing heterogeneity with regards to brewery typologies (see table 2). beer firms and agricultural breweries somehow represent two antithetical directions of the same kind of evolution. as the italian craft brewing sector is now exiting from the period of pioneers, amateurs, and home-brewers and entering that of market and business orientation, such evolution apparently may take two opposite forms. on the one hand, larger size microbreweries may decide to enter the market by only taking care of the final part of the supply chain, that of commercial valorisation and differentiation of the prod14 the determinants and the time-dependence of these dynamics can be more formally investigated with survival models. this kind of econometric investigation is beyond the scope of the present paper especially as it is not particularly informative concerning the specific features of major interest agricultural breweries while it still assures limited robustness in inferential analysis due to the quite recent emergence of the phenomenon and, thus, the limited number of observations (just 5 years). nonetheless, an example of this econometric investigation on market dynamics can be found in esposti et al. (2017). 69the italian microbrewing experience: features and perspectives ucts. this is what most beer firms do and this form would definitely allow big producers, and also large industrial brands, to enter this growing and promising market segment with its own new products. in this case, craft brewing does not guarantee any kind of local dimension in terms of agricultural production, competences, and skills. the entry of these bigger players might thus have major implications for the future of craft brewing in italy. this looks like a pattern of conventionalisation (that is, craft products more like industrial ones) that may guarantee economic sustainability in terms of market and business orientation, thus of long-term profitability, but, in fact, might also reveal a negative outcome concerning the sustainability of localised supply chains and social and environmental feedbacks. at the same time, the advent of agricultural breweries may represent the opposite attempt to transform this experience into a profitable activity while still maintaining a real craft dimension, high product heterogeneity and specificity as well as a stronger linkage with the local dimension and environment. while beer firms tend to prefer imported raw materials, it can be stated that where agricultural breweries are present this gives opportunities for local cereal, malt and, maybe, hop production and, therefore, opportunities table 2. active microbreweries in italy by typology (1996 – 2015). variations (δ %) refer to the previous year. year craft breweries brew pubs beer firms agricultural breweries total no. δ % no. δ % no. δ % no. δ % no. δ % 1996 8 33 8 167 0 0 16 78 1997 9 13 13 63 0 0 22 38 1998 8 -11 23 77 0 0 31 41 1999 12 50 32 39 0 0 44 42 2000 18 50 40 25 0 0 58 32 2001 21 17 49 23 0 0 70 21 2002 23 10 59 20 0 0 82 17 2003 34 48 61 3 0 0 95 16 2004 42 24 64 5 0 0 106 12 2005 55 31 70 9 0 0 125 18 2006 72 31 80 14 0 0 152 22 2007 91 26 92 15 3 0 186 22 2008 127 40 101 10 6 100 0 234 26 2009 155 22 106 5 9 50 0 270 15 2010 174 12 106 0 17 89 32 329 22 2011 201 16 115 8 30 76 38 19 384 17 2012 248 23 122 6 58 93 50 32 478 24 2013 309 25 125 2 117 102 68 36 619 29 2014 386 25 133 6 199 70 89 31 807 30 2015 434 12 136 2 246 24 104 17 920 14 source: elaboration on microbirrifici.org. 70 matteo fastigi, elena viganò, roberto esposti for the already mentioned, though still developing, regional supply chains (fastigi et al., 2015). a further convenience, in this respect, is represented by the fact that such initiatives, given their agricultural and rural relevance, may encounter the interest of regional policies. in particular, the regional rural development plans (rdp) in italy definitely played a role in supporting these initiatives and will be relevant, as well, also in the current programming period (2014-2020). table 2 supports this interpretation of a recent twofold evolution of the sector. in the last five years,15 after the introduction of the “agricultural brewery” within the italian regulation, the two most significantly growing typologies are the beer firms and the agricultural breweries. therefore, though both processes are present, the question is whether we are experiencing an inversion in the re-orientation to market and business of the sectors: more focused on local (and, maybe, sustainable) agricultural production and transformation and less convergent towards the conventional industrial production mode? before trying to provide an answer to this question in the following sections, it is worth noticing here a final descriptive piece of evidence about the last years of evolution. it concerns the regional distribution of different microbrewery typologies and the emergence of a degree of geographical/local specialisation in this respect. figure 1a shows the regional concentration of active microbreweries in italy, highlighting lombardy (16,6%), piedmont (10,4%), tuscany (8,8%) and veneto (8,5%) as the four regions with the highest number of production units. this evidence may seem somehow obvious due to the size effect: these are among the largest (in geographical and demographic terms) italian regions. nonetheless, as shown in figure 1a, these regions still form a continuous area in the north-western part of the country while other large regions in the south (for instance apulia and sicily) do not belong to this leading group. again focusing attention on the specific segment of agricultural breweries, however, the picture is a little different (figure 1b). among the four regions with the highest number of production units we still find tuscany and lombardy but also emilia-romagna and, above all, marche. marche is a relatively small region but still presents the highest number of agricultural breweries among italian regions with 16 production units. this is not so surprising, as it is the region where the already mentioned cobi consortium was created and is operating. this demonstrates how agricultural breweries are strongly related to the presence of a local supply chain. also the concentration of production units in the four leading regions is higher in the agricultural brewery case compared to other typologies, at 49% and 43%, respectively. to get rid of the regional size effect in order to have better representation of the geographical characterisation of the italian craft brewing experience, it is helpful to express the presence of production units in relative terms. figure 1c shows the four italian regions with the highest number of production units per 100.000 inhabitants. it is now clear that the area with the highest presence of microbreweries is not the north-western part of italian but the central-eastern part. also expressing the presence of agricultural production units in relative terms provides a different picture. figures 1d and 1e reports the number of agricultural breweries per 100.000 inhabitants and the share of agricultural 15 in 2010, the first year when agricultural breweries were added in the italian regulation of the sector, there were 28 units. 71the italian microbrewing experience: features and perspectives units on total microbreweries, respectively. the four regions with the highest values are the same for both indicators: marche is by far the first (more than 35% of microbreweries are agricultural units) then followed by two contiguous central regions (umbria and toscana) and by a north-eastern one (friuli-venezia-giulia). these maps actually reveal that the italian craft brewing experience has a relevant geographical characterisation. southern regions are still less active in this respect while the most dynamic areas correspond to that part of the country (the central and northeastern part) with a marked, and widely emphasised, historical experience based on an industrialisation process driven by small and medium enterprises and a strong specialisation in traditional sectors. there is an overall agreement that the advent of agricultural breweries represents a relevant and positive improvement within the italian context. from an agricultural perspective, this has become a real alternative for farms’ looking for profitable diversification strategies and new market opportunities. in pursuing such strategies, as mentioned, they may have access to the public support delivered by the regional rdps that figure 1. top four italian regions for: (a) number of craft breweries, (b) number of agricultural breweries, (c) craft breweries per 100000 inhabitants (italy = 1.5), (d) agricultural craft breweries per 100000 inhabitants (italy = 0.17), (e) share of agricultural breweries on total microbreweries (italy=11%). source: elaboration on microbirrifici.org. data refers to the year 2015. 72 matteo fastigi, elena viganò, roberto esposti is absent, or much more difficult to obtain, for non-agricultural breweries. moreover, the local impact of these breweries is higher than non-agricultural ones especially in relation to jobs creation and revitalisation of rural areas and economies. a final, but still relevant advantage of agricultural breweries, would consist in the fiscal advantages acknowledged to this typology by the recent italian regulation as it is treated as agricultural production and can thus benefit from the special agricultural tax regime. the latter advantages may also be problematic as it might encourage non-agricultural breweries to convert to the agricultural typology or major industrial producers to enter this segment by matching the minimum requisites designated by the current regulation. in fact, the advent of this typology is too recent to already assess whether this risk is real and its possible extent. from a production point of view, however, an agricultural brewery can take different forms. as mentioned above, the basic requisite for a microbrewery to be considered agricultural is that at least 51% of the cereals used in its beer production must come from the brewery’s own cultivation. in practice, there is no other limitation concerning the transformation stage, the plant size and ownership. gradually, two opposite typologies have emerged. agricultural breweries that are in fact originally conventional craft breweries that rent land to crop the large enough amount of product to meet the requirements to be considered an agricultural brewery and take advantage of the resulting benefits. on the other hand, there are the farms with conventional cereal production that decide to orient their production towards malt and beer transformation by renting a plant or by delivering its barley to an independent, often collective, production plant (technically, a type of agricultural beer firm). this second typology corresponds more closely to the idea of the local supply chain and to reinforce this link with the local production, collective plants or producer organisations voluntarily reinforce the requirements implied by the regulation. for instance, for a farm to be part of the previously mentioned cobi consortium and to benefit from cobi’s trademark “birragricola” (namely, “agricultural beer”), agricultural breweries must use at least 70% of their own grains. therefore, the advent of the agricultural brewery within the original and somehow unexpected italian craft brewing experience has been hailed as a positive evolution. however, its characters are still largely unknown and its perspective has to be fully understood. 4.2 the survey can we ultimately state that the even more recent “agricultural brewery revolution” is taking place within the recent “italian craft brewing revolution”? and, if the answer to this first question is positive, what actually characterises this revolution? in other words, what are the differences with respect to non-agricultural craft breweries and to what extent do they open new and more sustainable perspectives in the sector? as anticipated, statistical information is largely lacking regarding this specific phenomenon and it would not in any case capture the deeper aspects such as the motivations and expectations of the new agricultural beer producers. therefore, to shed light on these aspects, an online survey was launched in 2014, through electronic questionnaires sent to all the active craft beer producers. the aim was to obtain information about their background, their motivations to undertake such a particular activity, their expectations as well as detailed production 73the italian microbrewing experience: features and perspectives information including the origin of the feedstock used in the beer production and preferred distribution channels16. the questionnaire was designed to gather first-hand information on craft brewers work history, time spent homebrewing as a hobby before starting their own private brewery, business strategy and expectations about the future of the sector. last but not least, special attention is paid to the potential of this phenomenon in terms of generating local development which is also economically, socially and environmentally sustainable. the questionnaire was sent to the 604 microbrewers registered as active by may 2014 (in the web portal microbirrifici.org) and was completed by 325 units, with a response rate of 53.81%. these 325 producers can be considered a representative sample of the whole population of italian craft beer producers. the distribution across the four different typologies (table 3) and across regions within the sample is very close to the same proportion within the population. as expression of the most recent growth of the sector, only 11.4% of the sampled breweries were founded before 2005. of the other 88.6%, 23.4% were founded between 2005 and 2009, 65.2% between 2010 and the end of 2013. by distinguishing the respondents by the year of foundation some significant differences emerge in terms of the origin of their choices to enter this market, i.e. their motivations and expectations. table 4 compares the two groups of respondents (founded before and after 2010) with regards to some survey questions17. it emerges that “new” breweries are more business oriented as their entry choices is less dependent on previous homebrewing amateur experience and resulting more from a strategic choice concerning their activity. also the context is new as these new entrants expect a more intense growth in production, thus more competition and lower prices. nonetheless, differences among the two groups are not so large and do not apparently express a real change within the sector or post-2010 “revolution”. in fact, if a post-2010 “revolution” within the italian craft brewing sector really occurred, this should be attributed to the advent of agricultural breweries. therefore, to 16 for more details on the sample see also (fastigi, 2015; fastigi et al., 2015). the complete survey results are available upon request. 17 those with more significant differences between the two groups are reported. the whole comparison is available upon request. table 3. composition of the sample (breweries that responded to the survey) compared to the population by typologies. sample (respondents) population no. % no. % craft breweries 171 52.6 297 49.2 brew pubs 58 17.9 125 20.7 beer firms 67 20.6 118 19.5 agricultural breweries 29 8.9 64 10.6 total 325 100.0 604 100.0 74 matteo fastigi, elena viganò, roberto esposti assess whether these new entrants eventually determined a significant change in behaviours, motivations and expectations, the relevant comparison of the answers to the survey has to be made between agricultural and non-agricultural production units. in particular, here we want to assess, in sequence, whether differences have emerged regarding structural characteristics, motivations and expectations and, consequently, production and marketing choices. table 5 highlights some of the main differences emerging from the survey among the two groups. while the owner’s age is the same (about 40 years), their experience in the sector is different. agricultural breweries’ owners more frequently than others (34.3% and 23.7% respectively) come from a former experience in the beer sector or in somewhat similar activities, like wine or spirits production. this could suggest that agricultural breweries are often strategic choices in terms of activity diversification and business reorientation of already existing professional activities. this would find further confirmation in the higher presence of previous amateur and home-brewing experience among the non-agricultural commercial breweries compared to agricultural ones (77.4% and 55.2% respectively). nonetheless, these characteristics highly vary within the two groups and when a mean-comparison test (t-test) is performed, the results indicate a not statistically significant difference between agricultural and non-agricultural microbreweries. with regard to production and economic size, however, the difference between the two groups emerges more clearly. among non-agricultural breweries we find on average activities with a lower number of employees, production and revenue compared to agricultural ones. the latter, in particular, show an average production level in 2013 which is more than double the average production levels of non-agricultural breweries. the meantable 4. comparison of survey responses between breweries founded before and after 2010 (%). <2010 2010-2013 what are the main reasons that made you want to become a craft brewer? passion 44.3 41.5 search for quality 18.4 20.3 willingness to experiment 17.9 18.5 strategic choice (business opportunity or production diversification) 13.0 15.3 others 6.5 4.4 what do you expect as far as the production and number of breweries, in italy, in the next five years? > production and breweries 53.9 65.7 > only production 24.4 21.9 other 21.7 12.4 what are the expectations of the average price of craft beers in italy in the next five years? increase 25.2 26.0 stable 30.6 30.3 decrease 38.7 40.4 i don’t know 5.4 3.4 75the italian microbrewing experience: features and perspectives comparison test concludes that, at least in terms of production volumes, agricultural microbreweries are statistically bigger than non-agricultural ones. a further evidence on the difference between agricultural and non-agricultural breweries has emerged within the italian brewing sector in the last few years and concerns the differentiated production and marketing choices. this evidently depends on the already mentioned restrictions agricultural producers must meet in order to be considered agricultural breweries. but again, differences go beyond this and they are linked to a stronger business orientation of agricultural breweries. table 6 compares some responses concerning the production and marketing choices. their larger size and stronger business orientatable 5. structural characteristics: comparison between survey responses of agricultural and non-agricultural breweries (% of responses). agricultural breweries non-agricultural breweries owner’s age 39.8 39.6 two-group mean-comparison test (t test) -.047 former working experience of the owner in the beer, wine, spirits’ sector yes 34.5 23.7 no 65.5 76.3 two-group mean-comparison test (t test) a -1.151 did the owner homebrew before starting the commercial craft brewery? yes 55.2 77.4 no 44.8 22.6 two-group mean-comparison test (t test)a .628 number of employees none 41.4 54.7 1-3 37.9 27.3 4-5 6.9 8.3 >5 13.8 9.7 two-group mean-comparison test (t test)b .104 beer production – 2013 (hl) 1.357 564,6 two-group mean-comparison test (t test) 2.173* revenue – 2013 avg. (€) <50.000 27.3 42.7 50.000-100.000 22.7 15.5 100.000-250.000 18.2 20.5 >250.000 31.8 21.3 two-group mean-comparison test (t test)c 1.267 a the test is computed on the dichotomous variable: yes = 1; no = 2. b the test is computed on the polytomous ordered variable: 1 = none; 2 = 1-3; 3 = 4-5; 4 => 5. c the test is computed on the polytomous ordered variable: 1 =< 50.000 €; 2 = 50.000-100.000 €; 3 = 100.000-250.000 €; 4 => 250.000 €. * statistically significant at 0.1 level. 76 matteo fastigi, elena viganò, roberto esposti tion justifies why agricultural breweries sell a slightly larger proportion outside the local (regional) market and tends to exploit more retail channels (both specialised and largescale retailers) rather than rely on direct selling. in particular, this latter aspect is statistically different (mean-comparison test) between the two groups. beside market orientation, however, the main difference between the two typologies implied by the regulation concerns the feedstock, that is, cereal production, its provision and perception about quality. considering the lack of a beer tradition in italy (in most of the country), it is not surprising that italian beers are mainly produced with imported malted cereals, from countries such as germany or the united kingdom, which, thanks to their tradition, have an undeniable competitive edge in terms of quality and price. the results of the questionnaire confirm this, showing that italian non-agricultural microbrewers buy a very high percentage of their raw materials abroad (more than 90%) while this is evidently not possible for agricultural breweries where feedstock supply coming from abroad is just around 11%. this generates a major difference regarding the creation of a good quality local supply chain: most agricultural brewers are convinced that in italy there are conditions for a national and local provision of cereals and malt to produce good table 6. production and marketing choices: comparison between survey responses of agricultural and non-agricultural breweries (% of responses). agricultural breweries non-agricultural breweries % of sales within the region? 67.5 70.2 two-group mean-comparison test (t test) -.397 % of sales in different channels? direct selling 23.1 38.0 specialised retailers 69.4 58.0 large-scale retailers 5.2 1.9 web 2.3 2.1 two-group mean-comparison test (t test)a -2.169* how do you evaluate the quality of italian malts good 88.9 31.3 medium 7.4 22.6 poor 3.7 46.2 two-group mean-comparison test (t test)b -3.290* do the conditions to produce barley for beer in italy exist? yes 96.4 79.3 no 0.0 7.6 i don’t know 3.6 13.1 two-group mean-comparison test (t test)c -1.917* a the test is computed on the % of sales in specialised retailers. b the test is computed on the polytomous ordered variable: good = 1; medium = 2; poor = 3. c the test is computed on the dichotomous variable: yes = 1; no = 2. * statistically significant at 0.1 level. 77the italian microbrewing experience: features and perspectives quality beer. this confidence is significantly lower among non-agricultural producers as confirmed by the mean-comparison tests. on the one hand, this “agricultural side” of the craft beer revolution offers a great opportunity to increase the share of cereals cultivated within national borders (and the variety of supply), as well as to reduce the environmental impact of international transport of cereals from abroad. in this context, the exploitation of italian barley would represent an interesting opportunity to add value to beers that are the result of skills, creativity and passion, thus responding to differentiated consumption behaviours, interested in local productions and cultures. on the other hand, the creation of a local supply chain linked to agricultural breweries does not limit their market penetration. 4.3 a quantitative assessment of expectations formation of major interest here is to assess whether these differences between the two typologies with regards to structure, size and marketing choices might lead to substantial differences also in terms of motivations and expectations concerning the craft brewing business. table 7 compares some survey answers and supports this argument. passion remains the most considerable factor in deciding to launch a craft beer business in both cases: 43.5% owners of non-agricultural microbreweries and 32.1% of agricultural ones responded that they started producing craft beer because they wanted to transform a passion into a job opportunity. the search for quality and desire to experiment different beer styles are significant factors as well, but less for agricultural producers. for the latter, on the contrary, a very relevant motivation (25% of the respondents) is the search for business opportunities and making a consequent strategic choice to re-orient the farming activity. in addition to motivations, expectations also seem to differ. most respondents declare optimistic expectations for the future concerning the enhancement of the cultivated area dedicated to feedstock for beer production, higher number of producers and overall volume of production. agricultural breweries, however, show less optimistic, or more realistic, expectations: both feedstock and beer production is going to increase but the number of breweries will not. as a result most agricultural producers expect a price decrease, whereas non-agricultural microbreweries still trust in a price increase. the apparently different motivations and expectations emerging from table 7, however, provide just a qualitative evidence that can be hardly interpreted as an indisputable difference between agricultural and non-agricultural microbreweries. in order to more formally assess this different attitude, the answers provided on the expectations about the evolution of the sector have been used to construct an ordered categorical variable. three questions have been considered: expectation about production volumes; expectation about prices; expectation about the quality of italian barley and malt production. for the generic i-th microbrewery the ordered variable exi takes the following values: exi = 0 when the expectation is negative for all the three questions (no production increase, no price increase, no quality improvement); exi = 1, 2 or 3 when the expectation is positive for 1, 2 or all 3 aspects, respectively. as the microbreweries taking value exi = 3 are very few (just 2 units), values 2 and 3 have been collapsed into a unique value. thus, the adopted ordered variable takes the following values: exi = 0, 1, 2. 78 matteo fastigi, elena viganò, roberto esposti this categorical variable is then entered into a ordered logistic regression model (ordered logit) whose determinants (i.e., the independent variables) are selected characteristics of microbreweries presented and discussed in previous sections: the geographical location of the microbrewery expressed by a geographical gradient (an increasing variable moving from northern to southern provinces; torino province takes the lowest value, siracusa province takes the highest value); the age of the entrepreneur; the age of the microbrewery; the typology (a dummy taking value 0 for non-agricultural breweries and 1 for agricultural ones); the production level (hl/year); the % of sales within the region; the % sales in specialised shops. table 8 reports the maximum likelihood (ml) estimation of this ordered logit model (cameron and trivedi, 2005). rather than reporting the estimated coefficients, the table reports the respective marginal effects as they can be directly interpreted as the increase of the probability to be associated to a given option induced by a unit increase of the indetable 7. motivations and expectations: comparison between survey responses of agricultural and nonagricultural breweries (% of responses). agricultural breweries non-agricultural breweries what are the main reasons that made you want to become a craft brewer? passion 32.1 43.5 search for quality 14.3 20.2 willingness to experiment 12.5 18.9 strategic choice (business opportunity or production diversification) 25.0 0.0 others 16.1 17.5 in italy in the next five years, will the quantity of cultivated barley for craft beer production increase? yes 67.9 65.2 not much 32.1 33.8 i don’t know 0.0 1.1 what do you expect concerning the production and number of breweries in italy in the next five years? > production and breweries 60.7 63.9 > only production 32.1 23.4 other 7.1 12.7 what are the expectations of the average price of craft beers in italy in the next five years? increase 10.7 27.2 stable 21.4 31.3 decrease 67.9 37.1 i don’t know 0.0 4.5 79the italian microbrewing experience: features and perspectives pendent variable.18 extreme options (highly pessimistic and highly optimistic breweries) collect a lower number of observations compared to the intermediate one. nonetheless, in all options numerosity is enough to identify some statistical significant determinant. the estimation results emerging from table 8 suggest that expectations formation is significantly affected by three major factors: the geographical location; the age of the entrepreneur; the selected supply chain with the consequent marketing strategy. more positive expectations are found moving from northern to southern provinces and in young producers. moreover, expectations are also higher for microbreweries with a higher share of sales to specialised shops so, arguably, with a stronger attention to the quality and specificity of their products. on the contrary, the microbrewery typology does not seem to have a significant impact; in other words, expectations do not significantly differ between agricultural and non-agricultural breweries. the size and the age of the microbrewery do not significantly affect expectations, too. in fact, for all these variables the sign of the marginal effects would rather suggest that less optimistic expectations can be found in older and bigger agricultural microbreweries. it is worth noticing that these results differ from what emerged in previous studies about the main determinants of craft brewing dynamics in italy (esposti et al., 2017). while agricultural breweries definitely represented a major engine in the recent boon of the sector in italy and this rapid growth did not show a major geographical characterisation, the expectations about the future evolution of the sector are more affected by the location rather than by the typology. 5. conclusions this article aims at investigating the evolution of the new and strongly increasing phenomenon of production and consumption of craft beers in italy. although microbreweries are often seen as a niche sector within a market ruled by industrial mass producers, 18 coefficient estimates are available upon request. an ordered probit estimation has also been performed. results are qualitatively very similar but with lower statistical quality. these further estimation results are available upon request. table 8. ordered logit estimation: conditional marginal effects for the 3 options (estimated standard errors in parenthesis). option 0 (n=36) option 1 (n=205) option 2 (n=84) geographical gradient n-s -.0059* (.0030) -.0058* (.0029) .0116* (.0060) age entrepreneur .0014* (.0008) .0014* (.0010) -.0028* (.0016) age brewery .0032 (.0044) .0030 (.0044) -.0062 (.0088) agricultural microbrewery (dummy) .0122 (.0231) .0121 (.0232) -.0243 (.0461) production (hl/year) .0001 (.0001) .0000 (.0001) -.0001 (.0001) % sales within the region -.0037 (.0033) -.0036 (.0030) .0073 (.0065) % sales in specialised shops -.0058* (.0023) -.0055* (.0020) .0113* (.0052) * statistically significant at 0.1 level. 80 matteo fastigi, elena viganò, roberto esposti the so-called craft brewing “revolution” is triggering interesting transformations in several contexts with possibly significant reverberations in terms of sustainable local development. regarding this latter aspect, the italian case shows an interesting peculiarity. it consists in the emergence, in the last five years, of a highly dynamic and particular segment, that of agricultural breweries. the empirical analysis confirms that the advent of this new typology is significantly affecting the evolutionary trajectories of a still infant sector in italy. above all, it changes its long-term perspectives in terms of economic and socio-environmental sustainability. as a matter of fact, since the mid-nineties the italian craft brewing “revolution” has been strongly dependent on amateur and home-brewing forms (the so-called “knowledge productive leisure” – de solier, 2013), that then moved into commercial production. this origin explains the enthusiasm and the creativity that characterises the italian experience but it may also reveal unsustainable aspects in the long term. the small microbreweries’ size, their “naivety” as well as their dependence on imported feedstock and competences, may jeopardise their competitiveness in both domestic and foreign markets. the survey carried out and discussed, however, demonstrates that agricultural breweries are themselves “revolutionising” the sector with regard to these aspects. their larger size, business orientation, creation of local supply chains, but also their more realistic attitude towards the real evolutionary potential of the sector may represent a real opportunity for the longer-term success of the italian craft brewing industry. the role of policies is also critical in this respect. on the one hand, it has been crucial for the birth of the italian craft beer sector (legislative decree no. 504, 1995) and, in particular, of agricultural craft breweries (ministerial decree no. 212, 2010).19 on the other hand, however, a further selective support is now expected for this latter typology, especially because of their potential in helping developing rural territories and their long-term sustainability. in particular, the creation of local supply chains, from the cultivation of barley to its transformation into malt, seems a major target for agricultural and rural policies. this seems of strategic relevance not only to reduce dependence on foreign imports (and, consequently, limiting the environmental impact of transport activities) but also to create economic opportunities for micro malt houses which, in turn, might even differentiate and innovate their malt production and trigger the research and development of new dedicated varieties of italian malting barley (anderson, 2013). on these opportunities and on the role of policy and regulation in this respect deeper investigations and further research are expected in the future. acknowledgments work developed in the framework of the research project on “dynamic models for behavioural economics” financed by desp-university of urbino, italy. 19 the new legal definition of artisanal beer (disegno di legge s 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(2003). geographies of food: agro-food geographies – making reconnections. progress in human geography 27(4): 505-513. bio-based and applied economics 5(3): 215-216, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-20512 the changing role of regulation in the bio-based economy: an introduction regulation has been broadly defined as a sustained and focused control exercised by a public agency over activities that are valued by a community. as such it has been a major concern of policymakers in a range of fields from financial to industrial and agricultural activities. examples of regulatory areas in the bio-based economy are risk, health, environment, competition, intellectual property rights. regulations in all these areas have deep implications for trade issues, global value chains and for the provision of public goods. for most of the twentieth century, regulation has been considered the primary instrument to deal with market failures and market imperfections in western economies and a number of new regulatory agencies have been created. starting from the 90s, however, as the private and public costs of regulation have become more apparent, an effort has been devoted to the reduction of the cost of regulation and toward deregulation. as an example, the better regulation agenda adopted by the european commission in 2015 has been designed to improve regulation across eu countries with the final objective of reducing its cost. in the eu, regulations concerning food retailing, food processing, farming activities, tariff and non-tariff barriers to trade as well as financial markets have deeply affected the bio-based sectors. the objective of these regulations is guaranteeing food security and food safety to final consumers and a sustainable management of natural resources such as land, water and the rural environment. there is no doubt that, in these areas, the role of eu policies has increased dramatically and questions have been raised on the effectiveness, efficiency and equity of such regulations. in recent years, firms working in agricultural and food production, bioenergy and biotechnology have often protested against the supposed “invasive” role of eu regulations in their activities. how a viable balance between regulation and deregulation ought to be attained is both a political and a scientific question. the aim of the fifth aieaa conference, held in bologna, june 16-17, 2016, has been to provide a scientific contribution to these issues by expanding the knowledge base on the fundamentals of better regulation, by promoting a critical debate on the underlying theoretical and methodological issues and by analysing the main policy implications. the conference programme included around 50 papers addressing issues such as cost-benefit analysis of regulation, design of enforcement rules, nudge approaches to consumer regulation, political economy of regulation, intellectual property rights, regulation of quality standards and global value chains, innovation in methods and tools for regulatory impact analysis. the bae editor invited some of the speakers to submit their papers for publication on this bae issue. the papers published in this issue have gone through a regular review process and are a selection of the topics covered in the conference. 216 paolo sckokai the paper by stephane marette analyses the relationships between quality, market mechanisms and regulations in the food chain, emphasizing the relationships between quality and sustainability. through a partial equilibrium model and a simulation concerning the use of linseed for feeding dairy cows, the paper concludes that instruments like minimum quality standards and labelling may have a positive impact on consumer surplus and on producer profits. through a structured literature review, alessandro olper explores the political economy issues of trade-related regulatory policies, with a special focus on non-tariff measures (ntms), such as environmental and food standards. focusing on both theory and existing empirical evidence, the paper analyses the role of global value chains as driver of the potential impact of policies. francesca galli and co-authors analyse a case study of food assistance schemes in tuscany: adopting a participatory approach, they examine pathways for strengthening the impact of food assistance schemes in periods of economic crisis. the paper by michele vollaro and co-authors addresses the issue of the increasing demand of phosphorus (p) worldwide and the stability of the fertilizer market. based on a circular economy theoretical framework, the paper proposes an impact analysis of the use of recycled p as a substitute of chemical p fertilizers, proving its potential economic sustainability. finally, giovanni tagliabue proposes a political economy analysis of the eu gmo maize policy, concluding that the schumpeterian approach adopted by politicians may lead to policy choices totally disconnected by science-based evidences. paolo sckokai president of aieaa bio-based and applied economics 7(1): 1-17, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-24045 do japanese citizens move to rural areas seeking a slower life? differences between rural and urban areas in subjective well-being hiroki sasaki food and agriculture organization of the united nations (fao), roma, italy date of submission: 2016 30th, june; accepted 2017 7th, june abstract. for some time, individuals in multiple contexts have been moving from rural to urban areas for economic reasons. in recent years, however, young people in japan have been increasingly turning to rural areas to embrace a slower, less-hectic lifestyle. despite this interesting development, researchers have thus far failed to identify determinants of residents’ well-being in rural and urban areas in japan. moreover, recent empirical work has shown that stated happiness or subjective well-being (swb) can serve as an empirical proxy for perceived utility. to expand upon this line of research, in this paper, i use swb to gauge disparities between the japanese rural and urban environments. in addition, i determine how natural capital and social capital affect swb for both rural and urban residents. results show that on average, rural residents report higher swb than urban residents despite low average income. i also identify multiple factors other than household income that affect swb; these relationships are particularly pronounced for rural residents. finally, results demonstrate that residents that migrate from urban to rural areas reported high levels of swb. taken together, the results of this study provide new insight into rural values and the attractiveness of rural residency. keywords. happiness, subjective well-being, natural capital and social capital. jel codes. i31, d63, q15. 1. introduction japan is one of the first countries in the world to face problems associated with depopulation. the “masuda report” (masuda, 2014) generated significant interest throughout japan with its prediction that nearly half of all japanese municipalities may disappear due to population decline and the inability to maintain administrative functions. because the municipalities at risk for disappearance are mostly located in rural areas, the need to cope with rural community issues has come to the fore for policy makers. corresponding author: hiroki.sasaki@fao.org 2 hiroki sasaki contrary to the findings of the masuda report, a recent opinion poll showed that a growing number of young japanese urbanites wish to settle in rural areas (cabinet office of japan, 2014), indicating a general interest among japanese citizens to embrace a rural lifestyle. this interest in rural living was not always pervasive. in the 1980s, tokyo served as the center of the japanese population, causing overconcentration there. in turn, the concentration of urban functions in tokyo resulted in substantial income disparity between citizens in urban and rural areas. despite the economic benefits of living in an urban area, a growing number of people have begun to leave cities in search of better lives in rural areas (ministry of internal affairs and communication of japan, 2017). to illustrate, the aforementioned opinion poll showed that the proportion of japanese citizens interested in living in rural areas increased from 21% in 2005 to 32% in 2014 (cabinet office of japan, 2014). this trend was particularly pronounced for young people. the return of young citizens to rural areas could revitalize these areas and improve japanese agriculture on the whole. to date, the cabinet office has not performed an econometric analysis to determine which variables affect citizens’ motivations for returning to rural areas. still, the results of the survey suggest that increasing interest in rural residence among young citizens may be a result of shifting perceptions regarding that which makes living conditions attractive and changing values. internationally, researchers and policymakers have widely accepted that not only that food is the key product of agriculture, but also there are other benefits of agriculture. taken together, these benefits have come to describe “multifunctionality” of agriculture (organization of economic co-operation and development [oecd], 2001 and 2003). past research by agricultural economists on multifunctionality has largely focused on “visualizing value” in monetary terms through stated preference and revealed preference methods. these researchers have not sufficiently explored (a) which elements of rural areas contribute to well-being, or (b) how these variables are related. these questions are of utmost importance, given recent emphasis on the use of ecosystem services1, which relate to the association between ecosystems and well-being (teeb d0). in short, ecosystem services directly or indirectly support our quality of life. in the last decade, the economic literature has experienced the emergence of a new research agenda that uses subjective questions to measure individual well-being. some of this work has provided support for a link between factors related to the regional environment (e.g., air quality, green space) and well-being. given the emergence of this link, the purpose of this paper is to use subjective measures to compare urban and rural residence in terms of well-being. in doing so, i will show how rural characteristics affect subjective well-being (swb), which may influence japanese citizens’ motivations for migrating from urban to rural areas. as an empirical indicator of utility, happiness data permit comparison of urban and rural areas to a degree greater than traditional economic indicators (e.g. gdp). 1 ecosystem services can be classified into one of four main categories: provisioning services, regulating services, habitat services, and cultural services. provisioning services relate to products obtained from ecosystems, including food, fresh water, wood, fiber, genetic resources, and medicines. regulating services are defined as the benefits obtained from the regulation of ecosystem processes. these include climate regulation, natural hazard regulation, water purification and waste management, pollination, and pest control. habitat services emphasize the importance of ecosystems to provide habitats for migratory species and to maintain the viability of gene pools. cultural services include non-material benefits that people obtain from ecosystems, including spiritual enrichment, intellectual development, recreation, and aesthetic enjoyment. 3do japanese citizens move to rural areas seeking a slower life? to address the issues outlined above, the remainder of the article is organized in a series of interrelated sections. section 2 features a review of research on swb, with a particular emphasis on differences between rural and urban areas. in section 3, i describe the data and empirical model used to test these differences. following this, i report the results of the econometric analysis in section 4. finally, in section 5 i discuss the limitations of the analysis and offer some concluding remarks. 2. subjective well-being research: rural vs. urban areas the easterlin paradox is a key concept in happiness economics. related to the relationship between economic variables and well-being, easterlin (1974) showed that within developed nations, reported happiness was not significantly associated with per capita gdp. this paradox has recently manifested in japan, where survey data has shown that happiness levels have not risen in parallel with increases in income (cabinet office, 2008: figure 1). in short, these data show that economic wealth does not necessarily determine the degree to which one is satisfied with his/her life. figure 1. japanese real gdp per capita and the degree of life satisfaction. 1 2 2734 2885 3188 3729 3859 3934 3867 3964 4201 4358 3,46 3,6 3,18 3,38 3,34 3,26 3,19 3,12 3,07 3,41 0 0,5 1 1,5 2 2,5 3 3,5 4 2000 2500 3000 3500 4000 4500 1981 84 87 90 93 96 99 2002 05 08 r ea l g d p pe r c ap ita (t en th ou sa nd y en ) d eg re e of li fe s at is fa ct io n year source: cabinet office 2008. (notes) 1. compiled from the cabinet office “national survey on lifestyle preferences,” “annual report on national accounts” (data before 1993 is compiled from 2002 report and data after 1996 is compiled from 2006 report), and the ministry of internal affairs and communication “population statistics”. 2. ”degree of satisfaction” is calculated as follows: the question, “are you satisfied with life or not?” was answered using five scales from “satisfied” to “unsatisfied.” the weighted average of each answer was indexed into “degree of satisfaction.” 3. the respondents represent both sexes from the age of 15 to 75. (excludes “do not know” and “no answer”). 4 hiroki sasaki happiness research based on self-reports of life satisfaction has made significant contributions to our understanding of how people conceptualize well-being beyond their consumption habits. in addition, the growing literature on swb has thus far focused on degree and determinants of happiness. this is useful in a variety of fields that inform policy (bok, 2010). despite the growing literature on swb and happiness, studies that focus on rural areas, agriculture, and their respective relations to swb are scarce. in one of the rare studies to explore these associations, baaske et al. (2009) surveyed 18,000 citizens in 60 municipalities to show a close relationship between farming performance and perceived quality of life. this finding reiterates that agriculture is one of the most significant predictors of quality of life within a municipality. in another example, a team of researchers from the university of évora and cardiff university have been conducting a survey in rural portugal to measure swb. these researchers have surveyed local farmers and other community members using a placebased approach. to evaluate causality between swb and agriculture, the researchers plan to add specific questions on agriculture to complement general questions about swb (surove et al., 2012). in addition, although multiple researchers have measured swb in the rural areas of developing countries (e.g. markussen et al., 2014 in vietnam; dedehouanou et al., 2011 in senegal; guillén et al., 2006 in thailand), none of these studies have compared rural areas with urban areas in terms of swb. in a similar line of research, tsutsui et al. (2009) compared large japanese cities (the 13 largest in japan), medium-sized cities (>100,000 residents), and other cities/towns/villages in terms of swb. their results show that on average, the size of the city positively corresponded to respondents’ reported swb. this finding is not consistent across all studies, however. for example, hellevik (2003) found no significant difference between rural and urban residents in norway with respect to reported swb. all studies that have evaluated differences in swb between rural and urban residents delineated respondents contingent on the province or prefecture in which they lived. despite the convenience this method offers, classification based on administrative boundaries may not highlight how rural and urban areas differ in terms of how they moderate the relationships between multifunctionality conservation, social capital, and migration on swb. given the specificity of the swb construct, greater nuance with respect to respondents’ locations may reveal significant effects on swb that would otherwise remain hidden. this is especially true in japan, where capturing one’s residential environment is difficult using any standard means due to japan’s geographic diversity. given the shortcomings of past research, this paper offers two key contributions to the literature. first, it features a comparison of rural and urban residents’ swb using “subjective” classifications of urban and rural areas. specifically, respondents are classified as rural or urban based on their own self-reports. delineation of rural areas from urban areas has always been a controversial endeavor. one criterion for disaggregating urban and rural areas is the presence of densely inhabited districts (dids), which have been accounted for since the 1960 population census of japan. this criterion would dictate that areas that have not been classified as a did are rural in kind. despite the simplicity of this solution, land use in japan is complicated; farmland is often scattered across multiple kinds of districts, even in tokyo. furthermore, even areas designated as dids are often 5do japanese citizens move to rural areas seeking a slower life? surrounded by farmland. therefore, it is not appropriate to distinguish urban and rural areas as a function of their did-status2. second, this classification protocol will allow for the identification of rural characteristics and individual experiences that affect swb. the recent movement in japan for residents to return to rural areas is affected by the multifunctional value of rural land, but no researcher has attempted to identify variables that affect rural and urban residents. the increased understanding that will derive from this analysis can potentially contribute to rural-development policy planning. 3. empirical application 3.1 econometric model consistent with most extant studies in this domain, in this paper, swb is operationalized with participants’ responses to the following question: “how dissatisfied or satisfied are you with your life overall?” responses to this question were posed as an 11-point likert scale ranging from 0 (not at all satisfied) to 10 (completely satisfied). the first step in this life-satisfaction approach is to estimate a micro-econometric swb model in which swb is estimated as a function of socio-economic and demographic variables, factors related to natural and social capital, and other control variables. the model takes the form of an indirect utility function for individual i in location k: swbi,k = β0 + β1 ln(yi,k)+β2 xi,k +β3 ai,k i = 1…i, k = 1…k (1) in this model, yi,k represents household income; x is a vector of a wide range of socio-economic and demographic characteristics other than income, including relative income, age, marital status, employment, health status, and migration experience; and aik depicts respondents’ attitudes towards rural natural capital and social capital (brereton et al., 2008; ambrey et al., 2014). for the purposes of this paper, i estimated eq. (1) as an ordered logit model. as such, swb is assumed to be a categorical variable, making it impossible to directly observe happiness levels. instead, i could determine only the range of values in which respondents’ happiness levels lie. 3.2 data the empirical model used in this study is guided by existing studies on swb. data for the model were collected in october of 2014 via an internet survey in which i asked 2 according to the japanese ministry of agriculture, forestry, and fisheries’ (maff) “classification of agricultural area,” “rural areas” refer to areas that are not “urban areas.” the maff approach involves using rural areas as a unit of classification. in contrast, the oecd uses the prefecture (of which there are 47 in japan) as a unit of classification. both the maff and oecd approaches are based on an area’s population density (oecd, 2009). hayashi and sasaki’s (2015) classification is similar to the oecd’s; they identified 14 rural prefectures, 21 intermediate prefectures, and 12 urban prefectures. regardless of how different approaches delineate rural and urban areas, none of them captures the specific elements that affect swb (hayashi and sasaki, 2015). 6 hiroki sasaki participants questions related to their perceptions of swb, demographics, socio-economic factors, and personal attitudes. the oecd’s guidelines on measuring subjective well-being (oecd, 2013) contend that although economic variables, demographic variables, and quality of life affect swb, many other issues (e.g., measuring personality traits) are complex. as a result, the oecd did not provide recommendations in relation to these complex issues. nevertheless, recent research has shown conscientiousness to be the strongest predictor of life satisfaction among the big five personality traits (i.e. tanksale, 2015). because the big five personality traits are important predictors of political and social attitudes, in addition to typical variables that have appeared in past swb studies, i also added questions to measure respondents’ thoughts regarding the conservation of natural capital and expectations for food, agriculture and rural issues in the coming decade. i administered this survey with the policy research institute in the ministry of agriculture, forestry and fisheries in japan through a consumer monitoring company with access to 2.3 million registered subjects. the survey platform randomly selected respondents based on the demographics of each prefecture by ensuring the sex and age ratios of participants reflected those of japan overall. in total, 1,500 japanese participants aged 20 to 64 provided data. to collect data concerning swb, the survey included a question asking individuals “how dissatisfied or satisfied are you with your life overall?” table 1 provides summary statistics for all explanatory variables used in the estimation. explanations of all explanatory variables in the empirical model are offered in the following subsections. 3.2.1 socio-economic characteristics socio-economic variables in the model include age, marital status, health status, income, and relative income. i selected these variables based on past research on swb. the survey also included questions related to participants’ places of residence; they were asked to indicate if they lived in a rural area, sub-rural area, suburban area, or urban area. 3.2.2 awareness and personal thoughts concerning natural capital and social capital respondents provided answers to questions meant to capture the respective determinants of swb for rural and urban residents. these items relate to the conservation of natural capital and the perceptions of their living environment’s social capital. the items concerning natural capital test participants’ awareness toward natural capital conservation, which is a summation of answer towards degree of awareness for eight types of key elements of multifunctionality in agriculture3. questions related to social capital measure how much respondents trust their neighbors (“number of trustable person”) and how much respondents help others (“degree of norm of reciprocity”) in their region of residence. i selected these questions based on a maff policy report focusing on social capital in rural areas (japanese ministry of agriculture, forestry and fisheries, 2007). while 3 1. conservation of land, 2. fostering water resources, 3. preservation of the natural environment, 4. development of favorable landscapes, 5. maintenance of cultural heritage, 6. recreation/relaxation, 7. viability of rural community, 8. food security (maff-japan, http://www.maff.go.jp/e/nousin/tyusan/siharai_seido/s_about/ cyusan/tamen/). 7do japanese citizens move to rural areas seeking a slower life? table 1. definition of variables and descriptive statistics. variable definition mean max min std. dev observations swb reported current life satisfaction (happiness) by integers from 0 to 10. based on the following survey question “overall, how happy are you these days?” the respondent is to choose from a scale of 0 to 10, where 0 is “very unhappy,” 5 “neither happy nor unhappy” and 10 is “very happy” 5.823 10 0 2.230 1500 age age of respondents in years 43.147 64 20 12.508 1500 age squared/100 age of respondents in years squared/100 20.180 40.96 4 10.843 1500 unemployed/seeking dummy variable = 1 if respondent is currently unemployed and seeking a job 0.066 1 0 0.248 1500 married dummy variable = 1 if respondent is legally married 0.590 1 0 0.492 1500 very good health dummy variable = 1 if respondent’s health condition is very good 0.108 1 0 0.310 1500 good health dummy variable = 1 if respondent’s health condition is good 0.624 1 0 0.485 1500 ln(income) natural log of household income 6.137 7.65 3.91 0.770 1246 relative income dummy variable = 1 if respondent thinks their income is higher than the average income in the neighborhood 0.341 1 0 0.474 1500 citizen in urban dummy variable = 1 if respondent subjectively believes him/herself to live in an urban area 0.287 1 0 0.452 1500 citizen in suburban dummy variable = 1 if respondent subjectively believes him/herself to live in a suburban area 0.402 1 0 0.490 1500 citizen in subrural dummy variable = 1 if respondent subjectively believes him/herself to live in a subrural area 0.216 1 0 0.412 1500 citizen in rural dummy variable = 1 if respondent subjectively believes him/herself to live in a rural area 0.079 1 0 0.270 1500 i turn dummy variable = 1 if respondent experienced urban-to-rural migration 0.033 1 0 0.178 1500 u turn dummy variable = 1 if respondent experienced returning to the countryside in home town 0.097 1 0 0.297 1500 j turn dummy variable = 1 if respondent experienced returning to the countryside other than home town 0.035 1 0 0.185 1500 mf conservation degree to which respondents recognize the importance of agriculture’s multifunctionality (index of eight elements of agricultural multifunctionality) 17.971 24 0 4.527 1500 8 hiroki sasaki ferre-i-carbonell and gowdy (2007) evaluated the relationship between subjective measures of well-being and individual environmental attitudes, the current study also included variables related to social attitudes. 3.2.3 migration from urban to rural areas in japan, a “u turn” refers to the migration of people who return to their hometowns to settle down and earn a living after working or studying in cities. in contrast, the “i-turn” refers to unidirectional movement out of urban areas. one final migration pattern is called the “j-turn,” wherein a person leaves the city to move to a rural area other than his/her birthplace. the questionnaire included a question related to the type of migration participants engaged in. this variable was operationalized as a control variable, as migration type may exert an effect on swb. 3.2.4 preference parameters items related to respondents’ aversion to risk were also incorporated into the model as controls. i included these variables because respondents’ happiness may relate to these preference parameters (tsutsui et al., 2009). variable definition mean max min std. dev observations farmer dummy variable = 1 if the respondent is a farmer 0.062 1 0 0.241 1500 farmland dummy variable = 1 if respondent resides in an area that is less than a 15-minute walk to farmland 0.611 1 0 0.488 1500 food/agri perspective expectations for state of food, agriculture and rural issues in the coming decade (higher values reflect more optimistic expectations) 7.968 21 0 3.618 1500 neighbor friendliness perceptions of friendliness within the neighborhood (scale = 0–3) 1.239 3 0 0.788 1500 trust person number of trustable persons in the neighborhood (scale = 0–3) 0.876 3 0 0.739 1500 norms of reciprocity degree of norms of reciprocity 0.269 1 0 0.443 1500 shock frequency with which respondent experienced a shocking event in the previous five years (scale = 0–4) 1.145 4 0 1.284 1500 risk aversion degree to which respondent wishes to avoid risk (scale = 0–10) 5.761 10 0 2.298 1500 satoyama satoyama index (si) of respondent’s resident area (10 km × 10 km). 0.238 0.592 0.003 0.123 1500 population decrease dummy variable = 1 if respondent expects the population of young women (aged 20 to 39) within his/her municipality to decrease by more than half its current level in the next 30 years 0.052 1 0 0.222 1500 9do japanese citizens move to rural areas seeking a slower life? 3.2.5 objective indicators in addition to the subjective data gleaned via the above questions, i also included several objective measures as predictors in the model. first, i included the satoyama index (si) to indicate the 100-sq. km area (10 × 10 km) in which a resident resides. si was included because it can serve as a proxy designed to capture the richness of different geographic regions; “a high si value is an indicator of high habitat diversity, which is characteristic of traditional agricultural systems, including japanese satoyama landscapes, while a low value indicates a monotonic habitat condition typical of extensive monoculture landscapes” (kadoya and washitani, 2011, pp. 20). second, i included a predictor in the model that reflects the rate at which the population in certain regions decreases due to an outflow of young women. because aging and decreasing fertility rates are serious problems in japan, their salience can affect swb. if the population of young females is in decline, the capacity for the japanese population to replenish itself declines in parallel (masuda, 2014). 4. results 4.1 estimation results: whole sample the largest portion of the entire sample indicated that they were neither happy nor unhappy (5 on the likert scale), followed closely by a slight leaning towards happiness (7 and 8 on the likert scale; see figure 2). the result is consistent with previous survey data provided by japanese citizens (cabinet office, 2011). western european countries differ. most respondents in western europe mark 8 on the likert scale, indicating slightly happier respondents. although these differences between japanese and european data are interesting, comparing swb across nations should be done with caution and a consideration of cultural factors that may influence responses (diener and oishi, 2004). following the comparison of the overall sample, i then compared urban and rural respondents based on their reported levels of happiness (see table 2). respondents were classified into one of four categories, all of which were based on participants’ subjective perceptions. these four categories are citizen in urban areas, citizen in suburban areas, citizens in subrural areas, and citizens in rural areas. rural residents reported a slightly higher happiness level (µ = 6.04) than their urban counterparts (µ = 5.82), despite the latter having higher household income. however, an analysis of variance (anova) failed to show statistically significant differences between the four categories (p-value = 0.35). for the sake of simplicity, i combined the samples of urban and suburban citizens into one larger “urban citizen” category. i similarly combined the samples rural citizens and subrural citizens into a larger category of “rural citizens.” following these combinations, i evaluated the relationship between income level and swb (see figure 3). results of the survey reveal a positive relationship between income level and swb for urban residents, but this correlation is weak for rural residents. these findings suggest that income may be a contributor to swb for urban residents, but rural residents seek out other factors for their swb. 10 hiroki sasaki consistent with past work on swb, i developed an ordered logit regression model to examine how multiple factors influence swb. in this model, the main predictor variable was area of residence (i.e., urban vs. rural) and the outcome variable was swb. although we were primarily interested in the effect of area of residence on swb, we also included other predictors in the model. for instance, we tested whether migration from urban to rural areas (i.e., uji turns) influences swb. other important variables relate to individual respondents’ relationships with the rural areas in which they reside. in the original iteration of the analysis, i included several additional agriculture related variables such as experience and frequency to participate rural activities, but ultimately removed them to avoid multicollinearity and endogeneity. although instrumental variables can be included to avoid potential inaccuracy, there exists no consensus on which combination of variables should be used to predict swb. in addition to the standard logit regression model, i also performed inverse probability of treatment weighting (iptw) and propensity score (ps) figure 2. distribution of swb scores in comparison with urban and rural residents. 1 2 swb table 2. respondents’ reported levels of swb by category. sample average variance urban citizen 430 5.82 5.47 suburban citizen 603 5.88 4.55 subrural citizen 324 5.66 4.98 rural citizen 119 6.04 5.19 all sample 1500 5.82 4.97 11do japanese citizens move to rural areas seeking a slower life? matching to better understand the relationship between residential area and swb (see section 4.3). table 3 presents the main results produced by the logit regression model. the pseudor2 value of 0.072 is comparable to previous work in this domain (e.g. ambery and fleming, 2011), suggesting that the model has an acceptable level of explanatory power. results of the logit regression analysis show that an individual’s area of residence (i.e., rural = 1, urban, suburban and subrural = 0) has a positive effect on swb. none of the variables related to citizen migration (i.e., the uji turns) were statistically significant predictors of swb across the entire sample, but the following section evaluates rural and urban residents independently. in relation to agriculture related variables, the results suggest that if respondents (a) are aware of the importance of agriculture’s multifunctionality and/or (b) envise a bright future for japanese food and agriculture, they experience higher swb. with respect to the socioeconomic predictors, age, unemployment, health condition, income, and relative income all exert significant influence on swb. all social control variables—degree of friendliness with neighbors, number of trusted persons and degree of norms and reciprocity—similarly exert significant, positive effects on swb. 4.2 estimation results: rural and urban residents after performing the logit regression on the entire sample, i then replicated the analysis independently on the rural and urban resident samples. these analyses respectively yielded pseudo-r2 values of 0.075 and 0.094, which indicate that both models had satisfactory levels of explanatory (see table 4). figure 3. distribution of average swb score in each income group. 1 2 4,91 5,03 5,46 5,82 6,15 6,48 7,07 5,58 4,50 5,60 5,75 5,44 6,33 6,42 6,22 6,70 5,23 4,00 4,50 5,00 5,50 6,00 6,50 7,00 7,50 les s t han 1 m illi on jp y 1-2 m illi on 2-4 m illi on 4-6 m illi on 6-8 m illi on 8-10milli on 10-12 m illi on more than 12 m illi on jp y n.a. swb urban swb rural income 12 hiroki sasaki for some variables, significant differences between urban (urban and suburban) and rural (rural and subrural) residents emerged. first, among rural residents, there was a significant parabolic (i.e., u-shaped) relationship between age and swb. this result may be attributable to elderly respondents’ desire to move to a more peaceful residence in their final years. second, consistent with the correlational results reported in section 4.1, i found that household income is significantly and positively related to swb. this result is consistent with many previous studies that have revealed a significant relationship between income and swb. the analyses presented in this study, however, indicate that this phenomenon applies only to urban residents. interestingly, there was a positive correlation between relative income and swb for both urban and rural residents. third, with respect to respondents’ migration experiences, i found that respondents who moved to the rural via an “i-turn” tend to have higher swb than their “u-turn” and “j-turn” counterparts. people who performed an “i-turn,” which refers to unidirectional table 3. results of the ordered logit model across all respondents (dependent variable: swb). variable coefficient p-value age -0.058 0.060 * age_squared_100 0.066 0.061 * unemployed_seeking -0.695 0.002 *** married 0.675 0.000 *** very_good_health 1.241 0.000 *** good_health 0.693 0.000 *** income 0.000 0.022 ** relative_income 0.826 0.000 *** i_turn 0.346 0.226 u_turn -0.098 0.586 j_turn 0.110 0.686 mf_conservation 0.037 0.004 *** farmer -0.246 0.258 farmland -0.345 0.002 *** perspective_fa 0.046 0.002 *** neighbor_friendly 0.137 0.093 * no.__trust_person 0.164 0.049 ** norms_of_reciprocity 0.397 0.001 *** shock -0.141 0.002 *** risk_aversion 0.103 0.000 *** satoyama -0.686 0.102 pop_decrease -0.099 0.663 citizen_in_rural 0.420 0.042 ** pseudo r-squared 0.0726 sample 1498 note: ***p < .01, **p < .05, *p < .10. 13do japanese citizens move to rural areas seeking a slower life? movement out of an urban area to a rural area, were mostly between the ages of 20 and 40. these residents may no longer require growth in material wealth, but seek durable human communities and living environments characterized by nature. in this way, the observed i-turn may result from fundamental changes in young people’s values within the “de-growth” movement. fourth, the analyses also produced several notable findings concerning natural and social capital. urban residents with strong attitudes concerning conservation of the rural environment reported high levels of swb. similarly, urban residents with optimism towards future japanese agriculture had high swb, on average. interestingly, there was no relationship between attitudes towards conservation and swb among rural citizens. this result may demonstrate that rural residents do not seem to realize the value of natural capital in their own backyards. city residents within 15 minutes walking distance of farmland reported low swb. in japan, agricultural land use is common, even in urban areas across the country. this finding may be attributable to difficulties associated with managing farmland in urban areas, including the use of pesticides, noise from agricultural machines, or dust. however, urban residents have recently come to recognize the importance of the social and environmental functions of urban agriculture, and the benefits related to rural farmland (e.g. open space for disaster management, resources for recreation and education) have been promoted nationally. issues related to social capital also seemed to exert influence on swb, as some of these factors (i.e. degree of friendliness with people in the neighborhood, number of trustable people) were positively associated with rural residents’ swb. fifth, with respect to the preference-based predictors, risk-averse individuals in both rural and urban environments reported high swb. this result was consistent with past studies (e.g. tsutusi et al., 2009) finally, the associations between the objective variables and swb produced unclear results. for instance, there was no clear relationship between the satoyama index and swb. decreases in population negatively affect swb, but only for urban residents. this finding supports the work of glaeser et al. (2016) who found that residents of declining cities appear less happy than residents of other areas (e.g. the american rust belt). in addition to identifying factors that influence current swb, i also estimated an ordered logit regression model to predict future swb. although there were many similarities to the analysis of factors that affect current swb, there was one key difference. rural and urban respondents who were optimistic about future japanese agriculture also reported high levels of future swb. 4.3 using propensity score methods to estimate the effect of rural residence on swb past correlational studies have shown that there are differences between rural and urban residents in their swb. however, past work has not provided evidence to show that one’s area of residence is causally antecedent to swb. given this gap in the literature, i supplemented traditional regression methods with inverse probability of treatment weighting (iptw) using the propensity score to better represent the relationship between an individual’s area of residence and his/her swb. propensity score methods compare individuals in different treatment conditions by testing differences in their average scores. originally developed by rosenbaum and rubin 14 hiroki sasaki (1983), ps matching has been used in several other fields (binder and freytag, 2014; barra et al., 2016) and can be applied to research questions concerning swb. to identify any causal relationships between rural residence and swb, we used a matching estimator. to simplify the interpretation of the results, we created a dummy variable that adopts the value of 1 if an individual reports a swb-value greater than 8, and 0 otherwise. using this dummy variable, we performed an iptw analysis with a cox proportional hazards (ph) model, as well as a logistic regression that relies on iptw. we then used the logistic regression model to estimate the propensity score for each subject. results produced by the iptw cox proportional hazards (ph) model indicated an estimated hazard ratio of 0.701 (95% ci: 0.420-1.171, p = 0.148), and a non-significant effect of rural residence on swb. in contrast, the iptw logistic regression model produced an odds ratio of 0.635 (95% ci: 0.372-1.002, p < .10). this result suggests a marginally significant, positive, causal association between rural residence and swb. table 4. ordered logit model results by resident type (dependent variable: swb). variable urban residents rural residents coefficient p-value coefficient p-value age -0.040 0.292 -0.134 0.022 *** age_squared_100 0.050 0.249 0.138 0.039 *** unemployed_seeking -0.755 0.007 *** -0.726 0.094 * married 0.583 0.000 *** 0.898 0.000 *** very_good_health 1.415 0.000 *** 1.116 0.002 *** good_health 0.755 0.000 *** 0.579 0.011 ** ln_income_ 0.211 0.026 ** 0.013 0.930 relative_income 0.857 0.000 *** 0.858 0.000 *** i_turn -0.896 0.032 ** 1.300 0.001 *** u_turn -0.262 0.300 -0.098 0.704 j_turn -0.041 0.896 0.573 0.327 mf conservation 0.048 0.004 *** 0.009 0.677 farmer 0.146 0.685 -0.357 0.195 farmland -0.405 0.001 *** 0.119 0.778 perspective_food and ag 0.048 0.010 *** 0.029 0.258 neighbor_friendly 0.066 0.525 0.298 0.034 ** no._trust_person 0.041 0.697 0.457 0.002 *** norms_of_reciprocity 0.436 0.003 *** 0.403 0.081 * shock -0.113 0.037 ** -0.195 0.019 ** risk_aversion 0.124 0.000 *** 0.102 0.015 ** satoyama -0.303 0.552 -1.659 0.033 ** pop_decrease -0.651 0.040 ** 0.457 0.181 pseudo r-squared 0.075 0.094 sample 850 380 note: ***p < .01, **p < .05, *p < .10. 15do japanese citizens move to rural areas seeking a slower life? 5. conclusions in this paper, i used subjective classification standards to compare rural and urban residents in terms of their swb. results suggest that on average, rural residents have higher swb than their urban counterparts, despite higher average income among the latter. by using an ordered logit estimator, i demonstrated that for rural residents, factors other than household income significantly predict swb. in addition, urban residents with high awareness of the conservation of natural capital reported high levels of swb. this finding is consistent with past work showing that beliefs and intrinsic religiosity significantly affect swb (barra, 2016). in contrast, for rural residents, some elements of social capital (i.e. friendliness with neighbors, number of trustworthy people) positively affect swb. past work has suggested that swb depends on personal relationships, but the current study demonstrates that this association exists only for rural residents. rural residents who migrated directly from urban areas reported high swb. taken together, these results provide new perspectives that are related to the values of rural residents, making rural areas attractive. furthermore, the results of the analysis using propensity score methods revealed that living in a rural area is causally antecedent to swb. this result suggests that standard regression analyses do not satisfactorily capture these effects. the results of the analysis provide evidence for the importance of conserving the rural environment for well-being: environmental conditions in respondents’ residential areas and respondents’ awareness of and attitudes towards conservation influence swb differently in line with past researches (i.e. kyoto university, 2013). in the current study, for example, the satoyama index was not significantly associated with swb, but positive attitudes towards the conservation of natural capital did exert a positive effect. taken together, these results suggest that raising awareness of environmental issues is fundamental to maintaining swb. finally, although this paper provides several new findings that can be used to inform policy, one limitation should be acknowledged. this study represents the first attempt to use data from japanese respondents to compare urban and rural citizens in terms of their swb. as a result, the results should be interpreted with caution. as argued by hirschauer et al. (2015), the study of swb in specific domains may help identify conditions that foster well-being, but it will inevitably raise questions as whether and how this research should inform policymaking in all contexts. besides, the regional classification based on the self-report might severely limit its applicability for policy, even though other measures of regional classification have some limitations as well. those who regard themselves as the residents of urban or rural are not necessarily those of urban or rural. proposing the legislation affecting those people that think themselves living in urban or rural area seems meaningless. as such, the results reported here should encourage future applied research in other geographic regions. acknowledgements my heartfelt appreciation goes to takashi hayashi and atsushi tanaka, whose comments and suggestions were invaluable to my study. my appreciation also goes to the 16 hiroki sasaki journal editor and two anonymous referees for helpful comments. this work was accomplished as part of a research project performed at the policy research institute, ministry of agriculture, forestry and fisheries (primaff) in japan. the findings and views reported in this paper are those of the author and should not be attributed to primaff or the japanese ministry of agriculture, forestry and fisheries. all errors and omissions are the sole responsibility of the author. i would like to thank editage (www.editage.jp) for english language editing. references ambery, c.l. and fleming, c. 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(in japanese). bio-based and applied economics 6(3): 243-257, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-23339 reviews articles migrants to rural areas as a social movement: insights from italy giorgio osti university of trieste, italy date of submission: 2017 8th, september; accepted 2017 10th, december abstract. migrants in italy often form the majority of the labour force in the primary sector; their residential and work conditions are generally of low quality, and their entrepreneurship is limited. how are the presence and action of migrants in italian rural areas to be interpreted? the question is tackled with a framework based on the social movements literature. the mobilisation of migrants is seen as a way to enter the political arena when traditional channels are closed. even heterodox participation is considered a sign of integration in a country. cases of mobilisation are presented in order to show the robustness of the research perspective. the results are that migrants ‘collective action’ is rare and weak, especially in the primary sector because of gangmaster action, temporary and dispersed jobs, obtuseness of employers. moreover, the advocacy coalition supporting migrants is unable to overcome the logic of emergency. the paper ends by wondering whether the sporadic mobilisation of migrants will lead to a moral economy of the agrifood value chain. keywords. migrants, agriculture, workforce, italy, social movement. jel codes. b55, e26, f22, j43, r23. ormai tra l’agricoltura e i lavoratori immigrati si è creato un legame inscindibile 22° rapporto statistico sull’immigrazione 1. introduction surveys show that there are numerous migrants: sometimes they are the majority of the labour force working in the primary sector; their residential and work conditions are generally of low quality; their entrepreneurship is very limited, differently from other sectors like construction and commerce (cicerchia and pallara, 2009; bock et al. 2016; colloca and corrado, 2013). the paper address these questions: how can the presence and action of *corresponding author: giorgio.osti@dispes.units.it 244 giorgio osti migrants in italian rural and agricultural areas be interpreted? is the over-representation of foreigners working in the primary sector a sign of economic marginality or dynamism? the proposed framework is based on the social movements literature. this choice is justified by the assumption that the full integration of foreigners happens when they participate in the political sphere (osti, 2006). mobilisation is a way to enter the political arena when traditional channels are closed. the choice of this interpretative frame entails neglect of some aspects, like entrepreneurship and skills training. the hypothesis is that the mobilisation of migrants in italy has not happened in general, and in the primary sector/rural areas in particular, except in rare cases and despite the presence in italy of a robust advocacy coalition. collective action is too costly for migrants, far from the clan mentality, and useless for people who consider their presence in italy and in agriculture to be temporary. subsidiary interpretations concern the role of intermediaries like the migrants themselves, unions, third sector organisations and local authorities that are generally unable to overcome the logic of emergency and operate outside their specific mission. cases of mobilisation and integration are discussed in order to show how robust the hypothesis is, and how deviations from theoretical patterns shed light on further aspects of the rural life of migrants in italy. the paper ends by wondering whether manual work, now mostly provided by foreigners, will disappear from italian agriculture, and whether the weak and sporadic mobilisation of migrants will include important common values and a sort of moral economy of the agrifood filiere. 2. farm migrants between spatial fixity and social mobility the paper will adopt a sociological approach: the theory of social movements, which is indeed quite unusual for migration studies. otherwise, how can one interpret the impressive demonstration1 of may 20th, 2017 when about 100,000 people – italians and foreigners rallied and marched in the streets of milan? using this approach means highlighting not only the movements of migrants from rural to urban areas (and vice versa), but also socio-political actions, especially strategies to acquire power, prestige or rewards (social mobility). the emphasis on spatial and social movement entails changes of perspective: one change is to look at the integration between inhabiting and working and attention to internal or residential mobility of migrants after the long journey to arrive in italy. in other words, how migrants combine fixity and mobility tendencies (milbourne and kitchen, 2014). the other change of perspective is to consider migrants as a political movement, more or less intertwined with movements around the world. it is hard to conceive migrants as a movement for changing the political equilibrium of an intricate country like italy. generally, their aspiration is to live in peace alongside local people. a common aspiration of all migrants is to achieve some material benefits and give a future to their children. however, migrants can rapidly change their time-horizons; thus, we cannot exclude in the future their political mobilisation in order to gain better and fuller integration in italy. moreover, they may be engaged by autochthonous political organisations for instrumental pur1 http://www.ilgiorno.it/milano/cronaca/foto/corteo-pro-migranti-1.3132029, accessed 21 may 2017. 245migrants to rural areas as a social movement: insights from italy poses (e.g. voting in primary elections). this gives momentum to the social movement approach. in order to justify the approach, there is also an intellectual homology between movement in society (generally up and down a scale of job prestige) and movement in space typical of migrants. integration of the spatial and social dimensions of mobility can be fruitful for rural studies as well. rural spaces become a valuable research topic because migrants often show an attachment to public places stronger than that of local people (rishbeth and powell, 2012), or new temporary ghettoes emerge in remote areas (eason, 2017). at the same time, situated micro-relationships assume a special value in rural areas. in those situations, families, friendship networks and religious groups find an easier placement. we frequently frame foreigners according to national citizenship or ethnic borders, neglecting smaller memberships and agency (de haas, 2010). according to this combination of places and relationships, we distinguish two polar types and an intermediate one: • seasonal work – non-accompanying family members, highly mobile • animal breeding work – very stable, with family and fixity to the place (azzeruoli, 2017) • farming, forestry and grazing – intermediate case in terms of mobility and family presence, living in the mountains (membretti, 2015; nori, 2015). foreigners working in the agro-industry are excluded from this classification. they may be part of an enlarged primary sector when employed in the big worker cooperatives of the po river valley that include most of the food filiere, like dairy herding, butchering, milling and storage (povoledo, 2011). for the year 2017 the italian government ‘decreto flussi’ fixed no more than 17,000 non-ec foreign seasonal workers for agriculture and tourism. that number shows the authorities’ under-evaluation of the need for foreign labour. in fact, the number of seasonal employees is much larger. estimates of some years ago put the number of temporary workers without italian citizenship at about 320,0002. of course, these are rough estimates; the number of people without documents or with documents but working irregularly, respectively identifying the black and grey labour markets, is unknown but presumably higher. in single localities of intense agriculture production, the numbers are in the order of tens of thousands of units. at the top of the table is the province of foggia, where about 20,000 seasonal migrants work; the second province is south tyrol (alto adige) with about 18,000 foreigners; then the province of verona (17,000). the provinces of  trento, latina and ragusa follow with about 13,000 temporary workers each. most of them are young males coming, in numerical order, from romania (117,000), india (28,000), morocco (26,000) and albania (24,500)3. in recent years, data on nationality have changed in favour of people from sub-saharan countries, while in the past, after the inclusion of east european countries in the eu, the opposite happened against people of maghreb origin. the statistics for people employed in agriculture, an official indicator based on sample interviews, state that there are 843,000 units, of whom 405,000 are foreigners.4 in 2 source coldiretti for 2013. http://www.rainews.it/dl/rainews/articoli/contentitem-c50f89ea-ee16-443f-ba0154ef410b72c6.html, accessed 5 june 2017. 3 ibidem. 4 year 2015; source: crea, (2017: p. 165). this is a figure from crea, which has recently integrated istat sample 246 giorgio osti relative terms the latter are clearly over-represented in the primary sector. for the italian labour market as a whole foreigners represent just over 10% of employees (2,360,000 on 22,465,000 plus 3 million unemployed). we can conclude by saying that there is a dramatic concentration of migrants in the agriculture sector (table 1). putting together different sources of information, roughly half of the labour force of the primary sector comes from abroad. however, this happens in single localities for two types of activities: permanent jobs in areas specialised in dairy and livestock activities; temporary jobs in a wide range of fruit and horticulture localities from north to south italy, as seen above. in each place, the work and residential conditions are highly variable, even if reports generally state that the situation is better in the north (zanuttig and pozzi, 2014). however, the dualism is more evident by sector than by region: migrants working in the pruning of trees and livestock rearing have better job conditions than those gathering fruits and vegetables both in the north and south of italy. in any case, geographically the phenomenon is fragmented; in that sense less visible and more difficult to deal with. visibility and critical mass are crucial aspects in political mobilisation. low visibility and territorial dispersion is even higher for a third category of foreigners, those working as shepherds and woodcutters. they are presumably very few and neglected, but relatively numerous in comparison to italians operating in the same sector. according to data from infocamere, entrepreneurs born abroad employed in agriculture in 2012 amounted to slightly more than 17,000; they represented 2.9% of the total of data with its own information (crea, 2015: p. 163). the difference between the two sources is striking because the crea figure is almost three times the istat one: 405,000 versus 133,000 foreigners employed. the question is therefore how many people are employed in agricultural sector? the answer varies according to the source: inps, the italian institute for social security, counts about 900,000 farmers and workers paying pension contributions (crea, 2017: p. 158). according to direzione generale dell’immigrazione e delle politiche di integrazione (2015), the non-eu migrants for whom a farm pays pension contributions amounted to 146,394 in 2014. statistics using the number of farms reach 1,470,000 units (crea, 2017: p. 41). table 1. work contracts activated according to economic sector and workers’ citizenship. absolute values and percentage of variation. year: 2014 (direzione generale immigrazione e politiche di integrazione 2015: p. 66) economic sector absolute values var. % 2014/2013 italians foreigners (a) total italians foreigners (a) totalof which: of which : total eu non eu total eu non eu agriculture 927.744 505.181 280.572 224.609 1.432.925 0,0 7,2 5,3 9,7 2,4 industry 622.039 164.487 40.074 124.413 786.526 9,5 5,9 0,9 7,6 8,7 constructions 429.885 138.709 55.634 83.075 568.594 3,1 -3,6 -6,0 -2,0 1,4 services 5.498.474 1.003.282 367.680 635.602 6.501.756 3,4 -3,8 -3,5 -4,0 2,2 commerce 583.094 84.740 25.457 59.283 667.834 4,4 7,7 3,9 9,4 4,8 total 8.061.236 1.896.399 769.417 1.126.982 9.957.635 3,5 0,2 -0,2 0,5 2,9 (a) employees born abroad without italian citizenship 247migrants to rural areas as a social movement: insights from italy foreign entrepreneurs (source: benvenuti and cordini, 2013: p. 16). their contribution to total added value was even lower: 1.6% compared to 13.8% in construction and 10.1% in commerce (ibid.: p. 7). noteworthy is the large presence of women, almost 50%, which is unusual among italian farmers (probably there are fiscal reasons). finally, the regional distribution is curious: in tuscany non-italian farm entrepreneurs are at the top both as percentage of foreign employers of all economic sectors (13.8%) and as percentage of all figures working within the agriculture sector (4.5%)(ibid.: p. 17). puglia – one of the most agricultural regions in italy – is in the opposite situation; but the usual italian centre-north vs south divide does not fit very well. the unexpected classification is explained by the country of origin of farmers in tuscany; most of them come from rich european countries, like switzerland and germany. thus, tuscany emerges as a place of large foreign investments rather than of incoming small farmers; nevertheless, in the same region there are important minorities engaged in cattle herding. this is a typical segmentation of poor and rich migrants in regions with high levels of tourism (see osti and ventura, 2012). the exception is romania, from which 5% of farmers arrive. people from this country are the first nationality among foreigners and present good levels of integration in many economic sectors. for some classifications, miners and quarries belong to the primary sector; in any case such activities are developed in rural areas and share the exhausting and humble image of agro-forestry jobs, discarded by italians. the difference is, however, that they are concentrated and taken by foreigners. in the piedmont alps there is a quarry whose workers are almost all of chinese origin (pignatta, 2013). the spatial concentration is sometimes combined with rapid ‘succession’ – replacement of one ethnic group with another – according to an old pattern elaborated by the chicago ecological school for urban areas in the 1920s. ecological succession is registered also for some temporary activities in agriculture. rough quantification allows a first conclusion to be drawn: the numbers are not high, there is no social alarm, as happens in urban neighbourhoods. rural autochthonous people do not show great concern, except in two situations: when migrants do not respect local rules of power/reward distribution, which has sporadically occurred in some places of southern italy, and when local primary school classrooms have a majority of foreign pupils. episodes of parental protest or school change have occurred in some localities of the po valley, where both intensive horticulture and livestock activity are developed. besides social reactions, the work and residential conditions of many foreigners both communitarian and not are objectively dramatic. this situation has been weakly mobilising national public opinion and strongly mobilising many non-profit organisations. again, it is a matter of visibility and political agency. the question is what role migrants play in the situations we have summarily illustrated. it is important to frame the issue in these terms – the agency of migrants in agriculture – because most models of analysis insist on structural processes or attitudes of indigenous people and politicians (semprebon, 2017). 3. a social movement framework the analytics of social movement can be schematically listed: • unusual action repertoires, practices at the margin of orthodoxy (kröger, 2013); • the determination to promote or defend values in the public sphere (protest, claim, 248 giorgio osti campaigning), symbolically called the piazza (public events) in opposition to palazzo (public institutions: rootes, 1997); • such values have a material as well as a symbolic value5; • intense network of relationships6 which can become a purpose itself and a source of wide and unexpected alliances called advocacy coalitions (ambrosini, 2005: the expression was first used by giovanna zincone). the dynamics of social movements: • relationships trigger mobilisation; the search for them is used to explain volunteers’ practices as well (mcadam and paulsen, 1993); • self organisation of resources; the resources are indeed many, material and immaterial; communication campaigns and fund raising are important. in this approach, competition among organizations is used as well, according to ecology of population dynamics (minkoff, 1997); • framing capacity: social construction of an ideal issue like justice, rights, health, commons. this is a cognitive approach (eder, 1996): mobilisation is seen as the capacity to design an issue and easily communicate it to the public. for example, the environmental issue was presented as a new systemic way to interpret the world. migration as well can be considered an epochal phenomenon; • structure of opportunity: that is, all the situations favourable to the raising of protest; for example, the sympathy of public opinion for an ethnic group, the facilitated access to social media, the absence of autochthonous leaders on a topical argument (e.g. job creation). indicators are the number of protests reported in the press, public sit-ins, case studies, rule changes after mobilisation, presence of migrants in trade unions and local associations. in the absence of systemic research on the mobilisation of migrants in italy, single case studies will be used. 4. the mobilization of migrants in italy the public protest, the ‘piazza’ situation, quite rarely takes the form of a strike7. abstention from work and demonstration in a public space have a general significance either because they concern many professional categories or because they are spatially pervasive. this kind of strike is called a ‘sciopero generale’ in italian. the cases monitored are national but not widespread throughout the country; they correspond to a network or 5 “the motivation for new social movement mobilisation cannot be reduced simply to material gain, but may concern the achievement of symbolic goals or the defence of symbolic resources” (woods, 2003: p. 315). offe (1985) talked of paradigm change that is much more stronger than a symbolic stake. 6 according to some authors, like diani (1992), this is the main feature of social movements; other authors underline the value of resource mobilization capacity, including organizational capacity, the presence of a charismatic leader, fund raising and communication campaigning (woods, 2003). 7 the press reported a first case in 2010, march 1st: arriva il primo “sciopero” degli immigrati. “un giorno senza di noi e l’italia si ferma” (vladimiro polch, la repubblica 26 febraury 2010); noteworthy is the critical comment on national unions “i grandi sindacati a livello nazionale non ci hanno supportato, eppure nessuno ha mai pensato di indire uno sciopero etnico”, which shows the suspicion of the ‘generalist’ italian trade unions towards single issue protests. 249migrants to rural areas as a social movement: insights from italy punctuated logic8. the foreign workers demonstrations of march 1st, 2010 were organized only in a few selected towns and the event was connected with a similar initiative in several european countries. in the end, the repertoire of action is more similar to a happening or a rally than to a traditional mass abstention from work causing a severe damage to the national economy. the special event has been organized in subsequent years, becoming a regular european ‘day’ of foreigner workers. such occurrences are very different from those organized after a bloody event. these mobilizations arise as spontaneous responses to cases of violence or abuse against foreigner workers. the protest action can be violent as well, involving clashes with the police and the destruction of street furniture. the first main demonstration occurred after the killing of jerry essan masslo in 1989. soon after his tragic death, the first antiracist demonstration ever organized in italy was held in rome, with the participation of more than 200,000 persons, italians and foreigners. masslo was killed in villa literno (campania), where he was harvesting tomatoes without any legal protection. his application for recognition as a refugee had been rejected. the second protest of foreigner workers able to reach national echo was the demonstration held after the slaughter of castel volturno in 2008 (again in campania, but almost 20 years later). six african people sitting in a pub were killed by a camorra’s (mafia local name) command. they were precariously employed in the area, most of them in temporary jobs in agriculture. after the massacre a spontaneous demonstration occurred, with roadblocks, cars damages, and severe vandalism of street furniture. the violent demonstration did not receive any support from the local population; on the contrary, cases of retaliation by inhabitants were reported. their reaction was a signal of an isolated foreign community coupled with a weak capacity to organize a peaceful demonstration. in any case the criminal episode raised wider awareness in national authorities and public opinion. we lack statistics on minor events of migrants protest. looking at the press articles, rare, local, low participated episodes pointed towards local authority and claiming for better control on seasonal work and living conditions of foreigners emerge. such demonstrations are supported by centri sociali (antagonistic communities, ruggiero, 2000), unions and other non profit organizations. their philosophy of action is still within the frame of protest and claim, what we call an advocacy movement, whose main focus is the respect for rights and the defence from oppressing actions of employers and the same authorities. some changes in this framework came at the beginning of 2011 when a group of small farmers, activists and migrant farm workers in the piana of gioia tauro launched the ‘sos rosarno’ campaign with the support of social economy organisations and the involvement of a variety of antiracism bodies (olivieri, 2016). that represented a novelty because the mobilisation was focused on the modes of production, and there was a specific ideology supporting the action: the conditions of production and reproduction in marxist terminology life conditions, housing and welfare services – must be united. small farmers joined forces with ‘braccianti’ to gain compliance with some basic rules of 8 see for example the protest by the agro pontino farm workers (sironi, 2016). the punctuated logic of this mobilization caused a middle level gravity and visibility. other information on the latina province is provided by omizzolo (2013), who uses the categories of transnationalism and transurbanism for sikh people working there. 250 giorgio osti employment and very important to get better prices for citrus fruits. the novelty is precisely a new focus on market exchanges. the deux-ex-machina – the device solving the problem becomes commercial links with rich markets of northern italy, in which many consumers are aware of uneven conditions for farmers and temporary workers of the south and are willing to pay more for citrus fruit production respecting some virtuous parameters. sos rosarno applied a sort of fair trade pattern. when the workers were almost all foreigners a specific ideology was developed. they are considered a reserve army of labour, precisely as defined by marx9: a mass of unemployed people to be used by employers for pressing their employees to accept further wage cuts. migrants, especially if they have a weak status (without documents or needing a job to keep the residence permit) are used to blackmail the workers and generate further profits for the entrepreneur. such an analysis provides a strong motivation to local activists for mobilising. in fact, being without contractual force and with precarious accommodation put foreigners in the hands of unscrupulous entrepreneurs and mediators. this is felt to be deeply unfair and trigger protest. mediators require special treatment. they are called ‘caporali’ (gangmasters in english). most of them are foreigners, of the same nationality as the workers that they hire. they are traditional figures in italy countryside (perrotta, 2014; avallone, 2017) able to achieve great power between absent landowners and poor rural proletarians. they are crucial for maintaining the labour force’s expropriation. besides relational abilities and strong personalities, they are generally accepted by migrants because they are ‘one of us’ and they often do the same manual work but with the role of coordinator (olivieri, 2016: p. 73). the more powerful and accepted the gangmaster is, the less probable is the formation of a sharp contraposition between seasonal workers and the employer. to be noted is that the latter is often a small farmer, who can work on the land alongside the foreigners. all these factors reduce the social distance and the chances of a classic labour/capital conflict arising. caporalato is widespread in southern italy, but there are frequent cases in the north as well (marzorati et al.,  2017). furthermore, in northern italy gangmasters have grown in sectors different from agriculture, especially in logistics and transportation sectors. the difference with respect to seasonal work in horticulture is that permanent work in those sectors requires gangmasters to adopt a more formal approach. thus, they create ‘fronting’ cooperatives with which they sign regular contracts of labour provision, so they are able to conceal abuses against formal coop members, all of them being foreign weak manual labourers. 9 “the one classical marxist theorist who made useful additions to marx’s reserve army analysis with respect to imperialism was rosa luxemburg. in  the accumulation of capital  she argued that in order for accumulation to proceed “capital must be able to mobilise world labour power without restriction.” according to luxemburg, marx had been too “influenced by english conditions involving a high level of capitalist development.” although he had addressed the latent reserve in agriculture, he had not dealt with the drawing of surplus labor from noncapitalist modes of production (e.g., the peasantry) in his description of the reserve army. however, it was mainly here that the surplus labor for global accumulation was to be found. it was true, luxemburg acknowledged, that marx discussed the expropriation of the peasantry in his treatment of “so-called primitive accumulation,” in the chapter of capital immediately following his discussion of the general law. but that argument was concerned primarily with the “genesis of capital” and not with its contemporary forms. hence, the reserve army analysis had to be extended in a global context to take into account the enormous “social reservoir” of non-capitalist labor” (foster et al., 2011). 251migrants to rural areas as a social movement: insights from italy work cooperatives dominated by a gangmaster are growing also in agriculture, especially in those localities where controls by the authorities on black and grey jobs are more frequent. cooperation, a juridical formula in which members are both owners and employees, is another way to decrease the chances of worker mobilization. a social movement needs an enemy. generally, this is the owner of the means of production; if the worker and the owner coincide, a strong motivation for enmity disappears. thus, cooperatives can be seen as an astute means for controlling class conflict in the countryside. on these aspects the theory of social movements can provide further insights. mobilisation can either be calmed structuring the deprived farm workers in fronting cooperatives or it can explode in collective uncontrolled anger of migrants. the former is a rationalization of unequal relationships; the latter is a growth of irrationality. seasonal workers in agriculture are therefore caught in a trap. the mobilization for changing the unequal distribution of rewards is emptied transforming the labour force in co-workers or vanished because of explosion of rage, which alienates the sympathy of public opinion. besides this interpretation two other approaches to mobilisation are fruitful. one is based on the indian literature concerning reactions of poor people to extreme abuses. francesco caruso thinks it is possible to use their conflict action repertoire to interpret the different outcomes of the rosarno and castel volturno migrant rebellions. in the former case, he considers the demonstration by foreign workers (braccianti) to be an extreme and desperate attempt to defend their nude life10; this demonstration did not bring any material benefit to the migrants, but at least they testified they were alive. in the latter case the protest was violent as well, but more cleverly organized by a group of migrants from ghana. they channelled the protest march toward the city hall, where they met authorities; the result was a special residence permit for 2,000 seasonal workers. caruso (2016: p. 68) explains the different results: in castel volturno the permanent and deep-rooted presence of migrants had allowed the maturation of self-organised initiatives. self-organisation varies in even very similar contexts, both dominated by mafia and subordinate agriculture11. the category of self-organisation introduces the second alternative approach to mobilisation. self-organization is widely mentioned in the literature on social movements, especially in the theory of ‘resources mobilisation’ (donati, 1995). it is frequent also in the description of farm workers in italy both in the south and the north. the surplus of organizational capacities for dispersed workers derives from: • cultural and political motivations of the migrants themselves. religion is very important, especially when there is an explicit proselytizing programme (see omizzolo, 2013); • non-profit organizations, whose members are mainly italians, are of three types: charities helping migrants to find basic accommodation; advocacy groups helping with job 10 “in rosarno the uprising is configured as a self-defense tool and as nude life affirmation: those who are accustomed to ‘bending their heads’, undergoing obscene and silent abuses, living in an extreme state of exploitation and subjugation, arise and rebel when the biopolitics assumes the features of the thanatopolitics” (caruso 2016: p. 67). 11 in the paper this food regime is called ‘californian’, based on extreme exploitation of seasonal workers, low prices of products and high rewards for mediators of labor and crops/means of production (agribusiness); see also corrado (2017). 252 giorgio osti rights and residence permits; social cooperatives providing alternative and fair jobs (see ambrosini, 2005); • trade unions; since the beginning of modern migration they have sought to play their role with varying strategies and results (see allievi, 1997); • local authorities: in italy it is common opinion that they generally work for the good inclusion of migrants provided that the help measures have low visibility. this would protect mayors and council members from adverse public opinion (marzorati et al., 2017). self-organization, therefore, is never absolute; it does not mean self-sufficiency of a group, but it is the product of an assemblage of initiatives and actors. in italy there is a variably combined coalition in favour of migrants able to deal with the most extreme situations of residential degradation. many groups intervene in the emergency providing food and lodging. ‘coalition’ is a term denoting not only political convergence in defence of migrants’ rights, but also collaboration in practical assistance. it is frequent to find catholic church volunteers working together with members of antagonist groups and civil servants of municipality. farmers’ and workers’ unions are involved in the migrants issue, even if in principle they have opposite interests. agriculture employers’ organizations realize that it is unsound to defend their members’ activities without assuring minimal living conditions for their employees, whether temporary or permanent. trade unions are institutionally engaged in the protection of workers. in both cases there are ambivalences as well. farmers’ organisations cannot insist too much on compliance with the rules because their members make abundant use of black and grey labour. nonetheless, inclusion in local pro-migrant agreements induces farmers’ associations to be more attentive to human values. a second inducement is market reputation that helps as much as food consumers are concerned and take responsibility of regular work demand in agriculture (i.e. a fair trade pattern). trade unions are deeply involved in the protection of foreigner migrants against any abuse. however, researchers talk of insufficient action: “trade unions have not managed to aggregate them” (semprebon et al., 2017: p. 10). “the trade unions, often committed to imposing regular hiring on employers, do not have a sufficient number of members to have a decisive impact” (scotto, 2016: p. 86), “indeed, [in southern italy, trade unions] have been accused of contributing to the development of patron-client practices, carrying out their mediation task as it was a distribution of charitable resources” (ibid.). in northern italy workers’ unions attend all the public committees in which the seasonal work issue is dealt with; but that action can be very formal and far from the real involvement of foreigners (cnel, 2002: p. 153). the causes of insufficient capacity of trade-union engagements are several: dispersion of places, low cooperation by migrants themselves, hostility of farmers, trade unions’ almost exclusive reliance on controls by the public authorities. evidently, the unions’ resources are limited; moreover, their exclusive confidence on the advocacy role creates some problems with seasonal workers themselves. migrants absolutely want to get a job as soon as possible without paying much attention to rules; strong claiming for respect of rules can be an obstacle to this urgent goal. in this sense, organizations that offer alternative jobs are more successful than trade unions, at least when the latter comply too closely with their ‘advocacy role’. indeed, in the past metalworker trade unions promoted the 253migrants to rural areas as a social movement: insights from italy creation of worker cooperatives to which the entrepreneur outsourced simpler tasks. this organisational choice increased the power of unions with, however, some trade-offs: paternalist attitudes during personnel hiring, accusations of collusion with the company, mismanagement12. the weak strategy of farm worker unions shows that multifunctional third-sector organizations – those more equipped for providing advocacy, assistance and jobs – are more successful. these tasks are indeed united in the most flexible non-profit organizations. multitasking is adopted by the public bodies, too. local and national authorities have understood that ‘work’ is a key factor in the control of migration flows. accordingly, they have created together with unions, farmer associations and charities several employment opportunities for example in green care or green spaces maintenance. this emerges in the most dynamic rural areas of south and north italy. 5. discussion and conclusions “indeed” – as stated in the 22° statistical report on immigration (caritas and migrantes, 2012: p. 260) – “the link between agriculture and migrant workers is indissoluble. the tendency is positive and data demonstrate that farms employing migrant workers have benefited also in terms of productivity”. this statement, used for the paper inscription, is quite optimistic: it claims a solid and productive relationship between agriculture and migrants. indeed, the italian situation is geographically variable and socially segmented: quantitative analysis shows that the bulk of the issue concerns foreign employees, most of them seasonal workers. this represents a marked difference with mainstream italian agriculture, made of small self-employers (coltivatori diretti). the employment and residential conditions of migrants are still dramatic in rural areas. mountain areas are an exception because foreigners working in the primary sector and accompanied by their families are seen as means to repopulate deserted and remote villages. the situation is improving but in a point-like way according to the implementation of single temporary projects. the two main policy instruments – migration admission quotas and random controls on farms – are both insufficient. thus, virtuous initiatives are in the hands of local bodies, when fortunately a good public/non-profit partnership is established. this confirms a ‘molecular’ mode of migrant integration applicable to every economic sector and area of the country (ostanel and fioretti, 2017). high segmentation means that different migrant situations co-exist in the same area. the case of tuscany foreigners buying farms in ‘chiantishire’ beside shepherds living in poor conditions in the hills is emblematic. thus, we have two combinations operating in the italian context: • weak economy and low education areas attracting low skilled migrants and vice versa; they give rise to a spatial division of labour, with a concentration of best educated foreigners in the cities of northern and central italy, and 12 “social cooperatives, born as movement organisations against the exploitation of farm workers and proletariats in the po valley at the beginning of last century, have dramatically changed similarly to the decline suffered by workers’ parties and trade unions” (michelino, 2015), my translation. 254 giorgio osti • strong economy and high education areas attracting low skilled migrants occupying low-lewel jobs like food and assistance services; they give rise to a social division of labour. migrants will become a social class, if not an underclass (extreme poor people living near more luxury urban zones). the two trends are replicated in italian agriculture: some areas – sorts of agriculture district (perrotta, 2017) will further develop in terms of mechanisation and digitalisation, attracting foreigners for a wide range of tasks, from simplest ones to those requiring high skills. other areas will attract almost exclusively blackmailed low waged workers for very simple tasks like manual crop harvesting. this dualism can affect also agro-tourism and green care services, in which the physical and relational aspects of work are crucial. if the spatial division of labour trend prevails, we may expect new waves of migrants, maybe with further ecological succession of ethnic groups. if the social division of labour is more likely, the flow of migrants into the italian countryside will become more selective. if we look at tendencies abroad in very specialised districts (e.g. california), the combination of low and high skilled employees appears the most likely. some works, especially crop and food manipulation, will be done by poor workers. of course, we must not lapse into a sort of technological determinism, as if the fortune of migrants in the countryside was linked to mechanisation (or to agriculture 4.0). the paper challenge is based on the high importance of relational factors, specifically the political capacity of farm/rural migrants to selforganise their life and work. we have seen that migrant mobilisation is limited in italy; its strength depends on a combination of three factors: 1) long term permanence in the rural area or regular return each year, i.e. a special form of place attachment; 2) religious or national diasporas, called also transnationalism and understood as a project of return to the motherland cemented by an ideology and a praxis; 3) capacity to press authorities at national level for residence permits or refugee status recognition. this reduces the capacity to blackmail foreigners both employed and unemployed. however, advancements in legal status, arriving for example to allow voting in elections for long term resident foreigners, can change the farm workers’ marginality very little. their upgrading social mobility greatly depends on external factors and precisely on a farmers’ (saint) alliance with consumers for higher prices in exchange for organic/low input products. filiere mediators (large scale retailing) should attend the alliance, too. this means that such a pact cannot be based on strict computation of rewards of each filiere member but on values of justice and commons protection. in this scenario foreign farm workers have a more balanced and realistic image: if they do not actively share such values of justice and environmental sustainability they remain within the trap of pure and rough economic stance with a very low bargaining power. norms protecting worker rights and official on-site inspections are not enough for assuring their contractual position. then, the most profitable path is that migrants themselves participate as much as possible to projects in a new moral economy. in italy there are many projects promoted by farmers, supported by public bodies and disseminated by eccentric rural-urban networks (http://barikama.altervista.org/; 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citation: r. zucaro, v. manganiello, r. lorenzetti, m. ferrigno (2021). application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context. bio-based and applied economics 10(2): 109-122. doi: 10.36253/bae-9545 received: july 30, 2020 accepted: june 23, 2021 published: october 28, 2021 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: meri raggi, fabio bartolini. orcid rz: 0000-0001-9386-7612 vm: 0000-0003-0348-6600 rl: 0000-0003-3346-0874 mf: 0000-0002-5347-0984 application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context raffaella zucaro, veronica manganiello, romina lorenzetti*, marianna ferrigno council for agricultural research and economics research centre for agricultural policies and bioeconomy (crea-pb), via po 14, 00198 roma, rm, italy. e-mail: raffaella.zucaro@crea.gov.it, veronica.manganiello@crea.gov.it, romina.lorenzetti@crea.gov.it, marianna.ferrigno@crea.gov.it. *corresponding author: romina.lorenzetti@crea.gov.it abstract. in the context of climate change, one of the eu’s major political efforts focus on water management. public investment is carried out considering several drivers, from economic development to demographics, climate, and pollutants. meanwhile, the need for evaluation methods is also increasing, so their development has grown in recent years. among these, multi-criteria analysis methodologies (mca) have taken on great importance. this work aims to demonstrate the usefulness of mca in addressing crucial environmental issues, such as the use of water resources for agricultural and food production. the document presents an application of mca for the ranking and selection of projects to be financed under the italian national plan on water resources. the plan is part of the national initiatives planned for the adaptation of the agricultural sector to climate change. the selection criteria have been identified following a participatory approach, and to respond to both the challenge of climate change and the limited availability of funds. mca is used to select the best projects to be financed with the available amount. the italian experience confirms the effectiveness of mca and highlights how the involvement of both decision makers and stakeholders is necessary for a successful application of mca to environmental issues. keywords: drought risk, water management, investment database, reservoirs, climate change. 1. introduction in recent decades, climate change has caused worrying drought events across europe, even in countries where past meteorological drought had been rare. this situation has led eu member states to monitor the availability of and need for water, to provide timely alerts in the event of drought and identify possible actions to undertake in the event of a crisis. recent studies carried out on the italian territory have shown a growing climate heterogeneity due to climate change (zucaro, 2017; ispra, 2018). in the past, drought events http://creativecommons.org/licenses/by/4.0/legalcode 110 bio-based and applied economics 10(2): 109-122, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9545 raffaella zucaro et al. were mainly concentrated in the southern regions and islands, while, in the last 20 years, central and northern italy have also suffered from recurrent droughts. the agricultural sector is the most exposed to the effects of climate change (mahato, 2014), there is therefore a need for targeted investments increasing the preparedness to face extreme events. as f loods and droughts affect both the quantity and quality of water, they contribute to environmental degradation and loss of ecosystem services. thus, all member states (mss), including italy, are implementing adaptation and mitigation measures. international institutions, and in particular the european union (eu) are steering their policies and economies towards long-term sustainability. in recent years, there has been a crescendo in the political narrative aimed at promoting climate change adaptation and mitigation. several actions have been proposed to implement these policies, namely: enhancing knowledge in the field of climate change adaptation and mitigation policies (eu adaptation strategy, european commission, 2013); managing water risks and disasters; ensuring good water governance and sustainable investment for water services (oecd, 2015, odec 2016); encouraging the sustainable use of water for agriculture and the introduction of priority actions for the adaptation of agriculture to climate change (fao – wasag global framework for action to cope with water scarcity in agriculture); taking account of climate adaptation in public and private investments (european green deal, european commission, 2019). several measures, singly or in combination, can be taken to cope with drought risk in agriculture, climate change adaptation, and sustainable water management. these include regulatory measures, risk management measures, water governance, research and innovation, and structural measures. there is no single decisive action, but the most effective one or a combination of them should be taken. public investment in water distribution infrastructure allows for greater and more constant availability of water for irrigation and greater efficiency in water use, by reducing water abstractions, introducing instruments for water metering, and increasing the use of non-conventional water. these investments can also contribute to achieving the objectives of the water framework directive (wfd, 2000/60/ ec) of ensuring the availability of quality water for the needs of people and the environment. this is possible through the improvement of the ecological quality of water bodies and the conservation and restoration of areas of naturalistic interest (e.g. nature 2000 sites). at the european level, specific funds have been allocated to finance irrigation investments as a response to the water crises of 2003 and 2007. these investments aimed to increase water storage and irrigation efficiency, through the modernization of existing assets, the building of new reservoirs, and the recovery and improvement of existing ones. to decrease the dependency on conventional sources and reduce withdrawals from natural water bodies, the promotion of the reuse of treated wastewater for irrigation purpose is also pursued. in italy, with the aim of ensuring the integrated management of water resources, a steering committee has been set up to coordinate the various administrations responsible for water: the steering committee addressing investments in cross-sectoral investments, responding to the recommendations of the european commission communication “addressing the challenge of water scarcity and drought in the european union” (com, 2007) 414 final). following this strategy, in 2017 the italian government financed the “national plan of interventions in the water sector” (budget law 2018, december 27, 2017, no. 205). the national plan was finalized to modernize and complete the national water distribution network (including the irrigation network) and to build new reservoirs. the national plan also foresaw the adoption of an extraordinary plan, consisting in the implementation of urgent interventions against drought, with a focus on multipurpose reservoirs. at the river basin scale, reservoirs are considered as effective climate change adaptation measures, especially where natural water availability is highly variable throughout the year. in fact, they retain water to be released during periods of scarcity, thus sustaining irrigated agriculture and increasing the availability of water for irrigation (biemans, 2011). in addition, reservoirs have ecological and recreational functions, ranging from the conservation of protected migratory species (mascara, 2010) and biodiversity (deacon, 2018, croce, 2015), to cultural and recreational purposes. that is why some of them are now defined as natural conservation areas. the case study shows the procedure followed by the council for agricultural research and economics (crea), on behalf of the italian ministry of agriculture (mipaaf), in selecting interventions to help the agricultural sector adapt to climate change. the interventions were selected according to the objectives of the extraordinary plan applying a multi-criteria analysis (mca). mca is a non-monetary method of ranking and prioritizing the characteristics of the projects submitted for funding. the paper aims to present the feasibility and usefulness of mca in identifying the most effective project proposals in the field of water, stating that this method 111application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context bio-based and applied economics 10(2): 109-122, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9545 can allow the inclusion of different disciplines in a single evaluation frame. in addition, mss need appropriate methods to assess ex ante effectiveness of investment projects, including their potential impacts on natural resource protection. the italian experience can therefore be extended to other countries. 2. data and research methodology 2.1 multi-criteria analysis multi-criteria analysis (mca) was selected as a method for classifying and selecting projects, as it allowed consideration of the different priority elements according to the requirements by the funder, and the needs in term of adaptation to climate. mca was considered the appropriate method as it allowed several specific agricultural and environmental conditions to be applied (figueira et al., 2005). this facilitates the achievement of increased efficiency and sustainability in the use of natural resources in line with the eu guidelines. several papers have been published over the last 30 years on the empirical applications of mca to a range of nature conservation topics, including: conservation priority and planning; management and zoning of protected areas; forest management and restoration; mapping of biodiversity, naturalness, and wilderness. many references can be found in several reviews, such as: mendoza et al. (1986); romero and rehman (1987); tarp and helles (1995); hayashi (2000); kangas et al. (2001); steiguer et al. (2003); mendoza and martins (2006). a recent and extensive review of the applications of multi-criteria decision analysis was carried out by adem esmail and geneletti, (2017), based on 86 papers and dealing with empirical applications in nature and biodiversity conservation. decision-making in environmental management requires more and more comparison alternatives to achieve multiple and competing goals. indeed, many of the following objectives must often be considered: ensuring a sufficient quantity of water for both people’s needs and the environment (water framework directive – implementation of the water framework directive), economic development, addressing the challenges posed by demographic change, climate change, and emerging pollutants. the public administrations responsible for determining and evaluating strategic choices need systems and/or selection criteria that are as objective as possible and not influenced by endogenous factors. this problem is particularly acute when it comes to public funding. in this context, multi-criteria methodologies have become important because they provide valuable help in choosing between alternatives, especially since the classic economic and monetary surveys do not represent the plurality of aspects that these problems present (skonieczny et al, 2005). compared to monetary methods based on welfare economy principles (costbenefit analysis, cba), non-monetary methods that also consider natural resources and are based on decision theory are an alternative when assessing the effectiveness of the interventions. while cba is mainly applied to project evaluation to improve a specific environmental service, non-monetary methods such as mca are used for issues related to territorial and environmental assessment and planning, as they can also evaluate qualitative information. currently, several books deal with multi-criteria methodologies as applied to natural resources management (e.g. zeleny, 1984; yoon and hwang, 1995; malczewski, 1999; belton and stewart, 2002). basically, mca is applied with the following typical steps: 1. structuring of the problem and the decision-making network. 2. data acquisition and processing. 3. normalization (linear normalizations or value and utility functions). 4. criteria and weight allocation. 5. calculation and sorting of alternatives (e.g. with outranking methods; graphic methods; scoring methods). 6. results. 7. sensitivity analysis (optional). the next paragraph describes how these steps were applied to the case study. 2.2. applied methodology in this study, the listed steps of the multi-criteria analysis were slightly reformulated, as follows. 1. structuring of the problem and the decision-making network. there are many mca approaches that differ in terms of computational complexity, level of stakeholder engagement and time and data requirements. to protect the agricultural sector against drought events, policymakers identified structural measures, concerning infrastructure interventions on multipurpose reservoirs for water collection during rain periods and water saving interventions. a specific fund has been set up to these objectives, governed by specific rules. water management operates within an interdisciplinary framework that seeks to ensure the protection of resources (cugusi and plaisant, 2019; dir. 2000/60/ec; dlgs 152/1999; autonomous region of sardinia, 2005), and requires the integration of ecological, economic, 112 bio-based and applied economics 10(2): 109-122, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9545 raffaella zucaro et al. and socio-political elements of different territorial scales. therefore, all the institutions responsible for water management (ministries of agriculture, environment, infrastructure, regions and river basin district authorities (rbdas)), local agencies for irrigation water management (lawms), and stakeholders were involved in the decision-making network of this case study. the involvement of the stakeholders was a selling point in the methodology adopted by the crea. 2. data acquisition and processing. for the collection of data useful for the analysis, the crea, mipaaf, and regions with the support of the lawms, identified the infrastructure priorities to be financed through national and eu resources. all information was stored and managed by dania, the national database of investments for irrigation and the environment (http://dania.crea. gov.it/). it was implemented by the crea for mipaaf, for the collection of structural and financial information on financed and programmed projects. information about investments were provided by regions and by sigrian, the national information system for water resources management in agriculture (https://sigrian.crea.gov.it) managed by the crea (mipaaf, 2015). sigrian contains data from the italian national irrigation system and is the national reference database for the collection of data on water used for irrigation on a national scale. in this work, sigrian was used to collect information on the use of water resources and the extent of the irrigated area affected by the projects for the estimation of the catchment area. starting from dania information, mca was applied to identify a series of projects to be financed up to the amount of 80 million euros, allocated by the extraordinary plan. 3 4. criteria and weight allocation and normalization. the criteria and their weights, as well as related attributes and scores were defined in compliance with the requirements and objectives of the financing instrument, by a technical committee of experts through focus group discussions. the focus group involved representatives of the aforementioned institutions, in the application of a participatory approach. through debates between the actors of the technical committee, shared choices were developed. the participatory approach minimized decision makers’ subjectivity in weight and score allocation, which is a very important and delicate step. indeed, it can influence the final order of alternatives and, therefore, significant involvement is appropriate. within the technical committee, the criteria were defined in accordance with the objective and priority of the fund. once the criteria were decided, several possible attributes for each criterion were defined. at first, the normalization step was bypassed in this case study. since the main aim of normalization in mca is to make quantities comparable, this was achieved by using nominal attribute quantities, to which scores must then be assigned. the different attributes of the criteria were sorted according to their compliance with the selection aims. the weight of the criteria and the score of the attributes were assigned at the same time. applying a monotonically linear utility function, a discrete scoring scale was adopted, with a step of 1, in all the criteria. in a descending way, a maximum score was assigned to its best attribute and a lower score was assigned to the other attributes, according to the preferences of the technical committee, and with reference to the selection goals. in this way, the weight of a given criterion coincides with the highest score assumed by its best attribute. attribute scores ranged from 0-1 to 0-4, while the weights assigned to the criteria ranged from 1 to 4. with this operative choice, the discretions and uncertainties implied in weights were shifted to the definition of scores. for this reason, the technical committee verified that the highest score of each attribute truly represented the weight that the individual criterion should have had compared to the others. 5. 6. calculation and sorting of alternatives and examination of results. the ranking of alternatives, namely the projects, was achieved by applying a scoring method as a type of aggregation. the scoring method classified the alternatives by assigning a numerical evaluation for each of the attributes considered; the scores obtained for each criterion were summarized in a “summary indicator” which aimed to represent the effectiveness of the proposal in achieving the objectives of the fund. the number of projects financed was the maximum obtainable on the basis of the defined budget allocated by the budget law. the direct assignation of a value to the attribute and the use of a linear aggregation method with scores simply added together, have made the method used for the evaluation of the proposal clearer to the potential beneficiary. consequently, even the selfassessment required in the submission phase of the projects was more feasible. self-assessment was introduced because the rbda was called upon to prioritise proposals, mainly based on the declared information. 7. sensitivity analysis. the shared approach gave a certain degree of robustness, as the steps of criteria and weight allocation were based on the expert judgment of the technical committee. the order of importance of criteria and attributes was considered clear and objective, as it was shared among all the stakeholders. nevertheless, in this study sensitivity analysis was carried out to verify the stability of the results, testing some changes in the weight of criteria (skonieczny g. et al. 2005). new 113application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context bio-based and applied economics 10(2): 109-122, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9545 weights were allocated to the criteria in compliance with the aims and rules of the fund and without upsetting the priorities established by the technical committee. to perform sensitivity analysis, as first step, the attribute scores were normalized to the maximum value that each attribute could assume (maximum row normalization), so that all the attribute scores are between 1 and 0. then, weighted linear combination (wlc) was used (malczewski and rinner, 2015) for the aggregation. following equation 1, the normalized value of attribute score (xi) was multiplied for the tested weights (wi), and the new summary indicators (s) were returned for each alternative. (1) the new rankings of the alternatives, given from the different tested weight assignments, were compared with the original ranking by means of the spearman’s rank correlation coefficient, that is a non-parametric measure of rank correlation, following equation 2 (clef, 2013): (2) where i = paired score, x and y are the ranks, and x-bar and y-bar are the mean ranks. the analysis of the results was carried out taking into account that the spearman correlation between two variables is high when observations have a similar rank, up to a correlation of 1 for identical ranks. 3. results and discussion this section describes the detailed application and results of each step described above. 3.1 structuring of the problem and of the decision-making network the case study concerned the application of mca when selecting infrastructure interventions to facilitate adaptation of the agricultural sector to climate change. the financial instrument identified was the extraordinary plan as part of national plan of interventions in the water sector. it was introduced by the budget law 2018 to finance urgent interventions concerning: preferentially executive projects (the final phase of the project was also accepted); multipurpose reservoirs; water saving in agricultural and household use. the decision-making network identified included the competent ministries of infrastructure (mit), environment (mattm) and agriculture (mipaaf), the 7 rbdas, the 21 regions and autonomous provinces, and the lawms. according to italian legislation, the regions are responsible for irrigation water management and reclamation, while the lawms, reclamation and irrigation consortia, and land improvement consortia are territorial authorities and actuators of the interventions. 3.2 data acquisition and processing the database at the time of the study, dania included 894 irrigation infrastructure projects, representing almost 6 billion euros. information was collected in the database for each project for their evaluation, in accordance with the established criteria. the stored data were acquired in collaboration with regions and processed with identification data (title, actuators, etc.), technical features of projects (project objective and type, project stage, etc.), intervention cost, vulnerability of the intervention area to drought and hydrogeological risk, regional priority of intervention (1-high, 2-medium, and 3-low). starting with the stored projects, a first selection was made before applying the mca according to the following eligibility criteria, in line with the budget law objectives and in the framework of financing fund rules: • project stage = executive (because quickly implementable); • type of intervention = interventions on multipurpose reservoirs and water saving interventions in agriculture; • regional priority of intervention = level 1 (urgent interventions). a dataset of 55 projects was identified on the entire national territory, representing a total amount of almost 360 million euros. the rbdas were asked to give priority to projects in this dataset, to which mca was applied. 3.3 criteria and their attributes some of the adopted criteria related to technical elements and aims of projects, while others referred to effectiveness, in compliance with the aim and priority of the fund, as established in law 205/2017. as mentioned, the extraordinary plan dealt with multipurpose reservoir (irrigation and household) and the priority water saving objectives. more in detail, the plan includes a) completion of interventions concerning large existing dams or unfinished dams; b) recovery and expansion of the reservoir capacity, waterproofing of large dams and safety of the main water derivations for significant river basins in seismic areas classified in 114 bio-based and applied economics 10(2): 109-122, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9545 raffaella zucaro et al. zones 1 and 2 and at high hydrogeological risk. as a result, the following project criteria were identified: • water resource use. multiple uses were favoured over exclusive ones. • site sensitivity in terms of seismicity and hydrogeological instability. great importance was given to the presence of these hazards. one of the priority objectives was identified as safety in seismic areas (classified in zones 1 and 2) and in areas of high hydrogeological risk. the technical committee decided to assign more importance to areas at seismic risk than to the landslide. therefore, the same value was associated with the presence of hydrogeological risk and the presence of the lower class of seismic risk (fourth class). increasing importance was given to other seismic classes, because of the growing risk. • catchment area in equivalent inhabitants – ei (given 40 equivalent inhabitants –per irrigated hectare). this criterion intended to indicate the impact of the project on the territory in term of users of financing (population or agricultural areas). three classes were created for this continuous variable (ei > 500,000; 300,000 ≤ ei ≤ 500,000; ei < 300,000), both based on expert assessment, and on assessments based on the dania dataset. in addition, it was necessary to provide a unique criterion for household, irrigation, and multiple interventions. thus, the irrigated area was returned to the ei, with a conversion criterion of 40 ei per hectare of irrigated surface. • project stage. the attributes represented the possible status of the project. the extraordinary plan focused on the final and executive level. • project objectives. this criterion aimed to select projects compliant with fund objectives. so, completion of existing dams and the recovery or extension of the reservoir capacity were among the priority objectives. in addition to these, a third class was created for projects aimed at the improvement of the derivation efficiency. • project type. this criterion integrated the technical information agreed in the previous one, detailing the specific type of intervention. the following attributes were identified: securing; extraordinary maintenance; completion; new intervention. • co-financing. this was considered a reward element by the technical committee to promote public-private partnership. • possibility of subdivision into lots. this was considered a reward element by the technical committee, since it made it possible to assess the multiple financing of a project, even with different funding sources at different times. in addition, three effectiveness criteria were identified, as follows. • project effectiveness (ratio of the intervention cost to the number of equivalent inhabitants corresponding to the irrigated area covered by the project: project cost (€)/ei). the criterion was described in 3 classes, namely < 25€/ei, >=25 €/ei <50 €/ei, >=50€/ei. they were created according to the evaluation by experts, also through the dania. • territorial effectiveness. this reflected a classification of the italian regions in relation to the percentage of their regional territory under risk of desertification; according to the scientific reference available for the national scale (ceccarelli et al., 2006), 3 classes were adopted, namely: >40% very sensitive danger (basilicata, marche, molise, puglia, sicily and sardinia); > 40% moderately sensitive danger (abruzzo, campania, emilia-romagna, lazio, piedmont, tuscany, umbria and veneto); little sensitive (other regions). • district priority. this was the assessment provided by the rbda on the effectiveness of the project, in the context of the specific river basin management plans. this criterion was considered by the technical committee to be the most important of the effectiveness criteria, as it was evaluated through expert assessment by each rbda and summarised several environmental aspects. in particular, each rbda established their priority based on the information listed above and considering the objectives of the water framework directive (2000/60/ec) and the main issues in the national plan. for the estimation of district priority, the factors considered were: consistency with another district plans; criticality of the intervention area, such as the hydraulic risk level; hydro-morphological aspects; environmental pressures; expected benefits in terms of pressure reduction on water bodies; expected benefits in terms of improving the water balance at river basin level. the level of effectiveness dealing with the strategic environmental feature, was described with four attributes: strategic, relevant, important, required. 3.4 weight and score allocation the weights assigned to the criteria are shown in table 1. the criteria with the highest weight were: district priority, seismicity degree, project type, and project stage (weight 4). they were of equal importance and were followed by water resource use, project objective, 115application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context bio-based and applied economics 10(2): 109-122, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9545 catchment area, and project effectiveness, each with a weight of 3. for an easier understanding of the order of the criteria, a matrix was developed (table 2). the attributes assigned to each criterion and their scores are shown in table 3. the normalization of the score is also reported because it was used to perform sensitivity analysis. although the project stage was used to enter the selection, it was included in the mca criteria. the criterion cannot affect the mca result in any way since each alternative evaluated had the same score. however, it was decided to keep it in the process because the same method was adopted by the mit, on another group of projects to be financed with the same fund. unlike mipaaf, the mit did not choose to focus only on executive projects. therefore, it was necessary to maintain the criterion in order to make the results of the two selection processes comparable. 3.5 calculation and sorting of alternatives and selection of the projects the summary indicator returned from the sum of the scores obtained from each project. it represented the effectiveness of the intervention proposal to meet the objective of the fund. based on the defined budget allocated by the budget law, 10 projects were financed in the amount of almost 80 million euros (fig. 1 and table 4), all with a summary indicator of 22 to 26. the 10 projects financed were in 7 regions (veneto, lombardy, emilia-romagna, tuscany, abruzzo, sicily, and sardinia) and were implemented by 8 lawms. figure 1 shows the location of the lawm which received funding. table 1. criteria and their assigned weights . criterion weight id name project criteria 1 water resource use 3 2.1 site sensitivity seismicity 4 2.2 site sensitivity hydrogeological instability 1 3 project objectives 3 4 catchment area 3 5 co-financing 1 6 project type 4 7 possibility subdivision in lots 1 8 project stage 4 effectiveness criteria 9 project effectiveness (ratio cost/ equivalent inhabitants) 3 10 territorial effectiveness 2 11 district priority 4 total 12   33 table 2. criteria order: score matrix. criteria si te se ns iti vi ty hy dr og eo lo gi ca l in st ab ili ty c ofin an ci ng po ss ib ili ty su bd iv isi on in lo ts te rr ito ria l eff ec tiv en es s pr oj ec t e ffe ct iv en es s w at er re so ur ce u se pr oj ec t o bj ec tiv es ba sin u se rs d ist ric t p rio rit y si te se ns iti vi ty se ism ic ity pr oj ec t t yp e pr oj ec t s ta ge site sensitivity hydrogeological instability 1 1 1 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 co-financing 1 1 1 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 possibility subdivision in lots 1 1 1 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 territorial effectiveness 2 2 2 1 0.7 0.7 0.7 0.7 0.5 0.5 0.5 0.5 project effectiveness 3 3 3 2 1 1 1 1 0.8 0.8 0.8 0.8 water resource use 3 3 3 3 1 1 1 1 0.8 0.8 0.8 0.8 project objectives 3 3 3 4 1 1 1 1 0.8 0.8 0.8 0.8 basin users 3 3 3 5 1 1 1 1 0.8 0.8 0.8 0.8 district priority 4 4 4 6 1.3 1.3 1.3 1.3 1 1 1 1 site sensitivity seismicity 4 4 4 7 1.3 1.3 1.3 1.3 1 1 1 1 project type 4 4 4 8 1.3 1.3 1.3 1.3 1 1 1 1 project stage 4 4 4 9 1.3 1.3 1.3 1.3 1 1 1 1 116 bio-based and applied economics 10(2): 109-122, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9545 raffaella zucaro et al. among the financed projects, 2 of them concerned the increase in storage capacity to improve the availability of water for agriculture; the remaining projects concerned improving the efficiency of the main irrigation supply networks in order to achieve better efficiency in water use and water saving in agriculture. under the same plan, other projects were selected by the ministry of infrastructure using the same methodology for a total of 30 projects for about 250 million euros. table 3. attributes and their scores. row max normalization refers to normalization carried out before sensitivity analysis. criterion attribute row max normalizationid name name score 1 water resource use irrigation and household 3 1.00 household 2 0.67 irrigation 1 0.33 2.1 site sensitivity seismicity seismic zone 1 4 1.00 seismic zone 2 3 0.75 seismic zone 3 2 0.50 seismic zone 4 1 0.25 2.2 site sensitivity hydrogeological instability yes 1 1.00 no 0 0.00 3 project objectives completing of existing dams or unfinished dams 3 1.00 recovery or extension of the reservoir’ capacity 2 0.70 improvement of the derivation’ efficiency 1 0.30 4 catchment area ei > 500.000 3 1.00 300.000 ≤ ei ≤ 500.000 2 0.70 ei < 300.000 1 0.30 5 co-financing yes 1 1.00 no 0 0.00 6 project type securing 4 1.00 extraordinary maintenance 3 0.75 completion 2 0.50 new intervention 1 0.25 7 possibility of subdivision in lots yes 1 1.00 no 0 0.00 8 project stage executive project 4 1.00 final authorizing project 3 0.75 definitive technical project 2 0.50 feasibility project 0 0.25 9 project effectiveness < 25€/ei 3 1.00 >=25 €/ei <50 €/ei 2 0.70 >=50€/ei 1 0.30 10 territorial effectiveness > 40% very sensitive danger (basilicata, marche, molise, puglia, sicily, and sardinia) 2 1.00 > 40% moderately sensitive danger (abruzzo, campania, emiliaromagna, lazio, piedmont, tuscany, umbria, and veneto) 1 0.50 little sensitive (other regions) 0 0.00 11 district priority strategic 4 1.00 relevant 3 0.75 important 2 0.50 required 1 0.25 117application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context bio-based and applied economics 10(2): 109-122, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9545 3.6 sensitivity analysis two other assumptions of weight allocation to the criteria were tested to apply sensitivity analyses within this study. both were designed to follow the aims and rules of the fund, but by making changes in the order of criteria however, the new assignations were made without a profound distortion of the priorities expressed by the technical committee. in these new assignations, the correlation between the priorities expressed in the relevant law and the criteria that best represented them was considered. the decision of the technical committee was amended to stress the weight of the criteria in two ways. firstly, the importance was increased for criteria providing for the effects on the environment and community (e.g. number of people involved, mitigation of desertification, district priority, etc.), and the importance was decreased for criteria providing for the feasibility properties of the project (such as cost-efficiency ratio, possibility subdivision in lots, etc.) (r2). then, the opposite point of view was applied (r3). in r2, the most important criteria were established to be the district priority, the basin users, the seismicity of the site, the territorial effectiveness, and the project stage (weight 4), followed by the project objectives and project type (weight 3). they all described some aspect of the effect of the intervention, except for the project stage. the latter criterion had no effect on the final ranking of alternatives, but it could not be deleted or modified, as explained above (see paragraph 3.3). the lower table 4. list of scores awarded to selected projects for each criterion: evaluation matrix. project criteria su m m ar y in di ca to r po sit io n w at er re so ur ce us e pr oj ec t ob je ct iv es c at ch m en t a re a c ofin an ci ng pr oj ec t t yp e. po ss ib ili ty su bd iv isi on in lo ts pr oj ec t s ta ge pr oj ec t eff ec tiv en es s si te se ns iti vi ty se ism ic ity si te se ns iti vi ty hy dr og eo lo gi ca l in st ab ili ty te rr ito ria l eff ec tiv en es s d ist ric t p rio rit y 1 3 1 3 0 4 1 4 3 2 1 1 3 26 2 3 2 1 0 4 1 4 3 1 1 1 3 24 3 3 2 1 0 4 0 4 3 1 1 1 3 23 4 1 1 3 0 3 1 4 3 1 0 2 4 23 5 1 1 3 0 3 1 4 3 1 0 2 4 23 6 3 1 1 0 3 1 4 1 3 1 1 4 23 7 1 1 3 0 3 1 4 3 2 0 1 3 22 8 3 1 3 0 3 1 4 3 1 0 0 3 22 9 1 1 1 0 4 0 4 3 3 0 1 4 22 10 1 1 1 0 3 1 4 1 3 1 2 4 22 figure 1. maps of the italian lawms. the blue polygons indicate the lawms that had their projects funded under the extraordinary plans from mipaaf (author’s extrapolation of sigrian data). 118 bio-based and applied economics 10(2): 109-122, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9545 raffaella zucaro et al. weights were for project properties, such as co-financing, the possibility of subdivision in lots (weight 0.5), water resource use (weight 1), project effectiveness, project type, and hydrogeological instability of the site (weight 2). the technical committee associated with the latter criterion the same weight as class 4 in seismic risk. in this way, seismic risk was emphasized more than hydrogeological risk, compared to the priorities expressed by the legislation, where priority was given to interventions in seismic area 1 or 2 and those affected by hydrogeological risk. in r2, the same trend was maintained but the presence of hydrogeological instability was associated with the same weight as the seismic risk class 3, shortening the distances between the two criteria. on the contrary, in r3, the most important criteria were established as project effectiveness, project type, and project stage (weight 4), followed by water resource use, and the criteria on the effects (project objectives, basin users, site seismicity, district priority) (weight 3). the burden of co-financing and of the possibility of subdivision in lots were increased to 2. the lowest weights were placed on hydrogeological instability of the site and territorial effectiveness (weight 1). table 5 and figure 2 summarize the weights adopted in the two tests in relation to those chosen by the technical committee (r1). new summary indicators resulting for each alternative were obtained by multiplying the tested weights of the criteria by the normalized attributes score (see table 4). then, as result of the aggregation with the scoring method, the alternatives were sorted according to r2 and r3. table 6 shows the comparison of these alternative rankings for the first 10 projects. in both of the cases examined, two of the projects selected by the technical committee were not included in the top 10 ranking. nevertheless, the comparison of the results for all 55 cases, by spearman test (fig. 3), showed that there was a significant and strong correlation between the ranking performed based on r2 and r3 and the ranking performed on the basis of the assignment of the original weights (r1) (respectively 0.920 and 0.940, p-level<0,001, n=55). the results still showed a significant correlation when the spearman test was calculated only on the top ten positions (respectively 0.641 and 0.681, p-level<0,05, n=10). 3.7 discussions looking at the adopted approach, the involvement of all stakeholders was a strength in the methodology. firstly, it ensured competence in all the involved disciplinary areas. in particular, the involvement of the rbdas was very important as they are key players in water management and protection. secondly, it ensured a high level of objectivity in the definition of criteria and weights. indeed, the multidisciplinary technical committee allowed for setting criteria, attributes, and scores, including the objectives and constraints imposed by the financial instrument, and shared weight distribution between decision-makers was achieved. finally, this approach facilitated the acceptance of results obtained by the stakeholders embodied by the regions. the absence of traditional normalization and the assignment of a predefined score to attributes represented table 5. weights of the criteria according to the two tests (*criteria mostly linked to the definitions given in the reference law), compared to those assigned by the technical committee. main semantic area criteria r1 weight in tested hypothesis r2 r3 project properties *water resource use 3 2 3 project properties co-financing 1 0.5 2 project properties possibility subdivision in lots 1 0.5 2 project properties *project stage 4 4 4 project properties project effectiveness 3 2 4 project properties / effects project type 4 3 4 effects / project properties *project objectives 3 3 3 effects *basin users 3 4 3 effects *site sensitivity seismicity 4 4 3 effects *site sensitivity hydrogeological instability 1 2 1 effects territorial effectiveness 2 4 1 effects *district priority 4 4 3 total weight 33 33 33 119application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context bio-based and applied economics 10(2): 109-122, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9545 a practical advantage: the method was easy for all parties involved to understand, making them even more confident in the results of the application. this was important for the self-assessment that stakeholders had to carry out when submitting their project, and for the rbdas, which had to express their priority mainly based on the information included in the self-assessment. in addition, two elements could make the methodology suitable for financing projects by means of a call for proposals. the first one consists of the direct assignment of the score to the attributes to facilitate the self-assessment. the second is the production of a definitive ranking of the proposals, without comparison with other test rankings, coming from sensitivity analysis (e.g. skonieczny et al. 2005). in fact, sensitivity analysis is not suitable for funding guided by calls for proposals, because in these cases the scores of the attributes and/or weights of the criteria must necessarily be unequivocal, defined, and published a priori. however, sensitivity analysis was applied to this study to verify the stability of the results when the weights of the criteria were changed. the results showed a good correlation between the ranking made on the two test hypotheses and that applied by the technical committee. the differences between the rankings were not significant. however, the small variations imposed on the weights of the test criteria during sensitivity analysis are worth noting. surely this choice influenced the results of the sensitivity analysis, overestimating the quality of the results. on the other hand, if there were a profound variation in weight assignations, this would have resulted in choices that overturned the very strict and detailed rules and priorities of the fund. overall, the study seemed to confirm that the allocation of the weights through a technical committee and the involvement of stakeholders achieved adequate solidity of the results. the analysis of the results also suggests that this solidity is higher when the regulation behind 0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 *d esi gn le vel projec t ty pe projec t e ffe cti ve ness *w ater re sou rce s u se co-fin an cin g poss ibi lity of lo ts *p roject ob jecti ves *b asin users *s ite se ism ici t y *s ite hyd rogeo logic al… te rrit oria l e ffe cti ven ess *d ist ric t p r io ri t y w ei gh t o f cr it er io n r1 r2 r3 figure 2. graphic representation of the different weights of the criteria between the two tests and the assignment of the technical committee (*criteria mostly linked to the definitions reported in the reference law). table 6. the first 10 alternatives sorted by the summary indicator, obtained for r1 (the choices of the technical committee), r2, and r3 (the letters of the alphabet symbolize the alternatives, i.e. the projects). ranking of the alternatives (first 10 positions) by r1 adoption (technical committee) by r2 adoption by r3 adoption a a a b d b c e h d l d e b e f f c g c g h q f i g n l r o 120 bio-based and applied economics 10(2): 109-122, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9545 raffaella zucaro et al. the selection gives precise and detailed rules. this should reduce the discretion exercised by the technical committee. 4. main conclusions public infrastructure investments in water distribution networks are part of a broader framework of possible interventions (regulatory, risk management, investments, etc.) to cope with and adapt to climate change. recently, the european green deal strategy also highlighted how climate change will continue to create significant stress in europe despite mitigation efforts. hence, the consideration of climate adaptation in public and private investments is an essential topic. the mca method proved to be a very useful tool for choosing between different investment alternatives. when it is well-designed, it allows for the inclusion of different quantitative and qualitative criteria that can be measured in a single evaluation process. this has also made it possible to weight these criteria according to the priorities assigned by decision makers. however, the mca procedure is articulated and complex, due to the need to develop an approach that represents the multiplicity of objectives. there is a risk that the results achieved will be strongly influenced by subjective choices made at some of the various stages. this can be a critical point. that is why sensitivity analysis should be applied. however, in some cases like those presented, a profound change in weight allocation for testing robustness is limited by the need to respect the priorities and constraints imposed by the related regulation. that is why decision maker and stakeholder involvement are even more necessary to achieve realistic and acceptable results. during the application of the methodology described, certain strengths and weaknesses came to light. one of the main strengths was the participatory approach used to identify the decision-making network (ministries and rbdas) and stakeholders (regions and lawms). the main weakness lies in the fact that the weights adopted can only be controlled ex-post, shifting the variation to weights to compare the results obtained. the methodology applied has the advantage of being applicable in the future also in the case of funding based on calls for proposals, for which the scores of the attributes and/or the weights of the criteria must be defined and published a priori. the ex-post sensitivity analysis, carried out by modifying the weights with due regard for the priorities and limitations of the fund, confirmed the solidity of the classification on the total number of cases. this solidity seems to be favoured precisely by the presence of accurate rules and priorities of the fund, which reduce the margin of discretion entrusted to the technical committee. mca is a useful informative support for policy decisions, but it is important to keep in mind that it is not an “automatic” method for land management. acknowledgement the authors would like to thank luca adolfo folino and adriano battilani for their help and cooperation in revising the english. 8 10 12 14 16 18 20 22 24 26 28 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 su m m ar y 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(2017). characterization of drought in italy applying the reconnaissance drought index”. in: european water 60: 313318 (issn 1105-7580) https://sigrian.crea.gov.it http://dania.crea.gov.it/ volume 10, issue 2 2021 firenze university press mediterranean agriculture facing climate change: challenges and policies filippo arfini the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications cristina vaquero-piñeiro application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context raffaella zucaro, veronica manganiello, romina lorenzetti*, marianna ferrigno climate changes and new productive dynamics in the global wine sector emilia lamonaca*, fabio gaetano santeramo, antonio seccia a systematic review of attributes used in choice experiments for agri-environmental contracts nidhi raina*, matteo zavalloni, stefano targetti, riccardo d’alberto, meri raggi, davide viaggi the effect of farmer attitudes on openness to land transactions: evidence for ireland cathal geoghegan*, anne kinsella, cathal o’donoghue bio-based and applied economics 5(2): 101-112, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-20086 editorial towards an economics of the bioeconomy: four years later davide viaggi department of agricultural sciences (dipsa), university of bologna, viale fanin 50, 40127 bologna, italy date of submission: july, 2016; accepted august, 2016 abstract. this paper provides a summary of the evolution of the bioeconomy and compares it with trends in related economics literature. the objective is to discuss how the bio-based economics literature from recent years matches real world concerns and to emphasise emerging needs, in order to derive implications for economic research. though ‘bioeconomy economics and policy’ is still far from being a well-established discipline, the current literature seems to recognise scope for its development together with (and contributing to) the development of the bioeconomy as a whole. several emerging research areas are identified, ranging from the quantification of bioeconomy components and biomass flows, to the political economy of the bioeconomy. however, the economics of the bioeconomy needs most likely to develop not as an independent research area, but rather in close connection with the more ‘traditional’, but highly lively and innovating, areas of agriculture and food economics. keywords. bioeconomy, economics jel codes. q00, q2, q57 1. introduction and objective in 2012, the newly founded italian association of agricultural and applied economics (aieaa) launched a new journal. after considerable discussion about the title of the journal, and its thematic focus within agriculture, food and economics, and their possible combinations and declinations, a proposal emerged for it to be dedicated to the bioeconomy and to take the name ‘bio-based and applied economics’. the idea was to look to the future and to situate the direction of the journal within the context of the most ambitious and widespread cutting edge concept in the field of biological resources economics, without however dismissing the more traditional areas of agriculture and food economics. since then, a good deal has happened in the world of the bioeconomy. the pathway has been marked by a number of major funding initiatives and by several major events. a corresponding author: davide.viaggi@unibo.it 102 davide viaggi major landmark event took place on 25-26 november 2015, when the global bioeconomy summit was held in berlin, bringing together more than nine hundred participants from around the world. the event, the first of its kind, brought to the stage a number of top speakers, including policy makers, scientists, religious leaders and entrepreneurs and has enabled the identification of key global strategic needs for the future bioeconomy (el-chichakly et al., 2016). while a lot seems to be happening in the real world, what is economic research doing to match this evolution? viaggi et al. (2012) envisaged a potential disciplinary shift from agriculture to bio-based economics and pathways for potential research developments in this direction. they also identified two broad areas of attention: i) the first is the bulk of specific research fields related to individual issues in the sphere of the bioeconomy: consumer sciences, markets, patenting rights, and innovation, as well as the economic and social aspects of bioenergy, biotechnologies and biomaterials; ii) the second is the need to address the broad concept of bioeconomy and to approach it in a comprehensive way from an economic perspective. needless to say, the latter was already identified as the most challenging one. this paper provides a summary of the evolution of the bioeconomy in the latest five years and compares it with trends in related economics literature. the objective is to discuss how the bio-based economics literature from recent years matches real world concerns and to emphasise emerging trends and needs, in order to derive implications for economic research. this is also an opportunity to support reflection on the role of the bae journal in the context of the bioeconomy literature. the remainder of the paper is organised in three main sections. section 2 provides a review of recent trends in the evolution of the bioeconomy and section 3 summarises the advances in related economic literature. section 4 provides a discussion and conclusions. 2. policy context and perspectives the policy context is characterised by growing concerns for climate change, scarce natural resources and world food needs. in parallel, there is growing recognition of the advances in technology, especially related to life sciences. in this context, the importance of the bioeconomy has grown considerably in policy agendas around the world. at least 45 countries now have policy agendas impacting directly on the bioeoconomy, while at least 8 (including the eu and usa) have holistic bioeconomy strategies (german bioeconomy council, 2015a; 2015b). the g7 countries have made considerable efforts to position themselves as leaders in this strategy. germany, usa and japan have produced the most ambitious bioeconomy strategies; the eu has taken a leading role in promoting the bioeconomy through its bioeconomy strategy and h2020 research framework programme (european commission, 2012a; 2012b; german bioeconomy council, 2015a). most eu countries now have national strategies on issues related to the bioeconomy and 18 out 28 have the bioeconomy as a priority for eu structural funds. concrete steps have also been made toward qualifying the bioeconomy and its policy approach. there is a growing emphasis on the notion that the idea of bioeconomy alone is not necessarily ensuring improvements in welfare, and that efforts should be made to 103towards an economics of the bioeconomy: four years later target explicitly a concept of sustainable bioeconomy. this is emphasised in the final document of the global bioeconomy summit (global bioeconomy summit 2015, 2015) that recognises that a sustainable bioeconomy could make essential contributions to achieving the united nations sustainable development goals (sdg). the bioeconomy contribution is particularly focused on the sdgs related to food security and nutrition (goal 2), healthy lives (goal 3), water and sanitation (goal 6), affordable and clean energy (goal 7), sustainable consumption and production (goal 12), climate change (goal 13), oceans, seas and marine resources (goal 14), and terrestrial ecosystems, forests, desertification, land degradation, and biodiversity (goal 15), but it is also very relevant for sustainable economic growth (goals 8 and 9) and sustainable cities (goal 11) (global bioeconomy summit 2015, 2015; el-chichakli et al., 2016). another important step in the policy context is the increasingly pronounced integration between the bioeconomy and circular economy strategies. the recent eu communication on the circular economy (european commission, 2015) devotes one chapter to food waste and one chapter to biomass and bio-based products. food waste is a major concern in the eu and a wide range of actions is expected (both at the eu and member state levels). these actions range from common efforts toward improving food waste measures, to legislative initiatives supporting food donations or a better use of ‘best before’ labelling. the bioeconomy is expected to contribute to the circular economy, especially by providing alternatives to fossil-based products and energy, while it is recognised that bio-based materials can also offer advantages linked to their renewability, biodegradability or compostability. according to the communication, some of the main areas of interest include: a) the cascading approach to using bio-based resources, including food waste; b) the potential for innovation in new bio-based materials, chemicals and processes contributing to the circular economy; and c) the recycling of wood packaging and separate collection of bio-waste. bioeconomy strategies are also increasingly embedded in other policies, including the eu common agricultural policy (cap). for example, eu priorities for rural development, within priority 5 (promoting resource efficiency) also include: “(c) facilitating the supply and use of renewable sources of energy, of by-products, wastes and residues and of other non-food raw materials, for the purposes of the bio-economy”. according to the current definitions, the bioeconomy is one of the main components of the eu economy, with over 17 million jobs (8.5% of the eu workforce) and around 2 trillion euro of turnover. food and agriculture remain the two main subsectors, but about 20% of bioeconomy employment and 25% of turnover are now generated by non-food and nonagriculture bioeconomy industries. though the same statistics are not available for other areas of the world, bioeconomy sectors are already playing a key role in the economy of several large countries such as the usa, india, and brazil. the share of bio-based products in world trade has raised from 10% in 2007 to 13% in 2014 (el-chichakli et al., 2016). information remains a clear constraint for more focused analyses of the bioeconomy. to fill this gap initiatives are under way, including the bioeconomy observatory launched by the eu commission (https://biobs.jrc.ec.europa.eu/). in spite of the lack of comprehensive data, it is already clear that the development of the bioeconomy in different areas of the world, while pushed by common needs, is also highlighting different approaches, as well as clearly competing interests. some areas of the 104 davide viaggi world see the bioeconomy mostly as industrial or technological progress, while others see it in close connection with rural development, or as a way of exploiting their biological resources. there is a growing global interconnection in the effects of specific bioeconomy policies from one region of the world to another through global market forces, e.g. for the certification or protection of property rights on biological resources. to better address these issues, global awareness and global coordination in defining bioeconomy strategies is advocated (el-chichakli et al., 2016). looking at the future, on the heels of the global bioeconomy summit, a delphi study was carried out involving more than one hundred bioeconomy experts. it identified seven priority project areas for developing a sustainable bioeconomy, which are also a good proxy of the way forward in the development of the bioeconomy (german bioeconomy council, 2015c; el-chichakli et al., 2016): 1) new food and sustainable agri-food systems; 2) biosmart and integrated urban areas; 3) the next generation biorefineries; 4) artificial photosynthesis; 5) marine bioeconomy; 6) the development of consumer markets; and 7) international regulation and governance of the bioeconomy. 3. advances in the economic literature 3.1 general trends searching “bioeconomy” in the scopus database in july 2016 yields 479 papers, with a growing trend in recent years. of these, 114 are classified in the category “social sciences” and 49 as “economics, econometrics and finance”. twenty-six are in “business, management and accounting”. the most frequent are the 127 papers in “biochemistry, genetics and molecular biology” and 119 papers in “agricultural and biological sciences”. taking “economics, econometrics and finance” only, the number of papers per year is (irregularly) growing over time: 8 in 2012, 9 in 2013, 4 in 2014, 17 in 2015 and 1 in 2016. moreover, papers in economics tend to appear later than those in both the biological sciences and social sciences (the latter recording a good number of papers as early as 2004-2006). noteworthy special issues have been devoted to the bioeconomy in at least two major journals of agriculture and food economics (agricultural economics and the german journal of agricultural economics) to complement several individual papers and at least one journal entirely dedicated to bioeconomy-focused works (agbioforum). in fact, the bulk of the bioeconomy-related economic literature relates to the individual building blocks of the bioeconomy. this is not surprising since some sectors have been extensively studied for decades and have a history that is much longer than the concept of bioeconomy itself. some of these include well-established traditional sectors, such as agriculture and food. some insights into these trends can be seen from publication numbers. economics papers related to agriculture are now 10,213 in scopus, and have grown constantly from 174 in 2000 to 756 in 2015. this is even more evident for economics papers related to ‘food’, now counting 12,954 papers in scopus, having grown from 232 in 2000 to 1,187 in 2015. clearly, these sectors are still attracting a good deal of attention and are even becoming more attractive topics for research. more specific aspects of the bioeconomy are also the subject of a growing research effort, such as bioenergy, biorefinery, and biotechnologies. economic papers indexed 105towards an economics of the bioeconomy: four years later in scopus are now 1,357 for the “biotechnology” keyword (with a growing trend and average of more than 100 per year in the period 2011-2014) and 252 for bioenergy (with similar trends, though having started much later, and a production of about 40 papers per year in recent years). biorefinery, for its part, only has 45 economics papers, with a top production of 10 in 2011 with subsequent ups and downs. one of the most interesting components of this group, involving newly emerging products such as bio-materials, still counts only 10 papers in economics (compared with more than 100 in business and tens of thousands altogether). investigating this large bulk of literature is beyond the scope and ambition of this paper. we rather focus on papers that are more oriented (or carrying some message) towards the bioeconomy as a whole and as a new concept. based on the recent literature, some of the key areas of economic research in this direction are identified below. 3.2 scenarios and major driving forces the bioeconomy is driven by the perception of future needs as well as by opportunities generated by new technologies. among the driving forces recognised as being instrumental in the push for the current and future development of the bioeconomy, the following areas of change play a key role: a) advances in related and complementary technologies, especially in biological sciences and information and communication technologies; b) challenges arising from a growing population and climate change, and related resource (fossil fuel, land, water) limitations; c) changes in the organisation of industries, including horizontal and vertical integration in agricultural supply chains, increasing interand intra-industry exchanges and the increase in the globalisation of the economy and product chains (pätäri et al. 2016; wesseler et al., 2015). the highly dynamic nature of these drivers creates a continuous need for scenario development and technology forecasting. both the identification of these driving forces and the role they can play for the future bioeconomy are investigated in the literature, as well as the understanding (measurement) of the future needs they entail and the potential and credible strategies for the bioeconomy to deal with these needs. 3.3 definition and conceptualisation of the bioeconomy the definition of the bioeconomy and its boundaries is a particularly difficult task given the number of interconnections that it embodies, both as a concept and as a sector. the definition of the bioeconomy in itself is largely driven by policy action and the contents of bioeconomy strategies worldwide, such as the list of sectors addressed (see section 2). with regard to research, the problem that has emerged is that of understanding and qualifying the concept(s) of bioeconomy. this has been largely addressed by exploring the connection between the bioeconomy and surrounding concepts: sustainability, circular economy, ecosystem services, green economy, and agroecology. in this respect, the bioeconomy as a political vision is more and more often specified as a “sustainable and circular bioeconomy”. from a purely conceptual (but also cultural, political and economic) point of view, one stimulating as well as confusing aspect is that the concept itself is 106 davide viaggi developing in a context characterised by the emergence of a number of “bio-concepts” which, in a way, makes it even more difficult to identify a common understanding of the bioeconomy (and, for that matter, bioeoconomics) (birch and tyfield, 2012). the term bioeconomy, on the other hand, is used to identify different ‘types of objects’, notably ranging from a list of sectors to, more ambitiously, a new development model. one of the most politically and conceptually relevant distinctions is the demarcation between a territorial view of the bioeconomy and a more process-oriented (industrial) interpretation of the bioeconomy (schmidt et al., 2012). with a different perspective, these contrasting views also apply to one of the most qualifying concepts linked to the bioeconomy, namely that of bioerefinery (ceapraz et al., 2016). 3.4 describing the bioeconomy: cases studies, experiences, policies, and measurement it is common for fields of study at their inception, to build a lot on ‘simple’ observation. a number of studies address the issue of the bioeconomy by describing and analysing case studies of bioeconomy development. these may be related to specific policies, country strategies (e.g. kamal and che dir, 2015), or to specific plants (e.g. schieb et al., 2015). however, as the subject is still being developed, the measurement of specific bioeconomy features is also developing and shaping the specificities of the bioeconomy. the most relevant cases include: • quantification of biomass flows, especially in relationship to industry needs (in terms of quantity, quality and location), to the ability of agriculture and the forestry sector to meet future demand for biomass, and in connections with resource use (see also section 3.7 below) (kalt, 2015); • quantification of waste and by-products and circular flows into re-use, in connection with the idea that the minimisation of waste and re-use is in fact one of the key concepts in a circular bioeconomy (cardoen et al., 2015); • analysis of the degree of circularity, closely connected to the quantification of waste and by-products, but also of interest on its own; this area of research also highlights several specificities of biomass in terms of (lack of) potential for re-use and of the ability to close the physical loops, which is connected to the cycle of different resources, such as fertilisers (haas et al., 2015); • uptake of specific bioeconomy technologies, e.g. gmos, biotechnologies in general, or those based on biomaterials which may in fact be connected to a growingly intricate network of intersectoral relationships; • investment of venture capital in the biotech and bioeconomy industries, which is relevant both as an indicator of economic attractiveness and as an indicator of innovation effort; this is also a field in which it may be possible to identify the specificities of the sector in terms of research investment and innovation financing (festel and rammer, 2015). 3.5 political economy of the bioeconomy and transition analysis given the difficulties that some key bioeconomy technologies, most notably gmos, face in navigating their way through political legitimation and public acceptance, the 107towards an economics of the bioeconomy: four years later political economy of the bioeconomy is emerging as one of the most focused economic research areas applied to the bioeconomy. a special issue of the german journal of agricultural economics was dedicated to this topic (wesseler et al., 2015; zilberman et al., 2015; pannicke et al., 2015; puttkammer and grethe, 2015). the authors stress several points, including the importance of understanding and representing explicitly the interplay within different stakeholder groups and the usefulness of insights from behavioural economics, especially in connection with how to approach unknown futures. the need for a dynamic framework is also highlighted. the use of a dynamic approach leads to the use of the perspective devoted to the process of making the bioeconomy real through transition analysis, understanding how major changes in technologies may become possible through different steps and enabling conditions (pannicke et al., 2015). 3.6 technology, innovation and technology transfer technology is one of the main focuses of the bioeconomy; from the perspective of technology, the bioeconomy can be viewed as a continuing evolutionary process of transition from systems of mining non-renewable resources to farming renewable ones (zilberman et al., 2013). its evolution is strictly linked to funding research and innovation to develop new and improved technologies in this direction. the landscape of research and link to technology production and transfer is a key object of research in bioeconomy studies (golembiewsk et al., 2015; festel and rittershaus, 2014). similarly, noteworthy attention is being devoted to technology transfer processes, including in connection with agriculture knowledge systems. technology is also relevant in terms of the connections between different goods on the supply side and related costs. finally, technology can be addressed from the perspective of the specificities of bioeconomy technologies and how they can be represented in economic studies (viaggi, 2016). 3.7 biomass supply and resource use the future need for biomass will put pressure on resources needed for its production; a widely explored field focuses on the ability of resources to meet biomass demand, and the transformation, including technological improvement, to match such needs. among others, land is the primary resource under pressure, but increasing attention is being paid to alternative sources of biomass, e.g. seas. there is also an increasing focus on the need for water and fertiliser (especially non-easily renewable ones, such as phosphorous), which are the main other factors needed for vegetal production (hertel et al., 2013; rosegrant et al., 2013). 3.8 markets and their connections an area of study that is linked to the previous one relates to the connection between markets. market connections go beyond meeting demand, but also relate to qualifying market relationships. for example, topics include price stability (and the transmission of 108 davide viaggi instability) or vertical and horizontal integration. one market connection that has already been in the spotlight is the connection between energy and food products, as part of the discussion about the energy-food nexus (lochhead et al., 2016). the issue is however much wider as long as a number of bioeconomy technologies tend to substitute existing products and/or create links between different value chains. on the other hand, for products that do not have a market yet, the issue is how to smoothly create new markets. this is a clear policy concern in most of the policy documents available and in research and innovation funding (including h2020). in the context of markets, consumer analysis and behaviour is one of the big focuses of attention of economic research. specific bioeconomy issues related to food (e.g. gm crops) have dominated, but the issue at hand is much larger and even more important in perspective. it does not only implies understanding consumer preferences, but also includes investigating consumer awareness and new models of connection between supply and demand (viaggi, 2016). 3.9 tools and management while the sector is developing steadily, much can be learned from one of the most practical needs, namely the development of tools for the evaluation of the potential impacts of new technologies. in this respect, life cycle assessment (lca) has rapidly developed as a key tool for technology analysis, to answer the growing need to account for a product chain view of the need for new technology assessment and to consider specific bioeconomy issues, such as the allocation of impact across multiple products and by-products, as well as circular flows (ness et al., 2007; sandin et al., 2015; tilche and galatola, 2008). furthermore, environmental management tools are pervasive and are increasingly seen as key instruments for the sustainable management of bioeconomy sectors (straczewska, 2013). 3.10 at the boundary of economics at this point in the inception of the sector, a good deal is actually happening at the boundary of economics, with a number of potential points of contact between economics and politics, sociology, law, governance, and communications, just to mention a few. a direct focus of attention is politics and the interpretation of the push for bioeconomy development as a mainly political process to ensure the survival of current capitalist structures (goven and pavone, 2014). a clearly very important aspect is regulation, which has also been the subject of a growing number of studies. the regulation of new technologies has been at the forefront, including regulation as a way to ensure the compatibility between profitability and economic, social and environmental objectives related to bioeconomy technologies (wesseler et al., 2010). 4. discussion and implications the global bioeconomy summit highlighted the fact that the bioeconomy as a whole is a growing economic and development strategy the world over, while statistics 109towards an economics of the bioeconomy: four years later underscore its large and growing role in the economy. there is a latent divergence between two notions of bioeconomy: one that basically uses the term as the non-food component of the economy linked to biological resources, and another that also includes agriculture and food. policy trends in the eu and worldwide seem to legitimate the second option. bioeconomy research, for its part, tends to maintain a focus on a number of promising topics, though largely focused on very narrowly defined problems and scientific fields. one clear message that arises is the need for a more comprehensive view of the bioeconomy that is able to address the challenges of the interconnections among different components of the bioeconomy, different regional needs and different expectations. this demand also involves research and, while this may be common to all disciplines, it is especially true for all economic research. the literature has begun to mention an “economics of the bioeconomy” (viaggi et al., 2012) or a “bioeconomy economics and policy” (wesseler et al. 2015). however, attention remains particularly focused on individual sectors such as agriculture and food, and, to a lesser extent, biotechnology or bioenergy. this is largely justified by the individual relevance of these sectors, but also by their role in the development of the bioeconomy, and is measured by a steadily growing number of publications in these fields, especially food and agriculture. this is not again the development of the idea of bioeconomy. indeed, myriad issues in the bioeconomy discussion directly concern agriculture and the development of agriculture is seen as a key step in the development of the bioeconomy. the emphasis on the bioeconomy and world food needs have given prominence to, and raised interest in, the sector, hence encouraging a renewed emphasis on its economic analysis. looking at current trends in the economy and in research, it is clear that the development of the bioeconomy requires research with multiple scales and scopes. on the one hand, focused studies, starting from basic profitability, consumer attitudes, technology assessment and business model analysis are needed in specific bioeconomy sectors. on the other hand, holistic approaches are needed to ensure the integration of bioeconomy in a territorialised economy, where the ecosystemic, social and public good dimensions of the bioeconomy are also perceived as relevant. furthermore, both sector and multisector approaches are needed. these different views could feed each other through more attention to new linkages between sectors and product chains, which are one of the structuring features of the bioeconomy. altogether, ‘bioeconomy economics and policy’ is still far from being a wellestablished discipline. however, the current literature seems to recognise scope for its development together with (and contributing to) the development of the bioeconomy as a whole. this confirms the appropriateness of the choice of the focus on “bio-based and applied economics” for this journal, but also highlights that giving meaning to this choice remains an open challenge and will need ‘pragmatic dialogue’, rather than (or perhaps in order to achieve) a ‘paradigmatic shift’, with the more traditional fields of agriculture and food economics. this also validates the choice of a flexible scope and hints at this being even more the case in the future. having said that, maintaining continuous reflection on the bioeconomy as a subject of research and a shaping delimitation for economic thought is also a worthwhile challenge. each of the research areas identified in this paper are merely at the inception level and 110 davide viaggi may in fact represent suitable directions, though still largely undefined, for further research in this field. among them, technological specificities, the conceptualisation of the bioeconomy, linkages among bioeconomy sectors (in terms of both technology and markets) are key areas of economics in which research is highly needed for the development of the bioeconomy. in this respect, a further 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(2013). technology and the future bioeconomy. agricultural economics 44(s1): 95-102. doi:10.1111/ agec.12054. bio-based and applied economics 10(1): 51-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 bio -based and a ppl ied economics bae copyright: © 2021 d. vergamini, f. bartolini, g. brunori. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: d. vergamini, f. bartolini, g. brunori (2021) wine after the pandemic? all the doubts in a glass. bio-based and applied economics 10(1): 51-71. doi: 10.36253/bae-9017 accepted: march 16, 2021 published: july 28, 2021 competing interests: the author(s) declare(s) no conflict of interest. editor: valentina raimondi. orcid dv: 0000-0002-0721-826x fb: 0000-0002-8946-3110 gb: 0000-0003-2905-9738 wine after the pandemic? all the doubts in a glass daniele vergamini, fabio bartolini, gianluca brunori university of pisa, dep. of agriculture, food and environment, italy. e-mail: fabio.bartolini@unipi.it; gianluca.brunori@unipi.it corresponding author: daniele vergamini. e-mail: daniele.vergamini@agr.unipi.it abstract. covid-19 has triggered an unprecedented global crisis, the increasing recessions in many countries and related trade uncertainties are affecting the whole wine sector, from production to distribution, sales, and consumption. while the full recovery is still uncertain, and even worse scenarios are possible if it takes longer to recover trust and financial stability on wine markets, the crisis risks to jeopardies recent developments and sustainability in wine territories. building on a tailored revision with a mixed-method participatory research process of the conceptual framework on condition, strategies, and performance of grando et al. (2020), we offer a critical reflection made by researchers and stakeholders supporting several socio-economic narratives and policy implications in the light of the current crisis. distinguishing between short and long-term implications, we analyse the impact of disruptive changes in the external and internal conditions of the business environment, the strategies adopted by the wineries and their implication on performances, as well as a reflection on the policy needs to alleviate the ongoing suffering of the sector. the speed and scope of the pandemic crisis underscore the need for the wine sector to become more resilient by increasing the ability to cooperate and coordinate among supply chain actors and between policy levels. the latter offers a reflection on the balance between short-term interventions and the complementarity of post-2020 cap measures to stabilize market and future incomes. we conclude that once the crisis abates, it will be necessary to reaffirm credible commitment and trust at all levels, not only with regard to production side but also on sale and distribution, especially in the face of changing consumption patterns that in the future will become more pressing for issues related to safety and sustainability. keywords: covid-19, wine industry, pandemic, italy. 1. introduction the covid-19 pandemic has triggered devastating consequences both for human lives and for economic progress. the most optimistic view of what we can expect after a long recovery from the covid-19 will certainly be a half-full glass. the international organization of vine and wine (oiv) sees in the near future a huge drop in wine consumption, as well as a reduction in average prices, and therefore in sales margins and turnover. the downhttp://creativecommons.org/licenses/by/4.0/legalcode 52 bio-based and applied economics 10(1): 52-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 daniele vergamini, fabio bartolini, gianluca brunori ward pressure on prices could be even more pronounced for the fine wine market (the upper segment of the wine market) with a fall around 35% (cardebat et al. 2020). while the covid-19 pandemic has quickly delivered a global economic shock with catastrophic consequences, the increasing recessions in many countries and related trade uncertainties are affecting the entire wine sector, from production to distribution, sales, and consumption. the pandemic and the set of measures adopted to contain it, have led to massive downturns in global economies, and to increasing disruptions to global supply chains, trade that collapsed in the first half of 2020, and tourism for which the world tourism organization (unwto) has estimated a decline of 44% in international tourist arrivals with a loss of about 159 billion euros just for the first quarter of 2020. export found increasing difficulties and limitations alongside widespread international border closures, uncoordinated policy restrictions and social distancing measures, trade policy uncertainties and turmoil in the financial market (world bank 2020a). symmetrically imports have been curtailed by aggressive quarantine measures, which heavily weighed on consumption and investment (world bank, 2020b). wine achieved a lower than average production volume in europe, where the extraordinary measures to reduce the harvest volume had a significant impact in italy, france, and spain. but the sharpest decline was due to its heavy reliance on exports and tourism, in a magnitude that could jeopardies recent developments in most of the wine-producing countries. in italy, mediobanca (2020) estimated a loss of 2 billion euros in the 2020 turnover, resulting from a huge drop on sales between 20% and 25% compared to 2019. according to the institute of services for the agricultural food market (ismea) from january to september 2020 wine export volumes are 2.6% lower than the previous year, while in value the loss is about 3.4%. many priorities on political agendas previously considered ambitious, especially those related to the post-2020 cap reform that aim at securing those investments necessary to align agriculture with sustainable development goals (sdgs) risk now to become even further out of reach (pomarici and sardone, 2020). a huge question mark looms over the vast majority of emerging market and developing economies, the growth for many sectors is still uncertain, and even worse scenarios are possible if after the immediate policy support the structure of the wine industry takes longer to recover. the simple wage of this paper is to provide a reflection on the effects of the covid-19 epidemic for the wine sector. building on a conditions-strategies-performance framework (grando et al., 2020) adapted to the emergence of the covid-19 pandemic the analysis integrates a desk-based review of recent economic perspects on the wine sector (world bank, 2020a; vergamini et al., 2019) with diverse experience data collected through two workshops conducted before the spread of the pandemic (jan 2019) and during the first lockdown of may 2020. the purpose is to offer a critical reflection on the most-updated socioeconomic narratives and policy implications, in the light of the current crisis. distinguishing between short and long-term implications, we will try to analyze the impact of uncertainty that has spread since the earliest outbreaks of mid-march 2020 providing possible courses and outcomes. clear policy actions and recommendations to alleviate the ongoing suffering of the sector are discussed, as well as addressing future challenges such as the recovery of the environmental investments through sustainable policies and the support of international trade through global coordination and cooperation. the starting point of our reasoning adapts well to the italian sector and other traditional wine countries (france, spain, portugal), after which some trends and policy implications that are specific to the sector can also be extended globally. 2. methodology 2.1 the condition-strategies-performance (csp) framework the analysis provides a review and a reflection that further expand and test the csp approach of grando et al. (2020) derived from industrial organization (porter, 1980) and agrofood value-chain management approaches (rastoin and ghersi, 2010) in light of the current spread of the covid-19 pandemic. the previous csp framework was completed towards the end of 2019 in a completely different scenario. driven by the objectives of the eu-funded horizon 2020 sufisa project1 it has been tested on various sectors (prosperi et al., 2019) including wine (vergamini et al., 2018). however, the disruptive changes introduced with the pandemic in 2020 offered the opportunity to revise the previous approach and further develop the framework according to the mutated conditions. the framework proved to be a reliable ally for understanding the way the producer integrates and 1 sufisa – sustainable finance for sustainable agriculture and fisheries was an h2020 project (grant agreement 63555) for which we analysed the wine sector in tuscany through several quantitative and qualitative research activities. the national report for italy provides a synthesis of the diverse experience data we gathered through a survey, several focus groups and regional workshops (https://www.sufisa.eu/ publications/, last accessed june 2020). 53wine after the pandemic? all the doubts in a glass bio-based and applied economics 10(1): 53-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 translates internal and external conditions into chosen strategies that then leads to performances and how on the basis of the observed performances producers adapt by recalibrating reactions to conditions (e.g. remote tasting, web-marketing, quantity of seasonal labour, etc.). given its extreme flexibility, we have chosen the csp approach to capture the short and long-term coronavirus pandemic effects on producers’ strategies and performance in the wine sector. for ease of reading, we report here only the elements of novelty that are attributable to the changes introduced by the current crisis, while for more details regarding the csp we remind to grando et al. (2020). the adaptation of the csp under the covid-19 scenario is summarized in figure 1 and should be interpreted in the following way. the core is the decision-making unit (the wineries). according to the change in external conditions introduced by the worsening of the pandemic (restrictions on the free movement of people and goods, trade and tourism disruptions, introduction of social distancing rules, national, regional and local lockdowns, rising of unemployment and financial stress), we observed a sudden change in the wine business environment, especially for the wineries that focus on the on-trade channels and those more exportoriented and widely connected to global value chains (gvcs.). the shock induced by the change in external conditions has determined for wine producers the need to confront and subsequently adapt to a new – although initially perceived as temporary – internal environment. while wine production conditions appeared stable in the short to medium run as producers have adapted to the situation and continued their work in the vineyards, others were the conditions that mostly constrained wine producers. the “new” internal environment was found to be characterized by a lack of timely policy measures and coordination, by the drop of ontrade channels against the growing concentration of large retailers and supermarkets, by a quick fall in consumption of fine wines vs an increase of the mediumlow quality segment albeit with a strong focus on regional brands, by sudden labour shortage and an increase in production time and costs vs a drop in average grape prices, with a generalised lack of liquidity and increase in debt exposure and related risks. both external and internal conditions constrained the producer’s decisionmaking to produce timely and adaptive strategies while waiting for a wider structural policy support. these responses, as we will analyze more in detail below, have had an impact both in the short and medium-long run on the performance of the sector, according to differences in the composition of output and exports, as well as the endogenous factors that determine the competitivefigure 1. producer’s decision-making process. source: author created. 54 bio-based and applied economics 10(1): 54-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 daniele vergamini, fabio bartolini, gianluca brunori ness, the reliance on on-trade vs off-trade sale channels and producers’ participation in gvcs. 2.2 data collection, csp development, and reflection the process of reviewing and reflecting on the csp approach in the light of the current pandemic crisis for the wine sector was based on a phased mixed-method participatory research process that integrated stakeholders’ opinions, experiences, and reflections with a deskbased review of different sets of conditions, strategies, and performance at the end of 2019 with those in mid and late 2020 (fig. 2). we developed a baseline picture for the csp analysis (i.e. the state of the sector before the pandemic) building on vergamini et al. (2019, 2018) and integrating additional discussion elements grasped from a large workshop conducted in tuscany during 2019 with 80 key players of the wine supply chain. the actors involved in this first workshop included small, medium-sized and large wineries, academics, members of dg-agri of the tuscany region, members of tuscan pdo consortia, sales agents, and other key intermediaries. in compliance with privacy issues, to these type of data, we will refer in the text with the initials wp (workshop participants). the workshop was organized as an iterative and interactive foresight game to capture the strategic nature of decision-making through a cyclical process of confronting potential future european wine sector states including agricultural and trade policies with individual and collective objectives, planning or taking future action (strategies), and reflecting on potential outcomes and policy implications (for a deeper analysis of the scenario building process2 see gardin et al., 2019). the experience data collected through the workshop contributed to develop and validate the chosen baseline set of conditions and strategies (see annex 1). the actors involved answered questions about how they plan to meet these conditions or which strategies among those that can be implemented in the described scenarios they think can contribute to reaching individual and collective objectives regarding the future sustainability of the sector. such exercise generate discussion among stake2 for ease of reading, it is important to mention that the scenario narratives were built on the basis of key dynamics that afflict european agriculture and which can be summarized in three macro-categories (consumption models, distribution of power along the supply chain, prevailing technological models) that resulted from the sufisa project. for wine, the interaction of these macro-categories with different trends and drivers of change identified for the tuscan sector led to the formation of potential specific reference scenarios that have been verified with actors under their territorial context. holders that we further employed to validate, and, ultimately refine the baseline csp. then during the early outbreaks of may, we promoted a second and web-based workshop (may 15, 2020) to co-reflect with more than 20 international wine actors across the supply chain (international winemakers, market consultants, agricultural consultants, wineries, representatives of regional institutions, academics and students) on the impact of the sudden changes in external conditions leaden by the pandemic on the producers’ strategies and performances (please see annex 2 for a deeper explanation of the workshop scopes and process). the data collected allows researcher to develop a backward reflection on the initial csp set and grasp additional short-term implications for different geographical contexts (italy, france, spain, portugal, australia, and us). the workshop was intended to ‘ground-truth’ on the previous findings and better clarifying the role of the different pandemic stressor(s) in driving adjustment processes and the dynamics that drove wineries from the ‘baseline csp’ in the past to the new framework with respect to the current pandemic state and to the future sustainability and viability of the sector. then a final comparative and reflexive desk-based analysis of market and regulatory conditions faced by the producers integrating different and recent economic prospects (world bank, 2020a; oiv, 2020) was conducted by the research team to structure the different findings and provide key insights in terms of policy implications. drawing on the different data sources described above, the next sections examines the resulting condition, strategies and performances, particularly in terms of different temporal (short vs long term) and spatial contexts, the pressures faced by producers in each region and the strategies adopted by wineries and associated supply-chain actors to adapt and overcome the pandemic. figure 2. phased mixed-method participatory research process. source: author created. 55wine after the pandemic? all the doubts in a glass bio-based and applied economics 10(1): 55-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 3. a devastating blow to an already-fragile vision of the future before march 11 2020 we did not expect a certainly bright future, however, there were positive trends and strategies that aimed – let’s say – to increase the sustainability of the sector (obi et al., 2020). the baseline conceptual framework (figure 3) illustrates that in face of growing concentration on foreign markets, rising tariffs, and regionalisation in consumption patterns, we have witnessed the consolidation of investments in traditional local and national sale channels, increasing investment in the maintenance of the territory (e.g. rdp non-productive investments aiming at securing environmental assets such as landscape through the restructuring of old and abandoned terraced vineyards) and in quality, the progressive formation of new territorial networks to promote new consumer experiences (brunori et al., 2012), the growing application of trade marketing (spread of b2b and b2c events), and the improvement of existing facilities, especially those related to the increasing of wine tourism in the light of multifunctionality and income diversification. for some italian regions like tuscany, where the budget for cmo promotion measures is around 30 million euros, these investments represent the result of a decade of work conducted by the region together with the regional wineries and protection consortia. thanks to these efforts tuscany gained its resonance as a global umbrella brand for its agricultural productions, including its high-quality wines (e.g. chianti, brunello di montalcino etc.). however, these developments have not been limited to promotion but involved the transformation of the regional winescape (vlahos, 2020). if climate change was a key concern, there was no lack of plans for sustainability and improvement of the vineyards. indeed, the attention to sustainability and to the environmental impact of production have been proved, for example, by the widespread increase in organic viticulture (pomarici and sardone, 2020). in the panorama of sustainable initiatives, the italian producers were the first to believe in new production protocols such as organic, which in 2019 marked its strong growth. even against the changes in demand and consumption patterns, we have seen an increasing ability of protected designation of origin (pdo) and protected geographical indication (pgi) wines3 to boost their average prices and an increasing interest in wines that are easy to drink, mix, with low alcoholic content, premium, with low environmental impact and with alternative packaging. therefore, in the face of wine structural flows determined by change in the sector conditions 3 we refer to those wines that belong to the art. 93 of the regulation (eu) 1308/2013; figure 3. the baseline csp framework for wine. source: revision on grando et al. (2020). 56 bio-based and applied economics 10(1): 56-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 daniele vergamini, fabio bartolini, gianluca brunori driven by internal and external forces, producers demonstrated their ability to find proactive and unison strategies to maintain substantial stability of the wine supply, increase productivity, reduce costs, increase in products variety and access to new markets (vergamini et al., 2019; 2018). according to vergamini et al. (2018) their performances resulted from strategies for which potential demand, as well as supply and market risks, were perceived as intelligible and manageable and for which they developed adaptive/proactive responses. however, as they pointed out during the first workshop, there are also other factors that should have been considered, such as environmental conditions, for which the risk can assume an indefinite value, beyond their control. unfortunately, this has been – shortly thereafter – confirmed by the rapid spread of covid-19. the early march outbreaks exposed us to the idea of a fragile future, but above all, they added an additional element that we didn’t consider in the baseline approach, namely “time factor”. although aware of the existence of sudden and uncontrollable changes in external conditions, in all the wine scenarios that we analyzed during the first workshop (garding et al., 2019) an important variable escaped from the control of the analysts as well as of respondents, and it was the “time response”, or rather the speed in providing solutions. the astonishing speed and the scope of the pandemic crisis is unprecedented. although in previous scenario analyses we consider the opportunity for sudden shocks, what we did not model was the need for the predisposition of quick policy strategy, or let’s say at least timely. what we should learn from the current situation is to anticipate the crisis, we need to prepare us and instead of perpetuating the present living we should project ourselves towards the future. (wp01) despite the unprecedented policy support and the stringent control measures to mitigate the ongoing health and human costs and to support the near-term economic losses, the underlying policy strategy was “taking time”, since policymakers were not prepared to deal with a severe public health crisis of this scope. although this factor is not immediately evident, the lack of predisposition and coordination among policies is detrimental in the long term, leaving more room for the downturn consequences that we are now experiencing for wine and for many other sectors. thus, the uncertainty associated with the lack of predisposition, becomes the starting point of our revision of the framework and the first fundamental insight from our reflection. we have lived to date in the complete lack of signs of restarting, in an atmosphere of uncertainty, in the lack of real planning for the sector (wp02) if it is true that nobody imagined this scenario before january, nevertheless in the last few years some extreme and catastrophic events have taught us the need to predispose strategies to be able to tackle quick measures. covid-19 epidemic should lead us to review the production world in a different way. (wp03) a strong sustainable future needs timely and targeted policy interventions that reaffirms credible commitment to sustainable policies and predispose a new relationship with the environment. as we would deepen in the discussion, at european level, for example, pomarici and sardone (2020) illustrates that the post-2020 cap reform there already includes preventive instruments that goes in this direction like the “harvest insurance”, the “mutual funds” and the “green harvesting”. however, to these policy tools that could offer concrete stability to crisis situations, the members states (ms) posed so far very little attention despite being feasible (trestini et al. 2017). before the covid-19 crisis, the world was concerned with concentrating the national support programs (nsps) resources on fostering competitiveness. consequently, the overall picture leaves no room for optimism. in the next section, we try to analyze the events step by step to distinguish some key implications between the short and medium-long term. 4. short-term implications in the short term, the wine sector – affected by social distancing and the tourism stop – experienced a sharper decline. the territories that before the crisis were growing, are now in difficulty, affected by the horeca stop and by the collapse of wine tourism. (wp04) however, specific results emerge, reflecting differences in the composition of output and exports, as well as the reliance on on-trade vs off-trade sale channels and changes in those endogenous factors that determine the competitiveness (i.e. human capital and other terroir factors4). portugal wines, for example, suffered a 4 for a deeper analysis of the regional factors that determine competitiveness of the wine supply chain we refer to vergamini et al., (2019). 57wine after the pandemic? all the doubts in a glass bio-based and applied economics 10(1): 57-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 lower impact than france, while the france sector has been affected more than the italian one (table 1), bringing out also the problem of different speeds for different countries. in addition, the world bank reports that the sectors that participate more in the global value chains could be more affected by the disruptive effects of covid-19. although wine is a territorially-based product, for those wineries that focus on export and are globally connected, the effect of safety measures is to slow down production and transport with the consequent lack of the necessary inputs/outputs between one process and another. argentina, for example, encountered such a problem during the harvest with regards to labor shortage, since workers traditionally come from regions very distant from the wine-growing areas. (wp08) the covid-19 hit the wine sector manly form the supply side. the wineries are not experienced with labor shortages as in other sectors. since most of the labor force is generally mobilized during the harvest, producers from the southern hemisphere faced this problem during the first wave, while the european countries fear to face it with a second wave, which this time would bring down the resilience of the sector. in addition, the propagation of shocks through networks and trade interlinkages such as gvcs could be a major driver of economic fluctuations (acemoglu, akcigit, and kerr 2015). gvcs account for more than half of global trade, which becomes more volatile especially during crises (freund 2009; taglioni and zavack 2016). these effects could also prove disruptive for those companies that depend to a greater extent on external financial flows from companies operating in other sectors or that are simply controlled by foreign capitals. wine has experienced a high degree of financialisation in recent years, where external investment and acquisitions have not excluded many popular brands. net of these considerations, in the supply chain of the traditional wine countries most of the “on-trade” distribution channels disappeared. the horeca and other “on-trade” channels have been the most affected by the lockdown. with the closure of restaurants, the stop of social life, travel & leisure industry, we assisted to the collapse of sales for many of the eu denominations of origin and other regional wines (i.e. the most affected). according to the comité européen des entreprises vins (ceev), in europe the stop to this channel could lead to a 35% drop in sales volumes, and a loss of over 50 % in value. but let’s not forget that the spatial dimension also matters (ilbery et al., 2010). by unpacking this impact following a vertical direction (northern vs southern hemisphere) we envisage greater repercussions in the mediterranean area where there is a greater concentration of wine bars and restaurants (italy, portugal, spain, france and greece). furthermore, this area has seen in recent years the greatest concentration of investments in the vineyard and cellars: to improve the product quality, but also the appeal and accommodation capabilities of the territory. with regard to the hospitality in the north of montalcino (tuscany) we suffered a big blow, a devastating impact: we miss the most beautiful moment of our work, the relational one. if we consider also the contraction of the highly developed tourism industry, which will continue to be severely limited during the next months, the negative impact for thousands of wineries that focus on the strategic combination of these activities is likely to be unsustainable. (wp11) the feeling is that the crisis is eroding decades of development standards for our quality wines providing unbalanced territorial consequences that could threaten the current objectives of a “vibrant agriculture” and “generational renewal” (pomarici and sardone, 2020). although even before the pandemic several experts and practitioners endorsed several pessimistic scenarios for future wine demand, a general collapse of the market was truly unpredictable. on the opposite, considering only the increase in sales for domestic consumption mainly recorded by large retailers during the lockdown as a signal of recovery, we risk drawing wrong conclusions. in supermarkets, the offer is much more limited compared to the on-trade channels for which differentiation is a key strategy (vergamini et al., 2019), and focuses on price and rather homogeneous products among several major players. according to cardebat et al. (2020) we should contextualise the increase in sale of large distribution by market segments and distinguishing between export and national/regional market accounting for the collapse of off-trade channels. therefore, we envisage that the most affected producers are those that a) focus on terroir-driven and fine wines, b) are based on export c) benefit from an important local/ regional demand through horeca. the same reasoning applies to the rapid growth of sales in e-commerce. as we will discuss later with regard to the medium-long term, this type of offer is also badly suited for a highly differentiated production like that we are used to finding in the most prominent italian wine regions. ours is a medium-sized family business, for which the impact on the european markets has been violent, but we are mostly affected by the stop of horeca; our winery has now realized above all his vocation and the link with 58 bio-based and applied economics 10(1): 58-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 daniele vergamini, fabio bartolini, gianluca brunori table 1. short-term implications by countries. countries composition of output and export, on-trade vs off-trade, competitivness short-term implications impact experienced actors italy (tuscany) world class red wines (sangiovese), export oriented, great focus on wine tourism, quite heterogeneous reliance on different trade channels (mix of on-tade vs offtrade). strong territorial endowment. with the collapse of wine tourism, the sector experienced a huge drop exacerbating the previous criticalities like late payments and increasing lack of liquidity. as a consequence, debt exposure is rising. unlike france, before the pandemic, italy managed to exploit the rise of united states tariff to its advantage, so the subsequent lock-down resulted in a balance of losses compared to the previous positive export balance. mediumstrong wp05 spain (rioja) world class red wines, great focus on on-trade. strong image of the regional brand. in spain, there has been a huge drop in wine consumption as in other countries following the horeca stop, which has mainly affected the upper tiers of production (40% nationally and 60% from the rioja where they produce 300 mln of liters/year of which 60% goes to the national market and 40% is exported). while the top wines recorded the major negative consequences, the consumption of medium-level or low-quality wine has increased, but with a very low percentage that does not compensate for the losses related to the closure of the restaurants. the estimation is about 40/50% reduction in volume and 50/60% in value (turnover). last year the average price for grapes was around 1 euro/kg, while for this year predictions show about 0.5 euros per kg. in addition, several large processors risk purchasing less quantity. even in spain, there is a consensus for a reduction in yields, but without aids, part of the grapes will remain in the countryside. the impact of e-commerce was minimal but still a stimulus for many wineries. tourism has had the first and greatest impact, now is the turn for uncertainty and the consequent economic crisis. mediumstrong wp06 portugal (lisbon) export oriented, great focus on wine tourism, mixed trade channels. in portugal, the main impact is on export; 15-20% losses for large companies and up to 50% for small wineries; anyway, the export is gradually restarting with the reopening of the asian, us and canada’s markets (with a probable increase of 15% of exportation); however, it needs to be balanced with the negative impact from the closure of horeca channels and the stop of wine tourism. take-away and supermarkets contribute to maintaining sales while wine consumption at home increases. the feeling is that for the 2020 harvest in portugal the price of the grapes will not be affected by the situation; portuguese government has planned fiscal and support intervention with the same measure announced for italy (distillation aid, etc.) lowmedium wp07 france (bordeaux) world class red wines (cabernet-sauvignon & merlot) and champagne (luxury segment). great focus on wine tourism. strong territorial endowment. restrictions played a major role in delivering less consumption of premium wine or luxury products like champagne while there was an increase in the consumption of cheap products; wine region like bordeaux has been mostly affected by the stop of wine tourism. although the wine is a durable product, the situation doesn’t encourage a reactive response from the markets when it will improve. then the emotional context constrained the purchasing behavior; in addition, the confinement of workforces caused workforces troubles or increasing production times and costs. with regard to export, it decreased by 20% during the first 2020 quarter following another 20% fall just in march. exceptional measures from the agricultural minister provide: social security contributions for employees and companies with an envelope of 400 mln euros; aid for distillation to reduce a volume of 2mln hectoliters with 140 mln euros; high wp08 australia (adelaide hills) great wine capital for white wines (sauvignon blanc), notably sparkling wine. focus on export. focus on drinkability and new blends (vanguard wines). the territory is a matrix of new and old patterns that aim at increasing wine tourism. limited implications since vine harvest and wine processes have been taken place normally with an impact on the organization and logistic steps due to social distancing restrictions; restrictions slow down production processes while wineries react by engaging with customers online; for large wineries with 24hrs processes the impact was in the re-organizing of the processes (slow down, divided into steps with breaks to include regular cleaning processes) to reduce workers exposition to contamination; large impact by the stop of travels; stop of wine tasting (no visitors) and shut down of horeca channels; fires have been a greater concern than the covid-19 shut-down; the main challenge is how they will maintain export channels (50/60% of their sales to countries overseas). lowmedium wp09 59wine after the pandemic? all the doubts in a glass bio-based and applied economics 10(1): 59-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 the restaurants despite having a couple of multi-channel labels including those suited for large retailers and supermarkets, which obviously continued to sell in this period in italy and abroad and the online channel worked a bit; but lacking tasting or direct-sale, we feel worried. if uncertainty was initially normal, it is now becoming chronic and widespread across europe. we will invest in online but we believe in returning to a vis-a-vis relationship, wine needs relationships. (wp12) in line with the relational nature of wine, we feel strong doubts about the possibility of shifting our business online. we need certain and quick answers on the public front to support tourism, the author’s cellar is in great difficulty. it needs support and speed. (wp13) in any case, even in face of considerable growth in e-commerce and takeaways, the profitability of producers who relied on more traditional channels remains deeply undermined. neither large cooperatives escaped from these negative effects, especially those direct-selling/regional based that in the last decade have made substantial investments to shift their business towards pdo, organic, and terroir-driven wines. in the same way, we risk being exaggerated to figure a general collapse of the market, since a recent mediobanca survey confirms that 53.4% of the cooperatives that focuses on “off-trade” channels, expect for 2020 less pessimistic results. furthermore, for mediobanca the italian wine could lose up to € 2 billion in revenues in 2020, with a drop between 20 and 25 percent compared to the 2019 (a great year). assuming that the covid-19 specific effects on exports come on top of the fall in world trade envisaged by the world trade organization (wto), current projections estimate a contraction in exports between 700 million and 1.4 billion euros for the major italian producers in 2020. with regard to the domestic market, given that around 65% of national sales are “ontrade”, the short-term impact can be approximated to a loss of over 500 million euros. this figure is also confirmed by the recent nomisma wine monitor survey. according to the results of the first quarter of 2020, considering the us market, the sales of italian wines in the off-trade reached 94 million liters, which represent only 40% of the total imports. the problem will therefore concern the other 60% of italian wine for which we expect a drop, especially with the on-trade that is continuing to be down to zero. these observations, coupled with the fact that liquidity is quickly drying up on the wine market reinforce the thesis that the latter segment is the one who in the end will have suffered a devastating blow. a survey on 400 producers recently conducted by firab (foundation italian for research in organic and biodynamic agriculture) found that the 73% of organic farms were hit by the pandemic crisis and, in terms of liquidity for the 65% the expected economic stability is at most three months. to notice that half of the respondents is under 50 thousand euros in turnover. these figures corroborate the narratives expressed by the italian producers who attended the second and “online” workshop. however, as we introduced in table 1 the past quarter did not end entirely in a negative way. indeed, thanks to the threat of tariffs and the “january exploit” of italian wine in the usa, the italian trend was above the average of the other countries: overall us imports for the quarter closed at + 10.9% in value. therefore, net of exogenous and external factors (tariffs and covid-19) it is now necessary to shift the discussion on the mediumlong term. countries composition of output and export, on-trade vs off-trade, competitivness short-term implications impact experienced actors us (napa valley) world class red wines (cabernet-sauvignon). focus on on-trade and wine tourism. the territory focuses on innovation and in the ability of renovating old blend and developing new and easy to mix and drink wines. the us experienced border closures and various levels of strictness depending on the county level (yellow counties start later wearing masks etc.). during the lock-down, wineries were open without tasting, so with a huge economic impact for those areas like napa valley where tasting tours and on-sale channels are key. off-farm sales were up, while on-farm or direct sales were down (retailers like supermarkets have increased their sales and considering a large amount of stock wine from past years this may help of clearing past inventory). this, however, does not represent a trend that might continue with the reopening. it seems that when people stuck at home they increased wine consumption or simply since they cannot consume wine outside they substitute with in house consumption. for smaller companies high-end, high-quality producers are facing huge economic difficulties (potential bankruptcy in the next few months) and it really depends on how quickly restaurants will be re-open. mediumstrong wp10 60 bio-based and applied economics 10(1): 60-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 daniele vergamini, fabio bartolini, gianluca brunori 5. long-term implications severe and long-lasting socio-economic effects of the pandemic crisis may erode the basis of a balanced territorial development and rural viability. the decline in investment because of elevated uncertainty and financial stress, the ruptures in trade linkages, the fall of many businesses, and the rising unemployment will cause negative effect on both consumers and producers sides. in addition, potential difficulties in providing a continuum of specific support programs to existing declining agricultural areas and to those segments hit by the covid-19 crisis (the more dependent by the on-trade channels) risk compromising the human and territorial capital and losing a whole series of necessary assets for terroirdriven wine regions, such as the protection of landscapes and environmental quality according to world bank (2020a) the economic, social and environmental implications are likely to be more severe and protracted in those countries that experienced larger outbreaks, greater exposure to international spillovers (i.e. gvcs, financial markets, and tourism), and pre-existing difficulties such as business and workers informality, large flaws in the health system, widespread social inequalities. furthermore, the covid-19 epidemic eroded the confidence about prospects for future labor income and profits, in other words, it contributes to a widespread uncertainty forcing wineries to operate with weak cash flow in a generalized lack of liquidity (figure 4). in the mediumand long-term the wine risks continuing to be penalized by psychological aspects, linked to the general climate of uncertainty. we could introduce it as “domino effect”. the collapse of economies in the short run caused a sharp decline in household consumption and firms’ investments with huge repercussions on demand and supply (bhandari, borovicka, and ho 2019). recession and subsequent increasing unemployment caused a loss in lifetime earnings, but to a greater extent the risk of unemployment permanently increases consumers’ savings rate, while again reducing consumption. tourism has had the greatest impact with the collapse, and doubts are still strong. there is a growing concern about the economic crisis that will come. actually, we have 5 million workers at home that have been supported by the spanish government, which will have an impact in the medium and long term on the gdp. what about unemployment? now is likely to be at 20%, and in the next future? (wp06) in this scenario, a key point will be the economic health of “on-trade” players and their attitude towards risk. in the worst case by becoming risk-averse the horeca players will make fewer orders, asking for more delayed forms of payment to minimize their risks (the participants’ behaviour affect pricing and buying dynamics). in the short term, we took care of the cellars, how to keep them in business, while in the long run we must support our distributors. (wp12) accordingly, the wineries that focus on the on-trade channels will be crushed between the reduction in sales and downward pressure on prices, in any case, with less liquidity. however, among the pdos, pgis and fine products, each wine will make its own story (evidence suggest a drop on price for chianti wines). many reactions from several wine regions will depend on the combination of creativity, innovation and their ability to deal with political risk (cardebat et al., 2020). but probably to curb the slow decrease in the price lists the wineries will need signs of a strong recovery, especially from travel & leisure industry. considering that tuscany has 38 million tourists, as strong sign towards recovery we should focus on rural tourism as a guarantee of accompaniment (strong sign) towards recovery. rural tourism could be a much safer or more manageable form of tourism. for example, we should develop partnerships between restaurants and wineries to shift the restaurant from the crowded city centers to the rural areas, in those structures that could allow greater control and security. (wp13) at the opposite for some large brands, the situation will be probably more affordable since they could be able to impose higher purchase quantities thanks to their greater market power “in a take it or leave it way”, while the others will be forced to find new creative solutions (i.e. social networks, e-commerce platforms) or in the worst cases to exit the business. while a probable future in the face of the appropriate incentives for distillation, could then see the distillation of wine surpluses, possibly to produce sanitizing alcohol and help unprofitable firms to persist (we analyze the implication in the discussion), the first overall impact for the sector in the medium-long term is that of the consolidation of a two-speed market: one driven by large retailers and one by the slow restart of the on-trade channels. potential impacts from this situation vary greatly in function of the opening and participation of wine regions in gvcs and from the rebound velocity for those small and medium direct-selling and terroir-driven wineries. in a post-covid-19 world that support open trade with exchange rate stability the local tropism could be a limiting factor. wine region as well as wineries that are 61wine after the pandemic? all the doubts in a glass bio-based and applied economics 10(1): 61-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 well-integrated in gvcs may re-assess whether the gains from participation in global value chains are worth the risk of further disruptions. a retreat from these exportoriented firms towards a regionalization of trade would produce adverse effects on the sector (barattieri et al., 2019), further reducing already-low growth and productivity. in the latter, the main threats are the permanent loss of productivity for many wineries and the risk for the wine territories of being depleted of all those investments necessary to maintain quality, and most important to secure the necessary environmental interventions (let’s not forget that during 2019, many wineries have made great investments to deal with environmental improvements). vice-versa in a protectionist context and figure 4. validated shortand long-term wine conditions. source: revision on grando et al. (2020). 62 bio-based and applied economics 10(1): 62-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 daniele vergamini, fabio bartolini, gianluca brunori strong exchange rate instability due to tripartite trade war between the u.s., china and the european union the regionalisation of trade patterns become a key success factor due to strategic trade policy reasons but also for solid local direct-selling dynamics. in both cases we can expect a drop of investments (except in communication) and more control of yields during the harvest. too much wine on the market risk to eliminate the value of the supply chain. (wp15) then, despite near-term policy support, some wineries will resort to credit or financial strategies to survive given the supposed ‘safe-haven’ nature of their wines. at national level, under the caring for italy decree of march it emerges the revolving pledge as an instrument that gives wineries that have stocks of wine the opportunity of depositing such value to the bank as a guarantee on the loan. furthermore, when wineries will sell the wine they replace the guarantee with other lots to keep the bank guaranteed. this instrument will be very valuable for italian pdos’ wines. (wp14) however, net of any short-term private/public aid, we refer to a second and most probable medium/longterm effect as the “financial stress”, whose balance will depend for the european wineries to a large extent on the availability and access to the future cmo/rdp measures and other forms of more structural support provided by the post-2020 cap reform (pomarici and sardone, 2020). in addition, export-oriented firms tend to be more exposed since they are dependent on borrowing to finance promotion activities and trade marketing. for all the wineries that are in financial stress the inability to service debt (high borrowing costs against weak cash flow) could cause to exit the business. more or less evidently, a third effect of the covid-19 epidemic will be the greater attention to digital. while e-commerce, smart work, and remote technologies did not allow the wineries to balance the negative impact of the fall in sales, several wineries are investing more in digital, both for the marketing of products and for export processes. coronavirus has accelerated these new horizons, and we expect that the newly opened channels will also continue when the sector restart. in our opinion, those who bought on the web will continue if they have had a positive experience, a key aspect for future business. (wp05) during the lock-down we started to focus on the online channels; we implemented a new customer management system (crm) and we tailored our newsletter, the frequency of which has increased thanks to increasingly personalized crm management. there has been a positive response from the us as a market more accustomed to actively participate in virtual life vs the italian market that is almost at a standstill. we created a virtual wine experience, bookable online. literally we bring at your home the val d’orcia. the experience consists of a “home delivery” of a tasting kit with all the accessories necessary for a classic tasting with the addition of a virtual tour at the vineyard, cellar etc. and with a final guided tasting. however, it is difficult to create emotions and empathy even if these virtual experiences will increase in the future. (wp11) however, the different experiences converge on the difficulties to completely transfer the emotional/relational nature of wine consumption to a virtual environment. without policy intervention, most wineries see the risk of privileging quantity rather than quality, rewarding again the large networks or at least the more structured companies. however, policies could play a crucial role in supporting this transition. if on the one hand, the producers try to be resilient, in the long run, and without policy intervention, the situation risks exacerbating negative aspects of current changes in lifestyles and consequently in wine consumption. finally, a fourth and very likely change will be delivered by new consumption patterns. today it is difficult to determine whether in the future we will see a greater role for “safe” and environmentally friendly products, but for producers, the challenge of organic and terroirdriven products represents a key opportunity. wine will benefit a lot when people come back to life and relationships, as a product that focuses on these aspects. however, the consumer patterns will change focusing more on sustainability. if before the covid-19 epidemic, words like sustainability, authenticity, and transparency were often associated with empty slogans, with the crisis the consumer’s attention to their contents could increase. (wp05) according to future societal demands, the current crisis offers a further opportunity to strengthen the greening process of the cap promoting behavioural shifts in line with the recent green new deal by the european commission, and follow up initiatives (e.g., farm to fork strategy and biodiversity strategy). 6. policy implications and conclusion we analyzed the consequences of the covid-19 outbreak on wine sector building on a tailored revision of the csp framework of grando et al. (2020) that allowed us to integrate diverse experience data with 63wine after the pandemic? all the doubts in a glass bio-based and applied economics 10(1): 63-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 recent socio-economic narratives bringing out key elements for short and long-term policy implications. according to our review, the most prominent economic narratives support that despite an arsenal of macro-prudential support policies in the near term the pandemic caused the disruption of domestic demand and supply, trade, and finance. the speed and depth with which it has struck the sector depends on the microstructure of the wine and suggest the possibility of a slow recovery that poses formidable challenges for policymakers. according to world bank (2020a) protectionism in the way of new trade restrictions should be avoided since it could reverse the few gains that the sector maintained (i.e. by increasing price volatility and dampen growth) and it does not offer a solution to the security problem. to limit probable long-term negative implications, the analysis underscores the value of coordination and cooperation in agricultural policy as well as between and across governments and the private sector. the diverse opinions collected through the online workshop converged on the need for greater coordination between regions and the national government in planning income stabilization measures. for the italian sector, despite the envelope announced of approximately 50 million euros, the reduction of stocks through “a distillation of crisis” for the participants risks of not generating the desired impact since it focuses on “generic grapes” a quality that is now becoming marginal in many regional vineyards (i.e. in tuscany generic grapes weigh only 1% of the total at the national level). by extending this reflection beyond the short term, especially in view of its further application in the field of measures to target market and revenue stabilization within the cap reform, this tool should be refined according to the principle of complementarity to provide a better use of available funds per ms and a balanced achievement of its outcomes. despite the new cap was designed within a very different scenario the challenge for policymakers will be to integrate the urgent interventions with the long-term measures to reshape the structure of the eu wine industry after the pandemic. thus, the crisis offers a new opportunity for strategic planning that should likely facilitate this process. therefore, in the wine sector, the debate converges on the opportunity to implement ad hoc strategies and combining the different available tools. the same reflection applies to the “partial green harvest” or the voluntary reduction of yields that will be part of the stabilization package discussed for the post-2020 cap reform. the application of this tool should provide a quick-fix in the current situation, but for its effectiveness, in the medium-long term, there are at least a couple of points that need to be further analyzed. the first regards the attention and the resources that will be effectively delivered because in the past they have been always very scarce (pomarici and sardone, 2020; european commission 2017). as we previously introduced in the past the mss took advantage of favorable wine market conditions to concentrate nsps resources on fostering competitiveness, while we expect that the green harvesting, insurances, and mutual funds should assume more importance in the next future, especially after the current shock. again, the crisis could represent the opportunity to reflect if it is necessary to implement complementary actions that facilitate the effective adoption of such tools. the second point stresses again the need for more coordination between policy levels. in face of the recent regional advance in securing quality and the origin, the key message is that “centralization could be risky” since there are regions that are more advanced than the national government in the control of vine and wine-growing potential. these actors fear a limited impact of a “complex aid” that should also guarantee “speed, effectiveness, and simplicity” at the same time. one positive aspect that emerged is that the covid-19 epidemic increased the solidarity between supply chain members and amplified the opportunity for discussion, shifting the attention from a crystallized present towards the search for a viable future that should go beyond the current limits of the sector in a more effective way. consistent with this idea, one option could be on dusting off old cap tools. we refer to the open debate on the single cmo on the integration of wine actors (european commission 2016). indeed, forms of aggregation such as producer organizations (pos), which are not currently widespread in the sector are seen as a strategic instrument in concentrating supply, obtaining more favorable prices that notwithstanding the rules of the european competition, facilitating the access to cap support and deliver a significant reduction in production and marketing costs for their members. with regards to the single cmo, it will be relevant to provide the necessary support and flexibility for securing the ongoing projects for the restructuring of vineyards and promotion, which are key measures to guarantee the viability of wine territories, especially for those actors that have been most affected by the on-trade fall. thus, policymakers should provide quick reprogramming. for example, the tuscany region is now acting as a facilitator with the national government to allow an extension for the nsp to the conclusion of the projects (march 2021), as well as a fast and smart variant, to ensure promotion actions (e.g. no vinitaly, it is possible to steer the promotion action in ultra-rapid times in other markets), an increase the percentage of contribution from 50 to 60 percent (i.e. less promotional activities but with the same 64 bio-based and applied economics 10(1): 64-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 daniele vergamini, fabio bartolini, gianluca brunori budget) as well as a reduction of penalties. in line with these needs, the future cap could include – beyond the typical promotional actions and market analysis – new actions aimed at the preparation of technical files and facilitate access to non-eu markets with information on oenological practices limitations, phytosanitary and hygiene rules (pomarici and sardone, 2020). if the problem of the future has repeatedly stressed by participants since the crisis is seen as an opportunity for redemption, then the need to limit future shocks becomes crucial. despite the unprecedented amount of financial support to “do whatever is necessary to restore confidence and economic growth and to protect jobs, businesses, and the resilience of the financial system” (u.s. department of the treasury 2020), the need to invest in the predisposition of viable future strategies and policy interventions emerges from the discussion. in other words, against an immediate support strategy that shift the private sector income losses into public debt relaxing capital and liquidity coverage requirements, a more direct role of policies is expected to ensure a strong and sustainable economic recovery of the territories, evaluating the opportunity to support the investments, human and territorial capital. however, to achieve these goals, it is necessary to recover a climate of trust, starting from the travel & leisure industry. many euro area members that are heavily reliant on tourism, are still prone to a slow recovery, stressing the need for more cooperation. wine as many other sectors will need to uphold a stable rules-based international trading system to secure a solid and lasting recovery. although the covid-19 epidemic has caused the disruption of the most privileged on-trade sales channels, the sector has witnessed a new ability to coordinate among the various actors to express positive messages of restart, identity, and reaffirmation. this positive wave has also offered an opportunity to accelerate the process of consolidation of the supply-chain (vergamini et al., 2019), at least for what regards the formation of new promotion networks at the regional and national level. the most important italian wine consortia are now acting in this direction. for example, the chianti wine consortium launched a european tender to form a network aimed at developing promotion in canada and the united states. from this point of view, the ability to network resources, at least for promotion, could represent a fundamental element to overcome growing difficulties. in addition, to facilitate this type of operation, several italian regions are requesting a variation on the nsp to offer the necessary support to alternative forms of promotion like remote tasting or virtual b2b meetings. finally, the pandemic poses the production of healthy, safe, and sustainable products as a key challenge for many wineries, representing what we can see as the half-full glass. acknowledgments this work was developed thanks to research activities carried out within the european union horizon 2020 funded project sufisa (sustainable finance for sustainable agriculture and fisheries; grant agreement no. 635577). references acemoglu, d., chernozhukov, v., werning, i, and whinston m. 2020. a multi-risk sir model with optimally targeted lockdown. unpublished paper. massachusetts institute of technology, cambridge, ma. barattieri, a., m. cacciatore, and f. ghironi. 2019.protectionism and the business cycle. nber working paper 24353, national bureau of economic research, cambridge, ma. bartolini, f., andreoli, m. and brunori, g. 2014. explaining determinants of the on-farm diversification: empirical evidence from tuscany region. bio-based and applied economics journal 3(1050-2016-85760), pp. 137-157, https://doi.org/10.36253/bae-12994 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(2010). le système alimentaire mondial: concepts et méthodes, analyses et dynamiques. paris: éditions quae. taglioni, d., and v. zavack, 2016. innocent bystanders: how foreign uncertainty shocks harm exporters. policy research paper 6226. world bank, washington, dc. trestini s., pomarici e., giampietri e., 2017. around the economic sustainability of italian viticulture: do farm strategies tackle income risks? quality – access to success 18, pp. 461–467. u.s. department of the treasury. 2020. statement of g7 finance ministers and central bank governors. statements & remarks, u.s. department of the treasury, washington, dc. vergamini, d., prosperi, minarelli, f., p., grando, s., bartolini, f., raggi, m., viaggi, d., brunori, g., 2018. sufisa national report italy-d 2.2. https://www. sufisa.eu/wp-content/uploads/2018/10/d_2.2-italynational-report.pdf vergamini, d., bartolini, f., prosperi, p., & brunori, g., 2019. explaining regional dynamics of marketing strategies: the experience of the tuscan wine producers. journal of rural studies, 72, pp. 136-152. https://doi.org/10.1016/j.jrurstud.2019.10.006 vlahos, g. 2020. farming system transformation impacts on landscape: a case study on quality wine production in a highly contested agricultural landscape.  land,  9(4), pp. 120. https://doi. org/10.3390/land9040120 world bank, 2020a. global economic prospects, june 2020. washington, dc: world bank. https://doi. org/10.1596/978-1-4648-1553-9 world bank, 2020b. global economic prospects: slow growth, policy challenges. january. washington, dc: world bank. 66 bio-based and applied economics 10(1): 66-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 daniele vergamini, fabio bartolini, gianluca brunori annex 1 sufisa regional workshop: “long-term future scenarios of wine markets between opportunities and risks: what will be the strategic choices of tuscan wineries” a) objectives of participatory scenario workshop the participatory workshop was the final task of the sufisa activities for wp4 “scenarios & solutions” carried out by the university of pisa. the workshop intended to elicit the views and opinions of relevant stakeholders and decision-makers operating in the tuscan wine sector. the end goal of this event was the identification of market and regulatory issues influencing the performance of wineries in tuscany (italy) in response to potential future scenarios (see gardin et al., 2019 for a deeper understanding of the scenarios development process); to elicit how wineries developed strategies to deal with them and to discuss their relevance for the sustainability of their farms and farming systems. more specifically, the aim was to extend, support with further evidence, and refine our understanding of csp framework from a primary producer perspective, and contribute to the formulation of future alternative solutions. accordingly, the workshop was organised as an interactive cyclical process of ask & answer at an individual and collective level (work in groups) between researchers and 38 participants (small and medium-sized tuscan wineries, wine cooperatives members, staff members of the tuscan dg-agri, wine consultants and extension service providers, members of the special agency of the chamber of commerce “promo firenze”). in other word, the workshop triggered participants to confront potential future european wine sector scenarios including agricultural and trade policies with individual and collective objectives, planning or taking future action (strategies), and reflecting on potential outcomes and policy implications. before the workshop, we engaged participants by email, sharing all the relevant information about the sufisa project and its related scenarios, as well as the workshop goals and its schedule. b) general introduction: presenting the four food system narratives (scenarios) and their (possible) impacts for the wine sector since wine significantly differed from the other sufisa sectors and commodities, before the running of the regional workshop we discussed and refined through several face-to-face meetings with relevant regional stakeholders (members of wine appellation consortia, of wine cooperatives, large regional wineries, as well as members of the tuscan dg-agri) the four scenarios figure 1a. four scenarios for tuscan wine sector. 67wine after the pandemic? all the doubts in a glass bio-based and applied economics 10(1): 67-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 provided by the sufisa project in order to adapt it to the different specificities of the wine sector. below we report the resulting scenarios. figure 2 summarises the main variables and trends and it follows a brief description for each scenario. – scenario 1: international/global competition the first scenario (international competition) is qualified by an overall increase in demand as well as full market liberalisation. to 2030 it is assumed that the dominant consumption model is based on low price and cheap food due to a limited amount of household budget for the food basket. wines of medium-low quality and reasonably priced are now globally available thanks to the modern trade. the consumption of premium and organic wines has been limited to few segments of the average richest population, among these, consumers continue to prefer to drink less but of more quality. therefore, on average consumers are not willing to pay for quality products and low-price food is ensured by trade. the wine trade is completely liberalized and both tariff or non-tariff barriers are removed. there is less bureaucracy with a marginal weight of product standards lower than in previous years (i.e. less stringent rules for labelling and fewer controls). the dominant research is mainly performed by few private corporations that – due to a huge investment in the sectors as well as dominant position in acquiring farm data – provide new technologies and equipment mainly aimed at reducing production costs. the control of all farm data together with the advanced use of digitalisation and big data allows such few multinationals to directly support farms in any daily practices to reduce production costs. thus, the market has become very efficient but there is a very high competition among farms (many small and medium-sized farms have stopped producing wine) and just few farms can still afford to produce premium and organic wines. the value chain is dominated by intermediaries (international buyer and export manager) with a very low bargain power for both retailers and producers. – scenario 2: highly segmented market the second scenario (highly segmented market) is qualified by an overall reduction in the demand as well as full market liberalisation. the reduction of the demand mostly affects the cheapest segments of production (standard and table wines). thus, within the market, both cheap wine and high-quality wine (premium and organic wines) coexist. consumption is increasingly regionalised, the differentiation of production undergoes a considerable increase, the types of quality products increase, the origin is increasingly connected to the environment and to historical and cultural factors linked to the different terroir. each winery seeks its own market segment to represent its uniqueness and its regional key characteristics. the demand for wine shows a general reduction that mostly affects the price for standard wines (not quality or origin wines) while the segment for premium price and organic remains almost stable (status quo). the maintenance of a cohesive european market but more open at the international trade is coherent with the maintenance of functioning eu market. however, the free trade and international competition (mostly characterised by an increase in the wine producing regions due to the regionalisation of consumption) increase also the supply of high-quality products (emerging markets may benefit for not-tariffs barriers) with the possibility for some products to obtain more favourable prices on these markets comparing to the national market. the dominant research is mainly performed by private companies which provide new technologies and equipment to reduce production costs or to improve the quality of the process. the value chain is dominated by retailers that – due to their economy of scale and to their ability to reduce transactions cost – try to increase the variety of wines into the supermarket and, meanwhile, try to reduce the number of wine supplier (few companies afford to represent the different producing regions). – scenario 3: europeanization the third scenario (europeanization) is qualified by very strong european standards that set out the framework for both trade and production as well as for an increase in the demand. european agricultural production is effectively protected and recognizable and ensures a quite high-quality level respecting higher sustainability and ethical standards. thus, wine import into eu is very limited due to such high standards. this has determined an increase in production costs with an overall increase of the cost of food, and consequently of wine. at the same time, eu consumers show a high willingness to pay for healthy food as well as for food communicating a low impact on the environment and society. the increasing demand for origin, premium and organic wines, support the efforts of producers who have specialized their production patterns towards this market. the dominant research paradigm is characterised to increase competitiveness and food safety to reach extra-eu markets as well as to introduce technologies and practices to mitigate the effects of climate changes. the latter is for example oriented at introducing new varieties tolerant to drought and extreme weather. the value chain segmentation continues with the current trends but differ68 bio-based and applied economics 10(1): 68-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 daniele vergamini, fabio bartolini, gianluca brunori ent high prices for wine and has encouraged cooperation among producers, consolidating their position along the value chain. – scenario 4: ecologisation the fourth scenario (ecologisation) is qualified by very strong european environmental standards that set out the framework for both trade and production but with a reduction of the demand that mostly affects the cheapest segments of production (standard and table wines) comparing with that of organic and high-quality wine, which remain almost stable. furthermore, as europe is going less dependent on external agricultural markets, there is no need for international trade. meanwhile, a reduction in production is a consequence of export restriction due to low activity of trade with extra eu countries. european consumers – that are increasingly oriented towards the regionalization of consumption – show a very high willingness to pay for health food and for production communicating high environmental standards and origin. moreover, wine demand is further contracted due to shifting in consumers’ preferences towards health and safety consumptions (i.e. substitution of cheap wine with energy and improved nutritional drink). therefore, the wine demand remains high only for premium prices wine. relaxing of competition for prices as well as the new business model to meet the new demand for consumers have reduced the bargaining power of retailers and now there is no actor in a dominant position along the value chain. a balanced research system, between public and private research, ensure adequate provision of technology and practise to address sustainable development goals (sdgs) as well as to improve knowledge exchange and mutual learning across wineries and along the supply chain. c) workshop schedule · 13.00 registration · 14.00 workshop presentation, scopes and introducing scheduled activities at individual and collective level · 14.15 introducing first round of csp individual analysis · 14.30-15.00 individual analysis (live survey) · 15.00 individual scenario analysis (live survey) · 15.45-16.00 coffee break and cluster preparation · 16.15 introducing second round of csp collective analysis · 16.30-17.30 collective/group analysis (focus group discussion per cluster) · 17.30-18.00 discussion and conclusions d) animation techniques and instructions to participants the workshop has been driven using a “metaplanlike” technique, that is: · by training participants on the use of a smart application on their mobile phone to answer questions, taking comments, suggestions and other notes; · by providing participants with post-it large enough (e.g. 15x20 cm) to be able to write two or three sentences with a pen; · by leaving them enough time to reflect to the questions; · by organizing collective discussion (for strategies and for solutions), in order (notably) to – identify solutions and strategies that are shared by most, and conversely, that are highly controversial – “cluster” the different strategies / solutions proposed by individual and in different groups. e) individual analysis (live survey carried out during the workshop by launching the questions on the screen and having each participant answer via an app from their mobile phone). 1) now we ask you to select one or more business goals for the next 10 years objectives max 3 obj. business development reduction of production costs maintenance facilitate generational renewal (succession) export growth increase local markets increase branding renewing quality exit other please specify 2) which of the following changes in the conditions of demand, supply, territory and the environment do you think will affect your business choices in the next 10 years. give an answer from 0 not influential 5 very influential. 69wine after the pandemic? all the doubts in a glass bio-based and applied economics 10(1): 69-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 demand changes 0 nothing – 5 very greater attention to quality by markets and consumers (in addition to the origin, attention to premium wines, organic, from international blends, wines produced from native vines, light and easy to drink and mix wines, etc.) change in the requested quantity (growth in regional consumption, opening of new markets or even vice-versa closing of markets such as the usa and decrease in regional consumption etc.) changes in selling prices (request for lower prices and / or possibility to increase prices in emerging markets) changes in consumption patterns (e.g. lower consumption of wine and search for drinks with a lower alcohol content) increase in consumer knowledge and awareness (eg. consumers who are more informed, more aware of what they drink, who do not choose only for the price but who carefully evaluate quality, brand and territory) change in regulation on the main non-eu export markets (eg usa, canada) and eu. (change in tariffs, methods of access, trade agreements and accessibility to state monopolies) supply changes 0 nothing – 5 very change in the offered quality (both in production and communication terms) change in the quantity produced and in the production scale (growth and / or decrease in production, increase or decrease in the production scale) greater attention to the origin and the territory (changes and / or simplification of the regulations, new rules related to the origin etc.) changes in production costs (greater production efficiency and reduction of production costs or increase in input costs and consequently higher production costs) search for greater production differentiation (increase in production of local grapes, transition to organic, biodynamic and other blends) change in production technologies (increase in research into new varieties resistant to climate change, increase in technologies that can contribute to greater production efficiency) territorial changes 0 nothing – 5 very changes in the access to factors of production – land, labor and capital (change in the regulation of planting rights, expansion of the vineyard area, increasing access to skilled workforce or contraction in its availability, greater access to capital for new investments, etc.) new entrepreneurs and generational change (continuation of the family business with family members and/or change with new qualified resources from the territory, or risk of exit from business due to lack of generational change) change in agricultural policies (mainly cap, rdp, and wine cmo measures) (e.g. opportunity of more incentives for restructuring, greater aid for the start-up of new productions and for young entrepreneurs, simplification of regulation, new support measures for promotion, etc.) change in relations with institutions and other regional organizations (e.g. greater openness in decision-making processes, greater participation, and more direct and simplified communication with the various bodies that regulate the sector and/or development of new territorial bodies such as ops, new types of consortia, etc.) environmental changes 0 nothing – 5 very changes due to a greater or lesser presence of extreme weather events changes in the management of water resources and soils due to an increase in drought periods changes linked to an increase of invasive species and pests changes in the management of soil fertility and natural resources changes related to the management of the rural landscape 3) now we ask you to select from the following strategies those that you think could allow you to pursue your business objectives over the next 10 years. which of the following could be your key strategy in the next ten years? with regard to this part (strategies), after a first round of answer, we introduced the four scenarios and we replicated the questions for each scenario. we asked to the participants which of the following strategies could be implemented within the reference scenario for the next 10 years. strategies to increase competitiveness select relevants aiming at the company’s production efficiency, reducing inputs and improving processes on both vineyard and cellar increase production specialization thanks to greater research and increased management & control on grape varieties and cellar processes focus on quality and origin (e.g. reduced yields but higher quality) intensifying production by increasing yields per hectares wineries diversification (developing new products and/or new types of business) product diversification aiming at technological development for greater control of production and production efficiency. develop territorial partnerships 70 bio-based and applied economics 10(1): 70-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 daniele vergamini, fabio bartolini, gianluca brunori forming new productive organizations, associations or joining cooperatives or pdo/pgi consortia market orientation select relevants improving the ability to access to markets through export broker, or market intermediaries or by investing in the creation of in-house export competences investing in the development of new markets invest in the creation of corporate units and/or specific resources in the management of commercial relations on foreign markets through new commercial and/or distribution companies (internationalization of added value) develop the lever of marketing and promotion in a cooperative perspective through consortia and other producer organizations and associations formalize presence on the markets through greater use of contractual instruments (e.g. annual or multi-year distribution contracts, contracts with intermediaries, etc.) reduce market risks though risk protection and management contracts and/or insurance contracts status quo (survival) select relevants farm diversification increasing non-agricultural activities reduce business (part-time) reduce the demand for external labour and improve the internal capacity to satisfy production needs resizing policy support select relevants increasing networking activities, partnership or the recourse to business associations (horizontal cooperation) in order to achieve business objectives increasing lobbying capacities and ability to access to rdp, cmo resources investing resource to acquire subsidies, tenders and other forms of support to achieve company objectives investing in r&d risk management select relevants insurance contracts to reduce production and market risks flexibility assets, corporate functions and underused structures liquidation to recover and maintain liquidity investment in credit recovery and debt management long-term contracts with large distributors f) collective analysis first, we divided participants in 8 different clusters according with the following specification: wineries size type of production export level <50% turnover >50% turnover small conventional 1 5 organic/biodynamic 2 6 large conventional 3 7 organic/biodynamic 4 8 each cluster was composed by 4/5 stakeholders and two facilitators. the goal was to create discussion groups with similar characteristics/background to facilitate the sharing of problems and the networking of potential strategies. then we asked in each group to: a) to identify a new cluster objective and to evaluate whether this objective was realistic or not within each scenario (robustness of the scenario) b) what individual and collective strategies would they have put in place to achieve the cluster objective in each scenario here facilitators organized with poaster and post-it the collective discussion: · to select strategies that were shared by most, and conversely, that were highly controversial · to “cluster” the different strategies / solutions proposed in the different groups. c) finally, we asked participants in the different groups to reflect on potential policy needs to achieve the group’s goal. finally – in a plenary session – one or two representatives of each group presented the most debated scenarios and their related strategies and solutions. annex 2 “web-based workshop” (may 15, 2020) a) objectives the aim of the web-based workshop was threefold. firstly, it was aimed at discussing the impact of the sudden changes driven by the pandemic on wine producers’ strategies and performances. secondly, it intended to provide with a space for stakeholders to make their own recommendations for the sector. thirdly, stakeholders were expected to improve our understanding of the policy implications of the findings emerged from our 71wine after the pandemic? all the doubts in a glass bio-based and applied economics 10(1): 71-71, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9017 research activities before and during the crisis. to pursue these objectives, we did not foresee a rigid analytical structure, but the workshop was instead promoted as a co-reflection exercise with 20 international wine key actors (international winemakers, market consultants, agricultural consultants, wineries, representatives of regional institutions, academics and students) to engage stakeholders and elicit through an open-discussion the most significant csp aspects that emerged from the pandemic. thus, the workshop was intended to ‘groundtruth’ on the previous csp findings and better clarifying the role of the different pandemic stressor(s) in driving adjustment processes and the dynamics that drove wineries from the ‘baseline csp’ in the past to the new framework with respect to the current pandemic state and to the future sustainability and viability of the sector. against this background the animation technique allowed participant to discuss carefully on the key topics introduced by pandemic, to stress different directions, including unpredicted events and issues. the structure of the workshop included two rounds of 10 presentations of 10 minutes each with 5 minutes of open discussion and at the end an hour of round table discussion between participants. the workshop employed a moderator for the presentations and a round table facilitator who returned a final summary of the day. then data collected allowed researcher to develop a backward reflection and a comparison with the initial csp set, including short-term implications for different geographical contexts (italy, france, spain, portugal, australia, and us) analysed in the workshop. b) workshop schedule · 10.00 workshop introduction, scopes and scheduled presentations · 10.15 first round of workshop presentations · 12.30 lunch break · 13.30 second round of workshop presentations · 16.00 coffee break · 16.15 round table · 17.00 discussion and conclusions c) researcher backward reflection with data collected in the second workshop we developed a comparative analysis to understand the context and evolution of the sector under the pandemic crisis. the aim of this exercise was to reflect on possible changes on the initial csp set, as well as address the important contingencies introduced by the pandemic. the reflection was guided through a grid of key issues developed to consider and discuss all impacts of the pandemic on the key elements of the csp framework (table 1a). table 1b. reflexive questions on strategies after pandemic. guiding question what we want to understand on csp 1. where and how (channels) producers commercialise their products? markets and marketing 2. what are the main challenges with customers and demand introduced by the pandemic? 3. what are the marketing strategies in place to secure export? 4. what are the contextual change that influenced most their business model? 5. what are the role of policies? 6. how do they maintain/finance their activity, and what they require after the crisis? financing 7. do they show a cooperative approach? how did this start? how is it going? will they continue in the future? horizontal coordination 8. do you collaborate with others in the valuechain? how did this evolve? will you continue with this in the future? vertical cooperation 9. do they feel that the current policy context can help to overcome the pandemic crisis and improve their business performance? policy and regulations 10. what about the environmental constrains and social challenges that they need to address? 11. how do they deal with current policies and regulations? what are their main strategies? 12. what about the sustainability of the sector, how would they define this impact? financial sustainability volume 10, issue 1 2021 firenze university press ten years of bio-based and applied economics: a story of successes, and more to come fabio g. santeramo1, meri raggi2 the capitalisation of decoupled payments in farmland rents among eu regions gianni guastella1,2, daniele moro1, paolo sckokai1, mario veneziani3 contribution of periurban farming systems to local food systems: a systemic innovation perspective rosalia filippini1,2, elisa marraccini3, sylvie lardon2 an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications, and consumption of agri-food products carrying those certifications niculina iudita sampalean1, daniele rama1, giulio visentin2 wine after the pandemic? all the doubts in a glass daniele vergamini*, fabio bartolini, gianluca brunori public r&d and european agriculture: impact on productivity and return on r&d expenditure michele vollaro1, meri raggi2, davide viaggi1 bio-based and applied economics 6(3): 279-293, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-20774 economic and social impact of grape growing in northeastern brazil linda arata1,*, sofia hauschild2, paolo sckokai1 1 department of agricultural and food economics, università cattolica del sacro cuore, piacenza, italy 2 italian embassy in brazil, brasilia, brazil date of submission: 2017 8th, june; accepted 2018 20th, march abstract. the northeastern viticultural industry has become a model for the whole brazil and has been identified as a grape production district. given the importance of agriculture in the economy of the region our study aims at analysing whether the grape producing activity affects some socio-economic indicators, namely the theil index, the human development index (hdi) and the unemployment rate over the period 2000-2010. the study is focused on the northeastern states of bahia and pernambuco, two of the poorest and with the highest income inequality among brazilian states and combines the difference-in-differences with the propensity score matching method at the municipality level. results seem to indicate that grape growing plays an important role to guarantee a fairer income distribution. indeed, the municipalities that grow grape experience a decrease in the level of theil index by 11.7% compared to the level they would have if they had not participate in grape production. no effect has been found on the hdi and on the unemployment rate. results are robust to the potential presence of an hidden bias according to the rosenbaum sensitivity analysis. keywords. grape production, socio-economic indicators, brazil, propensity score matching. jel codes. o13, q13, c21. 1. introduction and background although historically concentrated in the southern states of brazil, since the ‘60s grape production has developed also in the northeastern region of the country, due to public-private investments in agriculture as well as to the development towards a commercial agriculture. one of the most important investment subsidized by the government consisted in irrigation systems which have allowed the setting up of grape growing. indeed, the northeast of brazil is classified as a semi-arid region, with little and unpredictable amount of rain, which could undermine any potentiality for grape production *corresponding author: linda.arata@unicatt.it 280 linda arata, sofia hauschild, paolo sckokai if adequate irrigation systems were not in place. total area under irrigation in the country expanded more than fivefold between 1960 and 1980 and in two of the northeastern states, bahia and pernambuco, it increased by 100,000 hectares (selwyn, 2008). the construction of new public infrastructures in northeastern brazil, such as roads and airports, has facilitated the development of the grape industry and trade. the investments in the two states and the introduction of rational agricultural practices led to a radical transformation of agriculture and social relationships. since the late ‘70s there has been a shift from small-scale riverside and flood plain agriculture, where the sharecropping system between landowners and live-in workers was in place, to commercial agriculture, based on a high value horticultural industry. grape production represents one of the products of the new regional agricultural system and it is mainly concentrated in the vale do são francisco (san francisco valley, sf, the region located around the san francisco river), which includes parts of bahia and pernambuco. viticulture in the semi-arid northeastern macro-region has specific features due to the climate conditions characterised by a monthly average temperature between 24°c and 30 °c, 500 millimetres/year of precipitation and 50% air humidity. the permanent warm weather is responsible for an acceleration of the physiological process and the propagation is very fast, allowing the first harvest after one year and a half. in addition, irrigated grapes can be produced continuously throughout the year allowing on average 2.5 production cycles per year (texeira et al., 2007). this pattern leads to an average production of 40 tons per hectare per year, well above the average of other brazilian grape producing regions and of other regions of the world. it also allows harvesting in periods where prices are higher, which turns viticulture into an activity with a lower degree of uncertainty and a high potential profitability (lima et al, 2009). in 2003 the integrated production (ip) protocol has been introduced for grape production in the sf valley. this introduction has improved grape production systems not only for the ip product but for all grape produced in the region (camargo et al., 2011). the improvements concern a more rational use of inputs and an upgrade in the organisation of information made possible through the use of field notes. likewise, knowledge deriving from ip practices have supported the adoption of other private protocols of quality certification in the san francisco valley, such as the hazard analysis and critical control points (haccp). together with the subsidisation of irrigation systems and of other infrastructures (roads, airports), the government has supported new grape plantations by subsidised credit and by tax breaks (tales, 2009). there have also been investments in the training of workers in the grape industry, in research to improve the grape quality, as well as in the promotion of events such as organised paths along the vineyards, festivals and competitions. nowadays the sf valley is responsible for 99% of table grape exported by brazil (lima et al., 2009) and it is gaining fame as a development model in the northeast of brazil, the poorest macro-region of the country (ifad, 2011) and one of the regions with the highest income inequality (ipea, 2015). between 1991 and 2001 grape export from the sf valley increased in volume from 1,000 to 13,000 tons and in value from 4.7 to 20.4 us$ millions (selwyn, 2008). although grape production in this region mainly consists of table grape, recently there has been a growth in the production of grape to be transformed into fine wines such as cabernet sauvignon, syrah, moscato canelli, chardonnay and chenin blanc. 281economic and social impact of grape growing in northeastern brazil in 1992, parallel to an expansion in grape production and trade, the brazilian grape marketing board (bgmb) was created with the task of exporting grapes mainly to the eu. over the years its importance has increased, becoming the main exporting organisation of the northeastern region: the board performs also a quality check and provides its associates (individual farms, cooperatives and producer associations) with technical training (selwyn, 2008). in 2010, the table grape product in the sf valley has been rewarded with the geographical indication (gi) and it represents the first gi product in the northeastern region. differently from other brazilian agricultural industries, dominated by large and extralarge farm size, such as sugarcane, corn and soybeans, small farms organised in cooperatives play an important role in the grape industry, especially in northeastern brazil (tales, 2009). the presence of small farms should reduce the exploitation of workers that characterises other agricultural industries in brazil and represents a social benefit at the local level. in addition, the grape industry in this area represents an example of ‘production district’, given the high concentration in the same area of all actors involved in the grape supply chain as well as the high level of cooperation (tales, 2009). the organisation as a production district allows to increase the specialisation, due to the sharing of knowledge and skills, as well as to reduce the transaction costs. this may support the improvement of the socio-economic conditions of the population involved in the grape supply chain. the development of a well organised, competitive and high-value agricultural sector such as the grape producing industry in northeastern brazil may have an important role in the socio-economic development of the area. this is further supported by the high share (around 20%) of agricultural gross domestic product (gdp) over total gdp in the area. although some studies investigate the effect of sugarcane and soybean cultivation on some development indicators in brazil (chagas et al., 2012 and weinhold et al., 2013), we are not aware of studies that consider the effects of grape production. our paper aims at filling this gap by investigating whether the brazilian northeastern municipalities that started to grow grape after 2000 has experienced an improvement in some socio-economic indicators over the period 2000-2010 compared to the municipalities that never grew grape in that period. given the specific features of the grape industry in this area, it is likely to have an impact on regional socio-economic development. the paper is focused on the two northeastern states of bahia and pernambuco. since these two states are among the poorest and the ones with the highest income inequality in brazil, it is interesting to investigate whether the setting up of grape production improves some socio-economic conditions of that area. in particular, we consider three socio-economic indicators: the theil index1, the human development index (hdi)2, and the unemployment rate. these indicators allow a comparison of our results with studies carried out in other brazilian regions for other agricultural products, which use the same indicators (chagas et al., 2012 and weinhold et al., 2013). 1 the theil index is a measure of income inequality and was developed by theil in 1967. a value of 0 reflects total equality, a value of 1 represents maximum inequality. 2 this indicator encompasses three dimensions of social conditions: education (measured by rates of literacy and school enrollment), longevity (life expectancy at birth), and income (per capita gross domestic product gdp) (chagas et al, 2012). according to the united nations development programme (undp), the hdi was created to emphasize that not only economic growth but also people and their capabilities should be the ultimate criteria for assessing the development of a region. 282 linda arata, sofia hauschild, paolo sckokai 2. methodology 2.1 propensity score matching and difference-in-differences we investigate the effect of grape growing on the socio-economic conditions of the municipalities of bahia and pernambuco by means of the propensity score matching (psm) methodology. psm is a semi-parametric method that allows to assess the effect of a treatment in a non-experimental setting. indeed, in non-experimental conditions the treatment is not randomly assigned and individuals self-select to the treatment according to their characteristics. if those characteristics are related to the outcome to be evaluated, the simple comparison between treated and non treated individuals leads to a bias evaluation. psm aims at overcoming the selection bias problem by matching each treated unit with one or more non treated units with similar observed characteristics, such that the difference in the outcome between the units can be interpreted as the effect of the treatment (smith and todd, 2005). rosenbaum and rubin (1983) propose to combine the observed characteristics (x) that potentially affect both the treatment and the outcomes in one summary measure, the propensity score p(x), that is the probability of being treated, such that the conditional distribution of x given p(x) is independent of the treatment assignment. psm provides a consistent evaluation of the treatment when two assumptions are satisfied. the first assumption is the mean independence assumption, which states that after conditioning on the propensity score, the mean outcome is independent of the treatment assignment. the second assumption is the common support condition which guarantees that each treated unit potentially finds a matched untreated unit by restricting the probability of the treated to be lower than 1. the presence of unobservables that simultaneously affect the outcomes and the decision to participate into the treatment lead to biased results by violating the first assumption. to partially overcome the problem of selection bias on unobservables, heckamn, ichimura and todd (1997) propose to combine the psm estimator with the difference-in-differences (did) estimator, such that the effect of the treatment is evaluated by comparing the before-after outcome of the treated units with that of the matched non treated units and the matching is based on the propensity score: (1) where t’ is the pre-treatment period, t is the post-treatment period, i identifies the treated units, j identifies the non-treated units, n is the number of units of the treated group falling in the region of common support, wij indicates the weights (0 ≤ wij ≤ 1), which depend on the distance between pi and pj, and s indicates the region of common support. the did estimator controls for unobservables that are constant over time and are responsible for outcome level differences. although did allows for time-invariant differences in outcome levels between the treated and the control group, it requires that, conditional on the propensity score, the outcome in the two group follows parallel path in the absence of the treatment (i.e. the did mean independence). 283economic and social impact of grape growing in northeastern brazil the aim of the psm is to estimate the average treatment effect on the treated (att) which, in the case of the did-psm, can be expressed as (2) and represents the difference in the average outcome growth between the treated and the matched control group. the combination of psm and did has been used to investigate the impact of some agricultural practices on farm production choices and economic performances in developed countries (arata and sckokai, 2016; udagawa et al., 2014; pufahl and weiss, 2009). in developing countries, where agriculture represents a large share of total gdp, psm-did has been applied to analyse the impact of agricultural activities on social and economic development (chagas et al., 2012 and weinhold et al., 2013). our analysis belongs to this second stream of literature, since the combination of psm and did is suitable for analysing the effect of grape growing on some socio-economic indicators at the municipality level. indeed, it is likely that municipalities that started to grow grape differs from the municipalities that never grew grape over the period considered and this difference may be related to the values of the outcomes. in addition, the use of did allows to use the pre-treatment outcome as a control variable in the propensity score and to remove the bias for the time-invariant unobserved characteristics. this later feature allows us to reduce the set of control variables to be used in the propensity score matching. 2.2 sensitivity analysis the combination of psm and did avoids the bias due to time-invariant unobserved characteristics. however, the presence of unobservables that vary over time and that are related to the decision to grow grape and to the outcome indicators undermines the results of the treatment effect. matching estimators are not robust to this kind of hidden bias. as it is not possible to check the presence and the magnitude of the hidden bias, a sensitivity analysis is required in order to measure how strongly an unobserved variable should affect the odds ratio of treatment assignment in order to undermine the conclusions about the treatment effect. rosenbaum (2002) proposes to put a bound on the significance level of the treatment effect according to the extent of the hidden bias. we can express the log of the odds as: (3) where, πi is the probability of unit i to participate into the treatment, f(xi) is a general function that relates the observed covariates to the odds of participation, ui is an unobservable component and γ is its effect on the odds of participation. the odds ratio between two observationally identical units is: 284 linda arata, sofia hauschild, paolo sckokai (4) the last term of the equality reduces to as unit i and unit j are identical with respect to the observed variables. therefore, if there are no differences in the unobserved variables, or if the unobservables do not affect the treatment assignment (γ = 0), the odds ratio is equal to 1. conversely, if there is hidden bias due to unobservables the odds ratio conditional to the observable characteristics may differ from 1. thus, г measures the magnitude of the hidden bias. the larger the value of г, the stronger is the influence of an unobserved variable on the decision to participate and the wider is the confidence interval around the treatment effect. for each level of г the bounds for the significance level of each outcome is computed. the rosenbaum sensitivity analysis represents one of the most widely applied method to check the robustness of the results of psm (caliendo and kopening, 2008; chagas et al., 2008, liu and lynch, 2011). we implement this analysis in our study in order to check the robustness of our results to the potential presence of an unobserved variable that simultaneously affects the decision to grow grape and the indicator outcomes. 3. data and empirical model the state of bahia is 564.733 km2, has 15 million inhabitants and ranks the fourth most populous brazilian state and the fifth-largest in size, with a total of 417 municipalities (brazilian institute of geography and statistics – ibge, 2016). in 2010 (the year of our evaluation), bahia’s hdi ranked 22nd among the 27 brazilian states, and the 3rd most unequal brazilian state according to the theil index (ibge). pernambuco has a total area of 98,149 km2, 185 municipalities and 9.2 million people; it is the seventh most populous state of brazil and the sixth most densely populated. pernambuco ranks 19th among the 27 brazilian states for the hdi and is the 12th most unequal considering the theil index (ibge). although the main commodities produced in bahia are soybean, cotton and sugarcane (table 1), grape growing represents an important sector as explained in section 1. over the period 2000-2010 land allocated to cotton rose by nearly fivefold, soybean and grape increased by around 50%, while the area to sugarcane dropped by 10%. in pernambuco the most important commodity in terms of land allocation is sugarcane, whose area increased slightly between 2000 and 2010, while land allocated to grape more than doubled. as the number of total municipalities and their boundaries changes periodically, municipalities have been consolidated into minimum comparable areas (mcas) to make them consistently comparable over time. in our analysis the treated group is represented by the 16 mcas of bahia and pernambuco that did not grow grape in 2000 but started grape production between 2000 and 2010. conversely, the non-treated group is represented by the 363 mcas of the same region that never grew grape over the period 2000-2010. we exclude from the non-treated group those mcas that are located inside the so called 285economic and social impact of grape growing in northeastern brazil “sertão nordestino” since they did not have irrigation systems in the period under analysis. the rationale behind that decision was that, if the region does have neither a minimal amount of rain nor a good irrigation system, it gets really difficult to succeed in producing grape, and this may have a more general effect on the socio-economic indicators we analyse. moreover, the metropolitan areas of recife and salvador are excluded from the analysis. the first step of the matching procedure consists in running a probit model of the probability (propensity score) of a mca to have started grape production between 2000 and 2010 on a set of control variables. the control variables used to compute the propensity score are selected in order to control for characteristics that may affect both the decision of starting grape production and the development outcomes. these control variables are: the share of agricultural employees over total population, the share of agricultural gdp over total gdp, the average number of tractors per farm, the value of agricultural production per hectare, the average temperature in each season and the average rainfall in each season. the first four variables come from the brazilian institute of geography and statistics (ibge) agricultural census while the last two come from the institute of applied economics research (ipea). as we apply the did we also use the 2000 values of the outcomes (pre-treatment period) as control variables. since we are interested in analysing the effect of grape growing on the socio-economic conditions of mcas, the outcome variables are the theil index, the hdi, and the unemployment rate, which are all taken from the ibge demographic census. these outcomes are measured at the mcas level both in 2000 and in 2010, the last two years of the brazilian census carried out by ibge. unfortunately the data from the agricultural census refer to 2006 and thus the timing does not overlap with the data from the demographic census. based on the propensity score from the first step, we implement the 10 nearest neighbour (10 nn) matching estimator with replacement which assigns to each mcas starting grape production later than 2000 (treated group) ten mcas that had never grown grape in 2000-2010 (non-treated group). for each treatment unit, the ten closest matched nontreated mcas in terms of propensity score are selected. in order to reduce the bias that may derive from an estimator that assigns multiple non-treated units to each treated unit, table 1. land allocation to the main commodities grown in bahia and pernambuco (hectares). 2000 2010 % variation 2000-2010 bahia soybean 628,356 950,920 51.3 cotton 55,952 289,483 417.4 sugarcane 91,755 82,045 -10.6 grape 2,238 3,273 46.2 pernambuco sugarcane 304,499 347,576 14.1 cotton 11,805 2,387 -79.8 grape 2,946 6,956 136.1 286 linda arata, sofia hauschild, paolo sckokai we introduce a caliper of 0.1; thus, among the ten nearest neighbours matched units, only the ones whose propensity score differs from the propensity score of the treated group no more than 0.1 are selected. the 10 nn matching estimator is a good compromise between bias and variance. indeed, assigning to each treated unit multiple non treated units reduces the variance of the estimator compared to the single nearest neighbour at a small cost in terms of bias (lawley and towe, 2014). in addition, the bias is controlled by the imposition of a caliper and by allowing for replacement. before matching, the share of agricultural employees on total population is 30.6% in the treated group and 24.3% in the non treated group and the share of agricultural gdp on total gdp is 20.4% and 18.5% in the two groups respectively (table 2). while these two variables do not show differences that are statistically significant, the average number of tractors per farm significantly differs between the two groups: it is 0.21 in the treated mcas and 0.05 in the non treated mcas. if we look at the value of the outcome variable before the treatment (the value employed as control variable in the matching) we notice that the theil index in the treated mcas is significantly larger than the theil index in the non treated group (0.56 vs. 0.50). the hdi is 0.45 and 0.43 and the unemployment rate is 13.5 and 14.7 in the two groups respectively, but these differences are not statistically significant. in order to check the goodness of our matching technique (i.e. the ability to make the distribution of the control variables independent of the decision to participate into the treatment) we follow the three criteria suggested by caliendo and kopeinig (2008). the first criterion is the covariates balancing property, which consists in a t-test on the mean difference of each control variable between the treated and the non-treated group. the second criterion consists in measuring the standardized bias before and after the matching; the standardized bias measures the distance of the marginal distribution of the covariates between the two groups. it is calculated for each covariate as the difference between the sample means of the treated and the matched control groups over the square root of the average of the corresponding sample variances. the third criterion is the pseudo r-square, which consists in re-estimating the probit model after the matching, when the pseudo r-square should turn out to be very small (sianesi, 2004). once the matching has been performed and the matching quality is verified, the att is computed in order to get the difference in the average growth of the outcomes between the mcas which has started grape production after 2000 and the mcas which had never produced grape in the period 2000-2010. 4. results 4.1 quality of matching as discussed in section 3, we compute three indicators to check the quality of our matching analysis: the balancing test, the standardized bias and the pseudo r square. the results of the balancing test (table 2) show that, after the matching, there are not statistically significant differences in the level of the control variables between the treated and the control group. that is not the case before the matching, since some of the variables differ between the two groups at the 1% (average number of tractors per farm, winter and spring 287economic and social impact of grape growing in northeastern brazil ta bl e 2. c on tr ol v ar ia bl es m ea n an d st an da rd iz ed b ia s be fo re a nd a ft er th e m at ch in g. c on tr ol v ar ia bl es ^ u nm at ch ed g ro up m at ch ed g ro up % b ia s i n un m at ch ed gr ou p % b ia s i n m at ch ed gr ou p % b ia s re du ct io n tr ea te d co nt ro l t tr ea te d co nt ro l t sh ar e of w or ke rs in th e ag ric ul tu ra l s ec to r o ve r t ot al po pu la tio n (% ) 30 .6 5 24 .3 2 1. 33 31 .4 1 29 .9 0. 19 29 .1 6. 9 76 .1 sh ar e of a gr ic ul tu ra l g d p ov er to ta l g d p (% ) 20 .3 8 18 .5 1 0. 6 20 .7 9 23 .6 7 -0 .5 15 .1 -2 3. 4 -5 4. 6 av er ag e nu m be r o f t ra ct or s p er fa rm 0. 21 0. 05 3. 51 ** * 0. 08 0. 09 -0 .1 40 .2 -1 .1 97 .2 a gr ic ul tu ra l g d p pe r h ec ta re (r $/ ha ) 21 03 .3 19 06 .3 0. 25 23 32 22 78 .5 0. 04 7. 6 2. 1 72 .8 th ei l i nd ex 0. 56 0. 5 1. 84 * 0. 54 0. 53 0. 23 48 .4 9. 3 80 .8 h d i 0. 45 0. 43 1. 52 0. 45 0. 44 0. 33 33 .1 13 .7 58 .6 u ne m pl oy m en t r at e (% ) 13 .4 7 14 .6 8 -0 .6 7 14 .4 4 14 .2 6 0. 08 -1 7. 6 2. 6 85 su m m er te m pe ra tu re (° c ) 25 .5 5 25 .2 3 1. 12 25 .6 25 .6 2 -0 .0 6 28 .4 -2 .4 91 .4 au tu m n te m pe ra tu re (° c ) 24 .7 8 24 .3 9 1. 45 24 .8 3 24 .9 -0 .1 7 39 .9 -6 .4 83 .8 w in te r t em pe ra tu re (° c ) 22 .9 8 21 .9 3. 67 ** * 22 .8 9 23 .0 6 -0 .3 4 92 .8 -1 4. 7 84 .2 sp rin g te m pe ra tu re (° c ) 25 .4 1 23 .9 8 4. 77 ** * 25 .3 7 25 .4 8 0. 84 11 7. 3 -9 92 .3 su m m er p re ci pi ta tio n (m m /m on th ) 11 0. 14 84 .0 2 2. 61 ** * 10 1. 42 10 4. 63 -0 .1 6 55 .3 -6 .8 87 .7 au tu m n pr ec ip ita tio n (m m /m on th ) 79 .7 4 11 1. 77 -2 .8 ** * 73 .1 78 .4 1 -0 .6 1 -8 2 -1 3. 6 83 .4 w in te r p re ci pi ta tio n (m m /m on th ) 17 .2 8 92 .9 1 -5 .1 ** * 18 .6 6 21 .4 4 -0 .2 1 -1 57 .2 -5 .8 96 .3 sp rin g pr ec ip ita tio n (m m /m on th ) 49 .9 7 62 .0 8 -1 .2 9 46 .3 4 47 .2 -0 .0 7 -3 3. 2 -2 .4 92 .9 *, * *, * ** in di ca te 1 0% , 5 % a nd 1 % s ig ni fic an ce le ve l r es pe ct iv el y. 288 linda arata, sofia hauschild, paolo sckokai temperature, winter precipitation), 5% (summer precipitation) or 10% (theil index in the pre-treatment period) significance level. the percentage reduction in the standardized bias between the two groups ranges from 54.6 to 97.2% according to the variable. finally, the f-test considering all the control variables against the probability of participation into the treatment is significantly different from zero before the matching (pseudo r2 equal to 0.405 and p-value of the likelihood ratio lower than 0.01) while it is no longer significant after the matching (pseudo r2 equal to 0.039 and p-value equal to 1). thus, all the three indicators allow us to conclude that our matching analysis successfully reaches the goal of removing the differences in observed variables between the two groups such that, conditional on p(xi), the distribution of each covariate is independent of the treatment status. 4.2 impact of grape production the probit model of the probability of growing grape against the set of observed covariates indicates that the average number of tractors per farm, the temperature in autumn as well as the summer precipitation increase the probability of producing grape (10% significance level ). on the other hand, the summer and winter temperature and the average precipitation level in spring decrease this probability. the other variables included in the binary model do not significantly affect the decision of producing grape (table 3). mcas which started to grow grape after 2000 experience a decrease in the value of the theil index over the period 2000-2010, while the matched mcas which had never grown grape in the same period show an increase in the same indicator (table 4). the treated group shows an average decrease in the theil index of 3.5% compared to the 2000 level, while the control group records a rise of 9.4%. the difference in the average change between the two groups is significant at the 5% level and seems to indicate that grape production contributes to a better income distribution and to reduce inequality in bahia and pernambuco. one of the reasons of this result may be the high quality level of grape production as compared to other agricultural industries, which may generate a better remuneration of workers in the grape industry. in addition, parallel to the development of large farms in the grape industry in northeastern brazil, also a large number of small farms started grape production (selwyn, 2008). the presence of a large number of small farms may guarantee a fairer income distribution. another explanation may be that the higher unit value of grape production compared to other agricultural products requires skilled workers and thus higher wages are paid. the result on the theil index is opposite to what weinhold et al. (2012) found for the effect of soybean production in the brazilian amazon region, where production has led to a rise in income inequality. this may be explained by the different characteristics of the grape industry as compared to soybean, where the high share of large farms and the low value added of the product does not guarantee a fair income distribution. at the same time the increase in income inequality due to an increase in soybean production found in weinhold et al. (2012) may explain the increase of the theil index in our control group. indeed, in the area subject to our analysis soybean production increased over the period 2000-2010 (table 1) and it is reasonable to assume that this increase took place mainly in municipalities which did not start grape production. this may be one of the reasons for the increase of the theil index in the control group. 289economic and social impact of grape growing in northeastern brazil table 3. coefficient estimates of the probit model on the probability of growing grape. coefficient estimates standard error p-value share of workers in the agricultural sector over total population 1.16 0.97 0.233 share of agricultural gdp over total gdp -2.16 1.71 0.205 average number of tractors per farm 1.39 0.75 0.063* agricultural gdp per hectare (r$/ha) 0.00 0.00 0.136 theil index 0.86 1.61 0.595 hdi 0.03 0.03 0.399 unemployment rate -1.53 3.62 0.673 summer temperature (°c) -1.42 0.74 0.056* autumn temperature (°c) 2.22 1.11 0.045* winter temperature (°c) -1.00 0.60 0.094* spring temperature (°c) 0.30 0.45 0.502 summer precipitation (mm/month) 0.04 0.03 0.080* autumn precipitation (mm/month) 0.00 0.02 0.806 winter precipitation (mm/month) -0.01 0.02 0.664 spring precipitation (mm/month) -0.07 0.04 0.039* constant -6.22 5.54 0.262 pseudo r2 0.40 total number of mcas 378 number of treated mcas 16 number of non treated mcas 362 *, **, *** indicate 10%, 5% and 1% significance level respectively. we did not find any effect of grape production neither on the hdi nor on the unemployment rate in this area. over the period 2000-2010, the hdi in the grape producing mcas and in the matched non grape producing mcas has increased with a parallel path, while the unemployment rate has decreased by 5 points in both groups. the lack of an effect on the hdi was found also in chagas et al. (2012) in the case of sugarcane production in brazil. our result strengthens the conclusions of chagas et al. about the need to implement effective public policy, additional to agricultural policies, specifically targeted to the education and the well being of the citizens. given the absence of a reduction in the unemployment rate due to grape production, the improving of the theil index may be the consequence of a shift of the labour force from a sector where the employee/landowner wage ratio was very low to a sector where income is more equally distributed and where small farms have higher chances to survive. in fact, it is worthy to mention that most owners of grape farms that adopt the ip system give their employees a wage premium as a mean to increase their motivation (embrapa, 2015). in addition, the training of employees in the grape sector has increased over the years, and this may have led to an average increase in wages. finally, differently from other agricultural industries, around 70% of grape producing farms are small family farms (leite et al., 2005). 290 linda arata, sofia hauschild, paolo sckokai table 4. average treatment effect on the treated (att) of growing grape, 2000-2010. average growth in the treated group average growth in the control group att t theil index -0.019 0.050 -0.070** -2.070 (-0.034) hdi 0.162 0.164 -0.002 -0.160 (0.013) unemployment rate -5.132 -4.990 -0.142 -0.090 (1.585) *, **, *** indicate 10%, 5% and 1% significance level respectively. 4.3 impact of grape sensitivity analysis the results of the rosenbaum sensitivity analysis indicate that the positive effect of grape production on the theil index is robust when an unobserved variable affects the odds ratio of the treatment assignment by no more than 25-30% (table 5). the absence of an effect on the hdi because of the starting of grape production is questioned by a critical level of г between 2.2 and 2.3, while the critical level of г in the case of the effect on the unemployment rate is between 2.1 and 2.2. in the last two cases, it means that a hidden bias, that causes the odds ratio of the probability to participate to change by more than 2, may undermine the validity of the conclusion on the absence of an effect of grape production on the hdi and on the unemployment rate. according to the sensitivity analysis, our results seem to be robust against the potential presence of an unobserved factor that affects simultaneously the probability to grow grape and the outcomes. it is worth to remind that we also control for unobserved factors that are constant over time by means of the did. in addition, as mentioned by diprete and gangl (2004), the results of the sensitivity analysis are the worst case scenarios. for example, if the hidden bias affects the odds ratio more than 30% it does not mean that there is no effect of grape production on the theil index, but it means that the confidence interval of the theil index would become wider and include the value of zero. 5. discussion and conclusions in recent decades, grape production has become a well-organised, competitive and high quality agricultural industry in northeastern brazil. given the importance of agriculture in the overall economy of the two northeastern staes of bahia and pernambuco (around 20% of gdp), it is likely that the development of a modern agricultural industry may have an impact on some socio-economic indicators. in addition, differently from other agricultural industries in brazil, small family farms play a key role in the grape industry and the high concentration and cooperation among the actors of the industry in northeastern brazil identifies a production district which supports regional development. our study investigate whether grape production affects income distribution measured by the 291economic and social impact of grape growing in northeastern brazil theil index, the hdi and the unemployment rate in the two states at the mca level. by combining the psm with the did we compare the development of the value of each outcome between a treated group (mcas that started to grow grape after 2000) and a control group (matched mcas that never grew grape in the 2000-2010 period). results seem to indicate that grape production contributes to a fairer income distribution within the treated mcas. indeed, mcas that started grape production experience a decrease in the theil index of 11.7% compared to the level they would have experienced without developing grape production. one of the reasons for the positive effect on the theil index may be the high value added of this agricultural industry which may contribute to reduce the worker exploitation and guarantee a better remuneration. another reason may be represented by the large share of small family farms that work in the grape industry, which may also contribute to a fairer remuneration. no effect has been shown for the hdi and the unemployment rate. in order to promote the hdi and reduce the unemployment rate public table 5. results of the rosenbaum sensitivity analysis. gamma (г) p-critical theil index hdi unemployment rate 1 0.021 0.413 0.365 1.05 0.025 0.382 0.335 1.1 0.030 0.353 0.308 1.15 0.035 0.326 0.282 1.2 0.040 0.301 0.259 1.25 0.046 0.278 0.237 1.3 0.052 0.256 0.217 1.35 0.059 0.236 0.199 1.4 0.065 0.218 0.182 1.45 0.072 0.201 0.167 1.5 0.079 0.185 0.153 1.55 0.087 0.170 0.140 1.6 0.094 0.157 0.128 1.65 0.102 0.144 0.117 1.7 0.110 0.133 0.107 1.75 0.118 0.122 0.098 1.8 0.126 0.113 0.090 1.85 0.134 0.104 0.082 1.9 0.142 0.096 0.075 1.95 0.151 0.088 0.069 2 0.159 0.081 0.063 2.05 0.167 0.075 0.057 2.1 0.176 0.069 0.053 2.15 0.184 0.063 0.048 2.2 0.193 0.058 0.044 2.25 0.201 0.054 0.040 292 linda arata, sofia hauschild, paolo sckokai policies specifically targeted to more general objectives, such as education and health, are required. as stated in this paper, the setting up of grape production in northeastern brazil has been supported by private-public investments in infrastructures as well as by subsidised credit and tax breaks. thus, the grape industry in this region is an example of how public support to agriculture leads to general socio-economic benefits to the society as a whole, since agriculture represents an important share of the economy. given this result, it would be interesting to analyse whether the positive effects of grape production on some general socio-economic indicators, such as the theil index, is confirmed also for the southern states, the historical grape producing area in brazil, as well for other high value added agricultural industries, such as mango and high quality coffee. references arata, l. and sckokai, p. 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(2011). do agricultural land preservation programs reduce farmland loss? evidence from propensity score matching estimator. land economics 87(2): 183-201. pufahl, a. and weiss, c. (2009). evaluating the effects of farm programmes: results from propensity score matching. european review of agricultural economics 36(1): 79–101. rosenbaum, p. (2002). observational studies. new york, ny: springer. rosenbaum, p. r., rubin, d. b., 1983. the central role of the propensity score in observational studies for causal effects. biometrika 70(1): 41-55. selwyn, b. (2008). institutions, upgrading and development: evidence from north east brazilian export horticulture. competition & change 12(4), 377-396. smith, j. and todd, p.e. (2005). does matching overcome lalonde’s critique of nonexperimental estimators? journal of econometrics 125: 305-353. tales, v. (2009). vitivinicultura no nordeste do brasil: situação recente e perspectivas. revista economica do nordeste 40(3): 499-524. teixeira, a.d.c., bastiaanssen, w.g.m. and bassoi, l.h. (2007). crop water parameters of irrigated wine and table grapes to support water productivity analysis in the sao francisco river basin, brazil. agricultural water management 94(1): 31-42. udagawa, c., hodge, i. and reader, m. (2014). farm level costs of agri-environment measures: the impact of entry level stewardship on cereal farm incomes. journal of agricultural economics 65 (1): 212–33. weinhold, d., killick, e. and reis, e.j. (2013). soybeans, poverty and inequality in the brazilian amazon. world development 52: 132-143. rural-urban migration and implications for rural production alan de brauw migrants to rural areas as a social movement: insights from italy giorgio osti immigrant workforce and labour productivity in italian agriculture: a farm-level analysis edoardo baldoni, silvia coderoni*, roberto esposti economic and social impact of grape growing in northeastern brazil linda arata1,*, sofia hauschild2, paolo sckokai1 is the question of the “active farmer” a false problem? maria rosaria pupo d’andrea*, simona romeo lironcurti bio-based and applied economics 7(2): 179-190, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-7674 short communication bioeconomy and the common agricultural policy: will a strategy in search of policies meet a policy in search of strategies? davide viaggi dipartimento di scienze e tecnologie agro-alimentari, università di bologna date of submission: 2019 25th, june; accepted 2019 15th, july abstract. both the revised eu bioeconomy strategy and the proposals for the common agricultural policy (cap) 2021-2027 were released in 2018. this paper explores the connection between these two policy areas, the needs for economic and policy research and the way economic literature in the field of the bioeconomy is meeting these needs. the paper concludes that the two policies are highly complementary in principle, but the current exploitation of potential synergies is largely delegated to the implementation stage of the cap, hence to country and local programming authorities. to make both policies effective, and to bring about constructive synergies, the availability of bridging concepts allowing for territorial-level integration of chain and ecosystem services views is key. however, on the practical side, monitoring indicators for policy and economic/management support to developing sectors is even more important. support to innovation design, uptake and exploitation will remain key to the sector and will need a proactive and participatory collaboration among multiple actors. the increased relevance of the role of ecosystem services and environmental attention in both policies will make the results more dependent on the ability to understand the value of public goods and to incorporate them into policy design and marketing strategies. keywords. bioeconomy, common agricultural policy, rural development, eu. jel. q00, q01, q02, q57. 1. introduction and objectives interest in the bioeconomy has been growing steadily in recent years, both in policy and literature. a growing number of countries have bioeconomy strategies and are implementing policies that promote the development of the bioeconomy (el-chichakli et al., 2016; german bioeconomy council, 2018). markets for bio-based solutions are growing and are attracting the attention of consumers and investors alike. applications are at times corresponding author: davide.viaggi@unibo.it 180 davide viaggi visible but in some cases appear to be simple (drop-in) substitutes to existing products.yet technological change is fully under way, with continuous new solutions being proposed (wesseler and von braun, 2017; ronzon et al., 2017). cross-cutting connections among different value chains are now countless and growing exponentially. the concept of the bioeconomy as the aggregate of the sectors using biological resources is now undergoing consolidation, at least in europe. agriculture, forestry and food are at the core of the bioeconomy while the most important progress in terms of markets concerns new sectors, such as bio-based materials and bioenergy. sectors of the bioeconomy such as forestry, aquaculture and marine production are seen as major areas for future development. the idea that the bioeconomy needs to be sustainable and circular is getting stronger as well as the awareness that these features are not implicit in the bioeconomy, but rather need to be purposefully promoted. the context driving these trends is different than it was at the beginning of the history of the bioeconomy. climate change concerns, long-term sustainability objectives and circular economy objectives (european commission, 2015) have reinforced the focus on bio-based solutions. while energy concerns are less often in the news, they are taking on greater importance due to their linkages with climate change causes and adaptation strategies. the guiding focus on the un sustainable development goals has made evident how comprehensive concepts such as the bioeconomy are key to managing the interplay between social concerns and sustainable economic growth in an interwoven economy. in spite of the above-mentioned trends, several (or perhaps the majority of) bioeconomy activities linked to bio-based solutions, bioenergy and co-product management are far from being cost-competitive with fossil resources. in addition, technologies are often insufficiently stable and reliable with respect to market expectations. for these reasons, uptake is slower than sought by promoters and increased efficiency is needed. one of the keys to this increased efficiency is the connection between bio-based and bioenergy chains through biorefinery optimisation, but the issues at stake are much wider and involve the efficiency of the whole system of biomass production and use, as well as the consistent accounting of public good components linking society and market values. moreover, general knowledge of the bioeconomy as a concept and a vision remains poor. on the eu policy side, a new boost to the bioeconomy has been given by the eu commission through the revision of the 2012 bioeconomy strategy and several studies aimed at quantifying the economic role of the bioeconomy. this was followed by the launch of the revised bioeconomy strategy in october 2018 (european commission, 2018). meanwhile, the whole programming period 2021-2027 is under discussion, notably with new proposals for the cap related to this period. economic research has been developing in parallel (lewandowski, 2018; viaggi, 2018; wesseler, banse and zilberman, 2015; viaggi, 2016). in 2018, scopus reported 123 papers related to the bioeconomy in the fields of economics, business and social sciences, with a growth of about +66% compared to the previous year and a constant increase over time. this paper aims to provide a review of the policy challenges brought about by the revised bioeconomy strategy and the cap legislative proposals, with a focus on the connection between these two policy areas. based on this, the paper discusses needs for support and research in the field of economics and policy, matches these with the related 181bioeconomy and the common agricultural policy trends in literature, and provides insights into future research developments targeting the most relevant current challenges. the next section (section 2) provides an overview of the revised bioeconomy strategy, the proposed cap reform and the connections between the two policy initiatives. section 3 discusses economic and policy research needs emerging in response to these policy developments. section 4 provides a discussion and concluding remarks. 2. the eu bioeconomy strategy and the cap 2.1 the revised bioeconomy strategy the announced revision of the eu bioeconomy strategy followed a 2-year process building on the previous 2012 strategy. the evaluation of the strategy painted a rather positive picture in terms of strategy and action plan implementation (european commission, 2017). funding has increased for bioeconomy research and action has been taken in several directions. bioeconomy concepts have affected different policy areas in the eu and a number of countries now have their own bioeconomy strategy. italy is among them, with a broadly supported strategy published in 2017. in addition, a manifesto for the bioeconomy in europe was published in 2017. several relevant topics for attention and further action were included; in particular, it is noteworthy that there is an emphasis on the role of regions in the development of the bioeconomy and the need for focused training and education. the revised bioeconomy strategy (european commission, 2018), basically maintains the same objectives of the 2012 strategy, namely: • ensuring food and nutrition security; • managing natural resources sustainably; • reducing dependence on non-renewable, unsustainable resources whether sourced domestically or from abroad; • mitigating and adapting to climate change; and • strengthening european competitiveness and creating jobs. instead, from a definition point of view, the revised strategy includes some relevant novelties. the bioeconomy is now defined as follows (european commission, 2018): “the bioeconomy covers all sectors and systems that rely on biological resources (animals, plants, micro-organisms and derived biomass, including organic waste), their functions and principles. it includes and interlinks: land and marine ecosystems and the services they provide; all primary production sectors that use and produce biological resources (agriculture, forestry, fisheries and aquaculture); and all economic and industrial sectors that use biological resources and processes to produce food, feed, bio-based products, energy and services. to be successful, the european bioeconomy needs to have sustainability and circularity at its heart. this will drive the renewal of our industries, the modernisation of our primary production systems, the protection of the environment and will enhance biodiversity.” the most interesting feature is the placement of sustainability and circularity at the heart of the notion of bioeconomy. the definition also explicitly highlights the role of eco182 davide viaggi systems and their services. on the contrary, innovation and new technologies, in particular genetic engineering, have much less emphasis. biomedicines and health biotechnology remain excluded. to achieve the objectives above, the communication envisages three action areas: 1. strengthen and scale-up the bio-based sectors, unlock investments and markets; this includes: mobilisation of public and private stakeholders, in research, demonstration and deployment of bio-based solutions (action 1.1); a circular bioeconomy thematic investment platform (action 1.2); identification of bottlenecks, enablers, and gaps affecting bio-based innovations, and providing voluntary guidance on their deployment (action 1.3); environmental performance information (action 1.4); facilitation of the development of new sustainable biorefineries (action 1.5); contribution to the global challenge of plastic-free oceans (action 1.6). 2. deploy local bioeconomies rapidly across europe; this includes: develop a strategicdeployment agenda (action 2.1); pilot actions enhancing synergies between existing eu instruments to support local activities (action 2.2); set up of eu bioeconomy policy support facility for member states (action 2.3); piloting on education and skills (action 2.4). 3. understand the ecological boundaries of the bioeconomy; this includes: enhancing the knowledge base and understanding of specific bioeconomy areas (action 3.1); implementation of an eu-wide, internationally coherent monitoring system (action 3.2); voluntary guidance for operating the bioeconomy within safe ecological limits (action 3.3); integration of the benefits from biodiversity-rich ecosystems (action 3.4). 2.2 the proposed cap reform after the release of preliminary documents in 2017, the commission published the legislative proposals for the post 2020 cap in june 2018. the objectives of the future cap are: • to ensure a fair income to farmers; • to increase competiveness; • to rebalance the power in the food chain; • climate change action; • environmental care; • to preserve landscapes and biodiversity; • to support generational renewal; • vibrant rural areas; and • to protect food and health quality. the basic structure of the cap is not expected to change dramatically, in particular the organisation into two main pillars. however, in terms of measures, the cap will bring some important novelties. these include the refocusing of the direct payments towards a basic payment for sustainability; the replacement of the current cross-compliance and greening measures with a new enhanced conditionality scheme; and the provision of voluntary ecological payments (eco-schemes) in the first pillar. 183bioeconomy and the common agricultural policy a critical aspect of the cap reform is the new delivery model, leaving to strategic plans to devise precise actions for implementation. strategic plans are expected to cover all cap measures and to be designed at member state (ms) level. this implies a larger level of flexibility for ms concerning the design of measures and implementation, while the european commission will monitor the results on the basis of a list of indicators. this should, in principle, allow for higher efficiency through greater flexibility and better targeting, but will also rely more on decentralised coordination and management capacity. the cap reform is accompanied by an important effort toward innovation and research, with a proposed allocation of 10 billion euro to agriculture and food in horizon europe. this continues the coordination between the cap and research policy already established during the 2014-2020 period. 2.3 the bioeconomy strategy and the cap: opportunities, drawbacks and emerging policy issues in spite of the obvious interplays, the convergence between the bioeconomy strategy and the cap is still weak; however, the rural development objectives in the bioeconomy strategy and the explicit mention of the bioeconomy (as well as of the need for biomass production) in the cap are important steps forward in the field of policy harmonisation. notably, this does not only concern the areas in which the bioeconomy is mentioned, but also other components of the cap including the international dimension. the cap does not contain/impose any specific measure related to non-food bioeconomy sectors; however several measures can be used in this direction by local strategy design. a number of cap measures can indeed contribute to the bioeconomy. these include: a) those strengthening the role of farmers in the supply chain; b) sectorial programmes if connected to bio-based products; c) enhanced conditionality, including crop rotation provisions; d) voluntary eco-schemes,which are mandatory for ms; e) coupled income support, directly or indirectly affecting specific value chains; f) rural development measures (including agri-environmental schemes, innovation and investment support, knowledge transfer measures). however, the decision to use these measures to support the bioeconomy development will be in the hands of member states or local authorities. one stated cap objective (also in the documentation about strategic plans and their evaluation) is directly connected to the bioeconomy, namely: “promote employment, growth, social inclusion and local development in rural areas, including bio-economy and sustainable forestry”. in terms of cap result indicators for the monitoring of evaluation plans, two main indicators are specific to the bioeconomy: r.15 green energy from agriculture and forestry: investments in renewable energy production capacity, including bio-based and r.32 developing the rural bioeconomy: number of bioeconomy businesses developed with support. on the other hand, the bioeconomy strategy envisages a number of supporting instruments that could be used by the cap implementation strategy. these primarily concern initiatives for sustainability diagnostics and intra-regional coordination. in addition, there seem to be a number of procedural meeting points in the two strands of policy in as much as both envisage some implementation plan at the country or regional level. this could provide an opportunity for greater coordination to the extent that it does not result in duplication. indeed, the cap strategic plans offer an improved 184 davide viaggi opportunity for coordination with the bioeconomy strategies through needs analysis and the setting of objectives. one potential issue, however, remains the scale of coordination and inter-scale dialogues. potential conflicts are difficult to envisage. the most evident issue is that of biorefinery development and investment programmes, which have the potential to affect the farming sector and could lead to undesired effects if the two areas of intervention are not locally coordinated. 3. challenges for economic & policy support 3.1 bioeconomy definitions and boundaries from the definition point of view, the bioeconomy is shaping up and consolidating at least in terms of the sectors involved. the new strategy makes it more explicit that the bioeconomy is the aggregate of all sectors using living organisms and this partially goes beyond a number of discrepancies found in the literature between different approaches to the bioeconomy. these do tend to remain, however, when the bioeconomy is viewed from different regional or stakeholder perspectives (de besi and mccormick, 2015). the current trends in the eu policy clarify once more that the bioeconomy concept will not substitute our current notion of sectors, such as agriculture, food etc., at least in the short-term, but will rather provide a complementary view at system level. this separation will also remain as such in the policy realm. this is a reasonable strategy, which is legitimate with path-dependency motivations, as sector identity and related policy are already quite consolidated and have been developed over time. on the one hand, the difficulty in understanding and communicating what the bioeconomy is will continue. indeed, there is a consolidation of the view of the bioeconomy as a bridging concept rather than a sector. on the other hand, the definition of the bioeconomy has clearly expanded in the direction of accounting for ecosystems, clarifying the increasing trends towards the need for a consistent inter/trans-sectoral approach to the management of biological resources. biorefineries are clearly seen as a key connection point among the bioeconomy sectors. their development across europe is somehow the most practical action envisaged in the strategy. this is very relevant as biorefineries are peculiar solutions connecting different value chains and at the same time are the strategic topic to connect the industrial and territorial visions of the bioeconomy. however, chain coordination and consistency with the ecosystem services perspective needs to be carefully investigated. 3.2 bioeconomy sectors and markets the pragmatic identification of the bioeconomy as an overarching concept encompassing or including different sectors, as well as the envisaging of only a (mainly) strategic approach from the point of view of bioeconomy policy, somehow refocuses attention, including for the bioeconomy, on the functioning of markets and their dynamics. the developing of new markets (except for bioenergy) seems to remain not supported by strong direct incentives from policy, but rather promoted by soft measures related to primary production, chain structure, certification and information. 185bioeconomy and the common agricultural policy here, a focal point remains cost-competitiveness with similar fossil-based products and the distinction between drop-in and new products (petrovič, 2015). on the one hand, this requires an improved understanding of consumer and citizen behaviour, on the other hand it needs to address supply side (cost) issues. these topics are emphasised for markets for new products, such as innovative (in terms of value proposition) biobased products. the increased relevance of the role of ecosystem services and environmental attention in both policies will shine light on the ability to understand the value of public goods and to incorporate them into policy design and marketing strategies. 3.3 system view the territorial planning envisaged in the bioeconomy strategy and the strategic planning envisaged in the revised strategy call for both a description and an understanding of bioeconomy systems. in this direction, the bioeconomy literature already seeks to deliver interpretations of complex bioeconomy systems through the evolution of the concept of value chains into a vision of value webs (scheiterle et al., 2016; virchow et al., 2016); at the same time, examples, especially of biomass provisions for biorefineryand logistics, need to explicitly address the connection between process design and territorial scale. this, in turn, extends to international biomass and value flows. the direct consideration of the engagement of consumers and citizens is also a key factor in these processes. the inclusion of ecosystems into this view, and the socio-ecological system approach as a potential interpretation of society’s action are also under way. an attempt to merge these approaches into a unified view goes under the proposed term socio-ecological technological value-enhancing web system (setvews)(viaggi, 2018), which is still, however, undefined in operational terms. the system view not only provides a vision of the bioeconomy, but also highlights the need to understand the role of logistic organisation (lamers et al., 2015), chain structure (espinoza pérez et al., 2017) and flexibility (swartz, wang and mastragostino, 2015) as the key to efficiency. in addition, the understanding of system organisation needs to take into account technological potential. in particular, the increasing ability to break down and recompose biomass has lead the emergence of the concept of platform products as key “connectors” in the biomass flows, with potential implications on system organisation and market power (bomtempo, chaves alves and de almeida oroski, 2017). 3.4 policy coordination and territorial governance both bioeconomy and agricultural policy require territorial level programming. this is connected to the system view and the need to consistently manage resources and opportunities in a landscape (ecosystemic) framework. in addition, the topic of policy coordination is of paramount importance. the bioeconomy is already most often promoted by a mix of policy instruments with different strategies and composition depending on the individual country and location (german bioeconomy council, 2015). the focus on strategy emphasises these needs. 186 davide viaggi in a more analytical way, the picture above requires the ability to understand the working of policy mixes. research and innovation policy clearly plays a major role in this context. the cap already includes a variety of different measures which consistency is sometimes not straightforward (or clearly lacking). addressing the bioeconomy consistently requires, greater effort with regard to connecting agriculture, food, fisheries, industrial and environmental sectors, energy policies, as well as activities related to research, innovation and education. in addition, this strategic approach highlights the need for working approaches to participation and governance. this has been an area of particular focus in the literature on participatory decision-making, and, among other issues, highlights the positive role of the bioeconomy as an ‘umbrella concept’ to provide a dialogue platform for different views of the future. on the other hand, for the same reasons, it runs the risk of remaining just a buzzword with unclear references to the use of biomass. indeed, in a communication context, ‘bioeconomy’ can be qualified as a ‘boundary object’ or a ‘bridging concept’, i.e. serving specific interests of different stakeholders under a generally accepted conceptual umbrella (hodge, brukas and giurca, 2017). the relevance of the topic has been highlighted in contexts in which the different players have rather different backgrounds and power, so it is of special importance for rural areas. this implies the consideration of two connected aspects. one is the role of local institutions in the governance of the bioeconomy. the other is the involvement of the ecosystem service view as compared with the value chain view. 3.5 innovation the definition of the bioeconomy used in the revision of the ec communication seems to downplay innovation and research. in particular, genetic engineering, which was at the core of some of the founding documents by other bodies (e.g. oecd) is has no particular relevance here. in fact, looking at the actions proposed, research and innovation is still high in the agenda and even more important in economic terms. most likely, in the current setting, innovation stands behind the scenes and is less to be interpreted as a specific set of technologies and rather as whatever is needed to promote the objectives of developing bioeconomy sectors in industrial terms while guaranteeing circularity and sustainability. this approach certainly brings bioeconomy innovation closer to current practices in agriculture and rural innovation such as the innovation systems perspective adopted by the agricultural knowledge and innovation systems (akiss) or the collaborative perspective used by the eip agri measures. however, it is also connected to information, education and human capital, and is linked to industrial innovation. on the other hand, innovation is connected to appropriate incentives related to the features of final products, and hence cannot be thought of as being disconnected from markets and value chain development. the link among research disciplines is even more important, as also implied by transdisciplinary research linked to multi-actor driven processes. the balance between multiactor emphasis and consistent new research has, however, proven to be difficult to manage and this will be a key issue to tackle in order to provide genuine and result-focused innovation systems. 187bioeconomy and the common agricultural policy one important perspective here relates to the trend towards technology design as an explicit process aimed at specific achievements and within circular innovation processes, which implies an even greater degree of coordination. 3.6 defining and measuring the problem with measuring the bioeconomy remains at the core, due also to the dearth of suitable data (wesseler and von braun, 2017; ronzon et al., 2017; lokko et al., 2017). besides agriculture and food, bio-based sectors such as energy, biomaterials, and biorefineries are largely included in other sectors’ statistics and require difficult disaggregation procedures and, at times, questionable assumptions. on the other hand, the new definition and policy approach require a move towards a more functional use of measurements, most notably in three directions: • first, in the direction of measuring the actual progress of bioeconomy sectors and in particular, understanding the dynamics of emerging sectors such as those of biobased products. • second, in the direction of understanding the sustainability of current bioeconomy systems, with a focus on the new field of measurement represented by circularity and consolidating areas such as the connection with ecosystem services and public goods; while the bioeconomy strategy focuses to a significant extent on the concept of ecological boundaries, the cap more and more explicitly focuses attention on the positive potential of the primary sector to produce valuable public goods. • third, in the direction of having measures suitable for policy evaluation or even for performance/impact measurement linked to the provision of cap payments. 3.7 communication, awareness and education communication, awareness and education are clearly important for an emerging sector of the economy. the first straightforward aspect is linked to awareness and acceptability by the general public, which is well known to be critical for new products such as those obtained through genetic modification. furthermore, information is linked to market expressions of willingness to pay. this is clearly key in a policy approach only weakly based on direct incentives and more focused on the promotion of innovation. the role of education and human capital in the bioeconomy is of primary interest to the academia. noteworthy initiatives are being developed that range from primary to post-university and lifelong learning, but the role of bioeconomy studies in curricula remains, to a large extent, questionable and under developed. 4. discussion and conclusions research and interpretation of the bioeconomy is taking shape (viaggi, 2018). the sought after interaction between the cap and bioeconomy is now at a crossroad, with the revised bioeconomy strategy and the upcoming cap reform, ushering in significant opportunities for coherent and synergetic support, while at the same time leaving the 188 davide viaggi details of these synergies rather open to local action. both strategic approaches also bring with them a number of implications for economic research related to policy. while the bioeconomy is consolidating as one of the biggest phenomena of our age, it continues to be in search of an identity. there are different dimensions to this identitybuilding process. one is policy, as can be expected from an emerging area of the economy. however, the eu’s bioeconomy action and most country strategies rely more on strategies than bioeconomy policies, leaving to specific sector policies the role to implement actions. this is also the case of the eu. this approach is in itself understandable, due to the fact that some parts of the bioeconomy have long-term policy structures, the implementation of which is rather consolidated with reforms depending on path-dependency. as for the cap reform there is a reliance on decentralised strategic planning, which is fuelling debate about implementation procedures (new delivery model) and priority setting. accordingly, this is an ideal time to discuss the coordination between the bioeconomy and agricultural priorities and policies. the explicit call for convergence (or the beginning of dialogue) between bioeconomy and the cap is a relevant step forward. certainly, strong support for economic information is imperative. economics is moving forward in building this identity through an increasing number of works and new concepts. the next step is to improve the application of these concepts to the next generation of policy problems. the main contributions likely rest in providing a coherent system view, helping to identify priorities and designing improved mixes of policy instruments. each of these areas of action is facing a number of new challenges, as discussed above. the enlargement of the bioeconomy concept to ecosystem services and the more neutral view of innovation also represent important topics to be dealt with in economic research. finally, both from an academic and sector perspective, greater attention is needed to bring the bioeconomy into the education system. perhaps there will never be a bioeconomist profession, but the comprehensive vision of the bioeconomy and an economic focus on its evolving components will undoubtedly be of great importance for any professional working with biological resources in the future. 5. references de besi, m. and mccormick, k. 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(2017). a systematic approach to understanding and quantifying the eu’s bioeconomy. bio-based and applied economics 6(1): 1-17. doi: 10.13128/bae-20567. scheiterle, l., ulmer, a., birner, r. and pyka, a. (2016). from commodity-based value chains to biomass-based value webs: the case of sugarcane in brazil’s bioeconomy. journal of cleaner production 172: 3851-3863. doi: 10.1016/j. jclepro.2017.05.150. swartz, c. l. e., wang, h. and mastragostino, r. (2015). operability analysis of process supply chains-toward the development of a sustainable bioeconomy. computer aided chemical engineering 36:355-384. doi: 10.1016/b978-0-444-63472-6.00014-8. viaggi, d. (2016). towards an economics of the bioeconomy: four years later. bio-based and applied economics 5(2): 101-112. doi: 10.13128/bae-20086. viaggi, d. (2018). the bioeconomy. delivering sustainable green growth. cabi publishing. 190 davide viaggi virchow, d., beuchelt, t. d., kuhn, a. and denich, m. (2016) biomass-based value webs: a novel perspective for emerging bioeconomies in sub-saharan africa. in gatzweiler, f.w. and von braun, j. (eds), technological and institutional innovations for marginalized smallholders in agricultural development. springer open: 225-238. doi: 10.1007/978-3-319-25718-1_14. wesseler, j., banse, m. and zilberman, d. (2015). introduction special issue “the political economy of the bioeconomy”. german journal of agricultural economics 64: 209-211. available at: http://www.scopus.com/inward/record.url?eid=2-s2.084949545519&partnerid=tzotx3y1. wesseler, j. and von braun, j. (2017). measuring the bioeconomy: economics and policies. annual review of resource economics 9: 275-298. doi: 10.1146/annurevresource-100516-053701. bio-based and applied economics 9(3): 283-304, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-7764 does the place of residence affect land use preferences? evidence from a choice experiment in germany julian sagebiel1,*, klaus glenk2, jürgen meyerhoff3 1 swedish university of agricultural sciences 2 sruc, land economy and environment research group 3 technische universität berlin abstract. discrete choice experiments can be used to inform policy makers on people’s preferences for landscapes and cultural ecosystem services. recent studies have shown that the spatial context influences preferences and related willingness to pay values. in this paper we investigate the effect of the landscape surrounding people’s places of residence on their willingness to pay using data from a discrete choice experiment on local land-use changes and cultural ecosystem services throughout germany. for analysis, we apply a latent class logit model and include landscape categories as explanatory variables for class membership. we find that the different landscapes people live in are correlated with preferences. especially people from urban areas and farmand grassland landscapes have larger willingness to pay values for improvements in cultural ecosystem services than people from forest landscapes and cultural landscapes. the results are important for policy makers as different willingness to pay values in different landscapes imply different welfare effects for land use changes. taking this information into account can help in reaching more efficient resource allocations. keywords. landscape preferences, latent class model, spatial heterogeneity, willingness to pay. jel codes. q51, q57. 1. introduction policy makers at different scales initiate land use changes to conform with subordinated laws and guidelines. decisions should balance social and private costs and benefits for different stakeholder groups and the local population. rigorous cost-benefit analysis is often difficult to conduct, as most regulating and cultural ecosystem services that are produced by landscapes are not traded in markets, making it impossible to directly observe societal demand for them. benefit estimates of changes in ecosystem service provision need to be inferred through the use of non-market valuation techniques; in particular stated *corresponding author. e-mail: julian.sagebiel@slu.se editor: meri raggi. 284 julian sagebiel, klaus glenk, jürgen meyerhoff preference methods, which allow estimation of willingness to pay through direct elicitation of preferences in hypothetical markets. in europe, several non-market valuation studies assessing preferences for components and management of agrarian landscapes have been conducted, but they rarely accounted for spatial differences in preferences (zanten et al. 2014; glenk et al. 2019). the few studies that considered spatial heterogeneity in preferences found that the place of residence of respondents in stated preference surveys influences willingness to pay estimates (campbell, scarpa, and hutchinson 2008; campbell, hutchinson, and scarpa 2009; brouwer, martin-ortega, and berbel 2010; broch et al. 2013; garrod et al. 2012; johnston and ramachandran 2014). as land-use changes are often conducted locally, such information can significantly impact the results of cost-benefit analyses and may reveal insights on where a land-use change offers the largest benefits. this paper contributes to the literature on spatial preference heterogeneity by investigating how preferences for policy-relevant landscape attributes differ across respondents residing in different landscapes. spatially-driven differences in preferences for changes in landscape attributes can occur for two main reasons. first, it is well-established that individual preferences are affected by the current level of endowment (glenk 2011; hess, rose, and hensher 2008; tversky and kahneman 1981). therefore, an increase or decrease in a good is valued relative to this status quo situation. because the marginal value of a good or service may not be constant over levels of provision, individuals with different status quo situations may value additional changes in provision differently. in particular, economic theory suggests that the utility or value that is attributed to an additional unit of a good or service is higher if its scarcity increases. the concept of diminishing marginal utility suggests, for example, that people residing in a forest landscape are willing to pay less for additional forest area created than individuals living in farmand grassland landscapes with little forest cover (sagebiel, glenk, and meyerhoff 2017). diminishing marginal utility may apply if more of a good or service is always preferred over less; however, this may not always apply to landscape attributes, where optimal shares of certain land use shares and landscape elements may exist. that is, an increase in land use share may be perceived beneficially up to a threshold, beyond which utility for an additional increase in provision decreases (schmitz, schmitz, and wronka 2003). second, the overall composition of a landscape has a unique value that is qualitatively different from other landscapes and that is difficult if not impossible to describe in terms of separate landscape attributes. that is, residents have different perceptions of landscapes and of the role that specific elements play in achieving uniqueness. consequently, preferences for changes in landscape attributes may differ across landscape types, either in a systematic fashion in case that subjective perceptions of landscape amenity and value are similar across individuals living in a particular landscape type, or in an unpredictable way if there is considerable heterogeneity in perceptions. for example, those individuals living in forest landscapes may have a systematically greater demand for enhancing biodiversity, whereas people living in farmand grassland landscapes may prefer additional structural elements. similarly, some people living in farmand grassland landscapes may perceive their openness as a cultural heritage characteristic of a particular region, thus objecting structural change. in the paper, we investigate the correlation between residing in different landscape categories (i.e., different status quo situations) and preferences for changes in landscape 285does the place of residence affect land use preferences? attributes, for example share of forest or levels of biodiversity. we use data from a webbased discrete choice experiment (dce) survey in germany to empirically test if differences in willingness to pay for landscape attributes exist; and if the ‘status quo’ landscape serves as a reference point for choices in the dce with impacts on willingness to pay estimates. the results are relevant for policy makers dealing with local land-use changes and researchers considering dces to assist cost-benefit analyses. for example, in germany, there is a discussion about combating climate change by increasing the share of energy crops for renewable energy generation. a policy maker can set spatially varying incentives or other policy tools aiming at increasing or decreasing the share of corn on agricultural fields. typically, such incentives are based on private benefits and ecological constraints, e.g. where gross margins are high. social welfare impacts associated with landscape change are often not considered at all, or are not directly compared with private costs and benefits. additionally, the importance of acceptance of the land use change by the local population is often neglected, and willingness to pay values, distinguished by landscape categories, can help to identify areas where such a land-use change is likely to find local support. 2. survey and data 2.1 data collection and discrete choice experiment the dce is part of a german-wide, web-based survey conducted in march 2013. the respondents were recruited from an online panel of a large international market research institute. people 18 years or older who resided in germany at the time of the study were eligible to participate. the survey consists of the six sub-samples with different dces, totalling around 10,000 respondents. the dces differ in their attributes and had different land-use foci. in all samples, the scenario was a local land use change within a radius of 15 km around the respondent’s place of residence. the radius should represent a typical distance for everyday activities. we discussed the radius in focus groups and came up with 15 km being a widely accepted distance. besides the dce, the survey includes questions on leisure activities, perceptions and knowledge on land use and climate change as well as socio-demographic variables. respondents were requested to provide their postal code or to use the integrated geo-tool which supplies the coordinates of the places identified by respondents such as their residence location. in this paper, we use a sub-sample with attributes related to agricultural land-use changes. the dce comprises five non-monetary attributes each having three levels, with zero indicating the status quo as today. table 1 gives a description of all attributes of the used sample as well as the dummy codes used in the analysis. the first attribute forest refers to the share of forest. it takes the values as today, 10% less and 10% more. we assume that an increase in forest area increases utility with a decreasing rate (diminishing marginal utility). that implies that people living in forest rich areas gain less utility from an increase in forest than people living in areas with a low share of forest. the second attribute fieldsize describes the average size of fields and forests. the levels include as today, half the size of today and double the size of today. 286 julian sagebiel, klaus glenk, jürgen meyerhoff smaller field sizes imply a less monotonic landscape and more structural elements, which are assumed to be more attractive in terms of visual amenity (zanten et al. 2014). on the other hand, larger forests can lead to better forest connectivity which may have positive implications for biodiversity and recreation. we therefore have no clear expectation for this attribute. the third attribute biodiversity is described with a bird indicator as a proxy for biodiversity. bird indicators are used in several countries as headline indicators for biodiversity (gregory et al. 2003; butchart et al. 2010). the bird indicator, developed by the german federal agency for nature conservation, provides information on the suitability of the area for birds, where 100 points describe the state in the year 1975 in germany (doerpinghaus and ludwig 2005). for germany as a whole, the bird indicator is currently estimated to lie at about 55 points. the levels used in the dce are as today (55 points), slight increase (85 points) and strong increase (105 points). we expect that utility increases with increasing points, as it has been found in other dce studies (shoyama, managi, and yamagata 2013). the fourth attribute cornshare is the share of corn on agricultural fields. the levels are as today, 30% and 70% on the agricultural fields in the surrounding. in the focus group discussions conducted prior to the survey, corn was often described as having a negative impact on landscape. we expect that a larger share of corn leads to a decrease in utility. meadowsshare, the fifth attribute, refers to the share of meadows and grassland used for grazing. it takes the levels as today, 25% of the area, 50% of the area. in the focus group discussions, most participants linked a high share of meadows to a more natural landscape. we thus expect a utility increase from an increase in the share. note that some attribute levels imply a reduction in the endowment compared to the status quo. this is explicit for forest and fieldsize and implicit for cornshare and meadowsshare. in the former case, we expect that some respondents have preferences for a table 1. attribute description. attribute description levels dummy code forest share of forest in % as today 10% decrease omitted forminus10 10% increase forplus10 fieldsize average size of forest and fields as today half the size double the size omitted fieldhalf fielddouble biodiversity degree of biodiversity measured with bird indicator as today (55 points) slight increase (85 points) strong increase (105 points) omitted bio85 bio105 cornshare share of corn on agricultural fields as today share of 30% share of 70% omitted corn30 corn70 meadows share share of meadows in % as today share of 25% share of 50% omitted mead25 mead50 price annual payment to a local landscape fund in euro 0, 10, 25, 50, 80, 110, 160 287does the place of residence affect land use preferences? reduction. for example, in forest rich areas, people may prefer a reduction in forest share (sagebiel, glenk, and meyerhoff 2017). to account for such preferences, we used a positive and a negative level. in the case of cornshare and meadowsshare, the direction of the change (reduction or increase) depends on the respondent’s current situation. however, absolute percentage values are useful as, in practice, land use changes are often announced in such values. we expected that people understand an absolute percentage value better than a relative change. thus we used absolute percentage values for these attributes, taking into account that the change people value varies between respondents. finally, the price attribute is framed as an annual payment to a newly introduced landscape fund per person for an unspecified period of time. we explained to the respondents that all residents who are affected by the land use change will have to contribute to the fund (i.e. a compulsory payment) and that the money in the fund was to be exclusively used to finance and maintain the land use changes. the exact description of the payment vehicle was informed by focus group discussions. the framing of the payment vehicle as a fund was preferred to other possible payment vehicles and regarded as credible. tax payments were not regarded as credible, because the land use change was local while taxes are usually collected at least at county level and often used for multiple purposes. the levels of the fund range from 10 to 160 euro and is set to szero in the status quo alternative. each choice set consists of three unlabelled landscape alternatives, where landscape 3 represents the status quo (figure 1). the experimental design was created with the software package ngene, maximizing c-efficiency, which relates to the minimization of varifigure 1. example of a choice set. 288 julian sagebiel, klaus glenk, jürgen meyerhoff ance of willingness to pay estimates. the design was optimized for a multinominal logit model with linearity in utility and priors close to zero. it consisted of 18 choice sets divided into two blocks. each respondent answered nine choice sets. the order of the choice sets was randomized across respondents. 2.2 landscape categories and socio-demographics the german federal agency for nature conservation has developed a system to classify landscapes within germany. the intention behind this approach is to provide a basis for effective conservation and development of cultural landscapes along the objectives of the european landscape convention. overall, the german land surface was divided in 858 landscapes including 59 urban conglomerations. the system comprises overall 24 landscape types that are assigned to the following six main categories (gharadjedaghi et al. 2004):1 1. coastal landscapes: this type is characterized by landscapes near the german coast of the north sea and the baltic sea. 2. forest landscapes: these landscapes have a large share of forests between 40% and 70%. 3. cultural landscapes: these landscapes have a share of forest between 20% and 40% and a high share of one of the following items: water bodies, meadows and grassland, wine-growing, glaciers and rocks, orchards, wetlands, a combination of the items. 4. farmand grassland dominated landscapes: in contrast to the cultural landscapes, they have a share of forest that is less than 20%. they are further characterized by a large share of grassland and arable land. 5. mining areas: landscapes with more than 10 percent of the land surface under open cast mining. 6. urban agglomerations: these landscapes comprise cities and areas with a high density of settlements and infrastructure. table 2 summarizes the distribution of the respondents according to the landscapes. each respondent is uniquely allocated to one of the categories. in this process, the actual place of residence was used to determine the landscape category rather than the percentage share of landscape categories surrounding the place of residence. figure 2 maps both the landscape categories and the respondents’ locations. we exclude five respondents from coastal landscapes and mining areas from the analysis as these categories are too small. the final sample size is 1409. 1 see https://www.bfn.de/en/activities/protecting-habitats-and-landscapes/landscapes-of-conservation-importance/landscape-types.html for a brief description of the 24 landscape types. table 2. distribution of landscape categories. landscape category no. % coastal landscapes 3 0.2 forest landscapes 204 14.4 cultural landscapes 326 23.1 farmand grassland landscapes 309 21.9 mining areas 2 0.1 urban agglomerations 570 40.3 total 1414 100.0 289does the place of residence affect land use preferences? figure 2. spatial distribution of sample. 290 julian sagebiel, klaus glenk, jürgen meyerhoff 2.3 hypotheses and empirical strategy the geo-referenced respondents are distinguished by the landscape categories described in table 2. the main aim is to find out whether respondents from different landscapes exhibit different preferences. hence, the main hypothesis is: preferences and willingness to pay values for landscape attributes correlate with the landscape in which a respondent lives. we expect decreasing marginal utility, i.e. marginal willingness to pay is lower in landscapes where the status quo levels of defining attributes are already high. for example, marginal willingness to pay for more forest is lower in forest landscapes than in the other landscape categories. additionally, we expect some kind of place attachment for attributes that dominate a landscape (scannell and gifford 2010). for example, a respondent living in a forest rich area is not willing to give up forest as it is a dominant characteristic of the landscape. in contrast, a respondent living in an area with a medium share of forest is more interested in gaining forest but also less averse against a loss in forests. table 4 shows that in farmand grassland landscapes, fieldsize is higher than in the other categories, where it is rather similar. hence, the hypothesis is that the willingness to pay for half the size differs between farmand grassland landscapes and the other landscapes. corn share is highest in the two cultural landscapes and lowest in urban agglomerations. as a high corn share is expected to be perceived negatively, and for most respondents the first level already implies an increase over the status quo, we expect negative willingness to pay values. these would be highest in cultural landscapes and lowest in urban agglomerations. therefore, we focus on the second level of this attribute, i.e. an increase to 70%. the average share of meadows is relatively similar in all landscapes, so that large differences in willingness to pay may not be present. 3. econometric approach in the analysis, we use a latent class logit model to investigate the effects of the landscape categories on preferences and willingness to pay. the model is consistent with microeconomic theory, assuming rational individuals who maximize a utility function under constraints. an individual i chooses in t choice situations between a given set of alternatives n – each described by a conditional indirect utility function uint – the alternative that provides the maximum amount of utility. each alternative is characterized by k attributes that have levels aiknt. we assume the utility functions for alternatives to be linear and additive in the attributes, and add an error term eint which is extreme value type i distributed to the random utility model. a utility function can be written as uint=beta1ai1nt+β2ai2nt+…+βkaiknt+eint (1) where the βks are the corresponding utility coefficients. the probability of an individual choosing alternative n can be written as a conditional logit model: (2) 291does the place of residence affect land use preferences? this model has a closed form and can be estimated using maximum likelihood. in order to incorporate preference heterogeneity, we apply a latent class logit model. we assume that a given number of preference classes s, differing in their utility parameters <βk|1,βk|2,…,βk|s>, exists. each individual has probabilities <h1,h2,…,hs> to be member of the preference classes. the probabilities hs can be estimated with a multinomial logit model (3) where xi are explanatory variables, in this case the landscape categories, and ζs are the coefficients. the unconditional choice probability to choose alternative m is given as (4) the latent class logit model as described in equation 4 introduces preference heterogeneity between classes. within a class, preferences are fixed. to relax this assumption without introducing a large amount of new parameters, we extend the model to a scaleadjusted latent class model (magidson and vermunt 2008). in this model, each preference class s is separated by a constant which can be interpreted as a scale parameter. the scale parameter merely states that preferences for all attributes are higher in the one scale class than in the other scale class. whether the differences between respondents are caused by different preferences (all very high, vs. all very low) or by differences in the error variances (more random vs. less random choices) cannot be answered empirically (hess and train 2017). still, the introduction of this parameter captures another dimension of heterogeneity, which can improve model fit significantly. as the scale classes are restricted in a way that all preference parameters differ similarly, willingness to pay values between scale classes are not affected. technically, the scale parameter is estimated by another multinomial logit model, and each respondent has a probability g to belong to scale class r – similar to the preference classes. the unconditional choice probability in equation 4 becomes (5) if an earlier analysis has already identified some respondents belonging to a specific class, one can add a known-class parameter τr. this parameter is zero if a respondent cannot be assigned a priori to a certain class, leading to (6) in this study, we use the known class indicator to classify all respondents who have always chosen the status quo option into class 1. to determine the number of preference 292 julian sagebiel, klaus glenk, jürgen meyerhoff classes s, one can use statistical measures of fit such as the bayesian information criterion (bic), or the corrected akaike information criterion (caic). both bic and caic penalize for more parameters and are therefore preferred over other information criteria. additional to the statistical criteria, one can rely on own judgment concerning reasonable parameter estimates and knowledge gained from earlier analyses (boxall and adamowicz 2002; scarpa and thiene 2005). to calculate willingness to pay values for each class individually, the respective class preference parameter is divided by the class cost parameter. confidence intervals of willingness to pay are calculated with the delta method. 4. results 4.1 descriptive statistics of landscape categories we first analyze the relationship between socio-demographic variables and landscape categories. this step is important to understand whether and how potential differences in preferences could arise from differences in socio-demographics rather than the landscape respondents are living in. most differences are found between urban agglomerations and the other landscapes (table 3). respondents from urban agglomerations are more educated and have fewer children. we use kruskall-wallis and t-tests to test for overall differences between the landscape categories. statistically significant differences on a 5% level are present for all variables except personal income and sex. although there are differences in socio-demographics between landscape categories (especially between urban areas and all other areas), we will not investigate those here. we acknowledge that the differences in preferences may be driven by socio-demographics rather than landscape categories, but this is not relevant for the policy question of how land use changes are perceived in different landscapes. our analysis thus only provides correlations. using data from the german federal agency for cartography and geodesy (bkg) and the german federal institute of research on building, urban affairs and spatial development (bbsr), we investigated the actual status quo attribute levels of the respondents. table 4 summarizes the actual status quo in the 15km radius by landscape categories. in most cases, there are relatively large differences between the landscape categories. for the sake of parsimony, we will not investigate the actual status quo and possible effects any further. sagebiel, glenk, and meyerhoff (2017) conduct a detailed investigation of the actual status quo and its effects on willingness to pay. 4.2 latent class analysis we estimate the latent class model described in section 3 using the software package latentgold choice 4.5 with the syntax module. to select a specific number of classes we compared bic and caic for two to eight class models, in the absence and presence of a scale class. we choose a model with five preference classes and two scale classes. this model turned out have the lowest bic and caic values and offered plausible parameter values. 293does the place of residence affect land use preferences? all attributes except price were dummy coded with the status quo level as today as the reference. the landscape categories entered the class membership function as dummy coded variables with forest landscapes as the reference category. we did not include any socio-demographic variables as these are correlated with the landscape categories, table 3. frequencies and column percentages (in parentheses) of socio-demographic variables. forest cultural farmand grassland urban total education secondary or less 83 122 121 141 467 (40.9) (37.4) (39.4) (24.7) (33.2) higher education 46 86 77 155 364 (22.7) (26.4) (25.1) (27.2) (25.9) university 74 118 109 274 575 (36.5) (36.2) (35.5) (48.1) (40.9) sex male 100 168 175 308 751 (49.0) (51.5) (56.6) (54.0) (53.3) female 104 158 134 262 658 (51.0) (48.5) (43.4) (46.0) (46.7) children in household yes 68 135 102 134 439 (33.3) (41.4) (33.0) (23.5) (31.2) no 136 191 207 436 970 (66.7) (58.6) (67.0) (76.5) (68.8) income less than 1500 euros 81 128 120 227 556 (39.7) (39.3) (38.8) (39.8) (39.5) 1500 to 2600 euros 58 91 74 153 376 (28.4) (27.9) (23.9) (26.8) (26.7) more than 2600 euros 65 107 115 190 477 (31.9) (32.8) (37.2) (33.3) (33.9) age 19 to 29 32 67 60 132 291 (15.7) (20.6) (19.4) (23.2) (20.7) 30 to 39 50 62 58 110 280 (24.5) (19.0) (18.8) (19.3) (19.9) 40 to 49 39 89 92 144 364 (19.1) (27.3) (29.8) (25.3) (25.8) 50 to 59 40 64 55 102 261 (19.6) (19.6) (17.8) (17.9) (18.5) older than 60 43 44 44 82 213 (21.1) (13.5) (14.2) (14.4) (15.1) 294 julian sagebiel, klaus glenk, jürgen meyerhoff potentially causing multicollinearity. 23% of all respondents chose the status quo alternative in all choice situations and were assigned to class 1 with a probability of 1. as several respondents seemed to have ignored the price attribute, we fixed the price parameter to zero in class 3 to capture price non-attendance. in models without this restriction, at least one class is characterized by willingness to pay values three times as high as the highest price level of 160 euro, which we consider implausible. in a first step, we describe the five classes in terms of estimated utility parameters and willingness to pay values. then, we investigate the relationship between class membership and landscape categories. table 5 shows the estimation results and table 6 its willingness to pay values. the overall model is highly significant. the statistically significant coefficient for the scale class of -0.302 translates to scale class probabilities of 57.5% and 42.5% for scale classes 1 and 2, respectively, indicating that additional heterogeneity and correlation patterns are present. in class 1, price, forminus10, fieldhalf, fielddouble, corn70 and mead50 are highly significant and negative. willingness to pay values range between -88 and -35 euro, i.e. people are opting against all land use changes and would need to be compensated. the positive and significant ascsq means that class 1 is characterized by preferences towards the status quo. class 2 has a negative and significant ascsq, indicating preferences for land use changes. forminus10, forplus10, fielddouble, bio105, corn70, mead50 and price are significant with the expected signs. the willingness to pay for forminus10 and forplus10 is -165 euro and 64 euro, respectively. people are willing to pay for increases in forest, but would need to be compensated nearly three times as much for decreases in forest. for a reduction of field sizes (fieldhalf), willingness to pay is nearly 20 euro while a doubling of field sizes would need to be compensated with 45 euro. willingness to pay for increases in biodiversity is 32 euro for an increase to 85 points and 51 euro for 105 points. a share of corn of 30% is not significant but a share of 70% requires a compensation of 61 euro. willingness to pay for a share of meadows of 25% is positive (42 euro) while a share of 50% is not significant and close to zero. in summary, class 2 is characterized by large positive and negative willingness to pay values for land use changes. class 3 is the price non-attendance class. respondents who disregard the cost attribute are likely choosing a land use change scenario over the status quo if they table 4. mean and standard deviation (in parenthesis) of actual status quo by landscape categories. forest cultural farmand grassland urban total forest share 41.7 29.8 17.5 18.4 24.2 (12.2) (11.2) (9.7) (10.0) (13.7) field size 17.7 17.5 25.9 17.0 19.2 (7.5) (6.8) (12.2) (6.7) (9.1) corn share 14.9 20.9 19.9 10.5 15.5 (10.3) (14.8) (15.8) (10.0) (13.5) meadows share 15.6 17.9 18.1 12.6 15.5 (6.1) (9.0) (12.1) (7.1) (9.1) 295does the place of residence affect land use preferences? have a positive attitude towards policy change. this is reflected in the very large and negative ascsq. similarly, the very large and negative coefficient for decreases in forest share can be explained by this phenomenon. nearly all coefficients of the remaining attributes are significant and have the expected signs. bio85 is significant and negative which could imply that members of this class have already a high degree of biodiversity and regard 85 points as a deterioration. similarly, the positive coefficient of corn30 implies that people have already high shares of corn and regard a 30% share as an improvement. finally, mead25 is not significant while mead50 is significant and positive. class 4 is characterized by comparatively large negative willingness to pay values to avoid decreases in forest share, field size and a corn share of 70%. interestingly, the willingness to pay for meadows share is negative for both 25% and 50%. in class 5, positive willingness to pay values are significant and positive only for forplus10 (14 euro) and bio105 (12 euro) and negative for fieldhalf (-16 euro) and mead25 (-19 euro). this class comprises small or no utility gains from land use changes. the landscape categories have a significant impact on the probability to be member of a class. forest landscapes and class 1 are the reference categories, the parameters in table 5. latent class model with five classes. class 1 class 2 class 3 class 4 class 5 ascsq 0.760 -1.281*** -23.897** -3.993*** -3.535*** forminus10 -3.151*** -4.169*** -17.829* -1.287*** -0.062 forplus10 -0.316 1.624*** 0.926*** 0.317* 1.108** fieldhalf -2.678*** 0.474 -0.075 -0.961*** -1.305*** fielddouble -2.120*** -1.132*** -0.275** 0.390** -0.361 bio85 0.774 0.814** -5.791** 0.444 -0.455 bio105 0.431 1.295*** 1.233*** 0.103 0.964* corn30 -0.647 0.592 1.055*** 0.038 0.351 corn70 -5.326*** -1.563*** 0.214 -1.195*** -0.763 mead25 -1.173 1.056*** -0.035 -1.120*** -1.503*** mead50 -2.525*** 0.090 0.773*** -0.802*** -0.586 price -0.060*** -0.025*** 0.000 -0.013*** -0.078*** covariates of membership function forest ref ref ref ref ref cultural ref 0.172 0.877** 0.041 0.924* grass/farm ref 0.655** 1.245*** 0.2462 1.167** urban ref 0.198 1.326*** 0.438 1.325*** scale classes scale class 1 scale class 2 constant ref -0.302** log-likelihood observations respondents -8976.153 12681 1409 * p < 0.10, ** p < 0.05, *** p < 0.01 , ref = reference category with parameter fixed to zero 296 julian sagebiel, klaus glenk, jürgen meyerhoff the membership function are interpreted relative to them. class 1 is the largest class with a share of about 40%. classes 2 to 4 have a share between 16% and 19%. class 5 is the smallest class with a share of 9%. note that classes 1 and 5 are characterized by no or low willingness to pay values and make up nearly 50% of class membership. table 7 shows class membership probabilities calculated for each landscape category separately. differences in class membership between landscape categories are present in classes 1, 3 and 5. membership probabilities are rather homogeneous for classes 2 and 4. respondents from forest landscapes are more likely to be member of class 1 compared to the other categories with a share of nearly 51% (against the class average of 39%) and less likely member of classes 3 and 5 with shares of only 8% and 4% (compared to the class averages of 18% and 9%). respondents from cultural landscapes are slightly more likely to be member of class 1 (42%) and less or equally likely in the other classes. respondents from grassand farmlands are less likely to be member of class 1 (34% against 39%) and class 4 (14% against 16%) and more likely to be member of class 2 (24% against 19%). finally, respondents from urban agglomerations are less likely to be member of class 1 (35% against 39%) and 2 (16% against 19%) and more likely in classes 3 (21% against 18%), 4 (17% against 16%) and 5 (11% against 9%). the results are partly in line with our expectations. forest landscapes and cultural landscapes have high shares of forest and are relatively bio-diverse, with many structural landscape elements. such landscapes are generally associated with high recreational values. respondents from these categories are more likely to be member of class 1 which is characterized by status quo choices and strong opposition against reductions of forest share, increases in corn and changes in field size. this aligns with our expectation of place attachment. the zero willingness to pay for increases of forest indicates diminishing marginal utility. people from forest landscapes are also less likely to be members of class 3, which is characterized by a strong tendency towards land use changes and cost non-attendance, and of class 5, which is characterized by low willingness to pay, implying some heterogeneity within this landscape category. about 55% are allocated to classes 1 and 5 (low willingness to pay), while the remaining share belongs to the other classes which are characterized by high willingness to pay and strong preferences for land-use changes. farmand grasslands are dominated by agriculture and monotonic landscapes with low shares of forest. respondents from farmand grasslands are more likely to be members of class 2, which is characterized by rather large willingness to pay values. this result fits to our expectations of marginal diminishing utility. people living in this landscape have a low endowment of forest, biodiversity and meadows and are thus more willing to pay for an additional unit. finally, respondents from urban agglomerations are more likely to be member of class 3, i.e. are more likely to not attend to costs. while we have no expectation here, this result may be explained by hypothetical bias. the choice scenario is less realistic for people in urban areas and they are less used to the landscapes. they may have ignored the price attribute more often, while at the same time exhibit strong preferences for land use changes. it should be noted that our results indicate preference heterogeneity within landscape categories. we do observe deterministic patterns of distinct preferences between landscape categories. each landscape category is present in each class with a probability close to the average group probability. class 1 is the largest class for all landscape catego297does the place of residence affect land use preferences? ries and class 5 is the smallest class for all landscape categories. the effects that we identified should be interpreted as tendencies. additional to the latent class analysis, we have estimated separate conditional logit models by landscape categories and used poe et al. tests (poe, giraud, and loomis 2005) to test for differences in willingness to pay between landscape categories. while exact quantitative results differ, the key findings are similar irrespective of the approach used. the appendix provides more details on the conditional logit models, willingness to pay values and the poe et al. test results. 5. conclusion and policy implications this paper investigated preferences for land-use changes and compared willingness to pay values between different landscape categories in germany. the data came from a discrete choice experiment inferring preferences for forest share, average size of forest and fields, degree of biodiversity, share of corn and share of meadows within the 15 kilometer radius of the respondents’ places of residence. the radius was chosen to represent a typical distance for everyday activities. as the places of residence were geo-referenced, we could combine the data with landscape categories compiled by the german federal agency for nature conservation. the categories comprised forest landscapes, cultural landscapes, table 6. willingness to pay values. attribute class 1 class 2 class 3 class 4 class 5 forminus10 -52.52*** -165.19*** -100.67*** -0.7954 forplus10 -5.27 64.33*** 24.75* 14.24*** fieldhalf -44.64*** 18.77* -75.17*** -16.76*** fielddouble -35.34** -44.83** 30.51** -4.64 bio85 12.90 32.24** 34.75 -5.84 bio105 7.17 51.32*** 8.04 12.38** corn30 -10.78 23.44 2.96 4.50 corn70 -88.78*** -61.93* -93.47** -9.80 mead25 -19.55 41.82** -87.58*** -19.31** mead50 -42.09** 3.57 -62.74** -7.53 * p < 0.10, ** p < 0.05, *** p < 0.01. table 7. class probabilities by landscape categories. landscape class 1 class 2 class 3 class 4 class 5 forest 0.51 0.19 0.08 0.16 0.04 cultural 0.42 0.19 0.17 0.14 0.09 grass/farm 0.34 0.24 0.19 0.14 0.09 urban 0.35 0.16 0.21 0.17 0.11 overall 0.39 0.19 0.18 0.16 0.09 298 julian sagebiel, klaus glenk, jürgen meyerhoff farmand grassland landscapes and urban agglomerations. the aim of the study was to test whether preferences for land-use changes are correlated with these landscape categories. to do so, we estimated a five-class latent class model and used the landscape categories as explanatory variables in the class membership function. the classes can be distinguished by different willingness to pay values. it turned out that people from forest landscapes and cultural landscapes were less willing to pay for land-use changes and showed a preference towards the status quo situation. further, people from urban agglomerations and farmand grassland have high probabilities to be member of classes with large willingness to pay values. in summary, the results showed that the preferences do differ among landscape categories, but not as systematically as we had expected. although we find systematic differences in preferences between landscape categories, all landscape categories are relatively evenly distributed across classes. as the latent class analysis has shown, preference heterogeneity exists also within the landscape categories. that is, each respondent, independent of which landscape category the respondent is from, has a probability of at least 8% to be member of any class. the analysis has implications for policy makers. our study provides evidence that there are differences in preferences determined by the place of residence. integrating such differences in landscape planning and cost-benefit analyses may help to improve decisions and induce land-use changes to areas where people appreciate them most or are least reluctant towards a change. a relevant example is the share of corn among agricultural fields. while an increase in the production of energy corn can potentially help to reduce carbon dioxide emissions, it is largely regarded as a disfigurement of the landscape. our study revealed that opposition to corn is generally large, but stronger in forest and cultural landscapes than in other landscapes. similarly, increases in forest share should take place in areas with limited forests and near urban agglomerations. areas characterized by high recreational values such as forest and cultural landscapes should be preserved. here, people tend more towards the status quo and changes are less appreciated by residents. there is limited interest in increases in forest shares or biodiversity, and at the same time a large resistance against reductions. in contrast, respondents from urban agglomerations and farmand grasslands are more likely to benefit from increases in forest shares and biodiversity. here, significant welfare effects of such measures are more likely. our findings may also be used to inform the design of agri-environmental schemes. for example, compensation may be higher for measures to increase agro-biodiversity in a rather monotonic landscape or near urban areas, because benefits of measures are greater. similar studies have investigated land use changes on a broader scale. in their metaanalysis, van zanten et al. (2014) have found preferences for various landscape elements such as smaller field sizes, but no spatial determinants of preferences. garrod et al. (2012) have found that preferences for improving ecosystem services depend on the landscape where they are present. this result is in line with our findings, yet our study differs as the proposed land use change always took place at the person’s place of residence. in garrod et al. (2012), this was not the case. to our knowledge, our study is the first study that identifies spatial differentiated preferences for local land use changes. this study is limited by the fact that we did not investigate the underlying sources for the differences. the landscape categories differ in the status quo of the investigated attrib299does the place of residence affect land use preferences? utes and in socio-demographic variables. thus, we are not able to identify the causal effect of living in a certain landscape on preferences. yet, the study insights provide correlation patterns which are sufficient to foster an understanding of the variation of preferences and willingness to pay between qualitatively different regions. acknowledgments we thank henry wuestemann for support in the processing of the gis data and dr. roland goetzke and raphael knevels from the federal institute of research on building, urban affairs and spatial development (bbsr) for preparing the landscape structure data. financial support from the german ministry of education and research within the project cc-landstrad (grant number: 01ll0909c) is gratefully acknowledged. references boxall, peter c, and wiktor l adamowicz. 2002. “understanding heterogeneous preferences in random utility models: a latent class approach.” environmental and resource economics 23 (4): 421–46. broch, stine wamberg, niels strange, jette b. jacobsen, and kerrie a. wilson. 2013. “farmers’ willingness to provide ecosystem services and effects of their spatial distribution.” ecological economics 92: 78–86. brouwer, roy, julia martin-ortega, and julio berbel. 2010. “spatial preference heterogeneity: a choice experiment.” land economics 86 (3): 552–68. butchart, stuart hm, matt walpole, ben collen, arco van strien, jörn pw scharlemann, rosamunde ea almond, jonathan em baillie, et al. 2010. “global biodiversity: indicators of recent declines.” science 328 (5982): 1164–8. campbell, danny, w george hutchinson, and 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northeastern alps: a latent-class approach based on intensity of preferences.” land economics 81 (3): 426–44. schmitz, k, pm schmitz, and tc wronka. 2003. “valuation of landscape functions using choice experiments.” german journal of agricultural economics 52 (8): 379–89. shoyama, kikuko, shunsuke managi, and yoshiki yamagata. 2013. “public preferences for biodiversity conservation and climate-change mitigation: a choice experiment using ecosystem services indicators.” land use policy 34: 282–93. tversky, amos, and daniel kahneman. 1981. “the framing of decisions and the psychology of choice.” science 211 (4481): 453–58. van zanten, boris t, peter h verburg, mark j koetse, and pieter jh van beukering. 2014. “preferences for european agrarian landscapes: a meta-analysis of case studies.” landscape and urban planning 132: 89–101. 301does the place of residence affect land use preferences? appendix in order to further investigate differences between landscape categories, we estimate separate conditional logit models for the landscape categories. table 8 provides the estimation results. table 8. conditional logit models by landscape. (1) forest (2) cultural (3) farmand grasslands (4) urban ascsq 0.101 0.0171 -0.0959 0.0436 (0.201) (0.155) (0.154) (0.112) forminus10 -0.647*** -0.594*** -0.532*** -0.458*** (0.135) (0.103) (0.106) (0.0778) forplus10 0.139 0.284*** 0.384*** 0.401*** (0.104) (0.0789) (0.0769) (0.0562) fieldhalf -0.152 -0.314*** -0.283*** -0.221*** (0.119) (0.0905) (0.0906) (0.0670) fielddouble -0.302*** -0.345*** -0.267*** -0.0984* (0.107) (0.0791) (0.0773) (0.0561) bio85 0.0381 0.129 0.0650 0.204*** (0.127) (0.0994) (0.102) (0.0753) bio105 0.0126 0.246*** 0.327*** 0.417*** (0.118) (0.0884) (0.0843) (0.0614) corn30 -0.0983 0.176* 0.000266 -0.0231 (0.125) (0.0972) (0.0965) (0.0706) corn70 -0.691*** -0.390*** -0.550*** -0.548*** (0.127) (0.0930) (0.0919) (0.0670) mead25 0.0596 0.0768 -0.131 0.0247 (0.136) (0.103) (0.105) (0.0761) mead50 -0.234* -0.151 -0.182* -0.125* (0.130) (0.0939) (0.0938) (0.0683) price -0.00615*** -0.00755*** -0.00520*** -0.00583*** (0.00115) (0.000892) (0.000858) (0.000630) n 5508 8802 8343 15390 pseudo r2 0.171 0.117 0.087 0.081 aic 3366.8 5717.0 5604.7 10379.3 bic 3446.1 5802.0 5689.0 10471.0 χ2 691.3 753.6 529.8 916.5 log-likelihood (null) -2017.1 -3223.3 -3055.2 -5635.9 log-likelihood -1671.4 -2846.5 -2790.3 -5177.6 standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01. 302 julian sagebiel, klaus glenk, jürgen meyerhoff all models are highly significant and differences between the landscape categories are visible. in forest landscapes, forplus10 is not significant, according to the hypothesis that respondents living in areas with a lot of forests have a limited preference for an increase in the share of forests. an increase in biodiversity to 85 points is only significant in urban agglomerations, where people are characterized by a low degree of biodiversity. hence, an increase to 85 points has already a positive effect on utility. in the other categories, biodiversity is significant only at the 105 point level. in order to better understand the differences, table 9 displays the estimated willingness to pay values for the different categories and figure 3 gives a graphical overview of the willingness to pay values and corresponding 95% confidence intervals. finally, table 10 provides the p-values of the poe test. if the p-value is larger than 0.95 or smaller than 0.05, the willingness to pay values are significantly different. the poe test has to be interpreted with care. significant differences will only appear when confidence intervals are small enough. hence, if the test does not reject the hypotheses that the willingness to pay values are similar, it does not necessarily mean that they are not. it rather means that we cannot show that they are. differences in willingness to pay are significant for forplus10, bio105, corn30 and corn70. forplus10 is not significant for forest landscapes and is significantly higher in open cultural landscapes and urban agglomerations. an increase in biodiversity is valued most in open cultural landscapes and in urban agglomerations and is significantly higher than in forest landscapes. an increase in corn share to 70% has the highest negative willingness to pay in forest landscapes and in open cultural landscapes. there are very few differences between open cultural landscapes and urban agglomerations and no significant differences for meadows share and bio85, which however maybe due to the large confidence intervals. fieldhalf and fielddouble are nearly always significant, but again, no significant willingness to pay differences exist. thus, preferences for this attribute are relatively similar. the results from the poe test are corresponding to the findings from the latent class analysis. in both exercises, people from open cultural landscapes and urban agglomerations seem to have relatively equal preferences. similarly, people from forest landscapes and from structurally rich cultural landscapes exhibit similar preferences. the main hypotheses of decreasing marginal utility seem partly confirmed. for example, people in forest landscapes have no willingness to pay for an increase, but a strong willingness to pay against a decrease. however, not in all cases, the results correspond to our expectations. 303does the place of residence affect land use preferences? table 9. willingness to pay for different landscape models. (1) forest (2) cultural (3) farmand grassland (4) urban forminus10 -105.2*** -78.72*** -102.3*** -78.52*** (29.01) (16.00) (26.24) (15.52) forplus10 22.54 37.66*** 73.74*** 68.74*** (16.21) (10.10) (16.33) (10.44) fieldhalf -24.75 -41.61*** -54.31*** -37.89*** (20.69) (13.79) (20.61) (12.65) fielddouble -49.10** -45.62*** -51.34*** -16.88 (21.77) (13.10) (19.00) (10.26) bio85 6.188 17.10 12.49 34.97*** (20.38) (12.77) (19.15) (12.37) bio105 2.044 32.57*** 62.91*** 71.57*** (18.98) (10.26) (14.17) (9.419) corn30 -15.98 23.26* 0.0511 -3.970 (21.72) (11.95) (18.54) (12.29) corn70 -112.4*** -51.64*** -105.6*** -93.96*** (35.27) (16.03) (29.91) (18.47) mead25 9.688 10.17 -25.24 4.244 (21.66) (13.45) (21.35) (12.97) standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. table 10. poe test results. forminus10 forplus10 fieldhalf fielddouble bio85 bio105 corn30 corn70 mead25mead50 forest vs. cultural 0.845 0.905 0.251 0.481 0.879 0.987 0.992 0.965 0.696 0.558 forest vs. farm 0.412 0.998 0.174 0.529 0.644 0.993 0.805 0.589 0.327 0.517 forest vs. urban 0.795 0.997 0.318 0.711 0.877 1.000 0.837 0.683 0.675 0.492 cultural vs. farm 0.074 0.986 0.33 0.55 0.185 0.775 0.056 0.046 0.14 0.449 cultural vs. urban 0.4 0.975 0.597 0.779 0.486 0.908 0.025 0.041 0.474 0.403 farm vs. urban 0.889 0.321 0.733 0.683 0.802 0.623 0.505 0.587 0.867 0.458 304 julian sagebiel, klaus glenk, jürgen meyerhoff figure 3. willingness to pay confidence intervals by sample. −1 50 −1 00 −5 0 w illi ng ne ss to p ay wooden landscapes structurally rich landscapes open cultural landscapes urban agglomerations forminus10 0 20 40 60 80 10 0 w illi ng ne ss to p ay wooden landscapes structurally rich landscapes open cultural landscapes urban agglomerations forplus10 −1 00 −5 0 0 50 w illi ng ne ss to p ay wooden landscapes structurally rich landscapes open cultural landscapes urban agglomerations fieldhalf −1 00 −8 0 −6 0 −4 0 −2 0 0 w illi ng ne ss to p ay wooden landscapes structurally rich landscapes open cultural landscapes urban agglomerations fielddouble −4 0 −2 0 0 20 40 60 w illi ng ne ss to p ay wooden landscapes structurally rich landscapes open cultural landscapes urban agglomerations bio85 −5 0 0 50 10 0 w illi ng ne ss to p ay wooden landscapes structurally rich landscapes open cultural landscapes urban agglomerations bio105 −6 0 −4 0 −2 0 0 20 40 w illi ng ne ss to p ay wooden landscapes structurally rich landscapes open cultural landscapes urban agglomerations corn30 −2 00 −1 50 −1 00 −5 0 0 w illi ng ne ss to p ay wooden landscapes structurally rich landscapes open cultural landscapes urban agglomerations corn70 −1 00 −5 0 0 50 w illi ng ne ss to p ay wooden landscapes structurally rich landscapes open cultural landscapes urban agglomerations mead25 −8 0 −6 0 −4 0 −2 0 0 20 w illi ng ne ss to p ay wooden landscapes structurally rich landscapes open cultural landscapes urban agglomerations mead50 figure 3: willingness to pay ci by sample 20 investigating determinants of choice and predicting market shares of renewable-based heating systems under alternative policy scenarios cristiano franceschinis, mara thiene multi-country stated preferences choice analysis for fresh tomatoes maria de salvo1,*, riccardo scarpa2,3,4, roberta capitello2, diego begalli2 “not my cup of coffee”. farmers’ preferences for coffee variety traits. lessons for crop breeding in the age of climate change abrha megos meressa, ståle navrud* does the place of residence affect land use preferences? evidence from a choice experiment in germany julian sagebiel1,*, klaus glenk2, jürgen meyerhoff3 the use of latent variable models in policy: a road fraught with peril? danny campbell*, erlend dancke sandorf bio-based and applied economics 7(2): 161-178, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-7673 the income effect of cap subsidies: implications of distributional leakages for transfer efficiency in italy stefano ciliberti*, angelo frascarelli department of agricultural, environmental and food sciences university of perugia, italy date of submission: 2018 13th, june; accepted 2019, 30th, january abstract. enhancing farm income level is one of the main purpose of the common agricultural policy (cap). the ability to reach such a goal can be measured in terms of transfer efficiency, that is affected by the presence of distributive leakages through the agro-food system. the present work aims to shed light on the income distributional effects of the main forms of cap subsidies in italy over the period 2008–2014: single payment scheme, coupled payment and second pillar aids. to this aim, an arellano–bond linear dynamic panel-data estimation (based on a database provided by the italian fadn) is performed. results show that all the main types of cap support have a significant income effect, even though some relevant differences occur between decoupled and coupled components of direct payments received by italian farmers as a consequence of distributional leakages. keywords. cap subsidies, transfer efficiency, income effect, arellano-bond, italy. jel code. q18 1. introduction in the last decades, the common agricultural policy (cap) has moved from price support policies to direct payments, causing a dramatic increase of the transparency of transfer. as a consequence, public aids for farmers has been scrutinized by the general public and taxpayers who are interested to know who receives such payments (agrosynergie, 2011). moreover, the distribution of subsidies and incomes among subjects and economic sectors became a relevant topic, because some of those may not be the primary intended beneficiaries of the policy. the incidence of agricultural policy has been investigated by measuring the transfer efficiency, that provides a means for comparing the benefits to producers with the combined costs to consumers and taxpayers and to society as a whole. this term is usually defined as the ratio of income gain of the targeted beneficiaries and the sum of the associ*corresponding author: stefano.ciliberti@unipg.it 162 stefano ciliberti, angelo frascarelli ated governments expenditure and consumer costs (dewbre et al., 2001). all in all, this concept allow assessing the distribution of the costs and benefits of the policy among different interest groups, defined in terms of their roles as consumers, taxpayers, producers or supplies of factors of production (alston and james, 2002). despite a significant literature has paid attention mainly to some specific mechanism that affect transfer efficiency (such as the capitalization effect of the single payment scheme), scholars have also focused on the transfer efficiency of agricultural support as a whole, in order to evaluate and somehow measure the whole effectiveness of agricultural policies in delivering additional income to farm households. in this regard, empirical evidences tend to support the theoretical findings that not only agricultural producers, but also other market participants along the vertical chain, may benefit from agricultural subsidies. oecd (1996) reported that a broad quantification of transfer efficiency suggests that as little as one-fifth of the benefits of market price support resulted in additional income for farm households. in more detail, it has been demonstrated that those support measures causing the greatest distortion to production and trade are also the least efficient in providing income benefits to farmers (dewbre et al., 2001). it clearly follows that the type of support matters when measuring its impact on farm income. the present work aims to shed lights on the distributional effects of the main forms of cap subsidies in italy over the period 2008-2014, where both coupled and decoupled payments have been coexisting along with second pillar aids (that is, rural development programs – rdps). to this purpose a dynamic panel data estimation is implemented to quantify the impact of these different policy tools on farm income and to indirectly evaluate the transfer efficiency of these aids. the paper is organized as follows: section two reports the literature on transfer efficiency of cap aids with particular emphasis on the comparison among the different types of public aids. section three describes the evolution of the cap instruments in italy. section four illustrates data and the empirical methods used in the study. section five shows empirical findings and discusses the results in the lights of the existing literature. lastly, conclusion and final remarks are reported. 2. theoretical framework 2.1 the transfer efficiency where income support is an objective, it is important that the policy pursues it in an efficient way, since the ability of the considered policy to enhance the income level of agricultural households can be measured in terms of transfer efficiency. in this regard, three main source of inefficiency have been reported by agrosynergie (2011): targeting efficiency, economic costs and distributive leakages. as for the first aspect, corden (1957), bhagwati (1971) and more recently guyomard et al. (2004) show that the standard policy recommendation is to follow the principle of targeting policies to their specific objectives. with concerns to the second element, subsidies are costly to introduce, administer an enforce; these costs and the effects of producer responses to the incentive to cheat also change the deadweight losses from each of the policies, their distributional consequences and their efficiency as means of transferring income to producers (alston and james, 2002). the latter issue refers to the case in which a part of the economic support “leak” to non-farm 163the income effect of cap subsidies owners of resources, as it is benefitted from subjects who may not be intended beneficiaries of the policy by means of both increased farm production costs and decreased farm income. according to the oecd (1996) “no support policy linked to agricultural activity succeeds in delivering more than half the monetary transfer from consumers and taxpayers as additional income to farm households”. despite the leakages could also be viewed as a sort of positive spillover effect that impact on incomes of other stakeholders of the agri-food system (input suppliers, consultancy services, buyers and so on), the intriguing question is: where does the rest of the money for farmers provided by the public authorities go? the overall subsidy effect on farm income depends on the magnitudes of multiple factors. first, subsidies may increase input prices (for example, fertilizers, land and capital), thus channelling policy benefit to input suppliers. since subsidy-induced changes in input use are likely to result in changes in some input prices, therefore named recipient of the subsidy payment is unlikely to capture all of the benefits. second, subsidies may lead to lower output prices, thus generating policy gains for consumers, third, subsidies may interact with other markets (as in credit constraint) or may alter farm behaviour (substitute private farm activities), which may enhance or reduce farm profits depending on the type of induced effect (ciaian et al., 2015; ciaian and swinnen, 2009). to sum up, whether agricultural support benefits farmers closely depends on whether farmers own the resources they use in production (latruffe and le mouël, 2009). when farmers do not own such inputs, payments may not belong to the group of the intended main beneficiaries of the policy. indeed, empirical evidence exists on the fact that part of the support provided by agricultural policies (including direct payments) contributes to increasing the costs of resources, the income of input suppliers and the income of non farming landowners. however, the level of transfer efficiency and the destination of the money transfer differ according to the policy instrument (agrosynergie, 2011). 2.2 types of support and transfer efficiency the literature review reveals that scholars have investigated how income distributional effects differ based on subsidy types. empirical evidences indicated that compared to area payments, market price support is indeed a relatively inefficient and trade distorting way of supporting farm incomes. direct payments based on output or on variable input use, however, are also highly inefficient and trade distorting when compared to area payments. this latter, requiring planting of specific crops, are however less efficient and more trade distorting than payments made irrespective of the use to which the land is put (dewbre et al., 2001). moreover, farmers report that the largest share of direct payment receipts tend to be used to cover agricultural production crops (goodwin and mishra, 2005). as a consequence, an increased demand for inputs drives the increase in factor expenditure, with significant effects on the costs of input (land, fertilizers, pesticides and so on) (kirwan, 2009). 2.2.1 leakages related to coupled payments many scholars have analysed how income distributional effects differ between coupled and decoupled payments. it is well known that some income support policies are explicitly linked with production decisions in the sense that these latter can alter the magnitude of 164 stefano ciliberti, angelo frascarelli income support: this linkage is generally called coupling and breaking the linkage is called decoupling. the term “coupled” itself links payments to a specific stimulating production activity and these payments are available to farms in all member states and include crop area direct payments and animal direct payments. in general, studies focus on the effects of coupled subsidies in narrowly defined agricultural sectors and results showed that a significant part of coupled payments could be leaked away to other agents through changes in market prices and this effect diminishes farms’ benefits from subsidies. the leakage is positively correlated with coupling because it implies a stronger link of subsidies to farm activities and thus stronger impact on the aggregate price level (rizov et al., 2013). more in general, since coupled payments clearly have production impacts and due to the fact that the greater the production impact of direct payments the less they push up rental values, it follows that such an increased production results in lower commodity prices and higher input prices as well as it also dilutes the impact that direct payments have on land rent (kirwan, 2009). 2.2.2 decoupled payments and the capitalization effect as a consequence, because the production impacts of explicitly coupled supports sometimes have been quite substantial and costly to the government, many policies have been modified to reduce or break the coupling (hennessy, 1998). therefore, the last reforms of the cap have led to the decoupling of direct payments from production. decoupled payments were introduced in order to curb over-production and to reduce the trade-distorting and inefficiency effects of the cap (howley et al., 2012). literature suggests that, depending on both farm size and the duration of the tenant-landlord agreement, the decoupled direct payments linked to land positively influence land rents, because only those who own or have rented (eligible) land can claim the payments (kilian and salhofer, 2008; kirwan and roberts, 2015). this result is due to the fact that the sps is still “coupled” to agricultural land and has a high potential for capitalization into land values. with some exceptions (guastella et al., 2013), scholars showed that decoupled payments exert a larger impact on rents than coupled payments. such a capitalization effects vary from to 0.20 to 0.90 euro (or dollar) for each euro transferred to the farmers (ciaian and kancs, 2012; kirwan, 2009; patton et al., 2008; o’neill and hanrahan, 2016; breustedt and habermann, 2011; kilian et al., 2012). under certain circumstances the decoupled payments are even fully reflected in rental values (hendricks et al., 2012). more in general, whether agricultural support benefits farmers closely depends on whether farmers own the resources they use in production (latruffe and le mouël, 2009). it follows that, the greater the share that goes to land and landowners, the less effective direct payments ultimately become as a means of supporting farmers’ incomes (patton et al., 2008). as a consequence, what appears clearly is that part of the payments is capitalized in land prices, implying that the governments could have partially missed their target of providing income support to farmers (latruffe and le mouël, 2009). 2.2.3 second pillar aids and distributional impacts lastly, potential leakages effects could also affect rdp aids, that include different policy measures, ranging from area payments to investment supports. as for the first 165the income effect of cap subsidies category, both less favoured area (lfa) payments and agri-environmental payments are compensatory type of aids, granted for a range of farm activities that should cover additional costs and farm income foregone resulting from adoption of environmental management practises (ciaian et al., 2015). the transfer efficiency of such a type of area payments may again be hindered by the above-mentioned capitalization of the aids into the land value. the second category covers only a share of the total cost of a programme of investment activity either for farm practises (capital items) or for a farmer (training courses and other qualifications). since these public aids are known by suppliers, they can be partially absorbed into the prices for input and services, so that the transfer efficiency of the payment decreases. 3. policy framework: the application of the cap in italy cap reforms have seen a progressive move away from direct market interventions and production specific subsidies. to this purpose, since 1992 the cap of the eu has been reformed several times. first pillar (direct payments and common market organization cmo) is the most important in financial terms and it currently consumes more than 60% of the overall cap resources (henke and coronas, 2011; ciliberti and frascarelli, 2015). 3.1 decoupled payments the 2003 fischler cap reform significantly reduced and partially replaced the previous coupled payments system with the decoupled payments (sps). under this scheme, each farm was allocated an amount of entitlements; they can receive decoupled payments if they have both entitlements and an equal amount of eligible land. however, the sps is not linked to a specific land area, since the entitlements can be activated by any eligible farmland in the region. moreover, farms can expand or decrease their stock of entitlements by buying or selling entitlements on the market from other farms. as concerns italy, it must be noted that the historical model of the sps was implemented from 2005 to 2014. under this model the payment per hectares varied strongly across farms, depending on the coupled payments farmers received in historical reference period (2001-2003) (erjavec et al., 2011). 3.2 coupled payments cdps include crop area direct payments and animal direct payments. in general, they are land-based subsidies linked to the cultivation of certain crops, implying that the level of the crop cdp does not depend on production level, but on the area cultivated with eligible crops. the coupled animal direct payments are either output (animal) type of payments (such as beef premiums, slaughter premiums) or subsidies linked to non-land input. aftere the introduction of decoupling in 2005 mss were allowed to grant optional coupled payments in specific cases. additional payments granted under article 69 of reg. 166 stefano ciliberti, angelo frascarelli (eu) 1782/2003 were considered as coupled1, with the provision that they were not granted to all producers of a sector, but were based on certain eligibility criteria. this optionality was maintained after the cap health check in 2009 with the introduction of article 682 of council regulation (ec) 73/2009. however, it broadened the range of such specific support, with the possibility of granting coupled payments depending on the objectives assigned of the last supply control measures (milk quotas, sugar quotas and vineyard planting rights). 3.3 the second pillar the rural development policy represents the other core element of the cap that is implemented in a more targeted and programmed approach compared to other measures (uthes et al., 2017). the paper focuses only on specific measures that absorb a high share of the budget for regional rdp: less favoured area (lfa) payments, agri-environmental payments and investments support. the lfa scheme is a longstanding measure that provides a broad-scale mechanism for maintaining the countryside in marginal areas. agri-environment measures provide payments to farmers who subscribe, on a voluntary basis, to environmental commitments related to the preservation of the environment and maintaining the countryside. lastly, investments aids cover only a share of the total cost of a one-off or short-term programme of investment and/or training activity aiming at improving the competitiveness and sustainability of the farming sector. 4. methodology 4.1 the econometric model based on theoretical studies, the methodology used assumes a profit-maximizing farm and analyses the effect of subsidies on farm profits. according to the literature assuming a profit-maximizing farm (floyd,1965; alston and james, 2002; de gorter and meilke, 1989;gardner, 1983; guyomard et al, 2004; salhofer, 1996; ciaian and swinnen, 2006, 2009: ciaian et al., 2015), the optimal farm profit (π) depends on input and output prices, subsidies and farm characteristics. in details, consider an agricultural economy with n farms. the output of each farm is a function of the amount of land (a) and non-land inputs (k), which captures also other capital inputs used by the farm. the production function is represented by f(a, k) with fi > 0, fii < 0, fij > 0, for i, j = a and k. furthermore, define s as the subsidy (area payment) per unit of land, and assume that all land in the analysis qualifies for the subsidies, the representative farm objective function is (ciaian and swinnen, 2009): π= pf(a,k)+sa-rawk(1+i) (1) 1 in italy, this type of payment was activated for several sectors: cereals, oilseeds and protein crops, tobacco, sheep and goat and so on. 2 under article 68 sectors supported under the quality measure for the period 2010-2014 are beef meat, sheep and goat meat, olive oil, milk, tobacco, sugar and floriculture. 167the income effect of cap subsidies where p is the price of the final product, s are subsidies for unit of land, r is the price of land, w is the unit price of other capital inputs, and i is the interest rate. more precisely, profit is affected by both the indirect (that is, through subsidy impact on input and output price) and the direct effects of subsidies on profits, as follows (ciaian et al., 2015): π= π[p(cdp,rdp,sps),r(cdp,rdp,sps),w(cdp,rdp,sps),cdp,rdp,sps,x] + ε (2) where, as concern subsidies, cdp are crop coupled subsidies, rdp are rural development payments, sps are decoupled payments. moreover, x is a vector of observable covariates and ε is the residual. it follows that the profit equation (2) accounts for both the direct and indirect effect of subsidies on farm profits. totally differentiating equation (2) yields the following relationship between profits and subsidies: dπ = dπ dp dp dcdp + dπ dr dr dcdp + dπ dw dw dcdp + dπ dcdp ⎡ ⎣ ⎢ ⎤ ⎦ ⎥dcdp + dπ dp dp drdp + dπ dr dr drdp + dπ dw dw drdp + dπ drdp ⎡ ⎣ ⎢ ⎤ ⎦ ⎥drdp ++   dπ dp dp dsps + dπ dr dr dsps + dπ dw dw dsps + dπ dsps ⎡ ⎣ ⎢ ⎤ ⎦ ⎥dsps+ dπ dx dx + ε dπ = dπ dp dp dcdp + dπ dr dr dcdp + dπ dw dw dcdp + dπ dcdp ⎡ ⎣ ⎢ ⎤ ⎦ ⎥dcdp + dπ dp dp drdp + dπ dr dr drdp + dπ dw dw drdp + dπ drdp ⎡ ⎣ ⎢ ⎤ ⎦ ⎥drdp ++   dπ dp dp dsps + dπ dr dr dsps + dπ dw dw dsps + dπ dsps ⎡ ⎣ ⎢ ⎤ ⎦ ⎥dsps+ dπ dx dx +ε dπ = dπ dp dp dcdp + dπ dr dr dcdp + dπ dw dw dcdp + dπ dcdp ⎡ ⎣ ⎢ ⎤ ⎦ ⎥dcdp + dπ dp dp drdp + dπ dr dr drdp + dπ dw dw drdp + dπ drdp ⎡ ⎣ ⎢ ⎤ ⎦ ⎥drdp ++   dπ dp dp dsps + dπ dr dr dsps + dπ dw dw dsps + dπ dsps ⎡ ⎣ ⎢ ⎤ ⎦ ⎥dsps+ dπ dx dx + ε (3) where dπ dp , dp ds ,   dπ dr , dr ds and dπ dw , dw ds are parameters representing the indirect impact of subsidies on profits (that is, through subsidy impact on input and output prices) and dπ ds is the direct effect of subsidies on profits for s= cdp, rdp, sps. equation (3) can be rewritten as: dπ=δ0+δcdpdcdp+ δrdpdrdp+δspsdsps + δxdx+ ε (4) where 𝛿𝛿" = dπ dx , 𝛿𝛿'() = dπ dp dp dcdp + dπ dr dr dcdp + dπ dw dw dcdp + dπ dcdp , 𝛿𝛿1() = dπ dp dp drdp + dπ dr dr drdp + dπ dw dw drdp + dπ drdp , 𝛿𝛿𝑆𝑆𝑆𝑆𝑆𝑆 = dπ dp dp dsps + dπ dr dr dsps + dπ dw dw dsps + dπ dsps 𝛿𝛿" = dπ dx , 𝛿𝛿'() = dπ dp dp dcdp + dπ dr dr dcdp + dπ dw dw dcdp + dπ dcdp , 𝛿𝛿1() = dπ dp dp drdp + dπ dr dr drdp + dπ dw dw drdp + dπ drdp , 𝛿𝛿𝑆𝑆𝑆𝑆𝑆𝑆 = dπ dp dp dsps + dπ dr dr dsps + dπ dw dw dsps + dπ dsps 𝛿𝛿" = dπ dx , 𝛿𝛿'() = dπ dp dp dcdp + dπ dr dr dcdp + dπ dw dw dcdp + dπ dcdp , 𝛿𝛿1() = dπ dp dp drdp + dπ dr dr drdp + dπ dw dw drdp + dπ drdp , 𝛿𝛿𝑆𝑆𝑆𝑆𝑆𝑆 = dπ dp dp dsps + dπ dr dr dsps + dπ dw dw dsps + dπ dsps parameters δs (for s= cdp, rdp, sps) measure the net impact of subsidies on farm profits by accounting for the above-mentioned both direct and indirect subsidy effects. in 168 stefano ciliberti, angelo frascarelli other words, they indicate the income effects of subsidies in terms of policy rents, which farmers receive for each additional euro of cap subsidies. even though the model contains the main variables determining the incidence of agricultural subsidies, there are also unobservable time-invariant farm characteristics which both affect dependent variable and are correlated with explanatory variables. in addition, there are also time-varying region fixed effects that cannot be ignored. therefore, in order to reduce possible sources of bias, farm fixed effects are included (ciaian and kancs, 2012). moreover, according to kirwan (2009), in order to absorb farm-specific time-invariant unobserved factors, the first difference of the series are applied, since the resulting farm income model in the first difference eliminates the unobserved heterogeneity component that remains fixed over time. as a result, the final econometric model is specified as follows: δπjt=δ0 + δcdpδcdpjt + δrdpδrdpjt + δspsδspsjt + δxδxjt + δrrr + δffj + εjt (4) where π is the profit of the farm j at the time t. however, an estimation issue is due to the fact that subsidies are not assigned to farmers randomly, but rather they are affected by regional productivities and farms’ crop choices (moro and sckokai, 2013). this fact implies that in the econometric model these variables (cdp, sps, rdp, os) are endogenous since they reflect the characteristics of countries’/regions’ land and farmer’s behaviour. first, in order to reduce the individual heterogeneity bias, farm fixed effects and regional control variables are included in the estimable equations, respectively δffj and δrrr.. in more details, the first differences of series are adopted in order to absorb farmspecific time-invariant unobserved factors, since they eliminate the unobserved heterogeneity component that remains fixed over time. lastly, in order to address the issue of endogeneity, the arellano and bond robust two-step generalized method of moment (gmm) estimator is applied and a set of valid and reliable instruments is adopted (see table 1). the gmm estimator is applied since it is particularly suitable for datasets with a large number of cross sections. lastly, the windmeijer (2005) bias-corrected robust variances is used in order to correct for the intrinsic downward bias of the robust two step gmm standard errors. 4.2 data and variables the source of data used in the empirical analysis is the italian fadn (farm accountancy data network) provided by the council for agricultural research and economics (crea). the fadn is the only source of micro-economic data that is harmonized and is representative of the commercial agricultural holdings in the whole eu (moro and sckokai, 2013). the survey does not, however, cover all the agricultural holdings in the eu, but only those which are of a size allowing them to rank as commercial holdings. based on previous study (ciaian and kancs, 2012; michalek et al., 2014; ciaian et al. 2015) in the present study a balanced panel dataset with 24’668 observations of n=3’524 italian farms over the period 2008-2014 (t=7) is adopted, meaning that farms in the sample are traced over the same period of time. moreover, the sample is stratified on three 169the income effect of cap subsidies key variables, i.e. location (21 nuts regions and 3 altimetric areas), economic size (6 size classes) and farm type (19 typologies). variables used in the econometric model are organized in order to effectively identify the relationship between net farm income3 and subsidies (table 1). descriptive statistics are provided in table a of the appendix. more in details, the dependent variable is calculated as the change in net farm income. based on the document “farm accounting data network: an a to z methodology” (european commission, 2010) the net farm income is obtained by subtracting taxes, variable expenses (intermediate, land, labour) and fixed costs (depreciation and interest payments) from the total farm revenues (output and subsidies). as concerns subsidies, they are sps, cdps (crop area payments, animal payments), the rdp (investment support, environmental payments, lfa and other rural development payments) and os (that accounts for other types of subsidies, such as those from the cmo). the above-mentioned variables are expressed per hectare. the advantage of using per hectare values instead of totals per farm is the reduction of the potential problem of heteroskedasticity. the farm size varies strongly in regions and sectors covered by this study, implying that the value of farm income, as with the other variables (output, subsidies and so on), also varies significantly in the cross-sectional dimension. in order to account for the dynamic adjustment of farm income, lagged dependent (1 lag) is created in order to incorporate feedback over time. moreover, since variables related to subsidies (cdp, sps, rdp and os) are endogenous, lags are used as instruments along the exogenous and lagged dependent variables. more in details, the choice of lags as instruments was selected by checking the validity of different sets of instruments. table 1 summarizes both lags and type of variables (exogenous, endogenous and instrumental). the covariates matrix (x) includes variables which contribute to explain the variation in profits among farms, respectively referred to two main categories: inputs and productivity and management practises. independent variables linked to the first set of covariates include rented land ratio (expressed as the ratio of rented land to uaa, rented_land), sharecropped land (expressed as the ratio of sharecropped land to uaa, share_land), own labour ratio (that is the ratio of unpaid input to total labour, own_labour), and liabilities-to-assets ratio (that is the ratio of total liabilities to total farm assets, liabilities_assets). given that productivity is an important determinant of farm profitability, if not controlling for its variation among farms, it may be confounded with the estimated subsidy effect on profits. therefore, productivity differences among farms are controlled including in the econometric model variables. they are: output per hectare (output), farm size (expressed in esu), irrigated land ratio (ratio of irrigated land to uaa), the building machinery value per hectare (machinery) and the ratio of total livestock output to total farm output (output_livestock), the total livestock units (lu) and the stock of agriculture products (product_stock). likewise, management practises affect the organization of farm activities and thus also have a direct impact on farm profitability. covariates capturing those practises are the 3 even though there could be income effects from the subsidies beyond the farm operating income, the household non-farm income is not accounted since it is well-known that one of the main cap goals is to enhance farm incomes. 170 stefano ciliberti, angelo frascarelli own consumption ratio, that indicate the ratio of farmhouse consumption and farm use to total output, as well as the ratio of woodland area to uaa. 5. results and discussion henceforth outcomes of the arellano-bond test for the italian fadn sample (based on period 2008-2014) are shown. first of all, it must be noted that the specification test allows not rejecting the null hypothesis of no serial autocorrelation in the first-differenced errors at order 1. it entails that the model has not misspecification problem. concerning the sargan test of over identifying restrictions, it shows that the instrumental variables are uncorrelated to some set of residuals and therefore they are acceptable instruments. moreveover, the windmeijer bias-corrected robust standard errors allow to both account for table 1. list of variables. category variable name lags type description unit of measure dependent variable(π) nfi 1 : net farm income €/ha (δ%) subsidies (s) cdp 0 and 1 endogenous coupled payments €/ha sps decoupled payments €/ha rdp rural development payments €/ha os other subsidies €/ha covariates (x) inputs rented_land 0 exogenous ratio of rented area to utilized agricultural area (uaa) % share_land ratio of sharecropped land to uaa % own_labour ratio of unpaid input to total labour % liabilities_assets ratio of total liabilities to total farm assets % covariates (x) productivity output 0 endogenous hectare value of total output of crops and crop products, livestock and livestock products and of other products €/ha size 0 instrumental economic size of holding expressed in european size units (esu) € irrigated_land ratio of irrigated land to uaa % machinery value of buildings and machinery €/ha output_livestock ratio of total livestock output to total farm output % lu total livestock units n. of head product_stock stock of agricultural products €/ha covariates (x) management practises own_ consumption exogenous ratio of farmhouse consumption and farm use to total output % wood_land ratio of woodland area to uaa % 171the income effect of cap subsidies heteroscedasticity and correct for autocorrelation as well. in order to facilitate the presentation and the relative discussion or results, table 2 reports different categories of variables used in the model (i.e., lagged dependent, subsidies and covariates). what emerges from the estimations is that the farm income at the time t-1 somehow negatively affects (-0.005) the farm income at the time t. even though this result may seem counterintuitive, it indeed recalls the well-known “cobweb theorem” (kaldor, 1934; ezekiel, 1938). this latter explains how price instability in a supply-demand framework – caused by low price elasticity of supply and demand – along with the assumption of a lagged response by production to price changes, can give rise to  irregular fluctuations in prices and quantities in agricultural markets. such a peculiar characteristic of the agricultural sector obviously causes unexpected and reverse relationships between prices, quantities and, as a consequence, incomes at various stages in time. the contribution of the model however concerns the effect of cap subsidies on farm income over the period under investigation. all the main variables related to public aids (that is, cdp, sps, rdp, os) are significant. sps represents the main source of public support for farmers and results highlight that it is highly able to sustain incomes. in this regard, it should be noted that the total income effect (e.g. including both contemporaneous and lagged effect) of the sps is 0.978, implying that a great part of such aid is transferred to farmers and only a small share go to the other actors of the supply chain (landowner, input suppliers and output buyers). in this regard, since the implementation of the sps scheme started in 2005, a flourishing literature has pointed out the potential distorsive effect due to the fact that non-farming landowners can extract a rent from such form of payment that is indeed “coupled” to the land (ciaian and kancs, 2012; ciaian and swinnen, 2006; kilian and salhofer, 2008; kirwan and roberts, 2015; klaiber et al., 2017; patton et al., 2008; viaggi et al., 2013). according to the literature in this field, the capitalization effect of sps into land rents varies from 0.2 to 0.8 (breustedt and habermann, 2011; kilian et al., 2012; o’neill and hanrahan, 2016; patton et al., 2008). these results imply that depending on specific characteristics of each mss on average about half of the direct aids are capitalized into land rents. in more details, as for new eu member states (eu-12), the rental price of farmland increseas between 0.18 and 0.20 eur for each unit of saps payment4 (ciaian and kancs, 2012). moreover, other studies confirms that also in the us a significant share of the direct aids are reflected in rental rates (varying from 20% to 100%, depending on the form of support). all in all, the model reveals a high transfer efficiency for sps in the observed period, meaning that the capitalization of the sps into the land rents in italy was scarce, according to guastella et al. (2013). furthermore, it must be noted that, since their introduction decoupled payments have not completely led farmers to be more market oriented (burfisher and hopkins, 2003; o’neill and hanrahan, 2016), and such an effect may have reduced the transfer efficiency, as confirmed by 4 it is a transitional, simplified income support scheme which was offered to the member states who joined the eu in 2004 and 2007 (eu-12) as an option at the date of accession in order to facilitate the implementation of direct payments. this scheme replaces (with some exceptions) all direct payments with a single area payment. the level of the payment is obtained by dividing the country’s annual financial envelope with its respective utilized agricultural area. it is simpler than the sps because there is no need to establish and administer payment entitlements. however it does not offer to farmers the flexibility of entitlements based on individual needs, such as sales or lease. 172 stefano ciliberti, angelo frascarelli empirical evidences of decoupled payments used so as to subsidise loss-marketing activities (breen et al., 2005; howley et al., 2012; kazukauskas et al., 2014). with regard to rdp measures, estimates point out a significantly positive influence of these aids on farm income. what emerges is that the total effect (lagged plus simultaneous effect) on farm income is 0.282 for each euro of rdp aids, even though these aids do not evidently aim to sustain farm income. indeed, it is well-known that their main objectives are, on the one hand, to foster various types of investments (in both physic and human capital) and, on the other hand, to cover opportunity costs related to the adoption of low income (but environmental-friendly) activities and techniques (e.g., organic farming and so on) mostly in disadvantaged areas. moreover, since both agri-environmental and less table 2. arellano-bond (first difference) dynamic gmm estimator: results (estimates based on period 2008-2014). variable coefficient (std. err.) p>z nfi (-1) -0.005 (0.000) 0.000 *** cdp -1.242 (0.376) 0.001 ** cdp (-1) 0.540 (0.195) 0.006 ** sps 0.117 (0.069) 0.089 * sps (-1) 0.861 (0.320) 0.007 ** rdp 0.067 (0.010) 0.000 *** rdp (-1) 0.215 (0.010) 0.000 *** os -0.197 (0.037) 0.000 *** os (-1) -0.061 (0.022) 0.007 ** rented_land 3.325 (3.633) 0.360 share_land 6.104 (3.379) 0.071 * own_labour 3.106 (2.709) 0.252 liabilities_assets -0.103 (0.091) 0.258 output 0.055 (0.001) 0.000 *** own_consumption -11.675 (5.424) 0.031 ** wood_land -1.870 (2.997) 0.533 observations 17620 number of farms observed 3524 n. of instruments 77 wald chi2 5257.30 0.000 *** arellano-bond for zero autocorrelation in first-differenced errors (h0: no autocorrelation) ar(1) (prob>z) -1.345 0.178 ar(2) (prob>z) -0.991 0.321 sargan test of over identifying restrictions h0: overidentifying restrictions are valid sr (prob>chi2) 57.295 0.610 *p<0.10; **p<0.05; ***p<0.001 173the income effect of cap subsidies favoured area payments are linked to the amount of land owned/rented, a possible explanations of such a result could be that a significant amount of these aids could be capitalized into rental values as well as in the input prices (seeds, fertilizers, machines and so on) as well as in the cost of services (transaction costs, assistance costs and so on). very interestingly, the other type of direct payment of the first pillar – the cdp – shows an immediate negative effect on farm income (-1.242), followed by a lagged but positive impact on profitability (+0.540). therefore, what emerges is that the total effect of cdp on farm income is still inegative (-0.702). here, many causes may potentially determine such an impact that could explain why this form of payment has been criticized since its introduction in the early ‘90s. scholars have indeed always recognized that such an aid is able to affect (and somehow to distort) product decisions, by incentivizing the cultivation of specific crops without taking into account the real needs of the demand, with negative consequence both on farm efficiency and total factor productivity (hennessy, 1998; mary 2013; zhu et al., 2012). such an impact on production decision may explain the negative impact on farm income in the short run, due to the fact that the cdp induces farmers to produce/feed not profitable crops/livestock. in addition to this opportunistic behaviour that such a payment generates on the supply side, inducing beneficiaries to “farm” the subsidies in spite of the crops, in the meanwhile the presence of an aid linked to specific productions induces input suppliers and buyers of agricultural commodities (i.e., wholesalers, middlemen, processors, manufacturers) to somehow intercept an amount of such a subsidy, by lowering the price of the commodities (alston and james, 2002; hendricks et al., 2012; oecd, 1996; rizov et al., 2013). it follows that the payment is (at least in part) taken away from farmers to the benefit of other actors along the agro-food supply chain (breen et al., 2005; ciliberti and frascarelli, 2015; mcdonald et al., 2014; o’neill and hanrahan, 2016; russo et al., 2009). moreover, patton et al. (2008) showed that different types of coupled payments are capitalized in land rents in northern ireland and such an effect absorbs half of the value of the aid. apart from the above-mentioned explanations, other causes of this outcome could be that cdp has for a long time subsidized low quality production and, more in general, has not represented an incentive for competitiveness at all (latruffe et al., 2009; zhu and oude lansink, 2010). it, on the contrary, has triggered speculative and opportunistic behaviours that actually hampered a market-oriented approach. lastly, also the os shows a total negative effect on farm income (-0.258). such a result could be attributed to the fact that, likewise cdp, this type of aids are also mainly aimed to subsidize specific products, therefore causing a similar impact on farm incomes. as concerns covariates, for each set of explanatory variables some estimated coefficients have the expected sign and are significant as well. with regard to the inputs, results confirms that sharecropped land positively contribute to increase the farm income (+6.104), since they represents a cheap alternative to land rent or land tenure. as concerns productivity, the model reveals the expected positive impact of the total output on farm income (+0.055). accordingly, with regard to management practises, the model confirms that self-consumption (-11.675) – especially in small or very small italian farms managed by so-called hobby-farmers – obviously causes a relevant and significant decrease of the farm income, due to the fact that the farm output is not sold but is used to satisfy family needs only. 174 stefano ciliberti, angelo frascarelli 6. conclusions even though farmers are the only beneficiaries of various forms of public support established by the cap, a vast literature has shown that some leakages however occur. such a phenomenon may be relevant when both input supplier and output buyers (that is, wholesalers, processors, manufacturers), thanks to their market and bargaining power, are able to extract some rents from the public aids. in this regard, the present paper aimed to shed lights on the transfer efficiency of cap subsidies in italy. to this purpose, data from the national fadn allowed analysing the distribution of public support among the several players of the italian agro-food system over the period 2008-2014, so as that a first contribution for this – to the best of the authors’ knowledge – still unexplored research field in italy is provided. the dynamic panel data estimation reveals that all the main typologies of aids established by the cap significantly affected the variation of the farm income in italy. more in details, the national implementation of the sps, cdp and rdp aids over the investigated period contributed to an increase of the profitability of italian farms, despite of a significant transfer of public resources from the primary sector to other stages of the agro-food supply chains or even external to the primary sector. such a phenomenon somehow confirms the presence of opportunistic behaviours of both input suppliers (e.g., landowners and input dealers that increase prices of land/products they rent/sell to farmers in order to indirectly take advantage of the sps and/or rdp scheme) as well as of buyers that – exploiting their purchasing power – contract lower prices for agricultural commodities. very interestingly, results show that the impact of cdp negatively affects income variation of italian farms, even though only in the short run. this type of subsidy, introduced in the early ‘90s as transitory means of support to replace price support, has been criticized for a long time due to the fact that it clearly influences production decisions and therefore alters market equilibrium. furthermore, both the existence and the amounts of such a payment, by definition “coupled” to specific crops/livestock, is also well known by several suppliers and buyers that therefore try opportunistically to take advantage from it. as a result, the cdp may simply become a sort of surreptitious transfer of public resources to none other than agro-food industry companies (i.e., suppliers, landowners, processors, manufacturers). moreover, it may also distort (and reduce) the incentive for quality with immediate negative consequences on output prices and, in the long term, on farms ability to be competitive in both national and international markets. to sum up, these empirical evidences have important policy implications for the implementation of the cap in italy. first of all, results allow confirming that even though farm income substantially benefitted of the implementation of the cap, the transfer efficiency of public financial resources officially intended for farmers was hindered by leakages that are occurred in italy over the period 2008-2014. more in details, what emerges is that decoupled income transfers without mandatory production (sps), as well as incentives for investments and compensatory payments (rdp) are preferable to coupled measures (cdp) for ensuring an annual and continuous support to farmers income. in conclusion, it is straightforward that a different allocation of cap resources in italy may bring more advantages for farmers, decreasing leakages and increasing transfer effi175the income effect of cap subsidies ciency. in this regard, an indication for the future is that both the reform paths towards a more targeted and tailored support for farmers and, on the other hand, national implementation of the cap rules should aim to properly address the causes of such leakages in order to improve the transfer efficiency of public aids, due to the fact that enhancing farm incomes still remains one of the main priority of the cap. 7. acknowledgments the authors acknowledge the council for agricultural research and analysis of agricultural economics (crea) for providing access to the italian fadn. 8. references agrosynergie 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(2010). impact of cap subsidies on technical efficiency of crop farms in germany, the netherlands and sweden. journal of agricultural economics 61: 545-564. appendix table a. descriptive statistics of variables. variable obs mean sd min max unit of measures nfi 24,668 182.5 20,229.4 -515,950.0 2,637,476.0 (δ%) €/ha cdp 24,668 59.2 341.8 0.0 17,934.4 €/ha sps 24,668 297.6 956.8 0.0 105,050.0 €/ha rdp 24,668 115.3 967.0 0.0 87,273.3 €/ha os 24,668 49.5 638.4 0.0 47,050.5 €/ha rented_land 24,668 34.2 39.5 0.0 100.0 % share_land 24,668 7.8 21.8 0.0 100.0 % own_labour 24,668 87.4 22.7 0.0 100.0 % liabilities_assets 24,668 4.2 72.2 0.0 6,252.2 % output 24,668 13,006.4 48,295.0 -20,524.0 1,724,127.0 €/ha size 24,668 117.3 986.5 0.0 98,807.9 € irrigated_land 24,668 39.9 43.6 0.0 100.0 % machinery 24,668 6,181.9 19,526.8 0.0 512,428.6 €/ha output_livestock 24,668 21.8 35.4 0.0 100.0 % lu 24,668 282.2 4,850.7 0.0 261,093.0 n. of head product_stock 24,668 16,443.9 341,083.5 0.0 39,800,000.0 €/ha own_consumption 24,668 1.1 3.4 0.0 100.0 % wood_land 24,668 3.8 12.9 0.0 100.0 % bio-based and applied economics 7(2): 139-160, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-7672 the impacts to food consumers of a transatlantic trade and investment partnership# yaghoob jafari1, wolfgang britz1, jayson beckman2,* 1 institute for food and resource economics, university of bonn, germany  2 economic research service, usda, usa date of submission: 2018 2nd, july; accepted 2019, 29th, january abstract. primary agriculture is a textbook example of competitive supply with many producers outputting homogenous products, in contrast to firms in the processed food sector produce heterogeneous products while differing in productivity. our model of trade reform explicitly accounts for the differences between the markets for primary agriculture and food processing. to demonstrate this point, we use a computable general equilibrium (cge) model to quantify potential impacts of a trade agreement between the eu and us. crucially, our heterogeneity-firm setup allows for the allocation of ntms as ‘fixed costs’, which provides an alternative angle to previous literature that only considered ntm costs in a more conventional framework (e.g., tariff equivalent). further, the use of this framework allows us to provide detailed welfare impacts, providing more information on the impacts to consumers who purchase mainly processed food and little primary agricultural output, a point often unrepresented in previous analysis of ntm reform. keywords. trade policy, imperfect competition, heterogeneous firms, simulations. jel codes. f12, f14, f47. 1. introduction primary agriculture is a textbook example of competitive supply with many producers outputting homogenous products while firms in the processed food sector produce heterogeneous products. thus, there are important differences in productivity, size, and exporting behavior among these firms that should be reflected in quantitative analysis. while manufacturing has typically received the firm heterogeneity treatment, the potential transatlantic trade and investment partnership (ttip)1 provided an instance of using *corresponding author: yaghoob.jafari@ilr.uni-bonn.de # the findings and conclusions in this preliminary publication have not been formally disseminated by the u.s. department of agriculture and should not be construed to represent any agency determination or policy. this research was supported in part by the intramural research program of the u.s. department of agriculture, economic research service. 1 we note that the chances for implementation in the near future is slim; however, negotiations have not officially ended (unlike the trans-pacific partnership). the united states trade representative still maintains a webpage devoted to the potential agreement. https://ustr.gov/ttip 140 yaghoob jafari, wolfgang britz, jayson beckman an agri-food sector (i.e., food processing) that exhibits firm heterogeneity characteristics. luckstead and devadoss (2016) analyze the potential impact of ttip assuming heterogeneity of firm involved in food processing. however, that study uses a single sector model that neglects feedback with other sectors, including agriculture as a major upstream link of the processed food sector. the potential ttip agreement generated a large amount of research, which may not be surprising given that the agreement would have linked the world’s two largest economies. although the above mentioned paper incorporated a firm heterogeneity setup in their analysis of ttip, most research still considers firms as homogenous (e.g., arita et al. 2014, 2017; beckman et. 2015; beckman and arita, 2017; berden et al. 2009; disdier et al. 2015; egger et al. 2015; fontagne et al. 2013; welfens and irawan 2014; beghin et al., 2016). despite their traditional model approaches, these papers make two points relevant to trade policy analysis. first, tariffs are usually low in developed countries (relative to developing countries), especially for manufacturing and services. however, in developed countries, agriculture is usually more protected relative to other sectors, with higher tariffs, tariff-rate quotas, and non-tariff measures (ntms).2 second, for developed countries, ntms are becoming more of a trade barrier tariffs, with almost all of the papers that compare the two concluding that ntm removal (even partial removal) could generate larger trade gains than those from tariff removal. the ntm topic is also relevant for firm heterogeneity, since under the standard armington assumption, ntms are typically treated simply as ad-valorem equivalents (aves). this differs from the heterogeneous firm layout e.g. used in akgul et al. (2016) or luckstead and devadoss (2016), that treats them (partly) as fixed costs of trade. our work here builds on the previous ttip analysis, by starting with the view that reforming agri-food trade might generate the largest relative trade gains. we focus on the processed food sector as it accounts for the largest share of bilateral trade in agri-food between us-eu, and because it can be characterized as exhibiting all the signs of a sector with heterogeneous firms.3 berden et al. (2009) note that one percent of processed food firms account for 52 percent of total sales. these large firms regularly modify and improve the characteristics of their products to meet the requirements and changing preferences of different consumer groups and to differentiate themselves from competitors.4 following most ttip analysis, we employ a cge model that encompasses all sectors and their interactions, but integrate a firm heterogeneity approach for processed food and all other types of manufacturing. this firm heterogeneity specification allows us to provide more information on impacts to consumers with evidence on the impacts to consumer welfare from a change in the number of new varieties entering the processed food sector, information that is not available in a standard perfect competition setup. our model also details welfare impacts in general, providing information on an aspect of ttip so far largely ignored. 2 one could examine the average most favoured nation (mfn) tariffs reported to the world trade organization to confirm this point. the eu trade-weighted mfn rate for agriculture is 8.7% compared to 2.8% for non-agriculture. the u.s. rates are 2.3% and 4.0%. the largest gap is likely for japan: 1.4% and 12.9%. 3 particularly relevant to ttip, the eu and us account jointly for one third of global trade in processed food (un comtrade, 2015); and they trade (bilaterally) more processed food products than any other partners globally (fas/usda, 2014; olper et al., 2014). 4 in addition, high product differentiation and considerable differences in firms’ size and productivity has led some (e.g., neff et al., 1996; francois et al., 2013) to label the processed food sectors as monopolistic competition, which is often used to characterize firms with heterogeneity. 141the impacts to food consumers of a transatlantic trade 2. modeling framework global cge models are generally considered well suited for ex-ante appraisal of trade agreements as they consider bilateral trade and trade barriers in a consistent microeconomic behavioral framework and account for interlinkages between sectors. here we use the flexible and modular cge model by britz and van der mensbrugghe (2017) extended by the heterogeneous firm module of jafari and britz (2018a) (see the online appendix for its detailed documentation5). next, we discuss briefly the general structure of the model. 2.1 perfect competitive sectors sectors with perfect competition are depicted as in the standard gtap model (hertel, 1997), with cost-minimizing behavior under constant returns to scale (crs) production technologies along with utility maximizing consumers in competitive markets. relevant to this work, the perfect competition sectors use an armington trade setup where a constant elasticity of substitution (ces) function, specific for each agent, i.e. final consumers, government, savings and the different production sector, drives competition between domestically produced products and imports. a second ces nest, which is not agent specific, depicts the import demand composition from bilateral trade flows. hence, the armington setup considers commodities produced in the same region as homogenous, but different from commodities stemming from other regions. for example, all dairy products from the eu are assumed to be of the same quality and fetching the same price. on the supply side, production is defined as the leontief aggregate of value added and intermediate inputs bundles; the value added composition is based on a ces aggregate of primary factors while the composition of intermediate demand is based on fixed physical input coefficients. 2.2 heterogeneous sectors in each sector there is a continuum of firms that are heterogeneous with regard to productivity, and each firm produces its own distinct variety. while firms are free to enter or exit the market, entrance requires covering fixed costs. firms learn about their productivity level once they enter the market, and then choose to stay or exit. firms with too low of a productivity level will not be able to cover their fixed cost, and therefore exit the industry. for those that survive, only the most productive ones are involved in exports since they can cover the fixed costs of exporting, while less productive firms only serve the domestic market. in this framework, the number of firms operating on the domestic market and on the bilateral trade links depends on the characteristics of the domestic market and bilateral trade costs. since each firm produces a single distinct variety, the total number of varieties available in any given country depends on the number of firms operating in the domestic market and the number of firms exporting to that country. accordingly, the total number of varieties available to consumers in a given country is determined endogenously. in this 5 jafari and britz (2018) published that online appendix as part of their paper, provided here again for ease. 142 yaghoob jafari, wolfgang britz, jayson beckman context, any policy shock that leads to changes in variable or fixed costs can change the fraction of firms operating on domestic and on trade links, and therefore the number of varieties available to consumers. on the demand side, the composite demand of each agent for each commodity is defined as the dixit-stiglitz composite of demand for average firm level varieties around the world6. that index can be interpreted as a standard ces aggregator where the import quantity impact is additionally multiplied by the change in the number of operating firms providing a love of variety effect. each heterogeneous firm produces one single unique variety and therefore, the number of varieties produced in a regional industry is equal to the number of operating firms. the production structure is shown in figure 1, where total cost is the sum of variable and fixed costs per firm, the latter consists of fixed costs to enter the enter the industry and fixed cost on each trade link. the variable cost nest uses both primary factors and intermediates based on a constant return to scale technology, while fixed cost only relate to primary factors. however, if the overall total cost share of value-added in a sector is small, the fixed cost nest also comprises a share of intermediate composite. this alternative is identified by the intermediate composite in brackets. the value added and intermediate bundles are ces composites of primary factors of production and intermediate inputs, respectively. the total value added (not shown here) is the sum of value added used in both variable and fixed cost nesting. similarly, the total uses of intermediate commodities and primary factors (not shown here) are the sum of their use in fixed and variable cost nesting. 6 the heterogeneous firm model defines the so-called “average firm” depicting the average productivity of all firms operating on a specific trade link. figure 1. production structure in melitz sectors. variable cost commodity 1 … commodity n intermediate inputs value added factor1 … factor k fixed set-up cost commodity 1 … commodity n [intermediate inputs] value added factor1 … factor k fixed trading cost commodity 1 … commodity n [intermediate inputs] value added factor1 … factor k total fixed cost total cost source: authors’ illustration. n 143the impacts to food consumers of a transatlantic trade consistent with the monopolistic competition assumption, each firm applies a markup pricing rule, i.e. it collects rent stemming from producing a specific variety, which covers its fixed costs. marginal production costs are corrected for the average productivity effect of firms operating on each bilateral trade link. the average productivity of firms on each trade link is determined from a pareto distribution function which encompasses a so-called cut-off productivity level. only firms with productivity equal to or higher than that specific threshold level for each bilateral trade link will operate on that link, while the remaining firms are forced to exit. the number of operating firms on a link is derived from a zero profit condition where the revenue of the average firm must be equal to its bilateral fixed cost. however, ensuring zero profit for operating firms on each trade link does not ensure zero profits for the industry as a whole, due to the sunk costs associated with the entry of new firms in the industry. therefore, zero profit at the industry level is assured by a free entry condition in the industry, indicating that the expected profit for firms over their lifetime must be equal to the overall industry fixed set up costs. trade liberalization filters through this type of model differently than in the standard armington setup, beginning with the reallocation of resources between firms. for example, a policy that decreases bilateral export cost will encourage some firms that initially did not export (those with low productivity) to start trading. this leads both to an increase in the number of exporters and a decrease in the average productivity of exporters (since those firms that just entered the export market were less productive to begin with). due to fixed cost per firm, an increase in the number of exporters implies that the industry as a whole uses more resources. this increases input prices in the domestic market, leading to some lower productive firms to exit the domestic market. as a result, the average productivity in the domestic market increases. since some of the least productive firms exit the industry, the productivity for the industry increases and generates a welfare gain (as those firms that now enter the export market are relatively more productive than those leaving the domestic market). on the importing side, similar adjustments in industry structure take place while consumer benefits from more varieties being present on the import side. 2.3 model parameterization and calibration a major advantage of this firm heterogeneity model is that it does not require as much information on industries and consumers as the original melitz (2003) model. indeed, only two parameters are needed for each sector: one that describes the productivity distribution of the industry (based on a pareto distribution) and another that is the elasticity of substitution among domestic and imported varieties. we use the estimate of 3.8 from bernard et al. (2003) for the elasticity of substitution, and an estimate of 4.6 for the pareto shape parameter from balistreri et al. (2011). 2.4 sectoral and regional aggregation table a1 in the appendix provides details on how we treat the sectors in our application. we generally keep the full sectoral detail of gtap sectors to prevent bias (britz et al., 144 yaghoob jafari, wolfgang britz, jayson beckman 2016) but aggregated the processed food sectors in the gtap data base for two reasons. first, while there is in consensus in the literature that the processed food sector in general should be treated with heterogenous firms, there is no information on if some sectors (e.g., meat or dairy production) should be excluded and treated as homogenous instead. second, as discussed below in more detail, data on the potential ntm reduction between the eu and the us suitable for our analysis is available only at the aggregated level. to capture the impact of a proposed ttip agreement on third countries, we aggregate the gtap data base to 10 regions (european union, united states, canada, mercosur, china, asean 10, mediterranean countries, other northern europe7, low-income countries, other oecd and rest of world). our mapping of regions to the low-income countries aggregate follows the current world bank classification. 3. quantifying the policy experiment the model is calibrated based on version 9 of the global trade analysis project (gtap) database (aguiar et al., 2016), which provides a snapshot of world economy in 2011. figure 2 reports bilateral ad-valorem trade weighted tariffs from the database. it reveals that processed food, beverage and tobacco products, and textile and clothing are the sectors subjected to the highest tariffs. in most cases, the applied rate of the eu is lower than that for the us. ntms are not explicit in the data base and need to be incorporated before they can be subjected to policy experiments. the aves of ntms that are potentially removable if a deep trade agreement is reached are taken from egger et al. (2015), the estimated ave for processed food is 33.83%.8 it is not based on the latest negotiations status of ttip, but rather more generally reflects the expected change if the two trade partners move to a deep fta agreement given the empirical evidence from past ftas. we analyze two scenarios (see table 1): the first scenario considers completely removing import tariffs for all commodities between the eu and the us, while the second one adds ntm reform. removing existing tariffs is straightforward as they are part of the data base, whereas the second scenario requires allocating the ntm costs estimated by egger et al. (2015). cge models treat the trade cost effects of ntms as either rent-generating or cost creating. modeling the rent-generating effect is straightforward using either an “export tax equivalent” – changing export taxes or a “tariff equivalent” approach—changing import taxes, depending on where the rent are assumed to occur. changes in the cost generating basis of ntms are modeled by changing the variable portion of trade costs (since there are no fixed costs in an armington model). however, ntm costs often reflect a ‘fixed cost’ component. for example, the us is able to export beef to the eu, but that beef is produced differently than how most beef sold domestically in the us is produced. to be able to export beef to the eu, us producers must have separate facilities or incur other fixed cost type of costs. as our firm heterogeneity structure is able to account for 7 other northern europe include switzerland, norway and rest of european free trade association (efta) 8 it should be noted that one would expect the ntms between the us and the eu to be region specific (i.e., asymmetric). however, egger et al. (2015) estimated the trade cost equivalents of a deep trade agreement between two regions. therefore, these estimates should not be interpreted as the current level of ntms but rather as the trade costs that two regions could reduce due to ntm removal when moving to a deep fta. 145the impacts to food consumers of a transatlantic trade fixed cost, we explicitly change variable and fixed costs for eu-us trade links drawing on jafari and britz (2018a). one should note that ntms could also have demand side effects when regulations affect consumer behavior, typically captured by changing either the consumer willingness to pay (as in walmsley and minor, 2016) or armington elasticities. although ttip might provoke such demand side shifting effects, we leave them out due to missing empirical evidence. breden et al. (2009) suggested 60% of ntms in eu-us are cost generating and 40% are rent generating. the later is then allocated by 2/3 to import duties and 1/3 to export taxes following francoise et al. (2013) and egger et al. (2015), (see table 1). the cost portion of the ntm is allocated to variable and bilateral fixed costs in equal shares following jafari and britz (2018a). 4. scenario analysis while we presume that costs related to ntms are already observed in the global sam, rents related to ntms probably hide in capital income flows and are clearly so far not allocated bi-laterally. we therefore first run a simulation to include the rent generating effects associated with ntms currently in place between the us and the eu by introducing respectively increasing bi-lateral import and/or export taxes. that augmented database serves as the benchmark. in the following, we discuss the simulated impacts of both scenarios on trade, production, and welfare. then, we turn to the specific outcomes for the food processing sector with a focus on the information given by the firm heterogeneity model. figure 2. applied mfn tariff on transatlantic trade. 2 3 2 0 16 6 7 6 2 1 3 2 0 5 1 8 1 1 0 2 4 6 8 10 12 14 16 18 all goods grains and crops livestock and meat products mining and extraction processed food beverages and tobacco products textiles and clothing light manufacturing heavy manufacturing eu and us applied tariffs on goods, percent rate us tariffs eu tariffs source: data extracted from version 9 of the global trade analysis project (gtap) database (aguiar et al., 2016). 146 yaghoob jafari, wolfgang britz, jayson beckman 4.1 effects on trade flows table 2 shows simulated changes in the volume of aggregate exports. removing import tariffs (scenario 1) increases eu exports to the us increase by 4.78%, while us exports to the eu increase by 6.8%.9 adding ntm reform on top of tariff removal boosts bilateral trade further, by 9.7% from the eu to the us, and by 8.5% from the us to the eu. however, with increases of 0.2% (tariff removal only) and 0.5% (ntm reform included), respectively, the changes in global eu exports are minor; while total us exports expand more significantly (by 1.4% and 2.6%). these findings are comparable with francois et al. (2013). some regions including china, asean 10, “low-income countries”, and “other northern europe” have marginally increases in their exports to either the eu or us, depending on the scenario. in the first scenario, canada has a decrease in their bilateral exports to both regions, but in the second scenario, canada has an increase in their exports to the eu. in summary of these changes in regional trade flows, overall world trade increases marginally by 0.3% and 0.4%, respectively. table 3 focuses on export flows for the processed food sector. the higher tariff protection in that sector leads to larger changes compared to the results reported above: eu exports to the us of processed food increase by 39% while us exports to the eu increase by 121% (for tariff removal). these findings are consistent with the partial-equilibrium model results of luckstead and devadoss (2016), but the magnitude of the impacts found here is different due to the use of different elasticities and feedback effects in our cge modelling. as the ave estimates of the expected changes in existing ntms between the 9 bilateral changes are not presented here, but are available upon request from the authors. table 1. scenario layout. tariffs shocks aves shocks total aves reduction divided into the last three columns import tax export tax bilateral fixed and variable trade cost (1) (2) (2)*0.4*2/3 (2)*0.4*1/3 (2)*0.6 scenario 1 -100% reduction for all economic sectors scenario 2 -100% reduction for all economic sectors -33.83% -9.0% -4.50% -20.3% modeled as reduction in bilateral import tariff reduction in import tariffs representing rents in importer country reduction in export taxes representing rents in exporter country converted to an equivalent reduction in bilateral fixed and variable trade cost source: authors. 147the impacts to food consumers of a transatlantic trade eu and us are quite high and exceed existing tariff levels, bilateral trade volumes increase considerably for processed food in the second scenario. eu exports to the us almost quadruple, while us exports to the eu multiply by more than seven. this leads to changes in total exports of processed food for the eu by almost 9% and by 63% for the us. the trade diversion effects of that second scenario in the processed food sector is accordingly sizeable: most eu trading partners lose about 4% of their exports while exports to the us from the non-eu countries decreases by around 10%. trade impacts for primary agriculture are minor (see table a2 and a3) which reflects low tariffs (see figure 2) and low exports values. eu exports of primary agricultural products to the us amount to about 83 million, vice versa it is 6 million. our analysis also shows that the impact on average manufacturing trade between two regions is small (see table 4.4) due to low tariffs between the regions. 4.2 effects on domestic output quantities table 4 presents information on production changes across all sectors. for processed food, the eu faces a decrease in both scenarios, while the us increases its production. however, the increase in us production is small, as the 63% increase in exports is mostly offset by an increase in imports (46 %) (see table 3). opposite and stronger effects are simulated for beverages and tobacco, with a 5% increase in eu production and a 16% table 2. change in aggregate exports by region [% change]. regions scenario 1 scenario 2 eu us total eu us total world 0.2 1.1 0.3 0.4 1.8 0.4 eu -0.21 4.8 0.2 -0.3 9.7 0.5 other northern europe 0.1 0.2 0.2 0.0 -0.4 -0.1 us 6.8 1.4 8.5 2.6 canada -0.1 -0.3 -0.2 1.3 -0.1 0.2 mercosur -0.2 0.1 0.0 -0.1 -0.5 -0.1 china 0.0 0.3 0.2 0.5 -0.5 0.1 asean 10 -0.4 0.5 0.7 0.2 -0.7 0.2 other oecd -0.2 0.0 0.0 0.5 -0.6 0.0 eu mediterranean partners -0.3 0.3 -0.1 0.1 -0.5 0.0 low income -0.3 0.3 -0.1 0.1 -0.1 0.0 rest of world -0.2 0.1 0.0 0.1 -0.5 0.0 notes: exporters in rows, importers in columns. source: model results. 1 the reader should note that the numbers presented in the column “eu” showing eu to eu exports is due to an aggregation effect. sales to the domestic market of a nation are not reported as exports in the sam. however, if we aggregate individual eu countries, the former bi-lateral trade links between two eu nations occur now inside one aggregate and become the diagonal trade flow in this column. the domestic sales of the eu aggregate are defined from adding up the domestic sales of individual eu countries. 148 yaghoob jafari, wolfgang britz, jayson beckman decrease in the us. this happens for two reasons: 1) the eu has larger base exports of beverages and tobacco relative to the us; 2) the us has relatively higher tariffs on beverages and tobacco compared to processed food. other sectors of the economy show only marginal changes. an exception is the output of “textiles and clothing”, which has a 2.5% increase in the eu in the first scenario. this gain disappears in the second scenario as resources flow to beverages and tobacco in order to meet the large increase in production. overall, the domestic output of processed food sectors in the eu is simulated to increase by 1.4% in the second scenario, while us output drops by 2.9%. this result is different from that found in other ttip studies. those studies generally conclude that the us has large production gains at the expense of the eu. 4.3 effects on welfare welfare impacts are measured based on the equivalent variation (ev) criterion, i.e., the amount of money to be added to the regional household’s benchmark income at benchmark prices to reach the same utility as under simulated income and prices. there are global welfare gains of 5.6 billion usd when tariffs are removed (see table 5), of which 2.8 billion usd accrue to the eu and 5 billion usd to the us (the results are comparable with francois et al., 2013); the remaining countries, with the exception of china, have losses below 1 billion usd. both changes in the intensive and extensive margin of trade are important in determining the welfare changes in other countries: following a reduction in trade barriers between the us and eu, the intensive margin of trade between the two regions increases, diverting trade with other countries and causing welfare to decrease. however, a reduction in trade barriers between the eu and us helps increase the average productivity of firms operating on the domestic market and/or operating on table 3. export volumes by region for “processed food” [% change]. regions scenario 1 scenario 2 eu us total eu us total world 1.5 5.3 1.1 7.2 46.2 7.4 eu 0.2 39.4 1.2 -1.7 394.2 9.3 other northern europe -0.7 -0.1 -0.4 -5.4 -10.8 -4.4 us 120.9 9.4 748.5 63.4 canada -1.0 -0.4 -0.4 -5.7 -11.3 -8.6 mercosur -0.7 0.0 -0.2 -3.9 -9.1 -1.6 china -0.7 0.0 -0.1 -3.9 -9.3 -2.4 asean 10 -1.0 -0.3 -0.3 -3.7 -8.9 -1.7 other oecd -0.7 -0.1 -0.1 -3.8 -9.3 -2.7 eu mediterranean partners -0.6 0.0 -0.3 -4.5 -9.7 -2.5 low income -0.6 0.0 -0.3 -4.5 -9.5 -2.5 rest of world -0.7 0.0 -0.2 -4.1 -9.4 -2.1 source: model results. 149the impacts to food consumers of a transatlantic trade trade links other than eu-us trade link. this results in an increase in the intensive margin of trade (i.e., increase in varieties) in other countries, which is welfare increasing. the total welfare impact on third countries is therefore determined based on the total volume of trade, i.e., the sum of changes in intensive and extensive margins of trade. all regions are better off compared to the first scenario if ntms are also reduced, several regions besides the eu and us now experience welfare increases, which results in a global welfare gain of 22.4 billion usd. the removal of ntms increases average domestic productivity, simulating the extensive trade margin, and improving welfare compared to the first scenario. still, welfare losses occur in canada, mercosur, asean 10, and other oecd countries. the eu has the largest additional welfare gains, increasing from 2.8 billion usd to 13.8 billion usd under the second scenario. the us has an additional 5 billion usd added in the second scenario to reach a total of 7.8 billion usd. the welfare improvements in the second scenario match findings by balistreri et al. (2011) who reports that ntm reduction in the melitz (2003) framework increases welfare considerably. further, our welfare decomposition analysis reveals that the largest portion of welfare gains are associated with the scale effect (associated with the increase in returns to scale), the productivity effect (expansion in market shares of efficient firms), and variety effects (i.e., increases in the number of varieties face by consumers). while term of trade and allocative efficiency contribution is small, the fixed cost effect (due to the increase in firms fixed cost payments) reduces welfare (table 6). 4.4 firm-level impact of policy shocks in processing food sectors table 7 shows the change to the average firm (as shown in rows) associated with the production and sale of processed food in the eu for different bilateral trade markets. the table 4. industrial output by sector [% change]. sectors eu us scenario 1 scenario 2 scenario 1 scenario 2 total 0.00 -0.01 0.02 0.00 processed food -0.11 -0.05 0.30 0.46 beverages and tobacco 0.13 5.67 -0.23 -16.00 grains and crops -0.13 0.29 0.23 -0.24 livestock -0.08 0.03 0.22 0.23 mining and extraction -0.01 -0.05 -0.05 0.07 textiles and clothing 2.52 -0.01 0.28 1.11 light manufacturing -0.09 -0.24 0.51 0.15 heavy manufacturing -0.13 -0.40 -0.25 0.30 utilities and construction 0.00 0.00 0.05 -0.01 transport and communication 0.02 0.07 0.01 0.02 other services -0.01 0.01 -0.01 0.02 source: model results. 150 yaghoob jafari, wolfgang britz, jayson beckman first column refers to the domestic market, the second column denotes intra-eu trade, the third and fourth columns show eu trade with the us and other regions not included in the transatlantic trade block (hereafter referred as nonttip). the last column relates to overall industry performance. table 5. changes in welfare [billion usd]. regions scenario 1 scenario 2 world 5.6 22.4 eu 2.8 13.8 other northern europe -0.2 0.1 us 5.0 7.8 canada -0.1 -0.1 mercosur -0.1 -0.1 china 0.0 0.6 asean 10 -0.2 -0.1 other oecd -0.7 -0.2 eu mediterranean partners -0.2 0.2 low income -0.1 0.1 rest of world -0.7 0.3 source: model results. table 6. welfare decomposition analysis. scenario 1 scenario 2 eu 2.8 13.8 allocative efficiency 0.0 0.5 term of trade effect 0.1 1.1 variety effect 1.3 5.2 scale effect 1.9 9.7 productivity effects 0.9 4.2 fixed cost effects -1.3 -6.9 other effects -0.1 0.0 us 5.6 22.4 allocative efficiency 0.3 0.9 term of trade effect -0.1 1.7 variety effect 1.8 4.7 scale effect 3.6 11.7 productivity effects 2.6 8.5 fixed cost effects -2.3 -5 other effects -0.2 -0.1 source: authors’ calculations based on model results. 151the impacts to food consumers of a transatlantic trade table 7. average firm results for eu domestic sales and exports of processed food [% change]. scenario 1 scenario 2 domestic sales eu us nonttip total sale domestic sales eu us nonttip total sale firm price -0.2 -0.1 9.5 0.0 0.8 -0.9 -0.9 17.6 0.0 1.3 number of operating firms -0.6 0.1 52.7 0.0 4.3 -2.9 -2.7 666.5 0.9 55.8 avg. output per firm 0.2 0.1 -8.7 0.1 0.1 1.0 1.0 -35.5 0.1 0.8 avg. productivity per firm 0.1 0.0 -8.8 0.0 -0.9 0.7 0.7 -35.7 0.0 -2.9 industry fix costs 0.0 0.0 0.0 0.0 0.0 0.1 0.1 -24.2 0.0 -0.1 fix costs per unit 0.3 -0.2 -28.3 0.0 -2.1 2.0 1.9 -84.7 0.0 -7.3 industry variable costs -0.6 0.1 52.7 0.0 -0.2 -3.0 -2.6 481.2 1.2 -0.4 variable costs per unit -0.3 -0.1 9.5 0.0 0.8 -1.1 -0.9 17.6 0.0 1.2 total output sold -0.4 0.2 39.4 0.1 0.0 -1.9 -1.7 394.2 1.3 0.7 source: based on model results. the changes in the eu-us trade link for the tariff removal scenario shows a typical reaction of the firm heterogeneity model: tariff removal reduces the average import price in the us, allowing less productive eu firms to operate on that trade link. this increases the number of firms and varieties exported to the us (52%), providing benefits to us consumers. per unit fix costs drops by 28%; however, lower average productivity increases the variable costs per unit by about 9.5%. there is an increase in total output sold to the us of 39%, but increasing the number of operating firms decreases the average productivity of the firms operating on that trade link (-8.8%).10 thus, the average size of these firms also drops – average output per firm decreases by about the same percentage. the average firm exporting to the us after these changes is less productive and smaller. together, these changes constitute a new equilibrium with zero profits for the firms operating on that trade link, while monopolistic prices charged are equal to the willingness to pay for the specific quality delivered on that trade link given the number of varieties available. the impacts of the second scenario on eu-us bilateral trade are more pronounced: besides tariff removal, we also shock variable and fix costs related to ntms for eu-us bilateral trade. this amplifies the effect compared to the first scenario, as now all firms face a higher willingness to pay in bilateral trade, and experience cost savings before supply and demand adjust. this allows far less efficient firms to operate in bilateral trade: the number of the eu firms exporting to the us increases by 666%11 while average productivity (35%) and firm size (-35%) on the trade link drop. average per unit variable costs increase by 18%, which translates into changes in the average firm price, while total output for eu-us bilateral trade almost quadruples. the fix cost of the industry operating on 10 note that in table 7 and subsequent tables, even though the number of operating firms increases, the total output change is small because each firms now produces less output. this is equivalent to saying that large increases in the extensive margin are compensated by a reduction in the intensive margin of trade. 11 only a small share of firms operate on the link before trade liberalization which are the firms with the highest productivity. given the shape of the productivity distribution, a significant decrease in bilateral trade cost leads to a nover-proportional increase in that share and increase at the same also the number of traded varieties on the link. 152 yaghoob jafari, wolfgang britz, jayson beckman that link decreases by 24%, reflecting our assumption of reduced trade costs (see table 1). however, the original reduction is partly offset by the loss in average productivity, and at the same time distributed to a much higher output quantity. the combined impact on per unit fix costs on that link drops by around -85%. the finding is in line with the literature emphasizing the importance of the extensive margin of trade (e.g., hummel and klenow, 2005; chaney, 2008; among others). no significant changes occur on the eu-nonttip link, such that overall changes in trade reflect only the discussed eu-us bilateral changes. the expansion in exports combined with an on average less productive firm that trades, increases the overall input demand in the economy. this in turn bids up factor and other intermediate prices. as a first order impact, production costs increase and profits on other trade links decline, which induces some of the less productive firms to exit the eu domestic market. the number of operating firms in the domestic market decreases by 0.6% and 2.9% in the first and second scenario. as firms with lower productivity exit, factors are reallocated towards higher-productive and larger firms, thus the average productivity of firms operating in the domestic market rises by 0.1% and 0.7%. this leads to a decline in variable per unit costs of 0.3% and 1.1%, and an increase in average output per firm of 0.2% and 1%. however, the increase in average firm output does not compensate for the decrease in the number of firms operating in the domestic market. consequently, domestic sales decline by 0.4% and 1.9%. this, along with lower firm prices of -0.2% and -0.9%, reflects the increased competition with us imports. the impact on export flows of processed food from the us to the eu is presented in table 8. note first the impact on us-eu trade: following the reduction in border protection and trade cost, less productive firms find it profitable to enter the trade market. thus, the number of operating firm on the us-eu link increases by a factor of 1.7 in the first scenario and by 1412 in the second scenario. this lowers average productivity on that link, such that there are increases in the average firm price and output. still, us exports to the 12 please see footnote 12. table 8. average firm results for us domestic sales and exports of processed food [% change]. scenario 1 scenario 2 domestic sales eu nonttip total sale domestic sales eu nonttip total sale firm price -0.1 24.6 0.0 2.3 -2.0 35.6 0.0 3.1 number of operating firms -0.6 174.9 -0.9 15.1 -5.0 1421.0 4.0 132.0 avg. output per firm 0.3 -19.6 0.3 0.2 1.9 -44.2 0.0 1.6 avg. productivity per firm 0.2 -19.7 0.0 -1.9 1.4 -44.5 0.0 -4.1 industry fix costs 0.1 0.1 0.0 0.1 -0.1 -24.4 0.0 -0.2 fix costs per unit 0.4 -54.7 0.0 -4.6 3.1 -91.1 0.0 -8.0 industry variable costs -0.4 175.2 -0.4 0.4 -5.5 1050.5 3.6 -0.4 variable costs per unit -0.1 24.6 0.0 2.2 -2.5 35.6 0.0 2.9 total output sold -0.3 120.9 -0.3 0.4 -3.1 748.5 3.7 1.3 source: based on model results 153the impacts to food consumers of a transatlantic trade eu increase considerably (by a factor of 1.2 and 7.5), which reflects tariff removal plus an increased willingness to pay due to a higher number of varieties. export expansion ultimately negatively affects the output sold in the domestic market by 0.3% in the first scenario and 3% in the second. accordingly, total us processed food sales increases by only 0.4% (in the first scenario) and 1.3% (in the second). 5. sensitivity analysis the policy shock, model structure and parameterization jointly determine the model results. we check their robustness with regard to welfare and the volume of exports in the processed food sector. given the uncertainties on the future of ttip negotiations, we first perform a sensitivity analysis with regard to tariff and ntm reduction. to do so, we impose a 50% tariff shock (similar to francois et al., 2013) instead of the 100% removal in the benchmark. this essentially takes into account agricultural products that could be exempted from tariff removal. our results (not shown here) indicate negligible impacts on trade in processed food and welfare and a small impact on overall primary agriculture trade. we also perform a sensitivity analysis for the ntm reduction scenario, but allowing for only half of the reduction in ntm costs. figure 3 shows that the simulated changes in trade in processed food are 75-95% and welfare gains are 27-30% lower compared to the earlier results. next, we compare changes in welfare and trade in processed food in the tariff/ntm removal between model setups, i.e. proceed food and all manufacturing sectors have the firm heterogeneity setup (mel), and the more conventional structure where all the sectors follow the standard armington specification (arm). welfare in arm scenario is about 40-50% and trade effects are 10-30% lower compared to the firm heterogeneity configurafigure 3. change in welfare [equivalent variant per capita in constant usd] and export volumes [million constant usd] under lower reduction of ntms. 0 5 10 15 20 25 world eu us us d pe r c ap ita 33.8% reduction in ntms 16.9% reduction in ntms 0 100 200 300 400 500 600 700 800 900 world eu us m illi on u sd 33.8% reduction in ntms 16.9% reduction in ntms source: simulation results. 154 yaghoob jafari, wolfgang britz, jayson beckman tion (figure 4). comparable relative differences in welfare and trade flows are reported by hosoe (2017), and jafari and britz (2018b) for a simulation of brexit in a cge model. lastly, an additional sensitivity analysis shows that trade expansion and welfare gains are higher under a higher shape parameter, i.e. if the distribution of firms’ productivity becomes steeper. in figure 5, we compare the results when the shape parameter is onethird higher than the benchmark value. the gain in welfare is 30-80% and trade in processed food is 20-40 % higher than under the default parameters across different regions. the results are comparable with zhai (2008), who simulated a 50% reduction in manufacfigure 4. change in welfare [equivalent variant per capita in constant usd] and export volumes [million constant usd] under the under melitz and armington specification. 0 5 10 15 20 25 world eu us us d pe r c ap ia mel arm 0 200 400 600 800 1000 world eu us us d m illi on mel arm source: simulation results. figure 5. change in welfare [equivalent variant per capita in constant usd] and export volumes [million constant usd] under different shape parameters of the pareto distribution of firm productivity. 0 5 10 15 20 25 30 world eu us us d pe r c ap ita benchmark shape parameter (4.8) higher shape parameter (6.4) 0 100 200 300 400 500 600 700 800 900 1000 world eu us us d m illi on benchmark shape parameter (4.8) higher shape parameter (6.4) source: simulation results. 155the impacts to food consumers of a transatlantic trade turing tariffs across the world. we also test the implication of increasing the benchmark armington elasticities by one-third but keeping the original shape parameter. our results (not shown here) reveals that that under this assumption, exports are about 10-12% lower across regions, with only a modest impact 4-5% increase in welfare. 6. conclusion this study employs a cge model with a firm heterogeneity setup for processed food and manufacturing to simulate impacts of a potential ttip agreement on the eu, the us and other countries. this setup allows us to trace the impacts on the intensive and extensive margin of trade as well as on firm productivity. in addition, in accounting for firm heterogeneity by allowing fixed costs to vary, we can more flexibly allocate ntm compared to the more conventional armington set-up. we simulate the impacts of (i) removing all bilateral tariffs currently in place between the eu and the us; and (ii), an additional removal of ntms in food processing sectors. dismantling bilateral import tariffs leads to bilateral trade impacts that are below +10%, and limited welfare and trade diversion effects. as empirical estimates in the previous literature of the welfare impacts of ntms suggest that these form considerable barriers, and the results of our second scenario are consistent with those of the earlier studies. in particular, eu welfare increases from 2 billion usd under the first scenario to 13.8 billion usd under the second. the larger increase in exports for food processing stems almost entirely from more firms exporting, which underlines the importance of the melitz model in the analysis. however, increased exports are offset by lower domestic sales in both regions, such that overall industry output changes little. a sensitivity analysis on the core parameters used in the model shows the robustness of the overall results. our results differ from previous analysis of ttip, which suggested that the us would see larger increases than the eu in agri-food production and exports. while this is still somewhat the case for primary agriculture, our results indicate that the eu could see larger production gains for processed food with the u.s. experiencing a decrease in output. our results also indicate that tariff removal alone could benefit both regions once productivity gains are considered—something that cannot be shown with the typical armington model setup. in the end, consumers could benefit most from a ttip agreement as prices are likely to fall and the diversity of products available increase. our study could only draw on rather aggregate estimates of the costs caused by ntms in the food processing sector. thus, as the sector is highly heterogeneous, future work could try to provide more disaggregated estimates of costs related to ntms and their composition (rents in importer and exporter country, variable or fixed cost of trade, demand shifting etc.). that would clearly not only improve the analysis of a potential ttip agreement, but more generally economic impact assessment of ftas and multilateral trade liberalization. 7. acknowledgments the authors would like to thank thomas heckelei, peter witzke, and valentina raimondi (the editor of the journal), and anonymous referees for their insightful comments that greatly improved this paper. 156 yaghoob jafari, wolfgang britz, jayson beckman 8. references aguiar, a., narayanan, b. and mcdougall, r. 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(2008). armington meets melitz: introducing firm heterogeneity in a global cge model of trade. journal of economic integration 23(3)3: 575-604. 158 yaghoob jafari, wolfgang britz, jayson beckman appendix. supplemental tables table a1. sectoral correspondence of gtap 9 sector to new sectors. number code description pre model aggregation post model aggregation market structure 1 pdr paddy rice paddy rice grains and crops pc 2 wht wheat wheat grains and crops pc 3 gro cereal grains nec cereal grains nec grains and crops pc 4 v_f vegetables, fruit, nuts vegetables, fruit, nuts grains and crops pc 5 osd oil seeds oil seeds grains and crops pc 6 c_b sugar cane, sugar beet sugar cane, sugar beet grains and crops pc 7 pfb plant-based fibers plant-based fibers grains and crops pc 8 ocr crops nec crops nec grains and crops pc 9 ctl bovine cattle, sheep and goats, horses bovine cattle, sheep and goats, horses livestock pc 10 oap animal products nec animal products nec livestock pc 11 rmk raw milk raw milk livestock pc 12 wol wool, silk-worm cocoons wool, silk-worm cocoons livestock pc 13 frs forestry forestry mining and extraction pc 14 fsh fishing fishing mining and extraction pc 15 coa coal coal mining and extraction pc 16 oil oil oil mining and extraction pc 17 gas gas gas mining and extraction pc 18 omn minerals nec minerals nec mining and extraction pc 19 cmt bovine meat products processed food processed food fh 20 omt meat products nec 21 vol vegetable oils and fats 22 mil dairy products 23 pcr processed rice 24 sgr sugar 25 ofd food products nec 26 b_t beverages and tobacco products beverages and tobacco products beverages and tobacco products fh 27 tex textiles textiles textile and clothing fh 28 wap wearing apparel wearing apparel textile and clothing fh 29 lea leather products leather products light manufacturing fh 30 lum wood products wood products light manufacturing fh 31 ppp paper products, publishingpaper products, publishing light manufacturing fh 32 p_c petroleum, coal products petroleum, coal products heavy manufacturing fh 159the impacts to food consumers of a transatlantic trade number code description pre model aggregation post model aggregation market structure 33 crp chemical, rubber, plastic products chemical, rubber, plastic products heavy manufacturing fh 34 nmm mineral products nec mineral products nec heavy manufacturing fh 35 i_s ferrous metals ferrous metals heavy manufacturing fh 36 nfm metals nec metals nec heavy manufacturing fh 37 fmp metal products metal products light manufacturing fh 38 mvh motor vehicles and parts motor vehicles and parts light manufacturing fh 39 otn transport equipment nec transport equipment nec light manufacturing fh 40 ele electronic equipment electronic equipment heavy manufacturing fh 41 ome machinery and equipment nec machinery and equipment nec heavy manufacturing fh 42 omf manufactures nec manufactures nec light manufacturing fh 43 ely electricity electricity utilities and construction pc 44 gdt gas manufacture, distribution gas manufacture, distribution utilities and construction pc 45 wtr water water utilities and construction pc 46 cns construction construction utilities and construction pc 47 trd trade trade transport and communication pc 48 otp transport nec transport nec transport and communication pc 49 wtp water transport water transport transport and communication pc 50 atp air transport air transport transport and communication pc 51 cmn communication communication transport and communication pc 52 ofi financial services nec financial services nec other services pc 53 isr insurance insurance other services pc 54 obs business services nec business services nec other services pc 55 ros recreational and other services recreational and other services other services pc 56 osg public administration, defense, education, health public administration, defense, education, health other services pc 57 dwe dwellings dwellings other services pc notes: fh: firm heterogeneity, pc: perfect competition (armington). 160 yaghoob jafari, wolfgang britz, jayson beckman table a2. export volume by region for “crop products” [% change]. exporters partners eu other northern europe us canada mercosur china asean 10 other oecd eu mediterranean partners low income rest of world first scenario world 0.3 0.0 1.4 -0.2 -0.2 -0.3 -0.2 -0.2 -0.3 -0.2 -0.3 eu -0.3 0.0 16.4 0.5 -0.2 0.0 0.0 0.4 -0.1 -0.1 0.0 us 15.1 -1.1 -0.7 -1.3 -1.2 -1.1 -0.7 -1.2 -1.2 -1.2 second scenario world 0.5 -1.8 2.8 -1.7 -0.6 -0.5 -1.2 -0.8 -0.4 -0.3 -0.5 eu -0.1 -1.7 17.4 -0.1 -0.7 0 -0.8 0.2 -0.1 -0.2 -0.1 us 14.1 -4 0 -2.8 -2.9 -2.5 -3.2 -2.1 -2.4 -2.5 -2.5 table a3. export volume by region for “livestock products” [% change]. exporters partners eu other northern europe us canada mercosur china asean 10 other oecd eu mediterranean partners low income rest of world first scenario world 0.2 -0.2 1.3 0.0 -0.1 -0.2 -0.1 0.0 -0.2 -0.1 -0.1 eu 0.0 -0.2 9.9 0.2 -0.2 -0.1 -0.1 0.2 -0.2 -0.1 -0.1 us 11.5 -0.5 -0.1 -0.6 -0.4 -0.4 -0.2 -0.5 -0.4 -0.4 second scenario world -0.5 -0.9 -2.2 4.9 -0.1 0 -0.2 1.2 0.2 -0.1 0 eu -0.6 -0.8 9.4 3.4 0 0.3 0 0.9 0.2 0.3 0.2 us 13.5 1.5 0 5.8 2.1 2.6 2.2 3 2.5 2.5 2.4 table a4. export volume by region for overall manufacturing sectors [% change]. exporters partners eu other northern europe us canada mercosur china asean 10 other oecd eu mediterranean partners low income rest of world first scenario world 0.3 -0.1 1.3 0.0 -0.2 -0.1 -0.1 -0.1 -0.1 0.0 0.0 eu -0.2 0.0 3.9 0.3 -0.1 0.0 0.1 0.1 0.1 0.1 0.1 us 5.2 -0.4 -0.2 -0.5 -0.5 -0.4 -0.4 -0.4 -0.3 -0.4 second scenario world 0.4 -0.1 1.4 -0.4 -0.4 -0.1 0.0 -0.1 -0.1 -0.1 -0.1 eu -0.3 -0.1 3.6 -0.3 -0.5 -0.3 -0.2 -0.3 -0.2 -0.2 -0.2 us 5.3 -0.4 0.0 -0.6 -0.9 -0.6 -0.6 -0.6 -0.5 -0.5 -0.5 bio-based and applied economics 6(1): 37-56, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-16372 what if meat consumption would decrease more than expected in the high-income countries? fabien santini*, tevecia ronzon, ignacio perez dominguez, sergio rene araujo enciso, ilaria proietti european commission, joint research centre, ipts, seville, spain date of submission: 2015 30th, june; accepted 2017 2nd, january abstract. changes in meat consumption patterns could induce significant adjustments in agricultural markets. in this paper alternative scenarios envisaging lower meat consumption over the coming decade in high income countries and some selected emerging economies have been tested, with or without compensation by other sources of proteins. from a european perspective, results show a livestock farming sector having to deal with contradictory market signals. on the one side, the reduction in feedstuffs prices is an incentive to produce more, with lower output prices affecting positively the trade balance with developing countries, where demand keeps increasing. however, on the other side, the lower domestic demand for meat would affect profitability of meat production in the eu. overall, the european beef meat sector would be the most affected, with some higher demand for dairy products. this possible evolution of european diets is a challenge for european livestock farmers, which will be required to adapt their production mix and rely on the portfolio of policies the cap offers. keywords. meat consumption, agricultural markets, agro-economic modelling, agricultural commodities jel codes. q13, q17, q18 1. introduction the nutrition transition worldwide has been extensively described since popkin’s seminal article (popkin, 1994). associated with income growth and higher food availability, predominantly starchy diets firstly diversify with the integration of fruits and vegetables as well as animal proteins. it then ensures a new stage of the nutrition transition characterised by increased per capita consumption of fat (particularly animal fat), refined carbohydrates and sugar. this stage of the nutrition transition has been first observed in western countries in the first half of the 20th century. it then occurred in lowand middle-income *corresponding author: fabien.santini@ec.europa.eu 38 f. santini et al. countries from the early 1980’s onwards, and by then it is often referred to as a “westernisation of diets”. here, the adoption of a “western” dietary pattern is mainly an urban phenomenon associated with the adoption of sedentary lifestyles, although it is increasing also in rural areas. as a consequence, many regions and countries, both in the developing and developed world, are experiencing a lower dietary intake of legumes, vegetables and coarse grains and a higher intake of refined carbohydrates, added sugars, fats, processed foods and animal-source foods (popkin et al., 2012). sign of economic development and opening markets, it has also been subject to early warnings with regards to its corollary health effects: development of overweight, obesity, diabetes and coronary heart diseases (popkin, 1994; popkin, 1999; tsolekile, 2007; schmidhuber, 2004). in response, “health conscious behavioural changes” emerged, especially among better educated groups of population who tend to substitute part of their animal-based consumption by plant-based products (popkin, 1994). ethical concerns in relation to animal welfare in farming/marketing practices or the growing uptake of philosophical and/or religious conceptions recognising a status to animals were other reasons for a growing number of people to exclude meat in their diet. moreover, especially with the release of the fao livestock‘s long shadow report (steinfeld et al., 2006) and with the spread of life-cycle assessments (lca) in the early 21st century, the adverse effects of intensive meat production systems in terms of land use, greenhouse gas emissions and biodiversity losses – were pointed out in environmental assessments, determining the scaling up of the vegetarianism linked to concerns with the environmental and ecological impact (ruby, 2012). concerning climate change, livestock are a major source of greenhouse gas (ghg) emissions within the agricultural sector, mainly carbon dioxide, nitrous oxide and in particular methane. methane is 25 times more powerful than carbon dioxide and is estimated to be responsible for approximately one-fifth of man-made global warming (reay et al., 2010). on a global scale, livestock contributes about 15% of the total global anthropogenic greenhouse gas emissions (steinfeld et al., 2006) and ruminants, in particular, represent more than 80% of total ghg emissions related to livestock (herrero et al., 2013; hristov et al., 2013). in the wake of such concerns, medias and ngos conveyed the health, environmental and ethical benefits of lowering individual meat consumption including ‘meatless day campaigns’, pro-vegetarianism and pro-flexitarianism communications (laestadius et al., 2013). the rationale of the meatless day campaigns was to rely on responsible citizen to achieve mass effects from small individual changes. in other words, cutting out meat only one day a week equals to a 14% reduction in meat consumption, which can be highly significant in terms of health, environmental and animal welfare impacts. these campaigns also claimed for governmental support to educate and inform the targeted “responsible citizens” (gold, 2004). but, this strategy has been later judged at risks in the sense that “a simple ‘eat less meat’ message alienates some people and could have unintended consequences, not least on farmers’ livelihoods” (sutton and dibb, 2013). that led to the emergence of ‘less-but-better’ recommendations still oriented towards a reduction of individual meat consumption but jointly with purchases favouring meat proceeding from more extensive farming systems and/or of higher quality (de boer et al., 2014; sutton and dibb, 2013; national food administration, 2009). the impact of current western diets also moved up in the policy agenda. this was motivated by a context of implementation of sustainability principles, of fixing green39meat markets and a stronger consumption decrease in high-income countries house gas reduction targets, and of growing costs for public health systems. in 2009, the government of sweden was the first government to officially recommend to lower meat consumption as one ‘environmentally effective food choice’ among others and to notify such a proposal to the european union (eu)1 (national food administration, 2009). in this case, the health perspective argument was not put forward but reducing meat consumption appeared as a win-win option for both human health and the environment in the recommendations to government of the sustainable development commission of the united kingdom (2009) as well as the ones of the health council of the netherlands (2011) and more recently the recommendations of the dietary guidelines advisory group (gdap) of usa (2015) (u.s. department of agriculture and u.s. department of health and human services, 2015; sustainable development commission, 2009; health council of the netherlands, 2011). all these activities resulted in the rise of people’s awareness and in the growth of vegetarian population over time. nevertheless, data on the development of vegetarianism and / or flexitarianism behaviour is scarce. first because the definition of vegetarianism is not stabilised, giving space to a co-existence of various typologies of vegetarians in the scientific literature (phillips, 2005; de bakker and dagevos, 2012; ruby, 2012). vegetarian types range from flexitarians (still eating meat but reducing their meat consumption) to vegans (not eating any product of animal origin). in between, a large diversity of vegetable-based diets can be found: lacto-, lacto-ovo-vegetarianism, macrobiotic, and pescatarians (or pesco-vegetarians) according to the variety of personal motivations for adopting a lower meat or meat-free diet. few surveys have been conducted in the united states in particular by the vegetarian research group, which commissions a yearly poll, showing that 4 to 5% of the adult population can be defined as vegetarian and that up to 15% of the population do not eat meat at more than half of the meals (stahler, 2011; casalena, 2011). similarly, the european vegetarian union compiled in 2008 estimations on the number of vegetarians in developed countries. the share of vegetarians among european countries ranged between less than 1% in poland and portugal and close to 10% in germany (pichler and blackwell, 2008). the same source estimated 3% vegetarians in australia, 3.2% in usa and 4% in canada. finally, empirical observations like the increased numbers of vegetarians in western societies (gossard and york, 2003) and in particular growing flexitarianism (dagevos, 2014; de boer et al., 2014; friends of the earth, 2014) motivated the analysis of two simplified alternative protein consumption scenarios, mainly resulting in a stronger reduction of per capita meat consumption in developed countries, compared to the baseline projections of the european commission (european commission, 2014). in the first section, the quantitative framework and the market projections resulting are described and in the second section the alternative scenarios implemented in order to capture the nutritional pattern changes anticipated are analysed. the model ouputs for the main agricultural markets concerned by the alternative scenarios are discussed in the third section and finally, in the fourth section, a reflection on policy implications and on the limitations of the present work is presented 1 proposal notified to the eu 15.05.2009. 40 f. santini et al. 2. methodology and modelling approach annually, the oecd and the fao jointly release a ten-year horizon assessment of medium-term projections of national, regional and global agriculture commodity markets (oecd/fao, 2015a). the baseline scenario is taken from the european commission contribution to the latter published as “prospects for eu agricultural markets and income” (european commission, 2014) which is produced within the aglink-cosimo modelling framework. the modelling framework ensures that the overall set of equations balances with plausible outcomes. aglink-cosimo is a global economic recursive-dynamic, partial equilibrium, supply demand modelling framework which covers the main agricultural commodities (araujo enciso e. al., 2015; oecd, 2015). the model is a collaborative work integrating the oecd’s aglink and fao’s cosimo sub-modules. it is used to simulate the developments of annual supply, demand and prices for the main agricultural commodities produced, consumed and traded worldwide. the aglink-cosimo model covers 44 individual countries and 12 regions, 93 commodities and 40 world market clearing prices with a total of around 36000 equations. most behavioural equations in aglink-cosimo can be linearised in logarythms (i.e. “double-log” functions), including those for estimating production and demand functions, where the underlying relationship between y and x resembles a logarithmic function (e.g. y experiences diminishing marginal returns with respect to increases in x): ln yi( )=ai +ξij i ln xij( )+γ i it+ ln ei( ) where i and j correspond to the agricultural commodities covered in the model. the relationship between x and y is parameterized through the introduction of a constant term αi a slope term ξij which corresponds to the elasticity between y and x (araujo enciso et al., 2015) and a term trend (γi). the residual is captured by the error term (ei) which is frequently referred to as ‘calibration term’. for the purpose of this paper, we focus our attention on the demand side of the model. total consumption is modelled as an aggregate of different uses including food, feed, biofuels and industrial uses. for meat, consumption only covers food use (fo), and is modelled as a function of the relative ratio consumer prices (cpc,r,t) for each commodity and the total consumer price index (cpir,t) the gdp index (gdpir,t) and the population (popr,t) with a constant, a trend and an error term. for any region and time period, the food demand equation can be expressed as: ln foc( ) =α + c ∑ξfoc ,cpci i ln cpc cpi ⎛ ⎝ ⎜⎜ ⎞ ⎠ ⎟⎟+ξfoc ,dgpi i ln gdpi pop ⎛ ⎝ ⎜⎜ ⎞ ⎠ ⎟⎟+ ln pop( )+γ c i t + ln ec( ) (1) the trend serves to depict changes in the consumption patterns; the population serves to model changes due to demographic changes and the gdpi the income effect on food consumption. 41meat markets and a stronger consumption decrease in high-income countries 2.1 reference scenario the reference scenario (ref) is taken from the european commission “prospects for eu agricultural markets and income” (european commission, 2014). such a scenario is the so-called baseline and it is the result from an interactive process between market analysts, policy analysts and modellers. the reference scenario reflects the expert knowledge on the possible developments of each agricultural commodity market in view of the recent trends and anticipated developments. noteworthy, it is built upon a certain number of exogenous assumptions deemed most plausible at the time of the analysis concerning macro-economic and energy conditions, agricultural and trade policy arrangements in force, as well as yield trends under ‘normal’ climatic conditions. for the meat markets, the assumptions of ref reflect a stabilisation or lower growth of per capita meat consumption in the richest countries (a limited growth in meat consumption is exclusively attributable to a continuing increase of consumption of poultry meat) and, on the other hand, a further development of the nutrition transition in developing countries (i.e. an upward trend in sweeteners and animal-based products consumption). these assumptions are reflected by the trends in the model for different meat consumption and selected countries. accordingly, in terms of total meat consumption, the ‘baseline’ projections foresee some relative stability in developed countries, while the total consumption per capita is projected to keep on increasing in the ten coming years in developing countries (figure 1). as a result, domestic eu meat demand is projected to slightly decrease while exports are expected to increase, mainly due to the population and economic growth in lowincome countries and driven by a sustained growth in poultry meat. the medium term outlook of eu and world prices is accordingly rather positive, in particular for eu poultry and pig meat. the gap between eu and world prices is projected to significantly reduce over the projection period, as shown in figure 2 comparing the level of prices for three periods 2005-07, 2012-14 and 2022-24. the use of such partial equilibrium model for a ten years ahead projections of a change in consumption patterns does not take into account possible changes in price and income elasticities over the period. to this respect, such a tool represents a simplification of what is likely to happen in the event of lower meat consumption. in addition, such a model considers each meat product as one single commodity, while the reality is likely to be characterised by segmentation into different types of meat products, each of them reacting differently to the shock. organic or ethically claimed products are going to follow another path of development and this could nuance the results. in addition, each country is considered as a whole, while consumption preferences will depend on regional and/or population (gender, demography) characteristics. lastly, the scenario by essence does not take into account the possibility of external unforeseen events (e.g. an animal health related problem). 2.2 low meat consumption scenarios in order to assess the impact of lower meat consumption on agricultural commodity markets, two “lower meat consumption scenarios” are designed: lowmeat1 and lowmeat2 (see table 1). 42 f. santini et al. figure 1. baseline total meat consumption (kg per capita). 0 10 20 30 40 50 60 70 80 90 100 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 north america oceania developed european union-28 latin america and caribbean china japan asia africa source: oecd-fao world outlook, 2015a. figure 2. world and eu producer meat price development indices (base 100 = 2005-2007) in the baseline (eu in eur, world in usd). 100 110 120 130 140 150 160 170 180 190 eu world eu world eu world 2012-14 2022-24 beef pig meat poultry source: own elaboration from european commission (2014). 43meat markets and a stronger consumption decrease in high-income countries table 1. scenario characteristics. scenarios lowmeat1 lowmeat2 common elements linear decrease of meat consumption for : all meat types (i.e. beef and veal, sheep, pig, poultry) meat consumption (food use) in the eu, usa, canada, australia and new zealand by -11% over ten years and, certain (beef and veal, pig) meat consumption (food use) in argentina, brazil, mexico and uruguay by -5% over ten years distinctive elements no compensation with non-meat proteins linear increase of non-meat protein sources human consumption (food use) in the eu, usa, canada, australia and new zealand wheat, other cereals, fresh dairy products and butter, cheese, eggs: +5% over the whole period oilseeds and pulses: +2% over the whole period reduced (by half) linear increase of the same products in argentina, brazil, mexico and uruguay. in line with the nutrition transition theory, meat consumption projections are based on steady path, which is depicted with the trend. the alternative scenarios presented in this study are precisely based on a change of consumer preferences towards lower meat consumption. these changes are assumed to happen in high income countries, and in developing countries where the protein intake is higher than the “safe level of protein intake”2 defined by who/fao/unu (2007). the high income countries considered are the united states, canada, australia, new zealand and the european union, altogether representing 29.1% of the total world meat consumption (2011-13). given the high levels of protein intake in certain latin american countries and the rising concerns on health in particular and other issues, the scenarios are also implemented to three mercosur partners where meat consumption is the highest per capita (argentina, brazil, uruguay) and mexico as north american oecd country only for specific types of meat (bovine and pig meat), poultry being considered to keep on following the reference scenario. such countries represent an additional 10.5% of the total meat consumption. various dietary guidelines for high-income countries confirm that in general the average per capita level of meat consumption is high enough to recommend lower meat consumption without putting people at risks of nutrient deficiencies (national food administration, 2009; health council of the netherlands, 2011). in the rest of the world, it is assumed that these trends are not yet likely to express themselves within the time horizon chosen (ten years ahead) and therefore the baseline trends are not changed in the scenarios. per capita meat consumption, especially poultry, would continue to increase in these countries as shown in figure 1. 2 the safe level for a population is defined as the average protein requirement of the individuals in the population, plus twice the standard deviation (sd) (who/fao/unu 2007). 44 f. santini et al. concerning the magnitude of the meat consumption decrease in the two scenarios, the basic assumption reflects a possible doubling of vegetarians and flexitarians over the 10 coming years in high income societies. concretely, estimating that around 3% of the population of high-income countries is vegetarian in 2014 and does not consume any meat products, we assume that this share would double (6%) by 2024 in our alternative scenarios. concerning flexitarians, we assume the conservative assumption that 15% of the population of these countries eat 50% of the average per capita meat consumption and that this share would double by 2024. under these assumptions, the total meat consumption per capita decreases by 11% by 2024 relative to the baseline in the eu, united states, canada, europe and oceania. concerning the three mercosur countries and mexico, information on the development of vegetarianism and flexitarianism is even scarcer than for high income countries, which tends to confirm that these trends are less developed for a series of social, economic, ethical and political reasons. therefore, we assume a lower total meat consumption decline in these countries than in the other countries included in the scenario. per capita meat consumption is reduced by only 5% by 2024 relative to the baseline in these countries, and in addition, the decrease is only applied to beef and veal meat as well as pig meat (see table 1). in both scenarios, meat consumption reduction is uneven among types of meat. as the baseline foresees a stronger poultry meat consumption trend than for other types of meat (+5% above the average 2005-07 in the eu, see figure 3), these assumptions correspond to a stability / slight increase of poultry meat consumption in developed countries over the projected period. on the contrary, the baseline scenario implies strong reduction in pig meat and beef meat per capita consumption. therefore the two scenarios result in an even stronger decrease of consumption of those types of meat. in the case of the eu, the 2024 level of consumption is close to 20% below the average 2005-07 for pig meat and to 25% for beef and veal meat. over the period 2002-11, 32% of the protein intake comes from meat and derived products in the high income countries (see figure 4). population turning to vegetarianism or reducing its consumption of meat would see their intake in protein reduced, unless different sources of protein intake are envisaged. this significant source of proteins is likely to be compensated by an increase of consumption of other sources of proteins. in the lowmeat2 scenario, a partial compensation of protein intake losses by other sources is envisaged, corresponding to an increased food consumption of cereals, eggs, dairy products and oilseeds-pulses. in total, these four groups of products represent 53% of the total protein intake in the countries concerned, therefore the increased protein intake from these products should be of 6.7% to fully compensate the 11% decrease of meat consumption. fish, fruit and vegetables intake, although they represent around 15% of the total protein intake of high-income countries, were not considered because not covered by the modelling tool used. given the fact that some commodities are richer in weight (oilseeds and pulses in particular) than others in protein and that the scenario only aims to represent a partial compensation (being reasonable to assume that in countries where the daily protein intake is well above recommendations, a full compensation will not occur), the assumption tested is of a 5% increase in cereals, dairy and eggs food use over the period and of 2% for oilseeds and pulses food use (see table 1). 45meat markets and a stronger consumption decrease in high-income countries figure 3. scenarios lowmeat1 and lowmeat2 (in bold) and baseline (in thin) – meat consumption per capita index (eu-28), base 100 = 2005-2007. 70 80 90 100 110 120 130 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 poultry pig meat beef and veal thin lines are for the baseline and bold lines for the lowmeat1 and lowmeat2 scenarios. as the two scenarios are equal in terms of meat consumption assumptions (see table 1), both scenarios are represented by the same bold line. source: own calculations from aglink-cosimo database. figure 4. average protein intake in high income countries (% of total protein intake, 2002-11). 0% 5% 10% 15% 20% 25% 30% 35% meat milk cereals oilseeds and pulses eggs fish other source : author’s calculations from fao food balance sheets. 46 f. santini et al. both scenarios lowmeat1 and lowmeat2 do not claim to fully reflect plausible pathways in their complexity but were designed to mainly capture simplified elements of what could imply for commodity markets the decrease of meat consumption and their partial compensation by consumption of other protein sources, as well as the contribution of each aspect of both scenarios (meat / other protein sources). 3. results in scenario lowmeat1, meat consumption reduction in selected countries results on a moderate meat demand reduction at global level. in terms of world prices, the difference to the baseline trend ranges between 4% and 12% by 2024, depending on the types of meat (figure 5). lower world prices for meat provoke meat consumption increase in the rest of the world, mitigating overall the impact on world prices. the subsequent analysis of results focuses on the situation on the eu markets. at eu level, the meat price difference to the baseline is wider than at global level. indeed, with a decrease in the domestic consumption and domestic meat prices (significantly higher in general than world prices), the eu faces difficulties to fully compensate losses of domestic demand only by additional exports. as a consequence the price gap between the eu and world meat markets is closing down, depending on the type of meat considered. poultry, pig and sheep meat domestic prices are below the baseline level in 2024, but they remain 10 to 25% above the average price 2005-2007. eu domestic price of beef and veal meat results to be more affected, but remains around 10% below the average 2005-2007 domestic price level (figure 6). at eu level, the decrease of domestic demand is expected to induce an adjustment either through a drop in meat production and/or a drop in eu meat imports and/or a figure 5. impact of the lowmeat1 scenario on the world price for different meat (% difference to the baseline in 2024). -12% -10% -8% -6% -4% -2% 0% poultry pig meat beef and veal sheep meat source: own calculations from scenarios results. 47meat markets and a stronger consumption decrease in high-income countries boost of extra-eu exports. however, in scenario lowmeat1, meat markets respond differently. for poultry and pig meat, the decrease of consumption is compensated in the eu by both a decrease of domestic production and increased exports in similar proportions (figure 7). this market response is favoured by domestic prices not too far away from the world prices and a reasonable degree of competitiveness of the eu industry in these sectors. on the contrary, concerning beef and veal meat, the price wedge between the eu and world markets remains too important and the adjustment to reduced consumption is mostly achieved through a reduced eu production. finally, sheep meat consumption reduction results almost entirely in an import reduction. these impacts result in an improved self-sufficiency ratio and trade-balance for meat in the eu (see table 2). the eu would become self-sufficient for beef meat and would be very close for sheep meat. the positive trade balance would also amplify significantly for poultry and pig meat. a reduction in meat consumption also implies a contraction in feed demand, resulting in price decrease for these commodities. prices are particularly affected for coarse grains and protein meals (i.e. commodities mainly used for feed) but less for wheat and oilseeds (i.e. commodities used for both animal and human consumption as well as for the industry). overall the feed cost index3 calculated in the aglink cosi3 defined as the average price of all types of commodities potentially used as feed weighed by the actual quantities of each commodity used for feed ; only commercial feed bulks considered. figure 6. evolution of eu meat domestic price index in the baseline compared to the lowmeat1 scenario (base 100= 2005-07). 70 80 90 100 110 120 130 140 150 poultry pig meat beef and veal sheep thin lines are for the baseline and bold lines for the lowmeat1 and lowmeat2 scenarios. source: own calculations from scenarios results. 48 f. santini et al. figure 7. eu market balances for meat markets in the lowmeat1 scenario (change in 1000t relative to the baseline). -2000 -1000 0 1000 poultry -3000 -2000 -1000 0 1000 2000 pig meat -1000 -500 0 500 beef and veal -200 -100 0 100 sheep meat source: own calculations from scenarios results table 2. eu self-sufficiency ratio and net exports (in % of production). self-sufficiency ratio net exports baseline scenario lowmeat1 baseline scenario lowmeat1 poultry 113 121 +11% +17% pig meat 104 110 +4% +9% beef and veal 99 101 -1% +1% sheep meat 88 97 -13% -3% source: own calculations from scenario results. 49meat markets and a stronger consumption decrease in high-income countries mo for the eu would decrease by nearly 8% over the period relative to the baseline (figure 8). concerning the meat market, the lowmeat2 scenario (with partial protein compensation) shows similar results as the scenario lowmeat1, although most prices (except for beef meat) are slightly higher than in the scenario lowmeat1. the impacts in crop markets are significantly contrasted in the two scenarios. as in lowmeat2 meat proteins are partially substituted in the diet with other products crop or animal the demand in cereals and oilseeds is less affected than in the first scenario: the increased dairy and eggs production is associated with a higher demand in feed, and food demand for cereals and oilseeds also increases. all in all, eu domestic and world prices for these commodities tend to decrease less than in the scenario lowmeat1 (without protein compensation). the feed cost index in the eu would only be 4% below the baseline in this second scenario. figure 8. eu domestic and world prices for crops and eu feed cost index (difference to baseline in % change, 2024). scenario lowmeat1 scenario lowmeat2 -10% -8% -6% -4% -2% 0% world eu -8% -6% -4% -2% 0% world eu source: own calculations from scenarios results it is to be noted that the lowmeat2 scenario includes higher dairy products consumption in compensation for the meat consumption reduction, and it would therefore translate into higher eu milk and cheese prices above baseline trends, already quite positive. this scenario with protein compensation leads to particular market stress: on the one hand the domestic demand for beef meat is reduced, but on the other hand beef meat 50 f. santini et al. production is stimulated by the boost in milk domestic demand as an output of dairy farming. this explains why the beef meat price is the only one to be more affected in scenario lowmeat2 than in scenario lowmeat1. this is also the reason why in the eu in scenario lowmeat2, the herd itself would evolve towards less suckler cows (-3%) and more dairy cows (+1%), which would put under pressure those livestock systems specialised in beef meat production. 4. discussion on policy implications these two scenarios depict a ten-year transition during which individual changes in food consumption patterns could induce market adjustment and changes in relative prices. the european livestock farming sector would have to cope with contradictory market signals: the reduction in feedstuffs prices is an incentive to produce more at lower costs of production, but at the same time domestic demand for meat is losing momentum and lower domestic price would affect profitability of meat production in the eu. this is particularly the case for cattle breeders specialised in meat, who cannot take advantage of the higher demand for milk and dairy products. some elements of the new cap might be relevant to help producers facing such challenges, for instance in terms of support to livestock farming systems and in particular for extensive grazing livestock farming systems or through its rural development. first, the cap 2014-2020 provides recoupling possibilities to eu member states for livestock (regulation (eu) no 1307/20134). considering that 24 out of 28 member states5 opted for the voluntary coupled support for beef and veal (agra europe, 2015), it is likely that the majority of eu cattle farmers will receive direct coupled support in the future. beef and veal meat coupled support is expected to represent 10.2 billion euros over the period 2015-20, which represents over 40% of the total voluntary coupled support granted by member states to farmers. sheep and goat meat coupled support (around 12% of the total) would add to this. other meat sectors would not represent a significant share (european commission dg-agri, 2015). thus, recoupling support could play a role of partial safety net for livestock farmers and especially for suckler-cow based farming systems. however, such recoupling possibilities might delay the structural adaptations . second, as highlighted in the introductory section, civil society organizations are conveying the message to eat “less-but-better” meat. thus, extensive grazing system and/or quality cuts might benefit from niche markets, based on specific voluntary labelling, quality schemes or simply further market segmentation, for example on the base of breeds specific to one or the other areas of production. extensive grazing systems are also acknowledged to deliver substantial environmental positive externalities. some instruments of the new cap other than recoupled support can then play a role in the reorientation of the eu meat sector. grazing livestock systems should first mechanically be the prime beneficiaries of the redistribution of direct payments for internal convergence envisaged in the new 4 regulation (eu) no 1307/2013 of the european parliament and the council of 17 december 2013 establishing rules for direct payments to farmers under support schemes within the framework of the common agricultural policy and repealing council regulation (ec) no 637/2008 and council regulation (ec) no 73/2009. 5 only ireland, germany, cyprus and luxembourg did not recouple support for beef and veal. 51meat markets and a stronger consumption decrease in high-income countries cap 2014-2020. also, grassland-based farms are over-represented in less-favoured areas (lfas) and areas of natural constraints (ancs) eligible for specific cap supports, which may therefore indirectly contribute to foster the extensive livestock systems. in addition, the rural development component of the current cap contemplates a list of measures relevant for the development of “high quality” livestock systems (regulation (eu) no 1305/20136), among others support to quality or agri-environment-climate measures (e.g. on the maintenance or introduction of extensive livestock management). the promotion of extensive livestock systems within or without the cap would also have some benefits for the farming sector. in addition to the environmental adverse effects, intensive meat production systems might face biological risks (such as infectious diseases) and high costs if practices aimed at protecting animals’ wellbeing and safety are not put in place (hinchliffe et al., 2013; tilbrook and hemsworth, 2015). it should be also mentioned that most policy measures presented above will be implemented differently in the member states. the ability to target financial resources to different categories of breeders according to specific policy or local objectives is the strength of rdps as a policy tool. however the impact assessment cap 2020 showed that rdps sometimes suffer from path dependence (authorities tend to favour past successful measures over new ones) and unbalanced ability of areas/groups to weight in the process of defining rdps (european commission, 2011). from these observations, we can anticipate that in our scenarios livestock producers will receive uneven support across member states. noteworthy, most of the policy tools mentioned above are either not designed for the benefit of non-ruminant meat sectors (pig and poultry) or not targeted by member states to such sectors. eu farmers active in these sectors therefore find less policy tools in the cap than the ones involved in ruminant livestock breeding. 5. conclusion this paper analyses the possible impacts on agricultural markets of a significant change in meat consumption in high income countries and selected latin american countries. the scenarios described here have to be considered as two single and subjective alternative pathways among many other possible and plausible ones. the level of uncertainty concerning the pace and features of future development of meat demand and of the development of alternative sources of protein intake in the eu, in other developed countries or in developing countries remains high. also, the baseline and the two scenarios presented consider a certain set of macroeconomic and yield conditions and the absence of any ‘black swan’ events, like the emergence of new zoonoses or food scares. furthermore, the scenarios have been elaborated with the same modelling tool as the baseline. since the alternative scenarios encompass nutritional issues, they could justify that the model is improved by including relevant missing commodities (fish products, fruit and vegetables) for this purpose. 6 regulation (eu) no 1305/2013 of the european parliament and the council of 17 december 2013 on support for rural development by the european agricultural fund for rural development (eafrd) and repealing council regulation (ec) no 1698/2005. 52 f. santini et al. actually, including the fish commodity would allow for a deeper analysis of protein substitution in lower meat consumption scenarios. but fish inclusion is not straightforward. it would also require further investigation to correctly integrate fish linkages with feed in aquaculture. another model improvement could be the conversion of quantity of commodities produced and traded into quantities of calories or nutrient equivalent. this would allow to better assess the nutritional value in each scenario: are we modelling healthy purchase behaviours/diets or not? are we correctly simulating protein substitution options? linking the different food uses of the different commodities by specific equations (developing nutritional module) could be a way forward. finally, as one of the drivers of a lower meat consumption choice is the individuals’ concerns of their food impact on the environment, an interesting field of research would be to couple the agro-economic model with environmental indicators like the commodities’ virtual content in water or land (like for example in (wirsenius et al., 2010). in the same perspective, agricultural production could be linked with greenhouse gas emissions (garnett, 2011; tukker et al., 2011) or biodiversity losses and other environmental impact indicators (as in (tukker et al., 2011)). indeed, reducing animal production and/ or improving its efficiency may be key in the mitigation and adaptation to climate change. the reduction in numbers and enhancement of productivity of animals would represent an important mitigation strategy that might have side effects on animal welfare and other environmental issues (gerber et al., 2013). further assessment of the environmental and climate impact of nutritional changes as explored by wolf et al. 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(2014). curbing global meat consumption: emerging evidence of a second nutrition transition. environmental science and policy 39: 95-106. westhoek, h., rood, g., van den berg, m., janse, j., nijdam, d., reudink, m. and stehfest, e. (2011). the protein puzzle: the consumption and production of meat, dairy and fish in the european union. european journal of food research and review 1(3): 124-144. wirsenius, s., azar, c. and berndes, g. (2010). how much land is needed for global food production under scenarios of dietary changes and livestock productivity increases in 2030? agricultural systems 103(9): 621-638. wolf, o., pérez-domínguez, i., rueda-cantuche, j. m., tukker, a., kleijn, r., de koning, a., bausch-goldbohm, s. and verheijden, m. (2011). do healthy diets in europe matter to the environment? a quantitative analysis. journal of policy modeling 33(1): 8-28. world health organization europe (2003). food-based dietary guidelines in the who european region. eur/03/5045414. copenhagen, denmark: who regional office for europe. annex – summary results for meat markets (2024) poultry meat ref lowmeat1 lowmeat2 world price (usd/t) eu producer price (eur/t) 1600 2076 1498 1800 1516 1827 eu production (1000t) 13977 13298 13244 eu imports (1000t) 1018 1002 1003 eu consumption (1000t) per capita (kg1/cap/yr) 13466 22.8 12052 20.4 12030 20.4 eu exports (1000t) 1529 2249 2217 pig meat ref lowmeat1 lowmeat2 world price (usd/t) eu producer price (eur/t) 1924 1857 1715 1581 1734 1596 eu production (1000t) 22779 21824 21843 eu imports (1000t) 21 18 18 eu consumption (1000t) per capita (kg2/cap/yr) 20211 30.3 18031 27.1 18018 27.0 eu exports (1000t) 2590 3812 3843 56 f. santini et al. beef and veal meat ref lowmeat1 lowmeat2 world price (usd/t) eu producer price (eur/t) 3022 3618 2895 2649 2894 2641 eu production (1000t) 7443 6827 6843 eu imports (1000t) 334 272 271 eu consumption (1000t) per capita (kg2/cap/yr) 7530 10.1 6759 9.1 6772 9.1 eu exports (1000t) 252 346 347 sheep and goat meat ref lowmeat1 lowmeat2 world price (usd/t) eu producer price (eur/t) 4948 5779 4560 4986 4593 5023 eu production (1000t) 957 939 939 eu imports (1000t) 205 106 107 eu consumption (1000t) per capita (kg2/cap/yr) 1083 1.8 964 1.6 964 1.6 eu exports (1000t) 79 82 82 a systematic approach to understanding and quantifying the eu’s bioeconomy tévécia ronzon1*, stephan piotrowski2, robert m’barek1, michael carus2 cost function and positive mathematical programming quirino paris what if meat consumption would decrease more than expected in the high-income countries? fabien santini*, tevecia ronzon, ignacio perez dominguez, sergio rene araujo enciso, ilaria proietti a stakeholder engagement approach for identifying future research directions in the evaluation of current and emerging applications of gmos davide menozzi1*, kaloyan kostov2, giovanni sogari1, salvatore arpaia3, daniela moyankova2, cristina mora1 a spatial analysis of terrain features and farming styles in a disadvantaged area of tuscany (mugello): implications for the evaluation and the design of cap payments laura fastelli1*, chiara landi2, massimo rovai1, maria andreoli1 bio-based and applied economics 9(1): 109-125, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8892 the impact of food price shocks on poverty and vulnerability of urban households in iran ghasem layani1, mohammad bakhshoodeh1, mona aghabeygi2,*, yaprak kurstal3, davide viaggi3 1 university of shiraz (iran) 2 university of parma (italy) 3 university of bologna (italy) abstract. the aim of this paper is to assess the welfare effects of food price changes on urban households’ poverty and vulnerability. this is achieved by using hicksian price compensating variation (cv) and compensated price elasticities, based on quadratic almost ideal demand system (qaids). the study includes in total eight food groups (cereals, meats, dairy, cooking oil, sugar, fruits, vegetables, and tea and coffee) and encompasses 18852 urban households. the results showed that the welfare index for food groups was 20 usd (2.52% of the monthly average income of urban households). after increasing food prices, based on the poverty line, 41% of urban households were observed to be below the poverty line and the number of poor households increased by 10.63%. to enable food security and to execute food safety goals, the iranian government should compensate for the welfare losses by supportive policies such as direct subsidy payments to vulnerable households. keywords. compensating variation (cv), quadratic almost ideal demand system (qaids), welfare effect, vulnerability, food price shocks. jel codes. i32, n95, q18. 1. introduction the level of the iranian food consumption is expected to increase significantly in next years. there are two main reasons for this issue: first, the iranian population of currently approximately 82 million is estimated to grow by around 1 million people annually in the next 5 years. the expected short-term population growth will increase demand and consumption of food products in iran. second, an increasing part of the iranian population is moving from rural into urban areas. the growing urbanization decreases the amount of wholly or partly self-sufficient people in iran. instead, they become consumers in the urbans contributing to a growing food demand (isc, 2016). the gdp-growth in iran has increased from 0.9 % in 2015 to 4.6 % in 2016. the economist intelligence unit predicts that the iranian gdp will increase further, reach*corresponding author. e-mail: monaaghabeygi17@gmail.com editor: simone cerroni. 110 ghasem layani, mohammad bakhshoodeh, mona aghabeygi, yaprak kurstal, davide viaggi ing 5.4 % in 2017 and 5.9 % in 2018. this positive development in iranian economy is expected to contribute to a general increase in food consumption and demand, as it is likely to increase the living standard for the growing middle class in iran, raise purchasing power and consumers’ confidence. this will raise the demand for more expensive and specialized food products. the demand for basic food products is also expected to increase, as the current consumption of food products in iran is relatively low by regional standards. this especially concerns sugar, corn, meat, and vegetable oils. during the sanctions period, real disposable incomes for most consumers were falling, due to high inflation rates not matching by salary increases. as a consequence, the consumption per capita of some of the more expensive food products like red meat, cheese and milk declined. with increasing economic growth, the repressed food demand is expected resume and continue to grow. traditional grocery and other stores accounts for more than 80% of retail sales in iran. however, recently hyperand supermarkets are growing in importance thereby stimulating the demand for more advanced products. a larger product variety is expected to lead to a growing demand (imaj, 2016). therefore, iran is expected to be faced with increasing food prices, which leads to increased adverse impacts on poverty and food security. in this regard, it is of utmost importance to know how changes in food prices affect the welfare of households. hence, understanding the effects of price increases of food especially on vulnerable households could have significant implications for the design of supporting policies (fallahi et al., 2016) and to help decrease the negative impacts of increasing prices towards achieving the goal of food security. in the case of iran, over the course of the last 15 years (2001-2015), the average annual imports of eight main food groups, namely meat, cereals, dairy products, oils and fats, sugar, fruits, vegetables and tea and coffee, has reached a level of 12430 million tons, which has shown an increase of 54 percent over 15 years, and around 5.21 percent increase, annually. to provide an example, iran is the major importer of oilseeds and about 90 percent of the country’s oil needs to be provided through imports. also, in the year 2018, nearly 20 percent of meat and more than 40 percent of cereals supplied in the iranian market have been provided through imports (fao, 2016). this significant rise in the level of imports, hence, brought about an increase in the prices of many products. being vulnerable to the rising food prices in the world due to high level of imports in food products and increasing global food prices also carry major implications for both the economic and social welfare of iranian households, which has been subject of increasing concern. in this context, urban households, which constitute around 75 percent of the total number of households in iran, and whose budget composition is directly impacted by the inflated food purchases, are hit ineluctably (ravallion and chen, 2007; robles and keefe, 2011). in this study, our aim is to estimate the effects of the 2000–2016 food price surges on urban households’ expenditure and food poverty. our methodological approach follows azzam and rettab (2012) and rodriguez-takeuchi and imai (2013) to take into account the impacts of food price changes on households’ vulnerability and poverty. the rest of the paper is structured as follows: the next section provides the background of the importance of studying the behavior of consumers. we, then introduce the methodology of the quadratic almost ideal demand system (qaids) model and the vulnerability index and data. ownand cross-price elasticities, welfare and poverty effects of food 111the impact of food price shocks on poverty and vulnerability price changes are presented in the results section, and also, the final section offers concluding remarks and policy discussion. 1.1 measuring economic welfare measuring the economic welfare and poverty effects of different policies among societies have always been one of the most important economic issues of public policies. countries often use policy interventions to dampen the impacts of international food price spikes on domestic markets and lessen the burden of these especially on vulnerable population groups. besides, understanding the causes of the food price shocks and addressing its significant effects on developing countries have been critical in order to analyze the efficiency and adequacy of policies in addition to be able to propose policy options. the impact of these shocks on welfare depends on a variety of factors, including but not limited to the nature of the shock, initial household, or community conditions and also policy responses by the government (unicef, 2009). in addition to such macro impacts, micro impacts are mainly experienced in the form of reduced household income, due to lower wages and employment or limited access to credit and reduced real income in the face of higher food prices, and decreased access to public services, as a result of reduced service delivery on the part of the government (unicef, 2009). meanwhile, the extent to which households are affected by these shocks will depend on the change in relative prices, substitution of commodities and response of households to all these factors (osei-asare and eghan, 2013). towards this end, numerous studies worked on the relative prices and substitution relation among commodities by estimating elasticities of demand functions based on translog or “almost ideal demand system” (aids) forms. some of the examples include deaton and mulbaer (1980), studying the case of great britain, blanciforti and green (1983), and hayes et al. (1990), of united states; fulponi (1989), of france; abdulai et al. (1999), of india; tefera (2012), of ethiopia and suharno (2010) of indonesia. meanwhile, other studies have used the aids model, which has assumed a linear engel curve (tefera, 2012), while banks et al. (1997) proposed a generalized quadratic almost ideal demand system (qaids) that permits a non-linear engel curve. matsuda (2006) also applied qaids to estimate food demand in japan. on the other hand, arabatzis and klonaris (2009) studied on cases, in which qaids has been applied for wood product imports in greece. furthermore, layani and esmaeili (2015) also used qaids and a welfare index such as hicksian compensating variation (cv) to analyze food demand in order to assess the welfare effects of increasing food prices for households in iran. the significant welfare impacts of price shocks have prompted studies to evaluate recent price shocks on household poverty in developing countries (e.g., de janvry and sadoulet, 2010; leyaro et al., 2010; coleman, 2012; ivanic et al., 2012; fujii, 2013; layani and bakhshoodeh, 2016). in recent studies of the economic welfare effects of food price changes, azzam and rettab (2012) have focused on the vulnerability of households in the united arab emirates (uae) as a result of increasing prices for imported food products; while, rodriguez-takeuchi and imai (2013) and fujii (2013) have first calculated the poverty line and then analyzed the effects of increasing food prices on household expenditure and the poverty line. in this study, in addition to discussing the welfare effects of food rising prices in the face of highly elastic poverty lines to relative food prices, the poverty line changes in 112 ghasem layani, mohammad bakhshoodeh, mona aghabeygi, yaprak kurstal, davide viaggi urban households are also addressed to understand the extent of iranian consumers’ vulnerability to food price increases and food supply shocks. measuring the welfare changes caused by increasing food prices is crucial to provide a compensatory support system. our methodological approach follows azzam and rettab (2012) to determine the impacts of food price changes on iranian urban households’ expenditure and poverty line. within this context, the objectives of this paper are: (1) to determine the price and income elasticity for food groups by using quadratic almost ideal demand system (qaids); (2) to explore welfare impacts of increasing world food prices using compensated variation (cv); and (3) to calculate the consumer vulnerability index and poverty effects of food price shocks. 2. methodology 2.1 poverty line and the vulnerability index poverty measurement is based on a comparison of resources to need (world bank, 2000). a person or family is identified as poor if their resources fall short of the poverty threshold. meanwhile, poverty is defined by using a poverty line; when a household falls below this line, it is considered to be poor. for instance, the world bank considers a household to be poor if it survives on less than 1.90 usd per day. in this study, food poverty has been considered. in order to measure the poverty line based on the relative concept, poverty line can be determined by the percentage of average household expenditure. by following the work of khodadad kashi et al. (2005) and arshadi and karimi (2013), we take 66 percent of the average household food expenditure as a threshold for determining the relative poverty line: poverty line=66 percent × (average food expenditure) (1) therefore, the relative poverty line is calculated before the change in food prices by (1). after computing the poverty line, we can divide urban households into two groups: the households that have a food expenditure higher than poverty line (above the poverty line), and the households that have a food expenditure lower than poverty line (below the poverty line). the reason for this is because poverty lines are highly elastic to relative food prices and changes in food prices result in variations of poverty prevalence. furthermore, we then compute a new poverty line, after accounting for the rise in food prices (rodriguez-takeuchi and imai, 2013): secondary poverty line= poverty line + welfare index (2) in addition, we compute the vulnerability index following azzam and rettab (2012): households vulnerability = total welfare loss relative to income = wi/ai (3) in the equation, wi is the total welfare effects of rising food prices and ai is the average income of urban households. 113the impact of food price shocks on poverty and vulnerability 2.2 welfare index with price changes in general, in the welfare literature, there are various indexes for measuring the welfare changes due to implementation of different policies (gohin, 2005). by changing economic conditions, such as price changes, consumers’ utility rates may increase or decrease. to determine how and how much of the consumer utility changes due to changing economic conditions, some criteria are used such as consumers surplus (cs), compensated variation (cv) and equivalent variation (ev). in our context of rising food prices, cv is the minimum amount, the iranian consumers are willing to accept (wta) to tolerate higher food prices; and ev is the maximum amount they are willing to pay (wtp) to avoid higher food prices. the focus of cv is on the welfare level prior to the increase in prices, while the focus of ev is on the subsequent welfare level after the increase in prices (azzam and rettab, 2012). hence, we use the cv in our study, based on the studies of azzam and rettab (2012), tefera (2012) and cranfield (2007). the starting point of the cv model with price changes is the consumer problem of minimizing expenditures on n food commodities subject to a utility level u0. substitution of the resulting optimal hicksian quantities into the expenditure equation yields the minimized expenditure function (azzam and rettab, 2012): (4) where pi for i = 1, 2, . . ., n are the respective prices of the n commodities, and the superscript h stands for hicksian. denoting the initial and the subsequent periods by superscripts ‘‘0’’ and ‘‘1’’, respectively, consumer wta to tolerate higher prices is given by: (5) using (4), we can expand (5) as follows: (6) direct measurement of cv using (6) is not possible because the hicksian demand functions (.) for i = 1, 2, . . ., n depend on the utility level u0, which is unobservable. however, as shown by huang (1993), if the respective changes in prices and hicksian quantities are defined as (azzam and rettab, 2012): (7) and substituted into (6), cv can be approximated by: 114 ghasem layani, mohammad bakhshoodeh, mona aghabeygi, yaprak kurstal, davide viaggi (8) the percentage change in hicksian quantities is not observed. however, an approximation of the change is obtained through the total differential of the hicksian demand functions (.). for example for i = 1, 2, . . ., n: (9) . . where is the hicksian price elasticity for i = 1, 2, . . ., n and j = 1,2, . . ., n. 2.3 quadratic almost ideal demand system to estimate the hicksian price elasticities as shown in (9), we estimate a quadratic almost ideal demand system (qaids) model for n commodities by imposing the usual restrictions: adding-up, homogeneity, and symmetry. the qaids model developed by banks et al. (1997), which has budget shares that are quadratic in log total expenditure, is an example of the empirical demand systems that have been developed to allow this expenditure nonlinearity. the qaids not only retains the desirable properties of the popular aids of deaton and muellbauer (1980) nested within it, but also has the additional advantage of being versatile in modelling consumer expenditure patterns. quadratic in the logarithm of total expenditure, the qaids allows such situations where the increase in the expenditure would change a luxury to necessity (arabatzis and klonaris, 2009). the qaids model is (gorman, 1981; jing et al, 2001): (10) where si is the share of food group i in total expenditure on the n food groups, for i=1,2,..,n; and pj is a vector of prices; m is total expenditure. also, f(p) is the stone price index defined by logf(p)* = ∑i si logpi. the restrictions are: 115the impact of food price shocks on poverty and vulnerability (11) i,j = 1,2,…,n the respective formulas for computing the hicksian price elasticities for n groups are: (12) (13) where δij is the kronecker delta taking the value δij = –1 if i = j and δij = 0 if i ≠ 0. in terms of the ui, the formula for income elasticities can be written as: (14) negative cross-price elasticities indicate a complementarity relationship and the positive values for cross-price elasticities indicate substitutability. also, the positive (negative) values for expenditure elasticity indicated non-inferior (inferior). in the former case when εi ≥ 1 the goods are regarded as luxury. specifically, so-called normal necessities have an income elasticity of between 0 and 1. but one of the problems with working with these kind of models is the phenomenon of zero consumption of a commodity or the zero budget share, which is due to the division of food groups into a large number of groups and the use of cross-sectional data at the household level. in other words, some households report a zero consumption, and some others spend a non-zero share. therefore, the variable is censored. in order to solve this problem, based on the bakhshoodeh (2010) study, we use the following equation instead of equation (10). (15) where ϕ(0) and φ(0) are cumulative distribution function (cdf) and probability distribution function (pdf) for each household the system of eq. (15) is estimated using iterative seemingly unrelated regression (sure) to calculate elasticities for eight food groups (cereals, meats, dairy, oil, sugar, fruits, vegetables, and tea and coffee). 116 ghasem layani, mohammad bakhshoodeh, mona aghabeygi, yaprak kurstal, davide viaggi 3. data and information this study is based on urban household’s income-expenditure survey (2012) of the iranian statistics center (18852 urban households) for computing price and income elasticities. we collected data on food imports to iran during the years of 2000 to 2016. then the average annual growth of imported food prices is defined as a price shock scenario. by referring to ivanic et al. (2012), we assume that the global food price shocks transferred completely to the domestic market in iran. finally, welfare effects and changes in food poverty are determined. mean and standard deviations of expenditure share and average monthly expenditures for eight food groups including cereals, meats, dairy, cooking oil, sugar, fruits, vegetables, and tea and coffee are presented in table 1. generally, the share of food and beverage expenditures in total household budget is equal to 23.5%. the share of food expenditures is in the second-placed after the share of buying/renting house budget (isc, 2016). among eight food groups, the maximum and the minimum average expenditure shares related to cereals were 26.21 percent (monthly 43.69 usd) and 2.9 percent for tea and coffee (about 4.69 usd) respectively. table 1. average expenditure shares of different food groups. group average expenditure share coefficient of variation standard deviation average monthly food expenditure cereals 26.21 0.45 0.11 43.69 meats 22.87 0.47 0.10 41.01 dairy 12.16 0.51 0.06 18.68 oil cooking 5.42 0.71 0.03 8.84 sugar 4.60 0.81 0.03 8.13 fruits 12.29 0.63 0.07 22.38 vegetables 13.55 0.45 0.06 21.76 tea and coffee 2.90 1.07 0.03 4.69 source: iranian statistics center, 2016. 4. result and discussion according to the price elasticities of the qaids1 model, all own-price elasticities are negative. in terms of absolute values, the highest own-price elasticity is related to tea and coffee (2.19 percent) and the lowest own-price elasticity is related to fruits (0.05%). it means that, demand for tea and coffee is highly responsive to any change in the price. the estimated own price elasticities for vegetables (-0.74%) and for sugar (-0.82%) are close to one. in fact, demand for these two groups has large response to changes in their relative prices. the estimated own-price elasticity is low for others. we concluded that demand for cereals, meat, dairy and oil cooking are stable to price changes, meaning that these food groups are essential in household consumption patterns. 1 qaids estimation is reported in the annex. 117the impact of food price shocks on poverty and vulnerability cross-price elasticities show competitive or complementary relations among products. positive cross-price elasticities indicate competitive relations, while negative cross-price elasticities indicate complementary relations. the cross-price elasticities presented in table 2 also show that most of the selected goods have complementary relationship with each other. in addition, in terms of the absolute value of the elasticity, the complementary relationship can also be stronger than the substitution relation. the cross-elasticity of other commodities with cereals and meats suggest a substitution relationship between them, but the relationship between cereals and meat with other commodities is mostly complementary. this pattern of relationships can indicate the importance of consumption of cereals and meat in the consumer food pattern. by illustration, consumers prefer to add other commodities as a complement to their consumption patterns after the inclusion of cereals and meat, while these two products will be the substitution to other commodities. the patterns of household consumption, and especially the high per capita consumption of cereals such as rice and wheat, can also confirm these results. the estimated total income elasticities presented in table 2 have the expected positive signs in all eight commodities. the values for cooking oil (e=1.76), fruits (e=1.38), meat (e=1.22) and cereals (e=1.18) are much higher than others. this implies a fairly large response of demand for these food groups to changes in total food expenditure. actually, the demand for cooking oil, fruits, meat and cereals are elastic with respect to total food expenditure. the estimated income elasticities of dairy, sugar, vegetables and tea and coffee are less than unity, so these goods are fairly inelastic with respect to total food expenditure. after obtaining compensated own and cross price elasticities in this section we examine the welfare impacts of the changes in selected food items’ prices. following some recent literature, we estimate the change in consumer welfare by using the compensating variation (cv). the compensating variation is the amount needed to compensate a household for a price increase, in order for the household to remain at the same utility level after a price change. we define price shock scenarios based on average annual changes in world food prices presented by fao (2016) for period of 2000-2014. prices of cereals, meat, dairy, oils, sugar, fruits, vegetables and tea and coffee have increased by 9.80, 8.35, 7.72, 8.06, 8.78, 3.16, 15.70 and 4.68 percent, respectively. we present the average compensating variation values in table 3. results show that the welfare losses from the price increases in cereals, meats, dairy, oils, sugar, fruits, vegetables and tea and coffee amount to 20 usd. in other words, on average, iranian urban households need to be compensated with approximately 11.82 percent of their 2016 total household expenditure on food in order to accommodate the adverse impact of food price changes they faced between 2000 and 2014. the highest amount of cv as a result of the increase of prices is obtained for fruits. the amount of cv for fruits is estimated at 4.90 usd, which is equivalent to 2.42 percent of the average food expenditures in 2016. also, the cv index of cereals is estimated to be 3.56 usd, which is equivalent to 2.11 percent of the average food expenditure in 2016. thus, with an increase of 9.80 percent in the price of cereals (considering the simultaneous price change), urban household expenditures increase and their welfare decreases. the cv for meat, dairy and vegetables are 2.86 (equivalent to 1.69 percent of the average food expenditure), 3.46 (equivalent to 2.05 percent of average food expenditure) and 3.03 usd (equivalent to 1.79 percent of average food expenditure), respectively. finally, the last column of table 3 shows the weight of the calculated cv index for each food group 118 ghasem layani, mohammad bakhshoodeh, mona aghabeygi, yaprak kurstal, davide viaggi from the total welfare index. according to the results, the amount of cv for fruits, cereals and dairy constitutes the highest share of the total cv index, respectively equal to 20.45 percent, 17.80 percent, and 17.30 percent of the total cv, respectively. table 4 shows the average monthly food expenditure of the 8 food groups, total cv, average monthly income for households, and the welfare measure of the vulnerability index (total cv relative to income). given that the average monthly income of iranian urban households is 792.66 usd, the total welfare loss due to rising food prices is equivalent to 2.52 percent of an average household income, which is an indicator of the vulnerability of urban households as a result of the increase in global prices. this index can be used as an effective tool as part of efforts towards enforcing supportive policies. in more detail, policymakers determine the rate of increase in employees’ wage annually, based on economic indicators such as inflation. specifying the vulnerability index would be a suitable measure to balance the wages and inflation in the society. finally, we examine the effect of rising food prices on poverty in urban households in table 5. according to the average total food expenditures, the initial poverty line is computed as 111.66 usd; and after rising food prices, the secondary poverty line is computed to be 131.66 usd. in the initial setting, 30.13 percent of urban households have a monthly food expenditure below the poverty line (about 5680 households). table 2. hicksian (compensated) price and income elasticities for different food groups. cereals meats dairy oil cooking sugar fruits vegetables tea and coffee cereals -0.22 (-7.51) * -0.01 (-12.98) 0.31 (11.63) 0.17 (15.01) 0.61 (9.82) -0.24 (-5.25) 0.02 (7.96) 1.01 meats 0.05 (12.98) -0.27 (-11.09) 0.15 (10.44) 0.26 (9.10) 0.25 (2.34) -0.01 (-2.34) 0.09 (2.34) 0.23 dairy -0.06 (-11.63) -0.19 (-10.44) -0.22 (-8.04) -0.56 (-7.26) 0.78 (6.37) -0.01 (-4.13) 1.35 (10.51) 0.61 oil cooking 0.13 (15.01) 0.12 (9.10) 0.14 (7.26) -0.24 (-5.54) 0.10 (10.43) 0.12 (5.14) -0.04 (-6.02) 0.39 sugar 0.01 (9.82) -0.13 (-2.34) 0.48 (6.37) -0.60 (-10.43) -0.82 (-7.07) -0.25 (-8.23) 0.94 (7.05) 0.71 fruits -0.07 (-5.25) -0.00 (-2.34) 0.32 (4.13) 0.25 (5.14) 0.02 (8.23) -0.05 (-8.66) 0.25 (6.15) -0.26 vegetables -0.04 (-7.96) -0.01 (-2.34) 0.40 (10.51) -0.21 (-6.02) 0.51 (7.05) 0.01 (6.15) -0.74 (-7.04) -0.07 tea and coffee 0.28 (10.01) 0.48 (6.21) -0.84 (-7.33) 1.36 (9.07) -0.58 (-7.58) 0.34 (5.98) -1.02 (-4.56) -2.19 (-8.43) income elasticities 1.18 1.22 0.23 1.76 0.39 1.38 0.58 0.14 source: authors’ calculations * indicates significance at the 5% level, t-ratios are in parentheses. 119the impact of food price shocks on poverty and vulnerability based on what was explained, the total share of poor households in urban areas increases to 40.76 percent (table 6). we find that 10.63 percent of households, which were above the poverty line before the food price increase, become poor after the price shock. consequently, the overall share of poor households increases from 0.30 to 2.05 percent in urban areas. for instance, in the case of a 9.80 percent price increase for cereals, the cv is 3.56 usd and the poverty line is 115.52 usd. our results suggest that an additional 1.79 table 3. welfare effect of price changes(the average compensating variation values). group proportion of cv (%) compensated variation (%) compensated variation (usd) price change (%) average monthly food expenditure (usd) cereals 17.80 2.11 3.56 9.80 43.69 meats 14.29 1.69 2.86 8.35 41.01 dairy 17.30 2.05 3.46 7.72 18.68 oil cooking 5.59 0.66 1.12 8.06 8.84 sugar 6.56 0.78 1.31 8.78 8.13 fruits 20.45 2.42 4.09 3.16 22.38 vegetables 15.15 1.79 3.03 15.70 21.76 tea and coffee 2.84 0.34 0.57 4.68 4.69 total 100 11.82 20 169.18 source: authors’ calculations. table 4. vulnerability index. average monthly urban household income (usd) 792.66 total welfare index (usd) 20 average monthly food expenditure (usd) 169.18 household vulnerability 2.52 source: authors’ calculations. table 5. the effect of rising food prices on poverty in urban households (poverty line for urban households). poverty line household percent first poverty line (usd) upper 13172 69.87 111.66 lower 5680 30.13 total 18852 100 source: authors’ calculations. 120 ghasem layani, mohammad bakhshoodeh, mona aghabeygi, yaprak kurstal, davide viaggi percent of urban households (about 338 households) drop below the poverty line after a 9.80 percent price increase. 5. policy implications in iran, goods and services subsidy policy has been one of the most important consumer supportive policies over the past 40 years. the main goals of this policy include controlling and stabilizing prices, supporting vulnerable groups, reducing poverty, and distributing equitable income. although this policy instrument may help improve food security, it has been subject to increasing critiques in recent years. in fact, some local actors claim that, apart from the budget constraint, the use of goods and services subsidy policy in iran dates to the early 1970s, however, poverty index is still high, standard welfare is not achieved for households yet, and food safety and food security for poor and vulnerable households are still major concerns. as such, this instrument is seen as inefficient given its high budget costs, as a potential source of market distortions, and benefitting some groups who do not need to be supported (e.g. target groups are not identified and households receive the same subsidy) (azzam and rettab, 2012; bakhshoodeh, 2010; tefra, 2012). the subsidy payments by 11 usd per month for each person has been constant without considering inflation over the last two decades. these untargeted subsidy payments to the households, regardless of considering their vulnerability and their income level, in addition to being costly for the government, does not improve welfare indicators at the national level. identification of vulnerable households and determining the amount of subsidy payment to the target groups is one of the most important challenges that policymakers in iran are facing. assessing the effects of simultaneous price changes on household expenditures would be one of the tools to identify target groups in receiving subsidies. in other words, determining the share of household expenditure changes (with different table 6. the impact of rising food prices on the poverty. group cv (usd) secondary poverty line (usd) households (%) poverty line change in household poverty upper lower (number) % cereals 3.56 115.22 68.08 31.92 338 1.79 meats 2.86 114.52 68.48 31.52 262 1.39 dairy 3.46 115.12 68.16 31.84 323 1.71 oil cooking 1.12 112.78 69.37 30.63 95 0.50 sugar 1.31 115.75 69.26 30.74 116 0.62 fruits 4.09 114.69 67.82 32.18 386 2.05 vegetables 3.03 112.97 68.39 31.61 280 1.49 tea and coffee 0.57 112.23 69.57 30.43 57 0.30 total 20 131.66 59.24 40.76 2004 10.63 source: authors’ calculations 121the impact of food price shocks on poverty and vulnerability socio-economic characteristics) in their income show households that are more vulnerable and in need of government support. also, the government can use the effect of total price increases on household expenditures to determine the optimal subsidy payment. finally, implementing this supportive policy by identifying target groups could reduce poverty and increase social justice and keep households with low-income above the food poverty line. 6. conclusion we employed iranian urban households’ price elasticities of eight food groups (cereals, meats, dairy, oils, fruits, vegetables, sugar and tea and coffee) to evaluate the welfare effect of food price changes. for this aim, the compensated variation (cv) has been utilized, based on the changes in global food prices between 2000 and 2016. meanwhile, iranian food demand has been estimated by using the quadratic almost ideal demand system (qaids). this model used to explore how increasing food prices affect iranian urban consumer welfare and the poverty line. substitution effects among food items are estimated by including own and cross price elasticities obtained through the estimation of a demand system, qaids. according to the demand theory, all the estimated price and expenditure elasticities are acceptable (negative for own elasticities and positive for expenditure elasticities). increasing food price leads to urban iranian household welfare losses and a decrease in the purchasing power. also, the results of cv suggest that on average, iranian urban households need to be compensated with approximately 11.82 percent of their 2016 total household expenditure on food in order to accommodate the adverse impact of food price changes they faced between 2000 and 2014. although the food price changes have had differential effects for each of the food groups, price changes for the majority of households, have brought severe hardship for them to access food. the food price changes drive 2004 urban households below the food poverty line and causes a 10.63 percent increase in the number of poor urban households. our findings, hence, underline the negative impacts of price shocks on households’ welfare, especially those that are more vulnerable than others. being informed about the extent of these impacts, by the use of the vulnerability index, hence carries a crucial significance in the context of efforts towards achieving food security in developing countries, and towards supportive policy making targeted at especially vulnerable households. in a similar manner, in order to contribute to food security efforts and to execute food safety goals, the iranian government should compensate the welfare loses that are incurred by vulnerable households by putting in place supportive policies including but not limited to raising wages or subsidizing vulnerable households. the strengths of this study compared to other research include: (i) using quadratic system of the equation which is more flexible than other demand functions, (ii) calculating welfare changes simultaneously with food price changes, (iii) calculating vulnerability index, (iv) calculating second poverty line due to simultaneous changes in food prices and (v) assessing the increase of food prices simultaneously on food poverty of rural households. there are, however, some limitations of our study that could be addressed in order to add more precision to our results. this paper has focused on the vulnerability index of aggregate eight food groups rather than individual food items. further research can 122 ghasem layani, mohammad bakhshoodeh, mona aghabeygi, yaprak kurstal, davide viaggi also focus on individual food items as well as major expenditure groups such as clothing, housing services and health for urban households. meanwhile, the compensated elasticities can also be calculated for each household in order to identify the social characteristics of households that fall in the category of vulnerable households. moreover, in this study, all the simulations were carried out by cross-sectional data. future research would need to use panel data and explore poverty dynamics to test how food price shock affects poverty over time by taking into account the household livelihood strategies. last but not least, we assume that the global food price shocks transferred completely to the domestic market in iran. future work would benefit substantially using accurate quantities of food price transfer in welfare calculations. references abdulai, a., jain, d. k. and sharma, a. k. 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(2000). world development report: attacking poverty. oxford university press, new york. isbn 0-19-521129-4. appendix the qaids for eight food groups reported below: 125the impact of food price shocks on poverty and vulnerability the results of qaids model represented in table a. table a. estimated parameters for the qaids model. αi γ1j γ2j γ3j γ4j γ5j γ6j γ7j γ8j βi λi cereals 0.312 (0.072* 0.142 (0.00) -0.053 (0.00) -0.014 (0.00) 0.001 (0.00) 0.004 (0.00) -0.079 (0.00) -0.021 (0.00) 0.021 (-0.02) 0.095 (0.03) -0.009 (0.00) meats -0.121 (0.06) 0.089 (0.00) 0.002 (0.00) -0.001 (0.00) -0.001 (0.00) -0.038 (0.00) -0.003 (0.00) 0.005 (-0.02) 0.131 (0.03) -0.021 (0.00) dairy 0.730 (0.04) -0.021 (0.00) 0.019 (0.00) -0.021 (0.00) 0.028 (0.00) 0.017 (0.00) -0.011 (-0.02) -0.171 (0.02) 0.016 (0.00) oil cooking -0.055 (0.03) -0.033 (0.00) 6×10-4 (0.00) 0.001 (0.00) -0.001 (0.00) 0.014 (-0.01) 0.099 (0.01) -0.012 (0.00) fruits 0.475 (0.04) 0.021 (0.00) -0.017 (0.00) 0.013 (0.00) 0.002 (-0.01) -0.142 (0.02) 0.021 (0.00) vegetables 0.108 (0.04) 0.109 (0.00) 0.005 (0.00) -0.010 (-0.02) 0.086 (0.02) -0.007 (0.00) sugar 0.202 (0.02) 1×10-4 (0.00) -0.01 (-0.01) -0.056 (0.01) 0.008 (0.00) tea and coffee -0.651 (0.13) -0.011 (0.15) -0.042 (-0.15) 0.003 (0.00) *the numbers in parenthesis are standard deviation. source: authors’ calculations from eviews 9. the role of trust and perceived barriers on farmer’s intention to adopt risk management tools elisa giampietri1, xiaohua yu2, samuele trestini3,* drivers and barriers of process innovation in the eu manufacturing food processing industry: exploring the role of energy policies federica demaria, annalisa zezza step-by-step development of a model simulating returns on farm from investments: the example of hazelnut plantation in italy alisa spiegel1,*, simone severini2, wolfgang britz3, attilio coletta2 the role of group-time treatment effect heterogeneity in long standing european agricultural policies. an application to the european geographical indication policy leonardo cei1, gianluca stefani2, edi defrancesco1 the impact of food price shocks on poverty and vulnerability of urban households in iran ghasem layani1, mohammad bakhshoodeh1, mona aghabeygi2*, yaprak kurstal3, davide viaggi3 bio-based and applied economics 6(1): 81-114, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-14625 a spatial analysis of terrain features and farming styles in a disadvantaged area of tuscany (mugello): implications for the evaluation and the design of cap payments laura fastelli1*, chiara landi2, massimo rovai1, maria andreoli1 1 dept. of agricultural, food and agri-environmental sciences, university of pisa, pisa, italy 2 italian national institute of statistics, rome, italy date of submission: 2014 15th, june; accepted 2017 1st, march abstract. in recent times there has been a growing awareness of the role of agriculture in providing public goods and services, in particular in less favoured areas. however, since agriculture is an economic activity, its permanence implies that it should be able to generate a satisfactory income for farmers. where this is not possible, due to natural constraints or adverse economic and market conditions, in order to maintain an adequate use of farmland it is necessary to provide public aid to farmers. in this framework, the design of proper interventions aimed to promote rural development in less favoured areas should be based on a deep knowledge at the farm and territorial level. as regards the territorial level, the rdp zoning [art. 11 reg. ce 1698/2005] developed by member states on the base of the guidelines provided by the european commission is very often not sufficient to adequately define the territorial characteristics of rural areas. the use of gis techniques may help to handle this issue by providing a better and more detailed knowledge at the territorial level. farm level is important insofar as aid effectiveness is usually strongly depending on the type of farm that is receiving it. thus, a careful selection of beneficiaries could determine a more effective and efficient distribution of resources. this paper aims to provide a spatial analysis of natural constraints and types of farming style in mugello area and to analyse their relations with cap aid distribution. both single payment scheme (sps) and rural development programme (rdp) payments have been taken into account. the paper combines a gis analysis of terrain features with the theoretical approach of farming styles. for this purpose, the study integrates several sources of data: the 2010 italian agriculture census, the tuscany regional agency for payments in agriculture (artea) database, and land cover data from the database corine land cover (clc06), as updated to 2007 by lamma (laboratory for environment monitoring and modelling). a geo-referenced database including socioeconomic attributes of farms, land use, and terrain characteristics has been generated in order to merge information at territorial and farm level. the results of this integrated analysis confirm that mugello is a very heterogeneous area as regards terrain characteristics despite the fact that it is totally included in less favoured areas. on the other side, farm strategies and economic results seem to be related to entrepreneurial characteristics as much as to natu*corresponding author: laura.fastelli@for.unipi.it 82 l. fastelli et al. ral constraints. the analysis of pillar i payments and rdp payments for farms located in this mountainous area shows a very complex situation where the strategies implemented by farmers of the strongest farming styles may successfully counteract natural constraints. besides, in the authors’ opinion, the analyses performed highlight the importance of spatial analysis as a tool for evaluating how public resources are distributed on a territory, thus providing also useful information on the way this distribution could be improved, e.g. for ensuring a higher level of environmental services. keywords. farming styles, integrated gis database, multi-criteria analysis, disadvantaged rural areas, common agricultural policies jel codes. q12, q15, q18 1. introduction since its beginning the common agricultural policy (cap) has been aiming at raising farmers’ incomes. in time, cap aims have changed showing an increasing interest in rural development, also as a tool for lowering disparities among different territories, and in maintaining the environment. in these last cases, farm viability and performances are seen more as a mean to promote both local development and ecosystem services provision, than as a goal in itself (cooper et al., 2006). the italian agricultural system is characterized by a wide range of farms, which differ in terms of socioeconomic (size, structure, labour, knowledge, networks, etc.) and farmed land (terrain) bio-physical characteristics. in order to design proper policies, decision makers need to understand and take into account this heterogeneity in the framework of the changing socioeconomic conditions of rural areas, both at farm and at territorial level. this heterogeneity is extended also to farmers strategies, as regards entrepreneurial attitudes, external operating environments and uses of available resources, that can be more deeply analysed through the theoretical approach of the style of farming (van der ploeg, 1990; 1994; 2000). according to the existing literature, farming style defines a specific way of combining together different resources, such as, land, labour, livestock, machines, networks, knowledge, etc., in order to achieve specific objectives. in other words, it is an entrepreneurial attitude that combines material and immaterial resources related to a specific rural context. this type of analysis allows understanding the causes of the coexistence of several types of farms, in terms of resources, investments and cultural heritage, within the same area or the same supply chain, and it helps investigating the complexity of agricultural systems, which are characterised by an increasing heterogeneity among farms (van der ploeg, 1994). farming style analysis is often based on surveys of farms located in territories that have similar characteristics, thus highlighting the differences in farm strategies (emtage & herbohn, 2012; schmitzberger et al., 2005). according to guillem et al. (2012), even at small geographical scale, diversified farm strategies related to personal attitudes and objectives of the farm operator, may coexist and lead to diversified development trajectories within the same area (beaudeau et al., 1996). the farming style analysis represents a useful tool to assess the combination of goals, strategies, and related practices put in place by farmers to improve their economic perfor83a spatial analysis of terrain features and farming styles mance. furthermore, this analysis allows understanding the impacts of public policies on the territory and the effects of farms’ activities on the provision of ecosystem services. following this approach, public policies should be tailored to the specific needs of each farming style, consequently improving the effectiveness of public expenditure (wilson, harper, and darling, 2013; van der ploeg and ventura, 2014; guillem et al., 2012). guillem et al. (2012), through a social survey, define farming styles according to farmers’ perceptions of the environment. results show that farmers do not entirely follow their stated objectives, since external factors – such as input and output price signals and subsidy levels – have on their strategies a stronger influence than their stated opinions about environmental and social issues. from this point of view, a classification of farming styles based on characteristics and decisions taken by farmers could be more reliable than one built on the results of a social survey. this approach may help to design more effective policy instruments, which integrate ecological issues within the planning activity. nevertheless, due to its relation both with farms and territorial characteristics, farming styles’ definition needs a huge amount of information, which prevents this kind of analysis being carried out at large scale. during the last few decades, researchers have created large databases able to provide good information also on specific small areas. these databases may reduce the time lag between research activities and policy makers’ decisions (joerin et al. 2001). however, this issue is very complex as many variables at territorial and farm level play a key role. many questions arise, such as: which are the needs of a specific area at farm and aggregated levels? and which are the best tools to fulfil these needs, combining public objectives related to regional development, and private objectives, related to business development? geographical information systems (gis) may help handle this issue by highlighting the specific needs of these areas in terms of policy supply (budic, 1994). the current availability of data and their processing by gis analysis make it possible to reach a more comprehensive knowledge of a specific territory, since socioeconomic information alone is not sufficient to explain the evolutionary trajectories of agriculture, due to their relationship with the physical characteristics of land (altitude, slope, etc.). indeed, spatial analysis – by locating all the parcels composing the farms through a spatial representation – better supports the planning processes (gonzales and del campo, 2012). many contributions on gis analysis have been applied to land use evaluation (for a review see malczewski, 2004). several contributions try to estimate ex-ante the potential impact on farm structures and on ecosystem services provided by the territory brought about by agricultural policies in general or by specific measures, i.e. rural development plan (rdp) environmental measures. ex-ante evaluations are usually performed by means of agent-based model (abm), such as the contributions of piorr et al. (2009), brady et al. (2012), and lobianco and esposti (2010). another strand of the literature uses gis to perform an ex-post evaluation of the cap since it seems to reduce costs and increase the effectiveness of policy results (matthews et al., 2013). matthews et al. (2013) use a spatial analysis framework to assess the effects of single farm payments (sfp) on farm income in several areas of scotland, integrating biophysical and socioeconomic data. their results show that the shift to area-based payments leads to a redistribution within sub-sectors; from intensive to more extensive systems. yang et al. (2014) investigating the implementation of agri-environmental measures 84 l. fastelli et al. in scotland argue that spatial models are more suitable to achieve an effective policy if compared to non-spatial regression models. besides, they note that, although results show a significant positive relationship between sites of special scientific interest (sssi)1 and agri-environmental payments, the current incentives system is not able to improve the provision of ecosystem services in the ‘wider countryside’, since only a low share of scottish farmers accede to this kind of payments. finally, other contributions (spaziante et al., 2009; 2012; adisa, 2012) use gis to evaluate the effectiveness of agri-environmental payments by analysing the spatial distribution of these measures (for a review see gonzales and del campo, 2012). results show that the territorial allocation of several environmental measures among farmers could be greatly improved. according to the authors, the spatial analysis provides useful inputs to policy makers especially when dealing with rdps, which involve a wide range of objectives, stakeholders, environmental effects and beneficiaries. the research presented in this paper follows the latter strand of the literature. although the introduction of the analysis at farm level in this kind of evaluation is usually based on the data of fadn2 accountancy (see, e.g., piorr et al., 2009) and it is mainly based on structural features and type of output mix, the authors deemed more important to take into account farm strategies, in order to be able to evaluate the influence of human capital on the possible evolution of the area. in this framework, this paper provides a gis analysis of single payment scheme (sps) and rural development programme (rdp) payments spatial distribution in mugello area (tuscany), with the aim of providing some insights on the spatial correlation between terrain features and farming styles and between farming styles and amount of aid received. in particular, these analyses should allow verifying if aid is more related to territorial features or to the type of farming style to which the receivers belong to. mugello is a mountain area located the north-east of tuscany, italy, where farming is characterised by the presence of dairy cattle and other livestock, that experienced a 20% decline in the number of farms over the last ten years, associated to a 12% loss of utilised agricultural area (uaa) (istat, 2010). indeed, as regards mountain and other disadvantaged areas, cooper et al. (2006) state that in these areas farms and farming systems are generally subject to natural handicaps, which act as a constraint on more intensive practices. in turn these handicaps exert an impact on the viability of the farm business and its relative competitiveness. as such, these farms are potentially under the greatest threat from the decline and cessation of management, with a consequent risk of loss of environmental values. the previous data of farms and uaa decrease seems to confirm that this risk is present in mugello. on the other hand, more than for its economic impact, farms and farming systems disappearance and land abandonment are important from an environmental viewpoint, insofar as the environmental and related public goods that are of value in the countryside stem from appropriate land management, and in particular, agricultural management over large areas. continued agricultural management contributes most to the countryside where it supports the maintenance of valued open landscapes, semi-natural habitats and biodiversity; it assists in the control of forest fires; or contribute to good soil and water management (cooper et al., 2006: p. 13). 1 the sites of special scientific interest (sssi) are designated sites prioritized by the scottish policy. 2 the farm accountancy data network (fadn) is an instrument for evaluating the income of agricultural holdings and the impacts of the common agricultural policy. 85a spatial analysis of terrain features and farming styles for this study, a geo-referenced database was created, integrating 2010 agricultural census data, public payments database and land use data, in order to understand the interactions among socioeconomic aspects, land use, terrain characteristics and agricultural policies. different entrepreneurial models or farming styles were identified through a multi-criteria analysis, which used quantitative and qualitative information at farm level. statistical and spatial data were processed in order to provide a deeper knowledge on the consistency of distribution of cap payments aimed at supporting farms located in disadvantaged areas. the paper is structured as follows: the following section 2 presents the methodology; section 3 presents the case study, firstly giving a description of the main features of the area, and then describing data and results of the analyses; section 4 presents concluding remarks on the main results of this work. 2. methodology in order to analyse the distribution of sps and rdp payments in mugello area and its correlation with farming styles and territorial features, several sources of data were integrated. land use data from corine land cover (clc-06) and lamma 2007 (laboratory for environment monitoring and modelling) databases were merged with the 2010 census of agriculture (in the following referred to as “census”) and the 2012 tuscany agency for payments in agriculture (artea) databases. the 2010 census provides a wide range of information regarding socioeconomic aspects of farms, and the artea database provides information on the public payments received by farms both under pillars 1 and 2. in the first step, both territorial and cadastral3 data were used in order to focus on the main differences among the agricultural resources existing inside mugello and to check their correlation with the zones defined by the tuscany rdp. then census and artea data were processed in order to define different farming styles/attitudes. finally, all the databases were merged to analyse the territorial structure of each rdp zone. all spatial analysis were carried-out using open-source gis software (qgis. 2.2) and its statistical and geomorphologic analyses plug-ins4. as previously stated, the first analysis focuses on territorial characteristics of rural areas according to land use, and it is carried out through statistical processing of land cover data (lamma, 2010). a geo-referenced database has been created integrating the cadastral database, the artea database and census data, in order to be able to allocate each plot of land to the farm that is farming it, to know the socioeconomic features of farms to which plots were allocated and to reconstruct the spatial area to which cap payments were distributed. then, these data have been clipped with the clc data and terrain features data. in particular, altitude (extracted from a digital elevation model) and slope (calculated as the average slope angle for each cadastral parcel in percent, based on first order derivative estimation) were determined for each cadastral parcel. the choice of altitude and slope 3 cadastral data refer to the italian inventory of agricultural land (catasto terreni), where the elementary unit is a parcel of land belonging to the same municipality, holder, category of agricultural utilization and class of productivity, that is not divided by roads, rivers, railways, etc. 4 more information of qgis project and on the qgis software can be found on the qgis project website, at the address http://qgis.osgeo.org/en/site/about/index.html (last consulted 05.05.2016). 86 l. fastelli et al. as has been made on the base of the rules for defining less favoured areas, among which mugello is included. indeed, “there are three categories classified as lfa. each category covers a specific cluster of natural or specific handicaps in europe in which the continuation of agricultural land use is threatened”. the first class is that of mountain areas, which “are characterised as those areas handicapped by a short growing season because of a high altitude, or by steep slopes, or by a combination of the two at a lower altitude” (jones et al. 2012). although the new rules proposed by jones et al. (2012) for assessing natural constraints favour the length of growing season as a constraint related to both temperature and altitude, within such a small area as mugello, altitude is highly correlated to temperature, and consequently it is a good proxy of the length of growing season. thanks to the merging and geo-referentiation of data from different sources, it was possible to obtain a sample, which covers a total agricultural area (taa) of 52,849 ha, 94% of which (49,966 ha) was geo-referenced. this final geo-referenced sample, which allows to represent each farm according to its terrain features (such as altitude, slope and land cover) and socioeconomic characteristics, is composed by 821 farms, representing 56% of farms and 81% of the taa surveyed by istat in 2010 (table 1). table 1. mugello coverage of the farms sample compared to universe provided by 2010 agricultural census.   sample universe % sample/ universe farms (number) 821 1462 56% utilised agricultural area (uaa) (ha) 23,476 27,290 86% total agricultural area (taa) (ha) 49,966 61,865 81% source: own elaboration on 2010 census of agriculture data the geo-referenced sample of mugello farms presents stronger structural characteristics than the universe in terms of uaa, taa and standard output per farm (table 2) and this is very likely due to the fact that it includes only farms which have received public payments through artea; consequently the sample represents the core of the mugello agricultural system. this explains the differences between sample and universe in terms of farm acreage, which are confirmed by anova results (see row t-test in table 2). then the farms operating in mugello area were classified in order to identify different farming styles or entrepreneurial models/attitudes using quantitative and qualitative information provided by the 2010 census. following the approach proposed by rocchi and landi (2013) and rocchi et al. (2014), we define entrepreneurial attitudes according to the level of entrepreneurial dynamism and of multifunctional diversification. the entrepreneurial dynamism is related to the farm operator attitude towards the business external environment, i.e. his/her capacity to properly react to external changes, and it was measured using several variables as proxies, i.e. the use of bookkeeping, the use of information and communication technology (ict), the use of subcontracting, on-farm presence of the farm operator, type of integration with output markets and the attendance of the farm operator to professional 87a spatial analysis of terrain features and farming styles courses (table 3). the multifunctional diversification, related to non-agricultural activities, i.e. the so called “other, or secondary, gainful activities” and the production of high quality products (table 3), is another very important feature of farmers since it highlights the attempt to diversify farm income by pursuing new business activities, as promoted by the eu. the multifunctional diversification and entrepreneurial dynamism levels were measured through a multi-criteria analysis (mca) methodology (dean and nishry, 1965). the mca encompassed the following stages: a) definition of variables and attributes; b) definition of a scale from 1 to 10 for each attribute (table 3) and attribution of scores to each farm for every attribute; c) pairwise comparison to assign relative weights to the variables; d) determination of the matrix of weighted scores for both multifunctional diversification and entrepreneurial dynamism indices; and e) definition of farming styles. in order to weigh the variables used for computing the two indices, we applied a pairwise comparison among the selected variables (table 4), where the value 1 was given when the variable on the row was deemed more important than the variable on the column, 0.5 if they had the same importance and 0 when the variable on the row was deemed less important than the one on the column. from the matrix of the weights resulting from the pairwise comparison it was possible to obtain a vector of the variables’ weights, that has been normalised by making its sum equal to 1. the results obtained are presented in tables 4 and 5, which show the weights assigned to the variables used to compute entrepreneurial dynamism and multifunctional diversification indices, respectively. the most important variables, i.e. those with higher weights, are the percentage of income coming from non-agricultural activities (multifunctional diversification) and the importance of forms of integration with output markets on total production (entrepreneurial dynamism). the values of both multifunctional diversification and entrepreneurial dynamism indices are obtained, for each farm, as a weighed sum of the scores obtained on all variables composing the indices. when representing the entrepreneurial dynamism and the multifunctional diversification on the two axes of a cartesian plane, where the threshold value (axes origin) is represented by the average value of the resulting scores, four main farming styles are defined table 2. mugello comparison between the geo-referenced sample and the universe. farm utilized agricultural area (ha) farm total agricultural area (ha) farm standard output (€) sample universe sample universe sample universe min 0 0 0.24 0 0 0 1st quart 4.87 2.38 8.12 4 5,870 3,397 median 11.05 5.82 22 11.36 17,370 9,348 mean 28.34 18.67 64.37 42.32 47,750 31,690 3rd quart 30 16.59 56 35.58 42,000 23,600 max 775.75 775.75 6,453 6,453 3,073,000 3,073,000 ttest 4.5831*** 2.1743** 2.8027*** source: own elaboration on 2010 census data. (*** significance at 0.01; ** significance at 0.05, * significance at 0.1). 88 l. fastelli et al. (figure 1). since the multifunctional diversification is mainly related to non agricultural activities, the farms beyond the average value have at least one non-agricultural activity representing a consistent share of total income. table 3. variables (and rules for their score assignment) composing the «multifunctional diversification» and the «entrepreneurial dynamism » indices. multifunctional diversification index entrepreneurial dynamism index cultivation practices (total score 0-10 as a sum of the four single scores) presence (2.5)/absence (0) of: hedgerows grass and cover crops crop rotation conservative tillage bookkeeping (presence and type) 0 no bookkeeping 5 bookkeeping only for vat accounting purposes 10 bookkeeping for ordinary accounting regime high quality products (share of farm acreage dedicated to high quality products) 0 none 2.5 < 1/3 5 1/3 2/3 10 > 2/3 information communication technology (uses for types) 0 no ict use 2 only for administrative purposes 4 only for crops and livestock management 6 both for administration and for crops and livestock management 8 use of internet 10 use of internet and e-commerce non-agricultural activities (number) 0 no non-agricultural activities 3.33 1 activity 6.66 2 activities 10 > 2 activities attendance to professional courses 0 no 10 yes share of farm income deriving from non -agricultural activities 0 – none 5 < 30% 10 >30% subcontracting (surface expressed as percentage of uaa.) 0 > 75 % 5 2575 % 10 < 25 % presence of the entrepreneur (in terms of on farm and off farm working days) 0 farm operator not working on farm 2.5 < 100 working days on farm 5 100180 working days on farm 7.5 >180 total working days; off-farm days > onfarm days 10 >180 total working days; off-farm days < onfarm days forms of integration with output markets (share of sold standard output) 0 only self-consumption 3.33 < 1/3 of sold standard output 6.66 1/3 2/3 of sold standard output 10 >2/3 of sold standard output note: variables’ scores in bold. 89a spatial analysis of terrain features and farming styles the farming styles stemming from the previous classification are: i) decline, ii) survival, iii) conservative development, and iv) innovative development (figure 1). the decline farming style includes farms without non-agricultural activities and with a low degree of entrepreneurial dynamism. the survival farming style includes farms with a low degree of entrepreneurial dynamism but where entrepreneurs adopt strategies aimed to increase farm income through on-farm diversification (bartolini et al. 2013). the last two types include farms with higher entrepreneurial dynamism. the difference between these two last farming styles stems from the productive choices of the farm operator: strengthening the agricultural production improving economies of scale and technological investments with a more traditional approach (conservative development); or farm diversification due to the introduction of activities that increase the added value of products and the provision of ecosystem services (innovative development), with a more innovative approach and a positive spill-over on the society for the provision of public services. table 4. weight matrix related to entrepreneurial dynamism. bookkeeping ict courses subcontracting entrepreneur labour integration with output markets control number weight vector bookkeeping x 0 1 0 0 0 1 0.100 ict 1 x 1 0 0 0 1 0.150 courses 0 0 x 0 0 0 1 0.050 subcontracting 1 1 1 x 1 0.5 1 0.225 entrepreneur labour 1 1 1 0 x 0 1 0.200 integration with output markets 1 1 1 0.5 1 x 1 0.275 table 5. weight matrix related to multifunctional diversification. cultivation practices high quality products number of nonagricultural activities output from nonagricultural activities (%) control number weight vector cultivation practices x 0 0 0 1 0.100 high quality products 1 x 0 0 1 0.200 number of non agricultural activities 1 1 x 0 1 0.300 output from non-agricultural activities (%) 1 1 1 x 1 0.400 90 l. fastelli et al. figure 1. the definition of farming styles according to the level of multifunctional diversification, and entrepreneurial dynamism. 3. the case study: description of the area, data and results in the following paragraphs, after describing the case study-area, firstly we describe in details the data used in this analysis, and then we provide an analysis of the statistical significance (anova and t-test) of the results for mugello area of the farming styles described in the previous chapter against several socioeconomic indicators, including the access to public payments. once verified the validity of the farming styles from a statistical point of view, they were correlated with rdp zoning. as regards public payments, especially those relating to rdp measures 211 and 212 (i.e. mountainous and other disadvantaged areas), the analyses of their spatial distribution aimed at highlighting their correlation with farming styles and the level of natural handicap constraints, measured by terrain features. 3.1 mugello: location, main features and importance of cap aid for the permanence of agricultural activities mugello, one of the historical regions of tuscany, is located northeast of florence, and it includes 9 municipalities5, which belong to two different sub-zones, upper 6 mugello in the north, where mountain areas are associated to high altitude and declivity of land, 5 barberino di mugello, borgo san lorenzo, dicomano, firenzuola, marradi, palazzuolo sul senio, san piero a sieve, scarperia, and vicchio. 6 firenzuola, marradi, and palazzuolo sul senio. 91a spatial analysis of terrain features and farming styles and lower7 mugello in the south, where the land is more suitable to agricultural activities. according to tuscany rdp zoning, mugello includes three different zones: intermediate rural areas in transition (c1); declining intermediate rural areas (c2) and rural areas with development problems (d). zones c1 and c2 derive from the splitting of the class of intermediate rural areas described by the national rural development plan, since in tuscany this zone would have accounted for more than 60% of the regional territory. the splitting has been made mainly on the basis of the incidence of workers employed in agriculture (whose high value characterises c2 zone) and of the total forestry area (including also the part not belonging to farms). mugello area has traditionally been specialised in agricultural activities and animal husbandry, especially dairy cattle and beef cattle rearing. in particular, in upper mugello, forestry and pastoral activities are very important. farmers mainly use the forestry for firewood and chestnut cultivation, which are both rapidly growing due to the current policies. in mugello the relationships between geomorphologic features and anthropogenic organisation are very important. indeed, several differences between the northern and the southern part exist at geological and at hydro-graphic network level. this, in turn, has strongly affected the types of settlement, their locations, and the farm structures. upper mugello is characterised by steeper terrains with worse accessibility conditions, associated to increasing depopulation and abandonment of previously cultivated fields, pastures and woods. the reduction of agroforestry practices has triggered massive processes of renaturalisation and expansion of natural vegetation. this massive renaturalisation of mugello is often bordering with abandonment and, coming after an agricultural exploitation of the land, it has broken the old balance of this territory, bringing about hydrologic and erosive problems resulting in very negative effects. the southern side is characterised by gentle reliefs, where the forest cover is not continuous, and large areas of agricultural fields alternate to grasslands and pastures (lamma, 2010). as regards environmental quality, a recent contribution of bartolini and brunori (2014) shows that in 2010 the situation in mugello was quite good as regards the high nature value (hnv) index and had been improving during the previous ten years. a spatial description of the distribution of field types in mugello area can be found in piorr et al. (2009, fig. 3). recent analyses by piorr et al. (2009) and uthes et al. (2011) show that a change of cap aid in mugello, especially in the case of abolishing direct payment, could bring about severe consequences in the area, such as a dramatic abandonment of mountain grassland field types, a relevant change in the share of uncultivated land on the total arable and changes in land use intensity. piorr et al. (2009) provide an estimate of the impact of these changes on water erosion. nevertheless, according to this study, the main changes would be related to a loss of landscape diversity and biodiversity. uthes et al. (2011) in a simulation of the effects of abolishing direct payments, stress the importance for farm survival of a highly diversified sector with agro-tourism opportunities and good marketing and sales structures, variables that in our analysis have been included in the computation of the multifunctional diversification index. nevertheless, according to uthes et al. (2011), although agriculture survival could not be at stake, in the case of change of policy patterns, there will be severe impacts on some variables, e.g. on the share accounted by grassland, due to shifting of the traditional grazing-based activity of livestock rearing to 7 barberino di mugello, borgo san lorenzo, dicomano, san piero a sieve, scarperia, and vicchio 92 l. fastelli et al. more intensive indoor systems. these changes could bring about both environmental problems (due to intensification) and social problems (due to the disappearance of many small farms and a reduction of labour requirement caused by land abandonment). 3.2 land use, natural constraints and rdp zoning of mugello in order to better understand the natural resources of mugello area, we have analysed the data of the distribution of corine land cover as updated by lamma (table 6). in this case the total agro-forestry area includes also land that is not belonging to any farm. the area of mugello is 113,122.65 ha, 95.3% of which (107,838.37 ha, table 6) are accounted by agroforestry land uses (lamma, 2010). forestry (clc classes 311, 312, 313, 322 and 323) and pasture and natural grassland (clc classes 231 and 321) prevail in d zone (zone which represents 50.20% and 64.87% of the total area, respectively, for these land uses). in terms of share of each rdp zone, forest uses range from the 63.74% of c1 zone to the 74.49% of d zone, thus representing the main use of land in all the territory (70.91% on average in the whole mugello area). furthermore, the data show an almost equal distribution of the total arable land (clc class 211) among the three rdp zones, although in terms of incidence on the total area of the rdp zones, arable land is decreasing from c1 (where it accounts for 28.26%) to d zone (where it accounts for 14.27%), with an inverse correlation with the share of forest land. arable land accounts on average for 19.21% of the total mugello areas, being the second most important land use after forest. on the map, the arable land is mainly represented by the horizontal belt in clearer colours that is located in the southern part of c1 and c2 zones. this arable land is mainly located in the class with the lowest altitude (figure 3). contrariwise, permanent crops (clc classes 221, 222 and 223) are distributed unevenly, with a prevailing presence in the c2 zone (which accounts for 64.34% of the total), although their share on the total area of each zone is quite small, ranging from the 3.72% of the c2 zone to the 0.45% of d zone. figure 2 presents the detailed distribution of clc classes in mugello area, while the corresponding detailed data can be found in table a.1 in appendix. as already highlighted in the methodological chapter, according to the 2010 agricultural census, the sample used in the present analysis covers a taa of 52,849 ha and over 94% of this area has been geo-referenced trough the q-gis software (qgis development team 2012). land use is often related to terrain features, such as altitude and slope, which were assessed through terrain analysis (plug-in q-gis5). the data were analysed choosing cultivated cadastral parcels as the elementary reference unit. this choice results in analysing units with a variable size, while most of the analyses has been done by using larger regular square grid units. in the author’s opinion, the advantages in using a unit that is homogeneous (see note 2) from the point of view of agricultural features and utilisation; and which allows performing spatial analysis of the real structural and socio-economic features of each farm, overcome the disadvantages in working with units that are not regular in shape and size. due to parcels homogeneity, their size distribution was deemed as not important, since it is mostly depending on the administrative history of the parcel. after analysing land uses in mugello areas, we deemed important to analyse the main terrain features, since they are related to the level of natural constraints that not only influence the possible land uses, but also the costs and the incomes of those uses. the 93a spatial analysis of terrain features and farming styles following maps and tables (figures 3, 4, 5 and tables 7, 8, 9) present the characterisation of the geo-referenced farms operating in the mugello area in terms of terrain features. as already stated above, differently from piorr et al. (2009)8 the land characteristics have been attributed to the farm’s parcels as resulting from the artea database (artea 2012) and not to a regular grid and this, in our opinion, consent to obtain results that are more adherent to real farm management, since regular grids do not allow reconstructing the land that is under management by each farm. the following table 7 analyses the correlations between the distribution of farm land by classes of average altitude and the three rdp zones into which mugello total area is 8 piorr et al. (2009) defined the standard size of the fields (1 ha). table 6. distribution of corine land cover (clc) classes in mugello rdp zones. c1: intermediate rural areas in transition; c2: declining intermediate rural areas; d: rural areas with development problems. 6.1 – absolute values group number description of groups of clc classes groups of clc classes (lamma, 2010) surface (hectares) by rdp zones c1 c2 d mugello 1 arable land 211 6,457.01 6,910.09 7,353.23 20,720.33 2 permanent cultivation 221+222+223 456.49 1,244.18 233.15 1,933.82 3 pastures and natural grassland 231+321 187.13 333.27 960.81 1,481.21 4 agriculture land mixed with natural land 241+242+243 410.79 559.00 510.83 1,480.62 5 forest 311+312+313+322+323 14,565.90 23,514.31 38,385.74 76,465. 95 6 sparsely vegetated area 333+324 774.38 891.63 4,090.45 5,756.46 total total 22,851.70 33,452.48 51,534.21 107.838,37 6.2 – relative values group number description of groups of clc classes row distribution (%) column distribution (%) c1 c2 d mugello c1 c2 d mugello 1 arable land 31.16 33.35 35.49 100.00 28.26 20.66 14.27 19.21 2 permanent cultivation 23.61 64.34 12.06 100.00 2.00 3.72 0.45 1.79 3 pastures and natural grassland 12.63 22.50 64.87 100.00 0.82 1.00 1.86 1.37 4 agriculture land mixed with natural land 27.74 37.75 34.50 100.00 1.80 1.67 0.99 1.37 5 forest 19.05 31.18 49.51 100.00 63.74 70.29 74.49 70.91 6 sparsely vegetated area 13.45 15.49 71.06 100.00 3.39 2.67 7.94 5.34 total agro-forestry 21.19 31.02 47.79 100.00 100.00 100.00 100.00 100.00 source: own elaboration on corine land cover as updated by lamma (2010). 94 l. fastelli et al. classified. this could help understanding with greater detail how much the variable altitude has influenced the rdp zoning9. besides, it allows verifying, if between c1 and c2 areas there are differences in altitude since the splitting of intermediate rural areas has not been influenced by this variable. table 7 has been organised in three sections; the first part gives the share accounted by each class coming from the crossing of altitude classes and rdp zones, while the second and third parts describe how each rdp zone is distributed among altitude classes (row distribution) and how each altitude class is distributed among rdp zones (column distribution), respectively. as one can see from table 7, land below 400 m a.m.s.l. is mainly located in c2 zone, which accounts for 74.81% of the total of this “lowland”, while lands in the class 400600 m and in the classes above 600 m are mainly located in c1 zone (where it accounts 9 the national rural development plan (ndp) provided a zoning based on demographic density, altimetry features and share of agricultural surface on total. figure 2. land use in mugello. source: own elaboration on corine land cover as updated by lamma (2010). 95a spatial analysis of terrain features and farming styles for 56.87%) and d zone (where it accounts for 72.13%), respectively. usually, when analysing altitude as a factor constituting a natural constraint for agricultural activities, the altitude of 600  m is considered as the one able to limit agricultural activity when taken alone, while in combination with a relatively high slope also a lower altitude could be considered as a limiting factor (cooper et al., 2006). when analysing data by rdp zone (see third section of table 7), it emerges that c2 zone accounts for almost 75% of the total land located under 400 m, although it presents also a not negligible share of land in the 800-1000 m class, while d zone represents the prevalent share both of the class 600-800 m (75,92% of which is located in this zone) and of the class 800-1000 m (52,92%). the 0,36% of total mugello area that is located above 1000 m is entirely located in d zone. the previous analysis (table 6) shows a d-c2-c1 hierarchy of “favourable” land uses. however, as regards altitude features, there is an inversion in the hierarchy between c1 (intermediate rural areas in transition) and c2 zone (intermediate rural areas in decline) as this latter presents better features. table 8 presents the distribution of farmland area by slope classes and rdp zones. while the first part of the table presents the share accounted by each class obtained by crossing slope classes and rdp zones, the following parts present, respectively, the row and column distributions, namely the share that slope classes account inside each rdp figure 3. average altitude of farmland areas in mugello (m a.m.s.l.). source: own elaboration on digital terrain model of tuscany region. 96 l. fastelli et al. zone (row distribution) and the share that rdp zones account inside each slope class (column distribution). according to the new proposed rules for the delimitation of areas with natural constraints (böttcher et al., 2009), slope becomes a limiting factor alone when it is above 15%; thus the attention should focus on the class related to the highest slope (15-30%) while the previous class should be given attention only when associated to high altitude. as it is apparent from table 8, in mugello the areas with a slope higher than 15% are about 39% (13.58% accounted by c2 zone and 25.62% by d zone). in d zone the class with the highest slope accounts for 55.56%. since d zone is also characterized by the highest altitude classes, the agricultural activities of this rdp zone suffer from severe natural constraints. as regards the situation of c1 and c2 zones, c2 is characterised by a higher share of farmland included in the highest slope class compared to that of c1 zone (36.4% in c2, 26.32% in c1). thus, while c1 zone has worse features than c2 as regards altitude, it has better features as regards slope. the previous figures and tables highlight a great difference in terms of terrain characteristics among the three rdp zones. the land above 600  m a.m.s.l. is concentrated in table 7. mugello distribution of farmland areas in rdp zones according to the class of altitude (meters above medium sea level). rdp zone distribution of farmland area in the sample (%) by altitude classes and regional development programme (rdp) zones < 400 m a.m.s.l. 400-600 m a.m.s.l. 600-800 m a.m.s.l. 800-1000 m a.m.s.l. > 1000 m a.m.s.l. c1 7.40 14.85 3.30 0.56 0.00 c2 22.87 8.23 3.91 2.34 0.00 d 0.30 9.88 22.73 3.26 0.36 rdp zone distribution of farmland area in the sample (%) by altitude classes inside each regional development programme (rdp) zone < 400 m a.m.s.l. 400-600 m a.m.s.l. 600-800 m a.m.s.l. 800-1000 m a.m.s.l. > 1000 m a.m.s.l. total area c1 28.34 56.87 12.64 2.14 0.00 100.00 c2 61.23 22.03 10.47 6.27 0.00 100.00 d 0.82 27.05 62.22 8.92 0.99 100.00 rdp zone distribution of farmland area in the sample (%) by regional development programme (rdp) zone inside each altitude class < 400 m a.m.s.l. 400-600 m a.m.s.l. 600-800 m a.m.s.l. 800-1000 m a.m.s.l. > 1000 m a.m.s.l. c1 24.21 45.05 11.02 9.09 0.00 c2 74.81 24.97 13.06 37.99 0.00 d 0.98 29.98 75.92 52.92 100.00 total area 100.00 100.00 100.00 100.00 100.00 source: own elaboration on digital terrain model of tuscany region and rdp regional db. 97a spatial analysis of terrain features and farming styles zone d (26,35% of mugello total farmland area), with lower values in zones c1 (3.86%) and c2 (6.25%). similarly, land in the slope class higher than 15% located in zone d accounts for a share of 25.62% of mugello total area, whilst in zones c2 and c1 this value decreases to 13.58% and 6.91%, respectively. in summary, coherently with expectations, areas with development problems show the worst operating conditions. these results, together with average values for altitude and slope by rdp zone, have been validated through anova, which shows statistically significant differences among rdp zones (table 9). figure 4. farmland areas in mugello according to classes of average slope. source: own elaboration on digital terrain model of tuscany region table 8. mugello distribution of farmland surfaces (%) in rdp zones according to the slope classes. rdp zone slope classes (share %) row distribution (%) column distribution (%) < 5% 5-15% 15-30% < 5% 5-15% 15-30% < 5% 5-15% 15-30% c1 1.81 17.53 6.91 6.9 66.78 26.32 58.39 34.51 14.99 c2 1.26 22.47 13.58 3.38 60.23 36.40 40.65 44.24 29.45 d 0.03 10.79 25.62 0.08 29.61 70.31 0.97 21.24 55.56 source: own elaboration on digital terrain model of tuscany region and rdp regional db 98 l. fastelli et al. table 9. mugello average altitude (m a.m.s.l.) and slope (%) of mugello rdp zones and statistical significance of distribution variables rdp zones anova f valuec1 c2 d altitude 458.63 440.17 642.34 152.51*** slope 11.09 14.04 18.53 156.84*** source: own elaboration on digital terrain model of tuscany region and rdp regional db (*** significance at 0.01; ** significance at 0.05, * significance at 0.1). 3.3 farming style analysis and spatial distribution the farming styles representing different entrepreneurial attitudes of farmers in mugello were analysed according to the statistical significance of the main socioeconomic characteristics and factors of productivity. the decline group, with 379 holdings, includes the majority of farms, whilst the survival group includes only 52 farms. the conservative development and innovative development groups include respectively 193 and 197 holdings. table 10, with anova10 results, shows that differences in structures among the four farming styles are statistically significant. farms included in the decline style have lower acreage, economic dimension and annual working units11 (awu), presenting an average size of 16.29 hectares, an average standard output (so) equal to 18,150 € per year and 1.09 awu per year. on the contrary, the highest values characterize farms included in the innovative development group, with an average size of 52.22 ha and an average standard output equal to 103,000 € (table 10). survival and conservative development groups show similar values, even though the latter group shows slightly higher values (27.90 ha against 25.71 ha of average size; 49,430 € against 43,670 € of annual standard output). in the first two rows of table 10 the total farm uaa, farm so and farm awu accounted by each farming style and the relative share accounted by each farming style on the total of the above mentioned variables are reported. as regards uaa, the innovative development style accounts for the major share (44.4%) of the total uaa followed by the decline and conservative development styles which account respectively for 26.6% and 23.2% of the uaa, while survival style accounts for a very limited share (5.8%). the importance of innovative development style is still higher in terms of standard output (52.1%). as regards so, conservative development represents a higher share than decline, while for uaa it was the opposite. when analysing the annual working unit, the situation is more balanced with shares, for the three more important styles, varying from the 36.4% of innovative development to the 26.3% of conservative development style, while survival accounts for 7.7%. in summary, results show that over 52% of farms and 32% of uaa are included in the decline or survival styles, which are the groups closer to exit in the long run. 10 three different anova were run on the sample of 821 farms. the dependent variable was respectively the uaa, so and awu, whilst the independent variable was the categorical variable showing the style to which each farm belongs. 11 the annual working unit (awu) is a measure introduced by the european union to design the work of a full time equivalent worker and it is calculated as the number of working days divided by 225. 99a spatial analysis of terrain features and farming styles ta bl e 10 . m ug el lo u a a (h ec ta re s) , s ta nd ar d ou tp ut (e ur o) a nd a w u a cc or di ng to fa rm in g st yl es fa rm u a a (h a) fa rm s o (€ ) fa rm a w u d ec lin e su rv iv al c on se rv at iv e d ev el op m en t in no va tiv e de ve lo pm en t d ec lin e su rv iv al c on se rv at iv e d ev el op m en t in no va tiv e de ve lo pm en t d ec lin e su rv iv al c on se rv at iv e d ev el op m en t in no va tiv e de ve lo pm en t to ta l 61 73 ,9 13 36 ,9 53 84 ,7 10 28 7, 3 6, 87 8, 85 0 2, 27 0, 84 0 9, 53 9, 99 0 20 ,2 91 ,0 00 41 3, 1 10 8, 2 36 6, 7 50 8, 3 % o n to ta l 26 .6 5. 8 23 .2 44 .4 17 .6 5. 8 24 .5 52 .1 29 .6 7. 7 26 .3 36 .4 m in 0. 24 0. 32 0. 66 1. 00 0 0 0 11 65 0 0 0. 08 0. 10 1st q ua rt 3. 01 6. 53 7. 40 14 .3 0 2, 85 6 7, 14 9 12 ,6 90 25 ,3 20 0. 40 0. 71 0. 92 1. 50 m ed ia n 6. 03 13 .5 6 15 .1 0 27 .3 6 7, 21 5 16 ,0 10 25 ,8 80 41 ,7 70 0. 73 1. 26 1. 62 2. 06 m ea n 16 .2 9 25 .7 1 27 .9 0 52 .2 2 18 ,1 50 43 ,6 70 49 ,4 30 10 3, 00 0 1. 09 2. 08 1. 90 2. 58 3rd q ua rt 13 .0 0 25 .3 2 32 .2 5 61 .0 0 17 ,9 20 37 ,6 80 55 ,2 30 93 ,0 90 1. 62 2. 67 2. 68 3. 20 m a x 46 8. 20 21 5. 30 16 9. 10 77 5. 7 57 3, 50 0 36 1, 40 0 10 7, 20 00 30 73 ,0 00 6. 48 13 .3 0 12 .8 9 9. 73 a n a ly si s o f va ri a n c e (a n o va ) d f su m sq m ea n sq f va lu e d f su m sq m ea n sq f va lu e d f su m sq m ea n sq f va lu e m od el 1 1. 53 39 e+ 09 1. 53 39 e+ 09 61 .7 3* ** 1 8. 80 36 e+ 11 8. 80 36 e+ 11 46 .3 8* ** 1 28 6. 18 28 6. 18 13 9. 82 ** * re sid ua ls 81 9 2. 03 51 e+ 10 24 84 81 84 81 9 1. 55 43 e+ 13 1. 89 78 e+ 10 81 9 16 76 .3 2 so ur ce : o w n el ab or at io n on 2 01 0 ce ns us d at a. (* ** s ig ni fic an ce a t 0 .0 1; * * si gn ifi ca nc e at 0 .0 5, * s ig ni fic an ce a t 0 .1 ). 100 l. fastelli et al. table 11 and figure 5 show the intensity on the use of factors among different farm styles according to the criteria of farm’s efficiency (iacoponi, 1994): land productivity, labour productivity and intensity of labour use on the land. according to the anova, differences among farming styles in terms of land productivity, intensity of labour use for land unit and age of the household members working in the farms are statistically significant, while differences in labour productivity among farming styles are not. farms included in the innovative and conservative development styles show the highest levels of land and labour productivity associated to a lower average age of family workers. this may be due to a higher land productivity associated to a less intensive use of labour. hence, the productivity should be related to capital intensive and labour saving (dairy cattle, mechanisation) factors. similarly, the added value of the production may increase the productivity. conversely decline and survival styles register lower values. in particular, farms included in the survival style showed higher land productivity than those of decline style, but an inefficient use of the labour per hectare (it presents the highest value among the four styles), which negatively affects total labour productivity. this may be due to the high share of manual labour used by these farms on high added value production (i.e. specialty foods). figure 5 illustrates the economic performance of the four farming styles in terms of index numbers, showing that the highest efficiency belongs to the farms included in the innovative development whilst the lowest performance to farms included in the decline style. table 12 illustrates the distribution of the four farming styles according to the rdp zones. farms included in the decline style represent respectively 42.24% and 45.70% of the total farms located in c2 and d areas, i.e. the areas deserving more attention from policy makers, due to their development problems, and which accounts for about 73.5% of the mugello sample taa. hence, without a proper policy support these areas are deemed to lose a very high share of active farms. in terms of taa the situation is quite different since, although farms included in decline style represent 49.70% of the taa in c2 area, in d area they represent only 2.53%, due to a higher incidence of farms belonging to conservative and innovative development styles. in order to assess the existing relationships between the amount of public payments received by farms and the farming styles, access to public payments has been investigated (table 13). the most likely hypothesis was that “stronger” farming styles may have a significantly higher level of access to public payments. in this last case the enforcement of a specific policy able to prevent the exit of weaker farms could be required, if their survival is considered to be important due to social or environmental targets. however, knowing the relations among farming styles and aid distribution could help to improve policy design, e.g. by introducing rules able to promote beneficiaries who have specific characteristics and attitude towards farming. the anova (table 13) shows that the difference in average payments stemming from pillar 1 and 2 among the styles is statistically significant, and confirms the above hypothesis, i.e. that the “strongest” farming styles are the ones which capture the majority of aid in mugello area. according to the artea database (2012), between 2007 and 2012, 144 farms operating in the mugello area received rdp payments stemming from axis 1, 92 farms received payments from axis 2 and only 16 farms received payments from axis 3. farms included in the decline style (n = 379) are the less dynamic, since they receive the lowest amount of rdp payments (pillar 2). the majority of the aid stems from pub101a spatial analysis of terrain features and farming styles ta bl e 11 . m ug el lo la nd a nd la bo ur p ro du ct iv ity a cc or di ng to fa rm in g st yl es . d ec lin e su rv iv al c on se rv at iv e d ev el op m en t in no va tiv e de ve lo pm en t a n a ly si s o f va ri a n c e (a n o va ) d f su m sq m ea n sq f va lu e so /a w u 16 ,4 06 20 ,8 58 26 ,0 81 40 ,4 74 m od el 1 2. 50 44 e+ 09 2. 50 44 e+ 09 0. 03 3 re sid ua ls 81 8 6. 13 18 e+ 13 7. 48 70 +1 0 so /u a a 1, 11 4 1, 67 2 1, 77 1 1, 97 8 m od el 1 27 72 2 27 72 2 5. 10 45 * re sid ua ls 81 8 44 42 50 8 54 30 .9 aw u /u a a 0. 07 0. 08 0 .0 7 0. 04 m od el 1 0. 00 02 2. 66 85 e04 11 .0 4* ** re sid ua ls 81 8 0. 01 9 2. 41 56 e05 av er ag e ag e of h ou se ho ld m em be rs w or ki ng in th e fa rm 61 55 53 47 m od el 1 22 01 7 22 01 7 15 0. 52 ** * re sid ua ls 78 6 11 49 66 14 6. 3 so ur ce : o w n el ab or at io n on 2 01 0 ce ns us d at a. (* ** s ig ni fic an ce a t 0 .0 1; * * si gn ifi ca nc e at 0 .0 5, * s ig ni fic an ce a t 0 .1 ). 102 l. fastelli et al. figure 5. mugello land and labour productivity indices according to farming styles. 0 20 40 60 80 100 so/working days so/uaa uaa/days average age of household members working in the farm decline survival conservative development innovative development source: own elaboration 2010 census data. table 12. mugello farming styles distribution by tuscany rdp zones. farms number and hectares of taa. rdp zone decline survival conservative development innovative development total c1) intermediate rural areas in transition n° 74 5 24 33 136 % n 54.41% 3.68% 17.65% 24,26% 100 ha 3,748.72 662.45 2,276.73 6,543.78 13,231.68 % ha 28.33% 5.01% 17.21% 49.46% 100 c2) intermediate declining rural areas n° 98 22 52 60 232 % n 42.24% 9.48% 22.41% 25.86% 100 ha 9,337.38 919.54 2,547.87 5,981.21 18,786.00 % ha 49.70% 4.89% 13.56% 31.84% 100 d) rural areas with development problems n° 207 25 117 104 453 % n 45.70% 5.52% 25.83% 22.96% 100 ha 453.23 515.85 7,897.72 9,082.44 17,949.24 % ha 2.53% 2.87% 44.00% 50.60% 100 total n° 379 52 193 197 821 % n 46.16% 6.33% 23.51% 24.00% 100 ha 13,539.33 2,097.84 12,722.32 21,607.43 49,966.92 % ha 27.10 4.20 25.46 43.24 100 source: own elaboration on cadastral data (2011), artea data (2012), and census data (2010). 103a spatial analysis of terrain features and farming styles lic payments under pillar 1, highlighting how farms included in the decline style try to maintain the status quo, which grants them a sufficient amount of money to survive in the short run. the lower access to the rdp payments of farms included in the decline style could be due to higher barriers in accessing payments. indeed, they could require higher investments, entrepreneurial attitudes, innovations. farming styles intercepting the majority of public payments stemming from pillar 1 are the innovative development and conservative development styles, with an average payment12 respectively equal to 8,559 and 5,708 €/year over the period 2007-2012, that in the first style is probably related also to the larger size in terms of uaa. furthermore, farms included in the innovative development style show the highest level of access to rdp payments both in terms of average payment, with an average amount of 1,960, 1,034 and 783 € per year, respectively, from axes 1, 2 and 3, and in terms of share of farms intercepting payments, with 35%, 24% and 7%, respectively, for the axes 1, 2 and 3 of rdp. more than 30% of the farms included in the innovative development style received payments stemming from axes 2 and 3, which are respectively related to agri-environmental schemes and diversification, and represent one 12 the average payment is calculated as the ratio between total payments per year over the period 2007-2012 for each farming style and the number of farms included in that style. this index allows assessing the access of farms to public payments according to the farming style. table 13. mugello first pillar (sps) and rdp payments according to the farming style. decline survival conservative development innovative development sps average annual payment per farm (pillar 1) 2,716 3,678 5,708 8,559 rdp axis 1 2 3 1 2 3 1 2 3 1 2 3 share of farms (%) 5 4 0.5 11 8 0 17 9 0.5 35 24 7 rdp payments per year, 000 € 227.3 15.7 27.9 82.2 9.5 0 156.3 77.4 1.5 393.7 207.9 157.3 annual average rdp payments per farm € 596 41 73 1,521 184 0 810 401 8 1,960 1,034 783 anova degrees of freedom sum sq mean sq f value sps average annual payment per farm (pillar 1) farm style 1 4.5132e+09 4513228387 22.916*** residuals 819 1.6130e+11 196947987 total rdp payments per year (all axes) farm style 1 1.5148e+11 1.5148e+11 20.399*** residuals 819 6.0817e+12 7.4258e+09 fischer exact test pearson chi2 share of farms 0.00 135.8344 source: own elaboration on artea data (2007-2012) and census data (2010). (*** significance at 0.01; ** significance at 0.05, * significance at 0.1). 104 l. fastelli et al. of the main development strategies to increase farm income. finally, the survival style (n = 52) shows the second highest payment (1,521 € per year) after the innovative development style for rdp axis 1 and also a sps average annual payment that is higher than that of the decline style. a further analysis is needed to assess whether the farming styles affect or are affected by the access to public payments, which represents an important aspect within the cap reform. when considering the distribution of rdp resources among styles, due to the high differences in the number of farms belonging to each style, the situation is slightly different insofar as farms belonging to the decline style, although having the lower amount of rdp payments per farm, are able to capture about 20% of the total rdp aid, while more than 55% goes to the farms belonging to innovative development. after the analysis of the main features of the farming styles and their relation with the ability to intercept public aid, it is important to analyse their spatial distribution, that could be confronted with the land use analysis and the spatial analyses of altitude and slope presented above, which give a picture of the kind of resources and constraints that farms have to face in their activity. thus, in the following part, we provide both the maps of the two indices by which farming styles have been individuated, i.e. entrepreneurial dynamism and multifunctional diversification, and the map giving the spatial distribution of farmland belonging to the four farming styles. figure 6 shows the spatial distribution of the entrepreneurial dynamism among farmers. the highest levels of entrepreneurial dynamism are located in zones c2 and c1 (especially the municipalities of dicomano and scarperia); however, even some areas of zone d in the upper mugello are characterised by a high entrepreneurial dynamism level such as the north part of firenzuola and palazzuolo sul senio. surprisingly, the municipality of borgo san lorenzo, albeit located in zone c2 and characterised by areas with more favourable conditions for crops, shows the lowest level of entrepreneurial dynamism, probably due to better chances of employment in other sectors. farming strategies, in this case, do not aim to produce income, but they could be interpreted as “hobby-styles” or as strategies for the maintenance of real estate assets, i.e. land and buildings, that could increase their value, due to the proximity to residential or industrial areas. as shown by figure 7, the majority of farms operating in mugello present low levels of multifunctional diversification if compared to the entrepreneurial dynamism map. however, the upper mugello seems to be more oriented to multifunctionality; this could be likely due, from the one hand, to the more severe cropping conditions that makes it necessary to have an innovative approach and look for alternative sources of farm income, and from the other hand, to the lower level of economic development that makes it difficult to find employment in other sectors. in terms of number of farms, firenzuola municipality presents a relatively high level of multifunctional diversification since over 50% of farms can be considered as multifunctional. the same result holds in palazzuolo sul senio. indeed, the agricultural activity in this area requires a high level of labour intensity and presents a lower land productivity if compared to lower mugello. farmers who operate in these municipalities and have few opportunities to work outside agriculture without moving out from the area, tend to adopt diversification strategies made possible by the high quality level of local natural, environmental and landscape resources, when agricultural production is not able to ensure an adequate income. 105a spatial analysis of terrain features and farming styles these results highlight the importance of human capital, since there are farms that, due to the ability of the entrepreneur, are economically profitable despite the scarce fertility and suitability to an intensive use of their farmland. thus, although resource marginality could be one of the drivers of agricultural and farm marginality, this factor can be counterbalanced by good entrepreneurial skills; indeed statistical analysis shows no significant correlation between farming styles and terrain features. this result highlights the importance of the human factor, which is often not considered at farm level analysis, where structural and productive mix features are usually privileged. figure 8 illustrates the spatial distribution of the four farming styles. most innovative development style farms are located in the municipalities of dicomano and scarperia in lower mugello (the part of mugello with less development problems). many decline style farms are located in the municipality of borgo san lorenzo (and s. piero a sieve), the part of lower mugello that is more suitable for agricultural activities. this might be due to the higher off-farm opportunities of employment offered by these municipalities. i.e. employment in local smes and in the public sector which allows to maintain /continue farming activities. in this framework, the survival of weak farms might be due to a strong presence of retirees or professionals who carry on farming, e.g. as style of life. in this case, as previously stated, farming is carried out either as a hobby and a way to produce food for the family, or as a strategy based on the expectation of an increase in periurban land values. figure 6. map of entrepreneurial dynamism in mugello. source: own elaboration on cadastral data (2011), artea data (2012), and census data (2010). 106 l. fastelli et al. in summary, the analysis of the spatial distribution of farming styles seems to highlight a poor correlation with the terrain and land use features of the territory. indeed, it seems that this correlation is sometimes opposite to the one that could have been hypothesized, namely that where the territorial features are more favourable to agricultural activities, other factors – such as labour and land markets for non agricultural activities – have higher importance in influencing farming activity. viceversa, where territorial features are less suitable for agricultural uses, farmers seems to be more motivated and capable of innovation with the aim to improve their farming activity and to make it more sound from an economic viewpoint. this aim is pursued by farm diversification, intensification or by the research of economies of scale and scope, in many cases through the use of public aid, both belonging to pillar 1 and 2. at the end of this chapter we consider useful to provide a spatial analysis of the distribution of cap aid. in this framework, since all mugello is classified as less favoured area (lfa) and since cooper et al. (2006), in their evaluation of the lfa measure in the 25 member states of the european union, raised some doubts on the way the measure had been implemented in tuscany, we have decided to focus on rdp axis 2 measures related to lfa, i.e. measure 211 (mountain areas) and 212 (other disadvantaged areas). we, thus, analysed the spatial distributions of farms receiving payments under measures 211 and 212, in order to test the effectiveness of rdp payments in giving help to the figure 7. map of multifunctional diversification in mugello. source: own elaboration on cadastral data (2011), artea data (2012), and census data (2010). 107a spatial analysis of terrain features and farming styles areas with the highest natural constraints. given that the entire territory of mugello is classified as a mountainous area, it should be only eligible for the measure 211. nevertheless, some farms in mugello have received payments under measure 212 since these payments are not granted on the basis of cadastral parcels location but just considering the location of the farm center. since farmers cannot apply to both payments for the same parcel, but they have to choose which measure they prefer, for farms that have their center outside mugello, but manage land inside mugello it could be more profitable to apply for compensation under measure 212. in this way they get aid for all their farmland while, if applying under measure 211, they would be able to obtain aid only for the land located in mugello. figure 9 shows the spatial distribution of public payments given to farms located in mountainous areas with natural disadvantages (measure 211, left) and the spatial distribution of public payments given to farms located in other areas with natural disadvantages (measure 212, right). as result, we observed that 51 out of 821 farms received the payments related to measure 211 for a total amount of 676,037 € over the period 2009-2012, while 55 farms received the payments related to the measure 212, with a total amount of 713,796 € over the period 2009-2012. this spatial analysis shows that many farms located in lower mugello, where the terrain is less steep and the soils more suitable for cropping, received these types of payments. conversely many areas located in upper mugello did figure 8. the farming styles distribution in mugello. source: own elaboration on cadastral data (2011), artea data (2012), and census data (2010). 108 l. fastelli et al. not receive any kind of payment related to natural disadvantages. consequently we note a lack of coherence in the use of the measures 211 and 212, which should have helped farms located in area with higher natural disadvantages, since these payments are distributed without taking into account the characteristics of farm parcels. when analysing the farm styles that were more able to capture this kind of aid, the results highlight the problem of the access to these public payments, which affects especially farms of the decline style. over the period 2007-2012, only 2% of the farms included in this style received the payments, whilst 11% of the conservative development farm style group received the payments. this could be due to the combination of limited resources to be distributed and the priority given to professional farmers that led to provide aid almost exclusively to the strongest farming styles. the decision makers chose to limit aid to livestock farms with a minimum acreage and to privilege professional farmers with the aim to concentrate resources on the farms representing the backbone of mugello agriculture. decision makers were afraid that, otherwise, these farms would disappear or intensify their production by shifting to indoor systems (uthes et al., 2011), since both abandonment and intensification would bring about negative effects on es provision or on socio-economic situation of mugello. however, a spatial distribution that is neither linked to the areas with higher natural constraints nor to beneficiaries with weaker features at farm level could increase the risk of land abandonment. this may imply a stronger limit to the development of rural areas, which are often characterized by part-time farmers, non-professional farmers, life-stylers farmers, which, however, play an important role for agri-environmental protection and the maintenance of rural culture and traditions. the present tuscany rdp, which has just started, does not bring an improvement on the side of a better targeting to areas with higher natural constraints insofar as, differently from other regions (e.g. the bordering umbria region), it does not provide any graduation of the intensity of payment related to the classes of altitude and slope. figure 9. average payment (euro) related to areas with development problems (measure 211, left, and 212, right), in mugello. source: own elaboration on artea rdp regional db 109a spatial analysis of terrain features and farming styles 4. conclusions this paper presents a spatial analysis of the farm styles, terrain features and cap payment distribution in mugello area. the analysis was carried out by utilizing and integrating data at farm and territorial level, which included information on farm and family structures, public payments and terrain characteristics. the resulting database includes data of 821 agricultural holdings operating in the area, whose cadastral parcels were georeferenced to localise the land managed by each farm. then, through a multi-criteria analysis, four different farm styles were defined according to their entrepreneurial dynamism and multifunctional diversification level, i.e. decline, survival, conservative and innovative development, with the aim to include farm strategies in our analysis. indeed, many expost and ex-ante evaluations of cap aid take into account the farm level only by considering structural or crop mix features. however, personal attitude and farming styles may heavily influence the capacity to intercept public aid and to build viable farms, especially when only poor agricultural resources are available. the main strength of this contribution is the intertwining between farms and territorial data, which allows to localise all the parcels of every farm included in the sample and to characterize them in terms of altitude and slope, being aware of the constraints that those features have for the agricultural activity. this contribution shows the potential of gis, as it allows performing a deeper analysis on the area at the farm and territorial level and to single out the areas (and farms) that are at risk of abandonment. policy makers should use this approach to design, monitor and assess the impact of local agricultural policies, which, in order to be effective, should be able to fit the needs of specific rural sub-areas and farm styles. besides, the spatial analysis could be extended from an ex-post to an ex-ante evaluation, e.g. providing maps of potential aid distribution under different hypotheses about eligibility or priority (based on farm acreage, professional status of the farmer, etc.) or about a graduation of aid intensity based on the level of natural handicap (e.g. as it happens in umbria region for aid given to areas with natural constraints). results show a significant heterogeneity within mugello area, with the coexistence of different farming styles also in areas with similar terrain features. these results are particularly important since they point out that biophysical marginality is only one of the components of farm marginality and that entrepreneurial skills could allow farm viability and profitability also in the case that terrain features and land uses are not favourable. from this point of view, the importance given to farms belonging to the weakest farming styles, especially in the most unfavourable areas, aims to highlight situations where natural and human capitals concur to bring about negative effects. in particular, almost 50% of farms and 1/3 of the total agricultural area included in the sample belong to farms that seem to be close to the exit (decline and survival styles). this may negatively impact the socioeconomic, environmental and landscape conditions of mugello area in the following years, due to the effects of agricultural activity abandonment and the relocation of residents in more accessible and favourable areas. as regards the evaluation of cap design, especially in the case of rdp measures, the analyses aiming to relate rdp zones with land use and terrain features show that their delimitation was consistent with the aim to individuate areas of increasing marginality levels. results show that c1 zone is the best as regards the share of highest profitability 110 l. fastelli et al. land use (e.g. arable land) while d zone has the worst ones (with high presence of forestry and natural grassland). as regards terrain features, while d zone has the worst characteristics as regards altitude and slope, c2 seem to be slightly better for altitude, but has worst characteristics as regards slope than c1 area. nevertheless, significant levels of heterogeneity within each rdp zone may be individuated. while it could be hypothesized a correlation between natural resources endowment and farming styles, results, as above mentioned, show that this is not the case in mugello area. furthermore results show that very often public aid, even if targeted to areas with natural constraints, do not show any significant relation with the level of natural constraints arising from terrain features or with the kind of farming style, even if rdp zoning is consistent with an increasing level of natural constraints. thus, if policies aimed to avoid land abandonment by aiding areas characterized by less favourable terrain features and weaker farming styles, results show that they have failed to meet these targets. the decision to perform an analysis for less favoured areas that, while integrating terrain and farm characteristics, keep these two levels as separate stems from the awareness that policies related to natural constraints areas currently focus on area delimitations based only on bio-physical characteristics, while the promotion of farming typologies or farming styles can be introduced by regions and member states when deciding rules regarding beneficiaries and priority criteria. thus, delimitations could be valid for a longer term (since bio-physical features do not change so fast in time), while changes in the socioeconomic situation (that can change also in a short time period) can be counteracted by adapting eligibility and priority criteria. as regards the access to public payments stemming from pillars 1 and 2, we note that the highest amount of aid goes to farms included in the conservative and innovative development styles, which represent farms with the highest level of entrepreneurial attitudes. a further (diachronic) analysis could explain whether this capacity to intercept financial opportunities provided by cap is the cause or the effect of the resulting high levels of payments, and if the design of policies has directly or indirectly favoured areas, types of productions, types of farmers, in a way that is consistent with the goal of supporting agriculture, especially in disadvantaged areas. another reason that might explain the results in term of distribution of public resources among farming styles could be the “technocratic” approach of tuscany region, which aims at capturing and spending the maximum amount of european financial resources more than at targeting the promotion of self-empowerment and improvement of weaker areas and farms as, e.g., in the case of leader approach (arrighetti, 2016). results show deficiencies in the access to public payments by weaker farms and territories. farms included in the decline group face the highest barriers when they try to enter into the system of public aid. hence, new activities such as information practices or more adequate rules regarding farms and farmers eligibility and priority for these payments should be put in place to address this issue. although focusing mainly on professional farmers has its rationale, this could drive out of the system “lifestyle” farmers (mantino, 1990), who may well contribute to the landscape maintenance (lobley and potter 2004) and, from a territorial point a view, could hinder a more homogeneous rural development. in these cases, proper policies should be addressed also to this type of farmers. finally, focusing on the rdp measures specifically intended for areas with natural disadvantages, we raise some doubt on the effectiveness of their design, since in 111a spatial analysis of terrain features and farming styles mugello there is apparently no correlation among distribution of aid and intensity of natural constraints. this result confirms the need to use more efficient evaluation tools, like gis, to support a 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(2014). spatial analysis of agri-environmental policy uptake and expenditure in scotland. journal of environmental management 133: 104-115. 114 l. fastelli et al. appendix table a.1 distribution of corine land cover (clc) classes in mugello rdp zones. c1: intermediate rural areas in transition; c2: declining intermediate rural areas; d: rural areas with development problems. clc classes (lamma, 2010) c1 (ha) c2 (ha) d (ha) tot clc (ha) 211 non-irrigated arable land 6,457.01 6,910.09 7,353.23 20,720.33 221 vineyards 137.67 364.30 127.69 629.66 222 fruit trees and berry plantations 66.88 156.37 85.88 309.13 223 olive groves 251.94 723.51 19.58 995.03 231 pastures 167.43 121.59 133.23 422.24 241 annual crops associated with permanent crops 64.93 70.85 33.41 169.18 242 complex cultivation patterns 136.62 149.86 14.40 300.88 243 land principally occupied by agriculture, with significant areas of natural vegetation 209.24 338.29 463.02 1,010.55 311 broad-leaved forest 13,140.08 23,430.36 36,699.66 73,270.10 312 coniferous forest 967.51 7.25 1154.85 2,129.61 313 mixed forest 437.92 35.25 436.94 910.11 321 natural grasslands 19.70 211.68 827.58 1,058.96 322 moors and heathland 20.39 41.45 88.29 150.13 323 sclerophyllous vegetation 0.00 0.00 6.00 6.00 324 transitional woodland-shrub 774.38 880.08 3,815.24 5,469.70 333 sparsely vegetated areas 0.00 11.55 275.21 286.76 tot area 22,851.69 33,452.47 51,534.21 107,838.37 source: own elaboration on land uses data (lamma, 2010). a systematic approach to understanding and quantifying the eu’s bioeconomy tévécia ronzon1*, stephan piotrowski2, robert m’barek1, michael carus2 cost function and positive mathematical programming quirino paris what if meat consumption would decrease more than expected in the high-income countries? fabien santini*, tevecia ronzon, ignacio perez dominguez, sergio rene araujo enciso, ilaria proietti a stakeholder engagement approach for identifying future research directions in the evaluation of current and emerging applications of gmos davide menozzi1*, kaloyan kostov2, giovanni sogari1, salvatore arpaia3, daniela moyankova2, cristina mora1 a spatial analysis of terrain features and farming styles in a disadvantaged area of tuscany (mugello): implications for the evaluation and the design of cap payments laura fastelli1*, chiara landi2, massimo rovai1, maria andreoli1 bio-based and applied economics 6(1): 19-35, 2017 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-18140 cost function and positive mathematical programming quirino paris department of agricultural and resource economics, university of california, davis date of submission: 2016 31st, march; accepted 2016 28th, november abstract. a line of research in positive mathematical programming (pmp) has pursued the goal of estimating a cost function capable of reproducing the base-year results in a sample of farms. originally, the pmp approach estimated a “myopic” cost function, that is, a cost relation depending only on the output levels observed during a production cycle. no input price entered this type of cost function. in this paper we define and estimate a proper cost function that calibrates the economic results of a sample of farms. in the process, we demonstrate the existence of a unique solution of the pmp problem when observed output quantities and limiting input prices are taken as calibrating benchmarks. furthermore, the paper shows how to obtain endogenous output supply elasticities that calibrate with available exogenous information in the form of previously estimated elasticities for an entire region or sector. this framework is applied to a sample of italian farms that admit no production for some of the crop activities. this pmp model can be used to explore farmers’ response to various policy decisions involving output prices, environmental constraints, limiting input supply, and other government interventions. keywords. positive mathematical programming, solution uniqueness, supply elasticities, calibrating model jel codes. c6 1. introduction a cost function embodies the technological and market conditions facing a rational entrepreneur in the production of given output levels. shephard lemma (1953) established the duality between a cost function and the underlying production technology. under restrictive conditions, it may be possible to obtain an explicit expression of the underlying production function (cobb-douglas, ces, generalized leontief). in other cases, however, the derivation of an explicit production function may be impossible (translog cost function). from an empirical and policy viewpoint, however, this lack of explicit duality is not a serious deficit since the cost function – as stated above – summarizes all the technological and market conditions. corresponding author: paris@primal.ucdavis.edu 20 q. paris the methodological contribution of this paper – with reference to a sample of farms – can be outlined in four connected objectives. first, we assume the availability of information regarding a sample of farm production plans that were realized in the most current production cycles, as in the traditional approach of positive mathematical programming (pmp). we also assume the availability of the price of limiting inputs (either at the farm or regional level). these important pieces of information are treated symmetrically and are used in a model that generates a unique calibrating solution. second, we define and estimate a complete (non myopic) cost function that includes calibrating output quantities and limiting input prices. this cost function, then, can be used in the analysis of policy scenarios. third, we calibrate the pmp farm model using exogenously determined (via econometric studies or expert judgement) output supply elasticities. fourth, we extend the pmp methodology to the realistic case where not all farms cultivate all crop activities. the pursuit of these four objectives is the content of the following eight sections. the paper proposes an approach to positive mathematical programming that guarantees the uniqueness of the calibrating solution, a result that relies upon the use of all the available information, including prices of limiting inputs. this is the starting point of the paper. toward the goal of dealing also with calibrating input prices we discuss first the pmp approach as often practiced to date (qureshi et al., 2014, arfini et al., 2013, howitt et al., 2012, henseler et al., 2009, cortigiani et al., 2009). the original formulation of the pmp methodology (howitt, 1995a, 1995b) was based upon the estimation of the marginal cost associated with the observed production plan (or the difference between known per-output unit accounting costs and effective economic marginal costs). phase i of this model took on the following specification (howitt, 1995a, p. 151): primal maxtnr = p’x c’x (1) subject to ax ≤ b structural constraints (2) ε+x x≤ calibration constraints (3) and x ≥ 0 where a is a matrix of technical coefficients of dimensions (i×j, i<j) and all the other vectors are conformable to it. in particular, x > 0 is a vector of realized and observed levels of outputs whose utilization qualifies the positive feature of the pmp approach. vector b refers to limiting input supplies. vectors p and c represent market output prices and unit accounting costs, respectively. the parameter vector ε is composed of small, positive (user-determined) numbers whose role is to guarantee that the dual variables of the binding structural constraints achieve a positive value. in howitt’s words (1995a, p. 151): “the ε perturbation on the calibration constraints decouples the true resource constraints from the calibration constraints and ensures that the dual values on the allocable resources represent the marginal values of the resource constraints.” this statement implies that, without the user-determined ε perturbation, the solution might result in the undesirable occurance of a zero dual variable for a binding resource constraint. typically, the determination of the magnitude of the ε parameters requires a trial and error approach that is performed by solving repeatedly the phase i model until the user finds that the shadow (dual) prices of the binding resource constraints achieve positive values. with these stipulations, the dual of model (1)-(3) is stated as dual mintc =b'y+ ʹλ [x +ε] (4) subject to ʹa y+λ +c ≥ p (5) 21cost function and positive mathematical programming with y ≥ 0, λ ≥ 0 where y represents the (i×1) vector of shadow prices of the structural constraints and the (j×1) vector λ represents the shadow prices of the calibration constraints. in the dual constraints (5) there are j constraints and (i×j) variables. at the optimal primal solution x* relation (5) is satisfied with the equality sign by complementary slackness conditions given that x* = x + ε > 0. hence, the traditional specification of the pmp model is underdetermined (ill posed). it admits an infinite number of (y*,λ*) solutions because there are more unknown variables than equations. this is the reason why the parameter ε was introduced in model (1)-(3) in order to elicit a dual solution with positive values of the shadow price y of the binding structural constraints. this means that i components of the vector λ assume a zero value. another criticism of the original pmp approach regards the specification of the calibration constraints. why is the solution vector x of model (1)-(3) stated as less-than-orequal to the observed vector of output levels (x ≤ x + ε) in the calibration constraints (3)? the answer was (is): to guarantee a nonnegative dual vector of shadow prices λ ≥ 0 interpreted as variable marginal cost levels of x . admittedly, this is an unsatisfactory answer. given that vector x represents observed (by the econometrician) output levels that were realized by the producer in a previous economic cycle, the observed vector x may contain some deviations that either overstate or understate the levels of economically efficient production for a given farmer. a more plausible specification of the calibration constraints, therefore, could be x = x + h where h is a conformable vector of unrestricted deviations from x . furthermore, a measure of the limiting input price vector y may be available at either a regional or more local level. for example, the price of agricultural land is surely available, either by region or by area. the regional estimate may not be fitting every single farm but it can be assumed that it will fall within a reasonable range of the actual optimal land value of each farm as obtained by solving model (1)-(3). if the information on land price and other important limiting inputs is available, it should be used in a pmp approach in order to avoid violating the principal tenet of the methodology: all the available information should be used. also in this case, therefore, it seems plausible to state a calibration constraint for the dual variable vector as y = y + u where u is a conformable vector of unrestricted deviations from y . within this alternative pmp framework, the notion of a calibrating solution assumes a different structure from the original formulation of model (1)-(3). in that model, a calibrating solution achieves the obvious values of x* = x + ε. many critics of pmp have objected that this equation represents a tautology. in fact, the equality between the optimal solution of model (1)-(3) and the vector of observed output levels (adjusted by the ε parameter) is achieved because the – presumably – available information on the limiting input prices is ignored. with the more general specification of the calibration constraints in the form of x = x + h and y = y + u, a calibrating solution (x*,y*) will not, in general, be tautologically equal to (x , y ). but it can be arranged to make the solution (x*,y*) as close as possible to the observed quantities and prices by using, for example, a least-squares minimization goal. this approach, then, resembles an econometric estimation where the goal is to minimize the residuals of a system of regressions. the objective of this alternative pmp methodology, therefore, is to make deviations (h,u) as small as possible. 22 q. paris 2. the use of x and y in pmp to justify the structure of the novel phase i pmp model we begin with two preliminary analyses. first, suppose that a preliminary phase i of the pmp methodology is concerned with solving the following problem maxtnr = p’x c’x (6) subject to ax ≤ b dual variables y (7) x = x + h dual variables λ (8) with x ≥ 0 and h unrestricted. furthermore, we wish to minimize the sum of squared deviations, h’wh/2 as in a weighted least-squares approach. the w matrix is diagonal with elements pj > 0 on the main diagonal, j = 1,…,j. the effective objective function, therefore, will be expressed as an auxiliary function such as maxaux = p’x c’x h’wh/2. the purpose of the weight matrix w is to measure each component of the auxiliary objective function in the same measurement units, that is, in dollars. forming the lagrange function and stating the relevant karush-kuhn-tucker (kkt) conditions will give l = p’x c’x h’wh/2 + y’[b ax] + λ’[x + h x] (9) ∂l ∂x = p−c− ʹa y−λ≤0 (10) λλ ∂ ∂ = − + h hl w 0= (11) from relation (11), λ = wh and, thus, we can dispense from using the λ symbol explicitly. relation (10), then, can be reformulated as a’y + wh + c ≥ p. (12) relation (11) represents a case of self-duality, where a dual variable is equal (up to a scalar) to a primal variable. analogously, and still in a preliminary stage of analysis, let us consider the following problem mintc = b’y (13) subject to a’y + c ≥ p dual variables x (14) y = y + u dual variables ψ (15) with y ≥ 0 and u as unrestricted deviations. again, we wish to minimize the sum of squared deviations, u’vu/2 as in a weighted least-squares approach. the matrix v is diagonal with elements b yi i/ > 0 on the main diagonal, i i=1,..., . the effective objective, then, will be expressed as an auxiliary function to be minimized such as minaux2 = b’y + u’vu/2. the purpose of the v matrix is to render homogeneous the measurement units of all the terms in the objective function and to scale the deviations u according to the size of the input constraints. forming the lagrange function and stating the relevant kkt conditions give l* = b’y + u’vu/2 + x’[a’y + c p] + ψ’[y y u] (16) ψ ∂ ∂ = − + ≥ y b x 0l a * (17) 23cost function and positive mathematical programming u u 0l v * ψψ ∂ ∂ = − = (18) from the self-dual relation (18), ψ = vu and, again, we can dispense from using the symbol ψ explicitly. thus, relation (17) can be reformulated as ax ≤ b + vu (19) this discussion leads to a specification of a phase i pmp model that combines the duality relations of a lp problem together with the least-squares necessary conditions involving deviations h and u. combining constraints (12) and (19) with the calibration relations (8) and (15), we can write the relevant phase i pmp model as the problem of finding nonnegative vectors x ≥ 0, y ≥ 0 and unrestricted vectors h and u such that ax ≤ b + vu dual variables y (20) a’y + wh + c ≥ p dual variables x (21) x = x + h dual variables wh (22) y = y + u dual variables vu (23) together with the associated complementary slackness conditions. this pmp approach avoids the necessity of searching for the user-determined parameter ε. the solution of model (20)-(23) generates estimates of the effective marginal cost levels ( ʹa ŷ+wĥ+c) and the input demand levels xaˆ. 3. solution uniqueness of the phase i pmp model a least-squares (ls) solution is unique if and only if the matrix of “explanatory” variables has full rank. to verify this crucial condition in relation to model (20)-(23) we assume that vectors x and y have all positive components and thus x > 0 and y > 0 (this assumption will be relaxed in section 8). this implies – via complementary slackness conditions associated to relations (20)-(21) – that ax = b + vu (24) a’y + wh + c = p (25) substituting constraints (22) and (23) into (24) and (25), and rearranging terms in order to have all the unknowns on one side and the constant parameters on the other side of the equal sign, we obtain u h b xv a a− + = − (26) ʹa u+wh= p− ʹa y −c (27) and in matrix notation −v a ʹa w ⎡ ⎣ ⎢ ⎤ ⎦ ⎥ u h ⎡ ⎣ ⎢ ⎤ ⎦ ⎥= b−ax p− ʹa y−c ⎡ ⎣ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ m z = q (28) the matrix m is of full rank because the nonsingular weight matrices v and w are on the main diagonal. hence, the least-squares solution û and ĥ is unique. it follows that the solution x̂ and ŷ of model (20)-(23) is also unique. given the structure of the m matrix, an inverse of m exists even if the a matrix is not of full rank. the explicit least-squares solution of (28) is 24 q. paris û ĥ ⎡ ⎣ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ = −(v +aw −1 ʹa )−1 v −1a( ʹav −1a+w )−1 w −1 ʹa (v +aw −1 ʹa )−1 ( ʹav −1a+w )−1 ⎡ ⎣ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ b−ax p− ʹa y −c ⎡ ⎣ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ (29) the optimal and calibrating ls levels of the primal and dual variables x and y then, are obtained as a simple addition according to the specification given in constraints (22) and (23) with x x hˆ ˆ= + and y y uˆ ˆ= + . 4. phase ii: specification of a general cost function phase ii of this pmp approach deals with the derivation of output marginal cost and input demand functions to be used in a calibrating model for the analysis of various policy scenarios. following economic theory, we postulate that the total cost function of interest takes on the following symmetric and extended leontief specification: c(x,y) = (g’y)(f ’x) + (g’y)x’qx/2 + (f ’x)[(y1/2)’gy1/2] (30) where the (j×j) matrix q is symmetric and positive definite, the (i×i) matrix g has elements gi,i’ = gi’,i ≥ 0, i≠i’. the elements gi,i can take on positive or negative values. the components of vectors f and g are free to take on any value. we require, however, that f ’x > 0 and g’y > 0. from theory, a cost function is non-decreasing in output levels and input prices and, furthermore, it is homogeneous of degree one in input prices. this requirements drive to a large extent the specification of the cost function presented in relation (30). the vector of output marginal cost functions is stated as mcx = ∂c ∂x = (g'y) f +(g'y)qx+ f [(y1/2 )́gy1/2]= ʹa y+wh+c (31) while, by shephard lemma, the vector of demand functions for inputs is stated as ∂c ∂y = ( f'x)g + g(x'qx)/ 2+( f'x)δ(y−1/2 )́gy1/2 =ax (32) where the matrix ∆(y-1/2) is diagonal with terms yi −1/2 on the main diagonal. the vector of output supply functions comes from relation (31) by equating it to the vector of market output prices, p and inverting the marginal cost function to obtain x = -q-1f -q-1f[(y1/2)’gy1/2]/(g’y) + q-1p/(g’y) (33) that leads to the supply elasticity matrix η≡δ(p) ∂x ∂p ⎡ ⎣ ⎢ ⎤ ⎦ ⎥δ(x−1)=δ(p)q−1δ(x−1)/(g'y) (34) where matrices ∆(p) and ∆(x-1) are diagonal with elements pj and xj -1, respectively, on the main diagonals. relation (34) includes all the ownand cross-price elasticities for all the output commodities admitted in the model. the demand elasticities of limiting inputs can be easily measured from the input demand functions of relation (32). suppose two limiting inputs form the structural constraints of the model. then, the portion of the demand function that involves input prices can be stated as 25cost function and positive mathematical programming b1 + u1 = k1 + (f ’x)[g11 + y1 -1/2g12y2 1/2] (35)b2 + u2 = k2 + (f ’x)[g22 + y1 1/2g12y2 -1/2] where k1 and k2 do not involve input prices. the matrix of derivatives of the demand functions results in ∂(b1+u1) ∂y1 ∂(b1+u1) ∂y2 ∂(b2 +u2 ) ∂y1 ∂(b2 +u2 ) ∂y2 ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ = − 1 2 y1 −3/2g12y2 1/2 1 2 y1 −1/2g12y2 −1/2 1 2 y1 −1/2g12y2 −1/2 − 1 2 y1 1/2g12y2 −3/2 ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ( f'x) (36) this means that with only one limiting input, its demand elasticity will be equal to zero (as in a leontief fixed coefficient specification) since the term g11 drops out of the derivative in (36). an intuitive idea of how the estimated production plan x̂ lies on the cost function (30) is illustrated in figure 1. for simplicity, given two outputs (x1,x2) and two inputs (b1,b2), figure 1 shows the transformation possibility set, tps, defined by two linear constraints involving the known levels of inputs b1 and b2. assuming that the production plan x̂ maximizes the farm total revenue, it is possible to fit a cost function c x x y y( , , )1 2 1 2 through the point x̂ where y y1 2, are given prices of inputs b1 and b2. the tps is in general a convex set that is limited by known levels of inputs b1 and b2. hence, the production plan x̂ must also be on the boundary of the true tps(b1,b2) no matter what is the underlying technology corresponding to the cost function c x x y y( , , )1 2 1 2 because the true tps(b1,b2) is defined by the same known input levels b1 and b2 that define the tps in figure 1 and must go through the point x x xˆ ( ˆ , ˆ )1 2= figure 1. transformation possibility set and cost function. 26 q. paris 5. exogenous and disaggregated output supply elasticities pmp has been applied frequently to analyze farmers’ behavior to changes in agricultural policies. a typical empirical setting is to map out several areas, say t areas, in a region (or state) and to assemble a representative farm model for each area (or to treat each area as a large farm). when supply elasticities are exogenously available (say the own-price elasticities of crops) at the regional (or state) level (via econometric estimation or other means), a connection of all area models with these exogenous elasticities can be specified by establishing a weighted sum of all the areas endogenous own-price elasticities and the given regional elasticities. the weights are the share of each area’s revenue over the total revenue of the region. let us suppose that exogenous own-price elasticities of supply are available at the regional level for all the j crops, say jη , j = 1,…,j. then, the relation among these exogenous own-price elasticities and the corresponding areas’ elasticities can be established as a weighted sum such as wj tj tj t t 1 ∑η η= = (37) where the weights are the areas’ revenue shares in the region (state) w p x p x tj tj tj sj sj s t 1 ∑ = = (38) and ηtj = ptjqt jjxt -1/(g’t yt) (39) where qt jj is the jth element on the main diagonal in the inverse of the qt matrix. 6. estimation of the cost function parameters using the optimal ls solutions of x,y,h and u for each of the t areas, x y hˆ , ˆ , ˆ t t t and ût obtained from solving phase i model (20)-(23), it is possible to proceed to the estimation of parameters q,g,f and g of the cost function (30). the programming model that executes the estimation of the marginal cost (31) and input demand (32) functions in the presence of exogenous supply elasticities for a region (state) that is divided into t areas takes on the following least-squares specification: minls = (dt ʹdt +rtʹrt )/ 2 t=1 t ∑ (40) subject to ( ʹg t ŷt ) ft +( ʹg t ŷt )qt x̂t + ft[( ŷt 1/2 )'gt ŷt 1/2]+dt = ʹat ' ŷt +wtĥt +ct marginal cost function ( ʹft x̂t )g t + g t ( ˆʹxtqt x̂t )/ 2+( ʹft x̂t )δ( ŷt −1/2 )'gt ŷt 1/2 +rt =at x̂t input demand function qt = ltdtlt’ positive semidefinitenes of qt qt qt -1 = lt definiteness of qt ηtjk =δ(ptj )qt jkδ(x̂tk −1)/( ʹg t ŷt ) endogenous ownand cross-supply elasticities 27cost function and positive mathematical programming w p x p x ˆ ˆ tj tj tj sj sjs t 1∑ = = revenue shares ηtj = ptjqt jj x̂tj −1 / ( ʹg t ŷt ) endogenous own supply elasticities η j = wtj t=1 t ∑ ηtj disaggregation of exogenous elasticities with dt > 0, gt and ft unrestricted parameters; ftʹx̂t >0 and gtʹŷt > 0, dt ≥ 0, rt ≥ 0. vector variables dt ≥ 0, rt ≥ 0 perform the role of auxiliary slack variables that will equal to zero identically when minimized by the gams solver (the gams solver requires an explicit objective function). in this way, the system of relations involving the specification of marginal cost and demand functions for inputs will be estimated as they appear in equations (31) and (32). to limit the number of estimated parameters it may be convenient to assume that matrices q and g belong to the entire area (or state) and do not carry the index t identifying each individual farm. model (40) is highly nonlinear in the constraints and a successful solution of it depends crucially on the proper scaling of the data series and on the choice of an initial point that falls in the neighborhood of the equilibrium solution. this specification was applied to three samples of t = 14 italian farms (areas), classified according to acreage size, each producing four crops (sugar beet, soft wheat, corn and barley) using only land as a limiting input. the gams software program achieved an equilibrium solution in all the three cases. in this paper we present the result for the class of farms of size greater than 100 hectares. table 1 exhibits the observed output levels and the percent deviation obtained from solving model (20)-(23) (alternatively solving model (28)). the primal solution x̂ is almost equal to the observed output levels x for every farm. all the percent deviations of the primal solution (except two) are well below the one percent level. the same event characterizes the dual solution. table 2 presents the deviations from the observed land input prices and the percent deviation of the optimal dual solution, ŷ . also in this case, the percent deviation is minimal in every farm. the weighted ls minimization of the primal and dual deviations (h,u) has produced a largely satisfactory result in this sample. this goal is accomplished also by virtue of the diagonal weight matrices w and v. the estimated parameters of the cost function are reported in tables 3 and 4. for reasons of space, only three q matrices are reported. all 14 farms achieved a nonsingular q̂ matrix. this feature is instrumental in defining the matrix of endogenous supply elasticities. table 5 presents the endogenous own and cross-price supply elasticities for three farms. we stipulated that regional, exogenous own-price supply elasticities were available in the magnitude of 0.5 for sugar beet, 0.4 for soft wheat, 0.6 for corn and 0.3 for barley. the endogenous own-price elasticities of all farms were aggregated to be consistent with the regional exogenous elasticities according to relation (37). table 6 presents the farms’ ownprice supply elasticities and the revenue weights used in the aggregation relation. 28 q. paris 7. calibrating equilibrium model with the estimates of the cost function parameters f g q gˆ, ˆ , ˆ , ˆ it is possible to formulate a calibrating equilibrium model for each farm (sector, area) of the following structure mincsct = ʹzpt yt + ʹzdtxt =0 (41) table 1. observed output levels, x and percent deviation (dev) of the ls calibrated solution, x̂. farm sugar beet x1 soft wheat x2 corn x3 barley x4 sugar beet % dev soft wheat % dev corn % dev barley % dev 1 1133.4240 305.4032 341.3693 18.2398 0.026 0.060 0.157 1.341 2 3103.7830 861.7445 478.4465 59.8025 0.016 0.042 0.052 0.637 3 1547.9780 450.7937 881.9748 7.6887 0.010 -0.003 0.011 0.164 4 3488.3540 821.3934 1493.332 51.1247 0.002 0.019 0.023 0.526 5 959.1102 468.2848 478.9261 28.2406 0.032 0.001 0.091 1.136 6 942.2039 801.1288 1283.591 152.581 0.049 0.059 0.046 0.384 7 1600.7310 695.8293 899.4739 66.9718 0.023 0.068 0.061 0.683 8 3507.5490 1212.8550 1237.584 98.0497 0.006 0.047 0.048 0.388 9 1050.5370 332.3773 498.0150 63.6696 0.043 0.188 0.120 0.846 10 3473.6780 952.5199 774.7402 84.0070 0.010 0.039 0.062 0.444 11 1245.7220 765.1689 501.9673 59.5366 0.030 0.047 0.101 0.718 12 3276.1450 1100.1680 742.9419 177.974 0.014 0.031 0.074 0.326 13 877.0970 380.9171 564.6091 76.2122 0.048 0.055 0.105 0.683 14 1430.9460 768.6901 1309.392 67.7906 0.026 0.038 0.035 0.604 table 2. deviations of ŷ from y : vector û. farm absolute deviation û observed land prices y percent deviation % 1 0.0053817 4.42 0.122 2 0.0026860 4.38 0.061 3 0.0004449 6.98 0.006 4 0.0018006 5.73 0.031 5 0.0031117 4.40 0.071 6 0.0014600 1.86 0.078 7 0.0032416 3.65 0.089 8 0.0018922 3.36 0.056 9 0.0052767 2.75 0.192 10 0.0027213 4.28 0.064 11 0.0029836 3.28 0.091 12 0.0011904 1.93 0.062 13 0.0028811 2.32 0.124 14 0.0022795 4.03 0.057 29cost function and positive mathematical programming table 3. intercepts f̂ , ĝ and ĝ matrix of the marginal cost and input demand functions. farm f̂ ĝ ĝ ʹf̂ ŷ ʹĝ ŷ sugar beet soft wheat corn barley 1 0.1110 0.0912 -0.0727 0.6183 0.00191 -1.2669 140.294 0.00847 2 -0.0112 0.6636 -0.0784 0.7116 0.00110 -0.9190 542.721 0.00484 3 0.5937 0.6745 0.4603 1.0948 0.00222 -0.0313 1637.550 0.01550 4 -0.0549 0.2153 0.2302 1.0018 0.00061 -1.0016 380.591 0.00350 5 0.0174 0.5424 -0.0297 0.6213 0.00361 -0.5182 274.242 0.01589 6 -0.7008 1.6163 6.0416 1.0965 0.01073 -0.2639 8561.452 0.01998 7 0.2155 0.2852 0.2414 0.9854 0.00328 -0.7473 827.362 0.01198 8 -0.0769 0.7406 0.4971 0.9387 0.00791 -0.9633 1336.688 0.02660 9 -0.0559 0.7323 0.3863 0.8988 0.00941 -1.2263 435.449 0.02592 10 0.0300 0.8861 -0.2342 0.9465 0.00055 -0.6090 846.879 0.00234 11 0.2427 0.4817 -0.0555 0.9430 0.00638 -0.7449 699.825 0.02095 12 0.0796 0.8584 0.4385 1.0428 0.00650 -1.4001 1717.901 0.01255 13 0.7831 0.3158 -0.3314 0.8222 0.00711 -0.9164 683.351 0.01651 14 0.1802 0.6635 0.1982 0.9977 0.00925 -1.2669 1095.888 0.03732 table 4. matrices q̂ and d̂ for three farms. matrix q̂ matrix d̂ sugar beet soft wheat corn barley sugar beet soft wheat corn barley farm 1 s. beet 0.90363 -1.97461 -0.88227 0.06447 0.90363 s.wheat -1.97461 5.83223 2.23097 0.35591 1.51732 corn -0.88227 2.23097 1.49261 0.17779 0.57068 barley 0.06447 0.35591 0.17779 21.75286 21.55051 farm 2 s. beet 0.71517 -2.04607 -0.76495 -0.00159 0.71517 s.wheat -2.04607 7.25493 2.41519 -0.05663 1.40123 corn -0.76495 2.41519 1.53268 -0.03759 0.67780 barley -0.00159 -0.05663 -0.03759 18.98344 18.97949 farm 3 s. beet 1.24597 0.24597 -2.27223 -0.42147 1.24597 s.wheat 0.24597 1.95471 -1.25809 -0.02225 1.90615 corn -2.27223 -1.25809 4.76858 0.85444 0.28099 barley -0.42147 -0.02225 0.85444 6.78018 6.59126 30 q. paris table 5. endogenous ownand cross-supply elasticities for three farms. sugar beet soft wheat corn barley farm 1 s. beet 0.2001 0.1952 0.1321 -0.1091 s. wheat 0.2815 0.6056 -0.2563 -0.1763 corn 0.2052 -0.2760 1.2485 -0.1514 barley -0.0089 -0.0100 -0.0079 0.5927 farm 2 s. beet 0.2487 0.2182 0.1855 0.0133 s. wheat 0.3081 0.4399 -0.2513 0.0162 corn 0.1454 -0.1395 1.5539 0.0191 barley 0.0012 0.0011 0.0023 0.4196 farm 3 s. beet 0.1839 0.1379 0.1727 -0.1676 s. wheat 0.2347 0.3725 0.2474 -0.5665 corn 0.4893 0.4121 0.4952 -0.9548 barley -0.0044 -0.0087 -0.0088 2.5417 table 6. disaggregation/aggregation of the regional, endogenous supply elasticities. farms endogenous own-supply elasticities revenue weights sugar beet: 0.5 soft wheat: 0.4 corn: 0.6 barley: 0.3 sugar beet soft wheat corn barley 1 0.2001 0.6056 1.2485 0.5927 0.0406 0.0291 0.0295 0.0165 2 0.2487 0.4399 1.5539 0.4196 0.1334 0.0937 0.0489 0.0628 3 0.1839 0.3725 0.4952 2.5417 0.0527 0.0446 0.0699 0.0070 4 0.2225 0.4774 0.5665 0.9868 0.1000 0.0893 0.1383 0.0536 5 0.1599 0.4512 0.8430 0.5691 0.0326 0.0413 0.0385 0.0256 6 0.6932 0.9332 0.5011 0.1080 0.0371 0.0828 0.1151 0.1601 7 0.0990 0.2906 0.3522 0.1918 0.0502 0.0688 0.0769 0.0606 8 0.1347 0.2714 0.2307 0.0823 0.1288 0.1292 0.1022 0.0931 9 0.1303 0.2670 0.3384 0.1544 0.0376 0.0335 0.0426 0.0576 10 0.2954 0.3940 1.9745 0.9603 0.1027 0.0930 0.0649 0.0825 11 0.1085 0.3682 0.2755 0.2195 0.0424 0.0737 0.0417 0.0539 12 0.1843 0.2692 0.2407 0.1197 0.1555 0.1079 0.0685 0.1868 13 0.0947 0.2861 0.4050 0.1486 0.0299 0.0336 0.0454 0.0689 14 0.0883 0.2455 0.3772 0.1349 0.0564 0.0795 0.1175 0.0711 31cost function and positive mathematical programming subject to ( ʹf̂txt )ĝ t + ĝ t ( ʹxtq̂txt )/ 2+( ʹf̂txt )δ(yt −1/2 )́ ĝt yt 1/2 + z pt =bt +vtût ( ʹg t yt ) f̂t +( ʹg t yt )q̂txt + f̂t[(yt 1/2 )́ ĝt yt 1/2]= pt + zdt with xt ≥ 0, yt ≥ 0, zpt ≥ 0, zdt ≥ 0. the variables zpt and zdt are slack-surplus variables of the primal and dual constraints, respectively. the objective function (csc) of model (41) combines all the complementary slackness conditions of the farm (region, area). hence, its optimal value must be equal to zero. the solution of the equilibrium model (41) produces optimal values of the primal and dual variables, xt and yt that are identical to the solution values of model (20)-(23). notice that the matrix of constant technical coefficients, at, no longer appears in the calibrating equilibrium model (41). this elimination removes the last vestige of a linear structure that has been considered too rigid for representing the choices of a producer. model (41) can be used to perform response analysis to variations in prices, subsidies, quotas, input quantities, and other parameters for a variety of policy scenarios. 8. pmp uniqueness with missing observations empirical reality compels a further consideration of the above methodology in order to deal with farm samples where not all farms produce all commodities. it turns out that very little must be changed for obtaining a unique and calibrating solution in the presence of missing commodities, their prices and the corresponding technical coefficients. to exemplify, suppose that the farm sample displays the following table 7 of observed crop levels. table 7. observed output levels, x with non produced commodities. farm sugar beet soft wheat corn barley x1 x2 x3 x4 1 1133.4240 0.0 341.3693 18.2398 2 3103.7830 861.7445 0.0 59.8025 3 0.0 450.7937 881.9748 0.0 4 3488.3540 821.3934 1493.332 51.1247 5 959.1102 468.2848 0.0 28.2406 6 942.2039 801.1288 1283.591 152.581 7 1600.7310 0.0 899.4739 66.9718 8 0.0 1212.8550 1237.584 98.0497 9 1050.5370 332.3773 0.0 63.6696 10 3473.6780 952.5199 774.7402 0.0 11 0.0 765.1689 501.9673 59.5366 12 3276.1450 1100.1680 0.0 177.974 13 877.0970 380.9171 564.6091 76.2122 14 1430.9460 0.0 1309.392 0.0 other missing information deals with prices and unit accounting costs associated with the zero-levels of crops. furthermore, the technical coefficients of the farms not pro32 q. paris ducing the observed crops also equal to zero. hence, we can state that, for t = 1,…,t, the number of farms, and j = 1,…,j, the number of crops, if xtj = 0 also p ctj tj= =0 0, and atij = 0 . furthermore, suppose that only one input, land, is involved in this farm sample. then, the land price is observed for all farms. as to the solution of the phase i pmp specification, we expect that x x htj tj tj= + for xtj > 0 and h xtj tj= = 0 for xtj = 0 . it turns out that the least-squares computation of the deviations uti and htj expressed by equation (29) produces the desired estimates of the deviations htj and crop levels xtj when the observed level of those crops equals zero, xtj = 0 . this is so because the first term on the rhs of (29) is equal to zero by construction, bi − aijj=1 j ∑ x j =bi − (acresijj=1 j ∑ / x j )x j =0 . the second term on the rhs of (29) reduces to zero because of the zero information about non-produced crops, pj − aiji=1 i ∑ yi −c j =0−0yi −0=0 therefore, h xˆ ˆ 0tj tj= = for xtj = 0 and the least-squares pmp solution is unique also in this more elaborate case. the estimation of the cost function carries through as in section 6 without modification. also the phase iii calibrating model expressed in (41) needs no adjustment. 9. results for a farm sample with missing production of some crops the observed crop production of a 14-farm sample is given in table 7. also the corresponding output prices, ptj = 0 and accounting costs, ctj = 0 are part of the data sample for the no-production levels xtj = 0 as reported in table 7. furthermore, atij = 0 for the same activities of no-production. table 8 presents the unique least-squares estimates of the crop levels and the corresponding percentage deviation from the observed sample data. table 8. estimated output levels, x and percent deviation (dev) for the sample with missing crop production (compare with table 7). farm sugar beet x1 soft wheat x2 corn x3 barley x4 sugar beet % dev soft wheat % dev corn % dev barley % dev 1 1133.7140 0 341.9053 18.4843 0.0256 0 0.1570 1.3400 2 3104.2820 862.1098 0.0000 60.1834 0.0161 0.0424 0 0.6369 3 0 450.7820 882.0680 0 0 -0.0026 0.0106 0 4 3488.4150 821.5529 1493.6830 51.3938 0.0017 0.0194 0.0235 0.5264 5 959.4208 468.2891 0 28.5614 0.0324 0.0009 0 1.1360 6 942.6667 801.6001 1284.1790 153.1671 0.0491 0.0588 0.0458 0.3840 7 1601.1000 0 900.0223 67.4290 0.0231 0 0.0610 0.6825 8 0 1213.4210 1238.1750 98.4298 0 0.0466 0.0478 0.3876 9 1050.9910 333.0022 0 64.2084 0.0433 0.1880 0 0.8463 10 3474.0410 952.8955 775.2208 0 0.0105 0.0394 0.0620 0 11 0 765.5305 502.4727 59.9640 0 0.0473 0.1007 0.7179 12 3276.6110 1100.5140 0 178.5547 0.0142 0.0314 0 0.3260 13 877.5201 381.1268 565.2019 76.7330 0.0482 0.0550 0.1050 0.6833 14 1431.3200 0 1309.8500 0 0.0261 0 0.0350 0 33cost function and positive mathematical programming except for two cells, the percent deviations of the estimated crop levels from the observed production quantities are below 1 percent. the cells with a zero estimated quantity level correspond to the cells with observed zero level of production, as in table 7. table 9 presents the estimated land price and the percent deviation from the observed input price. table 9. deviations of ŷ from y . farm estimated land prices ŷ observed land prices y percent deviation % 1 4.428035 4.42 0.1818 2 4.382827 4.38 0.0645 3 6.980315 6.98 0.0045 4 5.731801 5.73 0.0314 5 4.402587 4.40 0.0588 6 1.861460 1.86 0.0785 7 3.653809 3.65 0.1044 8 3.362198 3.36 0.0654 9 2.756308 2.75 0.2294 10 4.281756 4.28 0.0410 11 3.283229 3.28 0.0984 12 1.931129 1.93 0.0585 13 2.322881 2.32 0.1242 14 4.031362 4.03 0.0338 the deviations of the estimated land prices from the observed prices are all below one percent. table 10 presents the estimates of the parameters of the cost function under the condition of zero production for some crops in various farms. table 11 presents the own price elasticities of the 14 farms that correspond to the observed and exogenous price elasticities of the four crops. the calibrating model (41) applies also to this data sample without any modification. 10. conclusion we have achieved the objective of using all the available information about output quantities and limiting input prices, and the formulation of a calibrating pmp model that is free of the rigidities of a linear programming structure. in the process, we dispense with the necessity of dealing with the user-determined vector of small and arbitrary positive numbers ε that is required by the traditional pmp methodology. we also demonstrate the uniqueness of the calibrating solution. two empirical examples were presented. in the first sample of 14 farms and 4 crops, all farms produce every commodity. in the second sample, some of the farms do not produce all the commodities. this is the typical case. it is shown that the uniqueness of the calibrating solution is maintained also in this more elaborate case. 34 q. paris table 10. intercepts f̂ , ĝ and ĝ matrix of the marginal cost and input demand functions for the case of zero production of some crop in various farms. farm f̂ ĝ ĝ ʹf̂ ŷ ʹĝ ŷ sugar beet soft wheat corn barley 1 -0.15426 0.00274 0.73961 -0.11144 0.00686 -1.9508 75.930 0.03038 2 0.07435 -0.14574 0.03714 0.32875 0.00495 -3.1350 124.945 0.02171 3 0.03532 0.27086 -0.11507 0.03964 0.00052 -1.0871 20.595 0.00363 4 -0.02920 0.07372 0.10372 0.85513 0.00441 -2.4222 157.570 0.02530 5 0.02132 0.01858 0.11481 0.06645 0.00754 -2.3602 31.051 0.03318 6 0.22974 0.22787 -0.02587 0.26590 0.01186 -5.3411 406.732 0.02208 7 0.13824 -0.00074 -0.14506 0.29086 0.00273 -3.6725 110.382 0.00997 8 0.01525 0.40319 -0.12078 -0.17109 0.01373 -3.0222 322.849 0.04616 9 0.11620 -0.08339 0.00722 -0.03553 0.00499 -2.9037 92.071 0.01375 10 -0.00636 0.36252 -0.14708 0.00406 0.00076 -2.4324 209.320 0.00325 11 0.00236 0.30162 -0.12984 -0.24600 0.00764 -2.8158 150.906 0.02507 12 0.10700 0.16788 0.00002 0.57873 0.00004 -2.6811 638.676 0.00001 13 0.24139 0.42193 -0.25400 -0.19708 0.01046 -2.9149 213.946 0.02431 14 0.05358 0.06649 0.07745 0.01622 0.00686 -2.6086 178.140 0.03038 table 11. disaggregation/aggregation of the regional, endogenous supply elasticities when some crops are not produced in various farms. farms endogenous own-supply elasticities revenue weights sugar beet: 0.5 soft wheat: 0.4 corn: 0.6 barley: 0.3 sugar beet soft wheat corn barley 1 0.257 0 0.385 0.722 0.0523 0 0.0368 0.0198 2 0.289 0.409 0 0.251 0.1719 0.1139 0 0.0748 3 0 0.515 1.740 0 0 0.0542 0.0871 0 4 1.873 0.262 0.428 0.333 0.1288 0.1086 0.1726 0.0639 5 0.381 0.577 0 0.445 0.0421 0.0502 0 0.0306 6 0.052 0.221 0.322 0.120 0.0479 0.1007 0.1437 0.1904 7 0.149 0 0.656 0.225 0.0647 0 0.0960 0.0723 8 0 0.329 0.294 0.122 0 0.1571 0.1275 0.1108 9 0.212 0.407 0 0.335 0.0485 0.0408 0 0.0688 10 0.241 0.309 0.794 0 0.1323 0.1130 0.0810 0 11 0 0.399 0.313 0.268 0 0.0896 0.0520 0.0643 12 0.487 0.714 0 0.531 0.2004 0.1311 0 0.2220 13 0.142 0.325 0.837 0.259 0.0385 0.0409 0.0567 0.0822 14 0.305 0 0.583 0 0.0727 0 0.1465 0 35cost function and positive mathematical programming the central piece of the methodology is the estimation of a non-myopic cost function defined over output levels and input (shadow) prices. this cost function is not associated with an explicit functional form of the underlying technology. for this reason, the phase i procedure estimates the output and limiting input shadow price levels that are consistent with a linear technology and the observed information about output levels and input prices. these levels, then, are used to estimate the parameters of the cost function. this model is akin to an econometric model that is estimated for prediction without regards to the identification of the structural parameters of the cost function. the model “goodness,” then, depends on the ability to predict outside the sample observations. this test can be executed with multiple observations per farm. when several observations per each sample farm are available, the estimation procedure becomes a proper econometric approach. in this case, it will be convenient to split the sample observations in two parts: say, ninety percent (or whatever share of the observations the researcher would prefer) for estimating the cost function and ten percent for evaluating the prediction ability of the pmp methodology. this approach is a goal of further research. the pmp procedure presented in this paper uses also exogenous information about supply elasticities assumed to be available at a regional or state level. it shows how to calibrate the endogenous elasticities to this additional information while achieving a unique calibrating solution. references arfini, f. and donati, m. (2013). organic production and the capacity to respond to market signals and policies: an empirical analysis of sample of fadn farms. agroecology and sustainable food systems 37: 149-171. brooke, a., kendrick, d. and meeraus, a. (1988). gams, a user’s guide. redwood city, california: the scientific press. cortigiani, r., and severini, s. (2009). modeling farm-level adoption of deficit irrigation using positive mathematical programming. agricultural water management 96: 1785-1791. henseler, m., wirsig, a., herrmann, s., krimly, t., and dabbert, s. (2009). modeling the impact of global change on regional agricultural land use through an activitybased non-linear programming approach. agricultural systems 100: 31-42. howitt, r. e. (1995a). a calibration method for agricultural economic production models. journal of agricultural economics 46(2): 147-159. howitt, r. e. (1995b). positive mathematical programming. american journal of agricultural economics 77(2): 329-342. howitt, r. e., medellin-azuara, j., macewan, d., and lund, j. r. (2012). calibrating disaggregate economic models of agricultural production and water management. environmental modelling and software 38: 244-258. qureshi, m.e., ahmad, m.d., whitten, s.m., and kirby, m. (2014). a multi-period positive mathematical programming approach for assessing economic impact of drought in the murray-darling basin, australia. economic modelling 39: 293-304. shephard, r.w. (1953). cost and production functions. princeton, n.j.: princeton university press. a systematic approach to understanding and quantifying the eu’s bioeconomy tévécia ronzon1*, stephan piotrowski2, robert m’barek1, michael carus2 cost function and positive mathematical programming quirino paris what if meat consumption would decrease more than expected in the high-income countries? fabien santini*, tevecia ronzon, ignacio perez dominguez, sergio rene araujo enciso, ilaria proietti a stakeholder engagement approach for identifying future research directions in the evaluation of current and emerging applications of gmos davide menozzi1*, kaloyan kostov2, giovanni sogari1, salvatore arpaia3, daniela moyankova2, cristina mora1 a spatial analysis of terrain features and farming styles in a disadvantaged area of tuscany (mugello): implications for the evaluation and the design of cap payments laura fastelli1*, chiara landi2, massimo rovai1, maria andreoli1 bio-based and applied economics 9(3): 241-262, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-7928 multi-country stated preferences choice analysis for fresh tomatoes maria de salvo1,*, riccardo scarpa2,3,4, roberta capitello2, diego begalli2 1 university of catania, department of agriculture, food and environment, italy 2 university of verona, department of business administration, italy 3 durham university, business school, united kingdom 4 university of waikato, waikato management school, new zealand abstract. in this study we investigate consumers’ preferences for fresh tomato attributes in four european countries by assessing and comparing marginal willingnessto-pay (mwtp) estimates from panel mixed logit (mxl) models with utility specifications in the wtp-space. we performed an in-depth post-estimation inference to identify what attributes are the main determinants of fresh tomato purchases in each domestic market. we also assess the choice probabilities for tomatoes of various origins and types to illustrate how these post-estimation inference can be used to inform strategies designed to increase the market shares of italian fresh tomato exports in new markets and to consolidate positions in markets where italian fresh tomatoes are already appreciated by local consumers. keywords. mixed logit model, marginal willingness-to-pay, wtp space, preference space, fresh tomato. jel codes. d12, q13, q18. 1. introduction fresh tomato is one of the most commonly consumed vegetable in europe. over the last decade its consumption has remained stable at about 15 kg/year per capita, although stark changes have been observed concerning the range of quality consumers demand (european union, 2018). italy is one of the major tomato producers in europe (european commission, 2020) with exports to german, austrian, british, french and romanian markets, where italian fresh tomatoes are traditionally very appreciated. however, consumers’ preferences gradually change, and year after year, the diversity of tomato types sold has increased everywhere to meet a rapidly evolving and diversifying demand. health, convenience, taste and type of packaging are nowadays some of the most important product values for consumers. in the case of tomatoes, as for other foods, the market for ‘specialties’ is growing at *corresponding author. e-mail: mdesalvo@unict.it editor: meri raggi. 242 maria de salvo, riccardo scarpa, roberta capitello, diego begalli a significant rate (santeramo et al., 2018). new tomatoes varieties with attractive shapes, colours and tastes, innovative recyclable packaging, health claims and/or environmental certifications have been emerging as valuable product features that producers and retailers use to grow their market shares (yue and tong, 2009; tonsor and shupp, 2009; alamanos et al., 2013; oltman et al., 2014). nevertheless, the demand for tomatoes shows substantive differences across countries in terms of favorite shapes, packaging, origins and many other factors. determining consumers’ preferences and willingness to pay (wtp) for fresh tomato attributes is important to stakeholders in this industry (e.g., agricultural producers, intermediaries and retailers). it helps them determine which types of fresh tomato to grow and trade, how to manage the marketing mix, what communication content to emphasize in advertising campaigns, and how to apply fair prices along the supply chain. this information is particularly crucial for small-scale farmers who experience a strong competitive pressure from bigger companies of producers and importers. for them it is essential to correctly identify and characterize the market segments to supply, so as to define the assortment of tomatoes to produce the following season. against this background, the objective of this study is threefold. firstly, this study aims at estimating consumers’ willingness to pay for fresh tomato attributes across four key importing countries in europe. secondly, it aims at identifying the main determinants of tomato purchases across such markets. thirdly, it aims at exploring how structural estimates of heterogeneous preferences can be used to inform marketing strategies which could guide the growth of italian fresh tomato exports. alongside these research objectives, this paper also aims at achieving methodological and disseminative purposes. it will present and discuss the estimation strategies that could be implemented in a discrete choice cross-sectional analysis to face heterogeneity in preferences and take into account correlations between attributes. frequently, in discrete choice applications, post estimation analyses are limited to the assessment of the marginal willingness-to-pay (mwtp). but several additional results can be derived by the estimation of a discrete choice model with preference heterogeneity. in order to make concrete the methodological dissemination purposes of this study, the ‘rmarkdown’ and ‘markstat’ codes we used in our analyses are made available to the reader. the data collection took place in germany, russia, the uk and norway. these countries were selected for different reasons. two countries, germany and the uk, are traditional export markets for italian fresh tomatoes. in particular, germany has been for several years the main european country for italian fresh tomatoes exports. in 2015, germany imported 28,188 tons of italian fresh tomatoes, equivalent to 31% of the total fresh italian tomato export. in 2015 the uk ranked third in terms of imported quantity from italy, with 8,250 tons of fresh tomatoes.1 the other two countries, russia and norway, instead, are marginal markets for italian fresh tomato producers. here italian tomatoes compete with imports from other countries, such as the netherlands, spain, egypt and morocco. nevertheless, the four markets under investigation in this study have all, to larger or smaller extent, the potential for future growth of italian exports if producers will implement strategies aimed at meeting consumers preferences. 1 source: trade map (http://www.trademap.org). 243multi-country stated preferences choice analysis for fresh tomatoes to achieve the study objectives, the same choice experiment was administered to four representative samples of consumers, one for each country. in the data analysis, to account for heterogeneity in preferences, we used mixed logit models (henceforth mxl, see train, 1998; 1999, 2009) with utility specified in wtp-space, as suggested by train and weeks (2005) and scarpa et al. (2008). despite the well-argued methodological advantages of this approach, when compared with the more conventional preference-space specification, applications in food choice experiments are still infrequent (balcombe et al. 2010, balogh et al. 2016, caputo et al. 2016, and caputo et al. 2018). researchers have generally opted for the more traditional preference-space approach (loureiro and umberger, 2007; ortega et al., 2011; zanoli et al., 2013; liu et al., 2019), even to assess the mwtp for tomato attributes (onozaka and mcfadden, 2011; caputo et al., 2013; carroll et al., 2013; maples et al., 2014; skreli et al., 2017). in the post-estimation stage of our analysis, estimates of the marginal willingness-topay (mwtp) for fresh tomato attributes were derived and compared across the four surveyed countries. from an empirical point of view, mwtps provide producers with evidence to adjust their price strategy in line with market preferences. further, we estimated full correlation matrices for random taste coefficients of tomato attributes. we used these to estimate market shares for combinations of tomato shapes and certifications. signs and magnitudes of significant correlations between random attributes provide crucial information to producers and exporters. they are needed to define product profiles that meet consumers’ demand and identify those combinations of tomato traits that consumers dislike. moreover, to illustrate, probability choice functions were derived for selected product profiles. these functions are useful to predict consumer behavior, since differences in choice probabilities are dependent on tomato attributes. finally, marginal changes in choice probabilities within samples and for the whole population were simulated. this type of analysis serves as a tool to predict changes in consumer behaviour specific to the different export markets. the rest of the article is organized as follows. section 2 discusses materials and method, while section 3 illustrates the econometric analysis. section 4 reports and discusses results. we conclude with some final remarks in section 5. 2. materials and method data used for this analysis were collected through a choice experiment designed to gather statements on hypothetical purchases of fresh tomato by consumers living in germany, russia, the uk and norway. preliminary focus groups and pilot surveys supported the final design of the questionnaire. tomato attributes and levels were identified from previous studies (yue and tong, 2009; onozaka and mcfadden, 2011; caputo et al., 2013; carroll et al., 2013; maple et al., 2014; oltman et al., 2014; meyerding, 2016; skreli et al., 2017) and via discussion with experts in these export markets. ten attributes were selected to profile fresh tomato characteristics. these were: tomato shape (which acts as a label for the product alternatives), colour, skin thickness, pulp type, packaging format, country of origin, production method, workers’ health and safety certification2, eco-sustainability 2 in the questionnaire this attribute was explained as follows: “tomatoes can be produced according to systems that ensure high health and safety standards to workers. the final product can have a label that certifies that these standards were implemented in production”. 244 maria de salvo, riccardo scarpa, roberta capitello, diego begalli certification of production methods (including organic)3 and price4. table 1 reports the attributes and their levels. to generate the alternatives, we used a fraction of the full factorial design, that was d-error minimizing within the sets that are orthogonal in the difference (refer to ngene handbook for details). by using ngene 12.0, 144 choice tasks were generated, blocked in twelve blocks of twelve each. respondents were randomly assigned in a balanced rotation to one of the twelve blocks. each was asked to complete the twelve randomized choice tasks in their assigned block. given the complexity of the experimental design, the qualitative attribute named ‘colour’ was not directly included in the experimental design but was paired with tomato shape. consequently, the combination between shape and colour was constant in each block, but combinations changed between blocks and assigned to different people. in this way, each respondent always visualized the same pictures for the five tomato alternatives in all choice scenarios under his/her scrutiny. the other attributes were presented in a textual form. figure 1 illustrates a choice card. the final questionnaire contained three sections. the first was designed to identify the respondent’s socio-demographic profile; the second relates to food consumption habits, with specific reference to fresh tomatoes; the final section was dedicated to the choice experiment. the survey was carried out in april 2016. the target population consisted of adult consumers that consumed fresh tomatoes in the last six months and were aware about the product characteristics. country samples were selected to be representative of national populations in terms of age and gender. interviews were conducted online and administered by toluna (www.it.toluna.com), a market research company that deals with market 3 in the questionnaire this attribute was explained as follows: “tomatoes can be produced according to systems that ensure ecological sustainability and biodiversity protection. the final product can have a label that certifies that these standards were implemented in production”. 4 in the questionnaire, prices were expressed in the national currency. table 1. attributes and levels. attribute attribute levels shape beef, salad (salad), vine (vine), cherry (cherry), date (date) colour red (red), not-red (i.e., yellow, orange or variegated) skin thin, thick (thick) pulp juicy, rich (rich) packaging loose tomatoes, net (net), tray (tray) origin italy, netherlands (nld), spain (esp), morocco (mar), egypt (egy), others (oth) production method conventional, low environmental impact (env), organic (org) workers’ health and safety certified not present, present (safety) eco-sustainable certified not present, present (eco) price (euro/kg) 1.18, 1.58, 2.37, 2.76 note: qualitative attributes were coded using dummy variables. the price attribute was coded using a continuous variable. in bold font the reference level. in brackets the variable name. 245multi-country stated preferences choice analysis for fresh tomatoes analysis and has a world opt-in panel with over 9 million consumers. the company supplied the availability of high-quality internet panels (i.e., iso certification and application of international quality standards for market research) and guaranteed an incidence rate equal to 0.70 for each country. the online questionnaire was completed by a total number of 2,600 respondents: 700 in germany, russia and the uk and 500 in norway. the total choice observations generated were 31,200 (12 choice cards for 2,600 respondents). the number of products evaluated by respondents amounted to 156,000 (5 tomato shapes/scenarios for 2,600 respondents). table 2 reports the summary statistics at country level and for the whole sample. figure 1. example of a choice card. workers’ health and safety certification no no yes no no packaging loose tomatoes net loose tomatoes loose tomatoes net eco-sustainable certified yes no no yes yes pulp juicy juicy juicy rich juicy production method low env. impact organic low env. impact conventional organic skin thick thin thin thick thin origin morocco italy morocco netherlands netherlands price (€/kg) 2.76 2.37 1.58 1.18 1.58 table 2. summary statistics. germany (n = 700) norway (n = 500) russia (n = 700) uk (n = 700) all (n = 2,600) mean sd mean sd mean sd mean sd mean sd white european ethnicity* 0.89 0.31 0.81 0.40 0.95 0.22 0.81 0.39 0.87 0.34 bmi 25.42 5.68 25.50 5.05 24.72 6.86 26.00 6.53 25.40 6.16 vegetarian/vegan * 0.09 0.29 0.07 0.26 0.10 0.31 0.10 0.31 0.10 0.29 female* 0.60 0.49 0.53 0.50 0.60 0.49 0.60 0.49 0.59 0.49 age (in year) 39.01 12.09 40.34 16.74 38.70 11.09 39.23 12.40 39.24 12.96 education** 0.29 0.45 0.39 0.49 0.75 0.43 0.43 0.50 0.47 0.50 family size (n.) 2.54 1.23 2.47 1.39 3.23 1.17 2.84 1.34 2.79 1.31 minor or dependent (n.) 0.68 0.92 0.61 1.02 0.93 0.94 0.79 1.04 0.77 0.99 *1 if yes; ** 1 if university graduate or post graduate. 246 maria de salvo, riccardo scarpa, roberta capitello, diego begalli 3. econometric model and inference the choice data were analyzed by means of econometric models based on random utility maximization with heterogenous preference parameters (mcfadden, 2001). we assumed a linear and additive indirect utility function: unjt=-αnpnjt+βn ’xnjt+εnjt (1) where pnjt is the price attribute, xnjt represents the vector of non-price tomato attributes, and αn and βn are random parameters which represent nth respondent’s taste intensities for each attribute describing the tomato profile of each jth alternative in the tth choice occasion in the sequence. for the random component, we hypothesized that εnjt~ i.i.d. gumbel. assumptions imply that, conditional on βn, the probability of observing a particular sequence of 12 choices for each nth respondent (yn=yn1,yn2,…,yn12 ) is the product of standard logit formulas: (2) unconditional probability was calculated as the integral of equation (2) weighted by the density function g(αn,βn|μ,ω): pn(yn)=∫l(yn1,yn2,…,yn12|αn,βn)g(αn,βn|μ,ω)dαndβn (3) this integral was approximated through simulation, by: i) taking draws from the g(.) function; ii) calculating the likelihood function for each draw; and iii) averaging the results. the maximum simulated likelihood estimator is the value of the unknown parameters that maximizes the likelihood of the sample simulated in this manner. equation (3) represents the so-called panel mixed logit, which allowed us to use a mixed logit model specification in the context of repeated choices by respondents assuming specific taste distributions (revelt and train, 1998). to obtain a posterior distribution of αn,βn for each respondent, the procedure described by revelt and train (2000) can be used. following train and weeks (2005) we specified the utility function in the wtp space5. with a gumbel distributed unobservable component of utility, the error variance varies among respondents: (4) where kn represents a scale parameter for the nth respondent. to allow for random scale parameter, train and weeks (2005) suggested to divide equation (1) by the scale parameter: 5 sonnier et al. (2007) called this model the “consumer’s surplus model”. it is also known in literature as “expenditure function space” model, “valuation function”, or “money-space” (thiene and scarpa, 2009). 247multi-country stated preferences choice analysis for fresh tomatoes (5) as a consequence, in equation (5), unjt~i.i.d. gumbel, but with constant variance equal to π2/6. assuming that λn=an/kn and cn=βn/kn, equation (5) becomes: unjt=-λnpnjt+cn ’xnjt+unjt (6) where λn=an/kn, wn = c’n/λn, cn=βn/kn and kn represents the scale parameter for the nth respondent. equation (6) is the so-called utility function in the preference space. given that, by definition, the mwtp for an attribute is the ratio between the attribute’s coefficient and the coefficient of the price attribute, equation (6) can be re-written as follows: unjt=-λnpnjt+(λnwn)’xnjt+unjt (7) where wn = c’n/λn. equation (7) is the so-called utility function in wtp space (train and weeks, 2005).6 through the direct choice of specific random wtp distributions, the wtp space approach prevents situations where the implied mwtp distributions from the random preference coefficients show excessively long tails. this is often the case in preferencespace utility specifications (scarpa et al., 2008). the literature reports controversial results on what approach produces a better fit to the empirical data. however, there is a general consensus on the ability of wtp space specifications to generate more reasonable and less disperse estimates of wtp distributions (scarpa et al., 2008; balcombe et al., 2009; hensher an greene, 2009; rose and masiero, 2009; daly et al 2012; owusu coffie et al., 2016). our estimator was implemented in stata 15.0 and employed the packages mixlogitwtp (hole, 2007). we did not find significant evidence of heterogeneity in preliminary estimations for tomato colour, skin, pulp and country of origin. so, we assumed these to be fixed, meaning that we hypothesized homogeneous preferences for these features. conversely, we obtained significant variance estimates in preliminary results for tomato shapes, packaging types and certifications. hence, the associated random parameters were consequently assumed to be random and specifically distributed multi-variate normal with a full correlation matrix. the coefficient for the negative of price was assumed to have a log-normal distribution, to constrain the price coefficient to be always negative. estimates were obtained with 1,000 halton draws, which despite the high number of random parameters, can assure sufficiently low simulation variance of the maximum simulated likelihood estimator according to zeng (2016) and palma et al. (2018). while all of the above is informative, it is also quite standard. in this study, however, we extended the range of inference in a more novel direction. we used the estimates of the vector of means μ and their variance-covariance matrix ω=(ll’) for each country to infer the probabilistic choice behavior in the underlying population of consumers. note 6 sonnier et al. (2007) called this model the “consumer’s surplus model”. it is also known in literature as “expenditure function space” model, “valuation function”, or “money-space” (thiene and scarpa, 2009). 248 maria de salvo, riccardo scarpa, roberta capitello, diego begalli that estimates of the cholesky decomposition l of the full variance-covariance matrix ω enabled us to derive the correlation matrix for the random α and β. with this we identified patterns of covariation across taste parameters β that we then used in behavioral inference. for example, we used them in the derivation of probabilistic demand functions based on the simulation of distributions of preference values β in the population from which to infer market choice probabilities. we replicated this for selected tomato attributes (tomato profiles) and compared them across countries. another type of inference was conducted at the sample level. here information on the observed choice sequence of each respondent was brought to bear by deriving individual specific means for marginal wtps. these are graphically represented for the samples by kernel smoothing plots for the sample of each country. hypothetical choice probabilities were also simulated at the population estimates by modifying the choice sets to evaluate shares for what-if scenarios. scenarios simulated the introduction in the choice tasks of specific tomatoes profiles at a given price. we provide an illustration of the latter obtained with the post estimation commands in stata. this required the modification of one or more attribute levels in the choice set and the re-computation of the in-sample predicted probabilities of choice to obtain changes in market shares following the introduction of new tomato profiles. 4. results and discussion table 3 reports the coefficients estimates for each country. the model estimated for the pooled samples across countries are reported in the last columns to the right. the uninformed sequence of 12 choices between 5 alternatives has a log-likelihood of ln[(1/5)12]=-19.31, while the averages in our estimated model range between a maximum of -16.33 and a minimum of -16.67, respectively 0.85 and 0.86 percent of the uninformed probability. this implies a good explanatory power of the joint model. findings suggest that red color (baseline yellow/orange/variegated) and country of origin (baseline italy) are key determinants of choice, while a thick tomato peel (baseline thin) and a rich pulp (baseline juicy) do not seem to be relevant. preferences vary across the investigated markets, especially for the country of origin. italian tomatoes are always preferred to those coming from other origins for germans. these, for example, are willing to pay an average premium of 0.90 €/kg for italian tomatoes in comparison to those coming from morocco. russians are generally indifferent to country of origin when tomatoes come from egypt, italy or spain. however, they significantly dislike those produced in the netherlands, morocco or “other countries”. for the latter, wtp is comparatively lower by about 0.19-0.21 €/kg. the uk consumers emerged as “origin-blind” as the country of origin never emerges as significant. country-level models’ results further suggest that norwegians appreciate juicy tomatoes (+ 0.15 €/kg in comparison to rich-pulp tomatoes), while russians prefer to buy thinskin tomatoes (+ 0.23 €/kg in comparison to thick-skin ones). interestingly, preferences for tomato shapes vary across countries and, at the same time, are also significantly heterogeneous within each country. for the coefficients of tomato shapes, standard deviations are significantly different from zero, with the exception of “cherry” and “date” shaped tomatoes in the uk and “date” shaped tomato in nor249multi-country stated preferences choice analysis for fresh tomatoes ta bl e 3. m ix ed lo gi t m od el s’ co effi ci en ts e st im at es . va ria bl es g er m an y n or w ay ru ss ia u k a ll m ea n st an da rd d ev ia tio n m ea n st an da rd d ev ia tio n m ea n st an da rd d ev ia tio n m ea n st an da rd d ev ia tio n m ea n st an da rd d ev ia tio n c oe f. c oe f. c oe f. c oe f. c oe f. c oe f. c oe f. c oe f. c oe f. c oe f. re d 3. 77 ** *( 12 .8 7) 2. 26 ** *( 15 .7 0) 1. 39 ** *( 23 .2 6) 3. 95 ** *( 13 .6 9) 2. 52 ** *( 31 .0 2) th ic k 0. 05 (0 .5 6) 0. 01 (0 .1 2) -0 .2 3 ** * (5 .7 9) -0 .0 3 (0 .3 5) -0 .0 6 (1 .8 6) ri ch -0 .0 7 (0 .8 5) -0 .1 5 * (2 .5 7) 0. 05 (1 .3 9) -0 .0 9 (1 .1 7) -0 .0 3 (0 .9 1) n ld -0 .3 9 ** (2 .6 4) -0 .0 9 (0 .9 3) -0 .1 9 ** (2 .9 2) -0 .0 1 (0 .1 0) -0 .1 8 ** (3 .4 1) es p -0 .6 5 ** * (4 .2 8) -0 .1 5 (1 .4 3) -0 .1 2 (1 .8 9) 0. 09 (0 .6 3) -0 .2 1 ** * (3 .9 4) m a r -0 .9 0 ** * (5 .5 0) -0 .1 9 (1 .8 9) -0 .2 1 ** (3 .1 5) 0. 01 (0 .0 6) -0 .2 9 ** * (5 .4 4) eg y -0 .8 6 ** * (5 .1 5) -0 .1 6 (1 .5 1) -0 .0 9 (1 .3 8) -0 .0 5 (0 .3 9) -0 .2 5 ** * (4 .6 4) o th -0 .6 2 ** * (4 .0 0) -0 .2 3 * (2 .3 1) -0 .1 9 ** (2 .8 7) -0 .2 7 (1 .9 0) -0 .2 9 ** * (5 .3 4) sa la d -2 .7 0 ** *( 10 .2 9) 2. 05 ** *( 8. 80 ) -1 .3 5 ** *( 10 .0 5) 1. 17 ** *( 8. 81 ) -0 .2 0 ** (2 .7 8) 0. 75 ** *( 10 .1 2) -2 .3 6 ** * (9 .2 0) 2. 48 ** *( 9. 49 ) -1 .4 5 ** *( 18 .6 4) 1. 43 ** *( 18 .2 0) v in e 1. 11 ** * (7 .5 8) 0. 54 ** (3 .4 7) 0. 68 ** * (6 .4 1) 0. 50 ** *( 5. 30 ) 0. 57 ** * (9 .2 1) 0. 48 ** * (9 .4 5) 1. 57 ** * (8 .5 6) 1. 16 ** *( 7. 58 ) 0. 73 ** *( 14 .7 6) 0. 18 * (2 .1 2) c he rr y -2 .0 0 ** * (7 .8 9) 1. 81 ** *( 7. 96 ) -0 .8 2 ** * (6 .4 6) 0. 80 ** *( 6. 48 ) -0 .9 5 ** *( 11 .0 0) 0. 41 ** * (3 .9 6) -0 .6 8 ** * (4 .4 7) 0. 21 (1 .1 7) -1 .1 2 ** *( 14 .5 6) 0. 52 ** (2 .6 3) d at e -3 .6 2 ** * (9 .9 5) 0. 83 ** (2 .9 2) -1 .1 3 ** * (7 .5 7) 0. 10 (0 .7 7) -1 .2 1 ** *( 10 .5 3) 0. 71 ** * (5 .1 5) -1 .8 0 ** * (7 .8 1) 0. 02 (0 .1 4) -1 .7 5 ** *( 18 .4 1) 0. 07 (0 .5 2) n et -0 .2 5 * (1 .9 7) 0. 26 (0 .7 6) -0 .1 0 (1 .0 4) 0. 38 * (2 .3 7) -0 .1 4 * (2 .0 8) 0. 05 (0 .5 3) -0 .1 0 (0 .7 9) 0. 53 (2 .6 4) -0 .1 6 ** (3 .3 8) 0. 02 (0 .2 1) tr ay -0 .7 2 ** * (5 .2 3) 0. 34 (1 .8 0) -0 .4 5 ** * (4 .6 6) 0. 32 * (2 .3 4) -0 .0 5 (0 .8 0) 0. 06 (0 .8 1) -0 .6 4 ** * (4 .6 3) 0. 05 (0 .3 1) -0 .3 9 ** * (7 .8 3) 0. 03 (0 .4 1) en v 0. 18 (1 .2 4) 0. 25 (0 .8 0) -0 .0 5 (0 .5 0) 0. 02 (0 .1 4) -0 .2 2 ** (2 .9 5) 0. 38 ** * (4 .4 3) 0. 23 (1 .7 5) 0. 15 (0 .6 9) 0. 01 (0 .2 2) 0. 08 (0 .8 1) o rg 1. 22 ** * (6 .2 3) 0. 99 ** *( 4. 23 ) 0. 26 * (2 .3 2) 0. 80 ** *( 6. 03 ) -0 .0 7 (0 .9 3) 0. 17 (1 .9 0) 0. 39 ** (2 .6 5) 0. 24 (1 .0 2) 0. 37 ** * (6 .5 8) 0. 56 ** * (3 .7 3) sa fe ty 0. 56 ** * (4 .8 1) 0. 04 (0 .1 7) 0. 50 ** * (5 .3 6) 0. 23 (1 .6 6) 0. 23 ** * (3 .8 5) 0. 33 ** (3 .2 8) 0. 79 ** * (6 .2 6) 0. 19 (0 .9 7) 0. 43 ** *( 10 .1 2) 0. 13 (0 .8 1) ec o 0. 60 ** * (4 .6 9) 0. 06 (0 .3 0) 0. 16 (1 .8 2) 0. 03 (0 .2 5) 0. 37 ** * (5 .5 6) 0. 36 ** * (3 .8 5) 0. 47 ** * (3 .9 8) 0. 31 (1 .4 9) 0. 44 ** * (9 .3 6) 0. 20 (1 .9 4) ln -n eg .p -1 .2 1 ** *( 15 .6 9) 0. 13 * (1 .9 9) -0 .6 8 ** *( 11 .2 1) 0. 10 (0 .8 3) -0 .4 8 ** *( 10 .0 6) 0. 04 (0 .4 1) -1 .1 9 ** *( 16 .0 6) 0. 01 (0 .1 9) -0 .8 5 ** *( 27 .5 2) 0. 17 ** (2 .9 4) c ho ic es 42 ,0 00 30 ,0 00 42 ,0 00 42 ,0 00 15 6, 00 0 n 70 0 50 0 70 0 70 0 2, 60 0 ln -l /n -1 6. 33 5 -1 6. 42 9 -1 6. 48 8 -1 6. 48 8 -1 6. 67 4 a ic 23 ,0 39 .5 1 16 ,5 98 .8 7 23 ,2 52 .9 5 23 ,2 53 .0 1 86 ,8 75 .5 9 bi c 23 ,7 74 .3 7 17 ,3 05 .1 4 23 ,9 87 .8 1 23 ,9 87 .8 7 87 ,7 21 .9 9 *p <0 .0 5; * *p <0 .0 1; * ** p <0 .0 01 . z s ta tis tic in p ar en th es is . v ar ia bl es c od in g is re po rt ed in t ab le 1 . 250 maria de salvo, riccardo scarpa, roberta capitello, diego begalli way. “vine” tomatoes are always preferred to “beef ” (the baseline) across all countries, while the latter are always preferred to “salad”, “cherry” and “date” tomatoes. in general, consumers prefer to buy loose (the baseline) rather than packaged tomatoes. however, preferences for packaging types (in “nets” or “tray”) are not always significant and emerge as heterogenous at the country level. tomatoes with certified credence attributes are preferred to those without certification. in particular, germans are willing to pay, on average, 1.22 €/kg for organic-certified tomatoes, even if the distribution is strongly dispersed in comparison to those in other countries. german consumers are also sensitive to certifications ensuring workers’ health and safety (+ 0.56 €/kg) and eco-sustainability (+ 0.60€/kg). however, the choices observed in the uk sample imply a higher willingness to pay for workers’ health and safety certification (+ 0.79 €/kg). preferences for certifications show a significant heterogeneity for organic products in germany and norway. russians demonstrated significant heterogeneous preferences for low-environmental impact, workers’ health and safety, and eco-sustainable certified products. instead, organic certification is not appreciated by russians, who in turn are the only consumers with a significant positive appreciation for certification for low-environmental impact production methods. figure 2 displays the kernel smoothing of individual posterior means of mwtp sample distributions for each country for tomato shape, whose coefficients showed a significant heterogeneity in the majority of the investigated countries. sample distributions are displayed only for those tomato shapes which have both significant mean and standard deviation estimates. some distributions differ significant in terms of range, number of modes and relative positions in the wtp space. as pointed out earlier, german and uk consumers do not appreciate salad tomatoes in comparison to beef ones. their mwtp distributions are prevalently located in the negative range and are multimodal in both cases. this implies that, everything else equal, for most consumers, salad tomatoes need to be sold at a lower target price compared to beef tomatoes to induce a purchase; how much lower is different in the two countries. in contrast, mwtp for salad tomatoes in the other two markets are located to the right, especially for russia, that present both positive and negative modal values. negative means of mwtp are shown also for cherry and date tomatoes in comparison to beef tomatoes. however, preferences for cherry tomatoes seem to be more similar among norwegian and russian consumers than germans; while mwtp distribution for date tomatoes are less dispersed for russians and more variable for germans. vine tomatoes, in contrast, are preferred to beef ones in all markets. modal value estimates are always positive, although the ranges of variation are extremely different between countries, with the widest one in the uk, where there is also the higher modal value. table 4 reports the estimates of correlation coefficients (lower triangle), variances (diagonal) and covariances (upper triangle) between random mwtps in each country. for some pairs of random attributes, correlations are significant across all models and have concordant signs, even if they have different magnitude. correlations between salad and date tomatoes, for instance, are always positive and significant in all markets, meaning that these kinds of tomato could be jointly sold in these countries through focussed advertising strategies exploiting the “drag” effect of a tomato type on the other. converse251multi-country stated preferences choice analysis for fresh tomatoes ly, a negative correlation is estimated between salad and vine-shaped tomatoes. for germany and norway, in particular, the correlation coefficients are high and significant, -0.89 and -0.84 respectively. this finding is focal to support product marketing by the sellers: salad and vine tomatoes are antagonist in these markets and meet the preferences of different consumers and consequently separate market targets. this could suggest locating these products on different shelves or even separate shops when the locations of these are correlated with one type of buyers. country-level preferences for type of packaging vary and they are correlated with the tomato’s shape. in general, all consumers prefer to buy loose-packaged tomatos. however, germans prefer to buy salad tomatoes that are traypackaged (correlation coefficients: 0.77) and dislike trays for vine ones (-0.85); norwegians like cherry tomatoes when packaged in a net (0.79). for the uk and russia, some figure 2. kernel density plots for conditional wtps for tomato shapes. 252 maria de salvo, riccardo scarpa, roberta capitello, diego begalli table 4. estimates of correlation and covariance matrixes in each country. germany salad vine cherry date net tray env org safety eco np salad 4.20 -2.19 -0.38 4.58 0.47 1.25 0.14 0.46 -0.58 -0.14 0.06 vine -0.89 1.44 1.05 -1.23 -0.34 -0.81 -0.15 0.30 0.46 0.18 -0.15 cherry -0.08 0.37 5.77 4.82 0.10 -0.14 0.24 -1.09 0.62 -0.27 0.17 date 0.66 -0.30 0.60 11.30 0.74 0.94 -0.30 -0.05 -0.08 -0.58 -0.15 net 0.38 -0.46 0.07 0.36 0.38 0.19 -0.29 -1.17 -0.14 -0.36 0.10 tray 0.77 -0.85 -0.07 0.35 0.40 0.63 0.29 -0.56 -0.12 -0.08 0.20 env 0.07 -0.13 0.11 -0.10 -0.50 0.39 0.86 0.58 0.08 0.23 0.17 org 0.09 0.09 -0.17 -0.01 -0.72 -0.27 0.24 6.98 -0.35 0.58 -0.37 safety -0.37 0.50 0.34 -0.03 -0.29 -0.19 0.11 -0.10 0.59 0.61 -0.05 eco -0.06 0.13 -0.09 -0.15 -0.51 -0.09 0.21 0.26 0.68 1.37 -0.21 -price 0.05 -0.22 0.12 -0.07 0.28 0.42 0.32 -0.08 -0.12 -0.31 0.34 norway salad vine cherry date net tray env org safety eco np salad 1.37 -0.93 -0.21 1.21 -0.14 0.14 -0.08 0.24 -0.13 0.07 0.07 vine -0.84 0.88 0.21 -0.40 0.16 -0.05 -0.08 -0.33 0.04 -0.16 -0.23 cherry -0.21 0.27 0.69 0.43 0.41 0.03 0.00 -0.06 -0.09 -0.06 0.04 date 0.71 -0.29 0.35 2.14 0.26 0.23 -0.26 -0.01 -0.34 -0.16 -0.16 net -0.19 0.26 0.79 0.29 0.39 0.08 -0.01 0.00 0.19 -0.01 -0.06 tray 0.30 -0.13 0.08 0.39 0.33 0.16 0.01 0.09 -0.07 0.01 0.01 env -0.20 -0.24 0.02 -0.48 -0.05 0.06 0.14 0.16 -0.11 0.01 0.11 org 0.20 -0.35 -0.07 -0.01 0.01 0.21 0.43 1.02 0.03 0.36 0.10 safety -0.12 0.05 -0.11 -0.24 0.31 -0.17 -0.31 0.03 0.93 0.27 -0.16 eco 0.08 -0.24 -0.10 -0.15 -0.02 0.03 0.05 0.25 0.41 0.49 0.01 -price 0.12 -0.46 0.09 -0.20 -0.17 0.07 0.54 0.05 -0.31 0.02 0.29 russia salad vine cherry date net tray env org safety eco np salad 0.57 -0.08 0.12 0.71 -0.09 0.20 0.09 -0.06 -0.17 -0.12 -0.24 vine -0.23 0.24 0.28 0.25 0.02 -0.02 -0.02 -0.06 -0.01 -0.01 -0.17 cherry 0.21 0.75 0.60 0.51 -0.10 -0.08 -0.07 -0.26 -0.06 0.09 -0.44 date 0.67 0.35 0.47 1.98 -0.22 0.21 0.14 -0.10 -0.26 -0.14 -0.39 net -0.33 0.14 -0.36 -0.42 0.14 0.10 0.05 0.10 0.00 -0.14 0.06 tray 0.53 -0.10 -0.22 0.29 0.55 0.25 0.14 0.08 -0.14 -0.28 0.05 env 0.23 -0.09 -0.17 0.19 0.23 0.54 0.28 0.16 -0.02 -0.16 0.14 org -0.13 -0.19 -0.54 -0.11 0.43 0.24 0.48 0.40 0.09 -0.05 0.12 safety -0.36 -0.03 -0.13 -0.31 0.00 -0.45 -0.07 0.09 0.38 0.26 0.03 eco -0.21 -0.03 0.15 -0.13 -0.49 -0.73 -0.40 -0.07 0.55 0.58 -0.14 -price -0.39 -0.42 -0.71 -0.34 0.21 0.12 0.32 0.16 0.06 -0.23 0.64 253multi-country stated preferences choice analysis for fresh tomatoes estimates of correlation coefficients between tomato shapes and package types are significant, but their values are lower than 0.60, showing a low-to-moderate correlation. another interesting result concerns the relationship between certifications of workers’ health and safety protection and eco-sustainability. their correlation is always significant and positive, suggesting that consumers who are willing to pay for an eco-sustainable tomato are also willing to pay for a health and safety-certified tomato. however, the correlation coefficients are moderate ranging between 0.41 (for norway) and 0.68 (for germany). table 5 reports estimates of the market shares for combinations of tomato shape and certifications. in all investigated markets, vine tomato shows the higher shares. in all countries, this tomato type could increase its market share when certified as produced with methods that promote workers’ health and safety, or eco-sustainability. figure 3 displays the estimated choice probability functions for selected product profiles along the price/kg dimension, when the baseline is a beef tomato without any additional attribute. for all graphs we adopted a price ranging from the lower level assumed in the choice experiment (1.81 €/kg) to three times the higher level (3 x 2.76 €/kg). the top left plots of figure 3 show that as price increases the predicted purchase probability by germans of red rich-pulp italian tomatoes drops rapidly, so that at a price of 6€/ kg it is basically zero except for vine tomato sold loose and certified. the function for norway investigates the simulated effects of eco-sustainable and organic certification of red italian tomatoes, while the function for russia is about the effect of tomato shape. finally, the function for the uk investigates the role of certification for the same tomato profile and demonstrates that in this country consumers have the same reactions to price changes regardless the type of certification. figure 4 focuses on co-variation of preferences for tomato shapes, specifically salad and date, both of which tends to be disliked compared to the beef tomato baseline. it reports the iso-quantile plots for all the countries of bivariate kernel densities of mwtp uk salad vine cherry date net tray env org safety eco np salad 6.14 -2.66 -0.69 5.70 0.43 0.92 -0.30 -0.02 -0.58 -0.50 0.12 vine -0.68 2.50 -0.16 -2.63 -0.25 -0.63 0.47 0.51 0.29 0.04 -0.62 cherry -0.53 -0.19 0.27 -0.50 -0.04 -0.09 0.00 -0.15 0.11 0.14 0.14 date 0.98 -0.71 -0.41 5.48 0.38 0.73 -0.16 -0.03 -0.42 -0.39 0.09 net 0.25 -0.23 -0.11 0.24 0.47 0.35 0.10 -0.27 -0.29 -0.41 0.14 tray 0.49 -0.53 -0.23 0.41 0.68 0.58 -0.04 -0.32 -0.29 -0.40 0.28 env -0.18 0.44 0.01 -0.10 0.22 -0.07 0.46 0.14 0.06 -0.22 -0.15 org -0.01 0.28 -0.25 -0.01 -0.35 -0.38 0.19 1.28 -0.07 0.26 -0.20 safety -0.24 0.19 0.22 -0.18 -0.44 -0.39 0.09 -0.06 0.94 0.53 -0.15 eco -0.22 0.03 0.29 -0.18 -0.65 -0.57 -0.35 0.21 0.59 0.86 -0.15 -price 0.08 -0.66 0.44 0.07 0.33 0.61 -0.37 -0.11 -0.26 -0.26 0.36 note: estimates of variances are reported in the diagonal. covariance and correlation estimates are reported above and below the diagonal, respectively. correlations are in italic. in bold the estimates which are significant with p<0.05. 254 maria de salvo, riccardo scarpa, roberta capitello, diego begalli estimates in a range of change between -5 to +5 euro/kg compared to the baseline product profile. the price change combinations along each iso-quantile curve represent the proportion of the population with the same probability of selecting tomatoes with one of the two shapes rather than the baseline beef tomato. the isoquantiles highlight a positive correlation between salad and date-shaped tomatoes in the four countries, but the price set combinations with which they relate to the baseline are quite different. for german consumers, with a correlation estimate of 0.66, the curves cover a much larger set of mwtp values than in the russian and norwegian samples. for the uk consumers, the mwtp ranges are similar to those shown in germany. however, because of the much stronger correlation of 0.98 between the shape attributes, the room for a differentiated pricing policy is much reduced. norwegian and russian consumers show quite similarly sets in terms of preferences and willingness to pay. finally, the estimated models can be used to simulate marginal changes in probability of choice within the samples rather than in the population. for example, what would the distribution of choice probability be, within the german sample, if all choice tasks including the baseline italian tomato were offered with certification for workers’ health and safetable 5. market shares for combinations of tomato shapes and certifications. shape certifications germany norway russia uk salad org-safety 4% 6% 11% 7% org-safety-eco 4% 4% 8% 5% org-eco 6% 6% 10% 7% safety-eco 5% 5% 17% 8% env-safety 4% 2% 10% 7% env-safety-eco 3% 1% 7% 4% vine org-safety 45% 29% 28% 45% org-safety-eco 37% 22% 21% 36% org-eco 41% 28% 25% 40% safety-eco 54% 32% 46% 54% env-safety 39% 17% 20% 48% env-safety-eco 32% 11% 14% 34% cherry org-safety 11% 5% 0% 4% org-safety-eco 8% 3% 0% 3% org-eco 8% 6% 1% 4% safety-eco 12% 5% 6% 8% env-safety 12% 3% 1% 6% env-safety-eco 8% 2% 1% 5% date org-safety 7% 8% 5% 10% org-safety-eco 6% 6% 3% 7% org-eco 6% 8% 5% 9% safety-eco 8% 7% 8% 11% env-safety 6% 2% 5% 10% env-safety-eco 4% 2% 3% 6% 255multi-country stated preferences choice analysis for fresh tomatoes ty at a price increased by ten percent? this comes down to computing the choice probabilities for all five alternatives in each choice task, i.e. the probability vectors with the figure 3. country-level demand functions for some types of tomato. 256 maria de salvo, riccardo scarpa, roberta capitello, diego begalli price increase for certitication (p1) and without such change (p0) for the baseline tomato. then the difference between the two sets of predicted selection probabilities (p1 p0) for the alternatives with the profile of interest is computed and the distribution of these values examined. in our case we have 13,071 choice sets containing the baseline profile in the german sample. an increase of ten percent would always result in a decreased selection probability, as shown in figure 5. this suggests that either the price change should be lowered, or some additional positive features should be added, for example organic certification, that the german consumers seem to strongly appreciate. one can also envisage iterating this exercise at gradually lower price increases until a sufficient fraction of the within sample predicted choices show a positive value. figure 4. iso-quantile plots of bivariate kernel density distributions for mwtp estimates for salad and date-shaped tomatoes in the four countries. 257multi-country stated preferences choice analysis for fresh tomatoes 5. conclusions we conduct identical surveys across four countries to estimate the marginal wtps for a set of attributes of fresh tomatoes. estimates were obtained in wtp-space, which several authors encourage practitioners to adopt to obtain more reliable, interpretable and plausible mwtp distributions. specific differences in preferences across countries have been highlighted in terms of sign and magnitude of the coefficient estimates, conditional mwtps, correlation coefficients and market shares. further, simulations of choice purchase behaviour were inferred within-sample and at the population level. these were discussed with regards to their effects of price changes on tomato profiles in the four markets, to explore marketing implications of population distributions of marginal mwtps and to exemplify the range of uses analysts can make of these model post-estimations. the method can produce evidence that could be used to support the design of strategies aimed at consolidating the position of italian tomatoes on traditional european markets, such as germany and the uk; and at the same time, it could help italian producers to identify what types of tomato produce to improve their share in norwegian and russian markets. the tomato profile, which shows the highest probability to be purchased in all markets is vine, red and sold loose (unpackaged). however, some specific tomato profiles figure 5. in-sample simulation of selection probabilities for workers’ health and safety certification at 10% price increase. 258 maria de salvo, riccardo scarpa, roberta capitello, diego begalli have been identified for each market. in germany, where italian tomatoes are preferred to those coming from other countries, consumers ask tomatoes whose quality is certified for workers’ health and safety and eco-sustainability, but only within a restricted price range, as shown by the in-sample inference, where a ten percent increase was found too high. salad-shaped tomatoes is more likely to be purchased when packaged in trays, while the use of this package should be avoided for vine-shaped tomatoes. in the uk, the same types of tomato certifications are also appreciated. however, the uk consumers seem to be not interested in the country of origin, unlike german consumers. norwegian and russian consumers adopt an intermediate behaviour. consequently, tomatoes from italy may not enjoy the same level of competitive advantage abroad, as it is generally assumed, and hence export penetration strategies should vary across countries. to sell more tomatoes in norway, italian producers should offer juicy-pulp tomatoes and certify their quality with organic and worker’s health and safety labels. cherry tomatoes are more appreciated in the uk market if are packaged in a net. finally, russians prefer thin-skin tomatoes and appreciate certifications for workers’ health and safety and eco-sustainability, rather than for organic production. further research should address some of the limitations of our study in order to confirm or disconfirm our findings, which were only illustrative in their nature. in fact, we are aware about a number of limitations of our study. they arise from the choices we have been forced to make regarding the experimental design and the data analysis. firstly, to assure that the survey respect international quality standards for market research in a cross-country context, we decided to collect data engaging a market research company. the use of such online survey has grown rapidly in social science and policy research in the last ten years (lehdonvirta et al., 2020). however, it is well known that data generated in this manner could be affected by self-selection issues and non-random and non-representativeness of the samples, and these limitations should be taken into account in evaluating the external validity of our results. further, to reduce the choice task complexity, we simulated a forced choice decision context, asking consumers to imagine they had to decide to buy one of the proposed options, without including an opt-out alternative. this decision has been supported by dhar and simonson (2003) who suggested that forced choice may generate more accurate and complete results in categories of familiar commodities in which the deferral option is available but rarely exercised. we assumed that this is the case of our research given that participants in our survey are consumers of fresh tomatoes, fresh tomatoes are characterized by high versatility in cooking and individual diets, and the expenditure of this product has a low impact on the individual/household budget. however, we are aware that this can be seen as a limitation of our study. therefore, market shares estimates could be affected by the adopted choice design. this possibility must be taken into account by the reader. moreover, each choice card includes several attributes and levels and, despite this well simulates the real-life scenario faced by consumers when purchasing fresh tomatoes, at the same time, respondents may not have attend to a certain number of attributes. an attribute-not-attendance phenomenon (hensher, 2010) could consequently affect this survey as a limitation. we plan to analyse this eventuality through a further paper, given that it is not the focus in this one. another limitation is related to the econometric approach. we chose to use halton draws for simulations, despite the use of scrambled sobol draws could be more appropriate, as demonstrated by czajkowski and budziński (2019). our choice stemmed from the 259multi-country stated preferences choice analysis for fresh tomatoes fact that one of the aims of this paper is to provide the reader with estimation and postestimation codes used in data analysis to facilitate dissemination. further, it is worth observing that we took the exporter viewpoint, and consequently we did not adjust prices according to the national purchasing power given that results are mainly presented at a country level. therefore, it is important to underscore that, in the case of a country comparison, the same tomato profile could be perceived as relatively cheap or expensive in countries with different purchase powers. these cases could affect choice probability estimates. finally, we used maximum likelihood estimators, which suffer from the limitation of local optima, and assumed normal and log normal distributions of qualitative attributes and price, respectively, for the random parameters. assumptions of unimodal symmetric distributions surely affect our estimates and the analysis might also have been conducted with more flexible semi-parametric mixtures (train 2016, caputo et al. 2018, scarpa et al. 2020). despite these limitations, this study presented useful insights into consumer choices and their impact on market competitiveness for food producers. it demonstrated how the use of stated food choice experiments in a multi-country context is focal to support 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(2016). using halton sequences in random parameters logit models. journal of statistical and econometric methods 5(1): 59-86. investigating determinants of choice and predicting market shares of renewable-based heating systems under alternative policy scenarios cristiano franceschinis, mara thiene multi-country stated preferences choice analysis for fresh tomatoes maria de salvo1,*, riccardo scarpa2,3,4, roberta capitello2, diego begalli2 “not my cup of coffee”. farmers’ preferences for coffee variety traits. lessons for crop breeding in the age of climate change abrha megos meressa, ståle navrud* does the place of residence affect land use preferences? evidence from a choice experiment in germany julian sagebiel1,*, klaus glenk2, jürgen meyerhoff3 the use of latent variable models in policy: a road fraught with peril? danny campbell*, erlend dancke sandorf bio-based and applied economics 7(3): 249-263, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-7678 can menu labeling affect away-from-home-dietary choices? elena castellari1,*, stéphan marette2, daniele moro3, paolo sckokai1 1 dipartimento di economia agroalimentare, università cattolica del sacro cuore, piacenza, italy 2 umr économie publique, inra, université paris-saclay, f-78850 grignon, france 3 dipartimento di scienze economiche e sociali dises, università cattolica del sacro cuore, piacenza, italy date of submission: 2018 28th, september; accepted 2019 23rd, july abstract. this study aims to evaluate the impact of two menu-labeling formats on changes in dietary choices in an away-from-home meal, specifically in a university cafeteria. a field experiment at a university cafeteria in italy was conducted providing two different types of nutritional labels. the experiment lasted four days, spread over two weeks during which a total of 930 observations were collected. during each day of the experiment, only in one food line (treated line) a label indicating the healthy options was displayed, while in the other line no label was presented (control line). the paper describes two indexes to measure how the selected food choices for each participant are in line with what suggested by the labels. we define five different classes of these indexes and we test our hypothesis using an ordered logit model. results show the labels we provided had no significant impact on changing the tray composition, in accordance with other previous experiments suggesting that adding only nutritional information in a restaurant setting does not necessarily encourage healthier choices. the paper concludes highlighting the need of a multifaceted approach to design effective public policies enhancing healthier choices in a self-service restaurant. specifically, the provision of nutritional information by itself can have zero or low impact unless it synergizes with others instruments such as nutritional education, social norm provision and nudges. in the conclusions, some suggestions on public policies addressing the promotion of healthy food habits are given. keywords. menu labels, food away-from-home, healthy food policies, food labeling. jel codes. i12, i18, d12. 1. introduction food away from home (fafh) consumption plays an increasing role in the daily diets of many people worldwide. in italy, the share of fafh on total food expenditure was 33% in 2015, versus 46% in spain, 44% in the united kingdom, 27% in germany, and 26% in *corresponding author: elena.castellari@unicatt.it 250 elena castellari et alii france (agrifood monitor, 2016). in 2015, about 12 million italians (around 20% of the entire population) had lunch away from home 3-4 times a week (fipe-commercio, 2015). these patterns are actually similar for all industrialized and many developing countries (mottaleb et al., 2017). in the usa, the share of fafh on total yearly food expenditure rose from 25.9% in 1970 to 43.1% in 2012 (usda). in the 2007-2008 national health and nutrition survey data 41% of adults said they had consumed foods and/or beverages from fast food-type restaurants during the previous 24 hours, and 27% of them from full-service restaurants (seguin et al., 2016). while the rising in fafh consumption is not a bad habit per se, researchers have found that the frequency of eating fafh is positively correlated with some unhealthful outcomes, such as overweight and obesity (binkley et al., 2000; mccrory et al., 1999; satia et al., 2004, todd et al., 2010). the link between fafh and obesity can be explained because people tend to underestimate calories and fat content when they select their meal in an away-from-home environment (backstrand et al., 1997). indeed, restaurants and cafeterias typically use caloric dense ingredients (butter or dressings) to gain palatability1; yet, it is almost impossible for consumers to detect those “hidden” fats and overall taste remains a major force driving food choices (glanz et al., 1998). the positive relationship between fafh expenditure and bmi has also been found in children. according to a study by bowman et al. (2004), on a typical day when eating at quick-service food, children (aged 4-19) tended to consume more fat (+ 9 g), added sugars (+ 26 g), sugar-sweetened beverages (+ 228 g), and less fiber (-1.1 g), milk (-65 g) and fruits and non-starchy vegetables (-45 g), compared to those who did not, leading to 187 extra calories compared to a meal consumed at home. given the increasing trend in eating away from home, policy makers have considered the urgency of finding policy instruments which can lead to healthier consumption behavior. labeling2 is among the information-based instruments extensively used to lead consumers towards more informed and possibly healthier choices (galizzi, 2014; traill, 2012). we can think that in a fafh environment providing some nutritional information may limit the misperception on nutrients’ content when consumers are choosing their meal. however, while the introduction of nutritional information in a restaurant menu is supported by many researchers and health officials, their provision is mainly due to private sector or local government initiatives (brambilla-marcias et al., 2011). in fact, the implementation of a mandatory policy in a catering environment would require the 1 elaborating data from household food consumption surveys conducted by the u.s. department of agriculture (usda) during the period 1977-2008, biing-hwan et al. (1999; 2012) have shown a reduction of the share of saturated fat to the overall caloric intake of americans. however, from their analysis, fafh is still richer in saturated fat than food at home: in 2005-2008, fat contributed to 30.5% and 37.2% of the caloric intake from food at home and from fafh, respectively (biing-hwan et al., 2012; kozup et al., 2003). moreover, the fafh has been found higher in saturated fat, sodium and cholesterol and resulted in lower calcium content and dietary fiber than food at home (biinhwan et al. 2012). todd et al. (2010) estimated that in the usa each meal consumed away from home results in 134 additional calories. 2 in the united states, under provisions of the affordable care act of 2010, restaurant chains with twenty or more locations operating under the same brand are required to provide detailed nutritional information to consumers and to display calories on their menus. in the european union (eu), with regulation no. 1169/2011, new rules regarding nutritional information for food, both pre-packed and non-pre-packed, have been introduced. however, this regulation does not impose stringent rules for restaurants, unless differently required by each member state. 251can menu labeling affect away-from-home-dietary choices? capacity of standardizing ingredients and portions, which is not a trivial task especially for smaller size and not-chained restaurants. as a consequence, requiring stringent adoptions of nutritional labels in a catering environment can have the side effect of pushing smaller, non-chain business out of market, and can reduce options for consumers (mazzocchi et al., 2009). the aim of this study is to analyze if the provision of some nutritional information in a university cafeteria has an effect on the composition of the meal chosen. specifically, it can be expected that, by providing some nutritional information using labels, consumers might be facilitated to reduce the bias of “hidden calories” and consequently to identify healthier options. at this end, the authors conducted a field experiment in a university cafeteria in italy, where two types of informative labels were alternatively provided. this article proceeds as follows: after a literature background, first the experimental design and the indicators used to evaluate the quality of the meal are described; then the model and the empirical results are presented, followed by some discussion and policy implications. 2. background previous literature showed the provision of nutritional information can lead to mixed findings. in a systematic review, mazzocchi and trail (2005) evaluated the effect of food label in portion size consumption and they found varying impacts, from increasing, to decreasing or no effect. however, none of the studies examined found an effect on reducing energydense foods (mazzocchi and trail, 2005). similarly, a literature review by swartz et al. (2011) and another by kiszko et al. (2014) have shown the provision of caloric labels had none effect on the caloric intake of the food ordered and consumed. further, harnack and french (2008) concluded that, even if some studies support the evidence of a relation between the provision of caloric labeling and food choices, these effects are weak or inconsistent. similarly, empirical studies have shown mixed results. some have found the provision of nutritional information in a restaurant menu helps reducing the caloric intake (roberto et al., 2010, wisdom et al., 2010), others have measured no significant effect (elbel et al., 2009, finkelstein et al., 2011). ellison et al. (2014) showed that numeric labels alone (i.e. labels where nutrients content was shown as grams or mg per 100 grams of products or as percentage) have no influence on food choices, unless reinforced by traffic light symbols. in fact, traffic light labels (i.e. labels where some nutrient contents are classified with colors red, orange or green based on some thresholds with respect to dietary recommendations) may lead restaurant patrons to introduce in the menu lower-calorie options. marette et al. (2019) showed that the appearance of traffic light labels significantly impacts the willingness to pay of products offered in the experiment. on the other side, an experimental study conducted by seward et al. (2016), where traffic labels where provided in a university cafeteria setting, has shown that, while students reported to use the traffic light regularly and support their use, the intervention had no effect in improving dietary quality. vasiljevic et al. (2015) have shown that, on selecting different snacks, emotion labels (such as smiling faces) yields stronger effect on the perception of the healthfulness of the snack than colored label; and overall frowning labels are more effective than smiling ones. 252 elena castellari et alii using a random control trial, oliveira et al. (2018) find the provision of a menu labeling displaying different food information being positively associated with healthy food choices. elbel et al. (2009) find that the provision of caloric labels on fast food menus in new york had no effect on the caloric content of the purchased meal. in general, the literature has found paternalistic interventions (nudges), eventually combined with information provision, being more effective in producing behavioral changes (downs et al., 2009; thapa and lyford, 2014; thunström and nordtröm, 2013; castellari and berning, 2016). other studies have shown the importance of providing social descriptive norms to encourage change in food choices (burger et al. 2010). this work evaluates the effect of nutritional labels’ provision on the menu items selections, rather than the caloric content of the meal choices. nutritional information may have little effect on the caloric content of the overall meal but might impact its composition inducing a shift from ‘worse’ to ‘better’ choices3. other studies have found only a small portion of consumers (between 16% and 29%) have responded to nutritional labels changing their menu selections (balfour et al. 1996, yamamoto et al. 2005). we evaluate two different label intervention: (1) a label where the green color is matched with a positive emotion (smile) to identify the item within the same food group (first, second, side dish, fruit and dessert) that has the lowest caloric intake among the available options; (2) a label which ranks within the same food group (first, second, side dish, fruit-dessert) the options available based on their caloric composition using a medal (gold, silver and bronze). 3. methods 3.1 experimental design the hypothesis behind the experiment is that displaying some nutritional labels (i.e. indicating either a partial or a complete ranking of dishes in terms of their caloric content) in a self-service restaurant may influence consumer when selecting food options. we expect the presence of the label would help consumers to identify the hidden calories and thus the less caloric options. at this end, we collected data at a university cafeteria located in piacenza, italy; the experiment lasted four days, spread over two weeks (with a four-week break between them) between march and april 2016. the cafeteria is a self-service caterer, presenting two lines, each one providing identical food choices. the cafeteria meal has a fixed price and it allows to select one option within each menu category: first dish; second dish; side dish; fruit-dessert.. to test our hypothesis we provided (in separate settings) two different types of labels: 1) less caloric labels (lcl): within each menu category (first dish; second dish; side dish; fruit-dessert.) the label indicates the option with the lowest level of calories4 per portion (fig. 1, left panel); 3 within each food category (first dish, side dish, second dish, fruit-dessert), we rank food choices based on their caloric content from best (less caloric content) to worse (higher caloric content). 4 the canteen staff provided us the recipes of the dishes and, with their supervision, we used the website http:// www.myfitnesspal.com to rank every dish in each category, from the less to the most caloric. 253can menu labeling affect away-from-home-dietary choices? 2) calories ranking labels (crl): within each meal category (first dish; second dish; side dish; fruit-dessert.) the labels indicate a ranking among options based on the level of calories from the least (gold medal, 1st place) to the most (bronze medal, 3rd place) caloric (fig. 1, right panel). the labels were chosen together with the canteen managers. we proposed different types of labeling selected from previous studies. during the first week (1st and 2nd day) the effect of providing a lcl was tested, whereas the crl was used in the second week (3rd and 4th day). during each day of the experiment only one food line (treated line) displayed a label while in the other line no label was present (control line). it is assumed people randomly choose between the two lines, although to account for a possible self-selection bias the treated and the control lines from day one to day two (lcl) and from day three to four (crl) were switched. participants were not aware to be part of the experiment before selecting the food choices. the first contact with the labels took place at the beginning of the treatment line, where a flier explained the meaning of the label (lcl in day 1 and 2: crl in day 3 and 4 as in fig. 1). individuals taking the control line did not receive any nutritional information during the meal selection. the recruitment of participants to the experiment took place at the end of the lines (both control and treatment), where, with the support of a flier, two recruiters explained to users how to take part to the experiment. if they accepted, they were asked to take a picture of their tray using their smartphone before starting to eat and to share it using a digital platform. moreover, after lunch, participants were asked to complete a survey including both demographic and behavioral questions. all participants were rewarded with a coupon redeemable at the university coffee shop. we collected 459 observations during the first week (1st and 2nd day) and 471 during the second week (3rd and 4th day). the final dataset contains 930 observations recording tray composition, -------------------------------------------------------------------------------------- these symbols identify caloric content of dishes as: the lowest: ; the medium: ; the highest: relatively to the group they belong to: first dish, second dish, side dish, dessert. --------------------------------------------------------------------------------------example: dessert ice cream a slice of sacker cake fruit salad -------------------------------------------------------------------------------------- figure 1. explanation of lcl (left panel) and crl (right panel). 254 elena castellari et alii demographics and behavioral characteristics for each individual. the final sample is mostly composed by university students (around 84% of the sample), and in small percentage by university faculty and staff. for a detailed description of the participants, please refer to the model and empirical results sections. 3.2 indexes of meal composition to summarize the food selections made by participant i at day t we computed two different indicators of the tray’s meal composition. the purpose of this index is to measure how close the composition of the meal is to an “optimal meal”, which in the case of lcl would correspond to a tray with all green labeled choices, while in the case of crl to a tray with all gold medals. two different indexes for both the treatment and the control subsamples were computed: (a) a uniform index (ui) where we attributed the same weight to each of the dish selections; (b) a weighted index (wi) where we attributed different weights to dishes of different categories (first dish, second dish, side dish and dessert). the ui was computed as follows: uii,t = j=1 ni ,t ∑s jit 1 nit (1) where nit is the total number of dishes composing the tray of individual i at day t while sjit is the score, which in the case of the lcl would be equal to one if individual i at day t made a choice j labeled as healthy (green label), and zero otherwise. in the case of the crl sjit has a value equal to 1 if the choice j made by individual i at day t was labeled as gold, equal to 0.5 if choice j was labeled as silver, and zero if it was labeled as bronze. similarly, to compute the wi we used the following: wii,t = j=1 ni ,t ∑s jit pjit j=1 ni ,t∑ pjit (2) where pjit is the weight attributed to each dish selected by individual i at day t the weight pjit depends on the meals’ category. specifically, a weight of 0.35 was attributed to the first and second dishes, since they are typically more caloric, and a weight of 0.15 to side dish and dessert. both indexes (ui and wi) range from one, when an “optimal tray” was chosen, to zero, when all choices are not the one “suggested” by the labels. fig. 2 shows the distribution of the two indexes (wi and ui) under both label treatments (lcl and crl). the index computed using the uniform approach is more concentrated around some specific values. moreover, the distribution of all indexes is concentrated around zero: for the lcl indexes the zeros account for more than 80% of the observations, while for the crl indexes this share reduces to around 60%. 255can menu labeling affect away-from-home-dietary choices? 4. results to test whether the label provision had an effect on the food selections, we generated a variable indicating the propensity to select an “healthy option” (pho), using the ui and the wi. specifically, based on the index values, we compute the pho as an ordinal variable with five possible outcomes as described in table 1. the probability of being in a pho class (k), is given by: pr(pho =k | z) = φ (βk + [z]`β) – φ (βk+1 + [z]`β) (3) where φ (.) is the standard logistic density function (cdf), k= [0,…,4], β0 = ∞ and β5 = + ∞; z is a set of covariates influencing pho and β is a conformable set of parameters. specifically, z includes the following variables: a) t is a dummy variable equal to one if participant i in day t belongs to the treated sample; b) xi is a set of demographics and behavioral variables collected for each person i, as described in table 2. summary statistics are presented in table 3. in both weeks, the sample is almost equally split between treated and non-treated observations. students are the largest share figure 2. distribution of the weighted index (wi) and uniform index (ui) for the less caloric label (lcl) and calories ranking labels (crl). 0 2 4 6 8 10 d en si ty 0 .2 .4 .6 .8 1 uniform index (ui) less caloric labels (lcl) 0 2 4 6 8 10 d en si ty 0 .2 .4 .6 .8 1 weighted index (wi) less caloric labels (lcl) 0 2 4 6 8 10 d en si ty 0 .2 .4 .6 .8 1 uniform index (ui) calories ranking labels (crl) 0 2 4 6 8 10 d en si ty 0 .2 .4 .6 .8 1 weighted index (wi) calories ranking labels (crl) source: own data elaboration. 256 elena castellari et alii of the sample (around 84%) and females are around half of the sample. around 40% of the sample is commuting from nearby areas and almost 90% of the participants use the cafeteria at least three times a week. a large share of the sample declared to usually pay attention to the labels of the food they purchase (around 80%), to prepare its own meal often or sometimes (around 70%), to practice regular physical activity at least once a week (around 70%), and to not substitute water with other drinks during a meal (around 70%). more than 30% of the sample experienced some weight gain in the last six months and more than 20% sometimes visited a nutritionist to receive diet advises. surprisingly, only 2.6% of the whole sample reported to consume at least five portions of fruit and vegetables daily. given the nature of the dependent variable, model (3) was estimated in stata using an ordered logit model. equation (3) was estimated for both lcl and crl samples, using both uniform and weighted indexes. results are reported in table 4. all parameters on the treatment line variable (t) are not statistically significant, suggesting that all our specifications fail to identify any significant positive effect of the label table 1. definition of the classes of pho. outcomes classes pho=0 ui or wi = 0 pho=1 0 < ui or wi ≤ 0.25 pho=2 0.25 < ui or wi ≤ 0.50 pho=3 0.50 < ui or wi ≤ 0.75 pho=4 0.75 < ui or wi ≤ 1 table 2. demographic and behavioral variables. variable name variable description student one if student, zero otherwise female one if female, zero otherwise commuter one if commuter, zero otherwise frequent user one if he/she eats at the cafeteria at least 3 times a week, zero otherwise cook one if he/she prepares his/her own dishes often or sometime, zero if rarely or never label one if he/she reads the label of the food consumed often or sometime, zero if rarely or never fv5 one if he/she consumes at least 5 portions of fruit or vegetables per day, zero otherwise water one if during the meal he/she never or rarely substitutes water with other drinks, zero if often or always weight one if in the last six months he/she had a weight increase, zero otherwise nutritionist one if he/she ever visits a nutritionist for a diet, zero otherwise active one if he/she practices physical activity at least once-twice a week, zero otherwise source: own data collection. 257can menu labeling affect away-from-home-dietary choices? provision on the level of the index (ui and wi)5. these results are in line with several other studies which found information based policies are effective on improving consumer awareness but not necessarily to significantly impact behavior (galizzi, 2014). results show students tend to be more reluctant to change their food selections (for all models coefficients are negative and significant). in line with previous studies ( i.e. krieger et al., 2013), this paper also finds women have a different attitude towards menu labeling, with specifications (3) and (4) of table 4 showing positive and significant coefficients. frequent users of the canteen service do not seem to respond differently than less frequent users (i.e. coefficients are not significant). this study also finds people who sometimes or often cook their own meal tend to have a higher index under the lcl approach, while for the crl the difference is not significant. variables associated with more attention to the diet, as the attitude on reading food labels, or consuming more fruit and vegetables, are significantly correlated with higher pho under the crl scheme, but not under the lcl. similarly, people who declare to never substitute water with other drinks, or having required the opinion of a nutritionist, tend to have higher pho, with a positive improvement of the index, only under the lcl scheme. furthermore, results show variables such as having gained weight in the previous six months, or practicing sport at least once a week, are not associated with different pho. 5 this study considers only a selected sample of a university cafeteria in italy, for regulatory purpose and policy interventions an extended study with a more representative sample need to be consider. table 3. summary statistics. variable less caloric label (lcl) n=459 calories ranking label (crl) n=471 mean std. dev. mean std. dev. uniform index (ui) 0.279 0.289 0.286 0.262 weighted index (wi) 0.245 0.279 0.256 0.261 pho ( from ui) 1.251 1.243 1.314 1.122 pho (from wi) 1.203 1.278 1.306 1.167 treated line (t) 0.468 0.500 0.482 0.500 student 0.843 0.364 0.851 0.356 female 0.525 0.500 0.501 0.501 commuter 0.397 0.490 0.372 0.484 frequent user 0.854 0.353 0.868 0.338 cook 0.786 0.410 0.769 0.422 label 0.806 0.396 0.794 0.405 fv5 0.026 0.160 0.030 0.170 water 0.778 0.416 0.726 0.446 weight 0.327 0.470 0.344 0.476 nutritionist 0.255 0.436 0.225 0.418 active 0.691 0.463 0.705 0.457 source: own data elaboration. 258 elena castellari et alii 5. discussion the main objective of this research was to analyze whether the provision of nutritional information influenced the meal composition in an away-from-home environment. to this end, we conducted a field experiment at a university cafeteria. table 4. results ordered logit model. variables (1) (ui lcl) (2) (wi lcl) (3) (ui crl) (4) (wi crl) t 0.019 -0.121 0.260 0.146 (0.175) (0.175) (0.171) (0.170) student -1.024*** -1.263*** -1.387*** -1.366*** (0.232) (0.235) (0.251) (0.246) female 0.008 -0.046 0.310* 0.368** (0.179) (0.178) (0.182) (0.183) commuter -0.139 -0.189 -0.786*** -0.769*** (0.184) (0.183) (0.186) (0.185) frequent user -0.025 0.091 0.021 0.076 (0.264) (0.262) (0.269) (0.268) cook 0.500** 0.514** 0.216 0.252 (0.222) (0.222) (0.214) (0.212) label 0.044 0.024 0.399* 0.430** (0.229) (0.229) (0.216) (0.216) fv5 0.544 0.671 1.042** 1.188** (0.568) (0.559) (0.482) (0.475) water 0.550** 0.493** 0.305 0.280 (0.220) (0.219) (0.204) (0.201) weight 0.120 0.041 -0.018 -0.073 (0.190) (0.189) (0.180) (0.179) nutritionist 0.396* 0.469** 0.345 0.199 (0.203) (0.202) (0.211) (0.209) active -0.052 -0.066 -0.003 0.175 (0.192) (0.190) (0.201) (0.199) constant cut1 -0.254 -0.554 -1.222*** -1.064** (0.461) (0.470) (0.456) (0.450) constant cut2 0.195 0.245 -0.200 0.151 (0.461) (0.469) (0.452) (0.448) constant cut3 1.753*** 1.503*** 1.734*** 1.745*** (0.470) (0.476) (0.460) (0.457) constant cut4 3.333*** 2.534*** 3.306*** 2.963*** (0.512) (0.495) (0.508) (0.484) observations 459 459 471 471 standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. 259can menu labeling affect away-from-home-dietary choices? the use of information based policies is among the most debated instruments when policy makers look for solutions to promote behavioral changes towards healthier and more sustainable food choices. yet, effects of these information-based policies on actual behavioral changes are mixed. while some previous studies have found some potential benefits of menu labeling in a restaurant setting, in terms of calorie intake reduction and healthier food choices (oliveira et al., 2018; ellison et al., 2013; roberto. et al., 2010), this paper did not find any statistically significant effects of caloric labeling on food selections, in accordance with several other studies (elbel. et al., 2009; swartz et al., 2011; downs et al. 2009; mazzocchi and trail, 2005; swartz et al. 2011; kiszko et al., 2014; harnack and french, 2008). while these results can also be driven by the experimental settings, they suggest that compulsory nutritional labeling in a dining-out environment may not be effective per se. first the effect of a label on dietary choices depends on many unobservable or not-recorded factors, such as the environment characteristics, the sample composition, the way the labels have been explained and communicated, the type of labels, and many behavioral characteristics. most of the studies are referred to relatively small sample and to selected group (such as university students), so it becomes difficult to generalize the results from this type of studies to the whole population, as well to find ad hoc recipe valid for all settings. further, even if a strong link between nutritional label and caloric intake reduction as well as food environment improvement would be found, there would still be the need to consider the final outcomes of this policy interventions on health and bmi (jaime and lock, 2009). bonanno et al. (2018), using a quantile regression approach, have shown that the relationship between reading food labels and bmi highly differs among demographics groups. krieger et al. (2013) have measured the effect of calories posting in fifty restaurants, and after eighteen months, have found a decrease of menu calories only in some sites and in women, but not in men. these previous studies highlight the difficulties to find a “best for all” policy. thus, some ad-hoc interventions are needed to set up eating environments where healthy food choices are enhanced. in this sense, the synergies among different actions can be valuable to reach broader demographic groups. however, in general, especially in cafeterias linked to educational or working environments, a sure action that need to be reinforced is the setup of common protocols to monitor the nutritional quality of the service and to measure the effect of any in-site healthy initiative. only continuously and carefully monitoring the nutritional quality of food options and the effects of interventions can ensure their effectiveness on enhancing healthier behavioral changes. 6. conclusions and policy implications the provision of nutritional labels in a food canteen have many practical difficulties. first, recipes need to be standardized and carefully followed; second, dish sizes need also to be standardized, with additional burdens on the food preparation process. however, asking to provide nutritional labels without enforcing the use of standard procedures on food preparation might lead to misleading information signaling, while, at the same time, enforcing this standardization might push out of business small no-chained restau260 elena castellari et alii rants (mazzocchi et al., 2009). given these practical issues, applying the requirements for the labels only to chain restaurants, as experimented in the usa, is probably the easiest option to be applied in europe. moreover, chain restaurants are usually chosen to dine out by people driven by time and price constraints, which represent a population group most likely to be targeted by policy makers. however, even if nutritional labels alone will not be the solution to the obesity problem, their provision can increase consumer awareness and lead to some beneficial spillover effects, such as encouraging restaurants to offer healthier food and meal “reformulation” (schulman, 2010). as nutritional information is presented to consumers, restaurants might find incentives to offer lower calorie and healthier options, as observed by ellison et al. (2014). in this scenario, if the final goal of these policies is to improve the healthiness of food choices, our results, together with the existing literature, suggest the need of continuous monitoring of behaviors in order to design effective policies. however, we can also think of label policies for only their information value “per se”, independently from their effect on final food choices and health outcomes. in this sense, marette et al. (2019) have found a traffic light label significantly impacts the willingness to pay for the different types of products offered in an experiment, showing that consumer positively evaluate the provision of an easily readable label. an analysis sizing the cost and the benefits of implementing a labeling policy could be valuable to understand to what extent this policy is economically feasible and if it can be potentially sustained under a voluntary scheme. however, at this end, it is also important to consider that the literature has previously mentioned that an overload of information reduce the marginal effect related to it (keller and staelin,1989), at the point that consumers can even lose any interest, which is a big challenge for regulators. in accordance with previous findings, we believe that, in order to encourage behavioral changes in an away-from-home food environment, public policies need to rely on a multifaceted approach, where the provision of nutritional information synergizes with other instruments such as nutritional education, social norms provision and nudges (downs et al., 2009; thapa and lyford, 2014; thunström and nordtröm, 2013, burger et al. 2010, storcksdieck genannt bonsmann and wills 2012, castellari and berning, 2016). moreover, the discussion highlights the importance of reinforcing common protocols to ensure the nutritional quality of the food options in cafeterias, and to constantly monitor any intervention promoting healthy food styles. moreover, further research needs to evaluate if the implementation of a “health related” intervention in a cafeteria, such as the introduction of nutritional label, has an effect on the sustainability of the food environment and on the produced waste. overall, it is important for regulators to follow a multidisciplinary and systemic approach to the food system where all possible spillovers from the demand and supply side are evaluated in order to promote a more sustainable and healthy food environment. 7. acknowledgements we thank giorgia ciulli for the help during the data collection, stefano longo and elena barbieri and educatt for the fruitful collaboration. the authors only are responsible for any errors or omissions. 261can menu labeling affect away-from-home-dietary choices? 8. funding this work was supported by the daniel & nina carasso foundation through the fp7 susfood era-net  research project “susdiet implementing sustainable diets in europe” (scientific coordinator: louis-george soler). 9. references agrifood monitor (2016). http://www.agrifoodmonitor.it/en/food-consumption . accessed february 3, 2017. biing-hwan, l., frazão, e. and guthrie j. 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(2005). adolescent fast food and restaurant ordering behavior with and without calorie and fat content menu information. journal of adolescent health 37(5): 397-402. positive mathematical programming and risk analysis quirino paris the hedonic contents of italian super premium extra-virgin olive oils luca cacchiarelli1,*, anna carbone2, tiziana laureti1, alessandro sorrentino1 corporate r&d and the performance of food-processing firms: evidence from europe, japan and north america heinrich hockmann1, pedro andres garzon delvaux2,*, peter voigt3, pavel ciaian2, sergio gomez y paloma2 can menu labeling affect away-from-home-dietary choices? elena castellari1,*, stéphan marette2, daniele moro3, paolo sckokai1 a preliminary test on risk and ambiguity attitudes, and time preferences in decisions under uncertainty: towards a better explanation of participation in crop insurance schemes attilio coletta1, elisa giampietri2, fabio gaetano santeramo3,*, simone severini1, samuele trestini2 bio -based and a ppl ied economics bae bio-based and applied economics 11(3): 265-275, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-9753 copyright: © 2022 w. sobczak, j. gołębiewski. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: w. sobczak, j. gołębiewski (2022). price dependence of biofuels and agricultural products on selected examples. bio-based and applied economics 11(3): 265-275. doi: 10.36253/bae-9753 received: september 18, 2020 accepted: september 9, 2022 published: november 4, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio gaetano santeramo. orcid ws: 0000-0003-3812-3877 jg: 0000-0001-7869-790x price dependence of biofuels and agricultural products on selected examples wioleta sobczak*, jarosław gołębiewski institute of economics and finance, warsaw university of life sciences, poland *corresponding author. e-mail: wioleta_sobczak@sggw.edu.pl abstract. the growing demand for raw materials for the production of biofuels may lead to an increase in the prices of these raw materials and, due to the shortage of land, to an increase in the prices of other crops. this is due to the fact that the growing demand for raw materials for the production of methyl esters and bioethanol (the most widely used biofuels), such as rape and corn, is a form of competition on the food and feed markets. it should be mentioned that although the topic is not new, it is still very relevant, taking into account the expansion of energy crops, as well as national, european and world energy policy. especially due to the fact that, as has already been mentioned, the use of plant products for the production of biofuels has an impact on the regulations of the food market.this study is to analyze the volatility and dependence of ethanol, biodiesel, maize and rapeseed prices in the period of 2016-2019 and aims at assessing the correlation between the agricultural and biofuel markets. in this paper, the investigation regarding co-integration of biofuel and agricultural commodity prices has utilized ethanol and commodity prices with the use of the vector error correction model (vecm). price dependencies between the prices of biodiesel, rapeseed, maize and ethanol were found, indicating the existence of long-term causality in at least one direction between the analyzed prices. the results indicated that biodiesel prices during the period in question were influenced by the previous week’s prices of biofuel and rapeseed. moreover, biodiesel prices had an impact on the level of ethanol and rapeseed prices. in the case of rapeseed, the correlation between its prices and those of corn is also noticeable, while prices of corn may also affect prices of ethanol. keywords: biofuels, agricultural market, biofuel market. jel codes: q16, q4. 1. introduction to deal with the unprecedented pace of climate change caused by the accumulation of greenhouse gases in the atmosphere, there is a clear need to shift from an energy dependency on fossil fuels to renewable energy. now, with environmental policy pushing to reduce greenhouse gas emissions, aided by recent advances in crop engineering and fermentation processes, the production of bioethanol and biodiesel has once again become viable and sustainable substitutes for petroleum-based fuels. production of biofuels showed a growing tendency in the 1990s when the assumptions of the comhttp://creativecommons.org/licenses/by/4.0/legalcode 266 bio-based and applied economics 11(3): 265-275, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9753 wioleta sobczak, jarosław gołębiewski mon agricultural policy (cap) indirectly supported the production of biofuels through guaranteed minimum prices, subsidies per hectare of production and compensation payments for set-aside land that, however, could be used to produce raw materials for biofuel production. moreover, the 2003 cap reform introduced a cultivation premium for production of energy crops on primary land (lamers et al., 2011). it should be noted that in the case of the production of pollutants, more than a quarter of the total co2 emissions are generated by the transport sector (adams et al., 2020). to mitigate the effects of global warming caused by the accumulation of greenhouse gases from climate change, it is imperative to reduce co2 emissions from fuel combustion in car engines and to switch to alternative and cleaner fuels. it should be noted that the development of road transport in the world has led to a rapid increase in the demand for fuels, especially those derived from crude oil. increased greenhouse gas emissions are due to the burning of fossil fuels and to changes in land use caused by human activities. therefore, alternative solutions are sought, especially biofuels that could actually compete with conventional energy sources (kurowska et al., 2020, klikocka et al., 2019). it should be emphasized that the known oil resources are limited resources. various studies set the date of the world peak in oil production in 1996-2035. that is why it is so important to pay attention to biomass-based energy technologies, which use waste or plant matter to produce energy with lower ghg emissions than fossil fuel sources (sheehan, 1988). thus, biofuels entered the market as an option to reduce dependence on crude oil and as a way to pursue social, economic and environmental sustainability (chavez et al., 2010, kurowska et al., 2020). as noted by janda et al. (2012), increased interest in the application of biofuels as an alternative to liquid fossil fuels was observed after the oil crisis that occurred on world markets in the 1970s. in addition, the use of biofuels (compared to fossil fuels) contributes to the mitigation of greenhouse gas emissions (hallam et al., 2006). moving on to the meaning of biofuels, it should be clarified that the term biofuel refers to liquid and gaseous fuels (bioethanol, biodiesel, biogas) and solids produced mainly from biomass (demirbas, 2008). biofuel is a non-polluting, locally available, sustainable and reliable fuel obtained from renewable sources (vasudevan et al., 2005). liquid biofuels are primarily used to power vehicles, but they can also power engines or fuel cells to generate electricity (demirbas, 2007). bioethanol and biodiesel are the two most popular biofuels used as substitutes for regular gasoline and diesel fuel (clerici and alimonti, 2015). as already mentioned the global demand for biofuels such as ethanol and biodiesel is increasing mainly for environmental reasons (goswamia and choudhuryb, 2019; ajanovic, 2011). this is in line with the expansion of this market and the rapid increase in their production worldwide (banse et al., 2008). biofuels are perceived as an essential element in the development of fuel markets (ryan et al., 2006). in the transport sector, ethanol constitutes the most widely consumed liquid biofuel in the world (mcphail, 2011). it should be noted that the demand for biofuels is driven mainly by the transport sector (fundira and henley 2017). brunschwig et al. (2012) as well as balat (2011) indicated that biodiesel is an attractive alternative to diesel fuel. sivakumar et al. 2010 noted that with population growth, industrial development, and fossil fuel transportation costs soaring, it seems reasonable that countries seek for solutions independent from non-renewable fuels for climatic and economic reasons (reboredo et al., 2016), thus drawing the attention of many stakeholders related to this issue, i.e. decision makers, representatives of the industry, and the scientific community (timilsina et al., 2011). at the same time, the development of the biofuel market translates into a growing demand for the most important agricultural production factors (van eijck et al., 2014). however, it should be taken into consideration that biofuels compete for renewable and non-renewable resources, and therefore may affect their sustainable growth and the market for agricultural products. increased cultivation of biofuel crops will affect land utilization (searchinger, 2007) which will have an impact on global natural resources and environmental sustainability (zhang et al., 2009, hausman et al., 2012), i.e. by generating indirect effects from their exploitation (van noorden, 2013). moreover, extending the cultivation area of biofuels with a simultaneous increase in population may lead to higher prices of agricultural raw materials on international markets. thus, production of biofuels can pose challenges in terms of sustainable food production (naylor et al., 2007). moreover, in the case of biofuels, a crowding-out effect may appear (vacha, 2013), redirecting food production to production of biofuel (baffes, 2013). it should be emphasized that if part of the soil resources is occupied by the fields of energy crops, the potential for food production is weakened, which may result in an increase in food prices. competition between energy crops and food crops has consequences such as rapidly rising food prices and a food deficit on a global scale (gomiero, 2010, oecd-fao). the problem of competition between bioenergy crops and plants intended for consumption, resulting from land use, was also noted by vasile et al. (2016) and cai et al. (2010), 267price dependence of biofuels and agricultural products on selected examples bio-based and applied economics 11(3): 265-275, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9753 tomei and heliwell (2016). therefore, the indirect effects of biofuel production have become the subject of research and discussion among economists, environmentalists, ngos, and international organizations that call for an additional analysis of the outcomes related to biofuels (bentivoglio and rasetti, 2015; oláh, 2017). it has been observed that the growing demand for raw materials for the production of methyl esters and bioethanol (the most widely used biofuels), such as rapeseed or corn, is a form of competition in the food and feed markets (koizumi, 2015). it should also be noted that the activities related to the production of biofuels also have indirect negative effects of land use, such as the conversion of food crops into fuel (humalisto, 2015). this phenomenon is known as indirect land use change, which, in combination with the conversion of carbon-rich lands, can lead to significant greenhouse gas emissions, which counteracts the previously indicated positive environmental importance of biofuels (britz and hertel, 2011, ec. directive (eu) 2015 / 1513, santeramo and searle 2019, kupczyk 2020). as the research by searchinger et al. (2008), emissions of greenhouse gases from corn ethanol in selected locations may even double compared to the continued use of petroleum products. then, the impact of the biofuel program on greenhouse gas emissions may be unfavorable (britz and hertel, 2011). in consequence, it raises doubts as to whether biofuels are a friendlier alternative to petrol (chakravorty et al., 2017). the issue of dependence between the agricultural market and the biofuel market plays a significant role, inter alia, due to the expansion of biofuels into global agricultural commodity markets (drabik et al., 2016,banase et al., 2008). the research conducted so far by, among others, wright (2011), 2011 de gorter and drabik (2015) indicate a sharp increase in biofuel production as well as a strong and direct relationship between prices of energy and agricultural commodit. ). the growing demand for raw materials for the production of biofuels may lead to an increase in the prices of these raw materials, and due to the shortage of land, to an increase in the prices of other crops (searchinger, 2008). the price interdependencies between the food and biofuel market have therefore become an ongoing subject of discussion among energy, environmental and agricultural economists interested in the sustainability of biofuels (kristoufek, 2012, oladosu and msangi, 2013, kurowska et al. 2020). drabik and et al. (2016) also notes that the global agriculture and energy sectors have become more interdependent due to the surge in biofuel production over the past two decades. at the same time, both sectors exhibit high price volatility. in contrast, the transmission of global price shocks to domestic markets, from agricultural commodities to food prices, might have a significant impact on income distribution and welfare for farmers and consumers. as a result, the issue of price transmission between agricultural markets and biofuel markets becomes relevant from the perspective of political economy. this article analyzes the price relations between the biofuel market and the market of agricultural products. the price transmission between the prices of rapeseed, biodiesel, maize and ethanol was assessed. the goal was to obtain answers to the following research questions: 1. how were the prices of biodiesel, ethanol, corn and turnip in the analyzed period ? 2. is there a relationship between the prices of biodiesel, bioethanol, corn and turnip in the analyzed period ? 3. what is the relationship between the prices of biodiesel, bioethanol, corn and turnip in the analyzed period? it should be mentioned that although the topic is not new, it is still very relevant, taking into account the expansion of energy crops, as well as national, european and world energy policy. especially due to the fact that, as already mentioned, the use of plant products for the production of biofuels affects the condition of the food market. 2. methods and data the data set includes weekly wholesale prices of ethanol, biodiesel, rapeseed and maize, from the first week of 2016 to the last week of 2019, from global markets, i.e. the stock exchange: the paris stock exchange oraz new york mercantile exchange prices have been averaged and given in eur. in order to standardize the currency, the average eur rate in a given trading week was used. a period of no significant disturbances resulting from the covid-19 pandemic was selected. the mutual integration of all prices was analyzed. prior to estimation of model parameters, it is necessary to determine the stationarity of the analyzed time series. for the purpose of this study, the kwiatkowski, phillips, schmidt and shin (kpss) test was applied (maddala, 2009; welfe 2009). the direction of cointegrating relations between the analyzed prices was established based on the vector model of the vecm error correction, which determines the short-term dynamics of each price within long-term relations. according to the granger representation theorem, the equation of the vecm error correction model 268 bio-based and applied economics 11(3): 265-275, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9753 wioleta sobczak, jarosław gołębiewski assumed the following form (gujarati and porter, 2009; johansen and joselius, 1990): (1) where: (2) (3) xt= [xt1 …xtk]t – vector of observations on the current values of all explanatory variables, dt – vector of exogenous equation components such as intercept, time change, non-stochastic regression, delayed values of exogenous variables, a0 – matrix of parameters with vector variables dt. (does not contain zero elements), ai – matrix of parameters with delayed xt vector variables (does not contain zero elements), k – model row, specifying the maximum length of the delay, εt = [e1t … ekt]t – vectors of stationary random disturbances (residual vectors of the model equations). in order to assess the response of individual variables to a change in the price level of another component, the impulse response function (irf) was applied, as presented below (baillie, kapetanios, 2013). (4) where: b – matrix of parameters standing at non-lagged vector values xt, φi – response of the distinguished vector variable xt to an impulse from another variable. the choice of the order of variables in the model depends on the aic information criterion. the length of the model lag has been 1. the granger causality test was used to analyse relations between the studied variables. testing cau-sality in the granger sense is based on the following system of equations: (5) (6) where: yt – values of the variable y; xt – values of the variable x; β – structural parameters of the model; ut– random component of the model (granger, 1969). the null hypothesis in the granger causality test assumes that all βk coefficients are equal to zero, which means that there is no causality, while the alternative hypothesis assumes the occurrence of causality in the granger sense. 3. results in 2021, the global production of biofuels reached the level of 1,747 thousand. barrels of oil equivalent per day, compared to 187 thousand barrels of oil equivalent per day, produced in 2000. production of biofuels, given the belief that it can provide energy security and reduce greenhouse gas emissions in the relevant sectors. the global biofuel market is expected to reach over $ 200 billion by 2030 (statista.com, 2022). as noted, price developments in the four markets in question appear to be correlated. the evolution of rapeseed, maize, biodiesel, ethanol prices and their volatility in years 2016-2019 is depicted in figure 2. in the analyzed time period, a gradual decline in biodiesel prices was observed. this situation stabilized in the first quarter of 2016. a similar situation occurred in the case of ethanol prices. throughout years 2016-2019, there were significant fluctuations in the prices of ethanol and biodiesel. in the case of rapeseed and maize, the differences were milder. studying the interdependencies of time series requires an examination of their stationarity. the level of integration of the analyzed time series was tested using the kpss test. the calculated value of the test statistics presented in table 1 with the included lags at significance level α = 0.01 indicates rejection of the null hypothesis which suggests the stationarity of the tested 269price dependence of biofuels and agricultural products on selected examples bio-based and applied economics 11(3): 265-275, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9753 time series, proving the non-stationarity of the analyzed prices. the performed test using the johansen method shows that at the signifi cance level equal to 0.05, the null hypothesis of no cointegrating relation should be rejected. th e test results included in table 2 indicate the existence of three dependence relations between the examined prices. th e existence of relationships between prices proves the existence of long-term causality in at least one direction between the analyzed prices. however, it does not indicate the direction of causality in price developments. th is causality can be determined using the vector model of the vecm error correction (table 3). th e results of the model estimation for the analyzed prices suggested the existence of numerous relationships between the analyzed prices (statistically significant relationships between the price levels have been marked in grey). namely, the price level of biodiesel in the said period was infl uenced by prices of this biofuel from the previous week and prices of rapeseed. at the same time, biodiesel prices infl uenced the price level of ethanol and rapeseed. in the case of rapeseed, the estimation of the 0 200 400 600 800 1000 1200 1400 1600 1800 2000 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 20 20 figure 1. biofuel production worldwide from 2000 to 2021(in 1,000 barrels of oil equivalent per day). source: own elaboration based on statista.com. table 1. results of stationarity tests with regard to the analyzed time series. biodiesel ethanol maize rapeseed kpss test statistics / critical value 1.724*** 1.581*** 0.852*** 0.965*** p-value = 0,01 0.587 0.587 0.587 0.587 p-value = 0,05 0.399 0.399 0.399 0.399 p-value = 0,1 0.311 0.311 0.311 0.311 source: own calculations and analysis with the use of eviews soft ware. table 2. occurrence of correlations between the analyzed time series johansen’s test. th e number of cointegrating vectors test trace critical value p=0,05 0* 66.05 47.99 1* 36.61 28.99 2* 14.99 13.11 3 3.44 4.74 source: own calculations and analysis with the use of eviews soft ware. 270 bio-based and applied economics 11(3): 265-275, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9753 wioleta sobczak, jarosław gołębiewski vecm model showed a connection with maize prices. nonetheless, it should be noted that maize prices may also cause changes in ethanol prices. in this case, the obtained results indicate the existence of a two-way relationship. the impulse response function determined on the basis of the estimation of vecm parameters illustrated the occurrence of reactions between individual variables, as depicted in table 3 (figure 2). the irf functions were determined by the results of the vecm model parameter estimation for the levels. the course of the irf function confirms the interaction of prices, for which the vecm model estimation indicated the presence of interdependencies in their formation. based on the analysis of the course of the irf function, the reaction to the impulse appears up to 2 weeks after its occurrence while individual functions expire within 3-4 weeks, rebalancing the system. the granger causality test was used to determine which prices are interdependent in terms of price formation. the test results are presented in table 4. figure 2. price level of rapeseed, maize, biodiesel and ethanol prices in the analyzed period in nominal terms (in eur). source: own calculations and analysis with the use of eviews software. table 3. the results of the vecm model parameters estimation.   biodiesel rapeseed ethanol maize cointeq1  -32.05 24.25 -0.31 -13.14 27.24 8.15 0.21 5.88 [-1.24] [2.31] [-2.19] [-2.74] δ_biodiesel 0.41 0.07 0.01 0.01 0.09 0.04 0.01 0.02 [2.74] [2.11] [1.11] [0.40] δ_rapeseed 0.73 0.31 0.01 0.11 0.62 0.06 0.01 0.05 [2.13] [3.88] [0.99] [2.87] δ_ethanol -24.75 -0.20 0.17 -6.412 18.11 6.01 0.07 3.31 [-1.74] [-0.02] [-1.51] [-1.74] δ_maize -0.24 -0.27 0.01 -0.07 0.42 0.14 0.001 0.08 [-0.81] [-1.81] [1.64] [-1.07] source: own calculations and analysis with the use of eviews software. δ – price of given product from previous period. 271price dependence of biofuels and agricultural products on selected examples bio-based and applied economics 11(3): 265-275, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9753 figure 3. the reaction of individual markets to an impulse in the form price level changes. x axis days; y axis price in eur. source: own calculations and analysis with the use of eviews software. 272 bio-based and applied economics 11(3): 265-275, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9753 wioleta sobczak, jarosław gołębiewski the analyzes of the granger causality test showed that there was a relationship between the prices of biodiesel and ethanol, the impact of rapeseed prices on biodiesel prices, the price of rapeseed and ethanol prices, as well as the prices of rapeseed and corn prices.. discussion and conclusion the obtained results indicate that further research is necessary in order to provide a detailed description of the multiple dependencies that occur in the biofuel market as well as their connection with fossil fuel and agricultural markets. the presented research results on price volatility and price response of selected biofuels and agricultural products could have been measured more thoroughly with higher frequency data (e.g. daily), as well as taking into account products such as soybean, palm oil, rice or sugar. in addition, it is worthwhile to examine the problem from a broader perspective and to consider to what extent the price interdependence in these markets is a natural phenomenon and how much action is taken to promote the bioeconomy. literature provides many studies on the relationship between the biofuel market and the agricultural raw materials market, e.g. ciaian and kancs, (2011), janda et al. (2012), serra and zilberman et al. (2013), kristoufek et al. (2014), de gorter et al. (2013), de gorter et al. (2015), goswami and choudhury (2019). however, due to the dynamic character of the market, this area should be the subject of continuous study. the conducted analyses indicated relationships between the prices of biodiesel, rapeseed, maize and ethanol, proving the existence of long-term causality in at least one direction between the analyzed prices. based on the results of the estimation of the vecm model parameters, biodiesel prices in the period in question were influenced by prices of this biofuel from the previous week and prices of rapeseed. moreover, biodiesel prices influenced the price level of ethanol and rapeseed. in the case of rapeseed, one may also observe the dependence of its prices on the prices of maize, while the prices of maize might be cause changes in ethanol prices. moreover, in this case, the obtained results indicate the existence of a two-way relationship. this study may add value to previous studies, showing the relationship between the prices of biofuels and agricultural products, and thus become the basis for further considerations on the analysis of the impact of energy crops and biofuel production on the prices of agricultural and food products. it should also be mentioned that obtaining fuels from bio sources is becoming more and more important. particular attention in this direction has been paid recently, when there has been a strong increase in the prices of fossil fuels resulting from the pandemic situation in recent years and the ongoing war in ukraine. references 1. adams, s., boateng, e., acheampong, a.o. 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(2009). ethanol, corn, and soybean price relations in a volatile vehicle-fuels market. energies, 2(2): 320-339. volume 11, issue 3 2022 firenze university press bio-based business models: specific and general learnings from recent good practice cases in different business sectors nora hatvani1,*, martien j.a. van den oever2, kornel mateffy1, akos koos1 food loss and waste accounting: the case of the philippine food supply chain anieluz pastolero*, maria sassi the role of network characteristics of the innovation spreaders in agriculture antonio lopolito1,*, angela barbuto2, fabio gaetano santeramo2 the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period roberto esposti price dependence of biofuels and agricultural products on selected examples wioleta sobczak*, jarosław gołębiewski bio-based and applied economics 7(2): 97-116, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-7670 climate change and variations in mountain pasture values in the central-eastern italian alps in the eighteenth and nineteenth centuries marco avanzini1, isabella salvador2,1, geremia gios2,* 1 muse museo delle scienze, c.so del lavoro e della scienza 3, i-38123, trento, italy 2 university of trento, department of economics and management, via inama 5, i-38122 trento, italy date of submission: 2017, 26th, january; accepted 2018, 18th, june abstract. this study investigates variations in pasture lease rents during the eighteenth and nineteenth centuries in a sector of the italian alps and how these correlate with climate changes. analysis of the rents in the three data sets clearly demonstrates a sharp increase over the period considered, which can generally be ascribed to increased human pressure following population growth during the same period. oscillations in the values obtained for fifty-year periods between the last half of the eighteenth century and the beginning of the twentieth suggest a strong connection with environmental and climatic factors. increases or decreases in temperature seem to have a less marked and less direct effect on the values of grazing lands close to the upper limit of vegetation, while socio-economic and infrastructural signals impinge significantly on climate signals on the grazing lands at lower altitudes. keywords. grazing lands, climate changes, italian alps. jel codes. q54, q15, q51. 1. introduction 1.1 climate change and mountain agriculture the consequences of ongoing climate change are the subject of an increasing number of scientific studies (ipcc, 2014). in particular, the interaction among climatic factors, agro-forestry systems and ecosystem productivity is currently being investigated using a variety of tools (baglioni et al., 2009; bosello and zhang, 2005; roson, 2003; solomon, 2007). the aim is generally to obtain an economic assessment of variations in well-being due to changes in the environments where agriculture is practiced (e.g., palatnik and nunes, 2010). *corresponding author: geremia.gios@unitn.it 98 marco avanzini, isabella salvador, geremia gios in fact, in the alps: a) climate imposes very clear limitations on soil productivity1; b) the history of locations bears clear evidence of variations in climate2; c) for many centuries the development of communities has been strongly conditioned by agricultural productivity, which in turn is correlated with climate evolution (mathieu, 2000, p.127). in the southern alps in particular, the traditional organisational structures of communities were such that private property was located near the villages and common pastures and meadows in the mountains (raffaelli, 2005). this type of organisation reflected the fact that development of the local communities was to a large extent dependent on resources that could be generated locally. unlike the fields close to the villages, pastures and woodlands could be exploited with low fixed investments and represented a reserve of resources that could be adjusted relatively quickly in the case of rapid increases or reductions in anthropic pressure. from this perspective, the alps are an ideal testing ground for measuring the economic consequences of climate change (dearing 2006; fraser 2009; pfister and brazdil 2006; theurillat and guisan, 2001). alpine pastures represent one of the most complex and interesting study cases. forage productivity and quality in areas given over to pasture are closely linked to environmental factors, such as soil temperature, fertility and moisture (baglioni, et al. 2009; bosello and zhang, 2005; menzel and fabian, 1999; roson, 2003). alpine pastures are characterized by a rapid growth in productivity in spring and summer followed by a period of gradual decline and decreasing quality. there is now an extensive body of scientific literature on the effects of temperature on productivity trends in alpine pastures (cavallero et al. 1992; gusmeroli et al., 2005; orlandi et al., 2004; ziliotto and scotton, 1993;), although there has not always been general consensus on the nature of the variability (orlandi and clementel, 2007). all of the studies agree, however, that pasture productivity is closely related to natural constraints and particularly to temperature variation, which, in the mountains more than anywhere else, has a direct effect on the vegetative cycle and the productivity of herbaceous vegetation. in other words, it seems to be clear that the productivity of mountain land varies over time in response to trends in temperature, with consequent fluctuations in its economic value. in the alps, spring temperature appears to be particularly important for total grass production. indeed, it is well known that the growth of grass depends on accumulated temperature (day degrees); as a consequence, the spring temperature determines whether herds are taken up to the mountain pastures earlier or later (gusmeroli et al, 2005). summer temperature, on the other hand, appears to be less important in the alps, so that the end of the grazing season, unlike the beginning, is traditionally set for a fixed date (20 september), at least in the area examined here (bussolon, martini, 2007). as far as precipitation is concerned, the climate regime in the entire alpine area has a winter 1 for example, the upper limit of tree growth (treeline) and the limit of cereal cultivation are usually defined by altitude. this is because, with the exception of specific local situations, average temperature and length of the growing season vary as a function of height above sea level. 2 for example, toponomy still reflects situations arising as a consequence of the climate in the near or distant past. (bussolon and martini, 2007) 99climate change and historical variations in mountain pasture values minimum (under the influence of the russo-siberian anticyclone in the cold months) followed by a maximum between spring and autumn. the study area in particular has a prealpine type of climate regime with an autumnal maximum slightly higher than the spring maximum. areas with a pre-alpine climate are more influenced than others by their geographical proximity to the po plain, which places few obstacles in the way of humid air masses. spring precipitation in these areas is always abundant and, unlike in south-central italy, no significant fluctuations are evident in the available historical series (buffoni et al., 2003). as a result, the precipitation regime has had less influence on the modifications in the seasonal productivity of pastures in these regions. the mountains of the italian pre-alps studied here have been exploited at least since the sixteenth century. the pastures are part of a system based on vertical transhumance whereby livestock spend the summer on higher alpine pastures (salvador and avanzini 2014). against this background, the aim of this study is to investigate variations in pasture lease rents during the eighteenth and nineteenth centuries and how these correlate with climate changes. in analysing the relationship between climate variation and the value attributed to the pasture areas, account has been taken of natural and socio-economic factors, which may be summarised as follows: a) climate changes and, in particular, variations in spring temperatures; b) population evolution in pastoral communities. regarding the former issue, given that climate variability influences pasture productivity, as will be described later in greater detail, it may be considered a proxy for the potential volume of grass that the pastures produce. regarding the latter issue, population evolution in an economic system that is closely dependent on local natural resources may be considered a proxy for anthropic pressure on the environment and hence for the demand for pasture with possible repercussions on the attributed value. 1.2 climate and social well-being as mountain areas have developed economically, especially in the periods prior to the industrial revolution and extensive migration, the link between resource availability and climate change has been crucial (malanima, 2006), even though, as in other historical processes, altitude and environmental factors play a variable role (mathieu, 2000, p.127). an increase or decrease in temperature of even a few tenths of a degree may result in an increase or decrease in resources, thereby contributing to capital gain or loss. climate deterioration may lead to a shorter growing season with a consequent decrease in the value of pastures and radical changes in the exploitation of mountain areas, even over short periods of time (bozhong, 1999). a decrease in temperature may give rise to a 10% reduction in calories per square centimetre of land, a shortening of the field pasture and forest vegetation growth period by three weeks, increased rainfall, changes in microbial activity in the soil and consequently its level of fertility, and a lowering by 150-200 metres of the altitude limit for growing cereals (anfodillo, 2007). according to some authors (e.g. pfister, 2005), that contraction of pastureland in the european mountains at the height of the little ice age (lia from the fourteenth to the late nineteenth century), restricted the prospects for pasturing animals. lower forage yields also affected the quality and quantity of the milk produced. during the lia weather and climate conditions were different from those prevailing in the preceding ‘medieval 100 marco avanzini, isabella salvador, geremia gios warm period’ (from about 900 to the fourteenth century) and in the ‘warm twentieth century’. the lia was a simultaneous, world-wide phenomenon, although there were considerable regional and local variations. that epoch was the longest period of glacial expansion in the alps for at least 3000 years. however, it should be stressed that the six centuries between 1300 and 1900 were not continuously cold. the cold phases were repeatedly interrupted by phases of ‘average climate’. in some periods, e.g. from 1718 to 1730, the summer half-year was even somewhat warmer than the ‘warm twentieth century’. it is in this context that heinz wanner coined the expression “little ice age type events” (liate) to designate the three extensive advances known. many historians assume that the productivity of agriculture in the medieval and early modern periods depended only on the relative scarcity of two prime production factors: land and labour. the fundamental fact that agricultural output also depends on weather and climate has simply been ruled out. the most difficult study regards impacts and consequences. having reconstructed past climate in the area of concern, biophysical impact studies may be carried out to identify the direct effect of climate anomalies on plants, domestic animals and disease vectors through study of their sensitivity to climate. social impact assessment studies can then examine how biophysical impacts i.e. the effects of climate anomalies on biota propagate into the social and political system. this type of integrated approach, which would include the potential of people to adapt and adjust to climatic stress, reflects historical reality far better than a simple impact model and raises more fruitful research questions. pfister (2005) developed a climate impact model tailored to food production in the agrarian economies within the mixed economies of southern central europe, where grain was the staple crop cultivated according to a three-field system in combination with dairy or wine production. it was found that a given set of specific sequences of weather spells over the agricultural year was likely to affect all sources of food, at the same time leaving little margin for substitution. this yielded a model of worst-case crop failure and, conversely, a year of plenty. livestock in traditional agriculture did not serve only the currently exclusive purpose of providing animal protein for human nutrition; instead, its vital role consisted in the multiple function of providing muscular power, manure and milk. livestock provided large part of the required labour and enabled the active management of plant nutrients. the milk yields of cows and goats depended on the amount of the daily food ration available per animal and its nutrient content, mainly raw proteins. the amount of the feed ration varied according to the duration of the winter snow cover and autumn and spring temperatures. in a frosty spring, the animals ran out of feed, as happened in 1688 in the example provided by einsiedeln (pfister, 2007). the longer the famine lasted, the longer it took for the animals to recover and resume their usual level of milk production. a long wet spell during the hay harvest in july and early august could reduce the raw protein content of the hay by as much as two-thirds, causing the cows to cease producing milk during the subsequent winter. most importantly, the simultaneous occurrence of rainy autumns, cold springs and wet mid-summers in successive years had a cumulative impact on agricultural production. this combination of seasonal patterns contributed largely to triggering extensive advances 101climate change and historical variations in mountain pasture values of the glaciers. chilly springs and rainy mid-summers have been shown to be the most common climatic elements during the little ice age, even though they were not causally related. this economically adverse combination of climatic patterns is labelled “little ice agetype impacts” (liatimp). the biophysical climate’s impacts in terms of the duration of cold spells and wetness in particular phases of the year may be relatively similar without being fully identical. human responses to such impacts, on the other hand, often differ over time; and these differences may form the basis of in-depth studies on changing vulnerabilities. at the same time, complex interactions with environmental changes compounded by socio-economic factors may eventually lead to a loss or decrease in the value of the asset (gellrich et al., 2007; irwin and geoghegan, 2001; paavola and fraser, 2011) and the associated rental fee. an example found throughout the southern trentino region in the italian alps is the contrast between changes in the value of privately-owned agricultural land over time and the large tracts of forest and pasture assets managed by local communities. the former were subject to extensive fragmentation with a gradual reduction and dispersion of agricultural land; but the latter, because they had a fixed land area (the pastures in particular), made long-term management of the resources possible. 2. materials and methods 2.1 the study area the pastures studied for this paper are located on the pasubio massif and its surrounding areas (fig. 1). the pasubio is an extensive plateau in the southeast of the trentino region (northern italy) at a height of between 1500 m and 2000 m and confined by two deep valleys. the summit area is a wide plateau from which radiate a series of small valleys cutting deep into the slopes. the geological structure of the massif has given rise to the development of surface karst landforms where water drains deep into the mountain, leaving the summit land arid and feeding large springs at lower altitudes. in phytoclimatic terms the area is classifiable as pre-alpine moist temperate. 2.2 temperature variability in the alps very few quantitative reconstructions of climate variability in the alps over the last millennium have been made. high-resolution reconstructions for the pre-instrumental period are based on documentary reports (behringer, 2013; lutherbacher et al., 2004; pflister, 2005, 2007), geochemical data (stable oxygen isotopes), physical data (annual growth rate of stalagmite laminae; frisia et al., 2007; mangini et al., 2005; smith et al., 2006), and temperature profiles measured along deep perforations (pasquale et al., 2003). representative results can be expected from trees at the alpine timberline, where the temperature during the short vegetative period controls the growth rate. utilizing ringwidth series measured with string instruments, several authors have developed a consistent, spatially-resolvable network of summer temperature-sensitive chronologies for high elevations in central europe for at least the last 500 years (wilson et al., 2005). a com102 marco avanzini, isabella salvador, geremia gios mon temperature signal across the alps has allowed regional reconstructions to be made of mean april–september temperatures (1650–1987) from ring-width (rw) and density (mxd) records using nested principal component regression models (wilson et al. 2005). calibration of paleo-climatic series with instrumental series is based on the assumption that the climate in the last millennium was characterized by modes of variability similar to those in the instrumental period. while this assumption may not be entirely correct, we can be reassured by the fact that all of the series now available display comparable low and high frequency variations. in fact, a comparison of temperature proxy reconstructions for the alpine region highlights periods of synchronous warm and cold periods in the records (mann et al. 2000; pauling et al. 2003; luterbacher et al. 2004, wilson et al. 2004). these variations can be adjusted to local contexts where the micro-climate or altimetric conditions diverge from the alpine reference conditions. 2.3 the climate in the pasubio massif in the last thousand years located at an altitude of 1025 m in the central pasubio, the cogola di giazzera is a large cave with concretions that have been the subject of recent palaeo-climatic studies (frisia et al., 2007). analysis of a stalagmite in this cave using the u/th dating technique, micro-crystal analysis, and oxygen and carbon stable isotope ratios (284 samples) has made it possible to reconstruct the curve of local thermal anomaly over the last 4500 years. figure 1. the pasubio area is in southern trentino (italy). the mountain pastures studied, higlighted in grey, occupy the central part of the pasubio massif and the southermost part of the vallarsa valley. (1 campogrosso; 2 pra; 3 monte di mezzo; 4 pian delle fugazze; 5 pozze; 6 campobiso; 7 cosmagnon; 8 pasubio). 103climate change and historical variations in mountain pasture values the isotopic series derived from the stalagmite’s most recent concretions (u1), dated from 1060 ± 70 ad to today, had an average resolution of seven years. the isotopic series was synchronised with the milan series (1750-1998) (maugeri and nanni, 1998) and with reconstruction of temperatures in europe and the alps from dendrocronological, instrumental and historical data (mann and jones, 2003; lutherbacher et al., 2004; bohm et al 2001; briffa et al 1998) (fig. 2). the coefficient of correlation between the giazzera and luterbacher reconstructions of temperature anomalies was good (r2 = 0.77). for the purposes of the research reported in this article, we needed to be able to correlate the average temperature of the reference periods with those used to determine the rents for alpine pastures in the study area. because the leases were renewable every five years and the rent was correlated with this time frame, it was necessary to have temperature data on a scale of at least five years. the giazzera dataset has a resolution of seven years, and lutherbacher’s (2004) series, having a resolution on an annual and seasonal scale, perfectly suited with the purposes of our analysis. therefore, having confirmed the positive correlation between the temperature anomaly series reconstructed for the pasubio and those available for europe and the alps (frisia, 2007; frisia et al., 2007), lutherbacher et al.’s (2004) annual thermometric data were adopted in the analysis. 2.4 historical data 2.4.1 the social context: public good and private good except for the brief period of napoleonic rule, from the second decade of the sixteenth century onwards few alterations were made to the political administration of the area, which was part of the habsburgs’ tyrolean domains. with the demise of the feudal system in the eighteenth century, local communities gained possession of most of the mountain land and managed them by leasing pastures to local and non-local breeders (salvador and avanzini, 2014). at the beginning of the eighteenth century, grazing land was the property of the comfigure 2. comparison of reconstructed temperature anomalies for the pasubio (gz1) (from miorandi et al., 2007) and for the alps (from lutherbacher et al., 2004) over the past 500 years. 104 marco avanzini, isabella salvador, geremia gios munity (now the district council) of vallarsa, which every five years leased them by public auction to the highest bidder. the rental contracts contained clauses that remained substantially unchanged over time and were the same for all pastures in the same period. in this respect, changes in the rent values assigned to alpine pastures are good indicators of climate changes. the fact that the extent of land3 and its ownership do not change in part removes several socio-economic variables from the diachronic evaluation of their values.4 during the period considered, the population of the area increased in line with that of the entire alpine region (bussolon, martini, 2007). in the area examined, between the eighteenth and nineteenth centuries the number of inhabitants grew, albeit more slowly than in the nearby plain. in order to increase food resources to feed the larger population there was a rise in the number of livestock raised and therefore an increased demand for pastures, with a consequent linear increase in their average value. 2.4.2 source of economic data the historical archives5 of the administrative districts of the area under study contain the ‘auction deeds’ and the corresponding ‘auction tenders’ for grazing lands since the seventeenth century. they record the conditions on which the district council leased each grazing pasture, the price the tenant had to pay annually to the district council and any additional sums due, which in the eighteenth century would become a fixed fee for maintenance of the pastureland. from the eighteenth century onwards (the first rental agreement examined here is dated 1719) the vallarsa district council kept specific records of mountain leases with documentation of the costs and auction conditions. in the eighteenth century, the grazing lands were allocated in the autumn of the year preceding the start of grazing, although the auction for the five-year period 1774-1778 took place in the autumn two years previously (october 1772) and became the rule for successive decades. this gave the tenant who had won the auction sufficient time to procure cattle, hire a cheese maker and shepherds, procure all the cheese making equipment, ensure that the buildings (farmhouse and cheesemaking outbuilding) and infrastructures (roads, watering holes) were in good condition and, if necessary, carry out repairs. from 1810, the auction deed also specified the reserve price (usually the rent from the previous five years) and by how much each bidder was willing to raise the starting price. when the reserve price was considered too high for that year, the auction was cancelled and another took place with a lower starting price. the year of the auction, regardless of whether it took place one or two years prior to the start of the lease, was therefore taken into consideration in analysing the comparison with the standard thermometric series. the price from 1719 to 1773 is given in trons, and after that in florins (1 florin = 5 3 the actual extent of the pasturelands may well have varied as a result of tree clearance, or, in other periods, tree encroachment. however, while it is true that the areas cannot be measured with any certainty, it is also true that these changes to the grazing lands have no significant bearing on the analysis that follows. 4 the alpine pastures are not privately owned but are instead the property of the district council, which means no variables associated with land division and change of ownership need be considered. (bussolon and martini, 2007). 5 the trento state archive, the rovereto district archive and the vallarsa district archive were consulted. 105climate change and historical variations in mountain pasture values trons). in the nineteenth century, the price was given in various currencies: tyrolean florins, imperial florins and common florins (100 viennese florins = 105 tyrolean florins = 120 imperial florins = 125 common florins). to overcome currency conversion problems, prices have been converted to silver equivalents, i.e. the actual amount of silver (in grams) contained in the coins in every year under study. information regarding the various currencies is taken from pribram (1938).6 it has been necessary to use silver as the numeric value because it is practically impossible to reconstruct a historical series of the prices of the products of livestock raising to which the analysis refers. the chosen indicator allows at least reduced instability, which happens to be rather substantial in some of the periods under study. a similar solution has already been adopted by other scholars (allen, 2001). it goes without saying that such an indicator does not resolve the issue of silver’s actual purchasing power. nonetheless, no significant variation in silver’s actual purchasing power has been recorded in the area under study (bonoldi et al. 2018). 2.5 analysis 2.5.1 rents for pastures from the eighteenth to nineteenth centuries and the relationship with changes in climate. until the mid-twentieth century, land values and pasture rents were directly related to the productivity of the mountain. therefore, to investigate the relationships between them, we compared the values of the pastures with environmental drivers in the study area. this analysis took account only of those grazing lands for which there is a sufficient continuity of information on rents for the period 1719-1880. we also selected pastures used mainly for grazing cattle and which were not subject to any change of use during the period considered.7 in addition, periods of evident socio-economic and/or political instability (such as the napoleonic rule from 1800 to 1815) were excluded from the comparison, and any sums due in addition to the rents as a result of improvements to and work carried out on the buildings or pastures during the period under investigation were removed. the grazing lands examined fell into three groups: a) campogrosso/monte di mezzo/ pra, average altitude 1350-1400 m (low altitude); b) pozze/campobiso/pian delle fugazze, average altitude 1550-1600 m8 (medium altitude); and c) cosmagnon/pasubio, average altitude 1900 m (high altitude). 6 given the length of the period considered, use of a deflector in order to express the variables considered in terms of purchasing power would be desirable. unfortunately, the available statistics do not allow even approximate estimates of this indicator to be made. 7 because of the scarcity of hay fields from the beginning of the twentieth century and the need for hay to feed livestock during winter, several pastures neighbouring vallarsa were leased for haymaking and were only partly utilised for grazing. these were allocated directly (without public auction), although the contract was still for five years and the price in some cases did not change for as much as 30-35 years. 8 we also had to consider the grazing lands at passo pian delle fugazze (altitude 1100 m to 1300 m) as they were often leased together with those of pozze/campobiso. however, as they comprised only less than one fifth of the total area leased, these grazing lands should not greatly impinge on the following analysis. the pozze/campobiso pastures cover around 250 ha, those of passo pian delle fugazze around 30 ha. the relationship between the sizes of the two areas remained more or less constant throughout the period considered. 106 marco avanzini, isabella salvador, geremia gios preliminary analysis of the variations in rent values (with all prices converted to florins) shows that they gradually increased over the course of the period studied as a consequence of the increase in demographic pressure, as can be seen from the following graph (fig. 3). furthermore, comparison with the climate curve (fig. 2) is highly consistent with the trend of rising average temperatures and hence with the presumed improvement in mountain weather conditions following the negative peak of the 1740s. from the beginning of the period analysed, higher values were assigned to the grazing lands below 1500 metres, these being rich pastures at lower altitudes with relatively easy access, a reasonably assured supply of water and relatively speedy connections to the towns in the valley.9 the grazing lands located at higher altitudes (group c) have poorer pastures and structural conditions that remained unchanged over time, and their rent values do not significantly increase. at the turn of 1740, there was a drop in the value of the pastures for which data are available (a and b), possibly as a result of the marked fall in average temperatures over this 9 a new road was constructed in 1823 giving better access to the area where all the grazing lands are located, which may have something to do with the greater value assigned to them between 1825 and 1839. figure 3. variations in rent values of the three groups of grazing lands between 1715 and 1850 normalised and expessed in silver grams. 107climate change and historical variations in mountain pasture values period (fig. 2). a second, clear drop in the rent values of all the grazing lands (a, b and c) occurs between 1820 and 1845, followed by a marked rise in the next three five-year periods. this appears to coincide with the cold phase documented in the alps between 1820 and 1840 (büntgen et al., 2006; leonelli et al., 2012; rea et al. 2003), followed by a rapid rise in average temperatures from the 1850s onwards. differences in rent according to temperature and altitude may be understood in light of the differential rent concept defined by ricardo (1821) and reinterpreted by, among others, quadrio curzio (1998).10 temperature and altitude are, in fact, the original natural factors influencing rents and therefore income. in the case of grazing, as with many agricultural crops, productivity depends on natural factors and on permanent or temporary improvements resulting from human activities. during the period considered, characterized by few technological innovations, temporary improvements linked to the use of production aids, such as fertilizers, seeds, etc., were almost non-existent. even management organizational models remained more or less the same, as evidenced by the invariance of the conditions that applied to the tenant. however, permanent improvements were effective, resulting in deforestation and clearing of the land occupied by the less steep woodlands. as a result of the increasing need for pastures, from the sixteenth and seventeenth centuries (salvador and avanzini, 2014) woods located at increasingly lower altitudes were ceded to grazing areas. the initial investment in deforestation increased, the lower the altitude.11 these deforestation activities can be treated as investments and are considered fully amortized, given the period examined in this study. soils at lower altitudes are more productive and can be more easily associated with permanent housing. in this framework, the annuity of a pasture in the period examined (i.e., in the absence of technological innovations and in an essentially closed market) will necessarily tend to increase in the presence of increasing human pressure. this will lead, as ricardian theory suggests, to less fertile lands being cultivated and to an increase in the income from those already in use.12 2.5.2 econometric analysis to examine the available data in detail, a multiple regression analysis was conducted to identify the types of links between the variables that we considered important: the dependent variable being pastures’ rent value13 and the independent variables being temperature and population.14 as already mentioned, to overcome problems resulting from the use of different currencies, we used silver equivalents of their values.15 10 the best-known reference is p. sraffa (1925), but keynes (1936) also worked on the problem. 11 at lower altitudes, the forests are bushier with trees of larger diameter. 12 some pastures utilized in the nineteenth century were abandoned in a later period due to low fertility. 13 during the period under examination the pastures’ rent value in campogrosso, prà and monte di mezzo (group a) varied between 25.258 and 8490.830 silver grams; pastures in pozze, campobiso, pian delle fugazze (group b) yielded a rent between 24.175 and 3561.084 silver grams; pasubio and cosmagnon (group c) between 7.998 and 1587.12 silver grams. 14 in the period under study, population varied between 1394 and 3206 inhabitants. 15 other explanatory variables might be of some interest, e.g. the overall economic trend and farms’ structure, 108 marco avanzini, isabella salvador, geremia gios population pressure was examined by taking information on the population residing in the vallarsa district as a proxy.16 regarding temperature, we decided to employ average values17 for the spring immediately preceding the auction. as reported in the introduction, the spring/early summer temperature is crucial for grass growth and hence for determining the productivity of an alpine pasture (cavallero et al., 1992). the nutrients contained in the soil are the most critical factors influencing the level of grass output and growth, although this also depends to some extent on precipitation patterns: too much precipitation, particularly during autumn and winter, reduces the content of calcium, phosphates and nitrogen in the soil. sequences of wet years had a cumulative impact, although temperature, according to results obtained by agronomists, has far more to do with mobilizing nitrogen from the soil than was previously believed (bengston, 2004). since temperature trends are spatially far more uniform than rainfall patterns, we may conclude that yields tend to react in a similar way within large regions. detailed analysis of the temperatures reconstructed by lutherbacher et al. (2004) also reveals a high correlation between average spring temperature and average annual temperature (correlation coefficient 0.61) (fig. 4). furthermore, the use of constructed variables, such as moving averages and weighted moving averages of spring temperatures for the three years preceding the year of the auction, did not produce results significantly different to those obtained using simple average spring temperatures (c.c. 0.64 for moving averages, c.c. 0.82 for weighted moving averages). the results of the preliminary analysis suggested using the average spring temperature of the year of auction, which has a greater influence than the average temperature of the previous years on the amount of grass in the pastures in the year of auction and ultimately on the bidding. 3. results 3.1 data elaboration. an initial analysis was performed treating all the available information (59) as panel data (greene, 2008). as the panel regression did not involve a significant increase in the explanatory capacity of the model, we estimated an ordinary least square regression on the entire set of available data. in this case, we used a double-log functional form of the following type18: but statistical information is not available to add such dimensions in the model. given the specific situation we can nonetheless suggest that the effect of economic growth is to a certain extent captured by the population variable. in a closed economy, population growth is only possible when a larger amount of resources becomes available (malthus 1798). the farms’ structure does not change significantly in the period under examination (bussolon and martini, 2007). given this specific context, we believe that, altogether, the lack of availability of further variables does not invalidate the main conclusions of the study. 16 missing observations for the years of interest were interpolated using linear regression. 17 during the period under study, the average spring temperature was between 5.895 and 8.133 degrees celsius (°c). 18 we used a log-log formula to reduce the influence of the different rent values of the grazing lands. this functional form smooths the rent value differences. 109climate change and historical variations in mountain pasture values ln(ar)19 = f (ln(pop), ln(s_temp), d1, d2 the estimated results are presented in table 1. we can note that all the estimates are significantly different from zero at least at the 0.05 value. estimated coefficients confirm that average spring temperature and population positively affect the rent values of the grazing lands. moreover, the lower the altitude of the grazing land, the higher is its rent value. given this relevant effect of altitude, we deemed useful to estimate separated functions for the three different groups of mountain pastures. despite the small number of observations for each area examined, this exercise allows highlighting the role of population and temperature as a function of altitude. in this case, it seems appropriate to introduce a new dummy variable for the grazing 19 ln(ar) = logarithm of annual rent expressed in silver, ln(pop) = logarithm of population, ln (s_temp) = logartihm of average spring temperature, d1 dummy variable for grazing lands at medium altitude, d2 dummy variable for grazing lands at lower altitude. figure 4. average annual temperature versus average spring temperature in the period analysed (from lutherbacher et al. 2004). table 1. estimated results for all the mountain pastures panel estimation population +8.461 *** average spring temperature (degrees centigrade) +2.783 ** dummy variable for grazing lands at medium altitude +1.634 *** dummy variable for grazing lands at lower altitude +1.759 *** constant -66.731 *** r2 0.896 adr2 0.889 number of observations 59 * significant at 10% significance level, ** significant at 5% significance level, *** significant at 1% significance level. 110 marco avanzini, isabella salvador, geremia gios lands located on the border with the nearby veneto region (group a, c), assuming a value of zero up to 1752 and one over the following years. this is to account for the fact that the borders were definitively fixed in 1752, thus putting an end to a series of incursions and acts of intimidation that had made the mountain unsafe for use in previous years.20 the estimated equations using the least squares method were as follows21: ar22 = f(pop, s_temp, d) most estimates are significantly different from zero at least at the 0.10 value, except for average spring temperature for group (b). estimated coefficients confirm a strong effect of human pressure due to population growth on rent values for each group of grazing lands. interestingly, this effect is decreasing as moving from grazing lands at lower altitude towards grazing lands at higher altitudes, confirming the ricardian assumptions. the same trend emerges for average spring temperature, which exerts the greatest effect on rent values for grazing land at lower altitudes, while for group (b) and group (c) the effect is lower. in order to highlight the different sensitivity of rents to variations in spring temperature and changes in the size of the population we can calculate elasticities. the elasticity of rent to population indicates the average amount by which the rent varies as a response to a change in the population. the elasticity of rent with respect to temperature indicates the average amount by which the rent varies as a response to change of 1 degree celsius in spring temperature. the elasticities of rent are presented in table 3. calculating these elasticities on regression results of table 1 (all the grazing lands considered together) we obtain 0.70 for the elasticity with respect to population and 7.73 for the elasticity with 20 the campogrosso/prà/monte di mezzo and cosmagnon/pasubio pastures are located on the border with the veneto region. until 1752 this border was not clearly defined and as a result animals might be found grazing in a neighbouring property, thus provoking punitive raids which included the animals’ seizure, the burning of farmhouses and so on, and the beginning of lengthy controversies. with the rovereto treaty (1752) the borders were precisely drawn and guarded, and there was a considerable increase in the rent values as a consequence of the greater security. 21 since there were no structural differences (e.g. in altitude) within the three groups identified for mountain pastures and their rents, we preferred to use a linear functional form. 22 ar = annual rent table 2. estimation results for three different groups of mountain pastures. group (a) (low altitude) group (b) (medium altitude) group (c) (high altitude) population +4.34 *** +1.87 *** +1.22 *** average spring temperature (degrees centigrade) +798.21 ** +309.31 +199.43 * dummy variable for years after 1752 +1746.42 ** +418.86 ** constant -14554.24 *** -5296.81 ** -4187.87 *** r2 0.80 0.77 0.82 adr2 0.77 0.74 0.77 number of observations 23 18 18 111climate change and historical variations in mountain pasture values respect to temperature. this indicates that rents are inelastic with respect to an increase in population but are very elastic in response to an increase in average spring temperature. the elasticities calculated for the three different groups confirm a different sensitivity of rents according to altitude. it appears as evident that changes in temperature have a much stronger impact on the amount of rent charged than changes in population. the elasticity of rents with respect to population is high (3.118) for grazing lands located at low altitude but is inelastic for grazing land at medium (0.830) and higher altitude (0.236). we can therefore draw the conclusion that a change in human pressure has graver consequences for the grazing lands at low altitude than for those at higher altitudes. this is explained by the fact that it makes sense to make the best possible use of the lower grazing lands in the area under study given that they are, on the one hand, closer to the towns and villages and, on the other hand, adjacent to the tree line and can be “extended” by encroaching on the woods and forests. in contrast, there is no possibility of extending the pastures at the highest grazing lands, which in all probability are affected by the situation in the pastures at lower altitudes.23 the sensitivity of rent values to changes in spring temperature follows a similar pattern, even with a different order of magnitude.24 the elasticity to spring temperature is very high for grazing lands at low altitude while it is lower for grazing lands at medium altitude and even lower for land located at higher altitude. this can be explained in part by the fact that even small increases in spring temperatures can give rise to longer pasturing periods in the low grazing lands, while the grazing period in the high pastures is much more constant. 4. concluding remarks research conducted on the values of mountain pastures in the pasubio estimated from the rents charged for them over a two-hundred-year period show that variations in these values are related to natural and anthropogenic drivers to varying extents depending on historical period and altitude. 23 it should also be remembered that only pastures located above the tree line were at first utilised, and it was only later that pastures were created at lower altitudes by clearing the less steep woodland areas. 24 the different ranges of variation of the two variables (low for temperature, high for population), rather than their different orders of magnitude should be taken into account when interpreting the high values for elasticity. table 3. estimated rental price elasticities. elasticity with respect to population elasticity with respect to temperaturea overall elasticity 0.70 7.73 group (a) (campogrosso, prà, monte di mezzo) 3.118 187.493 group (b) (pozze, campobiso, pian delle fugazze) 0.830 36.735 group (c) (pasubio, cosmagnon) 0.236 14.213 a in thousandths of a degree celsius. 112 marco avanzini, isabella salvador, geremia gios oscillations in the values for 5-year periods between the last half of the eighteenth century and the beginning of the twentieth century suggest a strong connection with environmental and climatic factors. increases or decreases in temperature appear to have a less marked and less direct effect on the values of grazing lands close to the upper limit of vegetation, while, in addition to the climate signal, socio-economic and infrastructural signals impinge significantly on the grazing lands at lower altitudes. if we consider that the value of the rent is an estimate of income and therefore of the utility of the “land productive factor” within the production process, we may draw some general considerations from this survey. in particular, an interesting observation is that increasing population and temperature have the same influence in increasing the yield, independently of altitude. for both the variables, the values of the rents for land located at a lower altitude generally more fertile have an elasticity approximately 13 times greater than that of land at a higher altitude. this means that human pressure and more favourable climatic conditions lead to significantly intensified pastoral activity in the fertile areas, while the income of marginal land is less affected by these changes. this finding is counterintuitive because people are generally inclined to believe that higher temperatures should favour pastures at high altitude because they should supposedly become more fertile. this apparent contradiction can nonetheless be explained with reference to the ricardian theory of rent. our research supports what david ricardo, at the dawn of economic science, had guessed. this contribution to the validity of the ricardian theory of rent is even more interesting given the long time interval considered and the relative small number of situations where this theory can actually be tested. within agricultural production, only pastures have undergone no significant technological transformation over time. the analysis partly suffers from the lack of consistent statistical data: given the length of the period under study, the elaboration could not always be conducted on homogeneous information. more precisely, the absence of a reliable indicator to convert the rent value into actual purchasing power can lead to a distortion in the estimates provided. nonetheless, the elaboration carried out, if confirmed by other surveys, can provide a solid basis for appropriate measures of agricultural policy and land management adapted to ongoing climate changes. 5. funding this study is part of the project armo archeologia del paesaggio montano: reti insediative e paleoambienti nelle prealpi trentine” funded by muse science museum (trento) and the department of economics and management of trento university. 6. conflict of interest the 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(1993). metodi di rilevamento della produttività dei pascoli alpini. comunicazioni di ricerca isafa (tn), 93/1, 33-42. bio -based and a ppl ied economics bae bio-based and applied economics 11(1): 75-87, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-9992 copyright: © 2022 m.o. kehinde, a.m. shittu, m.g. ogunnaike, f.p. oyawole, o.e. fapojuwo. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: m.o. kehinde, a.m. shittu, m.g. ogunnaike, f.p. oyawole, o.e. fapojuwo (2022). land tenure and property rights, and the impacts on adoption of climate-smart practices among smallholder farmers in selected agro-ecologies in nigeria. bio-based and applied economics 11(1): 75-87. doi: 10.36253/ bae-9992 received: november 4, 2020 accepted: february 4, 2022 published: july 22, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: donato romano. orcid mok: 0000-0001-7081-4454 ams: 0000-0002-0857-0337 mgo: 0000-0002-1510-1969 fpo: 0000-0001-5899-7120 land tenure and property rights, and the impacts on adoption of climate-smart practices among smallholder farmers in selected agroecologies in nigeria mojisola o. kehinde1,*, adebayo m. shittu1, maria g. ogunnaike2, funminiyi p. oyawole1, oluwakemi e. fapojuwo3 1 department of agricultural economics and farm management, federal university of agriculture, abeokuta, p.m.b. 2240, abeokuta, ogun state, nigeria 2 department of agricultural economics and farm management, olabisi onabanjo university, ago-iwoye, p.m.b. 0012, ayetoro, ogun state, nigeria 3 department of agricultural administration, federal university of agriculture, abeokuta, p.m.b. 2240, abeokuta, ogun state, nigeria *corresponding author: e-mail: mojisolaolanike@gmail.com abstract. this study investigates the effects of land tenure and property rights (ltprs) on smallholder farmers’ adoption of climate-smart practices (csps) among cereal farming households in nigeria. the data were collected from maize and rice farmers in a nation-wide farm household survey conducted across the six geopolitical zones in nigeria. data collected were analysed within the framework of multivariate probit to determine the factors that facilitate and/or impede the adoption of csps. the results showed that the adoption of csps considered in this study – agroforestry, zero/ minimum tillage, farmyard manure, crop rotation and residue retention were generally low. empirical analysis showed that farmers with transfer right were more likely to adopt farmyard manure, crop rotation and residue retention while the likelihood of adopting agroforestry reduced with having transfer right. the coefficient of de jure secure increased the likelihood of adopting zero/minimum tillage while the coefficient of control right increased the likelihood of adopting agroforestry. again, we found that the adoption of zero/minimum tillage reduced with control and transfer rights. the study also contributes to the existing literature on adoption by recognizing the interdependence between different climate-smart practices as well as jointly analyse the decision to adopt multiple csps. the study therefore, suggests that governments, in whom the responsibility for land use policy reform lies, review the existing framework to ensure a prompt, fair, and efficient land tenure system. keywords: climate-smart practices, land tenure and property rights, multivariate probit, smallholder farmers, nigeria. jel codes: q15, q18. 76 bio-based and applied economics 11(1): 75-87, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9992 mojisola o. kehinde et al. 1. introduction agriculture in the world especially sub-saharan africa (ssa) is at a crossroads simply because climate change has brought a menace to the agricultural sector, which must be attended to (ipcc, 2014). nigeria as one of the african countries is not an exemption in this issue. climate change poses the greatest challenge to smallholder farmers and threatens the progressive efforts towards poverty alleviation, food security, and sustainable agriculture (lipper et al., 2014; vermeulen, et al., 2011). globally, smallholder farming households are estimated to be between 475-500 million; cultivating less than 2ha of land (lowder et al., 2016). many of whom are living in abject poverty and on less than $2 a day, hence, experiencing food insecurity (world bank group, 2016; morton, 2007). usually, smallholder farmers are the main victims of climate change because of their sole dependency on rain-fed agriculture, limited market access, insecure access to land, cultivation of marginal and fragmented land as well as inadequate access to technical and/or economic support which can help them to embrace resilient-farming practices (donatti et al., 2018; morton, 2007). the world’s climate is changing fast and will continue to do so for the foreseeable future, no matter what measures are now taken. for agriculture, the change will also be significant, as temperatures rise, rainfall patterns change and pests and diseases find new ranges, posing new risks to agriculture and food systems (cooper et al., 2013). the negative impacts of climate change have led to a reduction in agricultural productivity and substantial welfare losses which eventually lead to food and nutrition insecurity in the populace (tripathi & mishra, 2017). shifting to climate-smart agriculture (csa) seems to be the most efficient way for farmers to reduce the negative impacts of climate change on the production, incomes, and well-being of vulnerable smallholder farmers (mccarthy & brubaker, 2014). according to the food and agricultural organization of the united nations (fao, 2013), csa is an unconventional approach to manage land in a sustainable manner while increasing agricultural productivity (world bank, 2011). it is aimed to achieve three key goals sustainably increasing agricultural productivity and incomes; adapting and building resilience to climate change; reducing and/or removing greenhouse gases emissions, where possible (braimoh, 2015). climate-smart practices (csps) include inter-cropping, crop rotation, zero tillage, green manuring, application of farmyard manure, integrated soil fertility management, agroforestry, irrigation, changing planting dates as well as alternate wet and dry lowland rice production systems (bernier et al., 2015). despite this potential, adoption of csps remains generally low, particularly in ssa, nigeria inclusive. this may, however, not be unconnected with insecure land tenure and property rights (ltprs), which is often cited as one of the barriers to the adoption of improved technology and investment in land development in africa (shittu et al., 2021; byamugisha, 2013; liniger et al., 2011). it is pertinent to note that without secure property rights, farmers often do not have the emotional attachment to the land they cultivate and would thus, not invest in land improvement that can enhance their productivity in the long run and promote sustainable development (deininger, 2003). empirical evidence from the literature corroborates the earlier assertion that adoption of csps are generally low in nigeria, usually between 15.5% – 40.6% (shittu et al., 2018) while the adoption rate for water harvesting, irrigation, and terraces are 15%, 10%, and 30% respectively (onyeneke et al., 2018). they attributed the low adoption to a very weak agricultural extension service delivery system across various states in nigeria and also to the need for more capital, lack of technical know-how, low potential for irrigation and most importantly present markets cannot accurately account for the value of the environmental benefits that csa delivers (ahiale et al., 2020; shittu et al., 2018). gleaning through the literature, some of the factors driving the adoption of the csps among smallholders in nigeria include education, income, credit, extension services, livestock ownership, farming experience, farm size, distance to market and water resources, gender, land ownership, household size, and mass media exposure among others (oyawole et al., 2020; amadu et al; 2020; aryal et al., 2018). arising from the foregoing, using smallholder farmers in selected rice ecologies of nigeria as a case study, this paper1 will build on the recent work of shittu et al., (2018) by assessing the influence of ltprs on the adoption of csps. we used multivariate probit (mvp) regression analysis, which explicitly allows for correlation in the error terms of the adoption equations to control for interdependence in decisions on csps’ adoption. the paper contributes to the ongoing debates on ltprs and the adoption of csps in africa’s smallholder agriculture in a number of ways. first, technology adoption remains one of the most researched areas in the field of agricultural 1 an earlier version of this paper, titled ‘land tenure and property rights impacts on adoption of climate smart practices among cereals farmers in nigeria’, was presented at the 18th annual national conference of nigerian association of agricultural economists, october 16th – 19th, 2017. 77 bio-based and applied economics 11(1): 75-87, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9992 land tenure and property rights impacts in nigeria economics, very few studies have looked at the factors that determine the adoption of csps in nigeria. second, methods that recognise the interdependence between different climate-smart practices and jointly analyse the decision to adopt multiple csps agroforestry, farmyard manure, crop rotation, zero tillage, and residue retention are used. this study attempts to fill these identified gaps. in the next section, we describe the theoretical framework underpinning the adoption of csps and the econometric approach of multivariate probit. section three (3) presents the methodology in which we have the study area, research design as well as measurement of land tenure and property rights. in section four, we present and discuss the results, while the final section presents the main conclusions and the policy implications. 1.1 brief on land tenure and property rights in nigeria ltprs have to do with the rights that individuals, communities, families, firms, and other community structures hold in land and associated natural resources. as noted by feder and feeny (1991), the rights on the land are “either de facto or de jure secure” if they are clearly defined, exclusive, enforceable, transferable, and recognized by relevant authorities. in nigeria, the land use act made provision for granting two types of land use rights customary and statutory rights of occupancy to all categories of land users (land use act [lua], 2004). customary right of occupancy is granted under the act by the local government councils to individuals, firms, and communities while the statutory right of occupancy is the right to use land in any part of the state and it is granted under the act by the state governor (lua, 2004; kehinde et al., 2021). a certificate of occupancy is issued to a land user as evidence of being granted the statutory right of occupancy on the land by the state governor, thus making the certificate of occupancy the highest form of land title in nigeria. issuance of certificate of occupancy requires that the landowner possesses a purchase receipt, duly stamped deed of transfer, and an approved boundary survey of the land. the customary rights of occupancy are governed by the largely unwritten customary laws in various localities and are also considered de facto held by holders of agricultural lands in rural areas that have been under use for agricultural purposes prior to the enactment of the land use act of 1979 (lua, 2004; shittu et al., 2018). shittu et al. (2018) show that when the land has not been issued a certificate of occupancy, it is subject to unfair expropriation, though the lua made everybody an occupant of the land. landowners that acquired their land through direct inheritance and outright purchase enjoy customary rights on their land even though that title is not officially certificated but they are recognized as having a secure title on their land from the customary point of view. both the latter and the former will enjoy statutory rights of occupancy when the de facto-held land is moved to the highest level of tenure security (de jure secure) by getting the land surveyed, registered with the state government, and possibly obtain the certificate of occupancy. it is important to note that freehold land is still susceptible to unfair expropriation if it is not registered with the government. table 1 shows the different land tenure types, possible types of rights with their level of tenure security. 2. analytical framework 2.1 multivariate probit model multivariate probit regression framework was used to analyze the factors that facilitate or impede the adoption of csps, following scognamillo and sitko, (2021), aryal et al., (2018), kpadonou et al., (2017), timu et al., (2013), and teklewold et al., (2013). the model is an extension of the probit model used for the estimation of several correlated binary choices jointly (greene, 2003). considering several agricultural technologies, there is the possibility that some level of interdependence may exist among the technologies with farmers adopting some of these technologies as substitutes, complements or supplements. a farming household would be adopting one or more of the components of csps if and only if table 1. kinds of rights and tenure security by mode of land acquisition. mode of land acquisition use right control right transfer right de facto secure de jure secure freehold (inherited & purchased √ √ √ √ × communal √ √ × √ × leasehold √ √ × × × 78 bio-based and applied economics 11(1): 75-87, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9992 mojisola o. kehinde et al. the utility expected is higher than otherwise. a positive correlation of the error terms means the technologies are complements while negative correlations of the errors terms imply the technologies are substitutes (teklewood et al., 2013; belderbos et al., 2004). if a correlation exists, simply estimating the technology adoption equations independently will generate biased and inefficient estimates of the standard errors of the model parameters for each technology (greene 2008), inducing incorrect inference as to the determinants of technology adoption. dorfamn (1996) observed that the estimates of separate probit equations (univariate probit) exclude useful economic information contained in interdependence and simultaneous adoption decisions. hence, when farmers adopt a combination of technologies to deal with land degradation rather than adopting just a single practice or technology, the adoption decision is inherently multivariate. hence, the mvp estimator corrects for these problems by allowing for non-zero covariance in adoption across technologies. thus, the observed outcome of csps adoption can be modelled following a random utility-based estimation framework. consider the ith farm household i=(1,…,n) facing a decision on whether to adopt the available csps on plot p(p=1,…,p). let u0 represent the benefits to the farmer from traditional management practices, and let uk represent the benefit of adopting the kth csps: vis-a-vis, agroforestry (ag), farmyard manure (fy), crop rotation (cr), zero tillage (zt), residue retention (rr). the farmer decides to adopt the kth csps on plot p if y*ipk=u*k-u0>0. the net benefit (y*ipk) that the farmer derives from the adoption of kth csps is a latent variable determined by observed household, plot (zip) and socio-economic characteristics xi and the error term εip: y*ipk=z’ipδk+x’iβi+εip (k=af,fy,cr,zt,rr) (1) using the indicator function, the unobserved preferences in equation (1) translate into the observed binary outcome equation for each choice as follows: (k=af,fy,cr,zt,rr) (2) equation 1 can be rewritten as a system of equations that can be estimated simultaneously using equation 3; y*1pk=β’1x1i+z’1iδk+ε1i y1pk=1 if y*1pk>0, y1pk =0 otherwise y*2pk=β’2x2i+z’2iδk+ε2i y2pk=1 if y*2pk>0, y2pk =0 otherwise ⋮ (3) y*npk=β’kxki+z’kiδk+εki ynpk=1 if y*npk>0, ynpk =0 otherwise in the multivariate model, where the adoption of several csps is possible, the error terms jointly follow a multivariate normal distribution (mvn) with zero conditional mean and variance normalized to unity (for identification of the parameters) where (μaf,μfy,μcr,μzt,μrr), mvn (0,ω) and the symmetric covariance matrix ω is given by: (4) the off-diagonal elements in the covariance matrix represent the unobserved correlation between the stochastic components of the different types of csps. this assumption means that equation (2) generates a mvp model that jointly represents decisions to adopt farming practices. this specification with non-zero off-diagonal elements allows for correlation across the error terms of several latent equations, which represent unobserved characteristics that affect the choice of alternative csps2. the computation of the maximum likelihood function based on a multivariate normal distribution requires multidimensional integration. different simulation methods were proposed to approximate such a function (train, 2002). the geweke–hajivassiliou– keane (ghk) simulator is a particularly popular choice in empirical research (geweke et al., 1997). the ghk simulator exploits the fact that a multivariate normal distribution function can be expressed as the product of sequentially conditioned univariate normal distribution functions, which can be accurately evaluated (cappellari and jenkins, 2003). the ghk simulator relies on a cholesky factorization, and to do this, the estimate of the correlation matrix at each iteration must be positive definite. 3. methodology 3.1 the study area the study was conducted in selected farming communities reputed for maize and rice production across the six geopolitical zones, and covering five of the seven agro-ecological zones (aezs) of nigeria, viz; rainforest zone, derived, southern guinea, northern guinea, and sudan savannah zones respectively. nigeria is situated 2 the authors acknowledge that the correlation between the error terms in a system of simultaneous equation depend on the correct specification of the model. 79 bio-based and applied economics 11(1): 75-87, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9992 land tenure and property rights impacts in nigeria in the west african region and lies between longitudes 3° and 14° and latitudes 4° and 14°. it has a landmass of 923,768 sq. km. nigeria shares a land border with the republic of benin in the west, chad and cameroon in the east, and niger in the north. its coast lies on the gulf of guinea in the south and it borders lake chad to the northeast (udo et al., 2018). administratively, it is made of 36 federating states and the federal capital territory. the states are commonly grouped into six geopolitical zones: northeast, northwest, north-central, southeast, southwest, and south-south geopolitical zones and seven agro-ecological zones all of which are suitable for maize and rice, among several other crops like cassava, yams, etc. 3.2 the study design the study was part of the funaab-raaf-pasanao project implemented by the federal university of agriculture, abeokuta in partnership with the national cereals research institute, baddegi, and funded by the economic community of west african states. the central focus was on incentivising adoption of climatesmart agricultural practices in cereals production in nigeria. the data were collected across selected agroecologies in nigeria, focusing on maize and rice farmers. the respondents were selected in a three-stage sampling process, described as follows: stage i: purposive selection of 15 states that have been the leading rice and maize producers in nigeria (excluding conflict-prone areas), based on production statistics from (national bureau of statistics [nbs], 2016). stage ii: purposive selection of three agricultural blocks per state per crop from the main rice and maize producing areas of the state, and two extension cells per block that is, six blocks per state, 12 cells per state and 180 cells in all. stage iii: proportionate stratified random selection of 12 rice and maize farmers from members of rice/maize farmers’ association in each of the selected cells. this process yielded 2,007 households of maize and rice farmers, from which complete datasets were collected through personal interviews of the farmer and other farming members of their households. data were collected on a wide range of issues, including the households’ socio-economics, climate-smart practices, and ltprs on farmland cultivated during the 2016/17 farming season. 3.3 dependent variables the outcome variables considered in this study are the csps. the respondents were asked to recount the type of csps practiced on each of their plots agroforestry, farmyard manure, crop rotation, zero/minimum tillage, and residue retention (table 2). agroforestry refers to the intentional integration of trees and shrubs into crop and animal farming systems to create environmental, economic, and social benefits. the intentional nature of agroforestry made many of the sampled farmers fall short in this regard; hence, only 9% of the respondents practiced agroforestry on their farms. farmyard manure, on the other hand, refers to the application of a decomposed mixture of livestock waste on the farming plot. it is a major component of nutrient management with potential benefits of soil fertility maintenance as well as supply of major nutrients such as nitrogen, phosphates, and potash. out of the total plots, about 24% of these plots received manure. crop rotation involves growing different crops sequentially on the same plot of land to optimize nutrients and reduces the incidence of weeds, pests, and diseases (bockel et al., 2013). in our case, any farmer that plants different crops following a particular sequence and includes a leguminous crop in the rotation was considered as having practiced crop rotation. based on this concept, only a few of the farmers (8%) practiced crop rotation on their farms. zero/minimum tillage is part of csps that promotes minimum soil disturbance and allows crop residue to remain on the ground with the accompanying benefits of better soil aeration and improved soil fertility. minimum soil disturbance requires less traction power and fewer carbon emissions from the soil (delgado et al., 2011). in our case, zero/minimum tillage practice entails reduced tillage with a single plough and/or the use of traditional farm tools such as hoe and cutlass. zero/ minimum tillage was practiced on 22% of the plots. the use of residue retention is another option of csps that provides an opportunity for the farmers to retain crop residues as an alternative to biomass burntable 2. adoption rates of climate-smart practices. variable mean std. error min max agroforestry 0.090 0.005 0 1 farmyard manure 0.240 0.007 0 1 crop rotation 0.080 0.005 0 1 zero/minimum tillage 0.220 0.007 0 1 residue retention 0.540 0.009 0 1 80 bio-based and applied economics 11(1): 75-87, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9992 mojisola o. kehinde et al. ing and/or exporting the residues from the farm to feed livestock (bockel et al., 2013; abrol et al., 2017). residue retention was practiced on about 54% of the plots during the cropping season considered for this analysis. 3.4 independent variables the description and the summary statistics of the variables are given in table 3. specifically, the models include socio-demographic characteristics such as age, sex, year of schooling, household size, extension contact, farmers’ association among others. 3.4.1 socio-demographic characteristics with respect to socio-demographic characteristics, table 3 shows that the average age of the smallholder farmers across the six geopolitical zones is 45 years. this implies that the majority of the respondents were still in their active years implying significant participation in the farming activities. this result, however, contradicts the findings of eze et al., (2011) who did a similar study and obtained the mean age of his respondent to be 59 years. only 12% of the respondents were female indicating that the majority of the sampled smallholder farmers were male. the mean year of schooling is 8 years while those that had access to extension services and belong to one farmers’ association or the other were 63% and 94% respectively. as shown in table 3, about two-thirds (60.0%) of the respondents have the right to control their land while about 58.0% have the right to transfer their parcels permanently to the third party across the study locations. table 3 further shows that about half (52.0%) of the parcels were held as inheritance across the study locations, however, this result is much less than the findings of bamire (2010) who found that 84.0% of farmland was acquired through inheritance in the dry savannah part of nigeria. on the contrary, about 14.0% of the parcels were purchased by the farm households, 24.0% on leasehold while 10.0% were communal land while only 4.0% of the farmland were titled, i.e., registered with the land registry in the study area. this implies that only a few out of the sampled farmers had legal tenure security while the majority had insecure tenure (de jure) which can lead to eviction from their table 3. definitions and summary statistics of the variables used in the analysis. variable description mean sem socio-economic characteristics age age of the farmers in years 44.58 0.21 sex 1 = if the sex of the farmer is female, 0 otherwise 0.12 0.01 schooling year farmers’ education level in years 7.74 0.10 household size number of persons in the household 9.25 0.11 extension contact 1 = extension contact during the last planting season 0.63 0.01 amount borrowed amount of money borrowed in naira. 99633 11200 farmers’ association 1 = belong to farmers’ association, otherwise 0 0.94 0.02 tlu tropical livestock unit (livestock wealth)1 3.28 0.23 plot-level characteristics control right 1 = has control right, 0 otherwise 0.60 0.008 transfer right 1 = has transfer right, 0 otherwise 0.58 0.008 de jure secure 1 = if registered with the state, 0 otherwise 0.02 0.002 inherited 1 = cultivates land acquisition by inheritance, 0 otherwise 0.52 0.008 purchased 1 = land acquisition by purchase, 0 otherwise 0.14 0.01 leasehold 1 = land acquisition by leasehold, 0 otherwise 0.24 0.01 communal 1 = land acquisition by communal means, 0 otherwise 0.10 0.01 boundary survey 1 = has boundary survey, 0 otherwise 0.18 0.007 farm size (ha) cultivated farmland in ha 1.60 0.04 lowland 1 = cultivates lowland, otherwise 0 0.42 0.01 extent of farm fragmentation the extent of land fragmentation computed using simpson index 0.35 0.01 note: sem (standard error of mean). 1 tlu conversion factor according to beyene and muche (2010): 1 head of cattle = 0.7 tlu, 0.1 tlu for 1 sheep or goat or pigs and 0.01 tlu for poultry. 81 bio-based and applied economics 11(1): 75-87, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9992 land tenure and property rights impacts in nigeria farmland and regular harassment by land grabbers. a plausible reason for this is that the process of titling land is inexplicably tedious and expensive. thus, given that most farmers in nigeria are smallholders and resource poor, they may not allocate their scarce financial resources to land titling. the mean size of household landholdings was 1.60ha portraying the respondents as smallholders. the average farmland that is fragmented is 35% implying that about one-third of the cultivated farmland in nigeria is not completely consolidated. 3.4.2 ltprs’ measurement two indicators were employed in assessing the ltprs of farmers in this study. they include: i. rights type: this was measured on a nominal scale using three dummy variables – use, control, and transfer rights. use right refers to the right to access the resource, withdraw from a resource or exploit a resource for economic benefit. control right on the other hand refers to the ability to make decisions on how the land should be used including deciding what crops should be planted, and who benefits financially from the sale of crops, etc. while transfer right refers to the ability to transfer land (permanently through sale). each of the types of rights takes the value of one if the farmer has the right to use, control, and transfer the parcel of land. otherwise, the dummy variables were assigned a zero. meanwhile, the use right was dropped as the reference rights-type variable. ii. de jure secure: a tenure was classified as de jure secure if the parcel has been surveyed and duly registered with the land registry; otherwise it was classified as insecure. this variable was meant to determine the importance of title registration. 4. results and discussion 4.1 determinants of adoption of climate-smart agricultural practices the estimates of the determinants of the probability of adoption of climate-smart agricultural practices are presented in table 4. the wald test (χ2 (70) = 404.66; prob > χ2 = 0.0000) of the hypothesis that regression coefficients in all the equations were jointly equal to zero was rejected at 1% indicating that the model fits the data reasonably well. the coefficient of transfer right is positive and significant at 1%, 5%, and 10% levels respectively for the adoption of farmyard manure, crop rotation, and residue retention. hence, transfer right has positive impact on the adoption of farmyard manure, crop rotation, and residue retention, suggesting that farmers are more likely to adopt these csps on owned plots. this is in line with the marshallian inefficiency hypothesis where input use by the tenant on rented or borrowed land is lower or less efficient than on owned land (gray and kevane, 2001). this finding may also be due to tenure insecurity, as an insecure tenure status leads to poor agricultural practices (gray and kevane, 2001). the long-term dimension of the return on investment in land-enhancing practices such as farmyard manure may discourage land-insecure farmers to adopt them as they may not control the land long enough to reap the benefits of their investments. similarly, the coefficient of control right is positive and significant at 10% level for agroforestry, implying that the likelihood of adopting agroforestry rises with farmers’ having control right. on the contrary, the coefficient of transfer right is negative and significant at 10% for agroforestry. this implies that having transfer rights reduce significantly the likelihood of agroforestry in the study area. the possible explanation for this might be that farmers are not interested in agroforestry because of its upfront investment that does not yield any immediate returns; they possibly prefer to dispose of the land in the nearest future at a higher price. the result is contrary to the findings of patanayak et al., (2003) who found that landowners are more likely to adopt agroforestry than tenants are because the latter may be prevented from planting trees, as it is less likely that agroforestry will be adopted on insecure land. again, we found that the coefficients of control and transfer right significantly and negatively influence the adoption of zero/minimum tillage at 1% level. security of land tenure, (de jure secure), positively and significantly affects the adoption of zero/minimum tillage implying that the probability of adoption of zero/minimum tillage is higher when ownership on land is secure. this flows from the fact that rationally, a farmer may not be willing to adopt any csps on land that he/she does not have secure rights to in the long run. as arthur young succinctly puts it in his 1792 treatise, “give a man the secure possession of a bleak rock, and he will turn it into a garden; give him a nine years’ lease of a garden, and he will convert it into a desert”. this gives credence to the findings of owombo et al., (2015) that secure land tenure significantly influences farmers’ adoption of agricultural technology in ondo state, nigeria. the coefficient of farm size is negative and significant at a 1% level for agroforestry. this means that additional hectares of land by the smallholder farmers 82 bio-based and applied economics 11(1): 75-87, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9992 mojisola o. kehinde et al. reduced significantly the possibility of adopting agroforestry; this is simply because the target population for this study is smallholders with an average farm size of 1.60ha. it is also good to note that fragmented farmland does not reduce the adoption of zero tillage in the study area. the level of education of the cereal farmers has a significant positive relationship with the adoption of crop rotation. this suggests that farmers with higher levels of education are more likely to adopt crop rotation. this finding is consistent with that of langyintuo and mekuria (2005) who assert that educated farmers are better able to process information that can enhance production and productivity in agriculture (ali and abdulai, 2010). on the contrary, female-headed households are less likely to adopt crop rotation when compared to their male counterparts; this might be because of the level of skill/ expertise involved in planting different crops sequentially on the same plot. it is important to note that gender differentiation has no impact on the adoption of any other csps. the coefficient for household size is negative and significant at 5% levels for the adoption of crop rotation, suggesting that larger household size is associated with a lower probability to adopt crop rotation. this is consistent with the finding of bekele and drake (2003) who find the family size to have a significantly negative relation with certain adoption choices. the coefficient of farmer’s age is positive and significant at a 1% level for the adoption of zero/minimum tillage in nigeria. this might be because of their farm experience which makes the older farmers be in a better position to adopt new agricultural practices due to their comparative advantage in terms of capital accumulated, frequency of extension contacts/visits, and creditworthiness among others (langyintuo and mekuria, 2003). hence, an experienced farmer is more conscious of the benefits of soil conservation and he would go for adopting the minimum tillage technology. this finding, however, contradicts that of adesina and zinnah (1993) who noted that younger farmers are more amenable to change old practices than older farmers because they tend to be more aware and knowledgeable about new technologies. on the other hand, an inverse relationship exists between age and the decision to adopt residue retention among cereals farmers in nigeria. this can be because the younger farmers are usually more willing to take risks and are likely to perceive increased profits from adoption in terms of accommodating the relative labour-intensive nature that comes with adopting residue retention as against other csps (soule et al., 2000; aryal et al., 2018; ekboir, 2003). hence, the greater willingness to adopt the new agricultural practices. meanwhile, the level of education of the cereal farmers has a significant positive relationship with the adoption of zero/minimum tillage, suggesting that farmers with higher levels of education are more likely to adopt zero/minimum tillage. this finding is consistent with that of shiyani et al., (2000) who asserted that education has a positive impact on the adoption of new technology. the role of education enlightens the farming community with the importance of minimum disturbance of the soil in particular. the adoption of zero/minimum tillage is usually known to reduce the labour required on the farm, hence for larger families where labour is sufficiently available, adoption may not bring many benefit. hence, the a priori expectation is that a larger family size will be inversely related to the adoption of zero/minimum tillage. our finding (table 4) shows a negative relationship between the household size and adoption of zero tillage among the cereal farmers in the study area, indicating that the more the household size, the less likely the adoption of zero/minimum tillage. this is in line with the findings of la rovere et al., (2010) and laxmi and mishra (2007). 4.2 adoption decisions of climate-smart agricultural practices the mvp model is estimated using the maximum likelihood method on plot-level observations. table 5 shows the likelihood ratio test [chi square (10) = 161.736, p = 0.000)] of the null hypothesis that the covariance of the error terms across equations is not correlated is rejected. these findings confirm the interdependence between the adoption decisions of csps, which may be due to complementarity or substitutability in farming practices, but also potentially, to omitted factors that affect all adoption decisions. consequently, farmers do not decide upon a single practice to adopt; instead, the probability of adopting a practice is conditional on whether other practices have already been adopted. the estimated correlation coefficients are statistically significant in seven of the ten pair cases, where five coefficients have negative signs and the remaining two have positive signs. the result shows that farmyard manure is complementary with crop rotation while agroforestry complements residue retention. the complementarity between manure and crop rotation contradicts the finding of teklewold et al., (2013) where they found substitutability. the correlation between adoption of zero/ minimum tillage and residue retention is the highest (19.64%) while that of farmyard manure and agroforestry is the least (5.32%). the negative strong correlation between residue retention, zero/minimum tillage, 83 bio-based and applied economics 11(1): 75-87, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9992 land tenure and property rights impacts in nigeria and farmyard manure is logical as the use of one csp can discourage the adoption of the other one. these findings suggest that using ordinary probit or logit regression to assess the determinants of csps adoption among smallholder farmers in nigeria will yield inefficient estimates. we, however, estimated the model by a set of probit regression (appendix 1), the result of which shows that the evidence from our study are robust to estimation methods. though, one will expect that separate probit regression analysis will yield large standard error but we found that the coefficients (βs) and standard errors resulting from each probit regression analysis are the same or nearly the same as that of the multivariate probit estimate. hence, we conclude that using ordinary probit to assess the determinants of csps adoption among smallholder farmers is consistent and asymptotically efficient with large sample (3,311). 5. conclusions and policy implications this study was carried out to assess the effects of ltprs on farmers’ adoption of climate-smart practices among smallholder farmers in nigeria. a multi-stage sampling procedure was used to sample 2,007 farm households across 180 farming communities in nigeria, table 4. influence of ltprs on adoption of climate-smart practices among smallholder farmers: multivariate probit estimates. agroforestry farmyard manure crop rotation zero tillage residue retention coef. std. err. coef. std. err. coef. std. err. coef. std. err. coef. std. err. control right 0.1277* 0.0738 0.0207 0.0576 -0.0134 0.0765 -0.3654*** 0.0583 -0.0507 0.0521 transfer right -0.1220* 0.0723 0.1839*** 0.0573 0.1532** 0.0765 -0.5192*** 0.0583 0.0991* 0.0516 dejure secure -0.0729 0.2186 -0.2341 0.1780 -0.3115 0.2709 0.3153* 0.1636 0.1504 0.1523 age -0.0005 0.0026 0.0014 0.0021 0.0019 0.0028 0.0065*** 0.0021 -0.0051*** 0.0018 sex -0.0517 0.0967 -0.1080 0.0771 -0.2045* 0.1134 0.0817 0.0778 -0.0025 0.0682 schooling year -0.0062 0.0053 -0.0075* 0.0041 0.0119** 0.0056 0.0075* 0.0044 -0.0038 0.0038 household size -0.0036 0.0048 0.0046 0.0036 -0.0130** 0.0055 -0.0247*** 0.0044 0.0045 0.0034 amount borrowed -3.39e-08 8.31e-08 -4.84e-08 5.39e-08 3.23e-08 4.73e-08 2.31e-08 5.05e-08 2.88e-08 4.15e-08 farmers association 0.0111 0.0214 0.0223 0.0166 0.0086 0.0223 -0.0016 0.0182 0.0018 0.0154 extension contact -0.0391 0.0638 0.0457 0.0506 0.0071 0.0681 -0.1776*** 0.0526 0.0408 0.0456 tlu -0.0039 0.0046 0.0004 0.0021 0.0037 0.0023 -0.0039 0.0030 -0.0022 0.0020 farm size -0.0118*** 0.0045 -8.2e-05 0.0027 0.0005 0.0036 -0.0024 0.0029 -0.0017 0.0025 extent of land fragmentation -0.0218 0.1085 -0.1780** 0.0834 0.1681 0.1123 0.4113*** 0.0885 0.0070 0.0755 lowland -0.0147 0.0636 0.0705 0.0496 0.0465 0.0671 -0.0177 0.0528 0.1260*** 0.0451 constant -1.1136* 0.1594 -0.8917*** 0.1250 -1.6749*** 0.1722 -0.4639*** 0.1298 0.2095* 0.1126 wald chi-square (70) 404.66 404.66 404.66 404.66 0.0583 404.66 log-likelihood -7452.52 -7452.52 -7452.52 -7452.52 -7452.52 prob > chi2 0 0 0 0 0 number of obs. 3,311 3,311 3,311 3,311 3,311 table 5. results of the wald test of simultaneity of the decisions to adopt csps. error correlation1 coefficient p value rho21 (farmyard manure & agroforestry) -0.0532 0.064 rho31 (crop rotation & agroforestry) 0.0015 0.969 rho41 (zero/minimum tillage & agroforestry) -0.0747 0.011 rho51 (residue retention & agroforestry) 0.1694 0 rho32 (crop rotation & farmyard manure) 0.0814 0.047 rho42 (zero/minimum tillage & farmyard manure) -0.1897 0 rho52 (residue retention & farmyard manure) -0.0922 0.001 rho43 (zero/minimum tillage & crop rotation) 0.0493 0.192 rho53 (residue retention & crop rotation) 0.0346 0.307 rho54 (residue retention & zero/minimum tillage) -0.1964 0 1 likelihood ratio test of rho21 = rho31 = rho41 = rho51 = rho32 = rho42 = rho52 =rho43 = rho53 = rho54 = 0.00 chi-square (10) = 161.736 prob > chi square = 0 84 bio-based and applied economics 11(1): 75-87, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9992 mojisola o. kehinde et al. and data collected were analysed within the framework of multivariate probit regression. the results showed that the adoption of csps considered in this study – agroforestry, zero/minimum tillage, farmyard manure, crop rotation, and residue retention – were generally low. policymakers thus need to target practices with lower adoption rates and provide farmers with further incentives towards the intensification of their use. another major highlight of this paper is the apparent existence of complementarities between different csps such as the use of farmyard manure and crop rotation, use of residue retention, and agroforestry. a potential strategy could be to promote agricultural practices that show some degree of complementarity as a package rather than independently. this may reduce the time required between when the farmer adopts the first technology and the subsequent adoption of other technologies and hence realising the full and extensive benefits of csps as a package. the effects of transfer right on the adoption of farmyard manure, crop rotation, and residue retention are crucial in targeting those farmers that have appropriate socio-cultural characteristics that favour the adoption of the csps in question. secondly, awareness and promotional strategies should be tailored depending on whether the target farmers resemble factors for/against adoption. our findings confirm that tenure security will increase the likelihood that farmers will reap the returns from the long-term investments such as zero/minimum tillage without unfair expropriation. therefore, policy measures that will focus on a more effective and efficient land title registration system should be established by the government. this holds important implications for environmental sustainability and climate change adaptation, as farmers will concurrently invest less and try to extract maximum value from land resources if they are unsure about the security of their tenure. as shittu et al., (2018) argue, ltprs on agricultural lands in nigeria are mostly informally defined and prone to unfair expropriation, in view of the overriding powers of the state governor and local governments, as well as the corrupt network of land grabbers. the study suggests that governments, in whom the responsibility for land use policy reform lies, review the existing framework to ensure a prompt, fair, and efficient land tenure system. acknowledgement this project was being implemented with 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(2012). options for support to agriculture and food security under climate change. environmental science & policy, 15(1), 136-144. https://doi.org/10.1016/j. envsc i.2011.09.003. world bank (2011). climate-smart agriculture: a call to action. washington, dc: world bank. world bank group (2016). a year in the lives of smallholder farmers. https://www.worldbank.org/en/news/ feature/2016/02/25/a-year-in-the-lives-of-smallholder-farming-families (accessed august 21, 2020) 87 bio-based and applied economics 11(1): 75-87, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9992 land tenure and property rights impacts in nigeria appendix 1. influence of ltprs on adoption of climate-smart practices among smallholder farmers: probit estimates.     agroforestry farmyard manure crop rotation zero tillage residue retention coef. std. err. coef. std. err. coef. std. err. coef. std. err. coef. std. err. control right 0.1273* 0.0738 0.0183 0.0575 -0.0164 0.0767 -0.3746*** 0.0581 -0.0447 0.0522 transfer right -0.1214* 0.0722 0.1814*** 0.0571 0.1539** 0.0768 -0.5192*** 0.0582 0.1010* 0.0517 de jure secure -0.0747 0.2188 -0.2198 0.1762 -0.2998 0.2688 0.3200* 0.1638 0.1406 0.1522 age -0.0005 0.0026 0.0015 0.0020 0.0019 0.0028 0.0066*** 0.0021 -0.0051*** 0.0018 sex -0.0519 0.0967 -0.1080 0.0771 -0.2030* 0.1134 0.0817 0.0778 -0.0069 0.0683 schooling year -0.0062 0.0053 -0.0075* 0.0041 0.0120** 0.0056 0.0072 0.0044 -0.0040 0.0038 household size -0.0036 0.0048 0.0044 0.0036 -0.0128** 0.0055 -0.0254*** 0.0044 0.0044 0.0034 amount borrowed -3.18e-08 8.24e-08 -4.71e-08 5.31e-08 3.36e-08 4.72e-08 2.07e-08 5.07e-08 2.97e-08 4.12e-08 farmers association 0.0114 0.0214 0.0228 0.0166 0.0091 0.0223 -0.0022 0.0182 0.0009 0.0153 extension contact -0.0390 0.0638 0.0463 0.0506 0.0085 0.0681 -0.1763*** 0.0527 0.0411 0.0457 tlu -0.0038 0.0045 0.0004 0.0021 0.0037 0.0023 -0.0035 0.0029 -0.0022 0.0020 farm size (ha) -0.0119*** 0.0045 -0.0001 0.0027 0.0005 0.0036 -0.0024 0.0029 -0.0016 0.0025 extent of land fragmentation -0.0208 0.1085 -0.1746** 0.0833 0.1647 0.1125 0.4287*** 0.0885 0.0079 0.0756 lowland -0.0144 0.0636 0.0715 0.0495 0.0458 0.0671 -0.0202 0.0529 0.1260*** 0.0452 constant -1.1144*** 0.1593 -0.8915*** 0.1248 -1.6746*** 0.1725 -0.4679*** 0.1298 0.2074* 0.1129 lr chi-square (14) 18.35   39.46   32.03   340.93   26.16   prob > chi 2 0.1911 0.0003 0.004 0 0.0247 log-likelihood -1004.07 -1789.02 -872.688 -1568.52 -2272.86 pseudo r2 0.0091 0.0109 0.018 0.098 0.0057 number of obs. 3,311   3,311   3,311   3,311   3,311   bio-based and applied economics 7(2): 117-138, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-7671 traditional poultry farmers’ willingness to pay for using fly larvae meal as protein source to feed local chickens in benin sètchémè charles bertrand pomalégni1, cokou patrice kpadé2,*, dossou sèblodo judes charlemagne gbemavo3, victor attuquaye clottey4, marc kenis5, guy apollinaire mensah1 1 national institute of agricultural research (inrab), 01 bp 884 cotonou 01, benin 2 national university of agriculture, benin 3 university of abomey-calavi (uac), 04 bp 1525 cotonou, benin 4 cabi west africa centre, no. 6 agostino neto road, accra, ghana 5 cabi, rue des grillons 1, 2800 delémont, switzerland date of submission: 2018 12th, january; accepted 2019, 20th, january abstract. this study estimated poultry farmers’ willingness to pay (wtp) for fly larvae meal as animal protein source to feed local chickens in benin. a double-bounded contingent valuation approach was used to collect data from 480 poultry farmers, and an interval regression model was performed. we found that 82.10% of poultry farmers are willing to pay for using fly larvae meal. the average wtp was estimated at fcfa/ kg 225.10 (€/kg 0.34), indicating a potential and reliable demand in fly larvae meal. our analysis suggests that public actions can sensitize poultry farmers and support innovative small companies to produce and market fly larvae meal. keywords. animal feeding, benin, fly larvae, poultry farmer, willingness to pay. jel codes. q120, q160, q180. 1. introduction poultry farming plays an important role in traditional agricultural production systems in africa. poultry farming is ideal for all families, even the poorest (bell, 1992), given the low individual needs of the animals involved and the low investment costs (guèye, 2002). it provides a significant share in the supply of animal protein calories (buldgen et al., 1992) and of cash income. therefore, poultry farming contributes to poverty reduction. edible domestic poultry includes chickens, pigeons, geese, ducks, guinea fowls, quails, turkeys, etc. (njue et al., 2002). in developing countries, chicken (domestic fowl) is the most widely accepted and appreciated species (ideris et al., 1990) and makes up the bulk of the poultry industry (spradbrow, 1997). three traditional poultry production systems exist and have been studied: the scavenging system, the semi-scavenging system and, the confinement system (gunaratne et al., 1993). *corresponding author: kpadepatrice1@hotmail.com 118 sètchémè charles bertrand pomalégni et alii the most common system in rural africa is based on scavenging poultry (kitalyi, 1998). although productivity is modest, even a few live poultry and eggs generate a net benefit for poultry farmers because of the very low production costs (buza and mwamuhehe, 2001). the deficit in poultry products in developing countries, particularly in sub-saharan africa, is mostly due to the low productivity of traditional poultry (guèye, 1998) and other factors as well. as shown by narrod et al. (2008) and delgado et al. (2008) the production technology for exotic breeds of poultry meat is widely available and has in fact been used by farmers in developing countries. to overcome these deficiencies, many african countries have supported the development of short-cycle poultry species hoping to provide people cheap and with highly nutritive animal products. the constraints of traditional poultry farming include access to animal feed and healthcare, improving productivity as well as commercial issues. in particular, the dominant local breeds are of low productivity and the traditional chicken farming methods are prone to diseases, which sometimes decimate entire flocks. feed represents the major constraint to the development of small-scale poultry farming in africa. feed given to local poultry is often insufficient in quantity and quality because its protein content is low, especially during dry seasons (goromela et al., 2006). in particular, scanty provision of dietary protein by rural farmers to scavenging poultry does not optimize productivity and profitability of their enterprise. the use of unconventional food resources such as local legume seeds, leaves and tubers, and various animal by-products, which availability or cost is not a limiting factor, could be a solution. the interest in these resources in recent years has particularly increased with the grain crisis of 2007. the conventional protein sources such as soybean and peanut de-oiled cake (doc) and fish meal are indeed rare and therefore expensive. the demand and the price of fish meal, which is used as protein in animal feeding, have particularly increased these recent years (fao, 2014). various studies attempted to use locally available animal and vegetable proteins to substitute some or all of the conventional proteins (basak et al., 2002; amaefule and osuagwu, 2005; fao, 2014; mutungi et al., 2017). the introduction of snail flour or meat in animal diet has been explored (barcelo and barcelo, 1991; farina et al., 1991). the positive influence of the use of termites as a protein source on production parameters of guinea fowl in villages has been demonstrated (chrysostome, 1997). earthworms have been bred as a protein source for feeding chickens (vorsters et al., 1994). broilers can receive 3.6% of earthworm flour to substitute 5% of meat meal without affecting their growth performance (agbédé et al., 1994). also fly larvae, in particular house fly (musca domestica) and black soldier fly (hermetia illucens) proved to be an excellent source of protein, and can replace fish meal partly or entirely in animal diets (kenis et al., 2014; makkar et al., 2014). pomalégni et al. (2016; 2017) indicated the use of fresh fly larvae by small poultry farmers in benin, and most of them had a good perception of its use in poultry feeding. pomalégni et al. (2017) showed that 5.6% of traditional poultry farmers in benin use fly larvae at least occasionally to feed their poultry, with variations among regions. the use of fly larvae in animal feed is safe if the standards of production on substrates are respected (charlton et al., 2015; nkegbe et al., 2018). one of the current constraints in the widespread adoption of fly larvae in poultry farming is their unavailability on the market. seeking an economic measure of fly larvae valorization is a prerequisite to generate relevant indicators needed for better decision-making. 119traditional poultry farmers’ willingness to pay for using fly larvae meal this study explores the terms of use of fly larvae meal in traditional poultry farming diet that may offer new opportunities in terms of value creation, human health preservation and nutritional value improvement of local chickens. it uses the double-bounded contingent valuation procedure to analyze the possibility that traditional poultry farmers could accept to pay for fly larvae meal. the procedure considers nutritional value and contribution to the improvement of production performance of local chickens in benin. if farmers are willing to pay, how much are they willing to pay? what are the factors affecting their willingness to pay (wtp)? the contingent valuation method is often used to reveal the monetary value of services, public goods, public dimensions of private goods and non-market assets (roe et al., 2004; mogas et al., 2006; wu et al., 2016). it is also used to reveal farmers’ and consumers’ preferences for new agricultural technologies or products (wei et al., 2016; drichoutis et al., 2016). it is based on the intentions of respondents, i.e, not on observed behavior (vidogbéna et al., 2015; wu et al., 2016). this paper provides useful information to international non-governmental organizations, international organizations, food policies-makers; and enterprises dealing with food and nutritional security, who intend to promote and/or produce fly larvae meal as a protein source on a large scale to poultry farmers in developing and developed countries. 2. material and methods 2.1 sampling and data collection this study was conducted in 12 provinces of benin where traditional poultry farming is practiced. a pilot survey was first made to determine the importance of traditional poultry farming in the provinces, districts and villages of benin (pomalégni et al., 2016). the result of that preliminary survey was used to test the validity of the levels of bids proposed for the contingent valuation procedure and to elaborate, test and validate the survey questionnaire. this preliminary survey allowed to select the districts and villages where the real study would be carried out. based on information provided by extension officers, one district was chosen in each province according to the relative number of poultry farmers, the genetic diversity, and the supply of poultry. in each district, two villages were selected according to the importance of livestock. twenty-four villages were visited. the sample size was determined using a formula (dagnelie 1998): n = pi(1− pi)u1−α /2 2 d2 (1) where pi is the proportion of traditional poultry farmers considering the number of farmers at the national level and was estimated at 50.00 %. we used pi =0.5 as it is not possible to make any assumption regarding the traditional poultry farmers coverage in benin (lwanga and lemeshow, 1991). u1-α/2 = 1,96 represents the value of the normal random variable for a risk α equal to 0.05 (confidence level). the margin of error (d) provided for any parameter to be estimated from the survey was 4.47%. thus, the sample size n of traditional poultry farmers has been determined as 480. based on this sample size, 120 sètchémè charles bertrand pomalégni et alii 20 poultry farmers were surveyed in each selected village and respondents were randomly selected accordingly. an in-person contingent valuation survey was administrated to the 480 poultry farmers. individual surveys were conducted from march to april 2017, which is the best period of the year for interviews since few people work in the field during the dry season. the questionnaire was written in french (appendix) but the interviews were entirely conducted in the respondents’ local languages. face-to-face interviews were conducted in the presence of a translator when needed. this face-to-face interview was more appropriate as it helps to clearly explain the contingent scenario and background information to illiterate and poorly educated respondents thus avoiding hypothetical bias (shi et al., 2014). the face-to-face interviews are also more flexible and reliable (hoyos and mariel, 2010) and they are better than inquiries made by e-mails, telephone or postal survey (arrow et al., 1993) in helping to substantially reduce the protest rate and non-responses. nevertheless, the “social desirability bias” also called “cheap talk” was controlled during the administration of the questionnaires with frequent exchanges with agricultural extensions officers who knew the respondents better than us. most of the questions were closed-ended, although some open-ended questions were included to investigate respondents’ perception on fly larvae meal use. outside the principal research questions (the willingness to pay), data were also recorded on socioeconomic characteristics1 of respondents. at the end of the investigations in each village, the feedback was made in the presence of poultry farmers and agricultural extensions officers. 2.2 wtp elicitation methods this study used a field experimental bid to reveal small poultry farmers’ preferences for fly larvae meal use to feed traditional chicken. the double-bounded contingent valuation procedure was used. the traditional poultry farmers were submitted to a sequence of open-ended questions which gradually helped narrow the wtp. in contrast to singlebounded contingent valuation format, a double-bounded format provides more econometric precision than closed-ended questions lose compared with open-ended questions (hanemann et al., 1991; hoyos and mariel, 2010). the hypothesis of the double-bounded contingent valuation method is that the responses to the two bids are underlying to the same value of wtp and therefore the second bid increases the information on the true wtp of the respondent (alberini, 1995). to overcome some problems arising in the double-bounded approach, the second bid is only presented to the respondents if it is consistent with the respondent’s previous answer. the double-bounded contingent valuation approach is generally preferred over open questions, which are more practical in email survey (shi et al., 2014). as the survey was conducted face-to-face, protest responses with zero or extremely high values could be given by the poultry farmers (watson and ryan, 2007). fly larvae may be used fresh, especially at the small-scale farm (rakotonirina, 1990), or can be made into meal (hwangbo et al., 2009; makkar et al., 2014). for industrial or 1 such as age, gender, occupation, educational level, main occupation, number of family workers, income, motivation for fly larvae meal use, etc. 121traditional poultry farmers’ willingness to pay for using fly larvae meal semi-industrial production, the meal form is recommended because of the long-term conservation constraints of live fly larvae, which quickly pupate. the nutrients contained in the meal form are as acceptable as those of the fresh form (makkar et al., 2014). in this study, fly larvae meal option was considered rather than fresh fly larvae to allow a proper comparison with fish meal that is sometimes used in poultry feed. after being informed about the use of fly larvae in animal feeding, traditional poultry farmers were questioned regarding the payment vehicle for the fly larvae meal usage. the respondents who did not protest the payment vehicle were submitted to the contingent scenario with the payment bids, where respondents face a list of bids randomly drawn (hoyos and mariel, 2010). in the experiment, the structure of the contingent scenario was as follows: “fish meal is used as protein source for chicken feed. the international prices of fish meal are between fcfa 1,000 (€ 1.52) and fcfa 1,200 (€ 1.83) per kg. fish meal is imported and sold at the local market at fcfa 550 (€ 0.84) per kg by one major importer firm which dominates the market of animal provender in benin. the low prices can be attributed to the low-quality of the fish meal with lower protein content. fly larvae meal are an appropriate source of animal protein for traditional chickens. they improve the performances of local chickens (e.g. average daily gain, food conversion ratio, etc.) and they reduce the cost of feed protein. they can replace low-quality fish meal that is used to feed poultry. would you be willing to pay a sum of fcfa mi i per kg to feed your local chickens with fly larvae meal? “. mi i is a random value taken into a vector of 7 bids (600; 700; 800; 900; 1,000; 1,100; 1,200). the bids containing seven levels of the monetary payment can be considered reasonably efficient (carson and hanemann, 2005). the minimum bid of fcfa 600 (€ 0.91) corresponds to the minimum cost of producing 1 kg of fly larvae meal. knowing that 1 kg of fly larvae meal required 4 kg of fresh fly larvae, the minimum bid is equivalent to 4 kg of fresh fly larvae, which are produced at a minimum cost of fcfa/kg 150 (€/kg 0.23) (600 = 150 *4). the maximum bid of fcfa 1,200 (€ 1.83) corresponds to the present production cost of 1 kg of fly larvae meal. it is also equivalent to 4 kg of fresh fly larvae, which are produced at a cost of fcfa/kg 300 (€/kg 0.48) (1,200 = 300*4) (m. kenis and s.c.b. pomalégni, adapted from roffeis et al., 2018). these costs are likely to decrease when production systems improve. the first bid mi i was followed by the second bid, mi u increased when the first bid was accepted, or mi l decreased when the first bid was refused by fcfa 100 (€ 0.15), which corresponds to the additional cost to increase or decrease, to a certain level, the quality of the fly larvae meal (content, presentation, etc.). each poultry farmers surveyed had a first bid mi i and the following bid mi l or mi u according to their response to the first bid, where mi l ≺mi i ≺mi u (table 1). four possible responses were used: (a) both responses were “yes”; (b) both responses were “no”; (c) “yes” response followed by “no” response; d) “no” response followed by “yes” response. an ex-ante approach was used to correct the hypothetical bias on wtp (loomis, 2011). during the investigation, it was clearly explained to the poultry farmers surveyed that the amount (bids) proposed would be paid for the coming years so that they have fly larvae meal in the markets. this information was given to make a choice that was as realistic as possible. furthermore, the poultry farmer should feel that his/her response will have policy implications so that he/she feels comfortable supporting or opposing the proposed policy. 122 sètchémè charles bertrand pomalégni et alii 2.3 data and empirical model 2.3.1 data the main socioeconomic characteristics of the sample were as followed. in total, 23.75% of poultry farmers surveyed were females whereas 76.25% were males. the average number of local chickens owned by the investigated poultry farmers was around 25. the poultry flock species by descending order of importance were chickens (97.92%), guinea fowl (28.96%), ducks (24.79%), pigeons (11.25%), and turkeys (3.33%) and others (20.83%). the ecotypes of local chickens encountered among the respondents’ flock were: the southern ecotype or “yaya” (83.13%), the “fulani” ecotype (19.42%), a hybrid ecotype called “yovokloklo” which is a cross between local and exotic roosters and hens (11.25%), the “holli” ecotype (7.71%), “sahwé” ecotype (4.38%) and other local races (0.42%). the poultry farming methods were dominated by the scavenging method (55.05%) followed by the semi-scavenging (42.54%) and confinement methods (2.37%). the average age of respondents was 44 years, with an average age of 45 years for female farmers against 43 years for male farmers. the average years of experience in poultry farming was 16. although the respondents were all poultry farmers, 61.25% and 10.63% of them had agriculture or livestock and trade as main occupation, respectively. there were 52.29% literate or educated against 47.70% illiterate and only about 4.37% of them were members of a professional organization of poultry farmers. the overall annual income per poultry farmer varied and averaged fcfa 610,663.50 (€930.95). the average annual agricultural income was fcfa 421,563.50 (€ 642.67), representing 69.03% of the average annual overall income of poultry farmers. poultry production contributes on average to 25.15% of the annual agricultural income (fcfa 106,023) of the poultry farmers surveyed. poultry farmers surveyed were aware of the possibility of using fly larvae as poultry feed (92.50%) against only 7.50% who did not know this usage before our survey. despite this, only 8.54% of poultry farmers surveyed had used fly larvae to feed their chickens against 91.45% who had never used them. in total, 394 poultry farmers (82.08%) were motivated to use fly larvae meal for feed chicken. the motivations varied among respondents. most, (80%) respondents were motivated by the improvement of the nutritional qualtable 1. random bid schemes used in the double-bounded contingent valuation procedure. bid schemes decreased follow-up bid in fcfa (if ‘no’ for mi i initial bid in fcfa (mi i ) increased follow-bid in fcfa (if ‘yes’ for mi i ) scheme 1 500 600 700 scheme 2 600 700 800 scheme 3 700 800 900 scheme 4 800 900 1,000 scheme 5 900 1,000 1,100 scheme 6 1,000 1,100 1,200 scheme 7 1,100 1,200 1,300 123traditional poultry farmers’ willingness to pay for using fly larvae meal ity and performance (growth and laying) of their local chickens and (65.42%) respondents mentioned the reduction of feed costs as motivation. at the time of the study, only 41 poultry farmers surveyed (8.54%) had used fish meal as chicken feed, and 39 of them (8.13%) would have liked to replace the fish meal with other protein sources, the rest does not currently use protein to feed their poultry. the motivations behind replacing fish meal with other proteins were: high price of fish meal (37.78%), bad quality of fish meal (26.67%), non-availability of fish meal on the local market (13.33%) and others factors (22.22%). the low use of protein to feed poultry generates low zootechnical performances in many farms. 2.3.2 econometric model and specification an interval regression model was developed to determine the factors influencing the wtp and to estimate sample wtp as function of the characteristics of the respondents (breffle et al., 1998; fu et al., 2011; kpadé et al., 2017). because bids proposed to respondents are defined in certain intervals, interval regression was used to model outcomes that have interval censoring. to elicit wtp, each respondent i was considered to accept the payment vehicle for a wtp of fly larvae meal which is equal to yi * and related to the characteristics xi by the equation: yi * = xiβ + ε i (2) where β is the coefficient associated to each characteristic, and εi is assumed to have zero as average and follows a normal distribution. the data were organized as left-censored for the “no -no” responses, right-censored for “yes-yes” responses, and interval-censored for “yes-no” or “no-yes” responses for each poultry farmers surveyed. following hanemann et al. (1991), yi * is not observed, but each respondent wtp i was in the interval mi l ,mi u⎡⎣ ⎤⎦ . the probability of “yes-no” response is: pr (mi i ≺max wtp ≤mi u ) (3) the probability of “no-yes” response is: pr (mi i ≻max wtp ≥mi u ) (4) the probability of the right-censored data, “yes-yes” response is given by: pr(mi i ≤max wtp and mi u ≤max wtp) (5) and the probability for the left-censored data, “no-no” response is as follows: pr(mi i ≻max wtp and mi l ≻max wtp) (6) the econometric software stata mp v.13 software (statacorp. 2013.  stata statistical software: release 13. college station, tx: statacorp lp) was used to estimate the maximum likelihood function through interval regression model. the interval regression model esti124 sètchémè charles bertrand pomalégni et alii mates the probability that a latent variable is included in a given interval (cawley, 2008; fu et al., 2011; kpadé et al., 2017). at last, the estimations of the interval regression model were used to calculate the individual wtp (post-estimation prediction), the average and median wtp of the sample. table 2 lists the bids and explanatory variables used in the econometric analysis. four types of variables could potentially affect the respondents’ wtp: personal characteristics of poultry farmers, characteristics of poultry farms, type of flock, factors of motivations. the personal characteristics of poultry farmers, the characteristics of poultry farms, and the factors of motivation were considered to positively affect the wtp whereas the type of flock was considered to positively or negatively affect the respondents’ wtp. table 2. statistics of bids and explanatory variables for wtp. variables description n minimum maximum mean (standard deviations) expected signs bids upper bound of wtp upper bound level (fcfa) 196 500 1300 788.77 (193.42) lower bound of wtp lower bound level (fcfa) 274 500 1300 850.00 (209.70) initial bid of wtp bid level proposed (fcfa) 480 600 1200 826.00 (189.00) independent variables sex sex of poultry farmer (1 = male, 0 = female) 480 0 1 0.76 (0.40) + age age of poultry farmer (years) 480 17 80 43.57 (12.84) + gross income annual total income received by the poultry farmer, including non-farm income (fcfa) 480 45,000 7,000,000 610,633.54 (862,225.36) + farm income annual farm income of poultry farmer (fcfa) 480 0 7,000,000 421,563.50 (744,196.80) + percent poultry income part of poultry income in annual farm income (%) 480 0 100 25.15 (31.17) + scavenging farming scavenging poultry farming (1=yes, and 0 if not) 480 0 1 0.55 (0.49) + semiscavenging farming semi-scavenging poultry farming (1=yes, and 0 if not) 480 0 1 0.42 (0.49) + confinement farming confinement poultry farming (1=yes, and 0 if not) 480 0 1 0.02 (0.15) + credit access credit access for poultry farmer (0= not access; 1= yes) 480 0 1 0.17 (0.37) + experience in poultry farming experience in poultry farming (years) 480 0 60 16.00 (11.53) + education formal or functional education (years) 480 0 16 3.00 (4.05) + 125traditional poultry farmers’ willingness to pay for using fly larvae meal variables description n minimum maximum mean (standard deviations) expected signs fly larvae use awareness farmer awareness on fly larvae as feed (0= not known; 1= yes) 480 0 1 0.92 (0.26) + fly larvae use adoption of fly larvae in poultry feeding before (0= not used; 1= yes) 480 0 1 0.09 (0.28) + family workers (number) number of family workers on the poultry farming 480 0 16 4.00 (3.00) + local chicken (number) number of local chickens of the poultry farmer 480 0 500 24.58. (35.83) + farming as main occupation main occupation of poultry farmer (1=agriculture or livestock; 0=else) 480 0 1 0.61 (0.48) + fish meal use fish meal using in farm for local chicken feed (1= yes, and 0 if not) 480 0 1 0.08 (0.28) + motivation to improve chicken nutritional quality motivation for fly larvae use to improve nutritional quality of local chicken (yes = 1, if not 0). 480 0 1 0.80 (0.16) + motivation to improve poultry performances motivation for fly larvae use to improve performances of local chicken (yes =1, if not 0). 480 0 1 0.80 (0.16) + motivation to reduce feeding cost motivation for fly larvae use to reduce feeding cost (yes =1, if not 0). 480 0 1 0.65 (0.40) + note: if the lower bound of wtp was less than fcfa 500 (€ 0.76), or if upper bound of wtp was over fcfa 1,300 (€ 1.98), then they were set to missing values. 3. results 3.1 payment vehicle in the double-bounded contingent valuation procedure, respondents were first subjected to the acceptance or not of the payment vehicle. out of the 480 respondents, 86 poultry farmers (17.90%) protested the payment vehicle because of: the lack of trust placed on fly larvae (36.03%), the lack of means of payment (25.73%), for the fact that fly larvae are an available natural resource for which there is no need to pay (13.97%), and others reasons (24.26%). comparing the socioeconomic characteristics of the accepters and the protesters of the payment vehicle, table 3 showed that accepters were younger, more educated, had a higher number of local chicken in their farms, had higher gross annual and farm incomes and depended more on poultry farming financially. 126 sètchémè charles bertrand pomalégni et alii 3.2 bids acceptance out of the 394 traditional poultry farmers having accepted the payment vehicle (82.10%), 56.59% had accepted the first bid against 43.41% who refused. in total, 50.24% accepted both bids when the first was increased by fcfa 100 (€ 0.15) against 30.71% who refused both bids even when a decrease of fcfa 100 (€ 0.15) to the first bid was proposed (table 4). also, 6.35% of the poultry farmers accepted the first bid but refused the second bid, while 12.70% refused the first but accepted the second bid proposed. in the econometric modeling, the 86 poultry farmers that protested the payment vehicle were not considered as they refused to participate in the fly larvae meal market development. the development of the scenario contingent was also stopped at this step for these 86 poultry farmers. only the 394 poultry farmers who accepted the payment vehicle were considered in the wtp estimation. 3.3 factors affecting traditional poultry farmers wtp table 5 shows the results of the interval regression model to identify the factors influencing the wtp. in total, 19 out of the 20 explanatory variables were retained in the final table 3. comparison of characteristics of accepters and protesters of payment vehicle. characteristics mean (standard deviation) pr (|t| > |t|) accepters protesters age (years) 43.10 (12.27) 45.73 (15.11) 0.085* experience in poultry farming (years) 16.22 (11.56) 14.22 (11.33) 0.145 education (years) 3.56 (4.08) 2.54 (3.83) 0.035** local chickens (number) 27.18 (38.67) 12.64 (11.96) 0.000*** family workers (number) 4.30 (2.64) 3.32 (1.81) 0.001*** gross income (fcfa) 696,166.20 (925,410.70) 218,941.90 (201,994.50) 0.000*** farm income (fcfa) 483,270.30 (805,606.60) 138,860.50 (147,641.30) 0.000*** percentage of poultry income (%) 26.62 (31.37) 18.40 (29.46) 0.026** *significant at 10%; **significant at 5%; ***significant at 1%. table 4. traditional poultry farmers’ responses to double bids. answer to first bid answers to second bid total frequency (%)yes frequency (%) no frequency (%) yes 198 (50.24) 25(6.35) 223(56.59) no 50(12.70) 121(30.71) 171(43.41) total 248(62.94) 146(37.06) 394(100.00) 127traditional poultry farmers’ willingness to pay for using fly larvae meal model, as two variables, motivation to improve poultry performances and motivation to improve chicken nutritional quality, were correlated. eight factors significantly affected the respondents’ wtp (sex, education, farming as main occupation, scavenging poultry farming, gross income, fly larvae use awareness, fly larvae use, motivation to reduce feeding cost) (table 5). five of these factors positively affected the respondents’ wtp (sex, farming as main occupation, gross income, fly larvae use, motivation to reduce feeding cost) whereas three factors affected negatively the wtp, namely education, scavenging poultry farming and fly larvae use awareness. the signs of the coefficients of those three factors were opposite to what was expected (table 2). based on the post-estimation of the interval regression model predictions, 394 individual wtp were estimated. in total, 134 respondents had negative wtp between -513.78 and 0 with a standard deviation (sd) of fcfa/kg 99.78; 31 respondents had wtp between 0 and 100 (sd= fcfa/kg 24.19); 180 respondents had wtp between 100 and 500(sd = fcfa/kg 112.71) and 49 respondents had wtp between 500 and 2,032.41(sd = fcfa/kg 328.46) (figure 1). in total, 134 respondents had negative wtp. respondents with the negative wtp were considered as a zero value in the sample because those respondents were not able to pay for the fly larvae meal according their profile, even though they accepted the payment vehicle. the 260 individual positive wtp were considered with no right-truncation because those respondents could financially pay fly larvae, independently to the amount they can afford. finally, the average wtp for the sample in figure 1. distribution of wtp per kg of fly larvae according poultry farmers in benin. -5 00 0 50 01 ,0 00 1, 50 02 ,0 00 -5 00 0 50 01 ,0 00 1, 50 02 ,0 00 [-513.78; 0[ ]0; 100] ]100; 500] ]500; 2032.41] w tp e st im at ed graphs by subgroup 128 sètchémè charles bertrand pomalégni et alii post estimation was fcfa/kg 225.10 (€/kg 0.34) of fly larvae meal against a median wtp estimated at fcfa/kg 127.81 (€/kg 0.19). the standard deviation was fcfa/kg 300.70 (€/ kg 0.46). 4. discussion this study evaluated wtp of traditional poultry farmers in benin to use fly larvae meal as a source of animal protein in local chicken feed, and analyzed the factors influencing their wtp. the protest rate found in this study is low compared that of other similar studies, e.g. 58% founded by grappey (1999) or 44.06% reported by drichoutis et al.( 2016). in this study, protesters were excluded in the wtp estimation to distinguish protable 5. factors affecting respondents’ wtp. explanatory variables coefficients (standard error) sex (1= male, 0=female) 181.30*** (67.04) age (years) -2.78 (2.98) experience (years) 0.39 (3.51) education (years) -14.58** (7.28) family workers (number) 3.78 (12.05) farming as main occupation (1 =yes, 0=no) 166.25**(66.14) local chicken (number) 1.10 (1.05) scavenging farming (1=yes, and 0 if not) -293.05* (162.46) semi-scavenging farming (1=yes, and 0 if not) 88.88 (162.64) confinement farming (1=yes, and 0 if not) 88.67 (220.88) farm income (fcfa) -4.61e-5 (1.03e-4) gross income (fcfa) 1.97e-4** (8.60e-5) percent poultry income (%) 0.07 (0.89) credit access (1 =yes , 0= no) -7.04 (74.30) fly larvae use awareness (1 =yes, 0=no) -262.01* (142.66) fly larvae use (1 =yes, 0=no) 189.60** (93.48) fish meal use (1= yes, and 0 if not) -85.13 (96.80) motivation to improve chicken nutritional quality (1 =yes, 0=no) 2300.99 (68858.25) motivation to reduce feeding cost (1 =yes, 0=no) 303.06*** (94.82) constant -1445.13 (68858.73) /lnsigma 5.97*** (0.09) sigma 393.16 (34.61) observations summary: 394 observations 121 left-censored observations 198 right-censored observations 75 interval observations log likelihood = -388.06; lr chi2(19) = 144.10; probability > chi2 = 0.000  * significant at 10%; ** significant at 5%; *** significant at 1% 129traditional poultry farmers’ willingness to pay for using fly larvae meal test bids from true zero. protesters are typically considered to be outside the market and should thus be omitted from the analysis used to derive wtp estimates (villanueva et al., 2017). protest bids are often registered by respondents who may place a higheror lowerthan-average value on the commodity in question but refuse to pay based on ethics or other reasons (halstead et al., 1992; ready et al., 1995). moreover, the payment vehicle plays a major role in the decision making of the respondents (loomis, 2011; diederich and goeschl, 2014). the payment vehicle provides the context for payment (morrison et al., 2000) and needs to be credible, coercive and incentive compatible (hoyos and mariel, 2010). special attention has been given to the choice of the payment vehicle (travisi and nijkamp, 2008), which alters the resulting wtp (rowe et al., 1980). in willingness to pay scenarios, the payment vehicle must be presented fully and clearly, and should be convincingly described with the relevant budget constraint emphasized (arrow et al., 1993). 4.1 variation in respondents’ wtp the traditional poultry farmers’ wtp varied according to their very heterogeneous socio-economic conditions, fly larvae perception, costs and benefits associated with the use of fly larvae as a protein source to feed local chickens. this result conformed with numerous studies on natural resources valuation (perman et al., 2011; wu et al., 2016). based on the interval regression model, out of the eight factors significantly affect poultry farmers’ wtp. five were positively correlated with wtp. primarily, sex has a positive significant effect on the wtp. men have a higher wtp compared to women, indicating a gender effect on the level of traditional poultry farmers wtp, as observed by vidogbéna et al. (2015) and wu et al. (2016). farming as main occupation also had a positive effect on wtp. respondents whose main occupation is agriculture or farming had higher wtp compared to those who have another primary occupation. moreover, the gross income had a positive effect on the respondents’ wtp, confirming previous studies which found a positive effect of income from the field of environment and natural on wtp (mogas et al., 2006; halkos and jones, 2012). the econometric analysis highlighted that respondents that had already used fly larvae were willing to pay more, probably because they were convinced of the advantages of the use of fly larvae in poultry feeding. they saw fly larvae as an alternative feed, even though it was not marketed yet. the current users of fly larvae produced themselves limited quantities to feed their poultry. feed cost is a major constraint limiting the competitiveness of small poultry farms in africa. even though the local price of fish meal is lower compared to the international due to its notoriously of bad quality (proteinsect, 2017), the analysis of the respondents attitudes showed that, the more poultry farmers were motivated to reduce their poultry feed cost by replacing fish meal, the higher was their wtp. poultry farmers saw fly larvae meal as an innovation that could be adopted to help them to reduce the cost of poultry production. three factors negatively affected wtp of the respondents. the education level had a significant negative effect on wtp. this result was similar to that of jaleta et al. (2013). it suggests that poultry farmers who were illiterate or less educated had more time to look after their livestock and their feed. more educated poultry farmers probably had other professional occupation and, thus, were less available to care for their livestock. moreover, respond130 sètchémè charles bertrand pomalégni et alii ents with higher levels of education may initially be more critical and suspicious of innovative approaches and the cost involved. they may finally end up adopting the innovative approaches after some analysis. on the other hand, it is often observed that low education levels of respondents often block the adoption of new production techniques (sall et al., 2010). scavenging farming was negatively related to wtp. in scavenging mode, chickens can easily pick up food residues and invertebrates and, therefore, the poultry farmer feels he/ she does not need to pay a high price for the purchase of the protein ingredients. this kind of farmers did not usually pay for protein ingredients to feed poultry. to valorize fly larvae on farms, a poultry farmer understands that the proposed fly larvae and other insects are already being searched by these chickens in garbage piles and other wastes. however, they may not realise that adding fly larvae to the diet of scavenging flock could strongly enhance their growth and survival. therefore, a policy to promote fly larvae meal in scavenging poultry farming could be subsidies to support fly production or purchase among smallholder farmers to demonstrate the benefits of fly larvae meal before its selling on the market. fly larvae use awareness was also negatively related to wtp because those respondents had already information on fly larvae as feed and were less incited to pay for its use in poultry feeding. our finding showed that fly larvae could be a cheap and sustainable source of protein that can be promoted and sold to small poultry farmers at an affordable price. 4.2 development of fly larvae meal market the post estimation of the average wtp indicated a wide heterogeneity among respondents. this heterogeneity increased the standard deviation because the distribution was not normal. other studies also highlighted that some individuals had negative wtp for the change (fu et al., 2011; pavel et al., 2015). the average wtp was calculated by considering the negative wtp as zero (fu et al., 2011). the average wtp estimated for fly larvae meal in this study was 59% lower compared to the local market price of fish meal with low quality. this is also probably lower than the expected production costs in a small fly larvae production system. roffeis et al. (2018) calculated the cost of producing house fly larvae on chicken manure in a small system in mali to vary between 1.09 and 2.08 €/ kg. if the demand of fly larvae meal increases, the challenge faced by fly larvae producers will be to produce and to sell fly larvae meal at an acceptable price for small poultry farmers. this will oblige enterprises to be innovative in their production process to ensure financial benefits. 5. conclusion poultry farmers are facing a major constraint of feed, representing about 70% of the total production costs in developing countries. finding a sustainable and cheaper ingredient for poultry farming is needed to increase their competitiveness. this study applied a field bid experiment to assess the economic feasibility of fly larvae meal as an alternative feed in replacement for fish meal. then, we analyzed wtp for using fly larvae as a protein source to feed traditional poultry in benin. respondents were mostly willing to pay for it and are ready to use it, although the amount they are willing to pay is different 131traditional poultry farmers’ willingness to pay for using fly larvae meal according to poultry farmers. the average wtp estimated at fcfa/kg 225.10 (€/kg 0.34) for fly larvae was lower than the local price of fish meal. meeting the demand of fly larvae meal for poultry farmers in benin requires that it is produced on a large scale. this production requires the creation of small-scale innovative enterprises and the development of equipment that can reduce production costs thus making the enterprises financially viable. information sharing and sensitization on fly larvae meal use as a low cost protein source and as a sustainable alternative of fish meal in traditional poultry farms is required for better food and nutritional security. 6. references agbédé, g. n. and mpoame, m. 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(2016). chinese consumers’ willingness to pay for pork traceability information: the case of wuxi. agricultural economics 47: 71–79. 136 sètchémè charles bertrand pomalégni et alii 32 appendix questionnaire for estimating traditional poultry farmers’ wtp in benin survey sheet n°|_____________| date of survey: |____|____|____| name of investigator:…………………… the purpose of this survey is to determine if beninese’s poultry farmer is willing to use fly larvae as protein source to feed his livestock of local chicken. if yes, how much is he willing to pay for? a. socio-economic characteristics of the respondent characteristics code responses 1. province (depart) 1= alibori, 2 = borgou, 3= atacora, 4= donga, 5 = collines, 6=zou, 7=plateau, 8 = ouémé, 9= atlantique, 10=littoral, 11=mono, 12= couffo 2. dstrict (commune) enter the name of the district 3. village (vil) enter the name of the village 4. name and surname of the respondent (npenq) enter correctly the name and the surname of the poultry farmer 5. socio-cultural groups (ethnie) 1. fon and related 2. bariba and related 3. dendi and related 4. adja and related 5. yom & lokpa related 6. betamaribe and related 7. peulh and related 8. yoruba and related 9. other socio-cultural groups of benin 10. foreign 6. the different animals constituting the livestock of the poultry farmer (animal) 1= chicken ; 2= guinea fowl ; 3= duck ; 4= pigeons ; 5= turkey ; 6= other 7. number of the dominant species in the livestock (ed) enter the species and the number 8. what are the local chicken ecotypes of your farm (rapl)? 1=holli ; 2=fulani ; 3=sahwè ; 4= yaya ; 5=yovokoklo ; 6= other 9. number of local chicken (effec) enter the number 10. type of livestock farming (me) 1=scavenging ; 2= semi-scavenging ; 3=confinement 11. marital status (sima) 1=married ; 2=divorced ; 3=single, 4 = widower, 5=other 12. number of agricultural assets (ae) enter the number 13. household size of the poultry farmer (emel) enter the number 14. age (age) enter the poultry farmer’s personal response (number of years) or make an approximation in case of no response 15. education level (ninst) 0=not literate; 1= literate ; 2= primary ; 3= secondary, 4= professional training ; 5= higher ; and enter the number of years of study 16. main occupation (profpri) 1=farmer or livestock farmer; 2= trader ; 3= agri-food processor; 4= official in activity ; 5=mechanic ; 6=carpenter ; 7=other (specify and continue the list) 137traditional poultry farmers’ willingness to pay for using fly larvae meal 33 b. double bid procedure to propose 29. the fish meal is often used as protein source to feed chickens. the price per kg of fish meal on the international market is between fcfa/kg 1,000 and fcfa/kg 1,200. this fish meal is sold on the local market at fcfa/kg 550. this low price can be attributed to the low quality of the fish meal sold with low protein content. fly larvae meal proved to be nutritional as source of animal protein for local chickens. it can replace the fish meal with low quality used to feed local chickens. in addition, the use of fly larvae meal improves zootechnical performance of your local chickens (average daily gain, consumption index, etc.) and reduce the costs of protein feeding. would you be willing to pay for fly larvae meal as a protein source to feed your local chickens? 17. number of years in poultry farming (expe) enter the response (number of years) 18. to which network or livestock farmer association do you belong to (reseau)? enter the response 19. do you have access to a micro finance institution (aimf)? which one? 1=yes, 0=no 20. what is your annual global income? (revglo) ? enter the quantified income (data) 21. what is your annual farm income (ran)? enter the quantified income (data) 22. what is the share of poultry income in annual farm income (paran)? enter the response (on 10) 23. do you know that fly larvae can be used for chicken feeding (colamou)? 1= yes; 0=no 24. did you use fly larvae to feed local chicken (afas)? 1= yes ; 0=no 25. 25. do you currently use fish meal to feed local chicken (ufap)? 1= yes; 0= no 26. 26. would you like to replace fish meal with another protein source (sus)? 1= yes ; 0= no 27. 27. if yes to question 26. why do you want to substitute fish meal (rsfap)? 1=bad quality, 2=unavailability of the fish meal on the local market, 3=high price, 4= other 28. 28. what other ingredients are used as protein source to feed local chicken (asp)? 1= soy flour, 2 = oil cake, 3 = any, 4= other to be specified yes no 138 sètchémè charles bertrand pomalégni et alii 34 30. if yes, would you accept to pay fcfa/kg x to feed your local chickens? the vector x 2 to be proposed is composed of 7 bids: fcfa 600; fcfa 700; fcfa 800; fcfa 900; fcfa 1,000; fcfa 1,100 and fcfa 1,200. select and go to the question 31. select and go to the question 32. with k=100 31. could you pay fcfa/kg x+k ? select or and go to 34. 32. could you pay fcfa/kg xk ? select or and go to 34. 33. if the answer for question 29 is no; so why? no means to pay: refusal of payment vehicle : no value given to fly larvae: other (specify and continue the list): 34. if the answer to question 29 is yes, what is your level of motivation for using fly larvae (degmot)? 1= very motivated; 2= motivated; 3 =indifferent: 4=not motivated; 5=little motivated. 3 |_____________| 35. are you motivated to use fly larvae to increase the nutritional quality of your local chickens (mqual)? 1=yes; 0=no |_____________| 36. if yes, what is the level of the mqual (degmqual)? 1= very motivated; 2= motivated; 3 =indifferent: 4= not motivated; 5= little motivated: ……………. 37. are you motivated to use fly larvae to increase zootechnical performance of your local chickens (mperz)? 1=yes ; 0=no :……………… 38. if yes, what is the level of mperz? 1= very motivated; 2= motivated; 3 =indifferent: 4= not motivated; 5= little motivated:……………………. 39. are you motivated to use fly larvae to reduce the costs of feeding (mcot)? 1=yes; 0=no:………………. 40. if yes, what is the level of mcot? 1= very motivated; 2= motivated; 3 =indifferent: 4= not motivated; 5= little motivated: ……………. 41. what can we do to promote the use of fly larvae to all poultry farmers (recom)? a. train poultry farmers to produce fly larvae ……………………………. b. promote the consumption of poultry fed with fly larvae …………….. c. other (specify)…………………………………………………………………………………………………… ………………………………………………………………………………………………………………… 2 fcfa 600 correspond to the minimum production cost of 1 kg of fly larvae meal, i.e., equivalent to fcfa 150 as minimum production cost of 1 kg of fresh fly larvae. fcfa 1,200 correspond to the maximum production cost of 1 kg of fly larvae meal, i.e., equivalent to fcfa 300 as maximum production cost of 1 kg of fresh fly larvae. we increment and decrement the initial bid by 100 fcfa, which corresponds to the marginal cost to improve the quality of the fly larvae meal. 3 we use 1-5 likertscale to evaluate the level of motivation. yes no yes no no yes bio-based and applied economics 8(3): 239-259, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8888 human capital and rural development policy: evidence from european fadn regions ornella wanda maietta1, biagia de devitiis2, sergio destefanis3,*, domenico suppa4 1 università di napoli federico ii 2 università di foggia 3 università di salerno 4 università della campania “l. vanvitelli” abstract. this paper analyses the evolution and policy drivers of the productivity of farmers’ human capital in eu agriculture from 1986 to 2010. the empirical analysis employs farm data sourced from the farm accountancy data network standard results as well as eurostat’s information on farm holders’ educational-attainment levels. productivities of human capital are measured by the shadow prices for three levels of educational attainment of farm family labour, computed using data envelopment analysis with variable returns to scale, and related to a malmquist index of total factor productivity and to selected policy variables. the results indicate that productivities of farmers’ human capital trend upwards and are positively associated with rural development payments. keywords. productivity of human capital, shadow prices, technical efficiency, productivity growth, specific education, agricultural change. jel codes. 047, 015, d24, e24, c43. 1. introduction human capital requires investment in learning new skills, both through traditional schooling and postschool job training. it also represents a crucial source of productivity gains and long-term economic growth. according to the neoclassical approach (mankiw et al. 1992), human capital is a fundamental input into the aggregate production function, and its accumulation explains the process of economic growth. on the other hand, the schumpeterian approach holds that growth results from the initial endowment of human capital, which influences a country’s or region’s capability to innovate and catch up with the technology of the leader area (nelson and phelps 1966; benhabib and spiegel 1994). *corresponding author: destefanis@unisa.it. the authors gratefully acknowledge comments from three anonymous referees on a previous version of the paper. the usual disclaimer applies. 240 ornella wanda maietta et al. at any rate, few economists would dispute that for most of the world’s agriculture, immaterial inputs – including human capital – are now crucial for total factor productivity (tfp) growth. this growth is no longer a resource-based process driven by material input accumulation, but a productivity-based process driven mainly by immaterial input accumulation (fuglie 2015; ball et al. 2016). knowledge-intensive work environments are increasingly common, creating a situation in which human capital relates to entrepreneurial outcomes more than ever before (unger et al. 2011). it has also long been known that education in agriculture enables farmers to allocate inputs more efficiently (welch 1970) and to optimise their information searches (ram 1980). the educational system imparts the ability to summarise information from various sources and to engage in nonroutine problem solving (swaim 1995; gasson 1998). technical education favours participation in agri-environmental schemes (dupraz et al. 2002), improves eco-efficiency (van passel et al. 2009; picazotadeo et al. 2011) and increases the value added per annual working unit (carillo et al. 2013). in this context, how is european agriculture responding to these challenges? in principle, education offers higher returns for individuals working in any sector experiencing technological progress (blundell et al. 1999). hence, the returns on farmers’ education are linked to a changing agricultural technology and production structure. if these conditions do not change, farmers’ incentive to acquire education dwindles (huffman 2001). now, there is little doubt about the existence of an ongoing demand for new skills in european agriculture (european commission 2014). crucial to the present analysis, public policy also plays a role in incentivising the accumulation of human capital. since 2005, as a result of the fischler reform and subsequently the cap reform 2014– 2020, direct support (pillar i subsidies) and structural policies (pillar ii payments) have pursued a more entrepreneurial approach to agricultural business management through increased market orientation and competitiveness (clark, 2009). corporate efficiency and environmental safeguarding became key issues. in terms of direct-support policies, farm aid has largely been decoupled and subject to cross-compliance. as for structural interventions, rural development policy has been strengthened with funds and policy instruments aimed at facilitating the provision of environmental goods. in addition, activities have been diversified in a more targeted and locally tailored manner. it is expected that the stronger market orientation of direct support will foster aggregate productivity gains for the sector as a whole. this prediction rests partly on the assumption that only high-performing farms will survive due to their ability to thrive in an environment that promotes continuous learning and problem solving (henke et al. 2011) and partly on the belief that the transition from a traditional agricultural policy to a rural one may improve the policy communities and networks relevant to farmers (keating and stevenson 2006) or the farmers’ business strategies (clark 2009; severini and tantara 2013). productivity-enhancing effects may result also from the rural development plans and human-capital transfers carried out within the cap. however – and this is the central focus of this paper – these reforms could also have increased the productivity of higher-order cognitive skills, an issue that has wide-ranging policy relevance because higher returns for human capital may attract this input into the sector (olper et al. 2014; garrone et al. 2019). although these arguments suggest the existence of a link between the cap reforms and human-capital productivity in agriculture, this relationship has yet to be investigated empiri241human capital and rural development policy cally.1 there is a simple way of testing the hypothesis that the greater cap market orientation has enhanced the productivity of human capital in agriculture: determining whether the relative shadow price of the human capital embodied in european farmers has increased after the cap reforms. therefore, this paper primarily aims to measure the relative shadow price of farm family labour for three levels of educational attainment from 1986 to 2010. these relative shadow prices are computed by applying the data envelopment analysis with variable returns to scale (dea-vrs) for all eu-27 farm accountancy data network (fadn) regions, for all years for which information on farm holders’ trainings is available. dea has been widely used in growth accounting studies because it does not impose restrictive functional forms on the production frontier and is much more directly interpretable than other approaches in terms of production theory (arcelus and arocena 2000; filippetti and peyrache 2013). due to data availability, we focus on three levels of educational attainment: low, medium and high (further details are given in the research materials that are available online). because tfp growth influences the productivity of human capital (and vice versa), a second and complementary task of this paper is to measure the growth in tfp by computing a malmquist tfp index (which is possible only for a balanced panel of eu-12 fadn regions). to the best of the authors’ knowledge, no other study has measured a tfp index for european agriculture at the regional level over so long a period. it should be emphasised that some of the utilised data are not readily available from public sources, as explained in greater detail in section 4 and the research materials. the analytical framework proposed in this paper may be replicated to evaluate the productivity of human capital for similar situations in other sectors, particularly when labour is mainly self-employed and lacks a market price. the analysis could also be extended to provide absolute (as opposed to relative) shadow prices for human-capital services, which could be used in a dea-based cost–benefit analysis (see, e.g., kortelainen and kuosmanen 2006). the remainder of the paper adheres to the following structure. section 2 reports on the history of the cap and provides some descriptive statistics. sections 3 and 4 focus on the methodology and data used, respectively. section 5 describes and comments on the empirical results, and the paper offers concluding remarks in section 6. the paper also includes a research materials section that describes the empirical framework further and reports some robustness checks. 2. the evolution of human capital in eu agriculture and the cap table 1 reports the percentage of farmers with full agricultural training, our proxy for high human capital, as calculated from farm structure survey (fss) data, as well as the percentage of the population aged 15 to 64 years with tertiary education, as calculated from eurostat data. educational attainment is a poor indicator of the extent to which individuals possess the cognitive skills and technical knowledge required to carry out more demanding and better-paid jobs; nonetheless, the table highlights the well-known gap between rural and urban educational levels (swaim 1995). whereas the percentage of the population with tertiary qualifications, 1 there is, however, an empirical literature on the relationships between cap reforms and tfp. we comment on this literature, whose results are rather diverse, when discussing our evidence in section 5. 242 ornella wanda maietta et al. measured in either 2010 or 2018, is not appreciably sensitive to the eu aggregate considered (if anything, it increases at each eu enlargement), the percentage of farmers with full agricultural table 1. human capital in eu agriculture and economy, 2010 and latest available years. areas % farmers with full agricultural training, 2010 % population from 15 to 64 years with tertiary education, 2010 % farmers with full agricultural training, 2016 % population from 15 to 64 years with tertiary education, 2018 eu-6 14.9 21.8 16.1 26.2 eu-9 14.4 23.9 16.4 29.1 eu-10 13.6 23.8 14.3 29.0 eu-12 12.0 24.1 12.1 29.4 eu-15 12.3 24.1 12.5 29.7 9 eu-25 12.6 23.2 13.8 29.1 eu-27 11.3 22.7 11.5 28.5 nb: eu-6, eu-9, eu-10, eu-12, eu-15, eu-25, eu-27 to be defined in the research materials. source: own elaborations on fss and eurostat regional statistics. figure 1. farm holders with full agricultural training (%), 2010. source: own elaborations on fss and eurostat regional statistics. 243human capital and rural development policy training (either in 2010, available from our dataset, or in 2016, the latest year for which we can retrieve some aggregate information) tends to decrease with each eu enlargement. the cross-sectional distribution of the percentage of farm holders with full agricultural training across fadn regions is further depicted in fig. 1. the percentage ranges from 0.2% in ipiros-peloponissos-nissi ioniou to 45.9% in latvia and luxembourg. generally, the most rural regions exhibit the lowest percentage of farmers with full agricultural training. the significant differences observed in human capital across eu regions may be explained by divergent agricultural education systems, agricultural structures and farm-size distributions. yet, human capital has improved over time. according to fss data, in 1990, the percentage of farm holders with medium and high human capital in eu12 was 12% and 7%, respectively. in 2010, these figures were 20% and 12%. it could be asked whether policies, by affecting the incentives for human-capital accumulation, have favoured or hampered this improvement. before dealing with this question in the following sections, we proceed to give a detailed account of the most relevant changes of the cap in this sphere. the cap has undergone several changes since the 1980s, including production limits to reduce surpluses (milk quotas were first applied in 1984); during this time, much emphasis has been placed on environmentally sound farming. the first fundamental reform occurred in 1992 with the macsharry reform, followed by “agenda 2000” in 1999, the fischler reform in 2003 and the cap reform 2014–2020 in 2013. in 1992, the macsharry reform caused a shift from market to producer support. cereal, oilseeds and livestock intervention prices were scaled down. computed on the basis of average regional yield levels, per-hectare compensatory payments were also introduced, along with compulsory set-aside requirements attached to these payments. in 1999, the “agenda 2000” reform further cut intervention prices, bringing them closer to world market levels while aligning cereal, oilseed and livestock payments in order to promote the competitiveness of european agriculture. “agenda 2000” also initiated the rural development policy, a wider structural strategy of decentralised spatial management for rural territories in member states (mss). this policy sought to encourage sustainable development by valorising both agricultural and nonagricultural activities. in 2003, the fischler reform was introduced, promoting sustainability and cohesion. farmers received a single payment calculated by dividing the total payments received over a historical period by the number of hectares on the farm. previously related to the number of animals or the milk quota size, premiums were largely added to the flat-rate compensation per hectare. single farm payments favour the use of land relative to other inputs in agricultural production and reduce the yields of many commodities; their total output response is less than the price support (sckokai and anton 2005). furthermore, these payments severed the link between production and farm income support. this decoupling sought to orient farmers towards the market while still providing them with a degree of income stability. farmers were free to produce what they judged most profitable, so long as the land was used for agriculture. income stability was intended to serve as compensation for higher production standards with regard to consumer protection, animal welfare and environmental conservation (compared to many non-european countries). anyone failing to fulfil this ‘cross-compliance’ condition risked a reduction in their direct income payments (moro and sckokai 2013). the reform was in place from 2005 onwards, but decoupled payments fully replaced direct aid only in 2007. 244 ornella wanda maietta et al. the fischler reform has also strengthened the role of services in fostering agricultural human capital and competitiveness. each ms must set up an advisory system aimed at farms in order to satisfy compliance requirements. the programme for rural development (2007–2013) provided funding for the supply of advisory services and other actions2 aimed at human-capital transfer (contó et al. 2012). yet, the background of both advisory services operators and private business consultants was often agronomic and not business management, resulting in outdated or incomplete professional skills (clark 2009). indeed, the fact that the returns for professional and technical training are lower than those for managerial training (blundell et al. 1999) prompts the need to promote entrepreneurship through education. in 2013, cap contents were again redesigned over the programming period 2014–2020. in particular, single farm payment has been unpacked into different payments targeting different goals and partly tailored to farm-specific characteristics. according to european regulations, only some of these payments (base payment, greening payment and payment for young farmers) are mandatory for mss, unlike other kinds of payment (coupled, for less favoured areas, for small farms). the introduction of the greening payment, conditional on compliance with certain “agricultural practices beneficial for the climate and the environment”, reflects the eu legislators’ intention to provide a more consistent justification for cap instruments, emphasising their role in pursuing environmental sustainability (european commission 2010 a, b; matthews 2013; cimino et al. 2015; erjavec and erjavec 2015). the key role of services has also been strengthened during the period 2014–2020. in particular, the programme for rural development pays greater attention to knowledge transfer and information actions, including vocational training and skills acquisition by farmers (or smes operating in rural areas),3 and to advisory services, farm management and farm support.4 3. the empirical methodology the shadow price associated with an input indicates how much more output could be obtained by increasing the amount of that input by one unit. it is a measure of the opportunity cost of that input and reflects its marginal productivity. in the field of productivity measurement, shadow prices are estimated when market prices are inapplicable, unknown or inappropriate. they can also be used as appropriate indicators of input productivities. carrying out intercountry comparisons of agricultural productivity, coelli and prasada rao (2005) and nin-pratt and yu (2010) estimated shadow input prices in order to obtain input cost shares as market prices are distorted due to government intervention. ten raa and mohnen (2002) used shadow input prices as a valuation of input productivities unaffected by market power, disequilibrium in factor holding, suboptimal capacity utilisation and returns to scale. 2 examples include vocational training for consultants (measure 111) and support for cooperation in the development of new products, processes and technologies (measure 124). 3 examples include training courses, workshops and coaching, as well as short-term agricultural exchanges and visits to farms (article 14 of council regulation (eu) no 1305/2013) 4 these include three types of measures: supporting farmers and related operators in the use of advisory services to improve economic and environmental performance and resilience to climate change, encouraging the establishment of farm management and promoting the training of advisers. 245human capital and rural development policy shadow prices may be estimated through nonparametric linear programming or through parametric regression analysis. examples of the nonparametric approach include the study of industrial wastes (reig-martínez et al. 2000), volunteer work (destefanis and maietta 2009), hospital outputs (o’donnell and nguyen 2013), biodiversity (sipilainen and huhtala 2013), undesirable outputs (leleu 2013), and water and wind resources (ilak et al. 2015). deabased shadow prices have also been used in cost–benefit analyses of environmental services (kortelainen and kuosmanen 2006). within a nonparametric framework, shadow prices are determined as the solution to multiplier or dual linear programming problems. they are the multipliers revealed by individual producers in an effort to maximise their relative efficiency (fried et al. 2008). in this paper, in order to determine the shadow prices of inputs, we rely on the dea-vrs technique,5 implemented through the solution of the multiplier (dual) problem bccd i proposed by banker et al. (1984): i i i i i i i i i i i i d i i i , , i i i bcc ( ): max 0 0, 0 , w w + = − + ≤ ≥ ≥ μ ν ω x y μ y ν x 1 μ y ν x μ ν (1) where x is the input vector and y is the output vector, ni and mi are the shadow prices or multipliers of inputs and outputs, respectively, and wi is an indicator of returns to scale. note that whereas ni and mi must be greater than or equal to zero, wi may be positive, negative or zero, which makes it possible to use the optimal value of this variable to identify the nature of returns to scale. this input-oriented problem is solved by finding values for ni and mi that maximise output “values” miyi + wi, subject to a normalising constraint on input “values” (which avoids the occurrence of infinite solutions to the problem) and to the constraint that efficient output “values” must be smaller than or equal to input “values”. as a consequence of these constraints, shadow prices computed from different frontiers are not directly comparable (kuosmanen and kortelainen 2006). however, it is possible to compare the ratio between the shadow prices of two inputs, which is the marginal rate of technical substitution between these inputs (ouellette and vigeant 2016). for this reason, our analysis always considers the shadow prices of family-labour categories as ratios calculated vis-àvis the shadow price of paid labour. the computation of shadow prices may provide values equal to zero for some outputs or inputs. input shadow prices are zero in cases of slack in the primal envelopment form. it is also possible to have a zero value in the case of multiple optimal solutions (olesen and petersen 2015). indeed, the estimated dea frontier is not smooth. its kinks in primal space correspond to flats in dual space that fail to yield unique shadow prices for strongly efficient units, that is, observations with zero slacks in the primal envelopment form (chambers and färe 2008). in order to solve this problem, olesen and petersen (2015) proposed a “facet 5 when the data are expressed on “an average per farm” basis (as in this paper), it is sensible to stick to a variablereturns-to-scale technology (coelli and prasada rao 2005). 246 ornella wanda maietta et al. analysis” of the convex hull, making it possible to identify well-defined shadow prices for strongly efficient units as well. dea-vrs can also be used in order to compute malmquist indexes for tfp growth. this index, explained in detail in the research materials, is one of the most widely used tools for measuring tfp growth of firms, industries and countries (mizobuchi 2017). it enables decomposition of tfp growth into movements towards or away from the production frontier, technical progress and scale-related factors. we chose a nonparametric approach for the computation of both shadow prices and the malmquist index, because, unlike econometric estimation, this approach does not rely on any assumption about the functional form of input–output relationships or of stochastic disturbances. 4. the data and the empirical specifications the bulk of data for this study were obtained from fadn and refer to a representative farm at the regional level, commonly used in sector models based on linear programming (jonasson and apland 1997) and for intercountry productivity analysis (rizov et al. 2013). we are aware that reliance on these data may lead to the neglect of some interesting heterogeneities characterising the phenomenon under scrutiny. however, microdata across fadn regions are unavailable across a time span sufficiently long to allow investigation of the cap reforms. furthermore, the literature contains few aggregate analyses concerning the role of entrepreneurial human capital in local development (marvel et al. 2016). more generally, it has been stressed recently that the use of microdata in policy evaluation may lead to biased results, because analyses based on them neglect the presence of spillover effects (see, e.g., instance deaton 2019). data on representative farms at the regional level can be downloaded from the standard results section of the fadn database. for the purpose of this study, version a1 of fadn standard results was downloaded,6 because it refers to a representative farm at the regional level for the period 1989–2012. meanwhile, consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (crea; formerly istituto nazionale di economia agraria, or inea) provided version a1, with 34 variables for the period 1986–1988 (rica ri/cc/882 rev. 3, described in dell’acqua 1995). we also relied on the eurostat fss (for the period 1986–2010), which is the only harmonised source for human-capital data in eu agriculture; it periodically measures the percentage of farm holders with practical, basic and full agricultural training. unfortunately, no such information is available for paid labour. note that the territorial location of the fss corresponds to nuts2 regions, which are not necessarily the same as fadn regions (see the research materials for an explanation of the matching procedure across these territorial definitions and for other information about the data). table 2 reports the descriptive statistics of the variables. on average, the commercial farm employed 1.28 family work units, the farmer plus another (part-time) family member, and 0.64 paid work units in the time period under consideration. family labour is much more likely to have lowor medium-level educational attainment (which corresponds to the information available from european commission 2014). 6 http://ec.europa.eu/agriculture/ricaprod/database/consult_std_reports_en.cfm 247human capital and rural development policy the specification of the production set used for computing the malmquist index differs from that used for shadow prices (see table 3). for the malmquist index, the analysis is output oriented and labour is measured in work units (fwus for family labour and awus for paid labour), as is common practice in the measurement of agricultural productivity. disaggregation of family labour in human-capital categories was not used, because this information was not available for all years. furthermore, because calculation of the index requires the use of balanced panel data, only the eu-12 regions were considered. on the other hand, when computing shadow prices, all available regions among those belonging to eu-27 were included in the sample, and family labour was divided into three categories according to human-capital endowment. in this case, an input orientation was deemed more appropriate table 2. descriptive statistics of the main variables. variable units mean st. dev. minimum maximum products 2005-€ 87,358 103,424 4,806 948,056 subsidies “ 15,665 26,882 0 238,769 materials “ 56,674 75,052 1,894 659,560 capital “ 293,228 263,265 8,211 2,095,475 paid labour awus* 0.64 1.37 0 15.99 family labour fwus* 1.28 0.32 0.38 2.68 family labour low hk “ 0.55 0.40 0 2.03 family labour medium hk “ 0.62 0.20 0 1.22 family labour high hk “ 0.19 0.36 0 2.01 compensatory payments/gross farm income % 4.83 8.12 0 37.24 decoupled subsidies/gross farm income “ 5.62 10.74 0 51.09 human capital transfer payments/gross farm income “ 0.2 0.85 0 12.26 rural development payments/gross farm income “ 3.15 6.48 0 45.73 * awu, annual working unit, and fwu, family working unit, are defined as 2,200 hours worked annually. source: own elaborations on fadn and fss data, eurostat regional statistics. table 3. dea model specifications. malmquist index shadow prices output agricultural products (total output) agricultural products (total output) inputs materials, capital, paid labour, family labour materials, capital, paid labour, family labour low human capital, family labour medium human capital, family labour high human capital dea orientation output-increasing input-saving fadn regions eu-12 eu-12, eu-15, eu-25, eu-27 years 1986-2012 1986, 1990, 1993, 1997, 2000, 2005, 2010 248 ornella wanda maietta et al. for evaluating the productivity of varying levels of human capital because of the latest cap objectives, which do not encourage input intensification. 5. results and discussion 5.1 productivity growth and the malmquist index the first step of our empirical analysis, a propaedeutic for the calculation of the malmquist index, is the estimation of annual production frontiers. we use an output-oriented dea-vrs on the balanced panel data of 88 eu-12 regions for period 1986–2012. indeed, in cross-country multilateral productivity comparisons, the analysis is usually output oriented (arnade 1994). from fig. 2, it is evident that the mean level of output-increasing technical efficiency decreases in the reform years (1992, 1999 and 2006–2007) before rebounding upwards. the details for each region (available in the research materials) indicate that the fadn regions that always lie on the frontier are champagne-ardenne, comunidad valenciana (in line with the results in maudos et al. 2000) and the netherlands, followed by denmark,7 picardie and bretagne. efficiency has lagged in eastern england over the past few years, in line with the demonstrated decrease in uk tfp compared to that of neighbouring countries (burgess and morris 2009). increasing returns to scale slightly prevail (53% of observations). 7 both the netherlands and denmark are fadn regions in their own right. 0,8 0,82 0,84 0,86 0,88 0,9 0,92 1984 1989 1994 1999 2004 2009 2014 te ch ni ca l e ffi ci en cy years figure 2. the average level of output-increasing technical efficiency in eu-12, 1986-2012. source: own elaborations on fadn data. 249human capital and rural development policy table 4 reports the geometric mean for each component of the output-increasing malmquist index, which was computed using the fear library of r. on average, the annual tfp growth index in eu-12 throughout 1987–2012 is equal to 1.008, mainly due to technical progress, with an annual mean index of 1.009. there is little efficiency change, and the contributions to productivity growth of scale and shape variations are even less pronounced. more details for tfp growth in each region are available in the research materials. as in previous research (bernini carri 1995), denmark shows the highest rate of tfp growth. at the national level, france, germany and the netherlands follow patterns already observed for similar periods in other studies (coelli and prasada rao 2005). fig. 3 shows the aggregate evolution of tfp growth and technical progress. these variables do not exhibit very marked differences over time. however, the years following the macsharry reform (1993–1998) and the recession years (2010–2012) are associated with a productivity slowdown. table 4. geometric mean of annual productivity growth components in eu-12. year/period mi det tp dscale dshape 1987 1.03 1.04 0.98 1.01 1.00 1988 1.00 0.99 1.01 1.00 1.00 1989 1.01 0.97 1.06 0.99 1.01 1990 1.00 1.00 1.00 1.00 1.00 1991 1.02 1.02 0.99 1.00 0.99 1992 1.04 0.92 1.09 0.95 1.07 1993 0.99 1.08 0.94 1.07 0.93 1994 0.99 1.00 1.01 1.00 0.99 1995 0.98 1.03 0.96 1.01 0.99 1996 1.02 1.01 1.01 1.00 1.01 1997 1.03 0.98 1.03 1.00 1.00 1998 1.00 0.94 1.09 0.93 1.03 1999 1.03 0.99 1.03 1.02 1.03 2000 1.02 1.11 0.92 1.04 0.98 2001 0.99 1.00 0.98 1.02 0.99 2002 1.06 0.99 1.13 0.96 1.00 2003 0.96 1.01 0.93 1.01 1.00 2004 1.04 0.95 1.10 0.94 1.06 2005 1.02 1.04 0.97 1.07 0.96 2006 1.01 0.98 1.03 0.97 1.01 2007 1.02 0.95 1.07 0.98 1.03 2008 1.00 1.02 0.97 1.06 0.96 2009 1.02 1.01 1.01 1.01 0.98 2010 0.99 0.97 1.06 0.98 1.00 2011 0.99 1.04 0.95 1.04 0.98 2012 0.97 1.01 0.96 1.01 1.00 1987-2012 1.008 1.002 1.009 1.001 0.999 source: own elaborations on fadn data. 250 ornella wanda maietta et al. finally, we find a strongly significant negative kendall’s rank correlation coefficient (with a value of -0.21) between technical progress and the number of family-labour work units with low human capital, which suggests that a low level of human capital has constrained productivity growth. this result stresses the importance of immaterial input accumulation for tfp growth within eu agriculture as well. 5.2 the shadow prices of family labour table 5 reports the relative shadow prices of the three family-labour categories, differentiated according to their human-capital endowment, for various sample cuts and treatments of the strongly efficient units (traditional vs. facet analysis). the shadow price of paid labour, which is unlikely to differ substantially from market wage, is taken as numéraire. hence, this relative price is the marginal rate of technical substitution between paid labour and family labour with low, medium and high levels of human capital, respectively. the benchmarking library of r was used to compute the shadow prices for the traditional analysis, while the qhull code (developed by brad barber, davi dobkin and hannu huhdanpaa) was used for the “facet analysis”, which makes it possible to identify well-defined shadow prices for strongly efficient units as well. we present results for both the full sample and a sample restricted to regions from the eu-12 countries, because we do not want to draw conclusions that may be crucially affected by the eu enlargements after 1986. indeed, our previous analysis of tfp growth relates only 0,9 0,95 1 1,05 1,1 1,15 19 87 19 88 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 mi tp source: own elaborations on fadn data. figure 3. the malmquist index (mi) and technical progress (tp) in eu-12, 1987-2012. 251human capital and rural development policy to eu-12 countries. taking first the results for the traditional analysis, we find that for both samples, the marginal rate of substitution for family labour with a low level of human capital increases up to one and a half from 1986 to 2010 (yet only in 2005 and 2010 does this marginal rate of substitution show an appreciable increase); the marginal rate of substitution for family labour with a medium level of human capital almost doubles; and the marginal rate of substitution for family labour with a high level of human capital triples. switching to the results that include the facet analysis for strongly efficient units, we find that the marginal rate of substitution for family labour with a low level of human capital is basically constant, whereas the marginal rates for the other categories of family labour show a gently rising trend (on average, high human capital is slightly more priced that medium human capital). summing up, for both the traditional and the facet analysis, we observe increases in the relative shadow price of medium and high human capital over the period under scrutiny. it is unlikely that the conclusions are affected by the changing number of observations in the year samples, because they hold true for the eu-12 samples for 1990, 2000, 2005 and 2010, which have very similar numerosity (86 to 94 observations). moreover, the results are not very likely to be driven by exit of inefficient farms from the market. to be sure, we do not have farm-level data, but table 6 reports the mean (input-oriented) efficiency and the number of efficient fadn regions characterising the production set used for the calculation of shadow prices. both mean efficiency and the number of efficient observations rise up to 2000, but in 2005 and 2010 they fall back to levels very close to those of 1986 (once more, this is true for the full and the eu-12 sample). this at least suggests that the gradual disappearance of inefficient farms is not a key factor of the evolution of marginal rates. we carry out two further robustness checks on the above results, which we detail in the research materials. first, we computed shadow prices by including production subsidies table 5. marginal rates of substitution between paid labour and family labour by human capital categories. years full sample eu-12 sample traditional analysis facet analysis traditional analysis facet analysis human capital endowment human capital endowment human capital endowment human capital endowment n low medium high low medium high n low medium high low medium high 1986 76 0.46 1.09 1.62 0.50 0.54 0.68 76 0.46 1.09 1.62 0.50 0.54 0.68 1990 86 0.20 0.38 1.79 0.41 0.73 0.62 86 0.20 0.38 1.79 0.41 0.73 0.62 1993 68 0.17 0.53 4.74 0.36 0.60 0.76 68 0.17 0.53 4.74 0.36 0.60 0.76 1995 71 0.17 0.86 0.71 0.53 0.57 0.66 70 0.17 0.87 0.72 0.53 0.57 0.66 1997 71 0.08 0.29 2.32 0.46 0.48 1.08 71 0.08 0.29 2.32 0.46 0.48 1.08 2000 97 0.27 0.98 1.30 0.48 0.85 0.71 92 0.26 1.01 1.29 0.50 0.85 0.70 2005 122 0.76 1.40 4.15 0.44 1.11 1.08 95 0.70 1.42 4.04 0.39 0.97 1.00 2010 135 1.41 2.24 4.06 0.43 0.89 0.84 94 1.16 1.58 3.68 0.38 0.64 0.52 source: own elaborations on fadn and fss data. 252 ornella wanda maietta et al. among the outputs. we get more erratic figures for marginal rates than in table 5, but the general picture does not change. secondly, we used quality-adjusted data for paid labour as numéraire. again, we get results similar to those in table 5, although they show a lower increase for the mediumand high-human-capital categories. an explanation for the rising trends in the relative shadow prices of family labour (particularly with medium and high human capital) could in principle be found in technical progress. however, technical progress subsides in the last years of the sample, when the increase in shadow prices is even more marked. hence, other factors, including policy effects, must be considered. in order to explore the relevance of these policy effects, we follow han et al. (2014) and perform a robust analysis of correlation among our variables of interest. table 7 reports three different indicators: (a) kendall’s simple rank correlation coefficients between the marginal rates of substitution for family labour categorised by human-capital endowment (and obtained through the traditional analysis on the full sample) and the percentages of different kinds of cap-related variables on gross farm income; (b) kendall’s partial rank correlation coefficients between the same variables as above (they measure the rank correlation between the two above sets of variables, controlling for the influence on both of them of a third variable, the cumulative malmquist index,8 cmi); (c) kendall’s coefficients of concordance among marginal rates, cap variables and cmi. coefficients of concordance robustly test the concordance in rankings among two or more variables. in table 7, all variables are robustly netted out of region and year fixed effects. we do so by applying a median polish procedure.9 the evidence from table 7 can be summed up as follows. compensatory payments, which were the backbone of the pre-fischler reform policy, are almost never significantly 8 we cumulate the malmquist index, obtaining a proxy of the level of technological capability, because all other variables are measured in levels. the cumulation of the index is carried out following the procedure suggested in tone and tsutsui (2017). 9 the median polish is a data analysis technique that enables the robust measurement of various effects in a multifactor model (hoaglin et al. 1983). table 6. mean input-oriented efficiency and percentage of efficient observations. years full sample eu-12 sample n mean inputoriented efficiency % of efficient observations n mean inputoriented efficiency % of efficient observations 1986 76 0.94 0.58 76 0.94 0.58 1990 86 0.93 0.48 86 0.93 0.48 1993 68 0.95 0.57 68 0.95 0.57 1995 71 0.97 0.69 70 0.97 0.69 1997 71 0.96 0.65 71 0.96 0.65 2000 97 0.97 0.69 92 0.96 0.70 2005 122 0.96 0.56 95 0.95 0.56 2010 135 0.95 0.53 94 0.96 0.56 source: own elaborations on fadn and fss data. 253human capital and rural development policy associated with the marginal rates (and cmi). human-capital transfer payments and decoupled payments are positively associated only with the marginal rates for medium levels of human capital. for human-capital transfer payments, these findings align with previous evidence indicating that the relationship between entrepreneurship outcomes and entrepreneurship education and training programmes is lower for training-focused educational interventions than for academic-focused educational interventions (martin et al. 2013). on the other hand, decoupled payments are different from zero only in the last two years. a longer time span of application might have yielded a more significant correlation for this policy. in any case, according to our evidence, only rural development payments are associated with the marginal rates across all categories of human capital. on the whole, our evidence points to a favourable assessment of cap reforms. they are associated with a higher productivity of family labour, and the apparently ineffective compensatory payments were replaced by more relevant policies. the results, however, suggest that the association between higher productivity and rural development payments is more robust than that for decoupled payments, and support previous evidence on the ineffectivetable 7. kendall’s coefficients. human capital categories kendall’s simple rank correlation coefficient) between cap variables and marginal rates of substitution for family labour by human capital categories. kendall’s partial rank correlation coefficient) between cap variables and marginal rates of substitution for family labour by human capital categories. cmi as confounder. kendall’s coefficients of concordance among cap variables, marginal rates of substitution for family labour by human capital categories and cmi. compensatory payments/ gross farm income high hk 0.05* 0.05* 0.31 medium hk -0.01 -0.01 0.30 low hk -0.02 -0.01 0.31 decoupled subsidies/ gross farm income high hk -0.01 0.00 0.30 medium hk 0.10*** 0.10** 0.33 low hk 0.02 0.02 0.33 human capital transfer payments/ gross farm income high hk -0.01 -0.01 0.27 medium hk 0.04 0.13** 0.30 low hk -0.02 -0.02 0.29 rural development payments/ gross farm income high hk 0.00 0.01 0.35* medium hk -0.02 0.04* 0.37** low hk 0.05* 0.05* 0.39*** nb: cmi is the cumulative malmquist index. all variables are netted out of region and year effects (computed through median polish). stars denote coefficient significances: * means a p-value < 0.1; ** a p-value < 0.05; *** a p-value < 0.01. source: own elaborations on fadn and fss data. 254 ornella wanda maietta et al. ness of training-focused educational interventions. this finding implies that the cap reforms of the past decades, which have gradually increased the budget for rural development and promoted the economic self-sufficiency of communities through investment in local partnership, have favourably influenced the productivity of farmers’ human capital. it is interesting to compare these results with those from the empirical literature on the cap reforms and various measures of productivity, mainly tfp (mary 2013; rizov et al. 2013; kazukauskas et al. 2014; boulanger and philippidis 2015; smit et al. 2015; latruffe and desjeux 2016; dudu and kristkova 2017). it is difficult to summarise the very diverse results obtained in these papers. the gist of their evidence, however, is that the impact of cap instruments on productivity depends very much on the type of instruments. decoupling seems to have on the whole a positive impact on productivity. moreover, an important channel for productivity improvement is the increased specialisation in more productive farming activities. in particular, several studies (latruffe and desjeux 2016; boulanger and philippidis 2015; smit et al. 2015; dudu and kristkova 2017) have argued that there may be heterogeneous effects across different types of rural development payments (such as lessfavoured-areas payments, agri-environmental measures and investments in human capital and physical capital). therefore, future research on the productivity of human capital should consider in greater detail the impact of different types of rural development subsidies and analyse its evolution for various types of agricultural production. 6. concluding remarks this paper provides evidence about the evolution of the productivity of family labour endowed with different levels of human capital across the eu fadn regions and about the association of this evolution with tfp and changes in the cap. the issue is relevant for agricultural growth because tfp growth in today’s agriculture is driven largely by human capital and other immaterial inputs. we find in section 5 that low human-capital accumulation may constrain tfp growth across eu regions. it has also been noted in section 2 that there are still significant differences in farmers’ human-capital endowment across eu regions. we then ask whether the cap, by affecting the incentives for human-capital accumulation in agriculture, has favoured the attraction of this input into the agricultural sectors of eu regions. we measure the productivity of farm family labour for different levels of educational attainment (low, medium and high) using the relative shadow prices obtained by applying dea-vrs to data sourced from the standard results of the fadn. subsequently, these shadow prices are associated with indicators of cap measures and a malmquist tfp index. our evidence points to an increasing trend for the shadow prices of all categories of family labour, but in particular for those with medium and high educational attainment. in relation to policy, we find a robust association between productivity growth, shadow prices of human capital and rural development payments. decoupled subsidies and training transfers are also associated with higher productivity in the case of low and medium levels of human capital, but this evidence is less pervasive. the policy implication we draw from these results is that rural development payments are more relevant than other kinds of payments in enhancing tfp growth and human-capital productivity. the findings of our study have wide-ranging policy relevance, because higher returns for human capital may reduce the outflow of labour from agriculture. adverse economic condi255human capital and rural development policy tions caused by the global economic crisis have reinforced the arguments for job creation in agriculture. for example, the european commission’s recent “communication on the future of the common agricultural policy (cap)” identified fostering jobs in rural areas and attracting new people into the agricultural sector as key policy priorities (european commission 2017). looking ahead to the post-2020 cap, the ongoing shift to rural development seems to be the right direction to pursue. yet, as explained at the end of the previous section, further investigation in this field is required. a related issue concerns the greater attention paid to knowledge transfer and information actions in the cap reform 2014–2020. in fact, it remains to be seen whether these policies can overcome the strictures of previous trainingfocused educational interventions (martin et al. 2013). future research on these fields will of course take advantage of greater variation in the data across time. finally, in this study, shadow prices have been used to evaluate the services of an input lacking a market price, that is, human capital. in future research, this analysis could be extended to provide absolute (as opposite to relative) shadow prices for human-capital services, which could be used in a dea-based cost–benefit analysis. references arcelus, f.j. and arocena, p. 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(1970). education in production. journal of political economy 78: 35-59. bio-based and applied economics 9(3): 305-324, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8087 the use of latent variable models in policy: a road fraught with peril? danny campbell*, erlend dancke sandorf university of stirling, stirling management school, economics division abstract. this paper explores the potential usefulness and possible pitfalls of using integrated choice and latent variable models (hybrid choice models) on stated choice data to inform policy. using a series of monte-carlo simulations, we consider how model selection depends on the strength of relationship between the latent variable and preferences and the strength of relationship between the latent variable and the indicator. our findings show that integrated choice and latent variable models are difficult to estimate, even when the data generating process is known. ultimately, we show that their use should be driven by the analyst’s belief about the strength of correlations between preferences, the latent variable and indicator. we discuss the implications of our results for policy. keywords. stated preferences, choice modelling, integrated choice and latent variables, hybrid choice model. jel codes. c25, h41, q51. 1. introduction many policies affect the natural environment: e.g. a new hydro-electric dam will provide clean renewable energy and jobs, but may cause damage to the local river; a new motorway will reduce travel time, but may be built in a vulnerable natural area; and, a new conservation area will protect a number of vulnerable species, but possibly displace existing and future industrial activity and development. policy makers are routinely faced with these decisions and trade-offs, and in many countries they are required to undertake cost-benefit analyses or assessments. problematically, many of these costs and benefits are not traded in markets and policy makers have no information on society’s preferences for these non-market goods and services. stated choice experiments, where people are asked to make a choice between competing policy alternatives, are a way to elicit people’s preferences for non-market goods and services. economists have long recognized that people’s choices are affected by a multitude of observable (e.g. gender, age and income) and unobservable (e.g. attitudes and beliefs) *corresponding author. e-mail: danny.campbell@stir.ac.uk editor: meri raggi. 306 danny campbell, erlend dancke sandorf individual characteristics in addition to the characteristics of the options amongst which they choose. for example, when asked to choose whether to support a policy to protect a river from hydropower development, people’s decision will likely depend on their income and where they live in relation to the river, but also their attitudes towards development, clean energy and conservation. testing whether choices are different between high and low income people is trivial and straightforward, but how do we test for differences in attitudes and beliefs? how do we incorporate and consider them in our models? the most obvious, and perhaps most intuitive, way to test for the marginal effect of an attitude or belief is to use an interaction term the same way we would when exploring the marginal effects of age, gender or income. however, unlike age, gender and income, attitudes and beliefs are likely correlated with unobserved factors affecting choice (i.e.  the error term) and indicators of attitudes and beliefs (e.g. likert scale survey questions) are themselves imperfect measures of the true underlying attitude or belief. if either of these are true, then the model will be misspecified and the estimated parameters may be biased (endogeneity bias and measurement error) (ben-akiva et al., 2002; hess, 2012). recently, the integrated choice and latent variable (iclv), or hybrid choice model (ben-akiva et al., 2002; mcfadden, 1986), popularized in transport (bhat et al., 2015; hess and stathopoulos, 2013), has gained traction in environmental economics (alemu and olsen, 2019; hoyos et al., 2015; kassahun et al., 2016; mariel and meyerhoff, 2016; taye et al., 2018; zawojska et al., 2019). an iclv model combines structural equation modelling with discrete choice modelling. in this modelling framework, we assume that (unobserved) character traits, such as pro-environmental attitudes, can be captured by one or more latent variables defined as functions of observable characteristics and measures intended to capture such attitudes, e.g. likert scale questions. these latent variables can be included directly in our choice models to capture the effect of (latent) attitudes and beliefs on the probabilities of choice (ben-akiva et al., 2002). the popularity of iclv models stems from claims that the inclusion of attitudes and beliefs through latent variables leads to improved forecasts (vij and walker, 2016; yáñez et al., 2010), that it sheds more light on preference heterogeneity (kassahun et al., 2016; mariel and meyerhoff, 2016), and that it allows for the inclusion of attitudinal variables and beliefs while avoiding issues with measurement error and possible endogeneity bias (ben-akiva et al., 2002; guevara and ben-akiva, 2010).1 the latter is only true under specific conditions (vij and walker, 2016). measurement error and endogeneity bias aside, the interpretability of the parameters in iclv models remain a challenge, especially if we seek to use the model results to influence policy. in an iclv model, indicators only affect choice indirectly through the latent variable. the latent variable is, by definition, unknown and has no direct interpretability. as such, the indicators can only be interpreted in relation to their directional impact on the latent variable and its directional impact on utility. for examples from environmental economics, see kassahun et al. (2016) who study farmers’ marginal willingness to pay (mwtp) to adopt irrigation methods, taye et al. (2018) who study how people’s environmental attitudes affect their mwtp for forest management options, alemu and olsen (2019) who try to understand how people’s food choice motives affect their mwtp for 1 for an overview of the historical development of hybrid discrete choice models, we refer the reader to (bahamonde-birke and ortúzar, 2017). 307the use of latent variable models in policy: a road fraught with peril? insect based food products or lundhede et al. (2015) who look at how perceived uncertainty about policy outcomes affect bird conservation under climate change. to aid interpretability of the latent variable and to gain a better understanding of what drives heterogeneity in welfare measures, hoyos et al. (2015), mariel and meyerhoff (2016) and mariel et al. (2018) argue in a series of papers, all in environmental economics, that practitioners should use exploratory factor analysis to identify which indicators are appropriate for each latent variable. this approach can also be helpful in model estimation, because more appropriate indicators should make estimation of the model easier. an alternative, or perhaps complement, to the exploratory analysis is to use already validated scales to elicit attitudes or personality traits (alemu and olsen, 2019; boyce et al., 2019; hoyos et al., 2015; taye et al., 2018). that said, vij and walker (2016) show that a reduced form model without latent variables may fit the data at least as well as a latent variable model if the observable explanatory variables are good predictors of the latent variables, which is a specific case of the general result provided by (mcfadden and train, 2000). chorus and kroesen (2014) caution that using the results of an iclv model to inform policies that seek to influence choice by targeting the latent variable is inappropriate given the cross-sectional nature of the data (i.e. only between-individual comparisons based on differences in the latent variable can be accommodated, rather than within-individual comparisons based on changes in the latent variable) and the possibly endogenous relationship between the latent variable and choice. it is also important to keep in mind that as the complexity of our models – and our ability to capture more heterogeneity – increase, we need to be careful that we do not tailor our model too close to the sample data. this may compromise our ability to generalize our model and results beyond the existing dataset and limit the usefulness to policy makers. while end users will often want to establish the relationship between the dependent variable(s) and a relatively small number of key independent variables, increasing model complexity is justified only if it produces reasonably more accurate results. while a familiar aphorism among econometricians is that “all models are wrong”, some models are more wrong than others, and to be of practical use there is a need to ensure that our results are understandable and meaningful. that responsibility lies with us. so what then, is the additional benefit of developing an iclv model? we argue in this paper that while model fit is obviously important, it is not the be all and end all of model selection; and that while using hybrid models to suggest polices that target the latent variable itself is inappropriate (chorus and kroesen, 2014; kroesen et al., 2017; kroesen and chorus, 2018), these models can provide rich insight into behaviour (hess, 2012), help de-bias estimates (vij and walker, 2016), offer improvements in prediction in certain contexts (vij and walker, 2016) and reveal additional layers of heterogeneity (hess, 2012; mariel and meyerhoff, 2016; taye et al., 2018). however, we show that retrieving the true parameters of iclv models can be challenging, and that the benefits of developing and using them are not always clear-cut. this paper is a practical illustration of the points outlined above, and can work as a clarification for practitioners and policy makers alike. using monte carlo simulations, we show the important role that correlation between the attributes, indicators and latent variables play in model selection and that the econometricians belief about the strength of this correlation is the main thing to consider when trying to decide whether an iclv model is appropriate. furthermore, we show that the bias of not accounting for these correlations in 308 danny campbell, erlend dancke sandorf parameters and mwtp is generally increasing with the strength of the correlations. the practical implication is that the strength of the endogeneity bias from including the indicator directly in the choice model is related to the strength of the correlation between the indicator and the latent variable. for low degrees of correlation, omitting the latent variable or using a reduced form model does not lead to substantial bias in mwtp, but for high degrees of correlation between the indicator and the latent variable, the bias in mwtp is less than the model without indicators or latent variables. as such, our results can be viewed as an illustration of vij and walker (2016) and kroesen and chorus (2018). the rest of the paper is outlined as follows: section 2 outlines our econometric approach, section 3 details the monte-carlo data generation processes, section 4 presents the results from the simulation study, and section 5 discusses the implications of our results for the use of iclv models for policy and concludes the paper. 2. econometric approach to illustrate our point and substantiate our conclusions, we use a straightforward stated choice data setup. we generate synthetic datasets and show through monte-carlo simulation how misspecification of the model can lead to bias and under which circumstances this may not be the case. in the following, we assume that the reader is somewhat familiar with discrete choice modelling. to introduce notation, and to save space, we start with a standard random parameters mixed logit model where the probability of observing the sequence of tn choices yn made by individual n is a k dimensional integral of the logit formula over all possible values of :2 (1) where xnjt is a column vector of attribute levels and the joint density of the row vector of marginal utilities is given by f( |.). a key consideration when specifying random parameters is the assumption regarding their distribution. in this paper, we express the individual marginal utility parameter for attribute k, , as follows: (2) where is the mean of the distribution for attribute k, zn is a column vector of regressors relating to individual-specific characteristics, e.g. age, gender, attitudinal responses or latent variables, is a conformable row vector of estimated mean shifter parameters and εnk is a deviate from a multivariate normal distribution with zero mean and covariance . introducing individual specific characteristics, e.g.  responses to an attitudinal question, allows us to assess and interpret the marginal effect of the attitudinal response on margin2 the iclv model can be specified with other choice kernels as well, e.g. multinomial logit or latent class, but throughout this paper, whenever we refer to the iclv model it is one specified with a random parameters mixed logit kernel. 309the use of latent variable models in policy: a road fraught with peril? al utility in the same way as we would for age, gender or income. however, as discussed above, by including attitudinal measures directly in the model, we assume that responses to these attitudinal questions are direct measures of attitudes, e.g. pro-environmental attitudes, and that they are exogenous, i.e.  that the responses are uncorrelated with the error terms. if either assumption is violated, our model is misspecified and our parameters may be biased. to avoid some of the issues associated with measurement error and endogeneity bias, we can, for example, use a hybrid choice model. in this model, we assume that the responses to the attitudinal questions are mapped to a latent variable that is included directly in the marginal utility expression just like we would for any other individual characteristic. in our case, the latent variable is given by the following structural equation: (3) where is a normally distributed random disturbance with zero mean and standard deviation to be estimated. responses to our pro-environmental behaviour question are given on a three-point likert scale, as explained below. since the response is on an ordered scale, we need to use an ordered model for the measurement equations (daly et al., 2012). let us create an underlying continuous variable, i*, that determines the observed response to the indicator question. for individual n, we assume the following relationship with the latent variable: (4) where is a constant to estimate, represents the variation of the underlying continuous variable for a unitary variation in the latent variable and εn is an idiosyncratic random disturbance term assumed to be a deviate from an identically and independently standard logistic distribution. now, we can map the value of to the observed cardinal response to the three-point indicator question. specifically, with l denoting the index for the indicator response (i.e. l∈{1,2,3}), we have: (5) where and are threshold parameters to be estimated. in order to preserve the positive signs of all of the probabilities and ensure that the support is over the entire real line, there is a strict ordering of threshold values that demarcate the observed ordinal levels of the indicator question, specifically -∞< < <∞, with τ0=-∞ and τl=∞. with this in place, the probability for the response to the indicator question for individual n can be represented by the ordered logit model: (6) where λ(.) represents the standard logistic cumulative distribution function and is a variable equal to one when the indicator level l is responded by individual n and zero otherwise. 310 danny campbell, erlend dancke sandorf to estimate the iclv model, we need to maximize the joint likelihood of the observed sequence of choices and the observed responses to the likert scale questions gauging proenvironmental behaviour. we can write the overall likelihood function as follows: (7) where denotes the normal density with mean zero and variance . note the probability now involves a k+1 dimensional integral. 3. synthetic data generating process and approach 3.1 data we use monte-carlo experiments to generate synthetic datasets. this is particularly useful because we know the true parameters underlying the data generating process (dgp) and will enable us to judge model performance in terms of how close the model estimates are to the true values. for this demonstration, we construct a stated choice experiment characterized by three environmental attributes: “area” represents the protected area (in 1,000 km2) with levels 2, 4, 6, 8, 10 and 12; “broadleaf ” denotes the fraction of newly planted trees that are broad-leafed with levels 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0; and, “recreation” is a zero-one indicator variable signifying if recreation opportunities are available. the “cost” attribute is specified as having six levels: €5, €10, €15, €20, €25 and €30. next we generate a random experimental design consisting of 500 synthetic individuals completing six choice tasks comprising two alternatives.3 for the indicator question we make use of a three-point likert-scale indicating environmental tendency: anti-environmental tendencies, neutral-environmental tendencies and pro-environmental tendencies. our monte-carlo strategy involves 25 data generation processes. in all settings, the model specification used in the dgp is based on the iclv model with a random parameters mixed logit kernel described above. specifically, we assume: βnk=μk+γkln+σkυnk, (8) where υnk is an independent standard normal deviate, meaning that σk can be interpreted as the standard deviation of the (underlying) normal distribution.4 the parameter vector γ determines the direction and strength of the relationship between the latent variable and the marginal utilities. to asses how findings are sensitive to different values of γ we consider different vectors. this goes from the case where the latent variable has no bearing on any of the marginal utility distributions (i.e. where γk=0∀k) to one in which it plays a 3 while this design ensures that all attribute levels can be estimated independently of each other, we recognise that a more efficient experimental design could have been used to minimise the variance of the parameters. however, in a monte carlo experiment with specified parameters it may be more appropriate to show that the results stand up in cases where the experimental design is not tailored too closely to the data-generating parameters. indeed, this would be the case in a real-life empirical application. 4 for the cost attribute, we specify βnk=-exp(μk+γkln+σkυnk) to ensure strictly negative values. 311the use of latent variable models in policy: a road fraught with peril? large role. given our dgp of a positive correlation between the latent variable and environmental tendency, we achieve this by increasing the γk values for the non-cost attributes and decreasing it for the cost attribute. furthermore, we consider different values of ψ to contrast the suitability of the indicator question as a manifestation of the underlying latent environmental tendency, respectively, from the case where the likert responses are independent of environmental tendencies to one in which they are, for all intents and purposes, direct measures of environmental tendency. we make use of an orthogonal setup with five sets of parameters to control for the strength of relationship between the latent variable and the marginal utility parameters and five parameters to control the strength of relationship between the latent variable and the indicator response, thus producing 25 different dgps enabling independent evaluation. the respective γk and ψ for each dgp is reported in table 1. the other parameters remain constant across all dgps. respectively, for the cost, area, broadleaf and recreation attributes the values of μk are -1.0, 0.6, 2.5 and 1.4, and the values of σk are 0.4, 0.1, 0.8 and 0.4. for σl, ζ, τ1 and τ1 we use 1.2, -0.6, -1.0 and 0.1, respectively. in practice, we generated a deviate for each synthetic individual from n~(0,σl 2) to represent their specific latent variable, and independent deviates from n~(0,σk 2) to obtain their specific marginal utility. additionally, for each utility function and their underlying continuous variable relating to the indicator we retrieved deviates from independently and identically distributed type i extreme value distributions with variance π2/6. the choices are produced by identifying the alternative associated with the largest utility value. the individual counterfactual response to the three-point indicator question is established by comparing the simulated indicator distribution against the demarcation thresholds. since idiosyncratic results can arise from a single sample of individuals, we generate 100 replications for every simulation setting. to determine how well the simulated data reflect the dgp, we report a number of pearson correlation coefficients for each data generation setting in table 1. specifically, ρwk,l and ρwk,i* denote the correlation between the mwtp for attribute k and the latent variable and the underlying continuous variable relating to the indicator, respectively. the correlation between the latent variable and the underlying continuous variable relating to the indicator is signified by ρl,i*. we can see that the correlations reflect the dgp and, most importantly, that we separately control for differences in the influence of the latent variable on preferences and the indicator. it is also noticeable that the latent variable has a relatively stronger influence on the area attribute, followed by broadleaf and, lastly, recreation. this is a deliberate artefact of the parameters we used in dgp, since it allows us to compare the implications under a wider range of settings. 3.2 analysis for each dataset generated, we estimate six candidate models. this includes a random parameters mixed logit model (mxl), a random parameters mixed logit model with the indicators mapping directly to the marginal utilities (mxlind) and a hybrid random parameters mixed logit model (lvmxl), that matches the dgp, where the latent variable enters both the marginal utility expressions and measurement equation relating to environmental tendency. it is widely acknowledged that models relying on the strict notion of 312 danny campbell, erlend dancke sandorf ta bl e 1. p ar am et er s us ed in th e d g ps a nd m ea n co rr el at io ns a cr os s th e 10 0 m on te -c ar lo s im ul at io ns . 313the use of latent variable models in policy: a road fraught with peril? independent random parameters can be inferior to those that accommodate correlation (mariel and artabe, 2020; mariel and meyerhoff, 2018). while this correlation can stem from observable characteristics (e.g. gender, age and income), it may also be an artefact of unobserved latent variables. the importance of this latter point is often not fully appreciated. indeed, a pertinent question is whether or not—and in what settings—allowing for correlation is an acceptable substitute for hybrid latent variable models and, conversely, if it is possible to say anything about the potential aptness of considering a hybrid latent variable model based on an inspection of the correlation structure of random parameters. to explore these issues we also estimate the corresponding models that allow for correlated random parameters (mxl-corr, mxlind-corr and lvmxl-corr, respectively). estimating all six candidate models allows us to compare the effects under correctly specified and misspecified cases and to make inferences regarding the consequences of the naïve assumption(s). combined, this leads to a total of 15,000 mixed logit models to estimate (i.e. 25 simulation treatments times 100 replications times six model specifications). all models are coded and estimated using the maxlik library in r (see henningsen and toomet (2011) and r core team (2020) for further details). we used maximum simulated likelihood estimation using 500 quasi-random scrambled sobol sequences for the simulation of the random parameters and latent variable. for all models, we started the estimation iterations using the parameters that were specified as part of the dgp. 4. results in table 2, we show the mean difference in log-likelihood for all 25 dgps over the 100 simulated monte-carlo datasets, and the corresponding 2.5th and 97.5th percentiles. note that for the latent variable models we focus only on the fit of the choice model component, which we denote using ll*. first, and unsurprisingly, in accordance with mariel and meyerhoff (2018), we see that the models allowing for correlations between the random parameters fits the data better, i.e. produces higher log-likelihood values. this result holds for all three model specifications. however, we do note that the improvements in log-likelihood reported here do not penalise for the increased number of parameters. second, including the indicator directly in the utility expression leads to better choice predictions. referring back to the correlations between the indicator and latent variable for each dgp in table 1, we see that this improvement in model fit is increasing in the degree of correlation between the two (i.e. ρwk,i*). the most important take-away from this is that we find that the reduced form model without the latent variable fits the data equally well, which is consistent with vij and walker (2016). this fact really brings the question of what the additional benefit of a hybrid choice model is in many contexts to the forefront. moving beyond model fit is necessary to fully understand what is going on. while the reduced form models do “just as well” at predicting the chosen alternative, do they also retrieve unbiased and consistent estimates of the parameters and welfare measures? to explore this, in table 3 and table 4 we show the degree of bias in the parameters associated with the latent variable: specifically, with table 3 and table 4 comparing the mean error (i.e.  the mean of all differences between the estimated values and the true value for each data generation setting) and the corresponding 2.5th and 97.5th percentiles for the models without and with correlations, respectively. we report the absolute bias, but rela314 danny campbell, erlend dancke sandorf tive bias can be assessed by referring back to the true parameters associated with any given dgp in table 1. nonetheless, the tables are useful to compare the different dgps and signing the bias. first, looking at table 3, the most striking result is that, in general, the standard deviation of the latent variable is underestimated (first column), the parameters for cost and recreation are overestimated while those for area and broadleaf are (for the most part) underestimated. we also remark that the latent variable interaction with the indicator shows a high degree of bias for all simulation settings. indeed, in situations where ψ=0 we find that the interaction is underestimated, whereas for settings where ψ>0 we find that they are overestimated. furthermore, while the extent of the bias is increasing in ψ, we see no such pattern for the standard deviation of the latent variable or the other estimated parameters. recall that the dgp was based on the lvmxl model. while we can normally expect to see idiosyncratic bias because the integrals are simulated and the data randomly generated, the fact that we observe systematic bias is a cause for concern and should make any practitioner think twice about using hybrid choice models. the inablity to recover the true parameters—even when the dgp is known and we start the estimation at the true parameters—is disconcerting and underlines the point that these models are difficult to estimate even under “perfect” conditions.5 so what does this say about our ability to 5 we recognise that 500 quasi-random draws may not have been sufficient and that increasing the number of simulation draws may have led to a more stable set of parameter estimates. we justify this on the grounds that, table 2. mean improvement in log-likelihood (choice) over respective dgp baseline mxl model across the 100 monte-carlo simulations. 315the use of latent variable models in policy: a road fraught with peril? retrieve unbiased parameters in empirical settings when the dgp and its parameters are unknown? in total, we estimated 15,000 mixed logit models. increasing the number of draws would have entailed considerably more estimation time. table 3. bias for parameters connected with the latent variable in the lvmxl model. table 4. bias for parameters connected with the latent variable in the lvmxl-corr model. 316 danny campbell, erlend dancke sandorf turning our attention to the model with correlation reported in table 4, we see some stark differences compared to the models without correlation. the most notable change is the switching signs and larger magnitude of the bias in the standard deviation of the latent variable and ψ. this shows overwhelming evidence that allowing for correlation in the random parameters when this was not part of the dgp leads to severe bias in the parameters associated with the latent variable when the latent variable is the only source of correlation in the data. intuitively, this makes sense. we now have a whole correlation structure, in addition to the latent variable, trying to describe the influence of the latent variable. crucially, the magnitude of the bias of ψ is important because it can lead to an entirely misleading interpretation of the latent variable. note that given the true parameters of ψ in table 1, the magnitude of the bias implies that the estimated value of the impact of the latent variable will be negative. consequently, ceteris paribus, we would wrongly conclude that an increase in the latent variable is associated with an increase in the mwtp for the environmental attributes and a decrease in the tendency to report pro-environmental attitudes on our three-point likert scale question. if we look at the bias in the γ parameters, we see that this is much smaller compared to the lvmxl model. while the bias for the standard deviation of the latent variable and the latent variable indicator interactions switch signs and are considerably larger, the bias for γ is much smaller, which makes it difficult to ascertain the net effect on welfare estimates. what it does highlight, and we cannot stress this enough, is that there appears to be a dilemma and a set of unforeseen trade-offs when it comes to hybrid choice model selection. the extent to which this is just an artefact of our dgp parameters and assumptions remain unclear, as this would require further simulation work under a broader range of settings. nonetheless, it does show that model selection comes down to the analyst’s belief about correlations and that model selection and the use of these models truly is “a road fraught with peril”. to determine how the above results affect mwtp, we compare the overlapped estimated area of the actual mwtp kernel density estimates to that of the kernel density of the distribution of the means of the individual-specific posterior mwtp. this is an easy way to quantify the similarities or differences between the actual and predicted mwtp distributions. to make the comparison more intuitive, we consider the difference in the percentage overlap of each model to the basic mxl model, which represents the most naïve assumptions about the dgp. to illustrate how this difference is sensitive to the correlation between the dgp mwtp and the latent variable, as well as the indicator, we plot the differences against ρwk,l and ρwk,i*, respectively. we sort the corresponding points by the correlation measure and graph this using a technique known as locally estimated scatterplot smoothing (loess).6 we show these in figure 1, figure 2 and figure 3 (and their associated 95 percent confidence level) for the area, broadleaf and recreation attributes, respectively. specifically, the locally regressed and smoothed percentage point differences in overlap of each candidate model relative to the mxl model are plotted against: (i) the correlation between the actual mwtp and the latent variable in the left panel; and, (ii) the correlation between the actual mwtp and the underlying continuous variable relat6 the loess method is a non-parametric approach where fitting is done locally (in our case with a neighbourhood proportion of 0.4). the result is a smooth curve, which makes it easier to detect trends. this was achieved using the stats library in r . 317the use of latent variable models in policy: a road fraught with peril? ing to the indicator in the right panel. as we move from the origin to the right, the degree of correlation increases. the vertical axis shows the percentage point difference in overlap relative to the mxl model, meaning that a move up this axis signifies that the candidate model does better at predicting the true mwtp distribution relative to the mxl model. as might be expected, a visual inspection of figures 1-3 reveals that all models generally retrieve the same mwtp distributions when the correlation between mwtp and either the latent variable or indicator is low. but, as the degree of correlation increases, we can see that the models that accommodate correlated random parameters and/or environmental tendency (either directly or indirectly) are better at explaining the true mwtp distributions. recall the discussion relating to the switching signs for the bias in the standard deviation of the latent variable and the interaction of the latent variable; figure 1. percentage point difference in overlap of mwtp distributions relative to the true mwtp distribution for area. figure 2. percentage point difference in overlap of mwtp distributions relative to the true mwtp distribution for broadleaf. 318 danny campbell, erlend dancke sandorf this switch does not appear to affect the estimation of mwtp. relatively speaking, for the most part, the lvmxl and lvmxl-corr curves are closely aligned. focusing on figure 1, we see that as the correlation with the latent variable (left panel) increases beyond 0.2, the models that directly or indirectly include the indicator outperform the mxl and mxl-corr models. this is an important finding, since it suggests that simply allowing for correlation does not, in itself, allow us to recover the correct mwtp distribution. however, it must be noted that this result is strongest in cases where the correlation with the latent variable is moderate. as the strength of the relationship gets very high (ρ>0.6) there is a clear turning point, indicating that the relative importance of directly or indirectly including the indicator lessens. but this same finding is not observed for the mxl-corr model, to the extent that just allowing for correlated random parameters does all most just as well at retrieving the correct mwtp to pay distribution. importantly, this suggests that if the analyst believes that most, if not all, of the correlation between the random parameters are caused by a single unobserved latent variable, and that the effect of this variable is sufficiently strong, then simply estimating a standard mixed logit model with a full correlation structure may be sufficient if mwtp is the key measure of interest. though, of course, this comes at the expense of not knowing the underlying source of heterogeneity, which may, or may not, be of interest. we also observe that the mxlind and mxlind-corr are better able to uncover the true mwtp distribution compared to the lvmxl and lvmxl-corr, respectively, when the strength of relationship between the latent variable and mwtp is weak or moderate. as the strength of relationship increases, however, we remark that this no longer holds. this additional insight implies that the relative advantage of iclv models over simpler models to retrieve the correct mwtp distribution is dependent on the strength of the role that the latent variable plays on the distribution. while not a surprising finding, it reinforces the need to think twice about using hybrid choice models in situations where it is believed that the latent variable is weakly related. these findings are perhaps better illustrated when the change in overlap is plotted against the correlation with the indicator. the downward turn figure 3. percentage point difference in overlap of mwtp distributions relative to the true mwtp distribution for recreation. 319the use of latent variable models in policy: a road fraught with peril? towards the mxl baseline is even more pronounced at higher levels of correlation for all but the mxl-corr model and especially so for the models that directly include the indicator responses in the utility function. at this point, recall that the “area” attribute is also the one that is linked the strongest to the latent variable and the indicator. this explains why the difference between the models are so stark and why we see that this result is mitigated as the relationship between mwtp and the latent variable and indicator becomes weaker. for example, looking at the figures 2 and 3, where the strengths of association are lower, we see that the predicted curves are more closely aligned and do not exhibit an inverted u-shape. this implies that, for these attributes, models that include the indicator (either directly or indirectly) do not produce markedly better predictions of the mwtp distribution compared to the mxl-corr model, and this holds irrespective of the correlation between mwtp and either the latent variable or indicator. for the recreation attribute (figure 3), which had the lowest association with the latent variable and indicator response, the predicted curves are relatively flat, suggesting that the prediction of the mwtp distribution is less sensitive to which of candidate models is used. while these are also obvious findings, the fact that we are able to retrieve, show and prove them through our simulation is reassuring. in generating the results illustrated in figures 1-3 we took account of all synthetic individuals per dgp. however, this may mask the relative performance of each candidate model to correctly predict mwtp for individuals who hold a particular environmental tendency. indeed, one of the often-purported advantages of iclv models is their ability to provide additional insight on preference heterogeneity, particular among those with different latent attitudes. while, as stated earlier, we should be prudent about making policy recommendations on the basis of a latent variable – as well as the, obvious, impracticality, and futility, of targeting policy on the basis of an indicator response – policy makers may still be interested in knowing how members of society with different environmental tendencies judge their policies. for this reason, in figures 4-5, we plot the locally regressed and smoothed mean bias in mwtp for each attribute against the correlation between the attribute and the latent variable broken down by whether an individual holds anti, neutral or pro-environmental tendencies, depicted on the first, second and third panel, respectively.7 for comparison, in the fourth panel, we also present this for all individuals. looking firstly at this fourth panel, we see that the curves essentially overlap and are not significantly different from zero when the correlation between mwtp and the latent variable is weak or moderate. in these cases, the ability to retrieve the mean mwtp (across all individuals) does not appear to be affected by the degree of correlation with the latent variable nor by which of the candidate models we use. as can be seen in figures 4-5, however, these curves begin to diverge as the degree of correlation increases (ρ>0.5) to the extent that some are significantly different from zero. this insight suggests that if the main interest is on describing the means of the posterior mwtp distributions at the sample level, model choice is perhaps only consequential when the mwtp distribution 7 for this, we subtract the actual individual-specific mwtp from the mean of the predicted individual-specific posterior mwtp and take the arithmetic mean for each data generation setting and model and, again, apply the loess method with a smoothing parameter of 0.4. the results are qualitatively similar for correlations between the attribute and the indicator and is omitted from the paper for brevity, but are available from the corresponding author upon request. 320 danny campbell, erlend dancke sandorf is believed to be strongly correlated with the latent variable. this is expected given the results above and that more flexible models are preferred if you suspect high degrees of correlation between mwtp and the latent variable. however, the corresponding curves produced for individuals who hold anti-, neutral or pro-environmental tendencies tell a somewhat different story. only when the mwtp and latent variable distributions are uncorrelated do we find that all models produce relatively unbiased estimates of mwtp irrespective of environmental tendency. however, with any degree of correlation we see that the mxl and mxl-corr models produce biased mwtp estimates for each subgroup. specifically, these models overestimate individual-specific mwtp for individuals who hold antiand (albeit to a lesser extent) neutral environmental tendencies, whereas they underestimate for the subgroup with pro-environmental tendencies. an important figure 4. mean bias in mwtp broken down by anti-, neutral and pro-environmental tendencies for area. figure 5. mean bias in mwtp broken down by anti-, neutral and pro-environmental tendencies for broadleaf. 321the use of latent variable models in policy: a road fraught with peril? finding for analysts who make use of individual-specific posterior mwtp estimates is that the extent of these biases increase with the degree of correlation. while this trend of overestimating mwtp for antias well as neutral environmental tendencies and underestimating for pro-environmental tendencies still largely holds for the other candidate models, it is less evident and we observe it to be less sensitive to the degree of correlation. nonetheless, there appears to be systematic differences between the models where we have included the indicators directly and their analogous latent variable models. for example, in figures 4-5, relative to the mxlind and mxlind-corr models, the lvmxl and lvmxl-corr models, respectively, produce higher mwtp estimates for the antiand pro-environmental tendency subgroups, but lower estimates for the neutral subgroup. furthermore, these differences become more apparent as the degree of correlation between the mwtp and latent variable distributions increase. while the extent to which this finding can be generalized beyond our data generation settings is unclear, it does, nonetheless, further emphasise the difficultly associated with model selection when latent attitudes are believed to play an important role on mwtp. 5. discussion and concluding remarks in this paper, we generate a series of monte-carlo simulations that separately control for the strength of relationship between the latent variable and preferences and the strength of relationship between the latent variable and the indicator. in the real world, structural equations usually comprise standard socio-demographic characteristics and are often weak. to mimic this in the present paper, without complicating the dgp more than necessary, we treat the latent variable as normally distributed with zero mean and estimated standard deviation. this is exactly identical to a structural equation containing only an error-term. this also means that our reduced form model is a mixed logit model with an additional random error component (vij and walker, 2016). in the present paper, we used a simple three-point likert scale question as an indicator of environmental tendency. this indicator was included in an ordered logit measurement equation. for each dataset generated, we estimate a random parameters mixed logit model, a random parameters mixed logit model with the indicators mapping directly to the marginal utilities and an iclv random parameters mixed logit model, each with and without allowing for correlation among the random parameters. from our results, it is clear that if you are only interested in choice prediction, then a mixed logit model with correlation may perform equally well to a hybrid choice model. while this is consistent with the general result of vij and walker (2016), who suggest that a reduced form model will fit the data at least as well, this is not in and of itself a reason to not use iclv models. as we, and others, have shown, such models can offer greater insight into underling behavioural phenomena and contribute to decomposing marginal effects of the latent attitude on welfare estimates. but whether or not these additional behavioural insights outweigh the costs of estimating them remains an empirical question and will be entirely context dependent. in our simulations, we show that if the structural and measurement equations are weak (i.e.  if observable characteristics are poor predictors of the latent variables, if appropriate indicators are not available and/or if the correlation between preferences, the latent construct and the indicator is weak), then 322 danny campbell, erlend dancke sandorf the model’s ability to separately identify the marginal effects are likely limited and the benefits of developing and using an iclv model are less clear-cut. in the cases where we do have weak structural equations, the use of measurement equations can help explain the latent variable and improve the fit of our choice model. unfortunately, in real world applications we do not know a priori whether an indicator is good or bad, nor is there much guidance on the strengths of correlation. but there are ways to identify better indicators using, for example, exploratory factor analysis (hoyos et al., 2015; mariel et al., 2018; mariel and meyerhoff, 2016). ultimately, however, we show that model selection should be driven by the analyst’s belief about the strength of correlations between preferences, the latent variable and indicator. in any case, we need to be careful and mindful of the criticisms of chorus and kroesen (2014) and kroesen and chorus (2018): given the potential endogenous relationship between the latent variable and choice and the crosssectional nature of the data, it is impossible to ascertain a causal relationship between attitudes and behaviour meaning that we should be very careful recommending policies that target the latent variable itself. while we have not spent significant time talking about prediction in the present paper, we do feel it is prudent to reiterate that hybrid choice models can lead to improved predictions, but that any improvements are only likely in the case where it would be possible to predict the future state of the latent variable itself (vij and walker, 2016; yáñez et al., 2010). more likely than not, this type of data will not be available. this is also possibly why we see that our models fit the data equally well, i.e.  in terms of explaining the sequence of choices made by individuals. that said, the conclusions in this paper echo those of many others (chorus and kroesen, 2014; kroesen and chorus, 2018; vij and walker, 2016), that we need to take better heed of the quality of our data and recognize the limitations of it. the usefulness of iclv models hinge on the quality of the data, and an iclv model applied to poor data may add nothing to explanatory power and even less to policy. furthermore, it is clear from our simulation work that even under “perfect” conditions, we struggled to retrieve the true parameters of the model, and the appropriateness of the model itself came down to the degree of correlation between our attributes, latent variable and indicator. taken together, this makes the use of latent variable models, perhaps especially to inform policy, a road fraught with peril. acknowledgements erlend dancke sandorf acknowledges funding from 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from a choice experiment in germany julian sagebiel1,*, klaus glenk2, jürgen meyerhoff3 the use of latent variable models in policy: a road fraught with peril? danny campbell*, erlend dancke sandorf bio-based and applied economics 7(3): 217-232, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-7676 the hedonic contents of italian super premium extra-virgin olive oils luca cacchiarelli1,*, anna carbone2, tiziana laureti1, alessandro sorrentino1 1 dipartimento di economia, ingegneria, società e impresa, università della tuscia, viterbo, italy 2 dipartimento per l’innovazione nei sistemi biologici, agroalimentari e forestali, università della tuscia, viterbo, italy date of submission: 2018 11th, march; accepted 2019 17th, july abstract. this study focuses on the italian market for high quality olive oil and seeks at assessing the value of a set of emerging quality clues. to this aim a hedonic price model is proposed where the price is regressed on various product attributes using a quantile regression that allows for deeper insights. the analysis covers about one thousand italian extra-virgin olive oils reviewed by slow food guide. overall, results indicate that various quality clues (e.g.: variety of the olives, the production area, the certification of origin, the organic certification) are associated with relevant price premiums. moreover, the quantile regression reveals the values associated to quality changes at different price levels. it is worthwhile to underline that the usual negative price premium against olive oils produced in southern italy tends to decrease in higher market segments. keywords. hedonic price, extra-virgin olive oil, quantile regression, quality clues. jel. q11, q13. 1. introduction olive oil is an important component of the mediterranean diet, it is used as a seasoning and as such it is basically eaten in association with many different foods. more than half of the world olive oil production and consumption are concentrated in eu and other mediterranean countries which traditionally are both producers and consumers. however, olive oil is increasingly appreciated worldwide as a healthy and tasty vegetable fat and its use is growing all around the world given the increased popularity of the mediterranean diet, especially among consumers in north america, australia and large parts of asia (bottcher et al., 2017; romo muñoz et al., 2015). over the last years several new quality features started playing an important role for enhancing product differentiation and market segmentation both in traditional and newer *corresponding author: cacchiarelli@unitus.it 218 luca cacchiarelli et alii consumption countries. this process not only leads to a segmentation of consumers based on taste and other personal variables, but also differentiates olive oils on the basis of different consumption occasions and of the kind of foods that olive oil is going to match. olive oil is becoming a trendy seasoning with a hedonic connotation so that its market started resembling that of wine (cacchiarelli et al., 2016b; cabrera et al., 2014). traditionally, differences in olive oils were mainly related to chemical attributes (i.e. acidity or polyphenols) that are, in turn, related to cultivation and olive-picking techniques as well as to the technology adopted for extracting oil from olives. besides, in the italian market olive oil quality is also largely associated to the production area (particularly to soil, climate and olive varieties that are associated to the place of production). the area of production may be defined at different levels such as country level, regional level, or even with reference to smaller areas (menapace et al., 2011; van der lans et al., 2001; verbeke et al., 2012). in this changing market the importance of some quality clues is emerging, although these may have different roles in different demand segments. among the others, it is worth recalling: i) the environmental impact of the production process and the related certifications (cacchiarelli et al., 2016a; marette, 2017) including organic that has gained momentum as a relevant quality feature also for olive oil (schleenbecker and hamm, 2013; cabrera et al., 2014; martinez et al., 2002); ii) the kind of flavor that may match different foods (i.e. intense or mild fruity); iii) the color (i.e. green vs. yellow) and the turbidity; iv) the shape, the size and the color of the bottle or the design of the label. all these quality features generate a complex system of both vertical and horizontal differentiation, as some attributes (i.e. acidity) can be ranked from the best to the less preferred ones, while for other attributes consumers’ preferences are not aligned (i.e. filtered vs non-filtered olive oil, oils from tuscany vs. umbria regions). in countries where the use of olive oil is traditional and common, the consumers’ ability to choose quality attributes is widely based on buying habits. in newer markets consumers need to collect information in different ways and many quality clues have been developed at different stages of the value chain and by different stakeholders (roselli et al., 2016). relevant quality clues are mainly experience and credence attributes, implying that the market is affected by a significant degree of asymmetric information (mastronardi et al, 2015). as the sophistication of the product and the complexity of the market increase, additional information is required and the effectiveness and reliability of each quality attribute can be questioned (hassan and mornier-dilhan, 2002). in this context, reviews by experts in journals and guides as well as testing events and prizes, become a relevant source of information. they also provide comparisons between individual preferences and external, more competent and objective judgments, thus contributing to increase product value (spiller and belogolova, 2017). these reviews are used not only by the final consumers but also by many different kinds of stakeholders along the chain (poroissien and vissier, 2018; cacchiarelli et al., 2016b; delgado and guinard, 2011). such a complex market implies that also prices are diversified and span over a large range; as a consequence, price itself further segments the market and contributes to convey information about quality and safety (haws et al., 2017). in order to fully understand the crucial role of price in this market it is useful to keep in mind that olive oil, besides being itself a differentiated good, has also many cheap substitutes among other vegetable oils. this means that when purchasing olive oil and particularly extra-virgin ones 219the hedonic contents of italian super premium extra-virgin olive oils (evoo), consumers are already in high segments of the wider vegetable oil market and are seeking for a quality product for which they carefully consider price and attributes (martinez et al., 2002). following these premises, this study aims at assessing the role of different quality clues in the creation of value in higher segments of the italian olive oil market. on the one side, this focus allows to get insights on one of the oldest and largest evoo market; on the other side, we argue that looking at the higher and more sophisticated segments of the market contributes to understanding which tendencies will spread in the near future in the wider evoo market. to meet this goal, a hedonic price model is estimated where price is regressed on different quality clues (rosen, 1974; thrane, 2004). most works employing the hedonic price approach have focused on wine (benfratello et al., 2009; schamel, 2006; cacchiarelli et al., 2016a). however, in recent years, various studies aimed at identifying the more effective variables in creating value in the olive oil markets, both in eu mediterranean countries (italy, greece and spain) and in the so called “new countries” (chile and us) (romo muñoz et al., 2015; gazquez-abad and sanchez-perez, 2009; roselli et al., 2016; carbone et al., 2018). in literature, the hedonic price models have been usually estimated by using ordinary least squares (ols) regression. however, over the last few years the quantile regression model (qrm) has also been applied in order to establish whether the relationship between price and other product characteristics and quality clues varies at different price levels (cacchiarelli et al., 2014; costanigro et al., 2010). while the former shows how the various quality clues affect, on average, prices, the latter detects additional patterns (location, scale and skewness shifts) related to the effects of the covariates and, thus, allows to investigate consumer behaviour at different price levels. the paper is organized as follows. section 2 illustrates the source of data, the model specification and the methods employed in the estimations. section 3 reports and discusses results, while section 4 concludes. 2. methodology 2.1 the source of data data used for estimating the hedonic price model comes from one of the major italian olive oil guides: slow food guide (2014 edition).this guide has been chosen for three basic reasons: i) the data set is quite large as it includes 1024 evoos (of which 1001 have been utilized for the analysis due to missing data for the remaining 23); ii) coverage of italian production areas is wide; iii) information released about each product is rich and relevant for stakeholders. for each reviewed producer/oil the guide reports a set of information about the product, about the farm/mill and about the production process. olive oils included in this guide account for about 3% of evoo national production (in volume) and represent the top segment of the market with an average price that is about 5 times higher than the average unit value of bulk production. this focus on top quality evoos allows us to investigate on a quite peculiar market segment where quality and attention to quality clues are very high (slow food, 2014). evidences from such a peculiar market segment cannot be extended sic et simpliciter to the whole evoo market. how220 luca cacchiarelli et alii ever, considering that market niches and especially high market segments tend to anticipate upcoming trends that spread out over time, these findings bring interesting insights on what will likely be general future trends (yeoman and mcmahon-beattie, 2006; lataczlohmann & foster, 1997 ). 2.2 model specification 2.2.1 the model in the analysis of differentiated products, several studies have adopted hedonic price models in which the price is described as a function of product characteristics (deselnicu et al., 2013; oczkowski, 2001). in this study, with the aim of measuring the price premiums associated to different quality clues in the italian olive oil market we use a hedonic price model specified as follows: log poili = α0 + α1icui + α2ipii + α3imii + α4ivoli + α5iori + α6iszi+ α7igii + α8imri + εi (1) where: log poili, the logarithm base 10 of the evoo price, is the dependent variable; cui indicates a set of dummy variables accounting for olive variety; pii is a dummy variable that indicates the technique of harvesting; mii is an ordinal variable indicating the degree of vertical integration; voli is an ordinal variable measuring production volumes by class; ori is the dummy for organic evoos; szi accounts for bottle size (ordinal); gii assesses the presence of the certification of origin; mri is a categorical variable for the macro-area of origin. it is worth to underline that not all the quality cues here considered have the same visibility for consumers. in fact, while some appear in the label of the bottle, other do not. however almost all can be found in the producer/seller website and all of them are released by slow food guide. the model assumes that consumers in this super premium market segment are so interested in quality features that, not only are willing to pay very high prices but also devote time and expertise in collecting and evaluating these less visible pieces of information. besides, it should also be taken into account that retailers in these premiums market segments are usually willing and committed to release additional information they consider valuable to customers (clerides et al., 2008). 2.2.2 the variables. the variables included in the model are described below while table 1 reports frequencies and descriptive statistics of price distribution for all the selected explanatory variables. 1) prices are released by producers at the final consumers’ price (in euros, vat included). each price is referred to the actual bottle size used for packaging (250/500/750 ml and 1 liter) so that, in order to allow for correct comparisons, the dependent variable was transformed in euros per liter. the mean and the median values (respectively 16 and 18.7 euros/lt. as shown in table 1) confirm that the market reviewed by the guide is correctly defined as super premium1. 1 the maximum price value, as evidenced in table 1, is very high due to an outlier present in the sample, as it is also confirmed by the price value at 90th quantile (30 euros). 221the hedonic contents of italian super premium extra-virgin olive oils 2) cu relates to olive variety (i.e. the cultivar of the tree). mono-cultivar oils were not so common in italy until a few years ago though presently their number is increasing as a mean for differentiation and following consumers’ interest for variety based also on sensory features and their inclination for (re)discovering old traditional varieties. slow food guide devotes much attention to mono-cultivar oils. the model includes three categories of mono-cultivar oils that are distinguished according to the territorial diffusion of the olive variety: i) national olives such as pendolino, moraiolo, leccino, and a few others (14% of the sample); regional varieties such as itrana, carolea, carboncella and many others (13% of the sample); and local varieties that are hundreds each cultivated in a very limited area (altogether these account for 23% of the sample). this distinction is aimed to get information about the value that consumers may attach to diversification and strong territorial roots vs wider diffusion and more general reputation of more common and better-known varieties. the remaining half of the oils reviewed in the guide are blend of different cultivars; this dummy act as benchmark for the other cases. 3) pi indicates the technique of harvesting: where 100% hand picking and machine aided hand picking are both included in the same dummy (that accounts for 77% of the sample) as opposed to complete machine picking (23%), as the latter has a different impact on product quality and on cost level and structure. 4) mi is an ordinal variable reflecting the degree of vertical integration and, thus, measuring the strengths of the relation among stakeholders in charge of olive production and oil processing and packaging. the stricter relation holds when there is an on-farm table 1. frequencies and descriptive statistics of price distribution for the different quality clues. national cultivar 141 0.14 7.5 10.5 14 16.5 19.88 20 30 52 regional cultivar 134 0.13 8 10.5 14 16.5 19.16 20 30 80 local cultivar 227 0.23 6.5 10.5 14 16.5 19.48 20 30 100 olive oil blend 499 0.50 5.5 10.5 14 16.5 18.71 20 30 100 pi hand picked 778 0.77 5.5 10.5 14 17 19 20 30 100 cooperative mill 133 0.13 5.5 10.5 13.5 16 18.3 20 30 50 mill on farm 394 0.39 6.5 10 13.5 17.25 19.4 21.5 30 100 mill off farm 474 0.47 6 10.5 14 16 18.3 20 30 80 1-50 hl 562 0.56 5.5 10.5 14 17 19.1 20 30 100 51-100 hl 154 0.15 7 10 13 17 18.6 20 28 52 101-500 hl 94 0.09 8 10 13 15.75 17.2 20 28 42 >501 hl 191 0.19 6 9.5 13 16 18.25 20 30 48 or organic 475 0.47 5.5 10.5 14 16.5 18.5 20 30 80 bottle of 250 ml 30 0.03 12 19.5 30 32 37 40 54 100 bottle of 500 ml 583 0.58 9 13 16 20 21.2 24 30 60 bottle of 750 ml 329 0.33 5.5 9.5 10.5 13.5 13.9 15.5 20 48 bottle of 1 litre 59 0.06 6 7.5 9 12 11.4 13 16 20 gi pdo-pgi 183 0.16 5.5 10 14 17 20.4 20 30 60 north 147 0.15 10 14 20 24 24.7 28 37 100 centre 361 0.36 8 12 16 18 20.0 22 30 50 south 492 0.49 5.5 9.5 12 14 15.8 18 24 80 total 1001 1.00 5.5 10.5 14 16 18.71 20 30 100 cu mr mean 70th quantile 90th quantile variable mi vol sz maxobs freq min 10th quantile 30th quantile 50th (median) source: our elaborations on slowfood 2013. 222 luca cacchiarelli et alii mill (39% of the sample), the second level refers to farms cooperatives that mill olives conferred by members (which are 13%) and the third case is represented by private mills (47% of the sample) that process olives bought from different farms (that are mostly located, nearby). in this case we estimate the price premiums associated to oils from on-farm mills, or from cooperatives in comparison with oils from off-farm mills (the benchmark for the estimation of the pp). 5) vol expresses the production scale as follows: 1-50 hl (56%), 51-100 hl (15%), 101-500 hl (9%) and more than 500 hl (19%). although the most of the producers in the sample are small or medium-small, the relation between production volumes and price may be complex due to possible diverging reputational effects as it will be discussed later on in the text. 6) organic oils (or) represent a bit less than half of the slow food selection (47%). organic production is quite established in the italian olive oil sector thanks to the favorable climatic conditions in many areas and to the emerging consumers’ interest for this attribute. 7) variable sz represents the following bottle size: 250 ml (3%), 500 ml (58%), 750 ml (33%) and 1000ml (6%). the size of the bottle affects the use of the product; smaller bottles are preferred for making presents, for trying new products (martinez et al., 2002), for special occasions and in case of difficult transport conditions (e.g. in case tourists buy evoo when travelling). conversely, larger bottles are preferred for domestic every-day consumption. 8) gi is the european certification of origin which includes pdo (protected designation of origin) and pgi (protected geographical indication); however, since in italy there is only one pgi olive oil but many pdos, for the purposes of this analysis they have been all gathered in one dummy that distinguish between gi (pdo and pgi) certified evoos (16%) and non-certified ones (84%). 9) mr represents the area of origin defined at the following macro-area level: northern (15%), central (36%) and southern italian regions (49%). in the italian evoo market, especially in segments where quality is relevant, the macro-area of production matters for consumers as it is also confirmed by significant and persistent price differences for both bulk and bottled oils. although the reputation of evoos from different regions varies significantly within the country, stricter area definition was not possible due to the small size of some regional sub-samples in the guide. as it can be seen from table 1, the mean of the price distribution is higher than the median, for many quality clues, thus suggesting that the dependent variable is positively skewed (the value of the fischer coefficient is 2.35). moreover, the range values (max-min) suggest a great heterogeneity of prices in the sample. figure 1 shows the distribution of prices through a probability density function, which is a powerful tool to describe several properties of a variable of interest (cowell and flachair, 2013). although this function seems basically unimodal (about 18 euros), it also presents a few additional, much less pronounced, modes (see in the highest quantiles) and a stretched shape of the right-side tail of the distribution. such a distribution suggests exploring the relationship between prices and the selected quality clues as they might change along the different quantiles and particularly at the two extremes (table 1). the choice of the functional form of the hedonic model is essential because it determines the way marginal prices will be related to attributes (rasmussen and zuehlke, 223the hedonic contents of italian super premium extra-virgin olive oils 1990). a reset test (regression equation specification error test) was run in order to explore a series of possible transformations of the dependent variable (e.g. log, inverse square root). the test has revealed that the log-linear specification performs better than other functional forms so that it has been chosen for estimating equation (1). log-linear specification presents a twofold advantage with respect to other ones: i) it allows obtaining residuals that are approximately normally distributed as required by the selected regression models; ii) the interpretation of regression coefficients is immediate: the dependent variable changes by 100*(ecoef -1) percent for a one-unit increase in one of the regressors, holding all other variables fixed. last, heteroskedasticity proportional to the predicted values was tested via goldfeld–quandt statistics (goldfeld and quandt, 1965). 2.3 estimation methods clearly, even in this super premium market segment, the impact of quality attributes on price may differ across price levels. therefore, following the prices distributions described in table 1 and shown in figure 1, a qrm was run to go deeper into the analysis of the market segmentation mechanism. selected quantiles are: 0.1, 0.30, 0.50, 0.70, 0.90 percent2. quantile regression (koenker, 2005) is used for estimating the functional relationship between olive oil price and quality attributes at different points in the conditional distribution of y. moreover, quantile regression is more robust than ols regression in response to large outliers which may be present in the olive-oil top market segment. consequently, we estimate model (1) over the various quantiles which are of interest in our research context. the qrm analyzes the effects of the explanatory variables at different quantiles of the price distribution as opposed to focusing on the mean of the distribution (cameron and trivedi, 2005). although its computation requires linear programming methods, the quantile regression estimator is asymptotically normally distributed. moreover, qrm is a semi-parametric approach since it avoids assumptions concerning the parametric distribution of the regression errors. this technique specifies the conditional quantile as a linear function of covariates (koenker, 2005). quantile regression has several advantages over ols. indeed, ols can be inefficient if errors are highly non-normal while qr is more robust to non-normal errors and outliers. in the present case, the θth quantile regression can be written as: qθ yi│x i( ) = x i'βθ + εθ (2) where yi (i=1,…,n) is the dependent variable (logarithm of the price), xi is the sequence of the k-vector of regressors while βθ is an unknown vector of regression parameters associated with the θth quantile and εθ is an unknown error term. the quantile regression estimator for quantile 0<θ<1 minimizes the sum of absolute deviation residuals: 2 for quantile estimates, standard errors were calculated by bootstrapping and, specifically, 400 random draws were taken. moreover, by using wald test, comparing pairwise at each fifth quantile within the 5th and 95th, we formally verify whether the effect of each variable statistically differs across quantiles (hao and naiman, 2007). 224 luca cacchiarelli et alii β∈rk min i:yi≥x iβ ∑ θ│yi − x i 'β│+ i:yi≥x iβ ∑ 1−θ( )│yi − x i'β│ ⎧ ⎨ ⎪ ⎩⎪ ⎫ ⎬ ⎪ ⎭⎪ (3) which is solved by linear programming methods. when θ is continuously increased from 0 to 1, we obtain the entire conditional distribution of y conditional on x. 3. results table 2 reports estimation results from quantile models at the selected points of the price distribution. figure 2 provides a graphical view of the qrm estimates where, for each selected quality clue, the vertical axis shows the pps associated to the different quantiles3 (horizontal axes). the fit of the model, measured by pseudo r2, is quite good. these values indicate that the model takes into account the effects of important quality clues related to prices 3 in figure 2, the gray-shaded area illustrates the bootstrap 95% confidence interval while the line shows qrm estimates. figure 1. prices distribution. 0 .0 2 .0 4 .0 6 d en si ty 0 20 40 60 80 100 price €/l kernel = epanechnikov, bandwidth = 1.5076 kernel density estimate source: our elaborations on slowfood 2013. 225the hedonic contents of italian super premium extra-virgin olive oils in the italian market for sophisticated evoos. nevertheless, the model proposed clearly focuses on the value of quality features captured by the market while leaves out of the picture other features that, altogether, may be relevant and able to influence consumers’ prices. coming to detailed estimation results, we start from those that generate the higher pps, even if in some cases the effects in the different price quantiles vary and generate an uneven ranking. first, bottle size confirms to be an important leverage for price. as a matter of fact, smaller sizes get, on average (i.e. 50th quantile), always a positive price premium compared to larger bottles: 750 ml worth +23.7% compared to 1000ml, while they get, respectively, -80.5% compared to 250 ml and -32.9% compared to 500 ml. these results are in line with findings of other studies (cabrera et al., 2014). results for different quintiles provide additional insights by showing that the mentioned price differentials are higher and more significant in the highest market segments where packaging matters more; in particular the smallest bottle size is associated with the highest pps observed in the sample (+89%) (see also the bottom of figure 2). wald test confirms these results showing that in case of bottles both from 250 ml and 500 ml the 30th, 50th and 70th quantiles are statistically different from 90th (at 5% level of significance). second, variables related to the place of origin are all associated with significant and large price premiums. olive oils from northern and central regions worth more compared to products from southern regions (46.1% and 18.4%, respectively). this result reflects the widely known segmentation of the italian olive oil market and it is in line with the findings of other studies focused on high quality evoo markets (carbone et al., 2014; di vita et al., 2013). moreover, the qrm provides additional non-trivial insights also confirmed by wald test (see the upper part of figure 2). the price premiums associated to northern and central regions decrease in the upper quantiles (70th and 90th), indicating that in the higher market segments consumers are less influenced by the macro-area of origin. this is probably due to the higher consumers’ willingness to collect detailed information about producers and their products before buying more expensive bottles instead of using proxies such as those related to the production area. this result suggests that olive oil producers from southern regions that seek at marketing excellent evoos might reduce the negative price gap that affects evoos from the south, provided they are able to select appropriate information and quality clues for each market segment. according to the important role played by the area of origin, our findings show that also the certification of the place of origin (gi) affects prices. in line with findings from other works (carlucci et al., 2014), pdo/pgi evoos get, on average, a price premium of +12.5% compared to non-certified olive oils, showing that this certification is a muchappreciated quality clue. looking at the different quantiles (at the top right of figure 2) it appears how the certification of origin plays a greater role in the highest market segment (+ 18.9% at the 0.90 quantile). wald test confirms this result proving that the 70th quantile is statistically different from the 90th at 10% level of significance. organic certification affects positively evoo prices (on average +9.3%) as well. the result holds at any price quantile without relevant differences in the size of the pp. this outcome confirms the positive role played by organic certification in the evoo market as emerged in other works (delmas and lessem, 2017). 226 luca cacchiarelli et alii table 2. qrm estimation results for various conditional quantiles. national cultivar 0.091* 0.089* 0.098* 0.121* 0.102 (0.0241) (0.0283) (0.0238) (0.0318) (0.0682) regional cultivar 0.067** 0.091** 0.110* 0.158* 0.093* (0.0284) (0.0216) (0.0219) (0.0272) (0.0272) local cultivar 0.088* 0.106* 0.085* 0.051* 0.093* (0.0263) (0.0243) (0.0253) (0.0342) (0.0343) pi hand picked -0.002 -0.027 -0.032*** -0.051** -0.082** (0.0192) (0.0259) (0.0226) (0.0284) (0.0282) coop mill -0.022 -0.042 -0.024 -0.024 -0.056*** (0.0425) (0.0325) (0.0261) (0.0263) (0.0317) mill on farm -0.016 -0.003 0.032 0.047*** 0.103** (0.0232) (0.0243) (0.0258) (0.0334) (0.0321) 51-100 hl -0.019 -0.027 0.011 0.095** 0.100* (0.0370) (0.0361) (0.0341) (0.0362) (0.0342) 101-500 hl -0.024 0.010 0.003 0.021 -0.005 (0.0382) (0.0364) (0.0352) (0.0323) (0.0612) >501 hl 0.006 -0.027 -0.032 -0.005 0.027 (0.0231) (0.0252) (0.0325) (0.0554) (0.0323) or organic 0.073* 0.086* 0.093* 0.079* 0.068* (0.0172) (0.0275) (0.0192) (0.0248) (0.0241) bottle of 250 ml 0.677* 0.811* 0.805* 0.867* 0.892** (0.1128) (0.0372) (0.0613) (0.1352) (0.4127) bottle of 500 ml 0.281* 0.317* 0.329* 0.335* 0.452* (0.0196) (0.0218) (0.0223) (0.0283) (0.0291) bottle of 1 litre -0.285* -0.249* -0.237* -0.316* -0.313* (0.0243) (0.0623) (0.0321) (0.0363) (0.0372) pdo/pgi 0.121* 0.137* 0.125* 0.080* 0.189* (0.0182) (0.0277) (0.0253) (0.0318) (0.0512) north 0.430* 0.484* 0.461* 0.418* 0.366* (0.0413) (0.0372) (0.0314) (0.0451) (0.0421) centre 0.207* 0.179* 0.184* 0.150* 0.138* (0.0312) (0.0272) (0.0269) (0.0334) (0.0417) cons 2.143* 2.332* 2.414* 2.607* 2.733* (0.0362) (0.0382) (0.0381) (0.0551) (0.0524) pseudo r^2 0.325 0.335 0.321 0.305 0.287 70th quantile 90th quantile mi 30th quantile 50th quantile cu vol sz gi mr 10th quantile variable source: our elaborations on slowfood 2013. 1 table reports coefficients and standard errors (in brackets). 2 *means significant at 1%; **means significant at 5%; ***means significant at 10%. 227the hedonic contents of italian super premium extra-virgin olive oils with respect to the role of the cultivar, the model provides interesting findings. first, mono-cultivar oils are always associated with positive pps ranging between 8% and 11%, regardless to the size of the diffusion area of the cultivar itself and regardless to quantiles. since usually labels explicitly claim whether the oil is made with one olive variety, regardless to the specific cultivar utilized, mono-cultivar oils are appreciated and valued as such. as this kind of product is almost new in the italian market and introduces a new factor of differentiation, the result seems to indicate that consumers in this market segment appreciate novelty and variety. this finding is in line with recent literature (carlucci et al., 2014). moving to the next set of variables, results show that the scale of the production process affects prices in a quite complex fashion. in particular, the estimates show that production volumes have limited or non-significant impacts on price in the lower price quantiles, while at 70th and 90th quantiles medium-small producers are favored compared both to very small producers and to larger ones, with a pp of around 10%. this is probably due to a complex reputational effect, according to which very small producers are hardly visible in larger markets where they find difficult to establish their own reputation and to get a pp; at the other extreme, very large companies may give an image of a more standardized less valuable product compared to medium and medium-small producers who can be associated to a sense of rarity, exclusivity and preciousness that pushes price up (eisend, 2008; kristofferson et al., 2017). 0. 10 0. 20 0. 30 0. 40 0. 50 n or th 0 .2 .4 .6 .8 1 quantile 0. 00 0. 10 0. 20 0. 30 0. 40 c en tre 0 .2 .4 .6 .8 1 quantile 0. 00 0. 10 0. 20 0. 30 0. 40 pd o _p g i 0 .2 .4 .6 .8 1 quantile 0. 50 1. 00 1. 50 si ze 25 0m l 0 .2 .4 .6 .8 1 quantile 0. 10 0. 20 0. 30 0. 40 0. 50 0. 60 si ze 50 0m l 0 .2 .4 .6 .8 1 quantile -0 .5 0 -0 .4 0 -0 .3 0 -0 .2 0 -0 .1 0 0. 00 si ze 1l itr e 0 .2 .4 .6 .8 1 quantile figure 2. qrm estimates of place of origin, pdo and bottle size. 228 luca cacchiarelli et alii as for other features of the production process, and, in particular, the way olives are picked, results show that hand picking negatively affects prices (on average, -3.2%). the price premium becomes even more negative in the highest market segments (-8.2% in the 90th quantile). even considering that most consumers may not be aware of the methods adopted for harvesting, this result is hard to explain and requires further explorations. in fact, so far, hand picking has been considered a superior technique in terms of preserving sensorial qualities and avoiding high acidity rate. however, more recently, technological change has improved the performance of harvesting machinery also in terms of plant health and product quality. besides, machine harvesting requires shorter time than hand picking; this, in turn, allows for processing fresher olives, thus contributing, other things being equal, to push up oil quality. summing-up, the role of this feature shall be further explored and/checked also looking at different datasets. finally, concerning vertical integration, again, this does not seem to significantly affect price on average. however, in the highest market segments the presence of on-farm mill is statistically associated with a positive price premium between 5% (70th quantile) and 10% (90th quantile); while, on the other hand, a negative pp (-5.6%) is associated to cooperative mills at the 90th quantile. the first of these results can be explained by the deeper interest of consumers in buying an evoo strictly connected to the farm –and as such, regarded as to more genuine, traditional and so forth when they are spending more. the negative pp associated to the coop mills may be explained by the negative reputation that surrounds coops in some italian regions, where, due to different reasons whose analysis is beyond the scope of this paper (carbone et al., 2010), coops are not regarded as able to provide quality products. 4. concluding remarks trends in consumers’ demand as well as marketing strategies in the olive oil sector seem to increasingly push towards product differentiation, following to some extent the wine market. the increasing role of different quality clues creates different and inter-related layers of horizontal and vertical differentiation that frame the market as progressively sophisticated. in the present study a hedonic price model has been built for exploring the italian high-quality olive oil market in order to identify the price-quality relation for different quality features. quantile regression has been used for analyzing the functional relationship between olive oil price and quality attributes at different points in the conditional distribution of price. data used have been collected from slow food olive oil guide that portraits the italian high quality evoo market. in particular, our model specification brings about some interesting insights that in some cases confirm results already discussed in the literature; while in others provide original indications. the quantile regression estimates indicate that overall the quality clues included in the model have a significant impact on price at the different price quantiles. however, in the lower quantiles there are some clues that do not impact prices while they are effective at higher price levels. among these there are clues that are not released by the labels such as the kind of olive-picking, the size of the production units and the degree of vertical 229the hedonic contents of italian super premium extra-virgin olive oils integration. this can be explained by the deeper interest of consumers in some quality features when they are spending more money. this more demanding attitude towards quality may push them to collect additional information with respect to that released in the label. as for the remaining quality attributes, all have a significant impact on price and this impact significantly increases with price. while price differentials between italian macro-regions are well known and represent no novelty at all, the finding that these differences reduce in higher price quantiles is original and valuable. this may suggest that southern producers shall use different communication strategies, with respect to the place of origin, when targeting at different market segments. also results about certifications of origin (pdo/pgi), showing a higher pp in the highest price quantile, are not trivial. this is especially true when comparing them to those found for the wine market where the certifications of origin are more rewarding at medium-low price levels. in fact, in the case of wine, they seem to act more as a minimum quality standard than as a clue for excellence. the explanation of this difference between the two sectors is given by the extreme sophistication of the wine market where quality clues are many and diverse and wine producers have reached a greater visibility and reputation in the marketplace, while, on average, olive oil producers are far less reknown (except large industrial firms that do not belong to the kind of market we are looking at). besides, the certification of origin is relatively less used and more recent in the olive oil market compared, for example, to wine, so that it has not yet become a trivial quality clue as it is in some cases for wines where it also suffers from a lack of trust. as expected, bottle size is associated with the highest pp evidenced by the model estimates. specifically, smaller sizes cost more compared to bigger ones. again the qrm brings additional insights: just as in the case of the place of origin, the quantile estimates show that pp increases in higher quantiles. one more original result of the study concerns the value associated to olive varieties, with mono-cultivar and the nationally widespread olive cultivars that add values to the oil. these results can be taken by producers in order to adopt relatively easy differentiation strategies based on the separation of olive varieties before milling, hence increasing the value of their oil. results on harvesting methods were unexpected and remain unexplained, thus shedding light on an area that requires further explorations for improving our knowledge of this changing market. besides, the overall results obtained also indicate that some factors that were not included in the model due to lack of datamay play an important role in the olive oil market, so that more work is needed for a better understanding of additional relevant and more recent tendencies. 5. references benfratello, l., piacenza, m. and sacchetto, s. 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(2006). luxury markets and premium pricing. journal of revenue and pricing management 4(4): 319-328. positive mathematical programming and risk analysis quirino paris the hedonic contents of italian super premium extra-virgin olive oils luca cacchiarelli1,*, anna carbone2, tiziana laureti1, alessandro sorrentino1 corporate r&d and the performance of food-processing firms: evidence from europe, japan and north america heinrich hockmann1, pedro andres garzon delvaux2,*, peter voigt3, pavel ciaian2, sergio gomez y paloma2 can menu labeling affect away-from-home-dietary choices? elena castellari1,*, stéphan marette2, daniele moro3, paolo sckokai1 a preliminary test on risk and ambiguity attitudes, and time preferences in decisions under uncertainty: towards a better explanation of participation in crop insurance schemes attilio coletta1, elisa giampietri2, fabio gaetano santeramo3,*, simone severini1, samuele trestini2 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-10000 bio-based and applied economics 8(3): 237, 2019 in memory of ornella w. maietta ornella w. maietta passed away on 18th april 2019, after a long battle against cancer fought with the dignity that marked her whole life. ornella got her bachelor degree in agriculture in 1987 at the university of naples federico ii and the specialization degree in agricultural economics in 1991 at the centro di specializzazione in economico agrarie per il mezzogiorno, portici (na). then she enrolled the m.phil. in land economy at the university of cambridge, uk where she graduated in 1993. finally, she got her phd degree in agricultural economics and policy at the university of siena in 1997. soon after she got a position as researcher at the then department of agricultural economics and policy of the university of naples federico ii. then she moved to the department of economics and statics of the same university where she was serving as associate professor of economic political. ornella’s contributions to the profession focuses mostly on two broad areas of research, namely: a) the economics of innovation where she provided key contributions on the methodology for estimating the farm/firm efficiency and productivity – she was the recipient of the 2002 best young economist paper by the european review of agricultural economics/european association of agricultural economists and soon after she published an important book on the analysis of efficiency that has been the reference textbook for the last generation of production economists – as well as on the role of human capital in generating innovation and growth with a focus on university-firms r&d collaboration as a driver of innovation especially for low-tech industry; b) the economic analysis of non-profit sector, with a wide set of contributions ranging from the analysis of cooperatives to socially responsible consumption, from fair trade consumption to school meals and care sector, to some contributions that tried to bridge between the two broad areas of research analyzing the role of social capital and innovation. ornella was very active also in the profession being a member of the italian association of agricultural and applied economists – aieaa since its establishment in 2011 and having served as associate editor of bio-based and applied economics – bae from 2012 to 2018. ornella was an excellent economist, an effective mentor of generations of young economists, and a true friend of many of us. we will always remember her brilliant economic mind as well as the high quality of her academic commitment and her pursuit of excellent in study and research. but we will mostly miss the human touch she put in whatever she did, the mutually respectful relationships she was able to develop and the supportive and loyal collaboration she provided to whoever worked with her, primarily the young economists. bio-based and applied economics 10(1): 35-49, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9909 bio -based and a ppl ied economics bae copyright: © 2021 n.i. sampalean, d. rama, g. visentin. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: n.i. sampalean, d. rama, g. visentin (2021) an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications, and consumption of agrifood products carrying those certifications. bio-based and applied economics 10(1): 35-49. doi: 10.36253/bae-9909 accepted: april 14, 2021 published: july 28, 2021 competing interests: the author(s) declare(s) no conflict of interest. editor: fabio gaetano santeramo. orcid nis: 0000-0002-9222-2828 dr: 0000-0002-4207-8338 gv: 0000-0003-0869-5516 an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications, and consumption of agri-food products carrying those certifications niculina iudita sampalean1, daniele rama1, giulio visentin2 1 department of agri-food economics, catholic university of the sacred heart, via milano, 24, 26100, cremona (cr), italy. e-mail: daniele.rama@unicatt.it 2 department of veterinary medical sciences, alma mater studiorumuniversity of bologna, via tolara di sopra 50, 40064, ozzano dell’emilia (bo), italy. e-mail: giulio.visentin@unibo.it corresponding author: niculina iudita sampalean. e-mail: niculinaiudita.sampalean@ unicatt.it abstract. the present study investigated italian consumers’ awareness, perception, knowledge of european union (eu) quality certifications: protected designation of origin (pdo), protected geographical indication (pgi), traditional specialty guaranteed (tsg), and organic as well as the consumption of agri-food products carrying those certifications. a total of 212 consumers responsible for food purchases took part in a web-based survey between june and december 2019, inclusive. descriptive statistics were calculated in relation to the data collected, followed by a factor analysis to reduce data dimensionality, and a cluster analysis on the latent variables generated, to identify similarities and differences among respondents. awareness, perception, knowledge and consumption of agri-food products carrying eu quality labels has increased among consumers in recent years. the results related to the consumer’s knowledge of quality-certified products showed that more than half of respondents were able to spontaneously quote examples of pdo (76%), pgi (56%) and organic food products (73%) while only 33% of participants could name at least one tsg product. the general awareness of the guarantees offered by pdo and pgi certifications was also assessed in relation to production processes, the natural and human factors of a particular environment and the reputation and quality of a particular region. cluster analysis showed that consumers with the highest education were most likely to value eu quality certifications and support their local economies. the information obtained have practical implications for marketing and communication of european certified food products at national and international level. keywords: factor analysis, cluster analysis, food labels, knowledge evolution, european quality certifications. http://creativecommons.org/licenses/by/4.0/legalcode 36 bio-based and applied economics 10(1): 36-49, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9909 niculina iudita sampalean, daniele rama, giulio visentin 1. introduction european quality certification was first introduced with regulation (eec) no 2081/92, which was subsequently repealed by regulation (ec) no 510/2006, followed by regulation (eu) no 1151/2012. such regulations define three key labels of product quality, namely: protected designation of origin (pdo), protected geographical indication (pgi), and traditional speciality guaranteed (tsg). pdo are products originating in a specific place, region or in a country, whose quality or characteristics are essentially or exclusively due to a particular geographical environment with its inherent natural and human factors and whose all production steps take place in the defined geographical area. pgi products are originating in a specific place, region or country, whose given quality, reputation or other characteristic is essentially attributable to its geographical origin and whose have at least one of the production steps taken place in the defined geographical area. finally, tsg are products or foodstuff that results from a mode of production, processing or composition corresponding to traditional practice for that product or foodstuff or is produced from raw materials or ingredients that are those traditionally used. the main differences among them are related to the number of production steps that are involved in the defined geographical area, the raw materials used and the way the product is made. the quality policy aims to protect the names of specific products to promote their unique characteristics which are associated with their geographical origin, as well as their traditional knowhow. the eu quality recognition enables consumers to trust and identify quality products while also helping producers to trade on the added value markets and avoid free riding. moreover, these formal certifications help food products to be more competitive in the global market (carbone, 2018). the european parliament and council have also established quality certifications for organic agri-food products (regulation (eu) no 2018/848). according to this regulation the organic products were developed to respond to a specific market in which consumers were demanding for products whose production’s promotes environmental protection and animal welfare, maintains the biodiversity of europe, contributes to rural development.the distribution of quality-certified products across europe is not homogeneous, as more than 70% of the total products originate from only five countries, including italy (21%) , france (17%), spain (14%), portugal (10%) and greece (8%) (eu commission, 2019). as for consumers perception of these products and their characteristics the distribution is varying (profeta et al., 2010). indeed, aprile and gallina (2008) reported a level of awareness of 30% with regard to pdo, pgi and stg labels among italian consumers, whereas verbeke et al. (2012) observed that 23% of the italian respondents were aware of the pdo certification, 38% were familiar with the pgi certification and 22% recognized the tsg certification. in northern european countries, consumers’ awareness of quality recognition is generally low (jordana, 2000; profeta et al., 2010; vanhonacker et al., 2010) but is increasing, as these products seem to capture new segments on the market (european commission, 2018). in countries specialized in the production of quality-certified food, pdo/pgi labels are reported to be important and play a role in the consumers’ decisionmaking process as well as on their willingness to pay, as these products have a favourable image (scarpa and del giudice, 2004; van ittersum et al., 2007, vecchio and annunziata, 2011), however, other studies (platania and privitiera, 2006; grunert and aachmann, 2016) have reported evidence to the contrary. although the pdo/ pgi labels appeared to be important, aprile et al. (2016) observed that only a small proportion of consumers was able to correctly associate pdo/pgi/organic farming characteristics to their respective labels. however, the organic farming label seemed to be more widely recognized among eu consumers, irrespective of their own national level of food quality specialization (european commission, 2018). the simultaneous investigation of perception, awareness, understanding, knowledge, decision-making and consumption of the european quality certifications was often hampered by the limited sample size, as well as the difficulty in retrieving information from the consumers’ questionnaire. indeed, many of the studies concentrated primarily on one aspect, with the majority focusing on the decision-making process, measured generally using the conjoint analysis (krystallis and ness, 2005; mesias et al., 2005; capelli et al., 2014). to the authors’ knowledge, no research conducted among italian consumers has ever attempted to determine all those aspects in one single study. another important issue was the often limited geographical distribution of the sample of respondents collected, which was primarily restricted to the main cities or to certain provinces (van der lans et al., 2001; arfini and pazzona, 2014; ceschi et al., 2018). we focused our research on the last eu regulation’ (no 1151/2012) main objective (‘’to help producers of agricultural products and foodstuffs to communicate the 37 bio-based and applied economics 10(1): 37-49, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9909 an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications product characteristics and farming attributes of those products and foodstuffs to buyers and consumers’’) and tried to study if this goal was reached, if this regulation can be considered a proper tool in communicating those food’s attributes to consumers, or if eu should find a better suited solution. for our study’ objective we considered consumers perception, awareness, knowledge, and consumption of the pdo/pgi/tsg being the best way to measure the regulation objective’s accomplishment. given this, an overview of the past and current situation was required to understand whether there was any positive change in the consumers attitudes towards these certifications. confirmation of the existence of a real evolution will help prove the effectiveness and efficiency of pdo/ pgi/tsg certifications as a marketing tool, therefore the eu regulation (no 1151/2021) could be considered successful, reaching one of its main objectives. new policies and communication efforts could be used to enhance consumers’ curiosity in relation to products that are pdo/pg/tsg/organic certified. 2. material and methods 2.1 the survey between june and december 2019, a convenience sample made of 312 consumers across italy replied to the web-based survey, formulated to conduct the current research. of these, only 212 declared that they were responsible for the food purchases in the household, therefore, only these 212 consumers were invited to complete the whole questionnaire. the survey aimed to examine european quality certifications, to understand whether they were recognized by the consumers (awareness), whether the consumers perceive the guarantees offered by the pdo/pgi/tsg, organic certifications (perception), approved their use (knowledge) and whether they played a role in consumers’ buying decision process, thereby establishing whether these certifications truly had an impact on the purchasing decision (consumption). another purpose of the questionnaire was to verify whether the market is stratified into different consumer categories with different attitudes towards the certifications, the final goal being to suggest different solutions for their promotion and valorisation. the questionnaire1 was created in conjunction with the literature on consumer behaviour relating to typical foods and food labelling. initially, a pilot test (n=20) was performed to ensure that the formulated questions were 1 the questionnaire is available upon request. clear and understandable for consumers. should a question be regarded as unclear, this was revised and modified accordingly for the final questionnaire. the final questionnaire was sub-divided into six sections, addressing specific issues as following: i) the first section (one question) contained the filter question, as the survey was designed for those responsible for the food purchases for the family. the answer to this question was a dummy variable, indicating whether the respondent was (i.e.,1) or not (i.e., 0) responsible for the household food purchasing. if the participant was not responsible for food purchases in the household, he/she would be redirected to the last section, where he/she would complete only the socio-demographic questions. ii) the second section (4 questions) examined consumers’ perception of food quality and safety, the importance of the eu quality certifications and other different food characteristics when choosing a food product, the significance of the food label and consumers’ feelings towards food law compliance and different production types and techniques. five-point likert scale question were used in this section, with 1 corresponding to ‘’not at all’’ and 5 to ‘’very important’’. iii) the third section (8 questions) covered consumers’ awareness and knowledge of the eu quality certificates (pdo/pgi/tsg) and the organic certificate, attempting to identify the main differences between the pdo/pgi/tsg and organic products, and conventional products. in this section multiple image choice questions was used when respondents had to choose which of the shown logos they knew, and multiple choice questions when they had to select the right definitions of the eu quality certifications. also, the previously used fivepoint likert scale question was used (1= ‘’not at all’’ and 5= ‘’very important’’). iv) the fourth section (12 questions) analysed consumers’, knowledge and consumption of eu quality-certified products as well as organic products. each of these quality labels was again analysed separately. here threepoint likert scale questions were used (no=0, yes=1, maybe=2). in order to test their knowledge, the participants were asked to give some examples of each of these types of products. in addition, in order to establish their consumption of products baring these certification they were asked for examples of the last pdo/pgi/tsg and organic products they had bought during the last three months. for this purpose, open-ended questions were used in all the above cases. v) the fifth section (16 questions) consisted of an analysis of 16 provolone dolce cards, with different combinations of various characteristics, thereby collect38 bio-based and applied economics 10(1): 38-49, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9909 niculina iudita sampalean, daniele rama, giulio visentin ing the data needed for a conjoint analysis; however, this will not be considered further in the present study but will form part of an alternative ongoing project. vi) the sixth section (10 questions) used demographic questions to cover the socio-demographic aspects of the respondents; the formulated questions evaluated the participants’ city and area of residence, sex, age, number of family members, education level, job, civil status, and annual income. the questionnaire was distributed online, and was shared on facebook pages and groups, linkedin, whatsapp, messenger, as well as on certain cooking blogs. therefore, the actual number of people viewing the survey is unknown, however, the total number of respondents is reported above. 2.2 statistical analysis descriptive statistics were calculated in relation to the data collected between the second and the sixth sections of results, using a basic script in python (python software foundation, ver. 3.6). the software ibm spss statistics (ver. 24.0, ibm corp., armonk, ny) was employed to conduct multivariate statistical analysis within a multiple-step framework. in the first step, we carried out a factorial analysis in order to reduce the dimensionality of the data collected into a smaller set of key factors, that would be easier to explain. the variables covered in the analysis focused on different food characteristics at the point of purchase, the importance of different safety and quality food characteristics, attitudes towards eu quality-certified products, the perception of law compliance, production types and techniques, as well as the attention given to various information on the label. a 5-point likert scale was used to measure all the variables included in the factor analysis. the optimal number of latent variables selected for the subsequent analyses was chosen, based on the lowest number of components with associated eigenvalues greater than 1 (kaiser, 1960) and based on the proportion of the total variance explained by the retained factors of at least 50%. in the second step, a cluster analysis was applied to the latent variables previously generated and selected with the aim of organizing the respondents into homogenous groups. prior to the cluster analysis, data were processed with the agglomerative hierarchical procedure. according to ward’s criterion of aggregation, 10 iterations with mobile centres were completed. based on a visual inspection of the generated dendrogram, the optimal number of clusters to specify in the k-means method was set at 4. this type of analysis applied euclidean distance to define similarities and differences within the clusters. 3. results and discussion 3.1 description of the sample the results reporting the socio-demographic aspects of the sample used in the present study, are depicted in table 1. the sample analysed in the present study may not be completely representative of the italian population as the criteria that was used for the sampling is convenience. there is an over-representation of women and younger respondents, with 48% of the sample aged between 18 and 35 years old, that may be because the questionnaire was distributed online, and the population tends to not have access to the internet or computer skills. 64% of the respondents were women and this over-representation can be explained by the fact that our respondents needed to be responsible for the food purchases in their household, and women, generally, have that responsibility. more than 70% of the respondents had at least a bachelor’s degree, with 11% having a phd. having this highly-educated sample can be explained by the method used to administer the questionnaire. moreover, the north-eastern region of the country is also overrepresented (52%). this can be explained as the questionnaire was disseminated with the social network of the authors , so it may have inflated the number of respondents from a limited geographical area. the most popular occupations were office worker (37%), freelancer (14%), student (14%) and housewife (8%). the 17% declared an annual income less than 10,000 € while 12% declared an income greater than 40,000 €. the non-representativeness of our sample might have some influence on the final results. for example, the women over-representation could have generated greater results, as found by dekhili et al. (2011), or contrary could have shown lower ones as sometimes men presented better knowledge of these certifications (verbeke et  al., 2012). these both same studies shown that older groups of people have a higher awareness and use of the eu quality certifications. as in our sample the older groups were underrepresented (45-70 years old) we believe this could result in lower outcomes. having a higher educated sample might have introduced some bias as it is expected that the higher the education level, the higher the knowledge resulting in a more positive attitude towards these certifications. 3.2 awareness and knowledge of european quality certifications in the third section of the questionnaire (awareness and knowledge) the consumers were shown four 39 bio-based and applied economics 10(1): 39-49, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9909 an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications table 1. socio-demographic distribution of the collected sample by comparison with the italian population. variable levels frequency (%) population (%) age (in years) 18-25 9 10 26-35 39 16 36-45 22 20 46-55 17 24 56-70 13 30 gender female 64 51 male 36 49 no education/elementary school 0 17 education junior high school qualification 3 32 high school qualification 27 36 bachelor’s degree/ master’s degree/post graduate training/phd 70 15 civil status single 57 42 married 39 47 divorced 2 3 in a relationship 1 separated 1 family members 1 17 33 2 28 27 3-4 42 35 >4 13 5 geographical distribution north east 51 19 north west 19 27 south 12 23 centre 11 20 islands 7 11 occupation office worker 37 freelance 14 student/phd student 14 housewife 8 teacher 4 research/academia jobs 4 unemployed 5 worker 3.5 retired 1.0 jobseeker 1.5 entrepreneur 3.0 food related jobs (chefs/food bloggers) 1.0 other 4 average annual income (€) < 10,000 17 10,000 – 20,000 38 20,000 – 40,000 33 40,000 – 50,000 5 > 50,000 7 area of origin rural 30% urban 70% * istat (national statistics institute) data extracted in november 2019. 40 bio-based and applied economics 10(1): 40-49, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9909 niculina iudita sampalean, daniele rama, giulio visentin eu quality logos, pdo, pgi, tsg and organic farming logos and were asked to select those that they were aware of. the results indicated that the logo people were more aware of was the pgi, selected by 82% of respondents, followed by the pdo (76%) and the organic logo (68%), while people were least aware of the tsg with only 34% of them. 25% of the respondents declared that they were aware of all four logos, 30% were aware of three logos, 25% of two logos and 20%, just of one logo (appendix, figure 1). these findings were higher than those reported in a study by aprile and galina (2008) in which the pdo, pgi, tsg and organic mark were recognized by 30%, 16%, 3.5% and 41% of the interviewees, respectively. arfini (1999) demonstrated that 41.8% of italian consumers were aware of the presence of a pdo-labelled food product in the food market. similar results were found in a later study by platania and privitiera (2006) that assessed the consumer appraisal of the italian pdo soppressata salami, which reported that 42% of italian consumers were aware of the pdo label. as explained in the review conducted by grunert and aachmann (2016), and identified in the present study, the higher degree of consumer awareness of european quality labels depended on the time period in which the study was undertaken. to further investigate the self-declared awareness and consumption of the eu quality certifications, participants were then asked how well they knew the certified products and how often they bought them. the pdo certified products were bought most frequently, with 68% declaring that they regularly (18%) and sometimes (50%) purchased them. conversely, tsg products were bought least often (4% regularly and 16% sometimes; appendix, table 1). respondents were then presented with six official definitions extracted from regulation (eu) no. 1151/2012 and had to choose for each of them the corresponding eu certification (pdo, pgi, tsg or none). for both statements that defined the pdo’s out of all respondents 42% were able to identify correctly the one that refers to ’’the production steps of which all take place in the defined geographical area” and 43% “whose quality or characteristics are essentially or exclusively due to a particular geographical environment with its inherent natural and human factors”. (appendix, figure 2) for the pgi defining statements, the one describing the production steps, was correctly identified by 55%, but only 38% did so for the statement explaining that the quality and reputation are given by the geographical origin. as for the tsg statements, in both cases almost half of the respondents identified the right statements: 46% explaining ‘’the traditional production, processing, and composition for that products’’ and 49% for the statement related to the raw materials and ingredients traditionally used, for at least 30 years. data from table 2 show the mean and the standard deviations of the elements that consumers used to distinguish the certified products from the conventional products. the “place of the origin” mean was the highest in the case of pdo (4.64), pgi (4.49), tsg (3.73), followed by the “eu quality logo” (pdo 4.29, pgi 4.21, tsg 3.61) which was seen as the most important characteristic for the organically-certified products (4.18), followed by “price” (4.01). the less relevant features were “brand” and “point of purchase” for all four certifications. in accordance with these data, other studies (contini et table 2. means and standard deviation of the different attributes distinguishing between eu quality-certified products and conventional products. eu certification attribute mean standard deviation pdo price 4.01 0.76 brand (national brand/private labels) 3.62 0.91 eu quality logo 4.29 0.78 appearance 4.00 0.89 place of origin 4.64 0.53 point of purchase 3.44 0.98 pgi price 3.91 0.90 brand (national brand/private labels) 3.52 1.00 eu quality logo 4.21 0.93 appearance 3.94 1.00 place of origin 4.49 0.77 point of purchase 3.46 1.12 tsg price 3.43 1.56 brand (national brand/private labels) 3.07 1.46 eu quality logo 3.61 1.62 appearance 3.37 1.58 place of origin 3.73 1.69 point of purchase 3.08 1.60 organic price 4.01 1.05 brand (national brand/private labels) 3.48 1.10 eu quality logo 4.18 1.03 appearance 3.86 1.12 place of origin 3.91 1.24 point of purchase 3.36 1.24 41 bio-based and applied economics 10(1): 41-49, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9909 an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications al., 2016; vanhonacker et al., 2010b) revealed that “place of origin” was the most important attribute in distinguishing and choosing between european quality-certified products and conventional products. the choice of “price” as a distinguishing element for quality-certified products can be viewed as a signal of a high-quality product, as confirmed by previous studies conducted by grunert et al. (2000) and verberke et al. (2007) santeramo (2020) suggested that adding regional certification labels (e.g., protected designation of origin–pdo, protected geographical indication–pgi, american viticultural area–ava) or regional information increases consumers’ confidence on the product quality. 3.3 knowledge and consumption of the european quality certifications results reporting the opinions of respondents in relation to the food safety of european quality-certified products are detailed in table 3. food safety was used in this section as a way to study consumer’s knowledge of eu quality certifications as those products are believed to have a higher level of food safety. when respondents were asked whether they considered the pdo certified products safer than conventional products, 58% of the respondents replied “yes” and 22% “no”, while 20% responded “i don’t know”. similar results were recorded with regard to the pgi certified products, with 50% choosing “yes”, 26% “no” and 24% “i don’t know”. organic farming products registered the highest percentage for “no” with 40%, with only 39% replying “yes”. in relation to tsg products, 50% of the respondents declared they “didn’t know” if they were safer or not, while 25% replied “yes” and 25% answered “no”. figure 1 reveals evidence of the consumers’ actual knowledge of quality-certified products, as they were asked if they could name any pdo, pgi, tsg or organic products, without being prompted. the results show that in relation to pdo products, over 11% of the sample provided an incorrect answer, around 13% were unable to recall any pdo products, 24% gave one example, 19% two examples, 12% three or four examples, and 9% five examples. as for the pgi products, over 16% of the individuals provided an incorrect answer, around 28% were not able to quote any example, 26% gave one example, 15% two examples, 8% three examples, 6% four examples and 1% five examples. with regards to tsg products, 13% of respondents gave an incorrect answer, 55% were unable to cite any tsg product, 25% remembered one example, while 7% provided two which is the maximum of right examples possible in italy. 27% of participants were unable to recall any organic products and 73% provided one or more organic food examples. in relation to the organic product results, de magistris and gracia (2012) showed that more than 50% of consumers declare to be a habitual buyer of organic food products and around 59% of italian consumers state that table 3. consumers’ perception of the safety of eu quality-certified products. in your opinion, are eu quality-certified products safer than other products? yes no i do not know pdo products 58% 22% 20% pgi products 50% 26% 24% tsg products 25% 25% 50% organic products 39% 40% 21% 13% 20% 36% 16% 5% 6% 5% 11% 13% 24% 19% 12% 12% 9% 0% 5% 10% 15% 20% 25% 30% 35% 40% wr on g/ inc om ple te no ex am ple 1 p rod uc t 2 p rod uc ts 3 p rod uc ts 4 p rod uc ts 5 p rod uc ts examples of known and consumed pdo products pdo products consumed pdo products known 16% 28% 26% 15% 8% 6% 1% 14% 41% 34% 8% 1% 2% 0% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% wr on g/ inc om ple te no ex am ple at al l 1 c orr ect ex am ple 2 co rre ct ex am ple 3 c orr ect ex am ple 4 c orr ect ex am ple 5 c orr ect ex am ple examples of known and consumed pgi products pgi products known(%) pgi products consumed(%) figure 1. examples of known and bought pdo and pgi products. 42 bio-based and applied economics 10(1): 42-49, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9909 niculina iudita sampalean, daniele rama, giulio visentin “probably yes” or “definitely yes”, they pay attention to organic label when shopping organic food products. these results are in accordance with the selfassessed knowledge relating to logos (discussed above in the “awareness and knowledge of european quality certifications” section) except in the evaluation of the pgi products, in which the degree of self-assessed knowledge was higher than the actual knowledge with a frequency of 26%. the results in these findings are higher than those in previous studies like vecchio and annunziata (2011) who considered pdo/pgi products together and revealed that over 37% of the respondents gave an incorrect answer, around 29% were unable to recall any pdo or pgi food, 20% gave less than two names and 14% less than four. examining the category to which the examples provided belong, it was observed that in the case of the pdo products, the correctly cited products belonged to the cheese category, the meat products category (cooked, salted, smoked), the fresh or processed category (fruit, vegetables, cereals) and finally the oils and fat category, with figures of 60%, 18%, 3% and 1%, respectively. in the case of the pgi products, 34% of the correct examples were associated with the meat products category (cooked, salted, smoked), the fresh or processed category (fruit, vegetables, cereals) recorded 26%, closely followed by vinegar at 24% (category known as “other products”). the results correspond to the consumption value of italian pdos and pgis in which cheese and prepared meats account for 84% of its total sales (ismea, 2018). regarding these findings santeramo and lamonaca (2020), found that geographical labels are effective differentiation tool although their relevance varies across products and origins. for instance, gl is the main differentiation tool for wine, but it is of low relevance for low-prices products and in different national markets. costanigro et al. (2010 ) sustains the same results as to the less expensive products, showing that the consumer may not see the value (in terms of search costs) in critically differentiating across many individual producers when buying less expensive products (such as grains, fruits and vegetables) but affirms the contrary when it comes to purchasing more expensive products (such as wine and olive oil), as the incentive to learn about differences in quality across brand names is more pronounced, allowing brand names to capture a larger share of the reputation premium. to determine the consumers’ actual use of eu quality certifications and their accurate consumption, respondents were asked to recall from the previously given examples which products they had purchased during the last three months (figure 3). in relation to the pdo certification, 13% of the individuals returned an incorrect answer, 20% were not able to provide any example, 36% indicated one example, 16% two examples, 5% three examples, 6% four examples and 5% five examples. in the case of the pgi certification, incorrect or incomplete examples were provided by 14% of the respondents and 41% gave no example at all. of the correct examples, 34% provided one, 8% gave two, 1% three, 2% four and none (0%) of the participants provided five correct examples. as for the organically certified products, 44% of the respondents provided no example at all, while 56% gave one or more examples. aprile and gallina (2008) showed the interviewees a list of nine products, from each category considered; all products were pdo or pgi certified and respondents were asked to choose those that they purchased more frequently. the more frequent categories were the cheese category, meat products category (cooked, salted, smoked), fruit and vegetables their findings were very similar to ours. it has been observed that some of the products that appeared in the study of aprile and gallina (2008) are not mentioned by our respondents, however, certain new names were mentioned. another difference is the higher percentage found in the comparable study, but this is due to the fact that their respondents selected names from a given list, while our respondents gave the examples spontaneously, without any help or suggestion. our descriptive analysis showed that consumers were asked to provide examples of eu quality-certified products; in most cases, the responses provided contained at least one well-known food on the national market (e.g., parmigiano reggiano, mozzarella di bufala campana, gorgonzola, grana padano) but their answers were not limited to these. related to these findings, deselnicu et al. (2013) revealed that the institutional framework for the geographical indications was found to matter: within the same country, quality assurance certifications with higher quality standards (such as pdo) receive higher premiums than less stringent ones (such as pgi). moreover, when multiple labelling certifications with different minimum quality standard coexist (as for pdos and pgis in europe), the price premium associated with the labels is lower than when a single label is used (as for the gi trademark in the united states). leufkens (2018) tried to prove the positive value of a gi quality signal (i.e. label) by quantifying its monetary value for the consumers and found that consumers are willing to pay a marginal premium for the gi, by an average of 11.5 percent, while the pdo alone achieves an le of 13.6 and a pgi of 6.2 percent. 43 bio-based and applied economics 10(1): 43-49, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9909 an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications 3.4 perception, attitudes towards quality food products and purchasing habits in relation to the questions in the second section (appendix, table 2), different characteristics were listed, and respondents had to evaluate them using a 5-point likert scale. with regard to the various aspects that consumers recognized as “very important” and “relatively important” in their food purchasing process, the most important was hygiene standards (97%), followed by price (92%), appearance (88%), nutritional value (77%) and pdo certification (75%). the aspects that were seen as less important, registering the highest percentage of the options “indifferent”, “not much” and “not at all” were tsg certifications (65%), organic certifications (51%), brand (38%) and pgi certifications (33%). when asked about the characteristics of a safe and quality product, the absence of undesirable chemicals and microorganisms was evaluated as “very” and “relatively important” (98%), followed by compliance with national and european laws relating to food and the environmental area (96%), controlled and certified production sites (89%), products that satisfy the senses, are well prepared and preserved (89%), country of origin (86%), sustainable production techniques (80%) and pdo certification (74%). the lowest scores on the likert scale (“indifferent”, “not much” and “not at all”) were again recorded in relation to tsg certification (59%), popular brand (58%) and organic certification (47%). with reference to the various information found on the product label, the components considered to be “very” and “relatively important” were expiry and useby date (94%), ingredients (92%) and information relating to the producer and place of production (89%), while 23% regarded nutritional characteristics as being “indifferent”, “not much” and “not at all important”. the last question in this section revealed that 88% of the respondents claimed to purchase italian food whenever they could, 74% claimed to be very proud of the pdo, pgi and tsg products produced in their area, municipality or country. however, only 67% felt that they were supporting local farmers when they bought pdo, pgi and tsg products. as for the affirmation that pdo, pgi and tsg trademark products are too expensive, 40% either agreed or completely agreed, 39% disagreed or completely disagreed, while 21% were neutral. similar to our findings deselnicu et al. (2013) shown that stricter regulations may signal increased benefits to consumers in the form of food safety, quality assurance, and stronger cultural or heritage connection, prompting a higher willingness to pay for products that are more closely regulated. also, more stringent regulations for the pdo designation appear to secure a higher price premium than its less stringent quality-assurance counterpart (pgi). 3.5 exploratory factor analysis (efa) a series of exploratory factor analyses (efa) were conducted using the questions and affirmations from the survey’s second section. before we carried out the efa, the values of the bivariate correlation matrix of all items were analysed, and where the bivariate correlation scores were greater than 0.8, one of the pair’s items was removed, as suggested by field (2013). additionally, the multicollinearity was tested via the determinant of the matrix, whose value of 0.1 exceeded the minimal value of 0.00001. furthermore, our factor model kaiser-meyer -olkin’s measure of 0.820 proved the adequacy of the sample size. bartlett’s test of sphericity was significant (p< 0.001). the varimax rotation method was employed and the eigenvalues greater than 1 were established as borderlines for the factors extracted. the analyses eventually resulted in the selection of a six-component solution, based on 24 of the 27 initial variables. the six extracted components accounted for 56.32% of the total variance in the data, respecting the rule of at least 50% (streiner, 1994). items in this six-component solution were regarded as high and moderately high, loading higher than 0.400 on each component (kleine, 2014). their cronbach’s alpha reliability tests showed increased reliability, with values higher than 0.60 (up to 0.79). table 4 contains the components resulted from the factorial analysis. the first component “product composition and characteristics ” comprised variables such as nutritional and organoleptic characteristics, ingredients and label information. the second component “product’origin ” describe, as the name suggests, the importance given to the origin of the product and of the raw materials producer’s information, as well consumers’ pride in buying eu quality-certified food that is locally produced. the third component “eu quality certifications” describes the importance consumers attach to the european quality certifications (pdo/ pgi/tsg) and how buying eu certified food supports local farmers. the fourth component “product visual presentation relates to the value attributed by consumers to the products appearance and appeal and the expiry date. the fifth component “product law and hygienic compliance” examined the significance of hygiene standards, law compliance, absence of unwanted chemicals and controlled and certified production sites in consumers’ food choices. the sixth component “product price and brand” considered the impact that price and popular brand had 44 bio-based and applied economics 10(1): 44-49, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9909 niculina iudita sampalean, daniele rama, giulio visentin on consumer choices. the six components that were obtained using the factor analysis were then used as variables in a cluster analysis that divided our sample into four groups, with maximized homogeneity within the individual groups and minimized between them. table 5 presents a detailed representation of the socio-demographic characteristics of the four clusters. 3.6 the socio-demographic characteristics of the four clusters from a socio-demographic perspective, the first cluster is defined as the most gender-balanced (47% men and 53% women), predominantly from urban areas (73%) with the highest concentration of young consumers, as 83% were aged between 18 and 45 years. this group had the highest proportion of one member families (27%), with 53% earning at least 20,000 €/year (12% of these > 40,000 €/year). the respondents’ occupations were from research and academia (4%), entrepreneurs (5%), students/phd (16%) and retired people (4% the only cluster in which this group was represented). the second cluster had the highest percentage of primary school graduates together with the highest percentage of unemployed people (10%) and office workers (65%) but also the lowest number of freelancers (5%). in this cluster none of the participants earned more than 40,000 €/year, half of the participants were made up of families with three to four members and a quarter had four members or more. the third cluster is characterized by an urban population, consisting predominantly of women (74%), characterizes this group, with more than 40% being over 45 table 4. factor analysis on the components associated with respondents’ purchasing intent. items components product’ composition and characteristics product’ origin eu quality certifications product’ visual presentation product law and hygienic compliance product price and brand nutritional characteristics 0.746 ingredients 0.639 sustprod techniques 0.595 label information 0.556 producers’ experience 0.540 biological mark (organic) 0.526 organoleptic characteristics 0.525 country of origin 0.786 frequency of buying 0.728 local raw materials 0.629 pride eu marks 0.546 producer information 0.519 pdo trademark 0.713 tsg trademark 0.604 support for local production 0.476 appeal, conservation 0.788 food aspect 0.779 expiry date 0.612 absence of uw chemicals 0.733 law compliance 0.725 hygiene standards 0.577 cc production sites 0.431 cost, expensiveness of eu trademarks 0.790 popularity, brand 0.592 explained variance, % 24.942 9.315 6.807 5.972 4.985 4.295 cumulative variance, % 24.942 34.257 41.064 47.036 52.020 56.315 *the items are ordered by dimension, and the small coefficients with an absolute value below 0.300 have been eliminated. 45 bio-based and applied economics 10(1): 45-49, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9909 an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications years old; the highest number of high school only graduates were found in this group (35%) and single (48%) and married (47%) people were equally represented. it was the most diversified group in terms of occupation (teachers, food related workers, researchers, workers, freelancers, office workers). the fourth cluster consisted of single individuals with a high standard of education (> 82% had at least a bachelor’s degree) and low annual income, as 69% earned less than 20,000 €/year; this cluster comprised primarily office workers, research workers, housewives and freelancers. table 5. socio-demographic distribution among clusters. variables level cluster 1 “visual presentation enthusiasts” cluster 2 “origin enthusiasts’’ cluster 3 “food provenance and image enthusiasts’’ cluster 4 “food regulations enthusiasts’’ area of origin rural 27% 40% 27% 33% urban 73% 60% 73% 67% gender male 47% 60% 26% 39% female 53% 40% 74% 61% age 18-25 6% 20% 11% 3% 26-35 55% 40% 26% 58% 36-45 22% 20% 23% 21% 46-55 10% 15% 27% 3% 56-70 7% 5% 13% 15% number of family members 1 27% 10% 11% 24% 2 35% 15% 24% 43% 3-4 35% 50% 50% 24% >4 3% 25% 15% 9% education no title 0% 0% 0% 0% elementary or middle school 2% 10% 1% 0% high school 18% 15% 35% 18% bachelor or master’s degree/phd 80% 75% 64% 82% civil status single 65% 65% 48% 67% married/in a domestic relationship 35% 35% 47% 30% divorced/ separated 0% 0% 5% 3% average annual income <10,000 € 12% 25% 15% 24% 10,000-20,000 € 20% 40% 39% 45% 20,000-40,000 € 41% 35% 33% 21% 40,000-50,000 € 4% 0% 6% 3% >50,000 € 8% 0% 7% 6% occupation homemaker / housewife 8% 5% 8% 6% unemployed 6% 10% 1% 0% office worker 37% 65% 31% 45% school teacher 2% 0% 4% 6% freelancer 14% 5% 15% 15% worker 4% 0% 5% 0% retired 4% 0% 0% 0% research/academia jobs 4% 0% 9 12 student/phd student 16 10 18 6 entrepreneur 5% 0% 5% 0% food related jobs(blogger/chef) 0% 0% 3% 3 job seeker 0% 0% 2% 6% others 0% 5 4 1 46 bio-based and applied economics 10(1): 46-49, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9909 niculina iudita sampalean, daniele rama, giulio visentin 3.7 the clusters attitudes towards the analysed variables as regards to the considered variables (table 6), first cluster ’’visual presentation enthusiasts’ is characterized by respondents that pay most attention to appeal, appearance, and availability. they also considered law compliance and the healthiness of the product as particularly important in their food choice. this group recorded the lowest interest in producer’s information, origin of raw materials and of the product. in addition, eu quality certifications and support for local economies were insignificant to this group. by comparison with the first cluster, the second cluster ’’origin enthusiasts’’ valued most the producer’s information and the origin of raw materials and of the product. this cluster recognized extrinsic characteristics (price, brand) as decisive. law compliance and the healthiness of the product were less important elements for this group. organoleptic, nutritional and sustainability characteristics were also regarded as insignificant. the third cluster ’’food provenance and image enthusiasts’’ was the only cluster that valued all the components positively (table 6), demonstrating a great interest in producer’s information, origin of raw materials and of the product, as well as appeal, appearance, and availability. of all the clusters, the last cluster ‘’food regulations enthusiasts’’ attributed the highest value to law compliance and the healthiness of the product. eu quality certifications and support for local economies, as well as producer’s information and the origin of raw materials and of the product, were essential elements of this group’s components. 4. conclusions and reccomendations our results outlined that the level of perception, awareness, knowledge and consumption of eu quality labels has increased considerably among italian consumers in recent years. with respect to geographical indications, a widespread awareness of the guarantees offered by the pdo and pgi marks in relation to production steps, the natural and human factors of a particular environment and the reputation and quality of a region were assessed. as for the traditional specialties (tsg) an extensive knowledge regarding the traditional practices of production, process and composition, as well as ingredients and raw materials was identified. new policy and communication efforts could be used by the consortia to enhance consumers’ curiosity towards products that are pdo/pg/ tsg or organic certified. our results allow us to formulate some suggestions for the policy makers as well as for the consortia and the producers of the pdo/pgi/tsg/organic products. seeing that our consumers were divided in four clusters we assume that even at the national/international level there is heterogeneity as regards to these labels, therefore for each of the cluster we propose some communication strategy. for the “visual presentation enthusiast” cluster, the strategy adopted should concentrate more on the way these products are presented, using attractive packaging but also one that helps reflect the look of the products. for the “origin enthusiasts” the message of the communication campaign should point out how these products are unique in the sense of the typicity that is given by the particular geographical areas where they are produce and by the raw materials they are made of, strengthening the importance that these two elements have on the final product. as to the ‘’food provenance and image enthusiasts’’ cluster considering their positive attitude towards all the quality certified foods’ attributes, we believe that the message the policy makers as well as the producers and consortia should sponsor and publicize, is one table 6. final cluster centres. cluster 1 “visual presentation enthusiasts” (8%) 2 “origin enthusiasts’’ (53%) 3 “food provenance and image enthusiasts’’ (11%) 4 “food regulations enthusiasts’’ (28%) product’ composition and characteristics -0.389 -0.443 0.242 0.039 product’ origin -1.267 0.300 0.471 0.130 eu quality certifications -0.417 -0.086 0.171 0.101 product visual presentation 0.309 -0.109 0.344 -1.541 product law compliance 0.126 -2.387 0.289 0.295 product price and brand -0.208 0.113 0.238 -0.555 47 bio-based and applied economics 10(1): 47-49, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9909 an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications that could produce some ethical and altruistic motives, therefore the message must stress out the support these products bring to the local economy in the area in terms of jobs and income. the “food regulations enthusiasts” could be conquered by campaigns that point out how these quality products follow very strict production rules, with regular checks on healthiness, sanitary and organoleptic elements, and that this is one of the elements that differentiate them from the conventional products that might have more relaxed rules and less controls. one limitation of the present study is the fact that the sample is not strictly statistically-representative of the italian population. the sample is biased towards relatively younger and highly educated shoppers and female consumers. therefore, additional qualitative and quantitative research needs to be done with a larger and representative sample, to extend the legitimacy of the findings and to generalize the results to represent the national population. another possible limitation of the study, is that since the questionnaire was our investigation instrument there might have been a certain predisposition to socially desirable responding, or as martin and nagao (1989) better described it, a tendency to give answers that make the respondent look good, or the tendency ‘‘to stretch the truth in an effort to make a good impression’’. acknowledgements this study was supported by the doctoral school in conjunction with the agro-food system (agrisystem) of the università cattolica del sacro cuore (italy). the authors wish to thank yari vecchio (university of bologna) and all the participants of the study for their valuable contribution. authors also wish to thank the anonymous reviewers for their useful comments and suggestions. references aprile, m. c., caputo, v., and nayga jr., r.m. 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(2011). the role of pdo/ pgi labelling in italian consumers’ food choices. agricultural economic review, 12: 80-98. verbeke, w., pieniak, z., guerrero, l., and hersleth, m. (2012). consumers awareness and attitudinal determinants of european union quality label use on traditional foods. bio-based and applied economics, 1: 213-229. vanhonacker, f., lengard, v., hersleth, m., and verbeke, w. (2010). profiling european traditional food consumers. british food journal 112: 871-886. appendix table 1. self-declared knowledge and frequency of buying of the eu quality certifications. certifications i regularly buy them i know and i buy them sometimes i know them i don’t know them pdo certified 18% 50% 27% 5% pgi certified 15% 48% 30% 8% tsg certified 4% 16% 24% 57% organic certified 12% 43% 38% 7% table 2. importance of different attributes when food shopping. very important pretty important indifferent not much not at all important hygienic standards 78% 19% 2% 0% brand 6% 56% 25% 9% 4% pdo certification 19% 56% 17% 7% 2% appearance 46% 42% 7% 3% 1% pgi certification 15% 52% 22% 9% 2% price 39% 53% 6% 2% 0% nutritional values 35% 42% 18% 2% 3% organic certification 12% 37% 27% 14% 10% tsg certification 5% 30% 38% 12% 15% 68% 82% 34% 76% which of the following logo do you know? organic pgi tsg pdo 20% 25% 30% 25% number of logo's respondents declared to know. 1logo 2 logo 3 logo 4 logo figure 1. self-declared knowledge of the eu quality certifications logos 54% 52% 53% 33% 29% 43%42% 43% 38% 55% 46% 49% 4% 5% 8% 13% 25% 8% 0% 10% 20% 30% 40% 50% 60% pdo 1 pdo 2 pgi1 pgi2 tsg1 tsg2 eu quality certification's definitions 0-wrong definition 1-right definition * wrong (chose none of the options) figure 2. eu quality certifications definitions. volume 10, issue 1 2021 firenze university press ten years of bio-based and applied economics: a story of successes, and more to come fabio g. santeramo1, meri raggi2 the capitalisation of decoupled payments in farmland rents among eu regions gianni guastella1,2, daniele moro1, paolo sckokai1, mario veneziani3 contribution of periurban farming systems to local food systems: a systemic innovation perspective rosalia filippini1,2, elisa marraccini3, sylvie lardon2 an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications, and consumption of agri-food products carrying those certifications niculina iudita sampalean1, daniele rama1, giulio visentin2 wine after the pandemic? all the doubts in a glass daniele vergamini*, fabio bartolini, gianluca brunori public r&d and european agriculture: impact on productivity and return on r&d expenditure michele vollaro1, meri raggi2, davide viaggi1 bio-based and applied economics 5(2): 113-130, 2016 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-16366 structural change and agricultural diversification since china’s reforms lihua li1,*, bill bellotti2, adam m. komarek3 1 western sydney university, sydney, australia state key laboratory of grassland farming systems, lanzhou university, lanzhou, china 2 vincent fairfax chair, dean’s unit school of science and health, western sydney university, sydney, australia 3 international food policy research institute (ifpri), washington, dc 20006, usa date of submission: 2015 29th, june; accepted 2016 21st, april abstract. structural change is considered the major engine in fostering a country’s growth. in the agricultural sector, diversification is the commonly used development strategy to increase rural sector’s flexibility, and to respond to improving technologies and market conditions. this study examined agricultural development and transformation during china’s socio-economic reforms. in particular, it empirically investigated whether the change of china’s agricultural structure is consistent with structural change theory and observed outcomes from other countries. the degree of agricultural diversification was quantitatively measured at a regional scale using the herfindahl index. an underdeveloped province in northwest china was studied to provide insights into the interaction among structural change, agricultural diversification, and implemented development policies. aggregate-level analyses suggest that china’s agricultural transformation pattern is consistent with those of other developing countries. a specific provincial-level analysis shows that environmentally and economically disadvantaged regions are slower to diversify their economy than better endorsed regions. keywords. structural change, growth, agriculture diversification, china jel codes. o1, q12, q18 1. introduction moving agricultural labour and resources into non-agricultural sectors is considered fundamental to economic growth (lewis, 1954; kuznets, 1959; syrquin, 1988). structural change theory suggests this transformation is an economy-wide phenomenon, characterised by a decreasing proportion of agricultural output and employment, along with rapid progress of industrialisation and urbanisation (kuznets, 1966, 1971; chenery and syrquin, corresponding author: lilihua@lzu.edu.cn 114 l. li, b. bellotti, a.m. komarek 1986a; timmer, 2007; world bank, 2007). during this transition, industrialisation and urbanisation create employment opportunities and absorb the displaced rural labour force thus increasing labour productivity, while technological advancement and infrastructure improvement enable agriculture to grow, together with the industrial and service sectors (timmer, 2009). meanwhile, the agricultural sector is expected to be more responsive to markets with a diversity of farm products to meet the increasing demand for food variety and quantity, which is stimulated by higher income and growth of the urban population (pingali and rosegrant, 1995). agricultural diversification has been a policy objective of most developing countries during their structural change process (timmer, 1997). asian nations such as japan, thailand, and south korea have been successful in diversifying their agricultural sector (world bank, 1990). however, diversification of agriculture requires developments in technology, provision of better infrastructure, and well-functioning agricultural markets to support more diversified production. this poses challenges to countries with limited technologies, inefficient agricultural support systems and unfavourable government policies. therefore, different countries have differing capacities to diversify their agricultural sector. as a result, the extent and patterns of agricultural diversification may differ among countries. china’s fast growth and special paths of transition and development have puzzled scholars about the contradictions between expectations shaped by theory and the observed outcomes (jefferson, 2008). china had a relatively large rural population (world bank, 2015), and had a large backlog of underemployed labour in farming, caused by strict regulation on labour migration prior to economic reforms (oi, 1999). this distinct labour issue could have affected china’s agricultural transformation pathway. in addition, the large variations in agricultural endowments, along with disparity in the level of development across regions within china, imply that the processes of agricultural diversification may vary. few researchers have attempted to examine agricultural diversification in the process of structural change. in addition, little effort has been made to quantitatively measure and compare the degree of diversification across regions and time (timmer, 1997). the trend of the chinese production diversification has been described by several descriptive studies (huang and rozelle, 2004; huang et al., 2012; carter et al., 2012; fan et al., 2003; young 2000). to the author’s knowledge, no measurement of the degree of diversification has been used in a study of china’s structural change and development. to fill this gap, the present study attempts to quantify agricultural diversification at the national level, to compare the degree of diversification across regions and time, and to investigate agricultural diversification in relation to a region’s growth and agro-economic conditions. the purpose of this study was to better understand the pattern of transformation in china’s agricultural sector. the degree of diversification was examined at the regional and national level for the period between 1978 and 2012 using the herfindahl index. we studied gansu province to provide insights into the interaction between structural change and agricultural development during china’s economic transition. 115structural change and agricultural diversification since china’s reforms 2. structural change and agricultural diversification: the conceptual framework 2.1 economic development and patterns of structural change although structural transformation is heavily affected by a country’s specific macroeconomic and sectoral policies (chenery, 1988; syrquin, 1988; syrquin, 2006), historical experience indicates that consistent patterns exist. these are a declining share of agriculture in gdp and employment, followed by the rise in industrial and service sectors, and a continuous urbanisation which is induced by rural-to-urban migration (chenery, 1988; timmer, 2007; chenery, 1988). theoretically, the decline in share of agricultural employment and output raises productivity in agriculture. this change is viewed as the major driver for economic growth for countries at the early stage of development (kuznets, 1956, 1967; timmer, 1988; world bank, 1990). the phenomenon of shifting labour and resources out of the agricultural sector is explained by two mechanisms: a decreasing share of consumer expenditure devoted to food and agricultural products as income grows (engel’s law of demand) and the rising productivity in agriculture which generates the resources and then stimulates the expansion of industry and services (timmer, 1988; world bank, 1990). the ultimate outcome of structural change is that agriculture becomes homogenous to other sectors as an economic activity, when incomes are high enough and different economic sectors are integrated by well-functioning labour and capital markets. this is emerging in some developed economies (timmer, 2007). literature on development economics also shows that there is a substantial gap between agriculture’s share of gdp and its share of employment during the course of a nation’s growth. this gap indicates the differences in the factor productivity between agricultural and non-agricultural sectors, reflected in the concentration of poverty in agricultural and rural areas (timmer and akkus, 2008). therefore, narrowing this gap is critical in fostering growth and alleviating poverty for developing countries, especially when they are facing globalised market competition, together with the pressure of rapidly growing urban populations and non-agricultural sectors contending for already scarce land and water resources (timmer, 2007; world bank, 2007). however, agriculture alone cannot improve economy-wide productivity. productivity growth involves a reciprocal interplay between the agricultural and non-agricultural sectors, and the sectoral exchange fundamentally mirrors the equilibrium between rising income and changing proportions of demand and supply, while development in agriculture enhances growth in other sectors through links between consumption and production (chenery, 1988). at different development stages, countries face different growth problems; thus, agriculture is required to respond differently. transforming economies like china have recently moved from relying on agriculture for growth and employment (agriculture based countries, world bank, 2007), to the stage of facing rising rural-urban income disparities and persistent rural poverty. the recommended strategy to reduce the disparities for those countries is to diversify into high-value horticulture and livestock in response to rapidly growing domestic and international demand (world bank, 2007). this agricultural diversification process involves integrating output into markets, substituting traded inputs for non-traded inputs, and shifting mixed production to monoculture farming to capture economies of scale (pingali and rosegrant, 1995; chavas, 2008). from the 116 l. li, b. bellotti, a.m. komarek production perspective, agricultural diversification is viewed as a transformation of food production from subsistence to commercial systems, a course of agricultural sector diversification and commercialisation accompanied by farm-level production specialisation (pingali, 1997; timmer, 1997). the patterns of structural change and the trend of agricultural diversification are proposed to be predictable and uniform, and have been witnessed in most industrialised countries (timmer, 1997; world bank, 1992, 2007). compared with developed countries, the current developing nations have been transforming in different historical, demographic, economic, and agro-climatic contexts, in addition to the variation in natural resource endowment, opportunities, and constraints across countries and regions (losch et al., 2012). 2.2 agricultural transformation leads to production diversification timmer (1988; 1997) suggests that agricultural transformation inevitably experiences four critical stages. in the first phase, increasing agricultural productivity generates a farm surplus. during the second phase, farm surplus stimulates the non-agricultural sectors to expand. in the third stage, the improved infrastructure and markets further support resources and outcomes to flow out of the farm sector. finally, at the end of the agricultural transforming stage, agriculture integrates into the whole economy and its role in an economy is no different from industry and services. those four diversification stages are part of the overall transformation process. based on historical transformation experiences in asian countries, timmer (1997) illustrates that trends of the diversification process can differ at the economy, the agricultural sector, and the individual farm level. demonstrated in figure 1, the vertical axis indicates the degree of diversification1, and the horizontal axis shows the course of transformation. the entire economy, measured by the diversity of food consumption, and the agricultural sector become more diversified when resources are being shifted out of agriculture. at the farm level (individual fields within a single farm, and/or single farms within a region) the degree of diversification declines while the agricultural productivity increases, measured by rising value added per agricultural worker. the decreasing diversification/increasing specialisation are facilitated by the improvement of credit and labour markets during structural change, enabling farmers to capture the economies of scale by specialising their production (coelli and fleming, 2004; pingali, 1997; timmer, 1997) from a policy perspective, agricultural diversification is regarded as a crucial strategy to increase the flexibility of the rural sector and to respond to improving technologies and market conditions. the macro-level agricultural diversification is also considered as a cushion against the adjustment costs caused by transforming resources to protect farmers against price fluctuations when the economy is being integrated into the world market (timmer, 1988 and 1997; world bank, 1988 and 1990). meanwhile, the diversified agricultural sector potentially expands rural small and medium-scale industry (processing, marketing, and other labour-intensive services), and in turn absorbs the displaced labour force from agriculture the advantage of diversifying traditional grain-dominated production into higher income demand elasticity products are that countries increase the flexibil1 timmer’s (1997) study is conceptual; no attempts are made to quantitatively measure the diversification degree. however, approaches such as the concentration ratio or the herfindah index are suggested for empirical studies. 117structural change and agricultural diversification since china’s reforms ity of their faming systems, efficiently allocate resources, reduce rural poverty and sustain productivity (world bank, 1990 and 1992). diversification at different stages and different economic levels reflects long-run and short-run agricultural development issues, calling for different policy priorities. in the short-run, problems are narrowed to the micro-level response to price changes, and require producers to adjust production with alternative crops and activities rapidly (world bank, 1988). however, producers’ ability to respond to market signals can be influenced by technologies, market conditions, and households’ characteristics such as education and risk-aversion. thus, appropriate policies are vital to facilitate changes of crop patterns and activities, and to deal with unstable food prices and concern over food security. apparently, the short-run policy priorities are to increase the flexibility of production systems, and to guide farmers towards activities that are more responsive to market demand and prices. outcomes from those policies would be poverty reduction and improvement of income distribution (world bank, 1988, 1990 and 1992). 3. structural change and agricultural transformation in china: an overview 3.1 distinctive economic features, consistent transformation patterns over the past three decades, china has undergone an impressive and rapid structural change; its agricultural sector has achieved significant progress in increasing productivity, figure 1. the relationship between diversification and agricultural transformation. source: timmer (1997). 118 l. li, b. bellotti, a.m. komarek diversifying products, and alleviating poverty. agriculture has significantly contributed to the nation’s growth; however, its relative contribution to gdp continues to decline. a large part of the labour force has been reallocated from agricultural to non-agricultural sectors, and the share of agricultural employment decreased from 68.7% in 1980 to 34.8% in 2011. agricultural value calculated in gdp declined from 30% to 10% in the same period (world bank, 2015). more importantly, households’ consumption patterns have changed; demand has increased for meats, fruits and vegetables. the share of staple crops in total agricultural output dropped from 82% in 1970 to less than 50% of gdp in 2008 (huang et al., 2010). impressively, 58% of the world’s horticulture, and 67% of the world’s aquaculture production increases were generated by china since the mid-1980s (world bank, 2007). it is widely accepted that china’s overall transformation has followed a traditional line of growth, with the agricultural growth as the precursor to the economic development (united nations, 2006; world bank, 2007). however, compared to other developing countries, china had, and to some extent still has, some distinctiveness prior to its reforms. the most distinguishing characteristic is its planned governance system, namely, central control over prices allocation of inputs and outputs and financial flows. this centrally controlled system, along with pursuing “a capital intensive heavy industry orienteddevelopment-strategy in a capital-scarce agrarian economy” (lin et al., 1996) resulted in imbalanced economic structure, frail institutions, and weak incentives (brandt and rawski 2008). these negative consequences have in turn caused inefficiency in performance and productivity. research indicates that technical efficiency in state owned enterprises was relatively low as a result of overstaffing and underutilisation of capital resources (lin et al., 1996). it is also suggested that chinese socialism, especially the planned system, detained the economy inferior to its production frontier (brandt and rawski, 2008). moreover, the low efficiency of china’s economy was a consequence of the government-controlled monopoly of finance, telecommunications, and steel sectors. this large proportion of state-run enterprises was an outcome of the preferentially promoted large industry during the maoist era. the large manufacturing was aimed at building the state’s ability of producing capital goods and military supplies for the consideration of self-sufficiency and national security (brandt and rawski, 2008; lin et al., 1996). this distinctive institutional feature potentially affected china’s reform path. in 1980, when the reform was initiated, china’s share of manufacturing was larger than most low-income and middle-income countries. along with the heavily discounted service sector, china’s distorted economic composition is presumed to have affected its growth pathway and the progress of structural change (heston and sicular, 2008). the third feature of china’s economy prior to reforms was its long isolation from deep engagement with the global economy. combined with the communist party’s self-sufficient tendencies and the partial trade embargo led by the usa, china was restricted in its global market participation (china joined the wto in 2001). this very limited participation in the world markets deprived chinese producers of global competition. under the central plan and control system, neither import nor export was sensitive to exchange rates or relative prices. the composition of chinese trade was consequently not linked to its comparative advantage (branstetter and lardy, 2006). this isolation from the international economy enlarged the gap between china’s achievements and potential, and also prevented world market prices from stimulating domestic production (brandt and rawski, 2008). 119structural change and agricultural diversification since china’s reforms aside from features of the planned system, dominance of the state sector, and the isolation from world markets, a rural-urban gap, in both economic and institutional terms, was another feature unique to china’s initial condition. the “dual track” structure which was formed to ensure a collectivized agricultural production in rural areas and the concentration on heavy industry in urban areas resulted in segmentation between the rural and urban sectors. in addition, the strict residency system (hukou), a heavy urban bias on education, health care, housing, and pensions have contributed to the disparity between rural and urban development. it is well recognised that restrictions on rural resource mobility (mainly labour migration) have constrained structural change and caused stagnation in agriculture(benjamin and brandt, 2002). it appears that china has several fundamentally distinct institutional, political and economic policy settings compared to other economies. this begs the questions of whether this uniqueness has made china a special case regarding economic composition, and whether china’s overall structural constitution is consistent with its development stage. figure 2 compares china with countries at different growth levels (the usa, australia, brazil, and india), using the world development indicators to measure agricultural development in relation to gross national income (gni) 2 across countries (figure 2). in 2008, agriculture’s share in china’s employment and gdp were higher than the 2 gni per capita (formerly gnp per capita) is the gross national income, converted to u.s. dollars using the world bank atlas method, divided by the midyear population. agriculture value added per worker is a measure of agricultural productivity. value added in agriculture measures the output of the agricultural sector (isic divisions 1-5) less the value of intermediate inputs. agriculture comprises value added from forestry, hunting, and fishing as well as cultivation of crops and livestock production. data are in constant 2000 u.s. dollars. figure 2. comparison of structural change and growth among china and selected countries. 0 10 20 30 40 50 60 gni per capita ( 1000 current us$) employment in agriculture (% of total employment) agriculture value added (% of gdp) agriculture value added per worker ( 1000us$, constant value of year 2000) united states australia brazil china india sources: world development indicators for 2008, world bank, 2011. 120 l. li, b. bellotti, a.m. komarek usa, australia and brazil, respectively. by contrast, its agricultural productivity is higher than india, indicating china’s development of the agricultural sector is consistent with its overall economic level. a number of comparative investigations have drawn similar conclusions. for example, focusing on both distinctive and common features, heston and sicular (2008) examine the post-1978 chinese economy in comparison to averages for low-, middleand highincome countries. the results show that china’s structural change has followed the general international pattern since 1980. its development has been associated with a decline in agriculture’s relative importance in the economy, a rising industry, and expansion of the service sector. timmer (2007) compares the general growth pattern of fifteen countries, suggesting that “china is unique in its rapid growth and in the structural patterns that growth has induced in employment and gdp. but china is not unique in the distributional consequences of its growth”3 . from different perspectives, several other studies have concluded consistently that china’s structural change has fitted surprisingly well into the conventional views of development economics. herrmann-pillath (1994) stated “china is an enfant terrible of the mainstream theory of transformation”, and “it was the way in which china went about in reforming its system that makes the country’s reform experience unique” (hofman and wu, 2009). the reforms during china’s transition period have followed logical prescriptions mainstream economics would recommend, that is, the development of incentives, mobility, price flexibility, competition and openness (lin et al., 1996; brandt and rawski, 2008). this conventional economic transformation and growth in china has been unexpected to most economists’ contention, especially from the political economics perspective (for a detailed debate on this topic, see arrighi, 2007; harvey, 2005). coexisting with uniqueness and consistency, the transformation in the agricultural sector has contributed to china’s impressive growth significantly. large-scale labour move from agricultural to non-agricultural sectors reduced the employment in agriculture from 69% in 1978 to 35% in 2011 (world bank, 2013), despite the fact that the growth of productivity in agriculture was the major driver of labour reallocation. agricultural value added per worker increased from 224 to 785(constant 2005 us$) between 1980 and 2013 (world bank, 2013). during the same period, agricultural value added in gdp declined from 30% to 10%. the above figures show that the relative importance has continued to decline, however, agriculture has been the major contributor to structural change in china’s economy. 3.2 transformation in agriculture, stages and policies china initiated rural reforms in 1978. a series of strategies and policies were implemented to improve farmers’ incentives and develop the rural economy. among others, decollectivisation was a major driver to improve total factor productivity in the early stage of reform (lin, 1992); the effort to restructure the rural economy through institutional change created strong incentives for chinese small famers to use inputs more efficiently, 3 the fifteen countries are: bangladesh, brazil, china, india, indonesia, japan, korea, malaysia, nepal, nigeria, pakistan, papua new guinea, philippines, sri lanka, and thailand. 121structural change and agricultural diversification since china’s reforms including human capital (ash, 1988). it is estimated that the change in incentive structure increased agricultural output by 20% to 30% without any claim on additional resources from the rest of the economy (lin, 1988; mcmillan et al., 1989). the well-studied policy implemented in this period was the household responsibility system (hrs), a bottom-up initiated plan which shifted production from a collective system to a family-based management, and enhanced farmers’ motivations to adopt new technology and thus sped the diffusion of new technology (lin, 1992). as a result, grain output increased by 4.7% per year during the period 1978 to 1984, and the real value of gross output in the farm sector doubled between 1978 and 1989. this remarkable production growth was accompanied by a significant diversity of china’s agricultural production and food consumption patterns. cash crops like cotton and oilseeds, along with meat production increased quickly. for instance, annual growth of cotton production was 19.3% between 1978 and 1984 (huang et al., 2008; hofman and wu, 2009). during the same period, both rural and urban households’ share of grain consumption reduced dramatically due to rising incomes and falling grain prices (huang et al., 2008). commencing in 1985, further reforms focused on market extension and price regulation. the intention to initiate commercial exchange and agricultural investment was realised by replacing the state monopoly purchase and supply with a part-contractual, part-free market exchange system (ash, 1988). after a long period of restriction in agricultural prices, those reforms enabled market prices to become the basis of farmer production and marketing decisions (rozelle and huang, 2006). the development of domestic markets and the agricultural trade liberalisation (especially the accession to the world trade organisation) have considerably narrowed the differences between international and domestic market prices for many commodities. especially after china’s accession to wto in 2001, agriculture has entered a stage of all-round reform and opening-up. china has abolished non-tariff border measures, converted non-tariff measures into tariffs and adopted tariff cuts and “binding” to accommodate further reform and opening-up and participate in international market competition(moa, 2015). consequently, price changes and farmers’ incentives have been directly affected by world markets (huang et al., 2008). world market prices became an active stimulus for china’s agricultural diversification, for instance, the large-scale reallocation of cultivated acreage from staple crops to vegetables, horticulture and other labour-intensive alternatives occurred only after the government ended its policy of setting domestic grain prices above world market level (brandt and rawski, 2008). these developments also attributed to chinese government’s pro-farm policies to enhance small farmers’ marketing alibility and competitiveness. for example, the vegetable basket program (vbp) has significantly boosted production of vegetables, meat, dairy products, and aquatic products (moa, 2012). diversification in farm production has been significant, stimulated by price policy, market liberalisation, and technological improvements. between 1978 and 2002, the percentage of grain crops in total sown area reduced from 80% to 65%, and has maintained at above 68% since then. absolute grain production even decreased by 16% from 1998 to 2003 (carter et al., 2012). by contrast, vegetable sown area increased 5.7% annually; the output of fruits increased thirty-fold. over the same period, the livestock and fishery sector rose from 14% and 2% to 31% and 10%, respectively (nbsc, 1978-2012). 122 l. li, b. bellotti, a.m. komarek 4. quantifying agricultural diversification in china 4.1 method the herfindahl index is a statistical measure of concentration, commonly used in diversification research to indicate the extent of specialisation (pope and prescott, 1980; culas, 2006). the herfindahl index of product concentration is defined as: pit = ait / ait∑ (1) hrt = pit 2∑ (2) in equation (1) ai is the value of product i, a∑ i is the sum of farm products’ value. thus pit is the value share of product i in total farm value in time t. in equation (2), hrt is the herfindahl index, computed by the sum of farm products’ value share squared. because this study examines agricultural diversification, the herfindahl concentration/specialisation index was inverted to formulate a diversification index, to make the demonstration more illustrative and straightforward. the diversification level for region r at time (year) t is: d hrt rt= -1 (3) the value of diversification index drt ranges from 0 to 1, and larger values denote higher degree of agricultural diversification, lower values indicate greater specialisation. 4.2 data six categories of farm products were included in the index computation: grain, cotton, rapeseed, vegetables, fruits, and livestock. fishery and forestry products were not included due to data being incomplete for some provinces. considering crop and livestock production account for 86% (in 2010) to 95% (in 1978) of output-value share in china’s agricultural economy, the exclusion of fishery and forestry production in the computation would have very little impact on formulating the diversification indices. farm output data were extracted from china’s statistical yearbook (nbsc, 19782012), price information was from the china compendium of statistics between 1949 and 2008 (nbsc, 2010) and china yearbook of agricultural price survey (nbsc, 20042012). farm values were calculated as outputs multiplied by prices, and then applied into equation (1)-(3) to compute diversification indices for individual provinces. indices were further used to aggregate regional and national diversification. six regions were grouped based on similarities in agricultural endowments and level of economic development, following the classification by carter and lohmar (2002). the specific categorisation was: 1) north (beijing, tianjin, hebei, shanxin, inner mongolia, henna, and shanddong); 2) northeast (heilongjiang, jilin, and liaoniang); 3) central (anhui, jiangxi, hubei, and hunan); 4) coastal (shanghai-jiangsu, zhejiang, fujian, guangdong, and hainan); 5) southwest (chongqing, sichuan, guizhou, yunnan, and guangxi); 6) northwest( tibet, shaanxi, gansu, qinghai, ningxia, and xingjiang). 123structural change and agricultural diversification since china’s reforms 5. results 5.1 agricultural diversification in relation to growth: regional comparison figure 3 shows the association between diversification and gdp per capita for the national average and the six aggregated regions. overall, the agricultural sector has been more diversified. notably, the diversification level had a remarkable increase before gdp per capita reached about 5,000 yuan (approximately 1,811 us dollars). once gdp per capita exceeded 15,000 yuan, the agricultural diversification level remained unchanged or slightly declined for all the cases. moreover, the diversification level decreased during 2003-2007 in most regions. this sharp decline could be partially explained by the nationwide policy effort to increase grain production at the time. a series of policies were implemented to stimulate farmers’ grain production and the relative profitability of grain production, when grain production decreased by 16% between 1998 and 2003. these policies included ending agricultural taxes, directing subsidy payments to grain producers, grain crop support price, input subsidies for fertiliser and farm equipment, and increased investment in infrastructure (carter et al., 2012). the above pro-grain government policies effectively encouraged grain production, area planted with grain recovered to 1997 levels, and the share of grain’s output to the agricultural sector rose (liu et al., 2008). the decline of production diversification figure 3. diversification level and gdp per capita for national average and six regions, 1978-2012. 124 l. li, b. bellotti, a.m. komarek between 2002 and 2007 was attributed to this grain production rise/concentration, as grains (rice, wheat, and maize) account for more than 50% of crop production. the patterns of china’s agricultural diversification support timmer (1997) that agriculture tends to be more diversified at macro levels in the early stage of development. china’s practice further suggests that government’s policy, in particular, encouraging grain production, was effective in changing the diversification degree at the national and regional levels. moreover, the degree of diversification varies among regions at the same growth level/gdp per capita. studies in other developing countries indicate that besides the growth of gdp, agricultural diversification is closely related to the degree of market development, especially the level of growth prior to agricultural transformation, and the relative importance of agriculture in the region (dorsey et al., 2005). this is true in the chinese case; for example, the northwest and southwest regions were at similar growth levels between 1978 and 2002, but the southwest region was higher in the agricultural diversification level, owing to its comparatively developed markets and infrastructure, and the intensification of the piggery and feedstuff industries (carter et al., 2012). by contrast, the northwest region has had low agro-ecological potential (rainfall, soils, topography), underdeveloped markets and infrastructure (isolated from demand centres and coastal areas for exporting), and higher share of agriculture in the region’s gdp. consequently, agriculture in this area is diversified least among the six regions. the comparisons above suggest that the rate of agricultural diversification is related to comparative advantage (natural resources, access to markets), development levels (education, access to information, markets) and the relative importance of agriculture in the regions. for instance, the coastal region is the most developed area in china, with the highest average gdp per capita (figure 3). production diversification levels in this zone, however, are relatively low among the six regions. this can be explained by the fact that the rapid urbanisation and industrialisation in this region has led to grain production decline and the importance of agriculture in the economy to diminish relatively faster. 5.2 agricultural transformation and its interdependence with non-agricultural sector in underdeveloped regions: the case of gansu province to further investigate the interaction between agricultural and other sectors, gansu province in northwest china was closely studied. gansu province is one of the poorest regions in china. in 2012, average rural per capita income was 4,507 yuan (the lowest in china), accounting for only 57% of the national average 7,917 yuan (nbsc 2013). in terms of agricultural conditions, gansu is poorly endowed with natural resources, one-fifth of the cultivated land is terraced, and annual average rainfall ranges from 50 mm in the west and 550mm in the east (gansu yearbook editorial board, 2012). growth in gansu’s agricultural sector has been considerably slow with very low productivity, accounting for 2.3% of china’s rural employment but contributes only 1.3 % of the value of chinese farm production (brown et al., 2009). by contrast, the industrial sector in gansu experienced special growth in the 1950s, when substantial government investments were shifted from coastal cities into interior regions for security considerations (brandt and rawski, 2008). the average annual growth rate in industry was 15.28% between 1952 and 1978, compared with 6.27% for the overall gansu economy (yue, 2009). during this time, emphasis was placed on establishing 125structural change and agricultural diversification since china’s reforms the province’s heavy industry. state-owned enterprises, like mining, petroleum refining and drilling, have been the backbone of gansu’s industrial development. as a result, 90.7% of industrial output was from state-owned enterprises (soes) in 1978, which was second highest in china, and much higher than national average (77.6%) and guangdong province (67.9%) located in coastal region (table 1). gansu’s industry-prioritised development strategy intensified agriculture’s inferior situation and resulted in a distorted economic structure. when the economic reforms started in 1978, gansu’s industrial share was higher than the national average, and the agricultural share was low with respect to its development level (figure 4). consequently, gansu experienced a catch-up growth period in agriculture between 1979 and 1985; the average farm labour productivity growth rate exceeded the industrial sector (4.30% compared to -6.51%, appendix table a), and the productivity gain was table 1. regional share of industrial output by ownership (percent), 1978-1990. 1978 1985 1995 soes coes soes coes soes coes nation 77.6 22.4 64.9 32.1 54.6 35.6 guangdong 90.7 9.2 88.1 11.9 78.1 18.0 gansu 67.9 25.5 53.3 30.0 41.0 34.1 source: wei, 2013, p105. note: soes refers to state-owned enterprise, coes refers to colletive-owned enterprise. figure 4. sectoral shares in gdp and employment, gansu province and national average. sources: china statistical yearbook, and gansu yearbook, 1978-2012. 126 l. li, b. bellotti, a.m. komarek attributed to the province-wide effort of grain self-sufficiency (yue, 2009). the share of agriculture in gdp started declining when labour started shifting to the industrial and services sector after 1985, indicated by the declining agricultural employment (figure 4) between 1978 and 1990, gansu’s sectoral composition had restructured, coincided with a rapid growth of non-state enterprises and an enormous decline of the share of industrial output produced by soes (wei, 2013). the compositional distortion in gansu’s economy before reforms and the later on efforts to optimise the industrial structure were reflected by its change in diversification patterns. as shown in figure 5, gansu’s overall agricultural diversification was at lower lever in the early reform period (1978-1995), compared to the guangdong province and national average. this is consistent with its low annual growth rate of 6.0%, in contrast to the annual growth of 10.8% and 7.5% for guangdong and national average, respectively (wei, 2013). as mentioned earlier in section 2.2, the level of production diversification in a region’s rural economy is closely associated with its overall growth. agricultural diversification is driven by income growth and increasing urbanisation to meet the shift of dietary patterns away from cerealsdominated to a variety of livestock products, fruits, and vegetables (world bank, 2007). the process of diversification in gansu’s agricultural sector indicates that agricultural diversification is affected by the non-agricultural sector and the process and progress of figure 5. patterns of agricultural diversification, gansu province in comparison with guangdong and national average. 127structural change and agricultural diversification since china’s reforms structural change. the growth pathway provides some insights into how sectoral composition in the early stage of transformation affects diversification in the rural economy. diversification in the agricultural sector maybe constrained if farms cannot move out to higher productivity sectors and agriculture’s share in employment stagnates. this is supported by findings from brandt et al. (2008) suggesting that provinces with a relatively large state sector at the start of reforms are likely to experience slow growth. the present study shows that the capital intensive and low-labour-absorbing state sector indeed posed higher initial barriers to gansu’s rural labour mobility, and subsequently slowed the pace of structural transfer and economic growth. 6. concluding remarks from a historical and regional perspective, this study examined agricultural development and transformation during china’s socio-economic reforms. in particular, it examined the theory that economic development results in agricultural diversification at the national and regional levels. aggregate-level analyses suggest that, although economic growth in china is unique, its pattern of agricultural transformation is consistent with those of other developing countries. the agricultural sector becomes more diversified as the economy grows. the findings of this study further show the significance of regional comparative advantages (for example, natural endowments, market functionality, and the activity of non-state-owned enterprises) that determine how much a region can transfer its labour out of agriculture, and how quickly this region can narrow the gap between agriculture’s share of gdp and increasing employment to reduce poverty and rural-urban disparity. this research provides insights into the specific circumstances of farmers in less-favoured regions, demonstrated by the surveyed households in the gansu province of western china, where the region is still in an early stage of the economic transition, and the smallholders could be constrained from increasing their incomes and integrating into the restructuring of agro-food markets. to implement the agriculture-for-development agenda, the promotion of high-value farm activities and non-farm employment, and the provision of infrastructure to support diversification in agriculture and rural economies are recommended policies (world bank, 2007). the insights of this research clarify that a discussion of patters of agricultural diversification/specialisation, and the strategy of agriculture-for-development, must be region and settings-specific. for the less-favoured smallholder in western china, a more effective strategy might be to establish efficient value chains, enhance smallholders’ competitiveness and facilitate their market access. by improving markets, especially the missing institutions for credit, technical support and insurance, smallholders can be encouraged to specialise in high-value activities and integrate into the market. acknowledgements this study is supported by the fundamental research funds for the central universities, lanzhou university (lzujbky-2009-107). 128 l. li, b. bellotti, a.m. komarek references arrighi, g.(2007), adam smith in beijing: lineages of the xxi century. london: verso. ash, r.f. 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(2000). the razor’s edge: distortions and incremental reform in the people’s republic of china. the quarterly journal of economics 115: 1091-1135. yue, m. 2009. empirical research in the dynamic impact of economic structure changes on economic growth since reform and opening up in gansu, northwest normal university. appendix table a. growth of labour productivity in national average and gansu, 1978–2012. labour productivity real gdp per worker average annual growth rate 1978 1985 1995 2005 2012 1978– 1985 1985– 1995 1995– 2005 2005– 2012 nation agriculture 363 642 1029 1599 3454 8.50% 4.82% 4.51% 11.63% industry 2513 2904 5517 11763 17200 2.09% 6.63% 7.86% 5.58% services 1784 2412 3565 7626 14206 4.40% 3.98% 7.90% 9.29% gansu agriculture 244 328 354 830 1471 4.30% 0.75% 8.90% 8.53% industry 4804 2998 2748 9809 18946 -6.51% -0.87% 13.57% 9.86% services 1725 1724 2208 6229 10818 -0.01% 2.51% 10.93% 8.21% bio -based and a ppl ied economics bae bio-based and applied economics 11(2): 123-130, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-12160 copyright: © 2022 m. tappi, g. nardone, f.g. santeramo. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: m. tappi, g. nardone, f.g. santeramo (2022). on the relationships among durum wheat yields and weather conditions: evidence from apulia region, southern italy. bio-based and applied economics 11(2): 123-130. doi: 10.36253/bae-12160 received: october 4, 2021 accepted: april 27, 2022 published: august 30, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: simone cerroni. orcid mt: 0000-0003-0682-5719 gn: 0000-0003-3816-0993 fgs: 0000-0002-9450-4618 paper presented at the 10th aieaa conference on the relationships among durum wheat yields and weather conditions: evidence from apulia region, southern italy marco tappi*, gianluca nardone, fabio gaetano santeramo university of foggia (italy) * corresponding author. e-mail: marco.tappi@unifg.it abstract. the weather index-based insurances may help farmers to cope with climate risks overcoming the most common issues of traditional insurances. however, the weather index-based insurances present the limit of the basis risk: a significant yield loss may occur although the weather index does not trigger the indemnification, or a compensation may be granted even if there has not been a yield loss. our investigation, conducted on apulia region (southern italy), aimed at deepening the knowledge on the linkages between durum wheat yields and weather events, i.e., the working principles of weather index-based insurances, occurring in susceptible phenological phases. we found several connections among weather and yields and highlight the need to collect more refined data to catch further relationships. we conclude opening a reflection on how the stakeholders may make use of publicly available data to design effective weather crop insurances. keywords: climate change, farming system, phenological phase, risk, weather insurance. jel codes: g22, q14, q18, q54. introduction farming activities are exposed and vulnerable to several risks, among which the weather risks are increasingly frequent and impactful due to climate change (conradt et al., 2015). among the several strategies available to reduce the weather impacts on farming systems, e.g., pest control, financial saving, agricultural and structural diversification (vroege and finger, 2020), the crop insurance programs can play an important role (di falco et al., 2014). in recent years, the attention for the weather index-based insurances (wibis) has been growing mainly because these tools may help to overcome some of the challenges associated with traditional indemnity-based insurances, e.g., asymmetric information, high transaction costs, moral hazard, and adverse selection (norton et al., 2013; dalhaus and finger, 2016; belissa et al., 2019; ceballos et al., 2019). differently from the traditional insurances, which provide pay-outs depending on actual yield losses, wibis indemhttp://creativecommons.org/licenses/by/4.0/legalcode 124 bio-based and applied economics 11(2): 123-130, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12160 marco tappi, gianluca nardone, fabio gaetano santeramo nify the farmers when an index, computed on rainfall or temperature and highly correlated with farms performance (e.g., yields), is triggered (conradt et al., 2015; dalhaus and finger, 2016). therefore, farmers will be indemnified when the index exceeds a pre-determined threshold (belissa et al., 2019). moreover, wibis can be manipulated neither by the insurers or the insured because they are collected from historical and current dataset provided by recognized bodies (belissa et al., 2020; vroege et al., 2021). however, wibis present a limit, namely basis risk: a significant yield loss may occur even if the weather index does not trigger the payment (conradt et al., 2015; dalhaus et al., 2018) or a compensation may be granted even if there has not been a yield loss (heimfarth and musshoff, 2011). the contribution of our study is at least twofold: first, we provide empirical evidence on how yields and weather conditions are correlated, more specifically, we deepen the knowledge on the linkages between durum wheat yields and weather events occurring in susceptible phenological stages; second, we start a reflection on how stakeholders may make use of publicly available data to design an effective crop insurance scheme. we focused on the apulia region (southern italy) which is the main national producer of durum wheat: almost a thousand of tons of production, i.e., accounting for 25% of the italian durum wheat production, and about 344 thousand cultivated hectares, i.e., accounting for 28% of the italian area utilized to grow durum wheat (ismea, 2020). the italian crop insurance system the italy boasts a long tradition of public subsidies for agricultural risk management. the “fondo di solidarietà nazionale” (fsn) was instituted in 1974 to finance both insurance policies and ex-post payments (enjolras et al., 2012). moreover, the eu common agricultural policy allocated funds for agricultural insurances (art. 37 of eu reg. 1305/2013) to cope economic losses due to adverse weather conditions, plant diseases, epizooties, and parasitic infestations (santeramo et al., 2016; rogna et al., 2021). despite the public interventions, the participation level to insurance programs remains low (i.e., around 15 percent) mainly due to high costs of bureaucracy (i.e., complexity of procedures), delays in payments, lack of experience with crop insurance contracts or lack of high-quality information on existing insurance tools (santeramo, 2019). the role of defense consortia, introduced both to facilitate the match of insurers and farmers in the subsidized crop insurance market and to reduce the asymmetric information, is not negligible. it emerges a north-south territorial dualism that affects farmers participation: defence consortia are more effective in northern italy than in the southern italy and, also, the strong presence of producer organizations and cooperatives aggregates the crop insurance’s demand in the northern italy (santeramo et al., 2016). moreover, farmers who trust more in the intermediaries assisting them are inclined to adopt insurance tools to cope the risk of production loss, while risk averse farmers tend to implement other risk management strategies as crop or financial diversification (trestini et al., 2018). in italy, only the 9.9 percent of utilised agricultural area is covered by insurance contracts and 20.9 percent of production value is insured (ismea, 2021). according to a survey conducted by ismea in 2018 on low participation to the subsidized agricultural insurance systems, most italian farmers renounce to subscribe insurance contracts due to economic reasons, highlighting the high costs of policies. the share of farmers who believe that their farms are not exposed to specific risks or who have had negative experiences when receiving compensation, losing trust on insurance market systems, is also not negligible. indeed, giampietri et al., 2020 found that the trust affects the decision-making process: under uncertainty, the trust may substitute the knowledge also overcoming the lack of experience, therefore, strong communication campaigns to improve farmers’ participation are recommended. moreover, focusing on the wibis, also subsidized by the measure 17 of national rural development program 2014-2020, a lack of knowledge emerged among big insured farmers, i.e., wibis were unknown to 93 percent of them (ismea, 2020). furthermore, some farmers believe that indexbased insurances are inadequate to manage the weather risks due to the distrust of the objectivity of the indexes and parameters used, also showing an aversion to any future subscriptions. clearly, it is necessary to improve the appeal and communication of these innovative risk management tools, also considering that any intervention aimed at promoting farmer participation should improve the competition among insurance providers, also reducing at the same time the asymmetric information and opportunistic behaviour (menapace et al., 2016; rogna et al., 2021; santeramo and russo, 2021). in this complex scenario, we estimate the yield response equation to investigate the responsiveness of yield to climate, deepening the working principles of weather indexbased insurance, through a case study on durum wheat crop in the apulia region, also animating the debate on the use of publicly available data to the development of an effective and attractive tool to manage climatic risk in agriculture. 125on the relationships among durum wheat yields and weather conditions: evidence from apulia region, southern italy bio-based and applied economics 11(2): 123-130, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12160 data and research methodology an agronomic review on durum wheat allowed us to identify sensitive phenological stages of durum wheat in apulia region and those critical weather events occurring in certain phenological stages that may cause significant production losses (table 1). cold sensitivity is higher during the germination phase that occurs 10-15 days after sowing in which temperatures of few degrees centigrade below zero may cause considerable damages (baldoni and giardini, 2000, angelini, 2007; disciplinare di produzione integrata della regione puglia, 2021). likewise, temperatures of few degrees centigrade below zero during the stem elongation phase may cause stems death and serious damages to the tissue of the internodes (baldoni and giardini, 2000; angelini, 2007; disciplinare di produzione integrata della regione puglia, 2021). flowering stage occurs in late may and lasts about 10 days in which wheat crop is highly sensitive to cold stress that may cause death of f lowers (angelini, 2007; baldoni and giardini, 2000; disciplinare di produzione integrata della regione puglia, 2021). heat and drought stress during susceptible flowering and grain filling stages (i.e., after flowering, until the first decade of july) may cause considerable reductions in wheat yield and quality, leading the acceleration of leaf senescence process, reducing photosynthesis, causing oxidative damage, pollen sterility, also reducing physiological and metabolic imbalances, photosynthesis, grain numbers and weight (angelini, 2007; asseng et al., 2011; li et al., 2013; farooq et al., 2014; rezaei et al., 2015; zampieri et al., 2017; makinen et al., 2018). heavy rainfall during the entire crop cycle may cause significant production losses due to the proliferation of pathogens, nutrient leaching, soil erosion, inhibition of oxygen uptake by roots (i.e., hypoxia or anoxia), waterlogging and lodging (zampieri et al., 2017; makinen et al., 2018). furthermore, we collected yearly total production (tons) and area harvested (hectares) data for durum wheat crop from the national institute of statistics (istat), from 2006 to 2019, for each province of apulia region, also calculating the respective yields (tons/ hectare). then, for the same time-period, we collected 10-days frequency weather data from six synoptic weather stations of the institute for environmental protection and research (ispra), one for each province of apulia region: bari (ba), barletta-andria-trani (bt), brindisi (br), foggia (fg), lecce (le), taranto (ta). weather data include 10-days average minimum temperature (°c), i.e., the average of daily minimum temperatures, 10 days average maximum temperature (°c), i.e., the average of daily maximum temperatures, and 10-days cumulative precipitation (mm), i.e., the average of daily precipitation. details on collected variables are shown in table 2. our empirical approach is based on a panel data model that includes fixed effect (i.e., it is a major advantage of the panel rather than cross-sectional regression) both to control for unobservable variables such as seed varieties or soil quality that may vary across the space, i.e., provinces, and to catch the variation across the time within the apulian provinces (tack et al., 2015; blanc and schlenker, 2017; kolstad and moore, 2020). table 1. phenological stages, weather events and critical limits of durum wheat in apulia region. phenological stage weather event time interval critical limit reference sowing cold from the first decade of november to the first decade of december temperature < 0 °c baldoni and giardini, 2000; angelini, 2007; disciplinare di produzione integrata della regione puglia, 2021germination cold from the second decade of november to the second decade of december temperature < 0 °c stem elongation cold from the second decade of march to the third decade of april temperature < 0 °c baldoni and giardini, 2000; angelini, 2007 flowering cold from the second decade of may to the first decade of june temperature < 0 °c angelini, 2007; disciplinare di produzione integrata della regione puglia, 2021 heat, drought temperature > 30-31 °c angelini, 2007; rezaei et al., 2015 grain filling heat, drought from the second decade of june to the first decade of july temperature > 34 °c angelini, 2007; asseng et al., 2011; rezaei et al., 2015; zampieri et al., 2017; makinen et al., 2018 all phases excessive rainfall from first decade of november to the first decade of july rainfall > 40 mm/day makinen et al., 2018 126 bio-based and applied economics 11(2): 123-130, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12160 marco tappi, gianluca nardone, fabio gaetano santeramo the relationship between durum wheat yields and weather events is synthesized as follows: yit = f(wit) + μi + θt + εit where yit is the yield over the space (i) and time (t) as function ( f ) of weather (wit), also including fixed effects over space (μi) and time (θt), error term and “controls” refers to other relevant exogenous variables (εit) (kolstad and moore, 2020). more specifically, we conducted temporal and spatial autocorrelation identifying those contiguous provinces having a larger shared borders for a twofold check: (i) verify if the weather events occurring in a province may affect durum wheat yields in the contiguous province; (ii) control if the yields may be affected by weather events occurring at time t-1. undoubtedly, both environmental and agronomic factors may justify the extreme variability of the durum wheat yield across the apulian provinces: foggia shows the highest average durum wheat yields while lecce shows the lowest average yields, although it is characterized by lower yield variability than other provinces as brindisi that, on the contrary, is more affected by environmental and agronomic factors, reason why it may benefit of crop insurance programs more than other provinces to cope yields fluctuations (table 3). results our results clearly show that a relationship links weather conditions and production yields in the apulia region. more specifically, precipitation seem to have a negative effect on durum wheat yields (table 4). however, controlling by spatial and temporal autocorrelation, the effects of temperatures have been caught. minimum temperatures negatively affect durum wheat yields, while maximum temperatures positively affect the yields, both in a non-linear way. indeed, we included the squares of weather variables to catch the nonlinearity, in other terms, the trade-off between weather and yields (blanc and schlenker, 2017). our results clearly highlight that the weather affects the yields in a nonlinear way, therefore, variables have a statistically significant inverted-u shape relationship table 2. details on collected variables. variable (unit) frequency time-period province weather station province (no. of obs, sr in km2) source durum wheat yield (tons/hectares) yearly 2006-2019 bari (ba) barletta-andria-trani (bat) brindisi (br) foggia (fg) lecce (le) taranto (ta) istat average minimum temperature (°c) average maximum temperature (°c) cumulative precipitation (mm) 10-days bari ba (501, 5.138) trani bt (144, 1.543) brindisi br (471, 1.839) monte sant’angelo fg (504, 7.008) lecce le (471, 2.799) marina di ginosa – ta (471, 2.437) ispra, ucea,arpa notes: missing data have been integrated including research unit for climatology and meteorology (ucea) and regional agency for the protection of the environment (arpa) datasets. table includes no. of observations and spatial resolution (sr) of weather stations. table 3. durum wheat yields (tons/hectare) among apulian provinces. average minimum maximum standard deviation bari 0.234 0.170 0.306 0.045 bat 0.224 0.200 0.260 0.020 brindisi 0.285 0.180 0.420 0.071 foggia 0.314 0.200 0.420 0.047 lecce 0.189 0.160 0.220 0.018 taranto 0.244 0.100 0.350 0.057 notes: data include yearly durum wheat yield from 2006 to 2020. source: istat, 2020. 127on the relationships among durum wheat yields and weather conditions: evidence from apulia region, southern italy bio-based and applied economics 11(2): 123-130, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12160 (schlenker and roberts, 2009; lobell et al., 2011). last but not least, minimum temperatures may affect the contiguous provinces. according to the scientific literature, any excess (or deficit) of temperature and precipitation (or their combinations) may cause severe yield losses on durum wheat (baldoni and giardini, 2000; angelini, 2007; asseng et al., 2011; li et al., 2013; farooq et al., 2014; rezaei et al., 2015; zampieri et al., 2017; makinen et al., 2018). furthermore, we estimated the model for each phenological phase of durum wheat to capture the potential heterogeneity in the effect of weather variables, also controlling by spatial and temporal autocorrelation. our results show that the relationship between weather variables and yields is valid only for some weather variables in certain phenological phases. more specifically, the maximum temperatures and precipitation positively affect durum wheat yield in a nonlinear way when occur in the germination and grain filling stages, respectively (table 5). moreover, minimum temperatures may affect the contiguous provinces. clearly, ten-days data we have collected does not highlight the dynamics between weather events occurring in certain phenological stages and durum wheat yields mainly because the impacts of daily weather are not captured. moreover, most variables are not statistically significant: this limit opens a reflection on data disaggregation level and on the need to collect more spatially and temporally refined data, also laying the foundations for the development of an effective index that reflects the responsiveness of the yields to climatic conditions to be implemented in the wibis. the evidence resulting from our econometric model on phenological stages is also in contrast with the literature: germination stage is highly sensitive to cold stress (baltable 4. effects of weather variables on durum wheat yield. variables panel prov fe time trend panel temporal correlation prov fe time trend panel spatial correlation prov fe time trend panel temporal correlation spatial correlation prov fe time trend temperature (min) -0.00764 -0.00124 -0.46909*** -0.45553** (0.10641) (0.11715) (0.17058) (0.18731) temperature (min) sq. 0.00049 -0.00023 0.00892* 0.01384** (0.00296) (0.00320) (0.00490) (0.00544) temperature (max) 0.22572 0.28286* 0.61165** 0.66801** (0.14125) (0.15378) (0.25587) (0.27703) temperature (max) sq. -0.00523* -0.00612** -0.01530*** -0.02022*** (0.00278) (0.00299) (0.00515) (0.00568) precipitation -0.01646** -0.01625* -0.03939** -0.04670** (0.00799) (0.00844) (0.01819) (0.01954) precipitation sq. 0.00008 0.00007 0.00019 0.00024 (0.00006) (0.00006) (0.00017) (0.00018) yield (lag) 0.10464*** -0.09290*** (0.02153) (0.03579) temperature (min) contig. 0.23065*** 0.18642*** (0.06565) (0.07019) temperature (max) contig. 0.00822 0.04557 (0.10765) (0.11545) precipitation contig. 0.00537 0.00771 (0.00704) (0.00837) observations 1,837 1,638 914 833 number of id 6 6 4 4 notes: panel regression model was processed in stata software. it includes provincial fixed effect, time trend, temporal (i.e., yield lag), and spatial (contiguous weather variables) autocorrelation. standard errors in parentheses. *** significant at the 1 percent level. ** significant at the 5 percent level. * significant at the 10 percent level. 128 bio-based and applied economics 11(2): 123-130, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12160 marco tappi, gianluca nardone, fabio gaetano santeramo doni and giardini, 2000, angelini, 2007; disciplinare di produzione integrata della regione puglia, 2021), while there are not evidences on heat stress during this stage. however, our study may help the debate suggesting precise directions for the future research. conclusions participating in index-based crop insurance schemes is a key challenge to improve the resilience of farming systems and adopting effective subsidies to enhance participation in the schemes is a pressing goal for policymakers. in this complex scenario, we investigated how temperatures and precipitation are correlated with yields data to reflect on potential designs for the indexbased insurance schemes. while not novel (e.g., chen et al., 2014), we found that weather changes affect durum wheat yields in a nonlinear way and some weather events occurring in certain phenological phases may have an impact on the yields. our results are important to show that even with aggregated data the evidence is striking. however, focusing on phenological stages, our findings are in contrast with the literature highlighting the complexity of the phenomenon and the need to rely on more temporally and spatially disaggregated data. although we provided clear evidence on the weatheryield relationship, it is impossible to design a wibi using 10-days weather data. therefore, our contribution may help the debate suggesting precise directions for the future research: first, a major effort should be devoted to the collection of weekly or daily weather observations, also identifying empirical damage thresholds that can be verified at farm-level, as well as the collection of production area or municipal data; a promising approach could be the growing degree days tool so as to calibrate the more precisely the growing stages in a view to a better explanation of weather risks on crop performances (conradt et al., 2015; dalhaus et al., 2018; lollato et al., 2020); last but not least, the design of the indextable 5. effects of weather variables on yield by phase. variables sowing germination stem elongation flowering grain filling yield (lag) -0.11883 0.05952 0.17798* -0.04474 0.09403 (0.20660) (0.20523) (0.09219) (0.18593) (0.14041) temperature (min) 0.95845 -0.00051 0.50020 -1.32087 -0.65587 (2.53724) (1.74362) (1.26379) (4.06620) (3.83238) temperature (min) sq. -0.01783 0.01530 -0.01201 0.03550 0.02171 (0.11363) (0.08655) (0.05223) (0.10882) (0.08353) temperature (max) 3.15220 23.00804** -2.73726 7.62398 -1.65011 (12.35641) (10.88917) (2.21349) (8.51643) (6.74553) temperature (max) sq. -0.15964 -0.76330** 0.06023 -0.15868 0.01396 (0.35336) (0.33477) (0.05582) (0.15987) (0.11320) precipitation 0.04601 -0.07450 -0.03735 -0.43463 0.42332* (0.12015) (0.11228) (0.07473) (0.42173) (0.24351) precipitation sq. -0.00034 0.00054 0.00049 0.01188 -0.00826* (0.00088) (0.00084) (0.00101) (0.01680) (0.00463) temperature (min) contig. 1.05294** 0.86957** 0.62187*** 0.52210 0.55304** (0.41397) (0.35021) (0.17188) (0.35845) (0.23765) temperature (max) contig. 0.38942 0.17524 -0.06474 0.22627 0.00512 (1.25128) (1.33537) (0.34861) (0.52741) (0.37530) precipitation contig. -0.05370 0.01278 -0.01394 -0.10017 -0.05635 (0.05168) (0.04199) (0.03275) (0.11446) (0.04998) observations 42 44 125 43 67 number of id 4 4 4 4 4 notes: panel regression model was processed in stata software. it includes provincial fixed effect, time trend, temporal (i.e., yield lag), and spatial (contiguous weather variables) autocorrelation. notes: standard errors in parentheses *** significant at the 1 percent level. ** significant at the 5 percent level. * significant at the 10 percent level. 129on the relationships among durum wheat yields and weather conditions: evidence from apulia region, southern italy bio-based and applied economics 11(2): 123-130, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12160 based insurance schemes needs of further investigation because establishing a triggering index is a major challenge for the stakeholders involved in the implementation of the insurance schemes. the debate on crop insurance schemes is still vivid, and it will be so also in the next decade due to the central role that the risk management (old and novel) tools will have in the new cap (meuwissen et al., 2018; 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(2017). wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. environmental research letters, 12(6), 064008. volume 11, issue 2 2022 firenze university press agriculture, food and global value chains: issues, methods and challenges margherita scoppola mapping global value chain participation and positioning in agriculture and food: stylised facts, empirical evidence and critical issues silvia nenci1, ilaria fusacchia1,2, anna giunta1,2, pierluigi montalbano3, carlo pietrobelli1,4 on the relationships among durum wheat yields and weather conditions: evidence from apulia region, southern italy marco tappi*, gianluca nardone, fabio gaetano santeramo a choice model-based analysis of diversification in organic and conventional farms andrea bonfiglio*, carla abitabile, roberto henke financial performance of connected agribusiness activities in italian agriculture gabriele dono*, rebecca buttinelli, raffaele cortignani pesticides, crop choices and changes in well-being geremia gios1,*, stefano farinelli2, flavia kheiraoui3, fabrizio martini4, jacopo gabriele orlando5 bio-based and applied economics 7(3): 191-215, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-7675 positive mathematical programming and risk analysis quirino paris department of agricultural and resource economics, university of california, davis date of submission: 2018 3rd, july; accepted 2019 20th, march abstract. in 1956, freund introduced the analysis of agricultural price risk in a mathematical programming framework. his discussion admitted only constant absolute risk aversion. this paper generalizes the treatment of risk preference in a mathematical programming approach along the lines suggested by meyer (1987) who demonstrated the equivalence of expected utility of wealth and a function of mean and standard deviation of wealth for a wide class of probability distributions that differ only by location and scale. this paper extends the definition of calibration under positive mathematical programming (pmp) by considering limiting input prices along with the traditional decision variables. furthermore, it shows how to formulate an analytical specification for the estimation of the risk preference parameters and calibrates the model to the base data within small deviations. the pmp approach under generalized risk allows also the estimation of output supply elasticities and the response analysis of decoupled farm subsidies that recently has interested policy makers. the approach is applied to a sample of farms that do not produce all the sample commodities. keywords. risk analysis, positive mathematical programming, model calibration, chance constraint, policy analysis. jel. c6. 1. introduction this paper accomplishes several objectives: 1. it presents a procedure to estimate generalized risk preferences in combination with positive mathematical programming (pmp). 2. it obtains a unique calibrating solution of a pmp model even with a sample of farms that produce zero levels of some crops. 3. it estimates a complete cost function that can be used in a calibrating model for policy analysis. 4. it shows that phase i and phase ii of the classical pmp procedure give identical and unique results. 5. it shows how to incorporate exogenously given supply elasticities. 192 quirino paris 6. it extends the meaning of calibration in pmp by minimizing the distance of optimal solutions from observed output levels and limiting input prices. in this way, it dispenses from the necessity of a user-determined parameter that was originally introduced to guarantee a positive shadow price of binding constraints. the treatment of agricultural price risk in a mathematical programming framework has dealt mainly with either an exponential utility function and constant absolute risk aversion (cara) or the minimization of total absolute deviation (motad) of income. the first approach, originally proposed by freund (1956), appealed to the expected utility (eu) hypothesis and assumed that random prices were normally distributed. these assumptions lead to a mean-variance specification of the certainty equivalent (ce) defined as total expected revenue minus a risk premium. such a premium corresponds to half the variance of revenue multiplied by a constant absolute risk aversion coefficient. the motad approach was proposed by hazell (1971) who justified its introduction with the difficult access – at that time – to a quadratic programming computer software necessary to solve a mean-variance model. according to hazell (1971, p. 56), the motad specification “has an important advantage over the mean-variance criterion in that it leads to a linear programming model in deriving the efficient mean-absolute deviation farm plans.” the motad model approximates a mean-standard deviation (ms) criterion but it says nothing about the economic agent’s risk preference with regard to either decreasing (constant, increasing) absolute or relative risk aversion. recently, cortignani and severini (2012), arata et al. (2017) and paris (2018) have combined pmp with a cara specification of risk preferences. it is difficult, however, to accept the idea that farmers risk behavior does not account for changes in wealth as the cara approach stipulates. petsakos and rozakis (2015) have presented a combination of the traditional pmp specification with a decreasing absolute risk aversion (dara) parameter. the present paper combines a more encompassing specification of pmp (calibration of output quantities and limiting input prices) with generalized risk preferences where the behavior of the risk-avert farmer can vary over all theoretically possible preferences (cara, dara, iara, constant, decreasing and increasing relative risk aversion). the paper deals with market price risk leaving the treatment of production risk for further research. the mean-standard deviation approach has a long history [fisher (1906), hicks (1933), tintner (1941), markowitz (1952), tobin (1958)]. meyer (1987) presented a reconciliation between the eu and the ms approaches that may be fruitfully applied in a positive mathematical programming (pmp) analysis of economic behavior under risk. the main objective of meyer was to find consistency conditions between the eu and the ms approaches in such a way that an agent who ranks the available alternatives according to the value of some function defined over the first two moments of the random payoff would rank those alternatives in the same way by means of the expected value of some utility function defined over the same payoffs. it turns out that the location and scale condition is the crucial link to establish the consistency between the eu and the ms approaches. we reproduce here meyer’s argument (1987, p. 423): “assume a choice set in which all random variables yi (with finite means and variances) differ from one another only by location and scale parameters. let x be the random variable obtained from one of the yi using the normalizing transformation xi = 193positive mathematical programming and risk analysis (yi-μi)/σi where μi and σi are the mean and standard deviation of yi. all yi, no matter which was selected to define x, are equal in distribution to μi+σix. hence, the expected utility from yi for any agent with utility function u( ) can be written as eu(yi )= u(µi +σ ix)df(x)≡v( a b ∫ µi ,σ i ) (1) where a and b define the interval containing the support of the normalized random variable x.” “… under the location and scale condition, various popular and interesting hypotheses concerning absolute and relative risk-aversion measures in the eu setting can be translated into equivalent properties concerning v(μi,σi).” given the assumptions made by meyer about first and second derivatives, v(μ,σ) is a concave function of μ and σ. concavity is established when second derivatives vμμ and vσσ are non-positive and vμμvσσ-vµσ 2 ≥0. the structure of absolute risk (ar) is measured by the slope of the indifference curves in the (μ,σ) space that is represented as ar(µ,σ )= −vσ (µ,σ ) vµ(µ,σ ) (2) where vμ(μ,σ) and vσ(μ,σ) are first partial derivatives of the v(μ,σ) function. some properties of this risk measure are: 1. risk aversion is associated with ar(μ,σ)>0, risk neutrality with ar(μ,σ)=0 and risk propensity with ar(μ,σ)<0. 2. if u(μ+σx) displays decreasing (constant, increasing) absolute risk aversion for all μ+σx, then ∂ar(µ,σ ) ∂µ <(=,>) 0 for all μ and σ>0. 3. if u(μ+σx) displays increasing (constant, decreasing) relative risk aversion for all μ+σx, then ∂ar(tµ,tσ ) ∂t >(=,<) 0 for t>0. saha (1997) proposed a two-parameter ms utility function that conforms to meyer’s specification: v(μ,σ)= μθ-σγ (3) and assumed that θ>0. according to this ms utility function, the absolute risk measure (ar) is specified as ar(µ,σ )= −vσ (µ,σ ) vµ(µ,σ ) = γ θ µ(1−θ )σ (γ −1) . (4) 194 quirino paris hence, risk aversion, risk neutrality and risk propensity are specified by γ>0, γ=0 and γ<0, respectively. as economic agents do not, in general, operate directly upon expected wealth and its standard deviation but, rather, upon a string of decision variables such as output and input levels, it is important to analyze the behavior of the absolute risk measure (ar) under risk aversion and risk propensity. the justification for this requirement is due to the fact that knowledge of parameters θ and γ is obtained only by empirical estimation of economic relations involving entrepreneur’s decisions. the sign of these parameters, therefore, is an empirical question. for γ>0, (risk aversion), decreasing, constant and increasing absolute risk aversion is defined by ∂ar(µ,σ ) ∂µ = (1−θ )γ θ µ−θσ (γ −1) < (=,>)0 (5) and, therefore, by θ>1, θ=1, θ<1, respectively. for γ>0, (risk propensity), decreasing, constant and increasing absolute risk propensity is defined by θ<1, θ=1, θ>1, respectively. for γ>0, (risk aversion), decreasing, constant and increasing relative risk aversion is defined by ∂ar(tµ,tσ ) ∂t t=1 = (γ −θ )ar < (=,>)0 (6) and, therefore, by θ>γ, θ=γ, θ<γ respectively. for γ<0, (risk propensity), neither decreasing nor constant relative risk propensity are applicable because the combination of parameters’ signs produces always a positive derivative. increasing relative risk propensity is defined by any value of θ>0. the meaning of decreasing absolute risk aversion relates to an economic agent who experiences a wealth increase and chooses to augment his investment – measured in absolute terms – in the risky asset. decreasing relative risk aversion relates to an economic agent who experiences a wealth increase and chooses to increase the share of his investment in the risky asset. it is possible, therefore, for an economic agent to behave according to a decreasing absolute risk aversion framework and an increasing relative risk aversion scenario if the absolute amount of increase in the risky asset is not sufficient to increase also the share of that asset. in any given sample of economic agents’ performances, therefore, the prevailing combination of risk preference is an empirical question. the risk analysis of meyer (1987) admits all possible combinations of risk behavior (risk aversion and risk propensity). saha (1997) listed the risk aversion combinations for the ms utility function specified in relation (3) when γ>0. table 1, for example, admits absolute risk aversion behavior that may be decreasing, when θ>1 and γ>0, in association with either increasing relative risk aversion when γ>θ>0 or decreasing relative risk aversion when θ>γ. decreasing, constant and increasing absolute risk aversion are denoted by dara, cara and iara, respectively. decreasing, constant and increasing relative risk aversion are denoted by drra, crra and irra, respectively. 195positive mathematical programming and risk analysis table 1. possible risk preferences under risk aversion (θ>0, γ>0) drra crra irra dara θ>1, θ>γ θ>1, θ=γ θ>1, θ<γ cara θ=1, θ>γ θ=1, θ=γ θ=1, θ<γ iara θ<1, θ>γ θ<1, θ=γ θ<1, θ<γ table 2. possible risk preferences under risk propensity (θ>0, γ<0) drrp crrp irrp darp θ<1, na θ<1, na θ<1, yes carp θ=1, na θ=1, na θ=1, yes iarp θ>1, na θ>1, na θ>1, yes “na” stands for “not applicable” because the combination of parameters’ signs produces always a positive value of the derivative (6). when θ>0 and γ<0, risk propensity is active and the behavior of the risk measure ar, under the given ms utility, assumes the specification reported in table 2. decreasing, constant and increasing absolute risk propensity are denoted by darp, carp and iarp, respectively. decreasing, constant and increasing relative risk propensity are denoted by drrp, crrp and irrp, respectively. the v(μ,σ)=μθ-σγ function is concave with respect to μ and σ when θ<1 and γ>1. the same function v[μ(x),σ (x)]= μ (x)θ-σ (x)γ, however, exhibits a flexible behavior with respect to entrepreneur’s decisions, x. this behavior depends on the relative values of parameters θ and γ. in other words, the upper contour sets of v[μ(x),σ (x)]= μ (x)θ-σ (x)γ are convex for a wide range of values of parameters θ and γ. a few examples illustrate the function’s graph and the associated upper contour sets in the appendix. the rest of the paper is organized as follows. section 2 discusses a pmp model that combines a generalized risk analysis with an extension of calibration constraints involving observed prices of limiting inputs. this extension integrates the traditional pmp specification of calibration constraints dealing only with observed levels of realized outputs. in particular, the extension provides a unique estimate of the optimal decision variables and avoids the user-determined perturbation parameters introduced by howitt (1995a, 1995b) to guarantee that the dual variables of binding structural constraints will assume positive values. section 3 discusses a chance-constrained relation that anchors the θ and γ parameters to the decision quantities and, therefore, provides an independent relation for their estimation. section 4 assembles a phase-i estimation model of the novel pmp approach. section 5 defines and estimates a complete cost function involving output quantities and limiting input prices. the derivatives of the cost function are used in calibrating models that are suitable for policy analysis. section 6 discusses how to obtain endogenous (to a farm sample) output supply elasticities. this section matches exogenous (to the farm sam196 quirino paris ple) supply elasticities (available through econometric estimation, for example) with the endogenous supply elasticities. section 7 states that optimal decision variables are identical whether estimated as solution of the phase i model or solution of phase i and phase ii models combined. section 8 defines two alternative calibrating equilibrium models which reproduce calibrating solutions that are identical to those ones obtained in section 4. section 9 presents the empirical results of the more elaborate pmp and risky model applied to a sample of 14 farms when not all farms produce all commodities. conclusions follow. 2. generalized risk preference in a pmp framework a positive mathematical programming approach has been adopted frequently to analyze agricultural policy scenarios ever since howitt proposed the methodology (1995a, 1995b). in this section, we extend the pmp methodology to deal with generalized risk preference and risky market output prices. furthermore, we extend the pmp methodology to deal with calibration constraints involving observed prices of limiting inputs, say land. this extension modifies the traditional specification of calibration constraints and the notion of calibrating solution, as explained further on. suppose n farmers produce j crops using i limiting inputs and a linear technology. let us assume that, for each farmer, the (j×1) vector of crops’ market prices is a random variable !p with mean e( !p) and variance-covariance matrix ∑p. a (j×1) vector c of accounting unit costs is also known. the (i×1) vector b indicates farmer’s availability of limiting resources. the matrix a of dimensions (i×j,i<j) specifies a linear technology. the (j×1) vector x symbolizes the unknown output levels to be optimized. furthermore, farmer has knowledge of previously realized levels of outputs that are observed (by the econometrician) as xobs. random wealth is defined by previously accumulated wealth, w , augmented by the current random net revenue. assuming a ms utility function under this scenario, mean wealth is defined as μ=[w +( e( !p) -c)´x] with standard deviation equal to σ=(x´∑px)1/2. then, a primal pmp-ms model is specified as follows: maxx,h,θ,γv(μ,σ)=μθ-σγ=[w +( e( !p) -c)´x]θ-(x´∑px)γ/2 (7) subject to ax≤b dual variable y x=xobs+h dual variable λ where h is a vector of deviations from the realized and observed output levels, xobs. the first set of constraints forms the structural (technological) relations while the second set constitutes the calibration constraints. this specification of the calibration constraints differs from the traditional statement x≤xobs(1+ε) where ε is a user-determined, small positive number whose purpose is to allow the dual variables of binding structural constraints to take on positive values. in howitt’s words (1995a, p. 151): “the ε perturbation on the calibration constraints decouples the true resource constraints from the calibration constraints and ensures that the dual values on the allocable resources represent the marginal values of the resource constraints.” the present paper avoids the user-determined parameter ε of the traditional pmp methodology and allows the empirical data to reveal the com197positive mathematical programming and risk analysis ponents of the vector of deviations h. such deviations can take on either positive or negative values. to justify the specification of the calibration constraints x=xobs+h, we note that the vector of realized output levels, xobs, has been “observed”, that is measured, by persons other than the economic entrepreneur, say by an econometrician. it is likely, therefore, that the measured xobs vector may either overstate or understate the true levels of realizable optimal outputs. the deviation vector h captures these likely measurement errors. the dual constraints of problem (7) – derived by lagrange methods – turn out to be . γ(x´∑px)(γ/2-1)∑px+a´y+λ≥θ[w +( e( !p) -c)´x](θ-1)[ e( !p) -c] (8) parameters θ and γ are unknown as are the output levels, x, the deviations, h, the dual variables, y, and the lagrange multipliers, λ. appropriate initial values of the unknown variables are of great importance to achieve an admissible solution. furthermore, it is often the case that also the (approximate) market price of some input – say land – is known for the region of the sample farms or even for a single farm. the pmp methodology of this paper, therefore, uses also information yobs while the unknown dual variable y is treated as y=yobs+u (9) with u as an (i×1) vector of deviations from the observed input prices. let w be a nonsingular diagonal matrix of dimensions (j×j) with positive diagonal terms equal to observed expected price e( !p j )> 0 . and let v be a nonsingular diagonal matrix of dimensions (i×i) with positive diagonal terms bi/yobs,i>0. the purpose of matrices w and v is twofold. first, to render homogeneous the units of measurement of all terms in the objective function of models defined below. second, to weigh the deviations h and u according to the scale of the corresponding expected price and input size, respectively. using a least-squares approach for the estimation of deviations h and u, it turns out that, by the self-duality of least squares (ls), λ=wh and ψ=vu, where ψ is the vector of lagrange multipliers associated with constraints (9): see paris (2015). to show this result, consider the following weighted ls problem minls=h´wh/2+u´vu/2 subject to x=xobs+h dual variable λ y=yobs+u dual variable ψ. the corresponding lagrange function and first-order-necessary conditions with respect to h and u are l=h´wh/2+u´vu/2+λ´(x-xobs-h)+ψ´(y-yobs-u) ∂l ∂h =wh−λ = 0λ 198 quirino paris ∂l ∂u =vu−ψ = 0 with the result that λ=wh and ψ=vu as asserted. a crucial issue concerns parameters θ and γ. on the one hand, an economic entrepreneur wishes to maximize her utility of random wealth while minimizing the disutility of its risk. on the other hand, it is a fact that high levels of current income (a component of wealth) are associated with high risk of losses. another fact is that this entrepreneur has already made her choice and executed a production plan, xobs, in the face of output price risk. it is also likely that she does not know (or that she is not even aware of) parameters θ and γ. the challenge, therefore, is to infer – from her decisions – the values of parameters θ and γ that could explain the behavior of this entrepreneur in a rational fashion. 3. a chance-constrained relation for θ and γ charnes and cooper (1959) proposed a very interesting approach to deal with risky prospects based upon the notion of chance-constrained programming. this idea is particularly useful within the context of this paper because it establishes an independent link between the θ and γ parameters, on one side, and the entrepreneur’s decisions, x, on the other side. consider the following scenario. with some probability, a farmer may survive unfavorable events such as total revenue being less than total cost. in terms of the chanceconstrained methodology this risky scenario is expressed by the following probabilistic proposition: prob{ ! ′p x ≤ ′y ax + (c + λλ ′) x} ≤1− β (10) where the probability that uncertain (random) total revenue ′!p x be less than or equal to certain total cost y´ax+(c+λ)´x should be smaller than or equal to 1-β. intuitively, for how many years could a farmer survive while operating in the red? as an example, say once every ten years. in this case, the estimated probability equals to 1-β=1/10=0.10. the y´ax term is total cost associated with fixed limiting inputs (y´ax= y´x). the (c+λ)´x term is total variable cost associated directly with output levels. to derive a deterministic equivalent of relation (10) it is convenient to standardize the random variable ′!p x by subtracting its expected value e( !p) ´x and dividing it by the corresponding standard deviation (x´∑px)1/2: prob ! ′p x − e( !p ′) x ( ′x σ px) 1/2 ≤ ′y ax + (c + λλ ′)) x − e( !p ′) x ( ′x σ px) 1/2 ⎛ ⎝⎜ ⎞ ⎠⎟ ≤1− β prob τ ≤ ′y ax + (c + λλ ′)) x − e( !p ′) x ( ′x σ px) 1/2 ⎛ ⎝⎜ ⎞ ⎠⎟ ≤1− β prob[e( !p ′) x +τ ( ′x σ px) 1/2 ≤ ′y ax + (c + λλ ′)) x]≤1− β. (11) ψ 199positive mathematical programming and risk analysis by assuming that τ is a standard normal random variable and choosing a value, say τ = τ , that corresponds to probability 1-β, the deterministic equivalent of relation (11) assumes the specification e( !p ′) x +τ ( ′x σ px) 1/2 ≤ ′y ax + ′c x + ′λλ x (12) to establish the relation between the τ parameter and the ms coefficients θ and γ the dual complementary slackness condition of constraint (8) is subtracted from the deterministic equivalent (12) (recall that λ=wh): e( !p ′) x +τ ( ′x σ px)1/2 ≤ ′y ax + ′c x + ′h wx −θ[w + (e( !p)− c ′) x]θ−1(e( !p)− c ′) x = − ′y ax − ′h wx −γ ( ′x σς px)γ /2 . (13) with simplification, relation (13) corresponds to e( !p ′) x − ′c x +τ ( ′x σ px) 1/2 −θ[w + (e( !p)− c ′) x]θ−1(e( !p)− c ′) x + γ ( ′x σς px) γ /2 ≤ 0 (14) relation (14) establishes a simultaneous and independent link between the risk parameters θ, γ and the decision variables x, once the value of τ is selected by the researcher. as an example, if the survival probability is determined to be 1-β=0.10, the one tail value of the standard normal random variable is τ =-1.285. 4. phase i pmp model – estimation of calibrating primal and dual solutions the components of phase i pmp model are ready to be assembled. for estimation purposes, deviations h and u will be minimized in a weighted least-squares objective function subject to relevant primal and dual constraints, their associated complementary slackness conditions and relation (14). this task leads to the following phase i model minls=h´wh/2+u´vu/2 (15) subject to ax≤b+vu (16) θ[w + (e( p)− c ′) x](θ−1)[e( p)− c]≤ ′a y +wh ++ γ ( ′x σ px) (γ /2−1)σ px (17) x=xobs+h (18) y=yobs+u (19) y´(b+vu-ax)=0 (20) σ σ σς 200 quirino paris ′x { ′a y +wh ++ γ ( ′x σ px) (γ /2−1)σ px −θ[w + (e( p)− c ′) x](θ−1)[e( p)− c]} = 0 (21) e( !p ′) x − ′c x +τ ( ′x σ px) 1/2 −θ[w + (e( !p)− c ′) x]θ−1(e( !p)− c ′) x + γ ( ′x σς px) γ /2 = 0 (22) with x≥0,y≥0,θ>0,γ,h and u free. with the specification of the calibration constraints as in relations (18) and (19), the notion of a pmp calibrating solution differs from the traditional concept according to which the optimal calibrating solution is equal to the observed output levels, that is, x* ≅ xobs , as the perturbation results in a very small (user-determined) positive number. with the methodology proposed in this paper, a calibrating solution (x̂, ŷ) will not, in general, be exactly equal to the corresponding vectors of the observed production plan and input prices (xobs,yobs). the objective of model (15)-(22), therefore, is to minimize the deviations h and u in the amount allowed by the technological and risky environment facing farmers. constraints (16) represent the structural (technological) relations of input demand being less-than-or-equal to the effective input supply. constraints (17) represent the dual relations with marginal utility of the production plan being less-than-or-equal to its marginal cost. here marginal cost has two parts: the marginal cost due to limiting and variable inputs, a´y+wh, and the marginal cost of output price risk, γ(x´∑px)(γ/2-1)∑px. constraints (18) and (19) are the calibration relations. constraints (20) and (21) are complementary slackness conditions of constraints (16) and (17). constraint (22) results from the chance-constrained specification (10). because constraints (16)-(22) represent primal and dual relations and their complementary slackness conditions, any feasible solution of relations (16)-(22) constitutes an admissible economic equilibrium that is consistent with the behavior of decision making under price risk. furthermore, the calibrating solution (x̂, ŷ) is unique because the least-squares solution of (ĥ, û) is also unique. 5. phase ii pmp model – estimation of the cost function phase ii of the pmp methodology deals with the estimation of a cost function that embodies all the technological and behavioral information revealed in phase i. typically, a marginal cost function expresses a portion of the dual constraints in a phase i pmp model. in the absence of risk, pmp marginal cost is defined as a´y+wh+c, where a´y stands for the marginal cost due to limiting inputs and wh+c for the effective marginal cost due to variable outputs. in the risky price case, marginal cost is given by the right-hand-side of relation (17) where all the elements are measured in utility units. it is crucial to obtain a dollar expression of marginal cost, as in the familiar relation mc ≥ e( p) . to achieve this result, the elements of relation (17) will be divided by the term θ[w + (e( p)− c ′) x](θ−1) to write mc ≥ e( p) (23) c + 1 θ [w + (e( p)− c ′) x](1−θ )[ ′a y +wh]++ γ θ [w + (e( p)− c ′) x](1−θ )( ′x σ px) (γ /2−1)σ px ≥ e( p) σ σ σ σ σ σ σ 201positive mathematical programming and risk analysis in relation (23), all the terms are measured in dollars. the marginal cost due to limiting and variable inputs is given by c + 1 θ [w + (e( p)− c ′) x](1−θ )[ ′a y +wh]⎧ ⎨ ⎩ ⎫ ⎬ ⎭ . the marginal cost due to risky output prices is given by γ θ [w + (e( p)− c ′) x](1−θ )( ′x σ px) (γ /2−1)σ px ⎧ ⎨ ⎩ ⎫ ⎬ ⎭ . the cost function selected to synthesize the technological and behavioral relations of phase i is expressed as a modified leontief cost function such as c(x,y) = ( ′f x)( ′g y)+ ( ′g y)( ′x qx) / 2 + ( ′f x)[(y1/2 ′) gy1/2 ] (24) a cost function is non-decreasing in output quantities and input prices. it is linearly homogeneous and concave in input prices, y. the (i×i) matrix g has elements gi,ii=gii,i≥0,i≠ii,i,ii=1,…,i. the diagonal elements gi,i can take on either positive or negative values. the (j×j) matrix q is symmetric positive semidefinite. the components of vectors f and g are free to take on any value as long as f´x>0 and g´y>0. the reason for introducing a term like (f´x)(g´y) is to add flexibility to the cost function. the marginal cost function associated with cost function (24) is given by mcx = ∂c ∂x = f( ′g y)+ ( ′g y)qx + f[(y1/2 ′) gy1/2 ] (25) the derivative of the cost function with respect to input prices corresponds to shephard’s lemma that produces the demand function for inputs: ∂c ∂y = ( ′f x)g + g( ′x qx) / 2 + ( ′f x)[δ(y−1/2 ′) gy1/2 ]= ax (26) where ∆(y-1/2) represents a diagonal matrix with elements yi -1/2 on the main diagonal. with knowledge of the solution components resulting from the phase i model (15)(22), x̂, ŷ, ĥ, û,θ̂ ,γ̂ , a phase ii model’s goal is to estimate the parameters of the cost function, f,g,q,g. this task is accomplished by means of the following specification minaux=d´d/2+r´r/2 (27) subject to f( ′g ŷ)+ ( ′g ŷ)qx̂ + f[(ŷ1/2 ′) gŷ1/2 ]= (28) 202 quirino paris c + 1 θ̂ [w + (e( !p)− c ′) x̂](1−θ̂ )[ ′a ŷ +wĥ]++ γ̂ θ̂ [w + (e( !p)− c ′) x̂](1−θ̂ )( ′x̂ σ px̂) (γ̂ /2−1)σ px̂ + d ( ′f x̂)g + g( ′x̂ qx̂) / 2 + ( ′f x̂)[δ(ŷ−1/2 ′) gŷ1/2 ]= ax̂ + r (29) q=ldl´ (30) qq-1=i (31) with ′f x̂ > 0, ′g ŷ > 0,d ≥ 0 , f and g free. the gams software requires an objective function. the vector variables d,r perform the role of slack variables in the estimation of the marginal cost function and shephard’s lemma, respectively. the objective function (27) is a typical least-squares specification. relation (28) represents the marginal cost function. relation (29) is shephard’s lemma. relation (30) is the cholesky factorization of the q matrix with d as a diagonal matrix with nonnegative elements on the main diagonal and l is a unit lower triangular matrix. the cholesky factorization guarantees symmetry and positive semidefiniteness of the q matrix. relation (31) defines the inverse of the q matrix and, thus, guarantees the positive definiteness of that matrix. this constraint assumes relevance for computing the supply elasticities of the various outputs. any feasible solution of model (27)-(31) is an admissible cost function for representing the economic agent’s decisions under price risk. 6. pmp and output-supply elasticities it may be of interest to estimate price supply elasticities for the various commodity outputs involved in a pmp-ms approach. the supply function for outputs is derivable from relation (25) by equating it to the expected market output prices, e( p) , and inverting the marginal cost function: x = −q−1f −q−1f[(y1/2 )gy1/2 ] / ( ′g y)+ [1 / ( ′g y)]q−1e( p) (32) that leads to the supply elasticity matrix ξ = δ[e( p)] ∂x ∂e( p) δ[(x−1)]= δ[e( p)]q−1δ[(x−1)] / ( ′g y) (33) where matrices δ[e( p)] and δ[x−1] are diagonal with elements e( pj ) and x j −1 on the main diagonals, respectively. relation (33) includes all the ownand cross-price elasticities for all the output commodities admitted in the model. pmp has been applied frequently to analyze farmers’ behavior to changes in agricultural policies. a typical empirical setting is to map out several areas in a region (or state) and to assemble a representative farm for each area (or to treat each area as a large farm). when supply elasticities are exogenously available (say the own-price elasticities of crops) at the regional (or state) level (via econometric estimation or other means), a connection of σ σ 203positive mathematical programming and risk analysis all area models can be specified by establishing a weighted sum of all the areas endogenous own-price elasticities and the given regional elasticities. the weights are the share of each area’s expected revenue over the total expected revenue of the region. the advantage of using exogenously supply elasticities has been asserted by mérel and bucharam (2010) and petsakos and rozakis (2015) in order to account for second-order conditions’ information. let us suppose that exogenous own-price elasticities of supply are available at the regional level for all the j crops, say η j , j = 1,..., j . then, the relation among these exogenous own-price elasticities and the corresponding areas’ endogenous elasticities can be established as a weighted sum such as η j = wnj n=1 n ∑ ηnj where the weights are the areas’ expected revenue shares in the region (state) wnj = e( pnj )xnj e( ptj )xtjt=1 n∑ (34) ηnj = e( !pnj )q jjxnj −1 / ( ′gnyn ) (35) where qjj is the jth element on the main diagonal in the inverse of the q matrix. the phase ii model that executes the estimation of the cost function parameters and the disaggregated (endogenous) output supply elasticities for a region (state) that is divided into n areas takes on the following specification: minaux = ′dndn / 2 n=1 n ∑ + ′rnrn / 2 n=1 n ∑ (36) subject to fn ( ′gnŷn )+ ( ′gnŷn )qx̂n + fn[(ŷn 1/2 ′) gŷn 1/2 ]= (37) cn + 1 θ̂n [wn + (e( !pn )− cn ′) x̂n ](1−θ̂n )[ ′anŷn +wnĥn ] ++ γ̂ n θ̂n [wn + (e( !pn )− cn ′) x̂n ](1−θ̂n )( ′x̂nς px̂n )(γ̂ n /2−1)σ px̂n + dn ≥ e( !pn ) ( ′fnx̂n )gn + gn ( ˆ ′xnqx̂n ) / 2 + ( ′fnx̂n )[δ(ŷn −1/2 ′) gŷn ]= anx̂n + rn (38) q=ldl´ positive semidefiniteness (39) qq-1=i positive definiteness (40) σ σ 204 quirino paris ξn = δ[e( !pn )]q −1δ[(xn −1)] / ( ′gnyn ) endogenous ownand cross-price elasticities (41) wnj = e( pnj )x̂nj e( ptj )x̂tjt=1 n∑ expected revenue weights (42) ηnj = e( !pnj )q jj x̂nj −1 / ( ′gnŷn ) own-price elasticities (43) η j = wnj n=1 n ∑ ηnj disaggregation of exogenous elasticities (44) with dn≥0,gn and fn free and ′fnx̂n > 0 , ′gnŷn > 0 . the gams software requires an objective function. the objective function aux minimizes the pseudo slack variables, rn and dn, of the primal and dual constraints. 7. phase i versus phase i-ii estimates of the calibrating solution a strand of the pmp literature has discussed the issue of whether the phase i estimates of decision variables and input shadow prices, x,y, are consistent with the corresponding phase ii estimates where the cost function parameters are estimated simultaneously with them. the short answer is positive because the amount of information is the same in the two phases. with the limitations of a two-dimensional diagram, figure 1 illustrates the issue. in phase i, total cost is a linear function of the decision variables while in phase ii total cost is a nonlinear function of the same variables. hence, the calibrating optimal solution, x*, is the same in the two phases. in the context of this paper, phase i model is stated as a ls specification of relations (15) through (22). this model results in a unique least-squares solution of deviations h and u and, therefore, of the decision variables x̂, ŷ . the phase ii model that estimates figure 1. phase i and phase ii estimates of decision variables x and input shadow prices y. 205positive mathematical programming and risk analysis simultaneously the cost function parameters and the optimal decision variables is stated as the ls specification in phase i combined with constraints (28) through (31) (where the “ ⋅̂ ” symbol is removed from the decision variables). the original information is identical in the two models and, therefore, the ls methodology guarantees the unique and identical solution for the two sets of estimates. 8. phase iii pmp model – calibrating models with the parameter estimates of the cost function, f̂n , ĝn ,q̂,ĝ , derived from either phase ii model (27)-(31) or model (36)-(44), it is possible to set up a calibrating equilibrium model to be used for policy analysis. such a model takes on the following economic equilibrium specification mincsc=y´zp+x´zd=0 (45) subject to ( ′f̂ x)ĝ + ĝ( ′x q̂x) / 2 + ( ′f̂ x)[δ(y−1/2 ′) ĝy1/2 ]+ z p = b +vû (46) f̂( ′ĝ y)+ ( ′ĝ y)q̂x + f̂[(y1/2 ′) ĝy1/2 ]= e( p)+ ẑd (47) with x≥0,y≥0,zp≥0,zd≥0. the objective function represents the complementary slackness conditions (csc) of constraints (46) and (47) with an optimal value of zero. the variables zp and zd are surplus variables of the primal and the dual constraints, respectively. the solution of model (45)-(47) calibrates precisely the solution obtained from the phase i model (15)-(22), that is, x̂ls = x̂csc and ŷls = ŷcsc . this remarkable result is due simply to the fact that all the information of the phase i model has been transferred to the cost function. note that the matrix of fixed technical coefficients a does not appear in either constraint (46) or (47). the calibrating model, then, can be used to trace the production and revenue response to changes in the expected output prices, subsidies and the supply of limiting inputs in a more flexible technical framework. an alternative calibrating equilibrium model is suitable for dealing with a crucial aspect of a risky policy scenario. wealth is the anchoring measure of risk preference of an economic agent. as illustrated above, wealth is composed of accumulated income (or exogenous income) and net revenue derived from the current production cycle as in [w + (e( p)− c ′) x] where w measures the amount of exogenous income. agricultural policies in many countries deal with subsidies to farmers for cultivating (or not cultivating) crops. these subsidies may or may not be coupled to the level of crop production. subsidies that are decoupled from the crop production decisions of farmers constitute exogenous income and end up in the term of wealth that becomes an important target of policy makers. the w term, then, must appear in the calibrating model to allow the representation of decoupled subsidies as in the following specification mincsc=y´zp+x´zd=0 (48) 206 quirino paris subject to ( ′f̂ x)ĝ + ĝ( ′x q̂x) / 2 + ( ′f̂ x)[δ(y−1/2 )ĝy1/2 ]+ z p = b +vû (49) c + 1 θ̂ [w + (e( !p)− c ′) x](1−θ̂ )[ ′a y +wĥ] + γ̂ θ̂ [w + (e( !p)− c ′) x](1−θ̂ )( ′x σ px)(γ̂ /2−1)σ px = e( !p)+ zd (50) with x≥0,y≥0,zp≥0,zd≥0. also the solution of model (48)-(50) calibrates precisely the solution obtained from the phase i model (15)-(22), that is, x̂ls = x̂csc and ŷls = ŷcsc . 9. empirical implementation of pmp-ms with supply elasticities the pmp-ms approach described in previous sections was applied to a sample of n = 14 representative farms of the emilia-romagna region of italy. there are four crops: sugar beets, soft wheat, corn and barley. there is only one limiting input: land. empirical reality compels a further consideration of the above methodology in order to deal with farm samples where not all farms produce all commodities. it turns out that very little must be changed for obtaining a calibrating solution in the presence of missing commodity levels, their prices and the corresponding technical coefficients. using the gams software, it is sufficient to condition the various constraints of phase i, phase ii and phase iii models by the nonzero observations of the output levels. to exemplify, the available farm sample displays the following table 3 of observed crop levels while table 4 presents the variancecovariance matrix of the market output prices. table 3. observed output levels, xobs, with non produced commodities. farm sugar beets soft wheat corn barley 1 1133.4240 0 341.3693 18.2398 2 3103.7830 841.7445 0 59.8025 3 0 450.7937 881.9748 0 4 3488.3540 821.3934 1493.3320 51.1247 5 959.1102 468.2848 0 28.2406 6 942.2039 801.1288 1283.5910 152.5810 7 1600.7310 0 899.4739 66.9718 8 0 1212.8550 1237.5840 98.0497 9 1050.5370 332.3773 0 63.6696 10 3473.6780 952.5199 774.7402 0 11 0 765.1689 501.9673 59.5366 12 3276.1450 1100.1680 0 177.9740 13 877.0970 380.9171 564.6091 76.2122 14 1430.9460 0 1309.3920 0 σ σ 207positive mathematical programming and risk analysis other missing information deals with prices and unit accounting costs associated with the zero-levels of crops. furthermore, the technical coefficients of farms not producing the observed crops also equal to zero. hence, we can state that, for n=1,…,n, the number of farms, and j=1,…,j, the number of crops, if xnj obs = 0 , also pnj=0, cnj=0 and anij=0. furthermore, suppose that only one input, land, is involved in this farm sample. let us assume also that the land price is observed for all farms. the procedure to deal with this type of sample data consists in conditioning the relevant constraints on the positive values of the output levels. in gams, this procedure requires a conditional statement using the $ sign option. table 4. variance-covariance matrix of the market output prices. sugar beets soft wheat corn barley sugar beets 0.0024719 -0.0164391 -0.0117184 -0.0121996 soft wheat -0.0164391 0.2386034 0.1821288 0.2049011 corn -0.0117184 0.1821288 0.1530464 0.1610119 barley -0.0121996 0.2049011 0.1610119 0.1830829 tables 5 and 6 present the estimated output levels and input prices ( x̂, ŷ ). they also exhibit the percent deviation of the solution ( x̂, ŷ ) of model (15)-(22) from the corresponding targets (xobs,yobs). it is of interest to report that the same identical solution was obtained in three different ways. all the estimations were performed with the gams software. the first round of estimates were obtained by solving model (15)-(22) one farm at a time. the second round of estimates were obtained by solving model (15)-(22) using the entire sample of observations. this means that the objective function was specified as minls = hnwnhn n=1 n ∑ + unvnun n=1 n ∑ subject to constraints (16)-(22) specified for each single farm observation. the third round of estimates of the optimal decision variables were obtained by solving model (36)(44) with the “ ⋅̂ ” symbol removed from the variables. table 7 presents the estimates of the parameters θ and γ of the ms utility function. the sample is composed of relatively homogeneous farms. hence, the limited numerical range of variation of the ms utility parameters is not a surprise. within that range, however, a wide variety of risk preferences is detected. seven farmers exhibit decreasing absolute risk aversion accompanied by increasing relative risk aversion. this result matches a statement of tsiang (1972, p. 357): “…the most commonly observed pattern of behavior toward risk of a risk-averter individual is probably decreasing absolute risk-aversion coupled with increasing relative risk-aversion when his wealth increases…” two farmers exhibit increasing absolute risk aversion associated with decreasing relative risk aversion. four farmers exhibit decreasing absolute risk propensity and increasing relative risk propensity. it should be noted that the negative gamma coefficients of these four farmers are 208 quirino paris rather small, suggesting that risk neutrality may – probably – be a better risk-preference representation of these farmers. the approach does not allow for a statistical testing of this conjecture. finally, one farmer exhibits increasing absolute and relative risk aversion. this table 5. estimated ls solution, x̂ , and percent deviation from the observed levels, xobs with zero levels for some crops and some farms. farm optimal decisions x̂ percent deviation from xobs sugar beets soft wheat corn barley sugar beets soft wheat corn barley 1 1133.851 0 341.622 18.156 0.0377 0 0.0741 -0.4587 2 3104.392 861.829 0 52.923 0.0196 0.0098 0 0.2021 3 0 450.794 881.975 0 0 -0.0000 0.0000 0 4 3488.400 821.340 1493.477 51.165 0.0013 -0.0065 0.0097 0.0791 5 959.234 468.140 0 28.308 0.0129 -0.0310 0 0.2399 6 942.488 801.394 1283.947 152.923 0.0301 0.0331 0.0278 0.2238 7 1601.381 0 899.724 67.104 0.0406 0 0.0278 0.1975 8 0 1213.157 1237.937 98.080 0 0.0249 0.0285 0.0307 9 1051.373 332.592 0 63.767 0.0796 0.0645 0 0.1528 10 3474.183 952.606 774.966 0 0.0145 0.0085 0.0291 0 11 0 765.267 502.186 59.659 0 0.0128 0.0436 0.2052 12 3276.657 1100.245 0 178.324 0.0156 0.0070 0 0.1964 13 877.324 380.970 564.926 76.467 0.0258 0.0138 0.0561 0.3347 14 1431.231 0 1309.653 0 0.0199 0 0.0199 0 table 6. deviation of ŷ from yobs. farm observed land prices yobs estimated land prices ŷ percent deviation 1 4.42 4.4213 0.0287 2 4.38 4.3810 0.0219 3 6.98 6.9800 0.0000 4 5.73 5.7302 0.0036 5 4.40 4.3995 -0.0111 6 1.86 1.8609 0.0458 7 3.65 3.6517 0.0454 8 3.36 3.3609 0.0266 9 2.75 2.7521 0.0780 10 4.28 4.2807 0.0158 11 3.28 3.2810 0.0318 12 1.93 1.9305 0.0281 13 2.32 2.3213 0.0579 14 4.03 4.0308 0.0199 209positive mathematical programming and risk analysis empirical result is a clear illustration of the flexible structure of risk preferences as stated by the theoretical analysis. the corresponding meaning of the various acronyms is derived from table 1 and table 2. the estimated parameters of the cost function are reported in tables 8 and 9. in this numerical example, the g matrix contains only one parameter whose value is gi,i=11.39904. regional, exogenous own-price supply elasticities were available in the magnitude of 0.6 for sugar beets, 0.5 for soft wheat, 0.7 for corn and 0.4 for barley. the endogenous own-price elasticities of all farms were aggregated to be consistent with the regional exogenous elasticities according to relation (44). table 10 presents the farms’ own-price supply elasticities used in the aggregation relation. 10. conclusion this paper accomplished several objectives. first, it extended the treatment of risk in a mathematical programming framework to include any combination of risk preferences represented by absolute risk aversion (or absolute risk propensity) and relative risk aversion (or relative risk propensity). second, it modified the traditional pmp approach to deal with calibration constraints regarding observed output levels and observed input prices by eliminating the user-determined perturbation parameter. the combination of these two approaches provides suitable models for agricultural policy analysis that take into consideration farmers’ risk preferences associated with the randomness of output prices. third, this paper integrated the use of exogenous supply elasticities observed for, say, an entire region with the endogenous elasticities derived from the supply functions of the sample farms. this objective is achieved by specifying a complete and flexible total cost function that fulfills all the theoretical requirements. fourth, it resolves in a positive table 7. estimates of θ and γ. farm parameter θ parameter γ risk preference 1 1.0131215 1.1397862 dara, irra 2 1.0050568 1.0766995 dara, irra 3 1.1313873 1.2841485 dara, irra 4 0.9836798 0.9273945 iara, drra 5 0.9578977 -0.1867746 darp, irrp 6 0.9645178 -0.1465580 darp, irrp 7 1.0183502 1.1310367 dara, irra 8 1.0562629 1.1969911 dara, irra 9 1.0277583 1.1992494 dara, irra 10 1.0043433 1.0570120 dara, irra 11 0.9503372 -0.1567640 darp, irrp 12 0.9986044 1.0263446 iara, irra 13 0.9577406 -0.1663556 darp, irrp 14 0.9797649 0.8443536 iara, drra 210 quirino paris way the dispute debated in the pmp literature whether phase i calibrating estimates are consistent with phase ii estimates. fifth, a calibrating model resulting from the pmp-ms framework described here allows for the analysis of policy scenarios dealing with farm subsidies that are decoupled from the current crop production. consider the parameter table 8. intercepts f̂ and ĝ of the marginal cost and input demand functions. farm f̂ ĝ ′f̂ x̂ ′ĝ ŷ sugar beets soft wheat corn barley 1 0.00949 0 0.00666 -0.00320 0.00465 12.923 0.02055 2 0.00364 0.03154 0 -0.06940 0.00149 34.378 0.00654 3 0 -0.00290 0.00374 0 0.00129 1.965 0.00901 4 0.00734 -0.00284 0.00902 -0.06489 0.00132 33.448 0.00756 5 -0.00307 0.02349 0 -0.05730 0.00202 6.426 0.00888 6 -0.02082 0.08018 0.08193 0.26459 0.00658 190.289 0.01224 7 0.00473 0 0.02687 0.05681 0.00271 35.568 0.00992 8 0 0.08121 -0.00668 -0.05092 0.00220 85.254 0.00738 9 0.00408 0.04408 0 0.07081 0.00610 23.462 0.01679 10 0.00905 0.03772 -0.02931 0 0.00164 44.673 0.00703 11 0 0.08005 -0.04152 -0.05365 0.00300 37.213 0.00985 12 0.00395 0.11439 0 0.06448 0.00329 150.291 0.00635 13 0.00041 0.05078 0.03585 0.19131 0.00950 54.584 0.02205 14 -0.00159 0 0.03287 0 0.00192 40.770 0.00773 table 9. estimated matrices q̂ and d̂ . matrix q̂ sugar beets soft wheat corn barley sugar beets 0.0408842 -0.0269584 -0.0084418 -0.0405819 soft wheat -0.0269584 0.8509183 -0.1850005 -0.5925655 corn -0.0084418 -0.1850005 0.3698877 -0.0130721 barley -0.0405819 -0.5925655 -0.0130721 7.7008830 matrix d̂ sugar beets soft wheat corn barley sugar beets 0.0408842 soft wheat 0.8331423 corn 0.324558 barley 7.1182454 211positive mathematical programming and risk analysis w in the measure of wealth that may represent exogenous income subsidy. with a freund approach to risk based upon a constant absolute risk aversion utility function, the wealth parameter disappears from the programming model. on the contrary, one version of the calibrating equilibrium model presented in this paper allows for the analysis of decoupled farm subsidies that are more frequently the target of policy makers. this general model has been tested on different farm samples with satisfactory results including a data sample where not all farms produce all the commodities. 11. references arata, l., donati, m., sckokai, p. and arfini, f. (2017). incorporating risk in a positive mathematical programming framework: a dual approach. the australian journal of agricultural and resource economics 61: 265-284. brooke, a., kendrick, d., meeraus, a. (1988). gams, a user’s guide. the scientific press, redwood city, california. cortignani, r., severini, s. (2012). modeling farmer participation to a revenue insurance scheme by means of positive mathematical programming. agricultural economics – czech 58: 324:331. fisher, i. (1906). the nature of capital and income. macmillan company, london. freund, r.j. (1956). the introduction of risk into a programming model. econometrica 24: 253-263. hazell, p.b.r. (1971). a linear alternative to quadratic and semivariance programming for farm planning under uncertainty. american journal of agricultural economics 53: 53-62. table 10. disaggregation/aggregation of the regional, exogenous own-supply elasticities with zero observations of some output levels. farm exogenous sugar beets: 0.6 exogenous soft wheat: 0.5 exogenous corn: 0.7 exogenous barley: 0.4 1 0.4422 0 0.9144 0.7529 2 0.6093 0.6344 0 0.8320 3 0 0.8010 0.7415 0 4 0.3126 0.5757 0.6101 0.8427 5 1.1497 0.6980 0 1.1181 6 0.9824 0.3464 0.4243 0.1741 7 0.5677 0 0.7124 0.4221 8 0 0.3910 0.6719 0.4075 9 0.5837 0.5928 0 0.2624 10 0.3482 0.4793 1.1446 0 11 0 0.4196 1.2469 0.4779 12 0.6557 0.4610 0 0.2876 13 0.5061 0.3453 0.4804 0.1667 14 1.0249 0 0.6590 0 212 quirino paris hicks, j.r. 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(2018). estimation of cara preferences and positive mathematical programming. open journal of statistics 8: 1-13. doi: 10.4236/ojs.2018.81001 petsakos, a. and rozakis, s. (2015). calibration of agricultural risk programming models. european journal of operational research 242: 536-545. saha, a. (1997). risk preference estimation in the nonlinear mean standard deviation approach. economic inquiry 35: 770-782. tintner, g. (1941). the theory of choice under subjective risk and uncertainty. econometrica 9: 298-304. tobin, j. (1958). liquidity preference as behavior toward risk. review of economic studies 67: 65-86. tsiang, s.c. (1972). the rationale of the mean-standard deviation analysis, skewness preferences, and the demand for money. american economic review 62: 354-371. 213positive mathematical programming and risk analysis appendix the function v(μ,σ)=μθ-σγ is concave in μ and σ when the corresponding hessian matrix is negative definite. this event occurs when θ<1 and γ>1. when the mean and standard deviation of wealth, μ and σ, are expressed in terms of decision variables, x, μ(x) and σ(x), the resulting function assumes a flexible structure whose concavity depends on different values of parameters θ and γ. this appendix illustrates the possible shapes of the ms utility function (as a function of decision variables) by means of simple graphs and the associated upper contour sets that are conditional upon the magnitude of the θ and γ parameters. the value of θ and γ are chosen to reflect the estimates of table 7. the ms utility function is simplified to show two decision variables, x1 and x2. the expected prices are chosen as e( !p1) = 4 and e( !p2 ) = 6 with standard deviation σp1=0.5, σp2=0.7 and σp1p2=0.1. with these stipulations, all the figures’ functional forms and the upper contour sets exhibit the following specification v[µ(x),σ (x)] = µ(x)θ −σ (x)γ = [e( !p1)x1 + e( !p2 )x2 ]θ − [σ p1 2 x1 2 +σ p2 2 x2 2 + 2σ p1p2 x1x2 ]γ /2 = [4x1 + 6x2 ]θ − [0.52 x1 2 + 0.72 x2 2 + 0.2x1x2 ]γ /2 in all the figures, the upper contour sets appear to be convex even though the contour levels appear rather flat in some figures. the convexity of the upper contour sets is a crucial reason for obtaining an optimal solution. the flatness of the contour levels may make it more laborious for the algorithm to converge to an optimal solution. the figures were drawn using mathematica. 1500 2000 2500 3000 3500 4000 4500 5000 2000 2500 3000 3500 4000 4500 5000 θ=1.1, γ=1.36 dara, irra 214 quirino paris 1500 2000 2500 3000 3500 2000 2200 2400 2600 2800 3000 3200 3400 θ=1.05, γ=1.20 dara, irra 1500 2000 2500 3000 3500 4000 4500 5000 2000 2500 3000 3500 4000 4500 5000 θ=0.98, γ=0.92 iara, drra 215positive mathematical programming and risk analysis 1500 2000 2500 3000 3500 4000 4500 5000 2000 2500 3000 3500 4000 4500 5000 θ=0.99, γ=1.03 iara, irra 2000 3000 4000 5000 6000 2000 3000 4000 5000 6000 θ=0.95, γ=-0.15 darp, irrp positive mathematical programming and risk analysis quirino paris the hedonic contents of italian super premium extra-virgin olive oils luca cacchiarelli1,*, anna carbone2, tiziana laureti1, alessandro sorrentino1 corporate r&d and the performance of food-processing firms: evidence from europe, japan and north america heinrich hockmann1, pedro andres garzon delvaux2,*, peter voigt3, pavel ciaian2, sergio gomez y paloma2 can menu labeling affect away-from-home-dietary choices? elena castellari1,*, stéphan marette2, daniele moro3, paolo sckokai1 a preliminary test on risk and ambiguity attitudes, and time preferences in decisions under uncertainty: towards a better explanation of participation in crop insurance schemes attilio coletta1, elisa giampietri2, fabio gaetano santeramo3,*, simone severini1, samuele trestini2 bio -based and a ppl ied economics bae bio-based and applied economics 11(3): 219-230, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-9932 copyright: © 2022 a. lopolito, a. barbuto, f.g. santeramo. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: a. lopolito, a. barbuto, f.g. santeramo (2022). the role of network characteristics of the innovation spreaders in agriculture. bio-based and applied economics 11(3): 219-230. doi: 10.36253/bae-9932 received: october 15, 2020 accepted: august 9, 2022 published: november 4, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini. orcid al: 0000-0001-9358-3222 fgs: 0000-0002-9450-4618 the role of network characteristics of the innovation spreaders in agriculture antonio lopolito1,*, angela barbuto2, fabio gaetano santeramo2 1 department of economics, management and territory, university of foggia, italy 2 department of agriculture, food, natural resource and engineering, university of foggia, italy *corresponding author. e-mail: antonio.lopolito@unifg.it abstract. the diffusion of innovations is largely influenced by the characteristics of the network of initial adopters (or innovation spreader). we investigate how these characteristics tend to influence the adoption rate and the speed of the diffusion process of a technological innovation in agriculture. the diffusion process is simulated through an agent based model that replicates real-world data. we found that the closeness and the clusterization of the networks are the variables that tend to affect the most the capability of spreading innovations among members. our findings have direct policy implications: since innovations help advancing the economic development of the agricultural sector, promoting the emergence of networks that have desirable characteristics would enhance growth. our analysis provides specific insights on how to plan networks with desirable characteristics for the innovation spreaders. keywords: diffusion of innovations, agent based model, social network analysis. jel codes: c63, o33, q18, q55. 1. introduction improving the diffusion of innovations is a key strategy to promote the economic development. the agricultural sector, more and more oriented toward a bio-based sector (moro et al., 2019), is very much interested by innovations (scoppola, 2015; viaggi, 2015), and in a constant need of them as a way to face major challenges such ensuring food security, coping with climate change, and lowering the pressure on the environment. (li et al., 2022; ray et al., 2022) investigating the network characteristics underlying the adoption and diffusion of innovations among farmers is very relevant, since the benefits that would be derived from a wide use and a fast adoption of promising innovations are undoubted (e.g. hendricks, 2018; chavas and nauges ,2020). the success of innovations is tightly connected to the critical mass of their potential users and to their relationships: the successful innovations are generally associated with well performing networks of adopters capable of influencing both adoption and diffusion of innovations. the literature has pointed out clearly that the characteristics of the networks matter http://creativecommons.org/licenses/by/4.0/legalcode 220 bio-based and applied economics 11(3): 219-230, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9932 antonio lopolito, angela barbuto, fabio gaetano santeramo for the success of innovations – i.e. a fast diffusion with a high adoption rate – (tey and brindal, 2012; banerjee et al., 2013; barbuto et al., 2019). on the contrary, relatively little emphasis, with remarkable exceptions (esposti, 2012; vollaro et al., 2019; de maria and zezza, 2020), has been devoted to the agricultural sector. within the diffusion process, how social networks operate is key (valente, 1995): the set and pattern of support, the friendship, and the communication relations are important in defining the evolution and the success of innovations (morone and lopolito, 2010): spreading them is “a special type of communication, in that the messages are concerned with new ideas” (rogers, 2003:5). the innovations may be novel techniques or new strategies, on which the entrepreneurs have a scarce knowledge, and little experience: knowledge, familiarity, experience, and social learning are valuable catalysts for adoption (santeramo, 2018, 2019). in fact, sharing information and reaching a mutual understanding on the innovation tend to favour its first adoption and diffusion (rogers, 2003). if the importance of networks is clear, the reason behind such a relevant role is still unclear. so simply, why networks are so crucial for the diffusion of innovations? the ssocial networks act through several channels: first, they favour the circulation of information, by reducing the uncertainty and facilitating a better assessment of benefits and costs for the adopters; second, the redundancy of the information that can be derived through social reinforcement, also named as “indirect experience” (cfr. santeramo 2019), helps overcoming uncertainty; third, the homophily among potential adopters, strengthened by the similarity of characteristics (e.g.level of education, socioeconomic status, individual preferences), favours common meanings, the sharing of beliefs and a mutual understanding (rogers, 2003). in agriculture the third channel is an important catalyst for consumptions habits (santeramo et al., 2018). this work focuses on the first and the second channels. in this regard, an actor’s ability to circulate information to other actors depends on its position in the network, while its ability to be a source of social reinforcement depends on its level of clusterisation, also referred to as the density of neighborhoods, or, put differently, on how many of contacts are linked with oter members of the network (namtirtha et al., 2021; centola, 2010). the social network analysis (sna), a technique devoted to study and investigate networks, uses indexes to quantify the network characteristics. in this paper we investigate how the network characteristics (i.e. the sna indexes to measure the position and the clusterisation level) of the initial adopter influence the diffusion of innovations in agriculture. we aim is to show which characteristics may predict the best spreaders. this outcome is informative for policy makers, innovators and practitioners interested in planning effective spreading campaigns. this study focuses on a technological innovation (mulching films), and relies on a case study derived from specialist horticultural farmers located in the apulia region. the diffusion process for the innovative mulching films is replicated through an agent based model (abm), a powerful simulation modeling technique capable of capturing emergent phenomena with systemic characteristics stemming from the interplay of the individuals and which cannot be reduced to the system’s parts (bonabeau, 2002). the major abm distinctive feature is its ability to describe the system from the perspective of its constituent units (bonabeau, 2002). the adoption of novelties is a complex process typically involving a large body of interacting actors. although several computational models have been developed (bass, 1969; kumar and kumar, 1992; sharma et al., 1993; tanner, 1978), the empirical investigations on micro-level decisions are limited and challenging (janssen, 2020). one of the problems with these models is that they can explain the observed success in the diffusion processes, but cannot predict alternative emerging paths. the abm approach helps overcoming this limitation. proven its ability to describe the complex dynamics of the system by some simple rules acting at microlevel, it provides enough flexibility to capture the emergent phenomena (bonabeau, 2002). in the specific case of innovation diffusion, the abm modeling allows us to test various hypotheses on the characteristics of the agents, which represent the autonomous decision-making entities, i.e. in our analysis we refer to the farmers. we focus on their position in the networks and on their social connections. differently from other approaches, the abm can be aplpied in ex-ante analyses to predict whether a certain configuration is likely to succeed or to fail. we have calibrated the model on real-world data, acquired through a survey and by collecting secondary data. a further novelty of our analysis is the use of information that can directly replicate an existing social network. in short, we use a mixed approach which combines a case study, the sna and a simulation, to feed the empirical model and estimate the effects of the social relations on the diffusion of the innovation. the next section describes our integrated approach. the section 3 presents the findings of the analysis. we conclude with a discussion and reflections on policy implications to emphasize the relevance of study of this kind. 221the role of network characteristics of the innovation spreaders in agriculture bio-based and applied economics 11(3): 219-230, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9932 2. background a major issue in the process of diffusion of innovation is represented by the interpersonal communication channels, which play a crucial role in influencing the choice of the single agents to adopt or reject the innovation (rogers 2003). these channels provide means for communication between people, including the information transfer needed to make agents aware of the novelty (banerjee et al., 2013), and consists of the social relations connecting them (chavas and nauges, 2020; genius et al., 2014). the most suitable social relations to play the role of communication channels are represented by friendship, kinship and professional relationships (barbuto et al., 2017; cheboi and mberia, 2014; wang et al., 2020). this paper focus on the role of the network characteristics of the initial adopter in the diffusion of an innovation in a group of farmers. to analyze this process we model a network formed by nodes, each representing a farmer (i.e. agent), and links, each representing the social relations among farmers. the spread of the innovation is assessed by analyzing three outcomes: 1) the adoption rate – i.e. the fraction of farmers adopting the innovation within a time period; 2) the diffusion speed, which depend on the time required by the diffusion process to reach its maximum number of adopters; 3) the magnitude of the diffusion, that is a combination of the two previous outcomes (see table 3 below for details on their definitions and measurement). these outcomes are influenced by the nature of the network, and more precisely by i) the position of the innovation spreaders (kitsak et al., 2010; zhang et al., 2016), ii) by the structure of the network, proven that diffusion can reach more people and spread more quickly in clustered networks than in random networks, since the diffusion process is improved by reinforcing signals coming from clustered links (centola, 2010); and iii) by the socio-demographic characteristics of the farmers forming the network (banerjee et al., 2013). as for the agents’ characteristics, previous studies have shown that factors such as age, education level, mass-media exposure, experience in the sector, size of the farm are among the most important for the adoption of innovations (reimers and klasen, 2013; wang et al., 2020). moreover, agents involved in innovation adoption process typically exhibit an intrinsic “propensity to adopt”, an individual preference towards the innovation which stimulate the farmers to the adoption when the perceived quality of the innovation is sufficiently high (delre et al. 2007, van eck et al. 2011). in other terms, each potential adopter has a resistance to innovate, and this reluctance can be modeled as as a farmer-specific adoption threshold: the first adopters have a very low threshold for adoption whereas the later adopters have higher thresholds (i.e. a stronger resistance to the innovation) that tend to be exceeded only when many other members of the network have adopted the innovation and have reported on its goodness (macy 1991). we hypothesize that the spreaders who have higher chances of reaching a vast majority of farmers in the network, by mean of one(direct) or twoor morestep (indirect) relations, are expected to achieve a large spread; conversely, the spreaders who are closest to the vast majority of farmers are expected to allow a rapid spread and to reach the maximum number of adopters. figure 1 depicts this process by representing a simple diffusion model. it illustrates the impact that the network characteristics of the spreader have on the number of adopters and on the time required to spread the innovation. the time unit is conceived as the period needed for the information to pass from one agent to another, that is the time for the communication to occurs. the timing of the diffusion process is broken down in three periods: at t0 one agent is picked from the network to become the first adopter of the innovation(i.e. the spreader); at t1 the spreader informs on the existence of the innovation its neighbors (agents connected to the spreader), which become in turn aware of this novelty; at t2 a second-order information-passing occurs, at t2 when the spreader’s neighbors transfer the information to their neighbors in turn. in both diagrams the agents are distinguished according to the time at which they adopt the innovation. there are four types of agents, represented by different gradations of grey on a black-to-white scale, assuming that the probability that informed agents adopt the innovation is 1: i) the black circle represents the spreader who adopts the innovation at time t0; ii) the dark-gray circles represent the adopters at t1 (also named early adopters); iii) the light-gray circles represent the adopters at t2 (also named late adopters); iv) the white circles represent the non-adopters. spreaders a and b are embedded in two to different networks exhibiting different network characteristics: spreader a has four direct links to other agents; spreader b has only two direct connections. as a result, the diffusion processes are very different: in diagram 1 we found four early adopters and one late adopter, while in diagram 2 the opposite is true. put differently, the choice of spreader a leads to a fast diffusion, with four out of five potential adopters reached in the first period, while spreader b takes more periods to reach the vast majority of potential adopters but allows to spread the innovation to more adopters. 222 bio-based and applied economics 11(3): 219-230, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9932 antonio lopolito, angela barbuto, fabio gaetano santeramo th e most straightforward node indicator is represented by the degree centrality accounting for the number of connections the farmer has with other farmers (wasserman and faust, 1994). in our example (fi g. 1), the degree of centrality of node a and b are respectively 5 and 3: the more the connection the farmer has, the higher its infl uence on closeby farmers, proven that a very central node can pass information to a large fraction of the network directly (with no mediators). however, the degree of centrality is not the only source of infl uence. a great part of the infl uence that a node farmer has depends on its intermediary role in connecting other farmers. th is happens when a node lies between two other nodes. th e betweenness centrality concept has been developed to capture this characteristic: it is calculated as the sum of links connecting other nodes which pass through the original node (borgatti et al., 2013) and is a measure of its bridge capacity. another measure of the centrality of a node is represented by the closeness. th is index is expressed as the reciprocal of the farness of a given node. th is latter index is the sum of the lengths of the shortest paths to every other node: the closer a node is to all the others, the higher its infl uence is likely to be. th e index can be measured, as explained in the next section, as average reciprocal distance and through the eigenvector. finally, another relevant metrics related to the position of each single node is the local clustering coeffi cient which is the density (the total number of connections divided by the total number of possible connections) for the neighbourhood of the node (borgatti et al. 2002; newman, 2003): it measures the proportion of contacts which are linked together. a high level of local clustering generates reinforcing eff ects in the information passing which is an important issue in the adoption of a new behaviour or an innovation (centola, 2010). 3. material and methods we assess how the network characteristics of the spreaders infl uence the rate, the speed and the magnitude of the diff usion of the innovation in the farmers’ network. to this end we estimate the empirical model specifi ed as follows: (1) where yi represents the dependent variables capturing the diff usion process measured in terms of fi nal fraction of adopters, speed of diff usion, and diff usion magnitude; xid refers to the socio-demographics (d) of the spreaders, and xin denotes their network structure (n). th e variables of the model are described in table 1. to feed the model we adopted a mixed approach which combines case study analysis, sna and simulafigure 1. th e impact of the network characteristics of the spreader on the size and time of diff usion. source: own elaboration. diagram 1 diagram 2 223the role of network characteristics of the innovation spreaders in agriculture bio-based and applied economics 11(3): 219-230, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9932 tion. figure 2 unfolds the procedure we have employed and explains how we have derived the input variables expressed in eq. 1. 3.1 the case study to define the boundaries of the network, we referred to the 107 specialist horticulture farmers surveyed in a previous study on the diffusion of mulching techniques (scaringelli et al., 2016) in one of the largest horticultural areas in italy (i.e. province of foggia). the sample analysed in that study covered the 2,8% of the population of farmers producing vegetables crops in that area and was representative of the local horticultural sector. the interviewed farmers were identified as potential adopters of a newer mulching technique based on biodegradable films derived from organic waste (montoneri et al., 2011; franzoso et al., 2015). this case study provided the socio-demographics represented by the xid in the [eq. 1] and described in table 2. the average level of education is 2.45: the farmers represented in the sample reached high or medium education. they use at least one information channel among web site, e-commerce, and specialized journal subscriptable 1. the variables of the model. name cod. kind description adoption rate dif dependent (yi) the adoption rate is the proportion of farmers which adopted the innovation in consequence of the spreader operation speed spe dependent (yi) the speed of diffusion is the complement to unity of the number of time steps employed by the spreader to reach its maximum adoption rate in relative terms respect to the slowest spreader (i.e. the one who employs the maximum steps in absolute terms) magnitude mag dependent (yi) the magnitude of diffusion is the product of dif and spe education edu independent (xid) education, it is a discrete variable varying in the range [1-5], according to the education level of the farmer (post-doc, degree, undergraduate = 1; high school =2; middle school = 3; elementary school = 4; no school = 5) mass-media mas independent (xid) mass-media, which is a discrete variable ranging in the interval [0-3], according to the number of information channels used by the farmer among three kinds (firm web site, use of e-commerce, specialized journal subscription) experience exp independent (xid) experience, that is a discrete assuming values in the range [1-4], according to the class of experience (< 5 years = 1; < 10 years = 2; < 20 years = 3; > 20 years = 4) age age independent (xid) age, it counts the age of the farmer size size independent (xid) size counts the number of ectaras of the farm employees emp independent (xid) employees represents the number of employees enrolled in the farm degree centrality deg independent (xin) the centrality degree of a given node is the number of nodes linked with it (wasserman and faust, 1994) betweenness bet independent (xin) this is a measure of the bridge capacity of a node and is expressed as the sum of links connecting other nodes which pass through the node analysed (borgatti et al., 2013) closeness clo independent (xin) this index is expressed as the reciprocal of the farness of a given node. this latter index is the sum of the lengths of the shortest paths to every other node. the normalized closeness, here used, is obtained dividing the closeness by the minimum possible closeness expressed as a percentage average reciprocal distance ard independent (xin) this index represents a more accurate measure of closeness, including into the calculation not only the reciprocal of farness of the given node, but also the reciprocal of farness of the other nodes from the given node (borgatti et al., 2013) eigenvector eig independent (xin) it is a centrality measure in which the other nodes connected to the node under analysis are weighted by how central they are. in other words, the centrality of each node is therefore determined by the centrality of the nodes it is connected to local clustering coefficient lcc independent (xin) the local clustering coefficient is the density (the total number of ties divided by the total number of possible ties) of the neighborhood of an actor (borgatti et al. 2002; newman, 2003) 224 bio-based and applied economics 11(3): 219-230, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9932 antonio lopolito, angela barbuto, fabio gaetano santeramo tion. they have between 10 and 20 years of experience. th ey are, on average, 47 years old in mean, with the youngest and elder farmers being 24 and 75 years old respectively; 58% of farmers are in the 40-60 years old range (the standard deviation is 12 years). th e variable with the greatest variability is the fi rm size: it varies between 4 and 1805 hectares, with 65% fi rms having less than 50 hectares. th e average number of workers per fi rm is 13 with 80% of the sample with less than 20 employees. rather than having a probabilistic sample of horticulture sector, the rationale of choosing this case study was to obtain enough relational data to reproduce the complexity of a real farmer social network able to feed and calibrate the simulation model with a stylized representation of the interaction opportunities among farmers. th ese are based on the typical contact people have in a real-world network formed of group membership (representing, for example, co-workers), family and friend links, some connections to geographically close alters, and some ties to random alters in the population. instead of using stochastically generated network by means of specialised soft ware, which generates ideal network confi gurations (i.e. random networks or regular lattice), we adopted a participatory social network approach, a network survey technique directed at gathering data from actors well informed on the structure of network for their direct membership or for their expertise in the sector (campbell et al., 2019; delgadillo et al., 2020). we interviewed three experts, one agronomist with a long-time experience in local extension services and two expert farmers. th ese three interviewees know in depth the local context and the interactions among farmers. to ease the respondent’s task and maximizing their recalling potential we employed the following investigation procedure: 1) we divided the geographical area of the case study into four quadrants and grouped the farmers belonging to each quadrant, obtaining four figure 2. th e integrated approach. source: own elaboration. table 2. statistics of the socio-demographics independent variables. education (edu) mass media (mas) experience (exp) age (age) firm size (size) employees (emp) mean 2.45 0.81 3.22 46.88 69.99 13.23 min 0.00 0.00 1.00 24.00 4.00 1.00 max 4.00 4.00 4.00 75.00 1805.00 112.00 dev.st 0.79 1.05 1.06 11.50 176.76 15.68 source: own elaboration on data from (scaringelli et al., 2016). 225the role of network characteristics of the innovation spreaders in agriculture bio-based and applied economics 11(3): 219-230, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9932 groups; 2) for each group we asked the interviewees to recall the social links between farmers; 3) we repeated the procedure asking the interviewees to detect any intragroup links. since the objective is to piece together the social network structure as accurately as possible, traced back friendship, kinship and professional relationships between the farmers. to this end we posed two driving questions: 1) what are the farmers who are members of the same cooperative?, 2) what are the farmers who have known each other? in case the respondent acknowledged the existence of any relations between two farmers, each relation was further inquired by means of deeper analysis aimed at identifying also the kind of relation. for the relations acknowledged based on question 1, we asked the respondent to specify the if a professional relation existed between the two farmers connected asking the following sub-questions: i) did they entered a professional agreement?, ii) do they share means or other resources?, iii) do they contract each other for any operation?. for the relations acknowledged based on question 2 we also asked if the farmers connected were relatives of friends. of course, we did not expect that the three experts knew all the social interactions existing amongst the 107 members of the network, proven that this means to know information on 11.432 potential relations. rather than mapping the entire web of relations, our goal was to obtain a realistic network configuration resembling the typical morphology of a real-world network. the use of the participatory social network approach allowed us to cover all the typical forms of actors’ actual contact and not just random or regular ideal configurations.. indeed, we obtained a network formed of 2152 total connections, 1595 of which are local intergroup links and 557 arelong intragroup links. to define the network characteristics of the farmer, we applied the principal social network indicators of centrality and position described in section two. these formed the second group of independent variables (xin) in eq.1. 3.2 the simulation of diffusion process we simulated the diffusion outcomes within the network of farmers by means of an abm. although networks typically exhibit complex dynamics, we have intentionally focused on a simple model to trade-off the explanatory capacity and the clarity of interpretation of our results. it is formed of 107 agents interconnected which exactly reproduces the network described in the case study section. this web of social connections forming the network represents the interaction opportunity among agents which they use to exchange information about a technological innovation. the agents have specific attributes: (a) the preference toward the novelty; (b) the adoption threshold, as referred in the background section; (c) the level of education; (e) the spreader attribute, that is set true when the agent is used as spreader. as descends from attribute (e), the model runs two types of agents: ordinary farmers and spreaders. the spreader does not have to take any decision about its behavior, proven that it is set as the first adopter at the model setup. its unique role is to spread information on the innovation to its neighbors through the social relations interconnecting them. on the contrary, the ordinary farmers interact with the rest of the network, receiving and sending information and taking decision toward the adoption. in each time step, after having received information, each farmer recalculates its preference for the novelty on the base of its previous step preferences and the average of preferences of its neighbors weighted by a factor representing the level of homophily between the farmer and its neighbors. this average is then corrected multiplying it by a factor representing the years of education of the farmer. then each farmer adopts (rejects) the novelty if its preference is greater or equal (lower) than its innovation threshold. this process is repeated until a specific time span is covered, and three diffusion outcomes of the spreader operation are obtained: i) the diffusion rate, that is the proportion of farmers which adopted the innovation; ii) the speed of diffusion, that is calculated as: (2) where spei is the speed of spreader i, stepsmax i is the number of time steps employed by the spreader i to reach its maximum adoption rate; iii) the magnitude of diffusion, that is the product of the outcomes sub i) and ii). these outcomes represent the dependent variables (yi) in eq.1 (see table 1). the identification of the parameters of the model was based on the data available from the case study or according to the model internal logic. specifically, at the model setup, (a) the preferences of the farmers toward the new technology was set at 0, assuming that nobody, excepting the spreader, knows the novelty; (b) the innovation threshold was calibrated using data from scaringelli et al. (2016) which surveyed the attitude of the farmers towards the adoption of new kind of mulching films along a six-degree likert scale (0 very adverse – 5 very favorable) (c) the level of education was set at the level of education of the respondents; (d) regarding the spreader attribute, we used each farmer as a spreader 226 bio-based and applied economics 11(3): 219-230, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9932 antonio lopolito, angela barbuto, fabio gaetano santeramo one at a time alternately across the 107 model runs. this was to find the network characteristics best predicting effective spreaders (i.e. those with high levels of outcomes). the analysis produced 107 specific combinations of spreader/farmers. 3. results to guarantee the robustness of the simulations, each spreader/farmers combination has been replicated 100 times producing (107 x 100) 107,000 observations. each simulation has been ran for 500 time periods, which is the time span that guarantees the convergence of the diffusion process for all spreaders and to reach a steady number of adopters. we computed the average adoption rate, speed, and magnitude of diffusion at each step. to provide an encompassing depiction of overall process, tables 3-4 report the statistics of the model variables. table 3 reports the statistics of the network characteristics. the value of the degree highlights that each farmer is connected to 20 other farmers in mean, intercepts the shortest path length among 62 other farmers (bet), and is rather close to others (closeness). these values are the effect of a rather connected network, where the chances for a farmer to know others and influence theme or receive influence is very high. this relational structure represents a good premise for the innovation to spread. table 4 contains the statistics of simulated diffusion variables. they represent preliminary findings, since can give some initial indications for on a diffusion strategy. the first result is that spreaders achieve a 25% adoption rate in means, that is, a random chosen spreader is expected to cause adoption in 25% of other farmers. we found that the slowest spreader employs 389-time steps to reach its maximum adoption rate. in mean, the spreaders employ the 50% of this time to reach their maximum, that is the 194-time steps. the magnitude considers both diffusion rates and speed of diffusion in a synthetic indicators of diffusion effectiveness. in mean, spreaders reach a level of 0.11. but there is a huge variation among these performances. considers, for instance the adoption rates. table 4 reports that the maximum obtainable adoption rate employing a single spreader is 41%. this means that there are some effective spreaders capable of obtain high rates (>35%). we found 10 spreaders reaching this threshold. on the other side there are 11 spreaders reaching a zero-diffusion rate. this calls for a careful analysis in designing a diffusion campaign. indeed, while some spreaders can accomplish an effective campaign, choosing the wrong spreaders can result to a cul de sac dynamic, where the financial and human energies deployed would lead to a zero-result campaign. the diagrams of the density functions of three diffusion variables back up these findings (figure 3). they show that the speed of adoption and the adoption rate are bimodal, with the former showing a higher peak for low speeds, and the latter showing a higher peak for higher levels of adoption rate. this means that, in this context there are several good spreaders in terms of effectiveness (high adoption rates) but most part of them employ long time to completely accomplish the diffusion. on the other hand, there are some ineffective spreaders, characterised by low adoption rate which are relatively fast in covering their spreading. the third diagram confirm the initial findings. the magnitude (the interaction of speed and adoption rate) has a bimodal distribution as well with higher peak for lower values. the underlying process is that the speed of adoption table 3. statistics of the network independent variables. degree centrality (deg) betweenness (bet) closeness (clo) average reciprocal distance (ard) eigenvector (eig) local clustering coefficient (lcc) mean 20.11 62.26 0.08 45.34 0.73 54.70 min 1.00 45.00 0.01 0.00 0.00 39.26 max 98.00 101.67 0.26 1122.56 1.00 91.38 dev.st 17.58 9.40 0.06 167.84 0.22 7.25 table 4. statistics of the dependent variables. adoption rate (dif) speed (spe) magnitude (mag) mean 0.25 0.50 0.11 min 0.00 0.00 0.00 max 0.41 1.00 0.33 dev.st 0.01 0.23 0.07 227the role of network characteristics of the innovation spreaders in agriculture bio-based and applied economics 11(3): 219-230, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9932 dominates the adoption rate. put differently, the share of spreaders capable of enhancing high and fast levels of adoption is rather limited (approximatively equal to one third of those that have average performance in terms of speed and rate of adoption). we used the model in equation 1 to explain the three dependent variables, namely adoption rate, speed and magnitude. table 5 reports the regression results. the econometric analysis highlights the profile of the best spreader and, at the same time, provides deeper insights on the role of the network on the diffusion process. the model did not find any significant relation between the independent variables and the speed of diffusion. moreover, none of the socio-demographics is able of influencing the performances in terms of rate of adoption and magnitude, possibly due to the fact that the spreaders have similar under socio-demographic characteristics so that these variables are unable to discern the best spreader. likewise, four out of six network measures (i.e. deg, bet, clo and ard) do not exhibit significant effects. this result is likely to depend on the use of macro characteristics of the network, which very dense and close, rather than of micro relational characteristics of the members. on the contrary, eig (i.e. eigenvector centrality) has a positive, significant and relatively high impact on the diffusion rates and on magnitude, while, surprisingly, lcc (i.e. local cluster coefficient) exhibits a negative, even though small, impact on the diffusion process. the eigenvector is a measure of how central the actors connected to the spreader are: it resulted the best predictor of an effective spreader. lcc measures the density of a local neighborhood and is high when the actor connected to the spreader are in turns themselves connected. the fact that this variable has a negative impact is due to the redundancy it produces at local level. in other words, since the acquaintances of the spreader are also acquaintances among themselves, the information on the innovation continue to circulate within a confined clique producing redundancy and waste of social reinforcement. all in all, this analysis shows that, in an agricultural context as the one investigated, the best measure to select effective spreader and increase the success chance of a diffusion campaign, is represented by the eigenvector, which identifies the central spreader who knows very central actors in turn. 4. discussion and conclusions the innovations are catalysts of growth and their diffusion has been, during the last decades, a major driver of the economic development of the primary sector (esposti, 2012; scoppola, 2015; viaggi, 2015; moro et al., 2019): favoring a fast and complete spread of innovations should be a main goal in the policy agenda. the paper aimed at finding the network characteristics that identify the best innovation spreaders. we folfigure 3. 228 bio-based and applied economics 11(3): 219-230, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9932 antonio lopolito, angela barbuto, fabio gaetano santeramo lowed an integrated approach by using an abm model to simulate the diffusion performances of alternative potential spreaders. we found that the ard, a measure of how much each node is close to the whole network, and the clustering coefficients, which are related to the density of the neighborhood of a given node, are the main important factors to forecast the successfulness of an innovation spreader. these findings indicate that the diffusion of innovations in agriculture is fostered by spreaders relatively close and well connected to the rest of the web. furthermore, to enhance the diffusion of innovations in agricultural networks, the innovation spreaders should be highly clustered, so as to provide the needed information reinforcement required for the adoption to occur. we have also observed a low share of agents with a high level of adoption rate, a further proof that designing sets of spreaders capable of influencing the network areas is much in need to promote technologies adoption. these findings have direct implications for the policy agenda. for instance, they may be included in the design of policy measures and, in particular, within the context of the admissibility and the selection criteria in rural development plans:in order to enhance the spread of innovations, exploiting the relationships linking farmers in rural areas, the future policies may promote the creation of social interactions among farmers (i.e. promoting public and private social events to interconnect farmers); second, the policies for rural development may prioritize the requests of funds coming that are solicited by the most performing innovation spreaders, in order to exploit the multiplier effect that they will produce; third, the innovations should be promoted in areas where the existing networks are likely to be more receptive, a feature that can be easily proxied by the measures discussed in our paper. all these suggestions can be easily translated in admission and selection criteria in rural development plans: our analysis has direct implications for a better implementation of the future interventions. few words of caution. the present paper focuses on a case study with specific characteristics in terms of density of the network and clusterization of farmers, therefore the conclusions on the effects that the individual characteristics have on the rate of adoption would be externally valid only for those cases that are reasonably similar to our case study. thus, in order to further increase the external validity of our conclusions it would be recommendable the analysis of different network structures (e.g. high vs. low density, regular vs. randomized structure, high vs. low average degree, or so). to the extent that promoting innovations in agriculture is a priority for stakeholders in public and private sectors, similar studies should be encouraged. table 5. the results of the regression model. adoption rate (dif) speed (spe) magnitude (mag) coefficients σ p-value coefficients σ p-value coefficients σ p-value const 0,339 0,372 0,364 0,611 1,102 0,581 0,276 0,243 0,259 edu 0,017 0,012 0,165 -0,031 0,035 0,380 -0,006 0,008 0,479 mas -0,008 0,009 0,406 0,019 0,027 0,479 -0,005 0,006 0,436 exp -0,007 0,010 0,502 0,045 0,030 0,137 0,004 0,007 0,581 age 0,000 0,001 0,857 0,000 0,003 0,905 0,000 0,001 0,751 size 0,000 0,000 0,944 0,000 0,000 0,620 0,000 0,000 0,943 emp 0,000 0,001 0,611 0,002 0,002 0,424 0,001 0,001 0,127 deg -0,005 0,008 0,528 0,021 0,025 0,403 0,001 0,005 0,846 bet 0,000 0,000 0,593 -0,001 0,001 0,187 0,000 0,000 0,447 clo -0,022 0,022 0,313 0,083 0,065 0,203 0,007 0,014 0,628 ard 0,018 0,026 0,475 -0,082 0,076 0,282 -0,010 0,017 0,546 eig 1,698 0,698 0,017** 0,248 2,069 0,905 1,504 0,456 0,001*** lcc -0,100 0,047 0,034** 0,003 0,138 0,980 -0,079 0,030 0,011** r-quadro 0,450 0,106 0,553 229the role of network characteristics of the innovation spreaders in agriculture bio-based and applied economics 11(3): 219-230, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9932 references alkemade f. and castaldi c. 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(2016). identifying a set of influential spreaders in complex networks. scientific reports, 6(1), 1-10. volume 11, issue 3 2022 firenze university press bio-based business models: specific and general learnings from recent good practice cases in different business sectors nora hatvani1,*, martien j.a. van den oever2, kornel mateffy1, akos koos1 food loss and waste accounting: the case of the philippine food supply chain anieluz pastolero*, maria sassi the role of network characteristics of the innovation spreaders in agriculture antonio lopolito1,*, angela barbuto2, fabio gaetano santeramo2 the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period roberto esposti price dependence of biofuels and agricultural products on selected examples wioleta sobczak*, jarosław gołębiewski bio-based and applied economics 9(1): 53-83, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-7961 step-by-step development of a model simulating returns on farm from investments: the example of hazelnut plantation in italy alisa spiegel1,*, simone severini2, wolfgang britz3, attilio coletta2 1 business economics group, wageningen university, the netherlands 2 department dafne, università della tuscia, italy 3 institute for food and resource economics, bonn university, germany abstract. recent literature reviews of empirical models optimizing long-term investments in agriculture see gaps with regard to (i) separating investment and financing decisions, (ii) considering explicitly risk and temporal flexibility, and (iii) accounting for farm-level resource endowments and other constraints. inspired by real options approaches, this paper therefore stepwise develops a model extending a simple net present value calculation to a farm-scale simulation model based on mathematical programming, which considers time flexibility, different financing options and downside risk aversion. we empirically assess the different model variants by analysing investments into hazelnut orchards in italy outside of traditional producing regions. the variants suggest quite different optimal results with respect to scale and timing of the investment, its financing and the expected npv. the stepwise approach reveals which aspects drive these differences and underlines that considering temporal flexibility, different financing options and riskiness can considerably improve traditional npv analysis. keywords. perennial crop; real options; stochastic dynamic modelling; stochastic optimization. jel codes. c61, q12. 1. introduction recent literature reviews on empirical models for long-term investment analysis see gaps with regard to separating investment and financing decisions (e.g., trigeorgis and tsekrekos, 2018) and explicit consideration of associated risk and temporal flexibility (e.g., shresta et al., 2016). furthermore, opportunity costs, farm-level resource endowments, multiple risk sources and risk preferences are also rarely taken into account. this paper *corresponding author. e-mail: alisa.spiegel@wur.nl editor: fabio gaetano santeramo. 54 alisa spiegel, simone severini, wolfgang britz, attilio coletta illustrates how to include all these aspects into farm-level investment analysis and highlights resulting differences based on an empirical example of investing into hazelnut trees. the vast majority of research modelling farm-level investment behaviour opts for the classical investment theory, which maximizes the net present value (npv) or alternatively the internal rate of return (irr), or minimizes the pay-off period, subject to technological and resource constraints (e.g., schweier and becker, 2013; shresta et al., 2016; bett and ayieko, 2017). two major limitations of this approach are well known (among others see freixa et al., 2011; robinson et al., 2013; badiu et al., 2015; sgroi et al., 2015; stillitano et al., 2016). first, the risk underlying the investment project is not explicitly represented and can be reflected only by increasing the discount rate above market levels. other data determining cash-flow changes of the operation related to the investment enter with their expected values, only, neglecting their riskiness including potential correlations. second, the classical investment theory depicts a “now exactly as defined or never” decision problem where neither future adjustments to the investment project under, for instance, changing market, policy or technological environments, nor its postponement are considered. this easily overestimates necessary investment triggers and thus suggests a lower investment scale (wolbert-haverkamp and musshoff, 2014). the new investment theory aims to overcome these limitations. in particular, the application of the real options approach to agricultural investment projects has gained interest (e.g., wossink and gardebroek, 2006; hinrichs et al., 2008; maart-noelck and musshoff, 2013; spiegel et al., 2020). but its empirical application is still limited, for instance, in the domain of perennial crops. while quantitative analysis of investments into perennial crops has a long history (e.g., jackson, 1985), it mainly sticks to the classical investment theory. despite considerable market and production risk in orchard production, only a few recent studies, such as sojkova and adamickova (2011), consider risk. not astonishingly, they find substantial differences in optimal investment levels compared to the classical npv approach and suggest that deterministic models may provide flawed estimation of investment dynamics and scale. consideration of risks in investments is also beneficial for their social and behavioural analysis. social analysis mainly focuses on social networks and their effects on investment decision, for instance, via learning experience (marra et al., 2003; ghadim et al., 2005). dynamic social analysis is more promising and benefits from explicit consideration of risks, as learning and social interactions usually affect not only expectations, but also associated subjective risk; and optimal behaviour was found to be sensitive to strategic uncertainty (morreale et al., 2019). behavioural investment analysis studies subjective factors, including irrationality, subjective beliefs, and risk attitude (see e.g., chavas and nauges, 2020; weersink and fulton, 2020). also here, explicit consideration of risks in dynamics is beneficial as it allows adjusting risk perception and risk preferences (coelho et al., 2012). as for optimal financing behaviour, many studies investigate with other methods different aspects and determinants of farm-level demand for credits, such as present risk management strategies (katchova, 2005), credit source (farley and ellinger, 2007), interest rate (turvey et al., 2012; fecke et al., 2016), farmer’s personal characteristics and farm structural variables (howley and dillon, 2012). while financing behaviour is found to affect farm performance, financial risk, resilience, and their links to investment behaviour is still understudied. 55development of a model simulating returns on farm from investments building on this literature, we develop models for valuing and analysing long-term investment decision on farm, starting with a simple net present value calculation. we stepwise expand this model to a final dynamic stochastic farm-scale simulation model inspired by real options approaches, which considers different financing options and downside risk aversion in the form of minimum household withdrawals. to this end, the paper focuses on economic analysis of farm-level investment and financing options, while some social and behavioural aspects might be incorporated in follow-up research as discussed in the concluding chapter. accordingly, the objectives of the paper are twofold. first, we aim to illustrate how additional investment drivers can be stepwise incorporated into models of increasing complexity, and second, we aim to demonstrate sensitivity of results across the model variants to underline their relevance. the novelty of the paper is threefold. first, we explicitly consider factors that are still widely ignored when modelling farm-level investment decision, namely temporal flexibility, flexibility in terms of financing options, and downside risk aversion of the farm household. second, we stepwise introduce these factors to quantify their impact on optimal scale and timing of investments in a case study. third, the case study refers to perennial crops, a domain where advanced quantitative assessments are lacking. hazelnut production was chosen for the empirical application. it presents an interesting case study as it requires long-lasting expensive investments in form of a plantation, specialized machinery and irrigation. the different models are all set up for the same case study farm located in viterbo, a central italian region, where hazelnut production is not traditional, but becomes an increasingly important agricultural activity. the farm is assumed to currently manage rainfed annual crops. it is representative by its size and farm program for farms that are investing into new hazelnut plantations in the region. since hazelnut production was found to be characterized by a relatively high level of risks (zinnanti et al., 2019), we explicitly quantify considerable market (pelagalli, 2018), weather, and other production risks affecting product quality and quantity. taking hazelnut production in the viterbo region as an example is motivated by further facts. firstly, with 13% of global hazelnut production, italy is the second largest producer worldwide after turkey with ca. 65% (fao, 2019). global demand for hazelnut and derived products increased over the last decades and is projected to expand further. this triggers new investments in different producing countries, partially initiated by international food industry companies, of which a major one is located nearby our case study region. in italy, further expansion of hazelnut orchards in the traditional hilly production districts under rainfed systems is not possible. new plantations are now set-up in surrounding lower areas where irrigation is necessary to ensure relatively stable production and quality levels. over the years 2016-2019, the italian national institute for statistics (istat) recorded a 15% increase in the total area devoted to hazelnut cultivations. further investments are likely in coming years, according to major companies involved in hazelnut-based food production which foresee and foster the cultivation of 90.000 hectares in italy alone. the trend of investing into hazelnuts as an alternative land use option also reflects decreased profitability of so far dominating annual crops such as grains and oilseed. both socio-economic and environmental consequences of this ongoing land use change are lively debated (boubaker et al., 2014; utz, 2016). so far, economic assessments of investments into hazelnuts at farm level draw on data from specialized producers 56 alisa spiegel, simone severini, wolfgang britz, attilio coletta in the traditional districts, only. several authors therefore stress the need to better evaluate investments in new producing regions (bobic et al., 2016; pirazzoli and palmieri, 2017; frascarelli, 2017). the empirical analysis conducted in this paper closes the gap. the paper is organized as follows. section 2 step-by-step develops four models where each one expands the previous one by relaxing some assumptions to further improve the analysis. section 3 introduces data and assumptions used in our case study which also shows the additional data required for the model expansions. section 4 presents main empirical results to highlight differences across the model variants. section 5 concludes with a discussion of pros and cons of the different model variants and provides suggestions for further research on farm investments. 2. building-up a stochastic dynamic farm-level model 2.1 farm-level endowments, economy-of-scale and alternative crop (classnpv) we start with simulating discounted cash flows at farm level for either investing now or never – the still dominant approach in literature. in the case of hazelnuts, the nominal cash flows in each year depend on the age of the plantation (fig. 1). a newly set-up hazelnut orchard can be first harvested in its seventh year. from there to the tenth year, yields increase linearly from zero to a maximum yield level (maxyields) which is maintained until the trees are thirty years old. afterwards, there is a linear decrease in annual yields to 50% of the maximum up to the year 35. the resulting formula for the yields in year y is: (1) figure 1. evolution of a new hazelnut orchard, with related investments points. source: own elaboration based on liso et al. (2017) and frascarelli (2017). 57development of a model simulating returns on farm from investments where y depicts the year after the initial set-up and thus the age of the plantation; yieldhazel,y the hazelnut yields at age y in tonnes per hectare [t ha-1]; maxyields refers to the maximum hazelnut yield [t ha-1]. multiplying hazelnut yields with their price and deducting variable costs defines the gross margin per hectare. we capture the difference between the farm-gate and the average regional market price marketprice by so-called quality index qi, which reflects specific quality of hazelnuts, farmer’s negotiation power, and other related factors. both the quality index and the market price are represented in the npv calculation by their expectations. we also distinguish between harvesting costs per tonne harvested, and other costs per hectare, which include irrigation and fertilization costs. at each age of the plantation y, the cash flow per hectare equal to the gross margin is thus defined as: e[gmhazel,y] = yieldhazel,y * e[qihazel,y] * e[marketpricehazel,y] – yieldhazel,y * harvcost – othercost ∀ y ≤ 35 (2) where e[∙] is the expectation operator; gmhazel,y stays for the gross margin of hazelnuts [€ ha-1]; qihazel,y for the hazelnuts quality index; marketpricehazel,y for the average market price of hazelnuts [€ t-1]; harvcost for the variable harvesting costs [€ t-1]; othercost for the other quasi-fixed costs related to hazelnut cultivation, including irrigation and fertilization costs [€ ha-1]. furthermore, we consider two (quasi-)fixed resources endowments: land and labour. additional demand for labour can be satisfied via hired labour. the farm resources are distributed between hazelnuts and durum wheat an alternative crop to hazelnuts. the acreages of hazelnut and durum wheat can jointly not exceed the given land endowment: areahazel + areawheat ≤ endland (3) where areahazel depicts land under hazelnuts [ha] and areawheat land devoted to durum wheat [ha]; endland stays for the total fixed and given land endowment [ha]. labour requirement for the crops are expressed per hectare; for hazelnuts, additional labour hours per harvested tonne are considered. total labour requirement can be covered by on-farm or hired labour: areawheat * labwheat + areahazel * labhazel + areahazel * yieldhazel,y * labhm ≤ endlab + hiredlaby ∀y (4) where labwheat stays for labour requirements for durum wheat [hours per hectare, h ha-1]; labhazel for quasi-fixed (i.e., independent of yields) labour requirements for hazelnuts [h ha-1]; labhm for variable labour requirements for hazelnuts [hours per tonne, h t-1]; endlab for on-farm labour use [hours, h]; hiredlaby for additionally required labour that can be hired [h]. the gross margin of the alternative crop is defined in a similar way as the one of hazelnuts, namely, based on expected yield, quality index, market price and variable costs: e[gmwheat] = e[yieldwheat] * e[qiwheat] * e[marketpricewheat] – e[costwheat] (5) 58 alisa spiegel, simone severini, wolfgang britz, attilio coletta where gmwheat stays for gross margin of durum wheat [€ ha-1]; yieldwheat for its yields [t ha-1]; qiwheat for its quality index; marketpricewheat for its average market price wheat [€ t-1]; and costwheat for its quasi-fixed costs [€ ha-1]. while durum wheat is rain-fed, hazelnuts require irrigation water, such that farmers have to invest into a well and irrigation equipment in addition to the establishment costs of the plantation (fig. 1). furthermore, harvesting machinery for hazelnuts must be available prior to the first harvesting of hazelnuts. harvesting machinery is physically depreciated while other machinery is depreciated by lifetime. the formula for npv then becomes: e[npv] = * areahazel + areawheat * * e[gmwheat] – invcostwell – (6) where npv stays for the net present value over the overall planning horizon ∑y [€]; inicost for the costs associated with initial establishment of hazelnut plantation [€ ha-1]; dr for the discount rate [%]; reconvcost for the costs associated with final clear-cut of hazelnut plantation [€ ha-1]; invcostwell for costs of well and irrigation equipment for hazelnut [€]; invcostmachm,y for investment costs of machinery m{smaller;standalone;irrigation;tractor; operating} [€]; e[wage] for expected costs of hired labour [€ h-1]. we optimize the farmlevel npv under endowment constraints (eq. 3 and 4) by solving for the following decision variables: area of hazelnuts, area of durum wheat and investments into machinery m at each age of the plantation y. the model advances by accounting for all required investments as well as resource endowments. it also captures the associated economy-of-scale; in our example, via lifetime and capacities of machines and via fixed costs for a well and irrigation equipment. in another case study, the gross margin of the alternative land use option could also represent average returns from a portfolio of alternative crops instead of one crop, only, as in here durum wheat. as the result, we simulate the maximum possible farm-level npv under given conditions and constraints. this model still suffers from limitations as seen in literature. first, it operates with expected variables, ignoring their underlying riskiness when maximizing the npv. second, it implies investing into hazelnuts now or never. yet, in the case of uncertainty and high sunk costs of an investment project, investors might prefer to wait for new information before making a decision. here, sunk costs relate to setting up the plantation and investments into a well, irrigation equipment and specialized machines while future prices, yields, and costs are uncertain, and the first yield is generated only seven years after the investment. these circumstances might create an additional value of waiting and of getting more information, such as on price developments of hazelnut, and motivate using the real options instead of a classical npv approach. 59development of a model simulating returns on farm from investments 2.2 risk and flexibility in timing (realopt) spiegel et al. (2018; 2020) demonstrate the advantages of stochastic-dynamic programming for farm-level investment analysis, since it also considers risks besides (quasi-fixed) assets, such as land and on-farm labour already found in the model classnpv above, and addresses both time and scale flexibility as elements of a real-options approach. spiegel et al. (2018; 2020) overcome the curse of dimensionality found in binary lattices or similar scenario tree approaches by employing a scenario tree reduction technique. building on their work, we transform the classnpv model developed in the section above into a stochastic-dynamic farm-level model. in contrast to spiegel et al. (2018; 2020), we consider a second replantation period in order to expand the finite planning horizon so far in the future that differences to an infinite one become marginal from a numerical perspective. we assume the following aspects of management flexibility. the farmer can decide during the first five years to introduce hazelnut or to continue cultivating durum wheat as an alternative annual crop (time flexibility 1). after reaching an age between thirtytwo and thirty-five years, the hazelnut trees must be removed; afterwards the land can be either planted again with new hazelnut trees or cropped with durum wheat (time flexibility 2). the subsequent plantation must be closed down again after thirty-two to thirtyfive years (time flexibility 3). this results in a finite planning horizon of seventy-five years such that differences between an infinite and this finite planning horizon should be negligible for any reasonable private discount rate. in order to increase computational speed, we divide the total land endowment into distinct plots of sizes 2n with n = 0,1,2… which in combination allow any integer plantation size between 0 and the land endowment (scale flexibility). using fixed plot sizes instead of a continuous fractional plantation size allows for a mixed integer program instead of a mixed non-linear integer one. integers are needed anyhow to capture indivisibilities in investment (well, machinery). time flexibility is considered separately for each plot. differences compared to the previous model classnpv are threefold. first, we consider now not only the expected values of stochastic variables, but also the associated riskiness. more specifically, all expected values are replaced by probability distributions or stochastic processes, represented by a scenario tree. each node of the tree contains a vector of stochastic variables’ realizations. second, we now distinguish between the time period and the age of the plantation. in the previous simpler model, hazelnuts could only be planted in the first year such that the plantation’s age was equal to the year. due to the time flexibility in realopt model, time and plantation age become two different dimensions as the time flies regardless of the farmer’s decision to introduce hazelnuts or not. accordingly, a plantation now can consist of plots of different age. as a consequence, in the expanded model, decision variables and risky parameters carry now both a time and node indices, such that the gross margins of both crops are defined as follows: gmhazel,p,t,n = hahazel,p,t,n *[ yieldhazel,p,t,n * qihazel,t,n * marketpricehazel,t,n – yieldhazel,p,t,n * harvcost – othercost] (7) gmwheat,t,n = yieldwheat,t,n * qiwheat,t,n * marketpricewheat,t,n – costwheat,t,n (8) 60 alisa spiegel, simone severini, wolfgang britz, attilio coletta where gmhazel,p,t,n stays for the gross margin of hazelnuts [€ ha-1] on plot p in time period t{t1,t2,…,t} and node of the scenario tree n; yieldhazel,p,t,n for hazelnut yields [t ha-1] on plot p in time period t and node of the scenario tree n. the hazelnut yield depends on the difference between current year t and the year when they were planted on this plot on the same path from the root to the current node n according to eq.(1). hahazel,p,t,n stays for a binary variable of devoting a plot p into hazelnuts in time period t and node of the scenario tree n (1 = the plot is cultivated with hazelnuts; 0 = otherwise); qihazel,t,n for the hazelnuts quality index in time period t and node of the scenario tree n; marketpricehazel,t,n for the market price of hazelnuts in time period t and node of the scenario tree n [€ t-1]; gmwheat,t,n for gross margin of durum wheat [€ ha-1] in time period t and node of the scenario tree n; yieldwheat,t,n for yields of durum wheat [t ha-1] in time period t and node of the scenario tree n; qiwheat,t,n for quality index of durum wheat in time period t and node of the scenario tree n; marketpricewheat,t,n for the market price of durum wheat [€ t-1] in time period t and node of the scenario tree n; costwheat,t,n for quasi-fixed costs for durum wheat [€ ha-1] in time period t and node of the scenario tree n. the farm’s operating income is thus defined as follows: operincfarm,t,n = areawheat,t,n * gmwheat,t,n + sizep * gmhazel,p,t,n – inip,t,n * inicost * sizep– reconvp,t,n * reconvcost * sizep – invwellt,n * invcostwell – invmachm,t,n * invcostmachm – hiredlabt,n * waget,n ∀t,n (9) where operincfarm,t,n stays for farm’s operating income in time period t and node of the scenario tree n [€]; areawheat,t,n for land area under durum wheat in time period t and node of the scenario tree n [ha]; sizep for the size of the plot p [ha]; inip,t,n for a binary variable of exercising the initial establishment of a hazelnut plantation on plot p in time period t and node of the scenario tree n (1 = hazelnuts are introduced; 0 = otherwise); reconvp,t,n for a binary variable of exercising clear-cut of hazelnuts plantation onto a plot p in time period t and node of the scenario tree n (1 = hazelnuts are clear-cut; 0 = otherwise); invwellt,n for a binary variable of exercising investments into a well and irrigation equipment in time period t and node of the scenario tree n (1 = investments into a well and irrigation equipment are exercised; 0 = otherwise); invmachm,t,n for a binary variable of exercising investments into required machinery m in time period t and node of the scenario tree n (1 = investments into machinery are exercised; 0 = otherwise). the discounted operating income is the objective variable to be maximized, defined as follows: npv = probn * (10) where probn stays for the probability of the node to occur [percentage points]. at each node of the constructed scenario tree, the model takes into account available time and scale flexibility, the state of the stochastic variables, as well as resources endowments, and provides the following output: 61development of a model simulating returns on farm from investments land distribution between hazelnuts and durum wheat. observing changes in land distribution between different nodes of the tree allows to observe (re)planting decisions, as well as decisions to expand or clear-cut hazelnut plantations; investments into a well and harvesting and other machinery for hazelnuts, the latter differentiated by size; related economic variables such as costs and revenues. although the realopt model is fairly complex and presumably closer to real world decisions on investments, it has still two major drawbacks considering gaps found in literature. first, due to high costs related to the initial investments, the farmer will face considerable negative cash flows during the first years after a plantation is set up. related costs for financing are most probably underestimated by the average discount rate in the model. second, the model neglects downside risk aversion, while the production cycle of hazelnuts implies significant negative cash flows in several time periods and related financing costs. we address these drawbacks stepwise in the two final models. 2.3 costs of financing (realoptfin) the realoptfin model introduces a current account of the farm operation. it serves as the source to cover variable and investment costs and receives subsidies and the operating income from selling products. in order to finance investments beyond accumulated cash, the model considers different types of loans with fixed repayment times and interest rates. the benefit for the farmer from the farm operation is represented now by annual profit withdrawals from the current account of the farm, discounted by his private discount rate. accordingly, the private discount rate now does not longer need to reflect the costs of financing. instead, the market based discount rate is implicit and endogenously determined depending on the financing decisions. the farmer now optimizes the expected net present value of future profit withdrawals from the farm operation, considering simultaneously investment and financing decisions. farm operating income operinc enters the current account as follows: curacct,n = curacct-1,n1 + operincfarm,t,n – withdrawt,n + newloansloans,t,n – repaymloans,t,n – intpaymloans,t,n ∀t, (11) where curacct,n stays for the current account in the year t and node n [€]; withdrawt,n for annual farm household withdrawals [€]; newloansloans,t,n for the loans acquired in the year t and node n [€]; repaymloans,t,n for the debt to-be-paid in the year t and node n [€]; and intpaymloans,t,n for the interest to-be-paid in the year t and node n [€]. the household withdrawals are defined for each combination {t,n} based on investment and financing decisions. the reader should note that introducing endogenous financing decisions implies and requires a more accurate simulation of cash flows. in particular, if the previous two models could omit cash flows independent of investment decisions, such as decoupled subsidies under the common agricultural policy’s (cap) first pillar, all cash flows related to 62 alisa spiegel, simone severini, wolfgang britz, attilio coletta the farm operation have to be included now, since they affect the required financing. the operating income is hence defined as: operincfarm,t,n = areawheat,t,n * gmwheat,t,n + sizep * gmhazel,p,t,n – inip,t,n * inicost * sizep – reconvp,t,n * reconvcost * sizep – invwellt,n * invcostwell – invmachm,t,n * invcostmachm – hiredlabt,n * waget,n + endland * prem ∀t,n (12) where prem stays for the common agricultural policy (cap) first pillar direct payments [€ ha-1]. the discounted household withdrawals are now the objective variable to be maximized and defined as follows: npv = probn * (13) 2.4 downside risk aversion (realoptfinrisk) explicitly considering profit withdrawals allows introducing a lower limit of income from the farm operation such as to ensure household survival. this limit also acts as risk floor. the previous realoptfin model assumes such minimum withdrawals to be zero, i.e. there are combinations of years and nodes possible where the household will not receive any income from the farm. this is likely to occur especially in the first years after setting up the plantation where high investment costs coincide with zero or low yields of hazelnuts. our final realoptfinrisk model instead assumes a minimum withdrawal level in each year and each node of the scenario tree. it is calculated by multiplying the level of the farm resource endowments with assumed minimum riskfree returns: withdrawt,n ≥ endlab * minwage + endland * prem (14) where minwage is a minimum risk-free off-farm wage [€ h-1]. similar, the minimum withdrawal limits above assumes that the farmer would be able to receive at least the premium of the first pillar of cap as returns to its land, for instance, by renting it out. different assumptions to define minimum withdrawals could clearly be chosen. financing and deciding on the annual withdrawals are hence also measures of risk management. while we ensure that the amount of new long-term loans cannot exceed investment costs in a year – assuming that bank will link such loans to a business plan – short-run loan and postponed withdrawals allow flattening the impact of stochastic operational cash flows from the farm on household withdrawals, i.e. income. the reader should note further that we assume that the quality indices, yields and prices of hazelnut and durum wheat are not correlated. combining arable farming and a hazelnut plantation thus by itself reduces risk due to natural hedging. 63development of a model simulating returns on farm from investments we consider a lower limit on annual household withdrawals as a rather transparent and easy to communicate measure of risk aversion. changing the limit in sensitivity analysis can help to inform a decision taker on the trade-offs between ensuring a minimum income level under any potential future development and his expected discounted income level. it does not require to introduce explicitly a risk-utility function in the framework above which is another avenue to develop the model further, for instance, to introduce behavioural aspects. figure 2 graphically represents the model variant and its major components. each node of the scenario tree contains a vector of realizations of the seven stochastic variables. these realizations enter the calculations of net revenues in each node of the tree, which also reflect set-up and removal decisions with respect to hazelnuts made in this one and its ancestor nodes. these decisions translate into the future according to the production cycle and determine required future financing, as well as future costs of adjusting these production decisions. financing decisions need to ensure minimum household withdrawals and a nonnegative current account. the model simultaneously solves for optimal behaviour in all its nodes, maximizing the net present value (eq. 13) under endowment and other constraints. figure 2. graphical representation of the realoptfinrisk model’s major components and relations between them. note: h stays for hazelnuts; dw stays for durum wheat. 64 alisa spiegel, simone severini, wolfgang britz, attilio coletta 2.5 comparison of the models fig. 3 and table 1 below give an overview on the four model variants. classnpv calculates discounted annual cash flows at farm level under the assumption to convert a part of land into hazelnuts now or never, i.e. it considers scale flexibility under endowment constraints. consequently, it also considers that additional labour might be needed depending on available farm family labour and the chosen investment program. realopt adds time flexibility, i.e., it captures and optimizes returns from investments at different time points, drawing on a real options approach. that model is next expanded to realoptfin by introducing a difference between the private discount rate, used by the farmer to discount cash flows, and the costs of financing investments, i.e. it also optimizes financing decisions. realoptfinrisk finally ensures that the farm household can withdraw in each year a minimum sum of money from the farming operation. it is also worth to mention that classnpv does not require a scenario tree as only the expected realizations are needed in each time period. however, the tree realizations can be used post-model to report on the riskiness of the npv optimized without considering risk. figure 3. comparison of components of the four model variants. 65development of a model simulating returns on farm from investments 2.6 solution approach we use the solution approach suggested by spiegel et al. (2018; 2020), which combines monte-carlo simulation, a scenario tree reduction technique, and stochasticdynamic programming (fig. 4). first, 5’000 monte-carlo draws are obtained for all the stochastic variables, using empirically predefined stochastic processes and distributions. jointly this results in a huge scenario tree with 5’000 equally probable independent paths and a realization vector for the seven stochastic variables in each node. this step is done in java based on standard libraries and own developed code to overcome speed limitations in gams. the gams-package scenred2 by heitsch and römisch (2009) reduces this scenario tree in the second step. the underlying scenario reduction technique merges selected paths and nodes and provides new outcomes (i.e., the expected mean of merged outcomes) and the respective probabilities (i.e., the thickness of merged paths). the relation between nodes across time in a scenario tree is captured by an ancestor matrix, generated by scenred2. the final step combines the obtained scenario tree with the farmlevel model and solves for the optimal investment behaviour using stochastic programming. due to manifold dynamic relations between endogenous variables, all nodes on the same path to a final leave are interrelated. as all paths start with from the same root node, that implies that all nodes need to be simultaneously solved. the code of scenario tree composition and the farm-level model is available online. 3. data and parameters the parameters of the model draw on multiple data sources, including the italian farm accountancy data network (fadn-crea), eurostat, world bank, census data (istat, 2010), agricultural output prices (istat, 2018) and the italian central bank, as well as available literature (frascarelli, 2017; liso, 2017; ribaudo, 2011) and expert judgement. the fadn data are only available for the period 2008-2016; the data from istat, eurostat, and the world bank were selected for the period 2000-2016. all monetary values were deflated using the gdp deflator for italy provided by the world bank (2015=100) to ensure comparability over time. traditionally, hazelnut orchards were found in a specific district of the viterbo province, only, which is specifically suitable for hazelnut cultivation but nowadays doesn’t offer table 1. comparison of the four model variants. classnpv realopt realoptfin realoptfinrisk (i) production cycle yes yes yes yes (ii) spatial flexibility yes yes yes yes (iii) economy-of-scale yes yes yes yes (iv) resources endowments yes yes yes yes (v) time flexibility no yes yes yes (vi) optimising financing costs no no yes yes (vii) downside risk preferences no no no yes 66 alisa spiegel, simone severini, wolfgang britz, attilio coletta any additional space for new hazelnut cultivation. therefore, new investments are located in municipalities close by, following a gradient of falling hazelnut yields depending on soil characteristics, climate conditions and often higher irrigation requirements, which mostly depends on the distance to the traditional growing zone. data have been retrieved from the individual farm fadn database (2008-2016) considering only 21 municipalities of the province of viterbo1 (lazio region) where hazelnut represents a limited share of the utilized agricultural area according to 2010 census data (less than 5%), but which have recently experienced relevant relative increases due to new plantations. we furthermore filter fadn data to account for two factors. first, observations referring to years at or close after the establishment of hazelnut plantations were excluded to reflect that no yields occur in the first six years 1 arlena di castro, bassano in teverina, blera, castel sant’elia, celleno, civita castellana, gradoli, graffignano, marta, monte romano, montefiascone, monterosi, oriolo romano, orte, piansano, tuscania, vejano, vetralla, villa san giovanni, vitorchiano in tuscia, viterbo. figure 4. graphical representation of the solution process. (source: based on spiegel et al., 2018; 2020). 67development of a model simulating returns on farm from investments after planting (frascarelli, 2017). second, only observations above 1 ha are included to neglect non-commercial activities in form of “hobby farms”. the regional focus and the two filters led to 62 observations in total. census data suggest a representative farm size of 30 ha, and, for the considered municipalities, cropping of rain-fed arable crops with durum wheat as the dominant one as the benchmark before considering a hazelnut plantation. table 2 provides an overview of the parameter values and underlying data sources. for durum wheat and hazelnuts, expected yields are derived from the fadn sample based on total production and area. since there is no information on the age of the respective plantations, we corrected the resulting average hazelnut yields by a coefficient of 1.25 and assumed it to be the maximum hazelnut yields. that coefficient reflects the average relation between the maximal yield and the yield developments depicted in eq.(1). also, due to limited data on hazelnut yields, we assume no riskiness in maximum hazelnut yields maxyields and the yields derived thereof yieldhazel,p,t,n. instead, stochasticity in hazelnut production is captured by a stochastic quality index and market price. in order to estimate the expected market prices of unshelled hazelnuts and durum wheat, the market prices in italy provided by istat (for hazelnuts) and eurostat (for durum wheat) were used. furthermore, we correct the expected hazelnut price derived from historical observations by a multiplicative coefficient of 1.18. assuming higher future prices seems appropriate due to increasing global demand of hazelnut, while production is expanding into less suitable production areas with a lower yield potential and higher costs, such caused by the required irrigation. furthermore, all four models suggest no investments at all into hazelnuts under the expected historical price level. this contradicts observed farmers’ behavior, and suggests that farmer expect higher future prices. we used sensitivity analysis to find a suitable future expected mean price level where some but not all land was devoted to hazelnut in at least one of the models, reflected by the factor of 1.18. for quality indices, the fadn data were used to derive annual per unit farm specific prices of hazelnuts and durum wheat by dividing crop revenues by sold quantities. these calculated farm-gate prices were normalized by the market prices in italy provided by istat (2018) for hazelnuts and durum wheat to define samples of farm specific quality indices. we differentiate two sizes of a specialized harvester for hazelnuts between which the model can chose endogenously. the cheaper harvester is drawn by a tractor ordinarily used for other activities. the more expensive self-driving harvester reduces per ha labour needs and has a longer lifetime measured in harvested area. compared to the classnpv model, the other models require converting expectations of stochastic variables into stochastic processes or distributions. all the stochastic variables are assumed to be mutually independent, i.e. a correlation coefficient between any two stochastic variables of zero is chosen. in particular, the market prices of hazelnuts and durum wheat are captured by uncorrelated mean-reverting stochastic processes defined as follows: dprht = μhazel (θhazel – prht)dt + σhazel dwt hazel (15) dprdwt = μwheat (θwheat – prdwt)dt + σwheat dwt wheat (16) where t is the time period; hazel indicates hazelnuts and wheat durum wheat; prht is the natural logarithm of hazelnuts price; prdwt the natural logarithm of durum wheat price; 68 alisa spiegel, simone severini, wolfgang britz, attilio coletta ta bl e 2. o ve rv ie w o f p ar am et er s of th e fo ur m od el s, th ei r a ss um ed v al ue s, an d re sp ec tiv e re fe re nc es . m od el pa ra m et er n ot at io n us ed in eq ua tio ns a bo ve 1 va lu e re fe re nc es classnpvex pe ct ed y ie ld s o f d ur um w he at e[ yi eld w he at ] 3. 9 t h a-1 fa d n ex pe ct ed v ar ia bl e co st s o f d ur um w he at e[ co st w he at ] 37 1. 75 € h a-1 fa d n ex pe ct ed m ar ke t p ric e of d ur um w he at e[ m ar ke tp ric e w he at ] 23 7. 22 € t-1 eu ro st at , w or d ba nk ex pe ct ed m ar ke t p ric e of h az el nu ts e[ m ar ke tp ric e h az el] 2, 54 9. 66 € t-1 is ta t, w or d ba nk ex pe ct ed q ua lit y in de x of d ur um w he at e[ qi w he at ] 0. 92 47 fa d n , i st at ex pe ct ed q ua lit y in de x of h az el nu ts e[ qi ha ze l] 0. 98 17 fa d n , i st at ex pe ct ed w ag e of h ire d la bo ur e[ w ag e] 10 € h -1 lo ca l c ol le ct iv e co nt ra ct s f or h ire d la bo ur realoprfinrisk realoptfin realopt av ai la bl e an nu al la bo ur e nd ow m en t en d l ab 35 0 h o w n el ab or at io ns av ai la bl e la nd e nd ow m en t en d l an d 30 h a fa d n h az el nu t e st ab lis hm en t c os ts in ic os t 8, 00 0 € ha -1 li so e t a l., 2 01 7; r ib au do , 2 01 1; f ra sc ar el li, 2 01 7 in ve st m en ts fo r a sm al le r h ar ve st in g m ac hi ne ry in vc os tm ac h s m al ler 8, 00 0 € li so e t a l., 2 01 7; r ib au do , 2 01 1; f ra sc ar el li, 2 01 7 la bo ur re qu ire m en ts fo r a sm al le r h ar ve st in g m ac hi ne ry 32 h h a-1 li so e t a l., 2 01 7; r ib au do , 2 01 1; f ra sc ar el li, 2 01 7 m ax im um la nd a re a th at c an b e ha rv es te d pe r y ea r w ith a sm al le r h ar ve st in g m ac hi ne ry 5 ha li so e t a l., 2 01 7; r ib au do , 2 01 1; f ra sc ar el li, 2 01 7 to ta l e nd ow m en t f or a sm al le r h ar ve st in g m ac hi ne ry in te rm s o f l ife tim e 12 y ea rs li so e t a l., 2 01 7; r ib au do , 2 01 1; f ra sc ar el li, 2 01 7 in p hy sic al te rm s 2, 00 0 h li so e t a l., 2 01 7; r ib au do , 2 01 1; f ra sc ar el li, 2 01 7 in ve st m en ts fo r a st an dal on e ha rv es tin g m ac hi ne ry in vc os tm ac h s ta nd al on e 40 ,0 00 € li so e t a l., 2 01 7; r ib au do , 2 01 1; f ra sc ar el li, 2 01 7 la bo ur re qu ire m en ts fo r a st an dal on e ha rv es tin g m ac hi ne ry 15 h h a-1 li so e t a l., 2 01 7; r ib au do , 2 01 1; f ra sc ar el li, 2 01 7 m ax im um la nd a re a th at c an b e ha rv es te d pe r y ea r w ith a sm al le r h ar ve st in g m ac hi ne ry 15 h a li so e t a l., 2 01 7; r ib au do , 2 01 1; f ra sc ar el li, 2 01 7 to ta l e nd ow m en t f or a sm al le r h ar ve st in g m ac hi ne ry in te rm s o f l ife tim e 12 y ea rs li so e t a l., 2 01 7; r ib au do , 2 01 1; f ra sc ar el li, 2 01 7 in p hy sic al te rm s 3, 00 0 h li so e t a l., 2 01 7; r ib au do , 2 01 1; f ra sc ar el li, 2 01 7 o th er la bo ur re qu ire m en ts fo r h az el nu t ( ex cl . l ab ou r re qu ire d fo r h ar ve st in g m ac hi ne s) 69development of a model simulating returns on farm from investments m od el pa ra m et er n ot at io n us ed in eq ua tio ns a bo ve 1 va lu e re fe re nc es a ge o f p la nt at io n: b el ow 7 y ea rs (w ith ou t pr od uc tio n) 49 .5 h h a-1 ex pe rt b as ed in fo rm at io n a ge o f p la nt at io n: e qu al to o r m or e th an 7 y ea rs 89 .5 h h a-1 ex pe rt b as ed in fo rm at io n va ria bl e ha rv es tin g co st s o f h az el nu ts ha rv co st 50 € t-1 ri ba ud o, 2 01 1 o th er p ro du ct io n co st s o f h az el nu ts , i nc l. ot he rc os t 1, 70 0 € ha -1 ex pe rt b as ed in fo rm at io n c os ts o f f er til iz at io n an d ch em ic al tr ea tm en ts 80 0 € ha -1 ex pe rt b as ed in fo rm at io n o pe ra tio na l c os ts fo r o th er m ac hi ne ry (e xc l. ha rv es tin g) 60 0 € ha -1 ex pe rt b as ed in fo rm at io n ir rig at io n co st s 30 0 € ha -1 ex pe rt b as ed in fo rm at io n in ve st m en ts in to a w el l in vc os tw ell 12 ,0 00 € li so e t a l., 2 01 7; r ib au do , 2 01 1; f ra sc ar el li, 2 01 7 in ve st m en ts in to ir rig at io n eq ui pm en t f or h az el nu ts in vc os tm ac h i rr ig at io n 2, 00 0 € ha -1 li so e t a l., 2 01 7; r ib au do , 2 01 1; f ra sc ar el li, 2 01 7 in ve st m en ts in to tr ac to r in vc os tm ac h t ra ct or 20 ,0 00 € ex pe rt b as ed in fo rm at io n li fe tim e of tr ac to r 20 y ea rs ri ba ud o, 2 01 1 in ve st m en ts in to o pe ra tin g m ac hi ne ry fo r h az el nu ts in vc os tm ac h o pe ra tin g 10 ,0 00 € ex pe rt b as ed in fo rm at io n li fe tim e of o pe ra tin g m ac hi ne ry 10 y ea rs ri ba ud o, 2 01 1 c a p di re ct p ay m en t pr em 30 0 € ha -1 o w n el ab or at io n a nn ua l d isc ou nt ra te dr 2% o w n el ab or at io n la pl ac e di st rib ut io n fo r y ie ld s o f d ur um w he at (s ee ap pe nd ix fo r f ur th er d et ai ls) m ea n 3. 91 20 fa d n st an da rd d ev ia tio n 1. 19 84 fa d n ex pe ct ed m ax im um y ie ld s o f h az el nu ts m ax yi eld s 2. 9 t h a-1 fa d n m ea nre ve rt in g st oc ha st ic p ro ce ss fo r n at ur al lo ga rit hm of m ar ke t p ric e of d ur um w he at (s ee a pp en di x fo r fu rt he r d et ai ls) m ar ke tp ric e w he at lo ng -t er m m ea n 5. 46 90 eu ro st at , w or d ba nk sp ee d of re ve rs io n 3. 10 53 eu ro st at , w or d ba nk st an da rd d ev ia tio n 0. 48 08 eu ro st at , w or d ba nk st ar tin g va lu e 5. 40 36 eu ro st at , w or d ba nk 70 alisa spiegel, simone severini, wolfgang britz, attilio coletta m od el pa ra m et er n ot at io n us ed in eq ua tio ns a bo ve 1 va lu e re fe re nc es m ea nre ve rt in g st oc ha st ic p ro ce ss fo r n at ur al lo ga rit hm of m ar ke t p ric e of h az el nu ts (s ee a pp en di x fo r f ur th er de ta ils ) m ar ke tp ric e h az el lo ng -t er m m ea n 7. 67 82 is ta t, w or d ba nk sp ee d of re ve rs io n 0. 92 19 is ta t, w or d ba nk st an da rd d ev ia tio n 0. 19 33 is ta t, w or d ba nk st ar tin g va lu e 8. 06 69 is ta t, w or d ba nk la pl ac e di st rib ut io n fo r q ua lit y in de x of d ur um w he at (s ee a pp en di x fo r f ur th er d et ai ls) qi w he at m ea n 0. 98 17 is ta t st an da rd d ev ia tio n 0. 25 80 is ta t la pl ac e di st rib ut io n fo r q ua lit y in de x of h az el nu ts (s ee ap pe nd ix fo r f ur th er d et ai ls) qi ha ze l m ea n 0. 92 47 is ta t st an da rd d ev ia tio n 0. 23 98 is ta t g am m a di st rib ut io n fo r v ar ia bl e co st s o f d ur um w he at (s ee a pp en di x fo r f ur th er d et ai ls) co st w he at sh ap e 3. 82 86 fa d n sc al e 97 .0 98 fa d n u ni fo rm d ist rib ut io n fo r c os ts o f h ire d la bo ur w ag e m in im um 7. 50 € h -1 ex pe rt b as ed in fo rm at io n m ax im um 12 .5 0 € h-1 ex pe rt b as ed in fo rm at io n a nn ua l i nt er es t r at e fo r sh or tte rm c re di t [ 1 ye ar ] 7% o w n el ab or at io n m id dl ete rm c re di t [ 5 ye ar s] 6% o w n el ab or at io n lo ng -t er m c re di t [ 10 y ea rs ] 5% o w n el ab or at io n m in im um o fffa rm ri sk -f re e w ag e ra te m in w ag e 6 € h-1 ex pe rt b as ed in fo rm at io n 1 in di ce s y, {t ,n } a nd {p ,t, n} a re o m itt ed fo r s im pl ic ity . 71development of a model simulating returns on farm from investments μ the speed of reversion; θ the long-term logarithmic average level of price; σ the standard deviation; and dwt hazel the standard brownian motion independent from dwt wheat. other stochastic variables, namely a quality index of hazelnuts and a quality index, yield and variable costs of durum wheat are captured by distributions that were selected based on akaike information criteria (aic) (akaike 1998) using @risk software. more details on deriving the stochastic processes and distributions based on historical data are presented in the appendix. 4. results and discussion we focus in this section on differences between the models with respect to key results: scale and timing of optimal hazelnuts introduction, expected npv, as well as financing decision (table 3). in particular, according to the classnpv model, hazelnuts cannot compete with the representative alternative arable crop durum wheat. accordingly, the expected npv of classnpv (rows 4-5 in table 3) reflects returns from cultivating durum wheat only and hazelnuts are never introduced. in contrast, a hazelnut plantation might be set-up in later years in the realopt model which considers temporal flexibility. specifically, that model suggests that a land share of about 48% of hazelnuts in the second year or later is optimal. this does not imply that in any future stochastic scenario hazelnuts are cultivated. temporal flexibility means that the farmer can wait, observe how the stochastic environment evolves, and take an investment decision depending on which node of the scenario tree is realized in the future. the 48% is hence an expected share. row 2 in table 3 reports the earliest time point where any hazelnuts are introduced (if at all). while both realopt and realoptfin imply waiting at least for two years before setting up the first time a plantation, realoptfinrisk suggests even longer postponement as the minimal year profit withdrawal is increased from zero in realoptfin to opportunity costs reflecting off-farm wages and renting out land. durum wheat exceeds these opportunity costs in any year and node, but hazelnuts do not. accordingly, the realoptfinrisk model has to postpone investments until hazelnuts are only introduced on such nodes where the minimal income of farming exceeds opportunity costs. for the remainder of the stochastic tree, only durum wheat is cropped. compared to realopt or realoptfin, this implies a lower average discounted household income at however reduced downside risk. the temporal flexibility introduced in realopt allows increasing the expected npv by 9.5% compared with the classnpv model. note that generally the npv can never decrease when additional flexibility is considered if all other assumptions are equal. explicitly considering the costs of financing in realoptfin slightly decreases the competitiveness of hazelnuts and reduces the npv by 4.4% compared with the realopt model. that means that the discount rate used in realopt underestimates the true costs of financing under assumed loan conditions. yet, considering downside risk aversion in the realoptfinrisk model has an even stronger effect: only around 6% of the total land is converted to hazelnut in the third year or later. the expected npv drops by 8.2% compared with the realopt model and by 3.9% compared with the realoptfin model. however, the expected npv under realoptfinrisk still slightly exceeds the one of the classnpv model by 0.6%. fig. 5 compares the riskiness of the resulting npv in the four models described above, plus the forcehazel variant which forces immediate conversion of the whole farm 72 alisa spiegel, simone severini, wolfgang britz, attilio coletta table 3. comparison of empirical results of different models. classnpv realopt realoptfin realoptfinrisk (1) expected area under hazelnuts, % of total farm land endowment 48.07 40.80 6.03 (2) time period when introducing hazelnuts for the first time (in 2 years) (in 2 years) (in 3 years) (3) is earlier reconversion applied? yes yes yes (4) expected npv at farm-level, € 541,740.32 593,267.05 567,052.33 544,800.89 (5) expected npv per hectare, € [calculated as (4) divided over the total farm land endowment] 18,058.01 19,775.57 18,901.74 18,160.03 (6) used harvesting machine(s) large large large (7) total expected amount of new loans over the planning horizon, € short: 110,534.94 middle: 2,602.33 long: 1,720,204.63 short: 140,573.06 middle: 11,792.54 long: 432,047.90 (8) total expected amount of interest paid, € 481,262.14 130,775.94 figure 5. distributions of maximized net present values in the five model variants, incl. forcehazel – an additional model variant that forces immediate conversion of the whole farm into hazelnuts. the forcehazel model assumes no financing constraint, as it has no feasible solution otherwise. both forcehazel and classnpv models ignore the associated risk and treat all the stochastic variables as their expectations, yet we recovered the riskiness of resulting npvs based on the optimal behaviour that the models suggest. 73development of a model simulating returns on farm from investments into hazelnuts. the forcehazel model considers no financing options, as otherwise it has no feasible solution. the forcehazel model is therefore similar to the classnpv model except for having no scale flexibility. the models forcehazel and classnpv hence represent the two corner solutions: the former suggests devoting all resources to hazelnuts, the latter to the alternative crop. both deterministic models forcehazel and classnpv ignore any risk by using expected values, only, for any stochastic variable related to both hazelnuts and the alternative crop durum wheat. we however recovered the associated riskiness in resulting npvs post-model by applying the optimal behaviour in both models to the constructed scenario tree (fig. 5). one can observe that hazelnuts imply much more risk of the resulting npv, while also leading to a slightly lower expected npv (compare forcehazel and classnpv in fig.5). in contrast, the other three models directly report the riskiness of the npv and consider it when searching for the optimal investment and financing behaviour. while realopt and realoptfin are quite similar in terms of the spread of the npv, the model realoptfinrisk clearly outputs a less risky npv due to its lower limit on annual household withdrawals, however as noted already above, at the costs of a lower expected npv (fig. 5). the realopt and realoptfin show some outliers (indicated as dots in the box-and-whisker charts) with quite low npvs that are removed at the realoptfinrisk model, which however also considerably reduces upside risk. figure 6 visualizes the riskiness of the four models in greater details. classnpv implies no hazelnuts and reflects the moderate riskiness of durum wheat cultivation, only. the upper panel shows that quite clearly, as the cloud with the points showing the different outcomes for the farm income is quite dense. in contrast, realopt implies much more risky withdrawals, including considerable positive and negative outliers. moreover, annual withdrawals implied by realopt echo the production cycle of hazelnuts: negative withdrawals in the beginning of the time horizon (establishment of the first plantation) and between time periods 35 and 40 (establishment of the second plantation), combined with high positive withdrawals that are associated with periods of maximum yields of the hazelnut plantation. both models with financing (the lower part of fig. 6) cut off the negative withdrawals by covering them with short-term credits or by not withdrawing all profits in some years, i.e. using a retained profit position. without these internal and external financing options, a lower limit of household withdrawals of zero or above in any year under all potentially considered futures cannot be achieved. this is visible from the upper panel as even under the classnpv where only durum wheat is grown, there are some years where farm profits become negative. these last two models differ mainly in financing behaviour. the realoptfin model only needs to maintain a positive current account of the firm but can reduce household withdrawals in certain years down to zero. as a consequence, it uses almost solely long-term credits (table 3, row (7)) to finance the initial investment costs of plantation set-up and the well, as well as in some later years investment in a harvester. the costs relate to an expected 41% land share under hazelnuts (table 3, row (1)). in contrast, the realoptfinrisk model ensures minimum annual withdrawals above opportunity costs and has to use also shortand especially middle-term credits to balance annual fluctuations in withdrawals (table 3, row (7)). these reflect foremost the production cycle, i.e. plantation ages of no or low hazelnuts yields, but also relate to nodes in scenario tree with lower than average prices and/or quality indices. since only 6% compared to 41 % of 74 alisa spiegel, simone severini, wolfgang britz, attilio coletta total land is in the expected mean devoted to hazelnuts, the required investment costs are considerably lower such that the amount of long-term credits decreases substantially compared with the realoptfin model. the empirical results are in line with the available literature. comparison of the results of classnpv and realopt models indicate that uncertainty and time flexibility leads to later investments at a higher expected scale. trigeorgis and reuer (2017) and musshoff (2012) confirm that managerial flexibility usually increases the value of waiting, hence leading to postponement of investments; the reader should note here that relatively small uncertainty might lead to no value of waiting and hence immediate investments. as for investment scale, hassett and metcalf (1993) confirm that if immediate investment is worthless, uncertainty could create its value in the future. however, the effect of uncertainty might be the opposite if immediate exercising of investment is profitable in a riskfree environment. in this case, considering temporal flexibility might lead to lower expected investment scale depending on how the stochastic environment evolves. the resulting effect would depend on the underlying uncertainty, as well as on relationship between stochastic variables and the optimal investment scale. in this regard, our empirical results stating that uncertainty leads to larger expected area under hazelnuts shall be treated as a special case. trigeorgis and reuer (2017) also report that managerial flexibility reduces downside riskiness of investment, which is confirmed by our results, in particular the figure 6. distributions of annual withdrawals across the planning horizon in the four models. 75development of a model simulating returns on farm from investments upper part of fig. 6. comparison of the results of realopt and realoptfin models suggest that explicit consideration of financing behaviour reduces investment scales, yet does not affect the earliest time of investments. the results can indirectly be confirmed by chen (2003) and lin (2009), both claiming that a higher debt ratio leads to a higher investment threshold. however, we explicitly highlight here that the literature focusing on financing of investment under uncertainty is extremely limited. finally, comparing the results of realoptfin and realoptfinrisk, one can conclude that consideration of downside risk aversion leads to later investment at a lower scale, as well as lower resulting riskiness. previous studies confirm that risk aversion, and downside risk aversion in particular, reduces incentives to invest (e.g., chronopoulos et al., 2011). 5. discussion and conclusion our case study results highlight that the assumptions underlying the different model variants can considerably affect key results. the comparison confirms that more advanced models are more informative: they provide additional insights and can provide more detailed advice to decision takers, such as on how to best finance an investment and how to buffer income fluctuations from production and market risks. the step-by-step development of the advanced farm-level models allows to identify the relative importance of the additional elements considered and to illustrate their value added. for instance, the simple npv calculation suggests not planting hazelnut at all while all the other more complex models suggest doing so, however at varying time periods and scales. constraining the downside risk of income from the farm operation in the most advanced models not only highlights the trade-off between mean income and reduced downside risk, but also shows the resulting consequences on the scale and timing of investments, as well as on financing behaviour. clearly, there is a trade-off between additional insights and potentially more realistic results on the one hand, and increased data demands (table 2) and model complexity on the other one. additionally, higher data requirements imply typically also higher uncertainty. for instance, the more advanced model with explicit financing costs does not simply require one average interest rate, but interest rates for different finance instruments which depend on a number of factors, such as credit amount or farmer’s credit scores. the results – both additional ones and the ones also found in simpler models – are sensitive to what is assumed here in detail on top of the parameter found also in simpler models. compared to sensitivity to one average discount rate only, the more advanced model distinguishes between different components of discount rate, i.e., time preferences, risk preferences, costs of financing, etc., which all can be subject to sensitivity analysis to inform on their importance individually. furthermore, such sensitivity analysis could also help to find a set of parameters which best fits the observed behaviour (e.g. troost and berger, 2014). in our case, expected hazelnuts yields and market prices as well as their riskiness would be obvious first candidates for such an analysis. as a word of caution, we remind the reader that using more advanced methods such as real options does not necessary imply a better fit to observed behaviour. indeed, especially the full rationality assumption inherent in optimization approaches might be questioned. a potential promising avenue here is to complement the optimization model with other 76 alisa spiegel, simone severini, wolfgang britz, attilio coletta methodologies (colen et al., 2016), for instance, to expose farmers facing investment decisions to results of such models in order to learn more, e.g., on how they frame the decision problem including which results matter to them most, or to contrast subjective perceptions of market developments and related risk with findings from statistical analysis. the detailed what, how and when view of dynamic programming approaches might ease that kind of dialogue as it might be similar to the one used by the farmer itself. alternatively, results obtained with other methodologies, e.g., econometric or experimental techniques for objective or constraint functions, can serve as input for optimization model and allow introducing behavioural aspects, for instance in form of a risk utility function (chronopoulos et al., 2014). finally, further research might put greater focus on how learning affects future expectations, for instance, how experiences of rare but catastrophic events shape expectations, and how this can be reflected, for instance, in a scenario tree. overall, our paper underlines that the conceptual and technical elements are readily available to build farm-scale models based on dynamic stochastic optimization. this allows to determine scale and timing of long-term investments under production and market risk and endowment constraints, drawing on real options. we also 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(2019). economic performance and risk of farming systems specialized in perennial crops: an analysis of italian hazelnut production. agricultural systems, 176, 102645 appendix. capturing stochastic variables with stochastic processes and distributions based on historical data market price of hazelnuts and durum wheat in order to estimate the stochastic processes for market prices of unshelled hazelnuts and durum wheat, the market prices in italy provided by istat (for hazelnuts) and eurostat (for durum wheat) were used (fig.a1). we omit the observations from the years 2008 and 2014-2016 for hazelnuts, as they do not fit the general trend and hence should be excluded when estimating stochastic processes. we ran the following stationarity tests: augmented dickey-fuller (adf) test; phillips–perron (pp) unit root test; and kwiatkowski–phillips–schmidt–shin (kpss) test. for both data samples, non-stationarity hypothesis cannot be rejected based on the adf and pp tests, while the kpss test concludes that stationarity hypothesis cannot be rejected. in light of the conflicting results of these tests, we decide on the appropriate method based on economic reasoning and therefore apply an mrp estimation. this assumes stationarity 80 alisa spiegel, simone severini, wolfgang britz, attilio coletta reflecting that the market price likely fluctuates around a constant long-term per unit production cost. the result of the mrp estimations are summarized in the table a1. furthermore, as above, we correct every price draw by a multiplicative coefficient of 1.18 in order to account for expected increase in hazelnut price due to increasing demand. this price level also leads to introduction of hazelnut in some but not all model variants and also to highlight differences. quality index for hazelnut and durum wheat the fadn data were used to derive annual per unit farm specific prices of hazelnuts and durum wheat by dividing crop revenues by sold quantities. these calculated farm-gate figure a1. real durum wheat (dw) and hazelnut (h) prices, € 100kg-1. source: istat and eurostat; the prices are deflated (2015=100) using the gdp deflator in italy provided by the world bank. 0,00 5,00 10,00 15,00 20,00 25,00 30,00 35,00 40,00 0,00 50,00 100,00 150,00 200,00 250,00 300,00 350,00 400,00 450,00 500,00 2000 2002 2004 2006 2008 2010 2012 2014 2016 h dw table a1. estimated parameters of mean-reverting processes for hazelnut and durum wheat prices. source: own estimation based on the istat (for hazelnuts, years 2000-2013) and eurostat (for durum wheat, years 2000-2016, excl. 2008) data. the prices were deflated (2015=100) using the gdp deflator for italy provided by the world bank. natural logarithm of hazelnut price natural logarithm of durum wheat price long-term mean 7.6782 5.4690 speed of reversion 0.9219 3.1053 standard deviation 0.1933 0.4808 starting value 8.0669 5.4036 81development of a model simulating returns on farm from investments figure a2. distribution fitting for the quality index of hazelnut. source: own elaboration based on fadn and istat data. 5.0% 90.0% 5.0% 4.4% 91.9% 3.8% 0.511 1.363 0. 0 0. 2 0. 4 0. 6 0. 8 1. 0 1. 2 1. 4 1. 6 1. 8 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 fit comparison for qi 15 risklaplace(0.92467,0.23981) input minimum 0.1766 maximum 1.6130 mean 0.9236 std dev 0.2497 values 56 laplace minimum −∞ maximum +∞ mean 0.9247 std dev 0.2398 @risk course version wageningen university figure a3. distribution fitting for the quality index of durum wheat. source: own elaboration based on fadn and eurostat data. 5.0% 90.0% 5.0% 6.3% 88.9% 4.9% 0.602 1.406 0. 0 0. 5 1. 0 1. 5 2. 0 2. 5 3. 0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 fit comparison for qi 4 risklaplace(0.98170,0.25802) input minimum 0.0555 maximum 2.5309 mean 0.9795 std dev 0.2660 values 653 laplace minimum −∞ maximum +∞ mean 0.9817 std dev 0.2580 @risk course version wageningen university 82 alisa spiegel, simone severini, wolfgang britz, attilio coletta prices were normalized by the market prices in italy provided by istat for both hazelnuts and durum wheat to define samples of farm specific quality indices. these observations for quality indices were fitted to a laplace distribution with a mean of 0.9247 and standard deviation of 0.2398 (fig.a2) for hazelnut, and mean of 0.9817 and standard deviation of 0.2580 (fig.a3) for durum wheat. yields and variable costs for durum wheat for durum wheat, yields derived from the fadn sample based on total production and area were fitted to a laplace distribution with a mean of 3.9120 and standard deviation of 1.1984 (fig.a4). the observations for durum wheat costs were fitted to a gamma distribution with a shape parameter of 3.8286 and a scale parameter of 97.098 (fig.a5). figure a4. distribution fitting for the yields of durum wheat. source: own elaboration based on fadn data. 5.0% 90.0% 5.0% 5.1% 90.8% 4.1% 1.98 6.04 0 1 2 3 4 5 6 7 8 9 10 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 fit comparison for yields_dw_t_per_ha risklaplace(3.9120,1.1984) input minimum 0.4427 maximum 9.8519 mean 3.9127 std dev 1.2456 values 653 laplace minimum −∞ maximum +∞ mean 3.9120 std dev 1.1984 @risk course version wageningen university 83development of a model simulating returns on farm from investments figure a5. distribution fitting for the variable costs of durum wheat. source: own elaboration based on fadn data. 5.0% 90.0% 5.0% 4.0% 88.8% 7.2% 114 676 -2 00 0 20 0 40 0 60 0 80 0 1, 00 0 1, 20 0 1, 40 0 0.0000 0.0005 0.0010 0.0015 0.0020 0.0025 0.0030 fit comparison for dataset 3 riskgamma(3.8286,97.098) input minimum 13.30 maximum 1,363.66 mean 371.75 std dev 190.45 values 647 gamma minimum 0.00 maximum +∞ mean 371.75 std dev 189.99 @risk course version wageningen university the role of trust and perceived barriers on farmer’s intention to adopt risk management tools elisa giampietri1, xiaohua yu2, samuele trestini3,* drivers and barriers of process innovation in the eu manufacturing food processing industry: exploring the role of energy policies federica demaria, annalisa zezza step-by-step development of a model simulating returns on farm from investments: the example of hazelnut plantation in italy alisa spiegel1,*, simone severini2, wolfgang britz3, attilio coletta2 the role of group-time treatment effect heterogeneity in long standing european agricultural policies. an application to the european geographical indication policy leonardo cei1, gianluca stefani2, edi defrancesco1 the impact of food price shocks on poverty and vulnerability of urban households in iran ghasem layani1, mohammad bakhshoodeh1, mona aghabeygi2*, yaprak kurstal3, davide viaggi3 bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 bio -based and a ppl ied economics bae copyright: © 2021 c. vaquero-piñeiro. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: c. vaquero-piñeiro (2021). the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications. biobased and applied economics 10(2): 89-108. doi: 10.36253/bae-9429 received: july, 15, 2020 accepted: january 5, 2021 published: october 28, 2021 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: meri raggi, davide menozzi. orcid cvp: 0000-0002-1378-3361 the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications cristina vaquero-piñeiro department of economics, università degli studi roma tre, italy e-mail: cristina.vaqueropineiro@uniroma3.it abstract. once the eu has perceived the strategic importance of local peculiarities to support rural development and high-quality productions, it has emphasized the need for more place-sensitive agri-food policies. the importance of socio-economic, historical and cultural factors as transfers of intangible value-added is particularly evident in the agri-food sector. place-blind and sectorial-oriented approaches have indeed not succeeded in dealing with the territorial heterogeneity of agri-food systems. by delving into the longstanding debate on the conceptualizations of territory and focusing on the territories of origin of the most economically performant italian protected designation of origins (pdos), this paper empirically investigates what are the contextual conditions that have mainly contributed in the economic success of local productions. drawing on an original geo-referenced database, the analysis is conducted on a panel of italian municipalities and exploits non-linear dynamic panel models. findings point out the heterogeneity of affecting territorial factors. imbalances come from both socio-economic conditions (food pdos) and socio-cultural knowledge (wine pdos). this paper informs the evidence-based debate on the relevance of territorially-sensitive interventions for the future of eu agri-food and rural development policies. in the case of gis, it should consider being more place-sensitive as well as more integrated with other agricultural and regional policies to meet the eu’s socio-economic objectives. keywords: local development, geographical indications, rural development policy, agri-food policy, italy, panel data. jel codes: o130, p250, q180, c230, o200 introduction in the conventional framework of economic competitiveness, the importance of territorial factors for socio-economic development and policy effectiveness has been subject to competing claims in academic debates. two main different approaches can be identified (crescenzi and rodriguez-pose, 2011): a territorially-blind and a territorially-sensitive standpoint. the former approach considers economic activities, at least in principle, reproducible everywhere as devoid of any territorial dimension and the maximization of http://creativecommons.org/licenses/by/4.0/legalcode 90 cristina vaquero-piñeiro bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 factor endowments as a fundamental condition for economic growth. the latter advocates for the active role of territories for economic activities (pike et al., 2017): contextual specificities help in understanding local production systems, economic growth and development performances and opportunities (markus et al., 2018; farole et al., 2011; scott and storper, 2003). the importance of socio-economic, historical and cultural factors as transfers of intangible value-added is particularly evident in the agri-food sector, where productions are deeply rooted in their place-of-origin. to preserve high-quality and traditional products, as well as to support rural development, in 1992 the eu established the quality scheme for geographical indications (gis) (eec no 1992/2081). 1 gis are often framed as levers of economic value-added. however, the economic returns differ radically among gis. most of the economic power, in terms of revenues, competitiveness, internationalization and so on, tends to remain spatial and sectorial concentrated. the gis market is led by products that were well-know also before they got the designation (qualivita, 2019). as a result, while gis may stimulate the local economy, they may also cause market inefficiencies and rent-seeking. among territories, impacts on local development depend on the extent to which local actors succeed in appropriating the rent with respect to actors located outside the region of origin. within the region of origin, the positive effects of gis on local development are instead dependent on an inclusive organization that ensures the participation of local actors and equitable distribution of such rent. the main risk is potentially exclusionary effects: the largest agribusiness capture gis rents without any benefits flowing to smaller (bramley et al., 2009). this paper investigates what are the contextual conditions that have mainly contributed in the economic success of local agri-food productions by delving into the longstanding debate on the conceptualizations of territory and focusing on the territories of origin of the most economically performant (in terms of production value) italian protected designation of origins (pdos). we start from the hypothesis that the economic benefits of adopting the gis scheme are biased by contextual conditions. in this way, more developed and productive areas should be likely to persist as leaders in the monopolistic competition. 1 legal documents available at: https://ec.europa.eu/info/food-farming-fisheries/food-safety-and-quality/certification/quality-labels/quality-schemes-explained/regulations-food-and-agricultural-products_en; https://ec.europa.eu/info/food-farming-fisheries/food-safety-and-quality/certification/quality-labels/quality-schemes-explained/regulations-wine_en we develop a novel geo-referenced dataset, use nonlinear spatial dynamic panel models, and the analysis is conducted for food and wine pdos separately. findings show that food-pdos localized in lessdeveloped regions struggle to achieve the highest gis market shares. local instability, defined as socio-economic vulnerability in municipalities and their neighbouring areas, has a negative effect on the success of pdo local market. however, the economic returns of pdo wines seem not to be affected by socio-economic development pre-conditions, but presumably by social and historical factors, such as cultural heritage. this discrepancy suggests that to avoid counterfactual effects the territorial dimension of gis should not be overlooked. although we cannot exclude that small producers and less known products have benefited from this scheme, evidence suggests that it might not succeed in dealing with growing market competition (ec, 2020). better policy results could have been achieved, if the gis european legal framework taped into both territorial and sectorial heterogeneity of agri-food systems. the results contribute to better understanding why some territories fail while others thrive in converting local food systems in levers of local economy. economic literature has highlighted as place-blind approaches are ill-adapted to address the heterogeneity of agri-food production systems and regional inequalities and advocated in favour of more place and community-sensitive interventions (e.g., de schutter et al., 2020; oecd, 2016). in the case of the common agricultural policy (cap), changes in the socio-economic context have shed light on the inadequacy of place-neutral sectorial quantity-oriented interventions to deal with the structural weaknesses of agricultural and rural areas. with the rural development policy introduced by the agenda2000 reform, context-specific interventions became crucial to promote rural endogenous development (henke et al., 2018; dax and fisher, 2018; corsinovi and gaeta, 2019). not surprisingly, the public buzz for territorial brands, like gis, increases in parallel with the shift in the paradigms of eu policies towards a more place-based and bottom-up approach (iammarino et al., 2019).2 while there is scepticism about promoting innovation and productivity-oriented place-based strategies at the local level (rodriguez-pose and wilkie, 2019), recently agricultural economists have recalled the importance of territorial factors as transfers of intangible value-added 2 spatially-targeted and bottom-up are not synonymous. the term spatially-targeted refers to the fact that policies are targeted at specific regions/cities/areas. conversely, the term refers to the fact that the design of interventions is based on the involvement of local actors and the identification of their needs. 91the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 in the light of the european efforts towards more inclusive and sustainable agri-food policies (e.g., farm to fork strategy) (ec, 2019). there is a growing concern about the effectiveness of investing in a series of coordinated and wide-ranging interventions targeting local productions to meet those goals. notwithstanding this progress, interventions have mainly remained locked into territorialblind approaches without synergies. this paper proceeds with the introduction of the analytical and theoretical framework underpinning this study (section 1 and 2). the empirical analysis is discussed in section 3, while section 4 presents the results and the composition analysis. lastly, concluding remarks are provided. 1. understanding territory: regional and agricultural economics perspectives to what extents territory matters for socio-economic activities has long been disputed among economists, who have examined the different factors in search of answers (friedman, 2005). the territory concept has been neglected by neoclassical (solow, 1957) and endogenous growth theories (e.g., romer, 1986 and 1994) as well as by the new economic geography literature (ottaviano and puga, 1998; krugman, 1991). understanding how territorial features mediate policy effects and what are the main affecting factors is, however, essential to building coherent and efficient policies (capello, 2009). since the early 2000s, the socially constructed nature of regions has been highlighted. territorial factors have been considered as endogenous resources to blame for socio-economic development (pike et al., 2017). the concept of space has been progressively replaced by the multidimensional (i.e. diversified-relational) notion of territory. space and territory are, in fact, not interchangeable terms; territory is not a fixed entity; its evolves and changes in time and space. territory can be assumed as the combination of coexisting exogenous and endogenous context-specific factors (paasi, 2010; oecd, 2009; camagni, 2009). besides conventional spatial elements like administrative units and geographical boundaries, territory compasses of several human and environmental dimensions influencing each other, e.g., altitude, natural habitats, citizenship, networks, capabilities, ethnicity, and culture (storper, 2013; paasi, 2011). nowadays, the predominant declination of territory refers to a territorial identity: the feelings of belonging to a group not only rooted in common socio-cultural and political values but also in the socio-economic advantages that a system of common competencies and local relationships generate (zimmerbauer, 2011; savage et al., 2005). in this perspective, development depends on endogenous factors and amenities; local factors are recognized as drivers of the local long-term development and territorial competitiveness. path-dependence frictions can arise in socio-economic systems leaving behind less-development regions. a consensus on what are the structural, physical and socio-cultural characteristics of territories that have a significant impact on local development and firms’ choices is, however, still lacking (espon, 2017; barca et al., 2012). over the years, literature has pointed out the key role of education (harrison and turok, 2017), institutions (rodriguez-pose, 2020), the quality of governments (ezcurra and rios, 2019; rodriguez-pose and garcilazo, 2015; charron et al., 2014) as well as foreign investments (crescenzi et al., 2016). spatial contiguity and accessibility (world bank, 2009; boschma, 2005), innovation (crescenzi and jaax, 2017; rodriguez-pose, 1999), and historical traditions (cortinovis et al., 2017; scott, 2004) have also been considered key issues in defining territories. albeit with lower emphasis, the importance of territorial peculiarities has been stated also by agricultural economists. from the supply-chains perspective (carbone, 2017), territory acquires a distributional-positional meaning, and the geography of agri-food productions is set up in response to market challenges and obstacles, such as land availability, expiry dates and market access. in marketing and food-label studies, territorial features are strategical assets with the evocative power of creating a perception of exclusiveness and uniqueness (pike, 2011). territory works as a catalysator of valueadded inferred from reputation and diversification strategies (newton et al. 2015; san eugenio-vela and barniolcarcasona, 2015; shapiro, 1993). a conceptualization of territory linked to social collectively is emphasised by the last group of economists concerned  with how territories instil their peculiarities to agri-food productions (e.g, rivera et al., 2019; cross et al., 2011). in their perspective, territorial peculiarities become conditioning factors for the agri-food systems (sforzi and mancini, 2012). they represent “an inherent quality system located in a place” (muchnik, 2009, p. 9) that evokes a special link between the unique quality of agri-food productions and the inimitable peculiarities endowed with local history and culture, tacit knowledge, institutional and social connections, like in the french notion of terroir (cross et al., 2011; josling, 2006).3 these 3 we can define terroir as a territory endowed with a strong identity characterized by a set of physical-environmental (e.g. soil and climate), social and cultural constructed local resources. 92 cristina vaquero-piñeiro bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 territories are anchored into their socio-economic structures. among regions specialised in agri-food productions, they are a minority. territories specialised in agri-food productions can be classified in areas devoted to standardized and local productions. while standardized systems are reproducible everywhere as unrelated to the contextual features, local systems are linked with their place of origin. among local agri-food systems characterised by alternative localized distribution schemes (e.g., km0 farmers’ markets), drawing a direct link between producers and consumers (pretty et al., 2005) must be distinguished from local embedded ones (bowen and mutersbaugh, 2014). the latter refers to local production systems entirely connected, and affected, by socio-economic, historical, institutional, natural, and cultural environments. gis belong to this group. dealing with the heterogeneous, unmeasurable and sometimes unobservable dimensions of territory is a very demanding task. to date, a wide set of complementary rather than substitute, methodologies has been used. qualitative approaches, such as surveys, focus groups, ethnography experiences or thematic analysis, are the most exploited (lourenco-gomes et al., 2015; dedeurwaerdere et al., 2015; tregear et al., 2007). for instance, haeck et al. (2019) has recently conducted a qualitative analysis to reconstruct the evolution of four of the most famous european wine terroirs, namely port, chianti, champagne and burgundy from historical documents. econometric and quantitative investigations are scanter, also due to methodological-statistical complexity (kelly, 2020). recently, oecd (2019) has formalised quasi-experimental (i.e. counterfactual) analyses as efficient methodologies to evaluate how effects of agricultural and rural policies interventions may vary across space, confirming the validity of what a great number of empirical studies have done (e.g., dinkelman, 2011). these techniques capture territorial elements by estimating the difference between treated and non-treated observations given that only one group of observations is treated (bondonio and greenbauman, 2018; daunfeldt et al., 2017). in utility and agent-based models, it is conceived as an element beyond the actors’ making processes (kremmydas et al., 2018; altomonte et al., 2016; brady et al., 2012). spatial analyses are the most used as they are able to consider where the phenomenon takes place and capture the presence of spatial spillover effects and the potential geographical endogeneity (e.g., wicht et al., 2019; crescenzi and giua, 2016; henderson et al., 2012). this paper leverages on the latter approach and uses the italian pdos in order to identify which are the territorial features that mainly support the economic performant of local agri-food systems. 2. geographical indications: a conflicting territorially-based approach over the last decades in europe, agri-food products deeply-rooted in their place-of-origin are marked by geographical indications. this sign identifies the product as legally tied to a specific production area where micro-climatic conditions, informal traditions, entrepreneurial practices and channels of collaboration were consolidated over time. gis comprise of protected designation of origin (pdo) and protected geographical indications (pgi).4 the differences between pdo and pgi are linked primarily to how much of the product’s raw materials must come from the area or how much of the production process has to take place within the specific region. in the case of pdos, every part of the production, processing and preparation process must take place in the specific region. for wines, grapes have to come exclusively from the geographical area where the wine is made. pgis requires that at least one of the stages of production, processing or preparation takes place in the region. at least 85% of the grapes used have to come exclusively from the geographical area where the wine is actually made. gis offer worldwide recognition and protection through the specific property right scheme, which identifies and endorses local forms of production on a global scale (reg. eu no.2012/1151; reg. eu no.2013/1308). at the world level, more than 200 bilateral and multilateral wipo and wto agreements exist defining gis regulations.5 however, gi regime goes father becoming the institutional formalization of localized agri-food systems (liu et al., 2016; menapace et al., 2012). indeed, for gis, tacit knowledge, informal institutions, historical traditions and cultural habits are important as much as environmental factors, or even more (e.g., van leeuwen and 4 the european quality scheme for agri-food products preserves also traditional speciality guaranteed (tsg). tsg highlights the way the product is made or its composition, without being related to a specific geographical area. the name of a product being registered as a tsg protects it against falsification and misuse. 5 the wto trips agreements (1994), the wipo madrid protocol, the wipo lisbon agreement on appellations of origin and their international registration (1958), the wipo geneva act of the lisbon agreement on appellations of origin and geographical indications (2015). in addition, the enforcement of gis is carried out thanks to bilateral agreements between eu and trading partners, such as south-korea, japan and ceta. more information available at https://www.wipo.int/ geo_indications/en/. 93the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 seguin, 2006).6 the quality expressed by the gis is a fundamental territorial asset (ditter and brouard, 2014), expression of the cross-fertilization of specific contextual conditions. the literature on gis is quite vast, due to the great eu efforts on supporting this scheme. a bourgeoning group of studies has attempted to evaluate the ex-post impacts of gis on premium pricing and economic value (costanigro et al., 2019; cacchiarelli et al., 2014), market access (prescot et al., 2020; altomonte et al., 2016), exports (agostino and trivieri, 2014), value distribution (belletti and marescotti, 2011) or local development (fao, 2018). the chain of causality might be, however, ambiguous as gis and socio-economic developed conditions strengthen each other. a second group of contributions have investigated what factors encourage producers to obtain institutional acknowledgement. favourable institutional context, local actors’ engagement and co-operation have been highlighted among others (meloni and swinnen, 2018; charters and spielmann, 2014). despite the common regulatory framework, gis located in regions with similar environmental and natural elements, differ in the capacity of creating economic value due to other territorial conditions (e.g., socio-economic and cultural) (haeck et al., 2019). gis are increasingly valued for their endogenous development potential (gangjee, 2017). it aims to support long-run development by strengthening the endogenous local assets (marsden, 2003). the establishment of a gi system can stimulate rural development, but previous structural bottlenecks of the region of origins are likely to impact on the whole local economy, and, in turn, gis can also be negatively affected. although there is not enough empirical evidence of this link to date, the uneven spatial distribution of gis across countries and regions may be a first wakeup call. if we look at the most important (in terms of revenues) pdos in italy, they are spatially concentrated in the north-central italy (fig. 1),7 which are the most developed ones (fig. a1, a2 and a3).8 according to the 2019 qualivita report, emilia romagna is the first region in terms of the territorial economic impact 6 according to the european regulation, the decision of designating an agri-food product as gis is based on three main points: (1) the specific nature of local resources used in the production process, (2) the application of traditional production techniques and (3) the presence of local identity. 7 parmigiano reggiano pdo, grana padano pdo, prosciutto di parma pdo, prosecco pdo, mozzarella di bufala campana pdo, gorgonzola pdo, prosciutto di san daniele pdo, conegliano valdobbiadene – prosecco pdo, pecorino romano pdo and asti pdo (qualivita, 2018). 8 a vibrant literature employs population, employment and income data as a measure of economic development. of gis food, around 3 million euros. in the same way, pdo wines predominate in the north, while the south has the large majority of generic wines. the northern regions account for the largest share of vineyard area for pdo wines and the highest number of farms producing pdo vines (istat, 2010). the hypothesis that less developed regions struggle to convert gis in levers of development, cannot be thus excluded a-priori. it is not just about identifying traditional products; the success of gis lies also on the socio-economic and institutional context. several studies have confirmed the relevance of institutional context (giovannucci et al., 2009), cooperation along the supply-chain and local actors engagement (bowen, 2010) as well as the fact that lagging areas are beset by problems of institutional sclerosis (farole et al., 2014). even if the success of these gis is likely to be determined by the territorial-specific factors of the region of origin, the european regulation on gis seems to not concerned explicitly the interaction between a single unitary eu framework and the heterogeneity diversified territorial conditions of the regions of origin. moreover, there is a lack of a sort of policy package within existing eu policies (i.e. cap and cohesion policy) supporting quality schemes and the synergies with other agricultural and regional policy mix used by the eu is weak. figure 1. most important pdos in italy (production value) (source: author’s elaboration on data collected from pdo codes of practice). 94 cristina vaquero-piñeiro bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 this scenario may pave the way to path-dependence frictions in the economic returns of local agri-food of less-developed regions and less-renewed products. understanding which, and to what extent, territorial conditions have been more relevant is challenging, but there is a need to investigate it. otherwise, practical caveats on how to operationalise these tools to the benefit of agri-food systems cannot be drawn. the next sections provide robust evidence in this direction. 3. methodology and empirical application the aim of the analysis is to assess the importance of territorial features by using the most economically performant (in terms of revenues) pdos in italy as a case study. we use the official national ranking provided by the 2018 annual report of ismea-qualivita. they account for just over a third of the italian gis market production value by themselves (36 per cent).9 among them, parmigiano reggiano pdo shows the highest value (€1,343 m), followed by grana padano pdo (€1,293 m) and prosciutto di parma pdo (€850 m). the leader of pdo wines is the prosecco-system: prosecco pdo (€631 m bulk) and conegliano valdobbiadene prosecco pdo (€184 m bulk). the analysis is conducted at the municipality level on a panel of 7,755 italian municipalities observed from 1991 to 2011.10 since the 1992 european regulation, the number of municipalities included in pdo areas increases over time. in 2011, 60 per cent of sample municipalities are included within the production area of one of the pdos under analysis. in the case of wine, first gis was assigned in 1962, and therefore already existed in 1991: in 1991, 2.5 per cent of sample municipalities were producing the most performant pdo wines, and they reached 11 per cent in 2011. pdos came to be recognized during the sample period justifing the use of a panel. municipalities are the most appropriate observation to conduct the analysis due to the fact that the gis 9 pdo-food: parmigiano reggiano pdo, grana padano pdo, prosciutto di parma pdo, mozzarella di bufala campana pdo, gorgonzola pdo, prosciutto di san daniele pdo, pecorino romano pdo, asiago pdo, mela della val di non pdo. pdo-wine: prosecco pdo, conegliano-valdobbiadene prosecco pdo, asti pdo, amarone della valpolicella pdo, alto adige pdo, chianti classico pdo, barolo pdo, valpolicella ripasso pdo, chianti pdo. 10 we restrict our sample to those municipalities whose administrative borders have been never changed since 1951. although several high-performing pdo-wines existed already long before 1991, the analysis starts in 1991 due to the fact that the first pdo-foods were registered in 1996 by the eu. the analysis stops in 2011 due to census data availability. regulation (especially for the wine sector) is established at that level. we know that the designated areas are not always defined on administrative boundaries and that for some pdos the spatial scale can exceed the municipality level. however if we had conducted the analysis at a more aggregate level, for the majority of pdos the result would have co-mingled pdo and non-pdo municipalities, resulting in a lower level of precision and constant contextual factors (ashley and maxwell, 2001). considering the exact production areas would improve the explanatory power of the analysis, but contextual indicators do not exist. conversely, if we consider more aggregated administrative units (i.e. provinces or regions), we will include areas where the product is not produced and contextual factors will become constant for all products. as a result, the municipality level is the most appropriate one for this study. we rely on an original geo-referenced database arranged by digitalizing all the gis product specifications and collecting data from national censuses and remote sensing sources. existing literature, indeed, have extensively applied panel data models and spatial econometrics to control for omitted variable bias, measurements errors and endogeneity issues (hsiao, 2007). the validity of adopting a spatial specification has been properly tested (elhorst, 2014). the moran’s test has been performed to check for spatial autocorrelation, which has been also ruled out by the spatially lagged variable. we adopt a binary choice model to estimate the probability that a municipality is included in the production area of pdos under analysis.11 we exploit the following spatial dynamic logit-panel models with fixed effects, according to hausman’s test:12 yi,t= α + β1localagriculturei,t + β2localcontext i.,t + β3localeconomy i,t + β4m (localeconomy, s)i,t + (1) + β5m(z,s)i,t + β6regagi,t + δi + δt + εit 11 although territories “do not take decisions” and using agents’ micro-data are more adequate for choice models, they can be used also to estimate the probability of a certain class or event existing, regardless of the fact that the outcome depends on agents’ choices. in this perspective, we consider the probability that a municipality is included in the production area, which can be at least partially assumed due to the choices of agents working in this context. 12 the choice of a fixed-effects approach is justified on both conceptual and empirical grounds. from the conceptual point of view, the municipalities included in the dataset cannot be considered as a ‘random sample’ of the italian municipalities. moreover, fixed-effects make it possible to control for all the geographical variables fixed over time (e.g., altitude, remoteness and soil texture) and partially for unobserved time-invariant factors. since regional characteristics accounted for the unobserved specific components are likely to be correlated with other geographical aspects, fixed effects are preferable (rodriguez-pose and fratesi, 2004). from the empirical standpoint, we the hausman’s test confirms that fixed-effects estimation has to be preferred over random effects. 95the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 where yi,t is a binary variable taking value 1 if the municipality i is within the pdo area at the census year t (0 otherwise). we estimate the model twice: (i) yi,t refers to the production areas of the most relevant food pdos and (ii) yi,t refers to the production areas of the most relevant pdo wines. the wine sector is very different in international reach, history and organization, and thereby needs to be investigate separately. being a pdo area is regressed on independent variables referring to the agricultural sector (localagriculture) and the socio-economic context of municipalities (localcontext). the economic prosperity and the wellbeing conditions are captured by an economic and social vulnerability index provided by the italian statistic institute (istat) (localeconomy). this index summarises the socio-economic condition of each municipality related to some principal components, such as education, income, employment and housing. the spatial lagged value of this index is also included, m(localeconomy), as well as other spatially lagged territorial characteristics m(z,s).13 in all specifications, we control for the regional output of the agricultural sector (regag), municipality (δi) and time (δt) fixed-effects. εi,t is the idiosyncratic error. variables are described in details in table a1 of the appendix, while table a2 shows their descriptive statistics. potential concerns can regard the outcome selection, as the choice of pdos could seem to be almost tautological. first of all, as sometimes pgis outweigh the production value of pdos. however, pdos are the only ones that allow us to properly capture the product-territory nexus given the rules of gis assignment. they have the strongest links to the place where they are produced as every part of the production, processing and preparation processes must take place in the same region. conversely, in the case of pgis only one of the stages of production, processing or preparation has to take place in the area. in this sense, we have however to highlight the fact that the products specifications of some of these pdos are “unconventional”, as they allow raw materials to come from areas not included in the designed production area. prosciutto di parma is one of them.14 although the non-coincidence could have some endogeneity implications for the study, we minimise it by considering only the municipalities where the produc13 spatial lags have been measured through the nearest neighbour approach. 14 according to the product specification, the raw materials originate from a larger geographical area than the production area [province of parma] that corresponds to the following regions: emilia-romagna, veneto, lombardy, piedmont, molise, umbria, tuscany, marche, abruzzi, lazio. this exception has been justified from the producers’ perspective to ensure consistent and adequate supplies of raw materials. tion process takes place (areas from where raw materials can come from have been excluded). in this way, we are more confident that the model estimates the effect on the delimited areas whose traditional production techniques have been recognized and codified. the geographical, historical and cultural origin added-value regard, in fact, the production areas, and not the other regions outside of this space-bounded context. secondly, because of the threshold in the number of pdos. if we had considered all the pdos, in fact, the majority of italian municipalities would have become treated, and there would have not been enough spatial heterogeneity for the analysis. lastly, we consider the status of being a pdo area without differentiation (e.g., an ascending ranking classification) as it allows us to compare the status – being a pdo area – regardless of the structural differences between productions. reverse causality may affect the estimates yet. the main concern regards the possibility that some explanatory variables might be affected by the achievement of pdos certification. in this direction, the use of long term variables, which are territorial factors that cannot be influenced in the short-run by the achievement of pdos, such as population density or education level, reduce the probability of this reverse causality. the fact that pdos follow a common european acknowledgement and scheme rules potential endogeneity bias out. endogeneity is also minimized by controlling for longterm territorial characteristics. 4. results regression analysis provides an in-depth insight into the relevance and the nature of territorial features. agri-food sector characteristics are entered as the first block of explanatory variables (column 1), followed by demographic and contextual predictors (column 2), employment controls (column 3) and economic vulnerability index (column 4). conscious that estimations do not represent causal mechanism, in the interpretation, we focus on the comprehensive significance of both signs and coefficients. 15 estimations in table 1 suggest that the italian food pdos with highest revenues come from municipali15 as a robustness check, we investigate what will happen if we consider the presence of one of these pdos as a driver of local development, rather than the result. in practice, we use the dummy accounting for the presence of pdos no longer as the outcome variable, but as an explanatory one (i.e. 1 for those municipalities included in pdo areas, 0 otherwise). the outcome variable refers to local development in terms of population growth and employment rate. the test is conducted over the same 1991-2011. results are available upon request. 96 cristina vaquero-piñeiro bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 ties with better socio-economic conditions, a diversified economy and a competitive agri-food sector. this is particularly relevant given that a handful of large-scale actors still access and monopolise these markets and some scepticism persists about the viability and rigidity of this regime (meloni and swinnen, 2013; eu, 2010). this is the case of some italian central and northern regions, such as emilia romagna region, where the geographical concentration of farming activities and local know-how have promoted the shift towards an outstanding agri-food sector (inea, 2012). economically performant pdos are positively correlated with the share of commercial farms and the productivity rate of agricultural areas, but negatively with the absolute amount of uaa. gis economic returns are indeed unrelated not only with the agricultural sector, for which can become even counterproductive in terms of productivity and land exploitation but also with the whole economic system of the place-of-origin. a positive correlation emerges in the case of lower illiteracy rates, lower vulnerability index and the presence of diversified and interconnected economies. the vulnerability index of neighbourhood municipalities is also negatively correlated suggesting that indirect spatial effects exist. in the case of the most economic performant italian pdos, the establishment of a successful gis would seem to be brought forward from the presence of thriving socio-economic preconditions and higher value-added economies, which have been considered an expression of economic growth for years. the regional output of the agricultural sector has been positive and statistically significant since the first specification. however, it does not reduce or undo, the significance of the territorial variables. however, results are not univocal and there is not a one-size-fits-all solution to territorial dynamics. estimations point out a different story when we performed the same set of analysis on the pdo wines with the highest revenues (table 2): the socio-economic predictors are no longer statistically significant. the only exceptions are agricultural intensity and illiteracy rate, but they are not enough to conclude that there is an overall effect of exante development condition on leading pdo wine market. other contextual factors hidden behind would seem to be responsible for the success of the high segment of pdo wines market (e.g., relational and social assets). vitivinicultural activity has contributed for the success of the european agri-food sector and the maintaining of adequate socio-economic conditions in some lagging regions for decades. in italy, local winegrowers have continued their activity over the decades preserving an outstanding capillary spatial distribution and different varietals (corsi et al., 2019). vine-growing shifts from the popular viticulture that characterized the roman empire, to the viticulture managed by churches and monasteries during the middle ages to the lowquality wines of local farmers during the xvi and xvii centuries. the unification of italy in 1861 paved the way to some specific policy interventions with high-quality orientation. after the second world war, when italy had to decide if importing french grapes or recovering the italian historical ones, the latter strategy was followed. as a result, most of the current pdo-wines are rooted in their historical presence and family businesses. this does not mean that they have been well-known wines since the beginning, but that their grapes have a century-old history that cut across time hiding the presence of common habits, informal institutions and cultural proximity. the history of brunello di montalcino is an evocative example.16 a productive and high-quality vineyard is a long-time investment strongly hard to replicate either elsewhere or in a short time (carbone et al., 2019).17 during that time, vineyards are certainly affected by the geographical and pedological factors of the region, such as minerals, organic matters and micronutrients, but they are also embedded in cultural habits, tacit knowledge and historical traditions of local communities (i.e. terroir). cultural traditions, community-based expertise and local identity seem to be thus decisive. from a theoretical perspective, these results are consistent with  the integrated territorial approach literature that advocates for the relevance of considering the heterogeneity of all exogenous and endogenous features. in sum, findings suggest that not only economic returns but also affecting territorial factors are highly heterogeneous across pdos. while in the food sector the higher production value of the most relevant italian pdos seems to be explained by an ex-ante socioeconomic development and a vibrant agri-food system, in the case of pdo wines it depends on other contextual factors. these adverse socio-economic influences should be taken into account when projecting the future returns and effectiveness of agri-food policies targeting local 16 the product specification tells the history of the brunello di montalcino and reveals that it has achieved its fame thanks to a few local farmers who had been continuing the production over the two world wars. after the second world war, when historical grapes were reintroduced to restart to produce typical wines, the brunello di montalcino was selected and became one of the most renewed italian wine worldwide. it was one of the first italian wines to receive the doc certification, in 1966, and to be recognized as docg, in 1980. 17 vineyards are permanent crops that occupy the yielding for centuries, do not grow in rotation and their effective production starts years after vines have been planted. 97the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 development, like gis, otherwise the evaluation may be biased. 18 18 according to the italian regulation (dm 14/10/2013, art.6), a sort of preventive diagnosis is already needed by italian national authority in the socio-economic report (i.e. one of the documents required for the application). however, the socio-economic report requires a very limited number of data: the amount of production (i.e. quantity produced over the last three years) and the number of local actors engaged (i.e. people working along the supply chain). information on the socio-eco4.1 composition analysis after providing evidence of the long-run effect of the socio-economic contextual features in the case of pdo-food, we turn to an analysis of the potential mechanisms they operate through. nomic conditions of the area and on the other eu policies in force (e.g., cohesion and cap policy) is conversely not requested. table 1. effects of contextual factors on pdo-food. (1) (2) (3) (4) utilized agricultural area (uaa) -0.003*** -0.003*** -0.002*** -0.002*** (0.000) (0.000) (0.000) (0.000) agricultural intensity 5.633*** 5.921*** 4.321*** 5.177*** (0.380) (0.504) (0.878) (1.317) big farms 37.581*** 35.543*** 19.581*** 21.961*** (1.760) (1.891) (2.595) (3.891) family farms 1.712*** 1.137* 0.602 -0.588 (0.454) (0.654) (1.366) (2.509) livestock -4.438*** -3.757*** -2.919*** -4.085*** (0.225) (0.284) (0.602) (0.901) population density 0.003** -0.001 -0.000 (0.001) (0.003) (0.004) illiteracy rate -2.769*** -1.567*** -1.438*** (0.128) (0.190) (0.243) employment rate 0.046 -0.079*** (0.055) (0.850) employed people in agriculture, forestry and fishing -0.335*** -0.394*** (0.037) (0.0622) employed people in tradable sectors 0.357*** 0.342*** (0.030) (0.045) employed people in services sectors 0.153*** 0.216*** (0.039) (0.057) economic vulnerability index -0.865*** (0.152) economic vulnerability index – spatial lag -1.765*** (0.239) territorial characteristics – spatial lags ✓ ✓ ✓ regional output agricultural sector ✓ ✓ ✓ ✓ municipalities and year fe ✓ ✓ ✓ ✓ observations 9,166 9,166 9,166 9,166 municipalities 4,583 4,583 4,583 4,583 hausman fe/re (p>χ2) χ2 713.80 872.74 305.03 193.71 p-value 0.000 0.000 0.000 0.000 notes: observations are at the municipality-year level; fixed effects estimations; standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. test for multicollinearity has been performed; estimations for the odd-ratio are coherent. we only report the preferred fixed effects results. source: author’s elaboration. 98 cristina vaquero-piñeiro bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 the european commission has included among its top priorities the revitalization of rural areas (ec, 2016) and gis are often presented as a potential strategic tool, but how do the negative effects of socio-economic vulnerability differ by level of rurality? table 3 considers the level of rurality of municipalities.19 we use the national rural network classification 19 even if the baseline estimations have highlighted the no relevance of socio-economic conditions for pdo wines, we conduct the analysis also for pdo wines, but, as we can expect, the test is not significant. both that groups municipalities in urban poles, rural areas with specialised intensive agriculture, intermediate rural areas, rural areas with comprehensive developed problems.20 in comparison with conventional rural classifications based on population density, this one allows us to capture the complementary effect generated not only by being classified as a rural municipality but also by being the socio-economic index and the interaction terms are not significant. 20 more information available at https://www.reterurale.it/areerurali. table 2. effects of contextual factors on pdo-wine. (1) (2) (3) (4) utilized agricultural area (uaa) -0.001*** -0.000 0.001 0.001 (0.000) (0.000) (0.000) (0.000) agricultural intensity 10.525*** 8.304*** 6.788* 11.091* (3.10) (2.295) (3.652) (6.272) big farms -305.76*** -54.301* -54.772 -13.318 (48.493) (28.663) (20.337) (23.582) family farms -15.470*** -13.515*** -10.044 -9.106 (2.03) (3.325) (6.602) (6.442) vineyards -9.990*** -4.666 -7.169 -6.994 (2.322) (5.039) (8.845) (7.953) population density 0.006 -0.002 0.001 (0.006) (0.011) (0.011) illiteracy rate -2.765** 2.448* 4.373** (1.084) (1.751) (1.912) employment rate -0.308 -0.405 (0.340) (0.366) employed people in agriculture, forestry and fishing -0.619 -0.804 (0.477) (0.614) employed people in tradable sectors 0.319* 0.238 (0.184) (0.173) employed people in services sectors -0.119 -0.422 (0.261) (0.325) economic vulnerability index -1.161 (0.978) economic vulnerability index – spatial lag 0.678       (0.802) territorial characteristics – spatial lags ✓ ✓ ✓ regional output agricultural sector ✓ ✓ ✓ ✓ municipalities and year fe ✓ ✓ ✓ ✓ observations 1,586 1,586 1,586 1,586 municipalities 532 532 532 532 hausman fe/re (p>χ2) χ2 33.27 28.85 28.26 48.14 p-value 0.000 0.000 0.013 0.000 notes: observations are at the municipality-year level; fixed effects estimations; standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. test for multicollinearity has been performed; estimations for the odd-ratio are coherent. we only report the preferred fixed effects results. the sample is restricted to municipalities with positive vineyards uaa. source: author’s elaboration. 99the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 more devoted to intensive agriculture activities or suffering from structural bottlenecks. the results confirm the overall negative impact, in line with the baseline estimations, which however diminishes in the case of intermediate rural areas. the socio-economic vulnerability in these areas does not particularly hind the economic returns of gis. how are the territorial effects distributed across pdos category? in terms of economic returns, cheese and cured-ham pdos are the most repressed, in line with the national trend.21 the model is estimated for each category separately (in order to compute productspecific effects) and focused on those regions where the production area is located. in the case of cured-ham, successful pdos would seem to be particularly brought forward from the presence of higher productivity rates and the presence of family farms. in the case of cheese, 21 in italy, the dairy sector accounts for the 57 per cent of the gis’ market in terms of production value. the presence of thriving socio-economic preconditions and higher-value-added economies would be more relevant. in terms of socio-economic vulnerability, municipalities with cured-ham pdos are the most affected. these results need to be framed in the exception to the origin requirement for raw materials (e.g., meat and milk), which may come from another geographical area, of some of these pdos. the external sourcing makes local expertise and specificities more important in shaping the economic success of gis as related to product production and transformation. only a few restricted areas have developed as production areas for hams with a designation thanks to the unique, inimitable conditable 3. pdos, rurality and economic vulnerability. pdo food economic vulnerability index (evi) -3.067*** (0.954) economic vulnerability index*rurality evi* rural areas with specialised intensive agriculture 1.602* (1.001) evi* intermediate rural areas 2.422*** (0.971) evi* rural areas with comprehensive developed problems 2.234*** (0.957) rurality dummy ✓ agricultural controls ✓ socio-economic contextual conditions ✓ economic vulnerability index – spatial lag ✓ territorial characteristics – spatial lags ✓ regional output agricultural sector ✓ municipalities and year fe ✓ observations 9,144 municipalities 4,572 notes: observations are at the municipality-year level; fixed effects estimations; standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. test for multicollinearity has been performed; estimations for the odd-ratio are coherent. model (1) has been augmented with the interaction term between the level of vulnerability index and the level of rurality; all the other explanatory variables are the same. evi*non rural as the control level. source: author’s elaboration. table 4. effects of contextual factors on pdo-food by product categories. pdocheese pdocured ham utilized agricultural area (uaa) -0.001*** (0.000) 0.000** (0.000) agricultural intensity 0.161 (0.199) 2.916** (1.487) family farms 0.997*** (0.245) 10.176*** (1.935) population density 0.000 (0.000) 0.002 (0.002) illiteracy rate -0.551*** (0.057) -2.511*** (0.731) employment rate -0.016 (0.046) 0.071 (0.075) employed people in agriculture, forestry and fishing 0.031*** (0.007) 0.009 (0.044) employed people in tradable sectors 0.015*** (0.007) -0.087* (0.047) employed people in services sectors -0.029*** (0.008) -0.191*** (0.071) economic vulnerability index -0.074** (0.035) -0.671*** (0.309) economic vulnerability index – spatial lag ✓ ✓ territorial characteristics – spatial lags ✓ ✓ regional output agricultural sector ✓ ✓ observations 5,715 550 r2adj 0.23 0.38 notes: observations are at the municipality level (yi); cross-section estimations; standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. test for multicollinearity has been performed. pdo-cheese: parmigiano reggiano pdo, grana padano pdo, mozzarella di bufala campana pdo, gorgonzola pdo, pecorino romano pdo, asiago pdo. pdo-cured ham: prosciutto di parma pdo and prosciutto san daniele pdo. source: author’s elaboration. 100 cristina vaquero-piñeiro bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 tions and specific human skills.22 the evidence of the positive effects of family farms goes in this direction; it is valid only for product-specific nature, with no insights for italian pdos as a whole. conclusions territorial features play a fundamental role in agrifood production systems. they generate a sort of entry barrier deriving from the strong linkage with the place of production, its inimitable resources, and specific competences. in this perspective, the quality system of the designations of origin has assumed a crucial role, as it represents the first step to deal with rural development by distinguishing local products from standardized ones. local production systems are very promising in terms of reducing environmental impacts, safeguarding local expertise and avoiding those high-quality local products will be crushed by industrialized and quantityoriented competitors, like the new world wines countries (mariani et al., 2012). conversely, several studies have provided insights on the responsibility of one-fitsall and place-blind approaches for the growing decline in the returns of a public intervention targeting local needs (rodriguez-pose, 2020). they could risk triggering communities towards homogenous economic systems and standardized productions. on their part, agri-food policies have slowly proven to adapt to this paradigm (ec, 2016). there is a great deal of interest harnessing rural and regional territorially-sensitive development tools in the service of building local agri-food systems. this paper has contributed to this debate by empirically demonstrating that territorial factors are fundamental to understand local dynamics, and the socioeconomic benefits of local production systems, like gis. we identify that a product-territory nexus exists, but that the affecting territorial factors differ across regions and sectors. imbalances come from both socio-economic conditions (food pdos) and socio-cultural knowledge (wine pdos). gis require a full-ranging adaptation of local economies. producers must follow product specification, new administration offices (i.e., consortia) must be established to collectively manage the appellation and intersectoral productive and services mechanisms activated. the presence of a fertile socio-economic context could support this process. however, these peculiarities are not evenly distributed across all municipalities. 22 dossier no. it/pdo/0117/0067. it can be accessed at http://ec.europa. eu/agriculture/quality/door/list.html (accessed on 10 apr 2014). this territorial imbalance of gis requires above all addressing the territorial distress felt by the areas that have been left behind by a preventive territorial analysis of the production area, more severe than the socioeconomic report required for the application. the territorial diagnosis should be conducted to collect information on the socio-economic conditions of the area, the other eu policies in force and local potential strengths and weakness with the ultimate aim to find territorial features contributing to the success of different types of territories. even if eu institutions have highlighted the importance of supporting gis products by a common regulation to achieve rural development, these results show that the gis scheme, as it is now, is yet far away from ensuring the benefit of such regime to all products, and places-of-origin. a possible adaptation of gis scheme to the socio-economic condition of production areas may be introducing to guarantee far-reaching general provisions for less-developed areas or niche products. for instance, from the offer side, the lack of a florid socio-economic context should entail an effort by eu and national institutions to create synergies between producers, associations and regional authorities prior to the designation. ideally, only by creating a sort of policy package within the existing eu policy mix, the gis regime could operate as a flexible strategic tool to support the local development and well-being of all the regions-oforigin. being aware of the key role of territories should be a necessary condition for policymakers and practitioners to understand why agri-food systems located in similar regions sometimes react so differently to the same policies. acknowledgements the author would like to thank participants at the 2020 aieaa conference, the editor and two anonymous referees, for their helpful and constructive comments that greatly contributed to improving the final version of the paper. the author would like to thank fabrizio de filippis, riccardo crescenzi and mara giua for their very helpful contribution to the first version of the paper. references agostino, m. and trivieri, f. 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(2011). from image to identity: building regions by place promotion. european planning studies 19(2):243-260. 105the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 appendix table a1. description and sources of variables. variable definition source pdo dummy variable equal to 1 if the municipality is included in one of the pdo production areas author’s elaboration local context population density logarithmic transformation of population density inhabitants per km2 population and housing census, istat illiteracy rate share of illiterate residents population and housing census, istat employment rate share of residents working-aged 15 years or over population and housing census, istat employed people in agriculture, forestry and fishing share of economically active population working in agriculture, forestry and fishing sectors population and housing census, istat employed people in non-tradable sectors share of economically active population working in nontradable sectors population and housing census, istat employed people in tradable sectors share of economically active population in tradable sectors population and housing census, istat population density – spatial lag logarithmic transformation of population density in neighbouring municipalities inhabitants per km2. nearest neighbour approach. author’s elaboration – geographical information system employment rate – spatial lag share of residents working-aged 15 years or over. in neighbouring municipalities. nearest neighbour approach author’s elaboration – geographical information system local agriculture uaa utilised agricultural area agricultural census, istat agricultural intensity utilized agricultural area/total agricultural land agricultural census, istat big farms share of farms with more than 100 ha agricultural census, istat family farms share of family employees agricultural census, istat livestock farms1 share of farms with livestock agricultural census, istat vineyards2 share of wine grape uaa agricultural census, istat regional output agricultural sector output of the agricultural industry basic and producer prices eurostat local economy economic vulnerability index socio-economic condition of each municipality related to some principal components: education, income, employment and housing 8mila census, istat economic vulnerability index socio-economic condition in neighbouring municipalities. nearest neighbour approach. author’s elaboration – geographical information system notes: (1) the variable livestock is included only in the model related to the presence of food pdos. (2) the variable vineyard is included only in the model related to the presence of pdo wines. source: author’s elaboration. 106 cristina vaquero-piñeiro bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 table a2. descriptive statistics. variable obs. mean std. dev. min max local agriculture utilised agricultural area (uaa) 23,265 1729.34 2,860.02 0 64246.74 agricultural intensity 23,265 0.70 0.23 0 1 big farms 23,265 0.029 0.07 0 1 family farms 23,265 0.86 0.15 0 1 livestock 15,479 0.43 0.28 0 4 vineyards 23,265 92.87 355.43 0 13512.79 regional output agricultural sector 23,265 3,274.705 1,868.87 56.9 6,485.86 local context population density 23,265 280.35 630.08 0.9 15164.90 illiteracy rate 23,265 1.81 2.42 0 30.1 employment rate 23,265 43.02 8.73 11.7 74 employed people in agriculture, forestry and fishing 23,265 11.18 10.31 0 80 employed people in tradable sectors 23,265 35.38 10.45 0 88.9 employed people in services sectors 23,265 17.76 5.46 0 71.6 population density – spatial lag 23,265 282.93 529.24 1.35 10547.55 employment rate – spatial lag 23,265 43.03 7.98 20.7 66.55 local economy economic vulnerability index 23,265 99.03 2.49 92.4 120.9 economic vulnerability index – spatial lag 23,265 97.49 0.92 95.1 102.9 source: author’s elaboration on data collected from pdo codes of practice and istat. 107the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 figure a1. pdos and income spatial distribution. source: author’s elaboration on data collected from pdo codes of practice and istat. figure a2. pdos and population spatial distribution. source: author’s elaboration on data collected from pdo codes of practice and istat. 108 cristina vaquero-piñeiro bio-based and applied economics 10(2): 89-108, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9429 figure a3. pdos and employment spatial distribution. source: author’s elaboration on data collected from pdo codes of practice and istat. volume 10, issue 2 2021 firenze university press mediterranean agriculture facing climate change: challenges and policies filippo arfini the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications cristina vaquero-piñeiro application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context raffaella zucaro, veronica manganiello, romina lorenzetti*, marianna ferrigno climate changes and new productive dynamics in the global wine sector emilia lamonaca*, fabio gaetano santeramo, antonio seccia a systematic review of attributes used in choice experiments for agri-environmental contracts nidhi raina*, matteo zavalloni, stefano targetti, riccardo d’alberto, meri raggi, davide viaggi the effect of farmer attitudes on openness to land transactions: evidence for ireland cathal geoghegan*, anne kinsella, cathal o’donoghue bio-based and applied economics 13(1): 13-25, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13941 bio-based and applied economics bae copyright: © 2024 tyllianakis, e. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: tyllianakis, e. (2024). assessing the landscape recovery scheme in the uk: a q methodology study in yorkshire, uk. bio-based and applied economics 13(1): 13-25. doi: 10.36253/ bae-13941 received: november 10, 2022 accepted: february 08, 2023 published: may 20, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. guest editors: stefano targetti, andreas niedermayr, kati häfner, lena schaller orcid et: 0000-0002-8604-4770 assessing the landscape recovery scheme in the uk: a q methodology study in yorkshire, uk emmanouil tyllianakis university of leeds, uk e-mail: e.tyllianakis@leeds.ac.uk abstract. embedded within the european union’s green deal is a re-enforced scope to encourage farmers’ participation in primarily voluntary agri-environmental schemes. although outside of the european union, the newly announced agri-environment schemes in england mirror such a policy shift towards incentivising participation in order to deliver more and better climate public goods. farmers’ viewpoints regarding such schemes and contracts are therefore important to examine, as they should be main determinants of current and future enrolment. in this paper, upland yorkshire farmers were asked to express their opinions for the landscape recovery scheme that aims to encourage collaboration and achieve landscape-wide interventions to ensure lasting delivery of climate public goods. viewpoints show divergent views between environmentally conscious farmers and pragmatic farmers objecting to the functioning of agri-environmental schemes. farmer viewpoints lean towards ‘broad and shallow’ schemes that would have simple contract requirements and only achieve marginal gains in the delivery of agri-environmental climate public goods while still showing concern about the natural environment and its impact on farming. keywords: agri-environment schemes, q methodology, environment land management scheme, landscape recovery. jel codes: r58, r51, q18. 1. introduction to carry out climate actions in the agricultural sector the european commission has published its green deal aiming to utilise 40% of the common agricultural policy budged for the 2021-2027 period for this purpose (european commission, 2019). these climate actions include the “farm to fork” strategy (scown et al., 2020) and incentivising participation to agrienvironmental climate schemes (aecss) through means of direct income and financial support (hasler et al., 2022). the ultimate goal for the european union’s agriculture is to become carbon-neutral by 2050 (european commission, 2019) and in the intermediary, devote 25% of its budget to eco-schemes (now part of the more heavily financed pillar i of the new cap) and link payments to mandatory environmental and biodiversity requirements of the new cap period of 2023-2027 (european commission, 2022). https://doi.org/10.36253/bae-13941 http://www.fupress.com/bae https://doi.org/10.36253/bae-13941 https://doi.org/10.36253/bae-13941 https://orcid.org/0000-0002-8604-4770 mailto:e.tyllianakis@leeds.ac.uk 14 emmanouil tyllianakis bio-based and applied economics 13(1): 13-25, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13941 participation in these (primarily) voluntary aecss is determined by a variety of factors, including farmer characteristics (unay gailhard et al., 2015), motivations that include financial components (lastra-bravo, et al., 2015) and environmental inclinations (dessart et al., 2019) and the scheme’s characteristics (tyllianakis and martin-ortega, 2021). of particular interest when evaluating aecss are determinants of farmer behaviour, driven by pre-existing concepts and viewpoints (muhar et al., 2018). empirical approaches to assess and find common patters in viewpoints regarding agriculture, environmental management and stewardship and types of aecss are becoming more pronounced in the literature (e.g., walder and kantelhardt, 2018; iofrida et al., 2018; braito et al., 2020; norris et al., 2021), recognising the important role that the plurality of viewpoints across a topic play. this study aims to examine and analyse viewpoints concerning a soon-to-be introduced aecs in a country (england) that still is influenced by cap concepts and has laid out ambitious environmental goals for aecs and the future of farming in the country. it aims to determine whether groups of farmers with similarities concerning their farm type and experience in collaborative aecs are positively inclined towards new and ambitious aecs currently rolled out in england. by using the semi-structured survey method of q methodology i present the viewpoints of a specific, geographically-explicit group of uk farmers around the adoption of the newly introduced landscape recovery scheme. this is examined in a sample of yorkshire farmers, members of the countryside stewardship facilitation fund (csff) scheme with past experience in collaborating and sharing knowledge around land stewardship. by doing so i find several patterns in viewpoints of upland farmers in yorkshire, involved mainly in sheep and beef farming and depending on government subsidies for their income, regarding the operationalisation of the scheme in the lands they manage. i also identify two main typologies of drivers; practical and related to implementation concerns characterise one group of participants while social and environmental concerns are of interest in the other two groups. the paper next presents the method used and reviews past literature of relevance to this application (section 2). section 3 describes the case study locations while section 4 describes the data collected. the results of the q methodology are presented in section 5 and section 6 discusses the findings relating to the implementation of aecs and the delivery of agri-environmental climate goods in the uk and offers some concluding remarks relevant to policy-making. 2. literature review the method of analysis chosen in this paper is q methodology. it stems from the field of psychology and has seen a steady increase in its use through the years, starting from the mid-1950s (stephenson, 1953) and recently has seen increased application in social sciences (akhtar-danesh et al., 2009). in its core, q methodology systematically studies subjectivity on a particular topic (brown, 1993) by identifying patterns within the discourse, as broadly and accurately as possible, of a particular topic (doody et al., 2009). the researcher is responsible for presenting the full range of opinions in an activity and as such the approach is inherently subjective (vecchio et al., 2022) and therefore more suitable to analyse attitudes towards a topic (cross, 2005). nevertheless, subjectivity is mediated by the researcher presenting recognised points of view to participants instead of an existing framework (barker, 2008). potential viewpoint patterns are analysed through factor analysis over small sample sizes (davies and hodge, 2007; taheri et al., 2020). of particular interest to researchers are patterns such as relationships between participants who have similar rankings of statements (i.e. similar attitudes) that represent the full discourse on a topic (borthwick et al., 2003). q methodology has seen extensive application in surveys of farmers since the 1990’s (e.g., van der ploeg, 1992; fairweather and keating, 1994; vanclay et al., 1998) and in particular post-2000 with farmers being the 5th largest group of stakeholders examined in the socioenvironmental research literature employing the same methodology (sneegas et al., 2021). research amongst farmers is extremely rich and has focused on a plethora of issues. such issues, for example, refer to determining generic views of farming (e.g., fairweather and keating, 1994), environmental management of agricultural land (davies and hodge, 2007) and farmers self-identity (zagata, 2010). identifying types of farmers based on viewpoints and beliefs is also of major interest in the literature which has focused on classifying farmers’ identities (cullen et al., 2020), farmers’ ideologies or perspectives (braito et al., 2020; walder and kantelhardt, 2018), farmer archetypes based on sociodemographic, psychological and structural characteristics (leonhardt et al., 2022) or decision-making preferences related to the farm (barbosa et al., 2020; braito et al., 2020). while studies focusing on environmentally conscious farming are more numerous, a small number of studies exists in the literature investigating the viewpoints of farmers regarding agri-environment schemes. norris et al., (2021), for example, find that reliance on 15assessing the landscape recovery scheme in the uk: a q methodology study in yorkshire, uk bio-based and applied economics 13(1): 13-25, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13941 ecosystems (peatland) determines one type of viewpoint while lack of land ownership makes participants more inclined to adopt pro-environmental behaviour. visser et al., (2007) find that current use of a protected ecosystem in ireland strongly influences differences in viewpoints between farmers and non-farmers regarding conservation approaches. iofrida et al., (2018) report that farmers identify with concepts of modernising agricultural practices while emphasising the importance of protecting the environment in olive grove farming. walder and kantelhardt (2018) used a q methodology approach to assess the views of austrian farmers regarding specific agrienvironmental schemes and found farmers’ viewpoints combining environmental stewardship characteristics, appreciation of ecosystems as part of culture and placing less importance on generating income. q methodology outcomes of types of farmers have also been used in quantitative studies to predict adoption of agri-environment schemes (e.g., leonhardt et al., 2022). 3. case study description this study focuses on two similar (in terms of farming activities and landscape) but also distinct csff groups in yorkshire (in terms of size and financial and development opportunities in the wider area) of land managers. the study offers several insights into viewpoints for aecs, the role of farmer groups and facilitators and their impact. a sizeable portion of the (small) funds allocated to csff groups is assigned to fund the activities of a local group lead who can be either a farmer or a farm advisor. such group leads are expected to encourage group participation, provide support in funding acquisition endeavours and training activities, amongst other duties. as a concept, collaborative groups of farmers, led by specific individuals can support “cultural and social capital” creation (burton and paragahawewa, 2011). furthermore, established and well-functioning groups of land managers should influence implementation of aecs while reducing individualistic and un-coordinated approaches to farming (riley et al., 2018), further strengthened by the role of intermediaries and advisors (prager, 2015; riley et al., 2018). as explained in the following sections, the two selected groups have been operating for several years, attracting an increasing number of engaged farmers, involved in several nature recovery and enhancement projects and steered by locally based group leads. overall, these two groups should offer valuable insights when evaluating the landscape recovery scheme and inform potential uptake from such types of farmers. 3.1 agri-environmental public goods post-brexit in the uk a uk case study is used, focusing on yorkshire which contains large number of farmers, to examine the viewpoints on the innovative concepts the uk is introducing in its agri-environmental policy, with agrienvironmental climate goods delivery being prioritised (bateman and balmford, 2018; reed et al., 2020). as the uk leaves the eu, increasing attention is being paid to the future design of national environment policy. following the recent publication of the 25 year environment plan and england’s first agriculture bill for over 70 years (uk parliament, 2020), the devolved administrations are consulting on and developing their own policies and strategies. in england’s agriculture bill and the consultations run by each of the devolved administrations, proposals are being made to replace the current subsidy system of ‘direct basic payments’ to farmers, which is based on the total area of land farmed, with a system based on “public money for public goods” (defra, 2021a). there is therefore a unique opportunity to re-evaluate existing options and prioritise funding towards interventions that are more likely to deliver public goods. as all existing direct basic payments are to be phased out over the transition period (2021-2028), (defra, 2020). elms are being positioned to be the main source of future ‘financial assistance’ to uk’s farmers. at the time of design of this study, elms were conceived by the department of environment, food and rural affairs (defra) as a three level system with varying degrees of complexity and environmental and biodiversity targets (defra, 2020). the first level was broadly described to fund the ‘broad and shallow’ land activities through the sustainable farming incentive (sfi), which will pay farmers for actions (defra, 2021b), to continue supporting direct payments in farming. the other two levels are designed as being focused more on ‘narrow and deep’ aecs, under which farmers would be paid for outcomes (defra, 2018) entailing higher demands from land managers, coupled with higher desired environmental results. these two highest levels were to include elements of collaboration, as well as different and increasing suggested means of monitoring of results and scope of deliver public goods. the landscape recovery scheme is the most ambitious of the elm schemes, envisioning collaboration between land managers and landholders and landscape-wide interventions and benefits. a test and trials phase for trialling characteristics and goals of possible landscape recovery projects is taking place between 2021 and 2022, across england (defra, 2021b). 16 emmanouil tyllianakis bio-based and applied economics 13(1): 13-25, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13941 3.2. esk valley farmer group the esk valley countryside stewardship facilitation fund (csff) network consists of a large group of upland and lowland farmers with common interests in improving water and soil quality within the esk river catchment. farmers have joined the group to explore ways of supporting their farm income through providing evidence of environmental services they already provide (carbon storage, natural flood management etc.) in the face of a changing domestic and european agricultural policy. the csff is focused on the environmental and ecological aspects of the catchment, specifically from the perspective of those farming and managing the land (defra, 2017). the csff aims to support efforts by the esk pearl mussel and salmon recovery project to reintroduce the pearl mussel to bolster the remnants of the existing population, through improving the water quality in the river. for this iconic species ‘good’ is not good enough, pristine conditions are required. this needs collective action from farmers in both upper and lower reaches of the catchment to reduce pollution and sedimentation problems (defra, 2017). there is a long history of action in the river esk catchment seeking to improve its ecological status so that an iconic species previously found in the river, the freshwater pearl mussel, does not ultimately go extinct (schaller et al., 2020). the csff network covers the whole catchment and 30% of the land area (10,514 hectares, both in upper and lower reaches) is farmed by csff network members (59 members) (defra, 2017). a key focus is what can be done to improve water quality across the catchment, especially as it is a salmon and trout river and sediment in the water is a major factor in the lack of recruitment of juvenile migratory fish (defra, 2017). water quality is generally good across the catchment and of good ecological status according to the water framework directive apart from one exception (schaller et al., 2020). many other additional environmental improvements have been added: sedimentation, nitrate and phosphate pollution due to the agricultural and farming activities in the area, and complement the main focus – for example waders benefit from the network tackling issues of water quality (schaller et al., 2020). the majority of the land is under good ecological status according to the water framework directive while the ph is 6.0 for more than 68% of the esk grasslands (compared to 53% for the whole of the u.k.) (schaller et al., 2020). the area encompassed by the esk valley csff is the esk catchment that extends from the source of the esk all the way to the sea at whitby (defra, 2017). this means the catchment includes a range of land types from heather moorland to arable fields, areas classified as site of specific scientific interest (sssi), special area of conservation (sac) and special protection areas (spa) to highly intensive farmland. there is little woodland in the region, less than 13% of the total region, mainly in linear strips (schaller et al., 2020). as the area falls within the iconic national park and its traditional landscapes so another aim is to address the disconnect between maintenance of these landscapes and the system to reward this. farmers joined the csff with a two-fold intention: to see environmental improvements and economic benefits increase from the ongoing and expanding environmental management in the esk catchment (defra, 2017). the group and its activities were key in esk valley farmers working with the national parks authority (npa) to submit a successful bid for £300k of capital works plus advice programme (schaller et al., 2020). in terms of sociodemographic characteristics of the sample, the upper reaches there are moorland hill flocks of sheep and herds of beef cattle. lower down in the valley dairy farms are seen; over time there has been a shift to smaller numbers of large dairy farms (schaller et al., 2020). there are some small pockets of arable land in the valley and potatoes are typically grown. the farms tend to be small compared to the average size of farms in the yorkshire dales; the average farm size is about 100 hectares while there are 7-8 big dairy farms in the csff group. the farms are a mixture of owner-occupied and tenanted and this is mixed across the whole catchment. farms belonging to the group cover approximately 1/3 of the whole esk catchment (defra, 2017). large numbers of the farmers are reliant upon farm subsidies and agrienvironmental scheme to stay in operation, and many of the farmers also have second jobs (schaller et al., 2020). 3.3 south pennines farmer group the south pennines farmers csff network is a large network of farmers from the wider yorkshire area benefiting from the support and active involvement of local government agencies aiming to bring farmers and land managers together, with support from governmental agencies to better deliver aecs. in particular, facilitate they facilitate knowledge exchange between farmers and provide information on how to better manage the local ecosystems especially under the threat of extreme weather events such as the damaging floods of 2015 (defra, 2016). the group is comprised of a number of participants with homogeneous interests, land holdings and farm activities and farm holdings are found in mainly upland areas with the majority of the farmers 17assessing the landscape recovery scheme in the uk: a q methodology study in yorkshire, uk bio-based and applied economics 13(1): 13-25, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13941 depending considerably on farm subsidies and aecs to supplement farm income (schaller et al., 2020). the south pennines farmers csff network was set up initially in 2015 with the purpose to deliver and explore how they can improve delivery of several key environmental benefits in the wider catchment area (defra, 2016). group members’ land holdings are in the proximity of special protection areas (spa), special areas of conservation (sac) and the south pennines moor site of special scientific interest (sssi), which is both expected to have beneficial impact on environmental quality of services and to be benefited from improvements in land management (defra, 2016). given the relatively high altitude (approximately 400m above sea level) of the land holdings the interest of farmers revolved around moorland restoration and enhancement, grassland habitat creation, and enhancing and expanding riparian habitats to benefit flood risk management and water quality while considering afforestation practices as well (schaller et al., 2020). soil quality and acidity result in grass quality not being enough for sheep to grow properly. farmers in the group do not engage in any organized forestry and woodlands within the land holdings of members are currently unmanaged. it is early to see whether participation in the network and the actions it supports has produced tangible outcomes for the environment (schaller et al., 2020). the majority of the south pennines farmers csff network farmers have small holdings (average size is 30 hectares) and are involved in sheep and beef farming while there are no dairy farmers or arable/mixed farmers in the network either (defra, 2016). given the grass quality, sheep are being sold elsewhere for fattening which results in lower market prices for the local farmers. as a result, farmers have been engaging in other economic activities to supplement their farm income with the majority of network members having such “outof-farm” income (schaller et al., 2020). the low price of beef is also resulting in reduced farm income. additionally, farmers in the area have been dependant in income from various environmental management schemes, mainly the basic payment scheme (on average, 75% of farm income comes from payment schemes) (schaller et al., 2020). the majority of the farms are not rented. from all farming activities in the wider yorkshire area, the activities that the csff members partake (grazing livestock) is by far the least profitable one, generating £19.3k per year, lower than the england average (defra, 2019). grazing livestock in upland areas is the activity that the vast majority of farms in the west yorkshire area (where the network’s farmers are located) are engaged with. farmers in the group have seen a decline in farm income while intensification of weather events (such as the floods of 2015 and the recent (2019) floods that impacted west yorkshire, in particular, with some lowland areas still recovering and undergoing rebuilding) stress the importance of proper land management in adjacent lands, making land abandonment a real future threat. farmers see themselves, and are seen by other actors in the economy, as vital partners and providers to environmental goods and services that support climate change mitigation and adaptation while safeguarding income and lives. as a result, the grouping of farmers such as the specific csff network has allowed for the procurement of funding for a local council (calderdale council) to address flood issues and explore flooding measures such as natural flood management (nfm), following the 2015 floods (schaller et al., 2020). 4. data 4.1 workshops two workshops took place in yorkshire in march (whitby) and may (hebden bridge) 2022. the q-method was part of further data collection through questionnaires, data from which were not used in the analysis and they are not presented here. these questions assessed the knowledge of participants concerning landscape recovery and their interest in participating in agri-environmental schemes in general. they were followed by a list of open-ended questions where participants were asked about types of agri-environmental activities, their priorities regarding public good provisioning and how participants achieve farm production and delivery of public goods and finally assess any changes in knowledge and intentions to participate in agri-environment schemes. the first workshop attracted 19 participants with all but two being farmers (the remaining participants were members of local government agencies and farmer advisors). the majority of the participants are quite active in participating in farmer meetings and only a small number of participants did not attend regularly farmer meetings organised in the general whitby area or organised through the now-discontinued esk valley csff group (which was comprised by a group of approximately 30 farmers). 14 complete q-sorts were collected and analysed in the first workshop1. the second work1 questionnaire collection was fragmented with some participants not filling in the second questionnaire and with few not filling in them at all (also due to late arrivals). some questions in the pre-workshop 18 emmanouil tyllianakis bio-based and applied economics 13(1): 13-25, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13941 shop attracted 15 participants, with all being farmers and members of the south pennine farmers csff group and regular attendees to farmer meetings and discussions through the years. this csff group reached a total number of approximately 60 members before it was discontinued but former participants still meet regularly and have contact with the group lead. similar to the esk valley csff group, the csff group of south pennine farmers’ legacy is the continued involvement of several of its members in aspects of land management in their area. each meeting took approximately two hours in total to be completed. only q-sorts carried out individually were included in the analysis, q-sorts that were completed collectively were excluded, as were q-sorts from non-farmers. this approach was followed to ensure consistency in viewpoint expression. 4.2 q methodology data in order to understand better the viewpoints of land managers that participated in the two workshops, the q methodology was used. q methodology groups survey participants in distinct groups (sometimes called “factors”) based on differences and similarities in their ranking of statements within a sample of statements, called the q-set. after the participants rankorder the statements presented to them to their individual q-sorts a quantitative analysis through factor analysis can take place (taheri et al., 2020). additionally, q methodology allows for finding statements that participants had a consensus opinion on; either positive or negative one, and therefore are not part of the aforementioned groups of statements. overall, q methodology enables assessing common drivers and characteristics of survey participants for a specific topic. in this case, it allows to determine how opinions on contract, socio-economic, environmental and legal characteristics of landscape recovery groups yorkshire upland land managers in distinct groups. such statements need to be representative of the variety of opinions around the topic to allow for agreement and disagreement around them. see the next section for a detailed description of the q-set formulation. following sneegas et al. (2021)’s ‘best practice’ recommendations, below i present the development of the q-set. to this end, a list of statements covering sevquestionnaire were left unanswered from the farmers when some terms were not explained to them. for example, some questions in the preworkshop questionnaire asked about elms landscape recovery but several farmers indicated that the workshop was the first time they heard about the term, and this was also one of their main reasons for attending and therefore more missing data exist. eral aspects was produced through consultation with official documents describing the landscape recovery scheme, loosely based on a political, economic, sociological, technological, legal and environmental (pestle) analysis related to potential agri-environmental contract solutions between farmers from 13 case studies in europe (hamunen et al., 2022). aspects considered relate to four different topics relating to aepcss: first, contract aspects (po) (e.g., whether the 20-year length of landscape recovery is feasible for the participant, the availability of training as part of costs covered in the scheme, the requirement to collaborate with adjacent farms or whether compensation should cover income foregone etc.). second, environmental aspects (en) (e.g., scheme supporting climate change adaptation goals in the uk, scheme supporting wider delivery of public goods, etc.). third, socio-economic implications of the scheme (ec and so) (e.g., participation in the scheme reducing income uncertainty for farmers, scheme fitting different farm types and levels of income, scheme increasing the visibility and appreciation of farmers for delivering public goods etc.). finally, policyoriented aspects (le and te) (e.g., how well does the landscape recovery scheme fit with wider uk policy, how well the landscape recovery scheme fits with the participant’s farm goals etc.). this resulted in 25 statements that were tested in a separate farmer workshop with 13 participants from north yorkshire (including participants from the esk valley and south pennines csff groups) in february 2020. that workshop included a q methodology and discussion afterwards on the statements and method itself. this helped to finalise phrasing and inclusion/exclusion of statements. the 22 final statements were then presented in the two workshops in the esk valley and south pennines in the form of laminated cards to participants, and they were asked to place them in a grid (turning the q-set into a q-sort). statements placed in the extreme left were the ones that participants disagreed with most/did not interest them at all and those in the extreme right those with the opposite effect. the full list of the 22 statements is presented in table 1. the q-grid used is available in the appendix. each q-sort took participants approximately 20 minutes to complete. q-sorts were then analysed through factor analysis, using a varimax rotation, using the statistical software stata (version 15.1) and the qfactor command (akhtar-danesh, 2018). statements were distinguished between each other with the stephenson’s (1978) formula that allows for an individual to be loaded on a factor of their score is statistically significantly different at the 95% level. 19assessing the landscape recovery scheme in the uk: a q methodology study in yorkshire, uk bio-based and applied economics 13(1): 13-25, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13941 5. results in total, 25 q-sorts were collected from the two workshops. after removing incomplete sorts (sorts where not all statements were placed within the grid, i.e., statements went missing) or q-sorts that participants filled in in a collaborative manner, 16 q-sorts were retained for analysis. non-farmers were excluded from the analysis. results for a three-factor (discourse) solution can be seen in table 2 below. ‘value’ reflects the importance (from -4 to +4) an average participant loaded in a discourse placed on a specific statement. the three-factor solution explains 60% of the variance, higher than other q methodology farmer studies (e.g., iofrida et al., 2018) and was selected after comparing model fit with different number of factors and minimising consensus statements (howard et al., 2016). each of the three factors had an eigen value higher than 2.8 and the three-factor solution had only two consensus statements compared to the 6 of the two-factor one. the higher the value participants in a factor placed on a statement, the higher the reported value in table 2 below. each discourse had a similar number of q-sorts loaded in it, with q-sorts from esk valley farmers loading mainly in discourse 3 and 2 while q-sorts from the south pennines loaded equally in discourse 1 and 2. the bottom of table 2 presents statements (so4 and le3) that workshop participants had a consensus opinion on and as a result did not influence the grouping of participants in ether factor. from the results of the q methodology it appears that the workshop participants in discourse 1 are concerned with practical, implementational characteristics when evaluating the prospect of enrolling in the landscape recovery scheme primarily, followed by environmental clauses embedded within the contract of the scheme. offering training to farmers, guidance and support and economic returns are important to them. these “pragmatic yet environmentally conscious” workshop participants have slightly different priorities with those grouped in factor 2 (discourse 2). workshop participants grouped in discourse 2 are more preoccupied table 1. list of the q-concourse items. statement coding contract aspects (po) farmers’ training and guidance should be eligible cost in the scheme po1 the scheme should deliver environmental goods and services by farmers, beyond biodiversity and carbon/climate benefits po2 scheme must have a low level/amount of bureaucracy po3 allow support from skilled authorities and intermediaries in aiding farmers in the implementation of schemes po4 environmental aspects (en) adaptation to climate change (e.g. change practice/crops, irrigation systems) must be addressed by the scheme en1 mitigation of climate change (e.g. reducing flood risk, sequestering carbon) must be addressed by the scheme en2 scheme must take into account unpredictability of nature and the limited possibility for farmers to guarantee results en3 scheme objectives acknowledge spatial and regional differences of environmental conditions across england en4 the contract of landscape recovery scheme should be 20 years or longer as there is a long period from action to result en5 socio-economic (ec and so) financial compensation for participation in the scheme should follow cost incurred/income forgone ec1 landscape recovery should reduce financial risk and uncertainty of income for farmers ec2 scheme should support better visibility (appreciation, recognition) of farmers’ work in providing environmental benefits so1 it is important for the scheme to support cooperation with others (stakeholders, neighbours, farmer unions) so2 farmers’ awareness and knowledge of environmental issues increases through participating in scheme so3 the landscape recovery fits all different farmer and farm characteristics: education, age, size of farm, tenancy so4 policy (legal and technological aspects) (le and te) the elms and landscape recovery in particular, are simple to understand from the material online le1 large scale landscape recovery is compatible with existing laws, programs and uk policy le2 the national landscape recovery goals are compatible with your farming long term goals le3 there is good agreement between landscape recovery priorities and practical, achievable goals in your region le4 scheme must require smart (specific, measurable, attainable and action-oriented, relevant, and time-bound) indicators te1 scheme must be easy to apply and without complex monitoring implementation te2 farmers have no time or money for implementing measures in other elms on offer te3 20 emmanouil tyllianakis bio-based and applied economics 13(1): 13-25, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13941 with economic and implementational issues when considering enrolling in landscape recovery. in particular, these participants’ viewpoints focus on the specifics of the scheme, in particular with respect to monitoring of results, low levels of bureaucracy and advice offered by skilled intermediaries. these “pragmatic” farmers appear less interested in environmental aspects of the scheme while being sceptical of how landscape recovery fits with their personal farming goals. finally, participants’ viewpoints in discourse 3 showed a varied interest in environmental issues, compensation levels, minimising of financial and climate risk as goals of the scheme, as well as a desire to co-operate. these “risk-averse environmentalists” appear more interested in solutions that maximise farmers’ income, training and welfare while minimising personal financial and climate-related risk. such participants also appear to not find the landscape recovery’s goals as attractive or feasible to them. all groups of workshop participants appear to find the 20-year length of landscape recovery as undesirable and consider the goals of landscape recovery as incompatible with existing uk laws. the results and ranking of statements (4 for “very important” to -4 for “not important at all”) for each group of participants can be seen in table 2. workshop participants were in consensus regarding the uniformity of landscape recovery, either in terms of compatibility with personal farmer goals, or in terms of fitting all farmer types and profiles, as can be seen in the bottom part of table 2. both these statements did not differ significantly from discourse to table 2. relative importance for landscape recovery characteristics and aims for esk and south pennines land managers. discourse 1 (factor 1) discourse 2 (factor 2) discourse 3 (factor 3) pragmatic yet environmentally conscious pragmatic objectors risk-averse environmentalists label z-score value label z-score value label z-score value po1 1.860 4 po1 1.62 4 en1 1.61 4 po2 1.120 3 ec2 1.6 3 te2 1.22 3 so3 1.580 3 po3 1.42 3 en2 1.22 3 en1 1.050 2 te1 1.04 2 ec2 1.21 2 en3 0.927 2 po4 1.19 2 so2 1.16 2 ec1 0.617 1 so3 0.339 1 po1 -0.021 1 le1 -0.025 1 en3 0.229 1 so3 0.516 1 le3 0.091 1 le3 0.279 1 en3 0.041 1 so2 0.678 1 te2 0.62 1 so1 0.902 1 te1 0.461 1 en2 -0.165 1 en4 0.63 1 te3 0.003 1 so1 0.639 1 ec1 0.313 1 ec2 -0.145 0 en1 -0.586 0 po3 -0.085 0 en4 -0.099 0 le1 -0.371 0 te1 -0.538 0 le2 -0.206 0 te3 -0.252 0 po4 -0.449 0 po3 -0.473 0 en4 -0.374 0 le3 -0.103 0 so4 -0.513 0 so4 -0.443 0 so4 -0.372 0 te2 -0.442 0 le4 -0.378 0 le4 -0.363 0 en5 -0.649 -2 ec1 -0.92 -2 le1 -1.17 -2 po4 -0.961 -2 le2 -0.708 -2 po2 -0.83 -2 en2 -1.480 -3 po2 -1.49 -3 te3 -1.3 -3 so1 -1.110 -3 so2 -1.31 -3 en5 -1.7 -3 le4 -2.290 -4 en5 -1.98 -4 le2 -1.91 -4 number of q-sorts= 5 number of q-sorts= 4 number of q-sorts= 4 consensus statements label/discourse 1 discourse 2 discourse 3 so4 1 1 0 le3 0 0 0 21assessing the landscape recovery scheme in the uk: a q methodology study in yorkshire, uk bio-based and applied economics 13(1): 13-25, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13941 discourse and both were seen as “neither important nor important”. 6. discussion and conclusions the aim of the workshops was to understand the perspectives of upland yorkshire farmers regarding the goals and intended impact of the landscape recovery scheme being rolled out in the uk. to achieve this the q methodology was used and to demonstrate the range of viewpoints amongst farmers that share considerable similarities concerning their farming practices and dependency to government subsidies. the main outcome of the q-sorting is that there is considerable agreement in viewpoints regarding the a) aspects of the scheme that are non-favourable for the participants and b) a desire to combine feasible and economically beneficial to their farm practices with environmental objectives. in particular, discourses 1 and 3 (“pragmatic yet environmentally conscious” and “risk-averse environmentalists”, respectively) group viewpoints that show interest in farmer-friendly aecs coupled with environmentallyfriendly objectives. economic returns and business-oriented viewpoints while showing a disposition towards aecs are grouped in discourse 2 (as “pragmatic objectors” viewpoints), with such views being common in the literature (e.g., davies and hodge, 2007, walder and kandelheart, 2018; norris et al., 2021). discourse 1 grouped statements somehow common issues affecting the practical enrolment of farmers to aecs. such issues focus on simplifying implementation of aecs (po2, +3) echoing similar studies (e.g., de groot and steg, 2010). similar with other studies, such viewpoints are not “purely” from an environmentalist point of view (norris et al., 2021) as farmers appear to want to combine financial viability of their farm (en3, +2; ec1, +1). viewpoints of such pragmatic yet environmentally conscious farmers appear more inclined to consider enrolling in an generic aecs contract as a means to achieve the two main goals (financial survival of the farm and environmental stewardship) but doing so through the landscape recovery scheme is strongly opposed to (le4, -4). a desire for “broad and shallow” measures within aecs that achieve limited environmental benefits is often reported in qualitative studies amongst european farmers (zimmermann and britz, 2016; braito et al., 2020). within discourse 1 also appear elements of a lack of desire to be recognized for their role as farmers (so1, -3), contrary to barbosa et al., (2020), potentially exacerbated by farmer views that the public underestimates the role of famers in society. discourse 2 has viewpoints focusing on contractrelated characteristics of aecs such as adequate financial compensation provided to farmers (ec2, +4) (e.g., walder and kantelhardt, 2018), provisioning of advice being included in the scheme (po1, +4) and reduced bureaucracy at the application stage and during the duration of the scheme (po3, +3). these pragmatic farmers appear fundamentally against several aecs concepts such as the delivery of multiple agri-environmental public goods (po2, -3), cooperate with other land managers (so2, -3). this is confirmed by their belief that elms are not compatible with uk policy (le2, -2). this reflects the wider literature concerning land managers’ viewpoints regarding aecs contracts and their features, with current schemes failing to properly incentivise farmers to participate (uthes and matzdorf, 2013; tyllianakis and martin-ortega, 2021). such a desire for aecs with limited requirements is also confirmed in these pragmatic farmers by the strong viewpoints against the long-term duration (20-year) of contracts funded by the landscape recovery scheme (en5, -4), similar with the risk-averse environmentalists in discourse 3. discourse 3 (‘risk-averse environmentalists’) included viewpoints that are somewhat common in aecs since farmers are known to be generally risk-averse when considering aecs (schroeder et al., 2013) while generally concerned about the environment grouped more environmentally-focused viewpoints, another common occurrence in the relevant literature (e.g., walder and kantelhardt, 2018; braito et al., 2020; cusworth, 2020). this discourse included viewpoints preferring simple aecs contracts over complicated ones with respect to monitoring (te2, +3) and aecs acknowledging and being used to address the risk that climate change presents to farming (en1, +4 and en2, +3), showing preferences for “narrow and deep” schemes given their low preference for long contract durations (en5, -3). this apparent pro-aecs stance coupled with strong objections to specific contract characteristics might indicate an extrinsically motivated approach of farmers (matzdorf and lorenz, 2010) when expressing viewpoints around the landscape recovery scheme with respect to cultural capital creation in farming, evidence from the q-sorting points to the need for training and guidance (po1) as topic of agreement amongst most participants. such a viewpoint, (evident in discourse 1 and 3’s viewpoints) reflects the need of farmers to receive training and guidance when enrolled in an aecs (braito et al., 2020) but offering such an option might not be practically feasible in aecs contracts (knierim et al., 2017). given that viewpoints across the three farmer groups were indifferent for aspects of social 22 emmanouil tyllianakis bio-based and applied economics 13(1): 13-25, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13941 capital such as cooperation with other farmers (so2) or having schemes that fit every farmer (so4) came from participants of csff groups with well-functioning group leader dynamics. these groups had also operated over an extended period of time (each csff operates more than 5 years with the same group leader), nevertheless, cultural capital creation appears to be still be lacking. this prevents potential positive spillover effects in aecs (burton and paragahawewa, 2011) and in the delivery of agrienvironmental climate goods. in other similar examples in the literature, braito et al., (2020) did not find a desire amongst farmers to coordinate actions and foster social networks. norris et al., (2021) did not find any association between membership in collective, cooperative agreements (what can be approximated by csff membership in the present study) and any farmer viewpoints when assessing viewpoints over peatland management between farmers. therefore more studies are required to determine the impact that past experiences in cooperative, collaborative and socially-driven farmer networks influences similar viewpoints concerning environmental land management. limitations of this study refer to the research scope and the familiarity of participants with it. as it became evident through the workshops, many participants were not aware of the specific requirements and description of the landscape recovery scheme. expressing their opinions was therefore based on past experiences and viewpoints concerning the authority responsible for the scheme’ rollout (defra) and their (limited) past experience with aecs. therefore, larger ‘burden of proof ’ is placed upon the workshop organisers to present an accurate description of landscape recovery to facilitate viewpoint formation. additionally, some self-selection existed within the farmer sample. interested farmers were more likely to respond to the invitation to participate in the workshops (although this should have been partially mitigated by the offer for claiming expenses and free dinner offered) and therefore their viewpoints might be representative of other, less engaged farmers. therefore, generalising the findings is not possible (walder and kantelhardt, 2018) and also outside of the purposes of q methodology (norris et al., 2021). finally, although farmer viewpoints are expected to be primary drivers behind enrolment in aecs, determining the impact that socio-demographic characteristics such as age, having a named successor, farmer income and current dependency from direct payments is required. all these factors were brought up from workshop participants as key drivers of any future enrolment in aecs, therefore quantitative experimental survey methods such as through the use of vignettes (e.g., parkins e al., 2022) or examining relationships between observed aecs participation and farmer viewpoints/types (e.g., leonhardt) could act as complimentary to the presented results. from these findings, it appears that enrolling in the landscape recovery scheme is inhibited by a series of factors for upland yorkshire farmers. nevertheless, the viewpoints expressed by yorkshire farmers should fit broadly with “broad and shallow” aecs (defra, 2021), such as the wider elm scheme. it appears that aspects regarding payments, free advice, duration and scope inhibit the endorsement of landscape recovery from yorkshire farmers. uncertainty around the level of payments, type of management practices and the type of changes in existing practices they would entail also appear significant. furthermore, socio-environmental issues also further inhibit potential enrolment, with landscape recovery and particularly lengthy contracts within it, being perceived as un-aligned with yorkshire farming goals and capabilities. such findings, if corroborated by actual enrolment in landscape recovery in the future from upland beef and dairy farmers in yorkshire, would mean that wider, landscape interventions will not be taking place in the area. instead, such land managers would focus in lessdemanding elm schemes such as the sfi, which seems to be meeting the combination of requested management practices and involvement. nevertheless, lack of clarity whether sfi payments would be enough to cover for the loss of basic payment scheme (bps) payments would mean that upland yorkshire farmers might be faced with ever-decreasing farm-related income. in the event of this occurring, farmers are expected to turn even more to out-of-farm activities such as tourism and hospitality sectors to supplement farm income or continue the trend of land abandonment. this would have detrimental effects in maintaining the existing quality and quantity of public goods in the general yorkshire area. acknowledgement this work received funding from the european union’s horizon 2020 research and innovation programme console contract solutions for effective and lasting delivery of agri-environmental-climate public goods by eu agriculture and forestry, under grant agreement no. 817949. references akhtar-danesh, n., baxter, p., valaitis, r.k., stanyon, w. & sproul, s., (2009). nurse faculty perceptions of 23assessing the 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(2010). how organic farmers view their own practice: results from the czech republic. agriculture and human values, 27(3), 277–290. appendix figure 1. q-grid used for sorting in the workshops. https://gov.wales/sites/default/files/consultations/2020-12/agriculture-wales-bill-white-paper.pdf https://gov.wales/sites/default/files/consultations/2020-12/agriculture-wales-bill-white-paper.pdf _hlk60054323 _hlk104829823 _hlk128742463 _hlk104830529 _hlk127347111 _hlk128647880 _hlk128647635 _hlk128646517 _hlk128649491 new pathways for improved delivery of public goods from agriculture and forestry stefano targetti1,*, andreas niedermayr2, kati häfner3, lena schaller2 assessing the landscape recovery scheme in the uk: a q methodology study in yorkshire, uk emmanouil tyllianakis upscaling environmental incentives in the common agricultural policy: an assessment of the potential of transfers from the first to second pillar fanny le gloux1,2,*, pierre dupraz1 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures: a pestle approach based on stakeholder statements theresa eichhorn1,*, lena schaller1, katri hamunen2,3, tania runge4,* innovative contract solutions for the agri-environmental-climate public goods provision: which features meet the farmers’ approval? insights from emilia-romagna (italy) riccardo d’alberto1,*, meri raggi2, davide viaggi3 the use of innovative contracts to provide agri-environmental public goods: comparing attitudes between ireland and other european countries tracy bradfield1,*, thia hennessy1, riccardo d’alberto2, emmi haltia3 bio-based and applied economics 10(1): 73-86, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9928 bio -based and a ppl ied economics bae copyright: © 2021 m. vollaro, m. raggi, d. viaggi. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: m. vollaro, m. raggi, d. viaggi (2021) public r&d and european agriculture: impact on productivity and return on r&d expenditure. bio-based and applied economics 10(1): 73-86. doi: 10.36253/bae-9928 accepted: may 29, 2021 published: july 28, 2021 competing interests: the author(s) declare(s) no conflict of interest. editor: fabio gaetano santeramo. orcid mv: 0000-0002-3072-3141 mr: 0000-0001-6960-1099 dv: 0000-0001-9503-2977 public r&d and european agriculture: impact on productivity and return on r&d expenditure michele vollaro1, meri raggi2, davide viaggi1 1 department of agricultural sciences, university of bologna, italy 2 department of statistical sciences, university of bologna, italy corresponding author: davide viaggi. e-mail: davide.viaggi@unibo.it abstract. while higher effort in research is advocated for agriculture, there continues to be a lack of measurement of its impact in economic terms, at least in europe. this paper seeks to assess the economic impact of public agricultural r&d investments in europe. different panel models are applied on 16 european countries, by employing productivity and investment data. results show positive impacts with returns on public r&d investments on agricultural productivity of between 6.5% and 15.2%, varying according to model specifications and computation techniques. these values confirm that research expenditure in agriculture is well justified in economic terms. however, the results are highly dependent on the analytical approach and limited by the paucity of expenditure data. further research is recommended to take into account the role of other important determinants of impact, such as climate, spill overs and the common agricultural policy (cap). however, a proper consideration of these variables will first require a major improvement of data availability. keywords: public r&d investments, agricultural productivity, rate of return, europe. jel codes: o33, o47, q16. 1. introduction public agricultural research investments in developed countries has shown contrasting trends in recent decades, including a reduction in some documented cases (hurley et al., 2016; pardey et al., 2016; rao et al., 2016; pardey et al., 2018). while the reasons for current trends in public r&d investment in developed countries can be debated, representing a paradox (alston, 2018), institutional and political reforms are in place in middleincome countries aimed at supporting both research and agricultural productivity (wang et al., 2012; fuglie, 2016). at the same time, private investments in research and development (r&d) in the agri-food sector notably increased, especially in upper middle-income countries (pardey et al., 2018). it is well known, since the first study by griliches (1958), that public investments in agricultural research are highly profitable in the long run (alston et al., 2000; piesse et al. 2010; hurley et al., 2014) and are acknowledged to be a fundamental driver for the improvement of agricultural prohttp://creativecommons.org/licenses/by/4.0/legalcode 74 bio-based and applied economics 10(1): 74-86, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9928 michele vollaro, meri raggi, davide viaggi ductivity (ball et al., 2001; ball et al., 2010). at the same time, the literature admits the limitations and, in many cases, the lack of reliability of the agricultural productivity measurements as well as their difficulty in representing the actual evolution of the agricultural sector and the profitability thereof (alston et al., 2010; wang et al., 2012; hurley et al., 2014; rao et al., 2016). better measurements and more reliable estimates of agricultural productivity and rates of returns (ror) would be helpful in guiding public investment choices. indeed, the improvement of methodologies for quantifying the impact on agricultural productivity, with the aim of precisely estimating the ror of research investments in agriculture, is an issue that has been challenging economists for a long time, especially in developed countries. indeed, the ongoing literature discussion (davis, 1981; alston et al., 2000; hurley et al., 2014; oehmke, 2016; hurley et al., 2016b) is still focusing on adjusting the ror estimates because they are considered, for several technical reasons, (upward) biased and hence not fully reliable. however, in europe, recent evidence of returns on public investments in agricultural r&d are scarce because of limited data availability. besides the difficulty of establishing a connection between r&d expenditure and productivity, the literature observes a change in focus of european agricultural policies (and public r&d effort) from purely productivity-focused objectives towards guaranteeing the environmental sustainability of agricultural production, the health and safety aspects of food and feed production, along with other aspects related to the degree of protection and promotion of public goods1 (gardner and lesser, 2003). in contrast, the more production-oriented investments in agricultural r&d are ‘left’ to the interest of private (business) investors (pardey et al., 2018). further, scientific evidence from the instepp database (pardey et al., 2018) reveals that part of the r&d investments in agriculture are devoted to “maintenance” of productivity levels obtained in previous years. the objectives of this paper are to assess the contribution of public investments in agricultural research to agricultural productivity in europe, through a quantitative analysis, and to measure the economic impact of research expenditure in terms of ror. consistently with this branch of the literature, the focus of the paper is on public expenditure related to agriculture (see section 3 three for more details) and not on research policy (i.e. how money is spent and what 1 for a wider and more comprehensive description of recent perspective on these aspects, see the deliverable 4.2 of impresa project, downloadable at http://www.impresa-project.eu/home.html incentive instruments are used)2. the main contribution of the paper is on the empirical ground as it contributes to fill a gap in the recent literature, which does not include recent analyses of r&d impacts on european agriculture. in fact, the only ‘recent’ study addressing the issue is that of schimmelpfennig et al. (1999), analysing the 20-year period from 1973-1993. in addition, this paper also provides a methodological contribution by tailoring suited analytical methodologies to the limited available data, especially the series on public r&d expenditure in europe. this paper proceeds with a section on the review of the relevant literature (section 2), followed by the selection of the available data (section 3) and the presentation of the chosen methodology (section 4). two subsequent sections provide the illustration of the results (section 5) and related discussion (section 6). the paper ends with a concluding section (section 7). 2. literature review the connection between spending on research and development (r&d) and agricultural productivity has diffused evidence in the literature (griliches, 1958; parente, 2001; hall et al, 2010). the nature of such a connection, firstly explored by shultz in 1953, is to be referred to what would have been formalized as the solow model after solow (1957): technological change and inputs are responsible for the long-run variations in rates of growth of output, with technology being the unobserved exogenous factor of the aggregated production function and estimated ex-post as residual. applying the solow model, most studies (alston et al., 2000; ball et al., 2001; fuglie, 2016) measure agricultural productivity by the means of total or multi factor productivity (tfp or mfp), namely the solow residual. the computational methods and estimation techniques of the tfp have been largely improved over time (e.g. the aggregation and index numbers and the dual approach, inter alia) (hall et al, 2010). yet they remain in the framework of the solow model, therefore treating technology advances – and their causes – as exogenous elements of the models. such a framework, in fact, completely ignores the decision process of agents and institutions for generating and adopting new technologies and, hence, treats change in technology as a costless factor. 2 for these and more aspects related to institutional aspects and to the relationship between european r&d policies, cap and more policies the reader might refer to the other documents and publications of the impresa projects. 75public r&d and european agriculture: impact on productivity and return on r&d expenditure bio-based and applied economics 10(1): 75-86, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9928 the further objective of this type of studies is to estimate the rate of return from public investments in agricultural research. based on the same neoclassical framework, research expenditure is treated as a capital input affecting the agricultural supply function (causing shifts in the supply function) and, therefore, the tfp. the contribution, in terms of effects, of public research expenditure on the evolution (increase) of agricultural productivity is then used as the basis for the computation of the ror on investment, under a cost-benefit analysis framework. alternative theoretical and methodological approaches are applied, instead, for estimating the ror of private r&d investments (hall et al., 2010). the main difference rests in the specification of the maximizing behaviour of the firm, which includes elements pertaining to the private sector, such as market power, strategic behaviour, variable return to scale (long-run ror) and own spill over stocks. another important distinctive factor is the joint determination of r&d investment and expected ror, which, in fact, causes the emergence of measurement issues of ror on private r&d investments as well as manifold interpretations of the estimates, especially of the ror, due to the condition of endogeneity of the r&d variable. common issues to tackle in the evaluation of public and private ror on r&d investment are the estimation of the rate of return and its interpretation. in fact, both topics are still feeding the academic debate and, despite efforts by alston et al. (2011) and hurley et al. (2014) in proposing a more cautious approach for estimating ror (taking into account reinvestment factors) and for providing results more suitable for plausible interpretations, the issue of correctly estimating ror remains unresolved. such an issue appears clearer in the meta-analysis proposed by alston et al. (2000) and, more recently, in the worldwide collection of ror studies by instepp returns to research (rtr) database (hurley et al., 2016a). what emerges from these reviews is a likely overestimation of the marginal effects of r&d investments on productivity, which, in turn, affects ror estimates (hurley et al., 2014; oehmke, 2016; hurley et al., 2016b). in order to try to address this issue, it would be useful to minutely isolate the effects of r&d investments on agricultural productivity by considering potential factors, other than r&d investments, affecting the returns on r&d in agriculture, such as: the intraand extra-sectorial spill over, the role of the structural transformation in the agricultural sector (timmer, 1988), the influence of policies on agricultural production and productivity (restuccia et al., 2008) and the effect of the growing competitive pressure on the european agricultural sector (galdon-sanchez, 2002; schmitz, 2005; duarte et al., 2010). 3. data availability and selection to estimate the return to investments in agricultural research, two groups of data are needed: expenditures on agricultural r&d and measures of agricultural productivity. at the european level, data on r&d expenditure are collected according to two main categories: gross domestic expenditures on r&d (gerd) and government budget appropriations or outlays on r&d3 (gbaord). gerd data group the actual intramural expenditures on r&d, while gbaord data refer to all appropriations by central governments allocated to r&d in central government or federal budgets. unless otherwise stated, gbaord data include both current and capital expenditure and do not only cover governmentfinanced r&d performed in government establishments, but also government-financed r&d performed in the business enterprise, private non-profit and higher education sectors, as well as abroad4. agricultural gerd time series are difficult to use in econometric analyses, as data are missing for several years, especially before 1996, and several countries do not have any records to speak of. the use of the alternative source, gbaord data, as an indicator (or measure) of agricultural r&d investment may hold only under the condition of considering solely public r&d investments, provided that gbaord can represent a reliable proxy of gerd public r&d expenditures. a comparative analysis of public gerd (for all fields of science), revealed that the difference (or divergence), in average terms per country, at the european level is 3% with respect to gbaord5. for this reason, gbaord data have been considered as a suitable proxy of actual expenditure for the aims of this paper. gbaord data are covering all public budget spending related to r&d and are linked to policy issues by means of a classification by “objectives” or “goals”. programmes are allocated between socio-economic objectives on the basis of intentions at the time the funds are committed and not the actual content of the projects concerned. these breakdowns reflect policies at a given 3 since 2019, gbaord are renamed gbard: government budget allocations for r&d 4 this and further methodological information can be found in the revised version of the frascati manual, oecd 2002. 5 for a wider and more comprehensive description of gerd and gbaord data, see the deliverable 4.1 of impresa project, downloadable at: http://www.impresa-project.eu/home.html 76 bio-based and applied economics 10(1): 76-86, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9928 michele vollaro, meri raggi, davide viaggi moment in time. gbaord data are organized according to nabs. gbaord data from 1980 to 2007 on agricultural production and technology are collected according to nabs 92 chapters and sub-chapters: • general research: fishing and fish-farming; crops; forestry and timber production; • animal product: veterinary medicine; • food technology; • other research on agricultural production and technology. gbaord data since 2008 on agriculture are collected according to the nabs 07 unique chapter agriculture, which de facto aggregate the sub-chapters listed under nabs 92. for agriculture, gbaord data collected according to nabs 92 are available for chapters and sub-chapters, while gbaord data collected according to nabs 07 are available for the unique chapter6. agricultural productivity series are available from usda in terms of tfp, computed upon agricultural input and production data, available from faostat, over the period 1961-2010 for all countries worldwide. another series of agricultural tfp is available from the klems project (2016), but these differ from the ones computed by the usda because they take into consideration improvement in qualitative aspects of both agricultural products and inputs. even if they are apparently an attractive data source for econometric analysis, in light of inclusive of qualitative attributes, the limited series availability for several european countries and the indexation 1995=100 do not allow for klems data to be suitable for quantitative analysis7. based on this, tfp series from usda have been preferred as productivity measures to be employed in the present study as the data are complete and available for all european countries and the reference value 100 is set in 1961 (out of the observed period8). gbaord data on agricultural r&d expenditures have been selected from the oecd database because 6 for further details, please refer to ramon – reference and management of nomenclatures provided by eurostat. 7 despite this, a comparability test has been performed on both datasets to check potential longitudinal differences. to make both series comparable, the data have been transformed in growth terms with respect to the fix year 1980. the equality (t-test) test reveals that the time-series are different in terms of growth trends. 8 for more details about the computational methodology, visit the usda website:https://www.ers.usda.gov/data-products/international-agricultural-productivity/documentation-and-methods/ . they are measured in usd and, for this reason, comparable to the production measures provided by faostat and, in turn, to tfp measure provided by usda. the following 16 countries provide for the most complete series of agricultural gbaord and, hence, have been selected for the aims of the present study: austria (at), belgium (be), denmark (dk), finland (fi), france (fr), germany (de), greece (el), ireland (ie), italy (it), the netherlands (nl), norway (no), portugal (pt), spain (es), sweden (se), switzerland (ch), and the united kingdom (uk). for statistical and analytical purposes, the selected countries guarantee a rather good representativeness of europe, in particular because of the presence in the sample of nordic, continental and mediterranean countries. complete series of agricultural gbaord are available starting from 1981 to 2013. however, in order to align them with usda productivity series, the time series are intentionally selected up to 2010. table 1 shows that only six out of sixteen countries – fr, de, it, nl, es and uk – record average agricultural gbaord values largely over 100 musd in the period considered. by looking at physical and economical dimensions (from faostat), it is possible to note that public agricultural investments, at country level, are to a large extent proportional to both agricultural fixed capital (mainly represented by agricultural land) and the value of agricultural production. another factor emerging from the selected sample is the variability per country of the investment in agricultural r&d over time (yearly trend – aver. % δ in table 1). the most extreme examples from the selected countries are be, el and uk, which have steadily disinvested in agricultural r&d over the last three decades, and at, fi and no, which, on the contrary, recorded constant increases. the remaining countries, instead, show intermediate averages generated by alternating periods of increases and decreases in agricultural r&d investments. an exhaustive presentation of the faostat production and input data is available on the usda website (2016) and in fuglie (2016). given the objectives of this study, the use of faostat agricultural data are preferred to eurostat data since the latter does not provide a complete series over time. another reason has to do with comparability in constant 2005 usd with r&d investment measures, at least for gross agricultural production (gap)9. a synthetic analysis10 reveals that fr, de, 9 tfp measures are computed from gap, hence allowing for the comparability between tfp and r&d investments. 10 a detailed descriptive analysis is available at: http://www.impresa-project.eu/home.html, deliverable 4.1 of impresa project. 77public r&d and european agriculture: impact on productivity and return on r&d expenditure bio-based and applied economics 10(1): 77-86, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9928 it, nl, es and uk are the european countries with the highest shares of gap, having each an average (sample) value over 5%. cumulatively, these countries cover about 80% of the gap value of the european agricultural sector and their average gap trends have been stable over the period 1980-2010. the group formed by the remaining countries, at, be, dk, el, ie, no, pt, ch, se and fi, records a global increase in gap (mostly up to 2000 then flattening or decreasing thereafter). despite the differences observed at the country level, a weak increase in gap trends in the 1980-2000 period, followed by a marginally decreasing growth tendency, seems to characterise the general pattern of the agricultural sector at the european level. the possible determinants of this observed pattern are to be identified in factors underlying production processes that contributed to improve the productivity of inputs (technology, innovations, knowledge…), in other elements characterising the multifunctional nature of the european agricultural sectors (environmental protection, food safety, diversification, climate change) as well as in measures providing constraints (cross-compliance, agro-environmental schemes) or reducing incentives (decoupled payments) to agriculture productivity provided by the common agricultural policy (cap). the data on r&d expenditure are available at a european level, but considering country level investment in agricultural r&d. data are not referred to a european union context, but rather at the country level expenditures. this includes eu funds and the cap component related to r&d, but it is not explicitly disaggregated. therefore, we omitted explicit references to cap or to other related eu policies. unlike the gap and production inputs, tfp is not an observed measure but rather a complex index expressing the relative change, over time, of the technical contribution of production inputs to output. indeed, the evolution of the tfp index is, as suggested by fuglie (2016), highly sensitive to r&d investments in terms of both improvement of the production frontier, through technical change (by increasing output levels) and rise in input productivity, through technical and allocative efficiency (by decreasing input levels). this implies that the use of the tfp index allows for a more precise identification, with respect to the use of gap and inputs, of the contribution of r&d investment on productivity. table 2 shows the evolution of tfp for the sample countries over the considered period 1981-2010. the first information to highlight is that the average level of tfp index for some countries, such as ie, no, pt and ch, is close to the reference level. the meaning of such datum is that productivity in those countries lagged behind (20 years from 1961 to 1981) with respect to the others. on the other side, there are some countries, such as be, dk, de, it, nl and es, for which the average tfp index is greater than 200. such variability across countries in tfp index is a favorable element for the reliability of an inferential procedure aimed at estimating the impact of r&d investments over years at country levels, i.e. the exercise we are carrying out in this paper. by looking at the yearly trends, in terms of average percent change, the sample shows a notable variability across countries, from about 1% for uk to 4% for dk. indeed, a deeper exploration of the yearly evolution at country level, not shown in table 2, shows that most countries record a flat trend until 1990 (1987 for it, no, pt and es) and a steady (but variable across countries) increase thereafter. only nl and se show constant positive tendencies along the entire period11. 11 for a detailed description of the tfp series see the deliverable 4.1 of impresa project, downloadable at: http://www.impresa-project.eu/ home.html table 1. gbaord for agriculture – million 2005 dollars – constant prices and ppps (time averages). austria belgium denmark finland france germany greece ireland mean 41 59 73 80 526 470 51 57 st. dev. 6.4 20.6 22.7 13.6 170.6 128.1 12.7 27.4 aver. % δ1 0.80% -3.87% 1.42% 1.68% -2.94% 1.82% -1.82% 4.48% italy netherlands norway portugal spain sweden switzerland united kingdom mean 277 176 112 107 311 46 47 519 st. dev. 89.7 37.9 22.1 39.0 240.5 11.9 13.6 105.3 aver. % δ 1.54% 0.00% 1.98% 3.13% 7.13% 0.00% 0.00% -2.07% source: own elaboration on oecd data. 1 per each country, the trend has been computed linearly through ols (the estimated coefficient of time) and then averaged by the mean of the series. 78 bio-based and applied economics 10(1): 78-86, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9928 michele vollaro, meri raggi, davide viaggi the flatness of tfp until 1990 might prove to be a factor consistent with the supposed role of r&d investments in inducing productivity growth over time. in fact, given that the trends of the inputs do not show flat trends, but rather decreasing ones along the entire period, it is plausible to hypothesize the attribution of the initial stability and the subsequent growth of tfp to a likely progressive growth in technical and allocation efficiency originating from research. this preliminary assessment suggests that the selected data could be considered suitable for testing the hypothesis of a relationship between r&d expenditures in agriculture and agricultural productivity. based on available time series, in this paper we estimate the direct impact of public expenditure on r&d on tfp and, in turn, the relative ror, by employing the most appropriate methodology, suitably tailored to the available series of data. 4. methodology despite many years of academic analysis, the study of the impacts of agricultural r&d on the economy has not converged in a well-established and agreed upon methodology. two main theoretical streamlines of economic growth support the study of economic impact of r&d, the exogenous and endogenous growth models, which in turn give rise to different methodological approaches. the differences, as well as the pros and cons, between these main approaches for the study of the economic impact of r&d are well exposed by parente (2001), who considers the exogenous growth model the best analytical framework for the assessment of economic growth because it best describes the convergence process of countries’ economies. within the framework of the exogenous growth model for assessing the ror, expenditures in r&d are employed as a proxy for knowledge accumulation and, therefore, treated as an exogenous capital input in the estimation process. this assumption implies that the effect of the r&d investment is supposed to persist beyond the first year, therefore affecting more than one production cycle12. this condition implies the use of time series analysis techniques because the focus is on assessing the long-run growth and returns. however, being aware of the limitations of the available data and the consequent impossibility of applying the best available methodology, a wide review of the recent literature, including, inter alia, schimmelpfennig et al. (1999), fan (2000), oehmke (2004), ali (2005), alene et al. (2009), alene (2010), suphannachart (2011), andersen (2013), hurley et al. (2014) and jin et al. (2016), has been carried out to identify the analytical approach that could best exploit the informational power of the available data. these studies provide a variety of approaches and model specifications for the estimation of the impacts of agricultural research on productivity. the methodologies adopted are diverse across the reviewed works and have been likely chosen to best exploit the available data of each study. in fact, agricultural productivity is measured in gap, value added, tfp and mfp, while research is measured in knowledge stock, distributed or single lags of r&d expenditure. in fact, the way research is assumed to impact productivity over time is also modelled in several ways, either by imposing a certain number of lags, based on specific assumptions 12 in this case, the production cycle coincides with one year. table 2. total factor productivity (tfp) (reference level: 1961=100).   austria belgium denmark finland france germany greece ireland 1981 130 139 121 122 119 134 147 109 2010 263 226 330 190 222 292 215 160 mean 194 201 208 155 167 210 188 133 st. dev. 48.65 38.75 72.44 28.05 37.89 54.19 32.10 18.64 aver. % δ 2.73% 1.98% 3.88% 1.51% 2.51% 2.86% 1.88% 2.65%   italy netherlands norway portugal spain sweden switzerland united kingdom 1981 163 169 105 89 193 115 116 136 2010 358 365 164 209 386 196 218 172 mean 227 239 131 137 270 159 139 153 st. dev. 58.71 53.35 17.95 34.54 67.92 26.57 27.06 10.86 aver. % δ 2.82% 2.45% 1.41% 2.80% 2.73% 1.87% 2.03% 0.78% source: own elaboration on usda data 1981-2010; the first line includes values for 1981 as a term of reference. 79public r&d and european agriculture: impact on productivity and return on r&d expenditure bio-based and applied economics 10(1): 79-86, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9928 regarding the nature of the research system mainly present in a country (basic, experimental, adaptive, extension, etc…) (alene, 2010), or by inferring the length through information criteria of regression models, such as adjusted r2, akaike, likelihood ratio and other criteria (fan et al., 2000; alene et al., 2009). the presence of lags inevitably yields estimation issues (biases) due to multicollinearity, implying the imposition of a limit in the length of the time lag. furthermore, research lags are not modelled according to a linear impact path, but rather designed in specific shapes accommodating the largely shared hypothesis that the impacts of r&d grow in the early years right after the implementation of the research, reach a peak and then decrease. such non-linear impact paths are modelled in different ways in the literature, in particular as pdl (polynomial distributed lags), gamma distribution function, triangular or trapezoidal (sumelius, 1987; thirtle and bottomley, 1988; thirtle and bottomley, 1989; thirtle et al., 1995; schimmelpfennig et al., 2000; alene, 2010). the most accredited literature is not unanimous on the required lag length and differences depend on the underlying assumptions. in fact, in order to assess the total effects of r&d expenditures from the beginning of a research project to the complete obsolescence of the related technology, alston et al. (2000) suggest a period of at least 50 years. pardey and craig (1989), instead, indicate the necessity of a lag length of at least 30 years to be able to capture the long-run impact of r&d on agricultural output. it is useful to stress, however, that such a condition is mainly found in studies in which the united states is the subject of the estimation and for which the assumption of the research activities, composed mainly of basic (relative to applied) research, is coherent with the hypothesis of long-term impacts on productivity. in europe, however, previous studies adopted, on average, lag lengths of less than 30 years. although the methodological approach applied in the european studies is in line with the one applied in the us studies, the best performance of the estimation models applied on europe data is achieved with an average lag length of between 9 and 12 years and by imposing a polynomial distributed lag (pdl or almond) structure (inverted “u”), through which a dynamic evolution (rise-peak-fall) of the effects can be accounted for (sumelius, 1987; thirtle and bottomley, 1988; thirtle and bottomley, 1989; rutten, 1992; shimmelpfennig et al. (1994); thirtle et al., 1995). indeed, as highlighted by shimmelpfennig et al. (1994), piesse et al. (2010) and pardey et al. (2018), the likely prevalence in europe of adaptive research activities (with respect to basic research) accommodates the assumption of reduced r&d lagged effects (with respect to the us) and, according to piesse et al. (2010), the use of 30 year series ought to be sufficient to capture lagged effects of r&d on productivity and acceptable from a methodological perspective. it follows that the models in the literature with the characteristics we are looking for are the ones proposed by alene et al. (2009) and alene (2010). such models proved to be able to manage relatively short time series and to provide for robust results by employing structured lagged variables for r&d expenditure. given the objectives of this paper, we intend to apply a panel analysis, opportunely specified such as to accommodate at best the available data. the model specification has the objective of estimating the effect of the r&d expenditure on tfp, through the most efficient estimator of the panel models13. we used a tfp (total factor productivity) index as dependent variable, and r&d investments gbaord (constant 2005 usd) (oecd) and lags, in terms of pdl as independent variables. to overcome the issue of multicollinearity of r&d lags, the following polynomial distributed lag (pdl) specification of second order has been applied to r&d lag variables (gbaord): pdl=∑j j=0αj(r&dt-j) (1) αj=β0+β1j+β2j2 (2) with j=0,1,…,j, where j represents the maximum lag or, in other terms, the lags’ length; by substituting (2) into (1), we obtain the following formulations of the pdl variable: pdl=∑j j=0(β0+β1j+β2j2)(r&dt-j) (3) pdl=β0∑j j=0(r&dt-j)+β1∑j j=0(r&dt-j)j+β2∑j j=0j2(r&dt-j) (4) to avoid crossed effects between r&d and productivity (negative αj coefficients)14, an end-point restriction is applied such that expenditures in years t+1 have zero effects on productivity in year t: α-1=αj+1=0 (5). by expanding (5), the following specifications can be obtained: 13 to evaluate whether to employ the fixor randomeffect model. 14 by crossed effect between r&d and productivity is meant the potential effect that tfp at time t might have on r&d at time t+1, that is the negative coefficients. 80 bio-based and applied economics 10(1): 80-86, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9928 michele vollaro, meri raggi, davide viaggi α-1=0⇒β0+β1(-1)+β2(-1)2=β0-β1+β2=0⇒β0=β1-β2 (6) αj-1=0⇒β0+β1(j+1)+β2(j+1)2=β0+β1j+β1+β2j2+ 2β2j+β2j=0. (7) by substituting (6) in (7) and then (8) back in (6), the following final specifications are obtained: β1-β2+β1j+β1+β2j2+2β2j+β2=0⇒β1(2+j)+β2(2+j)j=0 ⇒β1=-β2j (8) β0=β1-β2⇒β0=-β2j-β2⇒β0=-β2(1+j). (9) the restriction implies the estimation of only β2 and obtaining the other coefficient from the following equations (8) and (9). once the β2 coefficient has been obtained, the effects αj∀j and the total effects ∑j j=0αj can be estimated. the lag length j has been decided through the max adjr2 criterion, which makes it possible to choose that lag that maximizes the adjusted r2 of the free-form lag structure of the estimation equations (fan et al., 2000; greene, 2003; alene, et al. 2009). two different specifications of the panel model have been applied and controlled for heteroscedasticity: 1. tfp level: tfpit=γ0+γ0pdlit+eit, pdl computed on gbaord; 2. tfp log: lntfpit=γ0+γ0pdlit+eit, pdl computed on ln(gbaord) where i indicates the countries and t the period between 1981-2010. given the proposed methodology, it is expected that the sign of the r&d lags (calculated back from pdl) will be positive15. random(reff) and fixeffect (feff) models produce estimates according to the computational formula of the random-effect and within estimator, respectively. this implies a rigid constraint on the interpretation of the results, which must be attributed to, or referred to, the panel and not to the individual countries. data have been tested for the presence of unit root through several tests, both as a single series and as a panel, and the results indicate that not all series and panels are stationary. given that this result is not sufficient for co-integrating the data, a further co-integration test, namely the pedroni (2004) test, has been applied. the results of the pedroni test indicate that the couple of series (tfp-gbaord) share the same stochastic trend and that such data become stationary if a linear combi15 the coefficient of the variable pdl, namely , given the imposed shape of an inverted parabola, is expected to be negative. the corresponding lag coefficients of r&d, instead, namely , are expected to be positive. nation of the relative variables is applied16. based on this, we opted for the use of standard ols econometric procedures, in the version of the panel model, in order to obtain super-consistent parameter estimates (andersen et al., 2013). within the framework of cost-benefit analysis, an effective methodology for the evaluation of the economic impact is the computation of the rate of return of r&d investments. in particular, by referring to several studies, especially to griliches (1964) and davis (1981), in this paper the computation of the ror has been carried out according to the method of the marginal internal rate of return (mirr). the mirr for both tfp specifications has been computed according to the criteria adopted by alene (2010): , with j=“18” and t={“19812010”}, where vmp stands for value marginal product of r&d. given that the rors have been computed upon estimates from panel models, they are unique for all the countries. further, different average measures are applied, namely arithmetic vs geometric, in order to control for the potential effects of the deflation of the value variables, namely re, on the vmp and ror (davis, 1981). in fact, important differences emerge from the comparison of the rors computed through the mentioned techniques. however, the application of geometric averages is not possible for the variables in level form because the relative computation formula of the vmp does not involve the average of r&d17 (but also because, by definition, the geometric average of a variable is the equivalent of the arithmetic average of the logarithmic form of the variable). therefore, the sensitiveness of the ror computation with respect to the geometric mean is performed only on the tfp log specifications. 5. results by applying the max adjr2 criterion, 18 lags have been found for tfp specifications, implying that the variable pdl=∑j j=0j2(r&dt-j) is computed with j=0,1,…,18. the hausman test applied on the tfp level specifications reports an estimated χ2=9.59 and a ρ<0.05, revealing that the random-effect model results are more appropriate. for this reason, a further model including an autoregressive process of order 1 in the error term (ar(1)) is employed to consider likely effects of omitted 16 more details about the unit-root tests are available from the authors upon request. 17 the computation of vmp varies according to the tfp specification used in the models: the log form implies the use of average values of tfp and re, while the linear form does not. 81public r&d and european agriculture: impact on productivity and return on r&d expenditure bio-based and applied economics 10(1): 81-86, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9928 variables unlinked to single countries (coherent with random-effect model specification). on the other hand, the hausman test applied to tfp log specification reports an estimated χ2=0.02 and a ρ<0.89, suggesting that fix-effect model results more appropriate. however, we decided to run a reff model with ar(1) disturbances to account for missing variable bias, given that no efficiency as well as consistency would be lost. the lags imposed on the models, although computed empirically, have been doublechecked by referring to the work by piesse at al. (2010) who propose the existence of a diffusion path from the us to less developed southern countries, passing by northern and southern europe, backing the hypothesis that agricultural research in europe is mostly adaptive rather than basic. given this path, the lags from research to productivity in europe may be expected to be shorter than the suggested 50 years. the empirical determination of the lags seems to confirm this hypothesis. the variables in the tfp model specifications have been employed in the form of levels and logarithm. the results, displayed in table 3, show the expected positive sign of the r&d estimates and high statistical significance. across the considered 18-year lag period, the variables on agricultural research (r&d) indicate positive and significant effects, shown in figure 1, summing up to a total effect of 0,24 and 0,17, for the reff and reff with ar(1) specifications in level forms, and to a total elasticity of 0,10 and 0,09 for feff and reff with ar(1) specifications in logarithmic form, respectively.18 by comparing the results of the different specifications of the models on tfp, interesting estimation aspects are revealed. in particular, the inclusion of the ar(1) component in the error term does not improve the regression accuracy (both r2-within and r2-between) as well as the estimation of the r&d effects, which, rather, turns out to be lower. the reason for this difference in the estimates (or lack of difference in r2) might be related to the condition that both regressions include only one explanatory variable, namely r&d (in terms of pdl), inducing a lower impact when the ar(1) error component is considered. in this case, the relative importance of differences across countries versus time-related variability is null. further, other aspects emerge from the regression performed through the feff estimator in that it excludes the between variation from the estimation process. the results, shown under the column titled log form in table 3, indicate a lower performance of r&d (in terms of 18 each coefficient of the variable r&dt-j, i.e. current and lagged effects, has been computed from the original estimates of the pdl variables β2=-0.000182 (z-value= -13.64) and β2=-0.000129 (z-value = -5.24) for both re model specifications in levels, respectively, and β2=-0.000077 (t-value = -9.00) for fe model specification in logarithm. pdl) because the country-level effects are flattened out. however, the models run under the feff and reff (w/o ar(1)) estimator return exactly the same results, while the inclusion of the ar(1) component lowers the elasticity estimates but does not affect the goodness-of-fit. the observed sensitiveness of the effect of r&d on productivity supports the need to include more variables that potentially might affect agricultural productivity in the long-run to better isolate the impact of r&d. in particular, we refer to other elements, especially country-specific factors, such as climatic elements, weather anomalies, private investment in research, spill overs and agricultural policy implementation. although some of these have been tested in the models (such as climate and cap) the obtained results were not improving. in fact, both variables were not statistically significant.19 19 for climate, we used two climatic indexes, growing and cooling degree days indexes, estimated by the joint research center (jrc) of the european commission within the framework of agri4cast tooltable 3. results from tfp specifications. tfp level form log form reff reff w/ ar(1) feff reff w/ ar(1) constant 152.6*** 169.5*** 4.838*** 4.908*** r&dt 0.003*** 0.002*** 0.002*** 0.001*** r&dt-1 0.007*** 0.005*** 0.003*** 0.002*** r&dt-2 0.009*** 0.007*** 0.004*** 0.004*** r&dt-3 0.012*** 0.008*** 0.005*** 0.004*** r&dt-4 0.014*** 0.010*** 0.006*** 0.005*** r&dt-5 0.015*** 0.011*** 0.007*** 0.006*** r&dt-6 0.017*** 0.012*** 0.007*** 0.006*** r&dt-7 0.017*** 0.012*** 0.007*** 0.007*** r&dt-8 0.018*** 0.013*** 0.008*** 0.007*** r&dt-9 0.018*** 0.013*** 0.008*** 0.007*** r&dt-10 0.018*** 0.013*** 0.007*** 0.007*** r&dt-11 0.017*** 0.012*** 0.007*** 0.007*** r&dt-12 0.017*** 0.012*** 0.007*** 0.006*** r&dt-13 0.015*** 0.011*** 0.007*** 0.006*** r&dt-14 0.014*** 0.010*** 0.006*** 0.005*** r&dt-15 0.012*** 0.008*** 0.005*** 0.004*** r&dt-16 0.009*** 0.007*** 0.004*** 0.004*** r&dt-17 0.007*** 0.005*** 0.003*** 0.002*** r&dt-18 0.003*** 0.002*** 0.002*** 0.001*** r&dtotal 0.24*** 0.17*** 0.10*** 0.09*** r2 within 0,3078 0,3078 0,6866 0,6866 r2 between 0,0101 0,0101 0,1172 0,1172 note: *** represent statistical significance at the 1% level. standard error for lagged r&d coefficients has been computed via delta method. 82 bio-based and applied economics 10(1): 82-86, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9928 michele vollaro, meri raggi, davide viaggi the estimated coefficients obtained from all specifications (both marginal effects and elasticities), beyond characterising and quantifying the relationship between r&d investments and productivity, are the fundamental elements of the assessment, via ror, of the economic impact of r&d. the measure of ror is expressed as the mirr that equates the marginal value of productivity to the unit value of research expenditure (re). depending on the regression results, the computed ror for tfp specifications, shown in table 4, follows the same variation in magnitude as the elasticity estimates. in particular, the rors computed via the estimates obtained through the models employing the ar(1) component in the error term turn out to be smaller than the counterpart (w/o ar(1)). the application of geometric means to tfp in the log specification yields higher returns, namely 9.13% vs 7.03% and 7.59% vs 6.58%. this result is essentially due to a rebalancing of the average values of r&d lags. in particular, the geometric average, applied first to cross-country and then to lags, reduces the average value of the early lags and raises the values of the farther lags. essentially, in this specific case, the geometric means flattened the r&d lags, by increasing the slope of the downward trend of the lag series. as related to the computation of the ror, applying the geometric means to re leads to the estimation of box, specific for the agricultural sector. for cap, given the unavailability of country level data, we applied a dichotomous variable at year 1992 as proxy for the macsharry reform. a higher contribution of past r&d to the present value of agricultural production. the values of ror obtained by including the ar(1) component in the error term ought to be considered as the most reliable, because they account for omitted variables having potential effects on the entire sample. however, the observed variation, from 7.0% to 6.6% for arithmetic means and from 9.1% to 7.6% for geometric means, do not change the magnitude of the estimated ror in a meaningful way. 6. discussion the ror on investments in agricultural research in europe is consistently positive across different analytical methods. however, our estimates are comparatively low with respect to most findings documented in the literature for developed countries. moreover, the results confirm that ror computation is sensitive to the specification of the models, the method applied to measure the variables, the lag length and its shape and the territorial coverage. in fact, if compared to other works, such as schimmelpfennig et al. (2000a) (in the closed economy case), the rors obtained in this paper are to be considered very low. indeed, these results might depend on several differences between our study and those used as a comparison, including the time period, and the relative length (1973-1993, in which agricultural productivity recorded high levels of tfp growth, vs 1981-2010), and a wider coverage of countries (we included spain and 0,000 0,002 0,004 0,006 0,008 0,010 0,012 0,014 0,016 0,018 0,020 0 2 4 6 8 10 12 14 16 18 effect elasticity lag random effect random effect w/ ar(1) fix effect figure 1. distribution of single r&dt-j effects in tfp specifications. source: own elaboration. note: feff estimates are elasticities; reff estimates are marginal effects. 83public r&d and european agriculture: impact on productivity and return on r&d expenditure bio-based and applied economics 10(1): 83-86, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9928 portugal, the economies of which were lagging behind in 1973-1993, as well as sweden, finland and norway, characterised by particular climatic conditions, limited agricultural activities and intensive use of advanced technologies). despite this, the rors resulting from the analyses might be deemed reasonable when considering that most of the agricultural research carried out across the european countries has the characteristic of being adaptive, rather than basic research. in this regard it is important to highlight that, beyond the methodology and the variable measurement, the rors are sensitive also to both the length and the shape of the lags elapsing between r&d and agricultural productivity, as shown by the differences obtained by applying arithmetic and geometric averages. in particular, further potential contributions could come from imposing a shape of fourth-order pdl (or positivevalued distribution function) with a positive skew in order to impute more weight on the effects of the early lags. the results presented in this paper may suffer from potential limitations stemming from available data, namely gbaord, which are a proxy of actual expenditure, and from the omission of unavailable information potential used as covariates. in fact, as suggested by the difference in the results due to omitted variables, despite the general goodness and robustness of the estimations, model specifications are susceptible to improvements by including and controlling for more variables, especially those likely affecting agricultural productivity in direct ways unlinked to own-country agricultural research, such as evolution of farm structure, spill overs, weather, trade flow and agricultural policy. in particular, including aspects regarding the cap reforms and accounting for spill overs as well as climate evolution could potentially modify the results. further, the availability of data on private expenditures on agricultural r&d and the use of longer and more complete time series might have furtherly increased the robustness of the results. the consideration of these variables in this paper was explored, but the results were not satisfactory, most likely due to limitations in data availability. in any case, even if data had been available their use would also have required a reformulation of the analytical models in a consistent way. table 4. computation of the internal rate of returns (irr) for the tfp specifications. j variables in level form variables in logarithmic form arithmetic average geometric average α pdl vmp α pdl (ar1) vmp re α pdl vmp α pdl (ar1) vmp re α pdl vmp α pdl (ar1) vmp 0 0.004 0.04 0.002 0.03 183 0.001 0.02 0.001 0.02 115 0.001 0.03 0.001 0.02 1 0.007 0.08 0.005 0.06 182 0.003 0.03 0.002 0.04 115 0.003 0.05 0.002 0.05 2 0.009 0.11 0.007 0.08 181 0.004 0.05 0.004 0.06 114 0.004 0.07 0.004 0.07 3 0.012 0.14 0.008 0.10 179 0.005 0.06 0.004 0.08 114 0.005 0.09 0.004 0.08 4 0.014 0.17 0.010 0.12 178 0.006 0.07 0.005 0.09 113 0.006 0.11 0.005 0.10 5 0.015 0.19 0.011 0.13 147 0.006 0.10 0.006 0.11 113 0.006 0.12 0.006 0.11 6 0.017 0.20 0.012 0.14 141 0.007 0.11 0.006 0.12 112 0.007 0.13 0.006 0.12 7 0.018 0.21 0.012 0.15 135 0.007 0.12 0.007 0.12 112 0.007 0.14 0.007 0.13 8 0.018 0.22 0.013 0.16 129 0.008 0.13 0.007 0.13 111 0.008 0.14 0.007 0.13 9 0.018 0.22 0.013 0.16 124 0.008 0.14 0.007 0.13 111 0.008 0.15 0.007 0.13 10 0.018 0.22 0.013 0.16 118 0.008 0.14 0.007 0.13 110 0.008 0.15 0.007 0.13 11 0.018 0.21 0.012 0.15 112 0.007 0.15 0.007 0.12 110 0.007 0.14 0.007 0.13 12 0.017 0.20 0.012 0.14 107 0.007 0.15 0.006 0.12 110 0.007 0.13 0.006 0.12 13 0.015 0.19 0.011 0.13 101 0.006 0.14 0.006 0.11 109 0.006 0.12 0.006 0.11 14 0.014 0.17 0.010 0.12 95 0.006 0.13 0.005 0.10 109 0.006 0.11 0.005 0.10 15 0.012 0.14 0.008 0.10 90 0.005 0.12 0.004 0.09 109 0.005 0.09 0.004 0.09 16 0.009 0.11 0.007 0.08 84 0.004 0.10 0.004 0.07 109 0.004 0.08 0.004 0.07 17 0.007 0.08 0.005 0.06 78 0.003 0.08 0.002 0.06 110 0.003 0.05 0.002 0.05 18 0.004 0.04 0.002 0.03 72 0.001 0.05 0.001 0.03 109 0.001 0.03 0.001 0.03 mirr 15.21% 9.51% 7.03% 6.58% 9.13% 7.59% source: own elaborations 84 bio-based and applied economics 10(1): 84-86, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9928 michele vollaro, meri raggi, davide viaggi in perspective, if data become available, different strategies may be envisaged to improve this study. the correctness of the model specifications and, hence, the robustness of the results would largely benefit from a wider analytical approach that is able to consider the modern transformations occurring in the european agricultural sector. we refer, in particular, to the growing interest of research, including agricultural research, and policies, especially the cap, towards the development of technologies, practices and measures devoted to aims other than productivity, such as improving environmental protection, food safety and climate change mitigation. however, the impact of these variables is not straightforward. for example, the cap, on the one hand promotes innovation measures accelerating the transfer of research results to farmers, and on the other hand includes measures aimed at improving the sustainability of agricultural production processes but that indirectly might induce the effects of moderating the productivity. as a result, the direction of this impact is an empirical issue and may be correctly accounted for only by disentangling the effects of different measures. moreover, agricultural productivity itself as a focus of analysis ought to be revisited. indeed, it is not only productivity, by the means of research, that brings about benefits to society. other measures able to contemplate the effects of research on side aspects related to the agricultural production processes could be investigated, such as the societal value of the provision of public goods or the environmental protection, such that even the relative ror would be much more representative of broader research efforts. all these aspects and dynamics require a deeper analysis and could be the subject of further investigations in the years to come, especially as they would need better data than those currently available. 7. conclusions in this paper we analyse the impact of public r&d on agricultural productivity at the aggregate level, by using data from 16 countries, that can be considered as representative, in aggregated terms, of the european agricultural sector. based on this, we estimate the ror of public research expenditure in europe. our results add updated empirical information on the topic, by widening both the period of analysis and the territorial coverage at the european level as compared to existing studies. the results corroborate the hypothesis that, on average, research expenditure has a generally positive impact on productivity, which yields a relevant ror. our estimates show returns of public r&d investments on agricultural productivity of between 6.5% and 15.2%, varying according to model specifications and computation techniques. these results are consistent with other estimates from the literature, though lower than results from the us. the time lags are shorter than reported by most of the us literature. the general policy message from this paper is that the return on public research expenditure justifies investments in agricultural research, especially considering the low return from alternative investments in the current stage of the economic cycle. at the same time, the level and variability of return according to different estimation methods and different countries/sectors hints at the need for a careful evaluation of expenditures at the stage of programme/project funding. this would require a more detailed ex-ante evaluation of expected returns, but also attention to the widest range of priorities by policies (beyond productivity), more attention to targeting of expenditure as well as greater attention to factors that enable fast and effective research impact. this is indeed the route taken by current r&d funding policies at the european level. the analysis has limitations related to data availability, especially concerning research expenditure. the main limitations concern the length of the available time series and the level of standardisation (comparability over time and space) of expenditure data. this also reflects on the methodological approach used, as the study was carried out by employing the most suitable methodology able to accommodate both the quality and availability of panel data. in spite of the wide room for improvement, this work should be useful as a reference basis for further studies, especially for evaluating the impact of private r&d investments, the role of spill-overs as well as the effects of cap reforms on agricultural productivity, both at the country and european levels. an additional pathway for further research is to take into account the diversity characterising the research policies of different european countries. a satisfactory exploration of these routes will require consistent improvements in the availability of methodologies and datasets, with a strong priority for the latter. acknowledgement this study was conducted in the framework of the “impresa” project, which received funding from the european community’s seventh framework programme under the ga 609448. the content of this study does 85public r&d and european agriculture: impact on productivity and return on r&d expenditure bio-based and applied economics 10(1): 85-86, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9928 not necessarily reflect the official opinion of the european union. responsibility for the information and views expressed in the present paper lies entirely with the authors. references alene, a. d., and coulibaly, o. 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(eds.) handbook of development economics, 1: 275-331. usda (2016). https://www.ers.usda.gov/data-products/ international-agricultural-productivity/ wang, s. l., schimmelpfennig, d. and fuglie, k. o. (2012). is agricultural productivity growth slowing in western europe? productivity growth in agriculture: an international perspective, 109-125. volume 10, issue 1 2021 firenze university press ten years of bio-based and applied economics: a story of successes, and more to come fabio g. santeramo1, meri raggi2 the capitalisation of decoupled payments in farmland rents among eu regions gianni guastella1,2, daniele moro1, paolo sckokai1, mario veneziani3 contribution of periurban farming systems to local food systems: a systemic innovation perspective rosalia filippini1,2, elisa marraccini3, sylvie lardon2 an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications, and consumption of agri-food products carrying those certifications niculina iudita sampalean1, daniele rama1, giulio visentin2 wine after the pandemic? all the doubts in a glass daniele vergamini*, fabio bartolini, gianluca brunori public r&d and european agriculture: impact on productivity and return on r&d expenditure michele vollaro1, meri raggi2, davide viaggi1 bio-based and applied economics 8(1): 75-99, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8147 estimating a dual value function as a meta-model of a detailed dynamic mathematical programming model claudia seidel, wolfgang britz institute for food and resource economics, university of bonn, germany abstract. mathematical programming (mp) is a widespread approach to depict production and investment decisions of agents in agent-based models (abm) related to agriculture. however, introducing dynamics and indivisibilities in mp models renders their solution computing time intensive. we present a meta-modeling approach as an alternative to directly integrating mp in an abm. specifically, we estimate a dual symmetric normalized quadratic (snq) value function from a set of mp solutions. the approach allows us to depict relationships between key attributes, like the farm endowment with (quasi-) fixed factors and discounted farm household incomes, without modeling the technology in detail. the estimated functions are integrated in the abm to derive agents’ decisions. the meta-modeling approach relaxes computational restrictions such that spatial interactions in large regions can be simulated improving our understanding of structural change in agriculture. it can also be used to extrapolate to farming populations where data availability might be restricted. keywords. mathematical programming, mixed integer program, meta-model, duality, symmetric normalized quadratic value function, agent-based modeling. jel codes. q15, c61, c63. 1. introduction agent-based modeling is a popular approach to simulate phenomena depending on spatial interactions between farmers (berger 2001; britz 2013a; huber et al. 2018). it allows integrating behavioral rules that differ from standard micro-economic assumptions such as full information and full rationality (bonabeau 2002; nolan 2009). these features render agent-based models (abms) particularly suited to investigate the complex dynamic processes underlying structural change in agriculture (zimmermann et al. 2009). abms rapidly become complex and computing intensive if behavior of agents is modeled in detail (zimmermann et al. 2009). this is especially true for abms that, first, use mathematical programming (mp) to derive agents’ behavior in the abm and that, second, explicitly model land markets as key driver of structural change in agriculture (balcorresponding author: wolfgang.britz@ilr.uni-bonn.de 76 c. seidel, w. britz mann 1999). in this type of abms, decision making of farmers regarding production and investment quantities or willingness to pay for (quasi-) fixed resources is typically (partly) derived from (discounted) profits or household incomes1, as well as from marginal values of (quasi-) fixed resources simulated by mp (schreinemachers and berger 2011; happe et al. 2006). accordingly, a mp model has to be solved at least once for each agent and in each time step of the abm. to represent realistically the decision space of agents, especially when considering investments, mp models require a large set of constraints as well as binary and integer variables (mixed integer program, mip). this can result in model set-ups with several ten thousand equations and variables, of which several hundreds are binary or integer variables (britz et al. 2016). as a consequence, running an abm on large farming populations is very resource demanding if each agent’s behavior is derived from solving a large mip. mp solutions serve as inputs for different elements of abms focusing on agricultural structural change. farm household income drives exit decision of farmers; and marginal returns to land (or other factors distributed by auctions) determine the bids of agents in simulated markets. as a consequence, income and marginal returns to land determine which farms grow, shrink or exit in the abm and are, therefore, key drivers of dynamic processes in abms (balmann 1999). in particular, simulation of land auctions is computationally challenging. bids have to be calculated for each farmer and each plot of land that is available for rent at each time step, which can require solving a mp model for each combination of plot and farmer. such abm applications require efficient sampling schemes and sufficient computing power, especially if a whole agricultural region with many agents and a long time horizon should be investigated (troost and berger 2016). even with increasing computational power such as using computing cluster and efficient mip algorithms solving in parallel (e.g. britz 2013b; troost and berger 2016) direct implementation of large mp models in an abm results in high computing intensity. the tension between computing needs and increased detail and coverage is a longstanding problem in the scientific and engineering simulation domain despite the tremendous increase in computer power and algorithmic progress. it persists since increased data availability, using sensitivity analysis and growing model sizes, by e.g. integrating more interactions between the agents, drive up computing needs. indeed, the higher computing power itself invites researchers to increase model size and complexity to overcome shortcomings in previous set-ups. this can also be observed for agricultural abms using mp models (e.g. arsenault et al. 2012; brown et al. 2016; kellermann et al. 2008; lobianco and esposti 2010; polhill et al. 2007; schreinemachers and berger 2011; zimmermann et al. 2015). they are now solved with far more agents, integrate different types of agents and/or different types of market interactions, or they use mp models which are harder to solve. furthermore, large-scale sensitivity to address model uncertainty has become widespread. thus, to keep computing time at bay, agricultural abms using mp models still face restrictions with regard to the number of agents and/or to the design of the mp 1 in the following, we only refer to “income” for the sake of readability. dependent on the model set-up, a mp model derives profit rather than household income if off-farm labor or other non-agricultural activities are not included. in the mp model we use, farmers also generate income from non-agricultural sources. the term “discounted” applies to dynamic settings where mp models are solved for several years. in this case, yearly profits or incomes are discounted. 77a dual value function as a meta-model of mathematical programming model approach. reflecting that mip problems are np-hard to solve2, mp models are set-up without dynamics, with no or a decreased number of binaries or integers, or with an overall reduced number of constraints and variables. to overcome the need to sacrifice detail in a simulation model in favor of speed, meta-modeling strategies that require less computational power have been developed (meckesheimer et al. 2002). they provide a simple mathematical approximation of the input/output relations in the underlying simulation model (kleijnen 2018; kleijnen and sargent 2000; meckesheimer et al. 2002; pierreval 1996), drawing on statistical approaches such as (polynomial) regression models, splines or neural networks (kleijnen and sargent 2000). their aim can be threefold: first, to improve the understanding of the behavior of the simulation model and the problem entity; second, to optimize the model with respect to the determination of the input set; and third, to make predictions of the model’s simulation behavior (bouzaher et al. 1993; kleijnen 1979; kleijnen 2005; kleijnen and sargent 2000). in the latter case, the meta-model is run instead of the simulation model itself, mainly to reduce computing needs (kleijnen and sargent 2000; meckesheimer et al. 2001; meckesheimer et al. 2002). the objective of this paper is to develop a dual value function as a meta-model of a complex mp model motivated by the opportunity to reduce computational limitations of simulations with abms. the meta-model also allows to extrapolate to larger farming populations where the necessary detailed information on endowments such as structures and machines, as well as costs, labor and investment needs to set-up a mp model, is not available for each agent. furthermore, setting-up and calibrating mp models to yield realistic solution behavior is a time consuming process which can be only partially automatized (see troost and berger 2016). the two aims of reducing computing needs and covering a larger population are therefore interrelated. our approach integrates a meta-model of a mp model in an abm to compute optimal production and investment quantities, related discounted income and marginal values of (quasi-) fixed production factors. we, first, solve a suitable number of farm optimization problems with the mp model to obtain a farming population with individual production plans. we, next, estimate a meta-model from the mp results and subsequently integrate the estimates in the abm. specifically, we estimate a dual symmetric normalized quadratic (snq) value function which simulates discounted farm household income, input and output (i.e. netput) quantities and marginal values. as in the underlying mp model, the meta-model reflects production and investment decisions under maximization of discounted income at given prices and endowments, providing results under full rationality. however, simulated values can also be used to depict agent behavior that deviates from this assumption. to give an example, instead of using the marginal values of land, the average discounted income per ha of land can be used to define the marginal willingness to pay for an additional plot of land. the structure of the paper is as follows. first, we present the general methodology to develop a meta-model of a mp model to be integrated in an abm (chapter 2). in chapter 3, we apply the proposed method to a specific setting in order to precisely describe 2 “np” stands for non-deterministic polynominal time algorithm. np-hard means that so far, no algorithm has been found which could solve mip problems in polynominal time. clearly, the actual solution time depends on problem size and structure, the solver and hardware used. 78 c. seidel, w. britz our methodological approach. chapters 4 and 5 present and discuss the estimation results before we briefly conclude in chapter 6. 2. general methodology many abms focusing on structural change in agriculture directly integrate a mp model in the model set-up (fig  1)3. the mp model, solved for each farmer and at each time step, delivers income, input quantities bought and output quantities sold, as well as marginal values to (quasi-) fixed factors such as land and labor. often, simulated incomes drive farm exit decisions in abms. in land market auctions, marginal returns to land can be used to determine the agents’ willingness to pay for an additional plot. aggregated quantities of inputs and outputs over the farming population might be used to define price feedbacks in the abm, such that aggregated macro-level phenomena have an impact on agents’ behavior on the micro-level (chen and liao 2005). figure 1. classical set-up of an abm integrating mp. note: π = (discounted) profit or income, mj = marginal returns to (quasi-) fixed factors, t = yearly time steps of abm. computing time restrictions for solving an instance of the mp model limit the complexity of the mp approach and/or the number of agents. therefore, we develop a metamodel of the mp model which we integrate in the abm. the meta-model delivers the same information as the mp model, but much faster by approximating the behavior of the mp model based on an estimated dual value function. the estimated dual value function mimics the simulation behavior of the mp model. using the same inputs as the mp model, i.e. prices and endowments of each farm in the population, it generates outputs (income, netput quantities, marginal returns to (quasi-) fixed factors) that are very close to those simulated by the mp model. the mp model has to be solved only once for the whole farming population. production quantities, income and marginal values of each 3 in this and subsequent figures of abms in this paper, only the two modules of farm exit and land market are presented. obviously, an abm can include other and/or further modules to which our approach can also be applied if agent’s decision making is based on mp. 79a dual value function as a meta-model of mathematical programming model farmer are updated in the abm using the estimates of the dual value function. in the classical approach, the mp model has to be solved in each simulation period to derive production quantities, income and marginal values. the advantage of estimating a dual value function over independent regressions of variables of interest is that the value function represents income maximizing behavior just like the mp. according to duality theory, the dual function depicts the optimal frontier, i.e. income maximizing netputs at given prices and limiting production factors. thus, it maintains microeconomic consistency and indirectly comprises the information on the production feasibility set (diewert 1971; sidhu and baanante 1981; thijssen 1992). while in the classical approach, the technology of agricultural production is directly integrated in the abm through the mp model; in the meta-modeling approach, it is represented in its dual form by the estimates of the value function. the overall modeling approach is depicted in figure 2; the steps to take in the modeling approach are presented in figure 3. figure 2. meta-modeling approach. note: pi = prices of inputs and outputs, zj = farm’s endowments with (quasi-) fixed factors, xi = quantities of inputs bought and outputs sold, π = (discounted) profit or income, mj = marginal returns to (quasi-) fixed factors, marab = marginal returns to arable land, mgrass = marginal returns to grassland, = estimated coefficients of value function, t = yearly time steps of abm. 80 c. seidel, w. britz figure 3. steps in the meta-modeling approach. first, the most important explanatory factors, which differentiate the farmers in the region to be investigated, and their factor ranges need to be defined based on price and structural statistics, e.g. the number of farms of different farm types in a region, the distribution of farm sizes in a region etc. second, a suitable observation sample has to be defined using design of experiments (doe) to cover the farming population. third, in comparison to the classical approach (figure 1), a mp model designed to capture important interactions at farm level is solved for each observation of the farming population once outside of the abm before the start of the abm-simulation. this yields the optimal production plan for each farmer, i.e. a dataset of optimal investment and production quantities, related farm household income and marginal returns to (quasi-) fixed factors. fourth, these solutions are used to estimate a dual value function that becomes the metamodel of the mp model. fifth, the function along with estimated coefficients are integrated into the abm and used for calculating income and marginal returns to (quasi-) fixed factors for each agent at each time step. 3. application we apply the suggested meta-modeling approach to the mp model farmdyn, the abm abmsim and the german region of north rhine-westphalia (nrw). nrw encompasses 3.4 million hectares of which 1.4 million hectares are agricultural land managed by 33,700 farms. the agricultural structure is dominated by livestock farming with 67 % of the farms holding cattle and/or pigs. nrw is characterized by high agricultural productivity and strong economic pressure on the land market with a rental share above 50% (it.nrw 2019). therefore, we assume that agents act in a (bounded) rational way, i.e. they optimize under limited information. furthermore, we represent land rental markets in the abm as auctions. to investigate structural change in nrw in an abm, the around 34,000 farms need to be depicted as agents. in an approach integrating a mp model in the abm, the mp model must be solved at least once in any year of the simulation horizon for each farmer. in our setting, the mip optimization problem for a single farm comprises roughly 20,000 equa81a dual value function as a meta-model of mathematical programming model tions with 30,000 variables including 3,000 binary or integer decision variables. solving 34,000 of these mips takes several days. even if the mp model would be less complex, the computing time would be still too high to allow investigations of structural change of the whole region over several years. with the meta-modeling approach, however, the several ten thousand optimization problems can be solved within a couple of minutes which allows us to solve an abm over 10 years for the whole region of nrw within 15 minutes. in the following, we present the meta-modeling approach along the five steps as depicted in figure 3. 3.1 generating the farm sample (steps 1 and 2) observation samples for farms of different specializations (arable cropping, dairy, pig fattening, cattle fattening, mixed) are generated considering variations in (1) input and output prices, (2) endowment with (quasi-) fixed factors and (3), where appropriate, factors describing the technology such as the milk yield per cow. the factor ranges are chosen to capture the possible minimum and maximum values found in the farming population according to statistical data from the association for technology and structures in agriculture (ktbl) (ktbl 2016) and regional data of nrw (it.nrw 2019). design of experiments (doe) generates for each farm specialization a sample of farms that differ in initial conditions and other attributes. initial conditions are, among others, available family labor, capital stock (stables, machinery and storage facilities), arable land and grassland owned by the farm. other attributes are input and output prices that describe the farm’s market environment as well as yield potentials and household expenditures (britz et al. 2016). to make the solution procedure more efficient, we make sure that only plausible combinations of factor ranges are generated. unrealistic set-ups such as a farm with 250 cows, 10 ha and 0.25 labor units are likely to either lead to infeasibilities, i.e. to a loss of observations, or to unrealistically high or low marginal returns. therefore, instead of drawing independent factor values from absolute factor ranges, we define for each farm branch one key attribute, e.g. total farm size in hectares for arable farms or dairy herd size for dairy farms. factor ranges of further attributes are defined relative to the key attribute and from there mapped into absolute values. as an example, for the farm branch dairy, the farm’s endowment with arable land and grassland is defined by its individual amount of hectares of arable and grassland per number of cows. the absolute amount of arable and grassland is then defined by multiplying sampled number of cows with sampled hectares of arable and grassland per number of cows. in order to consider many factors and decrease computing time, we construct our sample based on latin-hypercube sampling (lhs) as an efficient quasi-random sampling procedure. lhs is a space filling random sampling design that distributes the randomized factor level combinations smoothly over the range of factor level permutations (iman and conover 1980; mckay et al. 1979). specifically, we apply the lhs package of r by carnell (2016), assuming a uniform distribution over each considered factor. after steps 1 and 2, we have a farming population with individual endowments with e.g. arable land, grassland and labor units, as well as prices of netputs that farmers face, reflecting the farming population and prices in the region under investigation. 82 c. seidel, w. britz 3.2 description of the mp model the mp model farmdyn (britz et al. 2016), that we use for our application, simulates economic optimal production and investment decisions, assuming a fully informed, fully rational and profit maximizing farmer4. it considers farm profits including subsidies plus potential earnings from off-farm work, given constraints such as a detailed depiction of the production feasibility set of the farm, the maximum willingness to work on the farm or off-farm, liquidity or restrictions relating to the common agricultural policy and german environmental laws. farmdyn can be run in either comparative-static or dynamic mode with a finite planning horizon. decisions of investments and labor supply are modeled as integer variables to consider indivisibilities and to reflect returns to scale, for instance relating to stable sizes or labor needs for the management of farm branches. as an example, the mip assumes that the farm can work at higher wages for 20 or 40 hours a week and/or to supply a low amount of off-farm labor at the legal minimum wage. different farming systems can be simulated (arable, dairy, beef, pig fattening and biogas plants) and combined to depict diversified farms. farmdyn currently reflects german conditions drawing on technological and economic data from ktbl (britz et al. 2016; ktbl 2016). originally developed to derive marginal abatement cost functions in german dairy farming under differently detailed emission accounting schemes (lengers et al. 2013), it was subsequently extended by a detailed description of pig farms (garbert 2013), arable farming (remble et al. 2013) and biogas plants (schäfer 2014; schäfer et al. 2017). farmdyn is a bottom-up model. it is evaluated by means of its gross margins. gross margins as simulation results of typical farms of a particular region (e.g. taken from structural data of north rhine-westphalia, it.nrw 2019) are compared with data provided by ktbl (ktbl 2016). farmdyn is realized in gams and solved by the industry mip solver cplex 12.6 (britz et al. 2016), in our application on a 44 core computing server profiting from parallel processing in cplex. furthermore, efficient solution strategies are implemented in farmdyn such as parallel computing on multiple cores and the reduction of the solution space of the mip by, first, solving a relaxed mip (rmip). a graphical user interface based on ggig (gams graphical interface generator, britz 2014) allows to steer model runs and to exploit results (britz et al. 2016). a detailed description of the model can be found in the farmdyn model documentation (see britz et al. 2016). 3.2.1 mp model run (step 3) as third step in our meta-modeling approach, we use farmdyn to derive optimal farm household income, netput quantities and marginal returns to (quasi-) fixed factors for each farmer in the farming population sampled in the previous step. we run farmdyn in dynamic mode. in the dynamic set-up, optimal decisions are simultaneously determined at each point in time based on the current state of the system, reflecting the principle of optimality by bellman (bellman 1954). a value function dis4 a dynamic-stochastic variant of the model is also available which can capture risk behavior based on different approaches to which the approach could also be applied. 83a dual value function as a meta-model of mathematical programming model counts the incomes that are simulated at each point in time (bellman 1954). thus, farmdyn delivers discounted farm household income at given netput prices and endowments. the corresponding average quantities of outputs sold, inputs bought, investment made and off-farm work supplied reflect yearly average activities of the optimal production plan over the planning horizon. as marginal values of a mip are conditioned on the current integer solution and do not consider that integers might change if a (quasi-) fixed factor increases, they might not reflect the actual shadow prices. that is why we derive the marginal returns to arable land and grassland from solving the model with increased endowments of arable land and grassland by one hectare and report the change in discounted income. therefore, the marginal values of arable land and grassland consider how farming activities would change in the next ten years if an additional hectare of arable land or grassland could be used for agricultural production for ten years (the usual duration time of rental contracts in german agriculture is between eight and twelve years, albersmeier et al. 2010), also including possible investments in a new stable if additional land becomes available. in step 3, we obtain optimal netput quantities, income and corresponding marginal returns to (quasi-) fixed factors for each farm of the farming population. 3.3 determination of the meta-model (step 4) as a meta-model, we estimate a dual value function from the solutions of the mp model provided by step 3. since the meta-model comprises the information about the technology of farms, a meta-model has to be estimated for each farm type by specialization separately. the possibility of farmers to switch from one agricultural production to another could be implemented by generating and estimating a sample of mixed farms that use various technologies. 3.3.1 choosing the variables to be included in the meta-model the inputs and outputs that are used as explanatory variables determine the farm household’s costs and revenues from agricultural production and off-farm work. the endowments with (quasi-) fixed factors and prices of netputs are used as independent variables to explain the dependent variables discounted income, netput quantities as well as marginal returns to land. in opposite to estimating from real-world data, we control the data generation process by solving the mp model which allows us to also generate observations on marginal values. as an example, table 1 presents the lists of netputs simulated for dairy farms. the inputs include feed concentrates bought, variable costs of crops that are produced for feeding (such as maize silage, incl. fertilizer, plant protection products, electricity etc.), as well as investments made. outputs are the amount of milk produced (other revenues such as from slaughtered cows or solved calves are reflected in the milk price), hours worked off-farm and exported manure. in regions with high livestock density, a farmer who exports manure makes a payment to an importing farmer. therefore, exporting manure means a cost and is considered as a negatively valued output in the estimation of the value function (kuhn et al. 2019; schäfer and britz 2017). the (quasi-) fixed factors character84 c. seidel, w. britz ize a farm household as an agent depicted in the abm. for a dairy farm, we consider the number of hectares of arable land and grassland, the amount of labor available, and the construction year of the existing stable, as well as the milk yield per cow as an indicator of productivity. as described above, the farm endowment with land and labor is derived from the initial number of cows since this is the key attribute in the doe for the farm branch dairy. we define a range of 40 to 150 milk cows per farm. the construction year of the stable is included as (quasi-) fixed factor because a stable can only be used for 30 years. once the stable reaches an age of 30, the farmer has to invest in a new stable in order to continue milk production. if the stable reaches the maximum age of 30 years in the optimization horizon of ten years, the farmer decides to reinvest in a stable or to quit milk production. this way, we can consider the age of a stable in the abm and also include the possibility of farm exit due to a necessary large investment in a new stable. this is achieved by increasing the age of the stable with each time step of the abm until a maximum age of 30 years and if a farmer invests in a new stable at a certain time step of the abm, setting the age of the stable back to zero. whether an agent has recently invested or will have to invest in a new stable within the next ten years is reflected in its discounted income. this way, it is possible to also include sunk costs related to a recent investment in a new stable and path dependencies which play a crucial role in agricultural production decisions (huber et al. 2018). as the discounted income can be used to derive bids for land plots, the age of the current stable will have an influence on the willingness to pay for an additional plot of land. table 1. variables included in the meta-model for the farm branch dairy. variable description factor range min max unit outputs netput price ranges milk produced amount of milk produced [t] 310.00 360.00 €/t off-farm labor hours that a farmer works off-farm [h] 8.00 15.00 €/h manure exported amount of manure exported from the farm [m³] 1.00 20.00 €/m³ inputs feed concentrates sum of feed concentrates bought [kg] 0.80 1.20 €/kg crops sum of crops produced for feeding [kg] 0.80 1.20 €/kg investments sum of investments [1] 0.80 1.20 € (quasi-) fixed factors factor level ranges arable land number of hectares of arable land 0.38 0.42 ha/cows grassland number of hectares of grassland 0.31 0.35 ha/cows milk yield milk yield per cow in 100 kg 80.00 86.00 *100 kg/cow labor units (lu) amount of farm labor available 28.00 38.00 cows/lu stable year construction year of the existing stable 1985 2010 note: prices of feed, crops and investments are based on price indices composed of mean prices. produced crops are solely fed to the animals and not sold on the market. therefore, the crop price reflects the costs of crop production. please also note that the export of manure means costs to the exporting farm. in the estimation of the value function, its price will, therefore, have a negative sign. 85a dual value function as a meta-model of mathematical programming model 3.3.2 definition of functional form to approximate the behavior of the highly detailed mp model as good as possible, flexibility of the functional form of the value function is important. it must depict a multiple input, multiple output production function. its derivatives will define input demand and output supply functions as well as marginal returns to limiting factors (lopez 1982). inter alia, diewert (1971, 1974), christensen et al. (1973), lau (1976) and sidhu and baanante (1981) propose flexible functional forms applicable to duality theory. to our knowledge, the choice of functional forms is quite limited if multiple inputs and multiple outputs are to be considered and convexity can be imposed to guarantee regularity, which is important for latter simulations. based on the work of diewert and wales (1987, 1988) and diewert and ostensoe (1988), kohli (1993) developed the symmetric normalized quadratic (snq) profit function (in our case value function as the mp delivers discounted income) that allows imposing global convexity, stays flexible and treats all inputs and outputs identically: β p p δ p z i n i i i n j n ij i j i n j m ij i j, 1 1 1 1 1 1 1 2 1 2 1 1 1i n j m k m ijk i j kγ p z zπ ωα (i) where π is the profit (in our case discounted income), � � � �i ij ij ij, , , are the parameters to be estimated, pi are the input and output prices, zi are the (quasi-) fixed factors, � �� � � i n i ip 1 is the price index for normalizing the prices and θi is the weights of prices for normalization (henningsen 2014). the estimation equations encompass the output supply and input demand equations xi, derived by taking partial derivatives of the value function with respect to price pi, and marginal returns mj derived as partial derivatives towards the factor quantities zj, according to the envelope theorem (henningsen 2014; mckay et al. 1983). 1 ( ) = = = = = = ¶ = = µ + + + ¶ å åå å åå n n n m m m 1 2 i i ij j i jk j k ij j ijk j k j 1 j 1 k 1 j 1 j 1 k 1i π p,z 1 1x   ω β p θ ω β p p δ z γ z z p 2 2 (ii) m z z δ p γ p zj j i n ij i i n k m ijk i k , 1 1 1 π (iii) as shown by the shadow price equation (equation iii), marginal returns to fixed factors are determined by price effects and price-fixed factor effects. accordingly, marginal returns to land vary not only due to different netput prices that the agents face but also due to joint effects of netput prices and endowments of farms with land, working units and other (quasi-) fixed factors. therefore, the value function meta-modeling approach allows maintaining heterogeneity among farms of the same specialization with different farming structures and/or facing different prices. only farmers with a similar farming structure and prices are assumed to derive the same optimal netput quantities and marαi 86 c. seidel, w. britz ginal returns to (quasi-) fixed factors. this observation points out that the value function is a dual representation of the technology. the value function does not fully depict the behavior of the agents in the abm. additional factors determining agents’ decision making such as irrational or social behavior can be explicitly modeled in the abm resulting in further heterogeneity among agents. 3.3.3 estimation of the value function since we are particularly interested in the derivation of the marginal values to land, resp. the shadow prices of land, we estimate the netput equations (equation ii) and shadow price equations (equation iii) simultaneously, to inform the estimator on the marginal returns to land (mckay et al. 1983) that are also provided by farmdyn. to our knowledge, that is a rather novel approach which reflects that other data sources such as farm samples used to estimate dual value functions are not providing observations on marginal values. corner solutions, resulting from inor output quantities simulated as zero, frequently occur in our generated dataset and represent a particular challenge for the meta-modeling approach. for example, a farm may not supply off-farm labor as the returns to labor in the farm exceed the reserve wage; or the reserve wage becomes so high that the farm does not produce agricultural output and family members only work off-farm. if no off-farm labor is supplied, we can conclude that the internal return to labor is at or above the reserve wage, but we cannot assume that it is exactly at the reserve wage as required for a consistent estimation of the value function with a standard estimator. in real-world observed samples in which the data generation process is not controlled, such zero observations are potentially subject to self-selection bias such that two-stage procedures like limited information maximum likelihood (liml) drawing on heckman (1979) may become necessary. in our case, we can exclude all observations with any zero input or output from the estimation since we know that the underlying technology is identical for all farms by definition as defined by the structure and parameterization of the mp model. still, corner solutions remain in the solution space of the mp model because of its integer variables. this is a particular challenge for the meta-modeling approach and discussed in chapter 4. the snq value function is estimated as a seemingly unrelated regression (sur) using the r package miceconsnqp (henningsen 2014). convexity on prices, which is an assumption of duality theory (diewert 1973; lau 1976; lau 1986; thijssen 1992), is imposed post-estimation where necessary based on koebel et al. (2003). we slightly modified the r code to include equations for marginal returns to land. at step 4, we obtain the estimates of the dual value function which represent the optimal production decisions of farmers originally simulated in the mp model. the estimated dual value function is now the meta-model of the mp model and can be integrated in the abm. 3.4 description of the abm abmsim, the abm we use for our modeling approach, was constructed to analyze structural change in farming in a spatial explicit setting. the landscape is generated using 87a dual value function as a meta-model of mathematical programming model corine (coordination of information on the environment) land cover data (european topic centre on terrestrial environment 2000) and differentiates between arable land, grassland, forest, housing, other urban fabrics, water bodies and other land types. the farming population is disaggregated in groups by specialization such as dairy, arable, pig fattening or mixed farming types. for each county in nrw, based on data from it.nrw (2019), it generates the observed number of farms by specialization and size class (<5 ha, 5-10 ha, 20-50 ha, 50-100 ha, 100-200 ha, >200 ha). initialization takes place by distributing the generated farms on available spots in the landscape, making sure that the farming structure at commune and county level is reflected (schäfer et al. 2019). once the farmsteads are allocated, the algorithm generates the agricultural plots with a random plot size from 1 pixel (= 1 ha) up to a chosen maximal plot size (britz 2013a). abmsim consists of five modules in which the estimated coefficients of the metamodel are used to calculate incomes or marginal returns to (quasi-) fixed factors to depict decision making of agents in the abm: land use change module, farm exit module, land market module, nutrient auction module (schäfer et al. 2019) and milk delivery module. the modules of abmsim are solved iteratively over distinct time steps of one year. in each year, economic drivers like exogenous prices or policies can be updated (britz 2013a). all markets included in abmsim are represented as auctions and depict the interaction space of agents where they compete for e.g. land, manure disposal or milk delivery contracts. the discounted household income derived from the mp, resp. from the dual value function, can be used in the abm to represent economic optimal production and investment decisions. in order to mimic real-world behavior of agents, a variety of behavioral rules can be applied. bounded rational behavior of agents is included by e.g. the possibility to derive bids from observations in the agent’s neighborhood or from an agent’s average discounted income per ha. these behavioral rules can be applied only for a part of the agents. as a consequence, the population can differ in a way that some agents behave according to full economic rationality while others take bounded rational decisions (britz 2013a). in the following, the farm exit and the land market modules of abmsim will be briefly described in order to present the integration of the snq value function estimation in the abm. these two modules are of particular importance for the simulation of structural change since they depict actions and interactions of agents which result in exit decisions and farm growth – the typical indicators of structural change in agriculture. a full description of abmsim can be found in its model documentation (see britz 2013a). 3.4.1 integration of the meta-model in farm exit and land market modules (step 5) the estimated coefficients of the snq value function are used for the identification of agents that exit agricultural production based on discounted income calculations, and for the derivation of the bids of the agents based on the marginal returns to arable land and grassland. figure 4 presents how the snq value function estimates are used in the two modules. in the farm exit module, the probability of a farm exit in each period depends inter alia on each agent’s current discounted income from farming (net of off-farm income). combined with other information such as the agent’s age and the probability to be employed outside agriculture, the calculated discounted income from farming (using 88 c. seidel, w. britz equation i) drives the probability of a farm exit5. if an agent exits, its current renting contracts end and the land owned (with the exemption of the farmstead) will be rented out. these plots are handed over to the land market module. agents that do not exit agricultural production become potential bidders on plots in the land market. the land market in abmsim is a pure rental market and represented by a spatially explicit auction mechanism6. agents who want to rent an additional plot of land put bids on the plots they are interested in. free plots are plots where the rental contract ended or where the recent user exited the market. the agricultural plots are heterogeneous in location, size and type (arable land, grassland). the bidding behavior of agents is based on a base bid. one way to define it, is to use the marginal returns to land calculated from current prices, farm endowment and the estimated coefficients of the snq value function, as 5 the derivation of the probability of farm exit can be found in the appendix. 6 the auction mechanism is modeled as generic as possible in order to be applied to other market implementations, such as a market for milk delivery contracts or for manure disposal rights. a more detailed description of the auction algorithm can be found in the model documentation of abmsim (britz 2013a). figure 4. connection of dual value function and abm. note: besides determining the base bid by calculating marginal returns to arable land (marab) and grassland (mgrass), it can also be defined based on average returns to land or average rents in neighborhood. 89a dual value function as a meta-model of mathematical programming model presented in formula iii, and assuming full economic rationality. as the estimates of the value function are based on mp solutions of a ten year optimization, the calculated base bid includes information on the optimal production plan for the next ten years at current price expectations (in our case constant prices), also including potential large investments in the future, as presented in chapter 3.3.1. however, the base bide can also be defined according to the simulated discounted income per unit of land; or, as another possibility, an agent might use the average rent paid for rental contracts in the neighborhood. the last two options depict bounded rational behavior. the base bid is, first, reduced by transport costs to the plot depending on the distance to the farmstead and, second, increased by a markup for plots larger than one ha. the markup is used to reflect cost saving opportunities due to a large plot size. the resulting bid is restricted to not be larger than the base bid. as grassland and arable land are separate fixed inputs in the snq value function, agents place different bids on plots of arable land and grassland. a rental contract of 10 years is set at a specific rental price between land owner and farmer winning the auction which depends on the chosen rules on auction order and price determination. after this land transaction, all bids for the remaining plots are recalculated for the winner of the auction because the willingness to pay for another plot of land has changed due to changed land endowment. the new marginal returns to land can easily be calculated by means of the snq shadow price equations (equation iii) taking into account the increased land endowment. due to the binding nature of rental contracts, bids may turn out to become unfortunate in the future because changes in prices or nonrenewed rental contracts might change marginal returns to land and cause sub-optimal rental prices of current rental contracts. 4. results the dual value function is supposed to provide a good approximation for simulated netputs, discounted income and marginal returns to land to reduce the additional uncertainty introduced in the overall framework due to the replacement of the mp by a metamodel (meckesheimer et al. 2002). therefore, we focus on the fit of the meta-model in the result section, using a dataset of 1,002 dairy farms simulated by farmdyn. these observations were kept from a sample of 5,000 farms after removing zero observations. we run the mp model as a dynamic programming model over the period from 2015 to 2025. netput quantities refer to averages of 2015 to 2025. a descriptive summary of the simulated and estimated netput quantities, simulated incomes and marginal returns to land is presented in table 2. the values are discounted household incomes comprising not only returns from the farm operation, but also from working off-farm and from returns to accumulated cash. the model comprises optimal financing decisions based on different types of loans which differ in length and rates. the discount rate hence captures the time preference of a farmer and differs from the market based one. the farms simulated with farmdyn are medium to large farms with a herd size between 40 and 150 cows and a land endowment of 32 to 110 ha in total, representing well the dairy farming structure in nrw (it.nrw 2019). figure 5 presents scatterplots of the mp-simulated and fitted quantities from the snq value function of milk and off-farm labor supplied as outputs, and concentrates bought 90 c. seidel, w. britz and crops produced for feeding, investments made and manure exported as inputs, as well as of the discounted incomes and marginal returns to land. the adjusted r² are very high (>93%) for the netputs milk, feed concentrates, crops and exported manure. the slightly lower r² for investments (89%) results from the assumption made in the mp that stables have to be bought in pre-determined sizes to reflect returns-to-scale. the integer character of variables make their estimation more difficult (see also discussion section in chapter 5). this can be especially seen in the moderate fit of the variable off-farm labor (40%). the dual value function with its continuous derivatives fails to fit the step-function that results from the integer character of the variable. in opposite to that, the fit of average annual discounted income is with 95% very high, with a slight tendency to overestimate at high levels. the fit of the marginal returns to arable land and grassland is high (about 80%). the slightly lower fit compared to the estimated netput quantities is due to the complex interactions between the limiting production factors land and labor. the binary character of labor results in hard to predict changes in discounted incomes, if land endowment changes. these interactions are not fully captured by the shadow price equation derived from the snq value function. table 2. key descriptives of simulated and estimated variables. milk off-farm labor feed concentrates mp-simulated snq-fitted mp-simulated snq-fitted mp-simulated snq-fitted min 391.0 398.0 5.0 -299.6 -113,287.0 -96,854.0 median 1,142.6 1,141,6 105.9 167.3 -62,849.0 -62,712.0 max 1,922.3 1,913,3 2,442.0 793.5 -21,396.0 -19,769.0 r² 0.99 0.35 0.95 crops produced investments manure exported mp-simulated snq-fitted mp-simulated snq-fitted mp-simulated snq-fitted min -84,903.0 -81,915.0 -59,192.0 -55,646.0 -2,593.3 -2,338.2 median -43,145.0 -42,838.0 -32,733.0 -32,996.0 -794.0 -785.7 max -13,354.0 -10,313.0 -13,587.0 -12,510.0 -40.6 40.0 r² 0.97 0.89 0.93 discounted income marginal returns to arable land marginal returns to grassland mp-simulated snq-fitted mp-simulated snq-fitted mp-simulated snq-fitted min 44,666.0 74,335.0 3.3 82.0 107.9 232.1 median 189,989.0 233,354.0 716.8 673.6 771.6 754.2 max 363,546.0 407,976.0 2,079.9 1,798.9 1,693.0 1,691.1 r² 0.95 0.79 0.80 note: mp-simulated values represent the values that are provided by farmdyn; snq-fitted values are values that are based on the estimation of the snq value function. 91a dual value function as a meta-model of mathematical programming model 5. discussion to our knowledge, although a vast amount of literature can be found that investigates meta-modeling approaches for simulation models (e.g. friedman and pressman 1988; jalal et al. 2013; kleijnen 1979; madu and kuei 1994), inter alia simple linear programming (lp) models (e.g. bailey et al. 1999; johnson et al. 1996; thangata et al. 2004), there is a lack of research that explicitly presents a meta-modeling approach of a complex mp model. consequently, there is no evidence about the general performance of a linear metamodel of complex mips. our results suggest that a dual value function is able to provide, on average, a high fit for netput quantities and discounted income for the mp model farmdyn analyzed figure 5. scatterplots of the dynamic dataset. note: figures were created using r. 92 c. seidel, w. britz in here. this might come as a surprise since mips provide corner solutions (due to the presence of integers) and are prone to overspecialization. the high fit found in here suggests that the large set of constraints of farmdyn dampens that tendency and leads to a plausible and robust simulation behavior in the sense that changes in netput prices and factor endowments lead to a, on average, smooth response. this might imply that, if mp is used in an abm to depict farming decisions, a certain degree of complexity is needed if a jumpy and hard to predict behavior has to be avoided. however, computing needs would be driven up – that is the starting point of using a meta-model instead. although replacing the integers by continuous variables would reduce computing time, a “normal” mp would eliminate returns-to-scale in investments and labor use which are now endogenously captured by the integers. we consider capturing returns-to-scale as important for the investigation of structural change. note here that while the dual value function imposes convexity in netput prices, both convexity and concavity of income in fixed factors can be depicted. as expected, the fit of the meta-model is lower if quantities are depicted by integer variables. integers violate the assumed continuous relation between prices and netput quantities underlying the dual value function. however, this can also be considered as an advantage in some cases. the linear world of a mip requires that investments come in pre-defined sizes if returns-to-scale are to be captured, whereas in reality, especially for building and structures, sizes can be rather flexibly chosen by the investing farmer. farmdyn tries to overcome that problem partially by offering fine-grained stable sizes, but the basic problem remains. compared to buildings and farming structure, for off-farm labor, the restrictive assumption is made in the mp that only 20 and 40 hour contracts are possible, besides a minimum wage job with only a few off-farm working hours a week. this explains the low fit of the netput off-farm work. in reality, however, family members might have some more flexibility to work part-time such that the smoothing effect of the meta-model might actually lead to a more realistic behavior. as such, the meta-model can also be understood as a way to interpolate over distinct points of the technology and the resulting solution space. furthermore, actions of agents in the presented abm are derived from discounted income and marginal returns to (quasi-) fixed factors which are very accurately represented by the dual value function. therefore, the more moderate fit for the variable off-farm labor should not invalidate the overall approach. the main advantage of using a meta-model based on duality is that it provides a coherent framework to derive simultaneously netput quantities, (discounted) incomes and marginal returns. that is especially relevant if all these variables are needed in the abm. if, for instance, only marginal returns to land are required, a simpler estimation approach not requiring a system estimation focusing on a high fit might be sufficient and more promising. even if results for several variables are needed and a relatively high fit is obtained for all of them, the missing consistency might not be a concern. that would especially be true if the estimators are able to improve the fit for cases such as off-farm work where the dual approach cannot perform well by definition. thus, approaches for instance from machine learning could be used instead of a theory consistent system estimation. as such, our results with the more restrictive dual approach define a kind of lower bound on the potential fit of a meta-model using more flexible fitting approaches. 93a dual value function as a meta-model of mathematical programming model furthermore, in order to differentiate decision making of agents regarding planning horizons (e.g. milk delivery quantities in the current year as, to a certain extent, shortterm decisions, medium-term decisions with regard to rental contracts, and long term decisions related to farm survival) more precisely, different value functions differentiated by planning horizons could be estimated and integrated in the model.. this way, differences in decision making of farmers could be depicted more precisely already at the estimation stage of the modeling approach. the modeling approach as a whole would yet become more complex, partly offsetting its advantages. as the paper showed, the dual value function is able to explain a complex mp model to a certain degree and is, therefore, suited to be implemented in an abm to derive agent’s decision making. 6. conclusion we present an approach to meta-model netput quantities, discounted farm-household incomes and respectively marginal returns to (quasi-) fixed factors from a highly detailed mathematical programming (mp) model with a dual symmetric normalized quadratic (snq) value function. the objective of the modeling approach is to set up a meta-model that represents the mp model in an agent-based model (abm) to derive agent’s decision making from it. a set of parameters characterizes farm types by specialization, e.g. dairy farms, datasets are generated using mp for each and a dual snq value function is estimated. based on duality theory, the estimation results shall be integrated in an abm. this approach represents a less computing intensive and technically easier set-up of an abm compared to the direct integration of a mp model into an abm. this reflects that solving the mp model for each farm is computing time intensive and coding efforts to integrate the mp model into the abm are higher compared to coding some few assignments necessary for the dual value function. as presented in the paper, the estimation of a snq value function is able to fit the netput quantities, discounted incomes and marginal values of the mp model farmdyn very well. slightly lower fits can be explained by the integer character of some netputs which is more difficult to capture by the continuous character of the dual value function. the value function meta-modeling approach maintains micro-economic consistency and derives mutually consistent simulation results at single farm-scale for discounted incomes, netputs and marginal returns to land. even though the meta-model reflects micro-economic optimal behavior, the modeling approach still allows to introduce deviations from fully rational behavior. the reader shall be reminded that bidding behavior can be derived from both marginal and average costs which can be further modified in the abm by behavioral rules. furthermore, similar to mp, the dual value function provides a behavioral benchmark that can be compared to outcomes underlying alternative behavioral assumptions. by replacing the complex mp model, the dual value function relaxes computational restrictions while maintaining at the same time complex agent’s 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(2015). pathways to truth: comparing different upscaling options for an agent-based sector model. journal of artificial societies and social simulation 18(4): 1–11. 99a dual value function as a meta-model of mathematical programming model appendix i. calculation of probability of farm exit the probability of farm exit is calculated from two elements: 1. the square root of the relation between the farmer’s current yearly profit πf and the maximum of (1) a pre-determined quantile of the profits in the farming population π pop quantile, and (2) the expected yearly net wage in the industrial sector minus commuting costs, 2. a normally distributed random number which accounts for not controlled determinants of exit decisions. by settings parameters to zero, the effect of the different elements can be switched off. algebraically, the probability can be expressed as: p nf f exo pop quantile� � �� � � � �� � � � �/ max , , 2 if the stochastic variable p is below 0.5, the farm will exit. in this case, the agent’s current renting contracts will end, while the land owned (with the exemption of the farm stead) will be rented out. the expected yearly net wage of the farm in the industrial sector is determined from land cover data and the agent’s age. first, the share of the industrial land cover indshare in a search radius around the farm stead is determined. this search radius is equal to the maximum commuting distance an agent is willing to accept. thus, in rural regions with little urbanized cover characterized as non-residential, off-farm working opportunities are low. the probability to find work in the industrial sector is determined by the square root of the share of industrial land cover, multiplied by a factor find expressing the relation between industrial land cover and open positions, and corrected for a term fage that depends on the agent’s age. the expected wage is then determined as the product of the wage in the industrial sector wage and the probability shown in the bracket: e wage wage f indshare f age ageind age cur min�� ��� � ��� ��� �* expected commuting costs commcost are defined from the share of industrial land cover indshare times the maximal commuting distance maxcommdistance, and the commuting costs per km commcostperkm: e commcost indshare maxcommdistance commcostperkm�� ��� * * the exogenous alternative profit π exo from working off-farm is finally defined as: � exo e wage e commcost� �� ��� �� �� (britz 2013a). the future of bio-based and applied economics daniele moro1, fabio gaetano santeramo2, davide viaggi3 how did farmers act? ex-post validation of linear and positive mathematical programming approaches for farm-level models implemented in an agent-based agricultural sector model gabriele mack*, ali ferjani, anke möhring, albert von ow, stefan mann the impact of assistance on poverty and food security in a fragile and protracted-crisis context: the case of west bank and gaza strip donato romanoa, gianluca stefania,*, benedetto rocchia, ciro fiorilloa assessing price sensitivity of forest recreational tourists in a mountain destination gianluca grilli1,2 estimating a dual value function as a meta-model of a detailed dynamic mathematical programming model claudia seidel, wolfgang britz bio -based and a ppl ied economics bae bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 copyright: © 2021 w. britz. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: w. britz (2021). estimating a global maidads demand system considering demography, climate and norms. bio-based and applied economics 10(3): 219-238. doi: 10.36253/bae10488 received: february 22, 2021 accepted: july 19, 2021 published: january 11, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio gaetano santeramo. orcid wb: 0000-0002-8532-3823 estimating a global maidads demand system considering demography, climate and norms wolfgang britz institute for food and resource economics, university of bonn, germany abstract. based on data mainly from the international comparison program for 156 countries, we conduct a global cross-sectional estimation of an extended rank-3 maidads demand system for nineteen commodity groups including agri-food detail for integration in a computable general equilibrium model. we render both marginal budget shares and commitment terms depending on the implicit utility level and consider age shares on the population, the gini-coefficient, the share of islamic population, a sea access indicator and mean temperatures as further explanatory variables. we find that especially demographic factors, the share of islamic population and mean temperature considerably improve model selection statistics and the fit of commodity groups with a low fit in a variant where prices and income only are used. graphics of the estimated engel curves, with details for agro-food commodity groups, highlight income dynamics of budget shares. keywords: demand system estimation, aidads, general equilibrium modelling. jel codes: d12, c33, c68. 1. introduction partial and computable general equilibrium models (cge) are widely used tools for policy impact assessments, but simulated outcomes depend on model structure and parameterization. in their review of how final demand is modelled in long-term analysis, ho et al. 2020 underline the importance of the choice of functional form for final demand. they find differences in baseline results for 2050 for an otherwise identical cge model of up to factor two between a linear expenditure system (les), a constant-difference-in-elasticity (cde) demand system1 and an aidads specification for single sectors, and still for up to 11% in total global aggregated output, all calibrated against the same data and own and income elasticities. similarly, britz and van der mensbrugghe 2018 compare outcomes of different model configurations and find sizeable differences in comparative-static analysis under a trade liberalisation shock between variants using different functional forms, calibrated against the same data and elasticities. but besides moving to more flexible functional forms, especially with regard to engel curves, also 1 the cde demand system underlies the widely used gtap standard model. http://creativecommons.org/licenses/by/4.0/legalcode 220 wolfgang britz bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 the parameterization of the demand systems in equilibrium model can certainly be improved. the widely used gtap model, for instance, depicts up to 65 sectors, but its demand system is parameterized drawing on an estimation with ten aggregated sectors, only (hertel and van der mensbrugghe 2019), such that elasticities for many sectors are identical. this paper focuses on improved representation of final demand in equilibrium models for long-run analysis, specifically on the gtap model and its variants, as the most widely used cge models globally. the gtap data base covers in its latest version 10 141 single countries or group of countries for which consistent longterm time series on final demand, related price and income are not available. a country specific estimation of parameters is therefore not feasible, such that the established practise estimates generic demand systems at global level, based on cross-sectional analysis, such as in seale et al. 2006, reimer and hertel 2004, preckel et al. 2010, roson and van der mensbrugghe 2018, britz and roson 2019. given the large differences in per capita income across countries at global level and high projected income dynamics for current low and middle income countries, flexibility in engel curves is deemed important during estimation and simulation. here, an aidads system with its exponential engel curves is often found as a sensible choice (cf. rimmer and powell 1996) and also used to estimate the current gtap parameter (hertel and van der mensbrugghe 2019). ho et al. 2020 stress additionally in their review that demography, income distribution and other factors such as religious norms are found as important drivers of consumption choices in many micro-level studies, but are basically not considered as consumption drivers in any of the global cge models. against this background, we aim at an improved final demand representation in cge models in several directions, by (1) extending the sectoral detail in the global cross-sectional estimation of the aidads system, by (2) moving to a more flexible maidads specification where also the commitment terms change with income, and by (3) controlling for additional factors which are likely to shape preferences such as religious norms. the resulting demand system is then integrated in the g-rdem model (roson and britz 2019) for construction of long-run baseline, as a module of the flexible platform for cge modelling cgebox (britz and van der mensbrugghe 2018). but the findings in here are also of relevance of partial equilibrium models focusing on specific sectors, or more generally of interest to economists interested in income dynamics of demand. the paper is organized as follows. we first motivate the use and detail the extended maidads demand system and the estimation approach before we present key results. next, we discuss key findings with a focus on differences across variants which consider additional drivers such as demography or income distribution. finally, we summarize and conclude. 2. methodology 2.1 extended maidads demand system we empirically estimate an extended aidads (an implicit additive demand system, rimmer and powell 1996) demand system for nineteen product groups: ten broader non-food groups and nine food categories, where the extension refers to utility depending commitment terms. detail for food is introduced as income effects are here especially relevant such as expressed, for instance, by bennet’s law (bennet 1941). the aidads system can be understood as a generalization of a les demand system where marginal budget shares are not fixed, a property also described as a rank three demand system with regard to income effects. other rank three candidates are, for instance, the quadratic expenditure system (qes, pollak and wales 1978) and the quadratic aids (quaids, banks et al. 1997). cranfield et al. 2003 estimated all three demand systems based of an older version of the data set employed in here with less demand categories, and compared them against the rank-two systems les and aids from which they are derived. in their comparison, aidads and quaids performed best and they recommend aidads if the income differences in the estimation or later simulations are high. one reason for this recommendation is the global regularity of aidads. specifically, compared to quaids, it ensures that marginal budget shares stay between zero and unity. moreover, compared to the quadratic marginal budget shares of for instance a quaids or qes specification, the exponential marginal budget shares of an aidads system might be considered more appropriate when covering a data set with extreme per-capita differences (rimmer and powell 1996). in the aidads demand system, the marginal budget shares are a linear combination of two vectors, depicting the marginal budget structure at very low and very high utility (income) levels. a logistic function depending on the implicit utility level determines the linear combination. given that the marginal budget shares in each of the two vectors fulfil the adding up condition to 221estimating a global maidads demand system considering demography, climate and norms bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 unity, any linear combination of the two also leads to regular budget shares. we follow preckel et al. 2010 who extend the original cranfield approach by rendering also the commitment terms depending on income, to what they call the maidads for modified aidads demand system. with regard to the estimation strategy we follow cranfield et al., 2000 who improve on the original rimmer and powell 1996 approach by developing an estimation method that does not rely on an approximation of utility. as usual, the independent data in estimations are the per capita incomes y and consumer prices p for countries c and commodity groups i,j, and the dependents the budget shares w. equation (1) determines the estimated budget shares w*c,i. it is identical to a les specification with the exception that the marginal budget shares δ and commitment terms γ are not fixed, but depend on the endogenously determined utility level. the marginal budget shares δi are expressed in (2) as a linear combination of two vectors δlo and δhi driven by a logistic function depending on the utility level u, implicitly defined by (5): (1) (2) can be interpreted as the marginal budget share at minimum utility level, i.e. very low per capita income, while is the share at very high incomes. the utility level uc is calculated at the given δc,i and γc,i in (5). it drives in (2) a logistic function with the parameters ωδ>0 and κ∂ which in turn determines the marginal budget share; this shows the implicit utility definition. at the point where the expression ωδuc-κ∂ is zero, the average between the two marginal budget share vectors is chosen, based on (5), that point is defined by κ∂. for larger negative ωδuc-κ∂, the exponent term approaches zero and the lower δc,i share is chosen; for larger positive ones, the exponent term approaches infinity such that is selected. in opposite to the original rimmer and powell 1996 proposal and subsequent work, we also consider a multiplicative factor ωδ. different from previous work with aidads or maidads specifications we are aware off, the two vectors δlo and δhi are country specific in here as they depend on a set f of further country specific attributes a as detailed below, see equation (3). (3) γ are the constant terms, typically termed commitments. as suggested by preckel et al. 2010, we render also the commitment terms an exponential function of utility, see equation (4). this allows especially better differentiating price sensitivity across income differences. (4) equation (5) defines the additive utility from the consumption bundle and is identical to the les definition2: (5) besides considering additional factors in the determination of the marginal budget shares, our approach is therefore slightly more general compared to preckel et al. 2010 who, first, have κ identical in determining δ and γ, and, second, introduce ω into (4), only. 2.2 estimation approach we follow closely cranfield et al. (2000) and preckel et al (2010) in our estimation by performing a loglikelihood estimation on cross-sectional data from the international comparison program (icp) referring to the year 20113 which provides a harmonized data set on expenditures (2), consumer prices and purchasing power parities. however, we don’t use the publicly available data, only, but based on an agreement with the icp, add more detail for food. 2 the usual definition of the implicit utility definition in the (m)aiads is δc,iln(xc,i-γc,i)-ln(a)-uc=1 with δ and γ expressed by (2) and (4). our formulation is equivalent as the term (-ln(a)-1) could be recalculated from the expressions ωγuc-κγ and ωδuc-κδ. 3 the current gtap data base versions in use are version 9 for 2011 and version 10 for 2014, which fits to the year of the icp data. longrun baseline construction with recursive-dynamic cge models projects decades into the future. with regard to consumption behaviour, this is only defendable if one assumes that observed differences in consumption patterns across countries with different per capita income level provide guidance of how pattern might change in future under stronger income dynamics. if using data from 2014 instead of 2011 would lead to distinct differences in the estimated parameters, the assumption would be challenged. but as we don’t have access to newer data, we leave such evaluations to other scholars. 222 wolfgang britz bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 as preckel et al. (2010) we define a quadratic covariance matrix e of dimension (n-1)×(n-1) comprising the error terms ec,i from (1). dropping the last column and row reflects that budgets shares and their error terms are linear dependent due to adding up. assuming normally distributed error terms e, their concentrated loglikelihood function becomes -½ln|e*| which elements defined as (6) where c is the number of countries observed. in order to improve estimation speed, we follow preckel et al. 2010 and apply a cholesky decomposition e*=r’r which eases defining the log of the determinant of e due to ln|e|=2ln|r|. the decomposition does not itself constrain the estimation outcome as the (reduced) covariance matrix e* is by definition positive definite. the decomposition is defined as: (7) the cholesky matrix r as an upper triangular matrix comprises with (n-1) (n-1+1)/2 elements far less elements than e*. the lower triangular part of the matrix r with elements rkl=0∀k>l must be set to zero while for the diagonal elements non-negativity is required to guarantee finiteness. this requires small positive bounds, here chosen as 1.e-8 which turned out to not become binding (this would imply perfect fit). the overall concentrated log-likelihood to maximize is derived from the diagonal elements of r: (8) exhaustion of income requires adding up of the marginal budgets to unity. this leads to the following adding up restrictions during estimation: (9) as seen from equation (9), the regression coefficients αi,f and βi,f, must add up to zero to maintain the adding up condition as they update marginal budget shares at low and high utility depending on country specific additional factors in equation (3). as some of these regressions coefficients are therefore necessarily negative, we restrict all estimated marginal budget shares to be non-zero. in order to prevent negative estimates in later simulations with the cge model, we introduce two artificial observations at 75% of the lowest income and 125% of the highest one. these two observations do not impact the estimated log-likelihood directly as there are no error terms attached to them, but the estimator needs to ensure that the estimated budget shares for these two observations are between zero and unity. moreover, we ensure that the estimated commitment terms don’t exceed 95% of the estimated demand at the minimum and maximum observations additionally introduced, beside an observation at the mean income of the sample. this provides additional safeguards against implausible outcomes when simulating with the system in later applications. these details clearly ref lect the specific aims of the exercise4. the use of the exp function can provoke mathematical overf lows during estimation and simulation. we therefore replace is with the following smooth quadratic exponential function: (10) where s is a smoothing factor chosen here as s=10. the usefulness of this smoothing approach becomes obvious if we consider the point x = 100. the exponential function will yield ~2.7e+43 while the smoothed one results in ~1.e+8. for the resulting linear combination of the estimated parameters in (2) and (4), differences in values of this dimension are irrelevant for any reasonable estimate. this becomes visible if we consider their bounds. the marginal budget shares δ are bounded by [0,1] and the γlo,i by [0,ymin] where the minimum yearly per capita income ymin is around 250 usd. this acts as a maximal bound for commitment terms as utility in (5) is only defined if xc,i> γc,i such that even with a budget share of 100%, γlo,i can never exceed the minimum income level observed. setting γup,i to its lowest possible value of zero and γlo,i at its possible maximum yields an commitment parameter of γc,i= [1+sqexp(x)] driven by utility based on x = ωγuc-κ∂. that means that if 1+sqexp(x)>> for larger values of u, the resulting marginal budget share will be, as desired, almost zero. as exp(10) ~ 5.5e4, that is already given at the point where the smoothing starts to make a difference with the γlo,i and γup,i at their most critical values for the approxi4 for the selected model, none of these additional safeguards became active during estimation and impacted the estimates. 223estimating a global maidads demand system considering demography, climate and norms bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 mation. more generally, one could define demand systems similar to the (m)aidads based on any function returning values on the domain [0,1] for any value of utility u. we estimate different variants of the model by considering besides price levels and income further country specific attributes relating to income distribution, religious norms, climate, access to sea and demography, separately or jointly. such additional controls are often found in demand system estimations drawing on household samples, where such attributes refer to individual households and not, as in here, to a country. adding these controls aims at insights if and to what extent these drivers systematically improve the fit, both with respect to the overall model and to individual categories, and reflects that these attributes have been found in micro studies as relevant to explain differences in demand behaviour (ho et al. 2020). the usefulness of integrating further explanatory factors might deserve some discussion. in our and similar exercises, the utility structure of the representative household of any country is assumed to be identical. this implies, for instance, that consumers in a country with a mainly islamic population would spend as much on beverages and tobacco as the ones in a country dominated by christians when facing the same prices and enjoying the same income level. this is not very likely as consuming alcohol is often forbidden in countries where the islamic belief dominates. such impacts might be only partially captured by price differences in goods. similarly, a larger share of older people might imply different expenditures on health at the same prices and identical average per capita income, motivating the use of demographic factors. demand system estimations based on a cross-section of country data set might face collinearity issues. first, price levels for some of the aggregated commodities are likely related in a systematic way to income levels, while we miss variability over time as found in a panel data set to dampen this effect. for instance, the so-called “beaumol”-disease stipulates that labour-capital substitution is harder in certain service sectors, such that in countries with higher wages (and income levels), some services are systematically more expensive, the costs of a hair-cut serve often as an archetypical example. indeed, we find r2 values for a simple regression of prices on the logarithm of per capita income (see table 3) for non-food groups in the range of 50-60% with the exemption of communication (~30%). for agri-food groups, the correlation between income and prices is still high (>40% r2) for meats, fish and other food, and otherwise quite small. any estimation using cross-country data with larger income differences will likely face these issues. in our estimation, some additional factors are also correlated to income, especially demographic factors with r2 values of 60% and 70%, using again logarithms of income levels as explanatory factors. the problem is hence of a similar magnitude as for prices and will hinder a clear separation of demographic factors from income level effects. the r² for other factors are below 25% and give little reason for concern. still, if additional factors systematically improve model selection criteria despite collinearity issues, they contribute to a better explanation, but collinearity will make it harder to tell income and price effects apart from the influence of these additional factors. we will come back to that point when discussing which of the different model variants to use for actual simulation purposes with the cge model. technically, we implement the estimator in gams, updating and improving the codes by britz and roson 2019 which draws on the ones originally used by reimer and hertel 2004. the use of gams is motivated by an estimation which comprises highly-nonlinear equations and constraints, such as the endogenous choleskydecomposition in (7). this asks for robust non-linear programming solvers such as conopt4 employed here which are not available in statistical packages. gams is not a specialized statistical package which implies that any statistics and tests need to be programmed manually. beside these technical issues, we see several reasons why we don’t develop code to estimate p-values for the individual parameters. first, in our demand system estimation, dropping prices or income as independents is impossible, due to constraints, the same holds for dropping additional factors in individual equations. even for additional factors, single p-values can therefore not guide the selection of these controls. second, even in the models with many additional factors, we still have thousands of degrees of freedoms. this renders it likely that p-values always suggest most parameters significantly different from zero, even if their relevance might be low. moreover, the interpretation of p-values is challenging in the context of parameter restrictions. we instead carefully discuss the trade-off between considering more additional factors and model selection statistics such as the akaike’s information criterion when deciding which of the model variants to choose for simulation. 2.3 data as other global exercises, we draw on data by the icp as it provides standardized and consistent observations on many countries with different per capita income levels. 224 wolfgang britz bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 this should help to find a robust representation of global, country-wide engel curves. as our ultimate aim is to integrate the estimates into the gtap derived g-rdem model, we aggregate detailed icp data on food expenditures covering 34 items to (aggregates of) gtap sectors and keeping otherwise the icp classification for non-food as shown in table 1. per capita demands are real expenditures in u.s. dollars, the prices are derived from these and nominal expenditure per capita in u.s. dollars. the gtap data base differentiates between wheat, paddy rice and other coarse grains which are potential substitutes in consumption. keeping here more detail likely violates the assumption of additive utility such that we rather aggregate here to a category “cereals”. the same holds for the two gtap sectors ruminant meat and other animal products, the latter comprising pig and poultry meat and eggs. moreover, the “other meats and meat preparations“ reported by the icp might comprise both ruminant and non-ruminant meat and can therefore not clearly be linked to individual gtap sectors. the reader might wonder why we don’t consider bread and pasta under the cereals product aggregate. the reason is that in the gtap sam, cereals refer to primary production and thus the farm scale, while bread or pasta as processed product are reported under the other food industry sector which comprises many more products such as ready-to-eat menus etc.. britz and roson 2019 therefor argue that the input coefficients of this food processing industry aggregate are likely depending on per capita income, as empirical analysis consistently shows that bulk calorie products such as cereals, bread or potatoes are inferior goods while convenience food is a rather a luxury good. we aim with the aggregation shown in table 1 above to get a good match between the definitions in the icp data set and the gtap data base which motivates this specific aggregation scheme. an overview on key metrics of the budget shares as the dependent variables provides table 2 below. we observe that for the non-food items shown in the upper part, with the exemption of costs related to housing, the minimum shares are all below 1.5%. the maxima reveal that the categorisation of non-food items is rather balanced, with the exemption of housing, they are all in the 10-20% range. the same holds, with the exemption of vegetables oils and sugar for the food categories, also. here, all minima are, with the exemption of the other food category, all close to zero. the r2 of a simple regression on log of income reaches up to 33% of cereals, but is in most case in the 10-20% range which leaves ample room for improvement by a demand system estimation. table 3 reports key metrics for the prices and income levels as key independents. the spread of prices is astonishingly high which can also seen from their standard deviation. there is also a stronger impact of the income level on the prices, a point touched upon before. when moving from the lowest income of around 250 usd to the maxima of around 55.000 usd, the regressions suggest that prices of non-food items would increase by 0.36 to 0.45 (note that the us price is set to unity and serves for normalization). data on demography are taken from the iassa data repository5 for the socio-economic pathways which ensures that the same data can be used in model appli5 https://tntcat.iiasa.ac.at/sspdb/dsd?action=htmlpage&page=about table 1. commodity groups in estimation and icp detail. commodity group icp identical clothing and footwear housing, water, electricity, gas and other fuels furnishings, household equipment and maintenance health communication recreation and culture education restaurants and hotels miscellaneous goods and services cereals rice; other cereals; flour and other products meats and eggs beef and veal; lamb, mutton and goat; pork; poultry; other meats and meat preparations; eggs and egg-based products fish fresh, chilled or frozen fish and seafood dairy fresh milk; preserved milk and other milk products; cheese; butter and margarine vegetable oil and cakes other edible oils and fats fruits and vegetables fresh or chilled fruit; fresh or chilled vegetables other than potatoes; fresh or chilled potatoes sugar sugar beverages and tobacco spirits; wine; beer; mineral waters, soft drinks, fruit and vegetable juices; coffee, tea and cocoa; tobacco other food processing food products nec; narcotics; preserved or processed fish and seafood; frozen, preserved or processed vegetables and vegetable-based products; frozen, preserved or processed fruit and fruit-based products; bread; other bakery products; pasta products; jams, marmalades and honey; confectionery, chocolate and ice cream 225estimating a global maidads demand system considering demography, climate and norms bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 table 2. statistics on budget shares derived from icp data. mean min max std.dev r2 on log(y)1 clothing and footwear 0,047 0,010 0,145 0,023 0,11 housing, water, electricity, gas and other fuels 0,153 0,049 0,389 0,057 0,11 furnishings, household equipment and maintenance 0,049 0,009 0,132 0,020 0,00 health 0,076 0,009 0,197 0,035 0,22 transport 0,092 0,014 0,183 0,034 0,02 communication 0,028 0,001 0,098 0,015 0,16 recreation and culture 0,045 0,004 0,112 0,028 0,29 education 0,072 0,013 0,178 0,028 0,05 restaurants and hotels 0,045 0,000 0,141 0,032 0,18 rest 0,077 0,015 0,194 0,044 0,08 cereals 0,049 0,001 0,311 0,063 0,33 meats, eggs 0,053 0,006 0,239 0,035 0,03 fish 0,013 0,000 0,103 0,016 0,14 dairy 0,026 0,001 0,108 0,019 0,14 vegetable oils 0,011 0,000 0,047 0,010 0,20 fruit & veg 0,049 0,006 0,210 0,037 0,28 sugar 0,008 0,000 0,038 0,008 0,20 other food 0,060 0,020 0,159 0,031 0,10 beverages and tobacco 0,048 0,009 0,149 0,023 0,00 source: icp 2011, aggregated according to table 1. notes: 1 linear regression with log of income per capita as independent. table 3. statistics on income and prices. mean min max std.dev r2 on log(y)1 income 9.030 220 55.835 12.196 clothing and footwear 0,771 0,229 2,053 0,368 0,61 housing, water, electricity, gas and other fuels 0,540 0,074 2,400 0,413 0,55 furnishings, household equipment and maintenance 0,853 0,422 1,778 0,288 0,63 health 0,439 0,098 1,678 0,328 0,65 transport 0,943 0,385 2,349 0,380 0,54 communication 0,678 0,101 1,742 0,288 0,31 recreation and culture 0,768 0,330 1,948 0,323 0,59 education 0,313 0,037 1,905 0,320 0,55 restaurants and hotels 0,799 0,265 2,240 0,341 0,55 rest 0,640 0,233 1,993 0,333 0,69 cereals 0,916 0,258 3,588 0,395 0,15 meats, eggs 0,994 0,277 3,313 0,467 0,51 fish 0,593 0,155 1,723 0,289 0,53 dairy 1,080 0,412 2,159 0,293 0,02 vegetable oils 1,386 0,719 2,331 0,325 0,04 fruit & veg 0,732 0,234 2,614 0,356 0,39 sugar 0,915 0,239 2,329 0,304 0,06 other food 0,844 0,268 1,902 0,297 0,33 beverages and tobacco 0,716 0,128 2,289 0,329 0,33 source: icp 2011, aggregated according to table 1. notes: price of united states = 1, 1 linear regression with log of income per capita as independent. 226 wolfgang britz bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 cations for long-run analysis. we use the shares of two age groups as additional factors which can be expected to be not part of the working population (<15 and > 65 years). not only are these age groups likely to show consumption patterns different from other age groups, they also might (indirectly) control for differences in household sizes, especially the share of <15 years old. as some household expenditures comprise a fix-cost share, household size at the same average per capita income of the household members is likely to change budget shares (deaton and paxson 1998). we took access to sea into account especially in the hope to better control for spending on hotels and restaurants, and to explain fish consumption. mean temperature as the climatic variable chosen not only could impact the food consumption bundle, for instance with regard to dairy, but also impact housing and clothing expenditures (sheth 2017). to check for the influence of different income distributions, we use gini coefficients taken mostly from the cia factbooks, a few missing observations were filled by data from liberati 2009. data on the share of islamic population were taken from a study by the pew center, 2011 (pew center 2011). in total, we observed for c=156 countries budget shares, prices and additional factors. the 19 commodity groups lead to 2,964 observations. the extended aidads model where also the commitment terms depend on the utility level has four vectors of parameters (α, β, γlo, γhi), two utility multiplier κ and two exponents ω, considering the adding up conditions, this implies m = (2*n + 2*(n-1) + 4) = 78 parameters for the maidads variant without additional factors. each additional explanatory variable adds two additional vectors of marginal budget shares at low and high income, again considering adding up, that means for each factor 2*(n-1) = 36 additional parameters to estimate. for the model considering all six additional independents, we hence estimate 294 parameters. this reduces the degrees of freedom more than a quaids system which would estimate m = (3 * (n-1) + (n-1)*(n-1)/2 = 192 parameters. but the full model is not used for simulation in here, but rather serves as a benchmark to select a suitable set of additional factors beyond per capita income and price levels. 2.4 integration in the cge using the estimation results for benchmarking of a cge model is far from straightforward as observed budget shares for a country or country aggregate might deviate considerably from what the econometric model suggests. additionally, with the exemption of the agrifood sector, the commodity groups are still rather aggregated compared to, for instance, the 57 sector resolution of the gtap 9 data base or the 65 sectors of gtap 10. during estimation and later simulation, the utility is implicitly driven by the demands which depend on the marginal budget shares and commitment levels which are functions of utility. in order to ease benchmarking, we follow therefore the approach of britz and roson 2019 which perform a regression of the estimated utility levels from (5) on per capita income and add here as further independents the additional factors. the estimate of the utility level allows deriving an estimate of the country and sector specific δc,i and γc,i for benchmarking. we cannot introduce the error term in the simulation model directly. instead, we have, as usual for benchmarking with cge models, to correct some of the parameters in order to line up the observed data with the estimated ones. the errors cannot be simply added to the commitment terms γc,i as this changes non-committed income as well. doing so also runs the risk to introduce rather curious elasticities in the model. this becomes visible from the marshallian demands in equation (11). (11) if, for instance, the observed x is large compared to what the estimations suggests as x*, simply increasing the related commitment term will mean that income and price effects are considerably dampened compared to the estimation. increasing the marginal budget shares at unchanged commitment terms will instead increase price and income responsiveness. we therefore suggest first scaling both vectors of estimated parameters by the relation between the observed and the estimates, next scale the commitment terms such that they add up to unity and finally penalize squared deviations from the original estimates and under adding up conditions. table 4. additional factors considered. factor variable(s) income distribution gini coefficient religious norms share of islamic population climate mean temperature sea access coast line relative to country size [m/skm], in log demography share of persons < 15 year share of persons > 65 years 227estimating a global maidads demand system considering demography, climate and norms bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 3. results 3.1 fit of different model variants in order to assess the different model variants, we compare the value of the likelihood function, the akaike’s information criterion, the information inaccuracy, the schwartz’s criterion and the system wide root mean squared error. the calculation of the statistics follows cranfield et al. 2003, i.e. the root mean squared error for the estimation of the budget shares w for the products i is calculated as rmsei=[1/c ωic-ω*ic with c being the number of countries and the system wide rmse by using the mean budget share as weights, i.e. smrse= rmsei. the value of the likelihood function is defined as llf=-1/2cln|e*|, the information inaccuracy as iia=1/c ωc,i(ωc,i/ω*c,i), akaike’s information criterion as aic=2/ cm+ln|e*| and the schwartz’s criterion as sc=1/cln(t) m+ln|e*|. we calculate a system wide r² by weighting the individual r² with the budget shares. the full model which uses all additional explicatory factors clearly has the best fit with a likelihood function value of 11.472 and a system r wide ² of 54,2%, see table 5. it shows also the best iia value, but the aic and sc statistics suggests that it might be over specified when compared to other variants. specifically, it adds 6 times 2 parameter vectors to the base model, such that we estimate (around) ten parameters for each commodity from 156 observations. both in the groups of model variants using one factor or two factors, the religious norm and the demographic variables tend show the best values for the model selection statistics. overall, the three factor model using the religious norm, the climate factor and demographic attributes gives the best aic criterion. its llf and the system wide r² are close to the full model, but its aic and sc selection criteria are considerably better. we therefore consider it the most suitable candidate based on the model selection statistics. the sc criterion would favour the model without any additional factors. but, as expected, the system wide r² and the value of the likelihood function put it on the last position. table 5. model selection statistics. llf system r² srmse aic iia sc base 11.219 45,3 2,86 -142,9 9,47 -141,4 norms 11.295 48,6 2,75 -143,4 9,01 -141,3 demography 11.326 49,5 2,75 -143,3 8,83 -140,5 sea access 11.252 46,5 2,82 -142,8 9,22 -140,7 climate 11.275 47,7 2,80 -143,1 9,07 -141,0 gini 11.260 47,1 2,82 -143,0 9,26 -140,8 norms + demography 11.379 51,3 2,68 -143,6 8,53 -140,0 norms + sea acess 11.328 49,7 2,72 -143,4 8,74 -140,5 norms + climate 11.345 50,5 2,71 -143,6 8,68 -140,7 norms + gini 11.328 50,0 2,73 -143,4 8,82 -140,5 demography + sea acess 11.360 50,5 2,72 -143,3 8,60 -139,7 demography + climate 11.367 50,8 2,72 -143,4 8,54 -139,8 demography + gini 11.359 50,6 2,72 -143,3 8,62 -139,7 sea acess + climate 11.302 48,7 2,77 -143,0 8,86 -140,2 sea acess + gini 11.290 48,2 2,79 -142,9 9,04 -140,0 climate + gini 11.300 48,6 2,78 -143,0 9,12 -140,1 norms + demography + sea acess 11.413 52,4 2,66 -143,5 8,28 -139,3 norms + demography + climate 11.425 52,6 2,65 -143,7 8,25 -139,4 norms + demography + gini 11.405 52,2 2,66 -143,4 8,34 -139,2 demography + sea acess + climate 11.395 51,6 2,70 -143,3 8,39 -139,0 demography + sea acess + gini 11.390 51,5 2,70 -143,2 8,40 -139,0 sea acess + climate + gini 11.327 49,6 2,75 -142,9 8,78 -139,3 full 11.472 54,2 2,62 -143,4 7,99 -137,7 source: own estimation. notes: numbers in bold indicate the best statistic in the group of models and red ones the overall best model. 228 wolfgang britz bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 while the overall model statistics are reported in table 5, the tables shown in the following report the r2 for the individual equations as a widely used and easy to interpret statistics to compare the fit, here both across estimated equations in the systems and across competing model variants. for comparison, we add always the system wide r2. table 6 reports in the column “base” a model using prices and income levels only as independent variables, i.e. the slightly extended maidads model as proposed by preckel et al. 2010. the best fit is found for “recreation and culture” with 81% as a clear luxury good, followed by “fruits and vegetables” by 76%. as seen from table 6, these product groups also include staple food such as potatoes or root and tubers as classical examples of barnett’s law. this might explain the relatively high fit for that category. disappointing is the fact that “furnishings, household equipment and maintenance” even has a negative r2 while for “beverages and tobacco”, 8% only of the variance are explained. similar low fits are also reported in britz and roson 2019. the low explanatory power of the base model for some of the categories motivates considering additional factors which might drive consumption patterns. in order to assess how the additional factors impact results, we estimate versions where each factor is considered without the others, any combination of two or three factors and a full model comprising all of them. note here that we always consider the two demographic variables jointly. we first find that adding any additional factor to the base model improves the fit as seen from table 5. demography gives the best results of the models with single factors, but is actually introducing two additional dependents variables in the model. while it improves the fit for each single product group compared to the base model, it is not always better than model variants using another additional factor. the best results for any model variant considering one additional factor only are shown in bold in table 6. this highlights that for eleven out of the nineteen product groups, the two demographic factors give jointly the highest r2. the share of islamic population follows with seven groups. sea, access, climate and the gini coefficients trail both with regard of the overall fit and with regard to categories where they provide the best fit. however, one needs to consider that demography is based on two additional dependents. the bad performance of the gini coefficient we also tested a variant using logs instead of the linear model for which results are reported – might come as a surprise. one might have assumed that, for instance, higher table 6. fit of different model variants by commodity group, single factors. base norms demography sea access climate gini system wide r2 45,3 48,6 49,5 46,5 47,7 47,1 clothing and footwear 13,4 18,2 18,4 13,7 14,8 17,3 housing, water, electricity, gas and other fuels 45,4 51,3 48,7 46,7 46,8 45,7 furnishings, household equipment and maintenance -0,5 1,5 9,9 0,3 4,8 3,1 health 65,7 71,5 71,6 66,1 70,2 66,5 transport 32,5 33,7 38,2 33,4 36,0 36,5 communication 26,4 30,6 30,2 27,4 30,4 30,3 recreation and culture 80,9 85,3 84,1 81,2 81,5 81,3 education 29,9 33,6 35,8 30,0 31,6 31,7 restaurants and hotels 34,4 38,3 35,5 37,2 37,9 35,7 rest 74,4 76,0 76,4 74,5 75,1 74,5 cereals 73,1 74,4 74,6 73,4 73,5 73,2 meats, eggs 49,4 49,6 49,5 52,6 49,5 49,6 fish 33,2 34,0 34,4 38,7 37,6 35,0 dairy 34,7 38,9 36,0 36,7 39,9 40,6 vegetable oils 63,0 63,7 63,1 63,2 63,3 63,2 fruit & veg 63,7 65,2 64,8 63,9 65,1 64,2 sugar 60,9 61,2 65,2 62,3 61,3 60,9 other food 61,6 61,8 64,1 63,6 62,5 65,6 beverages and tobacco 8,5 16,5 23,5 14,2 16,5 14,1 source: own estimation. notes: numbers in bold indicate the best fit in the group of models. 229estimating a global maidads demand system considering demography, climate and norms bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 income inequality at low income levels might increase the observed budget share of luxury goods. a potential explanation why the gini coefficient does not improve the fit strongly might be that the impact of, for instance a small group of rich households, on average spending shares of the aggregate might still be rather limited.6 results for individual commodity groups of the models which consider two factors jointly are shown in table 7. here, combining the two demographic variables with the share of islamic population gives the best fit based on the system wide r², closely followed by adding the mean temperature to them. equally, the best fit found for any of the different product groups is more equally distributed over the different model variants. while the best model considering one of the factors adds around 4% to the overall r2 of the base model (see table 6), considering two jointly improves at best by around 6%. results for the models which consider three factors jointly are shown in table 8. perhaps as expected from the results found for single additional factors, combining 6 we also tested with gini coefficient provided by un with quite similar results. the share of the islamic population with the two demographic variables and the mean temperature to control for climate effects gives the best fit. it misses the fit of the model will all factors (i.e. adding the gini coefficient and the sea access indicator as well) by less than just 2%. this full model performs considerably better for “clothing and footware” (+5%), “beverages and tobacco” (+4%) and “meat and eggs” (+4%) compared to this best candidate model with three additional factors. it is interesting to see that simpler models give a better fit in two cases compared to the full specification, for which the fit is shown in bold if it is better than any other specification. besides considering the model selection statistics from table 5 and considerations of the fit for individual model groups, the choice of a suitable model variant depends also on how its estimates can be integrated into long-run simulations with a cge. suitable variants comprise factors which are likely rather stable over time or are explicitly controlled by dynamic updates. as the iassa data base reports projections of the demographic composition of the population for all countries and the different ssps, the two demographic factors are obvious candidates. they also have shown to improve contable 7. fit of different model variants by commodity group, two factors. norms demog norms sea acc norms climate norms gini demog sea acc demog climate demog gini sea acc climate sea acc gini climate gini system wide r2 51,3 49,7 50,5 50,0 50,5 50,8 50,6 48,7 48,2 48,6 clothing and footwear 18,7 18,4 18,7 19,9 19,3 20,6 19,9 15,3 18,0 17,7 housing, water, electricity, gas and other fuels 51,9 51,7 51,5 50,9 49,0 49,0 48,9 47,4 46,9 46,9 furnishings, household equipment and maintenance 10,4 2,0 6,7 4,2 10,4 12,0 11,4 5,9 3,6 6,0 health 73,1 71,4 72,9 71,1 71,8 72,5 71,9 70,4 66,6 70,1 transport 40,3 34,5 37,6 37,3 38,8 38,9 39,0 38,3 37,5 37,7 communication 31,4 32,1 33,4 32,8 30,9 33,2 32,0 30,3 30,9 32,0 recreation and culture 86,4 85,6 85,4 85,3 84,3 84,2 84,2 81,8 81,6 81,6 education 37,2 34,1 34,9 35,4 35,9 36,7 36,8 32,4 31,8 32,4 restaurants and hotels 38,6 42,0 44,6 39,8 39,5 43,9 39,0 39,3 37,8 38,1 rest 76,9 76,0 76,2 75,8 76,3 76,3 76,5 75,3 74,7 75,0 cereals 76,6 75,0 74,8 74,7 75,5 75,5 75,4 74,1 73,5 73,9 meats, eggs 50,2 52,7 49,9 49,8 52,5 50,2 50,7 52,1 52,6 49,7 fish 35,2 40,1 38,5 35,5 39,4 39,5 37,0 40,1 39,7 38,3 dairy 42,9 41,0 45,1 42,2 37,6 39,5 41,9 40,2 41,5 42,8 vegetable oils 64,2 63,7 64,1 64,3 63,1 63,4 63,2 63,8 63,3 63,8 fruit & veg 67,6 65,6 66,3 66,3 65,3 66,0 65,9 64,8 64,6 65,8 sugar 66,0 62,6 61,5 61,4 66,3 66,6 65,5 62,2 62,4 61,5 other food 64,4 64,1 62,6 66,0 67,1 64,7 67,2 64,2 67,0 65,8 beverages and tobacco 25,2 20,6 21,5 21,4 26,9 24,7 24,3 19,6 18,4 17,9 source: own estimation. notes: numbers in bold indicate the best fit in the group of models. 230 wolfgang britz bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 siderably the fit either alone or combined with others. the share of the islamic population in a country could clearly change when simulating over multiple decades into future, but cultural habits related to current or former shares of islamic population are properly more stable. it seems therefore defendable to use the share of islamic population as well as an additional control. finally, mean temperatures can be either considered stable or updated according to climate change projections. considering both factors besides the demographic ones clearly could improve the model selection satistis and the fit of most commodity groups. while in some cases, considering the gini coefficients gave best results for certain categories, the gini coefficient is likely to change if average per capita income increase considerably over the projection period and is therefore here excluded. sea access seems mostly to impact fish consumption and it is likely that the benchmarking process will address outliers here anyhow. based on these arguments and the model statistics, we opt for the model specification with uses the two demographic factors, the share of islamic population and the climate variable as additional explanatory variables. table 9 reports the estimated parameters. quantities during the estimation are expressed in usd dollars per capita and corrected for differences in prices, setting the us price to unity. the commitment terms are all modest to low, when considering that income reaches up to around 55,000 usd in the sample. generally, the reader should keep in mind the difference between expenditure levels and budget shares. let us take education as an example: the expenditure at low income levels (250 usd) is based on budget share of around 7%, plus forty dollars committed, i.e. around sixty dollars. at 50,000 usd, the about 5% marginal budget share implies an expenditure of 2,500 usd plus 2,000 usd of committed income, i.e. 4,500 usd. but, production costs and thus prices for educational services are also generally higher in high income countries. scatter plots are shown in figure 1 for non-food and in figure 2 for food-items jointly with logarithmic regression lines dependent on income. note that the income axis is logarithmic. the plots highlight two observations. first, the variation in the observed budget shares in countries of the same income range can be rather large, as seen for instance from the panel for the housing costs. there table 8. fit of different model variants by commodity group, three and all factors. norms demog sea acc norms demog climate norms demog gini demog sea acc climate demog sea acc gini sea acc climate gini full system wide r2 52,4 52,6 52,2 51,6 51,5 49,6 54,2 clothing and footwear 20,4 20,6 20,4 22,9 21,5 18,3 25,2 housing, water, electricity, gas and other fuels 52,0 52,5 52,3 48,9 49,2 47,4 52,5 furnishings, household equipment and maintenance 10,9 12,8 12,6 12,4 12,0 7,3 15,8 health 73,1 74,1 73,2 72,8 72,2 70,4 74,5 transport 40,8 41,6 40,5 40,7 39,7 40,1 43,2 communication 32,3 34,2 32,5 33,4 32,4 31,9 35,1 recreation and culture 86,4 86,5 86,3 84,4 84,4 81,8 86,4 education 37,7 37,9 38,5 36,6 36,8 33,1 39,4 restaurants and hotels 43,0 46,5 40,4 45,0 41,8 39,5 47,9 rest 76,9 76,7 77,0 76,1 76,4 75,2 76,6 cereals 77,0 77,3 76,8 76,2 75,9 74,5 78,0 meats, eggs 53,7 50,6 51,1 52,9 53,3 52,2 54,6 fish 40,6 40,4 37,3 41,0 41,4 41,0 42,9 dairy 45,5 47,0 46,0 39,4 42,8 43,1 49,2 vegetable oils 64,3 64,7 64,9 64,1 63,3 64,4 66,1 fruit & veg 67,8 68,3 67,9 66,0 66,4 65,7 68,4 sugar 67,0 67,5 66,3 67,4 66,5 62,4 68,4 other food 67,4 65,1 68,1 67,4 69,4 67,6 70,3 beverages and tobacco 28,3 26,8 26,5 28,4 28,0 20,8 30,7 source: own estimation. notes: numbers in red indicate the best fit in the group of models. results in bold indicate best value including the full model. 231estimating a global maidads demand system considering demography, climate and norms bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 are some observations in the 500 usd range where just 5% are spent on housing, whereas the average household in others countries spends 30%. at the same time, estimates also scatter around the simple logarithmic regression line which reflects the impact of price differences across countries, but also of the other explanatory factors. the diagrams also highlight the usefulness of the using the exponential marginal budget lines of the aidads system to capture, for instance, the clear saturation effect seen for cereals in figure 2. for meats and eggs as well as dairy, the plots suggest that budget shares first increase up to around 2000 usd to drop afterwards. figure 3 shows the expenditure shares resulting from the aidads estimation, for income levels between 250 and 50,000 usd evaluated at mean prices and mean explanatory factors. at very low income levels, more than a third of the income is dedicated to food (37%), around 13% is spent on housing and 8% on transport, 5% on furnishing, household equipment and maintenance and 2% on health. at very high expenditure levels, the share for food drops to about 17%, while shares for housing increase moderately to around 16%. shares for health care are more than tripling, reaching 11%, whereas for restaurants and hotels they increase by a factor five, from 1.7% up to 7%. a similar large increment is observed for “recreation and culture” growing from less than 1.6% to over 7%. an interesting observation is the rather drastic change in budget shares for some product groups when moving from 250 usd to 1000 usd per capita and year. housing cost half from 37% to 18%, while expenditures for food change only slightly. instead, budget shares for health (1.7% versus 5.6%), communication (0.08% to 2.3%), furnishings (2.2% to 4.3%), transport (2.8% to 6.7%), recreation and culture (0.5% to 2.3%) and other items (0.9% to 4.6%) increase substantially. that underlines that at very low incomes, expenditures are concentrated on food, shelter and utilities, where the later might serve also as input into, for instance, food preparation in the household, which is outsourced at higher income levels. figure 4 below provides more detail for food categories in the aidads system by reporting shares on total food expenditure. at very low income levels, cereals have the highest shares with around 28%, followed by the other food category (19%) which comprises, for instance, bread, and 12 % are spent on fruits and vegetables. expenditures on meat in total food consumptable 9. estimated base coefficients for selected model. alpha beta gamma, lo gamma, high clothing and footwear 4% 5% 6 136 housing, water, electricity, gas and other fuels 1,00e-07 20% 121 1.354 furnishings, household equipment and maintenance 5% 6% 1 158 health 4% 9% 781 transport 2% 13% 3 423 communication 2% 3% 290 recreation and culture 1,00e-07 6% 133 education 7% 5% 39 2.037 restaurants and hotels 0% 6% 5 181 miscellaneous goods and services 1,00e-07 12% 252 cereals 10% 1,00e-07 19 meats, eggs 12% 3% 203 fish 3% 1% 1 dairy 8% 2% 84 vegetable oils 4% 0% fruit & veg 15% 1% 131 sugar 2% 1% other food 13% 3% 7 209 beverages and tobacco 9% 3% 10 301 food (sum of the categories above) 76% 15% 37 928 source: own estimation. note: model considers two demographic factors and temperature as additional explanatory variables. the gamma parameters are expressed on a per capita basis. 232 wolfgang britz bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 0,16 100 1000 10000 bu dg et sh ar e income clothing and footwear obs est log. (obs) 0 0,05 0,1 0,15 0,2 0,25 0,3 0,35 0,4 0,45 100 1000 10000 bu dg et sh ar e income housing, water, electricity, gas and other fuels obs est log. (obs) 0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 100 1000 10000 bu dg et sh ar e income furnishings, household equipment and maintenance obs est log. (obs) 0 0,05 0,1 0,15 0,2 0,25 100 1000 10000 bu dg et sh ar e income health obs est log. (obs) 0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 0,16 0,18 0,2 100 1000 10000 bu dg et sh ar e income transport obs est log. (obs) 0 0,02 0,04 0,06 0,08 0,1 0,12 100 1000 10000 bu dg et sh ar e income communication obs est log. (obs) 0 0,02 0,04 0,06 0,08 0,1 0,12 100 1000 10000 bu dg et sh ar e income recreation and culture obs est log. (obs) 0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 0,16 0,18 0,2 100 1000 10000 bu dg et sh ar e income education obs est log. (obs) 0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 0,16 100 1000 10000 bu dg et sh ar e income restaurants and hotels obs est log. (obs) 0 0,05 0,1 0,15 0,2 0,25 100 1000 10000 bu dg et sh ar e income miscellaneous goods and services obs est log. (obs) figure 1. scatter plots, non-food items. 233estimating a global maidads demand system considering demography, climate and norms bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 -0,1 -0,05 0 0,05 0,1 0,15 0,2 0,25 0,3 0,35 100 1000 10000 bu dg et sh ar e income cereals obs est log. (obs) 0 0,05 0,1 0,15 0,2 0,25 0,3 100 1000 10000 bu dg et sh ar e income meats and eggs obs est log. (obs) -0,02 0 0,02 0,04 0,06 0,08 0,1 0,12 100 1000 10000 bu dg et sh ar e income fish obs est log. (obs) 0 0,02 0,04 0,06 0,08 0,1 0,12 100 1000 10000 bu dg et sh ar e income dairy obs est log. (obs) -0,01 0 0,01 0,02 0,03 0,04 0,05 100 1000 10000 bu dg et sh ar e income vegetable oils obs est log. (obs) 0 0,05 0,1 0,15 0,2 0,25 100 1000 10000 bu dg et sh ar e income fruits and vegs obs est log. (obs) -0,005 0 0,005 0,01 0,015 0,02 0,025 0,03 0,035 0,04 100 1000 10000 bu dg et sh ar e income sugar obs est log. (obs) 0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 0,16 0,18 100 1000 10000 bu dg et sh ar e income other food obs est log. (obs) 0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 0,16 100 1000 10000 bu dg et sh ar e income beverages and tobacco obs est log. (obs) figure 2. scatter plots, food items. 234 wolfgang britz bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 tion are estimated at 10%, while dairy accounts for 7% at such low income levels. there is again a distinct difference between the 250 usd to the 1000 usd consumption pattern, as the cereals share is halved to 14%, while the share of meat (+6% to 16%) and dairy (+3% to 10%) increase considerably. at very high incomes, other food (22%) followed by meat (18%) and beverages and tobacco (18%) are the largest expenditure groups inside the food bundle. the cereal shares on total food expenditure is still 3%, but the overall drop of the budget share of food implies that a very high income levels, less than 1% of the income is spent on cereals. the income dynamics become also visible from the engel curves shown in figure 5. recreation and culture as well as the other service category show very high engel elasticities at low income in the range of five. interestingly, at high income levels, education and communication have elasticities below unity, different from all other non-food items. for the food items, cereals show negative engel elasticities over a wider ranger of the income variation. below 100 usd, basically all food items besides cereals are luxury goods, as indicated above, this becomes possible by a quite low income elasticity for housing expenditure, also visible from the upper panel. but food item elasticities drop rapidly below 0.5 around 1000 usd, with the exemption of beverages and tobacco as well as meat and eggs, and increase slightly again up to income levels around 5.000 usd. a potential reason is the falling elasticity for housing costs suggested by the upper panel. above 1000 usd yearly per capita income, none of the food items is a luxury good any longer and the crop based food items with the exemption of sugar have elasticities below 0.5. the reader should keep in mind that these estimates also capture the effect of compositional changes, for instance, the average household in a rich country spent income on imported fresh fruits and vegetables, while in poor countries, this product 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 25 0 15 00 27 50 40 00 52 50 65 00 77 50 90 00 10 25 0 11 50 0 12 75 0 14 00 0 15 25 0 16 50 0 17 75 0 19 00 0 20 25 0 21 50 0 22 75 0 24 00 0 25 25 0 26 50 0 27 75 0 29 00 0 30 25 0 31 50 0 32 75 0 34 00 0 35 25 0 36 50 0 37 75 0 39 00 0 40 25 0 41 50 0 42 75 0 44 00 0 45 25 0 46 50 0 47 75 0 49 00 0 clothing and footwear housing, water, electricity, gas and other fuels furnishings, household equipment and maintenance health transport communication recreation and culture education restaurants and hotels miscellaneous goods and services food figure 3. simulated expenditure shares, non-food items and total for food. note: calculated at mean sample prices and mean sample values of the additional factors. 235estimating a global maidads demand system considering demography, climate and norms bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 group might mainly comprise locally available roots and tubers. 6. discussion a suitable specification for aggregate household demand in a cge model needs to reflect the targeted applications. for detailed policy analysis such as changing subsidies and/or taxes differentiated across energy carriers, income changes are mostly limited and the focus is rather on own and cross price effects. this motivates the use of nested demand systems e.g. in the gtap-e (mcdougal and golub 2007) model to capture in detail substitution effects between different energy carries. we focus instead on long-run analysis with large income dynamics which motivates the use of the maidads functional form. stronger hicksian substitution effects between the commodity groups considered in here are not very likely such that second-order flexibility with regard to prices is probably not needed to identify the engel curves. this motivates also the use of a simpler additive utility function. in this respect, we don’t follow the argumentation line of reimer and hertel 2004 who consider the aiads as not appropriate for more than ten product categories in estimation, an argument which would also apply to an les or cd specification. as the g-rdem model as our main application target also uses ces nests to substitute between different cereals and between different meats, we deliberately aggregate here beyond the individual gtap sectors in the estimation as discussed above. differentiating to individual cereals or meats would indeed render the use of an additive demand system dubious. an estimation exercise of an maidads system for food only by gouel and guimbard 2019 estimates calorie demands for seven food categories, introducing hence similar detail for food as in our exercise, however estimating demands based on producer prices. 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 25 0 15 00 27 50 40 00 52 50 65 00 77 50 90 00 10 25 0 11 50 0 12 75 0 14 00 0 15 25 0 16 50 0 17 75 0 19 00 0 20 25 0 21 50 0 22 75 0 24 00 0 25 25 0 26 50 0 27 75 0 29 00 0 30 25 0 31 50 0 32 75 0 34 00 0 35 25 0 36 50 0 37 75 0 39 00 0 40 25 0 41 50 0 42 75 0 44 00 0 45 25 0 46 50 0 47 75 0 49 00 0 cereals meats fish dairy vegetable oils fruits and vegetables sugar other food beverages and tobacco figure 4. expenditure shares for food categories. note: calculated at mean sample prices and mean sample values of the additional factors. 236 wolfgang britz bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 we opted in here to render marginal budget shares depending on additional factors besides prices and income. alternatively, the commitment terms could be updated. using the marginal budget shares has the advantage that additivity can be imposed on the impact of these additional factors. this at least prevents that more unusual observations for the additional factors can provoke e.g. negative consumption quantity estimates, or that the non-committed income overshoots the observed one when commitment terms are increased. the estimates for the commitment terms (see table 9) suggest that they are all mostly small compared to income levels. at least for the vector at low utility, that is not an astonishing outcome as estimation of negative budget shares is not allowed even at the quite low minimal per capita income levels in the estimation. here, neither larger increases of the commitment terms nor larger decreases are able without violating the non-negativity condition, while updates to the marginal budget share cannot provoke problems in that respect. 0 1 2 3 4 5 6 100 1000 10000 clothing and footwear housing, water, electricity, gas and other fuels furnishings, household equipment and maintenance health transport communication recreation and culture education restaurants and hotels miscellaneous goods and services -1 -0,5 0 0,5 1 1,5 2 2,5 100 1000 10000 cereals meat and eggs fish dairy vegetable oils fruits and vegetables sugar other food beverages and tobacco figure 5. estimated engel elasticities at mean prices. note: calculated at mean sample prices and mean sample values of the additional factors. formula based on preckel et al. 2010. 237estimating a global maidads demand system considering demography, climate and norms bio-based and applied economics 10(3): 219-238, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10488 switching to, for instance, a quaids to better capture cross-price effects while also considering some additional factors would introduce many new parameters in the estimator. the review of ho et. al. 2020 of demand systems in cges mentions only one example (jorgenson et al. 2013, a dynamic single country cge for the us) where a rank 3 translog demand system is used which gives also flexibility for coss-price effects, however for four aggregate expenditure groups, only, which are further dis-aggregated to more detail based on homothetic functions. given the non-homothetic character of e.g. food expenditure groups above, a nested approach where the lower nests assume homotheticy is probably less appropriate for our exercise. vigani et al. 2019 estimate a quaids for kenya with detail for food, but only mention that this can improve economic models without discussing how. it is also interesting to see that in their estimation, the quaids gives for most product and product groups income elasticities quite close to unity. their hierarchical demand system layout might render it hard to link their results into cge models, especially if flexible aggregation with regard to commodity is maintained, as in case of the gtap family of cge models. furthermore, given the often high correlation between prices and income levels in our cross-sectional data where time variability of prices is missing, it could be challenging to introduce a non-additive demand system with full flexibility for price effects several statistic packages allow estimation of a (nonlinear) system with parameter restrictions. for highly non-linear specifications such as in here, convergence and feasibility issues with the solvers inbuilt in these packages are not uncommon. it is therefore not astonishing that all authors estimating (m)aidads systems (reimer and hertel 2004, preckel et al. 2010, roson and van der mensbrugghe 2018, britz and roson 2019) rather use gams to access robust nlp solvers such as conopt. estimating one of the more detailed systems in here requires up to 10 minutes of computing time using the parallelism of conopt4 on a fast four core machine. we consider a larger-scale bootstrapping exercise to determine the distribution of the parameters and p-values as not feasible. arata and britz 2019 propose instead to construct a fisher information matrix by simulating the error terms at changed parameters. while this would be computationally feasible, we don’t consider that the additional coding efforts would help us in better assessing the choice of models. summary and conclusion we present an estimation of an extended maidads demand system from global cross-sectional data. existing literature in this field is extended in multiple dimensions. compared to britz and roson 2019 who use the same data set, we integrate the extension proposed by preckel et al. 2010 to render the commitment terms depending on utility. in both britz and roson 2019 and preckel et al. 2010, only prices and income are used as independents while we now also consider demographic factors, the share of islamic population to control for religious norms and cultural habits, mean temperature to check for climatic influences and test if access to sea and the gini coefficients have a systematic impact on consumption shares. according to our knowledge, this is the first time that the (m)aidads specification is extended in these respects. compared to reimer and hertel 2003 or preckel et al 2010, we also introduce more detail for food expenditure and render the functional form somewhat more flexible. we find that especially demography, religious norms and temperature considerably improve the fit in our global cross-sectional analysis. we compare different model variants, considering only one, two or three factors in combination compared to the base model and a variant with all factors. considering model selection statistics and the need to integrate estimates into long-run dynamic long run analysis with a cge model, we opt for a version where demography, religious norms and mean temperatures are maintained as additional factors. data selection and definition of food categories in here reflects our aim to integrate the estimates in a global dynamic cge model. we deliberately removed some detail for food available from the underlying data set to render hicksian substitution effects between groups less likely, to better motivate the use of an additive demand 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(2017). climate, culture, and consumption: connecting the dots. in the routledge companion to consumer behavior (14-18). routledge vigani, m., dudu, h., ferrari, e. and causape, a.m. (2019). estimation of food demand parameters in kenya. a quadratic almost ideal demand system (quaids) approach (no. jrc115472). joint research centre (seville site). volume 10, issue 3 2021 firenze university press the bioeconomy in economic literature: looking back, looking ahead davide viaggi1,*, fabio bartolini2, meri raggi3 the contribution of research to agricultural policy in europe alan matthews drinking covid-19 away: wine consumption during the first lockdown in italy giulia gastaldello*, daniele mozzato, luca rossetto estimating a global maidads demand system considering demography, climate and norms wolfgang britz the evolution of organic market between third-party certification and participatory guarantee systems gianluca iannucci1, giovanna sacchi2,* bio-based and applied economics 8(3): 325-334, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-9748 short communication competitiveness of cattle breeding in switzerland: the value of policies enabling informed decisions stefan mann agroscope abstract. using the case of of swiss brown cattle breeding, this paper develops the hypothesis that a two-angle strategy of fostering competition and providing maximum access to information is promising for states to attain high competitiveness on a national level. abandoning the monopoly on bull sperm while subsidising classifications of animals in order to provide a maximum base of knowledge helped to increase switzerland’s self-sufficiency in brown cattle sperm from under 50 to almost 100 per cent. keywords. switzerland, cattle, breeding, information economics. jel code. q13. 1. introduction the debate about appropriate policies for attaining competitiveness by sectors and nations has long been centred around traditional lines between hands-off liberalists and interventionists. the aspect of how governments should deal with information management is gaining importance in this respect. in section 2, the literature of the role of the state in handling information is reviewed and arguments about why and how governments should facilitate sufficient access to information to increase sector competitiveness are provided. cattle breeding in switzerland is a sector in which information plays a crucial role in becoming competitive and therefore is a well suited case study to illustrate the effect of information policy on competitiveness. in the swiss brown cattle market in switzerland, domestic sperm has increased its market share considerably over the last decades from under 50 to almost 100 per cent. changes in the political regime were an important driver in this process. on the one hand, the government abandoned the monopoly on bull sperm, on the other it provided incentives to generate and use genetic information . in section 3, the effect of this policy is described. section 4 focuses on the limitations of this case and offers some conclusions. corresponding author: stefan.mann@agroscope.admin.ch 326 stefan mann 2. theoretical framework while information has been defined in general as a reduction of uncertainty (sivak, 1996), its use in economic science is ambiguous. on the one hand, institutional economists have shown that the functioning of all markets is strongly dependent on the scale and scope of available information on the other hand, information is a tradable good, and market research companies, consultancies, publishers and many other enterprises owe one of their main justifications to the existence of the market for information. the latter perspective is needed when exploring reasons why the state might want to interfere in the market for information. one of them is distributional justice (craig et al., 2008). this is a factor that mostly justifies state intervention in the case of information asymmetries (mann and wüstemann, 2010; mann, 2017). beyond that, however, the character of information as a public good or as a merit good must be reviewed in some depth in order to arrive at tangible conclusions with regard to the rationale for state intervention. 2.1 the public good aspect of information when samuelson (1954) determined nonexcludability and nonrivalry as criteria for public goods, no one expected his brief text to reshape the way economists thought about the distinction between private and public goods (nordhaus, 2005). samuelson himself was rather clear that information qualified as a public good (samuelson, 1958), mainly with respect to media, such as radio and television, where the dissemination of information would nether be excludable nor create problems like rivalry. subsequent scholars have shown that the actual state of affairs is more complex. bates (1990), for example, demonstrated circumstances in which information has private good properties, and others in which it does not. at the same time, allen (1990) emphasised circumstances under which information generates a price, thereby qualifying it as a private good. complicating matters further, antle (1999) suggested that information, though noncompetitive, is excludable, such that it would qualify as a club good. the least that can be concluded from this debate is that information can be treated in extremely different ways. suppliers of information, like advertisers, may attempt to spread information as widely as possible. in other cases, both in business and private affairs, information may be sealed and never disclosed, or it may be carefully sold to a single person. similar choices are made on the demand side. some buyers of information are eager to share it (such as educational bodies), whereas others seek to conceal it. public entities create increasing pressure to make information public. lewis (2012) argued that gold open access, which grants access to all articles at all times, is a disruptive innovation, but one which is likely to cover a major share of the journal market. governmental bodies, including the european commission, have pushed for the accessibility of research results, not only technically in terms of open access, but also in terms of transfers of knowledge through the use of simple language and popular media (olff, 2014). it is indeed plausible that spreading information, or at least making it available on demand, increases its potential benefits much more than its potential costs. this leads to an often neglected opposition of interests: limiting access is strongly in the interest of those who want to gain benefits from either selling information or concealing it 327competitiveness of cattle breeding in switzerland (nisbet and lewenstein, 2002), whereas the public has an interest in making information as widely accessible as possible. the conflict around patent law, with its struggle to find a ‘balance between commercial profitability and public-interest concerns’ (maskus and reichman, 2004, 283) is an exemplary illustration of this unavoidable clash of interests. 2.2 the merit good aspect of information when musgrave (1957, 1959) suggested that the state, in some cases, should impose the consumption of certain goods in spite of the absence of demand among consumers, the concept attracted little support from mainstream economists. forty years later, thaler and sunstein (2008), based on their expertise in behavioural economics, introduced the concept of libertarian paternalism. it has been shown (mann and gairing, 2012) how strongly related their preference for ‘nudging’ people into making rational choices was to the ‘merit good’ concept. the nobel prize awarded to richard thaler in 2017 confirms how clear it is today that our decisions are occasionally irrational, and that it would benefit us if the state was more involved in our decision making. one classical example of such a case is our demand for education. even before musgrave (1957, 341) first mused about ‘the apparent willingness of the public to provide for a second car and a third icebox prior to ensuring adequate education for their children’, it has become common for the public to finance the bulk of primary, and often secondary, education. poutvaara (2008) even named a number of countries that finance 100 per cent of tertiary education. it would be technically possible to trade education on the free market; parents would buy kindergarten and school programmes as they would food and clothing. as such, there is little trust among policy makers that this would lead to the sufficient education of children, and probably rightly so. the necessary process of consuming food generates pleasure, which is probably the reason why paying for food is condoned. as we all know, however, obtaining new knowledge and learning about methodologies do not always generate the same feelings of satisfaction and pleasure. this is likely the reason why the state not only pays for but also encourages or even forces us to receive an education. only from around the age of 20-30, when we have sufficient knowledge to participate in the labour market, do states leave the decision to continue education to market forces. 2.3 institutional options for intervening leaving the availability of information solely to market forces is neither recommended by economists nor considered a good practice in many countries. this leads to the question of how to institutionalise interventions into markets for information. when discussing the failure of real-world socialism, most political economists (e.g., elson, 1988; prybyla, 1991; li, 2013) agree that both an incentive and an information problem were the main causes for the failure of the system. the information problem’s underlying mechanism was that competition generates information (tang, 2018). both the success and failure of actors on the market, as indicated by overdemand and lack of demand, are important signals for the preference functions of consumers in a given region, country or even worldwide. 328 stefan mann the incentive and information problems are interlinked: when facing increased competition, the incentive to collect sound information on consumer preferences rises, i.e., part of the more intense information flows is fostered by incentives for successful performance on the market. however, the main advantage of competition in terms of information is that outsiders have a fair chance. in administered markets, the ‘usual suspects’ are often in charge, whereas it has often been shown (timmons, 1994; faltin, 1999; henoch, 2006) how entrepreneurship introduces new approaches and ideas to tackle problems. translating this phenomenon into information economics, outsiders may sometimes have better ways of covering demands than actors from within the system, which is important information that should be spread. 2.4 an approach for enabling informed decision making the complex functions and characteristics of information in contemporary markets reject simple solutions with respect to the classic dichotomy between interventionists and non-interventionists. scholars who are in general supportive of governmental interventions into markets will find it difficult to accept that all limitations set on competition should be avoided. any attempts by governments to steer markets in special directions by excluding either players or options are counterproductive, as they decrease the amount of accessible information. government interventions, however, are highly appropriate when it comes to the accessibility of information. there is a tendency among information providers to disclose their findings to the non-paying public, and there is also a tendency among information consumers to underinvest in this crucial commodity. this creates likely gains if governments take care to provide data which the public can access at a low cost. combining these two elements leads to a strategy for maximising the accessibility of information that will become increasingly important in the information age. 3. empirical methodology information is not equally important in each sector. for competitiveness, for example, in the energy or transport sector, infrastructure will be more important than the availability of information (kljajic et al., 2016; dolinavova et al., 2017). in other sectors like finance, trust is probably the most important resource (namahoot and lavichien, 2015). it is therefore useful to focus on a sector where the role of information is rather above average. this applies to the market for genetic material in agriculture. breeding activities require a vast amount of information to be successful. habier et al. (2007), for example, emphasised the importance of genetic relationship information for the breeding values of holstein cattle. iezoni et al. (2010) illustrated the advantages of an integrated framework of marker-assisted breeding in rosaceae fruits. yates et al. (2018) demonstrated the need for professional data management for successful crop breeding programmes. the targeted selection of valuable attributes of a species requires much information about the available material. this also applies to cattle breeding where the largest database internationally, being situated in the united states, has been documented to markedly improve the quality of breeding 329competitiveness of cattle breeding in switzerland (weigel et al., 2017). while smaller nations attempt to catch up (lidauer et al., 2006; fürst et al., 2011), it seems questionable if smaller nation with their lower level of centralization are capable of staying competitive. switzerland appears as an ideal test case, as swiss actors had at one time lost any market power over breeds originating from switzerland, but regained this power through a series of political adaptations. this development may therefore serve as a case in point to test the theoretical points made above. both a thorough analysis of the agricultural press in the time between 1996 and the present and in-depth talk with core actors and trade data were used. this included the use of descriptive statistics, but also of content analysis of recorded and transcribed interviews. the case of swiss brown cattle is also economically relevant as it still constitutes the majority of all dairy cows in switzerland. this economic dimension, the high dependency on information in animal breeding and the strong dynamics in the breeding of swiss brown cattle make the case worthwhile to find out more about the role of governments in information management of a sector. 4. the case of breeding swiss brown cattle 4.1 trade development as in large parts of the contemporary developing world (vasconcelos dantas et al., 2018), in switzerland until the 1970s, local bulls were in charge of inseminating most cows. then, artificial insemination was introduced, which allowed the selection of the genetic material with the best performance on an international scale. figure 1 indicates that the option of sperm imports was readily taken up around the turn of the century, even for brown cattle, for which the genetic centre is situated in switzerland, where there are around 200,000 brown cattle animals, more than in any other country. while import values of the sperm of brown cattle have peaked at around 10 million usd per year, the degrees of self-sufficiency in sperm for other breeds were even lower at that time. figure 1 shows the market dynamics of the last 20 years. while the degree of self-sufficiency approached values close to 100 per cent, export figures also rose, mostly to adjacent countries like austria, germany and italy. with such a development, sperm is a clear exception to the rule of switzerland’s generally very low competitiveness in the farming sector (mann et al., 2011) and agricultural factor markets, in which the country is a net importer of almost everything from machinery to feed to fertilizer. switzerland’s currently strong position not only translates into market figures. at the last european brown cattle contest, swiss animals won all champion titles as well as the national cup. italy and france took second and third positions, respectively. 4.2 national monopoly and american expansion in order to steer the development of this technological innovation, the swiss association for artificial insemination was founded in 1961 and received a monopoly on both producing and selling semen boxes. the european green press in the fourth quarter of the 20th century in general highly favoured the international collection of good bull semen (e.g., diers, 1990). 330 stefan mann the main beneficiaries were american breeders. heimig (1995) mentioned four american companies entering the german market for cattle breeding at the same time. breeding objectives were an important factor leading to this development. over many decades, swiss breeders emphasised the small sizes of cows and prioritised several aesthetic factors, whereas american breeders tended to focus on milk yields. welter (1998) portrayed a swiss breeder who travelled to the us on a yearly base to collect promising genetic material, strongly criticising swiss breeding strategists. the only export of swiss brown cattle at that time was heavily subsidised. by paying 1000 swiss francs for every bull being exported, the government fostered an annual export of 10,000 live animals per year, an uncompetitive and particularly animal harming way of distributing genetic material. these subsidies ceased in 2010, causing an immediate end to this trade flow. 4.3 liberalisation and information policy the decade between 1995 and 2005 was a period of transformation for the swiss farming sector. market support in general was strongly reduced, and farmers received direct payments as compensation. in parallel, the state’s strong grip on genetics was also loosened. in 1995, the monopoly of the swiss association for artificial insemination was removed so that other organisations could start to apply for a license. in subsequent years, cantonal administrations transferred their shares of the association towards breeding associations, while the insemination organisation itself was transformed into a commercial company in 2004. finally, the requirement of a license to enter the trade of sperm was removed in 2005. today, two swiss companies select bulls for genetic purposes and sell their sperm, while two additional competitors specialise in the retail sector. figure 1. degree of self-sufficiency of brown cattle sperm in switzerland. 331competitiveness of cattle breeding in switzerland letting market forces work, however, was not the only strategy of the administration. while the swiss government traditionally offers a large range of subsidies to support farmers, the following support payments, adding up to 23 million swiss francs per year, fall into the range of cattle breeding: two-thirds of the money is used to regularly measure the milk yields of dairy cows and to feed them into a broadly accessible database. this enables breeders to distinguish promising from average bulls the database in which not only milk yields but also other characteristics and genetic linkages are stored is called herdbook. the majority of costs for managing the herdbook are also covered by the federal administration. minor budgets are available for collecting and storing information about outer appearance, meat quality and health status, all of which deliver supplementary information about genetic qualities. this way, the accessibility of information for farmers has been smoothened. they do not only receive data from the herdbook and similar sources for free, they are also encouraged financially to feed in the information of their own animals. as a result with a high accessibility of information, this contributes for dairy farms to finding the most promising bull (i.e. genetic information) for their herd. 5. discussion and conclusions it appears that the framework as developed in section 2 is fully confirmed by the case of brown cattle breeding as depicted in section 3. table 1 summarizes the two aspects of the strategy that should foster competitiveness in information-intensive sectors and illustrates it with the case of cattle breeding. it is plausible that the unprecedented success in regaining market shares of brown cattle breeding in switzerland has been caused by a combination of liberalisation and intervention. innovation often comes from unexpected directions, and competition gives a voice to such unlikely candidates. however, subsidising the structured management and accessibility of all relevant information has been the necessary second step to make use of the information generated in the field. however, while allowing for competition and providing full access to the relevant information will in general be a promising strategy, it would be premature to declare it a panacea for competitiveness. not all markets are as fully dependent on information as the market for table 1. a framework for strengthening competitiveness in information-intensive sectors. general strategy realisation for cattle breeding information generation let as much information as possible be generated in a decentralized way enable breeding efforts bottom up information dissemination make information as accessible as possible subsidize integration of animal information in database 332 stefan mann genetic material. other well-known factors for competitiveness, such as natural conditions or access to capital, remain important, probably more so for many markets. for future research, it will therefore be crucial to identify sectors and branches with a similarly high reliance on information as the breeding market. for such cases, it should be possible to test whether the combination of competition and supportive access to information proves to be equally helpful to national 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(2016). characteristics of cattle breeders and dairy production in the southeastern and northeastern mesoregions of pará state, brazil. semina: ciências agrárias 37 (mayo-junio).zugang: <http://www.redalyc.org/articulo.oa?id=445746397030> [17 july 2018]. weigel, k.a., r.s. pralle, h. adams, k. cho, c. do, h.m. white (2017). prediction of whole‐genome risk for selection and management of hyperketonemia in holstein dairy cattle. journal of animal breeding and genetics 134(3): 275-285. welter m., (1998). wenn züchter spekulieren. landfreund 6.10.1998, s. 12/13. yates, s., m. lague, r. knox, r. cuthbert, f. clarke, j. clarke, y. ruan, j. jatinder, v. bhadauria (2018). crop information engine and research assistant (ciera) for managing genealogy, phenotypic and genotypic data for breeding programs. https:// www.biorxiv.org/content/early/2018/10/11/439505 (oct 19, 2018). bio-based and applied economics bae copyright: © 2023 wane a., mballo a.d., dzoukou homsi c.l., diakhaté p., memboup r. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 citation: wane a., mballo a.d., dzoukou homsi c.l., diakhaté p., memboup r. (2023). reducing food-related economic loss to improve food security and cattle trade in the sahel: the case of agropastoral systems in senegal. bio-based and applied economics 12(3): 243-259. doi: 10.36253/bae13521 received: august 9, 2023 accepted: august 24, 2023 published: october 15, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: donato romano orcid aw: 0000-0001-5081-3788 adm: 0000-0002-5319-6532 cldh: 0009-0005-3822-8646 pd: 0000-0001-9799-3827 reducing food-related economic loss to improve food security and cattle trade in the sahel: the case of agropastoral systems in senegal abdrahmane wane1,*, aliou diouf mballo2, cabrelle lauriane dzoukou homsi3, pathé diakhaté4, rahimatou memboup5 1 ilri regional representative for west africa 2 international fund for agricultural development (ifad) 3 université cheikh anta diop de dakar, senegal 4 université cheikh anta diop de dakar, senegal 5 université laval, canada *corresponding author. e-mail: a.wane@cgiar.org abstract. food loss is a critical issue in africa, but investigation has mainly been limited to quantity loss. economic losses are likely to be more significant but are widely ignored. regarding ruminant-related losses, it remains challenging to identify the optimal harvest point. focusing on sahelian agropastoral systems, where stakeholders operate in a shockprone environment, our paper explains how critical actor behaviour is, and it addresses economic losses on live-animal transactions while integrating market behaviours into the analysis. loss elimination being illusory in such a context, our findings pioneer a loss reduction approach that is supported by an appropriate optimisation programme tested on primary data collected from 202 agropastoral households in senegal. keywords: behaviours, economic loss, optimal loss, pastoralism, sahel. jel codes: c61, d13, q12, q13, r20. highlights • post-harvest losses in african livestock and pastoral systems are narrowly limited to loss of physical quantity of product while loss of economic value is largely ignored. • livestock multifunctionality and behaviours of individual actors in increasing uncertainty led sahelian pastoralists to behave with a bounded rationality. • an optimization model subject to pastoral constraints allows for the determination of the optimum number of animal species that must be sold to cover household expenditures and animal loss. • simulation of ad hoc loss reduction scenarios reconciles food security and competitiveness of livestock economics in the sahel. http://creativecommons.org/licenses/by/4.0/legalcode https://doi.org/10.36253/bae-13521 https://doi.org/10.36253/bae-13521 https://doi.org/10.36253/bae-13521 https://orcid.org/0000-0001-5081-3788 https://orcid.org/0000-0002-5319-6532 https://orcid.org/0009-0005-3822-8646 https://orcid.org/0000-0001-9799-3827 244 bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 abdrahmane wane et al. 1. introduction a growing local and regional demand for meat and milk has provided opportunities for pastoral producers in the sahel. however, several factors make it difficult for sahelian producers and other actors in the livestock value chain to take full advantage of this positive trend. ickowicz et al. (2012), hollinger and staaz (2015), and diawara et al. (2017) all highlight low herd productivity as a critical constraint. together with structural constraints relating to logistics, infrastructure, public policy and enabling environment, low productivity contributes to sub-optimal performance in the livestock sector. the sahelian livestock sector is vulnerable to multifaceted shocks, mainly relating to climate, disease, natural disasters and market fluctuations. some of these shocks are severe, leading to quantitative, qualitative and economic loss (ifad 2016). there is ongoing interest in this issue, even though there is scarcity of information and the evidence on the extent of the losses is mixed with estimates ranging from 2% up to 27% (fao, 2011; blanchard et al., 2016). a quantitative evaluation of the different types of loss remains challenging for several reasons. while productivity gaps have been documented, food loss in the livestock sector has received far less attention. comprehensive modelling methods are needed to clarify spatial and temporal fluctuations in loss rates and to make credible estimates of the quantitative, qualitative and economic losses. further complicating the situation in the sahel is the perceived dualism between commercial and communal livestock keepers and between modern and traditional systems (lyet et al., 2010). the structure of the livestock value chain is extremely nuanced. there are considerable differences in the levels of market integration, motivation and vulnerability among value chain actors, and this influences the nature and perceptions of loss. furthermore, small-scale producers have a ‘producer–consumer model,’ as articulated by chayanov (1926, 1990). the goal of pastoralists in a changing environment, such as the sahel, is to balance short-term consumption needs with long-term herd-building strategies to meet future consumption demands (fadiga, 2013). consequently, an understanding of the motivation to increase sales is key to understanding decision-making strategies. moreover, the parameters of the livestock market remain relatively rigid, with a low supply of animals and high price levels. food loss has adverse effects on food safety and security, particularly for poor and vulnerable people (sheahan and barrett, 2016), on the livestock market (wane and mballo, 2016) and on sustainable development (gustavsson, 2011). concerns about food loss frequently give rise to quantitative and qualitative estimates, which tend to be followed by remediation (in a ‘zero loss’ approach). however, when considered from a different economic perspective, not all loss is undesirable. this opens the opportunity to explore an exciting loss assessment method in which mitigation is the goal (an ‘optimal loss’ approach). the optimal loss approach is based on two key assumptions. first, the cost of total elimination of loss is prohibitive, regardless of the availability of technology and institutional arrangements. second, a certain level of loss is inevitable and not necessarily undesirable, particularly in the agricultural sector. this paper contributes to a long-standing debate on risk in the agricultural sector (wane and mballo, 2016; chavas et al., 2021) and decision-making related to risk perception (wane et al., 2020). to address the complex issue of loss in the sahelian pastoral areas, we used a sequential approach by which qualitative data was collected from approximately 15 people in each of the three targeted sites, and these results guided the subsequent collection of quantitative data from 202 households. this paper pioneers the idea that it is possible to improve food security in the sahelian region by minimizing losses in the production stage of the live animal value chain. the paper contributes by developing an optimisation model that determines the optimum numbers of different animal species to be sold to counteract losses while also being subject to the farmers’ constraints. our methodology is pragmatic in that the recommended optimisation approach aims for loss reduction rather than illusory loss eradication. in addition, unlike measures of loss in the crop sector, which focus entirely on postharvest losses, in our analysis of loss in the livestock sector, we consider both pre-market and market losses to be equally significant. second, our model shows the ideal sales volume, age at sale and price at sale that will allow the livestock farmer to generate sufficient income to cover his expenditure. third, we simulate loss reduction scenarios, with their effects on volume and market price parameters, and we show how these scenarios can result in a decline in average market prices, with the result that buyers can access more affordable live animals. overall, our paper demonstrates that addressing economic losses offers a more impactful perspective than focusing solely on the more commonly emphasized physical losses. this paper is organized as follows. section 2 reviews the literature discussing the issues and challenges faced by people living shock-prone dryland areas. it analyses the relevance of the optimal loss approach by emphasising the effect of multifaceted exogenous shocks on https://doi.org/10.36253/bae-13521 245 bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 reducing food-related economic loss to improve food security and cattle trade in the sahel producer market behaviours. section 3 describes the economic loss model to the senegalese sahelian agropastoral production system. section 4 describes the study area and data used in our analysis. section 5 presents the main results of the optimization, identifying optimal quantity and price in different loss reduction scenarios. section 6 discusses the main results and concludes. 2. literature review there are many definitions of food loss, from the more operational (bourne, 1977; parfitt et al., 2010; hodges et al., 2011; fao, 2011, 2013; aramyan and van gogh, 2014; de gorter, 2014) to the more comprehensive (papargyropoulou et al., 2014). food loss occurs at the production, pre-harvest, harvest and post-harvest stages (parfitt et al., 2010). food waste refers to the unconsumed portion that is discarded as waste at any point in the food chain (hodges et al., 2011). although food loss and waste, especially the location and type of loss, have been discussed, loss has received relatively little attention due to the difficulties of measurement. several attempts have been made to estimate loss, particularly in the grain and crop sectors. early estimates, which used mass flow models, set loss and waste at one-third of the physical mass of all foodstuffs worldwide (fao, 2013; lipinski et al., 2013). the world bank (2011) reported the yearly grain loss in subsaharan africa as approximately us$4 billion. highlighting these issues is helpful for donors and funding agencies. however, these global estimates have increasingly been challenged, especially in sub-saharan africa, where recent scientific studies have found the magnitude of the loss to be overestimated. more recent estimates have ranged from 4% in the presence of prevention mechanisms to 20% in their absence (affognon et al., 2015; rosegrant et al., 2015). in 2012, the fao estimated milk loss in the subsaharan african dairy sector at 27%; this was found to occur mainly in the early or middle parts of the food chain. however, extensive fieldwork conducted by a cirad–pastoralisme et zones sèches (pastoralism and dry lands; ppzs) team in 2016 to evaluate loss in the senegal and burkina faso dairy supply chain, valued total milk loss at 4% to 14%, which was very different from the fao estimate. the potential for recycling and reusing food that is diverted from human consumption to animal consumption has led to the adoption of a more inclusive definition of food loss and waste, which considers both humans and animals in its calculations (mokkar, 2017). a key challenge is that the methodological approaches, which were designed and initially applied in developing countries, have relied on the experiences of those countries (sheahan and barrett, 2016). in 2009, the european union tried to support sub-saharan african countries by implementing the african postharvest losses information system (aphlis). this involved a network of local experts and facilitated the collection and sharing of cereal grain weight loss data by country and province (hodges et al., 2010; rembold et al., 2011). however, this attempt took place in an oversimplified post-harvest loss environment and there were challenges with data quality (affognon et al., 2015). at the micro level, cross-country surveys of farmers in relation to post-harvest loss in sub-saharan africa have revealed interesting findings, with relatively low loss indicators, ranging from 1.4% to 6.9% of total production (kaminski and christiaensen, 2014; abdoulaye et al., 2015). although designed for large samples, these surveys cannot be readily generalized to the national level because this was not built into their design. from a value chain perspective, and regardless of variations in magnitude, grain and cereal loss seems to occur more frequently during handling and storage in the on-farm phase. in contrast, fresh product loss is reported to occur more often in the processing and distribution phases. from a technical perspective, this consensus on loss distribution from farm to fork can be explained by the fact that most surveys have addressed on-farm storage loss (affognon et al., 2015). current trends and projections for food value chains challenge traditional methodological approaches to integrate chain modifications arising from urbanization and other modern drivers. however, these approaches do provide powerful analytical tools for describing complex interactions between physical and social systems and for enhancing well-being through the reduction of loss in the primary sector. new insights into food loss and waste estimates, particularly in the livestock sector, could contribute to a converging research agenda on the challenges presented by the stress of global, social and environmental change. optimising the management of scarce resources, possibly through the minimization of constraints, is a critical theme in economics. optimisation relies on economic rationality, a fundamental economic principle that guides the decision-making of actors. however, the inclusion of uncertainty leads to the choice of a specific analytical structure that cannot be appropriately represented by the usual constrained optimisation model (arrow, 1971; machina, 1987; kreps, 1988; dixit, 1990). moreover, it is well established that behavioural choice may be more fundamental than the rational pursuit of https://doi.org/10.36253/bae-13521 246 bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 abdrahmane wane et al. self-interested goals (bossert and suzumura, 2012). a flexible approach to rationality-based optimisation facilitates a paradigm shift to a form of bounded rationality (with limited information, cognition and decision-making time), as articulated in herbert simon’s (1955) seminal work. this also relaxes the constraint that links optimisation to instrumental rationality (mongin, 2000). in this study, both approaches were considered to reconcile the sahelian pastoralists’ bounded rationality, contextdriven behaviours and optimisation processes under conditions of uncertainty. risks are a central part of life for most households, especially those in low-income countries (banerjee and duflo, 2011). an increased understanding of the risks and the associated coping strategies is key for policymakers. the main challenge in risk analysis is that the presence or perception of risk can significantly affect the intertemporal behaviours of households in their allocation of resources. this applies not only to poor households but also to non-poor households that have a higher probability of becoming poor in a less safe environment. in developing countries, hazards are ubiquitous in the lives of most farmers, who must secure their livelihoods and minimize their loss. those with weak assets are usually pushed to engage in low-return and sometimes risky non-farming activities (barrett et al., 2001), while those who have better financial support, or who are living in regions with favourable alternatives, tend to focus on revenue growth and wealth accumulation (loison, 2016). pastoralists live and operate in shock-prone environments (wane et al., 2010) in which climate variability plays a central role. this has a direct impact on natural resource dynamics, as herders must deal with spatiotemporal variations. climate change has exacerbated economic, social, cultural and political unease (e.g., national and international food and feed price volatility, disease, political instability and social transformation). pastoralists also face market uncertainty and a lack of infrastructure, both of which severely affect their livelihoods. they adapt to these conditions by using mobility and diversification strategies to enhance production and secure their livelihoods (alary et al., 2015). their choices are limited by complex relationships and by the multifunctionality of their livestock assets. some pastoralists breed livestock species with short life cycles to make quick gains and to escape poverty (alary et al., 2015). others prefer large ruminants that represent long-term capital investments (wane et al., 2020). it should be noted that in a risky environment, holding animals beyond an optimal market period corresponds to a form of contingency rationality. imperfect and incomplete market information encourages pastoralists to adopt a prudent position that is based on their circumstances and is therefore contingent on the socioeconomic environment (wane et al., 2009). this explains their opposition to regular animal ‘destocking,’ even when it is encouraged by national technical support services. far from being indifferent to market prices (kerven, 1992), livestock farmers make trade-offs between short-term consumption needs and long-term herdbuilding strategies to meet future needs (fadiga, 2013). with varying levels of success, pastoral and agropastoral households have developed adaptation and coping strategies that reflect a range of responses to stress. this illustrates the close relationship between social and biophysical factors. extensive pastoral and agropastoral systems cannot be measured purely in terms of assets because they continually evolve and adapt to accommodate their increasingly uncertain biophysical environment and monetized world (chambers, 1989; van dijk, 1997; bovin, 2000; ancey et al., 2009). over time, smallholders in the sahelian livestock system have tried to secure production and their livelihoods by considering the uncertainties and disequilibrium of their environment (benkhe and scoones, 1983; wane et al., 2010). studies on inequality (sen, 1982; sutter, 1987; wane et al., 2009; mulder et al., 2010) and on the vulnerability of pastoral populations (swift, 1989; ancey et al., 2009) have discussed the complexity of the farmers’ securitization. the importance of the social and biophysical factors embedded in extensive african crop–livestock systems must be considered. given these uncertainties, sahelian farmers are opportunistic in their approach to the markets for goods and services. market fundamentals are not the primary drivers; rather, cultural, social and non-commercial factors often play a more significant role in producers’ selling decisions. these behaviours are so deeply rooted in market practices that two key concepts are critical in any discussion of the issues affecting post-production loss in sahel ruminant farming. a key question that needs to be examined is whether complete loss eradication along the agricultural value chain is a feasible option or is loss reduction through optimisation more realistic? regardless of the level of adoption of technologies, innovations and institutional arrangements, it is reasonable to assume that the cost of eliminating all loss in agricultural value chains would be prohibitive. accepting that a certain level of failure and loss will inevitably arise in a risky environment is economically rational, because some contamination or spoilage is inevitable (de gorter 2014). assuming that a certain amount of loss in agricultural value chains is necessary and even economically rational, the focus https://doi.org/10.36253/bae-13521 247 bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 reducing food-related economic loss to improve food security and cattle trade in the sahel should be on improving the microeconomic behaviours underpinning the potential sources of loss before developing strategies to mitigate the effects of individual decisions (waterfield and zilberman, 2012; horton and hoddinott, 2014; de gorter, 2014; goldsmith et al., 2015; sheahan and barrett, 2016). losses may also arise from the voluntary and intentional decisions of economic actors, particularly those focused on profit rather than production maximization. brazilian soybean farmers exemplify this situation (goldsmith et al., 2015). in terms of food safety, it is also possible that loss may be desirable when unsafe food is removed from the system to avoid human or animal contamination (magoha et al., 2014). in a dynamic analysis, the management of farm loss could yield mixed results. for example, by expecting losses due to a lack of storage facilities, farmers could be forced to sell products at lower price. in this case, quantitative loss could be low, while value-related loss would be very high, as was the case for maize farmers in benin (kodjo et al., 2015). because zero loss is likely to be an unattainable ideal, especially for sub-saharan livestock farming, an optimal loss approach would be more appropriate. identifying the main loss sources and estimating the amount of loss is only a starting point of the analysis. in fact, a major difficulty is the choice of counterfactuals against which the loss is to be measured. naturally, these counterfactuals are related to the production system. producers hold the females and sell the steers in an extensive production system. the useful life of a zebu cow is 4.5 to 8.5 years, during which time parturition, including abortion, occurs approximately five times (mukassa-mugerwa, 1989). following production, live animals are moved along the value chain to downstream markets for final use, and loss occurs at each stage. for livestock systems, especially those in sub-saharan africa, this is the central theme of a debate that does not occur in crop systems. ‘postproduction loss’ and ‘postharvest loss’ have been used interchangeably to reflect specific problems in the agricultural sector. these concepts, which refer to the temporal dimension, are equally relevant to studies on the livestock sector or to specific products, which may be perishable (e.g., meat, milk) or non-perishable (e.g. cereals). bourne (1977) made an operational distinction based on three periods during which food loss occurs: ‘preharvest,’ ‘harvest,’ and ‘post-harvest’. this classification allows for harvest and post-harvest losses to be combined into a single category: post-production loss. thus, combining pre-market and market losses to focus on postproduction loss would appear more relevant. recent definitions of food loss integrate the whole process, including food grown to maturity but not harvested and left in the field for any reason (minor et al., 2020). 3. modelling economic loss in sahelian animal production systems two distinct phases of economic loss in live-animal rearing should be considered. the first is the pre-market phase, in which animal mortality, theft and disappearance occur. this type of loss is related mainly to the costs of managing animals prior to their theft or death. in other words, the farmer loses the entire investment made in such animals. the second is the market phase, which starts with the decision to sell the animal and ends with the actual sale. two types of loss can occur at this stage: (i) death or disappearance at the mark-to-market stage or (ii) loss of profits or opportunity costs at sale. this second stage could be summarized as follows: what would have been earned if the farmer had sold the animal at the ideal age vs. what would have been earned if the animal had been sold earlier (for animals above the ideal age). optimisation would involve the sale of animals that are close to the ideal age at a good price while maintaining the herd structure. in other words, it involves the minimization of economic loss in the production of live animals. finally, there are various stages at which loss is calculated in both the pre-market and market phases. this leads to a global loss function as follows: (1) where = number of animals sold at age i by species, = sale price of animals at age i by species, = ideal age for sale according to livestock keepers, = cost of dead animals during the pre-market phase, = cost of stolen animals during the pre-market phase, = age of dead animals during the pre-market phase, = age of stolen animals during the pre-market phase, = cost of dead animals during the market phase, = cost of stolen animals during the market phase, = age of dead animals during the market phase, = age of stolen animals during the market phase, = average price of animals at ideal age at sale, = average cost of managing an animal by species, = number of animal deaths during the pre-market phase, = number https://doi.org/10.36253/bae-13521 248 bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 abdrahmane wane et al. of animals stolen during the pre-market phase, = other animals lost during the pre-market phase, = number of animal deaths during the market phase, = number of animals stolen during the market phase, = other animals lost during the market phase. optimisation process and numerical resolution for the numerical resolution of the loss function, two strong assumptions were made: – assumption 1: stolen, lost or dead animals in the pre-market phase would have reached the ideal age at sale. – assumption 2: most stolen, lost or dead animals in the market phase would have reached the ideal age at sale. thus, equation (1) can be rewritten as follows: where = number of animals sold at age i by species, = sale price of animals at age i by species, = ideal age for sale according to livestock keepers, = average price of animals at ideal age at sale, = average cost of managing animals by species, = number of animal deaths during the pre-market phase, = number of animals stolen during the pre-market phase, = other animals lost during the pre-market phase, = number of animal deaths during the market phase, = number of animals stolen during the market phase, = other animals lost during the market phase. the optimal loss approach is meant to minimize the loss function subject to constraints by considering p=( ) =(pj)1≤j≤n and x=( )=(xj)1≤j≤n vectors corresponding to the unit price and the number sold, respectively, by species, season and age; with n=∑as∈asaas; where aas is the maximum age reached by animal species on a family farm. definition of constraints in the optimisation programme the minimization of the post-production loss function was performed on variables and . because of the nature of these variables, and were positive ∀i∈{1,…,a} and ∀as∈as . this paper distinguishes between the main and complementary constraints to facilitate the resolution of the optimisation problem. the main constraint is based on the overall income constraint: the sum of the farmer’s annual sales is sufficient to cover all the total consumer expenditures (food and non-food) made by the farmer, leaving a profit margin that is at most equal to a share a of total expenditures. total expenditures d≤ ≤a*total expenditures this constraint can be written as follows: d≤ pjxj≤a*d additional constraints are defined on critical parameters, such as the loss function, prices and number of animals. constraint on the loss function the mathematical function for defining the loss function can be negative for some parameters. therefore, it is important to constrain it to a positive value. the constraint is defined as follows: f( )≥0. constraints on prices several constraints on prices were considered to avoid price outliers. – constraint 1: the vector p0 is a system data point obtained from the database. without harming generality, vector p0 is equal to the vector of the ideal selling prices, which are informed by each farmer for a species at the favourable age at sale. this constraint is defined as follows: p=(p1,p2,…,pn)≤p0=( ,…, ) – constraint 2: this stipulates that the sale price of a species at age i must be greater than the cost of the animal incurred from birth (the average age at which the animal entered the farm) to the age at which it is sold. this translates into the following: – constraint 3: the selling price curves for each species are concave functions of age. thus, prices increase with age until they reach their maximum at the ideal age, then they decrease. this can be expressed as follows: ≤⋯≤ ≥ ≥⋯≥ . https://doi.org/10.36253/bae-13521 249 bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 reducing food-related economic loss to improve food security and cattle trade in the sahel constraints 2 and 3 will cause some prices to be higher than they would have been before the ideal age (see all) because the producer would have spent more on an older animal than on a younger one. this result is unlikely in the case of female cattle because they are more expensive when younger (up to a certain age) due to their milk production capacity. therefore, for female cattle, the fact that the price of cattle older than 10 years is lower than the price of those three years old is added to the previous constraint. for female cattle, the constraint is presented as follows: constraints on the number of animal species sold – constraint 4: this is based on the animal off-take rate. a previously explained, farmers in pastoral and agropastoral systems will sell a limited number of animals just to meet their needs. the herd off-take rate is relatively constant. this constraint stipulates that the total number of animals (of any species at any age) sold is, at the most, equal to the herd offtake rate. it is defined as follows: ≤off-take rate*herd size – constraint 5: there is a hierarchy in the pastoral and agropastoral species that are sold. small ruminants are more likely to be sold than cattle, which are the main assets of livestock producers. the constraint therefore stipulates that the total number of cattle sold is lower than the total number of small ruminants sold. for the remainder of the paper, the following group of constraints is considered: enscont1={(x,p) that meet constraints 1,2,3,4 and 5}. formulation of the optimisation problem without any intervention, the number of dead, stolen or lost animals is given for the farmer who is unable to minimize this loss. quantity loss (by theft, death and disappearance) during the premarket and market phases should be considered as a constant in the minimization problem. therefore, the following is posed: subject to: with f(x,p)=lossesvalue(xas,pas)+g. solution for the optimisation model: convexity or concavity of the loss function the nature of the loss function can be analysed in its matrix form. thus, by posing ; therefore: where , . with the same calculations at the constraint level, the problem (p) becomes: starting with the minimization problem (p1), the unknowns in this minimization system are the vectors p and x. f(x,p)=(b-p)’.x+g= https://doi.org/10.36253/bae-13521 250 bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 abdrahmane wane et al. the hessian matrix of the function f(x,p) is given by: thus, with . the following is considered: however, det(m)=1>0 means that m is positive, and h is negative; thus, the loss function f(x,p) is concave. the concavity of the function f(x,p)0 makes conventional methods inadequate for achieving the loss minimization objective. therefore, a non-linear programming approach, the method of moving asymptotes (mma), was used. this numerical resolution method, which belongs to the family of convex approximation methods, is suitable for structural optimisation problems. the mma provides the best results for concave minimization problems. 4. data a mixed approach to data collection was used to answer the research questions about economic loss. qualitative and quantitative data were sequentially collected in northern senegal pastoral and agropastoral areas (ferlo region). the area of ferlo is 67,610 km², nearly one-third of the country. the climate is characterized by rainfall concentrated over two to three months. the annual average is less than 200 mm in the extreme north and more than 550 mm in the south. in the vast area of ferlo, the selection of sites for the study was based on a previous study by wane et al. (2007, 2009, 2010) who had distinguished one agropastoral area (thiel) and two pastoral sites (tatki and rewane) on a north-south gradient for their representativeness of the ecological, geographical, pastoral and biological diversity of the extensive production system of senegal (see, their socioecological characteristics in appendix 1). the data collection tools, administered in july and august 2016, addressed the 2015 rainy season1 through until early 2016 rainy season. the study focused on a sample of 202 encampments out of 389 potential encampments, for which complete data on the pastoral households was obtained. there was an error margin of 4.79%, with a confidence interval of 95%, thus keeping within standard statistical norms. focus groups were conducted at each of these three locations in november 2015. the composition of the focus groups was as follows: 14 participants (14 men) in tatki; 14 participants (13 men and 1 woman, who did not participate in the discussion) in rewane; and 14 participants (13 men, including the sub-county chief and 1 woman) in thiel. the main information collected from these group discussions related to household incomegenerating activities and animal species traded in the production area, livestock loss in the production area and seasonal loss. additional primary data2 were gathered from responses given by 202 livestock farmers raising small ruminants and/or cattle – 40% from thiel, 31% from tatki and 29% from rewane – to a detailed questionnaire. 1 two distinct seasons characterize senegal’s climate: a dry season from roughly october to may and a rainy season from june to september. while the arid zones receive a total rainfall of under 300 millimetres per year, the forested south receives an average of 1200 mm/year. rainfall is highly variable, both on the interannual and inter-decadal timescales. the average annual temperature for senegal was 27.8°c for the period 1960–1990, with monthly averages in the hottest seasons of up to 35°c. (https://climateknowledgeportal.worldbank.org/country/senegal/climate-data-historical) 2 the following data were collected from household investigations: pastoral encampment location, household socio-demographics, herd species composition, ideal average age and selling prices by species and sex, sales decision-making, number of pastoral sub-seasons, average sales volume and prices by species and sub-season, sales motivations, sub-season sales locations, mortality-related quantitative loss, theft and loss, risk hierarchy by species, average animal weight loss during transport to market, herd maintenance and transportation expenses, and the hierarchy of strategies dealing with shocks. the questionnaire ended with a question on the worst rainy season in the previous decade. pastoral encampments are identifiable socioeconomic settlement units that reveal an aggregate income. they can involve one or more households, which are defined as nuclear or relational units of married couples or blood relatives. https://doi.org/10.36253/bae-13521 251 bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 reducing food-related economic loss to improve food security and cattle trade in the sahel 5. results th e results of the optimisation model, which was applied to 202 agropastoral encampments, focussed on a combination of sales volume and selling price by age and species. th ese data should make it possible for average livestock farmers to minimize their economic loss by generating income to cover their expenditures. sales volume to minimize their economic loss, the average farmer would have to sell 4% male cattle, 26% female cattle, 22% male sheep, 13% female sheep, 17% male goats and 16% female goats from their herd annually (table 1). th e same trends have been observed in other pastoral and agropastoral production systems. because of the multiple non-commercial roles of cattle in the lives of pastoral producers, cows are not primarily for sale. in uncertain environments, pastoralists always try to maximize the non-monetary benefi ts from their cattle, despite the long-term costs of raising the animals. th erefore, loss minimization would require the increased application of these strategies to the more eff ective marketing of cows. ideal age at sale the distribution of optimal sales by species and area shows that the 4% male cattle sales should consist of 58% bulls at an average age of 5 to 6 years (figure 2). spatial diff erences are related to diff erences in the production systems. loss optimisation follows the climatic gradient because the bulls sold must be approximately 5 to 6 years old. th e data showed that 77% of male cattle are sold in tatki, the driest zone; 54% in rewane, the intermediate zone; and 41% in th iel, the wetter zone. th e situation is slightly diff erent for female cattle. in the study area as a whole, the optimal combination of 92% of sales should comprise cows at an average age of three to fi ve years. th e optimal sales volume of cows figure 1. map of the study location. 252 bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 abdrahmane wane et al. of three to five years old decreases from the wettest area around thiel (92%) to the intermediate area around rewane (91%) to the dry area around tatki (90%). regarding small ruminants, a very large number of male sheep are sold during the tabaski festival. tabaski, or eid ul adha [the feast of sacrifice], is a religious festival and the most important feast in the muslim calendar, requiring the sacrifice of rams. this suggests that optimal sales (49% of the herd) would be rams at the average age of two, three or even four years. female sheep and male and female goats play a role in short-term cash flow. the optimal sales are almost equally distributed across all ages, beginning with the first year, which is devoted to animal fattening. the animals sold are mainly male sheep (36% of herd) and female sheep (32%) aged two to three years. for goats, the target composition is males aged two to four years (48% of herd) and females aged five to six years (29%). ideal price at sale the unit price of an animal is a concave function of its age. the optimal model would be for the farmer to sell male cattle at seven years of age at an average price of 271,000 xof (figure 3). before this ideal age, table 1. approximate distribution of retained animals and optimum number for sale by species and area. rewane (%) tatki (%) thiel (%) survey area (%) retained sold retained sold retained sold retained sold cattle male 7 8 4 4 2 3 4 4 female 18 25 21 27 15 27 18 26 sheep male 14 25 13 22 7 21 11 22 female 41 10 47 13 54 16 49 13 goat male 2 19 3 18 3 14 3 17 female 18 12 12 17 18 18 16 16 0% 2% 3% 0% 2% 3% 3% 0% 3% 4% 5% 0% 5% 6% 7% 1% 8% 9% 8% 7% 29 % 22 % 21 % 47 % 29 % 32 % 20 % 30 % 10 % 10 % 14 % 8% 6% 5% 9% 3%4% 4% 6% 2%3% 3% 5% 1% overall rewane thiel tatki male cattle 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years 9 years 10 years 11 years 0% 1% 0% 0%0% 1% 0% 0% 35 % 42 % 35 % 28 % 37 % 36 % 34 % 42 % 20 % 13 % 23 % 20 % 4% 2% 3% 6% 1% 1% 1% 1%1% 1% 1% 1%1% 1% 1% 1%0% 0% 0% 0%0% 0% 0% 0%1% 1% 1% 1%1% 0% 1% 1% overall rewane thiel tatki female cattle 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years 9 years 10 years 11 years 12 years 13 years 6% 6% 5% 6% 17 % 19 % 15 % 19 % 19 % 19 % 17 % 20 % 13 % 12 % 14 % 12 % 9% 8% 11 % 8% 9% 9% 10% 9%8% 8% 8% 8% 7% 7% 7% 7%6% 6% 6% 6%6% 6% 6% 6% overall rewane thiel tatki male sheep 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years 9 years 10 years 3% 4% 4% 1% 15 % 17 % 18 % 8% 17 % 17 % 16 % 18 % 12 % 12 % 12 % 13 % 11 % 11 % 10 % 12 % 12 % 11 % 11 % 13 % 11 % 10 % 11 % 12 % 10 % 9% 9% 11 % 9% 8% 9% 11 % overall rewane thiel tatki female sheep 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years 9 years 10 % 9% 11 % 10 % 16 % 20 % 15 % 14 %16 % 16 % 14 % 17 % 16 % 19 % 13 % 15 % 12 % 11 % 12 % 12 % 11 % 9% 12 % 11 % 10 % 9% 12 % 11 % 10 % 8% 11 % 10 % overall rewane thiel tatki male goat 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years 6% 6% 6% 7% 14 % 14 % 14 %15 % 15 % 15 % 15 % 13 % 12 % 14 % 13 % 12 % 12 % 12 % 12 % 15 % 16 % 14 % 15 % 14 % 15 % 14 % 14 % 10 % 9% 11 % 10 % overall rewane thiel tatki female goat 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years figure 2. optimal number of animals for sale by age, species and area. https://doi.org/10.36253/bae-13521 253 bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 reducing food-related economic loss to improve food security and cattle trade in the sahel the average price rises and then falls, while remaining close to the price level for the fiveto seven-year-old cattle. for female cattle, the model shows that the ideal age at sale would be reduced to five years (the farmers had initially indicated eight years) for a maximum unit gain at the optimum price of 221,000 xof. the trajectories of the price curves were similar to those observed for the male cattle. the prices for male cattle tended to be higher in rewane and tatki, which are the more isolated areas in the more arid northern region. for sheep, the ideal age at sale is approximately two years for both males and females. the difference lies in the optimum price, which would be 49,000 xof for males and roughly half, at 26,000 xof, for females. the average annual prices for male sheep are relatively high, particularly during eid ul adha, which is celebrated by the dominant community (nearly 94% of the population) in senegal. as with cattle, male sheep have a higher value in rewane. for goats, the average ideal age at sale is zonedependent. in rewane and thiel, breeders must sell their male goats at approximately three years of age for an average of 21,000–26,000 xof. in tatki, breeders must wait five years to realise an average of 22,000 xof. for females, there is less variation by area. if the rewane and thiel breeders can sell their two-year-old female goats for an average of 17,000–19,000 xof, the tatki breeders will realise 19,000 xof for three-year-old animals. the optimisation model describes a situation in which the average farmer can minimize physical loss through animal theft and death. this is considered a reference point, or ‘business as usual’. consequently, the study arbitrarily chose three loss reduction scenarios: with a 25%, 50% and 75% reduction in average loss. the effects of these scenarios on the market parameters were then simulated. simulation of ad hoc loss reduction scenarios two radically contrasting periods experienced in sahelian pastoral areas (including northern senegal) were compared: 2014–2015 (period 1), which was characterized by very scarce rainfall in several areas, and 2015– 2016 (period 2), characterized by plentiful and evenly distributed rainfall. the comparison showed that losses involving the total herd population on transhumance were 23% in period 1 and 9% in period 2 for cattle; 26% in period 1 and 8% in period 2 for sheep and 43% in period 1 and 11% in period 2 for goats. these figures are far from the 40% to 70% loss rates observed during the droughts of the 1970s and 1980s (thebaud, 2017). based xof 0 xof 50,000 xof 100,000 xof 150,000 xof 200,000 xof 250,000 xof 300,000 xof 350,000 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years 9 years 10 years 11 years male cattle overall rewane thiel tatki xof 0 xof 50,000 xof 100,000 xof 150,000 xof 200,000 xof 250,000 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years 9 years 10 years 11 years 12 years 13 years female cattle overall rewane thiel tatki xof 0 xof 10,000 xof 20,000 xof 30,000 xof 40,000 xof 50,000 xof 60,000 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years 9 years 10 years male sheep overall rewane thiel tatki xof 0 xof 5,000 xof 10,000 xof 15,000 xof 20,000 xof 25,000 xof 30,000 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years 9 years female sheep overall rewane thiel tatki xof 0 xof 5,000 xof 10,000 xof 15,000 xof 20,000 xof 25,000 xof 30,000 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years male goat overall rewane thiel tatki xof 0 xof 5,000 xof 10,000 xof 15,000 xof 20,000 xof 25,000 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years female goat overall rewane thiel tatki figure 3. optimal selling price of animals by species and area. https://doi.org/10.36253/bae-13521 254 bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 abdrahmane wane et al. on this analysis, we developed three ad hoc loss reduction scenarios – 25%, 50%, and 75% – to determine their effects on volume and market price parameters. decreases of 25%, 50% and 75% in losses from theft and death would result in increases of 12%, 27% and 25% in the number of cattle, sheep and goats, respectively, available for sale (figure 4). the exception would be female sheep, for which there would be a 17% to 25% decrease in the number available for sale. the species most sensitive to loss reduction would be male sheep, which, given their market value, particularly during eid el adha, are prime targets for theft. small ruminants are easier to steal and conceal. the reduction in the loss of female sheep would lead to a decrease in their available number and in the selling price. the relative stability in the number of male cattle available for sale is indicative of the market relationship with this main element of the pastoralist’s heritage. first, only 20% would be available for sale following a 50% reduction in loss. for female cattle, the greater the loss reduction, the greater the number available for sale. all loss reduction scenarios resulted in average market prices generally declining (figure 5). in the 25% reduction scenario, the smallest negative price change was observed for female cattle, and the largest negative price change was observed for male goats. the 50% reduction scenario allowed for a minimum negative price change of 3% for male cattle and a maximum of 15% for male sheep and female goats. the 75% reduction scenario resulted in a minimum negative price change of 5% for male cattle and a maximum negative price change of 18% for male sheep. 6. conclusion given the complexity of loss issues in the ruminant sector, this study identifies several dilemmas presented by the existing analyses of the post-production loss of livestock. these include the zero loss vs. optimal loss approaches. other issues include the starting point for analysis: pre-market vs. market vs. post-market; enterprise vs. pastoral household model for production systems; intensive vs. semi-intensive vs. extensive; quantitative loss vs. qualitative loss vs. economic value loss; and constraint management vs. risk management. this study adopted a framework previously tested in the senegalese livestock production system. it applied a risk approach to analyse the quantitative and economic value of pre-market and market loss in the extensive pro0% 6% 41 % -1 7% 10 % 6% 12 % 20 % 13 % 55 % -1 7% 35 % 28 % 27 % 20 % 19 % 59 % -2 5% 25 % 22 % 25 % male cattle female cattle male sheep female sheep male goat female goat total 25% reduction 50% reduction 75% reduction figure 4. changes in numbers available for sale with varying loss reduction scenarios. https://doi.org/10.36253/bae-13521 255 bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 reducing food-related economic loss to improve food security and cattle trade in the sahel duction systems in senegal. it elaborated a loss function by summarizing the global monetary loss for big and small ruminants based on the producers’ perspectives of the number and prices of animals sold at different ages and sub-seasons. overall, the study supports the idea of an optimal loss, beyond which further loss reduction is not feasible due to the costs of mitigation. finally, based on the field data, an empirical exercise was performed to minimize the losses related to animal mortality and theft, subject to the constraints intrinsic to the sahelian pastoralist. thus, the effects of the three loss reduction scenarios on market parameters were modelled. although intuitive, a new perspective on the value of loss reduction emerged from this study: addressing economic loss is essential. it must be noted that quantitative loss is not necessarily detrimental in the context of general or partial equilibrium because a decrease in food availability can lead to an increase in prices and, thus, in pastoralists’ revenues. therefore, an identification of market fragility and reasoning in terms of opportunity costs or gains allows for a more comprehensive understanding of the economics of pastoralism. however, the simultaneous challenges of food security and improved market parameters (quantities and prices) remain. the optimisation model also shows that loss reduction can have beneficial effects in relation to the number of animals (except female sheep) available for sale, precipitating a downward trend in market prices. male sheep were the species most sensitive to loss reduction. all the ad hoc loss reduction scenarios resulted in lowered market prices. showing the flow of the economy through a social accounting matrix would provide a comprehensive and economy-wide database of the transactions between economic agents during a specific period. in addition, it would be useful for highlighting the importance of loss reduction. these insights indicate the relevance of loss-reduction policies and actions for addressing food security and competitiveness in the live ruminant sector. due to the growing complexity and uncertainty in this sector, policies and actions should contribute to the reduction of risk and uncertainty and the prevention of potential conflicts while contributing to growth and resilience. a priority should be the development of a genuine risk culture by providing information on the main risk factors and their occurrence; analysing their economic, social and environmental impact; identifying and evaluating existing risk management tools; and providing guidance on risk prioritisation and management. in recent years, policies have been developed to create an enabling environment in senegal. in addition, emerging initiatives address various degrees of sever-5 % -1 % -7 % -1 0% -1 6% -1 2% -3 % -7 % -1 5% -9 % -1 4% -1 5% -5 % -1 0% -1 8% -1 0% -1 5% -1 6% male cattle female cattle male sheep female sheep male goat female goat 25% reduction 50% reduction 75% reduction figure 5. selling price changes in loss reduction scenarios. https://doi.org/10.36253/bae-13521 256 bio-based and applied economics 12(3): 243-259, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13521 abdrahmane wane et al. ity. however, innovative financial instruments (livestock insurance and credit) and effective information systems could complement the standard approaches to combating disease, animal theft and productivity, as well as the rehabilitation and development of the market infrastructure. as a risk-transfer instrument, the development of livestock insurance could contribute to the reduction of vulnerability by providing compensation against economic loss. thus, smallholders could avoid using suboptimal coping strategies that further weaken their precarious food and nutritional status or prevent them from using the limited basic infrastructure (e.g., schools, health centres and markets). in addition, productivity could be improved through revitalized investments. this paper breaks new ground on economic loss in livestock production systems in the sahel. given the multifunctionality of livestock and the objective effects of increasing uncertainty, sahelian pastoralists have mostly used bounded rationality. thus, integrating their motivations to sell was key. therefore, an optimisation model subject to pastoral constraints enables the determination of the optimum number of animal species that must be sold to cover expenditure and animal losses. data availability statement the datasets, programmes, a full list of data sources, and information on empirical analysis, experiments and simulations generated for this study are available on request from the corresponding author. ethics statement the authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. references abdoulaye, t.c., alexander, j.h., ainembabazi, d., baributsu, d., kadjo, b., moussa, o., omotilewa, g., ricker, j., and shiferaw, f. 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(2007). how to obtain a representative sample of economic studies in the areas with strong mobility? case of the senegalese sahel (ferlo), in farming systems design 2007, int. symposium on methodologies on integrated analysis on farm production systems, eds m. donatelli, j. hatfield, and a. rizzoli. catania, italy, 10-12 september 2007, book 2 field-farm scale design and improvement 13-14. waterfield, g., and zilberman, z. (2012). pest management in food systems: an economic perspective. annual review of environment and resources, 37:223– 245. doi:10.1146/annurev-environ-040911-105628 world bank, natural resource institute, food and agriculture organization of the united nations (2011). missing food: the case of postharvest grain losses in sub-saharan africa. report no. 60371-afr. washington, dc. appendix 1 – socioecological characteristics of the target sites tatki, a sandy area in the northern frontier of ferlo, is exclusively pastoral. its proximity to national roads and the senegal river valley (40 km away) facilitates trade and social links between farming populations. the communities are scattered around a pastoral borehole built in 1953. there is a basic infrastructure that does not function very well. health services are provided through the intermittent presence of a health officer. a primary school is located close to the borehole, and there is a weekly livestock market mainly for small ruminants, which are prevalent in the herds. comprising 60% of the tatki herds, sheep are the dominant species. cattle account for 25% and goats for 15% of the herds. rewane, in east central ferlo, is an extensive livestock production area. the infrastructure here is mostly non-functional. there is a health office, a school with only two teachers, and a non-resident extension agent who makes occasional visits from dahra, which is 82 km away. almost all residents are animal producers. there is one trader and one transporter. the rewane herds have the lowest proportion of sheep: 55% sheep (41% female and 14% male), 25% cattle and 20% goats. thiel, which is further south in ferlo’s agropastoral area, is inhabited by fulani livestock keepers and farmers of other ethnic groups. thiel is an important hosting area for transhumance. the basic infrastructure here functions better than those identified in tatki and rewane. two boreholes were built before 1993. the presence of sedentary family farmers explains the school’s relatively good functioning. thiel’s bi-weekly market might result from its proximity to dahra (40 km away), the country’s biggest cattle market. following wane et al.’s (2009) study, different settlement units were targeted. these were first stratified by locality, which indicated the pastoral households’ place of physical presence and economic activities. this locality then made it possible to identify both the encampments, concessions and households. the encampments are large units of residence, and because they are directly identifiable settlement units, they revealed the level of market income aggregation that we chose to assess in this study. in addition, there are concessions, socio-economic units in which individuals (possibly blood-related) pool their resources for the common good. finally, there are households of atomic relational units comprising blood-related or married individuals. the sample was structured according to the density of the geo-referenced encampments. the definitions for the weightings of the encampment categories (‘very big,’ ‘big,’ ‘middle,’ and ‘small’) were validated by the livestock producers and allowed for weighting according to initial densities. as we obtained various perceptions of these categories, we built ours around the average thresholds. https://doi.org/10.36253/bae-13521 towards a holistic approach to sustainable risk management in agriculture in the eu: a literature review linda arata1, simone cerroni2, fabio gaetano santeramo3, samuele trestini4, simone severini5,* modelling technical efficiency of horticulture farming in kosovo: an application of data envelopment analysis nol krasniqi1,2,*, stéphane blancard1, ekrem gjokaj2, giovanna ottaviani aalmo3 incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon claudiane yanick moukam1,*, calvin atewamba2 farmers’ acceptance of a micro-irrigation system: a focus group study maria sabbagh1,*, luciano gutierrez2 reducing food-related economic loss to improve food security and cattle trade in the sahel: the case of agropastoral systems in senegal abdrahmane wane1,*, aliou diouf mballo2, cabrelle lauriane dzoukou homsi3, pathé diakhaté4, rahimatou memboup5 bio-based and applied economics 8(1): 21-61, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8145 the impact of assistance on poverty and food security in a fragile and protracted-crisis context: the case of west bank and gaza strip donato romanoa, gianluca stefania,*, benedetto rocchia, ciro fiorillob a department of economics and management, university of florence, italy b food and agriculture organization of the united nations, west bank and gaza strip office, jerusalem1 abstract. this paper assesses the impact of assistance on the wellbeing of palestinian households. the impact evaluation analysis uses a difference-in-difference approach for the treatment of sample selection bias. the paper uses data from the 2013 and 2014 rounds of the palestinian socio-economic and food security survey to estimate the impact of assistance to west bank and gaza strip (wbgs) households on their poverty and food security status. results suggest that both poverty and food insecurity would have been much higher for wbgs as a whole without assistance, further increasing in areas with lower levels of assistance. however, the average positive impact of assistance hides a lot of heterogeneity. in fact, while there is a clear positive impact of the intensity of assistance on poverty reduction, food consumption and diet diversity in the west bank, gaza strip analysis shows mixed results. results highlight how the international community cannot disengage from supporting palestinian households without severely impacting their wellbeing. keywords. poverty, food security, impact analysis, west bank and gaza strip. jel codes. q18, i32. 1. introduction the relationship between foreign assistance2 and development is one of the most debated topics in development policy. since world war ii, the debate shifted from discussing the rationale for mobilizing foreign resources to boost economic growth (chenery and bruno, 1962; chenery and strout, 1966; lal, 1972), to assessing the impact of aid on 1 the views expressed in this paper are those of the author(s) and do not necessarily reflect the views of the food and agriculture organization of the united nations. 2 foreign assistance is a broad term for any voluntary transfer of resources from one government, international organization, or ngo to a recipient country, usually a developing country. it encompasses loans (both soft or hard) and grants as well as in-kind transfers and technical assistance. the paper uses “foreign assistance” interchangeably with the term “foreign aid”. *corresponding author: gianluca.stefani@unifi.it 22 d. romano et alii economic growth and poverty reduction (bauer and yamey, 1982; cassens & ass., 1986; krueger, 1986; mosley, 1987; collier and dollar, 2001 and 2002), and subsequently to generating evidence in order to better design interventions and enhance aid effectiveness (burnside and dollar, 2000; hansen and tarp, 2001). more recently, increasing attention has been devoted to assessing the effectiveness of assistance delivered in fragile contexts.3 this shift was driven by empirical evidence suggesting that natural, economic and political risks are rising across the world (world bank, 2011; zseleczky and yosef, 2014), as well as by the rapidly growing literature on fragile states (ipke, 2007; kaplan, 2008; zoellick, 2008; baliamoune-lutz and mcgillivray, 2008; stewart and brown, 2009; andrimihaja et al., 2011; chandy, 2011; naudé et al., 2011). the key question here is whether aid can deliver its expected results within fragile/conflict contexts. the literature shows mixed empirical evidence (dollar and levin, 2006; fielding and mavrotas, 2008; ishihara, 2012; chandy et al., 2016). as a result, many practitioners, policymakers, and even laypeople express mounting concern for the poor development records within fragile country contexts. this implies a need to develop new approaches that explicitly address fragility pathways to insecurity when designing development and humanitarian assistance strategies in fragile/conflict contexts (oecd, 2007; eu commission, 2009; world bank, 2011).4 this paper contributes to the empirical literature on the impact of assistance in fragile contexts by adopting a microeconomic perspective. it aims to estimate the impact of assistance intensity on household wellbeing proxied by two outcome dimensions, poverty and food security. we adopt a counterfactual framework using a difference-in-difference approach to address sample selection bias as well as instrument variable econometric modeling to get rid of endogeneity problems where appropriate (e.g. poverty reduction). the empirical application focuses on the specific fragile, protracted-crisis context of the west bank and gaza strip (wbgs) as a case study. this specific region was chosen for several reasons. wbgs has been among the highest per-capita recipient of official development assistance worldwide (world bank, 2019) and it is also experiencing one of the longest contemporary conflict in the world. the majority of palestinians living under occupation would be unable to meet their own bare necessities since both humanitarian and development interventions in wbgs are largely financed by foreign assistance. indeed, the pledge for humanitarian assistance, amounting to usd 540 million in 2018 (ocha, 2017a), is completely financed by foreign resources. similarly, the share of for3 there is no universally accepted definition of fragility. instead of trying to stringently define fragility, oecd (2015) identifies fragile contexts according to a multi-dimensional framework that helps reveal different patterns of vulnerability in a given country. the five fragility “clusters” considered by oecd are the following: widespread violence, limited justice, ineffective and unaccountable institutions, weak economic foundations, and low resilience to shocks and stressors. these characteristics substantially impair the fragile country’s economic performance, the delivery of basic social services, and the efficacy of donor assistance. 4 this was explicitly considered by the so-called “new deal for engagement in fragile states” announced at busan in 2011. this deal identified five “peace-building and state-building goals”: legitimate politics, security, justice, economic foundations, and revenues and services (https://www.pbsbdialogue.org/en/). it also considered in the united nation’s “new way of working”, (https://www.un.org/jsc/content/new-way-working) within which humanitarian, development and peace actors are called to work together to pursue collective outcomes over multiple years to overcome the traditional divide between humanitarian and development interventions. this is at the core of the so-called “triple nexus”, which aims to integrate the humanitarian, development and peace aspects of interventions. 23the impact of assistance on poverty and food security in a fragile and protracted-crisis context eign support in 2018 accounted for as much as 48% of total development expenditure, a sum roughly equal to usd 381 million (imf, 2018). between 2007-2016, the yearly average total of aid received amounted to more than 2.3 billion usd per year or 23% of palestinian gdp (wdi, 2018). despite such large aid inflows, the palestinian gni per capita is still around usd 3,180 (wdi, 2018), qualifying wbgs as a lower-middle income country. similarly, the palestinian hdi is 0.686, placing wbgs 119th out of 189 countries and territories (undp, 2018). according to the humanitarian needs assessment (ocha, 2018), some 2.5 million people are in need of assistance on a total population of 4.95 million and 1.9 million people are targeted by humanitarian interventions. in factin light of this, data released by the palestinian central bureau of statistics (pcbs) regarding the 2013 and 2014 socio-economic and food security (sefsec) survey data (fss-pcbs, 2016) — designed for the first time as a panel — offers a unique opportunity to assess the impact of assistance on household poverty and food security in wbgs. it is important to note that from 2013 to 2014, the period in which the data was collected, the region faced persistent occupation as well as an open-arm conflict in the gaza strip occurring from july 2014 to august 2014. to conduct the aforementioned analysis, the paper is organized in the following way: section 2 reviews the literature on aid and development, looking at both theoretical arguments and empirical results. section 3 introduces the palestinian context and provides an overview of assistance to palestinian households. section 4 analyzes palestinian households’ profiles in terms of poverty and food security at the beginning of the period of analysis. section 5 describes the data and methods used in the impact evaluation. section 6 discusses the results of the impact evaluation analysis. finally, section 7 summarizes main findings and discusses policy implications. 2. foreign assistance and development: an introduction foreign assistance can be traced back to the colonial period. at the time, european powers provided large amounts of money to their colonies, typically to improve infrastructure, with the ultimate goal of increasing economic output. the use of foreign assistance as it is known today — as an instrument to help poor countries improve living standards — came into existence only after world war ii (thorbecke, 2000). the emergence of a new economic order and the founding of international organizations (such as the united nations, the imf, and the world bank) following wwii shaped foreign aid to become what it is today. the success of the marshall plan, the us-sponsored package implemented between 1948 and 1953 to rehabilitate the economies of western and southern european countries, showed that capital transfers alongside technical assistance could effectively spur growth so that targeted economies were able to surpass their pre-war economic levels by 1952. aid to developing countries today is more complex. its use is determined by several intertwined motives, including altruism, access to markets and resources, geopolitics, and colonial legacies. the impact of foreign assistance to developing countries is mixed, with success stories in various south east asian countries but also numerous failures in subsaharan countries (kanbur, 2000). foreign aid is thought to have helped poor countries raise income per-capita growth rates, in some cases converging with high-income countries, successfully lifting large seg24 d. romano et alii ments of the population out of poverty. however, this is difficult to establish unequivocally. there are two major difficulties when analyzing the relationship between foreign assistance and development. firstly, there are issues with different theoretical frameworks– macro as well as micro–that provide the rationale for foreign aid interventions. secondly, empirical studies lack conclusive evidence, making it hard to identify causal links between aid and development outcomes. indeed, there is a large gap between aid achievements at the micro and macro levels, with greater difficulties in establishing causalities at the macro/country level compared to the micro/project level. this is the so-called “micro-macro paradox” (mosley, 1987). as a consequence, the effectiveness of aid in the promotion of development is often uncertain and controversial, with personal opinions often deeply founded in ideology. the consequence is an ongoing debate regarding best practices in the provision of foreign assistance aptly named the “aid debate”.5 positions on the matter range from strong believers in the potential effectiveness of foreign aid who advocate for even more aid (sachs, 2005), to deep skeptics stressing the importance of experimentation and learning from past mistakes (easterly, 2006). along this spectrum lie pragmatists who support peace and the use of a broad set of instruments (collier, 2007) as well as opponents endorsing anti-corruption practices to increase aid effectiveness (moyo, 2009). finally, the aid debate also includes scholars who argue for the reduction of damaging oecd trade policies in agriculture, increased provision of technical assistance regarding institution building, an increase in investment devoted to fighting diseases and improving agricultural technology in tropical environments, and greater support for institutional reforms that favor secure property rights, the rule of law, and a reduction in arms sales to developing countries (deaton, 2013). 2.1 macroeconomic perspective the most important theoretical arguments in support of foreign aid as an effective strategy to boost growth and catch-up to rich countries are rooted in keynesian growth models. the harrod-domar model (domar, 1957) provides theoretical background for growth in developing country contexts by identifying savings rate and choice of technique (via the incremental capital-output ratio, or icor) as the two determinants of a country’s growth rate. the policy implications the model suggests for accelerating growth are clear: raise the savings rate (i.e. promote savings through stronger financial institutions) and lower the icor (i.e. increase the marginal productivity of capital through better technology). this is where foreign aid transfers come in. using these transfers for investment can fill domestic saving gaps in developing countries, thus providing a “big push” to kick-off economic growth (rosenstein-rodan, 1943). a popular extension of the harrod-domar model devised in the 1960s in latin america defined two kinds of capital goods used in production. the first kind are capital goods of domestic origin, such as buildings financed by domestic savings, while the other 5 foreign aid has throughout its history been subjected to close scrutiny both by academic researchers and others (dethier, 2008). a large literature extending over several decades bears witness to this, and the boundary between policy advocacy and research has not always been clearly delineated. 25the impact of assistance on poverty and food security in a fragile and protracted-crisis context kind are of foreign origin, such as imported intermediate goods and machinery paid for using foreign savings. if the two forms of capital are in fixed proportion, then the scarcest of the two types of savings will always be binding. this is the core of the “two-gap model” (chenery and bruno, 1962; chenery and strout, 1966). foreign aid that increases foreign savings can effectively increase growth with enough domestic savings, despite a deficit of foreign exchange. however, foreign aid cannot translate to growth if there is a deficit of domestic savings even if an economy has enough foreign exchange to acquire necessary amounts of imported capital goods. subsequent developments are based on new growth theory, which endogenously explains productivity growth by extending the above paradigm with an analytical basis for empirical cross-country studies (robinson and tarp, 2000). the underlying causal chain runs from aid to savings, from savings to investment, and finally from investment to growth. in the new growth theory approach, investment and productivity variables are assumed to depend on policy and institutional variables. usually, the effectiveness of aid has been empirically tested using country-level macro data, with aggregated aid as a single resource. such tests examined whether more aid lead to better outcomes, in particular whether more aid lead to higher growth. it is no surprise that reduced-form analysis shows tenuous links between aid and development outcomes, since aid is often advanced for non-developmental objectives, such as disaster relief or military and political ends. as emphasized by bourguignon and sundberg (2007: 317) development economists must better understand “the links from aid to final outcomes” because “trying to relate donor inputs and development outcomes directly, as through some kind of black box, will most often lead nowhere.” opening the black box allows for the identification of three types of links–from donors to policy-makers, from policymakers to policies, and from policies to outcomes–which, in turn, may provide additional answers. empirical studies on the link from donors to policymakers reveal a body of circumstantial evidence built primarily on years of failed aid efforts (dollar and levin, 2006). donor views regarding the “right development policies” have been promoted through aid conditionality with little attention to specific country contexts. for instance, public enterprise privatization and finance liberalization have at times been regarded as necessities, though were encouraged with little regards for local socioeconomic conditions, making such measures ineffective, risky, or simply counterproductive. the link from policymaking to policy formulation and implementation depends largely on governance systems. there is evidence suggesting the association between good governance and good policies, although the direction of causality is hard to determine. in practice, most research has focused on the relationship between governance and development outcomes, bypassing the impact on policies and pointing instead to the importance of good governance for better outcomes (acemoglu et al., 2005). regarding the impact of policies on outcomes, there is a good understanding of the effect of macro stability, investment climate, as well as well-managed trade openness on growth, even though country specificity can make it hard to generalize the impact of these factors. cross-country comparisons however indicate that better-quality policies are associated, on average, with higher gdp growth. some authors use empirical analyses to argue that aid leads to growth with decreasing returns (hansen and tarp 2001). others suggest that national growth-inducing pol26 d. romano et alii icies may reduce aid effectiveness because good policies and aid are substitutes of each other (dalgaard and hansen 2001). finally, some authors hold that aid stimulates growth conditional on key features. for instance, it is often argued that aid works if provided to countries that implement good policies (burnside and dollar 2000). this conclusion was questioned by easterly et al. (2004) who showed that the aid-policy relation was not robust enough for the expansion of the database in years and countries. despite such differing positions, cross-country regression analysis largely concludes that the relationship between aid and development outcomes is weak and often ambiguous (rajan and subramanian, 2005; clemens et al., 2004). in recent years, econometric assessments have included meta-analyses to synthesize results from the existing body of empirical data while controlling for heterogeneity among studies. surprisingly, even these studies, which are supposed to provide more objective analyses, have contributed little to resolving the aforementioned controversies. consider, for instance, two such studies by doucouliagos and paldman (2009) and mekasha and tarp (2013): while the former failed to find any significant impact of foreign aid on growth, the latter found an impact that is both positive and statistically significant. in conclusion, macro growth effects are both harder to achieve and harder to observe. they are harder to achieve than micro growth effects because the magnitude of aid may not be sufficient to affect recipient countries’ macro variables, and harder to observe because causality is difficult to establish in cross-country regressions (mavrotas, 2015). 2.2 microeconomic perspective non-conclusive results of reduced-form cross-country aid regressions brought about the need to establish the channels through which aid mattered the most for economic growth and poverty reduction (dalgaard et al., 2004). this was done through empirical studies at the micro level that analyzed the impact of single project and program interventions. until the 1990s, these evaluations were based on cost-benefit analysis (cba) of single projects by computating the internal rate of return of the intervention. such studies show that aid is effective at the micro level when taking into considerations local projects (hirschman, 1967; mehrotra and jolly, 1997). however, these results came under severe criticism once the concept of aid fungibility, i.e. aid money being used for purposes other than those earned, spread. in fact, rate-of-return metrics ignore more complex opportunity-cost issues like the fungible use of foreign aid. the approach also became problematic as donors started to embrace broader goals for aid, such as environmental sustainability and multiple social goals with hard-to-quantify objectives. in parallel, the weakness of cba-based impact evaluations, summarized under headings such as “before-and-after” and “with-and-without,” was the topic of many debates. consequently, methodological issues became increasingly important in the aid-effectiveness debate (cassen & ass., 1987; world bank, 1998). more recently, knowledge at the micro and project level has expanded based on evaluations using advanced econometric techniques and rigorous experimental or quasi-experimental designs. econometric techniques are used to examine the impact of specific policies or projects on local communities, household decision making, and individual welfare (banerjee and duflo, 2011). given the number projects and their different impacts in var27the impact of assistance on poverty and food security in a fragile and protracted-crisis context ying country circumstances, continued evaluation and revision is needed. impact evaluation evidence began in the mid-1990s. by the turn of the century, impact evaluation publications became increasingly more common, continuing to date (cameron et al., 2016). rigorous ex-post impact evaluations help inform government and donor decisions, an idea supported by donor agencies and even by aid critics (e.g., easterly 2006). however, an evaluation gap still exists. this is because governments, official donors, and other funders do not demand or produce enough impact evaluations and those that are conducted are quite often methodologically flawed (savedoff et al., 2006). this calls for a systematic review of conclusions drawn from such studies. several initiatives have been implemented in response to this issue, such that many reviews and meta-analyses are in circulation today. in terms of sectors, the ones most represented in studies are social protection, health and nutrition, and education. cash transfers is the most represented modality, though in-kind transfers and vouchers are also well-researched, especially in the context of humanitarian crises. randomized control trials and difference-in-difference studies are the most widely used methods. studies assessing the causal relationship between interventions and outcomes of humanitarian assistance generally lack a reliable and robust base of evidence (clarke et al., 2014). only a small proportion of the many evaluations of humanitarian assistance use designs with a counterfactual, control or comparator group that allows the studies to attribute measurable changes outcome indicators to programs or policies. however, there are also several examples of randomized trials. it is possible to generate evidence for specific questions using randomized trials, although this evidence base is limited and concentrated in certain areas, such as mental health (cameron et al., 2015). foreign aid has generally brought about a positive contribution in education, the most tangible outcome being increased enrolment rates in primary education (riddell and niño-zarazúa, 2016; birchler and michaelowa, 2016). however, there is a considerable gap regarding the contribution of aid to improvements in the quality of education. masino and niño-zarazúa (2016) conducted a systematic review of experimental and quasi-experimental evidence to establish what works best to improve education quality in developing countries. they found that educational policies are most successful when implemented in combination with multiple interventions. aid channeled into a variety of interventions, targeting different educational levels and utilizing different aid modalities works best. considering this heterogeneity, it should not be surprising that a generalized blueprint applicable to all developing countries hasn’t been devised. literature in the food security and nutrition sector has a lot of variation in program implementation (e.g. size and modality of transfers, duration and frequency of transfers, strength of conditions, pre-existing levels of undernutrition, health services). this makes difficult to establish which of the various interventions on food security and nutrition is most effective. conclusions of summary studies range from cautiously optimistic (ahmed et al., 2009; ruel et al., 2013) to lacking significant results (manley et al., 2012). in 2016, doocy and tappis reviewed 108 studies on intervention modalities. they found that unconditional cash transfers and vouchers may improve household food security among conflict-affected populations and maintain household food security during crises specifically affecting food, such as droughts. moreover, unconditional cash transfers led to greater improvements in dietary diversity and quality than food transfers. food transfers were 28 d. romano et alii found to be more effective in increasing per capita caloric intake than unconditional cash transfers and vouchers. while the evidence reviewed offers some insights, the scarcity of rigorous research on cash-based approaches limits the strength of such findings. however, drawing on findings from randomized control trials, karlan and appel (2012) identify seven ideas that work: microsavings; reminders to save; prepaid fertilizer sales; deworming; remedial education in small groups; chlorine dispensers for clean water; and commitment devices. likewise, banerjee and duflo (2011) draw on experimental studies to identify a host of promising interventions in areas ranging from health and education to policing. though promising, impact evaluation studies have several limitations. it is illusory to believe that all interventions can be subject to impact evaluations and that such evaluations will permit the flow of aid exclusively to what works, as some have suggested (easterly, 2006; banerjee, 2007). it is impossible to evaluate all projects. evaluations can also be misleading when projects or programs are applied outside the context in which they were evaluated, meaning there is a serious problem of external validity (pritchett and sandefur, 2013). furthermore, many policies have general equilibrium effects often ignored by impact evaluations. this suggests that unlocking the secret of aid effectiveness is most likely to be revealed by trial and error than by randomized control trials (deaton 2013). nonetheless, experimental and quasi-experimental studies are grossly underutilized instruments with tremendous scope to improve and regularize their use in bilateral and multilateral donor agencies. a larger evidence base and a more standardized approach to documenting and comparing costs and benefits of interventions are needed to draw important conclusions on the effectiveness of different development interventions (savedoff et al., 2006; white, 2010; cameron et al. 2016). 2.3 aid effectiveness in fragile contexts the new economics of aid stresses the importance of good governance to successfully achieve growth. focusing on good governance leads to country selectivity such that transfers are targeted at countries that pass the good-policy test. this means aid is shifted from project financing to budget financing. however, targeting countries with high institutional and policy scores means that poor individuals in countries with failed states and in postconflict societies will not be reached. the problem of building a developmental state that qualifies for aid is also left open. social development funds, local governments, and ngos therefore play an important role: they can bypass central governments while capacity building for improved governance goes on. traditional empirical research has largely dismissed the analysis of fragile or conflict contexts. for instance, econometric evidence used in the aid-effectiveness debate suggests that the ineffectiveness of aid is due to the failure of the recipient governments to create the right policy environment. however, this data uses a cross-section of countries without any specific focus on fragility contexts that, at best, were treated as a dummy variable in the regressions (boone, 1995; burnside and dollar, 2000; hansen and tarp, 2001; dalgaard and hansen, 2001; easterly et al., 2004; doucouliagos and paldam, 2009). the same reduced-form approach based on country aggregate data has been adopted in more recent 29the impact of assistance on poverty and food security in a fragile and protracted-crisis context literature on the “growth-efficient” level of aid.6 the literature found that the relationship between aid and growth takes on an inverted-u shape for both fragile and non-fragile countries, identifying a lower growth-efficient level of aid in the former as compared to the latter (gomanee et al., 2005; mcgillivray et al., 2006; mcgillivray and feeny, 2008; feeny and mcgillivray, 2009; naudé et al., 2011). existing evidence from impact evaluations in fragile contexts is equally poorly developed. a recent evidence gap map review of impact evaluations found little to no evidence on most categories related to the five peace-building and state-building goals. only two goals (community-driven reconstruction and psycho-social programs for victims) had a large enough number of studies to be promising for evidence synthesis. while prioritizing new research in understudied areas might help fill such knowledge gaps, the nature of experiments also imposes limits on what is studied. in addition to the common limitations of randomized studies (cf. section 2.2), some interventions may be impractical or unethical in fragile/conflict contexts (humphreys, 2015). some authors therefore look beyond the standard impact evaluation approach, choosing instead to focus on the drivers of success in fragile contexts by developing comprehensive theories that identify important factors and establish how they interact to create outcomes. the authors then test or demonstrate the plausibility of their arguments through case studies (cf., for example, guisselquist, 2015). addison (2000) was one of the first in the field to discuss the role of aid before, during, and after armed conflicts. he found that aid distributed during conflicts plays a minor yet positive role in humanitarian assistance as well as in the transition from war to peace. there are, however, serious problems in operating in wartime environments. this author notes that aid can complicate conflicts when it falls into the hands of belligerents. after periods of war, aid plays a major role in rehabilitation and reconstruction efforts. finally, addison considers the possibility of using aid to prevent conflict in areas at risk, arguing that foreign policy support should incorporate aid in conflict prevention efforts. such aid should focus on reducing poverty and inequality to dampen social tensions as well as support institutions and processes for conflict resolution. more recently, guisselquist (2015) argued that development assistance to fragile states and conflict areas can act as a core component of peacebuilding by providing support for the restoration of government functions, the delivery of basic services, the rule of law and economic revitalization. significant gaps exist regarding what has worked, why it has worked and the transferability and scalability of such findings. nevertheless, three broad factors can identify why some interventions work better than others. the first is the area of intervention and the related degree of engagement with local state institutions. the second factor relates to local contextual elements, including windows of opportunity, capacity and the existence of local supporters. finally, the third set of factors deals with project or program design and management. while the third set of factors is largely transferrable and scalable, the first two are less so and should be considered carefully when assessing the feasibility of extending project or program models to new contexts. area of intervention, degree of engagement with domestic institutions and local contextual elements are 6 the so-called “growth-efficient” level of aid is the level of aid beyond which more aid is associated with lower growth. 30 d. romano et alii vital factors to consider when making adjustments to improve the viability of development programs. finally, a more radical approach was proposed by authors adopting a political economy perspective to analyze the workings of aid in conflict contexts (murshed, 2002; sogge, 2002; kanafani and al-botmeh, 2008; hever, 2010; taghdisi-rad, 2011 and 2015). the authors argue that the debate on aid effectiveness in fragile contexts has treated conflict as an external factor to be considered only at a much later stage in the analysis. they believe that a conflict and its interaction with local socio-economic structures should instead be the starting point of the analysis. as taghdisi-rad (2015: 5) said, it is imperative to understand “the nature of [a] conflict and the ideological forces behind its continuation … to construct a framework for the analysis of economic performance under any given conflict”. 3. assistance to palestinian households 3.1 west bank and gaza strip: a fragile and protracted crisis context the world’s longest on-going crisis is in the west bank and gaza strip, marked by more than fifty years of occupation, repeated waves of violence, and wars. the last two decades of palestinian history have been marked by the construction of a separation barrier, the closure of the gaza strip in 2007, three devastating conflicts in 2008/2009, 2012 and 2014 respectively, as well as the increasing territorial fragmentation resulting from the continued expansion of israeli settlements in the west bank. the hope for greater welfare and stable economic growth brought about by the oslo accords (1993-95) has withered as a result of the unresolved israeli-palestinian conflict.7 moreover, a growing political divide between the west bank and gaza strip has further destabilized the economy. the attainment of palestinian economic development is largely dependent on economic relations with israel. according to the paris protocol, the palestinian economy works under the framework of a customs and monetary union with israel (hever, 2015; unctad, 2015).8 the palestinian government cannot exert power over its borders nor 7 the oslo peace accords, under which the palestinian authority (pa) was created in 1994, were intended to lead to a final negotiated settlement between the parties. the accords led to several administrative and security arrangements for different parts of the west bank, which became divided in areas a, b and c, with the pa having civil and security authority only in area a (which accounts for 18% of the west bank) and no authority whatsoever in jerusalem. these were meant to be provisional terms, pending a final negotiated settlement. permanent issues such as the status of jerusalem, security arrangements, international borders, and the rights of palestine refugees (5 million palestine refugees are to this day dispersed across the middle east) were left to be resolved after a five year interim period that ended in 1999. twenty-five years after the oslo accords, no progress has been made to settle the aforementioned pending issues (eu commission, 2018). 8 the protocol on economic relations, also called the paris protocol, is an agreement between israel and the palestine liberation organization signed in april 1994. it was incorporated into the oslo ii accord of september 1995 with minor emendations. originally, the paris protocol was to remain in force for an interim period of five years, yet it is still being enforced today. essentially, the protocol integrated the palestinian economy into the israeli economy through a customs union where israel controls both israeli and palestinian borders (elkhafif et al., 2014). the protocol regulates the relationship and interaction between israel and the palestinian authority in six major areas, namely: customs, taxes, labour, agriculture, industry and tourism. 31the impact of assistance on poverty and food security in a fragile and protracted-crisis context does it have an independent monetary policy.9 economic growth suffers as a result of restrictions and controls placed on the movement of people and goods, access to resources such as land and water and access to productive inputs and markets. the palestinian government has limited ability of collecting its own taxes, while israel recurrently withholds revenues collected on behalf of the palestinians. consequently palestinian public finances are seriously destabilized. the situation is further complicated by the internal political divide that further limits the sovereignty of the palestinian government. in such a situation, the scope and geographical coverage of policy interventions has limited effectiveness. as long as barriers to trade, access, and movement remain high, the palestinian economy will continue on its current path of low growth.10 the palestinian economy grew on average 5.5% per year over the last decade, with a marked difference between the west bank and gaza strip. the economy slowed down in the last few years, so much so that 2017 estimates project gdp to fall from 3.1% to 1.7% per year in the medium-run (imf, 2018). this is mostly due to the reduction of donor flows and the possibility of running tensions increasing further. with an expected population growth as high as 2.8% in 2017, the aforementioned implies a stagnation, if not a contraction, of per-capita incomes. unemployment continues to be high (27.8% in 2017) and labor force participation continues to be low, with structural unemployment particularly affecting young people and women: only 41% of youth between 15 and 29 years of age are active in the labor market while only 19% of women are active. household and government consumption are the main drivers of economic activity. the two crowd out the investment necessary for faster growth. primary capital inflows into palestine are remittances and development assistance rather than fdi. meanwhile, the national economy is highly import-dependent, israel remaining by far its main trading partner. overall, the palestinian economy is still highly aid-dependent despite a sharp decline in aid. unctad (2018) found that international developmental support to palestine in 2017 amounted to usd 720 million, only one third of the usd 2 billion received in in 2008. over the same period, budget support shrank from usd 1.8 billion to usd 544 million, a 70% decrease.11 moreover, the fiscal burden of humanitarian crises and occupation-related fiscal losses have diverted donor aid from development to humanitarian interventions and budget support. as emphasized by unctad (2015), no amount of aid would have been sufficient to put any economy on a path of sustainable development under conditions of frequent military escalations. poverty and low standards of living are increasing in palestine. the poverty headcount ratio at the national poverty line was estimated to be 29.2% in 2017 (pcbs, 2018a), well above the 2011 poverty headcount ratio of 25.8%. the proportion of poor in 2017 stood at 13.9% in the west bank and 53.0% in the gaza strip. in that year, about 16.8% of palestinians lived in extreme poverty (almost four percentage points more than in 2011), 9 the agreement defined specific arrangements through which the government of israel collects vat, import duties and other so-called clearance (custom) revenues on behalf of the pa, sharing it with the latter on a monthly basis. these revenues account for 73% of the pa’s total net revenues (eu commission, 2018). 10 world bank (2017) estimates indicate that removing israeli restrictions could increase annual gdp growth up to 10%. 11 the recent decision made by the united states to halt financial assistance to the palestinian government and to unrwa compounds an already critical situation. 32 d. romano et alii with 5.8% residing in the west bank and 33.8% in the gaza strip. the increase in overall poverty percentages between 2011 and 2017 is explained by the combined effect of two diverging dynamics: standards of living dramatically worsened in gaza strip, causing a rise in the poverty rate of 15 percentage points while poverty decreased by four percentage points in the west bank. according to the united nations relief and works agency for palestine refugees in the near east (unrwa), four out of five people living in gaza’s are currently aid-dependent. food and nutrition security are closely related to poverty. according to the socioeconomic and food security survey (fss-pcbs, 2016), in 2014, 26.8% of total households were classified as severely or moderately food insecure12. according to the food insecurity experience scale (fao-ifad-unicef-wfp-who, 2017), the prevalence of moderate or severe food insecurity in the population was 29.9% between 2014-16, of which 9.5% represented severe food insecurity. stunting (or height-for-age) stood at 7.4% for children under the age of five in 2014-2016, while the prevalence of wasting (or weight-for height) was only 1.2%. palestinians also face malnutrition: the prevalence of overweight youth was 8.2% among children under 5 years of age in 2014-2016 (faoifad-unicef-wfp-who, 2017). micronutrient deficiency is also a concern among vulnerable population groups, such as pregnant or lactating women and children. 3.2 an overview of assistance modalities in the west bank and gaza strip palestinians are vulnerable to many risks. according to ocha (2018), the most critical ones are the following: (i) the risk of conflict and violence, forcible displacement, and the denial of access to natural resources, inputs and markets that affect 2 million people in need of protection assistance; (ii) risks associated with poor water quality, poor wastewater collection and treatment, and lack of proper hygiene practices that affect 1.9 million people; (iii) the risks of food insecurity faced by 1.7 million people; and (iv) 1.2 million people are exposed to health and nutrition risks (e.g. conflict-related trauma casualties, pregnant and lactating women, children under the age of five, people with disability and elderly, etc.). although all palestinians are negatively impacted by the conflict, some of them – such as 1.4 million refugees, the 1.6 million gazan civilians in need, and 0.4 million individuals living in area c – are more severely affected (unsco, 2016). in the face of economic de-development and the denial of autonomous development prospects, humanitarian and development actors increasingly recognize the importance of bridging the humanitarian-development divide in palestine. the result is a combination of emergency response measures with longer-term interventions to better address the causes of vulnerabilities faced by the palestinian population (diakonia, 2018).13 many vulnerable 12 preliminary results of the last sefsec (pcbs, 2018b) show that the share of households classified as severely or moderately food insecure has increased by 6.2% between 2014 and 2018. 13 the protracted nature of the crisis and the dismal prospects for positive change have led to a considerable degree of critical reflection across the nexus from different perspectives and actors in wbgs. the un notes that “humanitarian action extends to less traditional areas of intervention and calls for a much closer collaboration between humanitarian actors and the government” (unsco, 2016: 17). along the same lines, the humanitarian response plan for 2018 (ocha, 2017a: 7 and 30) recognizes that “key drivers of vulnerability are common to both the humanitarian and development needs”. as noted by the mapping and synthesis of evaluations carried 33the impact of assistance on poverty and food security in a fragile and protracted-crisis context groups have been identified as beneficiaries of both humanitarian and development interventions, both of which must occur simultaneously in order to be effective. humanitarian and development programming are increasingly aligned in order to provide durable and sustainable assistance capable of building resilience and reducing vulnerability. in other words, a blend of interventions tends in practice to prevail on a strict divide between humanitarian and development interventions, leveraging on the “humanitarian-development nexus” and operationalizing the so-called “new way of working” (ocha, 2017b) as outlined in the un secretary-general’s report for the world humanitarian summit (un, 2016). the most important modalities of assistance in wbgs are: (i) in-kind provision of basic foodstuffs through baskets generally including wheat flour, rice, pulses and vegetable oil; (ii) food vouchers for use on selected items with designated merchants; and (iii) cash transfers distributed mostly through e-cards for cash disbursements. the aforementioned forms of assistance are listed in increasing flexibility, meaning that the mode of assistance provides a greater range of choice to targeted households, has cheaper implementation, and is less likely to focus on basic needs. vocational training programs and other forms of livelihoods support can also help families rise above the poverty line. other forms of support such as health and housing assistance are also quite important, especially in acute crisis (e.g. the 2014 war in gaza). assistance in palestine is delivered by many actors. in terms of financial volume, major implementing actors include the ministry of social development (mosd), the united nations relief and works agency for palestine refugees in the near east (unrwa), and the world food programme (wfp). while a large number of donors support unrwa’s activities, the two largest donors to direct assistance are the eu and, until 2017, the usa. charities linked to zakat — the payment made under islamic law on certain kinds of property used for charitable and religious purposes — as well as assistance through de-facto authorities in the gaza strip are equally important sources of financial inflows (culbert, 2017). while modalities of assistance vary between implementing bodies and beneficiary groups, selection criteria and program objectives are similar. the principal beneficiary selection tools used by actors for food and social assistance are poverty-based, using variations of a proxy means testing formula. institutional structures and political considerations are primary determinants in how social security assistance, direct food assistance and cash assistance are defined and channeled. some development donors fund through governmental channels, such as the mosd, while some humanitarian donors fund through humanitarian actors, such as unrwa or wfp. as a result, the current system of delivering assistance is fragmented despite recent efforts working towards effective coordination between humanitarian and development actors (culbert, 2017). the recent mosd’s strategy (mosd, 2017) holds promise in both coordinating and aligning assistance efforts of multiple actors by addressing underlying social-economic challenges. however, this strategy remains at an early policy stage. out by uneg (2018: 28), in the occupied palestinian territories there is recognition that “the scope of programming needs to transcend standard ‘good practice’ in order to mitigate the negative effects of what is likely to be a deteriorating situation”. 34 d. romano et alii 3.3 assistance to palestinians in 2013-2014 assistance to the wbgs is composed of a very heterogeneous set of modalities, implementing bodies and beneficiary groups, reflecting different conditions at the local level as well as between the west bank and the gaza strip. types of assistance according to sefsec (fss-pcbs, 2016), approximately 40% of all palestinian households reported receiving at least one type of assistance in 2014. there is a marked difference in the share of households receiving assistance in gaza strip (84%) compared to the west bank (less than 17%) (table1). between 2013 and 2014, the share of assisted households in the gaza strip increased by more than 18%, even greater than the amount observed in 2011 (fao-unrwa-wfp, 2013). however, the increase in share of assisted households between 2013 and 2014 in the west bank was less than 2%, standing 8 percentage points below the level existing in the region in 2011. table1 illustrates the prevalence of in-kind food, cash transfers and food vouchers provided to palestinian households. between 2013 and 2014, the composition of the various types of assistance in the west bank did not change significantly, while composition of assistance in the gaza strip underwent important changes. in the west bank, a large share of households reported that “cash” and “in-kind food” were the two types of the assistance they received the most of in 2013 and 2014. on the other hand, the major cattable 1. share of households receiving assistance by type of assistance and region, 2013-2014. wbgs west bank gaza strip 2013 2014 2013 2014 2013 2014 in-kind food 24.6% 28.0% 7.5% 7.6% 57.5% 67.0% health care 0.4% 2.3% 0.6% 2.7% 0.2% 1.6% clothing 0.7% 2.1% 0.4% 0.3% 1.3% 5.7% job creation 1.3% 0.3% 0.3% 0.2% 3.2% 0.6% compensation martyrs 0.2% 0.3% 0.1% 0.3% 0.4% 0.4% cash 16.8% 16.2% 10.5% 8.3% 28.9% 31.2% health insurance 11.5% 7.8% 0.7% 1.2% 32.2% 20.3% food vouchers 3.0% 8.2% 2.0% 1.6% 4.7% 20.8% school feeding 0.1% 0.1% 0.1% 0.0% 0.1% 0.4% productive inputs 0.1% 0.0% 0.2% 0.0% 0.0% 0.1% drinking water 0.4% 1.8% 0.0% 0.1% 1.0% 5.2% electricity 0.2% 0.2% 0.2% 0.3% 0.0% 0.2% housinga 9.2% 0.9% 25.0% other 0.6% 1.2% 0.2% 0.0% 1.3% 3.4% at least one form of assistance 32.4% 39.7% 15.2% 16.5% 65.7% 84.2% a not included in the 2013 sefsec survey. source: fss-pcbs (2016): table 7.1, modified. 35the impact of assistance on poverty and food security in a fragile and protracted-crisis context egories of assistance reported in the gaza strip fluctuated between the two years. new types of assistance outside the three core types (“in-kind food”, “cash” and “health insurance”) were reported in the gaza strip. these included “housing” (shelter, rent, caravan), “food voucher”, “drinking water” and “clothing”. all four increased significantly between 2013 and 2014 in response to worsening living conditions as a result of the armed conflict. value of assistance in 2014, assisted households received an average of 102 us$/month. however, national averages mask significant regional differences in both levels and trends. table 2 reports the average monthly value received by households in the two regions for each types of assistance during 2012-2014. there was a general decline in the average value of assistance for cash and food in the west bank from 2013 to 2014. conversely, assistance for employment and provision of agricultural inputs increased. in the gaza strip the average value of support increased for many assistance types but food assistance that did not change much. employment assistance represented the largest average allowances given to households in 2014. among “other” forms of support, the largest average values are seen for housing and shelter assistance. support to agricultural production activities almost disappeared in gaza strip after 2012. the value of assistance varies across different types of households (table 3). support to refugee households was slightly greater than that of non-refugee households (107 vs. 91 us$/month). moreover, a substantial difference was recorded in 2014 based on gender household heading: female-headed households received on average 30% more support than male-headed households (127 vs. 98 us$/month). this reveals that female-headed households are more dependent on assistance, probably due to higher vulnerability. the composition of assistance across different household typologies emphasizes the different needs of various groups (table 3). female-headed households are more likely to receive assistance in the form of cash and free health services than male-headed housetable 2. average value of support by type of assistance, us$/month. type of assistance west bank gaza strip 2012 2013 2014 2012 2013 2014 cash 115 79 55 95 92 123 in-kind food 45 34 27 37 36 48 food vouchers 42 43 28 30 48 32 job creation 115 97 126 82 147 215 agricultural inputs 46 69 123 129 na 9 housing na na 231 na na 211 othera 71 70 135 4 17 110 average per assisted household 128 96 86 65 102 108 a the “other” category in years 2012 and 2013 includes also housing. source: fss-pcbs (2016): table 7.3, modified. 36 d. romano et alii holds. this is probably due to the demographic composition of the former, with a majority of households headed by widows and elderly women. the comparison between refugee and non-refugee indicates a cash preference by non-refugee households, while refugee households receive a larger share of assistance in “other” forms, including substantial support for housing. sources of assistance social assistance coverage increased between 2013 and 2014, reflecting deteriorating livelihood conditions–especially in the gaza strip, where more than four households out of five were receiving assistance in 2014. overall, reported sources of assistance are given primarily by the palestinian ministry of social affairs (currently renamed the ministry of social development, or mosd), unrwa, international agencies, charitable and religious associations, and informal assistance (family, relatives or friends). however, key differences are observed between the west bank and the gaza strip (table 4). in the west bank, 7% of households reported receiving assistance from the ministry of social affairs in 2014, a slightly lower figure than that reported in 2013 (8%). the other two most cited sources of assistance in 2014 were unrwa and informal assistance (family and relatives), which remained unchanged from 2013 levels. a different picture emerges from the data in the gaza strip. not surprisingly, the largest source of social assistance in 2014 was unrwa, an organization that provided food assistance to some 867,000 refugees. a number of other sources of assistance were reported, including the palestinian ministry of social affairs, international agencies, charitable and religious associations, worker unions, and family and friends. informal sources of social assistance more than halved, dropping to 7% in 2014. this is a clear sign that informal social networks were unable to help in times of widespread severe hardship caused by the war. table 3. composition of assistance by region and household group, share of total value received, 2014. type of support west bank gaza strip refugee nonrefugee maleheaded femaleheaded cash 36.4% 34.5% 31.8% 40.2% 34.0% 40.4% in-kind food 15.3% 26.8% 23.6% 24.7% 25.7% 15.6% health insurance 19.8% 0.8% 5.3% 5.0% 3.1% 16.2% food vouchers 3.1% 5.5% 4.7% 5.7% 5.5% 2.3% housing 13.1% 21.6% 24.4% 12.2% 20.9% 12.9% other 0.1% 5.6% 5.5% 2.2% 5.0% 0.7% remaining sources 12.2% 5.2% 4.7% 10.0% 5.8% 11.8% average per assisted household (us$/month) 86 108 107 91 98 127 source: fss-pcbs (2016): table 7.4, modified. 37the impact of assistance on poverty and food security in a fragile and protracted-crisis context 4. poverty and food security the profiling of palestinian households in terms of poverty quartiles before receiving assistance shows expected patterns14 (table 5): moving from poorer to richer households saw a parallel decrease in household size, an increase in educational attainment, a decrease in the dependency ratio, and an increase in the employment rate (including that of the head of the household). poverty in the wbgs is determined by the employability of household members. food security on the other hand is largely influenced by access dimension, specifically by individuals’ labor entitlement. table 6 therefore provides a detailed account of household heads’ labor indicators across poverty quartiles. by and large, poorer households had more problematic labor conditions. for instance, household heads who worked fewer hours were more likely to be poor, just as irregular employment and lower level occupations were more related to poverty. usually, poverty is correlated to employment in the primary and construction sectors. in short, heads of poorer households tend to have more informal and irregular jobs that do not require high levels of formal skills and/or education, such as jobs in basic production sectors. 14 only the female-headed household share does not show a clear pattern. another characteristic (not reported in the table) that does not change at all is the number of sources of income per household: on average, two per household. table 4. reported sources of assistance by regiona. west bank gaza strip 2013 2014 2013 2014 ministry of social affairs 8.2% 6.8% 19.6% 23.5% other pa agencies 0.9% 2.0% 4.2% 8.6% political parties 0.0% 0.1% 0.4% 8.6% zakat/other religious institutions 0.5% 0.6% 0.5% 2.7% international agencies (excluding unrwa) 1.4% 1.2% 9.3% 21.3% unrwa 2.1% 4.0% 42.6% 62.3% arab countries 0.0% 0.1% 0.3% 2.8% charity/religious 0.4% 0.3% 3.8% 19.5% family and relatives 2.8% 2.8% 14.8% 6.8% friends/neighbors 1.1% 0.9% 1.8% 4.8% workers union 0.0% 0.0% 21.6% 12.9% national banks 0.0% 0.0% 0.0% 0.5% local reform commission 0.0% 0.0% 0.1% 0.6% other 0.4% 0.9% 0.3% 3.3% any type of assistance 15.2% 16.5% 65.7% 84.2% a sources of assistance are not mutually exclusive. some households reported receiving assistance from more than one source. source: fss-pcbs (2016): table 7.5. 38 d. romano et alii as expected, there is a direct relationship between poverty and food insecurity (table 7). this is measured by the food consumption score (fcs) and the household food insecurity access scale (hfias), two proxies for the qualitative and quantitative dimentable 5. households’ profile per poverty quartile, 2013. q1 q2 q3 q4 total average household size 7.7 5.2 4.8 4.5 5.6 share of hh with female head 6.4% 11.5% 11.4% 9.1% 9.6% share of hh with head with secondary education or above 28.1% 34.2% 39.1% 51.0% 38.1% global dependency ratio 1.20 1.19 1.02 0.90 1.08 share of hh whose head does not work 28.9% 28.4% 23.5% 22.4% 25.8% household employment rate 32.1% 36.9% 40.5% 43.7% 38.3% authors’ elaboration on sefsec 2014 data. table 6. head of household employment statistics per poverty quartile, 2013. q1 q2 q3 q4 total working status           employed from 1-14 hours 5.1% 4.6% 2.5% 1.3% 4.2% employed 15-34 hours 6.1% 6.9% 5.1% 3.2% 6.0% employed 35 hours and over 41.7% 46.5% 58.5% 63.5% 47.7% temporarily absent 14.6% 10.6% 6.6% 3.9% 11.2% looked for a job (already worked) 6.9% 3.9% 1.2% 2.1% 4.6% looked for a job (never worked) 2.1% 2.6% 0.7% 1.1% 1.9% did not look for work because of frustration 0.7% 0.9% 0.6% 0.6% 0.7% full time student 0.1% 0.0% 0.0% 0.0% 0.0% housewife 4.0% 5.0% 4.9% 3.4% 4.4% unable to work 16.8% 14.7% 12.8% 8.1% 14.8% other 0.0% 0.0% 0.2% 0.0% 0.0% professional status employer 2.4% 2.2% 3.9% 11.9% 5.1% self-employed 11.3% 11.5% 12.4% 13.9% 12.3% unpaid family worker 0.2% 0.1% 0.3% 0.1% 0.2% waged employee 61.9% 59.9% 60.5% 52.2% 58.6% sector of employment agriculture, fishing and forestry 8.7% 6.7% 3.5% 2.2% 5.3% mining, quarrying and manufacturing 6.5% 8.6% 11.5% 13.6% 10.0% construction 18.2% 16.7% 16.3% 12.6% 16.0% commerce, restaurants and hotels 10.9% 11.2% 14.6% 20.2% 14.2% transportation, storage and communication 7.2% 5.5% 6.1% 5.1% 6.0% services and other activities 24.3% 24.9% 25.1% 24.5% 24.7% authors’ elaboration on sefsec 2014 data. 39the impact of assistance on poverty and food security in a fragile and protracted-crisis context sions of food security, respectively (cf. section 5.1). probably the most striking indicator related to poverty is the share of households receiving assistance. this value encompasses almost two thirds of all households in the lowest quartile and 7.6% of households in the highest quartile. both indicators of food security show the expected regularities in that poorer households have lower fcs values. meanwhile, poorer households have larger shares of poor or borderline fcs (q1 three times larger than that of q4) as well as insufficient dietary quantities (hfias in q1 eight times larger than that of q4). quite surprisingly, the average value of assistance rapidly decreases from the lowest to the second-lowest quartile, but then increases again in the two higher quartiles15. 5. data and methods 5.1 data the socio-economic and food security (sefsec) survey has been administered since 2009 to monitor the status of food security among palestinian households. the sefsec methodology accounts for the multi-dimensional drivers of food insecurity in wbgs by exploring topics such as asset-based poverty, food consumption, and resilience. this is done to capture the capacity households have to adapt, transform and cope with shocks. besides these three main pillars, the questionnaire collects data on aspects such as socio-demographics, assistance, expenditure and consumption, all of which are useful for the analysis. the dataset includes data from the fifth and sixth sefsec surveys. data collection took place in 2014 and 2015, with a reference period covering the six months preceding the interview (the second half of 2013 and 2014, respectively). the 2013 sefsec survey was conducted on a sample of 7,503 households (4,949 in the west bank and 2,554 in the gaza strip), while the 2014 sample included 8,177 households (5,047 in the west bank and 3,130 in the gaza strip). the samples are representative for various levels of disaggregation, including gender, refugee status, governorate, locality type (i.e. urban, rural and refugee camp) and, for the west bank only, areas a/b and c (fss-pcbs, 2016). an important feature of the 2013-2014 sefsec is that 92% of the households interviewed in 2013 were included also in the 2014 wave. therefore, a sample of 6,881 units 15 however, this seems to be related to the higher average value of assistance in the west bank to households that own some type of business: essentially, it is a support to investment that is able to generate employment. table 7. households’ assistance and food security status per poverty quartile, 2013. q1 q2 q3 q4 total per capita expenditure (nis/month) 305 461 593 860 554 share of hh receiving assistance 62.5% 41.8% 21.3% 7.6% 33.3% average value of assistance per hh (nis/month) 418 293 347 321 368 households with insufficient dietary quantity (hfias) 50.7% 29.3% 14.8% 6.4% 25.3% households with poor or borderline fcs 30.4% 26.4% 17.8% 10.2% 21.2% average household fcs 70 72 76 80 74 authors’ elaboration on sefsec 2014 data. 40 d. romano et alii (4,454 in the west bank and 2,427 in the gaza strip) can be used to analyze the impact of assistance on palestinian households through the panel structure of the dataset. the main variables used in the analysis are summarized in table 8. they include the three outcome variables of interest: a measure of poverty and two measures of food security (i.e. hfias and fcs, the latter also broken down in its main components), a set of household socio-demographics that are the usual correlates used to analyze the outcomes, and some geographical dummies to account for regional/residence differences used to capture any unobserved heterogeneity.16 poverty outcomes are measured as an asset-based poverty index closely related to living standards. an asset-based poverty index better reflects long-term wealth over an expenditure-based poverty index, a short-term measure which in principle would work better in an impact evaluation of aid effectiveness. additionally, the asset-based poverty index was chosen since total household expenditure is not accurately sampled by the sefsec questionnaire. indeed, an assessment commissioned by sefsec administrators to evaluate the robustness and reliability of expenditure-based poverty measures resulted in the decision to abandon money-based (i.e. expenditure) measures of poverty because they were inconsistent with similar measures based on benchmark data from the palestine expenditure and consumption survey of 2011 (pecs) (langworthy et al., 2014; smith, 2014).17 furthermore, in the context of protracted crisis such as the currently ongoing one in palestine, assistance becomes a key source of income for the majority of households, establishing itself as a “structural” component of household income. assistance has significantly contributed to building household assets over the years and helps maintain a given level of standards of living via consumption smoothing. if assistance to households decreases, household assets would decrease in response because the household sells its assets to countervail the reduction in assistance. food security is proxied by two measures, namely the household food insecurity access scale (hfias), a quantitative measure of the dimension of food consumption (coates et al., 2007), and the food consumption score (fcs) that captures the quality of household diets (wfp, 2008). hfias is an indicator based on responses to nine questions, five of which relate to the size and frequency of meals consumed in the 30 days preceding the survey. hfias is value ranging from 0 to 27, where a higher score indicates an insufficient dietary quantity. fcs is an indicator based on the number of days specific food groups are consumed in the seven days preceding the survey. the fcs is a continuous score where a value less than or equal to 45 or between 45 and 62 respectively indicate poor or borderline food consumption. this value is obtained by assigning a specific weight to each food group in accordance to its contribution to dietary quality. 16 the variables listed in table 3.1 are the ones actually used in the following analysis, that is they are only a subset of the wider set of candidate variables that in principle could be used. unfortunately, the sefsec survey is designed only to monitor the evolution of food security in palestine. as such it does neither have the wealth of variables that can be usually found in a standard multi-purpose survey (e.g. household cultural traits, household behavior other than food consumption, etc.), nor the depth of data typical of household expenditure/consumption surveys (e.g. detailed information on household expenditures, food consumption composition, etc.). 17 the overall conclusion of these studies was that “in the absence of other options, an asset-based measure of poverty can thus serve as a valid, stand-alone measure for the purposes of the sefsec food insecurity analysis.” (smith, 2014: 21). 41the impact of assistance on poverty and food security in a fragile and protracted-crisis context the pros and cons of these two indicators have been assessed in several review and validation studies of food security indicators (carletto et al., 2013). ifpri (2006) concluded that the fcs weighting system for the food frequency scores might not be able to accommodate variations across space and time. nevertheless, ifpri found positive associations between fcs values and caloric consumption per capita in some studies. the information generated by hfias is used to assess the prevalence of household food security and detect changes over time. moreover, validations conducted in latin america and subsaharan africa (melgar-quinonez et al., 2006; knueppel et al., 2010) found that the indicator demonstrated reliability and validity in the local contexts in which it was deployed. table 8. summary statistics of key variables. variable meaning mean standard deviation min max l_ass_index log of asset based poverty index 7.09 0.33 5.52 8.28 fcs food consumption score (fcs) 74.28 17.06 0.00 112.00 hfias household food insecurity access scale (hfias) score 4.64 6.56 0.00 27.00 vegfru_fcs fcs cereals, tubers, pulses, vegetable and fruit 26.96 4.93 0.00 49.00 meatmilk_fcs fcs meat and milk 40.85 14.65 0.00 56.00 oilsug_fcs fcs fats and sugar 6.46 1.13 0.00 7.00 mass log of hh monthly assistance 1.96 2.63 0.00 10.82 ydum dummy for year 2014 0.50 0.50 0.00 1.00 massy interaction mass*ydum 1.04 2.12 0.00 10.82 lhsize log of household size 1.81 0.42 0.69 3.30 lexp log of household monthly expenditure (nis) 7.72 0.75 1.79 11.16 dep_ratio dependency ratio (aged 0-15+aged >65)/aged 15-65 1.10 1.34 0.00 7.00 rat_emp % of employed people aged >15 in the hh 0.37 0.24 0.00 1.00 agehead age of hh head (years) 45.34 14.37 19.00 98.00 femhead hh head gender (female = 1) 9.66%   0 1 head_ref hh head status (refugee = 1) 41%   0 1 high_ed hh head education (secondary education or higher = 1) 38.12%   0 1 employed hh head occupational status (employed = 1) 70.42%   0 1 qly_deprived hh with low fcs (< 61) (yes = 1) 22.26%   0 1 qty_deprived hh with insufficient food intake, hfias (yes = 1) 23.21%   0 1 ass hh receiving assistance (yes = 1) 37.71%   0 1 wb north regional dummy (west bank north = 1) 27.58%   0 1 wb center regional dummy (west bank center = 1) 17.69%   0 1 wb south regional dummy (west bank south = 1) 19.46%   0 1 gs north regional dummy (gaza strip north = 1) 18.47%   0 1 gs center regional dummy (gaza strip center = 1) 5.19%   0 1 gs south regional dummy (gaza strip south = 1) 11.61%   0 1 rural locality of residence (rural = 1) 18.62%   0 1 camp locality of residence (refugee camp = 1) 9.74%   0 1 urban locality of residence (urban = 1) 71.64%   0 1 42 d. romano et alii besides the considerations above, the sefsec dataset does not include enough data to build other food security indicators such as the food caloric intake. 5.2 methods to estimate the impact of assistance on a given dimension of well-being, such as poverty or food security, we need to control for possible unobserved heterogeneity in participation in the assistance program. due to the targeting strategies of the different agencies that provide assistance to palestinian households, treated households are quite different from untreated ones. notably, the probability of receiving assistance is correlated with a set of characteristics mostly related to poverty (cf. section 4). as a result, the selection bias is likely to be pervasive (khandker et al., 2010). moreover, further unobserved targeting variables may affect both the outcome variable and the probability to receive assistance. building on the panel structure of sefsec dataset, we used a difference-in-difference (dd) approach to get rid of aforementioned biases. the dd model assumes that the heterogeneity in participation is fundamentally time invariant once conditioned on a set of household characteristics (x): e(y0 t – y0 t-1 | t = 1,x) = e(y0 t – y0 t-1 | t = 0,x) (1) where y0 t is the potential outcome without the treatment measured at time t. t is the treatment status, which equals to 1 if the household received assistance and 0 otherwise. the assumption of time invariant heterogeneity implies that the dynamics observed in the control group are the same as the ones observed in the treated group had the latter not been treated. unfortunately, the sefsec dataset does not allow testing for the “parallel trend” hypothesis. however, considering the short time distance between the two sefsec waves, the risk that this assumption does not hold is low. in regression form the dd estimator is given by: yi,t = αi + βti + γt + δtit + ∑ζxi,t + εi,t (2) where t is a time dummy (1 in the second period, 0 otherwise). ti is the treatment dummy, with a value of 1 for the treatment group and 0 for the control. the casual effect of the treatment is assumed to be additive. in the classical dd model, the δ parameter — which is associated with the interaction term between the treatment ti and the time dummy variable t — identifies the expected impact (angrist and pischke, 2008). the traditional dd regression uses dichotomic (i.e. treated/non-treated) treatment variables. however, continuous treatment variables measuring the intensity of the treatment can be also used (card, 1992; acemoglu et al., 2004). continuous variables fully exploit the information content of available data. for the purpose of this study, the most suitable candidate is the monthly value of assistance received by the household. in this case, it can be demonstrated that for the i-th household the δ parameter is equivalent to: δ = (yi1 – yi0 | ti = ti1,xi) – (yi1 – yi0 | ti = ti0,xi) (3) 43the impact of assistance on poverty and food security in a fragile and protracted-crisis context where the numerator is the difference in outcome variation over time given the final and initial values of the continuous intervention variable and the denominator is the difference between the final and the initial value of the continuous treatment variable. in the case of an increase of the continuous treatment variable between the two periods, a positive value of δ indicates that the increased treatment intensity determines a higher increase of the outcome variable. this implies that the impact of the treatment is positive. moreover, thanks to the time dimension of the panel, we can include in (2) household specific intercepts or fixed effect, αi. irrespective of the adopted fixed effect estimator, this is equivalent to including a dummy variable for each household in equation (2) (wooldridge, 2013). equation (3) will still hold provided that we condition on both x and αi. the key identifying assumption in this context is that treatment intensity is not correlated with individual unobserved trends, although it can correlate with individual permanent characteristics. we posit that the intensity of assistance (“mass”, measured in logarithms) impacts the outcome variable, i.e. either the log of poverty asset index (“l_assindex”) or one of the food security indicators (“hfias” or “fcs”). the intensity of assistance and the outcome variable are both affected by a set of household characteristics that we assume to be time-invariant, including location, refugee status, and education of the head of household. all of these are captured by αi. we further conditioned on potential time variant confounders such as dependency ratio, household size, ratio of employed household members to the number of household members of working age, and employment status of the head of the household. in the case of poverty models potential endogeneity may remain even after having conditioned on the fixed effects due to the nature of the targeting process. therefore, we implemented the 2sls version of both the pooled ols and the fixed effect estimators. in the case of food security indicators, we can assume that regressors are exogenous because targeting is made on poverty, not on food security indicators. noticeably, in the case of the hfias score, we have to deal with a censored variable whose distribution has a clear peak at zero. in such a case the fixed effects tobit model estimates would be affected by the so-called “incidental parameters” problem especially in case of short time panel datasets (greene, 2004). to ensure consistency with the fixed effect models of continuous outcome variables (asset-based poverty index and fcs), in the case of hfias model we used the semi-parametric estimator of fixed effect tobit models proposed by honoré (1992), which is consistent and asymptotically normal even for time dimension of 2 as in our case. 6. results we first run a pooled ols regression using a sandwich estimator of the covariance matrix. results in the case of the asset-based poverty index18 are reported in the first two columns of table 9. all independent variable parameters except for a few regional dummies are significant at p=0.05. both the household size and the dependency ratio affect the index negatively, while the ratio of employed household members over working age 18 the dependent variable – i.e., the log of the asset-based poverty index – is built in such a way a higher index value corresponds to wealthier households. this should be considered when interpreting the results in table 9. 44 d. romano et alii household members shows a clear positive effect. this confirms that poverty is mostly a matter of (a lack of) employability. the characteristics of head of households that positively impact the index are the following: education, age, employment status, refugee status or living in the west bank. on the other hand, households situated in rural areas and refugee camps negatively impact the outcome variable. all estimates have expected signs: higher educational attainment, employment and living in the west bank over the gaza strip all decrease the chances that a household is poor. conversely, holding refugee status or living far away from an urban center increases the likelihood of being poor. the impact denoting the intensity of assistance is captured by the interaction term “massy”. the value of monthly assistance positively impacts the asset-based poverty index. table 9. asset-based poverty index regression models, palestine. pooled ols pooled 2sls fixed effect fixed effect iv coef. student’s t coef. z coef. student’s t coef. z massa -0.03 -24.96 -0.03 -25.33 -0.02 -15.56 -0.02 -15.07 ydum -0.01 -1.89 -0.01 -1.91 -0.03 -5.18 -0.03 -5.07 massya 0.00 2.06 0.00 2.11 0.01 6.04 0.01 5.75 lhsize -0.31 -54.19 -0.31 -54.22 -0.27 -37.46 -0.27 -37.47 dep_ratio -0.02 -14.26 -0.02 -14.23 -0.02 -10.77 -0.02 -10.77 rat_emp 0.10 8.53 0.10 8.47 0.15 12.29 0.15 12.32 employed 0.04 5.55 0.03 5.35 -0.01 -4.20 -0.01 -4.20 agehead 0.00 10.45 0.00 10.42       refhead 0.04 8.5 0.04 8.47         femhead -0.02 -2.77 -0.02 -2.70         high_ed 0.09 19.05 0.09 18.85         wb north 0.13 13.33 0.12 12.84         wb center 0.23 22.58 0.23 21.98         wb south 0.09 9.25 0.09 8.83         gs north -0.01 -1.01 -0.01 -1.05         gs centerb                 gs south -0.01 -1.03 -0.01 -1.01         rural -0.10 -16.06 -0.10 -15.98         camp -0.04 -5.06 -0.03 -5.01         constant 7.44 417 7.45 415.01         r2 0.45       0.36       kp rk under-identification chisq p=0.00 cd wald f >350 >350 hj over-identification chisq exactly id. exactly id. iv (excluded) ass, assy   ass, assy   f test of fixed effect         1.8 p=0.00     a this variable has been instrumented; b gs center, where gaza city is located, is assumed as reference. note: kp is the kleibergen-paap lm test for under-identification of the model; cd is the cragg donald weak identification test; hj is the hansen j statistics for over-identification of the model (cf. baum et al., 2007). 45the impact of assistance on poverty and food security in a fragile and protracted-crisis context however, despite being statistically significant, the coefficient estimate is close to 0. to deal with possible endogeneity, we performed a pooled 2sls instrumenting the variable and the interaction term with dummies for assistance and its interaction with time. however, the size of the coefficient of the interaction term does not change in the case of 2sls. in order to account for unobserved individual heterogeneity, we run a fixed effect regression. this is done because the hausmann test rejected the hypothesis of absence of correlation between random effects and regressors. table 9 reports the parameter estimates obtained with the fixed effect estimator on transformed data as deviations from the group means.19 we also implemented the corresponding 2sls version for the fixed effect estimator using the same instruments employed in the pooled model (last two columns of table 9). all time-invariant regressors are perfectly correlated with the household specific intercepts, therefore only the time varying variables are considered in the fixed effect models: dependency ratio, household size, ratio of employed household members to working age members, and employment status of household head. both models confirm that the intensity of assistance has a significant effect in reducing household poverty. in all the models, the coefficients of the interaction term are statistically significant stable around 0.01: a 10% increase of assistance on average leads to a direct 0.1% increase of the asset-based index. to take into account the fact that the west bank and the gaza strip are physically, politically and economically apart, we estimated the impact of assistance separately for the two regions (table 10 and 11, respectively). as expected, the impact is significantly positive in the west bank and of the same order of magnitude as palestine as a whole (table 9). this was true after having accounted for individual heterogeneity. quite surprisingly, we obtained a non-significant impact of assistance in the gaza strip. this seems related to the very peculiar situation present in gaza. in 2014, more than four households out of five received assistance (cf. section 3.3), largely irrespective of the household characteristics.20 this was done in order to offset the region’s widespread humanitarian crisis resulting from a ten-year long blockade and generalized “de-development” (unctad, 2017). to make matters worse, a series of military operations took place over the last decade, ultimately culminating in the devastating war of july-august 2014 — exactly during the second period surveyed. this is likely to have blurred the causal relationship between assistance and poverty. the estimates in the case of hfias show the expected signs.21 in the models for palestine as a whole (table 12), the coefficient of the interaction term is significantly negative in the simple pooled ols model as well as in models addressing the censored nature of the hfias variable. this means that assistance has a significant positive impact in ensur19 with this transformation we get rid of the large number of group dummies that would be included in the least square dummy variable estimator had the transformation not being made (baltagi, 2005). 20 the poverty headcount ratio in the gaza strip is 53.0% while one third of population (33.8%) lives in extreme poverty according to monthly consumption patterns (pcbs, 2018a). according to atamanov and palaniswamy (2018) more than 90% of the bottom 40% in the gaza strip receive some form of aid; and even among the most well-off, half receive assistance. another anecdotal evidence of the generalized humanitarian crisis is the higher concentration around the mean of average assistance per household in gaza strip vis-à-vis west bank with the latter having a coefficient of variation that is five times larger than the former. 21 hfias is a measure of quantity deprivation of food showing higher scores the lesser the food consumed by the household. 46 d. romano et alii ing the consumption of adequate quantities of food. moreover, being a refugee, employed, well-educated, younger household head reduces household food insecurity. regional models tell the same story, although it is worth noting that the impact of assistance is much stronger in the gaza strip than in the west bank. this confirms the key role of assistance to ensure food security in a humanitarian crisis context such as the gaza strip, where two third of households receive in-kind food assistance and one fifth of surveyed households received food vouchers (cf. table 1). in the west bank, households have a wider portfolio of coping strategies available to them, including non-assistance strategies. both regions have marked sub-regional differences. the governorates of the two main economic centers – ramallah and east jerusalem in the west bank and gaza city in the table 10. asset-based poverty index regression models, west bank. pooled ols pooled 2sls fixed effect fixed effect iv coef. student’s t coef. z coef. student’s t coef. z massa -0.03 -16.29 -0.03 -17.15 -0.02 -10.01 -0.03 -10.15 ydum -0.02 -3.02 -0.02 -3.01 -0.03 -5.55 -0.03 -5.42 massya 0.00 0.53 0.00 0.47 0.01 3.61 0.01 3.34 lhsize -0.30 -38.49 -0.30 -38.49 -0.23 -25.1 -0.23 -25.13 dep_ratio -0.02 -10.99 -0.02 -10.94 -0.02 -8.25 -0.02 -8.24 rat_emp 0.09 5.91 0.09 5.85 0.16 9.82 0.15 9.72 employed 0.05 5.89 0.05 5.66 -0.02 -4.01 -0.02 -3.97 agehead 0.00 8.03 0.00 8.00       refhead 0.05 7.31 0.05 7.26         femhead -0.02 -1.86 -0.02 -1.77         high_ed 0.10 15.75 0.10 15.68         wb north -0.11 -14.93 -0.11 -14.85         wb centerb                 wb south -0.15 -18.74 -0.15 -18.64         rural -0.11 -16.37 -0.11 -16.25         camp -0.07 -5.69 -0.07 -5.63         constant 7.63 345.45 7.63 345.47         r2 0.31       0.21       kp rk under-ident. chisq   1083 p=0.00 1013 cd wald f >350 >350 hj over-identific. chisq exactly id. exactly id. iv (excluded) ass, assy,   ass, assy   f test of fixed effect         1.8 p=0.00     a this variable has been instrumented; b wb center, where ramallah and east jerusalem are located, is assumed as reference. note: kp is the kleibergen-paap lm test for under-identification of the model; cd is the cragg donald weak identification test; hj is the hansen j statistics for over-identification of the model (cf. baum et al., 2007). 47the impact of assistance on poverty and food security in a fragile and protracted-crisis context gaza strip – perform on average better than other districts. we do not have econometric evidence to explain this. however, we can argue that this happens for different reasons on the basis of secondary information. for instance, in the case of the west bank, residing within the municipality of ramallah or close to it is an advantage in terms of employment and market opportunities. furthermore, the impact of israeli settlements and territorial fragmentation is less pronounced in these areas compared to wb north and wb south. for the gaza strip, residing close to the decision-making center of the de facto ruling authority and further away from the israeli border22 is an advantage in terms of food security. 22 israeli forces enforce a buffer zone by land and sea, the “access restricted areas”. according to israeli authorities, up to 100 meters from the double wired/concrete fence built along the gaza-israel border is a “no go” area and up to 200 meters there is no access for heavy machinery. however, “humanitarian partners in the field have table 11. asset-based poverty index regression models, gaza strip. pooled ols pooled 2sls fixed effect fixed effect iv coef. student’s t coef. z coef. student’s t coef. z massa -0.03 -14.98 -0.03 -14.38 -0.02 -9.32 -0.02 -8.74 ydum 0.03 2.67 0.03 2.17 0.00 -0.11 0.00 -0.16 massya 0.00 -1.5 0.00 -1.08 0.00 1.34 0.00 1.15 lhsize -0.35 -43.49 -0.35 -43.28 -0.33 -33.63 -0.33 -33.69 dep_ratio -0.02 -9.1 -0.02 -9.11 -0.02 -6.81 -0.02 -6.82 rat_emp 0.14 7.86 0.14 7.86 0.12 7.46 0.12 7.49 employed 0.00 -0.17 0.00 -0.20 -0.01 -1.45 -0.01 -1.46 agehead 0.00 7.82 0.00 7.80       refhead 0.02 3.06 0.02 3.06         femhead -0.02 -2.13 -0.02 -2.11         high_ed 0.07 11.34 0.07 11.14         gs north 0.00 0.36 -0.01 -1.05         gs centerb                 gs south 0.00 -0.09 0.00 -0.08         rural 0.01 0.4 0.01 0.40         camp -0.01 -0.9 -0.01 -0.88         constant 7.52 314.63 7.52 312.85         r2 0.48       0.46       kp rk under-identific. chisq p=0.00 cd wald f >350 >350 hj over-identification chisq exactly id. exactly id. iv (excluded) ass, assy   ass, assy   f test of fixed effect         1.5 p=0.00     a this variable has been instrumented; b gs center, where gaza city is located, is assumed as reference. note: kp is the kleibergen-paap lm test for under-identification of the model; cd is the cragg donald weak identification test; hj is the hansen j statistics for over-identification of the model (cf. baum et al., 2007). 48 d. romano et alii ta bl e 12 . h fi a s re gr es si on m od el s. va ria bl es pa le st in e w es t b an k g az a st rip po ol ed o ls to bi t h on or é es tim at or po ol ed o ls to bi t h on or é es tim at or po ol ed o ls to bi t h on or é es tim at or c oe f. st ud en t’s t c oe f. z c oe f. z c oe f. st ud en t’s t c oe f. z c oe f. z c oe f. st ud en t’s t c oe f. z c oe f. z m as s 0. 82 22 .8 2 1. 17 22 .3 7 0. 93 12 .6 2 0. 61 12 .0 4 0. 61 12 .0 4 0. 97 7. 27 0. 97 16 .4 6 1. 40 16 .8 0 1. 18 11 .8 4 yd um -1 .3 4 -1 3. 69 -2 .5 1 -1 1. 08 -3 .4 5 -1 2. 14 -1 .4 7 -1 4. 93 -1 .4 7 -1 4. 93 -4 .4 5 -1 3. 29 -0 .6 9 -1 .9 9 0. 24 0. 43 0. 90 1. 33 m as sy -0 .4 0 -9 .7 3 -0 .2 6 -4 .2 2 -0 .3 5 -4 .5 3 -0 .2 1 -3 .2 4 -0 .2 1 -3 .2 4 -0 .3 5 -2 .1 -0 .5 8 -7 .5 1 -0 .7 8 -6 .8 4 -1 .1 6 -8 .1 lh siz e 2. 43 19 .8 2 4. 51 19 .7 1 3. 90 11 .1 7 1. 73 13 .1 3 1. 73 13 .1 3 4. 05 7. 42 3. 62 14 .1 9 4. 71 13 .7 5 3. 95 8. 38 de p_ ra tio 0. 05 1. 36 0. 15 2. 27 0. 10 0. 99 0. 02 0. 46 0. 02 0. 46 0. 11 0. 7 0. 16 1. 85 0. 25 2. 16 0. 10 0. 67 ra t_ em p -1 .8 4 -8 .0 8 -3 .6 8 -7 .7 7 -3 .3 7 -4 .7 -1 .0 4 -4 .2 7 -1 .0 4 -4 .2 7 -3 .3 6 -3 .2 6 -3 .7 9 -7 .6 3 -5 .5 9 -7 .7 5 -3 .4 5 -3 .2 9 em pl oy ed -0 .9 3 -6 .5 1 -1 .3 0 -5 .1 5 -1 .3 2 -3 .3 6 -0 .8 5 -5 .4 4 -0 .8 5 -5 .4 4 -1 .5 2 -2 .4 8 -0 .6 8 -2 .3 9 -0 .4 1 -1 .1 3 -1 .1 7 -2 .2 1 ag eh ea d -0 .0 3 -9 .1 3 -0 .0 5 -8 .1 9 -0 .0 2 -5 .0 3 -0 .0 2 -5 .0 3 -0 .0 6 -8 .1 6 -0 .0 8 -7 .7 0 re fh ea d -0 .2 5 -2 .2 6 -0 .8 1 -3 .9 4   -0 .4 1 -3 .4 1 -0 .4 1 -3 .4 1   -0 .2 1 -0 .9 4 -0 .2 7 -0 .9 2   fe m he ad 0. 18 0. 99 0. 67 2. 05   0. 36 1. 86 0. 36 1. 86   -0 .0 5 -0 .1 3 -0 .0 4 -0 .0 9   hi gh _e d -1 .3 2 -1 3. 69 -2 .6 6 -1 3. 89   -0 .8 7 -8 .8 8 -0 .8 7 -8 .8 8   -2 .0 2 -9 .4 4 -2 .6 8 -9 .4 4   w b n or th -1 .1 0 -4 .4 3 -2 .7 9 -7 .0 0   0. 71 6. 04 0. 71 6. 04     w b c en te ra -1 .7 5 -6 .9 -5 .1 5 -1 1. 56               w b so ut h -0 .7 3 -2 .8 6 -0 .7 2 -1 .7 9   1. 16 9. 29 1. 16 9. 29     g s n or th 2. 22 8. 54 2. 58 6. 93   0. 19 1. 61 0. 19 1. 61   2. 10 7. 95 2. 28 6. 22   g s c en te rb           1. 30 5. 27 1. 30 5. 27             g s so ut h 2. 15 7. 92 2. 45 6. 36   1. 91 5. 08 1. 91 5. 08     2. 09 7. 6 2. 32 6. 09     ru ra l 0. 02 0. 18 0. 43 1. 67     -1 .5 9 -2 .9 7 -3 .2 1 -3 .9 7   ca m p 1. 42 7. 28 2. 70 9. 20   1. 59 5. 37 2. 20 5. 81 co ns ta nt 3. 25 7. 73 -0 .7 2 -0 .9 6     2. 63 3. 33 -1 .3 6 -1 .2 4 r2 0. 31       0. 23 0. 23       a w b ce nt er , w he re r am al la h an d ea st j er us al em a re lo ca te d, is a ss um ed a s re fe re nc e in t he w es t ba nk m od el ; b g s ce nt er , w he re g az a ci ty is lo ca te d, is as su m ed a s re fe re nc e in b ot h th e pa le st in e an d g az a st rip m od el s. 49the impact of assistance on poverty and food security in a fragile and protracted-crisis context ta bl e 13 . f cs re gr es si on m od el s. va ria bl es   pa le st in e w es t b an k g az a st rip po ol ed o ls fi xe d eff ec ts po ol ed o ls fi xe d eff ec ts po ol ed o ls fi xe d eff ec ts c oe f. st ud en t’s t c oe f. st ud en t’s t c oe f. st ud en t’s t c oe f. st ud en t’s t c oe f. st ud en t’s t c oe f. st ud en t’s t m as s -0 .8 4 -9 .6 4 -0 .5 8 -6 .2 2 -0 .8 0 -6 .3 6 -0 .7 0 -6 .2 2 -1 .1 4 -8 .5 4 -1 .0 4 -6 .2 2 yd um -0 .0 1 -0 .0 4 -0 .2 7 -0 .7 8 0. 35 0. 98 -0 .1 0 -0 .2 8 -1 .9 3 -1 .9 8 -3 .7 8 -3 .2 8 m as sy -0 .2 6 -2 .2 8 -0 .2 2 -1 .6 8 0. 21 1. 19 0. 58 2. 63 -0 .1 3 -0 .6 4 0. 27 1. 01 lh siz e 3. 29 8. 79 4. 51 9. 22 3. 34 7. 37 4. 42 7. 57 3. 84 5. 81 5. 11 5. 76 de p_ ra tio 0. 27 2. 44 0. 26 1. 82 0. 29 2. 21 0. 29 1. 76 0. 09 0. 38 0. 10 0. 33 ra t_ em p 7. 68 11 .0 3 8. 27 9. 85 6. 96 8. 57 8. 51 8. 48 9. 79 7. 31 8. 03 5. 3 em pl oy ed 1. 25 2. 98 -0 .4 6 -2 .1 1. 39 2. 74 -0 .4 7 -1 .8 4 0. 62 0. 85 -0 .4 3 -1 .0 2 ag eh ea d 0. 07 6. 1 0. 05 4. 31 0. 09 4. 51 re fh ea d -0 .4 5 -1 .4 4     0. 61 1. 58 -2 .5 9 -4 .6 9 fe m he ad -0 .8 6 -1 .6 2     -0 .7 5 -1 .1 9 -1 .0 2 -1 .0 3 hi gh _e d 3. 28 11 .1 6     2. 83 8. 07 3. 46 6. 42 w b n or th -0 .6 7 -0 .9 3     -0 .7 7 -1 .8 9 w b c en te ra 0. 00 0             w b so ut h -9 .8 8 -1 3. 15     -1 0. 43 -2 2. 66 g s n or th -5 .7 6 -7 .9 5     -4 .8 1 -6 .5 4 g s c en te rb             g s so ut h -7 .4 9 -9 .7 1     -6 .7 0 -8 .6 ru ra l -1 .2 6 -3 .4 2     -1 .9 0 -4 .9 5 4. 24 3. 15 ca m p -1 .6 9 -3 .0 7     -4 .4 2 -5 .7 5 0. 67 0. 86 co ns ta nt 67 .2 7 53 .9 5     66 .2 8 49 .8 7 68 .5 7 34 .3 8 r2 0. 13   0. 06 0. 13   0. 03 0. 13   0. 06   f te st o f fi xe d eff ec t  1. 28 p= 0. 0 1. 36 p= 0. 0   1. 28 p= 0. 0 a w b ce nt er , w he re r am al la h an d ea st j er us al em a re lo ca te d, is a ss um ed a s re fe re nc e in t he w es t ba nk m od el ; b g s ce nt er , w he re g az a ci ty is lo ca te d, is as su m ed a s re fe re nc e in b ot h th e pa le st in e an d g az a st rip m od el s. 50 d. romano et alii the fcs results are quite different. according to ols estimates (first column of table 13), the quality of food consumption in palestine seems to be negatively affected by the intensity of assistance.23 however, in the fixed effects model, the interaction parameter is not significant. all variables whose coefficients are statistically significant show the same signs as in the poverty index models except for two cases: the dependency ratio and the household size. they both have a positive effect on fcs, possibly because a larger number of household members includes a sizeable share of children and elders calling for particularly dietary requirements and/or making the household more eligible for food aid targeting. regional dummies are all negative vis-à-vis central gaza except for the north and central west bank. the latter two regions show non-significant coefficients, possibly explained by higher population density and more urban nature. the west bank and gaza strip models provide quite a different picture when considering the fixed effect model. the impact of assistance on fcs is positive and significant in the west bank but it is not significant in the gaza strip. this may depend on the nature of the outcome variable. a higher fcs presupposes the availability and physical accessibility of a variety of food, a condition that may not have held in gaza strip because of the open armed conflict and strict blockade that occurred in 2014. keeping in mind that under these very specific conditions food security was pursued primarily through humanitarian assistance, we have to consider that in-kind food aid is based on food baskets containing only basic foodstuffs such as wheat flour, rice, pulses and vegetable oil. therefore, in order to assess the impact of assistance on fcs via in-kind food aid, we disentangled the overall fcs in three additive components24 and estimated the impact model per each fcs component (table 14). doing so resulted in a slightly different picture. the intensity of assistance showed a positive impact of the two components provided via in-kind food assistance. the first component, which includes cereals, tubers, pulses, fruits and vegetables, is positive though significant only at p=90%. the second component, which includes oil and sugar, has a positive and significant impact at p=95%. conversely, the component not included in the food aid basket, i.e. the meat and milk component, was not significant. this may be attributed in part to the nature of in-kind food assistance constituted of cereals, pulses and vegetable oil during the war in gaza and in part to the low-income elasticity of these food categories as a source of low-cost calories and proteins. the less significant relationship found with reference to the first components can be explained by the dramatic drop in the availability of fruit and vegetables in the gaza strip as a result of the war.25 this drop was only partially compensated by the in-kind food assistance of cereals and pulses. in conclusion, food security was ensured more in terms of the quantity of food provided than the reported that in practice up to 300 metres from the perimeter fence is considered by most farmers as a “no-go” area and up to 1,000 metres a “high risk” area” (ocha, 2018: 5). this area is where most military operations take place. 23 higher fcs scores means in fact higher food quality as it measures food security in term of diet diversification. 24 the three components and the relevant fcs weights are the following: fruits, vegetables, cereals, tubers and pulses (weights from 1 to 3); milk and meats (weight equal to 4); oil, sugar and others (weight equal to 0.5). 25 commercial food imports to the gaza strip cover a significant share of gazan food needs. they stopped almost completely in the second half of 2014 because of the war and were partially offset by humanitarian imports providing food aid (latino and flämig, 2017). 51the impact of assistance on poverty and food security in a fragile and protracted-crisis context quality of diet during the war and following the conclusion of the hostility, at the height of the humanitarian crisis when interventions were primarily a matter of saving lives. 7. conclusions this paper contributes to the scanty literature on the impact of humanitarian assistance interventions and outcomes (clarke et al., 2014). it aims to answer a question that, to the best of our knowledge, has yet to be addressed: does assistance – broadly defined as any type of in-kind or cash transfer – improve the well-being of palestinian households? to do so, we apply advanced econometric techniques and impact evaluation approaches widely advocated in the debate on aid effectiveness (cf. section 2.2). specifically, we coupled the classical counterfactual framework of impact evaluation analysis with fixed effect econometric modelling using a difference-in-difference approach. this allowed us to treat sample selection bias. we also instrumented the fixed effect model to get rid of endogeneity where needed, such as in poverty models. the main results are in line with existing literature (ruel et al., 2013). assistance is indeed crucial to support the standards of living of palestinians: both poverty and food insecurity would have been much higher without the massive assistance provided by the international community to palestine. this result supports similar conclusions attained by recent studies on contexts marked by violent conflicts and food insecurity crises (doocy and tappis, 2016; mercier et al., 2017; trachant et al., 2018). we confirmed the key role played by assistance, specifically food aid, extending the evidence to a protracted crisis context such as palestine. the first policy implication is therefore that the international community should not keep disengaging from supporting palestinian households. over the last decade, overall assistance to palestine shrank by two thirds since 2008. the international community should be aware that if assistance continues to diminish, the severely negative consequences on the ground will affect the wellbeing of these households. more generally, the positive impact of assistance on poverty reduction and food security established in table 14. fcs components fixed effect regression models, gaza strip total cereals, pulses, vegetables & fruit meat & milk oil & sugar coef. student ‘s t coef student’s t coef student’s t coef student’s t mass -1.04 -5.44 -0.20 -3.31 -0.83 -5.01 -0.01 -0.97 ydum -3.78 -3.28 0.23 0.62 -3.37 -3.46 -0.63 -8.12 massy 0.27 1.01 0.14 1.72 0.09 0.39 0.03 2.01 lhsize 5.11 5.76 1.39 5.18 3.37 4.27 0.35 5.77 dep_ratio 0.10 0.33 -0.06 -0.71 0.23 0.88 -0.07 -3.39 rat_emp 8.03 5.30 -0.15 -0.31 8.14 6.21 0.05 0.51 employed -0.43 -1.02 0.24 1.87 -0.66 -1.75 -0.02 -0.53 r2 0.07 0.02 0.07 0.06 52 d. romano et alii this paper encourages renewed investment and further effort in enhancing aid effectiveness through better coordination of implementing actors and better design, targeting and delivery of assistance to the palestinian people. it is important to keep in mind that the average positive impact of assistance hides a lot of heterogeneity with marked differences on each outcome dimension (poverty, quantity of food consumed, diet diversity) and region (west bank or gaza strip). in the case of poverty reduction, there is a clear positive impact of intensity of assistance for both palestine as a whole and the west bank. however, this relationship is not significant for the gaza strip, probably because of the july-august 2014 war that could have blurred the causal relationship between assistance and poverty reduction. assistance has a positive and significant impact on the amount of food consumed (proxied by hfias) in both regions, though the impact is much larger in the gaza strip than in the west bank. this is thanks to massive in-kind food aid, food vouchers and cash interventions during and after the 2014 war that helped keep levels of food consumption at an acceptable level and restore household resilience (brück et al., 2018). in the case of diet diversity (proxied by the fcs), there is no significant impact of assistance for palestine as a whole. the impact is however significantly positive for the west bank but not for the gaza strip. when disentangling this last result according to main diet components, we see that the two components included in the food basket provided to households in need – cereals and pulses, and oil and sugar – have positively affected gazan households. this is true despite the fact that in-kind food aid was only partially able to compensate for the dramatic drop in the availability of fruit and vegetables imports during and after the 2014 military escalation. a second policy implication therefore relates to the importance of the composition of food baskets provided to a population in need in order to ensure a balanced diet (webb et al., 2014). this issue was raised in recent worldwide debates, specifically in palestine where the food basket provided by unrwa (ocha, 2016) and by wfp (2017a and 2017b), the two most important implementing agencies, recently changed in order to provide more fortified and balanced food baskets. careful consideration of the composition of food baskets is extremely important, especially when considering long-term consequences of a balanced diet to targeted households with children (alderman et al., 2006). our study presents some limits. understanding why assistance determined the abovementioned outcomes would require more detailed information as well as an information-eliciting tool different from the one used by the sefsec. indeed, the sefsec dataset, although quite informative on quantitative aspects of assistance to palestinian households, is not able to open the black box of mechanisms that lead to these outcomes. nor was it possible to analyze the effectiveness of different forms and sources of assistance, which affect the logics of intervention in a different manner. addressing these topics would have required a larger and more detailed database supplemented by qualitative information, which we did not have. nevertheless, the sefsec dataset may be further exploited to shed light on issues such as the spatial distribution of assistance. the dataset could even be used to conduct a finer analysis of the impact of different types of assistance on food security as soon as the third wave (carried out in late 2018) data is made available. methodological speaking, a possible future improvement to consider would be to model the different impact of assistance on 53the impact of assistance on poverty and food security in a fragile and protracted-crisis context asset accumulation/decumulation or even on household expenditure, provided the data is of 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a dual value function as a meta-model of a detailed dynamic mathematical programming model claudia seidel, wolfgang britz bio -based and a ppl ied economics bae bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 copyright: © 2021 g. layani, m. bakhshoodeh, m. zibaei, d. viaggi. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: g. layani, m. bakhshoodeh, m. zibaei, d. viaggi (2021). sustainable water resources management under population growth and agricultural development in the kheirabad river basin, iran. bio-based and applied economics 10(4): 305-323. doi: 10.36253/ bae-10465 received: february 15, 2021 accepted: october 14, 2021 published: march 31, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio gaetano santeramo. orcid gl: 0000-0002-0110-0113 mb: 0000-0001-8217-3535 mz: 0000-0003-4633-0593 dv: 0000-0001-9503-2977 sustainable water resources management under population growth and agricultural development in the kheirabad river basin, iran ghasem layani1,*, mohammad bakhshoodeh2, mansour zibaei2, davide viaggi3 1 shiraz university, iran 2 college of agriculture, shiraz university, iran 3 department of agricultural and food sciences, university of bologna, italy *corresponding author. e-mail: ghasem.layani.su@gmail.com abstract. in this study, an integrated system dynamics model was developed for scenario analysis in sub-sectors of the kheirabad river basin in southwestern iran where managing water resources is seriously challenging due to population growth and periodic drought. afterward, the variability of water demand and supply under baseline scenario and different water demand management policies, including water conservation and water pricing, was evaluated. findings illustrated that with increasing population and cropland area if no further demand management policies were implemented, the total water demand and withdrawal of water resources increase by more than 0.75% annually. the annual surface water availability during 2018-2030 is expected to decrease by around -1.23%. under these circumstances, the sustainability index of the water resources system is equal to 0.703, indicating that the water system would not be able to meet the total water demand in the near future. however, the water resource sustainability index increases significantly by improving irrigation efficiency and changing crop patterns at the basin. also, the reduction in per capita water demand and domestic water pricing under the competition structure would help to improve the sustainability index to 0.963 and 0.749, respectively. keywords: sustainability index, water system, system dynamics, agriculture, food security, kheirabad river basin. jel codes: q2, q25. 1. introduction water is essential for people’s daily life, agricultural irrigation, fish farming, and manufacturing (unido, 2003). however, this vital resource is faced with several stresses in quantity and quality (speelman & veettil, 2013). among the others, climate variability and increasing population growth have resulted in water scarcity in many countries especially in the arid regions (hashemi et al., 2019; mulwa et al., 2021). the water scarcity problem threatens nearly 80% of the world’s population (vallino et al., 2020). increasing http://creativecommons.org/licenses/by/4.0/legalcode 306 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 ghasem layani et al. water demand in various economic and social sectors exacerbates the problem of water scarcity (donati et al., 2013) and can make the water system more vulnerable (cai et al., 2018). therefore, the most challenging issue in water resources system in the world is to achieve a balance between supply and demand (kotir et al., 2016; xiong et al., 2020). the complexity of water systems is familiar to all those studying in the field because of fundamentally their large number of agents and interdependent subsystems (madani and mariño, 2009; balali and viaggi, 2015). in a water system, there are dynamic feedback relationships among different factors on the supply and demand sides (kotir et al., 2016). furthermore, the changes in water resource have a dynamics behavior as it is affected by many socio-economic and climatic factors over time (sterman, 2001). in other words, population growth, climate change, agricultural development, changes in harvesting rate from ground and surface water are factors that affect the water system of a region over time with interaction (brown et al., 2015). the use of water in one sector also affects other sectors, and the agents in the water system are contiguous. these interactions between different water users such as irrigation, drinking water, industrial production, and environmental facilities lead to complexity in the water resources system (berger et al., 2007). these complexities in the water resources system cause policymakers to face policy resistance in managing water resources. policy resistance occurs when policy actions trigger feedback from the environment that undermines the policy and at times even exacerbates the original problem. policy resistance is common in complex systems characterized by many feedback loops with long delays between policy action and result (sterman, 2001). besides, implementing different policies to manage water resources, depending on the conflict of interest, may have different effects on different stakeholder groups (darbandsari et al., 2020). addressing the complexities of water resources system, a holistic approach such as system dynamics (sd) can provide a sufficient water management framework based on conflict resolution approaches. system dynamics consider the interactions among different elements of different stockholders for simulating the behavior of the system and policy analysis (frank, 2000). this helps decision-makers assess different management policies considering various aspects (e.g., economic, social, environmental, etc.) for simultaneously reducing conflicts and improving water resources conditions (mirchi, 2013; darbandsari et al., 2020). there are a large volume of published studies that have applied sd modeling to evaluate the effect of changes in some variables such as water demand, population control, water transfer as well as climate change on water availability (gohari et al., 2017; sun et al., 2017; pluchinotta et al., 2018; mahdavinia and mokhtar, 2019; keyhanpour et al., 2020). a great deal of previous research into water management has focused on mathematical programing, but they do not pay attention to the feedback processes in the water resources system (donati et al., 2013; archibald & marshall, 2018; zeng et al., 2019; saif et al., 2020). given the significant water consumption in the agricultural sector, these studies emphasize that local water management authorities, in addition to being aware of farmers’ possible decisions to allocate farms, should also be able to provide an optimal cultivation pattern commensurate with the potential of each region (donati et al., 2013). although good progress has been made in the sd modeling of water resources system in different studies, there are still limitations. some important limitations of these studies are briefly as follows: (i) in general, less attention has been paid to theoretical foundations in modeling in the agricultural subsystem (madani and mariño, 2009; gohari et al., 2017; mahdavinia and mokhtar, 2019); (ii) some studies (kotir et al., 2016) considered the crop yields as a stock variable, which contradicts the definitions of the stock variable; (iii) in the population subsystem, few studies (clifford holmes et al., 2014; goldani et al., 2011) have considered the behavior of consumers to change in water prices; (iv) although most of the above-mentioned studies have focused on the interaction between elements and feedback loops in the water system, a few of them (madani and mariño, 2009; gohari et al., 2017) have been designed to analyze various water indicators, for instance, sustainability index that is defined as the ratio of water supply and demand and summarizes the performance of alternative scenarios and policies (loucks, 1997). it should be noted that the above points are important in studying the behavior of the water system at the basin. compared to previous studies, to achieve a better result, we used a nerlove (1956) partial adjustment framework to model the agricultural subsector and simulate cropland area and agricultural water demand. in more detail, farmers’ decisions to develop the cropland area were considered in response to changes in crop prices in modeling. it can be an effective effort to more accurately simulate the agricultural water demand. also in the population sub sector, consumers’ responses to water price changes were taken into account. policies such as taxes  and  subsidies can  change the price of goods and correspondingly the quantity consumed. thus, various indicators including sustainability (loucks, 307 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 sustainable water resources management in the kheirabad river basin, iran 1997), reliability (mcmahon et al., 2006), vulnerability (hashimoto et al., 1982) and max deficit (moy et al., 1986) indices, were considered to evaluate the effects of water resources management policies and to rank different policies base on their effects on water system behaviour. because of increasing complexity and integration of environmental, social, and economic functions, the early water resource models still need to be developed and appropriate policies should be adopted based on the socio-economic and environmental characteristics of basin. accordingly, this paper develops an integrated sd simulation model for exploring the water resource sustainable index in the kheirabad river basin in southwestern iran where managing water resources is seriously challenging due to population growth and periodic drought. put it simply, the present study aims to explore the water supply and demand dilemmas and calculate the water resource sustainability index at the basin. this paper is organized as follows. the case study and sd model features are presented in the next section. then, the applied data are described. the simulation results of the model are presented in section 4 and the conclusions are provided in section 5. 2. the study context and scope iran is located in the mid-latitude belt of arid and semi-arid regions of the earth. the arid and semi-arid regions cover more the 60% of the country iran. the main source of water in iran is precipitation in the form of 70% rainfall and 30% snow, which is estimated to be about 413 bcm (billion cubic meters). about 71.6% of the total rainfall (295 bcm) is directly evaporated. considering 13 bcm of water entering from the borders (joint border rivers), the total amount of the country’s renewable water resources (long-term averages for 1977 to 2018) is annually estimated to be 124 bcm, of which about 73 bcm go to surface runoff. groundwater recharge is annually estimated to be about 51 bcm. currently, total water consumption is approximately 88.5 bcm (abbasi et al., 2015). agricultural water consumption accounts for about 85% of total water resources in iran and 90% of them may be allocated in surface irrigation systems with low efficiency and full water supply (lalehzari et al., 2020). according to the latest figures, the average population growth rate in iran during 1999-2000 was 1.755 percent and lowered to 1.246 percent in 2010-2017. however, in all these periods, iran’s population growth rate is above the global average (undata, 2017). the annual water consumption in the urban areas of the country is about 5.4 bcm, of which 4.3 bcm is related to household consumption that implies to the per capita water consumption of 224 liters per person a day. as far as population growth is considered, the increasing demand is not limited to fresh water use for drinking purposes. the growing population is results in increasing demand for agricultural products as well, especially for some strategic food stuffs such as wheat that are provided at subsidized prices and the iranian government insists on their domestic supply (the statistical center of iran, 2018). considering the driving factors of water crisis, the water resources management issue is a national priority and the most important issues among policymakers in iran (madani, 2014). kheirabad river basin is a part of the zohre river basin in the kogiluyeh and boyerahmad province, southwestern iran (fig. 1). the average annual rainfall of the basin, where the rainfall regime is mediterranean (with dry and wet season), varies from less than 200 mm to more than 800 mm the average annual temperature also varies from 12°c to 25°c. the water consumption of the kheirabad river basin in the drinking, industrial and agricultural sectors is provided of surface and groundwater resources. this basin is rich in surface water, but the un-normalized utilization of soil and water resources and also the increasing water resources withdrawal have reduced the basin’s water potential to meet increasing demands. most of the surface water resource in the basin is provided by kowsar reservoir dam located in zohre river basin in the west of gachsaran county. rainfall is extremely seasonal; about 50% of which occurs in winter (concurrently with the smallest water demand), 23% in spring, 23% in autumn, and 4% in summer (concurrently with the greatest water demand). kheirabad river basin’s average annual precipitation is estimated to be 331 mm during 2012-2020 while evaporation amount is more than three times that. not only the climate variability but also the population as an important factor affecting water demand, is continually increasing. while according to the report presented by the regional water organization of kogiluyeh and boyerahmad province (2017), the average per capita domestic water consumption of this province is more than 220 liters per day, which is about 20 percent higher than the national average. the combination of these factors led to the water stored in kowsar dam has declined in recent years. because one of the most important goals of the kowsar dam construction is the supply of drinking water in the southern provinces of iran and agricultural development in these areas, meeting the growing water demand in this basin is becoming a concern among policymakers. 308 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 ghasem layani et al. 3. system dynamics methodology sd modeling is an iterative and feedback process to reach new understanding of how the problem arises and then design high leverage policies for improvement (davies and simonovic, 2011). a four-step sd modeling process introduced by sterman (2001) and ford and ford (1999) is used in this study: (1) problem articulation; (2) model formulation; (3) model testing; (4) scenario design and simulation. the first step in sd modeling is to be specific about the dynamic problem and problem articulation (ford and ford, 1999). this step includes defining the problem, identifying the key variables related to the problem, such as stocks, exogenous and endogenous variables, identifying the temporal and spatial scales to be considered (zhuang, 2014). the aim of model formulation is representing the structure of the problem and formulating a sd simulation model of the causal theory (sterman, 2001; zhuang, 2014). there are several diagram tools to capture the structure of the system, including causal loop diagram (cld) and stock and flow diagram. clds consist of variables connected by arrows for representing the feedback structure of the system (sterman, 2001). in spite of the fact that stock and flow and feedback are the two central concepts of system dynamic theory, clds are not able to capture the stock and flow structure of a system (ford and ford, 1999; sterman, 2001). this is an important reason for using stock and flow diagram to represent the structure of a system with more detailed information that is shown in a cld. in general, the stock variable is an accumulator variable (zhuang, 2014). a stock with a single inflow and single outflow can be mathematically formulated as: (1) where s is any time between t0 and t. the stocks are the key variables in the model. they represent where accumulation or storage takes place in the system. stocks tend to change less rapidly than other variables in the system, so they are responsible for the momentum or sluggishness in the system (ford and ford, 1999). model testing begins as the first equation is written and it is a critical step in sd modeling (sterman, 2001). tests to rely on sd model can be divided into two groups, structure tests and behavior tests (forrester, 1997). structure tests compare the structure of the sd model with the available knowledge about the real system presented in historical data. behavior test is to run the model and compare the results to the reference figure 1. kheirabad river basin and kowsar dam. 309 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 sustainable water resources management in the kheirabad river basin, iran mode1 (historical or observed data). when the simulation results match the reference mode, you have reached a major milestone in the modeling process (ford and ford, 1999). following kotir et al. (2016), mean relative errors (mre) and coefficient of determination (r2) were applied to evaluate the performance of the model. mre indicates the mean possible divergence between the observed and simulated data (qin et al., 2011), the lower values of mre indicates that the model satisfactory fits the historical values. r2 describes the proportion of the variance in measured data explained by the model2 (kotir et al., 2016). (2) (3) where and are the observed and simulated values of tested or variable and is the average of observed values of variable. after the validation of the model, we can use this model to evaluate the impact of different scenarios designed to solve the problem (zhuang, 2014). 3.1. sd modeling of kheirabad river basin 3.1.1. water supply subsystem the water supply subsystem includes feedback relationships between climate variables and water resources. this subsystem is constructed based on the surface and groundwater resources balance equation by taking in to consideration all inflows and outflows at the study area. this subsystem represents the measure of water resources available at the basin (hjorth and bagheri, 2006). surface water resources available are controlled by various factors such as measure of precipitation, runoff, water inflow and outflow of surface water, evaporation, transpiration and infrastructural conditions (hjorth and bagheri, 2006; gohari et al., 2017). as shown in fig. 2, the water supply subsystem includes surface and groundwater resources. it is also worth mentioning that the surface and subsurface water inflows, return flow and precipitation are incoming inflows, and the surface and subsurface water outflows, evaporation, transpiration, water withdraw for kind of uses are outflows. temperature and precipitation as climate variables affect 1. a reference mode is a pattern of behavior over time 2. the values of r2 range from 0 to 1, with values closer to 1 indicating that the model well simulates the system. the measure of available water. as a matter of fact, the increased precipitation can increase water availability. strictly speaking, part of the precipitation is entered in to the water system as runoff (eq. 4), taking into consideration of the runoff coefficient reported in the water balance studies of the study areas (hjorth and bagheri, 2006). another part of the precipitation, joins to the groundwater resources considering the average percolation coefficient (eq. 5). also evaporation and transpiration was considered as a function of temperature in this study. therefore, an increase of temperature in the future may affect the behavior of water resources system. annual evaporation in water supply subsystem is measured into available surface water multiplier in evaporation rate (eq. 6). at each time step, the evaporation rate is taken from temperature at the basin which is represented as a lookup table3. runoff=runoff rate × precipitation (4) percolation=percolation rate × precipitation (5) evaporation=evaporation rate × available surface water (6) also, the return flow in water system, according to eq. 7, is as a percentage of the water consumption in different sectors that is added to the surface and groundwater resources. total water withdrawal from the basin is measured into the sum of agricultural, domestic, environmental and industrial water demands. following davies and simonovic (2011), domestic water demand is expressed as a function of population and per capita water demand in the kheirabad river basin model. agricultural water demand is expressed as a function of cropland area and water requirement for each crop. environmental water demand is assumed to be as an exogenous variable. for calculating industrial water demand, per capita industry water use is applied (balali and viaggi, 2015), in which industrial water demand equals population multiplier per capita industry water use. the amount of surface water withdraw is equal to the part of total water demand that is supplied from surface water sources. according to the report presented by the regional water organization of kogiluyeh and boyerahmad province (2017), 49% of agricultural water demand, 66% of urban water demand and 51% of indus3. lookup tables are typically used in sd modeling to represent nonlinear relationships between two variables. a table function can be defined as a list of numbers whereby input values to a function are positioned relative to the x axis and output values are read from the y axis (ford and ford 1999; vensim reference manual 2011). 310 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 ghasem layani et al. trial water demand at the basin are supplied from surface water sources. return flow = return rate × water demand of each sector (7) total water demand = agriculture d. + domestic d. + oil industry d. + industrial d. + environmental d. (8) surface water withdraw = (water demandi × the share of surface water) (9) ground water withdraw = (water demandi × the share of ground water) (10) 3.1.2. population subsystem population is one of the factors that affect the water demand (sušnik et al., 2012). generally, population is the main driving factor in water demand. population influence the domestic water demand directly and other sources of water demands indirectly (davies and simonovic, 2011). there are some towns and villages on the kheirabad river basin. most of the domestic water demand at the basin is provided by kowsar dam. also kowsar dam supplied water to the persian gulf littoral cities and ports for nearly 20 years. population sub-model represents the population of the case study including one stock “population” which is increasing by population growth rate. the population at time t is mathematically represented by eq. 11 as follows: population(t) = population(0) + (population growth rate)dt (11) in this study, the total population is divided into urban and rural population groups according to urbanization rates (fig. 3). therefore, the water demand in the urban sector equals urban population multiplier per capita water consumption in the urban sector and similarly available surface water ground water availabilty natural surface water inflow run off precipitation rate evapotranspiration surface water withdraw surface water retern flow ground water inflow natural groundwater inflow + groundwater return inflow + ground water outflow ground water withdrawal + total return flow + + agricultural water demand total water demand + total domestic water demand total industrial water demand + + environmental water outflow rate of percolatin area of basin volum of precipitation+ + + run off rate + percolation+ minimum storage + inflow + + <time> + + + + + actual evapotranspiration rate + tempreture + effect of tempereture on evaporation rate + + + + + +oil industry + + figure 2. water supply subsystem stock and flow diagram. 311 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 sustainable water resources management in the kheirabad river basin, iran the water demand in rural sector will be obtained by multiplier of per capita water consumption in the rural sector rural in rural population. the sum of the demand for water in the urban and rural sector constitutes the total domestic demand. it is a fact that water price changes affect water demand. in other words, according to the price elasticity of water demand, we can calculate the feedback of consumer to water price change: (12) where e is the price elasticity of water demand, qt is the quantity of demand in period t and pt-1 is the price of water in period t-1 (varian, 1996). based on the regional water organization of kogiluyeh and boyerahmad province (2017), the price elasticity of water demand is considered to be -0.35 in kheirabad river basin. 3.1.3. agricultural production subsystem the agricultural sector is a major consumer of water resources and the change of rivers hydrology conditions, water resources change and climate parameters affect agricultural activities. climate change affects the water requirement of crops, water consumption and crops yield and consequently agricultural production and farmer’s income. nine crop types are included in the agricultural sub-system, namely wheat, barley, rice, corn, rapeseed, beans, tomato, watermelon and cucumber (fig. 4). in general, water demand of agricultural sector can be calculated from eq. 13: agricultural water demand = ( cropland areai × water requirementi) / irrigation efficiency (13) expected agricultural water demand of basin is obtained from sum of calculated demand for all crops. in addition, water requirement has a negative causal relationship with irrigation efficiency. in this research, nerlove (1956) model was used to model the area under cultivation. yt = γβ0 + γβ1pt-1 + (1-γ)yt-1 + γut (14) the model is based on the assumption that farmers determine their optimal cropland area based on the figure 3. population subsystem stock and flow diagram. agricultural water demand total water demand + change of population change rate of population + urbanization rate urbana population rural population final urban percapita domestic water demand final rural percapita domestic water demand total domestic water demand + + + + + percapita industrial water demamnd total industrial water demand ++ + population + + + training <time> oil industry water demand + water price elasticity percent change in water price initial urban percapita water use initial rural percapita water use ++ + 312 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 ghasem layani et al. expected price4. in this connection, the price of each crop during the simulation period is predicted applying arima5 model and entered into the basin sd model as an exogenous variable. as can be seen in equation 14, the lag of dependent variable is considered as an explanatory variable. according to the endogenous nature of this variable, generalized method of moments (gmm) was used to estimate nerlove model6. in the agricultural subsystem production of each crop are calculated based on eq. 15 via crop yield multiplied by its land area. following atherton (2013), population growth due to per capita food consumption increases food demand and the food self-sufficiency index is defined as the ratio of food production to total demand for food. other possible drivers, for instance international trade, labour productivity and other technological advance, are kept constant during the simulation period. food production = crop yield × cropland area self-sufficiency index=food production / food demand (15) food demand = population × per capita food consumption 4. further research could also be conducted to determine the effectiveness of labor, capital, and other input on cropland area change. but the lag of the cultivated area in the model could represent the farmer behavior and his decisions based on the available facilities. 5 . autoregressive integrated moving average – appendix 1 (result of real price forecasting) 6 . we’ve used the eviews9 software to regress this equation. 3.1. sustainability index the sustainability index (si) is a measure of a system’s adaptive capacity to reduce its vulnerability (loucks, 1997). to evaluate and compare the water management policies loucks (1997) suggested si formulated by eq. 16: si = [rei × (1-vul) × (1-max def)]1/3 (16) where si is sustainability index, rei, vul and max def are reliability index, vulnerability index and maximum deficit, respectively. water demand reliability is the probability that the available water supply meets the water demand during the period of simulation (hashimoto et al., 1982; klemeš et al., 1981). for each time period deficits (d) are positive when the water demand is more than water supplied, i.e.: wd-ws if wd>ws d = { (17) 0 otherwise the reliability rei is calculated by dividing the number of times d=0 by the length of the simulation period (mcmahon et al., 2006): rei = number of time d = 0 (18) n figure 4. agricultural production subsystem stock and flow diagram (with two crops). net benefit for wheat income from wheat + cultivation cost for wheat expected land area of wheat (nerlove) expected water requierment for wheat irrigation water requierment for wheat + irrigation efficiency cultivation cost for wheat per hectar + yield for wheat price of wheat + net benefit for barely income from barely + cultivation cost for barely expected land area of barely (nerlove) expected water requierment for barely irrigation water requierment for barely + cultivation cost for barely per hectar + yield for barely price of barely + prodeuction of wheat + + production of barely + + actual land area of wheatchange in land area of wheat + + + + actual land area of barleychange in land area of barley + + + + + smoothed lag price of wheat smoothing lag initial price of wheat + smoothted lag price of barley initial price of barley <smoothing lag> <time> improve in irrigation technology irrigation water requierment controling smoothed lag land area of wheat initial land area of wheat initial land area of barley smoothed lag land area of barley<smoothted lag price of barley> + <smoothed lag price of wheat> total agricultural water demand+ + 313 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 sustainable water resources management in the kheirabad river basin, iran the vulnerability index is the likely value of deficits if they occur (hashimoto et al., 1982). vulnerability is calculated by dividing the average annual deficit by the average annual water demand in deficit period (gohari et al., 2017; sandoval-solis et al., 2011): vul = (σd) / number of times d > 0 occurred (19) water demand the maximum deficit, if deficits occur, is calculated by dividing the maximum annual deficit by the annual water demand (moy et al., 1986): max def = max(dannual) (20) water demand table 1 shows the initial values of the stock and some key exogenous variables used for the sd model. this study also uses water shortage index (wsi) to address the interaction between supply (ws) and demand (wd) of water (fig. 5). the water shortage index defines as the ration of water supply and demand (zarghami and akbariyeh, 2012). in order to increase water shortage index, both demand and supply management options should be considered. the interaction between water supply and demand is captured using a water supply and demand balance index (fig. 5) (langedale et al., 2007). the water balance index (bi) increases with the water availability (ws) and decreases with the water demand. when the index is lower than zero or certain value, the water supply and demand management options will be necessary. the demand management options will decrease the water demand, which in turn the index. the supply management options will increase the water supply, which can offset the freshwater withdrawal and increase the water availability (zhuang, 2014). wsi = wd (21) ws bi = ws-wd (22) 2.2. policy scenario design to achieve the high sustainability at the basin, six scenarios is defined based on different levels of irrigation efficiency, water requirement, water price and crops water cropland area. a short description of the scenarios is presented in table 1. according to the latest studies conducted into this matter, the average irrigation efficiency in the kheirabad river basin is about 45%. the first policy scenario involves increasing irrigation efficiency as one of the most effective policies for water resources conservation. therefore, in line with the goals set in the fifth development plan of iran (gohari et al., 2017) and also the potential irrigation efficiency at the basin, agricultural water use efficiency was increased to 60%. to manage water demand in the agricultural sector, the policy of reduction water requirement of crops by 10% was considered as another scenario. reducing the water requirement of crops can be achieved by changing irrigation strategies, conservation activities, changing cultivation dates and using drought-tolerant varieties. since water is the major limiting factor for agriculture production, changing in the crop pattern can be considered as a water resources management policy (donati, et al., 2013). high water requirements of rice and watermelon point out that continued cultivation of these crops may not be justified under climate change from a water management perspective. therefore, as another scenario we simulate the effect of cropland declining (excluding rice and watermelon) on water resources availability, water demand and water resources sustainability index at kheirabad river basin. the underlying assumption for this scenario was that current cropland area would decrease. the iranian statistics center also reports that average per capita water demand in kheirabad river basin is 20 percent higher than the country average. thus, reduction in average per capita water demand is defined as another policy scenario. as well as, per capita water demand controlling can be achieved through domestic water tariff reform. based on the goals set in the fifth development plan of iran, the government was allowed to increase the price of drinking water by 7% annually to promote social justice. last scenario envisaged water sustainability by decreasing water withdraw of surface and groundwater resources due to applying inter-basin water transfer policy. as more detail, only 70% of the total domestic water demand will come from surface and groundwater resources at the basin, and 30% will be figure 5. interaction between water supply and demand. water supply water index water demand water demand management water supply management 314 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 ghasem layani et al. met through inter-basin transfer or desalination (vicepresidency for strategic planning and supervision, 2017). vensim professional 5 (ventana systems, 2009), one of the several software packages available for sd modeling, is applied to develop and run the kheirabad river basin model. the system dynamics modeling framework of kheirabad river basin water resources management is presented in fig. 6. some key parameters and stock variables used in the model and their corresponding values are describing in table 2. typically, long-term intervals from 10 years (zarghami and akbariyeh, 2012) to 100 years (rehan et al., 2011), are used to understand the effects of long-term management options on water system. in this study, a period of 40 years (1992-2031) is considered as the model time boundary and the data of 23 years (1999-2013) are used to validate the designed system dynamics model. according to the available data from water system, time steps are considered annually. 4. results 4.1. model validation the performance of the model is discussed by comparing model outputs for the selected variables to the corresponding historical data. the surface water availability and population are the key variables demonstrating the performance of the water system. in general, as shown in fig. 7 and 8, the model performed well in comparison to the historical data. the simulated results follow the same trend as the observed date, indicating that the model is well calibrated. the statistical values for m and r2 show that the model satisfactorily fits the historical values. predictions for surface water have low values of mean relative errors (less than 10%) and the value of r2 is calculated to be around 0.73 (table 3). 4.2. future simulation for future simulation, the water demand, water availability and water sustainability index were computed for the baseline scenario. prior to that, simulations of cropland area, the average production, and food self-sufficiency index for the catchment were presented. finally, the effect of different water demand management policies previously mentioned on water resources indicators was evaluated. 4.2.1. cropland area and agricultural production after testing the reliability of the model, the behavior of the system is then simulated over time to assess the availability of water resources and sustainability index. according to the coefficients obtained from the nerlove model, the simulated trend of the area under cultivation of selected crops at the basin is presented in the table 4. in order to examine this fact in more detail, it should be noted that the total cropland area at the basin increases table 1. description of different scenarios. policy scenario description improving irrigation efficiency increasing irrigation efficiency parameter to 60% smoothly (50%, 55% and 60%). decreasing water requirement of crops 10% reduction in water requirement of crops crop pattern cultivation of all crops, exclude watermelon and rice water price increasing price of drinking water by 7% annually controlling per capita water consumption 20% reduction in average per capita water demand at the basin controlling water withdraw only 70% of the total domestic water demand meet from surface and groundwater resources at the basin figure 6. the system dynamics modeling framework of kheirabad river basin. 315 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 sustainable water resources management in the kheirabad river basin, iran from 20.077 thousand hectares in 2020 to 21.049 thousands hectares at the end of the simulation period. the results indicate that the average of total cropland area during simulation period will be 20.506 thousand hectares and the annual growth rate of this variable will be 0.585 percent. changes in the level of cultivation of agritable 2. the stock and exogenous variables of water system. variable type variable name initial value unit source st oc k va ria bl e population 1.993 million person the statistical center of iran 2018 surface water 996.54 mm3 regional water organization of kohgiluyeh and boyerahmad province 2017 c ro pl an d ar ea wheat 7214 hectare the ministry of agriculture – jahad 2018 https://www.maj.ir/ barley 973 hectare corn 653 hectare rice 2830 hectare bean 169 hectare rapeseed 250 hectare watermelon 796 hectare cucumber 58 hectare tomato 40 hectare ex og en ou s v ar ia bl e per capita industry water use 2.13 m3 regional water organization of kohgiluyeh and boyerahmad province 2017urban per capita domestic water use 64.3 m3 rural per capita domestic water use 36.6 m3 runoff rate 16 % regional water organization of kohgiluyeh and boyerahmad province 2017percolation rate 12 % oil industry water demand 31 mm3 precipitation time series mm water balance reports 2018 temperature time series c irrigation efficiency 45 % abbasi et al. (2015) area of basin 4232.5 km2 water balance reports 2018 population growth 1.59 % the statistical center of iran 2018 yi el d wheat 2.93 tone the ministry of agriculture – jahad 2018 https://www.maj.ir/ barley 2.25 tone corn 6.33 tone rice 4.44 tone bean 1.66 tone rapeseed 1.13 tone watermelon 38.87 tone cucumber 33.21 tone tomato 24.26 tone g ro ss w at er re qu ire m en t (n et w at er re qu ire m en t/ irr ig at io n effi ci en cy ) wheat 4123 m3/hectare netwat software barley 3516 m3/hectare corn 6664 m3/hectare rice 11862 m3/hectare bean 7193 m3/hectare rapeseed 4602 m3/hectare watermelon 7694 m3/hectare cucumber 8989 m3/hectare tomato 9708 m3/hectare endogenous variable the other variables seen in the stock-flow diagram are endogenous variables. relationships related to endogenous variables are presented in equations 1-15. 316 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 ghasem layani et al. cultural products depend on changes in relative prices. the increase in area under cultivation can be attributed to the growth of prices during the simulation period. the simulated production and self-sufficiency index of selected crops at the basin are presented in the table 5. among selected crops, average production of wheat, watermelon and corn is higher than other crops. the lowest average production is also for beans. the results showed that the average annual production of wheat, watermelon and corn is 34.946, 34.827 and 18.344 thousand tons, respectively. given this average production, the self-sufficiency index for these crops is equal to 0.597, 1.101, and 0.899, respectively. the self-sufficiency index of greater than 1 for rice and watermelon means that this basin is a potential exporter for these products and there are excess productions available for exporting to adjacent basins. 4.2.2. water demand the simulated population at the basin is reported in table 6. according to available reports, the annual population growth rate at the basin was considered to be 1.59 percent that assumed to be constant over the period 2018-2030. due to this growth rate, total population increases from 3.141 million in 2020 to 3.678 million at the end of the simulation period. according to the sd model, both per capital water consumption in urban and rural areas and population growth affect total domestic water demand in kheirabad river basin. it can be seen from table 6 that domestic water demand will increase from 173.369 mm3 in 2020 to 205.662 mm3 at the end of simulation period. the annual average of domestic water demand at the basin will be 184.983 mm3 during the 2018-2031. another important finding is that 22 percent of average domestic water demand is related to rural household and 78 percent of that is related to urban household at the basin. changes in industrial water demand is also as a function of population in water system designed. table 6 indicates a slight increase occurring in both industrial and household water demands and thus, total water demand at the basin is likely to increase during simulation period. the results of this research support the idea that the gap between supply and demand for water increases continuously and the water system becomes more vulnerable in the future. the results corroborate the finding of a great deal of the previous work in this field, e.g. gohari et al. (2017) and kotir et al. (2017). considering available surface water for environmental and oil industry water demand, figure 7. the observed and simulated values of surface water. 0 500 1000 1500 2000 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 20 08 20 10 20 12 m m 3 observed simulated figure 8. the observed and simulated values of population. 0 0,5 1 1,5 2 2,5 3 1992 1996 2001 2006 2009 2010 2013 m illi on p er so n observed simulat ion table 3. statistical parameters of the model tests. variable r2 mre (%) surface water 0.73 4.45 population 0.99 0.86 table 4. results of cropland area and agricultural water demand (thousand hectare). crops 2020 2022 2024 2026 2028 2030 average wheat 11.653 12.301 12.159 11.589 11.663 12.411 11.927 barely 1.183 1.180 1.181 1.185 1.185 1.181 1.182 rice 2.701 2.802 2.714 2.724 2.722 2.711 2.712 corn 2.819 2.854 2.897 2.945 2.979 2.995 2.898 bean 0.175 0.175 0.175 0.175 0.176 0.176 0.175 rapeseed 0.393 0.397 0.396 0.393 0.394 0.399 0.394 cucumber 0.151 0.163 0.163 0.150 0.149 0.161 0.157 tomato 0.112 0.113 0.113 0.112 0.112 0.113 0.113 watermelon 0.889 0.910 0.908 0.885 0.883 0.903 0.896 total 20.077 20.797 20.706 20.159 20.263 21.049 20.506 table 5. average food production and self-sufficiency index at the basin. crops average production (thousand tone) self-sufficiency index wheat 34.946 0.597 barely 2.659 0.350 rice 12.041 1.078 corn 18.344 0.899 bean 0.290 0.325 rapeseed 0.445 0.315 cucumber 5.213 0.240 tomato 2.741 0.188 watermelon 34.827 1.101 317 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 sustainable water resources management in the kheirabad river basin, iran respectively 79 mm3 and 31 mm3, the amount of total water demand at the basin in 2020 is 627.493 mm3 and reach to 671.241 mm3 at the end of simulation period. 4.2.3. water availability the results of table 7 show that an increase in agricultural water demand along with population growth at the basin can increase the surface water withdraw and decrease surface water availability in the study area. in other words, the amount of available surface water decreases from 487.701 mm3 in 2020 to 433.893 mm3 at the end of simulation period. the average annual changes of this variable during 2018-2031 will be -1.23 percent. compared with available surface water, the average annual change of surface water withdraw is estimated about +0.75 percent. as shown in table 7, withdraw of water at the basin increase by 0.75% on average during 2018-2031 and reaches about 324.176 mm3 at the end of the simulation period. an increase in the population and cropland area in the basin increase the total water demand by almost 0.758 % annually. therefore, the upward trend of total water demand leads to an increase in surface water withdraw. withdraw of surface water is affected by the demand of the household, industrial and agricultural sectors. also, water withdrawal for environment uses is considered a constant amount (about 79 mm3 yearly), according to kohgiluyeh and boyerahmad provincial water organization. due to population growth (considering the growth rate of 1.59%) and also upward trend of cropland area the demand for household, industrial and agricultural consumption increases directly and the withdrawal of water resources increases eventually. therefore, it can be concluded that water demand control policy has a positive effect on decreasing in withdraw of water and sustainable water resources management. the simulated values for the groundwater withdrawal in figure 8indicate how changes in water demand can affect the behavior of groundwater availability. the volume of groundwater withdrawal at the beginning of the simulation period, equals to 212.809 mm3 and with growth of 10.29% reach to peak in 2030 about 234.396 mm3. the average annual percentage change of withdrawal of groundwater resources is calculated about 0.837%. as can be seen in fig. 9, the groundwater balance at the basin is negative during the simulation period, i.e. the trend of groundwater withdrawal is increasing and the outflows are more than the inflows. 4.2.4. water indices figure 10 illustrates the trend of water shortage index and water balance index in kheirabad river basin. as can be seen in the figure, the water shortage index increases and the water balance index decreases over time as water demand goes up as results of population growth and upward trend of cropland area. at the end of simulation period the demand and supply balance index are expected to be negative. it means that, in some years, the value of water demand becomes bigger than water supply at the basin. the downward trend of water balance index triggers the water supply or demand management options. the demand management options can decrease the water demand, which in turn increases the index. table 6. results of simulated population (million person) and water demand (mm3). variables 2020 2022 2024 2026 2028 2030 average population 3.141 3.242 3.346 3.453 3.563 3.678 w at er de m an dagricultural water demand 306.434 313.562 313.154 308.241 309.320 316.746 311.198 domestic water demand 173.369 179.914 186.706 193.754 200.101 205.662 184.983 industrial water demand 37.690 37.905 38.126 38.354 38.590 38.833 38.138 sum of water demand 517.493 531.381 537.986 540.529 548.011 561.241 534.319 table 7. results of available surface water and withdraw simulation mm3. 2020 2022 2024 2026 2028 2030 percent average change available surface water 487.701 478.543 467.777 455.085 443.311 433.893 -1.234 surface water withdraw 298.988 306.258 309.956 311.572 316.252 324.176 +0.756 318 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 ghasem layani et al. the next system dynamics model output displayed here is the sustainability index. as introduced in pervious section, this index is computed as combination of three other indices (reliability index, vulnerability index, max deficit). the results in table 8 indicate that the sustainability index at kheirabad river basin will be 0.703 if current environmental conditions within the basin would remain the same without any changes (baseline). as shown, values of reliability, vulnerability and max deficit indices are estimated to be 0.50, 0.119 and 0.213 respectively. in short, the simulation confirms that the growing demand for water is acting to exacerbate the problems of meeting the growing water demand of kheirabad river basin. the results represent the inability of water resources system to meet the increasing water demand in kheirabad river basin. the positive max deficit index indicates that in some years, the value of water demand surpassed its supply value and the system encountered to water shortage. therefore, according to the results, it is projected the increasing demand for water, due to population growth and upward trend of cropland area, is unlikely to be met using available water resources during the simulation period. 4.3. policy scenario analysis the impacts of water resources management policies on water system can be tracked through the calculation of water resource indices. as showed in table 9, the implementation of demand control policies likely help meets the growing water demand and the sustainability index for kheirabad water system increases in comparison with the baseline. as more detail, by increasing irrigation efficiency up to 50%, water resource sustainability index increased to 0.897. therefore, it can be stated that the effectiveness of this policy on water resource sustainability index within the basin is 27.59%. after increasing irrigation efficiency to 55%, the reliability index increased to 0.857 and vulnerability index decreased to 0.014. under these circumstances the water resource sustainability index will be 0.940. however, even under this scenario water shortage mater. the highest sustainability index (the lowest vulnerability index) belongs to business as usual conditions along with increasing irrigation efficiency up to 60%. the results also revealed that a 10% reduction in water requirement of crops increases the sustainability water index to 0.857 and decreases vulnerability index to 0.026, compared to business as usual condition without any actions. although this policy has a significant impact on improving the water system situation in terms of sustainability, it has still been vulnerable in terms of meeting water demand. therefore, in order to increase the system’s ability to respond water demand in the long-term, complementary strategies are needed along with reducing the water requirement of crops at the basin. the results also showed that the reliability and sustainability index of water resources will be 1 after removal of crops with high water requirement. agricultural water demand decreases as a result of changing crop pattern within the basin. therefore, it is expected that the gap between supply and demand for water decreases continuously and the water system becomes more sustainable in the future. therefore, controlling agricultural water demand using water conservation options such as increasing irrigation efficiency or changing crop pattern can help achieve a better balance between supply and demand of water to improve the water sustainability index even under population growth and agricultural development. the findings also reveal that, by controlling water withdraw as a water management policy at the basin, surface water outf low decreases during the simulation period compared with the baseline condition and the water system can be in a better situation in terms of sustainability. increasing water storage is expected to increase the ability to meet the demand for water in figure 9. the result of simulated values of groundwater changes. figure 10. the result of simulated values of groundwater changes. table 8. water indices at kheirabad river basin. reliability index vulnerability index max deficit index sustainability index water index 0.50 0.119 0.213 0.703 319 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 sustainable water resources management in the kheirabad river basin, iran the basin in this condition. according to the table 9, a reduction in per capita water demand and the economical instruments such as domestic water pricing under competition structure would help to improve the sustainability index to 0.963 and 0.749, respectively. 5. discussions as mentioned in the literature review, changes in water resources exhibit dynamic behaviors as such resources are affected by many socio-economic and climatic factors over time. because of complicated interactions and feedbacks among the factors in the water system, a comprehensive and interaction-based approach is needed to understand the consequences of a change in the system. so, in this research, an integrated system dynamics simulation model was developed to examine the feedback processes and interaction among the population, water resources, and agricultural production in sub-sectors of  kheirabad river basin  in iran. an initial objective of the project was to identify the behavior of the water system at the basin over time. with respect to the first research aim, it was found that water demand increases as the population growth and agricultural development occur. under such conditions, the withdrawal of surface and groundwater increases over time which is consistent with the gohari et al. (2017) results for the zayandehrud river basin. the most interesting finding is that population growth contributes to 0.75% annually increase in water demand and 1.23% annually decrease in available surface water during the simulation period. regarding that the population of the region is expected to increase by 1.87% during 2021-2027, this may put more pressure on the water resources at the basin. a growing population would be a threat for water use sustainability especially if other measures like increasing water use efficiency are not taken into consideration. another important finding is that the water sustainability index at the basin is 0.703 if the current environmental and socioeconomic condition within the basin would remain the same without any policy change. therefore, the water supply at the basin can likely be unsustainable and total water demand exceeds the water supply. this finding confirms the results of recent model-based studies and assessments that analyzed the impact of climate change and population growth on water resource availability (e.g., kotir el al., 2016; gohari et al., 2017; zubaidi et al., 2020). it cannot be denied that the water system is vulnerable (vulnerability index is more than zero) due to an imbalanced supply and demand, caused by increasing population and cropland area. this finding is also reported by madani (2014) and nkegbe & shankar (2014). in other words, both of the supply and demand side of the water system is threatening since a growing water demand is accompanied by a possible decrease in water availability caused from more intensive surface and groundwater use. regarding the widening supply-demand gap, to meet the water demand in the near future, demand management policies are needed. so, the present study is designed to determine the effect of water demand and supply management policies on the behavior of the water resources system at the basin. especially, given the possible increasing water shortage, water management policies should concentrate on demand side of water use to address the problem. according to the results, a reduction in per capita water demand can play a significant role in decreasing vulnerability and increasing the sustainability indices. this finding is also reported by stavenhagen et al. (2018). however, there is a larger room in agriculture uses where the water use efficiency is much lower than the global ones. it is worth noting that drinking water is supplied by the public sector and the price paid by the consumers’ accounts for a slight part of the water costs. thus, this may indicate that there is considerable room for water demand management even in drinking water demand. table 9. water indices under different scenarios. policies reliability index vulnerability index max deficit index sustainability index priority improving irrigation efficiency 50% 0.786 0.029 0.053 0.897 5 55% 0.857 0.014 0.015 0.940 3 60% 1.00 0.00 0.00 1.00 1 decreasing water requirement of crops 0.857 0.026 0.037 0.930 4 crop pattern 1.00 0.00 0.00 1.00 1 water price 0.571 0.104 0.180 0.749 6 controlling per capita water consumption 0.929 0.020 0.020 0.963 2 controlling water withdraw 1.00 0.00 0.00 1.00 1 320 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 ghasem layani et al. as far as the agricultural use of water is considered, the lower water use efficiency is controversial. in iran, resource constraints, in particular, water has always been a critical issue in agricultural production while the average irrigation efficiency is less than 35%, and only 5% of the farmed area enjoys modern irrigation system (madani, 2014). based on the results, the most effective policy scenario on water management at the basin related to improving irrigation efficiency and crop pattern. in more detail the improving irrigation efficiency up to 60% can bring the water resources system of the kheirabad river basin to a sustainable state. besides, the results revealed that crop pattern change and remove rice and watermelon from the cultivation pattern has a positive significant effect on the sustainability index. this study supports evidence from previous observation (e.g. donati et al., 2013 and hashemi et al., 2019). considering the expected negative effects of adopting this strategy on the production and income of farmers at the basin, the development of non-agricultural activities and small conversion industries in rural areas to compensate for the damage caused by possible variability of weather conditions can be effective. under the circumstances that the world as a whole, is facing water crises, many people have little information about how they can preserve water resources. to serve this purpose, the government should provide a context in which people learn to control their daily water consumption. comparison of water resources sustainability index after the implementation of various policies also confirms the fact that controlling per capita water consumption can be high effective in managing water demand. moreover, the economical instruments such as domestic water pricing under competition structure, can significantly control water demand toward sustainable management of water. what is surprising is that water pricing has the least efficient in water demand controlling. this is basically because of price inelastic demand at the basin. therefore, in summary, the effectiveness of water demand management policies in the domestic sector is expected. it doesn’t mean that we just focus on domestic water demand. indeed, it argues that we can conserve water resources in agricultural sector with irrigation water pricing and conservation agriculture (such as zero tillage, mulching and crop rotation). also, as a policy scenario in this study, it is assumed that 30% of domestic water demand can be met from outside the basin. the results of this study showed that supply-oriented policies such as inter-basin transfer or urban wastewater reuse to reduce the surface and groundwater withdrawal, regardless of the cost of their implementation, can contribute to the sustainability of the water resources system. 6. conclusion generally, for managers, a simulation model for the water system is extremely useful. the simulation model allows to look at the interaction of different elements over time and helps you simulate how different management decisions will affect the system. reducing available water resources is a very serious challenge facing policymakers and planners of water resources in iran. this mater underlines the need for sustainable management of this vital resource. this study applies system dynamics approach to analyze the behaviour of water system in kheirabad river basin located in southwestern iran. the study is an attempt to answer this question that can the kheirabad river basin reconcile its available water supply with the growing demand for water. although many studies have used the system dynamics method to manage water resources, there is a large difference between the structure of the designed model and the variables used in these studies. the differences in studies are in terms of objectives, evaluated policies, and temporal and spatial boundaries. the fact of the matter is that a model can be designed for each basin to suit the economic, social, and environmental characteristics. this is one of the ways to avoid policy resistance in water management. the designed system dynamics model was used to simulate the outcomes of different policy scenario. the results demonstrate that all scenarios reach limits to growth, however, water sustainability index was maximized under improving irrigation efficiency and changing crop patterns at the basin. in other words, the variables of the agricultural sub-sector can be considered as one of the high leverage points in the water resources management system of the kheirabad river basin. it confirms that with system thinking and holistic worldview, policymakers can identify the high leverage points in systems for each basin and avoid policy resistance. finally, our study is the first attempt at modeling in the kheirabad river basin in iran that by engaging stakeholders in model development, we have implemented a process compatible with improving stakeholder understanding of the dynamic behaviour of the basin over time. there are, however, some limitations of our study that could be addressed in order to add more precision to our results. this paper has focused on the sustainability index in baseline weather condition. further research can also focus on climate variability condition along with population growth and agricultural development. depending on the climatic conditions and the type of cultivation in each region, climate change can have negative and positive effects on agricultural production. therefore, it is necessary to study the effects of 321 bio-based and applied economics 10(4): 305-323, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10465 sustainable water resources management in the kheirabad river basin, iran climate change on the yield of strategic crops for regions or provinces (even in different sub-basins) separately in order to obtain the best cultivation pattern for vulnerable areas. meanwhile, the economic tools for water resources management can also be considered for agricultural sub sector to assess the impacts of agricultural water price reform on system behavior. considering farmers’ behavior under various conditions, including changes in available water resources, may contribute to the flexibility of the model. last but not least, a decrease in water supply may stimulate immigration from rural and agricultural-dominated areas to urban regions which is accompanied with some social-economic issues, needing to be examined. data availability statements some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. references abbasi, f., sohrab, f., & abbasi, n. 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(2020). urban water demand prediction for a city that suffers from climate change and population growth: gauteng province case study. water, 12(7), 1885. appendix table a1. real prices of agricultural products (rial/kilogram)-arima forecasting. crops 1992 1995 1998 2000 2002 2004 2006 2007 2008 2009 2010 2011 wheat 43.17 41.78 49.05 44.14 46.46 43.27 47.35 44.72 46.16 44.73 45.97 47.05 barely 33.54 35.22 37.60 35.43 33.24 33.14 35.05 33.62 36.92 33.68 36.77 35.80 rice 76.77 106.53 90.74 84.73 75.09 79.98 99.38 84.39 106.95 88.17 90.30 99.59 corn 37.97 36.97 41.44 38.32 37.55 37.86 38.73 37.66 40.51 37.58 39.67 38.52 cucumber 42.17 22.58 24.18 33.79 34.03 33.39 34.29 37.28 33.53 36.60 35.19 33.85 bean 139.82 71.77 90.41 116.57 92.47 76.43 90.54 100.49 104.42 100.40 92.26 85.38 tomato 43.89 33.13 32.04 28.92 30.17 30.69 31.22 30.10 30.05 31.10 29.54 30.92 watermelon 19.83 22.91 13.41 19.79 19.95 22.02 18.21 23.78 16.78 19.83 20.31 18.08 rapeseed 117.14 81.31 89.66 87.88 103.32 94.54 83.68 105.63 95.33 92.56 92.47 83.49 crops 2012 2015 2018 2020 2022 2024 2026 2027 2028 2029 2030 2031 wheat 44.13 45.28 46.28 44.68 44.80 46.56 46.80 45.30 45.31 46.83 44.43 46.52 barely 35.25 37.97 37.40 37.21 34.99 33.04 33.27 35.23 35.41 33.77 37.27 34.05 rice 77.90 79.41 91.46 80.13 83.06 98.02 100.10 85.17 84.53 97.99 77.28 94.38 corn 37.97 40.02 39.92 40.23 38.78 37.46 37.65 39.20 39.21 37.66 40.33 37.44 cucumber 35.06 34.94 32.34 36.38 36.48 32.97 31.78 35.43 35.01 31.94 36.90 32.49 bean 82.92 96.15 92.03 86.69 90.97 95.14 91.85 89.70 88.82 89.49 91.13 92.75 tomato 30.38 29.65 29.59 29.88 31.00 31.05 29.95 31.18 29.57 30.78 30.53 29.69 watermelon 21.80 21.19 18.57 21.54 21.51 17.76 17.30 20.33 20.34 17.27 22.26 17.82 rapeseed 106.61 94.44 95.64 96.13 105.51 86.34 85.68 103.79 97.18 85.11 97.79 87.45 volume 10, issue 4 2021 firenze university press the effect of attribute framing on consumers’ attitudes and intentions toward food: a meta-analysis irina dolgopolova, bingqing li, helena pirhonen, jutta roosen* rural areas between locality and global networks. local development mechanisms and the role of policies empowering rural actors francesco mantino understanding the bioeconomy: a new sustainability economy in british and european public discourse irene sotiropoulou1, pauline deutz2 sustainable water resources management under population growth and agricultural development in the kheirabad river basin, iran ghasem layani1,*, mohammad bakhshoodeh2, mansour zibaei2, davide viaggi3 the distributors’ view on us wine consumer preferences. a discrete choice experiment lina lourenço-gomes, tânia gonçalves*, joão rebelo please update your browser your browser isn’t supported anymore. update it to get the best youtube experience and our latest features. learn more opera microsoft edge safari mozilla firefox google chrome remind me later about copyright contact us terms privacy policy & safety © 2025 google llc bio-based and applied economics 8(3): 297-323, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-9329 spatial distribution of organic farms and territorial context: an application to an italian rural region andrea bonfiglio*, andrea arzeni research centre for agricultural policies and bioeconomy, crea – council for agricultural research and economics, italy abstract. organic farming is increasingly promoted and supported at several levels for its capability of producing safe food and public goods. it can give an important contribution to attenuating the environmental pressure generated by conventional agriculture. this paper analyses possible determinants of the spatial distribution of organic farms in a rural region of italy, characterised by several environmental issues. towards this aim, a quasi-poisson hierarchical generalised linear model with mixed effects is adopted. results indicate that there is spatial correlation and that the distribution of organic farming is related to socio-economic, environmental and political factors. in particular, they show that public support could have favoured the spreading of organic farming where there are more problems of erosion but far from the areas where there is intensive agriculture. keywords. organic farming, environmental pressures, rural development policy, hierarchical generalised linear mixed model. jel codes. c25, q18, q56. 1. introduction organic farming is defined as an overall system of farm management and food production combining best environmental practices, a high level of biodiversity, the preservation of natural resources, the application of high animal welfare standards and a production method consistent with consumer preferences for products produced using natural substances and processes (council of european union, 2007). organic farming has been acquiring growing importance because of the dual role it plays: it provides quality and safe food in response to an increasing consumer demand and, at the same time, it produces public goods contributing to the protection of the environment, animal welfare and rural development. in consideration of the important contribution of organic farming, better understanding how this phenomenon develops and which factors affect its spatial distribution can be diriment for policy makers in planning strategies pursuing objectives of sustainable development in rural areas. *corresponding author: andrea.bonfiglio@crea.gov.it 298 andrea bonfiglio, andrea arzeni in this respect, there is a growing body of literature about the identification of factors that influence spatial patterns of organic farms. several studies have shown that the adoption of organic farming is spatially clustered (nyblom et al., 2003; parker and munroe, 2007; lewis et al., 2011; schmidtner et al., 2012; bjørkhaug and blekesaune, 2013; taus et al., 2013; wollni and andersson, 2014). moreover, a number of determinants explaining the territorial distribution of organic farming have been highlighted. they are, in particular, the neighbouring effects connected with social influence and learning (nyblom et al., 2003; lewis et al., 2011), access to information (frederiksen and langer, 2004; genius et al., 2006; läpple and van rensburg, 2011), existing agricultural systems (häring et al., 2004), characteristics of the agricultural landscape (gabriel et al., 2009; schmidtner et al., 2012) and proximity to markets and urban areas (häring et al., 2004; koesling et al., 2008). another important factor that can justify different concentrations and development paths of organic farms between areas and regions is represented by public support (nyblom et al., 2003; frederiksen and langer, 2004). in europe, the most important policy in favour of organic farming is represented by the common agricultural policy (cap), which, by means of the rural development policy (rdp), provides incentives that are aimed at compensating farmers for additional costs and income foregone deriving from the conversion or the maintenance of organic farming practices (eu regulation no. 1305/2013). the main objective of this paper is to analyse the determinants of the spatial distribution of organic farms in a rural region located in central italy. firstly, a poisson regression model is adopted to analyse the effects of several social, economic, environmental and political factors on the territorial distribution of organic farms. the neighbourhood influence is then assessed to verify the appropriateness of a spatial model. thus, a quasi-poisson hierarchical generalized linear mixed (hglm) model is applied, allowing for underoverdispersion and including a spatially autocorrelated random effect. poisson regression models are applied by representing the regional territory as a regular grid of uniform cells and considering the number of organic farms localized in each cell as a response variable. this paper adds in the literature concerning the identification and the assessment of contextual factors explaining the spatial distribution of organic farms. the focus is thus territorial rather than the individual farm. the context can play an important role in terms of increasing returns to adoption due to the economies of scale associated with the concentration of organic farms. these economies include, for instance, formal and informal networks of organic farmers, technical support structures and downstream structuring (allaire et al., 2015). they are the result of shared needs and collective capabilities derived from past experience by all organic farms. if this experience is transmitted to neighbouring territories, it generates spatial autocorrelation, which goes beyond individual behaviours and specific territorial determinants. the main contribution of this paper is both territorial and methodological. firstly, it analyses a regional area, i.e. the marche region, which exhibits interesting peculiarities. this region presents very diverse characteristics from the coast to the apennine mountains and is therefore representative of the high heterogeneity of the italian territory. moreover, it is characterised by significant phenomena of erosion and negative environmental effects generated by intensive farming and related to a high specialization in arable farming (rusco et al., 2008). in this context, organic farming could therefore be very helpful in reducing the environmental impact generated by agriculture. from a methodological standpoint, rasterized data are used to determine the influence of territorial factors on a discrete variable, rep299spatial distribution of organic farms and territorial context resented by the concentration of organic farms. although rasters are most commonly used to represent continuous data, such as temperature and elevation values, they are often used to represent categorial or discrete data as well (pingel, 2018). rasterization, based on a common spatial reference, allows us to use an heterogenous set of statistical data, composed of discrete and continuous variables, and available in different formats and at different resolutions. to this aim, various techniques are adopted to rescale data to the same spatial unit. in comparison with traditional approaches based on the use of administrative borders, gridded data allow a better analysis of causes and effects of socio-economic and environmental phenomena (eurostat, 2016). moreover, rasterization of farms, i.e. their aggregation within cells, is one of the possible statistical-disclosure-control techniques to tackle the issue of ensuring anonymity in using micro-data (united nations, 2007), which is instead potentially present in research works at a farm level and based on the use of point data (i.e. läpple and kelley, 2015) in literature, there are a few studies that analyse the influence of territorial factors, rather than those at a farm level, on the distribution of organic farms through spatial econometric models for count data. for instance, gabriel et al. (2009) use a raster approach based on a 10-km grid to investigate the spatial distribution of organic farms in england and analyse the degree to which environmental and socio-economic factors correlate with their distribution. to assess the effects of covariates, they adopt a hurdle model (cragg, 1971). they analyse the presence/absence of organic farms using a generalized linear model (glm) with binomial errors, and the concentration of organic farming using a glm with gaussian errors. spatial information is incorporated by fitting spatial eigenvector mapping into the binomial model and by performing a spatial autoregressive (sar) model for the gaussian model. more recently, allaire et al. (2015) analyse the influence of territorial context on the diffusion of organic farming in france, measured by the number of beneficiaries of aid for conversion to organic farming (cof) existing in micro-territories at a nuts-4 scale. a hurdle model, composed of a spatial probit model and a non-spatial zero-truncated negative binomial regression, is applied to assess both the extent of the contracting of cof aid and its local intensity. hurdle models, as well as zero-inflated poisson (zip) models (lambert, 1992) and their variants, are commonly used to handle the overdispersion in the response variable generated by a high number of zeros. these models are based on some theoretical assumptions. they assume that either zero observations have different origins (zip models) or zeros and positive counts are generated by different processes (hurdle models). these models are also restricted to overdispersion, therefore excluding the possibility of underdispersion, which is another issue, even if less frequent, that may occur. moreover, in the cases where spatial effects are not considered, they fail in handling a further source of overdispersion generated by spatial aggregation, which increases the probability of observing zeros. a more flexible model is the quasi-poisson hglm model. the latter does not introduce any assumption about the distribution of zeros. moreover, it can handle both underand overdispersion in the response variable, generated by excessive zeros and spatial dependence. this kind of model has been recently used by lee et al. (2016) in the ecology field, showing its superiority in spatial prediction in comparison with zero-inflated and hurdle models. to our knowledge, it has not yet been used in agricultural studies, in particular in the field of organic farming. compared with existing research, a further novelty is therefore the analysis of the influence of territorial factors on the concentration of organic farms using raster data in conjunction with a quasi-poisson hglm model for jointly handling dispersion in both directions and spatial dependence. 300 andrea bonfiglio, andrea arzeni the reminder of this paper is organized as follows. section 2 is devoted to illustrating the area under study, the variables and the dataset used, and the spatial econometric model adopted. sections 3 and 4 present and discuss the main results, also providing some policy recommendations. final section concludes. 2. materials and methods 2.1 the area under study the area analysed in this study is the marche, an italian rural region. it is located in the central-eastern part of italy and has an area of 9.7 thousand km2, equivalent to about 3% of the national territory. most of the territory is mountainous or hilly. the annual average temperature varies from 5 to 14 oc. in the coastal strip and middle hill, the climate is mediterranean. it gradually turns into sub-mediterranean moving inward, while it is similar to the oceanic one in the mountains although mediterranean influences are still present. the average annual precipitation is 700 to 1,400 mm as we move from the coast towards mountain areas. agriculture is particularly important in the region from a territorial standpoint. the share of total surface managed by farms amount to 68%, against a national average of 57%. this reveals the importance of regional agriculture in managing natural resources and, thus, in affecting the overall quality of the environment. the marche region is characterized by a marked specialization in arable crops. based on 2013 data (istat, 2015), regional farms with arable crops are more than 37 thousand units, i.e. 90% of total farms, and cultivate over 361 thousand hectares, equivalent to 81% of regional utilized agricultural area (uaa), which is by far higher than the national share (55%). however, this strong specialization, which is also associated with an intensive use of inputs and extensive application of mechanization, raises some concerns about the high pressure that agriculture can potentially exert on the environment, in consideration of the morphologic characteristics of the region (bonfiglio et al., 2017). in fact, about 90% of the agricultural area is subject to erosion with annual values of eroded soil ranging between 5 and 20 tons/ha. this phenomenon is due to the natural morphology of the territory and is quite significant in terms of geographical coverage. intensive farming aggravates this problem and, in addition, produces pollution. the nitrate vulnerable zones cover an area corresponding to 11% of the territory, approximately 21% of total uaa. these zones fall into major regional river basins and involve both areas around river courses and the regional coastal strip (regione marche, 2015a). in this context, organic farming could be one of the possible options to attenuate the environmental pressure generated by agriculture, thanks to the relevant environmental benefits. in particular, organic farming can help to preserve soil (arnhold et al., 2014; reeve et al., 2016) and reduce water, soil and air pollution by banning the use of chemical pesticides and synthetic fertilizers (jouzi et al., 2017). besides an increasing demand for organic products, the constant rise of organic farming has been significantly affected by policy support (sanders et al., 2011). in italy, as in other european member states, the most important policy instrument supporting organic farming 301spatial distribution of organic farms and territorial context is the rdp.1 in the 2007-2013 programming period, measure 214 about agri-environmental payments (reg. ec no 1698/2005) has been introduced to encourage the introduction and the maintenance of organic production. in the 2014-2020 programming period, it has been replaced by measure 11 (reg. ec no 1305/2013). one of the main differences between the two frameworks is that, while measure 214 financed several agricultural production methods compatible with the protection and the improvement of the environment2, measure 11 specifically supports organic farming. organic sector is still growing in italy (sinab, 2001,2008,2015). from 2007 to 2014, the number of organic operators and the agricultural area managed organically have increased by 21% and 10%, respectively. however, this expansion has slowed down in the last years since the relevant measure has exerted lower appeal. the reasons for this relate to relatively low incentives in favour of organic farming, high disparity between payment levels at a regional level and return to conventional farming, determined by sudden price increases in some commodities and more incentivizing schemes supported by measure 214, such as integrated farming (zaccarini bonelli, 2011). from a regional point of view, in 2014, the marche region was the eighth italian region in terms of importance. there were 2,187 organic operators (4% of total organic operators) with an agricultural area of about 57 thousand ha (4% of total area). relative to 2013 total uaa (istat, 2015), the share of organic area was 13%, slightly higher than the italian average, amounting to 11%. after experiencing a significant phase of expansion in the early 2000s (the number of operators has grown by over 60% from 2000 to 2007), the regional organic sector has been involved by a continuous process of decline: from 2007 to 2014, organic operators have decreased by 23% and the agricultural area used for producing organic products has diminished by 44%. in addition, from 2007 to 2013, the share of organic area in relation to total uaa (istat, 2008,2015) has decreased by about 8% while the national one has increased, even if slightly, passing from 9% to 11%. regarding policy application, according to 2015 data about financial implementation (regione marche, 2016a), the managing authority of the marche region allocated €108.8 million (of which €49.6 million represented by eafrd contribution) to measure 214 for the 2007-2013 programming period, equivalent to 22.5% of total rdp expenditure. 2.2 the variables analysed the count (dependent) variable used is the number of organic farms operating in the marche region. as regards potential determinants, a number of variables that could affect the distribution of organic farming are investigated. they refer to the following aspects; existing farming system; land use; environmental characteristics; demographic and social characteristics; policy. 1 in addition to the rdp, there exist other policy instruments in favour of organic farming, which are not considered here, such as article 68 of regulation ec no 73/2009, contribution to producer organisations of the fruit and vegetable sector based on regulation ec no 1234/2007 and further national/regional support outside the cap. for a wider discussion about these policy tools, see for instance sanders et al. (2011). 2 in the case of the marche region, five actions were financed by measure 214 of the 2007-2013 rdp: a) integrated production; b) organic farming; c) protection and improvement of soil; d) maintenance of local endangered varieties and breeds; e) better management of permanent pastures (regione marche, 2015a). 302 andrea bonfiglio, andrea arzeni farming system is described by the number of total farms (this serves as an exposure; see the methodological section for details), the share of the agricultural area used for arable crops, uaa per farm, labour units per hectare and the share of young farmers. land use is described by the percentage of urban and natural areas. the environmental characteristics considered are altimetry, soil fertility and erosion. altimetry is measured as the natural logarithm of meters above the sea level. soil fertility is modelled using data about the percentage of soil organic matter. erosion is approximated by the quantity of tons of soil per hectare, which are lost due to surface water flows. demographic and social factors are represented by the shares of resident population aged 20-39 (young population) and 40-64 (adult population) and by the proportions of population with higher education (high school and university). finally, policy is represented by the total amount of public subsidies per hectare of agricultural area. the choice of determinants takes account of different aspects such as potential benefits of organic farming, the context considered, and the variables investigated in studies concerning organic sector. specifically, it is based on the following considerations. given its potential contribution to the environment, organic farming is expected to be more concentrated in territories of the marche region where there are or there might be more critically environmental issues, i.e. where there are higher levels of erosion and lower levels of soil fertility. this would be consistent with previous research showing a higher presence of organic farms where soil is less fertile and the levels of erosion are higher (lewis et al., 2011; gabriel et al., 2009; wollni and andersson, 2014; paudel and thapa, 2004). moreover, organic farming should be less concentrated in areas where its environmental contribution is lower because of the prevalence of natural elements, i.e. where there is a higher share of natural areas. the altimetric distribution of organic farms may also be important from an environmental point of view. in the medium-high hills of the marche region, organic farming can prevent from phenomena of landslides and leaching, thus preserving soil integrity, while, in flatter areas, it can reduce the quantity of pollutants produced by agriculture. with reference to the existing farming system, organic farming can contribute to reducing the environmental pressure exerted by intensive agriculture. therefore, we expect that organic farming is more concentrated where there is a higher soil exploitation, i.e. in areas characterized by higher levels of mechanization, thus a lower use of labour, and a higher specialization in mechanizable crops, such as arable crops. however, the adoption of organic farming can significantly depend on the relevant costs. in this respect, häring et al. (2004) have pointed out that the conversion to organic farming is more convenient for farms using practices that are less intensive in the use of mechanization. in this case, in contrast with our expectations, we would have a higher presence where the contribution of organic farming to the environment is lower. regarding further characteristics of farms, läpple and van rensburg (2011) have shown that the adoption of organic farming is more probable among smaller and younger farmers. this can be justified by the economic opportunities, in terms of relatively higher prices and public subsidies, which organic farming can give to new entrants and, in general, to farms that are too small to compete on the market. the result would be a higher concentration of organic farms in locations where there is a higher percentage of young farmers and small-sized farms. however, regarding farm size, pietola and lansink (2001) have shown that farmers who have large land areas are more likely to switch to organic farming since they have more possibilities of applying extensive farming technologies. in line with this result, koesling et al. (2008) have found that the probability that a farmer will produce organically rather than 303spatial distribution of organic farms and territorial context conventionally increases if the farmer has more farmland. as regards socio-demographic characteristics, wier et al. (2008) have shown that urbanization, education and population age play an important role in consumer choices. in particular, medium and long education increases the propensity to purchase organic foods. this propensity is also higher in more adult population and where the levels of urbanization are higher. looking at the supply side, studies, such as those by häring et al. (2004) and koesling et al. (2008), have shown that organic farms, for which direct marketing is particularly important, tend to localize close to urban areas, because they would have an easier access to consumers. if these results were confirmed, we should find that organic farms are mostly located where the percentage of urban areas as well as the share of population aged 40-64 and with higher education are higher. finally, policy may be another important factor to be considered since it can incentivise the spreading of organic farms by compensating higher costs or lower income resulting from organic management (sanders et al., 2011). 2.3 the dataset used data come from several sources and are available at a different geographical detail, i.e. points, irregular polygons and grids with different resolutions. for this reason, they are rescaled to the same territorial unit, represented by regular grids3 composed of uniform cells.4 specifically, data about organic farms refer to 2014 and come from the national register of organic operators. organic farms are both those who are already organic and those who are converting to organic farming. for every farm, there is information about the relevant headquarter address, which is used to identify the exact geographical position and localize farms within cells. total number of organic farms per cell is thus obtained as a sum of the organic farms localized in each cell.5 overall, in 2014, organic farms operating in the marche region and enrolled in the national register amounted to 2,160 units.6 2010 agricultural census is used to retrieve information about total farms and the relevant data about uaa and the age of farmers. coordinates of the firm site or, alternatively, the relevant address are used to localize farms within each cell. this allows us to derive total number of farms and of young farmers, who are less than 40 years old, existing in each cell. 3 the methodology used for dividing the territory into grid cells is based on the inspire equal area grid system (inspire, 2014). spatial representation is defined by a specific system of geographical coordinates (etrs89laea) that can be used as a common reference for different sources and studies. according to this system, cell sides should have a length included between one metre and 100 km with multiples of 10. 4 it should be cleared that not all cells are of regular size. in fact, the shape of the cells located on administrative borders of the region is adjusted in such a way to correctly represent the regional territory. 5 data on the agricultural area used by organic farms are also available. however, they are the sum of hectares that can be partly positioned in cells different from those where organic farms are localized. of these hectares, the exact localization, which is necessary for georeferencing, is unknown. moreover, for farms which are converting into organic farming, information about the area in conversion towards organic is not available. accordingly, data about the agricultural area of organic farms cannot be used. 6 in allocating farms among cells based on their headquarter address, it can occur that some organic farms fall in cells where there are not agricultural producers according to the agricultural census or there is not agricultural land according to 2012 corine land cover. for instance, in the case of a grid of 3-km size, there are 7 farms of this kind. this happens when headquarters do not correspond with operational sites. since removing these farms can produce biased estimates, they have been reallocated in the neighbouring cells where there are agricultural producers and land using the k-nearest neighbours algorithm (package spdep version 1.1.3 in r3.5.3). 304 andrea bonfiglio, andrea arzeni uaa per farm is calculated by dividing total uaa of farms located in each cell by the number of farms. the share of young farmers is obtained by dividing the number of young farmers by total farms falling into each cell. 2012-2014 data from italian farm accountancy data network (fadn) are used to estimate labour intensity. specifically, regional average ratios of labour units to uaa, differentiated by land use and five altimetric zones, are firstly calculated using fadn. these coefficients are multiplied by the shares of agricultural area present in each cell, taking account of different uses according to 2012 corine land cover and the altimetric zone of the cell. the sum of labour units distinguished by land use represents the total labour units employed in each cell. dividing this sum by total agricultural area, labour units per hectare are obtained. information about land use comes from 2012 corine land cover and is available at a 100-metre resolution, i.e., one-hectare area. the database classifies land in five main classes, of which classes “artificial surfaces” and “agricultural areas” are used to identify urban and agricultural areas, respectively. sub-classes “shrub and/or herbaceous vegetation associations” and “open spaces with little or no vegetation” within class “forest and seminatural areas” are instead employed to define natural areas. urban, agricultural and natural areas are obtained by summing the relevant hectares recorded in the dataset and falling into each cell. the shares of urban and natural areas are calculated as ratios of the corresponding hectares to total area, obtained as a sum of hectares belonging to all classes. sub-class “arable land” within “agricultural areas” is used to derive arable land and calculate the relevant share, obtained by dividing arable land by the total area attributed to each cell. all sub-classes within “agricultural areas”, including “arable land”, “permanent crops”, “pastures” and “heterogeneous agricultural areas”, are used to estimate labour intensity (see above). data concerning soil erosion come from the 2015 european dataset “soil loss by water erosion in europe”, which offers detailed information on soil erosion by water in 2010 for the european union at a resolution of 100 metres (panagos et al., 2015). data on erosion are available as tonnes per hectare. data related to soil organic matter are from the efsa spatial data version 1.1 and are available at a resolution of 1,000 metres (hiederer, 2012). the dataset is composed of several layers, of which that relevant to organic matter concentrations expressed as percentages is used. shares are calculated from the map of topsoil organic carbon by applying a factor of 1.72. this factor assumes an average organic carbon content of organic matter of 58%. information about altimetry, expressed in meters, is retrieved from shuttle radar topography mission dataset, managed by the u.s. geological survey agency. in particular, we use digital elevation data published in 2014 with a resolution of 3 arc-second for global coverage, corresponding to a spatial resolution of about 90 meters. levels of erosion, organic matter concentrations and altimetry of each cell are estimated by summing the respective levels relevant to the areas falling into each cell and dividing these sums by the number of areas that belong to the cell, so obtaining an average level of erosion, an average percentage of organic matter concentration and average altimetry for every cell. data about resident population, distinguished by age and level of education, come from 2011 population census and is available by census section, which represents the minimum territorial unit of a given municipality on which the census survey is based. spatially, it is represented by an irregular polygon. the sum of all census sections gives the entire regional territory. the polygons corresponding to census sections are firstly cut using regular grids 305spatial distribution of organic farms and territorial context in order to quantify the area falling into cells (figure 1). then, the population of a census section, which is present in a given cell (pc1), is estimated by multiplying total population (pcs1) by the share of territorial surface falling into the cell (ac1/acs1). the total population of the cell (tpc) is thus obtained as a sum of shares of population of all census sections, whose surfaces fall into the cell (pc1, pc2, etc.). percentages of population aged 20-39 and 40-64 as well as that of population with higher education are calculated by dividing the relevant quantities by the total population assigned to each cell. finally, public subsidies are collected from the dataset of national agency disbursing agricultural funds. we use information about the payments made in the period 2008-2014 and relevant to measure 214 of the 2007-2014 rdp. in this way, we assess the influence of the 2007-2014 rdp on the concentration of organic farms existing in 2014. 2008 is the first year of effective application of the rdp (i.e. the first year when payments are made), while 2014 represents the final year of programming and corresponds with the reference year of organic farms that are present in the national register of organic operators. data are not distinguished by sub-measure. therefore, it is not possible to identify the amounts that are specific to organic farming. however, this should not affect results significantly, since, in the marche region, almost the totality of the payments relevant to measure 214 are addressed to support organic farming.7 for each payment, information about the identification code of the beneficiary is available. this code is matched with that resulting from the national register of organic operators in order to associate payments with the organic farms recorded in 7 in the 2007-2013 programming period, 96% of total payments have been used to finance the specific action related to organic farming (regione marche, 2016b). figure 1. subdivision of census sections into regular cells and estimation of the population of a given cell (square with diagonal stripes) belonging to several census sections. cs1 cs2 cs3 cs4 a b c d cs1: census section 1 acs1: area of cs1 ac1: area of cell within cs1 pcs1: population of cs1 pc1: population of cell within cs1 tpc: total population of cell acs1 = a x b ac1 = c x d pc1 = pcs1 * (ac1/acs1) tpc = pc1 + pc2 + pc3 + pc4 note: for the sake of simplicity, census sections are supposed to be represented by regular polygons 306 andrea bonfiglio, andrea arzeni the national register. in this way, the sum of all agri-environmental payments that are made to organic farms existing in each cell can be derived. public subsidies per hectare, for every cell, are calculated by dividing the sum of agri-environmental payments by total agricultural area, obtained by using data from 2012 corine land cover. table 1 provides some descriptive statistics about the data used. for the sake of convenience, only data referring to the cells with a 3-km size where there are farmers are shown (see methodological and results sections for an explanation). 2.4 methods 2.4.1 the poisson regression model a glm for count data, specifically a poisson regression model, is firstly adopted. this choice depends on the objectives of the analysis and the characteristics of the dataset used. the main aim of this study is to analyse how specific characteristics of the territory affect the probability of observing organic farming in a given space. as already specified, these characteristics can be measured using data that are available at different spatial levels. theretable 1. descriptive statistics about the data used based on a regular grid of cells with a 3-km grid size and presence of farmers (total number of observations = 1,032). mean standard deviation min max organic farms (number) 2.1 3.2 0.0 46.0 farming system total farms (number) 43.5 35.5 1.0 259.0 % of arable land 57.9 32.3 0.0 100.0 uua per farm (ha) 16.4 25.4 0.3 400.0 labour units per ha 0.1 0.1 0.0 0.3 % of young farmers (< 40 years old) 3.0 2.8 0.0 22.0 land use % of urban areas 6.2 12.6 0.0 100.0 % of natural areas 7.6 11.3 0.0 84.6 environment altimetry (meters above the sea level) 350.9 279.1 2.0 1,687.0 % of soil organic matter 3.0 1.8 0.0 10.4 erosion (tons / ha) 12.3 5.5 0.0 26.7 demographic and social factors % of 20-39 years-old population 11.3 3.2 0.0 35.3 % of 40-64 years-old population 34.6 5.9 0.0 100.0 % of population with higher education level 33.9 8.9 0.0 100.0 policy 2008-2014 policy payments per ha (€) 54.3 415.6 0.0 12,513.5 source: authors’ elaborations 307spatial distribution of organic farms and territorial context fore, these data are converted into a common spatial reference, using regular grids composed of uniform cells. moreover, in consideration of data availability, the distribution of organic farming can be analysed in terms of farms, represented by points that can be georeferenced within a grid, so obtaining the number of farms, i.e. a count variable, operating in each cell. in analysing count data, ordinary least squares regression cannot be adopted because count data are non-negative and discrete, tend to be highly skewed and non-normally distributed, and commonly follow a poisson distribution (ma et al., 2012). for all these reasons, a regression model based on a poisson distribution may be considered as appropriate. the poisson regression model has two fundamental components: the response distribution is not necessarily gaussian distribution and a monotonic link function is used to transform the mean of response variables into a linear form. the probability density function of a poisson random variable z is given as: e-λλz p(z = z) = (1) z! where parameter λ is the mean and the variance of random variable z, i.e., e(z) = λ and var(z) = λ. thus, in the absence of other information, one should expect to see λ events, represented, in our case, by a given number of organic farms, in any spatial unit. assuming that event rate λ is not constant but depends on a number of variables, which are supposed to affect the probability of observing a given number of organic farms in a given spatial unit, the poisson regression model takes the following form: log(e(z)) = log(λ) = x’β (2) where x’ is a row vector of explanatory variables and β is a column vector of unknown regression coefficients. the log function is the link between the mean of the poisson random variable and linear predictors. it ensures that the mean remains positive for all linear predictors and parameters. the poisson regression assumes that observations are independent. however, this assumption could be invalid since the number of organic farmers, which operate in a given area, can be spatially dependent. in other words, it might also depend on the farmers located in the neighbouring space. if there is no evidence of significant spatial autocorrelation in model residuals, non-spatial methods may be appropriate. however, if statistical tests indicate significant spatial autocorrelation, methods that also consider the spatial component should be adopted (ma et al., 2012). in order to verify the existence of spatial autocorrelation, the moran’s i test (moran, 1950) is carried out on the residuals of the poisson regression model. values of moran’s i range from -1 (indicating perfect dispersion) to +1 (perfect autocorrelation). the regression model is applied using different sizes of cells in order to confirm spatial autocorrelation in correspondence with different grids.8 the choice 8 this kind of autocorrelation could also exist between farms operating within the same cells and this would contrast with the assumption of the poisson regression model, according to which observations should be independent. to ensure that this assumption is not violated, it would be necessary to carry out an analysis based on the use of points rather than areas. however, this would not be consistent with the objectives of this analysis and the dataset available. in fact, the aim is to assess the influence of territorial characteristics on the concentration 308 andrea bonfiglio, andrea arzeni of distances is often an empirical issue since exact information on the size of the neighbourhood does not exist (roe et al., 2002). following lapple and kelley (2015), we assume that beyond a certain distance, a spatial effect, if any, does no longer affect the adoption of organic farming. in order to allow for several neighbours per farm, 1 km is chosen as the minimum distance cut-off and poisson models with 1, 2, 3, 4 and 5 km distance cut-off are applied. this requires the creation of grids composed of a decreasing number of cells, i.e. 9,721 (1 km), 2,525 (2 km), 1,160 (3 km), 675 (4 km), 441 (5 km).9 the regional territory is also represented by non-agricultural areas, where zero observations are due to the absence of agriculture. this implies that the spatial autocorrelation, identified by the moran’s i test, could be the result of a clustering of agricultural and non-agricultural areas rather than a clustering of organic and non-organic farming. to avoid this, the poisson regression model is applied only to those cells where there are agricultural producers, amounting to 5,785 (1 km), 2,087 (2 km), 1,032 (3 km), 622 (4 km), and 409 (5 km). 2.4.2 the quasi-poisson hierarchical generalized linear mixed model after confirming spatial autocorrelation, a hglm model is then applied to analyse the influence of spatial dependence in addition to other possible factors on the distribution of organic farms. different from linear models, hglm models allow for inclusion, besides the usual fixed linear covariates, of an independent random location effect accounting for heterogeneity or, as in this study, a spatially autocorrelated random effect. specifically, a quasi-poisson hglm model with car-type specification10 of spatial covariance (hereinafter, quasi-poisson car-hglm model) is adopted.11 following lee et al. (2016), this model takes the following form: of organic farms. data about these characteristics are not all at level of single farms (thus point data) but are, mostly, at level of space. moreover, the reference unit of spatial analysis is given by the group of farms existing in a given cell rather than the single farm, i.e., the objective is to analyse spatial dependence between groups of farms, which can be composed of one or more units. as in allaire et al. (2015), we assume that the farms located in a given space are a homogenous group, sharing, because of their location, the same territorial characteristics that influence farmers’ strategies. 9 the threshold of 2 km corresponds with the squared root of the average territorial density of regional organic farms, which is specifically one farm every 4.5 km2. 10 there are two auto-poisson models that are commonly used: the sar model (whittle, 1964) and the conditional autoregressive model (car) (besag, 1975). car models are very popular in spatial analysis of count data (lee et al., 2016). they are unsuitable when the spatial weight matrix is asymmetric (dormann et al., 2007) but they are appropriate in the opposite case, as happens when proximity or distance between areas is modelled. the sar specification is a special type of car models, at least in a continuous-response context, and generates a less natural spatial structure (cressie, 1995). the sar approach is harder to apply for more complex and limited-response situations, especially when large datasets are used (wang et al., 2012,2014) and yields parameter estimates that are similar to those estimated by the car model (kim and lim, 2010). a formal and more exhaustive presentation of sar and car models can be found in wall (2004) and glaser (2017). 11 it should be remarked that the main interest, here, is not methodological. in other words, the aim is not to find and adopt the best model among a battery of possible alternatives but to evaluate the influence of some factors on the observed distribution of organic farms. this is conducted using one of the possible models available in literature, which can fit to the context and to the data analysed. comparison of results associated with different models and, thus, issues related to model selection can be an interesting and future research direction. 309spatial distribution of organic farms and territorial context (3) where zi|vi is the conditional distribution of the response (count) variable given the location specific random effect vi; ϕ is a dispersion parameter; λi is a random intensity for location i, which equals the conditional mean, i.e. the expected value e(zi|vi); ϕλi is the variance var (zi|vi), where ϕ =1 gives the poisson distribution, while ϕ < 1 and ϕ > 1 allow for underdispersion and overdispersion, respectively; v is a vector of random effects that are supposed to be normally distributed; σ is the covariance matrix of the n-dimensional normal density with a car-type specification; i is an identity matrix; τ and ρ are coefficients to be estimated. in particular, ρ is known as spatial dependence parameter (hodges, 2014), with ρ = 0 corresponding to independence and ρ = 1 corresponding to strong spatial autocorrelation. w is a spatial weight matrix, which defines the relationship among different locations. in other words, it defines the spatial neighbourhood for every location. there are several choices of spatial matrices, depending on the neighbouring criterion (anselin, 2002). in this study, we use the queen contiguity, according to which two cells are neighbours if they share a common side or a vertex. moreover, we opt for a binary approach, i.e. diagonal elements are all 0 while off-diagonal elements (i,j) are 1 if locations i and j are neighbours.12 the grid size used is that which exhibits the highest spatial autocorrelation, since it corresponds with that for which the spatial model can be more appropriate. the choice of this model is also conditioned on the characteristics of the sample used. in our data, there is a significant share of zero counts.13 this situation is not infrequent in that spatial counts are often characterised by a high number of zeros (agarwal et al., 2002; dénes et al., 2015; zuur et al., 2012). the presence of more zeros than expected is a source of overdispersion, meaning that the variance is higher than the mean. under such circumstances, a standard poisson regression model would be inappropriate. in the literature, for modelling counts with excessive zeros, zip models (lambert, 1992), hurdle models (cragg, 1971) and their modifications have been proposed (zuur et al., 2012). these models are based on some theoretical assumptions. a zip model assumes that zero observations have two different origins: “sampling” and “structural”. more specifically, the population is considered to consist of two types of individuals. the first type involves counts of event in a poisson or poisson-like process, which might also contain zeros (“sampling zeros”). the second type always gives 12 clayton and berardinelli (1996) point out that a binary specification of the spatial matrix is not internally consistent in the case where the number of neighbours varies, which occurs with most irregular lattices. in this case, matrix standardization is necessary. since the grid cells we use are mostly regular lattices, a simple specification is kept. 13 for instance, using a grid of 3-km size, zero counts amount to about 30%. 310 andrea bonfiglio, andrea arzeni a zero count (“structural zeros”). in contrast, a hurdle model assumes that all zero data are from one “structural” source, while the positive (i.e., non-zero) data have “sampling” origin, following either truncated poisson or truncated negativebinomial distribution.14 moreover, these models apply only when there is overdispersion in the response variable. however, there are studies finding that many zeros may be associated with underdispersion (i.e. the variance is lower than the mean) for which zip and hurdle models would not be appropriate (i.e., oh et al., 2006; tin, 2008). a further source of overdispersion is spatial aggregation, which leads to a higher probability of zero counts (gabriel et al., 2009). in other terms, due to spatial correlation, co-occurrence of zero counts may generate higher shares of zeros in some samples than expected under the assumption of independent observations (lee et al., 2016). in these cases, the inclusion of spatially autocorrelated random effects in regression models represents an effective way to handle the problem of overdispersion associated with zero inflation. neglecting the issues of overand underdispersion in analysing count data can cause several estimation problems, such as poor fit of the model, different estimates of regression parameters, and wrong inferences concerning the model parameters (ridout and besbeas, 2004; tin, 2008; ver hoef and boveng, 2007). compared with zip and hurdle models, the quasi-poisson hglm model is a more flexible solution. it does not make any specific assumption about the process that generates zeros. moreover, it can handle both underand overdispersion, by allowing situations where the variance differs from the mean and including random effects to capture spatial autocorrelation.15 a quasi-poisson regression model gives a correction term for testing the parameter estimates under the poisson model and produces an appropriate inference if overdispersion is modest (cox, 1983). however, in conjunction with a hglm model, not only it improves inference but it may also produce better fits in comparison with zip and hurdle models, in presence of zero observations (lee et al., 2016). models (2) and (3) allow us to estimate the expected number of organic farms within a given space, depending on some factors. however, the probability of observing organic farms is also conditioned on the presence of farms. to take account of this aspect, a simple remedy consists in adding an exposure (or offset), in our case the logarithm of total farms, to linear predictors (green, 2012, pp. 847-848). this is equivalent to estimate a model where the response variable is a rate rather than a count variable. this approach assumes that there is exact proportionality between the number of organic farms and total farms, since the parameter associated with the exposure is constrained to one. it is like stating that if total farms increase by 1%, also organic farms increase by 1%. however, this could be untrue since an 14 in our study, sampling zeros correspond with farms that are organic but do not appear in the national register of organic operators. this can happen for errors, possible delays in updating the register or because farms, which are organic de facto, still do not undertake the procedure of certification or, also, renounce the organic certification for avoiding the relevant costs and bureaucratic obstacles. conversely, structural zeros derive from the presence of only conventional farms. it is evident that both assumptions, i.e. there exist both sampling and structural zeros or there are only structural zeros, might be true. therefore, the choice between zip and hurdle models is not neutral because, not only the initial assumption could be untrue, but the relevant estimates and interpretations could be very different (hu et al., 2011). 15 an alternative to quasi-poisson models, allowing for both overdispersion and underdispersion, is the so-called conway–maxwell–poisson (com-poisson or cmp) distribution, which is a generalization of the poisson distribution (conway and maxwell, 1962). one of the main differences between quasiand com-poisson is that the latter requires estimation of two rather than one parameter, making the procedure of estimation more complex. 311spatial distribution of organic farms and territorial context increase in total farms can be due to an increase in conventional farms. to relax this assumption, which could produce biased estimates, the parameter of the exposure is let to vary, by treating the logarithm of total farms as a further explanatory variable to be included in x’. a model based on a poisson distribution does not allow the derivation of a natural counterpart to the r-squared of a linear regression model, because the conditional mean function is nonlinear, and the regression is heteroscedastic. therefore, to measure goodness of fit, several alternatives have been suggested (green, 2012, p. 844). here, in order to compare non-spatial and spatial poisson-based models, we use a r-squared measure based on deviance residuals, which satisfies all the criteria requested (cameron and windmeijer, 1996).16 let n be the number of events, this measure takes the following form: (4) 3. results table 2 shows the moran’s i statistic calculated on residuals of predictions in poisson regression models in correspondence with different grid sizes. as can be noted, the value of the statistic is always positive, indicating that the number of organic farms in a given cell increases (decreases) as the number of organic farms in the neighbouring cells increases (decreases). the relevant trend appears to be concave: it increases until 3 km, where it reaches the highest value, and then decreases. as expected, for lower values of grid size, spatial autocorrelation tends to vanish. this is because the distance between farms increases and the number of farms in each cell decreases. on the contrary, for higher values, the number of cells decreases and the concentration of farms within single cells increases with the consequence that spatial autocorrelation becomes weaker. results therefore confirm the presence of spatial autocorrelation, which is stronger in correspondence with a grid of 3-km size. the spatial analysis is therefore conducted using this grid size. figure 2 shows the distribution of organic farms over the regional territory represented by uniform cells of 3-km size. from the figure, it turns out that organic farming is a phenomenon that spreads over the entire territory. nevertheless, it mainly localizes in the hinterland, i.e. in the medium-high hills. it is less widespread in the eastern part of the region, 16 according to cameron and windmeijer (1996), a r-squared measure should satisfy the following criteria: 1) it is included between zero and one; 2) it does not decrease as regressors are added; 3) it coincides with r-squared based on explained variation; 4) there is correspondence between r-squared and significance test on all slope parameters and between changes in r-squared as regressors are added and significance tests; 5) it has an interpretation in terms of information content of the data. table 2. moran’s i statistics for residuals of predictions in poisson regression models for different grid sizes, marche (italy), 2014. size moran’s i statistic variance p-value 1 km 0.0659 0.0001 < 0.001 2 km 0.0896 0.0001 < 0.001 3 km 0.1561 0.0003 < 0.001 4 km 0.0954 0.0004 < 0.001 5 km 0.0563 0.0004 < 0.001 source: authors’ elaborations. 312 andrea bonfiglio, andrea arzeni characterised by flat areas. moreover, there is a zone in the southern part of the region and located in the ascoli piceno district where organic farms appear to be more concentrated. spatial distribution of all the predictors analysed are reported in figure a1 in the appendix. table 3 shows the results relevant to application of both the non-spatial poisson model and the quasi-poisson car-hglm model.17 the r-squared based on deviance residuals are 17 the poisson regression model and the quasi-poisson car-hglm model are estimated by using the packages stats version 3.5.3 and hglm version 2.2.1 in r3.5.3, respectively. to solve the quasi-poisson car-hglm model, the eql1 method available in the package is used. this method has been conceived to improve estimation for poisson models with a large number of levels in the random effects (lee and lee, 2012). figure 2. spatial distribution of organic farms within a 3-km-size grid, marche (italy), 2014. note: administrative borders at nuts-3 level are shown. source: authors’ elaborations. 313spatial distribution of organic farms and territorial context 0.42 and 0.90, respectively. these values indicate a significantly higher capability of prediction of the spatial model. the different performances can also be observed in figure 3, which shows a plot of the observed responses against the fitted values. it can be noted that the spatial model fits the observed data much better than the poisson regression model. looking at the parameters estimated, several differences in terms of extent, direction and significance between the two models emerge. one evident difference lies in the lack of significance related to the percentage of young farmers, the share of natural areas and the percentage of population aged 40-64 in the spatial model. these coefficients are instead significant and markedly higher (in absolute terms) in the non-spatial model. further deviations are the extent and the direction of significant coefficients in the spatial model. the relevant coefficients are clearly table 3. estimated parameters for the poisson regression model and the quasi-poisson car-hglm model based on a 3-km-size grid, marche (italy). poisson regression model quasi-poisson car-hglm model coefficient standard error coefficient standard error intercept -4.053** 0.547 -11.670** 1.199 number of total farms (log) 0.584** 0.037 0.383** 0.067 % of arable land -0.678** 0.086 -0.629** 0.214 uua per farm 0.007** 0.001 0.006** 0.002 labour units per ha -2.493** 0.821 6.938** 1.978 % of young farmers (< 40 years old) 0.920 * 0.374 0.666 0.563 % of urban areas 2.734** 0.256 5.434** 0.597 % of natural areas 0.700 * 0.317 -0.491 0.611 altimetry – meters above the sea level (log) 0.399** 0.073 0.767** 0.169 % of organic matter -28.210** 3.684 19.360** 7.745 erosion (tons / ha) 0.039** 0.006 0.039 * 0.013 % of 20-39 years-old population 0.051 1.185 1.941 1.905 % of 40-64 years-old population 1.488 * 0.743 0.749 1.227 % of population with higher education level 1.437** 0.360 3.193** 0.798 policy payments (€) per ha*** 0.190** 0.000 0.295** 0.000 ϕ (mean model) 1.0 0.705 ϕ (random effects model) 3.262 τ 0.894 ρ 0.127 moran’s i statistic 0.156** -0.050 aic 3842.9 r2 dev 0.415 0.904 * p-value < 0.05; ** p-value < 0.01; *** coefficients are multiplied by 1,000 for improving reading. source: authors’ elaborations. 314 andrea bonfiglio, andrea arzeni higher (in absolute terms) in the cases of labour units per hectare, proportion of urban areas, altimetry, share of population with higher education and public subsidies, while they are lower especially with reference to the number of total farms and the percentage of organic matter. labour units per hectare and the percentage of organic matter have also opposed sign. these results stress the importance of using spatial models if spatial autocorrelation is detected. focusing on the spatial model, coefficient ρ indicates the presence of some spatial dependence consistently with the results related to residuals of predictions in the non-spatial regression model. the moran’s i statistics is around zero and the hypothesis that its value is significantly different from zero can be rejected, indicating that there is no more spatial autocorrelation in residuals. dispersion parameter ϕ regarding the mean model is lower than one, indicating the presence of underdispersion, while the one related to random effects, which captures spatial autocorrelation, is higher than one, suggesting, in this case, overdispersion. the joint presence of underand overdispersion legitimates the use of models that can take into account both phenomena. analysing the estimated parameters, the results related to farming system firstly show that organic farms are present where there are also other farms. this is an obvious result. however, it is interesting to note that there is not a direct relationship. the relevant coefficient indicates that if total farms increase by 1%, organic farms increase by 0.4%. this means that the distribution of organic farms and that of conventional farms do not follow the same pattern. the coefficient related to the share of arable land shows a negative and significant relationship between productive specialization in arable crops and numerosity of organic farms. the parameters associated with the average farm size and labour intensity are also significant but with opposite signs. figure 3. plots of observed and fitted values for (a) poisson regression model and (b) quasi-poisson carhglm model based on a 3-km-size grid, marche (italy). (a) (b) source: authors’ elaborations. 315spatial distribution of organic farms and territorial context looking at land use, the number of organic farms is positively and significantly related to the percentage of urban area. in relation to environmental characteristics, positive and significant coefficients are also found in the cases of altimetry, share of organic matter and levels of erosion. with regard to socio-demographic factors, there is a positive and significant relationship between the share of population with higher education and the presence of organic farms. finally, the coefficient associated with the incidence of public subsidies is positive and significant. its value measures the logarithmic difference of expected organic farms due to an increase of one unit in public subsidies per hectare, keeping the other predictor variables constant. it suggests that, to increase organic farms of one unit, it would be necessary to increase total public payments of about 3,400 €/ha, corresponding to an annual payment of 485 €/ha. this value can be interpreted as an average measure of the willingness of regional farmers to convert towards organic farming and could be used as a reference parameter to define premiums in favour of organic farming. 4. discussion results show that the distribution of organic farming is spatially clustered, confirming previous studies. from mapping, it turns out that organic farming tends to concentrate particularly in the hinterland. the concentration of organic farms in the southern part of the region coincides with a specific cluster of organic operators, the so-called “bio-distretto piceno”18, born in 2014 and promoted by aiab, an italian association of organic farms. the econometric model confirms that there are spatial spillovers across territorial units. in other terms, the presence of organic farms in one territorial unit both affects and is affected by the presence of organic farms in the neighbouring units. results also indicate that the distribution of organic farming, in addition to neighbouring effects, relates to a number of territorial and political factors. in particular, in line with previous studies (häring et al., 2004; koesling et al., 2008), the concentration of organic farming is stronger where there are higher levels of urbanization. this can depend on the adoption of marketing strategies requiring a closer and stricter relationship with consumers, mainly localized in urban areas. education is a further socio-demographic factor that relates to the distribution of organic farming. organic farms are more numerous where there is a higher share of more educated people. the reason for this could be a greater propensity of people with higher levels of education to purchase organic products (zepeda and li, 2007; wier et al., 2008). with reference to farming system, results also show that organic farms are generally located in territories that are not specialized in arable crops and where there is a more intensive use of labour. moreover, in the areas where there is a higher concentration of organic farms, existing farms have generally a higher average size, confirming that a larger size offers more chances to apply extensive technologies that are typical of organic farming (pietola and lan18 a “bio-distretto” is defined as a geographical, functional and non-administrative area where a partnership between farmers, citizens, tourist operators, associations and public administration is established for ensuring sustainable management of resources. this synergy occurs on the basis of principles and activities of organic production and consumption (short supply chains, organized groups of consumers and producers, high-quality restaurants, public canteens) (basile, 2014). 316 andrea bonfiglio, andrea arzeni sink, 2001). as regards environmental characteristics, there is a higher diffusion of organic farming in less flat areas. organic farms are also more present where there are higher levels of erosion but also a higher content of organic matter in soils, in contrast with previous studies (lewis et al., 2011; gabriel et al., 2009; wollni and andersson, 2014). these findings can be read together. in fact, in medium-high hills, characterized by stepper slopes, problems of soil degradation due to surface water flows are graver. however, these areas are also less involved by intensive agriculture and are therefore richer in terms of organic matter. therefore, we can conclude that organic farms localize far from more competitive agriculture, characterised by a high specialization in arable crops and a more intensive use of mechanisation and chemicals. a reason for this could be lower costs of conversions to organic farming for farmers adopting agricultural methods that are already based on low levels of chemicals and mechanization (häring et al., 2004). another explanation can be the level of public payments per hectare, which can be too low for more competitive farms to render organic farming profitable, if compared with their relatively higher economic returns (pietola and lansink, 2001). therefore, organic farming seems to represent an economic alternative for that part of farms located in areas that are morphologically less suited to intensification. from a policy point of view, results show that there are more organic farms where payments per hectare of total agricultural area are higher. this contributes to confirming the importance of policy support in promoting organic farming. in the light of the other results, it appears that policy support mainly provides incentives in more remote areas, subject to erosion and more fertile. this can be positive for maintaining agriculture where the risk of abandonment is higher, also ensuring environmental protection from phenomena of erosion. however, there might be some inconsistency between the objectives of favouring organic farming and those of increasing environmental sustainability. if it is true that organic farming can help to reduce the environmental pressure generated by farming, it could be strategic to favour diffusion of organic farming also in areas where this pressure is higher, i.e. areas specialized in arable farming where there is a higher level of soil exploitation, and a higher use of fertilizers, chemicals and mechanization. support to organic farming, especially if it represents most agri-environmental payments, as is in the region under study, should also be targeted in relation to environmental characteristics (european court of auditors, 2011). this is because differentiating support spatially on the basis of given environmental issues can increase cost-effectiveness of agri-environmental measures (uthes et al., 2010; van der horst, 2007). analysing the system of incentives in favour of organic farms, it turns out that, during the 2007-2014 programming period, the maximum annual payment per hectare was on average 372 €/ha for introducing organic farming and 320 €/ha for maintenance (regione marche, 2015b). these values are lower than the average threshold estimated by the model to favour the appearance of an additional organic farms and are about 40-50% lower than the average threshold established by the regulation (ec) no 1698/2005 (650 €/ha). the system of compensation differentiated between crops and between areas, ensuring higher premiums for farms located in areas other than the mountains, but only with reference to maintenance. in other words, a single premium for introducing organic farms, distinguished by type of crop, was assigned to farms independently of their localization. as for maintenance, the difference between the premiums granted to the farms located in areas other the mountains and those received by mountain farms was 40€/ha, i.e. just 13% higher. in addition, the regional system awarded with similar scores applications from farmers located in nitrate vulnerable 317spatial distribution of organic farms and territorial context zones and in natura 2000 sites, as well as livestock farmers raising cattle with organic methods. therefore, several critical elements emerge, i.e. a relatively low level of premiums, the lack of territorial differentiation with regard to the premium for introducing organic farming, the small difference in terms of premiums for maintenance between areas, and the scoring system, which equates farmers operating in environmentally critical areas, those located in protected areas and livestock farmers that are already organic. granting a higher support, in consideration of available margins, and awarding, to a higher extent, conventional farmers located in environmentally critical areas in both phases of application (introduction and maintenance) could have produced better results in terms of achievement of environmental objectives. looking at the 2014-2020 rdp of the marche region, it turns out that, consistently with our suggestions, premium levels were increased by about 20% and the scoring system was revised by giving higher priority to conventional farmers with intensive production, who decide to convert to organic farming (regione marche, 2017a,b). however, territorial differentiation of premiums was completely abolished and only a single premium, though distinguished by type of crop, was introduced. moreover, with reference to the maintenance of organic farming, in selecting applications, a lower priority was attributed to farmers with intensive production, giving more importance to those located in natura 2000 sites, where constraints are surely more stringent, but the environmental benefits of organic farming are also lower. therefore, although a few improvements have been made in comparison with the previous programming period, some policy choices are still questionable and should be rediscussed to better target policy support in relation to environmental issues. in interpreting results, some caution should be taken owing to a few possible limitations. besides potentially different results that can derive from applying alternative models (such as zip, hurdle, negative binomial or com-poisson models), a possible drawback is that the regression analysis applied might suffer from endogeneity. technically, this problem occurs when a predictor variable in the model is correlated with the error term. this can happen for a variety of reasons (fingleton and le gallo, 2008). however, the most relevant one in this study is the possibility that the outcome variable is also a predictor and not only a response (the so-called “simultaneity bias”). firstly, simultaneity bias may concern factors related to the prevailing farming system, since the characteristics of existing farms may reflect those of organic farms, especially in the cases where organic farms represent the majority. however, considering that organic farms only represent 5% of total farms and that the percentage of organic farms is higher than half only in less than 2% of cells where there are agricultural producers, the influence of organic farming on the existing farming system, if present, appears to be rather limited. endogeneity issues can also concern environmental factors such as the levels of erosion and the content of organic matter. this is because the presence of organic farms could help to reduce soil erosion and to increase or, at least, to maintain the percentage of organic matter contained in soils. however, the results related to soil erosion indicate the contrary, i.e. organic farms tend to localize where the levels of erosion are higher. moreover, observing the territorial distribution of soil organic matter (figure a1-i), the content of organic matter increases as we move towards the hinterland. this highlights a natural phenomenon due to the morphological characteristics of the region, which is possibly affected by the distribution of all agricultural activities, rather than a result of organic farming. endogeneity can also be concerned with the variable related to policy. in fact, a higher concentration of organic farms could be the reason for a higher incidence of policy support per hectare. in this 318 andrea bonfiglio, andrea arzeni case, it is like stating that organic farms are not incentivized by policy, but they recur to policy support only because there exists this financing opportunity. in other words, a farm becomes organic also without policy. this would be an even stronger conclusion which does not take into account the selective criteria and the obstacles that exist when applying for receiving policy support. nonetheless, although the possibility of some endogeneity still remains, the main objective of this study is to investigate contextual factors that are more favourable to the concentration of organic farming rather than analysing causal relationships at level of single farm, for which the issues related to endogeneity can be more relevant. 5. concluding remarks this study has analysed the spatial distribution of organic farms in the marche, an italian rural region. this region is an interesting case because, in consideration of its peculiar environmental characteristics, organic farming could give an important contribution to mitigating or avoiding current and potential environmental impacts that agriculture generates. in particular, this analysis has assessed whether and the extent to which the distribution of organic farms is related to some economic, social, environmental and political factors. to this aim, a quasi-poisson car-hglm model has been adopted. this model provides high flexibility since it does not make any assumption about the distribution of zeros, allows for both underand overdispersion, and takes account of spatial dependence in measuring the influence of some possible explanatory factors. results indicate that there is a tendency to spatial concentration, i.e. organic farming develops where there already exist other organic farms. besides neighbouring effects, there are other factors affecting the spatial distribution of organic farming. indeed, results show that organic farms concentrate where there are more favourable market conditions, i.e. an easier access to consumers and a higher propensity to purchase organic products. in addition, organic farms tend to localize far from the areas where there is a more intensive use of mechanization and chemicals, and where they could give an important contribution to attenuating environmental pressure. factors such as different conversion costs and low incentives could have acted on this spatial distribution. from a policy perspective, organic farms reveal to be sensitive to public intervention. policy appears to be effective in stimulating organic farming where there are more problems of erosion, but it seems to be ineffective where the environmental pressure exerted by intensive farming is higher. this is attributed to an inadequate territorial differentiation of support and to the scoring system for selecting applications, in addition to low levels of premiums. for ensuring more consistency with environmental objectives, it is suggested that payments in favour of organic farming should be increased and that the management system must be revised, by territorially differentiating incentives to a larger extent and by giving more priority to farmers with intensive production. 6. acknowledgments although this article is common to both authors, the authorship can be attributed as follows: sections 1, 2.4, 3, 4, 5 to andrea bonfiglio; sections 2.1, 2.2 and 2.3 to andrea arzeni. 319spatial distribution of organic farms and territorial context the authors are particularly grateful to three anonymous reviewers for their valuable comments and suggestions, which contributed to significantly improving the quality of this manuscript. 7. references agarwal, d.k., gelfand, a.e. and citron-pousty, s. 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(2012). zero inflated models and generalized linear mixed models with r. highland statistics ltd. bio -based and a ppl ied economics bae bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-9552 copyright: © 2022 l. cesaro, a. giampaolo, f. giarè, r. sardone, a. scadera, l. viganò. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: l. cesaro, a. giampaolo, f. giarè, r. sardone, a. scadera, l. viganò (2022). italian farms during the covid-19 pandemic: main problems and future perspectives. a direct analysis through the italian fadn. biobased and applied economics 11(1): 21-36. doi: 10.36253/bae-9552 received: july 30, 2020 accepted: december 11, 2022 published: july 22, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini. orcid lc: 0000-0002-6568-3143 ag: 0000-0003-2752-793x fg: 0000-0001-9541-908x rs: 0000-0001-7650-6102 as: 0000-0001-9853-2678 lv: 0000-0002-0622-7316 italian farms during the covid-19 pandemic: main problems and future perspectives. a direct analysis through the italian fadn luca cesaro, antonio giampaolo, francesca giarè, roberta sardone*, alfonso scadera, laura viganò council for agricultural research and economics research centre for agricultural policies and bioeconomy (crea pb) e-mail: luca.cesaro@crea.gov.it; antonio.giampaolo@crea.gov.it; francesca.giare@crea. gov.it; roberta.sardone@crea.gov.it; alfonso.scardera@crea.gov.it; laura.vigano@crea.gov.it *corresponding author. e-mail: roberta.sardone@crea.gov.it abstract. the spread of the covid-19 virus in italy during the first phasis of the pandemic (february-may 2020) has caused a large-scale crisis, with an almost immediate decrease of industrial production and a consequent contraction in domestic consumption and external trade. however, the issue of food security was immediately recognized as one of the most sensitive, so that the government has decreed the priority role of the food system, which has been included among those considered fundamental services and economically essential, allowing the related activities to be carried out during the lockdown. agricultural production activities transformation, and commercialization remained fully operative during the lockdown; nevertheless, the sector has faced many difficulties related to the contraction of some of the marketing channels (restaurants, on farm sales, agritourism, problems with the logistics and many other ones). to better understand the effects of the initial phasis of the pandemic on the italian agricultural sector and provide useful information to the government and decision makers, a survey was carried out with a cawi (computer assisted web interviewing) sent to over 10,000 farmers belonging to the sample of the farm accountancy data network (fadn). the number of respondents has been of 733 farms, which represents around 7% of the italian fadn sample. the results of the questionnaire have been matched with fadn data on the structure and the economic performance of farms, allowing a more precise evaluation of the condition and effects of the pandemic. the results highlight a relevant effect of the covid-19 pandemic emergency on the agricultural sector: 37% of the interviewed farmers declared a significant liquidity crisis, while 60% predicted a contraction in turnover. these effects are more relevant for the wine, olives, and horticulture types of farming and more frequent in medium/large farms. a better situation has been found for farms which usually outsource processing and/or marketing/sale of the products. keywords: covid-19, farms, farm income, italy, fadn. jel codes: q12, q18. 22 bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9552 luca cesaro et al. 1. introduction the covid-19 pandemic in italy started to become dramatic in february/march 2020, a bit earlier than other european countries, and is still ongoing, albeit with reduced diffusion. the initial pandemic diffusion led to a severe lockdown in all the country, causing a severe stress not only to the health system, but also to the financial, economic, and social situation of the population. to curb the spread of the infections, the national and regional authorities adopted severe restrictive measures, closing a lot of economic activities and dramatically limiting the social life of people. the crisis triggered by covid-19 caused a significant slowdown in production activity, a sharp contraction in internal demand for some types of goods and services, and a reduction in commercial and trade activities. consequently, in the first quarter of 2020 national gross domestic production (gdp) decreased by 5.3% compared to the previous quarter (istat, 2020a) and data for the year 2020 show a global reduction equal to -9,2% (banca d’italia, 2021; istat, 2020b), pushing the country towards the most dramatic crisis faced since the post second war period. in this situation of emergency, the issue of food security was recognized as one of the most sensitive, so that the government has decreed the priority role of the food system, which has been included among those considered fundamental services and economically essential, allowing the related activities to be carried out during the lockdown (art. 1, co. 4, dpcm 11/03/2020). however, the authorization to maintain the operation of agricultural production, trade of agricultural products, and marketing activities (except for the hotel, restoration and catering, ho.re.ca.) did not prevent several difficulties related to the lockdown, which effects have been depending on the positioning of each company on the supply chain, the range of activities carried out, the geographical area, the organization, and the management of the production activities. available statistics show that just in the first quarter of 2020 an important reduction of the agricultural activity has been registered, with a reduction (with respect to the last quarter of 2019) of -1.9% in the added value and -1.8% of the work units (crea, 2020a). these reductions are mainly due to the scarcity of temporary workers, lack of liquidity, reduction/lack of other gainful activities together with the impossibility non-postponement of necessary operations (seedling, cure of livestock, veterinary visits, crop’s phytosanitary treatments, and fertilization, etc.). during the year, difficulties became more evident as showed by istat (2021), which estimated the reductions of the sectoral value added higher than -6% and of the work unit equal to -2,3%, mainly due to a smaller use of employees. this result seems related principally to the trend of vegetal productions (notably olive oil), to the reduction suffered by agricultural services (-4,1%), and mostly to the dramatic fall of secondary activities (-20,3%), mainly driven by the restrictions enforced for the agritourism services (buonaccorsi, 2020). these trends are confirmed by a fao report (fao, 2020) which states that in italy “lockdown measures and border closure disrupted the usual organization of work and flow of labour, causing risks of seasonal workers shortages for the spring harvest. rural tourism was impacted due to the cancellation of all farms’ stay accommodations”. therefore, needs and problems of farms have different relevance, according to the type of farming, the specialization in different productions and activities, the organizational and managerial schemes adopted (use of family labor, presence of permanent workers vs. seasonal/foreigner workers, outsourcing services), commercial channels utilized, and final markets of the products. the relevance of the structural and organizational characteristics in managing the responses to the post pandemic crisis has been put in evidence in many other countries, as emerges from the literatures published in the months following the spread of the covid-19 (aday and aday, 2020; gruère and brooks, 2021; marusak et al., 2021; weersink et al., 2021). at the same time, food industry has faced the challenge to quickly reorganize working spaces and shifts, for ensuring the safety of employees and granting the regular delivery of processed food to the distribution companies, in addition to the necessity to retrieve all required raw materials, often of foreign origin (crea, 2020c; ecovia intelligence, 2020; ismea, 2020a e 2020b). in this case, the index of industrial production has showed a more significant decline (about -4%) in comparison with same period of 2019 (march), and the negative trend has been confirmed in the following month, with a further reduction of -2% (crea, 2020a). in addition, within the phase of the industrial processing, the sector of beverages has suffered the highest reduction; in particular, the index of alcoholic products has showed a dramatic reduction (less 39% in march and -74% in april). these trends are summarized in a decrease of the added value of the food industry, estimated by istat equal to -1,8% (2021) and in a severe reduction of employment (-6,7%). the performance of the retail sector has been different, thanks to the role played by large scale retail trade and food and beverage distribution in replacing the market spaces of the ho.re.ca. (fipe, 2020), to ensure 23 bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9552 italianfarms during the covid-19 pandemic: main problems and future perspectives the compliance of the lockdown rules1. so, the value of sales has showed a positive trend (+10%) in march and also traditional retailers and specialized and small shops have increased the sales in the lockdown period (crea, 2020a). other distribution channels, such as proximity stores, short supply chains, home deliveries and online or digital sales, have acquired a strategic relevance, because they have been able to provide specific types of services or sales conditions more suited to the new and unexpected circumstances (ismea, 2020b). given this general framework, the agricultural sector deserves a special attention and analysis, because its activities may be only partially and slowly adjusted to the situation deriving from a lockdown. often, the main difficulties suffered by farms regarded the availability of specific production factors. particularly critical has been the availability and health protection of workers, in particular foreign ones, whose movements were heavily reduced by restrictions of mobility and by anti-contagion rules (e.g., the reduction of international connections and the obligation of quarantine) (ilo, 2020; ismea, 2020a and 2020b, macrì, 2020). in other cases, the undesirable effects of the crisis affected the organization of production activities, caused by the weakness of some essential services, including the structural lack of infrastructure and technological equipment in agriculture. in addition, some specific sectors have more severely hindered the negative impact of the economic slowdown, suffering a quite total stop of important market channels (as in the case of the floriculture or wine sector) (ilo, 2020; mediobanca, 2020). furthermore, the slowdown of the agricultural activities has generated serious damages in terms of food waste (as well as a related environmental damage), due to the loose of edible products remained not harvested and/or unsold (ilo, 2020). the scope of this work is to provide a picture, albeit partial, of the main difficulties that have affected the management of agricultural production activities and the financial situation of italian farms in the short term (during the lockdown of spring 2020), as well as the expectations expressed by farms for policy actions considered necessary to mitigate the difficulties arising from unpredictable and global event, such as the recent pandemic. the aim is to identify the areas of most significant weakness that can reduce the organizational and economic capacity of farms, threatening their functionality. knowledge about these aspects is of great importance for the programming of the cap 2023-2027 and 1 with the exception of those actors (bar, restaurant, catering) which have rapidly reorganized the supply towards the home delivery or take away services. other support actions aimed at overcoming some of the critical issues come to light. the paper is organised as following: the next section describes the rationale of the questionnaire and the methodology adopted for its submission to a sample of italian farms belonging to the farm accountancy data network. the third section presents the results of the analysis, matching the responses of the questionnaire and the structural, economic and financial data of the fadn dataset. the fourth section focuses on the main measures taken in the short term to respond, both at eu and national level, to the emergencies triggered by covid-19 pandemic, also offering useful directions in the medium-term aimed at stemming future crises. in the conclusions some implications are discussed, for the next future, offered by the results of the analysis. 2. materials and method during the first phasis of the emergency attention was focused on aspects related to availability, distribution, and consumption of food and agricultural products, while analysis on the effects of pandemic on agricultural production and farms have been relatively scarce.  however, the need to investigate the problems faced by italian agricultural farms and related solutions has been highlighted by many institutional and non-institutional actors, such as government authorities, professional organizations, and associations. indeed, to define possible actions to support farms and prevent the risks of other emergencies, it is crucial to have a deeper understanding of the effects of the covid pandemic and related policy actions on the agricultural sector and the farmers behaviors.  currently, these issues are widely documented mainly through journalistic investigations or expertsbased  research methods, while there is a lack of direct information from the farmers.  to contribute to cover this lack of information this work is based on a direct survey collecting data and information from farms.  the asked research questions are the following:  following the covid-19 emergency and the measures adopted to contain the pandemic, which kind of difficulties did the farms face in relation to the conduct of their activities?   which actions did the farms put in place to face the pandemic situation and the lockdown?  do the size, farm structure, production sector, marketing or other specificities have resulted in significant differences in terms of problems and adopted solutions?  24 bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9552 luca cesaro et al. what were the forecasts of farms with respect to the immediate future, in terms of both difficulties/solutions and economic results? did farmers expect a change in the total production of their farm? in what percentage? the hypothesis is that, although the restrictive lockdown measures regarded most of the other productive sectors but not directly the agricultural production, the effects of the covid-19 emergency have largely affected agriculture, although to diverse extents and in different ways.  to carry out the survey quickly and reach enough farmers, the cawi (computer assisted web interviewing) methodology was used (via web), also ensuring compliance with public health and safety regulations. despite this methodology reaches only those who have access to internet and does not allow statistically representative sampling, it guarantees a remarkable speed in the collection of information and the cawi is rather easy to be filled by the respondents.  the questionnaire was structured in 5 sections aimed at collecting information on: the difficulties faced by italian farms due to the covid-19 emergency; the actions taken to deal with them; the public support granted for supporting farms; the forecasts for the future, in terms of possible difficulties / solutions; and the expected change in the farm’s output. each section was organized in a set of closed-ended answers (with the possibility of multiple choice), in which also an openended answer was included, to collect additional unexpected input. to overcome the representativeness problems associated with cawi method, the questionnaire was sent to over 10,000 farmers belonging to the sample of the farm accountancy data network (fadn), distributed throughout the national territory. in this way it has been possible to match the data and information collected with the questionnaire to all the individual farm data already available in the fadn dataset (total output, production, costs, income, structural information on the farm, etc.).  the questionnaire was available online for 14 days (april-may 2020); 733 farms, operating in all the italian regions and covering all productions, filled out it with a response rate covering over 7% of the fadn sample (tab. 1). the data from the questionnaire were analyzed to estimate the impact of pandemic on some relevant farm indicators, using the economic results of farms recorded in the italian fadn survey in the 2016-2018 period as baseline. the matching of the farmer to which the questionnaire was sent with the farm code registered in the fadn database made it possible to link the responses of the questionnaire to the technical and accounting information found in the fadn survey. the baseline consists of 30,374 observations and includes the farms recorded in 3 consecutive accounting years. the annual sample, of about 10,100 units, is statistically representative of the universe (field of observation) of italian farms. however, based on the european fadn regulations, only a part of the farms is considered in the field of observation, i.e., those having a standard production (sp) greater than 8,000 euro. the field of observation of italian fadn represents only 50% of the farms estimated by the farm structure survey (fss) but more than 96% of the standard production and almost 90% of the agricultural area used in italy, guaranteeing an almost total coverage of the italian agricultural production. being the fadn sample designed using a rigorous methodology, it is statistically representative and it is therefore possible to extend its results to the entire field of observation of the survey with a good statistical precision at level of administrative region, type of farming, and economic size class. in addition, the estimates can also refer to structural elements of farms, such as the use of family and wage labour. in synthesis, among fadn variables, the following economic variables have been identified and used for the analysis: (1) total output; (2) specific costs; (3) value added; (4) agricultural working unit. to facilitate the reading of the economic results between the various typological classes of farms, the selected economic variables were also analyzed as working unit indices. to exclude anomalous values (outliers) within the layers considered, the dataset has been subject to statistical treatment. table 1. structure of questionnaire and number of respondents. survey sections number of answers a. kind of difficulties faced by farms due to the covid-19 emergency 733 b. actions taken to deal with different difficulties 535 c. priority support actions by state and regions 528 d. difficulties expected by farms during the following months 600 e. expected change in total output following the covid-19 emergency 639 source: own elaboration on collected data. 25 bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9552 italianfarms during the covid-19 pandemic: main problems and future perspectives th e 10 types of farming (tf-10) used in the analysis represent the most representative tf (in term of standard output) at national and regional level. in term of economic size, we aggregated the farms based on 3 classes of economic dimension (small, medium, large) to allow an easier representation of the stratifi cation of the sample. to eliminate annual variation of economic fi gures, three-year average values were used in the analysis. th e added value (av), in absolute or index form, constitutes the most appropriate fadn indicator for this type of analysis. for a better understanding of the results, it needs to be considered that there is a strong relationship between the economic size of the farm and the average levels of income produced, productivity, and profi tability indexes. in every tf, smaller farms are characterized by lower income and productivity (per work unit) with respect to larger ones. figure 1 describes the similarities and diff erences between the fadn sample and the subsample of respondent to the questionnaire. respondents are proportionally less than the fadn sample in southern regions and insulas (sardinia and sicily) while they are more than the fadn sample (as proportion) in north-west, north-east and central regions. considering the gender and age of respondents, no relevant diff erences appear between the subsample of respondent and the fadn sample, similarly for the organic/conventional classifi cation of farms. indeed, a relevant diff erence can be seen in the variable describing the diversifi cation of farm activities: the proportion of diversifi ed farms (agritourism, educational farms, etc.) is almost double in the subsample of respondent with respect to the fadn sample. one possible explanation for that could be the fact that these farms have a greater propensity to use social networks and participate in surveys. in addition, they are more sensible to the eff ects of the lockdown because their activities have been almost completely cancelled during the pandemic, so they are more interested to communicate it (ismea, 2020c) th e farm type (ft) and size diff erences between the subsample of respondent and the fadn sample are less relevant but still interesting. regarding the ft, figure 2 shows on its left part that the respondents are profigure 1. comparison of fadn sample and subsample of respondents. source: own elaboration on fadn and collected data. 26 bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9552 luca cesaro et al. portionally more numerous in the fts “specialist wine” and “specialist horticulture” while farms specialized in livestock production (milk and grazing) are less frequent in the subsample of respondents, regarding the farm size (fs) there is clear evidence that large farms are more represented in the subsample of respondent than in the fadn sample. overall, we can assume that farmers who forecast major losses as an effect of the pandemic are more propense to answer the questionnaire, as clearly demonstrated by the results of the analysis of the questionnaire. this framework shows differences according to various issues covered by the questionnaire, as described in the following paragraph. 3. main results and preliminary evaluations the answers given by the 733 farms of the fadn sample who accepted to fill the questionnaire show some elements of great interest. the answers reporting a reduction in the farm total output (fto) are prevalent, with over 60% of the responding sample expecting a decrease in the fto (for 13% of respondent the reduction is estimated to be higher than 50%). in all types of farming, estimates of negative changes in fto are prevalent, so that the distribution in the first three quartiles and the median are always below zero; this highlights the respondents’ expectation of marked contractions and significative fto decreases. this is particularly evident for some tf such as wine, olive, and horticultural ones, in which most of the observations are positioned on reductions that reach even 50%, with contractions in fto that in the last quartile reach almost 100% (fig. 1). the relatively more negative forecasts regarding the fto recorded for the wine, oil, and vegetable sectors mainly depend on the closure of the ho.re.ca. channels, which also affected the reduction in exports, the contraction in tourism and, in the case of horticultural products, by the penalization suffered in general by perishable products compared to those preserved or frozen and by fears about the availability of foreign labour for harvesting activities (coluccia et al., 2021). the distribution of the responses received is shown in the box plot chart below (fig. 3), with the expected changes in fto in relation to the types of farming. however, some of the respondents also expect an increase in agricultural revenues, albeit with variations among the tf. with reference to wine and horticultural farms, a quarter of the responses expected positive variation of the revenues even higher than 50%. positive changes in revenues, but more sporadic, are found for the fruit, granivorous, and arable crops (including cereals). to better understand the situation, the information on the expectation in term of farm total output figure 2. comparison of fadn sample and subsample of respondents per ft and fs. source: own elaboration on fadn and collected data. 27 bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9552 italianfarms during the covid-19 pandemic: main problems and future perspectives resumed from the questionnaire has been coupled with the sales channels adopted by respondent farms (derived from the fadn database). as shown in table 2, the expectations for a reduction in to are signifi cantly diff erent for each sales channel. th e most signifi cant prospects for a reduction in fto regard farms with agritourism (75%), with direct sale in the farm or by vending machines. th ese farms declare to expect the revenue in the next future to close to zero. on the other hand, the least negative expectations are found in farms selling a part or the entire production, including a part of transformed products, to cooperatives or transformation industries. it is therefore evident that the higher importance of the direct sale of products and services will make these farms more sensitive to current and future market diffi culties. however, it should be recalled that the questionnaire was submitted to farmers in the very fi rst phase of the lockdown, when there was still uncertainty on the duration of the epidemic and the possible schedule of reopening of markets was unknown by farmers. th e availability of adequate fi nancial liquidity was one of the diffi culties most reported by the respondent farms: as shown in fig. 4, 42% of respondents declared this issue as relevant, preceded only by the difficulties of repairing equipment, machineries, and buildings during this emergency period (49% of the participants). th e diffi culties in accessing advisory services and technical assistance services, or in fi nding technical means are also reported by a signifi cant number of respondents (approximately 1/3 of the total), followed shortly aft er by the complications linked to the marketing of the products and the signing of new sale contracts. farmers declare they intend to cope with fi nancial diffi culties in this emergency period by resorting above all to their own savings and/or forms of corporate selffi nancing and, more rarely, by accessing fi nancing and emergency instruments put in place by the government or bank credit. th is last option probably has been evaluated by farmers as less timely and less eff ective in responding to contingent needs. however, it should be remembered that the questionnaire was submitted in the figure 3. expected changes in farm total output in relation to the diff erent types of farming. source: own elaboration on collected data. 28 bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9552 luca cesaro et al. fi rst phase of the lockdown, when some measures had not yet been implemented and/or not adequately known. it is interesting to note that respondents who declared problems of fi nancial liquidity are not homogenously distribute per geographic area and specialization: farms located in central and southern italy are more aff ected by liquidity problems/concerns, while no relevant diff erences are registered in term of age and gender of the farmers. at the same time diversifi ed and organic farms are more aff ected by liquidity problems than the average of respondent. for other relevant questions of the questionnaire, like the problem of repairing machinery and equipment similar diff erences are less relevant. the main results from the survey conducted on the fadn farms have been considered for the formulation of simulations on the impacts on farm income, by interfering the subsample of respondents with the fadn table 2. expected changes in total output by sales channel for the surveyed farms. negative >75 negative 50-75 negative 25-50 negative 0-25 none positive total wholesales, large-scale distribution, exporters 7% 7% 26% 27% 26% 9% 100% industry 5% 6% 27% 27% 25% 10% 100% retailers 7% 9% 26% 22% 22% 13% 100% other farms 5% 5% 23% 31% 27% 9% 100% direct sale in farm and automatic distribution 13% 14% 25% 22% 22% 5% 100% agritourism 25% 22% 19% 8% 17% 8% 100% cooperatives 1% 7% 21% 26% 32% 12% 100% others 3% 8% 23% 28% 29% 8% 100% undetermined 7% 7% 25% 23% 28% 10% 100% total 6% 7% 24% 25% 27% 10% 100% source: own elaboration on collected data. figure 4. diffi culties faced by surveyed farms during the lockdown (%). source: own elaboration on collected data. 29 bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9552 italianfarms during the covid-19 pandemic: main problems and future perspectives database. more precisely, the contraction of total farm revenues (tfr) has been calculated in the three-year average 2016-2018, considering the median value of the responses, stratifi ed according to the three classes of economic size of farms. in addition, starting from fadn data, the incidence of current costs on revenues was calculated, obtaining the farm added value (av) as diff erence between farm total output and current costs; the av was then ratioed to farm agricultural working units (awu) (fig. 6). it is important to emphasize that the high incidence of current costs amplifi es the eff ect of the contraction of revenues in the more intensive farm types, such as horticulture, viticulture, and granivores, causing a signifi cant reduction in productivity per unit of work, expressed by the av/awu index, much larger than the expected contraction in farm revenues. th e assessment of the real economic eff ects deriving from the current health emergency should therefore consider not only the organizational structure of the farm and the prevalent type of production, which evidently infl uences farmers’ expectations, but also the different cost structures characterizing the farm typologies. th erefore, for the same reduction in revenues, the eff ects on farm income can also be signifi cantly diff erent. finally, examining the future expectations of the farms interviewed, fi nancial liquidity remains the most felt concern, also for the future, given that almost 2/3 of the survey participants identify it as the main problem to be faced in the next few months (fig. 7). it seems to emerge in the farms that participated in the survey a deep concern about their ability to meet the needs of current expenses necessary for carrying out production activities in the next future, probably cause of a reduced consistency of monetary liquidity. in this regard, farms of medium and large economic sizes express, to a greater extent, their concern for a possible unavailability of fi nancial resources in the coming months, expressed by almost 2/3 of the respondents, against a steady expectation recorded for small farms. th e latter, however, are characterized by a profi le that is hardly identifi able with a professional management of the agricultural activity and closer to a connotation of non-professional farms, or farms that are characterised figure 5. focus on fi nancial liquidity problems. source: own elaboration on collected data. 30 bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9552 luca cesaro et al. figure 6. changes in the ratio av/awu based on the estimated variations of total farm revenues. source: own elaboration on collected data. 0% 10% 20% 30% 40% 50% 60% 70% liquidity not-posponable investments availability of technical means adjustment/enhancement of it services equipment, machineries, buildings marketing production on foreign markets (export) seasonal workers sales through short chains farm products delivery (intermediaries, large-scale… personnel farm services figure 7. diffi culties expected by surveyed farms in the months following the lockdown. source: own elaboration on collected data. 31 bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9552 italianfarms during the covid-19 pandemic: main problems and future perspectives by the integration of other income. this peculiar condition may have affected the assessments expressed regarding the adequacy of future financial resources. 4. from emergencies to responses: expected measures and policy implications after the first lockdown in march 2020, some buffer measures at eu and national level, through state aids and a more extensive use of cap resources, were activated to mitigate the liquidity crisis faced by the italian farms during the phase of more severe restrictions and afterwards (crea, 2020d), also detected through the survey addressed to fadn farms of may 2020. at eu level, this was mainly implemented by the european commission through the introduction of “measure 21” in the framework of rdps. the new measure was activated by almost all the italian regions (17 out of 21), allocating a maximum of 2% of the financial resources of the rdps in favor of agritourism, educational farms, social farming and the sectors considered most exposed to the pandemic effects by each region. therefore, a non-repayable grant of up to seven thousand euros per farmer and 50 thousand for processing and marketing small and medium enterprises (smes) was arranged. furthermore, the advance on cap contributions was raised from 50% to 70%. as in numerous other eu member states, different schemes have been activated through the mechanisms of state aids to support agriculture, animal husbandry, forestry, and related sectors with different measures (e.g. renegotiation of mortgages, issue of zero-rate mortgages, establishment of the “fund for the development and support of agricultural, fishing and aquaculture chains”, favorable tax regimes, exemption from the payment of social security and welfare contributions by employers, for farms belonging to agricultural supply chains, intervention by the guarantee fund also in favor of agricultural smes). other measures were aimed at simplifying some procedures that the covid-19 emergency would have hindered (e.g., suspension of visits to the farms by certification bodies to issue the certificate of suitability, suspension of certificates of qualification for sale, consultancy and purchase and use of plant protection products and extension of existing ones, extension of terms and derogations from agricultural sector legislation). regarding agricultural labour, the survey of the fadn farms highlighted two problems: 1) keeping it on the farm, avoiding dismissal; 2) ensure the availability of seasonal workers, especially foreign ones (about 300,000 units). to facilitate agricultural enterprises, therefore, on the one hand, the extraordinary exemption or the suspension of the payment of social security and welfare contributions of workers by employers and the wage supplement for agricultural workers, for example, have been introduced. in the case of seasonal workers, on the other hand, the duration of residence permits was extended until the end of july 2021 and agricultural professional organizations were authorized to set up databases to recruit people to be hired temporarily on farms, a tool that minimally resolved the problem (contignani et al., 2020). nevertheless, the regularization of agricultural workers was unsuccessful (legislative decree no. 34/2020), with only about 2,000 units regularized (may 2021; bettini and coderoni, 2021). furthermore, in italy the green corridors, aimed at facilitating the entry of seasonal workers, have not yet been activated, with the exception of the autonomous provinces of trento and bolzano, because the active quarantine protocols have not been officially recognized by the state. therefore, the solution to the seasonal labour problem has been left mainly to the private initiative (bettini and coderoni, 2021). also, the difficulties of marketing farms’ products in italy and abroad, due to the closure of the ho.re.ca. channels and canteens, both public (especially school) and private, or due to the contraction of tourist flows and of the reduction in the subscription of new sales contracts, have been faced by public authorities with extraordinary campaigns of purchases financed by european and national extra funds. these measures were aimed at readdressing a more sensitive group of unsold products such as, fresh milk, typical cheeses (pecorino romano, parmigiano reggiano, grana padano, fontina etc.), extra-virgin olive oil, cured meats and so on – and supporting the increasing share of citizen affected by economic deprivation. however, in a medium-term perspective, the only remedies appear to be the restoring of traditional markets, mainly thanks to the results of vaccination campaign, still in progress, and the gradual reopening of the activities. about marketing, it is worth highlighting once again the results reported from farms involved in cooperative’s system. indeed, data confirm the relevance of the implementation of measures for strengthening networks or cooperatives, especially for those sectors in which this type of organization is weaker, such us proposed also in the framework of the cap 2023-2027. other critical issues highlighted by the survey addressed to the fadn farms (difficulties in maintaining / repairing machineries, equipment and buildings, finding technical means, access to consultancy and technical assistance services, making non-deferrable investments, marketing of production via short supply 32 bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9552 luca cesaro et al. chain), on the other hand, currently appear less serious than one year ago, as the supply of goods and services upstream and downstream of farms has been reorganized to comply with safety standards and operators have adapted to work in adverse conditions, thanks to the widespread use of protection systems and the gradual results of the vaccination campaign. however, these problems can recur with the recurrence of situations similar to the covid-19 pandemic, their evolution, or due to “catastrophic” natural events. as resulted by the survey, one of the main challenges of new policies in favor of the agricultural sector should be to prevent the negative impact of new possible global crises, health and non-health, through structural interventions, promoting resilience of farms. in the long run, the resilience of farms is fundamentally affected by the availability of liquidity, inputs, including labour, and services. farms, therefore, between now and 2030, should reorganize their production processes to reduce their dependence on the outside and the production costs with the support of the public sector, which should provide them with a range of services, positively also influencing the availability of liquidity. in particular, the latter problem could be mitigated by expanding to pandemic risk the mission of the eu toolkit for risk management in agriculture (insurances, mutual funds, and income stabilization tools), currently mainly addressed to mitigate the effects of climatic and health emergencies (epizootic and plant diseases, parasitic infestations) and sectoral income losses (durum wheat, fruit and vegetables, etc.). these measures should be better promoted, also favoring greater synergies among them, in addition with the adoption of strategies differentiated for business model, as well as their financial reinforcement in the next cap programming period, for reaching a wider range of farms, currently still too limited (capitanio, adinolfi, 2013; trestini et al., 2017; severini et al., 2018; capitanio and de pin, 2018). in china, for example, agricultural insurance has been activated, on the one hand, to stabilize the incomes of farmers who produce fruit and vegetables, reducing their risk in agricultural production and operation during the covid-19 pandemic, and, on the other, to guarantee a constant supply of these products to consumers in urban areas (gu and wang, 2020). with reference to other emerging issues highlighted by the survey, it must be emphasized the relevance of the opportunities offered by the recent policy documents and financial instruments launched by eu, ranging from the european green deal (european commission, 2019) and the farm to fork strategy (european commission, 2020c), up to massive amount of resources offered by the next generation eu (ngeu) (european commission, 2020d) and the new programming period of the cap, that will enter in force in 2023 (de castro et al., 2021). programming differentiated proposals for intervention according to specific needs should be effective not only to reduce current difficulties, but also for avoiding similar situation that could occur in the future. in this view, many are the future interventions that could positively affect the resilience of the farms, territories, and the environment, giving impulse to an ecological and digital transition, in line with the next generation eu strategy (european council, 2020). the european green deal aims to promote the efficient use of resources by moving to a clean and circular economy, restoring biodiversity and reducing environmental pollution. it also promotes a fair and inclusive transition transforming climate issues and environmental challenges into growth opportunities for all sectors. the challenge of producing more with fewer resources, dissociating the growth of output from a more intensive use of factors, strongly involves and affects the agri-food sector. the objectives for the agri-food sector are defined in the farm to fork strategy, whose ambitions are to protect the health and well-being of european citizens, to increase the eu’s competitiveness and resilience, to make the eu food system a standard for sustainability at a global level. the strategy identifies the strengthening the sustainability of food systems, both by reducing their environmental footprint and improving energy efficiency, and by increasing the availability and affordability of healthy and sustainable food options, as the set path, also functional in making farms less sensitive to various adverse conditions, similar to those detected due to the covid-19. in this view, eu regions could play an important role in the implementation of differentiated strategies, managing as much as possible the resources of the new cap in favor of those areas and sectors with greater difficulties or more negative outlook, but which play a strategic socio-environmental role at local level (frascarelli, 2021). this strategy should be accompanied by actions for supporting the development of the short chain, including local markets, which could allow smes farms to improve their economic results and consumers to continuously have local food at lower prices, in line with the farm to fork strategy (european commission, 2020c). these measures should also be accompanied by information and education actions on sustainability aimed at increasing the community awareness about its contribution to the maintenance of farms in the territory and on the mutual benefits of a closer relationship between producers and consumers, as it has happened 33 bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9552 italianfarms during the covid-19 pandemic: main problems and future perspectives in this emergency period. indeed, some studies highlight the importance of awareness campaigns addressed to farmers and consumers and initiatives aimed at public procurement of local products for canteens (reis, 2019). the issue of digitalization, including the infrastructure of the rural areas, is among the core area of interventions identified by the italian national recovery and resilience plan (nrrp; presidenza del consiglio dei ministri, 2021), approved by ue within the ngeu. the nrrp provides for the implementation of structural and training interventions in favor of businesses, especially small and medium-sized enterprises (smes), public administration, health and tourism operators, citizens. the first step in the nrrp is the coverage of the whole territory with ultra-broadband networks (ftth fiber, fwa and 5g), then to address resources for supporting the adoption of digital technologies by companies, so as to improve their logistics, marketing, and the efficiency of production processes. furthermore, precision agriculture, aimed at rationalizing the use of technical means and improving the quality of products, constitutes one of the three areas of intervention of the nrrp in favor of the agricultural sector and one of the farming systems promoted by the farm to fork strategy (ec, 2020c), together with agroecology, organic farming, carbon farming, and agroforestry, all agricultural production systems also aimed at reducing or eliminating synthetic chemical inputs. in relation to the difficulties in the supply of technical means encountered by farms and detected through the survey, the expected impact should make farms more competitive and self-sufficient, even smes located in the most marginal rural areas, facilitating their activities especially in times of unforeseen crisis. finally, the survey shows that difficulties in accessing extension services during the emergency period were reported by a third of farms interviewed. these services should be considered as fundamental policy tools in supporting farms and accelerating change towards food sustainability; however, in recent years they suffered a sharp downsizing due to the decrease of awareness about their relevance and the consequent reduction of resources allocated in their favor by public policy. nevertheless, research, advisory services, and education have a key role in socio-economic and technical development as demonstrated in work of several authors, some of which highlighted the importance of producing tailor-made innovations analyzing the farmers’ problems/opportunities (sewell et al., 2017) and the need to enhance the interactions among different actors (klerks et al., 2012; hermans et al., 2015) in order to introduce innovation and promote rural development. in the current programming period, specific interventions are foreseen to provide efficient knowledge and innovation systems (akis). advisory, farmers’ and advisors’ training, demonstration, exchange and dissemination of knowledge, information are foreseen in the eu regulation proposal. it is a question not of new types of intervention compared to the current programming period, but of a more flexible and organized way to use them (van oost and vagnozzi, 2020), to reach the aim of building a more sustainable, competitive, and inclusive europe. their effectiveness will largely depend on the capability to grant a better link between the new forthcoming measures and both the new policy objectives and the different characteristics of the farms. 5. conclusions the direct survey carried out during the first first phasis of the covid-19 pandemic, combined with fadn data, allowed obtaining a very detailed information and thorough results, otherwise difficult to achieve using a single questionnaire submitted to a random sample of farms. the number of farms reached also offered the possibility of having a significant overview of the situation, in terms of different types of farming, referred to an exceptional period, characterized by uncertainty and a lack of data based on scientific evidence. the one presented in this study is, in fact, the largest italian survey in the covid-19 period which reached such many farms (and in general of subjects within a specific sector) using a consolidated methodology and being compliant with the sanitary emergency. the analysis of the results has showed that the covid-19 emergency produced severe consequences on the agricultural sector, both in relation to the development of cultivation / breeding activities and marketing. in addition, farmers’ forecasts for the medium term indicated a growing concern about a possible worsening of the situation, with a significant part of interviewed which expressed uncertainty about the performance in the remaining part of 2020, forecasting negative impacts on agricultural incomes, especially in some sectors and for some types of farms. final official data about the yearly trend have effectively showed an important reduction suffered by a large part of italian agriculture. but the survey has also put in evidence the presence of a large variety of situations and farms’ characteristics which can give an important contribution in the mitigation of the negative impact caused by unexpected situation of general crisis. the organization of the supply chain, for example, seems to play a significant role, as it is witnessed by the less negative expectations on the 34 bio-based and applied economics 11(1): 21-36, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-9552 luca cesaro et al. economic performance predicted by farms marketing their products towards cooperative structures. as well as smaller farms, often considered less significant within the sector, have showed expectations of a substantial balance. for these farms, with a smaller quantity of marketable production, in comparison with the medium-large ones, look promising the opportunities of development towards alternative distribution models, such as home deliveries, particularly suitable for farms located near the urban areas and / or in areas with more efficient roadway systems. considering the differences related to the farm size, the structural and production characteristics and the position within the supply chain, it is possible to highlight how differentiated policy measures, able to respond to specific problems of individual sectors or activities, could produce more durable effects. then, it could be useful to deepen the analysis with a further step. a second survey could be repeated using the same fadn sample to obtain more accurate estimates on the effects of 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(2021), covid-19 and the agri-food system in the united states and canada, in agricultural systems, 188, https://doi.org/10.1016/j.agsy.2020.103039, elsevier bio-based and applied economics bae bio-based and applied economics 12(2): 85-101, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13971 copyright: © 2023 s. coderoni. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: s. coderoni (2023). key policy objectives for european agricultural policies: some reflections on policy coherence and governance issues. bio-based and applied economics 12(2): 85-101. doi: 10.36253/bae-13971 received: november 18, 2022 accepted: february 18, 2023 published: august 05, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author declares no conflict of interest. editor: fabio bartolini, emilia lamonaca. orcid sc: 0000-0001-8751-7376 keynote speech of the 11th aieaa conference key policy objectives for european agricultural policies: some reflections on policy coherence and governance issues silvia coderoni department of bioscience and agro-food and environmental technology, university of teramo, italy e-mail: scoderoni@unite.it abstract. food security and environmental sustainability are global challenges that must be addressed together to be solved. after stressing the importance of solving the challenges of producing enough food to feed a growing population while preserving the climate and the environment, this analysis discusses some issues related to the policy coherence (pc) approach that should be followed. within-policy and between-policies coherence problems are assessed and discussed, and governance problems related to the pc approach are presented. key points for a likely approach to pc include goalbased governance grounded in the analysis of synergies and trade-offs. key words: policy coherence, agricultural policy, environmental sustainability, food security, governance. jel codes: q15, q18, q57, q54. highlights food security and environmental sustainability should be tackled jointly policy coherence is central to achieving food security and environmental challenges within and between coherence policy problems are discussed synergies and trade-offs should be analytically assessed and made explicit a goal-based governance should be deployed 1. introduction the european green deal (european commission, 2019) has confirmed the environmental ambition, stated in 2011 (european commission, 2011), to transform the european union (eu) into a climate-neutral society with no net emissions of greenhouse gases (ghg) by 2050. this ambitious target makes the eu agriculture and forestry sectors pivotal in helping to reach clihttp://creativecommons.org/licenses/by/4.0/legalcode 86 silvia coderoni bio-based and applied economics 12(2): 85-101, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13971 mate neutrality as they are the only economic activities that can naturally store carbon in soil and biomasses, thus helping neutralise ghg emissions that cannot be reduced (european commission, 2021). along this line, the council and the european parliament have recently reached a provisional political agreement on strengthening the contribution of the land use, land use change, and forestry (lulucf) sector to the eu’s increased overall climate ambition.1 as a core part of the eu green deal, the ‘from farm to fork strategy’ (f2f) was released in may 2020 to establish the required legislative framework to meet the challenges of sustainable food systems by reducing the environmental footprint of eu food, recognising that the health of people, societies, and the planet are deeply intertwined (european commission 2020a). the strategy establishes severe environmental targets to be reached by eu agriculture by 2030, coupled with those established by the eu’s biodiversity strategy aimed at putting biodiversity on a path to recovery by 2030 (european commission 2020b). this environmental ambition for agriculture is also found in the 2023–2027 common agricultural policy (cap) objectives (regulation (eu) 2021/2115) as the newly established cap should be, at least in the commission’s view, a key tool for achieving the ambitions of the f2f and biodiversity strategies. however, the likely effects of these environmental commitments on food production and their socio-economic effects on farms and rural territories may be quite negative, raising food security (fs) concerns and highlighting the trade-off between economic and environmental objectives (beckman et al., 2021; barreiro-hurle et al., 2021, 2022; cortignani and coderoni, 2022). fs worries, indeed, have surged to the top of (also) developed countries’ policy agendas, mostly because of the compounded effects of conflicts, the covid-19 pandemic, and climate change, which have set back years of improvement in fs globally (fao, ifad, unicef, wfp, who, 2021). this study, first focuses on whether needing to choose between fs and environmental sustainability (es) is a ‘false dilemma’ (section 2) that may delay the urgent action needed to establish a coherent policy framework that could help in meeting the ambitious challenge of making agriculture and food systems more environmentally sustainable. then, it also reflects on some issues pertaining to the complexity of establish1 https://www.consilium.europa.eu/en/press/press-releases/2022/11/11/ fit-for-55-provisional-agreement-sets-ambitious-carbon-removal-targets-in-the-land-use-land-use-change-and-forestry-sector/ (accessed in november 2022). ing the policy coherence (pc) framework needed to meet this challenge. in particular, it focuses on what are defined here as ‘within-policy coherence’ (within pc) problems, i.e. when public policy efforts are not directed towards the needs of the sector, and ‘between-policies coherence’ (between pc), i.e. when different policy objectives receive different degrees of policy support or even contradict each other. governance problems related to this complex challenge are also presented (section 3). finally, issues related to pc approaches are discussed (section 4), and concluding reflections are presented (section 5). 2. key objectives of european agricultural policy: a false dilemma? providing fs and nutrition for a growing global population and contributing to es while supporting livelihoods for workers along the food supply chain is globally recognized as the threefold challenge facing the agricultural and food sector (oecd, 2021a). this complex challenge also exists at the eu level, where es issues have recently been placed firmly at the core of the policy agenda with the green deal strategy launch. indeed, the eu explicitly declares to be willing to become a global leader in the fight against climate change and environmental degradation, leading by example, setting standards for sustainable global value chains, and using diplomacy, trade, and development cooperation to advance climate action.2 these ambitious commitments have been established through the eu legislative process that, over the years, has increasingly embedded the principles of better regulation (listorti et al., 2020), including the stakeholders’ engagement. in particular, in the context of eu agricultural and rural policies, also to address the concerns related to legitimacy, besides the co-decision mechanism, the eu has strengthened its approach to evidence-informed policymaking (eip)3 and civil society dialogue through a stakeholders’ consultation approach and a system of impact assessment. as regards the stakeholders’ engagement, the public consultation ‘modernizing and simplifying the cap’ has highlighted that society identifies farmers as suppliers of healthy and safe products while also being responsible for protecting the environment and ensuring 2 european commission communication on the 2019 climate action summit hosted by the united nations secretary general in new york, doi:10.2775/171146. 3 eip is an idea in public policy proposing that policy decisions should be based on, or informed by, rigorously established objective evidence (baron, 2018). 87key policy objectives for european agricultural policies: some reflections on policy coherence and governance issues bio-based and applied economics 12(2): 85-101, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13971 animal health and welfare (ecorys, 2017).4 more recently, in the public consultation on the sustainable eu food system initiative, a large majority of respondents (92%) agreed that food production must become more sustainable to meet future environmental and climate change challenges (european commission, 2022a). stakeholders’ involvement is also increasingly used to derive overviews of relevant policy issues (van ginkel et al., 2020) and to set sustainability sciences into research projects (hagemann et al., 2020; menozzi et al., 2017; neßhöver et al., 2013), as it raises the quality and significance of research by contemplating more thorough information inputs (reed, 2008). in a recent analysis of the key policy questions for european agricultural and rural policies, coderoni et al. (2021) used expert sampling to select who could provide the best information to achieve the study objectives, such as people who advocate, supervise, or guide agricultural-policy processes in high-level institutions. the stakeholders’ engagement brought up two major broadly shared indications: i) future agricultural and rural policies should prioritise environmental and climate objectives, and ii) economic and environmental performances of agricultures should be pursued (and thus analysed) jointly. eventually, one key policy objective was commonly agreed upon, i.e. the ‘provision of enough healthy food with minimal impact on the environment and reduced reliance on subsidies, increasing efficiency, climate change adaptation, and resilience.’5 among the proposed post-2020 cap objectives, the environmental ones were deemed by stakeholders to be the most relevant. these findings were not surprising, although they came from a very different range of stakeholders (from policymakers and researchers to local government or farmers’ union representatives), because they were in line with other much wider stakeholders’ consultation (ecorys, 2017; hm government, 2018). surely, the influence of the policy context must be considered, as the interviews were administered between may and june 2020, thus, on the same days the f2f and biodiversity strategies were released, and this might have influenced replies as the attention of the agricultural and food sector was, at the time, completely catalysed by those documents. subsequently, the war in ukraine has raised global attention on fs, but, indeed, this conflict has contributed to exacerbating an already troubling situation, as 4 these findings have also been confirmed in the uk, were the vision of the green brexit – ‘with at its heart profoundly different agricultural policies, which put the environment first’ (hm government, 2018) found general support from stakeholders for replacing the cap system with support to public goods. 5 for details on the results and approach followed please refer to coderoni et al., 2021. in recent years, decades of progress towards improving fs globally have started to be undermined for the combined effects of conflicts, climate change, the covid-19 pandemic, and related economic shocks. the covid-19 pandemic alone contributed to the largest singleyear increase in global hunger since 2000 (fao, ifad, unicef, wfp, who, 2021). concerns around fs are thus firmly back into the policy agenda, even for developed countries. in this regard, also the european commission (ec) has elaborated several shortand mediumterm replies to boost (global) fs and support farmers and consumers with escalating prices, as the conflict in ukraine has not only reduced the supply of key commodities but also further intensified the rise in food and input prices (such as energy and fertilisers). the ec had initially declared that in europe, the availability of food, feed and fertilizers was not a primary concern (for the short term), although there were concerns regarding affordability due to high market prices and inflationary trends (european commission, 2022b). the main problems foreseen were in terms of impact on input (e.g. potash) flows to international markets in the short term due to the sanctions imposed on belarus and russia (jrc, 2022). however, some measures were taken at the eu and national levels to contrast the short-term effects, and the persistence of the war has reinforced the need for political responses. these responses range from the protection of consumers from rising energy prices (sgaravatti et al., 2021), to some derogation to greening obligations by allowing for the production of any crops for food and feed on fallow land that is part of ecological focus areas in 2022 while maintaining the full greening payment (european commission, 2022c). despite the commission’s assertion that this last measure should be aimed at aiding supply chains in becoming more resilient and sustainable, in accordance with the f2f strategy (european commission, 2022d), there is no doubt that such approaches could undermine es objectives (morales et al., 2022). these types of policy responses, hence, have once again brought attention to the ‘eternal’ (not only) agricultural policy dilemma of whether and how it is possible to reconcile the pursuit of fs without undermining es (haniotis, 2021). however, this is now a ‘false dilemma’, and arguing about it does not accomplish anything other than delaying the active response to the great challenge that this joint global issue poses. the ‘real’ question ought to be whether we believe that fs goals can today be achieved without addressing es challenges. there is no doubt that the question might be answered in any way other than ‘no’ after being rephrased in this manner. in fact, there cannot 88 silvia coderoni bio-based and applied economics 12(2): 85-101, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13971 be fs without higher es. first, climate change and biodiversity loss are major (actual and future) threats to fs (and food safety) (un, 2015; pörtner et al., 2021; jarraud et al. 2012; coderoni and pagliacci, 2023; lamonaca et al. 2021; leal filho et al. 2023). thus it is not plausible to hope to tackle fs without tackling es. secondly, many studies demonstrate that higher es levels can help to reach fs6, e.g. showing that less air pollution leads to higher crop yields (lobell et al., 2022), but also that there can be positive synergies between higher productivity and lower ghg emissions (valin et al., 2013; baldoni et al., 2017; coderoni et al., 2015). in other words, food (and energy) security concerns should reinforce efforts towards es and not weaken them. indeed, at least in the eu, the political agenda in the first months after the war in ukraine seemed to be consistent with this conclusion7, and the ec, in its observations on the draft strategic plans (submitted before the war started), required further review to ‘exploit all opportunities to strengthen the eu’s agricultural sector resilience, reduce member states’ dependence on synthetic fertilizers and scale up the production of renewable energy without undermining food production; and transform their production capacity in line with more sustainable production methods’ (european commission, 2022d) however, the policy objective to increase fs while reaching higher es standards is very difficult to achieve as it raises multiple pc and related governance problems, which are discussed in the following sections. 3. policy coherence problems eu policy objectives dealing with fs and es belong to different policy areas sub-ordinate to different authorities with partially contradictory interests. thus, the issues related to the pc and the governance of a policy aimed at reaching both fs and es are far than trivial. to build the best policy mix across all potential policy instruments, pc should consider all relevant synergies and trade-offs across all policy objectives since it is ideal for policies to minimize misalignments at all levels. despite the potential advantages of a coherent policy, achieving it may be highly challenging. sources of policy incoherence can be different. for this analysis, to identify different sources of likely policy 6 see, among many others: ginebra et al. (2022); hawkins et al. (2021); kakraliya et al. (2021); li et al. (2023); nguyen et al. (2022); wang et al. (2021). 7 with the foreseen possibility to increase the renewable energy targets under the ‘fit for 55’ package, and the recently reached provisional political agreement on strengthening the contribution of the lulucf sector to the eu’s climate ambition. incoherence more clearly, it is distinguished among what is defined as a within-pc issue, i.e. when public policy efforts are not focused on what the sector would need (in this case, to achieve fs and es jointly), and between pc, i.e. when distinct fs and es objectives have varying levels of policy support or even directly compete with one another. then, governance problems are analysed with reference to the establishment in the sector of a policycoherent approach. 3.1. within policy coherence issues as regards the issue of within pc, the attention is here mainly on the role of the cap, as it is the oldest and largest budget policy influencing the eu agricultural sector since the european community foundation, although whether cap expenditure brings any significant farmers’ response is still a subject under analysis (esposti, 2022a). the first source of policy incoherence within the cap is that, even if the agricultural and forestry sectors are key to reaching the gd targets, the cap is ultimately not an environmental policy. the cap approach remains, in fact, an exception to the eu environmental policy in some fields, as the polluter pays principle (ppp), which is one of the main eu environmental legislation cornerstones, is not always applied, according to the european court of auditors (2021). this is the case, for example, of diffuse water pollution or ghg emissions, also because the cost recovery principle is difficult to apply to pollution originating from diffuse sources where it is tough to identify the polluter – as is the case for agriculture. in this respect, the ec has replied to eca’s recommendations that it will conduct a study by december 2023 to assess the potential of applying the ppp to ghg emissions from agricultural activities (european commission, 2021). the application of the ppp, however, is not so easy to deliver in the agricultural sector, not only because it is a diffuse pollution source that makes it difficult to identify the polluter, but also because it requires a clear definition of the environmental baseline that separates the ‘polluter pays’ (when this baseline is not respected) from the ‘provider gets’, i.e. when farmers must be compensated if they aid in the preservation of the rural environment and so create public goods desired by society.8 in the cap policy framework, it is assumed that this environmental baseline is given by cross-compliance. however, in the actual setting, the ppp is undermined by the political justification idea that direct payments are, 8 as noted already by the green paper on perspectives for the cap in 1985 (com(85)333). 89key policy objectives for european agricultural policies: some reflections on policy coherence and governance issues bio-based and applied economics 12(2): 85-101, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13971 in part, a recognition of the costs that society requires farms to bear through cross-compliance (matthews, 2013). cross-compliance consists of respect for statutory management requirements (smr)9 and the land’s good agricultural and environmental conditions (gaec)10. while all farmers must respect smr, only farmers receiving cap support must respect eu standards on gaec. thus, it could be argued that the cross-compliance does not constitute the environmental baseline if farmers who do not get direct payments are not expected to adhere to all its standards (namely, the gaec). as suggested by matthews (2013), attention should be thus given to whether this baseline is appropriate or should be revised, considering both the effects on environmental outcomes and the competitiveness of the farms. although the cap is not an environmental policy, environmental concerns within the policy have risen in their relative importance over the subsequent reforms, and indeed the cap has also helped to achieve environmental objectives, mainly through the agroenvironmental policies (aep) it entails (among others: cross-compliance, greening requirements and agroenvironmental measures). however, farmers’ responses to different aep can be highly heterogeneous, as shown by many studies in this field (see among others: arata and sckokai, 2016; bartolini et al., 2021; bertoni et al., 2020; chabé-ferret and subervie, 2013). these studies have generally estimated average treatment effects without exploring individual treatment effect heterogeneity. in this respect, machine learning methods have recently proven to be helpful tools for analysing the impacts of aep (bertoni et al., 2021) that can also help in assessing heterogeneous treatment effects (stetter et al., 2022). supervised causal machine learning techniques have also been used to analyse italian farmers’ responses to distinct aep measures implemented within the cap’s 2015–2020 reform to estimate individual (farm-level) and group treatment effects (coderoni, esposti and varacca, 2021). results show high heterogeneity in farm responses, individually and across different farm subgroups, where geographical features and production specialisation seem to play a major role. detecting this heterogeneity becomes critical for improving policy design since it further stresses the highly advocated need for a more targeted design of the cap (erjavec and erjavec, 2015; ehlers et al., 2021). in fact, targeting specific farm 9 smr are rules on public, animal and plant health, animal welfare, and the environment. 10 gaec standards are designed to prevent soil erosion; maintain soil organic matter and soil structure; maintain permanent grassland; protect biodiversity and ensure the retention of landscape features; protect and manage water. features through policy could aid in reaching the stated environmental objectives more efficiently through expenditure savings (while retaining the same environmental performance) or through better environmental performances (while keeping the same level of expenditure) (esposti, 2022b). the need for a more targeted cap can also refer to the spatial nature of data used for policy design and implementation. space can interfere with the measurement of data used to plan, implement, and monitor the cap in two main ways: one is spatial dependence, that is, the possible correlation of the measures of environmental (and economic) performances across contiguous units; the other is spatial aggregation, that is, how aggregating farm units at some geographical scale affects these measures (baldoni et al., 2023). in fact, the literature has shown that when spatial data are used, spatial dependence cannot be excluded (baldoni and esposti, 2020) and that spatial aggregation (i.e. aggregating farm-level data at some geographical scale) affects the measurements (jansen and stoorvogel, 1998; wade et al., 2019). in particular, working at the macro level may result in wrong evidence if the true effect to be detected is one operating at the micro scale, since some farmlevel determinants disappear through spatial aggregation while other determinants emerge (baldoni and esposti, 2020). from a policy perspective, this means that the scale at which policies are designed and implemented becomes critical to prevent incurring the socalled ecological fallacy (i.e. the reasoning failure that arises when an inference is made about an individual based on aggregate data for a group). this problem has also emerged in italy, where studies on the productivity–environment nexus in agriculture have shown that this nexus is space and scale-dependent: it may disappear and change the direction of the relationship passing from farm-level to aggregate data (baldoni et al., 2023). this evidence represents a further issue for targeting as it highlights that the level (or scale) at which policies are designed, implemented and monitored is very relevant, and a more efficient policy should be targeted to the real needs of the different territories, being grounded on the proper indicators.11 however, more micro-level targeting, which has proven to be more effective in environmental and economic terms, comes at a cost to poli11 however, this does not necessarily imply a higher level of subsidiarity. in fact, the level of subsidiarity should be linked to the to the nature of the environmental problem and should be higher for those environmental problems that are more local in nature, and lower for those that are trans-boundary in nature (e.g. climate change). then, once decided the proper level of eu intervention, the scale at which the policies are applied should depend on the eventual spatial issues characterizing the measurement of the problem. 90 silvia coderoni bio-based and applied economics 12(2): 85-101, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13971 cymakers, both in terms of information requirements and administration (oecd, 2021a). for example, konrad et al. (2014) find that a more targeted approach at the parcel level can achieve reductions in nitrogen runoff at a lower cost, thus lessening the trade-off between economic costs and environmental benefits at the farm level. however, the additional cost of targeting may outweigh the benefit, and policymakers may prefer a less targeted approach, accepting a higher risk of trade-offs (see the next section). in this respect, digitalisation offers plenty of new instruments for sustainability monitoring (ehlers et al., 2021) and thus can help tackle the challenge of targeting and tailoring measures (allowing, among others, accounting for results-based schemes). indeed, abundant data are already available but not fully exploited, as in the case of data from tractors that could be used to leverage more sustainable farm management (mattetti et al., 2022).12 one last source of within-policy incoherence is the choice of the wrong policy instrument. many studies have shown that counterproductive effects can result from the selection of an inappropriate policy tool (oecd; 2021a). to this respect, it is interesting to look at the results of the oecd pc analysis on agricultural policies, which has further confirmed that different types of policy support for the agricultural sector have different environmental implications, with the most detrimental ones typically observed for the coupled support, while decoupled ones deliver income support with minor economic and environmental costs (oecd, 2019a). one last aspect regarding within-pc analysis is worth mentioning here. in fact, it could be argued that the multifunctionality paradigm, which states that most negative (and positive) agricultural externalities are ‘non-commodity outputs’ biologically embedded in agricultural processes (oecd, 2001), makes synergies and trade-offs between economic and environmental aspects and among environmental aspects ‘biologically embedded ’ in agricultural production. in this respect, the oecd has concluded that multifunctionality only becomes a real policy issue when there is a strong link between the commodity and the non-commodity output which cannot be altered and when there is a market failure. even then, more targeted policies (rather than relying on production-linked support) should be preferred (oecd, 2003; 2008; 2021).13 12 of course, other factors, such as the complexity of the of the measurement, data property rights issues, and costs of digitalisation should be considered as they could substantially hamper the uptake of a pc approach. 13 in fact, if the non-commodity output can be delivered disjointedly, separate incentives for the marketable and non-marketable outputs should be provided. otherwise, when a link is found, often it is relaxed 3.2. between policies coherence issues pc is complex to apply in a context in which synergies and trade-offs exist among different policies targeted at different objectives. this is likely to occur in the agricultural sector, which is asked, on the one hand, to produce more food, feed, fibre, and energy, and on the other hand, to become more environmentally sustainable and climate resilient. as an example, looking just at the eu climate policy, within the ‘fit for 55’ package of july 2021, which puts forward the legislative framework to reach 55% net emissions reduction by 2030, the proposals that will have the most influence on the agrifood system are the regulations on including ghg from lulucf, the effort sharing regulation, which covers agriculture, and the eu emission trading system, which has an impact on the entire food supply chain because it will cover not only emissions related to energy use and fertilisers but also emissions related to fuels used in buildings and transportation. additionally, the so-called ‘carbon farming’, i.e. practices to increase the store of carbon in agricultural soils and biomasses, is a key component of the legislative proposal (com(2022) 672) of november 2022 on a union framework for the certification of carbon removals. also, the committee on environment, public health, and food safety have developed initiative procedures focused on methane emissions, and the parliament issued a resolution in october 2021 that emphasised the significance of emissions monitoring and called for the creation of a legal framework with reduction targets. in such a complex framework, between-pc analysis seems to be fundamental. three major challenges arise in achieving between pc: the incurrence of high transaction costs to detect the synergies and trade-offs between policy objectives, the dependence of synergies and trade-offs on the different instruments chosen, and the need to arrive at value judgments on the different interests involved (oecd, 2021a). as regards the first point, attaining coherence across policies can be quite expensive as transaction costs are incurred when coordinating across a wide range of policy areas and, maybe, multiple levels of governance (see section 3.3). the absence of knowledge about all potential connections, which may necessitate significant research and consultation to discover potential interactions, further increases the transaction costs of establishing a pc. in addition, aiming for complete coherence could result in indecisiveness or even decision-making or weakened, e.g. through changes in farming practices (as mentioned previously, many synergies and trade-offs can depend on the chosen tool). thus, separate incentives should be put in place. 91key policy objectives for european agricultural policies: some reflections on policy coherence and governance issues bio-based and applied economics 12(2): 85-101, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13971 paralysis; thus, it might be more practical to aim for ‘good enough’ coherence (vanheukelom et al., 2018) that could be reached by addressing the most significant trade-offs and synergies. the second obstacle to achieving pc is represented by the evidence that trade-offs and synergies between different policy domains depend highly on the policy tools used. as a result, coherence faces an extra hurdle because mapping potential relationships depends on all the instruments used, although this could also represent a positive opportunity because judicious instrument selection can reduce trade-offs and increase synergies. one significant example provided by oecd (2021a) of the importance of the tool chosen is the need to target ghg reduction by means of demandor supplyside tools, e.g. taxes on types of food consumed (such as meat) or ghgs emitted. according to economic theory, in a closed economy, the results in terms of reductions in quantities produced (and consumed) should be the same whether the tax is charged to producers or consumers. however, environmental results can differ. in fact, if the tax is imposed on ghg emitted, then, typically, not only the total ghg but also the emissions intensity of production will fall, as farms will start investing in the less emission-intensive production method (to avoid decreasing production levels). the same result could not be obtained with an undifferentiated tax on specific food products category as it could only decrease the product’s consumption unless the program could be able to differentiate the tax depending on the levels of emissions, e.g. with a carbon label (canavari and coderoni, 2020); this could, however, bring higher transaction costs. thus, even though, in theory, demandside solutions might be utilised to address supply-side issues and vice versa, policies should ideally concentrate on directly addressing externalities as targeted measures have proven to be more effective in reducing the same level of ghg emissions with a smaller decline in production. demand-side policy interventions are, therefore, the most effective ways to address consumer externalities, while supply-side policies should be preferred to address production-related externalities (oecd, 2021a). finally, the third challenge is the choice between two or more desirable – but conflicting – outcomes that may be necessary while designing a policy. this refers to both the trade-off that emerges when a policy is publicly funded, as it entails either raising taxes or cutting spending on other programs, but can also refer to conflicting policy goals that can be pursued with different types of policies (e.g. producing more food or increasing the share of grasslands to provide carbon sinks). these types of choices are based on society’s priorities; thus, decisions cannot be reduced to technical issues but involve value judgments (oecd, 2021a). solving these complex challenges is not a realistic policy objective, whereas trying to manage them more effectively is. to that end, the oecd has proposed guidelines to provide a practical strategy to ensure pc for food and agriculture policy challenges, building on oecd recommendations on pc for sustainable development (oecd, 2019a). these guidelines propose that simplification is the first step to be made. then for the remaining complexity, the strategy aims to systematically test and quantify potential interactions, calibrate the policy mix, and make societal and transboundary tradeoffs explicit to support conscious and open decisionmaking (oecd, 2021a). as regards the first step, i.e. the reduction of complexity, according to a long tradition of economic theory, in principle, policy interventions should be limited to setting the level of playing, i.e. building the framework necessary for markets and communities to operate; correcting market failures (i.e. internalizing externalities and helping to provide public goods) and ensuring fair wealth and opportunities distribution (oecd, 2021a).14 even eliminating complexity, some interaction effects between policies will surely remain, and policymakers need to have the tools to identify the nature and extent of such interactions. this identification stage can be divided into two gradual steps. the first step is the preliminary screening process, which can be facilitated by several techniques, including regulatory impact assessments (rias) and stakeholder consultation processes. in this stage, a broad perspective is needed to identify spillovers since certain interactions may have an impact on present well-being, but it is also crucial to consider potential ‘future’ (i.e. inter-generational) interactions or ‘elsewhere’ (i.e. transboundary) effects (see section 3.3) (oecd, 2016). in the second step, the potential interactions found should be further scrutinized analytically to detect direct and indirect interactions and quantify them whenever possible. this scrutiny may entail simulations, discussions with experts and stakeholders, and analyses of statistical and experimental evidence (see, among others, ronzon and sanjuan, 2020; verghaus and hake, 2018; breure et al., 2022). many interactions can 14 public intervention is just one side of the actions needed to meet the fs and es challenge. businesses can in fact play a major role by at least minimising any adverse impact of their activities. in this respect, many initiatives included in the f2f and derived from the international context can help establish a common framework to help agri-businesses and investors contribute to sustainable development (see among others the oecd-fao guidance for responsible agricultural supply chains; oecd/fao, 2016). 92 silvia coderoni bio-based and applied economics 12(2): 85-101, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13971 be found, but what matters for policy is whether these interactions are significant enough to justify changing existing regulations. in this analytical task, if small or no interactions are found, the best way to proceed is simply to target instruments to the chosen political objective. if interactions arise between policy objectives, the first distinction to make is among synergies and tradeoffs among them. when synergies occur between objectives, it should be kept in mind that ‘silver bullets’ rarely exist, and multiple objectives usually require multiple policy instruments. oecd (2021a) suggests, as a rule of thumb, adopting the ‘tinbergen rule’ (tinbergen, 1952), i.e. using as many instruments as objectives. although one policy instrument has positive effects on different objectives, complementary policy actions are thus usually needed to achieve them fully. however, an interesting aspect to consider and eventually exploit is that the amount of effort required to implement the various targeted policies may be reduced if synergies exist between these objectives. thus, if synergies emerge, the main issue is the ‘calibration’ of the best combination of policy instruments and the extent to which they must be used, considering empirical evidence on their effectiveness compared to other tools.15 to assist policymakers in selecting the proper policy instrument and the extent to which it should be used, models that allow the quantification of synergies are crucial. an example of this type of synergy assessment is given by the studies that have estimated the impacts of the imposition of some of the f2f targets on eu agriculture (see, among others: beckman et al., 2021; barreiro-hurle et al., 2021, 2022; cortignani et al., 2022). among these, cortignani and coderoni (2022) presented an analysis for italy of the likely effects on agricultural added value and environmental externalities of adopting some environmental targets, as envisaged by the european f2f strategy and eu climate law.16 the results show that the imposition of these targets produces evident trade-offs between economic and environmental objectives, although highly differentiated across each scenario by farm type and size, but also reveal important synergies among different aspects of performance in meeting environmental targets that should be further scrutinised to assess 15 for example, to reduce ghg emission it can be used a tax on consumption of some food products (e.g. red meat) or on the total ghg emitted by the farms, but only using the tax on consumption the double goal of reaching higher es and health benefits can be reached. 16 these targets are represented by i) the reduction of 20% in chemical fertilizers use; ii) the reduction of 50% of more hazardous pesticides; iii) the 50% reduction in the expenditure of antimicrobials; iv) the previous three targets together and v) the reduction of 30% of agricultural ghg emissions. whether they could be exploited to obtain multiple environmental outcomes. when trade-offs are found, instead, the first step is to identify possible alternative instruments to use. often trade-offs (and synergies) depend on the choice of an inappropriate policy instrument, and they tend to be more severe when a single instrument is used to achieve multiple policy objectives. for example, a common source of trade-off found in many oecd countries is the benefit provided to farmers by fuel tax concessions (which also occur in italy), or lower vat rates applied to pesticides and fertilisers (oecd, 2020). in this case, it would be preferable to separate the two policy objectives (income support and the es) by targeting income support with a different tool and levying a tax on carbon emissions generated with fuel consumption (thus applying the ppp). if trade-offs still exist after the right policy tool is identified, there is a need for mediation between competing objectives, which inevitably requires value judgments, an approach that runs counter to identification and calibration, which place major emphasis on technical analysis. by allowing participants to reflect on data and arguments as well as their personal views, democratic deliberation could be beneficial (dryzek and list, 2003). however, if foreign parties are not represented, even such a deliberative method might not be able to resolve transboundary spillovers (see section 3.3). to conduct such an analysis of the interactions between policy objectives, it is crucial to invest in reliable systems to acquire the best evidence to inform policymaking. however, as these decisions are never made with perfect information, it is important that the uncertainties concerning the potential synergies and trade-offs of different policy options are made explicit. figure 1 proposes a schematization of the main pc issues analysed. 3.3. governance concerns governance issues related to pc are very complex when the policy objective, as in this case, involves different policy domains that often belong to different decision centres (e.g. different ministries, departments, agencies), different government levels (e.g. the eu, states, regions, municipal/local governments) and thus different governance structures and time horizons (e.g. mediumterm policies like the cap and long-term strategies like the green deal). in europe, policies are designed and implemented by the european multilevel governance (emg) system, through which the eu, its member states, and regional 93key policy objectives for european agricultural policies: some refl ections on policy coherence and governance issues bio-based and applied economics 12(2): 85-101, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13971 and local authorities cooperate at the operational and institutional levels (eu committee of the regions, 2013). being part of this emg system, decision competencies rest at diff erent levels for the agricultural, environmental, and climate sectors (venghaus and hake, 2018). th is distribution of decision competences and means can signifi cantly aff ect policy design in the implementation of fs and es objectives. th e cap is, in fact, primarily a distributive (of funds) policy which is also highly communitarized, while, for example, biodiversity and water policies are regulative (set rules and standards; berkhout et al., 2015) policies and, like energy policy, are left primarily to national implementation. in such a complex governance framework, some level of coordination would be needed between policy communities. to reach pc, this coordination could range from simple collaboration to real forms of ‘policy integration’ (parsons, 2019). starting from parsons’s defi nition of an integrated food policy, an integrated fs and es policy would be represented by the joining up of goals and policies related to relative domains – ‘horizontally across governments, vertically between government levels, or between inside and outside government actors – to better align these eff orts, reduce incoherence between them’ (parson, 2019: 3), and tackle related challenges more eff ectively. pc and integration of the policymaking process are thus not the same concept, and a coordinated approach to policy would require both of them. if pc refers to avoiding confl icts of objectives and results within and between different policy domains, policy integration refers to some form of coordination that can range from simple collaboration on some specifi c themes to complete functional integration by giving only one decision centre (e.g. a ministry) all responsibilities over a policy area. although the latter is the easiest way to help institutions align their objectives and policies, this type of policy integration is neither easy nor costless (oecd, 2019b). in fact, in some cases, complete functional integration cannot be reached (e.g. in the case of diff erent territorial levels involved), or it can bring various degrees of risks: higher integration can confl ict with principles of decentralisation or of subsidiarity17 or can increase coordination eff orts at the expenses of better programming (candel, 2017). th us, ideally, the degree of 17 although in principle more subsidiarity would seem to be desirable to achieve higher pc, as it puts forward a better targeting of goals to specifi c territorial needs, in practice the principle itself could be an obstacle to reach pc for two main reasons. first, environmental and climate objectives are oft en trans-boundary problems, therefore, national or local action alone are unlikely to lead to best possible solutions and a higher eu coordination is needed. secondly, although the best way to achieve pc should be a complete functional integration, this is actually not feasible within the eu governance setting. th us, to apply the subsidiarity principle and attain pc, what is needed is more coordination among the diff erent territorial levels. figure 1. a schematization of the pc issues. source: author’s elaboration on oecd, 2021a. 94 silvia coderoni bio-based and applied economics 12(2): 85-101, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13971 coordination between policy communities should match the intensity of synergies and trade-offs between their respective policy domains (oecd, 2021a). another obstacle to the governance needed to achieve pc is the transboundary spillovers that can characterise agricultural and environmental policies. domestic policies in these fields can have transboundary effects on one or more countries and even global effects. to the extent that interactions are domestic, the costs and benefits of pc are also domestic. in this context, the transaction costs of achieving pc can be better justified, and choices can be made within the country’s decision-making boundaries, simplifying the process of coordinating different policymaking communities and information recovery. when policies have an impact on other countries, instead, pc itself becomes a commonpool resource, with the related problems of under-provision and collective action failures (oecd, 2019a). international stakeholders can rarely advocate their interests in foreign policy-making processes, and the benevolent social planner ‘fails to operate’ at the international level. the case of the global public good given by climate stability offers the most relevant example of such an international case of pc that is tough to tackle. international collaboration should be put forward in this instance, reconciling domestic goals with the advantages of international cooperation (von lampe et al., 2016) as collaborative approaches can lead to meet of global challenges by realising mutual gains (oecd, 2021a). one strategy to cope with complex governance for the pc could be represented by focusing on goals integration, via goal-based governance, by incorporating environmental goals such as climate change mitigation and biodiversity preservation into agricultural policies to ensure that they consider these priorities and then put forward goal-based governance. such an approach, in contrast to ‘rules-based governance’, sees the engagement of a wide range of stakeholders in recognising shared problems and setting broad objectives (kanie et al., 2019). the process allows an early consensus on goals, targets, and subsequent indicator definitions (biermann et al., 2017). after stakeholders are involved, they establish priorities, gather resources, build the institutions needed or modify existing ones, and inspire individuals and institutions to work toward the goals (kanie et al., 2019). this strategy, indeed, builds on insights gained from researching effective management of common-pool resources, including the necessity of defining users, developing inclusive decision-making processes, and creating rules that are adaptable to local needs (ostrom, 1999). at the international level, the sustainable development goals and the paris agreement on climate change seem to have adopted this goal-based approach, with ‘a shift away from international rule-making towards a system based on goal setting’ (kanie et al., 2019: 1745), though only the second is legally binding. at the european level, such an approach seems to be put forward by the framework of the reformed 2023–2027 cap, in which policy-specific objectives are linked to common eu goals for social, environmental, and economic sustainability in agriculture and rural areas (including the f2f ones), and the focus on the results of the policy, with a wide set of indicators to monitor its signs of progress, seem to support – at least on theoretical grounds – the idea of goal-based governance. in fact, each member state is free to select and further design, in its strategic plan, the specific measures it considers the most effective in meeting its own specific needs (european commission, 2020c). although this could result in a lack of harmonization and comparability between member states, as seems to have been the case for the directive on the sustainable use of pesticide (european commission, 2017a; 2020d), in theory, the strategic plans should put forward a more coherent intervention logic, based on specific goals decided with a higher level of subsidiarity and shared with local stakeholders. however, according to lovec et al. (2020)18, the new cap will not probably make much of a difference in terms of overall policy effectiveness and coherence. since the programme logic of the new cap will be like the past rdps, the majority of the shortcomings in the existing planning system19 are likely to continue, and the strategic plans will only assist the commission in more fairly allocating responsibility to member states. in fact, according to the authors, the new cap lacks a robust ex post policy impact assessment framework, as most of the result indicators proposed are output or short-term outcome indicators, and this will hinder the achievement of substantial improvements in policy effectiveness and make trade-offs between objectives explicit20 (lovec et al. 2020). 18 the authors propose an interesting ex-ante analysis of the new cap delivery model. as strategic plans for the new period were not available at the time of the analysis, the authors used data from 2015-2020, a period with similar overall policy objectives, measures, and programming principles of the new legislative proposal, for the country analysed (slovenia). 19 the european court of auditors evaluating the cap 2014-2020 programming period, argued that interventions target too many objectives that were too general (european court of auditors, 2017) and highlighted a weak linkage between the objectives and interventions (european court of auditors, 2018). 20 as demonstrated also by the evaluation of the 2015-2020 rdp system (european court of auditors, 2017). 95key policy objectives for european agricultural policies: some reflections on policy coherence and governance issues bio-based and applied economics 12(2): 85-101, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13971 additionally, policy tools largely remain unchanged, along with current issues of poor targeting which cause poor transparency (swinnen, 2015). thus, although the cap 2023-2027 foresees higher shares of green spending, this does not necessarily imply that environmental objectives will be reached. indeed, also in the past programming period, as stated by the special report by the european court of auditors (2022), although half of all climate spending from the 2014-2020 eu budget related to agriculture, farm emissions had not decreased because of this. furthermore, stakeholder involvement commitments seem to be rather weak. at best, the effectiveness of the new delivery model will rely on the goodness and administrative prowess of governance systems within each member state. however, the issue of administrative capacity will be a substantial challenge for many member states that have little experience on programming of pillar i and also pillar ii measures (lovec et al., 2020). 4. likely policy coherence approaches in several oecd countries, rias are used by policymakers to evaluate pc before developing new regulations (oecd, 2021a). in the eu, the ria is a foundation of the policy-making process as stated by the eu ‘better regulation for better results’ (com(2015)215) and the subsequent ‘better regulation: joining forces to make better laws’ communications (com(2021)219). the guidelines on impact assessment call for a comparison of various policy choices based on their economic, social, and environmental implications, with quantification of impacts, whenever available. in particular, the ria should include the description of those who will be impacted, how they will be impacted, and any potential effects on competitiveness. also, impact analyses must include the consultation procedure adopted (european commission, 2017b). indubitably, this approach to the ria strengthens pc by using an ex ante assessment of potential trade-offs and synergies and enabling a comparison of various policy choices, considering their interaction effects. however, to tackle the ambitious joint target of reaching fs and es, a more proactive role for policymaking should be foreseen that goes beyond the usual requirements of the ria. in fact, when dealing with such a complex policy objective, there is no single policy cycle21 but rather 21 from its origins in the 1950s, the field of policy analysis has considered the policy process as evolving through a sequence of discrete stages, defined as the policy cycle. over the years, several different changes in the stages’ typologies have been developed; today, the distinction several policy cycles involved, and policy objectives may also contradict each other. thus, they require a joint analysis, i.e. a coherence assessment. an approach increasingly used to foster pc is to adopt a more complex multi-stakeholder consultative approach for long-term strategies or policies. an interesting example, in this case, is the ‘collaborative framework for food systems transformation’, established by the one planet network’s sustainable food systems programme, a multi-actor partnership focused on accelerating critical transformation towards sustainable food systems (unep/sfsp, 2019). the framework recognizes that creating pc for complex, interrelated issues requires cross-sectoral, participatory approaches (ilo, 2021) and acknowledges the importance of involving various levels of governance and analysing synergies and trade-offs between outcome goals, recognizing the importance of eip across the whole policy cycle (alliance of bioversity et al., 2021). this coherence assessment could be used to not only appraise new policies but also assess the coherence of established policies. an example, in this case, is represented by the g20 fuel subsidy peer review (oecd, 2018), in which countries conducted self-reviews of domestic fossil fuel supports and submitted these selfreviews to a review team. this process has allowed a within-country appraisal of inefficient policies and a rare coordination and dialogue on pc across countries. also, different levels of policy integration can be utilized to increase coherence; however, as mentioned, integration has a price and does not always guarantee better results (candel, 2017). an ria can be used to assess the transboundary effects of proposed policies to avoid unnecessary costs, e.g. through the assessment of trade impacts and impacts on foreign jurisdictions. thus, policymakers can improve global pc with proper policy processes in their domestic regulatory practice, even without explicit coordination, but just with the consultation of external stakeholders or compulsory notification of draft regulations to international fora (oecd, 2021a), like in the case of the world trade organization technical barriers to trade and sanitary and phytosanitary measures agreements with the single central government authority responsible for notifications (oecd, 2021b). aligning global targets to local contexts should be the norm but can, of course, create challenges in implementation. goal-based governance could help reach such ambitious policy objectives as long as it implies crossbetween agenda-setting, policy formulation, decision-making, implementation, and evaluation is quite commonly accepted (jann and wegrich, 2007). 96 silvia coderoni bio-based and applied economics 12(2): 85-101, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13971 silos interaction, encourages participatory and deliberative methods and adopts ‘backcasting’ approaches. this basically implies setting time-bound concrete quantified goals and targets, designing a viable pathway to achieve them, ‘backcasting’ from the future desired state to the current situation, and measuring progress, gradually adjusting the ambition of targets over time (kanie et al., 2019; sachs, 2015). these pathways should incorporate the key measures, their costs and financing, and the organization of the implementation strategy, e.g. through public and private investments (sachs, 2015), aligning all actors from private to public, with inclusive and adaptive decision-making. in this context, better tools for multi-sectoral scenario planning and modelling could help mapping pathways to achieve multiple goals simultaneously (pascual, 2022). setting goals based on what is needed rather than what is immediately feasible will encourage the necessary levels of creativity to attain them, and this will help exponential progress rather than (as is often assumed) linear progress (kanie et al., 2019). however, if these goals are not shared with the stakeholders, there is a high risk of creating dissatisfaction among some of them and can also limit the application of a pc approach (bruere et al., 2022), as happened in the case of the f2f strategy targets (copa/cogeca, 2021). recent research has highlighted the importance of reaching positive tipping points to speed up the transformation of complex systems (fesenfeld et al., 2022; van ginkel et al., 2020). the socio-technical tipping literature suggests that small-scale changes in a system can move sensitive systems into a qualitatively new state due to strongly self-amplifying (net) positive feedback mechanisms (sharpe and lenton, 2021; fesenfeld et al., 2022). thus, transformative change can occur using leverage points which alter future trajectories (e.g. consumption patterns), and this can help create a climate and biodiversity-resilient development pathway (pascual, 2022; pörtner et al., 2021). currently, there is a lack of knowledge about the politics of enabling such positive tipping points (fesenfeld et al., 2022), but hints can be derived from policy feedback literature that could help overcome barriers that impede reaching tipping points (béland and schlager, 2019). 5. conclusions fs and es challenges are joint global problems and must be addressed jointly to be solved (or to make progress towards their solution), as there cannot be fs without es. even if short-term shocks can point attention to one objective, in the long term, they are indubitably interlinked. policymakers should thus pay attention to how to reconcile short-term (often counterproductive) replies with long-term goals. there is no doubt that implementing such a complex multi-objective policy requires a higher level of pc which, in turn, requires cross-silos, participatory approaches and a backcasting method. pc should be pursued within policy and between policies. to analyse the within pc, the focus here was on the cap as the primary policy for the eu agricultural sector. the main challenges in including environmental objectives in the cap are related to applying the ppp, which also requires defining the appropriate baseline, dealing with the heterogeneity of replies and with spatial problems and choosing the proper policy tool. all these arguments call for better targeting and even tailoring of policies that will surely benefit from new instruments offered by digitalisation. to reach between pc, instead, after simplifying the policy framework, an analytical task should be developed aimed at identifying synergies and trade-offs among policy objectives. where synergies emerge, policymakers should be aware that there are rarely ‘silver bullets’ and that multiple objectives typically call for different policy instruments that should be properly calibrated using a mainly ‘technical’ approach. when trade-offs between competing objectives exist, often the solution is changing the adopted policy instrument. if trade-offs persist, value judgements should be made, making domestic and transboundary trade-offs explicit to support shared and open decision-making. policy integration is often advocated to reach pc; 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(2021). potential benefits of liming to acid soils on climate change mitigation and food security. global change biology, 27, 2807–2821. https://doi.org/10.1111/ gcb.15607 cap, farm to fork and green deal: policy coherence, governance, and future challenges annalisa zezza key policy objectives for european agricultural policies: some reflections on policy coherence and governance issues silvia coderoni towards a new generation of (agri-) food policies gianluca brunori earliness, phenological phases and yield-temperature relationships: evidence from durum wheat in italy marco tappi1,*, federica carucci2, anna gagliardi1, giuseppe gatta1, marcella michela giuliani1, fabio gaetano santeramo1 does the presence of inner areas matter for the registration of new geographical indications? evidence from italy francesco pagliacci, francesco fasano* learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy antonio r. hurtado1,2,*, julio berbel2 bio -based and a ppl ied economics bae bio-based and applied economics 10(4): 325-333, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10801 copyright: © 2021 l. lourenço-gomes, t. gonçalves, j. rebelo. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: l. lourenço-gomes, t. gonçalves, j. rebelo (2021). the distributors’ view on us wine consumer preferences. a discrete choice experiment. bio-based and applied economics 10(4): 325-333. doi: 10.36253/bae-10801 received: april 17, 2021 accepted: november 8, 2021 published: march 31, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: davide menozzi. orcid ll-g: 0000-0001-6509-1394 tg: 0000-0001-7492-1257 jr: 0000-0003-3564-7771 the distributors’ view on us wine consumer preferences. a discrete choice experiment lina lourenço-gomes, tânia gonçalves*, joão rebelo department of economics, sociology and management (desg), centre for transdisciplinary development studies (cetrad), university of trás-os-montes and alto douro (utad), quinta de prados, 5000-801 vila real, portugal *corresponding author. e-mail: tgoncalves@utad.pt abstract. this study explored the view that distributors have towards the most valued wine attributes by consumers in the us market, applying the discrete choice experiments technique. furthermore, to explore the extent to which the distributors’ perspective may reflect consumers’ preferences, the results are analyzed considering previous evidence with consumers in the same market. the results from a scaled multinomial logit, mixed logit and generalized logit models reveal similarities with consumer studies’ findings, especially for the influence of medals/awards, the origin of the wine, grape variety, and price, and it also identifies possible trends in the market. this evidence suggests that data collected using the knowledge and experience of wine distributors generates valuable information through a smaller sample at a lower cost than through applying consumer surveys, which is relevant in large markets with a higher number of consumers. keywords: consumer choice, stated choice method, distributors’ perspective, wine choice. jel codes: c25, d12, d20. 1. introduction consumer behavior has evolved over the years, and understanding the motivations, thought processes, and experiences of individuals as they make a choice is essential to improve marketing strategies and consumer welfare (malter et al., 2020). this statement becomes particularly relevant in wine as it is considered a complex “experience good” (ali & nauges, 2007; mueller et al., 2010) described by several intrinsic (e.g., wine-related, variety, alcohol content, flavor, or style) and/or extrinsic (e.g., price-related, packaging, awards, ratings, and brand) attributes. on the demand side, wine consumption trends are undergoing significant changes (castellini & samoggia, 2018) related to consumer spending habits, purchase power, new choice criteria or expectations (such as health-oriented, environmental-oriented, or based on cultural issues, identity/authenticity), and to the existence of substitute products, like beer and http://creativecommons.org/licenses/by/4.0/legalcode 326 bio-based and applied economics 10(4): 325-333, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10801 lina lourenço-gomes, tânia gonçalves, joão rebelo spirits. this became more relevant in the pandemic crisis (due to covid-19) where consumer patterns will focus more on sustainability issues, which demands the strength of the greening process of the cap (vergamini et al., 2021). accordingly, the wineries behavior, in terms of management decisions regarding technology, products, marketing, and other factors, is framed in a global market characterized by a monopolistic competition structure, where there exists a large number of firms with different characteristics and sizes; restricted control over price-output; with product heterogeneity; asymmetric information; and freedom to enter or exit the market (parenti et al., 2017). despite competition in domestic market, in this industry, firms’ competitiveness is increasingly dependent on the ability to trade at an international level (macedo et al., 2019). both changes in market supply and demand have been appealing for a vertical and horizontal wine differentiation based on unique factors such as grape varieties, terroir, quality, and brand or, at the marketing level and distribution channels. understanding the drivers of wine consumers’ purchasing decisions has been the object of a lively debate. as a highly differentiated product, wine preferences are distinctive and country-specific. in this sense, companies need to know consumer’s preferences for the attributes of wine to establish marketing strategies, which requires data collection and analysis. typically, companies use consumer panels through the application of surveys, which can be expensive (windle & rolfe, 2011), and whose validity depends on the sample size and randomness (mitchell & jolley, 2010). over the last few years, there have been a large number of consumer-oriented studies, including in wine research, particularly those using the technique of discrete choice experiments (dce), a stated preference method, to estimate which attributes are crucial to decision-making by decomposing the good into its attributes or characteristics in light of lancaster’s theory (lancaster, 1966). the use of the dce technique has attracted researchers’ interest as an alternative to more conventional techniques, as it improves the feasibility of valuation studies, and is relevant for research and policy. this method facilitates obtaining information about the most valued wine attributes in the decision-making process, providing information about how consumers value wine based on their intrinsic and extrinsic characteristics, and assessing a price premium or willingness to pay (wtp) for each wine characteristic. empirical evidence provides the wtp measures for different wine cues, such as labeling (e.g., combris et al., 2009; mueller et al., 2010), wine origin (e.g., d’alessandro & pecotich, 2013; kallas et al. 2013), grape variety (e.g., corsi et al. 2012; kallas et al. 2013), awards or medals (e.g., combris et al. 2009; corsi et al. 2012), brand and price (e.g., xu et al. 2014). for marketing purposes, the results of these studies allow wineries to adjust the definition of their wines to the consumer’ profile, gathering the needs of each market and segment. however, to obtain robust consumer knowledge representative samples are required and consider sample selection issues to avoid biased and inconsistent estimators (heckman, 1979). solving these issues requires surveying a large number of consumers with high costs. alternatively, similar information may be collected easily and reliably, by inquiring intermediaries who continuously contact with wine consumers and have knowledge about their preferences and habits. the distributors make an appropriate linkage between the producer and the final consumer based on consumer insights, playing a pivotal role in choosing the product to sell in each specific market. the distributors decide which products to carry, the market segments to reach, and the prices to charge consumers for each product. moreover, as sashi & stern (1995) attested, in some industries (such as producer goods industries), the intermediaries in the distribution channel are agents of product differentiation. after analyzing the sales of australian wines on the british retail market, steiner (2004) found that consumers associate a distribution channel with a specific product quality. in the same sense, pu, sun, and han (2019) state that an increasing number of manufacturers are considering selling differentiated products through different channels as their distribution strategy through quality differentiation. regardless of the question of the distribution channel and its relationship with product differentiation, which has been gaining attention [reviewed by pu et al. (2019)], wine distributors are agents with a deep knowledge of consumer’s preferences and behaviors when purchasing wine. thus, they may act as key players in collecting information for wineries to meet consumer needs, an increasingly complex and challenging demand. this alternative source of information has the advantage of obtaining data through smaller samples of the target markets. therefore, supported by the dce theoretical background, the goal of this paper is to test whether the distributors’ data may be an alternative source of information to convey consumers’ preferences and trends in the target market. specifically, this article explores wine distributors’ perceptions about the most valued wine attributes by consumers using the dce technique. this information is obtained by administering a survey on wine distributors in the american market (usa), positioned as the world’s largest consumer in 2018 (oiv, 2019), but whose background and related 327the distributors’ view on us wine consumer preferences. a discrete choice experiment bio-based and applied economics 10(4): 325-333, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10801 studies about wine consumers’ preferences are few. as far as we know, this approach has not been conducted before, constituting an innovative research topic capable of promoting helpful knowledge to wineries and wine distributors. the paper is organized as follows. section 2 presents the methods comprising the study design, sample, and the methodology employed. section 3 includes the results and discusses previous evidence on consumer preferences/choices in the us market. the conclusions of this study are presented in section 4. 2. methods 2.1. data an online survey comprised of four sections (general characterization of the distributor; ranking of wine characteristics importance; wine valuation scenarios (10 choice sets); business characterization of the distributor) was distributed by a specialized external firm, the nielsen consulting company, through distributors that operate in the us market to collect information about the attributes and values in the consumers choice. from the 1109 distributors for us market (bestwineimporters. com in october 2019), a total of 92 valid questionnaires multiplied by the 10 choice sets provides a dce sample size of 920 observations. as to the characterization of the data sample (table 1), the distributors have been on the wine market for 18 years, on average. red wine is the most important category in terms of market share of wine sales (on average, 53%). for 50% of the distributors, white wine represents up to 25% of wine sales, rosé represents up to 6%, and sparkling wine represents up to 5%. the specialist retailer is the most relevant distribution channel in terms of share of wine sales, followed by the on-trade channel, hypermarkets/supermarkets, and small grocers. moreover, 62% sell to hypermarkets/supermarkets, and 59.8% to small grocery stores. the wine sales represent the most crucial portion of the distributors’ total sales (84%, on average). on average, online sales represent near 7% of the total distributors’ business. nevertheless, for 66% of the distributors, the average share of online sales is zero. when asking distributors to identify the three most important attributes in the market they serve, the price attribute leads the ranking, followed by other relevant attributes, such as the expert ratings, grape variety, and country of origin (figure 1). 2.2. choice experiment the choice experiment used in this research includes six attributes (see table 2), representing highly influential cues for wine choice. medals/awards: consumers perceive this attribute as an important sign of quality when choosing a wine (corsi et al., 2012; lockshin, jarvis, d´hauteville, & perrouty, 2006). a gold medal with a “gold medal winner” description written in the middle was included. alcohol level: the growing concern about the effects of overconsumption of alcohol explains the inclusion of this attribute, characterized by three different levels: low (12% vol), medium (13.5% vol), and high (15% vol) alcohol wines. origin: wine origin is well documented as one of the most important cues for wine choice (e.g., kallas et al., 2013). six levels describe this attribute at the country level: countries with a long history and tradition in wine production – italy (54.8 mhl), france (48.6 mhl), and portugal (6.1 mhl) – being in the top 5 in european production (oiv, 2019) and wines from the new producing countries – usa (23.9 mhl), australia (12.9 mhl), and table 1. distributor’s business characterization. mean median years in the market 18 15 market share white 0.25 0.25 red 0.53 0.50 rosé 0.93 0.65 sparkling 0.73 0.50 others 0.30 0.00 presence in market channels hyper and supermarkets 0.62 small grocers 0.60 specialist retailers 0.92 on-trade 0.94 online 0.34 share of sales in each channel hyper and supermarkets 0.30 small grocers 0.17 specialist retailers 0.44 on-trade 0.39 online 0.07 share of wine sales in the total sales 0.84 0.98 less than 50% 0.14 50 75% 0.123 76% or more, less than 100% 0.25 100% 0.49 328 bio-based and applied economics 10(4): 325-333, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10801 lina lourenço-gomes, tânia gonçalves, joão rebelo chile (12.9 mhl) – also being in the top 5 in the new world production (oiv, 2019) accounting for the changes in the international wine market. grape variety: this factor is a choice driver, especially for the new world wines (corsi et al., 2012; kallas et al., 2013). regarding consumers’ preferences for wine varieties, in 2018, the best-selling wine varietals in the us market based on volume included chardonnay, cabernet sauvignon, and red blends (nielsen, 2019). therefore, two well-known red varieties were selected (cabernet sauvignon and syrah) and a red blend. closure: this packaging trait may function as a signal of expected quality (bekkerman & brester, 2019). two bottle closure types, screw cap, and cork closure are the most common closures in the wine market. the screw cap closure and the cork closure covered with a capsule were realistically presented in the survey. price: it is one of the primary drivers of choice and is commonly used as an indicator of quality (e.g., lockshin et al., 2006; corsi et al., 2012). four price levels were included between the range of $8.99 and $24.99. the choice of price levels was based on the actual price range of red wine in the off-channel in the us market. a d-efficient design with no priors was obtained using the ngene software. the attributes’ levels were combined into alternative wines and arranged in 10 sequential choice sets1. each choice set was formed by three alternative wines plus a none-option, as displayed in figure 2. distributors were asked to select their preferred option or bottle of wine that fits better the market they serve in terms of the consumers’ preferences, 1 the number of choice sets s was selected based on the equation: s≥k/ (j-1), where k= #parameters including constant; j=#alternatives (ngene v1.2.1 software, choicemetrics, 2018). 0 10 20 30 40 50 60 70 80 coun try of o rig in reg ion of o r ig in alco hol c onten t grap e med als exp ert appella tio n wine c ate go ry pub lic recog nitio n vintag e vines age cert i fie d lan dsc ap e produ cer siz e produ cer ty pe lab el s tyl e lab el c olor bottle sh ap e color scr ew ca p cork clo usur e orga nic biody na mic pric e responses 1st 2nd 3rd figure 1. three most attractive wine attributes in the market in which the distributor operates. table 2. attributes and levels used in the choice experiment. attributes medals/ awards alcohol level origin grape variety closure price levels yes no+ 12% vol. 13.5% vol. 15% vol. france italy portugal usa australia+ chile cabernet sauvignon syrah red blend no information+ cork screw cap+ $8.99 $12.99 $17.99 $24.99 + reference level on dummy coding. 329the distributors’ view on us wine consumer preferences. a discrete choice experiment bio-based and applied economics 10(4): 325-333, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10801 according to the question: “imagine you have three different types of wine. which of the following wines do you find as the most successful in serving wine consumers in your market?”. 2.3. discrete choice model the method of discrete choice experiments has its roots in the lancaster (1966) model of consumer behavior, which defines a good in terms of its characteristics, and on the random utility theory (mcfadden, 1974), where an individual is a rational decision-maker aiming to maximize her or his utility. respondent n (n=1, …, n) chooses among different j alternatives in t choice situations. a random utility expression represents each alternative j, according to the following equation: unjt = β’xnjt + εnjt (1) xnjt is the vector of explanatory variables and includes product attributes and respondents’ characteristics, εnjt is the random component. the alternative that gives the highest utility is chosen, such that pnj = prob(β’xnj + εnj > β’xnk + εnk) ∀ j≠k∈c, where c is the choice set of j alternatives, j=1, …, j. in the present application, the utility associated with a particular set of alternatives j can be derived as follows: ujn = βmedals * medalsj + βalcohol * alcoholj + βfrance * francej + βitaly * italyj + βportugal * portugalj + βusa * usaj + βchile * chilej + βcabernet * cabernetj + βsyrah * syrahj + βblend * blendj + βclosure * closurej + βprice * pricej + εn (2) in the mixed logit (mixl) model (train, 2009), also known as the random parameters logit model, the parameters are assumed to vary from one individual to another, such that: βn = β + ∆zn + γun (3) in which β, ∆, γ are parameters to be estimated, γ is the lower triangular cholesky matrix, zn a set of characteristics of individual n, un is a vector of random components, capturing non-observable effects, and β + ∆zn stands for heterogeneity in the mean of the distribution of the random parameters. the choice probabilities from the model are: (4) omitting the observed heterogeneity captured in ∆zn, by convenience, the generalized mixed logit model (gmxl) includes scale heterogeneity across respondents through random alternative-specific constants (fiebig, keane, louviere, & wasi, 2010; greene & hensher, 2010). consequently: βn = σn β + [γ + σn (1-γ)]γun (5) where σn = exp( + τwn) is the individual specific standard deviation of the idiosyncratic error term, τ captures the unobserved scale heterogeneity, and wn captures unobserved heterogeneity. the mean parameter in the variance, , is not identified independently from τ, such that σn is normalized to 1 by setting = -τ2⁄2. γ is a weighting parameter, bounded between 0 and 1, controlling how the variance in residual preference heterogeneity varies with scale. if γ = 0, the gmxl model reverts to the scaled mixed logit model (greene & hensher, 2010), βn = σn[β + γun]; when σn (τ = 0), the gmxl reverts to mixl; and when var (un) = 0 it reverts to the scaled multinomial logit model (smnl). 3. results and discussion table 3 presents the smnl, mixl and gmxl model results, using maximum simulated likelihood methods with 500 halton draws in nlogit 6. following greene, hensher, and rose (2006) and kragt (2013), a constrained triangular distribution was used for the random figure 2. example of a choice set. 330 bio-based and applied economics 10(4): 325-333, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10801 lina lourenço-gomes, tânia gonçalves, joão rebelo price parameter, and a normal distribution was defined for the other attributes (kragt, 2013). the scale heterogeneity parameter (τ) was equal to 0.821 and highly significant, indicating the presence of substantial scale heterogeneity, such that respondents varied in terms of certainty/consistency in their choices. results show that accounting for taste heterogeneity by introducing random parameters provides a better fit than smnl, with gmxl achieving best performance indicators. the majority of standard deviations for the random parameters are significant, showing taste differences across wine consumers in the perspective of wine distributors, which suggests individual preference heterogeneity. however, while the results from mixl show preference homogeneity for a red blend wine and american origin, gmxl reveals that preferences are homogeneous for us, chilean, and awarded wines and contradicts mixl revealing heterogeneity in preferences for red blend wines. the coefficients on cabernet sauvignon and chilean origin become insignificant when introducing random coefficients in the mixl. nevertheless, gmxl suggests that these attributes affect wine choice. both mixl and gmxl suggest the relevance of cork closure. the results show the importance of medals/awards, wine origin, grape variety, closure, and price. in particular, the present study shows that french origin and blended wines are significant and positive drivers for distributors’ choice, while australian origin has the opposite effect. these findings support a dce’s outcomes on wine consumers’ preferences (gonçalves et al., 2020) which also found a positive impact of an awarded wine and the negative influence of price and australian wines on consumers’ choice. moreover, the coefficient on closure is statistically significant, suggesting that this attribute (cork closure compared to screw cap) positively affects the utility of choosing a wine when introducing table 3. results from smnl, mixl and gmxl models. attributes smnl mixl gmxl mean mean sd mean sd medals 0.906*** (0.196) 0.997*** 0.691*** 1.200*** 0.462 alcohol -0.014 (0.039) -0.014 0.147*** 0.031 0.151*** country of origin france 1.497*** (0.409) 1.310*** 0.633*** 2.069*** 0.391* italy 1.232*** (0.412) 0.672** 0.742*** 1.192** 0.763** portugal -0.940*** (0.334) 0.784*** 0.794*** 1.165** 1.347*** usa 0.958** (0.389) 0.699** 0.069 1.315** 0.408 chile 0.982** (0.434) 0.499 0.753** 1.350** 0.225 grape variety cabernet sauvignon 0.284* (0.162) 0.137 0.987*** 0.482* 1.137*** syrah -0.062 (0.211) 0.013 0.785*** 0.263 1.182*** red blend 0.598* (0.313) 0.592** 0.010 1.012** 0.947** closure 0.176 (0.114) 0.420*** 0.801*** 0.580*** 0.879*** price -0.057*** (0.013) -0.079*** 0.079*** -0.095*** 0.029** asc1 -0.434 (0.509) -1.186** -0.136 variance parameter in scale (τ) 0.821*** 0.730*** weighting parameter (γ) 0.064 sigma: sample mean 0.985 0.933 sample standard deviation 0.895 0.670 log-likelihood -1110.9 -1008.2 -997.0 aic 2249.9 2064.3 2048.0 bic 2317.1 2179.6 2173.0 mcfadden pseudo-r2 0.11 0.20 0.20 observations 920 920 920 standard errors in parenthesis; sd = standard deviation; ***, **, * significance at 1%, 5%, 10% level, respectively. 1 alternative specific constant – included for the none-option and it represents the respondent n’s preference towards the opt-out choice compared to the three alternatives included in our experiment. 331the distributors’ view on us wine consumer preferences. a discrete choice experiment bio-based and applied economics 10(4): 325-333, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10801 random coefficients. this finding is in line with kelley et al. (2015) results, which found that consumers are more willing to increase purchases if bottles have cork closures using the conjoint analysis technique. additionally, wine distributors perceive red blend varieties as a relevant attribute for consumers’ choice. regarding willingness to pay measures, presented in table 4, the results from distributors’ perspective suggest the highest price premium for french origin (from $20.82 to $26.04 among models), followed by medals (from $15.76 to $17.33). there is also a positive price premium for the other origins compared to the australian one. the results also reveal the importance of red blend variety, with a premium ranging between $8.25 and $10.78, and cork closure compared to screw cap (from $6.10 to $7.57 among models). summing up, despite being a data source from distributors, the results are in line with those obtained from consumers in the same market, using either the same/ similar methodology (gonçalves et al., 2020; kelley et al., 2015) or with different methodologies (chrysochou et al., 2012; lockshin et al., 2015; pomarici et al., 2017; thach et al., 2020). among these, chrysochou et al. (2012) show the importance of grape variety using the best-worst scaling (bws) approach. this result was later confirmed by lockshin et al. (2015) and pomarici et al. (2017) using the same method. these scholars also reveal the importance of the origin of the wine (lockshin et al., 2015; pomarici et al., 2017), price (pomarici et al., 2017), and medals/awards (lockshin et al., 2015). additionally, in line with the present study, thach et al. (2020) also reported the relevance of blended wines, which might reflect a recent trend among american wine drinkers towards red blends instead of monovarietal. 4. conclusion this study employs a dce in the us market, to assess the perspective of wine distributors regarding consumers’ preferences. it explores whether the perception of a market distributor, who knows the market well, may reflect the evidence suggested by consumers’ preferences studies for wine. this study supports the importance of attributes such as price, medals, country of origin, and grape variety. as first highlighted in the previous questions of scoring an extensive list of attributes and identifying the three most attractive attributes, the alcohol content is not a significant attribute in choosing one bottle of wine over another. when faced with trade-offs between only six attributes, the closure attribute is relevant, suggesting a market trend favoring cork stoppers over screwcaps. we believe that bottle closures may influence the consumers’ perception of the quality of a wine and consequently how much they are willing to pay for the product. a recent study (bekkerman and brester, 2019) found that, on average, us consumers are willing to pay more for wines with cork closures rather than screw caps. the same study also found that this premium increases for lower-priced wines and decreases for more expensive wines, suggesting that the bottle’s closure has an enormous impact on the perceived quality of the wine. results from this study reinforce that both price and medals are well-known wine cues for choice in the analyzed market (both in consumer and distributors’ views). the red blend is a positive and significant choice driver for wine in the view of distributors, which suggests red blends as an opportunity in the us wine market. there are important implications based on this study. first, it reflects the view of distributors, who are important players in the wine value chain, about the most valued attributes in the us market, which is relevant for wineries to adapt their supply. second, this study suggests that distributors know consumer’s preferences in the respective market, potentially foreseeing emerging trends. hence, the distributors can provide robust information on wine consumers’ preferences and behaviors, representing a potential alternative to directly obtaining this information from the consumers. as usual, this research is not free of drawbacks. first, to reach this specific target of respondents, an table 4. willingness to pay estimates1, in us$. smnl mixl gmxl medals 15.76*** 17.33*** 15.92*** alcohol -0.25 -0.33 0.18 country of origin france 26.04*** 22.32*** 20.82*** italy 21.42*** 11.94** 12.84*** portugal 16.34*** 13.38*** 11.85*** usa 16.66** 12.69** 10.89*** chile 17.07** 8.34 8.093** grape variety cabernet sauvignon 4.93* 1.41 3.51*** syrah -1.07 -0.08 -0.11 red blend 10.39** 10.78** 8.25*** closure 3.06 7.57*** 6.10*** ***, **, * significance at 1%, 5%, 10% level, respectively. 1 wtp values for smnl were estimated as wtp = -(βk / βprice), while for the mixl the wtp were calculated based on unconditional estimates. in the case of gmxl, the model was re-parameterized in “wtp space” to directly produce the wtp estimates. 332 bio-based and applied economics 10(4): 325-333, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10801 lina lourenço-gomes, tânia gonçalves, joão rebelo external consulting company was contacted to distribute the survey. this action has costs, and it was possible because this research was funded. second, as it is common in similar studies, there is no certainty that all relevant attributes are included in the survey, so the results may not fully capture the market preferences. additionally, the comparison with results from other consumer studies only indicates preference matching since the survey design, technique, and analysis period are not synchronized. thus, future research should compare data from these different sources (distributors and consumers) using the same technique and period to obtain more solid conclusions. additionally, inquiring about this specific target (distributors) with market knowledge and experience may also benefit from a more qualitative study to investigate, for example, barriers and drivers of wine placement. funding this work is supported by the project vinci – wine, innovation and international competitiveness, under the operation number soe3/p2/f0917, feder – interreg sudoe, and national funds through the fct – portuguese foundation for science and technology under the project uidb/soc/04011/2020. references ali, h. h., & nauges, c. 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(2014). willingness to pay for red wines in china. journal of wine research, 25(4), 265–280. volume 10, issue 4 2021 firenze university press the effect of attribute framing on consumers’ attitudes and intentions toward food: a meta-analysis irina dolgopolova, bingqing li, helena pirhonen, jutta roosen* rural areas between locality and global networks. local development mechanisms and the role of policies empowering rural actors francesco mantino understanding the bioeconomy: a new sustainability economy in british and european public discourse irene sotiropoulou1, pauline deutz2 sustainable water resources management under population growth and agricultural development in the kheirabad river basin, iran ghasem layani1,*, mohammad bakhshoodeh2, mansour zibaei2, davide viaggi3 the distributors’ view on us wine consumer preferences. a discrete choice experiment lina lourenço-gomes, tânia gonçalves*, joão rebelo bio-based and applied economics 9(2): 201-221, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8242 exploring governance mechanisms, collaborative processes and main challenges in short food supply chains: the case of turkey yaprak kurtsal*, emel karakaya ayalp, davide viaggi university of bologna (italy) abstract. there is a growing scientific interest and public debate on the potential contributions that local food systems (lfs) and short food supply chains (sfscs) can make towards overcoming sustainability challenges and creating societal impact. in the case of turkey, where local agricultural systems are particularly vulnerable, lacking of resilience and innovative capacity, understanding the governance mechanisms of sfscs would have strong implications for policy making. to this end, our aim in this study is to explore the mechanisms through which civil society driven sfscs are governed in the city of izmir (turkey), referring to the actors involved in the process, institutional frameworks that are adopted and challenges experienced, that could inform policy discussions towards establishing more sustainable local food systems. in this direction, the questions we aim to answer are: (1) what are the mechanisms through which community level sfscs are initiated and governed, (2) how collaboration takes place within these groups and through which processes, and finally (3) what the outcomes of these processes are, with respect to individual, community and local impacts experienced on the ground, and challenges associated with them. we use a descriptive case study methodology, to study seven sfsc initiatives (four food community networks, two farmers’ markets and a local shop) in the city of izmir; and collect data through qualitative semi-structured in-depth interviews (41 with producers, 32 with consumers, 11 with coordinators and 5 with experts). our findings suggest that different mechanisms are at play within our cases, depending on aspects including their governing structures and their way and purpose of operation. while farmers had difficulties relying solely on their income from these initiatives for their livelihoods, organizational challenges experienced by food communities were mainly related to difficulties associated to managing tasks on a voluntary basis. moreover, arriving at a shared understanding about mutual goals, in addition to finding a way to include stakeholders in the process, were among the most prevalent challenges of all initiatives. keywords. short food supply chains, local food systems, collaborative governance, alternative food networks, governance challenges. jel codes. q13, q12, d71, r12. *corresponding author. e-mail: yaprak.kurtsal2@unibo.it editor: severino romano. 202 yaprak kurtsal, emel karakaya ayalp, davide viaggi 1. introduction over the last few decades, a wide range of grassroots movements have been gaining momentum around the globe, through a range of collective practices that are organized around the idea of promoting more socially just, culturally appropriate, environmentally conscious and healthier ways of food provisioning for communities (kirwan et al., 2013; renting et al., 2003). these movements aimed at empowering consumers to shift to proactive actors, and rural producers to become autonomous providers of sustainable goods and services (matacena, 2016; lamine et al., 2012). these food networks have been analyzed widely in the literature, not only with regards to their capacity to create societal change (seyfang and smith, 2007), but also because they growingly claim new roles in governance mechanisms, through mobilizing new forms of relationships, values, knowledge and skills (dominguez garcia et al., 2017; knickel et al., 2009). in this regard, the concepts of “food democracy”, “food citizenship” and “civic food networks (cfns)” are increasingly being used in public discourse, drawing attention to the role of local actors, citizens and civil society in shaping the new governance mechanisms in the food system (andrée et al., 2019). seyfang and smith, (2007) introduced the term grassroots innovations to describe networks of activists and organizations generating novel bottom-up solutions that involve people at the community level experimenting with social innovations and the capacity to build resilience at a community level. in this study, our aim is to explore the mechanisms through which civil society driven sfscs are governed in the city of izmir (turkey), referring to the actors involved in the process, institutional frameworks that are adopted and challenges experienced, that could inform policy discussions towards establishing more sustainable local food systems. in this respect, we intend to answer the following questions: (1) what are the mechanisms through which community level sfscs are initiated and operated, (2) how collaboration takes place within these groups and through which processes, and finally (3) what are the outcomes of these processes, in terms of individual, community and local impacts experienced on the ground, and challenges associated with them. numerous studies seek to explore the governance mechanisms of food networks and movements, especially in the european context. andrée et al. (2019) examine a food movement that is led by the partnership of civil society organizations (csos) and local governments, focusing on building relationships, trust, and shared values. renting and wiskerke (2010), that study emerging roles of public institutions and civil society in lfs, argue that currently we are lacking an adequate conceptual framework to think through the implications of governance issues. manganelli et al. (2019) identify the main governance challenges experienced by sfscs, including pressures in management, access to resources and creation of supportive institutional spaces. galli et al. (2014) discuss the cultural, organizational and institutional changes needed in the scope of sfscs. we see that a recurring theme that is common in this line of thought and related theories is collaboration and how relations and networks are shaped around these. in this regard, some studies propose collaborative governance, for dealing with complex problems, without readily available solutions (andrews and entwistle, 2010; emerson et al., 2012). brink and wamsler (2018) make use of collaborative governance to conceptualize how shared learning can filter back into participating organizations in addressing climate risk. other studies, discuss the collaborative 203exploring governance mechanisms governance processes and their implications from a perspective of local food banks (meads, 2017), food policy councils (koski et al., 2018; siddiki et al., 2015), small holder agriculture and its connection to sustainable development goals (sdgs) (florini and pauli, 2018), school meal services (galli et al., 2014), and local food action planning (andrée et al., 2019). however, the processes and mechanisms through which local food network actors get collectively organized and govern these systems, especially through collaborative governance structures, are not studied widely. this calls for a need to understand the existing place-based structures, their organization, the facilitating circumstances or challenges, and consider the role of different governance mechanisms that allow such networks to function (lamine et al., 2012). moreover, to the best of our knowledge, studies that seek to explore the governance mechanisms and main challenges and outcomes related to sfscs in turkey are very rare. for this reason, the experience of such networks and their associated organizational and operational dynamics are unknown. this study contributes to the literature on governance mechanisms of sfscs through the lens of collaboration, which can shed light on how social innovation practices within lfs can be enhanced and provide important implications for policy making especially on the local level. it can also shed light to the understanding of an emerging country context, where a new and novel local food movement is being shaped, and where local agricultural systems are being criticized in particular for the lack of innovative capacity and for the poor knowledge base, in the face of sustainability challenges. in this context, the city of izmir provides many opportunities as a city-level case, as it is the leading city in organic agricultural production in turkey (vatansever, 2017) and is the rising city of alternative food initiatives, providing a diversity of alternative agro-food practices (ozatagan and karakaya ayalp, 2018). in addition, izmir, being surrounded by rural areas where agricultural production is persevering, also gives an opportunity to observe the re-organization of urban-rural relations through which sfscs can flourish. izmir is also attractive for urban-rooted producers (producers coming from urban families but who started pursuing agricultural production later in life) (karakaya, 2016), who migrate from other metropolitan cities to izmir, with a dream to engage with agricultural production and start a new life, which in other cities could not be clearly observed. in izmir it is also possible to see a sufficient number of civil society-led initiatives that allows us to identify and understand the governance mechanisms that are at play. this paper is structured as follows. after providing the conceptual framework that we utilize in this study in section 2, we present our cases and methodology in section 3, and findings in section 4. lastly, we discuss our findings in section 5 and present our conclusions in section 6. 2. a multi-perspective collaborative governance framework for short food supply chains in this study, we make use of emerson and nabatchi (2015)’s integrative collaborative governance framework (icgf) (see figure 1), while we integrate a range of studies to further propose an adapted version of the framework (see figure 2) (ansell and gash, 2007; pascucci et al., 2016; manganelli et al., 2019; barbazza and tello, 2014). according to emerson and nabatchi (2015), within a collaborative governance regime (cgr), 204 yaprak kurtsal, emel karakaya ayalp, davide viaggi “collaborative dynamics” consisting of “principled engagement”, “shared motivation”, and “capacity for joint action” work together to result in actors to initiate collaborative actions to reach their collaborative goals. together, collaborative dynamics and actions shape the overall quality and the extent to which a cgr is effective”. actions, then lead to outcomes, which in turn through an adaptation process, feed back into the cgr and the system context. in the framework, departing from krasner (1983)’s definition, cgr is conceptualized as the “sets of implicit and explicit principles, rules, norms, and decision-making procedures around which actors’ expectations converge in a given area”. below, we present in figure 2, a multi-perspective collaborative governance framework to assess the governance mechanisms of sfscs through which collaboration actions take place. departing from icgf, the adapted framework aims to assess the motivations of actors to bring their forces together to organize sfsc initiatives, in which collaborative processes take place. within these collaborative processes, collaborative actions are taken, including building of partnerships, cooperation and information and experience sharing (emerson and nabatchi, 2015; barbazza and tello, 2014). the extent to which these occur is influenced by factors such as shared understanding, trust building, face-to-face dialogue and commitment to process (ansell and gash, 2007), as well as the institutional frameworks that shape the initiatives (rules, procedural arrangements, norms, inclusion criteria) (emerson and nabatchi, 2015). these processes then lead to governance tensions, and individual, community and local impacts within the groups. the governance tensions arise figure 1. integrated framework for collaborative governance regime. reference: emerson and nabatchi, 2015 205exploring governance mechanisms in the form of organizational, resource and institutional tensions, as these newly emerging initiatives grow, scale-up and out (manganelli et al., 2019). this iterative cycle is then completed, as these outcomes lead to an adaptation process, through feedback and learning. in this study, we do not make connections between the governance regime (where collaboration dynamics take place) and the system context that shape this regime, the reasons of which are discussed in the discussion section. 3. data and research methodology 3.1 methodology our study follows a descriptive multiple case study approach based on the framework explained above. we studied seven cases to examine the governance mechanisms, challenges, and collaboration processes and outcomes of sfscs in turkey. each case was examined independently and then a cross-case analysis was made between cases. the case study method is recommended when realities and dynamics of a phenomenon is not clearly explored beforehand (hollweck, 2016). we believe this methodology could help us unravel the dynamics of sfscs and outcomes associated with their performance in turkey, which are almost completely unknown. our aim was hence to maximize information richness and comparability, rather than to generalize statistically to a broader population of cases (hollweck, 2016). an additional reason for preferring a multiple case study analyfigure 2. a multi-perspective collaborative governance framework for sfscs. reference: elaboration of the authors 206 yaprak kurtsal, emel karakaya ayalp, davide viaggi sis was to collect as much information as possible from a variety of actors and groups to identify mechanisms at play. 3.2 selection of the cases a preliminary field research has been conducted to have an initial set of in-depth interviews with experts, academicians and local government representatives, followed by informal initial contacts with network coordinators to understand the ecosystem of sfscs in izmir. thus, we have selected our seven cases with special emphasis on how and through which support mechanisms they are initiated, their governance structure, their development processes, organization capacity, innovation capacity and stakeholder variety. in this regard, it was important to include only the cases that have been operational for at least two years, as cases that have been initiated more recently did not have stabilized institutional mechanisms and participant profiles in place. hence, our selected cases fall under the categories of: (1) a local shop (doğa’s shop), (2) farmers’ markets (foça earth market and ecobazaar1) and (3) food community networks (aegean university environment and human friendly agriculture group, west izmir community supported agriculture group (bitot), gediz ecology collective (geto) and homeros food collective). in this study, we use the term food community networks (fcns) introduced by pascucci (2010) to define a governance structure where consumers and producers strongly integrate their functions by organizing a “club”, in which resources, decisions, and responsibilities are shared among participants, towards more sustainable, just, and resilient food systems. hence, we use this term to refer to the four food community cases that we study in this research, which are organized by consumers, where individuals engage in common actions, such as co-producing and distributing food products, or sharing resources or risks, in order to produce and have access to ecological food products (pascucci, 2010). table 1 provides a summary of each case. 3.3 data collection the qualitative semi-structured in-depth interviews were the central method used to collect the primary data for this research. we collected qualitative data from 41 producers, 32 consumers, 11 coordinators and 5 experts. different questionnaires were used for each stakeholder group. the interviews directed at coordinators aimed to understand when, how and why the initiative is established, which stages it has gone through, the profile of consumers and producers, aspects regarding the institutional framework, how activities are organized and managed and the main challenges and needs of the groups. the interviews with producers questioned their motivations for being part of these networks, their selling channels, livelihood aspects, their main challenges and needs, and aspects regarding their sharing and learning behavior. consumers, on the other hand, were asked what 1 although we referred to ecobazaar as a farmers’ market, it should be noted that it is not obligatory to be a farmer selling their own products in order to be able to sell at ecobazaar. sellers of intermediary products are also permitted, as long as these products have an organic certificate. 207exploring governance mechanisms their main motivations for being part of these networks were and to what extent they are involved in the operation of the group. we first contacted the coordinator of each case and decided on the meeting days that could provide the presence of the highest number of producers and consumers. following semi-structured in-depth interviews conducted with 11 coordinators, we were directed to producers of the initiatives. in the case of fcns, consumers and producers were met during organized purchase days, while for the case of the remaining initiatives, the consumers and producers were reached at farmers’ markets or other organized events. 3.4 interpretation of data first, all in-depth semi-structured interviews were voice-recorded and the discussions have been categorized and subcategorized under each aspect in relation to our research questions and objective. then, the re-occurring concepts as well as answers that are falling outside of the repeated concepts have been coded. here, the triangulation of the data was possible when concepts such as institutional mechanisms, collaborative processes and challenges experienced within sfscs have been asked to each of the participating actor (coordinators, consumers, producers). in this way, different explanation of the same concept by different actors have made it possible to strengthen our data. 4. results the findings related to our study are presented in this section. the cases we investigate consisted of four food community networks (fcns), two farmers’ markets (fms) and one local shop. while, fcns have been initiated by organized consumers, csos played a key role in initiating the rest of the three initiatives. table 1. introduction of cases. initiative information about the initiative local shop doğa’s shop 12 rural-rooted producers selling their olive only through this network. farmers’ markets foça slow food earth market the 1st slow food (sf) market in turkey and 28th in the world. only targets small-scale producers within a radius of 40 kilometers, 13 producers: 12 rural-rooted and 1 urban-rooted. ecobazaar 1st organic farmers’ market in izmir. 11 producers (10 ruralrooted, 1 urban-rooted) and 2 intermediary sellers (selling organic products such as packaged food or beauty products). food community networks aegean university group 1 rural-rooted main producer that delivers weekly and numerous supporting ones. bi̇tot, geto and homeros 28 producers in bitot, geto and homeros altogether (12 part-time rural rooted, 10 urban-rooted, 6 full-time rural-rooted producers). this number is presented together, as there are producers that are shared among these 3 groups. 208 yaprak kurtsal, emel karakaya ayalp, davide viaggi 4.1 actors and motivations the first fcn that has been established in izmir with the initiative of consumers is the aegean university “environment and human friendly agriculture group”, which later inspired the other food community initiatives to be formed in the following years. the group was established in 2013, with the initiation of two academicians at the aegean university. the aegean university group is different than the others in the sense that it is a “workplace” organization and it is both founded and managed by the “employees” of the university. the second group is called bitot (west izmir community supported agriculture group), which has been established in the urla province of izmir in 2014 by a group of consumers. when bitot reached a certain number of consumers, the decision to scale-out also in other provinces of izmir has led to the establishment of geto in 2015 (in the karşıyaka province) and homeros in 2016 (in the bornova province). the motivations behind establishment of all of the groups are similar: to support local small-scale producers that conduct environmentally-friendly production and to be able to supply healthy food products to its members. the first fm of our case study, foça slow food earth market, was founded by slow food convivium “foça zeytindalı” in 2011, in line with the principles of the international slow food movement. the foça municipality also acted as a collaborator and has supported the convivium, which also provided the space for the market to be held each week on sundays. meanwhile, the district directorate of agriculture also contributed as a training partner, which helped identify and train the farmers in the initiation phase of the market. slow food international representatives were also involved in the process to provide direction and guidance. for the case of the foça earth market, the motivation behind establishing the market was to give smallholders the chance to sell directly, without intermediaries. the second fm of our case study, ecobazaar, on the other hand, was established in 2010 in the partnership of ecological agriculture organization association (eto), izmir metropolitan municipality and karşıyaka provincial municipality, as the first organic farmers’ market in the city of izmir. the main motivation behind its establishment was to initiate an all-organic market in izmir, as part of efforts of the izmir municipality to promote organic production. finally, doğa’s shop (“yavaş dükkan” which translates to “slow shop”) was established in 2015, by the school of nature, a project of the grassroots organization of doğa (nature) foundation. in 2013, doğa association founded the school of nature in the orhanlı village of seferihisar province in partnership with the seferihisar municipality, which also donated the school’s building to the doğa association to perform its activities. other partners of the school are orhanlı village society and several other local groups in anatolia. the motivations behind its initiation were to establish a collective, where all steps of production can take into account preservation of biodiversity and traditional olive oil production methods of the orhanlı village. preserving the biodiversity in this location carries significant importance, as orhanlı valley is one of the final production sites where traditional stone pressed olive oil production continues. 209exploring governance mechanisms 4.2 institutional framework we illustrate in this section, the criteria for inclusion of producers, how these criteria are controlled, and the general manner of operation for each initiative. for producers, the required specifications for being part of fcns are to be pursuing local production, the production to be conducted by the producers themselves and not by others, organic production, using heirloom seeds or organic seeds, respecting biodiversity and to having a sufficient buffer zone between the producer’s land and those others that pursue production using chemicals. these specifications are decided iteratively and may be subject to changes as a result of learning processes. field visits are made prior to being accepted into the group, and later unannounced visits are held at certain intervals. in the case of the aegean university group, members meet for the purchase day each week. coordinators, that are also academicians of the university, establish and facilitate communication among consumers and producers on a voluntary basis. the group meets in the workplace (the university) and the main producer is making weekly deliveries to the participating consumers. no rent is paid or needed for the meeting place. normally, the group members only pay for the products that they buy. however, in case of any problems or challenges on the part of the producer (e.g. drought, flooding), practices of solidarity are operationalized in the form of direct donations or upfront payments for overcoming challenges on the farm. meanwhile, as part of the other three fcn cases, nearly 10 people take part in the coordination of the groups on a voluntary basis, take rotations on different tasks, including communication with producers and consumers, weighing the products and arranging the finances during the purchase days. orders are collected from consumers through communication via a facebook group or a whatsapp group. within the case of the foça earth market, unlike the other two cso-organized initiatives, everyone who take part in the coordination do so on a voluntary basis. the producers, on the other hand, are expected to comply with certain rules: firstly, they have to sign a contract each year, and to reveal, every 6 months, the products that they are going to sell in the market. secondly, they need to be present every sunday, when the market takes place; thirdly, they are expected to engage in “good agricultural” practices, that are in line with the “good, clean and fair” aspects of the slow food movement. last but not least, the locality aspect is taken seriously, such that producers that are outside the radius of 40 kilometers are not accepted to take part. these criteria are being controlled by the auditing committee, that consists of the convivium partner, provincial agricultural organization, foça municipality and the municipal police. the producers, on the other hand, only pay a symbolic fee of 20 tl (3,14 euros) yearly, to pay for the maintenance costs of their counter, and do not have to pay any other rent or similar fees. as for the ecobazaar, the aspect that distinguishes this market from all other initiatives is the fact that it is an organic bazaar, where each of the products sold have to have an organic certificate, hence it is subject to very strict auditing processes. the audit is conducted in partnership with eto, izmir metropolitan municipality, provincial directorate of agriculture and provincial police. producers have to pay for their counters in the market. as part of doğa’s shop, the coordinators are either employees of the doğa foundation or have strong ties to it (through projects or collaborations). the initiative collaborates with the peasants of the orhanlı village, providing them with a higher price than that 210 yaprak kurtsal, emel karakaya ayalp, davide viaggi of the market price for their olives, and operates by producing olive oil with traditional methods, from the olives collected from peasants. to be part of doğa’s shop the peasants need to comply with 21 criteria covering 4 areas, that are developed by efforts and research conducted by school of nature employees: local products produced by smallscale producers; pursuing traditional production; producing in harmony with nature; not bringing energy or water from farther distances. these criteria are taken very seriously, and if a producer fails to comply with one or more of the criteria, they are excluded from the yearly olive oil production collective, and they are considered again for the following year. the audit is made during the packaging stage by authorities. the initiative, by creating and making visible a “village olive oil brand”, helps establishing a selling channel for peasants, and make sure the olives of the village are getting the value that they are worth. to market the products, the initiative uses online selling channels (a website), while the products are also sold during the workshops, local food festivals and educational activities being organized by the doğa foundation. earnings from sales are re-invested in trainings provided by the foundation. 4.3 collaboration dynamics school of nature names the process of olive oil production within their initiative as: “collective oil”, which signifies that it is a process undertaken “altogether”, “as a family” and in “conviviality”. the initiators and coordinators of the initiative also moved to live in this village, which have further strengthened their relations with peasants, rurality and the production process. hence, the coordinators not only stay in contact with the producers to lead the process, but also share a life together. foça earth market, being a part of the slow food movement”, adopts principles of the movement. slow food uses the term “coproducer” as part of its vocabulary and coins the term as “a consumer who goes beyond their passive role and takes an interest in producers, production processes and associated challenges”. hence, the idea of a community and co-production exits in the movement’s culture, yet, to diffuse it among all members of the initiative will need further efforts. for both foça market and ecobazaar, the interviewed consumers did not mention “the idea of a community” or used any terminology or language linking to “co-production”. in this direction, events such as trainings, workshops, or food festivals are held when consumers can come together and accustom with each other as well as with producers. one of the “collaborative actions” taken in the context of foça earth market has been establishing the “foça earth kitchen”, with funds and donations received from foça municipality and citizens, with an aim to bring the actors of the initiative together. at aegean university group, the members, that are employees of the university, share a big part of their days together; hence, communicating and decision-making within the group are easier. although this is helpful in terms of organizing purchases, it creates difficulties in terms of establishing a shared understanding of a community, “as the reason to be part of these networks for most members of the group is only to have access to healthy food”. in all of the fcns, the initiation phase was aimed to be made participatory through public meetings held and decisions on the general framework and inclusion criteria were decided following ideas and feedback from participants. meanwhile, some solidarity actions were taken within groups. some examples are the potato and corn projects, 211exploring governance mechanisms where producers have been supported financially to install trickle irrigation system and then provided with guarantee of purchase for all their products to be bought (bitot); the egg project, where farmers were supported to build a poultry house and obtain chickens and paid for the 6-month worth of eggs as an advance payment (geto). homeros, on the other hand, has established an urban orchard in order to conduct collective production practices. the aegean university group, in order to compensate for the loss of their producer after a serious hail incident, has established a funding system, through which consumers could provide financial support. moreover, a “solidarity pricing” practice was implemented, which allowed those consumers with a lower purchasing power to pay 25% less for buying eggs, as the remaining amount was compensated by the other consumers. as part of fms, the understanding of knowledge sharing and learning from each other are not very common among producers. producers of these initiatives, being mostly ruralrooted and having learned agricultural practices from their families, noted that they do not feel the necessity to exchange knowledge or information with other farmers. however, although producers did not feel this need, they have underlined the importance of social networks established in these groups. in the fcns however, our findings suggested that there is a stronger culture of knowledge and experience sharing, which is consistent with the community aspect of these groups. especially, urban-rooted producers in these groups, which consist of almost half of the total number of producers, have argued to have the need to learn from others, hence engaging in knowledge and experience sharing as much as they could. 4.4 individual challenges, governance tensions and adaptation in this section, we illustrate challenges experienced in the investigated cases in four sub-sections. in the first sub-section, we present individual and farm-level challenges put forth by producers, and in the remaining of the section (sub-sections ii-iv), we present governance tensions arising in the groups under three categories: organizational, resource and institutional tensions. i. challenges at the individual and farm level for the case of fms, setting up and dismantling the counters might require time and effort, as well as to be present in the market and to work on the farms. especially, for the case of foça earth market, the obligation to be present in the market place every sunday “even during harsh weather conditions or when there are almost no consumers” is one of the biggest difficulties. as foça is a province that is a vacation destination and attracting many visitors during summer, the demand is sufficient in summer, yet in winter it is more challenging. conversely, ecobazaar, being located in a residential area in central izmir, experiences a significant fall in consumer demand during summer. hence, producers that are tied to both fms are experiencing difficulties related to the amount of time that has to be spent in the market each week, in addition to the seasonal fluctuations of consumer demand that is experienced. for foça earth market, as the profile of producers mostly consists of very small-scale producers, another difficulty is also being able to bring an adequate amount of products to sell to their market counters each week. another significant problem raised by producers was that these networks were not sufficient to guarantee their livelihood. especially most producers of fcns noted that sell212 yaprak kurtsal, emel karakaya ayalp, davide viaggi ing only in these groups is not sufficient to make a living, as they can only meet their costs. for these producers, being here had more significant effects in terms of networks and social ties established, rather than financial gains. in this regard, a few producers noted: “small-producers could earn sufficiently, if they could back-up their sfsc activity with other activities such as eco-tourism or gastronomy linked to their agricultural production”. ii. organizational tensions for the case of fcns, which have a governance mechanism that relies on voluntary and informal organizational structures, one of the biggest organizational tensions experienced was regarding the lack of volunteerism and lack of members willing to take responsibility, which leads to the burden and responsibility of all tasks to be loaded on a few people, creating fatigue in the long-term. this challenge is exacerbated especially during the purchase days, which may lead to the meetings to be “chaotic” at times. this lack of workforce also results in some of the events or meetings that are wished to be organized by the groups, to not be realized. in addition, lack of time and active participation also lead to some problems experienced in control and audit mechanisms, which are also undertaken on a voluntary basis. some of the recommendations to deal with this issue include: raising the efforts to increase the number of volunteers and active participants, switching the tasks and responsibilities of volunteers every 6 months and in a more planned way, and to facilitate the ordering and distribution processes through online applications or through online portals, rather than using excel sheets and similar methods. other recommendations on the other hand were towards switching the coordination task from voluntary to a professional one, in order to give the worth of efforts, eliminate this fatigue and, in turn, to have a more stable coordination mechanism. meanwhile, some interviewees noted that the groups are growing more quickly than they can establish a strong organizational structure; hence, slowing down and taking firmer steps were also proposed. another organizational tension arises from lack of or difficulties related to communication within groups, and especially communication with producers. in many instances, lack of communication with producers leads to disruptions in delivery processes, in addition to not being able to follow-up on the challenges the producers are faced with. another point, on the other hand, is related to the decision-making processes within the groups. interviewees noted that while inclusive and democratic decision-making process is favored, this usually leads to a trade-off between members to have their voice in decisions, and actually arriving at a decision. members argued that most of the time due to lack of communication, the decision-making processes are ineffective. it has been noted that it is a big necessity to learn how to communicate within groups and arrive at decisions as a community. lastly, lack of a shared understanding and a common purpose was also regarded as a significant challenge. it was argued that “when the members focus only social networks without embracing social awareness, it is hard for groups to be long lasting”. in this regard, some consumers of the groups are criticized for “seeing these groups as organic shops or supermarkets”, who are “only focused on accessing healthy products, and complain about the products they receive or the time they have to invest”. for the case of ecobazaar, which is a market where only producers that have an organic certificate can be part of, the biggest organizational challenge is related to the formal processes of certification and regular controls. while, these processes are monitored 213exploring governance mechanisms by a cooperation of multiple organizations, the coordination among stakeholders is a delicate task, as these processes can be costly and time-consuming, for both producers and the auditing organizations. on the part of both of the fms the issue of creating a sense of community and involving consumers to the processes are the biggest challenges. it was noted that “consumers are often only here for healthy food, and to be a community is not one of their motivations”. hence, coordinators try to promote the idea of community through additional activities, such as seminars or workshops (e.g. a kitchen project where consumers and producers come together to cook, and share experiences and recipes). iii. resource tensions the operation, development and upscaling of local food projects require resources: funds, quality agricultural land and physical infrastructure, as well as knowledge and human capital (manganelli and moulaert, 2018). one of the challenges that has been mentioned by all groups was the difficulties of finding a place and space for the initiatives. in the case of the three cases initiated and organized by csos (2 fms and the local shop), the place of operation is provided by local authorities, which ensures a stable space to conduct activities. in the case of fcns, however, finding a space to perform their activities is more challenging. in the case of aegean university group, which is a work-place organization, the premises of the university provide the members with a comfortable space. with the rest of the other fcn cases, however, while the spaces where the purchase days take place are provided free of charge by some municipalities and organizations, these spaces often cannot be kept for a long time, which results in a continuous search for new places to operate and conduct activities. apart from purchase days, the fcns do not have a stable space or area to undertake other activities, such as meetings or seminars, as well as to store food products or to use as a base for logistical arrangements. another challenge that has been noted by all of the producers and coordinators of the group has been lack of consumer demand, especially in certain periods of the year. consumer demand is regarded as a resource challenge mainly because this aspect influences all groups in the way of not having access to sufficient financial resources and creating difficulties for producers regarding their livelihoods. this difficulty was pointed out mostly by the two fms. in the case of ecobazaar the coordinator argued that the main challenge of the initiative is to reach a sufficient number of consumers. “the number of consumers are decreasing; especially during summer when citizens escape from city center to go to vacation destinations, the demand decreases significantly, putting the market in hardship”. it is believed that the lack of consumer demand is due to two main reasons: firstly, lack of information about ecobazaar (“not even some people living across the street know about the market”); and secondly “low level of awareness about organic production, and skepticism towards organic products”. in the case of fcns, however, all coordinators and most members believe that there is sufficient amount of consumer demand around the city of izmir, yet the problem is linked more to these groups not having a strong base and structure, to be able to accept more consumers. in addition, for fcns, another challenge is finding producers to include in their groups. “almost all small-scale producers have given up, especially those that produce ecologically”. 214 yaprak kurtsal, emel karakaya ayalp, davide viaggi iv. institutional tensions our findings show that fms and the local shop, as being civil society organized initiatives, have a more formal structure and already established formalized relationship and links to other organizations, including csos, local authorities and municipalities. as a consequence, they collaborate often with these organizations in the context of some activities or projects, which also provides them with visibility. fcns, on the other hand, do not have ties to other fcns, as well as to formal organizations, such as local municipalities. in this regard, fcn coordinators, while acknowledging that these ties are either insufficient or missing completely, also noted that the steps need to be taken carefully and meticulously towards building strong relationships with other organizations. meanwhile, all producers and coordinators, regardless of the type of initiative they belong, have touched upon lack of government policies, support mechanisms and underlying laws in place, which results in financial uncertainty and lack of trust. underlining that agriculture has long been a neglected sector in turkey, producers noted that they were feeling “left alone”. they further noted that they were in need of support from the government both in the form of direct (i.e. subsidies) and indirect support (i.e. training) to be provided to organic producers; and awareness raising programs for consumers. producers further noted that they tried convincing other peasants to switch to organic or ecological production, but they were regarded as “crazy” for pursuing organic production. “if this proposition comes from the official authorities, then other producers would consider listening”. 4.5 local, community and individual outcomes regarding actual outcomes, our findings suggest that experience of farmers differed in terms of well-being and livelihoods, mainly in relation to whether the farmers were from urban or rural backgrounds. in the case of the two fms and doğa’s shop, where all farmers were rural-rooted, with an exception of a few, and very small-scale, the farmers’ livelihoods have been reported to have changed notably as a result of being part of these sfscs, as a result of receiving a more “just” price for their products and to have a new selling channel. as well, they gain more visibility and respectability. producers noted to have gained autonomy and are able to bring an income to the household. in the case of fcns, a big proportion of producers’ only occupation was not in agriculture and they earn money also from other channels, and around half of the farmers are urban-rooted and started pursuing ecological production, as a “way of living” and to “find a way out of the dominant system”. hence, a big proportion of farmers engaged in these networks do not mention “a big change” in their lives financially; however, “being here had significant effects in terms of networks, social ties, being part of a community and obtaining new selling channels”. finally, regarding local outcomes, in the case of doğa’s shop, as the initiative is located in a village, it is also possible to distinguish village-scale local impacts. it has been noted by the villagers that, before the initiative, the two out of three traditional olive oil factories in the village have been already shut down. it is argued by coordinators and the villagers that the final remaining factory could keep functioning and has been revitalized thanks to the olive oil village brand established and marketed in collaboration with doğa’s shop and the association of orhanlı village. foça earth market, on the 215exploring governance mechanisms other hand, has contributed to the reinvention of local cuisine to preserve local tastes in a 40-kilometre radius of rural, semi urban and urban geography through “the kitchen” established in the commercial center of foça. 5. discussion our findings reveal that there are differences of governance structures, institutional frameworks, as well as differing levels of shared goals and understanding among different initiatives studied as part of this research, which also lead to numerous governance challenges. in addition, the outcomes and farm-level challenges for farmers also differ, including but not limited to factors such as whether or not farmers are urban or rural-rooted and if their livelihood depends only on these initiatives or not. to begin with, one significant difference was among the formality of organizational structures and leadership mechanisms, in addition to the level of formality of criteria for inclusion of producers, and the extent to which they are enforced. the initiatives that have been organized and governed by csos have adopted stricter criteria and auditing mechanisms, which are implemented with the collaboration of multiple organizations, including other csos and local municipalities, which was also in line with other studies in the literature (skog et al., 2018; yacamán ochoa et al., 2019; van der jagt et al., 2017; jones, 2018). in the case of fcns, however, the governance structures were less defined and were implemented by volunteers (yacamán ochoa et al., 2019; manganelli and moulaert, 2018; bellante, 2017). furthermore, aspects of a shared sense of identity and community, were also being perceived differently among actors, which motivated initiatives to organize additional events such as workshops, seminars, or culinary events to bring the participants together. this was also in line with experiences discussed in other studies, where similar efforts were put in place to establish trust and embeddedness, such as farmers sharing their personal information with consumers in organized workshops (bui et al., 2019; skog et al., 2018; petrakou et al., 2011; papaoikonomou and ginieis, 2017). our study also revealed different governance tensions (manganelli et al., 2019) arising as a result of different governance mechanisms at play. the tensions experienced by fcns have been more on the organizational side, including tensions to manage tasks on a voluntary basis and challenges related to keeping up with the scaling up of initiatives. one of the most significant organizational challenges associated with the informal structure of fcns, has been regarding the insufficient number of volunteers taking responsibility and this in turn, resulting in difficulties to complete tasks in time and creation of fatigue within the volunteers. this finding is also in line with other studies that report governance challenges that are experienced by food communities, which are governed by informal mechanisms, mostly reliant on voluntarism (yacamán ochoa et al., 2019; manganelli et al., 2019b; mount et al., 2014). these studies also find that challenges are experienced as sfscs develop and increase the quantity and quality of the food they deliver, when the initiatives lack capacity in terms of efficient logistical delivery (yacamán ochoa et al., 2019), or the capacity and time to include more farmers or other actors into the network (skog et al., 2018); hence propelling them to search for more efficient logistics as well as decision-making structures. in this direction, the operation and upscaling of local food projects require further resources, including funds, a bigger space to conduct activi216 yaprak kurtsal, emel karakaya ayalp, davide viaggi ties in addition to knowledge, skills and human capital. emerson (2018) in this regard, draws attention to the importance of leadership in collaborative governance arrangements, noting that multiple skills are needed for the sustainment of such organizations, and if leadership is lacking at various scales, there may be need for sustained investment in leadership training, mentoring and awareness building before moving forward. as a response to such challenges, while our findings reveal some suggestions of group members towards switching to a more formal structure (e.g. cooperative), or to professionalize the system by lifting the voluntary aspect of coordination (e.g. providing a salary to those that take responsibility), others argued that this leads to bureaucratization of these initiatives, compromising the autonomy of their structures. other studies also mentioned the trade-offs that sfscs had to experience, between governance tensions caused by the informal structures and the risk of losing the “alternative” quality of these networks, as well as dissociating them from its local rootedness and community connectedness (yacamán ochoa et al., 2019; nchanji, 2017; renting et al., 2012; manganelli et al., 2019b). nchanji (2017) further argues that in the case of lfs, neither formal or informal systems are always successful in resolving governance issues, hence underlining the importance of including multiple stakeholders in the processes. on the side of the initiatives that were governed by csos, which had a more formal structure, the organizational tensions were less mentioned, while, the main challenge that was mentioned by all members and coordinators were regarding the lack of consumer demand and interest, or the seasonal aspect of this demand, that is causing mainly resource challenges within the initiatives (manganelli and moulaert, 2018). initiatives in this regard, noted the necessity of public administrations and local municipalities to step in to increase awareness among citizens regarding ecological and ethical food, and the need of a policy framework to support these initiatives to survive and to develop. other studies in the literature also highlighted lack of consumer demand being experienced by local food networks, and the importance of participatory governance mechanisms, in which multiple actors from different levels and sectors need to work together to achieve these common goals (yacamán ochoa et al., 2019; nchanji, 2017; reis, 2019; manganelli and moulaert, 2018; dedeurwaerdere et al., 2017; jones, 2018). some policy recommendations mentioned in these studies included governments to pursue awareness campaigns, or local municipalities to promote initiatives aimed at public procurement of local products for canteens, or facilitating direct sale by means of public aid through fairs, events and dissemination, and finally, adapting the legislation and regulations to facilitate the process (yacamán ochoa et al., 2019). in terms of individual impacts and challenges on the part of the farmers, our findings showed that the farmers in all groups mentioned having received support one way or another, yet the type of support mentioned was differed among farmers. rural-rooted and very small-scale farmers, which were mostly attached to more formal networks, mentioned a bigger change in their livelihoods. this was also supported by the fact that these networks were their sole source of income. skog et al. (2018) also find that small-scale farmers that are part of local food networks experienced better income, also mentioning additional impacts of an increased respect in the community. on the other hand, among fcns, the majority of these producers have other occupations and do not rely solely on the income from these networks. it was mostly mentioned that sfscs do not provide 217exploring governance mechanisms them with profits (and only meet their transportation costs), but rather, social networks, learning and experience sharing opportunities, to establish new channels of marketing through word of mouth and a motivation to carry on in the agricultural sector. other studies of sfscs also mentioned low levels of financial gains attained by farmers, while the associated gains were more on the side of sharing experiences, social learning as a result of established networks, or having found a “safe space” to share new ideas and create partnerships (skog et al., 2018; yacamán ochoa et al., 2019; bellante, 2017). the study has some limitations. one of the limitations was the relatively small sample size, mainly due to the limited number of producers that are part of these initiatives. besides, the willingness of consumers to take part in the research has varied significantly, depending on which initiatives they were part of. those consumers that purchased products from fcns were more willing to invest time in the interview process, while the ones who are contacted through fms were less inclined to do so. another limitation was to conduct a multiple case study with cases that are each particular and peculiar, having different profiles and ways of functioning. hence, comparing them with respect to some aspects had the risk of providing biased results. for example, fcns, in line with being “communities”, naturally had a higher level of shared understanding and collaboration among participants, in comparison to fms. another challenge was to link the motives, governance mechanisms and outcomes of these initiatives to the system context and conditions. due to limitations of time, we could not explore and discuss the political, legal, socioeconomical, environmental or other influences that may affect the governance dynamics and performance of collaboration within and across our studied cases, which emerson and nabatchi (2015) depict as the system context. local food networks or sfscs do not exist in isolation and are largely shaped by their surrounding context. hence, we believe that making this connection could provide important implications for policy making. in addition, while we could only focus on sfsc cases that are currently operational, we also know that there are other cases that have failed in the past. to be able to also reach these initiatives would have provided us with very important information towards understanding not only why these initiatives work but also why they fail. 6. conclusions this study aimed to explore the governance mechanisms of sfscs in the city of izmir (turkey), by making use of an adapted version of the collaborative governance framework introduced by the seminal work of numerous scholars, and to identify the governance mechanisms, collaboration dynamics and main challenges associated with this process. one of the most relevant findings was the need of a support mechanism or “decent policy framework” expressed by participants of all initiatives that we explored. in other words, actors that have chosen to “remain outside of the mainstream industrial food system” still felt very strongly, “the need of some kind of support from policy makers or local actors”, although the type of support needed differed depending on their specific experience. this aspect is of significant relevance to especially local public authorities, as interest in local food extends beyond consumers and producers, and by decentralizing food production and distribution, local food system has a potential to generate wider public benefits. these benefits include economic and social gains for farmers, and social inclusion on a local 218 yaprak kurtsal, emel karakaya ayalp, davide viaggi scale, through which citizens and communities in remote or less privileged areas can also be reached. especially, in cases where local food initiatives can collaborate with other local actors such as local municipalities, public schools, or local organizations or community projects, the variety of citizens and geographies reached can be widened. in addition, local food initiatives can also be spaces where collaboration, exchange of knowledge and experience and social learning can take place. while knowledge creation, social learning and exchange of experiences have strong implications for aspects such as agricultural innovation and adaptation to the impacts of climate change on the side of the small-scale local farmers, they also can create a process through which knowledge regarding traditional food, recipes and cultural heritage can be protected. in this direction, policy interventions may include outreach and public awareness building activities in order to enable knowledge sharing about the mid and long-term social, economic, environmental and cultural impacts of local food production systems, and information about specific initiatives on the ground. besides, local public authorities can establish links to such initiatives and to implement shared social or culinary projects, or establish partnership to enable local, ethical and healthy food to reach schools or canteens. moreover, local festivals, cultural events or workshops can be conducted, underlining the importance of local food systems in local and rural development, as well as preservation of local tastes and heritage. last but not least, reviewing of the regulatory framework to make it easier for small-scale farmers to survive and earn a living on their farms would be of great importance. while in turkey, the agriculture sector is a neglected one, and small farmers are not supported or protected sufficiently, the specific efforts on the local level will have significant contributions. in this respect, further research can focus on current efforts and implementations by local municipalities in turkey, regarding mutual projects and collaboration with local food initiatives, in order to reveal the outcomes from the perspective of local communities. while this could provide important insights for policy making, these experiences can also guide other municipalities in their future efforts. best practices from around the world could also provide a reference and guidance for future projects. references andrée, p., clark, j.k., levkoe, c.z. and lowitt, k. 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(2019). the potential role of short food supply chains in strengthening periurban agriculture in spain: the cases of madrid and barcelona. sustainability 11: 2080. https://doi.org/10.3390/su11072080 economics of culture and food in evolving agri-food systems and rural areas severino romano1, francesco vanni2, davide viaggi3 on the relevance of the region-of-origin in consumers studies fabio gaetano santeramo1, emilia lamonaca1,*, domenico carlucci2, biagia de devitiis1, antonio seccia1, rosaria viscecchia1, gianluca nardone1 benefits for the local society attached to rural landscape: an analysis of residents’ perception of ecosystem services stefano targetti1, meri raggi2, davide viaggi1 assessing preferences for rural landscapes: an attribute based choice modelling approach cathal o’donoghue1, stephen hynes1, paul kilgarriff2, mary ryan3, andreas tsakiridis3 exploring governance mechanisms, collaborative processes and main challenges in short food supply chains: the case of turkey yaprak kurtsal, emel karakaya ayalp, davide viaggi bio-based and applied economics 9(2): 127-136, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae editorial doi: 10.13128/bae-9959 economics of culture and food in evolving agri-food systems and rural areas severino romano1, francesco vanni2,*, davide viaggi3 1 university of basilicata (italy) 2 crea (italy) 3 university of bologna (italy) abstract. the role of culture in connection with the development of food and bioeconomy issues has gained growing importance in recent years. in this editorial we provide background information on the three key topics addressed by the papers published in this special issue: cultural ecosystem services, cultural issues in the organisation of food chains, culture and food characteristics. in a way, the common denominator of these papers is the link between culture, food and territory, taken from different angles. more research and methodological advances are needed for a better consideration of culture in economic research accounting for the current needs of more systemic and holistic vision of the topic. keywords. culture, agricultural economics, food economics, cultural ecosystem services, food chains, food characteristics. jel codes. o13, q13, q18, q57. 1. introduction this special issue of bio-based and applied economics (bae) features a selection of four papers previously presented at the 174th seminar of the european association of agricultural economists (eaae) titled “economics of culture and food in evolving agrifood systems and rural areas” (matera, 10-12 october 2019)1. the seminar aimed at promoting academic debate about food and rural cultures and their implications for agricultural and food economics, with either an individual, chain or system perspective. the role of culture in connection with the development of food and bioeconomy issues has recently gained growing attention. this has been emphasised by recent eu stra1 papers accepted for the seminar were invited to submit to the journal or this special issue. the submitted papers followed regular double blind peer review according to bae procedures. *corresponding author. e-mail: francesco.vanni@crea.gov.it editor: fabio gaetano santeramo. 128 severino romano, francesco vanni, davide viaggi tegic documents, such as the new green deal (european commission, 2019) and the farm to fork strategy (european commission, 2020), that give a paramount importance to education, awareness and changes in consumers’ behaviour. culture is also a driver of a range of topics increasingly investigated by scholars working in the field of agricultural and food economics. examples include consumer’s behaviour facing cultural features of products (guo et al., 2019), touristic-driven development of rural areas (both remote areas and areas closely connected to cities) (promsivapallop and kannaovakun, 2020), change in food choices and working habits (woodhall-melnik and matheson, 2017), innovation and technology acceptance (kemper et al., 2018; yang and hobbs, 2020), different forms of governance between supply and demand through food networks, chain organisation and trust (evans and mylan 2019; sonnino, 2019), as well as the interplay between private action and public policy. economists often address the role of culture in a rather narrow way, where culture is considered as a driver of economic behaviour, related with individual products or locations, either on the supply or demand side of markets. noticeably, the vast majority of these contributions consider culture as a static concept, i.e. culture is exogenous and immutable. it is assumed that it can be fully represented in a disciplinary or a ‘traditional’ fashion and coded into a well-defined set of behavioural rules determining the interaction and coordination among agents in rural communities, e.g. driving consumer choices and acceptance of innovation. more recently, from both demand and supply side, attention is driven towards the need for a systemic view in which not only demand and supply are connected through coordination means beyond market, but cultural aspects are embedded in coordination solutions, e.g. through concepts such as alternative food networks and knowledge and innovation systems. however, many of the issues implied by such needs remain largely unaddressed. for example, the role of culture in the organisation of food supply chains (dowty and wallace, 2010) and the way the culture is shared among actors in agri-food networks (hubeau et al., 2019; reina-usuga et al., 2020) have hardly been investigated. some of these aspects are further challenged by new technologies. new communication technologies for example, are bringing not only new ways of communication, but also new discourses, evolving mindsets and new forms of social interaction. another example is the bioeconomy, a sector that is bringing new avenues towards interpreting value creation and the interface with ethics. although these phenomena are often studied separately by different branches of agricultural and food economists, cultural issues are often interconnected. indeed, culture affects not only the relations between society and rural areas, farming, food and environment, but indirectly also the vitality and competitiveness of the agriculture, food and bio-economy systems. the objective of this paper is to provide background information on the three key topics addressed by the papers published in this special issue: cultural ecosystem services, cultural issues in the organisation of food chains, culture and food characteristics. in addition, it tries to derive some lessons learned about current research on the topic and the need for further research. the three topics are addressed respectively in section 2, 3 and 4, while section 5 provides some discussion and concluding remarks. 129economics of culture and food in evolving agri-food systems 2. cultural ecosystem services while the primary goal of the agricultural sector is to produce provisioning services such as grain, livestock, fuel, forage, and other products, it is widely recognised that, along the production of commodities, farming also provides a large number of cultural, recreational, regulating, habitat, and supporting services (swinton et al., 2007; van zanten et al., 2014; zhang et al., 2007). numerous studies underline the importance of intangible goods and services associated with agriculture, and amongst them, an increasing attention is devoted to the so-called cultural ecosystem services (ces). ces have been defined as “the non-material benefits people obtain from ecosystems through spiritual enrichment, cognitive development, reflection, recreation and aesthetic experiences” (mea, 2005, p. 40). when looking at the farming sector and more broadly to rural areas, ces include important services that do not sustain agricultural production, but deliver benefits derived from the aesthetic function of landscapes, open-space, cultural heritage of rural lifestyles, recreational activities and rural tourism (swinton et al., 2007). the analysis and assessment of ces associated with farming are a growing field of research also for agricultural economists, and major contributions have been made to understand both the monetary costs and benefits of ces delivery (van berkel and verburg, 2014). while most studies are strongly focused on economic and monetary valuation exercises, an interesting field of research is emerging, based on alternative evaluation approaches, drawing on a wide range of social science tools and methods. indeed, in order to address the role of ces in a more comprehensive way, an increasing number of studies focuses on perceptions, values, attitudes, and beliefs of experts and citizens towards ces (balàzsi et al., 2021; garcìa-llorente et al., 2012; martín-lópez et al., 2012; van zanten et al., 2014). such studies address socio-cultural preferences towards ecosystem services in order, on one side, to generate more meaningful insights regarding the appreciation of ces by society and, on the other side, to better identify trade-offs between ces, biodiversity, commodity production and other ecosystem services at landscape level (nelson et al., 2009; plieninger et al., 2013). two articles of this the special issue contribute to this literature by providing additional evidence on the perception of aesthetic value of ecosystems and rural landscapes by eu citizens. o’donoghue et al. (2020, this issue) explore individual preferences for rural landscape attributes, based on the viewing of photographs of the irish countryside. the authors aim at contributing to the literature on landscape preference valuation by investigating whether individuals’ characteristics interact with landscape attributes, but also by exploring how these interactions may affect public preferences for landscapes. from a methodological perspective, this approach aims, on the one side, at facilitating the creation of a formalised model of landscape preferences based on the component attributes and, on the other side, at bridging the gap between the literature on geographic information systems (gis) and landscape analysis. results show positive associations with natural attributes such as cliffs, mountainous landscapes, landscapes with water and native trees, and negative associations with events such as flooding, unmanaged landscapes, industrial turf cutting and mechanised features. more interestingly, a significant heterogeneity of preferences was observed across the urban-rural residency divide: while farmers and people living in rural areas 130 severino romano, francesco vanni, davide viaggi have the highest preference for agricultural landscape attributes, urban dwellers seem rather indifferent towards natural and farming landscapes. the relations between citizens’ characteristics and landscape appreciation are also explored by targetti et al. (2020, this issue), who assess the perception of ecosystem services associated with rural landscape by local residents in a rural area located in northern italy (po delta lowlands, province of ferrara). this article shows that while the urban population has a rather generic and positive understanding of ecosystem services produced by landscape elements, rural population also tends to acknowledge the presence of disservices associated with specific socioeconomic sectors. in addition, urban dwellers attribute a higher value to the recreational function and cultural meanings attached to specific landscape elements, while people living in rural areas seem having a more complex and comprehensive understanding of landscape elements. in other words, living close to specific elements have a significant impact not only on the services perception, but also on the capacity to discern among benefits for residents, agriculture and tourism. this study shows the complex relation between landscape elements, awareness and perception of individual characteristics and similarly to the paper by o’donoghue et al. (2020, this issue) it also provides interesting evidence that could be used to better design landscape valorisation policies. in sum, these two articles not only confirm the important role of recreation, tourism and aesthetic values of agricultural landscapes, but also add interesting insights on how socio-economic drivers determine ces awareness and perceptions. finally, both contributions have interesting methodological and policy implications. from a methodological perspective, this kind of research sheds light on the need for using innovative methods to better understand the relationships between ces and final users, with a special attention to people imagination, expectation, experiences and preferences. from a policy perspective, the possibility to identify citizens’ preferences on ecosystem services (and disservices) associated with farming, and on specific landscape attributes could be also a relevant approach to better target policy intervention. for example, the recognition of the high aesthetic value of specific elements of agricultural landscapes such as stone walls and hedgerows could be an important justification to incentivise farmers to maintain these public goods through targeted agri-environmental schemes or through other landscape conservation policies. 3. culture in the organisation of food chains agri-food systems have become increasingly complex due to the processes of globalisation of the supply chain, the industrialisation of food production, and the economic concentration in the processing and retail sectors (borsellino et al., 2020). these systems are dealing with various sustainability challenges such as climate change, ecosystem degradation, biodiversity loss, and resource scarcity that require collective solutions and actions, as well as system innovations (borsellino et al., 2020; hubeau et al., 2019). in this context, a wide variety of new food networks have emerged to improve the sustainability of the global food regimes and foster innovation and change (hubeau et al., 2017, 2019; maier et al., 2020; melkonyan et al., 2020; reina-usuga et al., 2020; roep and wiskerke, 2012). alternative food networks (afns) differ from these dominant food regimes “by building new producers-consumer alliances and creating experimental spaces (...)” where “food is 131economics of culture and food in evolving agri-food systems reconnected to the social, cultural, and environmental particularities of the context or the “local” in which it is produced” (roep and wiskerke, 2012). however, afns often either fail to reach their goals or cease to exist due to the organisational and collaborative difficulties between the members of the network. a solid societal embedding is therefore essential for the afns to develop and take paths of sustainable development. here culture plays an important role. it is no longer just an element affecting consumer behaviour but rather something affecting the entire organisational process of food supply chains. culture can be conceptualised as inherently relational, meaning that the elements that characterise it, such as narratives, values and norms, and everyday practices, only contribute to a collective culture when they are shared among actors (crossley, 2015; hubeau et al., 2019). according to hubeau et al. (2019), it is possible to distinguish five levels of culture. two of them, the culture related with (i) the landscape (dominant societal cultures) and (ii) the “conventional” agri-food regime (represented by policy measures and public actions) are not directly related with the organisation capacity of food supply chain. the latter depends on the culture related with (iii) agri-food networks (interactions and relations of the network members); (iv) the network member-organisations (culture of each organisations); and (v) the individuals within the member-organisations (culture of individual people). when members of a food network share the same culture, it is easier to agree on ideas and actions, simplifying the organisational process of food supply chain and the collaboration among the members, in order to achieve their common objectives and acknowledging their differences, such as expertise and viewpoints. in this special issue, kurtsal et al. (2020, this issue) seek to understand the governance mechanisms used in short food supply chains (sfscs) driven by civil society, studying seven sfsc initiatives in the city of izmir (turkey). the research contributes to the literature on collaborative governance in local food systems by analysing the processes and mechanisms through which local food network actors get collectively organised and govern these systems. in this direction, qualitative semi-structured in-depth interviews were conducted to examine the governance mechanisms, challenges, and collaboration processes and outcomes of sfscs, by collecting information from the actors (coordinators, producers and consumers) involved in the initiatives. the study shows that the local food system impacts positively on well-being and livelihoods of the rural community. however, differences of governance structures, institutional frameworks, as well as differing levels of shared goals and understanding among different initiatives studied are reported, which also lead to numerous governance challenges. probably, some of these challenges may occur due to the lack of sharing of the same culture among the actors of the local agrifood systems. for example, the results show that the initiatives in which actors feel part of a “community”, naturally have a higher level of shared understanding and collaboration among participants, in comparison to the initiatives where this sense of belonging is not present. this confirms the importance to conduct in-depth analyses on the cultural sphere in the organisational process of food supply chains. finally, two additional aspects deserve attention: (i) the need of a support mechanism or “decent policy framework” for sfscs actors, and (ii) the need for collaboration, exchange of knowledge and experience and social learning among actors, public authorities and citizens. these can represent drivers, with culture, to foster innovation and the promotion of the local food networks. 132 severino romano, francesco vanni, davide viaggi 4. culture and food characteristics studies on consumers’ preferences and behaviour have been one of the most fertile areas of agriculture and food economics in recent decades. while the link between consumer’s behaviour and cultural issues is rather straightforward, in most of the literature this is treated indirectly through a variety of attributes. in addition, much of the literature in this field focuses on a specific product or a specific issue among potential attribute groups. for these reasons, it is difficult to grasp the overall outcomes of this branch of research and even have a full overview of the topic. however, it needs also to be highlighted that this area of the literature accounts some of the hottest issues directly linking culture and food attributes. one example is preferences for different innovation characteristics of the product, such as genetic engineering. another is in the domain of religious or explicit cultural characteristics of a product. among the many potential attributes of interest for the connection between culture and consumer behaviour, one of special interest concerns the region-of-origin (roo). the existing literature on the consumers’ attitude towards roo provides numerous and varying evidence on the role of this attribute as compared to other product characteristics (henchion and mcintyre, 2000; stefani et al., 2006). on the one hand, regional imagery is a relevant component of buying behaviour (chamorro et al., 2015; van der lans, 2001) and can affect willingness to pay and hence agricultural income. on the other hand, different characteristics for the roo can strongly affect actual impact on purchasing patterns (van ittersum et al., 2007; verbeke et al., 2012). santeramo et al. (2020, this issue) address this topic through a meta-analysis of the relative importance of roo. in particular, the article aims at characterising the heterogeneity in the relative importance of roo. after systematically reviewing the literature on roo, the paper builds an ad hoc indicator to measure the relative importance of roo as compared to other attributes of agri-food products. then the authors use a meta-analytical approach to explain how the relative importance of roo varies according to factors related with publication process, methodological issues and characteristics of articles. these topics yield interesting and complementary information that deserve separate considerations. first, the findings reveal that the publication process and the methodological choices have limited influence on the relative importance of roo. this is important as it contributes to validate the strength of the following considerations and somehow corroborates the robustness of the approaches used by authors. in contrast, the authors find a strong effect of characteristics of articles, which include also the choice related with the topics studied. notably, the relative importance of roo is highly dependent on the products under investigation and on the characteristics of the roo addressed. overall, the results also highlight that roo is an effective differentiation instrument in the agri-food markets only if supported by geographical indication labels, such as protected designation of origin (pdo), protected geographical indication (pgi) and american viticultural area (ava). however, managerial implications go beyond this simple statement. in particular, it is critical for policymakers to develop communication strategies focused on consumers, in order to convey attractive information about roo by stimulating their interest in the origin of foods and building favourable perceptions about qual133economics of culture and food in evolving agri-food systems ity and distinctiveness of products. this entails enhancing communication strategies by targeting different messages to different target markets and consumers’ groups, as well as developing new concepts of label in terms of contents and communication channels. 5. final remarks culture somehow affects all aspects of agriculture and food economics. the agricultural literature connected with culture is extremely wide. however, it remains very difficult to grasp the main messages, as each paper is very often connected with extremely narrow topics, specific products, specific geographical areas and/or specific aspects of culture. in addition, culture touches aspects of agricultural and food economics that are traditionally addressed by different specialisations of scholars, e.g. consumer studies, innovation economics. the current trend is towards the quest for more systemic approaches to agriculture, food and bioeconomy, as embedded in concepts such as value chains and food systems, planet boundaries and ecosystems services, and driven by global and systemic problems, such as climate change, globalisation of markets and population growth. in this direction, also the interplay with culture can be expected to become more relevant in the future and explicitly addressed by research. in this special issue, we have collected four papers that deal with different aspects of such a relationship. in a way, their common denominator is the link between culture, food and territory, taken from different angles. this highlights a very important aspect of culture as related with food and probably of the future of food systems, which is their growing embedding in social systems that are geographically organised in connection with ecosystems. although this special issue certainly does not provide an exhaustive discussion on the role of culture in the agri-food systems, it provides some reflection points, not only related with the topics covered by the papers, but more generally on the relationship between culture and agri-food systems. the papers of this special issue, through different methodological approaches, provide an indication of the variety of topics connected with culture (and increasingly studied by agricultural economists), covering issues relating to rural areas, organisation and innovation of supply chains, and food consumption. culture influences the production of public goods and above all the perception that citizens have of rural landscape; it influences supply chain relations and governance of agri-food systems; characterises the products, their quality and above all the consumers’ perception of food products. for these reasons, public policies must take into consideration aspects linked with culture, including values and visions not only of the direct beneficiaries (e.g., farmers or actors in the supply chain), but also more generally of society. however, given the complexity of the topic, more studies and innovative approaches are needed to better support policy makers and decision-makers in general. the special issue tries to contribute to this process, representing a starting point to promote further and improving studies related with culture and its role in the agri-food systems, highlighting the importance of a multidisciplinary approach and a higher degree of cross-fertilisation also among branches of agriculture and food economics. 134 severino romano, francesco vanni, davide viaggi references balázsi á., dänhardt j., collins s., schweiger o., settele j., and hartel t. 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(2007) ecosystem services and dis-services to agriculture. ecological economics 64(2): 253-260. economics of culture and food in evolving agri-food systems and rural areas severino romano1, francesco vanni2, davide viaggi3 on the relevance of the region-of-origin in consumers studies fabio gaetano santeramo1, emilia lamonaca1,*, domenico carlucci2, biagia de devitiis1, antonio seccia1, rosaria viscecchia1, gianluca nardone1 benefits for the local society attached to rural landscape: an analysis of residents’ perception of ecosystem services stefano targetti1, meri raggi2, davide viaggi1 assessing preferences for rural landscapes: an attribute based choice modelling approach cathal o’donoghue1, stephen hynes1, paul kilgarriff2, mary ryan3, andreas tsakiridis3 exploring governance mechanisms, collaborative processes and main challenges in short food supply chains: the case of turkey yaprak kurtsal, emel karakaya ayalp, davide viaggi bio -based and a ppl ied economics bae bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 copyright: © 2021 f. mantino. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: f. mantino (2021). rural areas between locality and global networks. local development mechanisms and the role of policies empowering rural actors. bio-based and applied economics 10(4): 265-281. doi: 10.36253/bae12364 received: november 29, 2021 accepted: february 8, 2022 published: march 31, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini. discussant: davide viaggi, giovani belletti, donato romano. orcid fm: 0000-0001-5338-4333 bae 10th anniversary paper rural areas between locality and global networks. local development mechanisms and the role of policies empowering rural actors francesco mantino consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (crea) abstract. the main objective of this work is to review the recent achievements on the mechanisms explaining local and rural development, which underpin the current definition of rural areas in the european literature. the analysis carried out in this article acknowledges a gap between local development processes and the current representation of rural diversity by international organisations and national/regional authorities. new concepts can be drawn from this comparative analysis: 1) rural diversity cannot be explained exclusively by agglomeration forces and geographical distance from urban centres; b) multiple functions of rural areas, often rooted into sustainable agri-food systems or other forms of territorial capital, contribute to explain more autonomous roles of rural areas; c) organised or relational proximity is emerging in a context of a globalised economy and non-geographical networks, as a critical factor of connection between rural areas and distant regions/markets. this article translates these different disciplinary developments into a practical and integrated conceptual approach, in which local development processes result from three components: local resource systems, networks, institutions and enabling policies. keywords: rural development, local development, regional disparities, networks, rural policies. jel codes: o13, o18, q18, r11, r12, r58. 1. introduction rural development is a topic that still deserves attention both in research programmes and policymaking. since the key paper on l’avenir du monde rurale (“the future of rural society”) was published in 1988, european commission clearly identified, for the first time, the need for a territorial rural policy that went beyond agriculture and included local development and environmental concerns as key elements (european commission, 1988). indeed, a key feature of the debate about rural development is the close interaction between research and policy (bock, 2016), that translates into reciprocal influences over time in a complex relationship that deserves some future analysis (bryden and mantino, 2018). in the context of eu mutual relations between research and policy design, the rural development research, especially in rural sociology and agricultural economics, gained social relevance, http://creativecommons.org/licenses/by/4.0/legalcode 266 francesco mantino bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 especially for the cap reform. in contrast, economic geography and development economics contributed notably to the regional cohesion policy revision over time. still, rigid boundaries among different disciplines have been reduced, and in several research projects we can see examples of interdisciplinary cross-fertilisation. after forty years of debates about the conceptualization of rural development and its role within the cap, and more generally the eu framework, it is hard to say that the scientific process brought about a single, unified theory. nevertheless, the knowledge of the rural development processes has been significantly enriched in these decades by the contribution of different disciplines. in addition, there are different paradigms and visions of rural areas between disciplines as well as within the same discipline. in the vast literature on the topic, there is no consensus about the driving forces of rural development, and multiple development trajectories are possible, resulting from various combinations of local, regional, national and global forces in a given context (ward and hite, 1998). the main objective of this work is to review the recent achievements on the mechanisms explaining local and rural development, which underpin the current conceptualisation of rural areas in the european policy-making and research. this article is structured as follows. first, it begins with exploring how the diversity of rural areas is represented in the most recent literature, both with regards to the urban-rural relations and the differences within the rurality (section 2). in this regard, we think there is a gap between the current representation of rural areas and the recent rural development theories, as achieved by the different disciplinary approaches in rural sociology, rural/economic geography, agricultural economics and development economics. the main problem, in our opinion, is that official definitions and analyses of rural diversity in europe do not match the complexity of rural processes as they emerge from research and policy analysis (section 3). we conduct an interdisciplinary review of the theoretical approaches to rural development processes (sections 3.1, 3.2 and 3.3) and then we seek to explore how these achievements have influenced policy frameworks, notably placebased policies and policy approaches targeting the most peripheral/marginalised rural areas (section 3.4). the article proceeds, in the light of the development factors examined by the different theoretical approaches, with an exploration of how these approaches can contribute to creating a different theoretical framework (section 4), which re-defines the functions of rural areas, not simply depending on functional relations with urban centres but considering the capacity of rural actors to develop more autonomous networks and development pathways. the article ends with drawing up implications for future research and policy actions (section 5). 2. the representation of the rural diversity and increasing rural-urban disparities the definitions of “rural” and “rurality” has been a hot topic in both scholarly and policy debates for almost 60 years. while trying to define ‘rurality’, researchers have proposed various typologies based on different quantifiable criteria. in recent decades, a series of relevant research projects and activities have provided substantial evidence on the diversity of rural areas. approaches and methods to analyse and describe rural diversity have changed over time, moving from simple indicators of population density and percentage of rural population to more elaborate criteria, units of reference and thresholds (copus et al., 2008; féret et al., 2020). there is consensus on two points across the definitions, approaches, and scientific positions on the subject of rurality. first, rurality is a concept that is difficult to define. rural areas have undergone profound economic and social changes since the early agricultural policies aimed at modernisation and land management in the 1960s. as a consequence, rurality can no longer be defined solely according to farming activities and associated lifestyles. second, determining rurality depends on several factors (féret et al., 2020): 1) the global contexts (i.e. the characteristics of the socio-economic systems of which the rurality is a part); 2) the discourse and political objectives that were pursued; 3) the social representations of the different categories of stakeholders. in europe, each country has developed its own definition of rurality, often as a response to a particular political, administrative and the broadest territorial context, and in some cases as an output of national classifications of other factors (e.g. population, accessibility). approaches and definitions are rarely similar between countries (depraz, 2007; bontron, 1996). methods combining several criteria have been adopted since rural areas were recognised as complex and unable to be characterised by a single criterion. six types of approaches can be identified in the literature: 1) the administrative (or statutory) approach, based on the legal-administrative character; 2) the morphological (or demographic) approach, based on population criteria such as population density; 3) the locational approach, based on spatial relationships between urban and rural areas; 4) the functional approach; 5) the landscape approach, based on land-cover and climatic 267 bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 rural diversity, local development mechanisms and the role of policies empowering rural actors conditions; and 6) the combined approach, which used a combination of at least two of the other approaches (féret et al., 2020). the functional approach has been recently used in the oecd rural 3.0 policy note (2018), based on the relationships between rural and urban centres and the proximity to urban centres as factors conducive to economic performance and development potentials. a functional urban area (fua) includes a town and its surroundings consisting of less densely populated local units which are nevertheless part of the town’s labour market due to commuting, i.e. people travelling from their place of residence to the labour market and/or to access services (healthcare, education, culture, shops, etc.) (dijkastra and poelman, 2019). this approach has gained particular interest in the last decades due to the transnational (eurostat and oecd) institutional legitimation (oecd, 2018). according to this definition, ocde has further developed the classical distinction between predominantly urban, intermediate and predominantly rural areas into a new typology: a) rural areas within an fua, which are an integral part of the commuting zone of the urban centre; b) rural areas close to an fua, which have strong linkages to a nearby fua, but are not part of its labour market; c) remote rural areas, distant from an fua and somehow connected through the market exchange of goods and services. in this model, the proximity of less than 1 hour travel time to a large urban region is an essential predictor of rural growth: “proximity allows stronger linkages between urban and rural places” (oecd, 2018) since it allows better access to services, healthcare, education and transports, thus rural areas within or close to an fua are more advantaged than remote rural areas. remote areas dwellers, instead, can count on better environmental conditions and more affordable housing. rural regions close to cities displayed higher productivity growth before the 2008 economic crisis, and higher resilience after the crisis began (table 1), whilst remote regions were the most badly affected by the crisis, with an annual average drop of gdp per capita of -2.5%, almost ten times worse than rural regions close to cities. this representation of rural differences masks a more diversified situation and re-defines the functions of rural areas as dependent on the sphere of influence of various types of urban areas and as ‘commuting zones’. the oecd model seems to neglect rural areas’ capability to develop autonomous functions associated with specific assets and opportunities in terms of local development. furthermore, as we will see in the following sections, there is an evident gap between the knowledge achievements about rural diversity and the most relevant representations of rural areas in international and national policy documents. in short, the definition of rural areas related to the oecd approach does not seem to respond to the need to effectively understand rural areas diversity and the different opportunities for rural development (espon, 2021). thus, a definition less dependent on the role of urban centres, more appropriate indicators and territorial scales seem to be necessary for policy design (migas and zarzycki, 2020). even the definition of the rural development concept has changed over time. in the 1970s, rural development was identified with agricultural modernisation, focusing on encouraging labour and capital mobility (ward and hite, 1998). by late 1970, this model was criticised, and theories of endogenous development (see section 3.2) emphasised the need for overcoming exclusion through capacity building (skills, institutions, infrastructures) and diversified rural economies. in the first decade of the new millennium, neo-endogenous theories, assuming the need for mixing endogenous and exogenous forces (shucksmith, 2010), advocated a more holistic approach to address inadequate service provision, unbalanced table 1. trends in population growth, regional gdp per capita and labour productivity. type of region average annual population growth, % annual average gdp per capita growth, % annual average labour productivity growth, % 2000-07 2008-12 2000-07 2008-12 2000-07 2008-12 predominantly urban 0.76 0.67 2.39 -0.70 1.65 0.24 intermediate 0.55 0.45 2.20 -0.28 1.57 0.65 predominantly rural (total) 0.31 0.38 2.29 -1.11 1.97 0.12 predominantly rural close to cities* 0.61 0.55 2.29 -0.26 2.15 0.56 predominantly rural remote -0.03 0.18 2.30 -2.45 1.69 -0.61 all regions 0.47 0.46 2.29 -0.70 1.74 0.34 note: *defined as within 1 hour travel time of a large urban centre. source: oecd (2018), rural 3.0. a framework for rural development, paris. 268 francesco mantino bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 communities, remoteness, isolation and lack of critical mass. this approach has to include capacity building and adding value to local resources, enhancing connectivity and promoting innovation. also, the role of innovation became more and more relevant in many respects (governance, sustainable production, social inclusion, etc.). 3. different approaches in understanding rural development processes: an interdisciplinary analysis different strands of disciplines and theoretical approaches studied rural diversity and related development processes. rural sociology, regional economics and geography, agricultural economics and development economics have often looked at rural development from different perspectives and adopting different approaches. however, manifold research projects, especially within european horizon programmes, have been carried out in recent years through multidisciplinary efforts. at least four relevant strands of literature have deepened the knowledge of rural development processes and provided new evidence and arguments in many directions (figure 1). first, the group of neo-endogenous models, that evolved into networked approaches, studied in rural sociology and economic geography. as we will see, geographical differentiation factors are increasingly counterbalanced by the importance of a system of networks going beyond spatial differences. second, regional convergence/divergence models have studied particularly the role of factors explaining increasing territorial disparities in developed countries and rural-urban interlinkages in these diverging trajectories. the third strand of literature, focusing on clusters, territorial milieu and localised systems, explores the importance of relevant spatial processes and the role of endogenous development factors, notably locality and internal networks of actors and firms. this strand also includes the territorial agri-food systems, mainly studied by the french and italian economic geography and agricultural economics schools. figure 1. different disciplinary approaches to local-rural development processes. 269 bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 rural diversity, local development mechanisms and the role of policies empowering rural actors finally, the fourth strand of literature explores to what extent policy institutions play a crucial role in determining the development potential of any territory. this question has been widely studied in development economics theory (north, 1990; acemoglu and robinson, 2012). 3.1 rural areas in regional development models and territorial disparities the oecd conceptualisation of rural areas diversity heavily relies on theories of agglomeration (mccann and van oort, 2019), which explain why urban/metropolitan areas accumulate over time comparative advantages and external economies, based on the concentration of physical and financial capital, technological innovation, research and development activities, skills and human capital. theories of divergent development and cumulative causation models (myrdal, 1957; hirschman, 1958; krugman, 1995) explain why the inter-regional disparities can persist and grow over time. the new economic geography, in particular, highlighted that since 1970 onward, and especially in the new millennium, the technological progress and the long cycle of regional evolutionary features led to increasing regional divergence (iammarino et al., 2018). according to oecd study (2020), inter-regional disparities grew mainly, in terms of gdp per capita, in france, italy, germany, poland and the us. still, the polarisation across space is even higher when the gap is measured within the regions (at nuts3 level). after 2009, regions near metropolitan areas have grown faster than metropolitan regions, but remote rural regions trends do not confirm the traditional divergence cities-rural areas model: they grew faster than regions with the small-medium city (oecd, 2020). the diversity of rural areas and related wealth disparities make more complex the urban-rural dichotomy and their relationships. in the last two decades, many efforts have been focused on identifying main drivers of territorial disparities, which go beyond agglomeration forces and geographical distance from the centre. there is evidence that the economic relations between urban and rural areas do not follow a one-way functional dominance relation. for example, in-depth research conducted over recent years in the rural regions of italy, the netherlands and the uk have pointed out different forms of sustainable rural development (marsden, 2009). these studies support the idea that rural areas can achieve higher territorial competitiveness and more autonomous roles in different ways: a) through local agri-food systems (lafs), according to the definitions of the french and italian schools (sforzi and mancini, 2012; arfini et al., 2012; vaquero-piñeiro, 2021); b) alternative food networks, representing more complex and sustainable pathways within the agri-food system (lamine et al., 2012; sonnino and marsden, 2005); c) horizontal networks of economic activities located within an area (murdock, 2000), based on new synergies between agri-food, tourism, amenity, forestry, renewable energy, waste, information technologies and locality food chain developments (marsden, 2009). these different processes imply the sustainable valorisation of “territorial capital”1 (camagni and capello, 2012) in many rural areas. more value can be added locally, and more balanced production-consumption relations can occur between rural and urban areas. increasing and more complex territorial disparities also emerged in studies on the so-called peripheral territories, in particular within the framework of espon research programmes. peripheralisation has been recently interpreted as a process due to different drivers (noguera et al., 2017): a) low accessibility to centres of economic activity, in other words, localities geographically disconnected from the centre (conventional peripherality); b) poor access to services of general interest (education, healthcare, transports, etc.), whether this is a consequence of geographic remoteness, or to changing service delivery technologies, or to austerity, or other changes in the provision such as privatisation; c) absence of “relational proximity”, and exclusion from the mainstream of economic activity, due to low levels of social and institutional interaction with the broader world. these latter conditions are often associated with disconnection from the centre of political power and a lack of influence in terms of governance, and they may affect even geographically accessible regions. most areas identified as peripheral seem to be affected by a combination of at least two of the drivers described above (noguera et al., 2017). the espon study (profecy) estimated that peripheral areas cover approximately 45% of the european territory and only about half of them lack access to centres and services as key drivers. another 46% is represented by areas predominantly suffering from poor economic potential and demographic situation, and the remaining 4% covers areas affected by all types of drivers. peripherality is not a process involving only rural areas (according to the oecd nomenclature) but also a significant share of intermediate and urban and metropolitan regions (table 2), due to increasing unemploy1 the notion of territorial capital defined by camagni and capello includes not only physical assets (private and public goods and resources), but also human, social, relational capital and cooperative networks. in this regard, this notion shares relevant theoretical concepts with neo-endogenous approaches to rural development in the section 3.2. 270 francesco mantino bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 ment, decreasing wealth (gdp per capita) and further impact on out-migration. connection or disconnection can also be the result of poor governance of relations between urban and rural areas. new forms of territorial cooperation are emerging between rural and urban areas (rural-urban partnerships) to avoid over-exploitation and depletion of the rural assets (land, soil quality, water, amenities and landscape, ecosystem services, etc.) and foster the valorisation of complementary functions (copus, 2010). rural-urban interactions find very different governance solutions across the european countries (wood and haley, 2017). however, a series of obstacles hamper the cooperation: absence of trustful relationships, frictions between peripheral municipalities and the urban pole, power imbalances, inadequate financing and capacity constraints about personal and time resources (oedlwieser et al., 2020). 3.2 from exogenous to neo-endogenous and networked models in the 1970s’ and part of the 1980s’ rural development thinking was dominated by exogenous development models: rural areas were considered “backwards” and were thought to lack the dynamism of their own, be dependent on urban growth poles, external investment in agricultural modernisation, infrastructural connections, and the transfer of social and technological innovations from dynamic urban centres. even scientific knowledge was conceived as a mere uptake of technologies produced elsewhere (lowe et al., 2019). the main functions of rural areas were producing food and primary products for urban economies. this model was criticised mainly for fostering dependent development, reliant on continued subsidies and policy decisions of distant institutions (gkartzios and lowe, 2019), for delegitimising local knowledge, and its negative social and environmental impacts (lowe et al., 2019). in the late 1980s’ and 1990s, rural development theories were enriched by endogenous models, whose main principles were harnessing local potentials of its particular natural, human and cultural assets, including local knowledge and skills, for sustainable development; a territorial rather than sectoral approach, at a small scale; and finally, a focus on the needs, capacities and perspectives of local people (ray, 1997). the primary function of rural areas was providing diversified activities in the local economies. the leader initiative relied on these principles and fully represented the most typical example of a policy instrument empowering people and endogenous potentials within the cap. however, even this approach became quite simplistic, relying on assumptions of rural areas as self-sufficient and isolated from external forces (lowe et al., 2019). furthermore, leader experiences demonstrated problems of limited participation of marginal groups (unemployed and young people), the dominance of “who are already powerful and….enjoy a greater capacity to act and to engage with the initiative” (shucksmith, 2000), and limited impact on social inclusion of the most vulnerable population. finally, specific relevant policies such as the support to farming, public investments for infrastructures and general interest services, and taxation remain strongly exogenous in their design and delivery. this evolution from the exogenous to neo-endogenous or networked approaches highlights the importance of social, economic, and institutional networks in regional economics and rural sociology/geography. rural development approaches need to combine endogenous potentials with external forces in the context of a globalised economy, growing mobility of capital and people, substantial national reforms aimed at cutting public costs. consequently, it was suggested that there is a need to go “beyond exogenous and endogenous modes” (lowe et al., 1995) and focus on strategies that continue to valorise local assets in a multisectoral perspective but are also able to involve actively external actors. some authors name this different perspective as “neoendogenous approach” (shucksmith, 2010 and 2012), but the family of neo-endogenous contributions embrace a series of theoretical frameworks focusing differently on relations and networks between rural actors (ruralrural), between rural and urban actors (rural-urban) or between rural and other relevant actors in the national and international context (rural-global market). these models are referred to in different ways. table 2. percentage of peripheral areas in european countries by types of driver and types of region (espon, 2017). types of region a. peripherality due to longer travel times from urban centres (%) b. peripherality due to poor access to services of general interest (%) c. peripherality due to lack of relational proximity and depleting processes urban regions 9.6% 18.8% 32.2% intermediate regions 48.6% 40.0% 34.1% rural regions 41.8% 41.2% 33.7% mountain regions 49.5% 38.2% 24.4% metropolitan regions 24.0% 23.0% 43.0% source: espon-profecy project. noguera et al, 2017. 271 bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 rural diversity, local development mechanisms and the role of policies empowering rural actors the first example of the networked approach is within the “rural web” framework, defined as “a complex of internally and externally generated interrelationships that shape the relative attractiveness of rural spaces, economically, socially, culturally and environmentally” (ploeg et al., 2008, p. vii). the web encompasses a series of multi-actor (including institutions, companies, state agencies, civil society, etc.) dynamic networks of a multilevel character (local and regional, which also influence the relations in other levels). the web also presents six theoretical dimensions (endogeneity, novelty production, sustainability, social capital, institutional arrangements and governance of markets). they can generate multifunctionality and intra-sectoral intertwinement if they interact correctly and thus contribute to the competitiveness of rural development processes. shucksmith (2012), lowe et al. (2019) and esparcia (2019) refer to a “networked approach” to rural development which seeks to link localities “..into broader interwoven circuits of capital, power and expertise, such as rural professionals, regional agencies, ngos, companies, universities and research institutes”. they highlighted a vast number of networks in exploring the actors necessary for the setting-off, implementation and development of innovative projects in rural areas: actors involved in the scientific and technical support (provided by research centres, technical staff in government offices, certifying agencies, etc.), knowledge and information (on specific and technical and more generic issues, provided by a wide variety of public bodies), the physical infrastructure (needed for the everyday operation of the project, provided by public bodies, primarily local but, to a lesser extent, also national governments), organisation and marketing (provided by local governments, private organisations and ngos), and finally implementation of regulatory standards (provided mainly by local and regional governments). gkartzios and lowe (2019) describe a series of “hybrid neo-endogenous” frameworks where local and extra-local agencies collaborate in rural governance and development processes, mentioning in particular: the role of universities in creating a researchpractice rural network; the role of in-migrants in rural areas in terms of employment they might generate for locals, etc. copus (2010) outlines the importance of business networks in rural areas to transmit information and promote innovation. in these business networks, innovation depends, on the one side, on both the “bridging capability” to channel information from globally significant firms and, on the other side, the “bonding capability” to distribute them among the local firms and entrepreneurs. in other words, the role of business networks depends not only upon their local network density, degree of embeddedness and human and social capital but upon their connections to more distant sources of specialist information. in analysing the process of knowledge creation within a geographic cluster, bathelt et al. (2004) outline that this process relies on both information exchange and learning process within the cluster, achieved through informal day-by-day and faceto-face relations (the “buzz”), on the one side, and more complex channels used in distant interactions (the “pipelines”), on the other side. finally, co-location and visibility generate potentials for efficient inter-personal translation of important news and information between actors and firms. in contrast, trans-local pipelines allow more information and news about the markets and technologies to be “pumped” into internal networks. recently, bock (2016), focusing on the problems of promoting rural development in the marginal rural areas, outlined that these areas need more collaboration and linkages across space to give access to exogenous resources. in this regard, rural-urban linkages are essential, but broader connectivity and “virtual proximity” across the space are also relevant for remote rural areas. collaborations with nationally operating large business and external companies, third sectors corporations like cooperative movements, the presence of temporary residents, etc., can activate social innovation processes at the local level, including “the uptake of novel solutions developed elsewhere” (bock, 2016, p. 17). this can be necessary, especially in those marginal areas where mobilising citizens, ngos, third sectors, and business is problematic because “the local asset basis is too weak” (bock, 2016, p.17). supporting networks in the most peripheral areas is necessary to reduce physical and socio-economic isolation or counterbalance restrictive fiscal policies dismantling regional institutional structures (shucksmith, 2012). bock calls this “nexogenous approach” to rural development since it emphasises the importance of reconnection and re-establishing sociopolitical connectivity, which allows for vitalisation if matched with endogenous forces. networks can work at different levels. for example, in a study on rural networks in uk, miller and wallace (2012) define a typology of rural networks based on the geographical remit: a) locally-based networks; b) national networks; c) networks that transcend both national and international regions. from the networks identified, those operating within a locality tended to focus mainly on rural issues, whereas national networks were more likely to work on issues affecting both rural and urban areas. despite finding no substantive differences in why participants accessed rural networks, the three most 272 francesco mantino bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 common reasons for using rural development networks were to obtain advice and information, identify sources of funding, and share local learning and experience. this implies that a lack of funding for rural development networks can have a detrimental effect on communities. other examples of transnational networks can be found in leader (dwyer et al., 2022): some local action groups (lags) were able to promote innovative partnerships within the local area, but also supported the creation of transnational networks under the cooperation measures, lasting well beyond the project duration (as in numerous italy-austria transnational projects). other types of network, notably food-networks that go beyond the territory where productions are based, have been emphasised in other studies (lamine et al., 2019; lamine et al., 2012), identifying the linkages between collective brands, geographical indications (gis) and alternative food networks, on the one side, and groups of urban consumers, on the other side. some of these networks can transform into encompassing civil society organisations and broader territorial agri-food systems (see the case studies analysed in the lamine et al. works). the variety of these networks depends upon the diversity of actors involved and their changing nature over time. in conclusion, various studies confirm the increasing role of social, institutional and business networks in enabling connectivity between rural areas, adjacent urban areas and mainly beyond the geographical proximity. these networks can act as a factor complementary to (or maybe as a substitute for) agglomeration forces in peripheral rural areas2. 3.3 clusters and localised systems the concept of localised agri-food systems (lafs) focused on the production system and interactions among firms within a given territory: this can explain why it was strongly influenced by the concept of cluster (porter, 1990; porter and ketels, 2009), adopted by porter to define the spatial proximity of many production units and their reciprocal relationships. spatial proximity, specialisation of territorial systems and their complex interplay were also at the core of studies on the new economic geography in krugman (1995), on one side, and in becattini and his school focusing on the concept of marshallian industrial district (beccattini et al., 2 this concept has been developed by johansson and quigley (2004, p. 175): “…small regions may survive and prosper – to the extent that networks can substitute for geographically proximate linkages, for local diversity in production and consumption, and for the spillouts of knowledge in dense regions”. 2009), on the other side. lafs concept emerged in the mid-1990s and referred to geographical concentrations of specialised farms, food-processing units and distribution networks, private and public entities in a determined place. lafs appeared in french literature as systèmes agro-alimentaires localisés (syal) (cirad-sar, 1996). three distinctive features characterise lafs: a) place, b) social relationships and c) institutions. the place is considered in its broadest meaning as used in the french school, “terroir”. social relationships relate to trust and cooperation among actors. institutions include all private and public agents promoting actions regulated by formal and informal rules. lafs is “an agri-food system (production/transformation/services) in a specific territory in which actors try to set up coordination and collaboration processes in partnership terms, with internal management and regulation, but with strong ties to public managers and companies” (torres salcido and muchnick, 2012). this definition outlines the capability of main actors to set up innovative and effective solutions to govern the system and ensure the participation of farmers, processors, services providers and marketing operators. the contribution of lafs’ approaches to the understanding of sustainable rural development mechanisms relies upon three aspects: a) there can be broad and intense economic and social linkages between the territorial agri-food systems and the rest of the local economy, as in the case of the bigger agri-food chains (e.g. the case of the processed tomato in north italy) (giacomini and mancini, 2015; mantino and forcina, 2018); b) agri-food systems can have a relevant role in enhancing the local governance. in each lafs, specific coordination methods can emerge, and governance arrangements to change production, processing and consumption practices and create alternative networks. better local governance arrangements are supported by collective action that may take different forms and typologies of organisation. the oecd classical definition identifies three types of collective actions, based on the participants (oecd, 2013a): a) farm-led action; b) non-farm-led action; c) government-led action. in practice, multiple actors usually carry out collective action. a good start depends on a sufficiently large number of participants and the management capability of actors taking the lead in the process. indeed, lafs is a typical multi-actor situation where farmers are only a component, and the fundamental leading role can emerge either within the supply chain or civil society; c) finally, there are various cases of territorial alternative food networks in europe (lamine et al., 2019), 273 bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 rural diversity, local development mechanisms and the role of policies empowering rural actors contributing to connecting small farmers and peripheral rural areas with urban/extra-local markets and ensuring new development perspectives. 3.4 the role of institutions and public policies public rural policies are an essential component of all rural development models. moving from exogenous to neo-endogenous models implies the need for a differentiated use of policy instruments, decentralisation of policymaking, integration of multi-tiered institutions and sectors, participation of local stakeholders and more emphasis on investments in physical assets rather than mere subsidies. these were the main principles for a new territorial policy put forward by the new rural paradigm (nrp) for the oecd countries (oecd, 2006). the nrp was a turning point in the conceptualisation of the rural policy framework since it took on board the ongoing best practices coming from the oecd policy reviews in different countries (mexico, spain, italy, hungary, greece, germany, uk and canada) and distilled the key lessons to foster rural development in the new millennium. according to the nrp, the leader initiative and other territorial approaches in europe were recognised as success cases due to their innovative character and results, despite the relatively limited budget. however, despite the increasing number of innovative experiences, policies implemented in rural areas have not achieved significant impacts. on the contrary, in the last decades, some authors included rural areas in the so-called “geography of discontent”, which includes rural population left behind by national public institutions, lacking faith in the future, and supporting antiglobalisation and populist movements/parties (rodriguez-pose, 2018). thus, the oecd new rural paradigm needs to be updated, and today the debate on policies for rural areas needs to address three main questions: a) to what extent the place-based approach is effective and should be improved; b) what should be the role of public institutions in enabling/empowering local actors capacity building; c) which policy instruments should be set up to strengthening cooperation and networks (ruralrural, rural-urban and rural-wider markets). we are going to discuss point a) in 3.4.1 section and b) and c) in 3.4.2. 3.4.1 place-based policies and the cap the debate occurring in the late 1900s and first decade of the 2000s was dominated by two radical contrapositions between place-based and spatially-blind (or generalised) policies, on the one side, and bottomup and top-down approaches, on the other side. this debate strongly concerns the cap since, in most rural areas, this policy also aims to cover inequalities between rural and urban areas, but in reality, cap instruments, notably pillar i, mainly address agricultural incomes. in a recent evaluation study of cap impact on the balanced territorial objective (schuh et al., 2020), the most important target groups proved to be farmers and rural young people. only pillar ii instruments impact low skilled, unemployed people and the population in the most remote areas (schuh et al., 2020, p. 84-88). according to respondents in the concerned case studies, pillar i instruments (primarily basic and green payments) are not designed to solve territorial needs, and they have controversial impacts. on the one hand, they favour large-scale farms or farms owners not actively involved in agricultural activities (schuh et al., 2020, p. 90). on the other hand, they can have relevant income support effects in the less developed and marginalised rural areas and areas affected by the environmental and social crisis (e.g. the area hit by the plant pathogen xylella in the apulia region). in these areas, pillar i instruments intervene as income transfers to mitigate the symptoms of economic backwardness and decline of farmers and family’s incomes. within the cap, common market organisations (cmo) and rural development instruments seem more appropriate to remove farm structures’ weaknesses and enhance competitiveness. nevertheless, the effects on territorial disparities are uncertain and depend on local institutions and capacity building. for example, innovative approaches foster synergies between cap instruments, reducing intra-sectoral income disparities and strengthening cooperation in the supply chain (schuh et al., 2020). this happened in agri-industrial districts that were able to combine schemes targeting specialised production with more generalised cap instruments (e.g. operational programmes for com producers). leader is the most typical example of place-based approach within the cap. despite the leader broader scope in the last programming period (through the adoption of a multi-fund approach), two recent evaluation reports (schuh et al., 2020; dwyer et al., 2022) indicate that rural peripheral regions need more robust national policies than leader and more diversified supporting systems to face the lack of services of general interest and shortage of employment opportunities. due to the small budget share (5% of the rural development programmes), leader can only provide impulses at the local level. still, leader can generate higher social and economic impact when working alongside other nation274 francesco mantino bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 al/regional schemes. similar impacts have been reported in some spanish and italian rural areas, whereby linkages occurred with national programmes for depopulated areas3. these case studies provide relevant lessons on place-based policy’s effectiveness: the need for combining different types of policies under a common territorial approach. this result has two relevant methodological implications: a) first, to overcome the traditional dichotomy between spatially-blind (or people-based) and place-based development policies and adopt what iammarino et al. (2018) call “place-sensitive development policy approach”, whereby agglomeration effects are promoted in as many places as possible through a mix of policy instruments; b) second, to reconcile top-down and bottom-up policies in a “joint” meso-level conceptual framework (iammarino et al., 2018; crescenzi and rodriguez-pose, 2011). empirical evidence shows that social marginalisation and low local development opportunities render many place-based policies ineffective and often make them frequently function more as social rather than economic development policies achieving inter-territorial equity. within a broader perspective, even the world bank has advocated the need for reconciling policies to enhance institutions, infrastructures, and local interventions, but the right policy mix depends on the types and intensities of interregional disparities (world bank, 2009). 3.4.2 the role of public institutions in empowering local actors, capacity building and networks the quality of institutions plays a crucial role in the development processes. recent research has demonstrated that weak institutions represent a crucial obstacle to the effective use of european cohesion policies (rodriguez-pose and garcilazo, 2015) and undermine the capacity to innovate (rodriguez-pose and di cataldo, 2015). weak institutions imply ineffective regional and local governments, low efficiency in managing public programmes, limits in accountability and voice, and generation of rent-seeking positions, corruption, and lack of confidence in the future. in reality, the quality of institutions also includes the capability to enable local actors and communities “to make choices and transform those choices into desired actions and outcomes” (steiner, 3 these programmes are the national strategy for inner areas in italy and the regional strategy against depopulation in castilla-la mancha region (spain). for more details on these programmes see barca et al., 2014 and schuh et al., 2020. more recently, these two policies have been presented in a webinar organised by the european rural development network in brussels (mantino, 2021; martinez arroyo, 2021). 2016). this capability is crucial for two reasons: a) to create an enabling policy environment for communityled initiatives; b) to allow new institutions and groups to emerge in less active places and facilitate the taking action to address social, economic and environmental challenges (shucksmith, 2012). in other words, enabling policies should help local actors and communities to develop and support resilience (markantoni et al., 2018). nevertheless, public bodies remain in most cases unavailable, if not hostile, to these local needs, creating “a not supportive environment” that generate policy barriers in accessing public funding by “hard to reach” communities (celata and coletti, 2018). many authors outline that local development programmes usually are distributed unevenly across rural areas since the most experienced communities come forward and become increasingly empowered, while others fall further behind (markantoni et al., 2018). marginalised communities are less ready to participate in local development processes “unless explicit attention is given to their inclusion” (shucksmith, 2012) and that communities with well-established partnerships and networks are more successful at obtaining funds. in these contexts, we call enabling policies those policies explicitly addressing “hard to reach” communities and providing financial, technical, and animation support. a good example of enabling policy is the programme funded in scotland, capacity for chance (c4c), under the leader funds, since it provides financial support to selected communities that usually do not engage due to lack of human, economic and relational capital. for these reasons, the programme does not require finding match-funding but simply local people voluntary support and offers the support of a project manager to assist the communities in developing their selected projects (markantoni et al., 2018; steiner, 2016). this study emphasises how the national, regional and local institutions need to set up rules, organisation and behaviours consistently empowering local actors. other examples come from a recent evaluation study of the leader implementation in europe (dwyer et al., 2022). an enabling environment for the leader implementation is determined by two conditions: a) first, reducing the administrative complexity and enhancing coordination, especially in a multi-fund environment (as it happens when all european funds support leader) through a clear definition of tasks and roles between responsible authorities of programme implementation and local agencies; b) establishing a collaborative and mutual learning process between lags and programme authorities, through actions such as guidelines, manuals, websites, faq sessions, working groups, regular communications and meetings, formal collaborations (for275 bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 rural diversity, local development mechanisms and the role of policies empowering rural actors malised in joint committees including local agencies). these conditions are mainly promoted by the public administration bodies. regarding networks, the impact of public policies upon networks gained low attention in policy analysis. the need for supporting networks as a specific policy objective is only gradually shaping rural development strategies. in the last decade the attention is much focused on setting up either “networks of networks” (e.g. through funding the european network of rural development and the national rural networks) or trans-national networks. it is the case of many cooperation projects supported by transnational cooperation programmes, both within cohesion policy and the leader instrument. still, many obstacles and institutional barriers undermine their effectiveness (dwyer et al., 2022). on the contrary, there is a broad spectrum of studies measuring the influence of networks upon policy design, but some authors highlighted the capacity of rural networks to engage in lobbying activities, providing voice and keeping rural issues on the political agenda (regionally and nationally) (lamine et al., 2019; miller and wallace, 2012; dwyer et al., 2022). 4. translating recent achievements into a different operational framework to translate different disciplinary developments into a practical and integrated conceptual approach, we can borrow from the triple helix formalised to study regional learning and innovation (wellbrook et al., 2012). the model interpreting learning and innovation processes has to be adapted according to the main concepts drawn by our previous analysis. thus, local development processes can be conceived as the result of what happens in three main domains: local resource systems, networks, enabling institutions and policies (figure 2). the central dotted circle represents how the specific components of the three domains and how they interact in influencing the local development processes. the first component includes the territorial resource system, the different actors and their specific capabilities that bring about grassroots development initiatives. according to wellbrook et al. (2012), “the [territory]… can thus be regarded as an arena which comprises diverse actors and their different grassroots activities” (p. 6). identifying the concept of territory is a crucial step in this framework. following camagni and capello (2013), we can use a notion of a “system of localised production activities, traditions, skills and know-how”, based on “cultural elements and values which attribute sense and meaning to local practices and structures and define local identities” (p. 1387). in practice, this component identifies the physical and human capital underpinning neoendogenous development in a rural area, focusing on innovative designing and implementing local projects. when designing schemes to support new initiatives, this framework envisages a sort of inventory of local resources and existing initiatives. the second component is the “institutional system”, which encompasses a series of public, semi-public and private institutions managing policies for the rural territory and undertaking different tasks (planning, organising, directing, coordinating, monitoring and evaluation). we include in the institutional system the bodies operating at national and regional level, and also institutional actors and rules set up at local level and aiming to deliver eu, national and regional policy instruments to the rural area. even in this case we replaced the wellbrook et al. string “supporting policies” with “enabling policies” that, in our opinion, has a more pro-active meaning. thus, more than providing financial and administrative support, “enabling policies” for local actors imply at least three other conditions (see figure 2): a) supporting local development strategies through co-design, whereby public administration or other agencies collaborate with local stakeholders to define long-term actions and possible funding, especially in the areas lacking resources and human capabilities. in these areas, poor access to development policies is strongly correlated to the lack of human capital and poor networks; figure 2. the triple helix model (wellbrook et al., 2012), adapted to understand local development processes. 276 francesco mantino bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 b) eliciting innovators to emerge and participate in actions’ design and implementation. in the most peripheral areas, conservative groups and socially dominant coalitions often do not allow innovators to voice alternative needs and access policy support. this is detrimental for them to introduce social innovation and get opportunities to play a role in the future of the area; c) finally, connecting actions and actors, by promoting intersectoral and multi-actor initiatives in the area, either by valorising the current networks or creating new ones. the third component of the local processes in figure 2 concerns the different types of networking activities. by replacing the string “knowledge support structure”, we have adapted the wellbrook et al. conceptual framework, since networks gained more relevance in the literature concerning more general rural development processes. they include a set of geographical proximity relations (within the rural territory) and “organised proximity” (with distant areas/business systems). both can generate localised collective learning processes and can be identified as relational capital in camagni and capello (2013) definition of territorial capital. we can further expand the model to include a fourth helix as the new technologies have become more relevant in recent decades. looking at the model represented in figure 2, external actors or local innovators as providers of internal and tacit knowledge can introduce and develop new technologies in the area. local and national/international networks can play a relevant role in both cases. new technologies can also be fostered by enabling policies through the institutional system (private and public research and experimental bodies, advisors, trainers, etc.). public policies have supported digital and broadband infrastructures through regional and rural development incentives and financial resources addressed to peripheral rural areas. in many rural areas, especially the most peripheral, inadequate broadband infrastructures hamper networks and distant market relations. in conclusion, new technologies represent a relevant development factor, but they can enter the model and be diffused in the rural context through different modes. local development processes are the result of both the action of each component and of interactions among them. for example, evidence suggests that an enabling policy environment foster grassroots initiatives and new networks, notably at the local level and sometimes (and less evidently) with more distant networks. vice versa, good grassroots initiatives and local networks can inspire and facilitate a good use of existing policies. it is worth noticing that good local governance is a fundamental ingredient ensuring successful supporting policies, autonomous grassroots initiatives and dense local networks (mantino and vanni, 2019). this conceptual framework can provide a practical outline for development projects at the local scale. a similar framework has been adopted in co-design processes of local strategies in italy, within the national programme for inner areas aiming to support integrated initiatives in the most depopulated areas. the programme entails activating the three components in setting up initiatives through the participation of local actors through: a) an inventory of available infrastructure and service gaps, existing needs and initiatives aiming to overcome these gaps; b) an analysis of policy mix needed to support initiatives in the field of services of general interest and development of local sectors; c) deep and comprehensive scouting of innovators and potential networks to be involved in the project co-design processes. an essential condition for the success of the strategy design is formal governance arrangements signed by partnerships of local municipalities that ensure cooperation among the relevant local institutions (barca et al., 2014). the inner areas approach can solve another relevant failure in the rural development initiatives (world bank, 2009), that is the appropriate mix of policies addressed to people (education, healthcare, and mobility of population) and policies addressed to places (infrastructures, incentives to economic activities, etc.). this mix allows to strengthen the impact of place-based policies through the support of more general policies, usually falling under the category of macro-economic policies. 5. conclusions and key issues for future research and policy the analysis carried out in this article acknowledges a gap between the unfolding of local development processes and the current representation of rural diversity by international organisations and national/regional authorities. this gap is influenced by two relevant factors: a) high heterogeneity in terms of recent and accepted methods and definition criteria of rural diversity; b) a vision of rural areas as strongly dependent on the sphere of influence of urban areas. in the last two decades, a series of studies, mainly supported by the european commission (horizon, espon, evaluation studies, etc.), provided a more complex and diversified vision of rural diversity, regarding theoretical models and practical definitions. moving from a simplistic definition of rural development 277 bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 rural diversity, local development mechanisms and the role of policies empowering rural actors processes to more complex frameworks implies taking account of the contribution of different disciplines. new concepts can be drawn from comparative analysis: 1) rural diversity cannot be explained exclusively by agglomeration forces and geographical distance from urban centres; b) multiple functions of rural areas, often rooted into sustainable agri-food systems or other forms of territorial capital, contribute to explain more autonomous roles of rural areas; c) “organised” or “relational proximity” is emerging in a context of a globalised economy and non-geographical networks, as a critical factor of connection between rural areas and distant regions/ markets. thus, the definition of rural peripherality is changing accordingly. likewise, the dichotomy between exogenous and endogenous models is losing its interpretative appeal, and networks models are gaining interest among rural development scholars. which implications do these research achievements get in the directions of future research? first, they call for moving from a functional model to another approach based on the territorial capital endowments of rural areas, whereby territorial capital also includes different forms of “relational capital” and networks. in practice, this requires a detailed analysis of territorial capital variables and deep scouting of relations within the locality and between the locality and markets. second, there is a need for developing a rural area concept by revising the current urban-rural typology and introducing criteria based on the variety of functions that rural areas play in the socio-economic and environmental context (espon, 2021). the direction of agriculture and rural development of ec is emphasising this need (migas and zarzycky, 2020), but there is also a need to fill persistent data gaps at the correct geographical scale (local in many cases) through the cooperation between different data providers and screening a wide range of possible (including new) data sources beyond conventional indicators such as population density and settlement configuration. third, understanding rural diversity across european regions has to be used to read better the dynamics of megatrends, including climate change, environmental crises, and socio-economic and demographic drivers of change. the commission’s megatrends hub has identified fourteen global megatrends, and its strategic foresight report (european commission, 2020a) provides a preliminary systematic analysis of resilience, but we need a significant focus on how different rural areas can face megatrends. in this regard, bock and krzysztofowicz (2021) have contributed to the long-term vision for rural areas by drawing four types of scenarios through the combination of diverse future developments ranging from demography and multilevel governance to climate change, economic development and digitalisation (rururbanities, rural renewal, rural connections and rural specialisation). within the possible megatrends, particular attention deserves the digital transition as a powerful driver of technological innovation. digitalisation connected with artificial intelligence (ai), big data, and automation can potentially reshape the economy, which will represent a threat and an opportunity for rural areas. technology can be a way of overcoming economic disadvantages, notably for rural areas with a shrinking population. new communication technologies can limit the effect of distance. digital infrastructures will be crucial to facilitate connection, integration, and provision of e-services (e.g. administration, health, education, finance, culture) and enable the digitalisation of agriculture and the bioeconomy (e.g. precision farming, automation). these investments do not require only covering infrastructural needs but also grass-roots initiatives by local communities under the form of “smart villages projects” (european commission, 2020b). this approach encourages rural areas and communities to develop projects, build on their existing strengths and assets, and develop decentralised services, energy solutions, and digital technologies and innovations. another relevant question concerns to what extent the current policy framework fits local development needs of the different rural areas. the recent communication of the european commission on “a long-term vision for the eu’s rural areas” (ltvra) (ec, 2021) seeks to provide new answers to increasing territorial disparities and the feeling of left behind characterising most rural areas. but, as it was emphasised in the analysis of policies, place-based policy approach is used only for a marginal share of the cap. to be more effective, territorial lens need to be applied to a mix of different policies, including cap instruments other than leader and cooperation measures. the 2021-27 reform of the cap offered the opportunities of mixing different instruments in the cap strategic plan (csp) to prompt sustainable and integrated rural development. nevertheless, the opportunity to address territorial differences within the csp and implement a broader place-based approach does not seem realistic, given the dominant visions in the agricultural policies and the traditional barriers and silos between the two cap pillars. as part of the better regulation agenda, the ltvra puts in place a rural proofing mechanism, notably to assess the anticipated impact of major eu legislative initiatives on rural areas. it will be based on territorial impact assessments and a better monitoring of the 278 francesco mantino bio-based and applied economics 10(4): 265-281, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12364 situation of rural areas. the way in which rural areas are integrated in the eu’s policies will be monitored, notably through regular reports on the implementation of relevant policies. rural proofing will mean putting more attention to territorial distribution of eu policies before their implementation and potential impacts. this mechanism can become an interesting innovation whether reproduced at national level, but this will strongly rely on political positions of the agricultural world. a further relevant challenge concerns enabling all individuals to take active part in policy and decisionmaking processes, involving a broad range of stakeholders and networks as well as all levels of governance. the methodological framework proposed here seeks to activate a process that elicit endogenous capital and innovators through the empowerment of local communities and an enabling policy environment, notably in most peripheral and depopulated rural areas. these types of rural areas need a rather different approach to local development, whereby local institutions and innovators work alongside with regional and transnational actors, and public administrations as well. the provision of public funds is not sufficient to overcome the different obstacles, since empowering local communities requires a radical change in public institutions’ objectives, instruments and behaviour. in this regard the contribution of researchers and scholars should be more oriented to multi-actor action research methods, notably in marginalised 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development in the kheirabad river basin, iran ghasem layani1,*, mohammad bakhshoodeh2, mansour zibaei2, davide viaggi3 the distributors’ view on us wine consumer preferences. a discrete choice experiment lina lourenço-gomes, tânia gonçalves*, joão rebelo bio -based and a ppl ied economics bae bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-10896 copyright: © 2022 h. uçak, e. yelgen, y. arı. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: h. uçak, e. yelgen, y. arı (2022). the role of energy on the price volatility of fruits and vegetables: evidence from turkey. bio-based and applied economics 11(1): 37-54. doi: 10.36253/bae-10896 received: may 19, 2021 accepted: january 17, 2022 published: july 22, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio gaetano santeramo. orcid hu: 0000-0001-5290-5846 ey: 0000-0003-4506-377x ya: 0000-0002-5666-5365 the role of energy on the price volatility of fruits and vegetables: evidence from turkey harun uçak*, esin yelgen, yakup arı alanya alaaddin keykubat university, turkey *corresponding author. e-mail: harun.ucak@alanya.edu.tr abstract. in agricultural economics, fluctuations in food prices and the factors affecting these fluctuations have always been an important research topic. from production to delivery to consumers, the supply chain of agricultural products has a dynamic structure with continuous changes. in this dynamic process, analyzing the intensive use of energy at each stage has gained more importance with its deepening effects in comparison to the past. this study will empirically explore the volatility spillovers between energy price index and fruit-vegetables price index in the period of 2007-2020 in turkey using the kanas and diebold-yilmaz approaches. according to the results obtained from the kanas approach in the study, it has been observed that there is a statistically significant volatility spillover from the energy price index to the vegetable price index, whereas there is no statistically significant volatility spillover to the fruit price index. this finding was supported by the results obtained from the diebold-yilmaz approach showing that there is a volatility spillover of 13.52% to the vegetable price index and 0.86% to the fruit price index from the energy price index. keywords: volatility spillover, energy, agricultural prices, egarch, agricultural markets. jel codes: q11, q18, q41, q47, c32. 1. introduction volatility in food prices and the reasons behind this volatility have recently become a trending topic of discussions throughout the world, while they are often discussed in literature as well. in this regard, pricing process of sub-product groups must also be analyzed in addition to general food prices. indeed, due to the difficulties in storing these products for a long period, changing vegetable and fruit prices might well cause producers and consumers to be deeply affected by price volatility. on the other hand, it is also highly important to examine the reasons that may affect the price fluctuations of these products. fresh fruit and vegetables sector is considered one of the most essential sectors in the agricultural industry as it is vital for sustaining human life. in this context, the united nations declared the year of 2021 as the “international year of fruits and vegetables”, highlighting the importance of fruits 38 bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 harun uçak, esin yelgen, yakup arı and vegetables in nourishment, the problems experienced in the process from production to consumption, food wastes and losses, the importance of farming in the fight against famine and small family businesses generating incomes. thus, the factors that underlie price changes in agricultural markets is currently a hot topic. prices in agricultural markets have recently been affected by macroeconomic factors such as exchange rate, inflation (algieri, 2016), interest rates, energy prices and demand for biofuels, monetary policies, financial investments and speculations, sudden trade restrictions or lack of information, transaction costs, agricultural policies and international prices (kalkuhl et al., 2016; algieri, 2016; kornher and kalkuhl, 2013). this study will focus on turkey from an empirical perspective within its scope. while the country stands out in fruit and vegetable production across the world, turkey is experiencing frequent price volatility at recent times. according to the world food organization’s 2019 statistics, turkey is the 4th largest producer of fresh vegetables in the world (statista, 2021a). in addition, it is the 6th largest producer of fresh fruits in the world (statista, 2021b). therefore, turkey is one of the most important agricultural producers in the world. however, turkey’s currency is one with the highest volatility among emerging market markets and this causes fluctuations in the fruit and vegetable price indices. besides, fluctuations in energy prices due to the volatility of the exchange rate and global markets has become significant as energy is an input item in production processes. considering upward fluctuations in particular, the practices for direct sale points and mediators in the supply chain have been heavily discussed in recent years. in the same vein, the fluctuations in food prices have been the hot topic in turkey too due to the recent global crises, the climate change and foreign-source dependency on energy. it is stated that the reason behind these fluctuations in agricultural product prices is the increasing production input prices by farmers. besides seasonal effects on the price fluctuations in agricultural commodities, it can be observed that increasing energy prices have a direct or indirect aggravating effect on the costs of agricultural inputs such as fertilizers, chemicals, irrigation, production, storage and transportation (fasanya and akinbowale, 2019; tadasse et al., 2016; algieri, 2016). moreover, the use of modern technology applications in agriculture also increases energy consumption. the use of agricultural machinery and pesticides requires the consumption of fossil fuels, and indeed, intense energy consumption is particularly observed in the field of pesticide production (öztürk et al., 2010). besides, price volatility in the categories of electricity, coal, petroleum products and natural gas has an extremely deep negative impact on the economic performance of turkey, as an energy importer. as a matter of fact, oil and natural gas reserves are limited in turkey leading to foreignsource dependence in the field of energy. thus, it is observed that turkey has been the country with the fastest increase in energy demand among the organization for economic cooperation and development (oecd) countries in the past 20 years. within this framework, turkey ranks second in the world after china in the increase in electricity and natural gas demands. existing energy sources cannot unfortunately meet turkey’s increasing energy needs and thus, the country meets nearly 74% of its energy needs via imported sources (mfa, 2020). considering that turkey is a country dependent on imports of oil in its consumption, there is an urging need to address the effects of changing energy prices on the performance of several sectors and industries (algan et al., 2017). on the other hand, the increase in energy prices in recent years is one of the most crucial cost items threatening agricultural production (yıldırım, 2020). hence, the fluctuations in these costs reflect on product prices and cause difficulties in production plans (fasanya and akinbowale, 2019: 186; tadasse et al., 2016: 63; algieri, 2016: 210). for the reasons mentioned above, this study aims to investigate the effects of changes in energy prices on other price indices for turkey. in this regard, we analyzed the volatility spillover between the energy price index (epi), the fruit price index (fpi) and the vegetable price index (vpi) using monthly data sets from january 2007 to december 2019 by two different methods: the kanas (1998) approach for volatility spillover effect and the diebold-yilmaz (2009, 2012) spillover index, analyzed respectively. as for the content of the study, the second section consists of an extensive literature review. this part is followed by a detailed description of the methodology. the fourth section summarizes the data set used in the study. in the fifth part, empirical results of the analyses are given in two subsections. finally, the last section covers comments, discussions and policy recommendations based on the study results. 2. background and literature review energy consumption is one of the main determinants of the socio-economic development of countries. more specifically, oil and its derivatives are considered one of the main production factors in an economy. they are used in the energy supply of various sectors including agriculture, transportation, industry and households, 39the role of energy on the price volatility of fruits and vegetables: evidence from turkey bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 in addition to their extensive use as raw materials in the production of other energy products like electricity and petrochemistry. thus, oil and its derivatives have a vast impact on other commodities (sarwar et al., 2020; taghizadeh-hasery et al., 2019). at recent times, agricultural products and energy markets have been growingly intertwined (koirala et al, 2015: 431). from this perspective, energy consumption in agriculture can be evaluated in two categories: (1) direct energy use: energy inputs such as electricity, fuel, oil, coal, petroleum products, natural gas, biomass can be used in agricultural activities. (2) indirect energy use: the amount of energy consumed in human and animal labor, agricultural tools or machineries, fertilizers, pesticides, irrigation or seed production. in this regard, energy prices affect the costs of inputs necessary for faming including inorganic fertilizers and fuel for agricultural machinery. moreover, it is commonly observed that energy prices increase transportation costs and therefore, affect food transportation and distribution costs. the primary energy products directly consumed in agricultural production include fuels such as coal, petroleum products, natural gas and biomass. also, electricity is widely used as power carrier in farming and particularly irrigation operations. it is a source commonly benefited in the agricultural industry (akder et al., 2020: 9; radmehr and henneberry, 2020: 2; sarwar, 2020: 1; öztürk et al., 2010: 2; mawejje, 2016: 2; nwoko et al., 2016: 2; gilbert and mugera 2014: 201). yet, the history is marked by many crises related to food supply and demand. in this vein, it can be observed that the recent price volatility in food has had a destructive effect. the increased volatility in prices in this field can be associated with the transition from the labor-intensive to a more capital-intensive agricultural production in recent years as well as the regional and national differences in terms of farming. the use of energy is naturally essential in agricultural production. today’s technology enables growing even tropical products in cold regions thanks to the heat provided by energy sources. hence, technology allows countries that are rich in energy sources to produce fruits and vegetables despite their cold climate. on the other hand, especially developing countries that import energy seem to have hardship in their agricultural operations due to the high energy prices increasing the costs of inputs. this leads to an intricate relationship between energy and prices of agricultural products. from this perspective, various studies analyze the effects of oil and other energy prices on agricultural product prices. for example, hau et al. (2020) and koirala et al. (2015) discuss the relations between oil and agricultural prices in terms of futures. sarwar et al. (2020), hesary et al. (2019), alghalith (2010) and zhang et al. (2010) examine the effects of the changing crude oil prices on agricultural products. on the other hand, radmehr and henneberry (2020), balcılar and bekun (2019) and huchetbourdon (2011) scrutinize the effects of energy and exchange rates on agricultural products’ prices. mawejje (2016) further dwells upon the importance of energy and climate shocks in the case of uganda and the food prices in this country. in their study, volpe et al (2013) also investigate how fuel prices in the usa affect the prices of wholesale products and their transportation costs. since agricultural products themselves have been used for energy production at recent times, baffes (2011) examines the relations between oil, biofuel and prices of agricultural products. the literature in this field contains many other similar studies analyzing the volatility in the prices of energy and agricultural product using the econometric techniques that are also benefited in this study. table 1 summarizes these studies in detail: 3. methodology 3.1 the kanas approach for the volatility spillover effect engle (1982) developed a new method to measure the volatility in a time series by modeling conditional variance. he revealed that the conditional variance is a function of the lagged values of the error term squares and modeled the change of the error term squares with respect to time using the arch process. thanks to the introduction of the garch model in the literature, many other conditional variance models started to be widely used (bollerslev, 1986). although the standard garch model captures various features of financial series such as excess kurtosis and volatility clustering, they are not successful in capturing the leverage effect of financial time series. standard garch models tend to ignore the negative correlation between current return and future return volatility. further, the constraints on parameters to ensure the stationarity of the garch process can make parameter estimation difficult. lastly, another difficulty is to interpret whether shocks persist on the conditional variance in the standard garch model. an alternative model developed by nelson (1991) is the egarch model that removes these defects in the standard garch modeling of the financial time series, prevents the model from giving symmetrical responses in cases of positive and nega40 bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 harun uçak, esin yelgen, yakup arı ta bl e 1. s um m ar y li te ra tu re r ev ie w. au th or s g oa l m et ho do lo gy in du st ry re gi on re su lts h au e t a l. (2 02 0) to e xp lo re th e vo la til ity b et w ee n gl ob al cr ud e oi l a nd c hi na ’s ag ric ul tu ra l fu tu re s. ti m ede pe nd en t p ar am et er st oc ha st ic v ol at ili ty c on di tio na l v ol at ili ty a gr ic ul tu re , e ne rg y, fu tu re s a nd o pt io ns m ar ke t c hi na th er e is a he te ro ge ne ou s d ep en de nc e be tw ee n th e vo la til ity o f a gr ic ul tu ra l f ut ur es a nd th e vo la til ity o f c ru de oi l. w hi le c ru de o il vo la til ity d oe s n ot a ffe ct a gr ic ul tu ra l vo la til ity in th e no rm al m od e of th e cr ud e oi l m ar ke t, oi l vo la til ity a t h ig h or lo w a m ou nt s h as b ee n fo un d to h av e an e xt re m el y sig ni fic an t e ffe ct . ti w ar i e t a l. (2 02 0) to a na ly ze th e pr og re ss io nre gr es sio n re la tio ns hi p be tw ee n th e pr ic e in di ce s o f en er gy fu el s a nd fo od , i nd us tr ia l i np ut s, ag ric ul tu ra l r aw m at er ia ls, m et al s, an d be ve ra ge s ( le ad -la g re la tio n) in th e tim efr eq ue nc y do m ai n. w av el et c oh er en ce a nd p ha se di ffe re nc es , d ie bo ld & y ilm az (2 01 2) a nd ba ru ni k & k re hl ik (2 01 7) vo la til ity sp ill ov er in di ce s fo od & en er gy g lo ba l th e re su lts re ve al ed si gn ifi ca nt re la tio ns hi ps b et w ee n fu el an d fo od p ric es , f ue l a nd in du st ry p ric es , a nd fu el a nd m et al p ric es . th e re su lts a lso sh ow ed th at th er e ar e ph as e re la tio ns hi ps be tw ee n th es e pa ire d pr ic es . th e vo la til ity sp ill ov er re su lts sh ow ed th at th e ag ric ul tu ra l i nd us tr y w as th e m os t a ffe ct ed in du st ry b y sh oc ks fr om o th er m ar ke ts . ba lc ıla r a nd b ek un (2 01 9) to e xa m in e th e st ru ct ur e of th e in te rc on ne ct ed ne ss b et w ee n th e re tu rn s of o il an d fo re ig n ex ch an ge p ric es w ith se le ct ed a gr ic ul tu ra l c om m od ity p ric es . d ie bo ld & y ilm az (2 01 2) vo la til ity sp ill ov er in de x fo od , e ne rg y & fi na nc e n ig er ia a na ly se s d em on st ra te th at b an an a, c oc oa , p ea nu t, co rn , so yb ea n, a nd w he at a re n et tr an sm itt er s o f s pi llo ve r. m or eo ve r, th er e is w ea k sp ill ov er b et w ee n th e va ria bl es o f ric e an d so rg hu m , i n ad di tio n to p ric e in fla tio n, n om in al eff ec tiv e ex ch an ge ra te , a nd o il pr ic es . fa sa ny a an d a ki nb ow al e (2 01 9) to a na ly ze th e re tu rn s a nd v ol at ili ty o f cr ud e oi l a nd fo od p ric es . d ie bo ld & y ilm az (2 01 2) vo la til ity sp ill ov er in de x fo od & en er gy n ig er ia ev id en ce o f t he in te rc on ne ct ed ne ss b et w ee n cr ud e oi l an d fo od p ric es w as fo un d ba se d on sp ill ov er in di ce s. a dr an gi e t a l. (2 01 7) to e xa m in e th e da ily v ol at ili ty sp ill ov er s be tw ee n cr ud e oi l p ric es a nd a se le ct ed gr ou p of b as ic a gr ic ul tu ra l p ro du ct s. jo ha ns en -j us el iu s co in te gr at io n te st , d yn am ic c on di tio na l co rr el at io ns , sp ec tr al a nd c ro ss sp ec tr al an al ys es , th e bi va ria te e g a rc h m od el a nd g ra ng er c au sa lit y te st s. fo od & en er gy u sa th e jo ha ns en -j us el iu s c oi nt eg ra tio n te st re ve al s t ha t th e lo ng -r un e qu ili br iu m re la tio ns hi ps b et w ee n cr ud e oi l p ric es a nd th e co m m od iti es in q ue st io n ha ve di sa pp ea re d. th e dy na m ic c on di tio na l c or re la tio ns sh ow th at th e re la tio ns hi p be tw ee n ag ric ul tu ra l p ro du ct s a nd c ru de o il ch an ge s o ve r t im e. th e sp ec tr al a nd c ro ss -s pe ct ra l a na ly se s c on fir m th at vo la til ity in c ru de o il pr ic es is a ss oc ia te d w ith v ol at ili ty in ag ric ul tu ra l p ro du ct s g iv en in th e sa m pl e. th e bi va ria te e g a rc h m od el a nd g ra ng er c au sa lit y te st s c on fir m th is re la tio ns hi p. a na ly se s a lso c on fir m th at th e flu ct ua tio ns in c ru de oi l p ric es a re re la te d w ith th e vo la til ity o f a gr ic ul tu ra l pr od uc ts g iv en in th e sa m pl e. ju di th e t a l. (2 01 7) to in ve st ig at e th e ca us e of in cr ea se d fo od p ric e vo la til ity . d es cr ip tiv e st at ist ic s c or re la tio n an al ys is fo od u sa th e re su lts sh ow th at th e m ai n so ur ce o f f oo d pr ic e vo la til ity is m ai nl y th e oi l p ric e sh oc k. 41the role of energy on the price volatility of fruits and vegetables: evidence from turkey bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 au th or s g oa l m et ho do lo gy in du st ry re gi on re su lts c ab re ra a nd s ch ul z (2 01 6) to in ve st ig at e th e ris k of p ric e an d vo la til ity a ris in g fr om th e co rr el at io n be tw ee n en er gy a nd a gr ic ul tu ra l co m m od ity p ric es a nd to e xa m in e th ei r ch an gi ng d yn am ic s o ve r t im e. th e as ym m et ric d yn am ic co nd iti on al c or re la tio n g a rc h m od el , th e m ul tiv ar ia te m ul tip lic at iv e vo la til ity m od el fo od & en er gy g er m an yit is re ve al ed th at p ric es m ov e to ge th er in th e lo ng r un an d bo th ra pe se ed a nd b io di es el p ric es re ac t t o de vi at io ns fr om th e eq ui lib riu m . in th e sh or t r un , b io di es el p ric es d o no t a ffe ct ra pe se ed an d cr ud e oi l p ric e le ve ls, b ut ra th er re ac t t o pr ic e ch an ge s i n th e ot he r t w o m ar ke ts . m or eo ve r, th e vo la til ity o f b io di es el is re la te d to th e vo la til ity o f c ru de o il an d ra pe se ed , w hi le th e co rr el at io n be tw ee n th e flu ct ua tio n of ra pe se ed a nd c ru de o il ha s be en in cr ea sin g in re ce nt y ea rs . lu co tte (2 01 6) to a na ly ze th e dy na m ic s o f co -m ov em en ts in c ru de o il an d fo od pr ic es . c or re la tio ns o f v a r es tim at io n er ro rs (v ar ia nc e de co m po sit io ns ) fo od & en er gy g lo ba l em pi ric al re su lts m an ife st th at a fte r t he sp ik es in th e co m m od ity p ric es , s tr on g po sit iv e co -m ov em en ts w er e ob se rv ed b et w ee n th e cr ud e oi l p ric e an d fo od p ric e in di ce s, w hi le si gn ifi ca nt c or re la tio n co effi ci en ts w er e no t o bs er ve d in th e pe rio d be fo re th e sp ik es in th e co m m od ity p ric es . n w ok o et a l. (2 01 6) to e xa m in e th e lo ng -r un a nd sh or tru n re la tio ns hi ps b et w ee n oi l p ric e an d fo od p ric e vo la til ity a nd th e ca us al ity re la tio ns hi ps b et w ee n th em . jo ha ns en a nd ju se liu s co -in te gr at io n te st , th e ve ct or e rr or c or re ct io n m od el , g ra ng er c au sa lit y te st fo od & en er gy n ig er ia a na ly se s r ev ea l t ha t t he re is a lo ng -r un re la tio ns hi p be tw ee n oi l p ric e an d lo ca l f oo d pr ic e vo la til ity a nd th at ca us al ity is o ne -w ay fr om o il pr ic e vo la til ity to fo od p ric e vo la til ity . g ilb er t a nd m ug er a (2 01 4) to in ve st ig at e th e ro le o f b io fu el s i n ex pl ai ni ng th e in cr ea se d vo la til ity in fo od p ro du ct s. th e m ul tiv ar ia te g a rc h m od el th e d yn am ic c on di tio na l c or re la tio n m od el fo od & en er gy u sa a na ly zi ng a sa m pl e be tw ee n 20 00 a nd 2 01 1, th e st ud y fo un d in cr ea se s i n th e co rr el at io n an d jo in t m ov em en ts be tw ee n gr ai n an d cr ud e oi l p ric es a fte r 2 00 6 an d es pe ci al ly in 2 00 8 w he n cr ud e oi l p ric es w er e hi gh . re se ar ch er s c on cl ud ed th at th e in cr ea se d vo la til ity in gr ai ns d ur in g th e 20 08 -2 00 9 in cr ea se w as la rg el y du e to sh oc ks tr an sf er re d fr om c ru de o il to g ra in s, pa rt ic ul ar ly co rn , w he at , a nd so yb ea n pr ic es . ta de ss e et a l. (2 01 4) to se ar ch fo r e m pi ric al e vi de nc e on th e qu an tit at iv e sig ni fic an ce o f su pp ly, d em an d, a nd m ar ke t s ho ck s fo r p ric e ch an ge s i n in te rn at io na l f oo d co m m od ity m ar ke ts . to e xp lo re th e m ai n dr iv er s o f f oo d pr ic e sp ik es a nd v ol at ili ty fo r w he at , c or n, a nd so yb ea ns a nd sh ow h ow th es e fa ct or s tr ig ge re d th e cr isi s i n ex tr em e pr ic e sw in gs . th e pr ic e sp ik e m od el b y di ffe re nt ia te d re gr es sio n th e vo la til ity m od el b y pa ne l re gr es sio n th e pr ed ic tio n of e xt re m e vo la til ity b y qu an til e re gr es sio n fo od g lo ba l th ey c on cl ud ed th at o il pr ic es a re a st at ist ic al ly sig ni fic an t f ac to r i n ex pl ai ni ng th e in cr ea se s a nd v ol at ili ty in fo od p ric es . 42 bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 harun uçak, esin yelgen, yakup arı tive shocks in volatility, and thus is more convenient for modeling conditional variance. in this model, the logarithmic conditional variance depends on both the size and the sign of the residuals (nelson, 1991; bollerslev et al., 1994). egarch (p, q) is: ln(σ2 t)=ω+∑p i=1[αizt-i+(γi|zt-i|-e[|zt-i|])]+∑q i=1βiln(σ2 t-i) (1) where zt=εt ⁄σt and the coefficient αi captures the sign effect and γi captures the size effect. so, the egarch (1, 1) model can be expressed as follows ln(σ2 t)=ω+α1zt-i+(γi|zt-i|-e[|zt-i|])]+β1ln(σ2 t-i) (2) where γi is also referred to as the asymmetry coefficient and β1 indicates volatility persistence. it can be said that there is a leverage effect on the conditional variance when has a value other than 0. in this study, the kanas (1998) approach is taken as basis in determining the volatility spillover. before volatility modeling, it is first necessary to determine the most convenient autoregressive moving average (arma) models for the conditional mean process. by testing the arch effect on the residuals obtained from these models, the most convenient egarch (1, 1) model is determined according to the information criteria and likelihood value. the assumed distributions for egarch models are normal distribution (norm), skewed-normal distribution (snorm), student-t distribution (std), skewed-student-t distribution (sstd), generalized error distribution (ged), skewed-generalized error distribution (sged), normal inverse gaussian distribution (nig) and johnson’s su distribution (jsu). egarch (1,1) models with different distributions are compared according to akaike information criteria (aic), bayes information criteria (bic), shibata information criteria (sic), hannan-quinn information criteria (hqic) and likelihood values. kanas (1998) defines the residual squares of other variables obtained from the conditional variance model as exogenous variables and made parameter estimates in order to determine the volatility spillover. accordingly, the egarch (1,1) model to be estimated is as follows: ln(σ2 t)=ω+α1zt-i+(γi|zt-i|-e[|zt-i|])]+β1ln(σ2 t-i)+τ1ln(u2 t-i) (3) in the above equation, ut is the residuals obtained from the conditional variance model, and τ1 is the coefficient showing the volatility spillover. if the coefficient τ1 is statistically significant, it is concluded that there is a volatility spillover. au th or s g oa l m et ho do lo gy in du st ry re gi on re su lts g ar de br oe k an d h er na nd ez (2 01 3) to e xa m in e th e vo la til ity sp ill ov er s i n oi l, et ha no l, an d co rn p ric es . m ul tiv ar ia te g a rc h m od el sf oo d & en er gy u sa in th e st ud y, sig ni fic an t v ol at ili ty sp ill ov er is o bs er ve d on ly fr om c or n an d no t v ic e ve rs a. a lso , r es ea rc he rs do n ot d et ec t c ro ss -v ol at ili ty e ffe ct s f ro m o il to c or n m ar ke ts . th e re su lts d o no t p ro vi de a ny e vi de nc e th at vo la til ity in e ne rg y m ar ke ts h as a si gn ifi ca nt e ffe ct o n pr ic e vo la til ity in th e u s co rn m ar ke t. n az lıo ğl u et a l. (2 01 3) to e xa m in e th e vo la til ity tr an sm iss io n be tw ee n oi l a nd se le ct ed a gr ic ul tu ra l co m m od ity p ric es w hi ch a re w he at , c or n, so yb ea n, a nd su ga r. th e va ria nc e ca us al ity te st fo od & en er gy g lo ba l d at a ar e an al yz ed in tw o pe rio ds a s t he p re -c ris is pe rio d (j an ua ry 1 98 6 d ec em be r 2 00 5) a nd th e po st cr isi s p er io d (j an ua ry 2 00 6m ar ch 2 01 1) to d et er m in e th e im pa ct o f t he fo od p ric e cr isi s. th e re su lts sh ow ed th at a lth ou gh th er e w as n o ris k of sp ill ov er b et w ee n oi l an d ag ric ul tu ra l c om m od ity m ar ke ts in th e pr ecr isi s pe rio d, th er e w as a ct ua l o il m ar ke t v ol at ili ty sp ill ov er to ag ric ul tu ra l m ar ke ts ex cl ud in g su ga r in th e po st -c ris is pe rio d. se rr a (2 01 1) to in ve st ig at e pr ic e re la tio ns b et w ee n cr ud e oi l, et ha no l a nd su ga r a se m ip ar am et ric g a rc h m od el fo od & en er gy br az il th e re su lts re ve al th at in th e lo ng r un , e th an ol p ric es in cr ea se a lo ng w ith th e in cr ea se in b ot h cr ud e oi l a nd su ga r p ric es . 43the role of energy on the price volatility of fruits and vegetables: evidence from turkey bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 3.2 the diebold-yilmaz approach for the volatility spillover effect diebold and yilmaz (2009) describe the return and volatility spillover on the basis of the vector autoregressive (var) model. here, the total spillover index is measured based on the cholesky decomposition. nevertheless, diebold and yilmaz (2012) developed a methodology in a later study to evaluate directional spillover in a generalized var framework. this var framework approach offers variance decomposition that is invariant to the order of variables after that of koop et al. (1996) and pesaran and shin (1998). in the n-component standard var model, each entity xi with = 1,…, n is expressed as follows: (4) where yt is nx1 matrix of dependent variables and φi are nxn matrix of coefficients. εt is the vector of independently and identically distributed innovations (iid) and follows εt~n(0,σ) where σ is variance-covariance matrix. the moving average representation of the var model is as follows: (5) where ai are nxn matrix of moving average coefficients and ai=φ1ai-1+φ2ai-2+…+φpai-p. then, given the var framework, h-step-forecast error-variance decompositions are defined as follows: (6) where σij represents the standard deviation of the error term, σ is variance-covariance matrix and ∆i is the selection vector of which ith element is equal to 1 and the other elements are 0. if each element of the decomposition matrix is divided by row sums, each forecasting error decomposition variance will be normalized, thus using the available information in the decomposition matrix to compute the spillover effects as follows: (7) with (h)=1 and (h)=n. in the light of the above definitions and equations from 4.4 to 47, diebold and yilmaz (2012) defined total, directional and net spillovers as described below: the total volatility spillovers index based on h-stepahead forecasts with the following equation: (8) directional volatility spillovers to i market from other j markets: (9) directional volatility spillovers from market i to other j markets: (10) the net spillover index is obtained using equations 4.9 and 4.10 as follows (11) 4. data analysis as signified in the introduction, this study aimed to analyze the relationship between the fruit and vegetable price volatility and the energy price volatility in turkey. both energy and product prices consist of the data sets obtained from eurostat within the scope of the harmonized index of consumer prices (hicp). the scope of energy index includes “electricity, gas and other fuels”. the energy price index is a variable with broader content than the crude oil price, which is widely cited in the literature. it is considered noteworthy to refer to this energy price index in this analysis. the monthly data set obtained from eurostat consists of the energy price index (epi), the fruit price index (fpi) and the vegetable price index (vpi) between january 2007 and december 2020. appendixa, table-a1 and table-a2 demonstrate the descriptive statistics and augmented dickey-fuller unit root test results for the data set of these indexes and their loga44 bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 harun uçak, esin yelgen, yakup arı rithmic returns. figure 1 shows the time-series plot of the variables. 5. empirical results 5.1 empirical results for kanas approach the convenient conditional mean models for epi, fpi, and vpi were found to be ar (1), arma (2,2), and ma (1), respectively. the output of conditional mean models and arch test results are given in table a3 in appendix-a. the evaluation of the volatility models is given in table 2. the results1 in table 2 manifest that the most adequate models are as follows: sged-egarch (1,1) for 1 egarch-type volatility models were estimated using “rugarch” r package developed by ghalanos (2020a, 2020b). epi; std-egarch (1,1) for fpi and norm-egarch (1,1) models for vpi. table a2 points out to the parameter estimation results and diagnostic test results of the models. it is evident in all three models that all parameters are statistically significant. according to the diagnostic test results, the results of ljung-box (lb) and langrange-multiplier (lm) tests indicate that there are no autocorrelation problems in the residuals and heteroscedasticity problem in the residual squares. the nyblom stability test (nst) results show that there is no structural break according to the nst critical value of 1.49 at 10% confidence level. as in nst, common statistical values calculated for sign bias test (sbt) are given and according to these test statistics, there is no functional error in the conditional volatility model. looking at the results of the pearson goodness of fit (gof) test, it can be understood that the empirical distribution of standard residuals and the theoretical distribution are aligned. figure 1. time-series plot of indexes and log-returns. 45the role of energy on the price volatility of fruits and vegetables: evidence from turkey bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 negative values for epi and vpi can be found by analyzing the values of “gamma1” parameters that show the leverage effect. in this case, it can be concluded that the effect of bad news on epi and vpi volatility is higher the effect of good news and increases the volatility persistence. the persistent values indicate that the volatility persistence is high for epi and vpi variables. it is also found that the half-life of persistence in vpi was 9.94 days. thus, the effect of good news on the volatility is higher for fpi, while the volatility persistence and halflife are lower. this is an indication that good news has a less impact than bad news in the leverage effect. the time-series graph of the volatilities obtained from the models is as described in figure 2. table 2. egarch(1,1) model evaluation depending on information criteria and likelihood values. dist epi fpi vpi aic bic sic hqic l aic bic sic hqic l aic bic sic hqic l norm -5.18 -5.10 -5.18 -5.15 436.5 -2.54 -2.44 -2.54 -2.50 216.9 -1.91 -1.81 -1.91 -1.87 164.2 snorm -5.33 -5.23 -5.33 -5.29 449.8 -2.56 -2.45 -2.56 -2.52 219.9 -1.90 -1.78 -1.90 -1.85 164.3 std -5.63 -5.53 -5.63 -5.59 474.7 -2.71 -2.59 -2.71 -2.66 232.0 -1.87 -1.76 -1.87 -1.83 162.3 sstd -5.63 -5.53 -5.63 -5.59 474.7 -2.71 -2.59 -2.71 -2.66 232.0 -1.87 -1.76 -1.87 -1.83 162.3 ged -5.54 -5.44 -5.54 -5.50 467.3 -2.62 -2.51 -2.62 -2.57 224.7 -1.90 -1.79 -1.90 -1.86 164.9 sged -6.17 -6.06 -6.17 -6.13 521.2 -2.65 -2.52 -2.66 -2.60 228.6 -1.88 -1.75 -1.89 -1.83 164.2 nig -6.14 -6.03 -6.15 -6.10 519.0 -2.68 -2.55 -2.68 -2.63 230.8 -1.88 -1.75 -1.88 -1.83 163.9 jsu -6.16 -6.04 -6.16 -6.11 520.1 -2.69 -2.56 -2.70 -2.64 232.0 -1.87 -1.74 -1.88 -1.82 163.5 normal distribution (norm), skewed-normal distribution (snorm), student-t distribution (std), skewed-student-t distribution (sstd), generalized error distribution (ged), skewed-generalized error distribution (sged), normal inverse gaussian distribution (nig) and johnson’s su distribution (jsu), akaike information criteria (aic), bayes information criteria (bic), shibata information criteria (sic), hannan-quinn information criteria (hqic), llikelihood (l). table 3. the parameter estimation of egarch(1,1) models for price indices. parameters sged-egarch(1,1) for epi std-egarch(1,1) for fpi norm-egarch(1,1) for vpi est std.err t-stat sig est std.err t-stat sig est std.err t-stat sig omega -1.49 0.01 -194.71 0.00 -2.47 1.14 -2.16 0.03 -0.33 0.00 -3793.40 0.00 alpha1 0.35 0.03 11.90 0.00 0.12 0.11 1.05 0.29 0.26 0.00 2136.50 0.00 beta1 0.81 0.00 1176.69 0.00 0.56 0.21 2.71 0.01 0.93 0.00 4454.40 0.00 gamma1 -0.08 0.00 -16.82 0.00 0.39 0.15 2.63 0.01 -0.30 0.00 -2522.70 0.00 shape 0.47 0.01 76.08 0.00 5.69 1.84 3.08 0.00 skew 1.44 0.01 163.05 0.00 stat sig stat sig stat sig lb on sr 1.48 0.75 3.71 0.29 0.25 0.82 lb on ssr 1.19 0.82 0.13 1.00 3.59 0.31 arch lm 1.15 0.69 0.10 0.99 2.04 0.46 sbt joint 0.12 0.99 3.26 0.35 0.60 0.90 perason gof 47.67 0.53 42.88 0.72 35.10 0.93 nst joint 2.41 1.57 1.48 persistence 0.81 0.56 0.94 half-life 3.36 1.19 9.94 lb: ljung-box sr: standardized residuals ssr: standardized squared residuals lm: langrange multiplier sbt: sign bias test nst: nyblom stability test gof: goodness-of-fit. “omega” is the constant term. “alpha1”is the the arch coefficient that is a measure of sign effect. “beta1” is the the arch coefficient that is a measure of volatility persistence “gamma1” is the asymmetry coefficient that is a measure of leverage effect. “normal distribution (norm), student-t distribution (std), skewed-student-t distribution (sstd), skewed-generalized error distribution (sged). 46 bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 harun uçak, esin yelgen, yakup arı it can be said that there was a fluctuation in fpi volatility in may 2011 similar to a big shock effect. in this regard, the iterative cumulative sum of squares (icss) introduced by inclan and tiao (1994) was applied to all three indexes to locate any structural break in the variance. however, the results showed no break in the variance. to test the volatility spillover of epi on other variables in this study, the residual squares obtained from the sged-egarch (1, 1) model (given in table 3) were added as an exogenous variable to the volatility models. this step was followed by the parameter estimation. the results are given in table 4. the diagnostic test results in table 4 indicate that the models support the hypotheses. according to the results of fpi parameter estimation, it is understood that the “tau1” coefficient (which shows the volatility spillover from epi to fbi) is not statistically significant, and therefore there is no volatility spillover from epi to fbi. on the other hand, according to the vpi parameter estimations, the “tau1” coefficient is found to be statistically significant leading to the understanding that there is a volatility spillover from epi to vpi. hence, it can be concluded that the volatility in the epi negatively affects the vpi volatility. 4.2 empirical results for the diebold-yilmaz approach table 3 demonstrates the most suitable volatility models determined for epi, fpi and vpi indexes. derived from volatility data obtained from these models, the lag value of the var model was found to be 1. in addition to this calculation, the var (1) model parameter was estimated. the results of the model estimated by the lag value of selection criteria are respectively presented in appendix-b, table b1 and table b2. the diefigure 2. time-series plot of volatilities obtained from egarch processes. 47the role of energy on the price volatility of fruits and vegetables: evidence from turkey bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 bold-yilmaz approach results2 obtained on the basis of the var model can be seen in table 5. before moving on to the results, it is worth reiterating that the spillover index shows how much of the total variance that occurs in the variables themselves is caused by other variables. in other words, the dieboldyilmaz spillover index demonstrates the contribution of the volatility in price indices to the forecasting error variance. thus, the results of the total volatility spillovers index are based on a 10-step-ahead approach. as these results suggest, it is observed that the volatility spillover from epi index to other indexes is higher than the others. furthermore, the vpi is the index that is exposed to the highest volatility transfers. the total spillover from epi to the other indexes is 14.38% and 13.52% of this value belongs to the vpi and the rest belongs to the fpi index. this case points out to shocks in energy prices exhibiting a higher possibility to affect the pattern of other prices in the investigated area. here, the epi can be defined as a volatility transmitter. it can be deduced that the risk that the fpi index is exposed to from the outside is low. indeed, only 2.68% of its current volatility results 2 diebold-yılmaz analysis was performed using “spillover” r package developed by urbina (2020). from other indexes. on the other hand, it is seen in the vpi index that the externally exposed volatility spillover is 17.97%, and 75.23% of it (13.52%) is due to the epi. these results also support the outputs obtained from the kanas (1998) approach. the fact that the total spillover index value is 9.62% points out to a low connectedness between these indexes. nevertheless, it can be seen that the risk in energy prices is transferred to vegetable prices. due to the high energy prices in turkey, for instance, people can only heat their greenhouses only to protect them from frost rather than proper table 4. the parameter estimation of egarch(1,1) models for spillover from epi to fpi and vpi with diagnostics tests. parameters std-egarch(1,1) for fpi norm-egarch(1,1) for vpi est std.err t-stat sig est std.err t-stat sig omega -2.52 1.08 -2.33 0.02 -0.28 0.00 -7553.23 0.00 alpha1 0.13 0.11 1.11 0.27 0.28 0.00 5849.33 0.00 beta1 0.56 0.19 2.90 0.00 0.94 0.00 7751.43 0.00 gamma1 0.38 0.15 2.57 0.01 -0.28 0.00 -10783.22 0.00 shape 5.74 1.85 3.10 0.00 tau1 (epi spillover) 14.36 115.70 0.73 0.47 -7.75 0.01 -686.85 0.00 stat sig stat sig lb on sr 9.76 0.01 3.54 0.32 lb on ssr 3.21 0.37 0.12 1.00 arch lm 1.87 0.50 0.08 0.99 sbt joint 0.40 0.94 3.17 0.37 perason gof 48.87 0.48 36.89 0.90 nst joint 1.60 1.53 persistence 0.56 0.94 halflife 1.19 11.68 lb: ljung-box sr: standardized residuals ssr: standardized squared residuals lm: langrange multiplier sbt: sign bias test nst: nyblom stability test gof: goodness-of-fit. “omega” is the constant term. “alpha1”is the the arch coefficient that is a measure of sign effect. “beta1” is the the garch coefficient that is a measure of volatility persistence “gamma1” is the asymmetry coefficient that is a measure of leverage effect. “tau1” is the coefficient showing the volatility spillover normal distribution (norm), student-t distribution (std), skewedstudent-t distribution (sstd), skewed-generalized error distribution (sged). table 5. diebold-yilmaz generalized directional spillover output. epi fpi vpi contribution from others epi 91.80 0.27 7.92 8.20 fpi 0.86 97.32 1.82 2.68 vpi 13.52 4.45 82.03 17.97 contribution to others (spillover) 14.38 4.72 9.75 9.62 contribution to others including own 106.18102.04 91.78 300.00 net spillover 6.18 2.04 -8.22 total spillover index 9.62% 48 bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 harun uçak, esin yelgen, yakup arı heating. despite this widespread use of limited energy, volatility in energy prices affects greenhouse costs. 31 million tons of vegetables were produced in turkey in 2019 as the world’s 4th largest producer of fresh vegetables. 23.2 million tons of these crops were grown in agricultural or open areas, and 7.8 million tons were produced in greenhouses. as a matter of fact, around 0.6 million tons of fruits are produced in greenhouses (maf, 2021). according to the results of the analysis, this explains the reason why the vegetable price index is subject to volatility from the spillover of the fluctuating energy prices. within this framework, the average spillover effects over the full sampling period are obtained by generalized spillover analysis. diebold and yilmaz (2009, 2012) stated that full sample spillover measurements cannot clearly ref lect the important sustained and cyclical movement in spillovers. thus, they developed a rolling window framework that allows time-varying spillover indices to overcome their shortcomings in the spillover index, using a 48-month subsample. in this line, the following graphs show the estimation of the dynamic net and total spillover indexes. these rolling windows were obtained using the 10-step-ahead forecasting spillovers. the date that stands out at first glance in the rolling net spillover index is may 2011, when consumer prices increased by 2.42% and annual inflation rose to 7.17 %. coupled with the base effect, the high increases in fresh fruit prices due to seasonal transitions marked the rationale behind this rise. in this period, fresh fruit prices increased by 76.12% on a monthly basis, well above the average of the previous period (tcbm, 2011). therefore, the fpi became the volatility transmitter in may 2011 and created a net volatility spillover of 40.05% on the forecasting error variances of other indices. thus, the total spillover index was estimated as 44.33%. figure 3. the top-down rolling net spillovers indexes for epi, fpi and vpi. 49the role of energy on the price volatility of fruits and vegetables: evidence from turkey bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 6. conclusion input costs have a significant share in setting the prices of agricultural products and ensuring sustainable production. increases especially in energy prices may have an effect on many items from production to delivery of products to final consumers. these items include but are not limited to fertilizers, chemicals, irrigation, production, storage and transportation costs. in this context, stable pricing in the field of energy is essential for the price stability of agricultural products. however, energy prices are not reflected on every agricultural product at the same level. thus, this study analyzed the prices of fruits and vegetables as the category containing the highest price fluctuations compared to other agricultural products. two different analysis methods, kanas (1998) and diebold-yilmaz (2012), were used in the study and it is concluded that the results obtained from both methods support each other. after the parameter estimation of the relevant arma models for logarithmic changes of energy, fruit and vegetable price indices, the arch effect was determined in the residuals of conditional mean models. to identify the residuals of conditional mean models, volatility modelling was performed through the egarch conditional variance model introduced to the literature by nelson (1991). parameter estimations were made for the egarch models by assuming eight different conditional probability distributions. in this regard, sged-egarch, std-egarch and norm-egarch were found to be the most compatible models for epi, fpi and vpi, respectively. considering the outputs of these models indicating the leverage effect, it can be seen that the volatility of energy and vegetable price indexes is more affected by bad news in the market. on the other hand, the volatility of fruit price index appears to be mostly affected by good news. at the same time, it can be understood that the volatility persistence and half-life of energy and vegetable price indexes are higher according to the fruit price index. as an exogenous variable in other variables’ volatility modelling, we used the residual squares obtained from the volatility model estimated for the energy price index on the basis of the kanas (1998) approach. consequently, it is concluded that there is a statistically significant volatility spillover from the energy to the vegetable price index, while not from the energy index to the fruit price index. this clarifies that the fluctuations in energy prices increase the risk and uncertainty in vegetable prices. in the diebold-yilmaz (2012) approach, the volatility spillover index results were obtained by using the var model for the volatilities attained from the egarch models, which were found to be most compatible for the indexes. accordingly, it is understood that the volatility figure 4. the rolling total spillovers index. 50 bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 harun uçak, esin yelgen, yakup arı spillovers from the energy to the vegetable price index and the fruit price index are 13.52% and 0.86%, respectively. in addition, these calculations show that the risk that the fruit price index is exposed to from the outside is rather low, and only 2.68% of the current volatility are due to other indexes. in the case of the vegetable price index, however, it is found that 75.23% of the net volatility index is from energy prices. these results are well overlapping with the results obtained by applying the kanas (1998) approach. the fact that the total spillover index value is 9.62% points out to a low connectedness between these indexes. as we mentioned in the findings section, the share of greenhouse cultivation in vegetable production is considerably higher than in fruit production. at the same time, vegetable production is higher than fruit production in turkey. in this case, the amount of energy input needed in vegetable production is naturally higher than fruit production. in addition to these, turkey’s dependence on foreign energy, increases in the exchange rate, and price increases in the global energy market are other factors to be considered. thus, it is an expected result that the spillover effect of the energy price index volatility on the vegetable price index is greater than the fruit price index. another production input that has an indirect effect on energy prices (which, in turn, affect vegetable and fruit prices) is the price of fertilizers used in farming. indeed, it may well be observed that fertilizer production is decreasing due to the increasing costs of natural gas and electricity all over the world. this is the indirect factor that causes the upward volatility trend of fruit and vegetable price indices in turkey. in other words, the volatility of energy prices is quite high in the country. elaborated in this study from a scientific perspective, the increasing energy prices can be associated with expensive foods due to the increasing costs of processing, transportation, and distribution of agricultural products. in addition, the effect of energy prices on food prices also varies depending on the distance traveled by road. largely focusing on the fluctuating energy prices and their impact on agricultural products, the results of this study provide important implications for policymakers. in this sense, policymakers should urgently do make improvements in their exchange rate policies and the oil reserve system in order to reduce the negative impact of fluctuations in oil prices on the agricultural sector in turkey, which is an oil importer country. they should also pay as much attention as possible to the global oil markets and their impact on transportation costs. in parallel with the developments in the energy industry, there is also a need to design preventive/ protective regulations to mitigate the agricultural price risks and stabilize the market. in addition, policymakers should take measures to prevent speculative behaviors in the markets in an attempt to prevent price increases of food. in addition to these measures and regulations, governments must support farmers so that they maintain their resilience, while also protecting consumers against price changes. on the other hand, it is necessary to expand the use of alternative energy sources such as biofuels, wind, and solar energy in order to reduce turkey’s dependence on foreign-sourced oil consumption. similar to the rest of the world, turkey can grow fruits for a much longer time period than 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(2010). food versus fuel: what do prices tell us?. energy policy, (38), 445–451. 53the role of energy on the price volatility of fruits and vegetables: evidence from turkey bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 appendix-a table a1. summary statistics. variable mean median min max std. dev. skewness ex. kurtosis 5% perc. 95% perc. iqrange energy 96.50 93.68 45.33 184.46 36.02 0.82 0.07 45.75 175.00 39.66 fruit 94.05 82.58 40.38 217.00 43.83 0.98 0.06 47.88 194.70 56.74 vegetable 96.89 82.71 36.15 253.72 51.55 1.15 0.63 41.24 216.29 68.24 logret(energy) 0.00 -0.01 -0.08 0.09 0.02 1.60 5.25 -0.02 0.06 0.01 logret(fruit) 0.00 0.00 -0.36 0.46 0.08 0.16 7.82 -0.14 0.10 0.08 logret(vegetable) 0.00 0.01 -0.30 0.28 0.10 -0.06 0.42 -0.19 0.19 0.12 table a2. augmented dickey-fuller unit root test results. energy fruit vegetable logret(energy) logret(fruit) logret(vegetable) with constant t-statistic 1.39 5.28 1.02 -9.08 -6.10 -8.42 prob. 1.00 1.00 1.00 0.00 0.00*** 0.00*** with constant & trend t-statistic -0.49 2.15 -1.31 -9.05 -6.98 -8.46 prob. 0.98 1.00 0.88 0.00 0.00*** 0.00*** without constant & trend t-statistic 3.64 6.46 2.80 -9.11 -6.09 -8.43 prob. 1.00 1.00 1.00 0.00 0.00*** 0.00*** *** indicates that log-returns of epi, fpi and vpi has no unit root. table a3. arma model outputs for epi, fpi and vpi. coefficients ar(1) for epi arma(2,2) for fpi ma(1) for vpi est sig est sig est sig const −3.99718e-05 0.99 −0.000537185 0.71 0.00 0.99 phi_1 0.33 0.00 1.55 0.00 phi_2 −0.795506 0.00 theta_1 −1.76350 0.00 0.41 0.00 theta_2 0.83 0.00 mean dependent var 0.00 −1.91e-17 0.00 mean of innovations 0.00 0.00 −0.000061 r-squared 0.11 0.27 0.13 log-likelihood 417.55 206.90 154.05 schwarz criterion −819.7365 −383.0893 −292.7434 s.d. dependent var 0.02 0.08 0.10 s.d. of innovations 0.02 0.07 0.10 adjusted r-squared 0.11 0.26 0.13 akaike criterion −829.0905 −401.7972 −302.0974 hannan-quinn −825.2939 −394.2041 −298.3008 arch lm test 56.00 (9.65e-10)*** 51.3 (7.91e-09)*** 15.33 (3.20e-02)** ** and *** indicate that there is an arch effect on residuals. 54 bio-based and applied economics 11(1): 37-54, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10896 harun uçak, esin yelgen, yakup arı appendix-b table b1. var lag selection. lags loglik p(lr) aic bic hqc 1 1484.04 -18.51* -18.28* -18.42* 2 1485.41 0.97 -18.42 -18.01 -18.26 3 1487.78 0.86 -18.34 -17.76 -18.10 4 1494.17 0.17 -18.30 -17.55 -18.00 5 1499.96 0.24 -18.26 -17.34 -17.89 6 1508.76 0.04 -18.26 -17.16 -17.81 7 1521.01 0.00 -18.30 -17.03 -17.78 8 1526.37 0.30 -18.26 -16.81 -17.67 *the most convenient var lag is selected 1. table b1. var(1) model output. dependent var energy volatility (evol) fruit volatility (fvol) vegetablevolatility (vvol) est sig est sig est sig const 0.01 0.00 0.05 0.00 0.01 0.07 evol[-1] 0.77 0.00 −0.381 0.35 0.44 0.03 fvol[-1] −0.0063 0.49 0.30 0.00 −0.0383 0.29 vvol[-1] −0.0263 0.02 −0.0193 0.83 0.82 0.00 mean dependent var 0.02 0.06 0.06 sum squared resid 0.00 0.13 0.13 r-squared 0.60 0.10 0.10 f(3, 162) 82.29 5.79 5.79 rho −0.021 −0.004 −0.004 s.d. dependent var 0.01 0.03 0.03 s.e. of regression 0.00 0.03 0.03 adjusted r-squared 0.60 0.08 0.08 sig(f) 0.00 0.00 0.00 durbin-watson 2.04 2.01 2.01 all lags of evol f(1, 162) 241.41 [0.0000] 0.86818 [0.3528] 5.0403 [0.0261]** all lags of fvol f(1, 162) 0.4696 [0.4942] 15.226 [0.0001] 1.1422 [0.2868] all lags of vvol f(1, 162) 5.6895 [0.0182] 0.044003 [0.8341] 354.2 [0.0000] **the test statistics of all lags of evol in vvol model indicates that evol granger causes vvol. bio -based and a ppl ied economics bae bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 copyright: © 2021 i. sotiropoulou, p. deutz. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: i. sotiropoulou, p. deutz (2021). understanding the bioeconomy: a new sustainability economy in british and european public discourse. bio-based and applied economics 10(4): 283-304. doi: 10.36253/bae-9534 received: august 9, 2020 accepted: october 14, 2021 published: march 31, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio gaetano santeramo. orcid is: 0000-0003-2545-9611 pd: 0000-0002-9165-5322 understanding the bioeconomy: a new sustainability economy in british and european public discourse irene sotiropoulou1, pauline deutz2 1 independent researcher, hu6 7rx, united kingdom 2 department of geography, geology & environment, university of hull, hu6 7rx, united kingdom *corresponding author. e-mail: irene.sotiropoulou@gmail.com abstract. over the past decade, the term bioeconomy has emerged in both policy and academic discourse. implying a technology-driven approach to wealth generation from organic materials, the term has taken hold with so far limited critical engagement. it is a contestable rather than contested term. noting the rise of numerous other ‘economies’ (blue, green, circular) on a similar timeframe, this paper undertakes a critical discourse analysis of academic literature and uk/eu policy documents using the term ‘bioeconomy’ to produce a contextualised understanding of how it is used in both theoretical and practical contexts. our analysis shows that bioeconomy, as with the other ‘sustainability’ economies, which we term the ‘s-economies’, prioritises the economy and the markets as the solution brokers for the environmental and economic problems they seek to address. the apparent fragmentation of the theory and policy concerning the environmental sustainability of economic activity is expressed through the variability of terms that aspire to establish multiple economies functioning at the same time. limited empirical analysis of the existing ‘bioeconomy’ is symptomatic of the dissociation between theory and practice, emphasizing technological approaches favouring capital intensive approaches over local solutions. the s-economies, including the bioeconomy, are an attempt to bypass economic structural realities that otherwise would need to be addressed. keywords: bioeconomy, knowledge-based economy, green economy, circular economy, sustainable economy. jel codes: l6, o1, o3, p2, p4, p5, q1, q2, q5. 1. introduction in this study we are critically analyzing the role of the bioeconomy as a term and practice in academia and policy. broadly, ‘bioeconomy’ refers to economic activity directly drawing on biogenic material (derived from recently living plants and/or animal matter), to be distinguished from nonbiogenic based resources and fossil fuels. bioeconomy, or bio-based economy, is an expression coined in recent years by experts and policy makers, http://creativecommons.org/licenses/by/4.0/legalcode 284 bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 irene sotiropoulou, pauline deutz thereby integrating into a single term both the economic significance and awareness of the type of resources to be utilised (albrecht et al., 2010; begley et al., 2011; de besi and mccormick, 2015; allen et al., 2017; university of york, 2017; bell et al., 2018; aguilar and patermann, 2018; lewandowski et al., 2018). the bioeconomy has been represented as a way of addressing environmental emergencies and socio-economic challenges at the same time (oecd, 2006, 2009, 2018; benner and lofgren, 2007; birch, 2007, 2017a; cooper, 2007; asveld et al., 2011; kitchen and marsden, 2011; kircher, 2012; arancibia, 2013; hanlin et al., 2013; kautto and mccormick, 2013; arts et al., 2014; barben et al., 2016; viaggi, 2016; european commission, 2018a, 2020a, 2020b). the bioeconomy has already had an important economic impact. for example, in 2018 in the uk the bioeconomy amounted to two hundred twenty (220) billion of pounds of gross value added to the economy, supporting more than five million jobs (hm government 2018). in 2015 the bioeconomy in the european union reached an added value of 1,460.6 billion euros, which is eleven percent (11%) of overall gdp (kuosmanen et al. 2020). it may not be coincidental that the term has become prominent in an era of stagnating economies and high unemployment, following the financial crisis of 2008. over this same timespan other ‘economies’ have become prominent too, including the green economy (unep, 2009; pearce and barbier, 2000; bina, 2013; baarsden et al., 2014; antikainen et al., 2016; viaggi, 2016; ge and zhi 2016; ferreira gregorio et al., 2018; merino-saum et al., 2020; benson et al., 2021) promoted by the un as an approach to implementing sustainable development (2012); the marine resources-based blue economy (unctad 2014; smith-godfrey, 2016; le heron and winder, 2017; lee, noh and kim, 2020); the circular economy (promoted by the eu and others as a carbon control and competitiveness enhancing initiative; european commission, 2015). other terms such as the low carbon economy (stern 2007; hm government, 2009; zhang, 2010; foxon, 2011; lyu, ngai and wu 2019) are also seeking to use environmental investments to correct an economic imbalance – i.e., to promote growth and with assumed social benefits (i.e., usually employment). in this paper we examine how the term bioeconomy is used, how it is connected or situated in relation to other terms that represent various types of economic activity with aspirations to deal better with nature and the resources nature offers to human societies, whilst preserving, if not promoting, economic growth. drawing on analysis of policy and scientific documents, this paper undertakes a critical discourse analysis of the use of the term bioeconomy in policy documents alongside a comprehensive review of the social science academic literature relating to the bioeconomy in order to gain a contextualised understanding of how the term is being used both theoretically and in practical terms. research in critical discourse analysis stresses the significance of terms used in policy analysis as representing a social, political and economic context (jessop, 2004; farrelly, 2010; farrelly et al., 2019). certain expressions or forms of expression become accepted as ‘normal’ or inevitable, and this promotes ease of communication with groups who have the same understandings of the terms used. however, usage and the approach it represents may reinforce the exclusion of other interests or groups, either by representing a barrier to entry in the dialogue or by perpetuating a policy that favours some interests over others. to achieve a contexualisation of the bioeconomy, we examine the concept within the context of the other discursive attempts related to the sustainable types of economies proposed during the last decades, and we also consider the limitations of the scope of existing applications of the bioeconomy. the following section presents an outline of our approach and methods; section three examines the rise of the term bioeconomy since 1990s. section four explores how the bioeconomy is used in policy discourse and the fifth section examines critically the academic discourse about the bioeconomy. section six examines the various (aspiring to be) sustainable forms of economy that emerged in recent decades. the general discussion of our findings is presented in section seven and conclusions are presented in the final section eight. 2. approaches and methods this paper stems from the thyme project, which is a research consortium comprising the university of york, teesside university and the university of hull in order to investigate ways to support the local economy of the north east of england through the mobilisation of bioeconomic processes. thyme project is, in other words, an umbrella project within which several approaches to the bioeconomy are used to develop new understandings, production processes and community engagement. the approaches are employed in sub-projects, of which one example is the research project this paper stems from. in this paper we draw our methodology from two approaches. one is critical realism as a way to understand discourse and praxis in its historical context. critical realism is the epistemological approach which acknowledges that ‘real’ events and processes may only 285understanding the bioeconomy: a new sustainability economy in british and european public discourse bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 be observable through perceptions; the scholar is committed to what is possible to be known, while having in mind that this knowledge might not be perfect or objective (sayer, 2002). in critical realism, both the reality we perceive and the knowledge we have access to are thought of as historically constructed through the social, economic and political contexts we live in. through this approach, we aspire to present a version of discursive reality that is well founded on actual uses of the term of the bioeconomy and we also aspire to think in terms of the actual economic and political conditions that affect the use of the term or made the use of the term possible in the first place (archer et al., 1998; birch, 2017b). we are aware that discourse is a political economic endeavour and that, just like the people who have written about the bioeconomy until now, we also have a certain positionality both as researchers and as human beings. this makes us ready to re-visit and refine our approach in the future, discard analyses that we now think are the best we can have given the limitations of our research and interpret both our data and our collection of data through new prisms if what we have at hand does not adequately provide us with the analytical and synthetic tools we need to understand our subject matter. our second approach to the examination of the policy documents as sources is informed by grounded theory (charmaz, 2006; glaser and strauss, 2006). grounded theory is the epistemological approach according to which a research project can start without a pre-established or existing theory. gathering data with attention to detail and having as a priority to describe reality as it is possible to be approached by the researcher means that theory is chosen while or after the analysis of data. in case there is no theory with analytical capacity to explain the phenomena that the research data reveal, the researcher will attempt to create a theory based on the data, if that is possible. a grounded theory does not aspire to universality, although it can give results that can be widely applied, and is always well connected to research findings. we use this approach to ensure that our arguments stay as connected as possible to the texts we use as sources. this aids the uncovering of social and political context of the documents in order to identify possible understandings and interpretations (as opposed to an interpretation reflecting a theoretical position). concerning our sources: we used the existing academic literature, public statements and official documents, where “bioeconomy” as a term is mentioned. we use the search engines of web science, scopus and google scholar, and also using the snowballing method to find references used in published papers to make sure that we have not missed references that the search engines might miss for technical reasons. we are not addressing the engineering or scientific papers which currently dominate the field. for a recent review of these in combination with the literature of social sciences see bugge et al. (2016). the search was done in four languages (english, french, spanish, greek). all lay articles we found concerning the bioeconomy were written by experts (who were using their expertise credentials in the texts they were writing), and only in one case we found the use of the term to be related to a community practice (in greece) about which there is no public follow-up or further replication of the use of the term. in addition, we are not incorporating papers that address “bioeconomic” phenomena without mentioning the term. although we acknowledge the contribution of such papers to the understanding of related issues, this paper is specifically concerned with the use of the term “bioeconomy”. this paper presents a major part of the theoretical or desk-research section of our project. we have also conducted extensive field research about the farmers markets and open-air markets in east yorkshire, through the use of ethnographic methods (observation, observation by participation, interviews, analysis of public material released by the markets). our purpose is to document bioeconomic practices that do not belong to big industrial process, yet might be crucial for both environmental sustainability and social sustainability of a region. we are preparing detailed accounts of the field research findings in other studies. table 1 shows the range of sources we have used in this paper. we categorised the papers according to their content in terms of discipline and not according to the discipline of the journal they are published in. in many cases, journals focused on technology or environmental sciences publish a study that belongs to another discipline, like a social science. for the table we used only the sources directly related to the bioeconomy and not other sources on peripheral topics. our turn to official documents and policy declarations in addition to academic literature was made because it seems that the role of the states and international organisations is fundamental in the history and discourse of the bioeconomy. a governmental role in the development of the bioeconomy also seems to be expected by both the private sector and academics (brunori et al., 2011; pavone, 2012; gustavsson et al., 2013; barben et al., 2016; bbiju and scar, 2019). at this stage, we did not use linguistic quantitative methods, like corpus analysis, because we want to focus on notions themselves and how they are used within specific historically perceived political economic con286 bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 irene sotiropoulou, pauline deutz texts. in other words, we examine the context of the use of each word, the positionality of the user and the possible or intended effects of that use. 3. bioeconomy: rise of the term bioeconomy in the 1990s and beyond the term bioeconomy is quite new. it appears to have emerged from the pool of ideas and interactions associated with the biotechnology and the cell factory key action of the european union (1998-2002), at least for the countries who were members of the european union (european commission, 2007; aguilar and patermann, 2018). the term “biotechnology” was associated with policies promoted through the perception of biological knowledge and know-how as a particular type of value in the economy, which were adopted before the emphasis on bio-materials emerged. biotechnology refers to “the application of science and technology to living organisms, as well as parts, products and models thereof, to alter living or non-living materials for the production of knowledge, goods and services” (arundel and van beuzekom, 2006: 7; miller, 2007). biotechnology is broadly used in combination with the terms bio-based economy and knowledge-based economy, explicitly mentioning nowadays bioeconomic sectors like pharmaceutics, well before being combined as the “knowledge-based bio-economy” appeared in eu and oecd documents (oecd et al. 1997; neef, siesfeld and cefola 1998; european commission, 2002; oecd 2002, 2005). the term (bio-based economy) is also used in oecd documents, but there is no information in oecd archives who or which country first used the term bio-based economy (begley et al., 2011; birch, 2017b; aguilar and patermann, 2018; bell et al., 2018). before that, based on our search in english-speaking literature, we found the term “bioeconomy” only in a biology paper related to the behaviour of the house mouse (berry and bronson, 1992). table 2 provides a timeline for the key terms addressed in this section. the major emphasis of the eu policy in the late 1990s-decade of 2000s was on biomass to be used as non-fossil fuel and on the production of food (whether through agriculture or pasture/livestock production), including products that were not food but still were based on biological raw materials (albrecht et al., 2010). a second sector that has been strongly linked to the bioeconomy was pharmaceutical research and production. the documents of the european union use the latter as an option or direction of bioeconomic activity with increasing momentum after 2010. a special emphasis on pharmaceutical aspects of the bioeconomy is also given by the oecd report on the bioeconomy, especially through the description of future crisis or disaster scenarios that the pharmaceutical bioeconomy would resolve (oecd, 2006, 2009, 2018; albrect et al., 2010; styhre and sundgren, 2011; european commission, 2012, 2018a, 2020a, 2020b; benini et al., 2013; bell et al., 2018). the use of the term bio-economy within the phrase and/or acronym knowledge-based bio-economy (kbbe) was also linked to sustainability, though not as an inherent characteristic, but as a design element that can be possible, feasible and desirable, under certain policy choices (albrecht et al., 2010; allen et al., 2017; birch, 2017b). after the european commission published their communication for sustainable growth in 2012 (european commission, 2012), the bioeconomy (or bio-economy) became a prominent topic of debate concerning policies that can be related to improved economic pathways for the european union. the main idea was to use bioeconomy as an engine of economic sustainability, to support innovative solutions in a variety of sectors using policies that are coherent among each other. bioeconomy would be the umbrella term which would allow this coherence, or at least, this was the plan. moreover, the bioeconomy was supposed to provide answers to issues or provide opportunities that had emerged as a consequence of waste policies that were restricting the landfilling of biodegradable waste (girardin and peigne, 2003; taiwo, 2011; cal et al., 2017). the widetable 1. bioeconomy literature used in the paper. sector of origin author role type of document scope number of texts policy state report normative 6 international organisation report normative 4 researcher report normative 2 academia researcher – academic paper policydescriptive 16 researcheracademic paper theorydescriptive 44 researcheracademic paper theory descriptive 1 researcher – academic paper theoryempirical 10 researcheracademic book or edited book theorydescriptive 3 research – academic paper normative 3 business business report normative 2 287understanding the bioeconomy: a new sustainability economy in british and european public discourse bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 spread uptake of composting and anaerobic digestion in response to the landfill directive pointed to biodegradable residues as a valuable commodity, from which added value could be extracted by the application of science and technology (boons et al., 2015). economically, in the 2000s the bioeconomy was represented as able to provide new investment opportunities for the few who had access to capital and new job opportunities for the many who were experiencing a job market with high long-term unemployment rates and increasing employment instability at the same time (albrecht et al., 2010; styhre and sundgren, 2011; birch et al., 2012a, 2012b; dallemand et al., 2015; goven and pavone, 2015). this provided an attraction for policy makers similarly to other economically-driven environmental approaches (further discussed in section 6). the widespread concerns relating to the social and environmental consequences of biofuels (displacement of food crops and consequent reduction in the affordability of food) resulted not in the abandonment of biofuels, but in the drive for a technological solution via a “second generation” of biofuels, that would be more sustainable or possible to be sustainably managed although they are more costly to produce (boody et al., 2007; jordan et al. 2007; horlings and marsden, 2011; bhandary et al., 2013; hanlin et al., 2013; mohr and raman, 2013; eggert and greaker, 2014; lewandowski, 2015; bell et al., 2018; brent et al., 2019). one should note that up until now (2021), although different sectors of the bioeconomy are described as part of an integrated policy vision, questions of sustainability or the connections with other economic activities are not well developed in the literature or policy statements. in them, the economy is compartmentalised; the bioeconomy is perceived more as an exogenous economic design rather than organic part of economic activity. nonetheless, there is an implicit assumption that the wider economy can be made use of, with the addition of some technological solution, without an analysis of how the interconnection of the bioand the wider economy might work or of what the consequences might be. technology is the assumed solution to problems of human societies; thus the problems faced by human communities can be ameliorated by further technology-drive research. the use of the term bioeconomy in support of the economy as presently structured and in favour of the bioeconomy-investing companies means that the state authorities had a crucial role not only in shaping bioeconomy as a notion but also in the creation of a bioeconomic market. no doubt, nation states were already in search of possible solutions to the problems and contradictions mentioned above. at the same time, their intervention was supported or even demanded by the bioeconomic industries in a straightforward way (albrecht et al., 2010; brunori et al., 2011; pavone, 2012; arancibia, 2013; kautto and mccormick, 2013; birch et al., 2014; dallemand et al., 2015; goven and pavone, 2015; barben et al., 2016). a prominent role was also undertaken by international organisations like the oecd and the european union who were trying to promote bioeconomic policies and support bioeconomic activity within this big business setting (oecd, 2006, 2009, 2018; european commission, 2012, 2018a, 2020a, 2020b; fao, 2018; us department of energy, 2016; bbiju andscar, 2019). given the rapid rate of diffusion to the term bioeconomy through national, eu, oecd policy documents, it is difficult now to trace the lines of influence between the different organisations. the following section examines in more detail the recent developments in the use of the term in the eu and the uk. 4. current usage of the bioeconomy in policy discourse having briefly examined the development of bioeconomy as a policy in the eu, we now apply critical table 2. bioeconomy timeline. this table summarises the key concepts relating to the bioeconomy to provide a timeline. for sources, see the text. date term emphasis late 1990s biotechnology economic benefits from the development of commercial application of biological research early 2000s biofuels carbon reduction and energy security later 2000s bioeconomy biofuels – second generation emerging options for pharmaceutical industry emerging 2010 social benefits considered (e.g., employment) 2012 identified as sustainable growth strategy in the eu – but with economic emphasis focused on capital intensive industry 2010s organic waste management potential and biorefining 2018 ec address wider sustainability benefits and refer to small scale bioeconomic activity link made to circular economy uk re-emphasis the large-scale industry aspect 288 bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 irene sotiropoulou, pauline deutz discourse analysis (cda) to the text of key eu and uk policy documents. the original eu policy statement on the bioeconomy was the european commission joint research centre report (benini et al., 2013), which shows how an international institution steps into the production of knowledge while creating policy at the same time. this report was a study made by the researchers of the centre, which is a research facility under the auspices of the european commission. here we reproduce extracts (pp 18-20) to illustrate the aims and intentions for a bioeconomy [emphasis as in original text]: “towards this end, management of the bio-economy would imply: i) optimizing resource allocation by addressing multi-dimensional and potentially conflicting issues (for example, the “food versus fuel” debate); ii) driving research and innovation in the primary production and processing sectors; iii) developing new industrial concepts and business models, and open new markets, iv) and the creation of new high-skill jobs. … while having research and development at its core, eu bio-economy strategy aims also to reconcile sustainable agriculture, forestry and fisheries, food production and industrial use of biological feedstock. in addition, eu bio-economy strategy stresses the crucial importance of non-technological factors, such as wide stakeholder involvement and partnering, and the necessity of developing a coherently integrated eu policy framework for the bio-economy, including regional, agricultural, industrial, environmental and energy policy. the action plan focuses on three key pillars: i) developing new technologies and processes for the bio-economy, by using r&d and innovation to produce renewable raw materials sustainably in agriculture, forestry, fisheries and aquaculture, and to process renewable raw materials into value-added products in the bio-based sectors. ii) developing markets and competitiveness in bio-based industries. concrete actions include support for: development of new markets and bio-based value chains, and commercialization of new bio-based products; demonstration plants and up-scaling facilities, and establishing r&d public-private partnerships. iii) collaboration between policymakers and stakeholders by means of a more co-ordinated bio-economy governance mechanism (i.e. including cap, cfp; rtd1 and innovation; industrial policy and competitiveness; employment; energy and public health policies; eu environmental policies on: resource efficiency, sustainable use of natural resources and protection of biodiversity).” 1 cap: common agricultural policy. cfp: common fisheries policy. rtd: research training and development, which is the eu directorate general for research and innovation in these two passages the bioeconomy is used as a panacea to address and also interconnect other policies of the european union. the emphasis is on the economy and how the economy can be supported by the bioeconomy (see the frequent use of the word ‘competitiveness’). the bioeconomy is expressly seen as being related to and covered by eu environmental policies. there is reference to coordination of policies, e.g., bioeconomy as aiding sustainability (e.g., of agriculture), but the document does not indicate how that might function in an environmental sense. several references in the text are made to stakeholders – indicating that a common cause is sought with at least the business community, and recognition that although policy makers and scientists may collaborate in research and development for policy priorities, the implementation requires active engagement from other sectors of society. the public is referred to implicitly as consumers, i.e., passive stakeholders who will respond to the policy and buy or use the products, but will not participate in the formulation of the policy. the document is more a future-research-oriented document rather than an appraisal of current bioeconomic processes. even for the future, the focus is on certain industrial procedures and aims related to industry aspiring to development in biofuels and chemicals. the policy steps undertaken however, induce an effort to protect the food supply, thus tacitly acknowledging the issue of the first generation of biofuels i.e. the risk of competition for land with the potential to increase the price of food (jordan et al. 2007; ajanovic, 2011; baldes et al., 2013; brent et al., 2019). the eu approach to the bioeconomy has, however, changed since that initial report in 2013. in the 2018 report by the european commission (2018a, a sustainable bioeconomy for europe) the discourse has greatly changed. we observe some indirect reference to sustainability with respect to production sectors like forestry, fisheries, food, and feedstock as well as to environmental or energy policies. moreover, within a wider interpretative context the stakeholder engagement can be perceived as including community engagement procedures. the report, however, does not expand beyond a purely economy-focused approach. the wording would allow further sustainability negotiations or contestations, if the politics in the eu member states and institutions were to use this document as a starting point for a debate. in the european commission (2018b) staff working paper we can see that a major emphasis is given on sustainability and more details concerning how the bioeconomic policy of the european union can be deployed to support specific environmental and economic activities. however, the disparity of the focus between the two docu289understanding the bioeconomy: a new sustainability economy in british and european public discourse bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 ments reveals the internal tensions within the commission. some parts of the bioeconomic policy remain at wishful thinking or debate level in the working paper. the parts of the debate which are receiving a consensus are those appearing in the main policy document (european commission, 2018a). the same disconnection we observed between perceptions of farming policies and the role of the bioeconomy and the bio-based economy can also be observed in the more recent farm to fork strategy of the eu (european commission, 2020a, 2020b). there is a close association between the bioeconomy and circular economy. the latter refers to the maximisation of value from resources by design of products to promote longevity and recovery of materials at end of product life (european commission, 2015; 2019). the principle of circularity can be applied to the bioeconomy as well as to non-biogenic resources. the european commission report about the sustainable bioconomy in europe links bioeconomy to sustainability and circularity from the very first part of the introductory section (european commission, 2018a, p. 4), where in the box containing the definition of the term we read: “sustainable & circular: bioeconomy the european way the bioeconomy covers all sectors and systems that rely on biological resources (animals, plants, micro-organisms and derived biomass, including organic waste), their functions and principles. it includes and interlinks: land and marine ecosystems and the services they provide; all primary production sectors that use and produce biological resources (agriculture, forestry, fisheries and aquaculture); and all economic and industrial sectors that use biological resources and processes to produce food, feed, bio-based products, energy and services. to be successful, the european bioeconomy needs to have sustainability and circularity at its heart. this will drive the renewal of our industries, the modernisation of our primary production systems, the protection of the environment and will enhance biodiversity.” in this document responses to climate change and also the need to protect the economy (notwithstanding environmental constraints) are very visible. in the following sections the linkages to the circular economy concept are represented as more or less overlapping actions that the bioeconomy can deliver better than circular economy or at least more comprehensively than other alternatives (european commission, 2018a: 5-14). environmental constraints on the economy are perceived as an argument for more research and data collection for the bioeconomy in order to abide within those constraints (european commission, 2018a: 15): “the commission will implement an eu-wide, internationally coherent monitoring system (action 3.2) to track the progress towards a sustainable, circular bioeconomy in europe and to underpin related policy areas. knowledge gained will be used to provide voluntary guidance for operating the bioeconomy within safe ecological limits”. contrary to that declaration, the report by the british government that very same year (hm government, 2018) seems to express a more economic orientation, reflecting previous eu policies and statements. “what is the bioeconomy? the bioeconomy represents the economic potential of harnessing the power of bioscience, using renewable biological resources to replace fossil resources in innovative products, processes and services. the bioeconomy in the uk in 2014 has been estimated to have contributed to £220bn of output across the uk economy, supporting 5.2m jobs. building a world-class bioeconomy will transform our economy by removing our dependence on finite fossil resources. bioscience and biotechnology has the potential to create new solutions that are economically and environmentally sustainable as well as resource efficient. these solutions will help to tackle global challenges and create opportunities in agri-food, chemicals, materials, energy and fuel production, health and the environment.” (hm government, 2018: 9). the economic orientation is not just a misrepresentation due to the short length of a definition. in pages 16-17 of the same report we read the goals of the bioeconomic policy [emphasis with bold is in the original text]: “goals we have set out four high level goals, which are reflected in the actions of this strategy. 1. capitalising on our world class r&d: we will continue to advance our world class research, development and innovation base, leveraging greater investment to turn our cutting edge ideas into commercial success in the global marketplace. 2. maximising productivity: we will maximise the potential of our bioeconomy assets right across the uk, making the most of our knowledge, facilities and people to increase productivity from our existing renewable biological resources, 3. delivering benefits: we will support industry sectors to ensure that this strategy delivers real, measurable benefits for the uk, creating jobs, increasing productivity and doubling the size of the impact of the bioeconomy to £440bn by 2030. 4. creating the right market conditions: we will create the right national and international market conditions to allow innovative bio-based products and services to thrive, raising public interest, increasing skills in the workplace and sales to the market.” 290 bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 irene sotiropoulou, pauline deutz searching to see what role has been assigned to sustainability in that same british government report, we see that it is mostly linked to the plastic packaging policies but apart from that (plastic packaging), it is used in a generic, more aspirational than action-based and rather limited way. sustainability does not seem to take central role in the design of the uk bioeconomic policy (hm government, 2018: 4-5,10-12, 16, 24, 35, 37, 48, 51, 53-55). the difference in emphasis between the uk government and eu documents raises the question of the extent to which other member states have adopted the eu approach as opposed to devising their own variation, and indeed where the push for the eu approach came from. an example is the french bioeconomic strategy as it was announced in 2017 by the ministère d’ agriculture et alimentation (2017). sustainability issues, society’s involvement and even agroecology appear in the french ministry’s leaflet. in the report of the german bioeconomy council (2019: 72-106) one can see the stark difference between the uk bioeconomic strategy and the strategies of other countries like spain, latvia, italy, france and norway. the uk focuses on the economy only (plus reduction of waste) while the other countries are explicit in connecting environmental sustainability and/or climate change to the bioeconomic activities. nevertheless, the bioeconomy strategy adopted in january 2020 by the german government seems to be closer to the 2018 uk strategy, mentioning only the economic sustainability that the bioeconomy can bring and leaving as implicit or assumed any discussion about protection of the environment or environmental sustainability of the bioeconomic processes (federal government of germany, 2020). as we have mentioned above in section 3, the major effect of policy-makers and academics both being involved in the promotion and design of the bioeconomy has been that the term itself has been constructed around top-down policies and big corporate structures as the most probable private agents of bioeconomic activity. from the excerpts and examples used in this section, we see that this direction is normalised in official documents. in the european commission 2018 report, the word “small” referring to small farmers and businesses is used in a way that reflects more an awkwardness that the eu policies have to take into account the small production modes and arrangements that exist in the continent rather than supporting them by priority or as an inherent characteristic of the regional economy. we have to comment, though, that the quest for bioeconomic solutions that can be adapted to small production exists explicitly in the report although most of the details are placed in the staff working paper (european commission, 2018a: 11; 2018b: 46, 58, 60-62). conversely, in the british government report the word “small” does not exist at all. the bioeconomic strategy in the uk seems to have been heavily influenced by industry perspectives and by the perception that it is the economy which is the first priority in the debate about the bioeconomy. it is indicative of this orientation that the british biotechnology and biological sciences research council has commissioned a private company named capital economics to prepare a report (2015) in order to assess the importance of the bioeconomy for the british economy. this shows that the prioritisation of the economy, especially the large industrial mass-production based economy, in perceiving the bioeconomy is a more or less political trend or mediumterm occurrence in british research priorities. an example of prioritising large industrial massproduction in the bioeconomy is the thyme project itself, in which this paper originates. the project is the only major one that we could find, after searching the projects funded by the uk government until 2020. sustainability as a goal of the project refers to the industries of the region and this is very understandable given that this industrial sustainability is the most common understanding of the bioeconomy (goven and pavone, 2015; aurambout et al., 2016; mustalahti, 2018; university of york, 2017).). the local character of entrepreneuship is not taken into account by mainstream economic assessments of the regional economy (charles and hodgson, 2008, viaggi, 2016; university of york, 2017) and our economic understandings/theories for this scale are very limited. the bioeconomic process of food production can thus have various aspects and it can be small scale and follow various routes of generating income for the producers. those possibilities, however, are very rarely discussed as a possible and viable approach in national or supranational visions of the bioeconomy (gustafsson et al., 2011). 5. academic discourse about the bioeconomy: a critical approach we now turn to reviewing the academic literature on the bioeconomy, in order to see how the academics, some of whom in one or another advise or influence policy-makers, perceive or develop the notion of the bioeconomy. in most cases academic discussion of the bioeconomy is normative, i.e., relates to advancing the bioeconomy through technological developments, whilst assuming those to be economically and/or environmentally 291understanding the bioeconomy: a new sustainability economy in british and european public discourse bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 beneficial (the distribution of those assumed benefits being unquestioned). the term, as we have already mentioned, has been used extensively within a big-business and big-policy framework (oecd, 2006, 2009, 2018; european commission, 2012, 2018a; hm government, 2018; bbiju, 2019). the big-industry orientation of the bioeconomy has been noted by the food and agriculture organisation of the united nations in their relatively recent report about the bioeconomy (fao, 2018). scientific attention has been focused on specific technological advances e.g., the chemical engineering of biofuels, or derivation of high value constituents like pharmaceutical products (oecd, 2006, 2009, 2018; albrecht et al., 2010; asveld et al., 2011; brown et al., 2011; styhre and sundgren, 2011; bringezu et al., 2012; european commission, 2012; dallemand et al., 2015; barben et al., 2016; cal et al., 2017). in particular, the bioeconomy became the byword for people who wanted to believe, or actually believed, that mass production of biofuels would be the most effective solution to the problem of maintaining vehicle-dependence whilst reducing fossil fuel-related carbon emissions and also circumventing fuel security issues (hilgartner, 2007; jordan et al. 2007; gustavsson et al., 2013; lewandowski, 2015; brent et al., 2019). this narrow focus on the bioeconomy as synonymous with technological advancements (i.e., biotechnology) overlooks the perceptions and practices related to the existing economy that it was supposed to be connected with. the connection is seldom analysed, leaving at best a partial understanding of the likely and actual impact of biotechnology on the economy. for some time, the bioeconomy was perceived by academics precisely in the way that policy-makers do, i.e., an opportunity for capital-intensive economic development, as indicated by the technical literature (whether pharmaceutical or relating to enhancing the efficiency of technologies for extracting value from bio-residues). there are academic writings where the bioeconomy is a framework that is treated as known and non-problematic by the authors (duchesne and wetzel, 2003; dech and pocharel, 2011; galt et al., 2017). this is also more often observed in papers that have more of a technical or engineering character (chandra et al., 2011; dech, 2011; laserre et al., 2014; achury et al., 2015; le heron and winder, 2017). there were, however, voices who were critically assessing bioeconomy and were also offering to the debate other perceptions about it that went beyond the big corporation-oriented construction of a bioeconomy (helmreich, 2008; pavone, 2012; mustalahti, 2018). critique in particular focused on how the bioeconomy was functioning as a discourse to engage broader audiences and various social groups into economic decisions that were made while taking as their first priority the securing of the profits for companies who would invest in research and development of bioeconomic products (birch, 2006, 2007, 2009, 2012; larsen, 2007; fumagalli and morini, 2010; brown et al., 2011; birch and tyfield, 2013; arts et al., 2014; goven and pavone, 2015; bell et al., 2018). the critique of the corporate orientation of the bioeconomy is something we should delve into a bit further. first, the critique shows the impasses of this approach taken by both the private and public sectors with relation to the bioeconomy and its use as a panacea for the environmental and economic problems of 21st capitalist economies (cooper, 2007; hilgartner, 2007; kitchen and marsden, 2011; arancibia, 2013; birch and tyfield, 2013; goven and pavone, 2015; birch, 2017a). second, the most important arguments were related to accelerating and intensifying the pace with which nature and knowledge are privatised, commoditised and assetised (cooper, 2007; larsen, 2007; helmreich, 2008; fumagalli and morini, 2010; pavone, 2012; hendrickx and reis-castro, 2013; goven and pavone, 2015; birch, 2017a, 2019). third, some authors are severely critical about seeing all production processes and all natural materials as part of a scientifically organised profiteering and management process (brown et al., 2011; goven and pavone, 2015). this scientised perception of production and nature is considered to turn all activity related to those processes and materials into a profit-making process based on values that have been defined in advance. those same values are used as the targets and instruments of human activities that aspire to resolve the problems that the profit-making is creating (larsen, 2007; birch, 2012, 2019; kitchen and marsden, 2011; hendrickx and reis-castro, 2013). fourth, development of knowledge about nature and re-use of resources is a commodity that needs to be patented to become an asset, so that businesses can invest into that knowledge production by having secured that the knowledge or the practical implications of it will be privately owned by them (pavone, 2012; birch 2017a). that this would favour the development of specific types of knowledge and technology and specific ways of using natural resources suggests that the needs of the ecosystems would not be a priority or they would be subjected to the needs of the businesses to profit economically instead of the other way round (birch 2012; goven and pavone, 2015). as a consequence, the entire bioeconomic activity would not be sustainable or it would even harm further degraded ecosystems and problematic economies (birch, 2007, 2019; cooper, 2007; hilgartner, 2007; fumagalli and morini, 2010; arancibia, 2013; birch and tyfield, 2013; delvenne 292 bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 irene sotiropoulou, pauline deutz and hendrickx, 2013; delvenne et al., 2013; hendrickx and reis-castro, 2013; bugge et al., 2016; gawel et al., 2016; gawel et al., 2019). sustainability, especially the environmental part of it, is perceived as being distinct from the bioeconomy. we saw this in policies providing nothing to ensure the short or longer term sustainability of the bioeconomy. and likewise in the academic literature, the bioeconomy is seen as either sustainable or not without this ambivalence being thought of as problematic (jordan et al. 2007; baardsen et al., 2014; dankbaar et al., 2014; olikainen, 2014; caivano et al., 2015; sauvee and viaggi, 2016; viaggi, 2016; allen et al., 2017; szekacs, 2017; heijman and shepman, 2018; oecd, 2018: 25-68). in most cases it is assumed implicitly that the bioeconomy could be sustainable if we do not have not evidence to the contrary (passet, 2012; kircher, 2012; kautto and mccormick, 2013; goven and pavone, 2015; lasserre et al., 2014; aurambout et al., 2016; ferreira gregorio et al. 2018). and we note that there is very little research outside of laboratories and academic spaces that attempts to judge the sustainability of the bioeconomy (larsen, 2007; fror et al., 2017). in other cases, the sustainability of the bioeconomy is conflated with the renewability of resources, and those two are both thought to be interchangeable with the sustainability or renewability of capital (gawel et al., 2019; birch and tyfield, 2013). in reality, the sustainability of capital is taken for granted and because capital can renew itself indefinitely in time (or so it is perceived to be able to do), nature and knowledge are also perceived to do the same. if they do not, it is because better (i.e. more intensive) management and resource utilisation is needed (birch, 2007, 2012; birch et al., 2010; dankbaar et al., 2014; de besi and mccormick, 2015; gawel et al., 2016; birner, 2018; lewandowski, 2018; pulzl and ramcilovic-suominen, 2018) rather than a radically different approach – such as potentially a less intensive use of resources. however, bioeconomy can be given other meanings than the ones that have been constructed through policy documents and many academic documents. thus, we suggest it is a contested field for both theoretical debate and economic practice. contrary to the big-corporationoriented bioeconomy, there is the organic2 or agroecological approach. with agroecology we mean that agricultural production is taking place in modes that sustain the local ecosystem and local natural resources with a 2 we do not conflate organic with agroecological. those are two different approaches to agriculture and even if they sometimes overlap, they can be structured in various ways concerning their economic expression. however, we use them here together in the way they are used in the academic literature, that we examine in this paragraph. long-term view (altieri, 2009; levidow et al., 2012; martinez-torres and rosset, 2012; altieri et al., 2015, bugge et al., 2016; levidow, 2015). agroecological practices also seek to provide adequate income to the producers through the production of quality agricultural products, mostly food. given that agroecology focuses on synergies between the ecosystem as such and the human communities that are producing their food/agricultural products within it, it is more labour intensive than the big bioeconomic industries, but its mode of production is the one of the small farmer or the small producer in general. the specificity of ecosystems (soil, geography, climatic conditions, availability of local seeds, fauna of the region that feeds off or uses agricultural fields for habitat) does not allow sweeping decisions about practices and it requires adaptation of the production processes to the conditions of each place/community. in this framework, the production of food is the core activity of the bioeconomic process. organic agriculture and agroecology are perceived as methods of cooperating with nature to produce adequately, instead of perceiving nature as a space from which resources are extracted. in that way the mode of production is adapted to this production of food within the context of the local ecosystems (kitchen and marsden, 2011; birch et al., 2012a, 2012b; levidow et al., 2012; esposti, 2012; levidow, 2015; bugge et al., 2016; viaggi, 2016; hausknost, 2017). we need to note here that bioeconomic processes, such as anaerobic digestion, or value-added approaches to dealing with agricultural waste have proven so far to be quite beneficial to farming and food production on a big scale (de meester et al., 2012). farming and the ‘official’ view of the bioeconomy are not totally divorced from each other and given that policies are constructed through the official corporate-oriented view of the bioeconomy, the bioeconomic influence on farming is also corporate-based. we would need more research and a longer-term experience to have a sound conclusion about the interconnection of bioeconomic processes used to reduce waste and environmental degradation in farming. moreover, we would need a more holistic approach to assess the potential of the technological bioeconomy to decide whether as a production process is more ecologically sound than other ways of production. the published critique of the bioeconomy is very much an academic debate; it seems not to have been enjoined by practitioners or social movements. from all online investigations we have conducted (july 2019, october 2019, december 2019, april 2020), it seems that, to the best of our knowledge, the term bioeconomy is not used broadly by grassroots initiatives in their public activity, much less by people who might be small produc293understanding the bioeconomy: a new sustainability economy in british and european public discourse bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 ers, even if they practically follow bioeconomic processes in their activities. furthermore, the academic literature largely overlooks small scale activities which might be construed as part of the bioeconomy. we have found very few mentions of farmers’ markets, for example. thus, so far academic consideration of the bioeconomy is mirroring the policy focus on technology-driven, capital-intensive approaches to the economic opportunities arising from organic resources. we consider this further below, but first address the bioeconomy in the context of other approaches to addressing economic benefits. 6. sustainability economies to understand better the context of the use of the term bioeconomy, we turn to the other types of economies that have emerged during the last decade or two as policy options. these concepts including (the bio, green, blue, low carbon, and circular economies) we term the sustainability or s-economies (see table 4 later in this section for a summary of the features of a nonexhaustive list of these economies). the s-economies are named types of economic activity favoured by policy makers as offering potential for a better, or at least different, connection between the natural and human environment via focusing on particular activities as a route to value creation. in many cases, they are represented as attempting to achieve environmental sustainability, although the perceptions of sustainability to which each type of economy is connected might differ. in all cases, the debate that connects those economies to sustainable arrangements is being developed within the confines of the capitalist economy, i.e., to achieve economic growth whilst balancing environmental and social priorities in a manner protective of future generations’ abilities to do the same (following on from the wced in 1987 with brundtland report). we have already mentioned that the bioeconomy emerged initially under the rubric knowledge-based bioeconomy. knowledge (-based) economy was a term that the european states were using for some decades before the bioeconomy emerged as a term. the term was not just implying that the economy has parts where knowledge, or advances to it, were less significant, but also that people should continuously receive training (for which they should be paying, i.e. they should become clients of educational services providers) in order to adapt to the needs of the markets, i.e. unemployment and low wages were constructed as the result of lack of knowledge on the part of the workers and the businesses (oecd 2002, 2005; olssen and peters, 2005; oecd et al. 1997; jessop and sum, 2013; birch et al., 2014; birch, 2017a, 2017b). nevertheless, the knowledge-based economy can also be interpreted as an attempt by the capitalist economy to recognise and handle profitably the changes to the economy, brought about by the advance of information and communication technologies and by the creation of new jobs and new demand for advanced or new skills. by the time the bioeconomy arose as a policy concept, the idea that knowledge itself is a panacea to a stagnating economy was already a well-established one (godin, 2006; brine, 2006). the bioeconomy arrives to highlight that with new research and development of more intensive use of biogenic resources we can solve at once both the problem of production costs and job availability and the problems of waste management and environmental degradation. similarly, the green and blue economies, which were boosted in prominence by the rio 2012 summit, were seen as means to reignite the faltering global efforts for sustainability. the green economy argues for economic and social benefits to accrue from environmentally focused and social equitable investments, with the blue variant emphasizing marinebased economic opportunities (unep, 2009; bina 2013; unctad, 2014; smith-godfrey, 2016; lee, noh and kim 2020; benson et al., 2021). in practice green economy policies have tended to favour the more mainstream solutions over the more adventurous, socially progressive options (e.g., in building design; pearce and barbier, 2000; gibbs and o’neill, 2015; ge and zhi 2016; ferreira gregorio et al. 2018; merino-saum et al., 2020). approaches ostensibly designed to protect the environtable 3. academic discourse on the bioeconomy. this table is a visual presentation of basic perceptions of the bioeconomy as presented in academic literature. for sources, see the text. perception focuses on tries to attract normative technology big businesses, policymakers given-not discussed technology big businesses, policymakers, greater audience panacea environment economy knowledge big businesses, policymakers, greater audience, environmentally aware individuals and groups sustainable renewability of resources renewability of capital efficiency big businesses, policymakers, greater audience organic agroecological agriculture food big and small businesses, policy-makers, greater audience, food producers and farmers 294 bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 irene sotiropoulou, pauline deutz ment by slowing consumption (e.g., bike share schemes) can nonetheless primarily benefit those financially better able to consume (médard de chardon, 2019). these outcomes may reflect the contradiction of an apparently anti-consumption policy being driven nonetheless by the profit motive. the circular economy is a further s-economy, which has risen rapidly to prominence in policy and academic circles in the last few years and which promotes sustainable resource use via design for longevity of product/ material use recovery at end of life (bocken et al., 2016; geissdoerfer et al. 2017). as discussed above, the bioeconomy and circular economy are closely associated with each other in eu policy debates. the bioeconomy highlights a specific resource type and certain technologies for utilisation, whilst the circular economy proposes principles for using any resources. the circular economy for example favours design for sustainability over marginal improvements to recovery at end of life, but is nonetheless fundamentally seen as a strategy for economic growth by policy makers (european commission, 2015; 2019). whilst understandings of the circular economy vary considerably in their degree of social emphasis (e.g., kirchherr et al., 2017), its origins lie with explicitly corporate-oriented approaches to resource efficiencies such as industrial ecology and industrial symbiosis (mathews and tan, 2011; lieder and rashid, 2016). the efficient use of raw materials and the recovery of materials from waste, residues and production by-products are the central argument. industrial ecology and industrial symbiosis are approaches to economic-environmental benefits that argue for the ability of companies to collaboratively (through networks or business clusters) or collectively (as sectors) produce the necessary technology for avoiding a negative impact on the environment. business models have to evolve to suit not only changes of technology and social demands but also to be capable of surviving as businesses in a turbulent economic framework (boons et al., 2015; cecchin et al., 2020). industrial symbiosis therefore can be seen as a transition technology on the way to more profound solutions. by contrast, some understandings of the circular economy (e.g., ellen macarthur foundation, 2015) favour renewable resource use, as opposed to more efficient use of non-renewable resources prominently discussed in industrial ecology. the implied shift towards the bioeconomy is assumed by circular economy discussion to promote sustainability. we have argued, though, that bioeconomy policy and theory does not engage prominently with sustainability debates – leaving the outcome of expanding the use of bio-resources as uncertain. notably, though, bioeconomy literature has also called the adoption of circular practices (e.g., industrial symbiosis-style use of residues produce other products that could have economic value) (viaggi, 2016; sariatli, 2017; allen et al., 2017; pulzl and ramcilovic suominen, 2018). lately, the term of smart economy has emerged as a way to show that the economy as we know it can be better organised in order to become sustainable or more sustainable, by using technologies, especially the digital technologies that have been developed during the last decades (bronstein, 2009; caragliu et al. 2011; kumar, 2017; ruhlandt, 2018). again, we see the same pattern, of knowledge and advanced technology been assigned the role of the quasi-deus ex machina to save both the environment and the economy. within the broad remit of sustainability, the relative emphasis of the different pillars of sustainability varies between these different initiatives (cecchin et al., 2020). the bioeconomy, and mainstream understandings of the circular economy, can be seen as examples of ecological modernisation, the idea that policy-driven technological change can foster economic and environmental benefit (huber, 2008; horlings and marsden, 2011). that is, with a suitable regulatory framework, industry can make profit out of meeting the environmental needs of our societies –creating a win-win situation where the environment would not be harmed and the economic activity would continue unabated. that is, sustainability of the economy and the sustainability of nature are firmly entwined (pearce and barbier 2000; olikainen, 2014; dankbaar et al., 2014; antikainen et al., 2016; allen et al., 2017). notably the same vision of the green economy is understood differently, along with more radical perspectives on the circular economy, which tend towards the field of degrowth (e.g., latouche, 2009, cecchin et al., 2020) increasingly discussed academically though hardly a serious policy contender. the economies we have described in this section have important differences between them. for example, the knowledge-based economy and smart economy are mostly oriented towards technological solutions that are directly invented in laboratories and research centres. the green economy and ecological modernisation are very much practice-oriented, with a special emphasis on the business activities that will define aims for research and also disseminate the new sustainable technologies through the market. circular economy and the bioeconomy stand between the two groups, attempting to combine both a strong scientific component with a major role for the business world. some conceptualisations of the circular economy share the visions of the green economy that seek bottom up, even degrowth, approach295understanding the bioeconomy: a new sustainability economy in british and european public discourse bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 es. we argue also that the bioeconomy could be understood as contributing in this area, albeit this aspect has so far been overlooked. despite these differences, we cannot ignore the similarities among those types of economies and how they emerged at similar historical and geographical points in contemporary economic history, with significantly overlapping aims and intentions. the central aims and economic scope of the various types of sustainable economies are shown in table 4, along with key policy documents and references to the related academic literature. from the above we see that the use of the term of bioeconomy, and related ones like green economy, circular economy, or sustainable economy, reveals a commonly accepted, although implicit, assumption that 1) the economy cannot address its own problems but that 2) with some change of emphasis solutions can be found within that same growth-oriented economic approach. the proliferation of terms reveals the pressure to distinguish the contemporary proposals for solutions from recent previous solutions that are facing challenges. alternatively, the terms imply targeting a different partial approach to promoting growth (with sustainability priorities embedded to various degrees), without consideration of the interrelatedness of different aspects of the economy. an analysis of the terminology might reveal the intertwined character of the roles undertaken by state institutions, businesses and academia to promote each and all of those terms/economies and how this intertwinedness can have a potential for both achievements and failures, exactly because the spreading of the use of a certain discourse arguably frames the issues in ways that the various actors and social groups involved with them cannot address effectively from the within the frame/discourse (birch, 2007, 2012). this is a hypothesis, though, that will be the scope of another paper. 7. discussion: reflections on the meaning(s) of the bioeconomy the bioeconomy as a discourse and industrial sector can be credited to the authorities and policy makers who have been steering it over the years since its identification (benner and lofgren, 2007; birch et al., 2010; birch, 2012, 2017a, 2019; pavone, 2012; esposti, 2012; birch and tyfield, 2013; gustavsson et al., 2013; caivano et al., 2015). discourse is coming from policy-makers and academics, often in documents that explicitly combine both policy and scientific expertise, i.e. the experts are assigned by the states to provide policy-making advice on the bioeconomy. from our literature review and to the best of our knowledge it seems that questions like “why bioeconomy? why bio-? why biological? what is new about it? didn’t we have any bioeconomies before 1990?” have not be considered in the literature. the question that any broad definition of bioeconomy raises is whether this type of economy is different from the economy in general, if we accept definitions which state that all biological material is perceived as raw material in the bioeconomy (albrecht et al., 2010). apart from seeing such statements as problematic (because they identify all biological substances and all organisms primarily as production inputs), we need to clarify here that not all raw materials are biological materials. nevertheless, all raw materials are originating in nature ultimately, even if they have been produced in a laboratory or factory. in all cases, human production is based on taking resources from plants and animals. this human production can also be done through the destruction of an ecosystem (e.g. the cutting of a forest) in order to extract minerals from the earth or the diversion of essential parts of that ecosystem, like water, to benefit human activity instead of the water being available to plants and animals. in other words, whether we choose to deplete or not to overuse the water of a river, we are still (co-)producing the ecosystem. although the use of non-biological resources impacts on nature (as above), it could nonetheless still useful to distinguish a specifically ‘bio’-aspect as distinct from the wider economy. destruction is very different to using ‘nature’ to produce resources. the latter may equally be destructive of habitats but it is not always inherently destructive. the discourse of ecological modernisation has been based on the assumption that more management can be considered as the solution to a lack of sustainable outcomes. the previous section about sustainability economies gave some examples for this use of discourse. the question is whether we need the “bio-” component of the name to stay aware of this use of natural resources. furthermore, the bioeconomy, like all s-economies, is vested in the implication that the previous (if recent) economies were based on beliefs and lack of scientific knowledge, or that they were somehow non-, or not adequately, green or sustainable. these hidden critiques concerning the previous economies might have some seeds of truth. but one cannot help recognising that the critiques overlook efforts by several social groups or regions to be less linear and unsustainable through the use of local knowledge and inventiveness. despite that, the environmental knowledge of those communities has recently begun to be appreciated when the environ296 bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 irene sotiropoulou, pauline deutz mental management techniques originating in capitalist economies cannot tackle the implications of the environmental degradation associated with those economies (berkes and parlee, 2006; goodall, 2008). what also needs to be further examined is the variability of types of economies that have been marginalised in the public discourse but still existed and might have been experimenting with smart, green, sustainable and productionsymbiotic solutions. the interaction of these marginalised (‘grassroots’, ‘alternative’) economies with the mainstream economy, their coordination and potential lessons for sustainability needs much more examination. table 4. a non-exhaustive list of what we term the sustainability or s economies. these various expressions have been adopted to promote a particular economic activity as approaches to value generation. the decade of origin and key policy and academic references are provided. name aims – vision scope since know-how reference bioeconomy support economic sustainability through capital-intensive use of organic resources big industries, biotechnology, pharmaceutics, energy, waste efficiency-oriented mid-2000s edge-research based. laboratories, industrial applications oecd 2009, lewandowski 2018 european commission 2018 ferreira gregorio et al. 2018 blue economy sustainable management of marine resources profit making from marine nature can be sustainable small & big production. closely associated with to island and coastal economies 2010s research & business based aspects of community economies unctad 2014 smith-godfrey 2016 le heron & winder 2017 lee, noh & kim 2020 circular economy instead of resource depletion & accumulation of waste, design for reuse, repair, upcycling of products and byproducts of each supply chain all sectors of the economy, but very important for industrial products the raw materials of which are not renewable & after use they cannot be degraded in nature 1990s, but prominent since 2015 research-based, laboratory-oriented, industrial & consumer orientation ellen macarthur foundation 2015 european comission 2015 bocken et al. 2016 allen et al. 2016 geissdoerfer et al. 2017 cecchin et al. 2020 green economy sustainable solutions that can be profitable – profit making can be sustainable for the environment; some visions emphasise social benefits e.g., via community scale initiatives, and resemble degrowth initiatives all sectors of the economy 1990s, but prominent since 2000s economic & environmental research, industrial applications are preferred, even for the banking sector pearce & barbier 2000 unep 2009 bina 2013 gibbs & o’neill 2015 antikainen et al. 2016 ge & zhi 2016 allen et al. 2017 ferreira gregorio et al. 2018 merino-saum et al. 2020 benson et al. 2021 knowledgebased economy make profit out of using advances in research and technology, along with better management of human knowledge all sectors of the economy, emphasis on industrial sectors and on digital technologies 1990s research-based oecd et al. 1997 neef, siesfeld & cefola 1998 oecd 2002, oecd 2005 olssen & peters 2005 godin 2006 albrecht et al. 2010 jessop & sum 2013 low carbon economy reduce emissions of carbon in production & distribution chains without disturbing profit flow all sectors of the economy, but mostly industrial plants & other work spaces, food production, transport 2000s research-based, business oriented stern 2007 hm government 2009 zhang 2010 foxon 2011 luy, ngai and wu 2019 smart economy organise the economy through digital technologies so that costs are minimised and production is more efficient and profitable all sectors of the economy, emphasis on ict sector 2010s (or late 2000s). research-based, very technology oriented kumar 2017 bronstein 2009 caragliu et al. 2011 ruhlandt 2018 (olikainen 2014) 297understanding the bioeconomy: a new sustainability economy in british and european public discourse bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 8. conclusions for the purposes of this paper, the bioeconomy literature was critically examined in order to investigate how policy-makers and academics perceive economic activity involving the production, use and/or disposal or re-use (whether through upcycling or downcycling) of plant-based products, or products containing substances extracted from plants and animals. we stick with a very broad definition of the bioeconomy and do not assign any inherent sustainability goals to it. rather than assuming that the bioeconomy is inherently sustainable, we suggest that it can display various characteristics depending on its economic and political context. what we have seen from the examination of the policy documents and the academic literature is that for the policy-makers and for most academics, the bioeconomy is perceived as a dissociated activity from what everyday people do and from how they understand their relationship to natural materials used for production. at the same time, in cases like the united kingdom government (or the german government in 2020), the bioeconomy is perceived in a very narrow way which gives emphasis to the economy and sees sustainability as a mainly or only an economic problem. in other words, the corporate direction that the bioeconomy took since its beginning reflected neither the potential of the term nor the bioeconomies that already exist and are largely absent from policy documents and the academic writings relating to the term. we also showed that the bioeconomy has been developed within a broader context of various named ‘economies’ (which we call s-economies). these are presented by policy-makers, and analysed by academics, as possibilities for more sustainable economic activity. ironically, the periodic (if rather frequent) appearance of a new s-economy term in recent decades presents the solutions as something novel, despite that they are all based on common assumptions. each is aiming to identify a particular field of activity which can generate value within the capitalist economy by offering both a competitive edge and a perceived sustainability advantage (e.g., avoiding fossil fuels). however, the sustainability credentials may be uninvestigated or in any case contingent on circumstances. placing the bioeconomy within the broader context of the s-economies makes visible the potential of the bioeconomy to provide at least some environmental benefit, whilst indicating its economic and social limitations. neither the bioeconomy or other s-economies can overcome the internal contradictions of capitalism. they may generate new opportunities for investment, but also new areas for competition, which may favour some locations/people and may rearrange rather than eliminate inequalities (e.g., deutz, 2014). it would be the purpose of a future study to investigate 1) how the biotechnology economy articulates with the wider bioeconomy and 2) how the bioeconomy articulates with the wider economy, alongside the other comparable initiatives, which we term the s-economies. the bioeconomy is generally under-researched as a concept and even more as an economic practice. some of the literature is descriptive rather than presenting a critical exploration, or incorporates policy-related wishful thinking and academic visions rather than actual findings from the field. we note that there are few policy documents referring to the bioeconomy, and these share a representation of bioeconomic governance as top-down. this lack of extensive academic and political debate and contestation on the concept of bioeconomy obscures the struggle that is taking place among the various social groups who promote and practice the bioeconomy. these we are exploring in further research, especially as relates to small scale, grass-roots practices of the bioeconomy. our sub-project within thyme investigates the farmers markets in east yorkshire as small-scale bioeconomic practices on a local collective level. more details about the research findings from that field research will be considered in subsequent papers in order to explore the variety of notions and practices that the bioeconomy can include. through our research we have reached the conclusion that the bioeconomy is a contested concept. this is despite fact that it was constructed within a certain geographical and socio-economic framework that was prioritizing corporate and top-down understandings of bioeconomic processes. we also showed that the role of states and international organisations is prominent in this construction of meanings, although a detailed examination of this role should be the focus of future research. finally, we also showed that evolving perceptions of the bioeconomy, especially in the policy documents, indicate that if we want to develop a sustainable bioeconomy, we need to start considering limitations from the side of both ecosystems and our economies. our paper contributes to a more critical perception and use of the term bioeconomy. which we hope can inform decisionmaking for both policy-makers and experts. acknowledgements & funding this paper stems from thyme project, which is funded by research england’s connecting capability fund. 298 bio-based and applied economics 10(4): 283-304, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9534 irene sotiropoulou, pauline deutz no other funding or conflict of interest exists for this paper and the research in which it originates. we are grateful to the organisers and participants of the 4th international conference on cultural political economy (staffordshire university 9-10.1.2020) for their insightful questions and comments on a previous version of this paper and to two anonymous referees for their detailed 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(2010). china in the transition to a low-carbon economy. energy policy 38: 6638-6653. volume 10, issue 4 2021 firenze university press the effect of attribute framing on consumers’ attitudes and intentions toward food: a meta-analysis irina dolgopolova, bingqing li, helena pirhonen, jutta roosen* rural areas between locality and global networks. local development mechanisms and the role of policies empowering rural actors francesco mantino understanding the bioeconomy: a new sustainability economy in british and european public discourse irene sotiropoulou1, pauline deutz2 sustainable water resources management under population growth and agricultural development in the kheirabad river basin, iran ghasem layani1,*, mohammad bakhshoodeh2, mansour zibaei2, davide viaggi3 the distributors’ view on us wine consumer preferences. a discrete choice experiment lina lourenço-gomes, tânia gonçalves*, joão rebelo bio -based and a ppl ied economics bae bio-based and applied economics 11(4): 323-337, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-13289 copyright: © 2022 c. geoghegan, c. o’donoghue, j. loughrey. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: c. geoghegan, c. o’donoghue, j. loughrey (2022). the local economic impact of climate change mitigation in agriculture. bio-based and applied economics 11(4): 323-337. doi: 10.36253/bae-13289 received: june 22, 2022 accepted: october 2, 2022 published: may 3, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: davide menozzi. orcid cg: 0000-0002-7023-6313 cod: 0000-0003-3713-5366 jl: 0000-0001-9658-0801 the local economic impact of climate change mitigation in agriculture cathal geoghegan1,2,*, cathal o’donoghue1,3, jason loughrey2 1 national university of ireland, galway, ireland 2 teagasc rural economy and development programme, ireland 3 biorbic bioeconomy sfi research centre, ireland *corresponding author. e-mail: cathal.c.geoghegan@nuigalway.ie abstract. greenhouse gas (ghg) mitigation measures are currently being implemented in the agricultural sector across the globe. questions have been raised about the distributional and spatial impacts of agricultural emissions mitigation policies, especially at the local level. this study examines the local impact of a low-income farming sector, beef farming, in a typical irish beef farming county, county clare. input-output analysis reveals that clare beef farmers purchase the vast majority of farm inputs within the county, with intra-county suppliers providing 90% of their inputs and overheads. we examine the impact of reducing the size of the beef herd in co. clare as a direct consequence of meeting national ghg emissions targets by 2030. taking direct, indirect, and induced effects together, there is an €18.4 million reduction in economic activity in 2030 following the decrease in the beef herd with €14.72 million of that reduction taking place within the mid-west region. keywords: ghg mitigation, agricultural economics, input-output modelling, microsimulation. jel codes: q12, q18, q58, r15. highlights · irish beef farmers are highly dependent on local markets for inputs. · ghg mitigation measures reducing herd size will heavily impact the local economy. · including multipliers, the overall economic loss is almost double the direct loss. 1. introduction many countries are currently instituting policies to reduce their emission of greenhouse gases (ghg) in order to mitigate against climate change1. 1 climate change mitigation is defined as a human intervention to reduce emissions or enhance the sinks of greenhouse gases (ipcc, 2018). http://creativecommons.org/licenses/by/4.0/legalcode 324 bio-based and applied economics 11(4): 323-337, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13289 cathal geoghegan, cathal o’donoghue, jason loughrey in the european union (eu), the european green deal aims to reduce net ghg emissions by at least 55% from 1990 levels by the year 2030, and to achieve net zero emissions by 2050 (european commission, 2019). in ireland, the 2021 climate action plan provides a detailed plan for to achieve a 51% reduction in overall ghg emissions by 2030, with the aim of reaching net zero emissions by no later than 2050 (government of ireland, 2021). due to the historic importance of agriculture relative to other industries in ireland, the agricultural sector is the single largest contributor to overall ghg emissions, accounting for 37.1% of emissions in 2020 (epa, 2021). this compares with a figure of just over 10% for agricultural emissions in the eu as a whole (mielcarekbocheńska & rzeźnik, 2021). to meet abatement targets, the levels of ghgs intrinsic to agricultural production such as methane (ch4) and nitrous oxide (n2o) will need to be addressed. however, questions have been raised about the distributional impacts of agricultural emissions mitigation policies, especially those that rely on the “polluter pays” principle, the effectiveness of which stems from the contraction of output that they induce, by reducing profits and causing farms to exit the sector (oecd, 2019). polluter pays measures, such as pure carbon taxes, have generally been shown to be regressive, harming lower income households more than higher income ones (wang et al., 2016; verde & tol, 2009). within agriculture, mitigation measures2 that affect farms directly, or indirectly through increased input costs, are more likely to affect poorer farm households and lead to farm exit (mosnier et al., 2017) and may be ineffective in abating agricultural nonpoint pollution (kahil & albiac, 2013). agriculture is not just varied in terms of levels of farm income but also with regard to space. soils, weather, and other agronomic conditions differ across space, influencing yields, agricultural outcomes, and choice of farming activity (o’donoghue et al., 2015). as farming in ireland has become more specialised, local conditions have done much to determine which farming activity dominates in each area (gillmor, 1987). therefore, emissions mitigation measures that particular impact specific types of farming are likely to have an outsized impact in areas where that type of farming is dominant. this paper examines the economic and environmental effect of agricultural emissions mitigation measures at a local level. we focus on ireland as a country 2 mitigation measures are technologies, processes or practices that contribute to mitigation, for example, renewable energy technologies, waste minimization processes and public transport commuting practices (ipcc, 2018). with a significant agri-food sector and ambitious targets in terms of reducing ghg emissions in the near future. ireland has a significant agricultural footprint with about two-thirds of its land devoted to agricultural use. agri-food is the largest indigenous business and accounted for 6.7% of gni* in 2019 (dafm, 2020). spatially, the better quality agricultural land can generally be found in the south and east with the poorer land in the north and west (frawley & commins, 1996). the most profitable sub-sectors within agriculture, dairy, and to some extent, tillage farming, are predominantly concentrated in the south and east. the lower margin beef and sheep sectors are to a large extent located in the midlands, north and west of the country. in this paper we focus on beef cattle farming which is the most widely practiced form of farming in ireland. to examine the local impact of mitigation measures, we concentrate on beef farming in one county, co. clare in the west of the country. in general, farming in the county is not considered suitable for intensive production with 94% of the agricultural area classified as severely disadvantaged (dafm, 2022). the irish government’s agri-food strategy, food vision 2030, published in 2021, commits to a minimum 10% reduction in biogenic methane by the year 2030 (dafm, 20201). given the heterogenous nature of farming in ireland, the implementation of a national level target such as this may have differential impacts across the country. this paper examines the impact of a 10% methane reduction on cattle beef farming in co. clare, taking account of the interlinkages between cattle beef farming and the local economy. this is accomplished by using spatial microsimulation to create a detailed dataset for local farmers and simulating the economic effect using input-output modelling of a reduction in the beef cattle herd in co. clare resulting from meeting the methane reduction target. moretti (2010) highlights the impact of changes in tradeable sectors on jobs in non-tradeable sectors. the agri-food sector is an example of a sector with potentially large local impacts in terms of local feed, animal, and service inputs, but is also part of a major globally traded system. linkages can be relatively complex. harriss (1987) finds that in addition to production impacts, consumption impacts can be even higher if a stronger agricultural sector reduces outward migration from a rural area. the effects of local spillovers from agriculture in the literature are relatively mixed. santangelo (2016) finds evidence of positive spillovers from agriculture on the wider local economy in both the agriculture and non-agricultural areas, while hornbeck and keskin (2015) find local gains within the agricultural sector but 325the local economic impact of climate change mitigation in agriculture bio-based and applied economics 11(4): 323-337, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13289 limited gains elsewhere from an exogenous change on the sector via improved irrigation. this is a novel piece of research in an irish or european context. while previous studies such as miller et al. (2014a) have examined the multiplier effects of agricultural output changes at a national level, few studies have examined the multiplier effects of output changes at a local level. there is also a gap in the literature with regards to the interaction between farm households and the local economy (roberts et al., 2013). the local economic impacts of nationally implemented ghg mitigation measures are also understudied in the literature. this is particularly relevant for livestock farming due to its emissions intensity and localised nature. in 2019, agriculture accounted for 1.9% of economic output in the eu-27, yet generated 15.6% of eu-27 ghg emissions, the second largest industrial share (giannakis & zittis, 2021). this is mainly due to beef and dairy production, which have significantly higher ghg emissions per euro of economic output than other agricultural sectors or aquaculture (tsakiridis et al., 2020). like ireland, many european countries have regionalised livestock production, with specific regions specialising in beef and dairy farming. examples include wallonia in belgium (duluins et al., 2022), massif central and pays de la loire in france (balouzat et al., 2020), and galicia in northern spain (lomba et al., 2022). agricultural systems in these areas are frequently embedded in local value chains, with many inputs being sourced locally and outputs being processed within the region (vázquez-gonzález et al., 2021; pays de la loire regional council, 2019). as a result, mitigation measures to reduce agricultural ghg emissions are likely to have an outsized economic impact in these regions due to value chain linkages. this study looks to provide evidence of the extent of that economic impact at a local level. additionally, this research applies a microsimulation approach to modelling the local economic impact. microsimulation models have previously been applied in a spatial context to examine the multiplier effects of major job losses or gains at a local level (ballas et al., 2006a), the implications of cap reform for the national spatial strategy (ballas et al., 2006b) and the local impact of the marine sector in ireland (morrissey et al., 2014). in the next section, we describe the background regarding the local economic impact of agriculture and the related literature. the methodology is described in section 3 followed by a description of the data sources. this is followed by three separate results sections, the first dealing with the spatial distribution of farms and farm income in the county, the second set of results dealing with the spatial distribution of livestock sales and the source location of inputs and the final results section dealing with the environmental impact of a reduction in the beef cattle herd and local multipliers. this is followed finally by the discussion and conclusions. 2. background cattle farming is the most prevalent form of farming in ireland, accounting for 47% of agricultural land use in 2011 (geoghegan & o’donoghue, 2018). cattle farmers in ireland are highly dependent on publicly funded subsidies and have become increasingly vulnerable to a cost-price squeeze with declining margins per volume of beef (o’donoghue, 2013; hennessy et al., 2008). evidence suggests that many cattle farmers use subsidies to support loss-making production (howley et al., 2012). in combination with off-farm income, publicly funded subsidies allow many cattle farms to maintain a reasonable standard of living and be economically sustainable (hynes & hennessy, 2012). the retention of these farm households in rural areas supports the relevant local economies via the farm and non-farm expenditures attributed to these households. although the economic position of irish cattle farmers is well covered in the economic literature, there is something of a void in relation to the treatment of the local economic effects of cattle farming production. cattle farmers may not enjoy the profitability of their dairy farming counterparts, but they do contribute indirectly towards other economic activity in rural ireland. the concept of ‘good farmers’ should account for the local social and economic outputs that farmers provide (sutherland & burton, 2011). miller et al. (2014b) have developed a social accounting matrix to examine the wider economy effects of a decline in the beef sector and show that significant employment losses in the wider economy would result. the analysis is focused however, at a national rather than local or regional level. the inadequate treatment of the wider local economic effects of agriculture in ireland contrasts with the united states where numerous studies have examined this issue. foltz and zeuli (2005) find that small farms are more likely to purchase inputs locally in communities where an array of marketing outlets exist. in a study of wisconsin dairy farmers, lambert et al. (2009) find that farmers located in areas with relatively large farm populations appear to be better served by local input suppliers indicating that farm-community linkages are strongest where farms are numerous and where the sector is large enough to anchor a regional farm supply centre. 326 bio-based and applied economics 11(4): 323-337, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13289 cathal geoghegan, cathal o’donoghue, jason loughrey aside from specific farming activities, farmers also contribute directly to local economies through off-farm employment. in ireland, a relatively high proportion of irish cattle farmers engage in off-farm employment, with 40 per cent of cattle farms operators being employed offfarm (donnellan et al., 2020). pluriactivity is therefore likely to play an important role in determining the economic welfare of cattle farming households. shucksmith and ronningen (2011) argue that small farm holdings provide a base from which rural households are able to sustain their livelihoods through pluriactivity, keeping ‘lights in the windows’ and retaining populations in areas from which they would surely have been lost in the case of farm amalgamation. while the ability of mitigation measures to reduce ghg emissions in agriculture has been much discussed in the literature, the potential trade-offs between economic and environmental concerns in agriculture, especially at a local level, have not been as well studied. some national-level studies have been performed, making use of economic analysis models such as input-output (io) and computable general equilibrium (cge) modelling. like ireland, brazil’s biggest source of ghg emissions is from the agricultural sector. using a national-level io model, de souza et al. (2016) find that an overall 1% reduction in brazilian ghg emissions would fall heaviest on the livestock sector, which in turn, would greatly impact poor households who rely on livestock as their main source of income. wu et al. (2015) use a cge model to simulate the effect of a ghg emissions intensity levy imposed on the agri-food sector in northern ireland. they find that without the adoption of feasible technology, there is a risk of serious damage to agri-food competitiveness with relatively limited economy-wide environmental gain, leading to trade diversion and ghg emission leakages. bourne et al. (2012) use a cge model to examine the potential impact of kyoto and eu environmental policy targets on specific agricultural activities in spain and find a reduction in agricultural output, increased prices for agricultural products and a cumulative fall in agricultural incomes of €1.5 billon compared with a business-as-usual scenario. research from chile by mardones and lipski (2020) shows that a cge-modelled environmental tax applied only to the agricultural sector results in a sectoral contraction and a generalised increase in the production of all other sectors, without a substantial fall in overall ghg emissions. in addressing the local economic effects of agriculture, researchers have focused analysis on a relatively small geographical area e.g., a particular district within new york state in jablonski and schmit (2014) and wisconsin dairy communities in foltz et al. (2002). this approach can be justified on the basis of data collection costs and the desire for a relatively homogenous sample of farms. our analysis is focused on the cattle sector in a particular area of ireland, co. clare. this county is chosen as cattle farming is overwhelmingly the most important agricultural enterprise in the county. according to the 2010 census of agriculture, approximately 78 per cent of farms in county clare are classified as specialist beef production which far exceeds the national average of 56 per cent. 3. methodology the objective of this study is to model the impact of cattle farming on the local economy. this objective has a number of methodological challenges. while good micro farm level data exist containing incomes, costs, and technical attributes at national level and while there exists spatial census information in relation to small area statistics of farm structures, no dataset contains both detailed income sources and fine spatial attributes. it is therefore necessary to utilise a methodology to synthetically generate spatially differentiated, micro data. one methodology that allows us to simulate the necessary data is spatial microsimulation (o’donoghue et al., 2014). hynes et al. (2009a) outline three main benefits of using synthetic data: the ability to create micro data from aggregated macro data at different spatial resolutions; the ability to retain a number of characteristics of micro units within the data and facilitate a multivariate analysis; and the ability to assess the impact of policies on particular groups within the population. the smile-farm model simulates spatially representative households and farms at an electoral district (ed) level using several data sets: the teagasc national farm survey, the census of population, and the census of agriculture (coa) amongst others (o’donoghue, 2017). the data simulation process involves the sampling of farms from the micro dataset containing detailed farm level data from the teagasc nfs to make it consistent with the coa. the constraint variables used include farm size, farm system, soil type, and stocking rate. the smile-farm model is used in this paper to create an enhanced spatial microsimulation model by combining smile-farm with the farm-level survey information collected for co. clare using a quota sampling technique (o’donoghue et al., 2017). the most recent smile-farm model which is from the year 2014 is combined with the survey data which were collected in the year 2010. although the two datasets are not contemporaneous, nfs data show that there was relatively 327the local economic impact of climate change mitigation in agriculture bio-based and applied economics 11(4): 323-337, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13289 little change in the characteristics of cattle farming in co. clare over this time period so combining the two datasets is appropriate. input-output in order to estimate the multiplier effect associated with changes in the size of the beef cattle herd, a sub-regional input-output model is employed. a multiregional input output (mrio) model for sub-regions takes the same form as that of a regional mrio with a multiregional matrix (of technical coefficients. the objective is to capture the various economic transactions between and among the several regions in a multiregional economy. a p-region mrio would be expressed as follows: (i-a)x=d and the solution for x is shown as follows (similar to the standard i-o solution for x): x=(i-a)-1d where: the mrio can then be expressed as: x – a vector of gross output for each of the p regions arr a regional technical coefficient matrix intra-spatial unit arr= ars a technical coefficient matrix inter-spatial unit between unit r and s ars= i the identity matrix (“1” in the diagonal, “0” in all other fields) (i-a)-1 an inverse of a square matrix (also known as the leontief inverse). a sector’s output can be broken down in output required to meet final demand and output used by other sectors (intermediate demand). the leontief inverse matrix allows the estimation of individual sectoral output multipliers capturing the direct and indirect economic effects of exogenous shifts in final demand. an induced or type ii multiplier incorporates the impact of household spending in addition to the direct and indirect impact. augmenting the leontief inverse matrix with wage income per unit output3 w and fcr is the total final consumption of households4, we produce the augmented leontief inverse matrix: (ia-aa)-1 the input-output model can be extended to account for environmental emissions associated with production activities by multiplying the economic output of a sector at each stage (vector x as shown earlier) by the diagonal matrix of sectorial environmental burden coefficients (e.g. ghg emissions per monetary or physical unit of output) b ek = bk(i – a)-1d where e is the total (direct and indirect) environmental impacts vector per unit of final demand (tsakiridis et al., 2020). the subscript k denotes the type of environmental impact, while matrix bk has diagonal elements representing the environmental impacts of interest per unit of output for each process (hendrickson et al., 1998). this process relates ghg emissions, in this case methane, to economic output so demonstrates how big a fall in beef output is required to achieve a 10% reduction in methane. the extent to which the exogenous shock of the herd reduction spreads through the rest of the economy is indicated through the use of multiplier effects. the sub-regional model, based on work by o’donoghue (2021), provides multipliers for three regions: limerick city, the mid-west nuts 3 region, and the rest of the country. the mid-west region in the is model is comprised of the rest of co. limerick, co. clare, and co. tipperary. therefore, the sub-regional io model can provide economic multipliers related to changes in economic activity at a level quite close to the county level examined in this paper. the data used for the sub-regional io model is further described in the annex. 4. data the survey used for this study was undertaken in co. clare, in the west of ireland. clare was chosen as the 3 including operating surplus for sectors with high numbers of sole traders, such as agriculture, construction, transport etc. 4 the consumption rate per € of wage is defined as cr,i=fcr,i/wr. 328 bio-based and applied economics 11(4): 323-337, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13289 cathal geoghegan, cathal o’donoghue, jason loughrey study area for this paper for two specific reasons. firstly, nearly 10% of the working population in the county are employed in agriculture, forestry, and fishing (see table 1). this is substantially higher than the number employed in agriculture for the entire country, which is just under 4%. secondly, table 1 shows that, of the 6,297 farms located in co. clare, over 81% of them are beef specialists. again, this value is much higher than the national average, which is 55%. hence, cattle farming is a relatively important source of employment in clare. in this paper, we hypothesise that cattle farmers’ earnings feed into the wider clare community, thus meeting one of the primary goals of the cap’s rural development plan: rural viability. we then estimate how changes to the local beef sector in clare would hypothetically influence rural viability in the county. while the smile-farm model provides the spatial distribution of farms with their incomes, costs, and technical attributes, we also need to collect data in relation to the location of purchases and sales by type of good. this data provided us with the necessary information about the source location for inputs and the output destination for cattle outputs among cattle farmers in the county. the resulting sample of clare farms was matched with the smile-farm model so that spatial analysis could be carried out with reference to the activities of all cattle farms in the county. a more detailed description of the data sources used for this paper and the methodology used to collect the data from clare farmers is available in the annex. 5. results outputs from the smile model – spatial distribution of agriculture in county clare the census data indicate the importance of specialist beef production to farming in clare with some variability within the county in terms of the reliance upon specialist beef production. in figure 1, we show the share of farmers engaged in specialist beef production. the results indicate some variability between the north and south of the county. farming in many parts of the north is almost exclusively dependent on specialist beef production. many parts of the south have a relatively high share of farmers devoted to other activities such as dairying or mixed livestock grazing. this explains why the share engaged in specialist beef production is below 63 per cent in parts of the south. in figure 2, we present the spatial distribution of family farm income using five income brackets. the definition of family farm income includes agricultural subsidies but excludes off-farm income. figure 2 indicates that farm income is highest in the very north of the county. the relatively high farm income in this area may be attributed to the higher than average farm size. the table 1. comparison of farming in clare and ireland. clare national population 118,817 4,761,865 daytime working population 34,761 2,304,037 people in agriculture, forestry, and fishing 3,423 89,116 number of farms 6,297 135,037 specialist beef farms 5,109 74,159 source: census of agriculture 2020 and census of population 2016. figure 1. the share of farmers classified as specialist beef producers in clare. figure 2. spatial distribution of family farm income in 2006. 329the local economic impact of climate change mitigation in agriculture bio-based and applied economics 11(4): 323-337, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13289 relatively lower income in the south emerges despite the fact that dairy farms have higher incomes than specialist beef producers and dairy farms are more common in the south. the patterns suggest that there are many lowincome specialist beef producers in the south and northeast of the county. these farm households are economically vulnerable unless decent off-farm employment is available. cattle farmer income and expenditure in county clare in this section, we present results regarding the flows of cattle farming inputs and outputs in the county as facilitated by the matching of the clare survey data to the smile model. for analytical purposes, we provide these results according to six regions i.e. north-west clare, south-west clare, north-east clare, south-east clare, ennis and outside clare. the four within-clare regions are defined roughly according to their position relative to the town of ennis. as figures 1 and 2 show, the majority of the geographical area in the county is west of ennis town. it is therefore unsurprising to find that the majority of farmers and agricultural output comes from that part of the county. in table 2, we provide the share of output5 for each of the six regions based on the geographical point of sale. this includes the share of output sold to outside the county. table 2 shows that 72% of beef cattle farm output goes outside of the county. this high figure is primarily driven by how beef cattle farming in structured in clare and the rest of the country. cattle farming in clare is dominated by suckler farming, where calves from suckler cows are reared for six to nine months until weaning takes place and the calf is sold to another farm for further finishing (teagasc, 2015). these finishing farms are located mainly outside clare, principally in the east of ireland. the remaining output goes to the west of the county (10.9%), the east (9%) and ennis town 5 in this context, by output we mean the monetary value of production. (9.2%), with the majority of output not going outside the county staying within each region. inputs and overheads we have now established some important findings about the point of sale for livestock sales. in table 3, we present the share of direct inputs and overheads purchased from each of the six regions. direct inputs include feed, machinery hire, casual labour and fertiliser. overheads include electricity, telephone costs, interest payments and depreciation of assets. we find that almost 9% of expenditures are sourced from ennis town and 10.5% from outside the county. among specialist beef farms, we find that just over half of all overheads and inputs are sourced from the west of the county. approximately 29.5% are sourced from the east of the county. table 3 also shows that for all regions except ennis, the majority of inputs are purchased within the same regional area. this is particularly the case in the western part of the county, where over 60 per cent of inputs are purchased locally. the findings have much in common with those of pritchard et al. (2012) where there is clear evidence that farm households and businesses make extensive use of their local towns for maintenance purchases and a range of other supplies. pritchard et al. (2012) label this tendency to buy local as the ‘local if possible principle’. overall, the findings suggest that a change in the volume and value of the cattle herd in clare will lead to output changes for those companies supplying the specialist beef farmers with inputs. the information supplied in table 3 suggests that nearly 90% of these output changes would come from within the county. the multiplier effects at the county level are therefore likely to be more important than in the case of most other industries. table 2. share of cattle farm output sold in co. clare regions according to geographical point of sale. region outside north-west south-west north-east south-east ennis north-west 71.5 12.7 3.4 0.6 1.5 10.4 south-west 57.5 12.0 13.5 0.1 2.9 14.1 north-east 73.7 0.1 0.0 15.6 4.0 6.6 south-east 79.7 0.3 0.1 4.4 9.2 6.3 ennis 83.5 1.6 0.0 0.0 7.2 7.6 total 72.0 6.9 4.0 3.7 4.3 9.2 330 bio-based and applied economics 11(4): 323-337, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13289 cathal geoghegan, cathal o’donoghue, jason loughrey effects of changes in the beef industry to the environment and local economy in this section, we present results regarding the overall effect for the wider clare economy of a hypothetical decline in the size of the cattle herd in the county. the food vision 2030 policy document was published by the irish government in 2021, providing a roadmap for the irish agri-food sector up to the year 2030 (dafm, 2021). the document commits to a reduction of at least 10% in biogenic methane by 2030 (based on 2018 levels) in the agri-food sector in ireland. we assess what the overall impact of a such a reduction would be in an area heavily dependent on cattle farming such as co. clare. it is assumed that the 10% reduction in methane is achieved entirely through a reduction in the beef cattle herd, with dairy cattle numbers continuing to increase at the current rate and sheep numbers staying constant. this is compared with a scenario where beef cattle number increases seen post-quota removal (2016-2018) are extrapolated at a decreasing rate to the year 2030 until a steady state is achieved. there are two main reasons for concentrating of a reduction in the beef cattle herd. first, beef has the highest ghg footprint per euro of output of all the major sources of protein produced in ireland (tsakiridis et al. 2021). second, the other main source of agricultural methane emissions (dairy) is currently far more profitable at the farm level than beef farming (dillon et al., 2021). the environmental impact of a 10% reduction by 2030 in methane in co. clare is shown in table 4. an overall 10% reduction in the beef cattle herd is unequally distributed across the county. in order to achieve a 10% county wide reduction, reductions of 16.3% in the north-west, 13.5% in the south-west and 11.7% in the south-east are necessary. this compares with lower levels of methane reduction in the north-east and ennis regions. table 4 also shows that reducing methane emissions through reduction in the beef cattle herd also reduce the production of organic nitrogen with the highest percentage nitrogen reductions being achieved in the west of the county. in figure 3, the relative economic impact of reducing methane by 10% is observed. the reduction in the beef cattle herd causes an 18.6% fall in output from that sector by 2030, relative to 2018. as the dairy herd continues to increase in size, a 27.8% increase in dairy output is observed. the sheep herd remains constant so only a 0.008% drop in economic output is seen from 2018 to 2030. although there is a larger percentage increase in dairy output than there is a drop in beef cattle output, the much larger size of the beef herd means that overall economic output over the time period remains almost flat, with a 0.01% fall in output being observed. the wider economic effects of the 10% reduction in methane can be seen in table 5. the reduction in the beef cattle herd results in a €9.34 million decrease in total output in 2030 relative to a scenario where cattle numbers continue to increase at the current rate. the reduction in output is concentrated in the mid-west region of which clare is part with €8.59 million of the reduction taking place there. the indirect multiplier is 0.68, indicating that there is an additional €680,000 loss to the irish economy for every €1 million reduction in spending by beef cattle farmers in clare. the majority of indirect spending takes place in the midwest region but more of the multiplier effect is felt outtable 3. share of cattle farm inputs coming from co. clare regions. region outside north-west south-west north-east south-east ennis north-west 13.4 60.7 11.1 3.1 3.5 8.2 south-west 4.6 13.7 68.9 0.9 4.0 7.8 north-east 9.6 7.0 2.6 53.1 18.7 9.0 south-east 10.8 5.8 2.8 18.6 53.2 8.8 ennis 10.5 25.2 7.7 4.7 15.1 36.8 total 10.5 25.8 25.4 12.3 17.2 8.8 table 4. percentage change in emissions in the clare regions with a 10% cut in methane emissions in cattle production. region methane greenhouse gases organic n clare -9.8 -9.7 -11.9 north-west -16.3 -16.2 -18.6 south-west -13.5 -13.4 -15.7 north-east -5.4 -5.3 -7.2 south-east -11.7 -11.6 -13.9 ennis -9.5 -9.4 -11.4 national -4.6 -4.4 -6.4 331the local economic impact of climate change mitigation in agriculture bio-based and applied economics 11(4): 323-337, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13289 side the region than for the direct multiplier with 37% of spending taking place outside the mid-west region. the induced multiplier which takes employee spending effects into account is 0.29. the small size of the multiplier reflects the limited amount of employment provided by agriculture compared to other industries. again, a large proportion of the multiplier (78%) is located within the mid-west region. when direct, indirect and induced effects are taken together, there is an €18.4 million reduction in economic activity in 2030 following the decrease in the beef cattle herd with €14.72 million of that reduction taking place within the mid-west region. following on from the effects of a reduction in methane emissions on the wider economy, the effects of the methane reduction at the farm level can be seen in table 6. teagasc, the irish agricultural advisory service, defines a farm business as being economically viable if family farm income (ffi) is sufficient to remunerate family labour at the minimum wage and provide a five per cent return on the capital invested in non-land assets, i.e. machinery and livestock. table 6 shows the percentage of beef cattle farms that are considered viable with and without the 10% reduction in methane emissions. there are 9% fewer viable beef cattle farms in co. clare following the methane reduction, with the largest falls in the number of viable farms taking place in the north-west, the north-east and ennis. the viability gap measures how far the average farm is from the viability threshold. the imposition of a reduction in methane emissions would put the average beef cattle farm 60% below the viability threshold, compared with 52% with the emissions reduction taking place. farms in the north-west of the county would suffer the biggest viability gap increase, moving a further 16% away from the viability threshold. 6. discussion and conclusions in this paper, we have examined the importance of the beef cattle sector to the local economy of co. clare in ireland and the potential impact of ghg mitigation measures in this sector upon wider economic activity. our findings suggest that irish beef cattle farmers are inclined to purchase most of their inputs from within their own immediate area, thus indicating that the ‘local if possible principle’ is followed by many farmers in the county. the findings suggest that a substantial share of livestock sales tend to take place in the main county town of ennis and thus away from the immediate hinterland of many cattle farmers. this shows that farmers will travel longer distances for specific transactions, but the overall results indicate that small towns and villages are deeply connected with the agricultural hinterlands. as in the case of pritchard et al. (2012), the analysis suggests that a viable local farming sector supports local figure 3. percentage change in output in county clare for a 10% reduction in methane. -0,300 -0,200 -0,100 0,000 0,100 0,200 0,300 0,400 cattle dairy sheep total pe rc en ta ge o ut pu t c ha ng e table 5. economic effects of beef industry changes after a 10% reduction in methane. region direct indirect induced total rest of country -0.37 -1.66 -0.39 -2.42 mid-west -8.59 -4.02 -2.11 -14.72 limerick -0.38 -0.67 -0.20 -1.25 total -9.34 -6.35 -2.71 -18.40 multipliers rest of country 0.039 0.178 0.042 0.259 mid-west 0.920 0.431 0.226 1.577 limerick 0.041 0.071 0.022 0.134 total 1.00 0.68 0.29 1.970 table 6. changes in beef cattle farm viability with 10% methane reduction. viability rate viability gap region baseline methane cut change baseline methane cut change north-west 0.27 0.24 -11% 0.45 0.52 16% south-west 0.29 0.27 -5% 0.49 0.56 14% north-east 0.28 0.25 -11% 0.47 0.52 9% south-east 0.31 0.29 -9% 0.50 0.56 13% ennis 0.26 0.23 -12% 0.46 0.51 12% total 0.29 0.26 -9% 0.52 0.60 13% 332 bio-based and applied economics 11(4): 323-337, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13289 cathal geoghegan, cathal o’donoghue, jason loughrey towns and the basic commercial functions demanded by farm households. the empirical analysis has further examined the impact of a reduction in the size of the herd in co. clare to meet ghg emission targets. the overall impact of such a decline is capable of reducing total primary cattle output by €9.3 million, with €8.6 million of the reduction located within the mid-west region. when multiplier effects are included, the overall decline in economic output is €14.7 million in the mid-west region and €18.4 million overall. the number of beef cattle farms in co. clare considered viable is also reduced by 9% as a result of the mitigation measures. overall direct economic output within the agricultural sector in co. clare remains almost unchanged due to increases in dairy output that offsets the beef output reduction. the results are in line with other analyses such as wu et al. (2015) and bourne et al. (2012) that show that the implementation of ghg mitigation measures in agriculture can lead to a reduction in output from the agricultural sector, in this case beef. when multiplier effects are included, the overall economic loss is almost double the direct loss, showing how strongly linked the beef sector is to the local economy. this analysis shows that the distributional and spatial impact of mitigation measures must be taken into account when designing policy instruments. given the high global warming potential (gwp) associated with methane, ruminant animal agricultural systems are highly likely to be subject to increasing emissions mitigation measures in the coming years. with the wide disparity between irish beef cattle and dairy farm incomes, as well as value added opportunities, it is likely that methane reductions will be concentrated on the beef sector. as a result, policymakers must prepare mechanisms to offset the costs incurred by those most affected by these measures. the results of the multiplier analysis show that the beef sector is highly embedded within the regional economy with indirect and induced effects almost doubling the direct impact of the cattle herd reduction. localised value chains are also observed in other regions of europe where beef cattle farming is prominent (vázquez-gonzález et al., 2021; pays de la loire regional council, 2019). ghg mitigation measures that impact upon cattle farming will affect not just the farmers themselves, but a chain of businesses and households connected to the beef value chain. in an environment where such policy measures are becoming more likely to be implemented, as well as shifting consumer demand away from meat products, governments will need to quantify the size of the impact on affected industries, as well as what steps should be taken to assist those affected. some attention has been paid to the idea of a ‘just transition’ for those most affected by climate change mitigation measures, in order to support communities transitioning to a low carbon economy (blattner, 2020; heyen et al., 2020). as part of the european green deal, a just transition mechanism (jsm) worth €55 billion over six years exists to alleviate the socioeconomic impact of the transition to a low carbon economy. investments such as these will be required to offset losses arising from ghg mitigation measures, especially in areas where alternative sources of employment are scarce. such a possibility is put forward by hynes et al. (2009b) who use spatial microsimulation to model an agricultural methane tax in ireland with revenue raised being redistributed in the form of an environmental subsidy to farmers. the study found that such a measure would encourage farmers to participate in the scheme and could also have the effect of moving low income farms up the earnings distribution ladder. efforts to meet ghg emission targets are not the only potential reason for a decline in the cattle herd. beef cattle farming continues to be loss-making on average, with an increasing age profile, and greater competition for farmland. regardless of the cause for the decline in the cattle herd, the multiplier effects remain important. the potential losses to farm income further underline the importance of off-farm employment as an alternative income source. the effect of a reduction in beef cattle farming is also complicated by the nature of agricultural land in co. clare, 94% of which is classed as severely disadvantaged, and thus unsuitable for the intensive production seen in dairy farming. alternative approaches to agriculture have proven successful in the region with agri-environment schemes such as the burren farming for conservation programme (bfcp) and burren programme (bp) proving successful in the burren region, which covers an estimated 72,000 ha of land in counties clare and galway (dunford & parr, 2020). these programmes utilise a ‘hybrid’ approach whereby farmers are rewarded annually for their environmental performance while also having access to a fund to carry out self-nominated ‘conservation support actions’ to help improve conservation performance over time. in addition, the basic payment scheme, environmental and the rural development payments play an important role in sustaining these farming communities. given the increasing environmental orientation of eu policy, future agricultural payments to farmers should take account of the role of farmers in environmental 333the local economic impact of climate change mitigation in agriculture bio-based and applied economics 11(4): 323-337, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13289 stewardship and as economic pillars of local communities (mcgurk et al., 2020; rizov et al., 2018). conversion to organic beef farming may also be an option for some farmers as required inputs are very similar to non-organic beef farming and incentive schemes already exist to support the sector (o’donoghue et al., 2018). additionally, organic farms tend to localise in places like co. clare, far from more competitive agriculture, characterised by a high specialization in arable crops and a more intensive use of mechanisation and chemicals (bonfiglio & arzeni, 2019). while this paper has emphasised the local economic impact of beef cattle farming, it should be acknowledged that beef production has a strong export orientation and that a wider treatment of the contribution of beef cattle farming to the overall economy should reach beyond a local/global dualism. acknowledgement this work has been carried out as part of the food shield project, funded under the sfi covid-19 rapid response research and innovation funding call and the northern ireland department of agriculture, environment and rural affairs. we are also grateful for funding from science foundation ireland as part of the biorbic sfi centre and irish local development network. we would also like to thank ger murphy, ultan shanahan, david meredith and corina miller for their contribution to the research. references ballas, d., clarke, g., and dewhurst, j. 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(2015). how economic analysis can inform the climate change policy debate: the case of northern ireland. eurochoices, 14(1): 42-47. https://doi.org/10.1111/1746-692x.12081 the local economic impact of climate change mitigation in agriculture annex sub-regional io model data the sub-regional io model uses census-derived, commuting flow employment data at a sub-county or district scale to downscale regional and national inputoutput data. the national io table containing 58 industrial sectors is collapsed to an 8-sector6 model for which localised employment data derived from the most recent 2016 census exist. spatial interaction models of the inputs and outputs in different sectors in different areas are estimated as a function of the distance to different markets and the characteristics of these markets using information from an industry survey. data collection to facilitate data collection, an application was made to undertake a series of queries on the teagasc client information management system (cims). this request was granted subject to a number of conditions associated with ensuring the confidentiality of the data. a number of queries were run to (i) identify all teagasc clients in co. clare; (ii) identify all teagasc clients with a beef enterprise from this subset of data; and (iii) identify those enterprises where beef production was the primary type of farming undertaken on the farm. using spatial analytical techniques, co. clare was divided into four sub-regions by applying a horizontal and vertical transept that bisected ennis. (i.e. clare was divided into four sub-regions with ennis at the centre). each of the farms selected from the cims was allocat6 these 8 sectors are food and agriculture; manufacturing and industry; building and construction; commerce; training, storage and communications; public administration and defence; education, health and social work; and other. 337the local economic impact of climate change mitigation in agriculture bio-based and applied economics 11(4): 323-337, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13289 ed a sub-region identification number based on their address. these data were incorporated into a statistical analysis package and a random sample of 100 farms was identified from each sub-region. the survey was carried out by contacting the selected farmers by telephone. if a farmer did not wish to participate in the survey, the next farmer on the list was contacted until 13 farms in each of the sub-regions had been surveyed. this resulted in a sample of 52 farms being surveyed. once complete, the survey data were entered into a spreadsheet and the data restructured to extract four individual survey sections. these included (i) the address of the farm; (ii) the structure of the farm enterprise; (iii) the source (address) of inputs to the farm; and (iv) the destination (address) of outputs. all sections that incorporated address data were geocoded to facilitate spatial analysis (location allocation) within the smile model. the survey data were then matched to the smile model using a statistical matching process known as the distance method. this method of matching from the survey to the smile model involves a set of overlapping variables that are common to both the survey data and the smile model. these variables included the farm type (i.e. dairy, specialist beef, crops etc.), demographic variables such as age and marital status, and economic variables such as the amount of direct payments. this allows the matching of farms of similar type from the farm survey data to the smile model and therefore achieve the necessary scale for spatial analysis. the most basic implementation of the distance method uses distance functions with finite weights for the overlapping variables (decoster et al., 2020). to be precise, for a given record in the survey, the distance in the (selected) overlapping variables to every record in smile model is calculated. this could for instance be the difference in age, the difference in farm type, the difference in direct payments, etc. then the weighted sum of these differences is calculated and finally the record in smile which has the smallest weighted sum is picked out. if there are several records which result in the same minimum distance, one of these records is chosen at random. volume 11, issue 4 2022 firenze university press systems thinking, mapping and change in food and agriculture domenico dentoni1,*, carlo cucchi2, marija roglic1, rob lubberink3, rahmin bender-salazar4, timothy manyise5 vulnerability and resilience to food and nutrition insecurity: a review of the literature towards a unified framework pierluigi montalbano1, donato romano2,* the local economic impact of climate change mitigation in agriculture cathal geoghegan1,2,*, cathal o’donoghue1,3, jason loughrey2 dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: a scoping review simone russo1,2,*, lerato phali3, maurizio prosperi1 provision of public goods and bads by agriculture and forestry. an analysis of stakeholders’ perception of factors, issues and mechanisms stefano targetti1,*, valentina marconi1, meri raggi2, annette piorr3, anastasio josé villanueva4, kati häfner3, mikko kurttila5, natalia letki6, mihai costica7, dimitre nikolov8, davide viaggi1 bio -based and a ppl ied economics bae bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-12912 copyright: © 2022 r. esposti. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: r. esposti (2022). the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period. bio-based and applied economics 11(3): 231-264. doi: 10.36253/bae-12912 received: march 17, 2022 accepted: september 7, 2022 published: november 4, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: davide menozzi. orcid re: 0000-0002-1656-0331 the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period roberto esposti department of economics and social sciences università politecnica delle marche, ancona, italy. e-mail: r.esposti@staff.univpm.it abstract. this paper investigates the co-evolution of the cap expenditure and of the farms’ performance and choices to assess whether and to what extent cap assessment itself meets the requisites of causal inference. in order to identify some regularities in this co-evolution, the analysis is performed on a constant group of professional farms over a long enough time period. the italian 2008-2019 fadn balanced sample is here considered. results points to two major empirical implications. first of all, they question whether cap expenditure is actually accompanied by any significant farmers’ response. an exception may actually concern the support specifically focused on environmental standards. secondly, they raises some major methodological issues about the applicability of the treatment effect logic to cap assessment. keywords: common agricultural policy, farmers’ behaviour, program evaluation, panel data, co-evolution. jel codes: q18, d04. “verum scire est scire per causas” 1. introduction: two topics, one objective this paper deals with two distinct research topics and aims to join them into a unique research objective. the first topic consists in analysing the evolution of the common agricultural policy (cap) support, of the farmers’ production choices and of their possible interdependence (henceforth, the co-evolution). the second topic has to do with the growing use of the socalled program evaluation methods (pem) (imbens and wooldridge, 2009) in assessing the impact of the cap, its measures and reforms, on the farming activity (dumangane et al., 2021). the research objective that brings these two topics together is understanding whether and under which conditions investigating the farms’ response to cap support can be performed with the cause-effect logic implied by these pem. pem have progressively emerged as the application of the general principles of causal inference (ci) to the assessment of public policies (imbens http://creativecommons.org/licenses/by/4.0/legalcode 232 roberto esposti bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 and rubin, 2015; perraillon et al., 2022). these methods are thus grounded on sound statistical concepts but, at the same time, they imply specific preconditions for an appropriate application to policy assessment (khagram and thomas, 2010). the bottom line is that an unambiguous cause-effect direction must occur between a well-defined policy measure (the treatment) and a welldefined response (the treatment effect, or te). such a direction (te logic, henceforth) can be obviously assumed but it is not necessarily a good representation of the world especially in the case of many cap measures. in particular, a correlation between some cap measures (or reforms) and farmers’ behaviour does not automatically make the latter a response to the policy. not only because, as well known, correlation is not causation (angrist and pischke, 2009). more importantly, as stressed by the literature on the political economy applied to the cap decision making process (swinnen, 2015; collantes, 2020), a potential endogeneity may occur within this process. the main aftermath of such endogenous relationship is that cap and farmer’s behaviour rather co-evolve, so the observed correlation might express a cause-effect relationship whose direction, in fact, is not clearly identifiable. it follows that this paper is an empirical work but it is not an empirical application of some pem to some cap assessment. the empirical analysis rather aims to investigate the extent and nature of the abovementioned co-evolution in order to assess whether and how it is compatible with the application of the te logic. the main research question underlying this study is thus the following: which empirical support do we really have to interpret farmers’ behaviour as a response to cap measures and, thus, to consistently and properly apply the te logic to cap assessment? to answer these questions, the invariance of the field of investigation must be granted: a constant group (i.e., a balanced panel) of heterogeneous enough professional farms followed in its evolution over time together with the different cap support they are recipients of. the farm accountancy data network (fadn) is helpful to perform this investigation, particularly in the italian case where the fadn-rica dataset contains most of the required information for the present analysis (cagliero et al., 2010). moreover, italy presents a very diverse agriculture, and it is often considered the most heterogenous agriculture within the eu (baldoni et al., 2021). therefore, the 20082019 italian fadn balanced panel is here used. the abovementioned logic of the study also justifies its structure. section 2 overviews the literature and the policy relevance underlying the present empirical investigation. section 3 presents and discusses the balanced panel used for the analysis. sections 4 examines the evolution of both cap support and farms’ production choices and performance. then, section 5 presents the co-evolution hypothesis by connecting these two dynamics and wondering to what extent one can be considered a response to the other. section 6 derives the main consequences of this co-evolution in terms of the methodological challenges in adopting the te logic in this field. section 7 concludes drawing some methodological implications. 2. the policy issue with the eu approaching the first year of application of its n-th cap reform, expected to enter into force in 2023, the debate among agricultural economists, policy experts and analysts remains essentially the same of the previous reforms. positions range between two extremes. on the one hand, those (and the eu commission itself) who support the idea that this reform, as the previous ones, contain substantial novelties and somehow radical changes (european commission, 2021; pupo d’andrea, 2021). on the other hand, others consider it, as the previous ones, essentially a conservation of the same fundamental schemes (same money, same beneficiaries, same modalities,) with only marginal or “cosmetic” changes (arc2020, 2020; sotte, 2021a). a sort of “conservative revolution”. what is common between these two opposite views is that both see the cap as a policy expected to produce an effect on (or a response by) the farming sector (oecd, 2011; matthews, 2021).1 maybe, however, this is not the proper perspective from which the cap and its reforms have to be evaluated. the very fundamental question is to what extent the cap really conditions farmers’ choices and, therefore, whether it is really worth to adopt a te logic (coderoni, esposti and varacca, 2021). in particular, the cap presents three major problematic features in this respect. first, cap is a policy and not a program, that is, is made of a set of interdependent measures (lassance, 2020). these may be separately assessed (castaño et al., 2019) but are not, usually, separately delivered to beneficiaries; and beneficiaries know this. in order words, the cap is not a treatment, but it is a farm-specific (thus heterogeneous) combination of multiple treatments. consequently, also the evaluation of individual measures 1 “agricultural economists have been more concerned with the how and how well food and agricultural policies should be designed to achieve specific objectives and how policies have succeeded in their aims” (matthews, 2021, p. 185-186). 233the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 should be performed only within a complex multipletreatment environment. secondly, the cap is not just a set of measures, but it is a menu of measures since beneficiaries (farmers) are not assigned to some measures but voluntarily select among them (esposti, 2022).2 thirdly, this policy being a menu of measures, it turns out (in fact, it aims) to be a “passive” policy in the sense that is tailored on the existent rather than on inducing a change or a behavioural response. “active” measures are present, but they may take the form of conditionalities, that is, requirements to be met in order to be eligible to a support. these conditionalities are usually quite weak, if not actually purely apparent, in the sense that most beneficiaries already satisfy them or need just minimum adjustments to satisfy them (lataczlohmann et al., 2019).3 the key point here is that neither the cap nor any cap reform has a clear and univocal objective or target for which beneficiaries are expected to provide a specific response. cap is a sort of “institutional environment” regularly accompanying, and not necessarily inducing, farms’ evolution. eventually, the cap behaves as a welfare system reserved to the eu farming sector. its universalism (though limited to the farming activity) is expressed by the fact that its menu of measures covers nearly all farms, as well as all their different activities and instances.4 this does not exclude some more targeted measures, but it remains true that multiple targeted measures ultimately aim to be universalistic. the main consequence of this universalism is that the cap tends 2 the generalized voluntary nature of the cap can be questioned. here, voluntariness is intended in confront with the golden standard of randomized experiments where units assigned to the treatment do not choose whether or not to be treated. on the contrary, for all ii pillar measures the treatment is always the consequence of a voluntary choice. in the case of i pillar direct payments, a difference has to be made between the period before and after 2015. after 2015, in practice all farms (but landless farms) have become entitled to apply for these payments. before 2015, those farms that did not receive coupled payments before 2005 were not entitled to apply and, therefore, could not voluntary opt for the treatment. it remains true that, even when entitled, farms have to apply (so, to take a decision) and this also implies the respect of the cross-compliance conditions. consequently, farmers that do not want to accept this conditionality may decide to do not apply even when entitled to do so. 3 there may be significant exceptions to this conclusion due to large heterogeneity of agricultural systems across eu and italy. for instance, in farming systems showing the prevalence of monoculture the introduction of green payments, and the consequent compliance, had a relevant impact on farmers’ choices and behaviour (bertoni et al., 2018; 2021). 4 this universalism does not conflict with the voluntary nature of most measures. it is rather the opposite: through a large set of voluntary measures, the cap is able to provide assistance to all different kind of farmers according to their very different kinds of objectives. voluntariness within universalism is, therefore, the obvious consequence of the large heterogeneity of beneficiaries. to be conservative and passive in the abovementioned sense. rather than being one the effect of the other, the cap and the farming sector seem to actually co-evolve.5 the nature of the cap as an all-encompassing policy is not, per se, at odds with an evidence-based design and implementation (esposti and sotte, 2013; erjavec  and  erjavec, 2015; erjavec, 2016; ehlers et al., 2021). but this evidence concerns an expected effect (and, therefore, effectiveness and efficiency). since this expected effect is unclear, the need of an evidence-based cap inevitably raises the question: evidence about what? waiting for the implementation of the new cap reform (period 2023-2027), it seems useful to limit this question to the last 15 years. this is the period under investigation here and it has been interested by two major reforms, implemented in 2005 and 2015, and by some major further adjustments meanwhile (particularly in 2007 and 2008). it can be argued that these reform steps share the same three fundamental objectives (frascarelli, 2020, 2021; coderoni et al., 2021): farm income support (or protection); farm competitiveness through (more) market orientation, i.e., (more) product diversification; larger and better public (mostly environmental) good provision by farms.6 in italy, the decoupling of i pillar support (the socalled fischler reform) was firstly introduced in 2005. it has been extended and reinforced in 2007 (with the introduction of the single common market organization, cmo) and in 2008 (the health-check reform), and then progressively dissociated from historical direct payments in 2015 (the ciolos reform) (sotte, 2021b). consequently, the period under consideration here (2008-2019) starts from a year in which the full decoupling of direct payments was already under way. meanwhile, ii pillar support has been strengthened in terms of overall support and of its share on the total cap budget, but also 5 this is the empirical counterpart of the political economy argument on the endogeneity of the cap (swinnen et al., 2015) which suggests that its design may depend on farmers’ choices and behaviour more than the other way round. 6 matthews (2021, pp. 185-191) overviews the evolution of the fundamental objectives of the cap over time. “farm income”, “environment” and “competitiveness” are among the most persistent. the objective of production diversification and market reorientation can be considered an explicitation of the competitiveness objective. in fact, these are not the only objectives of the cap but are those that directly and exclusively refer to farmers’ behaviour under scrutiny here. other objectives could actually be added to this short list (european commission, 2019; coderoni et al., 2021). in particular, two are worth noticing. one is favouring structural change or adjustment within agriculture. the other is supporting the rural economy. but these objectives are beyond the horizon and, above all, the field of investigation of the present study both for the limited time under consideration and for the use of balanced panel of farms (see below) that, evidently, do not cover all socioeconomic aspects of the rural economy. 234 roberto esposti bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 in terms of a progressively stronger orientation towards environmental goods provision. with respect to the three abovementioned fundamental objectives, the decoupling of support (with the maintenance of the support level) was expected to induce market re-orientation while granting farmers’ income (anton, 2006; esposti, 2017a,b; ciliberti et al., 2022). also ii pillar had to facilitate market re-orientation (and structural change) and, at the same time, the environmental goods provision especially due to the strengthening of agroenvironmental measures (aem) already introduced in the 1992 reform (macsharry reform). pillar i itself has been designed to contribute to the environmental objectives with the introduction of the environmental conditionality already in 2005, then further enhanced with the novel greening payments in 2015. therefore, in principle, this sequence of reforms has been designed to get progressively closer to the abovementioned objectives. in practice, however, their actual implementation might not have generated a major impact.7 a lot of research work has been done in order to directly investigate, simulate, estimate the impact of these cap reform steps on beneficiaries. this large body of literature is definitely helpful in better understanding the mechanisms through which the cap operates and, therefore, in better designating and implementing it (matthews, 2021). but analysing the possible impact of the cap and its reforms with these approaches does not necessarily correspond to a program evaluation. most studies are grounded on farm-level structural models used either for ex-ante (simulations) or ex-post (simulations or estimations) assessment (see, for instance, mack et al., 2019). within their theoretical structure, these models somehow impose the existence, the form and sometime the direction of the response to policy measures. eventually, the problem is the lack of a counterfactual evidence. in most of these studies the counterfactuals are never observed, and they might not even exist, but the counterfactual case is just extrapolated from the estimated models parameters. the search of such counterfactual evidence may explain the emergence, in the last fifteen years, of a consistent body of empirical studies whose aim is to explicitly assess the cap impact within a te logic (just to mention a few: chabé-ferret and subervie, 2013; castaño et al., 2019; coderoni, 7 studies on the distribution of the cap support across regions and farms (see sotte, 2014, and terluin and verhoog, 2018, to mention a few) have mostly concluded that the beneficiaries and the allocation among them did not change significantly over time. this can be considered an implicit demonstration that the (reform of the) cap might not have had an effect. but this is not obvious. maintaining the distribution of support but changing the forms and modalities may still induce a response. esposti and varacca, 2021; ciliberti et al., 2022; esposti 2017a,b, 2022). this research effort is commendable and promising. as mentioned, however, the actual characteristics of the cap and of its reforms do not necessarily fit the strict requirements of this te logic. in most of these recent studies its suitability for cap assessment is given for granted and never really questioned. in principle, preliminary to any te investigation, it would be desirable to scrutinize the empirical support about the applicability of this logic to the three abovementioned key objectives. looking for this empirical support is the main purpose of the present study. 3. the data: 2008-2019 fadn italian balanced sample another major issue in the investigation of farms’ responsiveness and co-evolution with respect to cap measures concerns the field of investigation. several previous studies work on all farms, but this can introduce a bias as their response may be not fully observable for the presence of many very small farms (even “non-farms”) (sotte, 2006; sotte and arzeni, 2013) and may be also driven by long-term structural processes that are largely independent on the cap support. a further limitation of the field of investigation of many previous studies is the lack of a long-enough time dimension. most of them are, in fact, ex ante assessments thus they are a-temporal in the sense that are based on current farm-level data possibly on the basis of future scenarios. they seldom take the needed time until the farms’ co-evolution or response is significantly revealed by data. here, we focus on a sample that take these issues into account: the italian 2008-2019 fadn balanced panel.8 a constant field of observation is clearly needed to investigate the co-evolution of the cap support a farmers choice in order to get rid of the spurious effects simply generated by the change in the sample composition. this choice, however, may also have limitations and two of them are worth noticing here. the first limitation is that working on the fadn sample may miss some of the 8 this balanced panel consists of 1585 farms observed over 12 years, thus 19020 total observations. even if 2020 data were available, they are going to be problematic in terms of comparability due the effects of the covid-19 pandemic also on the farming sector. the eu-wide fadn sample could be used instead but the information available over all countries are less comparable and, above all, less detailed than those reported in the italian rica-fadn dataset. the choice of working with a balanced panel also explains why some of the results here presented may also substantially diverge from what obtained in studies working on the same period but on a different fields of investigation (european commission, 2019). 235the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 implications of cap and its reforms as changes occurring in non-professional farms, numerically prevalent in the italian context (sotte, 2006; sotte and arzeni, 2013), remain unobserved as these units are excluded from the fadn field of survey. structural changes may be also missing in the balanced panel. as the non-constant part of the fadn sample is excluded, the dynamics of entry/ exit (i.e. deactivation) from the sector, as well as other changes somehow related to the entry/exit from the sample (for instance, change in size due to land acquisition or loss), are at least partially missed. however, none of this possibly missing information is at the core of the three cap objectives here considered. the second limitation concerns the possible lack of representativeness of the adopted balanced panel with respect to whole italian agriculture even when only professional farms are considered (mari, 2020; vrolijk and poppe, 2021).9 representativeness is evidently sacrificed when a balanced panel is extracted over a longenough period since the fadn sample is rotating just in order to maintain representativeness over time. it is thus informative to make explicit how much the adopted dataset may over or under-represent some farms category compared to the whole italian agriculture. table a1 in the annex compares the distribution of farms by type of farming (tf) and economic size class (es) in the adopted sample (in year 2010) with the italian 2010 agricultural census.10 for the sake of comparison, census data are reported in two forms: the whole farm population and the population corresponding to the fadn field of survey, that is farms with a standard output (so) higher than 8 thousand € (also called professional or market-oriented farms).11 it firstly emerges that the fadn sample always somehow misrepresents the whole italian agriculture as about 63% of the farm population is excluded from the fadn field of survey. but limiting the attention to professional farms, the distribution of farms within the balanced fadn panel in terms of tf does not differ much from what observed in the census data, even though a slight over-representation of grazing livestock activities (tf4) and under-representation of permanent crop farms (tf4) is observed. a more important bias concerns the es as the balanced panel evidently self-select larger farms, in economic terms. this bias has to be taken in mind in commenting the following results and any 9 we wish to thank two anonymous referees for their helpful suggestions and remarks on this aspect. 10 together with the geographical district (regions in italy), these are the two levels for which the representativeness of the fadn sample is granted (mari, 2020). 11 in italy, this threshold was 4 thousand € up to 2014. generalization to the whole italian agriculture requires caution. however, it is worth stressing here that there is no feasible solution to this representativeness issue whenever a balanced fadn panel is adopted.12 even the vector of individual weights that accompany the fadn sample cannot be helpful in this respect. these weights allow to carry over the sample-level evidence to the population, at least for those dimensions for which the fadn sample is representative (mari, 2020). therefore, weights are useful to compute population-level aggregates, given representativeness, but it is not suitable to recreate this representativeness. moreover, these weights refer to the whole fadn sample and not to the balanced fadn sample. they also vary any year and have to be redefined any time the underlying sampling scheme is changed as occurred, in particular, with the change of the professional farm threshold in italy in 2014 and with the change of the tf classification in 2010. applying these weights to the balanced panel over 12 years would incur the risk of generating an uncontrollable distortion rather than correcting for an observed misrepresentation. considering that working on a constant sample is a strict condition to properly investigate the co-evolution of cap support and farms’ behaviour, we prefer here to sacrifice representativeness rather than to generate artefacts in the attempt to correct for it. also because representativeness is not a major concern with the respect of the major objective of the present paper. evidently, any policy conclusion based on these data should be taken with major caution (vrolijk and poppe, 2021, p.10). but the main interest, here, is rather on the methodological implications of the co-evolution of cap support and farms’ behaviour. it may be the case that such co-evolution does not perfectly correspond to what observed in the whole italian agriculture and may slightly overvalue the incidence of the outliers (in particular, farms with very high payments).13 however, evidence here reported remains valid within the adopted field of investigation and, more importantly, with respect to its main methodological implications. within this sample, the empirical analysis is developed in a sequence of three steps. first, the evolution of the cap support and of its distribution is investigated, considering both its total amount and its components (section 4.1). then, the evolution of the farmers’ choic12 in any case, it has been already noticed that also within the italian fadn sample the full representativeness on the three abovementioned dimensions is more theoretical than actual (mari, 2020, tables 2 and 3). 13 in the present case, however, what could be considered outliers are actually real farms. they might be peculiar and, for this reason, they are recipients of a very high cap support. but this does not mean that they represent anomalous or aberrant cases. 236 roberto esposti bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 es and performance is analysed (section 4.2). finally, some stylised facts about the co-evolution of these two dynamics are derived (section 5). 4. the evolution of the cap support and farms’ behaviour 4.1. cap support the first question to be answered is whether the cap support actually changed within the adopted field of investigation and how. figure 1 displays the total and per farm public support considering all the possible sources.14 the total support remains quite regular over the period (always ranging between 23 and 27 million €) with only limited oscillations due to the transition to one cap regime to another. overall, we observe an increase in total support (+17% from 2008 to 2019) in nominal terms, but this growth almost entirely vanishes (+4%) in real terms (2010 prices).15 consequently, the per farm average support passes from 14.4 thousand € to 16.9 thousand € per farm, in nominal terms. but in real terms this variation drops from 14.4 thousand € to 15.3 thousand € per farm. figure 2 reports the evolution of the composition of the total cap support. it evolved as a combination of three dynamics: 1. i pillar declined by 4% and ii pillar grew by 156% and this has made the share of i pillar and ii pillar be gradually re-equilibrated with the latter moving from a 13% to 29% of total cap support. 2. within i pillar, decoupled support remained stable (-0.4%) while coupled payments declined by -20% up to a final 15% in 2019 on total pillar i payments (corresponding to 11% on total cap support). the process of progressive decoupling of support actually stopped in 2012 since for the rest of period the shares of coupled and decoupled support remained quite stable. 3. within ii pillar, the largest growth concerns aem payments (+196%) while the other measures increased by 124% with aem support passing from a share of 44% on the total ii pillar support in 2008 to 51% in 2019. the huge growth and the increasing relevance of the aem support is investigated further in the annex (figure a1). 14 regional co-financing of ii pillar is included in cap support. the remaining national support represents a very marginal part, always lower than 5%. for this reason, the national support will be neglected in the rest of the analysis. 15 following matthews (2000), real values are computed using the official italian gdp deflator released by the national institute of statistics (istat). the synthesis that can be drawn from this general picture is that, at least from the farms’ perspective, the evolution of cap support in the 12 years under investigation really represents a sort of “conservative revolution”: the different components of the whole expenditure changed significantly, but the support eventually delivered to farmers is more or less the same. nonetheless, the key argument of the critics of this alleged conservatism of the cap consists not so much in the amount of support but in its strongly uneven distribution across farmers. table 1 reports some year-by-year distributional statistics of the total and cap support, and of its different components, within the present sample. overall, it is confirmed that values (but the maximum) are quite stable over time. at the same time, the distribution is very disperse with a standard deviation always much higher than the mean value as indicated by a greater than two coefficient of variation (cv). moreover, the left tail of the distribution being truncated at 0, the presence of several extreme values generates a remarkable asymmetry with a very long right tail. this is clearly revealed by the difference between the mean and the median (2nd quartile) values, with the former being in all cases more than double than the latter. high variability and asymmetry is observed in all the different policies but some specificities are worth noticing. in particular, both coupled i pillar payments and non-aem ii pillar payments show very high cv values. for both ii pillar subgroups the observed support is zero until the third quartile indicating that payments concentrate on a very limited number of farms.16 it can be also concluded, however, that these specific asymmetries tend to compensate, at least partially, as dispersion and asymmetry observed in the total support are significantly lower than in the single components. this apparent stability of the cap support distribution over time does not mean that from any individual farm perspective nothing changed. by looking at the single farm percentage variation of the received support from 2008 to 2019 (bottom of table 1), it emerges that several farms lost all the support (-100%) while for others the growth is maximum (in fact, it can not be computed simply because the initial value is zero). between these extreme cases, we find most farms with a change in the support that ranges from a decline (the first quartile is -19%) to a huge increase (the third quartile is +370%). the mean value (the second quartile) indicates a 16 a similar, in fact more extreme, case can be found in national payments where also time variation is large. these distribution characteristics can be explained by the fact that national payments tend to have an emergency or exceptional nature: they are activated under very special conditions, for very specific farms and for a limited period of time. 237the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 30% growth which is consistent with the growth of average support commented above. we should thus conclude that the evolution of the cap over this period significantly redistributed the support across farms but did not make it more homogeneously distributed. 4.2. farms’ behaviour 4.2.1. profitability in order to assess whether or not this cap evolution had any relevant impact on farms’ performance and choices, the first question to be answered concerns farms’ profitability. here we proxy the farm’s profit with the farm’s net income simply computed as revenue plus policy support less all costs.17 therefore, in order to investigate the evolution of farms’ profitability it is 17 in the fadn terminology what is here referred to as net income corresponds to the entrepreneurial income. as most agricultural production units are family farms, this also corresponds, for many units, to the family farm income (european commission, 2018a). the difference between net farm income and farm profit is that the former is defined as farm revenue, plus policy support, less all external costs; the latter as the difference between net farm income and the opportunity cost of factors of production (labour, land and capital) provided by the family farm. we wish to thank an anonymous referee for an helpful clarification on this point. worth to analyse the evolution of its components. figure 3 displays the dynamics of the average revenue and variable costs within the field of investigation. a selection of these costs is also shown. they concern what we design here as environment-using costs: fertilizers, pesticides (herbicides included), energy and water. it firstly emerges a regular increase of both revenue and costs, but with the latter showing a larger growth than the former (+38% and +12%, respectively). it follows that the incidence of variable costs on revenue passes from 38% in 2008 to 47% in 2019. among costs, environment-using ones maintain a quite constant share, always higher than 20% and lower than 25%. from these figures a quite regular profitability over the period can be deduced. figure 4 shows that the average farm net income did not significantly change as it remains between 50 and 60 thousand €. a -10% variation is actually observed comparing 2019 with 2008, but this decline can be entirely attributed to the very last year. if we express net income in real terms, however, a different conclusion can be drawn. although inflation has been constantly low during this period, in real terms the average farm net income suffered a -20% decline from 2008 to 2019 that becomes a -9% if we stop the comparison at 2018. we should thus rather conclude that, on average, farms actually struggled to defend their profitability over this period. at the same time, however, 0 2 4 6 8 10 12 14 16 18 20 0 5,000 10,000 15,000 20,000 25,000 30,000 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 total cap support (000 €) left scale avg. per farm cap support in nominal terms (000 €) right scale avg. per farm cap support in real (2010 prices) terms (000 €) right scale figure 1. total and per farm public support within the italian 2008-2019 fadn balanced sample. 238 roberto esposti bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 figure 2. composition of the total public (a) and cap (b) support within the italian 2008-2019 fadn balanced sample. 239the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 table 1. distribution of the public support (cap included) within the italian 2008-2019 fadn balanced sample (€). 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 total support mean 14,449 14,848 16,642 15,802 16,700 16,846 16,870 17,158 16,614 16,381 16,510 16,856 standard deviation 31,844 31,258 41,230 38,001 39,722 38,967 42,899 42,205 41,005 40,174 36,552 34,645 coefficient of variation 2.2 2.1 2.5 2.4 2.4 2.3 2.5 2.5 2.5 2.5 2.2 2.1 min 0 0 0 0 0 0 0 0 0 0 0 0 1st quartile 755 1,014 1,666 1,860 1,924 1,844 1,904 1,508 1,501 1,532 1,804 2,325 2nd quartile (median) 5,065 5,498 5,920 6,341 6,545 6,536 6,329 6,470 5,941 6,185 6,449 6,826 3rd quartile 14,824 15,904 17,100 16,878 17,634 17,607 17,431 18,625 17,807 16,971 17,813 18,489 max 420,574 505,280 859,158 834,940 737,493 720,471 894,886 1,158,547 972,158 911,073 834,179 756,761 national support mean 245 573 708 473 373 437 469 255 276 294 296 185 standard deviation 1,883 4,874 8,621 4,410 3,126 3,396 4,983 2,219 2,408 2,233 1,974 2,379 coefficient of variation 7.7 8.5 12.2 9.3 8.4 7.8 10.6 8.7 8.7 7.6 6.7 12.9 min 0 0 0 0 0 0 0 0 0 0 0 0 1st quartile 0 0 0 0 0 0 0 0 0 0 0 0 2nd quartile (median) 0 0 0 0 0 0 0 0 0 0 0 0 3rd quartile 0 0 0 0 0 0 0 0 0 0 0 0 max 32,072 102,854 213,984 119,430 66,942 69,493 145,670 60,900 56,491 59,487 36,800 74,000 total cap mean 14,204 14,275 16,106 15,329 16,327 16,410 16,401 16,903 16,338 15,753 16,286 16,599 standard deviation 31,763 30,850 38,511 37,724 39,512 38,714 42,579 42,135 40,897 37,516 36,501 34,463 coefficient of variation 2.2 2.2 2.4 2.5 2.4 2.4 2.6 2.5 2.5 2.4 2.2 2.1 min 0 0 0 0 0 0 0 0 0 0 0 0 1st quartile 724 947 1,622 1,840 1,892 1,750 1,814 1,491 1,461 1,497 1,770 2,286 2nd quartile (median) 4,957 5,144 6,395 6,125 6,478 6,325 6,130 6,340 5,727 5,955 6,257 6,701 3rd quartile 14,611 15,320 17,745 15,969 17,132 17,157 16,961 18,337 17,253 16,514 17,417 18,245 max 420,574 505,280 805,154 834,940 737,493 717,971 894,886 1,158,547 972,158 911,073 834,179 752,234 pillar i decoupled mean 9,961 9,954 11,211 11,178 12,750 12,536 12,886 11,541 11,340 10,883 10,291 9,922 standard deviation 21,774 21,001 33,082 31,119 35,614 34,153 36,599 32,735 30,054 26,924 24,078 21,307 coefficient of variation 2.2 2.1 3.0 2.8 2.8 2.7 2.8 2.8 2.7 2.5 2.3 2.1 min 0 0 0 0 0 0 0 0 0 0 0 0 1st quartile 98 215 507 768 886 830 878 739 925 1,028 1,204 1,242 2nd quartile (median) 3,477 3,483 4,015 4,199 4,231 4,171 4,068 3,568 3,614 3,644 3,682 3,830 3rd quartile 10,715 10,992 11,718 11,772 12,412 12,095 12,179 11,040 11,008 10,519 10,445 10,378 max 317,849 319,288 801,933 724,970 720,596 680,898 759,890 862,371 631,221 558,244 528,809 417,296 pillar i – coupled mean 2,374 2,380 1,923 1,577 567 631 776 1,726 1,883 1,744 1,736 1,889 standard deviation 11,566 12,675 8,401 8,003 3,357 3,786 5,307 8,515 9,999 10,320 8,860 9,275 coefficient of variation 4.9 5.3 4.4 5.1 5.9 6.0 6.8 4.9 5.3 4.9 5.1 4.9 min 0 0 0 0 0 0 0 0 0 0 0 0 1st quartile 0 0 0 0 0 0 0 0 0 0 0 0 2nd quartile (median) 0 0 0 0 0 0 0 0 0 0 0 0 3rd quartile 690 794 0 0 0 0 0 975 1,087 1,034 1,221 1,120 max 237,355 340,652 122,828 124,584 90,000 108,794 134,996 293,872 338,633 352,829 305,370 312,498 pillar ii – aem mean 826 931 1,100 1,127 1,602 1,620 1,366 1,946 1,940 1,917 2,279 2,450 standard deviation 2,991 3,300 4,132 4,430 5,390 5,585 5,043 6,552 6,674 6,555 7,528 7,862 240 roberto esposti bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 the number of farms with negative net income did not increase. it amounted to 9% of the whole sample in 2008 and to 7% in 2019, and has remained always between 10% and 5% though with a clear drop after 2009.18 more generally, average values may be uninformative, and even misleading, due to the large heterogeneity occurring within the panel as also detailed in the annex (figure a3). table 2 illustrates how during these twelve years the farm net income dispersion and asymmetry maintained the same basic features with no major evidence of a more uniform distribution. such large dispersion is confirmed by a cv always around two or 18 a classical issue in the analysis of farm profitability concerns whether the farming activity can grant agricultural workers and families a comparable income with respect to the rest of the economy. this issue has generated a long debate among agricultural economists, particularly in italy (rocchi et al., 2012). present results may provide some indication in this respect even though, as discussed, the net farm income here considered does not correspond, stricu sensu, to the family farm income for all units. in addition, as discussed, the adopted sample only consider commercial farms and tends to be biased upward, i.e., to have a little overrepresentation of larger farms in economic terms. nonetheless, for the sake of comparison, it can be noticed that the mean net farm income in the last year of observation (51,440 €) is significantly higher than the average family income resulting, for the same year, from the italian statistics on income and living conditions (silc) (33,653 €). this remains true even when only families with prevalent autonomous work are considered (42,340 €). however, it should be also noticed that this positive gap can be a further consequence of the asymmetry within the sample. if the median net farm income is considered (23,154 €) the gap seems to be actually reversed. moreover, while the average (or median) net farm income observed within the sample shows a decline in real terms over the period under analysis, the average real-term family income resulting from the silc data show a very slight increase (+0.4%). more, though it also shows a decline in the last three years under observation. the same does not occur for the asymmetry that remains large and constant over the whole period, with a very long right tail that motivates why the mean value is always more than double than the median value (2nd quartile). 4.2.2. factor use and structural change the fact that farm profitability did not change much over the period does not exclude that the behaviour and choices of farmers significantly responded to the change of external conditions (cap included). in order to more deeply investigate this response is useful to assess whether factor endowment, use and intensities significantly changed within the adopted field of investigation. four fixed (or quasi-fixed) factors are considered: land (uaa); labour (awu) also including the farm family labour (fawu); machinery (kw); livestock (lsu) (sahrbacher et al., 2008). figure 5 exhibits the evolution of these factors’ endowment over the 2008-2019 period. to facilitate interpretation and comparison, values have been indexed with respect to the initial level (2008=1). for all factors a positive trend can be appreciated whose slope seems to be dependent on the respective degree of fixity. from 2008 to 2019 the average land endowment increased by only 6%, while the growth has been of 10%, 15% and 23% for awu, lsu and kw, respectively. in fact, livestock endowment is the only case showing significant 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 coefficient of variation 3.6 3.5 3.8 3.9 3.4 3.4 3.7 3.4 3.4 3.4 3.3 3.2 min 0 0 0 0 0 0 0 0 0 0 0 0 1st quartile 0 0 0 0 0 0 0 0 0 0 0 0 2nd quartile (median) 0 0 0 0 0 0 0 0 0 0 0 0 3rd quartile 0 0 0 0 0 1 0 0 0 0 0 1,401 max 38,115 51,974 77,603 77,603 77,598 99,500 100,000 73,863 77,341 92,541 92,541 120,010 pillar ii – others mean 1,043 1,010 1,872 1,447 1,408 1,622 1,373 1,690 1,175 1,208 1,980 2,339 standard deviation 6,853 5,388 8,962 7,567 8,536 8,343 7,605 7,330 5,035 4,720 8,287 7,854 coefficient of variation 6.6 5.3 4.8 5.2 6.1 5.1 5.5 4.3 4.3 3.9 4.2 3.4 min 0 0 0 0 0 0 0 0 0 0 0 0 1st quartile 0 0 0 0 0 0 0 0 0 0 0 0 2nd quartile (median) 0 0 0 0 0 0 0 0 0 0 0 0 3rd quartile 0 0 0 0 0 0 0 0 0 0 1,047 1,860 max 184,212 140,000 133,700 149,093 240,000 215,000 240,000 110,000 87,000 83,265 176,513 161,758 % variation cap support (2019-2008) min 1st quartile 2nd quartile 3rd quartile max -100% -19% +30% +370% 241the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 oscillations and, more importantly, an apparent trend reversal after 2015. what emerges points to a substantial intensification in the use of these factors (in fact, the same was observed for the variable inputs). a more detailed analysis of the nature of this factors’ intensification is available in the annex (table a2). it is worth emphasizing here that, combining the evolution of factors’ use with 0% 5% 10% 15% 20% 25% 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 %environment-using costs on vc revenue variable costs (vc) figure 3. average revenue, variable costs and environment-using costs (fertilizers, pesticides, energy, water) (€) over the 2008-2019 period within the italian fadn balanced sample. 0% 2% 4% 6% 8% 10% 12% 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 % of farms with negative net income right scale net income (nominal terms) left scale net income (real terms, 2010 prices) left scale figure 4.average farm net income (€) over the 2008-2019 period within the italian fadn balanced sample. 242 roberto esposti bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 the profitability dynamics, a decline of factors’ productivity is observed. figure 6 displays the evolution of the farm net income per unit of labour. labour productivity (or profitability) declined by 25% from 2008 to 2019, though most of the decline occurs in the very first years of the period. however, if the real term values are considered, the decline is more pronounced (-34%) and occurs quite regularly up to 2014. it is finally interesting to assess whether this evolution in terms of factor endowment, intensities and profitability is associated to other structural adjustments concerning farm holders, their turnover and attitudes. figure 7 reports the presence of female and young (<40 years old) farmers within the sample.19 what emerges is a sharp decline of young holders (from 18% in 2008 to 6% in 2019) and a substantial stability of the presence of female holders (from 15% to 17%). moreover, there is no 19 it is worth noticing that this sample may significantly underestimate the holders’ turnover. as entry and exit dynamics are excluded by definition within a balanced panel, here only the internal replacements are captured, that is, the possible substitution of the holder within the same farm. although partial, however, this may still be a reliable representation of the actual structural change occurring within the professional farming sector. considering agriculture as a whole may misrepresent the presence of female and young farmers as numbers are affected by the presence of very small (non)farms. in the italian case, in particular, both the presence of female and of elder holders has been always altered by the persistence of these marginal (non)farms (iacoponi, 2021). table 2 – distribution of the farm net income within the italian 2008-2019 fadn balanced sample (€). 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 mean 57,056 53,945 55,907 55,703 55,380 54,253 51,358 54,100 54,186 55,072 57,512 51,440 standard deviation 139,843 134,874 135,000 142,913 127,696 119,818 123,255 132,212 133,324 109,159 106,696 106,542 coefficient of variation 2.5 2.5 2.4 2.6 2.3 2.2 2.4 2.4 2.5 2.0 1.9 2.1 min -160,758 -124,741 -143,652 -184,265 -66,484 -40,2051 -18,1687 -165,917 -205,180 -229,603 -121,842 -255,091 1st quartile 8,969 6,542 9,042 9,147 9,669 9,702 8,423 9,399 9,172 9,573 10,065 8,424 2nd quartile (median) 24,802 21,723 25,506 24,741 25,537 25,001 23,146 23,966 24,972 25,785 26,229 23,154 3rd quartile 58,698 52,296 58,763 57,760 59,681 58,205 53,101 57,595 62,726 6,1431 65,008 58,063 max 2,429,5722,075,4032,333,8292,228,0931,983,0412,019,8092,100,8503,368,7153,691,6321,815,4411,939,3881,930,918 0.8 0.9 1 1.1 1.2 1.3 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 awu uaa kw lsu figure 5. evolution of main factors’ average endowment (2008=1) over the 2008-2019 period within the italian fadn balanced sample. 243the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 0 10,000 20,000 30,000 40,000 50,000 60,000 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 net income/awu nominal terms (€) net income/fawu nominal terms (€) net income/awu real terms (2010 prices) (€) net income/fawu real terms (2010 prices) (€) figure 6. evolution the farm net income per (f)awu over the 2008-2019 period within the italian fadn balanced sample. 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 female left scale young left scale settlement by succession right scale figure 7. evolution of the presence of the female, young and organic farmers over the 2008-2019 period within the italian fadn balanced sample. 244 roberto esposti bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 evidence of a correspondence between young and female holders as the average age of female and male holders is substantially the same.20 it thus seems difficult to interpret these figures as the progressive emergence of a new generation of farmers within the adopted sample. nonetheless, the share of farmers settled by succession significantly increased from 30% in 2008 to 43% in 2019. this would indicate that 13% of farms experienced a succession during the period of observation. however, this succession is not apparently associated with the takeover of young and female farmers. in addition, most of these successions occurred between 2010 and 2012, thus it may be questioned whether it is real or it is just an artefact due to data collection or some other administrative reason. 4.2.3. production choices a final aspect of the evolution of farmers’ behaviour concerns their production choices. the classification of agricultural holdings by type of farming (tf) can be informative in this respect. fadn classifies farms in eight tf categories: five main groups of specialist agricultural holdings and three mixed groupings.21 therefore, the first indicator of a production response is expressed by the tf dynamics: a switch from one tf to another evidently expresses the farmer’s decision to change production orientation or specialization. figure 8 exhibits the evolution of the tf categories over the 2008-2019 period. the most frequent categories are field crops (tf1), permanent crops (tf3) and grazing livestock (tf4). none of the other types of farming (tfs) exceeds a 10% share. overall, shares remain quite constant over time: tf1 remains at 26% even though a slight decline is observed between 2010 and 2016; tf3 remains constant at 30% up to 2014 and then slightly declines to 28%; tf4 starts from 21% and experiences an increase in the first years but then comes back to 22% in 2019. all other tfs show a very limited variation of their share (always lower than 2%). even the combination of these tfs does not express any significant structural dynamics. for instance, tfs with livestock activities combined (tf4, tf5, tf7 and tf8) show the same share in 2008 and 2019 (31%) with minimum changes over the period. 20 see also giampaolo et al. (2021) and selmi (2021) for a comparison with analogous evidence on the whole italian agriculture. 21 the tf of an agricultural holding is determined by the relative importance of each production activity on the total farm so. the eight groups are defined as follows: tf1 = field crops; tf2 = horticulture; tf3 = permanent crops; tf4 = grazing livestock; tf5 = granivores; tf6 = mixed crops; tf7 = mixed livestock; tf8 = mixed crops&livestock. even though relatively few transitions from one tf to another are observed, it may be interesting to investigate further where these transitions occurs and speculate on the possible motivations. the annex (table a3) provides more details on the observed tf switches. here, it seems interesting to define the proper dimension of this event. figure 9 orders the farms per number of tf changes over the 2008-2019 period. for 1079 units (68% of the sample) no change is observed. for other 166 farms (about 10%) only one change is observed. it means that these are genuine switches, namely, in these observations a real change in production orientation has taken place. for all other units, multiple switches are observed during the period. in most cases, they are back-and-forth movements, that is, these farms are momentarily associated to another tf but then go back to the original category. arguably, this peculiar behaviour does not express any relevant change in production farmers’ choices. it can be interpreted as physiological oscillations of production activities in borderline farms between two tfs. however, the switch of tf may be a poor indicator of farm production re-orientation. there could be more radical changes in farmer’s output mix that are not captured by the tf classification. it is the case of the activation of unconventional farm activities usually designated as multifunctional diversification: farms combining agricultural production with market or non-market services (multifunctional farms). the fadn dataset provides information about the so-called “other gainful activities”, also defined as “agriculture-related activities” (“attività connesse”) in italian regulation.22 figure 10 displays the evolution of the number of farms with other gainful activities, as well as their incidence on the so both in the whole sample and in these multifunctional farms. for both the number of farms and the incidence on the whole sample, a sharp drop is observed between 2009 and 2010. after that, the trend regularly and consistently reverts to the initial 20082009 variation. it can be argued that this 2009-2010 drop is an artefact due to some changes in data collection as corroborated by the incidence of these activities within these multifunctional farms: it does not show any drop and it increases quite regularly, at least up to 2016. therefore, if compared to the 2010 level, in 2019 we observe a 3% growth in the number of multifunctional farms within the sample (from 14% to 17%), a 1.4% growth in the incidence of these activities within the full sample, and a 5% growth in the incidence within mul22 they include agritourism and rural tourism, educational farms, active subcontracting, aquaculture, transformation of farm products, production of renewable energy, environmental services, agro-craft activities. 245the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 tifunctional farms. therefore, the observed progress of multifunctional activities seems slow overall and it looks like more an increasing specialization of a limited group of farms. eventually, it appears as a gradual and spontaneous structural evolution driven more by the market conditions than by some change in the policy support (see below). 5. the co-evolution this section derives from the analysis above some stylised facts about nature and extent of the co-evolution of cap support and farm behaviour. by co-evolution here we mean that the dynamics of the cap and the change of farmers’ behaviour concur (so they appear to be correlated) in such a way that it is very difficult, if not unfeasible in practice, to distinguish which is the cause and which is the effect. therefore, with the term co-evolution we do not want to necessarily mean policy neutrality (or ineffectiveness) in promoting farm practice changes. it may be definitely the case that some agricultural practices are triggered by the change in the cap support. however, empirically assessing this causal linkage, may be very challenging. we want to motivate this conclusion more in detail by separately considering the three abovementioned major policy objectives (income support, production diversification, environmental goods provisions) to which we associate three respective research questions. 5.1. farm income and cap support is there any evidence that cap payments did really protect the farm’s net income in both level and variabil26% 25% 23% 22% 23% 23% 23% 23% 25% 25% 26% 26% 30% 30% 30% 30% 31% 30% 30% 29% 29% 29% 29% 28% 21% 21% 25% 24% 24% 24% 24% 24% 24% 23% 23% 22% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 tf 1 tf 2 tf 3 tf 4 tf 5 tf 6 tf 7 tf 8 figure 8. evolution of the type-of-farming (tf) categories over the 2008-2019 period within the italian fadn balanced sample (% is indicated only for ft >10%,). legend: tf1 = field crops; tf2 = horticulture; tf3 = permanent crops; tf4 = grazing livestock; tf5 = granivores; tf6 = mixed crops; tf7 = mixed livestock; tf8 = mixed crops&livestock. 246 roberto esposti bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 10 79 16 6 13 4 73 62 39 16 10 4 1 1 n o change 1 change 2 changes 3 changes 4 changes 5 changes 6 changes 7 changes 8 changes 9 changes 10 changes (6 8% ) 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 0% 5% 10% 15% 20% 25% 30% 35% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 % of farms with other gainful activities (a) % of other gainful activities on farm so (only a) % of other gainful activities on farm so (whole sample) figure 9. farms per number of tf changes over the 2008-2019 period within the italian fadn balanced sample. figure 10. evolution of the farms and of the incidence on farm standard output of other gainful activities within the italian fadn balanced sample. 247the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 ity? an income-protection effect should imply a negative relationship between the level of cap support and the farms net income, that is, a larger support for farms showing higher income problems. these problems can be expressed by a negative net income, by a net income that would be negative without the cap payment (i.e., the ratio between cap support and net income is >1) or, more generally, by a low labour profitability (i.e., net income per unit of labour). but income problems can be also intended as a large income variability. in order to assess this question, it is worth measuring the intensity of support per unit of family labour (fawu) both to eliminate the size effect and to focus on the actual farmers’ objective variable. table 3 provides detailed information about the evolution of the cap support per unit of net income and of awu and, above all, about its distribution within the sample. figure 11 displays the cap support and number of farms with a cap support larger than the net income (included net income<0). five major facts are worth noticing. 1. the support per unit of net income significantly oscillates due to the oscillations of the net income itself but, overall, it remains stable over time: 39% in 2008 and 40% in 2019, with a maximum of 66% in 2018 and minimum of 24% in 2014.23 2. the support per unit of fawu increased by 21% in nominal terms (8% in real terms) from 2008 to 2019, but if the comparison is made between 2009 and 2019, the increase falls to 4% in nominal terms and becomes a decline (-7%) in real terms.24 3. the correlation between the cap support per unit of fawu and the respective unit net income is significantly positive25 and it slightly reinforces over time with a maximum of 0.67 in 2018. it indicates that the incidence of the cap support on net income per unit of labour tends to be stronger in farms that need it less as they show an higher labour profitability. 4. the number of farms with a cap support greater than net income (negative net income included) is quite stable (around 20%). they receive an almost proportional share of support (between 20% and 30%) and the average support to these farms increased by 11% in nominal terms but remained constant in real terms (-0.6%). 23 these figures confirm what emerged in previous studies also for italian agriculture (european commission, 2018b). 24 due the presence of negative values, in computing this indicator, farms with negative net income are attributed the highest incidence observed in the rest of the sample. 25 it is worth reminding that, as detailed in section 4.2.1, the calculation of the net farm income includes the cap support. therefore, even when the latter shows a limited incidence on the former on average, a slight positive correlation between the two necessarily occurs. 5. the growth of unit cap support26 shows a weak but significantly positive correlation with family labour profitability. at the same time, a positive but much stronger correlation is observed between unit support and the variability the family labour profitability. it can be concluded that a quite contradictory evidence emerges about the consistency of the cap as an income protection policy. on the one hand, cap support may have really supported the farms’ income as its incidence is remarkable. on the other hand, however, support and support growth, though very disperse, go more towards farms that need less, i.e., more profitable farms.27 therefore, there is no clear indication that this policy is selective in favour of most problematic units but, at the same time, support itself is strongly oriented towards cases showing higher income variability. more than an income support policy, cap thus seems to behave like an income stabilization policy at whatever income level a farm is. 5.2. production diversification and cap support is there any evidence that the change in cap payments, either the decoupling of i pillar payments and the increase of ii pillar payments, induced production diversification? to assess a diversification-inducing effect we need a metric to measure production diversification. here we firstly follow the analogy with ecological studies where diversity is often measured using the shannon (or shannon-wiener) and the simpson indexes (keylock, 2005). these indexes are here adapted to compute the farm-level diversification index for any i-th farm at any time t (diit) (coderoni, esposti and varacca, 2021): (1) shannon (2) simpson where c indicates a generic crop/animal species of the set of all observed crops/animal species c. these indexes are separately computed on crops (on the basis of the share on the total farm’s uaa) and on animals (on the basis of the share on the total farm’s lsu), and then averaged weighting by the respective share of crop and livestock products on farm revenue. for both indexes, more diversified farms are expected to show an higher diit and, more 26 for farms with a zero initial cap support, the attributed growth rate corresponds to observed maximum finite value. 27 a similar evidence for the italian fadn farms is obtained by ciliberti et al. (2022). 248 roberto esposti bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 importantly, an increased production diversification within the sample is expressed by an higher average diit.28 figure 12 shows the evolution of the average shannon and simpson diversity indexes within the adopted field of investigation. the two indexes behave similarly though the shannon index evolves a little more smoothly: from 2008 to 2019, the shannon index increased by 12%, the simpson index by 10%. as usual, these average values may hide a major heterogeneity within the sample as can be better appreciated by looking at the descriptive statistics reported in table 4. in both cases, the dispersion (as indicated by the cv) and the asymmetry (as indicated by the median-mean ratio) are lim28 the main difference between the two is that the shannon index ranges between 0 and lnc/ln2, while simpson index ranges between 0 and 1. ited compared to most variables investigated above. the growth of the lower quartiles is more intense than the higher ones, thus indicating that not only diversification increased, but also that it distributes more uniformly within the sample. the bottom of table 4 reports the correlation coefficients between these indexes and the cap support per unit of fawu. as expected, the two diversity indexes behave very similarly. therefore, respective results can be commented on together. cap support by itself shows a little linkage with diversity indexes, at least until 2016 when a positive relationship started to emerge. apparently, this emerging relationship can be attributed to both the ii pillar support and to the i pillar decoupled support, for which, in fact, the positive linkage emerges from the beginning of the period. table 3. evolution of the cap support per unit of net income and of awu within the italian 2008-2019 fadn balanced sample. 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 a) cap support/net income (%) mean 39% 29% 54% 40% 43% 33% 24% 49% 47% 51% 66% 40% standard deviation 387% 1,207% 408% 270% 805% 628% 810% 363% 953% 11,234% 732% 266% coefficient of variation 9.9 41.6 7.6 6.7 18.9 19.3 34.4 7.3 20.3 220.3 11.1 6.7 min 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1st quartile 0% 0% 3% 4% 5% 4% 4% 3% 2% 3% 4% 5% 2nd quartile (median) 17% 21% 24% 24% 24% 22% 25% 25% 22% 22% 24% 25% 3rd quartile 55% 64% 61% 60% 60% 61% 62% 64% 62% 60% 61% 63% max 9,868% 16,679% 8,601% 3,553% 21,479% 17,270% 6,455% 9,591% 27,578% 2,388% 23,517% 3,902% b) cap support/fawu (€) mean 13,660 15,954 14,701 14,041 14,789 16,774 16,627 16,239 14,330 15,240 14,561 16,534 standard deviation 33,102 45,784 40,671 32,212 34,684 53,774 61,150 43,461 36,434 42,160 37,633 39,940 coefficient of variation 2.4 2.9 2.8 2.3 2.3 3.2 3.7 2.7 2.5 2.8 2.6 2.4 min 0 0 0 0 0 0 0 0 0 0 0 0 1st quartile 556 800 1,096 1,244 1,470 1,330 1,350 1,164 1,043 1,175 1,386 1,754 2nd quartile (median) 4,038 4,802 4,519 4,525 5,165 5,369 5,238 5,303 4,414 4,893 4,954 5,592 3rd quartile 12,498 14,468 13,835 13,489 15,083 14,765 15,201 16,545 14,457 13,841 14,328 16,522 max 647,037 973,039 781,053 533,976 597,378 1,233,624 1,413,233 1,053,225 883,780 828,248 1,005,035 886,387 correlation coefficient between b) and net income/ fawu 0.38* 0.36* 0.60* 0.39* 0.52* 0.64* 0.65* 0.51* 0.50* 0.46* 0.67* 0.54* correlation coefficient between net income/fawu and the cap support 2019-2008 growth rate 0.06* correlation coefficient between avg. 2019-2008 cap support/fawu and standard deviation of net income/fawu 0.52* a farms with net income<0 are excluded. *statistically significant at 5% confidence level. 249the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 0% 5% 10% 15% 20% 25% 30% 35% 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 % of farms with cap>net income right scale % of cap support to farms with cap>net income on total cap support right scale cap support to farms with cap>net income (000 €) left scale figure 11. cap support and number of farms with cap support > net income (included net income < 0) within the 2008-2019 italian fadn balanced sample. 0.35 0.36 0.37 0.38 0.39 0.40 0.41 0.42 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 shannon-wiener left scale simpson right scale figure 12. evolution of the average shannon and simpson diversity indexes within the italian 2008-2019 fadn balanced sample. 250 roberto esposti bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 a similar analysis can be performed for another set of indicators of production diversification. in this case, it is not an “horizontal” diversification (more crops or livestock activities) but a “vertical” diversification, that is, higher production quality as expressed by process and production certifications and or by the activation of other gainful activities. table 5 reports the correlation coefficients between cap support (and its different components) per unit of fawu and four indicators of this “vertical” diversification.29 all these indicators can be expression of a generalized tendency of farmers to look for an improved allocation efficiency, i.e., to find the best output mix given the market conditions. in turn, this tendency can be affected by the cap and its reform in two ways. on the one hand, the progressive decoupling of i pillar support should ena29 three has to do with certifications: organic farming certification; any kind of environmental certification (organic farming included); any product quality certification but organic certification (for instance, designation of origin). the last indicator is the already discussed multifunctional diversification, that is, the share of other gainful activities on farm’s so. ble this market reorientation (esposti, 2017a,b). on the other hand, it can be also the consequence of the ii pillar support itself, as certifications and diversification activities are incentivized by several ii pillar measures. correlation coefficients reported in table 5 only weakly support the linkage between the unit cap support and these diversification indicators. the total cap support is positively correlated with the organic farming certification (but this linkage is statistically significant only in the last four years) and negatively correlated with product quality certification. this evidence holds true also for decoupled i pillar support, while any kind of statistically significant relationship seems to vanish when only coupled i pillar support is considered. ii pillar unit support shows a very strong positive linkage with organic farming that only slightly weakened from 2009 to 2014. a little weaker and more volatile, but still positive and mostly statistically significant, is the linkage with all environmental certifications. with only few exceptions concentrated in the initial years of the period, the correlation with ii pillar support statistitable 4. evolution of the shannon and simpson diversity indexes within the italian 2008-2019 fadn balanced sample. 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 a) shannon diversity index (>1) mean 1.34 1.35 1.35 1.32 1.33 1.36 1.38 1.42 1.45 1.45 1.47 1.49 standard deviation 0.94 0.96 0.93 0.94 0.94 0.95 0.97 0.95 0.98 0.98 0.99 1.00 coefficient of variation 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 min 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1st quartile 0.66 0.66 0.66 0.62 0.64 0.65 0.65 0.72 0.76 0.73 0.74 0.75 2nd quartile (median) 1.27 1.31 1.31 1.30 1.31 1.37 1.40 1.44 1.45 1.43 1.44 1.48 3rd quartile 1.94 1.96 1.96 1.92 1.94 1.99 2.02 2.04 2.09 2.10 2.12 2.14 max 5.50 5.60 4.85 6.18 5.29 4.97 5.31 5.01 4.80 4.85 5.12 5.50 b) simpson diversity index (0-1) mean 0.37 0.38 0.38 0.37 0.38 0.38 0.38 0.40 0.41 0.40 0.40 0.41 standard deviation 0.26 0.26 0.26 0.26 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27 coefficient of variation 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.6 min 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1st quartile 0.12 0.11 0.11 0.08 0.09 0.09 0.10 0.15 0.16 0.15 0.17 0.17 2nd quartile (median) 0.42 0.44 0.44 0.43 0.44 0.44 0.44 0.47 0.47 0.47 0.47 0.47 3rd quartile 0.60 0.61 0.61 0.60 0.61 0.61 0.61 0.63 0.63 0.63 0.62 0.64 max 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 correlation coefficient btw a) and cap support per fawu 0.01 -0.02 0.03 0.01 0.00 0.03 0.05 0.04 0.09* 0.05* 0.11* 0.03 correlation coefficient btw b) and cap support per fawu -0.03 -0.03 0.02 -0.03 0.00 0.02 0.03 0.01 0.06* 0.04 0.09* 0.04 correlation coefficient btw a) and i pillar decoupled support per fawu 0.05* 0.03 0.08* 0.07* 0.02 0.03 0.04 0.04 0.08* 0.06* 0.11* 0.04 correlation coefficient btw b) and i pillar decoupled support per fawu 0.01 0.01 0.07* 0.04 0.02 0.01 0.03 0.01 0.07* 0.04 0.10* 0.06* correlation coefficient btw a) and ii pillar support per fawu 0.01 -0.02 0.03 0.01 0.00 0.03 0.05 0.04 0.09* 0.05* 0.11* 0.03 correlation coefficient btw b) and ii pillar support per fawu -0.01 -0.01 -0.02 0.00 -0.02 0.05* 0.04 0.05* 0.08* 0.02 0.06* 0.02 251the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 cally disappears in the case of product quality certification and multifunctional diversification. it can be concluded that there is some linkage between the increasing ii pillar support, the progressive decoupling of i pillar support and production reorientation. however, the empirical evidence is not enough to interpret the observed linkage as an undisputable causeeffect relationship. it can be again interpreted as a coevolution between market-driven production choices and the path-dependent cap support.30 30 its negative linkage with product quality certifications, for instance, can be simply explained by the fact that most of these highly specialised farms were historically recipients of poor support. and of this remains a trace in both decoupled and coupled payments. 5.3. environmental goods and cap support did the change in the cap support and composition (ii pillar in particular) really induce a greater provision of environmental goods? also an environmentalgood-provision effect of the cap requires an appropriate metric, i.e., appropriate indicators (janssen et al., 2010).31 31 this is a challenging task because environmental indicators often require detailed physical information that are hardly available at the farm level and only partially included in the fadn dataset. as part of “the farm to fork strategy”, the european commission has recently announced its intention to convert the fadn into a farm sustainability data network (fsdn) to expand the scope of the current fadn network by collecting farm level data also on environmental and social farming practices. table 5. correlation coefficients between cap support per unit of fawu and different certifications within the italian 2008-2019 fadn balanced sample. 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 total cap support/fawu organic farming 0.03 0.01 0.03 0.02 0.00 0.00 0.01 0.02 0.07* 0.09* 0.09* 0.08* environmental certification (organic included) 0.01 -0.03 -0.01 0.00 -0.03 0.01 0.00 -0.01 0.02 0.00 0.03 0.04 product quality certification (organic excluded) -0.01 -0.03 -0.06* -0.08* -0.08* -0.07* -0.08* -0.07* -0.07* -0.08* -0.07* -0.06* % of other gainful activities -0.03 -0.01 -0.03 -0.03 0.01 -0.01 -0.03 -0.02 -0.03 -0.02 -0.03 -0.04 decoupled i pillar support/fawu organic farming 0.00 0.00 -0.01 -0.01 -0.02 -0.03 -0.02 -0.02 0.05* 0.05* 0.05* 0.02 environmental certification (organic included) 0.00 -0.04 -0.03 -0.03 -0.04 -0.02 -0.02 -0.04 -0.03 -0.01 -0.01 0.01 product quality certification (organic excluded) -0.04 -0.04 -0.06* -0.08* -0.08* -0.08* -0.07* -0.08* -0.09* -0.10* -0.08* -0.07* % of other gainful activities -0.04 -0.01 -0.03 -0.03 -0.03 -0.02 -0.03 -0.03 -0.04 -0.04 -0.03 -0.04 coupled i pillar support/fawu organic farming -0.02 -0.02 -0.04 -0.04 -0.03 -0.02 -0.01 -0.03 0.00 -0.02 -0.01 -0.03 environmental certification (organic included) -0.03 -0.03 -0.04 -0.05* -0.03 -0.01 -0.01 -0.03 0.02 -0.03 -0.03 -0.03 product quality certification (organic excluded) 0.02 -0.03 -0.05* -0.08* -0.03 -0.03 -0.03 0.00 -0.01 -0.03 -0.04 -0.03 % of other gainful activities -0.04 -0.03 -0.03 -0.02 0.01 0.01 -0.01 -0.03 -0.03 -0.02 -0.02 -0.04 ii pillar support/fawu organic farming 0.19* 0.08* 0.14* 0.10* 0.07* 0.10* 0.07* 0.16* 0.20* 0.13* 0.19* 0.18* environmental certification (organic included) 0.11* 0.04 0.10* 0.07* 0.03 0.12* 0.05 0.13* 0.17* 0.06* 0.16* 0.12* product quality certification (organic excluded) 0.05* 0.02 -0.01 0.00 0.00 -0.04 -0.03 0.02 0.00 -0.02 0.01 -0.01 % of other gainful activities 0.06* 0.04 0.01 0.00 0.08* 0.01 0.01 0.02 0.01 0.01 0.01 0.02 aem ii pillar support/fawu organic farming 0.30* 0.17* 0.16* 0.14* 0.15* 0.11* 0.13* 0.19* 0.18* 0.19* 0.20* 0.19* environmental certification (organic included) 0.23* 0.08* 0.13* 0.11* 0.08* 0.06* 0.09* 0.14* 0.15* 0.09* 0.14* 0.13* product quality certification (organic excluded) 0.08* 0.02 0.02 0.04 0.02 -0.01 -0.01 0.04 0.02 -0.01 0.02 -0.02 % of other gainful activities 0.03 0.00 -0.02 0.00 0.01 0.00 0.00 -0.01 -0.01 -0.01 0.00 -0.03 other ii pillar support/fawu organic farming 0.03 -0.01 0.06* 0.04 -0.01 0.03 0.03 0.06* 0.12* 0.04 0.11* 0.10* environmental certification (organic included) -0.02 -0.01 0.03 0.02 -0.01 0.12* 0.02 0.06* 0.12* 0.00 0.12* 0.06* product quality certification (organic excluded) 0.00 0.01 -0.02 -0.03 -0.02 -0.05* -0.04 -0.01 -0.02 -0.03 -0.01 0.01 % of other gainful activities 0.06* 0.05* 0.01 0.00 0.10* -0.02 -0.01 0.03 0.01 0.00 -0.01 -0.01 *statistically significant at 5% confidence level. 252 roberto esposti bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 the diversity indexes discussed above may represent proxies of the provision of some environmental services, like the protection of biodiversity within the agro-ecological context. but they seem rough indicators of the provision of other environmental goods. at the same time, however, an explicit indication of the achievement of higher environmental standards comes from the abovementioned environmental certifications. therefore, it is worth investigating further the linkage between these certifications and the cap support. figure 13 shows the evolution of the share of farms with organic and environmental certifications. for the sake of comparison, also product quality certifications are reported. all certifications significantly grew over the whole period with +162% for organic farming, +52% for all environmental certifications and +47% for product quality certifications. in general terms, if we exclude organic farming, environmental certifications seem substantially stagnant compared to product quality certifications. eventually, organic farming has become the prevalent form of environmental certifications over time as it was just 34% on the total in 2008 and reached 58% in 2019. table 5 presents the correlation coefficients between the two categories of ii pillar cap support (aem and other measures) per fawu and the different certifications. as could be expected, it emerges a strongly positive and significant linkage between aem payments and environmental certifications, in particular organic farming. on the contrary, there is no evidence of a regular and significant relationship between other ii pillar measures, product quality certifications and multifunctional diversification. even for these measures, the only evidence concerns the linkage with environmental certification, organic farming in particular. it could thus be concluded that a robust relationship between the aem support and organic farming and, more generally, environmental certifications actually emerges. but, again, this does not imply a treatment effect as this linkage may be just apparent or, to be more precise, just a tautology. as a matter of fact, certification is not the consequence of a treatment (i.e., a ii pillar measure), but it is the treatment itself: untreated units cannot be certified whereas treated units are automatically certified. therefore, the te logic might not work properly because the treatment does not leave any behavioural trace, namely, it does not induce any observable behavioural response. in fact, the only behavioural trace is the farmer’s voluntary choice of the treatment itself which inevitably implies certification. 6. causal inference, cap assessment and the co-evolution hypothesis we can now go back to the original question of the present study, i.e., the actual applicability of the te logic to cap assessment. previous section points to some major features of the co-evolution of cap support and of farmers’ performance. as shown, this co-evolution does not necessarily exclude causation but makes it hardly identifiable. in practice, co-evolution is the consequence of the particular forms in which cap measures are delivered to farmers and these latter progressively take decisions combining voluntary participation to these measures with production choices. these forms eventually enter in conflict with the prerequisites of a te logic. without entering into technical details, it must be reminded that almost all ci studies are based on the so-called potential outcome (po) framework (rubin, 1974; imbens and wooldridge, 2009; imbens and rubin, 2015). within this theoretical framework, the empirical identification of the te depends on the identification of counterfactuals mimicking the outcome variable of a treated unit in the case it was not treated (and the other way round) (perraillon et al., 2022). but empirical identification and estimation of the te within this conceptual framework requires an appropriate quasi-experimental design32 and imposes its conditions.33 in particular, six specific sources of conflict between these conditions and the abovementioned forms of coevolution deserve detailed discussion. not only they may be all encountered in cap assessment exercises; more importantly, they may occur simultaneously. let’s discuss them from the more general (and problematic) to the more technical (and manageable) ones. 6.1. voluntary and universalistic treatments as discussed at the beginning of this paper, and as shown repeatedly in the empirical analysis, cap meas32 here we refer to “quasi-experimental design” with the same meaning given by perraillon et all. (2022) to “research design” on observational units, that is, the overall strategy used to answer a research question with non-experimental data. 33 in particular, three assumptions are critical: the first is the conditional independence assumption (cia, or unconfoundedness) that postulates the independence between the potential outcomes and the treatment conditional on a set of pre-treatment (exogenous) variables, or confounders. the second assumption is the overlap (also known as balance, or positivity, or common support) condition that empirically implies that there must be at least one treated unit and one control unit at each possible value of all confounders. the third condition is the stable unit treatment value assumption (sutva) that rules out any interference of an individual’s treatment status on another individual’s potential outcome. if these conditions are satisfied, observational data can be regarded as generated by a “natural experiment”. 253the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 ures are tendentially universalistic and adoption is mostly voluntary. all or most farms can apply for these measures and, therefore, the treatment status can not be considered exogenous. this poses fundamental problems in finding suitable counterfactuals as they may not exist at all. even when non-treated units are present and observable, they are so peculiar that can not be confronted with the treated ones: their peculiarity actually is the main reason for their exclusion (either voluntary or not) from the treatment. this makes the application of the te logic to cap assessment seriously questionable. any possible way out of this problem relies more on a proper design of the quasi-experimental setting rather than on alternative or adapted te estimation approaches. in practice, however, available datasets (like the fadn) might make these alternative settings unfeasible. 6.2. outcome variable the search of an appropriate quasi-experimental setting encounters another major issue. it has to do with the ambiguity about the outcome variable to be considered. the empirical analysis here performed clearly illustrate the point. on the one hand, for many cap measures a policy target variable is simply neither explicit nor univocal. in such case, the present investigation had to identify, more or less arbitrarily, a suitable metric for the policy assessment. on the other hand, when measures are very clearly targeted (several ii pillar measures, for instance), the outcome variable is clear or univocal but it is just a tautology: the treatment adoption itself implies the outcome variable which automatically takes zero value for the non-treated units. as shown, this is the case, for instance, of certifications’ adoption. an outcome variable may not exist, may be unobservable, may be multiple or may be tautological. in any case, this poses a fundamental practical challenge for the consistent application of the te logic to cap assessment. also in this case, the solution does not necessarily depend on some methodological adaptation or alternative to conventional te estimation approaches. it rather requires a well suited quasi-experimental design based on a conceptualization of farmers’ behaviour that eventually leads to the identification of the most appropriate outcome variable to be considered in the analysis.34 34 for a theoretical and empirical investigation on how farmers select the policy and change their behaviour in order to take advantage of it within an utility-maximizing framework, see esposti (2022). 0% 5% 10% 15% 20% 25% 30% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 organic farming environmental certification (organic farming included) product quality certification (organic farming excluded) figure 13. evolution of the share of farms with certifications within the italian 2008-2019 fadn balanced sample. 254 roberto esposti bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 6.3. heterogeneity coexistence of the voluntaristic and universalistic nature of the cap aims to cover very diverse farming conditions. as repeatedly emerged in sections 4 and 5, farms under investigation (treated or not) are characterized by vast heterogeneity. this has to do with their structural and geographical characteristics, but also with farmer’s personal motivations. while the former features may be observed, the latter remain unobserved and can only be indirectly revealed by the observable farmer’s behaviour (esposti, 2022). controlling for this heterogeneity requires many confounders, thus highly dimensional datasets that, in turn, imply remarkable computational complexity (the so-called curse of dimensionality). literature in the field has proposed several solutions (abadie, 2021) that have also widely adopted in cap assessment (esposti, 2017a,b). but farm heterogeneity is challenging also for another more fundamental, and often disregarded, reason: the te itself may be strongly heterogeneous. in such case, although the average te (ate) is correctly identified and consistently estimated, it simply remains uninformative. under strong te heterogeneity, estimating the group or the individual te is needed for policy assessment and learning (esposti, 2022). recently proposed machine learning (ml) approaches seems interesting in this respect (bertoni et al. 2021; coderoni, esposti and varacca, 2021; esposti, 2022). but they are also computationally demanding and complex making their outcome not always transparent and results not fully reliable (knaus et al., 2021). as a consequence, these approaches also requires a lot of additional validation work (athey and imbens, 2017). 6.4. multivalued treatments most ci approaches have been designed and applied in a binary treatment context. but, as clearly shown, almost all cap measures consist in interventions whose intensity varies across discrete or continuous range of possible values (i.e., they are multivalued treatments). a multivalued treatment can be still represented within an augmented po framework but the empirical implications can be severe. imbens (2000) and hirano and imbens (2004) developed an extension of po framework to continuous multivalued treatments and proposed an estimation approach based on the generalization of the propensity score matching (psm) of the binary case (generalised propensity score, gps, estimation) (esposti, 2017a). however, it provides consistent estimates only whenever the treatment assignment can be considered exogenous once all confounders have been taken into account. the approach proposed by cerulli (2015) admits this possibility of treatment endogeneity and the respective results are consistent even under this circumstance. the application of both approaches, however, may encounter several practical problems for the computational complexity and, above all, for the likely violation of the overlap condition. alternative non-parametric (or semi-parametric) estimation strategies can be helpful to overcome these issues, but they only apply to discrete (or categorical) multiple treatments (cattaneo, 2010; cattaneo et al., 2013; athey and imbens, 2017; esposti, 2017b). therefore, they may require an arbitrary discretization of continuous treatments. 6.5. multiple treatments almost all ci studies concentrate on single treatments. as shown, however, the main feature of the cap and its co-evolution with the farmers’ choices is that it delivers multiple treatments to farms. identifying and consistently estimating the te of any single treatment with the conventional approaches is possible only under the assumption of treatment independence. but the empirical evidence clarifies that this assumption is quite unrealistic as interdependence is likely to occur both in terms of treatment assignment and in terms of outcome variable. in particular, within the cap both interdependencies may evidently occur between i and ii pillar measures. in this respect, it could be interesting to assess whether treatments reciprocally interfere by magnifying or offsetting the respective te. at present, however, a viable empirical solution to this issue has not yet emerged (frolich, 2004; athey and imbens, 2017). 6.6. treatment timing when panel data are available, as in the present case, units can be observed before and after the treatment. this allows te identification and estimation via widely used approaches like the difference-in-differences (did) estimation or the two-way fixed effects estimation (de chaisemartin and d’haultfœuille, 2020). however, though powerful, these approaches still require counterfactuals, with all the abovementioned complications, and imply an additional assumption (the socalled parallel trend assumption) that excludes that time behaves as an additional confounder.35 but what really 35 see arkhangelsky et al. (2021), chan and kwok (2022), cho et al. (2022), for recent developments in this field. 255the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 makes the timing of the treatment a challenging issue in cap assessment is that it may differ (in fact, it usually differs) across the treated units. they enter the treatment in different moments of time (asynchronous policy adoption). this issue can become even more problematic in the agricultural context as the timing of the farms’ response can be itself heterogenous across units depending on their structural characteristics: even under the same treatment timing, some farms can respond immediately others may take some years. recent generalizations of the did approach tackle this issue under more than one preand post-treatment periods, but still a fixed treatment time (cerulli, 2019), as well as under many postand pre-intervention times and with the treatment itself that varies over time (cerulli and ventura, 2019; callaway and sant’anna, 2021; sun and abraham, 2021). however, as shown in sections 4 and 5, cap magnifies these issues and these methodological solutions may be insufficient or unfeasible. even though, in principle, cap reforms start at the same time for all farms (at least within a specific eu member state), their actual implementation can differ across space (for instance, regions) and farms may apply in different moments. moreover, several measures are reiterated across successive cap programming periods. consequently, dealing with timevarying treatments is even more challenging because treatment itself may be reiterated on the same units in different periods of time, possibly melded with periods without the treatment. 7. some concluding remarks assessing the farm-level impact of cap measures and reforms with a te logic is potentially informative thus highly desirable. unfortunately, it is also highly challenging. major theoretical and methodological problems are more often overlooked that explicitly tackled. in this respect, a deeper and more critical discussion within the profession would be desirable. the present paper contributes to this discussion not by proposing an empirical application of methods based on this logic, but presenting an empirical evidence that poses doubts and conditions on their actual applicability. provided that the target of the policy to be investigated is clearly identified (in fact, it is often not clear at all) (matthews, 2021), empirically assessing whether and to what extent this policy has been successful requires specific pre-conditions. firstly, we need appropriate datasets. fadn surely is very helpful in this respect, but some of its limitations may reduce the application of these evaluation methodologies. secondly, and more importantly, we need to investigate the co-evolution of the policy instruments and of the potentially treated units, that is, farmers’ behaviour. investigating co-evolution means finding enough support to the existence of a possible cause-effect relationships and to the feasibility of its investigation. in the meaning here given to the term, co-evolution implies that a correlation occurs but this does not necessarily imply causation as it may be the consequence of interdependence between the two processes making an unidirectional cause-effect relationship unidentifiable. on the basis of the empirical investigation here presented and the observed co-evolution, we can conclude that the cap has really moved in the right direction, that is, consistently with the declared objectives. and the farmers’ changed their behaviour and performance, as well. at the same time, however, this does not mean that the policy induced the expected farmers’ response. achieving this conclusion within a te logic requires conditions that are not always compatible with the cap features. it does not follow that these approaches are and will be always inappropriate in this specific case. it rather implies that an acritical adoption of these approaches may not only lead to wrong policy conclusions but also procrastinates the search for more suited solutions. moreover, it suggests that any consistent application of these approaches requires more attention on setting up appropriate quasi experimental design with the consequent appropriate datasets and theoretical representation of farmers’ choices, and on suitable adaptations and refinements of these approaches. references abadie, a. 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(2021). cost of extending the farm accountancy data network to the farm sustainability data network: empirical evidence.  sustainability 13, 8181. annex a1. evolution of the aem support figure a1 shows how aem payments evolved in terms of number of beneficiaries and of average support per beneficiary. the growth of aem support comes from the combination of two facts. on the one hand, the number of beneficiaries increased by 75% passing from 245 farms (15% of the whole balanced sample in 2008) to 428 units (27% in 2019). on the other hand, the average payment per farm increased almost with the same intensity (+70%) passing from about 5.3 thousand € in 2008 to 9.1 thousand € in 2019. in fact, the growth of the number of beneficiaries is not regular as it shows a fall from 2012 to 2015 and, then, a jump as a consequence of the transition from one regime to another. this sort of bureaucratic cycle is somehow compensated by the countermovement of the average payment per farm that reaches its peak exactly in 2015. a2. the lorentz curve of the farms’ cap support and income to better illustrate the distributional characteristics of cap support, and its evolution over time, within the sample, the lorentz curves of the pillar i and pillar ii support, respectively, are reported in figure a2 for years 2008, 2015, 2019. the sharp concentration of the support on a very limited number of farms clearly emerges. as expected, it is higher in the case of ii pillar where 5% and 3% of farms (i.e., 79 and 48 farms) concentrate 50% of the support in 2019 and in 2008, respectively. but this over concentration is only a little lower for i pillar with 8% and 6% (127 and 95 farms), respectively. within the adopted field of investigation, the sequence of cap reforms has slightly changed the distribution of the cap support by making it a little bit more homogenous. but this change remains almost negligible. figure a3 presents the analogous lorentz curves of the farm net income for selected years 2008, 2015 and 2019.36 two aspects are worth noticing. first, as expected, the distribution of net income within the sample is highly asymmetric with very few units concentrating most of the total (positive) net income. second, no significant change in this distribution can be appreciated moving from 2008 to 2019. eventually, in 2008 9% of farms concentrated 50% of the total (positive) net income; in 2019, this share has slightly increased to 11%. 36 these curves are obtained considering only farms with a positive net income in the respective year. 259the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 a3. factors’ intensification to better investigate the nature of factors’ intensification, table a2 reports the distributional characteristics of the factor intensities per labour unit (awu) together with labour profitability. it firstly emerges that these structural characteristics remain quite stable over time as could be expected considering that adjustments in (quasi)fixed factors’ endowment take time and may have a cost (esposti, 2017a). it emerges a small reduction in the incidence of family labour on the total farm’s labour use (-3.2%). also the land endowment per unit of labour slightly declines (-4.8%). but for the other production factors, it emerges a gradual intensification with a 11% increase of machinery endowment, a 8% increase of the livestock endowment and, above all, a 18% increase of environment-using costs per unit of labour. although these ratios should get rid of the size effect, with the only exception of the fawu/awu ratio, they show a remarkable heterogeneity. also for these structural characteristics and their evolution, a major dispersion (as expressed by cv) and asymmetry (as expressed by the median/mean ratio) emerges within the field of investigation. for instance, in the case of land endowment, we range from no-land farms to observations with hundreds of hectares per unit of labour. the bottom line of this large heterogeneity is expressed by the net income per unit of labour reported in the final rows of table a2. here we also find negative values and this makes the dispersion even more evident. values range from a minimum of -345 thousand € per unit of labour in 2008 to a maximum 2372 thousand € per unit of labour in 2009. only a little decline of dispersion of asymmetry is observed in the post 2015 period. more importantly, the mean value significantly declines over the 2008-2019 period (-13% in nominal terms; -22% in real terms) and this reveals a significant redistribution in favour of the more profitable farms: while 1st and 2nd quartiles decline by 15% and 20% respectively, the 3rd quartile declines by only 6% and the maximum value increases by 8%. a4. tf switches in order to only focus on real changes in production orientation, we limit our attention to those switches that make the initial tf of farm differ from the final one. these switches concern 187 farms (12% of the sample). these movements are positioned in a source-destination matrix by tf category (table a3).37 as could be 37 therefore, the diagonal elements indicate the non-switching units. 0 50 100 150 200 250 300 350 400 450 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000 11,000 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 number of farms right scale total support (000 €) left scale avg. per farm (€) left scale figure a1. evolution of the agro-environmental measures (aem) support within the italian 2008-2019 fadn balanced sample: number of beneficiaries, total support and average support per beneficiary. 260 roberto esposti bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2 27 52 77 10 2 12 7 15 2 17 7 20 2 22 7 25 2 27 7 30 2 32 7 35 2 37 7 40 2 42 7 45 2 47 7 50 2 52 7 55 2 57 7 60 2 62 7 65 2 67 7 70 2 72 7 75 2 77 7 80 2 82 7 85 2 87 7 90 2 92 7 95 2 97 7 10 02 10 27 10 52 10 77 11 02 11 27 11 52 11 77 12 02 12 27 12 52 12 77 13 02 13 27 13 52 13 77 14 02 14 27 14 52 14 77 15 02 15 27 15 52 15 77 2008 2015 2019 a) 92% 94% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 26 51 76 10 1 12 6 15 1 17 6 20 1 22 6 25 1 27 6 30 1 32 6 35 1 37 6 40 1 42 6 45 1 47 6 50 1 52 6 55 1 57 6 60 1 62 6 65 1 67 6 70 1 72 6 75 1 77 6 80 1 82 6 85 1 87 6 90 1 92 6 95 1 97 6 10 01 10 26 10 51 10 76 11 01 11 26 11 51 11 76 12 01 12 26 12 51 12 76 13 01 13 26 13 51 13 76 14 01 14 26 14 51 14 76 15 01 15 26 15 51 15 76 2008 2015 2019 b) 95% 97% figure a2 – lorentz curves of the pillar i (a) and pillar ii (b) support within the italian 2008-2019 fadn balanced sample: years 2008, 2015, 2019. 261the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 expected, flows mostly concern two kind of movements: one occur across the main tfs, (tf1, tf3 and tf4); the other concerns movements from more specialized tfs to the mixed ones (tf6, tf7 and tf8). nonetheless, no prevalent migration emerges and this confirms that, over the period of observation, there is no prevalent evolutionary dynamic expressing a generalised reorientation of the farmers’ production choices. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 26 51 76 10 1 12 6 15 1 17 6 20 1 22 6 25 1 27 6 30 1 32 6 35 1 37 6 40 1 42 6 45 1 47 6 50 1 52 6 55 1 57 6 60 1 62 6 65 1 67 6 70 1 72 6 75 1 77 6 80 1 82 6 85 1 87 6 90 1 92 6 95 1 97 6 10 01 10 26 10 51 10 76 11 01 11 26 11 51 11 76 12 01 12 26 12 51 12 76 13 01 13 26 13 51 13 76 14 01 14 26 14 51 2008 2015 2019 89%91% figure a3. lorentz curve of the (positive) farm net income within the italian 2008-2019 fadn balanced sample: years 2008, 2015, 2019. 262 antonio lopolito1,*, angela barbuto2, fabio gaetano santeramo2 bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 table a1. representativeness of the balanced fadn sample. comparison of the italian 2008-2019 fadn balanced panel (year 2010) with the italian 2010 agricultural census: distribution by types of farming (tf) and economic size (es) classes (so=standard output). fadn balanced sample 2010 census (total) 2010 census (so>8000 €) tf classes: tf 1 23% 24% 23% tf 2 8% 2% 6% tf 3 30% 55% 43% tf 4 25% 8% 16% tf 5 3% 1% 1% tf 6 6% 7% 7% tf 7 1% 0% 1% tf 8 4% 2% 4% not classified 0% 1% 0% total 100% 100% 100% es classes: small (so <25,000 €) 30% 18% 49% medium-small (so=25,000-50,000 €) 19% 8% 21% medium (so=50,000-100,000 €) 22% 5% 15% medium-large (so=100,000-250,000 €) 20% 4% 10% large (so>250,000 €) 9% 2% 5% total 100% 37% 100% legend: tf1 = field crops; tf2 = horticulture; tf3 = permanent crops; tf4 = grazing livestock; tf5 = granivores; tf6 = mixed crops; tf7 = mixed livestock; tf8 = mixed crops&livestock. source: fadn and istat. table a2.factor use and profitability per labour unit within the italian 2008-2019 fadn balanced sample. 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 family awu/awu mean 0.71 0.68 0.73 0.71 0.70 0.70 0.70 0.69 0.73 0.71 0.72 0.69 standard deviation 0.31 0.31 0.29 0.30 0.30 0.29 0.29 0.30 0.30 0.30 0.29 0.31 coefficient of variation 0.43 0.46 0.39 0.42 0.43 0.42 0.42 0.43 0.41 0.43 0.40 0.45 min 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 1st quartile 0.43 0.40 0.48 0.46 0.44 0.46 0.45 0.43 0.49 0.44 0.48 0.41 2nd quartile (median) 0.80 0.72 0.79 0.80 0.76 0.73 0.74 0.74 0.85 0.79 0.81 0.73 3rd quartile 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 max 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 uaa/awu (ha) mean 18.5 18.5 17.8 17.4 17.4 17.3 17.5 18.3 17.7 17.8 18.1 17.6 standard deviation 26.6 25.6 24.3 23.7 24.4 23.9 24.4 31.5 22.9 22.9 23.2 21.9 coefficient of variation 1.44 1.39 1.36 1.36 1.40 1.38 1.39 1.72 1.29 1.28 1.28 1.24 min 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.1 0.1 0.1 1st quartile 3.8 3.9 3.9 4.0 3.9 3.9 3.9 4.0 4.0 4.1 4.0 4.0 2nd quartile (median) 9.5 9.6 9.6 9.4 9.4 9.2 9.2 9.6 9.7 9.3 9.6 9.7 3rd quartile 23.0 22.9 22.3 22.4 21.8 21.7 21.6 21.6 22.3 22.2 22.1 21.9 max 486.4 387.3 387.3 421.8 421.8 421.8 387.3 803.6 274.5 200.5 192.4 179.1 263the role of network characteristics of the innovation spreaders in agriculture bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 kw/awu (hp) mean 113.1 112.4 112.2 112.7 114.0 114.0 116.4 124.0 120.3 123.0 123.7 125.7 standard deviation 117.1 104.8 110.3 100.7 98.2 98.1 100.3 241.8 109.4 115.8 117.3 121.4 coefficient of variation 1.03 0.93 0.98 0.89 0.86 0.86 0.86 1.95 0.91 0.94 0.95 0.97 min 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1st quartile 45.3 47.7 50.0 50.2 51.8 50.9 51.5 51.8 52.2 51.2 53.0 53.6 2nd quartile (median) 80.8 82.9 82.1 82.8 86.1 87.3 88.0 90.0 92.4 90.3 91.2 90.6 3rd quartile 137.8 143.6 143.5 145.9 143.7 144.3 148.1 148.2 151.9 155.6 155.4 155.5 max 1,341 1,010 2,010 812 798 926 846 8,560 1,488 1,123 1,488 1,488 lsu/awu mean 12.3 12.4 14.2 14.3 13.7 15.1 14.4 14.5 15.8 14.8 13.6 13.2 standard deviation 36.4 41.3 44.5 57.8 40.7 55.1 46.8 56.3 62.4 56.2 43.6 46.9 coefficient of variation 2.97 3.32 3.13 4.03 2.98 3.65 3.25 3.87 3.95 3.81 3.21 3.54 min 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1st quartile 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2nd quartile (median) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3rd quartile 11.1 11.9 13.0 12.7 12.5 12.3 13.0 12.0 11.5 10.6 10.1 9.4 max 528 1,032 992 1,782 580 881 860 1,042 1,125 1,291 604 723 environment-using costs/awu (€) mean 5,498 5,432 5,501 5,920 6,197 6,033 6,223 6,908 6,419 6,602 6,398 6,488 standard deviation 8,373 7,237 8,036 7,993 8,199 7,813 8,101 19,089 9,226 11,042 9,776 9,830 coefficient of variation 1.52 1.33 1.46 1.35 1.32 1.30 1.30 2.76 1.44 1.67 1.53 1.52 min 0 0 0 0 0 0 0 0 0 0 0 0 1st quartile 1,452 1,444 1,543 1,737 1,856 1,827 1,938 1,873 1,669 1,688 1,670 1,705 2nd quartile (median) 3,068 3,184 3,199 3,484 3,598 3,634 3,743 3,678 3,602 3,608 3,691 3,764 3rd quartile 5,940 6,072 5,957 6,428 6,838 6,635 6,914 6,960 7,002 6,927 7,031 7,018 max 102,031 64,425 84,848 75,441 92,735 73,174 82,336 671,360 119,471 189,599 154,697 132,348 net income/family awu (€) mean 44,928 50,549 43,592 45,238 45,209 45,561 43,007 45,313 43,174 45,413 45,861 45,113 standard deviation 103,713 141,432 107,183 124,906 102,187 115,314 126,977 104,157 107,065 95,329 99,178 97,087 coefficient of variation 2.31 2.80 2.46 2.76 2.26 2.53 2.95 2.30 2.48 2.10 2.16 2.15 min -456,321 -166,321 -82,087 -80,300 -64,492 -181,104 -182,261 -162,664 -170,206 -229,603 -69,855 -208,142 1st quartile 5,919 4,881 6,080 6,016 7,051 6,647 5,871 7,063 6,116 7,134 6,955 5,817 2nd quartile (median) 18,756 16,773 18,025 17,781 19,567 18,784 16,593 19,057 17,168 18,894 19,262 17,367 3rd quartile 45,051 45,133 43,189 43,864 45,927 46,124 43,316 46,335 46,287 49,011 48,762 48,544 max 1,454,834 3,459,005 1,944,858 2,197,699 1,246,851 2,693,079 3,166,903 2,041,645 1,986,362 1,609,615 2,085,363 1,821,656 net income/awu (€) mean 33,991 34,658 33,194 33,845 31,891 31,971 29,003 29,232 31,445 30,749 32,729 29,628 standard deviation 78,467 96,969 81,619 93,450 72,083 80,916 85,630 67,193 77,978 64,547 70,778 63,762 coefficient of variation 2.31 2.80 2.46 2.76 2.26 2.53 2.95 2.30 2.48 2.10 2.16 2.15 min -345,243 -114,033 -62,508 -60,077 -45,493 -127,082 -122,912 -104,936 -123,965 -155,465 -49,852 -136,698 1st quartile 4,479 3,346 4,630 4,501 4,974 4,664 3,959 4,556 4,455 4,831 4,963 3,820 2nd quartile (median) 14,190 11,500 13,726 13,303 13,803 13,181 11,190 12,294 12,503 12,793 13,746 11,406 3rd quartile 34,085 30,944 32,888 32,817 32,397 32,365 29,211 29,891 33,712 33,185 34,799 31,881 max 1,100,695 2,371,566 1,480,981 1,644,227 879,534 1,889,753 2,135,670 1,317,088 1,446,709 1,089,877 1,488,212 1,196,382 264 antonio lopolito1,*, angela barbuto2, fabio gaetano santeramo2 bio-based and applied economics 11(3): 231-264, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12912 table a3. source-destination matrix for tf category within the italian 2008-2019 fadn balanced sample (in grey >10 elements). destination source tf 1 tf 2 tf 3 tf 4 tf 5 tf 6 tf 7 tf 8 total tf 1 294 10 20 35 6 57 3 36 460 tf 2 10 96 22 0 0 14 0 0 142 tf 3 20 10 375 4 2 47 2 12 471 tf 4 33 0 5 272 2 6 17 46 382 tf 5 5 0 3 2 28 2 3 5 48 tf 6 55 6 58 5 3 9 0 5 141 tf 7 2 0 2 13 4 0 0 0 22 tf 8 35 0 15 36 7 6 2 5 105 total 455 122 501 366 52 141 26 110 1772 legend: tf1 = field crops; tf2 = horticulture; tf3 = permanent crops; tf4 = grazing livestock; tf5 = granivores; tf6 = mixed crops; tf7 = mixed livestock; tf8 = mixed crops&livestock. volume 11, issue 3 2022 firenze university press bio-based business models: specific and general learnings from recent good practice cases in different business sectors nora hatvani1,*, martien j.a. van den oever2, kornel mateffy1, akos koos1 food loss and waste accounting: the case of the philippine food supply chain anieluz pastolero*, maria sassi the role of network characteristics of the innovation spreaders in agriculture antonio lopolito1,*, angela barbuto2, fabio gaetano santeramo2 the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period roberto esposti price dependence of biofuels and agricultural products on selected examples wioleta sobczak*, jarosław gołębiewski bio-based and applied economics bae bio-based and applied economics 12(2): 83-84, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14794 copyright: © 2023 a. zezza. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae editorial cap, farm to fork and green deal: policy coherence, governance, and future challenges annalisa zezza crea centro di ricerca politiche e bioeconomia, italy the european green deal sets very ambitious highlevel goals in terms of growth and environment, with the aim of turning the eu into a modern, competitive, and sustainable economy. the objective is to reach the target of zero carbon emissions generating greenhouse effects by 2050, the “decoupling” of economic growth from the use of natural resources, and a wider social inclusion for people and territories. to pursue such an ambitious plan, the green deal laid out a series of objectives and actions to ensure that every sector of the economy gives its contribution. this approach should be able to translate the green deal ambition into a system of complex sectoral-level policies that interact with each other for the achievement of the common high-level goals. however, the extent to which this complex system will be effective in achieving these goals will depend on policy coherence and on governance. the 11th aieaa annual conference entitled “cap, farm to fork and green deal: policy coherence, governance, and future challenges” has been an occasion to explore the potential synergies coming from the integration of the cap with other eu and national policies under the green deal umbrella. by gathering together scholars with different points of views and academic backgrounds, the conference aimed to improve the understanding on how to reach an increased policy coherence and integration, explore new holistic governance approaches to the agri-food system, analyse the impact on the national and local systems and look to future challenges. in this context, a new generation of complex transformative policies is needed and the first paper, by gianluca brunori (2023) gives a valid contribution in finding an answer to some very important questions such as “what are the qualities that a new generation of policies should have? what should be done to foster a new generation of policies?”. transformative policies are characterized both by a multiplicity of actors and complex sectoral interconnections. considering that both the design and the impact of transformative policies depends on the characteristics of socio-technical and socio-ecological systems, new knowledge, new approaches and new forms of dialogue and governance are needed. the second paper in this issue by silvia coderon (2023), focuses on the trade of between food security and environmental sustainability as a ‘false dilemma’ that may delay the urgent action needed to establish a coherent policy framework that could help in meeting the ambitious challenge of making agriculture and food systems more environmentally sustainable. nevertheless, the policy objective to increase food security while reaching higher environmental sustainability standards is very difficult to achieve as it raises multiple policy coherence and related governance problems. the author distinguishes between 1) a ‘within-policy coherence’ when public policy efforts are not directed towards the needs of the sector, and 2) ‘between-policies coherence’ when different policy objectives receive different degrees of policy support or even contradict each other, presenting governance issues related to this complex challenge. with regard to 1) the author stresses the need (and the challenges) for a more targeted cap including the need for space based data for better policy design, implementation and monitoring. with regard to 2) the author highlights the impact on the agrifood system of policies including the lulucf regulation, the effort sharing regulation and the eu emission trading system, which has an impact on the entire food supply chain. three http://creativecommons.org/licenses/by/4.0/legalcode 84 annalisa zezza bio-based and applied economics 12(2): 83-84, 2023 | e-issn 2280-6172 | doi: major challenges are discussed i.e. the incurrence of high transaction costs to detect the synergies and tradeoffs between policy objectives, the dependence of synergies and trade-offs on the different instruments chosen, and the need to arrive at value judgments on the different interests involved. the next two papers explore some emerging issues at the local level. the contribution from fasano and pagliacci (2023) analyses how the valorization of high-quality agri-food products through the use of geographical indications impacts on the economic development of inner areas. they use municipality-level (lau2) data and apply hurdle models to assess the effect on several variables such as agriculture and food industry features, socioeconomic characteristics, regional settings. their results suggest that across inner areas geographical indications still represent a sort of untapped resource that, to be effective would require a more effective policy intervention, recalling the “between policies coherence” suggested in the first paper, the paper from tappi and al. (2023) analyses the impacts of extreme weather events on crop production demonstrating how heterogeneous those effects can be accordingly to the types of crops. the results imply that farmers and policymakers may adopt ex-ante and ex-post risk management strategies taking into account thus variability, adapting solutions to the local scale as stated by brunori (2023), in the opening essay, a new generation of policies implies a new generation of scholars that experiment new models of collaboration between policy-makers and researchers. our hope is that the aieaa conference has been a step in opening the way to a new generation of agri-food policy researchers that assign increased attention to socio-technical and institutional mechanisms that regulating food systems, by adopting a a stronger interdisciplinary approach. references brunori g. (2023). towards a new generation of (agri) food policies.  bio-based and applied economics, 12(2), 23-33. https://doi.org/10.36253/bae-14003 coderoni, s. (2023). key policy objectives for european agricultural policies: some reflections about the policy coherence.  bio-based and applied economics, 12(2), 5-21. https://doi.org/10.36253/bae-13971 pagliacci, f., fasano, f. (2023). geographical indications as an untapped opportunity for inner areas. an analysis of geographical indications from southern italy .  bio-based and applied economics, 12(2), 47-59. https://doi.org/10.36253/bae-13628 tappi, m., carucci, f., gagliardi, a., gatta, g., giuliani, m. m., santeramo, f. g. (2023). crop varieties, phenological phases and the yield-weather relationship: evidence from the italian durum wheat production.  bio-based and applied economics, 12(2), 35-45. https://doi.org/10.36253/bae-13745 cap, farm to fork and green deal: policy coherence, governance, and future challenges annalisa zezza key policy objectives for european agricultural policies: some reflections on policy coherence and governance issues silvia coderoni towards a new generation of (agri-) food policies gianluca brunori earliness, phenological phases and yield-temperature relationships: evidence from durum wheat in italy marco tappi1,*, federica carucci2, anna gagliardi1, giuseppe gatta1, marcella michela giuliani1, fabio gaetano santeramo1 does the presence of inner areas matter for the registration of new geographical indications? evidence from italy francesco pagliacci, francesco fasano* learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy antonio r. hurtado1,2,*, julio berbel2 bio -based and a ppl ied economics bae bio-based and applied economics 10(1): 3-6, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-11497 copyright: © 2021 f.g. santeramo, m. raggi. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae ten years of bio-based and applied economics: a story of successes, and more to come fabio g. santeramo1, meri raggi2 1 university of foggia (italy) 2 university of bologna (italy) bio-based and applied economics, the official journal of the italian association of agricultural and applied economics, has been established in 2011: the journal has constantly hosted high quality manuscripts, published by scholars aiming at contributing to the debate on topics that have been relevant for the association. in less than ten years bae has become a wellestablished international journal, indexed in several scientific databases, such as scopus and emerging sources citation index™ of web of science. across years, the journal has repeatedly devoted attention to innovative aspects for the profession, with a special attention to the definition and evolution of the bioeconomy (schmidt et al., 2012), lately as the “evolutionary process of transition from systems of mining nonrenewable resources to farming renewable ones” (zilberman et al., 2013). the importance of bioeconomy is increasing more and more exactly because its objectives are converging towards those of the new paradigm of economic growth: the circular economy (aguilar et al., 2018). as from the very first articles hosted in the opening and in subsequent issues, such as those authored by viaggi (2012), romano (2012) and esposti (2012), among others. the large number of submissions that the journal has attracted has allowed to publish research papers from many very traditional areas such as agri-food system analyses, demand and production economics (e.g. sckokai and varacca, 2012), price, trade and policy analyses (e.g. matthews, 2013; jafari et al., 2018; santeramo and cramon-taubadel, 2016). in addition, the journal has hosted several manuscripts analysing fields closely connected with the agricultural and applied economics, such as environmental economics, behavioural economics, political economy, development economics, health economics, or focused on important and timely topics such as changes in the agri-food systems and methodological challenges in analysing them (e.g. heckelei et al. 2012; donati et al., 2013; scoppola, 2015). such a broad range of topics has allowed the journal to become an agora for the scientific debate, and such a prominent role is very much strengthened by the open access nature of the journal that, coupled with absence of submission and publication fees, guarantee that manuscripts are accessible to all interested readers, without limitations of any kind. after a decade of great achievements, the board has launched several initiatives (moro et al., 2019) such as, among others, words mention to the most impactful papers hosted in bae and the recurrent recognition of one “best paper in bae”. the first edition of these awards has been in 2021. starting from the next years a special mention will also be dedicated to the reviewers that have excelled in their (extremely valuable) reviewer activities. the support of competent reviewers has been one of the main resources on which the journal has counted to become a very well-established field journal. bae is not only a fast-growing journal, but also a resilient, fast-changing environment. after a deep reorganization of the board, composed by the two editors in chief, five associate editors, and the editorial assistant, and a major transition to a new online platform, bae has just renewed its graphical aspect to provide more information to the readers, and emphasize the rigor and transparency of the double-blind peer review process, a mechanism that ensure the high quality of the manuscripts hosted in bae. furthermore, the journal will host more articles, organized in four issues per year: a signal that the growth in quality is solid and promising. http://creativecommons.org/licenses/by/4.0/legalcode 4 fabio g. santeramo1, meri raggi24 fabio g. santeramo, meri raggi bio-based and applied economics 10(1): 4-6, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-11497 the best has yet to be described. among the new initiatives we proudly announce, starting form the following issues, bae will host a series of invited reviews aimed at synthesizing the topics debated in bae as well as at emphasizing the status of the art of the bioeconomy, a repeated tradition for the journal (e.g. romano, 2013; viaggi, 2016; sckokai, 2016). following the debate on bioeconomy is of utmost importance, due to the vivid attention that the topic is receiving not only in europe (wessler et al., 2017; d’adamo et al., 2020; stegmann et al., 2020), but also in other continents (asada and stern, 2018; zilberman et al., 2018). moreover, bae aims at publishing invited reviews on behavioural and risk management in agri-food systems issues in agri-food systems, themes that have attracted several important submissions (e.g. coletta et al., 2018; o’donoghue et al., 2020; giampietri et al., 2020) and are highly debated in top field journals (howley , 2015; vigani, and kathage, 2019; sok et al., 2021). in order to follow the debate on vulnerability, resilience and systemic changes in the agri-food sector, greatly animated by allouche (2011), upton et al. (2016), pingali and sunder (2017) and shobe (2020), the journal will continue to dedicate attention to the systemic changes in the agro-food systems, and to the resilience of the agri-food systems, updating the debated that has been animated in bae by several authors such as, among others, sarris (2013), alvarez and arias (2015), avanzini et al. (2018) and romano et al. (2019). the board has also solicited reviews to synthesize the state of the literature devoted to speculating on policy and trade dynamics (e.g. peterson et al., 2000; petit, 2008; sun and reed, 2010; pannell and claassen, 2020), whose debate dates back to the first issues of bae (e.g. moschini et al., 2012; dwyer, 2013), but has never ended (e.g. carbone et al., 2015; olper, 2016), and, indeed, has increased in prominence (e.g. de maria, 2018; macedo et al., 2019). last, but not least, the journal will dedicate space to review the state of the art on the rural development and on nutrition and health issues, whose debate has deep roots in bae and it is quite promising and vivid (e.g. barreiro‐hurle et al., 2010; irwin et al., 2010; camaioni et al., 2013; sckokai et al., 2014; bertolini and pagliacci, 2017; cerroni et al., 2019; frison and clément, 2020). the board is proud of the journey that the journal is facing and will continue to work to ensure that bae will continue to be an independent and open access environment to debate and disseminate rigorous scientific findings, and authoritative critical views. as closing note, the editors in chief express their gratitude to the scientists who have made possible the ambitious project of bio-based and applied economics to become a solid reality. references 1. aguilar, a., wohlgemuth, r., & twardowski, t. 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(2016). towards an economics of the bioeconomy: four years later.  bio-based and applied economics, 5(2), 101-112. 50. viaggi, d., mantino, f., mazzocchi, m., moro, d., & stefani, g. (2012). from agricultural to bio-based economics? context, state of the art and challenges. bio-based and applied economics, 1(1), 3-11 51. vigani, m., & kathage, j. (2019). to risk or not to risk? risk management and farm productivity. american journal of agricultural economics,  101(5), 1432-1454. 52. wesseler, j., & von braun, j. (2017). measuring the bioeconomy: economics and policies. annual review of resource economics, 9, 275-298 53. zilberman, d., kim, e., kirschner, s., kaplan, s., & reeves, j. (2013). technology and the future bioeconomy. agricultural economics, 44(s1), 95-102. 54. zilberman, d., gordon, b., hochman, g., & wesseler, j. (2018). economics of sustainable development and the bioeconomy.  applied economic perspectives and policy, 40(1), 22-37. volume 10, issue 1 2021 firenze university press ten years of bio-based and applied economics: a story of successes, and more to come fabio g. santeramo1, meri raggi2 the capitalisation of decoupled payments in farmland rents among eu regions gianni guastella1,2, daniele moro1, paolo sckokai1, mario veneziani3 contribution of periurban farming systems to local food systems: a systemic innovation perspective rosalia filippini1,2, elisa marraccini3, sylvie lardon2 an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications, and consumption of agri-food products carrying those certifications niculina iudita sampalean1, daniele rama1, giulio visentin2 wine after the pandemic? all the doubts in a glass daniele vergamini*, fabio bartolini, gianluca brunori public r&d and european agriculture: impact on productivity and return on r&d expenditure michele vollaro1, meri raggi2, davide viaggi1 bio-based and applied economics bae bio-based and applied economics 12(1): 69-81, 2023 | e-issn 2280-6e172 | doi: 10.36253/bae-13479 copyright: © 2023 r. seegers, e. winter, u. grote. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: r. seegers, e. winter, u. grote (2023). exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms: evidence from rural rwanda. bio-based and applied economics 12(1): 69-81. doi: 10.36253/ bae-13479 received: august 1, 2022 accepted: april 13, 2023 published: june 24, 2023 competing interests: the author(s) declare(s) no conflict of interest. editor: davide menozzi, linda arata. orcid rs: 0000-0001-5055-3402 ew: 0000-0003-0815-4816 ug: 0000-0002-9073-6294 exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms: evidence from rural rwanda ronja seegers*, etti winter, ulrike grote institute for environmental economics and world trade, leibniz university hannover, königsworther platz 1, 30167 hannover, germany *contact author. e-mail: seegers@iuw.uni-hannover.de abstract. addressing the global challenges of climate change and biodiversity loss requires the widespread adoption of sustainable agricultural practices such as agroforestry. in many sub-saharan african countries, however, agroforestry adoption rates remain low among small-scale farmers, with insufficient knowledge about the benefits being a major barrier. to close this knowledge gap and increase farmers’ motivation to plant different tree species on their farms, this study applies a role-playing game (rpg) as an awareness-raising tool. 72 small-scale farmers from rwanda played the rpg and participated in preand post-game surveys. a comparison of responses before and after playing demonstrates that the rpg increased farmers’ knowledge and attitude toward most tree-related benefits. moreover, playing the game significantly strengthened farmers’ motivation to plant more tree species on their farms. the findings were supported by debriefing results, confirming that rpgs are an effective tool to raise farmers’ awareness and motivation on sustainable land use management. keywords: agroforestry adoption, on-farm tree planting, ecosystem services, roleplaying game, serious game. jel codes: q15, q51, q54. 1. introduction since 1960, more than half of the world’s tropical forests have been destroyed and at present, deforestation continues to increase (iucn, 2021). deforestation and land degradation pose serious threats to ecosystem functioning and the human food system. especially in sub-saharan africa, where the prevalence of food insecurity and undernourishment is among the highest in the world (fao et al., 2021; ndoli et al., 2021), small-scale subsistence farmers are severely affected by the consequences of forest conversion (meijer et al., 2015). strengthening the resilience of ecosystems and human livelihoods, therefore, requires the urgent and widespread adoption of sustainable agricultural practices (fao & unep, 2020). agroforestry, one sustainable land-use practice with trees as an integral part of the farming systems, http://creativecommons.org/licenses/by/4.0/legalcode 70 bio-based and applied economics 12(1): 69-81, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13479 ronja seegers, etti winter, ulrike grote provides various benefits for human well-being and the environment, including provisioning, regulating, supporting, and cultural ecosystem services (coulibaly et al., 2017; fao, 2013b; fao & unep, 2020; gamfeldt et al., 2013; garrity et al., 2010; udawatta et al., 2019). despite the numerous benefits of trees, however, adoption rates among smallholder farmers remain low in many sub-saharan african countries (amare & darr, 2020; ndlovu & borrass, 2021). a large body of literature has already examined a variety of adoption barriers, which include lack of land tenure rights, high investment costs, limited access to credits, insufficient availability of planting material, high transaction costs, information asymmetries, and lack of institutional support (amare & darr, 2020; arvola et al., 2020; bettles et al., 2021; jerneck & olsson, 2013; kang & akinnifesi, 2000; kehinde et al., 2022; meijer, catacutan, sileshi, et al., 2015; romero et al., 2019; russell & franzel, 2004). besides the importance to address such external barriers, researchers and policy-makers should not ignore the intrinsic motivation of smallholder farmers to adopt agroforestry systems. according to the millennium ecosystem assessment (millenium ecosystem assessment, 2005), ecosystem management depends on people’s evaluation of the services provided by these ecosystems. it is therefore assumed that smallholder farmers are more willing to plant trees if they are aware of and positively evaluate the benefits of agroforestry systems. nevertheless, smallholders value and plant trees predominantly for direct economic benefits such as product provision and income-generating opportunities (ndayambaje et al., 2012). in contrast, they seem to disregard environmental benefits that are not directly observable in the short term (karamage et al., 2016; ndayambaje et al., 2012; zubair & garforth, 2006). this disregard may imply a lack of knowledge about the complex interactions within social-ecological systems (lima & bastos, 2019; lima & bastos, 2020). insufficient knowledge about the linkages between ecosystem services and human well-being can therefore reduce farmers’ motivation to plant trees (coulibaly et al., 2017; fao, 2013a), whereas increasing farmers’ knowledge and perception can improve their motivation (oduro et al., 2018). this assumption is confirmed, for example, by a study from indonesia, in which an environmental information campaign increased the motivation of oil palm farmers to plant trees (romero et al., 2018). to improve agroforestry adoption among smallholders, it is therefore important to close existing knowledge gaps and raise awareness of the benefits of trees (bettles et al., 2021; ndayambaje et al., 2021; zhang et al., 2016). one approach to raising smallholders’ awareness about environmental concerns is serious games, among them role-playing games (rpgs). rpgs simulate realistic issues in a safe learning environment, in which players can experience alternative actions that do not affect their real lives (barreteau et al., 2007; salvini et al., 2016; villamor & badmos, 2016). this experience allows players to better understand the consequences and linkages of their actions within complex social-ecological systems (le page et al., 2016; van pelt et al., 2015; villamor & badmos, 2016).1 accordingly, rpgs have been increasingly used in recent years to better understand human decision-making behavior, and increase players’ knowledge of sustainable resource and land use management (barreteau et al., 2007; den haan & van der voort, 2018; falk & meinzendick, 2021; hardy et al., 2020; jean et al., 2018; medema et al., 2016; moreau et al., 2019). for example, the rpg conducted by salvini et al. (2016) in brazil increased farmers’ awareness of the need to adopt climate-smart agricultural systems to strengthen their resilience to climate change. the findings of previous studies, therefore, suggest that rpgs may also be appropriate instruments to raise awareness about the importance and advantages of agroforestry adoption. thus, this paper aims to investigate whether an rpg can improve small-scale farmers’ perception of the benefits of trees and their motivation to plant different tree species on their farms. specifically, the study explores whether there are significant differences in attitude, knowledge, and intention regarding tree planting between a group of farmers who played the rpg and a group of farmers who did not play the game. the rpg of this study was applied in the volcanic highlands of the northern province of rwanda. the area is characterized by high population density, land scarcity, and steep slope farming, which have resulted in high deforestation rates and susceptibility to soil erosion in the past (ndoli et al., 2021; stainback et al., 2012). although the government of rwanda promoted the implementation of the fast-growing, exotic alnus 1 rpgs aim to address real-world challenges and are therefore designed for a primary purpose beyond entertainment such as education, training, and information exchange (medema et al., 2016). particularly in the context of human-environment interactions, rpgs are used to improve stakeholders’ understanding of different viewpoints and the consequences of their behavior on a system’s functionioning. in addition to sharing perspectives and knowledge, their goal is also to strengthen collective action to change the current functioning of a system. although rpgs are only simplified representations of difficult issues, they can still reflect the complexity of a system by incorporating relevant dynamics and interrelationships. this makes them an important tool for supporting social learning and collective decision-making processes (bousquet et al., 2013) 71 bio-based and applied economics 12(1): 69-81, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13479 exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms and eucalyptus species to reduce soil erosion, the diversity of trees in the volcanic highlands remains low (iiyama et al., 2018; mukuralinda et al., 2016). the volcanic highlands of rwanda, therefore, represents an interesting case study to promote the planting of more different tree species. this paper begins with information on the study area, data, and methods used in section 2; presents the results in section 3; follows with a discussion in section 4, and ends with a summary and conclusion in section 5. 2. data and methods 2.1 study area the study was conducted in the volcanic highlands in the north of rwanda, a small, landlocked country in sub-saharan africa. the north of rwanda is characterized by mountainous regions with altitudes of more than 2000 meters (mukuralinda et al., 2016). during the two rainy seasons occurring between march and may/june and from september/october to december/january, annual precipitation accounts for more than 1200 mm (ngarukiyimana et al., 2018). the population of rwanda mainly consists of small-scale farmers who use almost 75% of the land for crop cultivation and cattle farming (mukuralinda et al., 2016). due to land scarcity and high population density, farmers occupy only less than one hectare of land, which is often located on steep slope areas. however, cultivation on steep slopes, high deforestation in the past, heavy rainfall, and increasing population densitiy exacerbate the already existing vulnerability to flooding, landslides, and soil erosion (uwihirwe et al., 2020). these environmental hazards decrease the availability of fertile soil for food production, impairing farm productivity and crop yield, which severely affects the farming population’s livelihood (ndoli et al., 2021; stainback et al., 2012; wfp & vam, 2018). 2.2 data collection and analysis for this study, data were collected from 72 smallholder farmers. the gender ratio of the sample selected for this study is balanced and consists of about 50% male and 50% female farmers. they have a mean age of 38 years and invested 8 years in education (table 1). the average household size is six persons, about half of whom are dependent on other household members. respondents own approximately 0.6 ha of land and generate two-thirds of their income through agricultural activities. most farmers are members of agricultural cooperatives and have learned about agroforestry through previous projects. on average, they have planted three different tree species on their farms. we conducted a mixed-method approach that includes both quantitative and qualitative data collection procedures. figure 1 provides an overview of the single steps of our data collection processes. first, for the quantitative data collection approach, all farmers answered a pre-game survey that contained questions following the theory of planned behaviour (tpb) developed by ajzen (1991). this theory states that an individual’s behavior is guided by intrinsic beliefs. whether an individual performs a certain behavior or not depends mainly on the individual’s behavioral intention. intention, in turn, is composed of three components, namely attitude, subjective norms, and perceived behavioral control. attitude refers to the individual’s perception of the consequences of certain behavior and whether the individual evaluates the behavior as favorable or unfavorable. perceived behavioral control relates to the individual’s perception of possible obstacles and the extent to which the person assesses the performance of the behavior as easy or difficult. subjective norms take into account whether other people would approve or disapprove of the performance of the behavior and the extent table 1. farmer and household information. socioeconomic variables mean values standard deviation female respondents (%) 49.32 age 38.15 10.76 years of education 8.29 3.71 household size 6.25 2.24 dependency ratio 1 (%) 48.33 size of land (ha) 0.73 0.76 annual income (rwf) 629,383.8 683,957.8 share of agricultural income (%) 64.84 cooperative membership (%) 70.83 social interactions 2 6.43 16.88 experience with agroforestry (%) 73.61 number of tree species planted on farms 3.11 1.74 risk affinity 3 (%) 88.89 notes: if mean values are shown, standard deviations are given in parentheses. 1 dependency ratio defined as share of dependent household members who are not counted as labor force due to young/old age or diseases in relation to total household size. 2 social interactions defined as number of people with whom respondents discuss their agricultural decisions. 3 risk affinity (binary variable) measured by stated willingness to plant a tree even if the yield is uncertain. *** p<0.01; ** p<0.05; *p <0.1. 72 bio-based and applied economics 12(1): 69-81, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13479 ronja seegers, etti winter, ulrike grote to which the opinion of other people infl uences an individual’s decision to perform a given behavior. since this study investigates the impact of an rpg to raise awareness and enhance, respectively change farmers’ viewpoints towards positive perceptions of tree planting, we only include tpb constructs that are not aff ected by external factors. since subjective norm is determined by the opinion of family members, friends, and other farmers, and perceived behavioral control is infl uenced by external adoption barriers such as insuffi cient access to markets, unavailability of tree seedlings, and small land area, we only include questions related to farmers’ attitude and intention. in addition to these two original tpb constructs, we also include knowledge as an additional construct (anebagilu et al., 2021; maleksaeidi & keshavarz, 2019). we measure each construct by several indicator questions. attitude includes eleven indicator questions and knowledge includes three indicator questions. as proposed by meijer et al. (2015), the indicator questions of attitude are determined by multiplying the two measurement components salient belief and outcome evaluation. in our study context, salient belief describes farmers’ expected outcome of diverse tree planting and outcome evaluation defi nes the personal assessment of this outcome. concerning knowledge indicators, the first component specifies whether respondents think that specifi c knowledge of agroforestry is required to plant more tree species on farms and the second part describes whether respondents have this knowledge. all questions were asked on a fi ve-point likert scale. both measurement components of attitude and knowledge indicators are multiplied, which resulted in a maximum fi nal score of 25. aft er answering the pre-game survey, all respondents participated in an applied rpg, which is further described in chapter 3.3. aft erward, participants conducted a post-game survey that contains the same questions on farmers’ knowledge, attitude, and intention as the pre-game survey. in addition, the post-game survey also includes questions that are directly related to the game’s outcome. to gain a more comprehensive understanding of the eff ectiveness of the game based on qualitative data, debriefi ngs were held aft er the game sessions to discuss the results of the game. game debriefings allow players to deepen their knowledge, increase mutual understanding between participants and translate their experiences into learning outcomes (crookall, 2010; eisenack, 2013; mendler de suarez et al., 2012; meya & eisenack, 2018). th e descriptive data were analyzed using stata 14.2. paired t-test was used to compare players’ responses before and aft er the game. 2.3 role-playing game th e role-playing game used in this study is based on the “upstream downstream” game developed by the partners for resilience (pfr) program to build community resilience and reduce disaster risk (mendler de suarez et al., 2012). to increase farmers’ knowledge of the multiple benefi ts of trees and to improve their perception of agroforestry systems, the game was adapted and expanded for the specifi c context of the study region. players take on the role of subsistence farmers in mountainous areas who are at risk of crop failure due to heavy rainfall and fl ooding. to deal with shocks and maintain livelihoods, farmers have the option to cut down trees and sell wood, which in turn increases the risk of fl ooding in subsequent turns. for this study, two more elements were added to the game: the inclusion of trees of diff erent species and the addition of scenarios related to various ecosystem services. each game session consisted of eleven rounds and took about two hours. th e game board depicts a steep hillside landscape along a river and is divided into a mountainous upstream area and a fl atland downstream area. while the upstream area has a higher tree density, trees are less common in the downstream area. each farmer cultivates two neighboring plots that diff er in terms of crop yield and fl ood figure 1. data collection procedure. 73 bio-based and applied economics 12(1): 69-81, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13479 exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms risk. the game includes three different types of trees: fruit trees, timber trees, and indigenous trees. each tree species exhibits different characteristics (e.g., morphological characteristics such as height, leaf appearance, and flowering) that result in different contributions to economic and environmental benefits. trees define the dynamic linkages between upstream and downstream farmers and determine the severity of precipitation’s effect on crop and yield losses. the number of trees planted by upstream farmers can increase the damage threshold and thus reduce flood risk. specifically framed scenarios reflect the different ecosystem services provided by different tree species (fruits, timber, soil fertility, pest control, pollination, climate regulation, and tourism). the scenarios reflect players’ livelihoods in a resilient environment depending on the tree number and species diversity growing in their fields. the first three rounds were played without scenarios to ensure participants’ understanding and familiarity with the rules of the game. 3. results 3.1 tree planting the most frequently planted tree species by farmers from the study area and their reasons for planting are provided in table 2. most smallholders implemented alnus, avocado, eucalyptus, and tamarillo trees on their farms, which account for more than 70% of trees on their farms. a majority of these tree species provide multiple benefits but farmers’ planting reasons for ecological purposes are rather monotonous. the only ecological purposes for farmers to plant trees are soil erosion control and soil fertility improvement, with alnus and eucalyptus being the most preferred species to provide these benefits. since alnus and eucalyptus are widely known as fast-growing species (cyamweshi et al., 2021; kuria et al., 2017), farmers of our study have planted them for timber production, stakes for climbing beans, and firewood. in contrast, they grow fruit tree species such as avocado, tamarillo, and papaya exclusively for food production and income generation. although agroforestry systems in rwanda are dominated by exotic tree species, some farmlands also include indigenous species such as vernonia amygdalina and erythrina abyssinica (mukuralinda et al., 2016). for example, 14% of farmers in our study region implemented vernonia trees, with cultural backgrounds, use as fodder, and medicinal purposes being particularly important reasons for planting. overall, our results show that most farmers are highly willing to plant more trees in the future, even if growth and yield are uncertain. 3.2 game feedback players’ feedback on the game is illustrated in figure 2. the results indicate that, although 38 players perceived the duration of the game as too long and onethird considered the game as too complicated, nearly all participants liked the game and had fun while playing. in addition, more than 75% of all participants agreed that the rpg represents their reality. in contrast, about 20% were uncertain about the game’s ability to represent farmers’ daily life, and only two players mentioned that the game was not representative. despite this criticism, all farmers have expressed that they have learned something from the game. when asked farmers about their strategies followed during the game, 97% of farmers mentioned profit maximization, 51% indicated environmental protection, and 28% increased biodiversity. results of the post-game survey furthermore show that, except for one farmer, all players consider it important to plant more trees in real life. of these farmers, everyone emphasized the need to increase tree diversity on their farms and stated a higher willingness to plant trees in the future after they have played the game. the debriefing results also support the post-game survey responses of the farmers. for example, one player explained: “while playing the game, i diversified trees to support biodiversity. now that i know the values of all trees, i would like to buy more species.” another farmer mentioned “what i learned is that indigenous trees are also important. i did not value them before but now, table 2. tree species planted by farmers. tree species farmers who planted corresponding trees (%) most important planting reasons for farmers alnus 75.00 stakes for climbing beans, soil fertility, soil erosion protection, timber, firewood avocado 61.11 fruits, income eucalyptus 50.00 timber, firewood, stakes for climbing beans, soil erosion control tamarillo 37.50 fruits, income papaya 16.67 fruits acacia 16.67 soil erosion control, soil fertility, firewood, timber vernonia 13.89 stakes for climbing beans, fodder, culture, medicine, soil fertility, soil erosion protection grevillea 9.72 timber, stakes for climbing beans, soil erosion control, firewood erythrina 6.94 soil fertility 74 bio-based and applied economics 12(1): 69-81, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13479 ronja seegers, etti winter, ulrike grote aft er this game, i will start planting them on my farm.” when players were asked why farmers do not mix trees with crops, they replied, “it is a poor mindset” and “lack of knowledge”. th is fi nding suggests that the rpg applied in this study is a useful tool to increase farmers’ knowledge about the benefi ts of trees and improve their motivation to plant more diff erent species. 3.3 comparison of pre-game and post-game survey figure 3 shows the results of farmers’ knowledge scores before and after playing the rpg. the higher score values for all knowledge indicators imply that the rpg signifi cantly aff ected farmers’ awareness of knowledge requirements and skills related to agroforestry. specifi cally, playing the rpg increased farmers’ views on the knowledge needed for tree management. th e higher awareness can be explained by the fact that farmers learned about the importance of the choice of tree species and the planting density on their farms. for example, the way players managed their trees aff ected not only their own livelihood in the game but also that of other players. th e more trees players cut on their farms, the higher the likelihood that other players also suff er from fl ooding and soil erosion, leading to loss of crop yield and removal figure 2. players’ feedback on the game. note: illustration adapted from orduña alegría et al. (2020). 0 2 4 6 8 10 12 14 16 18 20 tree management biodiversity ecological consequences k no w le dg e sc or e before playing the rpg after playing the rpg ** *** *** figure 3. comparison of farmers’ knowledge score before and aft er playing the rpg. notes: mean values and standard deviations are shown. maximum possible value of perception score is 25. *** p<0.01; ** p<0.05; *p <0.1. 75 bio-based and applied economics 12(1): 69-81, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13479 exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms of young trees. likewise in a game debriefing, one player concluded “if farmers hardly mix trees with crops and they manage their trees poorly, they suffer crop and tree losses”. at the same time, players noticed that the choice of tree species and the planting density also depend on external circumstances, such as farmland size, crop species cultivated, and tree-crop competition. for example, some farmers explained, “planting too many trees on small farming plots will decrease crop yield”. thus, the debriefing results underline the finding that the rpg significantly increased players’ awareness of the knowledge required to manage and benefit from trees. the game also significantly reinforced respondents’ opinion that planting trees requires knowledge and that they have the required knowledge about the ecological consequences of trees and their impact on biodiversity. similar to knowledge scores, farmers significantly increased their attitude toward various benefits of trees 0 5 10 15 20 25 30 bett er he alt h o f e nv iro nm en t inc om e d ive rsi fic ati on prov isio n o f ti mber prov isio n o f fr uit s bett er soi l fe rtil ity redu ced so il e ros ion bett er pe st c on tro l high er po llin ati on an d c rop yi eld clim ate ch ang e m itig ati on high er ani mal div ers ity inc rea sed to uri sm a tti tu de sc or e before playing the rpg after playing the rpg ****** *** *** *** *** *** ** figure 4. comparison of farmers’ attitude scores before and after playing the rpg. notes: mean values and standard deviations are shown. maximum possible value of intention score is 25. *** p<0.01; ** p<0.05; *p <0.1. 0 1 2 3 4 5 6 intention to plant diverse trees within next three years likelihood of planting diverse trees in next three years in te nt io n sc or e before playing the rpg after playing the rpg ** ** figure 5. comparison of farmers’ intention scores before and after playing the rpg. notes: mean values and standard deviations are shown. maximum possible value of intention score is 5. *** p<0.01; ** p<0.05; *p <0.1. 76 bio-based and applied economics 12(1): 69-81, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13479 ronja seegers, etti winter, ulrike grote after playing the game (figure 4). most of the changes in attitude scores relate to indirect environmental services. farmers revealed a significantly more positive perception, especially in terms of better environmental health, improved soil fertility, better pest control, increased pollination, climate protection, greater wildlife diversity, and tourism attraction. while farmers now also increasingly perceive trees as an opportunity for income diversification, the game did not significantly change farmers’ attitudes regarding the potential of trees to provide fruits and timber and to reduce soil erosion. figure 5 illustrates the games’ impact on the intention of farmers to plant more diverse trees on farms in the future. concerning both farmers’ stated intention and their self-assessed likelihood of planting diverse trees in the next three years, score values increased significantly after playing the game. although the farmers were already highly motivated to plant trees before playing the game, the significant increase in their intention scores implies that the rpg is an effective and meaningful tool to further stimulate the intrinsic motivation of participants. 4. discussion this study explored the potential of an rpg to raise smallholder farmers’ awareness of the benefits of trees and increase their motivation to plant more different tree species on their farms. the results manifest our hypothesis that after playing the rpg, farmers significantly increased their awareness of the knowledge needed to plant different tree species on farms. after gameplay, farmers are more aware that tree planting requires knowledge about both tree management and the consequences of planting. indeed, agroforestry adoption and management is more knowledge-intensive and requires more education and experience than conventional agricultural systems (barrett et al., 2002; mercer, 2004). our result that farmers increased their awareness of knowledge requirements coincides with findings from other studies that revealed higher awareness among participants after gameplay (moreau et al., 2019; salvini et al., 2016). one example is the study by salvini et al. (2016), which investigated the impact of an rpg on social learning and collective action among brazilian farmers toward the adoption of climate smart agricultural (csa) practices. they found that the rpg increased farmers’ awareness in terms of higher resource investments needed to implement csa. the rpg applied in this study furthermore strengthened farmers’ positive attitudes towards the majority of tree-related benefits. the increasing positive perceptions towards most environmental benefits might stem from the game scenarios that raised farmers’ awareness of the harmful environmental consequences of not planting trees or planting few trees, especially when tree diversity is low. similarly, coffee farmers who played a rpg in brazil learned about the positive environmental effects of agroforestry in terms of higher resilience to droughts compared to traditional farming systems (salvini et al., 2016). concerning knowledge acquisition, one player from our study declared during a debriefing session “the only way to learn about the consequences is through experience.” as concluded in other studies, serious games create a safe environment in which players experience and learn from the consequences of their actions (hardy et al., 2020; mayer, 2009; van noordwijk et al., 2020; villamor & badmos, 2016). rpgs, therefore, offer players the possibility to make decisions and explore alternative actions without taking real-life risks. however, farmers of our study did not significantly change their attitude toward the potential of tree planting to decrease soil erosion and provide timber and fruits. the unchanged attitude towards these benefits can be explained by our finding that in real life, farmers have already planted most of the trees on their farms for soil erosion control and product provision. this result is in line with findings of ndayambaje et al. (2012), who also noted that farmers in rwanda have planted trees in the past primarly for economic reasons. although farmers’ attitudes towards some treerelated benefits did not improve, the game still significantly increased their motivation to plant more different tree species on their farms. our finding that the rpg increased farmers’ intention to plant more different tree species on farms is in line with previous studies showing that serious games can lead to motivational or behavioral change (janakiraman et al., 2021; meinzen-dick et al., 2018; meya & eisenack, 2018; salvini et al., 2016). according to meinzen-dick et al. (2018), an applied rpg on watershed management increased farmers’ motivation in india to adopt water registers in their real life and resulted in the introduction of rules for more sustainable groundwater use. salvini et al. (2016) found that after gameplay, some farmers increased the area used for agroforestry systems on their land, while farmers who have not implemented agroforestry systems at the time of the study adopted coffee agroforestry and silvopastoral system after playing the game. although most studies emphasize the effectiveness of serious games to increase awareness of certain issues among participants, there are some studies criticizing that the knowledge gained through gameplay is not sufficient to alter stakeholders’ behavior (ducrot et al., 77 bio-based and applied economics 12(1): 69-81, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13479 exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms 2015; lamarque et al., 2014). in this context, lamarque et al. (2014) conducted an rpg with farmers to investigate how knowledge and valuation of ecosystem services influence their land-use decisions. however, the authors found that other external factors such as socioeconomic and topographic characteristics influence farmers’ landuse decisions and outweigh the effects of ecosystem service valuation. these contrasting study results highlight both the strengths and weaknesses of serious games. although the application of serious games is an appropriate participatory method to reduce knowledge gaps and shift viewpoints, games represent only a simplified version of players’ real life. in reality, they face additional challenges affecting their land-use decisions. to capture the entire socio-ecological system with all its elements, complex interactions and possible actions in the game is almost impossible and would lead to the game becoming unmanageable. to maximize the impact of a game while ensuring its representativeness and fun atmosphere, stakeholders should already be involved in the early design and testing stages of the game. 5. conclusion the purpose of the present study was to examine the impact of an applied rpg on small-scale farmers’ knowledge, perception, and motivation to increase the planting of different tree species in a highly deforested area of the volcanic highlands in rwanda. comparisons of pre-game and post-game survey responses revealed that farmers significantly increased their awareness of the knowledge requirements for tree planting. furthermore, the game significantly improved players’ attitudes toward a wide range of tree-related benefits. after playing the game, participants expressed a significantly higher intention to plant more different tree species on their farms. various statements made by farmers during the game debriefing sessions also confirmed the learning effect achieved through the game. thus, this study provides empirical evidence of the effectiveness of rpgs in improving farmers’ intrinsic motivation to adopt agroforestry. our study implies that insufficient awareness among smallholder farmers of the benefits of trees is an important barrier to tree planting that needs to be addressed. this paper, therefore, calls for more support to farmers to close existing knowledge gaps and promote agroforestry adoption. we recommend the use of serious games, which, as has been shown in this and other studies, are an auspicious tool to stimulate learning and support decision-making processes in sustainable land-use management, biodiversity conservation, and climate change mitigation (e.g. andreotti et al., 2020; meinzen-dick et al., 2018; salvini et al., 2016; souchère et al., 2010). as players criticized the duration and complexity of the rpg in this study, we suggest the participation of relevant stakeholders in every step of the development process of serious games. early involvement would not only result in serious games that are fun and easy to understand but also helps researchers and policymakers to identify complex problems, constraining conditions, and feasible solutions to policy-relevant sustainability issues where actions are needed. in particular, the inclusion of a larger diversity of stakeholders can lead to both a greater expansion of personal views and knowledge and a commom understanding of the research gaps that need to be addressed (menozzi et al., 2017). stakeholder involvement is therefore key to successful game development and implementation (barreteau et al., 2014). overall, the strengths of rpgs to exchange knowledge, improve mutual understanding and collectively develop solutions make them a promising bottom-up instrument, which might be more efficient than conventional top-down approaches. this study also comes with some limitations. first, we were interested in investigating the game’s impact on farmers’ intrinsic motivation but we have not considered whether a motivational change also leads to a change in farmers’ actual tree-planting behavior. overall, studies assessing the long-term effect of serious games and the translation of motivational changes into participants’ real behavior are rare (meya & eisenack, 2018), offering scope for future research. second, we have only examined farmers’ intrinsic motivation as a superordinate factor and knowledge and attitude as subordinate components being influenced by the game. beyond these components, farmers’ motivation is also determined by their other intrinsic factors (e.g. subjective norms and perceived behavioral control) and extrinsic factors (e.g. farmers’ socioeconomic and agroecological context). thus, future research should focus on a combination of extrinsic and intrinsic determinants to better understand farmers’ motivation and decision-making behavior in agroforestry adoption. funding this work was funded by the federal ministry for the environment, nature conservation and nuclear safety” (grant: bmuz_1273), supported by the “internatonal climate initiative (iki) – harnessing the potential of trees-on-farms for meeting national and global biodiversity targets” project. 78 bio-based and applied economics 12(1): 69-81, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13479 ronja seegers, etti winter, ulrike grote references ajzen, i. 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santeramo et al., 2021). agriculture, on which human welfare depends, is severely affected by climate change. some adverse effects, already observed, are likely to intensify in the future, contributing to declines in agricultural production in many regions of the world, fluctuations in world market prices, growing levels of food insecurity (reilly and hohmann, 1993; meressa and navrud, 2020). adaptation potential and adaptation capability to climate change may exacerbate differences between regions. in a globalised world, the macro-level impacts of climate change are driven by comparative advantage between regions (bozzola et al., 2021). if impacts of climate change on productivity http://creativecommons.org/licenses/by/4.0/legalcode 124 bio-based and applied economics 10(2): 123-135, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9676 emilia lamonaca, fabio gaetano santeramo, antonio seccia differ between regions, then adjustments through production patterns may dampen the adverse effects of climate change (costinot et al., 2016; gouel and laborde, 2021). although the agricultural sector is identified as the most sensitive and vulnerable sector to climate change (e.g., deschenes and greenstone, 2007), the effects of climate change on the wine sector and on different producing regions (i.e., old world producers, new world producers) is still an open question. how do productivity levels react to changes in climate? do climate change impacts on production patterns differ between old world producers and new world producers? as suggested by mozell and thach (2014), the narrow climatic zones for growing grapes may be severely affected both by short-term climate variability and longterm climate change. a vast majority of earlier studies on the impacts of climate change have analysed the effects on domestic markets, leaving underinvestigated the effects on world production (reilly and hohmann, 1993). in the wine-related literature, previous studies reveal that the impacts of climate change are likely to differ across producing regions of wine. jones et al. (2005) suggest that, currently, old world producers (i.e., european regions) benefit of better growing season temperatures than new world producers. however, future climate scenarios may push some regions into climatic regimes favourable to grape growing and wine production (lamonaca and santeramo, 2021). all in all, there is the potential for relevant changes in areas planted with vines due to changes in climate (moriondo et al., 2013; seccia and santeramo, 2018). projected scenarios of future climate change at the global and wine region scale are likely to impact the wine market. in particular, spatial changes in viable grape growing regions, and opening new regions to viticulture would determine new productive scenarios in the wine sector at the global level. given this background, our contribution aims at understanding how productive patterns allow different producing regions (e.g., old world producers, new world producers) to respond to changes in climate. specifically, we examine the linkage between climate change and productivity levels in the global wine sector. in this regard, rosenzweig and parry (1994) argue that doubling of the atmospheric carbon dioxide concentration would lead to only a small decrease in global agricultural production. in addition, reilly and hohmann (1993) suggest that interregional adjustments in production buffer the severity of climate change impacts both at global and domestic level. from a methodological perspective, the study of agricultural supply response has traditionally decomposed it in terms of acreage and yield responses (e.g., haile et al., 2016; kim and moschini, 2018). our contribution examines how climate change affects acreage and yield response for grapevines. to this aim, we assume that land allocations are consistent with the choices of a representative farmer who maximises expected profit. we posit that cropland can be allocated between grapevines and all other crops. because these two allocation choices exhaust the set of possible land allocations, total county cropland is assumed to be fixed. thus, the decision problem can be stated as that of choosing acreage. we assume that the acreage shares are a function of expected per acre revenue, given by the product between the output price and expected yield, and of climate change. investigating both the responsiveness of grapevine acreage and yield to climate change allows us to conclude on the global supply response. while our cross-countries analysis is informative on the production patterns in the wine sector at a global scale, it cannot conclude on the effects of climate change at the micro-level (e.g., grape growers, wine producers). indeed, a country-level analysis does not capture differences within countries in terms of both grapevine yield and climate variability, particularly in geographically heterogeneous countries such as the united states, canada, russia, china (kahn et al., 2019). 2. estimating the relationship between climate change and grapevines production 2.1 yield response equation following kim and moschini (2018), we postulate a simple linear equation for yield response. in detail, the expected grapevines yield of county i at time t(yit) is modelled as: yit = α + αi + βtt + γ’xit,s + εit (1) where αi are country-specific intercepts; tt is a linear trend variable and β the related parameter; the vector xit,s includes climate variables specific for county i, time t, and season s (i.e. 30-years rolling average seasonal temperatures and precipitations, tempit,s and precit,s), we also posit a quadratic relationship between climate and yields (i.e. temp2 it,s and prec2 it,s); γ’ is the vector of parameter of interest1; α and εit are a constant and the error term. following the climate literature (e.g., 1 it is worth noting that the parameter captures the climate sensitivity of grapevine yield without considering the implicit adaptation to climate change, differently from analyses based on the ricardian model of climate change (e.g., mendelsohn et al., 1994). 125climate changes and new productive dynamics in the global wine sector bio-based and applied economics 10(2): 123-135, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9676 kurukulasuriya et al., 2011; massetti et al., 2016), we use a four-season model, assuming that seasonal differences in temperatures and precipitations are likely to impact grapevines productivity. however, we exclude climate normals of the winter season which is characterised by the dormancy of grapevines; in fact, the annual growth cycle of grapevines begins with bud break in the spring season and culminate in leaf fall in the autumn season. we explore the relationship between grapevines yield and climate variables to estimate the potential effects of climate change using either ordinary least squares (ols) or quantile regression (qr). the model in equation (1) is estimated in an ols fashion on the whole sample and on subsamples of old world producers and new world producers. the properties of qr have motivated its application in the context of agriculture and weather, mostly focusing on the impact of climate change on various crop yield distributions (conradt et a., 2015). the qr facilitates a thorough analysis of the differential impact of climate change across the yield distribution; a qr approach is useful in such situations and for considering asymmetry and heterogeneity in climatic impacts (barnwal and kotani, 2013). 2.2 acreage response equation total county cropland (a) is assumed to be fixed and land allocations are presumed to be consistent with the choices of a representative farmer who maximises expected profit. we posit that agricultural land can be devoted to two alternative uses, grapevines and all other crops. the decision problem can be stated as that of choosing acreage shares sk ≡ ak ⁄ a, where ak is the acreage allocated to the k-th use (k = 1 for grapevines and k = 2 for all other crops). because a is fixed, increased land allocation to any one crop is equivalent to an increase in its share sk, maintaining the land constraint s1 + s2 = 12. empirically, observed acreage share of grapevines in county i at time t(sit) is modelled as: sit = λ + λi + θtt + φsit-1 + ψrit + ω’zit + νit (2) 2 due to a land constraint, a representative farmer may decide to allocate more (less) acreage to grapevine reducing (increasing) the share of acreage devoted to other crops to maximise expected profits. this may be a sort of implicit adaptation to climate conditions. for instance, due to warmer temperatures, acreages devoted to grapevine in italy may increase to the detriment of acreage intended to other production (e.g., apple tree, pear tree). as suggested in ricardian literature in climate change economics (e.g., timmins, 2006; kurukulasuriya et al., 2011; bozzola et al., 2018). where the set of conditioning variables includes country-specific trend effects, λi; a time trend, tt, capturing exogenous technological progress; expected per acre revenue, rit; past acreage shares, sit-1, climate variables, zit, which may directly affect planting decisions (i.e. 30-years rolling average annual temperatures and precipitations, tempit and precit, and their squares, temp2 it and prec2 it). the term λ is a set constant terms; θ, φ, and ψ are parameters to be estimated, ω’ is the vector of climate-specific parameters; νit is the error term. the term sit-1 allows us to account for the behaviour of producers that adjust their acreage when they realise that the desired acreage differs from the acreage realised in the previous year; it captures the dynamic effects on acreage allocation (santeramo, 2014). following kim and moschini (2018), we interact own output price and expected yields estimated in equation (1), to obtain the expected per acre revenue (i.e., rit = pit ∙ yit). since our study is a country-level analysis, consistent with hendricks et al. (2014) we assume that the country-level expected prices are exogenous: this assumption allows us to deal with potential endogeneity of prices. in order to compute the expected per acre revenue variables for the acreage response equations, we rely on the ols estimate of equation (1). we follow an approach similar to haile et al. (2016) and kim and moschini (2018) and estimate the model in equation (2) using a system generalised method-ofmoments (gmm) estimator, based on a one-step estimation with robust standard errors. in fact, applying ols estimation to a dynamic panel data regression model, such as in equation (2), results in a dynamic panel bias because of the correlation of the lagged dependent variable with the country-fixed effects (nickell, 1981). since current acreage is a function of the fixed effects (λi), lagged acreage is also a function of these country-fixed effects. this violates the strict exogeneity assumption, thus the ols estimator is upward biased and inconsistent. a solution to this issue consists in transforming the data and removing the fixed effects. however, under the within-group transformation, the lagged dependent variable remains correlated with the error term, and therefore the fixed-effects estimator is downward biased and inconsistent. to overcome these problems, the gmm is a more efficient estimator that allows the estimate of a dynamic panel difference model using lagged endogenous and other exogenous variables as instruments. in particular, the system gmm technique transforms the instruments themselves in order to make them exogenous to the fixed effects (roodman, 2009). ˆ ˆ ˆ ˆ 126 bio-based and applied economics 10(2): 123-135, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9676 emilia lamonaca, fabio gaetano santeramo, antonio seccia 3. data sources and sample description the empirical analysis relies on a rich dataset of historical temperature and precipitation data (from 1961 to 2015) and historical trade flows data (from 1996 to 20153) for 14 countries. the selected countries are argentina, australia, brazil, canada, china, france, germany, italy, new zealand, russian federation, south africa, spain, the united kingdom, the united states. they account for more than two-third of the volume of wine production (70% in 2016, global wine markets, 1860 to 2016 database). this group of countries includes both old works producers and new world producers and countries belonging to northern or southern hemisphere4. table 1 provides descriptive statistics for key variables, also distinguishing between old world producers and new world producers. historical country-specific monthly average temperature and precipitation data have been collected from the climate change knowledge portal world bank (world bank, 2018). annual and seasonal climatologies (i.e., rolling 30-years averages5) of temperature (in °c) and precipitations (mm) have been constructed using historical weather data. as for seasonal climatologies, monthly data have been clustered into three-month seasons: december (of the previous year) through february as winter, march 3 the longer time period used for climate data allows to build climatologies (i.e. 30-years averages) of temperature and precipitations: in 1996 (the starting point of the final dataset) climate normal is based on a real 30-years average. 4 the list of countries by group is presented in appendix a.1. 5 differently from other studies that aggregated to data by weighting each information at the grid level by the amount of agricultural area the grid contains (e.g., gammans et al., 2017), we use simple average of climate data aggregated at the country level. through may as spring, june through august as summer, and september through november as autumn. these seasonal definitions have been adjusted for the fact that seasons in the southern and northern hemispheres occur at exactly the opposite months of the year. the annual 30-years average temperature is 10.37 ºc (table 1). within this group, annual average temperatures are about 1 ºc higher for old world producers than for new world producers, reflecting the fact that new world producers are mostly located to lower latitudes (figure 1). the difference in average temperatures between old world producers and new world producers tends to be higher during winter (3.97 °c of old world producers and 0.77 °c of new world producers; table 1). the annual 30-years average precipitation is 68.55 mm and is about 5 mm greater in old world producers figure 1. list of countries. source: elaboration on anderson and nelgen (2015). notes: old world producers in blue, new world producers in red. table 1. descriptive statistics for key variables. variable unit all producers old world producers new world producers acreage ha 303,640 (±347,791) 560,850 (±435,259) 160,745 (±162,051) share of acreage 0.01 (±0.02) 0.02 (±0.00) 0.001 (±0.001) yield t/ha 10.50 (±4.59) 3.96 (±1.22) 12.09 (±1.13) price usd/t 779.27 (±448.80) 528.60 (±40.70) 708.32 (±396.59) 30-years average temperature (annual) °c 10.37 (±8.51) 10.86 (±1.87) 10.10 (±10.52) 30-years average temperature (spring) °c 9.90 (±9.08) 9.70 (±1.54) 10.01 (±11.28) 30-years average temperature (summer) °c 18.76 (±4.76) 18.26 (±2.54) 19.04 (±5.61) 30-years average temperature (autumn) °c 10.92 (±8.21) 11.57 (±2.03) 10.55 (±10.12) 30-years average precipitation (annual) mm 68.55 (±36.13) 71.89 (±17.46) 66.69 (±43.09) 30-years average precipitation (spring) mm 62.35 (±34.87) 67.18 (±11.14) 59.66 (±42.50) 30-years average precipitation (summer) mm 82.17 (±44.21) 61.95 (±19.52) 93.40 (±49.81) 30-years average precipitation (autumn) mm 74.56 (±44.14) 82.93 (±24.25) 69.91 (±51.47) note: average values and standard deviation in parentheses. 127climate changes and new productive dynamics in the global wine sector bio-based and applied economics 10(2): 123-135, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9676 than in new world producers. however, seasonal differences are observed: during summer, the level of precipitations is much lower in old world producers than in new world producers (table 1). in our sample, we observe a 6% increase in median values of 30-years average temperature over twenty years (figure 2). as suggested in jones et al. (2005), old world producers benefit of better growing seasons as compared to new world producers. it should be kept in mind, however, that the strength of seasonality varies significantly across the globe, with seasons being more homogenous around the equator. country-specific annual data on areas planted with vines (in ha) and yields of areas planted with vines (in t/ha), collected from the faostat database, are described in table 1. the faostat database also provides country-level annual acres for agricultural land. total agricultural land includes two components: i.e., cropland (arable land and land under permanent crops) and land under permanent meadows and pastures. in the methodological framework, we assume that agricultural land can be devoted to two alternative uses, grapevines and all other crops. the latter category should capture all acres that could have been not planted to grapevines. hence, we obtain the category all other uses as the difference between total agricultural land and acres planted with vines. in our model, we also use countryspecific annual price data for grapes (usd/t), collected from the faostat database. in order to obtain the reduced per acre revenue, we interact own output price and expected yields estimated in equation (1). within our sample, despite the expansion of areas planted with vines in new world producers during the last decades, acres intended to grape growing are, on average, more than three times larger in old world producers (561 thousands ha with respect to 161 thousands ha, table 1). however, grapevines yields are much larger for new world producers (12.09 t/ha) than for old world producers (3.96 t/ha). yields are often not normally distributed but are negatively skewed (e.g., swinton and king, 1991). this is also what we find in the distribution of grapevines yield in our sample (figure 3). a distribution of yield different from a normal distribution may be associated with the frequent occurrence of outliers; for instance, yield realisations may not follow the pattern described by the majority of yield observations (conradt et al., 2015). it is worth noting that countries with grapevines yields within 25th percentile are canada, spain, france, united kingdom, new zealand, russian federation, whereas countries with yields of grape within 75th percentile are argentina, australia, brazil, china, germany, united states, south africa. 4. results and discussion 4.1 yield response the estimation results for the yield response, based on equation (1), are reported in tables 2 (ols estimates)6 and 3 (qr estimates). the results in table 2 show that the higher the average temperatures in producing countries during summer, the lower the grapevines yield. greater precipitations are beneficial for yield during the early growing season (i.e., spring), but detrimental during the 6 in a sensitivity analysis, we analyse the effects of annual climatic variables on grapevine yields. the results, reported in table a.2 in the appendix, highlight differences between old world producers and new world producers. while higher annual average temperatures are detrimental (up a certain threshold) for old world producers, new world producers benefit of greater annual average temperatures and precipitations. 55.0 55.2 55.4 55.6 55.8 56.0 56.2 56.4 56.6 56.8 57.0 10.5 10.6 10.7 10.8 10.9 11.0 11.1 11.2 11.3 11.4 11.5 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 pr ec ip ita tio n (m m ) t em pe ra tu re ( °c ) temperature precipitation figure 2. median 30-years temperatures and precipitations in 19972015. source: elaboration on data from cru of university of east anglia. note: data refer to the sample of 14 major producers of wine. figure 3. distribution and descriptive statistics for grapevines yield. min: 1.22 max: 19.50 median: 10.57 mean: 10.50 std. dev.: 4.59 skewness: -0.15 kurtosis: 2.25 128 bio-based and applied economics 10(2): 123-135, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9676 emilia lamonaca, fabio gaetano santeramo, antonio seccia late growing season and the harvest time (i.e. summer and autumn). the relationship between summer climate and yields is nonlinear7. the overall effects are mostly driven by the impacts of climate change on grapevines yields of new world producers. differently, grapevines yield of old world producers seem not affected by climate change. the results are consistent with evidence from vine-related literature. in fact, merloni et al. (2018) report that higher temperatures can have a negative impact on grapevines yield and quality. an increase in extreme high temperatures in summer may have adverse consequences on grapevines phenology (briche et al., 2014). in addition, ramos et al. (2008) suggest that seasonal distribution of precipitation matter, with larger rainfall levels being crucial for grapevines at the beginning of the growing season (i.e., spring) whereas more stable precipitations are desirable from flowering to ripening (i.e., summer and autumn). the ols approach is applied when the dependent variable is normally distributed, whereas qr is employed when the variable is not normally distributed (see figure 3). the qr (median) is more robust to outliers than mean regression (ols)8. furthermore, qr provides a clearer understanding of the data by assessing the effects of explanatory variables on the location and the scale parameters of the model (conradt et a., 2015). the results of the qr reported in table 3 mostly confirm the non-linear relationship between grapevines yields and average temperatures in producing countries during summer. no substantial differences are observed across different quantiles of the distribution of grape7 the results are robust also controlling for different combinations of fixed effects: the results are reported in tables a.3 and a.4 in the appendix. we further detect a non-linear relationship between grapevine yield and summer precipitation controlling for time fixed effects (common to all countries) and country-specific fixed effects. differently, we cannot conclude on the relationship between grapevine yield and detrended climate variables obtained from the yearly weather deviation from the long-run climate (30-year rolling average), as recently proposed by khan et al. (2019). the result is not surprising: while detrended climate variables capture short-run changes in climate conditions (i.e., weather shocks), 30-year rolling average temperatures and precipitations inform on long-run changes in climate conditions: it is unlikely that weather shocks on a year-by-year basis affect the responsiveness of the viticultural sector, but long-run changes in climate capture structural changes in the sector and are more likely to influence production decisions of a multi-year crop. a comparison between shortand longrun analyses is reported in table a.5 in the appendix. 8 we conduct a multidimensional outlier detection analysis based on the ‘bacon’ algorithm, which identifies outliers based on the mahalanobis distances (billor et al., 2000, weber, 2010). the algorithm allows the identification and removal of observations characterised by implausibly large or low entries of key variables. the results of the model estimated without outliers, reported in tables a.6 and a.7 in the appendix, confirm the main results, although the effect of temperatures and precipitations on grapevine yields tend to be lower. vines yields. differently, the results reveal that lower yield realisations (i.e., within 25th percentile) tend to be most affected by greater precipitations during the harvest time (i.e., autumn). it is worth noting that countries with grapevines yields within 25th percentile are mostly cool climate wine regions such as canada and russian federation. cool regions tend to have also higher rainfall levels and yields tend to be lower on average, rising production costs (anderson, 2017). table 2. estimation results for grapevines yields, ols. variables dependent variable: yield all producers old world producers new world producers temperature (spring) 1.4440 -9.5441 -1.4800 (1.7044) (12.7571) (2.1761) temperaturesquared (spring) -0.3044*** 0.3965 -0.2577** (0.0747) (0.5755) (0.1209) temperature (summer) -16.3650** -22.5187 -1.8786 (7.1026) (14.6183) (11.2236) temperaturesquared (summer) 0.4258** 0.4752 0.3047 (0.1955) (0.3634) (0.3264) temperature (autumn) 0.6543 -0.6787 -0.5129 (1.9410) (12.3068) (2.3252) temperaturesquared (autumn) 0.0761 -0.0685 0.1321 (0.0888) (0.4882) (0.1181) precipitation (spring) 0.5227* 0.4326 0.8057* (0.2795) (0.7043) (0.4339) precipitationsquared (spring) -0.0041*** -0.0035 -0.0052*** (0.0015) (0.0048) (0.0019) precipitation (summer) -0.3230* -0.0678 -0.0427 (0.1906) (0.3849) (0.3922) precipitationsquared (summer) 0.0013 -0.0001 0.0005 (0.0009) (0.0022) (0.0013) precipitation (autumn) -0.3507** -0.3838 -0.4272 (0.1601) (0.4282) (0.3758) precipitationsquared (autumn) 0.0019** 0.0019 0.0019 (0.0008) (0.0020) (0.0017) time trend 0.1392*** 0.3459* 0.0109 (0.0477) (0.1756) (0.1007) observations 280 100 180 r-squared 0.9314 0.9656 0.8930 notes: ols estimate of equation (1) on the whole sample (all producers) and subsamples of old world producers and new world producers. all specifications include country-specific constants. robust standard errors are in parentheses. *** significant at the 1 percent level. ** significant at the 5 percent level. * significant at the 10 percent level. 129climate changes and new productive dynamics in the global wine sector bio-based and applied economics 10(2): 123-135, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9676 4.2 acreage response table 4 presents the estimation results under the acreage models. all dynamic models (all producers, old world producers and new world producers) are based on a one-step gmm estimator. the arellanobond test for autocorrelation is used to test for serial correlation in levels. the test results indicate that the null hypothesis of no second-order autocorrelation in residuals cannot be rejected, indicating the consistency of the system gmm estimators. according to the sargan test results, we fail to reject the null hypothesis of instrument exogeneity: the system gmm estimators are robust but weakened by many instruments. we fail to find a significant acres-price relationship, which could imply that many grapevines’ producers do not form their price expectations on the basis of information on expected per acre revenues. more importantly, the estimation results reveal that higher annual temperatures in producing countries are beneficial for grapevines acreage share. this is true for both old and new world producers, despite the effects are much larger in old world producers. as suggested in ruml et al. (2012), among the many climatic factors affecting wine production, temperature appears to be most important. differently, severe rainfall levels is significantly associated with less grapevines share. the negative effects of greater annual precipitations is entirely associated with new world producers, whereas the old world producers seem not affected by changes in the rainfall levels. table 3. estimation results for grapevines yields, quantile regression. variables dependent variable: yield 25th percentile 50th percentile 75th percentile temperature (spring) 0.8721 0.7711 1.3070 (1.9059) (1.8734) (2.3574) temperaturesquared (spring) -0.1418* -0.2405*** -0.3368*** (0.0756) (0.0812) (0.1073) temperature (summer) -22.4737*** -27.0681*** -23.1306*** (4.5501) (7.0368) (7.0902) temperaturesquared (summer) 0.5454*** 0.7064*** 0.6102*** (0.1219) (0.1864) (0.1763) temperature (autumn) 3.0239 1.9043 2.2129 (2.1210) (1.2873) (2.4223) temperaturesquared (autumn) -0.1279 -0.0515 0.0525 (0.0813) (0.0611) (0.0998) precipitation (spring) 0.2402 0.6707** 0.4740 (0.2974) (0.2899) (0.2913) precipitationsquared (spring) -0.0024 -0.0048*** -0.0035* (0.0018) (0.0017) (0.0018) precipitation (summer) -0.2866 -0.0272 -0.1956 (0.2024) (0.1155) (0.1925) precipitationsquared (summer) 0.0014 -0.0001 0.0011 (0.0012) (0.0006) (0.0011) precipitation (autumn) -0.3157* -0.1921 -0.1535 (0.1691) (0.1477) (0.1627) precipitationsquared (autumn) 0.0019** 0.0011* 0.0010 (0.0008) (0.0006) (0.0007) time trend 0.1523*** 0.1796*** 0.1024* (0.0574) (0.0534) (0.0545) observations 280 280 280 notes: qr estimate of equation (1) on the whole sample. all specifications include country-specific constants. robust standard errors are in parentheses. *** significant at the 1 percent level. ** significant at the 5 percent level. * significant at the 10 percent level. table 4. estimation results for grapevines acreage, old world producers and new world producers. variables dependent variable: acreage share all producers old world producers new world producers lagged acreage share 0.995*** 0.795*** 0.953*** (0.001) (0.046) (0.012) expected per acre revenue -0.00003 -0.163 -0.0003 (0.00003) (0.109) (0.001) temperature (annual) 0.107*** 38.983* 0.131*** (0.019) (22.496) (0.020) temperature-squared (annual) -0.006*** -0.134 -0.008*** (0.001) (1.574) (0.001) precipitation (annual) -0.107*** 18.447 -0.122*** (0.033) (11.384) (0.028) precipitation-squared (annual) 0.001*** -0.120 0.001*** (0.0002) (0.081) (0.0001) test for ar(1): p-value 0.096 0.106 0.239 test for ar(2): p-value 0.238 0.326 0.266 sargan test: p-value 0.134 0.592 0.926 number of instruments 149 47 123 notes: one-step generalised method-of-moments (gmm) estimate of equation (2) on the whole sample and on subsamples of old world producers and new world producers. all specifications include a constant and a time trend. coefficients and standard errors estimated are of the order of 10-6 for ‘expected per acre revenue’ and of 10-4 for climate variable. observations are 198 for all producers, 47 for old world producers and 151 for new world producers. robust standard errors are in parentheses. *** significant at the 1 percent level. ** significant at the 5 percent level. * significant at the 10 percent level. 130 bio-based and applied economics 10(2): 123-135, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9676 emilia lamonaca, fabio gaetano santeramo, antonio seccia 5. concluding remarks climate change has the potential to impact the agricultural sector and the wine sector in particular (mozell and thach, 2014). most of the previous studies analysing the impact of climate change on agriculture do not consider the effects of climate change on world production, markets and trade patterns (reilly and hohmann, 1993). our analysis allowed us to understand if climate change is able to affect productivity levels of grapevines. overall, we found that grapevines yield suffers from higher temperatures during summer, whereas precipitations have a varying impact on grapevines depending on the cycle of grapevines. in particular, we observed that greater precipitations are beneficial during the early growing season (spring), but detrimental during the late growing season and the harvest time (summer and autumn). differently, acreage share of grapevines tends to be favoured by higher annual temperatures, whereas greater annual precipitations tend to be detrimental. the impacts however vary between old world producers and new world producers, also due to heterogeneity in climate between them: the effects of temperatures are less pronounced for new world producers, whereas precipitations have no effects for old world producers. as suggested in previous studies (e.g., jones et al., 2005), old world producers benefit of better growing season, but climate change may push new world producers into more favourable climatic regimes. the opening of new regions, benefiting of better climatic regimes, to viticulture would determine new productive scenarios and, as a result, new trade dynamics (macedo et al., 2019). new productive scenarios are likely to favour the production of varietal wines from autochthonous grapes whose quality is strongly related to microclimatic and pedological conditions (seccia et al., 2017). in addition, changes in trade regulations, that have largely influenced the agri-food market, are modifying also global trade of wine (santeramo et al., 2019; seccia et al., 2019). such dynamics should not be neglected. future research should be intended to examine how climate change could affect global trade of wine and to understand how importers and exporters could react to new trade dynamics, due to climate change, in terms of trade regulations. acknowledgment the research has been supported by a research grant funded by the international organisation of vine and wine (oiv). the authors are grateful to dr. martina bozzola for collection and organisation of climate data, to tatiana 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(2010). bacon: an effective way to detect outliers in multivariate data using stata (and mata). the stata journal 10(3): 331-338. world bank (2018). metadata of the climate change knowledge portal. appendix table a.1. list and description of countries in the sample. country iso 3 wine producer hemisphere 30-years annual average temperature (°c) 30-years annual average precipitation (mm) argentina arg new world producer southern 14.44 49.16 australia aus new world producer southern 21.76 40.47 brazil bra new world producer southern 25.14 148.20 canada can new world producer northern -6.47 38.77 china chn new world producer northern 6.94 48.29 germany deu old world producer northern 9.28 61.12 spain esp old world producer northern 13.84 50.92 france fra old world producer northern 11.41 71.61 united kingdom gbr old world producer northern 8.94 103.42 italy ita old world producer northern 12.51 78.70 new zealand nzl new world producer southern 10.06 145.83 russia rus new world producer northern -5.43 36.64 united stated usa new world producer northern 7.50 55.57 south africa zaf new world producer southern 18.13 40.89 source: wine producer classification follows anderson and nelgen (2015). 133climate changes and new productive dynamics in the global wine sector bio-based and applied economics 10(2): 123-135, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9676 table a.2. estimation results for grapevines yields, ols. variables dependent variable: yield all producers old world producers new world producers temperature (annual) 1.3078 -22.4180*** 5.2902*** (1.4604) (7.5813) (1.8500) temperaturesquared (annual) -0.0215 0.6741*** 0.0892** (0.0344) (0.1969) (0.0423) precipitation (annual) 0.1755 0.3731 1.1522** (0.4226) (0.9870) (0.4877) precipitationsquared (annual) -0.0021 -0.0025 -0.0058** (0.0022) (0.0052) (0.0026) time trend 0.0400 0.2498 -0.0490 (0.0479) (0.1578) (0.0593) observations 280 100 180 r-squared 0.9148 0.9626 0.8758 notes: ols estimate of equation (1) on the whole sample (all producers) and subsamples of old world producers and new world producers. all specifications include country-specific constants. robust standard errors are in parentheses. *** significant at the 1 percent level. ** significant at the 5 percent level. table a.3. estimation results for grapevines yield: controlling for different combinations of fixed effects. variables our results sensitivity analysis temperature (spring) 1.4440 2.2203 (1.7044) (1.8717) temperature-squared (spring) -0.3044*** -0.3176*** (0.0747) (0.0794) temperature (summer) -16.3650** -16.1260** (7.1026) (7.4473) temperature-squared (summer) 0.4258** 0.4022** (0.1955) (0.2013) temperature (autumn) 0.6543 0.0276 (1.9410) (2.3118) temperature-squared (autumn) 0.0761 0.1007 (0.0888) (0.0948) precipitation (spring) 0.5227* 0.5692** (0.2795) (0.2844) precipitation-squared (spring) -0.0041*** -0.0041*** (0.0015) (0.0015) precipitation (summer) -0.3230* -0.3870* (0.1906) (0.2034) precipitation-squared (summer) 0.0013 0.0015* (0.0009) (0.0009) precipitation (autumn) -0.3507** -0.3009* (0.1601) (0.1607) precipitation-squared (autumn) 0.0019** 0.0017** (0.0008) (0.0008) country fixed effects yes yes time trend yes no time fixed effects no yes country-time fixed effects no no r-squared 0.9314 0.9386 notes: ols estimate of yield response equation. observations are 280. robust standard errors are in parentheses. *** significant at the 1 percent level. ** significant at the 5 percent level. * significant at the 10 percent level. 134 bio-based and applied economics 10(2): 123-135, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9676 emilia lamonaca, fabio gaetano santeramo, antonio seccia table a.4. estimation results for grapevines acreage: controlling for different combinations of fixed effects. variables our results sensitivity analysis lagged acreage share 0.995*** 0.995*** (0.001) (0.002) expected per acre revenue -0.00003 0.002 (0.00003) (0.003) temperature (annual) 0.107*** 0.095*** (0.019) (0.021) temperature-squared (annual) -0.006*** -0.006*** (0.001) (0.001) precipitation (annual) -0.107*** -0.133*** (0.033) (0.047) precipitation-squared (annual) 0.001*** 0.001*** country fixed effects yes yes time trend yes no time fixed effects no yes notes: one-step generalised method-of-moments (gmm) estimate of acreage response equation. coefficients and standard errors estimated are of the order of 10-6 for ‘expected per acre revenue’ and of 10-4 for climate variable. observations are 198. robust standard errors are in parentheses. *** significant at the 1 percent level. table a.5. estimation results for grapevines yield: controlling for detrended climate variables. variables our results (long-run analysis) sensitivity analysis (short-run analysis) temperature (spring) 1.4440 0.1500 (1.7044) (0.1418) temperature-squared (spring) -0.3044*** 0.0164 (0.0747) (0.0900) temperature (summer) -16.3650** 0.1692 (7.1026) (0.2820) temperature-squared (summer) 0.4258** -0.2140 (0.1955) (0.1384) temperature (autumn) 0.6543 0.2483 (1.9410) (0.1584) temperature-squared (autumn) 0.0761 -0.1644** (0.0888) (0.0820) precipitation (spring) 0.5227* -0.0050 (0.2795) (0.0088) precipitation-squared (spring) -0.0041*** -0.0002 (0.0015) (0.0005) precipitation (summer) -0.3230* 0.0039 (0.1906) (0.0093) precipitation-squared (summer) 0.0013 -0.0005 (0.0009) (0.0003) precipitation (autumn) -0.3507** 0.0071 (0.1601) (0.0056) precipitation-squared (autumn) 0.0019** 0.0002 (0.0008) (0.0002) r-squared 0.9314 0.9177 notes: ols estimate of yield response equation. observations are 280. detrended climate variables in the sensitivity analysis are obtained from the yearly weather deviation from the long-run climate (30-year rolling average). all specifications include countryspecific constants and the time trend. robust standard errors are in parentheses. *** significant at the 1 percent level. ** significant at the 5 percent level. * significant at the 10 percent level. table a.6. multidimensional outlier detection analysis. 5th percentile 10th percentile 15th percentile total number of observations 280 280 280 bacon outliers 0 0 20 non-outliers remaining 208 208 260 135climate changes and new productive dynamics in the global wine sector bio-based and applied economics 10(2): 123-135, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9676 table a.7. estimation results for grapevines yields: ols with and without outliers and qr. variables ols qr all observations (a) observations w/out outliers (b) 25th percentile (c) 50th percentile (d) 75th percentile (e) temperature (spring) 1.4440 1.6527 0.8721 0.7711 1.3070 (1.7044) (1.7917) (1.9059) (1.8734) (2.3574) temperature-squared (spring) -0.3044*** -0.3114*** -0.1418* -0.2405*** -0.3368*** (0.0747) (0.0765) (0.0756) (0.0812) (0.1073) temperature (summer) -16.3650** -14.7502* -22.4737*** -27.0681*** -23.1306*** (7.1026) (7.7445) (4.5501) (7.0368) (7.0902) temperature-squared (summer) 0.4258** 0.3653* 0.5454*** 0.7064*** 0.6102*** (0.1955) (0.2163) (0.1219) (0.1864) (0.1763) temperature (autumn) 0.6543 0.2605 3.0239 1.9043 2.2129 (1.9410) (2.1218) (2.1210) (1.2873) (2.4223) temperature-squared (autumn) 0.0761 0.1037 -0.1279 -0.0515 0.0525 (0.0888) (0.0967) (0.0813) (0.0611) (0.0998) precipitation (spring) 0.5227* 0.5162* 0.2402 0.6707** 0.4740 (0.2795) (0.2777) (0.2974) (0.2899) (0.2913) precipitation-squared (spring) -0.0041*** -0.0041*** -0.0024 -0.0048*** -0.0035* (0.0015) (0.0015) (0.0018) (0.0017) (0.0018) precipitation (summer) -0.3230* -0.3643 -0.2866 -0.0272 -0.1956 (0.1906) (0.2388) (0.2024) (0.1155) (0.1925) precipitation-squared (summer) 0.0013 0.0013 0.0014 -0.0001 0.0011 (0.0009) (0.0009) (0.0012) (0.0006) (0.0011) precipitation (autumn) -0.3507** -0.3302** -0.3157* -0.1921 -0.1535 (0.1601) (0.1629) (0.1691) (0.1477) (0.1627) precipitation-squared (autumn) 0.0019** 0.0019** 0.0019** 0.0011* 0.0010 (0.0008) (0.0008) (0.0008) (0.0006) (0.0007) observations 280 260 280 280 280 r-squared 0.9314 0.9037 notes: ols and qr estimate of yield response equation. robust standard errors are in parentheses. *** significant at the 1 percent level. ** significant at the 5 percent level. * significant at the 10 percent level. volume 10, issue 2 2021 firenze university press mediterranean agriculture facing climate change: challenges and policies filippo arfini the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications cristina vaquero-piñeiro application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context raffaella zucaro, veronica manganiello, romina lorenzetti*, marianna ferrigno climate changes and new productive dynamics in the global wine sector emilia lamonaca*, fabio gaetano santeramo, antonio seccia a systematic review of attributes used in choice experiments for agri-environmental contracts nidhi raina*, matteo zavalloni, stefano targetti, riccardo d’alberto, meri raggi, davide viaggi the effect of farmer attitudes on openness to land transactions: evidence for ireland cathal geoghegan*, anne kinsella, cathal o’donoghue bio -based and a ppl ied economics bae bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-10981 copyright: © 2022 m. aghabeygi, k. louhichi, s. gomez y paloma. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: m. aghabeygi, k. louhichi, s. gomez y paloma (2022). impacts of fertilizer subsidy reform options in iran: an assessment using a regional crop programming model. bio-based and applied economics 11(1): 55-73. doi: 10.36253/bae-10981 received: may 25, 2021 accepted: january 21, 2022 published: july 22, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: donato romano. orcid ma: 0000-0003-4953-2799 kl: 0000-0001-8449-5693 sgyp: 0000-0001-9289-7633 impacts of fertilizer subsidy reform options in iran: an assessment using a regional crop programming model mona aghabeygi1,2,*, kamel louhichi3, sergio gomez y paloma2 1 mona aghabeygi, università degli studi di parma, parma, italy 2 european commission, joint research centre (jrc), seville, spain, 3 université paris-saclay, inrae, umr economie publique, thiverval-grignon, france e-mail: monaaghabeygi17@gmail.com; kamel.elouhichi@inrae.fr; sergio.gomez-ypaloma@ec.europa.eu *corresponding author. e-mail: monaaghabeygi17@gmail.com abstract. the aim of this paper is to assess the potential impacts of different fertilizer subsidy reform options on the performance of the iranian crops production sector. this is achieved using a regional crop programming (rcp) model, based on positive mathematical programming, which includes in total 14 crop activities and encompasses 31 administrative regions. the rcp model is a collection of micro-economic models, working with exogenous prices, each representing the optimal crop allocation at the regional level. the model is calibrated against observed data on crop acreage, yield responses to nitrogen application, and exogenous supply elasticities. simulation results show that a total removal of nitrogen fertilizer subsidies would affect the competitiveness of crops with the highest nitrogen application rates and lead to a slight reduction of national agricultural income, at approximately 1%. this effect, which is more pronounced at the regional level, is driven by area reallocation rather than land productivity. the reallocation of nitrogen fertilizer subsidy to only strategic crops boost their production and income but increase disparity among regions and affects negatively welfare compared to the current universal fertilizer program. the transfer efficiency analysis shows that both target and universal simulated options are inefficient with an efficiency score below one. keywords: agricultural policy, fertilizer subsidy, land use effect, regional crop model, positive mathematical programing (pmp), iran. jel codes: q18, c13, c61. 1. introduction iran is a country in western asia with 82 million inhabitants, standing at the world’s 18th most populous country. its territory spans 1,648,195 km2, making it the second largest country in the middle east. in 2016, the gross domestic product (gdp) was 1797 billion irr, while the per capita gdp was about 219 million irr, and the country is ranked as an upper-middle 56 bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 mona aghabeygi, kamel louhichi, sergio gomez y paloma income economy by the world bank (icb, 2016). the agricultural sector plays an important role in the iranian economy. in 2016, agriculture contributed up to 9.64 % of the gdp, provided up to 87% of the food supply, occupied around 10% of the land, and employed 19% of the labor force (irica, 2016; icb, 2016; sci, 2016). smallholder farms with less than 25 acres (10 hectares) largely dominate the iranian agricultural sector. they represent more than 70% of the country’s agricultural producers and occupy more than 55 % of cropland (csi, 2014). they are basically family-based and family-managed farms with an average size of 3 hectares, with 2 hectares under cultivation (irnagric, 2015). the crop sector is the most significant agricultural subsector in the country with 65.7% of agricultural’ value added and 2.5 million agriculture production units. field crops, mainly cereals, constitute the bulk of iranian’s crop production. in 2016, wheat makes up 50.39% of total cultivated land, followed by barley 14.95%, rice 5.07%, and corn 1.35%. however, in spite of input and output support policies, the yields of these crops remain below the world average (wb, 2016; imaj, 2016). to boost productivity and foster national food security and agricultural self-reliance, iran has deployed a multi-pronged program of subsidies. this includes guaranteed price f loors for more than twenty crops, and which often results in producer prices that are well above world prices. in addition to this price floor, the iranian government provides support to farmers in the form of subsidized prices for fertilizer, pesticides, and improved seeds, as well as for equipment and basic inputs like water and energy (hosseini and shahnabati, 2015; pakravan et al., 2016; hosseini et al., 2017). fertilizer subsidy is the most important of these subsidy programs. it started in the 1970s, but it focuses mostly on export crops and on training farmers in the proper use of fertilizers. however, as food security became a top priority with the explosion of population, fertilizer subsidy it was extended to staple crops (ic, 2016). in 2016, mineral fertilizer subsidy represented around 10% of the public expenditure in agriculture. the subsidy was paid to the iranian petro-chemical industry, to permit it to sell fertilizers at reduced prices. subsidized fertilizers were universally available to all farmers, regardless their specialization, size, geographical location, etc. (i.e., universal fertilizer program). however, due to the government’s limited budget, not all farmers have access to subsidized fertilizer. in addition, given that subsidized fertilizers are often traded by intermediate dealers, they were sometimes sold to farmers at inflated prices or even smuggled out of the country. to address this issue, the “agricultural support services company (assc)”, responsible for providing and distributing mineral fertilizers, has recently implemented a smart agricultural input distribution system (saids) (sito, 2016) that records detailed farmer information and monitors the transportation of fertilizer from petrochemical companies to the different regions (assc, 2016). although the introduction of fertilizer subsidy may contribute to enhancing food availability and food security, it has been subject to increasing criticism in recent years from both national and international players. in fact, several local experts argued that the use of input subsidies in iran dates to the early 1970’s, however agricultural productivity is still low, self-sufficiency is not achieved yet, and food safety and food security are still major concerns. as such, this instrument is seen as inefficient, given its high budget costs, and source of market distortions since it benefits only specific groups of farmers (e.g., farmers with ease access to input market). to this, one can add the new pressure coming from the world trade organization (wto). in fact, the iranian government is expecting to become member of wto and such kind of subsidies are not allowed by this organization (najafi and dehghan, 2010; alijani et al., 2012; barikani and shahbazi, 2016). the debate on the ‘efficiency’ of fertilizer subsidy program is not new and not specific to iran. according to the literature there are two types of subsidy programs depending on whether these are universally applied or targeted to a specific crop, category of farmers or region. targeted subsidy programs include, for example, the five recent programs implemented in east and southern africa: kenya, malawi, rwanda, tanzania, and zambia. these programs have in common their large scale in terms of number of beneficiaries (e.g., 2.5 million in kenya), time frame (e.g., 10 years in zambia), coverage (nation-wide), and implementation arrangements (voucher-based system). on the opposite, other countries such as iran, india, and west african countries (burkina faso, ghana, mali, nigeria, and senegal) have adopted fertilizer subsidy programs, which seem to revert to universal (untargeted) price subsidies (dorward, 2009; praveen et al., 2017). both targeted and universal subsidies are highly discussed in the literature and two opposing views are generally identified. those who sustain their effectiveness in bringing about green revolution (gardner, 1992; wright, 1995; denning et al., 2009; javdani, 2012) and those who considers them expensive, mainly benefit the wrong people, and distort agricultural markets (holden and tostensen, 2011; chibwana et al., 2014). the main objective of this paper is to contribute to this debate by assessing the economic effects of the ferti57impacts of fertilizer subsidy reform options in iran: an assessment using a regional crop programming model bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 lizer subsidy program currently implemented in iran and to compare its performance with an alternative program based on targeting strategic crops. this is achieved using a regional crop programming (rcp) model designed to simulate farms’ responses to policy and market changes. the paper is structured as follows: section 2 describes the regional crop programming (rcp) model and its major features. section 3 presents and discusses the results of model simulation. finally, section 4 draws the main conclusions and policy implications. 2. the regional crop programming (rcp) model 2.1 model features rcp is a comparative static, regional, positive mathematical programming model, which includes in total 14 crop activities and encompasses 31 administrative regions. positive means that the model aim to reproduces the real conditions as accurately as possible and to simulate “what is likely” to happen to this situation when changing external conditions (howitt, 1995; janssen and van ittersum, 2007). regional signifies that the model operates at regional level and considers each region as one farm, as is often done in regional programming models (capri (britz and witzke, 2014); reap (johansson, 2007); tasm (eruygur and cakman, 2008)). this implies that all farms within the region are assumed to be homogenous, have the same behavior and can perfectly exchange production factors. the use of a regional approach is motivated by the relative homogeneity1 of arable farms in iran as well as by the limited access to micro-data (i.e., farm data) for confidentiality reason. builds on regional data from the iranian agriculture ministry-jihad (imaj, 2016), the rcp model is a collection of 31 non-linear regional programming models, working with exogenous prices, each representing the optimal crop allocation at regional level. after being solved, the regional results of the regional models are aggregated to national level. rcp is calibrated using positive mathematical programming (pmp) (howitt, 1995) 2. pmp is a methodol1 if we exclude large-scale farms which represent less than 0.2% of agricultural holding (sci, 2014), arable farms within the same region tend to be relatively homogeneous because the majority have small farm size and most of them are sharing the same technology and equipment (ansari et al., 2020). 2 other methods have been developed to calibrate optimization models to observed allocations, although not perfectly. the well-known ones are the risk (hazell and norton, 1986) and the multi-attribute utility theory (keeney and raiffa, 1993) based methods. ogy developed to exact calibrate programming models against observed economic behavior without the use of artificial flexibility constraints, while requiring minimal data. the pmp method is often preferred to linear mathematical programming as it avoids over specialization in crop production and yields smooth responses to policy changes. because of these desirable characteristics, models calibrated using the pmp approach and its variants are popular in agricultural and environmental policy analysis. existing agricultural supply models that rely on pmp principles include, among others, the european common agricultural policy regionalized impact (capri) modelling system (britz and witzke, 2014), the us regional environment and agriculture programming (reap) model (johansson et al., 2007), the canadian regionalized agricultural model (cram) (horner et al., 1992), the turkish agricultural sector model (tasm) (eruygur and cakman, 2008), and the dutch regionalized agricultural model (dram) (helming, 2005). over time, the literature on pmp has evolved and several variants have been developed to accurately calibrate programming models3. the more recent literature has focused on using supply elasticities and/or shadow prices for resources to reduce the under-determinacy of the model and increase the robustness of the parameter specification (heckelei and wolff, 2003; mérel and bucaram, 2010; jansson and heckelei, 2011; mérel et al., 2011, mérel et al., 2013; britz and witzke, 2014; louhichi and gomez y paloma, 2014; garnache et al., 2017; louhichi et al., 2018; henry de frahan et al., 2019). the pmp method used in this study builds upon this strand. it follows the variant proposed by louhichi et al., (2018), which use cross-sectional data and prior information on supply elasticities and on dual values of land constraints, to calibrate the model to the base year condition. supply elasticities are taken from the literature (sabohi and azadegan, 2014; garshasbi et al., 2014; jafari lisar et al., 2017), while prior information on dual values of land constraints is derived from the iranian agriculture ministryjihad (imaj) database. rcp model relies on profit maximizing behavior and search for the optimal land allocation among production activities in each region taking into account land constraints. the regional profit (i.e., agricultural income) is defined as the sum of gross margin minus a nonlinear quadratic cost function for specific activity. the gross margin is equal to the total revenue from the sales of agricultural products plus fertilizer subsidies minus the accounting variable cost of production activities. the accounting costs include cost of seed, fertilizer, 3 for a review of pmp models, see heckelei et al., (2012) and henry de frahan (2019). 58 bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 mona aghabeygi, kamel louhichi, sergio gomez y paloma pesticides, hired labor, and water. the quadratic activity-specific function is a behavioral function introduced to calibrate the model to the observed land allocation of the base year, as is usually done in positive programming models. this function allows capturing the effects of factors that are not explicitly included in the model, such as capital and labor constraints, price expectations, risk-adverse behavior, and other unobserved costs (heckelei and wolff, 2003). the crop yields and the nitrogen application rate are endogenously defined in our model to allow their adjustments under market and policy changes. this achieved thanks to a crop-specific quadratic4 yield response function to nitrogen fertilizer (considered to be the most important nutrient), econometrically estimated and embedded in the model, under the assumption that yield is independent of the acreage planted. the other fertilizer elements (p and k) are assumed to be applied in fixed proportion to nitrogen fertilizer and the remaining intermediate inputs such as seeds and pesticides are supposed to be independent to fertilizer and employed in fixed rate by hectare of each specific crop5. intermediate inputs are also assumed to be independent on the (unknown) marginal costs that are captured by the quadratic behavioral function (heckelei and wolff, 2003). the general mathematical formulation of profit maximization problem of region r = (1, 2, …, r) is as follows: (pr,iyr,ixr,i-wr,inr,ixr,i+sr,inixr,i) cr,i,kxr,i dr,ixr,i–0.5 xr,iqr,i,i’xr,i’ (1) subject to: ar,i,mxr,i ≤ br,m [φr,m] (2) y=αn+βn2+γ (3) 4 we opted for a quadratic functional form because it keeps the model quadratic and simplifies the resolution of the optimization problem. more sophisticated specifications may consider exponential form (godard et al., 2008; mérel et al., 2011) or quadratic-plus-plateau form (similar to the conventional quadratic, but a plateau is imposed). 5 this assumption lacks of rationalization given the strong relationship between fertilizer and other inputs. in fact, one could expect that an increase in fertilizer use would increase the risk of pest infestation (rossing et al., 1997) and, as a consequence, the amount of pesticides applied (similar effects could be observed in other inputs). however, due to the lack of data to make a reliable estimate of this relationship and in order to avoid additional bias we have adopted this assumption following previous studies by mérel et al., 2011; mérel et al., 2013; graveline and mérel, 2014, britz and witzke, 2014, etc. x≥0; y≥0; n≥0 (4) where indices i,i’=1,2,…,i denote the crop activity; k=1,2,…,k the intermediate inputs (i.e., seed, pesticides, hired labor, water, etc.) and m=1,2,…,m the resource constraints (only land is considered here). π is the objective function value of region r, xr,i is the unknown level (hectares) of crop activity i, pr,i is the crop price (i.e. market price), yr,i is the crop yield, wr,i is the fertilizer price, nr,i (per hectares) is the fertilizer quantity, sr,i is the fertilizer subsidy (per hectares), and cr,i,k are accounting variable costs (per hectares) for each intermediate input k and crop i. dr,i is the linear term of the behavioral activity function and qr,i,i’ is the quadratic term of the behavioral activity function. ar,i,m are the coefficients of resource (i.e., land) constraints, br,m is the level of available resources and φr,m are their corresponding shadow prices. α,β and γ are the coefficients of the yield response function to nitrogen. the coefficients α and β are crop, seed variety, season, and agro-ecological zone specifics to take into account technological, soil and climate heterogeneity. γ is the intercept parameter whose position (value) can be shifted up or down in the calibration step to capture region specification. by setting α and β at agro-ecological level we assumed that regions within the same agro-ecological zone have a common technology and, therefore, they have the same yield curve shapes but with different starting points (i.e., intercept γ is region specific). five agro-ecological zones are defined for iran, based on climatic conditions, soil characteristics and type of crops grown: mountain climate, moist climate, hot and dry climate, temperate climate and hot and moist. 2.2 model calibration the aim of the calibration process is to ensure that, in each region, the observed crop allocation during the base year period is exactly reproduced by the optimal solution of the programming model, which relies on profit maximization. this implies that two key variables need to be calibrated: the regional crop yield and area. this is performed in two successive steps: first, we calibrate yield response to the applied nitrogen rate and then, the land allocation. 2.2.1 calibrating yield response to nitrogen fertilizer calibrating yield response to nitrogen fertilizer consists of recovering the unknown crop specific nitrogen 59impacts of fertilizer subsidy reform options in iran: an assessment using a regional crop programming model bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 fertilizer prices w, the nitrogen response’s intercept γ and the nitrogen fertilization rate n that allows reproducing exactly the observed yield y0 assumed to be at the optimum level. mathematically, the above consists of solving the following model where the objective is assumed to be the maximization of the profit by unit of area with respect to nitrogen fertilization use (godard et al., 2008; louhichi et al, 2020): maxπr,i=pr,iy(n)-wr,inr,i+sr,inr,i (5) subject to: y(n)=αinr,i+βin2 r,i+γr,i (6) y(n)=y0 [ηr,i] (7) ηr,i≥0 [μr,i] (8) where π is profit by unit of area, r is the region, i is the crop activity, y is the crop yield (kg ha-1) and y0 is its observed level in the base year (assumed to be optimal), p is the crop prices assumed to be known with exactitude, α,β and γ are the coefficients of the regression model, n is the nitrogen fertilizer quantity (kg ha-1), w is the nitrogen fertilizer prices, sr,i is the fertilizer subsidy, η is the lagrange multiplier related to the constrained yield level and μ is the lagrange multiplier related to the non-negativity constraints for n,α and β are estimated by agro-ecological zone (more details are available in louhichi et al., 2020). 2.2.2 calibrating production activity levels the calibration of activity levels consists of recovering the set of unknown parameters (d, q and φ), so that the optimization model as described in equations (1) and (4) replicates exactly the observed activity levels (x0) of the base year. this is performed using the results of the yield calibration step and a new variant of positive mathematical programming (pmp) approach proposed by louhichi et al., (2018). this variant relies on prior information on (i) supply elasticities ( r,i,i), and on (ii) dual values of (irrigated and rainfed) land constraints (φr,m). to perform the estimation, we derive the focs of the optimization model, equation (1) and (4) and then we apply the hpd method to estimate the unknown parameters dr,i,qr,i,i’ and φr,m. the hpd model minimizes, in each region, the weighted sum of normalized square deviations of estimated national and agro-ecological zone own price(diagonal) supply elasticities and dual values of constraints from their prior subject to set of data consistency (foc) constraints. following louhichi et al., 2018, the general formulation of the corresponding hpd problem is the following: (9) subject to: (10) (11) (12) (13) (14) bz,i,i’=∑jlbz,i,jlbz,i’,j; lbz,i,i’=0 for i’>i (15) (16) (17) where indices j,j’=1,2,…,i (similar to i,i’) denote the crop activities; gmr,i is the gross margin for activity i (irr/ha) with gmr,i=pr,iy0 r,i-wr,in0 h,i+sr,i-∑kcr,i,k. y0 is the observed yield and w and n0 are, respectively, the nitrogen fertilization price and quantity estimated in the yield calibration step. and are, respectively, mean and standard deviation of the regional rental prices for irrigated and non-irrigated lands and , , and are mean 60 bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 mona aghabeygi, kamel louhichi, sergio gomez y paloma and standard deviation of own price elasticities of supply at country and agro-ecologic zone levels used as prior (jansson and heckelei, 2011) and δr,i is a scaling factor with δr,i= . the normalized squared deviations of dual values and of agro-ecological zone supply elasticities are weighted (ω) with the inverse number of administrative regions (i.e., 1/31) and the inverse number of agro-ecological zones (i.e., 1/5), respectively, to obtain a comparable weight with the first component of the hpd objective function. the prior for the own price supply elasticity at agroecological zone ( ) is defined as the own price supply elasticity at national level time the ratio of average production between agro-ecological zone and national level. this allow agro-ecological zone with low (high) average production to be more (less) elastic to price change compared to the national average. the endogenous variables of hpd problem defined in equations (9)-(17) are: (i) the dual values of land constraints, φr,m, (ii) the own and cross price elasticities of supply at regional (εr,i,i’), agro-ecological zone (εz,i,i’) and national (εi,i’) levels, (iii) the elements of the lower triangular cholesky decomposition related to bz,i,i’ parameters, lbz,i,i’, and (iv) the regional-specific behavioral parameters dr,i and qr,i,i’ (including the inverse matrix ). equations (10) and (11) represent the foc of the optimization model for crop activities and for land constraints, respectively. equations (12), (13) and (14) compute supply elasticities at regional, agro-ecological zone and national levels, respectively. equation (15) is the cholesky decomposition which ensures appropriate curvature properties of the estimated quadratic cost function (i.e., convex in activity levels), equation (16) calculates the region-specific quadratic parameters qr,i,i’ and equation (17) calculates its inverse . 2.3 data the primary data source used to parametrize rcp model are regional data from the iranian agriculture ministryjihad (imaj, 2016) for the three-years average around 2015 (2014, 2015 and 2016). imaj publish annual report on crop area and production in each region obtained from the aggregation of individual farm data collected through face-to-face survey. the information and communication technology centre of iranian agriculture ministry (ictcimaj) also use these individual farm data to derive input and output prices and quantities of crops in different regions as cost bank system (cbs). the cbs and imaj database provide detailed regional information for the five groups of crops, namely cereals (wheat, barley, corn, and rice), legumes (pea and lentil), vegetables (onion, potato and tomato), fruit (melon and cucumber), and industrial crops (cotton, canola and sugar beet). table 1 reports the statistical characteristics of the key variables for the 14 selected crops in rcp. these variables include total cultivated areas and total production for each crop as well as their yield, revenue, estimated fertilizer application rates, fertilizer subsidy, production costs (e.g., seed, pesticides, fertilizer, hired labor and water), gross income, estimated implicit costs/revenues and net income per unit of land, average across 31 regions and for the three-year average around 2015. 2.4 scenarios: layout and implementation as mentioned previously, apart from the pressure coming from the wto, there is an intensive ongoing debate about the effectiveness of the input subsidies in iran given their high, possibly unsustainable costs and the absence of credible empirical evidence on their impacts on agricultural productivity. therefore, a reduction or a total removal of these subsidies or their reallocation to only specific farm groups or to specific crop sectors are among the reform options that are currently under discussion in the country. in this regard, the aim of this paper is to simulate the impacts of two policy options: (i) a total removal of nitrogen fertilizer subsidy for all crops and all regions (abol scenario) and (ii) a reallocation of nitrogen fertilizer subsidy to only strategic crops (wheat, maize, and rice) while keeping the same subsidy budget (targ scenario). we are aware that this drastic scenario of a total removal of fertilizer subsidy is currently to a great extent unrealistic and cannot represent a prospective or even likely development; however, it might contribute to the on-going debate on their relevance and their legitimacy. keeping the subsidies (or reallocating all of them) for only strategic crops seems to be more realistic due to the high attention given by the government to these crops for political, economic and food security reasons, particularly under the various international sanctions. both scenarios are implemented and compared to a baseline scenario representing the business as usual (i.e., the baseline scenario is used for the counterfactual comparison of the simulated scenarios). 3. results and discussion in this section we examine whether and how the simulated fertilizer subsidy reform options affect land 61impacts of fertilizer subsidy reform options in iran: an assessment using a regional crop programming model bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 ta bl e 1. s ta tis tic al c ha ra ct er ist ic s o f t he k ey v ar ia bl es . c ro ps a re a un de r cu lti va tio n (1 00 0h a) pr od uc tio n (1 00 0 to n) yi el d (to n) re ve nu e (1 00 0 ir r/ ha ) fe rt ili ze r u se (k g/ ha ) fe rt ili ze r su bs id y (i rr /k g) n itr og en u se (k g/ ha ) pr od uc tio n c os t (i rr /h a) g ro ss in co m e (1 00 0i rr /h a) im pl ic it co st s/ re ve nu es (1 00 0i rr /h a) n et in co m e (1 00 0i rr /h a) c er ea ls w he at 58 94 .0 7 12 11 7. 70 2. 06 11 81 5. 04 30 2. 02 27 7. 76 19 4. 46 20 90 6. 21 -8 .8 1 42 .7 3 33 .9 2 ba rle y 17 39 .8 4 32 81 .6 6 1. 89 92 46 .7 8 17 1. 97 13 5. 86 11 0. 06 19 98 2. 51 -1 0. 60 44 .4 3 33 .8 3 m ai ze 17 3. 53 12 49 .7 0 7. 20 73 58 4. 91 67 7. 63 50 6. 43 44 1. 84 38 67 8. 80 35 .4 1 36 .1 3 71 .5 4 ri ce 33 2. 58 17 37 .1 7 5. 22 92 20 1. 17 11 27 .2 9 10 76 .5 3 69 5. 03 49 73 2. 81 43 .5 4 -2 6. 64 16 .9 0 le gu m es le nt il 13 4. 57 74 .7 1 0. 56 27 66 3. 63 5. 13 2. 91 3. 36 79 68 .6 9 19 .7 0 14 .0 7 33 .7 7 pe as 49 2. 56 23 6. 67 0. 48 70 45 .3 3 18 .4 9 20 .6 9 11 .4 5 79 24 .7 1 -0 .8 6 55 .0 1 54 .1 5 ve ge ta bl es to m at o 11 8. 76 48 78 .0 6 41 .0 8 13 90 39 .9 7 67 6. 35 96 1. 94 42 7. 57 88 65 6. 44 51 .3 5 5. 88 57 .2 3 po ta to 14 8. 45 47 26 .7 5 31 .8 4 17 54 94 .4 4 62 0. 01 79 4. 18 37 1. 17 11 00 89 .8 0 66 .2 0 16 .6 1 82 .8 1 o ni on 50 .1 6 18 61 .4 6 37 .1 1 12 61 22 .0 8 10 33 .7 7 18 53 .9 8 62 9. 38 98 89 1. 62 29 .0 8 19 .0 9 48 .1 7 fr ui t cu cu m be r 46 .2 5 10 07 .5 2 21 .7 8 23 36 10 .8 4 30 7. 87 27 1. 12 20 9. 55 73 46 2. 27 16 0. 42 -5 3. 94 10 6. 48 m el on 11 2. 39 30 99 .8 0 27 .5 8 10 04 23 .7 48 7. 34 84 1. 26 28 8. 78 55 58 1. 61 45 .6 8 -0 .9 5 44 .7 3 in du st ria l c ro ps c an ol a 60 .0 1 93 .6 3 1. 56 21 86 4. 07 42 5. 24 30 5. 52 25 9. 02 12 63 8. 04 9. 53 24 .9 3 34 .4 7 su ga r b ee t 10 0. 66 52 78 .9 0 52 .4 4 13 30 50 .2 58 7. 20 46 7. 32 35 0. 48 71 68 6. 40 61 .8 3 9. 54 71 .3 7 c ot to n 72 .0 9 16 6. 30 2. 31 65 60 0. 89 13 2. 61 86 .3 2 81 .2 0 45 81 0. 45 19 .8 8 22 .1 3 42 .0 1 m in 46 .2 5 74 .7 1 0. 48 23 36 10 .8 4 5. 13 2. 91 3. 36 79 24 .7 1 -1 0. 60 -5 3. 94 16 .9 0 m a x 58 94 .0 7 12 11 7. 70 52 .4 4 70 45 .3 3 11 27 .2 9 18 53 .9 8 69 5. 03 11 00 89 .8 0 16 0. 42 55 .0 1 10 6. 48 st d ev 15 64 .6 8 32 48 .3 8 18 .1 4 69 27 3. 66 34 5. 43 51 7. 20 21 1. 90 34 38 8. 40 43 .1 8 28 .7 0 23 .8 6 so ur ce : i c tc i m a j. th re eye ar s a ve ra ge a ro un d 20 15 (2 01 4, 2 01 5 an d 20 16 ) *n et in co m e eq ua ls to th e to ta l r ev en ue fr om th e sa le s of a gr ic ul tu ra l p ro du ct s pl us fe rt ili ze r su bs id ie s m in us th e ac co un tin g va ri ab le c os t o f p ro du ct io n ac tiv iti es p lu s im pl ic it re ven ue s/ co st s ( i.e ., pm p te rm s) . 62 bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 mona aghabeygi, kamel louhichi, sergio gomez y paloma allocation, nitrogen fertilizer application rate, production, agricultural income, and government budget in iran at both regional and national levels and compare their cost-effectiveness using the transfer efficiency index. before presenting simulation results it is important to notice that farmers may respond in three ways to a reduction or a removal of fertilizer subsidy: (i) extensive margin, that is, reallocation of acreage among crops by, for example, substituting more fertilizer-intensive crops with less fertilizer-intensive crops (i.e. acreage effects), (ii) intensive margin, that is, reducing fertilizer intensity per hectare for a given crop (i.e. yield effects), and (iii) land abandonment, that is, putting out of production land (i.e. land abandonment effects). with our models we tried to capture only the first two adjustments which represent the main opportunities for farmers to respond to shocks. land abandonment adjustment is excluded because agricultural utilized area is assumed to be fix in our model. 3.1 acreage, fertilizer intensity and yield effects 3.1.1 abol scenario the implementation of the abol scenario, based on a total removal of nitrogen fertilizer subsidy, would lead, as expected, to the reallocation of land from crop groups strongly dependent on fertilizer such as industrial crops, vegetables, and fruit to crop groups less dependent on fertilizer like legumes and cereals. as shown in table 2, the acreage of industrial crops, vegetables and fruit decreased by -1.72%, -1.47%, and -1.16%, while the acreage of legumes and cereals increases by +0.93 and +0.06%, respectively. these results are also confirmed while looking to individual crops. the acreage of crops strongly dependent on fertilizer such as rice, tomato and maize decreased by -5.75%, -2.54% and -1.46%, respectively, whereas the acreage of crops less dependent on fertilizer such as barley, peas and lentil increase by +9.56%, +1.02% and +0.63%, respectively. this finding is explained by the fact that fertilizer-intensive crops become less competitive with the removal of subsidy and, therefore, lose some of their areas in favor of less fertilizer-intensive crops. pishbahar and khodabakhshi (2015) in a study focusing on farmers of varamin area have found similar results showing a decrease in the acreage of maize and tomato with the removal of input subsidy. kohansal and ghorbani (2013) and shirmahi et al., (2014) have revealed, using estimated price elasticity for nitrate fertilizer, that removing nitrate subsidy would cause a remarkable land reallocation among crops. from this table it also appears that all crops experience a reduction in their fertilizer application rates when the nitrogen price increase with the removal of subsidy. the average reduction of fertiliser application rate across all crops is around -9.16%, ranging between -0.7 % and -18%. legumes are the most responsive to nitrogen price increase, in terms of fertiliser application rate. however, this response is small in absolute terms because legumes have relatively low fertiliser application rate in baseline. in the opposite, the response of fertiliser-intensive crop groups (e.g., industrial crops, vegetables, and fruit) is relatively low (less than 20%) but quite large in absolute terms. while comparing individual fertiliser-intensive crops (e.g., rice, onion, tomato, and maize), we found that their responses to nitrogen price increase are quite similar and close to -2.5%. the exception is maize where the percentage change in application rate seems to be very small (less than 1%), explained by the fact that the observed application rate for maize lies on the flatter proportion of the yield response curve. table ii also shows that the reduction of nitrogen application causes relatively drastic yield losses in nitrogen-intensive crop groups than in less nitrogenintensive crop groups. this clearly appears for legumes where a -48% decrease of fertiliser application rate causes a reduction of only -1.92% for yield, while a –1.84% decrease of fertiliser application rate for vegetable causes a reduction of its yield by -0.37%. however, given the relatively high yield of nitrogen-intensive crop groups their yield losses could be significant in absolute terms. appendix table a1 reports the reallocated area in each region as a result of the abol scenario. from this table it clearly appears that all regions seem to be affected by this scenario with different degree depending on their specialization. the largest reallocated area is observed in regions specialised in rice such as mazandaran, those specialised in industrial crops (mainly canola) like golestan and those specialised in vegetables (mainly tomato) like fars. golestan tend to be more affected because it is the first producer of canola with 14200 thousand hectares (around 25% of total canola area) and the second after mazandaran in cultivating rice with 59060 thousand hectares (around 10% of total rice land). this result is expected, as these three crops have the highest fertilisation rates and, therefore, a reduction of fertilisation application cause drastic losses in their yields and, thus, in their performances. regions specialised in maize such as kurdistan seem to be able to maintain such specialisation although its dependency on fertiliser. this means that maize remains competitive in these regions even with an increase of nitrogen fertiliser price. 63impacts of fertilizer subsidy reform options in iran: an assessment using a regional crop programming model bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 3.1.2 targ scenario paying nitrogen fertilizer subsidy to only strategic crops (“targ scenario”), namely wheat, maize and rice boosts their areas at the expense of non-target groups. as shown in table ii, the area devoted to cereals group increase by 0.13%, whereas the area dedicated to all other groups’ decline, reaching -1.69% for industrial crops. the percentage increase of strategic crops area is relatively small; however, measured in absolute terms, it is quite significant (about 57 thousand hectares) due to their large initial shares in the total area (table 1). looking at fertilization application rate change under targ scenario reported in table ii, we can see the same trend as for acreage change: an increase of fertiliser intensity for target crops and a decrease for the other crops. however, the magnitude of changes is quite different: the percentage change of fertiliser application is bigger than the percentage change of acreage, which is not surprising given that a large increase of crop area will be costlier due to rising marginal costs. the yield effect of the targ scenario seems to be limited which means that reducing fertiliser price for target crops, which are nitrogen-intensive crops, boost only marginally their yield. as predicted, the reallocation of fertiliser subsidies to only strategic crops stimulate their acreages in all regions (see table a1). regions specialised in target crops (i.e., with largest share of target crops) react relatively more rapidly to a nitrogen price decrease triggered by the targ scenario, in comparison to the other ones. for example, in the east azerbaijan, fars and kurdistan regions, where target crops area in baseline exceeds 70% of total cropland, the percentage increases are larger, in comparison to regions with small initial share (less than 30%). in these regions the land adjustment occurs, mainly at the expense of barley. for instance, in fars, the acreage of barley declines by -20% under the targ scenario and its share in total land drop down from 20% (121693 hectares) to 16% (97411 hectares). 3.2 production effects table ii shows the production effects of abol and targ scenarios. as can be seen from this table, the table 2. fertilizer application rate, acreage, production, yield, and income changes under abol and targ scenarios (% change relative to baseline). crop/ group/ scenario fertilization application rate acreage production yield average net income abol targ abol targ abol targ abol targ abol targ wheat -8.32 1.31 -2.38 0.90 -4.18 0.90 -1.94 0.00 -1.67 0.22 barley -18.15 3.65 9.56 -2.63 1.37 -1.06 -7.41 1.59 -0.51 -0.01 maize -0.72 0.15 -1.46 0.60 -1.73 0.67 -0.28 0.14 -0.39 0.10 rice -2.68 0.64 -5.75 0.76 -6.24 0.90 -0.38 0.19 10.51 -1.01 cereals -4.02 0.81 0.06 0.13 -3.22 0.53 -1.34 0.31 0.49 -0.02 lentil -11.61 -12.20 0.63 -0.38 0.26 -0.38 -1.79 0.00 1.27 -0.10 peas -58.60 -60.61 1.02 -0.16 -1.37 -2.40 -2.08 -2.08 -0.31 0.05 legumes -47.94 -49.63 0.93 -0.20 -0.97 -1.92 -1.92 -0.96 0.30 -0.01 tomato -1.86 -1.63 -2.54 -1.42 -2.79 -1.75 -0.27 -0.34 -0.47 -0.25 potato -0.97 -0.98 -0.71 -0.68 -0.79 -0.73 -0.06 -0.06 0.12 -1.80 onion -2.34 -2.33 -1.17 -1.39 -1.93 -2.03 -0.75 -0.65 -2.84 -1.41 vegetables -1.84 -1.77 -1.47 -1.07 -1.83 -1.37 -0.37 -0.36 -0.90 -1.02 cucumber -8.90 -8.72 -0.41 -0.38 -0.58 -0.51 -0.14 -0.09 -0.07 -0.16 melon -0.94 -0.95 -1.46 -1.72 -0.96 -1.03 0.51 0.69 8.05 -1.26 fruit -4.29 -4.22 -1.16 -1.33 -0.87 -0.90 0.22 0.34 2.33 -0.49 canola -1.78 -0.66 -3.44 -2.02 -3.69 -1.38 0.00 0.64 0.13 -0.39 sugar beet -2.25 -2.27 -0.87 -0.42 -0.85 -0.50 0.02 -0.08 0.39 -0.94 cotton -16.26 -14.10 -1.49 -3.19 -1.36 -3.40 0.00 -0.43 0.07 1.85 industrial crops -3.72 -3.06 -1.72 -1.69 -0.91 -0.60 0.02 -0.07 0.18 0.12 national -9.16 1.05 0.00 0.00 -2.61 -0.12 -0.23 -0.10 -076 -0.01 source: model results. 64 bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 mona aghabeygi, kamel louhichi, sergio gomez y paloma average production effects of abol and targ scenarios are estimated to be around -2.61% and -0.12%, respectively. the main production effects under the abol scenario are (i) a decrease of production for nitrogen intensive crop groups (e.g., vegetable decrease by -1.83%, industrial by -0.91%, and fruit by -0.87%) and (ii) an increase of production for less nitrogen intensive crop groups (e.g., legumes). these trends are also confirmed while looking to individual crops. production of crops less dependent on fertilizer such as barley and lentil increase whereas, whereas production of crops strongly dependent on fertilizer like rice, tomato and maize decrease ranging between -1% and -7%. these results are consistent with rahmani et al., (2011) who also found that increasing fertilizer price led to a decrease in the production of maize by -1.28% and cotton by -1.62%. under the targ scenario, the large positive effects in production are observed for the targeted crops, namely wheat, maize, and rice, ranging between +0.6% and +0.9%, while negative effects are experienced by less competitive crops such as cotton, tomato, and barley, with a production retraction of -3.4%, -2.4% and -1.06% respectively. at regional level, mazandaran by -31 %, golestan by -26% and kohkiloyeh by -20% show the highest decrease in production under the abol scenario. however, in the targ scenario production increased in mazandaran by +4% and bushehr by +2% (see table a2). as mentioned before, mazandaran and golestan are the most important regions in cultivation rice. these production effects are driven either by land reallocation (i.e., land substitution between crop groups), land productivity (i.e., yield effect) or both. to better understand the contribution of each driver, we decomposed the production effects into two effects using the logarithmic mean divisia index (lmdi) approach (ang, 2005): acreage effect (i.e., area) and yield effect (i.e., productivity): production= ×area=productivity×area (18) where area stands for cultivated area, therefore, production impacts are decomposed into productivity and area effects in an additive form as follows: ∆production=∆productivity+∆area (19) where ∆x=x(scenario)+x(baseline). the lmdi approach is used to calculate the above individual contributions. for example, the area effect is calculated as follows: (20) where prod-s and prod-b refer to production (in tons) under abol and targ scenarios and baseline respectively, in stand natural logarithm and area-s and area-b denote the cultivated area under abol and targ scenario and baseline, respectively. figure 1 reports the decomposition of production effects under abol and targ scenarios. from figure 1 it clearly appears that the acreage effect explains around 80% of production effect for vegetable, fruit, and industrial groups under both abol and targ scenarios. as an example, the -1.83% decrease of vegetable production under abol scenario is assumed to be a combined effect of yield (-0.37%) and area (-1.47%). the acreage effect accounts for 80.33% of the total change in vegetable production, while the remaining 19.67% is attributed to yield effect. given that the acreage effect explains most of the changes in production under abol and targ scenario for these three crop groups, it is not surprising to observe that their production and acreage effects are strongly correlated. on the other hand, for cereal and legumes groups, production changes under both abol and targ scenarios seem to be mainly driven by yield effect. for example, the 0.53% production increase of cereals is a result of a 75% yield change and 24 % of acreage change. 3.3 agricultural income effects the land and production effects presented previously dictate changes in agricultural income reported in table ii. before interpreting these changes, it is important to notice that agricultural income is equal to the maximized value of the objective function presented in equation (1) and, therefore, it is inclusive of all shadow costs. the impact of the removal of fertilizer subsidy (abol scenario) on agricultural income is rather small when aggregated at national level (less than 1% compared with the baseline), and the reallocation of fertilizer subsidy to only target crops has very limited effect on national agricultural income, compared to baseline. this is to say that due to the relatively low shares of subsided fertilizers in total fertilizer consumption and of fertilizer costs in total production costs, the removal or reallocation of fertilizer subsidy will not engender a large impact on agricultural income at national level. however, while looking deeper at the regional and crop levels the impact could be more pronounced and sometime with opposite sign. as shown in appendix table a3, income change under abol scenario is negative for all regions, which 65impacts of fertilizer subsidy reform options in iran: an assessment using a regional crop programming model bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 is not surprising, ranging between -0.35% and 5%. as expected, the most aff ected ones are those specialized in nitrogen-intensive crops such rice, tomato, and onion. this heterogeneous income effect is probably more noticeable when we go at lower levels such as sub-regional and farm levels. under targ scenario (table 6), the economic impact remains also small for the majority of the regions, ranging between -3% and 0.64%; nevertheless, there is an opposite eff ect: some regions loose and some regions gain from the reallocation of subsidy. regions specialized in target crops (wheat, maize, and rice) such as golestan gain from the reallocation, while other regions either they lose or almost no change compared to the current situation (i.e., khorasan and, south khorasan). 3.4 policy effi ciency in this section we use the results of the rcp model to compare welfare implications of the two simulated policies: current (i.e., baseline) vs. target (i.e., targ) fertiliser policies. for doing that, we use the abol scenario as counterfactual. in fact, the diff erence between the baseline and the abol scenario provides an estimation of the eff ect of the current policy (universal subsidies), while the diff erence between the targ and the abol scenarios gives an estimation of the alternative policy (target subsidies). from a cost/benefi t perspective, the most effi cient policy instrument is the one best at achieving the target benefi t at lowest cost. following brooks et al., (2011), we use the transfer effi ciency (te) index to compare the relative effi ciency of both policies. th is index is calculated as follow: (21) th e implementation of the target fertiliser policy (targ scenario) came at a total cost to taxpayers and consumers of about irr 2.83 billion and generates an increase of agricultural income of irr 2.64 billion, which means a te of 0.93. whereas the application of the universal fertiliser policy (baseline scenario) came at the total cost to taxpayers and consumers of irr 2.83 billion and generates an increase of agricultural income of irr 2.69 billion, which implies a te of 0.94. th e main conclusion coming out from this comparison is that, fi rst, the two policies are quite similar in terms of welfare implications and, second, both policies seem to be ineffi cient because their te are lower than one, knowing that all the administrative costs related to the implementation of this policy are not considered in our analysis. th ese results are in line with the fi nding of karimzadeh et al., (2006), mosavi et al., (2009), bakhshi et al., (2010) and rahmani et al., (2011) who also reported that fertiliser subsidy in iran has led to an ineffi cient use of nitrate fertilizer and, therefore, needs to be reviewed. -40% -20% 0% 20% 40% 60% 80% 100% abol targ abol targ abol targ abol targ abol targ cereals legumes vegetable fruit industrial la nd a nd y ie ld ch an ge s( % ) group/scenario % yield % land figure 1. production change decomposition under abol and targ scenarios. 66 bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 mona aghabeygi, kamel louhichi, sergio gomez y paloma 4. conclusion this paper presents the results of a comprehensive analysis aiming to assess the economic effects of the fertilizer subsidy programs currently implemented in iran and to compare its performance with an alternative program based on targeting strategic crops. two policy scenarios are simulated, and their results are compared to a baseline scenario representing the business as usual: a total removal of fertilizer subsidy “abol”, and a reallocation of fertilizer subsidy to only strategic crops (wheat, corn, and rice) “targ”. this analysis is done using a regional economic model which includes in total 14 crop activities and encompasses 31 administrative regions. this model is a collection of micro-economic models, working with exogenous prices, and calibrated against observed data on crop acreage, yields and exogenous supply elasticities. from a methodological perspective, the novelty of this paper lies in the employ of detailed regional modelling approach that allow for an adjustment of both crop acreage and input intensities and, therefore, to infer the effects of policies that are likely to have effects at the extensive and intensive margins. from a policy perspective, findings from this study reveal several exciting patterns. first, the effects of fertilizer subsidy removal are rather small at national level (less than 1%), although more pronounced at regional level, implying that a large share of farms do not use or use small quantity of fertilizer and, therefore, additional government efforts are needed to facilitate them access. second, the reallocation of fertilizer subsidy to only strategic crops under targ scenario boost their production and income, however, it increases disparity among regions and affects negatively national agricultural income and welfare compared to the current universal fertilizer policy. this imply that targeting strategic crops could not be the best solution and higher efficiency could be achieved by taking into consideration regional and farm heterogeneities. policymakers may gain from be cognizant of heterogeneity among regions/farms and that one policy may not fit all regions/farms. third, based on the result of the transfer efficiency (te) analysis, both target and universal simulated options seem to be inefficient, as their te indexes are lower than one, meaning that one irr injected in the iranian’s agriculture sector generate less than one irr. such results tend to confirm previous studies in the literature showing low productivity of iranian agriculture (bakhshi et al., 2010; and rahmani et al., 2011). our findings, however, need to be considered with some caution, on account of the model’s assumptions. first, output market prices are assumed to be exogenous. this implies that market feedback (output price changes) is not taken into account in the model. this could be an issue mainly when production change is quite high such as for cereals under abol scenario. accounting for price effects requires extending the supply model into a partial or a general equilibrium model which is clearly beyond the scope of the present paper. a relaxation of this assumption would dampen supply effects and partially offset the negative impacts of subsidy removal (abol scenario) given that a production decrease induced by higher fertilizer prices raises output prices which in turn enhances production. similar trend would be observed for non-target crops under targ scenario. second, due to data limitations the administrative costs related to the implementation of fertilizer policies are not considered. this may lead to an overestimation of the welfare impacts. a third potential caveat to our analysis is that we assume a fixed regional structure, implying that agricultural land extension/retraction (abandonment) in response to the simulated policies is not captured by the model. this may lead to an underestimation of the simulated impacts, mainly under abol scenario. a careful analysis of each of these limitations is, therefore, needed when examining simulation results. despite these limitations, our paper gives some insights on the potential role of fertilizer subsidy and provides useful recommendations to the policy making process aiming to enhance productivity and sustainability of the farming sector in iran. acknowledgements the authors are grateful to statistics and information technology office of iranian agriculture ministry jihad for granting access to regional data for crops. the authors would like to warmly thank prof. filippo arfini for his valuable and constructive suggestions during the planning and development of this research work. they also thank the two anonymous referees for their comments and suggestions that substantially improved the manuscript. the authors are solely responsible for the content of the paper. the views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the european commission. acknowledgements the authors are grateful to statistics and information technology office of iranian agriculture ministryjihad for granting access to regional data for crops. 67impacts of fertilizer subsidy reform options in iran: an assessment using a regional crop programming model bio-based and applied economics 11(1): 55-73, 2022 | e-issn 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(in persian) statistical centre of iran (sci) (2006). annual report for 2016. statistics and information technology office, ministry of agriculture, tehran. statistical centre of iran (sci) (2014). national agriculture census – 2014. https://www.amar.org.ir/english/ census-of-agriculture world bank (2016). world development indicators database. https://databank.worldbank.org/source/worlddevelopment-indicators. wright, b. d., (1995). goals and realities for farm policy. in d. a. sumner, (eds), agricultural policy reform in the united states, washington, d.c.: aei press, 9–44. 70 bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 mona aghabeygi, kamel louhichi, sergio gomez y paloma appendix table a1. regional acreage changes under abol and targ scenarios (% change relative to baseline). region/crop cereals legumes vegetables fruit industrial crops total abol targ abol targ abol targ abol targ abol targ abol targ alborz 0.05 0.01 0.00 -0.55 -0.38 0.00 -1.60 0.19 0.00 0.00 ardabil 0.06 0.03 -0.02 0.00 -0.60 -0.42 -0.20 -0.19 -1.40 -0.11 0.00 0.00 boshehr 0.01 0.01 0.00 -0.68 -0.71 -0.37 -0.39 0.00 0.00 0.00 chaharmahal 0.05 0.03 0.32 -0.10 -0.80 -0.36 -2.97 -0.84 -0.70 -0.06 0.00 0.00 east azarbaijan -0.43 0.10 4.86 -0.77 -1.28 -0.56 -14.15 2.13 -2.19 -0.54 0.00 0.00 elam 0.03 0.01 0.48 -0.06 0.00 0.00 -0.75 -0.06 -1.46 -0.01 0.00 0.00 esfahan 0.58 0.16 -8.97 -2.13 -2.51 -0.98 -4.42 -0.24 0.70 0.75 0.00 0.00 fars -0.70 0.31 29.08 -8.62 0.02 -0.80 1.56 -1.26 2.62 -1.40 0.00 0.00 gilan 0.01 0.03 0.08 -0.13 -0.20 -0.55 -0.14 -0.26 0.00 0.00 0.00 golestan 0.56 -0.02 -26.25 -4.72 -9.16 1.49 7.58 -2.82 -8.80 0.62 0.00 0.00 hamedan 0.03 0.05 0.34 -0.04 -0.62 -0.63 -0.59 -0.52 -0.43 -0.16 0.00 0.00 hormozgan 0.62 1.22 0.00 -0.53 -0.72 -0.05 -0.05 11.22 -23.83 0.00 0.00 kohkiloyeh 0.33 0.02 -5.24 -0.39 -18.87 -0.99 -9.15 -0.60 0.00 0.00 0.00 kerman 0.09 0.01 -1.91 -0.33 -0.60 -0.26 -0.18 -0.40 -1.57 1.02 0.00 0.00 kordestan 0.09 0.03 -0.45 0.06 -1.21 -1.21 -0.34 -0.31 1.25 -5.49 0.00 0.00 kermanshah -0.04 0.08 0.09 0.12 1.57 -5.53 0.00 -0.66 0.30 0.00 0.00 khouzestan 0.11 0.22 1.12 0.97 -0.87 -0.68 -0.51 -0.24 -2.61 -7.64 0.00 0.00 lorestan -0.02 0.03 0.14 -0.04 -0.39 -0.33 -0.16 -0.15 -0.41 -0.15 0.00 0.00 markazi 0.01 0.59 -0.20 -0.10 -0.14 0.00 -2.81 0.33 0.00 0.00 mazandaran 0.19 0.00 0.00 -1.49 -2.61 -31.24 3.77 -1.83 0.11 0.00 0.00 north khorasan 0.14 0.12 1.53 0.02 -1.74 -1.75 7.11 -1.14 -4.33 -1.33 0.00 0.00 qom -0.01 0.00 0.00 0.00 0.00 0.08 -0.07 0.00 0.00 qazvin 0.74 0.13 5.69 0.38 -9.34 -1.31 -19.03 -0.93 -12.41 -2.45 0.00 0.00 razavi khorasan 0.39 0.94 0.22 -0.42 -1.03 -3.81 -6.58 -9.25 -1.46 -4.72 0.00 0.00 sistan 0.05 0.12 -0.45 -0.99 -0.40 -0.58 -0.13 -0.32 0.02 -1.07 0.00 0.00 south khorasan 0.03 0.10 1.76 -8.69 -0.16 0.39 -0.65 -2.08 0.13 0.47 0.00 0.00 semnan 0.06 0.05 -0.69 -0.32 -0.41 -0.54 -0.12 0.22 -0.04 -0.03 0.00 0.00 tehran 0.03 0.01 0.00 -0.35 -0.16 -0.20 0.00 0.00 0.00 0.00 west azarbaijan -0.01 0.04 0.30 -0.04 -0.58 -0.51 -0.55 -0.30 -0.36 -0.22 0.00 0.00 yazd 0.05 0.06 0.00 -1.17 -1.27 -0.01 -0.17 0.00 0.00 0.00 zanjan 0.07 0.07 0.78 0.12 -2.10 -1.32 -1.79 -1.11 -3.93 -1.57 0.00 0.00 national 0.06 0.13 0.93 -0.20 -1.47 -1.07 -1.16 -1.33 -1.72 -1.69 0.00 0.00 source: model results. 71the role of energy on the price volatility of fruits and vegetables: evidence from turkey bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 appendix table a2. regional production changes under abol and targ scenarios (% change relative to baseline). region/crop cereals legumes vegetables fruit industrial crops total abol targ abol targ abol targ abol targ abol targ abol targ alborz -1.39 0.26 0.00 -0.54 -0.40 0.00 -2.15 -0.26 -1.23 0.12 ardabil -3.38 0.60 0.01 0.01 -0.69 -0.51 -0.29 -0.27 -1.58 -0.21 -1.95 0.04 boshehr -9.84 1.94 0.00 -0.88 -0.92 -0.41 -0.42 0.00 -4.88 0.66 chaharmahal -3.21 0.47 -0.06 -0.34 -0.85 -0.40 -3.83 -1.72 -0.98 -0.35 -1.79 -0.06 east azarbaijan -6.04 0.79 3.08 -1.88 -1.37 -0.64 -8.76 1.06 -3.45 -1.82 -3.82 0.09 elam -3.32 0.55 0.48 -0.06 0.00 -0.88 -0.08 -8.94 0.17 -2.56 0.33 esfahan -3.76 0.03 -7.95 -2.65 -2.96 -1.22 -5.03 -0.30 -0.05 0.37 -3.16 -0.64 fars -2.41 0.25 14.92 -6.21 -0.39 -1.30 1.34 -1.46 1.68 -1.02 -0.56 -0.70 gilan -1.09 0.24 0.06 -0.15 -0.20 -0.55 -0.16 -0.26 0.00 -0.49 -0.09 golestan -3.47 0.75 -26.26 -4.73 -11.63 1.90 7.45 -2.94 -9.91 0.37 -4.91 0.87 hamedan -2.55 0.43 -2.51 -2.87 -0.69 -0.70 -0.91 -0.84 -0.57 -0.29 -1.35 -0.27 hormozgan -0.54 1.44 0.00 -1.15 -1.35 -0.82 -0.82 2.85 -29.57 -1.02 -1.01 kohkiloyeh -5.89 0.37 -5.21 -0.44 -19.72 -1.93 -11.91 -0.96 0.00 -7.10 0.10 kerman -1.79 0.32 -4.01 -1.72 -0.62 -0.28 -0.24 -0.44 -3.00 -0.07 -1.25 0.03 kordestan -1.77 0.35 -1.66 -1.20 -1.44 -1.44 -0.43 -0.40 0.36 -0.81 -1.54 -0.26 kermanshah -2.57 1.06 -2.14 -1.05 1.36 -6.03 0.00 -0.77 0.07 -1.22 -0.91 khouzestan -1.85 0.37 1.12 0.97 -1.25 -1.07 -0.63 -0.34 -1.51 -0.48 -1.49 -0.12 lorestan -2.73 0.50 -2.50 -2.68 -0.41 -0.35 -0.30 -0.27 -0.50 -0.25 -1.52 -0.05 markazi -2.22 0.39 -3.36 -3.87 -0.19 -0.17 0.00 -1.96 -0.17 -1.90 0.26 mazandaran -1.83 0.30 0.00 0.00 -5.79 0.13 -31.25 3.76 -1.90 0.04 -2.67 0.38 north khorasan -5.40 0.44 0.77 -0.73 -2.00 -2.06 6.79 -1.44 -5.03 -1.38 -4.13 -0.66 qom -0.84 0.16 0.00 0.00 0.00 0.09 -0.10 -0.79 0.15 qazvin -11.04 -0.73 4.07 -1.15 -8.76 -1.33 -19.09 -1.00 -16.72 -2.02 -10.41 -1.15 razavi khorasan -4.60 1.45 -4.89 -5.55 -1.18 -4.06 -1.98 -4.77 -1.16 -1.46 -2.40 -1.46 sistan -5.66 1.19 -1.00 -1.69 -0.50 -0.70 -0.16 -0.36 -1.23 -2.31 -1.54 -0.05 south khorasan -3.98 1.23 1.76 -8.69 -6.08 -5.57 -1.06 -1.04 -0.51 -0.18 -2.53 0.16 semnan -2.28 0.46 -0.70 -0.32 -0.41 -0.49 -0.13 0.22 -0.31 -0.39 -0.97 -0.06 tehran -1.34 0.20 0.00 -0.40 -0.21 -0.25 -0.06 0.00 -0.91 0.03 west azarbaijan -3.47 0.62 -2.74 -3.08 -0.68 -0.62 -0.61 -0.30 -0.43 -0.29 -1.23 -0.13 yazd -1.27 0.33 0.00 -2.50 -2.57 -0.10 -0.26 0.00 -1.37 -0.59 zanjan -3.21 0.19 -0.56 -1.29 -2.24 -1.46 -1.85 -1.16 -4.09 -1.74 -2.53 -0.84 national -3.22 0.53 -0.97 -1.92 -1.83 -1.37 -0.87 -0.90 -0.91 -0.60 -2.61 -0.12 source: model results. 72 bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 mona aghabeygi, kamel louhichi, sergio gomez y paloma appendix table a3. regional agricultural income changes under abol and targ scenarios (% change relative to baseline). region/crop cereals legumes vegetables fruit industrial crops total abol targ abol targ abol targ abol targ abol targ abol targ alborz -0.61 0.10 -12.95 -11.00 0.14 0.07 -0.60 0.06 ardabil -0.27 0.05 -0.03 -0.42 -0.40 -0.22 -0.20 -0.27 -0.24 -0.44 -0.02 boshehr -1.23 0.33 -1.01 -0.95 -0.42 -0.34 -0.29 -0.01 chaharmahal -0.30 0.10 -0.27 -0.06 -0.46 -0.38 0.05 0.04 -0.34 -0.25 -0.56 -0.02 east azarbaijan -0.12 -0.06 -0.06 -0.02 -0.63 -0.60 0.11 0.11 0.55 0.50 -0.40 -0.01 elam -0.34 0.08 -0.08 0.02 0.00 -0.22 -0.18 0.07 0.06 -0.49 0.06 esfahan 5.19 -0.48 -29.76 -2.77 -3.28 -2.00 -1.45 -0.69 -0.68 -0.24 -1.43 -0.09 fars 14.64 -23.26 -9.76 2.40 -1.69 -1.14 -0.78 -0.47 -0.69 -0.29 -1.57 -0.10 gilan -0.59 0.09 -0.55 0.05 -0.13 0.01 -0.75 -0.51 -0.66 -0.02 golestan -30.67 5.72 0.05 -0.48 -0.37 0.64 0.54 -2.97 0.64 -4.83 0.64 hamedan -0.56 0.07 -0.26 0.02 -0.84 -0.73 -0.40 -0.32 -0.35 -0.26 -0.52 -0.08 hormozgan -3.16 -1.78 -3.73 -3.43 -3.08 -2.67 -1.42 -1.17 -4.15 -3.65 kohkiloyeh -3.14 0.32 1.46 0.10 0.42 0.38 10.43 8.14 -1.58 0.22 kerman -0.86 0.16 -14.27 -0.53 -0.41 -0.15 -0.11 2.24 0.89 -0.69 0.04 kordestan -1.70 0.42 -0.13 0.03 -4.62 -4.03 -0.23 -0.15 -0.41 -0.28 -0.44 0.01 kermanshah -0.27 0.08 -0.14 -0.25 -0.46 -0.43 0.00 -0.34 -0.28 -0.35 0.02 khouzestan -0.53 0.04 -0.30 0.05 -0.75 -0.67 -0.26 -0.21 -0.61 -7.13 -0.67 0.01 lorestan -0.46 0.09 -0.11 0.02 -0.39 -0.32 -0.20 -0.14 -0.40 -0.31 -0.29 0.01 markazi -0.47 0.09 -0.52 0.05 -117.64 -89.64 0.11 0.07 -0.62 0.09 mazandaran 0.37 -0.27 0.22 0.08 0.16 0.05 -4.00 -0.11 -2.03 0.38 north khorasan -0.41 0.18 -0.32 0.06 -2.53 -2.09 -0.40 -0.23 -1.14 -0.62 -0.80 0.02 qom -0.49 0.09 2.58 0.89 -0.47 0.07 qazvin 0.36 0.20 -0.33 -1.02 -0.88 -0.32 -0.18 -0.45 -0.30 -1.08 -0.02 razavi khorasan -0.69 -0.04 -0.38 -0.19 -15.75 -14.54 2.26 2.33 -0.93 -0.73 -0.70 -0.08 sistan 0.70 -0.05 -1.54 -0.52 -1.26 -0.84 -0.45 -0.23 -0.79 -0.28 -1.53 -0.28 south khorasan -1.01 -0.05 -22.51 -20.38 -0.31 -0.29 -3.56 -2.27 -0.55 -0.52 -0.90 -0.08 semnan -0.83 0.15 -0.28 0.03 -0.52 -0.32 -0.30 -0.12 -0.39 -0.15 -0.85 -0.01 tehran -0.71 0.10 -0.60 -0.41 -0.24 -0.12 -0.72 0.04 west azarbaijan -0.85 0.13 0.51 -0.07 -0.55 -0.52 -0.26 -0.23 -0.23 -0.20 -0.38 -0.03 yazd -1.12 0.03 -3.34 -2.78 -1.19 -0.82 -1.49 -0.20 zanjan -1.94 -0.02 -0.08 0.01 -0.65 -0.60 -0.27 -0.22 -0.34 -0.28 -0.50 -0.03 national 0.49 -0.02 0.30 -0.01 -0.90 -1.02 2.33 0.18 0.12 -076 -0.01 source: model results. 73the role of energy on the price volatility of fruits and vegetables: evidence from turkey bio-based and applied economics 11(1): 55-73, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10981 appendix table a4. regional agricultural cultivated area (1000 ha) and production (1000 t). region/crop cereals legumes vegetables fruit industrial crops total area prod area prod area prod area prod area prod area prod alborz 18.96 85.65 0.64 21.30 0.39 1.06 20.01 108.02 ardabil 469.41 863.32 33.16 19.42 25.61 840.94 2.48 77.50 7.43 15.70 538.11 1816.90 boshehr 121.69 125.82 0.79 18.31 3.14 123.35 125.64 267.49 chaharmahal 94.8 173.49 2.78 2.44 6.15 219.08 0.03 1.29 1.2 47.69 104.98 444.02 east azarbaijan 510.51 828.19 58.51 36.87 19.26 741.71 2.45 47.21 1.97 2.98 592.72 1656.98 elam 195.78 376.31 9.86 6.48 0 0.00 7.12 190.12 3.55 8.28 216.32 581.20 esfahan 138.94 466.83 3.18 1.90 18.76 696.11 1.8 52.41 3.9 80.13 166.6 1297.41 fars 536.92 1706.22 9.51 8.20 29.87 1327.49 18.48 733.72 27.5 620.26 622.31 4395.91 gilan 16.77 21.08 0.82 0.57 0.05 1.16 1.64 35.66 19.3 58.48 golestan 529.17 1583.54 0.78 0.66 12.62 393.84 6.62 49.78 24.1 44.58 573.31 2072.42 hamedan 490.24 841.32 20.84 10.64 29.25 1110.40 4.41 143.18 8.59 305.26 553.35 2410.82 hormozgan 18.54 81.69 24.66 719.85 12.11 250.88 0.18 0.10 55.51 1052.53 kohkiloyeh 155.67 221.24 6.4 5.68 0.24 4.98 1.44 44.77 163.78 276.68 kerman 79.96 325.08 0.88 1.15 3.43 101.87 4.28 122.91 1.74 3.47 90.31 554.50 kordestan 603.16 831.53 98.28 30.30 11.87 374.73 2.4 45.87 1.57 53.26 717.29 1335.71 kermanshah 603.46 1264.86 135.54 60.41 11.91 592.79 0 0.00 14.27 549.71 765.19 2467.78 khouzestan 628.27 1975.09 1.1 0.77 23.52 776.63 21.4 599.42 15.56 263.93 689.87 3615.86 lorestan 363.75 619.61 110.34 64.58 7.58 227.68 11.33 264.32 6.51 242.27 499.52 1418.47 markazi 258.48 539.49 8.04 3.61 3.71 104.73 1.47 26.48 271.71 674.32 mazandaran 309.01 1418.05 0 0.00 1.92 43.55 1.52 37.15 4.74 6.31 317.21 1505.07 north khorasan 211.6 354.23 13.98 6.76 6.37 207.13 0.57 8.19 10.04 125.75 242.58 702.09 qom 31.76 104.67 2.19 5.11 33.95 109.79 qazvin 200.95 493.04 7.55 3.56 12.56 669.46 1.16 25.01 4.27 99.98 226.51 1291.06 razavi khorasan 477.59 1069.87 9.81 3.44 22.8 823.85 15.28 290.13 45.59 994.63 571.1 3181.95 sistan 105.9 236.45 0.43 0.47 6.73 172.76 22.98 573.91 1.27 1.83 137.32 985.43 south khorasan 43.17 103.39 0.08 0.02 0.33 5.64 3.71 52.41 9.1 46.74 56.41 208.22 semnan 56.08 149.93 1.47 0.65 5.1 120.91 2.33 59.94 4.75 119.35 69.75 450.81 tehran 77.35 310.64 5.28 200.21 2.04 50.08 0 0.00 84.68 560.94 west azarbaijan 426.04 739.55 68.44 34.19 9.52 317.49 2.37 65.32 30.4 1873.51 536.79 3030.09 yazd 20.95 68.39 0.93 36.57 0.99 27.51 22.88 132.48 zanjan 344.96 407.51 25.24 8.50 15.77 594.95 4.4 135.16 0.35 0.33 390.74 1146.48 national 8139.84 18386.08 627.02 311.27 317.23 11466.12 158.48 4107.2 232.63 5539 9475.75 39809.91 source: ictcimaj. three-years average around 2015 (2014, 2015 and 2016). bio -based and a ppl ied economics bae bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-12206 copyright: © 2022 a. bonfiglio, c. abitabile, r. henke. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: a. bonfiglio, c. abitabile, r. henke (2022). a choice model-based analysis of diversification in organic and conventional farms. bio-based and applied economics 11(2): 131-146. doi: 10.36253/bae-12206 received: october 21, 2021 accepted: june 15, 2022 published: august 30, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: simone cerroni, meri raggi. orcid ab: 0000-0001-5249-1217 ca: 0000-0001-7159-5871 rh: 0000-0003-1677-0387 paper presented at the 10th aieaa conference a choice model-based analysis of diversification in organic and conventional farms andrea bonfiglio*, carla abitabile, roberto henke research centre for agricultural policies and bioeconomy, crea – council for agricultural research and economics, italy * corresponding author. e-mail: andrea.bonfiglio@crea.gov.it abstract. diversification is a polymorphic strategy to increase agricultural income and reduce the risks deriving from the surrounding environment. this strategy can also be successfully adopted in the context of organic farming. however, there is a lack of confirmation in this regard given the scarcity of studies that explicitly focus on diversification in organic farms. the objective of this paper is to analyse the influence of some territorial, socio-economic, and political factors on the probability of diversifying in both organic and conventional farms. to this aim, multinomial and binary logit models are applied to the italian case. results suggest that on-farm diversification requires specific competences and adequate organization. however, the reasons for diversifying differ depending on the production model. in conventional farming, farmers diversify to achieve income levels comparable with those of a more competitive agriculture. conversely, for organic farmers, diversification represents an integrated part of the production model to take advantage of synergies between organic production and diversification. from these results, some policy implications are drawn. keywords: on-farm diversification, common agricultural policy, organic farming, conventional farming, multinomial logit model. jel codes: c25, q12, q18. 1. introduction farmers can use different strategies to increase and stabilize income and reduce the risks deriving from external pressures and changes in the socioeconomic context. as a prevention strategy, they can diversify their sources of income to spread the risk over more activities (salvioni et al., 2020). diversification is a polymorphic strategy that can be expressed both inside and outside the farm through several multifunctional directions which can be broadly classified as deepening, broadening and re-grounding (van der ploeg and roep, 2003). it involves that one or more farm inputs are partially diverted from agricultural production: (a) within the same agri-food chain, to expand products range, quality and value or to shorten the length of the supply chain (deepening); (b) to produce other types of goods and services, such as hospitality, restoration, welfare and environmental services (broadening); (c) outside the primary sector, to integrate agricultural income (re-grounding). http://creativecommons.org/licenses/by/4.0/legalcode 132 bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 andrea bonfiglio, carla abitabile, roberto henke the potential of diversification is recognized both for farms, especially for family ones, and for rural areas, as evidenced by the specific support granted at the european level by the common agricultural policy (cap), specifically the rural development policy (rdp) (european parliament, 2016). this strategy not only can be successfully adopted in the context of organic farming but might also provide a comparative advantage over conventional farms that diversify by leveraging the willingness of consumers to pay higher prices for products and services provided by organic farms. however, there is a lack of confirmation in this regard given the scarcity of studies that explicitly focus on diversification in organic farms. this is because, in the wide stream of literature on multifunctionality and diversification, organic farming is commonly considered as a deepening strategy of conventional farms and is analysed as one of the factors explaining diversification (salvioni et al., 2009; rivaroli et al., 2017; dries et al., 2012). nevertheless, organic farming is a specific farm model, which brings about a rethinking of the management of the whole farm and its relations with the “outside world”, inspired by principles of sustainability (luttikholt, 2007). chemically synthesised inputs are strictly limited and replaced with inputs of natural origin. furthermore, techniques that prevent pollution, improve product quality, increase animal welfare standards, and ensure a soil ecology that retains nutrients and biodiversity are introduced. in this way, organic farming carries out a dual and complex function related to both the market and the production of public goods, in accordance with the changing consumers’ preferences (regulation ec no 2018/848). this change is also reflected in the growth and in spread of organic farming. focusing on the european union context, according to eurostat statistics, from 2012 to 2019, organic area, including that under conversion, increased by 46%, reaching a share of around 9% of 2016 total utilised agricultural area. italy, with 16% of organic area, is among the countries with the highest share of agricultural area devoted to organic production and with the highest growth rate (+70%). for all these reasons, organic farming cannot be considered as a mere option of diversification, but a unique model of production as opposed to the dominant model of conventional agriculture, which is taking increasing importance especially in some european countries such as italy. there is a wide literature analysing the determinants and the theoretical foundations of the process of income diversification (boncinelli et al., 2018). however, to the authors’ knowledge, there is no research work that focuses on the differences between organic and conventional farmers concerning the reasons that lead to diversification. the knowledge of factors affecting the choice of diversification in different farm models can be helpful for two main reasons. firstly, it contributes to verifying the hypothesis that the decision of diversifying is a necessity related to income volatility and lower levels of competitiveness, which push farmers to seek alternative opportunities to traditional activities in order to increase and stabilize income. in this respect, it may help policy makers to better define policy instruments. if the reasons explaining diversification vary according to the type of farms, policies can be usefully differentiated and better targeted, therefore increasing their effectiveness. secondly, it can contribute to better assessing policy effectiveness. in fact, a certain sensitivity of farmers to policy support can be a signal of effectiveness of policy instruments in favour of diversification. however, if this were confirmed also for organic farms, i.e., organic farms diversify thanks to the support to diversification, there could be indirect implications related to the effectiveness of policy supporting organic farming, which could be further investigated. this policy is aimed at incentivising organic farming by payments that should cover the higher costs that the adoption of organic practices brings about in comparison with conventional farming. in consideration of the higher prices paid by consumers for organic products, hence the potentially higher revenues for organic farms, if farms, which benefit from policy support for adopting organic practices, diversify by using support for diversification, this could mean that the payments aimed at encouraging organic farming are not sufficient to cover the higher costs, thus forcing organic farms to diversify by activating the related policy tools. the objective of this paper is to assess the differences between organic and conventional farmers in the choice of on-farm diversification. more precisely, the aim is to analyse the influence of territorial, socio-economic, and political factors on the probability of diversifying in these two types of farmers. the main novelty lies in an unconventional approach to diversification where organic farming is not analysed as a mere strategy of diversification but as a distinct entrepreneurial model that may have different motivations leading to a differentiated policy approach. for the purposes of this study, logit models are adopted. logistic regression analysis is widely used in several disciplines to investigate the relationship between binary or ordinal response probability and explanatory variables. multinomial logistic regression generalizes logistic regression to problems with more than two possible discrete outcomes. this kind of models have been already applied to study the phenomenon of diversification in agriculture (i.e., meraner et al., 2015; vik and mcelwee, 2011). a multinomial logit model is first 133a choice model-based analysis of diversification in organic and conventional farms bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 applied to compare organic and conventional farmers who diversify with farmers with no diversification strategies. this model gives the possibility of directly comparing two distinct groups of farmers relative to a base group. a logistic model is then applied only to farmers who diversify, in order to investigate the effects of specific factors affecting diversification, particularly policies in favour of diversification. this analysis is carried out by using the farm accountancy data network (fadn) sample of italian farmers for the period 2014-2018. the remaining of this paper is organized as follows. section 2 provides an overview of the existing literature on the main determinants of on-farm diversification and on the potential synergies deriving from combining organic production with diversification. section 3 illustrates the methodology, the variables and the data used. sections 4 and 5 present and discuss the results of this analysis, respectively. section 6 provides some concluding remarks and policy implications. 2. on-farm diversification and organic farming on-farm income diversification in agriculture roots in the multifunctional role of agriculture (henke and vanni, 2017; meraner et al., 2015; van huylenbroeck et al., 2007). brought in vogue at the time of agenda 2000 to legitimate the public support to the european model of agriculture, multifunctionality has become the key to a renovated role of agriculture and rural areas in the european and other developed contexts. on-farm diversification is practical application of multifunctionality through which new functions of production in agriculture complement, and sometimes compete with, the main one related to food production, especially in terms of inputs such as land, family labour and capital. deep and ongoing environmental and economic changes have enhanced the interest in on-farm diversification, by reallocating production factors towards new non-agricultural activities. the reasons that lead farmers to diversify have been widely investigated in literature. traditionally, economic survival and occupation strategies have been the main drivers of off-farm diversification. however, in on-farm diversification, several factors play a role in the decision to diversify. most are related to farmer characteristics, such as level of education (mcelwee and bosworth, 2010; boncinelli et al., 2017,2018; khanal, 2020) and age (barbieri and mahoney, 2009; joo et al., 2013; boncinelli et al., 2018; meraner et al., 2015); farm characteristics, such as farm size (mcnamara and weiss, 2005; ilbery, 1991; mcnally, 2001; garcía-arias et al., 2015; bartolini et al., 2014; boncinelli et al., 2018; dries et al., 2012), productive specialization and location (dries et al., 2012; bartolini et al., 2014; rivaroli et al., 2017); and policy support (bartolini et al., 2014). however, studies do not always reach unanimous conclusions on the factors that affect farm diversification and how they act. for instance, joo et al. (2013) show that older farmers are more likely to participate in agritourism while barbieri and mahoney (2009) suggest that young farmers have a longer-term view that pushes them to diversify. according to boncinelli et al. (2018), younger and older farmers have the same behaviour in relation to diversification. it is also interesting to find different results about policy in literature despite the existence of rural development instruments specifically conceived to support farm diversification. while, for some studies, policy is ineffective or produces weak effects (boncinelli et al., 2017,2018), for others, both cap pillars positively influence farm diversification (bartolini et al., 2014). a reason that could explain contrasting results is that research on diversification mostly analyses organic and conventional farms jointly and considers organic farming as a strategy of on-farm diversification. this type of analysis is founded on the idea that organic production is a secondary function that farms introduce to expand their business portfolio, as they do when they decide to process products and sell them directly. this approach can be valid if farms implement the organic method only on a part of total production, but it is less appropriate where this choice, which involves an increasing number of farms, concerns the whole farm. as a consequence, studies that specifically analyse diversification in organic farms are fewer, even though results highlight the relevance of such a combination. frederiksen and langer (2008) show that half of danish organic farms engage in other farm-based activities, especially direct sales, of which a half is of some or major economic importance. they conclude that onfarm diversification should not be simply considered as a pathway away from agriculture but an integrated part of organic farming strategies. david et al. (2010) investigate the adaptive capacity of organic farms that adopt diversification strategies. they analyse the evolution of some organic farms in the southeast of france over a 15-year period, monitoring farm performance and farmers’ strategy. their results show that on and off-farm diversification contribute significantly to farm viability. aubert and enjolras (2016), using an econometric model with simultaneous equations based on data from the 2010 census of french farms, demonstrate that farmers specialised in winegrowing and arboriculture who adopt organic farming label are more likely to sell their produce through short food supply chains. as for the italian 134 bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 andrea bonfiglio, carla abitabile, roberto henke context, the choice to diversify appears more relevant in the organic sector than in the entire agricultural sector. for instance, dries et al. (2012), by a multivariate probit model applied to 2006 data from italian fadn, demonstrate that there is complementarity between agricultural diversification activities, such as organic farming, and the structural ones, such as direct sales or agritourism. bartolini et al. (2014) show a greater probability of diversification for cases of organic management in the tuscany region, due to the synergies between different diversification strategies. marongiu and cesaro (2017), by applying a logistic regression model to the italian fadn data for the period 2013-2015, reveal the existence of a positive correlation between participation in food quality systems, such as organic farming, and the presence of related activities in farms specialized in permanent crops and dairy production. khanal et al. (2019) confirm the existence of correlations between agritourism and organic diversification strategies for us farmers due to possible synergies between them and warn that the estimates produced by choice models could be biased if these correlations were not taken into account. by analysing the willingness to pay for a designated farm holiday stay in an italian region of trentino alto-adige, sidali et al., (2019) show that this complementarity also gives a comparative advantage in that the combination of organic farming and farm stay operations ensures a higher accommodation price than what conventional farms offering only hospitality are able to obtain. the studies that specifically analyse the determinants of diversification in organic farmers are even fewer. zander (2008), based on a survey conducted on a sample of successful organic farms in germany, concludes that an important motivation for organic farmers to opt for the vertical integration is to keep the value added of their products on farm. moreover, they give evidence that farmers who diversify tend to be larger in order to achieve good market conditions and that the availability of high skills is a precondition for successful diversification. weltin et al. (2017) use a survey of over 2 thousand farms from eleven european regions in order to investigate differences regarding the willingness to diversify in the future. they find that farm households with organic production led by young farmers are most likely to diversify activities, particularly on-farm. 3. materials and methods 3.1 the model the model used is a multinomial logit model where a farmer makes a choice among three unordered alternatives: 1) no diversification; 2) conventional production and diversification; 3) organic production and diversification. farmer i’s utility derived from choice alternative j is: uij = x’iβj + εij i = 1,…, n; j = 1,…,j (1) where j = 3 is the number of possible alternatives, n is the number of farmers, x’i is a row vector of case-specific variables that are supposed to influence this utility, βj is a vector of coefficients to be estimated, εij are random errors which are assumed to be independent and identically distributed across alternatives. this assumption is plausible since the alternatives analysed are not close substitutes and can therefore be assumed to be distinct (mcfadden, 1974). let yij be the dependent variable with j outcomes numbered from 1 to j. after imposing the restriction β1 = 0, which allows the model to be identified, the choice probability is defined by the following multinomial logit framework: (2) (3) estimation of the model is obtained by maximising the following log-likelihood function: (4) where i(yi = j) is the indicator function of the farmer’s choice, which takes 1 if yi = j and 0 otherwise. choice (1) is used as a base outcome. therefore, the probability that either an organic or a conventional farmer diversifies is calculated relatively to that of a farmer who does not diversify. in this way, the effects of determinants on the choice of diversification are assessed by keeping organic farmers and conventional farmers separate. in addition, to analyse the different influence of specific characteristics of farmers who diversify (specifically, policy in favour of diversification, which does not concern farmers with no diversification strategies), a logistic model is also applied to a subset composed of farmers with diversification strategies where the binary response is the probability that organic farmers diversify. this allows us to further investigate the differences between the two different types of farmers by assessing the effects of specific factors on organic farmers relative to that produced on conventional farmers. 135a choice model-based analysis of diversification in organic and conventional farms bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 while in binary models coefficients βj are easily interpretable, in multinomial logit models these coefficients show how predictors relate to the probability of observing a specific category relative to a base category and, therefore, indicate neither the direction nor the size of effects of predictors on the probability that an alternative is chosen (wulff, 2015). to provide this information, average marginal effects are thus calculated. marginal effects are the slope of the prediction function at a given value of the explanatory variable and inform about the change in predicted probabilities due to a change in a given predictor. for a continuous independent variable, the marginal effect related to coefficient k, farmer i and choice j is derived as follows: (5) where is a probability weighted average of the coefficients for different choice combinations, βkj. the average marginal effect is calculated over all the observations. for dummy variables, the marginal effect is defined by the discrete change in individual probabilities evaluated at the alternative values of the dummy (1 and 0). 3.2 the variables and the dataset used as already specified, in the multinomial logit model the dependent variable is represented by the following categories: farms with no diversification strategies, which are used a base outcome, and two other options represented by farms that diversify and produce conventionally and farms that diversify and cultivate organically. the latter are used as a dependent variable in the logit model, which implies that organic farmers are compared with conventional farmers, both with diversification strategies. as independent variables, a set of socio-economic and political factors that are supposed to affect the probability of diversification are analysed. the selection of these variables depends on the main determinants of diversification that have been analysed in literature (see section 2) and on data availability. the variables taken into consideration refer to both farmer and farm characteristics as well as policy support. as regards farmer characteristics, education and age are analysed while, with reference to farm features, altitude, geographical localization, economic size and productive specialization are investigated. the level of education is represented by two binary variables. they are one if the farmer has a high level and a medium level of education, respectively. they are zero when the level of education is low. age is modelled by a dummy indicating if farmers are young according to the threshold set by the cap for accessing specific measures in favour of farmers with no more than 40 years of age. altitude is represented by two binary variables that take unitary value if farms are localized in flat areas and in hills, respectively, while they are zero if farms are located in the mountains. geographical localization is described by a dummy that takes one if the farm is localized in central-northern italy and zero if it is in southern italy. economic size is represented by a dummy that takes value of one if the farm is large. it is zero in the case of small and medium-sized farms. following a eurostat (2016) classification, farms are identified as large if output is equal or higher than €25 thousand. as a measure of output, an average of gross marketable production (gmp) related to crops and livestock is calculated. productive specialization is measured by four dummies related to arable crops, horticulture, livestock and permanent crops, respectively. zero values indicate mixed specialization. finally, policy is analysed by including cap support per hectare related to the first pillar and the rdp support in favour of diversification, expressed as a binary variable, which takes one if a farm received support. the data used come from the italian fadn. the sample analysed is composed of 51450 observations in the period 2014-2018. in this way, the effects of 20142020 cap policy on the choice of diversification are analysed. 2018 corresponds with the latest year available. observations are represented by different farms observed in one or more years. since the farms that are present within fadn are subject to be changed over years, the analysis is conducted on pooled data. to take account of unobserved effects in different periods, dummies for the years 2015 to 2018 are added. if they are all zero, they indicate the year 2014. the italian fadn offers several data that can be used to identify farm diversification strategies (table 1). these data refer to processing, direct farm sales, quality certification, agritourism, supply of mechanical, environmental, recreational and educational services, and other services such as rental of non-agricultural equipment and rooms for courses and seminars, craft and educational activities. recalling the well-known and commonly used classification described in van der ploeg and roep (2003), processing, direct farm sales and quality certification can be included within the multifunctional direction of deepening, while the others are the result of broadening.1 the presence of at least one 1 van der ploeg and roep (2003) describe three types of multifunctional directions for farms: deepening, broadening and re-grounding. deepening refers to all agricultural activities that are transformed, expand136 bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 andrea bonfiglio, carla abitabile, roberto henke of the possible diversification activities can be inferred from economic information, data on policy support, list of certified products and processes and list of nonagricultural activities. the italian fadn also allows the distinction between organic and conventional farms, indicating farms that are classified as organic.2 by combining information on the presence of diversification with that relating to organic certification, it is possible to distinguish farms among organic farms that diversify, conventional farms that diversify and farms of any type without diversification strategies. table 2 shows the distribution of organic, conventional and all farms by kind of multifunctional direction. as can be noted, most farms undertake the direction of deepening, specifically processing. a small percentage of farms is oriented to broadening and an even smaller share combines both strategies. the differences between organic and conventional farms are not marked. however, organic farms are more oriented to deepening than broadening. moreover, among organic farms with deepening, a higher share of farms process and sell products directly in comparison with conventional farms. table 3 shows some descriptive statistics of the sample used by logit models. the observations related to organic farms that diversify represent 12% of the entire sample and 74% of those related to all organic farms. most are in southern italy (60%), operate in hills (60%) and have a medium-high level of education (about 70%). moreover, they are prevalently specialized in permanent crops (54%) and received on average about 470 €/ha from the first pillar of the cap. only 14% of the observations applied for rdp measures in favour of diversification. compared to organic farms, conventional farms that diversify are relatively lower (58% of observations related to all conventional farms), are mainly localized in central-northern italy (over 60% of observations), do ed and/or relinked to other players and agencies in order to deliver products that entail more value added per unit precisely because they fit better with the demands in society at large. broadening refers to the development of non-agricultural activities that enlarge the income flows of the farm enterprise, while they simultaneously imply the delivery of goods and services society is willing to pay for. re-grounding occurs when the farm enterprise is grounded in a new or different set of resources and/or involved in new patterns of resource use. it refers to two specific fields of activity: pluri-activity and farming economically. through pluri-activity the farm enterprise is partly built on off-farm income while farming economically is a strategy that raises income at farm enterprise level by reducing the use of external inputs and increasing the efficiency in the use of available internal inputs. 2 in the italian fadn, a farm is classified as organic if it is certified organic as a whole, there is at least one organic product or there is one process that is carried out with organic methods. this means that there could be mixed farms that combine organic and conventional farming. in this study, these farms are treated as organic. not show a prevalently higher level of education and are less specialized in permanent crops (40%). they received on average 325 €/ha from the first pillar of the cap and nearly 30% of observations were supported by the second pillar. 4. results table 4 shows the results related to the multinomial logit model which assesses the effects of a selection of explanatory variables on the probability of diversification in organic and conventional farmers compared with farms with no diversification strategies. the significance associated with the likelihood-ratio test indicates that the model can be reasonably used to explain the reasons that lead farmers to diversify. mcfadden’s pseudo-r2 can also be considered as acceptable.3 the coefficients related to localization show that there is a negative and significant relationship between the localization of organic farmers in central-northern italy and the relative probability of diversifying, on the contrary, this relationship is positive in the case of conventional farmers. this means that organic farmers who diversify are more likely to localise in southern italy while it is more probable to find conventional farmers who diversify in central-northern italy than farmers who do not diversify. in relation to altitude, for both organic and conventional farmers the relationship between localization in flat areas and relative probability is negative while the one related to localization in hills is positive. therefore, in both cases there is a higher probability that these farms localize in hills and do not localize in flat areas in comparison with farms that do not diversify. however, this probability appears to be slightly higher in organic farms. as regards age, the coefficient associated with organic farms is positive and significant. this implies that organic farms that diversify are more likely to be younger compared to farms that do not diversify. on the contrary, the coefficient related to conventional farms is non-significant and no conclusion can thus be drawn. 3 mcfadden’s pseudo-r2 tends to be considerably lower than the r2 index and should not be judged by the standards for a “good fit” in ordinary regression analysis. in fact, values of 0.2 to 0.4 represent an excellent fit (mcfadden, 1978). therefore, a value of 0.11 can be considered as acceptable. in any case, it should be stressed that the objective of the paper is to assess the influence of a battery of variables on the decision to diversify, focusing on those which are most analysed in literature and are of particular interest for this study. the search for further variables that can help to increase the goodness-of-fit of the model can be a future research direction. 137a choice model-based analysis of diversification in organic and conventional farms bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 table 1. fadn variables used to identify on-farm diversification. fadn table variable direction economic accounts gross marketable production – processing deepening economic accounts gross marketable production – direct sales deepening policy measure 3.1 – new participation in quality schemes deepening policy measure 4.2 – investments for processing/marketing and development deepening policy measure 4.4 – non-productive investments broadening policy measure 8.1 – afforestation/creation of woodland broadening policy measure 8.2 – establishment and maintenance of agro-forestry systems broadening policy measure 8.6 – investments in processing and marketing of forest products deepening policy measure 10.1 – agri-environment-climate commitments broadening policy measure 10.2 – genetic resources in agriculture broadening policy measure 15.1 – forest-environmental and climate commitments broadening policy measure 132 – participation of farmers in food quality schemes deepening policy measure 214 – agri-environment payments broadening policy measure 221 – first afforestation of agricultural land broadening policy measure 222 – first establishment of agroforestry systems broadening policy measure 223 – first afforestation of non-agricultural land broadening policy measure 225 – forest-environment payments broadening related activities agritourism broadening related activities craft activities broadening related activities educational activities broadening related activities mechanical services broadening related activities production of renewable energy broadening related activities recreational services broadening related activities rental of non-agricultural equipment broadening related activities rental of rooms for courses and seminars broadening related activities other services broadening certifications community eco-management and audit scheme (emas) broadening certifications environmental management system broadening certifications food safety management system deepening certifications integrated certified production broadening certifications intercompany traceability deepening certifications management system for hygienic self-control of products and processes deepening certifications national zootechnical quality system deepening certifications protected designations of origin deepening certifications protected geographical indication deepening certifications quality management system deepening certifications reduced environmental impact broadening certifications superior quality label (i.e. gmo free) deepening certifications traceability of the agri-food chain deepening certifications traditional agri-food product registered deepening certifications traditional specialities guaranteed deepening note: during the period 2014-2018, there are also payments related to the previous programming period (measures 132, 214, 221, 222, 223, 225). to avoid the exclusion of farms that diversify and are supported by the past policy, these payments are also used for identifying diversification strategies. measure 214 also includes payments in favour of organic farming. since the focus is on policy in favour of diversification and organic farming is not here considered as a result of diversification, this measure was not considered in all cases where farms receiving support were organic farms or farms in conversion. 138 bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 andrea bonfiglio, carla abitabile, roberto henke with reference to the education level, the relevant coefficients are positive and statistically significant indicating that it is more probable to find farmers with high and medium levels of education among farms that diversify in comparison with those with no diversification strategies. the coefficients associated with organic farms are largely higher and this shows the probability that organic farmers who diversify are more educated is higher than the one related to conventional farmers relatively to farms that do not diversify. as far as economic aspects are concerned, the significant and positive coefficients associated with size demonstrate that there is a higher probability of diversifying in larger farms, and this is more evident for organic farmers. about specialization, there is a positive and significant relationship between diversification and permanent crops in both types of farms, meaning that farmers who diversify are more likely to be specialised in permanent crops. this relationship is negative in other cases indicating that it is less probable that farms specialized in arable crops, horticulture and livestock diversify. the size of coefficients is larger in the case of organic farmers, therefore showing stronger relationships. concerning policy support from the first pillar of the cap, coefficients are significant, but the signs are opposed. as for organic farms, the positive coefficient shows that, as policy support increases, the likelihood that farms diversify increases in comparison with farms that do not diversify. conversely, the negative coefficient associated with conventional farms indicates that farmers with higher support have a lower probability of diversifying. finally, dummies related to time show that the probability that farms diversify increased over time reaching the highest value in 2017. table 5 presents the marginal effects of explanatory variables calculated at the sample means. as mentioned in section 3.2, in contrast with coefficients, average marginal effects provide information about the relationship between alternatives and predictors independent of the base outcome. they measure the difference in probability of each of the outcome level associated with a change in each predictor variable. consequently, coefficients and marginal effects have different interpretation and can provide different results. as regards organic farmers, the signs of the coefficients estimated by the multinomial logit model are confirmed, and all average marginal effects are significant. results indicate that organic farmers localized in central-northern italy and in flat areas have a probability of diversifying that is 7% and 6% lower than those localized in southern italy and in the mountains, respectively, as negative average marginal effects demonstrate. on the contrary, farmers operating in hills have a probability of diversifying that is 1% higher. moreover, the likelihood that organic farms diversify is 3% higher in younger farmers and 6% and 12% higher in farmers with medium and high levels of education, respectively. from an economic point of view, larger farms are those where there is a higher probability of diversifying (+2%). with reference to specialization, the possibility of finding organic farmers who diversify is 3% higher in farms oriented to permanent crops than in mixed farms and is lower in other typologies of farms, especially among farms specialized in horticulture (-12%). the marginal effects associated with policy indicate that if policy support per hectare increases by one thousand units, the probability that an organic farm diversifies increases by 1%. with regard to conventional farmers, not all marginal effects are consistent with coefficients in terms of direction and significance. specifically, results show that conventional farmers operating in central-northern italy and in hills have a probability of diversifying that is 7% and 4% higher than those localized in southern italy and in the mountains, respectively. conversely, farmers operating in flat areas have a probability of diversifying that is 12% lower. moreover, the likelihood that conventional farms diversify is 3% lower in younger farmers. it is also 2% lower in farmers with higher levels of education. these negative and significant relationships concerning age and education contrast with the results related to coefficients. from an economic standpoint, the average marginal effect associated with size is positive but non-significant. therefore, no conclusion can be drawn. with reference to specialization, conventional farms oriented to permanent crops have a probability of diversifying that is 6% table 2. distribution of farms with diversification strategies by multifunctional direction, italy, 2014-2018 (in %). direction organic farms conventional farms all farms deepening 88.6 87.3 87.6 processing 98.2 97.0 97.2 quality certification 64.5 70.8 69.4 direct sales 52.0 46.7 47.9 broadening 6.5 8.4 8.0 deepening & broadening 4.9 4.3 4.4 total 100.0 100.0 100.0 note: the sum of processing, quality and direct sales is not 100 since the same farm can undertake one or more directions. 139a choice model-based analysis of diversification in organic and conventional farms bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 table 3. descriptive statistics about the sample used, italy, 2014-2018. mean standard deviation maximum* organic farms with diversification strategies (no. of obs. 6053) located in central-northern italy (dummy) 0.41 0.49 1 located in flat land (dummy) 0.17 0.38 1 located in hills (dummy) 0.60 0.49 1 young farmers (≤ 40 years) (dummy) 0.21 0.41 1 farmers with high-level education (dummy) 0.15 0.36 1 farmers with medium-level education (dummy) 0.53 0.50 1 large (≥ 25 € thousand of avg. gmp) (dummy) 0.70 0.46 1 specialized in arable (dummy) 0.11 0.31 1 specialized in horticulture (dummy) 0.03 0.17 1 specialized in permanent crops (dummy) 0.54 0.50 1 specialized in livestock (dummy) 0.20 0.40 1 first pillar cap payments per hectare (€) 466.56 604.00 10061.95 supported by the second pillar cap for diversification (dummy) 0.14 0.35 1 conventional farms with diversification strategies (no. of obs. 25088) located in central-northern italy (dummy) 0.63 0.48 1 located in flat land (dummy) 0.24 0.43 1 located in hills (dummy) 0.52 0.50 1 young farmers (≤ 40 years) (dummy) 0.13 0.33 1 farmers with high-level education (dummy) 0.06 0.23 1 farmers with medium-level education (dummy) 0.43 0.50 1 large (≥ 25 € thousand of avg. gmp) (dummy) 0.68 0.47 1 specialized in arable (dummy) 0.19 0.39 1 specialized in horticulture (dummy) 0.07 0.25 1 specialized in permanent crops (dummy) 0.37 0.48 1 specialized in livestock (dummy) 0.27 0.44 1 first pillar cap payments per hectare (€) 325.25 571.11 40618.18 supported by the second pillar cap for diversification (dummy) 0.27 0.44 1 farms with no diversification strategies (no. of obs. 20309) located in central-northern italy (dummy) 0.65 0.48 1 located in flat land (dummy) 0.46 0.50 1 located in hills (dummy) 0.34 0.47 1 young farmers (≤ 40 years) (dummy) 0.12 0.32 1 farmers with high-level education (dummy) 0.04 0.20 1 farmers with medium-level education (dummy) 0.39 0.49 1 large (≥ 25 € thousand of avg. gmp) (dummy) 0.70 0.46 1 specialized in arable (dummy) 0.31 0.46 1 specialized in horticulture (dummy) 0.16 0.37 1 specialized in permanent crops (dummy) 0.16 0.36 1 specialized in livestock (dummy) 0.31 0.46 1 first pillar cap payments per hectare (€) 395.34 1759.88 121033.9 supported by the second pillar cap for diversification (dummy) 0.00 0.00 0 * minimum values are always zero. 140 bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 andrea bonfiglio, carla abitabile, roberto henke higher than the one of mixed farms. the other types of farms have lower probabilities, which reach the lowest value in farms specialized in horticulture (-18%). the marginal effect related to policy indicates that an increase of one thousand units in policy support per hectare decreases the probability of diversifying in conventional farms by 3%. table 6 shows the results related to the logit model, where the explanatory variables are regressed against the binary response probability of diversification in organic table 4. estimation of the multinomial logit model for organic and conventional farmers with diversification strategies compared with farmers with no diversification strategies. organic farmers conventional farmers coefficients standard deviation coefficients standard deviation intercept -1.250* 0.075 0.572* 0.048 localization central-northern italy (dummy) -0.614* 0.033 0.144* 0.021 altitude flat land (dummy) -1.246* 0.049 -0.849* 0.029 hills (dummy) 0.282* 0.041 0.224* 0.027 mountains (baseline) age young farmers (≤ 40 years) (dummy) 0.316* 0.044 -0.025 0.032 education level high-level education (dummy) 1.436* 0.060 0.281* 0.049 medium-level education (dummy) 0.712* 0.036 0.151* 0.022 low-level education (baseline) economic size large (≥ 25 € thousand) (dummy) 0.275* 0.036 0.096* 0.023 specialization arable (dummy) -1.499* 0.064 -0.908* 0.040 horticulture (dummy) -2.149* 0.090 -1.340* 0.046 permanent crops (dummy) 0.610* 0.055 0.421* 0.040 livestock (dummy) -1.002* 0.059 -0.648* 0.039 mixed (baseline) policy first pillar cap payments per hectare** 0.045* 0.009 -0.127* 0.020 time 2014 (baseline) 2015 (dummy) 0.314* 0.055 0.107* 0.032 2016 (dummy) 0.486* 0.053 0.101* 0.031 2017 (dummy) 0.692* 0.052 0.178* 0.031 2018 (dummy) 0.651* 0.052 0.168* 0.032 number of observations = 51450 likelihood-ratio test χ2(32) =11045.95 prob>χ2=0 mcfadden’s pseudo r2=0.111 * statistically significant at 1%; ** coefficients and standard deviations are multiplied by 1000 for improving reading. 141a choice model-based analysis of diversification in organic and conventional farms bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 farmers only compared with conventional farms that diversify. the likelihood-ratio test shows that the model as a whole fits significantly better than a model with no predictors. the negative and significant coefficients associated with central-northern italy and flat land indicate that, in comparison with conventional farmers with diversification strategies, organic farmers who diversify have a lower probability to be localized in centralnorthern italy and in flat areas. the coefficient related to hills is positive but non-significant, meaning that both types of farmers have the same probability of being localized in hills. as regards socio-demographic aspects, the positive and significant coefficients indicate that it is more likely that organic farmers who diversify are younger and have higher levels of education compared to conventional farmers. organic farms that diversify are also larger than the conventional ones as the coefficient relevant to economic size demonstrates. regarding specialization, the signs of coefficients, which are all significant, show that there is a higher likelihood that diversification is present in organic farmers specialized in permanent crops as well as a lower probability that organic farmers who diversify are specialized in arable crops, horticulture and livestock. the positive and significant coefficient related to policy support from the first pillar of the cap confirms that organic farmers with higher support have a higher probability of diversifying than diversified conventional farms. on the contrary, the coefficient associated with policy support from rdp in favour of diversification is significant but negative. this means that there is lower probability of diversifying in organic farmers who receive support from the rdp. table 6 also provides information about average marginal effects. the effects estimated are consistent in terms of direction with those shown in table 5 and can table 5. marginal effects of explanatory variables related to the multinomial logit model. organic farmers conventional farmers coefficients standard deviation coefficients standard deviation localization central-northern italy (dummy) -0.066** 0.003 0.072** 0.004 altitude flat land (dummy) -0.063** 0.004 -0.121** 0.006 hills (dummy) 0.012** 0.003 0.035** 0.006 mountains (baseline) age young farmers (≤ 40 years) (dummy) 0.031** 0.004 -0.025** 0.007 education level high-level education (dummy) 0.116** 0.005 -0.023* 0.010 medium-level education (dummy) 0.057** 0.003 -0.009 0.005 low-level education (baseline) economic size large (≥ 25 € thousand) (dummy) 0.020** 0.003 0.005 0.005 specialization arable (dummy) -0.083** 0.005 -0.119** 0.008 horticulture (dummy) -0.117** 0.008 -0.179** 0.010 permanent crops (dummy) 0.031** 0.004 0.060** 0.008 livestock (dummy) -0.053** 0.005 -0.089** 0.008 mixed (baseline) policy first pillar cap payments per hectare*** 0.012** 0.001 -0.032** 0.005 * statistically significant at 5%; ** statistically significant at 1%; *** coefficients and standard deviations are multiplied by 1000 for improving reading. 142 bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 andrea bonfiglio, carla abitabile, roberto henke be interpreted analogously. an additional result is related to the positive and significant effect concerning the second pillar of the cap, which shows that the organic farmers not receiving support from the rdp have a probability of diversifying that is 9% higher than that of farmers who are supported. this implies that among conventional farmers who do not receive support this probability is 9% lower. table 6. estimation of the logit model for organic farmers compared with conventional farmers with diversification strategies and average marginal effects. organic farmers average marginal effects coefficients standard deviation effects standard deviation intercept -1.858* 0.072 localization central-northern italy (dummy) -0.676* 0.032 -0.094* 0.004 altitude flat land (dummy) -0.506* 0.049 -0.071* 0.007 hills (dummy) 0.021 0.038 0.003 0.005 mountains (baseline) age young farmers (≤ 40 years) (dummy) 0.388* 0.040 0.054* 0.006 education level high-level education (dummy) 1.265* 0.053 0.176* 0.007 medium-level education (dummy) 0.589* 0.034 0.082* 0.005 low-level education (baseline) economic size large (≥ 25 € thousand) (dummy) 0.206* 0.034 0.029* 0.005 specialization arable (dummy) -0.583* 0.061 -0.081* 0.009 horticulture (dummy) -0.862* 0.090 -0.120* 0.013 permanent crops (dummy) 0.186* 0.049 0.026* 0.007 livestock (dummy) -0.272* 0.055 -0.038* 0.008 mixed (baseline) policy first pillar cap payments per hectare** 0.409* 0.030 0.057* 0.004 second pillar cap support for diversification (dummy) -0.665* 0.043 -0.093* 0.006 time 2014 (baseline) 2015 (dummy) 0.162* 0.053 2016 (dummy) 0.385* 0.051 2017 (dummy) 0.531* 0.050 2018 (dummy) 0.543* 0.050 number of observations = 31141 likelihood-ratio test χ2(17) =3220.12 prob>χ2=0 mcfadden’s pseudo r2=0.105 * statistically significant at 1%; ** coefficients and standard deviations are multiplied by 1000 for improving reading. 143a choice model-based analysis of diversification in organic and conventional farms bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 5 discussion results indicate that farmers who diversify have different geographical localization. organic farmers are mainly localized in southern italy while conventional farmers can be prevalently found in central-northern italy. this partly contrasts with dries et al. (2012) who find that the likelihood to observe diversification is higher in southern italy due to more difficult socio-economic conditions that favour the development of non-traditional activities to complement agricultural income. in our study, a higher probability of diversifying also involves conventional farms located in central-northern italy and is a consequence of the territorial distribution of farmers. in fact, about 60% of organic farmers who diversify are in southern italy, against 35% of conventional farmers. a common finding is that that diversification is less widespread among farms operating in flat areas. this is likely due to the fact that the more competitive farms that are localized in flat areas have a lower need to expand their activity and increase their income than farms located in less favoured areas (dries et al., 2012). as far as the characteristics of farmers are concerned, results show that education level contributes to explaining diversification strategies. specifically, farmers with higher levels of education diversify more frequently in accordance with other studies (mcelwee and bosworth, 2010; boncinelli et al., 2017, 2018). this confirms that the lack of education and skilled labour may represent major barriers to finding opportunities within the new challenges of agricultural business (khanal, 2020). however, among conventional farmers, those who are most likely to diversify are not farmers with the highest levels of education, although they are more educated than those who do not diversify. farmer’s age also inf luences the probability of engaging in diversification activities but with contrasting effects. this might explain why controversial results can be found in literature. in the case of conventional farms, farmers do not exhibit clear differences compared to farmers with no diversification strategies. however, within the group of conventional farms, older farmers seem to be more oriented to diversification as others studies have shown (joo et al., 2013). conversely, in organic farms, there is higher propensity of younger farmers to diversify, which is consistent with the findings of barbieri and mahoney (2009), who have stressed that longer-term ties would lead younger farmers to strengthen the existing farm business through diversification for future generations. looking at economic and structural aspects, it turns out that the economically largest farmers are those who diversify the most, independently of their model of production. the reason could be that the reduction of marginal returns determines that farms’ resource allocation is addressed towards more profitable activities (mcnamara and weiss, 2005; ilbery, 1991; mcnally, 2001) or more simply that larger farmers have more resources to bound to other activities than agriculture (garcía-arias et al., 2015). specialization is another factor explaining the choice of diversifying in both organic and conventional farmers. farmers specialized in permanent crops are found to diversify to a larger extent in line with findings by dries et al. (2012). this higher tendency to diversification may be due different reasons (salvioni et al., 2020). firstly, farms that allocate most of the agricultural area to permanent crops may have limitations in managing risks through crop diversification. for this, they may be characterized by lower and more concentrated seasonal harvests than farms specialized in herbaceous crops, which raise a problem of underuse of labour force during the rest of the year. additionally, products from permanent cropping systems (i.e., olive oil and wine) are better suited to differentiation-based marketing strategies. these factors increase the likelihood for farms to diversify, in particular towards processing and direct sale, which represent widespread diversification strategies in farmers specialized in permanent crops. as regards policies, both pillar 1 and 2 payments affect the propensity to diversify production consistently with previous studies (bartolini et al., 2014). however, the effects on organic and conventional farmers are opposed. in the case of conventional farmers, results indicate that pillar 1 payments negatively affect the choice of diversifying. the explanation could be that these payments, by producing a wealth effect, reduce the need to increase income by diversification. conversely, in organic farmers, the effects are positive. farmers who receive a higher support tend to diversify to a larger extent than the average. in this case, the higher financial resources made available by the cap are likely to be used to finance diversification. therefore, for organic farmers, the motivation pushing to diversification does not seem to integrate income but to expand activity by taking advantage of benefits from both organic production in terms of consumers’ willingness to purchase at higher prices and diversification in relation to the possibility of obtaining an even higher value added (zander, 2008; sidali et al., 2019). with reference to the second pillar of the cap, payments affect positively diversification adoption but only in conventional farmers. conversely, these payments do not exert any influence on organic farmers. results even show that organic farmers that diversify apply for policy measures supporting 144 bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 andrea bonfiglio, carla abitabile, roberto henke diversification to a lesser extent. this is a further confirmation of different motivations leading conventional and organic farmers to diversify. in interpreting the results, some possible drawbacks deriving from the approach used here should be taken into consideration. a first potential drawback comes from the fact that the sampled farms that do not diversify include both organic and conventional farms and that the latter represent the majority (around 90%). therefore, one of two comparisons is substantially between organic farms that diversify and those conventional which do not diversify. in addition, the fact that organic farms that diversify represent most organic farms (about 70%) implies that this comparison is basically between organic farms and conventional farms, and that the results are therefore affected by the main characteristics and differences of organic farms in comparison with the conventional ones. however, this does not compromise the main findings of this study but, on the contrary, strengthens the conclusion that organic farming and on-farm diversification are strongly connected with each other. a further and possible drawback deriving from mixing organic and conventional farms that do not diversify is that these two types of farms can exhibit marked differences which can explain why a conventional farm decides to convert to organic farming and would suggest that farms that do not diversify should also be kept separate. for example, marginality conditions and difficulties in reaching the same profitability as that of more competitive conventional farms due to agricultural constraints can be some of these reasons. however, this may be valid for a part of farms, especially for those which decide to convert. in fact, it has been shown that organic farms are mostly present in areas with favourable socioeconomic and climatic conditions, both globally but also within countries, and that, within developed countries, the locations of organic crop farmers often do not differ significantly from the locations of conventional crop farmers (malek et al., 2019). moreover, for a share of farms, particularly for new entrants, organic farming may represent an effective strategy to capture the economic opportunities provided by the current changes in the market and consumers’ preferences regardless of the presence of agricultural constraints. this allows conventional and organic farming to be viewed in the same way as business strategies and is consistent with the main objective of the paper of understanding the reasons why a farm decides to diversify rather than converting to organic farming. from a methodologic point of view, keeping organic and conventional farms that do not diversify also separate means that two distinct logit models should be performed in the place of a single multinomial model. although this can be a useful exercise for future research, which can provide further information, in this way, the coefficients of the models estimated separately for organic and conventional farms as well as the relevant marginal effects could not be compared directly. 6. conclusions and policy implications this paper aimed to analyse the possible differences between organic and conventional farms in relation to the reasons that lead farmers to diversify. the focus is on the italian fadn sample of farms observed in the period 2014-2018. from a methodological standpoint, multinomial and binary logit models, linking the probability that several alternatives are chosen to a few territorial, socioeconomic, and political factors, are adopted. the approach used is based on the consideration that organic farming should not be considered as one of the possible options of diversification available to conventional farms but a model of production that may respond to different logics. results suggest that, in both organic and conventional farms, diversification might not be necessarily an obliged passage for marginal farms which desire to survive. on the contrary, it can be more assimilated to an entrepreneurial strategy that requires specific competences and adequate organization. however, the reasons for diversifying differ according to the kind of production model. in the case of conventional farming, farmers might decide to diversify because they are not able to reach income levels comparable with those of a more competitive and highly mechanized agriculture due to factors related to localization, specialization, and lower support from the first pillar of the cap. therefore, they diversify to increase their income using policy support from the rdp in favour of diversification. conversely, in the case of organic farming, diversification seems to be an integrated part of the production model. organic farmers are likely to implement new activities, particularly processing and direct sales, to take advantage of benefits from both organic production and diversification, regardless of the policy support for diversification. for these farms, localization in less competitive areas and specialization in permanent crops might not be necessarily weakness factors, but rather distinctive characteristics that can be further enhanced by the diversification pattern. these results highlight some possible policy implications. a first consideration is that the incentives to implement diversification strategies appear to be inef145a choice model-based analysis of diversification in organic and conventional farms bio-based and applied economics 11(2): 131-146, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12206 fective in organic farms. a reason can be related to profitability reasons and the existence of synergies between different activities. the benefits, net of costs and administrative burdens, which can be obtained by requesting public support for diversification, may be lower than the benefits deriving from combining organic farming with diversification without asking for support. therefore, organic farmers can afford not to request support, or they do not express the need to request it at all. this can be positive as it can mean that organic farming, combined with diversification, allows farmers to reach levels of competitiveness that make the request for public support for diversification unnecessary. however, it must be considered that organic farmers, compared to conventional ones, also benefit from specific support for the conversion and maintenance of organic farming and this raises the question of how to better distribute the funds in favour of diversification between different types of farming in order to make policy more targeted and effective. a further consideration is more general and concerns both organic and conventional farms. results show that diversification strategies are undertaken prevalently by larger farms with higher levels of education. this means that smaller and family farms, which would benefit from on-farm diversification, do not diversify and this might depend on the lack of resources as well as on low skills and entrepreneurial capabilities, which prevent them from accessing policy support. therefore, administrative simplification as well as training and consultancy services specifically designed for this category of farms should be strengthened to avoid abandonment of agriculture, particularly in marginal areas. in the context of organic farming, this strategy could leverage the greater presence and propensity of young farmers to diversify and would be in line with recent european policy indications aimed at giving a significant acceleration in the growth of the organic sector. acknowledgments this work was supported by the italian national rural network programme 2014-2020 (crea project sheet 5.2 – actions for organic farming). this paper is the result of a joint effort of all authors. nevertheless, the authorship can be attributed as follows: sections 3 and 4 to andrea bonfiglio; sections 1 and 2 to carla abitabile; sections 5 and 6 to roberto henke. the authors would like to thank the editor and two anonymous reviewers for their valuable comments which helped to further improve the quality of this manuscript. references aubert, m. and enjolras, g. 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(2008). diversification and specialisation as development strategies in organic farms. paper presented at 16th ifoam organic world congress, modena, italy, june 16-20, 2008 available at: http:// orgprints.org/id/eprint/13980. volume 11, issue 2 2022 firenze university press agriculture, food and global value chains: issues, methods and challenges margherita scoppola mapping global value chain participation and positioning in agriculture and food: stylised facts, empirical evidence and critical issues silvia nenci1, ilaria fusacchia1,2, anna giunta1,2, pierluigi montalbano3, carlo pietrobelli1,4 on the relationships among durum wheat yields and weather conditions: evidence from apulia region, southern italy marco tappi*, gianluca nardone, fabio gaetano santeramo a choice model-based analysis of diversification in organic and conventional farms andrea bonfiglio*, carla abitabile, roberto henke financial performance of connected agribusiness activities in italian agriculture gabriele dono*, rebecca buttinelli, raffaele cortignani pesticides, crop choices and changes in well-being geremia gios1,*, stefano farinelli2, flavia kheiraoui3, fabrizio martini4, jacopo gabriele orlando5 bio-based and applied economics bae copyright: © 2023 moukam c.y., atewamba c. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 citation: moukam c.y., atewamba c. (2023). incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon. bio-based and applied economics 12(3): 197-220. doi: 10.36253/bae-13534 received: august, 16 2023 accepted: june 9, 2023 published: october 15, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: simone cerroni orcid cym: 0000-0001-8926-0906 ac: 0000-0001-7609-0397 incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon claudiane yanick moukam1,*, calvin atewamba2 1 department of public economics, university of douala, cameroon 2 college boreal, canada *corresponding author. e-mail: yanickmclaudiane@yahoo.fr abstract. this paper applies a bayesian approach to incorporate non-data information in estimating the opportunity cost for farmers in rural cameroon to engage in biodiversity conservation and carbon sequestration efforts. findings from our field survey reveal that only a small percentage of farmers are willing to participate in environmental protection programmes without compensation. a multidimensional preferences analysis indicates that this behavior may be attributed to a disconnection between environmental values and socioeconomic values. bayesian analysis of the tobit model, examining willingness to accept (wta) compensation for agroforestry participation, highlights that factors such as aging, higher educational attainment, and higher socioeconomic status are highly likely to promote pro-environmental behaviors. the estimated opportunity cost of supplying environmental services is 10,775 cfa francs with a standard deviation of 333.6 cfa francs per farmer. these results differ qualitatively from the existing literature, underscoring the relative significance of considering expert knowledge in the interpretation of environmental policies. keywords: bayesian analysis, environmental services, stated preferences, opportunity cost, rural cameroon. jel codes: q57, c34, c11. 1. introduction nature plays a crucial role in supporting human development; however, the increasing demand for the earth’s resources is leading to accelerated extinction rates and a decline in global biodiversity and ecosystem services. according to the international panel on biodiversity and ecosystem services (ipbes, 2019), the average abundance of native species in major land-based habitats has decreased by at least 20%, primarily since 1900. additionally, more than 40% of amphibian species, nearly 33% of reef-forming corals, and over one-third of marine mammal species are currently facing threats. recognizing this global challenge, governments worldwide are taking action to incorporate biodiversity and ecosystem services into their development plans, policies, and strategies (ipbes, 2019). these initiatives include targets http://creativecommons.org/licenses/by/4.0/legalcode https://doi.org/10.36253/bae-13534 https://doi.org/10.36253/bae-13534 https://orcid.org/0000-0001-8926-0906 https://orcid.org/0000-0001-7609-0397 198 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 claudiane yanick moukam, calvin atewamba such as regenerating vegetative cover in the agricultural sector, enhancing agricultural productivity, and reducing the amount of land used for agriculture through the implementation of intensive agricultural systems. farmers, being at the forefront of environmental conservation in agriculture, play a crucial role. the effectiveness and efficiency of government incentive mechanisms depend not only on the specific design of the schemes (bareille et al., 2023) but also on the values farmers associate with ecosystem services and the opportunity costs associated with adopting sustainable agricultural practices (karsenty et al., 2010; bessie et al., 2014; kernecker et al., 2021). by taking into account farmer preferences and expectations in the design of government incentive schemes, we can identify the factors that determine the social acceptability and economic efficiency of these schemes. conducting research to assess farmer preferences and expectations, as well as estimating farmers’ willingness to accept compensation (wta) for providing environmental services, is essential in this context. farmers’ wta to participate in environmental protection programmes reflects the opportunity cost of supplying environmental services. in other words, farmers express their preferences by assigning selling prices to environmental services, which can be used for their valuation (brown and gregory, 1999; hanley and czajkowski, 2019). the economic literature on the adoption of payment for ecosystem services (pes) schemes using a stated preference (sp) approach is extensive (carson, 2012; villanueva et al., 2017; johnston et al., 2017; hanley and czajkowski, 2019; wang and nuppenau, 2021; raina et al., 2021; viaggi et al., 2022). however, most sp studies rely on respondents’ hypothetical choices as data to infer their preferences and, consequently, their wta for changes in environmental services. as noted by haghani et al. (2021), the hypothetical nature of sp choice settings introduces a hypothetical bias, leading people to systematically over or understate their wta values in sp exercises. this bias arises because no actual payment is made or received in exchange for a change in the quantity or quality of environmental services. current research on hypothetical bias in sp approaches focuses on understanding its causes and developing methods to mitigate it. one approach to mitigate hypothetical bias is the use of “cheap talk” scripts, which aim to improve the realism of hypothetical scenarios and reduce the influence of social desirability biases. however, the effectiveness of cheap talk as a bias mitigation tool varies depending on the context and the specific script used, as highlighted by bosworth and taylor (2012) and doyon et al. (2015). another approach to mitigating hypothetical bias is to use “non-hypothetical” or “real” choice experiments (menapace and raffaelli, 2020; fang et al., 2021; cerroni et al., 2023). these experiments involve asking participants to make actual choices rather than hypothetical ones, and they can be conducted in laboratory or field settings. real-choice experiments have been found to reduce hypothetical bias in some contexts, although they can be more expensive and logistically challenging to implement compared to hypothetical choice experiments. in addition to these methodological approaches, researchers are exploring the use of behavioral interventions to reduce hypothetical bias. vossler and holladay (2016, 2018) suggests that framing survey questions in a way that emphasizes the importance of the decision or providing feedback on the accuracy of participants’ responses may encourage more truthful and accurate responses. however, it is important to note that surveybased welfare measures for public environmental goods are often sensitive to elicitation methods, such as whether the elicitation is framed as an up-or-down vote or an open-ended willingness-to-pay question. controlling for economic incentives, vossler and zawojska (2020) show that most survey response formats, including single binary choice, double-bounded binary choice, payment card, and open-ended formats, elicit statistically identical wtp distributions. this finding highlights that behavioral factors may not be the primary drivers of elicitation effects. overall, research on hypothetical bias in sp approaches is an active and evolving field, with ongoing efforts to understand its causes and develop effective mitigation strategies. reducing hypothetical bias in choice experiments requires not only careful survey design but also the integration of non-survey data information and expert knowledge. non-data information refers to prior knowledge or assumptions derived from sources other than observed or survey data, such as expert opinions, previous studies, or theoretical considerations (knuiman and speed, 1988; gelman et al., 2013; mahmoud et al., 2020; awwad et al., 2021; hegazy et al., 2021). incorporating non-data information in sp studies is particularly valuable when survey data is limited, noisy, biased, or when complex problems demand additional information for accurate analysis. by accounting for non-data information, we can improve analysis accuracy, mitigate the impact of outliers or measurement errors, and enhance understanding of economic agent preferences and behaviors (kadane and lazar, 2004; gelman et al., 2013; kruschke, 2013). however, it should be noted that incorporating non-data information poses challenges compared to analyzing survey data alone. despite its potential, there have been limited https://doi.org/10.36253/bae-13534 199incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 studies explicitly considering expert knowledge or nondata information to address hypothetical bias in choice experiments. this is partly explained by the difficulty to capture expert knowledge in current wta modelling frameworks, which usually rely exclusively on survey data to estimate the unknown parameters of agent preferences. this paper explores an approach that utilizes non-data information to constrain the range of unknown parameters of agent preferences and aims to reduce hypothetical bias in estimating wta values. to achieve our objective, we start by conducting a field survey in barombi mbo, a rural area in cameroon, to gather data on the socio-economic and environmental conditions of farmers. the survey includes information on farmers’ willingness to accept (wta) compensation for participating in agroforestry and afforestation programmes. additionally, we employ a multidimensional preferences analysis (mpa), a technique used to develop spatial representations of proximities among psychological stimuli or other entities (carroll and chang, 1970; wish and carroll, 1982; davison, 1983), to gain insights into the contextual socio-economic and environmental values of the farmers in barombi mbo. this analysis helps us understand the various factors influencing farmers’ decision-making processes. we then extend a tobit model, originally proposed by tobin in 1958, to estimate the wta values. the tobit model accounts for the presence of censoring or truncation in the wta data. furthermore, we incorporate stochastic constraints in the model’s parameters using prior distributions. these prior distributions capture our expert knowledge or expectations regarding agent preferences when engaging in environmental protection programmes. by adopting a bayesian approach, we update our knowledge based on the data and obtain posterior estimates of the model parameters. the results of our analysis indicate that a significant majority of farmers in barombi mbo are willing to participate in agroforestry and afforestation programmes if their financial constraints are alleviated. furthermore, we find that a higher socio-economic status is likely to promote pro-environmental behaviors among farmers, while increased knowledge on environmental protection strategies alone does not necessarily lead to eco-friendly behaviors. based on our bayesian estimation, the distribution of farmers’ wta is found to be normally distributed with a mean of 10,775cfa franc and a standard deviation of 323.59cfa franc. moreover, we estimate the opportunity cost of providing environmental services for farmers in our study area to be approximately 3,290,448cfa fanc per year. our research findings demonstrate qualitative differences from the existing literature (moukam, 2021; gou et al., 2021; p érez-s ánchez et al., 2021). while previous studies have acknowledged the potential of employing a bayesian approach for modeling ecosystem services (landuyt et al., 2013; ban et al., 2014; uusitalo et al., 2015; hofer et al., 2020), a review of these studies reveals that the technique is not yet fully utilized. it has been highlighted in hofer et al. (2020); moukam (2021); gou et al. (2021); p érez-s ánchez et al. (2021) that the standard approach for modeling ecosystem service delivery relies solely on data, without incorporating expert knowledge, which can lead to controversial results regarding the drivers of economic agent behavior for environmental protection. in contrast to the aforementioned studies, our approach incorporates expert knowledge through the utilization of prior distributions for the model parameters. by doing so, we not only provide meaningful insights into the determinants of economic agent preferences but also significantly improve the estimation of wta compensation for participation in environmental conservation efforts. this allows us to account for situations where the available data may not adequately capture the tangible and intangible benefits of the environment. our results suggest that the conditional probability of the parameters provides the best summary of the knowledge we can gain from the data. the remaining sections of the paper are structured as follows. section 2 provides a description of the study area, emphasizing its agroecological characteristics and the availability of agricultural extension services. in section 3, we outline the research methodology, including details on the survey design, data collection process, and analytical methods employed. the obtained descriptive statistics, research findings, and their discussions are presented in section 4. finally, section 5 serves as the conclusion of the paper, summarizing the key points and providing policy implications based on the findings. 2. barombi mbo area in cameroon 2.1. agro-ecological characteristics the rural area barombi mbo is located in the meme division of the southwest region of cameroon and is one of the villages near the periphery of lake barombi mbo, just after the forest reserve (indicated by a black line in figure 1). it was created in 1940 by the colonial government to protect the lake, and the local inhabitants (natives) were granted the rights to fish in the lake and harvest cocoa in existing farms within the reserve (ris, 2008). however, over the years, the resources attracted an increasing number of people, leading https://doi.org/10.36253/bae-13534 200 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 claudiane yanick moukam, calvin atewamba to the exploitation of illegal farming, hunting, timber, and non-timber forest products (ntfps), coupled with uncontrolled fishing (agbor, 2008; sounders and kimengsi, 2011; tchouto et al., 2015). the major food crops grown in the region include cassava (manihot esculenta), plantain (musa paradisiaca), egusi melon (cucumis sativus), maize, cocoyams, and taro (colocasia antiquorum). cocoa, palm oil, and rubber are the major cash crops in the zone, which is characteristic of the humid forest agro-ecological zone of the southwest region of cameroon. barombi mbo experiences a typical equatorial climate with a long rainy season from march to november and a short dry season from december to february. the village is known for its hot weather, with an average annual temperature ranging from 20°c to 30°c, as reported by the delegation of agriculture of kumba. however, according to the most recent survey (ris, 2008), the mean annual temperature is approximately 18°c or even lower at higher altitudes, with annual precipitation ranging from 1825 to 3000mm. the area has undergone significant climate change, with rains sometimes starting earlier in march and unexpected rainfall occurring during the dry seasons. in 2010, the rainy season extended until december, disrupting the planting and production of cash and food crops, as well as other economic activities, which typically end in october-november in previous years (sounders and kimengsi, 2011; lebamba et al., 2012; tchouto et al., 2015). furthermore, the area consists of steep slopes that are prone to erosion, and it is characterized by a mixture of soils, including limon, laterite, sandy, clay, and volcanic soils. these soils, which have a high content of andosols, are predominantly composed of dark volcanic materials. they are generally fertile and suitable for cultivating both food and cash crops. however, in deforested and degraded areas, soils are gradually losing fertility due to increased slash and burn practices, soil exposure, pollution, and overcropping (sounders and kimengsi, 2011; tchouto et al., 2015). agriculture is increasingly encroaching on the area, leading to the reduction of forested areas. as a result, the intensified use of fertilizers in agriculture has led to the pollution of the lake. 2.2. agricultural extension services several types of sustainable agricultural practices have been promoted among farmers in the meme division by the ministry of agriculture and rural development (minader), including farmer field school and farmer business school. through farmer field school, minader trains farmers on good agricultural practices in collaboration with cooperatives, while farmer business school focus on promoting agroforestry as a source of income. minader provides farmers with improved corn seedlings, maize seeds, cassava cuttings, as well as some pesticides and fertilizers. however, farmers face difficulties in adopting agroforestry practices due to the scarcity of improved agroforestry species or nurseries and limited access to productive agricultural land for planting. it is important to note that barombi mbo village is not one of the communities targeted by minader due to its proximity to the forest reserve, which is managed by the ministry of forestry and wildlife (minfof). due to the lack of collaboration between these two government institutions at the field level, barombi mbo farmers are unable to learn about or benefit from agroforestry practices supported by minader. 3. methodology this section outlines the methodology employed to estimate the opportunity cost for farmers in the barombi mbo area of cameroon to adopt agroforestry and afforestation practices. we present the conceptual framework, survey design and data collection methods, modeling framework, and the integration of expert knowledge. 3.1. conceptual framework – contingent valuation sustainable agricultural systems, such as agroforestry, deliver and maintain a range of valuable positive environmental externalities, including wildlife habitat and climate mitigation. they have been proven to be less vulnerable to shocks and stresses (verma et al., 2016; gama-rodrigues et al., 2021). since these environmental benefits are typically considered public goods, private ranches are often less motivated to supply them at their optimal levels. additionally, a standing forest typically represents a potential source of income that can be accessed through logging or farming in the case of sudden need (bacon et al., 2012; gama-rodrigues et al., 2021). farmers may thus be unwilling to introduce changes in their production systems that involve a loss of these potential income sources. therefore, a valuable approach to promoting biodiversity conservation and carbon sequestration is the pes, which provides financial transfers to landowners, farmers, and communities whose land-use decisions may affect the biodiversity values and climate change. pes creates incentives for the conservation of plant and animal species, as well as the soil quality (engel et al., 2008; ito, 2022). https://doi.org/10.36253/bae-13534 201incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 although pes is an economic incentive mechanism for the provision of environmental services, the effectiveness and efficiency of its implementation, especially in the agricultural sector, largely depend on their social acceptability (todorova, 2019; viaggi et al., 2021). in addition, it is relatively difficult, and even impossible, to value environmental services through market mechanisms due to their public goods nature. therefore, the compensation for supplying environmental services is usually based on the opportunity cost of changing practices or restricting use rights. in other words, an economic agent may seek a monetary amount to ensure that their activities protect or deliver a range of environmental services (divinski et al., 2018; sheng et al., 2019). the figure 1. location of the study area in south west region of cameroon. https://doi.org/10.36253/bae-13534 202 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 claudiane yanick moukam, calvin atewamba contingent valuation methodology helps reveal the monetary amount an economic agent would like to receive to secure the value of goods or services when prices are not available (carson, 2012; johnston et al., 2017). if an economic agent, such as a farmer, has exclusive property or user rights over a good, such as a standing forest, and is being asked to give up or restrict that entitlement in terms of exclusivity or transfer of user rights, then the correct measurement within a contingent valuation framework is the wta (brown and gregory, 1999; carson et al., 2001; mcfadden and train, 2017). there is evidence suggesting that farmers, through their exposure to agri-environmental schemes, have become familiar with the tradeoff between agricultural production and the provision of environmental public goods (buckley et al., 2012; mcgurk et al., 2020). according to mcfadden and train (2017), the sp methodology involves conducting surveys to elicit economic agents’ preferences and their wta for the provision of public goods, such as environmental services. the development of sp surveys aims to maximize the validity and reliability of the resulting value estimates. validity refers to minimizing bias in estimates, while reliability pertains to reducing variability (mitchell and carson, 1989; bateman et al., 2002; bishop and boyle, 2019). therefore, as emphasized by johnston et al. (2017), well-designed surveys and proper implementation procedures are crucial for achieving these goals and are necessary when extrapolating model estimates from a survey sample to an intended population. 3.2. survey design and data collection we design a survey instrument that clearly explains the current conditions and presents a consequential valuation question. additionally, we select a random sample from the potentially affected population and choose a survey mode that ensures complete questionnaire responses. scenario description we define a hypothetical scenario to assess agroforestry development in barombi mbo, capturing the impacts of current agricultural practices and potential changes. we present both the baseline or status quo conditions and the proposed changes relative to the baseline to the farmers. this approach ensures that farmers understand and accept the valuation scenario (schultz et al., 2012; johnston et al., 2017). our hypothetical scenario, along with its consequential value question, is as follows: “studies conducted in the barombi mbo forest reserve have observed that approximately 90% of the forest reserve, particularly the forest near the lake, has been destroyed. if the current level of activities in the reserve continues, there will be no trees left to provide fuelwood, wood, climate stabilization, wildlife habitat, and water quality and quantity for future generations, as well as for eco-tourism in the watershed. to restore the forest reserve, the government plans to implement an afforestation programme. your participation in this programme will assist the government in estimating the cost of afforestation.” questionnaire testing as recommended by johnston et al. (2017), we conducted a focus group discussion with 28 farmers from barombi mbo to test our questionnaire. this allowed us to assess the impacts of the information provided on farmers’ responses to the valuation questions, the framing of the valuation questions, as well as the respondents’ prior experience and knowledge. the testing of the questionnaire helped us clarify the questions and information with the farmers, and also enabled us to determine the monetary amounts (bids) that farmers are willing to accept for adopting agroforestry. this process is crucial not only for ensuring the validity and reliability of our estimates but also for avoiding respondent fatigue caused by the provision of unnecessary details (mitchell and carson, 1989; bateman et al., 2002; champ et al., 2017). value elicitation we utilize an open-ended elicitation format to gather pilot data during the survey pretesting phase. this format enables us to collect point estimates of different monetary amounts that farmers are willing to accept for agroforestry adoption (vossler and zawojska, 2020). following the presentation of the hypothetical scenario for agroforestry development, our open-ended valuation question is as follows: “what annual compensation would you expect to plant trees in or out of the reserve?” the responses obtained from the participants provide us with a range of monetary amounts, allowing us to determine the distribution of the wta and select a finite set of monetary amounts to be proposed to farmers in the final survey. instead of choosing monetary amounts between the 15th and 85th percentiles or from the tail of the distribution, as recommended by kanninen (1995) for wtp, we retain the first two lowest monetary amounts, https://doi.org/10.36253/bae-13534 203incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 specifically 10,000cfa franc and 15,000cfa franc. this approach helps to reduce hypothetical bias, as economic agents often tend to overstate their wta, as highlighted by kahneman and tversky’ (1979). alberini (1995) and terra (2010) suggest that including approximately two monetary amounts for estimating wta is theoretically optimal. having a small number of bids is preferred over a large number as it increases estimation efficiency and the power of statistical tests. after conducting the field pilot survey, we revise the questionnaire to incorporate the monetary amounts/wta for the provision of environmental services, as well as farmers’ suggestions regarding the types and levels of activities carried out in the farm and forest reserve, as presented in section 4.1. the final survey employs a dichotomous-choice elicitation format. specifically, we use a wta question to determine the minimum amount of cash a farmer is willing to accept as compensation for changing their current land-use practices to more productive and environmentally friendly ones. this question is presented to farmers using a single binary choice format (carson and groves, 2007; carson et al., 2014; vossler and holladay, 2018). our single binary choice question is as follows: “would you be willing to receive ‘x amount’ per year for your participation in the afforestation programme?” the ‘x amount’ represents either 10,000cfa franc or 15,000cfa franc. the farmer is asked to respond with either “yes” or “no.” population and sampling procedure the population of barombi mbo was estimated to be 595 inhabitants in march 2015, with 349 males and females above 15 years old (tchouto, 2015). limiting the age of respondents to 15 years and older allows us to account for farms owned or managed by youths when one or both of their parents are still alive or have passed away. to obtain a sample size that represents the population of barombi mbo, we use the following formula (yamane, 1967): (1) in this formula, n = 349 represents the number of individuals older than 15 years old, and c = 4.6% is the margin of error. by plugging these values into the formula, we calculate a sample size of 200 farmers. the selection of farmers for face-to-face interviews is done randomly within the village. data collection for data collection, we assign 50% of the sample to each of the two monetary amounts to ensure an equal distribution of bids. the responses to the single binary choice question mentioned earlier are obtained through face-to-face or in-person interviews. our questionnaire includes auxiliary or supporting questions to aid in understanding responses to value elicitation questions and ensure construct validity (krupnick and adamowicz, 2006; mitchell and carson, 1989; bateman et al., 2002; champ et al., 2017; johnston et al., 2017; vossler and holladay, 2018). these auxiliary questions serve multiple purposes, such as identifying demographic, household, or other relevant characteristics of the respondents. additionally, a subset of these questions may provide covariates, which are used in valuation models to explain the variation in responses to the value elicitation questions (johnston et al., 2017; vossler and holladay, 2018). to account for factors that may influence the wta, our questionnaire collects information on the socioeconomic characteristics of farmers, farm characteristics, and environmental variables. this information helps deconstruct farmer preferences and identify factors that affect the wta. previous studies, such as chatterjee et al. (2021), have shown that the adoption of conservation agriculture is related not only to ecological factors but also to adopters’ characteristics, their perceptions, and the decision-making process. specifically, our questionnaire includes questions regarding the age, gender, education level, family size, and origin of farmers. we also inquire about the location and size of farms because the ownership of large and strategically positioned agricultural land may influence farmers’ participation in environmental protection programmes (ajayi et al., 2012). furthermore, we include questions about the current agricultural income and the use of fertilizers and pesticides to examine how the land opportunity cost or on-farm income could make compensation or payments more attractive within a pes scheme. existing evidence suggests that farmers with higher profit levels from their existing activities generally demand higher levels of compensation to participate in a conservation scheme (bateman, 1996; ajayi et al., 2012). furthermore, our questionnaire includes questions to capture farmers’ perceptions of the potential development outcomes associated with unsustainable agricultural practices, such as the heavy use of chemical fertilizers and slash and burn techniques. the poor performance of these unsustainable practices may motivate farmers to seek sustainable alternatives, such as agroforestry. as highlighted by gama-rodrigues et al. https://doi.org/10.36253/bae-13534 204 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 claudiane yanick moukam, calvin atewamba (2021), agroforestry has positive effects on both income and the environment. in agroforestry systems, habitats are provided for species that can tolerate a certain level of disturbance, and the rate of natural habitat conversion is reduced compared to traditional agricultural systems (jose, 2009). agroforestry also contributes to biodiversity conservation as trees, crops, and/or animals enhance soil fertility, improve water quality, increase aesthetics, and sequester carbon. for instance, multi-strata cocoa agroforestry systems that incorporate timber, fruit, and native forest species create improved wildlife habitats by increasing plant diversity, enhancing landscape connectivity, and reducing edge effects between forests and agricultural land (jose, 2009; gama-rodrigues et al., 2021; bareille et al., 2023). however, it is important to acknowledge that seeking more sustainable alternatives also involves costs and potential income losses for farmers. therefore, they may require compensation for implementing agri-environmental protection solutions (raina et al., 2021). moreover, our questionnaire includes questions aimed at capturing the social, environmental, and cultural values associated with agroforestry. these values encompass the importance of non-timber forest products (ntfps), environmental sensitivity, access to information and knowledge about agroforestry and biofertilizer technologies, as well as awareness of the pes mechanism. recognizing and understanding these cultural and environmental values is crucial for promoting biodiversity conservation through agroforestry in the long term. these values provide justification for farmers to conserve native forest habitat within cocoa production landscapes, maintain or restore diverse and structurally complex shade canopies within cocoa agroforestry systems, and retain other forms of on-farm tree cover to enhance landscape connectivity and habitat availability (schroth and harvey, 2007; gama-rodrigues et al., 2021; bareille et al., 2023; ito, 2022). however, our field survey reveals a lack of knowledge about the benefits of agroforestry in the barombi zone. this issue will be discussed further in section 4.1. non-data information or expert knowledge in situations where the available data are limited, noisy, or biased, or when the empirical problem is complex and requires additional information to determine the wta, non-data information can be particularly valuable. non-data information refers to any prior knowledge or assumptions about the wta that are not derived from observed or survey data (knuiman and speed, 1988; gelman et al., 2013; mahmoud et al., 2020; awwad et al., 2021; hegazy et al., 2021). such information can be obtained from various sources, including: • expert opinion: prior knowledge can be informed by the insights and expertise of professionals in the field who possess relevant experience and knowledge. • previous studies: prior knowledge can be based on the findings of previous research that has investigated similar or related problems. • empirical data: prior knowledge can be derived from data collected from sources other than the current study, such as pilot studies or surveys. • theoretical considerations: prior knowledge can be based on theoretical frameworks and considerations regarding the relationships between the variables of interest. accounting for non-data information can indeed enhance the accuracy and precision of wta estimates and mitigate the influence of outliers or measurement errors (kadane and lazar, 2004; gelman et al., 2013; kruschke, 2013). however, it is crucial to approach the use of non-data information with caution and provide adequate justification, as it introduces subjectivity into the analysis. in our study, we rely on prior knowledge derived from theoretical considerations regarding the relationship between wta and psychological stimuli experienced by farmers. further details on this aspect are discussed in section 3.4. 3.3. modeling farmer’s willingness to accept the use of a tobit model is appropriate in our study to model farmers’ wta compensation. the tobit model is a regression model commonly employed when the dependent variable is censored within a certain range. in our case, the wta lies within the interval [0, ∞[ since there is no negative compensation observed in our experiment (as discussed in section 3.2). therefore, the tobit model can effectively capture the behavior of the wta. in the context of the tobit model, the choice of a farmer to participate in the agroforestry programme with compensation can be represented as a dichotomous outcome. a farmer either agrees to participate (indicating wta > 0) or does not agree to participate (indicating wta = 0). the tobit model has been widely used in studies investigating technology adoption and participation in conservation programmes, as mentioned in prior research (e.g., (buckley et al., 2012; thompson et al., 2021)). https://doi.org/10.36253/bae-13534 205incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 the conceptual model can be described in terms of a latent variable wta* and an observed variable wta as follows: (2) (3) where, xi is a row vector of explanatory variables that determine the respondent i’s wtai or participation in a sustainable agriculture or conservation programme, β is a column vector of parameters to be estimated, and εi is an error term with a normal distribution n (0, σ2). the tobit model consists of two parts: a continuous part, represented by the linear regression equation 2, and a discrete part, represented by the censored point equation 3. the continuous part, equation 2, models the underlying relationship between the latent variable wta*i and the explanatory variables xi. it assumes a linear relationship, where the value of wta*i is determined by the values of xi multiplied by the parameter vector β, along with the error term εi. the censored point equation 3 introduces the censoring mechanism. it states that the observed wta value wtai is determined based on the value of wta*i. if wta*i is greater than zero, indicating that the respondent agrees to participate, the observed wta value equals wta*i. however, if wta*i is less than or equal to zero, indicating that the respondent does not agree to participate, the observed wta value is censored at zero. the tobit model combines these two parts to estimate the parameters β that determine the relationship between the explanatory variables and the wta, taking into account the censoring mechanism. the estimation procedure accounts for both the continuous and censored parts simultaneously, providing insights into the factors influencing farmers’ wta and their decision to participate in the agroforestry programme with compensation. from (2), we derive that wta*i follows a normal distribution; and the probability to reject an offer to participate in a sustainable agriculture programme is given by: (4) where φ is the standard normal density function. it follows that the probability for wta*i to take on positive values is given by: (5) we derive the log-likelihood function of wta from (3), (4) and (5) as follows: (6) to determine the components of the explanatory variables xi, we draw insights from existing literature on empirical research on farmers’ valuation of environmental services, adoption of agricultural technologies, and participation in conservation programmes in both developed and developing countries. these studies include research by adesina et al. (2000); jose (2009); scognamillo and sitko (2021), chatterjee et al. (2021) and raina et al. (2021), among others. these studies provide valuable information on the factors influencing farmers’ wta. additionally, some of these studies offer guidance on designing a relevant questionnaire to explore the key determinants of farmers’ wta (refer to table 1). from (2) and (3), it can be shown that: e(wtai/xi) = (1 − φ(α))(µ − σλ(α)) (7) where α = −µ/σ, λ(α) = ϕ(α)/(1−φ(α)), ϕ and φ are the standard normal density and distribution functions respectively, and µ = xiβ, with xiβ = β1 + β2age + β3gend + β4origin + β5edu + β6fhsize + β7onfinc + β8lofarm + β9fasize + β10envsty + β11awpes + β12biofert + β13outcpra + β14ntfps (8) denote by θ = (β, σ) the parameter of the empirical model (2). using data to estimate θ, we can predict the wta from (7). in this paper, we are interested in predicting the wta of a representative farmer characterized by x– = e(xi). in the following section, we propose an approach to estimate θ. 3.4. incorporating expert knowledge into farmer’s willingness to accept in most sp studies, data on farmers’ hypothetical choices of the wta are utilized to deduce their preferences for various levels of environmental services (johnston et al., 2017; hanley and czajkowski, 2019; wang https://doi.org/10.36253/bae-13534 206 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 claudiane yanick moukam, calvin atewamba and nuppenau, 2021). however, the hypothetical nature of the wta choices introduces a bias, as individuals tend to systematically overstate or understate their wta values. this bias arises because no actual payment is made or received in exchange for an actual change in the quantity or quality of environmental services (haghani et al., 2021). as suggested in section 3.2, one way to correct the bias and improve the accuracy and precision of the wta estimates is to incorporate expert knowledge. expert knowledge can be utilized to constrain the range of possible values for the unknown parameters, θ, related to agent preferences. the most commonly employed statistical methods for estimating the parameter θ are referred to as frequentist (or classical) methods. specifically, the maximum likelihood method is often utilized, making use of the log-likelihood function (6) (xu and lee, 2015; xu and fei lee, 2018; toker et al., 2021). these methods assume that the unknown parameter θ is a fixed constant and determine the probability of its estimator through limiting relative frequencies. as a result of these assumptions, it is not possible to provide a probabilistic statement regarding the unknown parameter θ since it is considered fixed. consequently, the frequentist approach is not suitable for incorporating expert knowledge in the estimation of the unknown parameter θ. bayesian estimation provides an alternative approach, treating θ as a random variable and allowing for the expression of uncertainty through probability statements and distributions known as priors (mahmoud et al., 2020; awwad et al., 2021; hegazy et al., 2021). priors are designed to incorporate any relevant information the researcher possesses before observing the data. therefore, priors can take various forms, accommodating the inclusion of expert knowledge in the estimation of the unknown parameter θ. by leveraging our expert knowledge of farmer preferences, as captured by the prior distribution of θ, bayesian analysis enables us to learn from data and update our knowledge accordingly. it emphasizes that the conditional probability of the unknown parameter θ serves as the optimal means of summarizing the information derived from the data (chan et al., 2019). the bayesian approach provides a comprehensive probabilistic framework for empirical modeling. it enables us to address hypothetical bias in the estimates of sample characteristics such as e(wtai/xi) by leveraging our prior knowledge of the unknown parameters (kadane and lazar, 2004; gelman et al., 2013; kruschke, 2013). as stated by chan et al. (2019), bayesian analysis involves the calculation of the posterior distribution of the parameter θ, denoted as p(θ/wta). it can be expressed, up to an arbitrary constant, in a proportional form as: p(θ/wta) ∝ log l × π(θ) (9) here, log l represents the log-likelihood function of the censored regression model for wta (refer to (6)), and π(θ) is referred to as the prior distribution of θ, or table 1. description of explanatory variables and their expected signs. variables description expected signs age age of farmer (continuous) (±) gend sex of farmer (dummy): 1 if male and 0 if female (±) origin origin of farmers (dummy): 1 if native and 0 if non-native (+) edu education level of farmers (categorical): 0 if none (never been to school), 1 if primary and 2 if high level (secondary, high school) (−) fhsize size of farm households (continuous) (±) onfinc average yearly on-farm income (continuous) (+) lofarm location of the farm (dummy): 1 if out of the reserve and 0 if otherwise (+) fasize size of the farm (dummy): 1 if more than 5ha and 0 if not (−) envsty environmental sensitivity of farmers (dummy): 1 if sensitive to the role of forest to protect the environment and 0 if not (−) awpes awareness of pes scheme (dummy): 1 if yes and 0 otherwise (±) outcpra perception of the output of current practices by farmers (dummy): 1 if average (average, bad) and 0 if good (good, very good) (±) biofert knowledge of bio-fertilizers (dummy): 1 if farmers have knowledge and 0 otherwise (±) ntfps importance of ntfps to the farmer: 1 if important and 0 otherwise (−) source: authors’ definitions https://doi.org/10.36253/bae-13534 207incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 simply the prior. as mentioned earlier, the prior distribution reflects our expert knowledge about the parameter θ before examining the data. it can assume various forms, such as uniform, normal, gamma, or other distributions, depending on the problem’s nature and the available prior information. in equation (9), the prior knowledge is incorporated into the posterior distribution using bayes’ theorem. as more data is collected, the influence of the prior distribution diminishes, and the posterior distribution becomes increasingly shaped by the likelihood function. this process is known as updating the prior distribution. as discussed in section 3.2, there are various sources of prior knowledge. when expert opinions, previous studies, or empirical data about the parameters are lacking, theoretical considerations can be employed to generate prior knowledge. theoretical considerations are particularly valuable for specifying uninformative priors. chan et al. (2019) defines an uninformative, flat, or diffuse prior as any distribution that expresses vague or general information about a parameter. the use of noninformative priors in bayesian analysis offers several advantages, including: • objectivity: non-informative priors aim to minimize the influence of prior knowledge on posterior results by expressing ”objective” information, such as ”the parameter is positive” or ”the parameter is less than a certain limit.” they strive to be as objective as possible, allowing the data to exert the greatest influence on the final inference. this can help address concerns about subjectivity or bias in the analysis. • robustness: non-informative priors can be valuable when prior knowledge or information is limited or unreliable. they provide a default assumption that avoids strong assumptions or bias based on incomplete or uncertain information. this is particularly beneficial in situations where there is a lack of prior knowledge or when multiple analysts with different perspectives are involved. • simplicity: non-informative priors are often simple and unrestrictive, facilitating a more straightforward analysis. they simplify the modeling process and reduce the computational burden associated with estimating complex prior distributions. • sensitivity analysis: non-informative priors are useful for conducting sensitivity analyses. by comparing the results obtained with non-informative priors to those obtained with informative priors, researchers can assess the impact of prior assumptions on the final inference. this helps identify the extent to which the results depend on prior specifications. • communicating uncertainty: non-informative priors offer a means to quantify and communicate uncertainty when little or no prior knowledge is available. they enable the estimation of credible intervals or posterior distributions that reflect the uncertainty in the parameters of interest based solely on the observed data. however, it’s important to acknowledge that noninformative priors have their limitations. in certain cases, they may not fully capture all available information, resulting in less efficient inference or potentially misleading results. table 1 outlines the expected signs for the parameters in our model based on theoretical considerations, representing the necessary prior knowledge for specifying noninformative priors. however, for robustness, we assume that all explanatory variables may have both positive and negative effects on wta. the principle of indifference, which assigns equal probabilities to all possibilities, is the simplest and oldest rule for determining a non-informative prior. in this study, we adopt a non-informative prior for β, specifically a uniform prior distribution, π(β) ∝ 1. additionally, it is common in the literature to use a gamma distribution as a prior for the standard deviation of a normal distribution (chan et al., 2019). therefore, we assume that σ follows a gamma distribution, π(σ) ∝ g(a, b), where a = 0.01 represents the shape parameter and b = 0.01 denotes the inverse-scale parameter. the choice of hyper-parameters a and b ensures convergence of the posterior distribution sampling. furthermore, we assume that β and σ are independently distributed, giving π(θ) = π(β)π(σ). to perform a bayesian analysis of the tobit model (2) and (3), we can utilize the lifereg procedure in the statistical analysis software (sas). this procedure incorporates an adaptive rejection metropolis sampling (arms) algorithm based on the programme provided by gilks (2003) to draw a sample θk = (βk, σk)k=1...m from the full-conditional distribution (9). the bayesian estimate of the mean wta of agent i, denoted as e(wtai/ xi), is then calculated as: (e(wtai/xi)/y) ≈ (1 − φ(αk))(µk – σkλk(αk)), as m approaches infinity, (10) where, y = {wtai, xi}i=1,..,n represents the data, αk = −µk/ σk, λk(αk) = ϕ(αk)/(1 − φ(αk)), ϕ and φ denote the standard normal density and distribution functions, respectively, and µk = xiβk. https://doi.org/10.36253/bae-13534 208 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 claudiane yanick moukam, calvin atewamba 4. results and discussion in this section, we provide the results of implementing the methodology outlined in the previous section. firstly, we provide a brief overview of the descriptive statistics pertaining to both traditional and eco-innovative farming practices in the study area. subsequently, we employ a multidimensional preferences analysis to examine contextual behavior patterns that could elucidate farmer preferences. finally, we analyze the empirical estimates of farmer willingness to accept compensation for environmental services. 4.1. descriptive statistics of traditional and eco-innovative farming practices throughout generations, farmers have continuously strived to enhance agricultural land productivity through the utilization of available technologies. table 2 provides an overview of the traditional and eco-innovative farming practices employed by farmers in the study area. it is observed that approximately 85 percent of farmers utilize chemical inputs, such as fertilizers and pesticides, to improve soil fertility and manage cocoa farms. among the pesticides used, fungicides and insecticides are the most commonly employed, both within and outside the reserve. regarding soil preparation techniques, 53.5% of farmers employ crop rotation, followed by a slash and burn method (34%). despite facing challenges related to limited land availability for crop cultivation, a majority (50.5%) of farmers employ various durations of bush fallow systems to enhance land productivity. while 24.5% of farmers have their farms located within the reserve, a significant proportion of respondents (70.5%) attribute most of the observed deforestation in the reserve to the exploitation of fuelwood, timber, and ntfps. to mitigate the adverse impacts of deforestation, chemical fertilizers, and pesticides in the vicinity of the lake, farmers have adopted various eco-innovative practices to protect the environment. a significant number of farmers prioritize conservation by preserving old and large trees within their own farms. for example, approximately 52% of farmers have planted fruit trees, ntfps, and other species on their land. these seedlings are typically sourced from their own nurseries or purchased from external suppliers. the planting of trees serves the dual purpose of preventing soil erosion and safeguarding the environment. however, agroforestry practices are not widely implemented, primarily due to limited awareness regarding their significance. only a small proportion of farmers (16%) have heard about agroforestry or bio-agriculture, with information dissemination occurring through various channels, including schools, village meetings, and the farmers field school initiative of minader (ministry of agriculture and rural development). it is worth noting that the majority of farmers believe that chemical fertilizers are the most effective solution to combat declining soil fertility. this inclination can be attributed to the lack of awareness regarding indigenous knowledge pertaining to soil erosion prevention, soil demineralization, and the production and application of organic manure. in fact, when asked to explain their understanding of bio-fertilizers, only 30.5% of farmers demonstrated some knowledge on the subject. moreover, it is noteworthy that only 48% of farmers consider the outputs from their current farming practices to be good or satisfactory (see table 2). almost all farmers (95.5%) acknowledge the significance of forests in providing vital ecosystem services, including climate regulation, flood control, erosion control, wildlife habitat, landscape beauty, and cultural/spiritual value. concerning watershed protection, the majority of farmers (97.5%) recognize the positive correlation between fortable 2. traditional and eco-innovation farming practices. description frequency of ”yes” % of the respondents chemical use overall 170 85 fungicides 94 55.29 insecticides 22 12.94 soil preparation techniques slash and burn 68 34 rotation 107 53.50 bush fallow practice 101 50.50 tree conservation ntfps 47 43.12 timber 31 28.44 fruit trees 21 19.27 reforestation fruit trees 70 67.31 ntfps 27 27.96 origin of seedlings from own nursery 48 46.15 buy 29 27.88 donation 22 21.15 forest cover destroyed in the reserve more than 75% of forest destroyed 141 70.50 agro-forestry knowledge 32 16 bio-fertilizers knowledge 61 30.50 source: authors’ calculations from survey data. https://doi.org/10.36253/bae-13534 209incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 est cover and water quality. however, only 27% of farmers are familiar with the pes mechanism (see table 2). nonetheless, considering the farmers’ willingness to plant diverse tree species on their own land, it is reasonable to expect their active participation in the pes scheme if they are provided with incentives to plant and preserve trees. 4.2. adoption of agro-forestry and multidimensional preferences analysis according to the data presented in table 3, a significant proportion of farmers (87.5%) are willing to accept compensation in order to participate in an afforestation programme both within and outside the reserve, as well as along the border of the lake. while the benefits of agroforestry are discussed with farmers during the survey, only a small percentage (8.5%) of farmers residing near the lake express their willingness to adopt agroforestry practices. however, among those who are willing to adopt agroforestry, a majority also demonstrate their commitment to refrain from using chemicals within an 8-meter distance from the lake, provided that they receive seedlings for agroforestry and receive training on best agroforestry practices. in conducting a multidimensional preferences analysis (mpa), we aim to identify the primary dimensions of farmer preferences that can explain their willingness to adopt agroforestry practices. while principal component analysis (pca) focuses on reducing complexity and identifying patterns in large datasets, mpa delves into understanding individual or group preferences and priorities. it can be seen as a pca of a data matrix, with columns representing individuals and rows representing variables or objects. as depicted in figure 2, the determinants of farmers’ willingness to participate in an afforestation programme are classified into three groups: • the first group comprises variables such as awareness of pes schemes (awpes), knowledge of biofertilizers (biofert), the importance of non-timber forest products (ntfps), and education level (edu). this group reflects the extent to which farmers possess knowledge about environmental management. it is reasonable to assume that farmers with higher levels of education are more likely to be aware of pes programmes, have knowledge of bio-fertilizers, and understand the importance of ntfps. • the second group consists of variables related to environmental sensitivity (envsty), the origin of the farmer (origin) (whether native or non-native to the study area), and the location of the farm (lofarm) (whether inside or outside the reserve). this group captures the farmers’ connection (sensitivity, origin, and location) to the study area and the local community. it is evident that farmers who are native to the study area and have farms within the reserve exhibit a higher sensitivity to the role of forests in environmental protection. • the third group includes variables such as age (age), gender (gend), farm size (fasize), farm household size (fhsize), and yearly on-farm income (onfinc). this group reflects farmers’ socioeconomic status and demographic characteristics. the strong correlation between on-farm income and farm size suggests the existence of an extensive agricultural system, which often exerts significant pressure on the environment. by analyzing these three groups of variables, multidimensional preferences analysis helps uncover the underlying dimensions driving farmers’ preferences and their willingness to adopt agroforestry practices. the perfect negative correlation between the first group of variables (related to knowledge and awareness of environmental management) and the second group of variables (related to connections with the local community) reveals an interesting pattern. it suggests that farmers who have weak connections with the local community tend to be more knowledgeable about environmental management, while those with strong connections are less informed in this regard. this finding has important implications as it may help explain why rural areas are more susceptible to environmental degradation. in rural areas, where strong community ties and social networks are prevalent, farmers who have close connections with the local community may rely on traditional practices and knowledge passed down through generations. however, these practices may not always align with sustainable agricultural practices or modern environmental management strategies. on the other hand, farmers who have weaker connections with the local community, such as migrants or individuals with limited social integration, may have more exposure to table 3. distribution of the willingness to accept. response fcfa10,000 fcfa15,000 total no 14 11 25 yes 86 89 175 total 100 100 200 percentage of yes 86% 89% 87.5% source: authors’ calculations from survey data. https://doi.org/10.36253/bae-13534 210 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 claudiane yanick moukam, calvin atewamba external information and knowledge regarding sustainable agriculture and environmental management. this finding highlights the need for capacity building and training initiatives targeting local and indigenous communities, as well as natural resources owners in rural areas like barombi mbo. by providing them with appropriate knowledge and skills related to sustainable agricultural practices and environmental management, we can promote behavior change and the adoption of more sustainable practices. recognizing the ownership of natural resources, coupled with empowering individuals with the necessary knowledge, can serve as a catalyst for positive changes and contribute to the reduction of environmental degradation in the area. the non-correlation between the third group of variables (related to socio-economic status and demographic characteristics) and both the first and second groups of variables suggests that farmers’ knowledge of environmental management practices and their connections with the local community are independent of their socio-economic and demographic conditions. in other words, farmers can enhance their understanding of environmental management or improve their community connections regardless of their socio-economic status or demographic characteristics. this finding implies that efforts to build farmers’ capacity in environmental management will not significantly impact their socioeconomic and demographic conditions. while farmers may possess knowledge about sustainable agricultural practices, they may lack the socio-economic incentives or motivations to translate that knowledge into concrete actions that protect the environment. this may explain why farmers, despite having knowledge of sustainable practices, appear to be less sensitive to environmental degradation. to address this gap between knowledge and action, it becomes imperative to introduce economic incentive schemes such as pes programmes. these programmes figure 2. multidimensional preferences analysis. https://doi.org/10.36253/bae-13534 211incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 create an enabling business environment where farmers can be rewarded for their efforts in reducing environmental deterioration. by providing economic incentives, farmers are more likely to be motivated to adopt and implement sustainable agricultural practices that contribute to environmental protection. integrating economic incentives with farmers’ existing knowledge of sustainable practices, we can bridge the gap between awareness and action, ensuring that farmers are actively engaged in protecting the environment. this approach recognizes the need to align environmental goals with socio-economic conditions and provides a practical mechanism for incentivizing sustainable practices among farmers. overall, combining knowledge-building initiatives with economic incentive schemes can effectively encourage farmers to apply their knowledge and contribute to environmental conservation while considering their socio-economic and demographic realities. 4.3. determinants of the wta for the provision of environmental services geweke diagnostics are commonly used to assess the convergence of parameters drawn from the posterior distribution in bayesian analysis. the fact that geweke diagnostics (table 4) indicate no evidence to reject the convergence suggests that the estimation process has been successful and the sample of parameters obtained from the posterior distribution (9) is representative. by using this sample of parameters, statistical inferences can be made about the effects of farmers’ socio-economic, environmental, and demographic values on their wta for environmental services. the probabilities pr(θi ≤ 0) provide a basis for determining the likely direction of influence of each variable on wta. the influence is likely negative if pr(θi <= 0) ≥ 0.5, whereas it is positive if pr(θi <= 0) < 0.5. based on the given information, it appears that variables such as the sex of farmers (gend), origin of farmers (origin), location of farms (lofarm), output of current practices (outcpra), awareness of pes scheme (awpes), and knowledge of bio-fertilizers (biofert) have a positive inf luence on wta. this means that these factors are likely to increase farmers’ wta for environmental services. on the other hand, variables such as the age of farmers (age), education level of farmers (edu), size of farm households (fhsize), size of the farm (fasize), yearly on-farm income (onfinc), and importance of non-timber forest products (ntfps) are likely to have a negative effect on wta. this suggests that these variables are expected to decrease farmers’ wta for environmental services. these findings provide valuable insights into the factors that shape farmers’ preferences and willingness to accept compensation for environmental services. understanding these determinants can inform policy and decision-making processes related to the design and implementation of effective incentive schemes, such as payment for environmental services, to promote sustainable agricultural practices and environmental conservation. the negative effect of farmers’ age (age) on their wta participation in an afforestation programme can table 4. bayesian parameter estimates. parameters (θi) estimates std. dev. equal-tail interval pr(θi ≤ 0) geweke diagnostics lower upper z pr ≥ |z| intercept 12060.4 1843.8 8529.4 15674.5 0 -1,237 0.216 age (age of farmer) -79.680 36.286 -151.5 -9.6776 0.987 0.171 0.865 gend (sex of farmer) 1276.7 732.4 -172.5 2691.3 0.043 1,422 0.155 edu (education level of farmer) -871.7 549.2 -1942.4 211.5 0.944 -0.101 0.920 origin (origin of farmer) 1546.9 967.3 -344.2 3469.9 0.054 0.880 0.379 fhsize (size of farm household) -109.7 163.4 -436.2 206.4 0.748 -0.124 0.901 lofarm (location of farm) 346.9 820.5 -1251.8 11973.7 0.336 -0.235 0.814 fasize (size of farm) -255.3 772.2 -1749.9 1272.6 0.630 -1,505 0.133 onfinc (yearly on-farm income) -0.00013 0.000208 0.000281 0.000273 0.736 0.678 0.498 outcpra (output of current practices) 444.7 675.8 -875.5 1770.6 0.254 -0.154 0.877 awpes (awareness of pes scheme) 1751.6 782.2 218.3 3271.9 0.012 -0.852 0.394 biofert (knowledge of bio-fertilizers) 2923.6 765.1 1416.1 4434.5 0.000 0.766 0.443 ntfps (importance of non-timber forest products) -538.0 746.3 -2025.3 912.9 0.763 0.236 0.814 scale 4595.2 222.5 4182.7 5054.6 0 1,202 0.229 https://doi.org/10.36253/bae-13534 212 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 claudiane yanick moukam, calvin atewamba be explained by several factors. as individuals grow older, they tend to prioritize existential values over economic values. existential values encompass fundamental questions regarding human existence, such as “to be or not to be?”, as well as practical concerns related to protecting human life and avoiding threats to existence (lipiec, 2000). this shift in focus towards existential values may lead older farmers to be less inclined to accept compensation in exchange for adopting eco-innovations that protect the environment and, consequently, human existence. additionally, the aging process often fosters a greater concern for the well-being of others, beyond one’s own self-interest. as individuals age, they become more attuned to the collective and the welfare of the broader community. older individuals may view the realization of environmental values as a means to establish the foundational basis for other values. consequently, elderly farmers are less likely to be receptive to compensation offers aimed at incentivizing their adoption of agroforestry practices, especially when the central question revolves around human existence. overall, the negative relationship between age and wta for participation in an afforestation programme can be attributed to the prioritization of existential values over economic values among older individuals. aging prompts individuals to care not only for themselves but also for the collective well-being. elderly farmers may view the pursuit of environmental values as crucial for establishing the existential foundation necessary to support other values. consequently, they may be less inclined to accept compensation to adopt agroforestry practices, given the overarching importance they place on human existence. the positive effect of farmers’ origin (origin) on their wta compensation to participate in an afforestation programme may seem counterintuitive at first. one would expect that native farmers, who have a stronger connection to the local area and a better understanding of the importance of protecting their natural heritage, would be more inclined to adopt agroforestry practices voluntarily, without requiring compensation. however, to interpret this unexpected result, we need to consider the relationship between farmers’ origin and the location of their farms. as illustrated in figure 2 and table 4, native farmers tend to have farms located outside the reserve. it is important to note that farmers with farms outside the reserve are more likely to demand higher compensation to participate in an afforestation programme. this can be attributed to the fact that farms located outside the reserve generally have fewer trees compared to those within the reserve. consequently, the opportunity cost of adopting agroforestry practices on farms outside the reserve is likely to be higher than on farms within the reserve. native farmers, therefore, may be requesting compensation to offset the higher opportunity cost associated with implementing agroforestry on their farms. additionally, the observed behavior of native farmers could be influenced by their lower level of education and their limited valuation of ntfps, as indicated in figure 2. farmers with lower levels of education or those who do not recognize the importance of ntfps are more likely to demand higher compensation to adopt agroforestry practices, as demonstrated in table 4. this finding aligns with the well-established understanding that higher educational attainment promotes pro-environmental behavior (tianyu and meng, 2020; zhou et al., 2021). in summary, the positive effect of farmers’ origin on their wta for participation in an afforestation programme can be explained by several factors. native farmers, despite their stronger connection to the local area, may request compensation due to the higher opportunity cost associated with adopting agroforestry on farms located outside the reserve. furthermore, their lower level of education and limited recognition of the importance of ntfps may contribute to their demand for higher compensation. these findings emphasize the complex interplay between farmers’ origin, farm location, education, and value orientations in shaping their willingness to accept compensation for agroforestry adoption. the variables representing farmers’ socio-economic status, namely the size of farm households (fhsize), size of the farm (fasize), and yearly on-farm income (onfinc), are found to have a negative effect on farmers’ wta compensation for participating in an afforestation programme, as indicated in table 4. this implies that higher socio-economic status is associated with a greater propensity for pro-environmental behavior. two theoretical perspectives in the literature can help explain this important finding. the first perspective revolves around the concept of post-materialism, which suggests that individuals with higher socio-economic status are more likely to adopt values that prioritize self-expression, subjective wellbeing, and quality of life. as highlighted by pampel (2014), post-materialist values are associated with concerns for issues such as environmentalism, feminism, and equality. in our context, the size of farm households, which serves as an indicator of farmers’ social status, can be seen as reflecting their adherence to postmaterialist values. farmers who value self-expression and quality of life are more inclined to prioritize envihttps://doi.org/10.36253/bae-13534 213incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 ronmental protection and are thus more willing to participate in afforestation programmes. the second perspective is based on the notion of affluence, suggesting that environmental quality is considered an amenity that high-income individuals can more readily afford (franzen and meyer, 2010). in this view, the size of the farm and yearly on-farm income, as indicators of prosperity, can positively influence farmers’ inclination to protect the environment, particularly when the associated economic costs are perceived as insignificant. higher-income farmers may be more willing to invest in environmental conservation measures because they have the financial means to do so without compromising their livelihoods. this affluence argument aligns with the observation that higher socio-economic status promotes pro-environmental attitudes and behaviors. both theories, post-materialism and affluence, can be applied in our context to explain why farmers with higher socio-economic status exhibit a greater willingness for participating in afforestation programmes. the size of farm households ref lects post-materialist values related to self-expression, while the size of the farm and yearly on-farm income capture the affluence aspect, indicating that farmers with greater financial resources are more likely to prioritize environmental protection when the associated costs are perceived as manageable. overall, these findings highlight the role of socioeconomic status in shaping farmers’ pro-environmental behavior and suggest that individuals with higher socioeconomic status are more inclined to support environmental initiatives. the variables representing farmers’ knowledge of environmental management, namely the awareness of pes scheme (awpes) and knowledge of bio-fertilizers (biofert), are found to have a positive influence on farmers’ wta compensation for participating in an afforestation programme, as shown in table 4. this indicates that having greater knowledge about environmental management does not necessarily translate into ecofriendly behavior among farmers. there seems to be a significant gap between farmers’ knowledge of environmental risk management and their actual on-the-ground actions in dealing with environmental issues. this disparity between knowledge and behavior highlights the need to understand the factors that contribute to the “knowledge-behavior gap” in the context of sustainability. merely providing additional information to farmers is unlikely to lead to significant improvements in environmental conditions unless certain key factors are addressed. as emphasized by knutti (2019), securing political will and implementing simple solutions that provide immediate and local co-benefits are crucial. it is not enough for farmers to possess knowledge; they also require support, incentives, and clear pathways for action. while environmental management strategies exist, their implementation is often hindered by various factors, including attitudes towards environmental protection, short-term and medium-term implementation costs, and doubts about the effectiveness and efficiency of proposed policy instruments. farmers who have a positive attitude towards environmental management may perceive compensation for participating in an afforestation programme as a means to bridge the gap between their knowledge and their behavior in the context of sustainability. offering financial incentives can serve as a motivating factor for farmers to align their behavior with their environmental knowledge. overall, the presence of a “knowledge-behavior gap” among farmers indicates that simply increasing their knowledge of environmental management strategies is insufficient to drive eco-friendly behavior. addressing this gap requires a comprehensive approach that goes beyond information provision and tackles other barriers such as attitudes, costs, and doubts about the effectiveness of policy instruments. offering compensation as a reward for participating in environmental programmes can incentivize farmers and help bridge the gap between their knowledge and their actions in a sustainability context. 4.4. the opportunity cost of environmental services bayesian estimation provides us with a sample of parameters {θk = (βk, σk)}k=1...m from the full conditional distribution (9), where θk represents the parameters of farmer preferences. using this sample, we can derive a distribution of the mean wta using equation (7) for a representative farmer.1 the resulting distribution of the mean wta, as depicted in figure 3, exhibits a normal shape. to formally test the normality of the distribution, we use the anderson-darling statistic, which confirms that the mean wta follows a normal distribution with a mean of 10,775cfa franc and a standard deviation of 333.6cfa franc, as shown in table 5. to estimate the mean wta using a bayesian approach, we apply formula (10). the bayesian estimate of the mean wta is calculated to be 10,775cfa franc, with a 95% confidence interval of 10,769-10,781cfa franc, as presented in table 5. the narrow confidence 1 the characteristics of the representative farmer are obtained by taking the mean of each explanatory variable. https://doi.org/10.36253/bae-13534 214 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 claudiane yanick moukam, calvin atewamba interval indicates that the estimate of the mean wta has low volatility or high precision, suggesting a more reliable estimate. overall, the bayesian estimation allows us to obtain a distribution of the mean wta, which is found to follow a normal distribution. the bayesian estimate of the mean wta, along with its confidence interval, provides a precise estimation of the mean wta value, contributing to a better understanding of farmers’ preferences in the context of willingness to accept compensation for participating in an afforestation programme. based on the survey design outlined in section 3.2, we can deduce that the probability for a farmer in the barombi mbo community to accept compensation and participate in an afforestation programme is p = 175/200. to estimate the total willingness to accept (wta) or the community opportunity cost to participate in the afforestation programme using a bayesian approach, we can use the following formula: e(total wta/y ) ≈ n × p × mean_wtak, (11) where y = {wtai, xi}i=1,..,n represents the observed data, n = 349 denotes the size of the eligible population in barombi mbo, and {mean–wtak} represents the sample of the mean wta obtained from the bayesian estimation. in this formula, m represents the number of samples drawn from the bayesian estimation. as m approaches infinity, the estimate becomes more accurate. by multiplying the probability p with the population size n and the average of the sample mean wta values, we can estimate the total wta or the community opportunity cost to participate in the afforestation programme. it is important to note that this estimation assumes that the sample of farmers in the survey is representative of the entire eligible population in barombi mbo. the results reported in table 5 indicate that the bayesian estimate of the total wta or the community opportunity cost to participate in the afforestation programme is 3,290,448cfa franc with a 95% confidence interval of 3,288,511-3,292,385cfa franc. the small confidence interval suggests that the estimate of the total wta exhibits low volatility or high precision. we can also derive a sample of the distribution of the community opportunity cost of providing environmental services from the sample distribution of the mean wta using the relationship total wta = n ×p ×mean wta. furthermore, a test of normality using the anderson-darling statistic confirms that the community opportunity cost of providing environmental services follows a normal distribution with a mean of 3,290,448cfa franc and a standard deviation of 98,818cfa franc. comparing these results with those obtained by moukam (2021) using a maximum likelihood method to estimate the tobit model, it can be observed that the estimated values of the mean and total wta obtained from the bayesian approach are almost three times higher. specifically, the bayesian estimate of the mean wta is 10,775cfa franc, whereas the estimate obtained using the maximum likelihood method is 4,488cfa franc. similarly, the bayesian estimate of the total wta is 3,290,448cfa franc, while the maximum likelihood estimate is 1,370,491cfa franc. this difference highlights the potential of the bayesian approach to account for both tangible and intangible values of ecosystem services. overall, the results suggest that the bayesian approach provides a more comprehensive and precise estimation of the wta and community opportunity cost, incorporating both economic and noneconomic factors associated with environmental services. in bayesian analysis, sensitivity analysis is usually recommended to assess the impact of prior assumptions on the final inference using non-informative priors as the counterfactual. however, in our case, since our results are primarily based on non-informative priors, conducting a sensitivity analysis is not feasible. consequently, our results can be interpreted as a quantification of the uncertainty in the parameters of interest based solely on the observed data. table 5. opportunity cost of supplying environmental services (fcfa). parameter estimate std. dev. 95% confidence limits anderson-darling lower upper stat. p. value mean wta 10,775 323.59 10,769 10,781 0.587 0.131 t otal wta 3,290,448 98,818 3,288,511 3,292,385 0.587 0.131 note. aderson-darling is a goodness-of-fit test for normal distribution. https://doi.org/10.36253/bae-13534 215incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 5. conclusion and implications in this paper, we incorporate non-data information and expert knowledge into the estimation of farmers’ opportunity cost for providing environmental services through agroforestry and forest regeneration in the rural area of cameroon. to achieve this, we begin by conducting a survey to gather information on farmers’ wta and factors that may influence their preferences to participate in an environmental protection programme in barombi mbo, a rural region in cameroon. subsequently, we adjust a tobit model of the wta by incorporating expert knowledge through the specification of prior distributions for model parameters. finally, a bayesian approach is employed to estimate both the model parameters and the farmers’ opportunity cost of supplying environmental services, accounting for both data and non-data information. the paper contributes to the important economic literature on the valuation of environmental goods and services using a sp approach. specifically, we propose a two-step survey design to determine a limited number of bids that farmers can choose from, which allows them to highlight their preferences and their wta for changes in environmental services. additionally, we conduct a multidimensional preference analysis to identify the primary dimensions of farmer preferences that may explain their willingness to participate in environmental conservation programmes. furthermore, we expand the well-known tobit model of wta by incorporating non-data information or expert knowledge through the specification of parameter distributions. to estimate a comprehensive probabilistic model of wta for changes in environmental services, we employ a bayesian approach. compared to the related literature (moukam, 2021; pérez-sánchez et al., 2021), our approach to wta modeling has the potential to significantly reduce potential hypothetical bias in data collection and analysis.2 this reduction in hypothetical bias is achieved through an improved estimate of farmers’ opportunity cost of supplying environmental services, resulting in more realistic and interpretable results. our results align with the findings of previous authors kadane and lazar (2004); gelman et al. (2013); kruschke (2013) who have demonstrated that bayesian methods provide more accurate estimates, better model comparison, and improved inferences compared to traditional frequentist methods. however, it is worth noting that conducting a bayesian analysis requires a careful specification of prior distributions that incorporate our expert knowledge. as more data are collected, the influence of the prior distribution decreases, and the posterior distribution becomes increasingly influenced by the likelihood function (chan et al., 2019). an important result of this paper is that the majority of farmers (87.5%) are unlikely to voluntarily engage in environmental management without economic incentives. our multidimensional preference analysis suggests that farmers’ behavior may be attributed to the lack of correlation between environmental and socio-economic dimensions of their preferences. therefore, it is crucial to implement economic incentive mechanisms, such as pes, to facilitate the alignment of environmental and socioeconomic values. the bayesian analysis reveals that aging is likely to promote pro-environmental behavior, indicating that older individuals are more sensitive to existential values compared to the youth (lipiec, 2000). additionally, natives are more inclined to accept compensation for adopting sustainable agricultural practices compared to migrants. this controversial finding can be partly explained by the observation that natives generally have lower educational attainment than migrants. this aligns with the widely accepted understanding that higher levels of education promote pro-environmental behavior (tianyu and meng, 2020; zhou et al., 2021). a significant finding of this paper is that higher socio-economic status, as indicated by factors such as the size of farm households, farm size, and yearly onfarm income, positively inf luences proenvironmental behavior. this observation can be explained by the affluence argument (franzen and meyer, 2010), which suggests that high-income farmers are more capable of affording environmental quality as an amenity good. moreover, farmers with higher socio-economic status are more likely to have embraced postmaterialist values, which prioritize self-expression, subjective well-being, 2 hypothetical bias arises from the tendency of people to systematically overor understate their wta in sp studies. figure 3. distribution of mean wta. https://doi.org/10.36253/bae-13534 216 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 claudiane yanick moukam, calvin atewamba and quality of life, leading to increased concerns for environmental issues (pampel, 2014). furthermore, our analysis reveals that an increase in knowledge of environmental management strategies is less likely to promote eco-friendly behavior. this finding aligns with the research by knutti (2019), who identified several barriers contributing to the observed knowledge-behavior gap. these barriers include attitudes towards environmental protection, implementation costs in the short and medium term, and skepticism regarding the effectiveness of proposed policy instruments. another important finding of this paper is that farmers’ wta follows a normal distribution with a mean of 10,775cfa franc and a standard deviation of 333.6cfa franc. additionally, the community opportunity cost of supplying environmental services also exhibits a normal distribution, with a mean of 3,290,488cfa franc and a standard deviation of 98,818cfa franc. these distributions have significant implications for policy-making, as they enable us to make probabilistic statements about the value of environmental services. for instance, considering the significance of wta in cost-benefit analysis (cba), our results provide an effective means to incorporate uncertainty when assessing the welfare effects of regulatory and investment interventions that impact the environment. this allows expected outcomes in cba, such as financial and economic net present values (npvs), to incorporate risk and uncertainty associated with environmental management. furthermore, our estimation of the distribution of wta plays a crucial role in understanding the intricate financial trade-off involved in the cameroonian government’s engagement in international financial mechanisms for biodiversity conservation and climate change mitigation. this paper presents a significant empirical framework for estimating the value of environmental services and evaluating the inf luence of socio-economic and demographic factors on that value. considering that farmers in developing countries often exhibit comparable socio-economic and demographic characteristics, along with similar concerns regarding environmental degradation, our estimation of the wta distribution can serve as valuable prior knowledge or information regarding the value of environmental services in other rural areas of cameroon or the developing world. references adesina, a. a., d. mbila, g. b. nkamleu, and d. endamana (2000, 9). econometric analysis of the determinants of adoption of alley farming by farmers in the forest zone of southwest cameroon. agriculture, ecosystems and environment 80, 255–265. agbor, e. 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(1967). statistics: an introductory analysis (2nd ed.). harper and row. zhou, m., f. chen, and z. chen (2021, may). can ceo education promote environmental innovation: evidence from chinese enterprises. journal of cleaner production 297, 126725. https://doi.org/10.36253/bae-13534 220 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13534 claudiane yanick moukam, calvin atewamba a. appendix figures. 4 and 5 show the draws, the autocorrelation of draws, and the empirical posterior distribution of two parameters: the intercept and the biofertilizer (biofert) from the implementation of the adaptative rejection metropolis sampling (arms) algorithm based on a programme provided by gilks (2003) using the procedure lifereg of the statistical analysis software (sas). we can see that the draws are randomly distributed and exhibit low correlation. this is an indication that the bayesian estimation has converged for these two parameters. a similar result is obtained for other parameters. figure 4. bayesian diagnostics for intercept. figure 5. bayesian diagnostics for biofertilizer. https://doi.org/10.36253/bae-13534 towards a holistic approach to sustainable risk management in agriculture in the eu: a literature review linda arata1, simone cerroni2, fabio gaetano santeramo3, samuele trestini4, simone severini5,* modelling technical efficiency of horticulture farming in kosovo: an application of data envelopment analysis nol krasniqi1,2,*, stéphane blancard1, ekrem gjokaj2, giovanna ottaviani aalmo3 incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon claudiane yanick moukam1,*, calvin atewamba2 farmers’ acceptance of a micro-irrigation system: a focus group study maria sabbagh1,*, luciano gutierrez2 reducing food-related economic loss to improve food security and cattle trade in the sahel: the case of agropastoral systems in senegal abdrahmane wane1,*, aliou diouf mballo2, cabrelle lauriane dzoukou homsi3, pathé diakhaté4, rahimatou memboup5 bio -based and a ppl ied economics bae bio-based and applied economics 10(3): 169-184, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10881 copyright: © 2021 d. viaggi, f. bartolini, m. raggi. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: d. viaggi, f. bartolini, m. raggi (2021). the bioeconomy in economic literature: looking back, looking ahead. bio-based and applied economics 10(3): 169-184. doi: 10.36253/bae10881 received: may 16, 2021 accepted: october 29, 2021 published: january 11, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio gaetano santeramo. discussants: paolo sckokai, gianluca brunori. orcid dv: 0000-0001-9503-2977 fb: 0000-0002-8946-3110 mr: 0000-0001-6960-1099 bae 10th anniversary paper the bioeconomy in economic literature: looking back, looking ahead davide viaggi1,*, fabio bartolini2, meri raggi3 1 department of agricultural and food sciences, university of bologna, italy 2 department of chemical, pharmaceutical and agricultural sciences, university of ferrara, italy 3 department of statistical sciences, university of bologna, italy *corresponding author. e-mail: davide.viaggi@unibo.it abstract. the objective of this paper is to provide a review of recent economic literature related to the bioeconomy, in particular aimed at identifying relevant pathways for future research in this field. the paper is organised in four main parts. first, we illustrate the economic role of the bioeconomy and its key statistics in the eu. second we review economic topics related to the bioeconomy production in a mainly private (company, consumer, market) perspective. then we extend our attention to the review of wider social and environmental aspects with a focus on ecosystem services. finally, we discuss the interplay of the above topics and cross cutting issues in the attempt to identify the most promising pathways for further research. while the economic literature is growing fast in all the fields of the bioeconomy, we highlight in particular the need of more economic research focusing on transitions and innovation. however, we also highlight the need to take a system perspective and accounting explicitly for the trade-offs among the many objectives that the bioeconomy is expected to target and to better account of the costs and benefits affecting different stakeholder groups. keywords: bioeconomy, sustainable development, bio-based economy, circular economy, ecosystem services. jel codes: q57. 1. introduction and objectives the editorial of the first issue of the journal bio-based and applied economics in spring 2012 was entitled “from agricultural to bio-based economics? context, state-of-the-art and challenges”. the paper reviewed the trends in agricultural economic literature and asked whether there was a shift from traditional sectoral economics, such as agricultural economics, towards a more general discipline that could have been called “bio-based economics” (viaggi et al., 2012). that title, in itself, provided somehow an agenda for the journal, and envisaged a potential evolution of the discipline in the next decade, though, at that time, the bioeconomy was largely unknown by academic research in economics. since that article appeared, a lot of events and changes occurred: the bioeconomy has become mainstream, while more and more countries have http://creativecommons.org/licenses/by/4.0/legalcode 170 bio-based and applied economics 10(3): 169-184, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10881 davide viaggi, fabio bartolini, meri raggi their bioeconomy strategy. the eu published an update of its bioeconomy strategy in 2018, while italy went under two versions of its country strategy. the recent strategies in europe, in particular the one from the eu, re-define the bioeconomy as an aggregate of sectors using biological resources, emphasising the interconnection with ecosystems and the general contribution to economic development, while technologies, in particular biotechnologies, are much less prominent (european commission, 2018). the green deal strategy has renewed the importance of the bioeconomy and the circular economy, as the main means to achieve the transformative changes required to addresses global challenges, as the bioeconomy can potentially create synergies among the various dimensions of sustainability (giampietro, 2019; peters, jandrić and hayes, 2020) secondary and tertiary resource flows and helps to identify what can and cannot be re-circulated within the metabolic pattern of social-ecological systems. adopting the biophysical view, it becomes clear that the industrial revolution represented a linearization of material and energy flows with the goal to overcome the low pace and density of biological transformations. the required level of productivity of production factors in contemporary developed economies (flows per hour of labor and per hectare of land use). the economic literature on the bioeconomy has been growing steadily. at the end of april 2021, scopus reports 849 papers in the fields of social sciences, economics and econometrics, and business management and accounting, with title, keywords or abstracts mentioning the bioeconomy. papers published in 2020 were 187 in comparison to 21 published in 2012, while papers published in 2021 were already 97 at the time of writing this paper. the literature on the bioeconomy is taking shape, but did not substitute the literature in agricultural economics or food economics, both sectors being, on the contrary, flourishing. the bioeconomy literature is rather focusing on new value chains and on topics that are more relevant for the bioeconomy as a whole than for individual sectors. the objective of this paper is to provide a review of recent economic literature related to the bioeconomy, in particular aimed at identifying relevant pathways for future research in the field. the approach is based on a literature review, but is far from being systematic. the papers used derive mainly from a screening of the scopus database after searching for the keyword “bioeconomy” and selecting papers in economics, business & management and social sciences, or bioeconomy and ecosystem services. then papers were selected based on a subjective evaluation of their ability to provide insights about recent trends, focusing mostly on the more recent papers. the outcome is discussed following an organisation of topics derived from an update of the structure proposed by viaggi (2018). though potentially relevant, for reasons of space we on purpose exclude studies focusing on descriptive developments on one single product and environmental assessment studies such as lca, as well as studies describing the bioeconomy in individual countries. the remainder of the paper is organised as follows. in section 2 we illustrate key statistics and trends of bioeconomy in the eu. in section 3 we review economic topics related to the bioeconomy production in a mainly private (company, consumer, market) perspective. in section 4 we extend the topic to the review of wider social and environmental aspects with a focus on ecosystem services. in section 5, we discuss the interplay of the above and selected cross-cutting issues, in the attempt to identify the most promising pathways for further research. section 6 concludes. 2. statistics and trends of the bioeconomy in the eu in spite of the relevance of the bioeconomy, statistics related to the sector are still at a development stage. the main problem to obtain clear figures is the lack of a consolidated and harmonised methodology, which makes also difficult the comparison of results across countries. also for sectors with well-established statistics (e.g. energy) disaggregating the bioeconomy component may be a challenge. the two main approaches for quantifying the bioeconomy are the input-based and the output-based approach. the former attempts to measure the proportion of biomass in inputs used for the production of biobased products (see for example efken et al., 2016; luke, 2019), while the latter tries to measure the biomass content of bio-based products (see for example capasso and klitkou, 2020; vandermeulen et al., 2011). in 2018 the international sustainable bioeconomy working group (isbwg) published a review of approaches, applications and indicators to measure economic, social and natural resources aspects of bioeconomy in different eu countries (bracco et al., 2018). from 2017, the european commission and the nova-institute have presented a common output approach for a cross-country comparison in some publications where a quantification of performance indicators relying on sectors and sub-sectors of the bioeconomy for all eu member states are illus171the bioeconomy in economic literature: looking back, looking ahead bio-based and applied economics 10(3): 169-184, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10881 trated (ronzon and m’barek, 2017; ronzon et al., 2017; ronzon and m’barek, 2018; piotrowski et al., 2019). ronzon et al. (2020) updates the methodology and data presented previously in 2018 and proposes a methodology based on the following approaches: a) for the sectors that fully belong to the bioeconomy, existing statistics are harmonized and used; b) for those sectors which only partially belong to the bioeconomy, estimation of a “bio-based share” is derived from experts’ consultations. in particular, agriculture, forestry, fishing, the manufacturing of food, beverages, tobacco, and paper are considered as sectors fully belonging to the bioeconomy. other sectors, like the manufacture of textiles, wearing apparel, leather, wood products, furniture, chemicals, pharmaceuticals, plastics and rubber and the production of electricity are included only partially in the bioeconomy. eurostat is the main data source for a quantification of socio-economic relevance in all sectors of the bioeconomy in eu. in particular, prodcom (eurostat, 2020) and the structural business statistics (sbs, eurostat, 2020a) database are the two main data sources. the principal indicators presented in the reports are the turnover, the employment and the value added. from porc et al. (2020) figure 1 reports the percentage share of turnover in the bioeconomy in eu-28 in 2017 and the trends between sectors over the 2008–2017 period. the bioeconomy as a whole shows a continuous increase from 2008 (turnover less than 2 trillion) to 2017 (turnover over 2,4 trillion euro). almost half of the bioeconomy turnover comes from the food and beverages sectors, that also account for the majority of the increase over the period, while about a quarter is produced by agriculture and forestry. the last quarter is obtained by bio-based industries. ronzon et al. (2020) provide the eu bioeconomy data in the post brexit situation (table 1). they estimated that bioeconomy employed around 17.5 million people, and created €614 billion of value added in 2017. it is relevant to note that this data represents about 8.9% of the eu-27 labour force and 4.7% of the eu-27 gdp. the updated analysis of ronzon et al. (2020) also elaborates on the different trends and country developments of the bioeconomy. in particular, they identify four groups of eu countries based on their performances on two dimensions: a) apparent labour productivity, and b) location quotient of the bioeconomy (i.e. the specialisation rate of labour market in the bioeconomy). the groups showing structural differences between national bioeconomies, are: high specialisation (location quotient≥ 1.5) and below average apparent labour productivity (≤ half the eu-27 level): eastern member states (romania, bulgaria, poland, latvia, croatia, lithuania), portugal, and greece; this group is characterized by manufacturing of textiles and/or wood products with labour-intensive production and an high rate of bioeconomy jobs located in agriculture, forestry, and fishing and aquaculture (biomass production sectors). low specialistation (location quotient ≤ 1.3) and medium-high apparent labour productivity (between half the eu-27 level and the eu-27 level): estonia and central member states (slovenia, hungary, cyprus, czech republic, slovakia) and malta; in this group, apparent labour productivity is higher than the previous group, mainly in agriculture, forestry, and bio-plastics manufacturing. low specialisation (location quotient ≤ 0.9) and an apparent labour productivity above the eu-27 level (but less than double the eu-27 level): western figure 1. turnover in the bioeconomy in eu 28: percentage between sectors and trends over 2008-2017 (porc et al., 2020). 172 bio-based and applied economics 10(3): 169-184, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10881 davide viaggi, fabio bartolini, meri raggi member states (austria, italy, spain, france germany, luxembourg), characterised by more diversified in high productive biomass manufacturing sectors. low specialisation (location quotient ≤ 0.9) and an apparent labour productivity more than double the eu-27 level: northern member states (belgium, denmark, finland, ireland, netherlands, sweden). the study by ronzon et al. (2020) also highlights trends over the 2008-2017 period for the apparent labour productivity and location quotient of the bioeconomy in the eu27 member states, showing that different speed of increase are in place. consequently, while the composition of the four groups did not change, the heterogeneity of the eu’s bioeconomy remains and get stronger. the quantifications presented in this section refer mainly to a set of studies that apply the same analytical methods refining and updating over time. nevertheless, the discussion on alternative methods, different rate in sectors or choice in including/excluding sub-sectors still evolves and needs to be further developed in order to ensure advances in the bioeconomy definition for practical purposes (see for example vivien et al., 2019). 3. demand, supply, markets and chain organisation in the bioeconomy 3.1 demand demand for bioeconomy products come from the combination of two main forces, consumers and policy, that need to be understood in the light of major scenarios and driving forces providing incentives for societal change. part of the literature focuses on classical analyses of demand elasticity of bioeconomy products (schier et al., 2021; traditional forest products markets change and diversify. fossil-based inputs in the chemical, textile, apparel and downstream industries can be replaced by lignocellulose-based products such as dissolving pulp, cellulose-based chemical derivatives and textile fibres. when looking ahead, these previous niche products are likely to gain in economic importance. so far, little attention has been paid to the characteristics of macroeconomic relations of emerging lignocellulosebased materials on macroeconomic level. key economic parameters for such materials are not available neither at regional nor at global level. schier et al. (2021) to contribute to a better understanding of the market behavior of emerging forest products that are not yet covered by forest products market analysis. therefore, they investigate how lignocellulose-based products respond to changes of main economic drivers and compute global market elasticities for dissolving pulp, cellulose-based chemical derivatives and textile fibres. to conduct the evaluation, they first test historical input data for nonconstancy in time series due to structural changes using change-point estimator (mosum test in skjerstad et al., 2021). however, a large part of the literature rather points attention to consumers behaviour in the bioeconomy as linked to the issue of acceptance of new product and differential willingness to pay. table 1. number of persons employed, value added, and apparent labour productivity by sector of the bioeconomy (eu-27, 2017). (ronzon et al., 2020). sector workers value added apparent labour productivity (number of persons employed) (€ million) (€000 per person employed) agriculture 9,273,470 188,519 20 forestry 517,480 25,301 49 fishing 166,610 6698 40 manufacture of food, beverages, and tobacco 4,398,761 215,311 49 manufacture of bio-based textiles 692,906 21,103 30 manufacture of wood products and furniture 1,424,540 47,268 33 manufacture of paper 590,456 41,702 71 manufacture of bio-based chemicals, pharmaceuticals, plastics, and rubber (excluding biofuels) 396,712 60,312 152 manufacture of liquid biofuels 20,506 3216 157 production of bioelectricity 22,550 4208 187 bioeconomy 17,503,992 613,637 35 173the bioeconomy in economic literature: looking back, looking ahead bio-based and applied economics 10(3): 169-184, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10881 early literature was largely driven by attention to potential negative impacts of biotechnologies and willingness to pay (wtp) to avoid products implying the use of e.g. gmo crops. the most recent literature is more related to wtp for positive bioeconomy-related attributes (such as bio-based nature of feedstock in comparison to fossil materials) or for new products. for example in petruch and walcher (2021) the the public perception of wood as a sustainable building material that can facilitate the shift towards a bio-based economy is crucial. this study aimed to explore the attitudes towards timber construction among young millennials in austria, a cohort that in the coming years will increasingly occupy decision-making positions and gain purchasing power. most recent studies tend to integrate concepts from psychology and behavioural economics into consumer studies, revealing the complexity of choices on bioeconomy products. an example is provided by wensing et al. (2020) explaining why the food industry is increasingly interested in pro-environmental packaging alternatives such as bio-based plastic. as the market share for biobased plastic packaging is still small, strategies to raise consumer awareness and willingness to pay are increasingly investigated. another relevant area of recent research concerns the fact that consumers are not deciding in isolation and, on the contrary, are more and more networked. in this context, how the digitalisation and the role of online intellectual capital impact on consumers behaviour related to the bioeconomy is a key issue. vătămănescu et al. (2018) address a demand-side perspective of bioeconomy by laying emphasis on the digitalization of markets and, subsequently, on the consumption patterns at the macroeconomic scale. they investigates the influences of online intellectual capital on bio products consumption in two european countries (romania and italy). the imperative for a sustainable economic model corroborated with the advances in digital technologies usage have reconfigured consumers’ approaches and expectations and availed new forms of consumer behaviour. among these, the development of consumer-based online communities and of the online intellectual capital have often come forth as an undertaking of empowered consumers pursuing knowledge-based consumption patterns. the quest for sustainable, bio-labeled products on the digital markets has cemented the formation of new social aggregations built on the similarity of interests, goals, values, expectations, preferences, etc., giving way to consistent communication and interaction flows among their members and engendering profound transformations in today’s society. the behavioural aspects highlighted above can be detected through consumer studies but also by addressing stakeholder views (kakadellis, woods and harris, 2021), which may help in gaining a more aggregated view of different positions and understanding interactions among groups. being a new concept, in addition attached to a number of socially relevant attributes (such as climate change, sustainability, biological resources), the development of the bioeconomy is connected to visions, imagery and perceptions by the different stakeholders involved. several papers address this issue. some of them emphasise the general positive perception of renewable vs. non renewable products, in particular at consumer level (navrátilová et al., 2020), while others emphasise the contrasting views and the different potential positions by different stakeholders. however, the review by holmgren, d’amato and giurca (2020) concludes that most of the scientific literature tends to reproduce policy concepts linked to weak sustainability rather than introducing original ideas into the process of bioeconomy development. media and communication are also important in shaping these aspects. early work on genetic modifications has shown the potential role of media in changing public opinion. more recent research on this topic thought reveals relatively little attention on elaborating novel ideas and rather a discourse mostly driven by government positions (sanz-hernández et al., 2020). an important part of demand is driven by public policies. this has been relevant up to now in particular in the bioenergy sector, with studies largely focusing on biofuel mandates or biogas production depending on aspect most relevant in each country. 3.2 supply the primary production of biomass needed for the bioeconomy is the first issue in supply analysis. this is addressed in different ways through technical economic studies investigating, for example, the land footprint, land bio-capacity, degree of (de)coupling and self-sufficiency (naah, 2020). from an economic point of view, supply elasticity of specific products is also an issue for analysis (schier et al., 2021). also concepts such as the need to exploit economies of scale and reduce transaction costs are addressed with respect to biomass production (wen and chatalova, 2021). farm level incentives and trade-offs among different product streams are a key to supply analysis (jansen et al., 2021), in particular in the context of the competition for land use by most bioeconomy supply chains. 174 bio-based and applied economics 10(3): 169-184, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10881 davide viaggi, fabio bartolini, meri raggi not surprisingly, in a growing and innovative sector, many supply-side papers focus on specific new products either at the level of biomass production or processing, such as microalgae (orejuela-escobar et al., 2021), new sources of protein including insects and seaweeds (van der heide et al., 2021), forest bioeconomy and new forest-based products (kallio, 2021; jonsson et al., 2021). biorefinery are a key approach to biomass processing, aimed at the valorisation of different sources (including waste) into a range of valuable products and are becoming the object of a dedicated stream of economic research (clauser et al., 2021). another interesting area of research is that concerning emerging links among different value chains. these are rather countless and are part of the main nature of the bioeconomy. some of them are even surprising from the point of view of old technologies, for example the connection between the wood production and aquaculture (solberg et al., 2021). 3.3 markets the study of markets for bioeconomy products largely relates to two issues: development of new markets and relationships among markets of bioeconomy products and other products, in particular competing products based on fossil resources. the first point entails in particular the issues of launching new products on the market, even when they are already at a stage advanced enough for marketing and even more when they are in the process of moving from research to market through innovation processes. the difficulties and the actions needed to activate new products is visible in the example on market implementation of active and intelligent packaging (aip) technologies specifically for fiber-based food packaging provided by (tiekstra et al., 2021). they identify the following areas of concern: a) market drivers that affect development; b) the gap between science and industry, c) the gap between legislation and practice; d) cooperation between the producing stakeholders within the value chain, and e) the gap between the industry and consumers. the second point (relationship with non-bioeconomy markets) concerns directly the perceived specificity of bioeconomy products. assuming bioeconomy products are perfect substitutes of fossil-based products, the early models mostly focused on interaction between bio-based and non-bio-based products, and related market shares, due to different marginal costs. in addition, in the most recent literature this has expanded to considering the issue for recycled vs. non recycled. the interaction among the three (four) types can be addressed as well. when the product is different in terms of attributes, instead, the issue is more market differentiation than costs-competition, also in relation to consumer segmentation. finally, the problem of externalities and public goods needs to be taken into account. many positive attributes of bioeconomy products take the nature of public goods or externalities, which implies that market itself cannot take them fully into account and will tend to produce the bioeconomy goods in a sub-optimal amount. this issue is better discussed in the next section. to address some of the topics above, the role of policies is key. besides direct incentives, mentioned in demand and supply, certification, often related to sustainability, can be a strategic tool to connect demand and supply (vogelpohl, 2021). 3.4 organisation and business models organisation aspects of the bioeconomy derives from two main issues. the first is the growing degree of separability among different stages in bioeconomy processes. the second is the emergence of specific technologies in the treatment of biomass around the concept of biorefinery. an intermediate and connected issue is that of flexibility both in feedstock and processing, that allows plants to switch from one feedstock to the other and from one product to the other. as an answer to these trends, new concepts are increasingly being used to represent complex systems. one is that of value web approaches. biomass value webs can be defined as “complex systems of interlinked value chains in which biomass products and by-products are produced, processed, traded, and consumed” (calloconcha et al., 2020). examples of use of value webs concern the representation of different bioeconomy systems in the context of developing countries (callo-concha et al., 2020; naah, 2020; virchow et al., 2016). some of these approaches are supported and build on analyses of biomass flows (gonçalves, freire and garcia, 2021). networking and collaboration are an important part of the new organisation landscape emerging for the bioeconomy and several papers address this issue, in particular looking at collaboration among companies (guerrero and hansen, 2021), but also among different actors. in most cases, the key topic for collaboration is that of innovation. also part of the literature focuses on innovative business models related to the bioeconomy. they rather often address the specific topic of circular bioeconomy (donner and radić, 2021; donner et al., 2021). business models are a particularly relevant concept in 175the bioeconomy in economic literature: looking back, looking ahead bio-based and applied economics 10(3): 169-184, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10881 relation to the innovation process and to the interpretation of the ability of the innovation systems to speed up innovation development and uptake, also in relation to appropriate policy instruments (gatto and re, 2021). salvador et al. (2021) review the literature on business models and bioeconomy and identify key aspects for implementing and managing business models, namely; “the role of innovation and new markets, taking the customer perspective into account in the value creation process and being close to customers, adequate management of logistics and feedstock collection systems, being aware of different routes for valuing biomass, seeking technological development, building resilient value chains, and focusing on value creation to cover costs. issues that need addressing in the existing literature include product-service-systems, take back-systems, seasonal availability of resources, social impacts, rebound effects, and aquatic activities.” the topic of circular bioeconomy has taken an increasing relevance over time, due to its potential to reduce environmental impact and better exploit the economic value of biological resources. large part of it is related to food waste (ferreira, pié and terceño, 2021; santagata et al., 2021). the current state of the art in patents shows that this field is still far from expressing its full potential. ferreira, pié and terceño (2021) conclude their review by highlighting that for further progressing towards an impactful circular bioeconomy, further evidence is needed that circular bioeconomy products “are indeed preferable to their fossil-based counterparts, from both the economic and societal points of view, including environmental sustainability, and to communicate extensively the findings to the society at large.” 3.5 innovation mechanisms and entrepreneurship innovation is at the core of the bioeconomy and was actually the main field of research at the early stages of the development of the sector. two main areas of concern appear from the literature. the first regards the shape taken by innovation systems. this partly connect to wider approaches to innovation, for example the triplequadruple or quintuple helix approach (grundel and dahlström, 2016). the second concerns the impact of regulation and its effects in shaping innovation pathways. with growing investment in demonstration plants, for example in the field of biorefineries, attention is also moving to managerial difficulties. for example, using a survey concerning pilot and demonstration plants in sweden, mossberg et al. (2020) provide a description of various challenges, such as the division of responsibility for the operation and ownership, unclear roles and objectives, and the lack of specific competences and resources in the actor networks. research also concerns new areas of innovation and technology as they emerge. large part of economic research related to bioeconomy is in the field of biotech innovations. recently, an emerging field of research is that of digitalisation also in view of its potential support to collaboration in management and innovation processes (ryymin, lamberg and pakarinen, 2021). the application of digital innovation hub concept to the bioeconomy is a promising pathway to boost networking and innovation (aragonés et al., 2020). governance of innovation systems is also a widely addressed issue (toivonen, vihemäki and toppinen, 2021), with implication in shaping the form of supply chain network and its impact on the sustainability (see section 4). innovation and research are connected to economic development also through education and intellectual capital. though little explored by the literature, cristea et al. (2020) highlight that education, innovation and research, along with main bioeconomy features, are at the core of economic development in the eu. new business models and innovation are strictly linked to the issue of entrepreneurship. kuckertz, berger and brändle (2020) provide a holistic framework on the role of entrepreneurships in the bioeconomy identifying three main aspects: entrepreneurial activity on the micro level, entrepreneurial ecosystems (or clusters and innovation systems) on the meso level, and governmental vision and support on the macro level. while entrepreneurship is identified as important in most of the bioeconomy strategies worldwide, actions to strengthen its role are often lacking or too weak (kuckertz, 2020). the connection between entrepreneurship and academic activities is also a relevant area of debate, in particular in relationship to emerging new processes (rosenlund and legrand, 2021). 3.6 governance and political economy governance and policies are cross-cutting areas of concern in all the previous points and specific policies are mentioned above in relation to issues they intend to address. some cross cutting issues are however worth to be mentioned here. while there are now many bioeconomy strategies worldwide, bioeconomy policies as such are almost absent. on the contrary, there are important policy interventions in specific sectors of the bioeconomy (e.g. energy, agriculture). as a result, the literature on policies related to the bioeconomy is rather 176 bio-based and applied economics 10(3): 169-184, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10881 davide viaggi, fabio bartolini, meri raggi fragmented and largely incorporated in sectorial policies, such as agriculture and forestry. this is a more or less important topic depending on the sector. for example the topic of bioeconomy has a much higher degree of attention in connection to forestry (elomina and pülzl, 2021) than in connection to agriculture. governance landscape for forest bioeconomy and ecosystem services in europe is mapped by primmer et al. (2021). besides a lot of work on national or local policies and governance approaches, the international dimension of the bioeconomy is now emerging, together with a growing attention at the role of international institutions (bößner, johnson and shawoo, 2021). political economy analyses of regulation have accompanied the development of the bioeconomy, with a stronger focus on new technologies and, in particular, biotechnologies (smith, wesseler and zilberman, 2021). recent political economy contributions also touch the more theoretical and global vision of the bioeconomy. for example, vertommen, pavone and nahman (2021) propose the concept of “global fertility chains”, which “articulates the reproductive bioeconomy as a nexus of intraconnected practices, operations, and transactions between enterprises, states, and households across the globe, through which reproductive services and commodities are produced, distributed, and consumed”. potential for sharing good practices and replication in policy and governance solutions is also an issue in a period when the sector is expanding (andersson and grundel, 2021). a notable area of research is that concerning the potential of the bioeconomy for development. at local level, this discussion is partially related to the valorisation of marginal areas. the recent literature advocated that this opportunity is not straightforward in the current settings and it would require the designing of a new “‘resilience governance’ based on integration, innovation and future orientation to rural transformation” (sanzhernández, 2021) 4. bioeconomy, sdg and ecosystem services the literature shows conflicting views on the potential contribution of the bioeconomy to sustainability, with the academic debate on its benefits and risks driven by strong polarised opnions (kirkels, 2012). pfau et al., (2014) identify different visions on the impact of the bioeconomy on society that span from unconditional sustainability as an inherent characteristic (i.e. using biomass to replace fossil resources, see for example székács, 2017) to potential harm due to increasing competition with the food sector, changes in the demand for productive factors or unknown environmental and social consequences (see for example deboer et al., 2020). these multiple visions of the bioeconomy can be a consequence of the evolution of the bioeconomy concept itself (vivien et al., 2019), or of the nature of the sociotechnological transition of the bioeconomy that requires deep knowledge and technology as drivers of its development (vainio, ovaska and varho, 2019). therefore, reconciling environmental and social goals with economic development requires a deep understanding of the human-biosphere-technosphere nexus (giampietro, 2019). this complexity has the practical consequences that the economic literature on bioeconomy fails to provide a comprehensive assessment of their positive or negative impact on sustainability and ecosystem service (heimann, 2019) and properly addressing these issues requires strong multidisciplinary and interdisciplinary research. the sustainable management of biological resource and their circular transformation in food, feed, energy, and biomaterials relies on improving social wellbeing through ecosystem services (figure 2). although el-chichakli et al., (2016) consider the bioeconomy as directly or indirectly involved in reaching several millennium development goals, (i.e. food security (sdg 2), prosperity and economic growth (sdg figure 2 circular bioeconomy and wellbeing. source: european forest institute. 177the bioeconomy in economic literature: looking back, looking ahead bio-based and applied economics 10(3): 169-184, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10881 2; 8, 9, 11, and 12), protection of natural capital (sdg 6; 7; 14 and 15) and the mitigation of climate change (sdg 13), its linkages to the provision of ecosystem services are not yet established (d’amato, bartkowski and droste, 2020). the prerogative of the ecosystem services concept is to help understanding the synergies and trade-offs between various societal objectives highlighting the natural capital contribution to human wellbeing (fisher, turner and morling, 2009). however, the provision of services depends on the socio-technological context of individuals or groups of beneficiaries (oterosrozas et al., 2014). while the contribution of the bioeconomy on provisioning services is quite evident, other ecosystem services (i.e. regulating, cultural and supporting) are less investigated (d’amato, bartkowski and droste, 2020). lower attention is often given to the indirect effects on ecosystem services through a different land and resource management. table 2 provides an overview of the possible bioeconomy contribution on the ecosystem service. the bulk of studies that link the bioeconomy with ecosystem services concerns the provisioning service by forest and agricultural systems. they are perceived as a primary source of biomass from the bioeconomy (bugge, hansen and klitkou, 2016; d’amato, bartkowski and droste, 2020). differently, the literature studying circular-economy in the bioeconomy sector remains anchored to the provisioning sector but with the use of municipality or industrial waste or co-products as a primary source of biomass (deboer et al., 2020). except for biomass from algae for animal feed or energy provision, the contribution of marine ecosystems to the bioeconomy is less investigated. van schoubroeck et al. (2018), in a recent review, compare the sustainability of different types of bioeconomic products. the authors observe that bio-based chemical production has been investigated less than bioenergy and biofuels. hamelin et al. (2019) argue that the source of biomass is the main factor leading to sustainability. while the urban greenery management (i.e. residue from managing public green spaces or roadside vegetation) and, agricultural, industrial, and municipal waste are considered sustainable as implicit in the circular economy1, the sustainability of biomass from dedicated crops or by-products (i.e. straw, manure, residues, co-product from food (wheat, maize), and non1 although the debate about circular economy assumes implicitly and simultaneously economic growth, protection of natural environment and social equity, its real contribution on ecosystem services remains quite vague. millar, mclaughlin and börger, (2019) note a minor effect of circular economy with respect the linear model due to the existence of a) absence of an agreed definition of circular economy; b) the persistence of trade-off among sustainability dimensions and c) lack of knowledge about long term and rebound effects. food crops (i.e. hemp) or by forestry and forestry residues is largely debated. many studies have described a potentially harmful impact of bioeconomy on ecosystem services due to shirking the global food security or reducing the adaptive capacities of local food system due to simplification of cultivated crops (marsden and farioli, 2015). as for a local or regional biorefinery, cultivation of firstor second-generation biomass affects ecosystem services. the development of biomass crops instead of traditional crops can impact the landscape quality (cattaneo, marull and tello, 2018) and reduce the crop diversity (bartolini, gava and brunori, 2017) with a further increase in exposure to climatic or market risks (bartolini et al., 2015). some authors, comparing the dedicated crops to invasive species, consider the food systems more likely to be food-insecure (see, for example, ferdinands et al., 2011). in contrast, other authors have highlighted the positive impact on soil quality (schrama et al., 2018) and on reducing water demand (bartolini, gava and brunori, 2017) and the synergies in providing a feedstock supply while enhancing ecosystem services for perennial crops (mitchell et al., 2016). the other ecosystem services are less investigated by the literature due to the complexity of socio-technical transformation of the ecological system (giampietro, 2019), to the difficulties in define and assess the counterfactual carbon-based production and technology (spierling et al., 2018) or to the lack of understanding of the nexus between direct and indirect changes in land use with multifunctional ecosystem (egenolf and bringezu, 2019). the contribution of the bioeconomy to climate change is a debated topic. on the one hand, the literature has shown that the bioeconomy can sequester carbon from the atmosphere into biomass or store carbon in bioproducts, consistently with its main objective to replace fossil-based feedstocks with renewable sources (european economic and social committee, 2018). on the other hand, several studies provide shreds of evidence supporting incremental effects of co2 emissions due to mainly indirect land-use changes or deforestation (weiss et al., 2012; marchetti et al., 2014; haddad, britz and börner, 2019). bais-moleman et al. (2018) pinpoint that the cascade approach could shrink the climate change mitigation potential of biomass used by altering the efficiency of solutions among alternative uses. egenolf and bringezu (2019) show that a very complex multilevel interaction exists, highlighting possible trade-offs and synergies across scale and space. such complexity can be a consequence of the different governance structures of the bioeconomy (dietz et al., 2018), as governance can affect either the typology of the supply chain itself and the value exchanges 178 bio-based and applied economics 10(3): 169-184, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10881 davide viaggi, fabio bartolini, meri raggi among the constellation of actors involved in the supply chain. growing concerns arose about bioprocessing due to access to technology and knowledge, with the possibility of further creating disparities among countries between those with high capacities to create and internalise added value from industrial biotechnology and those that remain biomass growers. moreover, the environmental upgrading of some bioeconomy value chain may cause outsourcing of environmental damages in these countries with high export but low environmental and social standers (fuchs, brown and rounsevell, 2020). the inclusion of a specific concept on the governance of bioeconomy (i.e. cascade and circularity) can move toward sustainability by prioritising highvalue biomass uses (health, pharmaceuticals, chemistry, construction), instead of, for example, bioenergy (elchichakli et al., 2016). 5. discussion the concept of bioconomy is in the process of becoming mainstream. over time it has moved from a focus on innovation in some specific sectors (biotech, bio-based materials, bioenergy), to the broader identification with all sectors using biological resources. in addition, the connections with sustainability, ecosystem services, circular economy and climate change have table 2. summarises the linkages between ecosystem services, sdgs and bioeconomy. ecosystem service sdgs1 examples of bioeconomy contribution provisioning sdg 2 end huger more efficient animal production and cultivation (i.e. new vaccines and molecular diagnostics to reduce antibiotic use or use of a bio-based product as feed for animal) sdg 3 good health and well being sustainable medicines: biopharmaceuticals and microbiome-based approaches; improving knowledge on genetic resources sdg 7 affordable and clean energy use of biomass instead of carbon-based products (biofuel, biogas, wood from forestry systems) sdg 8 economic growth (economy) re-connect local and actors in new supply chain networks based on form better material/ energy flow. new green and innovative business model, income and job opportunities sdg 9 industry, innovation & infrastructure substitution of non-renewable with bio-based and renewable products could link a rural regeneration or re-industrialisation process; investment in r&d in marginal areas. development of green infrastructure of new green section (i.e. bio-construction, pharmaceutical and medical technology) regulating sdg 6 clean water (water) biological wastewater treatment (in developing countries), with the inclusion of water nutrients removal 14 life below water increasing aquatic biodiversity by reducing pressure on marine ecosystems making more efficient aquaculture productions and bio-products products (i.e. genetically modified tilapia in developed countries) 15 life on land increase terrestrial biodiversity through decoupling farm and industry from the fossil-fuel industry soil regeneration through co-products or by-products (i.e. digestate used as fertiliser) mediation of wastes or toxic substances cultural aesthetics 17 partnerships (partnerships) build partnerships develop new business opportunities from the extensive farming system and agro-forestry re-balance the material and energy flows between rural and urban systems supporting sdg 11 sustainable cities reduction of emission and waste by using biomass form local production, recycling systems or from urban waste sdg 12 responsible production and consumption integration and use of renewable resources, or the diffusion of innovative and sustainable production and biotechnology can improve the efficiency of material and energy cycles as well as create new and multiple material and energies loop. diffusion of bio-based products and material would alter reduce the plastic waste or improve the efficiency of waste disposal and/or material recycle 13 climate action (climate) bioeconomy is contribute strongly in carbon storage and in reducing emission by making c02 into the bio-based chemicals and biofuels source own elaboration. 1 yang et al. (2020) provide an explanation of the linkages between ecosystem services and sdgs. 179the bioeconomy in economic literature: looking back, looking ahead bio-based and applied economics 10(3): 169-184, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10881 become more prominent and taking shape in more concrete policy and business actions. in this section, we discuss the most promising pathway for research after considering two horizontal issues: the topic of conflicting objectives on the bioeconomy and the issue of methods. the transition to the bioeconomy is subject to tradeoffs among different sustainability dimensions, including the possibility to alter ecosystem services functioning. while strategies promoting the bioeconomy typically put emphasis on the expected beneficial effects it can bring, the consideration of sdgs clearly highlights a number of potential conflicts among different objectives related to the bioeconomy. the different views on bioeconomy can generate tension among stakeholders and shrink the bioeconomy impact on sustainability or make its transition more difficult or risky. some authors have argued that difficulties in social acceptance of the bioeconomy arise from the typology of innovation itself, which implies a large amount of asymmetric information among actors along the supply chain. other authors argue that the development of technology often has not paid attention to stakeholders’ needs and to its acceptability, but merely to achieving a large adoption, making very complex the communication about the practical benefits of the bioeconomy (mukhtarov et al., 2017). this was particularly evident in the early stages of bioeconomy development, in which the advanced biotechnological innovation has shaped the debate on the transition towards a bioeconomy purely on the acceptability of gmos, which is partially a misleading debate without fully understanding the potential of many other existing technologies (chapotin and wolt, 2007). in addition, gava et al. (2017) show that the actors involved in bioeconomy value chains are strongly different from the traditional agricultural networks, making dialogues among these stakeholders difficult. partly for this reason, perception and vision studies are key to the understanding of the bioeconomy. vision analysis of the transition process start to emerge also for the agricultural sector, moving attention beyond sustainable intensification and rather highlighting the topic of landscape and country level diversification (bayne and renwick, 2021). some of the trends above have been dramatically touched by the covid pandemic. some literature is emerging about the bioeconomy in the post-covid era, in particular in relation to food security (farcas et al., 2021). different branches of bioeconomy research have been using different methods and concepts, with a large use of qualitative methods. to fully understand the possible trade-off implied by the bioeconomy, there is a strong requirement for scientific advances to understand different opportunity costs and environmental benefits and costs among alternatives pathways in each specific ecosystem. although the lca is the most used method in the literature, it is still far from measuring the proper impact of the bioeconomy on the ecosystems and the complexity of nexus between biomass sources, bioproducts, supply chain governance and ecosystem services provided (d’amato, bartkowski and droste, 2020) the changing landscape and the increase in data is also bringing changes in methods. among others, modelling intended for simulation and forecasting is a growing field of activity for the bioeconomy. cingiz et al. (2021) use an input-output model of the eu bioeconomy. ferreira, pié and terceño (2021) use a bio-socio accounting matrix approach to assess the impact of bioeconomy in spain. gatune, ozor and oriama (2021) model bioeconomy futures in eastern africa using the international futures (ifs) modelling platform, based on the dynamic interaction of demographic, social, economic and environmental factors. farm level models including bioeconomy-specific concerns are also emerging. for example, jansen et al. (2021) develop a farm level decision making model including the choice of quality, losses and bio-based alternatives. given the number of variables affecting the development of the bioeconomy, it is not surprise that several studies further cast the problems in the framework of scenario analysis (rojas arboleda et al., 2021) and/ or transition theory (wydra et al., 2021). an example of modelling scenarios related to forestry is provided by morland and schier (2020). based on the above, the most interesting trends of the recent bioeconomy literature rest probably in the search for a more systemic view of the sector, or, at least, in the attempt to account for cross-sector interrelationships and for the consequent market and organisational changes. the areas of innovation and organisation are probably two of the most relevant for understanding the bioeconomy, with a growing role of the latter. the combination of qualitative and quantitative methods can allow to provide evidence-based support to policy while at the same time offering the possibility to account for views from different stakeholder groups. 6. conclusions looking back and looking forward, the economic literature on the bioeconomy appears to be still at this 180 bio-based and applied economics 10(3): 169-184, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10881 davide viaggi, fabio bartolini, meri raggi inception while the sector of the bioeconomy is taking shape and tends to become mainstream. the period from the 2010 to 2020 has been to some extent exploratory in keeping pace with strategy and policy documents providing a vision of the bioeconomy, as well as in exploring new sectors developing within the bioeconomy. a variety of different economic aspects of the bioeconomy have been addressed, from consumer behaviour to governance issues. the drivers that have pushed for the development of the bioeconomy remain very prominent and attention can be expected to grow in the next future. this will also depend on the ability of policy to bring together in a consistent way the many areas of intervention presently affecting the bioeconomy. although many countries have developed specific policies or even programs on the bioeconomy, the literature shed light on the lack of a clear policy landscape to support a sustainable transition of the bioeconomy. reaching the challenge set out by un 2030 agenda also requires a multilevel policy framework with the design of a mix of policy instruments addressing the negative impacts (short term objectives), together with policies promoting ecosystem services provision and supporting the scaling up of a sustainable bioeconomy (long term objectives). altogether, this promises to become an even more interesting field for economic research in the next future. in this context, it may be expected that the next decade will be key to see to what extent the literature on the bioeconomy will consolidate into a well-defined field of economic research. in turn, economic literature can likely help the further development of the sector. two key challenges can be envisaged in this direction. first, economics will need to take up the need for more holistic and systemic views required by the sector. secondly, research will need to better account for the role of citizens and institution, with a stronger consideration of equity in the distribution of private and public costs and benefits. summing together these issues, economics may contribute to the engagement of different views and in facilitating reflexive spaces to co-create shared transition pathways toward a sustainable bioeconomy. acknowledgements the authors would like to acknowledge thoughtful comments on an earlier draft from paolo sckokai e gianluca brunori references andersson, i. and grundel, i. 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(2020). prioritizing sustainable development goals and linking them to ecosystem services: a global expert’s knowledge evaluation. geography and sustainability, 1(4), 321–330. doi: 10.1016/j.geosus.2020.09.004 volume 10, issue 3 2021 firenze university press the bioeconomy in economic literature: looking back, looking ahead davide viaggi1,*, fabio bartolini2, meri raggi3 the contribution of research to agricultural policy in europe alan matthews drinking covid-19 away: wine consumption during the first lockdown in italy giulia gastaldello*, daniele mozzato, luca rossetto estimating a global maidads demand system considering demography, climate and norms wolfgang britz the evolution of organic market between third-party certification and participatory guarantee systems gianluca iannucci1, giovanna sacchi2,* bio -based and a ppl ied economics bae bio-based and applied economics 11(3): 207-218, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-11501 copyright: © 2022 a. pastolero, m. sassi. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: a. pastolero, m. sassi (2022). food loss and waste accounting: the case of the philippine food supply chain. bio-based and applied economics 11(3): 207-218. doi: 10.36253/bae11501 received: july 13, 2021 accepted: june 28, 2022 published: november 4, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini and simone cerroni. orcid ap: 0000-0003-4699-922x ms: 0000-0002-6114-6826 food loss and waste accounting: the case of the philippine food supply chain anieluz pastolero*, maria sassi university of pavia, italy *corresponding author. e-mail: acpastolero@gmail.com abstract. in recent years, the interest in food loss and waste has been gaining momentum from researchers and policy-makers. much of the attention on the matter is centered in industrialized countries, creating a knowledge gap within developing countries, among which is the philippines. this lack of information impedes the countrylevel response in solving the issue, whose implications extend to food and nutrition security, productivity, and resource use. for this reason, our paper estimates the food loss and waste levels in the philippine food supply chain of rice, corn, and banana commodities. we were first to identify the percentage accumulation of food loss and waste in each stage of the food supply chain and translated such portions into edible food volumes initially intended for human consumption. our findings revealed that between one-seventh to one-fifth of edible rice, corn, and banana quantities are lost/ wasted in their respective food supply chains. for each of the commodities analyzed, the principal activities responsible for the problem are drying, dehanding, and harvesting, respectively. our results suggest the following for policy intervention and research: establish an agreed-upon food loss and waste definition; calibrate interventions at the level of the food supply chain; follow a supply chain system approach in reducing the problem; and determine an acceptable level of loss/waste. keywords: food loss and waste, food supply chain, philippines. jel code: q13, q18. 1. introduction the widespread attention placed on losses is not of recent concern. it has been first expressed as one of fao’s organizational mandates during its establishment in 1945 (parfitt et al., 2010). the matter was highlighted again during the seventh session of the united nations general assembly in 1975 when they aimed to halve postharvest losses by 1985 (fabi & english, 2019). long after, the food crisis in 2007-2008 paved the renewed interest in addressing the problem (fabi et al., 2021). by 2011, the issue was better realized by releasing the first global estimate of flw, where about one-third of the food produced for human consumption is lost or wasted (gustavsson et al., 2011). subsequently, the international community recognized the concerning levels of food loss and waste (flw) in the sustainable development goals (sdgs) and stated its reduction goal in target 12.3. http://creativecommons.org/licenses/by/4.0/legalcode 208 bio-based and applied economics 11(3): 207-218, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-11501 anieluz pastolero, maria sassi the broad interest in flw is due to its implications transcending the issue of unrealized physical food quantities. for example, the fao (2017) reports that recovering or minimizing food outflow from the chain can increase productivity, promote food and nutrition security, and minimize negative environmental impacts. further, there are also moral overtones surrounding the problem (gjerris, 2020), given that 155 million people globally are acutely food insecure (fsin & gnafc, 2021). however, to design an effective policy intervention, there is a need to establish empirical information on the magnitude and causes of flw generation. moreover, at the country level, there is a need to strengthen efforts to understand the problem at a disaggregate scale, particularly on the different commodity food supply chains (fscs). currently, limitations to an evidence-based policymaking process in this field exist, which also constraints the synchronized global effort to reduce flw. for example, xue et al. (2017) identified three significant biases in the literature: first, the analyses are more concentrated in industrialized countries; second, over half of the publications relied on secondary data, with some authors using outdated data; and third, studies are abundant at the retail and consumption stages. these biases imply that information on the issue is limited in developing countries such as the philippines. in this country, the potential benefits of flw reduction on food security and poverty reduction are vital if we consider that 64% of the filipino population is chronically food insecure (ipc, n.d.). further, two of the most important fsc actors, farmers and fishermen, are consistently classified as the country’s poorest groups. moreover, in the philippines, commodity losses from harvesting to distribution are reported to reach as high as 50% (mopera, 2016). the fao (2019) notes that this is a manifestation of the significant constraints actors face in performing their activities. collectively, these imply that the recovery or prevention of food outflow from the chain has great potential in feeding and improving the livelihoods of people in the country. despite the potential positive impacts of flw reduction on the philippines’ sustainable development, studies on the matter lack. following gustavsson et al. (2011), we considered flw at the main stages of the fsc, namely the agricultural production, postharvest handling and storage, processing and packaging, distribution, and consumption. further, we used the concept of flw of gustavsson et al. (2011) to understand loss/waste in the all stages of the rice, corn, and banana fscs. the selected commodities are three of the most important crops in the philippines, creating significant implications on the country’s agricultural sector. moreover, owing to the methodological elements we used in this paper, our estimations can be considered as the first national accounting of edible food reductions initially allocated for human consumption across all stages of the fsc in selected philippine commodities. we also included the consumption stage in our fsc analysis, a level of investigation where knowledge on the problem is lacking. finally, through an extensive review of relevant literature, we attempt to explain the causes of flw generation to recognize the actions or decisions that lead to the problem. as previously mentioned, we used the definition offered by gustavsson et al. (2011), where food loss refers to the reduction in food quantities from the activities of agricultural production until the point prior to retail, while that of food waste is found at the retail and consumption stages. the terms are further characterized such that only edible portions and food shares for human consumption are considered flw (gustavsson et al., 2011). we followed the interpretation offered by gustavsson et al. (2011) because we adopted their methodology in estimating the magnitudes of flw. this choice was important prior to our assessment because it was crucial to operationalize the elements characterizing the concepts. the literature on the subject, however, articulates that there is no fixed definition and that various entities provide different interpretations depending on their objective of assessing the issue (chaboud & daviron, 2017; fao, 2014). as such, publications on the matter have varying illustrations and usage of the terms (fao, 2014; parfitt et al., 2010; ishangulyyev et al., 2019; chaboud & daviron, 2017; garrone et al., 2014; papargyropoulou et al., 2014; galli et al., 2019; von massow et al., 2019). to apply the approach of gustavsson et al. (2013), we conducted an extensive literature review to gather the potential variables and organized them into a matrix to facilitate the data selection and estimation of flw. this effort was due to a lack of systematized information from official sources. it also reinforces the need for more research and information on the issue. the paper is organized as follows: section 2 presents the methodology we adopted for this study and the requisite dataset for the estimations, section 3 presents and discusses the results, and section 4 concludes. 2. methodology and data 2.1 estimation approaches gustavsson et al. (2013) offered two approaches to estimating flw: the percentage accumulation of loss/ waste in the fsc and the resulting volumes generated at 209food loss and waste accounting: the case of the philippine food supply chain bio-based and applied economics 11(3): 207-218, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-11501 each stage. although the two methods show the magnitude of the problem, we opted to use both means because the elements in each estimation bring different realizations. the first one shows the percentage accumulation of flw as food moves through each stage of the fsc. with this information, we can determine the total portion of the commodity that was lost/wasted. in comparison, the second one reflects the volumes of flw at each stage of the chain. in other words, it translates the figures into actual food volumes that could have been utilized in the country. indirectly, the volume estimates can also show the significance of the commodity as food for the country. table 1 presents the details of the first approach. to illustrate the use of this information and formula, let us start with a hypothetical agricultural production equal to 100. at this stage, the loss/waste is equal to %ap. at the postharvest handling and storage stage (phs), the percentage of loss/waste (%phs) is computed out of the remaining share of production at the preceding stage (1-%ap). the same approach is used in the subsequent stages. for the calculation of the flw volumes, we used the formulae presented in table 2 and based our estimations on the mass flow model (figure 1). this model presents in a diagram the domestic supply quantities and utilization elements that provide the quantity of food available for consumption. there are three columns in table 2. the first one lays out the stages of the fsc. the second and third columns present the formulae we followed in calculating for the flw volume of cereal and non-cereal commodities, respectively. in each fsc stage, we followed a twostep approach in estimating its flw volume. the first step of the estimation process calculates the loss/waste in its entirety. these elements are denoted by the index w in table 2. in other words, it relates to the first aspect of the flw definition of gustavsson et al. (2011), where it is the total reduction of food quantities in the fsc. the second step accounts for the peculiarity of flw such that only the shares for human consumption (hc) and edible portion (e) are considered. using the phs stage as an example, we first determined the volume of flw at phs (phsw) by multiplying the percentage loss/waste (%phs) at this stage by the total production (a). the second part of the estimation adjusts the volume of flw (in our previous example, phsw) to fit the flw definition of intention for human consumption (phshc) and edibility (phse). we adjusted the first-level estimate for cereals using allocation factors (af) and for non-cereal items with conversion factors (cf). the differing factor adjustments between cereal and non-cereal commodities (af and cf) come from the nature of their utilization and mass flows model data. according to gustavsson et al. (2013), a significant portion of cereal production is adopted for means other than human consumption. for this reason, we used the allocation factor to capture the share of cereals appropriated for human consumption. in contrast, for non-cereal commodities, the relevant aspect is edibility, which we estimated with the use of the conversion factors. we recognize that cereals have portions which are inedible. however, the data on rice and corn are already in their milled and grain forms, respectively, thereby rendering the use of conversion factors irrelevant. as seen in table 2, there are other nuances in the formulae used for different commodity types and fsc stages. for cereals, the difference comes from the specificities of the individual fscs. in the estimation of rice, for example, we only used element “food” (denoted by j) in the final three fsc stages because all rice grains deemed as food are used in milled form (gustavsson et al., 2013). for corn, we used elements “processing” (denoted by h) and “milled food” (denoted by k) in the last fsc stages because the commodity can be used as food both in its grain and milled forms. on the other hand, for the last three stages of noncereal commodities, we used “processing” (denoted by h) and the sub-elements of “food” (denoted by j). the sub-elements of “food” could be in either “fresh” (denoted by k) or “processed” (denoted by l) forms. as previously mentioned, h refers to the quantities of table 1. estimation guide for the percentage loss/waste accumulation in the fsc. agricultural production (ap) postharvest handling and storage (phs) processing and packaging (pp) distribution (d) consumption (c) %ap %phs× (1-%ap) %pp× (1-%ap)× (1-%phs) %d× (1-%ap)× (1-%phs)× (1-%pp) %c×(1-%ap) ×(1-%phs) ×(1-%pp) ×(1-%d) note: %ap, %phs, %pp, %d, and %c=weight percentages per fsc stage. source: gustavsson et al., 2013. 210 bio-based and applied economics 11(3): 207-218, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-11501 anieluz pastolero, maria sassi the commodity that are used as raw material to manufacture food products, while l indicates the quantity of the commodity that is consumed in its non-fresh form. moreover, since non-cereals can be consumed in fresh and processed forms, we separated the calculations of flw according to its product form. this aspect was considered in the distribution and consumption stages, since the differentiation of the product materializes after the processing and packaging stage. only after calculating the loss/waste between the two product forms (df and cf for fresh; dp, cp for processed) we can estimate the total loss/waste generated for the distribution and consumption stages (dtotal and ctotal). 2.2. data gustavsson et al. (2013) illustrated the fsc as a five-stage succession of activities, starting from production, postharvest handling and storage, processing and packaging, distribution, and consumption. to estimate the flw generated at each stage, we first collected the weight percentages of loss/waste at each point in the fsc for all relevant commodities in the philippines. we found this information through an extensive online search of studies, reports, and other pertinent publications of various researchers and institutions such as the united nations industrial development organization (unido), philippine center for postharvest development and mechanization (philmech), and philippine statistics authority (psa). we compiled all data and entered them into a matrix to analyze the information we had at hand. upon assessing our matrix, we selected our data sources based on two grounds: data reliability and ability to reflect a relatively full picture of the fsc. it was also vital that we minimized the number of sourctable 2. estimation guide for the volume of flw generated at each fsc stage and by crop. fsc stage cereals non-cereals agricultural production (ap) aphc = apw × af ape = apw × cf postharvest handling and storage (phs) phsw = %phs × a phshc = phsw × af phsw = %phs × a phse = phsw × cf processing and packaging (pp) rice: ppr = %pp × j corn: ppc = %pp × (h + k) ppw = %pp × (h + l) ppe = ppw × cf distribution (d) rice: dr = %d × (j – ppr) corn: dc = %d × (h + k – ppc) df,w = %df × k df,e = df,w × cf dp,w = %dp × (h + l – ppw) dp,e = dp,w × cf dtotal = df,e + dp,e consumption (c) rice: cr = %c × (j – ppr – dr) corn: cc = %c × (h + k – ppc – dc) cf,w = %cf × (k – df,w) cf,e = cf,w × cf cp,w = %cp × (h + l ppw – dp,w) cp,e = cp,w × cf ctotal = cf,e + cp,e note: %ap, %phs, %pp, %d, and %c=weight percentages per fsc stage, a=production, h=processing, j=food, k=fresh/milled food, l=processed food, sub-components of food (j) = k and l; sub-scripts: w=total flw, hc=human consumption, e=edible portion, f=fresh food, p=processed food, total=fresh + processed flw. source: gustavsson et al., 2013. figure 1. the mass flows model. source: gustavsson et al., 2013. domestic supply quantity utilization elements food (j) production (a) import (b) stock variation (c) feed (f) seed (g) processing (h) export (d) loss (i) fresh/ milled (k) processed (l) other uses (e) 211food loss and waste accounting: the case of the philippine food supply chain bio-based and applied economics 11(3): 207-218, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-11501 es for commodity loss/waste weight percentages because we recognize that their methods and contexts differ. this first stage of data gathering was the most laborious and limiting in terms of the number of commodities we can analyze for our study. because of data availability and reliability issues and guided by the 2017 psa publication on the food commonly consumed in the philippines, we ultimately selected rice, corn, and banana for the study. after establishing the food items for the study, we searched for their conversion and allocation factors using the same approach as the loss/waste weight percentages. as previously mentioned, these two factors align the initial flw volume estimates with the definition offered by gustavsson et al. (2011). another requirement for the calculations was the construction of the mass f lows model. primarily, it includes domestic supply and utilization elements. concerning domestic supply, we collected information on production, import, stock variation, and export. as for the utilization elements, we gathered data on non-food uses, feed, seed, processing, and loss. depending on the food category, we divided the food quantities into fresh and processed (non-cereals) and milled and feed (cereals). we determined the fresh food quantities for non-cereal commodities using the information on the portion of food utilized as fresh, which we also found through an online search. finally, we identified milled food using the minimum main product recovery during the milling process. there were two potential data sources for the mass flows model. ultimately, we used the 2017 psa data on supply and utilization accounts because of the persistent value discrepancies in the processing parameter of faostat’s food balance sheet. nonetheless, we had to adjust the psa data to fit our methodological requirements. the first modification entailed the disaggregation of the processing data to capture the processed food quantities from the total value of the parameter, which includes non-food shares. for this, we took the prescribed proportions from psa’s measurement of the parameter. our second adjustment was to separate the feeds and loss (or waste) into two parameters. because there was no psa guide to isolate the two, we took the proportions of each from faostat data and applied them to our psa data. lastly, we also assumed a value of one for the export parameter because psa did not indicate the exact figure. the mass flows model was also relevant in completing the loss/waste weight percentages for the phs stage. according to gustavsson et al. (2013), the element “loss” represents the food outflow for the said stage. for this, we took the portion of loss from the sum of production, import, and stock variation to extract the phs weight percentage. of all the secondary data collected, the loss/waste weight percentages were highly influential in the flw volume estimation. some fsc stages have multiple activities, implying multiple loss/waste weight percentages per stage. instead of adding the weight percentages, we calculated the flw generated by each activity and deducted it from the succeeding activities within a stage. specifically for bananas, we modified its flw volume estimation by following the data on banana loss/ waste weight percentages. this meant a reorganization of the banana fsc such that distribution preceded the processing and packaging stage. as a result, we used the mass flows elements for processing (h) and food (j) in the calculation. at the processing and packaging stage, we deducted the flw volume estimate from distribution activities. lastly, we only used the fresh food formula for the consumption stage because of the lack of loss/waste weight percentage data for the processed food consumption of bananas. 3. results and discussion our estimated total flw, both in percentage terms and million metric tons (mt), are shown in table 3. the largest share is generated in the banana fsc, followed by that of rice and corn. in terms of volume, rice has the highest flw due to its role as a staple crop in the philippines and, therefore, has the highest quantities of food in the supply chain. in comparison, corn and banana have less in terms of volume. presented in figure 2 below is the total estimated flw shares of all fsc stages of rice, corn, and banana commodities. from this figure, we can note that all stages contribute to the total flw produced in each fsc. however, the critical stages are crop-specific. in particular, the critical loss points are processing and packaging in rice, agricultural production for corn, and distribution for bananas. in deconstructing flw figures, the fao (2019) uses the term critical loss points to refer to the areas in the table 3. total estimated flw in the philippine fsc by commodity. commodity percentage flw volume flw (million mt) rice 18.10 2.3 corn 14.69 0.246 banana 20.05 0.854 source: authors’ calculation. 212 bio-based and applied economics 11(3): 207-218, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-11501 anieluz pastolero, maria sassi fsc where food loss and waste levels are highest. thus, directing the reduction efforts at these sites might have the most impact on food security and economic returns (fao, 2019). in other words, by using the critical loss points as guides in policy formulation, we might recover the most food quantities and incomes we once lost from the fsc. 3.1 rice the literature on rice postharvest losses dictates a varied set of estimates and the extent of fsc stages covered. some studies report a wide range of losses, such as parfitt et al. (2010), who noted that rice losses in the philippines are between 10-37%. others state a more definite estimate, like manalili et al. (2015), who claim that the average total loss incurred from harvesting up to milling is 15%. in comparison with these figures, it may seem that our total rice loss estimate of 18.10% does not deviate much from the two studies. however, because of the non-existence of a standard accounting method for flw, our fsc coverage and estimation approach differ. in turn, this influences the results we offer from our analysis. deconstructing the processing and packaging stage, the critical loss point of rice, our estimates indicate that drying and milling activities are the primary sources of flw. of these two, drying generates the highest share at 30.67%, followed by milling at 27.19%. in volume terms, these portions respectively equate to 727,030 mt and 644,720 mt of rice loss. confirming our results, mopera (2016) reports that the two sub-stages of processing and packaging are the problematic areas in the rice fsc. however, she reported higher shares for the two, at 36% and 34%, respectively (mopera, 2016). there are several causes of drying losses. manalili et al. (2015) point to low-quality equipment, improper use of machinery, and unfavorable drying conditions as the contributory causes of loss. these may indicate that drying losses are merely a result of the inappropriate adoption of machinery. yet, there is another potential source of flw for rice. the traditional method of sun drying, which is still prevalent in the country, can decrease grain quality and even cause the grain to crack (mopera, 2016). also, laying the grains on the ground creates difficulty in complete grain collection after drying (de padua, 1999). even though actors often express sun-drying as a low-cost production option, ultimately, they might receive a decreased income since low-quality grains command low market prices (mopera, 2016). in turn, improperly dried grains that enter the milling process will have a lower milling recovery (chapungco et al., 2008). this fact means that the expected quantity of milled rice was not met and lost instead. aggravating the issue of grain recovery rate is the prevalent 23.44 43.13 30.35 5.81 5.23 12.6 57.86 12.39 6.99 2.3 28.5 33.19 10.66 10.75 16.87 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% rice corn banana agricultural production (ap) postharvest handling and storage (phs) processing and packaging (pp) distribution (d) consumption (c) figure 2. total estimated flw shares in each fsc stage by commodity. source: authors’ calculation. 213food loss and waste accounting: the case of the philippine food supply chain bio-based and applied economics 11(3): 207-218, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-11501 use of dated milling equipment in the country (oecd, 2017). besides loss generation, these two factors can also affect the marketability of low-quality milled rice, since filipinos, regardless of social status, prefer to eat good quality rice (eds. manilay and frio, 1985 cited in manalili et al., 2015). although less critical than processing and packaging in the fsc of rice, the agricultural production stage also has a considerable level of loss (502,810 mt). at this stage, harvesting and threshing are the main activities and contributors to flw generation. respectively, the two activities contribute 11.22% and 11.79% shares of total rice loss. when translated into volume, these two activities amount to 235,100 mt and 258,440 mt of rice loss. some reported causes for harvesting losses are the natural separation of the grain from the panicle, grain spillage, and unharvested panicles, which can be an intentional labor practice for personal gain (unido, 2012). on the other hand, the accumulation of loss during rice threshing can be caused by machine inefficiency. this situation pertains to mixing grains with the chaffs or the blending of partially threshed panicles with the completed ones (unido, 2012). in contradiction with our results, a study on the perception of loss generation revealed that farmers view harvesting activity as the primary source of loss (dela cruz & calica, 2016). by focusing their assessment on actors’ perceptions, dela cruz and calica (2016) included social and cultural practices that are usually overlooked in analyzing commodity losses. however, when they compared their results against a previous actual loss assessment as a validation measure, it revealed drying as the critical activity of loss. they offered three explanations for such difference: first, the recall of their farmer-respondents was based on the past two cropping seasons that were affected by two strong typhoons that hit the country; second, harvesters intentionally leave portions of crops for gleaners; and third, farmers might be shifting the product forms they sell (from dried grains to wet grains) (dela cruz & calica, 2016). the study of dela cruz and calica (2016) is important in understanding the complexities of flw. first, it shows us that changing the approach to analyzing the problem yields different realizations that do not negate one for the other. second, the inclusion of the interplay of society and culture, which affects the decisions of fsc actors, might provide a profound realization behind flw generation. for example, the intentional leaving of grains at the field for gleaners might reflect altruism or other tacit relationships and agreements in the community rather than farmer inefficiency or carelessness. finally, the omission of performing an activity may not impact the flw levels for a stage or an actor but will do so for the latter ones. our estimation for the rice consumption stage revealed that filipino households waste 252,630 mt of rice. in 2018, the philippine family income and expenditure survey showed that the bottom three income classes in the country spend about 58% of their income on food and about 22% of which they spent on bread and cereal (psa, 2020). the constancy of rice in a typical filipino diet reflects its relative importance in food expenditure. further, since there is a consumer preference for good quality rice (eds. manilay & frio, 1985 cited in manalili et al., 2015), which commands a higher market price, the unrealized economic loss from a seemingly inconsequential rice wastage might be considerable. 3.2 corn comparing rice with corn, the other cereal commodity in our study, we can note that the total flw generated in the entire supply chain is a little below the estimate for rice, at 3.41 percentage points. however, when translated into volume, the corn flw only amounts to 246,400 mt. the observable similarities in rice and corn supply chain activities might lead one to assume that the accrual of losses should be nearly level. however, the significant disparity exhibited by the two crops primarily comes from the definition we used for the study, which was captured by the allocation factor. one of our estimation guidelines was to only account for the food outflow of those quantities reserved for human consumption (gustavsson et al., 2013). as the staple crop in the country, rice production is primarily utilized as food in the country. this form of commodity use is, in turn, reflected in our findings. on the other hand, the allocation factor we adopted for corn demonstrates the stark difference between the grains’ losses. our data indicate that only about a fifth of the commodity is used for human consumption (jbic institute, 2002). even in the corn mass flows model, we found most of its quantities in the non-food utilization elements. all these imply that our flw estimates only reflect a segment of the commodity supply chains. consequently, it is possible that accounting for the commodity outflow in the non-food supply chains might result in greater levels of flw. the critical loss point for corn is agricultural production, where we estimated 117,880 mt of corn loss. when we consider the sub-stages, corn harvesting accounts for the highest loss level (21.85%), followed by shelling (16.15%). the causes of harvesting loss were unharvested corn and spillage, while that of thresh214 bio-based and applied economics 11(3): 207-218, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-11501 anieluz pastolero, maria sassi ing loss were incomplete shelling, accidental mixing of corn grains with the cobs, and low quality of threshing machine used (unido, 2012). in contrast with our results, castro (2003) reports that drying contributes the highest share of corn losses (37%), followed by storage (24%) and shelling (21%). this divergence does not necessarily negate our estimates. the study where we derived the weight percentages of loss/waste for corn reported that two typhoons affected the harvest period during the cropping season of recall. since weather patterns heavily influence agriculture, rainfall could play a vital role in the discrepancy between the critical points of this study and that of castro (2003). the weather disturbances caused the continued deterioration of the kernels, which was evident in its discoloration, fungal growth, and mechanical damage (unido, 2012). the other critical loss point of corn is the distribution stage, where we estimated 63,040 mt worth of the commodity was lost/wasted. unido (2012) reports that torn sacks (26,877 mt) and pest infestation during storage (36,165 mt) were the reasons for the flw generation. the reaching effect of natural calamities can be seen in the drying activity (i.e., processing and packaging stage) of corn. although this stage is not as critical as the other two, drying contributes a 12.39% (27,410 mt) share of the total estimated corn losses. during the typhoons, the submersion of the kernels prolonged the drying time, which was aggravated by the preferred method of sun-drying, and resulted in discoloration (unido, 2012). 3.3 banana compared to grains, fruits are more perishable commodity items, which could be the primary reason why bananas generated the highest percentage share of losses among the three crops. our estimated banana flw of 20.05% might be the highest in our analysis, but literature indicates that bananas losses in the philippines can range from 4-60% (serrano, 2006). as previously mentioned, the distribution stage is the critical loss point for the fsc of bananas. two actors were operating at this stage; the consolidator and the wholesaler contributed to 12.53% (124,530 mt) and 17.24% (134,640 mt) of total flw, respectively. these levels are due to their continued handling, sorting, and transport of bananas, as calica et al. (2018) reported. for highly perishable items such as fruits, time and distance are essential in the generation of loss/waste. the philippines is an archipelagic country composed of over 7,000 islands, making the transportation of highly perishable goods challenging. our study source for the banana loss/waste weight percentages reported that the bananas were transported in an uncontrolled environment for 12 hours from the area of production to the location of the trader/wholesaler (calica et al., 2018). in a country with high temperature and relative humidity, the common lack of temperature control during the succeeding stages of harvesting is conducive to the deterioration of the produce (mopera, 2016). another critical loss point for bananas is agricultural production. during the production stage, there is a practice called dehanding. it is an activity where each hand of a banana bunch is removed. however, farmers disregard the bottom two hands because they are small and immature, thus, deemed unmarketable by consolidators (calica et al., 2018). this practice resulted in 29.08% or 342,346 mt of banana losses, the highest in the production stage. although dehanding is a common farm activity after harvesting banana bunches, the act of discarding the bottom two hands is a consequence of a market standard. compared to the other underlying causes previously mentioned, flw due to market standards is not the result of a decision or limitation of a single actor. it also involves actors that are beyond the stage where the standards are realized as loss/waste. in dehanding bananas, farmers follow the directive of the middlemen, who then follow the demand preferences set by consumers. to counter the flw resulting from market standards, changes in attitude, commodity use, or expectation would involve all three actors. our estimate for banana waste was 16.87% of the total flw or 67,321 mt at the consumption stage. according to esguerra et al. (2017), the primary reason for fruit wastage was the consumers’ forgetfulness to eat the item. since fruits inherently have a short shelf-life, extensive delay in consumption can highly contribute to wastage. the onset of decay can happen immediately after, or even before, the point of purchase. 4. conclusions we used the methodology designed by gustavsson et al. (2013) to estimate flw generation in the philippines. our study provides the first estimates of the problem, covering the entire extent of the fsc in the country. given the novelty of this analysis in the country, we suggest further research and relevant policy design in addressing the problem. first, our study highlights the need for a standard and well-established flw definition at the level of the 215food loss and waste accounting: the case of the philippine food supply chain bio-based and applied economics 11(3): 207-218, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-11501 fsc. according to the literature, institutional objectives and motivation guide the characterization of ‘loss’ and ‘waste’ (fao, 2014; chaboud and daviron, 2017; cattaneo, et al., 2021). consequently, this stipulation gives entities some flexibility in establishing their interpretation of the terms. however, they should also consider the definitional implications on flw measurement and policy creation. for example, by adopting the definition of gustavsson et al. (2011) in our study, which only considers edible quantities intended for human consumption, our estimates only represent a fragment of the agrifood sector. we recognize that the omission of the non-food supply chains underestimates the magnitudes and restricts the achievement of a comprehensive flw reduction policy. concerning policy design, the agreed-upon terminology should also not contradict the reduction efforts at the country level. in our estimations, we considered the rejected banana hands at the dehanding activity as losses in the banana fsc. although farmers repurpose the rejected bananas as feed (calica et al., 2018), the definition of gustavsson et al. (2011) prescribes the inclusion of such quantities to the flw estimations. this situation implies that although the bananas were recovered and reused by the farmers, they will remain ‘lost’ because of the confines of the established definition. from a policy perspective, this might render specific reduction efforts ineffective because of the measurement guidelines followed. therefore, the resulting estimates might undermine the accuracy of tracking policy successes or failures. another limitation in quantifying the extent of flw in the philippines is the lack of an accounting standard covering all stages of the fsc. the absence of a consistently used methodology is an obstacle in accurately identifying the critical loss points and, by extension, the achievements or failures in minimizing the problem. the country-relevant actors can refer to the growing body of literature on this topic. of recent note is the micro-level survey developed and tested by delgado et al. (2021), which covered the current gaps in the measurement of flw—quantity and quality losses and pre-harvest losses. moreover, their methodology also allows for results to be comparable across countries and provides a granular understanding of the problem at the producer, middleman, and processor level. food waste, however, was not covered in their newly proposed method because of its distinct data collection and measurement requirements from food loss (delgado et al., 2021). this specificity suggests that the micro-level analysis of flw requires a mixture of methods to capture the total amounts of food outflow from the entire chain. compounding the issue on the creation of an accounting standard is the intricacy of the fsc. from the illustration of gustavsson et al. (2013), the fsc may seem simple. further, our estimations may reflect a singular fsc for a commodity. in reality, one commodity has numerous supply chains, and each varies in extent and number of actors. while it is improbable to determine every existing chain in the food system, there is a need to understand the trend of commodity flow through each stage and sub-stage and analyze the flw-influencing actions and decisions. this situation implies the need to balance the benefits and limitations of micro and macro-level analysis from a policymaking perspective. another consideration in analyzing flw is detecting the drivers of its generation. although identifying the extent of the problem is a vital part of flw information, determining the accompanying causes of the estimated figures can lead to a deeper understanding of the issue. by extension, the availability of this information will contribute to the accurate design of flw reduction policies. furthermore, the lack of relevant data that constrained our paper also challenges evidence-based policymaking. our flw estimates strongly depend on the availability of reliable loss/waste weight percentages and conversion and allocation factors, among other information. the philippines shares with other countries the lack of these critical statistics from official sources, compromising the quality of the estimates. we extend the same concern with some of the elements needed in the mass flows model. unfortunately, the repercussions of national data deficiencies are not isolated within a country. it is also consequential on a global scale. as targets in the sdgs, the global food loss index and the food waste index, which were developed and under the respective management of fao and unep, rely on country-level statistics (fabi & english, 2019). the indices will reflect the growth or decline of flw over time. as such, fabi et al. (2021) stress the need for comparable and reliable national data in light of coordinating reduction policies and worldwide monitoring of the problem. the authors also called the international community to formulate a standard definition and metadata to create synergies in data collection and policy actions (fabi et al., 2021). comparing our results and those from other studies shows the need to consider the conditions under which flw estimates were calculated. depending on the time of data collection or the relevant cropping season of recall, the presence of extreme weather events is likely to affect flw levels. in alignment with our findings, the 216 bio-based and applied economics 11(3): 207-218, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-11501 anieluz pastolero, maria sassi study of delgado et al. (2021) also reported that producers of selected staple crops in ecuador, peru, guatemala, honduras, and china all indicated the lack of rainfall or other weather conditions as one of their causes of loss. our comparison of results also suggests that social and cultural aspects reveal a deeper insight into the causes of flw generation. while capacities and knowledge are important in current practices, embedded social and cultural structures also affect the actions and decisions of fsc actors. soma et al. (2021) also highlight this point and express that practices, particularly that of farmers, are not solely based on rational decisions. instead, it results from the interplay of their physical assets, competencies, and viewpoints (soma et al., 2021). thus, analyzing and incorporating the underlying causes of flw in reduction policy design may be more beneficial to the relevant fsc actors. our evidence suggests that all stages of the fsc contribute to the generation of flw in the philippines. however, the critical loss points and the determinant factors are commodity-specific. therefore, effective policies aimed at reducing flw should be calibrated at the specific fsc stages. our analysis shows that the following shortcomings pose the most significant challenge in preserving the food quantities in the fsc: technological limitations, farming practices, and market standards in the rice, corn, and banana chains, respectively. it follows that efforts targeted at these issues may significantly reduce the problem. in line with the notion set by fao (2019), addressing these constraints may be highly consequential in improving the flw levels in the country. our study also highlights a fundamental mechanism of the fsc—it is a relay of the commodity from one stage to another. in other words, the flw incurred in the later stages may be affected by the activities performed or omitted in the prior ones (gustavsson et al., 2011). although the critical loss points have the greatest potential in reducing flw levels, this peculiarity also demonstrates the importance of addressing the bottlenecks in other fsc points. this consideration suggests a supply chain system approach for the containment of flw and not a fragmented policy intervention focused on the single stages (luo et al., 2021). in the pursuit to reduce flw, the fao (2019) also pointed out the possibility of establishing acceptable levels of loss/waste, which would warrant further research effort. this suggestion is rooted in the diminishing marginal returns of investments and the potential negative trade-offs with other sustainability aspects (fao, 2019; chaboud & daviron, 2017). although the fao (2019) acknowledges the difficulty of setting such a threshold, its identification can guide policy coherence, which is important in allocating limited mitigation resources, particularly in developing countries such as the philippines. acknowledgements we would like to thank mr. gopal trital for his valuable inputs to our paper. references calica, g. b., lingbawan, k. r., & dela cruz, r. s. 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(2017). missing food, missing data? a critical review of global food loss and food waste data. environmental science and technology, 51, 6618-6633. doi:https://doi. org/10.1021/acs.est.7b00401 volume 11, issue 3 2022 firenze university press bio-based business models: specific and general learnings from recent good practice cases in different business sectors nora hatvani1,*, martien j.a. van den oever2, kornel mateffy1, akos koos1 food loss and waste accounting: the case of the philippine food supply chain anieluz pastolero*, maria sassi the role of network characteristics of the innovation spreaders in agriculture antonio lopolito1,*, angela barbuto2, fabio gaetano santeramo2 the co-evolution of policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period roberto esposti price dependence of biofuels and agricultural products on selected examples wioleta sobczak*, jarosław gołębiewski bio-based and applied economics bae bio-based and applied economics 12(2): 103-113, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14003 copyright: © 2023 g. brunori. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: g. brunori (2023). towards a new generation of (agri-) food policies. bio-based and applied economics 12(2): 103-113. doi: 10.36253/bae-14003 received: november 26, 2022 accepted: may 16, 2023 published: august 05, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini, emilia lamonaca. orcid gb: 0000-0003-2905-9738 keynote speech of the 11th aieaa conference towards a new generation of (agri-) food policies gianluca brunori department of agriculture, food and environment, university of pisa, italy e-mail: gianluca.brunori@unipi.it abstract. the succession of systemic crises in the last 20 years have affected our lives and have shaken the old order. the global community, represented by un-based institutions, has encouraged a common effort to address global challenges. in the agri-food sector, one of the most relevant to the emerging societal challenges, the need of a new generation of agri-food policies is evident. the present paper reviews the recent literature on transformative policies, identifying their key characteristics directionality, reflexivity, and market articulation and proposing a framework to adapt these characteristics to the policy cycle. keywords: transformative food policies, transition, policy cycle, policy mix. jel codes: q18. 1. introduction the succession of systemic crises in the last 20 years have affected our lives and have shaken the old order, built upon the primacy of economy and trade over social and environmental problems. the global community, represented by un-based institutions, has encouraged a common effort to address global challenges. despite all difficulties, and many stops and go, there is now a wide consensus on global challenges, and agreements on climate, biodiversity and sustainable development goals have been embodied into national laws and have been turned into quantified targets and into accountability mechanisms (tap network, 2021). international agreements have activated new frameworks for the public debate at national level and generated new dynamics within political and economic communities. in the new context, a growing number of private and public actors commit to sustainability objectives. pushed by an increasing consumers’ sensitiveness, many companies tend to shift the arena of competition on sustainability issues by providing higher standards that allow them to communicate sustainability values (giovannucci et al., 2014). policy initiatives encourage european food business to coordinate sustainability standards and their communication and punish greenwashing. backed by international agreements, the most sensitive sectors of the public administrahttp://creativecommons.org/licenses/by/4.0/legalcode 104 gianluca brunori bio-based and applied economics 12(2): 103-113, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14003 tion to sustainability gain power on more conservative administrative bodies and become drivers of change. research and innovation policies encourage the production of new ideas and the dismissal of old paradigms, selecting research projects on their capacity to have an impact on societal challenges. in the political domain, environmental movements have started to bring governments into courts1 claiming that they don’t respect their climate obligations. in the agri-food domain these dynamics are particularly relevant, given the importance of agriculture and food on climate and sustainable development goals. many influential reports in the last years have agreed on the need to transform the way we eat, produce, distribute food2. as the un general secretary have stated in his summary and statement of action of the food system summit of 2021, food systems are contributing up to one-third of greenhouse gas emissions, up to 80 per cent of biodiversity loss and use up to 70 per cent of freshwater, and three billion people — almost half of all humanity — could not afford a healthy diet. the food system summit has mobilized tens of thousands of people in food system dialogues aimed at making proposals for food system transformation. the issue is not whether to change, but how and how fast. one of the problems, on this regard, is that we cannot change the system with the same policy instruments used in different historical contexts (rogge and stadler, 2021). a new generation of policies is needed. this paper will try to address this issue by developing a reflection around the following questions: what are the qualities that a new generation of policies should have? what should be done to foster a new generation of policies? 2. the characteristics of transformative policies according to a growing number of scholars and practitioners, transformative goals require transformative policies (unsrid, 2016; rogge et al., 2020; giurca et al. 2022, haddat et al. 2022), that are able to activate processes of structural change by affecting the root causes and the deep structures of the systems on which they operate. the difference between the new generation of policies and the old ‘grand reform’ approaches is the awareness of the complexity of structural change, the 1 https://www.unep.org/news-and-stories/story/battle-against-climatechange-courts-become-new-frontier 2 we could cite among others: the fao sofi 2021 (fao, 2022); global panel on food systems for nutrition (2020); ifpri (2020); sapea (2020); willet et al. (2019), brunori et al. (2020) awareness that transformation cannot be imposed in a top-down way and that organic, all-encompassing solutions are hard to succeed. transformative policies operate into arenas wherein a multiplicity of actors struggle to influence policymaking (loorbach et al, 2015), and where sectors of the same governments pursue different objectives and operate according to different logics. having this in mind, policymaking is not seen as a timeless mechanism where outcomes follow decisions automatically. rather, policymaking is seen as a process, articulated into phases characterized by different dynamics (howlett, 2019). in the ‘problem definition’ phase, knowledge, interests and values are mobilized to ‘frame’ a policy problem in terms of its causes, outcomes, responsibilities, actors involved. in the ‘agenda setting’ phase policy problems gain or lose priority in the policymaking agenda. in the ‘policy design’ phase, policies are deliberated and adopted by institutional bodies. in the ‘implementation’ phase, policies are applied in the various contexts and deploy their outcomes. implementation can include also monitoring and evaluation, which provides information on the efficiency and of the effectiveness of policies. each of these phases involves different categories of actors and networks, and different expertise. the policy process interacts with the political process, as political actors (parties, movements, members of representative bodies, media) interact with policymakers in all phases. the impact of policies on socio-technical systems depends on the characteristics of the system: the capacity of its actors to adapt, the robustness of its rules, the vulnerability of its components, the sensitiveness to specific measures, the distribution of power within the system. feedbacks from socio-technical systems can alter policy decisions and their implementation (rogge et al., 2020), as when macron was forced to withdraw its proposal of taxing fuel under the push of the movement of the ‘gilets jaunes’. the policy process can undergo rounds of depoliticisation and repoliticisation (wiesner, 2021). depoliticisation occurs when the problem definition is no longer contested, so that policy design is carried out outside the political spotlight and made mainly by experts. repoliticisation occurs when the effectiveness of the policy, or even the problem definition, is put into discussion. during repoliticization, the agenda setting and policy design are supported or contrasted by competing advocacy coalitions (mintrom et al., 1996). during depoliticization, policy design and policy implementation are carried out through policy networks, composed by public officers and stakeholders’ organizations who share the same assumptions, the same problem definitions, the same objectives. 105towards a new generation of (agri-) food policies bio-based and applied economics 12(2): 103-113, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14003 transformative policies intervene in this process with the goal to remove barriers to change and to support change makers. they also can repoliticize the policy problem providing new evidence and new ideas for problem definition. they can be introduced to activate processes of change within the administration itself and give power to ‘institutional entrepreneurs’ within the administrations. transformative policies differ from other policies for three main aspects: a) values and principles to which they are inspired; b) the knowledge base necessary to manage them; c) the intervention pathways they adopt. 2.1 values and principles the transformative potential of policies depends on their capacity to appeal to shared values and principles. the more they are based on consensus, the less they are likely to face open contestation. international agreements such as the sustainable development goals provide plenty of transformative values and principles. but these principles have not prevailed without resistance. they have progressively challenged the market-centered principles embodied into the so-called ‘washington consensus’, that constituted the key assumptions of economic policies in the capitalist world. as williamson one of the first to introduce the term pointed out, the washington consensus postulated the primacy of market forces, recommending budget discipline, market liberalization, price stability (williamson, 2003). serra and stiglitz (2008) have provided a radical critique to it, pointing to the fact that this consensus fails to address social and environmental consequences of policies. birdsall and fukuyama (2011) have observed that developing countries, after the asian crisis, have given much more emphasis to social policies rather than on efficiency. critiques to the washington consensus have stressed the relevance of market failures, pointing out that not always markets generate optimal outcomes. after the crisis of 2007, the obama administration openly contradicted the washington consensus introducing an aggressive program of public spending (rehman, 2010), and opening a new phase of economic policies. the next generation eu and the inf laction reduction act of biden go in the same direction. the paris agreement and the agenda 2030, both of 2015, reflects a radical change in approach. the emerging new consensus around sustainable development3 introduces a hierarchy between ecological, social, and 3 for an illustration of the term ‘consensus framework’ with reference to food security, see mooney e hunt, 2009. economic goals. the notion of anthropocene, now at the basis of the concept of sustainability, implies that human activities cannot trespass ‘planet boundaries’, environmental pressure levels above which human habitats could become less stable and hospitable (willet et al. 2019). as kate raworth (2017) has highlighted, not only biophysical planet boundaries, but also social boundaries should be considered. in her ‘doughnut economy’, called in this way because it is represented by two concentric circles, raworth (2017) explains that while the ceiling of a ‘safe and just operating space’ is given by biophysical constraints, the floor of this space is represented by minimum social standards: not respecting them put stability of human systems at risk. these metaphors raise the questions: are policies we are designing keeping the planet within the operating space? do they improve the desired outcomes without creating harms to other outcomes? in this approach, market forces are considered in a much more pragmatic way, while public policies as well as civil society get more weight in the definition of policies. after trump, the covid and in the middle of the ukrainian crisis, the sustainable development consensus looks much weaker than in 2015. the international order looks in transition from a bipolar to something different, maybe a multipolar world. war and tensions between superpowers have weakened the authority of international institutions. common global trade rules are undermined by protectionist policies. globalisation turns into regional economic spheres of influence. public deficit spending aimed at coping with the multiple crises has generated inflation and debt. in the meanwhile, last summer droughts and the intensification of extreme meteorological events show that the climate crisis is still there. the tension between those who think that the crisis shows that the urgency of the transition is even more necessary and those who want to rethink it is more and more evident. for europe, keeping a strong emphasis on sustainable development goals is a way to gain a leadership based on principles universally recognized rather than on force. so far, the roadmap established by the green deal strategy is proceeding fast: the main concern is related to the capacity to member states to follow. here is the role of transformative policies. 2.2 knowledge base transformative policies require a new knowledge base (clark and dickson, 2003). in the economic field, most of the concepts emerging in the sustainability debate are generated outside the old economic toolbox and make pressure on economists to open their studies 106 gianluca brunori bio-based and applied economics 12(2): 103-113, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14003 to other fields of knowledge. economists are encouraged to abandon mechanical system approaches in favor of complexity and to consider (positive and negative) feedbacks, emergent properties, unintended consequences of choice, and trade-offs related to different perceptions of agents (arthur, 2021). attention to complexity brings to consider the hybridity of the systemic connections: the notion of socio-technical systems (geels, 2004) captures the interplay between social and technological domains, and the notion of ‘socio-ecological systems’ (anderies et al., 2004) looks at how human activities generate wellbeing as well as pressure on natural resources. system approaches are inductive that is, they start from empirical evidence to build theory and the empirical work is finalized to build representations of systems around specific problems (gharajedaghi, 2011), so to produce knowledge immediately useful for practical purposes. system approaches are aware that different sets of actors can develop multiple representations of systems, none of them intrinsically ‘true’ or ‘false’, and that actors behave according to their representations of the system. this principle applies also to science-based representations, the differences between which depends on their conceptual assumptions and their systems of values (bené et al. 2021). this also opens the way to a new generation of quantitative models, such as agent-based models (lebaron and winker, 2008) and system dynamics (uriona and grobbelaar, 2019). applied to policies, this approach emphasizes that policymakers deal with a multiplicity of system representations based on different actors’ perspectives and values, and their task is to broker between different representations. for example, reading food systems with the lens of food security is different from reading it with the lens of competitiveness, and seeing food as a commodity might convey a representation of the system much different than in case of considering food a human right (sapea, 2020). stakeholders’ participation in building representations of the systems is thus necessary to the success of transformation policies. for example, concepts such as the food environment, central in the debate on sustainable food systems, have a strong subjective component, related to the timespace patterns of daily lives (mattioni et al. 2020). citizens’ involvement on food system appraisal can open researchers’ eyes on otherwise neglected aspects. policymaking, rather than being considered external to socio-technical systems, is increasingly considered as an endogenous variable (smith and stirling, 2007), affecting and being affected by system actors and activities. once emancipated from market failure approaches and exposed to other knowledge domains, the thought in this field has undertaken research pathways based on systemic concepts such as food environment, foodresources nexus, resilience, circularity, ecosystem services (galli et al. 2020). a stronger attention to societal challenges also has implied a greater attention to ‘actionability’ of knowledge produced by research (kirckoff et al. 2013), meaning that research should provide responses to problems that fit to users’ needs. obsolete approaches to scientific research tend to separate scientists from the rest of society and to create a unidirectional flow of information from research to practice. in the new approach, engagement of researchers with policymakers and stakeholders in all phases of research is necessary to build a common language and a shared representation of the systems observed. interaction helps to develop a shared understanding of problems, needs, barriers to solutions. this implies acknowledging the complementarity of different types of knowledge and the need to find different criteria for knowledge validation (cundill et al., 2015; jacobi et al. 2022). 2.3 intervention pathways in a post-washington consensus, market forces can even become barriers to transformation or drivers of degradation. when market loses its primacy, state and civil society gain a stronger role. mazzucato (2013) proposes an entrepreneurial state, taking the example of the apollo program which brought humans on the moon. more in general, it is said that sustainability cannot be achieved without transitions, and that management of transitions implies managing structural change (loorback, 2007). according to weber and rohracker (2012) system approaches search for solutions to problems by shaping differently the patterns of interaction in a system (ericksen et al., 2012; haladi, rao, 2010). the growing literature on transitions shows that transformative policies must have three properties: directionality (that is, goals of change defined in the public sphere), reflexivity (that is, capacity to learn from experience), market articulation (that is, influencing the way markets are shaped) (grillitsch et al., 2019). directionality implies building visions, establishing long term goals, setting pathways (weber and rorhacker, 2012). for this reason, consensus frameworks are important, as they support legitimacy of policy directions. policies based on directionality principles make use of strategic tools: they tend to facilitate rather than prescribe, have a contractual basis, and rely upon the autonomy of social forces. figure 1 represents three pathways for policy processes: one initiated by civil society, one by business, and the third by government 107towards a new generation of (agri-) food policies bio-based and applied economics 12(2): 103-113, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14003 reforms (unep, 2016). in the first and the second pathways the state intervenes with regulation when the conditions are already ripe, after that ngo initiatives and business have opened the way. organic farming can be considered an example of the second pathway, as the european regulation came after a bottom-up process of innovation carried out by forerunning business backed by ngos. palm oil-related initiatives have been started by ngos mobilization and have been incorporated into business practices (oosterveer, 2016). in a complex policy system as the european one, where there are multiple level of governance, local administrations or forerunning member states can play this initiating role. soft law, as in the case of voluntary standards or the eu code of conduct on responsible food business and marketing practices4, can activate societal and business energies and prepare the terrain for hard law. directionality also implies active efforts to pursue coherence between policies. for example, it has been observed that policies aimed at reducing carbon emissions might create pressures on biodiversity, and that policies supporting biofuel could put food security at risk (standish et al. 2020), not to speak of the compatibility between the new cap and the green deal (guyomard et al., 2020). however, hardly coherence can be addressed with fully coherent, all-encompassing policy design. as van bers et al. (2016) point out, barriers to change can be related to lack of access to resources, effectiveness of formal institutions, lock-in to a reigning paradigm. for de jesus and mendonca (2018), barriers can be classified into ‘hard’ (technological and financial) and ‘soft’ (institutional and cultural). in a concept of policy as a process, there is a need for actors, networks and institutions (‘institution4 https://food.ec.europa.eu/system/files/2021-06/f2f_sfpd_coc_final_ en.pdf al entrepreneurs’) that foster coherence with the general objectives, and policies that support them removing barriers to change. the presence of enablers and barriers to transformation make us also aware of the need to address the problems with policy mixes rather than with single, and separated, policy measures (rogge et al., 2020). the second property, ref lexivity, is based on the awareness that governing the transformation implies managing uncertainties, systemic trade-offs, cross-sectoral interactions, power dynamics and conflicting perspectives (european commission, 2021). this implies that the policy process would be better based on experiment, learning, and adaptation. in the transformative intervention logic, innovation is at the center of policies, as a catalyst for transformation (de boer et al., 2021). innovation can contribute to address trade-offs (for example, between economic and environmental outcomes) by providing win-win solutions. given the open nature of transformation processes, bottom-up innovation is encouraged to provide insights on levers and barriers to change. examples of these policies already exist in the european landscape: in the second pillar of cap, operational groups are conceived of as living laboratories for innovation, and the eip partnership provides a space for comparison, sharing and reflection. potentially, many rural development measures might have the character of experimentation, provided that they are framed into mechanisms that foster learning. when reflexivity is understood as a key property of policies for transformation, effective governance mechanisms should be developed to ensure that bottom-up innovation activates policy learning. innovation, in fact, regards also policies: given the complexity of the processes, hardly transformation can be made without learning from policy experiments at lower scale (mytelka and smith, 2002). given its transformative power, it is important to point out that innovation has not only a technological dimension: social and institutional innovation can play an equal or even greater role. and in any case, it is increasingly recognized that technical, social, and economic domains are not separated from each other, as they operate upon socio-technical and socio-ecological systems. when they challenge the basic assumptions and the principles on which systems operate, all types of innovation concur to system innovation (oecd, 2015). the third property, market articulation, rests on the fact that market are powerful mechanisms that contribute to orient actors’ behavior. when in conflict with actors’ economic interests, policies are much harder to succeed. agricultural economics has long been associated with policy-based orientation, involving actions on supply (such as quotas, price support, or standards) figure 1. transformation pathways (source: unep, 2016). 108 gianluca brunori bio-based and applied economics 12(2): 103-113, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14003 or on demand (through taxation). however, perceiving markets as context-specific systems of rules and resources that influence actors’ behavior allows for significant progress. this understanding helps us grasp how policies can shape actors’ choice environment, making them more conducive to change. in the realm of food, there exists a vast body of literature on social innovation, specifically targeting the transformation of market behavior among actors (chiffoleau and loconto, 2018). farmers’ markets and purchasing groups, for instance, defy conventional market forces and establish novel market institutions. voluntary schemes, such as organic farming and geographical indications, create market spaces for innovative products in their introduction phase (giovannucci et al. 2014). public procurement is now considered a key strategic policy tool for dietary change, especially when considering specific population groups such as primary school students (neto and gama caldas, 2018). public procurement can also open markets to innovative products. nutrition or sustainability labelling aim at orienting consumer preferences (brown et al. 2020: the debate on nutriscore vs nutrinform in europe shows how economic interests can be affected by information. 3. policy mixes for transformation the new generation of policies should be evaluated for their capacity to remove the barriers to transformation and to create synergies between agents of change. important barriers to transformation are often linked to the way administration bodies are articulated, which also affect the way knowledge is produced. alternative problem framings, mentioned earlier, can reflect separation between different bodies of knowledge. a clear understanding of the policy process, of the drivers, the barriers, the relevant actors and their relative power is the key to transformative policies, which are based on policy mixes rather than single solutions. policy analysis should start from a sound assessment of the policy process before identifying solutions. table 1 illustrates a tentative toolbox for transformative policies in the agri-food domain, articulated into the different steps of the policy process. 3.1 problem definition transformation implies a redefinition of existing policy problems and the emergence of new ones. problem definition is highly politically sensitive, so transformation management requires a careful management of stakeholders’ involvement. transformation fora gather stakeholders and administrations to deliberate around specific goals. for example, policy labs activated with the fit4food2030 project5 are participatory and experimental spaces that bring stakeholders together in a series of meetings with dedicated themes and methods. policy labs build a network of diverse stakeholders from different parts of the food system. together, the stakeholders analyse the current food system and related r&i system in their country or region, identify barriers and opportunities and work on innovating r&i policies. in the aftermaths of expo2015, hundreds of municipalities have activated food policy fora to address problems such as nutrition, food quality, relocalization of food systems (lever et al. 2019). food communities and rural districts, introduced in the italian legislation, could have the same role in redefining rural needs. transformative policies imply decisions on issues the knowledge about which is uncertain and contested, also within the scientific community. controversies on gmos, pesticides, the impact of livestock on the environment have animated the policy debate in the last decade. for this reason, a specific attention should be given to the role of scientists. in a context of ‘consensus frameworks’ such as the sustainability development goals, scientists are supposed to support the process of consolidation or the adaptation of the frameworks through ideas and evidence (duncan et al., 2022). while on one side they need to resist to capture by policymakers willing to legitimate their decisions, hardly scientists can claim a neutrality between opposing knowledge claims. on the other hand, not necessarily personal convictions should be separated from scientific judgement, as neutrality is not synonym of objectivity, a key ethical principle for scientists. transformative policies imply commitment to change, and this can give scientists, depending on the context where they operate, the role of ‘advocates for change’ (that is, look for alliances for change based on scientific evidence) or ‘honest brokers’ (who make a synthesis of different and 5 https://fit4food2030.eu/policy-labs/ table 1. transformative policies and the policy cycle. problem definition agenda setting design implementation transformation fora roadmaps for transition supply-side and demand-side win-win solutions information systems accountability addressing resistance formative evaluation transformative governance 109towards a new generation of (agri-) food policies bio-based and applied economics 12(2): 103-113, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14003 sometime opposing position and provide ranges of solutions and related implications) (pielke, 2012). 3.2 agenda setting in the agenda setting phase priorities are established. in complex political systems, transformational goals are embodied into the agenda setting process through policy roadmaps. roadmaps are strategic tools that serve to involve and to align the multiplicity of actors involved in the transformation. they need to be flexible enough to adapt to the conditions of the context, and at the same time they should be capable to mobilize the actors, commit and make them accountable. policy roadmaps should be based on a clear understanding of the dynamics of the systems on which policies should intervene, of the forces that support the change and those who oppose. the choice of policy instruments and the sequence of the steps to be taken should be based on an analysis of the leverage points, the barriers, the potential consequences of specific choices, stressing the consequentiality of the measures to be taken and their graduality. the need to overcome barriers to change would encourage the search of win-win solutions, and if not possible, participation would identify the groups that could be damaged and the size of the costs they would suffer, so to establish fair compensations. the green deal provide the most relevant example of roadmap, as it defines the goals and desired outcomes related to food, and lists the major steps needed to reach them. a roadmapping approach is implicit in the performance-based approach to the cap, as achieving specified targets would imply the identification of the steps necessary for transformation and a constant monitoring of the progress. 3.3 policy design in the design phase, the complexity of food systems requires an approach to system innovation based on policy mixes, able to address the root causes of the policy problems, mobilize all relevant actors, aim at a variety of objectives. traditionally, cap has intervened mainly on the supply side, while much less attempts have been made to address demand. the green deal and the farm to fork mention the need on acting on the demand side to pursue healthier diets, for example through public procurement, labelling, and education. the project fit4food20306 has developed a dataset with 460 policy 6 https://fit4food2030.eu/policy-labs/ tools, clustered into six goals: innovation, equitable outcomes and conditions, viable and socially balanced agrifood business, reduced environmental impacts, food safety, balanced and sufficient diets for all. the datasets also classify the tools according to the type of instrument, such as regulation, r&i, information, standards, labelling measures, border measures7. as the transformation has the power to change substantially the relations of power in the system and the distribution of costs and benefits, policy mixes should also look for win-win solutions, such as compensation schemes for the losers and incentives for the transition. 3.4 implementation in the implementation side, the capacity to distribute responsibilities across the system will be crucial. rather than models based on central administration exerting its disciplinary power upon the actors, contractual models are being developed, based on agreed objectives, clear performance indicators and monitoring of results, which implies accountability of the beneficiaries. the cap has introduced this new model, but its implementation won’t be easy, given the number of actors involved and the complexity of the issues. measures such as the new eco-schemes or the environmental and climate commitments under the ‘second pillar’ will need relevant monitoring and control activities to deploy their effects. ‘carbon farming’, for example, still raise questions about their effectiveness and their costs (dumbrell et al. 2016). digitalization of administrative procedures and effective information systems could reduce transaction costs and improve communication between business, administrations, and civil society (ehlers et al., 2021). important steps ahead in the process of sharing integrated administration and control system (iacs) data are made, but the process is slowed down by the reluctancy of member states to share their data given the concern that more transparency could mean more sanctions (oecd, 2019). in this stage, ideally policy evaluation is a key resource for learning. however, different evaluation models can have different transformational potential. while evaluation of results, linked to payments, can help to structure the principal-agent relationship and provide information in the wider public space, formative evaluation8 could provide feedback 7 https://knowledgehub.fit4food2030.eu/wp-content/uploads/2020/08/ fit4food2030_t2.2-extra_policy-cards_190316.pdf 8 a formative approach to policy evaluation is “ astyle of evaluation which is conducted with the participation of stakeholders with the main purpose of improving the definition and implementation of the interventions being evaluated” (molas-gallart, 2021) 110 gianluca brunori bio-based and applied economics 12(2): 103-113, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14003 information to stakeholders to improve their processes, to understand trade-offs, and to learn from failures. 3.5 transformative governance given the importance of the dynamics of the policy cycle in the success or failure of policies, the issue of governance is gaining more and more prominence. transformative policies imply first of all governance management: as hoppe (1988) points out, policy problems and governance are the two coins of the same medal, because the way policies are problematized, designed and implemented depend on the actors, networks, and institutions that are involved in the process. given that drivers and barriers are embodied into actors and networks, the best way to activate processes of change is involving them. the choice of who is involved, in which stage of the process, for what decisions, and the instruments to encourage interaction, is key to effective policies. the design of food policies, for example, requires a big effort to involve actors and administrations belonging to a large variety of areas. depending on their composition, governance arrangements can give different weight to the potential outcomes. bringing together stakeholders belonging to different phases of the supply chain might bring to new problem framings. involving stakeholders that in general are not involved in policymaking might provide transformative outcomes. governance can also affect the weight given to different drivers into decisions, as each stakeholder brings different values, knowledge, and interests. likewise, governance influences the activities and the actors that policy making takes into consideration. the governance arrangements that have accompanied the green deal and the farm to fork at eu commission level are significant. given that the strategy affects many directorates, the implementation of the strategy has been assigned to a dedicated unit under the executive vice-presidency of the european commission, with the power to coordinate the other directorates. another example of potentially transformative governance is the blossoming of urban food strategies after the expo 2015, which shows the intention of municipalities to become key actors of food policies and to generate a bottom-up change. 4. concluding remarks in this paper we claim that a new generation of policies is needed. these new policies are based on system approaches, are conceived of as mixes of different policy tools concurring to given objectives, are aware of the policy cycle and therefore of the distance between expected and real impact. one of the limits of these policies is related to the fact that they need time. as participation and deliberation are key principles for their success, there is the risk that the rapidity of change and the succession of crises could outprecede decisions or make them ineffective, as the long process of construction of the new cap has demonstrated. more experience on how to design and implement these policies might speed up the process. given that research on transformative policies is at its infancy, there is a strong need for research on these themes, to open the way to a new generation of agrifood policy studies, that ref lect on the assumptions and on the methodological bases of agri-food studies. we have observed that the notion of transformative policies implies an attention to socio-technical and institutional mechanisms that regulate food systems. a system approach blurs the boundaries between agriculture, food, natural resources, nutrition, and health, and takes into consideration multicausality, unintended consequences, nonlinear processes. stronger interdisciplinary approaches are needed, first of all with social and policy sciences. an emphasis on the role of policy actors as agents of transformation would shift the attention to agent-based models. the adoption of the concept of policy process, its articulation into policy stages, and the understanding of the feedbacks that policies receive from socio-technical systems, can help scholars to better understand the impact of policies on society, and to design effective evaluation methodologies. from 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(2003). the washington consensus and beyond. economic and political weekly, 1475-1481. cap, farm to fork and green deal: policy coherence, governance, and future challenges annalisa zezza key policy objectives for european agricultural policies: some reflections on policy coherence and governance issues silvia coderoni towards a new generation of (agri-) food policies gianluca brunori earliness, phenological phases and yield-temperature relationships: evidence from durum wheat in italy marco tappi1,*, federica carucci2, anna gagliardi1, giuseppe gatta1, marcella michela giuliani1, fabio gaetano santeramo1 does the presence of inner areas matter for the registration of new geographical indications? evidence from italy francesco pagliacci, francesco fasano* learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy antonio r. hurtado1,2,*, julio berbel2 bio-based and applied economics 10(2): 153-168, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9746 bio -based and a ppl ied economics bae copyright: © 2021 c. geoghegan, a. kinsella, c. o’donoghue. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: c. geoghegan, a. kinsella, c. o’donoghue (2021). the effect of farmer attitudes on openness to land transactions: evidence for ireland. biobased and applied economics 10(2): 153-168. doi: 10.36253/bae-9746 received: september 16, 2020 accepted: june 23, 2021 published: october 28, 2021 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini. orcid cg: 0000-0002-7023-6313 ak: 0000-0001-5670-9025 cod: 0000-0003-3713-5366 the effect of farmer attitudes on openness to land transactions: evidence for ireland cathal geoghegan*, anne kinsella, cathal o’donoghue teagasc, the irish agriculture and food development authority; national university of ireland, galway *corresponding author. e-mail: cathal.c.geoghegan@nuigalway.ie abstract. ireland suffers from very low levels of farmland mobility by european standards. this paper examines the role of attitudes in farmers’ openness toward land transactions using a nationally representative survey of irish farmers across the major farm systems. the results show that attitudinal factors are a significant predictor of openness to land mobility, both on the supply and demand side of the market. additionally, there appears to be a greater demand amongst farmers for temporary land transactions such as land leasing arrangements than is currently seen in at market level. keywords: land markets, attitudes, ireland. 1. introduction land mobility is becoming an increasingly important issue for european agriculture. the enhanced market orientation of european agriculture and reduced reliance on subsidies requires farmers to be more efficient in their use of factors of production. this is coupled with european farmers getting older on average and the need amongst young european farmers for access to land (davidova & thomson, 2014; zondag, 2015). access issues are further complicated by the increasing land concentration in europe, with more land being held by fewer farmers (kay et al., 2015; van der ploeg et al., 2015). agricultural land transactions in europe occur within a range of national institutional and regulatory environments (ciaian et al., 2010; ciaian et al., 2012; needham et al., 2011). one consequence of these diverse land governance frameworks is that land sales and land rental markets may operate uniquely from country to country. despite an integrated agricultural market and the longstanding common agricultural policy, the share of rented land varies between 20 and 80 per cent across the eu (ciaian et al., 2010). preferences for land ownership over land rental or vice versa have been linked to capital market imperfections, farm profitability and government regulations (swinnen et al., 2016). at an eu policy level, tension exists between encouraging land mobility so as to enable the structural change required for farms to reach an ecohttp://creativecommons.org/licenses/by/4.0/legalcode 154 bio-based and applied economics 10(2): 153-168, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9746 cathal geoghegan, anne kinsella, cathal o’donoghue nomically viable size and protecting the family farming model which accounts for 97 per cent of european farms (davidova & thomson, 2014; hennessy, 2014). enabling land to change hands but also maintaining local connections to rural areas requires a nuanced policy response. individual member states have tried to balance these policy priorities by giving young, local farmers first refusal when land becomes available locally or by providing brokerage type services between young and retiring farmers (ingram & kirwan, 2011; piet et al., 2012). in this context, the abolition of the quota on milk production in 2015 presents an opportunity for structural change in european agriculture (dervillé et al., 2016; groenveld et al., 2016). the abolition of quota means that for farmers looking to increase production, land rather than quota rights will be the scarcest production factor (boere et la., 2015). the potential for farmers to adapt to a post-quota landscape depends on many factors including demographics, socio-economic characteristics and the availability of inputs (chevalier et al., 2012; kempen et al., 2011). land is an input of particular importance, especially in an irish context. irish dairy farming depends upon a grass-based rather than feedbased production system, meaning a sufficient supply of land is necessary to increase dairy production (dillon et al., 2008). however, ireland suffers from very low levels of land mobility by european standards (ciaian et al., 2010). this means that accessing extra land for dairy farming may prove difficult. despite efforts from policymakers to encourage long-term leasing arrangements, most rented land in ireland is accessed through shortterm, 11-month “conacre” contracts, unsuitable for the long-term infrastructural provisions that are required by dairy farmers (o’neill & hanrahan, 2012). irish land markets have traditionally been quite static, with land rarely changing hands. the dominant means of transfer of ownership is through non-market arrangements, usually inheritance, which is often attributed to the strong emotional attachment to land in ireland (donnellan et al., 2008). rented land (both conacre and long-term leasing) only accounts for 18% of utilisable agricultural area (uaa) in ireland (geoghegan & o’donoghue, 2018). due to the illiquid land market in ireland, little information exists about what drives agricultural land transactions. this is especially true in relation to the supply of land. this study attempts to fill this information gap by examining the attitudes of irish farmers to agricultural land mobility. given the lack of information about the characteristics of farmers who participate in land markets, an exante approach is proposed to determine which types of farmers are open to land transactions. previous research has concentrated on the use of stated intentions surveys to accomplish this task by asking what the farmer will do in the future (breen et al., 2005; lobley & butler, 2010). however, given the static history of the irish farmland market and stable policy conditions, it is not anticipated that many irish farmers outside the dairy sector intend to change their current land allocation. therefore, a more exploratory analysis is required to identify farmers who would be open to land market participation. this study therefore aims to ask three main questions: • are farmers open to entering the land market? generally speaking, farmers can either supply or demand land. this study will focus on farmers’ openness to selling or leasing out land on the supply side and buying or leasing in land on the demand side. this contributes to the literature by quantifying land demand and supply in a context where market information is either missing or incomplete. • what distinguishes famers who are open to entering the land market from those that are not? in addition to agronomic and socioeconomic differences, do farmers interested in land transactions approach farming from a different attitudinal standpoint than farmers uninterested in land mobility? • given the desire of policymakers to encourage longterm leasing amongst irish farmers, are there differences between farmers interested in leasing and those interested in permanent transactions such as buying and selling? this paper is structured as follows. section 2 looks at the policy context of land mobility in greater detail, paying specific attention to the irish situation. section 3 deals with the methodology and data used in the study. section 4 looks at the results of logistic regressions examining farmers’ openness to entering the land market while section 5 provides a discussion of the results and their impact on policy. 2. policy context and related literature a lack of land mobility has long been seen as an impediment to structural change in irish agriculture (commins, 2001; inter-departmental committee on land structure reform, 1978; maguire, 1983). currently, the issue of land mobility is of interest to policymakers in light of public policy commitments to increase the output of irish agriculture in the coming years (daff, 2010; dafm 2015). one particular commitment is to increase dairy output by 50% by the year 2020, with 155the effect of farmer attitudes on openness to land transactions: evidence for ireland bio-based and applied economics 10(2): 153-168, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9746 sectoral growth expected to continue beyond that date1. achieving this target, as well as future growth, will require the acquisition of additional land by dairy farmers (dillon et al., 2008; geoghegan & o’donoghue, 2018; läpple & hennessy, 2012). currently, cattle farming is the dominant form of agriculture in ireland, accounting for 57% of land (geoghegan & o’donoghue, 2018). dairy farming accounts for 14.9% of agricultural land, with sheep farming taking place on 12.3% of land. despite making up the largest share of farmland usage in ireland, the average cattle farm has consistently returned negative market incomes over recent years and is dependent upon subsidies for survival (hennessy & moran, 2016). only dairy farming has been consistently profitable, on average, over recent years. land rental market simulation modelling by loughrey and hennessy (2019) suggests that a land market based solely on farm profit maximisation would lead to significant increases in farm size concentration with dairy and tillage farms growing at the expense of cattle and tillage. most irish farms are owner-occupied, with the land owner generally being the farm operator (donnellan et al., 2008). farm ownership generally transfers through inheritance, with a single family member usually inheriting the intact farm structure. as a result, farmland rarely comes onto the open market. land mobility outside of intra-family transfer is dependent upon land rental and sales markets. attempts have been made at a policy level to increase land mobility in irish agriculture, most notably the introduction of tax exemptions to incentivise the long-term leasing of land. traditionally, land has been rented in ireland on a shortterm, 11-month basis. to encourage longer term leasing agreements, tax incentives were first introduced in 1985. these incentives allowed income derived from the long-term leasing of land (minimum of five years) to be exempt from income tax up to specified limits. over time, these exemption limits have increased, with higher limits being added for leases of longer periods. by 2015, up to €40,000 per year can be earned free of income tax for leases of 15 years or longer. other policy measures to encourage land mobility have been introduced including stamp duty exemptions for young farmers acquiring land, the promotion of farm partnerships and capital gains relief to encourage land consolidation (macra na feirme, 2015; dafm, 2018). studies concerning land mobility in ireland have mostly focused on the succession and inheritance aspect 1 the 50% increase is compared to the output of the average of total production between 2008 and 2010. this target was achieved in 2018, two years ahead of schedule. of land transfer (hennessy & rehman, 2007; kennedy, 1991). it has been found that policy instruments incentivising either the early retirement of older farmers or the installation of younger farmers on farms have had limited success in increasing the level of land mobility (bika, 2007; gillmor, 1999). land mobility studies in ireland outside succession and inheritance processes have been relatively rare. conway (1986) studied land leasing practices in the west of ireland and found that although potential lessees were willing the pay more for land than the prevailing rate, potential lessors were generally not interested in leasing out land as long as they were able to continue farming the land themselves. jenkins (1997) found in a study of leasing activity in the south-east of ireland that commercial tillage2 and non-local3 farmers were predominant in the rental market, with land being supplied by older farmers operating smaller farms. bogue (2013) found that three-quarters of farmers with no successor would consider renting out land on either a long or short-term basis when they themselves were no longer able to farm at their current level. this compared with 28% of farmers who would consider selling their land in the same situation. banovic et al. (2015) found general support for policy measures incentivising land mobility amongst irish farmers but also found that surveyed farmers were reluctant to take advantage of the policy measures themselves. o’neill and hanrahan (2012) examined irish farmers’ land market decisions from the perspective of the decoupling of agricultural support payments from agricultural production. following decoupling, irish farmers are required to maintain the area of land on which they claim their single payment in a state fit for agricultural production although actual production is not required. the authors found that decoupling led to a modest reduction in net land rental on average but a lack of information on consolidation, where famers whose land rental agreements had expired could transfer payments from areas where they no longer rented to land that they still possessed, made the true impact of decoupling on land decisions difficult to assess. due to the low number of transactions in irish land markets, as well as the lack of literature in the area, little information exists about the characteristics and attitudes of irish farmers who enter the land market. therefore, this study includes a wide range of factors which may drive willingness to enter the land market. as well as structural and socio-economic factors, farmer attitudes are considered. farmer behaviour has been shown to be 2 specialist tillage farmers for who tillage accounts for at least two-thirds of the farm’s total standard gross margin. 3 greater than 10 kilometres away. 156 bio-based and applied economics 10(2): 153-168, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9746 cathal geoghegan, anne kinsella, cathal o’donoghue affected by a multiplicity of farming goals and attitudes (willock et al., 1999a). there is a large literature on the attitudes and objectives of farmers and the impact of these on farming behaviour with farming attitudes being identified as important to risk aversion, innovation, diversification, off-farm work, environment, production, management, legislation, stress, pessimism and satisfaction toward farming (willock, 1999b provides a review of the literature). non-economic objectives such as farmer lifestyle have also been shown to strongly affect farmer decisions (howley et al., 2015; marr et al., 2019). studies related to attitudes of farmers toward land have tended to focus on land use, especially in terms of environmental issues (mills et al., 2013; wilson, 1996). given the absence of information about farmers in the irish land market, these factors will provide a sense of what drives farmers in their consideration of land transactions. 3. case study and methodology this paper uses a nationally representative survey to examine the willingness of irish farmers to engage in a land transaction. given the binary nature of this proposition (the farmer either does or does not want to engage in the transaction), a logit model is utilised. four logit models are used to examine the willingness of farmers to lease land in, lease land out, buy land and sell land. one difficulty with interpreting non-linear models such as the logit is that unlike linear models, an explanatory variable’s coefficient does not equal its marginal effect. a given change in an explanatory variable x will usually have less effect when the response probability p(y = 1|x) is near the extreme values of zero or one as compared with middle values. therefore, this study uses odds ratios to interpret the marginal effects of the explanatory variables. odds ratios in logit models can be interpreted as the effect of a one unit change in x in the predicted odds ratio with the other variables in the model held constant. the odds of p(y = 1|x) increase multiplicatively by eβfor a one unit increase in x, holding all other variables constant. in order to determine the attitudinal orientation associated with farmers in the sample, a set of attitudinal statements was included as part of the survey questionnaire. principal component analysis (pca) is used to identify underlying structural relationships between farmer responses to these attitudinal statements. pca finds the linear combination that explains the maximum amount of variance among the observed variables – called the “first principal component”. it also finds another, orthogonal (uncorrelated) linear combination that explains the maximum amount of remaining variance (“second principal component”), and so on until all variance is explained (hamilton, 2013). pca thus serves as a data reduction technique, allowing the analysis of the attitudinal statements to be simplified. each principal component has an eigenvalue, which represents the standardised variance explained by the component. principal components with values of less than one eigenvalue explain less than the equivalent of one variable’s variance so are set aside for purpose of analysis (abdi & williams, 2010). following the pca, varimax orthogonal rotation is used to further simplify the factor structure. 4. data this paper’s analysis of farmer attitudes to land mobility is based on a survey of 837 irish farmers in 2014 and 2015. the survey used random probability sampling to survey a representative number of farmers from each county in ireland. in order to achieve a representative geographical spread, a starting point was randomly selected in each county with every third farmer being selected to participate in the study. the survey continued in each county until a quota of respondents in each county was reached. quota sampling set demographic quotas on the sample based on known population distribution figures. the quotas used here were based on known population distribution figures in relation to specific farm systems (dairy, cattle rearing, cattle other, sheep, tillage and mixed) taken from central statistics office data (cso, 2012) the respondents were asked questions based on three different areas: current farm characteristics; attitudes to land, farming and future plans; and knowledge about land-based policy initiatives. the survey also contained 15 attitudinal questions using a four-level likert scale ranging from “strongly disagree” to “strongly agree”. the respondents could also choose a “don’t know” option. there is little consensus regarding the correct number of response options or whether an odd number of response options should be used in order to allow a neutral, midpoint response (sturgis et al., 2014). in the context of this study, it has been found that 4-point scales (as used here) yield similar levels of reliability compared to 5-pont scales which would contain a midpoint (alwin, 2007). neutral, midpoint responses can also represent hidden “don‘t know” answers (sturgis et al., 2014). therefore, it was decided to use a 4-point scale with an additional “don‘t know” option. 157the effect of farmer attitudes on openness to land transactions: evidence for ireland bio-based and applied economics 10(2): 153-168, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9746 the statements drew on previous work examining irish farmers’ attitudes toward farming decisions (howley & dillon, 2012; howley et al., 2015), as well as input from experts. the attitudinal statements are listed in table 1, as well as the mean scores and percentage of respondents completely agreeing with of each statement. mean scores were computed by assigning a score to each level of agreement (2 for “strongly agree”, 1 for “agree”, -1 for “disagree” and -2 for “strongly disagree”) and averaging the scores of the participants for each statement. four principal components with eigenvalues above one were generated by the pca and rotation process, representing different attitudes toward farming among the respondents. these attitudes are related to the importance of innovation in farming, optimism about the future of agriculture, the non-economic benefits of farm work and conservatism regarding the farm business. a description of the attitudinal variables is available in table 2. the factor loadings for each attitudinal statement onto the four principal components can be found in table 3. respondents were asked about their openness to four forms of land transaction: i. land purchase; ii. land sale; iii. land lease in; iv. land lease out. respondents replied either “yes” or “no” in terms of whether they were ever willing to engage in each form of transaction. of the 837 farmers surveyed, 47% were willing to buy land, while 26% were willing to sell. in terms of leasing, 51% were willing to lease in land, while 29% were willing to lease out land. a logistic regression model is used to examine the probability of a farmer being open to each land transaction. the dependent variable is the willingness to engage in the land transaction (purchase land, sell land, lease land in, lease land out). besides the attitudinal variables described previously, explanatory variables utilised in the model include variables representing the farmer’s age, plans for future farm production, whether the farmer has children or not, the presence/absence of a successor to take over the farm business, whether the farmer has an off-farm job or not, market farm income, value of entitlements, land prices, the percentage of household income derived from the farm business. farm structure and agronomic variables such as farm size, farm system, soil type and stocking rate are also included (see table 4). the final specifications of the regression models are shown in tables 5 and 6. in most cases, respondents chose from a range of values rather than state exact values, so variables based on instances where respondents chose from a range of variables are treated as categorical variables. for categorical variables such as age and farm size, the reference categories are the categories most frequently chosen by respondents. for age, this is the 51-64 years category and for farm size, the 20-49 ha category. table 1. mean scores and percentage agreement with attitudinal statements. mean scores percentage completely agreeing it is important not to leave farm land idle 1.50 55.4 it is important for me to pass on my land in as good a shape or better than i received it 1.46 53.4 i enjoy farming much more than i would other potential sources of employment 1.32 48.1 farming is a more rewarding job in terms of quality of life, independence and lifestyle than it is in terms of money 1.20 44.6 it is important not to be afraid of adopting new farming practices 1.15 32.4 i have to keep my farm running to ensure i have something to pass on to my children/next generation 1.13 42.1 to be successful in farming it is important for me to adapt and use new technologies (whether agri or non-agri technologies) 1.10 33.5 it is important to visit other farms to look at their methods 1.03 36.2 i don’t think it is a good idea to take too many risks when it comes to farming 1.00 30.5 it is important for me to be respected by other farmers 1.00 30.6 i am good at finding different types of information to help me run my business 0.97 28.5 agricultural land in ireland is under-utilised 0.48 20.2 i am cautious about adopting new ideas and farm practices 0.37 16.6 my economic future on this present farm is bright 0.36 16.9 it makes more sense for me to join an agricultural scheme if my neighbours are also joining 0.13 17.4 158 bio-based and applied economics 10(2): 153-168, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9746 cathal geoghegan, anne kinsella, cathal o’donoghue farm income and cap entitlements payment data was collected as part of the survey but about 30% of the sample decided not to answer. in order to include farm income data, the missing information was replaced using farm income information from the 2014 teagasc national farm survey (hennessy & moran, 2014), a yearly, nationally representative survey of irish farmers which is ireland’s contribution to the farm accountancy data network (fadn). for cap payments data, the missing data was replaced by assigning average per hectare cap payments by farm system from the 2014 nfs in place of the missing values. since per hectare cap payments are closely related to farm system in ireland, this seemed the most appropriate solution. average land value and rental prices were sourced for the time period from the irish central statistics office (cso) for land prices and eurostat for land rental values, which are based on data from fadn. the data was available at the nuts3 regional level (8 regions in ireland) for land values and nuts2 level (3 regions) for rental prices. table 2. description of attitudinal variables. attitudinal variable description innovative orientation farmers with a high ranking in this variable acknowledge the importance of technology and new ideas with regard to farming. they agree strongly with statements such as “it is important not to be afraid of adopting new farming practices” and “i am good at finding different types of information to help me run my business”. pleasure of farming orientation farmers with a high ranking in this variable emphasise the non-economic benefits of farming, especially compared to non-farming employment. they are also concerned with their farming legacy. they agree strongly with statements such as “farming is a more rewarding job in terms of quality of life, independence and lifestyle than it is in terms of money” and “it is important for me to pass on my land in as good a shape or better than i received it”. conservative orientation farmers with a high ranking in this variable prefer to rely on traditional farming practices and dislike change. they agree strongly with statements such as “i don’t think it is a good idea to take too many risks when it comes to farming” and “i am cautious about adopting new ideas and farming practices”. agri-optimistic orientation farmers with a high ranking in this variable are optimistic about the future of agriculture and enjoy being farmers. they agree strongly with statements such as “my economic future on this present farm is bright” and “i enjoy farming much more than other potential sources of employment”. table 3. factor loadings of attitudinal statements. statement pleasure of farming innovative agrioptimistic conservative agricultural land in ireland is under-utilised 0.1666 0.3447 0.2091 0.0107 i enjoy farming much more than i would other potential sources of employment 0.4027 0.0196 0.5615 0.0332 i am good at finding different types of information to help me run my business 0.1075 0.4731 0.5335 0.0634 my economic future on this present farm is bright 0.0619 0.285 0.662 0.0321 to be successful in farming it is important for me to adapt and use new technologies (whether agri or non-agri technologies) 0.1398 0.5872 0.2708 0.036 i have to keep my farm running to ensure i have something to pass on to my children/ next generation 0.5969 0.2275 0.2029 0.0479 i am cautious about adopting new ideas and farm practices 0.0407 0.0712 0.1231 0.7262 it is important for me to be respected by other farmers 0.0744 0.2416 0.3484 0.5341 it makes more sense for me to join an agricultural scheme if my neighbours are also joining 0.0508 0.2087 0.4206 0.4901 farming is a more rewarding job in terms of quality of life, independence and lifestyle than it is in terms of money 0.5927 -0.119 0.3865 0.0128 it is important for me to pass on my land in as good a shape or better than i received it 0.6827 0.2444 0.1274 0.0055 it is important to visit other farms to look at their methods 0.1252 0.7124 0.1445 0.1457 it is important not to be afraid of adopting new farming practices 0.1569 0.7693 0.0685 0.0285 it is important not to leave farm land idle 0.6557 0.3514 0.0997 0.0135 i don’t think it is a good idea to take too many risks when it comes to farming 0.2029 0.0144 0.1156 0.6099 initial eigenvalues 2.29 2.23 1.73 1.55 159the effect of farmer attitudes on openness to land transactions: evidence for ireland bio-based and applied economics 10(2): 153-168, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9746 5. results four logistic regression models were created using the available data. the dependent variable in each case was willingness to engage in the stated land transaction. two models analysed farmers’ attitudes to the leasing of land. 417 farmers were open to leasing in land while 245 farmers were willing to lease out land. the results from these two models are presented in table 5. two models analysed farmers’ attitude to permanent land transactions. 447 farmers were open to buying land while 218 farmers were willing to sell land. the results from these two models are presented in table 6. attitudinal variables the attitudinal variables derived from the pca analysis were found to have a statistically significant impact on a farmer’s willingness to enter the land market. the attitudinal orientation “innovative” has a positive impact on a farmer’s willingness to lease land. for every one unit increase in the orientation, the probability of leasing out land increases by a factor of 1.29 and of leasing in land by a factor of 1.21. the “pleasure of farming” variable has a negative impact on willingness to sell land. additionally, it is positively correlated with a willingness to buy and lease in land. table 4. independent variables of land mobility model. variable description mean/ mode1 standard deviation innovative orientation (innovative)2 factor variable measuring degree to which farmer feels technology and new ideas are important. 0 1 pleasure of farming orientation (pleasure of farming)2 factor variable measuring degree to which farmer enjoys farming as opposed to other occupations. 0 1 agri-optimistic orientation (agri optimistic)2 factor variable measuring degree to which farmer feels optimistic about the future of their farm. 0 1 conservative orientation (conservative)2 factor variable measuring degree to which farmer is cautious about risk-taking and new ideas. 0 1 farm size3 number of hectares farmed in 2014 (<10 ha, 10-19 ha, 20-49 ha, 50-74 ha, 75–99 ha, 100-149 ha, 150+ ha) 20-50ha1 n/a soil quality4 description of soil type on land (good soil, medium soil, poor soil) good1 n/a increase future production plans for farming over the next five years (aim to increase production, maintain current levels of production) 0.17 0.38 decrease future production plans for farming over the next five years (aim to decrease production, maintain current levels of production) 0.09 0.28 diversify future production plans for farming over the next five years (aim to increase diversification, maintain current levels of production) 0.09 0.28 stocking rate number of livestock units (lus) per hectare 1.5 1.76 farm system5 main farm activity (dairy, cattle rearing, cattle other6, tillage, sheep, mixed7, other) cattle other1 n/a age age in years (<35 years, 35-44 years, 45-50 years, 51-64 years, 65+ years) 51-64* n/a children does the farmer have any children (yes, no) 0.71 0.45 successor has the farmer identified a successor (yes, no) 0.43 0.50 household income from farming percentage of overall yearly household income derived from farming (0-25%, 26-50%, 51-75%, 76-100%) 76-100%1 n/a market farm income farm income after costs minus subsidies 2,996 10.680 off-farm job does the farmer have an off-farm job (yes, no) 0.33 0.47 entitlements value value of farm cap entitlement payments 10,282 14,212 rent price average regional per hectare farmland rental price 252 35 land price average regional per hectare farmland price 16099 4221 1 mode. 2 attitudinal variables have mean zero as each variable is standardised to mean zero as part of the pca process. 3 farm size share by percentage: <10ha – 6%, 10-19ha – 20%, 20-49ha – 44%, 50-74ha – 15%, 75-99ha – 9%, 100-149ha – 4%, 150+ha – 2%. 4 soil quality is self-reported but definitions of each soil type were provided to aid respondents. 5 farm system share by percentage: dairy – 22%, cattle rearing – 14%, cattle other – 29%, tillage – 11%, sheep – 15%, mixed – 5%, other – 3%. 6 cattle other refers to cattle finishing farms where cattle are fattened up in preparation for slaughter. 7 mixed refers to farms that combine grazing livestock and field crops. 160 bio-based and applied economics 10(2): 153-168, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9746 cathal geoghegan, anne kinsella, cathal o’donoghue the “conservative” variable is negatively related to the willingness to buy and lease in land while the “agrioptimistic” orientation is positively related to leasing in and buying land while negatively related to selling land. farm structure and agronomic variables of the farm system variables employed in the analysis, cattle rearing, cattle other, and mixed and dairy enterprises proved to be significantly related to willingness to enter the land market. in the “lease out” regression, dairy, tillage, sheep and mixed enterprise farmers were significantly less likely to be willing to lease out table 5. factors related to the probability farmers are open to leasing land. lease out coef. std. err. odds ratio lease in coef. std. err. odds ratio innovative 0.25*** 0.09 1.29   0.19** 0.09 1.21 pleasure of farming -0.13 0.08 0.88   0.18** 0.08 1.20 conservative -0.03 0.09 0.97   -0.21** 0.08 0.81 agri-optimistic -0.13 0.09 0.88   0.21** 0.09 1.24 good soil 0.43 0.35 1.53   -0.45 0.32 0.64 medium soil 0.65* 0.35 1.92   -0.35 0.32 0.70 cattle rearing 0.18 0.25 1.20   0.25 0.26 1.28 dairy -0.79** 0.33 0.45   0.39 0.32 1.47 tillage -0.63** 0.30 0.53 0.29 0.29 1.34 sheep -0.70*** 0.27 0.49 0.02 0.26 1.02 mixed -0.96** 0.44 0.38 -0.91** 0.41 0.40 farm size <10ha 0.08 0.40 1.08   -0.69 0.44 0.50 farm size 10-19ha 0.42* 0.23 1.52   -0.20 0.24 0.82 farm size 50-74ha 0.37 0.27 1.44   0.27 0.27 1.31 farm size 75-99ha -0.11 0.41 0.89   0.31 0.40 1.36 farm size 100-149ha 0.59 0.54 1.80   -0.17 0.55 0.85 farm size >150ha -0.23 0.96 0.79   0.22 0.98 1.24 stocking rate -0.02 0.05 0.98   0.38*** 0.09 1.46 age <35 -1.11** 0.46 0.33 0.93** 0.40 2.52 age 35-44 -0.03 0.25 0.97   0.01 0.25 1.01 age 45-50 -0.28 0.27 0.75   -0.27 0.25 0.76 age >65 0.21 0.21 1.23   -0.64*** 0.21 0.53 children 0.14 0.20 1.15   0.40** 0.20 1.49 successor -0.40** 0.18 0.67   -0.44** 0.18 0.64 increase future production -0.18 0.24 0.84   0.77*** 0.24 2.16 decrease future production 0.81*** 0.28 2.26   -0.54* 0.33 0.58 off-farm job -0.11 0.21 0.89   0.53** 0.22 1.70 household income from farming ≤ 25% 0.31 0.29 1.36   0.16 0.30 1.17 household income from farming 26-50% -0.04 0.25 0.96   -0.35 0.25 0.70 household income from farming 51-75% 0.36 0.28 1.43   -0.52* 0.29 0.59 farm income 0.02* 0.01 1.02 0.02 0.01 1.02 entitlements value 0.01 0.02 1.01 0.02 0.02 1.02 rent price 0.00 0.00 1.00 -0.01*** 0.00 0.99 constant -1.86** 0.78 0.16   2.83*** 0.76 16.94 pseudo r2 0.08*** 0.19*** aic 999.13 1007.94 bic 1159.95 1168.75 observations 837 837 note: *** p<0.01, ** p<0.05, * p<0.1. 161the effect of farmer attitudes on openness to land transactions: evidence for ireland bio-based and applied economics 10(2): 153-168, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9746 land than the reference category of cattle other farmers. stated differently, cattle rearing and cattle other farmers were significantly more willing to lease out land compared to all other farm systems. however, farm system was not significantly related to willingness to lease in land. mixed farmers were significantly less willing to sell land than other farmers while dairy and tillage farmers were significantly more willing to buy land, albeit at the 10% significance level. in general, significant effects for farm size were limited to either very small or very large farms. farms under 20 hectares were the least likely to be willing to buy land compared with the most common farm size category. farms of over 150 hectares were less willing to sell land table 6. factors related to the probability farmers are open to selling/buying land. sell land buy land coef. std. err. odds ratio coef. std. err. odds ratio innovative 0.12 0.09 1.12 0.01 0.09 1.01 pleasure of farming -0.25*** 0.08 0.78 0.24*** 0.08 1.27 conservative 0.00 0.09 1.00 -0.23*** 0.08 0.80 agri-optimistic -0.19** 0.09 0.83 0.16* 0.09 1.17 good soil -0.05 0.33 0.95 -0.05 0.32 0.95 medium soil 0.28 0.33 1.32 0.01 0.32 1.01 cattle rearing -0.01 0.27 0.99 0.44* 0.26 1.55 dairy 0.10 0.33 1.10 0.53* 0.31 1.70 tillage -0.14 0.32 0.87 0.33 0.30 1.39 sheep -0.18 0.27 0.83 -0.10 0.25 0.90 mixed -1.67*** 0.59 0.19 -0.08 0.38 0.92 farm size <10ha -0.04 0.41 0.96 -1.36*** 0.47 0.26 farm size 10-19ha 0.03 0.25 1.03 -0.50** 0.24 0.60 farm size 50-74ha 0.04 0.28 1.04 -0.04 0.27 0.96 farm size 75-99ha -0.04 0.41 0.96 0.23 0.40 1.25 farm size 100-149ha 0.04 0.54 1.04 -0.40 0.56 0.67 farm size >150ha -2.04* 1.22 0.13 0.07 1.00 1.07 stocking rate 0.05 0.05 1.05 0.18*** 0.07 1.20 age <35 -0.28 0.41 0.75 1.37*** 0.41 3.94 age 35-44 0.32 0.25 1.38 0.56** 0.25 1.75 age 45-50 0.56** 0.26 1.75 0.25 0.25 1.29 age >65 -0.15 0.23 0.86 -0.50** 0.21 0.61 children 0.14 0.21 1.15 0.63*** 0.20 1.88 successor -0.50*** 0.19 0.61 0.32* 0.17 1.37 increase future production -0.04 0.24 0.96 0.68*** 0.25 1.98 decrease future production 0.62** 0.30 1.86 -0.49 0.32 0.61 off-farm job -0.15 0.22 0.86 0.52** 0.22 1.69 household income from farming ≤ 25% 0.22 0.30 1.25 0.17 0.29 1.19 household income from farming 26-50% 0.43* 0.26 1.53 -0.08 0.25 0.92 household income from farming 51-75% 0.41 0.30 1.51 -0.01 0.28 0.99 farm income 0.00 0.01 1.00 0.00 0.01 1.00 entitlements value 0.03* 0.02 1.03 0.03 0.02 1.03 land price -0.00*** 0.00 1.00 0.00 0.00 1.00 constant -0.72 0.54 0.49 -1.24** 0.51 0.29 pseudo r2 0.08*** 0.18*** aic 950.86 1018.68 bic 1111.68 1179.49 observations 837 837 note: *** p<0.01, ** p<0.05, * p<0.1. 162 bio-based and applied economics 10(2): 153-168, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9746 cathal geoghegan, anne kinsella, cathal o’donoghue than the most common farm size category. stocking rate was a significantly related to land demand. in terms of leasing in land, an increase of one lu/ha increased the probability of being willing to lease in land by a factor 1.46. an increase of one lu/ha increased the probability of the farmer being open to buying land by a factor of 1.20. soil quality was also included as an explanatory variable in each regression model. three soil categories were used: good, medium and poor. the good and medium categories were included as dummy variables, with poor quality soil acting as the reference category. soil quality was a significant explainer of willingness to enter the land market in the “lease out” model, with farmers on medium quality soil being significantly more open to leasing out land than those in the reference category. demographic variables age effects relating to willingness to enter the land market can be seen amongst the youngest and oldest categories of farmers. the youngest category of farmers (those under the age of 35) was significantly more willing to lease in and buy land compared to older farmers. additionally, the youngest farmers were significantly less willing to lease out land. farmers in the oldest age category (65 years and older) were significantly less likely to demand land either through leasing or purchase. farmers in the 45-50 years were more likely to be willing to sell land than any other age category. the presence of a farm successor was a significant explanatory variable in all four models. having a successor was associated with farmers being significantly less likely to be willing to lease out or sell land, compared with farmers without a successor. having a successor decreased the likelihood of being willing to lease out land by a factor of 0.67 and sell land by a factor of 0.61, compared to farmers without a successor. interestingly, farmers with a successor were more likely to be willing to buy land but significantly less likely to be willing to lease in land than those without a successor. farmers with children were significantly more willing to demand land than farmers without children. farmers with children were more likely to be willing to lease in land by a factor 1.49 and more likely to be willing to buy land by a factor of 1.88, compared with farmers without children. financial variables farmers were asked what percentage of household income is made up of farm income. responses were divided into four categories: 0-25%, 26-50%, 51-75% and 76-100% of household income coming from farm income. the reference category was farmers for whom 76-100% of household income came from their farm (the most common response). farmers in the 51-75% farm income category were less likely to be open to leasing in land than those who rely on farm revenues for over 75% of household income, while those in the 26-50% farm income category were more open to selling land. farmers with an off-farm job were significantly more likely to be open to leasing in and buying land than farmers without off-farm employment. farmers with off-farm jobs were more likely by a factor of 1.70 to be open to leasing in land and by a factor of 1.69 to buying land than those without off-farm jobs. market farm income is positively associated with willingness to rent out land while subsidy income from entitlements was positively correlated with selling land at the 10% significance level. agricultural land prices and land rents at the regional level are also modelled. regional farmland prices are significantly negatively correlated with a willingness to sell land while regional land rent prices have a negative relationship to willingness to rent land in. 6. discussion this study examined the extent to which irish farmers would be willing to enter the agricultural land market. the results show that about half of farmers in the sample are open to buying or leasing in land while about a quarter of farmers sampled are open to selling or leasing out land. the results also show distinct profiles emerging for farmers demanding land, through either leasing in or purchase and farmers open to supplying land, whether through leasing out or sale. farmers demanding land are more likely to have a high ranking on the “pleasure of farming” and “agrioptimistic” attitudinal orientations and a low ranking on the “conservative” orientation. they are also more likely to have children, be planning to increase farming activity in the next five years and have an off-farm job. they are more likely to be under 35 years of age and a have a high stocking rate. they are less likely to have farms of less than 10 hectares and be over 65 years of age. the issue of agricultural land demand, especially in how it relates to young farmers, has arisen in recent years in the context of increasing farmland concentration in europe (conway et al., 2020; van der ploeg, 2015). a reduction in farm numbers by approximately 3.8 million and an increase in farm size by about 36% was seen in the eu between 2005 and 2015 (eurostat, 163the effect of farmer attitudes on openness to land transactions: evidence for ireland bio-based and applied economics 10(2): 153-168, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9746 2017). these results confirm the desire of young, optimistic farmers to access land but whose ability to do so may be hampered in a competitive land market (zagata et al, 2017). farmers open to supplying land are more likely to rank high on the “innovative” orientation for leasing out and rank low on the “pleasure of farming” orientation for selling. they are more likely to be intent on decreasing farming in the next five years and are less likely to have a successor. they are also more likely to be only somewhat dependant on farm income, receiving greater than 25% but less than 50% of total household income from farming. farmers ranking high in the “pleasure of farming” orientation value the lifestyle benefits of farming over any pecuniary benefits associated with the profession. farmers have been found to have a multiplicity of motivations for why they farm, many of which are noneconomic in nature (howley et al., 2015; key & roberts, 2009). for these farmers, land may not be seen as an economic resource but as a source of utility in and of itself. therefore, it is not surprising that farmers with a high ranking in this orientation are opposed to releasing land and are open to increasing their land stock. the finding that farmers with a high ranking in the “innovative” orientation are more open to supplying land, both through leasing and sale, suggests that these farmers are less constrained by traditions of keeping land “in the family name”. they may see land as just another input in the agricultural production process. innovative farmers in the irish context may be thought about as generating new combinations of existing resources (bender & laestadius, 2005). the positive effect of having children and having a designated successor on willingness to buy land (and lease in land for the “children” variable) fits in with the farm life cycle concept (calus et al., 2008; potter & lobley, 1992). this farm life cycle concept suggests that a farm can be in one of three stages: growth, maturity or decline. younger farmers are expected to grow, while older farmers are expected to be in the maturity or decline stages. however, farmers with a successor do not enter the decline stage but rather are more likely to want to grow the farm in order to leave a legacy for their successor (calus et al., 2008; inwood & sharp, 2012). the finding that farmers with successors are more willing to buy land but are significantly less likely than farmers without successors to want to lease in land may be related to the lack of trust amongst irish farmers in the leasing system (banovic et al., 2015; bogue, 2013). farmer age effects align with previous studies, with younger farmers most likely to want to add land, while farmers who are older are significantly less likely to want to increase their farm size (gale, 1994; lobley & butler, 2010; katchova & ahearn, 2015; weiss, 1999). older farmers were not significantly more likely to want to lease out or sell land than average aged farmers, supporting the theory that older irish farmers want to maintain land within the family unit rather than sell or lease it out to others (banovic et al., 2015). farmers with off-farm jobs are more likely to want to add land through lease or purchase than full-time farmers. there is evidence in the literature that offfarm income may help to prevent farm exit by stabilising income (breustedt & glauben, 2007; kimhi, 2000). farmers in the study with off-farm employment are younger than full-time farmers (45% aged 50 and younger vs. 31% for full-time farmers) and may have difficulty accessing land in a manner similar to other young farmers across the eu (zondag et al., 2015). therefore, they may be aiming to increase land holdings going forward. also, there may be a wealth effect for farmers with offfarm jobs with farmers using their off-farm income in order to acquire more land through increased credit capacity or ability to pay higher rents. regional farmland prices were negatively related to willingness to sell, a finding contrary to standard economic theory. it must be stated that irish farmland markets are extremely local so prevailing regional prices would not be as significant to farmers’ decision-making regarding land as the local market. farmers may also be anticipating increasing land prices in the future. irish farmland prices are heavily inf luenced by non-agricultural factors (geoghegan & o’donoghue, 2018), so increasing property prices in ireland following the 2008 economic crash may be influencing farmers not to sell land until prices peak. the openness of cattle farmers to leasing out land may be related to the difficult financial conditions facing cattle farmers in ireland. widespread protests amongst cattle farmers over low beef prices broke out during the collection of the survey which may have led to cattle farmers being particularly pessimistic when surveyed.4 as a result, cattle farmers may have felt more open to leasing out land at this time. the factors that significantly inf luence farmers’ openness to temporary land transactions such as leasing also seem to significantly influence permanent transactions such as buying and selling. however, there are some notable exceptions to this finding. cattle farmers are open to leasing out land but not selling land while dairy farmers are open to buying land but not leasing 4 496 of the study’s participants were surveyed in winter 2014, of which 196 were cattle farmers. 164 bio-based and applied economics 10(2): 153-168, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9746 cathal geoghegan, anne kinsella, cathal o’donoghue in land. cattle farmers’ openness to leasing out may, as previously stated, have reflected particularly poor economic conditions at the time the survey was conducted. their willingness to lease out rather than sell land may reflect a desire to reclaim the land for their own farming purposes once economic conditions for cattle farming improved. 7. conclusions and policy recommendations the aim of the study was to explore irish farmers’ attitudes towards land mobility and to build a profile of farmers who would be open to partaking in land transactions. despite previous evidence that irish farmers are reluctant to enter the land market (especially to supply land), this study shows that a considerable number of farmers are open to the possibility. it is important to understand what motivates farmers who are open to trading land. this is especially true in the absence of market data, as is the case in ireland. this paper suggests that farmer attitudes are an important motivating force behind farmers’ willingness to enter land markets. farmers are not motivated solely by profit maximisation, as evidenced by the significance of the “pleasure of farming” and “conservative” variables. additionally, there appear to be a group of farmers amongst whom the traditional attachment to land is not as prevalent, as evidenced by the willingness of “innovative” farmers to supply land through leasing. therefore, policymakers must take account of these attitudes when designing policies to enable a more dynamic land market. such policies should not just focus on economic incentives to encourage land mobility but also on encouraging discussion between farmers, successors, potential farmers, policymakers and agricultural professionals (farm advisors, solicitors, accountants etc.) so as to take less tangible factors such as attitudes and motivation into account. together with the economic and socio-demographic information presented here, a picture emerges of the types of farmer policymakers can target with land mobility policies. young, optimistic farmers with higher than average stocking rates and plans for increasing production in the near future appear to be most likely to demand land. innovative cattle farmers who are somewhat but not totally dependent on farm income and are planning to decrease farm activity in the near future are most likely to supply land. policies that can both identify and mediate between these groups should be considered by policymakers. this can be done by policymakers engaging with farming organisations, through the organisation of information events and by aiding organisations such as the land mobility service that facilitate land mobility (macra na feirme, 2019). additionally, the promotion of joint farm ventures (jfvs) such as cooperatives, farm partnerships, share farming and contract rearing must be maintained (cush & macken-walsh, 2016). there is a similar level of openness amongst farmers to both permanent and temporary land transfer options. this is contrary to conventional thinking that irish farmers are reluctant to take part in temporary land transactions such as land leasing. this shows that there may be greater demand amongst farmers for land leasing arrangements than is currently thought by policymakers. as a result, policies that can promote and facilitate such leasing arrangements should be encouraged. since financial incentives in the form of tax breaks already exist, institutional solutions such as the establishment of intermediary entities to connect potential lessors and lessees or informational campaigns advertising the benefits of leasing may be appropriate. although numerous farm and farmer characteristics are examined in relation to openness to land transactions in this study, factors related to the socio-economic environment around the farm are considered outside of the scope of this paper. such factors include social and identity pressures (ní laoire, 2005), local labour market conditions (cavicchioli et al., 2019) and gender (balaine, 2019). further research examining 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(2015). needs of young farmers. luxembourg: publications office of the european union. retrieved from: https://ec.europa.eu/agriculture/sites/ agriculture/files/external-studies/2015/young-farmers/final-report-1_en.pdf volume 10, issue 2 2021 firenze university press mediterranean agriculture facing climate change: challenges and policies filippo arfini the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications cristina vaquero-piñeiro application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context raffaella zucaro, veronica manganiello, romina lorenzetti*, marianna ferrigno climate changes and new productive dynamics in the global wine sector emilia lamonaca*, fabio gaetano santeramo, antonio seccia a systematic review of attributes used in choice experiments for agri-environmental contracts nidhi raina*, matteo zavalloni, stefano targetti, riccardo d’alberto, meri raggi, davide viaggi the effect of farmer attitudes on openness to land transactions: evidence for ireland cathal geoghegan*, anne kinsella, cathal o’donoghue bio -based and a ppl ied economics bae bio-based and applied economics 10(3): 239-251, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10470 copyright: © 2021 g. iannucci, g. sacchi. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: g. iannucci, g. sacchi (2021). the evolution of organic market between third-party certification and participatory guarantee systems. biobased and applied economics 10(3): 239-251. doi: 10.36253/bae-10470 received: february 17, 2021 accepted: august 27, 2021 published: january 11, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: davide menozzi. orcid gi: 0000-0001-9780-9962 gs: 0000-0002-9286-7810 the evolution of organic market between thirdparty certification and participatory guarantee systems gianluca iannucci1, giovanna sacchi2,* 1 department of economics and management, university of florence, firenze, italy 2 faculty of science and technology, free university of bozen-bolzano, bolzano-bozen, italy *corresponding author. e-mail: giovanna.sacchi@unibz.it abstract. quality assurance is a dominant feature of organic production and, currently, third-party certification is recognized as the official authenticity assurance strategy by the majority of worldwide organic regulations. this model, however, is less accessible to smallholders because it is costly and its application time-consuming. furthermore, this certification system has been accused on several fronts to be responsible for the standardization of the organic production process leading to a “conventionalization” of organic productions. contextually, in several countries, groups of small producers have started to implement alternative quality assurance systems for their organic products, better known as participatory guarantee systems. research to date has not yet determined how these models can survive within a highly competitive market such as that of certification. in this framework, the paper aims to theoretically unveil and explain the alternative certification phenomenon and its coexistence with third-party certification by applying an evolutionary game (rationally bounded agents that adopt the more rewarding strategy). the results of simulations suggest that symbolic attributes such as localness, healthiness, quality, producers and consumers embeddedness can differentiate products guaranteed by alternative schemes, meeting consumers’ preference. the discussion of findings provides an assessment of the performance of both quality assurance systems, explain their coexistence within the organic market, identify critical aspects, and suggest some policy implications. keywords: organic market, certification system, participatory guarantee systems, evolutionary game. jel codes: c73, o13, q01, q12, q13. 1. introduction recent trends in the analysis of organic food led to a proliferation of studies closely related to the process adopted for ensuring the integrity and authenticity of organic products (hatanaka et al., 2005; vogl et al., 2005; zorn et al., 2012; bauer et al. 2013; janssen and hamm, 2014; veldstra et al., 2014). the mainstream approach is the so-called third-party certification http://creativecommons.org/licenses/by/4.0/legalcode 240 bio-based and applied economics 10(3): 239-251, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10470 gianluca iannucci, giovanna sacchi (tpc) which plays, among others, an important role for consumers in proving organic food authenticity. in general terms, in tpc an independent private body verifies the production process of a good and independently determines if the final product complies with organic standards. the verification typically includes comprehensive formulation/material reviews, testing as well as facility inspections. if the verification obtains a positive assessment, products bear the right of the organic logo usage on their packaging that can help consumers and other stakeholders to make oriented purchasing decisions. currently, many organic regulations worldwide adopt tpc as their official authenticity assurance strategy (national organic program of usda, european union council regulation (ec) no. 834/2007, japanese agricultural standards, australian national standard for organic and biodynamic produce, etc.). in several developing countries organic-certified products have been growing in recent decades with the purpose of being exported to european and north america markets (ayuya et al., 2015) gaining a price premium. furthermore, tpc is beyond question less accessible to worldwide smallholders, both in terms of the big amount of time required to the accomplishment of the paperwork, and in economic terms because of its costs (harris et al., 2001; milestad and darnhofer, 2003; vogl et al., 2005; courville, 2006; eernstman and wals, 2009). finally, it has emerged a debate on the evidence that certification and standardization of organic production have led to unintended consequences towards a new form of governance (guthman, 2004; vogl et al., 2005; courville, 2006), from the movement-oriented to the market-oriented organic production practices. in the same vein, other authors argue that the standards set by the organic regulations brought to a “corporatization” of organic agriculture (de lima et al., 2021) which threaten the original principles of the organic movement of health, ecology, fairness, and care towards a more process-based production system. in other words, and according to courville (2006), “paradoxically, the regulatory systems that were developed to protect the integrity of organic agriculture including standards-setting and conformity assessment systems are now reshaping the organic landscape in ways that threaten many of the values held by the movement that created it.” (p. 201). in the attempt to cope with these problematic issues, in several countries, groups of small producers have started to implement “alternative” quality assurance systems for their organic productions. there are two main alternative certification and guarantee systems to tpc, better known as internal control systems (ics) and participatory guarantee systems (pgs). ics, or group certification, “was originally created to increase equity and access of smallholder to certification schemes” (pinto et al., 2014, p. 60). it consists in the development of cooperatives, associations, or networks of farmers that voluntarily adhere to common organic production standards (usually based on national regulations). afterward, an independent certification body verifies the process functioning as well as a limited number of randomly selected companies/farmers. the results of the inspection, in both positive and negative cases, affect the whole group. adopting such a quality assurance scheme simplifies certification procedures for smallholders, who are often unfamiliar with all the paperwork required for third-party certification requests. in addition, it is more affordable compared to the mainstream certification model. ics is used primarily by smallholders of developing countries willing to access the markets of developed countries for the price premium advantage linked to organic production (latynskiy and berger, 2017). on the other hand, according to the official ifoam definition, pgs are “locally focused quality assurance systems. they certify producers based on the active participation of stakeholders and are built on a foundation of trust, social networks, and knowledge exchange” (ifoam official definition, 2008). ifoam organics international provides further details by describing such initiatives as “a verification system to ensure that a produce is organic. it is an alternative to third party certification for organic products, especially adapted to local markets, small farmers and short supply chains. they allow certified organic produce to be available to a wider consumer group, at a lower cost”. also, “pgs initiatives involve groups of farmers and groups of consumers; they are normally supported by an ngo or local association that provides the participants with administrative and technical help” (ifoam organics international). to the best of our knowledge, the pgs approach has been widely observed empirically, nevertheless, a theoretical framework or a modelling effort suitable for the interpretation of these phenomena is still missing in the academic international literature. in this perspective, the present paper aims to propose a mathematical modelling framework by using the game theory approach to assess and explain pgs model, in the attempt to shed light on its coexistence within a highly competitive market such that of certification. examples of evolutionary games application do exist in academic literature. indeed, thanks to its adaptability, the evolutionary context has been applied to several topics as well as to environmental economics and agricultural markets (antoci and bartolini, 2004; antoci et al., 2013; blanco and lozano, 2015; antoci et al., 2019). 241the evolution of organic market between third-party certification and participatory guarantee systems bio-based and applied economics 10(3): 239-251, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10470 on the contrary, its application to organic market and organic certification represents a novelty which allow us to a) analyse the evolution of the share of pgs firms within the organic market; b) study the bounded rationality of small firms which usually characterize organic market (bonfiglio and arzeni, 2020); c) present some implications on the market composition due to the comparative dynamics performed by changing parameter values of the model. the reminder of the paper is organized as follows: section 2 provides an overview of worldwide pgs development, the main features and functioning mechanisms as well as an outline on academic literature focused on these initiatives. it then will go into presenting the model in section 3, its dynamic regimes in section 4, while section 5 provides a discussion of the main economic results together with some policy implications. finally, in section 6 conclusions are drawn. 2. participatory guarantee systems for organic products the pioneering experiments that led to pgs development date back to the 1970s and were linked to a growing interest towards agroecological principles in general (altieri, 1987; 1995) and organic agriculture in particular. the first communities of organic producers consisted of family farmers and small companies interested in methods of production aimed at the promotion of social and environmental sustainability. the achievement of their objectives implied the development of a strategy to make their products recognizable to consumers. these communities have been growing over the decades, stimulating a great debate around the need to formalize their work and their alternative actions in both developing and developed countries. in 2004, the first international conference on alternative certification was organised by the international federation of organic agricultural movement (ifoam) and the latin american and caribbean agro-ecological movement (movimento agroecologico de america latina y el caribe, or maela) in brazil. although different methodology could be applied and norms and processes might vary, the key features of pgs remain consistent worldwide. in general terms, pgs are usually based on the ifoam international organic standards, and they require the involvement of all actors within the production process and along the supply chain (from producers to consumers) by taking place at the community level. a pgs model aims at minimizing bureaucratic procedures and costs (in economic terms) by employing simple verification methods. it also incorporates elements of environmental and social education towards quality improvement for both producers and consumers. the basic common elements of several pgs initiatives are the following: i) a participatory approach; ii) social control; iii) a shared vision and shared responsibility among stakeholders regarding quality, transparency, trust building, and reinforcing mechanisms; and iv) a non-hierarchical relationship between stakeholders (fonseca, 2008; ifoam, 2008). furthermore, a key feature of pgs models relates to the close relationship between producers and consumers, or co-producers. the process, in fact, involves direct selling allowing the reduction of transaction costs. in this way, as also experienced by fair trade practices, producers obtain a higher price by decreasing the numbers of middlemen, and this effect decreases contextually the price of organic products, with a positive impact for the final consumers. in a sense, in absence of an alternative procedure for quality assurance, in most cases, the possibility of access the (local) market is excluded to disadvantaged and/or small producers (nelson et al., 2010; ifad, 2004) and it also threatens the possibility of purchasing organic products by potential local consumers. in other words, the mission, and at the same time the challenge of pgs, is favouring and facilitating smallholders’ production towards the promotion of local food systems that meet agroecological principles, biodiversity protection, workers’ rights, and an easier access to organic food. these schemes are quite popular in less developed countries such as brazil, india, and costa rica, but there are also several cases of pgs adoption in western countries like the united states, france, new zealand, and italy. the most famous networks adopting pgs are the brazilian rede ecovida de agroecologia, certified naturally grown (usa), nature et progrès (france), keystone foundation (india), organic farm nz (new zealand). recently, the ifoam has developed a navigable map sponsored by the food and agriculture organization of the united nation (fao) which records worldwide pgs initiatives. figure 1 reproduces a static image of it. through this map, it is possible to find pgs projects at a global level, as well as the number of producers certified by pgs schemes by different countries. the yellow pointers define self-declared pgs initiatives (operational or under development), the green ones pgs projects officially recognized by local authorities, while the blue pointers define pgs models recognized by ifoam. according to the data collected in 2019 by ifoam (ifoam global pgs survey, 2019), at least 223 pgs initiatives have been recorded at a global level. these projects involve about 567.142 farmers and spread over 76 242 bio-based and applied economics 10(3): 239-251, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10470 gianluca iannucci, giovanna sacchi countries. since the submission to the ifoam global survey (and the consequent registration within the ifoam database) is on a voluntary basis, it is reasonable to assume that the numbers reported are underestimated (sacchi, 2015; 2019). in some cases, pgs initiatives (such as the brazilian and french ones) are older than national organic regulations, establishing third-party certifications as the official guarantee system, and in some countries (especially in latin america) pgs are officially recognized in national organic regulations and, in these cases, pgs are also defined as participatory certification. but how do pgs function in practical terms? usually, farmers are organized into local groups that have the responsibility to ensure that all participants adhere to the pgs principles and processes. each farmer receives an annual visit at least by one peer, namely another farmer/breeder/bees keeper of the group pertaining to the same product category. other stakeholders, such as consumers, technicians, support staff of ngo, can join, and they are encouraged to do so, the peer during the visit. results of these visits are documented and serve as the basis for the group of farmers to take decisions on the certification status of each network member. a summary of the documentation and the outcome is communicated to a higher level, for example, to a national or regional council. the council approves/ denies the certification decision taken by the groups or, more generally, allow/reject the use of the pgs logo, if any, to each local group. as far as academic literature is concerned with pgs phenomenon, it mainly focuses on producers’ motivation in pgs adoption (zanasi et al., 2009; binder and vogl, 2018; lópez cifuentes et al., 2018; kaufmann and vogl, 2018; fonacier montefrio and johnson, 2019), on social innovation, empowerment and spill-overs effects deriving from pgs adoption (home et al., 2017; rover et al., 2017; sacchi, 2019; lameilleur and sermage , 2020), on issues linked to consumers attitude and behaviour towards organic products guaranteed by pgs (sacchi et al., 2015; kaufmann and vogl, 2018; sacchi, 2018; carzedda et al., 2018) to institutional matters (fonseca et al., 2008; nelson et al., 2010; loconto et al., 2016; cavallet et al., 2018). as mentioned above, what is currently missing is a mathematical theoretical model able to capture pgs worldwide initiatives to understand how such systems figure 1. worldwide pgs distribution. source: ifoam website (https://pgs.ifoam.bio/). 243the evolution of organic market between third-party certification and participatory guarantee systems bio-based and applied economics 10(3): 239-251, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10470 can survive within the organic market competing with third-party certification. 3. the model the model considers the existence of a food organic market composed by a n-size population of firms that assure the authenticity of their organic productions following two possible modes. the first one consists in delegating the certification to a third-party body and paying a certification cost (tpc firms); while the second mode implies the adoption of participatory guarantee strategies (pgs firms). we assume perfect competition; therefore, farms compete choosing the level of output. furthermore, we assume that the market is horizontally differentiated. consumer has a unique reservation price but there is a certain degree of substitution between the goods that determines different output prices (when it is lower than 1). finally, the third assumption of the model regards the slope of the marginal cost of pgs farms that is greater than the slope of the marginal costs of the tpc farms, therefore produce using pgs mode is more expensive. these assumptions have been introduced for the following reasons: 1) perfect competition is a standard way to model food market, 2) horizontally differentiation can capture a price differentiation between goods (without assuming higher quality from one good, as in vertically differentiation), and 3) the pgs standards can be more stringent than tpc. finally, we endogenize the choice to adhere to pgs or to tpc introducing a dynamic selection process, the replicator equation, given by the evolutionary game theory. at each instant of time, farms can revise the mode or adopt a new one, following the differential profits that allow to compare the two strategies1. denoting pgs and tpc firms with subscripts i = g, c respectively, and assuming their profit functions as follows: (1) where pg and pc are the unit prices of the good produced by pgs and tpc firms respectively, ei>0 is the slope of 1 to learn more on replicator dynamics as well as other selection mechanisms, see, among others, hofbauer and sigmund (2003). the marginal cost, qi represents the quantities produced, and ϕ>0 is the certification cost (ϕqc is the total certification cots). to consider the higher effort of pgs firms in favour to the environment and workers’ rights, as well as to non-financial costs linked to participation, engagement to association, time and efforts to manage visits to peers, it is assumed that eg>ec. the market is horizontally differentiated2, and therefore, it has a unique reservation price and consumers substitute the goods at a certain degree (see, for further details, the seminal work by spence, 1976)3. denoting with x∈[0,1] and 1-x the shares of pgs and tpc firms respectively, the inverse demand of the goods produced by the firms is given by the following linear functions: (2) where >0 represents the organic market reservation price and α∈[0,1] is the substitution degree between goods. it is important to underline that if the goods are independent (no substitution), while if α=1, the goods are homogeneous (perfect substitution). the following proposition and corollary hold. proposition 1 let (3) with >0. if x< then the optimal quantity chosen by pgs firms is: (4) while the optimal quantity chosen by tpc firms is (5) otherwise, if x≥ , then the optimal quantity chosen by pgs firms is: 2 differently, a vertical differentiated market supposes that one good is perceived with higher quality by consumers (see, for further details, the seminal work by jaskold gabszewicz and thisse, 1979). 3 it is not assumed a different reservation price between goods. 244 bio-based and applied economics 10(3): 239-251, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10470 gianluca iannucci, giovanna sacchi (6) while the optimal quantity chosen by tpc firms is: (7) proof. given the value of variable x, the quantities qg and qc are chosen according the first order conditions: (8) (9) from (8) and (9) it derives: and therefore that is always positive. substituting (2) and qg in (9), we obtain: solving with respect to qc, we get: which is positive if: (10) conversely, if x≥ , then and, consequently corollary 2. assuming ϕ>(1-α) , then >0 always. however, <1 if and only if: from proposition 1 it emerges that qg>0 always, while qc>0 only if x< . therefore, if ∈[0,1], then qc>0 ∀x∈[0, ), and qc>0 ∀x∈[ ,1]. otherwise, if ≥1, then qc>0 ∀x∈[0,1]. to clarify this point, see figure 2a-b. moreover, from corollary 2 it is possible to notice that ≤1 if the certification cost is sufficiently high. this means that ϕ if is relatively low, then >1 and so qc>0 in the interval [0,1]. conversely, if ϕ is relatively high, then ≤1 and so qc>0 only in the interval [0, ). therefore, it is possible that in the transitional dynamics tpc firms produce zero output. however, at increasing time their number converges to zero, and the market will be composed by only firms that produce a positive amount (namely, pgs type). 4. dynamics suppose now that a firm can choose to be pgs or tpc. therefore, we can consider the two different modes as two different strategies. this means that, from now, the share x of pgs firms is not fixed but it can change. to do so, we introduce a differential equation that represents the law of motion of x. at each instant of time, firms can revise their strategy and choose to change or to continue with that strategy. this selection process is given by the following replicator dynamics (see, among others, friedman, 1998; nowak and sigmund, 2004; antoci et al., 2019): (a) when ∈[0,1] (b) when ≥1 figure 2. tpc firms’ quantities intervals. 245the evolution of organic market between third-party certification and participatory guarantee systems bio-based and applied economics 10(3): 239-251, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10470 (11) where is the time derivative (dx/dt) of the share of pgs firms. the mechanism of the replicator dynamics (11) is the following. if πg(x)<πc(x), then the strategy pgs is dominated by the strategy tpc and so the share of pgs firms decreases, namely <0. if πg(x)>πc(x), then the strategy pgs dominates the strategy tpc and so the share of pgs firms increases, namely >0. finally, if πg(x)=πc(x), then there is no dominance of strategies and the share x does not change over time, namely =0. moreover, the dynamics (11) admits three types of stationary states: x=0 (all firms adopt strategy tpc, namely only tpc firms exist at the equilibrium, in mathematical terms πg(x)<πc(x)∀x∈[0,1]),x=1 (all firms adopt strategy pgs, namely only pgs firms exist at the equilibrium, in mathematical terms πg(x)>πc(x)∀x∈[0,1]),x=x* (some firms adopt tpc strategy, others pgs one, namely both types of firms coexist at the equilibrium, in mathematical terms ∃x:πg(x)>πc(x)). considering that pg=egqg (from condition (8)) and that pc=ecqc+ϕ (from condition (9)), in the interval [0, ) where qg>0 and qc>0, the replicator equation becomes: (12) while, in the interval [ ,1], where qg>0 and qc=0, the replicator equation becomes: (13) numerical simulations show that under dynamics (11) three regimes may be observed: i. the case in which the market is eventually composed of only tpc firms, namely, whatever the initial distribution of modes x(0)∈(0,1), x will always converge to the stationary state x=0 (see fig. 3a); ii. the case in which both types of firms coexist at the equilibrium, namely, whatever the initial distribution of modes x(0)∈(0,1), x will always converge to the inner stationary state x=x* (see fig. 3b); iii. the case in which the market is eventually composed of only pgs firms, namely, whatever the initial distribution of modes x(0)∈(0,1), x will always converge to the stationary state x=1 (see fig. 3(c) in case of dynamics with qc>0. in fig. 3a, fig. 3b, and fig. 3c, condition (10) is not satisfied ( ≥1), and consequently qc>0∀x∈[0,1]. a situation in which condition (10) is satisfied ( <1) is represented by fig. 3d, where qc>0 in the interval [0, ) and qc=0 in the interval [ ,1]. numerical simulations shown that at most one inner stationary state x=x* may exist and it is always attractive. the parameters used to perform fig. 3 have been chosen to illustrate clearly the three regimes that may be observed under dynamics (11). in more detail, the parameters α, , eg, ec and n are the same in all diagrams of fig. 3. the parameter chosen to show the different dynamic regimes is the certification cost. indeed, if ϕ is relatively low, as in fig. 3a, then the strategy pgs is dominated by the strategy tpc, and, at the end, the market will be composed of only tpc firms. if the certification cost ϕ assumes an intermediate value, as in fig. 3b, then no strategies dominate, and, since x* is attractive, at the end, the market will be composed of both types of firms. finally, if ϕ is relatively high, as in fig. 3c and fig. 3d, then the strategy pgs dominates the strategy tpc, and, at the end, the market will be composed of only pgs firms. 5. simulations and discussion in the present section, it has been performed a numerical simulation to analyse the evolution of the share x of pgs firms that occurs when varying some (a) only tpc firms remain on the market (b) coexistence between firms (c) only pgs firms remain on the market (d) only pgs firms remain on the market figure 3. dynamic regimes. parameter values: α=0.95, =10, eg=5, ec=5, n=5. legend: • sinks, ⚬ sources. 246 bio-based and applied economics 10(3): 239-251, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10470 gianluca iannucci, giovanna sacchi key parameter values, namely, the substitution degree between pgs and tpc goods α, the certification cost ϕ, and the production costs ratio ec/eg. the parameters underlying the simulations are the same as in fig. 3b, namely, the case in which both types of firms coexist. the simulations results have to be evaluated in qualitative terms. indeed, the focus of the present research is on the relations between parameters and market composition. for a quantitative interpretation of the results, the parameter values should have been estimated, nevertheless, this is out of the scope of the paper. fig. 4a shows how the share of pgs firms monotonically decreases at increasing values of the substitution degree between goods α. the share x reaches its maximum value when α=0, namely when there is no substitution between goods (and the output prices, pg and pc, are independent). conversely, the share x reaches its minimum value when α=0, in case of full substitution (and the output prices, pg and pc, are the same). therefore, as α→1, pgs firms change their strategy adopting the tpc one. this means that the more the pgs product is differentiated, the more it will survive within the organic market. besides, a rise in α initially causes a slow decrease of x, while at higher values of α produces a rapid decrease of x. this means that initially (when the goods are enough differentiated) only few firms change their mode to become tpc, while for higher values of α (when the goods tend to be homogeneous) many firms change their mode and become tpc. the model suggests therefore that pgs firms have to differentiate their good to compete with tpc firms. as seen, pgs firms do differentiate their products and they also operate according to a different strategy and philosophy of production by incorporating strong peculiarities of environmental and workers’ rights protection as well as elements of social education in relation to quality-of-life improvement. in other words, symbolic attributes such as localness, healthiness, quality, embeddedness among producers and consumers, seem to be able to differentiate pgs products to tpc ones. pgs, indeed, adheres to a model that mirrors the recent critical consumption trends advantaging pgs productions compared to those certified by tpc, accused of being one of the main causes of the conventionalization of the organic model (raynolds, 2004; courville, 2006; hatanaka, 2014). finally, fig. 4b shows the behaviour of x at increasing values of the ratio of the slope of the marginal costs functions ec/eg. the ratio is always lower than 1 if we assume eg≥ec. this ratio can be considered as an increase in the relative marginal costs of the tpc mode compared to the pgs one, or, alternatively, as a decrease in the relative marginal costs of the pgs mode compared to the tpc one. the graph of x in fig. 4c shows a u-shaped trend. this suggests that a costs saving of the pgs mode compared to the tpc one may initially have adverse effects on the dynamics of the pgs firms. an increase of the production costs ratio has a negative effect on the quantities produced by tpc firms (see (5)) and an ambiguous effect on the quantities produced by pgs firms (see (4)). clearly, in the early stages, the raise of ec/eg has a negative effect on qg more than on qc and consequently the pgs mode is less rewarding than the tpc one. however, if then production costs ratio continues to increase, then the positive effect on qg prevails and so the pgs mode will be more rewarding than the tpc one. according to these results, if the goal of policymakers is to gradually change unsustainable consumption and production patterns and move towards a better integrated approach of sustainable food systems, they should consider ensuring to pgs firms the access to payments and subsidies supporting and compensating additional costs and income foregone due to the application of environmentally friendly farming practices. indeed, access to subsides is able to explain fig 4(b) in the sense that financial support to pgs firms compensate non-financial additional costs linked to their production by lowering and consequently improving the raise of . by financially supporting those farmers, environmentally sound farming techniques could be adapted to region-specific needs meeting the preservation of sustainable production potential according to sustainability criteria. as far as western countries are concerned, they could also consider the possibility to officially include pgs schemes within organic regulation as to recognize the crucial role played by those operators in the agricultural sector who address a sustainable use of public goods (schmidt et al., 2012) by adopting environmentally friendly farming techniques that go beyond legal obligations. (a) substitution degree. (b) slope of the marginal costs ratio figure 4. evolution of the share of pgs firms at increasing values of α, ec/eg. 247the evolution of organic market between third-party certification and participatory guarantee systems bio-based and applied economics 10(3): 239-251, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10470 6. final remarks worldwide organic standards, certification schemes and regulations ensure organic integrity, but they should do so in a way that does not create unnecessary technical barriers to organic trade, and that respects geographical as well as regional differences. currently, organic food market has shifted away from its original niche consisting of outlets such as specialized stores, organic farmers markets, direct selling, etc., towards more conventional grocery stores, supermarkets, as well as hypermarkets and even discount stores that have their organic brands. according to previous research, “the globalization of the organic food market could also be associated with the role played by the third-party assurance system” (sacchi et al., 2015). if on the one hand tpc has produced an increase in trust and reliability in organic goods and their commercialization worldwide, on the other it has meant the occurrence of several problems linked to certification costs as well as difficulties to accomplish all technical and bureaucratic paperwork needed for its request. in this framework, alternative assurance schemes, known as participatory guarantee systems, have been developed worldwide since the 1980s, originally to assess and guarantee the organic quality of products to consumers, and currently to overcome the barriers posed by tpc. furthermore, often farmers refer to pgs to differentiate their organic production from those more industrialized traded on mainstream channels. alternative initiatives suggest that pgs are a valuable tool in differentiating organic productions embracing a philosophy of production that goes beyond the organic production standards and process. the present research applied the game theory approach to develop a mathematical modelling framework able to explain the coexistence of pgs phenomenon within the certification market for organics. from the analysis of the model, it emerges that three dynamic regimes may be observed: (1) an organic market composed of only tpc firms, (2) an organic market composed of only pgs firms, (3) coexistence between firms. numerical simulations performed in the third scenario, show that by increasing the substitution degree between goods, the share of pgs firms progressively decreases until it reaches 0. this result means that the main way for pgs firms to compete against tpc ones is to differentiate their goods. numerical simulations also show the possible existence of non-linear effects in response to the change in production costs, so that the share of pgs firms sees an initial (and surprising) decrease before seeing an increase as the tpc mode gets relatively less remunerative. the present study explicates in mathematical terms the diffusion of pgs behaviour in a population of agricultural firms. on the one hand, the strength of the approach developed is represented by the possibility of presenting in a simple and organized context complex relationship such as those typical of the organic market. on the other hand, one could argue that the present model simplifies the reality by its assumptions. to this purpose, future research could amplify the model by including aspects not considered for analytical simplicity or by modifying some assumptions. for instance, a future line of research could be focused on a model that allows to the same firm to be contextually in a pgs group and certified by tpc, or rather considering a noncompetitive but oligopolistic market that could emphasize even more the strategic component of the model. to this respect, our hope is that the present findings will pave the way for more research on pgs certification programs, their strengths and pitfalls, to stimulate a greater debate on both organic producers’ and consumers’ actual needs. the insights gained from this study, indeed, represent an attempt of indications to policymakers, producers’ associations, professionals involved in the sustainability standards discourse, in the improvement of the livelihoods, working conditions, and income of rural populations. from another perspective, the importance of pgs model is also represented by its potential in promoting sustainable consumption by directly involving the participation of consumers. in academic literature there are many examples of consumers concerns about the production, distribution as well as the guarantee processes of agricultural products (murdoch and miele, 1999; murdoch et al., 2000; caputo et al., 2013; schnell, 2013; sacchi, 2018; kurtsal et al., 2020). several scholars claim that localness is an attribute often associate to consumer preference and willingness to pay more for local products compared to non-local counterparts (willis et al., 2013; carroll et al., 2013; sanjuán-lópez and resano-ezcaray, 2020). in the same vein, it has also been broadly demonstrated that consumers have a positive wtp for the organic attribute (loureiro and hine, 2002; costanigro et al., 2011; hu et al., 2011; zanoli et al., 2013; gracia et al., 2014; meas et al., 2014). in this sense, it should be interpreted the advantages of pgs: these systems, in fact, allow for quality assurance for products that conjugate values and attributes of localness and organic production and that can be purchased at a reasonable price. in this sense, policymakers and local authorities should pay attention to these systems that can positively impact both local economies and small farmers’ welfare. from a western perspective, european union opened a discussion on the possibility of 248 bio-based and applied economics 10(3): 239-251, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10470 gianluca iannucci, giovanna sacchi recognizing alternative certification systems to tpc for small farmers. this discussion will lead to the official inclusion within eu countries of the group certification as a possible certification strategy within the last regulation on organic production issued by the parliament and the council of the european union (reg (eu) 2018/848), that will enter into force on 1st january 2022. at the point (85) the regulation states that “a system of group certification should be allowed in order to reduce the inspection and certification costs and the associated administrative burdens, strengthen local networks, contribute to better market outlets and ensure a level playing field with operators in third countries”. establishing rules and procedure for implementing group certification could represent a step forward to the recognition of participatory guarantee systems and, eventually, to the access of support payments to small farmers adopting these alternative guarantee strategies. acknowledgements an earlier version of this paper was presented at the 8th aieaa conference, “tomorrow’s food: diet transition and its implications on health and the environment”, 13-14 june 2019, pistoia, italy. the authors wish to thank sara anselmi from ifoam organics international for her valuable observations on the paper as well as the two anonymous referees and the editor for their helpful suggestions and comments. references altieri, m. a. 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(2009). participative organic certification, trust and local rural communities development: the case of rede ecovida. new medit 2: 56-64. 251the evolution of organic market between third-party certification and participatory guarantee systems bio-based and applied economics 10(3): 239-251, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10470 zanoli, r., scarpa, r., napolitano, f., piasentier, e., naspetti, s., bruschi, v. (2013). organic label as an identifier of environmentally related quality: a consumer choice experiment on beef in italy. renewable agriculture and food systems 28(1): 70-79. https://doi. org/10.1017/s1742170512000026 zorn, a., lippert, c., dabbert, s. (2012). supervising a system of approved private control bodies for certification: the case of organic farming in germany. food control 25(2): 525-532. https://doi.org/10.1016/j. foodcont.2011.11.013 volume 10, issue 3 2021 firenze university press the bioeconomy in economic literature: looking back, looking ahead davide viaggi1,*, fabio bartolini2, meri raggi3 the contribution of research to agricultural policy in europe alan matthews drinking covid-19 away: wine consumption during the first lockdown in italy giulia gastaldello*, daniele mozzato, luca rossetto estimating a global maidads demand system considering demography, climate and norms wolfgang britz the evolution of organic market between third-party certification and participatory guarantee systems gianluca iannucci1, giovanna sacchi2,* bio -based and a ppl ied economics bae bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 copyright: © 2021 a. matthews. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: a. matthews (2021). the contribution of research to agricultural policy in europe. bio-based and applied economics 10(3): 185-205. doi: 10.36253/bae12322 received: october 4, 2021 accepted: november 19, 2021 published: january 11, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: donato romano. discussants: jeroen candel, emil erjavec, donato romano. orcid am: 0000-0003-1051-1499 bae 10th anniversary paper the contribution of research to agricultural policy in europe alan matthews trinity college dublin, ireland e-mail: alan.matthews@tcd.ie abstract. this paper surveys some of the key themes in european agricultural policy research in recent decades. it identifies three main drivers of this research: a gradual broadening of the scope of the discipline in response to changing political priorities and values; an enlarged toolbox of policy instruments that has raised new questions and required the development of new modes of analysis; and the availability of new data sources, increased computing power, as well as the introduction of new methodological advances from economics, statistics and psychology that have opened the way to new and more powerful analytical tools. particular attention is paid to direct payments and agri-environment-climate measures as examples of new policy instruments that have driven the research agenda. the paper concludes by identifying requirements to ensure that agricultural policy research remains vibrant and relevant in the future. keywords: agricultural policy, common agricultural policy, direct payments, agrienvironment-climate measures, policy research. jel codes: q18, q58. introduction this paper discusses some significant trends in european agricultural policy research in recent decades as a contribution to the celebration of the 10th anniversary of the bio-based and applied economics journal published by the italian association of agricultural and applied economics (santeramo and raggi 2021). this field of enquiry relates to the implementation of practical agricultural policy. in koester’s words, ‘agricultural policy is the entirety of all efforts, actions and measures aimed at regulating, influencing or directly determining the course of economic activity in the agricultural sector’ (koester 2020, 2). agricultural policy research includes analysis of the objectives of agricultural policy; diagnosis of actual outcomes compared to policymakers’ objectives or overall social welfare; the evaluation of interventions and instruments that might bring the actual situation closer to the desired policy outcome; and the reasons for policy change. while political economists and political scientists have been concerned with the why of government intervention in agricultural markets, agricultural economists have been more concerned with the how and how well – how food and agricultural policies should be designed to achieve specific objectives and how well http://creativecommons.org/licenses/by/4.0/legalcode 186 alan matthews bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 policies have succeeded in their aims. this review is confined to the contribution of agricultural economists. the frame of the discussion in this paper is further narrowed by only tangentially considering issues to do with the food industry and agricultural trade, both of which are inextricably bound up with agricultural policy, but which are discussed in separate review articles in this celebratory series (mazzocchi, 2021; olper and raimondi, 2021). reviews of agricultural policy research in europe tend to take the common agricultural policy (cap) as the starting point and examine the cap’s impact on various policy dimensions, for example, agricultural income (szerletics and jámbor 2020), environment (alliance environnment 2017), jobs (schuh et al. 2016), developing countries (blanco 2018), nutrition and health (recanati et al. 2019), or several dimensions at once (pe’er et al. 2017). one of the few attempts to systematise the evolution of agricultural policy research, albeit still rooted in the dynamics of cap reform, is erjavec and lovec (2017). this paper notes the shifting focus of the cap over time and proposes that this requires greater cooperation between disciplines in order to broaden the theoretical underpinnings of explanations for this shift. their paper builds on the idea that successive cap reforms represent a paradigm shift (meaning changes not only in mechanisms but also in principles and objectives (see also skogstad 1998; daugbjerg and swinbank 2011 for elaboration of the role of paradigm shifts). this paper has a more modest objective. it is neither intended as a systematic review of recent european agricultural policy research nor does it enter the debate whether recent reforms of the cap can be attributed to paradigmatic changes or not (rac, erjavec, and erjavec 2020). it presents a narrative describing recent trends in agricultural policy research and the factors that have driven these changes. three factors are highlighted in subsequent sections: a gradual broadening of the scope of the discipline in response to changing political priorities and values; an enlarged toolbox of policy instruments that has raised new questions and required the development of new modes of analysis; and the availability of new data sources, increased computing power, as well as the introduction of new methodological advances from economics, statistics and psychology that have opened the way to new and more powerful analytical tools (figure 1). broadening scope of the policy agenda agricultural policy analysis has always been an applied discipline that has responded to the changing priorities and objectives of practical agricultural policy. the competence for agricultural policy in the eu is formally shared between the union and the member states, although the treaties require that the union shall define and implement a common agricultural policy with common objectives and a common implementation. thus, one driver of the changing agricultural policy research agenda has been changing priorities and objectives of the cap. another driver has been the growing awareness of the market failures around agricultural production, both in terms of the under-provision of public goods but even more sharply the external costs imposed on society in terms of both health and the depletion of natural capital. yet another driver has been shifts in social values and expectations around the way food is produced, notably with respect to animal welfare, quality attributes, short supply chains and farm structures. it should be stressed that new objectives have been layered on top of existing ones rather than substituting for them. further, the emergence of new objectives has been a gradual and evolving process rather than marked by sharp discontinuities. daugbjerg and swinbank (2016) introduced the idea of policy layering into agricultural policy analysis. they characterise the path of cap reform, with its redesign of eu farm price support into wto-compatible decoupled payments, together with the greening of the cap, as a stepwise process of dual policy layering aimed at addressing new policy concerns while retaining the core objective of farm income support. their objective was to suggest a causal relation between policy layering and the sustainability of the reform path. here, i use the concept of policy layers simply to highlight that new policy objectives have emerged in addition to earlier concerns rather than replacing them. an illustration of the evolution in agricultural policy priorities is shown in figure 2. i now show how agricultural policy research has reflected this growing multi-dimensionality. figure 1. major influences on european agricultural policy research. source: own elaboration. 187the contribution of research to agricultural policy in europe bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 ensuring food security assuring the availability of food supplies and at reasonable prices are among the five objectives specified for the cap in the original treaty of rome and which have remained unchanged to this day. the tagline on the european commission’s webpage explaining the cap continues to affirm that ‘the common agricultural policy supports farmers and ensures europe’s food security’.1 despite the harrowing experiences of food shortages in the immediate aftermath of the second world war, national-level food security has not been an issue in the eu since before the cap came into force, though individual households can suffer food insecurity due to lack of means to purchase sufficient nutritious food rather than due to any problems of availability (borch and kjærnes 2016). nonetheless, food security continues to be prominent in agricultural policy debates, although with very different framings of how the objective is interpreted (candel et al. 2014), including the appropriate role for european agriculture in contributing to global food security. the covid-19 pandemic and the associated lockdowns underlined the potential vulnerabilities in food supply and the issue has once again become live, even though it is generally recognised that eu food supply chains proved remarkably resilient to date during the pandemic (montanari et al. 2021; meuwissen et al. 2021). the commission has since announced a contingency plan for ensuring food supply and food security in times of crisis which includes a food crisis response mechanism (european commission 2021b). the pandemic also triggered a literature reflecting more widely on the conditions for resilient food systems and the measures needed to realise them, which we consider further below. 1 european commission, ’the common agricultural policy at a glance’, https://ec.europa.eu/info/food-farming-fisheries/key-policies/commonagricultural-policy/cap-glance_en, accessed 2 september 2021. support for farm incomes income issues in agriculture refer to the level, stability, and distribution of income (finger and el benni 2021). the objective of ‘ensuring a fair standard of living for the farming community’ is also a treaty objective, although there has been much debate over whether the treaty wording sees this as a stand-alone objective or as the consequence of increased productivity and structural change. there is no doubt that for policymakers and farm organisations support for farm incomes is a major justification for the cap. the commission regularly produces a comparison of average farmer income (measured per work unit of family labour) with average gross wages and salaries in the total economy to show that farmers’ income are ‘still lagging behind’, while emphasising that direct income support payments ‘partially fill the gap between agricultural income and income in other sectors’ and ‘remain an essential part of the cap’ (european commission 2017; 2018a). whether there is indeed an income gap between farm and non-farm incomes and the extent to which this reflects agriculture-specific characteristics is a matter of definition and measurement (hill 2019). taking its cue from the treaty reference to ‘standard of living for the farming community’, the european court of auditors has argued that the disposable income of the farm household is the relevant indicator for family farms but that relevant data to make comparisons on this basis are not available (eca 2016). such data are collected in the eu survey on income and living conditions (eusilc) but the small number of farm households in this survey makes it difficult to draw valid conclusions. by pooling these data across eu countries, rocchi, marino, and severini (2020) find evidence that, on average, farm household incomes are lower than in non-farm households and even more so if the comparison is made with self-employed households in the non-farm sector. controlling for observable differences such as age, education, marital status and health status markedly reduces the size of the disparity, as does accounting for a wider definition of income to include nonmonetary factors, but it does not completely eliminate it. in addition to these observable characteristics that can account for differences in income, non-observable characteristics might also differ systematically between the two population groups. for example, unobservable preferences might determine the sorting of households into the farm sector while unobserved characteristics, such as skills, might affect incomes. marino, rocchi, and severini (2021) revisit the eu-silc dataset using a fixed effects regression methodology to control for these figure 2. overview of changes in cap priorities over time. source: own elaboration. 188 alan matthews bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 individual unobservable characteristics. again, the raw data show that farm household incomes are much lower than for nonfarm households, particularly in the newer member states, but there are also significant differences in observable characteristics. by further controlling for unobservable characteristics, their conclusions are revised. broadly-defined farm households still generally have a lower household income than comparable nonfarm households but except in the newer member states the differences are not significant. however, narrowlydefined farm households (those mainly dependent on their farm income) are better off than comparable nonfarm households in nearly all comparisons. they further find that being self-employed in agriculture instead of in other industries no longer represents a relative disadvantage across the eu countries. to summarise, this research confirms that farm household incomes are on average lower than non-farm household incomes across the eu, though with important differences in the size of the disparity across countries. the conditional income comparisons identify the factors that account for this and it appears sector-specific issues related to working in agriculture are not an important explanatory factor. it can be noted that the income of farm households in the dataset includes support payments under the cap as well as remuneration from the ownership of substantial farm assets including land. furthermore, the european commission data, even if not helpful in throwing light on relative standards of living, are still important in highlighting the relative difference in labour productivity between the two sectors which will have implications for future structural change. promoting rural development and employment the treaties note that, in working out the common agricultural policy, ‘account shall be taken of the particular nature of agricultural activity, which results from the social structure of agriculture and from structural and natural disparities between the various agricultural regions’. the treaties also specify territorial cohesion as an eu objective, with the aim of reducing disparities between the levels of development of the different regions paying particular attention, inter alia, to rural regions. the socio-economic disparities between rural and other regions are well documented. this led in 1999 to the introduction of a common rural development policy as the second pillar of the cap, building on precedents emerging in the previous decade (the commission’s communication on the future of rural society com (88) 501 in 1988 marks the beginning of a territorial rural policy). rural development policy has a complex series of objectives, including modernisation of the agricultural sector, integrating environmental concerns, generational renewal, and broader socio-economic development particularly emphasising community-led local development and job creation. however, overall expenditure on territorial measures within the cap has always been low. recurring themes include the desirability of moving towards a more integrated concept of rural development built around a ‘place-based’ approach, developing more explicit synergies with eu cohesion policy, and focusing on the endogenous development of territorial capital and particularly the role of leader groups (dax and copus 2016). most recently, the commission’s proposed long-term vision for rural areas, accompanied by proposals for a rural pact and a rural action plan (european commission 2021a), will no doubt stimulate a further wave of rural research. integrating environmental concerns the integration of environmental goals into the cap began in the 1980s with the growing awareness of the adverse environmental consequences of more intensive agricultural practices but also of the role that farmers can play in terms of management of natural resources and landscape conservation. attention shifted to the interactions between agricultural production and water, air, soil, landscape and biodiversity. agricultural pollution issues were addressed mainly by regulation, while the model of paying farmers to provide desired environmental outcomes was made mandatory for member states in the agri-environment regulation that accompanied the macsharry cap reform in 1992 (latacz-lohmann and hodge 2003). environmental cross-compliance for those in receipt of cap payments was introduced in the fischler 2003 cap reform, while the 2014-2020 cap saw the introduction of a greening payment in pillar 1 requiring farmers to follow three specific practices seen as beneficial for climate and the environment (matthews 2013). the huge literature stimulated by these developments is discussed later in the paper. the political agreement on the cap post 2020 includes a revised green architecture where the greening payment conditions have become part of a revised cross-compliance (now referred to as enhanced conditionality), while at least 25% of a member state’s direct payments envelope must be allocated to eco-schemes to fund measures beneficial to the climate and environment as well as animal welfare. the urgency to strengthen interventions to improve environmental outcomes on agricultural land has been underlined by the specific 189the contribution of research to agricultural policy in europe bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 targets set out in the commission’s farm to fork and biodiversity strategies to reduce the use of pesticides, mineral fertilisers and antimicrobials, to increase the area under organic farming, and to reserve more space for nature on farmland (european commission 2020a; 2020b). whether the implementation of the new green architecture in member state cap strategic plans will be up to the challenge of achieving these targets will likely become a major focus of agricultural policy research in the coming period (baldock 2020). the economics of transition a specific issue that attracted the attention of agricultural policy researchers in europe after 1989 and the fall of the berlin wall was the economics of transition in agriculture from central planning to a market economy (the countries affected included not only those that later become eu members, but also the balkans, russia, belarus, ukraine and moldova, transcaucasia and central asia). the socialist system left a legacy of a badly distorted economic system and prices. the institutional and relative price changes associated with the reorganisation of this system resulted in major disruptions and immediate declines in investment and output. state-owned assets such as input supply, credit and food processing and distribution companies were privatised as were state-owned farms using different privatisation models (e.g. direct sale, vouchers). in those countries where land had been collectivised, land was either restituted to former owners, physically distributed to farm workers, or ownership was transferred to workers through certificates. nonetheless, the share of land now farmed by large corporate farms in many of these countries remains very high, though often co-existing with many very small-scale individual farms that produce only for their own consumption (semi-subsistence farms). these institutional changes gave rise to a significant research effort addressing issues such as trade competitiveness (bojnec and fertő 2008; 2015), price reforms (bojnec and swinnen 1997; anderson and swinnen 2008), farm restructuring, privatisation and land reform (koester 2005; swinnen 2009), and productivity growth (gorton and davidova 2004). this research effort was extended after 2000 to the implications for the cap of the accession to the eu of the former soviet-bloc economies in central and eastern europe. both the importance of agriculture in these economies, particularly in employment terms, as well as the low productivity and consequential low incomes of those working in agriculture, were seen as posing a financial threat to eu agricultural policy which was still largely conceived as an income support policy for farmers (bojnec 1996; gaisford, kerr, and perdikis 2003; hartell and swinnen 2017). many papers have also focused on the performance of the agri-food sector in these economies after accession (csáki and jámbor 2013). as institutional structures have stabilised the topic of transition economics is one of the few layers that has now largely disappeared, although comparisons of the performance of older and newer member states within the eu continue to attract the interest of researchers (csáki and jámbor 2019). pursuing competitiveness increased productivity is also one of the treaty objectives for the cap. post-war agricultural production in europe increased dramatically with the adoption of mechanisation, chemicals, intensive livestock breeding, new seed varieties and the extension of irrigation (martín-retortillo and pinilla 2015). increased production was supported with high tariffs against imports and the use of export subsidies to dispose of surpluses. during this period of “fortress europe” (cost) competitiveness was given little explicit attention. this began to change with the 1992 cap reform that began the process of switching cap support from the product to the producer. a decade later, the commission reflected on the success of this move in promoting greater market orientation and competitiveness when introducing the midterm review (european commission 2002). this focus on market orientation has been maintained through successive reforms. one of the nine specific objectives for the cap post 2020 is ‘to enhance market orientation and increase competitiveness, including greater focus on research, technology and digitalisation’. productivity is an important determinant of competitiveness in the longer term and can be measured using either parametric, non-parametric or index number approaches (latruffe 2010). policy researchers have been interested in measuring the rate of growth in total factor productivity; in differences in relative productivity across member states; and in whether productivity levels are converging over time (baráth and fertő 2017). developments have taken place in measuring farm level productivity using new estimation techniques to address classical problems of endogeneity and identification when trying to estimate production functions using farm level data (sauer et al. 2021). contributions have sought to identify the factors responsible for productivity trends, building on the decomposition of productivity growth into technical change (shifts in the technical frontier), technical efficiency change 190 alan matthews bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 (catching up with a shifting technical frontier), scale efficiency change, and efficiency change due to changes in the mix of inputs and outputs. there have also been attempts to integrate the use of environmental resources into productivity measures led by the oecd network on agricultural productivity and the environment. finally, researchers have examined the effectiveness of policies to stimulate productivity growth (viaggi 2015; zezza et al. 2017; détang-dessendre et al. 2019). relevant work has been done by the standing committee on agricultural research (scar) in developing the concept of agricultural knowledge and innovation systems (akis) as an interactive innovation system involving farmers, education, extension and research (scar 2019). addressing climate policy successive reports by the international panel of climate change have warned about the dangers of anthropogenic climate change. the paris agreement which has the objective to limit global warming to well below 2˚c and preferably below 1.5˚c compared to pre-industrial levels entered into force in 2015. to achieve this temperature goal, the parties including the eu have committed to achieve a balance between emissions by sources and removals by sinks in the second half of the twentieth century, on the basis of equity. the european climate law adopted in 2021 commits the eu to reach climate neutrality by 2050. agriculture is both affected by climate change and a contributor to it. agricultural emissions contribute around 11% of total eu emissions while the land use, land use change and forestry (lulucf) sector is a net sink. policy research has investigated the impacts of climate change (bozzola et al. 2018; van passel, massetti, and mendelsohn 2017), the ability of farmers to adapt to climate change (moore and lobell 2014), and the mitigation potential of agriculture, using marginal abatement cost curves to shed light on the cost-effectiveness of different interventions (de cara and jayet 2011; eory et al. 2018; pérez domínguez et al. 2020). the literature on policy design to bring about emissions reduction in agriculture in an efficient way remains surprisingly underdeveloped (ancev 2011; de cara and vermont 2011; grosjean et al. 2018) but the commission proposal in the farm to fork strategy to introduce a carbon farming initiative to reward farmers for carbon sequestration may spark greater interest in market-based approaches. climate action has been an explicit objective of the cap since the 2007-2013 programming period. evaluations suggest that the measures taken to date have had a very limited impact on reducing agricultural emissions, although some measures may have helped to reduce emissions in the lulucf sector (alliance environnment 2019; eca 2021). the commission’s recent fit for 55 package of legislative initiatives designed to achieve the more ambitious greenhouse gas emissions reduction target included in the climate law for 2030 will increase the need to reduce agricultural emissions in the coming cap programming period (matthews 2021). farm structure concerns interest in farm structures arguably goes back to the early years of the cap when the then agriculture commissioner sicco mansholt proposed to offer financial incentives to drastically reduce the farm population to release land to enable remaining farms to grow to a viable size. however, the regulations that followed were only a pale shadow of the original proposal (stead 2007). the central role of the family farm in the european model of agriculture is taken as a given, although nowhere explicitly stated as an agricultural policy objective. the eastern enlargements of the eu in 2004, 2007 and 2013 radically altered the farm structure distribution by introducing a significant duality. on the one hand, a large number of farm holdings were now subsistence or semi-subsistence farmers (davidova 2011). on the other hand, the conversion of former collective or state farms into joint stock companies in some new member states created a new type of farm holding that was virtually unknown in the older member states (maurel 2012). by the middle of the following decade political concern was growing over trends in farm consolidation and farmland concentration. for some, the focus has been on land grabbing and the rise of large-scale land deals (van der ploeg, franco, and borras 2015; kay, peuch, and franco 2015); for others, it is safeguarding the position of the family farm (davidova and thomson 2014; hennessy 2014); for some, it is opposition to industrial farming and the growth of ‘mega’ farms (greenpeace 2019); for others, the issue is generational renewal (european commission 2018d; zagata et al. 2017); while yet others focus on the decline in the overall number of farms and its impact on rural vitality. common to all is the view that current patterns of farm structural change should be halted or even reversed (falkenberg 2016). this concern over the pace of structural change has been forcefully articulated by the current commissioner for agriculture and rural development janusz wojciechowski on many occasions since he took up office. however, the commission’s own figures showing the disparity in the returns per work unit in agriculture relative to the rest of the economy suggests 191the contribution of research to agricultural policy in europe bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 that the outflow of labour and thus farm consolidation will continue for some time to come. resilient and sustainable food systems in recent years, the issue of resilient and sustainable food systems has moved centre stage in recognition of the multiple and interrelated challenges they face including poor diets, poor health outcomes, food waste, biodiversity loss, environmental degradation, resource scarcity, inequality and climate change at both the global and eu levels (european commission 2020a; sapea 2020; scar 2020; webb and sonnino 2021). the food systems approach links these issues together, in contrast to sectoral analyses that look at the individual issues separately. the adoption of the un agenda for sustainable development in 2015 with its 17 sustainable development goals has given further impetus to this research direction (scown and nicholas 2020), as has the apparent vulnerabilities in food supply chains revealed by the covid-19 pandemic (bisoffi et al. 2021). food systems encompass the entire value chain from ecosystem services to primary production, processing, packaging, distribution, retailing, food service, waste stream management, safety assurance, to consumers, their nutrition, the food environment and diet-related diseases. sustainable food and nutrition security has been defined as the capacity of a food system to deliver food and nutrition security in an environmentally, economically and socially sustainable manner, thus combining nutrition and health with a social-ecological systems perspective (zurek et al. 2018). the food systems perspective draws attention to the interactions, including synergies and trade-offs, between different policy domains and levels of government. there is a strong normative element in this literature. many studies conclude that ‘business as usual’ is no longer a viable option and that radical system-wide change is required. their aim is to identify workable paths towards a healthier, more socially just and environmentally sustainable food system (sapea 2020). achterbosch et al. (2019) review the way in which food systems thinking has been reflected in eu research grouped around themes such as food system governance; sustainable diets; food, nutrition and health; agroecology; agricultural innovation; alternative methods of food distribution; food waste and the circular bioeconomy; and development. the food systems literature draws attention to new research questions such as the future role of animal agriculture in europe; the most effective ways to reduce food waste and to build a circular bioeconomy; how to redesign food environments to encourage more healthy eating habits; the balance between extensification and intensification in contributing to more sustainable food production; how to implement true cost accounting to reflect fully the role of externalities and environmental impacts; and improving resilience. diversification of policy instruments agricultural policy research has been influenced not only by the expanding scope of topics to be addressed but also by the introduction of new policy instruments that have raised new issues in terms of assessing their effectiveness as well as their interactions with other policy goals. for reasons of space we choose to highlight two examples here: direct payments and agri-environmentclimate measures. direct payments direct payments were introduced into the cap from 1994 onwards, first as coupled payments and then, after 2005, as decoupled payments. given the important contribution they make to farm income, they have attracted much research: how equally are they distributed; do they have production effects; are they capitalised into land values; do they impact on productivity growth; how do they influence the process of farm structural change? the concern with how direct payments are distributed arises from the well-known statistic that 80% of payments accrue to the largest 20% of farms, which in turn is driven by the allocation mechanism of direct payments which is related to land. distributional analyses have used either the annual commission data on payments by size of payment or micro-level farm data from the farm accountancy data network (fadn). the most recent analysis using commission data shows a trend towards a more equitable distribution of aid in the older member states, but the opposite trend for the newer member states (alfaro-navarro et al. 2021). farm-level analysis has been used to investigate the dependence of farm incomes on direct payments by investigating scenarios that assume the complete abolition of the cap (ciaian et al. 2020). by linking a farm-level model with the capri partial equilibrium model they overcome the limitations of a purely static analysis. they conclude that a small sub-set of farms (pigs, poultry, dairy and horticulture) could experience an increase in income due to improvements in both prices and yields but those farms that are currently most dependent on cap subsidies (arable and cattle farms) would experience significant income losses. another farm-level analysis concluded 192 alan matthews bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 that the 2013 cap reform only partially succeeded in its objective to equalise payments across farms, but also showed that cap subsidies (and direct payments in particular) contribute to a reduction in the inequality of total farm income (espinosa et al. 2020). severini and tantari (2015) reached the same conclusion using italian farm level data. it is no surprise that coupled direct payments stimulate production (smit et al. 2017; jansson et al. 2020), but views have differed on the importance of the production effects of decoupled payments (moro and sckokai 2013). this is a vital parameter when modelling the impact of changes to cap instruments (balkhausen, banse, and grethe 2008; boulanger, boysen-urban, and philippidis 2021). truly decoupled payments do not affect the marginal incentive to produce but economists have pointed to various mechanisms whereby such payments might affect production compared to the absence of such payments. payments that are decoupled in a static and riskless world may not be production neutral in a dynamic and risky world. studies have therefore tried to assess directly whether direct payments have kept land and labour in production that might otherwise have exited the sector, or inf luenced investment through wealth effects or by increasing access to credit where imperfect credit markets exist. a drawback of this literature is that empirical work has often been constrained to comparing decoupled payments with the previous system of partially-coupled payments rather than being able to undertake a test of the impact of these payments compared to the absence of these payments. for payments to be fully decoupled they must be fully capitalised into land values. another way to estimate the ‘degree of decoupling’ is therefore to examine the extent to which these payments are capitalised into land values and land rents. a high rate of capitalisation into land values implies a low transfer efficiency of support to farmers (if we exclude the benefits they receive as owners of land), and thus a lower likelihood that the payments will distort production. economic theory can describe the degree to which support is capitalised into land rents as a function of three parameters: i) how the policy is implemented, specifically its initial incidence (targeted to land, inputs or labour); ii) the ease which land can be shifted to alternative uses (its elasticity of supply); and iii) the ease with which land can be substituted with other factors of production (its elasticity of substitution) (floyd 1965). however, specific features of the cap payments implementation mechanism seem to play a dominant role. varacca et al. (2021) undertake a meta-analysis of the capitalisation of cap payments into land prices. in line with expectations, they find that the introduction of decoupled payments increased the capitalisation rate, although the extent of this effect hinges on the implementation scheme adopted by a member state. in particular, the rate of capitalisation is influenced by the relationship between the number of eligible hectares and the number of payment entitlements in those member states that adopted the single farm payment. other factors can also reduce the rate of capitalisation, including the time-limited commitment to payments, the costliness of cross-compliance conditions, rural land market imperfections, and differences in the value of entitlements (ciaian, kancs, and swinnen 2008). allowing for multiple estimates from individual papers, the range of estimates for the capitalisation rate is strikingly large. the varacca et al. (2021) study concludes that the capitalisation rate for coupled payments is around 11 cents per euro of payment. decoupled payments have a higher capitalisation rate, depending on the implementation model, while capitalisation in rental transactions is higher still, varying between 15 and 49 cents per euro. guastella et al. (2021) find that between 28 and 52 cents per euro of additional subsidy capitalise into land prices in member states that adopted the hybrid and the regional model, respectively, but find no evidence of capitalisation in farmland prices in member states that adopted the historical model. the corollary of these findings is that the residual payment increases the returns to the remaining production inputs, including intermediate inputs, capital and labour, and will likely inf luence production through these routes. biagini, antonioli, and severini (2020) throw further light on this issue by directly estimating the income transfer efficiency of cap payments in italy. italy made all land uses (except forests) eligible for entitlements, generating an abundance of eligible hectares compared to entitlements. studies show that as a result the capitalisation of direct payments into land values was negligible. an income transfer efficiency of unity would indicate the payments do not affect production decisions. they find that the income transfer efficiency of most cap measures is less than unity, pointing to the existence of leakages. their paper highlights that policy participation costs differ across farms and across instruments and also play a role in determining transfer efficiency. a recurring theme has been the investigation of the impact of cap subsidies on productivity growth (minviel and latruffe 2017; garrone et al. 2019). in theory, the direction of this effect could go either way. positive effects might arise if direct payments provide farmers with the necessary financial means to keep technologies 193the contribution of research to agricultural policy in europe bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 up to date or to invest in efficiency-improving on-farm organisation. negative effects might arise if farmers are less motivated to perform well with more income due to subsidies or where a soft budget constraint means that farmers over-invest leading to inefficient use of resources. early literature that focused on the impact of coupled payments found a predominantly negative relationship. more recent studies suggest that decoupled payments may have a positive effect on technical efficiency, although this may vary across different farming systems (bonfiglio et al. 2020). another issue that agricultural policy research has investigated has been the impact of direct payments on the pace of structural change in agriculture. direct payments can, in principle, influence the entry, growth and exit of farms. if direct payments are capitalised into land values and land rents, increased land rents and prices may represent significant barriers to entry into the agricultural sector and may also impede restructuring within the sector. direct payments may also influence a producer’s decision to exit the industry, particularly for low-profit farmers, given that receipt of the payment is contingent on having access to land. there is evidence at least for the eu-15 member states that the change to a decoupled payments regime after 2005 may have slowed the rate of farm consolidation in the eu (brady et al. 2009; kazukauskas et al. 2013). there is also evidence from survey intentions and simulation modelling (bartolini and viaggi 2013; brady et al. 2009) that decoupled payments slow down the rate of structural change relative to a situation of no agricultural policy support. the cap’s income support payments have created incentives for some farmers not to exit agriculture, reduced land reallocation towards more efficient farms, and helped to keep less efficient farms active. if new entrants or enlarging farms are seen as more productive, this mechanism may mitigate any production-stimulating effect of these payments through other channels. environmental interventions the second policy instrument that has generated a significant volume of research is the voluntary agrienvironment-climate measure (aecm) in the cap. it is distinguished from the income support instruments by its focus on environmental outcomes, its voluntarism, its contractual nature, and its reliance on an objective mechanism to establish payment levels. this has given rise to a vast literature focusing on the ecological effectiveness of these measures, the factors that influence farmer participation, the most efficient ways of designing contracts, their impact on other cap objectives such as farm income and employment, and their cost effectiveness (uthes and matzdorf 2013). an obvious question is whether the agricultural practices supported by aecm payments have delivered the desired environmental effects (ecological efficiency). such studies are usually undertaken by ecologists rather than economists. although aecms are often seen as the poster boy of the cap and the type of payment for public goods to which the cap as a whole should aspire, the literature on ecological effectiveness is surprisingly critical (eca 2011). this may partly reflect the findings of the highly influential seminal review of aecm effectiveness by kleijn and sutherland (2003) which is still frequently cited despite the experiences with aecms since then. however, a more recent meta-analysis concluded that the expectation that results of previous evaluations would be used to improve future policy was not borne out in practice (batáry et al. 2015). these authors found that schemes implemented after revision of the eu’s agri-environmental programs in 2007 were not more effective than schemes implemented before revision. for the 2014-2020 period, it seems many managing authorities continued with the interventions used in the previous programming period even where the rural development programmes state that the aecm measures have been improved (enrd 2016). still, evaluation studies suffer from methodological weaknesses that make it difficult to draw strong conclusions (josefsson et al. 2020). aecms often have multiple goals such as the protection of environmentally valuable landscapes, reduction of pollution, enhancement of biodiversity, and climate mitigation that makes outcomes difficult to quantify. furthermore, few studies examine whether farmers maintain their conservation practices over time, or the extent of rigour of these practices. a large literature has explored the factors affecting adoption of aecms by farmers. farmers receive financial support to participate but uptake is patchy and there is evidence of systematic non-participation in schemes. understanding the factors that influence farmer participation in aecms can help to design schemes that better incentivise farmers to participate. there is a widespread view that, at least in some countries, it can be difficult to attract farmer participation and thus there is a low uptake of aecms but firm evidence on this is hard to obtain. numerous analyses indicate that the main factor encouraging farmers to participate in aecms is financial incentives rather than environmental concerns (pavlis et al. 2016; wąs et al. 2021), although dessart, barreiro-hurlé, and van bavel (2019) highlight the importance of behavioural factors. brown et al. (2021) argue that over-emphasising economic considerations may hamper 194 alan matthews bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 the effectiveness of environmental payments, potentially corroding farmer attitudes to policy and environmental objectives. adoption studies initially focused on factors such as farm structure (intensive vs extensive) or farmers’ socio-demographic characteristics (education, age). more recent work has investigated the influence of behavioural factors such as farmers’ motivations and attitudes, the role of social capital and farmer’s networks, as well as the role played in diffusion by whether one’s neighbours have adopted the practices. most papers focus on individual schemes and specific countries. a review paper by lastra-bravo et al. (2015) surveyed ten papers that used a probit/logit model to examine the determinants of adoption. over 160 variables affecting uptake were identified, and grouped into five major categories: economic factors, farm structure, farmer characteristics, farmers’ attitudes towards aecms, and social capital (i.e. the connections, shared values and understandings between individuals and groups). results indicate that farms less likely to join an aecm are those where there is a high dependence on agricultural activities for farm income, those where there is the presence of a successor on a farm, and farms with a high proportion of family labour. in one of the few studies that take an eu-wide perspective based on fadn data, zimmermann and britz (2016) show that participation in aecms is more likely in less intensive production systems where, however, per committed hectare premiums tend to be lower. another line of research addresses the contractual design of aecms, starting from the dominant actionbased approach that requires participants to demonstrate compliance with specific management actions (prescriptions) to potentially more cost-effective contract designs, such as payments by results, auctions, spatial targeting, and collective implementation (berkhout, van doorn, and schrijver 2018). the popularity of action-based approaches can be explained by lower transactions and monitoring costs as well as less risk for farmers. however, these approaches may have contributed to the disappointing results of aecms to date given that they often encourage enrolment of less intensively farmed areas at lower risk of environmental losses, encourage farmers to choose those actions that require the least change to their management practices, and do little to promote long-term attitudinal change or commitment to improving environmental outcomes. as one paper noted: ‘thus far, no consensus exists whether [aecms] incentivize adoption of pro-environmental production or simply offer windfall profits for those already operating at lower intensities’ (uehleke, petrick, and hüttel 2019). results-based schemes (rbs) in which farmers are paid for achieving agreed environmental outcomes rather than performing specific management actions are advocated on the grounds that they give farmers greater flexibility in the way they achieve environmental outcomes which may be more in line with their own farm characteristics, can encourage innovation in successful practices, and by giving farmers a greater sense of ownership of the outcomes they may be more successful in promoting behavioural change (burton and schwarz 2013; chaplin, mills, and chiswell 2021). despite these apparent advantages, rbs have largely remained as pilot and small-scale initiatives. partly it can be difficult to define indicators for the desired ecological outcomes, partly rbs imply greater risk for farmers, while administrations worry about higher transactions costs (šumrada et al. 2021). research is seeking to address these issues, for example, by looking at the potential for hybrid schemes using a mixture of actionand results-based approaches, and by exploring the use of self-assessment by farmers to reduce monitoring costs (herzon et al. 2018). many policy problems in the design of aecms can be understood within the framework of principalagent theory. the issue is to design a policy that results in agents (farmers) doing what is in their best interests while also achieving the objectives of the principal (the state). designing such a policy faces problems of asymmetric information resulting in adverse selection (arising from the availability of information known to the agent but not to the principal, such as the opportunity costs of farmers in providing the environmental outcome) and moral hazard (because it is difficult for the principal to monitor compliance, the agent has an incentive to cheat). adverse selection means that farmers with the lowest compliance costs (perhaps because they are already managing their land in an environmentally-friendly way) have the greatest incentive to join a scheme, resulting in comparatively limited environmental gains and overcompensation of compliance costs (latacz-lohmann and schilizzi 2005). different contract designs have been proposed to overcome these problems, including the use of targeting mechanisms, incentivecompatible screening mechanisms, and auctions (vergamini, white, and viaggi 2015). collective implementation can also be important to widen the adoption of aecms and to lower transactions costs. olivieri et al. (2021) provide a systematic review of relevant papers to understand better how these innovative contract solutions can improve the effectiveness of aecms under asymmetric information and help to avoid policy failures relative to action-based approaches. 195the contribution of research to agricultural policy in europe bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 an expanded toolbox agricultural policy research relies on a large and sophisticated toolbox of methods and databases comprising statistical and experimental approaches, various farm-level, agent-based as well as sector models, and a dedicated collection of microeconomic data in the form of the farm accountancy data network (fadn) as well as census, survey, and administrative data (finger and el benni 2021). developments in data collection and access, models and new methodologies have been an important driver of the policy research agenda. availability of data agricultural policy research has a strong empirical focus and relies heavily on the availability of accurate and reliable data. agricultural and other statistics collected by national statistical agencies and coordinated by eurostat have been and remain a primary source of data, supplemented by administrative data, and survey data collected by researchers themselves. recent developments in data availability, accessibility and diffusion have helped to drive the expanding agenda of agricultural policy research by opening new areas of enquiry and permitting the use of new methodologies. nonetheless, both eurostat and the commission recognise that the absence of data in many new policy areas is likely to be a constraint for future policy analysis. the european agricultural statistics system (eass) maintained by eurostat consists of 10 legal acts and their implementing measures, as well as of a number of gentlemen’s agreements. eurostat embarked in 2016 on a strategy for agricultural statistics for 2020 and beyond with multiple objectives to clarify and streamline definitions, diversify data sources and improve the speed, flexibility and effectiveness of the eass while preserving high quality data and long time series. it recognised new data needs linked to the greening of the cap, climate change challenges, production structures, food supply chains, price volatility, yields and geo-referenced information (eurostat 2016). agricultural policy research has greatly benefited from the farm-level micro data collected through the commission’s farm accountancy data network (fadn) intended to provide reliable information on farm incomes in the eu (vrolijk et al. 2004). many of the papers cited in this review made use of fadn data for income comparisons and distribution analysis, efficiency studies, environmental assessments, microsimulation modelling, and policy impact analysis. the farm to fork strategy proposes to extend the current fadn to a farm sustainability data network (fsdn) to include a broader set of indicators on the sustainability performance of farms. given resource constraints, this may require a trade-off between the size of the representative sample and the breath of coverage of data collected (vrolijk and poppe 2021). the administration of the cap requires the collection of a huge amount of data through the integrated administration and control system (iacs) that centralises data on agricultural subsidies paid by the eu in each member state. data collected under the cap’s common monitoring and evaluation framework (soon to become the performance monitoring and evaluation framework) also includes administrative data supplied by member states as well as eurostat data (european commission 2018c). researchers’ access to these data seems to vary across member states although the commission has invested heavily in developing data platforms such as the agri-food data portal.2 the attempt by the commission to increase transparency around the distribution of cap payments by requiring member states to publish information on the names of beneficiaries, the municipality and the postal code where available on nationally-managed websites with a search tool, first introduced in 2009, has generated very limited research (one exception is scown, brady, and nicholas 2020). this may be because member states have not made much effort to make these sites user-friendly and considerable effort is required to turn these data into usable information. the european data strategy proposed by the commission includes rules on open data and the reuse of public sector information that will hopefully improve researchers’ future access to administrative datasets. the ongoing digital revolution is greatly increasing the amount of data collected regarding both farms and consumer behaviour. in addition to the traditional public sector actors involved in collecting and aggregating agricultural data, the digital revolution engages additional actors such as agricultural equipment manufacturers, software developers and other private actors. managing rights to agricultural data and privacy concerns relating to the use of both personal and nonpersonal data is emerging as a key regulatory challenge (kosier 2019). the voluntary code of conduct for agricultural data sharing launched by a coalition of associations from the eu agri-food chain in 2018 represents an important first step in building the necessary trust and transparency (van der burg, wiseman, and krkeljas 2020). the development of a common agriculture data 2 the portal can be accessed at https://agridata.ec.europa.eu/extensions/ dataportal/home.html. 196 alan matthews bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 space will be facilitated by commission initiatives as part of its european data strategy. the role of modelling agricultural economists have put significant effort into the development of policy models which are increasingly used in impact assessment and to support policymaking. policy models come in many forms – programming models, agent-based models, microsimulation models, partial equilibrium models and computable general equilibrium models – and increasingly include links to biophysical, land use and other models in order to evaluate impacts on a wider range of indicators than the narrowly economic ones of production, prices, income, trade and economic welfare. as the focus of policy has shifted from markets to farms, models have evolved to capture the heterogeneity of farm responses, for example, to changes in direct payments (espinosa et al. 2020; gocht et al. 2013). in the past, models were often developed for specific purposes and rarely re-used, encouraged by a pattern of research funding that prioritised novelty rather than the maintenance and development of existing models. two developments at european level have improved this situation. one is that the eu research programme has begun to fund a cluster of research projects that aim to improve modelling capabilities while also interacting with each other.3 the other is the creation of a modelling platform (the integrated modelling platform for agro-economic commodity and policy analysis, imap) at the commission’s joint research centre since 2005 that includes selected partial and general equilibrium models (m’barek and delincé 2015). imap facilitates the analysis of a given policy question with different tools, allowing comparison of results to substantiate the findings as well as extending the range of outputs given that the different models complement each other. the impact assessment undertaken for the commission’s legislative proposal for the cap post 2020 illustrates the contribution of the imap models (european commission 2018b). imap also contributes to making model results and harmonised data sources publicly available, thus increasing transparency and facilitating their scientific review. future directions for agricultural policy modelling were identified as part of the suprema project (gonzalez-martinez, jongeneel, and salamon 2021). in line 3 these include suprema: https://cordis.europa.eu/project/id/773499; agricore: https://cordis.europa.eu/project/id/816078); bestmap: https://cordis.europa.eu/project/id/817501; mind step: https://cordis. europa.eu/project/id/817566. with the narrative in the earlier part of this paper, the conclusions noted the increasingly wide range of issues and outcomes that policies sought to address. in particular, more effort is needed to integrate environmental and social aspects as well as economic outcomes. as no model can attempt to provide all the answers, the project emphasised the need to ensure that models can be coupled and work together which adds another layer of complexity to their design. models will increasingly be designed in a modular fashion so that depending on the question being asked discrete components can be included or not as needed. resources also need to be found to support the ongoing work of model maintenance and development as well as to undertake model comparisons. experimental methods and behavioural insights experimental approaches are a relatively recent but rapidly-growing addition to the methodological toolbox both for agricultural policy design and impact evaluation. insights from behavioural economics are also increasingly applied to understanding how farmers respond both to stronger regulations and to the broader range of voluntary measures now offered as part of agricultural policy. although they are quite distinct (experimental economics is a methodological approach while behavioural economics is a research programme informed by a richer set of assumptions about human behaviour than mainstream economics) they are often seen as complementary as experiments can be used to test predictions of human behaviour drawn from behavioural economics (thoyer and préget 2019). experiments are particularly useful in trying to establish cause-effect relationships because they seek to control all extraneous factors in order to isolate the impact of the ‘treatment’ (the policy intervention or different designs of the intervention). colen et al. (2016) survey the contributions of experimental approaches to agricultural policy, relatively limited at that time, and discuss the main challenges of integrating these approaches into the toolbox for agricultural policy impact assessment and evaluation. lefebvre et al. (2021) give examples of policy insights from experiments and also review the challenges in making better use of experimental approaches. a network of experimentalists, the research network on economic experiments for the cap (reecap) has been established with the aim of promoting the increased recourse to economic experiments for cap evaluation. behavioural economics explores the implications of observing how farmers and consumers actually make decisions rather than assuming that they are rational, 197the contribution of research to agricultural policy in europe bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 self-interested, utility-maximising individuals. psychology and other social sciences help to rationalise deviations in behaviour from the standard model and underpin the notion that there are systematic biases that if accounted for in designing policies can help to improve policy outcomes. much of this literature has focused on ways to improve participation in agri-environment schemes due to their voluntary nature (dessart, barreiro-hurlé, and van bavel 2019). the eu created in 2019 the competence centre on behavioural insights within the commission’s joint research centre to promote the use of behavioural insights in policymaking and the use of behavioural research is foreseen in the better regulation guidelines for evidence-based policymaking (baggio et al. 2021). as behavioural change among both consumers and farmers is central to achieving the objectives of the european green deal when it comes to food systems, the extent and relevance of this research is only going to increase in future. conclusions this review of recent agricultural policy research has highlighted its expanding breadth and the growing use of innovative datasets and methodologies. yet it is far from comprehensive, and some readers will regret the omission of specific mention of topics such as the bioeconomy, risk management, or political economy. we have highlighted the close relationship between research efforts and topics with the changing needs and priorities of practical agricultural policy. we have used the concept of layering to suggest that new policy needs and priorities have been added cumulatively to the agricultural policy research programme rather than deleting or substituting for previous themes. it is impressive to observe how responsive the discipline has been to the changing needs and demands of policymakers and stakeholders. in looking to the future, it seems appropriate to highlight two themes. the growing breadth of policy research brings with it a growing need for interdisciplinary collaboration. erjavec and lovec (2017) have already observed that the shift in focus of cap research from market distortions to broader societal issues such as food, environment and development requires greater collaboration with political and other social sciences to avoid policy failures. i would argue that the expanded research programme described in this paper points to the need for an even greater range of disciplinary collaborators including ecologists, climate scientists, nutritionists, food technologists, and other natural scientists. my impression, which requires further empirical testing, is that many of the most cited papers that are driving the broader food systems and food policy agenda are not published in the traditional agricultural economics journals and often do not include economists among their authors (fresco et al. 2021). in the past, agricultural economists often had a training in basic agricultural science which facilitated their contribution to, for example, farm management research. being able to communicate across disciplinary boundaries will become an increasingly important skill. the downside, of course, is that investment in developing such skills takes time and resources that could otherwise be used to build research competences and output within one’s own discipline. the ready access to information and the accompanying problem of information overload may, paradoxically, have the effect of encouraging greater specialisation where researchers can feel confident that, at least in their own specific areas, they have a full and complete understanding of the state of play. a second question that comes naturally to an agricultural policy researcher is whether their research has any actual impact on practical agricultural policy. researchers will be aware that research funding applications increasingly require evidence of impact or policy relevance. the commitment to the better regulation agenda within the eu and many national administrations includes a requirement for impact assessments (ias) to gather and evaluate evidence to support policymaking. reidsma et al. (2018) examine the use of scientific evidence in ias in the area ‘agriculture and rural development’ undertaken by the commission between 2003 and 2014. examining the total of 24 ias conducted during this period, they concluded that this policy area ‘provided relatively much scientific background compared to other policy areas’ based on the inclusion of references to scientific studies. both the european parliament and the european court of auditors regularly publish studies which draw on research outputs in support of their policy recommendations. however, actual impact is difficult to evaluate. while the above evidence suggests that policy research is referenced when taking decisions, it is often far from determining policy outcomes. the simple linear model whereby knowledge drives policy and that policymaking is driven by ‘evidence’ produced by science has been heavily criticised by the literature in political science, policy studies, and public administration (boswell and smith 2017). most studies show that the use of evidence is highly selective. some commentators attribute this to weaknesses in research communication and call for improved methods of knowledge exchange (e.g., policy briefs) as well as greater interaction (e.g. through stakeholder workshops) with poli198 alan matthews bio-based and applied economics 10(3): 185-205, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12322 cymakers to build trust. increasingly, we see such initiatives as part of h2020 projects while the european association of agricultural economics and the uk agricultural economics society have jointly published eurochoices with the specific aim of better communicating research results to policymakers. one might also make the case that agricultural policy research, even if not immediately visible in changes to cap policy instruments, for example, has nonetheless had an influence over a longer timespan in shifting the range of what are deemed to be relevant responses to specific policy problems. this instrumentalist view of the role of research in policymaking is challenged by other conceptual framings, most strongly the notion that it is politics that determines the research that is undertaken, or at least determines the research that is considered relevant, rather than the other way round. the way in which research funded by commercial interests often results in research findings that are of direct use to those interests is well documented. similar mechanisms can be at play in policy research if research funders signal what is likely to be funded (or not) and what the expected outcomes are likely to be. in other cases, the research process itself is not informed by politics but the use made of research findings (or not) may ultimately be decided through a political process. researchers need to be careful in criticising this outcome as research findings are not valueneutral. implementing new policies or policy reforms will likely have distributional impacts by affecting various interests differently and may also challenge underlying values. while researchers can play an important role in highlighting the way power relations can affect the outcomes of political decision-making, it is ultimately the role of policymakers to weigh up and evaluate the trade-offs and make the final decision. in conclusion, it is interesting to compare the narrative in this paper with one i wrote 25 years previously assessing the policy interests of agricultural economists at that time based on contributions to the 1996 congress of the european association of agricultural economists (matthews 1997). it is striking how many of the themes mentioned in that paper are also highlighted here. the concluding paper to that congress by claus-hennig hanf was entitled “agricultural economics in europe: a thriving science for a shrinking sector?” (hanf 1997). hanf poses the question how agricultural economics can maintain its relevance as a discipline (or quasi-discipline, to use the term suggested by fresco et al. (2021) when they pose a similar question) when the economic size of its focus of enquiry diminishes in importance. hanf was writing at a time when in his own country and elsewhere there was great pressure to close and amalgamate university departments of agricultural economics. he noted that the profession had maintained its numerical strength partly because the cap gave rise to an almost inexhaustible supply of research projects, but also because agricultural economists enlarged their research domain to tackle new research problems. that these continue to be successful survival strategies is supported by the narrative in this paper. however, he also identified several pitfalls, including a potential loss of identity, a tendency to apply a narrow toolbox of theories and methods based on a strict neoclassical paradigm, and the dangers of losing the familiarity with the natural and technical environment in these newer research fields that has been the hallmark of agricultural economists in the past. with these strictures in mind, it seems reasonable to suggest that agricultural policy research can maintain its relevance to practical agricultural policy if it continues to take up issues of societal concern, maintains its independence, encourages methodological innovation and supports a variety of methodological approaches, collaborates closely with other disciplines, while building on its long tradition of empirical analysis and working with data. acknowledgements alan matthews is professor emeritus of european agricultural policy at trinity college dublin, ireland. the author would like to acknowledge thoughtful comments on an earlier draft from jeroen candel, emil erjavec and donato romano. references achterbosch, t.j., a. getz escudero, j.d. dengerink, and s. van berkum. 2019. synthesis of existing food systems studies and research projects in europe: independent expert report. independent expert report for the european commission directorate-general for research 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contribution of research to agricultural policy in europe alan matthews drinking covid-19 away: wine consumption during the first lockdown in italy giulia gastaldello*, daniele mozzato, luca rossetto estimating a global maidads demand system considering demography, climate and norms wolfgang britz the evolution of organic market between third-party certification and participatory guarantee systems gianluca iannucci1, giovanna sacchi2,* bio -based and a ppl ied economics bae bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-12558 copyright: © 2022 s. nenci, i. fusacchia, a. giunta, p. montalbano, c. pietrobelli. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: s. nenci, i. fusacchia, a. giunta, p. montalbano, c. pietrobelli (2022). mapping global value chain participation and positioning in agriculture and food: stylised facts, empirical evidence and critical issues. bio-based and applied economics 11(2): 93-121. doi: 10.36253/bae-12558 received: january 8, 2022 accepted: march 2, 2022 published: august 30, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: simone cerroni, valentina raimondi. orcid sn: 0000-0003-3283-2269 if: 0000-0002-1965-9257 ag: 0000-0002-1562-7792 pm: 0000-0003-1397-8537 cp: 0000-0002-3989-8642 keynote speech of the 10th aieaa conference mapping global value chain participation and positioning in agriculture and food: stylised facts, empirical evidence and critical issues silvia nenci1, ilaria fusacchia1,2, anna giunta1,2, pierluigi montalbano3, carlo pietrobelli1,4 1 roma tre university 2 rossi-doria centre 3 sapienza university 4 united nations university unu-merit * corresponding author. e-mail: silvia.nenci@uniroma3.it abstract. this paper aims to overview the recent body of empirical work on the importance of global value chains (gvcs) in international production and trade. we begin by reviewing different approaches and levels of gvc analysis. we then consider developments in methods and data. focusing on the agriculture and food sector, we present a map of gvc measures at the country and sectoral level computed using trade in value added data to allow researchers to better assess the countries’ engagement in gvcs. we also apply this data to show some stylized facts on gvc participation and positioning in agriculture and food and provide empirical evidence of the economic impact of the gvcs on these sectors. we conclude with some critical issues and speculative thoughts regarding the future of gvcs. keywords: global value chains, participation and positioning, trade in value added, agricultural and food sectors, survey. jel codes: f14, o50, q17. 1. the importance of global value chains: past and present over the past twenty years, the term “global value chains” (gvcs) has become increasingly popular among economists, particularly those in the area of international trade. gvcs can be defined as the full range of activities – dispersed across different countries – that firms and workers engage in to bring a product from its conception to its end use (see gereffi and fernandez-stark, 2011). starting from the early 1990s, the world-wide economy experienced a radical transformation through a significant fragmentation in the production of goods and services and a deeper international division of labour, resulting in larger returns from specialization. this new era has been driven by at least two main factors. first, the information and communication technology (ict) revolution facilitated the global outsourcing and offshoring of manufacturing activities. second, the sharp drop in effective http://creativecommons.org/licenses/by/4.0/legalcode 94 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 silvia nenci et al. trade costs, which has been driven both by a significant increase in the rate of reduction of man-made trade barriers and faster methods of shipping goods (antràs and chor, 2021). the dramatic fragmentation in the organization of world production raised by the gvcs has also been witnessed by a sharp increase in the trade of intermediate goods, accounting for more than 50 percent of world trade in the last years and increased tenfold in value over the last thirty years (figure 1), thus justifying the growing interest of trade economists in understanding global production arrangements. the evolution of gvc is having a deep impact in many sectors such as resource-based commodities, apparel, electronics, tourism and business service outsourcing, with significant implications in terms of global trade, production and employment, for both developed and developing countries. for industrialized economies, gvcs ensure access to lower priced inputs, wider variety and economies of scale. for developing economies, gvcs represent a valid shortcut to industrialization since they allow them to join existing supply chains instead of building them. moreover, entering in a proven supply chain eliminates the need to acquire a comparative advantage in a broad range of production stages domestically. the specialized literature analyses the impact of gvcs by distinguishing between economic upgrading, usually defined in terms of efficiency of the production process or characteristics of the product or activities performed (humphrey and schmitz, 2002), and social upgrading often referred to outcomes related to employment and pay, gender and the environment (milberg and winkler, 2010). as for the former, the literature has pointed out a positive relationship between gvc participation and productivity both in developed and developing countries (kummritz, 2016; montalbano and nenci, 2018; constantinescu et al., 2019). offshoring and gvcs can lead to significant gains in productivity through numerous channels: the availability of broader input varieties (halpern et al., 2015); finer division of labour across countries, even by inducing firms to specialize in core tasks (grossman et al., 2008; criscuolo et al., 2017; antràs, 2020); greater competition; learning-by-doing externalities and technology spillovers (baldwin et al., 2014; piermartini et al., 2014; benz et al., 2014). even though some of these channels are related to traditional trade as well, welfare benefits can be larger when considering a multiple-sector framework and the input-output linkages (caliendo et al., 2015). this evidence shows that gvcs are a key factor in increasing wages and boosting development and long-term growth. however, the relationship between gvc participation and inclusive development does not fall automatically from the premises. as for social upgrading, the specialized literature also highlights the positive effects of gvcs on employment. this 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 m ill io ns $ exports imports figure 1. trend in international trade in intermediate goods (millions of usd). notes: values of intermediate goods for all the industries included in the bec broad economic categories rev.5 classification. source: authors’ elaboration on un-comtrade data using bec classification. 95 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 mapping global value chain participation and positioning in agriculture and food may vary depending on the industry characteristics and the relative engagement in the value chain of the activities that are not outsourced. relevant employment effects have been observed also in the agro-food sector in southeast asia, with countries having the highest shares of workforce associated with agro-food gvcs (lopez-gonzalez, 2016). a strand of the literature has also explored the beneficial effects of trade in intermediate inputs on a country’s innovation performance, through the transmission of technology and research and development spillovers (tajoli and felice, 2018; fracasso and marzetti, 2015; piermartini and rubinova, 2014). turning to sectoral value chains, studies on agrifood chain are not new (davis and goldberg, 1957), and the literature embraces some complementary traditions: commodity chain analysis focuses on worldwide temporal and spatial relations (hopkins and wallerstein, 1986); filière analysis focuses on national political regulation and institutions (lauret, 1983), whereas value chain analysis focuses on international business organisation and profitability (porter, 1990). there have also been several applications of industrial organisation to economic and policy issues in the food and agricultural sector as well as analysis of the interaction between industrial organisation and policy in a trade setting: see (among many) karp and jeffrey (1993), scoppola (2007), mccorriston and sheldon (2011), and sexton (2012). much empirical evidence on agri-food gvcs is largely focused on capturing the impact on national economies through an analysis of case studies on the globally integrated value chain at the product level. the increasing importance of global agricultural trade registered during the past three decades comes with changes in the way gvcs are organised, with increasing levels of vertical coordination, upgrading of the supply base and the increased importance of large multinational food companies (swinnen and maertens, 2007; mccullough et al., 2008; scoppola, 2021). a relatively small number of companies now organise the global supply of food and link small producers in developed or developing countries to consumers all over the world (gereffi and lee, 2009). this is generally referred to as agro-food gvcs (humphrey and memedovic, 2006; liapis and tsigas, 2014; greenville et al., 2016; balié et al., 2019). the review of the huge empirical literature on the different dimensions of gvcs is beyond the scope of this article. this survey aims to deepen our understanding of gvcs by measuring and mapping them mainly from a macro perspective, using international trade data, with special attention to the agriculture and food sectors, a subject that, as far as we know, has not yet been summarized. 2. different approaches and levels of analysis of gvcs the high complexity and the different scales of analysis make it impossible to define, measure and map gvcs in a single way. this phenomenon of organizationally fragmented international production has been subject to investigation in a wide range of academic disciplines, including economic sociology, international economics, economic geography, international political economy, supply chain management and international business (see kano et al., 2020). therefore, the economic literature has evolved along different strands of research, using different approaches and levels of analysis. 2.1 gvcs as a multidisciplinary topic : development, geography, innovation the development literature, beyond the early discussion on the role of the state in modern capitalism, has been widely influenced by the emergence of gvcs. specifically, the debate mainly centred on the perspectives that gvcs would open to firms from developing countries to gain access to markets, to access knowledge and technology from abroad, and to “capture value” and part of the rents generated in the process (davis et al., 2018; kaplinsky, 2019). the challenge for local firms would be to “upgrade” by innovating to improve their products and processes, but also notably to “functionally upgrade” by entering value chain segments offering larger shares of value added. more generally, local firms’ struggle would be to enter value chains niches with a stronger potential for learning and innovation, and for strengthening their technological and innovation capabilities (morrison et al., 2008). sometimes contrary to the interests of local firms, though sometimes fostering them, lead firms often have an interest to protect, appropriate, and create rents in the process of international production (davis et al., 2018). pathbreaking research put the concept of value chain governance at the core of the analysis and developed a theory that generates five different forms of gvc governance: hierarchy, captive, relational, modular, and market, which range from high to low levels of explicit coordination and power asymmetry (gereffi et al., 2005). three variables would determine how gvcs are governed, and these are: the complexity of transactions, the ability to codify transactions, and the capabilities in the supplybase. this theory, which itself draws on other streams of literature, like transaction costs economics, production networks, and technological capability and firm-level learning, has generated an infinite number of studies 96 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 silvia nenci et al. aiming at measuring and testing it with quantitative and qualitative approaches (e.g. brancati et al., 2021). importantly, from all these approaches it has gradually emerged the idea that gvcs are not only a trade phenomenon. given the variety of organizational arrangements prevailing in gvcs, and the inherent remarkable power asymmetries between lead firms and their different tiers of suppliers, the “relational dimension” of gvcs has gained utmost importance (world bank, 2020). therefore, the focus necessarily shifts away from the mere allocation of value added across countries through anonymous exchanges of goods and services. in contrast, the characteristics of the agents participating in a gvc become crucial, and they necessarily influence the distribution of benefits and rents along the value chain. the introduction of such a relational concept of gvcs, echoed by the world bank but originally developed by economic sociologists, and international political economists like gereffi, sturgeon, ponte and others, puts on a central stage a variety of themes, like for example the nature of the lead firms, i.e. multinational firms and foreign buyers and traders, and that of their suppliers, the institutional factors shaping the inter-firm relations and the location of global production, as well as the institutions affecting the decisions to invest in learning and innovation and their effectiveness. moreover, sometimes this literature has also crossed over a tradition of studies in economic geography that look at how the territorial context may influence enterprise behavior and performance. in many instances, local clusters of firms interact with lead firms and their first-tier suppliers, generating the need to understand how the local and the global dimensions of value chains influence and condition each other. thus, some authors studied the interface between enterprise clusters and gvcs, discovering their deep and mutually-influencing relationships (bathelt et al., 2004; de marchi et al., 2018; giuliani et al., 2005; humphrey and schmitz, 2002; pietrobelli and rabellotti, 2007). more recently, in the effort to understand the evolution and the dynamics of gvcs and foresee the potential they offer to local firms and countries, the issue of innovation in gvcs has also been studied extensively. it has been shown how gvcs may act as conduits of knowledge, channels for technology transfer, and in the end, opportunities to learn and innovate (lema, pietrobelli and rabellotti, 2019). a structured effort to understand the relationship between gvcs and national innovation systems has been attempted with various methods (fagerberg et al., 2018, pietrobelli and rabellotti, 2011). some authors concluded that a coevolution between gvcs and innovation systems would be constantly at play, though with remarkable sector-specific varieties (lema et al., 2018, 2019 and 2021). finally, the gvc approach is beginning to be employed also for the analysis of natural resource-based sectors, due to the remarkable restructuring and changes that these sectors are undergoing as a result of technological changes and the growing relevance of local communities and environmental considerations (kaplinsky and morris, 2016; pietrobelli et al., 2018; katz and pietrobelli, 2018). 2.2 firms level analysis although it is undeniable that, in the real world, it is not countries or industries that participate in gvcs, but rather firms, the analysis of gvc at the firm-level is still at an “infant stage” (antràs, 2021) and fall well behind the measurements’ advances put forward by the literature based on country-sector level data (see section 3). this is mainly because of the partial lack of good quality firm-level data, not to mention its scarce accessibility and the lack of standardisation of the different (proprietary) firm-level sources. indeed, to accomplish a fully integrated picture of the back-and-forth features of firms’ global linkages, a vast array of countries’ harmonized information is needed, which comes from different sources, such as custom-level data together with census-level information. for instance, in order to build up a firm-level counterpart of the country–industry gvc index of backward participation, one has to collect, for the exporting firm, information on: the percentage value of imported intermediate goods on the total value of intermediate goods used in the production; country of imports; the exports’ destination. and such a calculation, already difficult to achieve with the available data, will only provide information on a single portion of the trade in value added at the firm level1. going beyond the mere calculation of a backward participation index, census-level information would then be needed to link a firm’s trade behaviour to several dimensions of firms’ heterogeneity. notwithstanding the relative lack of good quality data, in recent years, a few papers, mostly relying on general firm-level surveys, have tried to carry out empirical analyses to investigate the firms’ involvement in gvcs and their impact on performance. a list of recent papers and firm-level datasets applied for gvcs empirical analysis is reported in the appendix. in order to provide a brief overview of the most recent findings, we will focus here on contributions 1 on the virtual impossibility of coming up with analogous firm-level measures of forward participation, see antràs, 2021. 97 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 mapping global value chain participation and positioning in agriculture and food related to the analysis of gvc participation and positioning at the firm level2 (the analysis at the countrysector level is presented in section 3). these firms’ level analyses still lack a unified framework and unit of analysis. this is due to the fact that, in general, studies rely on different datasets (mainly firms’ balance sheets, surveys, etc.), each containing different information. as a result, there are currently several measures of the gvc participation index of firms, without a unified framework. among the few studies, it is useful to distinguish between the ones ending up with a firm’s participation index similar to the one envisaged by the input-output based literature (see section 3) from those whose indexes rely on firms’ internationalization modes. in the first category, veuglers et al. (2013) use as a proxy of a firm’s participation the percentage of imported intermediates over total inputs. the second typology of studies focuses on the firms’ internationalization operation modes as a proxy of gvc involvement. although ending up with different proxies of the gvc participation index, those studies share a certain degree of common ground based on the assumption that a two-way trader firm can be univocally defined as a firm participating in gvcs. giunta et al. (2021), following on from veugelers et al. (2013), divide firms’ participation in gvcs into two categories: i) single forward mode, when firms are only exporters of intermediates; ii) dual-mode when firms are both importers of materials and services and exporters of intermediates or final goods. similarly, agostino et al. (2016) take into account the variety of modes of internationalization associated with the operation of gvcs, such as: exports only, intermediate goods imports only, both exports and imports (two-way trade) and international production. baldwin and yan (2014), investigating canadian manufacturing firms, consider as involved in gvcs those firms which engage simultaneously in importing and exporting activities. del prete et al. (2017) analyse a panel of manufacturing firms in egypt and morocco. in their empirical investigation, firms involved in gvc activities are international traders that received an internationally recognized quality certification. brancati et al. (2017) and agostino et al. (2020), by using a representative sample of italian industrial firms, investigate the gvc participation index by looking at exporters of semi-finished goods/components and two-way traders. moreover, they also infer firm gvc participation by using a question in the survey that asks about the existence of “long-lasting and significant rela2 we do not overview here the vast literature based on case studies since their findings are not immediately comparable with the ones based on representative samples (for an overview of the literature based on case studies, see ponte, 2019). tionships with foreign companies” (brancati et al., 2017). giovannetti et al. (2015) rely on this same question (i.e., a direct answer from a firm’s representative) to proxy the manufacturing firm’s participation in gvcs. likewise, giovannetti and marvasi (2016), in their investigation of firms operating in the italian food industry. despite the variety of gvc participation measures adopted in the firm-level applied literature, a striking regularity of results emerges: firms’ participation in gvc leads to productivity gains activated by several channels. exporting allows a firm to exploit scale economies, acquire new technologies abroad and learn by exporting. furthermore, other benefits may accrue to firms active in gvcs through imports of foreign inputs: cost saving, technology transfer, higher input quality and possible complementarities with domestic inputs. two-way trading may have the additional advantage of exploiting sunk cost complementarity and other positive interactions between export and import activities. finally, as is highlighted by the literature on firms’ internationalization, productivity gains are ordered: the more advanced the firm’s mode of gvc participation, the greater the productivity premium. the literature on firms’ positioning in gvcs distinguishes between final firms those selling their output on the end market – and supplier firms – those selling their intermediate products to other firms3 (accetturo et al., 2011; agostino et al., 2015, 2016; veugelers et al., 2013; giovannetti et al., 2015). why do these different organizational firms’ modes (final and supplier) need to be taken into account? there are at least three important reasons: i) suppliers, mostly small and medium enterprises, constitute the bulk of the productive system in the large majority of countries; ii) the impact of shocks differs according to the firms’ positioning in the gvcs (altomonte di mauro et al., 2012; békés et al., 2011; accetturo and giunta, 2016); iii) final and supplier firms substantially differ in terms of economic performance as well as the benefits that can be obtained by operating in the gvcs. despite its relevance, the latter issue has been brought to the fore by very few papers, mainly because of the relative lack of microdata, making it difficult to carry out a proper investigation. kimura (2002), razzolini and vannoni (2011), veugelers et al., (2013) document a large profitability and productivity gap between supplier and final firms. yet, some researchers have highlighted the heterogeneous behaviour and performance of supplier firms (accetturo et al., 2011). among 3 the exact definition used is: suppliers are firms producing to order for other firms. these firms are positioned in gvcs when they produce to order for other firms located abroad. 98 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 silvia nenci et al. these, agostino et al. (2015), who, based on a representative sample of italian manufacturing firms, confirm that, on average, gvc supplier firms are less productive than final firms. however, since the “ability” of supplier firms increases, their productivity shortfall diminishes. in fact, for those who succeed in both exporting and innovating, agostino et al. (2015) prove there is no statistically significant difference in productivity between suppliers and final firms. finally, a “gvc effect” in terms of superior technical efficiency or productivity can also be traced by comparing suppliers operating on local markets vis a vis with suppliers operating in the gvcs (agostino et al., 2020; veugelers et al., 2013). 3. country and sectoral gvcs analysis parallel to the firm analysis of gvcs, international economists developed various approaches to map and measure these chains at the country and sectoral level by relying on different methods and data sources. the empirical literature in this field mainly follows three approaches that provide different points of view on the quantification of gvcs and present both strengths and caveats in terms of complexity, accuracy and coverage (amador and cabral, 2016).4 the first one compares international trade statistics of parts and components with trade in final products (see the seminal works of yeats, 1998; ng and yeats, 1999; athukorala, 2005; and gaulier et al., 2007, among others). the second approach looks at the customs statistics on processing trade (see the works on the us processing trade by feenstra et al., 2000; clark, 2006; and swenson, 2005; those on eu processing trade by görg, 2000; baldone et al., 2001, 2007; helg and tajoli, 2005; egger and egger, 2005; those on china by lemoine and ünal kesenci, 2004; and xing, 2012; and recently kee et al., 2016; koopman et al., 2012; jiang, 2021; luck, 2019). the third method considers classical input-output (i-o) tables, sometimes complemented with import penetration statistics computed from trade data. using these i-o matrices, feenstra and hanson (1996) developed the first measure of foreign content of domestic production (computed as the share of imported inputs in production or total inputs, often used as a measure of outsourcing). this measure has been adopted in many subsequent works, such as campa and goldberg (1997), hijzen (2005), egger et al. (2001), egger and egger (2003), and feenstra and jensen (2012). exploiting the same data, hummels et al., 2001 formulated the second measure of fragmentation, which focus4 for details of the three types of data, see the survey by amador and cabral (2016). es on the direct and indirect import content of exports, labelled “vertical specialization” that has been applied or updated in other studies (see among others chen et al., 2005; zhang and sun, 2007; chen and chang, 2006; amador and cabral, 2009). however, traditional i-o tables by themselves are no longer able to capture the complexity of the fragmentation and the mechanism ruling trade in intermediate inputs. with the target of tracing value-added trade flows across countries, a strand of work which has recently become very popular has therefore combined information from custom offices with national input-output tables to construct global i-o tables (see the works of hummels et al., 2001; johnson and noguera, 2012; miroudot and ragousssis, 2009; koopman et al., 2010, 2014; foster mc gregor and stehrer, 2013). since the tables contain information on supply–use relations between industries and across countries, we can identify the vertical structure of international production sharing and measure cross-border value flows for a country or region. since the flows of goods and services within the global production chains are not always reflected in conventional international trade measures, many initiatives and efforts have recently been addressed to measure international fragmentation using trade in value added. the appendix shows the main available databases. 3.1 decomposition methodologies and trade in value added components in parallel, new methodologies have also been developed to exploit data from multi-region input-output (mrio) tables. these methodologies decompose gross trade flows in different value-added components and allow new gvc indicators to be computed. one of the most widely used decomposition methodologies is that proposed by koopman, wang and wei (kww) (2014), who fully decompose gross exports into various sources of value added and connect official gross statistics to value-added measures of trade. specifically, kww (2014) break gross exports down into nine different components of domestic value added (domestic value embedded in a country’s exports) and foreign value added (foreign value embedded in a country’s exports) plus double-counted items (that arise when intermediate goods cross borders multiple times). the result is a complete picture of the value-added generation process in which various preceding formulas for measuring valueadded trade are systematically integrated into a single accounting framework.5 this method encompasses most 5 for technical details, see koopman, wang and wei (2010 and 2014). 99 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 mapping global value chain participation and positioning in agriculture and food of the methodologies previously proposed in the literature (e.g. hummels et al., 2001; daudin et al., 2011; and johnson and noguera, 2012). a second methodology is that developed by borin and mancini (bm) (2015, 2019). they extend the kww (2014) methodology providing exhaustive and rigorous value-added decompositions of exports at the aggregate, bilateral and sectoral levels which are consistent with the kww framework and overcome shortcomings that affect the kww decomposition and other previous attempts to obtain a bilateral counterpart (such as wang et al, 2013).6 using the koopman et al. (2014) methodology extended by bm (2015, 2019), two key value added components of gross exports are traditionally selected to provide measures of country and sectoral gvc participation: the indirect domestic value added (dvx), that is, the share of domestic value added in intermediate goods further re-exported by the partner country. it measures the joint participation of the trade partners in a gvc since it contains the exporter’s value added of a specific sector that passes through the direct importer for a (or some) stage(s) of production before it reaches third countries. more specifically, it captures the contribution of the domestic country to the exports of other countries; the foreign value added (fva) used in the production of a country’s exports, that is, the share of value added provided by intermediate inputs imported from abroad and then exported in the form of final or intermediate goods. it measures the contribution of the foreign country to the country’s exports. 3.2 gvc participation and positioning indicators7 an important question raised in the gvc empirical literature is to what extent single countries and sectors are involved in international production networks. the hummels et al. (2001) measure of “vertical specialization” (the vs measure), mentioned above, is probably one of the first and most popular measures of participation of a country in the phases of international 6 in particular, borin and mancini (2015, 2019) provide proper definitions for some components that are incorrectly specified by kww: i) the domestic value added that is directly (and indirectly) absorbed by the final demand of the importing country; ii) the foreign value added in exports; iii) the double counted items produced abroad. they also overcome the main problems that make imprecise and at least partially incorrect the value added decompositions of bilateral exports previously proposed in the literature. 7 this paragraph is largely based on nenci (2020). production chains. however, this is a partial measure of participation in global value chains since it only considers the backward linkages (i.e. it measures the import content of a country’s exports). therefore, they also suggest considering the exports of intermediate products later processed and re-exported as the vs1 measure. following the seminal article of hummels et al. (2001), various measures of a country’s integration in international production networks have been proposed. using some of the trade in value added components of their decomposition, kww (2010) propose one of the most widely used indicators of gvc participation in the field literature. they calculate gvc participation by using the trade in value added components mentioned above: the foreign value added (fva) component and the indirect domestic value added (dvx) component. more specifically, fva is referred to as a measure of “backward participation”, given that it measures imported intermediate inputs that are used to generate output for export. dvx captures the contribution of the domestic sector to the exports of other countries and indicates the extent of involvement in gvc for relatively upstream industries. therefore, it can be considered as a measure of “forward gvc participation”. by expressing both measures as a percentage of exports, the formula for gvc participation is as follows: 𝐺𝐺𝐺𝐺𝐺𝐺 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 = 𝐹𝐹𝐺𝐺𝐹𝐹 + 𝐷𝐷𝐺𝐺𝐷𝐷 𝐺𝐺𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺 𝐸𝐸𝐸𝐸𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺 the larger the ratio, the greater the intensity of involvement of a particular country (or sector) in gvcs. other studies have measured a country’s forward gvc participation by identifying the export components that are later re-exported by the direct importer (see, among others, rahman and zhao, 2013; and ahmed et al., 2017). however, these contributions rely on the kww decomposition of gross exports. as discussed, this methodology does not properly allocate countries’ exports between the share that is directly absorbed by importers and the one that is re-exported abroad. the resulting measures of gvc participation are thus imprecise. borin and mancini (2019) calculated their measure of overall gvc participation. this is given by the sum of a ‘backward’ component, corresponding to the vs index, and a ‘forward’ component, the vs1 indicator suggested by hummels et al. (2001): gvc overall participation = gvcbackward + gvcforward =vs + vs1 100 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 silvia nenci et al. in this work, we refer to this indicator to measure and map gvcs in the agriculture and food sectors. recently, a strand of the international trade literature has developed measures of the positioning of countries and industries in gvcs (see fally, 2012; antràs et al., 2012; antràs and chor, 2013; fally and hillberry, 2015; alfaro et al., 2019; miller and temurshoev, 2017; wang et al., 2017). using the global input-output tables, with information on the various entries, it is now possible to compute the upstreamness or downstreamness of specific industries and countries. to do this, a common approach is to consider the extent to which a country-industry pair sells its output for final use to consumers worldwide or sells intermediate inputs to other producing sectors in the world. a sector that sells disproportionately to final consumers would appear to be downstream in value chains, whereas a sector that sells little to final consumers is more likely to be upstream in value chains. following this approach, antràs and chor (2018) present the two gvc positioning measures most popular in the literature. the first indicator is a measure of distance or upstreamness of a production sector from final demand which was developed by fally (2012), antràs et al. (2012) and antràs and chor (2013).8 fally’s model, as well as the variation proposed by antràs et al. (2012), capture the average number of production stages by pegging the endpoint of the sequence at final consumption, which enables us to measure the distance to final demand of a product along the production chains. more specifically, this measure aggregates information on the extent to which “an industry in a given country produces goods that are sold directly to final consumers or that are sold to other sectors that themselves sell disproportionately to final consumers. a relatively upstream sector is thus one that sells a small share of its output to final consumers and sells disproportionately to other sectors that themselves sell relatively little to final consumers” (antràs and chor, 2018). the second measure, originally proposed by fally (2012), is based on a country-industry pair’s use of intermediate inputs and primary factors of production. it captures the distance or downstreamness of a given sector from the economy’s primary factors of production (or sources of value-added). according to this measure, an industry in each country is downstream if its production process embodies a larger amount of intermediate inputs relative to its use of primary factors of production. conversely, if an industry relies disproportionately on val8 although the arguments used to develop the index differ in fally (2012) and antràs and chor (2013), antràs et al. (2012) emphasize that the resulting indexes are equivalent. ue-added from primary factors of production, then this industry is relatively upstream. in this work, we adopt the first measure – the upstreamness indicator (fally, 2012; antràs et al., 2012; and antràs and chor, 2013) – computed by nenci (2020) at the country-industry level for the “agriculture” and “food & beverages” sectors to present some stylized facts for the countries in the eora dataset for the period 1995-2015. 4. mapping gvc participation and positioning in agriculture and food: some stylized facts focusing on the global i-o tables and trade in value added data, this section aims to show some recent facts and trends on gvcs using the gvc participation indicator and the upstreamness positioning indicator for the “agriculture” and “food and beverages” sectors. to do this, we refer to the indicators computed by nenci (2020) from eora data at the country-industry level relying on borin and mancini (2019)’s extension of the koopman et al. (2014) methodology. these indicators are available for 190 countries in the period 1990-2015.9 4.1 mapping gvc participation using nenci (2020)’s data, dellink et al. (2020) show how gvc participation between 1995 and 2008 is globally around 30-35 percent for both agriculture and food and beverages (figure 2), although with significant variations across countries (highlighted by the shaded areas in the figure). however, further integration essentially stalled in the subsequent period. this trend is similar for both sectors. this may be because common factors driving gvc participation dominate sectoral and structural change effects. hence, although agricultural commodities are perhaps less complex than manufacturing products, fragmentation of the associated value chains has also occurred in the agricultural sectors. this has important implications for developing and less developed countries: although they cannot compete internationally in the manufacturing sectors of final goods, they can still participate in gvcs and increase exports. the effects of the 2008 crisis are evident in both sectors. these effects were also widespread across regions: 9 due to some inconsistencies in the eora data, the republic of the sudan and the republic of zimbabwe are not included in the empirical analysis. these inconsistences are attributable to missing, incomplete, and conflicting raw data that can lead to distorted (i.e., not consistent and unbalanced) io tables for a country in a given year. 101 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 mapping global value chain participation and positioning in agriculture and food until 2008, about 7 percent of the countries in the database observed a decline in gvc participation. these are primarily economies affected by war, droughts and other major disturbances. after 2008, only 8 percent of the economies – a widely varying group of mostly relatively small countries – further continued their integration in the global economy. the long-term increase in gvc participation comes with the overall rise of gross exports of agricultural and food commodities. figure 3 shows the composition of gross exports divided into: backward-linked gvc exports, that is the sum of fva across countries; forward-linked gvc exports, which are exports that will later be re-exported, aggregated across countries; non-gvc exports, which are exports that do not flow through gvcs but are absorbed in the destination country. the sum of the three components (plus some pure double-counting) equals gross exports. while roughly two-thirds of the export value is not part of a gvc, both backward and forward linkages contribute significantly to the export value. global exports of food and beverages are roughly twice as large as those of agricultural commodities and, in absolute terms, the rapid increase in food exports after 2002 is remarkable (see figure 3, panel b). as expected, gvc linkages in agriculture are mostly forward linked, since agricultural products serve as basic ingredients in other production processes. food and beverages are much more in the middle and at the end of a value chain and include the processing of agricultural inputs. the backward linkages in food and beverages are mainly imports from agricultural commodities. in contrast, the backward linkages of agriculture refer to imports of inputs for agricultural production and are linked to international trade in fertilizers and seeds as well as to the increased servitization of the economy. the forward linkages in food and beverages are mainly exports by the sector itself – agricultural commodities are lightly processed in one country, then re-exported and further processed and distributed. however, other downstream sectors embed value added created in the food and beverage industry, such as, for example, sugar in pharmaceuticals and cosmetics (dellink et al., 2020). finally, it is important to underline that the exports of agriculture and food sectors can also stimulate value added creation in other sectors, just as agriculture and food value added can be part of the exports of another downstream sector. in both the agriculture and food sectors, the biggest share of sectoral fva is provided by services (42 percent and 38 percent in 2015, respectively, see figure 4). this means that any boost to gvc participation in the two sectors leads to increased valueadded creation in some foreign services sectors. in agriculture, a significant share of foreign inputs is delivered by chemicals and raw materials – this mainly reflects the globalization of the seeds market. in the food and beverages industry, the second largest fva input share is agricultural commodities (20 percent). the share of manufacturing in fva is also sizable in both sectors (this includes machinery). finally, while foreign inputs from the food sector into agriculture are small, intra-sectoral trade in the food and beverages industry is more substantial (dellink et al., 2020). moving on to gvc analysis at the country level, figure 5 reports the gvc participation indicators for the 0% 10% 20% 30% 40% 50% 60% 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 panel a. agriculture range of country values global gvc participation rate 0% 10% 20% 30% 40% 50% 60% 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 panel b. food & beverages range of country values global gvc participation rate figure 2. evolution of global gvc participation rates in agriculture and food sectors. notes: the shaded areas show the range of country values. source: dellink, dervisholli and nenci (2020) based on nenci (2020)’s data. 102 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 silvia nenci et al. agricultural sector for each country in the world in 2015. th e european countries present, on average, the highest rate of gvc participation (about 40–45 percent of its total exports, on average, considering both the foreign value added and its domestic value added content embedded in third country exports). despite low trade shares at the global level, the african region turns out to be deeply involved in agriculture gvc participation too (about 37 percent, on average). th is is higher than the average values for america and asia and is consistent with the relative importance of the african continent in the global agri-food value chains highlighted by the literature in the fi eld. however, we can also detect country heterogeneity in both areas, with estonia and latvia showing the highest share of gvc related trade in agriculture in europe and the democratic republic of congo in africa. conversely, the commonwealth of independent states (csi) shows the lowest shares (except for belarus). figure 6 shows a similar picture for the gvc participation indicators for the food and beverages sector. in this case, apart from the usual heterogeneity by country, the african and the european continent share a similar degree of involvement in gvc trade (about 40 percent of their respective total exports). 0 200 400 600 800 1,000 1,200 1995 2000 2005 2010 2015 bl n u sd panel a. agriculture backward-linked gvc exports forward-linked gvc exports non-gvc exports 0 200 400 600 800 1,000 1,200 1995 2000 2005 2010 2015 bl n u sd panel b. food & beverages backward-linked gvc exports forward-linked gvc exports non-gvc exports figure 3. composition of gross exports in agriculture and food sectors. note: values calculated at the country level and then aggregated. source: dellink, dervisholli and nenci (2020) based on nenci (2020)’s data. agricultureagriculture 12% agricultureagriculture food and food and food and beveragesbeveragesbeverages 5% chemicals chemicals chemicals and raw and raw materialsmaterials 22% services services services services 42% other other manufacturingmanufacturingmanufacturing 19% agricultureagriculture agriculture agriculture 20% food and beverages 14% chemicals and raw materials 12% services 38% other manufacturing 16% food and beverages figure 4. origin of foreign value added in agriculture and food sectors (2015). source: authors’ elaboration based on nenci (2020)’s data 103 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 mapping global value chain participation and positioning in agriculture and food 4.2 mapping gvc positioning another valuable way to analyse and map gvcs is understanding the positioning of countries and/or industries within gvcs. at a descriptive level, these indicators provide information on the specialization of a country in relatively upstream activities or ones that are more proximate to fi nal demand. figure 7 shows the evolution of the upstreamness indicator at the sectoral level. unsurprisingly, agriculture has a higher score on upstreamness (measuring the distance of the sector from fi nal demand in terms of the number of production stages) than the food and beverages sector. while the median for agriculture is positioned more than 2.7 stages upstream of fi nal demand, the one for food and beverages is constantly two stages lower. th is positioning indicator closely depends on the length of the chains: between 2000 and 2008, the upstreamness of the two sectors rose modestly but steadily, suggesting an increase in fragmentation in production. moving to an analysis by region-country, figure 8 shows the degree of countries’ upstreamness for the agriculture sector in 2015. in this case, africa, america and europe share the same average degree of upstreamness (about 3 stages of production from the fi nal consumers), which is above the average world level of 2.25. at the country level, the most upstream countries in europe are serbia and bosnia-herzegovina (their agriculture production is, on average, concentrated on activities that are up to 5 stages away from the fi nal consumers), whereas in africa a pick of about 4 stages of production is registered for the agricultural sector in ethiopia. figure 9 shows the degree of countries’ upstreamness for the food and beverage sector. as expected, the average degree of upstreamness for africa and europe is lower than in the agricultural sector (less than 2 stages of production from fi nal consumers). at the country level, the food and beverage sector also shows a lower degree of heterogeneity. the most upstream country figure 5. gvc participation for the agricultural sector by country (2015). source: montalbano and nenci (2022). figure 6. gvc participation for the food and beverages sector by country (2015). source: montalbano and nenci (2022). 1,5 1,7 1,9 2,1 2,3 2,5 2,7 2,9 3,1 1995 2000 2005 2010 2015 agriculture food & beverages figure 7. gvc upstreamness at global level. source: authors’ elaboration based on nenci (2020)’s data. figure 8. upstreamness of the agricultural sector by country (2015). source: montalbano and nenci (2022) based on nenci (2020)’s data. 104 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 silvia nenci et al. in europe is moldova together with the small european states (about 4 stages away from fi nal consumers), whereas in africa again, about 4 stages of production away from fi nal consumers are registered in ethiopia. 5. impacts of gvcs on agriculture and food performance: empirical evidence th e empirical analyses on the impacts of agricultural and food gvcs have traditionally relied on case studies (salvatici and nenci, 2017). th anks to the availability of mrio tables and the possibility to compute a new set of gvc indicators based on trade in value added data, scholars have recently acquired the possibility to benefi t from true global analyses. th ese global analyses look at diff erent kinds of eff ects. a preliminary distinction is between economic upgrading, usually defi ned in terms of effi ciency of the production process or characteristics of the product or activities performed (humphrey and schmitz, 2002), and social upgrading oft en referred to improvements in the rights and entitlements of workers as social actors, as anchored in the ilo decent work framework, and/or enhancement of outcomes related to employment and pay, gender and the environment (milberg and winkler, 2010; barrientos et al., 2011; gereffi and lee, 2016). as for economic upgrading, some studies have analysed the role of intermediate goods in generating a positive impact on the total factor productivity of industries (see, inter alia, bas and strauss-kahn, 2014; halpern et al., 2015; olper et al., 2017). empirical results from southeast asia suggest that foreign sourcing in the production of exports is a complement to, rather than a substitute for, the creation of domestic value added in exports (lopez-gonzalez, 2016). other studies confi rming the positive relationship between the use of foreign imported inputs and an increase in fi rm productivity growth in developing countries are: amiti and konings (2007) for indonesia; kasahara and rodrigue (2008) for chilean manufacturing plants; halpern et al. (2011) for hungary; topalova and khandelwal (2011) for india; montalbano et al. (2018a) for latin america and the caribbean. constantinescu et al. (2019) and world bank (2020) also underline the signifi cance of backward linkages for growth and labour productivity. by focusing on the gvcs’ participation in agricultural and food and beverages sectors at the global level for a relatively long time span (1995–2015), montalbano and nenci (2022) confi rm that, on average and ceteris paribus, there is a positive relationship between changes in agriculture value added per worker and changes in both agriculture and food gvc participation, both backward and forward. th ese outcomes complement similar established empirical evidence on manufacturing and confi rm the positive eff ect of gvc participation on domestic value added with reference to both backward and forward linkages. some scholars have interpreted the notion of economic upgrading as a need for targeting specifi c production stages and “moving up along the value chain” (kowalski et al., 2015). this debate has been largely infl uenced by the “smiley curve” thesis10 and has been interpreted as implying that in order to increase the domestic value added share, it may be benefi cial to move away from the assembly or manufacturing parts of the chain to be involved in “more sophisticated” downstream stages. this interpretation looks in principle inconsistent with the principle of comparative advantage. th is latter argues that the most profi table segments of the value chain should be jointly determined by the characteristics of the production process as well as the relative skills and resource endowments of fi rms and countries in question. unfortunately, the empirical analyses on the economic eff ects of gvc positioning are still rare. montalbano and nenci (2022), using global indicators of upstreamness for agriculture and food sectors for the usual long time span (1995–2015), highlight a robust negative association cross-country between agriculture value added and the relative distance from fi nal consumers. although this could be seen as possible confi rmation of the fact that moving up the agriculture and food value chains could be seen as a good strategy for participating countries, the authors warn that gvcs are get10 th is argument has been made in business management and refers to a graphical depiction similar to a smile where the two ends of the value chain show higher value added than the middle part of the value chain. for a deeper analysis of the “smiley curve”, see elms and low (2013). figure 9. upstreamness of the food and beverages sector by country (2015). source: montalbano and nenci (2022) based on nenci (2020)’s data. 105 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 mapping global value chain participation and positioning in agriculture and food ting longer over time and additional investigations are needed in this field. as for social upgrading, the existing literature on gvcs often implicitly assumes that economic upgrading will automatically translate into social upgrading through better wages and working conditions (gereffi and lee, 2016; knorringa and pegler 2006; de oliveira 2008). however, pressures to reduce costs might indeed lead employers to combine economic upgrading with social downgrading (for example, by outsourcing employment to an exploitative labour contractor or delocalizing in countries with lower labour standards, (barrientos et al., 2011). preliminary empirical evidence confirms that firm performance is associated with improvements in working conditions (world bank, 2015). unfortunately, this strand of the literature, although addressing a key issue in the gvc debate, is still largely based on case studies and anecdotal evidence. 6. critical issues on gvcs there are some critical aspects that are currently affecting the gvcs – global but also agri-food ones which may affect and shape the future of gvcs. they include: the rise of protectionism, technological development, environmental issues, trends in emerging economies, and recently the covid-19 pandemic (antras, 2020; oecd, 2017; fortunato, 2020). among the issues identified by scholars, we want to focus on the following: the bidirectional nexus between gvcs and trade policy, the advent of new technologies that have become widespread in recent years and the impact of covid-19. 6.1 trade policies and gvcs a critical and important issue in the analysis of gvcs is that of the relationship with trade policies. recent developments in international trade literature have attempted to shed light on the interrelation between trade policies and trade patterns within regional and gvcs. gvc-trade policy nexus is bidirectional: the reduction in trade barriers has been identified as one of the determinants of the spread and diffusion of gvcs (antràs, 2020a) and, conversely, the global fragmentation of production influences trade policy (goldberg and pavcnik, 2016; blanchard et al., 2016). 6.1.1 impact of trade policy on gvcs the literature highlighted two main potential effects of trade policy on gvcs: a) a “magnification effects”, whereby goods that cross national borders multiple times incur multiple tariff costs. as such, tariffs are applied to gross imports, even though the value added content may be only a fraction of this amount. different ways of international involvement, notably upstream or downstream participation, shape the extent to which countries are affected by this cost magnification (yi 2003, 2009; muradov 2017); b) a “chain effect”, which influences all the stages of a gvc and, consequently, a country’s backward and forward participation. in terms of forward participation, a depressing impact is expected on the domestic value added content of a country embodied in partner countries’ exports. this is because, by reducing the gains for foreign producers of final goods, tariffs also hurt their upstream suppliers. in terms of backward participation, when import-competing sectors use foreign inputs, tariffs allow to pass some protectionist rents from the domestic producers on to upstream foreign input suppliers. this could represent an incentive for foreign suppliers to move to those countries/sectors to get the benefits of the protection (blanchard et al., 2016; balié et al., 2019). this may have important policy implications since trade policies no longer exclusively depend on the location of the imported goods but on the nationality of the value added content embodied in traded goods. consequently, there may be a need to reformulate trade policy priorities, especially in the more downstream food sector (montalbano and nenci, 2020a). differently from the standard narrative, which focuses mainly on gross exports’ performance, trade policy should thus consider that access to imports is an essential component of value-added exports. this implies broadening the scope of tariff and non-tariff trade policies, including softening barriers to imports to facilitate access to world-class inputs (montalbano et al., 2018b). moreover, integration into gvcs should be promoted, especially upstream integration that implies producing quality inputs for other countries’ productions and exports. oecd (2016) finds that the greatest effects were found to be on trade in intermediates for low and middle-income countries and suggests that protectionist policies, particularly in the form of tariff escalation, are likely to hamper the development of gvcs. greenville et al. (2017) outline that the levels of tariffs charged and faced, along with sanitary and phytosanitary (sps) and other technical  barriers  to trade (tbt) were correlated with lower gvc participation and suggest that higher levels of barriers to the flow of agricultural and food products across borders are associated with lower levels of agricultural and food gvc participation as the cost of being part of gvcs for individual countries increases and thereby decrease their competitiveness. 106 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 silvia nenci et al. to quantify the effects of trade policies on countries’ economic activity, prices and welfare, computable general equilibrium (cge) models have been adopted. some of these models allow for supply chain cross-border linkages, including the gtap-sc (“supply-chains”) model (walmsley et al., 2014) and the iesc model (minor and walmsley, 2017) which use a nested armington demand structure to distinguish between imports for different usages from different source regions. antimiani et al. (2018b) develop the gtap-va (“value added”) model to account for trade in value added flows when assessing trade policy shocks. in the same vein, the oecd-metro model includes gvc indicators similar to the approach used in the oecd-wto tiva database (oecd, 2018). a key empirical issue in this literature is that applied tariffs alone are not useful metrics to assess trade protection when intermediate trade is pervasive. diakantoni et al. (2017) argue that after falling into relative obscurity, at least from a normative perspective, effective protection rates (eprs) may be back to the central stage as international trade moves from “trade in (final) goods” to “trade in tasks”. from a national account perspective, what is internationally traded is the value added (the primary inputs) and the adequate measure of trade distortion is no more the nominal tariff structure on the output, but the effective rate of “protection” on value added. feenstra (2017) extends the concept of effective protection to reflect the impact of import tariffs on the foreign value added in an industry’s exports. more recently, antimiani et al. (2018a) define in a general equilibrium framework different benchmarks with which to measure restrictiveness, according to where the value added originates: the resulting value added trade restrictiveness indexes (vatris) are equivalent to the actual protection policies in terms of the impact on domestic or foreign value added embedded in imports. similarly, fusacchia et al. (2021) define an index capturing the effects that the tariff structure has on exporting firms that rely on imported intermediate inputs. rouzet and miroudot (2013) compute the ’cumulative tariff ’ (i.e. the accumulated burden of upstream tariffs for a given importer), which quantifies the total cost-push effect of direct and indirect tariffs, taking into account the upstream gvc structure. muradov (2017) extends the concept to account for indirect bilateral trade flows and proposes two alternative measures to account for related costs, the cumulative tariff at origin and destination. 6.1.2 effects of gvcs on trade policy as far as the political economy is concerned, there is growing evidence that gvcs affect trade policy. antràs and staiger (2012) make a theoretical contribution by examining trade agreements in the presence of offshoring. their findings support the view that terms-of-trade motives for cooperation are no longer sufficient when off-shoring is relevant and suggest the need for deep integration, with more individualized agreements that can better reflect member-specific needs (ruta, 2017). a key issue in this respect is the provision of “rules of origin”. these imply that trade agreements can have systemic consequences for the allocation of production across countries. despite the rules of origin, when the share of intermediate goods increases between a non-member country and a member country, the trade diversion of exports from the non-member country to the member country is largely mitigated. conversely, the disruption created by trade wars and dismantled agreements may be transmitted to other trading partners and may not be easily avoided by reorganizing buyer–seller relationships (salvatici, 2020). blanchard et al. (2016) develop a value-added approach to modelling tariff setting with gvcs, in which optimal policy depends on the nationality of value-added content embedded in home and foreign final goods. there are two mechanisms in play: the importing country’s incentive to manipulate the terms of trade is reduced if foreign producers use inputs from the home country in production, and when domestic producers use foreign inputs in production, some of the protectionist rents from higher tariffs accrue to foreign input suppliers. they find strong empirical support for the predictions of the theory stating that discretionary tariffs decrease in the domestic content of foreign-produced final goods and the foreign content of domesticallyproduced final goods. following the predictions of this model and emphasizing political economy considerations, ludema et al. (2019) and bown et al. (2020) find similar empirical results using firm-level gvc interlinks and anti-dumping duty and confirm that gvcs matter for trade policy determination. lastly, raimondi et al. (2021) extend the focus on the agricultural and food sectors by assessing how gvc participation affects trade policy. besides tariffs, they include the bilateral index of non-tariff measures (ntms) for both sps requirements and tbt measures which have the greatest impact on trade for most agrifood sectors. they find that a rise in domestic value added (but not the foreign value added) reduces both tariffs and ntm regulatory distance. in their sample of 150 107 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 mapping global value chain participation and positioning in agriculture and food countries for the period 1995-2015, a movement from low to high domestic value added induces a reduction in tariffs/ntms of about 30%. international trade of agro-food products is influenced not only by tariff and non-tariff measures, but also by the role played by domestic agricultural policies in determining the competitiveness of the sector. distortive agricultural policies that promote subsidies for the use of inputs or subsidies outputs addressing directly the producers or the sector as a whole have been found to have a negative effect on gvc participation and on domestic value added creation (greenville et al., 2017). along with protectionist policies, agro-food trade faces other economic barriers such as standards and intellectual property rights. where governments lack the capacity to enforce regulations, mncs may privately enforce standards necessary both to avoid the risk from media exposure of poor working conditions and to ensure the health and safety of the products all along the gvc. to date, little empirical evidence exists on the relationship between private standards and a country’s participation in gvcs. though some studies have found that compliance with private standards can have positive effects on firms’ trade growth and employment (colen et al., 2012; otsuki, 2011; volpe-martinicus et al., 2010), more recent evidence is mixed (beghin et al., 2015). in particular, the critique on private standards has concentrated on its developmental implications, arguing that standards are not poor inclusive. some empirical studies have suggested that the inclusion of smallholders in high-standard trade is only possible with external support from development programmes, public-private partnerships or collective action. 6.2 new technologies and gvcs the increasing adoption of industrial automation, data exchange, advanced robotics and smart factories (the so called “new technologies”) can change the production processes considerably and reshape world production, thus also affecting international trade (hallward, 2017). these new technologies are also promising in boosting productivity, reducing costs and supporting the speed of catch-up (dollar, 2019). however, since typically demand for automation arises for labour cost saving reasons and it covers all those tasks that are repetitive and codified, emerging innovations can prove to be quite disruptive and lead to a reduction in the demand for workers (rodrick, 2018; acemoglu and restrepo, 2020, acemoglu et al., 2020). consequently, the impact of these technologies on gvcs is twofold. on the one hand, automation represents an alternative to offshoring for those firms in developed countries looking to reduce their labour costs, thus raising the so-called process of “de-globalization” (antràs, 2020b). however, the degree of substitution between automation and workers is low, especially in the more advanced firms deeply integrated into gvcs. this is because of the demanding precision and quality standards associated with these technologies, which generate a disadvantage, especially for unskilled workers (rodrik, 2018). on the other hand, new technologies can foster productivity and increase the demand for intermediate inputs. artuc et al. (2018) show that automation in industrial countries boosted imports from developing countries and a growing literature seems to confirm this trend (see stapleton and webb, 2020; and wang, 2020). digital technologies are also useful in enhancing gvc participation by reducing barriers at the entrance. digital platforms allow the matching of buyers and sellers, fostering verification and monitoring in firm-to-firm relationships and thus lowering the initial fixed costs associated with gvc participation and information frictions (antràs, 2020a). furthermore, in these contexts in which language barriers are still prohibitive, the usage of artificial intelligence, big data and machine learning techniques could provide efficient translation services (brynjolfsson et al., 2019). those technological advancements and the associated business and product innovations are also affecting structural and agricultural transformations across the globe (christiaensen et al., 2021). they hugely reduce transaction costs, change economies of scale and modify the optimal inputs mix in agricultural production, processing and marketing. since some agricultural tasks are highly automatable, automation could accelerate the exit of workers out of agriculture in developing countries and transform farms and food processing firms in the industrialized world. robots are beginning to be used in fields and packaging plants, together with techsavvy agricultural workers, to integrate new technological solutions into specific goods and tasks. solar-driven water pumps, cold storage and agro-processing equipment are also beginning to spread in developing countries, accelerating the transition away from subsistence production (banerjee et al., 2017; world bank, 2020). understanding the direction of innovations is particularly important for developing countries and gvcs will play a key role in this process. extending access to high-speed internet and expanding e-commerce has the potential to greatly facilitate increased gvc participation by relatively small firms and also firms in countries with bad logistical infrastructure (antràs, 2020a). since gvcs are a channel through which new technolo108 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 silvia nenci et al. gies are transmitted between developed and developing countries, participation in gvcs could generate spillover effects in terms of learning (dollar, 2019). conversely, the reshoring of routine activities induced by technological progress could threaten unskilled workers in developing countries. since technological progress seems to be skill-biased, the impact of this could be non-trivial. chang (2016) estimated that almost 80% of cambodian, vietnamese, and, to a lower extent, indonesian workers could face possible replacement by automation. de vries et al. (2016) highlighted that the biggest impact is on the higher value added and more skill-intensive activities. they found a lower demand for production workers by about 55 million workers in china, but no significant effects on demand for r&d jobs. bertulfo et al. (2019), using regional input-output tables and a labour force survey for developing asian economies, found that technology within gvcs is associated with a decrease in employment levels across all sectors. technological changes may have a deep impact even on income differentials. a vast strand of literature points out that participation in gvcs increases the skill premium, thus exacerbating wage inequality, especially in developing countries (chaudhuri et al., 2010; li et al., 2016; shen and zheng, 2020). chongvilaivan and thangavelu (2012) show that a 1% rise in gvc participation leads to a rise in skill premium of approximately 2.5% in thailand. similarly, mehta and hasan (2012) found that gvc participation in services accounted for 30–66% of the increase in return to skills from 1993 to 2004 in india. based on empirical evidence at manufacturing firm-level data, wang et al. (2021) argue that the rise of wage inequality in china mainly arises from moving to more upstream sectors rather than changing gvc participation. 6.3 covid-19 and gvcs the covid-19 pandemic has disrupted global economies, with restrictions in the movement leading to large unemployment and gdp downturns across the world. it has also had a significant impact on international trade with a reduction in trade flows induced by government-mandated lockdowns (baldwin and freeman, 2020; wto, 2020; hayakawa and mukunoki, 2021): the volume of world merchandise trade contracted by 5.3% in 2020 (a contraction smaller than initially feared). trade in nominal us dollar terms fell more sharply, 8%, while commercial services exports declined by 20% (source: wto). although the disruptions from covid‐19 are ongoing, there is a growing body of research on the economic impacts of the pandemic. it is worth noting that these works are still in progress and those available are based on anecdotal evidence, estimates, or simulations based on incomplete and imperfect data. consequently, their results cannot be considered conclusive and much will be learned from more detailed future studies on the topic. that said, a negative impact is what has emerged from initial evidence. the hot issue is to assess whether or not this negative economic impact of the pandemic turns to be strictly related to the degree of participation of countries in global production networks (eppinger, 2020). it is undeniable that the covid-19 pandemic has affected gvcs through several channels (baldwin and tomiura, 2020; baldwin and freeman, 2020; miroudot, 2020; antràs et al., 2020), and the production has been directly cut because of lockdown measures. on the supply side, the lack or scarcity of foreign suppliers due to disruptions in foreign production and transport networks determined a great bottleneck that has affected and continues to affect the entire value chain. on the demand side, demand for most products has fallen sharply because of the economic crisis, whereas shocks in consumer markets have affected all foreign upstream suppliers. on the other hand, global demand for certain medical products has increased substantially, even resulting in temporary shortages and export restrictions (for instance, the pandemic led to shortages of medical equipment and pharmaceutical products in many countries as demand spikes exceeded existing supply and production capacity), whereas other sectors and markets significantly expanded because of the pandemic. sforza and steininger (2020) show that the economic effects of the pandemic are heterogeneous across sectors, regions and countries. di nino and veltri (2020) employ international input-output tables to evaluate the transmission via foreign trade of adverse shocks generated by lockdown and containment measures across the euro area. they highlight not only the presence of a large propagation effect in the euro area but also estimate that foreign demand weakness depressed the aggregate activity of the euro area by about one fifth the size of the foreign shock (a quarter of this effect is due to transmission of lower intermediate and final goods demand within the area). exploring the vulnerability of developing countries to both demand and the supply shock of the pandemic occurring in major economic hubs, pahl et al. (2021) find that the most integrated economies tend to suffer more through the gvcs. with specific regard to gvcs in the agriculture and food sectors, since the covid-19 outbreak, agri-food supply chain disruptions have been widely observed 109 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 mapping global value chain participation and positioning in agriculture and food across the world (christiaensen et al., 2021). the pandemic has imposed shocks on all segments of this supply chain, simultaneously affecting farm production, food processing, transport and logistics, and final demand. in this respect, the non-pharmaceutical interventions imposed by local and/or national authorities to flatten the spread curve of the virus weakened the local food system, thus acting as a real threat to food security, especially for the most vulnerable households (barrett, 2020; béné, 2020). however, not all sectors and products have been equally affected and different products have experienced disruptions at different stages of the supply chain (oecd, 2020c). furthermore, if agri-food supply chains in the developed world have demonstrated remarkable robustness and resilience in the face of covid-19,11 in the developing world, the impacts on agri-food-supply chains are expected to be felt widely but unevenly. farm operations may be spared the worst, while small and medium-sized enterprises in urban areas will face significant problems (reardon et al., 2020). in terms of actions, local governments have actively strengthened food safety nets and social protection mechanisms to maintain access to food. specific government measures also addressed the impact of income reductions through subsidies, tax breaks and transfers to those affected. these measures have been indispensable but acted basically as coping strategies. the challenge is to stabilise global supply and consumption, heading the global food system towards a sustainable and resilient path. this revamps the important role of risk exposure and international trade in general, and gvcs’ participation in particular, as the key tools for fostering resilience among the poor, and reducing their vulnerability to external shocks (morton, 2020; montalbano and nenci, 2022). to sum up, some key features of gvcs that matter for efficiency also determine the exposure to shocks and the propagation of these shocks along the chain. a high reliance of sales on foreign demand and high dependence on foreign value-added inputs are associated with high-risk exposure, but not necessarily to higher vulnerability since the latter is mainly related to how people actually manage risks (montalbano, 2011). in this respect, the covid-19 pandemic has led firms to a partial diversification of sources of supply whose extent will vary by sector depending on the costs of value chain reorganization. moreover, according to some scholars, the world economy has already entered an era of de-globalization before the pandemic and the observed slowdown is the consequence of the remarkable and unsustainable period of hyperglobalization experienced in the 11 see the economist, “the world’s food system has so far weathered the challenge of covid-19 – but things could still go awry”, 9 may 2020. two decades that preceded the 2008-2009 crisis (antràs, 2020b). the covid-19 crisis could have only fortified some of the trends noted above. however, its effects are ambiguous. 7. conclusions and the future of gvcs this work has analysed a wide range of aspects related to the study of gvcs, mainly using a “macro” lens and an empirical focus. we have first reiterated the economic centrality of gvcs, despite the slowdown recorded after the 2009 crisis and the further disruptions induced by the covid-19 pandemic. we have then reported the different approaches to the gvc analysis and presented indicators for measuring and mapping gvcs at micro and macro levels. subsequently, we have shown some stylized facts focusing on gvc participation and the position of the agriculture and food sector, underlining a growing involvement on a global level, although with variation across regions and countries. after, we have summarized the main empirical evidence regarding the impact of value chains on the performance of the sectors under interest, highlighting an improvement in productivity and a supported economic growth. finally, we have introduced and discussed some of the main critical issues affecting current and future gvcs, such as policy space, new technologies and the effect of covid-19 pandemic. as illustrated in this work, we should acknowledge that with respect to gvc measurements and mapping, the literature is still scant. a growing empirical literature emerged by taking advantage of the availability of global i-o matrices and aggregate trade data to map and measure gvcs, whereas sectoral, country coverage and firm-level data remain weak. mainly, the relative lack of good quality microdata is underwhelming. researchers struggle to envisage satisfactory proxies for both firms’ participation and positioning in the gvcs. moreover, a lot more is currently left out because it is hard to investigate it. issues in point are, for instance, the channels of technology transmission in the gvcs; the pros and cons of specific, idiosyncratic investments, in the presence of contractual incompleteness and low quality of institutions; the understanding of the different layers of gvcs and the availability of suppliers according to the tier they operate in.12 all that remains is to provide 12 antràs (2021:16) advocates “a novel, [..] conceptualization of gvcs in which the focus is shifted away from the mere allocation of value added across countries resulting from anonymous, spot exchanges of goods and services. instead, a new paradigm emerges in which the identity of the specific agents participating in a gvc is crucial”. 110 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 silvia nenci et al. more and more data to allow both a more robust understanding of the gvc phenomenon and, consequently, the design of effective policy measures. the recent slowdown and retrenchment in the global fragmentation of production have induced a growing body of authors to suggest that this trend is the prelude to a new era – the so-called “deglobalization”whose speed of development may even be powered by the covid-19 pandemic. following this track, some trends could arise: first, reshoring, resulting in a shorter and less dispersed value chain characterized by a higher geographical concentration of value added (javorcik, 2020); second, diversification, with a broader distribution of tasks along the gvc, especially for intensive manufacturing industries (antràs, 2020); third, regionalization, as a consequence of the reduced length of the chains, dominated by three large regions: north america, europe and a china-centric asia (baldwin and freeman, 2020; enderwick and buckley, 2020; wang and sun, 2021). several preliminary analyses seem to deny that reshoring and nationalization of production can improve resilience:   it would result in lower exposure to foreign shocks, but this comes at the cost of higher exposure to domestic shocks, i.e., value chains are still dependent on single suppliers, which does not protect against disruption in production (antràs, 2020; bonadio et al., 2020; oecd, 2020b; arriola et al., 2020; espitia et al., 2021). furthermore, such different kinds of risk exposure are not symmetric, being the foreign markets characterized, on average, by many players and a higher degree of competition. the demand for diversification, on the other hand, seems to find greater consensus: a larger network of diversified suppliers in multiple countries is the better response to avoiding the bottleneck (see antràs, 2020; bacchetta et al., 2021; caselli et al., 2020; miroudot, 2020). lastly, the process of regionalization of the gvcs was indeed already in place with hubs in china, the us and germany (baldwin and lopez-gonzalez, 2015). although the undeniable slowdown of recent years and several trends that will limit the expansion of gvcs in the near future, the complexity and high restructuring costs related to gvcs will probably prevent the large-scale dissolution of the existing gvcs (bonadio et al., 2020; simola, 2021). the key question then becomes how to consolidate gvcs in the future. in this context, policies can play a critical role. brancati, pietrobelli et al. (2021) collect from the field literature four types of policies to build more robust and resilient value chains: i) participation policies aimed at entering in and enhancing the local economy’s participation in gvcs. these are market-enabling policies that assist the private sector in restructuring productive activities according to a country’s latent comparative advantage and connectedness policies that reduce the costs related to linking domestic firms to foreign value chain partners (e.g. policies that reduce trade costs or information costs); ii) value capture policies intended for strengthening the local economy’s value creation and capture within gvcs. part of the gvc literature suggests supporting product and process upgrading, which implies moving vertically along the value chain to better products or processes as well as the more challenging functional and interchain upgrading, entailing horizontal movement towards new functions or new markets (gereffi et al., 2005; humphrey and schmitz, 2002; gereffi, 2019). examples of these policies are: strengthening of local innovation and production ecosystems; building and improving specific types (logistical, digital, and productive) of infrastructures; development of specific skills; establishment of linkages between universities, vocational centers and firms involved in gvcs; provision of advisory services in the areas of standards, metrology, testing, and certifications; iii) gvc inclusiveness policies directed to improve the local social and environmental conditions in gvcs. they concern improvement of labor, social, and environmental regulations and their enforcement, at national and supranational levels; responsible sourcing policies; private standard promotion; involvement of local communities in gvc governance; iv) and finally, resiliency policies designed for strengthening the local economy’s resiliency, that is how to ensure that a society’s ability to deliver essential goods and services is sufficiently resistant to both local and foreign disruptions. among these kinds of policies, there are: supply chain and food system stress test; 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lopez-garcia and di mauro, 2015; altomonte et al., 2020). similarly, the efige database combines measures of firms’ international activities (such as exports, outsourcing, fdi, imports) with quantitative and qualitative information on about 150 items ranging from r&d and innovation, labour organization, financing and organizational activities, and pricing behaviour. data consists of a representative sample of almost 15,000 surveyed firms (above ten employees) in seven european economies. data were collected in 2010, covering the years from 2007 to 2009 (see, among others, barba navaretti et al., 2011; accetturo and giunta, 2011; cainelli et al., 2018; meliciani et al., 2019; giunta et al., 2021). additional works focusing on developing countries have used the world bank enterprise survey (wbes). it provides detailed information on the characteristics of firms across several dimensions, including size, owner120 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 silvia nenci et al. ship, trading status, and performances for over 135,000 firms belonging to the manufacturing and services sectors interviewed in 139 countries since 2005 under a common global methodology (see, among others, montalbano et al., 2018b). some works also took advantage of the orbis database provided by the bureau van dijk. it contains data for more than 100 countries covering more than 400 million private and public companies worldwide (with about 120 in europe, 100 in north and south america, and 80 in asia), and collecting data primarily from balance sheets (see, among others, del prete and rungi, 2017). although the huge set of information, the main disadvantage is that the firms included in orbis represent only a fraction of the entire firm population and, most importantly, they do not form a representative sample (oecd, 2020a). among commercial databases, we also find the dun & brandstreet’s worldbase that provides comprehensive coverage of public and private companies and has been used in the empirical literature (alfaro et al., 2019). finally, for the italian case, it is worth mentioning the met survey. it is carried out every two years. it collects information on a representative sample of around 25,000 italian manufacturing firms. it also encompasses micro-sized companies (with less than 10 employees). waves cover the years: 2008, 2009, 2011, 2013, 2015, 2017 and 2019 (see, among others, brancati et al., 2017; agostino et al., 2020; giovannetti et al., 2015; giovannetti and marvasi, 2016). 2a. country and sectoral level datasets one of the most widely used country-sectoral datasets is the world input-output database (wiod) which has been developed thanks to a consortium of 11 institutions led by researchers at the university of groningen. it covers 43 countries (including oecd and emerging countries) and 56 industries from 2000-2014 (release 2016). moreover, it is based on official national account statistics and refers to end-use classification to allocate flows across partners and countries. as a result, it has been extensively used in the literature (see, among others, baldwin and lopez-gonzales, 2015; costinot and rodrìguez-clare, 2014; timmer et al., 2013; wang et al., 2013; koopman et al., 2014; johnson, 2014; los et al., 2015; adao et al., 2017; fajgelbaum et al., 2016; timmer et al., 2014). the adb multi-region i-o database (adb mrio) has been developed by the asian development bank and used in the literature (see de vries et al., 2016; de vries et al., 2019). it is basically an extension of the wiod and includes five additional asian economies – bangladesh, malaysia, philippines, thailand and vietnam – for the years 2000 and 2007-2019. importantly, the data provided for these countries are derived from estimations produced by researchers and do not refer to official statistics. another important source of data is the oecdwto trade in value added (tiva) database. it embodies the national i-o tables from 2005 to 2018 for 45 industries and 64 countries, 27 of which are non-oecd member economies (most east and south-east asian economies and a selection of south american countries). it provides useful indicators on value-added exports and other measures of the global supply chain used in the empirical studies (de backer and miroudot, 2013; miroudot et al., 2017; mukherjee, 2018). the data source presenting the broadest coverage in terms of countries is the eora global supply chain database, constructed by a team of researchers at the university of sydney (lenzen et al, 2012; and lenzen et table 1a. main datasets for research on gvcs at the firm level. e fige bruegel with the support of the e uropean commission survey 2007-2009 15,000 firms manufacturing barba navaretti et al., ( 2011) ; accetturo and giunta, ( 2011) ; cainelli et al., ( 2018) ; meliciani et al. ( 2019) compnet e uropean central bank account statistics, business registry, surveys, and balance sheets 1999-2017 19 56 inferrera, ( 2021) ; lopez-garcia and di mauro, ( 2015) ; altomonte et al.( 2020) dun & brandstreet’s worldbase dun & bradstreet 190 alfaro et al., ( 2019) orbis bureau van dijk balance sheets 400 million firms bloom et al. ( 2012a) , ( 2012b) ; del prete et al., ( 2017) world bank e nterprise survey world bank survey 174,000 firms seker ( 2011) ; amin et al. ( 2014) me t monitoraggio e conomia e territorio ( me t) survey 2008, 2009, 2011, 2013, 2015, 2017, 2019 25,000 firms per year observed 38 giovannetti et al. ( 2015) balduzzi et al. ( 2020) ; antonioli et al. ( 2021) fir m-level data ( used for gv c analyses) source: authors’ elaboration. 121 bio-based and applied economics 11(2): 93-121, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12558 mapping global value chain participation and positioning in agriculture and food al, 2013). this database provides a set of both national and global input-output tables, covering 189 countries and 26 sectors for complete time series from 1990 to 2015. recently, unctad and eora developed a  timeseries from 1990 to 2018  of some  key gvc indicators, including foreign value added, domestic value added and domestic value added embedded in other countries’ exports. results from 1990 to 2015 are generated from eora tables, whereas those for 2016-2018 are nowcasted using different data sources. the adopted methodology interpolates the missing points to provide broad, up-todate coverage. along with this, the gtap is a comprehensive multi-region database developed by the purdue university which has been increasingly enriched in terms of data thanks to the contributions of individual researchers and organizations. it covers 121 countries plus 20 regions and 65 industries for 2004, 2007, 2011 and 2014. thanks to its high coverage and relatively sectoral details, it has been intensely exploited by researchers (see, among others, trefler and zhu, 2010; daudin et al., 2011; johnson and noguera, 2012; koopman, wang and wei, 2014; aguiar et al., 2016). major regional initiatives in addition to adb mrio – include: the exiobase promoted by the eubased consortium. this database extracts information from national supply-use and input-output tables extended to environmental indicators. it covers 44 countries plus 5 rest-of-world regions and 163 industries for the period 1995 – 2015; the south-american input-output table from the economic commission for latin america and the caribbean (eclac) covering 10 latin america countries and 40 sectors for the years 2011 and 2014. table 2a. public datasets for research on gvcs at the country and sectoral level. pr oject insti tution data sour ces year s countr ies industr ies r elated paper s wordl input-output database ( wiod) e u-based consortium national supply-use tables 2000-2014 43 56 baldwin and lopez-gonzales, ( 2015) ; costinot and rodrìguez-clare, ( 2014) ; t immer et al., ( 2013) ; wang et al., ( 2013) ; koopman et al., ( 2014) ; johnson, ( 2014) ; los et al., ( 2015) ; adao et al., ( 2017) ; fajgelbaum et al., ( 2016) ; t immer et al., ( 2014) traded in value added ( t iva) dataset oe cd national i -o tables 1995-2018 64 45 de backer and miroudot, ( 2013) ; miroudot et al. ( 2017) ; muckherjee, d. ( 2018) unctad-e ora gvc database unctad/ e ora national supply-use and i -o tables from e urostat, oe cd, ide je tro 1990-2018 189 26 lenzen et al., ( 2012) ; lenzen et al., ( 2013) ; del prete et al., ( 2018) adb multi-region input-output database ( adb mrio) asian development bank wiod exstension 2000, 20072019 63 35 de vries et al., ( 2016) ; de vries et al., ( 2019) global trade analysis project ( gtap) purdue university individual researchers/ instituti on 2004, 2007, 2011, 2014 141 65 trefler and zhu, ( 2010) ; daudin et al., ( 2011) ; johnson and noguera, ( 2012) ; koopman, wang and wei, ( 2014) ; aguiar et al., ( 2016) e xiobase 3 e u-based consortium national supply-use tables 1995-2015 49 163 stadler et al. ( 2018) ; owen et al. ( 2016) south american input-output table e clap/ ipe a national i -o tables 2011, 2014 10 40 ce pal, ( 2016) ; banacloche et al. ( 2020) countr y and sector al level source: authors’ elaboration. volume 11, issue 2 2022 firenze university press agriculture, food and global value chains: issues, methods and challenges margherita scoppola mapping global value chain participation and positioning in agriculture and food: stylised facts, empirical evidence and critical issues silvia nenci1, ilaria fusacchia1,2, anna giunta1,2, pierluigi montalbano3, carlo pietrobelli1,4 on the relationships among durum wheat yields and weather conditions: evidence from apulia region, southern italy marco tappi*, gianluca nardone, fabio gaetano santeramo a choice model-based analysis of diversification in organic and conventional farms andrea bonfiglio*, carla abitabile, roberto henke financial performance of connected agribusiness activities in italian agriculture gabriele dono*, rebecca buttinelli, raffaele cortignani pesticides, crop choices and changes in well-being geremia gios1,*, stefano farinelli2, flavia kheiraoui3, fabrizio martini4, jacopo gabriele orlando5 bio -based and a ppl ied economics bae bio-based and applied economics 10(3): 207-218, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10044 copyright: © 2021 g. gastaldello, d. mozzato, l. rossetto. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: g. gastaldello, d. mozzato, l. rossetto (2021). drinking covid-19 away: wine consumption during the first lockdown in italy. bio-based and applied economics 10(3): 207-218. doi: 10.36253/bae-10044 received: november 23, 2020 accepted: july 13, 2021 published: january 11, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: davide menozzi. orcid gg: 0000-0002-7337-9897 dm: 0000-0002-4130-7290 lr: 0000-0001-7070-153x authors contributions: all the authors cooperated in producing the manuscript. in particular: d. mozzato provided guidance in data analysis, while l. rossetto supervised the research project. l. rossetto and g. gastaldello equally contributed to writing the theoretical background. l. rossetto, g. gastaldello and d. mozzato equally contributed to reviewing the original manuscript. finally, g. gastaldello performed the data analysis and wrote the remaining paragraphs of the manuscript. drinking covid-19 away: wine consumption during the first lockdown in italy giulia gastaldello*, daniele mozzato, luca rossetto department of land, environment, agriculture and forestry (tesaf), university of padova, italy *corresponding author. e-mail: giulia.gastaldello.1@phd.unipd.it; daniele.mozzato@ unipd.it; luca.rossetto@unipd.it abstract. in italy, wine is an integral part of most people’s habits and lifestyles. the advent of a traumatic event like the covid pandemic brought profound changes to people’s lives: economic instability and normality disruption led consumers to revise their priorities and modify their consumption and purchase behavior. this study analyses the impact of socio-demographic, psychological, and context-related modifications induced by the pandemic on wine consumption and purchase patterns. participants completed an online, structured survey, and the sample is constituted by italian wine consumers. logistic regression and descriptive techniques are applied to analyze data. results highlight that wine consumption is a deeply rooted habit among italian consumers, which resisted the great context modifications that occurred with the pandemic. moreover, changes in wine consumption are connected to that of other alcoholic beverages. significant shortterm and potential long-term effects are discussed. information collected is paramount to understanding wine consumers’ reactions and behavioral changes induced by the pandemic and effectively plan marketing strategies during new infection peaks. keywords: covid-19, wine consumption, italy, consumer behavior, logistic regression. jel codes: d12, l66. 1. introduction the covid pandemic is a traumatic event that led to significant changes in people’s lives and italy is among the hardest-hit european countries (ministero della salute, 2021). the rapid and insidious spread of the virus, causing a severe and potentially life-threatening respiratory disease, forced the national government to take a drastic step by forcing the country into a first, extended lockdown that lasted from march 10th to may 4th, 2020. this period had a profound impact on two significant aspects of the national community: on economy and productivity due to the forced shutdown of most activities, and on mobility and social occasions through the prohibition of physical gatherings and trips both outside and within the region. the disruption of people’s habits and lifestyles generated severe psychological discomforts (colbert, wilkinson, thornton, and richmond, 2020, arpaci, karataş and baloğlu, 2020). http://creativecommons.org/licenses/by/4.0/legalcode 208 bio-based and applied economics 10(3): 207-218, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10044 giulia gastaldello, daniele mozzato, luca rossetto the food-and-beverage industry and retail trade were among the few activities allowed to operate by the national law, so access to wine and other alcoholic beverages was still available. although wine is an essential component of italian culture and lifestyle (seghieri, torrisi, and casini, 2007), the lockdown profoundly transformed wine consumers’ routines, leading to potential modifications in wine consumption patterns. such changes can potentially affect future wine demand, thus the role of wine in italian culture and lifestyle. considering the unprecedented circumstances of uncertainty the wine industry is facing, there is a need for reliable information on the impact of the lockdown on wine consumption. this study aims at responding to this need, identifying factors that triggered modifications of wine consumers’ behavior during the first national lockdown. the effect of new consumption habits such as online purchasing is also explored to provide insights into whether such factors can affect demand in the long term. an online survey was conducted on a large sample of italian consumers of wine and alcoholic beverages (beer and spirits) to achieve these goals. a descriptive analysis is undertaken to highlight significant changes in alcoholic beverage consumption during the lockdown, focusing on both wine and substitution effects among wine, beer and spirits. finally, factors inducing positive and negative modifications of wine consumption frequency are identified. 2. theoretical background the covid pandemic is a one-of-a-kind, extraordinary event. although world economies have already experienced health emergencies due to virus outbreaks such as sars, the covid pandemic crisis is unprecedented due to the multi-level and interdependent changes it has induced on a global scale. consequently, to the best of the authors’ knowledge, no similar phenomena in nature and magnitude have been analysed in the existing literature on consumer behavior. nevertheless, researchers extensively explored the effect of habit disruption and stress on the consumption of both wine and alcoholic beverages. the following sections present the state of the art on these two key aspects, which are identifiable as significant consequences of the pandemic on consumers’ lifestyles. additionally, the role of wine and wine consumption in italy is discussed. 2.1 wine and alcohol consumption in italy italy is among the major players in the wine market, with a production exceeding 50 million hl/year (oiv, 2020), and being the third world wine consumer after the usa and france. the italian population consumes over 20 million hectoliters a year (22.4 in 2018), corresponding to 35 liters per capita (oiv, 2020). the latter has considerably shrunk in the last decades (sellers and alampi-sottini, 2016). still, its decrease is primarily due to changes in the way of consuming wine rather than to a switch of consumer preferences towards other alcoholic beverages. the function of wine has indeed gradually switched from nutrition to pleasure (hertzberg and malorgio, 2008), leading the share of daily wine consumers to decrease (17.6% of the population) in favor of non-daily ones, which are growing (36.6% of the population) (istat, 2020). generally, older generations tend to drink more wine and more often than younger ones (istat, 2020), and contemporary daily wine consumers are likely to be males rather than females (25.9%; istat, 2020). females, indeed, are generally less prone to alcohol drinking. compared to other alcoholic beverages such as beer and spirits, wine consumption is to a greater extent rooted in the italian population’s habits: indeed, only 5.3% and 0.6% of consumers, respectively, are daily beer and spirits drinkers (istat, 2020). the widely diffused habit of having wine during meals could be an explanation, and makes food and gatherings with family members important consumption motivations in the italian scenario. social relations also represent a relevant consumption motivation, mainly due to the habit of the pre-meal aperitivo, when wine is consumed either by the glass or mixed in cocktails. almost 40% of the italian population usually drink alcoholic aperitivo (istat, 2020). finally, health has been thoroughly explored as a factor inf luencing alcohol drinking behavior. generally, alcohol consumption can be either considered a potential threat because of the poisonous effect of alcohol overconsumption and the related health risks, or an unhealthy dietary choice, increasing the caloric intake while providing low nutritional value (bazzani et al., 2020). in this respect, the new post-pandemic lifestyle could prompt a recalibration of priorities (sigala, 2020), leading people to re-evaluate the outcomes of their behaviors (wood, tam, and witt, 2005). therefore, the pandemic may constitute a deterrent to alcohol consumption, as human health and survival are at stake. as regards to wine past research highlights the potential beneficial effects of moderate wine consumption on human health, mostly related to antioxidants in red-colored berries (e.g., nijveldt et al., 2001; de lorimier, 2000). the mediterranean diet can potentially improve such effects. in this sense, wine can be seen as a healthy dietary choice (fiore, alaimo, and chkhartishvil, 2019). 209drinking covid-19 away: wine consumption during the first lockdown in italy bio-based and applied economics 10(3): 207-218, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10044 on a broader scale, a moderate wine intake can also contribute to hedonistic health and well-being (fiore, m., alaimo, l. s., & chkhartishvil, n., 2019), which is associated with focusing on the self and the present moment (huta, 2015), favoring sociability and inducing a stress-free mood (cooper, 1994). the unprecedent circumstances of the lockdown and the uncertainty generated by the pandemic may have emphasized the role of wine drinking in emotional and mental well-being, positively impacting on its consumption frequency. 2.2 psychological difficulties and alcohol consumption behavior as mentioned above, the covid-19 pandemic constituted a source of stress and anxiety that have long been associated with increased alcohol consumption. among the first theories, there is horton’s tensionreduction hypothesis (1943), identifying alcohol consumption as a way to diminish the feeling of anxiety prompted by stress, which arises either from traumatic events or from environmental stressors. later studies further explored this connection, specifying that alcoholic beverages consumption can be a way to mitigate negative feelings (powers and kutash, 1985). stress, moreover, may increase alcohol intake when the intention to drink is already present (dawson, grant, stinson, and zhou, 2005). in this respect, a stronger association exists with being male (dawson et al., 2005), which can be explained by gender-related stress resistance. indeed, men and women tend to react differently both to stress and to single stressors, intended as factors inducing stress (apa, 2012). as regards age, older people tend to deal better with negative emotions when subject to stressors, which is believed to be an indicator of stress resilience (ong et al., 2006), while mainly endorsing positive feelings (scott et al., 2014). nevertheless, it should be considered that aging is associated with increased emotional complexity connected to the awareness of “running of time” (carstensen et al., 2000). such complexity peaks at middle age (labouvievief et al., 2007). in the context of this research, we expect the fear of the sars-cov-2 illness, jointly with economic uncertainty and isolation, to trigger an increase in wine and, more generally, in alcohol consumption frequency, especially in males and middle-aged people. indeed, other researchers have already outlined the high risk of potential alcohol overconsumption prompted by the pandemic’s emergence (e.g., clay and parker, 2020). 2.3 disrupting (wine) habits habits are defined as behavioral dispositions to repeat a set of everyday activities when specific circumstances occur (wood et al., 2005). as an individual repeats the behavior, triggering factors – e.g., performance time, location, or people the activity is usually shared with – are associated in the memory with specific activities, leading to a set of cognitive, neurological, and motivational changes. habits, indeed, tend to be contextdependent (wood et al., 2005). consequently, habitual actions lose their explicit instrumental nature, separating them from intentions (neal, wood, and quinn, 2006; wood et al., 2005) and being performed almost unconsciously. as regards wine, its recurrent consumption in italy is strongly connected with a multitude of attitudes, behaviors, and consumption situations (presenza, minguzzi, and petrillo, 2010), mainly conviviality, e.g., the aperitivo or gatherings with friends, colleagues, family and dining out. in this sense, wine drinking can be considered a habit for a large slice of the population. with the closure of restaurants, cafes and the ban of social gatherings during the lockdown, most factors driving wine consumption habits disappeared inducing changes in most wine consumers’ drinking habits. mainly, people usually drinking wine on social occasions may reduce their consumption frequency. at the same time, consumers who were used to consume wine alone are expected either to keep their consumption frequency stable or to increase it. the direction of this change may differ based on the strength of the role of wine in one’s habits. the literature highlights that stronger habits survive context changes as intentions may come into play, creating the conditions to preserve them (wood et al., 2005). given the strength of the habit of aperitivo in italy, people may look for alternative ways to pursue this activity during the lockdown: the virtual aperitivo. to sum up, the strength of wine consumption as a habit among the italian population before the pandemic leads to assuming that intentions arose to preserve this, despite the drastic context changes. in this respect, physical barriers imposed with the national lockdown may prompt the emergence of new ways to maintain usual wine consumption habits. conversely, the lockdown may show disrupting effects on wine drinking, resulting either in a reduced consumption or in substitution effects. 3. materials and methods a structured questionnaire was developed focusing primarily on wine while incorporating information on 210 bio-based and applied economics 10(3): 207-218, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10044 giulia gastaldello, daniele mozzato, luca rossetto the consumption of other alcoholic beverages. the survey includes seven sections: consumption and purchase patterns before and after the pandemic (for wine, beer, and spirits), wine consumption context preand postcovid, online wine-related interactions, psychological difficulties (i.e., feeling of isolation, fear of the virus and the economic crisis), positive feelings (i.e., willingness to support local wine producers, possibility to refocus on the self while in lockdown) and socio-demographics. specifically, isolation is expressed as a latent construct focusing on relational connectedness. indeed, relational connectedness represents social loneliness, one of the most significant consequences of the lockdown. for this purpose, a 3-items scale based on hawkley et al.’s (2005) loneliness scale (ucla scale) was adopted. the 3 items were reduced to a single factor (α=.87; kmo=.72) and the resulting isolation scale is inverted (1=strong isolation; 5= weak isolation). the three items used are “since the beginning of the lockdown, there are people i feel close to”, “since the beginning of the lockdown, there are people i can talk to”, and “since the beginning of the lockdown, there are people i can turn to”. fear of covid-19 was captured by the statement “i feel vulnerable to covid-19 outbreak”, while fear of the economic crisis is captured by the statement “i am concerned about the economic impacts of the crisis on myself and my family”. two statements represented positive feelings: “quarantine has allowed me to focus on the essentials”, and “since the quarantine has begun, i feel like i should buy more local wine to support my country’s economy”. all items were measured by 7-points likert scales (1= strongly disagree; 7=strongly agree). online data collection was carried out between april 16th and april 29th, 2020 – i.e., during the first lockdown in italy. as previously mentioned, given the impossibility of reaching the population of interest – consumers of alcoholic beverages – due to the ongoing pandemic and the short time window available, snowball sampling was adopted. this technique represents an efficient and cost-effective data collection method in contexts where subjects of interest are challenging to reach (ghaljaie, naderifar and goli, 2017). data were collected according to the guidelines provided by the declaration of helsinki (wma general assembly, 2013). drawbacks reported in the literature connected to this sampling technique, primarily due to its convenience nature, can be compensated by the large sample size. the survey was diffused through social networks and via direct contacts. the original study, designed in collaboration with the euawe (european associations of wine economists) research group, involved several big players in the wine sector – i.e., spain, italy, portugal and france. the current analysis refers exclusively to the italian sample, with a total of 1076 valid questionnaires collected. table 1 summarizes the descriptive statistics of the sample. the majority of interviewees (57.8%) are males employed in the service sector (57.4%) and with either good (50.1%) or sufficient (36.2%) economic situation. almost half of the sample live in an urban context, while 30% come from suburban residential areas. a minor share of respondents live alone, with an average household size of three adults (45.7%) and no children (68.4%). almost all age groups are homogeneously represented, with a slight predominance of 41-50 year old subjects. the age class of over 70s was poorly represented and was aggregated into the 60-70 age group. similarly, for wine consumption frequency before the lockdown (wcons_b), respondents drinking wine once a month or less were aggregated into one category of occasional consumers. data analysis relies on descriptive techniques and binary logistic regression (lr), given the categorical nature of the dependent variables (dv) and the use of humansourced data. this statistical approach was chosen due to its capacity to provide higher robustness when multivariate normality assumptions and equal variance-covariance matrices across groups are not met, as commonly happens in social science research (hair et al., 2019). specifically, two lr models were developed to identify factors triggering positive (model b), and negative (model a) changes in wine consumption frequency during the first lockdown. for the sake of the analysis, consumers of alcoholic beverages who do not drink wine and missing income values were excluded through listwise deletion, thus reducing the sample to 1018 respondents. the enter method was preferred to the stepwise procedure, as the latter tends to produce sample-specific results (hair et al., 2019). regressors were selected based on the literature. variance inflation factor (vif) and tolerance were used to check for multicollinearity, and all values were within the recommended thresholds (vif<5; tolerance>0.2; hair et al., 2019). although the primary aim of the analysis is explanatory, additional fitting diagnostics were performed. overall predictive accuracy of the models was assessed through receiver operating curves (roc) and area under curve (auc). according to hosmer et al. (2013) thresholds, both models show excellent discrimination power (auc model a = .82; auc model b = .87). 4. results 4.1 wine consumption: the pre-lockdown scenario before the beginning of the pandemic, most respondents were regular wine consumers drink211drinking covid-19 away: wine consumption during the first lockdown in italy bio-based and applied economics 10(3): 207-218, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10044 table 1. descriptive statistics of the sample.   frequency %     frequency % gender     n° adults (respondent included) male 622 57.8 1 22 2.0 female 454 42.2 2 145 13.5 age 3 492 45.7 18-29 223 20.7 4 199 18.5 30-40 176 16.4 5 161 15.0 41-50 269 25.0 ≥6 57 5.3 51-60 244 22.7 income   >60 164 15.2 very problematic 13 1.2 employment (n=1075) problematic 76 7.1 agriculture 146 13.6 sufficient 390 36.2 industry 104 9.7 good 539 50.1 service 618 57.4 no answer 58 5.4 student 125 11.6 motivations for wine consumption retired 58 5.4 socialization (yes) 372 34.6 unemployed 24 2.2 relax (yes) 290 27.0 has children health (yes) 118 11.0 no 736 68.4 food (yes) 738 68.6 yes 340 14.6 taste (yes) 788 73.2 res. area average bottle price wine before the lockdown (n=1052) rural 240 22.3 <5€ 148 13.8 residential 331 30.8 5-10€ 439 40.8 urban 505 46.9 11-20€ 359 33.4 freq. digital gatherings during the lockdown 21-30€ 75 7.0 did not do it 161 15.0 >30€ 31 2.9 rarely 517 48.0 has a wine app at least once a week 181 16.8 no 750 69.7 every day 217 20.2   yes 326 30.3 wine consumption frequency before the lockdown wine consumption frequency in lockdown never (0) 19 1.8 less frequent 478 44.4 <1 a month (1) 50 4.6 unchanged 251 23.3 at least 1 a month (2) 134 12.5 more frequent 347 32.2 at least 1 a week (3) 568 52.8   daily (4) 305 28.3   beer consumption frequency before the lockdown beer consumption frequency in lockdown never (0) 127 11.8 less freq. 563 52.3 <1 a month (1) 158 14.7 unchanged 333 30.9 at least 1 a month (2) 265 24.6 more freq. 180 16.7 at least 1 a week (3) 486 45.2   daily (4) 40 3.7   spirits consumption frequency before the lockdown spirits consumption frequency in lockdown never (0) 279 25.9 less freq. 565 52.5 <1 a month (1) 309 28.7 unchanged 414 38.5 at least 1 a month (2) 260 24.2 more freq. 97 9.0 at least 1 a week (3) 214 19.9   daily (4) 14 1.3   212 bio-based and applied economics 10(3): 207-218, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10044 giulia gastaldello, daniele mozzato, luca rossetto ing wine at least once a week (52.8%) or daily (28.3%) (table 1). concerning other alcoholic beverages, the majority used to consume beer weekly (48.9%) and spirits sporadically (54.6% less than once a month). based on istat data, the share of daily wine consumers in the sample is higher than the italian national average (17.6%). wine was mostly consumed for its taste, paired with food during meals, and to socialize (table 1). coherently, it was prevalently drunk with friends and relatives. adapting the rabobank wine classification (heijbroek, 2003), two main segments of wine consumers can be identified based on the average price per bottle: premium wine consumers, purchasing wine ranging between 5 and 10 euros per bottle (40.8%) and super-premium ones, who usually buy wines priced between 11 and 20 euros (33.4%). accordingly, half of the respondents declared a good economic situation (50.1%). results suggest that the sample comprises wealthier, higher-end consumers compared to the average italian population, since market data on domestic wine sales report an average price-per-liter of 3.27 € (iri, 2009). however, such average price is likely underestimated being referred to off-trade sales in supermarkets and discounts. such sales channels are usually offering wines at a lower average price point compared to restaurants and enoteche, i.e., italian specialized wine shops, which are excluded. accordingly, enoteche are the third most important shales channel for wine in the sample (45.5%) after cellar door sales (direct sales, 48.0%) and supermarkets (51.2%). other channels such as e-commerce (12.8%) play a minor role, despite the digitalization level of respondents: indeed, 40% declared that they have a wine app on their smartphone. anyhow, the share of online buyers in the sample more than doubles the average data reported in the sector literature for online wine sales in developed countries, which is approximately 5% (higgins et al., 2015). additionally, 70% of respondents declared a great willingness to support local wine producers in response to the covid-crisis by preferring domestic wines. 4.2 psychological difficulties during the lockdown as expected, psychological difficulties are strongly felt by all respondents. in particular, the greatest worry concerns the negative economic impact of the pandemic (79.2%; table 1). economic concerns are strong enough to overcome fear of the virus, which, notwithstanding its life-threatening nature, is suffered by less than a half of the sample. a feeling of isolation, intended as the impossibility to relate with, talk with, and rely on others, also emerges as a dominant feeling for most respondents (69.1%; table 1), despite most of them not living alone. positive feelings also emerged, as a large share of interviewees see the lockdown as a chance to refocus on themselves (58.0%). therefore, among the multitude of negative feelings, respondents could see the bright side of the situation. 4.3 changes in wine consumption during the lockdown most of the wine consumers interviewed kept purchasing wine during the lockdown (75.7%) without changing the average bottle price (table 2). however, part of the sample either reduced the average bottle expenditure (34.1%) or completely stopped purchasing the product (22.5%).   strongly disagree disagree neutral agree strongly agree willingness to support italian wine producers buying national wine 2.6 2.6 24.6 37.7 32.4 feeling of isolation (inverted) 0.9 3.6 26.4 47.8 21.3 fear of the economic crisis 0.8 5.8 14.2 46.0 33.2 fear of the virus 3.8 15.0 33.0 40.1 8.1 refocus on me during the lockdown 2.4 8.7 30.9 44.3 13.7 notes: where not specified, n=1076. the % column reports the valid percentage. table 2. changes in wine purchase pattern following the lockdown.   frequency % wine purchase behavior in lockdown     does not buy wine 19 1.8 stopped buying wine 242 22.5 kept buying wine 815 75.7 average bottle price variation in lockdown reduced 367 34.1 unchanged 647 60.1 increased 62 5.8 notes: n=1076. 213drinking covid-19 away: wine consumption during the first lockdown in italy bio-based and applied economics 10(3): 207-218, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10044 in this regard, the presence of over 22% of respondents drinking wine from their personal stock while in lockdown (a 134% increase compared to the pre-pandemic scenario) suggests that a stop in wine purchases does not necessarily correspond to a reduction in its consumption. crosstabulations support this hypothesis, as the stop in wine purchase is not significantly related to reducing wine drinking frequency during the lockdown (chi-square: .67, p = .418). among respondents who kept purchasing wine, results highlight several changes in their wine consumption habits during the lockdown. first, mobility restrictions and confinement impacted both consumption occasions and buying channels. as can be observed in table 3, during the lockdown respondents consumed wine mostly with their relatives (78.1%) or alone (26.4%). virtual meetings became an alternative social drinking occasion for 13.5% of the sample. although the share of respondents is limited, this finding suggests virtual gatherings constituted a tool to keep the social dimension of wine alive since the majority of respondents who drunk wine on such occasions also reported socializing as a motivation for drinking it (57.2%; chi-square: 38.07, p<0.001). as regards sales channels, the mandatory closure of several business activities inevitably impacted on wine purchase patterns, especially for the large share of consumers who used to purchase wine directly from the producer (48.0%) or specialized wine shops (45.4%) (table 3). online wine shoppers increased by 43% (table 3), 43.4% of whom were first-timers (table 4). this picture leads us to assume that online wine sales partially counterbalanced the inaccessibility of most sales channels. the lockdown imposed by the covid-19 pandemic significantly affected wine consumption frequency as well: 23% of the sample kept drinking wine as often as before the pandemic, while the great majority either increased (32.2%) or decreased it (44.4%). model a and model b investigate factors impacting the decrease (dv1) and increase (dv2) in wine consumption frequency among wine consumers during the first lockdown (table 5). first, results reveal that none of the psychological difficulties directly affects neither dv1 nor dv2. both decrease and increase in wine consumption frequency are connected to a parallel modification of beer (varbc) and spirits (varsc), suggesting variations in wine consumption are attributable to a change in the overall alcoholic beverages’ consumption pattern. nevertheless, the effect of beer consumption frequency is considerably greater than that of spirits. accordingly, when considering the total expenditure for all alcoholic beverages in the lockdown, only that of wine (for model a and b) and beer (for model b) significantly affect the dv. focusing on model a (dv1), a reduced wine consumption frequency is related to a decreased beer consumption (varbc_red). accordingly, these respondents did not increase total expenditure on wine (increxp_w) and no significant effects emerge for variations in the total expenditure on beer (increxp_b). none of the sales channels show significant impact on the dv1. families with children (childy) are less likely to have reduced wine consumption in lockdown. on the contrary, a significant positive effect emerge with age, suggesting that older subjects have greater odds of shrinking their wine consumption frequency in lockdown. among the reasons for drinking wine, health and relaxation emerge as significant factors impacting dv1: while drinking to relax (r_relax) decreases the odds of reducing wine consumption frequency in lockdown, consuming wine for its health properties (r_health) seems to promote this behavioral modification. despite table 3. evolution of consumption contex and sale channel before and during the lockdown.     before the lockdown during the lockdown δ n. % n. % wine consumption context         friends 857 79.6 83 7.7 -90% family 754 70.1 840 78.1 11% colleagues 215 20.0 31 2.9 -86% alone 193 17.9 284 26.4 47% digital gatherings 11 1.0 145 13.5 1218% source of the wine consumed     supermarket 551 51.2 510 47.4 -7% direct sales 517 48.0 154 14.3 -70% specialized wine shop 489 45.4 112 10.4 -77% online 138 12.8 198 15.8 43% personal wine stock 103 9.6 241 22.4 134% food shop 66 6.1 66 6.1 0% take away shopping 6 0.6 10 0.9 67% note: n=1076 table 4. online wine shopping during the lockdown. online wine shopping in lockdown n. % purchased wine online in lockdown 198 15.8 for the first time 86 43.4 as usual 66 33.3 more frequently 46 23.2 notes: n=1076. 214 bio-based and applied economics 10(3): 207-218, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10044 giulia gastaldello, daniele mozzato, luca rossetto the closure of bars and ban on social gatherings, no significant effects emerge from drinking wine to socialize (r_soc) as well as from drinking with friends and colleagues before covid (bc_fricol). wine consumption frequency before the pandemic (wcons_b) negatively impacts dv1 for regular consumers (i.e., drinking wine table 5. lr on the decrease (dv1; model a) and increase (dv2; model b) of wine consumption frequency in lockdown.     a reduced wine consumption frequency in lockdown (dv1) b increased wine consumption frequency in lockdown (dv2) b s.e. wald exp(b) 95% c.i.for exp(b) sig. b s.e. wald exp(b)   95% c.i.for exp(b) sig. lower upper lower upper age 0.12 0.06 3.67 1.13 1.00 1.28 0.06  * -0.08 0.07 1.28 0.92 0.80 1.06 0.26   gender -0.13 0.17 0.57 0.88 0.63 1.23 0.45   -0.68 0.20 11.32 0.51 0.34 0.75 0.00 *** income (good) 0.10   0.95   0.31   0.86   income 1 (sufficient) 0.05 0.16 0.08 1.05 0.76 1.45 0.78   -0.01 0.19 0.00 0.99 0.68 1.44 0.96   income (bad) 0.06 0.29 0.04 1.06 0.60 1.89 0.84   -0.19 0.34 0.31 0.83 0.43 1.61 0.58   childy -0.44 0.17 6.71 0.65 0.46 0.90 0.01 ** 0.46 0.19 5.67 1.58 1.08 2.30 0.02 *** crisfear -0.10 0.09 1.28 0.90 0.76 1.08 0.26   0.04 0.10 0.13 1.04 0.85 1.27 0.72   isofeel 0.16 0.10 2.45 1.17 0.96 1.42 0.12   -0.14 0.12 1.52 0.87 0.69 1.09 0.22   virusfear 0.08 0.09 0.92 1.08 0.92 1.28 0.34   -0.08 0.10 0.67 0.92 0.76 1.12 0.41   refofeel -0.11 0.09 1.42 0.90 0.76 1.07 0.23   0.18 0.10 3.02 1.20 0.98 1.46 0.08 * varbc_no     61.74   0.00       46.82   0.00   varbc_red 1.28 0.19 44.28 3.59 2.47 5.24 0.00 *** 0.45 0.23 3.68 1.56 0.99 2.47 0.06 * varbc_incr -0.18 0.31 0.33 0.84 0.46 1.53 0.57   2.24 0.34 44.17 9.40 4.86 18.20 0.00 *** varsp_no     11.44   0.00       12.95   0.00   varspc_red 0.52 0.19 7.91 1.68 1.17 2.41 0.01 *** 0.28 0.22 1.57 1.32 0.86 2.03 0.21   varspc_incr -0.29 0.35 0.66 0.75 0.38 1.50 0.42   1.37 0.38 12.94 3.94 1.87 8.30 0.00 *** increxp_w -1.80 0.21 77.15 0.17 0.11 0.25 0.00 *** 2.77 0.21 169.74 15.92 10.50 24.13 0.00 *** increxp_b 0.36 0.27 1.72 1.43 0.84 2.44 0.19   -0.96 0.32 9.14 0.39 0.21 0.71 0.00 *** increxp_sp 0.33 0.38 0.76 1.40 0.66 2.96 0.39   -0.36 0.41 0.78 0.70 0.31 1.56 0.38   bl_alone 0.34 0.21 2.57 1.41 0.93 2.14 0.11   -0.19 0.25 0.62 0.83 0.51 1.33 0.43   bl_fam 0.36 0.19 3.76 1.44 1.00 2.07 0.05 ** 0.13 0.21 0.38 1.14 0.75 1.72 0.54   bl_fricol -0.15 0.22 0.46 0.86 0.56 1.32 0.50   0.32 0.26 1.49 1.38 0.82 2.31 0.22   lc_online 0.39 0.24 2.59 1.47 0.92 2.36 0.11   -0.31 0.28 1.25 0.73 0.43 1.26 0.26   freq_digimeet 0.02 0.08 0.04 1.02 0.86 1.20 0.84   -0.10 0.10 0.96 0.91 0.75 1.10 0.33   r_soc -0.22 0.17 1.66 0.80 0.57 1.12 0.20   -0.09 0.20 0.20 0.92 0.62 1.36 0.66   r_taste -0.11 0.19 0.35 0.89 0.62 1.30 0.56   0.50 0.23 4.59 1.65 1.04 2.61 0.03 ** r_food -0.11 0.18 0.41 0.89 0.64 1.26 0.52   0.18 0.21 0.73 1.19 0.80 1.79 0.39   r_health 0.81 0.26 10.22 2.26 1.37 3.72 0.00 *** -0.18 0.28 0.41 0.84 0.48 1.45 0.52   r_relax -0.43 0.19 4.84 0.65 0.45 0.96 0.03 ** 0.74 0.22 11.86 2.10 1.38 3.20 0.00 *** scbl_super 0.06 0.17 0.11 1.06 0.76 1.46 0.74   -0.13 0.20 0.44 0.88 0.60 1.29 0.51   scbl_online 0.39 0.23 2.70 1.47 0.93 2.32 0.10 -0.11 0.27 0.17 0.90 0.53 1.51 0.68   scbl_dsale 0.15 0.17 0.79 1.16 0.84 1.61 0.37   0.01 0.20 0.00 1.01 0.69 1.48 0.98   scbl_wshop -0.07 0.16 0.16 0.94 0.68 1.28 0.69   -0.18 0.19 0.93 0.84 0.58 1.21 0.34   wcons_b (occasional)     30.26       0.00       24.37   0.00   wcons_b (regular) -0.57 0.26 4.77 0.57 0.34 0.94 0.03 ** 0.85 0.32 7.20 2.33 1.26 4.34 0.01 ** wcons_b (daily) -1.04 0.19 28.84 0.35 0.24 0.52 0.00 ** 1.15 0.23 24.21 3.16 2.00 5.01 0.00 *** loc_supp -0.21 0.09 6.18 0.81 0.68 0.96 0.01 ** 0.22 0.10 4.65 1.24 1.02 1.51 0.03 ** constant 0.30 0.75 0.15 1.34 0.69   -4.26 0.90 22.43 0.01 0.00   notes: n=1018. * p < .09; ** p < .05; *** p < .001. a: hosmer and lemeshow test: chi-square 10.24, sig. 0.249. pseudo r-square: cox & snell=0.28, nagelkerke=0.38. accurancy= 75.6%. b: hosmer and lemeshow test: chi-square 11.93, sig. 0.154. pseudo r-square: cox & snell=0.36, nagelkerke=0.50. accurancy= 82.3%. 215drinking covid-19 away: wine consumption during the first lockdown in italy bio-based and applied economics 10(3): 207-218, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10044 at least once a week, and daily wine drinkers). moreover, people who used to drink wine with family members before covid (bc_fam) show higher odds for a decreased consumption frequency. finally, consumers willing to support italian wine producers (loc_supp) show significantly lower odds of drinking less frequently in lockdown. factors driving an increased consumption frequency in lockdown (model b; dv2) are having children (childy), willingness to refocus on oneself (refofeel), drinking wine for relaxation (r_relax) and for its palatability (r_taste), and the willingness to support domestic wine producers. indeed, all these predictors are connected to greater odds of drinking wine more often in lockdown. conversely, being female and spending more on beer decrease the odds of having consumed wine more often during the lockdown. model b highlights a potential substitution effect in favor of wine. dv2 is significantly affected by increasing (varbc_incr) and decreasing (varbc_red) beer consumption frequency, although the former’s effect shows a greater magnitude. crosstabulations (table 6) reveal that 30.7% of interviewees who drink wine more often in lockdown have simultaneously reduced both beer and spirits consumption frequency, and the 11.2% has reduced beer drinking only. this substitution effect involves less than half of the sample, since 53.2% respondents have increased wine and at least one other alcoholic beverage. 5. discussion and conclusions covid-19 has profoundly changed people’s lifestyle, disrupting everyday habits and exposing them to considerable psychological pressure. our results highlight that such pressure arises primarily from concerns for the economic and financial uncertainty caused by the pandemic, followed by the fear of the virus. wine consumption is confirmed to be deeply rooted in the italian population’s habits. descriptive analysis reveals a major pre-existing preference for wine, which was the most assiduously consumed alcoholic beverage before the lockdown. despite the disruptive effect of the pandemic, our results highlight that the vast majority of the sample kept purchasing wine (75.7%) without lowering the average price per bottle (60.1%). market data on agri-food products and supply during the pandemic confirm a moderate but positive trend for wine sales during the lockdown (+9%), performing better than other beverages (+6%) (ismea, 2020a, 2020b, 2020c). accordingly, most respondents kept consuming wine notwithstanding the substantial context changes. in this respect, both regular and daily wine drinkers, who are the most common consumers within the italian population (istat, 2020), are likely to have drunk wine more frequently during the lockdown rather than the reverse. consistent with wood et al. (2005), these findings represent an indicator of strength of the wine consumption habit. results also show that 22.5% of the sample stopped purchasing wine in lockdown, with a similar number of respondents (22.4%) who consumed wine from their wine stock. although the current study did not investigate the size of this stock, this finding partially explains the non-significant association between a stop of wine shopping and reducing wine consumption in lockdown. it also reveals the presence of a wine stock in an interesting slice of italian wine consumers, which calls for further investigations. nevertheless, the impact of shock and habits disruption emerges on sales channels, consumption occasions, and wine consumption frequency. following the pandemic, most consumers kept buying wine in supermarkets, while mobility restrictions significantly penalized other important sales channels such as wine shops and direct sales. the online channel partly benefited from the lockdown, recording a 43% increase and managing to attract new buyers. although the relevance of online wine sales is limited, these extraordinary circumstances may lead to long-run effects on this sale channel, accelerating its growth in two ways: by pushing consumers to try online wine shopping, and by encouraging wine retailers to improve/create their online offer. this positive trend is in line with market data, with the online demand for agri-food products recording a 141% growth during the two months of lockdown (iri, 2020). consumption occasions also suffered from the stringent limitations imposed, as the only options during a lockdown is consuming wine alone or with household members. table 6. substitution effect in favor of wine and beer during the lockdown.       increased wine consumption frequency in lockdown no yes count % count % other 287 40.8% 175 53.2% <sp, beer unchanged 54 7.8% 16 4.9% <beer, spirits unchanged 81 11.6% 37 11.2% <beer&sp 273 39.6% 106 30.7% notes: n = 1018. pearson’s chi-square test: 15.39; p = .002. 216 bio-based and applied economics 10(3): 207-218, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10044 giulia gastaldello, daniele mozzato, luca rossetto it is reasonable to believe that such effects are temporary, as the reduction of wine consumption frequency in lockdown by people who used to drink wine at family gatherings is likely a consequence of a forced separation from other family members. virtual gatherings emerge as a new drinking occasion, although for a limited share of consumers. in this study, the new trend of the virtual aperitivo is intended solely as virtual gatherings organized independently by consumers. given the prolonged duration of the pandemic and the importance of social occasions as a wine consumption motivation, the diffusion of virtual drinking activities may have increased. moreover, wineries and wine shops started offering online tasting experiences as both a marketing tool to keep existing loyal consumers and to attract new ones. therefore, further investigations are needed to deeply explore the future potential of virtual wine experiences and their role in wine marketing, providing suggestions on how to effectively design them. regarding wine consumption frequency, most respondents modified it either positively (32.2%) or negatively (44.4%). being a woman reduces the odds of having increased wine consumption frequency. since the whole population was subjected to the same considerable pressure originating from the pandemic, it seems that drinking wine did not represent a relief for women in lockdown as it potentially did for men. this result is in line with past findings from, e.g., dawson et al. (2005) and apa (2012). variations in wine consumption frequency appear to go hand-in-hand with other alcoholic beverages, moving in the same direction. notably, the increase in wine consumption frequency in lockdown is associated with a simultaneous change in spirits and especially beer. in this respect, we can assume that the lockdown may constitute a burden encouraging alcohol consumption. this finding is in line with the existing literature identifying traumatic situations as a promoter of alcoholic beverages consumption (e.g., bartone and homish, 2020; bartone et al., 2017; clay and parker, 2020; horton, 1943; powers and kutash, 1985). in this regard, it should be noted that this study focuses on consumption frequency, while no information is collected on volumes consumed. substitution effects are qualitatively evaluated based on changes in consumption frequency. a minor substitution effect in favor of wine is detected. still, its limited extent suggests that the lockdown pushes consumers to drink more often the alcoholic beverages they are used to consume rather than switching from one to another. regarding families, the model shows that having children increases the odds of a higher consumption frequency in lockdown. this finding suggests that forced 24-hour cohabitation and the prolonged home-confinement may turn parenthood into a reason for drinking more frequently. further analyses should be conducted to explore this relationship and the behavioural role of parenting as a stressor in the context of the pandemic. accordingly, relaxation and hedonistic health and well-being – i.e., focusing on the self (huta, 2015) – trigger an increase in wine consumption frequency, highlighting wine may have played a role in mitigating the psychological pressure caused by the lockdown in a context where other alternatives to relax where not available. besides, the fact that drinking wine for its health benefits is linked to a reduced wine consumption frequency in lockdown, jointly with the connection between a reduced wine and beer drinking frequency, suggest the context of the pandemic brought a share of respondents to re-evaluate their personal priorities (sigala, 2020; wood et al., 2005), while discouraging alcoholic beverages consumption. indeed, drinking alcohol in a context where people’s survival is at stake may assume a negative connotation (bazzani et al., 2020). to conclude, many factors impacting wine consumption seem to be context-related and therefore are expected to have short-term effects. nevertheless, some of them may affect wine demand in the long term: mainly, the emergence of virtual wine experiences and the growth in online wine shopping. still, wine consumption appears to be a strong habit among italian consumers, which managed to survive the profound context changes induced by the pandemic. these are encouraging signals for italian wine producers, especially considering the strong willingness to support domestic wineries that emerged among respondents. indeed, willingness to support domestic wine producers by purchasing their wines shows positive effects on wine consumption frequency in lockdown, promoting its increase. future studies should validate the results of this survey and highlight potential changes occurred with the evolution of the pandemic in light of the uncertainty around its future evolution, which creates a metamorphic context that makes it particularly difficult to forecast how consumers will react. indeed, on the shortrun, similar phenomena are capable of jeopardizing sectors dynamics (vergamini et al., 2021) with relevant financial consequences for the involved stakeholders. within the perspective of a prolonged health emergency, information on the development of wine consumers’ behavior in the current, unprecedented circumstances such as that provided by this study, is strategic to help actors of the wine sector in planning future market strategies. indeed, the continued circulation of the virus 217drinking covid-19 away: wine consumption during the first lockdown in italy bio-based and applied economics 10(3): 207-218, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10044 requires wine producers and stakeholders to adapt to covid-induced changes in consumer behavior that can no longer be looked at exclusively as transitory. acknowledgments the authors thank the european association of wine economists (euawe) board for involving us in the study. the authors kindly thank editor and reviewers for their valuable comments and suggestions references american psychological association (apa), (2012). gender and stress. arpaci, i., karataş, k., & baloğlu, m. 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(2005). changing circumstances, disrupting habits. journal of personality and social psychology, 88(6), 918–933. https://doi. org/10.1037/0022-3514.88.6.918 world medical association (wma) general assembly (2013). declaration of helsinki – ethical principles for medical research involving human subjects. fortaleza, brazil. volume 10, issue 3 2021 firenze university press the bioeconomy in economic literature: looking back, looking ahead davide viaggi1,*, fabio bartolini2, meri raggi3 the contribution of research to agricultural policy in europe alan matthews drinking covid-19 away: wine consumption during the first lockdown in italy giulia gastaldello*, daniele mozzato, luca rossetto estimating a global maidads demand system considering demography, climate and norms wolfgang britz the evolution of organic market between third-party certification and participatory guarantee systems gianluca iannucci1, giovanna sacchi2,* bio-based and applied economics 7(3): 265-277, 2018 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-7679 short communication a preliminary test on risk and ambiguity attitudes, and time preferences in decisions under uncertainty: towards a better explanation of participation in crop insurance schemes attilio coletta1, elisa giampietri2, fabio gaetano santeramo3,*, simone severini1, samuele trestini2 1 università degli studi della tuscia, department of agriculture and forest sciences (dafne), viterbo, italy 2 università degli studi di padova, department of land, environment, agriculture and forestry (tesaf), padova, italy 3 università degli studi di foggia, department of agricultural, food and environmental sciences (safe), foggia, italy date of submission: 2018 26th, july; accepted 2019 22nd, july abstract. the exposure of farmers to different (and increasing) risks has been recognized by the eu policy, which supports several risk management tools through the common agricultural policy (cap). despite the vulnerability of the agricultural sector, and the attention paid at the eu level, the uptake of such tools is generally low across eu countries. the italian case is emblematic: the uptake of subsidized crop insurance contracts is low, limited to few products, and concentrated in few areas. coherently, the interest of policy makers toward explaining these characteristics and in gaining insights on the interventions that may help promoting participation is intense. this contribution investigates behavioral aspects linked to choices under risk and ambiguity, and account for time preferences in order to mimic the scenario faced by the potential adopters of the subsidized crop insurance contracts in italy. data are collected through questionnaires submitted to students from agricultural colleges in three administrative regions located in northern, central and southern italy. results show that attitude toward risk, ambiguity, and impatience are correlated with the intrinsic characteristics of respondents. in addition, some of those attitudes may help explaining decisions under uncertainty. despite the empirical analysis is preliminary and focused on students, it allowed to validate a promising methodological approach capable of explaining farmer’s willingness to adopt (or renew) insurance contracts. by accounting for (currently under-investigated) behavioral aspects, it is likely to prove useful to re-design or implementing, more effectively, the current policies. keywords. insurance, subjective probabilities, risk preferences, choice experiment. jel codes. d81, d83, q18. *corresponding author: fabio.santeramo@unifg.it 266 attilio coletta et alii 1. introduction risk affects all economic activities, and the agricultural sector shows specific factors that make yields, input and output prices highly variable. the increased volatility of these variables was shown in recent years, and it is possibly due to frequent adverse phenomena and extreme climatic events. at european level all countries are affected, and italy seems one of the most spoiled country. the italian agricultural sector is largely exposed to risky events, as shown by trestini et al. in 2017. among eu members, from 1998 to 2006 italy registered the highest number of farms experiencing a decline in farm income exceeding -30% (on average) (european commission, 2009); moreover, 35% of italian farmers experienced income decrease events from 2007 to 2013 (european commission, 2017). according to the economic theory, price volatility should incentivize farmers to adopt risk management tools (rmt): put differently, the increasing uncertainty should increase the latent demand for rmt. the increasing uncertainty and the availability of new instruments introduced by the 2008 cap health check should have favoured the diffusion of these policy instruments (e.g., mutual funds and subsidized insurance contracts). however, the implementation of risk management tools is limited, and the adoption of these instruments is currently rather scarce. such a contingent scenario is worrisome, provided that a correct use of risk management policies would allow eu countries to increase the resilience of their agricultural sector to external shocks. the eu regulation 1305/2013 promotes three types of measures, respectively under art. 37, 38 and 39: crop insurance, mutual funds, and the income stabilization tool. the italian ministry has budgeted a large amount of financial resources to promote these measures but, despite a great attention and a large turmoil, the experiences on mutual funds and income stabilization tool are scant (severini et al., 2018; trestini et al., 2018), and subsidized single crop insurances are still the most adopted rmt. however, the subsidized insurance programs are not always stories of success. in italy, participation in crop insurance programs is low, heterogeneous, and (recently) declining (santeramo, 2019), making it a pressing issue for policymakers. this decline is also associated to recent policy changes. the last cap reform has moved the support to rmt to the rural development policy, changing the administrative rules of the system. in italy this transition has resulted in a lack of familiarity with the rules, in delays in payments for subsidies and indemnifications and, at the end, in a reduced uptake of crop insurance schemes. the current literature falls short in explaining the peculiarities of crop insurance adoption in italy, and more precisely, it has not explored the potential role of ambiguity aversion and time preferences on participation in crop insurance programs. understanding the behavioral aspects of potential adopters of rmt is crucial to both design and implement effective policy interventions and avoid low and sparse uptake. the italian case is an emblematic one and it allows to focus on long-standing issues that need to be solved at national and eu level. the italian (subsidized) crop insurance system is characterized by high adoption rate in the north, and low participation rate in central and south regions. apart from the main drivers of farmer behavior under uncertainty and of adoption of risk management tools, several attitudinal aspects are likely to matter. departures from rationality and non-coherent choices with respect to risk perception help explaining farm267towards a better explanation of participation in crop insurance schemes ers’ choices. a recent study (sutter et al., 2013) suggests that attitudes toward ambiguity, due to incomplete information, as well as differences in risk perception, and in time preferences are likely to play a pivotal role for decisions under uncertainty. this paper is a preliminary attempt to assess the validity of an empirical methodology to evaluate if and how behavioral factors (risk and ambiguity attitudes and time preferences) may affect the decision-making process under uncertainty. our setup has been inspired by the framework faced by potential adopters of crop insurance. the analysis, conducted on a sample of students of agricultural disciplines allows to conclude on whether the methodological approach is worth replication to a set of italian farmers, representative of the latent demand for crop insurance contracts. the analysis is divided in two steps. first, we investigate how socio-economic characteristics tend to influence risk aversion, ambiguity aversion and time preferences. second, we explore how socio-economic characteristics as well as risk aversion, ambiguity aversion and time preferences may help explaining choices under uncertainty (smoking, practicing sport and playing lottery). 2. on italian insurance market and factors affecting farmers’ adoption 2.1 the italian market for subsidized crop insurance contracts risks linked to natural disasters have been recognized since long-time in agriculture as unexpected sources of losses for farmers, especially for those highly vulnerable that are not adopters of risk management strategies. the shift from ex post compensations to exante measures, and to subsidized crop insurance contracts, has been a concrete effort to promote the diffusion of risk management strategies. according to ismea (2018), the italian market (2004-2010) is characterized by a limited adoption of insurance contracts. subsidized insurance market reached a maximum of 265,000 contracts in 2008, followed by declines in the number of contract subscriptions. differently, total compensation rose constantly, signalling the low (economic) sustainability of the system, exacerbated by an adversely selective participation process: as contacts’ prices rise, farmers with lower probability of facing adversities quit the market, contributing to the increase of the total amount of compensations paid by insurers (and by public funds). since 2010 the public contribution to contracts decreased to 65% (according to eu reg. 73/2009) and has been devoted (since 2014) to contracts that cover at least three climatic adversities. these changes do not seem to push the market too far. last (public) data referred to 2015 (ismea, 2018) depicts a similar picture: from 2010 to 2015 contracts have decreased by 20% (from 210,000 to 168,000), while the insured area remained unaltered (+5%); the insured value raised by 20% as well (from 4.8 to 5.6 billion euro), and it has generated a 4% increase in the premium paid by farmers and through public funds (from 279 to 381 mil euro). the geographical distribution of contracts tends to be concentrated in northern regions, which account for more than 80% of the insured value (ismea, 2018). in addition, only few products account for most of the total insured value: indeed, apple, corn, rice, grapes, and tomatoes account for 2/3 of the covered value. 268 attilio coletta et alii 2.2 on the drivers of crop insurance uptake the identification of the drivers of crop insurance uptake is still open and vivid (enjolras et al., 2011; santeramo et al., 2016). more important, there has been a limited effort in investigating how farmers’ behavioral aspects may help explaining the adoption and/or renewal of crop insurance contracts, exception made for menapace et al. (2015). key drivers of uptake are the age and the income level: ogurtsov et al. (2009) found a positive correlation for age and adoption of crop insurance contracts, while wąs and kobus (2018), liesivaara and myyrä (2017) and van winsen et al. (2016) suggested that the opposite is true; as for the income level, menapace et al. (2015) found a positive correlation with uptake, while wąs and kobus (2018) and farrin et al. (2016) concluded on the opposite direction for correlation. ambiguous results have also been found for risk aversion, which has been found positively correlated with age, according to nielsen et al. (2013) and van winsen et al. (2016), and negatively correlated according to franken et al. (2017) and goldstein et al. (2008). heterogeneous results are also reported for the farm size, positively correlated with risk awareness in franken et al. (2017), and negatively correlated with risk awareness according to van winsen et al. (2016). furthermore, the low participation level may be due to a low level of familiarity with the instrument (santeramo, 2018 and 2019; santeramo et al., 2016). subscription of new contracts tend to be influenced by size, degree of crop diversification and irrigated area (enjolras and sentis, 2011; finger and lehmann, 2012); moreover, santeramo et al. (2016) argued that farmers tend to consider crop diversification (and irrigation) and insurance contracts as alternate management strategies with a high degree of substitutability. the policy framework is also playing a role: for instance, greening requirements push toward crop diversification to help preserving the environment; measures of income support (e.g. direct payments or agri-environmental measures) are aimed at reducing famers’ income instability and may prove substitutes for other risk management tools (severini et al., 2017). a contingent scenario, faced by italian farmers, is that the bureaucratic aspects related to subscription and reimbursement procedures, and the delays in refunds (ismea, 2018), may have discouraged participation and renewal of crop insurance contracts. from 2010 to 2014 the share of new adopters (14%) of (subsidized) crop insurance contracts has exceeded the number of farmers who gave up (11%). differently, and possibly due to the delays in payments and to the (perceived) ambiguity of the newly adopted rules, in 2015 the quitters overcame new adopters, and the net balance between new entrants and leavers was largely negative (-11%). 3. methodology and data collection the above presented scenario has emphasized the importance of focusing on three specific aspects: risk aversion, ambiguity aversion, and time preferences. this paper investigates how attitudes toward uncertainty (risk and ambiguity) as well as time preferences influence risky decisions. the dataset includes data on 50 students from three different universities (faculty of agricultural sciences) in italy: namely, the university of padova 269towards a better explanation of participation in crop insurance schemes (padova) in the north, tuscia university (viterbo) in central italy and university of foggia (foggia) in the south. the research is part of a wider ongoing study aiming at investigating italian farmers’ decision making under uncertainty: particularly, the broader aim is to study the factors influencing the insurance schemes’ uptake. the experimental methodology is inspired by the canonical holt and laury (2002) choice lists and, more specifically, by the approach proposed by sutter et al. (2013). in order to elicit individual preferences related to risk aversion, ambiguity aversion and time preferences, respondents received a structured questionnaire with three experiments and ten control questions. more specifically, the first and the second experiments (fig. 1) are made by a list of 11 choices with two options each: at any given choice respondents choose between a sure payoff (option a), and a gamble (option b). the sure payoff is iteratively decreased (from 100 to 1€) so to elicit the indifference point between the lottery and the sure payoff. the lottery has been simulated by extracting a random number from a uniform distribution ranging from 1 to 100 being the number 50 excluded (in order to have symmetrical probability distributions between the two outcomes). in the first experiment, aimed at eliciting risk preferences, respondents may win (for instance) 100€ if the randomly extracted number ranges between 1 and 49, or nothing, if the randomly extracted number is larger than 51. in order to get respondents acquainted with the functioning of the lottery, respondents have been exposed to a computer simulation of ten random draws from 1 to 100 (the extraction of the number 50 implies a further extraction), and have been informed on the cases in which they would have won the lottery. the second experiment, aimed at eliciting ambiguity aversion, compares the choices for a sure payoff and a (ambiguous) lottery. the lottery pays out if, by extracting two random draws, the second extraction gives a larger number than the one extracted in the first place. the ambiguity arises by a peculiarity: the result of the first extraction is not revealed, whereas only the second extraction (and the outcome of the lottery) is revealed. for instance, by drawing the number 20 and successively the number 35, the lottery results in a winning outcome. finally, in the third experiment aiming at measuring time preferences (fig. 2), respondents received two lists (blocks) of ten choice sets each. each choice set consisted in two sure payoffs (a and b) that respondents may receive in different periods: option a is a “early payoff ” of 100€, whereas option b is a “late payoff ” which is increased from 100€ to 190€. depending on respondents’ preference for receiving a sure payoff earlier (i.e., “now”) or later (i.e., “in 12 months”), we elicited respondents’ attitude in delaying the win (or, put differently, their impatience). prior to the survey, we paid attention to ensuring that participants were able to understand the questions, and that the experiments were correctly explained. we design a ranfigure 1. example of a choice list for experiment 1 (risk attitude) and 2 (ambiguity attitude) option a option b 1 sure payoff (100€) lottery 2 sure payoff (90€) lottery 3 sure payoff (80€) lottery source: own elaboration. 270 attilio coletta et alii dom lottery incentive system (cubitt et al., 2019), often used in individual choice experiments, to motivate respondents to reveal their true preferences: at the end of the experiments we ran a real lottery with the ten percent of (randomly selected) respondents: if their questionnaires did not present incoherent answers (as found in all cases), they played the game included in the questionnaire with the possibility of winning part of the money of the bet (more precisely, 10% of the money at stake), in case of favourable outcome. the individual certainty equivalent (ce) has been calculated for experiment 1 and 2 (cer and cea, respectively), as midpoint between the two consequent payoffs for which the interviewee switched from option a (i.e., sure payoff) to option b (i.e., gamble). accordingly, ce represents the payoff that makes the individual indifferent between receiving the sure amount and gambling. to measure risk attitude (experiment 1), we calculated the coefficient of risk aversion (r) as follows (sutter et al., 2013): r = 1− cer π (1) with π representing the prize of the gamble (i.e., 100€). this coefficient ranges from 0 to 1, with values of r larger than 0.5 indicating risk aversion, whereas smaller than 0.5 risk loving and equal to 0.5 risk neutrality. moreover, in the second experiment we measured the coefficient of ambiguity attitude (a) as follows: a = cer − cea cer + cea (2) the coefficient a ranges from -1 to 1, with negative numbers representing ambiguity loving, 0 standing for ambiguity neutrality and positive numbers indicating ambiguity aversion. as regards the third experiment, we calculated the future equivalent (fe) of the fixed payoff as the midpoint between the two consequent later payoffs where the interviewee decided to switch from option a to b. the larger the fe, the larger the aversion for delayed payments (i.e., impatience). finally, in order to control for the main drivers of decisions under uncertainty, we collected information on age (age), gender (gender), number of university credits achieved (ects credits), average grade (max 30) (average grade), and on whether the respondent does not have a technical high school degree (degree), on smoking habits (being a smoker), on habits to practice physical activity (sport practicing), and on habits to play lottery or sport betting at least once a month (playing figure 2. example of a choice list for experiment 3 (time preference). option a option b 1 receive 100€ today receive 100€ in 12 months 2 receive 100€ today receive 110€ in 12 months 3 receive 100€ today receive 120€ in 12 months source: own elaboration. 271towards a better explanation of participation in crop insurance schemes lottery). finally, we recorded whether the respondent is owner (or son of the owner) of a farm (family farm) and, whether the respondent has ever worked on a farm even for a short period of time (farmworker). the empirical strategy is admittedly simple, yet rigorous and comparable with the approach suggested in sutter et al. (2013). first, we use a linear regression to conclude on the effects of some socio-demographic variables on: i) the coefficient of risk aversion (r), ii) the coefficient of ambiguity aversion (a), iii) time preferences (i.e., future equivalent at 12 months). second, we use a linear regression to investigate how risk aversion, ambiguity aversion and time preferences (fe_12m) influence behaviors characterized by decisions under uncertainty: i) being a smoker; ii) sport practicing; iii) playing lottery. 4. hypothesis testing and results as shown in table 1, the sample consists of 78 observations, mostly male students (78%). most participants have not a technical high school background (51%), are not smokers (64%), practice sports activities (60%), and do not play lotteries (80%). the average number of credits acquired by sampled students is 132, while the average grade is 26. in terms of coefficients of risk aversion and risk ambiguity, we have quite heterogeneous results: the coefficient of risk aversion ranges from 0.05 to 0.95 and the coefficient of ambiguity aversion ranges from -0.50 to 0.83. similarly, we have time preferences computed at 12 months ranging from 105 to 185. the sample is mainly composed of risk averse (51%) and ambiguity averse students (51%), whereas the future equivalent shows a greater impatience for risk neutral and ambiguity averse subjects (table 2). we regress attitudes toward risk and ambiguity on control factors (table 3). the considered observable characteristics do not allow to explain these attitudes. regarding risk aversion, only the variable “degree” is positively correlated with risk aversion, regardless of students’ career characteristics (number of credits acquired) and average grade, and of respondent’s social characteristics (gender, age, farm owner and farming experience). there are no significant coefficients in the case of ambiguity aversion. results seems to be in line with studies (e.g. sutter et al., 2013) that refer risk attitude and ambiguity not influenced by ordinarily observable characteristics. as shown in table 4, we also found a positive significant correlation between the degree of impatience and gender, degree and past experience in farm work, showing that males with non-technical degree are less impatient, while subjects who already had a work experience related to agricultural sector are more impatient. conversely, we did not find any relevant effect for risk and ambiguity aversion. in general, we found that attitudes toward uncertainty (risk aversion, ambiguity aversion, and impatience) are correlated with intrinsic characteristics of the students, hereafter referred as control factors. following sutter et al. (2013) we use the control factors (age, gender, degree, ects credits, average grade, family farm, and farmworker) and the attitudes toward risk, ambiguity and time, to explain decisions under uncertainty. we regress “being a smoker”, “sport practicing” and “playing lottery” on control factors and variables on attitudes. we found that average grade and risk aversion are statistically significant having a negative effect on being a smoker, whereas impatience has a slight positive effect on the 272 attilio coletta et alii same characteristic (table 5). impatience seems to play a slight role on sport practicing too, being instead negatively correlated. regarding playing lottery, a significant positive correlation emerged for gender (all respondents that practice gambling are males), numtable 1. descriptive statistics of the sample (n = 78). variable type   % mean std min max age continuous     23.39 2.22 20 29 gender dummy 1 = male 78.20     0 = female 21.80         degree1 dummy 1 = yes 51.30         0 = no 48.70         ects credits2 continuous     131.51 57.99 23 300 average grade (max 30) continuous     25.72 2.04 21 29.7 family farm dummy 1 = yes 28.20     0 = no 71.80     farm worker dummy 1 = yes 61.50     0 = no 38.50     being a smoker dummy 1 = yes 35.90     0 = no 64.10     sport practicing dummy 1 = yes 60.30     0 = no 39.70     playing lottery dummy 1 = yes 20.50     0 = no 79.50 r continuous     0.48 0.16 0.05 0.95 a continuous     0.08 0.22 -0.50 0.83 fe_12m continuous     146.54 20.83 105 185 1 subjects without a technical high school background (“liceo” in italy). 2 ects credits express the volume of learning based on the defined learning outcomes and their associated workload. 60 ects credits are allocated to the learning. table 2. risk and ambiguity attitude (%) and future equivalent (n = 78). category % average fe_12m1 risk averse 51.3% 146.50 (20.07) risk neutral 24.4% 149.21 (24.79) risk seeker 24.4% 143.95 (18.83) ambiguity averse 51.3% 148.00 (20.78) ambiguity neutral 19.2% 147.00 (23.36) ambiguity seeker 29.5% 143.70 (19.84) 1 standard deviations are reported in parentheses. 273towards a better explanation of participation in crop insurance schemes table 3. ols risk aversion (r) and ambiguity aversion (a). dep. var. . risk aversion (r) ambiguity aversion (a) β s.e. p>|t| β s.e. p>|t| age 0.003 0.010 0.756 -0.012 0.013 0.355 gender 0.033 0.046 0.476 0.008 0.063 0.900 degree 0.068 0.040 0.088* -0.082 0.055 0.137 ects credits -0.001 0.001 0.185 0.001 0.001 0.448 average grade 0.008 0.010 0.456 0.015 0.014 0.307 family farm -0.032 0.046 0.486 -0.036 0.064 0.568 farmworker 0.006 0.042 0.888 0.024 0.058 0.678 cons 0.278 0.328 0.401 -0.036 0.457 0.983 obs 78 78 prob > f 0.574 0.695 adj r2 -0.017 -0.031 note: *** significant at the 1% level; ** significant at the 5% level; * significant at the 10% level. table 4. ols impatience (fe_12m). dep. var. future equivalent 12 months (fe_12m) β s.e. p>|t| age 0.039 1.234 0.975 gender -9.918 5.831 0.094* degree -8.656 5.146 0.097* ects credits -0.037 0.053 0.484 average grade 0.998 1.330 0.455 family farm -7.290 5.873 0.219 farmworker 9.760 5.331 0.072* r 11.212 16.183 0.491 a 9.264 11.626 0.428 cons 127.054 42.084 0.004 obs 78 prob > f 0.206 adj r2 0.045 note: *** significant at the 1% level; ** significant at the 5% level; * significant at the 10% level. 274 attilio coletta et alii ber of credits acquired (with a positive slight coefficient close to zero) and being part of a family involved in farming activities. average grade shows negative correlation indeed. respondents showing little risk aversion and high levels of impatience smoke more, whereas less impatient individuals practice sport more. men are found to play lottery more than women. as shown by “ects credits”, students up to date with credits play lottery more, whereas “average grade” shows that best students play lottery and smoke to a lesser extent. interestingly, the higher the impatience (i.e., subjects who have a higher future equivalent with 12 month-delay condition), the less they practice sport. lastly, ambiguity aversion coefficients don’t show significant relations with the analysed dependent variables. to summarize, both observable characteristics and behavioral characteristics (risk aversion, ambiguity aversion and time preferences) help explaining choices under uncertainty, particularly smoking and playing lottery. it is important to note that, as expected, risk aversion is negatively correlated with smoking while impatience is positively correlated with smoking while negatively with practicing sport. 5. concluding remarks risk management policies for the primary sector are under the spotlight in the eu: large subsidies have been granted for crop insurance programs and mutual funds. the eu regulation 1305/2013 establishes rules and funds that may be adopted by member states to promote participation in crop insurance programs (art. 37), to start and manage mutual funds (art. 38) and to enhance the start of the income stabilization tool (art. 39). despite table 5. ols estimates on being a smoker, sport practicing, and playing lottery. dep. var. being a smoker sport practicing playing lottery β s.e. p>|t| β s.e. p>|t| β s.e. p>|t| age 0.029 0.028 0.310 -0.022 0.030 0.465 -0.010 0.022 0.643 gender -0.091 0.136 0.506 0.169 0.146 0.248 0.265 0.108 0.016** degree 0.139 0.120 0.250 -0.071 0.128 0.584 0.116 0.095 0.225 ects credits -0.001 0.001 0.986 0.001 0.001 0.739 0.002 0.001 0.019** average grade -0.056 0.031 0.074* 0.038 0.033 0.250 -0.063 0.024 0.011** family farm -0.036 0.136 0.792 0.078 0.145 0.594 0.215 0.107 0.050* farmworker 0.075 0.125 0.548 -0.033 0.134 0.805 0.021 0.099 0.830 r -0.914 0.371 0.016** 0.232 0.397 0.561 -0.247 0.294 0.404 a -0.394 0.267 0.145 0.242 0.286 0.399 -0.170 0.211 0.423 fe_12m 0.005 0.003 0.093* -0.005 0.003 0.079* -0.001 0.002 0.523 cons 0.850 1.025 0.410 0.636 1.095 0.563 1.767 0.811 0.033 obs. 78 78 78 prob > f 0.134 0.590 0.008 adj r2 0.069 -0.021 0.179 note: *** significant at the 1% level; ** significant at the 5% level; * significant at the 10% level. 275towards a better explanation of participation in crop insurance schemes the clear interest of the policymakers, the academic debate seems behind. the economic literature provides several hints to explain farmers’ uptake in crop insurance programs, but several determinants (other than farm size, farmers’ education, relationships with other risk management strategies, and insurance premia) are still under-investigated. in particular, while the literature on insurance programs (i.e. health, car and life insurance) has emphasized the role of information, and of individual attitudes toward uncertainty, ambiguity and impatience, there is little evidence on the role of ambiguity and impatience on farmers’ decision to adopt crop insurance contracts. based on these premises, we tested the validity of a methodology in exploring how risk and ambiguity aversion, and impatience may influence the decision-making process for risky activities. our test, conducted on a sample of students, has been calibrated on behavioral aspects that are likely to matter for potential adopters of (subsidized) crop insurance contracts. we asked students involved in university programs related to agricultural sciences to declare if they experienced working in a farm. similarly, we investigated decisions under uncertainty proxying risky decisions such as those related to the adoption of crop insurance programs. we found that the attitudes toward uncertainty (risk aversion, ambiguity aversion, and time preferences) are weakly correlated with some intrinsic characteristics of the students. these attitudes cannot be satisfactorily explained by few observable characteristics. in contrast, we found evidence that attitudes toward risk and impatience may help explaining agents’ decisions under uncertainty. this suggests including agents’ attitudes in future research to prevent biased inference due to missing explanatory factors which would lead to ineffective policy recommendations. despite the analysis is still preliminary and applied to students, the approach we have taken seems promising in explaining potential residual factors that may affect farmer’s willingness to adopt (or renew) insurance contracts. hence, future research on this latter issue should take into consideration not only farmers’ risk aversion but ambiguity aversion and time preferences as well. these factors may be used to explain the limited (and heterogeneous) uptake of insurances. furthermore, the empirical findings may help to better design and manage future policy measures: understanding the role of time preferences may be useful to address how delayed payments of reimbursements and indemnities may discourage participation. 6. references cubitt, r., van de kuilen, g. and mukerji, s. 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(2017). toward the implementation of the income stabilization tool: an analysis of factors affecting the probability of farm income losses in italy. new medit 16(4): 24-30. 277towards a better explanation of participation in crop insurance schemes trestini, s., szathvary, s., pomarici, e. and boatto, v. (2018). assessing the risk profile of dairy farms: application of the income stabilisation tool in italy. agricultural finance review 78(2): 195-208. van winsen, f., de mey, y., lauwers, l., van passel, s., vancauteren, m. and wauters, e. (2016). determinants of risk behavior: effects of perceived risks and risk attitude on farmer’s adoption of risk management strategies. journal of risk research 19(1): 56-78. wąs, a. and kobus, p. (2018). factors differentiating the level of crop insurance at polish farms. agricultural finance review 78(2): 209-222. positive mathematical programming and risk analysis quirino paris the hedonic contents of italian super premium extra-virgin olive oils luca cacchiarelli1,*, anna carbone2, tiziana laureti1, alessandro sorrentino1 corporate r&d and the performance of food-processing firms: evidence from europe, japan and north america heinrich hockmann1, pedro andres garzon delvaux2,*, peter voigt3, pavel ciaian2, sergio gomez y paloma2 can menu labeling affect away-from-home-dietary choices? elena castellari1,*, stéphan marette2, daniele moro3, paolo sckokai1 a preliminary test on risk and ambiguity attitudes, and time preferences in decisions under uncertainty: towards a better explanation of participation in crop insurance schemes attilio coletta1, elisa giampietri2, fabio gaetano santeramo3,*, simone severini1, samuele trestini2 bio -based and a ppl ied economics bae bio-based and applied economics 11(2): 171-184, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-10310 copyright: © 2022 g. gios, s. farinelli, f. kheiraoui, f. martini, j.g. orlando. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: g. gios, s. farinelli, f. kheiraoui, f. martini, j.g. orlando (2022). pesticides, crop choices and changes in well-being. bio-based and applied economics 11(2): 171-184. doi: 10.36253/bae10310 received: january 5, 2021 accepted: april 13, 2022 published: august 30, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini. orcid gg: 0000-0003-0855-0130 sf: 0000-0001-9448-7638 jgo: 0000-0003-2648-3971 pesticides, crop choices and changes in wellbeing geremia gios1,*, stefano farinelli2, flavia kheiraoui3, fabrizio martini4, jacopo gabriele orlando5 1 department of economy and management, university of trento, italy 2 geologist registered with the tuscan order of geologists, italy. al1782a@geologitoscana. net 3 public health institute, university cattolica del sacro cuore, italy 4 department of economy and management, university of trento, italy 5 department of agricultural, food and agro-environmental sciences, university of pisa, pisa, italy. j.orlando@studenti.unipi.it * corresponding author.e-mail: geremia.gios@unitn.it abstract. this study investigates how pesticide risk indicators (pris) can be applied to help develop sound economic policies. we modified one of the numerous pris proposed over the years, the environmental impact quotient (eiq), originally developed for the fruit industry, to consider co-formulants and adjuvants. the new formula includes three components representing the externalities of farm worker risk, consumer risk, and ecological risk. it also considers the potential externalities of the use of pesticides on residents living near the farms where these products are used. we applied the modified eiq to two areas located in central italy (the chiana valley in tuscany and the tiber and upper tiber valleys in tuscany/umbria), surveying a sample of farms to determine the quantity and types of pesticides used on five crops: durum wheat, soft wheat, corn, tobacco, and olives. after calculating the impact quotient, we used data from a survey conducted in a different italian region regarding the willingness to pay (wtp) for a pesticide-free environment and determined the wtp for even minimal changes in that quotient. using those results, we simulated the changes in welfare (calculated as changes in willingness to pay) that would result from modifying the amount of land used for each crop. our findings indicate that the proposed wtp indicator may have broad utility and that its application may lead to enhanced awareness of the consequences of pesticide use in farming. keywords: pesticides, impact indicators, teiq, pesticide externalities. jel codes: q10, q15. 1. introduction the agri-food sector is nowadays asked to change approach towards production, taking into account the impact of its activities on the environment. in 2020, as part of the european green deal, the from farm to fork strategy highlighted the need to transform the european food system into a healthier, http://creativecommons.org/licenses/by/4.0/legalcode 172 bio-based and applied economics 11(2): 171-184, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10310 geremia gios et al. fairer and more sustainable system. among its goals, the strategy aims to reduce by 50% the use of plant protection products. previously, the directive 128/2009 already raised the issue of a more sustainable use of plant protection products. however, the lack of knowledge about the overall effects of these products on health and the environment makes it more difficult to reach the goal. the harmful effects of plant protection products (ppps), like those of many pollutants, have not been fully established. there are generally significant obstacles to attempting to measure pesticide negative externalities, which in many cases are compounded by the irreversibility of some of those effects (turner et al., 2003). accurate and realistic measurement of the environmental externalities caused using ppps would require the simultaneous assessment of all their potential harms concerning human health and natural capital (pretty et al., 2000)1. while there is abundant research into consumer health and the protection of farm workers, few studies have investigated the effects of pesticide use on people living near the land where such products are employed. however, the widespread urbanization of rural areas and the proximity of intensive farming to residential areas or other locations where people frequently visit have made this an increasingly important issue (targetti et al., 2020). the most common approach to ascertaining the consequences of pesticide use is to determine the relationship between a pollutant’s concentration in the environment and its effects, evaluating the risk entails an analysis of the “dose” (pollution level) and “response” (effect). in general, the three factors that must be considered when examining the environmental damage caused by ppps are hazard (the potential harm caused), exposure, and risk, where risk is the likelihood that the hazardous effect will occur and depends on the interaction between the hazard and exposure. other factors important for assessing the externalities caused by ppps are their characteristics of selectivity, the spectrum of action, penetration capacity and systemic action. by law, plant protection products must be evaluated for potential hazards and, where necessary, classified for their toxicological, ecotoxicological, and physicochemical effects. ppps are currently classified based on acute and chronic toxicity. according to directive (ec) 2009/128 of the european parliament, a national action plan for the sustainable use of pesticides must include “indicators to monitor the use of plant protection products containing active substances of particular concern.” the standard variable is the amount of pesticide per hec1 some definitions of technical terms have been provided in the glossary to appendix 3 to avoid overburdening the paper. tare of farmland. as observed by devillers et al. (2005) and ioriatti et al. (2011), it has become evident that simply measuring the quantity of ppps used is not sufficient to estimate the risk and characteristics of exposure. to address this shortcoming, the scientific community has developed a wide range of tools to estimate the impact of ppps more accurately. these tools are generally known as pesticide risk indicators (pris). pesticide risk indicators (pris) have also been used to assess the environmental impact of certain plant disease control programs over time in different locations, to evaluate the impact of farming and plant protection policies (gallivan et al., 2001; greitens and day, 2007), and to identify changes in environmental risks that require attention (ioriatti, 2011). the scientific community has developed several pris.2 for example, deviller et al. (2005) presented an exhaustive list of dozens of pris and a grid describing each one’s components, formulation methods, advantages, and limitations. generally, it is indeed challenging to find an acceptable balance between the benefits of a simplified system and a more elaborate model which can provide a greater wealth of information but is harder to use. furthermore, each of the available methods for devising pris has strengths and weaknesses that take on different degrees of importance depending on the intended purpose. finally, regardless of the specified purpose, the methods for formulating pris can also simply identify changes in the environment or seek to quantify their extent and meaning (ioriatti and martini, 2011). this article aims to study the possibility of assessing economically the consequences of ppps reduction in a specific area. in this evaluation also the impact of coformulants and adjuvants have been considered as well as drift effect for bystanders and locals. for this purpose, an indicator has been integrated with few components in order to estimate the impact of ppps used in an area located in the tuscany region. data have been collected among a representative sample of farms. in this way the economic value of the externalities has been assessed. finally, a simulation was conducted hypnotizing the substitution of high-impact crops with low-impact ones. 2 pris have been used in various parts of italy, sometimes on an experimental basis, to evaluate environmental policies and plant protection practices. (devillers et al., 2005) and the eiq at the centro vitivinicolo provinciale of brescia (2008) the piedmont region’s for rural development plan2000-2006 the eiq has also been used in international research, (leach and mumford, 2008). 173pesticides, crop choices and changes in well-being bio-based and applied economics 11(2): 171-184, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10310 2. materials and methods pesticide risk indicators usually combine hazard and exposure information with data on the quantity of the pesticide used and under what conditions. to a large extent, hazard information can be found on the pesticide’s safety data sheet (sds)3. in this study, too, sdss were used as a source of information for the assessment of health and environmental risks. sections 2 and 3 of an sds list of all pesticide hazardous ingredients4, along with their concentrations or ranges of concentration. these sections also contain the hazard statements that are assigned according to their physicochemical, health and environmental risks. to provide consistent estimates, this work follows the methodology recommended, amongst others, by ioriatti et al. (2011). for the purposes of this study, where safety data sheets did not provide sufficient hazard information or a detailed breakdown of ingredients (as was sometimes the case of formulations that are no longer registered), the pesticide properties database (ppdb)5 or the safety data sheets of similar products were used as sources for toxicological information. among the possible pris which may be considered in this study, we chose to use a modified version of the environmental impact quotient (eiq), which was originally developed to help consultants, who were promoting integrated fruit production in new york state, select low-impact pest control methods (ioriatti et al., 2011; kovach et al., 1992).6 like most pris, the original eiq does not consider co-formulants, for which information on identity, chemical properties, and health and environmental impact is rarely available. surgan et al. (2010) raised some criticisms regarding pri methodology, demonstrating that, concerning farm workers’ health, the inert ingredients of a ppp can sometimes have a higher impact score, as determined from the eiq, than its active ingredients. this means that relying solely on the active ingredient for measurement purposes may underestimate the potential adverse impact of a cer3 by the law, the safety data sheet that must be reported on the packaging of any pesticide shall include any health and safety information for the user. 4 the hazard statements are described in appendix 1. 5 see (http://sitem.herts.ac.uk/aeru/ppdb/en/index.htm)) or the safety data sheets of similar products were used as sources for toxicological information. 6 this type of approach is used not only to compare the impact of different plant production strategies, but also to assess the environmental benefits of integrated fruit production (agnello et al., 2009), to evaluate the overall impact of plant protection methods on different crops in a certain territory (ioriatti and martini, 2011), and to monitor the success of specific plant protection regulations (cross and edward-jones, 2006; gallivan et al., 2001). tain ppp formulation. in response to this criticism, we developed a modified eiq for this study that considers all substances in a preparation that pose a risk to human health or the environment, as stated on safety data sheets (available at http://sds-agrofarma.imagelinenetwork.com) in accordance with directive 91/155/eec as amended by directive 2001/58/ec. as originally formulated, the eiq is a rating system that evaluates product’s active ingredients about their potential adverse impact on farm workers, consumers, and terrestrial and aquatic organisms (ioriatti et al., 2011). the primary module of the eiq is a simple algebraic equation that generates a composite index of environmental impacts for each pesticide. a second module produces a field rating by incorporating variables related to the use of the ppp in specific situations (dose per hectare and concentration of active ingredients). the third step of the eiq method is to estimate the impact of different pest control strategies by combining the eiq scores of each pesticide treatment deemed necessary for a working farm. the result is the “eiq field rating,” which can be used to compare the environmental impact of alternative strategies for a given farm over a specified period of time. in the last 15 years, several authors have proposed modifications to the eiq. of the various possibilities, for this study, we chose a modified formula that considers the other substances in a product (co-formulants, adjuvants, etc.) in addition to its active ingredients (ioriatti et al., 2011). in essence, the modified formula (neweiq) is based on the same principles as the original eiq (kovach et al., 1992) but considers the overall impact of a commercial ppp as used in farming. by using the neweiq it is, therefore, possible to determine the impact of all hazardous active ingredients on the agricultural workers, consumers, and the environment. more in detail, there are three components of the neweiq. they can be written as follows: neweiqi=(x1 +x2 +x3)/3 (1) with x1=c[(dt*5)+(dt*p)] (2) x2=[(c*p*sy)+(l)] (3) x3=[(f*p) +(t*p*5)+(z*p*3)] (4) therefore: neweiqi={c[(dt*5)+(dt*p)]+[(c*p*sy)+(l)]+[(f*p) +(t*p*5)+(z*p*3)+]}/3 (5) 174 bio-based and applied economics 11(2): 171-184, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10310 geremia gios et al. where: i = each individual ingredient of the plant protection product; dt= acute toxicity; c= chronic toxicity; p = average score related to the active ingredient persistence; f = toxicity to aquatic organisms; l = longterm risk to aquatic organisms; z = toxicity to bees; t = toxicity to other terrestrial organisms; it can be observed that the first component, x1, measures the risk to farm workers and is defined as the sum of exposure by workers who apply the ppp (dt*5) and to workers who pick the produce (dt*p), multiplied by the long-term health effect or chronic toxicity (c). within the farm worker component, applicator exposure is determined by multiplying the acute toxicity score (dt) by a coefficient of 5, to account for the increased risk associated with handling concentrated ppps. picker exposure is defined as acute toxicity (dt) multiplied by the score representing the product’s half-life after application (ioriatti et al., 2011). the second component, x2, represents consumer risk and is defined as the sum of potential consumer exposure (c*p*sy) plus a score representing the risk of long-term adverse effects on aquatic organisms. the impact on aquatic organisms is included in consumer risk because it involves the stability of chemicals in the groundwater, which may affect human health (through drinking contaminated water) as well as wildlife (ioriatti et al., 2011). the third component, x3, represents the ecological element in the equation and refers to the impact on the water and terrestrial systems. the environmental impact on water systems is determined by multiplying the score for chemical toxicity to aquatic organisms (f) by the risk of long-term adverse effects on the aquatic environment (l). the impact on terrestrial systems is the sum of the chemical effects on bees (z*p*3) and on other terrestrial organisms (t*p*5). because terrestrial organisms are more likely than aquatic ones to come into contact with commercial farming systems, they are given greater weight by multiplying the risk rating for bees by three and the risk rating for other terrestrial organisms by five (ioriatti et al., 2011). if we examine the externalities of the use of pesticides on residents living near the farms where these products are used, a fourth component must be included. the premise used for quantifying this new component is that residents’ exposure is like that of one of farm workers, without the risk associated with handling concentrated ppps, but with the added risk of not using personal protective equipment. in addition, exposure: a. correlates with drift, or the distance of the residence and/or place of transit from the treated farmland. based on the results of a study in the province of bolzano (clausing, 2016; dallemule, 2014; federazione protezionisti sudtirolesi, 2017), we assumed that drift would affect areas within 500 m of pesticide-treated crops.7 this is a conservative value as recent investigations in val di sole (tn) have shown the possibility of drifts up to 10 km (favaro et al., 2019). because the exposure dose declines as distance increases, a normalization factor of 0.2 (assuming logarithmic decline as a function of distance) was used to determine chronic toxicity (c) and acute toxicity (dt), taking persistence (p) into account; b. depends on the number of individuals in the area affected by drift. here, potentially exposed persons were placed into two categories: b1) workers at other local farms; and b2) residents. ideally, tourists and hikers should also be included, but given the difficulty of finding reliable data for these categories, it was decided to omit them from this study. to normalize the x1 component, the number of individuals (workers at other farms and residents) was used as a denominator with respect to the acreage of the crop in question. the workers were then allocated to the various crops by tallying the total number of farm workers in the area and dividing that value based on rica-inea8 data on each crop’s required hours of work per hectare; c. depends on potential exposure time. this obviously differs for the two categories of individuals, farm workers and residents. the potential exposure time of the farm workers other than sprayers was estimated to be half that of the sprayers, assuming that they spent six out of twelve daylight hours outdoors. for residents, it was assumed that potential exposure time was one sixth that of the individuals who spray crops with ppps, corresponding to the number of daylight hours they spent outdoors (two out of twelve). given all these factors, the relative likelihood that residents and bystanders, in comparison with farm workers, will be exposed to pesticides through drift can be estimated as: c[(dt*p)*(ha1/n1)*0.2*0.5]+c[(dt*p)*(ha1/ n2)*0.2*0.17] (6) where: ha1 = hectares occupied by the crop in question; n1 = number of farm workers in the area affected by drift (excluding those working on the crop in question); 7 clearly, this distance is purely indicative and should be quantified, where possible, on the basis of measurements taken from different areas with respect to topography and wind patterns. 8 italian farm accountancy data network. it is based on a sample of italian farms and represents the only source of microeconomic data harmonised at agricultural level. 175pesticides, crop choices and changes in well-being bio-based and applied economics 11(2): 171-184, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10310 n2 = number of residents in the area affected by drift.   we can therefore define a new indicator, teiq, to consider this fourth component. therefore, the indicator [1] neweiqi becomes: teiqi=(x1+x2+x3+x4); (7) or, in extended form as: teiqi={c[(dt*5)+(dt*p)]+[(c*p*sy)+(l)]+[(f*p)+(t*p *5)+(z*p*3)+]+ c[(dt*p)*(n2/n1)*0.05]+c[(dt*p)*(n2/ n1)*0.017].9 (8) this risk index accommodates all hazardous ingredients in a ppp and provides a classification system that may be fairly easy to implement using farmers’ mandatory logbooks of pesticide treatments. for this new index, too, the weight assigned to each kind of hazard depends on the rating system used to classify the risks that a given substance or formulation poses to humans and the environment. the rating system derives from directive 67/548/eec or directive 1999/45/ec and is set by an official agency in accordance with the biological and physicochemical properties of the ingredients and the outcome of toxicological studies (ioriatti et al., 2011). the modified rating system (teiq) does not overcome all the accuracy limitations of pris for estimating the health and environmental hazards of pesticides (greitens and day, 2007; levitan et al., 1995; van bol et al., 2003), but it is the first to consider any potentially dangerous ingredients of a formulated product, which can, in some cases, have a greater impact than the active ingredient alone (surgan et al., 2010). once the teiqi has been calculated for every hazardous ingredient (i), the overall score for a pesticide, teiqp, is obtained by combining all the single-ingredient teiqi scores plus a teiqf score for the entire product. the teiqf is based on the hazard statements reported in section 16 of the sds with reference to the health, safety, and environmental labelling required by directives 67/548/eec and 1999/45/ec. hazard statements currently differ according to whether the ppp was registered under the old standards (directive 67/548/eec, incorporated into italian law by legislative decree 52/1997) (dsd classification with r-statements), or under the newer regulation (ec) 1107/2009 (clp regulation with h-statements). agrofarma (2014) has proposed a chart for converting from dsd to clp clas9 to compare the impacts measured by using the new eiq indicator with those assessed through the teiq, the former new eiq should be multiplied by 3. sifications, which makes it possible to leave the scoring method more or less unchanged as it is defined in ioratti et al. (2011). the transition to the new safety sheet and labeling standards was completed in 2017. to summarize: teiqp = teiqi1 + teiqi2 + … teiqin + teiqf. (9) this step constitutes the first module of the neweiq. the second and third modules incorporate the dosage of formulated products actually used on crops throughout the season, to estimate a farm’s yearly neweiq score .10 3. a study in the tiber valley (tuscany and umbria) and chiana valley (tuscany) this study evaluates the impact of pesticide use in two parts of central italy: on one side the tiber valley and upper tiber valley, located in the tuscany region neighboring the umbria region, and on the other the chiana valley, located in the tuscany region in the province of arezzo. appendix 2 describes the agricultural features of the two areas. data was gathered from 16 farms in the tiber valley areas and 10 farms in the chiana valley on the quantity and type of pesticides used in the regions. the data was collected in person every two weeks from the logbooks compiled throughout the crop year.11 we focused on annual crops, being more easily changeable compared to tree crops (such as vines and olive trees). we also included olives, because this crop is so prevalent in the area, albeit on small parcels of land at most of the farms studied. the farms specialized mostly in arable crops like tobacco, corn, and wheat (durum and soft), while some of them also grew olives or used the land as meadows and pastures. table 2 shows their overall crop allocation. to calculate the impact quotient, we began with safety data sheets (sdss), specifically sections 2 and 3, that list all hazardous ingredients along with their concentrations or concentration range, together with the hazard statements assigned as a function of physicochemical, health, and environmental risks. pre-harvest intervals were taken from the registered labels of each pesticide. unlike the original eiq, the modified indica10 various authors have described how to combine the eiq rating system in its original formula (kovach et al., 1992) with an environmental cost estimate for every pesticide application. for example, leach and mumford (2008). 11 while this laborious data collection method prevented us from surveying a greater number of farms, it provided greater accuracy than would different methods applied to a larger sample size. 176 bio-based and applied economics 11(2): 171-184, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10310 geremia gios et al. tor was not limited to the active ingredient but accommodated all dangerous ingredients and their corresponding hazard statements. for the evaluation of coformulant products, the new indicator considers the hazard statements included on the label. a score from 1 to 5 was assigned for each of the hazard phrases referring to acute and chronic toxicity and environmental risks, as shown in appendix 1. regarding the first component of the teiqp as per equation [7] (risk to farm workers), the values obtained were compared (where possible) with the values obtained by ioriatti et al. (2011). the comparison showed remarkable similarities between the two values compared. to calculate the second and third components of equation [7] (consumer risk and environmental risk), the dose per hectare of the various crops obtained from our survey of the 26 farms in tuscany and umbria was used. the fourth component of equation [7] (risk to residents) was calculated in agreement with the corresponding component of equation [8] using populations of 86,895 and 168,044 for the studied areas of the tiber valley and the chiana valley, respectively.12 as noted in the geostatistical information presented in appendix 2, in both regions studied, residential areas (except for a few scattered homes in mountainous areas) fell within a 500 m radius of mapped farmland. following the method described by leach and mumford (2008), the individual teiq scores per hectare-application of pesticide were combined to obtain each crop’s teiqp per hectare (table 3). it is important to note that the wide gap in teiq scores between durum wheat and soft wheat reflects the different treatments used for the two crops, as gleaned from the logbooks used to calculate field score: durum wheat was subject to more products and more sprayings than was soft wheat. more specifically, at the farms under study, soft wheat was not treated with glyphosate-based herbicides (roundup or ouragan), copper compounds, axial pronto 60, or granstar 50sx. this 12 because crop data is from 2010, population data from the 2011 census was used. explains the greater impact of one variety of wheat compared with the other. obviously, the data from this sample is not necessarily representative of all or most crops in the area. nonetheless, this data has been used as it is indicative of a different, but possible, method of farming. therefore, the impact score for all hectares planted with soft wheat, durum wheat, tobacco, olives, and corn in the chiana valley and the tiber and upper tiber valleys amounts to 69,204,800.8. if durum wheat and tobacco were replaced with soft wheat, that score would decrease substantially, to 3,429,371.7. 4. results: an attempt to quantify the economic externalities of the use of pesticides to identify the externalities resulting from the use of plant protection products, it is theoretically possible to use two different approaches. the first one is a direct assessment of the costs (in terms of health, environment, etc.) of using a given quality and quantity of plant protection products. many studies have investigated the direct adverse effects of pesticides. far fewer have sought to quantify the negative externalities associated with their use. the great number of substances to table 1. breakdown of uaa (ha) at surveyed farms in the tiber and upper tiber valleys and the chiana valley. area total uaa soft wheat durum wheat corn tobacco forage, setaside land, other olive and other trees tiber and upper tiber valley 625.37 20.89 110.12 103.52 44.58 330.82 15.44 chiana valley 283.06 66.72 4.26 76.54 29.63 102.36 3.55 istat data, 2010. table 2. teiq per hectare in the two areas studied. crop sum of eiq field scores per hectare in the tiber and upper tiber valleys sum of eiq field scores per hectare in the chiana valley durum wheat 2,372.8 2,372.8 soft wheat 66.6 66.6 corn 316.1 316.1 olives 193.2 193.2 tobacco 6,923.8 7,006.8 average for all five crops 1,974.5 1,990.6 unfortunately, the results obtained cannot be compared with those obtained from other studies as no surveys like the one presented here are known. 177pesticides, crop choices and changes in well-being bio-based and applied economics 11(2): 171-184, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10310 be considered, the time needed to determine the adverse consequences of direct and/or indirect exposure, our incomplete knowledge of metabolites and food chains, the problem of identifying means of contact, and a poor understanding of the relationships between different molecules and the environment make it challenging not only to identify potential harms, but also to put an economic price on them. the second approach is based on the assessment of the willingness to pay (wtp) of a given population in order not to be exposed to the consequences of pesticide use in a given area. in our study, this approach seems to be the only one that could be pursued. a survey carried out in veneto in 2009, offered some information useful for our study. in the effort to quantify the economic variables at play, we referred to a meta-analysis conducted by boatto et al. (2008) that determined the willingness to pay (wtp) of households in the veneto region in 200613. socio-economic conditions in veneto are like those in tuscany. more specifically, in the two regions, the incomes of families are very similar (banca d’italia, different years)14 similarities are also found in social capital 13 concerning the work made in the veneto region, the wtp was obtained by a meta-analysis complemented by other assessments used according to the technique of value transfer, whose primary studies used both direct and indirect assessment methods. this has led (in veneto) to the estimation of meta-functions which shows a satisfying statistical significance, and which differ for type and number of the explanatory variables featuring socio-economical, environmental and methodological factors. the wtp thus estimated, has made it possible to compare organic agriculture and conventional agriculture. the wtp concerning the non-use of pesticides in the conventional agriculture was estimated, in our case, by pairing it with the wtp estimated for obtaining the organic agriculture. 14 in 2018, while the national average income per household was € 31,641, in veneto region it was € 35,673 and in tuscany € 33,792 (similar observations can also be made for previous years). at the same time, there is also considerable homogeneity in the distribution of family income. in fact, the gini index has a value of 0.252 in veneto and 0.277 in tuscany (istat). and attitudes towards the environment. (carocci,2009; sabatini, 2009; istat, 2021). on the basis of the equations reported in that analysis, and using the average income in the tuscany region,15 we obtained the following wtp per household/year for the reported goals: having water free of pesticide residues (taking the low end of the range)16: €18.70; protecting biodiversity (taking the low end of the range): €23.60; being free of acute and chronic health issues caused by pesticides: €126.40. therefore, in total, the willingness to pay for a pesticide-free environment amounted to €168.7 per household per year. according to istat data for 2011, in the areas studied, the tiber and chiana valleys, there were a total of 254,939 residents in 105,352 households. applying the wtp per household from the veneto study, the total potential willingness to pay for a pesticide-free environment would amount to €17,772,882 per year.17 assuming that the wtp rises in a straight line from 0 (no use of pesticides) to the teiqp total impact score of more than 15 this statistic was used in place of the average income in the veneto region, deflated by the istat cost of living index to update the original 2006 figures to 2017. for the meta-analysis made in veneto, among all the explanatory variables used, exclusively the income was considered since it has better explanatory qualities. furthermore, the rest of the variables considered in veneto, show similar values to those available in tuscany in the same year. 16 in veneto region, the wtp was reported as being a range between a minimum value and a maximum value. these values are connected with the different value attributed to the explanatory variables. in this work, as a precautionary measure, the minimum value of such rage was chosen. 17 in this study, the municipality of arezzo (98,144 inhabitants) was considered as if it were formally falling within the chiana valley area. the municipality of arezzo was counted in this article because of its proximity to agricultural areas, as also shown in figure a2.2 “urbanization of rural areas in the chiana valley”. if we exclude the municipality of arezzo from the calculation, the total potential willingness to pay for a pesticide-free environment would amount to 14,825,154.23 euros per year. table 3. calculation of total teiqp scores. crop teiq tiber and upper tiber valleys (per ha) tiber and upper tiber valleys (no. ha) teiqptiber and upper tiber valleys teiq chiana valley (per ha) chiana valley (no. ha) teiqp chiana valley total teiqp durum wheat 2,372,8 4,901.06 11,629,235.2 2,372,8 9,416.72 22,343,993.2 33,973,228.4 soft wheat 66.6 1,385.28 92,259,6 66.6 2,139.63 142,499.4 234,759,0 corn 316.1 770.87 243,672,0 316.1 1,088.62 344,112.8 587.784.8 tobacco 6,923.8 4,073.41 28,203,476,2 7,006.8 695.02 4,869,866.1 33,073,342.3 olives 193.2 865.86 167,284,2 193.2 6,047.63 1,168,402.1 1,335,686.3 total ---------------11,996.48 40,335,927.2 ----------19,387.62 28,868,873.6 69,204,800.8 178 bio-based and applied economics 11(2): 171-184, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10310 geremia gios et al. 17 million euros estimated for 2016. in this scenario, a reduction of one percentage point in the teiqp index is equivalent to a wtp of eur 25.68. it goes without saying that the value of the teiqp index calculated in this way, can vary greatly from one year to the next one, depending on the crop growth, the climate variability and the cultivation techniques adopted. consequently, the value of a percentage decrease (or increase) of the index itself will also vary. to get to define results useful for the economic policy purposes, it would therefore be necessary to calculate the index shown here in relation to average or standard values per crop, or per area. this calculation is possible but goes beyond the objectives of this paper. on that basis, alternative scenarios were investigated in which one crop was hypothetically replaced with another to gauge variations in terms of wtp (here representing a replacement for welfare) as well as gross saleable production (gsp), gross margin, and operating margin (which is more representative than other variables of the actual difference between one crop and another in a farm’s gross income). table 4 shows that while the welfare gain (measured as wtp) resulting from the elimination of the tobacco crop is lower than the loss in terms of gsp and gross margin, it does lead to a reduction in net operating margin losses. table 4 demonstrates that while the welfare gain (measured as wtp) resulting from the elimination of the tobacco crop is lower than the loss in gsp and gross margin, it does lead to a reduction in net operating margin losses. to interpret table 4 correctly, it should be noted that both the gsp and gross margin indicator refer to day-to-day operations, while the operating margin also includes other elements that are not included in ordinary operations25. for reasons of space, in this paper, we present an example in which the land used to grow tobacco (scenario a) is planted instead with soft wheat (scenario b), or with corn (scenario c). 5. conclusions quantification of the negative externalities associated with cultivation methods can have interesting operational implications both in terms of land-use planning and in defining economic policies for the industry (maietta et al., 2019). this is particularly true when defining support measures for agricultural activities under the rdps (rural development programmes). it is not easy to evaluate the value of externalities linked to agricultural production activity. in most cases, positive externalities are considered, but also negative ones should also be considered. among the latter, those connected with the use of plant protection products are particularly important. in fact, italian agriculture can be considered ‘urban agriculture’, i.e., agriculture in which cultivated areas are intertwined with residential areas (filippini et al. 2021). this situation makes it difficult, in many cases, to reconcile the needs of producers to protect their crops and those of citizens to have an unpolluted environment. from this point of view, this work is characterised by at least two limitations. firstly, the use of meta-analyses to evaluate the wtp for a pesticide-free environment approximates the real wtp of the inhabitants of the area considered. secondly, the farms taken into consideration are not a probabilistic sample of the farms in the area considered, and the surveys of the pesticides used relate to a single agricultural year. therefore, the teiqp index values obtained, following more in-depth investigations, could lead to different values. finally, since this is an initial study, the methodology and the definition of indicators for specific regions will have to be refined. specifically, field experiments to determine the actual range and persistence of pesticide drift will need to be conducted to reach results at the operational level. despite these limitations, the results table 4 changes in wtp, gsp, gross margin and operating margin, compared to baseline (scenario a). wtp gain change in gsp change in gross margin change in operating margin absolute values variation from scenario a=100 absolute values variation from scenario a=100 absolute values variation from scenario a=100 scenario a (cultivations of tobacco) baseline 51.833.950 100 39.850.087 100 -18.050.906 100 scenario b (with soft wheat instead of tobacco) 8.436.983 23.481.044 45,3 20.885.942 52,4 -8.433.043 46.7 scenario c (with corn instead of tobacco) 8.369.472 25.402.709 49,0 23.251.287 58,3 -8.170.781 45.3 179pesticides, crop choices and changes in well-being bio-based and applied economics 11(2): 171-184, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10310 obtained lead us to believe that the application of tools like the one used in this case may have general validity. firstly, it should be noted that it is necessary to try to identify indicators that consider all possible externalities. specifically, we believe that the teiq indicator proposed in these notes can be of help in all cases where intensive agricultural activities and residential and recreational activities take place in very close areas. secondly, this study also demonstrates that for the purpose of deciding how best to allocate farmland, the inclusion of potential externalities, such as the results of pesticide use, leads to significantly different results than can be obtained without considering such factors. thirdly, in a heavily subsidized industry like agriculture, modulating subsidies to reflect the extent of environmental externalities may be essential, given the goal of maximizing social welfare. when attempting to quantify the externalities of pesticide use, indicators such as those described in this study can make a valuable contribution. fourthly, the exercise conducted in this study confirms once again that the economic policy objective pursued must be assessed very carefully. in the present case, in addition to the obvious difference between profit maximization and social benefit, there is a difference also between gross marketable output and operating margin (mack et al., 2019). finally, being able to rely on an indicator such as the one shown in the present study, make it possible to appropriately modulate the objectives of reducing the environmental impact of agricultural activity. consequently, it would be possible to overcome the contrast between conventional farming and organic farming, coming to define the maximum levels of environmental impact that can be tolerated in a given area. these levels depend not only on the characteristics of the agricultural sector but also on the environmental and socio-economic context in which it the area is located. acknowledgments the authors have no financial conflicts of interest relevant to this study. this paper is a self-funded work. there has been no assistance of individuals or 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(2010). pesticide risk indicators: unidentified inert ingredients compromise their integrity and utility. environmental management 45: 834–841. targetti, s., raggi, m., & viaggi, d. (2020). benefits for the local society attached to rural landscape: an analysis of residents’ perception of ecosystem services. bio-based and applied economics journal, 9(1050-2021-220), 155-170. turner, r.k., pearce, d.w. and bateman, i. (2003). economia ambientale. bologna: il mulino. van bol, v., claeys, s., debongnie, p., godfriaux, j., pussemier, l., steurbaut, w. and maraite, h. (2003). pesticide indicators. pesticide outlook 14: 159–163. 181pesticides, crop choices and changes in well-being bio-based and applied economics 11(2): 171-184, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10310 appendix 1 table a1.1. scoring system used to develop the new environmental impact quotient for pesticides (neweiq). scores range from 1 (no hazard statement) to 5 (hazard statements include high potential risk of acute or chronic toxicity or harm to the environment). hazard r-phrases (dsd classification) h-statements (clp classification) (not all can be converted directly) score acute toxicity = dt harmful (by inhalation, contact with skin, ingestion) r20, r21, r22 h300, h301, h310, h311, h330, h331 3 toxic (by inhalation, contact with skin, ingestion) r23, r24, r25 4 very toxic (by inhalation, contact with skin, ingestion) r26, r27, r28 5 irritating (by inhalation, contact with skin, ingestion) r36, r37, r38 h319, h335, h315 2 may cause sensitization by inhalation or skin contact r42, r43 h334, h317 5 risk of serious damage to eyes r41 h314, h318 3 harmful: may cause lung damage if swallowed r65 h304 2 repeated exposure may cause skin dryness or cracking r66 3 vapors may cause drowsiness and dizziness r67 h336 3 chronic toxicity = c possible risk of impaired fertility r62 h361 3 may impair fertility r60 h360 5 teratogenic (possible risk of harm to the unborn child) r63 h361d 3 teratogenic (may cause harm to the unborn child) r61 h360d 5 mutagenic (possible risk of irreversible effects) r68 h341 3 mutagenic (may cause inheritable genetic damage) r46 h340 5 cancerogenic (limited evidence of a carcinogenic effect) r40 h351 3 cancerogenic (may cause cancer) r45, r48, r49 h350 (h372, h373) 5 aquatic organisms = f very toxic r50 h400, h410 5 toxic r51 h411 4 harmful r52 3 long-term adverse effects in the aquatic environment = l may cause long-term adverse effects in the aquatic environment r53 h410, h411, h412, h413 5 bees = z toxic r57 5 other terrestrial organisms = t toxic to flora, fauna, soil organisms r54, r55, r56 5 persistence = p may cause long-term adverse effects in the environment r58 5 pre-harvest interval < 2 days     1 pre-harvest interval > 2 < 15 days     3 pre-harvest interval > 14 days     5 systematicity (sy) systemic     3 the different components considered are assessed by considering the following risk phrases: dt = acute toxicity defines the average individual rating for the risk of direct exposure to chemicals, considering the following dsd risk phrases: r20, 21, 22, 23, 24, 25, 26, 27, 28, 36, 37, 38, 41, 42, 43, 65, 66, 67. c= chronic toxicity defines the average individual rating for long-term fertility, and teratogenic, mutagenic, and oncogenic risks (dsd risk phrases r40, 45, 46, 12 48, 49, 60, 61, 62, 63, 64, 68). p= average score related to the active ingredient persistence based on the pre-harvest interval (phi) of the agricultural produce intended for human consumption; and to long-term environmental impact (dsd risk phrase r58). f= toxicity to aquatic organisms dsd risk phrases r50, 51, 52. l= long-term risk to aquatic organisms dsd risk phrase r53. z= toxicity to bees dsd risk phrase r57. t= toxicity to other terrestrial organisms dsd risk phrase r54, 55, 56. 182 bio-based and applied economics 11(2): 171-184, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10310 geremia gios et al. appendix 2 farming in the tiber valley (tuscany) and the upper tiber valley (umbria) th e tiber valley and upper tiber valley form a geographical area in the central-northern apennines. th e area consists of 11 municipalities in two italian regions: umbria (province of perugia) and tuscany (province of arezzo).18 it falls mainly on the fl ood plain of the tiber river, with the exception of some mountain communities (e.g. caprese michelangelo, badia tedalda, sestino, and monte santa maria tiberina) adjacent to the plain. in the valley there are numerous residential districts and scattered homes, while in the mountain communities, anthropization is more limited to the village centers. th e area covers a total of 75,285 ha, with uaa of 35,644 ha or 47% of the total. more specifi cally, arable crops take up 70% of the cultivated land, meadows and pastures 23%, and permanent (woody) crops 7%. of the arable crops, the most prevalent are cereals (36%), fodder (27%), and industrial crops (22%). th e latter consist almost exclusively of tobacco. farming in the chiana valley th e chiana valley is a geographical area in central italy that was reclaimed as farmland during the 1900s. all its municipalities19 are in the province of arezzo; they cover 74,258 ha with uaa of 46,714 ha (63% of the total). arable crops take up 72% of the cultivated land, meadows and pastures 3%, and permanent (woody) crops 25%. of the arable crops, the most prevalent are cereals (45%), fodder (13%), and industrial crops (15%). th e permanent cropland is planted primarily with olive trees (6,047 ha), grapevines (3,618 ha), and orchards (1,512 ha). table a2.1 shows the total crop acreage of the two areas studied. figures a2.1 and a2.2 come from the istat database and refer to the latest agriculture census available, for 2010, as intercensal data only provides aggregate fi gures by province. th e two areas are characterized by a widespread urbanization of rural areas and by a signifi cant proximity of intensive farming to residential areas where peo18 th e 11 municipalities are sansepolcro (ar), anghiari (ar), pieve santo stefano (ar), caprese michelangelo (ar), badia tedalda (ar), sestino (ar), monterchi (ar), san giustino (pg), citerna (pg), monte santa mara tiberina (pg), and città di castello (pg). 19 arezzo, castiglion fiorentino, cortona, civitella in val di chiana, monte san savino, foiano della chiana, lucignano, and marciano della chiana. ple frequently visit. figures a2.1 and a2.2 present some buff er zones mapped within a 500 m radius of farmland. th e centroids of the circular buff er zones (2 km radius) were selected using the geostatistical method with a semi-regular grid. th e centroids were inter-distanced according to the density and distribution of the farms included in the study. th e result was then superimposed on the colour orthophoto map of the region of tuscany. table a2.1. total crop acreage in the municipalities of the chiana valley and tiber/upper tiber valley included in the study. area soft wheat durum wheat corn tobacco olives chiana valley (tuscany) 9,416.72 2,139.63 1,088.62 695.02 6,047.63 tiber valley (tuscany) and upper tiber valley (umbria) 4,901.06 1,385.28 770.87 4,073.41 865.86 istat data, 2010. figure a2.1. urbanization of rural areas in tiber valley. 183pesticides, crop choices and changes in well-being bio-based and applied economics 11(2): 171-184, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10310 appendix 3 glossary “consumer protection” consumer health is protected by determining the maximum permitted residue of an active ingredient in foods meant for fi nal consumption. in case of residue, the law defi nes the tolerance limit or maximum residue limit (mrl) as the maximum amount of ppp active ingredients tolerated in food products, consistent with the amount that is safe for consumers. “exposure” th is term refers to the likelihood of coming into contact with the substance, based on the quantity of substance to which the living organism or the environment is exposed and the length of time of the exposure. exposure may have diff erent origins, such as: direct human interaction while working with the substance (mixing, spraying, etc.); contaminated rain and volatilization; drift during spraying; or soil and groundwater contamination aft er spraying (runoff , leaching, drainage). “gross margin and operating margin “ as known, this balance sheet partially deviates the traditional fi nancial statements and from the annual consolidated fi nancial statements (barbieri et al., 2004). specifi cally, the gross margin represents a value of the profi tability of the company’s production activities (crops and livestock), obtained as the diff erence between the total value of production (main product plus any secondary products) and the costs incurred in the production processes. at the same time, operating income is the economic result of the characteristic management of the agricultural enterprise, which includes all costs and revenues generated by the production processes and by active and passive services related to agricultural activities. it is calculated as the diff erence between the farm net product and the income distributed (wages and social security contributions, rents payable).in this specific balance sheet, since the operating margin considers both revenue and expenses diff erent from those typical of the ordinary operations, there may be some cases where the operating margin is higher than the gross one. in the case under consideration, the considered rica-inea data report such situation for the olive growing in tuscany. “hazard-based classification criteria” these criteria are based on: a) the median lethal dose (ld50), defi ned as the dose of active ingredient expressed in mg/ kg body weight (ppm) that causes death in 50% of the lab animals exposed to the ingredient orally or through the skin; and b) the median lethal concentration (lc50), or the concentration in air or water of an active ingredient that acts in the gas or vapor state and leads to the same outcome as the median lethal dose. th e lc50 thus expresses the same standard as the ld50 but refers to lab animals that are exposed to the active ingredient in the form of a gas or vapor. “plant protection products” (ppps) in this paper include all active ingredients, as well as commercial preparations containing one or more active ingredients, used in farming for the purposes of: protecting plants or produce from harmful organisms or preventing the eff ects thereof; assisting or regulating plant metabolism (except for fertilizers); preserving produce (except for preservatives governed by specifi c regulations); clearing the crop of weeds or other undesired plants; and removing parts of plants or halting or preventing their undesired growth. “selectivity” ppps selectivity can be physiological or ecological. it is physiological if it derives from the characteristics of the ppps itself. “spectrum of action” th is term means the range of pests a ppp is meant to control. for example, an insecticide that simultaneously acts against aphids, moth larvae, and fruit fl ies has a broad spectrum of action. “systemic action” indicates the ppp’s ability to penetrate the plant and fi ght infections within organs that cannot be reached directly by substances that work through contact action (surface-active ingredients). “toxicological classification” ppps are currently classifi ed on the basis of: a) acute toxicity, expressed as ld50 for solid and liquid preparations and as lc50 for figure a2.2. urbanization of rural areas in the chiana valley. 184 bio-based and applied economics 11(2): 171-184, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10310 geremia gios et al. gases, fumigants, and aerosols; b) chronic toxicity, which depends on the product’s hazardousness, indicated as risk to the farm worker, the consumer, and the environment as a function of exposure to the ppps. “worker protection” while the pre-harvest interval protects consumers by affecting the amount of residue remaining on foodstuffs, the restricted entry interval is the amount of time that must elapse between pesticide treatment and workers’ access to the treated area for pruning, thinning, picking, etc. without personal protective equipment (ppe). volume 11, issue 2 2022 firenze university press agriculture, food and global value chains: issues, methods and challenges margherita scoppola mapping global value chain participation and positioning in agriculture and food: stylised facts, empirical evidence and critical issues silvia nenci1, ilaria fusacchia1,2, anna giunta1,2, pierluigi montalbano3, carlo pietrobelli1,4 on the relationships among durum wheat yields and weather conditions: evidence from apulia region, southern italy marco tappi*, gianluca nardone, fabio gaetano santeramo a choice model-based analysis of diversification in organic and conventional farms andrea bonfiglio*, carla abitabile, roberto henke financial performance of connected agribusiness activities in italian agriculture gabriele dono*, rebecca buttinelli, raffaele cortignani pesticides, crop choices and changes in well-being geremia gios1,*, stefano farinelli2, flavia kheiraoui3, fabrizio martini4, jacopo gabriele orlando5 bio-based and applied economics 13(2): 147-160, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13482 bio-based and applied economics bae copyright: © 2024 pagliacci, f., & zavalloni, m. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: pagliacci, f., & zavalloni, m. (2024). the political economy determinants of agri-environmental funds in the european rural development programmes. bio-based and applied economics 13(2): 147-160. doi: 10.36253/ bae-13482 received: august 30, 2022 accepted: september 29, 2023 published: july 25, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio santeramo; silvia coderoni orcid fp: 0000-0002-3667-7115 mz: 0000-0002-6291-7653 the political economy determinants of agrienvironmental funds in the european rural development programmes francesco pagliacci1, matteo zavalloni2,* 1 dipartimento territorio e sistemi agro-forestali (tesaf), università di padova, italy 2 dipartimento di economia, società, politica (desp), università degli studi di urbino carlo bo, italy *corresponding author. e-mail: matteo.zavalloni@uniurb.it abstract. in recent years, agricultural policies have expanded their scope to include funding for the promotion of environmental sustainability in agriculture. however, these policies have been often overlooked in the political economy literature. this article aims to investigate the factors influencing the allocation of funds towards environmental goals in the rural development programmes of the european union common agricultural policy. the main findings of this study indicate a positive correlation between gdp per capita and the allocation of the environmental budget. conversely, delegating the management of these programmes to sub-national polities has a negative impact on the budget allocation. therefore, it seems that maintaining some central control over the budget allocation might favour the environmental sustainability of the agricultural sector. keywords: eu rural development policy, political economy, agri-environmental schemes, environmental federalism. jel codes: d72, o13, q18. 1. introduction agriculture has been historically the subject of pervasive policy interventions, even though their nature has been extensively developed over time. the general pattern is that, with economic development, interventions tend to switch from dis-incentivization toward subsidization of agricultural activities (anderson et al., 2013). even within high income economies the support to agriculture has substantially evolved over time, from price support, toward coupled and ultimately non-coupled subsidies (anderson et al., 2013). especially in high income economies, since the 1980s, the scope of government interventions has broadened from a support to production to larger shares of funds allocated to e.g. r&d (swinnen et al., 2000), infrastructures development (oecd, 2020) and the environmental goals (baylis et al., 2008). for example, in the european union since the 2000s, funds of the common agricultural policy (cap) have been allocated, through the rural develhttps://doi.org/10.36253/bae-13482 http://www.fupress.com/bae https://doi.org/10.36253/bae-13482 https://doi.org/10.36253/bae-13482 https://orcid.org/0000-0002-3667-7115 https://orcid.org/0000-0002-6291-7653 mailto:matteo.zavalloni@uniurb.it 148 bio-based and applied economics 13(2): 147-160, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13482 francesco pagliacci, matteo zavalloni opment programmes (rdps), to agri-environmental schemes, aimed at incentivizing the provision of environmental public goods (matthews, 2013). to explain the existence and persistence of agricultural policies, the literature has relied on the lens of political economy (swinnen, 1994). a number of determinants have been empirically analysed, among the others: electoral incentives (fałkowski and olper, 2014), personal preferences of the legislators (bellemare and carnes, 2015), lobbying and institutional settings (olper et al., 2014). however, the great bulk of the literature has focused on the determinants of the extensive margins of agricultural policies, i.e., to what extent the agricultural sector is affected by government interventions (anderson et al., 2013). surprisingly little has been said on in the intensive margins of agricultural policies, i.e. what determines the allocation of funds, within agricultural policies, for objectives that are beyond production or maintenance of agriculture. the objective of this article is to assess the political economy determinants of the allocation of agricultural policy funds toward environmental goals. our focus is on the european rdps. the decisions on rdp fund allocations are set within a common, eu-level, framework (e.g., common priorities), but are eventually delegated to national or subnational authorities, according to the principle of vertical subsidiarity. thus, they provide an interesting example for the issue here at stake. we address five main sets of explicatory variables: the societal demand for a greater environmental quality; the importance of the agricultural sector in the economy, which reflects into its bargaining power; the political characteristics –the ideology of the government coalitions in charge; the agri-environmental conditions of the area; and whether the rdp is managed at the national or subnational level (i.e., issue of decentralization). using a fractional regression model, we find that the most robust determinants of environmental budget allocations are gdp per capita (positively correlated), population density and management decentralization (both negatively correlated). the main value of the article is to complement the literature on the political economy of agricultural policies by unveiling the determinants of funds for agrienvironmental goals, a topic largely ignored so far (fredriksson and svensson, 2003), even though on the rise (mamun et al., 2021). indeed, several articles focus on the determinants of expenditures on the agri-environmental schemes of the european rdps (bertoni and olper, 2012; camaioni et al., 2019, 2016, 2013; glebe and salhofer, 2007; zasada et al., 2018), or of similar measures (hackl et al., 2007). while expenditures and budgets are obviously connected, looking at the former adds the noise of the specific design of the measures and of the farmers uptake, and cannot be fully interpreted as a government choice (glebe and salhofer, 2007). at the same time, this article also speaks to the more general literature on the relationship between institutions and environmental quality, which has not deepened the topic on agricultural policies (dasgupta and de cian, 2018). one of the few exceptions is the analysis by fredriksson and svensson (2003), who investigate the link between political instability and the stringency of environmental regulation (hence, not subsidy) faced by the agricultural sector. finally, we also contribute to the literature on effect of environmental policies decentralization (droste et al., 2018; fredriksson and wollscheid, 2014; sigman, 2014). the framework of the rdp implementations, that are managed by both national and subnational authorities, enables to give insights also on the consequence of policy decentralization, an issue that has been seldom investigated with respect to agricultural policies (bareille and zavalloni, 2020). the results provide several policy implications. despite the paucity of the literature on the issues, the environmental impact of the agricultural sector is a major concern (crippa et al., 2021), and understanding the drivers of policies addressing it seems of paramount importance. finally, decentralization of agricultural policies is often debated for the cap reforms and our results can feed the debate revolving on it (com(2018) 392 final, 2018). the remainder of the paper is structured as follows. section 2 provides a policy background focusing on the environmental goals in agriculture and on the eu 2014-2020 programming period of the cap. section 3 describes selected data and implemented methods. section 4 shows and discusses the main results. section 5 concludes and provides some policy recommendations. 2. background: environmental goals in agricultural policies and in the eu rural development programmes environmental goals attached to agricultural subsidies are a longstanding, albeit minor, presence. in the usa, a first example is the 1936 soil conservation act, aimed at incentivizing soil conservation practices (cain and lovejoy, 2004). only since the 1980s, however, in oecd countries the share of budget linked to environmentally friendly practices has substantially increased (guerrero, 2021). indeed in 1985 environmental protection became the main (nominal) rationale for the imple149the political economy determinants of agri-environmental funds in the european rural development programmes bio-based and applied economics 13(2): 147-160, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13482 mentation of the usa conservation reserve programme, subsidising practices aimed at e.g. improving environmental quality or providing wildlife habitat (hellerstein, 2017). similarly, in 1985 an eu regulation allowed member states to design incentives for farmers implementing environmentally friendly practices, even though the uptake of this possibility was rather limited (matthews, 2013). for a set of countries (oecd and others), figure 1 shows that most of the budget toward environmental goals is linked to general support to agriculture conditional on some forms of input constraint -mandatory input constraints, in figure 1. voluntary measures – voluntary environmental input constraints, in figure 1 – such as the agri-environmental schemes have also increased over time, even though they remain limited to about 6-7% of the total support (guerrero, 2021). in the eu, voluntary agri-environmental measures are currently implemented within the rdps. rdps represent the so-called pillar 2 of the cap. they were first formulated in the agenda 2000 reform, as part of a strategy to move away from coupled support and broaden the scope of the cap (matthews et al., 2017) and they are currently supported by the european agricultural fund for rural development (eafrd) of the eu. since the agenda 2000 reform, four programming periods have taken place: 20002006, 2007-2013, 2014-2020, 2021-2027. a comprehensive overview of the cap and its environmental goals is out of the scope of this paper, and we refer to e.g. matthews (2013) for a detailed description of the topic. the current version of the rural development policy is the 2021-2027 one, which in fact has only started in 2023, i.e., with a two-year delay. it followed extensive negotiations between the european parliament, the council of the eu and the european commission for the approval of the multiannual financial framework of the eu (as a consequence of both brexit process and the outbreak of the covid-19 pandemics). thus, due to the lack of data on the current programming period, our analysis focuses on the 2014-2020 programming period, when the rdps were legislatively based on the regulation (eu) no 1305/2013 of the european parliament and of the council, which provided the guidelines for their formulations and structure. even though the general framework was set at the eu level and plans were approved by the ec, national authorities had some degree of freedom in implementing them (eventually increased in the current 2021-2027 programming period). first, following the vertical subsidiarity principle, member states could delegate the management of the rdps to subnational authorities (beckmann et al., 2009). during the 2014-2020 programming period, 20 eu member states maintained a nation-wide implementation, while the remaining countries opted for a subnational implementation. on the one hand, germany, belgium, finland, portugal, and the uk opted for the nuts-1 level implementation (considering either single nuts-1 regions, e.g., the länder in germany or groups of them, as in the case of the uk). on the other, france, italy, and spain opted for the nuts-2 level implementation (e.g., the régions in france, the regioni in italy, and the comunidades autónomas in spain). second, the managing authorities – either at the national or the subnational level – chose their own allocation of funds, with some constraints, prioritising specific goals among the existing ones. according to article 5 of the regulation no 1305/2013, the rdp budgets, funded by the eafrd, must be shared among, centrally determined, 6 priorities, or goals: (1) fostering knowledge transfer and innovation in agriculture, (2) enhancing farm viability and competitiveness, (3) promoting food chain organisation, (4) restoring, preserving and enhancing ecosystems related to agriculture and forestry, (5) promoting resource efficiency and supporting the shift towards a low carbon and climate resilient economy, (6) promoting social inclusion. at the same time, eafrd budget was allocated to a set of measures, i.e., specific areas of interventions, aimed at achieving the aforementioned goals (table 1). within the current framework and according to the classification provided in table 1, environmental measures are granted a specific attention. according to article 59 of the regulation no 1305/2013, at least 30 % of the total eafrd contribution to each rdp shall be reserved 0 30% 60% 90% 1986 1991 1996 2001 2006 2011 2016 mandatory input constraints voluntary input constraints no input constraints not applicable other input constraint and animal welfare (voluntary) figure 1. share of subsidy type on the total producer support estimate for a set of countries (oecd and others). own elaboration on data from oecd (2020), downloadable at https://www.oecd.org/ agriculture/topics/agricultural-policy-monitoring-and-evaluation/. for technical explanation of the variables, we refer to oecd (2016). https://www.oecd.org/agriculture/topics/agricultural-policy-monitoring-and-evaluation/ https://www.oecd.org/agriculture/topics/agricultural-policy-monitoring-and-evaluation/ 150 bio-based and applied economics 13(2): 147-160, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13482 francesco pagliacci, matteo zavalloni for the following measures: m04 (only considering environment and climate related investments), m08, m10, m11, m12 (except for water framework directive related payments), m13 and m15. this is to achieve specific environmental goals in the eu. 3. data and methods 3.1. empirical model and data the goal of this article is to assess the determinants behind the decision to allocate funds to environmental goals in the rdps of the cap. the shape and type of policies result from the interactions of several elements. similarly to other analyses (e.g. bertoni and olper, 2012; fredriksson and svensson, 2003), we argue that the resulting share of budget allocated to environmental goals is determined by the interaction among five main factors: i) the societal demand for higher environmental quality, ii) the bargaining power of the agricultural sector, iii) the political environment, iv) the environmental conditions of the area, v) the polity level that manages the funds. our expectation is that higher demand for environmental quality will be translated into relatively larger budget for environmental goals. at the same time, low environmental quality will also call for larger budget for environmental goals. however, while the funds we are investigating are targeting agriculture, the sector might prefer support to investments and efficiency, rather than sustainability goals, and hence greater bargaining power would result in lower budget for environmental goals. the political environment builds upon those two blocks. party ideology and the composition of the government might filter the general preferences of the public. moreover, decentralization of agri-environmental policies, while might result in better targeting of local public goods, could end up in free-riding behaviour due to spillover effects. in the next paragraph, we describe the dependent and the explanatory variables that we use to proxy the aforementioned elements. given the structure of the rdp managing authorities, the analysis is grounded on a territorial basis. indeed, our units of analysis are the polities covered by each rdp managing authority, either at national or sub-national level. for the current analysis, we consider 100 rdps and the related polities, excluding from the full set: i) the french dom (namely, guadeloupe, guyane, la réunion, martinique and mayotte) due to data availability, ii) the uk rdps, for the difficulties to account for the functioning of the local (i.e., subnational) polities in that country, and iii) the national level rdps, when the lower tiers are the main table 1. description of measures and related articles in the regulation no 1305/2013. articles short description rdp codes 14 knowledge transfer and information actions m01 15 advisory services, farm management and farm relief services m02 16 quality schemes for agricultural products, and foodstuffs m03 17 investments in physical assets m04 18 restoring agricultural production potential damaged by natural disasters and catastrophic events and introduction of appropriate prevention actions m05 19 farm and business development m06 20 basic services and village renewal in rural areas m07 21-26 investments in forest area development and improvement of the viability of forests m08 27 setting -up of producer groups and organisations m09 28 agri-environment-climate m10 29 organic farming m11 30 natura 2000 and water framework directive payments m12 31-32 payments to areas facing natural or other specific constraints m13 33 animal welfare m14 34 forest-environmental and climate services and forest conservation m15 35 co-operation m16 36-39 risk management m17 40 financing of complementary national direct payments for croatia m18 42-44 leader m19 151the political economy determinants of agri-environmental funds in the european rural development programmes bio-based and applied economics 13(2): 147-160, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13482 managing authorities (i.e., in the case of france, italy, spain). the dependent variable is represented by the share of the rdp budget allocated to environmental measures in year 2014 (i.e., considering the first budget allocation). to operationalize the preferences for environmental goals we address the constraint set by article 59 of the regulation no 1305/2013, in terms of both key measures and minimum budget allocation (see section 2). we define our dependent variable, m-environment as the ratio between the rdp funds for environmental goals (i.e., budget allocated to measure 4, measure 8, measure 10, measure 11, measure 12, measure 13, and measure 15) that go beyond the minimum level fixed by the eu regulation and its complementary. for example, imagine the rdp budget is 100€, and budget allocated to environmental goals is 37€. our dependent variable is given by 7/70. as robustness check, we also run two additional models. in the first one, we define the dependent variable as the share of the budget (year 2014) allocated to priorities (4) “restoring, preserving and enhancing ecosystems related to agriculture and forestry” and (5) “promoting resource efficiency and supporting the shift towards a low carbon and climate resilient economy” (p-environment); in the second one, we define the dependent variable as the share of the budget (year 2014) allocated to agri-environmental schemes only, i.e. to measure 10 (m10). figure 2 shows the rather uneven allocation of m-environment, p-environment, and m-10 at the programming level across the eu. data on the rdp budget allocations have been collected from the european commission website (https://cohesiondata.ec.europa.eu/) and in all cases we considered the total financing, i.e., including both the eu eafrd funds and the national co-financing. in particular, table 2 returns the main descriptive statistics for the alternative specifications of the dependent variables. we now turn to the set of explicatory variables. when considering them, the first dimension we address is the demand for environmental quality. following previous research (e.g. franzen and vogl, 2013), we take into account gdp per capita and population density as a proxy for the societal demand for environmental quality. the large literature on the environmental kuznets curve indicates that, after a certain threshold, income is a key driver of environmental quality and policy implementation (dasgupta et al., 2002; dinda, 2004; lópez and and mitra, 2000; maddison, 2006). moreover, we use population density as a proxy for the degree or urbanization, which is also expected to be positively correlated to higher environmental quality, and hence higher share of budget allocated to environmental goals (e.g. franzen and vogl, 2013). the second element is the economic relevance of the agricultural sector. a larger magnitude of the agricultural sector might turn into a larger bargaining power of the sector itself, which, we argue, eventually turn into a reduction of the support to environmental measures in the rdp (fredriksson and svensson, 2003). however, following olson (1971), even the counterargument can be made: the larger the sector, the more is difficult to coordinate and hence the lower the bargaining power. to have proxies for the bargaining power of the agricultural sector, we rely on three indicators: share of utilised agricultural area with respect to the total area of the relevant polity, number of farmers per million inhabitants and share of gross value added of agriculture out of the total gross value added. as a third group of variables, politics aspects are considered. in terms of politics, first, we consider the ideology of the government in charge. several papers find that ideology plays a role in the level of protection and support to agriculture (klomp and haan, 2013; olp(a) (b) (c) m environment (exceeding 30%) p environment (% of expend.) m 10 (% of expend.) 1st quartile 4th quartile figure 2. allocation of environmental budget across the eu in 2014: a) m-environment, b) p-environment, and c) m-10. source: authors’ elaboration. https://cohesiondata.ec.europa.eu/ 152 bio-based and applied economics 13(2): 147-160, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13482 francesco pagliacci, matteo zavalloni table 2. list and description of the variables included in the models, by type. name meaning year specification source mean (std. dev.) dependent variables m-environment ratio of the share of the total rdp budget allocated to measure 4, measure 8, measure 10, measure 11, measure 12, measure 13, and measure 15 exceeding minimum (30%) over the total range. 2014 share cohesiondata.ec.europa.eu 0.27 (0.18) p-environment share of the total rdp budget allocated to priority 4, and priority 5 2014 share cohesiondata.ec.europa.eu 0.52 (0.12) m10 share of the total rdp budget allocated to measure 10 2014 share cohesiondata.ec.europa.eu 0.15 (0.08) environmental demand density population density (thousand inhab. per square km) avg. 2010-2014 continuous (1000 inhab.) eurostat population density 0.17 (0.19) gdp per capita income (in thousand €) avg. 2010-2014 continuous (1000€) eurostat gdp at current market prices by nuts 2 regions 25.71 (7.86) bargaining power of agriculture uaa_share utilised agricultural area (uaa) out of total land area 2013 share eurostat – farm structure survey 0.41 (0.15) farm per mill inhab number of farms per million inhab. 2013 continuous eurostat – farm structure survey 19.92 (22.81) gva_share % of agricultural gross value added out of total gross value added 2013 % ardeco database 2.85 (1.95) politics parties number of parties in the cabinet that was in charge at the date of approvation of the rdp continuous authors’ elaboration on döring and manow, (2020) schakel and massetti, (2018) 1.90 (1.00) left_right average position of the cabinet in terms of its overall ideological stance (from left to right), by considering the position of each party in the coalitions (weighted by the number of their seats) continuous (0 = extreme left to 10 = extreme right) authors’ elaboration on döring and manow (2020), schakel and massetti (2018), polk et al. (2017) 4.30 (1.70) agri-environmental conditions n_sur_kg_ha average nitrogen surplus (kg per ha), based on 16 nitrogen surplus estimates avg. 2010-2014 continuous batoo et al. (2022) 35.35 (18.15) animals_ab thousand cows and live swines per thousand inhab. avg. 2010-2014 continuous eurostat animal populations by nuts 2 regions 0.57 (0.67) hnv share of high nature value (hnv) farmland out of the total area 2012 % authors’ elaboration on european environment agency (eea) data on the basis of the corine land cover (clc) accounting layers 18.76 (14.06) (continued) http://cohesiondata.ec.europa.eu http://cohesiondata.ec.europa.eu http://cohesiondata.ec.europa.eu 153the political economy determinants of agri-environmental funds in the european rural development programmes bio-based and applied economics 13(2): 147-160, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13482 er, 2007) as well as for the level of environmental protection (pacca et al., 2020). following klomp and haan (2013), we address the ideology of the whole government cabinet (rather than simply the government head) by computing the average position of the cabinet in terms of its overall ideological stance (from left to right). polk et al. (2017) computed ideological stance of eu parties, by assigning each of them a position on a scale from 0 (extreme left) to 10 (extreme right). parties on the economic left wanted government to play an active role in the economy, while those on the economic right emphasized a reduced economic role for government: privatization, lower taxes, less regulation, less government spending, and a leaner welfare state. for the sake of our analysis, and as a reference point, we take the average score for the whole cabinets that were in charge of the relevant polity in the period up to the approval of the first rdp version, i.e., in most of the cases year 2014. note that regional politics might be more complex than the national one, as regional parties are often a key player in local elections and hence governments and the local institutional architectures exhibit a great degree of heterogeneity across eu member states (schakel, 2013; schakel and massetti, 2018). second, we also consider the number of parties that compose the government coalitions. this has been considered to affect state expenditures (perotti and kontopoulos, 2002) and protection to agriculture (beghin and kherallah, 1994). the fourth element we address is the agri-environmental conditions of the relevant polities to which the rdps refer. agri-environmental measures are aimed at reorienting the sector toward more environmentally friendly practices, thus the lower the agri-environmental quality of the area, the higher the agri-environmental funds should be (bertoni and olper, 2012). as a proxy for environmental quality, we use four indicators: average nitrogen surplus, number of animals (cows and live swine) per thousand inhabitants, share of high nature value (hnv) farmland out of the total area, share of agricultural areas, forest and semi natural areas under moderate or severe level of erosion. all of them are expected to be negatively correlated to environmental quality, but the share of hnv farmland. lastly, we address whether the rdp was managed at the national level, or if its implementation was delegated to lower tiers. we consider such an element because it is a structural characteristic of (some) rdps, which in fact has been usually disregarded by the political economy literature of agricultural policies (as they are mostly set at the national level). however, the variation in the polity level decision making, within the same policy framework, enables to explore the effect of decentralization on (agri-) environmental policies and hence to add results to the increasing literature on environmental policy decentralization (fredriksson and wollscheid, 2014) and more in general on the environmental federalism (shobe, 2020). in addition to the previous explanatory variables, in any of the selected models we also add two variables to control for population size and eastern european countries (eec). population size is crucial to disentangle the effect of decentralization, holding the demographic size of the polity constant. the inclusion of a geographical dummy for eec addresses the 20th-century historical differences across europe. the list of the variables and their sources is listed in table 2. name meaning year specification source mean (std. dev.) erosion moderate-severe share of agricultural areas, forest and semi natural areas under moderate or severe level of erosion, out of the total agricultural areas, forest and semi natural areas 2010 % eurostat estimated soil erosion by water, by erosion level, land cover and nuts 3 regions (source: jrc) 17.19 (15.88) nuts nuts rdp being managed at the sub-national level dummy authors’ elaboration control variables pop total resident population avg. 2010-2014 continuous (million inhab.) eurostat population 4.33 (5.23) eec rdp belonging to an eastern europe country dummy authors’ elaboration table 2. (continued). 154 bio-based and applied economics 13(2): 147-160, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13482 francesco pagliacci, matteo zavalloni 3.2. econometric strategy in the framework of the cap, different polities manage different budget size. to control for it, we focus on the relative share of the total budget for environmental goals, rather than on its absolute value. however, fractional dependent variables – as the one under consideration here – pose some methodological challenges. the first challenge is related to the functional form of the model (ramalho et al., 2011). firstly, fractional dependent data (as in this case) are bounded only within the [0, 1] interval, whereas standard econometrics generally assumes normally distributed dependent variables (ronning, 1990). secondly, a “negative bias” (aitchison, 1986, p. 53) affects them, as fractional dependent variables add up to one. even in the case of more than two categories, there will be always at least one pair of negatively correlated shares. due to these specific properties, conventional regression models – which simply ignore the bounded nature of the dependent variable and assume a linear conditional mean model for it – should be avoided. some scholars opted for assuming the logistic relationship, preferring to estimate by least squares the log-odds ratio model. however, this empirical strategy has some important drawbacks (see ramalho et al., 2011 for details). for the sake of this analysis, we adopt the fractional regression models, as originally modelled by papke and wooldridge (1996). following their approach, the simplest solution for dealing with fractional response variables only requires the assumption of a functional form for y that imposes the desired constraints on the conditional mean of the dependent variable, i.e. e(y|x) = g(xθ), where g(·) is a known nonlinear function satisfying 0 ≤ g(·) ≤ 1. papke and wooldridge (1996) suggested as possible specifications for g(·) any cumulative distribution function. among alternative choices, the logistic function is considered as an obvious choice, hence: e(y|x) =𝐸𝐸(𝑦𝑦|𝑥𝑥) = !!" "#!!" . . as suggested by papke and wooldridge (1996), this function may be consistently estimated by using the robust quasi-maximum likelihood (qml) method, which is based on the bernoulli log-likelihood function (see ramalho et al., 2011 for deeper details). with regard to the empirical strategy, we estimate – for each of the dependent variables, i.e., m-environment, p-environment and m10, – six alternative models, as it follows: y = βdd + βaa + βpp + βee + βrr + βcc + ε (1) y = βdd + βcc + ε (2) y = βaa + βcc + ε (3) y = βpp + βcc + ε (4) y = βee + βcc + ε (5) y = βrr + βcc + ε (6) where: – y is the (n × 1) vector, where n = 100, indicating the share of budget allocation devoted to the environmental issues, according to alternative specifications (m-environment, p-environment and m10). – d is the (n × 2) matrix of the proxies for the demand for environmental quality and βd is the (2 x 1) vector of respective unknown parameters. – a is the (n × 3) matrix of agricultural sector variables and βa is the (3 × 1) vector of respective unknown parameters. – p is the (n × 2) matrix of politics and polity variables and βp is the (2 × 1) vector of respective unknown parameters. – e is the (n × 4) matrix of environmental-quality variables and βe is the (4 × 1) vector of respective unknown parameters. – r is the (n × 1) vector of decentralization variable and βr is the respective unknown parameter, – c is the (n × 2) matrix of control variables and βc is the (2 × 1) vector of respective unknown parameters. – ε is the (n × 1) vector of error terms. the implementation of the fractional regression models was performed by using the software r (r core team, 2021). 4. results and discussion table 3 reports the results of all the models. across model specifications, three are the most robust results. first, the results indicate that gdp is positively correlated with the budget allocated to environmental goals (see section 3 for the description of the dependent variables). this result is in line with the large literature on the relationship between economic development and environmental quality (grossman and krueger, 1995) and with previous results on the political economy determinants of the stringency of environmental regulations to agricultural activities (fredriksson and svensson, 2003). note that even expenditures on agri-environmental measures are found to be positively correlated to the gdp per capita of the area (e.g. bertoni and olper, 2012). the result is robust to the model specification being positive and significant also when gdp is isolated from the other variables (model 2) and with different specification of the dependent variables (p-environment and m-10). the odd ratios (table 4) indicate that an increase by €1000 in gdp per capita induces an increase by 3.2% in the budget allocated to m-environment. second, density is neg155the political economy determinants of agri-environmental funds in the european rural development programmes bio-based and applied economics 13(2): 147-160, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13482 atively correlated to budget for environmental goals. this is in contrast with our expectations, i.e., on the intuition that more urbanized areas would have demanded for a higher allocation of funds to the environmental goals. one interpretation of this result might lie in the idea that, at the eu level, population density actually captures other dimensions than per capita income, both in the north and in the south of the continent. the odd ratios indicate that additional 1000 inhabitants per square kilometre translate in a large reduction for the environmental budget (m-environment) (almost by 91%), an effect that is larger than the (positive) effect of gdp. third, decentralization (nuts) is negatively correlated to the environmental budget. the dummy indicating a subnational polity is statistically significant and negatively correlated to the environmental budget share in any model specification. the literature on the topic is rather ambiguous and finds that the impact of decentralization on the allocation of funds to the environmental goals depends on the type of pollutants taken into account (fredriksson and wollscheid, 2014; sigman, 2014, 2005). in our case, the result seems to indicate that decentralization would lead to a race to the bottom (millimet, 2003) in allocating environmental budgets in the rdps. while further analyses are required to understand the mechanisms behind it, such a result can also be interpreted in terms of governance scope (schakel, 2009). for example, in italy only some policy aspects are delegated to regional administration (health policies, for example), and hence, probably, a greater grip from lobbying is on them. the odd ratios suggest that decentralization has a strong effect: the delegation to lower government tiers induce a reduction in the budget allocated to m-environment, p-environment and m-10 by respectively 61%, 45% and 36%. turning to the politics aspect of our problem, the number of parties that compose a cabinet is negatively correlated to the different proxies for environmental budgets (and significant in most of the models’ specifications). this might suggest that environmental public goods require greater political coherence, in order to be funded. however, ideology seems not to be linked to any preferences for environmental budget allocation, as the coefficient for left_right is non-significant. however, the effect of politics on budget allocations deserves a more comprehensive analyses, where e.g. electoral incentives are explicitly accounted for (list and sturm, 2006; pacca et al., 2020). moreover, we only consider the government coalition in charge of the first version of the rdps, to better address the effect of ideology it would be interesting to assess how changes in the government coalitions impact on the rdp budget allocations. surprisingly, the proxies for the bargaining power of the agricultural sector are all non-significant in any model specifications. to this regard, it is important to consider that we are analysing fund allocation among different goals but whose ultimate target is anyhow the agricultural sector. probably, farmers preferences among the goals gets watered and no clear priority emerges. note however that, when focusing on real expenditures rather than allocations, zasada et al. (2018) also find that the agricultural bargaining power (proxied by the share of agricultural area) have little explanatory power. similarly, bertoni and olper (2012) find a complex relationship between share of population working in agriculture and expenditures devoted to agri-environmental schemes. finally, a complex picture is drawn from the analysis of the agri-environmental conditions. the hnv and the nitrogen surplus are respectively negatively and positively correlated to the share of budget allocated to m10. when considering the other two dependent variables, the signs of the coefficients are reversed. this difference might be due to the different characteristics of each dependent variable under consideration. actually, while measure 10 only supports activities that are strictly linked to agri-environmental measures and that represent a cost from the farmers point of view, other dependent variables encompass a broader set of interventions, including investments for higher resource efficiency. 5. conclusions and policy recommendations in this work, we analyse the political economy determinants of the share of the budget allocated for environmental goals in the eu rdps, by considering the 2014-2020 programming period. the main idea is that such a budget is the result of some main determinants: i) demand of environmental quality, ii) bargaining power of the agricultural sector, iii) characteristics of the politics of the rdps managing authorities, iv) environmental quality of the area; and v) tier levels of the rdps managing authorities (national vs subnational levels). while a substantial literature has addressed the political economy of the support to the agriculture, very little has been said on the determinants of policies targeting the sustainability of the agricultural sector. in comparison to previous articles – which mostly addressed the determinants of the ex-post expenditures on agri-environmental schemes – the focus on budget allocation allows us to put a greater emphasis on the determinants of the political decision process behind the choice of allocating funds to the environmental goals rather than to other goals (often competing with each other). bio-based and applied economics 13(2): 147-160, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13482 ta bl e 3. r es ul ts o f t he m od el s ( ro bu st st an da rd e rr or s i n pa re nt he se s) . m -e nv iro nm en t pen vi ro nm en t m -1 0 (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) (i nt er ce pt ) 0. 24 3 -1 .5 71 ** * -0 .5 58 * -0 .7 89 * -0 .8 35 ** -0 .1 08 0. 90 3 * -0 .2 92 ° 0. 26 8 ° 0. 32 2 * 0. 30 1 * 0. 61 3 ** * -2 .0 05 ** * -2 .6 29 ** * -1 .9 08 ** * -2 .2 07 ** * -1 .6 76 ** * -1 .3 25 ** * (0 .7 74 ) (0 .2 68 ) (0 .2 51 ) (0 .3 10 ) (0 .2 84 ) (0 .2 24 ) (0 .3 73 ) (0 .1 50 ) (0 .1 49 ) (0 .1 58 ) (0 .1 40 ) (0 .1 31 ) (0 .4 11 ) (0 .1 92 ) (0 .2 35 ) (0 .1 87 ) (0 .1 65 ) (0 .2 08 ) d en sit y -2 .4 01 * -2 .4 50 * -0 .6 99 * -0 .6 13 -0 .8 72 ** -0 .9 27 ** (1 .1 92 ) (1 .0 85 ) (0 .3 30 ) (0 .4 63 ) (0 .2 68 ) (0 .3 26 ) g d p 0. 03 2 ° 0. 03 5 ** * 0. 01 9 * 0. 01 9 ** 0. 01 7 ° 0. 03 5 ** * (0 .0 16 ) (0 .0 10 ) (0 .0 09 ) (0 .0 06 ) (0 .0 09 ) (0 .0 07 ) u a a _s ha re -0 .5 14 -0 .9 20 -0 .1 48 -0 .2 46 0. 20 4 0. 41 4 (0 .7 94 ) (0 .7 51 ) (0 .4 12 ) (0 .3 95 ) (0 .4 90 ) (0 .5 75 ) fa rm p er m ill in ha b -0 .0 02 0. 00 3 0. 00 0 0. 00 0 -0 .0 03 -0 .0 07 (0 .0 05 ) (0 .0 05 ) (0 .0 02 ) (0 .0 02 ) (0 .0 03 ) (0 .0 04 ) g va _s ha re -0 .0 20 -0 .0 32 -0 .0 09 -0 .0 27 0. 03 3 0. 00 5 (0 .0 57 ) (0 .0 64 ) (0 .0 29 ) (0 .0 31 ) (0 .0 37 ) (0 .0 51 ) pa rt ie s -0 .2 70 ** * -0 .0 77 -0 .1 51 ** * -0 .0 77 ° 0. 02 2 0. 09 6 ° (0 .0 75 ) (0 .0 94 ) (0 .0 40 ) (0 .0 45 ) (0 .0 54 ) (0 .0 57 ) le ft_ rig ht -0 .0 66 -0 .0 23 -0 .0 58 * -0 .0 26 0. 04 0 0. 04 5 (0 .0 53 ) (0 .0 47 ) (0 .0 25 ) (0 .0 24 ) (0 .0 34 ) (0 .0 36 ) n _s ur _k g_ ha 0. 00 4 -0 .0 03 -0 .0 01 -0 .0 03 0. 00 7 ** 0. 00 6 * (0 .0 05 ) (0 .0 06 ) (0 .0 03 ) (0 .0 03 ) (0 .0 02 ) (0 .0 03 ) a ni m al s_ ab -0 .2 25 -0 .1 02 -0 .0 72 -0 .0 41 -0 .1 07 ° -0 .0 29 (0 .1 72 ) (0 .1 85 ) (0 .0 69 ) (0 .0 93 ) (0 .0 57 ) (0 .0 71 ) h n v 0. 01 5 * 0. 01 5 * 0. 00 7 * 0. 00 6 ° -0 .0 10 * -0 .0 10 * (0 .0 06 ) (0 .0 07 ) (0 .0 03 ) (0 .0 03 ) (0 .0 05 ) (0 .0 05 ) er os io n m od er at ese ve re -0 .0 08 -0 .0 16 ** -0 .0 06 * -0 .0 10 ** * -0 .0 03 -0 .0 08 ° (0 .0 06 ) (0 .0 06 ) (0 .0 03 ) (0 .0 03 ) (0 .0 05 ) (0 .0 04 ) n u ts -0 .9 50 ** -0 .9 38 ** * -0 .5 93 ** * -0 .5 58 ** * -0 .4 42 * -0 .5 65 ** (0 .3 41 ) (0 .2 16 ) (0 .1 60 ) (0 .1 34 ) (0 .1 87 ) (0 .2 00 ) po p 0. 00 7 0. 01 8 0. 00 2 0. 00 7 0. 00 6 -0 .0 15 -0 .0 01 0. 00 2 0. 00 0 0. 00 3 0. 00 3 -0 .0 07 0. 00 9 0. 02 5 0. 02 3 0. 02 3 0. 00 7 0. 01 5 (0 .0 25 ) (0 .0 22 ) (0 .0 19 ) (0 .0 19 ) (0 .0 20 ) (0 .0 20 ) (0 .0 10 ) (0 .0 10 ) (0 .0 09 ) (0 .0 09 ) (0 .0 10 ) (0 .0 08 ) (0 .0 15 ) (0 .0 15 )° (0 .0 16 ) (0 .0 15 ) (0 .0 13 ) (0 .0 14 ) ee c -0 .9 96 ** -0 .4 79 -0 .5 62 ° -0 .4 08 -0 .7 08 * -1 .2 08 ** * -0 .6 39 ** -0 .2 47 * -0 .3 28 * -0 .2 57 * -0 .4 85 ** -0 .7 93 ** * -0 .4 78 ° -0 .0 70 -0 .1 16 -0 .4 32 -0 .2 09 -0 .7 32 * (0 .3 80 ) (0 .2 99 ) (0 .3 17 ) (0 .2 86 ) (0 .3 24 ) (0 .8 24 ) (0 .1 98 ) (0 .1 26 ) (0 .1 35 ) (0 .1 26 ) (0 .1 48 ) (0 .1 57 ) (0 .2 87 ) (0 .2 55 ) (0 .2 64 ) (0 .2 71 ) (0 .2 52 ) (0 .2 85 ) o bs .d el et ed (m iss in g) 4 0 0 4 3 0 4 0 0 4 3 0 4 0 0 4 3 0 ef ro n ps eu do rsq ua re d 0. 40 2 0. 23 3 0. 05 5 0. 03 0 0. 09 1 0. 14 2 0. 38 9 0. 14 4 0. 06 6 0. 07 8 0. 13 1 0. 16 1 0. 38 4 0. 23 9 0. 08 9 0. 07 6 0. 20 4 0. 13 6 157the political economy determinants of agri-environmental funds in the european rural development programmes bio-based and applied economics 13(2): 147-160, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13482 ta bl e 4. r es ul ts o f t he m od el s – o dd ra tio s. m -e nv iro nm en t pen vi ro nm en t m -1 0 (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) (i nt er ce pt ) 1. 27 5 0. 20 8 0. 57 2 0. 45 4 0. 43 4 0. 89 8 2. 46 6 0. 74 7 1. 30 7 1. 38 0 1. 35 2 1. 84 6 0. 13 5 0. 07 2 0. 14 8 0. 11 0 0. 18 7 0. 26 6 d en sit y 0. 09 1 0. 08 6 0. 49 7 0. 54 2 0. 41 8 0. 39 6 g d p 1. 03 2 1. 03 6 1. 01 9 1. 01 9 1. 01 7 1. 03 5 u a a _s ha re 0. 59 8 0. 39 8 0. 86 3 0. 78 2 1. 22 7 1. 51 3 fa rm p er m ill in ha b 0. 99 8 1. 00 3 1. 00 0 1. 00 0 0. 99 7 0. 99 3 g va _s ha re 0. 98 1 0. 96 9 0. 99 1 0. 97 3 1. 03 4 1. 00 5 pa rt ie s 0. 76 4 0. 92 6 0. 86 0 0. 92 6 1. 02 2 1. 10 1 le ft_ rig ht 0. 93 6 0. 97 7 0. 94 4 0. 97 4 1. 04 1 1. 04 7 n _s ur _k g_ ha 1. 00 4 0. 99 7 0. 99 9 0. 99 7 1. 00 7 1. 00 6 a ni m al s_ ab 0. 79 8 0. 90 3 0. 93 1 0. 96 0 0. 89 8 0. 97 2 h n v 1. 01 5 1. 01 5 1. 00 7 1. 00 6 0. 99 0 0. 99 0 er os io n m od er at ese ve re 0. 99 2 0. 98 4 0. 99 4 0. 99 0 0. 99 7 0. 99 2 n u ts 0. 38 7 0. 39 1 0. 55 3 0. 57 2 0. 64 3 0. 56 9 po p 1. 00 7 1. 01 9 1. 00 2 1. 00 7 1. 00 6 0. 98 5 0. 99 9 1. 00 2 1. 00 0 1. 00 3 1. 00 3 0. 99 3 1. 00 9 1. 02 5 1. 02 3 1. 02 4 1. 00 7 1. 01 5 ee c 0. 36 9 0. 61 9 0. 57 0 0. 66 5 0. 49 3 0. 29 9 0. 52 8 0. 78 1 0. 72 0 0. 77 3 0. 61 6 0. 45 2 0. 62 0 0. 93 2 0. 89 0 0. 64 9 0. 81 2 0. 48 1 158 bio-based and applied economics 13(2): 147-160, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-13482 francesco pagliacci, matteo zavalloni the analysis shows that the determinants behind the allocation of the european rural development policy budget to environmental goals are similar to those found in the literature concerning environmental policies in general. the results seem to show the critical role played by an increase in the average wealth (as proxied by gdp per capita) favouring a larger environmental support. this result is not new – being in line with previous literature– but it is confirmed also for the eu rdp. moreover, different proxies for the lobbying power of the agricultural sector (as proxied by the uaa, the number of farms, and the agricultural gva) show no significance, hence the supposed competition between the agricultural support on the one hand and a broader support toward multifunctionality, and the environment in particular, on the other does not find strong support. decentralization is linked to lower budgets allocated to environmental goals and display a strong effect. the combination of the effect of per capita income and of decentralization seems to suggest that delegating rdps management to subnational authorities might be particularly problematic, given the high heterogeneity of development across european regions. the results seem to indicate that, if environmental issues are at stake, maintaining a relatively centralized grip on the environmental budget would be desirable. to this regard, the decision undertaken in the implementation of the current 2021-2027 rdps can be considered as positive for the implementation of a policy more in favour of agri-environmental targets. indeed, the regulation no 2115/2021 sets that all new rural development actions will be incorporated into national-level cap strategic plans, establishing specific rules on support for strategic plans to be drawn up by eu countries under the common agricultural policy. the emerging results are insightful, despite the existence of some possible shortcomings in the work. for example, the choice of a cross-sectional analysis, rather than a panel one, might somehow affect this analysis, due to the potential presence of unobserved heterogeneity. however, it seems not possible to compare expenditure patterns across different programming periods, due to the large changes that have always affected rural development policy over time. thus, further analysis will not only address these possible flaws. it should also seek to further disentangle the drivers of environmental budget allocation, including robustness checks, such as controlling for alternative proxies for the main effects admitted at 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https://doi.org/10.1016/j.jeem.2004.10.001 https://doi.org/10.2307/1243915 https://doi.org/10.1111/j.1574-0862.2000.tb00009.x https://doi.org/10.1016/j.landusepol.2018.05.053 https://doi.org/10.1016/j.landusepol.2018.05.053 _hlk149729446 _hlk135142798 _hlk135142780 _hlk152021616 _hlk136884351 _hlk140394422 _hlk149850987 _hlk149823827 _hlk116460470 _hlk125295920 _hlk125296237 _hlk125295754 _hlk94009644 lessons learned and policy implications from 20 years of swiss agricultural policy reforms: a review of policy evaluations robert huber1,*, nadja el benni2,3, robert finger1 the political economy determinants of agri-environmental funds in the european rural development programmes francesco pagliacci1, matteo zavalloni2,* economics sanction and barley price regime change in iran mohammad mehdi farsi aliabadi*, behzad fakari sardehaie dating common commodity price and inflation shocks with alternative approaches roberto esposti an input-output hydro-economic model to assess the economic pressure on water resources benedetto rocchi1,*, mauro viccaro2,3, gino sturla1 bio-based and applied economics 8(3): 261-277, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-9447 export propensity and intensity in the wine industry: a fractional econometric approach anthony macedo*, joão rebelo, sofia gouveia department of economics, sociology and management (desg), centre for transdisciplinary development studies (cetrad), university of trás-os-montes and alto douro (utad), quinta de prados, 5001-801 vila real, portugal abstract. using export market shares as a measure of international competitiveness, this paper studies wine exports in terms of propensity and intensity. based on data for the period 1999-2014, a two-part fractional regression model is applied. the results suggest that for importing countries gdp per capita, their own wine production, and eu membership have a positive effect on the probability of importing wine but tend to evolve inversely to market shares, as taste for variety becomes more important. additionally, export propensity is positively affected by regional trade agreements, common language, similarity of religious culture, wine production in the exporting country, and the exporting country being from the old world, while export intensity is boosted by common language and wine production in the exporting country. bilateral distance has a negative effect on both margins of trade. keywords. globalisation, international trade, market share, fractional regression model. jel codes. f14, l66. 1. introduction international competitiveness has become a topic of growing interest over the last decades. the concept of competitiveness is multifaceted and multidimensional (de grauwe, 2010), and researchers with such different backgrounds as economics, politics, management and history have all studied this concept. in economic literature, the roots lie in the international economic theories of adam smith, david ricardo and their followers. a definition of competitiveness, as given by the european commission is the ability “to sustainably produce and sell goods and services on a given market, in such a way that buyers prefer these goods to those offered by competitors” (european commission, p. 12, 2014). on the other hand, the organisation for economic co-operation and development suggests it is the “ability of companies, industries, regions, nations, and supranational regions to generate, while being and remaining exposed to international competition, relatively high factor income and factor employment levels on a sustainable basis” (hatzichronologou, p.20, 1996). *corresponding author: anthonym@utad.pt 262 anthony macedo, joão rebelo, sofia gouveia these definitions suggest that competitiveness can be observed from different points, namely at a company, sector, or national level. while ratios of financial profitability are a predominant indicator of performance for companies and the sectors of which they are composed, a high-performing country is one with positive trade balance (cardebat, 2019). this means that, although there is no “national decision” regarding trade in the sense of a centralized decision, the “national decision” is the sum of the decisions made by its companies. the well-known export market share is a simple but informative measure of competitiveness that allows direct trading positions to be established and was used, for example, by banterle and carraresi (2007), wijnands et al. (2008), and carraresi and banterle (2015). generally, this indicator is calculated by dividing the exports of a country by the total exports of a trading area and it is a measure of the degree of importance of a country, henceforth called country i, within the total exports of that trading area. the market share is given by a fractional variable bound by 0 and 1, being 0 if country i does not export and being 1 when all exports of the area are made by country i. in econometric modelling the fractional nature of this dependent variable provides limitations, in particular with linear specifications, where predicted values can be outside the boundaries [0, 1], resulting in meaningless outcomes. therefore, the fractional regression model (frm) developed by papke and wooldridge (1996, 2008) seems to be a preferable approach. moreover, by considering a two-part model (2p-frm) the aim is to estimate two different effects of explanatory variables: on the one hand, the effect on the decision of two countries establishing an international trade relationship (export propensity) and, on the other hand, the effect on the decision about how much to trade (export intensity). following a similar logic, bouët et al. (2017) estimated cognac export propensity and intensity using the heckman’s procedure to correct a sample selection issue. comparatively, the 2p-frm has the advantage of not requiring an exclusion restriction (ramalho et al., 2011). the global intensification of international trade have been accompanied by a wave of research estimating macroeconomic determinants mainly through the gravity framework to explain trade flows in value or volume (bergstrand 1989; mccallum 1995; anderson and van wincoop 2003; silva and tenreyro 2006; anderson and yotov 2012; cirera et al. 2016). inspired by newton’s law of gravity, the gravity model sets out to explain trade between an exporting country and an importing country based on their economic masses and the distance between them. it follows that economic masses (generally represented through the gdp) are expected to positively influence trade, while bilateral distance is expected to have a negative effect. over time, researchers have added more variables to the model and some works highlight the importance of studying the impact of trade agreements, transport costs, purchasing power, and cultural proximity, among other determinants, on international trade patterns.1 wine is a good example of a product increasingly globalised during the last five decades, presenting diversified geography of production and consumption and fierce competition between countries and even among local wine regions. studies on the global alcohol markets, and more specifically wine, have been multiplying in the last decades. anderson et al. (2018) presented historic facts about alcohol consumption and highlighted the study from holmes and anderson (2017) to state the more recent trend of convergence in national consumption 1 however, generally, such analyses do not take into consideration the contribution from the exporting countries to total imports in the destination country using, for example, market shares. 263export propensity and intensity in the wine industry patterns of alcohol as a result of globalisation. despite social, political, and fiscal differences among countries, consumption levels have decreased in “traditional” consuming countries (e.g. france, italy, and portugal) and increased in other countries without a tradition of wine consumption (e.g. china, russia, and usa) (smith and mitry, 2007; dal bianco et al, 2013). additionally, other topics were also studied such as the potential market implications of brexit (anderson and wittwer, 2017), the emergence of asia in the beverage market (anderson, 2019), and the impact of climate change on wine industry (anderson, 2017; ashenfelter and storchmann, 2016a, 2016b). a complementary strand of the literature deals with microeconomic factors of competitiveness. for example, bargain et al. (2018) use a qualitative approach to discuss key comparative advantages of 16 wine-producing countries, and ugaglia et al. (2019) dedicate several chapters to the study of the industrial organization of old and new world wine producing countries. based on the gravity model, but considering the fractional nature of the explained variable, which leads to the application of the frm, this research aims to contribute to the debate and provide further insight into the dynamics of international trade. to the best of our knowledge, there are no published studies that use the frm to analyse the competitiveness of an industry. specifically, a 2p-frm is applied to data on wine exports from the main fifteen producing countries to 193 partner countries, between 1999 and 2014, taking into account explanatory variables inspired by the gravity equation. after this introduction, the paper is organized as follows: section 2 presents the model framework and the data used; section 3 presents the results and discussion; and section 4 concludes. 2. material and methods given that the variable of interest y, i.e. market share, is a proportion defined and observed only in the interval 0 ≤ y ≤ 1, an approach capable of dealing with a bounded and fractional response variable is required. standard linear specifications can become inconsistent since they do not guarantee predicted values within the boundaries [0,1]. following the seminal papers of papke and wooldridge (1996, 2008), the frm is recommended since it presents advantages in relation to linear methods or other common solutions of the literature such as logit, probit, tobit and heckman sample selection model. unlike the frm, all these methods do not guarantee predictions within the meaningful interval [0,1] (ramalho et al., 2011). comparing in particular with the heckman model, the frm presents also the advantage of not requiring an exclusion restriction, which is useful in empirical practice and, following schwiebert (2018), avoids severe biases due to the imposition of an incorrect restriction. the frm is a non-linear model that does not require transformations for values at the boundaries, accounting for the non-linearity in the data, while being fully robust under generalized linear model assumptions (gallani et al. 2015). observations at the extremes of the distribution are included based on the assumption that e(y|x) = g(xβ), where fitted values are guaranteed within the unit interval by 0 ≤ g(∙) ≤ 1. papke and wooldridge (1996) suggest the quasi-maximum likelihood (qml) method for estimations of β, which is based on the maximization of the bernoulli loglikelihood function ll(β) ≡ y log[g(xβ)] + (1 y) log[1 g(xβ)]. furthermore, if there is a high concentration of observations at the boundary 0, ramalho et al. (2011) advise that it is better to consider a two-part model. the 2p-frm is 264 anthony macedo, joão rebelo, sofia gouveia constituted of a binary model for the discrete component (0 or 1) and a fractional model for the continuous component. choosing between oneor two-part models depends on the interpretation of the zeros based on the existence or not of two decision mechanisms for zeros and positive values [ramalho et al. (2011) also suggest a p test to compare the two models]. in international trade, by the estimation of a two-part model, it is presumed that a country as representing all of its firms has two distinct decisions to make: the first is whether to establish a trade relationship with another country (export propensity) and the second concerns the amount to be traded (export intensity). if none of the companies from a country i decides to export to a country j (e.g., because costs are too high for potential profit), then the decision of country i is to not export to country j. therefore, in the 2p-frm of this work, the first part estimates the factors influencing the probability of the wine of country i being imported by a certain country j and it can be defined as 0 for shareijt = 0 shareijt* = { (1) 1 for shareijtε(0,1) pr(shareijt* = 1|xijt) = e(shareijt*|xijt) = f(xijt β1) (2) where shareijt is the market share of country i’s exports as a proportion of the total of country j’s imports in year t, f(∙) is a non-linear conditional mean specification and β1 is a vector of coefficients for the covariates in xijt. the second part of the model considers only the observations of (1) where country i’s wine was imported, i.e. positive outcomes, to estimate the factors influencing the magnitude of a market share of country i in country j. it can be represented as e[shareijt|xijt, shareijt ∈(0,1)] = m(xijt β2) (3) where in m(xijt β2) the regressors xijt are the same as the first part (despite not being required) and m(∙) is also a non-linear conditional mean specification but not necessarily the same specification as f(∙). hence, following ramalho et al. (2011), e[shareijt|xijt] can be defined by e[shareijt|xijt] = m(xijt β2) ∙ f(xijt β1) (4) but to correctly interpret the estimated coefficients of non-linear econometric models it is advisable to estimate average and total partial effects. as xijt is a covariate of vector xijt, an average partial effect (ape) will be computed for the first part of the model to estimate the effect of xijt on the probability of a good (in this case wine) of country i being imported by a certain country j: ∂ pr(shareijt* = 1|xijt) = β1f(xijt β1) (5) ∂xijt 265export propensity and intensity in the wine industry similarly, another ape will be computed for the second part to estimate the effect of xijt on the magnitude of a non-zero market share of country i in country j: ∂e[shareijt|xijt, shareijt ∈(0,1)] = β2m(xijt β2) (6) ∂xijt finally, a total partial effect (tpe) will be computed to estimate the effect of xijt on the magnitude of any (including zero) market share of country i in country j: ∂e[shareijt|xijt] ∂m(xijt β2) ∂f(xijt β1) = f(xijt β1) + = m(xijt β2) (6) ∂xijt ∂xijt ∂xijt inspired by the literature on the gravity model (bergstrand 1989; mccallum 1995; anderson and van wincoop 2003; silva and tenreyro 2006; anderson and yotov 2012; cirera et al. 2016), the covariates that could influence the wine market share are: geographic distance between countries i and j (distij); gdp per capita of importer j in year t (gdppcjt); wine produced in countries i and j in year t-1 (respectively, prodit-1 and prodjt-1); exporter i being an old world country (oldi); the annual average exchange rate between the currencies of countries i and j in year t (erijt); importer j’s european union (eu) membership status in year t (eujt); the existence in year t of regional trade agreements (rta) between countries i and j (rtaijt); the same official language in countries i and j (langij); and countries i and j sharing common religion beliefs (religij).2 as regards the expected sign of the explanatory variables, distance should have a negative effect on the probability of wine trade and on market shares as this is a proxy for transport costs. on the other hand, the existence of a regional trade agreement between two countries should reduce trade costs and, consequently, may have a positive effect. the same is expected for the effect of wine production in the importing country because more wine produced means possessing a greater stock to export. exporting countries from the old world may also present some advantage in wine trade due to their experience. sharing a common language or common religion beliefs should also have a positive effect in both parts of the model because it represents higher cultural proximity. moreover, the sign of the effect provoked by an explanatory variable may not be the same in the first and second parts. for example, the gdp per capita of importing countries represents purchasing power, which should increase the probability of trade and quantity traded but can have a different impact on market shares. in fact, a hypothesis to be tested in the results section is that higher purchasing power may lead importing countries to search for differentiation, therefore spreading their imports across more countries. 2 the effect of tariff and non-tariff measures on wine trade have also been studied using the gravity model. the literature shows that specific tariffs may be a deterrent to trade (dal bianco et al., 2016; dal bianco et al., 2017) or not have a significant effect on certain wines (gouveia et al., 2018; macedo et al., 2019, 2020). however, there is an ongoing debate about non-tariff measures as the papers published on this subject relatively recent and scarce (dal bianco et al., 2016; santeramo et al., 2019). dal bianco et al. (2016) find that only some non-tariff measures present a significant negative effect on wine exports, while santeramo et al. (2019) find more of a positive impact of some non-tariff measures in wine imports. although recognizing the relevance of this issue, it was felt that it deserves a deeper analysis, which is beyond the scope of this paper, i.e. the application of the frm to international wine trade. 266 anthony macedo, joão rebelo, sofia gouveia similarly, wine production and eu membership of importing countries may have a positive impact on the probability of trade because it may represent a higher degree of cultural openness to wine consumption, however that may also result in a taste for variety that would have a negative impact on market shares. regarding the exchange rate, depreciation of an exporter i is expected to have a positive impact on market shares, because exports of country i become cheaper for importers j, while the converse will be true if exporter i’s currency undergoes appreciation. however, as suggested by chaney (2016), the effect on the probability of country i exporting to j may be the opposite because, in the presence of fixed costs and liquidity constraints, a depreciation will also mean that the value of domestic assets abroad decreases and, therefore, foreign markets become less accessible to some firms of country i whilst appreciation will have the opposite effect. these two effects of exchange rate are not incompatible considering a two-part model. the first part of the model should be more sensitive to fixed costs constraints because it refers to the factors affecting the probability of trade. on the other hand, the second part of the model should not be sensitive to fixed costs constraints because it only considers existent trade relationships. studying french wine exports, cardebat and figuet (2019) also identified that variations in exchange rates can lead to quality sorting, in the sense that higher-quality wines are less sensitive to exchange rate movements. the model is applied to the fifteen main wine producers3 (argentina, australia, chile, china, france, germany, greece, hungary, italy, portugal, romania, russia, spain, south africa and usa), focusing on 193 trade partners, all of which represent around 89% of world bottled wine exports during the period 1999-2014. regarding the explained variable, data for wine exports of the main exporting countries and total imports of each destination country are from the comtrade database4 in us dollars. the computation of the market share in this work measures the weight that an exporting country i has on total exports to a certain country j (or, in other words, on the total imports of a certain country j). about the explanatory variables, the sources are: world development indicators (wdi) database for gdp per capita in current us dollars and nominal exchange rate in local currency unit per us dollar (used to compute the exchange rate of exporting countries per 1 currency unit of importing countries); international organisation of vine and wine (oiv) for wine production data; cardebat (2019) for old world countries5; eu official website6 for eu membership dummy variable; and gravity database from centre d’études prospectives et d’informations internationales (cepii) for bilateral distance in kilometres weighted by population, religious proximity7, dummy for common official language and dummy for regional trade agreements. descriptive statistics of all variables considered in the paper are present in table a.1 in appendix. 3 in this paper the sample considers the main wine producers instead of the main exporters to limit bias. data on worldwide wine trade is not completely trackable and, consequently, they do not distinguish export from re-export. for that reason, countries such as united kingdom, switzerland and hong kong appear among the main wine exporters in comtrade database without producing relevant quantities of wine. 4 harmonised system codes starting by 220421 5 contrary to cardebat (2019), in this study hungary is included in the group of old world countries for having a historically relevant wine industry (luptak et al., 2016). 6 website: https://europa.eu/. 7 index from disdier and mayer (2007) calculated by adding the products of the relative proportions of catholics, protestants and muslims in the exporting and importing countries. higher values in this index mean sharing more common religion beliefs. 267export propensity and intensity in the wine industry 3. results and discussion in order to assess the robustness of the results, the 2p-frm is estimated assuming four alternative non-linear conditional mean specifications (cauchit, logit, probit, and loglog) for f(∙) and m(∙). based on reset and goodness-of-functional-form (goff) tests (ramalho et al. 2011), the results suggest adopting the logit specification in both parts of the model (see table a.2 in appendix).8 therefore, the results of the estimations with logit are presented in table 1.9 columns (1) and (3) refer to the coefficients estimated for the export propensity (equations 1 and 2) and the export intensity (equation 3), respectively. table 1 also presents the estimations of two apes for each explanatory variable: in column (2) the effect on the 8 reset and goff tests to the specification also reject the one-part model, which corroborate with the results of the p test suggested by ramalho et al (2011). additionally, estimations of the 2p-frm with a sample of 154 exporting countries (the maximum number for which information is available) were attempted but the specification was rejected by reset and goff tests. however, the results are fairly similar. the only differences in signs and significance of coefficients estimated are that, in the 1st part, religious proximity is not statistically significant and, in the 2nd part, exchange rate is not significant while rta is positive and significant. these results are available upon request. 9 for robustness of analysis, estimates were also made by splitting the sample into two sub-periods: before and after the financial crisis of 2008. the results do not indicate any marked differences between the two sub-periods (table a.3 in appendix). table 1. 2p-frm estimations, average partial effects and total partial effect. variables (1) (2) (3) (4) (5) export propensity export intensity tpe β1 ape β2 ape gdp pc importer (log) 0.349*** 0.057*** -0.167*** -0.014*** -0.003** (0.029) (0.004) (0.032) (0.003) (0.001) eu importer 1.037*** 0.170*** -0.292*** -0.024*** 0.000 (0.145) (0.024) (0.105) (0.009) (0.005) production importer (t-1) (log) 0.127*** 0.021*** -0.054*** -0.004*** -0.001 (0.012) (0.002) (0.012) (0.001) (0.001) production exporter (t-1) (log) 0.824*** 0.135*** 0.661*** 0.054*** 0.038*** (0.047) (0.007) (0.066) (0.005) (0.003) old world exporter 0.772*** 0.127*** -0.066 -0.005 0.007 (0.082) (0.013) (0.117) (0.010) (0.005) exch. rate (log) -0.069*** -0.011*** 0.038** 0.003** 0.001 (0.013) (0.002) (0.016) (0.001) (0.001) rta 0.611*** 0.100*** 0.071 0.006 0.010** (0.107) (0.017) (0.102) (0.008) (0.004) distance (log) -0.278*** -0.046*** -0.620*** -0.051*** -0.029*** (0.060) (0.010) (0.054) (0.005) (0.003) 268 anthony macedo, joão rebelo, sofia gouveia probability of wine of country i being imported by a certain country j (equation 5); and in column (4) the effect on the magnitude of a non-zero market share of country i’s wine in country j (equation 6). column (5) includes the tpe (equation 7). time effects are considered through yearly dummy variables (omitted due to space considerations), and standard errors account for intra-group correlation. the results suggest that all parameters estimated in the 1st part of the model (propensity to export) are statistically significant. by observing the apes, it is also possible to confirm the expected signs for all explanatory variables. it is estimated that the distance between two countries has a negative effect on the probability of wine of an exporting country i being imported by a certain country j. a depreciation of the exporter’s currency in relation to an importer’s currency has a negative effect on the export propensity, which following chaney variables (1) (2) (3) (4) (5) export propensity export intensity tpe β1 ape β2 ape common language 1.486*** 0.244*** 1.469*** 0.121*** 0.080*** (0.138) (0.022) (0.106) (0.009) (0.005) religious proximity 1.644*** 0.270*** -0.141 -0.012 0.014** (0.192) (0.031) (0.149) (0.012) (0.007) constant -9.317*** -1.961*** (0.650) (0.750) observations 44,313 22,671 pseudo r2 0.341 0.328 time effects’ significance 44.76*** 97.11*** [0.000] [0.000] reset 0.205 2.476 [0.651] [0.116] goff1 1.049 1.093 [0.306] [0.296] goff2 1.294 0.216 [0.255] [0.642] note: robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; figures in [ ] indicate p-values; time dummies included but not reported; goff1 and goff2 are goodness-of-functional-form tests; gdp pc is the per capita gross domestic product of the importer; eu is european union membership (or not) of the importer; wine produced in period t-1 in importing and exporting countries are represented by production importer and production exporter, respectively; old world is a dummy variable coded 1 if the exporting country is considered an old world country in wine trade and 0 otherwise; exch. rate is the annual average exchange rate between the currencies of importing countries and exporting countries; rta is a dummy variable coded 1 if there is a regional trade agreement between the exporter and the importer; distance is the geographical distance between the exporter and the importer; common language is a dummy variable coded 1 if importer and exporter share the same official language; and religious proximity is an index measuring common religion beliefs. source: authors’ computation. 269export propensity and intensity in the wine industry (2016) is an expected result in the presence of fixed costs and liquidity constraints. on the other hand, gdp per capita of the importers, wine production of both trade partners, the exporting country being an old world country, eu membership of importing countries, regional trade agreements established between both countries, and cultural proximity aspects, such as religion and language, present a positive effect. in the 2nd part of the model most of the covariates have a statistically significant effect, the exceptions being old world exporting countries, regional trade agreements and religious proximity. the apes indicate that the market share of wine from an exporting country i in a certain country j is negatively affected by gdp per capita, wine production, eu membership of importing countries and bilateral distance. however, the market share is positively affected by wine production in the exporting country, common language between trade partners and depreciation of the exporter’s currency in relation to an importer’s currency. it is noticeable that only three explanatory variables have apes with similar signs in both 2nd and 1st parts: distance, wine production in the exporting country, and common language between trading partners. regarding the other variables, as expected, the effect of depreciation of the exporter’s currency becomes positive in the 2nd part, as a result of wine becoming cheaper for importers. the effect of gdp per capita of importing countries goes from positive to negative, confirming the hypothesis that higher purchasing power leads importing countries to search for variety, therefore ranging across more countries to obtain their imports. a negative effect is also caused by higher wine production in importing countries and importer’s eu membership, because these variables seems to indicate higher cultural openness to wine consumption but also a taste for variety, which has a negative impact on market shares. regional trade agreements, religious proximity, and old world exporting countries have a positive impact on propensity to trade but have not a significant effect on the intensity of trade, meaning that historical, cultural, and commercial relationship are advantages in market entrance. the tpes show the effect of each covariate on the magnitude of any market share (including zero) of country i’s wine in country j. this can be interpreted as a global view of the effect of explanatory variables in both parts of the model. therefore, the results suggest that, overall, per capita gdp and distance have a negative impact on market shares, while wine production of exporting countries, regional trade agreements, common language, and religious proximity have a positive impact. as far as it is known, there are no fully comparable results in wine trade literature, as the dependent variable is usually not the market share. the closest comparison can be made with studies focused on determinants of wine exports, in which the dependent variable is usually the value or volume of exports. most of these works also suggest that bilateral distance has a negative effect on trade (castillo et al. 2016; dal bianco et al. 2016; lombardi et al. 2016), common language facilitates commercial relationship (castillo et al. 2016; dal bianco et al. 2016; lombardi et al. 2016; gouveia et al. 2018), regional trade agreements enhance trade (dascal et al. 2002; castillo et al. 2016), and wine production in exporting countries creates an export stimulus (dascal et al. 2002; agostino and trivieri 2014; dal bianco et al. 2016). with regard to the effects estimated for gdp per capita, wine production, and eu membership of importing countries, they are not comparable to such studies due to the difference in the nature of the dependent variable. 270 anthony macedo, joão rebelo, sofia gouveia 4. conclusion competition may be within domestic markets but, in a globalised economy, competitiveness is more and more dependent on the ability of industries to trade at an international level. one of the measures of competitiveness is the export market share, allowing the establishment of direct trading positions. however, when studying international competitiveness it is important to distinguish export intensity from export propensity, as some determinants can present opposite effects in these two respective measures, suggesting that caution should be exercised in policies aiming to improve export propensity because those same policies can lead to deterioration in export intensity, and vice-versa. these aspects call for the use of a 2p-frm. it is inferred through the results that strategic decisions of firms in the wine sector should vary according to the objective of the boards. in the case of managerial boards aiming to enter new markets, they should focus on importing countries with high purchasing power, eu membership, and high levels of wine production. these are characteristics of countries with a tradition of wine consumption, which means greater openness to try new wines. besides that, exploring markets with cultural (religious and linguistic) and commercial (trade agreements) proximity seems to increase trade propensity. on the other hand, corporate decisions should also be concerned about exchange rates and costs underlying distant markets. a different strategy should be adopted for managerial boards aspiring to increase market shares. in fact, markets with high purchasing power, eu membership, and high levels of wine production become less attractive because their taste for diversification in wine consumption may limit market shares. therefore, focus should be turned to markets with less tradition of wine consumption, despite the challenge of surpassing greater barriers to entry. this challenge can be overcome, for example, through the establishment of trade agreements and taking advantage of cultural proximity. the market share is a simple and informative measure of international competitiveness; nevertheless, in future research, it would be interesting to compare different measures to test the robustness of the results. also, the search for explanatory variables not yet considered in the literature should be done, since their eventual omission can lead to endogeneity and to the subsequent correction of the econometric model. in terms of methodology, including country-pair fixed effects in the 2p-frm, to treat neglected individual heterogeneity, or considering the heckman fractional model in development, by schwiebert (2018), could bring new insights to the nature of international competitiveness in the wine sector. 5. acknowledgements this paper benefits from the comments of three anonymous referees. the usual disclaimer applies. 6. funding this work is supported by the project vinci – wine, innovation and international competitiveness, under the operation number soe3/p2/f0917, feder – interreg sudoe, and national funds, through the fct – portuguese foundation for science and technology under the project uidb/soc/04011/2020. 271export propensity and intensity in the wine industry 7. references agostino, m. and trivieri, f. 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(2008). an economic and legal assessment of the eu food industry’s competitiveness. agribusiness 24(4): 417-439. 274 anthony macedo, joão rebelo, sofia gouveia appendix table a.1. descriptive statistics. variables obs. mean med. std. dev. min max market share 44313 0.1 0.0 0.1 0.0 1.0 distance 44313 7823.0 7687.4 4467.6 204.8 19539.5 gdppc importer 44313 11905.0 3831.5 17791.7 102.6 119225.4 rta (yes=1; 0 otherwise) 44313 0.2 0.0 0.0 1.0 exch. rate 44313 23.5 0.3 109.0 0.0 2320.0 eu importer (yes=1; 0 otherwise) 44313 0.1 0.0 0.0 1.0 production exporter (t-1) 44313 16285.7 10007.0 15483.5 1762.0 60535.0 production importer (t-1) 44313 1359.8 0.0 6004.3 0.0 60535.0 old world exporter (yes=1; 0 otherwise) 44313 0.5 0.0 0.0 1.0 common language (yes=1; 0 otherwise) 44313 0.1 0.0 0.0 1.0 religious proximity 44313 0.2 0.0 0.2 0.0 1.0 note: for the binary variables rta, common language, old world exporter, and eu membership of the importer the mean represents the percentage of observations equal to one. source: authors’ computation. 275export propensity and intensity in the wine industry table a.2. alternative functional form specifications of the 1st and 2nd parts of 2p-frm estimations. variables export propensity export intensity cauchit β1 probit β1 loglog β1 cauchit β2 probit β2 loglog β2 gdppc importer (log) 0.398*** 0.200*** 0.211*** -0.386*** -0.084*** -0.061*** (0.034) (0.017) (0.018) (0.086) (0.016) (0.012) eu importer 0.962*** 0.641*** 0.826*** -0.707 -0.153*** -0.114*** (0.207) (0.082) (0.123) (0.467) (0.051) (0.036) production importer (t-1) (log) 0.131*** 0.077*** 0.078*** -0.167*** -0.026*** -0.018*** (0.014) (0.007) (0.008) (0.055) (0.006) (0.004) production exporter (t-1) (log) 0.850*** 0.487*** 0.595*** 1.095*** 0.351*** 0.274*** (0.059) (0.027) (0.032) (0.338) (0.030) (0.021) old world exporter 0.825*** 0.460*** 0.495*** -0.288 -0.035 -0.026 (0.099) (0.048) (0.051) (0.456) (0.056) (0.039) exch. rate (log) -0.084*** -0.039*** -0.034*** 0.086* 0.021*** 0.017*** (0.017) (0.007) (0.007) (0.051) (0.008) (0.005) rta 0.642*** 0.355*** 0.461*** 0.180 0.038 0.030 (0.128) (0.063) (0.081) (0.257) (0.051) (0.037) distance (log) -0.359*** -0.145*** -0.190*** -1.199*** -0.314*** -0.231*** (0.067) (0.037) (0.040) (0.138) (0.027) (0.021) common language 1.490*** 0.886*** 0.988*** 2.399*** 0.794*** 0.642*** (0.161) (0.082) (0.107) (0.246) (0.058) (0.050) religious proximity 1.839*** 0.936*** 1.132*** -0.128 -0.076 -0.058 (0.242) (0.112) (0.138) (0.318) (0.076) (0.058) constant -9.322*** -5.629*** -5.916*** -0.355 -1.285*** -0.962*** (0.838) (0.376) (0.436) (3.527) (0.355) (0.251) observations 44,313 44,313 44,313 22,671 22,671 22,671 pseudo r2 0.338 0.341 0.338 0.305 0.325 0.318 time effects’ significance 45.40*** 44.97*** 52.53*** 39.34*** 107.03*** 116.64*** [0.000] [0.000] [0.000] [0.001] [0.000] [0.000] reset 75.262*** 16.840*** 193.217*** 775.004*** 19.202*** 115.371*** [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] goff1 17.701*** 18.047*** n.a. 530.827*** 21.229*** n.a. [0.000] [0.000] [0.000] [0.000] goff2 91.675*** 7.477*** 212.317*** 307.573*** 19.215*** 114.896*** [0.000] [0.006] [0.000] [0.000] [0.000] [0.000] note: robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; figures in [ ] indicate p-values; time dummies included but not reported; n.a. = not applicable. source: authors’ computation. 276 anthony macedo, joão rebelo, sofia gouveia ta bl e a .3 . 2 pfr m e st im at io ns s pl itt in g th e sa m pl e in p re a nd p os tfin an ci al c ris is o f 2 00 8. va ria bl es pe rio d 19 99 -2 00 8 pe rio d 20 09 -2 01 4 ex po rt p ro pe ns ity ex po rt in te ns ity tp e ex po rt p ro pe ns ity ex po rt in te ns ity tp e β 1 a pe β 2 a pe β 1 a pe β 2 a pe g d p pc im po rt er (l og ) 0. 34 7* ** 0. 05 7* ** -0 .1 63 ** * -0 .0 14 ** * -0 .0 03 * 0. 34 5* ** 0. 05 7* ** -0 .1 73 ** * -0 .0 13 ** * -0 .0 04 ** (0 .0 29 ) (0 .0 04 ) (0 .0 32 ) (0 .0 03 ) (0 .0 01 ) (0 .0 35 ) (0 .0 05 ) (0 .0 38 ) (0 .0 03 ) (0 .0 02 ) eu im po rt er 1. 12 2* ** 0. 18 4* ** -0 .2 48 ** -0 .0 21 ** 0. 00 4 0. 93 9* ** 0. 15 5* ** -0 .4 02 ** * -0 .0 31 ** * -0 .0 07 (0 .1 55 ) (0 .0 25 ) (0 .1 12 ) (0 .0 10 ) (0 .0 05 ) (0 .1 53 ) (0 .0 25 ) (0 .1 18 ) (0 .0 09 ) (0 .0 05 ) pr od uc tio n im po rt er (t -1 ) ( lo g) 0. 12 2* ** 0. 02 0* ** -0 .0 58 ** * -0 .0 05 ** * -0 .0 01 0. 13 7* ** 0. 02 3* ** -0 .0 47 ** * -0 .0 04 ** * -0 .0 01 (0 .0 12 ) (0 .0 02 ) (0 .0 13 ) (0 .0 01 ) (0 .0 01 ) (0 .0 14 ) (0 .0 02 ) (0 .0 13 ) (0 .0 01 ) (0 .0 01 ) pr od uc tio n ex po rt er (t -1 ) ( lo g) 0. 83 3* ** 0. 13 6* ** 0. 61 0* ** 0. 05 3* ** 0. 03 7* ** 0. 82 8* ** 0. 13 7* ** 0. 75 4* ** 0. 05 8* ** 0. 04 0* ** (0 .0 49 ) (0 .0 07 ) (0 .0 66 ) (0 .0 06 ) (0 .0 03 ) (0 .0 53 ) (0 .0 08 ) (0 .0 75 ) (0 .0 06 ) (0 .0 03 ) o ld w or ld e xp or te r 0. 73 9* ** 0. 12 1* ** -0 .0 30 -0 .0 03 0. 00 8 0. 81 5* ** 0. 13 5* ** -0 .1 17 -0 .0 09 0. 00 4 (0 .0 84 ) (0 .0 13 ) (0 .1 23 ) (0 .0 11 ) (0 .0 05 ) (0 .0 91 ) (0 .0 15 ) (0 .1 21 ) (0 .0 09 ) (0 .0 05 ) ex ch . r at e (lo g) -0 .0 58 ** * -0 .0 10 ** * 0. 03 1* 0. 00 3* 0. 00 1 -0 .0 82 ** * -0 .0 14 ** * 0. 05 1* ** 0. 00 4* ** 0. 00 1* (0 .0 13 ) (0 .0 02 ) (0 .0 17 ) (0 .0 01 ) (0 .0 01 ) (0 .0 14 ) (0 .0 02 ) (0 .0 17 ) (0 .0 01 ) (0 .0 01 ) rt a 0. 62 0* ** 0. 10 1* ** 0. 13 9 0. 01 2 0. 01 4* ** 0. 60 9* ** 0. 10 1* ** -0 .0 12 -0 .0 01 0. 00 6 (0 .1 36 ) (0 .0 22 ) (0 .1 20 ) (0 .0 10 ) (0 .0 05 ) (0 .1 09 ) (0 .0 18 ) (0 .1 10 ) (0 .0 08 ) (0 .0 05 ) d ist an ce (l og ) -0 .2 70 ** * -0 .0 44 ** * -0 .5 32 ** * -0 .0 46 ** * -0 .0 26 ** * -0 .2 95 ** * -0 .0 49 ** * -0 .7 60 ** * -0 .0 58 ** * -0 .0 35 ** * (0 .0 61 ) (0 .0 10 ) (0 .0 57 ) (0 .0 05 ) (0 .0 03 ) (0 .0 67 ) (0 .0 11 ) (0 .0 62 ) (0 .0 05 ) (0 .0 03 ) c om m on la ng ua ge 1. 45 7* ** 0. 23 8* ** 1. 44 0* ** 0. 12 5* ** 0. 08 0* ** 1. 52 2* ** 0. 25 1* ** 1. 49 8* ** 0. 11 5* ** 0. 07 8* ** (0 .1 43 ) (0 .0 22 ) (0 .1 07 ) (0 .0 10 ) (0 .0 05 ) (0 .1 51 ) (0 .0 24 ) (0 .1 14 ) (0 .0 09 ) (0 .0 05 ) re lig io us p ro xi m ity 1. 59 0* ** 0. 26 0* ** -0 .1 67 -0 .0 14 0. 01 3* * 1. 74 2* ** 0. 28 8* ** -0 .1 03 -0 .0 08 0. 01 4* * (0 .1 90 ) (0 .0 30 ) (0 .1 51 ) (0 .0 13 ) (0 .0 07 ) (0 .2 26 ) (0 .0 36 ) (0 .1 59 ) (0 .0 12 ) (0 .0 07 ) c on st an t -9 .4 09 ** * -2 .2 40 ** * -9 .2 85 ** * -1 .6 41 ** (0 .6 80 ) (0 .7 82 ) (0 .7 29 ) (0 .8 27 ) o bs er va tio ns 27 ,4 08 13 ,5 01 16 ,9 05 22 ,6 71 277export propensity and intensity in the wine industry va ria bl es pe rio d 19 99 -2 00 8 pe rio d 20 09 -2 01 4 ex po rt p ro pe ns ity ex po rt in te ns ity tp e ex po rt p ro pe ns ity ex po rt in te ns ity tp e β 1 a pe β 2 a pe β 1 a pe β 2 a pe ps eu do r 2 0. 34 5 0. 30 2 0. 33 3 0. 37 8 ti m e eff ec ts’ si gn ifi ca nc e 28 .1 9* ** 56 .9 9* ** 18 .4 4* ** 40 .8 4* ** [0 .0 01 ] [0 .0 00 ] [0 .0 02 ] [0 .0 00 ] re se t 0. 02 0 2. 04 4 0. 12 9 0. 46 0 [0 .8 88 ] [0 .1 53 ] [0 .7 19 ] [0 .4 97 ] g o ff 1 0. 02 6 1. 16 9 0. 86 4 0. 11 7 [0 .8 73 ] [0 .2 80 ] [0 .3 53 ] [0 .7 32 ] g o ff 2 0. 06 1 0. 41 4 1. 14 5 0. 02 3 [0 .8 04 ] [0 .5 20 ] [0 .2 85 ] [0 .8 81 ] n ot e: r ob us t s ta nd ar d er ro rs in p ar en th es es ; * ** p <0 .0 1, * * p< 0. 05 , * p <0 .1 ; f ig ur es in [ ] i nd ic at e pva lu es ; t im e du m m ie s in cl ud ed b ut n ot re po rt ed . bio-based and applied economics bae bio-based and applied economics 12(4): 305-321, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 copyright: © 2023 meloni, c., rocchi, b., severini, s. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: meloni, c., rocchi, b., severini, s. (2023). a systematic literature review on the rural-urban economic well-being gap in europe. bio-based and applied economics 12(4): 305-321. doi: 10.36253/bae-13178 received: may 27, 2022 accepted: september 04, 2023 published: december 31, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini orcid cm: 0000-0003-0140-988x br: 0000-0002-7545-3093 ss: 0000-0001-5501-3552 a systematic literature review on the ruralurban economic well-being gap in europe cesare meloni1,*, benedetto rocchi2, simone severini1 1 university of tuscia 2 university of florence *corresponding authors. e-mail: melonicesare94@gmail.com abstract. a large amount of policy support is spent to foster the development of rural areas in europe. however, empirical evidence on the well-being differential between rural and urban areas in europe is scant and incomplete. the present study develops a systematic literature review on this topic, bridging a gap in research as a systematic analysis on the subject has not been developed as far as we know. it uses the preferred reporting items for systematic reviews and meta-analyses (prisma) method. the review focuses on definitions of rural-urban most used in the literature, main dimensions of well-being that are analyzed, nature of the data and, finally, evidence that emerged regarding the differences in the various dimensions of well-being between rural and urban populations. the analysis confirms that available evidence is controversial and provides advice on how to develop new and better empirical analyses on this topic. keywords: well-being, rural areas, prisma, income gap, europe. jel codes: i31, i32, o18. 1. introduction european countries use large amounts of public resources to support the development of rural areas, particularly through the european union (eu) rural development policy. the reasons for supporting rural areas, which tend to be in disadvantaged conditions (shucksmith et al., 2006), are many and vary from improving their competitiveness, creating jobs outside the agriculture industry (new businesses, development of tourism related activities etc.), development of access and connections between cities and rural areas, development of basic infrastructure in villages, particularly in new eu member states. our analysis refers to the issue of the economic disadvantage of households living in rural areas. very often, rural areas are less developed and characterized by smaller incomes and greater employment, educational and administrative problems than non-rural ones (bock et al., 2015; shucksmith et al., 2006, 2009; sørensen, 2014). furthermore, rural areas and small towns are more eurosceptic than larger cities (dijkstra et al., 2020). all these aspects make the gap between rural and non-rural areas very important for policy makers. https://doi.org/10.36253/bae-13178 http://www.fupress.com/bae https://doi.org/10.36253/bae-13178 https://orcid.org/0000-0003-0140-988x https://orcid.org/0000-0002-7545-3093 https://orcid.org/0000-0001-5501-3552 mailto:melonicesare94@gmail.com 306 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 cesare meloni, benedetto rocchi, simone severini this paper investigates on this topic by means of a systematic literature review (slr) focusing on europe, filling a gap, as no similar analyses have been developed to the best of our knowledge. the study first aims at answering whether a well-being gap exists between rural and urban areas in europe, focusing on the economic aspect of wellbeing. this also calls for answering the following additional questions: is there sufficiently robust and comparable empirical evidence to answer the research question? are there any spaces to improve the analyses on this issue especially in terms of data and methodologies? the results of this analysis allow to explore the complexity of the topic at stake, the large array of data, dimensions and methodologies used, and to provide a synthesis of the main empirical results. as a consequence, the analysis paves the way for future research activities that could be developed on this relevant but somehow neglected issue. the paper is structured as it follows. next session describes the key concepts used in the analysis while section 3 describes the data and research methodology. session 4 presents the results of the analysis while session 5 the discussion of them. finally, session 6 concludes by providing a general judgment on what emerged from this analysis, its limits, and possible future developments. 2. key concepts used in the analysis 2.1 well-being definition the concept of well-being is used very often, but there is no commonly agreed definition of what it is. in fact, the terms “well-being”, “welfare”, “quality of life”, “happiness” and “life satisfaction” are often used interchangeably (oecd, 2013; schnorr-baecker, 2021). the oecd (2011a) argues that it concerns the satisfaction of various human needs, some of which are essential, and the ability to pursue one’s goals, thrive and feel satisfied with one’s own life. for this reason, well-being is a complex phenomenon and requires a multidimensional analysis approach (oecd, 2011a, 2020a; schnorr-baecker, 2021). oecd (2011a, 2011b, 2020b) identifies three pillars and eleven dimensions to describe and measure the various components of people’s well-being: material living conditions (or economic well-being), which determine people’s possibilities of consumption and their control over resources; quality of life, which is defined as the set of nonmonetary attributes of individuals that determines their life opportunities, and has a specific value in different cultures and contexts; the sustainability of the socio-economic and natural systems in which people live and work, essential for well-being to last over time. the eleven dimensions are defined, as follows (oecd, 2011a, 2011b, 2020a): material living conditions: i) income and wealth; ii) jobs and earnings; and iii) housing; quality of life: i) health status; ii) work and life balance; iii) education and skills; iv) civic engagement and governance; v) social connections; vi) environmental quality; vii) personal security; and viii) subjective well-being. this review focuses on papers including the economic dimension of well-being. economic well-being refers to the material living conditions that determine people’s consumption possibilities and their command over resources. this includes the ability of individuals to be able to consistently meet basic needs, such as food, housing, healthcare, transportation, education as well as the ability to make choices that contribute to security, satisfaction and personal fulfilment (oecd, 2011a, 2013, 2020a). income and wealth enable individuals to meet their basic needs and thus help achieve overall economic well-being (oecd, 2011a, 2011b, 2013, 2020c). both the availability of jobs and the resulting earnings are relevant to an individual’s well-being. indeed, they offer people the opportunity to fulfill their ambitions, develop their skills and feel useful in the society in which they live (oecd, 2011). societies with high levels of employment are also more politically stable, and healthier. finally, having a home is at the apex of human material needs. housing is the most important component of the expenses of many families and is fundamental for people’s ability to meet some basic needs. furthermore, any poor housing conditions can affect people’s health, both mental and physical (oecd, 2011). very often looking at national averages can lead to wrong or distorted conclusions because they often mask large differences in how different parts of the population are doing. for this reason, the distribution of current well-being should be analyzed into three different types of gap (oecd, 2020b): gaps between population groups; gaps between those who are at very distant points of the distribution in each dimension; deprivation (i.e., the share of the population that falls below a certain threshold, such as a minimum level of income, education or health). among possible comparisons between different population groups, the one based on the distinction between urban and rural areas can lead to interesting results. in fact, there are various aspects of well-being which are https://doi.org/10.36253/bae-13178 307a systematic literature review on the rural-urban economic well-being gap in europe bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 evaluated considering the rural-urban dichotomy and which provide different results. obviously, analyses of this kind require an objective and consistent definitions of “urban” and “rural”, usually in terms of settlement size or population density. there are different definitions in each country, reflecting different social constructions of what is rural and urban in that country or geographic area (shucksmith et al., 2009). the empirical evidence available on this issue is controversial also because different definitions of urbanrural, dimensions of well-being, evaluation methods and data sets have been used. indeed, recent analyses in europe show very different results for alternative european countries because of the different social and economic conditions existing in member states. for instance, rural areas are significantly poorer in some countries while in others the situation is balanced, or poverty is mainly a non-rural problem (bernard, 2019; shucksmith et al., 2009). 2.2 history of the european urban-rural issue the distinction between city and countryside, urban and rural, has long been rooted in european civilization. the etymological roots of the terms “urban” and “rural” extend at least as far as the classical latin words urbs (city) and rus (open space) (woods & heley, 2017). usually, the city or urbs has always been the object, with the rural always being the “other”, the non-urban, the open space beyond the city and the precise boundary between rural and urban, therefore, has always been open to interpretation and controversy (woods & heley, 2017). the history of the concept of urban-rural relations is one in which theoretical research and practical policy development are closely intertwined and difficult to separate. following copus (2011) it is convenient to divide it into two main phases. the first started in the mid-1950s and died out in the 1980s (phase 1: growth poles, cumulative causation and national policies). the second one started in the late 90s and still continues (phase 2: the esdp, spesp, espon, intereg, the territorial agenda, rurban and city regions). for a detailed explanation of the two historical phases, see copus (2011). in recent years the relationship between urban and rural areas has become a recurring theme in discussions on european rural policy. in very general terms it is seen as a promising component of a more territorial approach to meeting the development needs of lagging rural areas. this, of course, is not a new idea, but dates back to the 1950s and 1960s. however, in recent decades the reality and the connections between these two areas have become much more complex (copus, 2011). rural areas have undergone profound economic and social changes since the first agricultural policies aimed at modernization and land management. as a result, rurality can no longer be defined solely in terms of agricultural activities and associated lifestyles. indeed, since the publication of the key document on l’avenir du monde rural (“the future of rural society”) in 1988, the european commission has clearly identified, for the first time, the need for a territorial rural policy that goes beyond the agriculture and included local development and environmental concerns as key elements (european commission, 1988). the determination of rurality, being at the core of a relevant policy debate for almost 60 years (mantino, 2021), depends on several factors (féret et al., 2020): 1) the global contexts (i.e. the characteristics of the socioeconomic systems of which rurality is a part); 2) the discourse and the political objectives pursued; 3) the social representations of the different categories of stakeholders. in europe, each country has developed its own definition of rurality, often as a response to a particular political, administrative and wider territorial context, and in some cases as a result of national classifications of other factors (such as population, accessibility). approaches and definitions are rarely similar between countries (bontron, 1996; depraz, 2007; shucksmith et al., 2009). given the complexity of the topic, six approaches can be found in the literature to define the criteria of rural: the administrative approach, the morphological approach (population density), the locational approach, the functional approach, the landscape approach, and the combined approach (combination of at least two of the other approaches) (féret et al., 2020; mantino, 2021). furthermore, it is important to realize that the rural areas can be located inside a functional urban area (fua), outside but close to a fua or in a remote area (oecd, 2020c). for all these reasons, the debates on “rural” and “rurality” definition are an issue that still needs attention in both research programs and policies. 3. data and research methodology this paper uses the preferred reporting items for systematic reviews and meta-analyses (prisma), which is characterized by a rigorous and objective selection procedure that allows to increase the reliability of the final output. the approach is based on a statement that helps authors to improve reporting of the systematic review and meta-analysis (liberati et al., 2009). the prisma statement consists of a checklist of 27 items divided into 7 sections / topics and a four-step flow chart. https://doi.org/10.36253/bae-13178 308 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 cesare meloni, benedetto rocchi, simone severini the review uses the three most important platforms for researching scientific literature database: scopus; web of science core collections (wos); science direct (sd). these are easily accessible and have easyto-use search tools (gebre et al., 2021; li et al., 2018). more research databases and additional methods were used to be able to adequately identify all the literature related to the topic of interest (bramer et al., 2016). indeed, a single database is not considered sufficient to retrieve all references for a systematic review (bramer et al., 2017). to try to intercept most of the existing works on the research topic, the prisma prospectus allows authors the possibility of adding papers from sources other than the identified databases. therefore, we have added 15 additional articles that are considered important to address the research questions. after identifying the goal of the search, keyword detection and eligibility criteria setting follows. the keywords chosen were: ((“income” or “well-being” or “welfare”) and (“urban” or “non-rural”) and (“rural” or “non-urban”) and (“difference*” or “gap*” or “inequalit*”) and (“europe” or “eu”)). these keywords allowed for a thorough investigation and, at the same time, were relevant to the research question. regarding the eligibility criteria only publications in english were considered and editorials and letters were excluded. also, some sub-categories were excluded because they were deemed inconsistent in principle with the topic of our interest (i.e., medical analysis). studies were selected from the three databases by searching for keywords in abstracts, keywords, and titles of the research articles. after eliminating the duplicate articles, the studies were first selected by analyzing the title and abstract and subsequently reading the entire text of the remaining articles. the search yielded a total of 158 articles, of which 143 from the three electronic databases used in this slr and 15 added by the authors because considered important and particularly focused on the research topic, but they were not intercepted in the three databases used. by eliminating duplicates, the number of articles was reduced to 147. we then went through the articles, analysing their titles and abstracts, and excluded further 86 records, reducing the total of articles to 60. subsequently, after reading each single article, the eligibility criteria were applied, and another 20 studies were eliminated. therefore, 40 articles were included in the review and formed the basis for the remaining of the analysis. in this phase, as suggested by the prisma guidelines, the description of the study selection process was reported (figure 1). the quality assessment procedure is one of the steps in this slr process differentiating it from other types of reviews (bimbo et al., 2017; littell, 2006; ma & chen, 2020). quality assessment of papers included in a slr is necessary to assess the relevance of the studies to answer the research question and therefore to establish the strength and credibility of the slr’s findings and conclusions (yang et al., 2021). quality assessment consists in assigning a score to each paper included in the slr, based on pre-defined criteria. the literature quality assessment was not easy to perform given the high heterogeneity of the methodological approaches, and the lack of standardized quality assessment tools for studies belonging to the social science field. therefore, conventional measures of study quality were not appropriate in our case. so, similarly to bimbo et al. (2017), cox et al. (2015), mirra et al., (2021) and sulaiman et al. (2021) an ad hoc quality assessment tool was developed using the instrument critical appraisal checklist provided by joanna briggs institute (2017) as a reference document. additionally, based on the authors’ expertise, some studies characteristics considered important were included in the assessment of study quality. eventually six criteria were identified (table a1 in the appendix). the first criterion considered whether the analysis performed was qualitative or quantitative in nature. the adequacy of the sample size used was the second criterion considered. the third criterion was if there was a well-defined research question. the remaining three criteria were whether the outcomes were measured in a valid and reliable way, whether there was a clear definition of the rural-urban concept and whether well-being differences between rural and urban areas were addressed directly or just mentioned. the studies identified were rated as low, medium, or high quality, based upon a combination of the scores assigned to each of the six criteria (bimbo et al., 2017; cox et al., 2015; mirra et al., 2021). the more papers classified as “high quality” are present, the stronger and more robust the results and conclusions of the slr will be. a study was considered as “high quality” when showing “high” rating on four or more criteria; “medium quality,” with three “high” or two “high” and two “medium”. finally, we classified the study as “low quality” in case of zero, one or two high rating (excluded the case of two high and two medium). equal weighting was given to each criterion (bimbo et al., 2017; cox et al., 2015). the results of the quality assessment are reported in table 1. https://doi.org/10.36253/bae-13178 309a systematic literature review on the rural-urban economic well-being gap in europe bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 4. results of the analysis the 40 publications are distributed over time as represented in the figure below. there is a growing trend from 2000 to today (figure 2). first, the definitions of rural-urban used in the articles were identified and classified. the next step was to frame which dimensions of well-being were analyzed in the selected studies. this means evaluating how many articles study income, education, subjective well-being, etc., following the eleven economic dimensions presented by the oecd (2011c). all the research objectives pursued in the selected literature were also analyzed, as well as the nature of the data (macro or micro approach, cross-sectional, panel, time series) and the methodologies adopted to achieve the expected results. figure 1. articles selection process. source: own elaborations. https://doi.org/10.36253/bae-13178 310 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 cesare meloni, benedetto rocchi, simone severini table 1. quality assessment. author and year what it was the methodology researchers used in this study? was sample size adequate? was there a well-defined question? were the outcomes measured in a valid and reliable way? was there a clear definition of rural and urban? well-being differences between rural and urban areas addressed directly or just mentioned? overall rating schnorr-baecker s. (2021) quantitative yes yes yes yes yes high slettebak m.h. (2021) quantitative yes yes yes yes yes high ayala et al. (2021) quantitative yes yes yes yes yes high novák et al. (2020) qualitative yes yes no yes no medium piras s. (2020) quantitative no yes yes no no medium cipane k. and sloka b. (2020) quantitative yes yes yes no yes high wochner t. and holzhausen a. (2019) quantitative yes yes yes yes yes high viganóa et al. (2019) quantitative yes yes yes yes yes high bernard j. (2019) quantitative yes yes yes yes yes high grzega u. (2019) quantitative yes yes no no no medium sloka et al. (2019) quantitative yes yes yes no no high tobiasz-adamczyk b. and zawisza k. (2017) quantitative yes yes yes yes yes high bruder e. and unal h. (2017) quantitative yes yes no yes yes high mattioli g. (2017) quantitative yes yes yes yes no high zarnekow n. and henning c.h.c.a. (2016) quantitative yes yes yes yes yes high binelli c. and loveless m. (2016) quantitative yes yes yes yes yes high bock et al. (2015) qualitative yes yes no no yes high alexandri et al. (2015) quantitative n/a yes no no yes medium chivu et al. (2015) quantitative yes yes no no no medium zwiers m. and koster f. (2015) quantitative yes yes yes yes yes high weziak-bialowolska d. (2014) quantitative yes yes yes yes no high sørensen j.f.l. (2014) quantitative yes yes yes yes yes high marcotullio et al. (2014) quantitative yes yes yes yes no high stanef m.r. (2012) qualitative yes yes no yes yes high sørensen j.f.l. (2012) quantitative yes yes yes yes yes high lengsfeld j.h.b. (2011) quantitative yes yes yes yes no high vicente m.r. and lópez a.j. (2011) quantitative yes yes yes yes no high rodríguez-pose a. and tselios v. (2010) quantitative yes yes yes yes no high shucksmith et al. (2009) quantitative yes yes yes yes yes high rodríguez-pose a. and tselios v. (2009) quantitative yes yes yes no no high macours k. and swinnen j. f. m. (2008) qualitative yes yes yes no yes high bertolini et al. (2008) qualitative yes no no yes no low havard et al. (2008) quantitative yes yes yes yes no high nummela et al. (2008) quantitative yes yes yes yes yes high van hooijdonk et al. (2007) quantitative yes yes yes yes no high shucksmith et al. (2006) quantitative yes yes yes yes yes high hoggart k. andcheng s. (2006) quantitative yes yes yes yes no high o’brien e. (2005) qualitative no yes no no no low gerdtham u. and johannesson m. (2001) quantitative yes yes yes yes no high rietveld p. and ouwersloot h. (1989) quantitative yes yes yes yes yes high source: own elaborations. https://doi.org/10.36253/bae-13178 311a systematic literature review on the rural-urban economic well-being gap in europe bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 finally, for each dimension of well-being, the main results in terms of differences in well-being (or individual dimensions of it) between rural and urban areas were summarized and compared. to resume the structure of the results of our work, for each study included in the review, the dimensions that were analyzed, classified, and compared are the following: definition of the rural/urban concept; main dimensions of well-being observed (i.e. income, education, work-life balance, etc.); aims of the research; nature of the data (macro/micro, cross-sectional, panel or time series) and applied methodologies; main findings related to differences in the various dimensions of rural and urban areas well-being. as regards the definition of rural and urban, the most widespread typology in the works examined is based on the concept of population density. in fact, 22 out of 40 studies use definitions of rural and urban based on population density, 8 use other definitions and 10 do not provide any definition at all. in 5 of the 8 studies that use other ways of defining rural and urban, the population density represents only one dimension of the definition, while in 3 studies the interviewee subjectively indicates and classifies the area in which he/she lives as rural or urban. it should be noted that of the 30 studies that provide a definition of rural and urban, 12 use simple rural-urban dummies while 18 use categorical variables, which can range from 3 to 8 categories (i.e. rural, sub-rural, sub-urban, urban, etc). as for the dimensions of well-being (figure 3), the most studied are income and wealth (27), job and earnings (11), education and skills (10), health status (8) and housing (6). 27 papers analyse income, of which only 8 study the job and employment as well as income, with only 5 publications dealing also with housing. civic engagement and governance and social connections are discussed jointly. aims of the studies are very different both for the dimension of well-being investigated and for the centrality and importance of differences between rural and urban. some studies investigate the relationship between a dimension of wellbeing and its determinants, including the rural or urban residence, or how one dimension of well-being affects the total. in these studies, the dimension of well-being is central, while the variable defining the rural-urban areas is only one of the determinants. therefore, the real main objective of these works is not so much to observe a difference in well-being, or in its dimensions, between rural and urban areas. obviously, there are also studies in which the difference between rural and urban is the central aim. regarding the characteristics and nature of the data used, of the 40 studies reviewed in this slr, 63% use microdata. indeed, 26 studies use micro data, 4 macro data, 5 both micro and macro data and 5 do not use quantitative analyses. regarding the temporal nature of the data, of the 35 quantitative studies, 27 studies use crosssectional data, 6 use panel data and 2 use time series. the review showed that there are both studies based on individual data (individual households) and on regional data. obviously, this difference must be taken into account when comparing the results of the different types of analyses. in 23 papers the level of analysis is nuts0 (country or group of countries), in two papers is nuts1 and nuts2 together, in 15 papers nuts3 or more detailed territorial levels. in studies with less spafigure 2. number of publications per year. source: own elaborations. figure 3. number of publications per topic. source: own elaborations. https://doi.org/10.36253/bae-13178 312 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 cesare meloni, benedetto rocchi, simone severini tial detail, the tendency is to perform analyses on individual/household data and compare rural and non-rural individuals/households within the country. as the territorial detail of the analysis tends to increase, the greater the tendency to compare rural areas with non-rural ones (regions, provinces, etc.) without using individual/ household data. from a methodological point of view, it emerged that among the studies mainly focusing on the gap between rural and urban, the comparison of the averages between the two groups through descriptive statistics and / or hypothesis tests (e.g., t-test, χ2-test) are the most popular methods (14 papers). only few studies combine the comparison of the means with the comparison of the medians. in papers where the rural-urban differential is not the central topic, but only one of the many determinants of well-being or of a specific dimension, linear and more frequently non-linear regression models are used, such as logit, ordered logit or probit. pearson’s correlation is frequently used as a preliminary analysis (11 papers). regarding methodologies, it is important to underline that in various works the authors calculate and use indices and coefficients of various kinds, in relation to the objective pursued. these include the gini coefficients, the lorenz curve, the general psychological wellbeing index, the multidimensional poverty index and the theil index. next section reports and summarises the main results of the different studies divided by dimension of well-being. 5. discussion this section discusses the main results looking at the eleven1 dimensions of well-being already described in sections 2 and 4. in this approach, rural and nonrural areas have been treated as homogeneous withnin each of the two. however, this is an obvious oversimplification, as there are significant differences within these areas given that the degree of rurality of the different geographical realities also varies. this has been decided based on a compromise between the complexity of the classification of rural/non-rural areas and the aggregation of the results of 40 papers. in the following subsections we try to contextualize the results considering the geographical context in which the analysed works were carried out. even knowing that the comparison between different geographical areas, in different periods, has its limits, an attempt has been made to create a synthetic 1 civic engagement and governance and social connections are discussed jointly. discussion of the 40 papers that is as homogeneous and coherent as possible. 5.1 income and wealth in general, in europe, the analyzed studies have highlighted a lower income situation in rural areas than in urban areas (alexandri et al., 2015; bock et al., 2015; chivu et al., 2015; grzega, 2019; rodríguez-pose & tselios, 2009; schnorr-baecker, 2021; shucksmith et al., 2006, 2009; sloka et al., 2019; stanef, 2012). what has been observed in recent years in europe is a convergence between the two groups, characterized by a notable growth in rural areas and a less sudden growth in urban areas (grzega, 2019; wochner & holzhausen, 2019). furthermore, income differences between urban and rural areas change according to the wealth of the country of reference. in fact, urban-rural income differences are mild in the richest countries and most progressively marked in countries with a lower average income (alexandri et al., 2015; bock et al., 2015; chivu et al., 2015; grzega, 2019; rodríguez-pose & tselios, 2009; schnorrbaecker, 2021; shucksmith et al., 2006, 2009; sloka et al., 2019; stanef, 2012). therefore, there is evidence that the income gap of rural areas compared to urban ones decreases as the country’s average income increases. income differences between urban and rural areas in poorer countries may be less extreme than expected when considering domestic self-supply of food. indeed, growing food and raising animals is a very common activity in rural areas of low-income countries, which helps to mitigate the existing income gap. therefore, urban-rural differences in the poorest countries are lower than what one would expect observing the only monetary income differences (alexandri et al., 2015; bock et al., 2015; chivu et al., 2015; grzega, 2019; rodríguezpose & tselios, 2009; schnorr-baecker, 2021; shucksmith et al., 2006, 2009; sloka et al., 2019; stanef, 2012). furthermore, as reported by stanef (2012), the growing importance of extra-agricultural revenue in rural families is reducing the income gap between rural and urban areas. the income differences between the two groups concern and reflect the structure of consumer expenses. indeed, rural families spend relatively more on goods and services that satisfy their primary needs and less on those that satisfy secondary needs (alexandri et al., 2015; grzega, 2019). obviously, there are several exceptions to this rule. indeed, in some studies lower income levels characterise urban areas while higher income levels are found in the rural areas (rietveld & ouwersloot, 1989; zwiers & koster, 2015). these results seem to indicate that highhttps://doi.org/10.36253/bae-13178 313a systematic literature review on the rural-urban economic well-being gap in europe bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 income individuals, considering rural areas as places characterized by a better quality of life, leave urban areas and settle in more rural areas (viganó et al., 2019; zwiers & koster, 2015). sørensen (2014) found a positive correlation between income and satisfaction with life and that the inhabitants of rural areas have greater satisfaction with life than the inhabitants of the cities. as regards rural-urban perceived income differences, it was found that high-income urban residents are less likely to perceive large income differences than high-income rural residents, while there is no urban / rural difference for individuals with low income (binelli & loveless, 2016). the concept of ownership also falls within the definition of income and wealth provided by the oecd (2011c). in germany, car ownership is greater in rural areas, where it is essential for travel as the access to public transport services is lower (mattioli, 2017; schnorrbaecker, 2021). regarding poverty, similar poverty reduction is occurring over time in europe in rural and urban areas. however, there continues to be more poverty in rural areas (macours & swinnen, 2008; piras, 2020). the concept of poverty seems to partly follow the logic of income analysed above. in fact, in countries with the lowest average income, the worst situation regarding poverty is observed in sparsely populated areas, while a better situation occurs in densely populated areas. in richer countries, on the other hand, poverty is relatively higher in densely populated areas than in rural areas (weziak-bialowolska, 2014). regarding poverty in europe, the theme of the universal increase in poverty and deprivation levels as a problem of rural areas alone has been defined as wrong (bernard, 2019). indeed, as noted, rural-urban poverty disparities not only vary in magnitude, but, in some countries, disparities are completely reversed in favor of rural areas. according to bernard (2019), the increase in poverty in rural areas can be observed in countries with a lower population density in rural areas (reduced accessibility to opportunities for local people), in countries with a higher percentage of farmers (especially those who work on very small farms), in post-socialist transition countries and in countries with generally lower levels of economic development and reduced living standards. as mentioned above, poverty tends in some cases to become more and more an urban phenomenon. between 1996 and 2002 the poverty rate increased in large cities and decreased in small towns and rural areas (bertolini et al., 2008). however, in a cross-section perspective, rural districts still have the highest percentage of poor people. furthermore, according to bertolini et al. (2008), poverty rates drop further and significantly in rural areas when corrected for the fact that many rural families dwell in property homes and do not pay rents. further work that goes in the same direction is that proposed by rietveld & ouwersloot (1989) in the netherlands, according to which urban poverty has become a more serious and more widespread phenomenon than rural poverty. according to bruder & unal, 2017, deprivation rates are related to the average equivalent income of the country. thus, deprivation is not only an indicator of ownership of goods and equipment but also of the level of income and poverty of households. as for the works on deprivation in europe, some evidence suggests a lower level in rural than in urban areas (ayala et al., 2021). in the paper of havard et al. (2008), relating to the metropolitan area of strasbourg in france, the peripheral and sparsely populated areas (rural municipalities) that create a peri-urban ring around strasbourg are characterized by low deprivation. on the contrary, socio-economic deprivation is accentuated as we approach strasbourg and reaches its maximum in the center of the metropolitan area. 5.2 housing housing is one of the key dimensions of an individual’s material location and quality of life, in both rural and urban europe (alexandri et al., 2015; bock et al., 2015; chivu et al., 2015; grzega, 2019; rodríguez-pose & tselios, 2009; schnorr-baecker, 2021; shucksmith et al., 2006, 2009; sloka et al., 2019; stanef, 2012). housing problems are more severe in poorer european countries, in the sense that dwelling sizes, housing conditions and facilities are much worse, although levels of housing satisfaction do not differ significantly. the lack of space, the size of dwellings and the scarcity of affordable housing, including the high cost of renting or owning, are more common in urban areas, especially in richer countries (bock et al., 2015; schnorr-baecker, 2021; shucksmith et al., 2006, 2009). in contrast, poor physical condition and lack of amenities (e.g., damp, rot and lack of indoor sanitation) are more common problems in rural areas, especially in poorer countries (bertolini et al., 2008; bock et al., 2015; shucksmith et al., 2006, 2009). overall, there is almost no difference between urban and rural areas in housing satisfaction levels (bock et al., 2015; schnorrbaecker, 2021; shucksmith et al., 2006, 2009). regarding the financial burden of housing costs, it seems to be no significant difference between urban and rural areas (cipane & sloka, 2020). https://doi.org/10.36253/bae-13178 314 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 cesare meloni, benedetto rocchi, simone severini 5.3 jobs and earnings in richer countries, unemployment in urban areas is higher than in rural areas, while in lower-income countries, where unemployment is higher, it is more of a rural phenomenon (alexandri et al., 2015; bock et al., 2015; chivu et al., 2015; grzega, 2019; rodríguez-pose & tselios, 2009; schnorr-baecker, 2021; shucksmith et al., 2006, 2009; sloka et al., 2019; stanef, 2012). the contention that rural areas have shared the shift to a service-based economy is not confirmed across enlarged europe, except in the richest countries where most rural respondents work in white-collar and managerial occupations. indeed, even if agriculture plays a declining role, it still has a significant weight in rural employment in europe (bertolini et al., 2008; stanef, 2012). in eu countries with medium / low gdp, the rural employment structure has a high level of blue-collar workers, presumably in industrial employment, which is substantially higher than in the urban areas of these countries (alexandri et al., 2015; bock et al., 2015; chivu et al., 2015; grzega, 2019; rodríguez-pose & tselios, 2009; schnorr-baecker, 2021; shucksmith et al., 2006, 2009; sloka et al., 2019; stanef, 2012). it may be that the rural context of unemployment in these countries is more of deindustrialization than of peasant transition. interestingly, women in rural areas feel less stressed at work than men, while the opposite is true in urban areas (alexandri et al., 2015; bock et al., 2015; chivu et al., 2015; grzega, 2019; rodríguez-pose & tselios, 2009; schnorr-baecker, 2021; shucksmith et al., 2006, 2009; sloka et al., 2019; stanef, 2012). viganó et al. (2019), reported that being a worker in rural areas has a negative impact on well-being, while positive in urban areas; on the contrary, being office worker in rural areas has a positive impact on well-being and a negative impact in urban areas. this could be explained by considering the corresponding type of tasks for blue-collar and whitecollar workers in the two areas. a worker in a rural area gets a low wage compared to hard work, while a worker in the city gets a higher wage. on the other hand, being a white-collar employee in a rural setting could mean running a business in the agricultural sector, while a white-collar employee in the city could be someone with a high corporate position, a high level of stress and a low level of well-being. sørensen (2014) found a negative correlation between unemployment and life satisfaction and that rural dwellers have higher life satisfaction than urban dwellers. also in this case, there are several works with different results. for example, according to the paper by zarnekow & henning (2016), the determinants of quality of life, including employment, do not differ according to the degree of urbanisation of the respondent’s home region, or unemployment is lower in urban areas than in rural areas. (schnorr-baecker, 2021). the subject less studied in the literature is labor market. the results of slettebak (2021) show that the effect of eu11 migrant workers on natives’ income inequality is significant in rural municipalities, but weaker and not statistically significant in urban areas. this, according to the author, could be due to small and less diverse labor markets in rural areas. while natives in urban areas may have different ways of adapting to changes in competition, such as changing jobs or employment, their rural counterparts may have fewer opportunities. finally, as regards rural-urban differences in women’s wages, it appears that wage payments for similar jobs, for people with equivalent human capital endowments, differ very little between rural and urban areas (hoggart & cheng, 2006). 5.4 health status with regard to health status, it is useful to distinguish between access to health services and the actual state of health (self-assessed health, incidence of morbidity and/or mortality, etc.). regarding the former, urban areas generally have more infrastructure and access to health services tends to be easier for urban dwellers. however, viganó et al. (2019) claim that in italy there are no differences for health indicator in any of the 4 areas (rural, semi-rural, semi-urban, urban), probably because the italian national health system is quite widespread in the country. comparing these results with those of weziak-bialowolska (2014) at the european aggregate level, this absence of differences between the two groups is probably due to the fact that the richer a country is in terms of health, the smaller the differences between rural and urban areas; the poorer a country is in terms of health, the greater the differences between areas with different levels of degree of urbanisation. according to the work of sørensen (2014), both at the aggregate level of european union and at the level of three macro groups according to the gdp of the countries, self-reported health is positively and strongly correlated with life satisfaction. also, rural dwellers have higher life satisfaction than urban dwellers. indeed, the countryside landscape in rural areas plays an important role in promoting health because it has been affirmed that the great advantages of the rural context is the direct link with nature (novak et al., 2020). according to novak et al. (2020) the countryside can serve mental well-being by restoring attention, inducing positive https://doi.org/10.36253/bae-13178 315a systematic literature review on the rural-urban economic well-being gap in europe bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 thoughts and emotions, and reducing people’s stress levels. the countryside responds to the needs of promoting physical health by being a perfect place for outdoor activities. sport as a direct influencer of physical and mental health promotion is strongly linked to local communities and rural citizens who are attentive to their physical and mental health. finally, the countryside can foster the induction of social well-being when it promotes social integration, when it provides support and social security, and when it strengthens social engagement and participation (novak et al., 2020). these results can be read in the light of another aspect that reinforces the idea that health in rural areas is better than in urban areas: the risk of morbidity and mortality is higher among urban dwellers compared to rural dwellers. according to van hooijdonk et al. (2007), urban and highly industrialized areas tend to be characterized by a worse natural environment, which could have direct and indirect effects on human health. greater air or noise pollution can have a direct effect on respiratory and hearing diseases, just as the absence of green areas could cause a drastic reduction in outdoor physical activities. these features of densely populated areas can indirectly generate higher mortality and hospitalization rates in urban areas (van hooijdonk et al., 2007). other aspects, related to the age of individuals, can influence the self-reported state of health. indeed, in the work of tobiasz-adamczyk & zawisza (2017) on a sample of elderly polish people, several predictors of selfrated health in urban and rural residents were found, such as loneliness and networking and social participation. a relationship between loneliness and poor health self-assessment was observed only in rural residents. in urban residents, social networking and social participation significantly predicted positive self-reported health. another study that confirms the positive and significant relationship between social capital, self-assessed health and urban area is that of nummela et al. (2008). according to the authors, in fact, only in the urban area with high social capital a good health self-assessment was found. as with the other dimensions of well-being observed so far, there is no lack of contradictory results for health status. indeed, the determinants of quality of life, including health status, proposed by zarnekow & henning (2016) do not differ with respect to the region of origin of the respondents. 5.5 work and life balance in recent years, the issue of work-life balance has emerged as a prominent topic in sociology. the ability to reconcile work and family life, working hours and other time constraints are the most studied issues in this area. the results show that average weekly working hours are increasing for clusters in poorer countries but are also consistently higher in rural areas than in cities. (bock et al., 2015; schnorr-baecker, 2021; shucksmith et al., 2006, 2009). problems of reconciliation between work and private life are, however, widespread both in urban and rural areas and in rich and poor countries (bock et al., 2015; schnorr-baecker, 2021; shucksmith et al., 2006, 2009). in relation to pressures at work, however, being too tired from housework is the most surprising aspect cited by respondents, regardless of where they live (bock et al., 2015; schnorr-baecker, 2021; shucksmith et al., 2006, 2009). some gender differences emerge in work-life balance in rural but not urban areas (bock et al., 2015; schnorr-baecker, 2021; shucksmith et al., 2006, 2009). more clearly, women with partners and children in rural areas of richer countries have fewer problems than men in achieving a satisfactory work-life balance. moreover, work-life balance problems are widespread in both urban and rural areas, and no support was found for the idea that work-life balance is more satisfactory in rural areas (bock et al., 2015; schnorr-baecker, 2021; shucksmith et al., 2006, 2009). 5.6 education and skills access to education improves people’s employment prospects, as well as developing their skills in many other ways, and its inherent benefits (bock et al., 2015; schnorrbaecker, 2021; shucksmith et al., 2006, 2009). indeed, satisfaction with life increases with education (sørensen, 2014). there are notable differences both between groups of countries and between rural and urban regions across europe. education levels of people living in urban areas are higher across europe than in rural areas (bock et al., 2015; rodríguez-pose & tselios, 2011; schnorr-baecker, 2021; shucksmith et al., 2006, 2009; weziak-bialowolska, 2014). indeed, in rural areas more people have only primary education and fewer have a university degree (bock et al., 2015; schnorr-baecker, 2021; shucksmith et al., 2006, 2009). this could be related to the nature of jobs and labor markets in urban areas, which attract more skilled and educated people. consistent with this, slettebak (2021), argues that labor migrants have a greater impact on the employment status of locals and lead to greater competition in rural areas than in urban ones, since the level of general education is much lower in rural areas. the issue linked to distance from schools is also interesting. in france, for example, the distance from primary https://doi.org/10.36253/bae-13178 316 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 cesare meloni, benedetto rocchi, simone severini schools for rural municipalities increased from 1980 to 1998, due to a strategy of grouping schools when the number of pupils became too small, while the average distance from secondary schools decreased (bertolini et al., 2008). at the gender level, the differences are small, although in rural areas of the poorest countries, education levels are generally lower among women (bock et al., 2015; schnorrbaecker, 2021; shucksmith et al., 2006, 2009). the use of the internet, a potential indicator of a more general computer literacy, is higher in urban areas. this is true across the eu, although urban-rural differences are greater in poorer countries (schnorrbaecker, 2021; shucksmith et al., 2006). regarding the digital divide, there does not seem to be a rural-urban divide (vicente & lópez, 2011) given that the degree of urbanization is clearly not one of the main criteria that determines the digital divide (understood by the authors as digital inequality and it is defined as disparity in the quantity of internet usage), but education, age, and main vocational activity do indeed mark digital boundaries in many of the observed countries (lengsfeld, 2011). in summary, education levels are generally higher in richer european countries and in urban areas. of course, there are exceptions. indeed, according to the results obtained by weziak-bialowolska (2014) in luxembourg and the united kingdom, the least educated are people from densely populated areas, while in malta and germany there is hardly any difference. moreover, and surprisingly, education levels in middle-income countries are lower than in low-income countries, mostly former soviet countries, in both urban and rural areas. this may reflect a greater emphasis on secondary education in these countries in the past in order to reduce inequality (bock et al., 2015; shucksmith et al., 2009). furthermore, in the study by viganó et al. (2019), education does not reach statistically significant levels in any of the 4 areas (rural, semi-rural, semi-urban, urban) as a determinant of individual well-being. 5.7 civic engagement and governance and social connections the human being is a social creature, therefore the frequency with contacts with others and the quality of their personal relationships are crucial determinants of well-being (oecd, 2011c). activities are more satisfying when shared with others. additionally, social networks can provide material and emotional support in times of need, as well as provide access to jobs and other opportunities. the nature of social interactions also has broader implications beyond the immediate social circle, affecting levels of trust within the community, which is an important driver of other outcomes, including democratic participation, crime, and health (oecd, 2011c). participation in society and community life, for example through the expression of the political voice, is essential for individual well-being and allows people to develop a sense of belonging and trust in others (oecd, 2011c). considering the foregoing, the development of social capital is considered primarily significant where inadequate financial means are available for further economic and labor market growth. however, if social capital is exploited to pursue the objectives of small groups, it can also weaken social harmony and compromise economic performance. social capital seems to be more prevalent in rural areas than in urban areas (stanef, 2012): emotional networks (i.e., the commonality of mutual trust) are in many cases anchored to local social life, which also influences the interaction between businesses and / or the labor market. furthermore, the social network seems to positively affect the subjective well-being of rural elderly people, as opposed to loneliness which instead negatively impacts both rural and urban elderly (tobiasz-adamczyk & zawisza, 2017). other works in this topic present different results. indeed, according to sørensen (2012), no evidence of increased social and institutional trust has been found in rural areas of denmark. the data did not confirm the provisional hypothesis of greater institutional trust in rural areas. at the same time, unpaid voluntary work in associations was found to be higher in rural areas. 5.8 environmental quality contact with nature has benefits for people, often related to health. while the urban population has to look for pieces of nature in their neighborhood, the rural population lives their life in a much more direct contact with nature (maller et al., 2005). rural areas appear to enjoy better environmental quality than cities, positively affecting the mental and physical health of those who live there (novak et al., 2020). as for other environmental aspects, such as land use, obviously this is a problem mainly related to urban areas (schnorr-baecker, 2021). regarding waste management in germany, (schnorr-baecker, 2021) does not obtain an obvious difference related to the degree of urbanization of the area. the following is an intriguing and somewhat unexpected result discovered by marcotullio et al. (2014): european cities produce less co2-equivalent emissions per capita than non-urban areas. direct co2-eq emissions per capita are lower in urban areas than in non-urban areas in all sub-regions analyzed (eastern, western, northern, southern, and entire europe), most likely because urban https://doi.org/10.36253/bae-13178 317a systematic literature review on the rural-urban economic well-being gap in europe bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 areas are more carbon-efficient than non-urban areas. eastern europe is an outlier, with fairly similar values. this could be due to increased greenhouse gas emissions from heavy industries and/or energy production. 5.9 personal security no studies relating to personal security in europe have emerged from this slr. 5.10 subjective well-being the last key component of quality of life examined from an urban-rural perspective is people’s level of subjective well-being, optimism, and happiness. the findings in the literature on subjective well-being in europe do not seem to support the assertion that quality of life, indicated by the degree of life satisfaction and happiness, is higher in rural areas (bock et al., 2015; shucksmith et al., 2006, 2009). life satisfaction and happiness are higher in richer countries, as expected, but urban-rural differences are modest or zero, and while the eu-12 significantly favours rural areas, the balance is marginally in favor of urban areas elsewhere (bock et al., 2015; shucksmith et al., 2006, 2009). indeed, levels of optimism and happiness, in both rich and poor countries, are significantly higher in urban areas (bock et al., 2015; gerdtham & johannesson, 2001; shucksmith et al., 2006, 2009). most interestingly, subjective measures of happiness and life satisfaction do not seem to reflect urbanrural differences in the objective quality of life in poorer countries. such differences in subjective well-being appear to be quite small compared to large differences in some of the objective material indicators. the study conducted in poland on the elderly component of the population by tobiasz-adamczyk & zawisza (2017) is very interesting. indeed, the social network influences subjective well-being in rural dwellers. furthermore, poor appraisal of subjective well-being in old age increases with larger levels of loneliness and a rising number of chronic diseases in both urban and rural settings. in conclusion, living in rural or urban areas does not appear to have statistically significant effects on subjective quality of life (bock et al., 2015; shucksmith et al., 2006, 2009). 6. conclusions the literature does not provide precise and robust answers on the existence of a well-being differential between rural and urban areas. very often the results achieved by different studies do not agree with each other even referring to the same geographical area. moreover, the definition of what can be called rural (and also which are the differences within the rural category) and what is urban varies between different studies. however, what emerges from this analysis is that, considering various dimensions of well-being, a gap between rural and urban in europe seems to exist in favor of the urban one. however, this difference tends to be minimal or even to some extent reversed in those countries with high income, while the rural-urban gap tends to widen as the country’s income decreases. however, it should be noted that a growing gap exists between rural and urban areas in terms of provision of services of general interest and infrastructure (schools, mobility, health services, broadband connections) and that this is also present in countries with high income levels (european commission, 2022). for example, the rationale behind the italian strategy for inner areas is based on findings that rural areas in italy have greater difficulties in accessing services (including health services) than urban areas (dpcoe, 2020; uval, 2014). an example of a more articulated analysis is provided by viganó et al. (2019) paper included in this slr. unfortunately, part of the studies reviewed in this slr do not adequately capture the multidimensional nature of the gaps. this is mainly due to the different definitions of rurality used in the surveyed literature. for this reason, this slr may not provide a complete picture of the real well-being gaps between rural and urban areas, suggesting that further research is still needed looking at well-being from a multi-dimensional perspective. despite this, the conducted slr has provided some useful results. first, it identified the most widespread rural-urban definitions. the important finding in this regard is that the non-homogeneous definition of rural and urban in this slr makes it difficult to compare the results of the analyses considered in this slr. however, most of them use population density to differentiate between rural and urban areas. second, it also explored the databases that were used to run well-being analyses by identifying the pros and cons of each. some of the dimensions of well-being referring to quality of life are still not sufficiently considered in literature, such as personal security, work-life balance, civic engagement and governance & social connections. this constrains the possibility to expand the analysis to all dimensions of well-being. at a methodological level, some of these studies seem more appropriate than others to formally verify the existence of the gap considered. however, all the methodologies require an appropriate database, based mainly https://doi.org/10.36253/bae-13178 318 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 cesare meloni, benedetto rocchi, simone severini on individual-household data due to the need to verify the existing heterogeneity of the conditions existing within the two samples. this issue has been addressed with not too sophisticated methodologies, probably due to the lack of data or expertise. furthermore, many studies investigate the impact of the rural-urban component on one or more dimensions of well-being and do not explicitly assess the existence of a difference in wellbeing between the two groups. microeconomic and cross-sectional data were used in most of the studies. we believe that panel analyses would be more appropriate for analyzing the existence of differences in well-being between rural and urban, also allowing to observe how they change over time. finally, most of the studies refer to one or a limited number of countries, thus not providing complete results at european level on the rural-urban well-being gap. this limitation affects the possibility of drawing more general conclusions on well-being differences. the study is not without limitations. indeed, our slr focuses on europe alone. furthermore, from a methodological point of view, the evaluation of the quality of the studies included in this work used an ad hoc protocol based on the instrument critical appraisal checklist provided by joanna briggs institute (2017) due to the lack of standardized quality assessment tools for social science studies. we are aware that such limitations could affect the replicability of this slr and make it difficult to update the study. the limited number of analyses on the subject developed in europe and the heterogeneity observed between member states suggest the need to develop additional and methodologically sound empirical assessments on the issue at the whole european level. in the end, the results of this slr provide a useful basis in terms of the type and nature of databases to be used, methodologies and definition of rural and urban that can be used as a starting point for the development of new empirical analyses at the european level. references alexandri, c., păuna, b., & luca, l. 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(2019). urban – rural dwellers’ well-being determinants: when the city size matters. the case of italy. city, culture and society, 19(may), 100293. https://doi.org/10.1016/j. ccs.2019.100293 https://doi.org/10.36253/bae-13178 https://doi.org/10.1016/j.healthplace.2007.06.006 https://doi.org/10.1016/j.healthplace.2007.06.006 https://doi.org/10.1787/9789264121164-en https://doi.org/10.1787/9789264121164-en https://doi.org/10.1787/4ab9ee71-en https://doi.org/10.1787/4ab9ee71-en https://doi.org/10.1787/9870c393-en https://doi.org/10.1787/9870c393-en https://doi.org/10.1007/s11205-010-9717-1 https://doi.org/10.1007/s11205-010-9717-1 https://doi.org/10.1787/d25cef80-en https://doi.org/10.1111/joac.12351 https://doi.org/10.1068/a210881 https://doi.org/10.1068/a210881 https://doi.org/10.1007/978-3-642-14965-8_7 https://doi.org/10.1007/978-3-642-14965-8_7 https://doi.org/10.3233/sji-210803 https://doi.org/10.3233/sji-210803 https://doi.org/10.1080/00343400802378750 https://doi.org/10.1080/00343400802378750 https://doi.org/10.1177/0001699320930261 https://doi.org/10.1177/0001699320930261 https://doi.org/10.5755/j01.eis.0.13.23562 https://doi.org/10.5755/j01.eis.0.13.23562 https://doi.org/10.1080/00343404.2012.669471 https://doi.org/10.1080/00343404.2012.669471 https://doi.org/10.1080/00343404.2012.753142 https://doi.org/10.3390/nu13030945 https://doi.org/10.26444/aaem/74719 https://doi.org/10.26444/aaem/74719 http://www.dps.gov.it/it/pubblicazioni_dps/materiali_uval http://www.dps.gov.it/it/pubblicazioni_dps/materiali_uval https://doi.org/10.1016/j.socscimed.2006.10.041 https://doi.org/10.1016/j.socscimed.2006.10.041 https://doi.org/10.1016/j.telpol.2010.12.013 https://doi.org/10.1016/j.telpol.2010.12.013 https://doi.org/10.1016/j.ccs.2019.100293 https://doi.org/10.1016/j.ccs.2019.100293 321a systematic literature review on the rural-urban economic well-being gap in europe bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13178 weziak-bialowolska, d. (2014). spatial variation in eu poverty with respect to health, education and living standards. social indicators research, 125(2), 451–479. https://doi.org/10.1007/s11205-014-0848-7 wochner, t., & holzhausen, a. (2019). convergence of european regions: does the narrative of the ever-widening rural-urban divide hold? woods, m., & heley, j. (2017). conceptualisation of rural-urban relations and synergies deliverable 1.1. yang, l., zhang, h., shen, h., huang, x., zhou, x., rong, g., & shao, d. (2021). quality assessment in systematic literature reviews: a software engineering perspective. in information and software technology (vol. 130). elsevier b.v. https://doi.org/10.1016/j.infsof.2020.106397 zarnekow, n., & henning, c. h. c. a. (2016). determinants of individual rural versus suburban. women and migration in rural europe, lucas 2007. zwiers, m., & koster, f. (2015). the local structure of the welfare state: uneven effects of social spending on poverty within countries. urban studies, 52(1), 87–102. https://doi.org/10.1177/0042098014523688 appendix table a1. criteria of the quality assessment used in this review. criteria assessed quality rating low medium high what it was the methodology researchers used in this study? qualitative n/a quantitative was sample size adequate? no n/a yes was there a well-defined question? no n/a yes were the outcomes measured in a valid and reliable way? no, it is not validated and/ or it is not an objectively quantifiable measure n/a yes, it is a validated and/ or objectively quantifiable measure was there a clear definition of rural and urban? no n/a yes well-being differences between rural and urban areas addressed directly or just mentioned? no n/a yes overall rating no, one or two high rating (excluded the case of two high and two medium) three high ratings or two high rating and two medium four or more high ratings source: own elaborations. https://doi.org/10.36253/bae-13178 https://doi.org/10.1007/s11205-014-0848-7 https://doi.org/10.1016/j.infsof.2020.106397 https://doi.org/10.1016/j.infsof.2020.106397 https://doi.org/10.1177/0042098014523688 modeling conversion to organic agriculture with an eu-wide farm model dimitrios kremmydas*, pavel ciaian, edoardo baldoni a systematic literature review on the rural-urban economic well-being gap in europe cesare meloni1,*, benedetto rocchi2, simone severini1 the forest-based bioeconomy in latvia: economic and environmental importance vineta tetere1,2,*, jack peerlings1, liesbeth dries1 the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia margherita squarcina1,2,*, donato romano1 bioeconomy and resilience to economic shocks: insights from the covid-19 pandemic in 2020 jesús lasarte-lópez1,*, nicola grassano2, robert m’barek1, tévécia ronzon1 acknowledgements bio-based and applied economics bae copyright: © 2023 sabbagh m., gutierrez l. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae bio-based and applied economics 12(3): 221-242, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 citation: sabbagh m., gutierrez l. (2023). farmers’ acceptance of a microirrigation system: a focus group study. bio-based and applied economics 12(3): 221-242. doi: 10.36253/bae-13464 received: july 28, 2022 accepted: may 9, 2023 published: october 15, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: simone cerroni orcid ms: 0000-0001-7425-3279 lg: 0000-0002-3450-2514 farmers’ acceptance of a micro-irrigation system: a focus group study maria sabbagh1,*, luciano gutierrez2 1 department of agricultural sciences, university of sassari, viale italia 39/a, sassari, italy, msabbagh@uniss.it 2 department of agricultural sciences and desertification research centre, university of sassari, viale italia 39/a, sassari, italy, lgutierr@uniss.it *corresponding author. e-mail: msabbagh@uniss.it abstract. despite water scarcity and the numerous benefits offered by micro-irrigation systems, the implementation of these systems on potato crops in the bekaa valley of lebanon is notably low. this could be related to the local farmers’ acceptance to use this technique. the objective of this study is to investigate the factors that may or may not affect the adoption and investment in a new micro-irrigation system. for this purpose, the unified theory of acceptance and use of technology (utaut) served as the conceptual framework. a qualitative approach using focus group discussion was applied. a total of six focus groups with 34 farmers were conducted in the three main districts of the bekaa valley. from the analysis, performance expectancy, effort expectancy and facilitating conditions emerged as the three most prominent factors which influenced the farmers’ acceptance and adoption of micro-irrigation systems. according to the results of the focus groups, potato farmers are willing to adopt a new micro-irrigation system if they are assured that it will result in gains and reduce the amount of time and effort required for farming barriers included lack of knowledge about the system, financial capabilities and extension services. participants were enthusiastic about the idea of adopting a micro-irrigation system, but hindered by the unstable socio-economic conditions in lebanon and the financial situation. it was concluded that age, experience and voluntariness of use exert an effect on the related major determinants. this study will provide recommendations that can be considered while drafting agricultural policies. keywords: utaut model, focus group, climate change, micro-irrigation, technology acceptance. jel codes: q01, q2, q3. 1. introduction climate change is having a huge detrimental impact on freshwater availability on a worldwide scale, affecting water resources quantitively and qualitatively (field & barros, 2014). water scarcity is one of the most dangerous threats which has already resulted in catastrophic losses, notably in the arid regions. high temperatures, increased evaporation and fluctuations in precipihttp://creativecommons.org/licenses/by/4.0/legalcode https://doi.org/10.36253/bae-13464 https://doi.org/10.36253/bae-13464 https://orcid.org/0000-0001-7425-3279 https://orcid.org/0000-0002-3450-2514 222 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 maria sabbagh, luciano gutierrez tation are altering water availability and reducing crop yields (arbuckle et al., 2013; niles & mueller, 2016). these factors affect the management of farms, especially in arid and semi-arid regions (scoville-simonds et al., 2020). moreover, climate change is endangering the agricultural sector presenting risks for developed and developing countries (field & barros, 2014; niles & mueller, 2016). lebanon is a small mountainous country on the mediterranean sea’s eastern coast, covering a total area of 10,452 km². from a climatic point, lebanon is characterised by a mediterranean climate with a cold rainy winter and a semi-hot dry summer. according to the lebanese ministry of agriculture (moa), lebanon experiences water shortages during the dry season which goes from july through october, with about 60 percent of the country’s territory undermined by desertification (moa, 2003). this condition is expected to worsen in the future as a result of the effects of climate change. (bank, 2014). according to the lebanese agricultural research institute (lari), water scarcity more than land resources is actually the constraining factor in the country’s expansion of agricultural production (lari, 2019). in lebanon, groundwater sources are increasingly stressed by climate change as well by the increased demand from agriculture, the inadequate utilisation of underground water, the population growth and the industrial development (undp & unhcr, 2021). further, recent results (halwani & halwani, 2022) showed that from 1930 to 2019, the average temperature in lebanon increased between 1 to 3 ºc and a recent report from usaid (usaid, 2018) expects a 4–11% decrease in precipitation by 2100. thus, various conditions threatening water balance make adaptation to climate change more difficult in lebanon. in this situation, the enhancement of irrigation water usage efficiency and the conservation of water resources are turning into strategic priorities. for this to happen, adequate institutional arrangements are required to complement technical interventions in order to achieve effective water usage (speelman & veettil, 2013). according to the lebanese ministry of economy (moe) and the undp, the bekaa valley of lebanon, which represents 42% of lebanon’s area, is a very fertile valley in which 60% of lebanon’s agricultural production is concentrated including cereals, potatoes, vegetables and grapevine (moe & undp, 2011; moe et al., 2015). the production of potatoes typically ranks first among the top 10 commodities produced in lebanon each year, with a total production of 390,000 tonnes in 2017 (faostat, 2017). two-thirds of lebanon’s potato production comes from the bekaa plain, which is entirely irrigated (moa & lari, 2008). the bekaa valley is divided into three main zones: north bekaa, central bekaa and west bekaa. the valley is confronting the consequences of drought and reduced water availability that menace the yield and quality of irrigated crops (karam & karaa, 2000; moe et al., 2015; jaafar et al., 2016) . this is the case of potato crops which is one of the most sensitive crops to soil moisture stress and requires a systematic irrigation schedule (ayas, 2013). since potato crops are sensitive to water stress, water use efficiency such as water-saving technologies are becoming of high importance. until now, in the bekaa region, the high majority of potato farmers are still using the ordinary sprinkler irrigation (moa & lari, 2008). micro-irrigation, particularly mini-sprinklers, could be a solution to the above-mentioned climatechange related problems (houston et al., 2018). minisprinklers are small sized static sprinklers with a flow varying between 150 and 300 l per hour and a pressure of 1.5 bars inducing a water cooling canopy (deligios et al., 2019). micro-irrigation can induce an even application of water resulting in an improved crop quality and yields, in water savings and which also leads to energy and fertilizer savings compared with other irrigation methods (varma & namara, 2006; shah, 2011). further, micro-irrigation systems allow for a high level of control of chemical applications and weed and disease reduction due to limited wetted area. previous research executed in the middle east and north africa (mena) region, in lebanon and beyond indicated that the use of microirrigation in potato cultivation could have promoting results in terms of water savings of up to 40% (darwish et al., 2003; darwish et al., 2006), and allow for energy savings associated with higher crop quality and yields (karam & karaa, 2000; varma & namara, 2006; shah, 2011; rouzaneh et al., 2021). in this context, it should also be noted that the improvement of the diffusion of innovations such as water saving techniques is a crucial strategy for promoting economic development (lopolito et al., 2022). given the lack of information available on the performances of innovative technologies, farmers may evaluate these new systems through their experience and knowledge. this study aims to analyse the indirect non observed factors such as farmers’ motivations, attitudes and socioeconomic factors which may influence the farmers’ perceptions and behaviours in their investment in and adoption of a new micro-irrigation system. by disentangling these factors, effective strategies, and support systems for promoting the use of micro-irrigation systems in the area could be designed. to this end, the unified theory of acceptance and use of technology (utaut) model (venkatesh et al., 2003) was adopted. the utaut model (venkatesh et al., 2003) https://doi.org/10.36253/bae-13464 223farmers’ acceptance of a micro-irrigation system: a focus group study bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 is a tool that was mainly used to analyse the acceptance and diffusion of information systems and technology by evaluating the influencing factors. nowadays, the utaut model is commonly used to study individual intention and behaviour to adopt any type of technology (rippo & cerroni, 2023). previous studies utilised the utaut model to investigate factors affecting the adoption of pressurised irrigation technology among olive farmer (nejadrezaei et al., 2018), the acceptance of e-agriculture (eweoya et al., 2021), farmers’ use of communication technologies (mahamood et al., 2016), the acceptance of water saving technologies (sabbagh & gutierrez, 2022) as well as farmers’ participation in the apple-income stabilisation tool (ist) (rippo & cerroni, 2023).the utaut model integrates behavioural factors such as system ease of use, experience, and facilitating conditions , which give valuable insights into individuals’ decisions to innovate. understanding the impact of behavioural variables in innovation adoption could increase the efficacy and success of policies such as agricultural ones (cerroni, 2020; streletskaya et al., 2020). this is what makes utaut useful to analyse the adoption of other types of technologies other than information systems and technology. a qualitative study that utilised a focus group discussion (fgd) approach was employed. in this study, fgd could be an appropriate tool because it can allow for drawing upon the respondent’s knowledge, views, and experiences about the specific topic of introducing micro-irrigation systems. to the best of our knowledge, this is the first study to use the utaut model combined with a focus group discussion approach to shed light on the impact and importance of socio-economic, psychological and behavioural factors in influencing the adoption and use of a micro-irrigation system. several researchers found that these three factors work simultaneously to understand and obtain a more complete vision of the intention to adopt a new investment (heller et al., 1988; konana & balasubramanian, 2005). to this end, the scope of this paper is to enlarge the literature on technology investment analysing the impacts of socio-economic, psychological and behavioural factors that may affect the intention to adopt and invest in a micro-irrigation system by the potato farmers in the bekaa valley of lebanon. the remainder of this paper is organised as follows. section two briefly analyses the utaut model. section three explains the methodological approach employed in this study to explore the acceptance of a new micro-irrigation system. section four presents the results of focus groups conducted with potato farmers in three main districts of the bekaa valley. section five discusses the main findings providing insights about policies that the government could implement to encourage potato farmers to adopt a micro-irrigation system. in section 6, the main conclusions are presented. 2. research behavioural model and research questions a number of theories have been put forward to explain an individual’s behavioural intention to introduce a new technology. the current study employed the unified theory of acceptance and use of technology (utaut) (venkatesh et al., 2003) as a technology adoption model, which integrated previous technology acceptance models. thus, utaut is basically a synthesis through unifying at least eight existing technology acceptance and use models and specifically i) the theory of reasoned action (tra) (fishbein & ajzen, 1975); ii) the theory of planned behaviour (tpb) (ajzen, 1985); iii) the technology acceptance model (tam) (venkatesh & davis, 2000); iv) the combined tam and tpb (c-tam-tpb) (s. taylor & p. a. todd, 1995); v) the innovation diffusion theory (idt) (moore & benbasat, 1991); vi) the motivation model (mm) (davis et al., 1992); vii) the social cognitive theory (sct) (bandura, 1994; compeau & higgins, 1995; compeau et al., 1999) and finally viii) the model of pc utilization (mpcu) (thompson et al., 1991). according to utaut, an individual’s views on technology impact his or her behavioural intent to use and actual use of the technology. based on the integration of the eight models, utaut suggests four major determinants that have an effect on a person’s “use behaviour” to adopt a technology: performance expectancy (pe), effort expectancy (ee), social influence (si), and facilitating conditions (fc). the first three constructs influence use behaviour through a behavioural intention variable, while the fourth construct directly impacts the use behaviour. these constructs can be affected by four moderators a) age, b) gender, c) experience with similar technology, and d) voluntariness of use. fig. 1 presents the model. the performance expectancy (pe) represents the user’s level of belief in how advantageous a system usage will be and how it will help to attain benefits (venkatesh et al., 2003). pe aggregated all job performance related aspects, like usefulness (adapted from tam/tam2 and c-tam-tbp) (s. taylor & p. todd, 1995; venkatesh & davis, 2000), job fit (from mpcu) (thompson et al., 1991), relative advantage (from idt) (moore & benbasat, 1991), extrinsic motivation (from https://doi.org/10.36253/bae-13464 224 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 maria sabbagh, luciano gutierrez mm) (davis et al., 1992) and outcome expectations which are related to the consequences of the behaviour (from sct) (bandura, 1994; compeau & higgins, 1995; compeau et al., 1999). based on previous findings, pe will significantly and positively inf luence behavioural intention and technology acceptance (venkatesh et al., 2003; abushanab & pearson, 2007). people with high pe had high intentions to use a new technology (abushanab & pearson, 2007). additionally, the influence of performance expectancy on behavioural intention is suggested to be impacted by the moderating effects of gender and age (venkatesh et al., 2003). the effort expectancy (ee) construct suggests that the level of ease of use affiliated with the user’s adoption of a system is an important component in the adoption of a new technology (venkatesh et al., 2003). in this case, it is composed of three constructs: perceived ease of use (tam/tam2) (venkatesh & davis, 2000), complexity (mpcu) (thompson et al., 1991) and ease of use (idt) (moore & benbasat, 1991). previous research concluded that ee is a positive predictor of behavioural intention so that the higher the perceived ease of use of a new technology, the higher the intention to adopt it (venkatesh et al., 2003; bandyopadhyay & fraccastoro, 2007; kallaya et al., 2009; nassuora, 2012). according to venkatesh et al.( 2003), the influence of effort expectancy on behavioural intentions is moderated by gender, age, and experience. the social influence determinant (si) refers to the magnitude to which individuals perceive they should adopt a technology based on inputs from people who carry significant positions in their life (venkatesh et al., 2003). it also consists of “the degree to which peers influence use of the system” (venkatesh et al., 2003; slade et al., 2015; šumak & šorgo, 2016). social influence (si) consists of three variables: a) subjective norms which relate to the person’s perception that people who are important to her or him think that they should or should not execute the particular behaviour (ajzen, 1991; davis, 1989; fishbein & ajzen, 1975; s. taylor & p. todd, 1995), b) social factors which connect to the interpersonal arrangements that the individual has made with others as with co-workers (thompson et al., 1991) and c) image which is the extent to which the use of a new technology is seen to enhance one’s image or status in one’s social system (moore & benbasat, 1991). based on the review of the literature, it is expected that social influence positively influences the behavioural intention to use a new technology (bandyopadhyay & venkatesh et al., 2003; fraccastoro, 2007; kallaya et al., 2009; im et al., 2011; slade et al., 2015; šumak & šorgo, 2016). in addition, venkatesh et al. (2003) hypothesised that the influence of social influences on behavioural intentions is moderated by gender, age, voluntariness and experience. facilitating conditions (fc) represent the organisafigure 1. unified theory of acceptance and use of technology model (utaut) (venkatesh et al., 2003); adapted with permission from viswanath venkatesh, mis quarterly, 2003. the figure employs terminology, with ovals identifying latent variables and rectangles identifying moderator variables. https://doi.org/10.36253/bae-13464 225farmers’ acceptance of a micro-irrigation system: a focus group study bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 tional and technical conditions or infrastructure that the individual believes would encourage the use of the system and make it simpler for him or her to use it (venkatesh et al., 2003). the facilitating conditions determinant consists of three distinct constructs: a) perceived behavioural control (ajzen, 1991; s. taylor & p. todd, 1995), which are the possible internal and external limitations on behaviour related to resources, b) facilitating conditions adapted from (thompson et al., 1991), which relate to objective factors that persons agree make an act easy to realise, and c) compatibility from (moore & benbasat, 1991) which indicates the extent to which a new technology is perceived as being consistent with the current needs and capabilities of potential adopters. each one of these constructs is operationalised to incorporate technological and/or organisational aspects that are intended to eliminate obstacles to use. facilitating conditions are found to positively influence use behaviour (de veer et al., 2011). according to venkatesh et al. (2003), the influence of facilitating conditions on usage is hypothesised to be moderated by age and experience. as mentioned above, utaut hypothesised that gender, age, voluntariness and experience would moderate the relationships depicted in the model. these variables have been shown to moderate the intention to adopt new technologies in several studies (pearson et al., 2002; venkatesh et al., 2003; al-gahtani, 2004). in order to expand the literature on technology investment, such as water saving systems, by analysing the impacts of behavioural, psychological and socioeconomic factors, this paper studied the reasons behind using the current irrigation technique on potato fields, farmers’ perception of others’ opinions as well as the barriers that could hinder adopting a new irrigation system and the policies that could be used to encourage potato farmers to invest in a micro-irrigation system. 3. materials and methods the objective of this study was to explore via focus group discussions how socioeconomic, behavioural and psychological factors influence the adoption of a microirrigation system as a mean to save water and avert the water scarcity crises among potato farmers in the bekaa valley of lebanon. the focus groups discussed the socio-economic, psychological and behavioural aspects related to the possible shifting from the current irrigation technique (ordinary sprinkler) to micro-irrigation (drip or mini-sprinkler) that saves more water, induces higher production and better quality in the cultivation of potato crops. 3.1. the focus group protocol the focus group research protocol was divided into three sections. the first section aimed to start the discussion by introducing the research theme and to collect information about gender, age, education, type of land management, farm size and the annual irrigation water used. participants also received explanations of the role undertaken by the facilitator and that audio recordings would have only been used for the purpose of this study, thus reasserting the significance of privacy of all participants. it was explained that all participants were free to reveal their opinions related to the discussion and that all answers were to be accepted. section two aimed to provide information regarding the potato cultivation, the status of underground water in the bekaa region as well as the differences between the sprinkler irrigation system and the microirrigation system thus highlighting the advantages that could be obtained by implementing a micro-irrigation system. section three contained open ended questions related to the utaut model that the moderator asked participants of the three main districts of the bekaa valley. to trigger the discussion around the behavioural elements of the utaut model, section three started by asking participants about their knowledge of the micro-irrigation system and the reasons behind using ordinary sprinklers. this allowed the moderator to explore the degree to which each farmer believed that using a micro-irrigation system would help him or her to attain gains, thus exploring the performance expectancy determinant. the moderator then asked about the farmers’ perceptions of easiness of tasks related to the implementation and operation of the micro irrigation system and how they perceived the related technical operations. this permitted the moderator to explore the farmers’ effort expectancy towards micro-irrigation systems. further, participants were asked to list people whose judgment is important to them and who would approve or disapprove the adoption of a micro-irrigation system and the effect of personal moral obligation norms to adopt a micro-irrigation system for the sake of protecting the environment by preserving water resources. this revealed the social influence construct. to measure the facilitating conditions, the moderator explored the farmers’ opinion of being able or not to access required resources, as well as to obtain training and the necessary support needed to use micro-irrigation systems. following the utaut model variables, questions related to the moderating variables were raised in the focus groups. the moderator asked participants if they believed that the age of the farmers affect their incentive to adopt new https://doi.org/10.36253/bae-13464 226 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 maria sabbagh, luciano gutierrez irrigation practices. experience was tested by the familiarity of the farmers with the functioning of the microirrigation system either by their own trial on their crops or by observing others using it on potatoes or on other crops. for the voluntariness of use, farmers were asked about their tendency to adopt a micro-irrigation system in the case of the presence of external obligations as well as in the case of subsidies offered by the government. 3.2. sampling and data analysis fig. 2 shows the geographical area in which focus groups were carried out in the months of march and april 2020, among the potato farmers using the ordinary sprinkler irrigation system, in the three main districts of the bekaa valley (north, central, and west bekaa). the total number of potato growers in the area is approximately 500, and the sample of farmers participating the fg were selected using the information furnished by the president of the syndicate of potato growers in the bekaa valley. so, 35%, 20% and 45% of the farms are located in north bekaa, central bekaa and west bekaa, respectively. there was a total of 34 farmers in six focus groups consisting of five or six farmers each. two focus groups in each of the three main districts of the bekaa valley were run/organized/led to help ensure a variety of points of views amongst participants and to test their likeliness or unlikeliness to adopt a micro-irrigation system in their farms. due to covid-19 restrictions and safety limitations, three focus group meetings were conducted via a virtual “zoom” meeting platform among farmers having it resources. after the restrictions were lifted the three remaining focus groups took place, in conference rooms where all required safety measures were taken. farmers taking part in the focus groups were engaged in choices regarding agricultural techniques, type of crops, and irrigation strategies to be implemented in their farms; interviewees were chosen from different ranges of age, different educational levels, having different types of land management, and different farm sizes. the proportion of males among the participants was 100% since there were no women running farms in the area given that potato cultivation fields are largely male owned while female participation is higher in the industrial sector (konishi, 2017). all focus groups were audio-recorded, manually transcribed, and qualitatively analysed using nvivo12 software. 4. results 4.1. characteristics of participants table 1 presents the demographic characteristics. the focus groups were held among a total of 34 farmers of which 11 participants came from west bekaa, 11 others from north of the bekaa and 12 farmers were from central bekaa. in west bekaa, the average age was 55 years ranging from 45 to 60 years old for most of the n farmers (n=11). in north and central bekaa most of the farmers had a mean age of 46 (n=11) and 52 (n=12) years, respectively. in the cited 3 regions, the percentage of farmers who were older than 60 years was more or less equal (36% for both west and north bekaa while 33% in central bekaa). with regard to the educational level, the minority had a primary level (28%) in west bekaa, while the majority had a university diploma (64%) in north bekaa. however, in the region of central bekaa most of the participants had a secondary educational level (42%). as also shown in table 1, in each focus group there was a diversity in the size of the farms in order to gather the maximum points of views possible. in west bekaa the average farm was 146 hectares (sd=208), whereas in north bekaa, the mean farm size was 590 hectares (sd=1,55). in the region of central bekaa, it was 663 hectares (sd=1,55). figure 2. map of lebanon map showing the districts of the bekaa valley focus group discussions. https://doi.org/10.36253/bae-13464 227farmers’ acceptance of a micro-irrigation system: a focus group study bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 unfortunately, almost all of the participants were not aware of the quantity of the water used in the irrigation of their potato crops, which is an alarming problem. 4.2. results of the focus groups this section has the aim of presenting the findings from the six focus groups. after being transcribed from arabic to english, text files were imported into nvivo12 to first begin with the codings and finding core themes that reflect what participants were discussing to indicate the frequency of each core theme (allsop et al., 2022). the results are categorised into the investigated determinants affecting the acceptance of the micro-irrigation system in potato farming and three key moderators. to further emphasise and distinguish statements analysis from quotes, all direct quotes given by the participants will be highlighted in italics in the following findings’ part. 4.2.1. major determinants 4.2.1.1. performance expectancy performance expectancy was measured by the perceptions of using a micro-irrigation system in terms of providing benefits. firstly, participants were asked about their knowledge of the micro-irrigation system and the reasons behind using the ordinary sprinklers. all the participants showed a basic technological knowledge of the micro-irrigation system stating that it incorporates drip irrigation and mini-sprinklers irrigation. concerning the reasons for the adoption of the current irrigation system, which is the ordinary sprinklers, the most frequent answer was that sprinklers are less expensive (53%), and changing the ordinary sprinkler network that they have had for many years would cost them a fortune. one of the respondents said: i have been using sprinklers for a very long time and changing it and buying a micro irrigation network will be very expensive, especially for covering large areas. also, in the same context a second participant argued i still use sprinklers because i have had my equipment for a long time and in order to change it i will spend a lot of money because micro irrigation is a big investment, so i prefer to stay with sprinklers. furthermore, when participants were asked about their opinion about the following statement “adopting micro irrigation can be useful in your farm in terms of increasing potato yield, saving energy, labour, and quantities of pesticides and increasing your benefits”, 56% of the respondents totally agreed. some participants reported: yes, i totally agree with this statement in the sense of that micro irrigation controls water, consumes less fuel, and there is more control of the use of fertilizers. when the quantity of the crop increases, revenues and profits will surely increase. the more we irrigate the plant with a small amount, only as much as it needs, and at regular times, the more abundant the production and the better the quality and therefore we use less labour and pesticides. so i agree with this statement. on the other hand, 26% partially agreed with this statement arguing for example that micro irrigation definitely saves energy by saving water and because the water pressure is slight through it. it certainly increases the yield and increases profits, but i do not think it saves pesticides, as this amount remains the same as the sprinklers. however, one of the respondents asserted: since micro-irrigation uses less pressure, this saves energy. also, when using this irrigation technique, we don’t need a large amount of pesticides, but the yield won’t increase, it remains the same as in the case of sprinklers. finally, 18% of the participants fully disagreed with the statement, as other reported table 1. the mean and standard deviation results regarding the demographic data of the sample. characteristics west bekaa (n=11) north bekaa (n=11) central bekaa (n=12) mean (sd) age (years) 55 (11) 46 (13) 52 (16) farm size (hectares) 146 (208) 590 (1,555) 663 (1,556) age ranges [n (%)] <= 45 2 (18%) 6 (55%) 6 (50%) >45 and <60 5 (45%) 1 (9%) 2 (17%) >= 60 4 (36%) 4 (36%) 4 (33%) educational level [n (%)] primary 3 (28%) 1 (9%) 3 (25%) secondary 4 (36%) 3 (27%) 5 (42%) university 4 (36%) 7 (64%) 4 (33%) https://doi.org/10.36253/bae-13464 228 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 maria sabbagh, luciano gutierrez in practice, micro irrigation cannot be used on potatoes and cannot be adopted. it does not increase yields, nor save energy, nor reduce the amount of pesticides and it could not increase profits or micro irrigation does not increase the yield and does not save energy, nor does it reduce the amount of pesticides and fertilizers. micro irrigation does not add anything to sprinkler irrigation”. the most relevant statements that underpin this construct are the ones that relate to the general benefits associated with micro-irrigation use. therefore, participants were asked about their perceptions about the possible advantages deriving from the adoption of micro irrigation systems. based on the content analysis, the most important benefit mentioned by the respondents was water saving. this pattern is evident from the word cloud in fig. 3 which depicts the most frequently occurring words emerging from the focus group discussions. in fig. 3, central words with larger fonts are the most frequent, while distant words with smaller fonts are the less frequent. thus, the most recurrent words (water, distribution, saving, control, etc.) are important advantages according to farmers. participants highlighted that micro-irrigation is a water saving technique since it supplies water directly to the soil surface close to the plant roots, rather than the land around. in addition, they believe that micro-irrigation ensures uniform distribution of water by delivering water only wherever necessary and evenly over the whole land despite the presence of wind. moreover, farmers consider that micro-irrigation enhances the financial benefits by increasing yield, productivity, and therefore, farm profits. they also believe that micro-irrigation is a way to reduce operational costs in terms of reducing energy (less energy for water supply/ low pumping needs) and saving pesticides and fertilizers. overall, it was revealed that farmers perceived micro-irrigation as a system having many key advantages in potato farming from saving water, labour, and pesticides to increasing profits. therefore, we expect that ‘‘performance expectancy’’ will be positively associated with the intention of using micro-irrigation technology. 4.2.1.2. effort expectancy regarding the participants’ perception of the ease of use of a micro-irrigation system, and whether or not they would be skilled in using it, 62% of them considered micro-irrigation easy to be extended over fields. half of the 62% said that it saves labour and effort because it is installed once at the beginning of the season and there is no need to worry about moving it. moreover, the other half believed that micro-irrigation helps to save time. hence, the farmer can gain more time to take care of other profitable agricultural operations. accordingly, many participants claimed that micro irrigation is easier than sprinkler irrigation, and it is installed only once per season; therefore, the farmer will not worry about moving the network from one place to another as is the case with sprinklers. thus, micro irrigation saves labour. micro irrigation does not require significant time and effort to extend and remove the network. it is easier than sprinklers, because the network is extended once at the beginning of the season and does not need to be moved from one part to another part of the land as in the case of sprinklers. on the other hand, 38% of the participants perceived a high difficulty in extending the network of the micro-irrigation system on large fields and especially in the case of potato farming. they believed that, once extended, it decreases the efficacy of some agricultural operations. to highlight this problem some respondents commented figure 3. word cloud of the perceived advantages of micro-irrigation. https://doi.org/10.36253/bae-13464 229farmers’ acceptance of a micro-irrigation system: a focus group study bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 micro irrigation is very difficult to install and needs a lot of time since the technical process to extend the network takes about a week and more. there is a difficulty in the tasks related to micro irrigation because we can’t apply pesticides and carry out all the mechanical agricultural practices when it is installed. aside from that, they also argued that the installation of a micro-irrigation system needs a lot of attention and a specialised work force which induces a huge effort due to the complexity of the network equipment that should be implemented. additionally, one-third of the respondents who perceived a difficulty in the use of micro-irrigation claimed that micro-irrigation is time consuming. furthermore, another third considered micro-irrigation as labour consuming because the system needs constant attention in order to prevent damage to the hoses. some participants said micro irrigation initially requires a lot of effort in order to extend the network. likewise, if the hoses become clogged and we want to replace them then there is great effort and difficulty during the season. when installing the micro irrigation system, it will no longer be possible to operate properly on the field as the presence of the hoses restrains us. the sprinklers are much easier than micro irrigation, so that, just in one day, we can install, remove, and transfer 100 sprays. sprinklers require less labour because you only need one worker to do this, contrary to micro irrigation , which needs a lot of labour. furthermore, the effort expectancy construct is relevant to the question of whether participants believe they would/could become skilled at using micro-irrigation on potato crops. on one hand, 88% claimed that they would be skillful in using micro-irrigation. approximately one third of respondents believed they would do their best to develop their knowledge in order to improve the yield, and possibly to increase their profits; they would get used on any new agricultural practices that give positive results. one-fifth of the 88% participants described micro-irrigation as an easy technique and not difficult to implement on potatoes. these responses can be summarised with the following comment of course, it can be used in a successful way on potato and personally i will use it in a great way since it’s not difficult to manage. moreover, another fifth thought they would surely become skillful in micro-irrigation after getting appropriate training and guidance. further, approximately one-fifth of 88% of the participants assumed that they would improve their skills in every new technique to adopt it properly because it could improve their personal skills, thus their productivity. a respondent said: as farmers, we are most interested in developing our agricultural practices and noticing their positive results, and we therefore do our utmost to strengthen our skills in any new agricultural technology we adopt. on the other hand, 12% of the participants thought they would not become skillful in using micro-irrigation technology on potatoes. half of those participants were not convinced of the technology and believed it has no benefits on potato cultivation at all. no, since i see that it has no benefit in growing potatoes, obviously i wouldn’t improve my skills in using it. the other half considered micro-irrigation difficult and exhausting to be implemented in potato cultivation. overall, it was found that ‘‘effort expectancy” plays a positive role in user’s intention to use micro-irrigation technology. 4.2.1.3. social influence in the context of this construct, participants were asked to list people whose judgment is important to them and who would approve and disapprove their adoption of a micro-irrigation system. to this extent, 47% of the participants stated that they don’t care about others’ opinions, because each of them prefers to take his own decision concerning his work, and they know best what the soil requirements on their lands are; not every technique can be applied on all types of soil. for example, they said: i don’t care about someone else’s opinion. when i make my decision, i am convinced and sure that i will take advantage of it. since i believe that each one has a different point of view, i have my own. moreover, 21% of the respondents considered the opinion of “other farmers” or “nearby farmers” important. they expressed their trust in each other’s objective opinions about potato cultivation needs (irrigation, etc.) based on the soil type and the climate of the region. i only care about the opinion of the farmers, friends and relatives because i trust them and know they won’t suggest anything but useful things to help me in agricultural issues. https://doi.org/10.36253/bae-13464 230 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 maria sabbagh, luciano gutierrez i am very interested in the opinion of my neighbouring farmers in the area, because they express their opinion relatively to our area; as each region is different from the other concerning the soil, water availability, air velocity, etc. the opinion of other farmers is very important to me because we are in the same sector, and we face the same risks and problems. in addition, 20% of the farmers highlighted the importance of their family members’ opinion such as fathers, sons and/or cousins. two participants expressed this sentiment as my father’s opinion is very important to me, because everything i have learned is from him as he has large experience in agriculture in general and especially in potato agriculture. furthermore, 12% of the farmers were interested in ngo’s judgment and advice, as well as agricultural associations, organisations and engineers. according to those farmers, those organisations realise the significance of new agricultural practices and support the farmer in adopting these practices to develop his farm. they commented: i am also interested in the opinion of an agricultural organisation, because whenever it becomes clear that the farmer improves and adopts new technologies in his land, this agency supports and helps him by exporting cultivated yields. moreover, farmers responded to the question about the importance of collecting information from other farmers and observing what they think about their possible successes before adopting a new irrigation system. nearly all participants, 94%, were very interested in having access to the experience and suggestions of other farmers. inside this group, 50% voted for the collective benefit, and 44% were interested in continuous development and knowledge of existing and new agricultural practices. two statements can represent the general feeling collecting information from other farmers is important in order to share experience and increase development. it helps us to discover all new agricultural techniques, to test them and find out if they are useful in the region or not; this is a common interest. for this reason, i created the syndicate of potato farmers to exchange our knowledge and experiences, to share with each other every new agricultural practice, as well as our successes and failures so that we can learn more. on the other hand, 6% of the participants were not interested in the exchange of experience, because they believed that each farmer has his own individual specific agricultural practices and requirements. for example, each farmer has his own technologies and the specification of his land which differ from the other. some farmers may give agricultural information that can’t be adopted in the same way in my farm. obtaining a better understanding of the farmers’ perspectives on climate change and water shortage was also connected to this construct. participants were asked to define what these two terms meant for them. firstly, half of the farmers believed that climate change and water scarcity lead to loss in yield, thus in profits. according to them, the scarcity of water resulting from climate change causes cultivated areas to be minimised, resulting in huge losses. they also stated that climate change and water scarcity have negative consequences on agriculture in terms of the quality of yields. moreover, 16% argued that climate change and water scarcity affect potato farming in particular because potato crops are very sensitive to high temperatures and low precipitations. this group of farmers confirmed that climate change directly and negatively affects cultivation, especially potato crops, because it makes it vulnerable to climatic fluctuations. this fact may force them at some point to move from growing potatoes to rain-fed agriculture. further, 16% of the participants claimed that climate change and water scarcity put agriculture continuity at risk, because they lead to disasters that negatively affect agriculture. furthermore, 9% defined climate change as a fluctuation of precipitation and temperature during seasons. according to these farmers, climate change has led to changing temperatures during seasons, therefore to low precipitation rates, and consequently water scarcity. they also believed that climate change has caused the reduction of groundwater. finally, 3% of the participants argued that climate change and/or water scarcity do not exist because they still find water in abundance. in the same context of social influence, 91% of the respondents affirmed that a farmer should have moral norms and personal obligations as regards preserving water for the environment, future generations and for continuing appropriate agricultural practices. they stated that it is compulsory to have ethical and personal values to be forced to save water in order to preserve nature, water wealth and to keep the water resource for our children as well as to ensure the natural and continuous development of agriculture. personally, as i’m worried about climate change, if the government or a non-profit organisation will support us, i will adopt a micro-irrigation technique to conserve water https://doi.org/10.36253/bae-13464 231farmers’ acceptance of a micro-irrigation system: a focus group study bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 for the ecosystem’s well-being and to maintain a normal life-sustaining environment. overall, it seemed that social influence does not influence the farmers’ intention to use a micro-irrigation system. 4.2.1.4. facilitating conditions this construct is relevant to the question regarding the guidance role of the agricultural/irrigation extension services in the area. seventy-nine percent claimed that there was no presence at all, neither of agricultural guidance and extension nor of training courses. they stated that the agricultural sector is marginalised and neglected; therefore, the farmers had to rely on their personal experiences or the experiences of other farmers in the surrounding areas. they added that the non-presence of extension services made them unaware of the existence of new agricultural practices. they stated that the agricultural sector is marginalised, there are no agricultural policies, not even agricultural extension, and we have become used to relying on ourselves, our individual information, and the information we take from each other. in lebanon, we do not have agricultural policies, and farmers are not supervised by the ministry of agriculture, which does not provide any guidance. every farmer in this area depends on himself and on his personal experience. the other 21% of the participants stated that there was limited agricultural extension from some companies and institutions for the purpose of marketing. that is why they did not trust those types of companies and they relied on their personal experience. this common feeling can be summarised in the words of the participants: there is no appropriate agricultural extension role, there are some agricultural companies that deal with pesticides, they do some extension courses related only to the subject of insects so as to sell and market their products, but nothing more. so, i only rely on my personal information and experience. we have some agricultural guidance from some agricultural associations and institutions; they are doing all they can for agricultural extension. i take into account the information they provide, because agricultural guidance is necessary and sometimes it is a way to refresh my memory about things i know, but i do not remember. in the same context of facilitating conditions, participants were asked about the barriers they thought might prevent them from implementing a micro-irrigation system. participants had the possibility of multiple choices. several barriers were mentioned by each participant and results are illustrated in fig.4. all participants considered the most important barrier as the high initial expenses for installing the system: 53% stated they lacked the capital in order to cover the whole area; 53% believed they needed training to raise awareness about the benefits of the system; 44% said the sys100% 53% 53% 44% 38% 38% 35% 26% 21% 18% 12% 3% 3% 0 20 40 60 80 100 120 i find that the initial expenses are high for installing the system i lack the necessary capital to cover the entire area i need more training and awareness about the benefits of the system i admit that the system need attention from time to time for minor… i need credit facilities as a farmer i perceive the system as requiring too great effort i need subsidies i don’t have the technical know-how i think it is not feasible in a large field i find it technologically complicated i want the good spirit among farmers i need motivation and family/friends/farmers support my land is very scattered barriers to implementation figure 4. barriers to implementation of a micro-irrigation system. https://doi.org/10.36253/bae-13464 232 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 maria sabbagh, luciano gutierrez tem needed attention and time for minor repairs; 38% emphasised that micro-irrigation was effort consuming; 38% thought that they needed credit facilities as farmers; 35% believed that subsidies were necessary to implement this new technique with high costs; 26% said they did not have the required technical knowledge; 21% believed that micro-irrigation was not feasible on large fields; 18% found it technologically complicated; 12% stated that they wanted the good spirit among farmers because if famers cooperated they could support each other. however, only 3% needed motivation from family and friends in order to implement micro-irrigation, and another 3% believed that their land was very scattered, which impeded the installation of the system. overall, facilitating conditions could improve a farmer’s use behavior of a micro-irrigation system. 4.2.2. key moderators in addition to the previously mentioned four main determinants, the utaut model included four main ‘‘moderating’’ factors: gender, age, experience, and voluntariness of use. given that in this research all the farmers were of the same gender, the paper only included exploration of the possible effects of age, experience and voluntariness of use as moderating factors on the four main constructs. 4.2.2.1. age the question that was relevant to this factor was whether the participants believed that the age of the farmers affected their incentive to adopt new irrigation practices and in what way. it emerged that 62% of the participants considered that age had no influence on the intention of use of a new agricultural technology. they stated that farmers adopted a new technology once convinced of the advantages of that technology. they asserted that, no matter his age, a farmer remains enthusiastic about adopting new technologies, thus developing himself and his land. according to them, if a farmer is convinced of the benefits of modern technology, he will adopt anything that is beneficial for his land. some respondents commented that if it becomes clear to the farmer that the modern irrigation system will give him high profits, he will adopt it no matter what his age is. no, age does not decrease the incentive of adopting new agricultural technologies. a farmer who is convinced of the benefits of adopting new irrigation practices or other agricultural practices can only be hindered by financial capacity. no, there are young farmers who can’t be convinced of changing and developing, whereas there are older farmers (70 years and over) who always are willing to to keep up with progresst. however, 38% of the participants believed that age decreases a farmer’s incentive to adopt new agricultural practices because the age lessens the farmers’ enthusiasm. age was an important moderator in the context of adopting a micro-irrigation system among potato farmers. the younger group affirmed that it would be more difficult to persuade the older generation who does not have initiative to try new technologies, contrary to what the elderly said. moreover, in their opinion, elder farmers believe they have all the knowledge they need and that satisfies them. thus, it would be very difficult for them to be convinced of adopting new practices. those participants also added that, the older the farmer the more he rejects new technologies because he has no faith in them. in this case the usual comment was yes, when a farmer gets older, adopting a new irrigation system on his land becomes a secondary matter for him. he no longer has the urge to learn agricultural practices. 4.2.2.2. experience experience was tested by the familiarity of the farmers with a micro-irrigation system either by their own trial on their crops or by observing others using it on potatoes or on other crops. based on the analysis of the focus group discussion, some participants assumed that adopting micro-irrigation is not difficult for them as they witnessed its usage by other farmers on potato cultivation or on other crops. therefore, they have the know-how which increases their incentive to implement it on potato cultivation if they have the capital for the investment. in the same context, a participant stated as a member of my family uses micro irrigation on watermelon, i have professional and technical knowledge of this subject, and therefore i will not find great difficulty in using it on potatoes. another added i am adopting micro-irrigation on a small part of my land in vegetable cultivation, so i have experience of how to install it in an efficient way. https://doi.org/10.36253/bae-13464 233farmers’ acceptance of a micro-irrigation system: a focus group study bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 4.2.2.3. voluntariness of use moreover, “voluntariness of use” was measured by the tendency to adopt a micro-irrigation system in a situation where there is no external obligation to adopt the technology. external obligations can be defined for example as limited quantity of water usage imposed by the responsible authorities in the region. almost half of the participants (53%) stated that they can adopt microirrigation without external obligations, in order to induce good results and to ensure the continuity of their land cultivation: yes, i will move to a micro irrigation system in order to improve the quality of potatoes and produce more quantities, and the most important thing is to reduce water waste. however, it is worth mentioning that only one participant asserted that he will gradually adopt microirrigation regardless of its high initial cost, because he believed that it will greatly improve the quality and quantity of his potato yield: yes, i will move to the micro irrigation system, but in stages, due to the high cost. on the other hand, the other half of the participants (47%) have no tendency to adopt micro-irrigation spontaneously without external obligations: half of them consider it an expensive technology and they do not have the financial resources. the other approximate half does not perceive any benefit from adopting it on potatoes, and only very few have abundance of water so they do not need -water-saving irrigation technology. some comments were: no, because i am convinced that the sprinklers are better than the micro irrigation on potato crops, and i don’t have the financial resources to try and attempt micro irrigation even on a small part of my land. no, because i have enough water and i pay careful attention to the amount of water that the plant needs (manual soil testing) so that i don’t waste water and therefore micro irrigation won’t help me. no, i am not convinced that micro irrigation would be better than sprinklers on my land, so i won’t implement it. furthermore, participants were asked about the possibility of them adopting micro-irrigation if the government decided to subsidise the use of water-saving irrigation systems. eighty-five percent of interviewees responded that they would prefer to use a micro-irrigation system if government incentives were available. according to them, subsidies would reduce the financial burden on them at the beginning of the investment, and encourage them to take the first step toward the total adoption of the micro-irrigation system: yes, if the government provides subsidies, conducts training courses and supports us to export our production, of course i will adopt it. yes, i agree, because the state and the government have an obligation to take care of the farmer, who is the core of the lebanese economy. hence, micro irrigation is essential and necessary in improving the quality of potatoes to become competitive with potatoes from other countries. nonetheless, 15% of the participants insisted on not moving to micro-irrigation system even if there is support, because they do not perceive any benefit from it: no, i don’t agree… in the end, the productivity will be identical to that of the sprinklers. no, although this technique provides large quantities of crop production, it is not suitable for the large areas i cultivate, and thus the moth will surely appear resulting in high losses. it is crucial to note in this part that individuals who initially tended to adopt micro-irrigation without external obligations are also likely to adopt it if subsidies are given since they reduce the financial burden. further, participants who said they would not use micro-irrigation because of its expensive cost changed their mind when the interviewer mentioned the subsidies. the most notable change in intentions was that of the participants who had no tendency to adopt the system claiming that it has no benefits. however, 50% of them changed their answers when the question of subsidies was raised. they stated in this section that they would move to microirrigation gradually by applying it at first on a small part of the land to test its advantages. for example: yes, it will be possible for me to start adopting it on only one hectare. if my results are positive and there are no diseases, then i will gradually adopt it year after year until i have thoroughly checked its benefits. 4.2.3. the direct determinant: the behavioural intention the measurement of behavioural intention in this study included the intention and predicted use of a micro-irrigation system. the behavioural intention was measured by asking whether the participants had plans for the adoption of a micro-irrigation system in the following 12 to 24 months as well as the major concerns related to this system. https://doi.org/10.36253/bae-13464 234 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 maria sabbagh, luciano gutierrez fifty-nine percent of the participants said that they did not have any plan for the adoption of micro-irrigation in the next 12-24 months. this group of participants was divided into 3 groups according to the reason for not having a plan for adoption: a) the unstable economic conditions in lebanon that do not encourage farmers to invest large amounts of capital (the majority); b) the lack of micro-irrigation usefulness in terms of profits and feasibility ( a quarter of the respondents); c) lack of financial means (only 10%). the following quotes revealed the participants’ views: no, because sprinkler irrigation is easier for the farmer and does not require much effort, and i am satisfied with the quality and productivity that i get. no, if the government does not support me, i will not adopt the micro-irrigation system. then again, 41% of the participants stated that a plan to adopt the micro-irrigation system was possible in the near future. this group was also divided into several groups in terms of implementation conditions: a) presence of subsidies by the government (approximately half); b) better economic situation in the country (one quarter); c) in case of water shortage (6%); d) no conditions at all (14%). the following quotes revealed the participants’ views: in light of the current conditions in the country, i can adopt it in this period if there is protection for our products and if the state provides support. yes, if the country’s situation stabilises, i have the intention to adopt a micro-irrigation system soon; yes, when necessary, and that means if the water runs out on my land, i will adopt a micro irrigation system.” fig. 5 below shows the different answers obtained when investigating the concerns of the participants regarding micro-irrigation systems. each participant had the possibility to mention multiple concerns. as is clear, the main concern was the high cost of initial equipment and the possibility of financial losses (47%). in addition, 15% confirmed that micro-irrigation is a labour intensive technique that requires a lot of effort, time and attention. furthermore, 29% have no concerns at all. the remaining concerns differ in small percentages from the frequent maintenance to the emergence of diseases (fungal and moth), short lifespan, feasibility on large areas, no wind resistance. further, when asked about their willingness to adopt a new micro-irrigation system, 82% of the participants said yes and 18% said that they were not willing to. in conclusion, in order to recapitulate the main results of each construct, table 2 summarise the findings. 5. discussion the purpose of this study was to obtain a deeper understanding of the influential determinants (socioeconomic, psychological and behavioral factors) for 47% 29% 15% 12% 12% 9% 6% 3% 3% 3% 0 10 20 30 40 50 expensive technology no concerns labour intensive frequent maintenance disease emergence short lifespan not suitable for large areas/ for potatoes equal results as sprinklers lack of knowledge non resistance for the air factor concerns related to micro-irrigation figure 5. concerns related to micro-irrigation systems. https://doi.org/10.36253/bae-13464 235farmers’ acceptance of a micro-irrigation system: a focus group study bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 table 2. main findings from the focus group discussions. construct or moderator questions findings performance expectancy a. knowledge of micro-irrigation systems. basic technological knowledge of micro-irrigation systems stating that it incorporates drip irrigation and mini-sprinklers irrigation. b. the reasons behind using ordinary sprinklers. sprinklers are less expensive. c. possible advantages deriving from the adoption of micro irrigation systems. water saving, uniform distribution, yield increase, farm profits, energy cost reduction, reduction of pesticides and fertilizers. effort expectancy a. perception of the easiness of use of a micro-irrigation system. easy extension over the field, labour and effort saving, time saving. b. skillfulness in using micro-irrigation. 88% of farmers claimed that they would be skillful in using micro-irrigation. social influence a. list people whose judgment is important to farmers and who would approve or disapprove the adoption of a microirrigation system. 47% stated that they did not care about others’ opinions. 21% considered the opinion of “other farmers” or “nearby farmers” important. 20% highlighted the importance of their family members’ opinion such as fathers, sons and/or cousins. 12% of the farmers were interested in ngo’s judgment and advice, as well as agricultural associations, organisations and engineers. b. the importance of collecting information from other farmers and observing their possible successes before adopting a new irrigation system. 94% of farmers were very interested in having access to the experiences and suggestions of other farmers. facilitating conditions a. the guidance role of the agricultural/ irrigation extension services in the area. 79% claimed that there was no presence, neither of agricultural guidance and extension nor of training courses. they stated that the agricultural sector was marginalised and neglected; therefore, the farmers had to rely on their personal experience or the experience of other farmers in the surrounding areas. b. barriers that farmers thought might prevent them from implementing a microirrigation system. the most important barrier was the high initial expense of installing the system. age a. the age of the farmers affects their incentive to adopt new irrigation practices. 62% of the participants considered that age had no influence on the intention of use of a new agricultural technology because no matter his age, a farmer remains enthusiastic about adopting new technologies, thus developing himself and his land. experience a. the familiarity of the farmers with microirrigation systems either through their own trial on their crops or by observing others using it on potatoes or on other crops. participants assumed that adopting micro-irrigation is not difficult for them as they witnessed its usage by other farmers on potato cultivation or on other crops. therefore, they have the know-how which increases their incentive to implement it on potato cultivation if they have the capital for the investment. voluntariness of use a. the tendency to adopt a micro-irrigation system in a situation where there is no external obligation to adopt the technology. half of the participants (53%) stated that they can adopt micro-irrigation without external obligations, in order to induce good results and to ensure the continuity of their land cultivation. b. the possibility of adopting microirrigation if the government decides to subsidise the use of water-saving irrigation systems. 85% of the participants stated that they would adopt a micro-irrigation system if there were subsidies from the government. according to them, subsidies would reduce the financial burden on them at the beginning of the investment and encourage them to take the first step toward the total adoption of a micro-irrigation system. https://doi.org/10.36253/bae-13464 236 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 maria sabbagh, luciano gutierrez potato farmers’ adoption of micro-irrigation technology on their lands in the bekaa region in lebanon. this research further examined which factors seem to influence the farmers and their willingness to use a microirrigation system. based on the focus group analyses performed, performance expectancy, effort expectancy and facilitating conditions could have a significant effect on the acceptance of micro-irrigation technology while social influence does not. the effect of performance expectancy on behavioural intention was found to be relevant for many participants, which reflects the perceived benefits obtained using micro-irrigation systems. the benefits were identified as saving water, reducing labour effort and time, saving energy, increasing yield, improving crop quality and improving agricultural operations. farmers’ performance expectancy might increase by emphasizing the usefulness of micro-irrigation systems. that means if the advantages of micro-irrigation systems were presented in meetings conducted by specialists, this probably would increase the acceptance and adoption by people who were against this method, and who preferred ordinary sprinklers. almost all the participants declared that generation of good results and water saving were the main advantages of micro-irrigation systems. however, they were very anxious about losing financial investments if they could not apply this method without professional guidance. this asserts the idea of the essentiality of establishing agricultural guidance, in order to promote the advantages of micro-irrigation systems and its usage. this result was found to be consistent with previous research findings ( louho et al., 2006; bahramzadeh & shokati mogharab, 2010; im et al., 2011; yu, 2012; nejadrezaei et al., 2015; sa’ari et al., 2017; ronaghi & forouharfar, 2020) that have found a positive relationship between performance expectancy and behavioural intention to use technology. effort expectancy was measured by the perception of ease of learning and using the system, as well as how much effort should be spent on using the micro-irrigation system on potatoes. from the focus group analysis, it seemed that farmers preferred to adopt an easy way to use systems which required less effort and time than ordinary sprinklers on potato crops. furthermore, almost all participants, including a part of those who showed a high effort and attention concerns in extending the micro-irrigation system on their potato lands, demonstrated their willingness to learn about the microirrigation functions. for that, organising training and pilot studies could be a way for farmers to decrease their level of doubt. during on-field training courses, farmers discover how micro-irrigation functions, and the right way to install it in potato fields. similar with other research (venkatesh et al., 2003; louho et al., 2006; birch & irvine, 2009; im et al., 2011; nkandu & phiri, 2022), effort expectancy could have an effect on behavioural intention. the third determinant, social inf luence, seemed to have an insignificant impact on behavioural intention to use micro-irrigation. this result was consistent with venkatesh et al.(2003), rosen(2005) and yang et al.(2020). in his research, venkatesh et al. (2003) had found that the adoption of a new system depends on the user’s beliefs and not others’ opinions. social influence was not found to affect potato farmers’ willingness to adopt a micro-irrigation system since the vast majority does not care about the opinion of nearby farmers, famconstruct or moderator questions findings behavioural intention a. a plan for the adoption of a microirrigation system in the following 12 to 24 months as well as the major concerns related to this system. 59% of the participants said that they do not have any plan for the adoption of micro-irrigation in the next 12-24 months due to the unstable economic conditions in lebanon which do not encourage farmers to invest a large amount of capital; the lack of micro-irrigation usefulness in terms of profits and feasibility and the lack of financial means. 41% of the participants stated that a plan to adopt the micro-irrigation system is possible in the near future if there is the presence of subsidies by the government. b. concerns regarding micro-irrigation systems. the main concern was the high cost of initial equipment and the possibility of financial losses (47%). 15% confirmed that micro-irrigation is a labour intensive technique that requires a lot of effort, time and attention. 29% have no concerns at all. the remaining concerns differ in small percentages from frequent maintenance to the emergence of diseases (fungal and moth), short lifespan, feasibility on large areas, no wind resistance. https://doi.org/10.36253/bae-13464 237farmers’ acceptance of a micro-irrigation system: a focus group study bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 ily members, ngos, engineers, agricultural associations or organisations. this is why promoting the importance of agricultural associations and farmers’ gatherings, would revitalise the spirit of collaboration among farmers and the cooperation between them which enhances the spread of innovations as highlighted by nkegbe & shankar (2014) and lopolito et al. (2022). lastly, the facilitating conditions determinant was measured by evaluating the available resources and support to use micro-irrigation systems. the study results clearly depicted the direct effect of facilitating conditions on use behaviour of using micro-irrigation systems consistently with venkatesh et al. (2003), hung et al. (2006) wang & shih (2009) and im et al. (2011). guidance departments at the ministry of agriculture, non-governmental organizations (ngos) working in agricultural extension, particularly on the subject of climate change, social media advertising raising awareness of new ways of saving water, and any other available services to assist individuals in adopting and using micro-irrigation systems could be an important way to increase the adoption of a micro-irrigation system. nevertheless, all farmers confirmed that these conditions were unavailable in lebanon, and there was no guidance regarding agricultural practices in the country, which meant that they could not know about the benefits of micro-irrigation, or its right usage. with respect to the moderating effect of age, it was found that it was an important moderator in the context of adopting a micro-irrigation system among potato farmers. the younger group affirmed that it would be more difficult to persuade the older generation who did not have the initiative to try new technologies, contrary to what the elderly said. in fact, moderation by age impact was reported in several studies (venkatesh & morris, 2000; venkatesh et al., 2003; morris et al., 2005). experience was considered by venkatesh et al. (2003) as one of the important factors that affect behaviour intention. in this study, it was shown that the effect of effort expectancy on behaviour intention was in fact moderated by experience. the findings of this study revealed that, in terms of micro-irrigation usage, experienced farmers were more likely to accept and use microirrigation than inexperienced farmers. however, it appeared that experience was not a moderator of the effect of the facilitating conditions construct on use behaviour because farmers with different levels of experience have almost the same perceptions towards the resources supporting the use of micro-irrigation. this result is not consistent with the study of alshehri et al. (2013) who claimed that experience moderates the effect of facilitating conditions on use behaviour. voluntariness of use had moderated the effect of social influence on behaviour intention. it was measured on the basis of not using external obligations or incentives in order to implement the new irrigation system. the results confirmed that in the case of subsidies, the level of adoption would increase, and farmers would definitively implement the system. that is, if the microirrigation system was financially subsidised, almost all farmers in lebanon would adopt it. furthermore, the study findings showed that almost half of the participants did not have the tendency to adopt a micro-irrigation if there was no external obligation which is consistent with what venkatesh et al.(2003) had reported. in this case, if the government granted subsidies to support the implementation of a micro-irrigation system, the vast majority would adopt it gradually or immediately. this is compatible with what nkegbe & shankar (2014) found, that providing facilities to farmers, such as accessing credits in their case, could promote and intensify the adoption of agricultural technology. 6. conclusions the aim of this study was to investigate the potato farmers’ behaviour in adopting a micro-irrigation system. to achieve this objective, we adapted the unified theory of acceptance and use of technology (utaut) model. the outcomes offer visions for policymakers to encourage potato farmer to adopt a new micro-irrigation system. firstly, farmers are willing to accept micro-irrigation technology when they can make gains and reduce task uncertainty on their farming activities. secondly, they are keen to adopt a micro-irrigation system if they find that it reduces effort and time spent on their farming activities. finally, it is relevant to encourage farmers to adopt it through financial aids or subsidies which provide opportunities for farmers to decrease the financial burdens on them. furthermore, agricultural extensions, field training and pilot area studies are also important in increasing the farmers’ intention to adopt a micro-irrigation system. however, the study suffers from some limitations. legal restrictions and safety measures linked to the covid19 pandemic were a reason for the limited sample size. also, the sample used lacked gender differentiation since no females operated farms in the study area. thus, it would be useful to repeat the analysis with a larger sample for focus group discussions incorporating female participation and extending the study to other countries. https://doi.org/10.36253/bae-13464 238 bio-based and applied economics 12(3): 197-220, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13464 maria sabbagh, luciano gutierrez acknowledgements this paper is the fruit of the second chapter of maria sabbagh’s ph.d. thesis entitled “impact and adaptation strategies on water resources: the case of the bekaa valleylebanon”. we thank two anonymous referees, and the editor simone cerroni for their valuable comments and suggestions. references abushanab, e., & pearson, j. m. 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(2012). factors affecting individuals to adopt mobile banking: empirical evidence from the utaut model. journal of electronic commerce research, 13(2), 104. https://doi.org/10.36253/bae-13464 towards a holistic approach to sustainable risk management in agriculture in the eu: a literature review linda arata1, simone cerroni2, fabio gaetano santeramo3, samuele trestini4, simone severini5,* modelling technical efficiency of horticulture farming in kosovo: an application of data envelopment analysis nol krasniqi1,2,*, stéphane blancard1, ekrem gjokaj2, giovanna ottaviani aalmo3 incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon claudiane yanick moukam1,*, calvin atewamba2 farmers’ acceptance of a micro-irrigation system: a focus group study maria sabbagh1,*, luciano gutierrez2 reducing food-related economic loss to improve food security and cattle trade in the sahel: the case of agropastoral systems in senegal abdrahmane wane1,*, aliou diouf mballo2, cabrelle lauriane dzoukou homsi3, pathé diakhaté4, rahimatou memboup5 bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 bio-based and applied economics bae © 2024 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae citation: yalew, a.w., nechifor, v., & ferrari, e. (2024). the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia. bio-based and applied economics 13(3): 225-243. doi: 10.36253/bae-15364 received: november 19, 2023 accepted: march 11, 2024 published: october 16, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: francesco pagliacci, valentina raimondi, luca salvatici orcid awy: 0000-0002-1778-8477 vn: 0000-0001-8034-4070 ef: 0000-0003-0031-7190 the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia amsalu woldie yalew1,2,*, victor nechifor3, emanuele ferrari3 1 ca’ foscari university of venice, via torino 155, 30172 venice, italy 2 euro-mediterranean center on climate change (cmcc), via della liberta 12, 30175 venice, italy 3 european commission, joint research centre (jrc), seville, spain *corresponding author. e-mail: amsaluwoldie.yalew@unive.it abstract. the russian invasion of ukraine contributed to soaring world market prices of many commodities with severe repercussions for many african countries. this study examines the implications of the 2022 world market price increases for wheat, fuels, and fertilizers for ethiopia. using a computable general equilibrium (cge) model, the study shows negative impacts on gdp, wage rates, and households’ consumption in the country. the effects of fertilizer and petroleum price changes are notable and unequal across production sectors. with increasing import prices of inorganic fertilizers, crop growing activities substitute inorganic fertilizers with animal manure reducing the use of manure as cooking fuel. the effects on urban households are more severe than the effects on rural households. policies supporting biofuels and biogas digesters may dampen the adverse effects stemming from petroleum price surges. keywords: commodity markets, trade, cge, russia-ukraine war, ethiopia jel codes: c68, f10, l66. 1. introduction the russian invasion of ukraine and the subsequent war have caused a wide-range of crises with shortand long-term implications to the global economy. the repercussions of the war range from disruption of global commodity markets to long-term effects on the prospects of globalization and geopolitical order (garicano et al., 2022; ruta, 2022). the disruptions in the global supply chains increased the synchronization of grain, energy, and fertilizer prices at the global level (ihle et al., 2022). this resulted in contagion across food and non-food markets which would restrict the ability of consumers to mitigate the adverse effects of food and energy price spikes by resorting to inexpensive alternatives (ihle et al., 2022). the disruptions in global food, fertilizer and energy markets threaten to further increase the number of poor and malnourished people, especially in developing countries (guan et al., 2023; osendarp et al., 2022). https://doi.org/10.36253/bae-15364 https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode http://www.fupress.com/bae https://doi.org/10.36253/bae-15364 https://orcid.org/0000-0002-1778-8477 https://orcid.org/0000-0001-8034-4070 https://orcid.org/0000-0003-0031-7190 mailto:amsaluwoldie.yalew@unive.it 226 bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 amsalu woldie yalew, victor nechifor, emanuele ferrari the type and size of the effects will differ across countries as these are determined by the trade, production and consumption structures, and government responses in different countries (garicano et al., 2022). it is therefore necessary to understand how the war in ukraine affects individual economies (ruta, 2022) to underpin country-specific policy measures increasing the resilience of each economy. the shortand longterm implications of the war in ukraine for african countries are worrisome (badiane et al., 2022; unctad, 2022). from the 107 economies highly exposed to the shocks due the war in ukraine, 41 are in africa (un, 2022a). since many african countries are net importers of cereals, vegetable oils and fertilizers, the implications of the war to food security are substantial (badiane et al., 2022). higher import prices represent negative terms-of-trade for african economies in which poor households face the hardest hit (arndt et al., 2008). besides, many african countries have limited fiscal and borrowing capacities to respond to global energy and food market crises, particularly after various spending measures and tightening of monetary policies to cope with and recover from the covid-19 pandemic crisis. the effects on ethiopia are of particular interest (diao et al., 2022) as it depends almost entirely on imported petroleum and inorganic fertilizers (mengistu et al., 2019); the two commodities that felt the highest and immediate effects of the war on ukraine in 2022 (ruta, 2022; world bank, 2023). ethiopia has also been subject to multiple shocks in recent years (e.g., covid-19 pandemic, droughts, and armed conflicts) leaving the country with little fiscal space to cushion the adverse spillover effects from the russian invasion of ukraine. this study examines the economy-wide implications of changes in world prices for three commodities– wheat, fertilizers, and petroleum oil– highly significant for the ethiopian food and energy systems. it applies a computable general equilibrium (cge) model, which tracks the direct and induced economy-wide effects of the changes in world prices for the three major ethiopian imports. quantifying such effects and understanding their transmission mechanisms would provide lessons for possible policy responses in the advent of similar incidents with implications for global markets in the future. the study explicitly represents the sectors and commodities linked to agrifood and energy systems and applied case-specific nesting of production and consumption functions to investigate the implications of world market prices changes to the food-energy nexus in ethiopia and other low-income countries. the model combined production nesting features which are common in equilibrium model applications with detailed representation for energy (e.g., feng & zhang, 2018; hutagalung et al., 2019) and agriculture (e.g., hertel et al., 1996; brunelle et al., 2015) sectors. the production nests allow for the imperfect substitution between different fuels (petroleum fuels, electricity, and biomass fuels) and, for growing crops, limited substitution between organic (animal manure) and inorganic (chemical) fertilizers, and then between composite fertilizer and land. the study contributes to the literature on the transmission of shocks from global-to-domestic markets and their economy-wide impacts (e.g., arndt et al., 2008; dillon & barrett, 2016; von arnim et al., 2018), and the food-energy nexus (e.g., mekonnen et al., 2017) in african countries. the remainder of the paper is organized as follows. section 2 presents the materials and methods of the study. section 3 presents the results followed by section 4 for the discussions. section 5 concludes the paper. 2. materials and methods given their detailed coverage of commodity and factor markets, and that of the circular flow of income, cge models are widely applied for many trade, development, and fiscal policy issues of developing countries (devarajan & robinson, 2013). single-country cge modelling approach particularly helps to assess the direct and indirect effects of exogenous changes on different parts of the economy by comprehensively accounting for the country-specific interlinkages between production and consumption, and agrifood and energy sectors. 2.1. model description the dynamic equilibrium model for economic development, resources and agriculture (demetra) model is an extension of the stage_dev model (mcdonald et al., 2016). demetra is a single-country recursive-dynamic small open-economy cge model. the model allows for an advanced characterization of impacts of shocks at different levels: sectoral (output and production costs), household (income and consumption demand), factors (demand and income), and national (gdp, employment, and trade). demetra incorporates behavioral equations that represent the economic relationships in developing countries: nested production and consumption functions and factor market segmentations (jrc, 2021; mcdonald et al., 2016). the model and the underlying database have been applied in studies focusing on food security and agricultural policies in devel227the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 oping countries (nechifor et al., 2021; boulanger et al., 2022; ntah et al., 2024). further information and documentation about the model are available in jrc (2021). 2.2. model calibration the model assumes perfect competition in factor and commodity markets. therefore, both the sellers and buyers in the factor and commodity markets take the prices determined by market supply and demand forces as given. ethiopia is a small open-economy and thus its domestic price changes do not affect world market prices whereas world market price changes (of the country’s exports and imports) are exogenous. in line with the armington assumption (armington, 1969), the imported and domestically produced varieties of commodities are imperfect substitutes. the elasticities used in production, commodity, and households’ consumption nests are ad hoc values (summarized in table a2 in the appendix) within the range found in the existing literature relevant for low-income countries and increase from agriculture to service sectors (e.g., lofgren, 1994; diao et al., 2012; hertel & van der mensbrugghe, 2019). the production activities are disaggregated into sub processes captured by nested constant elasticity of substitution (ces) and leontief production functions, which combine primary factors and intermediate inputs at different stages. the substitutions are driven by relative price changes. the decisions of production activities at different stages are driven by cost minimization goals constrained by market prices (of inputs and outputs) and production technology. the production technology nest of activities (figure a1) is flexible and allows substitution possibilities among different factors and intermediate inputs at different levels. the top level is specified as leontief aggregation of a composite intermediate input, and a composite valued-added-energy input, assuming a perfect complementarity between the two aggregates. the composite (aggregate) intermediate input is a leontief aggregation of non-energy and non-fertilizer intermediate inputs. the composite value-added is a ces aggregation of a composite labor (of unskilled, semi-skilled, and skilled), a composite capital (of livestock, agricultural capital, non-agricultural capital), and a composite land (of irrigated or non-irrigated, and composite fertilizer) inputs. the composite energy input is a ces aggregate of energy commodities (electricity, fossil fuels, and bioenergy – fuelwood in hotels or biofuels in transport). such nesting between energy and factor inputs resembles recent cge applications (e.g., feng & zhang, 2018; hutagalung et al., 2019). the valueadded nest for crop-growing activities comprises a fertilizer nest which is a ces aggregation of animal manure (domestic) and inorganic (imported) fertilizers. this nest better represents the contexts in the country (metaferia et al., 2011; agss, 2020) and allows for substitutability between them due to relative price changes which would not be allowed within the leontief structure. in the recent five harvest seasons, about 45-50% and 11-13% of crop area cultivated by smallholder farmers in ethiopia applies synthetic (inorganic) and natural (organic) fertilizers (agss, 2020). the composite fertilizer (of organic and inorganic types) is then treated as an imperfect substitute for cropland. the nesting structure for crop activities is also related to previous research on factor substitution in agriculture (e.g., binswanger, 1974; hertel, 1989; ali & parikh, 1992; hertel et al., 1996; dalton et al., 1997), and in agricultural land-use (e.g., brunelle et al., 2015; lungarska et al., 2023). households maximize their consumption utility subject to a nested stone-geary (or linear expenditure system – les – demand) and ces functions (figure a2), and to income constraints. in the stone-geary/les utility function, at the top of the utility nest, household consumption demand consists of ‘subsistence’ demand and ‘discretionary’ demand. the commodities in the les demand function are defined as ‘broad’ commodity groups, which are either aggregates of ‘natural’ commodities or individual ‘natural’ commodities that are deemed sufficiently distinctive as to justify the assumption that they are characterized by having a distinct level of ‘subsistence’ demand (jrc, 2021). the second level of the utility functions nest is defined with ces preferences. it consists of six commodity categories representing cereals (6 commodities), livestock (7 commodities including fish), energy (8 commodities in which the 2 are electricity from off-grid and grid sources), processed food and beverages (4 commodities), sweets (sugar and honey), and transport services (equines and modern transport services). two of the energy commodities (crop residues and biogas), and one of the transport services (from equines) are consumed only by rural households. additionally, animal manure, crop residues, and biofuel are by-products from livestock, crops, and sugar manufacturing. households’ consumption expenditure is a residual of household income after deducting direct (income) taxes, savings, and their net transfers to other institutions (i.e., to the other household group, to enterprises, to the government, and to the rest of the world). households’ income sources include factors of production they own and supply, and net transfers from the rest of institutions. households’ consumption demand is therefore expected to be affected by changes in both households’ 228 bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 amsalu woldie yalew, victor nechifor, emanuele ferrari income and commodity prices. factors can be mobile across activities (labor and land factors1) or activity-specific (capital and livestock factors). for the mobile factors, flexible average economy-wide wage rates equate their demand and supplies whereas flexible activity-specific wage distortion factors (proportions) equilibrate the markets for activity-specific factors. the supplies of primary factors of production are fixed at their base levels. government and foreign savings are fixed at their base levels. the external (foreign sector) balance is maintained by a flexible exchange rate. all tax rates are fixed at the benchmark level. 2.3. model database the cge model is calibrated to a modified version of the 2015/2016 social accounting matrix (sam) for ethiopia (mengistu et al., 2019).2 the adjusted sam consists of 71 production activities (table a1). the agriculture activities comprise 30 crop-growing activities, 7 livestock raising activities, and 4 other allied activities to agriculture. there are 8 industrial and 6 service activities. the remaining 16 activities are related to energy sectors. the modified sam comprises 51 commodities of which 28 are exportable. synthetic (inorganic) fertilizers and petroleum oils are virtually all imported. there are 17 primary factor accounts representing different labor (3 by level of skill), land (rainfed and irrigated), capital (5 by primary use of the capital), and livestock (7 by species). there are four tax accounts representing domestic sales taxes, import tariffs and duties, direct (income) taxes, 1 sensitivity analysis was performed with partially and entirely activityspecific croplands. 2 additional notes regarding adjustment of the sam are given in the appendix. and subsidies to selected electricity producing activities (recorded in the sam as negative taxes in table 1). the sam comprises five accounts representing two households (rural and urban), enterprises, government, and the rest of the world. the remainder of the sam accounts represent trade and transport margin (or transaction costs), and disaggregated investment accounts. primary factors account for 66% of the production costs. approximately 90% of the factor incomes goes to households. imports account for about 14% of the supply of commodities. consumption (77%) and savings (20%) are the main households’ expenditure items whereas public services (61%) and savings (30%) are the main government expenditures. the inflows from the rest of the world include foreign saving (which is current account deficit for ethiopia) (35%), remittances (29%) and export earnings (28%). households’ consumption (35%), intermediate inputs (23.5%), and investment demand (18.7%) are the main sources of demand for domestically supplied goods and services while export demand accounts for approximately 4%. factor incomes (88.5%) followed by remittances (8.9%) are the main sources of households’ income. taxes are the main source of government revenue as they account for 74% of the total government income. about 65% of tax revenues are collected from commodities (on imports and on domestic sales) followed by income taxes from households and enterprises (30%). production subsidies (applicable only to the power sector) account for 4% of the total tax revenue. ethiopian households and foreign sources contribute to 47% and 26% of the total national saving, with the remaining saving coming from enterprises and the government. imports constitute about 97% of the total outflows from ethiopia to the rest of the world. the 2015/2016 sam was updated using the recursive features of demetra to the year 2022 using actual and table 1. macro sam of ethiopia (2015/2016, billion birr). activities commodities factors households enterprises government taxes investment restofworld total activities 2159.70 2159.70 commodities 742.26 456.68 1096.46 148.84 591.58 123.21 3159.04 factors 1425.11 7.83 1432.93 households 1268.15 11.15 11.32 126.23 1416.84 enterprises 158.60 5.52 0.28 164.40 government 8.19 26.72 181.22 28.35 244.49 taxes -7.67 118.59 29.21 41.09 181.22 investment 280.33 84.83 73.06 153.36 591.58 restofworld 424.07 6.19 2.64 0.61 5.75 439.26 total 2159.70 3159.04 1432.93 1416.84 164.40 244.49 181.22 591.58 439.26 source: authors’ elaboration. 229the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 forecasted growth rates of gdp (imf, 2022) and population (un, 2022b). the forecasted real gdp growth rate for 2022 was 3.8% (imf, 2022). we assume this gdp growth rate, which is lower than the country’s five-year average of 8% growth rate (imf, 2022; nbe, 2023), accounted mainly for the impacts of recent crises on ethiopia but little for anticipated cascading effects from the russia’s invasion of ukraine war impacts on world markets. the calibration process and the adjusted sam represent the contexts of the country and make the model suitable to address the study’s research question. the production nest for crops along with the households’ utility nest for energy commodities allow capturing the competition between agriculture and energy for animal manure (mekonnen et al., 2017). the possibility of substitution between different fuel types (agricultural residues, fuelwood, petroleum products and electricity services) captures the “fuel stacking” behavior of ethiopian households (yalew, 2022). 2.4. world price change impact scenarios the effects of global commodity supply, transport and logistics disruptions, the sanctions against russia, the export bans adopted by some countries, and speculative market behaviors that ensued russia’s invasion of ukraine have tremendously affected the prices for different world commodities in 2022 (world bank, 2022). although prices for some commodities showed a downward trend by the end of 2022 their level remained higher than in 2021 (world bank, 2023). prices of many agrifood and energy commodities in ethiopia increased in the past decade (ess, 2023). yet, the impacts of the recent domestic crises (e.g., armed conflicts, droughts) and international crises (e.g., covid-19 pandemic and russia’s invasion of ukraine) are conspicuous (nbe, 2023; egte, 2023). the annual average price indices for petroleum oil and wheat in the global and ethiopian markets exhibit similar trends (figure 1) substantiating the high inflation trends in ethiopia in the past decade (ess, 2023) as the local price changes grew faster compared to the world market prices. likewise, domestic fertilizer prices increase might be larger than increases in world market fertilizer prices (abay et al., 2024). global price changes would contribute to (or exacerbate) the domestic price changes which is why it is imperative to examine the implications of global commodity market shocks, such as those followed the war on ukraine, for ethiopia. this study considers the impacts of world import price changes for three commodities (wheat, fertilizer, and petroleum products) which play substantial roles in the food and energy markets in ethiopia and experienced more than 30% annual average real price changes in 2022 compared to 2021 (table 2). the simulation scenarios are designed in a way to: (i) assess the potential losers from each commodity price change, (ii) identify the dominant impact channel, and (iii) assess the comfigure 1. comparison of local and world price indices for wheat and petroleum oil. source: authors’ illustration based on data compiled from various reports by the national bank of ethiopia (retail gasoline price in addis ababa, ethiopia), the ethiopian grain trade enterprise (wholesale wheat price in ethiopia), and world bank (2023) (crude oil and wheat prices in world markets). trends for fertilizer prices were not presented here due to lack of publicly available local price data. 230 bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 amsalu woldie yalew, victor nechifor, emanuele ferrari bined effects of the increase in the import prices of the three commodities. equations 1 to 4 capture the mechanisms to transmit the impacts of world import price changes to the ethiopia’s economy in demetra: (1) , (2a) ∀c ∈(cm ≠ 0 ∩ cd ≠ 0) (2b) (3) ∀c ∈(cm ≠ 0 ∩ cd ≠ 0) the domestic price of competitive imports for commodity c (pmc) is a product of the world price of imports (pwmc, denominated in foreign currency, assumed to be exogenously determined and fixed by the world markets), the exchange rate (er, domestic per foreign currency), and the import tariff rate (tmc) (equation 1). the equation applies for wheat, fertilizer, and petroleum fuels. imported (qmc) and domestic (qdc) varieties are imperfect substitutes whose ces (or armington) aggregation (qqc, the aggregate domestic supply of commodity c) is influenced by the share (δ), the elasticity of substitution (ρ), and the shift (α) parameters, for all commodities, such as wheat, which have both domestically produced (cd) and import (cm) varieties (equation 2a). however, for some commodities such as fertilizers and petroleum oil their domestic supplies supply is composed of imports only (equation 2b). the cost minimization behavior of domestic agents (i.e., deriving the first order conditions of equation 2a), determines the optimal mix of supplies from domestic and foreign (import) producers depending on the relative price of domestic (pdc) and import (pmc) varieties of the same commodity (equation 3). for each cropping activity a, we endogenize land productivity to consider the yield improving role of chemical fertilizer application. crop yields (yl,a) endogenously respond to the relative changes to the chemical fertilizer application, i.e., the application in the new scenario ( ) relative to the application in the base scenario ( ). the response factor (µf) consider the crop phenological responses to chemical fertilizer application, and thus translating into a change in crop yield per unit of cultivated land (equation 4). (4) the value of response factors (was obtained from a relevant study (sheahan et al., 2016) and can be interpreted as a 1% increase (decrease) in fertilizer application leads to a 0.21% increase (decrease) in crop yield. although the yield responses to fertilizer use could vary by crop type (hertel et al., 1996; rashid et al., 2013), due to lack of information, we applied a uniform response rate for all crops, which we acknowledge as a limitation. the three commodities considered are essential items in both production and final consumption sectors. they account for one-fifth of the total spending for merchandise imports in ethiopia (nbe, 2023). ethiopia is a net importer of wheat with imports accounting for a quarter of the wheat supply. according to the sam, wheat accounts for 3.3% of total imports of goods and services and it is consumed as an intermediate input (26%) and as food by households (74%). the les-ces utility functions nest employed in the model allows the possibility that households substitute wheat by other cereals such as teff, barley, maize, and sorghum depending on their relative price changes. ethiopia depends on imported chemical fertilizers and petroleum products. fertilizer imports comprise approximately 2% of the total good and services imports in the sam. fertilizers are used as inputs in crop-growing activities and more than 50% of the supply is used in growing major cereal crops e.g. wheat, maize, teff, barley, and sorghum. in wheat and maize, chemical fertilizers account for up to 6.5% of the total production costs. increasing chemical fertilizer prices are expected to reduce the use of chemical fertilizers by crop growing activities, and partly cropland productivity (equation 4). petroleum fuels account for about 10% of imports of goods and services in the sam. they are consumed as inputs in agriculture (0.4%), industry (28.1%), electable 2. summary of the simulation scenarios. scenario description import price shocks wheat world wheat import price changes + 34% fertilizer world fertilizer import price changes + 54% petroleum world petroleum oils import price changes + 50% combined combination of the above impact scenarios source: authors’ calculations from world bank (2023). note: the annual average real price changes, between 2022 and 2021, were calculated as of february 2023. 231the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 tricity (2.7%), transport (51.3%), and the rest of services (10.7%). households’ demand represents 6.8% of the demand for petroleum fuels while petroleum fuels account for only 0.4% and 0.5% of rural and urban households’ consumption expenditure. the bigger proportion of petroleum fuel price change impact on households’ welfare is expected through indirect effects (i.e., higher commodity prices due to increased production costs in most of the sectors as consequence of higher petroleum prices). 3. results the subsections below present the impacts of world commodity price increases on different components of ethiopian economy. all results are presented as percentage changes relative to the base scenario, which represents the counterfactual ethiopian economy in 2022 without economic repercussions from russia’s invasion of ukraine. one could consider the impact scenarios as “what if” scenarios in which the information on world import price changes (table 2) were projected and communicated in advance as soon as the war on ukraine began (say as early warnings). this would have helped ethiopian producers and consumers plan and undertake anticipatory measures (e.g., factor allocations and adjustments in consumption demand) in response to the anticipated repercussions from the global market shocks but no significant investment and policy changes. 3.1. impacts on the macroeconomy the combined world price changes could reduce ethiopia’s real gdp (by 0.65%), imports (by 5.5%), private consumption (by 2.7%), and investment demand (by 1.3%) (table 3). likewise, the absorption, which measures the domestic expenditure on goods and services, falls by 2%. the effects are driven by the fertilizer and fuel price changes although wheat prices have a marked impact on the trade balance. increasing wheat import price decreases wheat imports (and hence total imports) but increases domestic wheat production as well as its substitute cereals (to meet the supply gap) which would pull factors from other sectors including those contributing to exports such as coffee, oilseeds, and manufactured foods and beverages. as production in some activities contract (and hence factor employment and income) direct tax and total government revenue decline by 4.1% and 0.54% in the combined impacts scenario. 3.2. impacts on production activities the domestic production in different sectors respond differently to the aggregate and individual commodity price changes (table 4). the sectors with the highest contraction of output in the combined impacts scenario are services, construction and utilities largely driven by the fuel price changes. production in the rest of manufacturing, natural resources-based primary sectors, and public services slightly expand (under fertilizer price changes) and in crops and food and beverages (under fuel price changes). petroleum price change results in a wider range of output impacts (figure 2). disaggregated results show that the impacts are highly scattered across sectors (figure 2). rising wheat prices expand domestic wheat production by 2.5%. table 3. the impacts on the macroeconomy (% changes). variable import price change scenarios wheat fertilizer petroleum combined gdp -0.04 -0.32 -0.27 -0.65 private consumption -0.32 -0.74 -1.64 -2.70 investment demand 0.13 -0.10 -1.34 -1.34 absorption -0.16 -0.46 -1.39 -2.01 government consumption -0.12 0.22 0.25 0.39 imports -0.87 -0.83 -3.81 -5.53 exports -1.21 -0.25 2.12 0.55 source: demetra simulations. table 4. the impacts on domestic production by activity groups (% changes). activities import price change scenarios wheat fertilizer petroleum combined crops 0.35 -1.48 1.7 0.52 livestock -0.17 -0.07 -0.14 -0.35 primary sectors – grazing, fishing, forestry, mining -0.19 0.27 -0.41 -0.35 food and beverages -0.87 -0.13 0.42 -0.57 textiles, clothes, leather, and wood processing -0.07 0.31 -0.32 -0.04 rest of manufacturing -0.08 0.59 0.09 0.68 utilities electricity and water -0.08 -0.06 -2.72 -2.87 construction 0.08 -0.04 -1.06 -1.03 services private -0.15 0.08 -2.59 -2.69 services public -0.11 0.16 0.07 0.14 source: demetra simulations. 232 bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 amsalu woldie yalew, victor nechifor, emanuele ferrari under petroleum price changes, domestic production expands in activities with substantial contribution to exports (oilseeds, coffee, vegetables, cotton, and tea) and electricity-powered transport services. the expansion of production in export-oriented agricultural activities derived from the depreciating exchange rates (due to higher import bills) making ethiopian exports cheaper in the world markets and thus to balancing the increasing import costs. in contrast, rising fuel prices reduce the outputs from fuel-powered transport services, diesel-powered electricity (from grid and off-grid systems), and other private (commercial) services which includes hotels, financial intermediaries, and other business services. consequently, exports from fuel-powered transport services and, slightly, electricity utilities decline. production in most cropping activities contracts following the rise in world chemical fertilizer prices (table 4) with negligible size except for oilseeds, wheat, and maize which declined by 5.4%, 3.8%, and 3.2%. the marginal effects on the other crop growing activities are explained by the small shares of inorganic fertilizer inputs in the base scenario and from the substitution by manure (organic) for inorganic fertilizers (figure 3). this, however, reduces manure available for household energy (table 6). 3.3. impacts on households’ consumption rising import prices affect households’ consumption demand directly (due to increased prices) and indirectly (declining household incomes as factor incomes fall due to contraction of production). in the combined impacts scenario, factor income decreases in thirteen of the seventeen factors. for instance, labor factor incomes decline by approximately 2% for unskilled to approximately 4% for semi-skilled and skilled labor categories whereas households’ income from enterprises decline by about 10% (of which 7% is due to the petroleum price increases). the decline in households’ income and the resulting decrease in demands for commodities (due to higher prices) result in declining households’ consumption by 2% for rural and 3.5% for urban households (figure 4). the effects on the household groups vary across import price change scenarios. urban households are worse off when it comes to wheat and fuel price changes whereas rural households are worse off under fertilizer price changes. the adverse effects on both household groups are mostly driven by petroleum price increases because petroleum products are inputs in almost all activities (and thus the rise in fuel prices increases in the costs of production and reduces factor demands and incomes) and as final demand product by households (and thus increasing price reduces quantity demanded). 3.4. implications for food security of the four dimensions of food security, i.e., availability, accessibility, utilization, and stability (peng and figure 2. the impacts on domestic production activities (% changes). source: demetra simulations. figure 3. fertilizer demand under inorganic fertilizer price increase scenario (% changes). source: demetra simulations. figure 4. the impacts on households’ consumption expenditure (% changes). source: demetra simulations. 233the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 berry, 2019); the rise in world import prices are expected to directly affect food availability (i.e., reduced wheat imports and reduced agricultural output due to expensive fertilizers and to some extent fuel prices), accessibility (i.e., increased transport costs and/or reduced transport services), and stability (i.e., the ability of the country’s food system to withstand other natural and man-made shocks in the future due to reduced economic growth and government revenue). except for rural households under the wheat price change scenario, rural and urban households’ food consumption decline (table 5). mirroring the impacts on crop production (table 4), the index of food production, which includes crops, sugar, processed foods, and fish, declines (by 1.2%) only under the fertilizer price scenario (table 5). the increase in food production index under petroleum price change is explained by increased agricultural exports, as discussed earlier. the impacts on food production (table 5) are negligible in most cases except under higher world prices for inorganic fertilizers due to ethiopia’s low dependence on food imports while many food staples (including teff and sorghum) are not traded internationally in large volumes (diao et al., 2022). yet, the index of food production increase includes overall crop production, most of which go to exports rather than to households in this scenario. that is why food consumption in both rural and urban households decreased despite the increased food production index (table 5). the possibility of substitutions (e.g., wheat by other cereals, inorganic fertilizer by animal manure) allowed by the model contributed to relieve some of the price increase burdens on households’ consumption. 3.5. implications for household energy the repercussions on households’ energy consumption have implications for the food-energy nexus in ethiopia (mekonnen et al., 2017; yalew, 2022). agricultural wastes (e.g., crop residues, and animal dung) and products (e.g., biogas, ethanol) are important sources of household energy but using agricultural waste as fuel reduces organic fertilizer available for cropping activities. the changes in demand for energy fuels are higher in rural households (table 6) because they have a wider option of fuels, and hence their demand for a specific fuel is set to be relatively elastic compared to that in urban households. petroleum prices affect households’ energy prices directly (e.g., gas and kerosene) and indirectly (e.g., electricity from diesel generators). indeed, as discussed in section 2, the demand for petroleum in production activities is also significant. the decrease in petroleum fuel demand entails an increase in ethanol consumption in both household groups. since ethanol is mostly produced from sugar molasses, in the long-term, this is an additional motive to expand sugar manufacturing capacities in the country. the combination of these mechanisms results in a differentiated price increase of the aggregate energy for households (by 1.5% for rural and by 3.5% for urban households). the combined share of electricity and ethanol in the total households’ energy consumption is 5% while grid electricity accounts for about 18% of the urban households’ energy consumption expenditure. as such, part of the decline in electricity demand is also associated with decreasing households’ income in addition to its price change relative to other fuels. rising inorganic fertilizer price increases the demand for animal manure as organic fertilizer (figure 3) and hence reduces the amount of manure consumed as fuel by households. demand for animal manure used as household energy declines by 5.4% and 3.2% in rural and urban households (table 6). increasing petroleum prices induces a slight increase in the use of biogas by rural households. this has positive implication for the domestic (household) biogas sector which converts cattle dung to fuel (biogas) and fertilizer (bio-slurry). as such, although the biogas sector in ethitable 5. implications for food security (% changes). scenario food production index food price index food consumption rural urban rural urban wheat 0.10 0.80 0.34 0.14 -0.19 fertilizer -1.19 0.80 0.41 -0.41 -0.21 petroleum 1.49 0.00 -0.08 -1.17 -2.39 combined 0.35 1.48 0.60 -1.53 -2.84 source: demetra simulations. table 6. impacts on household energy demand (% changes). fuels fertilizer petroleum rural urban rural urban residues -1.39 0.65 wood -0.12 0.02 -0.03 -0.76 manure -5.42 -3.20 -0.98 -1.33 petroleum -1.78 -0.98 -45.95 -31.38 biogas -0.81 0.16 ethanol -1.26 -0.67 13.55 7.12 electricity, off-grid -0.41 -0.16 -9.06 -6.24 electricity, grid -0.66 -0.31 -5.16 -3.84 source: demetra simulations. 234 bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 amsalu woldie yalew, victor nechifor, emanuele ferrari opia is yet at its niche phase (kamp & forn, 2016; yalew, 2021), support for the biogas sector has the potential to help agrifood and energy sectors in the face of world petroleum price crises. 3.6. senstivity analysis finally, we performed sensitivity analyses for the armington (import) substitution elasticities (as the exogenous shocks analysed are related to import price changes) and two main assumptions pertaining to the crop sector (as the sector is important source of domestic food supply and exports). first, the overall results and conclusions remain less sensitive to increasing or decreasing the armington import substitution elasticities by 30% (table a3). an exception is that with higher elasticity of import substitutions, as import prices increase, the demand for imported goods become relatively elastic and decline further with which the aggregate exports decrease as exchange rates depreciates lesser compared to the case with low import elasticities. second, the severity of the impacts partly depends on the crop phenological response factor to inorganic fertilizer use. for instance, if crop yields would be less sensitive to the amount of inorganic fertilizer applied, the combined impact on the real gdp drops to -0.39% (table a4). third, we deviated from our initial assumption regarding the flexibility of cropland allocations. we assumed cropland is partially mobile by fixing the land for 14 perennial crops (of the total 32 land-based activities). we then assumed all land is crop-specific (fixed to all activities), i.e., land cannot be reallocated in responses to shocks compared to the initial assumption such that farmers would easily and quickly switch between the crops they want to cultivate in response of actual and anticipated shocks. the sensitivity results (table a4) show that adverse effects worsen when cropland is assumed to be immobile across activities. the impacts are notable on the export sector which decline by 0.91% compared to an increase by 0.55% when cropland is assumed to be freely allocable (or mobile) to growing different crops. the contraction of exports implies that there will be lesser resources to finance imports, and thus total imports decline by 6.2% compared to by 5.5% under the assumption of fully mobile cropland. 4. discussions the russian invasion of ukraine, since february 2022, had profound implications for the global and african economies. the war caused massive supply chain disruptions and mounting trade costs globally (unctad, 2022) producing price spikes for many globally traded commodities (world bank, 2022). in ethiopia, information from the past decade shows that local price changes for domestic commodities with competitive imports exhibit similar trends to that of world price changes. this implies that global price changes would contribute or exacerbate the price changes due to domestic market conditions. this necessitates to evaluate the implications of global commodity market shocks for ethiopia. this study showed that the global market repercussions due to the war on ukraine are likely to have negatively affected the aggregate imports, households’ consumption, and labor wage rates in ethiopia. the effect on the real gdp is approximately -0.65% and is comparable to diao et al. (2022). nevertheless, the impacts are unevenly distributed among different sectors and households. crop growing activities substitute animal manure (domestic) for inorganic (imported) fertilizers that eventually could dampen the adverse effects on crop production. this, however, would reduce manure available as cooking fuel which substantiates the relevance of the food-energy nexus in the country (mekonnen et al., 2017; yalew, 2022). the impacts on consumption are worse for urban households compared to rural ones except under fertilizer import price changes. the results of this study are comparable to previous studies showing the detrimental effects of world commodity market impacts on african economies (arndt et al., 2008; dillon & barrett, 2016; von arnim et al., 2018). three caveats apply to this analysis. first, the behavioral and crop phenology parameters used in the model (i.e. model elasticities) influence the simulation results. despite the model and the adjusted sam employed allow capturing several contexts of the ethiopian economy, as in most cge models, the results are still influenced by the neoclassical assumptions of perfect competition in the cge model. likewise, in line with the tradition in cge model calibrations (lofgren, 1994; devarajan & robinson, 2013), most of the production, international trade, and consumption are ad hoc values in the range of previous literature and economic theory. we therefore performed several sensitivity analyses for a selected set of parameters and assumption affecting import substitutions (table a3) and crop activities (table a4). the results from the sensitivity analysis regarding cropland mobility across activities are in accordance with the findings of previous research (e.g., salazar-espinoza et al., 2015; martey et al., 2022) which showed farmers shift land use away from cash and permanent crops (and thus ethiopian exports fall) and devote more to growing staple crops in response to 235the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 adverse natural and man-made shocks. the sensitivity analysis also substantiates the important role of inorganic fertilizers to enhance cropland productivity (rashid et al., 2013; sheahan et al., 2016) and of crop agriculture in ethiopia (mengistu et al., 2019; nbe, 2023). our findings that farmers substitute animal manure for inorganic fertilizers are similar to abay et al. (2024). the study, citing survey data, indicated that the surge in inorganic fertilizer price in recent years might have encouraged ethiopian farmers to shift to organic fertilizers. however, more research on the empirical estimates for the elasticities of substitution between organic and inorganic fertilizers, and between land and fertilizer for ethiopia and other agrarian countries is highly needed. second, the study does not explicitly incorporate the impacts from interactions with domestic crises (e.g., armed conflicts, droughts) that have severely impacted ethiopia in 2022. the armed conflicts in northern ethiopia, between 2020-2022, might have pushed additional 3 million peoples deeper into poverty (endale, 2023) while the droughts in the southern and southeastern parts of the country had affected an estimated population of 24 million in 2022 (acaps, 2023). we assumed that the impacts due to the domestic crises are accounted in the projected gdp growth rate (imf, 2022) which is used to calibrate the baseline scenario. this could be a limitation as such compounding factors could influence the magnitude of the impacts from world market shocks (headey & fan, 2008; abbott & borot de battisti, 2011; meyimdjui & combes, 2021). thirdly, we assumed the government of ethiopia would not consider the possibility of adopting policy responses counteracting these global shocks. government responses to global commodity market shocks such as social protection programs, export restrictions, price caps, subsidies, and tax reliefs (abay et al., 2023) could have dampened the adverse impacts on production and consumption but mostly by transferring the burden to fiscal deficits (headey & fan, 2008; ntah et al., 2024). future research examining the interactions, and the combined effects of domestic and international market disruptions will be helpful. 5. conclusions this study assessed the consequences of the global commodity price changes in 2022, which followed from russia’s invasion of ukraine, on ethiopia. the results show that repercussions from global commodity market price increases adversely affect imports and households’ consumption in ethiopia. rising petroleum prices increase households’ demand for ethanol and biogas that can be considered as co-benefits to expanding sugar manufacturing and household biogas digesters. rising fertilizer prices tighten the competition for the use of animal manure between cropping activities (as fertilizer) and households (as fuel). policy measures to support the expansion of household (domestic) biogas digesters producing biogas (fuel) and bio-slurry (fertilizer) could be one mechanism to promote an optimal use of animal manure at the time of contemporaneous shocks to fertilizer and petroleum oil prices. the study gleaned insights on how the different parts of the ethiopian economy would respond to the world global commodity market shocks without explicitly incorporating other important internal natural and man-made crises that have battered the country in and around 2022. further research on how these multiple impacts have interacted is highly needed to identify policy measures to build an economy resilient to simultaneous domestic and global market crises. the nature and size of these additional adverse conditions may become clearer in the medium-term. disclaimer the views expressed in this paper are the sole responsibility of the authors and do not necessarily reflect the views of the european commission. references abay, k.a., assefa, t.w., berhane, g., abate, g.t., & hebebrand, c. 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https://unsdg.un.org/resources/global-impact-war-ukraine-food-energy-and-finance-systems-brief-no1 https://unsdg.un.org/resources/global-impact-war-ukraine-food-energy-and-finance-systems-brief-no1 https://unsdg.un.org/resources/global-impact-war-ukraine-food-energy-and-finance-systems-brief-no1 https://population.un.org/wpp/ https://unctad.org/system/files/official-document/osginf2022d1_en.pdf https://unctad.org/system/files/official-document/osginf2022d1_en.pdf https://doi.org/10.1016/j.jpolmod.2018.02.008 https://hdl.handle.net/10986/37223 https://hdl.handle.net/10986/37223 https://doi.org/10.1016/j.nexus.2021.100017 https://doi.org/10.1016/j.nexus.2021.100017 https://doi.org/10.1016/j.rset.2022.100018 https://doi.org/10.1016/j.rset.2022.100018 239the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 appendix 1. notes on sam adjustment this study used a modified version of the 2015/2016 sam for ethiopia (mengistu et al., 2019). important adjustments were made particularly pertaining to the agriculture, forestry, electricity, and transport sectors. for 14 crop growing activities, irrigated farming was separated from rainfed farming using information from agricultural surveys (agss, 2016) and other research reports (ncds, 2017; tilahun et al., 2011; hagos et al., 2009). the livestock sector was further disaggregated to explicitly account for 7 types of activities (cattle, sheep, goats, camels, equines, poultry, and beekeeping) using information from agricultural survey (agss, 2016) and national income accounts (mofed, 2012). animal feed table a1. list of activities in the sam and their group for reporting results. group activity group activity crops growing rainfed teff primary sectors managed natural grass fodder crops growing irrigated teff primary sectors fish crops growing rainfed barley primary sectors forestry crops growing irrigated barley primary sectors mining and quarrying crops growing rainfed wheat food & beverages processed and manufactured foods crops growing irrigated wheat food & beverages sugar crops growing rainfed maize food & beverages beverages and tobacco crops growing irrigated maize textiles, clothes… textile, leather, clothes, and wood processing crops growing rainfed sorghum rest of manufacturing rest of manufacturing crops growing irrigated sorghum construction construction crops growing rainfed pulses utilities water supply crops growing irrigated pulses utilities off-grid electricity, diesel crops growing rainfed oilseeds utilities off-grid electricity, solar crops growing irrigated oilseeds utilities grid electricity, hydro, abbay basin crops growing rainfed vegetables utilities grid electricity, hydro, omo basin crops growing irrigated vegetables utilities grid electricity, hydro, awash basin crops growing rainfed fruits utilities grid electricity, hydro, tekeze basin crops growing irrigated fruits utilities grid electricity, hydro, wabi-shebele basin crops growing coffee utilities grid electricity, hydro, rest of basins crops growing enset utilities grid electricity, wind crops growing rainfed sugarcane utilities grid electricity, geothermal crops growing irrigated sugarcane utilities grid electricity, solar crops growing rainfed chat utilities grid electricity, municipal waste crops growing irrigated chat utilities grid electricity, diesel crops growing tea utilities grid electricity, transmission & distribution crops growing rainfed cotton private services transport services, electricity-based crops growing irrigated cotton private services transport services, fuel-based crops growing rainfed crops nec private services rest of private commercial services crops growing irrigated crops nec. public services public administration crops forage & bioenergy crops public services education services crops cut flower public services health services livestock cattle public services health services livestock sheep livestock goats livestock camel livestock poultry livestock beekeeping livestock equines livestock domestic biogas 240 bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 amsalu woldie yalew, victor nechifor, emanuele ferrari sources include grass fodder, crop residues, animal forages, and proceed animal feed (agss, 2016). forest products were disaggregated into three distinct products – wood fuel, industrial wood, and non-timber forest products (ntfps) using product shares from the national income (mofed, 2012) and forest sector accounts (mofecc, 2017). electricity production is disaggregated into off-grid and grid connections (mowie, 2013), and then by technology using information on installed capacities information (lmsis, 2017; nbe, 2020; pappis et al., 2021; gse & jica, 2015; eapp, 2014) in line with the recent discussion regarding the power sector in cge models (e.g., chepeliev, 2020; peters et al., 2016; cai & arora, 2015; sue wing, 2008). electricity output also accounts for electricity from bagasse as byproduct from sugar manufacturing (esc, 2019; kruger et al., 2019). further adjustment was made to account for the implicit subsidies to the state-owned electricity utility enterprise (trimble et al., 2016) and export to neighboring countries (nbe, 2020). electricity-based transport services (of ethio-djibouti railway and addis ababa urban light rail services) are distinguished from fuel-based (road and air transport) services. to better account for the interlinkages between agriculture and energy sectors, the adjusted sam also contains biogas (activity and commodity) (yalew, 2021) and biofuel (mainly as a byproduct from sugar molasses in sugar manufacturing (esc, 2019; tesfaye, 2020). finally, compared to the initial sam (mengistu et al., 2019), the adjusted sam contains highly aggregated manufacturing (as rest of manufacturing) and private services (as rest of commercial services). for some of the adjustments, when correspondence allows, cross-checks were made with the supply and use tables of the 2005/2006 sam for ethiopia (ifpri, 2014). 2. model calibration figure a1. production technology nest structure (author’s elaboration). 241the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 table a2. range of production, trade, and consumption elasticities. type nest description range production l elasticities of substitutions among different labor categories (e.g., skilled, semi-skilled, unskilled workers). 0.30–1.50 k elasticities of substitutions among different capital categories (e.g., animal draught power, agricultural machinery, and non-agricultural capital). 0.20–1.50 fert elasticities of substitutions between organic and inorganic fertilizers. 0.70 land-fert elasticities of substitution between composite fertilizer and land factor. 0.30 eng elasticities of substitution between energy commodities for intermediate consumption (e.g., wood fuel, biofuel, petroleum oil, electricity). 0.30 va elasticities of substitutions among composite primary factors (e.g., labor, land, capital) 0.30–1.50 va-eng elasticities of substitutions between composite energy and value-added. 0.30 intermediate elasticities of substitution among different intermediate inputs other than fertilizers and petroleum fuels. 0.00 top level elasticities of substitution between composite va-eng and intermediate inputs at the top of the nest. 0.00 trade imports elasticities of substitution between import and domestic varieties of a commodity 0.80–3.00 exports elasticities of transformation between exports and domestic varieties of a commodity 0.80-3.00 consumption households elasticity of substitution among consumption goods (only for those under ces nests) 0.75–2.50 households income elasticity of consumption demand 0.50–1.20 households frisch parameter -1.50 figure a2. households’ utility nest (authors’ elaboration). 242 bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 amsalu woldie yalew, victor nechifor, emanuele ferrari 3. sensitivity analysis table a3. sensitivity of simulation results to armington (import) substitution elasticities. level variable armington elasticities [table a2-30%] armington elasticities [table a2] armington elasticities [table a2 +30%] wht frt ptr cmb wht frt ptr cmb wht frt ptr cmb macroeconomic indicators gdp -0.04 -0.33 -0.28 -0.66 -0.04 -0.32 -0.27 -0.65 -0.05 -0.32 -0.27 -0.65 private consumption -0.33 -0.74 -1.63 -2.69 -0.32 -0.74 -1.64 -2.70 -0.31 -0.74 -1.65 -2.70 investment demand 0.07 -0.13 -1.36 -1.44 0.13 -0.10 -1.34 -1.34 0.18 -0.09 -1.33 -1.26 absorption -0.18 -0.46 -1.39 -2.03 -0.16 -0.46 -1.39 -2.01 -0.14 -0.46 -1.40 -2.00 government consumption -0.06 0.27 0.31 0.56 -0.12 0.22 0.25 0.39 -0.16 0.18 0.21 0.26 imports -0.82 -0.81 -3.61 -5.24 -0.87 -0.83 -3.81 -5.53 -0.90 -0.85 -3.98 -5.76 exports -0.77 -0.19 2.56 1.49 -1.21 -0.25 2.12 0.55 -1.55 -0.29 1.73 -0.23 activity outputs crops 0.25 -1.45 1.67 0.45 0.35 -1.48 1.70 0.52 0.43 -1.51 1.73 0.59 livestock -0.15 -0.06 -0.17 -0.36 -0.17 -0.07 -0.14 -0.35 -0.18 -0.07 -0.12 -0.34 primary sectors – grazing, fishing, forestry, mining -0.14 0.27 -0.46 -0.35 -0.19 0.27 -0.41 -0.35 -0.23 0.27 -0.37 -0.34 food and beverages -0.80 -0.16 0.24 -0.74 -0.87 -0.13 0.42 -0.57 -0.93 -0.12 0.58 -0.43 textiles, clothes, leather, and wood processing -0.02 0.25 -0.61 -0.36 -0.07 0.31 -0.32 -0.04 -0.12 0.34 -0.09 0.18 rest of manufacturing -0.01 0.52 -0.22 0.35 -0.08 0.59 0.09 0.68 -0.15 0.62 0.32 0.88 utilities electricity and water -0.06 -0.07 -2.72 -2.87 -0.08 -0.06 -2.72 -2.87 -0.09 -0.05 -2.73 -2.88 construction 0.04 -0.07 -1.14 -1.18 0.08 -0.04 -1.06 -1.03 0.11 -0.01 -0.99 -0.91 services private -0.11 0.05 -2.46 -2.57 -0.15 0.08 -2.59 -2.69 -0.18 0.10 -2.71 -2.82 services public -0.06 0.20 0.11 0.28 -0.11 0.16 0.07 0.14 -0.14 0.13 0.03 0.04 consumption rural -0.01 -0.59 -1.25 -1.93 -0.01 -0.62 -1.29 -2.00 0.00 -0.64 -1.31 -2.04 urban -0.26 -0.38 -2.70 -3.39 -0.35 -0.41 -2.72 -3.52 -0.42 -0.42 -2.74 -3.62 source: demetra simulations. notes: wht – wheat, frt – fertilizer, ptr – petroleum, and cmb – combined price scenarios. 243the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia bio-based and applied economics 13(3): 225-243, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15364 table a4. sensitivity of simulation results to assumptions affecting crop activities. level variable crop phenology is less sensitive to the level of chemical fertilizer land partially mobile across activities land immobile across activities wht frt ptr cmb wht frt ptr cmb wht frt ptr cmb macroeconomic indicators gdp -0.05 -0.05 -0.28 -0.39 -0.04 -0.32 -0.29 -0.67 -0.04 -0.32 -0.33 -0.71 private consumption -0.33 -0.33 -1.64 -2.28 -0.32 -0.75 -1.64 -2.70 -0.31 -0.75 -1.67 -2.72 investment demand 0.15 -0.12 -1.43 -1.41 0.14 -0.09 -1.44 -1.42 0.18 -0.07 -1.67 -1.58 absorption -0.16 -0.23 -1.41 -1.79 -0.15 -0.46 -1.42 -2.03 -0.14 -0.46 -1.48 -2.08 government consumption -0.13 0.10 0.36 0.34 -0.12 0.20 0.34 0.46 -0.17 0.18 0.67 0.70 imports -0.83 -0.53 -4.06 -5.38 -0.83 -0.82 -4.06 -5.72 -0.68 -0.74 -4.77 -6.15 exports -1.10 0.56 1.54 1.03 -1.10 -0.20 1.54 0.14 -0.74 0.03 -0.18 -0.91 activity outputs crops 0.35 -0.41 1.61 1.54 0.33 -1.40 1.55 0.44 0.40 -1.36 1.19 0.19 livestock -0.19 0.01 -0.04 -0.22 -0.12 -0.26 0.21 -0.15 -0.12 -0.25 0.34 -0.03 primary sectors – grazing, fishing, forestry, mining -0.19 0.13 -0.41 -0.46 -0.12 0.14 -0.38 -0.37 -0.14 0.12 -0.31 -0.33 food and beverages -0.88 -0.08 0.48 -0.48 -0.87 -0.13 0.46 -0.53 -0.92 -0.07 0.61 -0.36 textiles, clothes, leather, and wood processing -0.08 0.13 -0.19 -0.14 -0.07 0.29 -0.22 0.03 -0.13 0.26 0.16 0.31 rest of manufacturing -0.11 0.26 0.29 0.47 -0.10 0.57 0.26 0.82 -0.20 0.52 0.89 1.27 utilities electricity and water -0.08 -0.05 -2.72 -2.86 -0.07 -0.06 -2.74 -2.88 -0.09 -0.06 -2.72 -2.88 construction 0.09 -0.08 -1.11 -1.10 0.09 -0.03 -1.12 -1.08 0.11 -0.02 -1.24 -1.18 services private -0.16 0.02 -2.56 -2.71 -0.15 0.07 -2.59 -2.69 -0.19 0.05 -2.46 -2.61 services public -0.12 0.07 0.15 0.11 -0.11 0.14 0.14 0.20 -0.15 0.12 0.41 0.40 consumption rural 0.01 -0.28 -1.44 -1.75 0.02 -0.64 -1.41 -2.12 0.09 -0.60 -1.93 -2.48 urban -0.36 -0.08 -2.74 -3.19 -0.34 -0.44 -2.72 -3.53 -0.34 -0.42 -2.79 -3.57 source: demetra simulations. notes: wht – wheat, frt – fertilizer, ptr – petroleum, and cmb – combined price scenarios. under ‘land partially mobile’ sensitivity test, cropland for selected crops (i.e., fruits, coffee, tea, sugarcane, enset, chat, cotton, forage and bioenergy crops, grass fodder, and cut flower) was assumed to be activity-specific and hence cannot be reallocated in response to the anticipated impacts. guns, germs and climate: food security and food systems in a risky world francesco pagliacci1,*, valentina raimondi2, luca salvatici3,* the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia amsalu woldie yalew1,2,*, victor nechifor3, emanuele ferrari3 participation of farmers in market value chains: a tailored antràs and chor positioning indicator tulia gattone adapting to climate change: what really drives the choices of the producers of geographical indications? francesco pagliacci1, dana salpina1,2,* exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach stefano ciliberti, angelo frascarelli*, gaetano martino, andrea marchini intention and behavior toward eating whole grain pasta on a college dining campus: theory of planned behavior and message framing giovanni sogari1, rungsaran wongprawmas1, giulia andreani1, michele lefebvre2, nicoletta pellegrini3, miguel i. gómez4, cristina mora1, davide menozzi1,* bio -based and a ppl ied economics bae bio-based and applied economics 11(1): 89, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-13412 open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae acknowledgements the editorial board thank sincerely all of whom have acted as discussants and reviewers for articles published in volume 10 no. 1, 2, 3, 4 of bio-based and applied economics. without their support the quality of our published articles would be severely compromised. thank you all. discussant of bae 10th anniversary papers giovanni belletti leonardo boncinelli gianluca brunori jeroen candel emil erjavec paolo sckokai davide viaggi reviewers federico antonioli mona aghabeygi lara agnoli filippo arfini petjon ballco luigi biagini martina bozzola roberta capitello leonardo cei daniele cavicchioli shouro dasgupta maria de salvo chiara falco giacomo giannoccaro erika hlédik emilia lamonaca sylvaine lemeilleur andrea marescotti javier martinez daniele mozzato tien thong nguyen chinedu obi alessandro olper stefano pareglio richard perrin andrea pronti maria raimondo carlo rega francesco riccioli lena luise schaller antonio seccia giovanni sogari claudio soregaroli stefanella stranieri simla tokgoz mario veneziani mauro vigani bio-based and applied economics bae bio-based and applied economics 12(2): 127-139, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13628 copyright: © 2023 f. pagliacci, f. fasano. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: f. pagliacci, f. fasano (2023). does the presence of inner areas matter for the registration of new geographical indications? evidence from italy. bio-based and applied economics 12(2): 127-139. doi: 10.36253/bae13628 received: august 30, 2022 accepted: february 27, 2023 published: august 05, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini, emilia lamonaca. orcid fp: 0000-0002-3667-7115 paper presented at the 11th aieaa conference does the presence of inner areas matter for the registration of new geographical indications? evidence from italy francesco pagliacci, francesco fasano* dipartimento del territorio e sistemi agro-forestali (tesaf), università di padova, italy *corresponding author. e-mail: fasanofrancesco@ymail.com abstract. remote areas have been progressively obtained greater attention. since 2014, the italian national strategy for inner areas has tackled remote areas with the aim of promoting local development. a tool to foster economic development in these areas is valorisation of those high-quality agri-food products that are characterised by unique features, through the use of geographical indications. this study addresses this topic, by considering the geographical indications registered in italy since 2014. the study considers municipality-level (lau2) data, taking the number of geographical indications that each municipality is eligible to produce as a dependent variable. hurdle models are used to assess the effect of inner areas and other covariates (i.e., agriculture and food industry features, socio-economic characteristics, regional settings). the results suggest that geographical indications still represent a sort of untapped resource across inner areas, even when controlling for regional settings across italy. thus, a more effective policy intervention is requested. keywords: geographical indications, inner areas, rural development. jel codes: q18, r50. highlights: – gi registration can promote economic development in inner areas. – degree of remoteness negatively affects gi registration in italian municipalities. – socioeconomic features of agriculture and regional differences also play a role. – policymakers should favour gi registration in inner areas. 1. introduction due to its geographical characteristics, italy shows large heterogeneity in terms of landscape and territory composition, turning into different conditions of accessibility to essential services, which represents a critical issue http://creativecommons.org/licenses/by/4.0/legalcode 128 bio-based and applied economics 12(2): 127-139, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13628 francesco pagliacci, francesco fasano when considering territorial imbalances (christaller, 1933; bonifazi and heins, 2003; barca et al., 2014; mantino, 2021). thus, some municipalities, placed at further distance from major urban poles, suffer from socioeconomic marginalisation and underdevelopment, in comparison with larger urban areas (bertolini and pagliacci, 2017; svimez, 2019; iammarino et al., 2018; de renzis et al., 2022). for this reason, in 2014 a specific nationallevel strategy was included in the national reform programme in italy. the national strategy for inner areas (hereinafter, nsia), supported with public funds, has targeted inner (i.e., remote) italian municipalities, with the aim of reversing depopulation trends and socioeconomic remoteness. to achieve this goal, inner areas have been supported in the capitalisation of the existing local assets and resources, through the activation of specific place-based policy measures (barca et al., 2014). among the available local resources inner areas can capitalise on, localized agri-food systems (lafss) can play a pivotal role (arfini and mancini, 2018). as other remote regions, inner areas are rich in high-quality and traditional agri-food products, whose value is based on the link between territorial features and production techniques (barca et al., 2014). in particular, some of the pilot inner areas identified by the nsia have already implemented some measures aimed at the valorisation of agro-food local products through the recognition of new geographical indications (gis). originally introduced in 1992 in the eu, gis are currently regulated under the eu regulation 1151/2012. gis stress the unique characteristics of the agro-food (and wine) products they protect, being a strategic tool to increase the income of the producers (cei et al., 2021; crescenzi et al., 2022). with 315 registered agro-food gis and 526 registered wine gis, italy is the forerunner in the eu for gi registration. in terms of valueadded, agro-food gis amount to €7.97 billion, while wine gis amount to €11.16 billion (ismea qualivita. 2022). among other goals (e.g., addressing the problem of asymmetric information between consumers and producers) (cei et al., 2018), gis can have positive economic effects for the involved territories, eventually favouring population and economic growth (crescenzi et al., 2022). to this regard, registering new gis really represent key opportunity for inner areas. in particular, this study explores if this opportunity is actually exploited by italian inner areas. it adopts a territorial approach, considering the agro-food gis registered in italy from 2014 onward (i.e., after the introduction of the nsia) and the set of the municipalities (i.e., lau2 areas) that are included within the boundaries of their eligible areas. by referring to municipality-level data, the analysis aims to investigate whether both territorial and socioeconomic features (e.g., characteristics of the agricultural sector and food industry; socioeconomic characteristics; regional settings and quality of the public governance) matter in the process of new gi registration, with a particular interest on the role of inner municipalities. this paper aims to contribute to the rather scant literature that quantitatively addresses the drivers of gi registration at territorial level (crescenzi et al., 2022; vaquero piñeiro, 2021; resce and vaquero piñeiro, 2022; cei et al., 2021). however, compared to previous studies, its novelty is twofold. firstly, it explicitly addresses the role of inner areas, as defined and mapped by the nsia (barca et al., 2014), while previous paper mostly addressed rural areas (e.g., vaquero piñeiro, 2021). secondly, its empirical strategy is grounded on the use of hurdle model, which properly handles skewed data with many zeros and admits different underlying processes to explain the zero values (i.e., registering no gis at all at municipality level) and the positive values (mullahy, 1986). the rest of the paper is structured as it follows. section 2 discusses the theoretical background, with an overview on both inner areas and the concept of gi. section 3 describes data and the adopted method. section 4 shows the results of the analysis, discussing them in comparison with previous studies. section 5 concludes the work, with possible policy implications. 2. theoretical background this section aims to introduce some of the key concepts used in the analysis. firstly, the characteristics of inner areas, as described and referred to by the nsia, are introduced; then, the gis, and their role for inner areas’ development are described. 2.1 the national strategy for inner areas the nsia represents an innovative place-based policy, aimed at promoting territorial development and cohesion in italy. launched in 2014 by the italian government, it represents a nation-wide support scheme aimed at addressing remote areas’ main problems, such as: remoteness, underdevelopment, marginalisation, low level of education and employment, depopulation trends (colucci, 2019; svimez, 2019; istat, 2019). more in general, it aims to reduce urban-rural disparities (barca et al. 2014; lucatelli 2016; urso 2016; de renzis et al., 2022). 129does the presence of inner areas matter for the registration of new geographical indications? evidence from italy bio-based and applied economics 12(2): 127-139, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13628 firstly, the nsia contributes to the mapping of the italian municipalities with the aforementioned characteristics of inner areas. a peripherality indicator – expressed as the travel-time distance from the nearest urban centre providing essential services (i.e., health, education, and transportation services) – is used to define them (de renzis et al., 2022). in particular, a 6-class taxonomy is produced, distinguishing: urban poles (a), intermunicipal poles (b), belt areas (c), intermediate areas (d), peripheral areas (e), ultraperipheral areas (f). classes d-f are generically labelled as ‘inner areas’ (bertolini and pagliacci, 2017). secondly, since 2014 the nsia has supported (and funded) the implementation of local development projects, based on more integrated approaches, to overcome the traditional weakness of project management in these areas (lucatelli, 2016), and to reinforce local territorial identities (capello, 2018). in particular, 72 pilot areas – involving at least one inner municipality – were selected on a regional basis, each of them being requested to develop its own strategy through a project framework agreement. according to it, several types of local interventions were suggested as tools to promote development processes. they have involved land management, territorial safeguarding, promotion of natural and cultural assets (i.e., through sustainable rural tourism), agricultural activities (bertolini and pagliacci, 2017). however, to be successful, each of these interventions must capitalise on the local specificities and local resources of the involved areas, i.e., some “latent development factors” (barca et al., 2014: 40). among the existing available local resources that deserve valorisation, arfini and mancini (2018) suggest the importance of lafss. in particular, valorisation of traditional high-quality agri-food products – through local participation and close cooperation among economic agents – can represent a valuable opportunity for local development across inner areas, as explicitly emphasized by the nsia (barca et al., 2014). thus, it is not a case that some of the pilot areas (e.g., alto-medio sannio, in southern italy, and valchiavenna, in northern italy) have implemented their local strategies with a focus on the valorisation of agro-food products through the recognition of gis (agenzia per la coesione territoriale and regione molise, 2021; agenzia per la coesione territoriale and regione lombardia, 2017). 2.2 gis and inner areas gis are distinctive signs used to identify a product whose quality, reputation and traditional production techniques relate to its geographical origin (oecd, 2000; cei et al, 2018). after having originated in mediterranean europe (cei et al., 2021; crescenzi et al., 2022), in 1992 they were introduced in the eu. currently, they are regulated under the eu regulation 1151/2012, hence representing one of the main elements of the eu quality policy (european commission, 2012; resce and vaquero piñeiro, 2022). gis stress the unique characteristics of the products they protect, also addressing the problem of asymmetric information between consumers and producers (oecd, 2000; cei et al, 2018), and affording a product protection against conflictual uses, frauds and fake imitations (euipo, 2017; wirth, 2016; crescenzi et al., 2022). as part of the high-quality schemes, gis represent one of the pillars of the eu agricultural and food policy. for 30 years, registered gis have steadily increased in number: in 2022, and only considering agro-food gis, there were 1,463 registered gis in the eu (+ 50% from 2010, according to andinternational (2019)), suggesting the ever-growing eu attention to those quality labels (cei et al. 2021). gis not only prevent frauds and fake imitations. they also represent strategic tools to increase producers’ income and to promote development in the territories where gi production occurs (gangjee, 2017; cei et al., 2021; resce and vaquero piñeiro, 2022; török et al., 2020). with regard to single producers, the price premium recognised to a gi can compensate not only the greater costs of the gi certification but also a weakness of local farmers in successfully participating in the globalized economy, hence working as a collective property right (bojnec and ferto,2015; crescenzi et al., 2022). moreover, gi implementation is proved to positively affect also the broader local communities, and the territories involved, in terms of value distribution (belletti and marescotti, 2017), socio-economic and environmental sustainability (belletti et al., 2015; cei et al., 2018), rural development (vaquero piñeiro, 2021), and population growth (crescenzi et al., 2022). given such a positive impact, they are attractive for those remote areas, looking for a “new rural development paradigm” (ilbery and kneafsey, 1999; marsden, 1998). actually, the link between gis and the place in which they are made suggests that geographical factors – e.g., climate, soil, biodiversity – play a role together with the human factor in assuring product quality (van leeuwen et al., 2018). such a link is stronger for protected designation of origin (pdo) than for protected geographical indication (pgi)1, but in both cases gis represent an effective way to preserve local cultural heritage (european commission, 2020). 1 in the case of pdos, every part of the production, processing and preparation process must take place in the defined region. in the case of pgis, at least one of these stages must take place in the defined region. 130 bio-based and applied economics 12(2): 127-139, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13628 francesco pagliacci, francesco fasano in more general terms, registering a new gi can be considered as a “collective” production process (teil, 2012: 497), turning into a “type of collective property” (barham, 2003). due to the length and the cost of the application procedure (cei et al., 2021), the whole locallevel community must be actively involved in this process (prévost et al., 2014), which must be driven by the interests of multiple stakeholders, including local policymakers, local communities, agri-food producers, and even marketers and consumers (castellò, 2021). such a collective organization is crucial not only for the initial registration of a gi but also for its ongoing management over time (reviron and chappuis, 2011), for example in the case of non-minor amendments involving changes in the boundaries of the production area (landi and stefani, 2015). mantino and vanni (2018) also show the importance of the support from local administrations and local politics, by means of two case studies from northern and southern italy. thus, it is clear that, when analysing the process of registration of new gis, several factors play a role. actually, analysing the main conditions that favour gi registration is complex, due to little availability of economic data on gis at the local level. because of these limitations, previous studies addressing this nexus were mostly qualitative (see, for example, torok et al., 2020; bonanno et al., 2019). however, they all confirm that socio-economic determinants (e.g., infrastructure endowment and consumer demand), dynamism of the local agri-food sector, and favourable institutional context all matter (huysmans and swinnen, 2019; meloni and swinnen, 2018; vaquero piñeiro, 2021; resce and vaquero piñeiro, 2022). also, farmers’ characteristics matter for gi registration, and in particular: famers’ education level (marongiu and cesaro, 2018), and propensity to cooperate (charters and spielmann, 2014; cei et al., 2021; vaquero piñeiro, 2021). lastly, also pre-existing experience in gi registration matters: traditional gi regions tend to be more active in new gi registration, thanks to accumulation of skills among producers and improved institutional capacity (cei et al., 2021; tregear et al., 2016; huysmans and swinnen, 2019). also, kizos et al. (2017) claim that implementation of gis in those territories having experienced gis registration for decades is even more developed thanks to the presences of consortia and pre-existing collective actions. all these elements can be grouped under the general (albeit rather fuzzy) definition of social and territorial capital, whose importance for agricultural and rural development has been largely emphasized over time (putnam et al., 1994; capello, 2018; rivera et al. 2019; cortinovis et al. 2017; pagliacci et al., 2020). when considering the aforementioned territorial and socioeconomic characteristics, remoteness cannot be ignored as a major driver, due to the specificities that characterize inner areas. indeed, when considering gi registration in inner municipalities, contrasting findings emerge. these areas are endowed with some crucial factors, but can lack some others. at eu level, many studies have claimed that gi registration represents an economic opportunity largely exploited by remote and other less favoured areas (parrott et al., 2002; santini et al.; 2015; van de pol, 2017; cei et al., 2021). however, in the case of italy, a positive nexus between gis and inner areas is less obvious. according to marongiu and cesaro (2018), italian farmers located in the less favoured areas (i.e., remote and inner regions, among other) are less likely to engage in gi schemes than those located close to the flatlands, hence benefitting from a larger infrastructure endowment. similarly, vaquero piñeiro (2021) claims that the italian food pdos with the highest revenues come from those municipalities that show better socioeconomic conditions, a more diversified economy and a more competitive agri-food sector. 3. data and methods this section aims to discuss the data adopted into the analysis together with the suggested method. 3.1 data this study considers all the agro-food gis (both pdos and pgis) that have been registered in italy, since 2014, i.e., the year of introduction of the nsia. specifically, the study takes into account all gis registered in both northern and southern italy, regardless of the extent of the territory specified by each gi’s product specification (i.e., considering both gis produced in only a few municipalities and those produced in entire regions2), in order to have a more general overview of the possible different factors playing a role in new gi registration. however, this study only considers agro-food gis, excluding wine gis. two main reasons drive this choice. firstly, previous studies tackling gis and their territorial distribution have favoured wine gis more than food gis 2 despite its focus, this analysis also includes the gis produced over entire regions. actually, although inner municipalities usually play a limited role in the decisions to register new large-scale gis, however their inclusion within the boundaries of the area of production can still represent an important decision, eventually prompting local economic development. 131does the presence of inner areas matter for the registration of new geographical indications? evidence from italy bio-based and applied economics 12(2): 127-139, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13628 (see for example resce and vaquero piñeiro, 2022; crescenzi et al., 2022). secondly, an analysis on agro-food gis – which include very different products (e.g., fruits and vegetables, cheeses, meat-based products) – can shed light on a broader set of territorial and social determinants of the degree of protection sought through gi labels, hence favouring more generalisable findings. in total, we consider 56 gis, of which 15 are pdos and 41 are pgis. as shown in table 1, 10 different product categories are included. most of the gis under analysis are fruit, vegetables and cereals. however, when considering pdos only, most of them are cheeses. for each of them, we retrieved the list of municipalities included within the boundaries of the eligible area of production according to each single gi’s product specification (as retrieved by the eambrosia dataset3). considering the agro-food gis registered in the 2014-2022 period, there are 4125 italian municipalities (out of 7926) that are eligible for the production of at least one of them. in particular, some municipalities in tuscany and in apulia are eligible to produce even four different newly registered gis (figure 1). gi eligibility at municipality level can be jointly analysed with the italian inner municipalities (table 2). on average, 46.8% of the italian municipalities are included in the production area of none of the gis registered in the period 2014-2022. however, this share is the largest in the intermunicipality poles (b) and belt areas (c), i.e., across some types of non-inner areas. conversely, it is definitely lower in type d, e, and f municipalities. these results seem suggesting that inner areas are more likely to adopt new gis than non-inner areas. 3 available at https://ec.europa.eu/info/food-farming-fisheries/foodsafety-and-quality/certification/quality-labels/geographical-indicationsregister/. 3.2 methods to assess the role of the drivers that may affect the number of newly registered gis at municipality level, the following empirical strategy is adopted. as a dependent variable, the number of agrofood gis registered in the 2014-2022 period, by italian municipality, is a count variable. it is not normally distributed, as it includes many zero observations (46.8% of the total observations). in this case, it is common to adopt a count regression approach. the basic distribution for a count variable is a poisson distribution, with the conditional mean (the mean of the outcome variable table 1. number of gis, by type (pdo and pgi) and product category.   pdo pgi total fruit, vegetables and cereals 4 16 20 cheeses 8 1 9 bread, pastry, cakes, … 1 7 8 oils and fats 6 6 meat products 6 6 pasta 3 3 other products of animal origin 1 1 fresh meat (and offal) 1 1 chocolate and derived products 1 1 fresh fish, molluscs, and crustaceans 1   1 total 15 41 56 figure 1. number of registered gis (2014-2022), by municipality. table 2. share of municipalities with no registered gis, by type of inner-area municipality, out of the number of municipalities in each type of inner-are   value (%) a – urban poles 41.9 b – intermunicipality poles 59.0 c – belt areas 54.1 d – intermediate areas 40.4 e – peripheral areas 42.0 f – ultraperipheral areas 32.9 total average 46.8 source: own elaboration. 132 bio-based and applied economics 12(2): 127-139, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13628 francesco pagliacci, francesco fasano y given the values of the predictor variables x) being equal to the conditional variance (cameron and trivedi, 2013). however, given the clear stack of zero values in the dependent variable in this case, poisson models tend to show poor fitting. thus, the hurdle model, i.e., a modified count model, can help (mullahy, 1986). it is a two-part model. firstly, the zero hurdle part is adopted to model a right-censored outcome variable indicating municipalities being eligible for not even a single gi (y = 0) or with at least one of them (y = 1, where all values larger than 0 are censored, i.e., are fixed at 1). the second part is a truncated count, which is adopted to model the exact number of gis for those municipalities that are eligible for producing at least one of them (municipalities with y > 0). the hurdle model is also based on the idea that different underlying processes – driven by different sets of regressors – can explain either the zero values and the positive values of this variable. if a municipality does not produce a single gi, then the threshold (i.e., the ‘hurdle’) to the truncated count part is not crossed, and a zero value is observed. otherwise, the hurdle to the truncated count part is crossed, and any given number can be observed. in the case of gi registration, it might be expected that those municipalities that have already been included in the area of production of one gi, might benefit from further facilitation in registering additional gis, compared to other municipalities (see cei et al., 2021; kizos et al., 2017). dealing with the research question of this study, the hurdle model combines a binomial probability model – governing the binary outcome of whether a count variable has a zero or a positive value – with a zero-truncated poisson count-data model, for those observations that cross the hurdle (y >0). formally, we have (zeileis et al., 2008): (1) where the model parameters are estimated by maximum likelihood, and where the specification of the likelihood has the advantage that the count and the hurdle component can be maximized separately (zeileis et al., 2008). the corresponding mean regression relationship is given by using the canonical log link, resulting in a log-linear relationship between mean and linear predictor (zeileis et al., 2008): log μi = xi t β + log(1 – fzero(0;zi,γ)) – log(1 – fcount(0;xi,β)) (2) with regard to the empirical strategy implemented here, different models, including different sets of regressors, grounded on the literature review carried on in section 2, are used. model 1 focuses on the role of inner areas, referring to the 6-class taxonomy of the italian municipalities produced by the nsia. model 2 considers the characteristics of the agriculture sector (i.e., utilised agricultural area per inhabitant, share of cooperative agricultural holdings out of the total, share of agricultural holders aged 40 years and less out of the total, share of agricultural holders having achieved tertiary education) and of the food industry (i.e., share of employment in food industry of the total manufacturing industry employment). moreover, the share of agricultural holdings being already involved in pdos or pgis production (considering 2010 census data) is included as a proxy of any pre-existing experiences in gi registration. model 3 includes socio-economic characteristics of the municipality, addressing average per capita income (in 2014) as a proxy for the local-level socioeconomic dynamism, and share of electoral turnout in the eu 2014 vote, as a more general proxy for social capital at local level4. lastly, model 4 is the most comprehensive model, including all the aforementioned covariates. lastly, it can be noticed that in all the models 1-4, a categorical variable distinguishing the italian macro-regions (i.e., northwest, north-east, centre, south and the islands) is also included. such a variable is important to address different regional settings. indeed, italian macroregions largely differ in terms of climatic conditions, characteristics of the agricultural sector and of the supply chains, and institutional settings (eventually affecting overall governance and politics quality). this categorical variable is expected to control for all these aspects. for each of the aforementioned regressors, table 3 provides variable specification as well as data source. 4. results and discussion the results of the hurdle models, in each specification, are returned with regard to the coefficients of the variables (table 4) and the estimated odd ratios (table 5). in model 1, the baseline odds of having a positive count (i.e., at least one eligible gi, by municipality) are 1.27. the odds are affected negatively by being either 4 actually, eu voting does not lead to any direct economic rewards, being mostly driven by a sense of public duty (bigoni et al., 2016; guiso et al., 2004; putnam et al., 1994). moreover, one could also argue that a higher electoral turnout in the elections could refer to the presence of a higher-quality political class as well. 133does the presence of inner areas matter for the registration of new geographical indications? evidence from italy bio-based and applied economics 12(2): 127-139, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13628 an intermunicipal pole (type b) or a belt area (type c), while this odds ratio is 1.805 times higher in the ultraperipheral municipality (type f). controlling for macro_reg, odds ratio is 8.030 times higher in central regions while being located in the north-east decreases it by 0.399 times. given the response is positive (i.e., the hurdle is crossed), the negative effects played by inner area are largely observed: intermediate (type d), peripheral (type e) and ultraperipheral (type f) municipalities are associated with a smaller number of newly registered gis. when controlling for macro_reg, north-eastern regions, southern regions and the islands are associated to a smaller number of gis as well. in model 2, the baseline odds of having a positive count are positively affected by uaa and food_ind, while coop has a negative effect, despite common expectations. controlling for macro_reg, these odds is higher in central regions and smaller in the north-east, as observed in model 1. given the response is positive, uaa and past gis increase the number of registered gis in each municipality, while young and food_ind negatively affect it. with regards to macro_reg, same effects, as observed in model 1, are found. in model 3, the baseline odds of having a positive count are negatively affected by income and election, also when controlling for macro_reg (whose coefficients are all significant). however, given the response is positive, both income and election turns to positively affect the number of registered gis in each municipality. in model 4, most of previous effects are largely confirmed. the baseline odds of having a positive count are 21.957. compared to urban poles, all other municipality types reduce these odds, with the only exception of ultraperipheral municipalities (type f), showing no significant effect at all. moreover, it is also significantly decreased by coop, while both university and food_ind positively affect it. among socioeconomic characteristics of the municipalities, election negatively affects it. when considering macro_reg, north-east is confirmed to have a negative effect on these odds, as south and the islands have. conversely, being a municipality in the centre increases the odds. given the response is positive (i.e., the hurdle is crossed), the negative effects played by inner area is much broader and generalised. inner municipalities (d-f) show a lower number of registered gis. conversely, uaa and past gis increase the number of registered gis in each municipality (confirming the findings form model 2), and also election turns to positively affect this number. the results about the new gi registration in italy, in years 2014-2020, confirm most of the findings from pretable 3. covariates for the analysis at municipality level. group label descriptions specification source year remoteness inner areas categorical variable, reflecting inner area type of italian municipalities (a-urban poles, b-intermunicipal poles, c-belt, d-intermediate, e-peripheral, f-ultraperipheral), according to the nsia classification 6 factors own elaboration on barca et al. (2014) 2014 agriculture and food industry uaa hectares of utilised agricultural area (uaa) per inhabitant (2010) ratio italian agricultural census (istat) 2010 coop share of cooperative agricultural holdings out of the total ratio italian agricultural census (istat) 2010 young share of agricultural holders aged 40 years and less ratio italian agricultural census (istat) 2010 university share of agricultural holders having achieved tertiary education ratio italian agricultural census (istat) 2010 food_ind share of employment in food industry of the total manufacturing industry employment ratio italian population and housing census (istat) 2011 past gis share of agricultural holdings being involved in pdos or pgis production in 2010 ratio italian agricultural census (istat) 2010 socio-economic characteristics income average gross taxable income (thousand €), for year 2014 continuous (1000€) ministero dell’economia e delle finanze 2014 election share of electoral turnout in the 2014 eu vote ratio ministero dell’interno 2014 regional settings macro_reg categorical variable, for the italian macroregion (north-west, north-east, centre, south, the islands) 5 factors istat 2011 134 bio-based and applied economics 12(2): 127-139, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13628 francesco pagliacci, francesco fasano vious studies. surely, geographical and territorial divides across italy matter. the results about inner areas are somehow contrasting. also, when controlling for other socioeconomic covariates, being an inner municipality generally decreases the chance of having a gi registered. moreover, even when one new gi is registered, inner areas tend to be associated to a smaller number of registered gis, per municipality. in fact, this finding contrasts with what observed by parrott et al. (2002) and by cei et al. (2021), who considered gi adoption in the eu table 4. model estimates for the number of gis at municipality level. variable m1 m2 m3 m4 count model zero hurdle model count model zero hurdle model count model zero hurdle model count model zero hurdle model (intercept) 0.380 *** 0.243 0.144 * -0.054 -0.968 *** 2.800 *** -0.355 3.089 *** (0.102) (0.154) (0.057) (0.064) (0.182) (0.221) (0.243) (0.320) inner areas _ type b 0.369 * -0.898 *** 0.374 * -1.018 *** (0.154) (0.154) (0.154) (0.257) inner areas _ type c -0.166 -0.389 * -0.158 -0.630 *** (0.102) (0.155) (0.105) (0.158) inner areas _ type d -0.389 *** 0.070 -0.358 ** -0.360 * (0.104) (0.157) (0.110) (0.165) inner areas _ type e -0.491 *** 0.082 -0.451 *** -0.438 * (0.111) (0.160) (0.119) (0.171) inner areas _ type f -0.870 *** 0.590 ** -0.852 *** 0.104 (0.178) (0.198) (0.185) (0.206) uaa 0.008 ° 0.030 * 0.021 *** -0.008 (0.005) (0.013) (0.005) (0.011) coop 0.013 -0.077 ** 0.010 -0.077 ** (0.025) (0.028) (0.025) (0.028) young -0.007 ** 0.000 -0.004 -0.004 (0.003) (0.003) (0.003) (0.003) university 0.002 0.000 -0.004 0.014 *** (0.003) (0.004) (0.004) (0.004) food_ind -0.002 * 0.005 *** -0.001 0.003 ** (0.001) (0.001) (0.001) (0.001) past gis 0.006 *** 0.000 0.006 *** -0.001 (0.001) (0.001) (0.001) (0.001) income 0.033 *** -0.116 *** 0.015 -0.109 (0.008) (0.010) (0.009) (0.011) election 0.007 *** -0.008 *** 0.006 *** -0.006 ** (0.002) (0.002) (0.002) (0.002) macro_reg_north-east -1.806 *** -0.919 *** -1.978 *** -0.835 *** -1.776 *** -1.002 *** -1.831 *** -1.020 *** (0.163) (0.070) (0.163) (0.071) (0.164) (0.071) (0.165) (0.074) macro_reg_centre 0.040 2.083 *** 0.021 2.128 *** 0.092 ° 1.914 *** 0.089 1.809 *** (0.049) (0.114) (0.052) (0.114) (0.050) (0.115) (0.057) (0.118) macro_reg_south -0.258 *** 0.002 -0.283 *** 0.076 -0.051 -0.623 *** 0.003 -0.757 *** (0.056) (0.063) (0.061) (0.066) (0.074) (0.086) (0.083) (0.094) macro_reg_islands -0.664 *** -0.123 -0.720 *** -0.001 -0.452 *** -0.745 *** -0.342 *** -0.928 *** (0.097)   (0.087)   (0.100)   (0.086)   (0.111)   (0.111)   (0.119)   (0.117)   note: for count model, truncated poisson with log link; for zero hurdle model, binomial with logit link. for inner areas: omitted type a single ‘poles’ for macro_reg: omitted type north-west significance: °p<0.1; *p<0.05; **p<0.01; ***p<0.001 source: own elaboration. 135does the presence of inner areas matter for the registration of new geographical indications? evidence from italy bio-based and applied economics 12(2): 127-139, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13628 less favoured areas. rather, this finding is more in line with the results by marongiu and cesaro (2018). a possible explanation for these contrasting results might lie in the different geographic areas (i.e., considering italy only) and in the different territorial scale adopted (the municipality level, i.e., a narrower territorial area). when considering other socioeconomic and territorial drivers, the findings from this study seem confirming previous ones. for example, the results about farmers’ education, proxied by the share of agricultural holders with tertiary education, confirm those by marongiu and cesaro (2018). conversely, cooperation in the agricultural sector shows detrimental effect in having at least one registered gi at municipality level. this finding is contrasting with previous results (charters and spielmann, 2014; cei et al., 2021; vaquero piñeiro, 2021) and largely unexpected: lack of cooperation among farmers is usually recognised as a major issue in the registration process of high-quality agri-food products, which ground on consortia for their protection and valorisation (see also fasano, 2021, for a qualitative study analysing some agri-food products of the molise region and the efforts to register new gis in southern italy’s inner areas). it is not a case that improving collective actions in agriculture represents a key objective of the common agricultural policy (cap) in 2023-2027 programming period. conversely, the positive role played by pre-existing experience in registering gis confirms the findings of cei et al. (2021), tregear et al. (2016), and huysmans and swinnen (2019). moreover, this study seems suggesting that accumulation of skills among producers and improved institutional capacity is even more important in explaining the registration of more and more gis, thus confirming the vitality of traditional gi regions. in the case of the proxies for social capital endowment at local level, electoral turnout in eu vote shows a significant effect, albeit with contrasting sign in either the zero-part or the count-part of the model. the fact that electoral turnout can play a positive role in explaining the registration of more and more gis, when at least one is registered, can be explained by the fact that quality of local political institutions also matter, with a sort of multiplying effect. actually, a greater quality of local institutions can increase citizens’ trust in the local political class, positively affecting in turn electoral turnout, also in eu elections. the nexus between electoral turnout, quality of institutions and gi registration is somehow consistent with the idea of gis as collective propertable 5. results of the models: odd ratios. variable m1 m2 m3 m4 count model zero hurdle model count model zero hurdle model count model zero hurdle model count model zero hurdle model (intercept) 1.462 1.274 1.155 0.948 0.380 16.442 0.701 21.957 inner areas _ type b 1.447 0.408 1.453 0.361 inner areas _ type c 0.847 0.678 0.853 0.533 inner areas _ type d 0.678 1.072 0.699 0.698 inner areas _ type e 0.612 1.086 0.637 0.646 inner areas _ type f 0.419 1.805 0.426 1.110 uaa 1.008 1.031 1.021 0.992 coop 1.013 0.926 1.010 0.926 young 0.993 1.000 0.996 0.996 university 1.002 1.000 0.996 1.014 food_ind 0.998 1.005 0.999 1.003 past gis 1.006 1.000 1.006 0.999 income 1.034 0.891 1.015 0.897 election 1.007 0.992 1.006 0.994 macro_reg_north-east 0.164 0.399 0.138 0.434 0.169 0.367 0.160 0.361 macro_reg_centre 1.041 8.030 1.022 8.396 1.096 6.778 1.093 6.102 macro_reg_south 0.773 1.002 0.754 1.079 0.951 0.536 1.003 0.469 macro_reg_islands 0.515 0.884 0.487 0.999 0.636 0.475 0.711 0.395 for inner areas: omitted type a single ‘poles’ for macro_reg: omitted type north-west source: own elaboration. 136 bio-based and applied economics 12(2): 127-139, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13628 francesco pagliacci, francesco fasano ties (barham, 2003), which calls for a high level of social capital for their implementation. these results are somehow consistent with those about the differences observed across italian macroregions. southern italian regions and the islands tend to show lower propensity to register new gis, and they are also characterised by a smaller number of registered gis per single municipality, when at least one has been registered. as already observed, several reasons might explain these differences across italian macroregions, including different climatic conditions, different structures of the supply chains, different institutional settings and quality of the local governance. in particular, several authors have stressed the importance of this latter hypothesis. indeed, vaquero piñeiro (2021), meloni and swinnen (2018) and crescenzi et al. (2022) point out the role of institutional quality in gi registration. when considering single cases studies, also mantino and vanni (2018) suggest the importance of the attitude of the local policy system, finding same differences when comparing northern and southern regions. overall, these results could perhaps challenge the willingness of the policymakers (both at eu and national level) to provide a tool, such as gis, to foster remote areas’ development. in particular, given the negative relationship between inner areas and the number of registered gis, the effectiveness of many of the strategies implemented at local level by the 72 pilot inner areas might seem not effective at all (see dipartimento per le politiche di coesione, 2020). especially across southern italy, promotion of agro-food quality systems is considered relevant and supported by local policymakers. however, the existence of some major weaknesses in the inner areas (e.g., remoteness, scarcity of agricultural modernisation, presence of elderly farmers in the inner areas) seems to overcome any political will. thus, in the case of italian inner areas, not even the nsia has been able to revert these weaknesses, hence turning into a still too limited exploitation of gi registration. 5. conclusions and policy implications in order to foster socioeconomic development and agriculture diversification of inner areas (as of other marginal areas), eu quality schemes for agro-food products (and gis in particular) are considered as a key opportunity, by both eu and national policymakers. actually, inner areas share a large amount of natural resources as well as of traditional agro-food products, which might benefit from gi protection. in particular, this paper has contributed to the empirical debate of the territorial and socioeconomic drivers that can affect gi registration in italy (i.e., the frontrunner country in the eu), by demonstrating which of them play the most prominent role. by considering the number of agro-food gis registered across italian municipalities in years 2014-2022, and by using hurdle models, this study suggests that this opportunity still represents a sort of untapped opportunity for italian inner areas, despite the strong political commitment to promote them. moreover, future works will try to extend these findings to other national contexts, as well as to include also the wine sector. however, it should be noticed that not even the inclusion in the area of production is necessarily a guarantee of production exploitation of the gi for the municipality itself. actually, gi producers are free to locate in any municipalities within the boundaries of the production area, eventually favouring non-inner municipalities. nevertheless, being included within the area of production of a gi (even in the case of larger scale gis) might represent a key element for any communities that aim to create a collective property, as gis are. therefore, this inclusion represents a tool to add value to the local agrofood production. as suggested by this study, in addition to geographic remoteness, other factors might hinder this process, e.g., the lack of local-level political commitment, and the limited extent of social capital at local level. however, further studies will also tackle the location of the producers within the boundaries of the production area, to test their effective links with inner areas. however, even if just considering municipality eligibility, it is clear that, in order to enhance a stronger registration of new gi labels across italian inner areas, the cap should put more attention on this nexus. however, the italian cap strategic plan for the 2023-2027 programming period has not included any radical innovations in the way quality schemes are supported by eu public funds. in addition to specific funds, what seems to be really important to achieve these ambitious goals is fostering cooperation among farmers and between producers and policymakers, who must have an even more proactive role in raising awareness of the gi potential, even in the inner areas. such an approach would be useful to get the chance to use this untapped potential, hence promoting a more efficient and a more cohesive food chain organisation also across inner areas. references agenzia per la coesione territoriale and regione lombardia (2017). accordo di programma quadro 137does the presence of inner areas matter for the registration of new geographical indications? 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(2008). regression models for count data in r. journal of statistical software 27, 1–25. cap, farm to fork and green deal: policy coherence, governance, and future challenges annalisa zezza key policy objectives for european agricultural policies: some reflections on policy coherence and governance issues silvia coderoni towards a new generation of (agri-) food policies gianluca brunori earliness, phenological phases and yield-temperature relationships: evidence from durum wheat in italy marco tappi1,*, federica carucci2, anna gagliardi1, giuseppe gatta1, marcella michela giuliani1, fabio gaetano santeramo1 does the presence of inner areas matter for the registration of new geographical indications? evidence from italy francesco pagliacci, francesco fasano* learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy antonio r. hurtado1,2,*, julio berbel2 bio-based and applied economics bae bio-based and applied economics 12(2): 115-125, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13745 copyright: © 2023 m. tappi, f. carucci, a. gagliardi, g. gatta, m.m. giuliani, f.g. santeramo. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: m. tappi, f. carucci, a. gagliardi, g. gatta, m.m. giuliani, f.g. santeramo (2023). earliness, phenological phases and yield-temperature relationships: evidence from durum wheat in italy. bio-based and applied economics 12(2): 115-125. doi: 10.36253/bae13745 received: september 09, 2022 accepted: march 06, 2023 published: august 05, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini, emilia lamonaca. orcid mt: 0000-0003-0682-5719 fc: 0000-0001-6460-6333 ag: 0000-0001-7923-3859 gg: 0000-0001-8381-7785 mmg: 0000-0001-9014-1955 fgs: 0000-0002-9450-4618 paper presented at the 11th aieaa conference earliness, phenological phases and yieldtemperature relationships: evidence from durum wheat in italy marco tappi1,*, federica carucci2, anna gagliardi1, giuseppe gatta1, marcella michela giuliani1, fabio gaetano santeramo1 1 university of foggia, department of agriculture, food, natural resources and engineering (dafne), foggia, italy 2 university of tuscia, department of agriculture and forest sciences (dafne), viterbo, italy *corresponding author. e-mail: marco.tappi@unifg.it abstract. the impacts of extreme weather events on crop production are largely heterogeneous along the timing dimension of the shocks, and the varieties being affected. we investigate the yield-temperature relationships for three categories of earliness of durum wheat: early-maturing, middle-maturing, and late-maturing. we disentangle the time dimension distinguishing five phenological stages, as identified by the growing degree days approach. our panel regression models show that the starting, growing, and anthesis stages are sensitive to changes in minimum temperatures, regardless of wheat earliness. raises in maximum temperatures during the starting stage are associated with increases in yields until a certain threshold above of which decrease; the opposite is true for increases in maximum temperatures in the maturity stage for latematuring varieties, and in the end stage for early-maturing varieties. results imply that farmers and policymakers may adopt ex-ante and ex-post risk management strategies, i.e., choice of variety to avoid severe yield losses and incentives to crop insurance uptake, respectively. keywords: climate change, crop insurance, growing degree days, risk management, weather index. jel codes: g22, q18, . introduction the climate variability and the increased frequency of extreme weather events threaten the agricultural sector (auci et al., 2021). the simulations on projected yields under climate change conditions show losses in crop production (challinor et al., 2014). in turn, these, may impact the market dynamics with price increases and changes in firms’ profitability margins (stevanovic et al., 2016). the risk management interventions subsidised by the common agricultural policy (cap) of european union (eu), e.g., crop insurances, mutual funds, may help farmers to cope with the potenhttp://creativecommons.org/licenses/by/4.0/legalcode 116 bio-based and applied economics 12(2): 115-125, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13745 marco tappi et al. tial losses due to climatic changes (severini et al., 2016; meuwissen et al., 2018; shirsath et al., 2019; giampietri et al., 2020; cordier and santeramo, 2020; rippo and cerroni, 2022), even better if combined with other exante practices, e.g., agroecological strategies (altieri et al., 2015). the weather-index insurances (wiis) emerged as promising tools to indemnify farmers affected by weather damages (anghileri et al., 2022). the working principle of the wiis is a compensation based on a proxy (the weather index) correlated with potential yield losses (abdi et al., 2022). the wiis may contribute solving the market failures due to moral hazard and adverse selection issues, which are common in traditional indemnity insurance (santeramo, 2019; bucheli et al., 2022). the main threat to the well-functioning wiis relies on the possible low correlation between triggered pay-outs and the occurrence of loss events, a peculiarity referred to as ‘basis risk’ (cesarini et al., 2021). the basis risk may assume multiple forms. the temporal basis risk may result from the discrepancy between the timing of the weather index fails and the evolution of the crop growth stages (masiza et al., 2022). the phenology information collected in publicly available datasets (e.g., through satellite remote sensors) may help reduce the temporal basis risk (dalhaus et al., 2018; afshar et al., 2021). indeed, the phenological stages show different susceptibilities to the weather conditions, a relevant aspect for the weather index definition. as for durum wheat, the timing of the undesired weather events matter. for instance, low temperatures are detrimental in all stages of growth, but the most severe negative impacts are observed during the reproductive stage (barlow et al., 2015). high temperatures severely compromise the physiological processes during the flowering and grain filling stages (rezaei et al., 2015; makinen et al., 2018, gagliardi et al., 2020). as a matter of fact, taking into consideration the phenological stages within which the weather event occur is crucial to understand the weather-yields relationships better: this concept directly translates into better modelling of the temporal basis risk. although remote sensing imagery represents a promising technique for identifying phenological stages, many factors, such as the atmospheric conditions (e.g., clouds) or the biotic and abiotic environmental perturbations, may also be relevant to analyse the physiological process (zeng et al., 2020), but are complex in nature and computation. on the other hand, a fixed calendar approach may be oversimplistic and misleading. a second-best solution is to use the growing degree days (gdd), adopted to schedule management activities. it represents a suitable method to predict specific crop stages based on the amount of daily temperature degree (miller et al., 2001). conradt et al., 2015 showed that the gdd approach accurately identifies the phenological phases. however, the timing of the phenological stages is not homogenous across varieties. apart from the studies just mentioned, the literature on the role of varieties in shaping the relationships between yield and weather is quite limited. thus, departing from a vast literature on the yield-weather nexus (di falco et al., 2012; powell and reinhard, 2016; delerce et al., 2016; chavas et al., 2019), we deepen on the heterogeneities that the yield-temperatures relationship may show across different phenological stages and earliness of durum wheat, hereafter defined as earlymaturing, middle-maturing, and late-maturing. building up the works of tappi et al. (2022), who show the need to collect more refined data to investigate the relationships between yields and weather variables, and of tappi et al. (2022), who focus on the role of temporal and design approaches in yield-weather assessment, the aim of our paper is to assess whether the relationships yield-temperature control for three categories of durum wheat earliness (i.e., early-maturing, middlematuring, and late-maturing) among five phenological stages identified by the gdd approach, focusing on the most representative italian provinces in terms of durum wheat production. apart from the new knowledge, our paper has direct implications for farmers aiming to adopt ex-ante risk management strategies (e.g., choice of variety) and for policymakers planning ex-post risk management strategies (e.g., incentives to crop insurance uptake). the italian participation level in crop insurance schemes is still low, limited to few products, and concentrated in few areas (santeramo, 2018, 2019; coletta et al., 2018). therefore, the focus on the yieldtemperature relationship may directly speak with the ongoing debate on how to improve the attractiveness of innovative insurances in a more and more warming climate change scenario. data and methodology durum wheat is the main crop in the mediterranean area for making pasta, couscous, semolina, and other products (carucci et al., 2020). we collected yields and weather data from 2006 to 2020 of 30 main durum wheat-producing italian provinces, located in central and southern italy (figure 1, in the appendix). specifically, yearly durum wheat yield data (quintals of production/cultivated hectares) have been collected from the national institute of statistics (istat). in contrast, daily weather data have been collected from jrc agri4cast 117earliness, phenological phases and yield-temperature relationships: evidence from durum wheat in italy bio-based and applied economics 12(2): 115-125, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13745 meteorological database of european commission that includes maximum temperatures (°c) and minimum temperatures (°c). descriptive statistics of the dataset are shown in table 1. more specifically, maximum temperatures show a mean value of 13.5 °c, a median value of 13.6 °c, in a range between -5.3 °c and 33 °c; minimum temperatures show a mean value of 5.9 °c, a median value of 6.1 °c, in a range between -11.6 °c and 19.9 °c, furthermore, maximum temperatures exceed 30 °c in some southern provinces, e.g., agrigento, caltanissetta, catania, enna, matera, palermo, trapani, while minimum temperatures exceed -2 °c in some northern provinces, e.g., bologna, ferrara, perugia, pisa, ravenna, rovigo, siena (table 2, in the appendix). we selected weather variables within the timeframe of the wheat production cycle. several approaches are available to assess econometrically the weather impacts on society and the economy: cross-sections, linear and non-linear panel, long-differences, and partitioning variation (hsiang, 2016; kolstad and moore, 2020). cross-sectional and panel regression analyses are the most used to assess the climate impacts on agriculture (carter et al., 2018). generally, panel model approach uses crop yields as the output of production function, while the cross-section uses a proxy for land productivity, e.g., revenue or profit (blanc and schlenker, 2020). according to hsiang (2016), climate may affect social outcomes in two ways: directly, i.e., the effects of weather in a certain time, and indirectly (i.e., belief effect), i.e., the consequent effects of weather on decisions and actions also referred to as adaptation. belief effects and other unobservable variables may cause bias in estimates (hsiang, 2016). in this complex scenario and considering the trade-off among econometrics models, the panel approach presents some advantages for controlling unobserved omitted variables, removing a possible source of bias (hsiang, 2016; kolstad and moore, 2020). moreover, nonlinear panel models with fixed effects may capture partially long-run adaptive response to climate change (carter et al., 2018), also contributing to overcoming the main limitations of panel regression: the short-run response to weather fluctuations (kolstad and moore, 2020). therefore, our yield response equation is based on a non-linear panel regression: yit = f(wit;β) + αi + αt + εit (1) where yit represents the vector of durum wheat yield data for the 30 main italian provinces (i) in terms of production volumes and time horizon covered (t). the function f(wit;β) is explained in the formula (2) below. the estimated coefficients (in bold) are collected in the matrix of first and second-order coefficients noted as β, whereas αi and αt are the vectors of the location-specific and year-specific fixed effects, controlling for unobserved heterogeneity over space and time. the error term is noted by the εit (hsiang, 2016; tack et al., 2015; kolstad and moore, 2020). five phenological stages of durum wheat have been identified through the gdd approach, starting from the sowing date in the middle of november for wheat crop cultivated in the mediterranean area (miller et al., 2001): (i) starting, from emergence to two leaves unfolded; (ii) growing, from the end of two leaves unfolded to the beginning of anthesis (first anthers are visible); (iii) anthesis, from the beginning of anthesis to beginning of seed fill; (iv) maturity, from the beginning of seed fill to dough stage; (v) end, from dough stage to full maturity. the gdd approach predicts plants stages from seeding to maturity using the accumulation of heat or temperature units above a threshold or base temperature below which no growth occurs (miller et al., 2001). the function f(wit;β) is explicated as follows: (2)1 1 we focused on how the temperatures may affect the yields, considering the precipitations as control factor mainly because its effect on yields is difficult to catch (being affected by other variables such as soil texture, management practices, irrigation, etc.). a single rain event may impact on a smaller portion of territory than changes in temperatures affecting entire areas. therefore, the evaluation of the effect of precipitation on the yields needs of further investigation. moreover, we controlled for the market shocks, i.e., on how unfavourable years in terms of durum wheat price. the results are robust. table 1. descriptive statistics of daily temperatures and yearly yield variables from 2006 to 2020 among 30 main durum-wheat producing italian provinces. variable (unit) obs. mean median st. dev min max maximum temperature (°c) 68,832 13.55749 13.63 4.59804 -5.336364 32.98 minimum temperature (°c) 68,832 5.899284 6.07 3.919422 -11.65 19.95 yield (q/ha) 68,299 36.81634 33 12.98301 17 81.42377 118 bio-based and applied economics 12(2): 115-125, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13745 marco tappi et al. where tminit and tmaxit, are the daily minimum and maximum temperatures across space (i) and time (t). the index s (s = {1,2,3,4,5}) indicates the phenological stage of durum wheat. the apex x indicates the linearity of the term. furthermore, based on phenology calculation combined with the universal growth staging scale reported in miller et al. (2001) for the wheat crop, we identified three categories of earliness, i.e., early-maturing, middle-maturing, and late-maturing, also identifying the dates of occurrence of phenological stages (table 3). we assume that the sowing date is the same for all varieties (i.e., november 152), although it represents a limit of our paper. however, it is useful to assess yieldtemperature relationships among different earliness identified by gdd approach. instead, the daily thermal sum that determines the transition from one phenological phase to the next, changes. it is interesting to highlight that the shift between early-maturing and late-maturing varieties is just one week. this aspect may play a decisive role in assessing of the yield-temperature relationship and, hence, both on farmers decisions (e.g., choice of earliness) and policymakers to plan risk management policies. results and discussion results display a strong relationship between durum wheat yields and temperatures among different earliness, focusing on the each phenological phase (table 4, more details in the table 7, in the appendix). more specifically, minimum temperatures that occur in the starting phase negatively affect the yields in a nonlinear way, until 8-9 °c for all categories of earliness, while maximum temperatures seem to have a positive 2 generally, the sowing date of wheat is set on the middle of november in the mediterranean area (allen et al., 1998) effect, until 14-15 °c, above of which the yield decrease (table 4 and table 5; more details in the table 7, in the appendix). yield is negatively impacted by minimum temperatures linearly occurring in growing stage (table 4, more details in the table 7, in the appendix). according to the scientific literature, 85% of worldwide wheat cultivation is yearly affected by spring frost causing severe yield losses due to damage of microorganelles of the cells, excessive production of reactive oxygen species (ros) and lipid peroxidation (hassan et al., 2021). moreover, low temperatures in the fall season may cause yield losses until 9 percent (tack et al., 2015). makinen et al. (2018) found that damages due to frost negatively affect all phenological stages, even more the reproductive phase (i.e., flowering). however, focusing on the anthesis stage, our results showed contradictory evidence: minimum temperatures seem to positively affect the yields in a non-linear way, although turning points of temperatures showed that the positive relationship is true until 7-9 °c for all varieties, above of which yields decrease (table 4 and table 5; more details in the table 7, in the appendix). it is still interesting to highlight that the effect of minimum temperatures on yields is not affected by earliness. although the end stage lasts just a week, minimum temperatures may negatively affect the yields of earlymaturing (until 10 °c) and middle-maturing varieties. maximum temperatures occurring in starting stage positively affect the yields of all varieties in nonlinear way until 14-15 °c above of which decrease (table 5). at the same time, the adverse effects have been highlighted only in maturity for late-maturing varieties and end stages for early-maturing varieties until a certain threshold, i.e., 17 °c and 13 °c, respectively. we also estimated the impacts of statistically significant weather coefficients among earliness and phenological stages, hence, the confidence level of temperature distributions. results show a high confidence level, highlighting no differences among coefficients table 3. dates of occurrence and gdd values of durum wheat among phenological stages. starting growing anthesis maturity end start end start end start end start end start end early-maturing (gdd) nov, 15 (0) dec, 1 (168) dec, 2 (169) mar, 29 (806) mar, 30 (807) apr, 19 (1067) apr, 20 (1068) may, 16 (1433) may, 17 (1434) may, 22 (1538) middle-maturing (gdd) nov, 15 (0) dec, 5 (188) dec, 6 (189) apr, 1 (853) apr, 2 (854) apr, 22 (1120) apr, 23 (1121) may, 20 (1494) may, 21 (1495) may, 26 (1602) late-maturing (gdd) nov, 15 (0) dec, 8 (207) dec, 9 (208) apr, 5 (900) apr, 6 (901) apr, 25 (1173) apr, 26 (1174) may, 23 (1555) may, 24 (1556) may, 30 (1665) note: referred to the year 2020. 119earliness, phenological phases and yield-temperature relationships: evidence from durum wheat in italy bio-based and applied economics 12(2): 115-125, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13745 (table 6). therefore, the temperatures’ effects on yields do not vary between earliness within each phenological phase. it follows that early-maturing varieties are the most susceptible to changes in temperature, although the general relationship between yield and temperature is the same among earliness. damages due to low temperatures are more likely among earliness than losses due to high temperatures. sure enough, the vegetative stage lasts about four months, while maturity about a month and ends in just a week. therefore, it is difficult to escape from low temperatures during starting and growing stages. although wheat crop needs low temperature to complete vernalization processes, frost events occurring toward the end of the vegetative phase may cause severe damage such as the tiller, spike number, leaf area reduction and photosynthetic capacity, leading to a heavy yield losses (xiao et al., 2018). conclusions given the potential impact of climate change on yields, deepening the yield-weather relationships is helping farmers cope with the weather risks. therefore, we assess the effects of temperatures on durum wheat yields among early-maturing, middle-maturing, and late-maturing varieties. we distinguished the effects across five phenological stages (i.e., starting, growing, anthesis, maturity, and end) identified through the gdd approach, starting from the middle of november as sowing date. the levels and changes in temperatures affect durum wheat yields in several ways. more specifically, upward changes in the minimum temperatures are detrimental for to yields when they occur in the starting and growing phases, regardless of the earliness. increases in maximum temperatures are indeed positively correlated (until a threshold of 14-15 °c) with the yields if they occur in the starting stage, whereas a negative effect table 4. effect of temperatures on yields among phenological stages and earliness of durum wheat. starting growing anthesis maturity end em mm lm em mm lm em mm lm em mm lm em mm lm minimum temperature maximum temperature notes: em, mm, and lm indicate the early-, middle-, and late-maturing durum wheat earliness, respectively. red cells indicate a negative impact of temperatures on yields, blue cells a positive impact, white cells for the uncaptured relationships. table 5. turning points of temperatures among phenological stages and earliness (°c). starting anthesis maturity end em mm lm em mm lm em mm lm em mm lm minimum temperature -8+ -8+ -9+ +8+9+7ns ns ns -10+ ns ns maximum temperature +15+14+14ns ns ns ns ns -17+ -13+ ns ns notes: em, mm, and lm, indicate the early-, middle-, and late-maturing durum wheat earliness, respectively. the values show the threshold temperatures beyond which there is a change of sign in the regression estimates (table 7, in the appendix). ns: not significant. table 6. confidence levels of temperatures distribution. starting growing anthesis maturity end em ml em ml em ml em ml em ml minimum temperature -0.50580 -0.79056 -0.41006 0.16589 0.17650 -1.11070 -0.03887 maximum temperature 1.50379 0.83624 notes: em indicates the differences among coefficients of early-maturing and middle-maturing varieties divided by standard errors of baseline (i.e., middle-maturing variety); ml indicates the differences among coefficients of middle-maturing and late-maturing varieties divided by standard errors of baseline (i.e., middle-maturing variety). 120 bio-based and applied economics 12(2): 115-125, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13745 marco tappi et al. is found when the event occurs at the maturity for latematuring varieties or end stage for early-maturing varieties. generally, the impacts of chronic heat stress, i.e., high temperatures for a longer duration, are lower than the heat shocks, i.e., extreme high temperatures for a short duration (li et al., 2013). however, early-maturing varieties provides a better adaptation under warming conditions (mondal et at., 2013), also because they may escape from the damages due to high temperatures by anticipating the crop cycle. cold stress may cause morphological, physiological, biochemical, and molecular modifications in wheat. phenotypic screening of coldtolerant genes, pre-sowing seed treatments, and exogenous application of growth hormones may be a suitable solution tolerating severe low temperature extremes (hassan et al., 2021). in conclusion, a better knowledge of the yield-temperature relationships, along with a deeper comprehension of the informative content of the secondary data on weather dynamics, may help both the farmers for the application of agronomic strategies, 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(2020). a review of vegetation phenological metrics extraction using time-series, multispectral satellite data. remote sensing of environment, 237, 111511. appendix figure 1. main durum wheat-producing provinces in italy. note: the main durum wheat-producing italian provinces in decreasing order are: foggia (puglia region), campobasso (molise region), palermo (sicilia region), ancona (marche region), potenza (basilicata region), matera (basilicata region), enna (sicilia region), macerata (marche region), avellino (campania region), catania (sicilia region), ferrara (emilia-romagna region), caltanissetta (sicilia region), perugia (umbria region), bari (puglia region), viterbo (lazio region), bologna (emilia-romagna region), ravenna (emilia-romagna region), brindisi (puglia region), siena (toscana region), agrigento (siclia region), benevento (campania region), grosseto (toscana region), pisa (toscara region), chieti (abruzzo region), trapani (sicilia region), teramo (abruzza), roma (lazio), barletta-andria-trani (puglia region), rovigo (veneto), pesarourbino (marche region) (istat, 2020). 123towards a new generation of (agri-) food policies bio-based and applied economics 12(2): 115-125, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13745 table 2. descriptive statistics of daily temperatures, cumulative precipitation, and yearly yield variables for 30 main durum wheat producing provinces, 2020 year. province variable obs. mean median st. dev min max agrigento maximum temperature 198 17.27042 16.475 3.656134 10.52143 31.60714 minimum temperature 198 10.53427 9.921429 3.269273 4.15 21.2 yield 198 27 27 0 27 27 ancona maximum temperature 198 15.0523 14.80455 4.87341 5.245454 28.81818 minimum temperature 198 6.42034 5.990909 4.345694 -1.463636 16.89091 yield 198 45.3306 45.3306 0 45.3306 45.3306 avellino maximum temperature 198 15.16203 14.54545 4.195631 4.790909 28.70909 minimum temperature 198 8.191552 8.095455 3.676873 -.0090909 18.63636 yield 198 32.81769 35 3.921524 25.80645 35 barletta-andriatrani maximum temperature 198 16.11383 15.70625 4.502608 6.375 28.525 minimum temperature 198 7.62822 7.15625 3.886316 -1.1625 17.4875 yield 198 21.92088 22 .1421842 21.66667 22 bari maximum temperature 198 15.81414 15.45833 4.549949 6.333333 29.175 minimum temperature 198 7.309596 6.741667 3.7425 -1.9 15.99167 yield 198 20.24346 20 .4374887 20 21.02564 benevento maximum temperature 198 15.37965 14.735 4.230869 4.54 28.35 minimum temperature 198 8.102222 7.995 3.768435 -.1 18.14 yield 198 32.00147 31.97674 .0444387 31.97674 32.08092 brindisi maximum temperature 198 16.79045 16.59 4.067458 8.61 28.66 minimum temperature 198 8.679343 8.05 3.772157 -.2 18.74 yield 198 34.8064 34.52381 .5077994 34.52381 35.71429 bologna maximum temperature 198 14.48802 13.37143 5.873645 2.014286 28.45714 minimum temperature 198 5.333694 4.835714 4.361542 -2.635714 15.53571 yield 198 54.32178 55.5577 2.220902 50.35106 55.5577 caltanissetta maximum temperature 198 16.8204 15.89 4.028488 9.41 32.56 minimum temperature 198 9.514748 8.94 3.520087 2.61 22 yield 198 28 28 0 28 28 campobasso maximum temperature 198 15.20285 14.95455 4.480825 3.372727 26.70909 minimum temperature 198 8.140358 8.2 3.817845 -1.163636 17.9 yield 198 35.76263 36 .4265517 35 36 catania maximum temperature 198 17.3101 16.73889 4.048381 9.516666 31.79445 minimum temperature 198 8.244501 7.669444 3.742443 .3722222 19.37222 yield 198 28.57143 28.57143 0 28.57143 28.57143 chieti maximum temperature 198 15.16586 14.795 4.622042 3.25 26.89 minimum temperature 198 7.781061 7.65 3.902836 -1.2 18.08 yield 198 32.6417 32.84671 .3684098 31.98302 32.84671 enna maximum temperature 198 16.58646 15.75455 4.356249 8.218182 32.06364 minimum temperature 198 8.238797 7.754546 3.665236 .4818182 20.07273 yield 198 30 30 0 30 30 ferrara maximum temperature 198 15.09104 14 6.145688 1.991667 29.18333 minimum temperature 198 5.57319 5.341667 4.807333 -2.8 17.08333 yield 198 60.20202 64 6.824827 48 64 foggia maximum temperature 198 15.9456 15.52292 4.463743 5.341667 27.81667 minimum temperature 198 8.216098 7.877083 3.775962 -.8 18.29583 yield 198 31.25 31.25 0 31.25 31.25 grosseto maximum temperature 198 17.01996 16 4.174036 9.141176 27.51765 minimum temperature 198 7.026352 6.997059 4.34386 -1.876471 16.59412 yield 198 38.79645 38.84181 .0815015 38.65074 38.84181 124 bio-based and applied economics 12(2): 115-125, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13745 marco tappi et al. province variable obs. mean median st. dev min max macerata maximum temperature 198 14.77406 14.60909 4.793149 5.263637 28.03636 minimum temperature 198 6.397888 6.027273 4.163119 -.9909091 16.48182 yield 198 42.00229 42.00229 0 42.00229 42.00229 matera maximum temperature 198 16.14141 15.80333 4.373894 6.466667 30.13333 minimum temperature 198 7.865387 7.27 3.649867 .06 17.63333 yield 198 29.68525 29.68525 0 29.68525 29.68525 palermo maximum temperature 198 16.95558 16.17143 3.899543 9.852381 33.84762 minimum temperature 198 10.14218 9.728571 3.256468 3.638095 19.40952 yield 198 25.99503 25.99503 0 25.99503 25.99503 perugia maximum temperature 198 14.29614 13.5125 4.94103 4.515 26.96 minimum temperature 198 5.502298 5.37 4.366943 -2.92 15.535 yield 198 45.45914 44.86486 1.067896 44.86486 47.36842 pesaro-urbino maximum temperature 198 14.78035 14.34091 4.952188 4.390909 28.68182 minimum temperature 198 6.921442 6.786364 4.275691 -.9363636 17.32727 yield 198 38.00858 38.00858 0 38.00858 38.00858 pisa maximum temperature 198 16.72483 15.875 4.305614 7.983333 27 minimum temperature 198 7.081019 7.179167 4.555321 -2.35 16.78333 yield 198 37.21282 40.33502 5.610488 27.1819 40.33502 potenza maximum temperature 198 14.74603 14.36087 4.311705 4.573913 28.35217 minimum temperature 198 7.983707 7.556522 3.500149 -.1913043 18.28696 yield 198 27.29257 27.29257 0 27.29257 27.29257 ravenna maximum temperature 198 14.83678 14.03182 5.718577 2.609091 28.87273 minimum temperature 198 5.954132 5.440909 4.503898 -2.563636 16.74545 yield 198 66.57576 68 2.55931 62 68 roma maximum temperature 198 17.05811 16.0775 3.893504 9.67 27.955 minimum temperature 198 7.608207 7.3925 4.132565 -.35 19.59 yield 198 29.23737 29 .4265517 29 30 rovigo maximum temperature 198 14.97117 13.71818 5.981333 2.472727 28.05455 minimum temperature 198 5.668916 5.363636 4.904146 -2.936364 17.71818 yield 198 56.23271 59.41509 5.718627 46.00845 59.41509 siena maximum temperature 198 15.97519 14.77813 4.710545 6.7125 27.15 minimum temperature 198 5.882323 5.953125 4.678793 -3.45 16.81875 yield 198 37.53158 38 .8417409 36.02664 38 teramo maximum temperature 198 14.60795 14.2125 4.647457 3.6875 26.6625 minimum temperature 198 7.074495 6.925 3.945144 -1.1 17.55 yield 198 39.80582 39.80582 0 39.80582 39.80582 trapani maximum temperature 198 17.92341 17.12143 3.75761 10.22857 33.5 minimum temperature 198 10.757 10.63571 3.492239 2.428571 21.32857 yield 198 22.90524 23.80952 1.624959 20 23.80952 viterbo maximum temperature 198 16.60761 15.75 4.229603 8.413333 27.76667 minimum temperature 198 7.143199 6.993333 4.106516 -.5666667 17.82 yield 198 38.73369 38.02031 1.281932 38.02031 41.02564 125towards a new generation of (agri-) food policies bio-based and applied economics 12(2): 115-125, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13745 ta bl e 7. e ffe ct s o f e ar lin es s o n th e re la tio ns hi p be tw ee n du ru m w he at y ie ld a nd w ea th er c on di tio ns . st ar tin g gr ow in g an th es is m at ur ity en d em m m lm em m m lm em m m lm em m m lm em m m lm m in im um te m pe ra tu re -0 .2 15 74 ** *0. 18 91 5* ** -0 .1 47 59 ** *0. 06 01 5* ** -0 .0 52 24 ** *0. 05 54 4* ** 0. 15 03 8* ** 0. 13 94 9* * 0. 20 80 2* ** 0. 07 91 8 -0 .0 25 72 0. 00 23 3 -0 .3 42 43 * -0 .3 34 64 * 0. 04 42 9 (0 .0 58 02 ) (0 .0 52 57 ) (0 .0 47 52 ) (0 .0 19 44 ) (0 .0 19 29 ) (0 .0 19 27 ) (0 .0 58 08 ) (0 .0 61 70 ) (0 .0 66 69 ) (0 .0 74 92 ) (0 .0 79 99 ) (0 .0 82 07 ) (0 .1 96 84 ) (0 .2 00 40 ) (0 .2 08 92 ) m in im um te m pe ra tu re (s q) 0. 01 43 1* ** 0. 01 29 8* ** 0. 00 91 7* ** -0 .0 03 22 * -0 .0 04 13 ** -0 .0 02 98 * -0 .0 09 87 ** -0 .0 08 65 * -0 .0 16 92 ** * -0 .0 08 80 * -0 .0 01 62 -0 .0 01 58 0. 01 72 0* 0. 01 59 5 -0 .0 02 85 (0 .0 03 94 ) (0 .0 03 64 ) (0 .0 03 35 ) (0 .0 01 73 ) (0 .0 01 71 ) (0 .0 01 70 ) (0 .0 04 46 ) (0 .0 04 55 ) (0 .0 04 72 ) (0 .0 04 56 ) (0 .0 04 69 ) (0 .0 04 65 ) (0 .0 10 28 ) (0 .0 10 08 ) (0 .0 10 05 ) m ax im um te m pe ra tu re 0. 48 70 6* ** 0. 33 81 1* ** 0. 25 52 8* ** 0. 01 71 2 0. 00 38 7 0. 00 92 1 -0 .0 29 72 0. 01 81 6 0. 13 01 3 0. 00 07 2 -0 .1 54 47 -0 .2 68 02 ** * -0 .3 78 84 * -0 .2 15 56 -0 .0 00 99 (0 .1 14 19 ) (0 .0 99 05 ) (0 .0 87 80 ) (0 .0 35 72 ) (0 .0 34 72 ) (0 .0 33 81 ) (0 .0 78 84 ) (0 .0 83 82 ) (0 .0 89 23 ) (0 .0 93 70 ) (0 .0 97 37 ) (0 .0 99 74 ) (0 .2 28 96 ) (0 .2 17 50 ) (0 .2 27 02 ) m ax im um te m pe ra tu re (s q) -0 .0 17 16 ** *0. 01 23 7* ** -0 .0 09 38 ** * 0. 00 18 0 0. 00 26 9* 0. 00 22 2 0. 00 27 5 0. 00 02 8 -0 .0 03 33 -0 .0 00 35 0. 00 45 6* 0. 00 83 6* ** 0. 01 48 3* * 0. 00 98 3* 0. 00 42 3 (0 .0 04 22 ) (0 .0 03 75 ) (0 .0 03 40 ) (0 .0 01 58 ) (0 .0 01 50 ) (0 .0 01 43 ) (0 .0 02 65 ) (0 .0 02 74 ) (0 .0 02 85 ) (0 .0 02 71 ) (0 .0 02 74 ) (0 .0 02 73 ) (0 .0 05 92 ) (0 .0 05 49 ) (0 .0 05 55 ) pr ov f e ye s ye s ye s ye s ye s ye s ye s ye s ye s ye s ye s ye s ye s ye s ye s ti m e tr en d ye s ye s ye s ye s ye s ye s ye s ye s ye s ye s ye s ye s ye s ye s ye s o bs . 6, 49 6 7, 47 2 8, 44 7 34 ,1 05 35 ,2 15 36 ,2 17 10 ,6 67 10 ,5 23 10 ,4 01 12 ,2 35 12 ,0 73 11 ,9 53 3, 00 6 3, 01 6 2, 95 8 n o. o f p ro v 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 n ot es : e m , m m , a nd l m , i nd ic at e th e ea rly -, m id dl e, a nd la te -m at ur in g du ru m w he at e ar lin es s, re sp ec tiv el y. r es ul ts s ho w th e es tim at es o f t he r eg re ss io ns m od el ( 1) fo r ea ch y ea r. st an da rd e rr or s a re sh ow n in p ar en th es is. p he no lo gi ca l s ta ge s h av e be en id en tifi ed th ro ug h th e g d d a pp ro ac h, st ar tin g fr om n ov em be r 1 5 as so w in g da te . cap, farm to fork and green deal: policy coherence, governance, and future challenges annalisa zezza key policy objectives for european agricultural policies: some reflections on policy coherence and governance issues silvia coderoni towards a new generation of (agri-) food policies gianluca brunori earliness, phenological phases and yield-temperature relationships: evidence from durum wheat in italy marco tappi1,*, federica carucci2, anna gagliardi1, giuseppe gatta1, marcella michela giuliani1, fabio gaetano santeramo1 does the presence of inner areas matter for the registration of new geographical indications? evidence from italy francesco pagliacci, francesco fasano* learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy antonio r. hurtado1,2,*, julio berbel2 1 appendices appendix 1. company category main initiatives related to da bayer seeds & crop protection climate fieldviewtm is an all-in-one da platform that combines agronomic farm data, satellite imagery, and data analytics to support informed decisions. bayer has partnered with microsoft to develop a platform (called azure data manager for agriculture) that enables digital innovation through the value chain. corteva seeds & crop protection corteva offers granular insights, a farm management software to improve agronomic decision-making. the company also provides landvisortm, a software that combines satellite imagery with data analytics, oriented to land productivity. syngenta seeds & crop protection cropwise is an integrated digital farming platform that offers a set of digital solutions to optimize planting, nutrition, and monitoring tasks. basf seeds & crop protection xarvio® field manager is a digital farming tool that optimizes input use, enables digital mapping of weeds, provides variable-rate solutions, and offers monitoring services. deere & co. farm machinery the john deere precision ag technology integrates sensors, gps, and software to provide real-time analysis of machine and field data. the john deere operations centertm, offers a space to collect, analyze, and add value to the data collected by machinery. cnh industrial farm machinery partnership with syngenta for integrating its precision agriculture systems, including the company´s leading machinery brands (i.e., case ih and new holland), with the cropwise platform to connect data sources and enhance decision-making. agco farm machinery agco has developed fuse® smart farming, which offers a combination of precision agriculture and data analytics to all its brands. kubota farm machinery introduced kubota smart agri system (ksas), which combines artificial intelligence, iot, unmanned vehicles, and drone technologies. cargill agricultural traders cargill elevate+tm is a subscription that provides access to market insights, and bushel farm tm is a farm management software designed to support informed decision-making. 2 adm agricultural traders association with farmers business network to deploy a digital app called gradable, connecting farmers with buyers. louis dreyfus co. agricultural traders launched the app “myldc” that helps connect farmers with the company, enabling to manage and sign contracts and monitor shipments and payments. cofco international agricultural traders partnered in an initiative to digitize and standardize trade documentation across global commodity supply chains using blockchain and artificial intelligence. nutrien fertilizers the echelon pa precision agriculture app is the main driver of the digitally engaged acres system, using geospatial technologies to provide acre level data and improve precision nutrition solutions. yara fertilizers offers a set of solutions in the yara digital ecosystems, oriented to assist farmers in crop nutrition, water management, and farming advisory. includes both software and hardware solutions. the mosaic company fertilizers da initiatives are not clearly disclosed online. cf industries holdings fertilizers da initiatives are not clearly disclosed online. source: elaborated by authors based on companies’ websites. companies were selected according to the c4 criteria in each market. 3 appendix 2. classification of solutions provided by argentine agrifoodtech start-ups (according to mac clay et al., 2024). classification of solutions provided by argentine agrifoodtech start-ups category number of companies % of total precision agriculture, smart farming, and farm robotics 91 41% biological inputs and solutions 30 13% plant, animal and food biotechnology solutions 15 6% alternative ways of farming 17 7% lab-based proteins and food ingredients 6 3% e-commerce and delivery solutions (digital agribusiness marketplaces) 17 7% e-commerce and delivery solutions (other) 4 2% digital food service 2 1% functional and healthy foods 13 5% logistics, food safety, and traceability solutions 14 6% financial solutions for food and agriculture 15 6% waste reduction and cascading uses 9 4% total 239 100% 4 appendix 3. list of the 114 argentine digital agriculture start-ups under study. accessman harvest, acronex, agrired, agro24, agroapp, agrobrowser, agrocheck, agroconsultas, agrofy, agrohub, agrology agro, agropro, agrosense, agrosty, agrotoolbox, agrotrack, agrovants, aprilis, ar-puf, auravant, avansys, avismart, bastó, bipolos, bold, bright data analytics, briste, caburé, cacta, canopilogger, cattle trace (onsen ingeniería), cattler, clarion, climate sense, control campo, corvus (agdp), cowdoo (raíces), cropilot.tech, culti, dale vaquita, deepagro, digirodeo, dvl satelital, dymaxion labs, ecodrip, edra, eirú, eiwa, el ojo del amo, enbaca, fauno, fielddata, finca, flashagro, forrager, genganar, haciendago, hidromotic ingeniería, hi-terra, iagro, idanimal, indegap, innova space, integra labs, inteliagro, kilimo, kan territory & it, kuna, la rotonda, lievrex, magno, magoya, malevo, mercado agrario, minnow, mkl agro, modo agrario, muu mercado digital ganadero, nandi, novimetrics, nutrixya, ñandú, okaratech, omixon, pacta, pampe.ro, pastech, ponce, preseeds, puma, qira, rastro agropecuario, rastros, riante, rumia, ruuts, sailo, satellites on fire, satellogic, sendevo software, servidrone, sima, smalldata, smartium, terratio, uco drone, ucrop.it, uniagro soft, ursulagis, vertrev, vistaguay, wiagro, wymaq, yield data bio-based and applied economics 14(1): 3-17, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15786 © 2025 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae bio-based and applied economics bae citation: abitabile, c., arzeni, a., cisilino, f. & orlando, m. (2025). building sustainable futures: the bio-based fertilizer case-study. bio-based and applied economics 14(1): 3-17. doi: 10.36253/bae-15786 received: february 13, 2024 accepted: july 18, 2024 published: june 13, 2025 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. associated editor: silvia coderoni editor in chief: silvia coderoni orcid ca: 0000-0001-6878-8015 aa: 0000-0002-1937-4331 fc: 0000-0002-8597-1353 mo: 0000-0002-9291-4760 building sustainable futures: the bio-based fertilizer case-study carla abitabile, andrea arzeni*, federica cisilino, matteo orlando council for agricultural research and economics research centre for agricultural policies and bioeconomy, italy *corresponding author. e-mail: andrea.arzeni@crea.gov.it abstract. bio-based fertilizers (bbfs) can be a solution for converting agricultural waste into new products useful for increasing organic matter in the soil, thus reducing the consumption of mineral fertilizers. this can contribute to the ecological transition launched by the european commission for the coming decades. scenario analysis is an effective tool to assess the factors that can affect the development of the agri-food supply chain, evaluating the effects of their possible evolutions. the aim of this work is to draw plausible future scenarios for the bbf supply chain and to strengthen the consistency evaluation process of these scenarios. we built the scenarios considering both the literature and findings from stakeholder consultations. we then verified their consistency by adopting the cross-impact balances (cib) method, along with other techniques to better evaluate the consistency and plausibility of the narratives. the analysis provides stakeholders with information to evaluate possible future trends in the bbf supply chain. monitoring the evolution of the identified drivers and maintaining constant and periodic discussions among stakeholders constitute the prerequisites for supporting the desirable future development of bbfs. keywords: future scenarios, bio-based fertilizers, circular economy, sustainability, social-ecological transition. jel codes: d81, e37, o33, q16, q57. 1. introduction and backgrounds in an increasingly globalized and interconnected world, the development of socio-economic systems is influenced by a multitude of factors whose trends are difficult to predict, at least in the long term. as demonstrated by recent financial, pandemic, and climate crises, mathematical models are not always capable of producing reliable forecasts in a context where uncertainty plays a determinant role (puy et al., 2022). the most recent big-data analysis tools and the development of artificial intelligence will certainly enhance our ability to understand the world, but they will also generate a mass of results that are not always coherent, making it difficult to identify the most reliable ones (hariri et al., 2019). chaos theory has demonstrated the unpredictability of complex systems, where a small change in the state of one or more factors is sufficient to produce completely different effects (schueler, 1996). https://doi.org/10.36253/bae-15786 https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae https://doi.org/10.36253/bae-15786 https://orcid.org/0000-0001-6878-8015 https://orcid.org/0000-0002-1937-4331 https://orcid.org/0000-0002-8597-1353 https://orcid.org/0000-0002-9291-4760 mailto:andrea.arzeni@crea.gov.it 4 bio-based and applied economics 14(1): 3-17, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15786 carla abitabile et al. future scenario analysis does not aim to predict the future but evaluates what happens if one or more factors that influence the system (driving forces) evolve in certain directions. it is not a probabilistic model but a logical approach for identifying possible evolutionary trends based on an appropriate knowledge of the initial state of the determining factors, the cause-effect relationships between them, and their impacts on the system. there is no single definition of scenarios. in this work, scenarios are plausible narratives of how the future could develop, based on a coherent and consistent set of assumptions about the main driving forces and their relationships (hunt et al., 2012; boschetti et al., 2016; guivarch et al., 2017). the narratives or storylines focus on the drivers that have greater importance and uncertainty, highlighting the main scenario characteristics, the relationships between key driving forces, and the dynamics of their evolution (ipcc, 2014). they may include quantitative data from literature, specific surveys, or mathematical models (swart et al., 2004; reed et al., 2013; guivarch et al., 2017). the literature on future studies is extensive, with several attempts at classification tracing back to the triad of possible, probable, and preferable futures. börjeson et al. (2006), adapting previous classifications, distinguish three main categories of scenario studies based on the user’s perspective (questions): predictive scenarios (what will happen?); exploratory scenarios (what can happen?); and normative scenarios (how to reach a preferred future situation?), further articulated based on more specific questions. different techniques can be adopted to develop future scenarios. a widespread method generates four alternative (exploratory) scenarios related to the investigated topic using the 2×2 matrix technique (schoemaker, 1995; o’neill et al., 2014; rhydderch, 2017; fritsche et al., 2021). for this purpose, two factors of great importance and uncertainty that influence the future of the topic are identified, with two opposed outcomes imagined for each. placing the two factors on a cartesian plane, they intersect at the present time to form four quadrants, with the ends of the axes indicating the possible evolution of the two factors at the chosen future horizon. each quadrant produces a scenario whose narrative is determined by the outcomes of the factors on the axes and other relevant identified factors. another technique of interest is a normative scenario, participatory backcasting (quist and vergragt, 2006), which starts from sharing a desirable future among stakeholders and identifies possible actions (policies) that may lead to the fixed goal. explorative scenarios and backcasting can also be combined, as vervoort et al. (2014) experimented in the context of food security. numerous public and private institutions use scenario analysis for their strategic choices and policies. in some governments, it has become an institutionalized activity (as in singapore, the united kingdom, and finland) (störmer et al., 2020). the european commission (ec) has also been using this tool for a long time. burgelman et al. (2014) trace its history, noting that the motivation behind this choice was to improve the administration and governance of the ec through the broad involvement of stakeholders in the decision-making process. the use of foresight processes by the ec began in the late 1970s, but only in 2017 did the ec produce documents officially acknowledging the usefulness of foresight for better regulation (störmer et al., 2020). the ec documents cited recognize four functions or benefits of applying foresight to policymaking: informing policy, facilitating policy implementation, embedding participation in the policymaking process, and supporting policy definition. scenario analysis has constituted an important tool for the scientific community in defining possible future paths of socio-economic development, both globally and in specific sectors and territories. among the former, a series of future scenarios have been produced, starting from the conceptual work of o’neill et al. (2014) and later defined in the corresponding narrative contents (o’neill et al., 2017). these are known as shared socioeconomic pathways (ssps) and describe alternative future trajectories of several factors connected to the challenges that climate change poses to society concerning adaptation and mitigation. they represent plausible conditions that can be realized in the future (to 2050) in large regions of the world regarding human and demographic development, economy and lifestyle, policies and institutions, technology, environment, and natural resources. due to the general nature of the ssps, they can be used as references for other analyses of development paths, both on issues directly related to the climate and on more specific themes, at both global and sub-national scales (e.g., lassaletta et al., 2019; chen et al., 2020; mitter et al., 2020), thus distinguishing basic and extended ssps (o’neill et al., 2014; van ruijven et al., 2014). using ssp narratives, mitter et al. (2020) defined possible future scenarios for the european agri-food system, the so-called eur-agri-ssps, providing plausible references to derive storylines related to more specific contexts (sectors or areas). the eur-agri-ssps have recently been used as a reference for defining future scenarios for pesticides (nagesh et al., 2023). using the same context scenarios, in this work we define plausible future development pathways for the 5building sustainable futures: the bio-based fertilizer case-study bio-based and applied economics 14(1): 3-17, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15786 bio-based fertilizer value chain, identifying the main factors that can influence its future development. to date, there is no unique definition of bio-based fertilizers (bbfs), but work is underway at the european level towards a standard definition (espp, 2023). wester-larsen et al. (2022) define bbfs “as materials or products derived from biomaterials (plant, animal, or microbial origin, often wastes, residues or side-streams from agriculture, industry, or society) with a content of bioavailable plant nutrients suitable to serve as a fertilizer for crops” (wester-larsen et al., 2022, p.1). this is the meaning of bbfs used in our work, which is consistent with the elements of the ongoing debate at the european level and the recent literature on the subject (tur-cardona et al., 2018; chojnacka et al., 2020; puglia et al., 2021; egas et al., 2023; kurniawati et al., 2023). the cited literature reports how the production of bio-based fertilizers from residues and by-products of the agri-food system would contribute to solving the problems arising from the large quantities of organic waste produced and the use of mineral fertilizers, which depend on non-renewable resources. an increasing and widespread use of bbfs to replace mineral fertilizers would improve the health of natural resources by reducing the accumulation of nutrients in the soil and water. the recovery of useful materials from the waste of the agri-food system to produce fertilizers also responds to the need to make the entire system more sustainable. this need was expressed by the european commission in the circular economy action plan (european commission, 2015), most recently updated (european commission, 2020), and is reiterated by the 2019 eu fertilizer regulation (european commission, 2019), as well as the recent report from the european environment agency (2020). however, it should be considered that the use of these products is not free from problems in the current state of technology. it has been ascertained that contamination by heavy metals and pathogens represents the main problem for the use of bbfs, whose acceptability by consumers (farmers) would be hindered, among other things, by issues relating to costs (for transport and production) and the still unclear political framework (kurniawati et al., 2023). for the purposes of this work, the qualitative data for identifying the most important and uncertain driving forces relating to bbfs were provided by a multi-actor participatory technique. this approach was supported by data collection from official sources and literature. stakeholder engagement is quite common in futures studies. in the review by fauré et al. (2017), they highlight how this approach is particularly prevalent when dealing with issues related to sustainability. more generally, pernaa (2017) points out that anticipating the future requires more interdisciplinary and multi-perspective collaboration due to the growing complexity in our societies. the participatory approach strengthens scenarios and facilitates the activities of researchers, policy makers, and decision-makers (borch and merida, 2013; mitter et al., 2020). the participation of stakeholders also contributes to ensuring the internal consistency of the storylines (or grading them in terms of coherence) through the judgments expressed by experts on the relationships between the identified drivers. a tool to visualize these relationships is causal loop diagrams (cld), used, for example, by mathijs et al. (2017) and mitter et al. (2020). in this work, we adopt the cross-impact balance (cib) analysis (weimer-jehle, 2006), which identifies internally consistent scenarios through cross-impact matrices. more generally, the cib method is aimed at the “systematic construction of qualitative and semi-quantitative scenarios” (weimer-jehle, 2023), and has been applied in many contexts to analyse the relationships between the factors of scenarios using an algorithm. in the literature, cib has more frequently been used for the analysis of scenarios in the energy field, for climate change, and for sustainable development. there are few works about the agricultural and agri-food sector, with only one publication (kurniawan, 2020) that used cib together with the ssp method to evaluate the coherence of scenarios at different scales of detail. in our analysis, we adopted cib to evaluate the consistency of scenarios of the same scale, constructed through the ssp method (bbfs scenarios). in summary, the aim of the work is twofold. firstly, it is intended to draw plausible future scenarios for the bbf supply chain, and secondly to verify whether cib can be used to facilitate the consistency analysis of the scenarios, reducing the risk of outlining internally inconsistent situations. the originality of this work concerns both the study object of the scenario analysis (bbf supply chain) and the combined use of cib and euragri-ssp methodologies to strengthen the validation process of the scenarios. in the following paragraphs, the methodological path adopted to build plausible and consistent future scenarios for bbfs is described, followed by the achieved results. the discussion is focused on the combined use of different methods and tools. finally, the advantages and limitations of the methodological approach are outlined in the conclusion. 2. methodology the methodology used to build the bbf scenarios is based on two preliminary considerations. 6 bio-based and applied economics 14(1): 3-17, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15786 carla abitabile et al. first, the case study represents a segment of the agrifood chain, which is itself a component of the agri-food system. this concatenation of contexts, which can be further expanded to include higher levels, implies that the driving forces influencing the development of bbfs can be internal to the sector or derived from external contexts. for example, the production cost of bbfs or their chemical-physical characteristics are internal drivers, while the prices of mineral fertilizers or the environmental sensitivity of consumers are external factors. the ability of the ssps to nest scenarios allows for the linking of external factors to internal ones, thereby articulating higher-level narratives by incorporating specific insights and variations for the analysed sector. the second consideration concerns the role of the multi-actor approach. generally, building scenarios with the participation of stakeholders involves a lengthy process of exchanges with the actors, including a preparatory phase and multiple meetings in which the elements of the scenarios are progressively defined (e.g., bock et al., 2020; mitter et al., 2020). in our study, the approach was decidedly more concise, hampered by the restrictions linked to the covid-19 pandemic. due to these constraints, the participatory process was carried out through online workshops and surveys, an approach that limited the interaction between the subjects involved but sped up the collection of information. the analysis followed the steps shown in table 1. 2.1. identifying and analyzing the focal issue the case study focuses on the production and use of bbfs, considering the main aspects that can affect the organization and development of this supply chain. the goal was to outline some plausible and alternative scenarios for 2050, useful to support decision strategies for both those who want to invest in the sector and policymakers who intend to facilitate the development of bbfs. an analysis of the available documentation focused on the fertilizer sector (chojnacka et al., 2020; fertilizers europe asbl, 2021) and more generally on the development of the agri-food system (fao, 2022) has provided the first qualitative and quantitative information. we classified this information according to the steep categories (society, technology, economy, environment, politics). for each category, the phenomena that characterize the sector have been summarized, with statistical and forecast data, to evaluate the current and prospective situation. in this way, the main factors (driving forces table 1. synoptic diagram of the analysis path. phases methods and tools outputs 1. identifying and analyzing the focal issue (from jan 2021 to mar 2021) bibliographic review on biofertilizers and agrifood system global trends steep classification analysis of main factors affecting bbf supply chain 18 “trend cards” summarizing the current and forecast situation of each factor (analysis and statistics) 2. choosing the appropriate scenariobuilding method (from apr 2021 to jun 2021) bibliographic review on scenario methods and on european agri-food scenarios eur-agri-ssps scenarios as baseline method for bbf supply chain analysis selection of the method for scenario building identification of the global agri-food framework for bbfs development 3. identifying the drivers and organizing the information framework (from jul 2021 to oct 2021) participatory approach techniques involving 5 experts, 14 stakeholders, and 10 project partners 1 focus group, 5 online meetings, 3 online surveys validation and integration of relevant and uncertain drivers for bbf development (134 final factors) 4. building and analyzing scenarios (from nov 2021 to dec 2021) adaptation of eur-agri-ssps scenarios introducing and analyzing bbf main drivers in-depth narrative writing linked to global scenarios; synthetic narrative drafting diversified by the project pilot areas narrative revision by experts in european agri-food development four main scenarios: two extreme and opposite and two intermediate ones scenario variants for each project pilot area (4) 5. checking the consistency of bbf scenarios (from jan 2022 to feb 2022) cross-impact balances (cib) tool for analyzing the relationships and combinations between the states of the drivers comparison between ssp and cib results (future situations) 9 consistent scenarios from 2,187 variant combinations the 4 ssp scenarios are included in the 9 cib scenarios (positive consistency check) 7building sustainable futures: the bio-based fertilizer case-study bio-based and applied economics 14(1): 3-17, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15786 or drivers) to be considered for the development of bbfs were identified. 2.2. choosing the appropriate scenario building method the definition of plausible future scenarios for bbfs started with the identification of more general scenarios for the food system and the main factors that influence its evolution. to this end, academic and grey literature and research projects on the subject were examined via the web, and also retrieved from the websites of international organizations, government agencies, and private institutions. the h2020 sure-farm project was identified as consistent with our objectives. sure-farm defined the eur-agri-ssps scenarios (mathijs et al., 2017, also described in detail in mitter et al., 2020), which are derived from the global shared socioeconomic pathways (ssp) scenarios (o’neill et al., 2017). mitter et al. (2020) start from the ssps to narrate the future conditions of the farming system in europe and use a multi-actor approach for their definition. they extend the analysis to food consumer issues and use other scenario studies to enrich the narratives. based on the uncertainty of the main socio-economic, environmental, and technological factors, they define five alternative scenarios, the eur-agri-ssps, describing plausible future conditions (up to 2050) for the european agricultural and food systems in relation to climatic challenges. the eur-agri-ssps are taken as context scenarios for the bbfs case study. each of them defines differentiated conditions of the macro-environment (population, geopolitics, economic development, markets, technology, etc.) which in turn influence the conditions of the specific factors identified for the development of the bbf supply chain. in this study, only four of the five eur-agrissps have been considered, excluding the eur-agrissps no. 2 because it has intermediate characteristics compared to the other scenarios. 2.3. identifying the drivers and organizing the information framework the set of indicators that measure the possible trends of the drivers in the reference period of the scenarios has been defined. this information, organized by steep categories, formed the basis of the bbfs scenarios, built on differentiated trends and therefore outlining evolutionary trajectories that lead to alternative or opposite future situations. in this way, it was possible to evaluate which factors determine the preferable evolution of bbfs. the set of drivers selected for the bbfs scenarios derives from the bibliographic survey (phase 1) and the participatory process, and partially from those already identified by mitter et al. (2020) for the eur-agri ssp scenarios. the main driving forces that can favour or hinder the development of fertilizers of biological origin in the european agri-food system were identified and discussed in several meetings coordinated by the research group, in which sector stakeholders participated. for this purpose, in each of the european areas considered for the development of the case study (almeria (es), flanders (b), friuli-venezia giulia (it), and pays de la loire (fr)), 10-15 stakeholders from the fertilizer sector were selected, including researchers, operators, associations, and policymakers, based on the following criteria: interest, availability, relevance, appropriateness, representativeness, broad vision (zawalińska et al., 2022). the four regions were selected by the rustica project partners because the agricultural sector significantly contributes to the deterioration of natural resources, although to different extents. the intensity of agricultural production causes widespread contamination by fertilizers (and pesticides), and considerable quantities of low-quality waste pose problems for their proper utilization/disposal, risking worsening the environmental impact. despite local policies promoting the development of the circular economy, the use of food sector waste in the form of bio-based fertilizers is still rather limited in all regions, as results from the direct survey carried out during the eu rustica project. the diversity of the socio-economic contexts of the regions, through the multi-actor approach, provided elements to enrich and strengthen the prospective framework defined hereafter in the bbfs future scenarios. having been informed in advance about the objectives and contents of the study, the stakeholders in each area were then invited to participate in a workshop during which they were interviewed based on a work outline common to all areas. overall, around 50 stakeholders were involved to identify and classify the most relevant and uncertain drivers of the bbf supply chain. relevance was assessed by the power to influence the evolution of the phenomenon of interest (bbfs development), while uncertainty concerned the predictability of the trend in the period considered. at the end of the stakeholder consultation, 134 of the most relevant and uncertain factors were considered for the scenario analysis (table a in the appendix). these factors were further analysed to classify them according to their common characteristics in terms of context and/or purpose. this slightly more detailed reclassification of the steep categories was helpful in identifying these seven main driv8 bio-based and applied economics 14(1): 3-17, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15786 carla abitabile et al. ing forces: sustainability awareness, political framework, fertilizers market, technological solutions, innovation uptake process, agri-environmental system, and bioeconomy patterns. these were considered the main determinant factors for the evolution of bbfs. 2.4. building and analyzing scenarios the bbfs scenarios were developed by associating the drivers identified for the bbfs with the eur-agrissp context scenarios. the process of adapting and deepening context scenarios into bbfs ones was long and articulated. in continuity with the context scenarios, the drafts of the bbfs narratives were elaborated, assuming four distinct future situations: two extreme and opposite (favourable/unfavourable for the bbfs development) and two with a mix of positive and negative elements (phase 4). a fifth eur-agri-ssp scenario was not considered as it is intermediate between the others. in the first two scenarios, the direction of the drivers is opposite, all aimed at facilitating or hindering the occurrence of a positive context for bbfs, while the other two are characterized by diversified situations with some dominant evolutionary elements. each scenario is characterized by a different evolution of the drivers. for example, in the first scenario, the sustainability of agriculture is favoured by a growth in social environmental awareness, which implies a propensity to reuse agricultural waste and to eat healthier food. the drafting of a scenario narrative is rigidly codified in the literature, and although there are margins for subjective interpretations, these must be based on objective elements such as the possible evolutions of coherent and specific drivers. the subjectivity of the interpretation can only make the narrative more interesting by avoiding a slavish commentary on the situations that outline the scenario. nevertheless, the guidelines of the methodology adopted, the feedback from the experts, and the robustness check of scenarios limit the personal influences of researchers, experts, and stakeholders. 2.5. checking the consistency of bbf scenarios with the drafting of the final narratives, the analysis of the scenarios was not concluded, as it was necessary to verify that the construction and revision process had not led to inconsistent situations within each scenario and between them. for example, if a scenario considers a sharp increase in energy prices and at the same time a reduction in the prices of mineral fertilizers, a contradictory or at least unrealistic situation has occurred. we then proceeded with a consistency check of the bbfs scenarios by analysing the relationships and combinations between the states of the drivers. while mitter’s methodology used causal loop diagrams (cld) to analytically describe the interdependencies between factors, another analysis tool called cross-impact balances (cib) (weimer-jehle, 2006) was chosen for bbfs. both methods analyse the relationships of influence between drivers and are used when it is not possible to adopt a mathematical model to measure these interdependencies. the cib method analyses the relationships between the factors through a quantitative assessment (scores), while the cld uses a graphic language (flow charts). the cld method should be applied to each hypothesized scenario, while the cib method considers all possible scenarios generated by the drivers’ combinations. for this reason, cib was chosen to assess all scenarios, including those unrelated to mitter’s results. the cib method is based on the construction of a symmetrical matrix where the different future situations are placed by row and by column. these situations are identified by a title (descriptor) and articulated into a few possible evolutionary paths (variants). the descriptors summarize the previously identified drivers, which act in the same context, labelling the group with a title evocative of the dominant theme, while the variants are derived from the different evolutions of the drivers between the scenarios. 3. results and discussion 3.1. the future scenarios for the bio-based fertilizers following the methodology described above, the first draft of the bbfs narratives was prepared by the research group and submitted for review to a panel of experts. twelve experts from different institutions and professional backgrounds participated in the panel. the online focus group was held in july 2021. the participants were selected based on their roles and expertise in the field: research/academics, stakeholders, and policymakers. two experts were from the university of bologna and crea agriculture and environmental research centre, with technical backgrounds in fertilizers and organic farming. a representative from enea had expertise in biomass for energy use, and a fruit supply chain expert was from crpv. the six stakeholders involved included representatives from the italian biomass association itabia, the president of the associazione chimica verde bionet-biomass and green chemistry, a representative from esco lazio biogas and digestate, a bio9building sustainable futures: the bio-based fertilizer case-study bio-based and applied economics 14(1): 3-17, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15786 logical expert in biofertilization from bio/intesa, the head of communication for terre d’etruria cooperative, and a representative from enomondo, which focuses on the recovery of agri-food waste for bioenergy and compost. additionally, two experts from the italian ministry of agriculture and three agri-environmental technicians from three italian regions were involved. they were asked to evaluate the narratives’ plausibility, consistency, richness, creativity, and salience, as indicated by the reference methodology (mitter et al., 2020). these criteria aim to consolidate the texts by eliminating any inconsistencies and evaluating their degree of realism while maintaining elements of creative originality, considering unexpected and improbable situations. this is exemplified by the war in ukraine, an extreme event not directly considered in the hypothesized scenarios, which were developed before the conflict, although one scenario describes a situation of strong territorial inequalities and social conflicts. the experts’ suggestions were useful in refining the narratives and arriving at their final version, the summary of which is reported below, while their full version is available online as supplementary material. each of the following narratives is composed of the main elements of the context scenario (from mitter et al., 2020, in a box in italics) and the extended bbfs narrative (in regular font). first scenario: bbfs on valorization path main elements of context scenario: agriculture on sustainable paths a strong network of small and medium-sized towns and large cities. diversity in agricultural supply chains supported by globally connected markets with internalized costs of trade. multi-level cooperation, policy integration, and societal participation. pronounced technological development directed towards environmentally friendly processes and cooperation between farmers and consumers. increasing environmental awareness, resource use efficiency, and environmental health. bbfs narrative sustainability awareness is growing in agriculture, leading to the adoption of circular business models, often through vertical integration in supply chains. growing urbanization facilitates the recovery and enhancement of biomass, thanks to infrastructure and the concentration of actors and knowledge in cities. digital technologies ensure the dissemination of knowledge to the most remote rural areas, where technological solutions are also widespread. there is a growing demand for safe and sustainable (organic) products, especially local products. society’s interest in food production methods directs agriculture towards more sustainable techniques and, due to strict environmental legislation, towards greater use of biobased products from agricultural waste, such as fertilizers. this leads to competition between the possible destinations of raw materials, resulting in wide price volatility for bio-based products. with the increase in demand for bio-based products, the supply is organized and structured into small or mediumsized networks with consortium-type biomass transformation plants spread throughout the territory, depending on the availability of local feedstock. sustainable logistics allow for efficient biomass collection and delivery services. within the local networks, integrated products and services adapted to the needs of farms are provided. policy encourages and supports the adoption of circular business models by stimulating the integration of actors. on the demand side, policy pays great attention to communication and fosters relationships based on trust. the integration between economic subjects facilitates the adoption of technological innovations that improve the quality of the bbfs (stability of characteristics, ease of use, effectiveness). artificial intelligence (ai) caters to fertilization needs by powering decision support systems (dss) based on the automatic exchange of data between devices and bbfs suppliers. the greater use of bbfs derived from the recycling of fruit and vegetable waste reduces the utilization of mineral fertilizers, avoiding the exploitation of non-renewable resources for their production. second scenario: bbfs on differentiation path main elements of context scenario: agriculture on separated paths decelerated urbanization. national agricultural supply chains benefit from protectionism. national agricultural policies aim for national food and energy security. slow agricultural technology development and uptake due to reduced investments and scepticism. high pressure on natural resources due to high national demand for agricultural commodities and limited coordination and technological progress. bbfs narrative a general climate of mistrust, slow generational turnover, and the degradation of infrastructure hinder the integration of economic actors and the adoption of innovative and eco-sustainable solutions. society also lacks a culture of waste recycling. low environmental sensitivity creates an unfavourable climate for the spread of sustainable (organic) agriculture and the adoption of circular production processes, which are also hampered by the reduction of public support. at the farm level, the valorisation of waste is limited and often faced with ineffective techniques. this hinders the spread of bio-based productions, to which inefficient logistics contribute. even if the price of biomass is low, the final bio-based product does not have a good quality-price ratio. 10 bio-based and applied economics 14(1): 3-17, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15786 carla abitabile et al. a few large producers of fertilizers (mostly mineral fertilizers) dominate the market, while the growing isolation of countries makes access to raw materials (such as phosphorus) more difficult and contributes to their price increase. the large companies cater to their country’s fertilizer needs, increasing their use efficiency through customized solutions and new technologies for mineral extraction. environmental policy is inconsistent and unresponsive, and the bioeconomy and circular economy languish due to the closure of national economies and the lack of environmental objectives. traditional agricultural lobbies, dominated by a few major players, increase their influence on political decision-makers, while the integration of local actors to organize integrated supply chains is not supported by adequate regional policies. the scarcity of investments in r&d limits the development of technologies with low environmental impact. there is also a lack of technological solutions for the adequate reuse of agricultural waste. this leads to poor quality finished products, which therefore cannot compete with mineral fertilizers. small-scale plants are present only in some areas with strong production specialization, but their diffusion is hindered by general mistrust and difficulty in establishing relationships. on the environmental front, the inappropriate treatment of agricultural residues contributes to the pollution of natural resources. third scenario: bbfs on polarization path main elements of context scenario: agriculture on unequal paths territorial fragmentation. a business-oriented elite dominates agricultural supply chains. a business-oriented elite dominates european institutions and sets the policy agenda. rapid technology development focuses on production and energy efficiency. environmental awareness is limited to the neighbourhoods of the wealthy upper class. bbfs narrative the tendency towards individualism hinders the organization of supply chains, while the lack of environmental sensitivity means that sustainable techniques remain confined to some rural areas and communities. in these areas, the lack of infrastructure produces serious logistical problems for the transport and storage of agricultural products, as well as for the distribution of inputs and the collection of production waste. only in peri-urban areas do a few fertilizer producers invest in bio-based products to differentiate their supply and respond to an elite demand willing to pay high prices. as a result, bbfs are more expensive than mineral fertilizers due to the absence of a market or well-structured supply chain. furthermore, economic conditions do not support farmers adopting high-cost inputs due to low food prices. the demand for bio-based fertilizer products is weak as environmental standards are not restrictive. this situation is also exacerbated by the lack of specific regulations on the use of organic waste. existing regulations, which are neither coherent nor incisive, favour aspects of technological development over those of environmental sustainability. the main rules that define certifications and labels are managed and guaranteed by private bodies, leading to differences between territorial productive systems. subsidies for innovative technologies in agriculture favour investments only in the most developed regions/countries, where effective bbf technologies are adopted. elsewhere, the complexity and cost of technology limit its local accessibility. technological platforms interconnect economic actors mainly to manage trade flows while maintaining the managerial autonomy of companies. in general, agriculture contributes to the degradation of natural resources, as the use of mineral fertilizers and chemical pesticides is intensive. sustainable agriculture methods and circular approaches are widespread only in natural areas. here, the use of bio-based fertilizers is mandatory as agricultural products are certified and subject to strict quality controls. fourth scenario: bbfs on technocratic path main elements of context scenario: agriculture on hightech paths metropolization. high-tech, large companies dominate globalized agricultural supply chains. european institutions foster international trade but delay environmental action. there is a high affinity for output-oriented technology. a lack of global environmental awareness. bbfs narrative the environmental awareness of the population and young farmers is limited, partly because the high and generalized orientation towards using technology for all aspects of life has solved many problems related to the scarcity of non-renewable resources. however, in rural areas excluded from technological development, traditional agricultural practices remain inefficient and sometimes negatively impact natural resources. food waste is concentrated in cities due to increasing urbanization. bio-based fertilizers are processed in agroindustrial districts where the plants operate on an industrial scale and are part of multinational networks. waste from agricultural production in less urbanized areas is recycled by large high-tech farms through their own small and medium-scale plants. the mineral fertilizer industry dominates the market thanks to the development of more efficient technologies and highly effective formulations. green chemistry is developing rapidly, but the technologies are protected by patents and are therefore not very accessible due to the competitive market environment. public support for the circular approach is almost absent, with most investments being private. in agri-food supply chains, processing waste is usually recycled to improve effi11building sustainable futures: the bio-based fertilizer case-study bio-based and applied economics 14(1): 3-17, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15786 ciency. policies supporting bio-based production are oriented towards their technological uses (e.g., bioplastics for food packaging). specific legislation for bio-based fertilizers is lacking, but some available measures concerning labels facilitate communication to consumers (health footprints). internet of things (iot) and blockchain technologies allow the automated management of a well-integrated and traceable agri-food chain, where biomass for recycling is also managed. in this way, bio-based waste and fertilizers are valorised. however, mineral fertilizers dominate the market due to the presence of large companies and pressure from lobbies. the lack of environmental awareness leads to negligent management of natural resources in agriculture. in periurban areas, the intensive use of pesticides and inorganic fertilizers creates problems with dangerous residues from chemical inputs. the production and use of bio-based fertilizers are quite widespread but limited due to competition with fossil-based inorganic fertilizers. 3.2. the consistency of the scenarios the next step was the validation of the robustness of the scenarios through the cib analysis. the cib matrix (table 2) developed for the bbfs is made up of seven descriptors and three variants each. the resulting matrix is sized at 21 rows by 21 columns, filled with scores assigned by the research group, evaluating the direction of each interdependence between the variants. the cib method can use different scoring (e.g., ±3) to measure the strength of relationships. usually, the score is an integer value between -1 and 1, which indicates whether the situation indicated in the row favours (1) or hinders (-1) the one indicated in the column. the zero value indicates substantial neutrality, while the null value indicates the absence of interdependence between the two situations. the cib algorithm computes the algebraic sum of the scores of all the matrix combinations and considers more coherent scenarios when positive values prevail over negative ones. these are the scenarios that do not present contradictions between the different hypothesized situations. the number of consistent scenarios varies according to the scores assigned and can be very high if the interdependence relationships generate many possible combinations or even null if they outline alternative and non-overlapping situations. this methodology was used to evaluate whether the four hypothesized table 2. cib matrix for the bbfs scenarios. descriptors and variants a b c d e f g 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 a. innovation uptake process 1. linear transfer                         -1 1 0             2. cooperative participation                   1 0 -1         3. selfish approach                         -1 0 1             b. sustainability awareness 1. societal rooted             1 1 -1 1 0 -1 1 0 -1 1 1 -1 1 1 -1 2. consumers driven           0 1 0 1 1 0 1 1 -1 0 1 0 0 1 0 3. elite fashion             -1 1 0 0 1 1 0 1 0 -1 0 -1 0 0 1 c. bioeconomy development 1. circular based       1 0 -1       1 1 -1 1 0 0       1 0 -1 2. transition in progress     0 1 0       0 1 0 0 1 0     0 1 1 3. business as usual     -1 0 1       -1 0 1 -1 0 1     -1 1 0 d. fertilizers market 1. bio-based competitiveness             1 -1 0             1 0 -1 1 1 -1 2. niche productions         0 1 0           0 1 0 0 0 1 3. inorganic power             -1 0 1             -1 0 1 -1 0 1 e. agri-enviromental system 1. agroecological approach       1 0 0 1 1 -1             1 0 -1 1 0 -1 2. low impact standards     0 1 0 0 1 0           0 1 0 0 1 1 3. sustainable oasis       -1 0 1 0 0 1             -1 0 1 0 0 1 f. political framework 1. systemic regulations 0 1 0 1 0 -1       1 0 -1 1 0 -1       1 1 -1 2. environmental compliance 1 0 1 0 1 0     0 1 0 0 1 1       0 1 0 3. chemical lobbies 0 -1 1 -1 0 1       -1 0 1 -1 0 0       -1 0 1 g. technological solutions 1. accessible and effective 0 1 0 1 0 -1 1 0 -1 1 0 -1 1 0 1 1 1 -1       2. effective but complex 1 0 -1 0 1 0 0 1 0 1 1 0 0 1 -1 0 1 0       3. efficient but ineffective 0 0 1 -1 0 1 -1 0 1 -1 0 1 -1 1 0 -1 0 1       source: own elaboration. 12 bio-based and applied economics 14(1): 3-17, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15786 carla abitabile et al. bbfs scenarios fall within the set of possible coherent scenarios. the software application of the cib algorithm extracted nine consistent scenarios with positive scores from 2,187 variants combinations. these scenarios were compared with the bbfs ones to assess correspondences and differences. table 3 indicates the variants that characterize the scenarios identified by the cib. those inside the green columns coincide with the situations described in the bbfs narratives. in summary, the cib analysis confirmed that the bbfs scenarios are consistent as no contradictions emerge in the relationships between the drivers considered. the cib analysis also identified five more scenarios in addition to those derived from the ssp methodology. these are situations that differ in a few elements from bbfs narratives but are equally plausible. from the synoptic table 2 of the cib scenarios, it also emerges that some situations (variants) that are not particularly favourable to the development of bbf are more frequent. in the other scenarios identified by the cib, the influence of chemical lobbies (f3) and the persistence in the market of mineral fertilizers (d3) are recurring variants, probably due to the setting of low environmental standards (e2). technological processing is not efficient and is not equally capable of creating effective and valid bbfs (g3). the ecological transition process is unfinished (c2), and the production of bbf is still marginal and valued only within some social contexts (b3); the development of innovations is weak and individualistic (a3). the cib tool also provides a graphical representation of the influence force of descriptors. in the following graph (figure 1), the descriptors in the upper right quadrant are the most influential, meaning they determine the status of the other factors the most. technological solutions are the most influential factor (high active score sum), while the innovation process is the least influential. this result is probably affected by the presence during participation processes of several people with technical skills who therefore emphasized the relevance of the technological drivers for the development of bbfs. social awareness and the political context are very influential too, while economic megatrends are weaker as they depend more on other factors. table 3. cib consistent scenarios with ssp overlapping results (■) and scenarios (grey columns). descriptors and variants scenarios 1 2 3 4 5 6 7 8 9 a. innovation adoption process 1 linear transfer ■ 2 cooperative participation ■ □ 3 selfish approach □ □ ■ □ □ ■ b. sustainability awareness 1 societal rooted ■ □ 2 consumers driven □ ■ ■ 3 elite fashion □ □ □ ■ c. bioeconomy patterns 1 circular based ■ □ 2 transition in progress □ ■ ■ □ 3 business as usual □ □ ■ d. fertiliser’s market 1 bio-based competitiveness ■ □ □ 2 niche productions ■ 3 inorganic power □ ■ □ □ ■ e. agri-environmental system 1 agroecological approach ■ □ 2 low impact standards ■ □ ■ □ □ 3 sustainable oasis □ ■ f. political framework 1 systemic regulations ■ □ 2 environmental compliance □ ■ □ 3 chemical lobbies ■ □ □ ■ g. technological solutions 1 accessible and effective ■ □ 2 effective but complex □ ■ 3 efficient but ineffective □ ■ □ □ ■ source: own elaboration. figure 1. influence profile of drivers. source: own elaboration. 13building sustainable futures: the bio-based fertilizer case-study bio-based and applied economics 14(1): 3-17, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15786 4. conclusions the exploratory scenarios describe future, plausible, and alternative situations, highlighting the technical and socio-economic conditions that could determine them. in this article, we have built four scenarios for bbfs to 2050, using context scenarios for the agri-food system identified in the literature and specific drivers for bbfs identified thanks to the active contribution of stakeholders. to validate the results, we first consulted external experts to verify the consistency of the scenarios. subsequently, we used the cib method in an original way to improve the robustness of the verification process. the defined scenarios include a very advantageous situation for bbfs (bbfs on valorization path), where the technological and socio-economic conditions are favorable to the development of an efficient, well-organized, and politically supported supply chain. in a context of this type, where circularity permeates the economic system and represents a value for all citizens, a potential threat for bbfs lies in the competition in the use of the raw material, the residual biomass of the agri-food system. conversely, in the less favorable scenario (bbfs on technocratic path), mineral fertilizers continue to dominate the market, supported by technology and public support, while a marginal bbf supply chain finds limited space in politics, hindered by powerful chemical lobbies. in the other two scenarios (bbfs on differentiation path and bbfs on polarization path), which are intermediate compared to the previous ones, the production and use of bbfs are reduced in both cases, but this situation is determined by different evolution of the drivers. in the first case, the difficulty of integrating companies and the lack of a widespread knowledge and innovation system contribute to the fragmentation of the production fabric and limit the diffusion of efficient technologies for bbfs. their use is therefore uneven across the territory and between types of agricultural holdings. finally, in the ‘bbfs on polarization path’ scenario, the production of bbfs is strongly localized in some areas where favorable conditions exist (for example, for the availability of biomass), while more generally it is hindered by various factors, such as limited environmental sensitivity and the lack of adequate technology and logistics. the scenario analysis highlighted the particular importance of some drivers for the future development of bbfs, such as product quality, farmers’ knowledge, adequate technology and logistics, and public intervention aimed not only at the regulation of the sector but also at the promotion of knowledge and use of bbfs, confirming what has also been found by others (kurnawiati et al., 2023). the consultation of external experts has contributed to and strengthened the coherence of the defined scenarios. however, different driver evolutions may lead to the definition of other plausible scenarios. we therefore checked whether, among all the possible scenarios generated by the bbf drivers, the scenarios presented above were also included and what the relative degree of coherence was. for this purpose, we used the cib algorithm. this approach aims to strengthen the verification of the results of the scenario analysis as the two paths are independent and start from different assumptions. the process of building storylines ensures that the factors considered are consistent with the object of the study (in our case the bbf supply chain), since they are based on specific information combined with expert assessments and stakeholder experiences. the cib, meanwhile, focuses on interdependent relationships between the drivers that allow any inconsistencies to be highlighted. if the results of the two techniques overlap, the risk of producing inconsistent scenarios is lower. in our case, the results of the comparison demonstrate that the scenarios built for bbfs using the eur-agri-ssp and the stakeholder support are included among those indicated by the cib as consistent but also indicate how other equally plausible narratives can be generated. this outcome is not surprising when we consider that one of the characteristics that guides the choice of drivers is uncertainty (in addition to relevance) and that, the greater the uncertainty, the more numerous the possible future realizations of the driver considered will be. in addition to the methodological aspect, the analysis carried out on the bbfs case study produced a further result which contributes to confirming what has already been argued (pernaa, 2017) regarding the ability of scenario analysis to create/increase knowledge through comparison between actors of different origins and experiences and the debate generated during the participatory process. this knowledge goes beyond the specific object of the investigation to include the ability to project oneself into the future, an ability which, however, only a structured and continuous path can ensure. the choice of stakeholders has an impact on the entire process of construction and evaluation of the scenarios and can represent a limit, where this choice is somehow lacking not only in terms of the breadth of knowledge on the object of the research but also by the lack of forecasting skills. on the other hand, the latter can be acquired along an interactive learning path between the research group and the actors involved in the scenario analysis. ultimately, the analysis provides stakeholders (researchers, policymakers, supply chain operators) with information to evaluate possible future trends in the bbf 14 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(2022), advancing the contributions of european stakeholders in farming systems to transitions to agroecology. eurochoices 21: 50-63. https://doi. org/10.1111/1746-692x.12378. appendix a. list of drivers proposed by stakeholders grouped by categories. sustainability awareness 1. acceptance of bbf 2. awareness of producers (farmers) 3. awareness of wastes as a resource 4. consolidation of traditional fertilisers 5. demand of healthy products 6. development of sustainable farming method 7. healthy dietary regimes 8. higher sustainability awareness thus greater demand 9. improvement of the landscape and of the image of our agricultural sector 10. increase of environmental sensibility 11. increased awareness and trust of farmers 12. increased organic production (consumer export) 13. increased worldwide demand for organic products 14. increasing awareness and interest in organic production 15. independence for fertiliser 16. public awareness of sustainability 17. qualified employment is needed to maintain a sustainable conscience 18. raising awareness 19. sensibilization/education/promotion to work with alive soils 20. social conscience about use of renewable resources 21. society education about environmental problems related to agricultural activity (rc) political framework 1. administration for farmer 2. ban on synthetic fertilisers 3. certain and enhanced regulation of biomass / bbf 4. certificates and labels 5. common agricultural policy/ rural development programs 6. compensation measure for soil c sequestration 7. design of a common regulation in europe 8. development of regulation to promote the use of bbf 9. economic help to develop bbf is needed 10. economic sustainability guaranteed 11. environmental responsive and consistent policies 12. facilitation of environmental objectives required by legislation 13. future demands imposed by regulation (water and carbon footprint certifications) (fa) 14. influence of lobby groups 15. intellectual property rights 16. lack of local regulation 17. lack of political will and regulations to support these processes (production, distribution, commercialization) 18. legal framework to be develop 19. legislation to boast the use of bbf if https://doi.org/10.1016/j.gloenvcha.2003.10.002 https://doi.org/10.1016/j.jclepro.2018.06.172 https://doi.org/10.1016/j.jclepro.2018.06.172 https://doi.org/10.1007/s10584-013-0931-0 https://doi.org/10.1007/s10584-013-0931-0 https://doi.org/10.1016/j.gloenvcha.2014.03.001 https://doi.org/10.1016/j.gloenvcha.2014.03.001 https://doi.org/ https://www.cross-impact.org/english/cib_e.htm https://www.cross-impact.org/english/cib_e.htm https://doi.org/10.1016/j.jenvman.2022.116249 https://doi.org/10.1111/1746-692x.12378 https://doi.org/10.1111/1746-692x.12378 17building sustainable futures: the bio-based fertilizer case-study bio-based and applied economics 14(1): 3-17, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15786 20. pressure on transparency in the chain 21. protection of european farmers vs non-european farmers 22. raw material regulation 23. recognition (fps public health) 24. regulation to facilitate, promote and prioritize the use of organic wastes to produce bbf (wp) 25. the primary sector is not going to lose competitiveness 26. variation in specific legislation for bbf fertilizers market 1. affordability of rbff production process 2. assessed costs/benefits of bbf 3. competition with other fertilisers 4. competitive market prices of bbf 5. competitiveness of the production chain 6. cost of mineral fertilizers 7. cost of the product (including full production) 8. costs of production will determine final price of bbf 9. decrease of the biowaste treatment costs 10. economic imbalance of costs of wastes management 11. economic studies are needed to demonstrate economic profitability growing with bbf and alive soils (fa) 12. economical valorisation of the food final products 13. evolution of the prices of agricultural products 14. high prices of chemical fertilizers 15. higher prices of bbf in comparison with inorganic fertilizers 16. increase of prices of inorganic fertilizers 17. increase of the price of mineral fertiliser 18. new bio-based fertilizers economically viable are needed 19. price of bbf: competitive? 20. production costs (competitors) 21. qualitative competitiveness 22. reduction in cost price of bbf by reducing cost price of residual flow 23. remuneration of bio-based resources 24. valorisation process must be economically sustainable technological solutions 1. accessibility of technologies 2. availability of effective technology 3. availability, homogeneity, and stability in time of fbb 4. bbf ease of use 5. continuity and volumes of inputs 6. development at big scale of technologies to reduce costs of production of bbf 7. ease of technology production 8. efficiency of technologies 9. enhanced bbf processing technology 10. ensuring consistent quality of end product with changing input 11. final bbf consistent with characteristics of each production area 12. lack of innovation and applicable development of last valorisation processes developed (rc) 13. local availability of technological solutions 14. logistic 15. management methods viable and suitable for private companies 16. need for additional investments 17. new valorisation processes must be in agree to the real situation of the agricultural sector (fm) 18. nitrogen level 19. preferable bbf traits 20. production of final stables and homogeneous bbf 21. rationalization of bbf production processes 22. reliability (efficiency) and easy to use 23. reorienting production sites towards bbf 24. risk of contamination in the process 25. transportation logistics 26. used of technologies not proven 27. weakly developed logistics for production and transport innovation uptake process 1. creation of new professional activities 2. farmers’ and bbf producers mutual learning and influence 3. generate the union of the different actors of the project 4. importance of the complete supply chain all components 5. increasing number of producer organizations that promote the use of bio-based fertilizers 6. lack of knowledge about waste valorisation technologies 7. lack of social association associative willingness to join these initiatives 8. low technical capacity of actors who have to fuse technique and economy 9. networking with advisory organizations 10. occurrence of fertilizers producers environmental system 1. additional benefits (e.g. nutrient input in soil) 2. assessment of product life cycle environmental impact 3. characteristics of soils at local level 4. greenhouse gases 5. impact of climate change on soil 6. improvement on food consumption and yield obtained 7. nutrient balance in soil and surface water. 8. optimization of residues management 9. reduction of vegetal effluents bioeconomy patterns 1. availability and quality of biomass 2. boom of bbf industry and circular economy 3. competition in residual flows 4. competition with other processing options for residual flows 5. increased demand bio-based resources 6. lack of recycling culture 7. lack of research on waste characterization and utilization 8. low availability of sources (different to sugarcane) and raw materials 9. measures favouring circular economy (green deal) 10. raw materials (residues) are readily available 11. role (involvement) of large retail chains as waste provider 12. seasonality and variation of volume of vegetable residues produced 13. seasonality of waste production 14. shift towards a circular approach 15. strength of circular economy 16. sufficient raw material source: own elaboration building sustainable futures: the bio-based fertilizer case-study carla abitabile, andrea arzeni*, federica cisilino, matteo orlando is there an animal food kuznets curve, and does it matter? vito frontuto1,*, tommaso felici2, vania andreoli3, marco maria bagliani1,#, alessandro corsi1 do agroholdings cope better with the agency problem? empirical evidence from corporate farms in russia alisher tleubayev1,*, yerzhan syzdykov2 farm characteristics and exogenous factors influencing the choice to buy land in italy silvia russo1,*, meri raggi2, barbara bimbati3, andrea povellato3, davide viaggi1 soils and ecosystem services: policy narratives and instruments for soil health in the eu greta winkler1,*, luciano pagano2, daniele vergamini2, fabio bartolini1 bio-based and applied economics bae bio-based and applied economics 12(1): 53-67, 2023 | e-issn 2280-6e172 | doi: 10.36253/bae-13475 copyright: © 2023 l. deißler, k. mausch, a. karanja, s. mcmullin, u. grote. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: l. deißler, k. mausch, a. karanja, s. mcmullin, u. grote (2023). a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture. bio-based and applied economics 12(1): 53-67. doi: 10.36253/bae-13475 received: july 31, 2022 accepted: march 31, 2023 published: june 24, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: davide menozzi, linda arata. orcid ld: 0000-0002-3646-534x km: 0000-0002-2962-7646 ak: 0000-0001-9095-4905 smm: 0000-0002-0976-1880 ug: 0000-0002-9073-6294 a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture luzia deißler1,*, kai mausch2, alice karanja3, stepha mcmullin3, ulrike grote1 1 leibniz university hannover, institute for environmental economics and world trade, germany 2 center for international forestry research (cifor)-world agroforestry (icraf), nairobi, kenya and bonn, germany 3 center for international forestry research (cifor)-world agroforestry (icraf), nairobi, kenya *corresponding author. e-mail: deissler@iuw.uni-hannover.de abstract. some recent research began to shift the focus of development efforts away from income and yield to more diverse concepts that consider people’s intrinsic drivers and values, such as aspirations and personality traits. we aim to contribute to the literature by exploring the connections between intrinsic drivers. hence, we analyze if and how the formation of aspirations relates to personality traits against the background of different socio-economic household characteristics. this research will help us provide practical insights for the successful design of development projects specifically tailored to the unique needs and aspirations of individuals and households. our analyses are based on a primary data set of 272 smallholder farming households in rural and peri-urban kenya. structural equation modeling (sem) results show a significant positive correlation of personality traits with aspirations (openness; extraversion; conscientiousness), indicating that personality structures indeed correlate with the formation of aspirations in a rural, agricultural context. furthermore, we show that household and respondent characteristics are associated with differences in education, income, and social aspirations. considering intrinsic factors for the prediction of human behavior has the potential to increase the efficiency of agricultural development projects and policies. we conclude that a contextualized understanding of aspirations can provide useful insights for development practice aiming to support smallholder farmers’ livelihoods. keywords: big five, aspirations, smallholder agriculture, rural livelihoods, kenya. jel codes: d91, q12. 1. introduction the agricultural sector in sub-saharan africa (ssa) faces numerous present and urgent challenges that affect current farming systems (fao, 2018; horton et al., 2017; rockström et al., 2009) and require sustainable solutions. http://creativecommons.org/licenses/by/4.0/legalcode 54 bio-based and applied economics 12(1): 53-67, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13475 luzia deißler et al. traditional development efforts often focus on increasing income (frediani, 2010). however, these approaches do not always lead to success as the well-being of individuals and communities is defined differently among different contexts. income is not a goal in itself for sustaining the needs of individuals and their families’ basic primary needs, but rather the use of it (nathan, 2005). instead of solely focusing on tangible resources or other traditional welfare measures, assessing people’s values and life goals to understand what drives and motivates them can provide practical insights for development research, projects and policies. farmers’ decisions on land use and sustainable practices play an important role within the current global debate on climate change and sustainability (giampietri et al., 2020; gios et al., 2022; menozzi et al., 2015). moreover, psychosocial constructs are frequently being referred to for the evaluation of farmers behavior and decision-making regarding development projects and policies (chipfupa & wale, 2018; giampietri et al., 2020; mekonnen & gerber, 2017; menozzi et al., 2015). recently, aspirations have received more attention as an approach to gain nuanced insights into people’s life goals (bernard & taffesse, 2014; horton et al., 2017), and their subsequent decision-making. since aspirations are theorized to be highly relevant for understanding the complex livelihood decisions of farmers, they can help align project or policy implementation with farmers’ individual life goals in order to improve adoption and success. aspirations can be viewed as drivers of a particular behavior that is supposed to lead to well-being in the future (bernard & taffesse, 2014). they can therefore provide additional details to broaden the understanding of decision-making processes and human behavior. amongst various external factors that inf luence the formation of aspirations (ajzen, 1991; bernard & taffesse, 2014; mausch et al., 2021; ray, 2006), an important aspect under consideration is the impact of personality traits in this process (roberts & robins, 2000; visser & pozzebon, 2013). personality traits were found to have significant influence on aspirations and life goals. however, this has so far only been investigated in studies in higher education settings in the global north, for example in sweden with regard to individuals’ business perceptions (hansson & sok, 2021). furthermore, their impacts on decision-making processes have also only been examined in similar settings (buelow & cayton, 2020; bühler et al., 2020; zhao & seibert, 2006) using artificial experimental designs (byrne et al., 2015). the correlation of aspirations with decision-making behavior in the context of countries of the global south or agricultural settings, however, has not been investigated yet. however, there are emerging studies which have found differences in the influence of personality and aspirations across different economic decisions (knapp et al., 2021), indicating the importance of context-specific analyses. the objective of this research is the investigation of connections between the formation of aspirations and personality traits, and to evaluate the impact of socioeconomic household and individual characteristics on these mechanisms. we aim to contribute to the literature on intrinsic drivers of decision-making, particularly in the context of agricultural settings in the global south. towards this aim, we use econometric analyses of primary data of smallholder farming households from rural and peri-urban kenya. 2. theoretical and empirical approach 2.1 aspirations smallholder farmers face continuous and often urgent challenges (i.a. increasing pressure on food production systems, extreme weather events, land degradation). changes in livelihood strategies are not uncommon and contribute to risk management and increasing living standards (ellis & freeman, 2004). the frequently used sustainable livelihoods framework suggests numerous aspects that influence livelihood choices and strategies (scoones, 1998). however, decisions and choices are not always the result of purely rational behavior (world bank, 2007). hence, not all decisions can be evaluated using standard indicators. besides the typically considered factors such as those in the livelihood framework, intrinsic factors have recently gained attention in explaining decision-making (mausch et al., 2018). in the pursuit of strategies and goals, it is not only ‘hard’ external factors that determine the outcome, but also the intrinsic drivers that shape people’s goals and actions (ajzen, 1991; verkaart et al., 2018) as well as the effort they exert (lybbert & wydick, 2018). thus, in the development context, many studies highlight the need to address aspirations and desires of farming households in the global south (chipfupa & wale, 2018; lybbert & wydick, 2018; mausch et al., 2018; mekonnen & gerber, 2017; roberts & robins, 2000). aspirations can be interpreted as visions for the future and include diverse, individually defined, aspects and dimensions of well-being (bernard & taffesse, 2014). in the broader sense, aspirations are determined and shaped by other intrinsic factors, such as mindset, personal interests and skills (mausch et al., 2018; roberts & robins, 2000), beliefs about the environment (dolan 55a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture bio-based and applied economics 12(1): 53-67, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13475 et al., 2012), and extrinsic factors such as farmer characteristics, household factors, access to resources, social or political conditions (mausch et al., 2018; mekonnen & gerber, 2017), as well as community peers (chipfupa & wale, 2018). th ese infl uences aff ect aspirations indirectly by shaping the aspirations window, within which individual aspirations are formed. th e aspiration window is a space of imaginable goals (mausch et al., 2021; ray, 2006). bennike et al. (2020) stress the importance of imaginative horizons for the formation of the aspiration window. th ose are aff ected by real and perceived limitations of specifi c outcomes in addition to the infl uence of social dynamics emerging from communities and general surroundings. finally, the gap between a desired level and the current status of a specific welfare dimension has been defi ned as the aspiration gap which, to some degree, determines a person’s level of eff ort. ray (2006) argues that the aspiration gap can lead to investments in the future to achieve the aspired level. if the gap is too small, it can limit motivation and investment, and progress is bound to be slower than optimal (janzen et al., 2017). neither should the gap be too wide, as this could induce frustration and stagnation (janzen et al., 2017; ray, 2006). cognizant of this complex web of interactions that infl uence aspirations and subsequent choices and actions we conclude that aspirations shape decisions and the eff ort put in livelihood choices and thereby, are quite important for the agricultural development context. 2.2 th eoretical framework various theories of human behavior focus on the inf luence of numerous intercorrelated intrinsic and extrinsic factors on choices and decisions (ajzen, 1991; lybbert & wydick, 2018; ray, 2006; sen, 1999). however, as stated by ajzen (1991), a critical factor for someone’s actual behavior is one’s intention to act in a specifi c way. th e ‘th eory of planned behavior’ provides a widely used model for explaining people’s behavior (ajzen, 1991). behavior, or decision making, is infl uenced by diff erent factors. firstly, perceived behavioral control, which describes the perceived power and opportunity for someone to make a particular decision and take a corresponding action (ajzen, 1991; lybbert & wydick, 2018). secondly, subjective norms and attitudes, including societal structures and opinions on a particular topic, shape decisions. th ese aspects have a combined impact on an individual’s intention to make a specifi c choice or whether to take or not to take a specifi c action to achieve well-being. it is notable that the drivers of intention described by ajzen (1991) are similar to the factors shaping aspirations. moreover, aspirations can be highly relevant for understanding the individual valuation of well-being, hence, the way people decide to use their resources. since aspirations are signifi cantly associated with livelihood choices (ajzen, 1991; mausch et al., 2018; verkaart et al., 2018), they should be included in a framework describing individual decision-making. to shed light on the specifi c formation of choices and the interlinkage between extrinsic and intrinsic factors and their impact on well-being, aspirations and their role in livelihood strategies and decision-making play an important role. th e fi rst step in understanding that process is refi ning the understanding of aspirations and their formation. figure 1 shows our conceptual framework for the formation of aspirations in the context of smallholder agriculture. external factors, such as resources and subjective norms provide the frame of the theoretically feasible, whereas individual preferences and personality traits account for the intrinsic attributes. both parts infl uence the aspiration window and subsequent formation of aspirations. additionally, besides the stated factors, there is evidence for a correlation between personality traits, major life goals and aspirations (roberts & robins, 2000; visser & pozzebon, 2013). it was shown that personality traits can be directly linked to specifi c economic decisions (zhao & seibert, 2006). gutman and akerman (2008) suggest that individual self-perception infl uences aspirations, indicating a relationship between personality traits and aspirations. yet, most fi ndings are based on samples within higher education settings in the global north. th us, examining the transferability of these fi ndings to agricultural households’ decision-making could provide useful insights for the application in development projects. exploring the correlation of personality figure 1. conceptual framework of the formation of aspirations (ajzen, 1991; bernard & taff esse, 2014; mausch et al., 2021). 56 bio-based and applied economics 12(1): 53-67, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13475 luzia deißler et al. traits with aspirations is a first step towards this direction. most studies rely on the five-factor model or big five (table 1), which is a commonly used concept for measuring personality traits (i.a. buelow and cayton, 2020; bühler et al., 2020; byrne et al., 2015; nishimura and suzuki, 2016; xu, 2020). it includes aspects that capture a person’s extraversion, agreeableness, conscientiousness, neuroticism, and openness (mccrae & john, 1992). although these traits are more commonly used in the global north, it was found that it can also be applied in studies in the global south such as thailand and vietnam (bühler et al., 2019, 2020). 2.3 data our analysis uses primary data collected as part of the fruit tree portfolio (ftp) project carried out by world agroforestry (mcmullin et al., 2019). the project aimed to close seasonal dietary gaps in rural households by providing location-specific portfolios of a diversity of selected fruit trees and annual crops (mcmullin et al., 2019). the data for this study was collected in 2021 across three kenyan counties (laikipia, tharaka nithi, kitui) covering semi-arid agro-ecological zones. the total sample consisted of 272 households. the survey included general socio-economic characteristics, personality traits and aspirations. socio-economic household characteristics captured the extrinsic factors stated in the theoretical framework (chapter 2.2), covering financial-, physical-, socialand human capital (table 2). data on personality traits (big five) were collected following the german socio-economic panel (soep) (caliendo et al., 2011).1 aspirations were captured following 1 table a (appendix) shows the two questions per personality trait asked within the questionnaire, following a five point likert scale. the the methodology of (bernard & taffesse, 2014).2 the use of likert scales to capture current and aspirational levels of income, education, and social status worked quite well in the smallholder context based on the quality of big five traits are then computed by adding up the likert scale points and calculating the average score per trait. 2 the questionnaire included two questions for capturing aspirations per each welfare dimension (income, education, social status), followed by one question regarding the importance of each dimension (table a, appendix). first, respondents are asked to establish a scale of 1-10, 1 representing the person in their community with the lowest score and 10 representing the person with the highest score. on this scale, respondents rank themselves according to their current status. second, respondents state the status they would like to achieve in the future (can be higher than 10). finally, respondents rank the welfare dimensions according to their personal importance. table 1. description of the big five (costa & mccrae, 2017; xu, 2020). personality traits – big five openness open to new information; fantasy, feelings, actions, ideas, values conscientiousness efficient, hardworking, organized; competence, dutifulness, achievement striving, self-discipline extraversion outgoing and social; assertiveness, activity, excitement seeking, positive emotions agreeableness kind, empathic, cooperative; straightforwardness, altruism, compliance, modesty neuroticism anxiety, further negative emotions (e.g. depression, vulnerability) table 2. description of the variables used in the correlation analyses. variable explanation aspirations level of education, income and social status wanted to achieve household characteristics total income total monthly hh income (kw) access to credit access to credit services farm size size of the entire farm (acres) number of extension visits number of extension visits during the last 12 months shocks number of shocks experienced in the last three years (climatic, biological, economic, other) hh size number of household nucleus members gender hh gender of the hh head, binary (0=female, 1=male) education hh head highest level of education achieved by the household’s head food security number of months without enough food during the last year (using months of adequate household food provisioning – mahfp) respondent characteristics gender gender of the respondent, binary (0=female, 1=male) age age of the respondent (in years) education highest level of education achieved by the respondent membership number of different groups/ organizations the respondent is a member of travel number of travels outside of one’s own village for one month media use per week number of times media was used during one week (television, radio, internet) 57a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture bio-based and applied economics 12(1): 53-67, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13475 data collected. this was enabled by thorough enumerator training, which capacitated the team to facilitate a comprehensive understanding of the scales by the smallholder farming respondents. the general sample characteristics are presented in table b (appendix). of all households, 21% are headed by women, with the highest proportion of female-headed households in laikipia at 39%. the main source of household income is wages (43%), while the usual occupation of the household head is casual labor, and farming for the spouse. while households located in kitui farm the biggest areas (2.35 acres), their average monthly household income is lowest with 5,648 kenyan shilling3. general aspirations are lowest in kitui as well, and highest in in tharaka nithi, mainly based on comparatively high educational aspirations. 2.4 methodology previous studies used correlation models to examine the relationship between the big five and aspirations (buelow & cayton, 2020; byrne et al., 2015; roberts & robins, 2000; xu, 2020). to detangle the complex relationships and to account for the intangibility of the variables we performed descriptive analyses and structural equation modelling (sem) in stata 14. 3 51.51 us dollar based on exchange rate for time of data collection (2021) derived from world bank 2022 (109.64). sem allows us to treat personality traits and aspirations as latent variables when analyzing their relationship. thus, sem takes into account that these variables cannot be observed directly, which can lead to measurement errors. sem compiles these latent variables according to their observed indicator variables (bollen & noble, 2011; fan et al., 2016; gallagher & brown, 2013). it consists of two parts, the measurement model that contains the measurement of the latent variables (constructs) based on their indicators (items), and the structural model that describes the relationship between the latent variables (hair et al., 2017). each personality trait (ξa) consists of two respective indicators (xi, xj), whereas the aspirations construct consists of three indicators (xi, xj, xk). we specified the sem model according to the literature and proxy general aspirations by education (xi), income (xj) and social aspirations (xk) (bernard & taffesse, 2014), while each personality trait (ξa) consists of two respective indicators (xi, xj), as described in the data section (caliendo et al., 2011) (figure 2). information on the respective questions is shown in the appendix, table a. we used the aspirations gap as the indicator for aspirations, based on the assumption by ray (2006) that the aspirations gap is the immediate driver of actions and decisions. we further hypothesized that the personality traits are intercorrelated with each other (indicated by the dotted line arrows). the first step is the confirmatory factor analysis (cfa) as part of the measurement model (shown exemaspirations artistic imagination considerate forgiving efficient thorough outgoing talkative openness agreeableness conscientiousness extraversion nervous worrying education aspirations income aspirations social aspirations neuroticism figure 2. model specification of the sem measurement and structural model. 58 bio-based and applied economics 12(1): 53-67, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13475 luzia deißler et al. plary for a latent construct with two items): xi=λiaξa+δi, eq. 1 xj=λjaξa+δj eq. 2 with ξ1 as the latent variable or factor, xi/xj as the observed variable or item, λi/λj is the factor loading that represents the respective difference in the item per one unit change in the factor and δi/δj as the respective error terms of the items (bollen & noble, 2011). in a second step, sem calculates the covariance between the latent variables (bollen & noble, 2011; jeon, 2015), representing their respective intercorrelation. the estimated coefficients provide information on the correlation of our variables of interest. to find and confirm external determinants of aspirations for contextualizing the formation of aspirations, we analyzed differences in variables of interest (table 2) to examine the relationship between factors derived from previous literature and aspirations. the variables include socio-economic household characteristics such as income, access to credit, farm size, extension visits, shocks, food security and household head characteristics. further, we included variables regarding the respondent and account for gender, age, education, memberships in groups or organizations, travel frequency and media use. for the aspiration measure, we normalized each dimension (income, education and social status) and computed an aggregate index (bernard & taffesse, 2014). the aggregated index of the aspirations gap allows an assessment of the overall ambitions, or drive, towards achieving more in life (bernard & taffesse, 2014; ray, 2006). by using the following equation (3), the values for each dimension were normalized to make them comparable across communities and dimensions (bernard & taffesse, 2014; larue et al., 2021): eq. 3 with k as the respective dimension, as the value for the aspirations regarding dimension k for individual i, σk and μk as the standard deviation and the community sample mean of the values for the aspirations and as the specific weight (ranking) the respondents assigned to the respective dimension. however, we did not only use the aspiration index (bernard & taffesse, 2014), but also looked at income, educational and social aspirations separately (larue et al., 2021). this allowed us to identify the importance that is placed on each dimension and shows what welfare aspects might be more important than others. we conducted welch’s t-tests to identify significant differences between those variables regarding high or low aspirations. aspirations were classified high or low if the values are above or below average: low/high: aindex<0.04 / aindex≥0.04 eq. 4 aeducation≤0.01 / aeducation>0.01 eq. 5 aincome≤0.01 / aincome>0.01 eq. 6 asocial≤0.01 / asocial>0.01 eq. 7 3. results 3.1 connection between personality, aspirations and adoption we investigated the correlation between personality traits and aspirations. in the following chapter we discuss the association between these two intrinsic factors and its implication for the decision-making behavior of smallholder farmers in kenya. the results from the confirmatory factor analysis (cfa) on the latent variables are presented in table 3. they show a good fit of the measurement model for the big five personality traits and aspirations. the observed variables for each latent construct are statistically significant with standardized factor loadings above 0.3 (kang & ahn, 2021). however, the indicator questions for neuroticism did not result in a valid latent variable. subsequently, we used the respective indicator questions themselves in the following path analysis. table 4 and figure 3 show the estimates from the structural model which analyzed the covariance between the latent variables. table 4 includes all theoretically possible relationships and their respective standardized correlation coefficients. except for neuroticism, all personality traits are intercorrelated. the lack of correlation here might be a result of the non-significant factor loadings (table 3) that indicate that the construct of neuroticism is not identified correctly. the strongest positive correlation exists between agreeableness and conscientiousness, extraversion and conscientiousness and openness and agreeableness. the results show that three of the five personality traits significantly correlate with aspirations. openness (0.41), conscientiousness (0.35) and extraversion (0.31) show a positive correlation coefficient. furthermore, the neuroticism indicator nervousness, also significantly correlates with aspirations (-0.13), indicating that individuals that are prone to nervousness or anxiety are less likely to have higher aspirations. this confirms our hypothesis that intrinsic factors such as personality traits do in fact, significantly correlate with the formation of aspirations. conscientiousness is usually associated with efficient and hardwork59a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture bio-based and applied economics 12(1): 53-67, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13475 ing individuals (costa & mccrae, 2017; xu, 2020). in relation to aspirations, the literature is inconsistent, reporting positive or insignificant correlations of conscientiousness with (including economic) aspirations (nishimura & suzuki, 2016; roberts & robins, 2000; visser & pozzebon, 2013). considering education and income aspirations as achievement-oriented goals, our results are consistent with roberts and robins (2000), who found high values for conscientiousness resulting in a significant effect on economic and achievement-oriented life goals. moreover, the results suggest that individuals that are open to new experiences and ideas, seeking excitement and socially outgoing also have a higher aspirations gap (costa & mccrae, 1997; xu, 2020; zhao & seibert, 2006). these are characteristics that can expand a person’s aspiration window by providing information and ideas that might be passing by more close-minded individuals. information and social networks play an important role for aspirations and in turn for livelihood choices and strategies of smallholder farming households. agreeableness and the indicators of neuroticism did not have a significant effect on farmers’ aspiration gap in our study. as described earlier, sem offers several advantages in dealing with theoretical constructs and hypothetical relationships. on the one hand, due to the limitations of the model, only correlations could be analyzed, not causality. on the other hand, however, considering that the data were collected after the actual intervention, it is reasonable to examine only correlations, as it would have been difficult to prove causality ex post. 3.2 correlation analysis aspirations are not only determined by personality, but also shaped by current context. we examined specific contextual variables and their correlation with educational-, income related-, and social aspirations to form a comprehensive idea of aspirations in a smallholder context. to this end, we examined the mean difference between individuals with above-average (high) and below-average (low) aspirations. table 5 presents the results from the correlation analyses. educational aspirations are significantly correlated with a higher number of extension visits, more frequent travels outside of one’s home village, smaller households, higher food security in terms of months table 3. factor loadings of measurement model. a. estimates of factor loadings factors items standardized factor loadings se p-value smc agreeableness forgiving 0.49 0.07 <0.01 0.24 considerate 0.50 0.07 <0.01 0.25 openness artistic 0.52 0.06 <0.01 0.27 imagination 0.80 0.07 <0.01 0.64 conscientiousness thorough 0.40 0.07 <0.01 0.16 efficient 0.60 0.09 <0.01 0.35 extraversion talkative 0.43 0.07 <0.01 0.18 outgoing 0.74 0.08 <0.01 0.55 neuroticism worrying 0.48 0.55 0.38 0.23 nervous 0.80 0.89 0.37 0.63 aspirations educ. aspirations 0.72 0.09 <0.01 0.51 inc. aspirations 0.33 0.09 <0.01 0.11 soc. aspirations 0.33 0.08 <0.01 0.11 b. covariances of measurement error item 1 item 2 standardized correlation coefficient se p-value forgiving talkative -0.22 0.08 <0.01 imagination efficient 0.55 0.13 <0.01 worrying nervous 0.38 0.05 <0.01 note: smc = squared multiple correlations. table 4. estimates of the structural model. relationship standardized correlation coefficient se p-value big five personality traits openness ←→ agreeableness 0.74 0.12 <0.01 agreeableness←→ conscientiousness 0.82 0.16 <0.01 conscientiousness ←→ extraversion 0.81 0.13 <0.01 extraversion ←→ openness 0.59 0.09 <0.01 openness ←→ conscientiousness 0.47 0.12 <0.01 agreeableness ←→ extraversion 0.95 0.16 <0.01 personality traits aspirations openness ←→ aspirations 0.41 0.10 <0.01 agreeableness ←→ aspirations 0.04 0.13 0.74 conscientiousness ←→ aspirations 0.35 0.13 <0.05 extraversion ←→ aspirations 0.31 0.11 <0.01 worrying ←→ aspirations -0.07 0.08 0.40 nervous ←→ aspirations -0.16 0.08 <0.10 fit indices: c2 (p-value) = 0.1129; rmsea = 0.031; cfi = 0.0.970; tli = 0.947 note: rmsea = root mean squared error of approximation; cfi = comparative normed fit index; tli = tucker-lewis index. 60 bio-based and applied economics 12(1): 53-67, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13475 luzia deißler et al. of adequate household food provisioning (mahfp), higher education attainment by the household head or respondent, as well as a younger respondent and a larger number of memberships (to groups/ organizations). in households with high educational aspirations of the respondents, human capital, proxied by information (extension visits; travels outside of the village), education, age and social networks (memberships), is significantly higher. by providing positive examples, new ideas, different experiences, or new ways of looking at things, these aspects can have an increasing impact on the formation of aspirations (chipfupa & wale, 2018). it was shown that present resources function as restraining or enhancing factors to what is achievable (elias et al., 2018). moreover, higher food security also seems to provide a base for higher aspirations. based on the ‘hierarchy of needs’, people are more likely to aspire complex future goals if their basic primary needs are fulfilled first (maslow, 1943). the fulfillment of immediate needs is one of the primary drivers of decisions in rural kenyan households (mausch et al., 2021). differing effects of household and respondent characteristics could therefore be due to differences in the ability to satisfy basic needs. not having to spend the imaginative or cognitive capacity on worrying about the availability of food allows individuals to aspire for more than the satisfaction of basic needs (nathan, 2005). high aspirations regarding future income is associated with smaller farms, higher food security (mahfp), and younger age of respondents. however, our results suggest that the determinants of aspirations are complex. on the one hand, food secure farmers might have the capacity to aspire more diverse life goals (including income and education) (nathan, 2005). on the other hand, households with significantly smaller farms might rely more heavily on other income sources to cover immediate needs such as food, and with that, have higher aspirations for future income. mausch et al. (2021) found a similar effect for households from turkana (kenya) that is characterized by difficult agricultural and economic conditions, where decision-making is based on the satisfaction of immediate needs rather than on the fulfillment of specific aspirations. similar to educational aspirations, younger respondents also have higher income aspirations, in line with a previous study on aspirations in rural kenya (larue et al., 2021). people at an older age may already have reached a considerable level of education and income. therefore, aspirations for further increases may be lower than for people who have not yet reached a certain level of relative prosperity. social aspirations appear to depend mostly on resources and household characteristics. they are positively associated with agricultural training, travelling outside of the village and more frequent media use. social aspirations can be linked with a broader information network and higher exposure to peers (chipfupa & wale, 2018). furthermore, respondents in households that are worse off regarding the education level of the household head, food security (mahfp), farm size and have experienced a higher number of shocks, figure 3. path diagram presenting the estimated covariance coefficients from the structural model. aspirations openness agreeableness conscientiousness extraversion nervous worrying 0.41*** 0.31*** 0.04 0.35*** -0.07 -0.16* 0.59*** 0.74*** 0.82*** 0.81*** 0.95*** 0.47*** 61a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture bio-based and applied economics 12(1): 53-67, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13475 have higher social aspirations than their counterparts. in fact, one would expect that households that are more disadvantaged would also be more likely to focus on their immediate needs than on the pursuit of social status. nonetheless, the complexity of the formation of aspirations suggests that greater exposure to peers and information may also override the focus on immediate needs. additionally, households within which the respondent stated high social aspirations are more likely to be female headed. it is notable that the three dimensions show different combinations of their determining factors. some of these factors might not directly determine or control aspirations, they do however, limit them (nathan, 2005). the aggregate aspiration index (table 6) shows consistent negative association of farm size and consistent positive effects of agricultural training and experiences of shocks with above average aspirations. moreover, respondents from female headed households in general, show higher aspirations. nevertheless, the effects differ across the factors and dimensions of aspirations under consideration. our results suggest that aggregating diverse directions of aspirations may mask individual differences in the importance of aspects of well-being based on differing backgrounds and preferences. effects and preferences can overlap and influence each other at the individual level, but also interact within the household and the wider community. while income aspirations may be seen as part of basic human needs, social aspirations can be considered a human need higher up the “hierarchy of needs”, which only comes into focus once the first basic needs have been satisfactorily fulfilled. thus, the aggregate aspiration index could be a useful tool for assessing the general attitude towards the future, as well as the individual’s agency and proactivity. however, when it comes to identifying specific socioeconomic characteristics that play a role in the formation of aspirations, looking at the individual aspiration dimensions is more likely to lead to a clearer picture. 4. conclusion we identified the role that personality traits as intrinsic factors play for the formation of aspirations and examined the influence of socio-economic household characteristics as control variables in this process. table 5. t-test/mann-whitney results on household and individual characteristics of respondents with below or above average aspirations. variables education aspirations income aspirations social aspirations low high mean diff. low high mean diff. low high mean diff. extrinsic factors monthly hh income (ksh) 5898 5897 -0.10 5739 6023 284.1 6066 5738 -328.0 access to credit 0.62 0.66 0.04 0.67 0.62 -0.06 0.67 0.62 -0.05 farm size (ac) 1.96 1.83 -0.13 2.16 1.69 -0.46*** 2.07 1.73 -0.34** agric. training 0.57 0.65 0.08 0.60 0.61 0.01 0.52 0.70 0.18*** extension visits 0.68 1.06 0.38* 0.79 0.94 0.15 0.85 0.90 0.05 travel 5.55 7.56 2.01** 5.85 7.16 1.31 5.86 7.25 1.39* shocks 1.11 1.18 0.07 1.08 1.20 0.12 1.04 1.25 0.21** household characteristics hh size 6.17 5.33 -0.84*** 5.76 5.72 -0.03 5.65 5.82 0.17 gender head 0.79 0.78 -0.01 0.76 0.82 0.06 0.83 0.75 -0.08* education head 3.24 3.54 0.30* 3.46 3.35 -0.11 3.60 3.20 -0.39** mahfp 9.45 9.94 0.50* 9.36 9.98 0.62** 10.0 9.39 -0.65** respondent characteristics gender resp. 0.26 0.23 -0.03 0.26 0.23 -0.03 0.25 0.23 -0.02 age resp. 47.4 43.5 -3.96** 47.0 44.2 -2.75* 44.8 46.1 1.28 education resp. 3.16 3.46 0.30* 3.29 3.33 0.03 3.27 3.35 0.08 membership 1.01 1.16 0.14* 1.12 1.06 -0.06 1.11 1.07 -0.03 media use 9.54 9.62 0.09 9.33 9.78 0.44 9.04 10.1 1.05* note: low and high refer to below and above average aspirations. t-test/welch mean differences are displayed. *** p<0.01, ** p<0.05, * p<0.1. hh = household, ksh = kenya shilling, mahfp = months of adequate household food provisioning. 62 bio-based and applied economics 12(1): 53-67, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13475 luzia deißler et al. the aim of our research was to gain insights into the intrinsic influences of smallholder farmers’ aspirations towards an improved understanding of their decisionmaking. we provide insights for agricultural development projects and policies to understand the underlying mechanisms of decision-making. ensuring the alignment of project goals with individual goals could significantly change adoption dynamics and the identification of clusters that could best utilize specific support mechanisms such as sustainable agricultural practices (integrating trees in farming systems, crop rotation and irrigation schemes). we found that three of the five investigated personality traits indeed significantly correlate with aspirations. these traits paint a picture of personality structures that might be conducive to high aspirations while facilitating the basis for proactive behavior. open-minded, socially outgoing and conscientious individuals will most likely have higher aspirations, which in turn can lead to higher susceptibility to novel technologies and approaches. nevertheless, extrinsic factors also play an important role in this system. our results suggest that different types of aspirations (e.g. education, income) are connected to different factors (e.g. food security, household size, age, group membership), indicating that understanding these differences with regard to the direction of aspirations is crucial. moreover, most of the determining factors derived from the literature are rather inconsistent across settings. therefore, it is necessary to contextualize methods and results in order to understand the process, which we aimed to contribute to by focusing on an agricultural setting within the global south. while social and human capital interact positively with educational and social aspirations, poverty is an essential factor that was found to shift the focus from complex future aspirations towards the satisfaction of immediate needs. this may warrant future research as it relates to different target groups for agricultural development efforts and could add to a more differentiated approach for the poorest segments as compared to those slightly better off. analyzing aspirations and different livelihood strategies prior to the design of agricultural development projects and policies can improve the suitability of these interventions for the target group. research and projects must acknowledge that there is no ‘one size fits all’ solution for development. individuals interact differently with opportunities and propositions based on their individual aspirations. for example, more introverted people, who may also have lower aspirations, might not only be more difficult to reach, but also need tailored interaction and support to realize and seize opportunities. whereas achievement-oriented, outgoing individuals are more likely to need less support to adopt new approaches. future research needs to explore these complex connections in more detail, using quantitative methods to examine context specific correlations. this process could also be extended towards actual behavior, by assessing real life responses to interventions. by doing so, the role of personality traits and aspirations in a concrete context could be identified, further deepening the 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(2006). the big five personality dimensions and entrepreneurial status: a meta-analytical review. journal of applied psychology, 91(2), 259–271. https://doi.org/10.1037/0021-9010.91.2.259 66 bio-based and applied economics 12(1): 53-67, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13475 luzia deißler et al. appendix table a. questionnaire sections on personality traits and aspirations. variable question scale aspirations social status present imagine the person with the highest level of social status in your community, this represents a 10. the one with the lowest level of social status in the community is represented with a 1. what is the level of social status that you have at present? (on the scale from 1-10) self-set scale (1-10) social aspirations what is the level of social status that you would like to achieve? (could be higher than 10) self-set scale (starting with 1) income present imagine the person with the highest level of income in your community, this represents a 10. the one with the lowest income in the community is represented with a 1. what is the level of income that you have at present? (on the scale from 1-10) self-set scale (1-10) income aspirations what is the level of income that you would like to achieve? (could be higher than 10) self-set scale (starting with 1) education present imagine the person with the highest level of education in your community, this represents a 10. the one with the lowest education in the community is represented with a 1. what is the level of education that you have at present? (on the scale from 1-10) self-set scale (1-10) education aspirations what is the level of education that you would like to achieve? (could be higher than 10) self-set scale (starting with 1) ranking of the three dimensions we have asked you about three dimensions income, social status and education. now i would like you to tell me which of these three dimensions are the most important for you. please assort 20 beans to the three dimensions, according to their importance for you. no beans assorted to a dimension means this dimension is of no importance for you. the more beans you assort to one dimension, the more important. rank_in how many beans would you allot for annual income? number (0-20) rank_soc how many beans would you allot for social status? number (0-20) rank_ed how many beans would you allot for education? number (0-20) big five do you see yourself as someone who… bf1 … works thoroughly? likert scale (1-5) bf2 … is talkative? likert scale (1-5) bf3 … worries a lot? likert scale (1-5) bf4 … has a forgiving nature? likert scale (1-5) bf5 … is outgoing, sociable? likert scale (1-5) bf6 … gets nervous easily? likert scale (1-5) bf7 … values artistic, aesthetic experiences? likert scale (1-5) bf8 … is considerate and kind to almost everyone? likert scale (1-5) bf9 … does tasks efficiently? likert scale (1-5) bf10 … has an active imagination? likert scale (1-5) note: own source. survey 2021. 67a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture bio-based and applied economics 12(1): 53-67, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13475 table b. characteristics of the 272 sample households. variable laikipia (n=93) tharaka nithi (n=89) kitui (n=90) total (n=272) mean sd mean sd mean sd mean sd household head and respondent characteristics gender hh head (% female) 0.39 0.49 0.12 0.33 0.11 0.32 0.21 0.41 age hh head 51.3 14.0 47.0 13.7 50.8 13.2 49.7 13.7 main occupation hh head farming casual labor casual labor casual labor education hh head 3.24 1.47 3.62 1.60 3.29 1.67 3.40 1.59 gender resp. (% female) 0.75 0.43 0.77 0.42 0.74 0.44 0.24 0.43 age respondent 46.5 13.5 42.5 13.4 47.3 13.6 45.4 13.6 occupation respondent farming farming farming farming education respondent 3.17 1.53 3.42 1.60 3.31 1.57 3.31 1.56 household characteristics hh size 5.88 2.96 5.21 2.23 6.18 2.56 5.74 2.65 number of children 3.19 2.15 2.31 1.27 2.86 1.59 2.79 1.75 farm size (ac) 1.78 1.28 1.56 1.26 2.35 1.76 1.90 1.48 monthly hh income (ksh) 5950 3407 6093 3556 5648 3770 5898 3580 main income source wage (43.2 %) wage (32.9%) wage (51.8%) wage (42.6%) mahfp 8.84 3.72 10.50 2.92 9.79 2.62 9.71 3.02 number of extension visits 0.97 2.25 1.17 2.45 0.49 1.02 0.87 2.02 number of shocks (last 3 yrs) 1.16 1.03 1.06 0.97 1.23 0.82 1.15 0.94 decision-making agricultural head joint joint head market head joint joint joint livestock head joint head head income off farm business head head joint head income employment head joint joint joint major expenditures head joint joint head minor expenditures head spouse spouse spouse loans head joint joint joint respondent characteristics access to credit 0.61 0.49 0.67 0.47 0.65 0.48 0.64 0.48 number of days travelled outside of the village (for one month) 3.89 4.91 8.06 9.91 7.91 10.1 6.58 8.81 number of memberships 0.96 0.84 1.03 0.74 1.28 0.78 1.09 0.80 aspirations education aspirations -0.01 0.29 0.04 0.27 0.01 0.23 0.01 0.26 income aspirations 0.00 0.34 0.02 0.34 0.01 0.33 0.01 0.34 social aspirations 0.03 0.36 0.02 0.20 -0.01 0.23 0.01 0.23 aspiration index 0.02 0.61 0.08 0.56 0.01 0.51 0.03 0.56 personality traits (big five) agreeableness 4.42 0.77 4.34 0.63 4.71 0.64 4.41 0.76 openness 3.70 0.98 3.70 0.92 4.29 0.89 3.88 0.99 conscientiousness 4.23 0.73 4.49 0.61 4.59 0.66 4.42 0.73 extraversion 3.83 1.02 4.00 0.89 4.24 0.96 4.01 1.00 neuroticism 2.54 1.04 2.63 1.00 2.42 1.05 2.52 1.04 note: own source. farmers’ motivations and behaviour regarding the adoption of more sustainable agricultural practices and activities linda arata1, davide menozzi2 how do farmers’ pluriactivity projects evolve? how do farmers’ pluriactivity project evolve? clarisse ceriani*, amar djouak, marine chaillard heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria chinasa sylvia onyenekwe1, patience ifeyinwa opata1, chukwuma otum ume1,*, daniel bruce sarpong2, irene susana egyir2 organic cocoa farmer’s strategies and sustainability ibrahim prazeres1,*, maria raquel lucas1, ana marta-costa2, pedro damião henriques3 a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture luzia deißler1,*, kai mausch2, alice karanja3, stepha mcmullin3, ulrike grote1 exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms: evidence from rural rwanda ronja seegers*, etti winter, ulrike grote bio-based and applied economics 10(2): 137-152, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9678 bio -based and a ppl ied economics bae copyright: © 2021 n. raina, m. zavalloni, s. targetti, r. d’alberto, m. raggi. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: n. raina, m. zavalloni, s. targetti, r. d’alberto, m. raggi, davide viaggi (2021). a systematic review of attributes used in choice experiments for agri-environmental contracts. bio-based and applied economics 10(2): 137-152. doi: 10.36253/bae-9678 received: september 5, 2020 accepted: december 4, 2020 published: october 28, 2021 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: simone cerroni. orcid nr: 0000-0002-7617-4731 mz: 0000-0002-6291-7653 st: 0000-0002-2154-546x rda: 0000-0002-7227-7485 mr: 0000-0001-6960-1099 dv: 0000-0001-9503-2977 a systematic review of attributes used in choice experiments for agri-environmental contracts nidhi raina*, matteo zavalloni, stefano targetti, riccardo d’alberto, meri raggi, davide viaggi university of bologna, italy *corresponding author. e-mail: nidhi.raina@unibo.it abstract. contract attributes are strong motivators for eliciting farmers’ preferences for a particular agri-environmental scheme. our study aims to conduct a systematic literature review to highlight the attributes used in choice experiment studies of agrienvironmental schemes using the prisma framework. we obtained 34 studies for an in-depth review, through which we extracted 32 attributes that were classified into five typologies: ‘monetary’ (7 attributes), ‘general’ (4 attributes), ‘flexibility’ (6 attributes), ‘prescription’ (12 attributes), and ‘purpose’ (3 attributes). though monetary attributes should theoretically define farmers’ choices; general design and flexibility attributes are more critical for farmers’ participation and willingness to accept. the study also discusses the lesser-used attributes that could be potentially explored in future studies. thus, our review can be used as a reference by future aes studies to select their bundle of choice attributes and test with a broader range of attributes in their choice experiments. keywords: choice experiment, agri-environmental schemes, willingness to accept, contract attributes, systematic literature review jel codes: q15, q20, q57. 1. introduction farmers’ decision to participate and their willingness to accept (wta) a particular agri-environmental scheme (aes) is affected by the contract’s design. studies have tried to investigate the choice behaviors of farmers using various methodologies. the choice experiment (ce) methodology, a type of stated preference method, is widely applied in valuation studies and is useful for analyzing different policy scenarios (kanchanaroek & aslam, 2018). ces are based on the theory of consumer choice, which states that individuals’ choices depend on utility or value gained from the attributes of the goods being consumed (lancaster, 1966). utility generally depends on attributes of the choices and socio-economic characteristics of an individual. so, ces provide an attribute-based approach that can investigate individual preferences (chèze et al., 2020) as well as quantify the trade-offs between the alternatives (hynes et al., 2011). thus, the ce method is parhttp://creativecommons.org/licenses/by/4.0/legalcode 138 bio-based and applied economics 10(2): 137-152, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9678 nidhi raina et al. ticularly suited for evaluating choices among different aess and to elicit farmers’ or landowners’ preferences for different attributes in a contract (espinosa-goded et al., 2010; horne, 2006; ruto & garrod, 2009, etc.). many studies have reported that even though socioeconomic, demographic, or cultural characteristics can influence farmers’ preferences, such findings are usually insufficient to quantify these choices (dachary-bernard & rambonilaza, 2012; dramstad et al., 2006; swanwick, 2009, etc.). thus, ces can be a useful tool to understand specific preferences by evaluating farmers’ behavior towards contract attributes. studies generally use evidence from previous literature to select the contract attributes and their levels for their ce. there exists a plethora of literature on the motivations and attitudes of farmers exhibiting conservation behavior that the aes studies use while choosing attributes (greiner, 2016; le coent et al., 2017). few studies have conducted in-depth literature reviews to understand why farmers join a particular aes (like, lastra-bravo et al., 2015) and the attractive attributes in a contract that motivate farmers’ participation (like, brandyberry, 2015). lastra-bravo et al. (2015) collected 160 variables through a review of aes studies which they classified into five different categories that depicted the socio-economic and demographic conditions of the farm and the farmers. however, they studied trade-offs between attributes within only one category: ‘farmers’ attitudes towards agri-environmental schemes.’ thus, there is still a substantial knowledge gap in the literature about attribute selection for contract design because of the lack of a definitive catalog of management and policy-based attributes used by previous studies. state of the art has majorly focused on reviewing the measures included in the aess (e.g., lastra-bravo et al., 2015 and rakotonarivo et al., 2016), but no study has specifically concentrated on reviewing the contract attributes used for designing the ces. this gap creates a divide between contract attributes studied by researchers and actual attributes preferred by the farmers, which may lead to inefficient contract designs. also, studies shortlist choice attributes using previous literature, but there is a lack of studies that employ a systematic review approach. some studies such as uthes & matzdorf (2013) reviewed the literature on agri-environmental measures (aems). however, they did not use a systematic method, thus, they covered a broad spectrum of aems that does not focus on using the ce methodology to examine farmers’ choices. one recent study by mamine et al. (2020) did conduct a meta-analysis of 79 aes studies that use the ce method to evaluate farmers’ preferences. however, they did not conduct a systematic review and grouped the extracted 290 attributes into only two categories – commitments and incentives. mamine et al. (2020) haven’t been the first to classify contract attributes into different sub-types. many aes studies that use the ce methodology classify the choice attributes as monetary and non-monetary. usually, aes studies include a monetary attribute related to payment level (expressed in currency per hectare per year) to estimate the wta of the various aes designs (espinosagoded et al., 2013; espinosa-goded et al., 2010; etc.). the monetary attribute can also be either funding schemes (e.g., climate premium), international price fluctuations, additional incentives, conditional bonus, etc. (kuhfuss et al., 2015; pröbstl-haider et al., 2016; etc.). the various types of non-monetary attributes can either be management attributes (like ‘biodiversity’ and ‘carbon sequestration’ as environment management attributes used in the study by mäntymaa et al. (2018) and ‘cover crops area size’ as an agriculture-management attribute in the study by villanueva et al. (2015a), or policy design attributes (like ‘collective participation’ and ‘monitoring’ by villanueva et al. (2015a)), or theory-relevant attributes (like ‘recommendation’) and policy-relevant attributes (like ‘share of farm’) (villamayor-tomas et al., 2019), etc. ruto & garrod (2009) labeled agri-environmental policy options as their key design attributes (like, ‘duration of aes contract,’ ‘per hectare payment rate,’ etc.), differentiating them from payment levels. similarly, le coent et al. (2017) categorized the contract attributes as: attributes that have a direct effect on farmers’ compliance costs (levels and types of environmental efforts) and attributes related to contract design (‘length of contract,’ ‘contract cancellation options,’ ‘contract flexibility,’ etc.). they extended the categorization to introduce a novel attribute called ‘purpose’ which they tested via a ce. dupras et al. (2018) also categorized attributes as either visual aspects (like ‘crop diversity’) or personal attributes (like ‘family heritage,’ ‘emotional attachment,’ etc.). christensen et al. (2011) also categorized their contract attributes into three categories: flexibility in contract terms (‘contract length’), flexibility in practical management (‘buffer zone width’), and economic incentive (‘subsidy in euro/hectare/year’). these numerous categorizations can be incoherent for future studies when selecting attributes, which calls for comprehensible and practical typologies. one of the ways to do it is by systematically collating all the attributes from previous studies and sorting them according to their usage. thus, we aim to conduct a systematic review of aes studies’ recent literature that uses ces to reveal the common attributes they use for testing contract designs and farmers’ preferences for those contract features. our 139a systematic review of attributes used in choice experiments for agri-environmental contracts bio-based and applied economics 10(2): 137-152, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9678 study also tries to categorize the attributes into broad typologies and highlight the lesser-used attributes that can be explored in future aes studies. a systematic literature review is used to collect and analyze data from relevant previous studies and identify empirical evidence to satisfy a specific hypothesis or research question (armstrong et al., 2011; petticrew & roberts, 2008; siddaway et al., 2019). we use a systematic review since it has a considerable edge over a narrative review as it is more organized and has reduced bias (koutsos et al., 2019). there have been several proposed methods for conducting a systematic review, and they are usually classified by the research discipline. e.g., the eklipse project report on different methodologies suggests using either the cochrane method (higgins et al., 2019), campbell collaboration protocol (kugley et al., 2017), or the collaboration for environmental evidence (2013) method for conducting a systematic review in the domain of environmental-related sciences (dicks et al., 2017). another novel approach is the prisma (preferred reporting items for systematic reviews and meta-analyses) methodology that illustrates the flow of information in different phases of a systematic review. prisma has been widely in different research disciplines, and has been cited more than 25,000 times, and endorsed in over 400 journals (page et al., 2018). in agricultural sciences, systematic reviews have been a recent change from the traditional narrative reviews (koutsos et al., 2019). koutsos et al. (2019) proposed a framework for conducting systematic review specifically for agricultural sciences by extending the basic steps of the prisma flowchart (illustrated by moher et al., 2010). hence, we use the framework by koutsos et al. (2019) to identify specific studies that use the ce methodology for aes studies and shortlist the attributes used in such studies. this paper is organized as follows: in section 2, we give a detailed account of our methodology and how we shortlisted the studies for the review; in section 3, we describe our results and then discuss our outcomes in section 4. we conclude our study in section 4, highlighting possible future implications of this review. 2. methodology prisma is an evidence-based method for reporting in systematic reviews and meta-analyses. it has been published in several journals to encourage its dissemination and citation (like bmj, plos, springer, etc.). in this study, we use the prisma flowchart and checklist downloaded from moher et al. (2010) and apply it to our study as explained for agricultural science reviews by koutsos et al. (2019). koutsos et al. (2019) tested the framework on a simple case to assess the methodology’s ease and efficacy and thus, promote its adoption among agro-scientists. their framework included the following six steps which we also used in this study: 2.1 scoping we set the following research questions to achieve the objective of this review. • rq1: how many and what are the common contract attributes used by studies while designing a ce for eliciting farmers’ preferences for aess? • rq2: what are the different typologies that the attributes can be classified into? • rq3: how can the lesser-used attributes influence farmers’ wta? 2.2 planning we conducted an extensive search to identify the studies relevant to our rqs. for that, we shortlisted keywords (and boolean operators) and selected the digital database for the search. we tested a range of keywords before finalizing on the following: ‘choice experiment’, ‘agri-environmental’, ‘contracts’, ‘schemes’, ‘measures.’ we chose two digital databases for our search: scopus database (https://www.scopus.com/) and web of science (wos) (https://apps.webofknowledge.com/). 2.3 identification we performed the search (query execution) using various combinations of keywords. we also decided to use no additional filters (like year, subject area, document type, document language, etc.) for the search. we executed the query in may 2020. in total, we found 110 documents (from scopus and wos). scopus search • search string: choice and experiment and agr*environmental and contracts or schemes or measures • outputs: 56 documents from 2006–2020; included 55 articles and 1 conference paper. wos search • search string: “choice experiment” and agr*-environmental schemes or “choice experiment” and agr*-environmental contracts or “choice experiment” and agr*-environmental measures. 140 bio-based and applied economics 10(2): 137-152, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9678 nidhi raina et al. • outputs: 54 documents from the years 2006 – 2020; included 51 articles, 2 reviews, and 1 conference paper. 2.4 screening we assessed the quality of the resulting documents from the search query by first deleting 40 duplicated documents, and then conducting initial screening of the remaining 70 documents by skimming through titles and abstracts. out of the 70 studies, we excluded 12 publications. some of the common reasons for exclusion were either the document was completely unrelated to the search query, or the study did not use the ce method. e.g., two studies (bartkowski & bartke, 2018 and dessart et al., 2019) are reviews of other empirical studies related to aess, but not related to aess that use ce methodology, hence were excluded. 2.5 eligibility we applied content-based quality checks of the full paper for the remaining 58 documents to make sure the selected studies aligned with our objectives. for that, we set inclusion/exclusion criteria for effective checks, as suggested by khan et al. (2003). the inclusion/exclusion criteria we applied to the studies were as follows: a) the study should have used a ce to explore farmers’ willingness to participate in or accept an aes b) the survey respondents should be specifically farmers c) the study should have recorded aes for public goods, not private benefits based on our criteria, 34 studies were finally selected for review with specific reasons for exclusion, like, study design, study measures, type of survey respondents or sample-type, etc., with specific reasons for the exclusion provided in appendix 1. 2.6 presentation we concluded the review by presenting the evidence, summarizing it, and interpreting it to answer our research questions. using the prisma flowchart (extracted from moher et al., 2010), we mapped out the number of articles identified included or excluded (figure 1). we tabulated the study characteristics and choice attributes and their levels found in each study for data synthesis to answer the research questions (appendix 2). similar attributes were grouped and the frequency of their occurrence was noted using ms excel (appendix 3). we then classified the attributes on basis of different typologies, which are discussed in the following sections. 3. results we derived 177 attributes in total from the 34 reviewed studies (appendix 3). the duplicated attributes are collated together, and the resulting 32 unique attributes are depicted in table 1. by categorizing similar attributes, five main typologies emerge: ‘monetary attributes,’ that can be used as a means to calculate potential monetary trade-offs among attributes (‘payment,’ ‘bonus,’ ‘fine,’ etc.); ‘general attributes,’ that outline the general preferences of a contract (‘area,’ ‘duration,’ etc.), ‘flexibility attributes,’ that indicate contract flexibilities (over a duration, over an area, over prescriptions, etc.), ‘prescription attributes,’ that include management, technical, and policy-related specifications across alternative contracts (‘communal participation,’ ‘risk,’ ‘farmer recommendation,’ ‘eco-label,’ ‘monitoring,’ etc.), and ‘purpose attributes’ that define the purpose of the aess and have a direct effect on farmers’ compliance costs (either through chemical reductions or through p r is m a 2 0 0 9 f lo w d ia g ra m records identified through scopus database searching (n = 56) sc re en in g in cl ud ed el ig ib ili ty id en tif ic at io n additional records identified through web of science database (n = 54) records after duplicates removed (n = 70) records screened by titles and abstracts (n = 70) records excluded (n = 12) full-text articles assessed for eligibility (n = 58) articles excluded (based on the set criteria) (n = 24) final studies for review (n = 34) studies included in qualitative analysis (n = 34) figure 1. prisma flowchart filled with study results. source: moher et al. (2010). 141a systematic review of attributes used in choice experiments for agri-environmental contracts bio-based and applied economics 10(2): 137-152, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9678 other environmental efforts like ‘biodiversity conservation’). we found 7 monetary attributes, 5 general attributes, and 5 attributes related to flexibilities in contracts. we also found 11 prescription attributes and 3 purpose attributes that are specific to the purpose of the aes. we discuss the attributes below in detail and how they table 1. attributes found in the review. attributes frequency relation with wta monetary attributes 1 payments (€/ha/year) or compensation; for animals (€/animal/year) 34 same as wta 2 conditional bonus/incentive 6 + 3 potential price fluctuation 2 4 cost ceiling for compensation 1 + 5 gross margin (€/ha/year) or (%) 1 + 6 compost price per trolley (in currency) 1 + 7 fine (in case of infringement) 1 general attributes 8 duration of contract 17 9 area enrolled in contract (%) 15 10 availability of technical training/ scheme support/assistance 8 + 11 average time spent on paperwork/ administration 2 flexibility attributes 12 flexibility over adherence to scheme prescriptions 11 + 13 flexibility over what areas of the farm are entered into the scheme 7 + 14 flexibility of duration or cancellation of contract 6 + 15 flexibility to change agricultural practice (fertilizers, pesticides, manure) 6 + 16 non-participation: flexibility to opt-out 2 + 17 flexibility of dates for working on fields 3 + prescription attributes 18 monitoring 9 not significant 19 communal participation or compensation 7 +/20 maximum grazing (stocking density) 4 + 21 more labor days for work 2 22 coordination with neighbors 1 + 23 recommendation 1 + 24 likelihood of complete crop failure (time in years) 1 25 data provision type 1 +/26 process optimization 1 + 27 input risk 1 28 conservation outcome risk 1 29 eco-label 1 + purpose attributes 30 allocation of land to some environmental activity(s) 15 +/31 ecological focus areas (%) 5 32 reduction of chemicals (%) 4 142 bio-based and applied economics 10(2): 137-152, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9678 nidhi raina et al. impact farmers’ participation and wta. the lesser-used attributes have been used in only one study, and we also discuss their potential for future studies. 3.1 monetary attributes monetary attributes signify those contract features that are specified in monetary terms. these include contract payments that promote farmers’ participation and keep agricultural policy budgets under control (villamayor-tomas et al., 2019), or economic incentives that motivate farmers’ adherence to the terms of the contract (like ‘fine’) (alló et al., 2015). we observed 7 attributes that could fall under this typology. 3.1.1 payment a typical contract dictates farmers modify their farming practices for per-hectare (annual) payments. so, every ce includes a monetary cost/benefit attribute called ‘payment’ that allows for evaluating welfare estimates, i.e., willingness to pay (wtp) or willingness to accept (wta) compensation, for changes in the levels of contract attributes (birol, 2012). the monetary attribute can not only evaluate the farmer preferences, but it can also help estimate the public expenditure needed for each new design of a contract. thus, ‘payment’ is an essential attribute for informing aes policy design (espinosa-goded et al., 2010). we observed that all reviewed studies used this attribute, and it is generally depicted in currency per hectare per annum. though farmers’ wta for changes in different attribute values can be calculated using payment as an attribute in ces, payment can also cover the combination of opportunity costs, management costs, monitoring cost, risk premium, and profit margin in an aes (greiner et al., 2014) which a respondent needs to be aware of while choosing aes options. however, the impact of other attributes affects payment amounts hugely. the trade-offs the farmers are willing to make in exchange for different levels of payments are interesting to analyze. e.g., ruto & garrod (2009) observed that farmers would easily tradeoff approximately 10% of their current payments in exchange for increased flexibility over what lands to enter in the contract or what measures to enroll in the aes. similar results have been noticed in other studies. espinosa-goded et al. (2010) also observed that relaxing the restriction on grazing areas could increase farmer participation and decrease the budget of the contract. similarly, santos et al. (2016) noted that technical support is more important for the farmers than subsidy amounts. the same study estimated that farmers would give up around 400€ per hectare per year for increasing the cattle density by one livestock unit per hectare, reflecting the high opportunity costs of extensification of grazing in portuguese montados (santos et al., 2015). furthermore, wainwright et al. (2019) observed a nonlinear relationship between payment values and farmers’ participation, which indicates the high significance of other contract attributes. villanueva et al. (2017) also observed that farmers required higher compensation for programs with very high levels of demand and low flexibilities. pröbstl-haider et al. (2016) also observed that, farmers are not ready to sway from intensive cropping even with higher compensations. thus, even though payment is the only monetary attribute in most of the aes studies, farmers’ preferences depend on a wider set of factors than just the monetary factors. 3.1.2 conditional bonus/incentives (one-time only) a conditional bonus is paid in addition to the annual compensation payments per hectare as an incentive to farmers to favor higher participation rates and land enrolment in aess and achieve higher targets of contract purposes. kuhfuss et al. (2015) and roussel et al. (2019) used the attribute on the condition of additional chemical reductions per year. similarly, villanueva et al. (2017) offered a fixed incentive at the end of the contract period (after 5 years) on the condition of improvements in the provision of biodiversity and soil functionality. this attribute should theoretically positively affect farmers’ participation; however, it is highly influenced by other contract features. since the bonus is conditional, farmers may not agree to stringent conditions of the contract. roussel et al. (2019) observed a high preference of the farmers towards the bonus. in contrast, kuhfuss et al. (2015) observed higher initial participation but, the bonus had no effect on the individual area enrolled in the scheme. only if the bonus would be used as in collective performance, it efficiently increased the total area enrolled, which signifies the use of this attribute for analyzing collective contract types. some studies also showed that additional bonus was insignificant for farmers, and they would instead prefer higher flexibilities in contracts than additional payments. e.g., the attribute ‘premium for results’ used by villanueva et al. (2017) had no significance on farmers’ participation. also, chang et al. (2017) observed that farmers are reluctant to reduce fertilizer consumption even when incentivized with additional payments. 143a systematic review of attributes used in choice experiments for agri-environmental contracts bio-based and applied economics 10(2): 137-152, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9678 3.1.3 other monetary attributes pröbstl-haider et al. (2016) studied the influence of the attribute ‘potential price fluctuations’ in their ce since international market prices are of increasing importance in their study region (march–thaya floodplains, vienna). their study observed that farmers chose an aes on the basis of price fluctuations play rather than on the value of the environmental premium. another attribute that is important for exploring in future studies is ‘fine.’ alló et al. (2015) used it in their study to analyze the farmer’s moral hazard and free-riding behavior under an aes. though the ‘fine’ attribute is similar to ‘monitoring’; however, unlike monitoring, it is a monetary attribute, and will expectedly increase the wta, but has not been tested sufficiently in aes studies. other market-based monetary attributes like ‘gross margin’ and ‘cost ceiling’ have been used in individual studies as an addition to the payment attribute to test the effect of additional monetary factors on wta. they also positively impact wta; however, they are contractspecific attributes that reflect farmers’ profits rather than the policy design of an aes. 3.2 general attributes we found 4 general attributes including the basic contract design elements (such as ‘contract length,’ ‘contract area,’ etc.). every contract has at least one general attribute, which defines the basic contract regulations. even though theoretically, monetary attributes are of the highest importance to farmers while choosing an aes, many studies have observed the general attributes could sway farmers’ preferences for an aes (christensen et al., 2011; greiner, 2016; hasler et al., 2019; lienhoop & brouwer, 2015). the basic design elements of a contract can thus influence farmers’ wta significantly. 3.2.1 duration of contract the contract duration is an important attribute to determine farmers’ wta. we observed 17 out of selected 34 studies used this attribute in their ces. in almost all the studies, the farmers preferred shorter contracts, except in the study by franzén et al. (2016), wherein it was insignificant. most studies show that increasing contract duration requires higher compensation by the farmers. e.g., ruto & garrod (2009) observed that farmers demand an increase of 1% of the current payments for a year’s increase in the contract duration. 3.2.2 area enrolled in contract (%) we observed 15 studies used this attribute to test its impacts on contract design, and 8 out of those showed that farmers prefer to enroll shorter areas into the contracts, while 6 showed no role of significance. studies have indicated this as a conf lict between agricultural intensification and conservation (de salvo et al., 2018; espinosa-goded et al., 2010; villamayortomas et al., 2019; villanueva et al., 2015a). farmers also have a high reluctance and strong disutility for larger conservation areas or larger forest sizes. e.g., lienhoop & brouwer (2015) noted that farmers do not find large-scale afforestation projects attractive and demand very high costs for such a contract. other studies also proved that farmers are willing to accept smaller subsidies for smaller areas enrolled in the contracts. hasler et al. (2019) observed that the danish farmers required an increase of 1% in their payments for every additional 1% of arable land enrolled in the contract, thus making this attribute important for considering payment amounts. similarly, villanueva et al. (2015) also reported that only 44% of the farmers surveyed would accept a low-tomedium increase in compensation amounts for 1% of the increase in cover crops area, while the rest would either not enroll more areas or ask for higher compensation amounts. enrolling larger areas into the contract increases the probability of adopting more restrictive measures, so farmers prefer to enroll smaller areas (roussel et al., 2019). 3.2.3 availability of technical training/ scheme support we found 8 studies that used ‘technical training/ scheme support’ as an attribute for analyzing farmers’ wta. the majority of the studies observed that technical support is welcomed by farmers and can lead to higher participation and lower compensation payments (christensen et al., 2011; espinosa-goded et al., 2010; hasler et al., 2019; kuhfuss et al., 2015; ruto & garrod, 2009). however, farmers did not consider scheme support important for a conservation program in some studies (franzén et al., 2016; wainwright et al., 2019). furthermore, the attribute is highly preferred when it is provided free of cost (christensen et al., 2011; kuhfuss et al., 2015). santos et al. (2016) attributed technical support as the second most observed factor influencing farmers’ participation in future aess, though it was not included as an attribute in their ce. 144 bio-based and applied economics 10(2): 137-152, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9678 nidhi raina et al. 3.3 flexibility attributes the flexibility in a contract is one of the key factors that facilitate its adoption. flexibility can be in plot selection, prescription selection, or withdrawal from the contract (christensen et al., 2011; kuhfuss et al., 2015; ruto & garrod, 2009; etc.), which can influence compensation amounts immensely. usually, studies have noted that higher flexibilities in contracts can lead to lower wta. e.g., lienhoop & brouwer (2015) observed that only a smaller percentage of farmers were influenced by the payment levels of the aess, as compared to more farmers preferring to have the option to return to agriculture after the contract ends. 3.3.1 flexibility over adherence to scheme prescriptions flexibility in scheme prescription measures or the choice of choosing management type is another attribute many studies deem as important for their ces. it generally has a positive correlation with farmer participation. the 11 studies that use this attribute observed that farmers preferred higher flexibility. latacz-lohmann & breustedt (2019) observed that offering flexibility to farmers like allowing organic fertilizer to be used (compared to no fertilization) reduced the compensation requirement by 127.40€. even the studies not using this attribute have reported farmers’ preferences for higher flexibility in measures and management practices (christensen et al., 2011; espinosa-goded et al., 2010; villanueva et al., 2015b, etc.) 3.3.2 flexibility over what areas of the farm are entered into the scheme the flexibility of the area under contract has a positive significance in most studies (e.g., alló et al., 2015; christensen et al., 2011; greiner, 2016; ruto & garrod, 2009). though 7 studies have mainly used this attribute, other studies have analyzed it through the attribute ‘area size under contract.’ however, this attribute is different from contract area size enrolled as it allows the farmers to choose the area size, conservation activity on that area, and the duration of being enrolled for that area. thus, this attribute is an integration of different flexibility options which can lead to higher participation and lower compensation amounts. e.g., christensen et al. (2011) observed that an average farmer could give up 43€/ha/year for f lexible buffer zone width. 3.3.3 flexibility of duration or cancellation of contract many farmers consider the opportunity to terminate the contract at any time to be an important pre-condition for participation (as shown in studies by broch & vedel, 2012; christensen et al., 2011; hasler et al., 2019, etc.). generally, this attribute has a positive correlation with farmer participation. farmers prefer this possibly because canceling the contract at will would allow them to switch to more intensive farming when market prices increase (mariel & meyerhoff, 2018). this attribute can also be used as an incentive for participation (greiner, 2016; hasler et al., 2019). 3.3.4 flexibility to change agricultural practices (fertilizers, pesticides, manure) studies have shown that flexibility in contract regulations is more important for farmer participation than pre-determined changes in agricultural practices. studies provide this choice of changing agricultural practices at will in their ces to determine the trade-offs between compensation amounts and conservation efforts. e.g., kuhfuss et al., (2015) observed that farmers would not include their whole vineyard in the contract unless they have the flexibility to use chemicals in some farm areas. similarly, latacz-lohmann & breustedt (2019) observed that allowing organic fertilizers, instead of prohibiting all fertilization, reduced the compensation amount by 127.40€. likewise, villanueva et al. (2017) also observed that compensation amounts were reduced with increasing levels of insecticidal treatments allowed in the contracts. their study showed that farmers’ wta is lowest for limited treatment and highest for non-treatment, indicating that farmers are reluctant to give up chemical treatments altogether. however, only 6 studies used this attribute; thus, there is a greater scope of experimenting with different conservation options. 3.3.5 non-participation: flexibility to opt-out though all the studies (like broch & vedel, 2012; christensen et al., 2011; espinosa-goded et al., 2010; kuhfuss et al., 2015; ruto & garrod, 2009; etc.) use it in their choice cards when conducting a ce; however, only 2 studies used it specifically as an attribute for the ce (le coent et al., 2017 and roussel et al., 2019). not including it as an attribute could be because the coding of variables in the ce testing model with an opt-out option poses several challenges (le coent et al., 2017). the opt-out option is generally used in ce to give the 145a systematic review of attributes used in choice experiments for agri-environmental contracts bio-based and applied economics 10(2): 137-152, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9678 farmers the voluntary choice of choosing an aes. villamayor-tomas et al. (2019) noted that 37% of farmers chose the opt-out option; however, he suggested exploring further whether this would affect the main findings. roussel et al. (2019) studied the attribute to understand farmers’ preference to keep their current practices. this attribute generally positively correlates to farmers’ preferences since it avoids them facing a forced choice. 3.4 prescription attributes most of the attributes in the reviewed studies were prescription attributes that defined the technical and management aspects of the contracts. we found 16 such attributes used in 5 or less than 5 studies; however, most are uniquely used (only in one study) and are also discussed under lesser-used attributes in the discussions section. researchers use attributes like ‘monitoring’ are to check for non-compliance among farmers (9 studies use this attribute). however, monitoring is costly, and the balance between non-compliance and monitoring is often ignored (vedel et al., 2015). we observed that the monitoring attribute was insignificant in most of the studies indicating that it plays a minor role in farmers’ choice of participating in an aes (greiner, 2016; rodríguez-entrena et al., 2019; villanueva et al., 2015b, 2015a; villanueva et al., 2017). however, only broch & vedel (2012) and vedel et al. (2015) observed that monitoring had a significantly negative impact on respondents’ utility and led to increased wta. the reason for the negative attitude towards monitoring could be the farmer’s mistrust of the system or the farmer’s perception of the system controlling him (broch & vedel, 2012). ‘communal participation’ or ‘communal schemes’ are also attractive to farmers since they induce a ‘neighbor-effect’ among farmers, leading to increased participation in the aes. communal management can have mixed results on farmers’ wta. studies such as hope et al. (2008) and villanueva et al. (2017) reported a positive correlation to farmers’ preferences. hope et al. (2008) reported that farmers prefer working as a group rather than as individuals. villanueva et al. (2017) reported that older farmers (> 60 years) show a higher willingness for collective participation than younger farmers in olive groves of plain areas. even though only 7 studies have used this attribute, many other studies mention similar factors that indicate that farmers’ have high utility for community participation and management. e.g., aslam et al. (2017) observed that social pressure and social networks could increase farmers’ acceptance for contracts. similarly, alló et al. (2015) tested the variable ‘social trust’ to evaluate whether farmers believe their neighbors fully comply with the contract terms, and observed that majority of respondents think that their neighbors will comply. this compliance indicates that the attribute should be tested in ces for collective contract types. however, some studies also observed that farmers prefer individual management and discrete compensation, like rodríguez-entrena et al. (2019) noted that collective participation leads to a higher degree of uncertainty among the farmers. similarly, villanueva et al. (2015a) suggested that most farmers showed medium to high wta for collective participation because they anticipated loss of freedom of their farm management due to community participation. other attributes that span under the umbrella of ‘neighbor-effect’ include ‘coordination with neighbors’ and ‘recommendation.’ neighbor-effect generally positively correlates with farmers’ participation. villanueva, et al. (2017) noted that farmers are more willing to participate at lower transaction costs if the neighbors also participate. also, the attribute ‘farmers’ recommendation’ used by villamayor-tomas et al. (2019) exhibited a positive significance to farmers’ acceptance, whereas the attribute ‘scientist recommendation’ had no significant impact. the attribute is also similar to ‘communal participation’, with the only difference being that this tests the farmer’s preferences to his immediate neighbor’s preferences, whereas the latter is on the level of the whole community. de salvo et al. (2018) suggested that neighbor-effect can improve acceptability of aess and achieve cost-effectiveness of contracts, and hence farmers’ preferences for ‘local context’ should be considered by policymakers. ‘grazing intensity’ or ‘stocking density’ is another attribute that 4 studies included for testing farmers’ preferences for a reduction in grazing intensity or the number of animals per hectare. the studies (breustedt et al., 2013b; latacz-lohmann & breustedt, 2019; santos et al., 2015) show that stricter prescriptions for lesser grazing lead to higher compensations, thus higher wta. non-monetary incentives have also been overlooked by all the studies except one. chang et al. (2017) used the ‘eco-label’ attribute to incentivize farmers who successfully complied with the aes standards and observed that farmers would readily exchange an eco-label for lower compensation amounts. so, including non-monetary incentives like eco-label can also help lower the farmers’ wta. ‘risk’ is another attribute that has been used in the study by star et al. (2019) that explored how input or outcome risk limits the farmers’ willingness to implement environmental measures. their study reported that higher levels of either risk would reduce participation 146 bio-based and applied economics 10(2): 137-152, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9678 nidhi raina et al. and increase the compensation amount. this attribute should be extensively tested especially in the face of climate and socio-economic uncertainties. 3.5 purpose attributes these attributes are different from the contract design attributes as they specifically iterate the purpose for which the farmer will accept the contract prescriptions. the purpose of an aes could be a conservation activity, afforestation, land allocation for environmental activity, chemical reduction, etc., which is what these attributes offer. 3.5.1 allocation of land to some environmental activity(s) attributes like ‘maintenance of soil organic matter,’ ‘protection of soil from water erosion,’ ‘recreational access,’ ‘biodiversity improvements,’ ‘forest co-benefits,’ ‘afforestation,’ etc. are different types of environmental and conservation activities that define the contract motives. some studies like broch & vedel (2012) used the attribute ‘purpose’ specifically to combine different conservation activities into one choice for their ce (biodiversity, water protection, or recreation). le coent et al. (2017) also used ‘purpose’ as a separate attribute to highlight farmers’ preferences between different conservation activities. one of the significant inferences from testing this attribute has been that most farmers prefer conservation over compensation (according to the studies by le coent et al., 2017; lienhoop & brouwer, 2015; santos et al., 2015; vedel et al., 2015). greiner (2016) also used this attribute to understand the significance of different conservation activities; however, he observed that 33% of farmers found the choice insignificant, rather focused on payment values and contract duration. 3.5.2 ecological focus area though 5 out of 34 reviewed studies used this attribute in their ce; however, 4 of these studies use the same set of choice attributes in their ce (rodríguez-entrena et al., 2019; villanueva et al., 2015b, 2015a; villanueva et al., 2017). according to villanueva et al. (2017, p6), this attribute was included in the ce to “explore a hypothetical future implementation of the efa requisite of the common agricultural policy (cap) ‘green payment’ in permanent crops such as olive groves”. some previous studies have also mentioned efa in their articles but do not test it in their ces; like breustedt et al. (2013) and villamayor-tomas et al. (2019). overall, studies using this attribute observed that farmers have a negative preference for efas, since agreeing to it would mean dedicating additional areas to ecological functions than stated in the contract. a similar attribute called ‘naturalization’ used by rocchi et al. (2017) defines conversion of agricultural areas to pastures, using particular species with a high natural value. however, farmers in their study show the least interest in this attribute. 3.5.3 reduction of chemicals (%) this attribute is typically used to study the compensation payments that would be required for a higher reduction in chemicals. kuhfuss et al. (2015) found that higher chemical reduction can lead farmers to enroll more farm areas in the aes because chemical reduction needs higher investment in equipment that becomes more cost-efficient if used on the whole farm rather than just small areas. chang et al. (2017) observed that after a point, farmers show high reluctance to further reductions of chemical fertilizer use even when additional payments are offered. similarly, kanchanaroek & aslam (2018) also observed that shorter contract lengths and a lower reduction in chemical input together lowered the wta substantially. 3 out of 4 studies using this attribute reported that chemical reduction negatively impacts farmers’ participation and increases their wta. only rocchi et al. (2017) observed that most of their respondents are interested in reducing nitrates. chang et al. (2017) suggested that farmers should be incentivized if they agree to an additional reduction of chemical fertilizers. 4. discussions our study used a systematic review for a reliable and transparent method of reviewing previous aes literature that uses ce to elicit farmers’ preferences to alternative aess. we set three specific research questions that this review hoped to answer and discuss. we listed out 32 attributes used by studies as shown in appendix 3 and defined and analyzed in the results section, which answers our first research question. the most common attribute used by all studies is the payment attribute that can help estimate the monetary value of other attributes. however, aes studies aim to find incentives other than monetary payments for estimating farmers’ wta (villamayor-tomas et al., 2019). the contract purpose is present in all studies, which could include either ‘allocation 147a systematic review of attributes used in choice experiments for agri-environmental contracts bio-based and applied economics 10(2): 137-152, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9678 of land to environmental activity,’ or ‘chemical reductions,’ or ‘changing the land to an ecological focus area.’ the purpose of the contract helps in deducting the conservation versus compensation behavior of the farmers. the general contract design is shaped by attributes such as ‘duration of contract,’ ‘area enrolled under contract,’ and ‘availability of scheme support/additional training,’ which are usually the first few attributes in the choice cards of aes studies. attributes indicating flexibility in overall contract terms and environmental goals have shown to increase farmers’ acceptance and participation (like christensen et al., 2011; espinosa-goded et al., 2010; ruto & garrod, 2009; etc.); thus, most studies also include a flexibility attribute. the management, technical, and policy prescriptions can also be tested for an effective policy design through a ce. these can include attributes such as ‘collective participation,’ ‘monitoring,’ ‘farmer recommendation,’ etc. they can also be a novel attribute that has not been tested before, like ‘risk.’ upon surveying the common attributes, five main typologies were established under which all the extracted attributes could be classified, which answers our second research question. at least one attribute under each typology must be used in the aes study for an effective outcome and to remove subjectivity bias among researchers designing ce. our classification includes monetary attributes, general attributes, flexibility attributes, prescription attributes, and purpose attributes, which have been discussed in detail in the results section. economic factors of farmers’ wta has been wellunderstood and widely discussed by many studies (like christensen et al., 2011; lastra-bravo et al., 2015; santos et al., 2016, etc.) which can include the level of compensation, transaction costs, duration, and flexibility of contracts, availability of scheme support, etc. however, equally important cognitive, behavioral, and societal factors have not been discussed enough in aes studies. farmers’ attitudes and values, perceptions about conservation and compensation, and social norms like collective participation can influence farmer participation in an aes (kuhfuss et al., 2015; villamayor-tomas et al., 2019). many studies have also inferred upon the farmers’ dilemma between compensation and conservation. le coent et al. (2017) conducted their ce with two types of contracts: compensation and conservation contracts. they reported that farmers preferred to participate in a contract with a biodiversity conservation objective than with a biodiversity compensation objective and exhibit higher wta for enrolling into the compensation contract. on the contrary, a study by villamayor-tomas et al. (2019) showed conservation programs tend to harm farmers’ utility and were not preferred by the farmers. studies have also noted that when the conservation options restrict the land-use options for the farmers, their wta for conservation measures increases (aslam et al., 2017; hope et al., 2008; pröbstl-haider et al., 2016). this disparity between what has been tested and what can be tested prompted us to discuss the lesserused attributes. our review also found 12 uniquely used attributes, and we explored their utility for further studies, as per our research question 3. most of these attributes are prescription attributes, that are specific to the contract area and type and might not be replicable over other aess. however, some of the attributes can be studied over different contract types and must be explored more. one such novel monetary attribute is ‘fine’ used by alló et al. (2015), which could be applied for any law infringement. even though other studies have also tried to test compliance through economic incentives (kuhfuss et al., 2015) or monitoring (broch & vedel, 2012); however, fine is the only attribute that enforces an economic penalty on noncompliance to the contract, and thus should be tested in more studies. attributes such as ‘coordination’ and ‘recommendation’ are prescription attributes that play on social psychology and behavioral economics to positively influence the choice of farmers to participate in an aes if there is already a high level of participation (kuhfuss et al., 2015). this indicates that farmers care not only about the economic incentives of the contracts but also of their “reputation” (villamayor-tomas et al., 2019), which can be tested through attributes exhibiting neighbor-effect. ‘risk’ is another prescription attribute that has only been used in one study (in star et al., 2019). though many other aes studies talk about farmers’ perceptions of risk and uncertainty as core reasons for non-participation (e.g., hellerstein et al., 2015; schilizzi & latacz-lohmann, 2016; whitten et al., 2013; etc.); however it has not been studied in their ces. though star et al. (2019) studied the input and output risks endured by landholders, their study did not consider institutional, production, or market risks that are also critical in designing efficient agrienvironmental policies. another interesting attribute is an ‘eco-label’ that has been tested in one study (by chang et al., 2017) that farmers appreciated more than higher compensation amounts. however, such non-monetary incentives are not usually tested in eu studies, but with the rise in local certification schemes, more aess could have such attributes. another variable of interest that hasn’t been tested in any study but has shown to lower farmers’ wta and increase participation (breustedt et al., 2013; lataczlohmann & breustedt, 2019) which is ‘ farmers’ previous participation in an aes contract.’ however, wain148 bio-based and applied economics 10(2): 137-152, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-9678 nidhi raina et al. wright et al. (2019) noted that farmers already enrolled in an aes scheme were more likely not to select a contract option. thus, this is a possible attribute that can be explored through future ce studies. this study can thus be used as a reference for other aes studies that use literature review for selecting the attributes for their ce from various categories. it also provides a systematic framework for organizing literature that can be applied to newer aes studies. this study can also help shortlist attributes for future ce testing that can evaluate specific aims of cap post-2020 like penalties to non-compliance (like fine) and alternatives to greening through certification schemes (like eco-label). 5. conclusions our study aimed to highlight the common attributes that are used in a ce for studying farmers’ acceptance of choice of agri-environmental contracts using a systematic review of literature while also categorizing the attributes into definitive typologies and glancing at the utility of lesser-used attributes. in conclusion, we found 32 attributes that could fit in five distinct typologies: 7 monetary attributes, 4 general attributes, 6 flexibility attributes, 12 prescription attributes, and 3 purpose attributes. contract design attributes can impact compensation amounts hugely; e.g., general contract attributes (like smaller area and shorter duration) and flexibility attributes (such as higher flexibility of participation, contract area, contract duration, management, etc.) are highly preferred by the farmers and can lower their wta and increase their participation. technical support and scheme assistance are also positively welcomed by the farmers. overall, the commonly used attributes are an indicator of those contract features that previous studies have tested repeatedly with ces, and have shown consistent outcomes, e.g., shorter contract duration and the lesser enrolled area is preferred by farmers in most of the studies. however, some attributes also show varied results, e.g., monitoring has been insignificant for farmer acceptance in most of the studies and was found to be negatively related to farmer acceptance in two studies. moreover, attributes that can directly address some of the emerging issues in eu’s cap reform features, such as result-based contracts (e.g., ‘conditional monetary bonus’ attribute used by roussel et al., 2019) and collective contracts (like ‘collective participation’ and ‘communal management’ attribute used by villanueva et al., 2017) have not been tested in many studies. they can be comprehensively analyzed in future aes studies. we also found attributes that have theoretically been shown to be critical for aes selection but have been overlooked by most of the studies. these are non-monetary incentives, fine, recommendation, risk, coordination with neighbors, etc. the reasons for this exclusion could probably be a lack of literature to support their importance, or maybe these attributes require exhaustive coding in models. market-based and value-chain attributes such as crop failure, price fluctuations, climate risks, etc. have also not been explored much which can become important under uncertain future scenarios (like climate change, socioeconomic change, etc.). thus, the lesser-used attributes are also an important indicator of farmers’ acceptance of a contract and should be studied intensively. our review indicates that ces should take more advantage of the virtual environment they are set to test and should experiment on a broader range of attributes across different areas and contract types. we hope that our systematic review can be used as a repository for choosing choice attributes for future studies and our typologies can be used to make a choice bundle that can fully explain both the farmer perceptions and value of a particular landscape. funding this work was supported by research executive agency (rea), under the powers delegated by the european commission, under the european union’s horizon 2020 research and innovation program through the project console, grant agreement no. 817949. this work does not necessarily reflect the view of the eu and in no way anticipates the commission’s future policy. acknowledgements we thank the anonymous reviewers for their insightful comments and suggestions that significantly improved the manuscript. references alló, m., loureiro, m. l., & iglesias, e. 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(2013). designing conservation tenders to support landholder participation: a framework and case study assessment. ecosystem services, 6, 82–92. volume 10, issue 2 2021 firenze university press mediterranean agriculture facing climate change: challenges and policies filippo arfini the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications cristina vaquero-piñeiro application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context raffaella zucaro, veronica manganiello, romina lorenzetti*, marianna ferrigno climate changes and new productive dynamics in the global wine sector emilia lamonaca*, fabio gaetano santeramo, antonio seccia a systematic review of attributes used in choice experiments for agri-environmental contracts nidhi raina*, matteo zavalloni, stefano targetti, riccardo d’alberto, meri raggi, davide viaggi the effect of farmer attitudes on openness to land transactions: evidence for ireland cathal geoghegan*, anne kinsella, cathal o’donoghue aieaa webinar disei dipartimento di scienze per l’economia e l’impresa disei dipartimento di scienze per l’economia e l’impresa pierluigi montalbano sapienza university of rome donato romano university of florence aieaa webinar: 27th april 2023 vulnerability and resilience to food and nutrition insecurity: a review of the literature towards a unified framework disei dipartimento di scienze per l’economia e l’impresa background why vulnerability and resilience? • increased shocks and risks, uncertainty • practical reasons: hum & dev’t agencies – hyogo framework for action (2007) – world humanitarian summit (2016): nwow, hdp nexus • conceptual reasons: – similar, largely overlapping concepts – hot topics, but § no comprehensive comparative analysis of v and r § many definitions for both v and r: different models and measures 2 vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa background 3 vulnerability is determined by the options available to households and individuals to make a living, the risks they face and their ability to handle this risk (dercon, 2001) resilience is the capacity of a household that ensures adverse stressors and shocks do not have long-lasting adverse development consequences (rmtwg, 2014: 4) • risks ↔ stressors and shocks • options available and ability to handle risks ↔ capacity • make a living ↔ prevent long-lasting adverse development consequences vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 why vulnerability and resilience? disei dipartimento di scienze per l’economia e l’impresa background why vulnerability and resilience? 4 vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa background why vulnerability and resilience? • key question: is resilience just the flip side of vulnerability? • specific questions on v and r: – how are they conceptualized? – how are they measured? – how good are they for practical use? § targeting § impact evaluation * specific reference to food insecurity 5 vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa outline • food security measurement axioms • vulnerability – definitions – measurement approaches • resilience – definitions – measurement approaches • vulnerability vs. resilience – towards a unifying framework • research and policy implications 6 vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa food security fs measurement axioms (upton et al., 2016) • scale: aggregation and decomposability • time: dynamics, forward-looking • access: conditioning factors • outcome: wellbeing dimension 7 vulnerability and resilience to food insecurity world food summit (fao, 1996) “a situation that exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life” aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa vulnerability • different approaches – vulnerability to expected poverty (vep) (christiaensen & subbarao, 2001; chauduri et al., 2002) – vulnerability as low expected utility (veu) (ligon & schechter, 2003, 2004) – vulnerability as the threat of future poverty (vtp) (calvo & dercon, 2007, 2013) * generally, no reference to food security 8 vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa vulnerability #1: vep 9 vulnerability and resilience to food insecurity axioms check scale y time n access y outcome n probability that the hh’s level of consumption at time t+1 will be below the consumption poverty line, z assumption: the cross-sectional variation of the sampled hhs’ consumption as the time series variation of hhspecific consumption aieaa webinar, 27th april 2023 𝑉𝐸𝑃!,# = 𝑃𝑟(𝑙𝑜𝑔 𝑐!,# < 𝑙𝑜𝑔 𝑧 |𝑋!,#) = φ 𝑙𝑜𝑔 𝑧 − 𝑙𝑜𝑔 2𝑐!,# 3𝜎!,#$ disei dipartimento di scienze per l’economia e l’impresa vulnerability #2: veu 10 vulnerability and resilience to food insecurity difference btw the u derived from some level of certaintyequivalent consumption at and above which the hh would not be considered vulnerable and the expected u of cons. axioms check scale y time y access y outcome n main limitations: • no focus axiom • mean consumption as independent from risk, underestimating the overall effect of risk aieaa webinar, 27th april 2023 𝑉𝐸𝑈! = 𝑈! 𝑧"# − 𝑈! 𝐸𝑐!,% + 𝑝𝑜𝑣𝑒𝑟𝑡𝑦 𝑈! 𝐸𝑐!,% − 𝐸𝑈! 𝑐!,% 𝜇& + 𝑚𝑒𝑠𝑜 𝑟𝑖𝑠𝑘 𝐸𝑈! 𝑐!,% 𝜇& − 𝐸𝑈! 𝑐!,% 𝜇& , 𝜇% + 𝑎𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑒 𝑟𝑖𝑠𝑘 𝐸𝑈! 𝑐!,% 𝜇& , 𝜇% − 𝐸𝑈! 𝑐!,% 𝜇& , 𝜇%, 𝑥!,% + 𝑖𝑑𝑖𝑜𝑠𝑦𝑛𝑐𝑟𝑎𝑡𝑖𝑐 𝑟𝑖𝑠𝑘 𝐸𝑈! 𝑐!,% 𝜇& , 𝜇%, 𝑥!,% − 𝐸𝑈! 𝑐!,% 𝑢𝑛𝑒𝑥𝑝𝑙𝑎𝑖𝑛𝑒𝑑 𝑟𝑖𝑠𝑘/ 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡 𝑒𝑟𝑟𝑜𝑟 disei dipartimento di scienze per l’economia e l’impresa vulnerability #3: vtp 11 vulnerability and resilience to food insecurity axioms check scale n time y access y outcome y the burden caused by the threat of poverty, i.e. exposure to states of the world where hardship may strike causes distress ex-ante, even if ex-post the dice are benign and poverty (consumption below a critical norm) does not materialize 𝑉! = 1−e & "#$ % 𝑥" $ % ! main limitations: • outcomes assumed to be nontransferable across states of nature • highly data demanding (knowledge of all possible states of nature) aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa resilience • different approaches – resilience as capacity (rci) (alinovi et al., 2008; fao, 2016) – resilience as as return to equilibrium (perrings, 2006; knippenberg et al., 2019) – resilience as a normative condition (𝜌) (barrett & constas, 2014; cissé & barrett, 2018) – resilience as transformability (walker et al., 2004; reyers et al., 2018) * referred to poverty as well as food security 12 vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa resilience #1: capacity 13 vulnerability and resilience to food insecurity axioms check scale n time n access y outcome y an ex-ante capacity that limits the adverse well-being effects of risk exposure (i.e., stressors) and/or the nearor longerterm consequences of shocks 𝑅𝐶𝐼 = 𝛽$, 𝛽&, … , 𝛽% . 𝐴𝐵𝑆, 𝐴𝑆𝑇, 𝑆𝑆𝑁, 𝐴𝐶 + 𝜀$ 𝑊$,𝑊&, … ,𝑊% = 𝛼$, 𝛼&, … , 𝛼% . 𝑅𝐶𝐼 . 𝜀&, 𝜀', … , 𝜀% main limitations: • explanatory variable, rather than an outcome • not forward-looking • not anchored to a norm aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa resilience #2: return to equilibrium 14 vulnerability and resilience to food insecurity axioms check scale n time y access y outcome n aieaa webinar, 27th april 2023 𝑍",) * = 𝛾+ + 𝛾$*𝑍",),$ * + 𝛾)* 𝑍",),$ * 𝛿) + 𝛿) + 𝜇" * + 𝛿𝜀",) hh’s capacity to recover – sometimes how fast is the speed of recovery – from a shock, i.e. ex-post recovery of a wellbeing variable rather than explicitly modeling the various capacities that result in rapid recovery main limitations: • not decomposable/aggregable across sub-populations • focus only on the capacity of the household/individual to return to the pre-shock status disei dipartimento di scienze per l’economia e l’impresa resilience #3: normative condition 15 vulnerability and resilience to food insecurity axioms check scale y time y access y outcome y a condition that reflects one’s capacity to avoid adverse well-being states, rather than a capacity itself 𝑊',% =? & 𝛼&𝑊',%() & + 𝛄𝐗𝐢,𝐭 + 𝛉𝐒𝐢,𝐭 + 𝜀',% 𝜀',%, =? & 𝛽&𝑊',%() & + 𝛅𝐗𝐢,𝐭 + 𝛝𝐒𝐢,𝐭 + 𝑢',% 𝜌',≡ pr 𝑊',≥ 𝑊 𝑊',-(), 𝐗𝐢,𝐧, 𝐒𝐢,𝐧 = 𝐹 𝑊, o𝑊',𝑊',-(), 𝐗𝐢,𝐧, 𝐒𝐢,𝐧 , ̂𝜀',-, 𝑊',-(), 𝐗𝐢,𝐧, 𝐒𝐢,𝐧 (hh-specific conditional variance) (hh-specific conditional mean) (hh-specific conditional probability of satisfying a normative wellbeing standard) aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa vulnerability vs. resilience 16 vulnerability is determined by the options available to households and individuals to make a living, the risks they face and their ability to handle this risk (dercon, 2001) resilience is the capacity of a household that ensures adverse stressors and shocks do not have long-lasting adverse development consequences (rmtwg, 2014: 4) * r: long-term consequences→ not being trapped in a low-level equilibrium, ability to bounce back vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa vulnerability vs. resilience is resilience just the the flip side of vulnerability? 17 vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 v=0 ⟹ r=1 r=0 ⟹ v=1 0<v<1 0<r<1 time (t) as se ts (a ) recoverycopingshock static asset poverty line poverty trap threshold aw ap pre-shock disei dipartimento di scienze per l’economia e l’impresa vulnerability vs. resilience is resilience just the the flip side of vulnerability? 18 vulnerability and resilience to food insecurity a non-poor hh with high m2 may be both vulnerable and resilient aieaa webinar, 27th april 2023 current wellbeing (wt) fu tu re w ell be in g ( w t+ 1) t1 t2 t1 t2 s i d food and nutrition security areachronic food and nutrition insecurity area humanitarian emergency area disei dipartimento di scienze per l’economia e l’impresa towards a unifying framework (cissé & barrett, 2018) the case for a conditional moments-based approach 20 poverty traps literature • estimating only the first moment (expected path dynamics) • allowing for potentially non-linear dynamics vulnerability literature • estimating both the conditional mean and conditional variance • ignoring non-linearity in prospective dynamics resilience • considering higher order conditional moments • considering non-linearity in prospective dynamics vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa towards a unifying framework the case for a conditional moments-based approach normatively anchored, intrinsically dynamic, forward-looking • scale: possibility aggregation across / decomposition between subpopulations • time: ability to estimate the time series of resilience score per each period, s, beyond t • access: condition the moments of the food security distribution on any of a host of economic, physical, and social factors • outcome: conditional probability of satisfying a normative wellbeing (e.g. fcs, hdds, muac, etc.)21 vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa towards a unifying framework the case for a conditional moments-based approach practical use • targeting: possibility to set different thresholds (e.g. low probability to be above a high threshold vs. high probability to be below a very low threshold) → exclusion vs. inclusion error) • policy impact: possibility to evaluate performance in ‘building resilience’: – specific assets (e.g., a livestock transfer), – products (e.g., insurance or credit), – infrastructure (e.g., irrigation, roads, or health clinics) 22 vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa policy implications • objective: minimize the likelihood of people falling into either of the less desirable zones (he, cfni) • three possible options for disruptive interventions – shift people’s current status, i.e. increase 𝑊! – alter the conditional transition distributions, i.e. truncate 𝜀! from below § risk transfers § risk reduction – change the underlying system structure to induce behavioral change and thereby change the functions 𝑚" = 𝑊!#$'𝑊!, 𝜀! 24 vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa research implications • applied research agenda – expand geographical coverage – expand the range of shocks and stressors – cross-scale measurements – socio-ecological system jointly dynamic analysis • build/use of the relevant data base – identify the relevant data needs – build a multi-country system of sentinel sites collecting high-quality, high-frequency data over long period of time, particularly in the more disaster-prone areas 29 vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 disei dipartimento di scienze per l’economia e l’impresa thank you for your attention donato.romano@unifi.it pierluigi.montalbano@uniroma1.it 32 vulnerability and resilience to food insecurity aieaa webinar, 27th april 2023 mailto:donato.romano@unifi.it mailto:pierluigi.montalbano@uniroma1.it bio -based and a ppl ied economics bae bio-based and applied economics 11(2): 91-92, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-13517 copyright: © 2022 m. scoppola. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae editorial agriculture, food and global value chains: issues, methods and challenges margherita scoppola università di macerata about one-third of trade in food and agriculture takes place within global value chains (gvc). coffee, palm oil or biofuels production are examples of the modern organization of agri-food production through gvc (de becker, miroudot, 2014; greenville et al., 2016; baliè et al., 2019). agricultural raw materials nowadays may cross borders many times before reaching the final consumers, as they are embedded in intermediate and processed goods which are produced in different countries. agri-food gvc are typically characterized by a strong coordination between farmers, food processors or traders, and between processors and retailers. value chain coordination can be initiated by downstream buyers, such as supermarkets and food processors, or by upstream suppliers including farmers or farmer cooperatives (swinnen and maertens, 2007; reardon et al 2007). in a number of cases, a group of “lead firms” plays a critical role by defining the terms of supply chain membership and whom the value is added (scoppola, 2021). the growth of the agri-food gvc raises new issues for the agricultural and food sectors. participating to the gvc is expected to have several positive effects, both for countries and farmers, in terms of technology and knowledge spillovers, increased productivity, growth, employment opportunities, and ultimately increase of farmers’ income. on the other hand, market concentration in agri-food gvc raises concerns related to the emergence of market power (swinnen, vandeplas, 2014). further, there are concerns that producing for agri-food gvc may result in the intensification of agricultural production, with negative environmental effects in terms of deployment of natural resources and water stress. sound knowledge and evidence about the nature and implications of modern agri-food gvc are relevant for policymaker, firms and civil society. the economic analysis of agri-food gvc challenges agricultural and food economists in several respects. the complex nature of gvc and of the issues they raise makes it essential the use of new and multiple lens of analysis (world bank, 2020). country-level (macro) approaches to gvc are needed to investigate the drivers of the world-wide fragmentation of agri-food production and the welfare implications of countries participating to gvc. recent progresses in the empirical trade analysis of gvc are certainly fundamental to the understanding of agrifood gvc. industry level (meso) approaches are needed to investigate the relationship among the various stages of the gvc. analytical tools and approaches from the industrial organization literature are to be used to investigate issues such the price transmission along the agrifood gvc, the drivers of vertical coordination or the distributions of benefits along the gvc. a firm level approach (micro) is needed to investigate the implications of the participation to gvc for farmers. the 10th aieaa annual conference contributes to this debate, by putting together different disciplines and approaches to the analysis of agri-food gvc and of their implications in terms of economic, social, and environmental sustainability. three keynotes explore these issues from different perspectives. the keynotes by silvia nenci ilaria fusacchia, anna giunta, pierluigi montalbano and carlo pietrobelli entitled mapping global value chain participation and positioning in agriculture and food (nenci et al., 2022) reviews key methods and data issues arising in country-level analyses of gvc. they overall conclude that improvements in gvc measurements and mapping are currently still severely limited by data availability. empirical literature to date mostly uses global input-output matrices and aggregate trade data to map and measure gvcs; however, sectoral and country coverage remains rather weak. they further review recent 92 margherita scoppola bio-based and applied economics 11(2): 91-92, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13517 evidence about trends of gvc, by using the gvc participation indicator and the upstreamness positioning indicator (measuring the distance of the sector from final demand in terms of the number of production stages) for two sectors, that is “agriculture” and “food and beverages”. they show that at the country level, gvc participation is globally around 30-35 percent for both agriculture and food and beverages; while gvc linkages in agriculture are mostly forward linked, food and beverages are much more in the middle and at the end of a value chain. furthermore, they show that, unsurprisingly, agriculture has a higher score on upstreamness with respect to the food and beverages sector. they conclude by discussing some critical issues faced by agriculture and food gvc concerning trade policies, technological innovation and the covid crises. the keynote by tim lloyd entitled “price transmission and imperfect competition in the food industry” 1 aims at providing insights on how information is conveyed by means of prices between food consumers and agricultural producers along the agri-food value chains. after presenting some basic insights from theory, the keynote addresses the issue of how to detect the degree of market power by reviewing theory-consistent empirical models as well as the approaches developed in the new empirical industrial organisation literature. the increased use of highly detailed retail (‘scanner’) data reveals that the food industry (retailing, manufacturing, and processing) is a major source of the price changes and that it also mediates price signals originating in other parts of the food chain in increasingly nuanced ways; the author concludes that agricultural and food economists should be wary of inferring too much about the competitive setting based on prices alone. the keynote by miet maertens entitled “a review of global and local food value chains in africa: supply chain linkages and sustainability” highlights the expansion of agri-food gvc in lowand middle-income countries and how gvc are modernizing rapidly through institutional, technical, and commercial innovations. while a large body of literature focusses on the development implications of participation in gvc, the development of local food supply chains in lowand middle-income countries has received less attention. the review assesses potential linkages between global and local value chains in african countries, and the sustainability outcomes of supply chain innovations. the keynotes emphasizes that market competition as well competition for land, labour, water, and other resources may create negative linkages between the development of global and local food value 1 slides of the keynotes are downloadable at https://www.aieaa.org/aieaaconference2021. chains. spill-over effects, such as investment, technical or institutional spillovers, may create positive linkages and complementarities in the process of supply chain development. the existence of such linkages importantly depends on the type of crop and the structure and organisation of supply chains and entail important consequences towards socio-economic and environmental sustainability. references balié, j., del prete, d., magrini, e., montalbano p., nenci s. (2019) does trade policy impact food and agricultural global value chain of sub-saharian african countries? american journal of agricultural economics, 101 (3), 773–789. de becker, k., miroudot, s. (2014) mapping global value chain, working paper series 1677, may, european central bank. greenville, j., beaujeu, r., kawasaki, k. (2016) gvc participation in the agriculture and food sectors, oecd trade and agriculture directorate working paper. nenci, s., fusacchia, i., giunta, a., montalbano, p., pietrobelli, c. (2022) mapping global value chain participation and positioning in agriculture and food: stylised facts, empirical evidence and critical issues. bio-based and applied economics, 11(2), 5-33. doi: 10.36253/bae-12558 reardon, t., henson, s., berdegué, j. (2007) proactive fast‐tracking’ diffusion of supermarkets in developing countries: implications for market institutions and trade. journal of economic geography, 7(4), 399–431. scoppola, m. (2021) globalisation in agriculture and food: the role of multinational enterprises. european review of agricultural economics, 48(4), 741-784. swinnen, j., maertens, m. (2007) globalization, privatization, and vertical coordination in food value agricultural economics 37(2), 89–102. swinnen, j., vandeplas, a. (2015) price transmission in modern agricultural value chains: some conceptual issues in steve mc corriston (ed) food price dynamics and price adjustment in the eu, oxford academic: oxford, uk; pages 147–166. world bank (2020) world development report 2020. trading for development in the age of global value chain, world bank, washington d.c., usa. bio-based and applied economics 8(2): 211-234, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-8931 determinants of farm households’ willingness to accept (wta) compensation for conservation technologies in northern ghana evelyn delali ahiale1,*, kelvin balcombe2, chittur srinivasan2 1 department of agricultural economics and extension education, university of education, winneba, ghana 2 school of agriculture, policy and development, university of reading, uk abstract. this paper examines the determinants of farmers’ willingness-to-accept (wta) in a hypothetical payment scheme for the adoption of stone and soil bunds in northern ghana using contingent valuation data from 305 farm households. bayesian estimation of the interval-data regression model is employed to obtain farmers’ wta and its determinants. besides farmer and household characteristics, farm characteristics and socioeconomic and institutional variables such as soil fertility and previous participation in conservation projects increase and decrease willingness-to-accept respectively. the results suggest that costs of soil and water conservation payment schemes may be significantly decreased by careful targeting of households more ready to accept compensation. keywords. conservation technology/practice, interval-data regression model, willingness-to-accept, contingent valuation method, payment for environmental services. jel codes. q18, q56, q57. 1. introduction land degradation is one of the world’s environmental concerns today. it can be regarded as a process that includes soil degradation and erosion. the main processes that lead to land degradation are soil erosion by water and wind; chemical changes such as acidification, salinization, and nutrient loss; and physical degradation through pressures such as compaction (eswaran et al., 2001; unccd, 2013). there is no consensus on the exact extent and severity of land degradation in the african region, there is however consensus that it is severe and widespread. analyses of global land degradation indicate that africa is especially susceptible to land degradation and is the most severely affected part of the world (lal, 1995; obalum et al., 2012). an estimate of two-thirds of africa’s productive land is affected by land degradation and almost all the land area is susceptible to *corresponding author: abedella@yahoo.com 212 evelyn delali ahiale, kelvin balcombe, chittur srinivasan soil and environmental degradation (fao, 2011; jones et al., 2013; unccd, 2013; vlek et al., 2008). in sub-saharan africa (ssa), in excess of 320 million hectares of land have been made unsuitable for agricultural purposes due to soil erosion, deforestation, overgrazing and mismanagement of land resources (sant, 2001). nabhan (1997) also reports that 67% of agricultural lands are affected by land degradation, with close to 490 million hectares displaying signs of erosion and declining fertility. the issue of land degradation is of immense importance in africa as majority of its population’s livelihood is heavily reliant on natural resources. agricultural productivity in the region is stagnating or declining, largely due to land degradation. land degradation in africa has thus been immensely detrimental to agricultural ecosystems and crop production consequently leading to increasing levels of food insecurity, loss of farm incomes, poverty, high mortality rates, other social vulnerabilities, migration and conflict (gomiero, 2016; hamdy & aly, 2014; hemant & padmini, 2013; unccd, 2013). land degradation thus has socioeconomic implications for african countries. soil and land degradation in ghana was recognized decades ago, as since the 1930s, it has attracted considerable attention and concern (agyepong, 1987; benneh & agyepong, 1990). land degradation is affecting all parts of ghana, however, the northern regions placed within the guinea and sudan savannahs are the most vulnerable zones and the most degraded area of the country (asiedu et al., 2016; world bank, 2006). ghana had 35% of its land threatened by desertification particularly in the northern regions (upper east, upper west and northern regions) since the 1960s (adanu et al., 2013; kenworthy, 1995). land degradation in the northern regions of ghana has thus rendered large tracts of croplands which were once fertile currently unproductive as such contributing to depleting farm income and food sources. as a result of land degradation, grasslands, woodlands and forests are being lost while natural water bodies are drying up due to prolonged droughts and deposition of sediments into water courses (adanu et al., 2013). land degradation in ghana, which is mainly as a result of soil erosion and soil nutrient depletion, has negative impact on farm productivity and environmental quality. the human-associated drivers of long-term soil and vegetation degradation in ghana include unsustainable farming practices, removal of vegetation cover (including deforestation and overgrazing), mining activities, and urbanization and industrial activities caused by increased population growth pressures. agriculture remains an important sector in the ghanaian economy contributing about 22% to the country’s gross domestic product (gdp) and providing 44.7% of employment in 2013 (aryeetey & baah-boateng, 2015). agriculture also remains the main source of livelihood for many subsistence smallholder farmers living in rural ghana. the agricultural activities of these smallholders is cited to be a key factor in promoting land degradation through the use of environmentally unsustainable cultural practices (asiedu-amoako et al., 2016; boardman et al., 2003; diao & sarpong, 2007; helming et al., 2006; senayah et al., 1998). as agriculture is the major user of rural land, its relevance is not only in relation to its economic significance, but also its influence over the use of land in rural ghana and its environmental health in general. with the relationship between land degradation, agricultural productivity and poverty well understood (dasgupta & mäler, 1995; gomiero, 2016; hamdy & aly, 2014; heath & binswanger, 1996; hemant & padmini, 2013; shetty et al., 1995; world bank, 1992), it is clear that land 213determinants of farm households’ willingness to accept compensation in northern ghana degradation is a threat not only to national and household food security but the overall welfare of many households in ghana. in order to maintain agricultural productivity, reduce food insecurity and poverty, and improve environmental conditions, the government of ghana (gog), international donor agencies and non-governmental organizations (ngos) have promoted soil and water conservation practices and technologies including soil and stone bunds. this has been done for several decades particularly in the northern, upper-east and upper-west regions because they collectively constitute the most degraded part of the country. adoption of the promoted technologies has arguably been unsuccessful due among others to weak regulatory institutions which have restricted the ‘command and control’ interventions (wunder, 2008). farmers’ inability to adopt soil and water conservation measures is mainly as a result of constraints resulting from market failures which lead to externalities like degradation. when externalities are present, government intervention has the potential to internalise these externalities. one potential intervention is payment for environmental services (pes) in which incentive payments are made to resource managers in return for the adoption of conservation practices/technologies. such external financial incentives may be crucial in ensuring that socially desirable levels of environmental services/goods (es) are supplied and maintained since poor smallholder farmers may not be able to afford to maintain healthy environmental quality especially when large opportunity costs occur when conservation technologies/practices are adopted. soil and water conservation technologies (e.g., soil and stone bund) are technologies that preserve the integrity of soils and their water content, and they offer a number of on-farm and off-farm ecosystem services of value to society as well as on-farm productivity improvements. stone and soil bunds are stone or soil walls built across a slope (along a contour) to act as a barrier to prevent run-off, therefore helping in reducing soil erosion and increasing water retention capacity of soil. they are often appropriate for gentle slopes (2-5%) (diao & sarpong, 2007). ecosystem services from stone and soil bunds include: substantial flood and erosion control, substantial reduction in sedimentation of water bodies and its consequent improvement in water quality and aquatic life; reduction in leaching and deposition of fertilizers, herbicides and pesticides, i.e. generally improved landscape quality, etc. (bingham et al., 1995; holland, 2004; webb et al., 2001). the adoption of soil and water conservation (swc) technologies is aimed at returning a landscape to a condition where it can again provide the es enumerated above after a period of degradation. farmers can therefore be paid/compensated for the adoption of such technologies as soil and stone bund per unit area to produce the socially beneficial es mentioned. payments can be in the form of money, in kind, and access to resources and markets. in order to know the optimum rate of public investment, the required level of compensation (wta) necessary for encouraging agricultural households to adopt a conservation technology which produces es must be ascertained. in addition, knowledge of the factors determining wta also informs policy implementation by enabling the direction of payments towards those that are the most predisposed towards adopting the proposed technologies. the current study therefore uses the contingent valuation (cv) method to estimate farmers’ wta for adopting stone and soil bunds in a hypothetical conservation plan/ valuation scenario context. the method of elicitation within the cv employed allows 214 evelyn delali ahiale, kelvin balcombe, chittur srinivasan for uncertain responses so as to maximise respondent’s engagement with the survey. it employs an interval regression model to estimate wta and determines the factors influencing their wta, having adapted this model to allow for uncertain responses. extensive literature exists on pes as an alternative intervention for environmental conservation. however, much of this has focused on parts of the world other than africa leading to a dearth of knowledge on environmental values and the main factors influencing wta for pes conservation practice/technologies for africa and specifically for pes schemes in ghana. this study fills this gap by building on previous studies, with a specific aim to determine the manner in which various factors influence wta compensation for stone and soil bunds in northern ghana. the manner in which various factors influence wta for conservation technologies may be location and conservation practice specific. this study therefore serves to analyse these factors, so they can be understood in a way that enables better designed interventions and decision-making in ghana. the paper proceeds by first reviewing literature on valuation of the welfare impact of adoption of soil and stone bunds and the factors that influence farmers’ preferences for or wta for conservation practices in section 2. the interval data regression model specification and estimation is presented in section 3 while section 4 discusses the field survey, data and variables. the results are presented in section 5 and section 6 discusses the results. the conclusions and policy implications is given in section 7. 2. literature review 2.1 valuation of the welfare impact of adoption of soil and stone bunds the contribution of a resource to human welfare forms the basis of the economic approach to the valuation of resources. an economic value is measured by the variations in welfare related to the variation in the quantity or quality of goods or services. variations in environmental service flows can influence the welfare of individuals in complex ways and through both marketed or non-marketed activities (shiferaw et al., 2005). interventions like adoption of soil and stone bund by farm households that lead, for example, to reduction in soil erosion apparently change the welfare of different members of the society. welfare economics suggests that welfare values or changes are determined by individual preferences and measured by their personal assessment of changes in well-being (bockstael et al., 2000) or the extent to which they are willing to make trade-offs between scarce resources to obtain or preserve something. investments in soil and stone bund provide multiple economic and environmental benefits to different groups of people beside the adopting smallholder farm households. an impact evaluation of the interventions should therefore take into account any non-marketed ecosystem goods and services along with marketed economic benefits (baker, 2000; shiferaw et al., 2005). the welfare gains from investments in soil and stone bund include the direct economic benefits (e.g., yield gains) and environmental benefits (e.g., sustainability benefits and ecosystem services) that have both use and non-use values to people. indirect welfare benefits obtained from environmental improvements are justifiable components of the welfare changes related to any conservation interventions, and must be measured in impact evaluation (shiferaw et al., 2005). total welfare benefit to people, therefore, 215determinants of farm households’ willingness to accept compensation in northern ghana is the sum of the direct economic and indirect environmental benefits. hence, the benefits accruing to soil and stone bund can be assessed as those captured privately by the farm household, which include the value of yield loss averted and/or yield gains which may be felt on-site by the farm household, and those external to the farm household that are captured publicly, whose value include the improvement in ecosystem services. the valuation of changes in ecosystem services as a result of the adoption of soil and stone bund by farmers “needs to take into account both intended and unintended outcomes as different individuals may attach values for such changes because of the use benefits they derived, or any expected or conceived non-use welfare benefits” (shiferaw et al., 2005). the concept of total economic value (tev), the usual and most appropriate framework for aggregating the value of non-market ecosystem goods and services and measuring welfare changes is a vital part of economic valuation (pearce, 2002; philcox, 2007). economic values reflect the services of an ecosystem and not the economic value of that ecosystem (nijnik & miller, 2017). the potential welfare changes or impacts as a result of soil and stone bund on groups of individuals differ, i.e., the farm household’s welfare change is different from the welfare impact on the consumers of the ecosystem services accruing from soil and stone bund. assessing the economic value of soil and stone bund can thus be done in two ways. first, the measurement “of how much better or worse-off a person is due to the variation in the quantity or quality of the service flow” and second, “the addition of the individual welfare variation (gains and losses or wtp/wta) to assess the value of this variation for the entire society (shiferaw et al., 2005). the former is the focus of the current paper. total welfare gains include the direct economic benefits (e.g., yield gains) and indirect environmental benefits (e.g., ecosystem services) that have both use and non-use values to people (shiferaw et al., 2005). the benefits accruing to the adoption of soil and stone bunds can be assessed as those captured privately by the farm household, which include the value of yield loss averted and/or yield gains which may be felt on-farm by the farm household, and those external to the farm household (off-farm) that are captured publicly, whose value include improvement in ecosystem services. direct use values comprise consumptive uses including the potential yield increase that may be associated with the adoption of the conservation technology and indirect use value is improvement in landscape quality. the non-use value of soil and stone bund includes improved and preservation of aquatic life, erosion control, and the reduction of the deposition of soil and agricultural chemicals into water bodies. for this study, the wtp or wta indicates how the adoption of stone and soil bund impact on the welfare of participating farm households. the benefits accruing to farmers by adopting soil and stone bunds are often below the total benefits created once public good values have been accounted for, leading to below optimal levels of resource supply. in the presence of high public good values, incentive payments for resource conservation may be necessary. there is dissimilarity between the average gross margin of adoption and non-adoption of conservation technologies/ practices that result in conservation opportunity costs for farmers (krishna et al., 2013). the opportunity costs for farmers adopting soil and stone bunds include: loss of valuable cropping land to bunds (ludi, 1997; wyatt, 2002) which for farmers is an important issue when land is scarce and which imposes revenue loss to farmers; additional labour requirements of household for construction and annual maintenance (shiferaw & holden, 216 evelyn delali ahiale, kelvin balcombe, chittur srinivasan 2001; stocking & abel, 1989). pes schemes should pay for the farmers’ opportunity costs. the stated preference (sp) of farm households’ stated wta compensation for adopting soil and stone bunds can be employed as an appropriate measure of the opportunity cost of adopting such technologies. the minimum compensation needed to motivate a farm household to accept a pes contract involving the construction of soil or stone bunds on a unit area of land is presumed to indicate the farmer’s real opportunity cost per unit area of soil or stone bunds adoption. 2.2 factors determining farmers’ willingness to accept/preferences for conservation practices/ technologies the factors influencing preferences and wta compensation for conservation practices and technologies have generally been categorised into: farm characteristics; farmer and household characteristics; socioeconomic; and, institutional factors by previous studies (see, e.g., ayuba et al., 2011; cooper & keim, 1996; matta et al., 2009; minten, 2003). key farmer and household characteristics have generally been thought to include gender, age, level of education of household head, own labour, labour sufficiency of the household, and wealth status of the household. for example, thurston (2006) observes that females have higher wta than males in valuing environmental conservation. stephen (2015) and wang et al. (2019) also estimate higher wta for females relative to males. sangkapitux et al. (2009), presa/icraf (2010), minten (2003), stephen (2015), and wang et al. (2019) all find a positive relationship between age and wta compensation. however, this relationship is found to be significant by sangkapitux et al. (2009), minten (2003), stephen (2015), and wang et al. (2019) and insignificant by presa/ icraf (2010). feng et al. (2018) however observe a negative effect of age on wta. education has usually been found to have a positive influence on wta for conservation/supply of environmental services (ninan et al., 2007; wang et al., 2018; wang et al., 2019; xu et al., 2015). minten (2003) explains that more educated households, who have a higher reservation wage, prefer to put more effort in off-farm earnings and hence, prefer to practice agriculture in a more extensive manner. in contrast, xiong and kong (2017) and yu and cai (2015) observe a negative influence of education on wta. household size has been used as an index of the farm household’s access to labour in most studies. presa/ icraf (2010) find a significant negative effect of household size on wta compensation for watershed services whilst minten (2003) observes an insignificant positive influence of household size on wta to give up slash and burn agriculture (‘tavy) and an insignificant negative effect of household size on wta to give up forest use in madagascar. stephen (2015), xiong and kong (2017), and wang et al. (2019) all find a significant positive effect of household size on wta. sangkapitux et al. (2009) observe that poorer farmers have a higher willingness to engage in a compensation scheme for providing better ecological services, probably indicating a lower wta for the supply of environmental services. farmers who rely on income from farm and aquatic products have higher wta (stephen, 2015; xiong & kong, 2017). key farm characteristics include total farm size, level/severity of erosion on farm/plot, level of soil fertility of farm/plot, slope of plot, and location/region. sukic (2001), xiong and kong (2017) and wang et al. (2019) find statistically significant positive impact of 217determinants of farm households’ willingness to accept compensation in northern ghana land size on wta compensation for conservation practices whilst presa/icraf (2010) discovers an insignificant influence. previous findings suggest that the higher the level of erosion and the lower the soil fertility, the higher the willingness to participate in a payment scheme (sangkapitux et al., 2009). this in turn suggests that farmers with severe farm erosion and low soil fertility are likely to demand less compensation for conservation practices than those with less severe erosion on their fields and more fertile soils. farm location heavily influences wta (minten, 2003; stephen, 2015; xiong & kong, 2017; yu & cai, 2015). minten (2003) reports that households with more lowland (which are more flat) are willing to accept less for compensation, though the estimates are statistically insignificant. by contrast farming in highlands (likely to be steeper) is a significant determinant of wta compensation. monthly income, adoption status, and previous participation of the household in a conservation programme/project are institutional and socio-economic factors that have been associated with wta compensation. a positive relationship between income and wta is observed by presa/icraf (2010) and sukic (2001), though the effect is statistically significant and insignificant respectively. xu et al. (2015), yu and cai (2015), and wang et al. (2018) find the opposite, that is a negative influence of household income on wta. for non-market valuation, respondents may be unable to give their true preferences because they have had little prior experience with the item in question and so have trouble establishing their minimum wta during a single survey (cummings et al., 1986). household knowledge of the good being valued can be proxied by identifying households who have previously adopted the conservation practice of interest or taken part in wider conservation programmes or projects. the literature is, however, silent on the direction of influence on wta of households’ prior adoption of a practice/technology and participation in previous conservation projects. however, various studies have found farmers’ environmental awareness and knowledge affect wta positively (feng et al., 2018; wang et al., 2018; xu et al., 2015; yu & cai, 2015). 3. estimation methodology cv studies require questions as to whether respondents are prepared to pay or accept specified monetary amounts in the light of changes that will impact upon them (e.g. the adoption of a technology). valuation studies often assume that respondents know their preferences with certainty, i.e. they know how much they would be willing to accept for es provision. however, empirical evidence in the sp literature indicates that respondents are uncertain about their responses (akter & bennett, 2013; akter et al., 2008; alberini et al., 2003; champ et al., 1997; ready et al., 1995). this is mainly because respondents use a heuristic mode while processing information provided in any of several cv formats which tends to dominate over more systematic ways of information processing for decisionmaking (bateman et al., 2004). unsurprisingly, the cv literature has spawned multiple forms of elicitation which are aimed at minimising or mitigating biases. one factor that leads to potential bias is that respondents may not know or be able to state with certainty their underlying preferences and forcing them to do so can induce bias (akter & bennett, 2013; ariely et al., 2003; poe, 2016; ready et al., 2010). uncertainty is an important aspect of many public goods, especially environmental goods (mitchell & carson, 1989) such 218 evelyn delali ahiale, kelvin balcombe, chittur srinivasan as produced by swc technologies like stone and soil bunds. preference uncertainty is a stochastic error term which comes about in hypothetical valuation scenarios as individuals do not know their true values of a good with certainty (li & mattsson, 1995). in the current study, the lb and ub are obtained based on the expansion approach of broberg and brännlund (2008), which takes uncertainty into consideration. this method can be termed the ‘multiple-bounded uncertainty choice’ (mbuc) approach. it has been argued that this approach is more intuitive, better fits the data, estimates mean and median wtp with better precision, is less sensitive to distributional assumptions, and it is better suited for policy analysis than other approaches (broberg & brännlund, 2008). the elicitation method employed here (and outlined in section 4) is to give respondents a ‘payment card’ (a series of possible ranges) but allowing them to indicate whether they would pay for that amount with certainty or with some level of uncertainty. however, this approach also requires an adaptation to the standard methods used to model wta/wtp. a standard model for dealing with the case where the dependent variable is only known within a range is the interval data model (stewart, 1983). hanemann et al. (1991) observed that the interval-data model improves the statistical efficiency of wtp estimates by reducing the variance and point estimates of wtp models relative to single-bound models. alberini (1995) explored the efficiency and biases of the estimates obtained from the bivariate probit and interval-data models and observed robust estimates for mean/ median wtp and concludes that in the absence of perfect correlation as is the case in many cv studies, the interval-data model might be appropriate. however, the intervaldata model assumes that answers provided by respondents reflect wtp/wta which are known with certainty (alberini, 1995). however, as discussed above, the elicitation approach outlined in section 4 is more general in that it allows for uncertain responses. because mbuc responses do not translate directly into the statistical models conventionally employed to model stated-preference responses, assumptions about the interpretation of the responses by the researcher are necessary. the literature provides a number of empirical ways to convert mbuc cv data to easily estimable forms. intervals are obtained by assigning, lb<yi *<ub given the responses, where lb and ub are the lower and upper bounds respectively. the notation used in this section are adapted from balcombe et al. (2009). if utility is unobserved and indicated by the latent variable yi *, and wtai is the wta of the th individual, then the utility can be expressed as: wtai= yi *=β'xi+εi εi ~ n(0,h) (1) where xi is a vector of explanatory variables determining respondents’ wta, β' is the parameter vector related to xi and εi is the error term assumed to be normally distributed with zero mean and variance h. consequently for wta, the highest “definitely no” and lowest “definitely yes” responses form the lb and ub respectively. uncertainties are accommodated in the model resulting in the uncertainty interval model which is employed in this study. the rule used to create the wta interval for the th individual is a function of his responses with the lb and ub constructed based on the following assumptions: 1. a ‘y’: bid forms wtaupper 219determinants of farm households’ willingness to accept compensation in northern ghana 2. a ‘n’: bid forms wtalower 3. a ‘py’: bid forms wtaupper, with a fixed probability, τ 4. a ‘pn’: bid forms wtalower, with a fixed probability, τ where τ, a probability value of truncation, is between 0 and 1 and set by the authors at 0.75; y is definitely yes, py is probably yes, dk is don’t know, pn is probably no, and n is definitely no. the latent variable yi * therefore has the clear interpretation of a person’s wta, and equation (1) would give a direct estimate of the mean wta. for a bayesian estimation and inference, priors for the β and h parameters are specified normal n(.,.) and inverse gamma ig(.,.) respectively: (2) the likelihood of the latent variable y* is (3) the posterior distribution is obtained by combining equations (2) and (3). the posterior distribution of model (1) is simulated using the gibbs sampler (see for example koop, 2003). two models were estimated using the model above. the first one contains an estimate of the wta which can be interpreted as describing the unconditional distribution of the wta in the population (verbeek, 2004). the second model includes the explanatory variables shown in table 2 below. the models were estimated using markov chain monte carlo (mcmc), setting the burn-in phase to 2000 iterations which is followed by another 200,000 iterations in which every 20th observation was sampled in order to lessen the dependence in the sequence, resulting eventually in 10,000 observations for analysis. convergence diagnosis was done using visual plots of the sequences of values produced by the sampler and by modified t-tests for the hypothesis of ‘no-difference’ between the first and second halves of the sampled values for each of the parameters. observations without either an upper or lower bound were not included in the analysis; hence none of the models was estimated with the total of 350 observations.1 4. field survey, data and variables in a hypothetical conservation plan context, data were collected from 305 households in the three northern regions of ghana through a survey conducted from january – march, 2010. the questionnaire used included a description of the technologies with pictures as well as pictures of the possible environmental consequences (i.e., landscape quality improvement/maintenance) of the technologies. a multi-stage sampling approach was adopted in which a district was first selected from each region. from a purposively 1 gauss code for estimation of the models written by kelvin balcombe. 220 evelyn delali ahiale, kelvin balcombe, chittur srinivasan selected group of communities in each district where physical evidence of the swc structures of interest to the research are present, ten communities from two of the districts and five from the remaining district were then randomly chosen.2 finally, ten or five respondents were randomly selected from the 25 communities. for the estimation of wta, a cv of four polychotomous-choice multiple-bounded elicitation format with variations of yes/no answers to indicate respondent uncertainty (fao, 2000; mitchell & carson, 1989). the dichotomous choice format with follow-up questions reasonably mimics the bargaining process common in developing countries (fao, 2000) and hence familiar to respondents. the cv consisted of a question format of eight bid amounts ranging from a minimum of gh¢200 to a maximum of gh¢600 for each of the technologies of interest, namely, stone bund and soil bund.3 these amounts were obtained from focus groups sessions and discussions with experts. face-to-face interviews with respondents were conducted during the cv survey. table 1 shows an example of a cv question format and how it should be answered and table 2 presents a description of variables. 5. results 5.1 description of respondents descriptive and summary statistics of selected socio-economic and demographic characteristics of respondents, physical characteristics of farm/fields, and institutional characteristics are presented in table 3. out of the total of 305 survey respondents, approximately 78% are males. the dominance of male-headed households in the survey 2 physical evidence is important because for stated preference studies, knowledge and familiarity of the good being valued is useful. 3 the average exchange rate in 2009 was gh¢2.2024 and gh¢1.4132 to gb £1 and us$1 respectively (bog, 2010: p.51). table 1. an example of an answered cv question. example: which of the values would you be willing to accept for constructing stone bunds on an acre of your own field? please tick only one option for each value. amount in cedis/acre definitely no probably no don’t know probably yes definitely yes 200 √ 300 √ √ 350 √ 400 √ 450 √ 500 √ 550 √ 600 √ 221determinants of farm households’ willingness to accept compensation in northern ghana sample conforms to glss5 (2008), which reports that the proportion of male-headed households in the rural savannah areas (study area fall within this category) is 85%. the highest proportions of more than 36% of respondents are over 50 years of age, whilst 33% are under 41 years, and about 67% are over 40 years. it can be concluded that households involved in agriculture are headed more by older heads than by young ones. this could be the reason why the adoption of swc technologies by farm households in the study area is low. respondents have low levels of education. about 77% and only 15% of respondents have had no formal education and primary education respectively. only 8% of farm household heads have had either secondary or higher level of education. with an average family size of nine (9) persons, households in the study area generally reflect the large household sizes typical of african villages and farm households. on average, 4 persons per household provide on-farm labour. most households (71%) do not have enough own/ household labour to perform farm production activities. this has important implications for the adoption of labour intensive soil and water conservation technologies like stone and soil bunds. this lack of adequate household labour is probably one of the reasons why adoption of these technologies is low in the study area. in terms of land holdings, the study area is generally characterized by small holdings. the average total farm size is 2 hectares, with minimum and maximum values of 0.40 and 6.20 hectares respectively. only 31% of households have total land sizes greater than the mean value. the small farm sizes table 2. description and statistics of variables used to explain wta. variable description farmer and household characteristics gender gender of household head (dummy, 1 if male, 0 if female ) age age group of household head (1 = 20 – 30 years) education level of education (1 = no formal education) own labour number of household members working on farm labour sufficiency whether farm household has enough labour (dummy, 1 if yes, 0 otherwise) wealth status index of household perception of wealth status (1 = rich) farm characteristics farm size total farm size in hectares severity of erosion average index for perception of erosion on plots (1 = not severe) soil fertility average index for soil fertility level on plots (1 = very fertile) slope of farm average index of type of slope of plots (1 = flat) north1 location is northern region (dummy, 1 if northern region, 0 otherwise) uwest location is upper-west region (dummy, 1 if upper-west, 0 otherwise) socio-economic and institutional variables income index for monthly income (1 = below gh¢21.00) adoption adoption of swc by household (dummy, 1 if adopter, 0 otherwise) participation participation in previous conservation project (dummy 1 if yes, 0 otherwise) member membership in farmer association (dummy, 1 if member, 0 otherwise) 1 to avoid the dummy variable trap (greene, 2003), the upper east region is used as reference location and therefore dropped from the models. 222 evelyn delali ahiale, kelvin balcombe, chittur srinivasan may also indicate that households will have low willingness to adopt swc technologies that take up too much of cropland. in terms of the adoption of either one or both swc technologies under consideration, 55% of the responding households are adopters. this distribution provides enough data to make comparisons between adopting and non-adopting households. an adopter household in this study is one that has already adopted any one or both of the technologies table 3. sample descriptive and summary statistics. sample characteristic district total sample lawra (%) (n=103) t-nabdam (%) (n=100) w-mamprusi (%) (n=102) freq. (%) (n=305) gender of household head male 74 (71.84) 84 (84.00) 79 (77.45) 237 (77.70) female 29 (28.16) 16 (16.00) 23 (22.55) 68 (22.30) age group (years) 20 – 30 11 (10.68) 16 (16.00) 12 (11.76) 39 (12.79) 31 – 40 18 (17.48) 25 (25.00) 20 (31.37) 63 (20.66) 41 – 50 22 (21.36) 30 (30.00) 42 (72.55) 94 (30.82) over 50 52 (50.49) 29 (29.00) 28 (72.55) 109 (35.74) level of education of household head no formal education 83 (80.58) 64 (64.00) 89 (85.29) 234 (76.72) primary 13 (2.62) 21 (21.00) 12 (11.76) 46 (15.08) secondary 3 (2.91) 13 (13.00) 3 ( 2.94) 19 (6.23) tertiary 1 ( 0.97) 2 ( 2.00) 0 3 (0.98) other 3 ( 2.91) 0 0 3 (0.98) adoption of conservation technology adopter 51 (49.51) 37 (37.00) 48 (47.06) 168 (55.08) non-adopter 52 (50.49) 63 (63.00) 54 (52.94) 137 (44.92) participation in previous conservation programme/project participant 54(52.43) 41 (41.00) 78 (76.47) 173 (56.72) non-participant 49 (47.57) 59 (100.00) 24 (23.53) 132 (43.28) membership of a group member 55 (53.40) 41 (41.00) 59 (57.84) 155 (50.82) non-member 48 (46.60) 59 (59.00) 43(42.16) 150 (49.18) labour sufficiency yes 54 (52.43) 21 (21.00) 12 (11.76) 87 (28.52) no 49 (47.57) 79 (79.00) 90 (88.24) 218 (71.48) wealth status rich 18 (17.48) 5 (5.00) 20 (19.61) 43 (14.10) average 54 (52.43) 74 (74.00) 72 (70.59) 200 (65.57) poor 31 (30.10) 21 (21.00) 10 (9.80) 62 (20.33) 223determinants of farm households’ willingness to accept compensation in northern ghana sample characteristic district total sample lawra (%) (n=103) t-nabdam (%) (n=100) w-mamprusi (%) (n=102) freq. (%) (n=305) household monthly income group (gh¢ ) below 10 47 (45.63) 31 (31.00) 9 (8.82) 87 (28.52) 10 – 20 47 (45.63) 29 (29.00) 35 (34.31) 111 (36.39) 21 – 30 3 (2.91) 4 (4.00) 13 (12.75) 20 (6.56) 31– 40 0 (0.00) 4 (4.00) 10 (9.80) 14 (4.59) 41 – 50 2 (1.94) 9 (9.00) 11 (10.78) 22 (7.21) above 50 4 (3.88) 23 (23.00) 24 (23.53) 51 (16.72) degradation problem in locality yes 101 (98.06) 90 (90.00) 102 (100.00) 293 (96.07) no 2 (1.94) 10 (10.00) 0 (0.00) 12 (3.93) severity of erosion not severe 29 (28.16) 22 (22.00) 18 (17.65) 69 (22.62) fairly severe 17 (16.50) 36 (36.00) 23(22.55) 76 (24.92) severe 38 (36.89) 13 (13.000 29 (27.45) 79 (25.90) very severe 19 (18.45) 29 (29.00) 33 (32.35) 81 (26.56) soil fertility very fertile 0 (0.00) 13 (13.00) 9 (8.82) 22 (7.21) fertile 51 (49.51) 54 (54.00) 63 (61.76) 168 (55.08) not fertile 52 (50.49) 33 (33.00) 30 (29.41) 115 (37.70) slope of farm flat 11 (10.68) 2 (2.00) 10 (9.80) 23 (7.54) fairly steep 72 (69.90) 68 (68.00) 39 (38.24) 179 (58.69) steep 14(13.59) 25 (25.00) 50 (49.02) 89 (29.18) very steep 6 (5.83) 5 (5.00) 3 (2.94) 14 (4.59) district mean stdv min max own labour lawra 4.18 2.16 1 10 talensi-naddam 3.85 2.29 1 13 west-mamprusi 3.49 1.58 1 9 total sample 3.84 2.05 1 13 household size lawra-nandom 8.36 2.98 2 16 talensi-naddam 8.75 3.56 2 18 west-mamprusi 9.93 3.39 2 16 total sample 9.01 3.38 2 18 total farm size (acres) lawra-nandom 1.18 0.68 0.40 4.80 talensi-naddam 1.98 1.21 0.60 6.00 west-mamprusi 2.84 1.53 0.60 6.20 total sample 2.00 1.36 0.40 6.20 224 evelyn delali ahiale, kelvin balcombe, chittur srinivasan under consideration. almost equal proportions of respondents belong to an organized farmer association/group. an important feature of these associations is labour sharing. a proportion of about 65% of households earn a monthly income (from all sources, both on and off-farm) below gh¢21.00 ($14.87) and are categorised as poor based on the ghana living standard survey (glss5, 2008). however, from the farmers’ own perspective, only 20% of them consider themselves as poor. ninety-six percent (96%) of respondents think that land degradation is a problem in their area. most households, 95%, report that erosion is a problem on their own farms/fields, whilst about 77% perceive the level of erosion as being fairly severe to very severe. however, only about 38% of respondents view their farms as infertile. 5.2 upper and lower bounds of wta the distribution of lower and upper bounds of wta are reported in table 4. the lower and upper bounds represent amounts that respondents are not willing and willing to accept respectively. for stone bund, whilst the range gh¢500-875 ($353.85-619.24) table 4. distribution of lower and upper bounds of wta for stone bund and soil bund. wta (gh¢/ hectare) lower bound no. of responses (%) upper bound no. of responses (%) stone bunds 250 31 (10.20) 0 (0.00) 500 104 (34.21) 1 (0.33) 750 88 (28.95) 13 (4.28) 875 49 (16.12) 23 (7.57) 1000 22 (7.24) 54 (17.76) 1125 6 (1.97) 62 (20.39) 1250 1 (0.33) 84 (27.63) 1375 3 (0.99) 48 (15.79) 1500 0 (0.00) 19 (6.25) total 304 100.00) 304 (100.00) soil bunds 250 90 (30.00) 0 (0.00) 500 141 (47.00) 27 (8.91) 750 47 (15.67) 52 (17.16) 875 18 (6.00) 66 (21.78) 1000 4 (1.33) 64 (21.12) 1125 0 (0.00) 52 (17.16) 1250 0 (0.00) 38 (12.54) 1375 0 (0.00) 4 (1.32) 1500 0 (0.00) 0 (0.00) total 300 (100.00) 303 100.00) figures in parentheses are percentages. 225determinants of farm households’ willingness to accept compensation in northern ghana form the most important range in terms of numbers reporting for the lower bound, it is gh¢1000-1375 ($707.71-973.10) for the upper bound. amounts ranging from gh¢250-750 ($176.92-530.78) and gh¢750-1250 ($530.78884.64) form the most important lower and upper ranges respectively for soil bund. the total numbers reporting are different for the two technologies because, in some cases, respondents do not have either an upper or lower bound and therefore the percentages are in respect of the numbers reporting for the bounds and not of the total sample of 305. 5.3 wta estimates mean wta/hectare for stone bunds and soil bunds are shown in table 5. these estimates are the unconditional wta in the population. respondents are willing to accept gh¢922.08/hectare (us$652.56) and gh¢714.92/hectare (us$505.95) for stone bunds and soil bunds respectively. wta for stone bund is gh¢207.16/hectare (us$146.61) more than for soil bund because the construction of stone bunds is labour intensive. table 5. interval data model estimates on farmers’ wta for stone bund and soil bund. technology wta(gh¢/hectare) s.d stone bund 922.08 12.808 soil bund 714.92 12.807 5.4 determinants of wta the results of the estimation of the determinants of wta for stone bunds and soil bunds are shown in table 6. irrespective of the conservation technology, farmer and household characteristics which have significant effect on wta are gender, the number of household members working on farm, whether household has enough farm labour, and the households’ perception of its wealth status, with their direction of influence being the same for stone and soil bunds. soil fertility and the farm location of northern region are the two farm characteristics variables that are significant determinants for both stone and soil bunds. for stone bunds, severity of erosion and the location of upper-west region are significant determinants as well. among the three socio-economic and institutional variables considered, only previous participation is found to be significant for both stone bunds while all three variables are important for soil bunds. 6. discussion resource managers’ wta is important in the implementation of any pes aimed at restoring es. influencing factors of preferences and wta compensation for conservation have been grouped into: farm characteristics; farmer and household characteris226 evelyn delali ahiale, kelvin balcombe, chittur srinivasan tics; socioeconomic; and, institutional factors by previous studies (see, e.g., ayuba et al., 2011; cooper & keim, 1996; matta et al., 2009; minten, 2003). in the current study, as is shown in table 6, gender, the number of household members working on farm, whether household has enough farm labour, and the households’ perception of its wealth status are the farmer and household characteristics significantly influencing wta. the direction of influence of these factors on wta for both stone and soil bunds is the same. the observed differences in all the coefficient estimates for stone and soil bunds, i.e., higher positive and lower negative for stone bunds and lower positive and higher negative for soil bunds is because it costs more to adopt stone bunds than soil bunds. households headed by females are willing to accept less than male household heads. female are willing to accept gh¢52.22/hectare ($36.98/hectare) and gh¢61.55/hectare ($43.59/hectare) less than males for stone and soil bunds respectively. this result is contrary to thurston (2006), stephen (2015), and wang et al. (2019) who found female respondents have higher wta than male respondents in valuing environmental conservation. the dissimilarity between the genders may be due to a number of reasons. first, wage disparity between genders and weak negotiation skills of females; second, females table 6. interval data model estimates on determinants of wta/hectare for stone bund and soil bund. variable stone bund soil bund coefficient s.d coefficient s.d constant 789.22* 71.09 546.78* 67.33 farmer and household characteristics gender 52.61* 29.88 61.55* 30.16 age -4.53 12.35 00.93 12.05 education 24.75* 17.59 17.47 17.87 own labour -5.81* 04.01 -9.55* 03.92 labour sufficiency 55.48* 27.81 51.52* 28.30 wealth status 52.60* 20.66 80.92* 19.96 farm characteristics farm size -2.20 08.55 -9.64* 07.99 severity of erosion 12.65* 11.22 09.85 10.03 soil fertility 76.16* 20.54 82.93* 18.53 slope of farm 09.82 17.66 11.71 16.42 northern region -36.62* 31.77 -185.43* 29.51 upper-west region 93.51* 32.17 -11.74 29.36 socio-economic and institutional variables income -3.83 11.14 -27.05* 10.59 adoption 24.78 30.11 53.57* 27.98 previous participation -84.21* 30.25 -104.30* 28.29 note: * pseudo t-value significant at 5%; within bayesian inference, the coefficient’s confidence/credible interval excludes zero if the ratio of the estimate of the mean to the standard deviation exceeds 2. 227determinants of farm households’ willingness to accept compensation in northern ghana may not be fully aware of the effort required as construction of bunds is done by males in the study area; third, women consider conserving the environment as a personal benefit unlike men who focus on financial rewards; and finally, women see current levels of pes funding as more helpful and useful than do men (schwartz, 2017). households with higher number of members providing farm labour are willing to accept less for adoption. this could be because the higher the household members providing on-farm labour, the less that household would require hired labour. the wta for own labour for stone and soil bunds are gh¢5.75/hectare ($4.07/hectare) and gh¢9.55/hectare ($6.76/hectare) respectively implying that an additional household member working on the farm leads to a reduction in the wta/hectare accepted by these amounts. there is no plausible explanation for the positive influence of labour sufficiency on wta. households who perceive themselves as poor demand gh¢52.50/hectare ($37.18/hectare) and gh¢80.92/hectare ($57.31/hectare) less for stone and soil bunds respectively than those who perceive themselves of average wealth. this result is consistent with that of sangkapitux et al. (2009) that poorer farmers have a higher willingness to engage in a compensation scheme for providing better ecological services, indicating a lower wta for the supply of environmental services. education is a significant determinant of wta for only stone bunds. consistent with ninan et al. (2007), wang et al. (2019), wang et al. (2018), and xu et al. (2015), the more educated the household head, the more the wta and this is explained by minten (2003) that more educated households have a higher reservation wage and thus prefer to put more effort in off-farm earnings. this is particularly true as stone bunds require much more effort than soil bunds. for example, farmers who have had primary education will accept gh¢24.75/hectare ($17.53/hectare) more than farmers with no formal education. in contrast, xiong and kong (2017) and yu and cai (2015) observe a negative influence of education on wta. two farm characteristics, soil fertility and the farm location of northern region are the significant determinants for both stone and soil bunds. for stone bunds, severity of erosion and the location of upper-west region are significant determinants as well. as the fertility of soil becomes poorer, farmers demand more compensation. farmers will accept gh¢76.16/hectare ($53.94/hectare) more for stone bunds on fertile than on very fertile soil. also the higher the severity of erosion, the more wta wanted. more infertile soils and highly eroded lands require more resources to construct closer bunds which also mean that more productive land is lost to conservation structures. the severity of erosion is significant for stone bund but not soil bund because stone bunds are comparatively better at controlling erosion. this result does not agree with sangkapitux et al. (2009) whose results on peoples’ willingness to participate in payment schemes posits that the lower the soil fertility and severe the erosion, the lower the wta demanded is likely to be. for soil bund, farm size is a significant variable showing that the bigger the households’ farm size, the less wta those households are willing to accept. farm size negatively influences wta probably because the opportunity cost of committing land is less for large landholders than for small landholders. this result conforms to that of minten (2003) but contradicts that of sukic (2001), xiong and kong (2017), and wang et al. (2019). willingness-to-accept of farmers in northern and upper-west regions are less and more compared to upper-east region respectively for stone bunds. farmers in northern region are willing to accept gh¢36.62/hectare ($25.93/hectare) less than their counter228 evelyn delali ahiale, kelvin balcombe, chittur srinivasan parts in the upper east region, whereas those in upper-west region demand higher, a mean of gh¢93.51/hectare ($66.22/hectare) more than those in upper east. willingnessto-accept is more in the upper-west because stones are more readily available on farms in upper-east than in upper-west where stones will need to be transported from elsewhere. in the case of northern region, it is perhaps because though stones are not abundant here, lands are much more flatter here compared to those in upper-west and upper-east and therefore will require less labour and materials for construction. this result does show that location influences wta as is found by minten (2003), stephen (2015), yu and cai (2015), and xiong and kong (2017). previous participation is found to be the only socio-economic and institutional variable significant for both stone bunds and soil bunds. households that have participated in previous soil and water conservation projects are willing to accept gh¢84.21/hectare ($59.64/hectare) less than households that have not. this result is also observed by presa/icraf(2010). such result, cummings et al.(1986) explained as respondents probably being unable to provide their true preferences because they have had little prior experience with the item being valued and thus have difficulty establishing their minimum wta during a single survey. this explanation does not however tell us the direction of influence on wta. however, various studies have found farmers’ environmental awareness and knowledge affect wta positively (feng et al., 2018; wang et al., 2018; xu et al., 2015; yu & cai, 2015). for soil bunds, income and adoption affect wta in a negative and positive way respectively. for instance, farmers in the monthly income range of gh¢21-30 ($14.8721.25) will accept gh¢27.05/hectare ($19.16/hectare) than those earning gh¢31-40 ($21.95-28.33) per month. the higher the income of the household, the less wta they are willing to accept agrees with xu et al. (2015), yu and cai (2015), and wang et al. (2018) but is inconsistent with presa/icraf (2010). this result is however contradictory to the influence observed for the wealth status variable probably because wealth status is a variable measuring perception than actual wealth. the adoption variable has positive effect, indicating, for example, that adopter households demand gh¢53.57/hectare ($66.22/hectare) more than non-adopter households. adopter households have previous knowledge of the technology thus it is logical to assume that their wta represent their true preferences. this result contradicts that of the participation variable in that previous experience leads to positive effect on wta (i.e., adoption) and negative influence on wta (i.e., previous participation) at the same time. the only explanation that can be given for this inconsistency is that adopters perhaps have better and more experience with the technology because they have actually adopted on their own fields than previous participants of projects who may not have adopted. 7. conclusions factors like gender, the number of household members working on farm, whether household has enough farm labour, the households’ perception of its wealth status, soil fertility, farm location and participation in previous conservation project are significant determinants of wta for both soil and water conservation technologies/practices. however, other factors like education, farm size, severity of erosion and income also show that 229determinants of farm households’ willingness to accept compensation in northern ghana the determinants of wta may vary with technology and location and therefore results from one location and for one technology should be employed in another location and on another technology with care. the fact that location effects are observed in the average amount of compensation that smallholders in northern ghana are prepared to accept indicates that location specific approaches might be employed, and that the amounts paid for the promotion of payment schemes should also be location specific. particularly, smallholders in the northern region are willing to accept less for soil and stone bund than those in upper east region and should be targeted. a widely held argument in the literature is that poor smallholders are concerned about short-term but not long-term economic interests, and are, therefore, not willing to adopt sustainable management services. the fact that in the current study, households who perceived themselves as poor are willing to accept less than those who perceived themselves as rich suggests that the poorer groups among the farm households in northern ghana are more willing to engage in conservation compensation schemes. therefore, the evidence provided in the current study supports the proposition that compensation schemes can, in addition to their environmental objectives, address poverty. to ensure effectiveness and efficiency of pes, policies should be designed to consider important factors. factors such as gender, age, education, location, farm size, income, and previous participation in conservation programmes are important. any category of household prepared to accept less compensation is more likely to participate in the pes scheme. therefore, if a pes scheme will be implemented in the study, groups that need to be consciously targeted include: less educated and female headed households and households headed by older individuals. compensation schemes should also concentrate more on poorer households than richer ones since this achieves both efficiency and equity objectives. monetary compensation can be an avenue for improving livelihoods, alleviating poverty and diminish reliance of such groups on natural resources. we briefly mention some limitations of our study. first, the high illiteracy rates of smallholder farmers impose some difficulties when conducting stated preference surveys in developing countries. even after considerable training, enumerators may still make considerable mistakes. we tried to address this by reviewing each administered questionnaire at the end of each interview to highlight incorrect responses, and make sure that responses are corrected before leaving the respondents. second, all valuation methods involve some uncertainty. the very complex nature of es themselves limits how precisely they can be valued. the complexity limits the ability of respondents to understand and appreciate nature of es or the reasoning behind the choices they make. we employed the mbuc cv format and the interval-data model to try and address the issue of uncertainty. finally, the values attached to benefits from es are subjective and variable over time, space and issue. these values differ substantially with people’s attitudes, awareness of background issues, cultural norms, preferences and status. additionally, it is extensively known that valuation of es is very context specific and should be informed by the viewpoints and needs of the 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(2015). ecological compensation based on farmers’ willingness: a case study of jingsan county in hubei province, china. the journal of applied ecology 26(1): 215-223. consumers’ rationality and home-grown values for healthy and environmentally sustainable food simone cerroni1,2,3,*, verity watson4, jennie i. macdiarmid5 the wellbeing of smallholder coffee farmers in the mount elgon region: a quantitative analysis of a rural community in eastern uganda anna lina bartl agricultural sector performance, institutional framework and food security in nigeria romanus osabohien1,3,*, evans osabuohien1,3, precious ohalete2,3 innovation adoption and farm profitability: what role for research and information sources? michele vollaro1,*, meri raggi2, davide viaggi1 determinants of farm households’ willingness to accept (wta) compensation for conservation technologies in northern ghana evelyn delali ahiale1,*, kelvin balcombe2, chittur srinivasan2 bio-based and applied economics 8(3): 279-295, 2019 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-9448 small-holders perception of sustainability and chain coordination: evidence from arriba pdo cocoa in western ecuador carlos moreno-miranda1,*, hipatia palacios2, daniele rama3 1 wageningen university & research, the netherlands 2 technical university of ambato, ecuador 3 universita cattolica del sacro cuore, italy abstract. protected denominations of origin (pdo) refer to the adoption of producers’ voluntary standards to highlight the quality to consumers and improve the socio-economic sustainability of small-holders. usually, in agricultural circuits, these focus on aspects of production systems and intrinsic features of agricultural raw materials. in agri-food clusters, pdo labels focus globally on market recognition of sensorial elements of farming and agroindustrial products. the study’s objective was to analyze socio-economic and governance components to understand the pdo cocoa arriba (theobroma cacao) chain and its sustainability to bring forward potential strategies in ecuador. the information employed comes from the observation of two strings (arriba pdo and ccn-51) by interviewing 450 respondents. principal components analysis was introduced to contribute with relevant insights. the framework applied accounts with a revision of primary and support activities and coordination mechanisms identification. the study clustered pre-production, production, and postproduction tiers. according to the results, arriba pdo production systems represent a disadvantage for farmers because, from the production point of view, the premium price paid for product certification is debatable. finally, the enhancement of national regulation to assist chain actors and the stimulus of young producers and associations empowerment is an urgent requirement. keywords. socio-economic, agricultural regulation, family farming, governance structure. jelcodes. q18, n56, l17. 1. introduction cocoa (theobroma cacao l.) is a historically strategic agricultural sector in latin america and constitutes an important crop worldwide for processed and raw material markets (krauss, 2018)which fail to mention one of the actual key drivers: the need to shore up pro*corresponding author: carlos.morenomiranda@wur.nl 280 carlos moreno-miranda, hipatia palacios, daniele rama duction in the long term in an embattled sector. consequently, representations also downplay the need for systemic change, reproducing the power asymmetries they claim to change. the research seeks to establish to what degree public-facing communication differs from underlying priorities in terms of forefronting altruism over necessity, and whether this is problematic for the initiatives’ overall outcome. through semi-structured interviews, focus-group discussions, documentary analysis and participant observation in latin america and europe, it reviews relations in two cocoa sustainability initiatives with environmental foci. crucially, the research establishes a link between representations, underlying priorities and the degree to which they (re. the fao’s latest estimates point out that the world’s production of cocoa is more than 4,600,000 tons per year (1,200,000 ha) (alemagi et al., 2015; fao, 2018). ecuador, with an output of 270,000 tons/year, placed ninth in the world ranking producing countries (saravia-matus et al., 2020; williams, 2019). in recent years, the ecuadorian cocoa chain faced problems such as price fluctuation and low production yield (on average 304 kg/ha) in contrast with direct competitors (e.g., perú – 634 kg/ha, colombia450 kg/ha) (kozicka et al., 2018). besides, its pdo cocoa is marketed without adequate mechanisms leading to low market performance (pino et al., 2018). the low coordination and commercialization strategies and biased public policies are unable to differentiate variety-based markets (marette, 2016; undp, 2020). therefore, specific instruments promoting sustainable chains are vital. nevertheless, the ecuadorian government, led a process of cocoa revaluation, through the project “production and improvement of the quality of national cocoa” (mag, 2015). the purpose was to improve yields of ccn-51 cloned variety and target national and international markets of pdo cocoa (mag, 2018). the ecuadorian pdo cocoa is known as “cacao arriba” and characterized by a deep floral-fruity aroma (benitez, 2018). in 2007, ecuador submitted the designation of origin (do) application for cocoa arriba, and it was approved in 2013 (iepi, 2019). today, ecuador has the most significant world market share of cocoa arriba (63%) (pino et al., 2018). pdo arriba production is a clear alternative to promote sustainability and rural development. various authors argue that studies have only addressed agronomic aspects (tuesta et al., 2017). an integrative perspective includes a series of variables such as standards application, economic evaluation, and social implications, which underline existing shortcomings (corsi & salvioni, 2017). for such reasons, it employs a principal components analysis to reduce a large set by emphasizing variation and bring out strong patterns of social and economic sustainability between arriba pdo and ccn-51 cocoa chains. in such a context, the present article aims to contribute by addressing two research questions. the first rq is how the cocoa arriba pdo chain is different from the ccn-51 cocoa chain in terms of socio-economic performance? the last rq is what kind of governance mechanism does the cocoa arriba pdo chain describe, and what sets it apart from the ccn-51 cocoa chain? as such, the study hopes to further our understanding of the socio-economic sustainability assessment and the relevant insight regarding the cocoa pdo chain. it focused on los ríos province since it covers most of the cocoa arriba production in ecuador. 2. background cocoa has been linked to ecuador’s economic performance and is one of the most priced agricultural products on the international market. the geographical structure and biodiver281small-holders perception of sustainability and chain coordination sity of ecuador allow the cultivation of pdo arriba cocoa (figure 1). the production takes place in the equatorial zone at an altitude between cero and 1,200 meters above sea level (estupiñán, 2018). this zone locates between latitudes 01°27‘06“n and 05°00’56”s and longitude 75°11’49“w to 81°00’40”w (villamar et al., 2016). also, it has a humid climate with rainfall of 2,000 to 4,000 mm and slight variations due to the small mountain ranges (anecacao, 2017). this native variety is the most appreciated in the international market by representing about 5% of total cocoa production worldwide (mag, 2018). however, at the local markets, unsuitable mechanisms for the identification of cocoa varieties are largely affecting the chain development. despite this challenge, ecuador plays a crucial role because it is responsible for 63% of pdo arriba cocoa exports at the world level. the responsible actors of pdo arriba cocoa production are associations and export enterprises. besides the pdo arriba cocoa, ecuador grows a cloned variety ccn-51 (castro naranjal collection) for the industry. the agronomic performance of ccn-51 characterized by its resistance to disease and its relatively high productivity. 2.1 transition of certifications and agricultural policies the cocoa production certification in ecuador has a direct connection with the quality of the variety and agriculture practices. in 2008, the ecuadorian cocoa achieved utz (rainforest alliance) certification that focuses on attaining ecological agriculture and sustainability principles. in 2012, organic certifications (usda / bcs öko) were obtained by cocoa producers, which requires constant revision of agronomic practices. the fair trade certification of the ecuadorian cocoa started to be visible at the end of 2015. besides, there are figure 1. map of geographical area of arriva cocoa cultivars in ecuador. 282 carlos moreno-miranda, hipatia palacios, daniele rama national players such as the ecuadorian service of standardization – inen and the agency of phytosanitary regulation and control – agrocalidad, focus on certifications related to good farming practices. the lack of regulations of protected denominations of origin – pdo is evident in the subsector of cocoa (iepi, 2019; iica, 2014; ogus, 1992, 1994). the international regulations agreement on trade-related aspects of intellectual property rights – trips –. r.o. no. 977, june 28/1996 and the paris convention for the protection of industrial property. r.o. no. 244, july 29, 1999, supported the process of cacao arriba pdo legalization (iica, 2014). also, the andean regulations normative decision 486 of the cartagena agreement of the common regime on industrial property r.o. no. 258, february 2, 2001, assisted in recognizing arriba pdo cocoa at the international level. afterward, the ecuadorian institute of intellectual property (iepi) established the cacao arriba pdo standard (table 1). “the standard technique allows an activity to take place without any ex-ante control, but the supplier who fails to meet the standards perpetrates an infringement” (ogus, 1994). the existing standards of cocoa arriba are inen 176 and 177. however, the inter-american institute for cooperation on agriculture argued this cocoa arriba pdo standard requires a specific rule to guarantee the quality of the four types of the ecuadorian cacao arriba (aidoo & fromm, 2015). the plan for the agro-industry development of the cocoa-chocolate chain (pmc cacao) – 2019 is the most recent supporting policy launched by the government. it seeks the ecuadorian leadership as producer and exporter of cocoa and derivatives worldwide. this plan is a joint work between the public and private sectors. its fundamental pillars are increasing quality and production, promoting national and foreign investment, and strengthening associativity. the plan also highlights the generation of new jobs with a growth rate of 15% per year and public investment private with an amount of usd 600 million. the european union – a strategic partner for the project – provides technical assistance in the socio-productive evaluation of the chain. recent government reports show an enhancement of productivity thanks to training and agronomic practices. it accounts for a traceability system to support certification efforts and improvement of marketing margins by adequate price incentives. however, the marketing and commercialization of arriba pdo still present intable 1. standards of cacao arriba pdo and ccn-51. requirement unit cocoa arriba ccn-51 assps asss ass asn ase fermentation one hundred of grains g 135-140 130-135 120-125 110-115 105-110 135-140 good % 75 65 60 44 26 65*** slight* % 10 10 5 10 27 11 total % 85 75 65 54 53 76 biophysical violet % 10 15 21 25 25 18 slaty % 4 9 12 18 18 5 mould % 1 1 2 3 4 1 total number of defects (over 500 g) % 0 0 1 3 4** 1 source: ecuadorian institute of intellectual property iepi, (2010). 283small-holders perception of sustainability and chain coordination conveniences in price transparency and signaling in the local cocoa market. therefore, there is a challenge to clarify its socio-economic sustainability and propose integrated strategies aimed to support the development objectives. 3. methodology 3.1 study zone the present empirical assessment is based on qualitative and quantitative data collected in 2017-2018 from beneficiaries engaged in the project “production and improvement of the quality of national cocoa.” babahoyo district, located in the coastal region, was selected for several motives. first, this zone is the leader in cocoa production, accounting for 15% of the national share, and with up to 8000 farmers. second, its agricultural conditions, such as the location above sea level, a temperature between 12 to 25 °c, and climates from tropical humid to semi-humid make a proper ecosystem for cocoa production. third, producers are ahead of other cocoa zones in adopting sustainability practices to protect the arriba pdo. fourth, the babahoyo location is a dynamic point of arriba and ccn-51 cocoa trade between producers, intermediaries, processors, and exporters. as such, this study arguably presents a more enriched view of socio-economic performance. the methodology applied includes phases and tools detailed below. 3.2 questionnaire modeling and sampling the study segmented producers by using the last agricultural census data. next, it interviewed professionals from the ministry of industries to section the post-production actors. then, we executed a workshop with stakeholders to select variables from a predetermined list – the list considered social variables to determine the condition of people within the chain (feschet et al., 2013) we design a pathway for social lca impact assessment. this pathway may be used to explain or predict the potential impact caused by the modification of one product sector upon the health of a population. the preston relationship usually is calculated for a cross section of countries. we assess whether the preston relationship is valid when a single country is considered alone. drawing from scientific literature regarding development, we define the context where the use of the preston relationship is justified. we describe the general design of the preston pathway, using a recalculated (panel based. production attributes showed aspects related to the agronomic models. economic factors described sustainability in terms of costing, associativity, and margins (barrera-mosquera et al., 2010). the experiment contemplated a one version survey, cronbach’s alpha index validated the questionnaire, and wording was changed to reflect the use of cocoa over other types of products. each study was pilot-tested with at least three interviewees, who assisted with confusing and ambiguous items, as well as survey layout and flow. the final questionnaire consisted of three major sections, socio-productive and respondents’ perceptions of how economic and agronomic issues impact their product performance. the information obtained from the ministry of agriculture-mag, and ministry of production-mipro resulted in a list of 450 chain actors. then, it applied the sukhatme formula (sukhatme, 1954), at a 95% confidence level, and employed the variable “number of produc284 carlos moreno-miranda, hipatia palacios, daniele rama ers registered by mag” to target 420 cocoa producers (farmers and cooperative representatives). we contacted post-production actors to participate in the study through interviews and know the information of local cocoa markets. beforehand, we refined the respondent data set of post-production to eliminate irrelevant remarks. for instance, the study blocked fruit, vegetable, and cereal producers, since they focused on different issues. the final group of participants consisted of 70 post-production participants (cocoa traders and entrepreneurs). overall, information gathered confirmed a reasonable basis for developing the governance analysis, using the gereffi framework (gereffi et al., 2005). governance typologies in value chains showed the mechanism for coordinate actors, activities, and stages. 3.3 fieldwork database analysis the study examined socio-demographic data obtained from surveys by applying statistical descriptions. the descriptive information includes averages for arriba and ccn-51 respondents, as well as two-population t-tests to examine means’ differences. this procedure allowed the characterization of arriba and ccn-51 chains. the analysis provided a chain mapping by employing the hawke scheme (hawkes & ruel, 2011) and the dotoli approach (dotoli et al., 2005). the mapping showed a graphical description of stages, linkages, and connections. also, it displays vital chain elements, such as value-trajectories, information, and financial flows. analysis of producer perception used a scale similar to that of melnyk (melnyk et al., 2003). here, the study asked respondents about the relevance of economic and agronomic factors to their crops. the variables were measured on the relative frequency of a five-point scale: 1, extremely irrelevant; 2, irrelevant; 3, neutral; 4, very relevant; and 5, extremely relevant. then, we employed principal component analysis (pca) (jolliffe, 1982) to assess crop constructs (e.g., land tenure, cultivation technique), economic constructs (e.g., costs and yields), and associative measures. the method includes a correlation analysis and standardization of variables. once orthogonal variables (z-scores) were obtained from pc analysis, we unified them by using the following expression: xij – μjζij = σj where: xij z-score of i observation, j variable μj mean of j variable σj standard deviation of j variable ζij z-score ij ajusted 4. results 4.1 actors’ segmentation and value-chain characterization table 2 shows the data provided by the ministry of agriculture about the production stage. the data accounted for 10.5 % of cocoa-producing families. 285small-holders perception of sustainability and chain coordination 4.1.1 socio-economic profiles table 3 states the socio-demographic profiles of respondents. most of the ccn-51 chain actors were between 26 and 40 years old (55.3%). the arriba chain actors were between 41 to 55 years old and represented 65.8% of the respondents. there was a difference concerning the education level, since a high proportion of participants (38.5%), belonging to the ccn-51 chain reported a college education. however, more than 50.3% of arriba producers only reported a high-school level of education. it is noteworthy that interviewees responded to crop management questions with a high level of knowledge. this aspect is because a large proportion (more than 50%) of producers of both table 2. number of producers and cocoa production area. province district number of producer families area of production (ha) los rios babahoyo 1250 1955.5 ventanas 750 1040.2 vinces 520 840.7 source: ministry of agriculture mag, (2015). table 3. socio-economic characteristics of cocoa-producing families. variable proportion mean ccn-51 arriba p-value gender (n = 250)   female 54.0 62.0 male   46.0 38.0 age (head of family) (n = 250) < 18 años 17 2.5 1.4 0.058* 19-25 años 23 14.2 4.2 0.045** 26-40 años 34 55.3 8.2 0.028** 41-55 años 46 17.3 65.8 0.039** 56-65 años 59 9.2 12.2 0.025** >66 años 68 1.5 8.4 0.437 education (head of family) (n = 250)     primary 13.4 22.5 secondary   48.1 50.3 college 38.5 27.2 associativity (households) (n = 250)     members 44.9 57.4 non-members   55.1 42.6 montly household income (n = 250) < 700 usd 625 11.4 14.6 0.001*** 701-1000 usd 830 19.6 36.2 0.021** 1001-1300 usd 1220 25.2 29.7 0.027** 1301-1700 usd 1580 37.5 17.8 0.032** >1700 usd 1950 5.5 3.4 0.001*** note: difference (p) represents the p-value significance of two population t-test with unequal sample sizes and unequal variances: **** for < 0.001, *** for < 0.01, ** for < 0.05, and * for < 0.1. 286 carlos moreno-miranda, hipatia palacios, daniele rama chains followed agricultural science programs (díaz-montenegro et al., 2018). regarding monthly income, most of arriba producers reported a range between 701-1000 usd (36.2%) and ccn-51 producers presented a range between 1301-1700 usd (37.5%). the average number of household members was 3.6 in both chains, and 63% of respondents reside in babahoyo district. according to the national institute of statistics and census (inec, 2010), the average number of members per household is 2.9. (in the coast region). besides, during 2018, the monthly income was usd 450, on average (viteri-salazar et al., 2018). therefore, the sample showed better representativeness in terms of the average salary of a household member. 4.2 chain actors: influencers/enablers outcomes showed the intervention of chain influencers, such as public entities, advisors, and private agro-centers. these actors aimed to provide a technical assessment to crop management. peasant families were the first enabler cluster identified and were responsible for channeling the harvest to collection points and wholesalers. the main difference detected was the crop volume of arriba cocoa, which is 20% of the ccn-51 cocoa volume. also, exports of dried ccn-51 cocoa are above 35% of arriba cocoa exports. however, exports of liquor are the opposite; arriba liquor exports are 21% greater than ccn-51 liquor. processors, the second enabler cluster, transform the raw material (dried cocoa) into liquor or paste. outcomes also detected dealers (third enabler cluster) strategically located in areas close to the plantations. dealers aimed to link processors and producers, thus dynamizing the trade by transferring the raw material ata the national level. the primary goods sold by the ccn-51 chain are dried cocoa and nibs, while the arriba chain sold mainly cocoa paste. the central bank (external influencer) established the reference prices of liquor and driedfermented cocoa, based on the international cocoa organization–icco and the new york stock exchange (see figure 2). 4.3 chain roles and resources’ streams outcomes of the pre-production stage showed the presence of private greenhouses as the main responsible for the supply of seedlings. due to an increase in rainfall and temperature, respondents pointed out december and may as the best cultivation time. it is noteworthy that crops require a daily shade-period to achieve an optimum level of production. another essential requirement is surface cleaning – the elimination of pests and weeds, while bush pruning is necessary after the first year of crop life. producers plan the harvest stage in two phases, the first to collect arriba cocoa in winter, and the second to harvest ccn-51 cocoa in summer. producers performed the harvest at intervals of 10 to 15 days. subsequent stages are fermentation and grain bagging, thanks to the sector’s humidity and temperature. then, producers dried cocoa beans by using solar energy and collection points employed gas dryers. roasting and shelling are the main steps in the transformation of cocoa beans. roasting potentiates aroma and flavor, and husking separates the crust from the almond. the final product is called the nib, which is ground to obtain a thick paste. the paste is refined and later distributed as a semi-processed product. the confectionery sector highly demands the 287small-holders perception of sustainability and chain coordination cocoa paste, and its price ranges between 10.00 and 15.00 usd/kg in the case of ccn-51 cocoa, and between 13.00 and 20.00 usd/kg in the case of arriba cocoa. the pastry, baking, and catering sectors are the principal applicants for the refined paste. production cost ranges between 8.00 and 10.00 usd/kg in the case of ccn-51 cocoa, and between 11.00 and 15.00 usd/kg in the case of arriba cocoa. at the marketing stage, small intermediaries promote cocoa trading and supply the grain to smes and artisans. concerning the key streams of resources, outcomes identified two types classified as high and low importance. the cocoa trajectories used the high-relevance streams (hrss) and connected production, fermentation, and drying activities. the ecuadorian standardization service (inen) set up a high-quality cocoa standard (see table 4), which plays an essential role due to local market requirements. the commercialization and transformation are the last hrss within the chain. besides, the social, environmental, and political interests of cocoa derivatives are increasing; however, their quality standards, established by inen through standards 175, 176, and 177, need revision, to boost their market growth (2.2 to 3.5 percent per year). the low-relevance streams (lrss) connected supporting activities. the first stream was the financial one. public and private banking entities and credit unions perform financial support. observations showed financing programs with access to microcredits. besides, the flow of information was also an essential supporting stream. figure 2. monthly averages prices (usd/tm). source: new york stock exchange, (2019). table 4. biophysical standards of cocoa. type of grain standard degree i degree ii moldy max. 3% max. 4% slaty max. 3% max. 8% affection by insects máx. in total 3% máx. in total 6% source: ecuadorian standardization service inen, (2006). 288 carlos moreno-miranda, hipatia palacios, daniele rama technical and marketing information streams were in high demand from actors. ministry of agriculture and the institute of agricultural research (iniap) were the leading providers. however, there were also private organizations focused on disseminating aspects of prices and marketing opportunities. figure 3 shows the mapping of all the components contained herein. 4.4 chain coordination the study examined the coordination mechanism, information coding, the complexity of the inter-firm information transfer, and the level of competence of actors. therefore outcomes are as follows: 1. market coordination. the ccn-51 chain characterized by governing bodies, such as farmers (suppliers) and dealers (intermediaries). repetitive transactions easily codified within exchange environments, such as local markets, are the main feature. the most common district markets close to babahoyo are quevedo, ambato, and guayaquil. cash payments or contracts with short credit periods, no more than eight days, were the primary business coordination mechanisms (see figure 4). 2. modular coordination. the arriba chain showed a setting whose transactions codified a relatively high level of complexity. the study observed a sort of power market imposed by governing bodies, such as processors and dealers. these actors set product specifications, credit periods, and buying prices through contracts. besides, figure 3. mapping of the cocoa chain at babahoyo district. figure 4. coordination mechanism at ccn51 cacao chain. 289small-holders perception of sustainability and chain coordination liquor and nibs processors acquired generic machinery, to reduce the risk of investment. the most common acquisitions are refiners, molders, and peelers. the relationships between actors is based on local and global market information and technical procedures (see figure 5). 4.5 producer perception and comparative analysis the experiment examined the cocoa producers’ performance in both chains, to elucidate socio-economic and production aspects. we applied a principal components analysis on the 12 primary variables. the details of the variables studied are in table 5. the components (kmo = 0.818, bartlett’s test χ2 sig. 0.000) arose with values greater than 1, satisfactorily explaining 70.22% of the variance. results in table 5 reveal that for producers’ performance evaluation, plant production factors are the most relevant. outcomes classified this component as agronomic. variables in the agronomic part were cocoa variety, land tenure, cultivation technique, number of crops, and post-harvest practices. cocoa variety is a factor that had the most impact on producer performance. thus, we performed a pca by producer group, i.e., arriba and ccn-51, to investigate differences between both chains. in the case of cocoa arriba producers, the first component is noteworthy on account of its impact. the variables included land tenure, cultivation technique, associativity, and postharvest practices, i.e., factors inherent to crop development (see table 6). most of the variables represented strategic information for excellent production performance. however, it is essential to emphasize that the results presented the associative variable as a crucial aspect for this group of producers. besides, the price variable captured little interest, possibly because the cocoa market is expanding its quotas and business opportunities (scherer & ross, 1990). in the case of cocoa ccn-51 producers, the second component had the highest score. the variables included production cost, financing, yields, cocoa acreage, and acreage, i.e., factors inherent to economic and management planning (see table 7). most of the variables represented strategic financial performance. however, the results showed the associative variable as having little impact on producers’ perception. the price variable also had little effect, possibly because international markets have already established the price of ccn-51. finally, figure 6a distinguished two distinct segments – non-association members and associated members – by considering agronomic and financial components. we observed that most cocoa arriba producers opted to be part of associations. respondents pointed out benefits, such as reducing economic risk, because representatives addressed production by following strategies formulated by consensus. in figure 6b, the interpretation is differfigure 5. governance mechanism at arriba chain. 290 carlos moreno-miranda, hipatia palacios, daniele rama ent because ccn-51 producers did not tend to be part of associations; they opted to make independent decisions. table 5. producers’ perception of the relative importance of productive performance aspects. variable relative frecuency aggregate score fully no relevant no relevant neutral very relevant extremely relevant mean s.d acreage 2.2 1.5 14.3 38.1 43.9 4.31 0.85 cocoa acreage 2.1 3.7 19.2 40.3 34.6 4.27 0.77 production cost 2.5 4.5 28.2 38.9 25.9 3.69 0.89 yields 3.1 4.9 33.7 36.2 22.1 3.55 0.92 financing 2.5 5.9 38.1 32.5 21.0 3.69 0.91 land tenure 2.9 5.7 40.2 34.9 16.3 3.62 0.88 price 1.5 3.2 38.7 36.5 20.1 3.66 0.91 cocoa variety 1.3 9.8 32.6 38.2 18.1 3.63 0.95 cultivation technique 9.4 12.5 26.3 30.7 21.1 3.58 2.47 additional crops 4.5 10.2 31.8 34.3 19.2 3.58 0.77 post harvest practices 2.5 19.7 38.1 25.9 13.8 2.98 0.84 associativity 18.2 22.2 30.2 18.1 11.3 2.74 1.35 table 6. matrix of extracted components from pca analysis of arriba cocoa producers. component 1 2 3 variable land tenure .961 cultivation technique .855 associativity .827 postharvest practices .818 acreage .875 production cost .862 cocoa acreage .795 yields .761 financing .733 additional crops .725 price .772 eigenvalue 4.422 1.524 1.102 statistical factors variance % 38.471 15.218 16.531 cumulative variance % 38.471 53.689 70.220 cronbach alpha 0.891 0.895 0.758 mean 3.11 2.53 2.89 291small-holders perception of sustainability and chain coordination 5. discussion and conclusions improving the sustainability performance of agri-food circuits from the socio-economic point of view implicates structural changes. cocoa producers, traders, processors, and distributors are agri-food sub-clusters and have responded to rural development problems, associativity, and cost-efficiency. market opportunities, together with certification, such as table 7. matrix of extracted components from pca analysis of ccn-51 cocoa producers. component matrix 1 2 3 variable additional crops .824 cultivation technique .811 land tenure .752 postharvest practices .623 production cost .951 financing .983 yields .845 cocoa acreage .839 acreage .712 price .753 associativity .694 eigenvalue 4.277 1.671 1.215 statistical factors variance % 35.522 18.196 14.112 cumulative variance % 35.522 53.718 67.830 cronbach alpha 0.866 0.899 0.761 mean 3.05 2.73 2.71 figure 6. (a) scatter plot of arriba producers and associativity; (b) scatter plot of ccn-51 producers and associativity. 292 carlos moreno-miranda, hipatia palacios, daniele rama pdos and supporting policies such as the plan for the agro-industry development of the cocoa-chocolate chain, look for synergy to supporting agricultural activities. environmental aspects related to soil conditions of crops, pest-management plans, and deforestation practices have been examined extensively. however, social and economic factors have received little attention. this paper aimed to provide an initial comparison between two different chains, ccn-51, and arriba pdo. the emphasis was on highlighting the socio-economic conditions and their effect on the sustainability of the pdo chain, which is widely recognized worldwide. while two research questions tackled this aim, the results showed marked differences between both cocoa chains. we also faced a scarcity of indicators of a holistic sustainability assessment. such findings highlight the complexity of evaluating sustainability conditions, encourage future discussion, and motivate frameworks for assessing the cocoa chain comprehensively. results suggest that the arriba pdo chain shows a disadvantage in the age profile of its population, which constitutes a possible threat. the education level of arriba workers, as well as their associativity, have fundamental weaknesses. regarding academic formation, actors required an integrated perspective to make decisions effectively. likewise, differences in monthly income pointed out a drawback for arriba cocoa pdo producers. thus, our findings conclude with the need for public intervention aimed to promote training programs on topics such as sustainable farm management and incentives to linkage young producers. most public bodies’ response has focused on production and market price monitoring. instead, private institutions, such as anecacao has led various projects aimed to coach producers on cultivation techniques and the use of technology in crops (anecacao, 2017). furthermore, public entities engaged in manage local and international market intelligence systems have focused on on-demand detection. for instance, pro ecuador is a public institution aimed to connect buyers and sellers of high-value products (proecuador, 2018). also, it provides webinars on specific topics such as market strategy and market niches information. however, the effectiveness of market participation requires a profound accompaniment aimed to differentiate, recognize, and re-evaluate arriba pdo at the local and international levels to address cost efficiency and improve margins for producers, smes, and entrepreneurs. in 2016, fedecade, a private association of cocoa producers, performed a national study investigating the arriba pdo cocoa market (iica, 2010). it concluded that small-holders do not possess sufficient resources to invest in promoting the arriba pdo cocoa and derivatives. based on these events, the government launched the public policy plan for the agroindustry development of the cocoa-chocolate chain (pmc cacao) – 2019. with the aim for underpinning both circuits, ccn-51, and arriba (mag, 2019). ccn-51 cocoa was assessed for mass markets and the industrialization of comparable products, such as nibs, cocoa powder, and degreased chocolate for toppings, among others. cocoa arriba is a good whose sensory potential is exploited in products with high quality and differentiation, that is, in exclusive market segments. consequently, we confirm that the execution by public bodies of equal strategies for both chains in terms of market orientation is a severe error. thus, coordination mechanisms play a crucial role in correcting the affection at the market level and improving the pdo chain performance. the pdo chain showed little interest in the price mechanism, since the world market is expanding, and actors are looking for a significant transition towards a sustainable chain. the modular governability of the pdo chain shows the need for design and strength293small-holders perception of sustainability and chain coordination ening precise information flows that aim to achieve high value-added consumer goods. we believe that the market for processed cocoa arriba-based goods has full reception at the local level, and even more so in global markets (iica, 2014). europe, asia, and north america are markets which demand this type of good. further, findings concluded that future research on integrated ecological and institutional practices within the multi-level approach is necessary. future studies must focus on different labor and agricultural practice regulations and policies to monitor their significant role in the adoption of sustainable models. 6. bibliography aidoo, r., and fromm, i. 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(2019). sumak kawsay: social empowerment through participatory user-centred design in ecuador. international journal of art and design education 38(1): 193-206. https://doi.org/10.1111/jade.12175 bio-based and applied economics 10(2): 87-88, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12230 bio -based and a ppl ied economics bae copyright: © 2021 f. arfini. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: f. arfini (2021). mediterranean agriculture facing climate change: challenges and policies. bio-based and applied economics 10(2): 87-88. doi: 10.36253/bae-12230 received: october 9, 2021 accepted: october 11, 2021 published: october 28, 2021 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio gaetano santeramo. orcid fa: 0000-0002-5179-2541 mediterranean agriculture facing climate change: challenges and policies filippo arfini dipartimento di scienze economiche e aziendali, università di parma, italy e-mail: filippo.arfini@unipr.it 1. background and objectives this special issue of bio-based and applied economics (bae) features a selection of four papers previously presented at the 9th conference of the italian association of agricultural and applied economics (aieaa) (10-12 june 2020, valenzano-bari, italy), titled “mediterranean agriculture facing climate change: challenges and policies”. changes in climate conditions consistently point to increasing risks to societies all over the world in uneven and multiple ways. the increasing average temperatures, frequency and intensity of extreme weather events are expected to severely affect agri-food systems in the next decades. according to figures, climate change is responsible for around 80-90% of projected changes in water availability and soil loss due to desertification processes and erosion. in many areas agricultural land and crop suitability is affected by the climate change that modifies the production patterns. the expected fall in the food production will have important consequences in the gross domestic product in the worst affected regions. all these phenomena will have important consequences for the global social stability. the harmful effects of global climate change on agriculture are unequally distributed across regions and countries, both in relation to the physical and environmental conditions, and depending on the sensitivity, exposure and adaptive capacity of local natural and social systems. the mediterranean area is one of the most vulnerable regions in the world to the impacts of global warming, according to international reports and projection scenarios. the european environmental agency (2019) states that in the coming decades, the entire mediterranean region is expected to experience severe climate events with diversified consequences on agriculture, depending on the adaptation capacities of different areas. the debate on impacts and consequences of climate change on mediterranean agricultural and food systems is particularly sensitive and controversial, considering historical, socio-economic and political diversities. the mediterranean region turns out to be a crucial crossroads for people movements induced by climate change. relocation and movement of people will cause an increased pressure on certain areas in terms of production and consumption, http://creativecommons.org/licenses/by/4.0/legalcode 88 bio-based and applied economics 10(2): 87-88, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-12230 filippo arfini while other geographic regions will suffer further erosion and desertification, due to land abandonment and reduced level of land protection. such migrations put increasing pressure on the geopolitical role of the region as well as its internal relations and domestic politics. mediterranean countries, due to their geographical location, play a central role in the eu international relations. programs within euro-mediterranean partnerships often promote initiatives for climate change mitigation and adaptation. current and future policies for agricultural and sustainable development of mediterranean countries need to prioritize climate risks considering agriculture multiple objectives such as providing adequate food for growing populations, protecting the environment and ensuring resilience to future climatic change. against the above scenarios, the 2018 evaluation report of the eu adaptation strategy invites enhancing the knowledge base and encourages new research and development, as well as innovation, in the field of climate change adaptation and mitigation policies. the purpose of this special issue is to address some of the challenges that agri-food systems in the mediterranean area are facing due to climate change. 2. the papers in this issue the four papers in this issue are very different in scope and methods and provide examples of different and complementary issues in addressing the topic of climate change in the mediterranean agri-food system. vaquero piñeiro (2021) focuses on gis and their impact on the economic development of mediterranean rural areas. especially the paper aims at identifying whether territorial features drive the success of gis, thus affecting their capacity to stimulate the local development. the findings demonstrate that pdo-food localized in less-developed regions struggle to achieve the highest gis market shares. the unique presence of food quality designation does not guarantee the development of the rural area where such food is produced. the study thus invites european, national and local policymakers to intervene in the areas with weaker socio-economic conditions, by applying more flexible production regulations and creating synergies between producers, associations and regional authorities prior the designation. raina, zavalloni, targetti, d’alberto, raggi and viaggi (2021) focus the attention on the farmers’ decision to participate and their willingness to accept (wta) a particular agri-environmental scheme (aes). according to literature the design of the contracts proposed to farmers influences their choice. the paper thus investigates which are the most successful attributes of the contracts, as highlighted by the scientific literature that uses choice experiments to test farmers’ preferences. results show that monetary attributes, in terms of compensation measures are highly preferred by the farmers and can increase their participation in aes, along with general contract attributes, such as the possibility to include smaller area or a shorter duration, and flexibility attributes, such as higher flexibility of participation, or different kinds of management. the study thus has the ambition to serve as a repository of possible attributes to be used in other choice experiments at disposition of other researchers and policymakers. the paper by lamonaca, santeramo, and seccia (2021) highlights the connection between climate change and wine productivity in different regions in the world. in particular, the paper aims at analyzing the effect of climate change parameters, such as increasing temperature and precipitation on production patterns in different producing regions such as old-world producers and new world producers. results seems to suggest that the effect may be different between them: while new world producers may suffer from precipitation patterns, old world producers may suffer from increasing temperature. the paper thus invites other future research to examine how the entry of new world producers in the global markets may affect the global trade of wine and to understand how importers and exporters could react to new trade dynamics, due to climate change, in terms of trade regulations. zucaro, manganiello, lorenzetti, and ferrigno (2021) in their article aims at presenting the feasibility and usefulness of multi criteria analysis (mca) in identifying the most effective project proposals in the field of water management. the issue is relevant considering the increasing effort of european and national institutions to adequately tackle the environmental effects of climate change by means of funds that follow public calls. the paper thus demonstrates that mca can be useful tool for choosing between different investment alternatives, since it allows for the inclusion of different quantitative and qualitative criteria that can be measured in a single evaluation process. nevertheless, the methodology is highly complex and there is the high risk of influencing the results of the method, by introducing subjective choices. for this reason, proper methods should also be applied to make mca a useful informative support for policy decisions. volume 10, issue 2 2021 firenze university press mediterranean agriculture facing climate change: challenges and policies filippo arfini the long-term fortunes of territories as a route for agri-food policies: evidence from geographical indications cristina vaquero-piñeiro application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context raffaella zucaro, veronica manganiello, romina lorenzetti*, marianna ferrigno climate changes and new productive dynamics in the global wine sector emilia lamonaca*, fabio gaetano santeramo, antonio seccia a systematic review of attributes used in choice experiments for agri-environmental contracts nidhi raina*, matteo zavalloni, stefano targetti, riccardo d’alberto, meri raggi, davide viaggi the effect of farmer attitudes on openness to land transactions: evidence for ireland cathal geoghegan*, anne kinsella, cathal o’donoghue bio -based and a ppl ied economics bae copyright: © 2022 n. hatvani, m.j.a. van den oever, k. mateffy, a. koos. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-10820 citation: n. hatvani, m.j.a. van den oever, k. mateffy, a. koos (2022). biobased business models: specific and general learnings from recent good practice cases in different business sectors. bio-based and applied economics 11(3): 185-205. doi: 10.36253/bae10820 received: april 23, 2021 accepted: may 31, 2022 published: november 4, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio gaetano santeramo. orcid nh: 0000-0003-4031-1199 mjavdo: 0000-0002-1587-5884 km: 0000-0002-3104-006x ak: 0000-0002-1308-5256 bio-based business models: specific and general learnings from recent good practice cases in different business sectors nora hatvani1,*, martien j.a. van den oever2, kornel mateffy1, akos koos1 1 bay zoltan nonprofit ltd, derkovits fasor 2, 6726 szeged, hungary 2 wageningen food & biobased research, bornse weilanden 9, 6708 wg wageningen, the netherlands *corresponding author. e-mail: nora.hatvani@bayzoltan.hu abstract. business models can be a perfect tool to meet the challenges in highlighting the competitiveness and sustainability potential of bio-based solutions, and facilitating primary producers to benefit from the opportunities offered by bioeconomy. in this work six concrete bio-based good practices that have succeeded in progressing from early ideas to products on the market were analysed. these examples pose new insights that can be used by a wide range of experts and stakeholders for the analysis of benefits and challenges of value chains in the bio-based economy sectors. it is concluded that the traditional business model canvas needs to be extended with additional factors related to sustainability and business ecosystem. in order to establish a practical framework promoting economic viability of bio-based business cases, the importance is highlighted for adjusting the exclusive focus on technology readiness levels by introducing levels reflecting business or market readiness. keywords: bioeconomy, business modelling, business model canvas, bio-based industry, business readiness level. jel codes: o31, o32, q01, q14, q16, q20, q50, q55, q56. 1. introduction 1.1 special role of business models in bioeconomy dissemination of good practices the shift towards a circular economy and bioeconomy is one of the main focuses of political initiatives aimed at replacing fossil feedstock by renewable biological sources while still achieving economic growth.1 awareness raising activities, highlighting the potential of bioeconomy for competitiveness and sustainability are necessary for informing the general public as well as the different policy departments and business sectors.2 bio-based business models, the importance of which is clearly stated in the updated bioeconomy strategy of the european commission (ec),3 can be a perfect tool to meet http://creativecommons.org/licenses/by/4.0/legalcode 186 bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 nora hatvani, martien j.a. van den oever, kornel mateffy, akos koos this challenge. according to this strategy, bioeconomy is sustainable and circular, and includes, among others, “economic and industrial sectors that use biological resources and processes to produce food, feed, bio-based products, energy and services”. one of the “pilot actions” included in the european bioeconomy strategy aims to better link national bioeconomy strategies and national strategic plans under the common agricultural policy (cap), in order to support inclusive bioeconomies in rural areas. it is also highlighted at this action that dissemination of good practices is among the most important tools to foster the deployment of the bioeconomy and enables primary producers to benefit from the opportunities offered by bioeconomy approaches.3 a strong support for economic information is imperative for enhancing the convergence between bioeconomy and the cap or other relevant agricultural policies and priorities.4 when developing cap strategic plans supporting the setting up of sustainable bioeconomy businesses in rural areas, particular attention has to be paid to primary producers because they play a key role in bioeconomy value chains. countries with well-developed primary sectors have certainly many opportunities to develop downstream value chains.5 the bioeconomy’s strength lies in its diversity, adaptability and close interactions with local and rural communities,2 and business modelling can represent all of these aspects in a unified structure, taking into consideration the local economic and social environment where the business operates. when talking about an innovative solution as part of the workflow in research and development (r&d) projects where companies or business oriented organizations (e.g. clusters, chambers) are involved, the first immediate question that arises on their part is whether deployment of the solution is economically viable. further questions that arise are: what evidence underpins the real potential and economic feasibility of the technology solution? has the technological viability been already demonstrated? has the technology been operated on a large scale? how does the innovation fit into the business environment? no matter how eager the representatives of business-supporting organizations (e.  g. clusters, farmers’ associations, consultancy services, etc.) are to widely share the bioeconomy-related technologies and opportunities to their network, they are not able to make steps forward in qualitative terms without having answers to these questions above. bearing in mind the precious trust gained from stakeholders they have been working with, representatives feeling responsible simply cannot afford to introduce these promising solutions, as long as it is not fully clear and proven that these innovations would not cause any economic disadvantage to these stakeholders. involvement of farmers is especially essential, since this is the key to ensuring that farmers, instead of being mere biomass providers, benefit from the profit-creating value-addition that is achieved by the innovative transformation processes in bioeconomy businesses. the study on the participation of the agricultural sector in the bio-based industries joint undertaking (bbi ju) emphasizes that business models in the biobased sector are worth highlighting.6 an easy-to-understand business model is a great tool for several purposes, i.e.: awareness raising in different sectors, dissemination of bioeconomy good practices, involving primary producers. the model is expected to clearly explain the key components of a business and how they relate to each other in order to create value and a favorable balance of cost structure and revenue streams that can make the business model viable.7 this current work focuses on business aspects within the bioeconomy concept, which are of key importance for this industrial sector, and intends to show interlinks between these aspects and others considered essential for the successful implementation. the aim is to analyse reallife, concrete examples of ‘bio-based concepts’ that have succeeded in progressing from the early ideas to final products placed on the market, and assessing their business models, in order to provide learnings that can be used by experts of consultancy services and other business-supporting organizations, clusters, research organizations, legal authorities, etc., for the development of innovative companies. the results contribute to developing a common and shared perspective of different sectors involved in bioeconomy developments, with special regard to academic or r&d organizations and industrial actors implementing bio-based industrial solutions in real life. 1.2 business model canvas in very general terms, business models explain how enterprises work to deliver value to their customers. a competitive model represents a business activity that is better than the existing options or may offer more value to a discrete group of customers or may even completely replace the old way of doing things and become the standard for other entrepreneurs. business model canvas (bmc) is a template framework identifying and addressing the nine most important so called building blocks of a business solution and its environment:8, 9 – value proposition is the bundle of benefits that a company offers to customers. it is the concept at the heart of the model, including the product or service 187bio-based business models: specific and general learnings from recent good practice cases in different business sectors bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 itself (or the combination of these), and also value factors being the reasons behind the customer’s motivation to buy it. – key partners, key activities, key resources are the internal building blocks that are mostly under direct control of the value proposition’s owner, including all the operational components that make the value proposition a reality. – customer relationships, channels, customer segments are the external building blocks, including all components related to the understanding and reaching people and companies representing the market. – cost structure and revenue streams are the financerelated building blocks, describing the financial viability and feasibility of the business. bmc is a useful tool for facilitating the entrepreneurial process by breaking down the most relevant aspects of a business solution, and helping to understand and visualize the interplay of the different components creating value. while bmc might be helpful to understand existing business models, it is also suitable to design novel innovations.7 the internal building blocks of bmc can be more complex for bio-based industry than other industries. in most bio-based value chains biomass raw material comes from a sector different from the one where it is utilized in a bio-based process (figure  1). this means that bioeconomy solutions evidently involve different sectors and thus require the cooperation of various and divergent players which rarely interacted so far, such as established chemical companies and small-scale farmers.10 moreover, the bioeconomy concept, as all holistic innovation systems, needs to involve all groups of stakeholders according to the quintuple helix approach: economic, education and political systems, civil society and natural environment.11 in 2017, a systematic literature review was conducted by reim et al. on research articles describing bioeconomy-related forest-based business solutions.1,12 the review assessed to which level of detail the bmc building blocks were investigated in these studies. the building block that is extensively covered in literature is the value proposition, mainly by describing existing or potential offers related to bioeconomy. key activities and key resources are also well-discussed. customer relationships block often mentions the need for reliable information to convince potential customers, however, there is not much written explicitly about customer relationships. channels is the bmc building block that is least addressed. details about the cost structure can also be found in literature even though detailed calculations are currently missing. the most frequent explicitly addressed cost is related to the cost of biomass or feedstock. 1.3 extension of bmc to meet sustainability and business ecosystem aspects bmc is often chosen for business modelling, due to the ease of its practical application and worldwide recognition. however, applying the principles of the circular economy and bioeconomy exceeds the existing bmc components. the potential contribution of the bioeconomy to sustainability and its social value generation (e. g. local employment, rural regeneration, energy security) are highly evident and well-described in the recent literature.1 however, bioeconomy is not per se sustainable just because it is based on renewable resources, and sometimes it even brings about new challenges, despite the fact that it can be a way to solve sustainability problems and may contribute to the sustainable development goals defined by the united nations.13 besides competition with food and feed, increased use of biomass also has its effects on land use, water availfigure 1. bio-based value chain structure demonstrating the stakeholders involved. 188 bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 nora hatvani, martien j.a. van den oever, kornel mateffy, akos koos ability and on other sectors. for example, forest-based industries (such as production of pulp and paper, building materials, etc.) are affected by the increased use of wood for energy conversion.14 as already seen in the context of bioenergy, rise of the demand for biobased products will increase the pressure on limited biomass and land resources and thus may cause several sustainability conflicts.13 to make sustainability as one of the key concepts behind bioeconomy supply chains noticeable, sustainability-related aspects can be added as extensions to the bmc. this extension enables the bmc to indicate/demonstrate that bio-based solutions are not aimed at merely replacing fossil resources by bio-based ones, but also generating societal and ecological values and contributing to a long-term structural change.13 various models based on the original bmc have already been published which suggest to add further blocks to the top, side and/or bottom sections to reflect a wider perspective.15 an example is the framework presented by antikainen and valkokari, which shows how to create values also in environmental and social terms, which is particularly relevant in bioeconomy businesses requiring the cooperation of various and divergent players.16 2. methodology 2.1 selection and categorisation of bio-based solutions the starting point for the selection was a collection of nineteen bio-based solutions which were described in detail in a study in the framework of the power4bio project.17 in the first step, this collection was screened to select good practice solutions having high technological maturity (trl8 or trl9, the highest technology readiness levels used in h2020 framework programme for research and innovation18) and proven business potential. additionally, sufficient quality and quantity of the data of the solutions should be available. in this step, knowing a reliable contact person at the company owning the bio-based solution who had permission for sharing necessary and relevant information for all nine building blocks of the bmc was an important aspect. the third step was to identify different solutions to cover the four categories as defined by cowi, bio-based world news and ecologic institute:19 1. final product (product that can be sold to the end consumer, without any further processing, e.g. tableware, biofuel, mushroom grown on agricultural wastes, etc.); 2. material (product that can be used as raw material to produce bio-based final products, e.g. bio-based fibers, bio-based foam for packaging applications, hemp-based insulation material for buildings, plantbased material for clothes, etc.); 3. general building block or biopolymer (chemical monomer or polymer to produce materials, e.g. biobased 1,4-butanediol, an industrial chemical used as a building block for the production of plastics, elastic fibers and polyurethanes); 4. technology licensing. the authors selected six recently developed biobased good practice cases and described them following the bmc modelling system. 2.2 data collection intensive desk and literature research were carried out to extract the valuable information from publicly available sources such as webpages of the companies, (bio)economy news portals, press releases issued by the companies, conference presentations, economical/statistical databases, scientific articles, etc. online or telephone interviews were conducted with the owners or experts of selected solutions. one person was interviewed per company. the company experts were informed of the aim and subject of the interviews in advance, during the appointment arrangement process. a set of relevant questions was compiled before the meetings, structured by the elements of bmc, to help covering all relevant details during the interviews. for the case when a company preferred to fill in a questionnaire rather than giving an oral interview, this set of questions was sent to its representative expert. the collected data were processed and organized using bmc structure.8 companies have checked and endorsed the business model descriptions. 2.3 literature review to learn from previous experiences and take into consideration bio-based business models elaborated earlier, a literature review was made to obtain an overview about the work already performed in the field of application of bio-based industry business modelling methodologies, especially bmc. based on this review, the cases presented in the following studies were included to develop learnings about the bio-based business models and their development, together with the good practice cases described in this paper: – the study on the participation of the agricultural sector in the bbi ju was carried out between march and august 2019.6 fifteen business models from 189bio-based business models: specific and general learnings from recent good practice cases in different business sectors bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 european countries and five from non-european countries were studied. – be-rural project, funded under the same h2020 call topic as power4bio, delivered a report in november 2019, addressing business models for regional bioeconomies20 which analyses four models based on bmc. – in 2017 a one-day stakeholder workshop was organized in lleida, spain in the agrimax project funded by bbi ju, to develop business models for valorisation of agricultural and food-processing waste. farmers, agricultural cooperatives, food producers, investors and other stakeholders were invited, and bmc was used to elaborate three case studies and map existing and innovative ways to create value for the new supply chains.15 – in 2019 a report was prepared for the ec by cowi group, bio-based world news and ecologic institute, in order to “provide concrete examples of ‘biobased concepts’ that have succeeded in progressing from the early ideas to a final product placed on the market”, to a fully commercial level, or close to that. fifteen success stories are presented in this report.19 2.4 framework for the extension of bmc to meet sustainability and business ecosystem aspects in order to extend the traditional bmc with sustainability and business ecosystem aspects, authors used the framework offered by antikainen and valkokari,16 which complements current business model tools by integrating the following additional factors: trends, drivers, stakeholder involvement (business ecosystem level), and environmental, social and business requirements and benefits (sustainability impact). a collection of examples for these additional factors was compiled based on the authors’ own experiences and the work of philp and winickoff,21 biber-freudenberger et al.22 and pavlovskaia,23 and categorised as follows: drivers and stakeholder involvement tools; sustanability requirements; sustainability benefits. 3. results table 1 summarizes the good practice cases presented in this article and figure 2 shows the locations where these cases are being operated by the owner companies. figures 3-8 present the business models in bmc format behind the analyzed solutions numbered s1, s2, s3, s4, s5 and s6, respectively. for each solution short additional background information is presented next. s1. production of feed quality protein meal and oil with high nutritional value by the bioconversion of residual organic streams using black soldier fly larvae (bestico, berkel en rodenrijs, the netherlands) bestico b.v. founded in 2013 focuses on the production and sales of black soldier fly (bsf) (hermetia illucens) larvae and their processing to protein for animal feed (especially aquaculture feed and pet food), insect oil and fertilizer agent (figure 3). the efforts of bestico b.v. are supported by its mother company, koppert biological systems, an internationalized firm with subsidiaries in 27 countries which is a leading provider of arthropods and microbes for biological control of agricultural pests and has developed expertise in the production of insects since 1967. the company provides a tailored, scalable solution (being at trl8 at the time of the interview) to use biomass by-products and convert this into feed-quality protein and oil with high nutritional value. when the data collection was performed, the production rate was around 6-12 tonnes of fresh larvae per week. the growing process takes about 14 days, and within this period the larvae reduce the feedstock weight by 40-60% while half of this, i.e., 20-30% of the feedstock weight is converted into larvae biomass, depending on the nutritional profile of the feedstock. s2. local production of fresh oyster mushroom combined with the valorization of the coffee ground residues (rotterzwam, rotterdam, the netherlands) rotterzwam b.v. is a private company, established in 2013. the company is dedicated to cultivation, production and sale of fresh edible mushrooms grown on coffigure 2. locations of the companies operating the good practice cases listed in table 1. 190 bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 nora hatvani, martien j.a. van den oever, kornel mateffy, akos koos fee grounds and its application in other related products (figure 4). its mushroom nursery is the largest coffee ground-based mushroom farm in europe and the company aims to develop it to be energy neutral and co2 negative. the cultivation units contain a modular and sustainable climate system, which is fully optimized for mushroom cultivation. these units, which are especially designed for an urban environment, can also be very well used in remote rural areas. rotterzwam also provides services like workshops, educational activities and consultancy in the field of urban management through the radical renewal of chains. they work with local partners to find new ways to put products derived from coffee grounds and oyster mushrooms into the market, developing products such as oyster mushroom vegetarian snacks and beer. s3. natural insulation material produced using hemp hurds and own-produced limestone-based binder (hempire, ukraine) hempire is a company based in ukraine and having a site in california, usa as well. the company was established to provide insulation technology based on hemp and lime (figure 5). in the last few years, the company has been involved in more thatn 60 building construction projects and successfully developed its own lime-based binder material called “fifth element” which does not contain cement, sand or toxic components. the binder material is produced in the company’s own facilities, both in ukraine and in the usa. through extensive r&d the company has created a very light insulation material called “hempire mix”. this highly energy efficient material consists of three components, which are mixed on the construction sites using a special mixer: hemp hurds, water and the special proprietary binder mentioned above. hempire mix is applied on the walls and it can be used to insulate any wall, floor and roof inside a new or an existing building. it contains only non-toxic components and has numerous benefits: insulation acting as humidity and temperature regulator in the building; excellent thermal insulation properties resulting in significant savings on heating and cooling all year round; with vapor permeable walls there is no need to install a ventilation system; high thermal mass and thermal inertia help prevent temperature f luctuations; no rotting but protecting walls from fungus due to the regulation of humidity; excellent acoustic absorption due to high porosity; non-flammable material which repels rodents. table 1. summary of the bio-based solutions selected for this study. product types, defined in section 2.1, are as follows: 1: final product, 2: material, 3: building block or polymer, 4: technology licensing. no. of solution name of solution bio-based product(s) product type company (country) mode of data collection s1 production of feed quality protein meal and oil with high nutritional value by the bioconversion of residual organic streams using black soldier fly (bsf) larvae feed quality protein meal, oil with high nutritional value; fertiliser 1 bestico (the netherlands) personal interview (08.01.2020); desk research s2 local production of fresh oyster mushroom combined with the valorisation of the coffee ground residues fresh oyster mushroom and processed products 1 rotterzwam (the netherlands) desk research, email discussion s3 natural insulation material produced using hemp hurds and own-produced limestone-based binder insulation material based on hemp hurd 2, 4 hempire (ukraine) personal interview (11.11.2019); desk research s4 meadow grass silage biorefinery producing grass fibre reinforced plastic composite granulates and bio-based insulation material, combined with biogas plant producing electrical energy from grass juice and food residues grass fibre reinforced plastic granulates, natural insulation material, electrical energy from biogas, organic fertiliser 2 biowert (germany) data and descriptions provided by the company in written form by filling a questionnaire; desk research s5 production of bio-based chemicals, highquality natural lignin, biogas and biofuels from 2nd generation biomass by cracking technology biochemicals, natural lignin, biogas, biofuels 3, 4 lxp group (germany) personal interview (24.02.2020) and email discussion; desk research; content of slides used by the company at public presentations s6 production of polyhydroxyalkanoates (pha) using waste cooking oil polyhydroxyalkanoates (pha) 3, 4 nafigate corporation (czech republic) personal interview (06.12.2019), followed by email discussion; desk research 191bio-based business models: specific and general learnings from recent good practice cases in different business sectors bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 figure 3. bmc of solution ‘s1’ (bestico, the netherlands). key partnerships · raw material providers ensuring biomass feedstock of appropriate quality · industrial and academic partners to increase trl and to find new applications for bsf larvae products and side streams key activities · feedstock reception (gmp+ grade for feed application) and strict quality control upon receipt (dry matter content, free of pesticides/insecticides) · primary production of insects · processing insects into concentrated protein meal, insect oil and natural fertilizer · adapting the technology to fit into existing operations (scale-up of current process) · continuous r&d for cost effective and higher quality production value proposition · tailored sustainable solutions for growing and processing insects · scalable solution to utilise excess biomass from potato industry, beer and alcohol industry into valuable feed-quality and storable protein and oil with high nutritional value · protein from bsf (with protein content up to 60%) suitable for farmed fish, poultry and other livestock, providing essential amino acids which are low in feeds produced from plant origin and which are easily digested by most animals · substrate remaining after isolation of protein and oil has value as fertiliser · automated processing secures consistent and safe products · bsf larvae system is adaptable to a wide range of residual organic streams, making food chain more sustainable customer relationships · business to business (b2b) and business to customers sales strategies · using a sales force of 3-5 persons in europe (france, united kingdom, the netherlands) customer segments · pet food industry · pet food consumers · aquaculture · animal feed industry (farm animals) key resources · raw material feedstock (e.g. vegetable waste coming from potato and alcohol industries) of appropriate quality (very low content of insecticides) and uniform in physical and nutritional properties affecting the time required to larvae growth · infrastructure for feedstock storage and quality control · facilities comprising conditioned rearing cells and equipment for feeding of the insects · equipment to isolate protein meal and insect oil · know-how on bsf eggs production and insects growing · sales competences channels · reaching customers by company website, social media and youtube channels · conferences and presentations at fairs and other events · cooperation projects cost structure · capex is estimated in the range between 3-5 million eur; · main long-term expenses: plant and equipment purchases, building and improvements, instrumentation and automation of the process; · most important operational expenses: feedstock, energy/utilities and labour costs, with equal shares within opex being in the range of 3-5 million eur/year revenue streams · sales of dry insects for animal feed and pet food; · estimated price of dry bsf larvae as animal feed: 1-3 eur/kg; price of pet food: 15-40 eur/kg (based on the retail prices in the webshop) 192 bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 nora hatvani, martien j.a. van den oever, kornel mateffy, akos koos s4. meadow grass silage biorefinery producing grass fiber reinforced plastic composite granulates and biobased insulation material, combined with biogas plant producing electrical energy from grass juice and food residues (biowert, germany) biowert industrie gmbh was founded in 2000 as a swiss-german company. the first biowert grass refinery which started its operation in 2007 is located in key partnerships · local coffee grounds providers · local industries for the development of new products: partners cooperating in the development and sales of products listed at the value proposition (restaurants, supermarkets, breweries, bakeries etc.) key activities · collection of coffee grounds from companies or organizations that use at least 50 kg of coffee beans per month · cultivation, storage and packaging of oyster mushrooms · sales and marketing of fresh oyster processed products · activities related to the development of processed products and to the services provided value proposition · valorisation of coffee residues on a local scale into the best possible uses such as food products · production of fresh oyster mushrooms · mushroom-based and coffee ground-based processed products (beer, snacks, soap, fertiliser etc.) · growkit for common people to convert their own coffee grounds into oyster mushrooms · substrate remaining after mushroom production can be used as soil improver customer relationships · direct sales to local people, restaurants and shops · attracting new customers and engaging entrepreneurs interested in mushroom growing and utilisation of coffee grounds · unique marketing strategy and communication package on valorisation of coffee ground customer segments · strong focus on the citizens of the local municipality · local shops, restaurants and markets · local hospitality sector (food category) · green consumers or entrepreneurs, with preference for local food production, environmentally friendly products and consumption of proteins of non-animal origin and concerned about the environment key resources · coffee ground material as the main feedstock · know how on oyster mushrooms cultivation techniques on coffee grounds · cultivation container units optimized for mushroom cultivation, especially designed for an urban environment · infrastructure and adapted facilities for production of mushrooms · vehicles for the collection and transport of coffee grounds and mushrooms channels · company’s own website and facebook site · online shops · catering wholesalers offering oyster mushrooms snacks · network of local mushroom nurseries promoting the growkit · social and traditional media · online e-learning courses and seminars, presentations, workshops · factory visits cost structure · 400 000 eur from crowdfunding (symbid) in 2018 to build the site; · shares of investments: 34% for breeding units (8 containers), including climate system and installation; 16% for substrate preparation area, mixer, packaging machine, office space, cold room; 7% for installation of solar panels on the entire nursery; 7% for roll out quasi franchise; 8% for r&d; 8% branding and marketing campaign; 8% accelerated sales & rollout of new products and 12% for the other costs · shares of the different costs within the opex structure are the following: staff costs: 33%, cost of sales: 29%, other operating costs: 34%, depreciation and financial expenses: 4% revenue streams · producers of coffee ground material pay for its collection (15 eur/kg) · fresh oyster mushrooms sales (8,5-10 eur/kg) · sales of processed products (beer – 9 eur/litre; vegetarian snacks etc.) · growkit for people to convert their own coffee grounds into oyster mushrooms: 15 eur per kit · e-learning courses and seminars on mushrooms growing (50350 eur per module) figure 4. bmc of solution ‘s2’ (rotterzwam b.v., the netherlands). 193bio-based business models: specific and general learnings from recent good practice cases in different business sectors bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 brensbach, germany. the “grass factory” system combines the biorefinery process, the multistage production of innovative bio-based materials and an affiliated anaerobic digestion facility with a chp plant producing green electricity (figure 6). the biorefinery is fed by meadow grass from their own permanent pastureland and arable land for crop production, and produces different innovative biomaterials: a fire safe blowin insulation material for wall, roof and floor cavities which naturally controls the absorption and release of water vapor to ensure the ideal building environment and safe from rodents, insects and mould (agricell), key partnerships · raw material providers: hemp growers and lime provider · logistic partners key activities · feedstock transport and storage · producing lime-based binder · binder transport · mixing binder, water and hemp hurds on the construction site · close cooperation with hemp growers · design, planning and implementation services · technology development for more cost effective and higher quantity production value proposition · natural hemp-based insulation solutions using local ingredients for environmentfriendly buildings · building and energy cost-effective construction solution · special lime-based binder material (free from cement, sand or toxic components) for the on-site production of insulation material · processes to be done on the construction sites are easy-to-learn and do not require high level of skills · wide range of services: consultation, services related to insulation, design, planning and implementation, quality control customer relationships · direct relationship and close cooperation with customers · b2b and b2c sales strategies · providing technical support · informing the public about new buildings and construction solutions, to add more visibility to the solution customer segments · private persons, organizations, business companies, communities interested in making a hemp-based building · consulting services: architects, designers, builders, contractors and other actors in the construction industry key resources · estate for the buildings and feedstock storage · buildings and machinery for lime-based binder production · high quality raw materials: hemp and lime (dust content of the hemp has to be as low as possible, it can’t be mouldy or wet, hurd particles shall be 1-4 cm long) · vehicles for feedstock and product transport and handling · know-how and knowledge for optimization based on own experiences channels · marketing: company’s own website, facebook site · presentations and workshops to inform stakeholders about the company’s activities and current developments · staying in contact with former customers cost structure · capex: building and equipment for a new facility: 2 million usd in usa or canada, while around 1 million usd in europe or ukraine · opex: price of raw materials: hemp hurds as main feedstock (120 eur/t) and lime; energy costs; labour costs (4 people in the binder production facility and other 4 people working at the construction site); general & administrative expenses revenue streams · sale of binder material: 12 eur per one bag, which contains 25 kg (480 eur/t) and for a retail price of 32 usd per one bag, which contains 23 kg (1 390 usd/t) in the usa · revenues from sale of insulation material (180 eur/m3) and insulation services · revenues from consultation and other services mentioned at value proposition figure 5. bmc of solution ‘s3’ (hempire, ukraine). 194 bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 nora hatvani, martien j.a. van den oever, kornel mateffy, akos koos key partnerships · raw material providers: local farmers · partners for transporting feedstock and products · waste material handling companies to provide organic wastes feeding the biogas plant key activities · feedstock transport and storage (seasonality: meadow grass grows in spring and early summer, it can be harvested up to 4 times a year, but ensiling makes it durable, so it is available all year round) · biorefinery process: cellulose fibres are separated from the grass using mechanical processing and then dried · processing of the fibres into plastic based granulates or insulation material, using recycled plastic · electricity and heat production by the biogas plant · processing of digestate to a concentrated organic fertilizer · keeping regular contacts with industrial customers and local farmers value proposition · meadow grass processed into materials by a biorefinery process and green energy by an affiliated biogas plant, using food residues, other organic wastes and grass juice, ensuring energy needs (e.g. drying of cellulose fibres) · products: grass fibre reinforced plastic granulates, insulation material, organic fertiliser · positive environmental impacts: reducing the use of fossil-based plastics; reducing co2 footprint by using grass being a natural co2 adsorber; energy produced from wastes; agricultural and food industrial waste reduction customer relationships · sales strategy: b2b relations with industrial partners · cooperation with local farmers customer segments · grass fibre reinforced plastic granulates: industrial customers in a wide range · insulation material: construction industry · electricity and heat: own use and local electric service provider · organic fertiliser: local farmers · licensing the solution to actors interested in technology implementation key resources · suitable biomass feedstock (mainly meadow grass, harvested before the panicle is pushed and ensiled, at a dry matter content of 25-30%) · estate for the buildings and feedstock storage · buildings and machinery for technology processes listed under key activities · know-how and optimization based on own experience, included in patents as well · specific-skilled workforce and high-quality experts channels · marketing: company’s own website and facebook site · presentations and workshops to inform stakeholders cost structure · capex cost estimated: 7-10 million eur for biorefinery and 8 million eur for biogas plant · 18-24 months long period needed to install and optimise the technology (related costs also have to be taken into account) · opex: feedstock (meadow grass: 140 eur/tonne dry matter), energy and labour costs, general and administrative expenses · energy costs depend on the share provided by the biogas plant revenue streams · selling of grass fibre reinforced plastic granulates (2 500 t/ year produced; price: 1,95-3,50 eur/kg, depending on additives and final recipe) · selling of insulation material (1 400 t/year produced; 1,38 eur/kg) · energy sold to the local electric service provider, (price set by feed-in tariffs; chp plant produces 5,2 gwh/year, the energy need of the biorefinery is 2,5-3 gwh/year, thus the surplus can be sold) · by-product of the biogas plant is a nitrogen-rich material, sold as organic fertiliser (11 000 t/year) · indirect revenue: waste management costs are lower, as by-product of the biorefinery (e. g. grass juice wastewater, 2 000 t/year) is processed in the biogas plant figure 6. bmc of solution ‘s4’ (biowert, germany). 195bio-based business models: specific and general learnings from recent good practice cases in different business sectors bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 a light, dimensionally stable and temperature resistant fiber-reinforced composite material with 30 to 75% natural fibers (agriplast) and an organic fertilizer made from biogas digestate (agrifer). the facility has an annual throughput of about 2 000 tonnes of dry matter (equivalent to 8 000 tonnes of grass per year at 25–30% dry matter content). grass juice, as waste of the biorefinery process and other co-substrates (biogenic residual materials such as local food waste, 15  000 tonnes/ year) are used for biogas production (1  340  000 m3/ year) in the anaerobic digestion facility. this solution is a good example of the case being quite specific for the circular bio-based business models, when the producer and converter of the biomass are one and the same party, i.e., producer directly uses its byproduct as a feedstock for a bio-based process. s5. production of bio-based chemicals, high-quality natural lignin, biogas and biofuels from 2nd generation biomass by cracking technology (lxp group gmbh, straubing, germany) the goal of the company, founded in 2009 is to optimize 2nd generation biomass value chains. they have developed and patented a pre-treatment technology (lx-process) that “gently cracks” the lignin strands in 2nd generation biomasses (such as agricultural residues, forest materials, energy grasses, organic municipal solid waste, fibrous portion of digestate from biogas plants) into oligosaccharides, lignin, cellulose and other polysaccharides which are available for further processing in fermentation processes (figure 7). since no inhibitors are left after the cracking process, downstream fermentation does not require expensive, custom-tailored enzymes for hydrolysis. to be economically feasible, the capacity of the plant has to be at least 10 kilotonne dry matter of processed biomass per year. the lx pre-treatment plants can serve as raw material producers for the chemical industry or the energy sector. the first industrial lx demonstration plant is located near straubing, bavaria, germany, it was inaugurated in february of 2020 and has a planned maximum capacity of 500 tonnes (dry matter) of biomass. initially the plant processes biogas digestate and will test additional biomass types to prove that lignocellulosic biomass from agriculture, forestry and municipal waste are also suitable for the lx technology. german-funded projects and an eu-funded project helped the development of the technical solution. the company is interested in own technology implementation (product type 3) as well as licensing the optimized technology solution to other companies (product type 4). the model presented on figure  7 describes the own technology implementation activity. s6: production of polyhydroxyalkanoates (pha) using waste cooking oil (nafigate, czech republic) nafigate corporation, a knowledge-based company founded in 2011 has developed hydal biotechnology. this technology, as the first in the world on the industrial scale, uses waste cooking oil (mostly a mixture of different plant oils such as rapeseed and sunflower oil) to produce polyhydroxyalkanoates (pha) by using a bacterial fermentation process (figure 8). the pilot pha production started in 2013 and the pilot of the downstream process (isolation of the polymer from microbial cells) in 2015. in 2019 the suitability of sludge palm oil was also verified by the company’s research activity for pha production. the pha family of biopolymers is unique to plastics from renewable resources, as it comprises the only group of polymers converted from raw materials into their final form directly by microorganisms. polyhydroxybutyrate (phb), a specific type of pha is similar in its material properties to polystyrene, has a good resistance to moisture and aroma barrier properties. it has a unique position in the pha family, as it biodegrades within a reasonable timescale in a wide range of microbiologically active environments24 such as soils, fresh water, aerobic and anaerobic composting, wastewater treatment plants. currently, the phb biopolymer’s application is multifaceted, it can replace toxic substances in uv filters; microplastics in e. g. cosmetic industry, healthcare products or medical applications (e.g., covering for capsules, stitching of wounds, bone implants); synthetic plastics in different applications such as bottles, disposable cups, cutlery, lamination foils etc.; and materials in the agriculture sector for the slow release of fertilizers, insecticides, pesticides or fungicides in the soil.25 poly-3-hydroxybutyrate (p3hb), the final desired biopolymer has a purity higher than 99% and high molecular weight. nafigate uses pure p3hb powder in cosmetic products (shower milk, sunscreen, etc.) which are sold by their sales and marketing partner company established in 2015. nafigate is working on broadening the use of biopolymer into the medical and agricultural sector. in the context of the extension of traditional bmc, table 2 shows examples for the three additional factors16 (left column) and good practice examples identified by authors in the reviewed business cases (right column). 196 bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 nora hatvani, martien j.a. van den oever, kornel mateffy, akos koos key partnerships · raw material providers · sales partners · partners in waste management · r&d partners cooperating in successful plant operation · financial advisory service, investors key activities · feedstock transport and storage · technology steps: lx chemical pre-treatment process, precipitation of cellulose and lignin, separation / filtration of each component · enzymatic hydrolysis · recovery of solvent and precipitant · technical problem solving · product transport · technology optimization and design for scale up value proposition · production of wide range of bio-based chemicals, non-toxic, sulphur-free, highquality natural lignin, biofuels and biogas · high feedstock flexibility: any lignocellulosic raw material can be processed (using only non-food biomass), meaning that the plant is not fixed to a single feedstock · using the output materials from lx-process technology, bio-processing plants can produce sugars by enzymatic hydrolysis which can be then converted through microbial fermentation processes into a multitude of valuable end products (e.g. biogas, ethanol, lactic acid, etc.) · lx-process leaves little inhibitors (such as furfural or formic acid) the presence of which is a principle hurdle faced in downstream bioprocessing, as they cannot not be removed economically · simple, modular technology system · decentralized bio-based production is possible · circular bioeconomy approach supported by legislation on eu and national level · ghg reduction customer relationships · b2b sales strategy · operating a large-scale pilot plant · personal followup contacts with stakeholders showing interest after presentations at conferences or website visits customer segments · chemical industrial enterprises interested in 2nd generation biochemicals · key players in development of bio-based, “dropin” replacement of petrochemicals · customers seeking natural lignin of unique quality · cosmetic industry · 3d printing market · sectors of construction industry interested in green construction materials · producers of biopolymers (resins, plastics) and adhesives · bioethanol and biogas consumers key resources · regionally available biomass feedstock (biogas digestate, agricultural and forestry residues) · estate for the buildings and feedstock storage · buildings and machinery for processes listed under key activities · vehicles for transport and handling of feedstock and products · specific-skilled workforce and their technical experiences channels · marketing: company website · presentations and workshops to inform stakeholders · demonstration activities performed in the large-scale pilot plant cost structure · capex costs highly depend on scale and integration scenario · main opex items: feedstock, energy (heat / electricity) · feedstock price: as cheap as possible, but up to 100 eur/tonne · simplicity of the process keeps operational costs low: low temperature, around 70°c, enabling use of residual waste heat; normal atmospheric pressure/tolerance/corrosiveness revenue streams · sales of the materials produced by lx-process technology · bulk products as lignin, cellulose, sugars generate revenue with relatively good margins but can also benefit from a wide market of niche products, such as vanillin, which generate high margins but in much lower volume markets · 6-7 years long payback time is estimated for a plant with a capacity of 25 kt dry matter processed biomass per year · deployment of the technology is already economical from ca. 10 000 tonnes of throughput (dry matter biomass) per year figure 7. bmc of solution ‘s5’ (lxp group, germany). 197bio-based business models: specific and general learnings from recent good practice cases in different business sectors bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 4. discussion 4.1 cross-cutting analysis based on the internal building blocks in the business models of bio-based solutions in table 1 and described in the reports listed in section 2.3, the following points were identified as especially important crosscutting elements and also as factors that are specific to the bio-based industry in several aspects: – biomass feedstock as a key resource, local biomass suppliers as key partners and maintaining good relationships with them as a key activity, since the constant availability of biomass and solving the related logistic issues are of key importance. for bio-based companies it is essential to have close relation with farmers by offering them reliable and convenient services, e. g. secure and regular payments irrespective of harvest time, assurance of timely transport key partnerships · industrial feedstock providers ensuring large amount of waste cooking oil · sales partner · r&d partners providing laboratories and equipment needed for the production (if these resources are not owned by the producer) key activities · feedstock transport to the production site · technology steps: microbial fermentation (transforming waste cooking oil into a pha biopolymer, stored inside the bacterial cell); isolation of polymer from microbial cells; mixing with additives for stabilization · production of cosmetics from p3hb · product transport · product selling activities · continuous technology development and optimisation value proposition · hydal biotechnology producing biodegradable pha (polyhydroxyalkanoates) from waste cooking oils · pha: raw material in cosmetic or medicine industry for biodegradable microbeads, uv filter in sunscreens or bioplastics · pha product of the highest priority: poly-3hydroxybutyrate (p3hb) · production of cosmetics (e.g. peeling shower milk, sunscreen) from p3hb · the technology does not use crops or other feedstock produced on agricultural land · the technology contributes to reducing pollution caused by plastics and microplastics and to solving the problem of waste cooking oil utilization as well, and, at the same time requires less water and energy compared to phb production from sugar beet, potato, wheat or corn customer relationships · close cooperation with industrial customers using pha · b2b and b2c sales strategies · close cooperation with sales partners customer segments · pha, p3hb: industrial customers (cosmetic and medicine industry, agriculture sector) · cosmetics: supermarket chains, wholesalers and retail customers · licences and knowhows: market actors interested in large scale pha production key resources · buildings, laboratories and equipment for processes listed under key activities · know-how related to the specific bacterial fermentation process, based on own experience · high quality experts and specific-skilled workforce channels · webshop of nafigate cosmetics to reach retail consumers directly · marketing: company’s own website, facebook site · press releases, presentations and workshops to inform stakeholders cost structure · technology development was financed by own investment and public funding · relatively high capex costs purchasing equipment (new facility producing pha on industrial scale would cost around 9 million eur, according to the company’s estimation) · opex: cost of waste cooking oil as main feedstock (0,6 eur/kg); rental costs of laboratory and equipment; energy costs; service costs (including the high cost of laboratory testing services that has to be purchased to provide certificates for cosmetics); labour costs; general and administrative expenses revenue streams · revenues from pha, ph3b sales · selling of cosmetics (60 eur/litre, based on retail prices in the webshop) · licensing of know-how figure 8. bmc of solution ‘s6’ (nafigate corporation, czech republic). 198 bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 nora hatvani, martien j.a. van den oever, kornel mateffy, akos koos before new crop needs planting, access to financing opportunities within the business group to support farmers investing in e.g. storage facilities.15 – logistic and quality assurance partnerships and activities, since keeping transport costs low is crucial for costs-efficiency, and so are the activities ensuring adequate biomass supply (e.g. handling seasonality and perishability, quality monitoring etc.). volumes of resources, especially feedstocks from agricultural production can fluctuate, which represents a constraint for markets traditionally not subjected to large fluctuations in feedstock supply, like for chemical products, hence issues in logistics, storage and quality preservation should be constantly addressed.26 steady supply of the required seasonal feedstock, which may increase storage costs, is often reported as a weakness.18 challenges from seasonality are tackled by building appropriate stortable 2. examples for additional factors to extend the traditional bmc, and good practices identified in bio-based business models for these factors. examples for the additional factors to extend the traditional bmc concrete good practice examples of bio-based business models additional factor: drivers and stakeholder involvement tools · identifying and informing “opinion formers” in different stakeholder groups · active participation and support of regional or industrial clusters, advocacy forums and other organisations in stakeholder involvement, by action platforms to promote new technologies and innovations · knowledge exchange and development through networks and across value chains, involving learning activities, mostly on the emerging technologies · market formation involving activities that contribute to the creation of a demand for the emerging technology (e. g. taxation, procurement) · general policy instruments (e. g. long-term public strategies on industry regulations, incentives for product labelling, consumer information, industry collaboration) · public funding for early-stage research and competence building · lxp group: cooperation with chemie-cluster bayern, a marketoriented development network of entrepreneurs and research institutions in the bavarian chemical industry, acting as a catalyst facilitating the diffusion of innovative products into new markets17 · rotterzwam: cooperation with many local partners to find new ways for marketing products from coffee grounds and oyster mushrooms17 · wilson bio-chemical: partnerships increasingly strengthened through r&d into higher value applications, anticipating delivering more economic, technical, environmental and social benefits15 · af biomass: strengthening the supply chain network by exploring new end-markets such as for linseed straw in the paper industry in spain and with a new straw pelleting plant in the uk15 · bio-lutions received grant from the german government for upscaling their technology20 · nafigate corporation started its operation with low investment amount and public support (most of these funds spent on r&d activities and know-how development)17 additional factor: sustainability requirements · energy and materials are conserved during the production process and the form of energy and materials applied are most appropriate for the desired result · work practices (including the use of chemical substances, physical agents or technologies) that present hazards to human health or the environment are continuously reduced or eliminated · products (including their packaging) and services are designed to be safe and ecologically sound throughout their life cycle · wastes and ecologically incompatible by-products are continuously reduced, eliminated, or recycled · non-food-competitive land use · biodegradability of bio-based materials in industrial, soil, or marine environments · avoiding health and ecological risks caused by improper use of technologies · management of companies is committed to an open, participatory process of continuous evaluation and improvement, focused on the long-term economic performance · nafigate corporation: hydal technology transforming waste cooking oil into high-value biomaterial polymers does not use crops or other feedstock produced on agricultural land, moreover, requires less water and energy compared to phb production from sugar beet, potato, wheat or corn17 · lxp group: lx-process can be readily adapted for large-scale 2nd generation ethanol manufacturing17 · biowert industrie gmbh: ‘grass refinery’ solution using agricultural and food industrial wastes17 · bestico: bsf larvae can transform nearly any kind of organic waste into high-quality protein17 · wilson bio-chemical: downstream reprocessing of recyclables15 · af biomass: growing use of biomass for energy can positively contribute to energy security, the low carbon economy and ‘green’ jobs15 · hédinn protein plant: process requiring less water and energy than comparable technologies and thus economically and environmentally beneficial20 · spawnfoam renewable biocomposite: by creating sustainable and biodegradable products, fossil-based products are substituted20 · bio-lutions: using local resources to produce biodegradable products, based on raw material being outside of the human food chain and other value chains20 199bio-based business models: specific and general learnings from recent good practice cases in different business sectors bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 age capacity and technologies converting perishable biomass to a stable feedstock (e.g. ensiling). – research background (laboratories as a key resource or a key partnership, research-based developments as a key activity), since bioeconomy and industrial biotechnology are highly innovative and researchintensive sectors. continuous r&d is part of business models for more cost effective and higher quality production as well as developing new applications for products and sidestreams. the value proposition is versatile since it can be extended to include social and environmental values besides the economic value the business creates. for example, in bioenergy producing solutions, the local production strengthens the local economy and reduces the dependence from fossil fuels at the same time.27 values that are generally recognized in all the business cases reviewed in this study include: valorization of wastes or untapped feedstocks; creation of more sustainable products; mitigating dependence from fossil fuels in the case of energy-producing solutions; local value creation by using locally produced feedstock; local job and income creation. public and private partners from diverse sectors need to be involved in order to establish strategic collaborations for bioeconomy initiatives.26 for example, a biorefinery evolves in a territory with an economic, political and social identity, thus, the success of such a business model depends on the ability to form partnerships and collaborate with local players: (large number of) primary producers, agricultural cooperatives, industries, educational and research organizations, local authorities.26 showing them the environmental, social and business benefits as part of sustainability impact that the biorefinery project can bring to their local level can greatly improve this ability, and thus the chance for a successful and profitable project implementation. business support organisations such as development authorities and clusters acting in the field of regional development or a specific industrial sector can play a key role in this integration process. examples for the additional factors to extend the traditional bmc concrete good practice examples of bio-based business models additional factor: sustainability benefits · valorisation of biomass which would otherwise end up as waste · higher recycling rates · novel energy sources for households · increasing food production and lowering production costs · reducing greenhouse gas emissions and air pollution · decreasing the use of pesticides · generation of livelihood opportunities and income sources for farmers · health benefits due to medical applications · benefits in terms of energy provisioning and food security, for example, using waste as feedstock for insects or algae which are subsequently used as feedstock for further applications · rotterzwam: oyster mushrooms grown on coffee grounds, as source of proteins of non-animal origin with minimal footprint and food miles17 · lxp group: lx-process reduces ghg emissions and enables 1st generation biorefineries and biogas plants to convert to 2nd generation feedstocks, thus reducing required acreage for 1st generation feedstocks17 · hempire: hemp-based building material reduces energy required for heating/cooling; 1 m3 of hempire mix locks up 165 kg of co2 (negative carbon footprint)17 · biowert industrie gmbh: their solution reduces co2 footprint by using grass as a natural co2 adsorber and the use of fossil-based plastics as well (and thus the consumption of fossil raw materials)17 · bestico: low environmental footprint of producing alternative protein sources for animal feeds by bsf larvae17 · wilson bio-chemical: biodegradable waste fraction diverted from landfill, reducing greenhouse gas emissions and freeing up land for other purposes whilst producing feedstock for renewable energy15 · soldebre: use of olive kernels as a low-carbon biofuel, environmental impacts are reduced such as achieving a reduction in carbon emissions by using less fossil fuel15 · small-scale pellet production: local pellet production reduces the dependence from fossil fuels, replaces them in households and chp plants, etc., thus reducing overall logistics costs and emissions from fossil fuels20 · hédinn protein plant: more targeted fishing of cyprinids helps to reduce eutrophication which may have positive environmental impacts while supporting rural development at the same time20 · spawnfoam renewable biocomposite: solution facilitates defossilisation for a range of products, reduces ghg emissions and prevents the deposit of waste in landfills or in the seas20 table 2. (continued). 200 bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 nora hatvani, martien j.a. van den oever, kornel mateffy, akos koos 4.2. readiness level of value proposition the solutions analyzed in the present study comprise a technology and its bio-based product with just enough features to be subjected to customer feedback (i.e., suitable to build a demo plant on it or to be offered to customers) and to gain experiences in connection with actual market needs, forming the basis for future developments. the level of development of these solutions more or less corresponds to the stage of the “minimum viable product” (mvp), a concept from lean startup that stresses the impact of learning for further product development. e. ries defined an mvp as such version of a new product which may lack some or many features that may prove essential later on but allows the development team to collect the maximum amount of validated learning about customers with the least effort.28 the initial mvp and the abovementioned customer feedback loops with new versions of the mvp, developed in accordance with the feedbacks to the initial mvp, are two consecutive levels of the business readiness level (brl) scale defined by r. ramsden; this scale can be used to benchmark the current status of a business proposition, from concept to mature business fully embedded in the market29 although trl scale18 is widely used to understand the current status of a technical innovation, even trl9 does not entail by itself that the technology is ready for market. this is because business-related aspects are not necessarily taken into account when trl is defined for a technology, moreover, trl classification can be subjective. as put eloquently in a report produced under the framework of access2eic, the official network for h2020 national contact points for the new european innovation council:30 “trl level as commonly used in h2020 can be used to define if a technology is ready to go to market or not, but it does not capture properly how ‘ready’ is the business based on such technology to go to market.” that is why it is useful to adjust the focus generally being on trls and involve brls when considering a technology being part of the value proposition, in order to measure readiness in terms of creating real customer value in an objective manner. 4.3 customer-related building blocks: customer segments, channels, customer relationships the customer-related building blocks can be a weak point of the business cases described. these building blocks could not be described properly in several cases in the bmcs due to the lack of relevant information, not because of insufficient data provision but because the owner of the value proposition has not mapped all the possible segments yet. grant-based subsidies covering the innovation costs as well as not fully market-driven developments focusing on increasing the trl of r&d results mainly aim at the optimization of technology processes and reaching the mvp but fail to pay sufficient attention to customer-related building blocks, if any. the results of a survey conducted with the participation of 66 companies from south-east finland in 201231 clearly support the importance of external building blocks in bio-based industry: they show that two of the six measures investigated, i.e., “customer value-added” and “supply chain collaboration” had statistically significant effects on business performance, while the other four (i.e., “opportunities from business environment”, “business forces”, “innovations”, “r&d collaboration”) did not show any remarkable statistical effect on expected business performance.31 this finding demonstrates that it is useful to start the development of the bmc at the customer segments building block where the value proposition can be delivered to. once there is a clear and thorough knowledge and understanding regarding who the customers are and what problems or needs they have, it is easier to define the value proposition which is the value that can be added to these customers’ activities. customer relationships building block includes, among others, identifying and tracking the specific customer segments and the customers’ needs. many customers are willing to pay slightly more for environmentally friendly, natural, chemical-free, local products, which aspects are easily discernible for products of biobased solutions. accordingly, companies clearly need to identify these segments12 and products need to be tailored to “bioeconomy customers” identified. word-of-mouth promotion by personal contacts and providing continuously updated content online via e. g. company website, social media, blogs, newsletters sent by email proved to be crucial for the bio-based industry to deliver, communicate and sell value propositions and to raise the customer awareness of a company’s products and services. however, channels is the least addressed building block in the analysed models, probably because many applications for bioeconomy have not yet reached a stage where attention to distribution channels can no longer be ignored. 4.4 finance-related building blocks: cost structure, revenue streams from the business models described in section 3, it appears that the main drivers for the innovation development from lower trls to the stage where the level of technology readiness allows the introduction of an mvp are: 201bio-based business models: specific and general learnings from recent good practice cases in different business sectors bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 – financial resources available to cover several capex items, which can come from different sources, i.e. external ones such as eu funds or governmental support and the profit of the business, but for the majority of solutions presented in section 3 it has been some kind of public subsidy so far; – the cheap biomass feedstock material ensuring lower opex, meaning that the low price has to include logistic costs already. the availability of the financing needed for the main capex items is a determining factor because the development and implementation of new technologies in the bioeconomy in most cases requires large upfront investments. moreover, uncertainties inherent to bioeconomy hinder these investments,1 that is why many initiatives in this sector are dependent on grant subsidies. for example, clariant is investing more than 100 million eur in a commercial-scale cellulosic ethanol production plant in podari, romania and this plant receives more than 40 million eur funding32 from the eu and bbi ju within sunliquid and lignoflag projects, although this multinational large company has been successfully operating a first pilot plant since 2009 and a large-scale pre-commercial plant in straubing, germany since 2012.33 when the cowi report was launched in 2019, 13 out of the 15 technologies mentioned were at trl9, meaning an actual system proven in operational environment,18 or about to reach that level in the very near future.19 among these 13 bio-based solutions, only 3 success stories were characterized by an investment requirement below 5 million eur, while the success of 2 companies out of these 3 was based on financial support from h2020 sme instrument funding and european structural and investment funds (esif). most of the large companies involved in the study developed their bio-based product as an addition to a wider set of products, and they have thus been able to mobilize the finance internally through leveraging on profits generated elsewhere in the business. all these large companies are reported not having received public funding for the set-up of the bio-based production plants, but all of them have benefited from eu funds in the initial phase of their bio-based developments (the biobased development included in the report and/or others) which funds have been available to support r&d phases preparing the ground for the investment in the industrial scale plant.19 utmost attention should be paid to logistic costs among opex items, as most waste and by-product streams used as feedstock for bio-based processes are bulky, making transportation a significant cost driver.34 the largest distance for profitable transportation of raw materials to a bio-based industrial plant depends on the density of the feedstock and the actual products produced out of it. however, based on our own development activities and also participation at relevant workshops, the highest distance from which the transport of raw materials to the bio-based industrial plant is profitable is maximum 60 km. this relatively low value means that the place of a new plant has to be very carefully planned, in consideration for the logistic aspects. in many cases, the production of value-added products from specific agricultural wastes and by-products may not be economically feasible mainly because of the low quantities and seasonality, high transportation costs, water content of raw materials and low market price of products. in order to overcome these problems, biobased sidestreams can be treated on-site by the same producing industry, in order to produce intermediate products that can be more easily stored than the original raw material and more economically transported to the place of further processing.34 for example, hédinn protein plant can be run by fisheries or fish processing companies; in this case, products are processed at the feedstock source and sold on-site to customers.20 another example is melodea technology producing ‘cellulose nano crystals’, which can be deployed at on-site pulp mills, where the feedstock, the necessary infrastructure and utilities are already in place.19 the outcome of a good practice case, even if the solution is technologically mature enough, cannot be turned into a real consumer product without marketing and public relation (pr) activities like e.g., a campaign including advertising, promoting by social media, press releases etc. the costs that have to be allocated for marketing and pr activities often exceed by a factor of several times the technology development costs. however, usually neither activities nor actors related to marketing and pr do appear in the key activities and key partners blocks, respectively. furthermore, the related costs are not indicated in cost structure, because solution owners give low priorities to sales development activities and customer segment analyses. (see also section 4.3 for this weak point of the business models.) as a basic principle, a long-term sustainable bioeconomy needs to be economically self-sustaining through the provision of marketable products that are independent from long-term subsidies.13 currently, many ongoing bioeconomy-related development activities are heavily dependent on grants and other subsidies, especially for smes, and they are quite far from reaching economic profitability, even if their bio-based technology is report202 bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 nora hatvani, martien j.a. van den oever, kornel mateffy, akos koos ed to be on high trl and brl. generally, the income from bio-based product sales is quite low, at least during the initial stages of market introduction, especially if compared with the total development and investment costs. a typical indirect revenue resulting from the biobased industrial processes comes from the utilization of wastes produced by other activities of the same company e. g. by-product of the biowert biorefinery is processed in their biogas plant (s4 in section 3 and figure 7), and thus decreasing costs associated with the disposal of these wastes. it is relevant to add here that waste management costs are often reduced on the side of feedstock providers, such as the olive-producing farmers selling olive stones to cooperativa la carrera (spain) using this feedstock as fuel for biomass heaters.6 this saving on the feedstock provider’s side can be included in the value proposition, as in the business model of hédinn protein plant described in the be-rural project, though value proposition is meant to be delivered for customers, not key partners, so this aspect is difficult to interpret by bmc. it is mentioned in the bmc of hédinn plant that fisheries, processing industries, etc. can save on their fish waste disposal costs.20 however, these feedstock producers are key partners in this model, so this saving can be regarded as an indirect revenue only when the protein plant is run by the feedstock producer itself, using its own wastes. when it comes to revenues, it is often stated that the products connected to bioeconomy are not profitable as long as they have to be sold at the same price as the non-bio-based product. higher price of bio-based products compared to fossil-based solutions causing a deficient market pull is one of the most important basic limitations to the bioeconomy development.6 it can be difficult to justify higher market prices for bio-based products, since many of them are commodity products and end-consumers rarely care where the original raw material comes from. moreover, even if they are conscious “bioeconomy customers”, they cannot distinguish the end-product from earlier, non-bio-based products. in this regard, products need to be tailored to these customers, and this specific customer segment has to be kept in the focus, since they are willing to pay higher but competitive price if the products fulfil their special demands while achieving similar functionality.31 similarly to farmers, brand owners are are less involved in the development of the bio-based economy. however, they consider climate challenges, sustainable production and consumption to an increasing extent35, and can play a key role in supporting market uptake of bio-based products and to influence consumer choices in relation to these products.36 with a very few exceptions, only large companies have the financial means to develop the technology, invest in the necessary infrastructure and commercialize the product exclusively through internal financing, and without putting the entire company at risk.19 size of the company can be a determining factor when defining internal building blocks key resources and key partnerships: larger companies are usually able to finance the human and financial resources to do long-term development work in-house concerning their products and processes, while smes’ ability to extend their knowledge and competence base is significantly more limited. smaller companies are much more dependent on external networks and the ability to create such networks,31 which is reflected in their cost structure as well. bio-based solutions have to be market-driven in order to achieve market viability, become independent from external financing in the long-term and that subsidies should focus on providing a learning curve in order to establish competitive business. 5. conclusions assessment of six bio-based solutions using bmc allows to present all general or sector-specific businessrelated aspects to interested stakeholders in an easy-tounderstand way. this way of presenting provides new insights for the relevant stakeholders in the bio-based economy and facilitates mutual understanding by different bioeconomy actors which often operate in different sectors like agriculture, industry, government or r&d. increased understanding of how bioeconomy businesses work not only facilitates smes and large enterprises to benefit from replicating bio-based businesses, but opportunities for other type of stakeholders increase as well: – producers of bio-based wastes or by-products can transfer these materials to bio-based industry, thus reducing their waste management costs or even generating income; – rural communities benefit from local industrial development, job creation and local renewable bioenergy or food production; – investors may profit from mapping attractive investment opportunities as they can be instrumental in meeting environmental and climate challenges; – policy makers may benefit from identifying industrial initiatives supporting their existing objectives related to sustainability and climate policies. this study shows that many bioeconomy business models developed thus far have in common that espe203bio-based business models: specific and general learnings from recent good practice cases in different business sectors bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 cially the customer-related building blocks have been weakly elaborated. at the supplier end, involvement of ‘rural entrepreneurs’ like primary agricultural producers, forest owners, their associations and other small rural businesses is prerequisite for success of the emerging bio-based economy. as proven economic feasibility is essential for acceptance of innovative solutions, welldeveloped, easy-to-understand business models such as those presented in this article can serve as a helpful tool to bring the good practice cases closer to primary producers, by making these cases more comprehensible and realistic. at the same time, bmc can serve as a useful and effective tool for enhancing the replication of existing good practice cases, even if the business model always needs to be individually tailored to each local deployment situation. for such tailoring, the business models have to be elaborated in much more detail than presented in this study. these findings point to the usability of bmc to identify strengths and weaknesses in the business concept at early stages of business planning. near market introduction, scaling systems such as brl can facilitate linking technology innovation (as the main part of value proposition) and the often under-elaborated customer segments. brl can be difficult to define, but it can describe the actual business potential of bio-based solutions in a more exact and objective manner, reflecting to which extent the customer may be willing to pay for the value proposition. the introduction of a scaling system such as brls in the assessment of bioeconomy solutions and alignment of trls and brls can create a practical framework to direct the development of bio-based startup companies as well as funding instruments for technology developments and business acceleration. the traditional bmc benefits from extending with additional factors related to sustainability requirements and benefits as well as factors related to the business ecosystem such as drivers and stakeholder involvement tools. the additional factors can show how to create short-term or longer term structural values also in environmental and social terms, which is particularly relevant in bioeconomy businesses requiring the cooperation of various and divergent players. acknowledgments this work was funded by the power4bio project (“empowering regional stakeholders for realizing the full potential of european bioe-conomy”) which has received funding from the eu’s horizon 2020 research and innovation programme (topic: rur-092018 – “realising the potential of regional and local biobased economies”) under grant agreement no. 818351. m.v.d.o was co-funded by dutch tki-t&u under grant no. eu-19038 (bo-57-101-001). (this article reflects the views only of the authors, and do not necessarily reflect the official opinion of the eu. neither eu institutions and bodies nor any person acting on their behalf may be held responsible for the use which may be made of the information contained therein.) the authors thank the colleagues in the power4bio project team for their work related to the deliverable 4.1 which provided idea and knowledge base to this article, their help in contacting companies, their useful comments and the careful correcting of the texts. the authors would like to thank also harriëtte bos (wageningen food and biobased research, the netherlands) for her many useful comments and suggestions. the authors would like to express their great appreciation to the representatives of the companies owning the solutions described in this article, for their valuable and constructive suggestions and willingness to give their time during the data collection process. the authors declare no conf lict of interest. the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. references 1. reim, w., parida, v., sjödin, d.r. 2019. circular business models for the bioeconomy: a review and new directions for future research. sustainability, 11:2558. doi: 10.3390/su11092558 2. communiqué of the global bioeconomy summit, 2020. expanding the sustainable bioeconomy – vision and way forward. https://gbs2020.net/wpcontent/uploads/2020/11/gbs2020_iacgb-communique.pdf 3. european commission. 2018. a sustainable bioeconomy for europe: strengthening the connection between economy, society and the environment updated bioeconomy strategy. doi: 10.2777/792130 4. viaggi, d. 2018. bioeconomy and the common agricultural policy: will a strategy in search of policies meet a policy in search of strategies? bio-based and applied economics, 7(2): 179-190. doi: 10.13128/ bae-7674 5. ronzon, t., piotrowski, s., m’barek, r., carus, m. 2017. a systematic approach to understanding and 204 bio-based and applied economics 11(3): 185-205, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-10820 nora hatvani, martien j.a. van den oever, kornel mateffy, akos koos quantifying the eu’s bioeconomy. bio-based and applied economics, 6(1):1-17. doi: 10.13128/bae20567 6. folkeson-lillo, c., diaz, i.p., calvo, m.h. 2019. study on the participation of the agricultural sector in the bbi ju: business models, challenges and recommendations to enhance the impact on rural development. study carried out by innovarum (eurizon s.l.) under bbi ju tender bbi.2019.sc.01, 2019. doi: 10.2884/371672 7. kuckertz, a., berger, e.s.c., morales reyes, c. a. 2018. entrepreneurial ventures and the bioeconomy. in lewandowski, i. 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policy support and farmers behaviour. an investigation on italian agriculture over the 2008-2019 period roberto esposti price dependence of biofuels and agricultural products on selected examples wioleta sobczak*, jarosław gołębiewski bio -based and a ppl ied economics bae bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-12211 copyright: © 2022 g. dono, r. buttinelli, r. cortignani. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: g. dono, r. buttinelli, r. cortignani (2022). financial performance of connected agribusiness activities in italian agriculture. bio-based and applied economics 11(2): 147-169. doi: 10.36253/bae-12211 received: october 24, 2021 accepted: june 6, 2022 published: august 30, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: simone cerroni, fabio gaetano santeramo. orcid gd: 0000-0002-0272-178x rb: 0000-0002-8934-6264 rc: 0000-0002-2685-9783 paper presented at the 10th aieaa conference financial performance of connected agribusiness activities in italian agriculture gabriele dono*, rebecca buttinelli, raffaele cortignani university of tuscia, viterbo (italy) * corresponding author. e-mail: dono@unitus.it abstract. the rural development policy combines measures that favour the growth of the productive dimension of farms and their specialization, and measures aimed at supporting diversification paths, with the expansion of the productive functions performed. the evaluation of the economic and financial results of farms engaged in activities of the second type can help to calibrate the intervention between the two options. to this end, we have studied a constant sample of fadn farms in the period 2014-2016, identifying the units engaged in organic farming or other forms of quality production, or engaged in direct sales or processing of their products or, again, in the management of farmhouses. we discuss the condition of financial sustainability of the farms involved in those activities by evaluating their ability to generate cash flows to offset for the depreciation of the farm production system. we used the ratio free cash flow on equity on depreciation to compare the results of farms engaged in those activities and farms which are limited to conventional agriculture. the analysis of this comparison and of some structural, technical, and economic characteristics of the farms involved in those types of activities resulted in various considerations on their characteristics and conditions of financial sustainability. our attention has focused above all on the financial results of farms within the sectors of italian agriculture in greater financial difficulty. the main objective was, in fact, to verify whether to diversify the farm’s commitment with these activities has contributed to improving the financial sustainability in those agricultural sectors. various considerations have arisen that can help fine-tune policies to support the types of diversification examined in this study. keywords: depreciation, free cash flow on equity, farm financial sustainability, agribusiness, organic farming, agricultural products processing, direct sale of agricultural products, quality agricultural products, farmhouses. jel codes: q13, q14. 1. introduction rural development policy was introduced as the second pillar of the cap as part of the agenda 2000 reform. since then, with the aim of protecting rural heritage and creating new jobs, it has also been dedicated to supporting multifunctionality and the diversification of agricultural activities. the focus on diversification increased in the 2007-2013 period, with axis 3 http://creativecommons.org/licenses/by/4.0/legalcode 148 bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 gabriele dono, rebecca buttinelli, raffaele cortignani (quality of life in rural areas and diversification of the rural economy), as well as in the 2014-2020 programming period, extended up to 2023 and 2025 for many rdp projects, with priorities 2 (farm viability and competitiveness) and 6 (social inclusion and economic development).1 in the latter period, the italian regions allocated 624 million euros, 3.2% of the entire rdp budget, for sub-measures 6.2 (aid for start-up of nonagricultural activities in rural areas) and 6.4 (investments to create and develop non-agricultural activities). the budget has been reduced in 2020 over the same period in 2018, linked to the covid-19 crisis, especially for activities such as agritourism, educational farms. yet, requests for support for operations related to diversification have been substantial, making funding insufficient in many cases (ismea, 2020). in the period 2010-2019 the trend to diversify agricultural activities has notably grown and in 2019 about one fifth of the total value of agricultural production (€ 12.5 billion) came from secondary and support activities. among others, the first-stage processing of agricultural products increased from 1.5 to 2.4 billion euros in the whole period, while direct selling of farm products grew by 4.3% in 2018-2019 (crea pb, 2021; istat, 2020). farms engaged in related or secondary activities are concentrated in the centre-north of italy, which indicates an imbalance in the development of these activities but also a great potential for further expansion. the scientific literature treats the intensification and propagation of these activities as the effect of a change in eu agricultural policies and in the choices of farmers seeking to stabilize and supplement their incomes. in this regard, an important line of analysis examines the factors influencing farmers’ decision to diversify or undertake other activities besides conventional agriculture (mishra et al., 2004; rivaroli et al, 2017; barbieri, 2010). a wide debate therefore concerns the influence of the farmer’s age and education, the presence of female labour, the degree of production specialization and the operational size of the farm. mcnamara and weiss (2005) and meraner et al. (2015) claim that larger farms diversify; in contrast, mishra et al. (2004) claim that larger farms tend to specialize instead. for tourism-related activities, the influence of other factors is also considered, such as public support or the environmental characteristics of the area where the farms are located (de rooij et al., 2014; boncinelli et al., 2018; biczkowski et al., 2021). proximity to urban areas and 1 focus area 2a “improving the economic performance of all farms and facilitating farm restructuring and modernisation” and 6a “facilitating diversification, creation and development of small enterprises as well as job creation”. consumers is also shown to play a key role, especially in terms of direct selling (zasada et al., 2015; pölling and mergenthaler, 2017). conversely, it is also highlighted that farms far from urban areas can be pushed to diversify due to the lack of alternatives (bartolini et al., 2014; arias et al., 2015). at the same time, the repercussions of these activities on the development and social and environmental well-being of one’s own territory are also considered (arfini et al.,2019a, 2019b; raimondi et al., 2018; belletti et al. 2017; heringa et al., 2012; lange et al., 2013). the analysis also concerns the production, economic and financial results obtained by the farms that are dedicated to these activities. studies have investigated the impact of these activities on farm work (chaplin et al., 2004; raimondi et al., 2018), on technical efficiency (lakner et al., 2018, arru et al., 2019) and on income (barbieri, 2013; barnes et al., 2015; salvioni and fontanella, 2013). khanal and mishra (2014) study the financial situation of these farms and state that the income of agritourism families is higher than other agricultural households. according to joo et al. (2013) agritourism has a positive effect on financial sustainability only on small farms. salvioni et al. (2020) conclude that diversification also has a positive impact on the financial performance of italian farms. below we focus our attention on the financial condition of the farms engaged in these activities. we study their cash flows which, according to fazzari et al (1988) and kaplan and zingales (2000), measure the firm’s dependence on internal funds, helping to explain its investment choices, the ability to obtain credit and, hence, to finance investments. our analysis follows the approach of dono et al. (2021) which frame financial sustainability in the ability to offset the depreciation of the production system with cash flows. specifically, these authors evaluate the ratio between free cash flow on equity (fcfe) and the value of depreciation (f/d index) in a constant sample of fadn farms over the period 2014-2016, and show that f/d is higher than 1 in most types of specialized farms, while it is less than 1 in nonspecialized types. dono et al. (2021) examine the financial condition of the ensemble of italian farms, focussing on the different technical-economic orientation sectors. here we deepen the study of their fadn sample by examining the financial condition in the farms that diversify their activities. in this regard, we focus on first processing and direct selling of farm products, as well as on farmhouse. these activities require more profound changes in entrepreneurial performance, unlike the electricity production, the provision of farm subcontracting services and 149financial performance of connected agribusiness activities in italian agriculture bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 the land leasing, which are excluded from our analysis. we also consider organic farming that, while managing typical agricultural practices, modifies the classic profile of the farm and its productions, abandoning the conventional approach. finally, we include the supply of quality products that, with the single farm, often involves other units in areas where productions with typical and homogeneous attributes are made2. for convenience we call agribusiness the whole of these activities, considering that they are an attempt to search for market niches by enriching the range of goods and services provided to users of the typical farm products. the analysis compares the financial results of the farms conducting these 5 activities with the results of farms that conduct only conventional farming activities, which we call simple farming. we first examine the financial condition of the fadn sample farms involved in at least one of the 5 based on the f/d ratio, named sustainability index, as done by dono et al. (2021).. structural, commercial, and economic characteristics that can influence the financial results of the farms involved in the 5 agribusiness are hence identified. comparisons are made with the financial results of the farms that are limited to conventional agricultural management. the analysis is exploratory and looks for clues on the contribution of these activities to the financial sustainability of italian farms, focusing on the agricultural sectors that dono et al. (2021) indicate as in difficult financial conditions. this study is, therefore, preliminary to a modelling, econometric or mathematical programming, of the contribution of these activities to the financial sustainability of italian agriculture. the next paragraph presents the materials and methods, framing the contribution of the financial analysis, the sequence of operations to calculate cash flows and the indicator used to express the financial sustainability of farms. the section on the results reports the general characteristics of the sample of farms, with the representativeness and weight of the 5 agribusiness activities on the total. subsequently, the levels of fcfe and depreciation are described to compare the condition of the farms that only deal with typical agriculture and 2 dealing with diversification and multifunctionality would require referring to consolidated scientific classifications that generally lead back to the concepts of deepening, expansion and regrounding. (van der ploeg et roep, 2003). yet, the scientific literature agrees that referring to a unique classification could create confusion and complicate the comparison between results obtained from different studies (sardone et monda, 2019; henke et salvioni, 2011). even referring to regulations does not always solve the problem of classification. national accounting divides into support activities and secondary activities contributing to the agriculture production. eurostat distinguishes processing of agricultural products and other production of goods and services. italian legislation is based on article 2135 of the civil code (oecd, 2009). those involved in the 5 agribusinesses. this analysis is conducted by single activity, by technical-economic sector and by size class of farms. the discussion and conclusion sections follow. 2. materials and methods. 2.1 general characteristics of the sample of farms we analyse the financial sustainability of italian farms based on the constant sample of fadn data used by dono et al. (2021). the fadn was established by the reg. 79/65/eec, updated by reg. ce 1217/2009, and annually collects technical and economic data of a large farms sample following a similar approach in the european union countries. the more than 86,000 fadn farms represent nearly 5 million farms in the eu, 90% of the utilized agricultural area (uaa) and 90% of standard production. currently the italian sample is based on about 11,000 farms and covers 95% of the uaa, 97% of the value of standard production, 92% of the work forces and 91% of the livestock units. about 1,000 variables are recorded for each farm in the sample, more than 2,500 for the italian fadn. the fadn sample only includes professional and market-oriented farms and is stratified by region, size class and technical-economic orientation [ote as italian acronym, type of farm (tf) according to reg. ce n. 1242/2008, henceforth tf]. based on these data, dono et al. (2021) obtain three years of financial statements (2014-2016) for a constant fadn sample consisting of 4.612 italian farms, for a total of 13,836 observations. here we divide the fadn sample considering the farms involved in the 5 most diffused agribusiness: namely organic farming (thereafter organic), processing, direct selling (selling), quality production (quality), and farmhouses. of these groupings, the weight on the total sample of some key variables, structural (gross capital, uaa, working units) and economic [gross saleable production (gsp), operating income], as well as their average value is calculated. this provides a representation of the importance and the operational and economic dimension of these activities. the next paragraph illustrates how the cash flows for each farm are calculated in each of the three years considered. 2.2 the calculation of cash flows table 1 shows how the cash flow of each farm is computed. the procedure begins by subtracting the tax component from the operating result, then adds depre150 bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 gabriele dono, rebecca buttinelli, raffaele cortignani ciation, provisions for severance pay and for risks and other expenses. the variation of net working capital, as made up of operating receivables with customers and operating payables with suppliers is hence added, as well as investments, obtained as increase of inventories net of their depreciation. this generates the cash flow from operations. once the cash flow of the operating activity has been obtained, the financial balance of relationships with the financiers of the farm is considered: where paying interest and principal on debts falling due in the year reduces liquidity, while obtaining new loans increases it. public aid from the second pillar of the cap and other national measures also increase liquidity, as well as revenue from other current accounts or other income, such as financial assets or divestments. paying fines and repaying other loans reduces liquidity. this sequence generates a monetary liquidity variable that still includes payments to work, and the capital resources provided by the farmer. the final cash flow is obtained by subtracting cash withdrawals to pay for the farmer’s resources: dono et al. (2021) estimated these latter payments at opportunity cost values to obtain the free cash flow to equity (fcfe). we use the same approach, although it is an approximation as the farmer does not necessarily collect the opportunity cost payments for the resources provided as, moreover, as is the case with the distribution of corporate dividends (chay & jungwon suh, 2009). financial sustainability is considered as achieved when fcfe is greater than the depreciation of productive capital, even by a margin that can also repay a debt service provided at a subsidized rate. this indicator can be traced back to the financial analysis of the debt of the company that bonazzi and iotti apply to the tomato processing industry, aquaculture, and dairy cattle breeding in italy (bonazzi and iotti, 2014a, 2014b, 2015; iotti and bonazzi, 2015). these authors calculate the financial sustainability of investment debt by relating its cost to the cash flows generated by various level of the operating activities3. yet, these indicators can be calculated only in relation to specific investment programs that are in place only in a part of the fadn farms. to carry out a financial sustainability analysis in all cases, as in dono et al. (2021), we assess whether the final monetary liquidity surplus given by fcfe is sufficient to balance the residual implicit costs, i.e., the depreciation of technologies and provisions for risks or other funds. the index does not check whether the farms will reproduce the initial capital or not. depreciation, in fact, is calculated at historical cost, which in the case of old plants can make the current restoration cost even very different from that associated with depreciation. furthermore, new market, policy support and production technology conditions may not induce farmers to restore the original system. thus, the index verifies a minimum sustainability condition, defined as weak, which reveals whether farms are generating additional cash flows at the same rate at 3 bonazzi and iotti (2014b) consider, among others, the operating cash flow, and the unlevered free cash flow, which subtracts the investment and adds the divestment to the former. table 1. fcfe calculation: formulas and fadn databases (fdb) used. income and cash flow items fdb note operating income is taxes + depreciation + other provisions bs ∆ (employee leaving indemnity fund + other funds) ± ∆ net working capital bs ∆ (debts + credits + product stock + raw materials stocks) investments cash flow from operations (caffo)  ± principal portion bs ∆ medium/long term debt interest portion is + public aid eu second pillar aid and other national aid + other receipts free cash flow + compensation to farmer resources (caffe)  payment to capital bs % of net capital compensation to managerial work is % of gross marketable output compensation to manual labor lab hourly wages for hours of family work free cash flow to equity (fcfe)     (is) = income statement; (bs) = balance sheet; (lab) = labor file; d = variation over the year. 151financial performance of connected agribusiness activities in italian agriculture bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 which their technological system depreciates. moreover, unlike the economic valuation indices, the financial components allow this ratio to also embody the investment efforts of farms, as well as their commercial and financial relationships. dono et al. (2021) calculate the index for the whole sample and for 18 tfs that aggregate the original fadn tfs. the following analysis compares the economicfinancial situation of farms dedicated exclusively to agriculture (simple farming), and those involved in at least one of the 5 activities listed above as agribusiness. specifically, the analysis concerns basic structural, commercial, and economic characteristics of the farms in those groups, as well as their values of fcfe, depreciation and fcfe/depreciation ratio (f/d), calculated as in dono et al. (2021). the comparison is carried out within each type of farming (tf), whose index values of simple farming are used as reference for assessing the condition of the farms involved in agribusiness. after a first general analysis in the whole sample and in each of its tfs, the farms’ financial results in each of the 3 years of the sample are examined. this generates three groups of different stability in the financial result: agribusiness farms with always better results than simple farming (better); farms with alternating results (alternating); and farms whose results are always worse than simple farming (worse). this aggregation changes the numbers in simple farming and agribusiness because includes in the latter also farms engaged in these activities for only one or two of the three years considered, i.e., that are in the start-up or disinvestment phase. finally, the results of these three groups are presented by size classes to reduce the influence of the operational scale on the comparison between agribusiness and simple farming. these classes are obtained by dividing into three equal segments the variation range of the farm’s gross saleable agricultural production (gsp) in each tf. therefore, for instance, expressed in thousands of euros, the small dairy cattle have a gsp of less than € 1.025, the medium between € 1.025 and € 2.044, the large between € 2.044 and € 3.063. particular attention is paid to the results in the tfs whose f/d value is below the financial sustainability threshold, to assess whether their agribusiness farms show better results or share this difficult situation. 3. results table 2 shows structural and economic features on the constant sample of fadn farms in the period 2014-2016. the two sections of the table distinguish the cases only involved in simple farming and the cases also engaged in the 5 agribusiness. the latter are shown both for their general aggregate (agribusiness), and for each of the 5 activities. the first section of the table reports the percentage of each group for each variable that are gross capital, utilized agricultural area (uaa), family work units, gross saleable production (gsp), operating income and the number of cases. note that the sum of simple farming and agribusiness is 100, while the sum of the 5 activities exceeds the total of agribusiness, because of cases engaged at the same time in more of these activities. the second section of the table reports the average value of those variables, and the average value of roi calculated net of from the first pillar cap payments. the two sections of the table show that, despite an average area analogous to simple farming, the farms with agribusiness operate in average with lower gross capital, employ fewer family work units and generate lower production values and operating income. differences emerge for organic, whose production value is higher than in simple farming. farmhouses prevails for gross capital invested and employed family work, although not in terms of production value. the roi values indicate that overall, the efficiency of these farms is relatively lower than in the simple farming units. the farmhouse is an exception because it obtains its income, albeit lower, with greater efficiency than simple agriculture. table 3 presents the results of the fcfe and depreciation, as well as the f/d sustainability index, calculated for the whole of the three years on the individual observations in each group.4 the f/d index of simple farming is higher than the sample average (1.84 vs 1.57) and the entire agribusiness (1.15). this worse result of agribusiness is mainly due to the lower cash flow production (-42.1% compared to simple farming) than to a different level of depreciation (-9.5%). organic is the exception given the +0,31% of fcfe and the -13,2% of depreciation compared to simple farming. processing is in a weaker situation but exceeds the financial sustainability threshold of 1.15 used by dono et al. (2021). the other agribusinesses show average unviable conditions depending on low (selling) or negative fcfe values (quality and farmhouses), and on an average high level of depreciation (farmhouses). 4 the table shows the levels of statistical significance of the differences between fcfe values and between depreciation values but not between f/d values. this happens because the f/d index in the tables are not the average of the farms’ values in the individual groups but the ratios between the sum of the fcfe and depreciation in each group. this kind of calculation does not change the general relationships among groups but prevents from performing the test of the differences between the values of the different groups. 152 bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 gabriele dono, rebecca buttinelli, raffaele cortignani table 4 shows the number of farms in the sample, the percentage of farms by tf, by simple farming and by each of the 5 agribusiness, and the value of the financial sustainability index for each group. it is noted that the presence of agribusiness farms in many tfs is appreciable: in order, in processing, organic and direct sales. in various tfs the presence of quality (vineyards), of farmhouse (mixed crops and livestock, dairy cattle) is also relevant. agribusiness farms have an f / d index value higher than the value in simple farming in several tfs (in bold-italics and in larger font in the table). 5 of the 7 tfs that are below the financial sustainability threshold for their farms as a whole, have an f/d index value higher than that threshold for their respective organic farms. important results are also found for quality (in 3 out of 7 tfs in financial crisis), farmhouse and selling (in 2 out of 7) and processing (in 1 out of 7). no agribusiness exceeds the sustainability threshold in dairy cattle; rather, the f/d of all dairy cattle farms involved in agribusinesses are lower than simple farming. 3.1 results by stability of financial conditions and by operational size table 5 compares the results of agribusiness and simple farming based on the stability of the results achieved in the single years of the examined period. as before, the comparison is carried out within the types of farming (tf) and considers three groups: agribusiness farms that in all three years achieved better results than simple farming; those with always worse outcomes; those with alternating results. the asterisks in worse and alternating indicate the statistical significance of the differences between their fcfe and depreciation and the corresponding values for better; the asterisks in better refer to the difference with simple farming. table 2a. structural and economic features of fadn farms percentage weight on the total. gross capital uaa working units gsp operating income number simple farming 59.0 48.5 50.8 54.1 60.8 48.9 agribusiness 41.0 51.5 49.2 45.9 39.2 51.1 organic 14.1 15.9 13.3 20.0 13.2 15.0 processing 29.2 38.1 37.6 34.5 27.7 39.2 selling 14.9 19.5 18.1 14.0 12.4 17.6 quality 5.1 5.7 4.7 5.2 4.5 5.3 farmhouses 4.6 3.8 4.9 2.5 3.2 4.0 table 2b: structural and economic features of fadn farms average value gross capital uaa working units gsp operating income roi simple farming 1,032,498 32.2 1.36 117,717 64,829 0.040 agribusiness 703,715 32.7 1.26 95,254 39,931 0.056 organic 832,688 34.4 1.16 141,639 45,878 0.060 processing 659,383 31.5 1.25 93,408 36,805 0.059 selling 750,580 35.9 1.34 84,297 36,625 0.039 quality 844,165 34.5 1.16 103,918 44,375 0.055 farmhouses 1,021,023 30.4 1.61 64,896 41,244 0.023 total sample 885,714 32.4 1.31 106,227 52,094 0.047 source: our elaboration of fadn data. table 3. fcfe, depreciation and financial sustainability index over the entire three-year constant sample. fcfe depreciation f/d simple farming 19,643 10,825 1.81 agribusiness 11,283*** 9,792*** 1.15 organic 19,703 9,396*** 2.10 processing 11,312*** 8,708*** 1.30 selling 10,231*** 10,638* 0.96 quality -4,440*** 9,494** -0.47 farmhouses -3,238*** 19,468*** -0.17 total sample 15,553 9,897 1.57 difference with simple farming statistical significance: *** p = 0.99, ** p = 0.95, * p = 0.90. source: our elaboration of fadn data. 153financial performance of connected agribusiness activities in italian agriculture bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 the table shows that 32.4% of organic farms performs better than simple farming. the percentage is much lower in other agribusiness, 17-22%, where instead 40% of farms always obtain worse results than simple farming. the gap between the f/d in the three groups is considerable for all activities, with very high average values for better. the average f/d in alternating is close to the sustainability threshold (1.15) in organic and processing. in the other activities f/d is less than 1, close to 0 in farmhouses. for each agribusiness high cv values for fcfe and low values for depreciation emerge, suggesting that the differences in f/d mainly depend on the different values of the cash flows. table 6 reports the f/d values of the three groups with relevant structural and economic variables at farm level. the latter include depreciable capital, which affects both the denominator of the f/d index, increasing the depreciation value, and its numerator, adding liquidity to fcfe. the average value of investments over the three years, which certainly influences the productivity level of other resources in the future, but immediately subtracts liquidity from fcfe. aid from the cap ii pillar adds liquidity to fcfe and includes public funding to support investments as well as agribusiness management activities. gross saleable production (gsp) reflects the operational size of farms and directly contributes to generating operating income, which adds liquidity to fcfe. the net change in working capital (δwcc) adds, or subtracts, liquidity to fcfe as the result of all commercial relationships with customers, suppliers, and banks. finally, return on investments (roi) calculated net of the cap aid of the first pillar, as an indicator of farm efficiency. the data are reported for simple farming and agribusiness, and for better, alternating, and worse groups. the asterisks indicate the statistical significance of the differences between better and the groups worse and alternating; between better and simple farming; finally, between total agribusiness and simple farming. the average endowment of depreciable, the value of investments, gsp and roi of agribusiness are significantly lower than simple farming. agribusiness activities are instead more supported by cap ii aid. there is no significant difference in δ wcc. differences with simple farming emerge for the individual groups. better also displays a significantly lower endowment of depreciable in organic, processing and table 4. number of farms (sample), percentage of farms (by tf, simple farming and agribusiness), value of the financial sustainability index (by groupings). types of farming (tfs) number of farms sample percentage of farms f/d sifa org pro dis qua far smp sifa org pro dis qua far mixed crops and livestock 447 49.4 11.9 38.3 16.1 2.0 10.1 -0.08 0.09 2.75 -0.02 -0.80 1.16 0.02 extensive beef cattle 828 47.9 17.0 40.3 9.4 1.1 4.6 0.10 -0.29 1.23 0.28 -0.25 -1.45 1.51 mixed crops 840 39.3 16.2 49.6 15.7 2.6 6.8 0.38 0.43 1.71 0.00 0.36 4.04 -0.50 mixed fruits 1.491 53.4 17.2 36.6 9.9 4.4 2.5 0.80 0.49 3.25 2.11 1.95 -1.50 -1.18 arable crops 3.039 66.2 7.5 26.1 7.6 1.7 1.9 0.82 0.77 2.71 0.08 0.40 5.63 0.52 sheep 720 47.4 23.2 33.3 12.1 0.8 2.1 0.87 0.98 0.94 0.78 1.12 6.35 1.23 dairy cattle 1.209 67.0 6.4 22.2 8.1 2.2 8.0 1.15 1.71 -0.11 0.52 0.35 -0.98 -2.19 vineyards 1.683 45.5 9.4 44.4 14.1 9.5 4.2 1.19 -0.29 1.46 2.08 1.25 -3.08 0.49 mixed livestock 297 50.5 10.8 39.1 10.1 1.3 4.0 1.42 0.32 9.28 0.37 2.41 7.32 -1.26 greenhouse vegetables 126 73.8 7.1 14.3 9.5 2.4 0.0 1.44 1.91 1.67 -0.94 -3.50 2.02 olive growing 531 5.3 49.9 86.4 17.1 6.8 5.6 2.08 -4.87 2.49 2.84 2.16 3.35 0.32 swine 252 77.4 1.2 19.0 5.2 0.0 2.4 2.42 2.54 5.91 1.63 2.29   -7.13 other 849 65.6 5.8 24.3 9.8 1.4 1.4 2.65 3.62 1.81 1.05 0.79 -2.24 4.90 poultry 336 74.7 6.3 19.3 4.2 0.3 0.0 3.90 3.80 0.81 4.82 3.92   citrus fruits 222 16.2 55.4 61.3 9.9 1.8 0.0 4.12 1.75 5.24 4.94 3.22 0.79 open field vegetables 624 65.5 9.0 26.3 8.3 1.9 2.4 4.48 5.00 1.72 3.41 6.96 2.78 -0.12 fruits in shell 114 69.3 20.2 18.4 0.0 0.9 0.0 6.86 4.39 9.12 10.58   32.31 intensive beef cattle 228 82.9 7.0 10.1 3.1 1.3 3.1 7.08 7.78 -0.47 -2.36 -0.30 -5.77 -0.76 total 13.836 55.4 13.1 34.5 10.2 3.1 3.6 1.57 1.81 2.10 1.30 0.96 -0.47 -0.17 total sample (smp), simple farming (sifa), organic (org), processing (pro), selling (dis), quality (qua), farmhouses (far). source: our elaboration of fadn data. 154 bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 gabriele dono, rebecca buttinelli, raffaele cortignani quality activities, while it is higher in farmhouse. the activities in better all make less investments than simple farming, while they benefit from significantly greater cap ii aid. they also display a higher roi and, more important, positive even net of cap i aid. conversely, these activities show non-statistically significant differences for gsp and δ wcc, even if with positive values and higher than simple farming. statistically significant differences emerge among the three groups in agribusiness. alternating and worse show significantly higher endowments of depreciable than better, as well as lower levels of roi, δ wcc and gsp5. since the gsp values in better are close to simple farming, the gsp levels of worse and alternating are also lower than this group’s values. conversely, alternating, and worse show significantly higher investments levels compared to better; despite this greater commitment, worse receives significantly smaller cap ii aid. an in-depth analysis may concern the position of the better, alternating, and worse groups, in the individual tfs and also by dimensional classes. the next paragraph presents the results of this analysis by focusing on 5 some variables are distributed in the farms of alternating and worse with a high variability; this makes the differences in their average values compared to better statistically insignificant, although appreciable. the tfs whose f/d value is below the financial sustainability threshold. 3.1.1 stability of financial conditions by tfs and by operational size tables 7 and 7bis report various information relating to the three financial result groups, better (bet), alternating (alt) and worse (wor), in each agribusiness and for simple farming in the tfs whose f/d value is below the financial sustainability threshold. table 7 shows, first, the relevance of the three groups with different financial results in terms of percentage of agribusiness farms placed in them. the alt group is on average pre-eminent in all cases, and in most of them it is closely followed by wor. the percentage of farms in bet is close or above wor only in organic. the table presents in bold-italics and with a larger font the tfs cases whose f/d agribusiness values are greater than simple farming. in all cases, the f/d values of better are well above the financial sustainability threshold and the value of simple farming. alternating also presents many cases above simple farming, albeit only a few well above the sustainability threshold (quality in sheep and mixed crops livestock). the f/d values table 5. farms with better, alternating, or worse results – percentage, f/d index, fcfe and depreciation, coefficient variation (cv). statistical significance of differences among the various groups (*). variables better alternating worse cv = s/m percentage weight organic 32.4 37.3 30.3 0.11 processing 21.8 35.3 42.9 0.32 selling 21.4 35.0 43.6 0.34 quality 21.1 37.8 41.1 0.32 farmhouses 17.3 41.1 41.6 0.42 f/d index organic 8.54 1.10 -1.45 1.90 processing 5.98 1.20 -2.14 2.43 selling 5.89 0.60 -2.02 2.70 quality 5.76 0.70 -1.95 2.60 farmhouses 4.51 0.20 -1.71 3.19 fcfe organic 74,933 *** 8,745 *** -17,859 *** 2.18 processing 61,763 *** 10,432 *** -16,270 *** 2.13 selling 64,150 *** 6,577 *** -19,028 *** 2.47 quality 41,972 *** 6,260 *** -21,997 *** 3.67 farmhouses 65,614 *** 3,355 *** -39,724 *** 5.43 depreciation organic 8,777 *** 8,167 12,343 ** 0.23 processing 10,325 8,829 ** 7,594 *** 0.15 selling 10,882 10,763 9,437 0.08 quality 7,283 *** 9,255 * 11,259 *** 0.21 statistical significance of differences: *** p = 0.99, ** p = 0.95, * p = 0.90. source: our elaboration of fadn data. 155financial performance of connected agribusiness activities in italian agriculture bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 of worse are all below the sustainability threshold and below the average result of simple farming. the ability to generate cash flows (fcfe) appears crucial in determining the f/d result, as suggested by the extent of the values in better, which in all tfs and agribusiness are higher than simple farming. similar evidence is found in alternating, notably in sheep and mixed crops livestock. conversely, fcfe is always negative and inferior to simple farming in worse. depreciation contributes to determining the value of f/d by increasing both the denominator and the numerator of the index. this makes his specific discussion less interesting and, given the exploratory nature of this study, it was decided not to include his data in table 7 and to make them available in the tables in appendix a. table 7bis reports some variables that influence the amount of cash flow. in this case, values of agribusiness that are above simple farming are marked with a bold italic font. the level of gross saleable production (gsp) directly affects operating income, that is one of the main components of fcfe. in this case it is noted that in most cases in better gsp is higher in agribusiness farms than in simple farming, while the opposite happens for all the farms in worse. here too we find very high gsp values in the sheep tf in alternating. furthermore, the roi, here taken as an indicator of efficiency, assumes average positive values in agribusiness farms, while in simple farming it always assumes average negative values. agribusiness farms have higher roi also in many tfs in alternating. yet, in these cases the indicator mainly maintains negative values. even in worse there are tfs whose roi is higher in agribusiness than in simple farming, even if always with a negative sign. finally, the table shows the investments (inv), which subtract liquidity from fcfe, and the ii pillar aid of the common agricultural policy (cap ii), which add it often applying measures to support the former. those data show that the value of the investments in agribusiness is lower than in simple farming in almost all tfs. the opposite happens for alternating and worse, where investments of some tfs are even 6-7 higher than in better. above all, it is interesting to note that, despite this investment discrepancy, cap ii aid are mostly greater in better than in alternating and worse. the last in-depth study of this exploration concerns the distribution of agribusiness farms among the three table 6. simple farming, total and single agribusinesses in the 3 financial result groups percentage of farms on total sample, f/d index; per farm 000 € of depreciable, investments, cap ii aid, gsp, dwcc; roi. % f/d depreciable (€ 000) investments (€ 000) cap ii (€ 000) gsp (€ 000) δ wcc (€ 000) roi simple farming 48.9 1.84 87.0 19.4 2.9 182.5 -0.2 -0.04 agribusiness 51.1 1.24 76.7*** 13.7*** 5.0*** 101.9*** -0.8 -0.06*** better organic 4.9 8.54 54.8 *** 5.2 *** 10.1 *** 176.3 4.5 0.01 *** processing 8.6 5.98 69.9 *** 7.7 *** 6.4 *** 173.7 2.5 0.01 *** selling 3.8 5.89 76.5 8.9 *** 6.0 *** 184.1 2.3 0.02 *** quality 1.1 5.76 49.4 *** 7.9 *** 5.5 *** 144.9 * 0.4 -0.04 farmhouses 0.7 4.51 140.4 *** 10.6 *** 8.4 *** 162.5 -1.1 0.01 *** alternating organic 5.6 1.07 66.0 * 17.0 ** 9.6 102.7 *** -0.6 -0.04 *** processing 13.9 1.18 77.8 15.3 *** 5.6 105.6 *** -2.1 *** -0.03 *** selling 6.2 0.61 108.5 *** 16.1 *** 6.4 102.9 *** -1.6 -0.02 *** quality 2.0 0.68 87.8 *** 23.5 *** 7.0 104.3 * -3.5 -0.02 * farmhouses 1.6 0.22 202.8 *** 25.4 *** 7.7 97.7 ** -2.4 -0.01 * worse organic 4.6 -1.45 114.1 *** 19.8 *** 6.9 *** 64.5 *** -4.1 -0.15 *** processing 16.8 -2.14 72.6 10.3 * 2.8 *** 46.0 *** -2.1 *** -0.13 *** selling 7.7 -2.02 96.3 * 12.3 *** 3.2 *** 56.0 *** -3.7 *** -0.08 *** quality 2.2 -1.95 110.8 *** 34.6 *** 5.9 118.2 -7.2 *** -0.08 farmhouses 1.7 -1.71 286.2 *** 44.8 *** 5.9 * 69.8 *** -8.3 -0.06 *** statistical significance of differences: *** p = 0.99, ** p = 0.95, * p = 0.90. source: our elaboration of fadn data. 156 bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 gabriele dono, rebecca buttinelli, raffaele cortignani gsp size classes and the financial result groups. table 8 allows this assessment for tfs with f/d values below the financial sustainability threshold (fist), and for the group above it. the first section of the table shows that most of the agribusiness farms are in the small dimensional class: 98.2% in the tfs with f/d under fist, 96.9% in the other tfs. agribusiness farms in bet are always a minority share; in tfs below fist their percentage is even lower, 20.5%, while the financial result of more than 40% of those farms is worse than simple farming. the second part of the table shows the f/d index values of each group6. a gap emerges between the f/d values in better, which confirm the figures of the previous tables, and those in alternating and worse. the prevalence of cases in the latter groups greatly reduces 6 in the group large the f/d value of alternating is lower than in worse because the comparison is carried out within each tfs, and given the low number of large farms in alternating and worse, this implies comparing the results of diverse tfs. the average values of f/d both for the total agribusiness and for the small farms. the impact is greater in tfs in financial difficulty, to the point that their f/d index value is below the fist both in the general average and in small farms. 4. discussion more than half of our fadn observations manage activities that we have called agribusiness. yet, their weight on the sample’s income is much lower, and when compared to simple farming the average operating income of agribusiness is a lower fraction of both gross invested capital and family work units. we found that these activities are carried out on farms that have smaller gsp, and in large part also worse financial results than simple farming in their respective tfs. yet, a group, albeit a minority, of agribusiness farms achieves better financial results than simple farming; and it is interesting to note that most of these farms are classified in tfs table 7. percentage of farms, f/d, fcfe in each financial result group in each agribusiness by tf; comparison f/d and fcfe in simple farming. type of farming (tfs) farms % agribusiness f/d fcfe (000 €) org pro dis qua far sif org pro dis qua far sif org pro dis qua far mixed crops livestock bet 34.0 15.8 16.7 15.6 0.09 5.32 5.83 5.01 6.53 0.9 109.2 88.9 35.3 50.0 extensive beef cattle 30.5 21.3 15.4 11.1 26.3 -0.29 4.45 3.63 6.70 2.02 5.32 -2.4 40.6 54.7 45.5 15.2 107.8 mixed crops 21.3 13.2 19.7 18.2 5.3 0.43 7.14 7.66 8.05 7.04 28.12 2.8 90.4 56.6 48.9 192.9 26.1 mixed fruits 35.9 25.5 23.6 23.1 13.5 0.49 8.24 7.48 10.58 4.61 6.24 4.2 55.4 54.6 99.5 48.0 38.3 arable crops 31.0 17.9 16.8 37.3 19.3 0.77 9.36 6.50 5.47 13.9 7.79 6.7 83.7 43.1 57.4 59.5 59.8 sheep 26.9 13.3 21.8 40.0 0.98 6.42 5.34 4.17 4.50 7.2 52.5 81.6 93.2 261.7 dairy cattle 13.0 16.4 19.4 3.7 6.2 1.71 6.41 4.98 6.39 2.32 2.48 38.3 73.9 88.9 79.0 36.1 50.3 mixed crops livestock alt 50.9 36.3 40.3 33.3 60.0 0.09 1.18 0.12 0.24 5.66 0.13 0.9 17.1 1.5 4.6 125.8 3.3 extensive beef cattle 43.3 43.1 47.4 44.4 52.6 -0.29 0.39 -0.57 -0.24 -1.60 -0.33 -2.4 3.9 -6.1 -3.8 -21.6 -5.8 mixed crops 52.2 44.1 51.5 50.0 63.2 0.43 1.15 0.56 0.09 0.58 0.50 2.8 7.0 2.9 0.6 2.9 3.8 mixed fruits 37.9 32.8 34.5 41.5 37.8 0.49 1.02 1.29 -0.42 -1.79 -0.38 4.2 7.7 6.4 -4.1 -23.7 -10.5 arable crops 36.2 30.7 29.7 37.3 33.3 0.77 1.20 0.01 0.03 1.23 0.71 6.7 5.4 0.0 0.2 5.0 10.3 sheep 40.7 40.0 37.9 100.0 20.0 0.98 1.06 1.10 1.09 6.35 1.69 7.2 11.5 14.2 15.3 49.2 62.2 dairy cattle 39.0 39.2 32.7 11.1 23.7 1.71 0.79 0.89 0.89 -6.07 -0.74 38.3 15.8 12.4 15.7 -175.2 -15.1 mixed crops livestock wor 15.1 48.0 43.1 66.7 24.4 0.09 -4.02 -7.39 -8.29 -5.04 -4.68 0.9 -24.5 -30.8 -38.3 -40.6 -38.3 extensive beef cattle 26.2 35.6 37.2 44.4 21.1 -0.29 -2.69 -4.15 -4.64 -2.99 -6.07 -2.4 -13.8 -17.8 -20.8 -8.8 -11.9 mixed crops 26.5 42.7 28.8 31.8 31.6 0.43 -1.82 -2.19 -1.60 -4.04 -0.76 2.8 -25.1 -20.5 -23.3 -9.7 -56.5 mixed fruits 26.2 41.7 41.9 35.4 48.6 0.49 -4.46 -3.83 -3.33 -6.52 -2.99 4.2 -12.6 -13.4 -16.7 -48.0 -49.3 arable crops 32.8 51.4 53.4 25.5 47.4 0.77 -1.71 -2.30 -2.08 -8.12 -0.39 6.7 -18.9 -14.1 -12.7 -15.2 -10.5 sheep 32.3 46.7 40.2 40.0 0.98 -0.70 -0.86 -0.66 -0.15 7.2 -16.6 -12.3 -13.5 -21.0 dairy cattle 48.1 44.4 48.0 85.2 70.1 1.71 -1.37 -0.85 -0.59 -0.52 -3.17 38.3 -37.7 -21.2 -23.1 -19.0 -59.5 better (bet, alternating (alt), worse (wor), simple farming (sifa), organic (org), processing (pro), selling (dis), quality (qua), farmhouses (far). source: our elaboration of fadn data. 157financial performance of connected agribusiness activities in italian agriculture bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 ta bl e 7b is . g sp , r o i, in ve st m en ts a nd c a p ii a id p er fa rm in si m pl e fa rm in g an d in e ac h fin an ci al re su lt gr ou p of e ac h ag rib us in es s. g sp (0 00 € ) ro i in v (0 00 € ) c a p ii (0 00 € ) si f o rg pr o d is q u a fa r si f o rg pr o d is q u a fa r si f o rg pr o d is q u a fa r si f o rg pr o d is q u a fa r m ix ed c ro ps li ve st oc k be t 16 9, 8 31 3, 3 22 4, 3 12 3, 8 10 7, 2 -0 ,0 4 0, 04 0, 04 0, 00 0, 04 23 ,0 9, 6 6, 8 0, 2 2, 8 4, 0 8, 6 4, 7 3, 5 1, 4 ex te ns iv e be ef c at tle be t 10 5, 4 11 6, 4 16 0, 3 98 ,8 38 ,6 37 8, 9 -0 ,0 7 -0 ,0 2 0, 02 0, 02 -0 ,0 3 0, 02 11 ,3 5, 5 8, 1 5, 4 7, 6 16 ,3 4, 5 11 ,1 9, 4 4, 5 11 ,0 8, 5 m ix ed c ro ps be t 89 ,1 17 2, 5 15 6, 4 14 7, 4 50 1, 1 10 7, 2 -0 ,1 3 0, 06 0, 05 0, 04 -0 ,0 3 0, 01 7, 3 3, 0 1, 3 1, 0 1, 1 1, 2 1, 8 7, 1 3, 5 4, 8 8, 9 7, 4 m ix ed f ru its be t 95 ,2 15 5, 1 15 5, 3 31 1, 8 12 9, 2 66 ,4 -0 ,0 1 0, 03 0, 03 0, 03 0, 01 0, 04 18 ,7 3, 3 2, 8 5, 2 11 ,4 7, 2 1, 4 8, 0 4, 1 6, 6 5, 3 2, 8 a ra bl e c ro ps be t 11 5, 2 21 6, 6 12 4, 3 15 5, 8 16 6, 2 93 ,2 -0 ,0 8 0, 04 -0 ,0 2 0, 00 0, 02 0, 01 18 ,4 4, 1 4, 4 8, 8 0, 2 15 ,0 3, 8 12 ,7 7, 9 7, 8 6, 9 14 ,0 sh ee p be t 67 ,8 11 8, 9 20 5, 6 24 1, 2 66 2, 4 -0 ,0 3 0, 02 0, 07 0, 05 0, 03 9, 4 6, 6 9, 6 8, 2 20 ,5 5, 5 19 ,8 10 ,6 5, 5 17 ,0 d ai ry c at tle be t 26 9, 5 18 8, 5 25 1, 5 20 3, 6 22 1, 4 10 7, 6 0, 00 0, 04 0, 04 0, 05 0, 00 0, 00 34 ,2 12 ,3 17 ,5 11 ,8 0, 0 17 ,7 5, 3 10 ,6 8, 5 12 ,2 0, 0 28 ,3 m ix ed c ro ps li ve st oc k a lt 16 9, 8 86 ,2 85 ,5 10 3, 0 11 8, 0 13 2, 6 -0 ,0 4 0, 00 -0 ,0 1 0, 00 0, 10 0, 01 23 ,0 15 ,2 14 ,5 21 ,4 8, 1 29 ,2 4, 0 10 ,9 7, 0 8, 7 2, 7 10 ,6 ex te ns iv e be ef c at tle a lt 10 5, 4 65 ,0 79 ,9 80 ,2 63 ,4 10 3, 1 -0 ,0 7 -0 ,0 4 -0 ,0 3 -0 ,0 3 -0 ,0 3 -0 ,0 2 11 ,3 12 ,7 17 ,1 21 ,5 0, 1 20 ,1 4, 5 12 ,4 5, 5 9, 2 19 ,4 12 ,6 m ix ed c ro ps a lt 89 ,1 95 ,3 68 ,3 72 ,5 51 ,3 40 ,7 -0 ,1 3 -0 ,1 2 -0 ,0 6 -0 ,0 1 -0 ,0 3 0, 01 7, 3 6, 0 8, 4 11 ,1 4, 1 7, 7 1, 8 8, 3 4, 7 6, 7 9, 0 5, 3 m ix ed f ru its a lt 95 ,2 87 ,4 72 ,9 78 ,4 10 8, 4 10 9, 6 -0 ,0 1 -0 ,0 3 -0 ,0 1 -0 ,0 2 -0 ,0 3 0, 00 18 ,7 14 ,5 10 ,7 17 ,6 63 ,3 46 ,4 1, 4 8, 6 3, 8 4, 2 3, 1 2, 2 a ra bl e c ro ps a lt 11 5, 2 61 ,2 60 ,9 67 ,6 56 ,4 11 8, 2 -0 ,0 8 -0 ,0 4 -0 ,0 5 -0 ,0 3 -0 ,0 6 -0 ,0 3 18 ,4 6, 2 10 ,9 7, 2 7, 0 13 ,3 3, 8 8, 8 3, 1 5, 7 4, 4 11 ,0 sh ee p a lt 67 ,8 80 ,9 10 7, 7 91 ,1 10 8, 0 67 ,4 -0 ,0 3 -0 ,0 1 -0 ,0 2 -0 ,0 2 -0 ,0 3 0, 00 9, 4 13 ,4 22 ,1 12 ,6 1, 2 0, 1 5, 5 12 ,4 12 ,8 14 ,0 7, 3 23 ,3 d ai ry c at tle a lt 26 9, 5 20 9, 3 14 3, 5 15 4, 1 11 6, 2 12 7, 4 0, 00 0, 00 0, 00 0, 00 -0 ,0 2 0, 01 34 ,2 45 ,1 25 ,5 18 ,6 21 0, 1 63 ,4 5, 3 24 ,6 14 ,2 15 ,8 64 ,7 10 ,5 m ix ed c ro ps li ve st oc k w o r 16 9, 8 60 ,5 38 ,0 32 ,9 45 ,8 15 ,4 -0 ,0 4 -0 ,1 8 -0 ,1 2 -0 ,1 0 -0 ,0 2 -0 ,1 8 23 ,0 11 ,0 13 ,3 21 ,3 26 ,6 28 ,1 4, 0 3, 6 1, 4 1, 4 2, 6 1, 5 ex te ns iv e be ef c at tle w o r 10 5, 4 32 ,4 38 ,4 40 ,2 20 ,1 16 ,7 -0 ,0 7 -0 ,1 1 -0 ,0 9 -0 ,1 0 -0 ,1 8 -0 ,1 4 11 ,3 11 ,7 5, 5 8, 5 1, 8 1, 7 4, 5 5, 5 1, 8 2, 8 3, 9 3, 4 m ix ed c ro ps w o r 89 ,1 42 ,7 30 ,6 42 ,9 20 ,4 80 ,8 -0 ,1 3 -0 ,2 4 -0 ,1 7 -0 ,1 1 -0 ,3 9 -0 ,1 9 7, 3 49 ,5 18 ,4 15 ,5 1, 0 59 ,2 1, 8 4, 2 1, 6 0, 8 1, 9 4, 6 m ix ed f ru its w o r 95 ,2 27 ,8 27 ,0 32 ,6 73 ,0 71 ,2 -0 ,0 1 -0 ,2 2 -0 ,1 6 -0 ,1 1 -0 ,0 6 -0 ,0 1 18 ,7 2, 7 4, 2 5, 9 50 ,7 78 ,4 1, 4 2, 9 1, 0 1, 6 1, 2 0, 7 a ra bl e c ro ps w o r 11 5, 2 51 ,1 36 ,5 43 ,1 21 ,2 55 ,4 -0 ,0 8 -0 ,1 3 -0 ,1 4 -0 ,0 8 -0 ,0 6 -0 ,0 7 18 ,4 24 ,3 7, 4 8, 4 3, 1 8, 2 3, 8 5, 5 1, 8 2, 2 1, 1 3, 5 sh ee p w o r 67 ,8 53 ,6 41 ,7 45 ,6 15 1, 2 -0 ,0 3 -0 ,0 8 -0 ,0 9 -0 ,0 7 -0 ,0 4 9, 4 17 ,9 7, 9 9, 2 37 ,9 5, 5 9, 9 3, 4 6, 6 18 ,8 d ai ry c at tle w o r 26 9, 5 94 ,9 10 2, 5 15 9, 5 23 7, 5 74 ,0 0, 00 -0 ,0 2 -0 ,0 3 -0 ,0 2 -0 ,0 2 -0 ,0 1 34 ,2 42 ,5 22 ,1 30 ,6 67 ,7 76 ,3 5, 3 18 ,1 14 ,3 20 ,3 27 ,0 9, 7 be tte r ( be t, a lte rn at in g (a lt ), w or se (w o r) , s im pl e fa rm in g (s if a ), or ga ni c (o rg ), pr oc es sin g (p ro ), se lli ng (d is ), qu al ity (q u a ), fa rm ho us es (f a r) . so uc e: o ur e la bo ra tio n of f a d n d at a. 158 bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 gabriele dono, rebecca buttinelli, raffaele cortignani whose average f/d value is below the financial profitability threshold. the ability to produce liquidity is the most important factor in determining the financial sustainability differential between farms in simple farming and those engaged in the various types of agribusiness. among the latter, organic shows the best situation, with analogous share of the three groups and the lowest percentage of farms in worse. processing remains above the sustainability threshold even in alternating. selling, quality and farmhouses do not share this condition and show an average unsustainable condition. farmhouses shows the largest percentage of cases in unstable and worse. the results on the structural and economic variables provided interesting insights into the conditions linked to the different levels of financial sustainability in the various groups. high values of the f/d index are associated with high levels of gsp and roi. the group better reaches values of 3-4 times higher for these variables than the unstable and worst. better achieves greater sustainability even than simple farming. its higher level of gsp suggests that the link between the operational dimension and financial difficulties of agribusiness farms should be studied. the literature deals extensively with this topic. meraner et al. (2015) claim that an increase in the economic dimension affects the likelihood of undertaking transformation activities. mcnamara and weiss (2005) also argue that as farm size increases, on-farm income diversification is more likely since the decline of marginal yields favours the allocation of farm resources towards more profitable activities. iibery (1991) and mcnally (2001) reach the same conclusion. according to garcía-arias et al. (2015) this happens because larger farms have more resources to devote to non-typical activities. lakner et al. (2018) note that in austria and switzerland, diversification increases farm production, which in turn strengthens the stability of agricultural production. instead, diversification negatively affects technical efficiency in some territories, while it improves it in others. clearly, the commitment to new activities other than simple agriculture also involves profound changes in the corporate structure and organization, as indicated by salvioni et al. (2020). this suggests that many agribusiness farms might still be in an evolutionary phase that does not yet allow for significant levels of production, efficiency, and profitability. in our sample the farms engaged in agribusiness are mostly small and most of them obtain worse results of the same type in simple farming. this gap is more marked in tfs in financial difficulty, while in other tfs even small farms with alternating results are financially sustainable. large farms are in a clear minority, even if in the group better their sustainability is very high. this picture of limited financial sustainability of small farms engaged in agribusiness is in partial contrast to the results of the studies cited above. the greater commitment in investments does not correspond to greater public aid. the amount of cap ii payments is, in fact, significantly lower for alternating, worse, and simple farming, despite the greater investments undertaken. this divergence could be due to the time gap between the time of the investment expenditure and the reimbursement provided for by the cap ii aid mechanisms, whose payments are linked on project progress. still, our analysis is based on a three-year time frame; hence, even in the presence of that time gap, it should capture at least a part of the aid associated with these investments. in any case, the condition of greater financial difficulty of the farms in the stage of investments raises the question of the effectiveness of this table 8. percentage distribution of agribusiness farms by financial result groups, size class and by groups of tfs, with relative value of the f/d index. % on agribusiness farms f/d value bet alt wor total bet alt wor total f/d < fist small 19.2 37.6 41.4 98.2 5.68 0.28 -1.76 0.21 medium 0.9 0.4 0.0 1.4 8.70 4.81 7.67 large 0.4 0.02 0.02 0.4 7.83 -5.07 1.12 6.94 total 20.5 38.1 41.4 100.0 6.25 0.39 -1.74 0.60 f/d > fist small 24.5 37.9 34.6 96.9 3.06 1.31 -0.99 1.00 medium 1.3 1.2 0.1 2.6 3.71 -0.24 0.83 2.34 large 0.4 0.03 0.1 0.5 10.34 1.00 3.18 8.64 total 26.2 39.1 34.7 100.0 3.75 1.22 -0.89 1.34 source: our elaboration of fadn data. 159financial performance of connected agribusiness activities in italian agriculture bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 support system, which does not appear to contribute to financial sustainability in that specific phase. the issue needs to be investigated also by examining in detail the composition of the cap ii aid, which includes support for activities that do not require investing in depreciable capital. in any case, this evidence agrees with the conclusions of boncinelli et al. (2018) that highlight the absence of any relationship between rdp payments and diversification. higher endowments of depreciable are found precisely in the groups alternating and worse in which the fcfe values are lower or even negative. instead, for all agribusiness activities, the group better shows lower endowment of these capital, along with lower depreciation levels which reduce the denominator of f/d. on the other hand, there is the impact of higher depreciation levels in increasing the numerator of the f/d ratio, favouring greater generation of fcfe. dono et al. (2021) concluded that in the studied period these capitals show little capacity to increase farms financial sustainability. our evidence seems to agree with those conclusions, which suggests that in acquiring new capital it will be useful to verify their impact on the productivity. this is mainly true for organic, quality and farmhouse whose endowments of depreciable in worse and alternating are much higher than in better, but negative differences in fcfe are also greater. the literature also deals with the conditions for the development of commercial relationships with buyers, suppliers, and banks: in the cash flows analysis they also contribute to transforming the value of production into greater liquidity through the δ wcc. in this regard, pölling and mergenthaler (2017) claim that in direct sales activities an important role is played by proximity to urban centres, although with differences due to the farm size and technical-economic orientation. besides, the wide variability in the results agrees with the statements of bauman et al. (2018) on us farms engaged in direct selling that show how results vary under different management conditions, types of market and farm location areas. our study did not consider the location of the farm which, especially in disadvantaged areas, far from the most dynamic agricultural markets, could lead to less intensive activities or activities related to tourism, such as farmhouses. in this regard, a third of the farmers interviewed by de rooij et al. (2014) believes that multifunctionality is best located in areas “without a future for conventional farming”. the literature on farm diversification pays close attention to the performance of the farmhouses. bagi and reeder (2012), show that the global net income of the average agritourism farm was small relative to all other farms in 2007. mastronardi, et al., (2011) claim that the profitability of simple farming, especially when specialized in tree crops, is more than double than in farmhouses. still, according to giaccio et al. (2018a; 2018b) farmhouses can increase their income also engaging in other activities such as selling, organic and typical foods production, catering and wine tasting services, access to environmental assets, such as forest areas, and provision of leisure services, such as cultural and sport activities. dries et al. (2012) and khanal (2020) also claim that there are synergies between structural and farm diversification activities. on the contrary, khanal and mishra (2014) affirm that small farms obtain better results by undertaking both farmhouse and off-farm work7. giaccio et al. (2018a) show that farm income decreases significantly as the number of family members employed on the farm increases. at the same time, according to lupi et al. (2017) farms that employ more (non-family) work are more likely to invest in agritourism businesses. we have not explored the links between diverse agribusiness activities due to the limited number of observations with more integrated activities in the studies fadn sample. our evidence agrees with these results as we found the most difficult situations in farmhouses and quality, which are in less favourable condition even when they get the best results, as in better. moreover, in the latter activity there is a strong push towards technological innovation that requires great changes and investments that put the farms in difficulty at least momentarily. an example is the parmigiano reggiano supply chain, cited by arfini et al. (2019a). we believe that our conclusions are consistent with these considerations, as they depict a very dynamic situation, characterized by investments that have not yet reimbursed the costs incurred and are not yet fully operational. 5. conclusion we have found that the studied activities in the examined period constitute a dynamic group in which the farms with negative or unsustainable results are not doomed to bankruptcy. in fact, many of the agribusiness farms are engaged in major investments which, on the one hand, subtract liquidity from the cash flow and, 7 yet, this aspect should be investigated by remembering that the italian legislation on the subject is very different from that of other countries and the farm is considered an agricultural activity and can only be carried out by a farmer “through the use of his own farm in term of the connection of the farming, forestry and livestock raising activities within the holding “(law number 96/2006). 160 bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 gabriele dono, rebecca buttinelli, raffaele cortignani on the other, take time to express their potential or even become operational. all this outlines a situation in evolution, perhaps even rapid, therefore, to be verified with further in-depth studies in a short time. in this perspective, it should also be deepened that farms that approached agribusiness in the middle of the last decade, investing or in a growth phase in this period, have had probably a major stop due to the covid-19 crisis. this could have strongly affected their growth precisely at the moment of entry into operation of many of their investments. still, the evidence of financial difficulty faced by agribusiness farms that make more investments suggests that it would be useful to modulate financial aid in a different way. this could be partially disbursed at the beginning of the investment process to reduce the financial difficulties associated with its activation. in any case, it is also interesting to explore the perspectives of the group better that, at the moment, is investing less than the others. in particular, it can be asked whether these farms will be able to generate sufficient financial resources to renew their technologies when their capital runs out of payback. the situation of the farms in agribusiness deserves further investigation especially about the situation of the mixed and more extensive tfs that appear to be in financial difficulty according to dono et al. (2021). it is of interest to deepen the investigation on the possible contribution of agribusiness in improving the financial condition of these tfs. to investigate these aspects, given the scarcity of observations for activities such as quality and farmhouses, it would be desirable to increase the fadn sample, especially of agribusiness relevance. another aspect of interest concerns the results deriving from the aggregation of these activities: it leads to investigate the issues of the integration of functions along the value chain by farms. acknowledgement this research was carried out in the context of two projects funded by miur (ministry for education, university and research): department of excellence project (law 232/2016), and smarties project (prima 2019, section 2 – multi-topic). the funders had no role in the study design, data collection and analysis, decision to publish or manuscript preparation. references arfini, f.; antonioli, f.; cozzi, e.; donati, m.; guareschi, m.; mancini, m.c.; veneziani, m. 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(2015). between capital investments and capacity building—development and application of a conceptual framework towards a place-based rural development policy. land use policy. 46. 178-188. 10.1016/j.landusepol.2014.11.023. 163financial performance of connected agribusiness activities in italian agriculture bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 appendix a the following tables report key variables for the three financial result groups (better, alternating, worse) in each agribusiness, in each tf. the reported variables are: fcfe, depreciation (depr), f/d, amortizable capital (amoc), investments (inves), cap ii aid, gross saleable production (gsp), working capital variation (dwcc), return on investment (roi), percentage of farms in the financial result group over the farms of the agribusiness activity in the tf (% n). organic better than simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock 109,193 20,512 5.32 97,967 9,569 8,579 313,256 36,987 0.04 34.0 extensive beef cattle 40,575 9,127 4.45 17,596 5,544 11,069 116,419 45 -0.02 30.5 mixed crops 90,421 12,672 7.14 95,509 2,992 7,090 172,464 553 0.06 21.3 mixed fruits 55,440 6,725 8.24 36,501 3,319 7,997 155,074 -380 0.03 35.9 arable crops 83,680 8,941 9.36 54,566 4,148 12,742 216,572 4,252 0.04 31.0 sheep 52,541 8,187 6.42 24,724 6,644 19,846 118,887 977 0.02 26.9 dairy cattle 73,903 11,531 6.41 86,511 12,323 10,593 188,461 3,414 0.04 13.0 vineyards 91,709 11,499 7.98 52,548 8,244 7,651 191,196 3,901 0.04 28.5 mixed livestock 261,754 8,091 32.35 84,251 5,194 20,724 333,967 110,174 -0.01 53.1 greenhouse vegetables 0.0 olive growing 26,066 6,528 3.99 61,104 1,055 8,475 77,641 -182 -0.06 54.3 swine 0.0 other 133,535 9,695 13.77 21,941 4,129 2,325 391,676 -23,171 0.05 16.3 poultry 39,353 0 300 569 2,265 40,998 20,243 0.07 14.3 citrus fruits 68,206 6,085 11.21 21,609 1,786 16,400 161,915 66 0.04 34.1 open field vegetables 23,688 2,409 9.83 28,860 576 3,787 99,397 -848 0.05 12.5 fruits in shell 172,103 15,461 11.13 123,239 77,055 13,213 277,489 5,340 0.09 39.1 intensive beef cattle 0.0 total 66,164 8,491 7.79 50,585 5,039 10,724 156,493 5,096 0.03 32.1  organic alternating over simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock 17,108 14,524 1.18 177,659 15,166 10,894 86,153 4,563 0.00 50.9 extensive beef cattle 3,911 10,062 0.39 46,070 12,727 12,435 65,036 514 -0.04 43.3 mixed crops 6,956 6,039 1.15 56,972 6,006 8,263 95,321 1,840 -0.12 52.2 mixed fruits 7,679 7,539 1.02 62,409 14,549 8,610 87,370 1,661 -0.03 37.9 arable crops 5,383 4,480 1.20 63,378 6,181 8,822 61,184 -1,476 -0.04 36.2 sheep 11,527 10,850 1.06 68,431 13,368 12,379 80,934 -1,659 -0.01 40.7 dairy cattle 15,776 19,906 0.79 159,685 45,105 24,557 209,309 10,287 0.00 39.0 vineyards 25,842 14,839 1.74 141,147 28,124 5,819 193,831 -8,474 0.01 28.5 mixed livestock -68,795 15,394 -4.47 77,632 249,670 36,338 393,577 -2,658 0.00 46.9 greenhouse vegetables 122,678 60,799 2.02 419,479 17,667 0 359,083 3,897 0.15 33.3 olive growing -5,363 2,672 -2.01 20,878 9,710 5,419 36,509 -1,268 -0.14 39.2 swine 34,723 5,878 5.91 29,567 2,053 1,985 167,743 4,831 0.01 100.0 other 20,961 11,101 1.89 48,417 16,005 5,753 233,513 4,194 0.00 36.7 poultry 26,326 8,207 3.21 45,290 5,827 2,551 204,896 3,705 0.03 42.9 citrus fruits 26,762 5,297 5.05 34,080 3,034 9,998 110,320 -2,812 0.01 33.3 open field vegetables 51,623 8,086 6.38 73,211 10,473 12,217 245,748 -8,365 0.03 33.9 fruits in shell 39,010 7,066 5.52 33,545 7,974 6,738 110,803 -432 0.00 39.1 intensive beef cattle -7,387 0 0 642 15,644 52,139 -3,383 -0.14 12.5 total 9,528 8,450 1.13 67,821 17,894 9,961 106,017 -260 -0.02 38.9 164 bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 gabriele dono, rebecca buttinelli, raffaele cortignani organic worse than simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock -24,513 6,102 -4.02 61,924 11,036 3,562 60,453 -7,029 -0.18 15.1 extensive beef cattle -13,761 5,122 -2.69 46,679 11,651 5,525 32,360 152 -0.11 26.2 mixed crops -25,102 13,788 -1.82 138,688 49,515 4,201 42,735 28,541 -0.24 26.5 mixed fruits -12,582 2,821 -4.46 22,869 2,697 2,878 27,796 -921 -0.22 26.2 arable crops -18,884 11,045 -1.71 102,593 24,338 5,509 51,088 8,300 -0.13 32.8 sheep -16,554 23,816 -0.70 111,819 17,869 9,912 53,587 -2,268 -0.08 32.3 dairy cattle -37,685 27,452 -1.37 474,224 42,490 18,110 94,878 -16,880 -0.02 48.1 vineyards -39,157 8,966 -4.37 96,108 30,606 3,415 64,375 -12,851 -0.05 43.0 mixed livestock 0.0 greenhouse vegetables -10,333 220 -46.90 894 258 183 47,574 -19 -0.38 66.7 olive growing -8,468 374 -22.65 11,419 8,935 3,535 9,704 2,177 -0.92 6.4 swine 0.0 other -20,669 11,293 -1.83 80,328 14,388 1,848 90,521 -1,975 -0.07 46.9 poultry -18,823 17,204 -1.09 202,599 52,103 3,644 33,280 378 0.03 42.9 citrus fruits 3,003 7,647 0.39 41,900 1,836 17,079 106,604 -14,695 -0.40 32.5 open field vegetables 2,845 18,155 0.16 138,296 18,838 19,757 189,140 -6,639 -0.03 53.6 fruits in shell -1,628 928 -1.75 4,789 125 3,000 14,635 -21 -0.11 21.7 intensive beef cattle -5,473 13,965 -0.39 98,120 16,923 5,106 112,462 5,760 -0.02 87.5 total -18,154 11,666 -1.56 109,610 20,448 7,465 64,853 -1,511 -0.18 29.0 processing better than simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock 88,889 15,258 5.83 92,144 6,792 4,745 224,278 27,380 0.04 15.8 extensive beef cattle 54,724 15,065 3.63 80,792 8,082 9,412 160,292 -2,011 0.02 21.3 mixed crops 56,630 7,389 7.66 56,618 1,340 3,526 156,402 -1,734 0.05 13.2 mixed fruits 54,591 7,294 7.48 48,466 2,822 4,134 155,289 112 0.03 25.5 arable crops 43,136 6,641 6.50 51,009 4,379 7,940 124,253 885 -0.02 17.9 sheep 81,562 15,263 5.34 63,739 9,630 10,553 205,572 -1,497 0.07 13.3 dairy cattle 88,913 17,848 4.98 100,811 17,456 8,491 251,492 1,010 0.04 16.4 vineyards 130,368 20,798 6.27 129,997 20,506 6,817 341,806 5,144 0.05 17.6 mixed livestock 67,981 5,452 12.47 27,026 4,052 5,068 135,264 620 -0.01 18.1 greenhouse vegetables 110,337 13,041 8.46 71,338 206,941 0 1,227,583 5,195 0.11 5.6 olive growing 31,199 7,360 4.24 62,558 2,783 5,216 90,941 696 -0.04 51.6 swine 247,597 15,465 16.01 241,610 19,139 0 529,816 19,945 0.11 8.3 other 48,088 4,520 10.64 39,468 3,838 3,098 155,421 1,625 0.05 16.5 poultry 148,415 6,282 23.62 53,552 1,453 231 235,666 14,281 0.12 26.2 citrus fruits 91,413 7,872 11.61 25,833 2,573 19,427 202,941 35 0.07 20.6 open field vegetables 174,734 7,233 24.16 27,483 2,742 1,369 495,664 3,379 0.09 6.7 fruits in shell 166,380 15,417 10.79 129,450 78,364 3,473 294,791 2,920 0.14 42.9 intensive beef cattle 0.0 total 66,637 10,455 6.37 69,550 7,589 6,320 178,136 1,936 0.05 21.0 processing alternating over simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock 1,509 12,893 0.12 160,760 14,463 7,049 85,496 2,624 -0.01 36.3 extensive beef cattle -6,135 10,803 -0.57 80,844 17,070 5,465 79,945 -1,816 -0.03 43.1 mixed crops 2,881 5,140 0.56 44,313 8,425 4,711 68,257 387 -0.06 44.1 mixed fruits 6,407 4,980 1.29 36,882 10,723 3,819 72,857 -1,894 -0.01 32.8 arable crops 42 6,118 0.01 60,493 10,900 3,119 60,893 1,152 -0.05 30.7 165financial performance of connected agribusiness activities in italian agriculture bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 sheep 14,242 12,995 1.10 74,768 22,056 12,770 107,724 -3,697 -0.02 40.0 dairy cattle 12,447 14,022 0.89 89,683 25,468 14,158 143,487 -508 0.00 39.2 vineyards 25,015 12,530 2.00 117,510 23,602 5,170 181,251 -12,605 0.01 42.8 mixed livestock 1,135 8,505 0.13 78,242 2,182 6,276 88,829 -1,986 -0.02 36.2 greenhouse vegetables -7,503 5,057 -1.48 85,363 2,561 0 117,605 -7,652 -0.01 66.7 olive growing -744 3,262 -0.23 22,515 11,398 3,499 52,342 1,257 -0.09 38.8 swine 49,173 20,570 2.39 276,229 19,019 2,062 242,757 8,139 0.02 54.2 other 35,606 11,939 2.98 82,981 12,388 6,543 172,322 5,783 0.02 27.2 poultry 35,497 7,733 4.59 98,844 2,797 1,534 80,597 1,523 0.00 32.3 citrus fruits 27,544 5,886 4.68 65,145 6,392 4,045 114,355 9,471 -0.01 39.0 open field vegetables 57,435 9,656 5.95 109,927 22,290 8,926 221,115 8,717 0.00 34.8 fruits in shell 28,272 2,908 9.72 3,838 219 3,750 58,413 -179 0.06 42.9 intensive beef cattle 0.0 total 11,414 8,762 1.30 76,789 15,010 5,592 107,373 -1,630 -0.01 37.5 processing worse than simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock -30,766 4,162 -7.39 50,886 13,321 1,401 38,041 -759 -0.12 48.0 extensive beef cattle -17,789 4,285 -4.15 32,949 5,524 1,766 38,418 -1,616 -0.09 35.6 mixed crops -20,487 9,369 -2.19 91,870 18,435 1,588 30,619 -3,812 -0.17 42.7 mixed fruits -13,401 3,499 -3.83 32,768 4,228 989 27,011 -784 -0.16 41.7 arable crops -14,127 6,130 -2.30 62,271 7,419 1,776 36,535 -108 -0.14 51.4 sheep -12,270 14,348 -0.86 85,963 7,892 3,407 41,673 -1,627 -0.09 46.7 dairy cattle -21,153 24,952 -0.85 282,835 22,067 14,251 102,464 -10,187 -0.03 44.4 vineyards -24,222 6,449 -3.76 51,764 15,298 2,139 53,581 -6,794 -0.11 39.6 mixed livestock -23,110 3,962 -5.83 47,854 9,918 2,213 26,977 -1,301 -0.08 45.7 greenhouse vegetables -19,794 1,997 -9.91 8,296 406 0 58,705 569 -0.10 27.8 olive growing -11,929 1,032 -11.56 13,684 5,385 1,698 12,250 2,167 -0.72 9.6 swine -38,069 20,761 -1.83 230,148 46,772 2,109 160,185 2,501 -0.06 37.5 other -17,727 5,854 -3.03 47,832 12,962 862 50,489 -733 -0.08 56.3 poultry 694 15,298 0.05 201,017 16,952 2,256 36,209 5,410 -0.02 41.5 citrus fruits -5,130 4,089 -1.25 29,133 1,843 973 48,743 191 -0.08 40.4 open field vegetables 1,764 9,804 0.18 75,356 3,663 7,373 96,405 -3,077 -0.10 58.5 fruits in shell -1,818 70 -25.98 106 25 0 18,359 0 -0.16 14.3 intensive beef cattle -20,623 8,736 -2.36 74,741 10,050 3,116 70,165 -6,733 -0.05 100.0 total -16,819 7,773 -2.16 73,071 10,756 2,772 46,686 -2,456 -0.13 41.5 selling better than simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock 35,287 7,037 5.01 74,201 156 3,464 123,840 2,181 0.00 16.7 extensive beef cattle 45,472 6,787 6.70 23,718 5,356 4,513 98,766 9,022 0.02 15.4 mixed crops 48,920 6,080 8.05 48,530 983 4,791 147,406 -1,565 0.04 19.7 mixed fruits 99,538 9,407 10.58 67,452 5,219 6,639 311,843 -261 0.03 23.6 arable crops 57,363 10,489 5.47 100,370 8,764 7,837 155,844 2,277 0.00 16.8 sheep 93,209 22,328 4.17 116,462 8,212 5,513 241,199 4,961 0.05 21.8 dairy cattle 79,037 12,365 6.39 108,252 11,794 12,155 203,553 6,852 0.05 19.4 vineyards 102,697 18,920 5.43 103,390 25,351 5,488 278,632 -1,037 0.04 19.4 mixed livestock 46,498 5,996 7.76 40,998 3,541 2,829 82,530 1,758 -0.01 33.3 greenhouse vegetables 0.0 166 bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 gabriele dono, rebecca buttinelli, raffaele cortignani olive growing 42,654 8,014 5.32 83,858 4,107 5,035 74,964 8,167 -0.08 36.3 swine 0.0 other 49,953 4,162 12.00 30,663 1,804 4,469 180,961 -1,480 0.05 16.9 poultry 161,001 24,526 6.56 259,482 20,481 74 678,060 20,830 0.11 21.4 citrus fruits 0.0 open field vegetables 194,439 9,224 21.08 24,137 3,369 1,387 352,758 2,538 0.10 13.5 fruits in shell 0.0 intensive beef cattle 0.0 total 74,090 11,319 6.55 81,060 8,886 5,867 200,362 2,534 0.03 13.4 selling alternating over simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock 4,607 19,509 0.24 247,712 21,364 8,659 102,997 5,499 0.00 40.3 extensive beef cattle -3,772 15,679 -0.24 143,830 21,522 9,174 80,176 1,901 -0.03 47.4 mixed crops 627 7,283 0.09 73,373 11,085 6,722 72,529 1,919 -0.01 51.5 mixed fruits -4,144 9,923 -0.42 82,349 17,562 4,156 78,367 1,076 -0.02 34.5 arable crops 220 7,754 0.03 92,019 7,242 5,674 67,567 -858 -0.03 29.7 sheep 15,267 14,061 1.09 83,027 12,610 13,965 91,058 -3,390 -0.02 37.9 dairy cattle 15,707 17,615 0.89 115,054 18,623 15,793 154,130 -1,080 0.00 32.7 vineyards 12,873 14,236 0.90 152,928 25,930 2,989 123,473 -6,559 0.01 42.6 mixed livestock 6,127 7,771 0.79 76,646 4,548 5,763 63,373 -689 0.00 46.7 greenhouse vegetables -4,362 10,754 -0.41 187,712 25,215 0 168,187 -16,972 0.01 66.7 olive growing -6,734 3,398 -1.98 30,025 6,773 5,150 28,368 1,733 -0.18 52.7 swine 121,918 36,840 3.31 445,716 27,693 0 251,157 28,730 0.03 38.5 other 32,057 6,662 4.81 64,538 12,007 7,889 157,321 26,134 0.02 25.3 poultry 32,180 8,342 3.86 86,852 5,633 0 85,363 557 0.07 42.9 citrus fruits 29,781 5,581 5.34 108,196 5,901 3,713 171,767 2,741 0.02 59.1 open field vegetables 133,639 9,781 13.66 158,482 24,991 19,066 209,821 21,826 -0.01 25.0 fruits in shell 0.0 intensive beef cattle 0.0 total 9,991 11,094 0.90 112,224 15,716 6,768 97,772 913 -0.01 38.9 selling worse than simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock -38,334 4,625 -8.29 74,060 21,312 1,353 32,919 2,468 -0.10 43.1 extensive beef cattle -20,787 4,480 -4.64 38,884 8,490 2,845 40,152 -2,870 -0.10 37.2 mixed crops -23,301 14,588 -1.60 143,512 15,459 785 42,938 1,432 -0.11 28.8 mixed fruits -16,718 5,026 -3.33 52,699 5,905 1,581 32,579 -4,254 -0.11 41.9 arable crops -12,714 6,106 -2.08 58,620 8,409 2,195 43,063 1,480 -0.08 53.4 sheep -13,503 20,539 -0.66 83,448 9,164 6,633 45,650 -1,265 -0.07 40.2 dairy cattle -23,088 39,004 -0.59 505,291 30,591 20,348 159,536 -17,221 -0.02 48.0 vineyards -26,399 6,910 -3.82 63,210 14,062 2,123 71,003 -7,539 -0.06 38.0 mixed livestock -15,415 3,513 -4.39 41,835 3,207 1,080 20,246 -436 -0.07 20.0 greenhouse vegetables -97,845 8,949 -10.93 90,302 86,673 0 52,271 -789 -0.07 33.3 olive growing -12,421 1,736 -7.15 39,410 8,004 2,295 15,975 2,542 -0.23 11.0 swine -14,009 4,143 -3.38 53,040 472 0 95,932 -14,341 -0.08 61.5 other -21,773 4,463 -4.88 52,850 13,770 637 65,308 -2,486 -0.07 57.8 poultry -20,796 4,459 -4.66 62,483 3,657 394 101,142 -6,458 -0.08 35.7 citrus fruits -597 5,124 -0.12 46,463 141 1,358 83,520 81 -0.03 40.9 open field vegetables -9,758 6,521 -1.50 56,911 3,699 3,409 51,962 -514 -0.11 61.5 fruits in shell 0.0 167financial performance of connected agribusiness activities in italian agriculture bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 intensive beef cattle -4,999 16,758 -0.30 166,096 6,388 2,163 126,560 -813 -0.03 100.0 total -19,564 9,891 -1.98 101,629 12,343 3,594 59,592 -3,133 -0.08 41.5 quality better than simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock 0.0 extensive beef cattle 15,209 7,518 2.02 2,166 7,601 11,000 38,631 1,766 -0.03 11.1 mixed crops 192,871 27,399 7.04 190,015 1,103 8,943 501,130 -3,392 -0.03 18.2 mixed fruits 47,970 10,407 4.61 42,141 11,370 5,263 129,228 3,026 0.01 23.1 arable crops 59,543 4,271 13.94 30,696 166 6,920 166,162 333 0.02 37.3 sheep 0.0 dairy cattle 36,092 15,578 2.32 36,714 0 0 221,359 27,069 0.00 3.7 vineyards 26,027 5,422 4.80 26,669 13,031 4,845 75,352 2,660 -0.07 8.1 mixed livestock 37,945 5,186 7.32 84,275 3,997 4,880 182,175 22 0.00 100.0 greenhouse vegetables 0.0 olive growing 28,106 4,756 5.91 31,899 2,606 4,326 79,933 3,562 -0.01 61.1 swine 0.0 other 0.0 poultry 0.0 citrus fruits 11,382 1,126 10.11 52 0 6,000 52,523 1,912 -0.31 25.0 open field vegetables 11,133 1,417 7.86 19,101 1,545 0 41,033 29 0.09 16.7 fruits in shell 20,227 626 32.31 0 0 0 48,803 -262 0.06 0.0 intensive beef cattle 0.0 total 46,217 6,872 6.73 41,423 5,199 5,312 132,653 2,189 -0.02 19.5 quality alternating over simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock 125,751 22,201 5.66 240,691 8,127 2,691 118,021 63,436 0.10 33.3 extensive beef cattle -21,557 13,448 -1.60 41,514 111 19,373 63,367 -11,508 -0.03 44.4 mixed crops 2,914 5,055 0.58 47,622 4,142 8,960 51,327 -925 -0.03 50.0 mixed fruits -23,707 13,275 -1.79 145,448 63,341 3,119 108,437 8,922 -0.03 41.5 arable crops 4,981 4,064 1.23 54,145 7,018 4,438 56,443 2,780 -0.06 37.3 sheep 49,173 7,746 6.35 49,055 1,221 7,326 107,976 9,335 -0.03 100.0 dairy cattle -175,232 28,851 -6.07 280,639 210,096 64,672 116,180 -11,380 -0.02 11.1 vineyards -914 6,345 -0.14 69,216 27,918 928 78,265 -9,769 0.01 45.0 mixed livestock 0.0 greenhouse vegetables 122,678 60,799 2.02 419,479 17,667 0 359,083 3,897 0.15 100.0 olive growing -6,572 4,817 -1.36 21,661 15,822 6,380 52,196 -59 -0.35 27.8 swine 0.0 other 44,401 8,571 5.18 8,025 17,449 0 761,955 3,593 0.08 25.0 poultry -1,599 0 0 166 0 261,351 3,218 -0.02 0.0 citrus fruits 5,680 1,755 3.24 12,835 0 1,500 42,782 -893 0.00 25.0 open field vegetables 112,228 7,696 14.58 65,191 4,999 15,734 264,272 24,885 0.04 41.7 fruits in shell 0.0 intensive beef cattle 0.0 total 2,868 8,917 0.32 85,271 28,871 4,770 103,062 -625 -0.02 39.5 quality worse than simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock -40,620 8,060 -5.04 116,804 26,607 2,615 45,815 -1,637 -0.02 66.7 extensive beef cattle -8,802 2,948 -2.99 17,765 1,836 3,929 20,100 -665 -0.18 44.4 168 bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 gabriele dono, rebecca buttinelli, raffaele cortignani mixed crops -9,655 2,392 -4.04 31,950 1,015 1,889 20,353 -489 -0.39 31.8 mixed fruits -48,012 7,367 -6.52 68,521 50,730 1,171 72,980 1,249 -0.06 35.4 arable crops -15,208 1,874 -8.12 27,857 3,053 1,051 21,169 -4 -0.06 25.5 sheep 0.0 dairy cattle -19,023 36,746 -0.52 538,991 67,693 26,983 237,489 -12,790 -0.02 85.2 vineyards -42,756 5,680 -7.53 36,530 41,608 927 60,935 -2,871 -0.06 46.9 mixed livestock 0.0 greenhouse vegetables 0.0 olive growing -9,662 163 -59.46 676 135 7,174 9,261 -358 -1.64 11.1 swine 0.0 other -79,015 25,764 -3.07 245,432 92,080 1,777 294,073 -64,573 -0.01 75.0 poultry 0.0 citrus fruits -3,535 4,852 -0.73 32,999 4,715 0 92,400 6,772 -0.02 50.0 open field vegetables -4,704 31,944 -0.15 237,877 32,417 4,251 355,549 -3,144 -0.01 41.7 fruits in shell 0.0 intensive beef cattle -43,921 7,617 -5.77 36,872 57,418 1,617 165,490 9,123 -0.05 100.0 total -35,660 11,303 -3.16 124,412 41,558 4,863 101,302 -6,058 -0.21 40.9 farmhouse better than simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock 49,983 7,658 6.53 84,241 2,807 1,406 107,202 -499 0.04 15.6 extensive beef cattle 107,815 20,280 5.32 107,942 16,347 8,544 378,880 -7,073 0.02 26.3 mixed crops 26,066 927 28.12 8,842 1,211 7,410 107,226 -3,954 0.01 5.3 mixed fruits 38,252 6,127 6.24 118,149 7,232 2,794 66,355 -1,215 0.04 13.5 arable crops 59,830 7,684 7.79 144,572 15,041 13,981 93,245 -4,904 0.01 19.3 sheep 261,716 58,210 4.50 276,053 20,478 17,014 662,378 6,050 0.03 40.0 dairy cattle 50,293 20,315 2.48 188,155 17,720 28,265 107,586 11,629 0.00 6.2 vineyards 143,257 18,216 7.86 125,046 9,352 3,561 341,652 -50,443 0.07 7.0 mixed livestock 44,940 9,433 4.76 137,639 2,510 2,841 44,754 695 0.02 50.0 greenhouse vegetables 0.0 olive growing 7,107 8,913 0.80 125,718 2,291 6,944 31,462 1,726 -0.03 73.3 swine 0.0 other 141,020 18,546 7.60 274,564 12,067 170 245,407 15 0.08 41.7 poultry 0.0 citrus fruits 0.0 open field vegetables 0.0 fruits in shell 0.0 intensive beef cattle 0.0 total 70,640 14,907 4.74 142,460 9,187 8,668 171,327 -2,907 0.03 17.2 farmhouse alternating over simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock 3,293 25,083 0.13 372,873 29,246 10,591 132,646 3,529 0.01 60.0 extensive beef cattle -5,787 17,719 -0.33 143,984 20,068 12,585 103,071 -3,036 -0.02 52.6 mixed crops 3,753 7,523 0.50 98,239 7,717 5,268 40,742 5,328 0.01 63.2 mixed fruits -10,534 27,722 -0.38 280,899 46,421 2,188 109,643 5,422 0.00 37.8 arable crops 10,323 14,586 0.71 168,903 13,273 11,008 118,242 2,503 -0.03 33.3 sheep 62,237 36,748 1.69 146,030 92 23,333 67,419 1,860 0.00 20.0 dairy cattle -15,110 20,433 -0.74 380,562 63,411 10,528 127,419 12,472 0.01 23.7 vineyards 15,204 15,159 1.00 201,807 26,725 2,057 110,558 -23,815 0.01 62.0 mixed livestock 57 10,535 0.01 97,329 3,796 7,089 25,627 -2,911 -0.01 41.7 169financial performance of connected agribusiness activities in italian agriculture bio-based and applied economics 11(2): 147-169, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12211 greenhouse vegetables 0.0 olive growing -8,316 1,612 -5.16 4,956 276 2,930 33,794 -120 -0.29 20.0 swine -8,855 1,853 -4.78 15,738 1,082 357 104,530 -4,545 -0.02 50.0 other 22,456 1,943 11.56 26,156 9,904 19 159,023 -5,203 0.08 33.3 poultry 0.0 citrus fruits 0.0 open field vegetables 41,056 7,097 5.79 173,061 14,071 138 55,430 16,973 0.10 40.0 fruits in shell 0.0 intensive beef cattle 0.0 total 9,991 11,094 0.90 112,224 15,716 6,768 97,772 913 -0.01 42.1 farmhouse worse than simple farming fcfe depr f/d amoc inves cap ii gsp dwcc roi %n mixed crops and livestock -38,318 8,179 -4.68 144,514 28,072 1,474 15,361 -495 -0.18 24.4 extensive beef cattle -11,946 1,968 -6.07 14,096 1,697 3,379 16,722 -3,706 -0.14 21.1 mixed crops -56,520 74,737 -0.76 752,915 59,157 4,630 80,847 -96,498 -0.19 31.6 mixed fruits -49,275 16,497 -2.99 244,037 78,449 689 71,227 -10,382 -0.01 48.6 arable crops -10,529 27,052 -0.39 424,340 8,171 3,452 55,426 -3,555 -0.07 47.4 sheep -20,992 144,062 -0.15 337,282 37,894 18,848 151,222 -21,282 -0.04 40.0 dairy cattle -59,479 18,757 -3.17 324,263 76,349 9,700 73,979 5,278 -0.01 70.1 vineyards -39,324 14,267 -2.76 178,816 21,264 1,334 135,022 -8,476 -0.03 31.0 mixed livestock -411,154 2,993 -137.37 443,043 396,241 588 7,831 3,001 -0.03 8.3 greenhouse vegetables 0.0 olive growing -19,688 1,619 -12.16 71,543 35,615 9,272 10,903 11,799 -0.19 6.7 swine -17,262 1,811 -9.53 9,367 1 0 5,320 -736 -0.27 50.0 other -23,333 15,840 -1.47 351,705 10,590 0 27,534 -23,596 -0.02 25.0 poultry 0.0 citrus fruits 0.0 open field vegetables -28,544 5,396 -5.29 90,163 20,090 57 30,692 913 -0.02 60.0 fruits in shell 0.0 intensive beef cattle -6,447 8,521 -0.76 51,330 13,644 6,124 126,006 -4,896 -0.04 100.0 total -42,442 25,121 -1.69 305,184 47,696 5,402 72,568 -10,259 -0.09 40.7 volume 11, issue 2 2022 firenze university press agriculture, food and global value chains: issues, methods and challenges margherita scoppola mapping global value chain participation and positioning in agriculture and food: stylised facts, empirical evidence and critical issues silvia nenci1, ilaria fusacchia1,2, anna giunta1,2, pierluigi montalbano3, carlo pietrobelli1,4 on the relationships among durum wheat yields and weather conditions: evidence from apulia region, southern italy marco tappi*, gianluca nardone, fabio gaetano santeramo a choice model-based analysis of diversification in organic and conventional farms andrea bonfiglio*, carla abitabile, roberto henke financial performance of connected agribusiness activities in italian agriculture gabriele dono*, rebecca buttinelli, raffaele cortignani pesticides, crop choices and changes in well-being geremia gios1,*, stefano farinelli2, flavia kheiraoui3, fabrizio martini4, jacopo gabriele orlando5 bio-based and applied economics 10(1): 19-34, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10855 bio -based and a ppl ied economics bae copyright: © 2021 r. filippini, e. marraccini, s. lardon. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: r. filippini, e. marraccini, s. lardon (2021) contribution of periurban farming systems to local food systems: a systemic innovation perspective. bio-based and applied economics 10(1): 19-34. doi: 10.36253/bae-10855 accepted: may 6, 2021 published: july 28, 2021 competing interests: the author(s) declare(s) no conflict of interest. editor: simone severini. orcid rf: 0000-0001-7949-6544 em: 0000-0002-2797-0758 sl: 0000-0001-6731-001x contribution of periurban farming systems to local food systems: a systemic innovation perspective rosalia filippini1,2, elisa marraccini3, sylvie lardon2 1 university of parma – department of management and economic science, parma, italy 2 university clermont auvergne, agroparistech, inrae, vetagro sup, territoires, f-63000 clermont-ferrand, france 3 up 2012-10-103 interact – unilasalle, beauvais, france corresponding author: rosalia filippini. e-mail: rosalia.filippini@unipr.it abstract. the debate on food security has highlighted the connection between periurban farming systems (pfs) and local food systems (lfs) for academic research. several researchers have called for in-depth analysis of the participation and impact of farmers in lfs, and the systemic innovation perspective can provide relevant analysis of the sustainability of this agro-food system. the objective of the current study is to investigate the integration of pfs into lfs from the systemic innovation perspective, by analysing systemic failures and merits that hinder or promote the contribution of pfs to lfs for farmers and commercial actors. the case study is the lfs of the urban pisa region in central italy. results show that farmers there are adapting to urban pressure, which improves the sustainability of their farming practices. at the same time, commercial actors have a commercial opportunity to include local farmers in their economic strategy. nevertheless, individual initiatives must be coordinated to support the sustainability of both lfs and pfs. this study thus developed an innovative method to identify systemic failures and merits for farmers and commercial actors to address sustainability strategies at the territorial level. keywords: adaptation, urban sprawl, local food network, systemic failures, italy. 1. introduction agriculture currently faces several systemic challenges, such as volatility in commodity prices, climate change, obstacles in generational turnover, and increasing labour costs, all of which influence how agriculture produces food. the population growth expected in urban areas has raised serious concerns about the ability of agro-food systems to feed people in the near future (fraser et al., 2005; godfray and garnett, 2014). according to the fao (2010), periurban farming systems (pfs) throughout the world need to be involved directly in relocating food systems to respond to the new challenges of food security. local food systems (lfs) thus appear central in addressing periurban farms’ contribution to local food security, even in the global north (opitz et al., 2015). http://creativecommons.org/licenses/by/4.0/legalcode 20 bio-based and applied economics 10(1): 20-34, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10855 rosalia filippini, elisa marraccini, sylvie lardon the literature defines pfs in several ways: geographically, pfs are simply agriculture performed near urban areas (filippini et al., 2018), while relationally, pfs are agriculture which has a functional relationship with an urban area (nahmías and le caro, 2012). several definitions lie between these two extremes. in this study, pfs are defined as farming systems near the main urban area of the case study, including territorial, production, social, and institutional factors. lfs also appear as a systemic phenomenon, in which different actors at different territorial levels integrate themselves in a sustainable way (kneafsey et al., 2013). analysis of lfs is complicated by the lack of a single definition of lfs (eriksen, 2013). in literature it has been defined as the emergence of high-quality and typical food (arfini et al., 2019), social and organisational relationships among actors (renting et al., 2003), a specific geographic area near consumers (horst and gaolach, 2015), or food supply that provides food to urban dwellers (morgan, 2015; wiskerke, 2009). this study defined a lfs as the food system by which periurban farmers can provide food to consumers of the periurban and urban area. the systemic innovation (si) perspective may help to identify innovation mechanisms in farming systems, since “systems approaches to innovation are essentially an attempt to think through and analyse the nature and implications of the collective character of innovation” (edquist, 2002). farms that follow this approach do not normally innovate in isolation, but in interaction with other organisations, which involves different sectors and different types of know-how, from production to consumption, from policy and institutions to firms and private agents, and from technical to social skills. therefore, the si perspective is relevant in this context. some studies indicate that integrating pfs into lfs is a source of innovation in farming and food systems (houdart et al., 2012; paül and mckenzie, 2013). other studies highlight the need for further analysis of the actors of these innovations, their relations, and the infrastructure in which they act (bloom and hinrichs, 2011; filippini et al., 2016a, 2018; venn et al., 2006; watts et al., 2005). from a farming system perspective, the literature has called for in-depth analysis of the participation of periurban farmers in lfs. urban sprawl may influence farming practices and output (e.g. intensity, crop rotations) and thus farmers’ ability to change or expand their local commercial strategies. according to the literature, understanding the pfs state “is a first step towards aligning agricultural and nutritional goals” (morrison et al., 2011 p. 498) in the development of a more sustainable agrofood food system (galli et al, 2020). from a food-chain perspective, lfs studies have focused more on analysing individual initiatives than on adopting a systemic understanding of lfs at the territorial level (bui et al., 2016; lamine et al., 2019). sonnino (2014) highlighted a lack of understanding of the “exchange nodes” in lfs networks, such as processors, wholesale markets, retailers, and others. from a territorial perspective, lfs studies have not adequately captured the “inherent complexity of the place” (duram and oberholtzer, 2010). according to several researchers (duram and oberholtzer, 2010; ilbery and maye, 2006; venn et al., 2006), lfs studies have focused mainly on relationships among actors who already participate in lfs, and less on the wider spatial and social dynamics of the place. this could be an obstacle for pfs studies, given the particular context in which periurban farmers work (filippini et al, 2020). the innovative process involved in integrating pfs and lfs thus requires further study to improve the sustainability of the innovation for both pfs and lfs. the aim of the study is to analyse the integration of pfs into lfs from an si perspective, which identifies systemic merits and failures of the innovation for both systems. this is essential to develop scenarios of transition to new forms of sustainability for lfs and pfs. to our knowledge, this is the first time that si has been applied at the territorial level to agro-food-system analysis. adopting a systemic perspective of territorial innovation processes makes it possible to apply an interdisciplinary, multilevel, and multi-actor approach, which is necessary to respond to claims made in the literature. the article is organised as follows. section 2 explains the theoretical background of pfs adaptation and issues related to the connection with lfs. section 3 describes the case study, the source of information and how si was applied in the present study. section 4 shows results of applying the si perspective to periurban farmers’ participation in lfs. section 5 discusses insights of the study and the methodology in light of the current literature. finally, the last section provides concluding remarks and offers ideas for future studies. 2. theoretical background 2.1 contribution of periurban farming systems to local food systems pfs are characterized by specific environmental, economic, and social pressures (tolron, 2001). in areas of urban sprawl, land-use change is rapid and results in agriculture competing for natural resources, such as water and land. several land issues influence pfs crop21contribution of periurban farming systems to local food systems: a systemic innovation perspective bio-based and applied economics 10(1): 21-34, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10855 ping systems, such as degradation of fertile land (eea, 2006), fragmentation of agricultural areas, and the lack of access to land. urban pressure increases the price of land, which results in land insecurity, for which the solution is shorter leases in anticipation of more profitable urbanisation, and in land abandonment and reforestation (tolron, 2001). several positive externalities of farming practices, such as flood control or ecosystem services, are limited, while negative externalities are exacerbated, such as production of noise or odours (heimlich and anderson, 2001). the agricultural economy and political representation become marginal in areas where agriculture’s position traditionally predominated. new social conflicts result from the coexistence of different community interests and activities in the same area, and at the same time farmers face new expectations for farming systems and new and varied demands from the urban system (darly and torre, 2013; heimlich and barnard, 1992; henderson, 2005). in this situation, farming systems may adapt to a particular territorial context that is characterised by continuous change. the literature mentions adaptability as one of the attributes of sustainable farming systems (lópez-ridaura et al., 2005). adaptability is also identified as a main characteristic of pfs (clark et al., 2007; soulard et al., 2017; tolron, 2001). clark et al. (2007) described the “pattern of adaptation” of pfs as changes in the cropping system as well as on-farm diversification. periurban farmers must adapt their cropping systems to spaces into which a complex urban environment encroaches. adaptation requires changing the intensity of production, as well as increasing or decreasing certain types of production (diaz-ambrona and maletta, 2014; filippini et al., 2016b; wortman and lovell, 2013). all of this occurs in a context that includes an uncertain future threatened by climate change and land insecurity (diaz-ambrona and maletta, 2014; wortman and lovell, 2013). on-farm diversification entails changing a farm’s structure to an urban-oriented context to meet urban expectations and demands for food production and services, as well as to minimise conflicts with urban neighbours. this adaptation helps to maintain agriculture in periurban areas. based on the literature, recognising the multi-functional character of pfs has promoted several agriculture services, such as the development of short food-supply chains and/or social and educational farming, as a way to maintain agriculture in periurban areas (filippini et al., 2020; zasada, 2011). by definition, pfs are adaptive farming systems which tend to be heterogeneous: different farming strategies are implemented, which reflects that agriculture has multiple responses to the demands of nearby urban areas (soulard et al., 2017). moreover, heterogeneity occurs within each pfs: empirical analysis has observed that farms tend to combine the commercial strategies of local and non-local food chains (filippini et al., 2016a). in addition, farms may adapt their practices to urban pressure, but not their commercial strategies (filippini et al., 2016a), especially when conventional markets are more convenient (brunori et al., 2016; touzard et al., 2016). according to the literature, pfs should be more prone to participate in lfs, as proximity increases access to local urban markets and market niches (jarosz, 2008), decreases transaction costs due to more direct social contact between producers and consumers, and decreases distribution and transportation costs (holloway et al., 2007). more generally, periurban agriculture is perceived as an innovative context that promotes the development of lfs (ruaf, 2008). participating in lfs is seen as a form of innovation and “smart agriculture” (corsi et al., 2021) empirical studies have shown that commercialisation is one of the few factors involved in adapting to urban pressure in french periurban areas (houdart et al., 2012). however, few researchers temper the positive role of periurbanisation in the emergence and development of lfs. the locally grown high-quality food that consumers demand requires more labour and investment in diversification, which increases production costs, and requires more available land, which is hindered by the same process of periurbanisation (jarosz, 2008). paül and mckenzie (2013: 94) even argue that short food supply chains in periurban areas “are only possible if farmland preservation is guaranteed, and that the former does not come as a direct consequence of the latter”. farmers may experience issues when adapting to urban pressure and demands, such as discovering that adaptation is not attractive or that they do not adapt effectively. one potential response is to simply move their agricultural activities, given the income that selling periurban land guarantees, and stop farming in periurban areas (pascucci, 2007). thus, the adaptability of pfs to lfs should be not taken for granted. similarly, if farmers do not produce locally, the economic actors who contribute to the value chain, such as small butcheries, slaughterhouses, and groceries, may also face a crisis, which could decrease the sustainability of lfs (filippini et al., 2020). to date, few studies have focused on the processors and commercial actors who interact with farmers in lfs. according to bloom and hinrichs (2011), studies underestimate the contribution of local actors in the traditional conventional value chain to the development of a reliable lfs. their analysis criticised the frustration of urban retailers and distributors when making direct commercial agreements with farmers 22 bio-based and applied economics 10(1): 22-34, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10855 rosalia filippini, elisa marraccini, sylvie lardon who participate in lfs, even though the participation of these actors can help scale up the lfs and thus improve the local food economy, which is the ultimate purpose of the lfs movement. according to sonnino (2014), a more effective connection between urban and rural areas in lfs requires understanding the role of distributors in the value chain, who connect farmers to consumers, as well as the role of coordination and governance of lfs. 2.2 application of the systemic innovation perspective to the contribution of farming systems to lfs according to knickel et al. (2009), the gap between the need for change and farmers’ willingness to adapt exists because innovation policies and research have applied a linear approach from innovators to farming and thus have failed to address the relevant issues in farming systems that influence sustainable innovation. doing so requires a more systemic approach to innovation that extends beyond the farmer who applies the innovation, to involve many interrelated actors for whom innovation has a performative character: “supermarkets that introduce self-service tools for fruit and vegetables reconfigure the roles between consumers and retailers’ personnel, and imply learning processes of all the involved actors. retailers also play a key role in shaping production systems, as they are able to impose their standards on national production systems” (knickel et al., 2009: 138). researchers define the inclusive character of systemic approaches as a “co-evolutionary process”, as it requires “combined technological, social, economic and institutional change” (klerkx et al., 2012). in this approach, innovation is perceived as a process characterised by continuous feedback mechanisms and interactive relations among the actors within the framework of specific institutional rules. consequently, innovation is an evolutionary process that always changes and adapts itself, and is not based on the concept of optimality (edquist, 2002). innovation implies a complex system of strategies, organisation, and hybrid networks that extends beyond the use of new technology or the definition of a new process (knickel et al., 2009). when applying a systemic perspective, those who research farming systems recommend a multi-actor, multi-level, and inter-/trans-disciplinary approach, due to its inclusive characteristics (klerkx et al., 2012). multi-level implies including different elements at the same scale, while multi-scale considers them at different scales (cash et al., 2006). inter-/trans-disciplinary is the progressive integration of different disciplines and sectors, such as academia and actor experts (vandermeulen and van huylenbroeck, 2008). in farming systems analysis, lamprinopoulou et al. (2014) and kebebe et al. (2015) applied a specific si perspective that identifies si structures and functions, based on contributions of woolthuis et al. (2005), wieczorek and hekkert (2012), edquist (2001), and weber and roharacher (2012). the objective was to evaluate the policies that sustain technological innovation to identify the specific failures that hinder innovation. wieczorek and hekkert (2012) used an si structural-functional analysis to determine si structures and functions. si structures are the elements that drive the innovation: actors, interactions, infrastructure, and institutions. the actors are the agents of the innovation, and the innovation process emerges from their interactions. they move in a particular infrastructure which includes physical (e.g. roads and territorial elements), financial, and knowledge infrastructures. the institutions are the written or unwritten rules which the actors should respect. si functions are the processes that enable the innovation to perform well. weber and rohracher (2012) define systemic functions as the “basic ‘activities’ or key processes required for successful system growth and performance of the innovation system”. researchers have identified several types of functions, depending on the case study and purpose of the study: knowledge diffusion, market creation, network exchanges which function at the micro-level, reflexivity, directionality, and policy coordination at the macro-level to effect transformations (lamprinopoulou et al., 2014). analysis of systemic functions complements a structural focus as it is process-oriented: structures make functions meaningful and vice versa, which supports the concept that a structural element must always be changed for policies to enable or strengthen functions. according to lamprinopoulou et al. (2014) “an integrated structural-functional analysis provides a much more comprehensive overview of the operation of systems and the determinants that shape innovation trajectories”. in this framework, “systemic failures” – also called systemic problems or blocking mechanisms (wieczorek and hekkert, 2012) – are obstacles that hinder development of innovation. according to lamprinopoulou et al. (2014), identifying failures helps to identify “systemic merits” and thus instances when si functions are working well and driving effective innovation processes. the literature has focused especially on mapping structures and functions and identifying systemic failures in order to provide policymakers with a list of measures to fix problems and highlight positive aspects of the system (wieczorek and hekkert, 2012). to date, this approach has rarely been applied to farming systems or the agricultural sector. for example, kebebe et 23contribution of periurban farming systems to local food systems: a systemic innovation perspective bio-based and applied economics 10(1): 23-34, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10855 al. (2015) applied structural-functional analysis to small dairy farmers and concluded that systemic failures in developing innovation were related mainly to missing actors, limited capacity of existing actors, inadequate infrastructure, limited interactions between actors, infrastructure failures related to property rights, and bureaucratic processes and corruption, which hinder the development of innovation among smallholders. lamprinopoulou et al. (2014) developed a comprehensive structural-functional systemic framework of analysis to compare national agro-food systems in europe and identified differences in actors’ skills and in infrastructure, which identified policies to support the agricultural sector. thus, analysing application of this framework to a specific case study should provide new insights into the approach and help to develop effective policies at the territorial level. 3. materials and methods: how to understand the connection between periurban farming system and local food system pfs and lfs from an si perspective 3.1 case study the case study is the periurban region of pisa, a medium-sized city of 86,000 inhabitants in tuscany, central italy (fig. 1). the area consists of six municipalities that were associated until 2020 in the area pisana inter-municipality. the area includes the coastal plain of the arno river and a hilly area known as monte pisano (917 m a.s.l.). thus, it is geographically defined by the sea to the west and the hills and mountain to the north and north-east. the area includes a regional natural park that contains privately owned agricultural land. the area is also representative of urban sprawl: the population in figure 1. case study: the periurban area of pisa (source: filippini et al., 2020). 24 bio-based and applied economics 10(1): 24-34, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10855 rosalia filippini, elisa marraccini, sylvie lardon the main urban centre of pisa has decreased since the 1980s but has increased in the nearby small towns. the geographic borders have created a unique social identity unlike those of nearby areas. farming systems in the area seem to differ from others in the province of pisa: agriculture is not specialised or specific to one food chain, mixed farming systems still persist, and farmers rely on several types of commercial organisation (filippini et al., 2016a). these characteristics have helped researchers consider different types of primary production. previous research has shown that lfs management is one of the most challenging issues in the area, as acknowledged by farmers and institutional actors (marraccini et al., 2013). farming systems in the area have followed the main trend of mediterranean agriculture: the number of farms decreased from 1990-2010 (-56%), especially for vegetables (-92%), while mean farm size increased slightly (marraccini et al., 2012). 3.2 interdisciplinary, multi-level, and multi-actor approach: selection and analysis of the sample the method is based on an interdisciplinary, multi-actor, and multi-level approach (vandermeulen and van huylenbroeck, 2008; klerkx et al., 2012; cash et al., 2006) (fig. 2). the study is based on integrating multiple disciplines, especially agronomy, economics, and geography. as it is focused on understanding the integration of pfs into lfs, it is first based on analysing farms and farming systems, given the production and commercialisation conditions of farms in the periurban area. then, the study analyses the relationships between the pfs and the rest of the local agro-food system (i.e. the lfs) by interviewing commercial actors. a multi-level approach is applied in the study, first by analysing farming systems and then by extending it to a more territorial basis, especially the value-chain actors in the lfs. analysing relationships between pfs and lfs provides a multiactor perspective that considered both farmers and the first buyers of local agricultural products, such as processors and retailers, as well as the intermediate actors who aim to help organise the lfs. the analysis is based mainly on interviews with farmers and then with the lfs’s first buyers in 20142015. as the analysis did not include perspectives from consumers or institutions, the overall lfs was not considered (fig. 2). farms were selected to represent the territorial farming system of the periurban area of pisa as described in istat (2010). three criteria were used to select the farms: the main types of production, farm size, and the distance from the farmstead to the urban centre. the initial sample contained 58 farms oriented to types of production that represented the territorial farming system: extensive crops (65%), livestock (14%), vegetables (13%), and olive groves (8%). farmers were contacted directly in order to conduct semi-structured faceto-face interviews in their farm’s head office. interviews focused on the farming territory, crop management, farm management, land-use intensity, commercialisation, the farmer’s individual characteristics, the main type of production, and commercialisation constraints. based on these interviews, 19 commercial and intermediate actors were selected, the first buyers of farmers’ products (e.g. processors, groceries, supermarkets, farmers’ markets) and the intermediate actors who interacted with farmers. the processors included two cheese factories, one slaughterhouse, one butchery, three olive mills, figure 2. multi-level and multi-actor approach: elements of the local agro-food system. 25contribution of periurban farming systems to local food systems: a systemic innovation perspective bio-based and applied economics 10(1): 25-34, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10855 one wheat mill, and one general processor (mainly vegetables). the interviews also included three supermarkets, four groceries, and the coordinator of a farmers’ market. one farmers’ cooperative and a local livestock association were included as intermediate actors. interviews with the commercial actors included questions about their practices, the importance of local farming systems for their income and stock of products, and limitations of and opportunities for interacting with local farming systems. previous studies have observed that only 26 of the 58 farms in the sample participated in the lfs: 19% of them sold all production to the lfs, 65% maintained hybrid commercialisation between those of the lfs and non-lfs, and 15% sold less than 10% of their products to the lfs (filippini et al., 2016a). qualitative textual analysis of the interviews was performed to compare the actors’ viewpoints. 3.3 application of the si perspective to define the contribution of pfs to lfs in the context of si, si structures and functions must be defined according to the literature. first, components of si structure in the connection between pfs and lfs were identified: actors, interactions, infrastructure, and institutions (table 1). the actors were the farmers and other actors, and their interactions took place in the lfs. the infrastructure was the periurban area, which was defined according to geography (i.e. physical proximity to the urban area) and relations and organisations (i.e. a common background of action for actors). farmers were related because they shared the same production constraints. farmers and commercial actors were related because they shared the same interest in developing a lfs based on proximal relationships. political actors shared similar concerns as the other actors about territorial management and the creation of an urban food system. the institutions were the municipal, regional, and national rules which govern food production and the participation of farmers in the lfs. si functions are the dynamics that enabled innovation to perform well. among the functions identified in the literature, the market, networks, directionality, and policy coordination were selected to be evaluated in this study. the literature provides several lists of systemic failures. wieczorek and hekkert (2012) developed a list of “systemic problems” related to the four structures as a function of their presence and quality/effectiveness. lamprinopoulou et al. (2014) and weber and rohracher (2012) identified several failures in the literature and adapted them to the characteristics of their case studies. based on the literature, a variety of systemic failures was thus chosen (table 2). according to lamprinopoulou et al. (2014), systemic merits are identified directly by identifying specific opportunities and qualities of the same categories of the structures and functions identified. in other words, identifying the failures makes it possible to identify merits. thus, merits were also identified for each systemic failure. table 1. the structures of systemic innovation in this study. actors farmers, processors, supermarkets, groceries, intermediate actors interactions commercial relationships in local food systems infrastructure periurban area institutions municipal, regional, and national rules table 2. description of the systemic failures selected from the literature. systemic failure definition references actor problems absence of actors and/or lack of skills wieczorek and hekkert, (2012) institutional failures missing or malfunctioning of written or unwritten rules, which hinders innovation lamprinopoulou et al., (2014) infrastructural failures absence of physical, financial, and/or knowledge infrastructure lamprinopoulou et al., (2014); wieczorek and hekkert, (2012) interactions or network failures networks of actors are too dense; actors do not interact enough lamprinopoulou et al., (2014); weber and rohracher, (2012) market structure failuresimperfections in the markets or monopolies; unbalanced market power; information asymmetries lamprinopoulou et al., (2014); weber and rohracher, (2012) directionality failures lack of shared vision, and inability for collective coordination of fragmented agents of change lamprinopoulou et al., (2014) policy coordination failures lack of consistency among policies at different institutional levels (national vs. local) and among different sectors lamprinopoulou et al., (2014) 26 bio-based and applied economics 10(1): 26-34, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10855 rosalia filippini, elisa marraccini, sylvie lardon 4. results: a structural-functional analysis to understand system innovation based on the interviews, the farmers’ and commercial actors’ opinions on systemic failures and merits of their participation in the lfs were summarised (table 3). 4.1 actor problems/failures and merits according to farmers, the blocking mechanisms of the systems were related to whether participation in lfs requires them to develop specific know-how, such as the ability to sell directly to consumers and other small businesses, while most farmers usually sold their products to cooperatives or wholesale markets. some farmers indicated that another failure of integrating pfs into lfs was the need to address conflicts with urban residents who do not understand the daily work on farms, which produces noise, smell, etc. from the farmers’ perspective, it seemed contradictory that consumers want local food but seem to have difficulty understanding how agriculture works. among the actors’ merits, farmers considered that the presence of other people in the farm structure, who can help with sales or processing, was a driver for table 3. the most important systemic failures and merits of systemic structures and functions according to farmers and commercial actors in the periurban region of pisa (italy). systemic structure and function according to failures/problems/blocking mechanism merits/opportunities actor farmers need for specific know-how and manpower; conflicts with neighbours presence of family and structure commercial actors not enough farmers; problems with the quality of the product: season, diversification, packaging; lack of awareness of local agriculture and farmers local food supply meets consumers’ demands for local products institution farmers rules for diversifying the product; manpower; territorial management; lack of dialogue commercial actors the same rules for small and large businesses infrastructure farmers fragmentation of areas; production constraints; funding; knowledge proximity to urban consumers, proximity to crop storage commercial actors greater potential to reach and contact farmers in order to control and trace production interactions, networks farmers individual efforts to participate in local food systems; no network among farmers multiple diversified networks that diversify the risk; flexibility in responding to commercial actors’ demands commercial actors strategy of contacting the same farmers already involved in other networks; short-term organisation market structure farmers difficulty in being paid by small business; no markets for certain products market power; high demand for local food; paid immediately by final consumers; not always possible to predict and manage final consumers’ demands commercial actors information asymmetries; difficulties in negotiating the supply with farmers and with supermarkets for processors; consumer preferences for certain products change and do not reflect local traditional products high demand for local food directionality farmers short-term participation in commercial actors’ businesses; lack of shared and territorial vision commercial actors differing private visions that may compete with each other organising long-term food-chain projects to maintain shared visions policy coordination farmers different interpretations of rules among institutions commercial actors no coordination of private initiatives; lack of policies that promote local products 27contribution of periurban farming systems to local food systems: a systemic innovation perspective bio-based and applied economics 10(1): 27-34, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10855 developing a lfs. the lfs was also considered a way to increase family income. in this case, the lfs was seen as a way to diversify farm income by processing the primary products and/or developing space to sell. here, the farm family had great relevance: employing family members provides more flexibility in organising activities and costs less than hiring people. this was especially relevant when farmers perceived the lfs as a risky and unsure market. according to processors, groceries, and supermarkets, the greatest system failure was the lack of local farms, but it has different meanings for each of them. according to groceries and supermarkets, few farmers from whom to purchase products were available. in particular, they expressed a lack of knowledge about new farmers who could be included in the lfs and an inability to contact farmers. during the interviews, some retailers even asked researchers to provide the names of farmers who could sell their products. their usual strategy was to contact farmers who already participated in other lfs initiatives. some of these retailers did not seem to know about characteristics of the farming systems in the area. they were surprised when researchers showed them data on the decrease in horticulture production, which they considered as a typical farming system in the area. this was not the case for processors, who seemed familiar with local farming systems and their potential production, to the extent that they recognised the production capacity of each farmer. this was likely because such small-to-medium processors had worked in the area for a long time and had seen the farming system change, while some retailers were younger and less experienced. to processors, the lack of available local farms was due to the crisis of local farming and the decreased amount of agriculture, and thus primary production in the area, which influenced the economy. retailers, especially small businesses, highlighted the lack of diversified products offered, since most farmers provided the same seasonal fresh food, but few processed products. processors of fresh vegetables complained about the difficulty in verifying the quality of products, as vegetables may arrive without being properly cleaned or packaged. nevertheless, for both types of actors, the presence of local farms that participated in the lfs was a merit of the system. consumers today increasingly want local food, and local farming systems are a source, which generates more business. 4.2 institutional failures and merits institutional failures included the presence or absence of regulations that hinder the contribution of pfs to lfs. most farmers experienced limitations related to regulations for processing primary products and the on-farm direct sale, including cheese from dairy production and jam and juices from fruits. according to farmers, the obstacles were related to meeting health regulations, as a large amount of money is necessary to convert the working environment. several farmers mentioned the lack of rules adapted to small farming businesses. other rules were related to the natural park in the production area: although it protects the use of land for agriculture, it also imposes strict environmental rules, which limits farm diversification. for example, to process sheep milk on the farm for direct sales, a farmer had to obtain permission from the local municipality, the local health authority, and the regional natural park, and each one imposed different and contrasting rules. the local health authority imposed strict health requirements for farm buildings according to european union (eu) regulations, while the regional natural park, whose main interest is to preserve nature in the territory, had refused permission for several years because on-farm processing could impact the natural equilibrium of the area. rules that influence diversification also influenced the involvement of seasonal workers. regulatory constraints included a large amount of bureaucracy and the time required for such investments. regulation failures for the small processors and retailers were related to the lack of regulations that are flexible and adapted to small businesses, because the same rules were applied to small and large businesses. 4.3 infrastructure failures and merits the infrastructure of the periurban area has both failures and merits for the contribution of pfs to lfs for farmers (table 2). the infrastructure failures included both physical and knowledge failures. physical infrastructure failures were related to urbanisation, which fragments the land and may influence crop rotations. increased transportation costs and the use of infrastructure caused farmers to stop growing irrigated crops far from the farmstead. the presence of infrastructure and fragmented agricultural area influenced the ability to graze land and rotate the grazing due to the difficulty in moving animals. knowledge failures were related in particular to information about financial opportunities that was fragmented among the many levels of institutions, from the region to the farmers’ union that helps farmers to request funds from the eu. another blocking mechanism was related to combining common agricultural policy (cap) funds and bank loans, for example to invest in innovation of the farm’s structure and process28 bio-based and applied economics 10(1): 28-34, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10855 rosalia filippini, elisa marraccini, sylvie lardon es. the guarantee of obtaining cap funds is not always sufficient for banks to loan money to farmers, which further slows the innovation process. among the merits, farming in a periurban area was one of the most important factors that allowed farmers to develop short food-supply chains. as stated in the interviews, the proximity to urban consumers drove innovation for several farmers, even those who sold less than 10% to the lfs. the innovation passed through the diversification of production and/or farming functions to sustain the farm economy. despite the uncertainties, which prompted farmers to maintain global food chains, the proximity to the urban area encouraged farmers to try some initiatives for specific products that were easier to sell to the lfs. proximity to an urban area also means proximity to crop storage for crops that are not sold locally. crops are usually stored near urban or periurban areas, as these areas are better connected to regional and national roads. commercial actors also mentioned the merits of the infrastructure. for both processors and retailers, the proximity to farmers decreased transportation costs because the farmers were nearby, and because periurban farmers were usually better connected to roads than farmers in marginal rural areas. when farmers are closer and more reachable, it is easier to remain in contact with them and monitor their products for final consumers, which provides an advantage for marketing and thus income. 4.4 interaction or network failures and merits the first network failure for farmers was that each farmer organised individual networks without coordinating his/her actions with other farmers or commercial actors. farmers thus invested much individual effort in developing each network. few farmers had established a farmers’ network in the area. one farmer, in addition to processing grain and selling bakery products on the farm, opened a shop in the city to sell products from other periurban farmers. in the interview, he explicitly affirmed his intention to establish a famers’ network initiative to promote the individual efforts of farmers. farmers in the sample did not even mention other farmers’ initiatives. another farmer organised direct onfarm sales of vegetables with another farmer, who provided what he did not produce himself, and vice versa. this mutual exchange of goods diversified the products offered to consumers. conversely, a merit of this individual-based lfs network was that it enabled farmers to be more flexible in organising networks and adapting their commercial strategies to the variety of opportunities and demands of processors and commercial actors. for example, one dairy producer mainly in conventional food networks sold some of his milk to the local sheep milk processor when shortages of sheep milk occurred in winter. another merit was that these individual networks allowed farms to diversify the economic risk of the lfs that they still perceived, as they can rely on several actors. from the viewpoint of commercial actors, especially retailers, however, this was a huge network failure. since farmers were in contact with other commercial actors, they had less interest in investing in a relationship with a specific grocery and provided products only with short-term perspective. moreover, the difficulty in including other farmers made the lfs a closed network. 4.5 market structure failures and merits one main market failure for farmers was that certain products, such as meat from dairy farming, fodder, other crops (e.g. winter wheat), and organic goat meat, had no local markets and needed to be marketed outside the local area. farmers maintained conventional food chains for these products, but with less profit. farmers also highlighted the difficulty in being paid by local commercial actors, especially restaurants, small groceries, and supermarkets. providing local farm products to supermarkets seemed possible only when products were collected and organised by an intermediary actor. one unique lfs initiative identified in the case study sample was the “carne bovina di pisa” a private meat label promoted by the local livestock producers’ association, which is organised as a non-profit organisation by the local livestock association to add value to local livestock production. from the viewpoint of supermarkets, the intermediation by the association allows cows to be monitored and traced, and it organises the supply effectively. from the farmers’ viewpoint, the association increases their bargaining power, which results in higher prices and guarantees that products are easier to sell in supermarkets. another market failure was the uncertainty some farmers expressed about the ability to sell all their products via direct on-farm sales and farmers’ markets. this may have been due to the difficulty in predicting and managing expectations of final consumers, especially those who were not well known. for farms located further from urban centres, direct on-farm sales depend on the flow of people on roads, which may be less frequent. in these cases, farms maintain conventional food chains to sell the remaining products. farmers stated that a major merit of the lfs was that final consumers paid 29contribution of periurban farming systems to local food systems: a systemic innovation perspective bio-based and applied economics 10(1): 29-34, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10855 them immediately, unlike when they sold products to a cooperative or wholesale market that belonged to global food chains. market failures for commercial actors depended mainly on network failures, which cause market failures, as when few farms participate in multiple lfs, each one can provide only a small amount of product, or the frequency of production is highly irregular. consequently, commercial actors, especially retailers, mentioned difficulty in negotiating contracts with farms. thus, farmers in this case study seemed to have the market power in the lfs, deciding how much, when, and to whom to sell their products. this market power resulted from another market failure: information asymmetries between retailers and farmers (i.e. a lack of information about the farmers who can participate in lfs). another difficulty for small processors was organising supply to supermarkets. lfs products were usually distributed by the same processor who negotiated the supply to all supermarkets. small processors were also concerned about consumers’ expectations and preferences for a product; for example, fresh cheese was increasingly sought after, but it is not a typical product in the area. a high demand for local products was the main market merit for commercial actors. the certainty that consumers are sensitive to local food for its higher quality, traceability, lower environmental impacts, and ability to sustain the local economy was a strong driver for commercial actors to invest in lfs relationships. the pfs provided retailers and supermarkets with an advantage with consumers for developing new markets. 4.6 directionality failures and merits directionality failures referred to the lack of a shared vision about the future of the local agro-food system. farmers recognized a lack of shared vision because they organised individual initiatives and because there was little recognition of their diversity and complementarity. there was also almost no recognition of the potential for sustainable and long-term integration with other food-chain actors. farmers who sold products to groceries and supermarkets considered their participation in the lfs as temporary. their objective was to be known by final consumers through retailers, restaurants, and other sellers to attract consumers to direct on-farm sales, which generate higher profits. conversely, retailers tried to organise networks with local farmers to develop new markets for their own activities. their directionality merit was that they envisioned a long-term economic strategy based on including local farmers who are invested in lfs opportunities as a long-term business strategy. for processors, the slaughterhouse was an interesting example, as it had to interact with multiple actors (e.g. farmers, butchers, supermarkets) and their multiple strategies to manage their contracts and relationships. consequently, the slaughterhouse actor interviewed perceived the slaughterhouse as a potential central node for coordinating individual initiatives, such as a territorial food-chain project. in this sense, an upstream example in the case study was “campagna amica”, which is a national initiative that organises farmers’ markets for members of the farmer’s union. the local headquarters of the union decided to organise the market differently. the manager of the farmers’ market allocated farmers among the markets in the area to regulate the products they supplied at the territorial level. this united the farmers in a single vision at the territorial level the sustainability of local production as farmers can sell only their own products. similarly, the “carne bovina di pisa” label united farmers, slaughterhouses, and supermarkets in a common vision of protecting local livestock production. although these initiatives are initial attempts to promote individual efforts in a single vision, they include different shared visions which sometimes compete. in addition, the private nature of these initiatives drives the interests in specific directions, and sometimes lacks a systemic and territorial perspective. 4.7 policy coordination failures and merits for policy coordination, farmers mentioned public institutions which interpreted rules differently. the lack of dialogue among policy-makers resulted in rules that sometimes differed or overlapped, which indicated that policy-makers did not sufficiently address the innovations of farmers. there is a need for policy coordination and innovative public policies that can create a shared vision of the agro-food system. for example, several actors mentioned the lack of policies that promoted local products, such as farmers’ markets or local labels which could highlight the specific connection between pfs and lfs. public initiatives are needed in the area. for example, the province and the university of pisa organised the “piano del cibo della provincia di pisa”, which aimed to encourage local dialogue to sustain lfs; however, the project ended when the local public administration changed. other initiatives have been developed, but a gap remains for including local farmers. 5. discussion the novelty of this study is the first application of the si structural-functional approach to territorial analysis, especially for pfs that contribute to lfs. the study thus had an analytical objective: si was used to envi30 bio-based and applied economics 10(1): 30-34, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10855 rosalia filippini, elisa marraccini, sylvie lardon sion innovation at the territorial level. the interviews with farmers and commercial actors identified relations between systemic failures and merits. by identifying factors that hinder development of innovation between pfs and lfs, it enabled actions, policies, and systemic measures to be identified that can solve the problems and highlight the positive drivers in the system (wieczorek and hekkert, 2012). results show that the systemic failures and merits expressed by periurban farmers and commercial actors are complementary, e.g. commercial actors’ demands for diversified products and farmers’ difficulty in investing in on-farm processing to diversify the supply. thus, farmers and commercial actors have common visions of the potential future development of lfs, such as including processors to give farmers the opportunity to diversify the supply, specific policies that support group processing of farmers’ products, and investing in new crops and products to diversify the local supply. actors seem to agree on the lack of merits of institutions, whose rules are perceived as just another cost. actors also perceive a lack of policy coordination, especially when too many rules exist or seem to overlap, and require managing a large amount of bureaucracy. this is probably due to the lack of dialogue with public institutions, including local ones, which was indicated by previous analysis performed in the area (lardon et al., 2016). actors seem to have different visions of periurbanisation, the infrastructure of this analysis. while commercial actors perceive periurbanisation as an opportunity, farmers perceive the obstacles involved in adapting cropping systems. for commercial actors, such as groceries, supermarkets and most processors, the farmers closer to the urban area are an opportunity as they are closer to their business activities and thus easily accessible. including local farmers is also important for marketing strategies toward consumers. nevertheless, commercial actors complain about the lack of regular supply because the same farmers are contacted for different lfs. in addition, as other studies in the area have shown, farmers do not always adapt to lfs: only 26 of the 58 farmers in the sample participated in lfs, and most of them maintained hybrid networks between conventional and alternative food chains (filippini et al., 2016a). this study shows that farmers recognise the potential for commercialisation in lfs that periurbanisation provides, but they also recognise the impact of urban pressure on their farming practices. farmers’ and commercial actors’ differing perceptions and knowledge about the farming system must improve to develop innovation of the local agro-food system further. the farmers’ adaptation to the new commercial opportunities of the nearby urban areas indicates that the process still needs to be improved for all actors in the lfs. results of his study are consistent with those of other studies performed in the area. for example, filippini et al. (2020) compared the viewpoints of livestock producers and supermarket managers when evaluating a food project based on the assumption that including all actors in the food chain is the only way to promote the sustainability of lfs. sonnino (2014) suggested including all actors in the food chain to reinforce urban food security. bloom and hinrichs (2011) highlighted the difficulties of local buyers when interacting with local farmers. the present study provides a systematic review of the advantages and disadvantages that farmers and commercial actors encounter when they participate in lfs. the main outcome of the study is a consistent story of actors’ viewpoints about the systemic functions and structures of lfs (e.g.. lamprinopoulou et al., 2014). in this sense, it is interesting to observe how failures accumulate: grocers’ lack of knowledge about the potential of pfs and lack of coordination at public and private levels causes network failures, which cause market failures. moreover, the lfs appears to be a closed network for a few farmers who were not related to each other. this lack of connections makes it even more difficult for commercial actors to make profitable contact with farmers in lfs. although the results of this study are valid only for its case study, similar analysis in other areas could deepen and enrich the set of systemic failures and merits that influence system structures and functions. among the actors usually connected to si in the literature, the contribution of research to innovation was not considered (lamprinopoulou et al., 2014), as the main purpose of the present study was to identify the phenomena accurately; likewise, policy-makers’ contributions were not considered (wieczorek and hekkert, 2012). according to the literature, si can help identify systemic measures that influence the functioning of the system innovation, especially to support policy design. although identifying systemic measures lay beyond the objective of this study, it is possible to identify elements from this study which may improve innovation efforts due to the identification of systemic failures. in particular, it is recommended to pursue si by coordinating individual initiatives; public institutions in particular should play a key role in developing a shared vision of pfs and lfs. there is potential for actions that coordinate all efforts in order to promote the innovation of pfs and lfs to new forms of sustainability. doing so requires establishing a coordinated system of rules at the territorial level, along with coordinat31contribution of periurban farming systems to local food systems: a systemic innovation perspective bio-based and applied economics 10(1): 31-34, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10855 ed activities that promote local food and lfs activities, to support the evolution to more sustainable lfs and pfs in the wider regional development (sonnino, 2014). the potential of this coordination requires that research build on this analysis and integrate local and regional analysis, and the si approach will help to integrate these levels (klerkx et al., 2012). future analysis should design better research projects that include policy-makers when applying a systemic policy framework (wieczorek and hekkert, 2012) to assess the si of a particular area better. finally, the use of educational tools is also recommended to provide a platform for learning and experimenting among actors (lardon et al., 2016). regarding the multi-level approach, it may be complicated for researchers to leave the farm gate to address other actors and territorial processes. comparing the data of actors, even those at the same level, may be problematic, as the data come from different sources. for example, data about food production and consumption or about farm and processor management may use different units of measure or have been obtained at different times. research may also require scaling up from microto macro-analysis to create regional knowledge to inf luence regional development. this process may become complicated when qualitative and micro-level quantitative data obtained from actor interviews are combined with regional data. this may also occur when institutions with different objectives operate at different scales, are not used to working together, and thus generate data which are not always comparable. nevertheless, leaving the farm gate is necessary to understand farms and the dynamics of their context better in order to improve innovation and sustainability. an interdisciplinary approach supports this perspective beyond sector-specific perspectives, which improves territorial and integrated analysis. integrating economic, agronomic, and geographic perspectives requires dealing with different languages, concept definitions, priorities, and methods. it is not always easy to find a common basis for research, which makes the research complex and longerterm. innovation is an evolutionary process, and fostering the sustainability of the agro-food system requires coordination between research and private and public actors. the benefits are related to using multiple skills toward a transversal purpose, recognising a variety of issues, and thus developing possible solutions. 6. conclusion in this study, si identified systemic failures and merits, and outlined future development possibilities. consequently, this study contributes to lfs and pfs literature by responding to claims from the literature. lfs is an innovation which may ensure adaptability and sustainable development for agriculture in periurban areas. in turn, adapting pfs involves differentiating the periurban farmers profit in lfs as an alternative model to global supply chains. lfs also represents a factor of sustainability for commercial actors who can respond to the increasing consumer demand for local and traceable high-quality food. nevertheless, the connection between pfs and lfs needs to be reinforced further, and the si perspective has helped to identify elements that hinder the long-term sustainability of the agro-food system. they include commercial actors’ lack of knowledge about farming systems, individual initiatives of farmers that hinder more coordinated lfs, difficulties in adapting farming practices to urban pressure, a lack of dialogue with local public institutions, and a lack of coordination at the territorial level. by applying the si perspective, this study is the first to describe the dialogue between farmers and commercial actors, which is the first step in outlining innovative systemic solutions. acknowledgements the authors acknowledge anr funding via the daume project (no. anr-2010-stra-007-01), the scuola superiore sant’anna for r.f.’s ph.d. grant, the committee of the 154th eaae seminar, as well as all the actors who participated in the interviews. references bloom, j.d., hinrichs, c.c. 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(2011). multifunctional peri-urban agriculture – a review of societal demands and the provision of goods and services by farming. land use policy 28: 639–648. volume 10, issue 1 2021 firenze university press ten years of bio-based and applied economics: a story of successes, and more to come fabio g. santeramo1, meri raggi2 the capitalisation of decoupled payments in farmland rents among eu regions gianni guastella1,2, daniele moro1, paolo sckokai1, mario veneziani3 contribution of periurban farming systems to local food systems: a systemic innovation perspective rosalia filippini1,2, elisa marraccini3, sylvie lardon2 an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications, and consumption of agri-food products carrying those certifications niculina iudita sampalean1, daniele rama1, giulio visentin2 wine after the pandemic? all the doubts in a glass daniele vergamini*, fabio bartolini, gianluca brunori public r&d and european agriculture: impact on productivity and return on r&d expenditure michele vollaro1, meri raggi2, davide viaggi1 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae doi: 10.13128/bae-10443 bio-based and applied economics 9(3): 223, 2020 editorial eliciting preferences using stated choice experiments mara thiene1, jürgen meyerhoff2 1 university of padova, padova 2 technische universität, berlin stated choice experiments are nowadays widely used in different fields ranging from marketing and transport economics to health and environmental economics. they are a survey-based preference assessment technique that presents respondents with mutually exclusive alternatives described by attributes and their levels and asks them to choose the most preferred of those alternatives. subsequently, the choices recorded enable estimates of the trade-offs among attribute levels respondents are willing to make, giving insights into their preferences. if one of the attributes is a cost variable, marginal willingness to pay estimates can be calculated, representing peoples’ preferences for different attributes on the same monetary unit. due to the comprehensive information choice experiments can provide such as marginal and non-marginal welfare measures, they have recently become a favored method to evaluate individual preferences. however, stated choice experiments are by no means a method that can be employed by simply following standard recipes from a cookbook. understanding participants responses to the designed choice tasks presented in surveys and their adequate analysis still requires further research to achieve validity and reliability of the requested results such as welfare estimates. this special issue wants to contribute to the development of choice experiments by presenting a number of selected papers that present results from methodological investigations as well as from policy-oriented applications of choice experiments in the area of environmental and agricultural economics. the authors are mainly members of a group of academics who have met regularly over the last decade as members of the envecho network, which is a scientific network of researchers using discrete choice modelling in the field of environmental valuation (www.envecho.com). we wish to thank all authors and reviewers and, of course, the publisher for giving us the opportunity to publish this special issue in the journal bio-based and applied economics. investigating determinants of choice and predicting market shares of renewable-based heating systems under alternative policy scenarios cristiano franceschinis, mara thiene multi-country stated preferences choice analysis for fresh tomatoes maria de salvo1,*, riccardo scarpa2,3,4, roberta capitello2, diego begalli2 “not my cup of coffee”. farmers’ preferences for coffee variety traits. lessons for crop breeding in the age of climate change abrha megos meressa, ståle navrud* does the place of residence affect land use preferences? evidence from a choice experiment in germany julian sagebiel1,*, klaus glenk2, jürgen meyerhoff3 the use of latent variable models in policy: a road fraught with peril? danny campbell*, erlend dancke sandorf bio -based and a ppl ied economics bae bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-12843 copyright: © 2022 s. targetti, v. marconi, m. raggi, a. piorr, a.j. villanueva, k. häfner, m. kurttila, n. letki, m. costica, d. nikolov, d. viaggi. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: s. targetti, v. marconi, m. raggi, a. piorr, a.j. villanueva, k. häfner, m. kurttila, n. letki, m. costica, d. nikolov, d. viaggi (2022). provision of public goods and bads by agriculture and forestry. an analysis of stakeholders’ perception of factors, issues and mechanisms. bio-based and applied economics 11(4): 351-371. doi: 10.36253/bae-12843 received: february 28, 2022 accepted: february 27, 2023 published: may 3, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: donato romano. orcid st: 0000-0002-2154-546x vm: 0000-0002-9607-8528 mr: 0000-0001-6960-1099 ap: 0000-0002-6936-5118 ajv: 0000-0002-1384-8372 kh: 0000-0001-6413-4540 mk: 0000-0001-5290-4771 nl: 0000-0001-8778-1126 mc: 0000-0002-0789-0910 dn: 0000-0002-4030-4645 dv: 0000-0001-9503-2977 provision of public goods and bads by agriculture and forestry. an analysis of stakeholders’ perception of factors, issues and mechanisms stefano targetti1,*, valentina marconi1, meri raggi2, annette piorr3, anastasio josé villanueva4, kati häfner3, mikko kurttila5, natalia letki6, mihai costica7, dimitre nikolov8, davide viaggi1 1 department of agricultural and food sciences, university of bologna, viale fanin, 50 40127 bologna, italy 2 department of statistical sciences, university of bologna, via belle arti 41 40126 bologna, italy 3 leibniz centre for agricultural landscape research (zalf), institute of socio-economics, eberswalder str. 84,15374 muencheberg, germany 4 ifapa-institute of agricultural and fisheries research and training, camino de purchil po box 2027, granada, spain 5 natural resources institute finland (luke), bio-based business and industry, fi-80101 joensuu, finland 6 institute of sociology, university of warsaw, 00-927 warszawa, ul. karowa 18, poland 7 department of economics and international relations, “alexandru ioan cuza” university of iași, carol i boulevard 22, iaşi ro – 700505, romania 8 institute of agricultural economics, zarigradsko shose 125, bl.1, 1113 sofia, bulgaria *corresponding author. e-mail: stefano.targetti@unibo.it. abstract. the provision of public goods by agriculture and forestry has been a major topic of the agricultural policy debate in the eu. the objective of this paper is to investigate local stakeholder perceptions regarding the cause-effect relations between agriculture and forestry activities and a broad set of public goods and bads, and hence to contribute to the identification of improved policy options for a more efficient delivery of public goods from rural areas. the study presents an assessment based on 71 stakeholder questionnaires collected from seven case study regions in different eu countries. the survey was based on a list of the most relevant public goods and bads developed with the local stakeholders, and aimed to collect stakeholder perception of positive and negative impacts of agriculture and forestry on a range of environmental assets and their relationship with local drivers, socio-economic and cultural features, and policy mechanisms. the analysis shows that the role of agriculture and forestry in the provision of public goods is perceived as generally positive across the selected case study regions. stakeholder opinions concerning the negative impacts on the environment were more divergent. in particular, differences regarding the impact of different socio-economic and cultural features, and policy mechanisms are evidenced. the results outline the importance of regulations. also, payments for environmental services are considered relevant in particular for biodiversity, landscape, and water quality. beside that, aspects such as expectations of society and the attitude of farmers towards the environment resulted noteworthy. http://creativecommons.org/licenses/by/4.0/legalcode 352 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 stefano targetti et al. keywords: common agricultural policy, ecosystem services, public goods, stakeholders, transdisciplinary approach. jel codes: q18, q20. highlights · the paper presents an analysis based on 71 stakeholder questionnaires collected from seven case study regions in the eu · the study concerns the relation between public goods and factors, issues and policy mechanisms · results outline that in particular issues and mechanisms are influenced by the perception of public goods · regulations, payments for environmental services and environmental attitude of farmers and society result as the most relevant mechanism. 1. introduction agriculture and forestry are the dominant forms of land-use, respectively covering 38% and 31% of the world’s land surface (fao, 2021). beside the provision of raw materials such as food and timber, the society is increasingly demanding environmental and cultural services, most of which displaying public goods characteristics, from agriculture and forestry (muradian & rival, 2012; oecd, 2015). also, ‘disservices’ (public bads) that are defined as ecosystem functions or attributes that generate negative impacts on human wellbeing, affect the wider society (shackleton et al., 2016). these negative impacts can result from agricultural and forestry activities (and in that case they overlap with the concept of negative externality) or might be related to natural processes (e.g. shrub encroachment, crop pests, pollen allergens cfr. shackleton et al., 2016 for a comprehensive definition of ecosystem disservices). a wide range of policy tools (including incentives, regulations, information and training, etc.) can be used to induce farmers to adopt practices able to enhance the provision of services and reduce the generation of disservices from agri-ecosystems (kuehne et al., 2017). nonetheless, the identification of efficient mechanisms in the context of the complex range of relationships between policy, institutions and actors, requires taking into account different factors that are often related to local-scale socio-economic and cultural features (zasada et al., 2012). indeed, a consistent body of literature reports that the complex cause-effect relationships between the management of agri-ecosystems and the generation of benefits linked to public goods are connected to local-scale contexts (hart et al., 2011; schaller et al., 2018). the provision of public goods by agriculture and forestry has been one of the main topics in the debate concerning the agricultural policy in the recent decades. the new programming period of the common agricultural policy (cap 2023-2027) of the european union (eu) has confirmed the growing attention towards the environment: the cap new “green architecture” aims to improve the effectiveness of eu agriculture in delivering public goods from rural areas through different tools such as enhanced conditionality, agri-environment-climate measures (aecm) and the eco-schemes. the latter is a relevant novelty of the reformed cap, introducing a set of measures that the member states should include in pillar i and that would work on a voluntary basis for farmers. the eco-schemes, together with the enhanced conditionality, substitute the so-called ‘greening’ of the previous programming period and are aimed at harmonizing the mechanisms and the objectives of the pillars i and ii and -to some extentshould facilitate the uptake of agri-environment-climate practices by farmers (runge et al., 2022). one relevant principle strongly underlined in the new cap programming period is that a more effective design of aecm requires an adaptation fitting to the local contexts (ec 2021). therefore, the role of national and sub-national institutions in the design and implementation of eco-schemes and aecms has been boosted in the cap reform to facilitate a better targeting of agri-environmental policies based on the physical and ecological features of different areas. nevertheless, the inclusion of the different socio-institutional structures (i.e. actors, networks, authorities, policy, etc.), their boundaries and interplay would allow a more comprehensive account of local needs and opportunities (zasada et al., 2017). consequently, aspects related to the stakeholder perception of factors and issues affecting the generation of public goods in rural areas would potentially allow to increase the efficiency of the policy design by integrating the local-scale biophysical context with the complex socio-ecological processes affecting the provision of public goods (lebel & bennett, 2008; schaller et al., 2018). the assessment of socio-ecological processes, that on one hand influence the supply of ecosystem services 353 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 provision of public goods and bads by agriculture and forestry and on the other hand determine their demand (van zanten et al., 2014; wolff et al., 2015), can follow different analytical approaches. biophysical approaches aim to assess public goods and bads through physical measures that can be spatially explicit. the results of such analyses are often characterized by a combination of very specific information that is difficult to scale-up on the aggregate (marconi et al., 2015). that hampers the policy design and reduces considerably its efficiency in particular when multiple public goods and bads are considered (armsworth et al., 2012). despite there are methods and approaches of combining multiple public goods in the same area, examples of implementation are scarce and limited to case studies (e.g. ungaro et al., 2021). other approaches try to attach values to public goods provision to support related decision-making, using either monetary (tienhara et al., 2021; tyllianakis & martín-ortega, 2021) or non-monetary techniques (targetti et al., 2018). however, generalizations about value-generation processes and the identification of societal and stakeholder demands for multiple public goods and ecosystem services in a spatial explicit manner are very often complicate (schwartz et al., 2021). in addition, value assessments should include demand and supply that are difficult to observe separately one from each other (wolff et al., 2015). in practice, the relevance of public goods tends to mix up with the discrepancy between the desired level of public goods and the actual supply, but the quantitative assessment is challenging due to their cognitive and subjective nature1 (faccioli et al., 2020), to the different types of use and non-use values perceived by people (targetti et al., 2021a) and to their variation at different spatial scales (granado-díaz et al., 2020). in this sense, the sociocultural evaluation is an approach that is getting momentum (martin-lopez et al., 2012). this approach is hinged on assessing how different people perceive and value the environment and the cognitions of wellbeing stemming from landscape. it therefore targets the relationship between society, public goods generation, and environment (targetti et al., 2021b). by embracing the complexity of human-nature relations, the sociocultural evaluation is less prone to incur in a mechanistic simplification of processes and institutions existing between society and nature and therefore is able to provide a more comprehensive assessment in comparison to other approaches (muradian & baggethum, 2021; norgaard, 2010). nonetheless, the heterogeneity of pub1 public goods perception is typically heterogeneous and depends on individual attitudes, experience and values. also, cognitive processes such as beliefs and knowledge of ecological processes have a relevant influence on the perception and are therefore important aspects to be considered when assessing public goods (adams, et al., 2003). lic goods perceptions involves the need of analyses able to identify typologies of such perceptions for supporting the design of policies (soini et al., 2012). the objective of the study is to investigate local stakeholder perceptions regarding the cause-effect relations between agriculture and forestry activities and a broad set of public goods and bads (pgbs) relevant to society in seven case study regions (csr). we do so by providing a cross country comparison of perception from a sample of stakeholders based on a common analytical framework. in this paper, the concept of public bads is introduced to consider both positive and negative impacts of agricultural activities on a range of environmental assets such as landscape, water quality, biodiversity, etc. the analysis is based on the identification of groups of stakeholders featuring different pgb perceptions and the characterization of the group dissimilarities in terms of: i) drivers and/or forces that impact pgb provision (hereafter ‘factors’), ii) local socio-economic and cultural features (hereafter ‘issues’) and iii) policy and governance mechanisms (hereafter ‘mechanisms’). more specifically, the paper aims to: a) finding relations between pgb perceptions and stakeholder opinions regarding issues, factors and mechanisms that are considered able to foster public goods and/or reduce public bads; b) finding groups of stakeholders with convergent perception of pgbs; and c) discuss the potential of that information for the identification of improved governance options for rural areas. the paper is structured as follows: the methodology and the description of the csrs are reported in section 2. section 3 illustrates the results and section 4 presents the discussion including the study implications for the design of agri-environmental policies. section 5 concludes. 2. case study regions and methods 2.1. description of the case study regions the csrs were located in seven member states (finland, spain, italy, germany, romania, bulgaria and poland) to cover different geographical areas of the eu (north, south, west and east eu). based on information collected from local stakeholders, one csr was identified in each country to investigate areas featuring a relevant supply of public goods (cfr. § 2.2). the finnish csr was north ostrobothnia, in northern finland, featuring 88% of the land covered by forests. typical elements of landscape are hills in the northeastern side, rivers and valleys in the western side, and flat peatland areas in the center of the region. 354 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 stefano targetti et al. the spanish csr was andalusia, in southern spain, which hosts a wide variety of agroforestry landscapes, especially including olive groves (with more than 1.5 million ha), ‘dehesas’ agroforestry and livestock systems (around 1 million ha), winter rainfed cereal systems and different types of irrigated agricultural systems. while there are several hotspots related to pgs (e.g. biodiversity in dehesas) and bads (e.g. soil erosion in certain olive grove areas), there is a significant potential for improving pgb provision by agroforestry systems. the italian csr was emilia-romagna, located in the north-eastern side of italy. agricultural areas cover around 60% of the region, which is mainly cultivated with intensive arable crops (42% of the utilized agricultural area). agricultural systems in emilia-romagna are mostly oriented towards high-quality traditional and intensive production systems and have been characterized by a process of farm concentration (abandonment of small and marginal farms and increase in average farm size). given the heterogeneity of the region, a wide range of pgb (e.g. biodiversity, amenities, water quality etc.) are relevant in relation to the different agricultural systems and practices. the german csr was located in the county of märkisch-oderland, federal state of brandenburg. the csr is a natural park where forested areas are under nature conservation measures and are surrounded by agricultural areas. relevant environmental aspects concern water scarcity, soil functionality (water retention and wind erosion), loss of habitats and biodiversity, and soil carbon stock linked to water management. the romanian csr is the north-east region, which is characterized by low productivity due to fragmentation of farmland ownership, aging workforce, migration of young people to urban areas and a high degree of poverty for small farmers. the main environmental problems are linked to deforestation, with implications on landslides and soil erosion issues. the bulgarian csr is the south central region, where 48% of the land is represented by agricultural areas (mainly arable and grasslands) and 45% by forest areas. the region features a developed livestock sector and agriculture delivers many public goods which are highly valued in the region: agricultural landscapes, farmland biodiversity, water quality and availability. however, also public bads such as soil erosion affects 80% of agricultural areas. the poland csr is the podlasie region, where agricultural areas constitute 53% of the area and forests cover 31% of the territory. the region is predominantly rural and a significant number of municipalities include natura 2000 sites. the number of farms recently declined by a rate of 14%. the farms are, on average, small and oriented towards high quality production. environmental issues that are important include water quality pollution and biodiversity losses due to the recent intensification of agriculture and urban expansion. 2.2. stakeholder survey and analysis the survey was carried out to collect information regarding the perception of pgb provision from local agriculture and forestry systems across europe, and identify the most relevant factors, issues, and the most useful policy mechanisms from the point of view of local stakeholders. the selection of stakeholders was made in all the csrs following the same procedure. first, a list of relevant stakeholder types was defined, involving farmers and/or foresters, consultants and technicians assisting agricultural and forestry farms, public officers and decision-makers, ngo technicians, and researchers, with all of them focusing their working expertise on pgbs provided by agriculture and forestry. second, according to the stakeholder types, a list of relevant stakeholders was identified. the list of public goods and bads linked to agriculture and forestry was selected and refined through stakeholder workshops carried out in the seven selected member states and in one eu-level workshop organized in brussels. the workshops were aimed at gathering the views of regional and eu-level stakeholders regarding the notion and the ranking of public goods and bads from agriculture and forestry systems, and issues affecting their provision and demand. a list of 29 different public goods and bads was developed in the workshops. public bads were, in general, not considered as something conceptually different from public goods and were referred to low or inadequate supply levels of public goods (e.g. for instance, the public bad related to biodiversity was ‘biodiversity loss’). in other cases, public bads referred to aspects which could be understood as activities or actions generating public bads, such as pollution. the list of 29 pgbs was afterwards refined taking also into account the typology of most relevant public goods linked to the agricultural sector in the eu as proposed by cooper and colleagues (2009; cfr. also enrd, 2010). accordingly, eight pgb types related to ecosystem capital (rural landscape, farmland biodiversity, water quality and availability, soil functionality, climate stability, air quality, resilience to flooding and fire) and two related to social capital (rural vitality, and animal health) were selected among the 29 pgbs (table 1; cfr. annex a; novo et al., 2016). a further objective of the country-level workshops was to map and delimit the csrs in which to carry out 355 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 provision of public goods and bads by agriculture and forestry the subsequent stakeholder survey focusing on pgbs and outline a list of issues, factors and mechanisms affecting the public good delivery. to this end, areas featuring relevant supply of public goods were mapped during the workshops to identify ‘hotspots’ areas and the main issues in terms of public goods supply and demand, and the potential related criticalities (tindale et al., 2018). a questionnaire was developed to be submitted to local stakeholders in the 7 selected csrs. in the questionnaire, the stakeholders were asked to score the relevance of the ten selected pgbs in their csr. first of all, the relevance of the public goods delivered by agriculture and forestry systems was assessed on a 0-9 scale, then they were asked to score on the same scale the public bads. thus, each stakeholder provided an overall 20 scores for the relevance of pgbs. for each pgb, the stakeholders were then asked to indicate whether the different factors, issues, and mechanisms were relevant or not in their csr (table 2). one-hundred-one local stakeholders were invited in the 7 csrs to participate to the survey with a request to fill-in a multiple-choice questionnaire. the survey was filled-in by a total number of 71 respondents in the seven csrs 68% out of which indicated ‘agriculture’ as their area of expertise, whereas 32% indicated ‘forestry’. the composition of the sample according to the professional categories represented by the respondents is synthetized in table 3, showing that stakeholders are mostly researchers working in the field of agriculture and forestry or related (38% of the total sample) or public officers from regional or national agencies (30%) (table 3). concerning the composition of the sample, the italian csr, emilia-romagna region, was the most represented region (23% of the total sample), followed by the romanian (21%), bulgarian and spanish (14% each), polish and german (10% each) and finnish csrs (8%). 2.3. statistical analysis the identification of a typology of stakeholder perception towards pgbs was carried out performing a hierarchical cluster analysis based on the scores attributed by the stakeholders to the 20 pgbs. the cluster analysis was preceded by a principal component analysis (pca). the output of the pca (scores on the pca axes) was employed for the cluster analysis (ward agglomeration method, manhattan distance metric). this analysis is often employed to explore heterogeneous opinions of respondents (e.g. soini et al., 2012). previous applications of such an approach have also shown its useful application in studies focusing on agri-environmental policy (e.g. maton et al., 2005; gómez-limón et al., 2013)2. the identification of the clusters was supported by the analysis of the dendrogram structure (appendix b) to identify how the cases (i.e. the stakeholders) grouped together. an inertia analysis was employed to support the visual identification of the optimal number of clusters. that analysis is based on the within-cluster sum-ofsquares calculated for each partition and indicates the partitioning of the dendrogram with the higher relative loss of inertia (inertia of cluster n+1/ inertia of cluster n). according to that, the inertia analysis identifies the 2 analyses combining pca with a hierarchical clustering is often used in social sciences to identify the main variables ‘explaining’ a database variability and describe groups of cases accordingly. in particular, the pca outlines the variables able to explain the major part of the variance on the different axes, the cluster analysis is then performed on the scores attributed to these variables. the approach is therefore able to reduce considerably the ‘noise’ that is usually present in database concerning individuals’ perceptions. regarding the use of such approaches in perception-related surveys, some examples are reported in husson et al., 2010; soini et al., 2012; targetti et al., 2020 and 2021a. the objective is reducing the ‘noise’ which generally affects database regarding opinions or cognitive-related processes and outline trends or tendencies in the dataset. that procedure is usually at the base of the interpretation of the information conveyed or formulation of policy recommendations. table 1. list of public goods and bads considered in the survey (cfr. annex a). public goods public bads landscape and scenery landscape degradation farmland biodiversity (animal and vegetal) biodiversity losses water quality and availability water resources pollution and depletion air quality air pollution soil functionality soil erosion climate stability climate degradation resilience to flooding, landslides and fire increase of flood and wildfire risk rural viability and vitality degradation of abandoned land production quality and security (food, timber, energy) poor productions quality and distribution farm animal health and welfare degradation of animal health and welfare 356 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 stefano targetti et al. classification where a further cluster formation does not provide an advantage in terms of data description. the information regarding the relevance of factors, issues and mechanisms was analyzed with the shannonweaver indicator (h index) as following: h=-∑pi×lnpi (1) where pi indicates the frequency with which a variable (factor, issue or mechanism) was rated as relevant for a specific pgb in that cluster. the h index is a measure of the information entropy and was employed to indicate if specific factors, issues or mechanisms were considered relevant for specific pgbs (i.e. highlighting a low entropy) or otherwise there was not specific indications emerging from the stakeholders (i.e. high entropy: all factors, issues or mechanisms considered relevant). 3. results 3.1. relevance of public goods and bads in general, public goods linked to agricultural and forestry systems were perceived as relevant in the csrs (figure 1). indeed, the average score ranged between table 2. list of factors, issues and mechanisms devised in the local and eu-level stakeholder workshops and considered in the survey. factors issues mechanisms public goods are a direct results of land management by farmers and foresters complying with the environmental regulations public goods and bads are still theoretical concepts, society has no perception of the role of farmers and foresters as land managers increase financial support to farmers and foresters public goods are direct result of agriculture and forestry fostered by cap funding inadequate funding for compensation of farmers and foresters adopting sustainable practices implement payments for environmental services public goods are direct result of the increasing pressure and control exerted by society on farmers and foresters conflicts of interest and uses between different stakeholders implement new market-based incentives public goods are direct result of market demand for healthier, more sustainable agricultural and forestry products development and trade-offs between different land uses promote farmers’ and foresters’ education to sustainability public goods are direct result of technological advancement and innovation in agriculture and forestry problems related to the urban sprawling, rural land abandonment adapt compensation schemes and regulations to the global market public bads are mostly unintended by-products from agricultural and forestry activities, (direct result only in absence of compliance with the law) public access to public goods; land tenure and property issues adopt more efficient land use plans public bads s are never a direct result of agriculture and forestry, which do not pollute the environment or to damage the society intentionally pioneer/foster cross-compliance in all public subsidies public bads are a direct result of land management choices exerted by farmers and foresters (e.g. practicing intensive agriculture) public bads are a consequence of the absence of adequate compensation schemes to farmers and foresters public bads are caused by the rising landabandonment in rural areas public bads emerge from the competition between regions/ countries forcing farmers and foresters to lower the sustainability of productions table 3. composition of the sample of stakeholders and shares of job categories. n. % research/ academics 27 38% public officers 21 30% ngos 9 13% consultant/ agronomists 8 11% farmers/foresters (incl. agri-food firms and representatives of producers associations) 6 8% 357 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 provision of public goods and bads by agriculture and forestry 6.34 for ‘resilience to flooding, landslides and fire’ and 7.31 for ‘water quality and availability’ (out of a maximum of 9). perception of public bads was lower in comparison to public goods. in particular, public bads related to ‘rural viability and vitality’ and ‘productions quality and security’ were considered the least important (range 4.8-4.9). on the contrary, the relevance of public bads linked to the reduction of ‘farmland biodiversity’, ‘soil functionality’ and ‘resilience to environmental risks’ was signifi cant. as evidenced in figure 1, a greater variability characterized the scores attributed to public bads. th at is also outlined by higher standard deviations for public bads (range 2.15-2.83) in comparison to public goods (range 1.67-2.37) (appendix c). public goods perceptions across the csrs did not result signifi cantly different. on the other hand, public bads perception were signifi cantly diff erent across the csrs (with p<0.05) for all the pgbs considered in the study except for ‘degradation of rural viability and vitality’, ‘reduction of climate stability’ and ‘reduction of resilience to environmental risks’. 3.2. factors, issues, and mechanisms linked to public goods and bads th e results about the most important factors considered as relevant for the diff erent pgbs across the seven csrs are shown in figure 2. th e results show that the most important factors relate to the complying of farmers and foresters to the environmental regulations for public goods and the land management decisions taken by farmers and foresters for public bads (with an acrosscsrs average of 30% and 25% of all pgbs impacted by these factors respectively). specifi c pgbs that were considered particularly related to regulations were farmland biodiversity and water quality (54% of stakeholders indicated regulation as a relevant factor for these pgbs; appendix d). similarly, the cap funding was considered a signifi cant and positive factor for biodiversity protection and maintenance of rural viability and vitality by 54% of stakeholders. on the other hand, production choices were indicated as a factor specifi cally related to farmland biodiversity depletion by 56% of stakeholders. th e stakeholders’ opinions concerning the factors were not signifi cantly diff erent across the csrs. two notable exceptions were ‘public goods are direct result of market demand for healthier, more sustainable agricultural and forestry products’ that was signifi cantly diff erent with p<0.01 and the factor ‘public bads emerge from the competition between regions/countries forcing farmers and foresters to reduce the sustainability of productions‘ with p<0.05. th e relevance of the issues for the ten pgbs was rated between an average of 18% and 33% (figure 3). in particular, the perception of the role of farmers and foresters as land managers was considered the most rellandscape and scenery farmland biodiversity water quality and availability air quality soil functionality climate stability resilience to environmental risks rural viability and vitality productions quality and security animal welfare 4 5 6 7 8 4 5 6 7 8 pu bl ic b ad public good figure 1. stakeholders’ perception of public goods and public bads provided by agricultural and forestry systems in the 7 csrs. th e rating is reported on a 9 point scale and related to the ten environmental categories included in the study. 0 10 20 30 40 50 re gu la tio n co m pl ia nc e ca p fu nd in g so ci et y de m an d m ar ke t d em an d te ch no lo gy % a: factors related to public goods 0 10 20 30 40 50 un in te nd ed b ypr od uc ts in di re ct re su lt of pr ac tic es pr od uc tio n ch oi ce s ab se nc e of a de qu at e co m pe ns at io n ab an do nm en t co m pe tit io n % b: factors related to public bads figure 2. perception of the stakeholders concerning the relevance of the factors linked to pgbs across the 7 csrs and for the 10 pgbs: boxplot of factors related to public goods (a) and public bads (b) provision from agricultural and forestry systems. grey points in each plot represent 10th, 25th, 50th, 75th, and 90th percentiles, bottom to top. 358 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 stefano targetti et al. evant issue, whereas issues linked to land tenure and access were considered on average the least important. even though urban sprawling and land abandonment was not considered among the most relevant issues for pgbs, that issue was the most important affecting ‘landscape and scenery’ (appendix e). in the case of public bads, ‘inadequate funding’ was perceived as the most important issue and in particular 45% of stakeholders considered that as relevant for biodiversity degradation3. likewise for factors, stakeholders’ opinions over the issues considered were quite homogeneous across csrs, except for ‘urban sprawling and abandonment’ and ‘development and trade-offs between different land uses’ for which statistical significant differences were found between the csrs (at 0.05 and 0.01 levels, respectively). according to the stakeholders, the most relevant mechanisms to improve public goods and reduce public bads were the implementation of payments for environmental services (pes) and the promotion of farmers’ and foresters’ awareness of sustainability (education). these mechanisms were considered effective for a range of different pgbs, but pes were rated as particularly effective for biodiversity and landscape (62% and 53% of stakeholders on average rated pes as relevant for biodiversity and landscape; appendix f). interestingly, the mechanism ‘adapt compensation schemes and regulations to the global market’ was considered as the least effective mechanism to foster public goods and reduce public bads. concerning the difference between regions, pes and ‘adopt more efficient land use plans’ were sig3 more details concerning the differences between the stakeholder perception have been reported in the discussion section. nificantly different across the seven csrs considered (p<0.05; appendix g). 3.3. cluster analysis. finding groups of stakeholders with convergent perception of pgbs the cluster analysis performed on the pca scores outlined four clusters (appendix b). the largest cluster (cluster 2) included 37% of stakeholders characterized by stating high scores for all pgbs (with overall average scores of around 8 out of 9 for both public goods and public bads; table 4). noteworthy, public bads were relevant and higher than in the other clusters. the second largest cluster (cluster 3) grouped 25% of stakeholders was characterized by the perception of landscape as the most relevant public good (average score of 7.7) and biodiversity depletion as the most important public bad (scoring 6.9) connected to agricultural and forestry systems. also, pgbs linked to resilience to flooding, landslides and fire and air quality were considered the least relevant, with scores within the range of 3.6-4.6. the third largest cluster (cluster 4; 24% of stakeholders) was composed by stakeholders who stated the overall lowest scores for pgbs (5.5 for public goods and 4.2 fo public bads), considering rural viability as the most relevant public good and soil erosion as the most critical public bad (scoring 6.7 and 6.2, respectively). in this cluster, water and production quality were considered the least relevant public good and public bad respectively (scoring 4.3 and 5.1). finally, cluster 1 included 18% of stakeholders. in that cluster, the stakeholders perceived a high relevance of public goods (7.2) compared to bads (4.5), indicating soil functionality as the most relevant public 0% 10% 20% 30% 40% 50% 60% so ci et y pe rc ep tio n in ad eq ua te fu nd in g co nf lic ts o f i nt er es t la nd -u se tr ad eof fs ur ba n sp ra w lin g/ ab an do nm en t la nd te nu re /a cc es s figure 3. boxplot of issues related to pgb provision from agricultural and forestry systems: average stakeholder perception concerning the relevance of the issues across the seven csrs and for the ten pgbs. grey points in each plot represent 10th, 25th, 50th, 75th, and 90th percentiles, bottom to top. 0 10 20 30 40 50 in cr ea se fi na nc ia l s up po rt to fa rm er s a nd fo re st er s im pl em en t p ay m en ts fo r en vi ro nm en ta l s er vi ce s (p es ) im pl em en t n ew m ar ke tba se d in ce nt iv es pr om ot e fa rm er s’ a nd fo re st er s’ e du ca tio n to su st ai na bi lit y ad ap t c om pe ns at io n sc he m es a nd re gu la tio ns to th e gl ob al m ar ke t ad op t m or e ef fic ie nt la nd us e pl an s pi on ee r/ fo st er c ro ss co m pl ia nc e in a ll pu bl ic su bs id ie s % figure 4. boxplot of mechanisms related to pgbs provision from agricultural and forestry systems . average perception of the stakeholders concerning the relevance of the mechanisms across the seven csrs and for the ten pgbs. grey points in each plot represent 10th, 25th, 50th, 75th, and 90th percentiles, bottom to top. 359 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 provision of public goods and bads by agriculture and forestry good (scoring 8.2) and the deterioration of climate stability as the most relevant public bad (scoring 6.4) produced by agriculture and forestry systems. in cluster 1 on the contrary, public bads related to landscape and production quality were perceived as the least important (both scoring 3.3). considering the results, we propose the following cluster labelling: c1-positivists, c2-holistics, c3-naturalists and c4-agrarians. the four clusters outlined a relation with some of the csrs as presented in appendix g. in particular, cases from the german and spanish csrs were more often classified in c3-naturalists and c4-agrarians, respectively. c1-positivists and c2-holistics, on the contrary, showed a less clear relation with a specific csr, though stakeholders from the italian csr were more likely c1-positivists and romanian csrs were more likely c2-holistics. figure 5 shows the shannon-weaver index of information entropy for factors, issues, and mechanisms for the different clusters. the classification provided by the cluster analysis allowed to reduce the information entropy and therefore provided indications about the relevance of specific issues and mechanisms for the different pgbs: as shown in figure 5, the uncertainty conveyed by the stakeholders concerning issues and mechanisms in connection with the different pgbs was significantly decreased. on the contrary, the information entropy regarding the factors linked to pgbs was not affected significantly with the cluster analysis. according to the shannon-weaver index, c2-holistics showed the highest entropy for issues and mechanisms, indicating a lower capacity to discriminate between these for the improvement of pgb provision from agriculture and forestry. the cluster analysis enhanced the information quality in particular for c1-positivists and, to a lesser extent, c3-naturalists and c4-agrarians, which recorded a significant lower entropy of the information across issues and mechanisms in comparison to c2. 4. discussion in general, the average scoring of public goods linked to agricultural and forestry systems in the 7 csrs was higher than the perception of public bads. that points to an overall positive perception of the role of agriculture and forestry activities in providing environmental services, but that was also linked to the selection of csrs with relevant levels of public goods supply. in particular in the selected csrs, public goods such as production quality and quantity, and rural vitality clearly prevail compared to the public bad one. this may reflect a general perceived efficiency of agriculture and forestry in providing those public goods (villanueva et al., 2014; novo et al., 2016). the results also indicate that the rating of public goods such as biodiversity, soil functionality, and resilience to environmental hazards, was very close to the rating of public bads. that denotes contrasting impacts for these environmental categories that are likely linked to different agricultural practices or systems and therefore highlights aspects where agrienvironmental policies may play a more relevant role. even though public bads perception was generally low, its variability across the csrs was more promitable 4. average scores of pgbs in the four identified clusters. the pgbs that in each cluster were reported more frequently as relevant in the stakeholder opinion are in bold. variable cluster 1-“positivists” cluster 2-“holistics” cluster 3-“naturalists” cluster 4-“agrarians” % of stakeholders 18 37 24 21 pgs pbs pgs pbs pgs pbs pgs pbs landscape and scenery 6.6 3.3 7.6 8.0 7.7 5.4 5.8 4.3 farmland biodiversity 7.8 4.8 7.7 8.2 7.4 6.9 4.6 5.4 water quality and availability 8.1 4.2 8.7 8.3 7.4 6.6 4.3 5.1 air quality 6.8 5.7 8.5 8.2 4.6 3.9 5.4 3.5 soil functionality 8.2 3.8 8.7 8.0 5.8 5.9 4.5 6.2 climate stability 7.5 6.4 8.3 7.9 5.5 5.1 5.5 4.5 resilience to flooding landslides and fire (%) 7.8 4.8 7.9 8.2 3.6 3.8 5.6 4.1 rural viability and vitality 6.1 4.5 8.0 7.3 6.4 2.8 6.7 3.7 quality and security of products (food. timber. energy) 6.8 3.3 8.1 7.3 6.1 3.5 6.5 2.6 farm animal health and welfare 6.6 4.2 7.9 7.6 5.9 4.4 5.6 3.0 overall average score 7.2 4.5 8.1 7.9 6.0 4.8 5.5 4.2 360 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 stefano targetti et al. nent in comparison to public goods. in other words, the analysis indicates that agriculture and forestry systems are perceived as relevant “providers” of a wide range of public goods, whereas differences across the csrs are more evident when considering the negative impacts. that points to an appropriate consideration of public bads and supports the stream of literature highlighting the usefulness of ecosystem disservice analyses for disentangling the dynamics taking place in different rural regions (blanco et al., 2019; targetti et al., 2021b; zabala et al., 2021). nonetheless, the inclusion of csrs featuring agricultural systems that provide significant public bads would be necessary to bring clearer insights in that respect. the selectd csrs were characterized by different agricultural and forestry systems, but that did not implicate siginificant differences related to pgbs. in that respect, the classification based on the cluster analysis allowed to increase the informative value conveyed by the stakeholders and highlighted significant differences across the clusters concerning issues and mechanisms. on the contrary, the perception of factors linked to pgbs was rather homogenous between the clusters. that evidence highlights how issues and mechanisms may have a different relevance even though the factors linked to pgbs are rather similar. for instance, the important role of factors such as regulations and the cap in promoting public goods was a clear outcome of the analysis. in contrast, the implementation of mechanisms such as payments for environmental services was rated as particularly effective for public goods such as biodiversity and landscape. similarly, inadequate funding was considered as an issue with negative consequences for specific public goods such as farmland biodiversity, rural viability, and production. that confirms that the design of large scale policies for pgbs is complicate because of the different local-scale and socioeconomic features as supported by a range of studies (e.g. armsworth et al., 2012; schaller et al., 2018). our results show that the consideration of mechanisms and issues would help to target the pgbs that are at stake in the different regions and improve the implementation of agri-environmental policies. indeed, some general trends regarding mechanisms can be highlighted. for instance, the promotion of farmers’ and foresters’ awareness of sustainability issues resulted as a very effective mechanism for a range of different pgbs. if on one hand the stakeholders underlined aspects related to human and social capital like education, on the other hand they highlighted a scarce belief in market-related mechanisms such as adapting pgb schemes and regulations to the global market and market-based incentives. the latter, though, was rated as a relevant mechanism for improving production quality. in a nutshell, market-related mechanisms link better to specific pgbs that are considered more relevant for consumers (e.g. food production and animal welfare), other pgbs relating for instance to water, soil, landscape etc. require more refined mechanisms such as pes and cross-compliance of public subsidies. contrarily, the scope of mechanisms designed to enhance education to 0 1 2 3 4 cluster1 cluster2 cluster3 cluster4 all h' factors ns 0 1 2 3 4 cluster1 cluster2 cluster3 cluster4 all h' issues ** 0 1 2 3 4 cluster1 cluster2 cluster3 cluster4 all h' mechanisms *** figure 5. shannon-weaver index of information entropy. boxplot of the clusters and anova: boxplots with different letters indicate significant differences of h’ between the clusters with p<0.05 (tukey hsd test for significant difference between clusters with**=p<0.01; *** p=<0.001; ns= not significant; i.e. cluster indicated with ‘a’ is different from ‘b’, cluster indicated with ‘ab’ are not different from ‘a’ and ‘b’). 361 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 provision of public goods and bads by agriculture and forestry sustainability of farmers and foresters is rated as effective on a more general level. concerning the issues connected to pgbs, tools targeting human capital were confirmed as important. for instance, societal perception of the role of farmers and foresters was considered the most relevant issue. that reiterates the opportunity to consider “soft” aspects like education ,social benefits and the acknowledgment of the role of land managers in environmental protection. other issues that reached a high ratings in connection to specific pgbs was urban sprawling and land abandonment for their impacts on landscape and rural viability. on the contrary, the issue ‘public access to public goods, land tenure and property’ was usually included among the least relevant issues. concerning the perception of factors, regulations were acknowledged as the most important for the delivery of public goods. cap funding was also perceived as very relevant in particular for specific pgbs such as biodiversity and rural viability. on the other hand, the role of farmers’ decisions and the unintended effects of those decisions were perceived as the most important factors. those results outline a very traditional view of agri-ecosystems where farming activities generate externalities that policies need to tackle through classical stick-andcarrot tools. technology, on the contrary, was not perceived as a factor able to improve the provision of pgb from rural areas. exceptions concerned water and production quality. that is likely linked to the availability of technologies like for instance drip irrigation that are well-known for their potential positive effects, whereas for other public goods like biodiversity the potential of technology in helping the transition towards agro-ecological solutions is still less palpable (bellon-maurel & huyghe, 2017). in terms of policy implications, the limited number of stakeholders and regions that were included in the survey (71 stakeholders in 7 csrs) makes difficult to generalise the results. though, the work was carried out in a good range of different agricultural and forestry areas, located in north, east and southern parts of the eu. even though with limitations, the study can therefore highlight some trends and interesting aspects on the connection between agriculture and forestry, and the supply of pgbs in eu. the results support the usefulness of mixing different tools taking into consideration their different capacity to deal with different pgbs. on one hand regulations seem to guarantee high levels of efficiency, on the other hand a mix with tools targeting information and education are also necessary. beside the importance of policy mix, that result also confirms that the configuration and design of the different tools together is important (fraser and campbell, 2019). nevertheless, this work cannot provide insights on aspets related to the design of different policy mixes as the study focused on the relevance of the mechanisms for the different pgbs and not on the configuration of different mechanisms together. a further interesting aspect regards the reflection on the temporal dimension. even though the survey did not explicitly consider the time range, the results outline a dicrepancy between issues, factors and mechanims that accrue in the long term (e.g. human capital related) and others that denote a more immediate impact (such as regulations and payments for environmental services). however, that observation would need confirmation through an ad-hoc study focused on these aspects. from a governance perspective, several considerations can be raised. first of all with specific reference to sectorial policies like the cap, the relevance of incentives to support (reduce) the supply of public goods (bads) results as paramount. indeed, a general skepticism emerges concerning the possibility to improve public goods such as biodiversity or other environmental services relying on market-related mechanisms only. likely, the importance of supporting (e.g. biodiversity) and regulating services are considered too complex to fit easily to society awareness. that involves the perceived necessity to intervene with subsidies to complement the rationale of the market demand-supply mechanism. in that regard, the new cap architecture (reg 2021/2115)4 could tackle that aspect. for instance, the higher rate of funding earmarked for environmental objectives (e.g. the eco-schemes) and the enhanced conditionality requirement could match with increasing the cap targeting towards environmental objectives. beside incentives, the role of regulations as necessary tools to ensure an adequate level of public goods supply is also reported. however, it seems obvious in the stakeholder perception that the availability of budget for incentives and regulations for pgbs is not enough without a more ‘horizontal’ approach of the policy design (hodge, 2001). fostering cross-compliance of public subsidies was for instance a mechanism that was rated as very important for several pgbs. in other words, the adequacy of a policy framework focusing on environmental objectives is necessary but not sufficient if a local-scale dialogue with other land-use-planning institutions and a wider range of local economic sectors is not established. 4 regulation (eu) 2021/2115 of the european parliament and of the council of 2 december 2021 establishing rules on support for strategic plans to be drawn up by member states under the common agricultural policy (cap strategic plans); https://eur-lex.europa.eu/eli/ reg/2021/2115/oj access in all eu languages 362 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 stefano targetti et al. the study also highlights that a more efficient governance of pgbs is forcedly related to human capital. knowledge, perception, ability, are for instance some of the farmers’ and foresters’ education objectives that need to be considered and promoted in consideration of longterm goals (knowler and bradshaw, 2007). the difficulty is clearly related to the necessity to focus short-term targets taking also into account the long-term objectives (janssen and anderies, 2007). for instance, the development of new pes schemes was considered relevant. but the implementation of innovative payment types also needs to take into account the socioeconomic context. in other words, if innovative solutions will be more and more necessary their success also depends on the capacity, interest and motivation of farmers to uptake such solutions (raina et al., 2021). tools fitting to the improvement of human capital are therefore relevant, but the time range needed is usually long and a constant adaptation and coordination with regulations and incentives is needed. 5. conclusions the work presents the results of a survey carried-out in 7 csrs and collecting opnions from 71 stakeholders. the work covered a range of different agricultural/forestry systems located in north, east and southern parts of the eu that were selected for their particular supply of public goods. even though with limitations, the study can therefore highlights some trends and interesting aspects on the connections between agriculture and forestry, and pgbs in eu. overall, the perceived impacts on pgbs linked to agriculture and forestry were positive: this is consistent across regions and stakeholders, whereas more remarkable differences between the stakeholders were evident for public bads. on one hand, that outcome confirms that the selected csrs were ‘hotspots’ of public goods. on the other hand, assessing aspects related to public bads is a potential pathway of research to shed light on differences and opportunities for the design of local-scale agri-environmental policies (blanco et al., 2019; targetti et al., 2021a). in that regard, the selection of csrs denoting a significant supply of public goods is however a limitation of the study. the inclusion of csrs featuring a wider set of pgb supply would therefore be necesssary to deepen the aspects related to public bads. the results clearly point to regulation compliance and subsidies as relevant factors for the maintenance and improvement of a range of public goods. the cap in particular is confirmed among the most relevant factors but the opinion of the stakeholders is rather differentiated according to the different pgbs. this can be related to a less clear perception of effectiveness of voluntary schemes, but in part it is also very likely associated to the higher complexity of that policy approach and the consequent difficulty in assigning clear impacts on specific pgbs. that is consistent with current debates that concerns for instance the role and design of subsidies for the conservation and promotion of farmland biodiversity (pe’er et al., 2022). a general convergence regards the impact of factors on pgbs. conversely, a different consideration of mechanisms and issues was evidenced and related to the different perception of stakeholders towards pgbs. the classification provided by the clusters analysis allowed to understand the configuration of issues and mechanisms that were considered relevant in connection with the different pgbs. for instance, market processes and society demand were more relevant for specific public goods such as production quality and security, and animal welfare, whereas to a lesser extent to climate stability. this in part explains the relevance attributed to market-related mechanisms for those pgbs. instead, more articulated tools such as payments for environmental services were considered necessary for public goods such as landscape, biodiversity, water quality, etc. the disconnection between society demand and supply of environmental services is fundamentally an issue that involves awareness of processes underpinning such services, the adequacy of markets to stimulate specific services, and the trade-offs that inevitably incur between levels of ecosystem services supply (adams, 2014). in this study, we have evidenced that different stakeholders have different views and opinions but further evidence would be necessary to understand whether such differences might be related to csr features or agricultural systems. beside regulation and subsidies, soft aspects leveraging on the environmental attitude of farmers and society are considered important across the different csrs and for the different public goods. surely, these aspects accrue on longer time ranges but likely their perceived relevance denotes a scarce attention or inefficiencies of the current agrienvironmental policy framework towards those topics. even though the study did not evaluate different policy mixes, a message emerging from the analysis supports the need of considering instruments addressing different temporal and spatial scales. on the one hand regulations are considered effective for a wide set of public goods and across the different csrs. on the other hand, the effectiveness of incentives depends on the type of public goods and local scale 363 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 provision of public goods and bads by agriculture and forestry issues. the role of knowledge, awareness and education in general is considered relevant for enhancing the adaptation capacity of a socio-ecological system (janssen and anderies, 2007). therefore, tools targeting social and human capital should also be taken into account even though their impact will likely span in the long-term. acknowledgments the work has been supported by the eu-h2020 provide project ‘providing a smart delivery of public goods by eu agriculture and forestry”, 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forest areas 2 farmland biodiversity pollination habitats wild berries and mushrooms games local varieties of plants and animals protection against pests picking fruits pest & diseases increase of dangerous wild animals pollination reduction 3 water availability sustainable land management resilience to drought intensification natural resources consumption water quality sustainable land management intensification water pollution intensification health problems 4 air quality health, & wellbeing sustainable land management intensification air pollution health problems 5 soil functionality sustainable land management carbon storage water retention geodiversity climate change adaptation and mitigation soil erosion soil pollution intensification 6 climate stability carbon storage ghg emissions carbon sink intensification 7 resilience to flooding, landslide and wildfire sustainable land management water flows regulation climate change adaptation flooding resilience to fire sustainable land management climate change adaptation wild fire 8 rural viability/vitality cultural heritage local identity land & infrastructure maintenance creation of rural jobs land stewardship connectedness & spiritual values land abandonment culture loss poverty poor land management safety / vandalism 9 food, energy and timber security and quality (local supply) energy supply food security & quality sustainable land management employment forest quality poor food quality & distribution outsourcing production deforestation natural resources exploitation 10 farm animal health/welfare foraging & hunting pasture and grasslands sustainable land management intensification health problems 367 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 provision of public goods and bads by agriculture and forestry appendix b cluster dendrogram. appendix c average perception of pgbs relevance and standard deviation. public goods perception public bads perception were significantly different across the csrs (with p<0.05) for all the pgbs considered in the study except for ‘degradation of rural viability and vitality’, ‘reduction of climate stability’ and ‘reduction of resilience to environmental risks’. public goods perception* public bads perception** average score st. dev. average score st. dev. landscape and scenery 7.01 1.67 5.79 2.68 farmland biodiversity (animal and vegetal) 6.92 1.97 6.70 2.25 water quality and availability 7.31 2.21 6.65 2.33 air quality 6.59 2.33 5.51 2.83 soil functionality 6.99 2.29 6.77 2.15 climate stability 6.83 2.15 5.80 2.56 resilience to flooding, landslides and fire 6.34 2.37 5.97 2.79 rural viability and vitality 6.93 1.89 4.84 2.76 productions quality and security (food, timber, energy) 7.00 2.00 4.92 2.73 farm animal health and welfare 6.68 1.98 5.28 2.55 *difference between case study regions not significant ** difference between case study regions significant with p<0.05 except for ‘degradation of rural viability and vitality’, ‘reduction of climate stability’ and ‘reduction of resilience to environmental risks’. 368 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 stefano targetti et al. appendix d heathmap of the perception of relevant factors for the different pgbs. relevant for public goods regulation compliance cap funding technology society demand market demand productions quality and security (food, timber, energy) 42% 41% 39% 34% 61% farmland biodiversity (animal and vegetal) 54% 54% 23% 24% 24% farm animal health and welfare 44% 35% 25% 37% 46% resilience to flooding, landslides and fire 42% 25% 18% 17% 8% water quality and availability 54% 28% 38% 27% 17% landscape and scenery 32% 39% 15% 24% 21% soil functionality 48% 34% 31% 15% 14% rural viability and vitality 18% 54% 24% 17% 23% climate stability 28% 23% 13% 18% 7% air quality 34% 15% 31% 25% 13% relevant for public bads production choices nnintended by-products absence of adequate compensation competition abandonment indirect result of practices productions quality and security (food, timber, energy) 31% 15% 24% 31% 14% 14% farmland biodiversity (animal and vegetal) 56% 48% 34% 25% 13% 15% farm animal health and welfare 30% 21% 23% 23% 7% 13% resilience to flooding, landslides and fire 32% 31% 14% 7% 18% 8% water quality and availability 45% 31% 20% 30% 3% 7% landscape and scenery 44% 51% 24% 15% 23% 14% soil functionality 48% 44% 24% 25% 17% 7% rural viability and vitality 15% 15% 17% 10% 30% 10% climate stability 25% 38% 27% 15% 11% 18% air quality 25% 32% 14% 14% 7% 13% 369 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 provision of public goods and bads by agriculture and forestry appendix e heathmap of the perception of issues related to the different pgbs. pgs relevance pbs relevance so ci et y pe rc ep tio n in ad eq ua te fu nd in g co nfl ic ts o f in te re st la nd -u se tr ad eoff s ur ba n sp ra w lin g/ ab an do nm en t la nd te nu re / ac ce ss so ci et y pe rc ep tio n in ad eq ua te fu nd in g co nfl ic ts o f in te re st la nd -u se tr ad eoff s ur ba n sp ra w lin g/ ab an do nm en t la nd te nu re / ac ce ss productions quality and security (food, timber, energy) 42% 42% 35% 46% 46% 45% 42% 39% 38% 37% 42% 25% farmland biodiversity (animal and vegetal) 37% 46% 48% 49% 31% 21% 37% 45% 38% 32% 35% 18% resilience to flooding, landslides and fire 39% 25% 46% 32% 23% 27% 28% 30% 28% 32% 18% 20% water quality and availability 38% 23% 18% 11% 18% 10% 25% 23% 13% 10% 15% 13% air quality 38% 34% 30% 34% 35% 14% 31% 35% 30% 32% 18% 10% soil functionality 46% 30% 25% 28% 14% 10% 37% 30% 15% 20% 14% 14% climate stability 39% 28% 34% 37% 28% 20% 25% 20% 23% 20% 18% 14% landscape and scenery 31% 30% 28% 17% 56% 18% 24% 31% 27% 18% 41% 30% rural viability and vitality 25% 30% 34% 23% 15% 14% 20% 30% 14% 13% 15% 15% farm animal health and welfare 20% 23% 34% 13% 6% 10% 23% 27% 20% 8% 8% 8% 370 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 stefano targetti et al. appendix f heathmap of the perception of mechanisms able to foster pgs and reduce pbs. fostering public goods in cr ea se fi na nc ia l su pp or t t o fa rm er s an d fo re st er s im pl em en t pa ym en ts fo r en vi ro nm en ta l se rv ic es (p es ) im pl em en t n ew m ar ke tba se d in ce nt iv es pr om ot e fa rm er s’ an d fo re st er s’ ed uc at io n to su st ai na bi lit y a da pt c om pe ns at io n sc he m es a nd re gu la tio ns to th e gl ob al m ar ke t a do pt m or e effi ci en t la nd u se p la ns pi on ee r/ fo st er c ro ss co m pl ia nc e in a ll pu bl ic su bs id ie s production quality and security (food, timber, energy) 34% 23% 42% 38% 32% 23% 28% farmland biodiversity (animal and vegetal) 48% 59% 32% 42% 20% 37% 39% resilience to flooding, landslides and fire 31% 35% 13% 45% 8% 28% 32% water quality and availability 30% 49% 25% 42% 21% 37% 39% air quality 23% 30% 15% 35% 13% 23% 35% soil functionality 28% 48% 17% 46% 14% 45% 39% climate stability 28% 45% 17% 39% 25% 24% 34% landscape and scenery 38% 59% 24% 44% 10% 37% 41% rural viability and vitality 42% 24% 30% 30% 28% 31% 27% farm animal health and welfare 31% 31% 37% 38% 20% 20% 31% reducing public bads in cr ea se fi na nc ia l su pp or t t o fa rm er s an d fo re st er s im pl em en t pa ym en ts fo r en vi ro nm en ta l se rv ic es (p es ) im pl em en t n ew m ar ke tba se d in ce nt iv es pr om ot e fa rm er s’ an d fo re st er s’ ed uc at io n to su st ai na bi lit y a da pt c om pe ns at io n sc he m es a nd re gu la tio ns to th e gl ob al m ar ke t a do pt m or e effi ci en t la nd u se p la ns pi on ee r c ro ss co m pl ia nc e in a ll pu bl ic su bs id ie s production quality and security (food, timber, energy) 32% 20% 38% 35% 28% 21% 24% farmland biodiversity (animal and vegetal) 46% 65% 37% 52% 20% 44% 51% resilience to flooding, landslides and fire 23% 35% 15% 35% 14% 27% 28% water quality and availability 35% 46% 28% 49% 15% 37% 46% air quality 24% 38% 15% 46% 14% 20% 37% soil functionality 31% 44% 17% 54% 13% 37% 42% climate stability 28% 44% 24% 48% 24% 30% 35% landscape and scenery 54% 46% 27% 46% 18% 42% 41% rural viability and vitality 37% 27% 20% 34% 18% 35% 24% farm animal health and welfare 30% 32% 42% 39% 27% 13% 37% 371 bio-based and applied economics 11(4): 351-371, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12843 provision of public goods and bads by agriculture and forestry appendix g correlogram outlining the relation between clusters and csrs. the intensity of the blue colour is related to a positive relation, whereas red color indicates negative relations. the dimension of the bubble is proportional to the rate of the relation. volume 11, issue 4 2022 firenze university press systems thinking, mapping and change in food and agriculture domenico dentoni1,*, carlo cucchi2, marija roglic1, rob lubberink3, rahmin bender-salazar4, timothy manyise5 vulnerability and resilience to food and nutrition insecurity: a review of the literature towards a unified framework pierluigi montalbano1, donato romano2,* the local economic impact of climate change mitigation in agriculture cathal geoghegan1,2,*, cathal o’donoghue1,3, jason loughrey2 dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: a scoping review simone russo1,2,*, lerato phali3, maurizio prosperi1 provision of public goods and bads by agriculture and forestry. an analysis of stakeholders’ perception of factors, issues and mechanisms stefano targetti1,*, valentina marconi1, meri raggi2, annette piorr3, anastasio josé villanueva4, kati häfner3, mikko kurttila5, natalia letki6, mihai costica7, dimitre nikolov8, davide viaggi1 bio-based and applied economics bae bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 copyright: © 2023 squarcina, m., romano, d. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: squarcina, m., romano, d. (2023). the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia. bio-based and applied economics 12(4): 333-366. doi: 10.36253/bae13404 received: july 21, 2022 accepted: october 23, 2023 published: december 31, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: davide menozzi orcid ms: 0000-0003-1128-1568 dr: 0000-0001-7120-8050 the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia margherita squarcina1,2,*, donato romano1 1 department of economics and management, universita degli studi di firenze, florence, italy 2 department of agricultural economics and rural development, georg-august-university, göttingen, germany *corresponding author: margherita.squarcina@uni-goettingen.de abstract. the effects of covid-19 have been highly heterogeneous, crucially depending on household livelihoods. in the context of households reliant on agri-food systems, the extent of these effects significantly depends on their position within the value chain. an assessment of the covid-19 effects along the agri-food value chain and the identification of pivotal factors influencing these outcomes are key for designing appropriate responses and targeting the population most in need should a crisis akin to covid-19 emerge in the future. using a longitudinal dataset from ethiopia, composed of a pre-covid baseline and six follow-up phone-based surveys, this paper estimates the covid-19-induced change in household income and job participation, tracing its evolution throughout seven months after the pandemic onset. applying both longitudinal and cross-sectional econometric models, we show that the covid-19 shock reduced both employment and income, with increasingly negative impacts over time. despite initial resilience in the face of restrictive measures, farming eventually emerged as the most affected segment within the agri-food value chain over the medium term. access to formal institutions such as insurance and credit services, formal contractual arrangements, and secured land ownership title played a key role in mitigating the likelihood of income loss. keywords: covid-19, food value chain, labor market, income loss, ethiopia. jel codes: i15, o12, q12. 1. introduction the covid-19 pandemic caused unprecedented disruptions in many value chains at domestic as well as global levels (moosavi et al., 2022), including the bioeconomy and specifically the agri-food value chains (afvcs) (devereux et al., 2020), although significant heterogenous effects were reported1. although some segments of afvc such as farming have 1 for instance, in the short run the bioeconomy – i.e. the economic activities that depend on the use of biological resources, including agriculture and food processing – showed a level of resilhttps://doi.org/10.36253/bae-13404 http://www.fupress.com/bae https://doi.org/10.36253/bae-13404 https://doi.org/10.36253/bae-13404 https://orcid.org/0000-0003-1128-1568 https://orcid.org/0000-0001-7120-8050 mailto:margherita.squarcina@uni-goettingen.de 334 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano been initially less affected by restriction decisions, downstream segments such as food services, restaurants, and retail as well as midstream segments such as processing, logistics, and transportation, have been impacted since the onset of the crisis2. the general conclusion of early studies is that the covid-19 impact is differentiated across different segments of the afvc as well as within each segment (diao et al., 2020; tamru et al., 2020; tesfaye et al., 2020). the pandemic and the related restrictions implemented by governments raised many challenges to individuals and households participating in the afvc. the ability to absorb, adapt, and even transform the way a livelihood is gained by individuals and households – in short, their resilience to the covid-19 shock – has been often limited by many factors such as access to technology, financial services, or social safety nets3. indeed, many agents had limited options to cope with the covid-19 shock, resulting in income reduction or job loss and eventually increasing poverty and food insecurity. assessing covid-19 impacts across afvc segments and identifying the main factors that determined those impacts on afvc participants and their options to adapt to the “new normal” is then crucial for designing appropriate responses and targeting the groups most in need should a shock similar to covid-19 occur again in the future. using ethiopia as a case study, this study aims at: (i) assessing which segments of the afvc have been most affected by the pandemic, in terms of labor participation and income loss; and (ii) identifying which factors at the household level have mostly influenced the impact of covid-19 on income, and specifically on farm income. ethiopia has been selected for several reasons. its economy is mainly based on agriculture, which accounts for 34% of gdp (world bank, 2021), 80% of the population depends on agriculture (njeru et al., 2016), and smallholder farming accounts for 95% of agricultural production (tigre and heshmati, 2022). however, new commercial and gig economy clusters are emerging in the country, as is the case of intensive vegetable cultivation ience relatively higher than the overall economy in europe. however, this result was mainly driven by the technology-intensive sectors of the bioeconomy, such as biochemistry and bioelectricity, which partially offset the negative impact on the more traditional sectors of biomass processing, namely agriculture and food processing (lasarte-lópez et al., 2023). 2 indeed, it was initially expected that farming experienced less direct effects, except where hired labor was important, although interlinkages with the other segments of the chain may have caused income losses and production disruption (swinnen, 2020). 3 for instance, cesaro et al. (2022) found that financial liquidity and repairment of equipment and machinery were the difficulties most reported by farmers in italy in the short run. in the central rift valley (minten et al., 2020). these new activities challenge small farmers’ and small enterprises’ participation in the afvc, which are compounding with already existing structural constraints such as low access to credit and extension, weak labor market, and high transaction costs (croppenstedt et al., 2003; bryan et al., 2009; asfaw et al., 2011; harvest sa, 2012). in such a situation, the covid-19 shock could push smallholder farmers and small and medium enterprises out of the market. the first case of covid-19 in the country was reported on march 13th, 20204. in the same month, the federal government implemented a set of containment measures, such as school closure, physical distancing, and restrictions on gathering and transportation (baye, 2020). in april, a five-month state of emergency was declared, though economic activities continued to operate. although farmers could keep working, they faced many challenges. with borders shut, imported inputs were more difficult to find and their price increased (hirvonen et al., 2021b, 2021c, and 2021d). moreover, restrictions on movement made it almost impossible for farmers to reach the markets. this eventually led to a drop in agri-food sales, particularly of some vegetables such as tomatoes, papaya, and watermelon (molla, 2020). the travel restrictions also doubled transport costs, with a further domino effect on production, raising the farmgate and retail prices of some products, such as tomatoes (hirvonen et al. 2021b). additionally, since many farmers could not store their goods – particularly perishable produce – they were forced to accept the low prices set by buyers (ababulgu et al., 2022). hired labor was also affected. many rural workers returned to their homes and the reduced labor supply pushed up the costs of labor (agajie, 2020). effects were driven also by the fear of contagion. people associated raw vegetables with infection, reducing their purchases (hirvonen et al., 2021a; tamru et al., 2020). this determined a significant reduction in local market sales as well as exports (ababulgu et al., 2022). although anecdotal evidence exists on the impacts of covid-19 on afvc participation and income, rigorous empirical studies based on household-level survey data are few. josephson et al. (2021) used the world bank phone-based surveys of ethiopia, malawi, nigeria, and uganda to document the socioeconomic impacts of the pandemic. they found that 77% of households across the four countries experienced an income loss in the immediate aftermath of the pandemic. however, the authors were not able to measure how much of the loss 4 for details, see https://www.afro.who.int/news/first-case-covid-19-confirmed-ethiopia. https://doi.org/10.36253/bae-13404 https://www.afro.who.int/news/first-case-covid-19-confirmed-ethiopia https://www.afro.who.int/news/first-case-covid-19-confirmed-ethiopia 335the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 can be directly determined by the pandemic, given the descriptive nature of their analysis. according to this study, ethiopian households are significantly less likely to experience an income loss compared to those from the other three countries. more recently, the same dataset has been used by rudin-rush et al. (2022) to document trends in food security over the twelve months after the onset of the covid-19 pandemic. this study reports a sharp increase in food insecurity in the aftermath of the pandemic, with a subsequent gradual decline. furthermore, rural households were more negatively affected than urban households in terms of food security. ifpri conducted a series of monthly phone-based surveys between may and august 2020 (i.e., up to five months after the pandemic onset) interviewing nearly 600 households in addis ababa (hirvonen et al., 2021a). more than half of respondents reported a fall in income relative to their average pre-pandemic income at the same time of the year (hirvonen et al., 2020), with the proportion of affected households increasing from may to july (hirvonen et al., 2021a). poorer households more likely reported income losses, with a significant worsening of household food security and nutritional status. income loss and unemployment were identified as the most common shocks experienced by the respondents (abate et al., 2020; de brauw et al., 2020; hirvonen et al., 2020). despite income loss, zhang et al. (2022) found that the population in addis ababa was not affected on average in terms of food security. however, the situation in other regions of the country was much different, especially in rural areas and among vulnerable individuals and households (abay et al., 2023, zhang et al., 2022). hirvonen et al. (2021b) relied on a large value chain survey administered by ifpri in february 2020 and follow-up phone interviews collected in may 2020 to analyze the disruption of the vegetable value chain from the main producing areas in the central rift valley to addis ababa, including changes in prices and adjustments in the marketing activities of the participants – from farmers to wholesalers and retailers. they found that nearly 60% of the smallholders and more than 60% of the investors reported less income than usual. they also found that the pandemic in ethiopia disrupted trade not only between neighboring countries but also among subnational geographies, thus determining high volatility in agricultural prices (hirvonen et al., 2021b). however, they found that the changes in wholesale and retail marketing margins were relatively low, suggesting a resilient response of the domestic food value chains during the pandemic in ethiopia. although these studies provided important early estimates of the effects of the pandemic on relevant indicators of welfare, they present some limitations. some of them are based on a non-representative sample. the study of hirvonen et al. (2021d) focuses on the vegetable value chain only, while hirvonen et al. (2021b) focus only on households living in addis ababa. most of the existing studies focus only on one or a few points in time, failing to capture the evolving impact of covid-19 over time. other studies look at the impact on employment, such as khamis et al. (2021), but they do not specifically disaggregate the analysis across afvc segments. our study aims to address these limitations contributing to estimating the magnitude of afvc disruption caused by the covid-19 pandemic in ethiopia over a relatively longer time (seven months from the pandemic onset) and looking specifically at differentiated impacts on various afvc segments. it also helps to identify the main factors that contributed to offset the negative consequences of covid-19 shock and to keep adequate levels of income for afvc participants. although the data present some limitations in terms of representativeness (cf. section 2), we think the findings emerging from this study are relevant not only because they provide a better understanding of the covid-19 impact in ethiopia, but also because they can contribute to a better management of covid-19-like crises should they emerge in the future. the paper is organized as follows. the next section describes the data used. section 3 presents some descriptive statistics, with specific reference to employment and income. section 4 describes the empirical strategy adopted. section 5 presents and discusses the results of the analysis. section 6 concludes. 2. data the analysis uses a seven-rounds longitudinal dataset, which includes a baseline pre-pandemic face-to-face survey and six follow-up phone surveys. pre-covid data come from the 2018/19 ethiopia socioeconomic survey (ess), which is part of the world bank’s living standards measurement study – integrated surveys on agriculture (lsms-isa). it covers all regions of the country and is representative at national, urban/rural, and regional levels. the other six rounds of data are part of the world banks’s covid-19 high-frequency phone survey of households (hfpsh) 2020. this phone-based survey is a 15-minute questionnaire administered to a subsample of the ess 2018/19 households from april to mid-october (figure 1). the world bank team interviewed the same households in each round, leading to a https://doi.org/10.36253/bae-13404 336 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano balanced dataset of 2,347 households5. to obtain unbiased estimates, sampling weights at the household level have been constructed by the world bank team following himelein (2014), thus having a sample that is representative at the national and urban/rural levels. a major problem with the hfpsh surveys is that phone penetration in rural ethiopia is still low, with only 40% of rural households having access to a phone. therefore, data are representative only of those households that have access to phones in urban areas (90% of all urban households) and better-off rural households that have access to mobile phones (wieser et al., 2020). however, these rural households are systematically different from the majority of rural households (ambel et al., 2020a). additionally, only one member per household – typically the household head or the spouse – has been interviewed, but household heads could systematically differ from the rest of the population, undermining the representativeness of the sample at the individual level6. 5 each covid-19 hfpsh survey has a slightly different number of observations, ranging from 2,704 to 3,249 households. in order to have a balanced panel we reduced the sample to 2,347 observations. for more information on sampling design please visit https://microdata. worldbank.org/index.php/catalog/3716. 6 further discussion about this issue is presented in section 4.3. a key methodological concern is that factors other than the covid-19 crisis could drive the evolution of outcomes over time. specifically, month-to-month seasonality could represent an issue. in principle, it can be controlled by including month fixed effects. however, this could not be done due to the different time reference between the baseline and phone surveys, especially for the employment variable. while the pre-covid survey considers the employment activities over the preceding twelve months, including both planting and harvesting seasons, questions on employment in the phone surveys consider only the week before the interview. there could be then an underestimation of the farming-related employment rate. luckily, the phone survey covers the sowing and the harvesting periods of the two main crop seasons (figure 1)7. therefore, although it is not possible to fully rule problems of seasonality out, it is likely that it does not significantly affect our estimates. seasonality can also bias the analysis because of its impact on farm income. there are two rainy seasons over the year: the small rainy season (belg), which occurs between march and may, and the main rainy season 7 only sugarcane and taro are neither planted nor harvested in the period under analysis. source: http://www.fao.org/agriculture/seed/cropcalendar/welcome.do;jsessionid=62ffb1ac3cb6fa74244a91586e5e1758. 13 march 1st case 22 april 14 may 1st wave 4 june 2nd wave 26 june 3rd wave 4th wave 27 july 14 august 14 oct 1st crop season (belg all cereals) growingsowing harvesting 5th wave 2k 1.5 k 1 k 500 0 17 sept 6th wave daily covid-19 cases 2st crop season (meher – main season) sowing growing harvesting harvesting figure 1. timeline with daily covid-19 cases, surveys date, and crop seasons in ethiopia. source: data on covid-19 daily cases retrieved from https://covid19.who.int/region/afro/country/et; information on crop seasons retrieved from https://www.prepdata.org/stories/ethiopiaclimate-and-agriculture; date of covid-19 hfpsh data collection retrieved from https://microdata.worldbank.org/index.php/catalog/3716. https://doi.org/10.36253/bae-13404 https://microdata.worldbank.org/index.php/catalog/3716 https://microdata.worldbank.org/index.php/catalog/3716 http://www.fao.org/agriculture/seed/cropcalendar/welcome.do;jsessionid=62ffb1ac3cb6fa74244a91586e5e1758 http://www.fao.org/agriculture/seed/cropcalendar/welcome.do;jsessionid=62ffb1ac3cb6fa74244a91586e5e1758 https://covid19.who.int/region/afro/country/et https://www.prepdata.org/stories/ethiopia-climate-and-agriculture https://www.prepdata.org/stories/ethiopia-climate-and-agriculture https://microdata.worldbank.org/index.php/catalog/3716 337the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 (meher), which takes place between june and september8 (hirvonen et al., 2016). around 90% of the total crop production is done during the meher season (taffesse et al., 2013). farmers usually run out of stock between july and september, which can result in increasing household food insecurity (dercon and krishnan, 2000; hirvonen et al., 2016; gilbert et al., 2017; sibhatu and qaim, 2017; roba et al., 2019). however, seasonality-induced food shortage is quite homogeneous across farmers, and it is captured by a variable that controls for the aggregate time trend (cf. section 4.3). another factor to consider in the analysis is the desert locusts’ outbreak, i.e. the most destructive migratory pests in the world (cressman et al., 2016; lazar et al., 2016), that swarmed from yemen to the horn of africa in the summer of 2019. in the fourth round of phone surveys9, 45% of farmers self-reported that they experienced desert locusts on their farm, and 41% of households experienced locusts in their kebele10. desert locusts have negative consequences on income because they destroy the crops and the fodder for livestock. additionally, labor time is required to spray the chemicals on the area under cultivation. 3. descriptive statistics 3.1. employment the first round of the phone-based survey asked if the individual did any work in the seven days before the interview, if the individual was working before the covid-19 outbreak, and if the current work is the same as before the pandemic. for the other rounds of data, the questions were the same, but using as reference time the previous call. as shown in figure 2, the employment rate experienced a significant reduction in the aftermath of the covid-19 outbreak. overall employment dropped by 11 percentage points. however, labor activities recovered quickly over the next months, exceeding the employment rate before covid-19 (ambel et al., 2020b), driven by own farming activity. the dynamics of labor mobility are somehow different within the various afvc segments11 (table 1). 8 this refers to the growing period of the season. 9 information on desert locusts is available only in rounds 4 and 6. however, in round 6 very few respondents answered the questions related to locusts, so it is not possible to produce reliable estimates. 10 the kebele is the smallest administrative unit of ethiopia, i.e. a neighbourhood or a localized and delimited group of people consisting of at least 500 families. 11 the variable of labor participation in afvc activities has been decomposed into three segments, namely: a) upstream (primary prothe upstream segment was quite stable, with 83% of people remaining in the same segment of employment and 12% moving towards non-afvc activities after seven months. in the case of midstream activities, only 26% remained in the same segment, while 39% moved towards non-afvc activities, 23% moved to upstream activities, and the remaining 12% moved to downstream activities. similarly, in the downstream segment, only 27% on average did not change the segment of employment, while most of the people who did it, moved to midstream activities (49%). finally, almost two-thirds of the ones who were not originally working in afvc activities remained outside the afvc, while the ones who entered the afvc split mainly between midstream (14%) and upstream (18%) activities. employment changes can be in part driven by seasonality. indeed, seasonal migration in ethiopia occurs both from rural to urban areas, used as a coping strategy during the dry season (asefawu, 2022), and also towards northwest ethiopia for temporary employment on large-scale agricultural farms during the rainy season (schicker et al., 2015). however, respondents reported that the main reason for stop working is covid-19, especially in the early phone rounds. between april and may (round 1), more than half of individuals stated that they lost their job because of the pandemic (figure 3). in the last rounds instead, being “temporarily absent” is the main reason to stop working. this can be indirectly associated with the pandemic since many who temporarily left their job in the city migrated to rural areas12. duction, including farming, fishing, forestry and hunting), b) midstream (manufacturing of food products, including processing; wholesale and retail trade; transport; and distribution), and c) downstream (restaurants and bars). 12 detailed information on the number of individuals that started to work again in each round, and the reason for having stopped working 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% pre-covid round 1 round 2 round 3 round 4 round 5 round 6 total employment own farm on farm wage off farm wage off farm self-employment figure 2. evolution of employment in ethiopia, 2018/19 – mid october 2020. source: own elaboration from ess 2018/2019 and hfpsh 2020. note: sampling weights applied. https://doi.org/10.36253/bae-13404 338 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano 3.2. income respondents to the phone-based survey were asked to assess the income change experienced by the household compared to the situation before the covid-19 outbreak in the first-round interview, and compared to the previous call in the subsequent rounds. the possible answers ranged from “total loss” to “income reduction”, “no change” and “income increase”. the categorical nature of the question does not allow for computin the previous round is reported table a.1 in the appendix. ing accurate estimates of the magnitude of covid-19 impact on income, limiting the analysis to the qualitative incidence of the pandemic (de weerdt, 2008). in the case of farming income, the answer highly depends on the harvest time of cultivated crops. in fact, the bulk of crop sales occurs between december and february, though april usually records the largest sales (hirvonen et al., 2016). figure 4 (panel a) shows a generalized decreasing trend of the share of households that reported a reduction of income or a total loss between rounds, not only for farming income but also for other table 1. labor transition matrix across afvc segments and non-afvc, 2018/19 – mid-october 2020.   n. obs. round 6 downstream midstream upstream non-afvc pre-covid afvc downstream 145 27.5 48.5 6.2 17.8 afvc midstream 184 12.4 26,0 22.9 38.8 afvc upstream 517 0.6 3.6 83.3 12.1 non-afvc 834 4.0 14.4 17.8 63.9 source: own elaboration from ess 2018/2019 and hfpsh 2020. note: upstream: agricultural production and agricultural employment, including fisheries, forestry, and hunting; midstream: manufacturing of food products, including processing, trade, and transport; downstream: restaurants and bars; non-afvc: all other employment activities. sampling weights applied. 0% 10% 20% 30% 40% 50% 60% covid-19 seasonal/casual worker contract ended temporarily absent retired being ill need to care for ill relative round 1 round 2 round 3 round 4 round 5 round 6 figure 3. reason to stop working, share on round total. source: own elaboration from hfpsh 2020. note: sampling weights applied. https://doi.org/10.36253/bae-13404 339the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 sources of income. if we compare the income change to the situation before the covid-19 outbreak13 (figure 4, panel b), the trend is different. the share of households that reported a reduction in income compared to the pre-covid situation shrank only for non-farm business while it did not change significantly over time for other income sources. in the case of total income, the share of households experiencing a reduction/total loss even increases over time, up to 9 percentage points increase in the sixth round compared to the baseline. table 2 reports the descriptive statistics of the outcome variables per each round. specifically, the employment variables show the rate of people employed in each sector, while the income variables report the share of households that experienced a reduction in income or a total loss compared to the baseline. there are some differences between the overall baseline sample and the phone-based sub-sample (table 3). respondents to the hfps sample are mainly located in urban areas, the majority of them are males, and their employment rate is higher. they are generally older, more educated, and more employed through a formal job contract. the rate of non-farm employment activities is high13 the change of income is computed backward up to the baseline. if, for instance, in round 2 income did not change compared to the previous round, and in round 1 it increased compared to the baseline, in round 2 it also increased compared to the baseline. the change is assumed to occur with the same amount, therefore if a household first reports an increase, and then a reduction, the net effect is null. we are aware of the arbitrariness of this methodology. for this reason, the analysis has been also conducted round by round, finding similar results, as reported in the appendix. er compared to the baseline population. vice versa, the rate of farm-related activities is similar and the same is true for the employment rate in the upstream segment. given these differences, the results of the analysis could not be generalized to the whole ethiopian population. to check for possible problems of representativeness, we ran a robustness check using individuallyadjusted weights (cf. section 4.3). 4. empirical strategy 4.1. outcome variables to assess the impact14 of covid-19 on income and employment we estimated a household fixed effects model with a continuous treatment variable, adapting the approach implemented by amare et al. (2020). we use two dependent variables, namely: participating in labor activities, considering any type of activity as well as specific sectors; and income change, looking at both the total income and the different income sources. labor activities are grouped into own-farm, onfarm wage employment, off-farm self-employment, and off-farm wage employment. we also consider employment according to segments of afvc (i.e., downstream, midstream, and upstream activities as defined above) given the expected differentiated impact of covid-19 14 we use the terms “impact” and “effect” throughout the paper, but we acknowledge that we are not able to fully identify a causal mechanism with our estimation strategy due to the limitations described in section 2. a) share of hhs experiencing income reduction or total loss, round-to-round. b) share of hhs experiencing income reduction or total loss, comparison with the pre-covid situation. 10% 20% 30% 40% 50% 60% 70% 80% 90% round 1 round 2 round 3 round 4 round 5 round 6 total income farming wage employment non-farm business 10% 20% 30% 40% 50% 60% 70% 80% 90% round 1 round 2 round 3 round 4 round 5 round 6 total income farming wage employment non-farm business10% 20% 30% 40% 50% 60% 70% 80% 90% round 1 round 2 round 3 round 4 round 5 round 6 total income farming wage employment non-farm business figure 4. share of hhs experiencing income reduction or total loss per income source category, share by income source. source: own elaboration from ess 2018/2019 and hfpsh 2020. note: sampling weights applied. https://doi.org/10.36253/bae-13404 340 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano related restrictions on different stages of the value chain (reardon et al., 2020b, swinnen and mcdermott, 2020). for each labor activity, we computed a dummy equal to 1 if the individual operated in that activity, and zero otherwise. we consider total income and specific income-generating activities, namely family farming, non-farm family business, wage employment of household members, and other sources of income (pension, remittances, etc.). the variables take the values -2 (total loss), -1 (income reduction), 0 (no change), and 1 (income increase). 4.2. treatment variable the main variable of interest is the number of confirmed cases of covid-19 over the number of inhabitants in each region. this information has been retrieved from the ethiopia covid-19 monitoring platform15 and weekly governmental bulletins16. this variable captures the evolution and the spread of the virus across the country. the variable has been transformed using the inverse hyperbolic sine (ihs) transformation, to account for zero cases in the first post-covid survey. regres15 available at this link: https://www.covid19.et/covid-19/. 16 see https://www.ephi.gov.et/. sion results can be interpreted as the log transformation (johnson, 1949; burbidge et al., 1988). this variable presents some limitations. firstly, the ratio of confirmed cases over the number of tests would do a better job than using the number of the total population in each region, but unfortunately, data on testing disaggregated at the regional level are not available. secondly, the number of confirmed cases probably underestimates the real infection level due to the limited testing capacity of the country17. although the testing capacity is presumably unequal across regions, as access to basic health care in ethiopia is highly unequal (woldemichael et al., 2019; alene et al., 2021), the use of fixed effects estimator (cf. section 4.3) should partially mitigate the issue, controlling for differences across regions that do not vary over time. thirdly, the number of confirmed cases does not adequately proxy the treatment variable, i.e. the variation in terms of access to the market and restrictions imposed by the government, which in turn affect labor participation and income. however, we can assume that as the number of confirmed cases increases in a region, both the restrictions imposed by the government and the individually self-imposed restrictions would increase. indeed, data confirm that the economic and health effects of the pandemic covary in ethiopia. when using daily data retrieved from the oxford covid-19 government response tracker (oxcgrt), the correlation between the covid-19 cases and the stringency index is positive and significant18. although there could be a time lag between the implementation of the restrictions and the effect in terms of covid-19 cases, this lag is shorter (7 to 14 days depending on the restriction type and stringency as well as on the rate of infection of the specific covid-19 variant) than the period analyzed in each round (i.e., one month). therefore, the average effect of the restrictions over a month should be captured by the number of confirmed cases. it is also important to consider the heterogeneity of the response across the regions. indeed, although measures were coordinated at the national level, each regional state in ethiopia tailored policy implementation to the local situation through its own public health emergency opera17 the virus spread unheavenly across regions. in particular, the addis ababa region reported the highest proportion of cases per million population, followed by harar and dir dawa. factors that can explain this heterogeneity are a different testing capacity, driven by better infrastructure, especially in the capital and in other urban areas, population density, and degree of internal and international connectivity. 18 similar results were found in other countries. for instance, amare et al. (2021) in nigeria found that the variables of covid-19 cases and government restrictions produced the same results, confirming that the two variables can proxy each other. table 2. descriptive statistics of employment and income outcomes. round baseline 1 2 3 4 5 6 employment: % of individuals total employment 75% 64% 85% 86% 87% 88% 88% downstream 4% 1% 2% 2% 2% 2% 2% upstream 40% 37% 55% 55% 56% 58% 57% midstream 5% 7% 8% 8% 8% 8% 9% out of fvc 25% 18% 20% 21% 21% 20% 20% own farm 39% 36% 53% 53% 54% 56% 56% on-farm wage 1% 1% 2% 2% 2% 2% 2% off-farm self-employment 28% 18% 20% 20% 21% 20% 20% off-farm wage employment 19% 9% 10% 10% 11% 10% 10% income: % of households total income 56% 67% 70% 72% 72% 71% farming 42% 50% 51% 47% 45% 41% wage employment 36% 36% 34% 35% 36% 33% non-farm business 86% 82% 82% 76% 68% 65% source: own elaboration from ess 2018/2019 and hfpsh 2020. note: employment variables report the share of people employed in each round. income figures show the share of households that reported income reduction or total loss compared to the baseline. sampling weights applied. https://doi.org/10.36253/bae-13404 https://www.covid19.et/covid-19/ https://www.ephi.gov.et/ 341the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 tions centre (pheoc)19. this calls for using regionally disaggregated variables. fourthly, the number of confirmed cases does not capture spillover effects that may occur across regions. 19 source: https://www.acceleratehss.org/wp-content/uploads/2022/03/ covid-collaborative-ethiopia-case-study.pdf indeed, each region is treated as an independent entity assuming that each of them does not have interactions with the rest of the country and no aggregate impacts occurred. this assumption does not hold when two or more regions have strong economic relationships. for instance, this may happen when a food value chain crosses over regional boundaries – e.g. a food item is produced in a region and consumed in another – or workers commute between different regions. in these cases, should one region be affected differently than others, this effect would affect not only that specific region, but also other geographically closer or economically linked regions. however, as regions in ethiopia are quite large and people are mostly working in the local economy (e.g. high share of family farmers), the spillover effect should be limited. additionally, the ethiopian political system based on ethnic federalism, where the regions have been identified based on “settlement patterns, identity, languages” (article 46.2 of the ethiopian constitution), makes it easier to conceptualize regions as separate economies. evidence indeed shows that labor mobility and internal migration in ethiopia are limited (bundervoet, 2018) because migration across regional boundaries often creates social tensions and violence (breines, 2020; fessha and dessalegn, 2020). 4.3. model specification the base model is the following: yhrt = αhr + β0timet + β1(casesr * timet) + εhrt (1) where yhrt is the outcome variable – either labor or income – defined for each household h in region r and round t; αhr captures household fixed effects, allowing controlling for unobserved time-invariant heterogeneity among households; casesr is the number of confirmed covid-19 cases per million population in each region; timet is a dummy variable representing the time of observation, equal to 1 for the post-covid round and 0 for the pre-covid round, whose coefficient captures the aggregate time trend in the labor market and income composition; the interaction term between time and the number of cases captures the differential impact of covid-19 on labor participation and income change across regions due to different exposure to the virus; εhrt is the error term. considering that the virus spread unevenly across regions over time, we need to control for this. regions that experienced the virus earlier are indeed more likely to report more cases than the other regions. a first specification of the base model introduces the variable day1r, table 3. comparison of individual characteristics between the baseline sample and phone-based subsample. variable baseline population phone-based sub-sample student’s t significance rural 0.72 0.64 *** (0.45) (0.48) sex: 1=female 0.51 0.27 *** (0.50) (0.45) employed in any activity 0.75 0.85 *** (0.43) (0.35) age 30.69 38.33 *** (16.38) (13.76) not engaged in education, employment or training 0.10 0.11 (0.30) (0.31) literacy rate 0.55 0.63 *** (0.50) (0.48) formal job contract 0.04 0.10 *** (0.19) (0.30) years of education 3.70 4.75 *** (4.32) (5.12) agricultural wage work 0.01 0.01 (0.09) (0.09) non-farm self-employment 0.10 0.15 (0.29) (0.36) non-farm wage work 0.12 0.22 *** (0.32) (0.42) own farm work 0.63 0.63 *** (0.48) (0.48) upstream of afvc 0.63 0.64 (0.48) (0.48) midstream of afvc 0.03 0.04 *** (0.16) (0.20) downstream of afvc 0.01 0.01 ** (0.10) (0.12) n. of observations 19,910 2,347 note: the first column includes all individuals at the baseline. the second column includes only individuals from the baseline who were tracked in the phone-based surveys. sampling weights applied. standard deviation in parenthesis. children below 11 years old dropped from the sample. mean difference is computed through a linear regression, where the independent variable is a dummy equal to one if the individual belongs to the phone subsample. *** p<0.01, ** p<0.05, * p<0.1. https://doi.org/10.36253/bae-13404 https://www.acceleratehss.org/wp-content/uploads/2022/03/covid-collaborative-ethiopia-case-study.pdf https://www.acceleratehss.org/wp-content/uploads/2022/03/covid-collaborative-ethiopia-case-study.pdf 342 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano which reports the number of days that occurred from the first covid-19 case at the national level to the first covid-19 case registered in the region: yhrt = αhr + β0timet + β1(casesr * timet) + β2(day1r * timet) + εhrt (2) to differentiate the impact of the isolated interactions and the impact of the combined spatial and temporal variabilities, we consider also a specification that includes the triple interaction between the time dummy, the number of confirmed cases per million inhabitants, and the variable as follows: yhrt = αhr + β0timet + β1(casesr * timet) + β2(day1r * timet) + β3(casesr * day1r * timet) + εhrt (3) as an additional specification, we include in (3) some control variables available in the phone-based post-covid surveys, which are not captured by the fixed effects. these variables are the presence of another member in the household who lost a job in the aftermath of the pandemic, and if the household received any assistance since the outbreak of the pandemic. the analysis has been conducted for each post-covid round, comparing it with the baseline. in this way, it is possible to observe the evolution of the response to the crisis over time. we expect that regions more affected by the pandemic will report a higher reduction in labor participation and income and that the effect will increase the pandemic deepening over time20. we used a linear probability model with household fixed effects. the advantage of this model compared to a logit or conditional logit model with fixed effects is the inclusion of all observations. in fact, the logit model with fixed effects drops the units that show no variability in the dependent variable (beck, 2018), drastically reducing the number of observations in case of small variability. in our data, this would result in an 80% reduction of the sample size. all analyses have been carried out using the balanced sample. however, given the existence of significant attrition rates, we replicated the analysis also using the unbalanced sample, finding consistent results (cf. figures a.2 and a.3 in the appendix). an important issue that could have affected our estimates is the desert locust outbreak experienced by some regions of the country in the period of analysis, which 20 we also estimated the impact of covid-19 from wave to wave, comparing the outcome with the previous interview and results still hold (cf. figure a.1 in the appendix). might have harsh consequences on production21. for this reason, it is important to consider this shock on farming employment and income. the hfph surveys report information on desert locust outbreak only in the 4th wave. we retrieved gis data on desert locusts from the fao locusts hub22 and merged it with the households’ location. given that the household coordinates refer to the dwelling, and not to the parcel, and they are slightly modified for privacy reasons, we created a buffer of 3 km around the household centroid to account for these factors (on average the parcel is 1.7 km distant from the dwelling). regarding the location of locusts, we considered the area surveyed, which is 580 hectares on average. figure 5 reports the location of households (in purple) and where the desert locusts have been observed (in green) over the year 2020. although gis data are quite accurate and reliable, many data gaps undermine the quality of the information and might represent a limitation of our analysis. firstly, household coordinates have been slightly modified for privacy reasons and this might determine some measurement bias. secondly, only the distance of the parcel is available, not the direction: it is not possible to know exactly where it is located. thirdly, the information provided for locusts does not account for the locust swarm movements over time, excluding areas outside sampled locations. for these reasons, self-reported information could be more reliable to measure the effect of these pests on farm crops. therefore, we report estimates of the impact of locust outbreak using gis data as well as self-reported data (cf. section 5.1). the second part of the analysis aims at identifying the main determinants influencing changes in income in the presence of covid-19. in doing this, we use a probability model with regressors in time t (pre-covid) and the dependent variable in time t + 1 (post-covid). in this way, we can estimate which attributes that were in place in normal conditions are more likely to affect the outcome during the pandemic. the probability that the outcome variable takes a certain value is given by prob(yh,t+1 = j) = xt h,tβc+ uh,t+1 (4) where h is the household, x is a column vector of observable variables, namely the attributes and factors in time t, uh,t+1 is the error term, and j takes the value 1 if the 21 the desert locust outbreak appeared in ethiopia in second half of 2019. by january 2020, the outbreak was already significantly affecting the country, peaking around mid 2020 around the harvesting time of the first crop season (belg) when most cereals were ready to be harvested (see figure 1). according to fao, the outbreak was the worst in 25 years in the country. 22 https://locust-hub-hqfao.hub.arcgis.com/. https://doi.org/10.36253/bae-13404 https://locust-hub-hqfao.hub.arcgis.com/ 343the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 outcome is dichotomous, or multiple values if it is categorical. the regressors include household characteristics, level of infrastructure and variables at the community level, economic-related variables, and agricultural-related variables when considering farm income. the dependent variable is the change in income at the household level. we decided to not consider the employment status because there could be problems of endogeneity because of omitted variable bias. this could occur mainly by external factors, for which information is not provided in the survey. an example could be the loss of job due to the employing company shut-down. in addition to econometric issues, as the job loss mainly depends on factors beyond household or individual control, investigating the household-related determinants of the loss of employment due to the covid-19 crisis would make little sense. the estimation has been conducted using a maximum likelihood estimator. we used the ordered probit model to account for the categorical nature of the dependent variable. however, given that the response rate for total loss and income increase was very low, we also created a dummy equal to 1 if income did not change or increase, and 0 otherwise. in this case, we used a probit model. 5. results 5.1. impact of covid-19 cases the impact on employment table 4 reports the impact on employment at round 1 as resulting from the different model specifications23, 23 table 4 reports the estimation for round 1 as an example. then we provide a visual estimation of our model results (e.g. figure 5) that makes easier understanding the evolution of the relevant outcomes over the analyzed period. the estimates of each model are available upon request to the authors. figure 5. map of households’ location (purple circles) and locust swarm sites (green circles), 2020. source: own elaboration using data from fao locusts hub and ess 2018/2019. https://doi.org/10.36253/bae-13404 344 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano starting from model (1), which is a simple ols over the pooled sample, to model (5), which includes all the variables and their interaction terms, the individual/household fixed effects, and the controls. we consider the last model as the best suited model for the analysis. in fact, from the theoretical viewpoint, the within estimator of the fixed effects model is robust to many types of omitted variable bias24. furthermore, the inclusion of all regressors in model 5 allows controlling for more variables and provides insights on the role of such controls in determining the outcome variables. this also leads to higher adjusted r-square statistics as shown in table 4. as expected, our variable of interest, i.e. the interaction term cases*time, has a negative sign and is statistically significant, meaning that covid-19 negatively impacted employment, while the other interaction terms are not statistically significant. figure 6 reports the coefficient of the interaction term between the time trend and the covid-19 cases for each round, firstly considering any labor activities and then looking at specific sectors or segments of the afvc. these results show how covid-19 negatively impacted employment activities in ethiopia. they also show that the severity of the impact increased over time. 24 however, it is more inefficient than an ols estimator, because it reduces the variation of the independent and dependent variables used for estimation. indeed, it is more affected by measurement errors and by omitted variables that are not constant within the household. decomposing the impact along the afvc, we can see that upstream activities are the most affected. although this segment had initially been relatively less affected, it shows increasingly negative impacts in subsequent rounds. downstream and midstream segments have table 4. regression results over different models, employment – round 1. dependent variable: individual employed in any activity   variable (1) (2) (3) (4) (5) time -0.0684*** -0.0758*** -0.0657*** -0.0658*** -0.0709***   (0.0137) (0.0127) (0.0185) (0.0193) (0.0196) cases*time -0.0438*** -0.0353*** -0.0362*** -0.0361*** -0.0360***   (0.00866) (0.00577) (0.00607) (0.00651) (0.00654) days*time -0.000395 -0.000386 -0.000364   (0.000505) (0.000644) (0.000640) cases*days*time -9.72e-06 -1.53e-06 (0.000383) (0.000383) constant 0.746*** 0.746*** 0.746*** 0.746*** 0.746***   (0.0163) (0.00507) (0.00507) (0.00507) (0.00507) controls no no no no yes fixed effects no yes yes yes yes observations 4,694 4,694 4,694 4,694 4,694 r-squared 0.042 0.071 0.082 0.107 0.116 number of pid   2,347 2,347 2,347 2,347 note: estimates are computed using a linear probability model. sampling weights applied. standard errors clustered at the household level. *** p<0.01, ** p<0.05, * p<0.1. data refers to the 1st wave. figure 6. impact of covid-19 cases on employment over time. source: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable = dummy equal to 1 if the individual is employed. dots are coefficients estimated from a linear probability model with household fixed effects. each post-covid round is compared with the baseline. bars are 95% confidence intervals. sampling weights applied. standard errors clustered at the household level. *** p<0.01, ** p<0.05, * p<0.1. https://doi.org/10.36253/bae-13404 345the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 also been negatively affected, but in this case, the impact did not significantly change over time. in the case of non-afvc, after an initial negative impact, the coefficients became no longer significant from the third round onwards. this could mean that the covid-19 cases no longer had an impact or that employment effects within this category offset each other. for instance, among the off-farm self-employment, construction and manufacturing reported a positive effect, while trade and restaurants, hotels, and bars showed negative coefficients. the impact on income table 5 shows the results of the various models estimating the impact of covid-19 on income change. again, the interaction term cases*time is negative and most of the time statistically significant25. the impact of covid-19 on income (figure 7), takes more time to occur. households indeed can rely on savings or other coping strategies in the short run. however, from the third round onwards total income has been negatively affected by covid-19 cases, and, similarly to employment, the effect increases over time. wage income and off-farm business income do not seem to 25 the coefficient loses significance when the triple interaction term is added. however, from the third round onwards it is statistically significant. have been significantly affected, while it is interesting to see the impact on farm family farming. after an initial positive effect, in the last three rounds covid-19 cases have significantly and negatively impacted farm income. this can be explained because initially, the virus spread table 5. regression results over different models, income – round 1. dependent variable: change in total hh income     variable (1) (2) (3) (4) (5) time -0.567*** -0.567*** -0.544*** -0.558*** -0.549***   (0.0274) (0.0274) (0.0374) (0.0404) (0.0412) cases*time -0.0246** -0.0246** -0.0266** -0.0157 -0.0148   (0.0112) (0.0112) (0.0114) (0.0118) (0.0119) days*time -0.000879 5.95e-05 1.58e-05   (0.00110) (0.00162) (0.00161) cases*days*time -0.000967 -0.000970 (0.000864) (0.000864) constant 0 -0 -0 -0 0   (3.08e-10) (0.0106) (0.0106) (0.0106) (3.08e-10) controls no no no no yes fixed effects no yes yes yes yes observations 4,691 4,691 4,691 4,691 4,691 r-squared 0.336 0.503 0.503 0.504 0.505 number of pid   2,347 2,347 2,347 2,347 note: estimates are computed using a linear probability model. sampling weights applied. standard errors clustered at the household level.*** p<0.01, ** p<0.05, * p<0.1. data refers to the 1st wave. figure 7. impact of covid-19 cases on income over time. source: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable = categorical variable of income change, ranging from -2 (total loss) to 1 (increase). dots are coefficients estimated from a linear probability model with household fixed effects. each post-covid round is compared with the baseline. bars are 95% confidence intervals. sampling weights applied. standard errors clustered at the household level. *** p<0.01, ** p<0.05, * p<0.1. https://doi.org/10.36253/bae-13404 346 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano in the cities, marginally hitting farmers livelihood in rural areas. then the virus spread across the whole country, affecting also people located in more remote areas. additionally, if initially smallholders and subsistence farm households were more advantaged against the measures implemented by the government because they relied less on external inputs and markets, this advantage disappeared over time, due to the limited coping mechanisms they had available. the impact of locust outbreak on farm employment and income the inclusion of the dummy for respondents who self-reported to have experienced the desert locust shock on the farm has a significant impact on changing the coefficients associated with the number of covid-19 cases. results are reported in table 6. for employment, the coefficient of the covid-19 cases loses significance, while having locusts on the farm is positively and significantly associated with labor activities. this confirms the additional labor time required to spray the chemicals all over the land. regarding income, compared to previous results, where the coefficient of covid-19 cases was -0.621, the inclusion of desert locusts increases the magnitude of the coefficient to -1.103, strengthening the negative impact of covid-19 cases on farm income. these results show that it is important to consider multiple shocks experienced by individuals and households when assessing the impact of a certain event. when using the georeferenced data (table 7), the locust variable loses significance for own farm labor activities. instead, the impact of locust outbreak is significant and negative in the case of farm income. the effect of the locust dummy is larger in the 4th wave (-0.377), which corresponds to the most damaging period for crops, given the locust life cycle as well as the timing of the crop season (peak harvesting in the first crop season, cf. figure 1). the inclusion of the locusts’ data over all six waves does not significantly affect the impact of covid-19 cases on farm income, showing only slight changes from the model not including the locust dummy estimates. 5.2. determinants of income change in this section, the results of the regressions aimed to identify the main determinants of income change are presented. regressors have been grouped into three categories: household characteristics, infrastructures, and economic-related variables. as dependent variables, we considered the change in total and farm incomes. for illustrative reasons, this section reports only the results of the models using a dichotomous dependent variable. the estimates of the ordered probit model are reported in the appendix (tables a.4 and a.5). total income change figure 8 reports the estimated coefficients of household characteristics over the six rounds. the only significant variable here is the level of education of the household head. a higher level of education is positively associated with a higher probability of not experiencing an income reduction/total loss. living in rural areas shows a positive and significant coefficient only in the first round, consistent with previous analyses that show that rural areas were initially less affected. economic-related variables (figure 9) show some interesting patterns. having a formal job contract is associated with a higher probability of income increase or unchanged income level. a similar relationship can be found with having a bank account and formal insurance, although the magnitude and the level of significance are lower than in the case of a formal contract. these results table 6. simultaneous impact of locusts (self-reported data) and covid-19 on own farm employment activities and farm income change, 4th round. employed in own farm activities farm income change time 0.0489 5.242*** (0.504) (1.893) cases*time 0.0216 -1.103*** (0.0938) (0.372) days*time -0.0237** -0.0998*** (0.0115) (0.0350) days*time*cases 0.00333* 0.0194*** (0.00200) (0.00671) locusts on the farm 0.134* -0.0244 (0.0685) (0.110) constant 0.542*** -0 (0.00927) (0.0111) controls yes yes fe yes yes observations 2,961 2,639 r-squared 0.088 0.309 number of pid1 2,347 2,347 note: estimates are computed using a linear probability model. sampling weights applied. standard errors clustered at the household level.*** p<0.01, ** p<0.05, * p<0.1. data refers to the 4th wave. https://doi.org/10.36253/bae-13404 347the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 show that access to formal institutions is a winning strategy to contrast the negative consequences caused by the crisis. per capita household income reports a positive relationship, meaning that as per capita income increases the probability of not experiencing an income reduction increases. richer households are then expected to suffer less from the crisis. however, the magnitude of the coefficient is quite small, suggesting that the differential effect between poorer and richer households is limited. regarding infrastructure variables, none of them has a substantial effect on total income (figure 10). being distant to the urban center, to the main road, or to the markets seems to be slightly positively associated, sometimes in a significant way, with the probability of income increase or unchanged. however, the coefficient is lower than 1%. farm income change the same variables considered in the previous section show partly different patterns when considering farm income. looking at the household characteristics (figure 11), the education of the household head no longer seems to play a relevant role, while the household size and the age of the household head are associated with a higher probability of income reduction, although the effect is statistically significant only in a few rounds. table 7. simultaneous impact of locusts (gis data) and covid-19 on farm income change.   wave 1 wave 2 wave 3 wave 4 wave 5 wave 6 time -0.377*** -0.981*** -1.163*** 2.829*** 3.007*** 5.228***   (0.0627) (0.269) (0.363) (0.895) (1.141) (1.273) cases*time -0.0217 0.277** 0.254** -0.620*** -0.519*** -0.815***   (0.0564) (0.134) (0.129) (0.174) (0.182) (0.196) days*time 0.00110 0.0131** 0.0189** -0.0531*** -0.0581** -0.103***   (0.00267) (0.00638) (0.00858) (0.0169) (0.0250) (0.0273) cases*days*time -0.00175 -0.00621** -0.00654** 0.0104*** 0.00923** 0.0153***   (0.00185) (0.00281) (0.00272) (0.00310) (0.00365) (0.00393) locust dummy -0.307*** -0.350*** -0.0973 -0.377*** 0.0327 -0.00324   (0.104) (0.129) (0.156) (0.144) (0.245) (0.213) constant 0.00328 0.00398 0.00131 0.00455 -0.000442 4.29e-05   (0.0114) (0.0126) (0.0111) (0.0131) (0.0163) (0.0139) controls yes yes yes yes yes yes fe yes yes yes yes yes yes observations 3,025 2,882 2,850 2,853 2,844 2,843 r-squared 0.386 0.415 0.384 0.225 0.102 0.099 number of pid1 2,347 2,347 2,347 2,347 2,347 2,347 note: dependent variable: categorical variable of income change, ranging from -2 (total loss) to 1 (increase). estimates are computed using a linear probability model with household fixed effects. sampling weights applied. standard errors clustered at the household level.*** p<0.01, ** p<0.05, * p<0.1. figure 8. effects of households’ characteristics on total income change over time. source: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable = dummy equal to 1 if total income did not decrease compared to pre-covid round. dots are average marginal effects from a probit regression. each postcovid round is compared with the baseline. bars are 95% confidence intervals. sampling weights applied. robust standard errors. *** p<0.01, ** p<0.05, * p<0.1. https://doi.org/10.36253/bae-13404 348 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano even in the case of farm income (figure 12), distance does not show significant effects, except for distance to a large market, where it seems that the more distant the household the higher the probability of farm income unchanged or increased. this result may look counterintuitive. a possible explanation could be that more (economically) isolated households had already put in place some strategies to account for the distance from large markets, so they were more advantaged relatively to those farmers who were used to relying on markets. additionally, given the travel restrictions, domestic food value chains could have reshaped to adapt to the new situation, shortening their lengths. in this way, people in remote areas relied more on locally produced agricultural products instead of going to the main urban market. the role of microfinance institutions in the community is interesting. indeed, differently from total income, here it shows a positive coefficient, and in the last rounds the effect is also statistically significant. this means that this type of institution matters in times of crisis. as in the total income case, having a bank account and formal insurance rise the probability of farm income increase (figure 13). having a formal job contract does not show a statistically significant effect on agricultural income. this makes sense given that most households in ethiopia run family farms and do not participate in the formal labor market. regarding the agricultural-related variables (figure 14), results seem to suggest that farmers with larger areas of land have a higher probability of success compared to smallholders, as shown also by the marginal effects of land size on the probability that farm income did not decrease (figure a.4 in the appendix). this result is in contrast to findings from other studies conducted in different contexts. cesaro et al. (2022), for example, found that medium-large farms in italy expressed greater concern about the negative consequences of covid-19 in the short term than small farms. having a land ownership title or holding the right to use it played an important role in cushioning the negative covid-19 impact. households that use fertilizers and those that have agricultural machinery, although they initially experienced a positive or insignificant effect, were subsequently negafigure 9. effects of economic-related variables on total income change over time. source: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable = dummy equal to 1 if total income did not decrease compared to pre-covid round. dots are average marginal effects from a probit regression. each postcovid round is compared with the baseline. bars are 95% confidence intervals. sampling weights applied. robust standard errors. *** p<0.01, ** p<0.05, * p<0.1. figure 10. effects of infrastructure variables on total income change over time. source: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable = dummy equal to 1 if farm income did not decrease compared to pre-covid round. dots are average marginal effects from a probit regression. each postcovid round is compared with the baseline. bars are 95% confidence intervals. sampling weights applied. robust standard errors. *** p<0.01, ** p<0.05, * p<0.1. https://doi.org/10.36253/bae-13404 349the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 tively affected. this result can be the consequence of the mobility and trade restrictions, which decreased inputs availability and increased their prices. a less diversified crop mix was detrimental to farm income increase in early rounds, as shown by the coefficient of the herfindahl index of crop26. 5.3. robustness checks placebo test to test the validity of the treatment variable used in the analysis, we ran a placebo test, imputing the covid-19 shock in the prior wave of the ess, collected in 26 the herfindahl index is a measure of crop concentration. it is computed as the sum of square of the proportion of individual crop groups in a portfolio. the index decreases with an increase in diversification. it ranges from 0 (complete diversification) to 1 (complete specialization) (singh et al., 2018). 2015/2016, and considering as baseline the 2012/2014 ess survey. if the variable of the number of covid-19 cases correctly captures the impact of the covid-19 shock, we should not find any significant effect, given that at that time the shock did not occur. table 8 reports the results of the test, applied for the change of total income at the household level and total employment at the individual level. the variable is valid when applied to the model of household income, where none of the coefficients related to covid-19 is significant. instead, when running the same model on total employment, the coefficient of the interaction between time and covid-19 cases is significant (column 1). however, the sign is positive, in contrast to the predicted effect that the shock should have. a possible explanation is that the variable of covid-19 cases is in a way correlated with regional characteristics. for instance, we know that covid-19 has affected some economic sectors more than others and, if a region is specialized in one sector, this correlation will be significant. if the employment rate was expanding between 2014 and 2016 in that specific sector, the correlation would be positive. figure 11. effects of households’ characteristics on farm income change over time. source: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable = dummy equal to 1 if total income did not decrease compared to pre-covid round. dots are average marginal effects from a probit regression. each postcovid round is compared with the baseline. bars are 95% confidence intervals. sampling weights applied. robust standard errors. *** p<0.01, ** p<0.05, * p<0.1. figure 12. effects of infrastructure variables on farm income change over time. source: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable = dummy equal to 1 if farm income did not decrease compared to pre-covid round. dots are average marginal effects from a probit regression. each postcovid round is compared with the baseline. bars are 95% confidence intervals. sampling weights applied. robust standard errors. *** p<0.01, ** p<0.05, * p<0.1. https://doi.org/10.36253/bae-13404 350 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano introducing regional income (column 2)27 indeed makes the interaction term not significant. inverse probability weights to address the problem of representativeness of the individual sample, we created individual-level adjusted weights using the inverse probability based on the ess 2018/2019, and we compared the outcomes using these weights following khamis et al. (2021)28. we ran 27 regional income can capture the level of economic development of the region, which is in turn correlated with other factors, including the economic sector. 28 khamis et al. (2021) relied on the world bank’s global monitoring database. although they found similar results when applying the corrected weights compared to the original ones, they had a limited set of variables available to use for reweighting the estimates, undermining the effectiveness of the weights created. in our case, instead, we can consider more variables, increasing the ability to effectively adjust for the a logit regression to estimate the probability of being in the hfps subsample over a set of variables at the individual level, weighted by the household weights of ess 2019. variables include age, gender, years of completed education, living in rural areas, income quintile, being employed, working in own farm activities, and being not engaged in education, employment or training (neet). children below 12 years old have been excluded. the inverse of the estimated probability is the adjusted weight. this procedure gives greater weight to observations that appeared in the hfps sample. figure 15 reports the coefficients estimated with original weights vis-à-vis the adjusted ones. the correlation of the estimates using the two methods is very high, i.e. 98%. this result suggests that the labor market outcomes of the subsample of individuals are generally consistent with the outcomes of the whole working population. 6. conclusions the analysis showed that covid-19 negatively impacted both household employment and income, the more so the longer the time length from the pandemic differences between the individuals in the subsample and the rest of the population. figure 13. effects of economic-related variables on farm income change over time. source: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable = dummy equal to 1 if farm income did not decrease compared to pre-covid round. dots are average marginal effects from a probit regression. each postcovid round is compared with the baseline. bars are 95% confidence intervals. sampling weights applied. robust standard errors. *** p<0.01, ** p<0.05, * p<0.1. figure 14. effects of agricultural-related variables on farm income change over time.csource: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable = dummy equal to 1 if farm income did not decrease compared to pre-covid round. dots are average marginal effects from a probit regression. each postcovid round is compared with the baseline. bars are 95% confidence intervals. sampling weights applied. robust standard errors. *** p<0.01, ** p<0.05, * p<0.1. https://doi.org/10.36253/bae-13404 351the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 onset. upstream activities, and specifically own farming, are the most affected segment of the afvc. indeed, despite an initial positive effect, the impact then became negative and increased in magnitude over time. this finding is partly in line with previous studies published in the immediate aftermath of the pandemic (bundervoet and finn, 2020; reardon et al., 2020a) that show that farming was the less affected sector. however, tracking the impact over time allowed gaining a more complete understanding of the evolution of the effect, with farming increasingly severely affected by the disruption of the food value chain. the initial resilience capacity of the ethiopian food marketing systems, as reported by hirvonen et al. (2021b) for the vegetable value chain does not seem to persist over time. this highlights the importance of monitoring the evolution of the impact of the shock over time. indeed, considering only the initial effect could give an incomplete and misleading understanding of the actual situation. we also showed that the most vulnerable farmers have been hit hardest. small farming households are more exposed to the negative consequences of the crisis. there is the need then to target specifically this group of afvc actors, especially in situations of crisis. to do this, afvc participants need to have access to specific tools that allow them to cope with the shock. access to formal institutions, such as formal insurance, bank account, formal contract, and land title are all positively associated with a higher probability of income increase. the national government should then increase its effort in providing improved opportunities to access financial services as well as formal institutions. last but not least, multiple shocks dramatically worsen the picture. this is the case of the desert locust outbreak, that compounded with an already difficult situation created by covid-19. therefore, policymakers should consider the effects of simultaneous shocks when designing policy responses to the crisis. from our data, it is not possible to identify how the above impacts may affect other important dimensions of well-being such as food security. abay et al. (2023) found that household food insecurity increased by 11.7 percentage points. the authors did not assess the relationship between a reduction in employment/income and food security. however, there is evidence from other studies that a reduction in employment and income may or may not affect food security. especially when subjective estimates of income change are used, the relationship is not straightforward. in hirvonen et al. (2021a), for example, self-reported income shocks did not appear to be associated with changes in the household dietary diversity score (hdds). furthermore, other mechanisms may be in place that can influence food security, depending on the type of household considered, its integration into the food value chain, and the participation in safety net programs29. additional analysis of the mechanisms and close monitoring of the effects of the crisis are then required to respond with appropriate policies as other crises arise. the main limitations of this work are related to the type of data available, which reduces the internal and external validities of the findings (abate et al., 2023). indeed, the fact that data are collected through phone interviews limits the representativeness of the sample, especially considering the low phone penetration in the rural areas of the country. the covid-19 cases variable is not fully able to capture the infection rate and the economic downturn caused by the policy interventions in the country. additionally, measurement error could be widespread in self-reported data. this is particularly relevant 29 abay et al. (2023), in the same study cited above, showed that participation in the productive safety net program (snp) offsets virtually almost all of the covid-19 induced food insecurity increase (11.7 percentage points): the likelihood of becoming food insecure increased by only 2.4 percentage points for psnp households. qualitatively similar results are reported by maffioli et al (2023) for myanmar. table 8. placebo test on ess 2012/2014 and ess 2015/2016. variables total income change total employment (1) (2) time 0.0852 -0.294*** -0.363** (0.154) (0.0850) (0.169) time*cases 0.0136 0.0258** 0.0419 (0.0204) (0.0113) (0.0365) time*days 0.00274 0.00153 0.00192 (0.00538) (0.00295) (0.00311) time*days*cases -0.000364 -0.000233 -0.000312 (0.000701) (0.000386) (0.000431) cases*regional income -4.31e-07 (9.34e-07) constant -0.00491 0.601*** 0.601*** (0.0109) (0.00583) (0.00584) controls yes yes yes fe yes yes yes observations 9,760 21,289 21,289 number of pid 4,887 11,368 11,368 r-squared 0.023 0.050 0.050 note: dependent variables: categorical variable of income change, ranging from -2 (total loss) to 1 (increase) (1st column), and dummy equal to 1 if the individual is employed (2nd column). income change is computed by comparing the amount of household income earned in each round. robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 https://doi.org/10.36253/bae-13404 352 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano for the variable of income change, which is highly subjective to respondents’ perception. income data collected through more reliable measures are then needed to avoid major measurement errors. finally, data used in this study were not intended to specifically track afvc participants. household surveys based on random sampling of the whole economy are typically unable to capture a representative picture of the actors across the different segments of the value chain30. vice versa, information/data retrieved through survey based on representative samples of the main afvcs31 coupled with cascading survey of the various afvc segments would have been better suited to grasp a better understanding of the overall effect of the covid-19 crisis on the ethiopian food system. 30 for instance, less than 100 individuals employed in the downstream segment are surveyed in each post-covid round. 31 studies on specific value chains in ethiopia have been conducted only for the dairy value chain (see hirvonen et al., 2021c), and the vegetable value chain (hirvonen et al., 2021d). references ababulgu, n., abajobir, n., and wana, h. 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http://documents.worldbank.org/curated/en/428541643263668314/monitoring-covid-19-impacts-on-households-in-ethiopia-results-from-11-rounds-of-high-frequency-phone-surveys-of-households-from-april-2020-through-may-2021 https://doi.org/10.1371/journal.pone.0213896 https://doi.org/10.1371/journal.pone.0213896 https://data.worldbank.org/indicator/nv.agr.totl.zs?locations=et https://data.worldbank.org/indicator/nv.agr.totl.zs?locations=et https://data.worldbank.org/indicator/nv.agr.totl.zs?locations=et https://doi.org/10.3390/epidemiologia3020013 https://doi.org/10.3390/epidemiologia3020013 356 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano appendix figure a.1. impact of covid-19 cases on income change, wave by wave. source: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable = categorical variable of income change, ranging from -2 (total loss) to 1 (increase). dots are coefficients estimated from a linear probability model with household fixed effects. each post-covid round is compared with the baseline. bars are 95% confidence intervals. sampling weights applied. previous call is considered the baseline. standard errors clustered at the household level. *** p<0.01, ** p<0.05, * p<0.1. figure a.2. impact of covid-19 cases on employment over time, unbalanced sample. source: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable = dummy equal to 1 if the individual is employed. dots are coefficients estimated from a linear probability model with household fixed effects. each postcovid round is compared with the baseline. bars are 95% confidence intervals. sampling weights applied. standard errors clustered at the household level. *** p<0.01, ** p<0.05, * p<0.1. figure a.3. impact of covid-19 cases on total income over time, unbalanced sample. source: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable = categorical variable of income change, ranging from -2 (total loss) to 1 (increase). dots are coefficients estimated from a linear probability model with household fixed effects. each post-covid round is compared with the baseline. bars are 95% confidence intervals. sampling weights applied. standard errors clustered at the household level. *** p<0.01, ** p<0.05, * p<0.1. figure a.4. marginal effects of land size on the probability that farm income change has not decreased. source: own calculation from ess 2018/2019 and hfpsh 2020. https://doi.org/10.36253/bae-13404 357the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 table a.1. number of individuals that started to work again in each round, by reason for stop working in the previous round. reason for stop working n. of individuals that started working again round 2 round 3 round 4 round 5 round 6 seasonal/casual worker 27 8 8 7 6 contract ended 3 0 3 2 1 covid-19 83 22 22 5 6 temporarily absent 25 8 6 5 9 retired 0 0 0 1 0 being ill 2 8 2 1 5 need to care for ill 1 1 1 0 0 other 1 0 1 1 0 n/a 329 94 71 54 30 total 471 141 114 76 57 table a.2. full regression estimates, total employment. variables round 1 round 2 round 3 round 4 round 5 round 6 time -0.0709*** 0.313*** 0.368*** 0.643*** 0.886*** 0.961***   (0.0196) (0.0463) (0.0539) (0.0807) (0.0975) (0.107) time*cases -0.0360*** -0.0717*** -0.0673*** -0.0813*** -0.0987*** -0.107***   (0.00654) (0.00837) (0.00879) (0.00980) (0.0110) (0.0118) time*days -0.000364 -0.00707*** -0.00792*** -0.0178*** -0.0260*** -0.0296***   (0.000640) (0.00134) (0.00142) (0.00281) (0.00377) (0.00391) time*cases*days -1.53e-06 0.00251*** 0.00197*** 0.00266*** 0.00338*** 0.00377***   (0.000383) (0.000367) (0.000345) (0.000434) (0.000513) (0.000514) other hh members lost job 0.0119 -0.166*** -0.0858 -0.1000 -0.233*** -0.225***   (0.0412) (0.0629) (0.0663) (0.0647) (0.0822) (0.0742) hh received assistance 0.0449 0.0777 0.0571 0.0223 -0.0219 -0.000938   (0.0358) (0.0634) (0.0537) (0.0495) (0.0480) (0.0459) constant 0.746*** 0.747*** 0.748*** 0.749*** 0.752*** 0.752***   (0.00507) (0.00840) (0.00833) (0.00853) (0.00842) (0.00863) observations 4,693 4,694 4,694 4,693 4,694 4,694 r-squared 0.122 0.086 0.079 0.098 0.124 0.116 number of pid1 2,347 2,347 2,347 2,347 2,347 2,347 source: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable = dummy equal to 1 if individual is employed. coefficients estimated using a linear probability model with household fixed effects. each post-covid round is compared with the baseline. 95% confidence intervals in parenthesis. sampling weights applied. standard errors clustered at the household level. *** p<0.01, ** p<0.05, * p<0. https://doi.org/10.36253/bae-13404 358 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano table a.3. full regression estimates, total income. variables round 1 round 2 round 3 round 4 round 5 round 6 time -0.549*** -0.599*** -0.502*** -0.237* -0.0341 0.203   (0.0412) (0.0522) (0.0685) (0.124) (0.160) (0.186) time*cases -0.0148 -0.00295 -0.0216** -0.0533*** -0.0677*** -0.0937***   (0.0119) (0.0102) (0.0110) (0.0150) (0.0179) (0.0206) time*days 1.58e-05 -0.00265 -0.00653** -0.0154** -0.0162* -0.0172*   (0.00161) (0.00198) (0.00311) (0.00670) (0.00945) (0.0102) time*cases*days -0.000970 0.000198 0.000921 0.00199** 0.00188 0.00192   (0.000864) (0.000564) (0.000685) (0.000947) (0.00119) (0.00124) hh received assistance -0.0774 -0.141** -0.201*** -0.117 -0.163** -0.165**   (0.0808) (0.0549) (0.0760) (0.0768) (0.0779) (0.0787) other hh members lost job -0.0758 -0.119 -0.123 -0.0720 -0.128 -0.108   (0.0871) (0.0839) (0.0759) (0.0823) (0.0809) (0.0802) constant -0 -0 0 0 -0 0   (0.0105) (0.0107) (0.0122) (0.0137) (0.0151) (0.0156) observations 4,691 4,693 4,694 4,691 4,693 4,685 r-squared 0.505 0.568 0.540 0.472 0.408 0.363 number of pid1 2,347 2,347 2,347 2,347 2,347 2,347 source: own calculation from ess 2018/2019 and hfpsh 2020. note: dependent variable: categorical variable of income change, ranging from -2 (total loss) to 1 (increase). estimates are computed using a linear probability model with household fixed effects. sampling weights applied. standard errors clustered at the household level. *** p<0.01, ** p<0.05, * p<0.1. https://doi.org/10.36253/bae-13404 359the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 ta bl e a .4 . o rd er ed p ro bi t, to ta l i nc om e ch an ge . va ria bl es ro un d 1 ro un d 2 ro un d 3 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 ye ar s o f e du ca tio n -0 .0 01 71 ** -0 .0 06 20 ** 0. 00 73 0* * 0. 00 06 11 * -0 .0 02 11 ** -0 .0 05 95 ** 0. 00 67 8* * 0. 00 12 9* * -0 .0 01 60 -0 .0 02 91 0. 00 33 1 0. 00 11 9 (0 .0 00 86 9) (0 .0 02 96 ) (0 .0 03 48 ) (0 .0 00 34 8) (0 .0 00 98 3) (0 .0 02 83 ) (0 .0 03 19 ) (0 .0 00 63 5) (0 .0 01 30 ) (0 .0 02 41 ) (0 .0 02 73 ) (0 .0 00 96 8) h h h ea d is fe m al e -0 .0 01 76 -0 .0 06 47 0. 00 75 9 0. 00 06 43 0. 01 17 0. 03 04 -0 .0 35 8 -0 .0 06 40 0. 02 88 0. 04 46 -0 .0 55 4 -0 .0 18 1 (0 .0 10 2) (0 .0 38 1) (0 .0 44 5) (0 .0 03 84 ) (0 .0 11 8) (0 .0 28 4) (0 .0 34 4) (0 .0 05 87 ) (0 .0 24 7) (0 .0 29 2) (0 .0 41 6) (0 .0 12 3) a ge o f h h h ea d -0 .0 00 46 1* -0 .0 01 67 * 0. 00 19 7* 0. 00 01 65 -0 .0 00 20 5 -0 .0 00 57 7 0. 00 06 57 0. 00 01 25 3. 79 e05 6. 89 e05 -7 .8 5e -0 5 -2 .8 3e -0 5 (0 .0 00 27 6) (0 .0 00 97 5) (0 .0 01 15 ) (0 .0 00 10 3) (0 .0 00 32 3) (0 .0 00 92 3) (0 .0 01 05 ) (0 .0 00 19 8) (0 .0 00 42 9) (0 .0 00 77 9) (0 .0 00 88 7) (0 .0 00 32 1) h h h ea d is m ar rie d 0. 00 80 2 0. 03 06 -0 .0 35 5 -0 .0 03 14 0. 00 92 5 0. 02 75 -0 .0 30 6 -0 .0 06 15 0. 01 62 0. 03 19 -0 .0 34 7 -0 .0 13 4 (0 .0 09 75 ) (0 .0 39 3) (0 .0 44 9) (0 .0 04 16 ) (0 .0 10 7) (0 .0 33 7) (0 .0 36 6) (0 .0 07 82 ) (0 .0 18 4) (0 .0 37 0) (0 .0 39 2) (0 .0 16 3) h h si ze -0 .0 00 39 7 -0 .0 01 44 0. 00 16 9 0. 00 01 41 0. 00 02 08 0. 00 05 86 -0 .0 00 66 7 -0 .0 00 12 7 0. 00 03 37 0. 00 06 13 -0 .0 00 69 8 -0 .0 00 25 2 (0 .0 02 06 ) (0 .0 07 44 ) (0 .0 08 77 ) (0 .0 00 73 3) (0 .0 02 42 ) (0 .0 06 83 ) (0 .0 07 77 ) (0 .0 01 48 ) (0 .0 03 44 ) (0 .0 06 26 ) (0 .0 07 13 ) (0 .0 02 57 ) a ss et in de x 0. 00 19 3 0. 00 69 9 -0 .0 08 23 -0 .0 00 68 9 -0 .0 00 41 2 -0 .0 01 16 0. 00 13 2 0. 00 02 51 -0 .0 01 47 -0 .0 02 68 0. 00 30 5 0. 00 11 0 (0 .0 01 64 ) (0 .0 05 93 ) (0 .0 06 96 ) (0 .0 00 62 0) (0 .0 01 80 ) (0 .0 05 03 ) (0 .0 05 74 ) (0 .0 01 09 ) (0 .0 02 67 ) (0 .0 04 69 ) (0 .0 05 39 ) (0 .0 01 96 ) h h c on su m pt io n (lo g) -0 .0 09 01 -0 .0 32 6 0. 03 84 0. 00 32 1 0. 00 21 5 0. 00 60 5 -0 .0 06 89 -0 .0 01 31 0. 00 41 9 0. 00 76 1 -0 .0 08 67 -0 .0 03 13 (0 .0 06 62 ) (0 .0 23 3) (0 .0 27 6) (0 .0 02 41 ) (0 .0 07 56 ) (0 .0 21 2) (0 .0 24 2) (0 .0 04 62 ) (0 .0 09 75 ) (0 .0 17 8) (0 .0 20 2) (0 .0 07 31 ) % o f m em be rs w ith jo b co nt ra ct -0 .0 63 5* ** -0 .2 30 ** * 0. 27 1* ** 0. 02 27 ** * -0 .0 63 4* ** -0 .1 78 ** * 0. 20 3* ** 0. 03 86 ** * -0 .0 68 6* ** -0 .1 25 ** 0. 14 2* * 0. 05 13 ** (0 .0 14 8) (0 .0 49 4) (0 .0 58 0) (0 .0 06 29 ) (0 .0 14 3) (0 .0 36 7) (0 .0 42 3) (0 .0 09 33 ) (0 .0 24 8) (0 .0 52 2) (0 .0 56 4) (0 .0 20 3) h h re ce iv es so ci al as sis ta nc e -0 .0 11 1 -0 .0 47 0 0. 05 30 0. 00 51 4 0. 00 67 4 0. 01 74 -0 .0 20 5 -0 .0 03 70 -0 .0 03 51 -0 .0 06 62 0. 00 74 0 0. 00 27 3 (0 .0 11 3) (0 .0 54 9) (0 .0 59 5) (0 .0 06 74 ) (0 .0 18 8) (0 .0 44 3) (0 .0 53 8) (0 .0 09 27 ) (0 .0 21 7) (0 .0 42 3) (0 .0 46 5) (0 .0 17 5) h h h as fo rm al c re di t -0 .0 01 74 -0 .0 06 41 0. 00 75 1 0. 00 06 39 0. 03 25 * 0. 06 62 ** -0 .0 85 1* * -0 .0 13 6* * 0. 04 14 0. 05 31 ** -0 .0 72 3* -0 .0 22 3* * (0 .0 11 3) (0 .0 42 4) (0 .0 49 4) (0 .0 04 28 ) (0 .0 19 3) (0 .0 26 8) (0 .0 40 1) (0 .0 06 10 ) (0 .0 27 5) (0 .0 22 1) (0 .0 38 2) (0 .0 11 1) h h h as sa vi ng s 0. 02 89 ** * 0. 10 7* ** -0 .1 26 ** * -0 .0 10 2* ** 0. 01 63 * 0. 04 56 * -0 .0 52 0* -0 .0 09 89 * 0. 00 58 6 0. 01 06 -0 .0 12 1 -0 .0 04 37 (0 .0 08 87 ) (0 .0 29 8) (0 .0 35 4) (0 .0 03 29 ) (0 .0 09 57 ) (0 .0 25 8) (0 .0 29 7) (0 .0 05 68 ) (0 .0 12 5) (0 .0 23 0) (0 .0 26 2) (0 .0 09 40 ) h h h as b an k ac co un t 0. 00 01 99 0. 00 07 22 -0 .0 00 85 0 -7 .1 0e -0 5 -0 .0 00 56 3 -0 .0 01 58 0. 00 18 0 0. 00 03 43 -0 .0 01 25 -0 .0 02 27 0. 00 25 9 0. 00 09 33 (0 .0 09 18 ) (0 .0 33 3) (0 .0 39 2) (0 .0 03 28 ) (0 .0 10 6) (0 .0 29 7) (0 .0 33 8) (0 .0 06 42 ) (0 .0 14 2) (0 .0 25 8) (0 .0 29 4) (0 .0 10 6) h h h as fo rm al in su ra nc e -0 .0 08 23 -0 .0 32 9 0. 03 77 0. 00 34 3 -0 .0 19 9* * -0 .0 72 6* 0. 07 56 ** 0. 01 69 * -0 .0 32 1* ** -0 .0 81 7* * 0. 07 93 ** 0. 03 46 ** (0 .0 08 32 ) (0 .0 36 0) (0 .0 40 3) (0 .0 04 00 ) (0 .0 08 35 ) (0 .0 37 7) (0 .0 36 4) (0 .0 09 78 ) (0 .0 11 7) (0 .0 38 7) (0 .0 32 8) (0 .0 17 4) d ist an ce to m ai n ro ad -0 .0 00 18 1 -0 .0 00 65 7 0. 00 07 74 6. 47 e05 -0 .0 00 24 6 -0 .0 00 69 3 0. 00 07 90 0. 00 01 50 -0 .0 00 41 8* -0 .0 00 76 1* 0. 00 08 67 * 0. 00 03 13 * (0 .0 00 15 6) (0 .0 00 56 1) (0 .0 00 66 0) (5 .6 7e -0 5) (0 .0 00 17 6) (0 .0 00 49 5) (0 .0 00 56 6) (0 .0 00 10 6) (0 .0 00 22 7) (0 .0 00 40 5) (0 .0 00 46 6) (0 .0 00 16 4) d ist an ce to u rb an c en te r -4 .5 7e -0 5 -0 .0 00 16 6 0. 00 01 95 1. 63 e05 -0 .0 00 13 2* -0 .0 00 37 3* * 0. 00 04 24 ** 8. 06 e05 * -0 .0 00 13 8 -0 .0 00 25 0 0. 00 02 85 0. 00 01 03 (5 .1 5e -0 5) (0 .0 00 18 8) (0 .0 00 22 0) (1 .8 9e -0 5) (6 .8 5e -0 5) (0 .0 00 18 7) (0 .0 00 21 3) (4 .3 3e -0 5) (9 .0 6e -0 5) (0 .0 00 16 1) (0 .0 00 18 3) (6 .7 7e -0 5) d ist an ce to m ar ke t -0 .0 00 47 6 -0 .0 01 73 0. 00 20 3 0. 00 01 70 -0 .0 00 35 6 -0 .0 01 00 0. 00 11 4 0. 00 02 17 -0 .0 00 70 5 -0 .0 01 28 0. 00 14 6 0. 00 05 27 (0 .0 00 30 0) (0 .0 01 08 ) (0 .0 01 27 ) (0 .0 00 10 8) (0 .0 00 34 5) (0 .0 00 96 7) (0 .0 01 11 ) (0 .0 00 20 7) (0 .0 00 46 6) (0 .0 00 85 4) (0 .0 00 97 2) (0 .0 00 34 5) (c on tin ue d) https://doi.org/10.36253/bae-13404 360 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano va ria bl es ro un d 1 ro un d 2 ro un d 3 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ic ro fin an ce in st itu tio n -0 .0 03 41 -0 .0 12 4 0. 01 45 0. 00 12 3 0. 00 69 0 0. 01 92 -0 .0 22 0 -0 .0 04 12 0. 01 49 0. 02 65 -0 .0 30 6 -0 .0 10 8 (0 .0 07 61 ) (0 .0 27 8) (0 .0 32 6) (0 .0 02 80 ) (0 .0 08 66 ) (0 .0 23 9) (0 .0 27 3) (0 .0 05 24 ) (0 .0 11 8) (0 .0 21 1) (0 .0 24 1) (0 .0 08 70 ) el ec tr ic ity 0. 00 87 6 0. 03 40 -0 .0 39 4 -0 .0 03 40 -0 .0 03 03 -0 .0 08 35 0. 00 95 7 0. 00 18 0 0. 00 11 4 0. 00 20 8 -0 .0 02 37 -0 .0 00 85 7 (0 .0 08 00 ) (0 .0 33 0) (0 .0 37 6) (0 .0 03 42 ) (0 .0 10 5) (0 .0 28 1) (0 .0 32 6) (0 .0 06 05 ) (0 .0 14 0) (0 .0 25 8) (0 .0 29 2) (0 .0 10 6) sa fe w at er 0. 00 33 9 0. 01 19 -0 .0 14 1 -0 .0 01 14 0. 00 51 3 0. 01 36 -0 .0 15 8 -0 .0 02 90 0. 01 97 0. 02 98 -0 .0 37 3 -0 .0 12 2 (0 .0 09 53 ) (0 .0 32 3) (0 .0 38 8) (0 .0 03 02 ) (0 .0 11 2) (0 .0 28 0) (0 .0 33 3) (0 .0 05 95 ) (0 .0 15 6) (0 .0 19 7) (0 .0 26 7) (0 .0 08 46 ) to ile t 0. 02 58 * 0. 07 01 ** -0 .0 90 1* * -0 .0 05 81 ** 0. 02 54 0. 05 22 ** -0 .0 66 9* -0 .0 10 7* * 0. 02 12 0. 03 07 -0 .0 39 2 -0 .0 12 7 (0 .0 15 2) (0 .0 29 9) (0 .0 42 7) (0 .0 02 49 ) (0 .0 16 4) (0 .0 23 9) (0 .0 35 3) (0 .0 05 05 ) (0 .0 19 3) (0 .0 21 4) (0 .0 31 4) (0 .0 09 19 ) h h in co m e (lo g) -0 .0 08 05 ** * -0 .0 29 2* ** 0. 03 44 ** * 0. 00 28 7* ** -0 .0 09 95 ** * -0 .0 28 0* ** 0. 03 19 ** * 0. 00 60 6* ** -0 .0 11 9* ** -0 .0 21 6* ** 0. 02 46 ** * 0. 00 88 8* ** (0 .0 02 62 ) (0 .0 08 19 ) (0 .0 09 73 ) (0 .0 01 08 ) (0 .0 03 30 ) (0 .0 08 05 ) (0 .0 09 30 ) (0 .0 02 12 ) (0 .0 03 88 ) (0 .0 06 53 ) (0 .0 07 31 ) (0 .0 02 99 ) d em og ra ph ic d ep en de nc y ra tio 0. 00 02 41 0. 00 08 73 -0 .0 01 03 -8 .6 0e -0 5 -0 .0 01 34 -0 .0 03 76 0. 00 42 8 0. 00 08 14 -0 .0 05 81 -0 .0 10 6 0. 01 20 0. 00 43 4 (0 .0 04 99 ) (0 .0 18 0) (0 .0 21 2) (0 .0 01 78 ) (0 .0 05 46 ) (0 .0 15 4) (0 .0 17 5) (0 .0 03 35 ) (0 .0 08 26 ) (0 .0 14 5) (0 .0 16 7) (0 .0 06 09 ) ec on om ic d ep en de nc y ra tio 0. 00 32 9 0. 01 19 -0 .0 14 0 -0 .0 01 17 0. 00 22 4 0. 00 63 0 -0 .0 07 17 -0 .0 01 36 0. 00 43 8 0. 00 79 7 -0 .0 09 07 -0 .0 03 28 (0 .0 02 23 ) (0 .0 08 03 ) (0 .0 09 46 ) (0 .0 00 80 6) (0 .0 02 24 ) (0 .0 06 31 ) (0 .0 07 14 ) (0 .0 01 41 ) (0 .0 03 66 ) (0 .0 06 36 ) (0 .0 07 28 ) (0 .0 02 73 ) in co m e di ve rs ifi ca tio n 0. 02 01 * 0. 07 28 * -0 .0 85 7* -0 .0 07 17 * 0. 01 87 0. 05 27 -0 .0 60 0 -0 .0 11 4 0. 01 47 0. 02 68 -0 .0 30 5 -0 .0 11 0 (0 .0 10 9) (0 .0 37 9) (0 .0 44 8) (0 .0 04 07 ) (0 .0 12 1) (0 .0 32 7) (0 .0 37 6) (0 .0 07 23 ) (0 .0 15 7) (0 .0 28 6) (0 .0 32 7) (0 .0 11 6) ru ra l -0 .0 29 7* * -0 .0 99 3* ** 0. 12 0* * 0. 00 88 3* * -0 .0 33 1* * -0 .0 81 5* ** 0. 09 73 ** * 0. 01 73 ** * -0 .0 23 9 -0 .0 40 2 0. 04 76 0. 01 65 (0 .0 12 5) (0 .0 38 2) (0 .0 46 9) (0 .0 03 70 ) (0 .0 13 9) (0 .0 27 5) (0 .0 34 9) (0 .0 06 55 ) (0 .0 16 3) (0 .0 25 8) (0 .0 31 1) (0 .0 10 9) h h re ce iv ed a ss ist an ce sin ce c o v id -1 9 ou tb re ak 0. 02 30 0. 06 53 -0 .0 82 7 -0 .0 05 63 * 0. 02 69 * 0. 05 75 ** -0 .0 72 7* * -0 .0 11 7* * 0. 04 41 * 0. 05 72 ** * -0 .0 77 9* * -0 .0 23 4* * (0 .0 19 3) (0 .0 43 3) (0 .0 59 3) (0 .0 03 35 ) (0 .0 15 0) (0 .0 25 0) (0 .0 34 9) (0 .0 05 13 ) (0 .0 26 1) (0 .0 20 5) (0 .0 36 4) (0 .0 09 75 ) o bs er va tio ns 2, 34 4 2, 34 4 2, 34 4 2, 34 4 2, 34 6 2, 34 6 2, 34 6 2, 34 6 2, 34 7 2, 34 7 2, 34 7 2, 34 7 st an da rd e rr or s i n pa re nt he se s ** * p< 0. 01 , * * p< 0. 05 , * p <0 .1 ta bl e a .4 . ( co nt d. ) o rd er ed p ro bi t, to ta l i nc om e ch an ge . https://doi.org/10.36253/bae-13404 361the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 ta bl e a .4 . ( co nt d. ) o rd er ed p ro bi t, to ta l i nc om e ch an ge . va ria bl es ro un d 4 ro un d 5 ro un d 6 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 ye ar s o f e du ca tio n -0 .0 01 65 -0 .0 03 10 0. 00 27 8 0. 00 19 7 -0 .0 00 92 0 -0 .0 02 20 0. 00 15 1 0. 00 16 1 -0 .0 01 37 -0 .0 03 29 0. 00 20 4 0. 00 26 2 (0 .0 01 30 ) (0 .0 02 49 ) (0 .0 02 22 ) (0 .0 01 56 ) (0 .0 01 13 ) (0 .0 02 73 ) (0 .0 01 87 ) (0 .0 01 98 ) (0 .0 01 17 ) (0 .0 02 80 ) (0 .0 01 76 ) (0 .0 02 20 ) h h h ea d is fe m al e 0. 01 02 0. 01 80 -0 .0 16 8 -0 .0 11 5 0. 02 28 0. 04 77 -0 .0 35 7 -0 .0 34 7 0. 02 84 0. 05 82 -0 .0 40 4 -0 .0 46 2 (0 .0 22 8) (0 .0 36 6) (0 .0 35 8) (0 .0 23 6) (0 .0 22 5) (0 .0 36 6) (0 .0 31 1) (0 .0 27 9) (0 .0 24 1) (0 .0 36 8) (0 .0 29 7) (0 .0 31 0) a ge o f h h h ea d 0. 00 06 73 0. 00 12 6 -0 .0 01 13 -0 .0 00 80 2 0. 00 07 35 0. 00 17 6* -0 .0 01 21 * -0 .0 01 29 * 0. 00 04 02 0. 00 09 65 -0 .0 00 59 9 -0 .0 00 76 8 (0 .0 00 47 3) (0 .0 00 84 0) (0 .0 00 75 4) (0 .0 00 55 4) (0 .0 00 44 7) (0 .0 00 95 4) (0 .0 00 67 4) (0 .0 00 72 1) (0 .0 00 40 0) (0 .0 00 92 9) (0 .0 00 57 8) (0 .0 00 74 9) h h h ea d is m ar rie d 0. 00 68 9 0. 01 34 -0 .0 11 8 -0 .0 08 54 0. 00 39 4 0. 00 96 3 -0 .0 06 51 -0 .0 07 07 0. 00 50 5 0. 01 24 -0 .0 07 55 -0 .0 09 93 (0 .0 20 2) (0 .0 39 8) (0 .0 34 4) (0 .0 25 6) (0 .0 18 0) (0 .0 44 3) (0 .0 29 6) (0 .0 32 8) (0 .0 18 2) (0 .0 45 3) (0 .0 27 0) (0 .0 36 4) h h si ze -0 .0 02 96 -0 .0 05 55 0. 00 49 8 0. 00 35 3 -0 .0 04 08 -0 .0 09 75 0. 00 66 9 0. 00 71 4 -0 .0 02 19 -0 .0 05 26 0. 00 32 7 0. 00 41 9 (0 .0 03 39 ) (0 .0 06 25 ) (0 .0 05 64 ) (0 .0 03 99 ) (0 .0 03 11 ) (0 .0 07 14 ) (0 .0 04 99 ) (0 .0 05 23 ) (0 .0 03 01 ) (0 .0 07 19 ) (0 .0 04 48 ) (0 .0 05 71 ) a ss et in de x -0 .0 01 26 -0 .0 02 37 0. 00 21 3 0. 00 15 1 -0 .0 02 49 -0 .0 05 95 0. 00 40 8 0. 00 43 6 -0 .0 02 28 -0 .0 05 48 0. 00 34 0 0. 00 43 6 (0 .0 02 61 ) (0 .0 04 73 ) (0 .0 04 30 ) (0 .0 03 04 ) (0 .0 02 58 ) (0 .0 05 64 ) (0 .0 03 93 ) (0 .0 04 29 ) (0 .0 02 59 ) (0 .0 05 79 ) (0 .0 03 64 ) (0 .0 04 74 ) h h c on su m pt io n (lo g) 0. 00 02 10 0. 00 03 94 -0 .0 00 35 4 -0 .0 00 25 0 0. 00 48 2 0. 01 15 -0 .0 07 92 -0 .0 08 44 0. 00 86 6 0. 02 08 -0 .0 12 9 -0 .0 16 5 (0 .0 09 68 ) (0 .0 18 1) (0 .0 16 3) (0 .0 11 5) (0 .0 08 52 ) (0 .0 20 7) (0 .0 14 1) (0 .0 15 1) (0 .0 08 21 ) (0 .0 19 8) (0 .0 12 1) (0 .0 15 9) % o f m em be rs w ith jo b co nt ra ct -0 .0 68 4* ** -0 .1 28 ** 0. 11 5* * 0. 08 15 ** -0 .0 55 7* * -0 .1 33 ** 0. 09 14 ** 0. 09 74 ** -0 .0 51 8* * -0 .1 24 * 0. 07 72 ** 0. 09 90 ** (0 .0 26 1) (0 .0 57 8) (0 .0 49 1) (0 .0 34 0) (0 .0 22 9) (0 .0 66 3) (0 .0 43 9) (0 .0 44 8) (0 .0 23 1) (0 .0 65 7) (0 .0 39 3) (0 .0 49 0) h h re ce iv es so ci al as sis ta nc e -0 .0 01 83 -0 .0 03 50 0. 00 31 1 0. 00 22 2 0. 00 17 9 0. 00 42 0 -0 .0 02 92 -0 .0 03 07 -0 .0 03 23 -0 .0 08 04 0. 00 48 7 0. 00 64 0 (0 .0 23 1) (0 .0 44 9) (0 .0 39 5) (0 .0 28 5) (0 .0 20 6) (0 .0 47 4) (0 .0 33 3) (0 .0 34 7) (0 .0 20 2) (0 .0 52 1) (0 .0 30 9) (0 .0 41 4) h h h as fo rm al c re di t 0. 01 65 0. 02 66 -0 .0 26 0 -0 .0 17 2 0. 00 19 7 0. 00 46 2 -0 .0 03 21 -0 .0 03 39 -0 .0 05 02 -0 .0 12 7 0. 00 76 4 0. 01 01 (0 .0 24 5) (0 .0 32 2) (0 .0 34 7) (0 .0 22 0) (0 .0 20 4) (0 .0 46 5) (0 .0 32 7) (0 .0 34 2) (0 .0 19 1) (0 .0 51 7) (0 .0 30 0) (0 .0 40 8) h h h as sa vi ng s 0. 01 58 0. 02 89 -0 .0 26 0 -0 .0 18 6 0. 02 16 * 0. 04 97 * -0 .0 34 3* * -0 .0 37 0* 0. 02 05 * 0. 04 73 * -0 .0 29 5* -0 .0 38 3* (0 .0 12 3) (0 .0 22 9) (0 .0 20 2) (0 .0 14 9) (0 .0 11 3) (0 .0 25 7) (0 .0 17 4) (0 .0 19 4) (0 .0 11 5) (0 .0 25 7) (0 .0 15 7) (0 .0 21 3) h h h as b an k ac co un t -0 .0 31 3* * -0 .0 57 0* * 0. 05 22 ** 0. 03 60 ** -0 .0 19 7 -0 .0 46 4 0. 03 22 0. 03 39 -0 .0 26 4* -0 .0 61 8* * 0. 03 90 ** 0. 04 92 ** (0 .0 15 6) (0 .0 26 0) (0 .0 24 4) (0 .0 16 9) (0 .0 12 8) (0 .0 29 3) (0 .0 20 4) (0 .0 21 5) (0 .0 13 5) (0 .0 29 2) (0 .0 18 8) (0 .0 23 5) h h h as fo rm al in su ra nc e -0 .0 16 5 -0 .0 36 5 0. 02 99 0. 02 31 -0 .0 19 8* * -0 .0 59 9* 0. 03 60 * 0. 04 37 * -0 .0 08 28 -0 .0 21 6 0. 01 27 0. 01 72 (0 .0 12 8) (0 .0 33 4) (0 .0 25 2) (0 .0 20 8) (0 .0 10 0) (0 .0 35 9) (0 .0 19 4) (0 .0 26 4) (0 .0 13 9) (0 .0 40 4) (0 .0 22 4) (0 .0 31 8) d ist an ce to m ai n ro ad -0 .0 00 55 9* * -0 .0 01 05 ** * 0. 00 09 40 ** * 0. 00 06 65 ** * -0 .0 00 42 4* * -0 .0 01 01 ** 0. 00 06 96 ** 0. 00 07 42 ** -0 .0 00 43 1* * -0 .0 01 03 ** 0. 00 06 41 ** 0. 00 08 23 ** (0 .0 00 22 0) (0 .0 00 40 2) (0 .0 00 35 9) (0 .0 00 25 4) (0 .0 00 20 2) (0 .0 00 49 1) (0 .0 00 32 0) (0 .0 00 36 8) (0 .0 00 21 0) (0 .0 00 49 2) (0 .0 00 29 7) (0 .0 00 39 8) d ist an ce to u rb an c en te r -0 .0 00 16 0* * -0 .0 00 30 1* * 0. 00 02 70 ** 0. 00 01 91 ** -0 .0 00 15 8* * -0 .0 00 37 7* * 0. 00 02 59 ** 0. 00 02 76 ** -0 .0 00 14 5* * -0 .0 00 34 9* * 0. 00 02 16 ** 0. 00 02 78 ** (7 .9 1e -0 5) (0 .0 00 13 8) (0 .0 00 12 7) (8 .7 7e -0 5) (7 .3 6e -0 5) (0 .0 00 14 8) (0 .0 00 10 6) (0 .0 00 11 4) (7 .3 4e -0 5) (0 .0 00 15 2) (9 .8 5e -0 5) (0 .0 00 12 5) d ist an ce to m ar ke t -0 .0 00 88 9* * -0 .0 01 67 ** 0. 00 15 0* * 0. 00 10 6* * -0 .0 00 75 5* -0 .0 01 81 * 0. 00 12 4* 0. 00 13 2* -0 .0 00 95 1* * -0 .0 02 28 ** 0. 00 14 2* * 0. 00 18 2* * (0 .0 00 45 1) (0 .0 00 84 9) (0 .0 00 75 6) (0 .0 00 53 3) (0 .0 00 39 6) (0 .0 00 98 2) (0 .0 00 66 3) (0 .0 00 70 6) (0 .0 00 41 5) (0 .0 01 01 ) (0 .0 00 61 5) (0 .0 00 79 3) (c on tin ue d) https://doi.org/10.36253/bae-13404 362 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano va ria bl es ro un d 4 ro un d 5 ro un d 6 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ic ro fin an ce in st itu tio n 0. 00 55 2 0. 01 02 -0 .0 09 25 -0 .0 06 51 -0 .0 01 57 -0 .0 03 78 0. 00 25 9 0. 00 27 7 -0 .0 01 32 -0 .0 03 18 0. 00 19 7 0. 00 25 3 (0 .0 11 4) (0 .0 21 1) (0 .0 19 1) (0 .0 13 3) (0 .0 09 40 ) (0 .0 22 5) (0 .0 15 4) (0 .0 16 5) (0 .0 10 3) (0 .0 24 8) (0 .0 15 3) (0 .0 19 8) el ec tr ic ity -0 .0 00 41 1 -0 .0 00 76 8 0. 00 06 91 0. 00 04 88 -0 .0 04 58 -0 .0 10 7 0. 00 74 3 0. 00 78 3 0. 00 54 3 0. 01 34 -0 .0 08 18 -0 .0 10 7 (0 .0 14 7) (0 .0 27 4) (0 .0 24 6) (0 .0 17 4) (0 .0 13 5) (0 .0 30 6) (0 .0 21 8) (0 .0 22 4) (0 .0 12 7) (0 .0 32 4) (0 .0 19 3) (0 .0 25 8) sa fe w at er 0. 03 06 0. 04 31 ** -0 .0 45 5* -0 .0 28 1* * 0. 02 20 0. 04 21 * -0 .0 32 9* -0 .0 31 2* 0. 02 15 0. 04 20 * -0 .0 29 7* -0 .0 33 8* (0 .0 18 7) (0 .0 19 3) (0 .0 24 0) (0 .0 13 5) (0 .0 14 9) (0 .0 22 1) (0 .0 19 8) (0 .0 17 0) (0 .0 13 8) (0 .0 22 2) (0 .0 17 6) (0 .0 18 1) to ile t 0. 02 13 0. 03 15 -0 .0 32 2 -0 .0 20 5 0. 01 78 0. 03 44 -0 .0 26 6 -0 .0 25 5 0. 02 35 0. 04 35 * -0 .0 31 7 -0 .0 35 3* (0 .0 19 1) (0 .0 21 5) (0 .0 25 7) (0 .0 14 7) (0 .0 16 8) (0 .0 24 9) (0 .0 22 3) (0 .0 19 2) (0 .0 16 8) (0 .0 22 7) (0 .0 19 9) (0 .0 19 3) h h in co m e (lo g) -0 .0 10 4* ** -0 .0 19 4* ** 0. 01 74 ** * 0. 01 23 ** * -0 .0 09 20 ** -0 .0 22 0* ** 0. 01 51 ** * 0. 01 61 ** * -0 .0 07 62 ** -0 .0 18 3* * 0. 01 13 ** 0. 01 46 ** (0 .0 03 94 ) (0 .0 06 74 ) (0 .0 05 99 ) (0 .0 04 52 ) (0 .0 03 61 ) (0 .0 07 29 ) (0 .0 05 08 ) (0 .0 05 70 ) (0 .0 03 62 ) (0 .0 07 67 ) (0 .0 04 84 ) (0 .0 06 35 ) d em og ra ph ic d ep en de nc y ra tio -0 .0 00 78 7 -0 .0 01 48 0. 00 13 3 0. 00 09 38 0. 00 39 7 0. 00 94 8 -0 .0 06 51 -0 .0 06 94 -7 .2 8e -0 5 -0 .0 00 17 5 0. 00 01 08 0. 00 01 39 (0 .0 07 62 ) (0 .0 14 2) (0 .0 12 8) (0 .0 09 04 ) (0 .0 06 38 ) (0 .0 16 0) (0 .0 10 8) (0 .0 11 6) (0 .0 06 54 ) (0 .0 15 7) (0 .0 09 74 ) (0 .0 12 5) ec on om ic d ep en de nc y ra tio 0. 00 64 0* 0. 01 20 * -0 .0 10 8* -0 .0 07 62 * 0. 00 72 3* * 0. 01 73 ** -0 .0 11 9* * -0 .0 12 7* * 0. 00 59 8* 0. 01 44 * -0 .0 08 91 * -0 .0 11 4* (0 .0 03 83 ) (0 .0 06 74 ) (0 .0 06 07 ) (0 .0 04 43 ) (0 .0 03 36 ) (0 .0 07 25 ) (0 .0 04 98 ) (0 .0 05 54 ) (0 .0 03 40 ) (0 .0 07 65 ) (0 .0 04 71 ) (0 .0 06 27 ) in co m e di ve rs ifi ca tio n 0. 01 15 0. 02 15 -0 .0 19 3 -0 .0 13 6 0. 00 29 0 0. 00 69 3 -0 .0 04 76 -0 .0 05 07 0. 00 71 5 0. 01 72 -0 .0 10 7 -0 .0 13 7 (0 .0 16 0) (0 .0 30 2) (0 .0 27 1) (0 .0 19 1) (0 .0 13 7) (0 .0 32 9) (0 .0 22 6) (0 .0 24 0) (0 .0 13 8) (0 .0 33 2) (0 .0 20 7) (0 .0 26 3) ru ra l -0 .0 18 4 -0 .0 31 9 0. 02 98 0. 02 05 -0 .0 14 8 -0 .0 33 0 0. 02 35 0. 02 43 -0 .0 05 40 -0 .0 12 7 0. 00 79 7 0. 01 01 (0 .0 15 8) (0 .0 25 6) (0 .0 24 4) (0 .0 16 8) (0 .0 13 7) (0 .0 27 4) (0 .0 20 3) (0 .0 20 7) (0 .0 13 7) (0 .0 31 3) (0 .0 19 9) (0 .0 25 1) h h re ce iv ed a ss ist an ce sin ce c o v id -1 9 ou tb re ak 0. 00 88 8 0. 01 55 -0 .0 14 5 -0 .0 09 89 0. 01 41 0. 03 04 -0 .0 22 3 -0 .0 22 2 0. 01 33 0. 02 91 -0 .0 19 3 -0 .0 23 2 (0 .0 18 3) (0 .0 29 1) (0 .0 28 6) (0 .0 18 8) (0 .0 17 2) (0 .0 30 9) (0 .0 24 9) (0 .0 23 2) (0 .0 15 8) (0 .0 29 7) (0 .0 21 3) (0 .0 24 2) o bs er va tio ns 2, 34 4 2, 34 4 2, 34 4 2, 34 4 2, 34 6 2, 34 6 2, 34 6 2, 34 6 2, 33 8 2, 33 8 2, 33 8 2, 33 8 st an da rd e rr or s i n pa re nt he se s ** * p< 0. 01 , * * p< 0. 05 , * p <0 .1 ta bl e a .4 . ( co nt d. ) o rd er ed p ro bi t, to ta l i nc om e ch an ge . https://doi.org/10.36253/bae-13404 363the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 ta bl e a .5 . o rd er ed p ro bi t, fa rm in co m e ch an ge . va ria bl es ro un d 1 ro un d 2 ro un d 3 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 ye ar s o f e du ca tio n -0 .0 00 16 2 -0 .0 01 05 0. 00 11 0 0. 00 01 15 -0 .0 00 38 2 -0 .0 03 82 0. 00 32 3 0. 00 09 72 2. 50 e05 0. 00 04 65 -0 .0 00 37 3 -0 .0 00 11 7 (0 .0 00 91 0) (0 .0 05 91 ) (0 .0 06 17 ) (0 .0 00 64 8) (0 .0 00 63 5) (0 .0 06 46 ) (0 .0 05 47 ) (0 .0 01 63 ) (0 .0 00 38 0) (0 .0 07 02 ) (0 .0 05 63 ) (0 .0 01 77 ) h h in co m e (lo g) -0 .0 00 18 0 -0 .0 01 16 0. 00 12 1 0. 00 01 27 0. 00 27 7 0. 02 77 -0 .0 23 4 -0 .0 07 05 0. 00 12 8 0. 02 38 -0 .0 19 1 -0 .0 06 01 (0 .0 02 77 ) (0 .0 17 8) (0 .0 18 6) (0 .0 01 94 ) (0 .0 02 19 ) (0 .0 19 2) (0 .0 16 2) (0 .0 05 28 ) (0 .0 01 40 ) (0 .0 19 3) (0 .0 15 6) (0 .0 05 13 ) h h c on su m pt io n (lo g) 0. 00 91 5 0. 05 90 -0 .0 61 7 -0 .0 06 46 0. 00 43 3 0. 04 33 -0 .0 36 6 -0 .0 11 0 0. 00 11 3 0. 02 11 -0 .0 16 9 -0 .0 05 31 (0 .0 06 24 ) (0 .0 36 5) (0 .0 37 9) (0 .0 05 03 ) (0 .0 04 42 ) (0 .0 47 6) (0 .0 39 4) (0 .0 12 7) (0 .0 02 69 ) (0 .0 53 9) (0 .0 42 7) (0 .0 13 8) h h h ea d is fe m al e -0 .0 09 46 -0 .0 69 6 0. 07 02 0. 00 88 1 0. 00 18 5 0. 01 79 -0 .0 15 3 -0 .0 04 41 -0 .0 02 73 -0 .0 56 0 0. 04 33 0. 01 55 (0 .0 08 17 ) (0 .0 67 3) (0 .0 65 2) (0 .0 10 5) (0 .0 08 63 ) (0 .0 81 0) (0 .0 70 3) (0 .0 19 4) (0 .0 04 65 ) (0 .0 96 9) (0 .0 71 4) (0 .0 30 0) a ge o f h h h ea d 0. 00 03 62 0. 00 23 3 -0 .0 02 44 -0 .0 00 25 5 0. 00 04 40 0. 00 44 1* * -0 .0 03 73 ** -0 .0 01 12 * 0. 00 02 07 0. 00 38 5* -0 .0 03 08 * -0 .0 00 97 0* (0 .0 00 30 3) (0 .0 01 73 ) (0 .0 01 83 ) (0 .0 00 21 0) (0 .0 00 31 2) (0 .0 02 11 ) (0 .0 01 81 ) (0 .0 00 64 3) (0 .0 00 20 6) (0 .0 02 01 ) (0 .0 01 66 ) (0 .0 00 57 4) h h h ea d is m ar rie d 0. 00 56 5 0. 03 95 -0 .0 40 5 -0 .0 04 64 0. 00 30 6 0. 03 19 -0 .0 26 4 -0 .0 08 61 -0 .0 10 0 -0 .1 34 0. 11 6 0. 02 83 (0 .0 09 52 ) (0 .0 70 5) (0 .0 71 2) (0 .0 08 91 ) (0 .0 07 83 ) (0 .0 83 1) (0 .0 67 2) (0 .0 23 8) (0 .0 09 57 ) (0 .0 85 6) (0 .0 77 1) (0 .0 17 4) h h si ze 0. 00 24 3 0. 01 57 -0 .0 16 4 -0 .0 01 71 0. 00 05 26 0. 00 52 6 -0 .0 04 45 -0 .0 01 34 0. 00 03 62 0. 00 67 3 -0 .0 05 40 -0 .0 01 70 (0 .0 01 82 ) (0 .0 11 5) (0 .0 11 9) (0 .0 01 43 ) (0 .0 01 44 ) (0 .0 15 1) (0 .0 12 7) (0 .0 03 84 ) (0 .0 00 72 7) (0 .0 14 9) (0 .0 11 9) (0 .0 03 73 ) a ss et in de x -0 .0 03 97 -0 .0 25 6 0. 02 68 0. 00 28 0 -0 .0 04 81 * -0 .0 48 1* * 0. 04 07 ** 0. 01 22 * -0 .0 00 42 9 -0 .0 07 99 0. 00 64 0 0. 00 20 1 (0 .0 03 26 ) (0 .0 19 5) (0 .0 20 1) (0 .0 02 74 ) (0 .0 02 65 ) (0 .0 21 1) (0 .0 17 5) (0 .0 06 47 ) (0 .0 01 17 ) (0 .0 21 4) (0 .0 17 1) (0 .0 05 47 ) la nd is ir rig at ed 0. 02 20 0. 10 5 -0 .1 18 -0 .0 09 01 0. 01 13 0. 09 08 -0 .0 82 7 -0 .0 19 3 -0 .0 01 32 -0 .0 26 7 0. 02 09 0. 00 70 3 (0 .0 21 7) (0 .0 73 0) (0 .0 89 0) (0 .0 06 14 ) (0 .0 13 9) (0 .0 92 4) (0 .0 89 5) (0 .0 17 0) (0 .0 06 26 ) (0 .1 34 ) (0 .1 03 ) (0 .0 37 4) o rg an ic fe rt ili ze r -0 .0 53 5* * -0 .2 21 ** * 0. 25 5* ** 0. 01 99 ** -0 .0 38 5* -0 .2 16 ** * 0. 20 2* ** 0. 05 22 ** * -0 .0 13 1 -0 .1 54 ** 0. 12 9* * 0. 03 81 * (0 .0 24 4) (0 .0 50 3) (0 .0 64 4) (0 .0 09 81 ) (0 .0 19 9) (0 .0 51 0) (0 .0 50 5) (0 .0 18 6) (0 .0 12 4) (0 .0 75 8) (0 .0 64 4) (0 .0 22 6) ti tle o f t he la nd -0 .0 08 55 -0 .0 52 5 0. 05 57 0. 00 52 6 -0 .0 10 1 -0 .0 89 2 0. 07 81 0. 02 12 -0 .0 08 94 -0 .1 30 0. 10 8 0. 03 06 (0 .0 10 6) (0 .0 62 7) (0 .0 67 9) (0 .0 05 57 ) (0 .0 09 07 ) (0 .0 64 2) (0 .0 58 5) (0 .0 15 0) (0 .0 09 19 ) (0 .0 84 7) (0 .0 74 4) (0 .0 19 0) % o f m em be rs w ith jo b co nt ra ct 0. 00 60 6 0. 03 91 -0 .0 40 8 -0 .0 04 27 0. 05 09 ** 0. 50 9* ** -0 .4 30 ** * -0 .1 29 ** -0 .0 18 0 -0 .3 35 0. 26 8 0. 08 43 (0 .0 16 6) (0 .1 07 ) (0 .1 12 ) (0 .0 12 0) (0 .0 22 9) (0 .1 93 ) (0 .1 64 ) (0 .0 54 7) (0 .0 16 4) (0 .2 21 ) (0 .1 75 ) (0 .0 61 6) h h i_ cr op 0. 02 90 ** 0. 18 7* ** -0 .1 96 ** * -0 .0 20 5* 0. 02 71 ** 0. 27 1* ** -0 .2 29 ** * -0 .0 68 8* * 0. 01 64 0. 30 4* ** -0 .2 44 ** * -0 .0 76 7* * (0 .0 13 4) (0 .0 71 2) (0 .0 73 4) (0 .0 12 0) (0 .0 12 6) (0 .0 77 2) (0 .0 64 3) (0 .0 27 5) (0 .0 12 1) (0 .0 94 3) (0 .0 74 3) (0 .0 32 1) la nd si ze (h ec ta re s) -0 .0 05 53 ** -0 .0 35 7* * 0. 03 73 ** 0. 00 39 0* -0 .0 01 89 -0 .0 18 9 0. 01 60 0. 00 48 0 -0 .0 02 65 -0 .0 49 4* * 0. 03 96 ** 0. 01 24 ** (0 .0 02 80 ) (0 .0 15 0) (0 .0 15 7) (0 .0 02 34 ) (0 .0 01 78 ) (0 .0 15 9) (0 .0 13 6) (0 .0 04 13 ) (0 .0 02 09 ) (0 .0 19 7) (0 .0 16 2) (0 .0 05 62 ) in co m e di ve rs ifi ca tio n 0. 00 66 6 0. 04 30 -0 .0 44 9 -0 .0 04 70 0. 00 41 2 0. 04 12 -0 .0 34 8 -0 .0 10 5 0. 00 06 94 0. 01 29 -0 .0 10 4 -0 .0 03 26 (0 .0 09 66 ) (0 .0 61 3) (0 .0 64 3) (0 .0 06 70 ) (0 .0 07 38 ) (0 .0 74 8) (0 .0 63 4) (0 .0 18 9) (0 .0 04 45 ) (0 .0 83 4) (0 .0 67 0) (0 .0 20 8) so ci al a ss ist an ce 0. 00 30 5 0. 01 88 -0 .0 19 9 -0 .0 01 95 -0 .0 04 07 -0 .0 43 7 0. 03 56 0. 01 22 -0 .0 03 97 -0 .0 93 8 0. 06 98 0. 02 80 (0 .0 12 3) (0 .0 72 4) (0 .0 77 6) (0 .0 07 05 ) (0 .0 06 77 ) (0 .0 75 3) (0 .0 58 6) (0 .0 23 5) (0 .0 04 21 ) (0 .0 89 0) (0 .0 60 6) (0 .0 32 4) a gr ic ul tu ra l m ac hi ne ry 0. 00 84 9 0. 05 03 -0 .0 53 5 -0 .0 05 22 0. 00 48 3 0. 04 61 -0 .0 39 7 -0 .0 11 2 0. 01 35 0. 18 9* ** -0 .1 62 ** * -0 .0 40 2* ** (0 .0 10 0) (0 .0 54 4) (0 .0 59 5) (0 .0 05 13 ) (0 .0 07 25 ) (0 .0 61 0) (0 .0 54 3) (0 .0 14 0) (0 .0 10 9) (0 .0 58 7) (0 .0 55 8) (0 .0 14 2) (c on tin ue d) https://doi.org/10.36253/bae-13404 364 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano va ria bl es ro un d 1 ro un d 2 ro un d 3 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 fo rm al c re di t 0. 01 03 0. 05 80 -0 .0 62 6 -0 .0 05 68 0. 00 72 7 0. 06 26 -0 .0 55 3 -0 .0 14 6 0. 00 29 9 0. 04 82 -0 .0 39 8 -0 .0 11 4 (0 .0 12 4) (0 .0 60 5) (0 .0 67 8) (0 .0 05 30 ) (0 .0 08 73 ) (0 .0 66 3) (0 .0 60 5) (0 .0 14 6) (0 .0 05 25 ) (0 .0 72 6) (0 .0 61 0) (0 .0 16 8) sa vi ng s 0. 01 55 * 0. 09 73 * -0 .1 03 * -0 .0 09 88 * 0. 00 19 6 0. 01 94 -0 .0 16 4 -0 .0 04 91 0. 00 89 7 0. 13 8* * -0 .1 13 ** -0 .0 34 2* * (0 .0 09 34 ) (0 .0 54 1) (0 .0 58 1) (0 .0 05 95 ) (0 .0 05 68 ) (0 .0 55 5) (0 .0 47 3) (0 .0 13 9) (0 .0 07 19 ) (0 .0 58 2) (0 .0 49 2) (0 .0 15 5) ba nk a cc ou nt -0 .0 04 10 -0 .0 26 3 0. 02 74 0. 00 29 6 -0 .0 06 37 -0 .0 65 2 0. 05 50 0. 01 66 -0 .0 06 67 -0 .1 28 ** 0. 10 1* * 0. 03 39 * (0 .0 08 47 ) (0 .0 55 4) (0 .0 57 5) (0 .0 06 42 ) (0 .0 06 20 ) (0 .0 60 4) (0 .0 51 3) (0 .0 15 6) (0 .0 05 49 ) (0 .0 63 1) (0 .0 49 8) (0 .0 18 8) fo rm al in su ra nc e 0. 00 24 5 0. 01 53 -0 .0 16 1 -0 .0 01 62 -0 .0 07 40 -0 .0 87 8 0. 06 92 0. 02 61 -0 .0 03 84 -0 .0 94 4 0. 07 09 0. 02 73 (0 .0 10 0) (0 .0 59 9) (0 .0 63 7) (0 .0 06 27 ) (0 .0 05 63 ) (0 .0 71 3) (0 .0 52 3) (0 .0 25 0) (0 .0 03 54 ) (0 .0 87 0) (0 .0 61 1) (0 .0 29 2) d ist an ce to ro ad -0 .0 00 10 1 -0 .0 00 65 3 0. 00 06 83 7. 14 e05 -3 .8 7e -0 5 -0 .0 00 38 7 0. 00 03 27 9. 83 e05 1. 52 e05 0. 00 02 83 -0 .0 00 22 7 -7 .1 3e -0 5 (0 .0 00 10 7) (0 .0 00 69 5) (0 .0 00 73 0) (7 .5 0e -0 5) (7 .2 1e -0 5) (0 .0 00 72 1) (0 .0 00 61 6) (0 .0 00 17 8) (5 .1 2e -0 5) (0 .0 00 90 1) (0 .0 00 72 2) (0 .0 00 23 0) d ist an ce to u rb an c en te r 1. 70 e05 0. 00 01 10 -0 .0 00 11 5 -1 .2 0e -0 5 1. 04 e05 0. 00 01 04 -8 .8 4e -0 5 -2 .6 6e -0 5 1. 17 e05 0. 00 02 18 -0 .0 00 17 5 -5 .5 0e -0 5 (4 .0 0e -0 5) (0 .0 00 25 9) (0 .0 00 26 9) (2 .9 5e -0 5) (2 .9 4e -0 5) (0 .0 00 28 7) (0 .0 00 24 3) (7 .3 6e -0 5) (1 .8 7e -0 5) (0 .0 00 30 8) (0 .0 00 24 8) (7 .8 1e -0 5) d ist an ce to m ar ke t -0 .0 00 45 8 -0 .0 02 95 * 0. 00 30 9* 0. 00 03 23 -0 .0 00 73 0* * -0 .0 07 30 ** * 0. 00 61 7* ** 0. 00 18 6* ** -0 .0 00 47 0 -0 .0 08 74 ** * 0. 00 70 1* ** 0. 00 22 0* ** (0 .0 00 27 9) (0 .0 01 75 ) (0 .0 01 83 ) (0 .0 00 22 0) (0 .0 00 34 9) (0 .0 02 48 ) (0 .0 02 19 ) (0 .0 00 70 1) (0 .0 00 32 7) (0 .0 02 70 ) (0 .0 02 21 ) (0 .0 00 82 2) m ic ro fin an ce in st itu tio n -0 .0 03 80 -0 .0 25 4 0. 02 63 0. 00 28 7 0. 00 30 4 0. 02 92 -0 .0 25 0 -0 .0 07 23 -0 .0 02 41 -0 .0 49 2 0. 03 87 0. 01 30 (0 .0 06 77 ) (0 .0 46 7) (0 .0 48 2) (0 .0 05 28 ) (0 .0 06 12 ) (0 .0 56 6) (0 .0 48 7) (0 .0 14 0) (0 .0 03 56 ) (0 .0 65 1) (0 .0 50 6) (0 .0 18 0) el ec tr ic ity -0 .0 17 2* -0 .1 03 ** 0. 10 9* * 0. 01 11 * -0 .0 03 93 -0 .0 38 7 0. 03 29 0. 00 97 2 -0 .0 03 77 -0 .0 64 7 0. 05 24 0. 01 60 (0 .0 09 81 ) (0 .0 43 5) (0 .0 48 5) (0 .0 05 72 ) (0 .0 05 40 ) (0 .0 51 7) (0 .0 44 2) (0 .0 12 9) (0 .0 03 93 ) (0 .0 57 3) (0 .0 47 1) (0 .0 13 9) sa fe w at er 0. 03 79 ** 0. 15 9* ** -0 .1 85 ** * -0 .0 11 6* * 0. 02 24 0. 15 2* * -0 .1 44 ** -0 .0 29 6* * 0. 00 47 8 0. 07 23 -0 .0 61 0 -0 .0 16 1 (0 .0 18 2) (0 .0 49 1) (0 .0 62 1) (0 .0 05 42 ) (0 .0 15 1) (0 .0 64 3) (0 .0 67 0) (0 .0 12 7) (0 .0 06 44 ) (0 .0 76 4) (0 .0 66 9) (0 .0 15 7) to ile t 0. 26 1 0. 22 2* * -0 .4 68 ** * -0 .0 14 5* * 0. 10 5* 0. 26 3* ** -0 .3 27 ** * -0 .0 40 6* ** 0. 01 78 0. 16 8 -0 .1 55 -0 .0 30 7 (0 .1 94 ) (0 .0 92 9) (0 .1 07 ) (0 .0 06 35 ) (0 .0 59 9) (0 .0 39 3) (0 .0 74 4) (0 .0 11 0) (0 .0 33 9) (0 .1 69 ) (0 .1 80 ) (0 .0 22 0) d em og ra ph ic d ep en de nc y ra tio 0. 00 34 6 0. 02 23 -0 .0 23 3 -0 .0 02 44 0. 00 15 1 0. 01 51 -0 .0 12 8 -0 .0 03 85 -0 .0 00 88 3 -0 .0 16 4 0. 01 32 0. 00 41 4 (0 .0 04 42 ) (0 .0 27 0) (0 .0 28 4) (0 .0 03 13 ) (0 .0 03 09 ) (0 .0 30 3) (0 .0 25 7) (0 .0 07 74 ) (0 .0 01 81 ) (0 .0 31 7) (0 .0 25 5) (0 .0 08 04 ) ec on om ic d ep en de nc y ra tio 0. 00 01 86 0. 00 12 0 -0 .0 01 25 -0 .0 00 13 1 -0 .0 01 55 -0 .0 15 5 0. 01 31 0. 00 39 5 5. 26 e05 0. 00 09 78 -0 .0 00 78 4 -0 .0 00 24 7 (0 .0 01 89 ) (0 .0 12 2) (0 .0 12 7) (0 .0 01 33 ) (0 .0 01 65 ) (0 .0 14 9) (0 .0 12 7) (0 .0 03 87 ) (0 .0 00 83 6) (0 .0 15 6) (0 .0 12 5) (0 .0 03 96 ) ru ra l -0 .0 01 60 -0 .0 10 1 0. 01 07 0. 00 10 8 0. 01 17 * 0. 14 9 -0 .1 09 * -0 .0 51 9 0. 00 45 6 0. 11 1 -0 .0 82 0 -0 .0 34 0 (0 .0 13 8) (0 .0 85 5) (0 .0 90 4) (0 .0 08 95 ) (0 .0 06 99 ) (0 .0 97 3) (0 .0 59 1) (0 .0 46 3) (0 .0 04 32 ) (0 .1 13 ) (0 .0 75 8) (0 .0 40 8) h h re ce iv ed a ss ist an ce sin ce c o v id -1 9 ou tb re ak 0. 05 50 ** 0. 20 3* ** -0 .2 45 ** * -0 .0 13 6* * 0. 05 33 ** 0. 26 7* ** -0 .2 78 ** * -0 .0 41 9* ** 0. 00 49 4 0. 07 65 -0 .0 64 1 -0 .0 17 4 (0 .0 25 3) (0 .0 53 4) (0 .0 72 6) (0 .0 06 07 ) (0 .0 22 2) (0 .0 52 9) (0 .0 59 0) (0 .0 12 3) (0 .0 05 37 ) (0 .0 61 7) (0 .0 53 2) (0 .0 13 7) o bs er va tio ns 67 8 67 8 67 8 67 8 53 5 53 5 53 5 53 5 50 3 50 3 50 3 50 3 st an da rd e rr or s i n pa re nt he se s ** * p< 0. 01 , * * p< 0. 05 , * p <0 .1 ta bl e a .5 (c on td .). o rd er ed p ro bi t, fa rm in co m e ch an ge . https://doi.org/10.36253/bae-13404 365the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 ta bl e a .5 (c on td .). o rd er ed p ro bi t, fa rm in co m e ch an ge . va ria bl es ro un d 4 ro un d 5 ro un d 6 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 ye ar s o f e du ca tio n 8. 28 e05 0. 00 08 70 -0 .0 00 48 2 -0 .0 00 47 1 -0 .0 00 28 3 -0 .0 04 82 0. 00 15 6 0. 00 35 5 -1 .5 5e -0 5 -0 .0 01 21 0. 00 02 87 0. 00 09 38 (0 .0 00 61 3) (0 .0 06 37 ) (0 .0 03 52 ) (0 .0 03 46 ) (0 .0 00 43 2) (0 .0 06 92 ) (0 .0 02 21 ) (0 .0 05 12 ) (7 .7 3e -0 5) (0 .0 06 06 ) (0 .0 01 44 ) (0 .0 04 70 ) h h in co m e (lo g) -0 .0 01 03 -0 .0 10 8 0. 00 59 9 0. 00 58 5 -0 .0 01 79 -0 .0 30 4 0. 00 98 1 0. 02 24 -0 .0 00 30 4 -0 .0 23 7 0. 00 56 3 0. 01 84 (0 .0 01 69 ) (0 .0 17 6) (0 .0 09 69 ) (0 .0 09 63 ) (0 .0 01 54 ) (0 .0 19 9) (0 .0 06 48 ) (0 .0 14 8) (0 .0 00 31 7) (0 .0 18 2) (0 .0 04 50 ) (0 .0 14 2) h h c on su m pt io n (lo g) 0. 00 50 4 0. 05 29 -0 .0 29 3 -0 .0 28 6 0. 00 24 5 0. 04 17 -0 .0 13 5 -0 .0 30 7 -0 .0 00 21 7 -0 .0 16 9 0. 00 40 1 0. 01 31 (0 .0 03 79 ) (0 .0 43 6) (0 .0 23 3) (0 .0 23 9) (0 .0 02 72 ) (0 .0 50 4) (0 .0 15 7) (0 .0 37 3) (0 .0 00 57 9) (0 .0 43 9) (0 .0 10 5) (0 .0 34 0) h h h ea d is fe m al e 0. 00 47 8 0. 04 60 -0 .0 27 3 -0 .0 23 6 0. 00 34 6 0. 05 31 -0 .0 19 3 -0 .0 37 2 -0 .0 00 20 0 -0 .0 16 1 0. 00 36 1 0. 01 27 (0 .0 11 4) (0 .0 97 9) (0 .0 62 1) (0 .0 47 2) (0 .0 06 29 ) (0 .0 83 5) (0 .0 33 8) (0 .0 55 8) (0 .0 00 98 1) (0 .0 81 7) (0 .0 17 2) (0 .0 65 5) a ge o f h h h ea d 0. 00 05 45 0. 00 57 2* ** -0 .0 03 17 ** * -0 .0 03 10 ** 0. 00 02 75 0. 00 46 8* ** -0 .0 01 51 ** -0 .0 03 45 ** 5. 44 e05 0. 00 42 4* * -0 .0 01 01 ** -0 .0 03 29 ** (0 .0 00 35 3) (0 .0 02 08 ) (0 .0 01 14 ) (0 .0 01 31 ) (0 .0 00 24 8) (0 .0 01 80 ) (0 .0 00 62 7) (0 .0 01 45 ) (4 .5 1e -0 5) (0 .0 01 82 ) (0 .0 00 48 8) (0 .0 01 44 ) h h h ea d is m ar rie d -0 .0 00 76 3 -0 .0 07 91 0. 00 44 4 0. 00 42 4 -0 .0 03 12 -0 .0 48 6 0. 01 75 0. 03 42 -0 .0 00 82 1 -0 .0 56 0 0. 01 55 0. 04 13 (0 .0 08 52 ) (0 .0 88 2) (0 .0 50 0) (0 .0 46 8) (0 .0 05 65 ) (0 .0 81 8) (0 .0 32 3) (0 .0 54 9) (0 .0 01 37 ) (0 .0 77 3) (0 .0 24 6) (0 .0 54 2) h h si ze 0. 00 05 91 0. 00 62 1 -0 .0 03 44 -0 .0 03 36 0. 00 08 54 0. 01 45 -0 .0 04 69 -0 .0 10 7 8. 68 e05 0. 00 67 7 -0 .0 01 61 -0 .0 05 25 (0 .0 01 25 ) (0 .0 13 9) (0 .0 07 66 ) (0 .0 07 47 ) (0 .0 00 73 1) (0 .0 14 0) (0 .0 04 44 ) (0 .0 10 3) (0 .0 00 15 7) (0 .0 11 5) (0 .0 02 75 ) (0 .0 08 95 ) a ss et in de x -0 .0 01 91 -0 .0 20 1 0. 01 11 0. 01 09 -0 .0 00 49 0 -0 .0 08 34 0. 00 26 9 0. 00 61 4 -3 .4 8e -0 5 -0 .0 02 71 0. 00 06 44 0. 00 21 0 (0 .0 01 72 ) (0 .0 15 9) (0 .0 09 05 ) (0 .0 08 58 ) (0 .0 01 18 ) (0 .0 19 4) (0 .0 06 31 ) (0 .0 14 2) (0 .0 00 23 9) (0 .0 18 7) (0 .0 04 43 ) (0 .0 14 5) la nd is ir rig at ed -0 .0 04 61 -0 .0 56 4 0. 02 78 0. 03 32 -0 .0 05 32 -0 .1 31 * 0. 02 21 ** 0. 11 4 -0 .0 01 48 -0 .2 27 ** * -0 .0 24 0 0. 25 2* ** (0 .0 07 88 ) (0 .1 05 ) (0 .0 45 4) (0 .0 67 1) (0 .0 04 50 ) (0 .0 78 3) (0 .0 09 37 ) (0 .0 78 7) (0 .0 01 06 ) (0 .0 50 0) (0 .0 30 9) (0 .0 76 3) o rg an ic fe rt ili ze r -0 .0 07 00 -0 .0 65 7 0. 03 88 0. 03 40 0. 00 08 99 0. 01 56 -0 .0 04 90 -0 .0 11 5 0. 00 02 32 0. 01 86 -0 .0 04 27 -0 .0 14 5 (0 .0 09 97 ) (0 .0 74 6) (0 .0 46 9) (0 .0 37 6) (0 .0 04 48 ) (0 .0 79 9) (0 .0 24 7) (0 .0 59 7) (0 .0 00 84 4) (0 .0 68 7) (0 .0 15 5) (0 .0 54 1) ti tle o f t he la nd -0 .0 14 8 -0 .1 28 * 0. 07 79 0. 06 47 * -0 .0 07 04 -0 .1 08 0. 03 98 0. 07 53 9. 23 e05 0. 00 71 9 -0 .0 01 68 -0 .0 05 59 (0 .0 12 6) (0 .0 72 6) (0 .0 50 0) (0 .0 34 8) (0 .0 07 23 ) (0 .0 73 1) (0 .0 30 7) (0 .0 48 8) (0 .0 00 91 5) (0 .0 71 1) (0 .0 16 4) (0 .0 55 6) % o f m em be rs w ith jo b co nt ra ct -0 .0 31 4 -0 .3 30 * 0. 18 3* 0. 17 8* -0 .0 05 80 -0 .0 98 6 0. 03 18 0. 07 26 -0 .0 01 67 -0 .1 30 0. 03 09 0. 10 1 (0 .0 20 5) (0 .1 70 ) (0 .0 96 5) (0 .0 93 8) (0 .0 11 3) (0 .1 82 ) (0 .0 58 6) (0 .1 34 ) (0 .0 02 50 ) (0 .1 74 ) (0 .0 41 1) (0 .1 36 ) h h i_ cr op 0. 01 64 0. 17 2* -0 .0 95 5* -0 .0 93 3* 0. 00 46 2 0. 07 87 -0 .0 25 4 -0 .0 57 9 0. 00 18 7 0. 14 6* -0 .0 34 6* -0 .1 13 * (0 .0 11 9) (0 .0 88 4) (0 .0 50 5) (0 .0 50 0) (0 .0 07 06 ) (0 .0 94 9) (0 .0 30 6) (0 .0 71 2) (0 .0 01 64 ) (0 .0 77 4) (0 .0 18 9) (0 .0 61 8) la nd si ze (h ec ta re s) -0 .0 01 38 -0 .0 14 5 0. 00 80 5 0. 00 78 6 -0 .0 01 89 -0 .0 32 1* * 0. 01 04 * 0. 02 37 ** -0 .0 00 32 5 -0 .0 25 3* 0. 00 60 1 0. 01 96 * (0 .0 01 31 ) (0 .0 12 2) (0 .0 06 90 ) (0 .0 06 55 ) (0 .0 01 66 ) (0 .0 15 9) (0 .0 05 73 ) (0 .0 11 8) (0 .0 00 28 3) (0 .0 15 0) (0 .0 03 96 ) (0 .0 11 5) in co m e di ve rs ifi ca tio n 0. 00 64 8 0. 06 81 -0 .0 37 8 -0 .0 36 9 -0 .0 01 44 -0 .0 24 5 0. 00 78 9 0. 01 80 -0 .0 01 07 -0 .0 83 3 0. 01 98 0. 06 46 (0 .0 07 38 ) (0 .0 74 8) (0 .0 41 3) (0 .0 40 8) (0 .0 04 37 ) (0 .0 70 3) (0 .0 22 7) (0 .0 51 9) (0 .0 01 12 ) (0 .0 68 9) (0 .0 16 7) (0 .0 53 9) so ci al a ss ist an ce -0 .0 04 63 -0 .0 55 5 0. 02 78 0. 03 23 0. 00 46 7 0. 06 72 -0 .0 25 4 -0 .0 46 4 0. 00 07 47 0. 05 09 -0 .0 14 1 -0 .0 37 5 (0 .0 06 37 ) (0 .0 79 7) (0 .0 35 2) (0 .0 50 8) (0 .0 06 33 ) (0 .0 75 4) (0 .0 32 4) (0 .0 48 9) (0 .0 01 45 ) (0 .0 80 0) (0 .0 25 5) (0 .0 56 1) a gr ic ul tu ra l m ac hi ne ry 0. 00 82 3 0. 07 85 -0 .0 46 3 -0 .0 40 4 0. 01 27 0. 17 3* ** -0 .0 69 1* * -0 .1 16 ** * 0. 00 10 6 0. 07 28 -0 .0 20 1 -0 .0 53 8 (0 .0 07 82 ) (0 .0 56 1) (0 .0 36 0) (0 .0 27 9) (0 .0 10 6) (0 .0 56 6) (0 .0 28 1) (0 .0 38 7) (0 .0 01 15 ) (0 .0 54 7) (0 .0 17 4) (0 .0 38 8) (c on tin ue d) https://doi.org/10.36253/bae-13404 366 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13404 margherita squarcina, donato romano va ria bl es ro un d 4 ro un d 5 ro un d 6 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 m ar gi na l eff ec ts fo r 2 m ar gi na l eff ec ts fo r 1 m ar gi na l eff ec ts fo r 0 m ar gi na l eff ec ts fo r + 1 fo rm al c re di t -0 .0 01 72 -0 .0 19 0 0. 01 03 0. 01 05 -0 .0 00 40 3 -0 .0 07 00 0. 00 22 2 0. 00 51 8 -0 .0 00 36 2 -0 .0 30 9 0. 00 64 8 0. 02 48 (0 .0 05 51 ) (0 .0 62 2) (0 .0 32 9) (0 .0 34 8) (0 .0 04 08 ) (0 .0 71 3) (0 .0 22 3) (0 .0 53 1) (0 .0 00 74 3) (0 .0 65 4) (0 .0 12 1) (0 .0 54 1) sa vi ng s 0. 02 29 ** 0. 18 8* ** -0 .1 08 ** * -0 .1 03 ** * 0. 01 30 0. 17 7* ** -0 .0 60 5* * -0 .1 29 ** * 0. 00 34 7 0. 22 0* ** -0 .0 60 1* ** -0 .1 63 ** * (0 .0 11 7) (0 .0 49 6) (0 .0 30 5) (0 .0 29 7) (0 .0 10 3) (0 .0 61 4) (0 .0 23 7) (0 .0 46 0) (0 .0 02 72 ) (0 .0 53 8) (0 .0 19 5) (0 .0 40 2) ba nk a cc ou nt -0 .0 17 3* -0 .1 76 ** * 0. 09 28 ** * 0. 10 1* ** -0 .0 06 23 -0 .1 10 0. 03 32 0. 08 28 -0 .0 02 33 -0 .1 79 ** * 0. 03 57 ** * 0. 14 5* ** (0 .0 09 53 ) (0 .0 57 1) (0 .0 32 2) (0 .0 35 9) (0 .0 05 82 ) (0 .0 67 9) (0 .0 20 8) (0 .0 52 7) (0 .0 01 83 ) (0 .0 52 8) (0 .0 13 4) (0 .0 46 2) fo rm al in su ra nc e -0 .0 09 37 * -0 .1 39 ** 0. 05 91 ** * 0. 08 90 * -0 .0 05 49 -0 .1 39 ** 0. 02 64 ** 0. 11 8* -0 .0 01 41 -0 .2 00 ** * 0. 00 29 2 0. 19 8* ** (0 .0 05 16 ) (0 .0 62 6) (0 .0 20 6) (0 .0 49 0) (0 .0 04 28 ) (0 .0 69 5) (0 .0 10 3) (0 .0 68 8) (0 .0 01 01 ) (0 .0 56 9) (0 .0 22 3) (0 .0 74 6) d ist an ce to ro ad -6 .2 4e -0 5 -0 .0 00 65 5 0. 00 03 63 0. 00 03 55 -7 .6 6e -0 5 -0 .0 01 30 * 0. 00 04 21 0. 00 09 59 * -1 .2 5e -0 5 -0 .0 00 97 5 0. 00 02 32 0. 00 07 56 (7 .1 3e -0 5) (0 .0 00 72 6) (0 .0 00 41 2) (0 .0 00 38 4) (6 .4 2e -0 5) (0 .0 00 78 3) (0 .0 00 27 4) (0 .0 00 56 5) (1 .3 7e -0 5) (0 .0 00 87 3) (0 .0 00 21 7) (0 .0 00 67 6) d ist an ce to u rb an c en te r -2 .9 3e -0 6 -3 .0 8e -0 5 1. 71 e05 1. 67 e05 3. 12 e05 0. 00 05 32 * -0 .0 00 17 2* -0 .0 00 39 1* 3. 54 e06 0. 00 02 76 -6 .5 5e -0 5 -0 .0 00 21 4 (2 .1 3e -0 5) (0 .0 00 22 4) (0 .0 00 12 5) (0 .0 00 12 1) (2 .6 5e -0 5) (0 .0 00 27 4) (8 .9 0e -0 5) (0 .0 00 20 9) (4 .9 0e -0 6) (0 .0 00 34 7) (8 .5 1e -0 5) (0 .0 00 26 8) d ist an ce to m ar ke t -0 .0 00 61 7* -0 .0 06 49 ** * 0. 00 35 9* ** 0. 00 35 1* ** -0 .0 00 41 5 -0 .0 07 06 ** * 0. 00 22 8* ** 0. 00 52 0* ** -0 .0 00 10 1 -0 .0 07 85 ** * 0. 00 18 6* ** 0. 00 60 9* ** (0 .0 00 32 2) (0 .0 01 63 ) (0 .0 01 08 ) (0 .0 00 91 7) (0 .0 00 30 2) (0 .0 01 78 ) (0 .0 00 79 9) (0 .0 01 28 ) (7 .0 1e -0 5) (0 .0 01 84 ) (0 .0 00 67 1) (0 .0 01 40 ) m ic ro fin an ce in st itu tio n -0 .0 05 83 -0 .0 67 4 0. 03 46 0. 03 86 -0 .0 09 12 -0 .1 94 ** * 0. 03 24 ** 0. 17 0* ** -0 .0 01 31 -0 .1 19 ** 0. 01 67 ** 0. 10 3* * (0 .0 05 15 ) (0 .0 59 3) (0 .0 28 2) (0 .0 36 2) (0 .0 06 37 ) (0 .0 54 3) (0 .0 14 9) (0 .0 59 3) (0 .0 00 93 7) (0 .0 52 4) (0 .0 07 93 ) (0 .0 50 4) el ec tr ic ity -0 .0 03 52 -0 .0 35 9 0. 02 02 0. 01 92 -0 .0 06 18 -0 .0 92 4* 0. 03 13 0. 06 73 * 0. 00 01 39 0. 01 10 -0 .0 02 59 -0 .0 08 52 (0 .0 05 13 ) (0 .0 50 3) (0 .0 28 8) (0 .0 26 6) (0 .0 05 10 ) (0 .0 54 9) (0 .0 20 1) (0 .0 39 3) (0 .0 00 62 8) (0 .0 50 2) (0 .0 11 8) (0 .0 39 1) sa fe w at er 0. 02 79 0. 18 2* ** -0 .1 33 ** -0 .0 76 6* ** 0. 01 20 0. 14 0* * -0 .0 63 2* -0 .0 88 6* * 0. 00 22 2 0. 13 3* * -0 .0 47 7 -0 .0 87 8* * (0 .0 18 9) (0 .0 62 3) (0 .0 56 2) (0 .0 23 7) (0 .0 09 40 ) (0 .0 61 6) (0 .0 33 8) (0 .0 34 7) (0 .0 02 07 ) (0 .0 65 9) (0 .0 30 1) (0 .0 38 3) to ile t 0. 00 33 4 0. 03 20 -0 .0 18 9 -0 .0 16 4 0. 01 98 0. 17 9* * -0 .0 91 0 -0 .1 07 ** 0. 00 82 5 0. 25 6* ** -0 .1 20 ** -0 .1 44 ** * (0 .0 11 8) (0 .1 04 ) (0 .0 65 0) (0 .0 50 4) (0 .0 17 6) (0 .0 90 3) (0 .0 62 5) (0 .0 42 5) (0 .0 07 26 ) (0 .0 90 5) (0 .0 61 2) (0 .0 37 0) d em og ra ph ic d ep en de nc y ra tio 0. 00 17 9 0. 01 88 -0 .0 10 4 -0 .0 10 2 -0 .0 00 28 4 -0 .0 04 84 0. 00 15 6 0. 00 35 6 0. 00 03 74 0. 02 92 -0 .0 06 93 -0 .0 22 6 (0 .0 02 79 ) (0 .0 28 8) (0 .0 15 8) (0 .0 15 8) (0 .0 01 92 ) (0 .0 32 2) (0 .0 10 4) (0 .0 23 8) (0 .0 00 45 0) (0 .0 28 8) (0 .0 07 07 ) (0 .0 22 3) ec on om ic d ep en de nc y ra tio -0 .0 00 51 3 -0 .0 05 39 0. 00 29 9 0. 00 29 2 -0 .0 00 11 1 -0 .0 01 88 0. 00 06 08 0. 00 13 9 0. 00 01 31 0. 01 02 -0 .0 02 42 -0 .0 07 90 (0 .0 01 37 ) (0 .0 13 9) (0 .0 07 78 ) (0 .0 07 54 ) (0 .0 00 77 1) (0 .0 12 9) (0 .0 04 18 ) (0 .0 09 53 ) (0 .0 00 19 4) (0 .0 13 3) (0 .0 03 14 ) (0 .0 10 4) ru ra l 0. 00 21 3 0. 02 36 -0 .0 12 5 -0 .0 13 2 0. 00 10 5 0. 01 86 -0 .0 05 68 -0 .0 14 0 -0 .0 00 58 4 -0 .0 41 5 0. 01 14 0. 03 07 (0 .0 08 23 ) (0 .0 93 9) (0 .0 47 9) (0 .0 54 2) (0 .0 05 31 ) (0 .0 98 0) (0 .0 28 3) (0 .0 75 0) (0 .0 01 55 ) (0 .0 96 9) (0 .0 29 7) (0 .0 68 8) h h re ce iv ed a ss ist an ce sin ce c o v id -1 9 ou tb re ak -0 .0 01 48 -0 .0 16 0 0. 00 87 1 0. 00 88 0 -0 .0 03 37 -0 .0 62 2 0. 01 75 0. 04 81 0. 00 01 67 0. 01 27 -0 .0 03 11 -0 .0 09 75 (0 .0 05 38 ) (0 .0 60 1) (0 .0 32 0) (0 .0 33 4) (0 .0 03 97 ) (0 .0 68 0) (0 .0 16 6) (0 .0 55 3) (0 .0 00 84 0) (0 .0 61 3) (0 .0 15 5) (0 .0 46 7) o bs er va tio ns 50 6 50 6 50 6 50 6 49 7 49 7 49 7 49 7 49 6 49 6 49 6 49 6 st an da rd e rr or s i n pa re nt he se s. ** * p< 0. 01 , * * p< 0. 05 , * p <0 .1 ta bl e a .5 (c on td .). o rd er ed p ro bi t, fa rm in co m e ch an ge . https://doi.org/10.36253/bae-13404 modeling conversion to organic agriculture with an eu-wide farm model dimitrios kremmydas*, pavel ciaian, edoardo baldoni a systematic literature review on the rural-urban economic well-being gap in europe cesare meloni1,*, benedetto rocchi2, simone severini1 the forest-based bioeconomy in latvia: economic and environmental importance vineta tetere1,2,*, jack peerlings1, liesbeth dries1 the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia margherita squarcina1,2,*, donato romano1 bioeconomy and resilience to economic shocks: insights from the covid-19 pandemic in 2020 jesús lasarte-lópez1,*, nicola grassano2, robert m’barek1, tévécia ronzon1 acknowledgements bio-based and applied economics bae copyright: © 2023 l. arata, d. menozzi. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae bio-based and applied economics 12(1): 3-4, 2023 | e-issn 2280-6e172 | doi: 10.36253/14720 editorial farmers’ motivations and behaviour regarding the adoption of more sustainable agricultural practices and activities linda arata1, davide menozzi2 1 università cattolica del sacro cuore, department of agricultural and food economics. 2 university of parma, department of food and drug. the ix eaae phd workshop took place on june 22-24, 2022, in parma (italy). a number of 126 phd students from 72 european and non-european universities gathered together to attend keynote speeches, present their research and get feedback on that, informally talk and share research experiences and opinions, enjoy free time events. talking with some of these students afterwards, you could realise how well-organised, fruitful and inspiring the three-day workshop was. for most of them, it was the first opportunity to present their work, share research challenges, and receive feedback from peers and senior economists in a conference setting. once the phd workshop was over, the participants were invited to submit their presented papers to bae bio-based and applied economics. the received original manuscripts underwent a double-blind peer-review process and five of them are finally part of this special issue. all of these five studies use primary data and analyse farmers behaviour. four of them are focused on african countries. the studies report interesting insights into the analysis of farmers’ aspirations (deißler et al., 2023), of the factors affecting the adoption of strategies for adapting to climate change (onyenekwe et al., 2023), of diversifying in off-farm activities (ceriani et al., 2023), of improving economic, social and environmental sustainability with livelihood strategies (prazeres, 2023) and agroforestry practices (seegers et al., 2023). the paper of ceriani et al. (2023) entitled “how do farmers’ pluriactivity project evolve?” investigates the motivations that lead farmers to diversify their income through off-farm activities, the barriers to diversification, and the management strategies for a long-lasting choice. the data were collected in northern france through farmers interviews, and the results show that the balance between agricultural and off-farm activities changes over time depending on family context, job opportunities and financial situation. the maintenance over time of the off-farm job is favoured by the flexibility of both on-farm and off-farm activities to adjust to each other and by the possibility of hiring labour on the farm. the paper “heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria” authored by onyenekwe et al. (2023) analyses the adaptation strategies to climate shocks uptaken by farmers and fishermen in the niger delta region (nigeria) and the main factors driving their adoption. the econometric analysis of the data collected from more than 500 interviewed farmers and fishermen shows that livelihood diversification, crop management, soil and water management strategies are the solutions mostly adopted. fishermen use mainly livelihood diversification as an adaptation option. the study finds that household size and education are the main determinants of adoption, independent of the practices being adopted, while the factors discouraging the adoption are practice-specific. the study by prazeres (2023) entitled “organic cocoa farmer’s strategies and sustainability” investigates the social, economic and agro-ecological factors affecting the choice of livelihood strategies by organic cocoa producers in são tomé and príncipe. the study collected data through a survey administered to 810 farmers belonging to organic cocoa cooperatives and finds that education level, perception of social class, insurances, loans and access to services are the main determinants of livelihood strategies. the paper authored by deißler et al. (2023) entitled “a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture” http://creativecommons.org/licenses/by/4.0/legalcode 4 bio-based and applied economics 12(1): 3-4, 2023 | e-issn 2280-6172 | doi: linda arata, davide menozzi assesses whether personality traits contribute to shaping the individual aspirations of smallholder farmers, and how aspirations are connected with their sociodemographic characteristics. the statistical analysis of data collected from 272 smallholder farming households in kenya, shows that openness, conscientiousness and extraversion affect aspirations, which are additionally influenced by extrinsic factors. the study concludes that analysing aspirations when evaluating development projects and policy is important for an effective outcome of the intervention. in their work “exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms: evidence from rural rwanda” seegers et al. (2023) use a role-play game to raise farmers’ awareness towards agroforestry adoption. the study involves 72 small-scale farmers from rwanda and interviews them before and after the game. results show the effectiveness of the game to increase the knowledge of the benefits of planting different tree species on the farm as well as the motivation to take that action. overall, the studies included in the special issue provide new insights into farmers’ behaviour analysis, in particular from developing countries. the authors emphasised relevant insights for policy-makers and researchers on the factors able to affect the adoption of strategies for improving the economic, social and environmental sustainability of farming activities. references ceriani, c., djouak, a., & chaillard, m. (2023). how do farmers’ pluriactivity project evolve?.  bio-based and applied economics, 12(1). https://doi.org/10.36253/ bae-13424 deißler, l., mausch, k., karanja, a., mcmullin, s., & grote, u. (2023). a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture. bio-based and applied economics, 12(1). https://doi.org/10.36253/bae-13475 onyenekwe, c. s., sarpong, d. b., egyir, i. s., opata, p. i., & ume, c. (2023). heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria.  bio-based and applied economics, 12(1). https://doi.org/10.36253/bae-13436 prazeres, i., lucas, m. r., marta-costa, a., & henriques, p. d. (2023). organic cocoa in sao tome and principehow producers perceive sustainability and elect livelihood strategies. bio-based and applied economics, 12(1). https://doi.org/10.36253/bae-13473 seegers, r., winter, e., & ulrike, g. (2023). exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms: evidence from rural rwanda. bio-based and applied economics, 12(1). https://doi.org/10.36253/ bae-13479 farmers’ motivations and behaviour regarding the adoption of more sustainable agricultural practices and activities linda arata1, davide menozzi2 how do farmers’ pluriactivity projects evolve? how do farmers’ pluriactivity project evolve? clarisse ceriani*, amar djouak, marine chaillard heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria chinasa sylvia onyenekwe1, patience ifeyinwa opata1, chukwuma otum ume1,*, daniel bruce sarpong2, irene susana egyir2 organic cocoa farmer’s strategies and sustainability ibrahim prazeres1,*, maria raquel lucas1, ana marta-costa2, pedro damião henriques3 a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture luzia deißler1,*, kai mausch2, alice karanja3, stepha mcmullin3, ulrike grote1 exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms: evidence from rural rwanda ronja seegers*, etti winter, ulrike grote bio-based and applied economics 13(1): 3-11, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15862 bio-based and applied economics bae copyright: © 2024 targetti, s., niedermayr, a., häfner, k., & schaller, l. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: targetti, s., niedermayr, a., häfner, k., & schaller, l. (2024). new pathways for improved delivery of public goods from agriculture and forestry. bio-based and applied economics 13(1): 3-11. doi: 10.36253/bae-15862 received: february 29, 2024 accepted: march 7, 2024 published: may 20, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. guest editors: stefano targetti, andreas niedermayr, kati häfner, lena schaller orcid st: 0000-0002-2154-546x an: 0000-0001-7691-4296 kh: 0000-0001-6413-4540 ls: 0000-0002-8313-8798 new pathways for improved delivery of public goods from agriculture and forestry stefano targetti1,*, andreas niedermayr2, kati häfner3, lena schaller2 1 university of bologna, department of agricultural and food sciences, viale fanin, 50 40127 bologna, italy 2 university of natural resources and life sciences, vienna, department of economics and social sciences, institute of agricultural and forestry economics, feistmantelstrasse 4, 1180 vienna, austria 3 leibniz centre for agricultural landscape research (zalf), eberswalder str. 84, 15374 müncheberg, germany *corresponding author. e-mail: stefano.targetti@unibo.it 1. introduction this special issue of bio-based and applied economics (bae) features a selection of five papers developed within the eu h2020 project ‘contract solutions for effective and lasting delivery of agri-environmentalclimate public goods by eu agriculture and forestry’ (console) (h2020rur-2018-2, ga no. 817949). console has been comprehensively investigating the effectiveness, efficiency and longevity of innovative contract solutions for the provision of agri-environmental-climate public goods (aecpgs), the acceptance of such contracts amongst european farmers and stakeholders, as well as the drivers and mechanisms influencing the implementation. despite large budgets for policies devoted to environmental objectives, the ongoing decline in the provision of aecpgs in many european agricultural and forest ecosystems and the growing societal concerns about ecological issues make it necessary to improve the environmental effectiveness in particular of the common agricultural policy (cap) (pe’er et al., 2022). the green deal of the eu and the farm-to-fork strategy are part of the eu’s plan to respond to these challenges. they highlight the relevance of the agricultural sector for the provision of a multitude of aecpgs, such as biodiversity, climate regulation, water and soil protection. to achieve real change, in addition to rethinking and strengthening actions set by the agri-environmental and climate schemes (aecs), the attention towards innovative, more effective, and efficient instruments is increasing (targetti et al., 2022). the most prominent examples are result-based and value chain-based solutions, as well as approaches that promote collective implementation. in addition, land tenure contracts with environmental requirements are in the focus of interest. although promising, all of these tools involve a number of challenges for their successful design and implementation, such as acceptance and enablement of https://doi.org/10.36253/bae-15862 http://www.fupress.com/bae https://doi.org/10.36253/bae-15862 https://orcid.org/0000-0002-2154-546x https://orcid.org/0000-0001-7691-4296 https://orcid.org/0000-0001-6413-4540 https://orcid.org/0000-0002-8313-8798 mailto:stefano.targetti@unibo.it 4 bio-based and applied economics 13(1): 3-11, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15862 stefano targetti et al. farmers, knowledge and training needs, equity/fairness, and compatibility with agricultural business. in this editorial paper, we describe the console framework as a major background for the evaluation of novel contract solutions and the development of the research papers for this special issue. the framework describes three main topics covered by the papers in this special issue: innovative designs (of aecs), attitudes of farmers toward these new approaches, and drivers for their successful implementation. in the following, we first describe the console framework and then present different aspects concerning contract design and innovative contractual options of aecs. finally, we introduce the papers of the special issue of bae: ‘new pathways for an improved delivery of public goods from agriculture and forestry’. 2. the console framework the analytical framework developed in the console project aims at the identification of a set of contract elements to be considered in the design and analysis of aecs (fig. 1). specifically, it identifies elements and factors characterizing an agri-environmental contract along three main groups (viaggi et al., 2022): – contract design includes the specific elements characterising aecs contract like objective, duration, level of payment, etc.; – mechanisms and impact comprise the factors that influence the effectiveness of the contract such as attitude of farmers which in turn affect the acceptability of the scheme, etc.; – system features consists of a range of drivers including governance, market, and local-scale conditions that affect the impact and efficiency of a contract. besides these elements, factors, and drivers that define and influence the success or failure of a contract, the framework also indicates a set of performance criteria. these criteria are directly or indirectly related to several items included in the contract design, mechanisms and system features. for instance, acceptance, longevity, effectiveness, profitability, etc. are only some of the criteria that can be considered in the evaluation of a contract and are related to farm-level characteristics (for example, farm structure, farmers’ attitudes) and regional-level characteristics (for example, environmental conditions, formal and informal institutions). 3. contract design: current agrienvironmental and climate schemes and potential for innovative solutions the provision of public goods by agriculture is largely determined by the management and practices applied at the farm and regional scale. therefore, policies usually include a variety of instruments to improve the environmental performance. currently, the main approach of figure 1. analytical framework developed in the console project for the analysis and design of agri-environmental and climate contracts (d’alberto et al., 2024). 5new pathways for improved delivery of public goods from agriculture and forestry bio-based and applied economics 13(1): 3-11, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15862 agricultural policy efforts to maintain and/or improve aecpgs on farmland are voluntary action-based aecs. these schemes consist of prescribed practices, and participating farmers receive monetary compensation for income forgone and increased costs associated with the implementation of the practices considered capable of improving the environmental performance of the farm. such payment schemes are relatively simple to implement, do not require complicated monitoring and do not incur inequality concerns (usually the same ‘average’ price is offered to farmers for undertaking a given action). action-based aecs are acknowledged to have positive effects on the environment (herzog et al., 2005), while their overall environmental effectiveness remains relatively low considering the financial resources put into these schemes (batary et al., 2015). such schemes incur a range of problems, as they typically over-reward ‘all but the marginal producer’ (hanley et al., 2012). this effect is linked to the actual provision of public goods from farmland, which is affected by spatial variation of opportunity costs and information asymmetries between ‘sellers’ and ‘buyers’ of aecpgs, leading to a spatial policy mistargeting, but also potential spatial under-provision/under-supply of aecpgs (zasada et al., 2017). the availability of local-scale information could improve the spatial targeting of policies and improve the cost-effectiveness of payments, but such a detailed level of information is usually challenging or not affordable to collect (ferraro, 2008). other negative aspects of actionbased aecs that are often criticized comprise a range of less tangible impacts related to the potential risks of commodification of public goods and the disconnect between the objectives of the scheme and the values and moral responsibility of farmers. moreover, another criticism is related to the long-term effects of action-based aecs. an essential condition to ensure permanent environmental improvements is to link farmers’ acceptance of a scheme with a major attitudinal change that corresponds to their interests and values (burton et al., 2008). farmers with interest in agroecology, for example, tend to dislike having to rely on payments for good behavior and, in some cases, prefer to be remunerated by the market because their business model is successful. farmers recognizing an inherent value of nature, for instance, are found to apply more holistic management strategies benefitting a diversity of aecpgs (klebl et al., 2024). 3.1 from action-based to result-based schemes to tackle the low efficiency and effectiveness of action-based aecs, a stronger focus on what is actually achieved in terms of environmental goals is considered a relevant improvement. result-based approaches in this respect are innovative solutions because they are based on a direct link between payment and the achievement of environmental goals, while no requirement of the implementation of specific practices is included. this allows farmers to be more f lexible in their management, but these contracts score significantly worse in terms of practicability, and therefore their application on a large scale is rare (drechsler, 2017). the two main limitations of result-based schemes concern the uncertainty of payment as it is dependent on the achievement of an environmental result, and the ability to measure it. these limitations considerably reduce the acceptance of these contracts because farmers incur risks of failing to achieve the aims and are exposed to public scrutiny as they become a seller of public goods (haaren and bathke, 2008; atari et al., 2009). on a more general level, the additionality of result-based approaches is a relevant concern. in other words, doubts are raised about the prospect of paying farmers for results that would be actually delivered even without the policy intervention. indeed, result-based payments potentially incentivize land enrollment where the target result is already achieved, resulting in a zero-additionality effect (uthes and matzdorf, 2013). 3.2 collective implementation to improve the capacity of aecs to deliver public goods such as farmland biodiversity, the coordination of interventions at the landscape level to maximize the positive outcome (for example, adopting ‘green’ practices in different landholdings) is also proposed. in contractual solutions based on collective implementation and/or cooperation, farmers and/or private/public landowners voluntarily enter a joint, collective partnership to commonly deliver a specific environmental or climate action goal. this means that farmers, foresters (and other stakeholders) cooperate (by establishing an entity with or without legal personality) to achieve a specific environmental target. contract solutions that propose collective implementation or cooperative/collaborative elements often address a territorial/landscape level to deliver public goods “across field borders”. they aim especially for environmental results, which can hardly be improved by measures on singular fields and plots (e.g., water quality, maintenance of habitats, peatland rewetting). the environmental effectiveness of coordination depends on the public good addressed. for example, in the case of habitat maintenance for a particular target species it relates to the habitat requirement of that species. in cases where the spatial characteristics of habitats and land ownership 6 bio-based and applied economics 13(1): 3-11, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15862 stefano targetti et al. do not overlap, collective contracts are more efficient in ensuring “economies of configuration” (franks, 2011). the design of collective aecs is relevant, as it targets groups of landowners, rather than individuals, so that agri-environmental efforts are spatially coordinated (prager, 2015). in general, collective and cooperative/collaborative approaches can be used to address problems that cannot be solved individually or to achieve specific environmental improvements that can be achieved better by working together. collective approaches are nevertheless affected by an increased complexity of the policy design and higher transaction costs, which limit the adoption of the schemes (zavalloni et al., 2019). in general, the success of collective contracts depends on the willingness of neighboring land managers to work together, on the existence of formal or informal institutions capable of implementing and coordinating the contract and the perceptions of these coordinating institutions (franks, 2011; häfner and piorr, 2021). 3.3 value chain-based solutions some contract solutions consider the delivery of public goods in connection with the production of private goods. these solutions are motivated by engaging different or all parts of a value chain, and the environmental benefits provided by the supplying farms are often part of the food companies’/retailers’ marketing strategies. in a value-chain contract, farmers accept to meet specific environmental requirements and receive monetary support from market actors. possible examples are reduced fertilization, higher animal welfare standards, preservation of biodiversity, etc. the monetary support can take the form of sale guarantees, price premiums, and/or the use and marketing of products under specific brands. moreover, some value chain-related contractual solutions provide an example of a way to better support and market organic production. value chain contracts are essentially based on the capacity of the market to reward the public goods attached to the production of a specific private good (food). the advantage is the reliance on market mechanisms and the more direct link with consumers without incurring in the distortionary effects of incentives or regulations. however, conveying information about the environmental performance of a product is not always straightforward. following economic principles, jack et al. (2008) notice that people are likely not disposed to pay for something that they can receive for free. that differentiates between public goods like drinking water where the beneficiaries see a direct advantage, and other goods that are non-excludable like climate change mitigation or biodiversity. this means that discrimination of the link between product and environmental performance is necessary, and not all public goods have the same communicative grip. for example, aecpgs related to regulating services are characterized by an indirect contribution to society (diaz et al., 2018) and thus could be more difficult to attach to a product compared to aecpgs related to e.g. cultural services (targetti et al., 2021). short supply chains are easier to develop because the link between consumers and promoted aecpgs is more direct, while the inclusion of large-scale public goods such as the mitigation of climate change is more difficult. a common problem concerns the need to mark up the value chain products with labels. given the high number of labels that are present on market shelves, labelling of public good related products risks being not effective in communicating with consumers. another problem of value chain contracts is the distribution of the added value along the value chain. value chain contracts include a wide range of different approaches and arrangements between different actors along the value chain. in a recent report, biber-freudenberger et al. (2019) concluded that the actors that should be targeted by policy to promote biodiversity are farmers and consumers, at each end of the value chain. this can strengthen the position of the farmer in the value chain through stronger bottom-up approaches. there are risks, however, that farmers have not enough bargaining power in comparison to big food companies or retailers. this risk involves mistrust in the contract by farmers and to some extent by the consumers that may value a food product for the level of fairness and transparency of its production process. additionally, also the majority of currently implemented value-chain approaches are based on action-based measures, potentially lacking environmental effectiveness (bredemeier et al., 2022) 3.4 land tenure contracts with environmental requirements a common drawback of aecs also concerns the trade-off between acceptance by farmers and length of contract. in general, longer contracts are less attractive to farmers because their room for maneuver is limited (raina et al., 2021). however, longer contracts are often more effective or even necessary to achieve an environmental target. this involves the relevance of land tenure contracts including land tenure arrangements with environmental clauses. indeed, these types of contract are usually able to reduce the trade-off between contract length and acceptance as the possibility to benefit from reduced loans for a longer time range is seen positively by a land-tenant. 7new pathways for improved delivery of public goods from agriculture and forestry bio-based and applied economics 13(1): 3-11, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15862 for the fao, “land tenure is the relationship, legally or customarily defined, between people, as individuals or groups, with respect to land. (for convenience, ‘land’ is used here to include other natural resources such as water and trees.) land tenure is an institution, that is, rules invented by societies to regulate behavior. the rules of tenure define how property rights to land are to be allocated within societies. they define how access is granted to the rights to use, control, and transfer land, as well as the associated responsibilities and restrictions. in simple terms, land tenure systems determine who can use what resources for how long, and under what conditions”1. the terms land tenure and land rights are often used interchangeably. land tenure contracts devoted to the improvement of aecpgs have clauses for the improvement or conservation of environmental assets. landowners (private or public) lease their land to farmers, foresters, or third parties under certain conditions and accept a lower lease payment to compensate for additional environmental or climate action efforts by farmers. these efforts serve to achieve some form of ecological or environmental improvement. however, these contract types may be hampered by legal issues or not considered at all by private owners. in many cases and usually in more marginal areas, landowners are not interested in selling the land, but have interest in preserving their land in good conditions. for instance, particular forms of properties (e.g. public lands, church properties, foundations, etc.) have institutional mandates (formal or informal) of good management. in other cases, land fragmentation causes transactional problems that hinder organization and rational management. in these cases, forms of land tenure with clauses can be effective. however, the success of these contracts is often linked to arranging an easy and comprehensible contract type for the owners and/or the availability of an intermediary actor able to manage the contracts efficiently (napoléone et al., 1995; the nature conservancy, 2019). 4. presentation of the special issue in this chapter we present the five papers included in this special issue and how they are related to the aspects outlined in the previous chapter. the paper of tyllianakis (2023) assesses the perspectives of upland yorkshire farmers in the united kingdom (uk) regarding the landscape recovery scheme, a soon to be rolled out agri-environmental initiative designed to promote collaborative efforts for landscape1 “3. what is land tenure” (available at: http://www.fao.org/3/ y4307e/y4307e05.htm#topofpage). wide environmental improvements. with the uk having left the eu, proposals are being made in its agricultural policy to move away from ‘direct basic payments’ for farmers and implement a system with a stronger focus on ‘public money for public goods’. the landscape recovery scheme is the most ambitious scheme within this new envisioned system. employing q methodology, the paper identifies diverse viewpoints among farmers, who largely depend on government subsidies and are involved mainly in sheep and beef farming. specifically, the analysis uncovered three main discourses: pragmatic yet environmentally conscious farmers, pragmatic objectors, and risk-averse environmentalists. pragmatic yet environmentally conscious farmers are in general open to aecs, as they perceive them as a means to achieve their two main goals (financial survival and environmental stewardship), but are at the same time resistant to the landscape recovery scheme, citing its complexity and perceived misalignment with these goals. pragmatic objectors prioritize financial compensation and reduced bureaucracy, showing resistance to long-term contracts, collaborative efforts and contracts addressing the delivery of multiple aecpgs. risk-averse environmentalists show a varied interest in environmental issues, particularly ones related to climate change, but prefer simpler contracts in terms of monitoring, indicating a preference for schemes that are less demanding (and risky) yet environmentally beneficial. across these discourses the results reveal a general preference for “broad and shallow” aecs that offer straightforward requirements, as opposed to the more ambitious landscape recovery scheme, and a desire to merge economically viable practices with environmental objectives. aspects regarding payments, free advice, duration and scope seem to inhibit the endorsement of the landscape recovery scheme. overall, this case study provides unique insights into farmers’ viewpoints on these innovative concepts the uk is planning to introduce. it is also highly interesting from the perspective of eu agricultural policy, which also plans to move more into this direction, even though the author makes clear that generalizing the findings is not possible and also outside the purpose of q methodology. le gloux and dupraz (2023) do not explore new aecs, but rather carry out an ex-ante analysis of the potential effects of reallocating the common agricultural policy (cap) budget from income support to already existing environmental incentives, specifically aecs and organic farming (of) support, using french farm accountancy data network (fadn) data from 2015 to 2019. the methodological approach of the study involves estimating a generalized tobit model for the voluntary http://www.fao.org/3/y4307e/y4307e05.htm#topofpage http://www.fao.org/3/y4307e/y4307e05.htm#topofpage 8 bio-based and applied economics 13(1): 3-11, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15862 stefano targetti et al. adoption of these environmental contracts and acceptable farm-level payment triggering this adoption, predicting new adoption probabilities and acceptable farm-level payments under reduced direct payments, and simulating budget reallocation towards environmental incentives. the analysis is based on an unbalanced panel of 36,251 farm observations, providing insights into the total farmlevel payments received for aecs and of support contracts. the findings indicate that reallocating an additional 7.5% of direct payments towards aecs and of support significantly increases participation rates in these programs. this effect is attributed to two main incentives: increased public funding for environmental commitments and the indirect influence of reduced direct payments, which lowers the acceptable farm-level payment for participating in of support. however, the study also acknowledges limitations, including insufficient information to capture the diversity of aecs eligibility and measures adopted by farmers, unobserved factors influencing adoption decisions, and the potential market repercussions of significant policy changes. in conclusion, the study suggests that decreasing direct payments with little environmental conditionality and increasing targeted payments for environmental public goods can enhance the adoption of sustainable farming practices. while the current regulation’s transfer limit may not suffice to meet the farm to fork target of 25% organic land, it can substantially contribute to this goal. finally, the study calls for further research to refine the modelling of environmental contract adoption, highlighting the need to consider intrinsic farmer motivations and locational factors more comprehensively. the work of eichhorn et al. (2023) addresses the challenge of understanding factors that support or hinder the implementation of novel aecs, specifically result-based and collective schemes within the european union. the research fills a gap in existing literature by systematically investigating the macro-environmental factors affecting the adoption of these novel schemes, moving beyond individual case studies and farmer surveys to a more holistic, structured analysis. the study thus posed research questions centered on the macroenvironmental impacts on aecs adoption, employing a political, economic, social, technological, legal, and environmental (pestle) analysis framework. this approach provides a comprehensive method to categorize and understand the external factors affecting aecs. data was collected through an online survey conducted in spring 2021 with 85 stakeholders from austria and germany, encompassing a broad range of participants involved in the design, implementation, and control of aecs, including government agencies, environmental organizations, and agricultural associations. the study’s main findings reveal that economic, legal, and social factors are the most influential in the adoption of aecs, with economic incentives, clear legal frameworks, and social dynamics being pivotal. the unpredictability of nature was identified as a significant challenge for resultbased schemes, whereas collective schemes emphasized the importance of social relations and farmer attitudes. discussion within the study highlighted the nuanced implications of these factors on policy and practice, stressing the importance of addressing both external and internal influences on farmer decisions. the conclusions drawn suggest that the pestle approach effectively identifies critical factors influencing aecs adoption, providing strategic insights for policymakers and stakeholders. looking forward, the study calls for further research into comparative analyses across countries, more in-depth investigations of differences between external factors for result-based and collective contracts and deeper examination of stakeholder influences. the study of d’alberto et al. (2023) goes back to an individual case-study analysis, but covers a broader scope of innovative contract solutions. specifically, it investigates the perceptions of farmers in emiliaromagna, italy, regarding four novel agri-environmental contract solutions, namely result-based (rb), collective (co), value chain (vc), and land tenure with environmental clauses (lt). the study assesses farmers’ perceptions of the understandability, applicability, and economic benefits of these contracts, as well as their willingness to enroll, using ordered logistic regression models that incorporate socio-demographic characteristics, structural features of the holdings, and preferences for 13 individual contract features. key findings reveal that farmers’ acceptance of innovative contract solutions is influenced by their age, with older farmers generally showing lower levels of acceptability and willingness to enroll. previous experience with similar measures significantly affects farmers’ perceptions, particularly enhancing the understandability of collective and value chain contracts. structural characteristics of the holdings, such as exposure to trade channels, sales amount, and farm size, also play a crucial role in shaping perceptions of contract solutions. the study concludes that farmers are open to the investigated contract solutions, but acceptance varies based on individual and farm characteristics, necessitating careful consideration in policy design. for one, rb contracts are favored by organic producers and those involved in nature conservation, highlighting the importance of understanding and perceived applicability of result-based instruments. co-contracts face opposition from larger farms due to 9new pathways for improved delivery of public goods from agriculture and forestry bio-based and applied economics 13(1): 3-11, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15862 perceived inapplicability, but are welcomed by recipients of direct cap payments. vc and lt contracts’ attractiveness is strongly linked to farmers’ previous experiences with similar measures, with a lack of such experience making these contracts seem complex and untrustworthy. vc contracts are moreover particularly attractive to farms already engaged in value chains, emphasizing the importance of understandability and applicability in these contexts. overall, the study suggests that the successful implementation of improved contract solutions could be achieved through a flexible mix of instruments tailored to farmers’ needs, incorporating a variety of contractual elements to enhance design and acceptance. finally, the paper of bradfield et al. (2023) again carried out a cross-country analysis on innovative contract solutions. it evaluates the perceptions of land managers and stakeholders regarding the understandability, applicability, and economic benefits of four innovative agrienvironmental contract types (results-based, collective action, value chain, and land tenure contracts) across twelve european countries, with a focused analysis on ireland. the methodological approach entailed a survey of 2,275 land managers and 486 stakeholders. the irish subset comprised 210 land managers and 16 stakeholders, highlighting the significance of agriculture in ireland, where 72% of land is agriculturally used, the highest in the eu. key findings indicate that most land managers agree that results-based contracts are understandable, applicable, and economically beneficial. however, there’s a noticeable disparity in ireland, with a lower proportion of land managers compared to other european countries agreeing that value chain and land tenure contracts are comprehensible and applicable to their farm. the study underscores the necessity for enhanced promotion and education concerning collective action contracts throughout europe, emphasizing their critical role in public goods management. the conclusion suggests a pressing need for policies that offer financial certainty and autonomy to farmers, particularly in ireland, to bolster the adoption of these innovative contracts. furthermore, the study calls for increased practical exposure and education about these contract types to improve understanding and applicability, notably for collective action, value chain, and land tenure contracts which are less familiar to irish land managers. authors’ contribution the authors have contributed in equal parts to the development of the paper. references atari, d. o. a., yiridoe, e. k., smale, s., & duinker, p. n. 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(2025). expenditure allocation for rural development interventions: main trends and patterns in the choices of the italian regions under the cap 2023-2027. bio-based and applied economics 14(2): 79-96. doi: 10.36253/ bae-16754 received: october 31, 2024 accepted: march 18, 2025 published: september 30, 2025 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor in chief: fabio bartolini associated editor: oriana gava orcid tm: 0009-0007-0265-965x fp: 0000-0002-3667-7115 expenditure allocation for rural development interventions: main trends and patterns in the choices of the italian regions under the cap 2023-2027 tobia minuzzo, francesco pagliacci* department of land, environment, agriculture and forestry, università di padova, italy *corresponding author. email: francesco.pagliacci@unipd.it abstract. the introduction of the new delivery model in the 2023-2027 common agricultural policy increased the decision-making and management autonomy of member states and their regions when implementing rural development policies. thus, understanding the drivers behind allocation choices for rural development funds is crucial. this study analyses the allocation of rural development funds across italian regions, considering ex-ante share allocation for different types of rural development interventions. a cluster analysis is then performed. different groups of italian regions are characterised using the indicators developed within the common monitoring and evaluation framework, the allocation of spending in the previous programming period, and other variables. four clusters of italian regions are identified: cluster 1 includes rural regions with low urbanisation, prioritising supporting interventions in disadvantaged areas and “environmental” ones; cluster 2 shows large allocation for cooperation interventions; cluster 3 includes regions funding primarily agricultural investments; cluster 4 shows no distinct or unique characteristics. this study is the first one addressing expenditure allocation of the 2023-2027 common agricultural policy. it confirms that expenditure patterns partially couple with geographical and historical similarities, although two main spending priorities (i.e. “environment” and “investments”) persist. keywords: eu rural development policy, political economy, cluster analysis, allocation. jel codes: d72, o13, q18. 1. introduction agriculture has been primarily affected by policy interventions, also in the european union (eu), where the common agricultural policy (cap) has represented the cornerstone of the european construction, since the origin of the eu (groupe de bruges, 1996; fusco, 2021). cap objectives have profoundly changed over time to adapt to the transformations of the agricultural sector and the whole society (de castro et al., 2020). today, more than in the past, the main objectives of the cap are mitigating climate change, https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae https://doi.org/10.36253/bae-16754 https://doi.org/10.36253/bae-16754 https://doi.org/10.36253/bae-16754 https://orcid.org/0009-0007-0265-965x https://orcid.org/0000-0002-3667-7115 mailto:francesco.pagliacci@unipd.it 80 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 tobia minuzzo, francesco pagliacci protecting the environment, landscape and biodiversity, improving the quality and safety of food, social cohesion, and the socioeconomic development of rural areas (bourget, 2021; european commission, 2023). for decades, the cap has been the most significant eu policy, in terms of budget allocation (de filippis and henke, 2010; matthews, 2017), although its share of the eu budget has halved, from 66% in 1980 to 35% in 2020 (dg agriculture and rural development, 2023). in the current programming period (2023-2027), italy is the third-largest beneficiary, after france and spain, of the resources allocated to the cap from the eu budget (reg. 2116/2021), receiving 10.5% of the total cap funds (about 28 billion euros). just for the rural development (rd) policy, a substantial portion of national funding is added, bringing the total funds for italy to nearly 37 billion euros. of this total amount, 48% is allocated to direct payments, while 43% is designated for rd policy (european commission, 2022). in the eu, rd policy has been traditionally managed in a decentralised manner, granting regional autonomy in decision-making and implementations (dwyer et al., 2007). in italy, nuts-2 (nomenclature des unités territoriales statistiques) regions oversee its management, a role reinforced by the new delivery model. it requires member states (ms) to develop a national strategic plan integrating both direct payments and rd policy (langlais, 2023). this shift has extended decentralisation to direct payments as well, now aligning under a need-based assessment, obtained through an in-depth regional-level analysis (barral, 2023). rd interventions are classified into eight groups, and in italy, 76 interventions have been selected, letting nuts-2 regions choose implementation and fund allocation. in this evolving governance framework, it is important to understand how funding decisions are made, in order to evaluate the effectiveness and equity of cap distribution, helping to inform future policies that promote rural development and sustainability. this paper investigates territorial differences in allocation of the rd funds, by analysing decision-making processes in italy within the 2023-2027 cap programming period. the primary objective is to understand resource allocation patterns and the key determinants of spending across the italian regions, offering innovative and updated empirical insights compared to previous studies. while regional differences in total allocation may naturally vary due to their different sizes, it is hypothesised that the percentage distribution of funds across interventions types also differs based on regional characteristics and needs. additionally, regional governments’ development objectives and policy priorities may shape these allocation choices (pagliacci and zavalloni, 2024). stemming from this primary objective, this study also seeks to: i) identify clusters of italian regions that allocate rd funds in a similar way; ii) examine the main drivers behind these allocation patterns, considering socioeconomic, sectoral and geographical factors. this research contributes to the literature by explaining how decentralised governance and regional policymaking affect fund distribution, offering valuable policy recommendations for both decision-makers and farmers. given the new governance model, these insights can enhance the efficacy and fairness of future rd policy. the rest of the paper is structured as follows. section 2 shows the theoretical background of this work as well as the characteristics of italian rd policy in the 2023-2027 programming period. section 3 presents the datasets used and the main methods adopted. section 4 reports the results of the analysis, while section 5 discusses them. section 6 concludes by highlighting possible implications and formulating hypotheses for future research. 2. theoretical background 2.1 cap 2023-2027 the 2023-2027 cap was definitively approved in 2021. in the same year, the strategic context regulations of the current cap (namely, the european green deal with its “european climate law” and the “fit for 55” strategy, as well as the “farm to fork” strategy) were also approved. the green deal’s main objective is achieving climate neutrality by 2050 in all eu economic sectors, according to the paris climate agreement (unfccc, 2015). in addition, it supports the transformation of the eu into a sustainable, fair and prosperous society with a modern and competitive economy, adopting a holistic and crosssectoral perspective (zezza, 2023). the package includes initiatives on climate, environment, energy, transport, industry, agriculture, education and research, sustainable finance, etc., all sectors deeply connected. among the green deal commitments with the greatest impact on agricultural policy, there are those relating to the “from farm to fork” (european commission 2020a) and the “eu biodiversity strategy for 2030” (european commission 2020b). in particular, “from farm to fork” was developed with the specific intention of reducing the environmental and climate footprint of the eu food system, setting some strict environmental targets that eu agriculture must achieve by 2030 (coderoni, 81 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 expenditure allocation for rural development interventions 2023). in doing so, it aims to strengthen eu agriculture’s resilience, ensuring food security, driving the global transition towards competitive sustainability from farm to fork and exploiting new opportunities (zezza, 2023). the current 2023-2027 cap started on the 1st of january 2023. despite the traditional path dependency that had characterised cap history (sotte, 2023), the current programming period represents a clear break from previous ones. the new delivery model is the new governance system that makes the cap more result-oriented, stressing the role of performance. precisely in this perspective, increasing freedom of choice is left to local authorities (i.e. national governments, and regional governments), in accordance with the principle of vertical subsidiarity (bolli et al., 2021). the new governance model is implemented through national strategic plans, developed by each ms after identification of their specific needs. national strategic plans outline intervention strategies to achieve eu objectives according to the specific needs of each territory, selecting interventions from a comprehensive range proposed by the commission, with specific targets and financial plans and after a negotiation phase with the commission itself. this significantly enhances subsidiarity, allowing mss to determine how to achieve eu objectives through the national strategic plans (carey, 2019; matthews, 2021). the paradigm shift has simplified eu activities but has increased management complexity for ms, particularly in a country like italy, where both the national and regional governments compete on agricultural policies. however, this shift has led to more targeted and tailored interventions. the european commission approved the national strategic plan for italy on 2 december 2022. it establishes a uniform national strategy for the agricultural, agrifood and forestry sectors, managing resources and support from the european agricultural guarantee fund and the european agricultural fund for rural development (eafrd). the strategic plan provides interventions for direct payments, sectoral support and rd interventions, with a total financial allocation available to the agri-food and forestry sector and rural areas of almost 37 billion euros for the five-year period 2023-2027. the entire financial envelope must pursue the objectives of the cap. the resources allocated to rd policy come from the eafrd, which is increased by 55% of national co-financing. for rd policy, there are 76 interventions, but four of them refer to risk management and are managed at national level (as in the previous programming period). for remaining rd interventions, italy decided to implement a management strategy that provides for national interventions with regional elements. therefore, regional governments plan and manage rd interventions, adapting them to their economic, social and territorial specificities. these rd interventions are implemented through the definition of regional programming complements for rd. they neither contrast with the national strategic plan nor add further choices, but detail how the general national strategy has declined at regional level, highlighting which interventions the region will finance, fund allocation for each of them, and specific conditions relating to each intervention. in italy, selected rd policy interventions belong to eight types (table 1): a. environmental, climate and other management commitments (agro-climatic-environmental interventions); b. natural or other specific territorial constraints; c. specific territorial disadvantages resulting from certain mandatory requirements; d. investments, including investments in irrigation; e. setting up young farmers and new farmers and starting rural businesses; f. risk management tools; g. cooperation; h. exchange of knowledge and dissemination of information. a smaller amount of the eafrd financial resources is also allocated to activities related to technical assistance. despite the large ms autonomy in resource allocation, the european commission introduced some financial constraints (ring-fencing), i.e. minimum fund allocations that mss must guarantee for specific types of intervention, in order to pursue the strategic objectives of the union. the heaviest one refers to agri-environmental measures, which must represent at least 35% of expenditure for rd policy. among the eight rd types of intervention, as shown in table 1, those that have recorded the largest fund allocation are: agro-climatic and environmental ones and investments, respectively accounting for 28.9% and 26.7% of total resources. moreover, 18% is reserved for risk management measures (table 1). 2.2 literature review: ex-ante and ex-post expenditure determinants for rd previous literature mainly focused on ex-post investigations on the implementation of agricultural policies, aiming at understanding how government interventions have affected the agricultural sector over time (ander82 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 tobia minuzzo, francesco pagliacci son et al., 2013). however, very little was said about what affects the allocation of funds within agricultural policies during the planning phase (fredriksson and svensson, 2003). for example, fałkowski and olper (2014) addressed the role of electoral incentives, bellemare and carnes (2015) focused on the personal preferences of legislators, olper et al. (2014) addressed the pressures from interest groups and institutional contexts, while pelucha et al. (2016), showed that rd policy in the czech republic was not implemented in accordance with the socioeconomic goals of territorial cohesion. referring to ex-post fund allocation, shucksmith et al. (2005) for the first time tried to assess the impact of the rural development policy at territorial (i.e. regional) level, asking the question of how far cap expenditure is compatible with objectives of territorial cohesion across the enlarged eu and consistent with the goals of the eu spatial development perspective. later, camaioni et al. (2016) focused on the cap resource allocation considering nuts-3 level regions. according to them, allocation is the joint result of topdown policy decisions and bottom-up ability of territories to attract available funds. thanks to an ex-post econometric analysis on the 2007-2013 rd expenditure, they identified three major drivers for expenditure allocation, which include a “pure spatial effect” (i.e. the influence of the surrounding space on the allocation of rd expenditure) and a negative rurality effect (i.e. the less rural the region, the greater the intensity of spending). bonfiglio et al. (2017) analysed the main territorial models of the effective spatial (ex-post) allocation of cap expenditures by considering knowledge transfer and innovation (kt&i) measures only into the 2007-2013 cap rd. they confirm that the economy’s structure plays an important role in such a spatial allocation. considering the same 2007-2013 programming period, uthes et al. (2017) also compared the data of the common monitoring and evaluation framework (cmef) and the rd policy expenditure levels at nuts2 territorial level across the eu. the authors highlighted four different patterns of expenditure allocation, distinguishing the eu regions into four groups: competitiveness, environment, rural viability, equal spending. they were established considering the percentage distribution of rd expenditure among axes, as provided for in the 2007-2013 cap. among the selected groups, the largest difference was observed between regions in the “competitiveness group” and those in the “environment group”, the latter one having a larger share of arable land and less permanent grassland, a smaller physical and economic farm size, greater workforce, less land in less favoured areas, a higher share of extensive arable land and a lower share of extensive grazing. on the contrary, the regions of the “environment group” also show a higher proportion of uaa within natural areas. uthes et al. (2017) once again demonstrate the feasibility of identifying expenditure patterns and validate the use of cmef indicators in explaining them. most importantly, their findings highlight a strong coherence between spending priorities, regional needs and development prospects. zasada et al. (2018) focused on european regions with above-average expenditures for natural capital measures within rd policy in the 2007-2011 period. they aim to understand the drivers behind such spending priorities related to local socioeconomic and agricultural contexts. the analyses identified six different spending patterns for european regions. the results show that the adoption of natural capital-oriented spending models is only partially influenced by environmental and agricultural factors, with a higher incidence of larger farms and regions with high purchasing table 1. types of intervention envisaged for the rd policy by the italian national strategic plan and related financial allocation. type of intervention eafrd resources allocated (eur million) % eafrd a. agro-climatic-environmental interventions 2099.42 28.92 b. natural or other specific territorial constraints 6664.71 9.16 c. specific territorial disadvantages resulting from certain mandatory requirements 14.30 0.20 d. investments, including investments in irrigation 1937.72 26.69 e. setting up young farmers and new farmers and starting rural businesses 339.97 4.68 f. risk management tools 1287.86 17.74 g. cooperation 591.24 8.14 h. exchange of knowledge and dissemination of information 96.79 1.33 i. technical support 188.14 2.59 source: authors’ elaboration on national rural network data (2022). 83 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 expenditure allocation for rural development interventions power and population density. however, a weak correlation exists between natural capital investments and ecologically significant areas such as natura 2000 sites or high nature value farmland. this shows that these areas don’t receive enough funds. socioeconomic and agricultural indicators have limited influence, reflecting criticisms about the rd policy’s lack of attention to local needs (copus and dax, 2010; piorr and viaggi, 2015). lastly, pagliacci and zavalloni (2024) investigated the factors that influence the allocation of funds for some specific objectives of the cap (i.e. environmental objectives), considering the european 2014-2020 rd. compared to previous articles, which mainly analysed the ex-post determinants, they mostly focused on the determinants behind the decision-making process. their results suggested that per capita gdp and population density positively correlate with higher environmental support, whereas greater decentralisation of the management of funds (i.e. at regional and not at national level) is negatively correlated with environmental support. therefore, pagliacci and zavalloni (2024) suggested that maintaining central control over the financial allocation could promote greater environmental sustainability in the agricultural sector. however, to the authors’ best knowledge, no studies have yet addressed the 2023-2027 cap. 3. data and methods 3.1 methods this study applies quantitative analyses to understand the distribution of rd expenditure in italian regions. firstly, a cluster analysis is conducted considering 21 nuts-2 level regions in italy, i.e. 19 italian regioni and 2 province autonome1. a hierarchical clustering is applied to a set of input variables that refer to rd expenditure allocation (see section 3.2), and that are preliminarily standardised. for the cluster analysis, euclidean distance and ward’s method are used to determine distance between statistical units and clusters (ward, 1963; murtagh and legendre, 2014). in particular, the ward’s method aims to minimise the variance within each cluster by merging the clusters that minimise the increase in the total sum of squared distances. despite 1 according to the italian constitution, article 116, second comma, trentino-alto adige/südtirol region is composed of the autonomous provinces of trento and bolzano. these provinces are equated to other special statute regions (regioni a statuto speciale). thus, nuts-2 regions in italy include both regioni and province autonome and also the 2 province autonome manage rd policies separately and autonomously. its strengths, which make it particularly suitable when clusters show different sizes and densities, the ward’s method might be sensitive to outliers, leading to biased results if there are non-random patterns of missing data or if the underlying assumptions of normality and equal variances are violated (ward, 1963). having selected a hierarchical cluster analysis, the choice of the number of clusters is defined ex post, under the well-known trade-off between the number of clusters considered and their homogeneity. the cluster analysis is conducted using the r software (r core team, 2024), and “fpc” package (hennig, 2024). after clustering, group description is done by submitting clustering variables to anova (analysis of variance) to find significant differences among the clusters. subsequently, a tukey hsd (honest significant differences) test is also conducted to verify which clusters differ significantly from each other (yandell, 1997). the results of these two tests are used to support cluster labelling. then, a further phase of the work aims to verify whether regions belonging to the same cluster also show other similarities at structural level, i.e. considering other descriptive variables, such as: socioeconomic, sectorbased (i.e. agriculture) and environmental variables. this analysis is accomplished by performing an anova test for each variable to verify if at least one cluster behaved differently from the others, and then a tukey hsd test to identify which one(s) differ from each other. as a final stage of our investigation, and in order to further characterize the identified clusters and explore potential correspondences between planned expenditure and characteristics of the italian regions, a correlation analysis is conducted. correlation coefficients are calculated between share of funds allocated to various types of interventions and cap context indicators (cmef), but also allocation of public resources during the previous 2014–2022 programming period (see section 3.2). the choice of cmef indicators, raw data and territorial subdivision of the analysis (nuts2) is inspired by uthes et al. (2017), although they referred to a previous programming period. correlation analysis is carried out using spearman’s method, after verifying that the assumption of linearity is hardly met. all analyses are performed using r (r core team, 2024). 3.2 data 3.2.1 input variables for cluster analysis cluster analysis is grounded on input data that encompass the shares allocated by each region to each type of intervention in the 2023-2027 cap out of the 84 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 tobia minuzzo, francesco pagliacci total rd expenditure. as mentioned above, rd policy is jointly funded by the eu and the ms. for the analysis, the overall allocation of public resources is considered, according to the 8-group taxonomy already shown in table 1. as risk management tools are managed at national central level, they are not considered. in addition to the seven types of intervention, three additional input variables are added: i) share of expenditure allocated to “technical assistance”; ii) total allocation of rd expenditure; iii) number of different activated interventions (as a proxy for heterogeneity of interventions at regional level)2. table 2 shows summary statistics for input variables. 2 other cluster analyses have been performed, considering different input variables (e.g., including total amount allocated by each region to each type of intervention, instead of the related shares; or removing amount of total expenditure; or removing the number of different interventions). results among different alternatives are largely comparable, and they are available upon request. however, after careful com3.2.2 other variables: cmef and rd expenditure in the previous programming period the analysis includes an additional set of variables. the cap context indicators in the cmef are developed by the european commission to evaluate the results of the cap and examine fund allocation. there are 45 main indicators, each of them with multiple sub-indices. almost all of them are available at nuts-2 level, hence being useful for this analysis. twelve cross-cutting socioeconomic indicators allow regions to be framed jointly (e.g., population, population density, employment rate). moreover, there are eighteen sectoral indicators, specific to the agricultural sector, and fifteen environmental indicators, which are useful to understand environmenparisons, the set of input variables described in the text has been considered. this is due to the observed dendrogram and considering the combination of input variables that demonstrate the greatest statistically significant differences among clusters. table 2. overview of input variables for cluster analysis of rd interventions across italian nuts-2 regions. nuts-2 regions clustering variables a . a gr ocl im at ic en vi ro nm en ta l ( % ) b. n at ur e & te rr ito ria l co ns tr ai nt s ( % ) c . s pe ci fic di sa dv an ta ge s ( % ) d . i nv es tm en ts (% ) e. y ou ng fa rm er s (% ) g . c oo pe ra tio n (% ) h . k no w le dg e (% ) te ch ni ca l as sis ta nc e (% ) to ta l ( eu r m ill io n) n um be r o f in te rv en tio ns itf1-abruzzo 36.30 12.79 0.29 27.92 7.56 9.26 2.33 3.55 343.90 33 itf5-basilicata 31.96 9.93 0.00 36.59 8.17 8.90 1.14 3.31 452.94 37 ith20-bolzano 39.36 35.86 0.00 11.16 6.62 6.49 0.18 0.33 271.87 18 itf6-calabria 42.29 3.84 0.00 35.69 5.12 8.85 0.90 3.31 781.29 39 itf3-campania 33.32 15.62 0.00 31.79 3.76 11.79 0.98 2.75 1149.61 35 ith5-emilia-romagna 35.71 11.17 0.39 30.93 6.77 10.32 2.18 2.53 913.22 45 ith4-friuli-venezia giulia 33.75 11.05 0.88 37.57 5.30 7.12 1.24 3.09 226.25 29 iti4-lazio 33.42 8.74 1.16 27.56 10.78 13.90 1.12 3.32 602.06 31 itc3-liguria 17.12 5.20 0.52 54.75 8.40 8.36 2.33 3.31 207.04 48 itc4-lombardy 17.79 11.12 0.00 49.44 4.58 10.66 3.79 2.62 764.50 39 iti3-marche 34.75 11.49 0.20 34.08 3.53 10.45 3.45 2.05 390.88 38 itf2-molise 36.27 18.63 0.00 27.14 5.07 5.00 4.32 3.57 157.71 21 itc1-piedmont 34.70 5.71 0.79 35.46 5.29 12.18 2.70 3.17 756.40 50 itf4-puglia 35.91 1.27 0.00 41.04 4.22 12.74 1.50 3.31 1184.88 41 itg1-sardinia 39.88 20.26 0.00 26.24 4.88 7.64 0.49 0.62 819.49 30 itg2-sicily 46.54 15.78 0.00 21.56 6.81 7.09 0.52 1.70 1467.61 30 iti1-tuscany 37.61 7.51 0.40 33.51 6.61 11.07 2.30 0.99 748.81 54 ith10-trento 21.94 25.13 0.00 35.93 6.07 7.36 0.55 3.02 198.96 17 iti2-umbria 31.44 6.07 0.29 40.63 2.51 14.61 1.45 3.01 518.60 44 itc2-valle d’aosta 35.42 33.64 2.18 17.69 1.09 8.43 0.63 0.92 91.85 27 ith3-veneto 25.09 10.91 0.85 38.95 8.56 9.93 3.58 2.13 824.56 44 source: authors’ elaboration on national rural network data (2022). 85 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 expenditure allocation for rural development interventions tal conditions of regions, the strategies implemented for its protection as well as the impacts of the agricultural sector on the environment. the selected sources for these indicators are the following: eurostat; farm accountancy data network (fadn); european environmental agency (eea); corine land cover (clc); dg agriculture and rural development; natura 2000 barometer statistics report and joint research centre (jrc ispra). the most recent indicators available have been updated in june 2020. for those indicators that show poor updates, data from istat website (https:// www.istat.it/) are retrieved and used. given that they refer to a period which is previous to the start of the current programming period, they are not affected by spending choices, hence they can be used in this analysis. lastly, expenditure allocations in the previous 20142022 cap programming period is also considered, breaking them down by priority of intervention and technical assistance, both in absolute and percentage terms. this broad set of variables is used to: i) characterise and provide proper labels to the clusters of regions (e.g. in order to verify the presence of similar characteristics for the regions belonging to the same cluster); and ii) assess the existence of correlations between these additional variables and the percentage allocation of funds across rd interventions, in the italian regions. 4. results figure 1 displays the results of hierarchical clustering through a dendrogram. observing its structure, it is possible to highlight, as a best partition option, a fourcluster partition for the italian nuts-2 level regions. clusters have different size as well as different average characteristics (table 3). the smallest group consists figure 1. dendrogram of the italian regions, hierarchical clustering with ward’s method. source: authors’ elaboration, software r (r core team, 2024). https://www.istat.it/ https://www.istat.it/ 86 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 tobia minuzzo, francesco pagliacci of just three regions, while the largest one includes eight regions. figure 2 maps the clusters in italy. thanks to the anova and tukey hsd tests, the clustering input variables are analysed to identify those that contribute most to the identification of each cluster. it is important to note that the limited number of statistical units, in relation to the four clusters, might have reduced the ability to detect statistically significant differences between them. the results of these analyses are reported in annex a, which displays boxplots for each of the selected input variables. the results of the anova suggest no significant differences between the four clusters regarding the percentage allocation of “specific disadvantages” interventions, “young farmers” interventions and the overall allocation of rd policy funds. thus, the analysis of the four clusters is based on the remaining seven variables. cluster 1 is labelled as “regions with disadvantages”. it shows a significantly higher-thanaverage allocation for “nature & territorial constraint” interventions (support to areas with natural disadvantages or other specific constraints), equal to 26.39%. cluster 1 also has the highest allocation level in agro-climatic-environmental interventions, equal to 40.30%, and the lowest allocation in investment interventions. cluster 2 is named as “cooperation regions”. it has the highest allocation for interventions related to cooperation in agriculture. on the other hand, it shows an average allocation for agro-climatic-environmental interventions, investment-related ones and those for knowledge and information exchange (akis). cluster 2, together with cluster 3, is also the one that activated the largest number of interventions, about 43. cluster 3 is labelled as “investment regions”, being characterised by a significantly higher allocation of funds to the interventions for investments. they are almost equal to 50% of the total allocation. the allocation for “exchange of knowledge and information” interventions is also higher than the average (3.23% vs. 1.85%). on the other hand, there is substantially less commitment to agro-climatic-environmental interventions, to which only 20% of resources are dedicated, half of what cluster 1 allocates and about 12% less than the average. cluster 4 includes “equal spending” regions, i.e. those regions not showing significant differences compared to the other three clusters. in this cluster, types of intervention show values close to the general average, with deviation usually less than one percentage point. however, it is clear that the regions of this cluster planned “smaller” budgets than the other regions, they allocate the lowest budget and also activate the fewest interventions (this can be deduced from the visual comparison of the medians in the boxplots, tukey’s hsd test did not show significant differences between the four clusters). table 3. composition of the four clusters: regions and autonomous provinces and average values of the clustering variables. cluster average value (italy) 1 regions with disadvantages 2 cooperation regions 3 investment regions 4 equal spending regions pa di bolzano calabria liguria abruzzo sardegna campania lombardia basilicata sicilia emilia-romagna veneto friuli-venezia giulia valle d’aosta marche lazio piemonte molise puglia pa di trento toscana umbria a. environment (%) 40.30 35.72 20.00 32.27 32.07 b. nature & territorial constraints (%) 26.39 7.84 9.08 14.38 14.42 c. specific disadvantages (%) 0.55 0.26 0.46 0.39 0.41 d. investments (%) 19.16 35.39 47.71 32.12 33.60 e. young farmers (%) 4.85 4.73 7.18 7.16 5.98 g. cooperation (%) 7.41 11.50 9.65 8.59 9.29 h. knowledge (%) 0.46 1.93 3.23 1.78 1.85 technical assistance (%) 0.89 2.64 2.69 3.31 2.38 tot (eur million) 662.7 805.46 598.70 330.3 599.29 number of interventions 26.25 43.25 43.67 28.00 35.29 87 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 expenditure allocation for rural development interventions to further expand cluster classification, it is possible to identify the statistically significant differences among clusters and the additional cap context indicators. of the large set of variables, 20 of them have proven to be helpful for characterization (see annex b for some boxplots of the variables that show significant differences between the clusters). in what follows, there is a brief description of each cluster, under these additional covariates. the regions included in cluster 1 are characterised by an extremely higher endowment of semi-natural areas (31.05% on average, but with a large standard deviation, equal to 13.79%) than the other clusters. consequently, it is also the group with the lowest share of urbanised areas (only 2.79%, compared to an average of 4.91) (annex b). this is coherent with low population density (95 inhabitants/km2), significantly lower than the general average (196 inhabitants/km2). cluster 1 also has a very high share of employment in the tourism sector (9.89% ± 2.28%), which is higher than the italian average (7.10%). finally, the regions belonging to cluster 1 appear to be those with the greatest involvement of young farmers in the management of farms. indeed, 12.19% of companies are led by farmers under 40, while the average value is 8.70%. this is also confirmed by the consequent lower number of farm managers over 55, who represent only 55.49% of the total (but with a large standard deviation, 7,40%). cluster 2 does not show specific elements of difference. one item, however, appears worthy of interest: female employment in agriculture. the total and male non-family workforce does not show differences compared to the other clusters. in contrast, for the female workforce the difference is great both in absolute (annual work units – awus) and relative terms. in cluster 2, 19.42% of the total workforce are women (although its standard deviation is 13.48%), while the italian average is just 9.39%. cluster 3 has the highest population density: 338 inhabitants/km2 (compared to an average of 196 inhabitants/km2 in italy). this value is significantly higher than the one observed in clusters 1 and 4 (for this variable, the tukey hsd output is statistically significant). cluster 3 is also the group with the most urbanized regions, with 8.81% of its area characterised by urban surface, compared to only 6.21% of the area covered by semi-natural areas, below the average of around 10.37%. the presence of manufacturing companies is therefore large, and this is confirmed by the number of employees in the secondary sector, twice as high as the italian average, and by all the other economic indices. the agricultural sector follows a similar trend. cluster 3 shows the largest share for irrigated/irrigable area (56.70% of the total, but with standard deviation of 17.52%), amount of fertilisers distributed and also livestock units. all these are indicators of the high level of specialisation and productivity of farms in these regions. in this way, the percentage of farms of large economic size is significant. companies between 250,000 and 499,999 eur in turnover represent 4.17% of the total, the italian average does not reach 1.80%. for cluster 4, as observed in the cluster labelling phase, no distinctive features emerge compared to the general average of the italian regions. the average population density is 142 inhabitants/km2, the regional territory is covered by 7.29% of semi-natural areas (but with large standard deviation, equal to 6.23%) and 4.02% of artificial areas. 5.24% of the population is employed in the primary sector, 22.08% in the secondary sector and 72.68% in the tertiary sector. as a conclusive test, the analysis of correlation coefficients suggests that demographic variables, economic variables, characteristics of the agricultural sector, and the allocation of funds in the past programming period correlate with current rd expenditure allocation. on the contrary, total expenditure is positively correlated with total population and population density, as expected. moreover, more populous regions allocate figura 2 i quattro cluster di regioni e pa: rappresentazione cartografica fonte: nostra elaborazione, software qgis e shapefile istat. molise e basilicata, regioni provenienti sia dal nord che centro-sud italia. per una più semplice visualizzazione spaziale dei cluster si rimanda alla figura 4. a questo punto sono state analizzate le variabili di clusterizzazione che effettivamente contribuiscono maggiormente all’identificazione dei singoli cluster, differendo effettivamente da un cluster all’altro. per capire ciò è stata eseguita contestualmente un’anova ed un test hsd di tukey per comprendere quali cluster sono tra loro statisticamente diversi per ciascuna variabile di input dell’analisi cluster. si legend cluster 1 cluster 2 cluster 3 cluster 4 figure 2. the four clusters of regions and pa. source: authors’ elaboration, qgis software (qgis development team, 2024). 88 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 tobia minuzzo, francesco pagliacci more funds to investments but less funds to address natural constraints. at the same time, there is a negative correlation between “natural or other specific territorial constraints” interventions and urbanisation rate. conversely, larger shares for semi-natural or protected areas couple with larger shares of funds, for interventions for natural or other specific territorial constraints. referring to the economic variables, there is a positive correlation between per capita gdp and the total allocations of funds. total labour productivity is negatively associated with agro-climatic-environmental interventions, while it is positively correlated with the funds for natural disadvantages. regions with larger farms allocate more funds for investment but less for environmental interventions. a more detailed overview of the correlation analysis is reported in table 4. lastly, the distribution of funds between the 20142022 and 2023-2027 programming periods shows continuity in priorities, with a focus on farm competitiveness and over environmental measures (table 4). 5. discussion the analysis of the allocation of rd policy expenditure in italy, in the 2023-2027 programming period, seems to confirm the results of previous studies on similar topics. the territorial imbalances produced by the cap and its – at least partial – inconsistency with the eu’s cohesion and convergence objectives have already been debated (see for example esposti, 2007; 2011). moreover, many studies have investigated how little “rural” the allocation of rd policy spending is, in fact supporting less rural regions compared to what is stated in its political intentions (camaioni et al., 2013; shucksmith et al., 2005; crescenzi et al., 2011). similarly, such a trend also seems to be confirmed by this study, as shown by the correlation analyses conducted. if it is reasonable to expect a positive correlation between total expenditure allocation and the total amount of people living in each region, or their regional area, since larger regions can correspond rd policy with higher budget, it is much more complex to justify the existence of a positive correlation between total expenditure and three major indicators of the presence of a larger urban population: population density (inhabitant/ km2), population in urban areas (% total) and the share of urban territory (% total). it can be deduced that, also in the 2023-2027 programming period, urban regions, also due to their likely better administrative capacity (charron et al., 2021), were more successful in attracting rdp funds, thus feeding a counter-selection mechanism that has already been verified in the past. however, when dealing with the allocation of rd policy budget across regions, and in order to identify which italian regions share similar spending behaviours, the results of the hierarchical cluster analysis seem inconsistent from a geographical point of view, with neighbouring regions showing different patterns. nevertheless, if one looks at the structure of the dendrogram at a deeper level, some geographical coherences can be found. it is possible to identify micro-clusters of regions that spend in a similar way and are also neighbours. for example, the cluster 2 “cooperation regions” can be divided into two further sub-groups of neighbouring regions: on the one hand, piedmont, emilia-romagna, tuscany, umbria and marche; on the other, puglia, campania and calabria. this further segregation of the dendrogram turns into greater similarity in expenditure allocation within the two subgroups. also, within cluster 1 it is possible to identify a sub-cluster composed of the two italian islands: sicily and sardinia. the same applies to clusters 3 and 4 even if the phenomenon is less clear. this could eventually suggest the existence of something similar to a “local agglomeration effect” (already identified by camaioni et al., 2016) even at nuts 2 level for italy. according to this, neighbouring regions with high rd policy support also tend to induce more support in the region in question and vice versa. the phenomenon had been studied at a more detailed level of disaggregation (nuts 3, compared to nuts 2 in this study) and over a more extended programming period in past studies, e.g., the one by camaioni et al. (2016) and by crescenzi et al. (2011). nevertheless, it is important to highlight the added value of the current study. indeed, it confirms similar results, also when considering ex-ante fund allocation and even under the current 2023-2027 programming period. this is true even though the current programming period is characterised by a new governance system, i.e. the new delivery model. moreover, when identifying the determinants of the expenditure behind clusters, it is possible to understand the regional structural characteristics that led to those specific allocation choices. similar analyses have been conducted in the past, referring to previous programming periods. the clustering made by uthes et al. (2017) has many similarities with this one, even though more than ten years and two programming periods have passed. they traced all italian regions into two groups: veneto, liguria and friuli venezia-giulia to the “competitiveness” group while all the others to the “environment” group. it is interesting to see that veneto and liguria were assigned to the group of “competitiveness” regions, as it is the case in the present study. however, in this study, given the 89 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 expenditure allocation for rural development interventions ta bl e 4. s pe ar m an ’s co rr el at io n co effi ci en ts b et w ee n 20 23 -2 02 7 rd in te rv en tio ns a llo ca tio n an d c m ef in di ca to rs a nd p as t f un ds a llo ca tio ns ( pva lu es in p ar en th es es ). d es cr ip tiv e va ria bl es u ni t rd in te rv en tio ns 2 02 320 27 a . a gr ocl im at ic en vi ro nm en ta l (% ) b. n at ur e & te rr ito ria l co ns tr ai nt s (% ) c . s pe ci fic di sa dv an ta ge s (% ) d . in ve st m en ts (% ) e. y ou ng fa rm er s ( % ) g . c oo pe ra tio n (% ) h . k no w le dg e (% ) te ch ni ca l as sis ta nc e to t n um be r o f in te rv en tio ns demographic variables re gi on al su rf ac e ha 0. 38 7 (0 .0 84 ) 0. 84 7 (0 .0 00 ) 0. 45 6 (0 .0 38 ) to ta l p op ul at io n 10 00 in ha b. -0 .3 71 (0 .0 98 )0. 52 6 (0 .0 16 ) 0. 80 4 (0 .0 00 ) 0. 48 6 (0 .0 26 ) po pu la tio n de ns ity in ha b/ km 2 -0 .4 43 (0 .0 46 )0. 40 8 (0 .0 68 ) 0. 49 4 (0 .0 24 ) 0. 38 5 (0 .0 85 ) 0. 54 7 (0 .0 11 ) 0. 49 9 (0 .0 21 ) u rb an a re a % to ta l -0 .4 54 (0 .0 39 )0. 48 2 (0 .0 27 ) 0. 43 9 (0 .0 47 ) po pu la tio n u rb an a re a % to ta l -0 .4 82 (0 .0 27 )0. 40 8 (0 .0 67 ) 0. 45 7 (0 .0 37 ) 0. 42 6 (0 .0 54 ) re gi on al p ro te ct ed a re a % to ta l 0. 44 3 (0 .0 44 ) 0. 42 5 (0 .0 55 ) -0 .3 82 (0 .0 87 )-0 .4 02 (0 .0 7) -0 .3 97 (0 .0 75 ) economic variables g d p pe r c ap ita eu r /in ha b -0 .3 86 (0 .0 85 )0. 51 9 (0 .0 17 ) 0. 65 1 (0 .0 02 ) 0. 56 3 (0 .0 08 ) pr im ar y se ct or g va % to ta l 0. 56 8 (0 .0 08 ) -0 .7 29 (0 ) -0 .4 04 (0 .0 71 )se co nd ar y se ct or g va % to ta l -0 .4 16 (0 .0 62 ) 0. 56 1 (0 .0 08 ) to ta l e m pl oy m en t 10 00 p er so ns -0 .4 16 (0 .0 62 )0. 55 8 (0 .0 1) 0. 69 1 (0 .0 01 ) 0. 59 (0 .0 05 ) em pl oy ed in p rim ar y se ct or 10 00 p er so ns 0. 44 9 (0 .0 42 ) 0. 93 6 (0 ) 0. 38 2 (0 .0 87 ) em pl oy ee s i n th e se co nd ar y se ct or 10 00 p er so ns -0 .3 91 (0 .0 81 )0. 59 6 (0 .0 05 ) 0. 45 (0 .0 41 ) 0. 64 5 (0 .0 02 ) 0. 62 2 (0 .0 03 ) em pl oy ed in to ur ism % to ta l 0. 44 9 (0 .0 42 ) -0 .5 31 (0 .0 13 )0. 47 (0 .0 32 ) u ne m pl oy m en t r at e 15 7 4 ye ar s o ld % to ta l 0. 37 4 (0 .0 96 ) 0. 39 1 (0 .0 8) 0. 38 8 (0 .0 83 ) an nu al w or ki ng u ni t aw u 0. 42 7 (0 .0 55 ) 0. 94 7 (0 .0 00 ) 0. 39 (0 .0 81 ) to ta l l ab ou r p ro du ct iv ity eu r /p er so n -0 .4 22 (0 .0 58 ) 0. 43 3 (0 .0 5) -0 .4 47 (0 .0 44 )la bo ur p ro du ct iv ity in p rim ar y se ct or -0 .3 81 (0 .0 9) la bo ur p ro du ct iv ity in s ec on da ry se ct or -0 .4 88 (0 .0 26 ) -0 .4 38 (0 .0 49 )la rg e co m pa ni es ( eu r 25 0, 00 049 9, 00 0) % o f t ot al -0 .4 23 (0 .0 57 ) -0 .3 74 (0 .0 96 )0 .3 97 (0 .0 74 ) 0. 44 8 (0 .0 43 ) 0. 42 1 (0 .0 57 ) 0. 57 1 (0 .0 07 ) sectoral variables ag ric ul tu ra l h ol di ng n . -0 .3 99 (0 .0 74 )0. 42 7 (0 .0 55 ) 0. 91 8 (0 .0 00 ) 0. 38 2 (0 .0 87 ) sa t ha 0. 37 (0 .0 99 ) 0. 89 0 (0 .0 00 ) 0. 39 3 (0 .0 78 ) u a a ha 0. 40 8 (0 .0 68 ) 0. 91 2 (0 .0 00 ) 0. 38 9 (0 .0 81 ) o rg an ic a re a % to ta l u a a 0. 48 6 (0 .0 27 ) 0. 48 6 (0 .0 27 ) ir rig at ed a nd ir rig ab le a re as % to ta l u a a -0 .3 71 (0 .0 98 ) ar ab le la nd % o f t ot al u a a 0. 40 3 (0 .0 71 ) 0. 45 1 (0 .0 42 ) 0. 67 6 (0 .0 01 ) 0. 51 4 (0 .0 17 ) pe rm an en t g ra ss la nd a nd m ea do w % o f t ot al u a a 0. 43 9 (0 .0 48 ) -0 .4 1 (0 .0 66 ) -0 .4 86 (0 .0 27 )0. 52 9 (0 .0 14 )-0 .5 66 (0 .0 08 )0. 59 7 (0 .0 04 ) pe rm an en t c ro ps % o f t ot al u a a -0 .4 87 (0 .0 27 )0. 43 3 (0 .0 5) c om pa ni es w ith a y ou ng a nd g ra du at e le ad er % to ta l -0 .4 7 (0 .0 31 ) 0. 38 7 (0 .0 83 ) 0. 45 2 (0 .0 4) 0. 51 1 (0 .0 18 ) 0. 51 7 (0 .0 16 ) (c on tin ue d) 90 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 tobia minuzzo, francesco pagliacci d es cr ip tiv e va ria bl es u ni t rd in te rv en tio ns 2 02 320 27 a . a gr ocl im at ic en vi ro nm en ta l (% ) b. n at ur e & te rr ito ria l co ns tr ai nt s (% ) c . s pe ci fic di sa dv an ta ge s (% ) d . in ve st m en ts (% ) e. y ou ng fa rm er s ( % ) g . c oo pe ra tio n (% ) h . k no w le dg e (% ) te ch ni ca l as sis ta nc e to t n um be r o f in te rv en tio ns past funds pr io rit y 1 f os te rin g kn ow le dg e tr an sfe r a nd in no va tio n in a gr ic ul tu re , f or es tr y an d ru ra l ar ea s pr io rit y 2 e nh an ci ng th e vi ab ili ty a nd co m pe tit iv en es s o f a ll ty pe s o f a gr ic ul tu re , a nd pr om ot in g in no va tiv e fa rm te ch no lo gi es a nd su st ai na bl e fo re st -0 .4 09 (0 .0 67 ) 0. 60 6 (0 .0 04 ) 0. 41 6 (0 .0 6) pr io rit y 3 p ro m ot in g fo od c ha in o rg an isa tio n, in cl ud in g pr oc es sin g an d m ar ke tin g of ag ric ul tu ra l p ro du ct s, an im al w el fa re a nd ri sk m an ag em en t i n ag ric ul tu re 0. 49 9 (0 .0 23 ) 0. 43 1 (0 .0 52 ) 0. 38 (0 .0 89 ) pr io rit y 4 r es to rin g, p re se rv in g an d en ha nc in g ec os ys te m s r el at ed to a gr ic ul tu re an d fo re st ry 0. 52 2 (0 .0 16 ) 0. 56 4 (0 .0 09 ) -0 .6 44 (0 .0 02 )-0 .5 26 (0 .0 16 )0. 55 7 (0 .0 09 )-0 .5 24 (0 .0 15 ) pr io rit y 5 p ro m ot in g re so ur ce e ffi ci en cy a nd su pp or tin g th e sh ift to w ar ds a lo w c ar bo n an d cl im at e re sil ie nt e co no m y in a gr ic ul tu re , f oo d an d fo re st ry se ct or s -0 .4 87 (0 .0 27 )0. 50 6 (0 .0 2) 0. 40 2 (0 .0 71 ) pr io rit y 6 p ro m ot in g so ci al in cl us io n, p ov er ty re du ct io n an d ec on om ic d ev el op m en t i n ru ra l ar ea s -0 .4 08 (0 .0 68 ) 0. 46 1 (0 .0 35 ) 0. 43 6 (0 .0 48 ) -0 .3 78 (0 .0 92 )to ta l p ub lic e xp en di tu re 0. 42 5 (0 .0 56 ) 0. 98 3 (0 ) 0. 39 1 (0 .0 8) n ot e: o nl y co rr el at io n co effi ci en ts , w ith p -v al ue s <1 0% a re r ep or te d. s ou rc e: o ur e la bo ra tio n on i st at ( ht tp :// da ti. ist at .it /) a nd e u c om m is si on d at a (h ttp s: // ag ri da ta . ec .eu ro pa .eu /e xt en sio ns /d at ap or ta l/c m ef _i nd ic at or s.h tm l). ta bl e 4. (c on tin ue d) . http://dati.istat.it/ https://agridata.ec.europa.eu/extensions/dataportal/cmef_indicators.html https://agridata.ec.europa.eu/extensions/dataportal/cmef_indicators.html 91 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 expenditure allocation for rural development interventions change in name of the interventions in the new programming period, this group has been labelled “investment group”. however, the expenditure targets are similar, as demonstrated by the correlation analyses conducted between investment interventions of the 2023-2027 programming period and the allocation for priority 2 – competitiveness and profitability of farms – of the 2014-2020 cap. also, the characterization made by uthes et al. (2017) about the “competitiveness” and the “environment” groups shows clear similarities with our description respectively of “investment” group and “disadvantaged” regions. the identification of similarities, based on spending behaviour in the 2007-2013 and 2023-2027 programming periods, as well as the clear correlation between the expenditure allocation for related objectives between the 2014-2020 and 2023-2027 programming periods, eventually suggests the existence of a sort of resistance to change, which has characterised the cap since its establishment (moyer and josling, 2002; greer, 2013; von cramon-taubadel, 2017). it can be explained by the concept of path dependency (iagatti and sorrentino, 2007). it is clear at this point that despite the clear intention of a greener and more sustainable cap, carried out especially in the last two decades, the paradigm has not changed substantially. the decision-making process is still strongly affected by stakeholders and agricultural lobbies, who enforce immobility to maintain their status quo, as still envisaged in the design phase of the current cap by rac et al. (2020). given the increasing importance which has been given to environmental aspects in the current programming period of the cap, because of the green deal, farm to fork, and the new green architecture (fusco, 2021; zezza, 2023; coderoni, 2023), it is crucial to elaborate a bit more on the allocation of funds for environmental interventions by the italian regions. in fact, this analysis does not show significant differences among clusters. instead, there is expenditure similarity in relative terms. this is probably the result of the ring-fencing itself, as imposed by the european commission, requiring that a minimum share equal to 35% of the total plafond is devoted to environmental interventions. in italy, all the regions have allocated resources for these interventions in line with this minimum threshold or slightly higher than it. actually, the european commission itself asked italian regions to raise their allocation during the approval phase of the national strategic plan. this sort of financial constraints, set by the eu, seems to be one of the latest top-down initiatives inherited from the past centralised governance system of the cap, in stark contrast to the new delivery model. despite this, ringfencing seems to be an essential tool to pursue strategic and far-sighted policies or objectives such as the environmental one, considered essential by the commission but too much relegated to a secondary level compared to others considered more tangible in the short term by local politics. despite returning insightful results, the current analysis might suffer from some limitations, e.g. a focus on italian regions only. moreover, an analysis adopting the same methodologies (e.g. cluster analysis and correlation coefficient analysis) might be replicated over previous programming periods. despite the main limitations of the adopted methodological approach (e.g. sensitivity to the choice of clustering algorithm, number of clusters to be selected, and input variables), this could verify whether groups of regions are maintained over time. any changes in the placement of the individual regions could then be explained by analysing the determinants to verify whether changes in regions’ characteristics may have also led to a change in the regions’ placement among the groups. if this were confirmed, it would validate specific indices that ex-ante would show the putative allocation of the region for each type of intervention. 6. conclusion this work aims to understand the main rd policy expenditure characteristics in the 2023-2027 cap across italian regions. it aims to verify the existence of similarities among regions when considering expenditure allocation and then to identify the major determinants behind this allocation. to achieve this goal, a cluster analysis is firstly conducted followed by correlation analyses. in particular, to the authors’ best knowledge, this analysis represents the first effort to study expenditure allocation of the 2023-2027 cap, thus providing new and interesting insights into this topic, thanks to a quantitative technique which also allows for direct comparisons across different observations. however, as an additional added value of the work, the current study also confirms the findings from previous analyses, conducted under different programming periods and governance systems. in the current analysis, four clusters of regions are identified by analysing the percentage distribution of expenditure allocated to each type of rd intervention. the results show that some regions, despite major geographical and historical differences, have similar spending behaviours and this could be linked to some other common characteristics. 92 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 tobia minuzzo, francesco pagliacci this finding could lead to some key policy implications, in particular enhancing improvements in the effectiveness of spending. at the end of the programming cycle or at the mid-term review stage, comparing the results achieved by different regions that improve the same expenditure mix would make it possible to determine which of them has achieved the best results. therefore, greater coordination across regions could improve the overall effectiveness of spending. from this perspective, the new delivery model might play a strategic role. it might open new opportunities for regions to adapt current spending to their specific needs, as well as increase coordination with those territories that share similar characteristics. at the same time, however, it could have the opposite effect. greater decision-making and management power could increase the gaps between lagging-behind and other regions, with the former group being disadvantaged with this new governance system. the implementation of the new delivery model, which further emphasizes the principle of subsidiarity, might suggest that in this programming period, expenditures of each ms are even more distinct from one another than before, potentially making comparison between them less meaningful. however, this work has confirmed that there are two main spending guidelines (“environment”-oriented and “investments”-oriented), which have survived, under different names, to the changes in the cap and its governance system, with the attention to the environmental aspects being reinforced under the current green deal context. it is for these reasons that it could be useful, in the future, to extend the same analysis to all eu regions. submit the expenditure mix of each european rd policy to cluster analysis and then research if the determinants of that expenditure could reveal whether the type of clusters identified in italy also holds at the eu level. 7. references anderson, k., rausser, g., swinnen, j. 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(2023). cap, farm to fork and green deal: policy coherence, governance, and future challenges. bio-based and applied economics, 12(2), 83–84. https://doi.org/10.36253/bae-14794 https://doi.org/10.1093/wber/lht003 https://doi.org/10.1093/wber/lht003 https://doi.org/10.36253/bae-13482 https://doi.org/10.36253/bae-13482 https://doi.org/10.1016/j.landusepol.2016.11.036 https://doi.org/10.1016/j.landusepol.2016.11.036 https://doi.org/10.1016/j.ecolind.2015.05.060 https://doi.org/10.1016/j.ecolind.2015.05.060 http://qgis.org https://doi.org/10.5424/sjar/2020183-16447 https://doi.org/10.5424/sjar/2020183-16447 https://www.r-project.org/ https://www.r-project.org/ https://doi.org/10.1079/9780851990552.0000 https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf https://doi.org/10.1016/j.landusepol.2016.10.016 https://doi.org/10.1016/j.landusepol.2016.10.016 https://doi.org/10.1016/j.landusepol.2018.05.053 https://doi.org/10.1016/j.landusepol.2018.05.053 https://doi.org/10.36253/bae-14794 https://doi.org/10.36253/bae-14794 95 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 expenditure allocation for rural development interventions annex annex a. box-plot of the resource allocation for the seven types of rd interventions, of the total resource allocation and of the number of interventions activated, for each cluster of regions. source: our own elaboration, r core team (2024). 96 bio-based and applied economics 14(2): 79-96, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16754 tobia minuzzo, francesco pagliacci annex b. box-plot of descriptive variables that have significant differences among the four clusters. source: our own elaboration. r core team (2024). reflections and new directions: an editorial retrospective and the launch of our new policy paper section silvia coderoni, fabio bartolini where is the italian agriculture heading? a discussion in light of the prospects for the future cap filippo arfini1*, fabio bartolini2, anna carbone3, tatiana castellotti4, silvia coderoni5, raffaele cortignani6, raffaele d’annolfo4, giovanni dara guccione4, michele donati1, francesca galli7, roberto henke4, giampiero mazzocchi4, alessandro monteleone4, towards the knowledge and innovation system for the bioeconomy? giacomo maria rinaldi*, davide viaggi impact of weather variability on crop yields and land use dynamics in eastern india: shortand long-term effects pratap kumar jena1, kirtti ranjan paltasingh2, souryabrata mohapatra3,,*, ashok mishra4 rebuilding strategies for food self-sufficiency: portugal’s past patterns and future ambitions alexandre macedo joão1, dulce freire2, humberto rocha3, joana dias4 from subsidy to land rent: how production-linked payments shape factor remuneration in agriculture adrian sadłowski expenditure allocation for rural development interventions: main trends and patterns in the choices of the italian regions under the cap 2023-2027 tobia minuzzo, francesco pagliacci* promoting natural capital conservation: a bet for socioeconomic 1 development of marginal areas 2 angelo martella (a.martella@unitus.it), francesca pietrangeli(f.pietrangeli89@unitus.it), 3 elisa biagetti (elisa.biagetti@unitus.it), barbara pancino (bpancino@unitus.it), silvio 4 franco (franco@unitus.it) 5 corresponding author: francesca pietrangeli, f.pietrangeli89@unitus.it 6 affiliations: università degli studi della tuscia 7 this article has been accepted for publication and undergone full peer review but has not 8 been through the copyediting, typesetting, pagination and proofreading process, which may 9 lead to differences between this version and the version of record. 10 please cite this article as: 11 martella a., pietrangeli f., biagetti e., pancino b., franco s. (2025). towards digital 12 farming: exploring technological integration in agricultural practices of a sample of 13 italian livestock farms, just accepted. doi: 10.36253/bae-16911 14 15 abstract 16 the aim of this paper is to enhance the well-being of marginalized areas by improving their 17 local economy, considering the correlation between socioeconomic marginalization and 18 environmental sustainability. these two objectives are at the core of the international and 19 european policy agenda, but they are not often merged in one action. within this study, we 20 selected a marginal area in central italy and assessed its environmental sustainability, using 21 the method of the ecological balance. the results show that this territory has the capacity to 22 provide an amount of natural capital greater than the ecological footprint generated by local 23 production activities, thus the value of the ecological balance is positive. then, we discussed 24 how local policies can favor processes focused on agricultural products coming from areas 25 recognized as sustainable. environmental sustainability can be work as a branding strategy, 26 which can raise the market value of the products and the farmers’ income, thus supporting 27 mailto:franco@unitus.it mailto:f.pietrangeli89@unitus.it the economic development of marginal areas and promoting the protection of their natural 28 capital. 29 keywords: sustainability, marginality, ecological balance, agriculture, rural policy 30 1. introduction 31 the socioeconomic development of marginal areas is one of the most important targets of 32 european policies, due to their generalized diffusion. about 45% of european territory 33 consists of regions presenting strong marginality characteristics (de toni et al., 2021). 34 in countries where a large part of the territory is covered by marginal areas, as in the case of 35 italy, the policy makers are engaged in facing the implementation of specific interventions to 36 fill the development gaps that often characterize such areas (kang et al., 2013a). 37 socioeconomic marginality, indeed, is associated with negative characteristics such as 38 isolation, low income, depopulation, old age of residents, weak connections with urban areas, 39 few infrastructures and a significant share of the local income being based on agricultural 40 production (arshad et al., 2021). 41 however, there are also some positive aspects in marginal areas, such as immaterial assets, 42 which are important to promote their development: biodiversity, varied agroecosystems, 43 wide availability of natural resources, just to name a few (ahmadzai et al., 2021; balzan et 44 al., 2018). this evidence has led to the general and often implicit perception that marginality 45 is associated with sustainability. the point is not whether this perception is true; rather, what 46 do we mean by (socioeconomic) “marginality” and (environmental) “sustainability”. 47 this study, carried out within the spoke 7 of pnrr agritech project (“integrated models for 48 the development of marginal areas to promote multifunctional production systems enhancing 49 agro-ecological and socio-economic sustainability”), aims to face and discuss this point. 50 more specifically, this paper has three main objectives. 51 the first one is to give a clear definition of socioeconomic marginality and select a marginal 52 area inside the province of viterbo (italy) that matches such definition. 53 the second one is to adopt a definition of environmental sustainability and assess if the 54 selected marginal area may be declared as sustainable. to accomplish this objective the focus 55 has been placed on the agricultural sector rather than on the whole local economic system. 56 this choice is justified by two main reasons. first, agriculture is the only economic sector 57 that directly manages natural resources, represented by farmland. second, the idea of food 58 from a sustainable production system mainly refers more to agricultural practices than to the 59 other stages of the supply chain. 60 the third one is to discuss how specific actions supported by local institutions can favor 61 processes to promote agricultural products coming from an area recognized as sustainable to 62 raise their market value, thus increasing the farmers’ income and supporting the economic 63 development of a marginal area. 64 as asserted by basile and cavallo (2020), regional and cohesion policies of national 65 governments and the european union typically address internal or marginal areas. however, 66 the objective of this paper is not to discuss the national or regional measures more suitable 67 for the development of marginal areas. this work looks at valorization process of local 68 economy as a possible "bottom-up" action, able to identify a territorial brand linked to 69 specific characteristics of marginal areas (banini and pollice, 2015). this appears to be an 70 interesting topic also considering that, despite its significance, research on the connections 71 between place branding and sustainable development is still limited in the scientific literature 72 (aguilera-cora et al., 2024). 73 to achieve these objectives the paper is structured as follows. 74 the background is divided into two subsections. in the first one we discuss what a marginal 75 area is, what does marginality mean, which the marginality indicators are and why the idea 76 of sustainability is so often linked to marginal areas. in the second one, we present the 77 definition of environmental sustainability as the spatial condition where the natural capital 78 maintenance is ensured. the methodology section is also split in two parts. one describes the 79 process adopted to identify a marginal area as a group of municipalities in a province. the 80 other explains how the environmental sustainability of an agricultural system can be 81 evaluated by means of an ecological balance. the results section reports on the outcome of 82 the marginal area selection process and the description of its main characteristics. the 83 ecological balance assessment of the selected area’s agricultural system follows. the results 84 obtained are then discussed to underline their coherence with previous studies and in terms 85 of policy implications of sustainability conditions. the paper ends with some final 86 considerations related to the opportunity of valorizing the environmental dimension of the 87 marginal areas. 88 89 2. background 90 2.1. the concept of marginality and marginal areas characteristics 91 rural areas cover a wide part of the italian territory. many of these areas face the issue of 92 overcoming the developmental gaps with urban areas and the consequent negative 93 phenomena such as depopulation, ageing and loss of primary services. this topic is 94 particularly relevant in a country like italy, where the internal areas suffer isolation and 95 socioeconomic problems. 96 for this reason, these areas are specifically considered as the target of many european (oecd 97 and european commission, 2020) and national policies, such as the “national strategy for 98 inner areas” (snai), to support their revitalization with a “place-based” approach, where 99 the focus of the policies are the specific qualities of each area and its local production system 100 (de toni et al., 2021). 101 in addressing these issues, the regions with the above characteristics are often defined as 102 marginal areas, yet it is not so common to find a clear meaning of what “marginality” really 103 is. therefore, a definition of marginality is needed for the task of identifying and shaping the 104 boundaries of a marginal area. 105 the general idea underlying the concept of marginal areas, a notion frequently used by 106 researchers and policy makers, is often associated with a complex condition of disadvantage 107 according to both geographic characteristics and socioeconomic aspects. sometimes this term 108 is also used like a synonym for abandoned, degraded, unused or under-used area (sallustio 109 et al., 2018). 110 to provide a more comprehensive definition, it should be noted that there is a widely shared 111 understanding that socio-economic marginality refers to situations of territorial disadvantage 112 that undermines the vitality, competitiveness and development potential of a region (arshad 113 et al, 2021). these are areas with a very low population density and a negative demographic 114 trend, along with a minimal presence of basic services (malikova et al., 2016). furthermore, 115 a region is considered marginal because it is significantly distant from essential services and 116 is also characterized by geomorphological conditions that lead to structural deficiencies in 117 transportation routes, which in turn affect the location of settlements and productive activities 118 (lucas, 2012; vendemmia et al., 2021). a territory far from primary services and weakly 119 connected to infrastructures and networks is marginal because these conditions affect the 120 establishment of farms and industries (meini et al., 2017). marginality also involves a 121 structural weakening of the local system’s capacity to react, caused by the simultaneous 122 occurrence of a combination of recessive effects (carrosio and osti, 2017). 123 due to the uneven geographical distribution of development conditions, such as structures, 124 activities resources, knowledge and so on, resources available to develop local systems do 125 not operate everywhere with the same intensity, increasing the risk of socioeconomic 126 marginality (sotte, 2016). on the other hand, if we associate the territorial conditions of 127 marginality to the specific characteristics of the economic local production system, the 128 economic system itself can represent a development driver, provided that is possible to 129 promote its positive environmental dimension (ahmadzai et al., 2021). 130 given a definition of marginality, it is important to clarify that, as a phenomenon, it can be 131 measured, detected, and quantified based on coherent economic, social, territorial, 132 environmental, and demographic indicators within a specific area. as well as the possible 133 definitions, even the indicators of socioeconomic marginality are varied and often 134 interrelated. commonly used indicators include income levels, employment status, education 135 and access to basic services such as healthcare, housing and social protection. other 136 indicators may include participation in informal labor markets, vulnerability to economic 137 shocks and social isolation (vendemmia et al., 2021). indicators help both with identifying 138 the area at risk of marginalization and providing a framework for assessing the effectiveness 139 of policies aimed at reducing inequality and promoting social and economic development 140 (de toni et al., 2021). 141 in choosing indicators it is important to consider that marginality is both a relative and 142 dynamic condition: relative because it can only be defined through comparison with different 143 situations, either spatial or socio-economic; dynamic because various causes can influence 144 its level, as well as changes in terms of comparison or in the factors considered important in 145 defining it (ahmadzai et al., 2021). to identify marginal areas as a group of municipalities, 146 data related to selected indicators should be collected at municipal level. 147 moreover, it is appropriate that such aggregation corresponds to an administrative authority. 148 this is important because only in the presence of an authority that aggregates the 149 municipalities it is possible to apply policies to develop the marginal area, involving the 150 participation of economic actors and the engagement of local administrations and 151 stakeholders (covino r., 2017). 152 153 2.2. definition and assessment of environmental sustainability 154 agriculture, like all productive activities, uses natural resources to obtain the material and 155 energy inputs needed to carry out production processes and to absorb residues, such as waste 156 and greenhouse gas emissions. however, unlike other sectors, agriculture has the potential to 157 directly contribute to the supply of natural resources with different types of farmlands. the 158 property of agricultural system of managing natural resources and economic activities at the 159 same time offers the opportunity of directly comparing availability and consumption of 160 natural capital in a certain area. it is thus possible to assess a real ecological balance (franco, 161 2021a). 162 in this regard, there is a broad consensus in the scientific community that ecological footprint 163 (wackernagel and rees, 1996), is a suitable methodology to compare supply and demand of 164 natural resources, therefore assessing the environmental sustainability of economic processes 165 (moldan et al., 2012; neumayer, 2013). the environmental sustainability discussed here, 166 refers to the ability of the ecosystems to support given economic effects. specifically, the 167 approach used in this study, evaluates a type of environmental sustainability defined as 168 “strong”, which assumes the conservation of natural capital as a basic condition, where the 169 use of natural resources must avoid the deterioration of their reserve. this methodology is 170 not limited to the analysis of single economic activities, but it is also applicable for entire 171 regions, thus providing the possibility to operate at different scales, as highlighted in many 172 studies focused on agricultural sector (niccolucci et al., 2008; cerutti et al., 2013; galli et 173 al., 2014; martella et al., 2023; biagetti et al., 2023a). 174 to proceed with the assessment of an ecological balance in agriculture, the first step is 175 represented by the delimitation of the space involved in the assessment. then, it is essential 176 to define the key indicators for calculation. the availability of natural resources present in 177 the defined area is measured by biocapacity (bc), while ecological footprint (ef) quantifies 178 the impact of economic activities carried out in the same area. ef translates the natural capital 179 required by cultivation and livestock management in terms of biologically productive areas, 180 in a way that such demand can be compared with the carrying capacity of the agricultural 181 ecosystem (franco, 2021b). this makes it possible to determine the condition of 182 environmental sustainability or unsustainability as the result of the ecological balance (eb 183 = bc ef). 184 bc, ef and eb are expressed using a standardized unit of measurement, defined as global 185 hectare (gha). this metric represents the average biological productivity of one hectare of the 186 world’s surface, calculated by dividing the planet’s total capacity to provide natural resources 187 by the number of hectares of land surface (kitzes and wackernagel, 2009; galli, 2015). the 188 yield factor (yf), specific to each country, and the equivalence factor (eqf), are the two 189 parameters used to convert physical land hectares to final global hectares, with values varying 190 by both land type and year considered (borucke et al., 2013). 191 the ecological balance, and consequently the validation of the sustainability condition, can 192 be applied to different typologies of agricultural systems, from a single crop to a farm, up to 193 an entire region (biagetti et al., 2023b). in this study, which focuses on the assessment of the 194 sustainability of the agricultural sector in marginal areas, the boundary within which the 195 supply and demand of natural capital must be assessed is represented by the total agricultural 196 area inside a predefined group of municipalities. 197 198 3. methodology 199 3.1. delimitation of marginal areas within a region 200 to identify marginal areas within a region, as pointed out in the background section, many 201 approaches can be applied. in this study, considering the focus on the socio-economic 202 dimension and the outcome of a literature overview on this topic, three indicators were 203 chosen: old-age index, depopulation rate and per capita income. these indicators synthetize 204 the marginality condition in terms of aging and decline of the local population and residents’ 205 impoverishment (de toni et al., 2021; vendemmia et al., 2021). in this study such indicators 206 for each municipality in the selected region are calculated as follows: 207 old-age index (2023): resident population older than 65, divided by resident population 208 younger than 14 (istat, 2024a); 209 population variation (2001-2023): resident population variation between 2023 and 2001 210 divided by resident population in 2001 (istat, 2024a); 211 per capita income (2023): total taxable income divided by resident population (ministero 212 dell’economia e delle finanze 2024). 213 for every municipality in the province of viterbo, the indicator values are classified in the 214 range 1 (low marginality) to 5 (high marginality) coherently with the categories reported in 215 table 1. in this way three new marginality indicators, respectively iage, idep and iinc, are 216 defined. 217 218 table 1 – categories associated with different levels of marginality 219 level of marginality old-age index population variation income per capita (€) 1 < 1.5 > +15% > 14,000 2 1.5 2.0 +5% +15% 13,000 14,000 3 2.0 2.5 –5% +5% 12,000 13,000 4 2.5 3.0 –15% –5% 11,000 12,000 5 > 3.0 < –15% < 11,000 220 categories in table 1 were set considering the values distribution of the three indicators in the 221 municipalities of viterbo province. it follows that iage, idep, iinc measure a relative (local) 222 level of marginality. furthermore, considering that the italian old-age index is 1.93, the 223 population variation is 3.5% and the income per capita is 14,590 €, what is marginal within 224 viterbo province is even more marginal in a national perspective. therefore, the highest 225 values of iage, idep, iinc can be associated with an absolute marginal condition. 226 then, for each municipality (i) the index of marginality imari is calculated using the following 227 relation: 228 imari = round ((iagei + idepi + iinci)/3) (1) 229 once each municipality is characterized with an individual marginality score, it’s necessary 230 to define a marginal area where to assess the environmental sustainability of the local 231 agricultural system. such delimitation should consider at least two criteria: all the 232 municipalities included in the area have a high enough level of marginality; the municipalities 233 aggregation corresponds to an administrative body (or, at least, to a recognized legal entity) 234 that is in the position to define and implement actions at local level. therefore, the second 235 step of the methodology is to border the marginal area that will be the object of sustainability 236 evaluation and, at the same time, to identify the related governance institution that can 237 promote specific actions for supporting the valorization of local products. 238 for this purpose, after identifying all the possible k municipality aggregations in the selected 239 region (the province of viterbo in our case), a variable t is defined. such variable can have 240 only two values: tij = 1 if the municipality i belongs to aggregation j; tij = 0 if the municipality 241 i does not belong to aggregation j. 242 using this variable, it is possible to assess the overall marginality of the aggregation j as 243 mj = ∑ (imari x tij) n i=1 nj (2) 244 once measured the marginality for each territorial aggregation, the one with the highest mj 245 can be chosen. this will be the area of which the sustainability of the agricultural system will 246 be assessed. 247 248 3.2. the ecological balance for sustainability assessment 249 to evaluate the condition of deficit/surplus of natural capital in a delimited agricultural 250 system a comparison between its supply and demand may be established. such an ecological 251 balance (eb) is calculated as the difference between the system’s overall biological 252 productivity (biocapacity bc) and the impact of production activities (ecological footprint 253 ef): eb = bc – ef (martella et al., 2023) 254 the amount of natural capital in the area (bctot) is determined by the bio-productivity of 255 different types of farmland: cropland (bccrop), forest land (bcfor), semi-natural areas 256 (pastures, shrublands, …) (bcasn), other surfaces (buildings, roads, …) (bcoth), water 257 (bcwat). 258 the values of bcfor, bcasn, bcoth are determined following the standard ecological 259 footprint methodology (wackernagel and rees, 1996) by converting the relative area through 260 standard coefficients yf (yield factor) and eqf (equivalence factor) provided by global 261 footprint network (gfn, 2024). 262 the biocapacity associated with cropland is also calculated following the standard 263 methodology, but the overall value is assessed by summing up the bio-productivity of each 264 one (i) of the n crops cultivated in the area, as suggested by passeri et al. (2013): 265 bccrop = ∑ aj × ypj ywj × eqf crop n i=1 (3) 266 where aj indicates the cultivated area, ypj the crop average yield in the area, ywj the crop 267 world average yield (fao, 2024). 268 concerning the term bcwat, is quite difficult to carry out a detailed analysis on the 269 watersheds inside the farms within the area under analysis. therefore, the biocapacity of this 270 typology of land cover is included in bcoth. 271 the demand for natural capital, measured by the total ecological footprint (eftot) is 272 calculated through the sum of three farm activities impacts: cultivations (efcrop), livestock 273 (efliv), others (efoth). 274 efcrop can be calculated following the methodology proposed by passeri et al. (2013), which 275 identifies two sources of impact: overproduction (efovp) and use of inputs (efinp). the first 276 one is related to the bias between the observed yield and the yield that can be obtained in 277 natural conditions, i.e., without using inputs. the second one expresses the area of bio-278 productive land required to absorb the effects of input use. the assessment of these two 279 components, both referring to calculation methodology and coefficients used, is explained in 280 detail in blasi et al. (2016) and franco (2021b). 281 the impact of livestock farming activities in terms of natural capital demand can be estimated 282 as suggested in biagetti et al. (2023b). the impact for each (j) of the m types of livestock 283 farms in the area (eflivj) is calculated in terms of bio-productive land needed for absorbing 284 the emissions caused by input utilization and by enteric fermentation and management of 285 animal manure, making use of specific conversion coefficients available in the literature 286 (coderoni et al., 2013; mancini et al., 2016). 287 finally, efoth includes the impact of labor use, energy and fuel consumption for operation 288 and maintenance of farms areas not directly affected by production processes. 289 therefore, the negative term of the ecological balance is calculated as follows: 290 eftot= ∑ (efovp i + efinp i )n i=1 + ∑ eflivj m j=1 + efoth (4) 291 eb, as well as all the components of bc and ef, are measured in global hectares (gha). 292 however, to explain the outcome of the ecological balance in clearer terms, it is possible to 293 convert the eb value in “real” hectares of a specific land cover. if we choose forest as land 294 cover and consider the average biological productivity of an average italian forest, an index 295 of ecological performance (iep) may be defined. such an index expresses the hectares of 296 italian forest, in terms of natural capital, that is made available (if eb>0) or subtracted (if 297 eb<0) for each hectare of the study area. 298 299 4. results 300 4.1. delimitation of the marginal area 301 according to the methodology presented in the previous section, the old-age index, 302 population variation and per capita income were calculated for all the 60 municipalities in 303 the province of viterbo. all data were extracted from italian bureau of census (istat, 304 2024a) database, except the taxable income that was obtained from the ministry of finance 305 (mf) website. 306 then the indicators iage, idep, iinc were assessed applying the classes reported in table 1. for 307 each municipality i these values were combined to obtain the marginality index imari. all the 308 values of variable, indicators and imar are listed in table a1 of the appendix. 309 the next step was to identify the territorial aggregations within the province of viterbo. 310 these are the following: inner area alta tuscia, biodistrict of bolsena lake, biodistrict of 311 maremma, biodistrict of amerina road and forre, local action group alto lazio, local 312 action group teverina, local action group etrusco cimino, local action group amerina 313 agro falisco, local action group tuscia romana, mountain community alta tuscia 314 laziale, mountain community of cimini (rural district). 315 table 2 lists the names of territorial aggregation, the number of the municipalities and the 316 marginality level mj. 317 the aggregation with the highest level of marginality is the local action group (lag) alto 318 lazio with a mj equal to 4.27 followed by mountain community alta tuscia laziale (4.13). 319 all municipalities that are also included in the lag alto lazio belong to this second 320 aggregation. considering this point and, moreover, the peculiarities of a lag with respect 321 to a mountain community in terms of policy intervention, the lag alto lazio was preferred. 322 therefore, this area was chosen to calculate the environmental sustainability of the 323 agricultural system and verify the possibility of increasing the value of local products through 324 an environmental claim. 325 figure 1 shows the values of imar for the viterbo province municipalities and the boundaries 326 of the lag alto lazio. table 3 lists the municipalities which belong to lag alto lazio and 327 their main characteristics. 328 329 table 2 – list of municipality aggregation in viterbo province 330 aggregation code aggregation name municipalities (nj) mj a1 inner area alta tuscia 22 3.91 a2 biodistrict of bolsena lake 17 3.59 a3 biodistrict of maremma 2 2.50 a4 biodistrict via amerina and forre 13 3.08 a5 lag alto lazio 15 4.27 a6 lag in teverina 11 3.55 a7 lag etrusco cimino 9 3.22 a8 lag amerina agro falisco 10 2.80 a9 lag tuscia romana 6 2.83 a10 mountain community alta tuscia laziale 8 4.13 a11 mountain community of cimini 10 3.10 source: our elaboration 331 332 figure 1 – imar of viterbo province municipalities and lag alto lazio boundaries 333 334 335 table 3 – main characteristics of lag alto lazio municipalities 336 municipality old-age index depopul. rate income p.c. (€) imar area (km2) agricultural area (km2) population acquapendente 3.11 -8.9% 13,117 4 131.71 46.08 5,271 arlena di castro 3.21 -2.9% 11,001 4 21.87 14.88 842 canino 2.44 -0.7% 10,806 4 123.50 76.27 5,036 cellere 4.02 -17.7% 11,138 5 32.70 25.46 1,071 farnese 4.25 -19.4% 11,924 5 52.38 23.78 1,393 gradoli 4.49 -16.3% 11,607 5 37.50 7.11 1,252 grotte di castro 3.82 -20.2% 13,389 4 33.42 16.88 2,369 ischia di castro 2.92 -13.2% 10,966 4 104.95 55.94 2,138 latera 6.23 -25.0% 12,063 4 22.66 9.24 767 onano 3.33 -23.6% 10,215 5 24.60 11.29 893 piansano 3.69 -13.2% 11,898 4 26.61 18.39 1,928 proceno 5.89 -17.6% 12,032 4 41.90 26.36 521 san lorenzo nuovo 3.21 -2.6% 12,001 4 27.99 8.51 2,013 tessennano 5.39 -33.1% 11,961 5 14.60 11.40 281 valentano 2.91 -5.7% 13,297 3 43.29 26.75 2,768 total 739.68 378.34 28,543 source: our elaboration on istat and mf (2024a) 337 338 it could be useful to point out that a local action group is a public-private partnership set 339 up as an association with recognized legal personality, that has the mission to implement a 340 strategic local development plan as defined in measure 19 of the 2014-2020 lazio regional 341 development plan. the lag decision-making is managed by the board of directors 342 composed of a maximum number of five members. this kind of administrative local authority 343 has the advantage of bringing decision making processes closer to the territory, enhancing 344 the real strengths and allowing broad and direct participation of local communities. 345 this feature fits with the area selection requirement, because only by involving local 346 authorities, stakeholders’ environmental policies and actions are more effective in developing 347 a marginal area. 348 349 4.2. the ecological balance of lag alto lazio 350 primary data on the agricultural sector in lag alto lazio area was obtained through the 7th 351 general census of agriculture (istat, 2024b). the information collected includes data on 352 the crop mix, in terms of major cultivation types and dimensions, and livestock, in terms of 353 species and number of animals. processing istat data is a key element for obtaining 354 detailed information on land use, such as crops and bio productive areas, as well as statistics 355 on the number and type of livestock. then, all information on the cultivation techniques 356 (yield, work, machinery, inputs) was obtained from the fadn database for the year 2021 357 and 2022 (fadn, 2024). the technical information contained in the fadn provides a solid 358 basis for the analysis of different agronomic practices and their consequent implications on 359 the lag territory. 360 361 table 4 subdivision of farmland among land cover typologies in lag “alto lazio” 362 municipality total (ha) crop land forest land semi-natural areas other surfaces acquapendente 7,127 64.7% 31.0% 1.3% 3.0% arlena di castro 1,634 91.1% 5.9% 0.4% 2.6% canino 10,039 76.0% 12.9% 1.0% 10.1% cellere 2,925 87.0% 9.8% 0.6% 2.6% farnese 2,997 79.4% 16.4% 2.3% 1.9% gradoli 941 75.7% 19.0% 1.9% 3.4% grotte di castro 2,165 78.0% 17.1% 2.4% 2.5% ischia di castro 8,499 65.8% 28.5% 1.1% 4.6% latera 1,361 67.9% 28.7% 0.8% 2.6% onano 1,394 81.0% 15.1% 1.6% 2.3% piansano 2,003 91.8% 3.6% 1.5% 3.1% proceno 3,132 84.2% 11.9% 1.5% 2.4% san lorenzo nuovo 1,113 76.6% 17.7% 1.7% 4.0% tessennano 1,395 81.8% 14.4% 2.2% 1.6% valentano 3,182 84.1% 12.3% 1.8% 1.8% total 49,907 75.8% 18.4% 1.3% 4.4% source: istat, (2024b) 363 364 figure 2 – tree map of lag alto lazio distribution of uaa 365 366 source: our elaboration on istat (2024b) 367 368 table 5 composition of livestock (number of heads) in lag alto lazio 369 livestock heads cattle dairy cow 348 other cattle 5,794 equines horses 293 other equines 29 sheep 68,109 goats 763 pigs sows 489 other pigs 7,033 poultry laying hens 77,395 broilers 14,591 source: our elaboration on istat (2024b) 370 371 the data analysis revealed that total farmland of the lag alto lazio covers 49,905 hectares, 372 which is about 67% of the whole area. as shown in table 4, the total farm area is allocated 373 76% to cropland, 18% to forest land, and the remaining 5% to semi-natural areas (identified 374 with grazing land) and other surfaces. 375 focusing on cropland, the lag area shows a general heterogeneity of cultivations, which 376 can be divided into three main categories (figure 2): leguminous fodder and temporary 377 pastures (42.6%), mainly linked to livestock practices; a group of three major crops: olive 378 trees, durum wheat, barley (28.5%); a group of many minor crops, including other cereals, 379 vegetables and fruit trees, characterized by low-incidence surfaces (28.9%). 380 regarding livestock (see table 5), the main categories include laying hens and broilers, which 381 constitute 52.6% of the total, followed by sheep, representing 39%. the remaining portion is 382 divided between pigs (4.3%) and other species, such as cattle, horses and goats (4.1%). 383 the analysis performed on the lag alto lazio farmland made it possible to evaluate the 384 environmental sustainability of the local agricultural system. the assessment, based on the 385 comparison between biocapacity (bc) of different types of land use and the ecological 386 footprint (ef) of agricultural activities, was carried out following the methodology explained 387 in the previous section and using the data elaborated from istat and fadn databases and 388 the coefficient described in the cited literature. the results are synthetized in table 6. 389 390 table 6 ecological balance of lag alto lazio 391 biocapacity gha ecological footprint gha cropland 104,353 crops 93,521 forested land 19,785 livestock 16,229 semi-natural areas 3,512 other activities 7,669 other surfaces 3,730 total bc 131,380 total ef 117,419 ecological balance (eb) + 13,961 source: our elaboration 392 393 as can be seen, the lag agricultural system has the capacity to make a natural capital amount 394 (bctot=131,370 gha) greater than the ecological footprint generated by local production 395 activities (eftot = 117,419 gha) available. the positive value of the ecological balance (eb 396 = 13,961 gha) signifies a condition of environmental sustainability where available natural 397 resources are not exploited beyond their regenerative capacity. 398 the bc of cropland represents the most significant component of the overall bc (almost 399 80%), and it is able to fully sustain the impacts generated by the cultivation activities. a 400 significant aspect that emerged from the analysis is the ability of forests and grazing land to 401 offset approximately 97% of the impact generated by livestock (efliv) and other activities 402 (efoth). 403 to explain the outcome of ecological balance more explicitly, the iep was assessed. its value, 404 equal to 0.13, shows that every hectare of the lag agricultural system hectares made 405 available to the community, beyond its agricultural productivity, the natural resources 406 equivalent to 0.13 ha of average italian forest. this result emphasizes the area’s ability to 407 maintain a positive balance between used and regenerated resources and to actively 408 contribute to the improvement of overall environmental quality. the iep represents a key 409 parameter for assessing the efficiency with which natural resources are managed by the 410 farming systems as a whole, thus providing a clear picture of the overall sustainability of 411 agricultural practices in the lag alto lazio territory. 412 413 5. discussion 414 although the topic of socioeconomic marginality has sparked extensive scientific and 415 political debate in recent years, accurately and comprehensively assessing the phenomenon 416 especially in terms of extent and location is not an easy task. moreover, the concept of 417 marginality is highly subjective and, together with the variety of definitions, indicators and 418 methodological approaches used (peterson and galbraith, 1932; csikós and tóth, 2023), 419 complicates comparisons with similar studies. marginal areas can emerge in a wide range of 420 socio-ecological systems, from deserts to rainforests, and thus vary in their conceptualization 421 (lipper et al., 2006). these areas are generally perceived as “limited” in their ability to 422 sustain human activities due to persistent biophysical and/or socioeconomic constraints. 423 in our study, socioeconomic marginality was assessed using three key indicators, as indicated 424 in the methodology. the results show that alto lazio lag is the area with the highest values 425 for the combination of such key indicators (old age index, population change and per capita 426 income), with a marginality index of 4.27 in the range 1 to 5. this suggests that the region is 427 characterized by a vulnerable economy and a significant social development gap. 428 in these conditions, especially for the marginal regions where the agricultural sector plays a 429 relevant role, the crops and livestock intensification could be seen as a possible strategy for 430 local socioeconomic development. nevertheless, it is evident how a similar solution presents 431 significant environmental risks. indeed, the practices for rising agricultural productivity with 432 the aim of greater profitability often lead to increase exploitation of natural resources, with 433 intensive use of pesticides and fertilizers, with harmful consequences for local ecosystems 434 (hazell and wood, 2008; barbier, 2010). in this sense, marginal areas are not only land to be 435 exploited, but places with a precious biological capacity that must be used in a sustainable 436 way (sallustio et al., 2018). agricultural activity in marginal areas should be view from this 437 perspective, considering how productive techniques, crop selection and human-environment 438 relationships pose tangible problems in terms of environmental impact (kanianska, 2016). 439 referring to lag alto lazio, the value of the ecological balance gives a positive result, 440 highlighting how the heterogeneity of land covers and the presence of forested and semi-441 natural areas within the agricultural land are essential not only biodiversity conservation but 442 also for ensuring the sustainability of farming systems. this outcome of our case study 443 suggests how marginal areas are often characterized by a land use pattern that can advantage 444 the environmental sustainability of local agricultural system. several studies have discussed 445 the relationships between marginality and environmental issues, such as water quality, soil 446 degradation, biodiversity and climate change mitigation (fisher et al., 2002; o’connor et al., 447 2005; searchinger et al., 2008; kang et al., 2013b). even if these studies raise concerns about 448 the environmental risks linked to some fragility in land use in marginal areas, the outcome of 449 our study clearly indicates that the combination of marginality and sustainable agriculture 450 should not be seen as separate concepts, but rather as two sides of the same coin. while 451 marginal areas present significant challenges, they also offer opportunities for more 452 ecological and resilient agriculture. therefore, our study confirms that agriculture can play 453 two basic roles: as a producer of agricultural goods and as a custodian of the ecosystem. 454 by improving sustainable agricultural practices, production can be optimized while 455 minimizing environmental impact. this approach not only helps protect natural capital, but 456 also promotes economic and social development in rural areas, creating a virtuous circle that 457 benefits both local communities and the ecosystem at large. furthermore, environmental 458 sustainability can be seen as a branding strategy, also at territorial level, with the result that 459 consumers will tend to perceive brands as adding value to the product and their place of origin 460 (nakaishi and chapman, 2024). the effects of this strategic leverage have been analyzed in 461 several studies, showing how integrating sustainability into branding increases consumers’ 462 willingness to pay a higher price for the product (franco and cicatiello, 2018). 463 464 6. conclusions 465 466 the main aim of this study was to find out how to develop the local economy of marginal 467 areas and thus increase their well-being. we focus on the link between socio-economic 468 marginality and environmental sustainability and find that in the literature there are many 469 examples of marginal areas that are also environmentally sustainable. 470 as agriculture is often the main productive activity in marginal areas, promoting sustainable 471 agricultural practices can be the key both to increase the value of local food production and 472 strengthen the overall identity of the territory. this approach not only protects the natural 473 capital, but also promotes the resilience of rural communities. 474 we chose the alto lazio lag as a case study due to its high socio-economic marginality 475 and the type of governance that allows political intervention. we then evaluated the 476 environmental sustainability of the lag agricultural system using an environmental balance 477 approach and came to positive conclusions. breeding activities, that generally have a high 478 impact on environmental sustainability, are present in this marginal area, but other 479 agricultural production combined with a good management system clearly compensate for 480 this and achieve a positive ecological balance. 481 marginal areas possess the potential for sustainable production and self-development without 482 adversely affecting the ecosystem, particularly through the implementation of virtuous local 483 production systems exemplified by the alto lazio lag. consequently, it can be stated that 484 this agricultural model is commendable; if local authorities promote and enhance it, it could 485 serve as an effective mechanism to bridge the development gap in marginal areas while 486 simultaneously safeguarding the environment. 487 the study also focused on identifying opportunities for sustainability claims and certification 488 processes that could enhance the value of local agriculture and food production. this tool is 489 needed to effectively convey to consumers, in a clear and quantifiable manner, the beneficial 490 impact of local agricultural production systems on the environment. 491 the promotion of agricultural sustainability, fully respecting ecosystem limits, if 492 accompanied by a recognized certification and resulting from the active involvement of 493 stakeholders, can serve as a strategic lever for the economic development of local agricultural 494 production. this approach not only strengthens territorial competitiveness but also helps to 495 create a more inclusive and environmentally respectful growth model. 496 we are aware of the limits of our results, due to a partial accounting for the territory 497 sustainability, since we considered only the agricultural production system. furthermore, 498 implementation challenges might arise due to potential discrepancies between the 499 overarching territorial vision and its practical application at the individual farm level. 500 further investigations are necessary, incorporating a stakeholder engagement process that 501 involves local policymakers and relevant actors. this approach aims to elucidate their vision 502 of development while emphasizing the potential costs and benefits associated with the 503 transition. 504 505 this study was carried out within the agritech national research center and received 506 funding from the european union next-generationeu (piano nazionale di ripresa 507 e resilienza (pnrr) – missione 4 componente 2, investimento 1.4 – d.d. 508 1032 17/06/2022, cn00000022). this manuscript reflects only the authors’ views and 509 opinions, neither the european union nor the european commission can be considered 510 responsible for them 511 512 513 references 514 515 aguilera-cora, e., fernández-cavia, j., codina, l. 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(2021). an institutional periphery in discussion. 642 rethinking the inner areas in italy, applied geography, 135, 102537. 643 wackernagel, m., rees, w.e., (1996). our ecological footprint: reducing human impact 644 on the earth. new society publishers, gabriola island, canada. 645 646 647 table a1 – marginality indicators of viterbo province municipalities 648 municipality old-age index iage depop. rate idep income per capita iinc imar acquapendente 3,11 5 -8,9% 4 13.117 2 4 arlena di castro 3,21 5 -2,9% 3 11.001 4 4 bagnoregio 3,35 5 -6,7% 4 13.025 2 4 barbarano romano 3,55 5 5,5% 2 13.146 2 3 bassano in teverina 2,77 4 11,7% 2 12.075 3 3 bassano romano 2,50 4 7,7% 2 12.352 3 3 blera 2,48 3 -7,1% 4 12.143 3 3 bolsena 3,97 5 -9,7% 4 12.119 3 4 bomarzo 2,26 3 4,0% 3 12.406 3 3 calcata 1,99 2 6,6% 2 10.386 5 3 canepina 2,22 3 -5,6% 4 11.665 4 4 canino 2,44 3 -0,7% 3 10.806 5 4 capodimonte 3,87 5 -0,9% 3 14.215 1 3 capranica 1,91 2 13,1% 2 11.795 4 3 caprarola 2,22 3 -0,7% 3 11.502 4 3 carbognano 1,86 2 1,3% 3 10.980 5 3 castel sant’elia 1,86 2 14,1% 2 10.578 5 3 castiglione in teverina 2,16 3 0,6% 3 11.933 4 3 celleno 2,55 4 -2,2% 3 12.290 3 3 cellere 4,02 5 -17,7% 5 11.138 4 5 civita castellana 2,23 3 0,5% 3 12.491 3 3 civitella d’agliano 3,33 5 -16,8% 5 12.100 3 4 corchiano 1,86 2 7,5% 2 10.307 5 3 fabrica di roma 1,74 2 23,5% 1 11.716 4 2 faleria 2,29 3 15,2% 1 11.778 4 3 farnese 4,25 5 -19,4% 5 11.924 4 5 gallese 2,65 4 -6,5% 4 12.105 3 4 gradoli 4,49 5 -16,3% 5 11.607 4 5 graffignano 2,67 4 -7,9% 4 11.759 4 4 grotte di castro 3,82 5 -20,2% 5 13.389 2 4 ischia di castro 2,92 4 -13,2% 4 10.966 5 4 latera 6,23 5 -25,0% 5 12.063 3 4 lubriano 2,70 4 -6,2% 4 11.970 4 4 marta 2,83 4 -5,8% 4 12.261 3 4 montalto di castro 2,21 3 14,0% 2 12.649 3 3 monte romano 2,06 3 -3,5% 3 13.569 2 3 montefiascone 2,41 3 2,6% 3 13.181 2 3 monterosi 1,32 1 102,1% 1 11.677 4 2 nepi 1,79 2 20,2% 1 11.762 4 2 onano 3,33 5 -23,6% 5 10.215 5 5 oriolo romano 2,13 3 27,4% 1 12.464 3 2 orte 1,71 2 16,6% 1 13.055 2 2 piansano 3,69 5 -13,2% 4 11.898 4 4 proceno 5,89 5 -17,6% 5 12.032 3 4 ronciglione 2,32 3 13,2% 2 12.232 3 3 san lorenzo nuovo 3,21 5 -2,6% 3 12.001 3 4 soriano nel cimino 2,50 3 -2,8% 3 11.728 4 3 sutri 2,40 3 32,0% 1 12.959 3 2 tarquinia 2,32 3 5,7% 2 13.901 2 2 tessennano 5,39 5 -33,1% 5 11.961 4 5 tuscania 2,31 3 6,6% 2 11.788 4 3 valentano 2,91 4 -5,7% 4 13.297 2 3 vallerano 2,54 4 -3,7% 3 11.724 4 4 vasanello 1,96 2 2,4% 3 12.322 3 3 vejano 2,51 4 3,0% 3 12.715 3 3 vetralla 2,09 3 11,6% 2 12.292 3 3 vignanello 2,38 3 -8,8% 4 10.664 5 4 villa san giovanni in t. 2,97 4 3,4% 3 12.668 3 3 viterbo 2,02 3 11,6% 2 14.440 1 2 vitorchiano 1,18 1 63,2% 1 13.619 2 1 source: our elaboration on istat (2024a) and mf (2023) 649 table a2 – membership of viterbo province municipalities to single aggregations 650 municipality imar a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 acquapendente 4 1 1 0 0 1 0 0 0 0 1 0 arlena di castro 4 1 0 0 0 1 0 0 0 0 0 0 bagnoregio 4 0 1 0 0 0 1 0 0 0 0 0 barbarano romano 3 0 0 0 0 0 0 0 0 1 0 0 bassano in teverina 3 0 0 0 0 0 0 0 0 0 0 0 bassano romano 3 0 0 0 0 0 0 0 0 1 0 0 blera 3 0 0 0 0 0 0 0 0 1 0 0 bolsena 4 1 1 0 0 0 1 0 0 0 0 0 bomarzo 3 0 0 0 0 0 1 0 0 0 0 0 calcata 3 0 0 0 1 0 0 0 1 0 0 0 canepina 4 0 0 0 1 0 0 1 0 0 0 1 canino 4 1 1 0 0 1 0 0 0 0 0 0 capodimonte 3 1 1 0 0 0 1 0 0 0 0 0 capranica 3 0 0 0 0 0 0 1 0 0 0 1 caprarola 3 0 0 0 0 0 0 1 0 0 0 1 carbognano 3 0 0 0 0 0 0 1 0 0 0 1 castel sant’elia 3 0 0 0 1 0 0 0 1 0 0 0 castiglione in teverina 3 0 0 0 0 0 1 0 0 0 0 0 celleno 3 0 1 0 0 0 1 0 0 0 0 0 cellere 5 1 1 0 0 1 0 0 0 0 0 0 civita castellana 3 0 0 0 1 0 0 0 1 0 0 0 civitella d’agliano 4 0 0 0 0 0 1 0 0 0 0 0 corchiano 3 0 0 0 1 0 0 0 1 0 0 0 fabrica di roma 2 0 0 0 1 0 0 0 1 0 0 0 faleria 3 0 0 0 1 0 0 0 1 0 0 0 farnese 5 1 1 0 0 1 0 0 0 0 0 0 gallese 4 0 0 0 1 0 0 0 1 0 0 0 gradoli 5 1 1 0 0 1 0 0 0 0 1 0 graffignano 4 0 0 0 0 0 1 0 0 0 0 0 grotte di castro 4 1 1 0 0 1 0 0 0 0 1 0 ischia di castro 4 1 1 0 0 1 0 0 0 0 0 0 latera 4 1 1 0 0 1 0 0 0 0 1 0 lubriano 4 0 0 0 0 0 1 0 0 0 0 0 marta 4 1 1 0 0 0 1 0 0 0 0 0 montalto di castro 3 1 0 0 0 0 0 0 0 0 0 0 monte romano 3 0 0 1 0 0 0 0 0 0 0 0 montefiascone 3 1 1 0 0 0 1 0 0 0 0 0 monterosi 2 0 0 0 0 0 0 0 0 0 0 0 nepi 2 0 0 0 1 0 0 0 1 0 0 0 onano 5 1 0 0 0 1 0 0 0 0 1 0 oriolo romano 2 0 0 0 0 0 0 0 0 1 0 0 orte 2 0 0 0 1 0 0 0 1 0 0 0 piansano 4 1 1 0 0 1 0 0 0 0 0 0 proceno 4 1 0 0 0 1 0 0 0 0 1 0 ronciglione 3 0 0 0 0 0 0 1 0 0 0 1 san lorenzo nuovo 4 1 1 0 0 1 0 0 0 0 1 0 soriano nel cimino 3 0 0 0 0 0 0 0 0 0 0 1 sutri 2 0 0 0 0 0 0 1 0 0 0 0 tarquinia 2 1 0 1 0 0 0 0 0 0 0 0 tessennano 5 1 0 0 0 1 0 0 0 0 0 0 tuscania 3 1 0 0 0 0 0 0 0 0 0 0 valentano 3 1 1 0 0 1 0 0 0 0 1 0 vallerano 4 0 0 0 1 0 0 1 0 0 0 1 vasanello 3 0 0 0 1 0 0 0 1 0 0 0 vejano 3 0 0 0 0 0 0 0 0 1 0 0 vetralla 3 0 0 0 0 0 0 1 0 0 0 1 vignanello 4 0 0 0 1 0 0 1 0 0 0 1 villa san giovanni in t. 3 0 0 0 0 0 0 0 0 1 0 0 viterbo 2 0 0 0 0 0 0 0 0 0 0 0 vitorchiano 1 0 0 0 0 0 0 0 0 0 0 1 source: our elaboration 651 bio -based and a ppl ied economics bae bio-based and applied economics 11(4): 339-350, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-13516 copyright: © 2022 s. russo, l. phali, m. prosperi. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: s. russo, l. phali, m. prosperi (2022). dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: a scoping review. bio-based and applied economics 11(4): 339-350. doi: 10.36253/bae-13516 received: august 18, 2022 accepted: february 12, 2023 published: may 3, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: simone cerroni. orcid sr: 0000-0003-2166-1994 lp: 0000-0002-7531-7577 mp: 0000-0003-0836-766x dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: a scoping review simone russo1,2,*, lerato phali3, maurizio prosperi1 1 department of agriculture, food, natural resources and engineering (dafne), university of foggia, 71122 foggia, italy 2 department of soil, plant and food science (disspa), university of bari aldo moro, 70126 bari, italy 3 discipline of agricultural economics, school of agriculture, environment, and earth sciences, university of kwazulu-natal, 3201 pietermaritzburg, south africa *corresponding author. e-mail: simone.russo@unifg.it abstract. literature on farm productivity and efficiency was reviewed using a scoping review methodology, focusing on studies that have included risk and risk management tools within the stochastic frontier analysis in agricultural economics. this study contributes to investigating the methods used to account for endogeneity by using a risk-accommodating stochastic frontier approach when analysing farmers’ performance. despite the increasing methodologies proposed in the literature, only a few studies have treated endogeneity in farm risk-performance evaluations. according to our findings, it can be concluded that there is a literature gap regarding the adoption of a comprehensive approach capable of dealing with endogeneity when assessing farm performances. endogeneity and risk issues need to be concurrently addressed to make strides in achieving economic and environmental sustainability. neglecting endogeneity in these analyses may lead to biased estimates and thus inappropriate policy recommendations failing to boost the productivity and technical efficiency of farmers. keywords: stochastic frontier analysis, agricultural economics, risk, endogeneity. jel codes: c18, q12, d81. highlights · scoping review of studies that account for risk in stochastic frontier analysis · we synthesise methodologies dealing with endogeneity in risk-accommodating sfa · the lack of risk and endogeneity accommodation in analysis yields biased results · literature gap in sfa dealing with risk and endogeneity in agricultural economics · risk and endogeneity inclusion may help develop effective agricultural policies http://creativecommons.org/licenses/by/4.0/legalcode 340 bio-based and applied economics 11(4): 339-350, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13516 simone russo, lerato phali, maurizio prosperi 1. introduction agriculture is one of the sectors where risk and uncertainty play a decisive role in production decisionmaking (ahsan et al., 1982; moschini and hennessy, 2001). it is well-known that since farmers make input use decisions before knowing the true state of nature, they choose the input allocation according to their subjective propensity to take a certain level of risk (ramaswami, 1992; cerroni, 2020). while exerting their typical actions, farmers do not aim only to maximize profits but also try to minimize the risk impact on income loss (just and pope, 1978, 1979; antle, 1983; finger, 2013). the conceptualization of agricultural risk is usually attributed to the length and complexity of the biological production cycle, which exposes farmers to risks such as pests, erratic climatic changes, price fluctuations, and even policy changes (duong et al., 2019; komarek et al., 2020). according to komarek et al. (2020), agricultural risks are classified into production, market, institutional, personal, and financial risks. production risks stem from the natural growth processes and are also related to weather and climatic conditions. these are factors beyond the farmer’s control given the stochastic nature of agriculture. within market risks, there are those associated with price volatility for both input and output prices, as well as those related to asymmetric information, international trade, and liberalization processes. institutional risks are generally associated with abrupt policy and regulation changes, as well as changes in the behaviour of informal institutions that affect transactions. personal risks are farmer-specific and related to health, personal relationships, and well-being, whereas financial risks stem from farm finance factors, credit access, and interest rate payments. researchers and policymakers have various reasons to be interested in how risk affects farmers’ decisionmaking and their economic performances. farm performance evaluations are fundamental for policymakers and producers to enhance both the economic and environmental sustainability of farming (farrell, 1957). moreover, understanding the interrelations between farmers’ behaviour in a risky environment and farm performance is essential to enhance the effectiveness of policy measures (khanal et al., 2021). for example, while risk-neutral farmers aim to maximize profits by considering only the mean effect of production, risk-averse producers account for both mean and higher moments of their production functions (antle, 1983). therefore, risk-averse production decisions differ from risk-neutral ones due to the marginal risk premium, which is the absolute value of the risk effect of input use on output (macminn and holtmann, 1983; ramaswami, 1992). the marginal risk premium may have a positive or negative sign and indicates whether risk-averse producers use more or less input than risk-neutral ones. thus, riskaverse farmers use less risk-increasing (and more riskdecreasing) inputs to cope with risk compared to a riskneutral farmer, who employ the profit-maximizing input vector (nelson and loehman, 1987; ramaswami, 1993). as such, the risk aversion due to the uncertainty of outcomes may result in non-profit-maximizing input use, potentially resulting in lower technical efficiency and productivity (roll, 2019). by ignoring the risk impact on production, battese et al. (1997) conclude that estimates of technical efficiency would be skewed. consequently, neglecting the interrelation between farm performance and risk-averse deviations from efficient behaviour would lead to incorrect policy implications and recommendations (just, 2003). in literature, most productivity and efficiency analyses are conducted through the development of production frontier models. the two commonly used methods in productivity and efficiency analysis are data envelopment analysis (dea) and stochastic frontier analysis (sfa). although these two methods have their merits, there has been constant debate amongst scholars on which method is better for modelling production technology. a relevant distinction between the two methods is that dea is deterministic while sfa is stochastic. while in the stochastic frontier model, the individual observations may be affected by random noise, in the deterministic approach the potential noise is neglected, and each variation in data is assumed to influence the firm’s efficiency and the shape of the frontier (bogetoft and otto, 2010). therefore, one of the principal limitations of the dea methodology is that it is not possible to consider the effect of risk on efficiency, which could be confused and interpreted as technical inefficiency. accordingly, it seems that sfa might be more suitable to model productivity and efficiency in the presence of risk as it is suited to disentangle the inefficiency from the standard statistical error related, for example, to weather events, market volatility, and regulation changes. stochastic production functions appeared to be a reasonable solution to account for risk in agricultural economics (chavas et al., 2010). just and pope (1978) introduced a production function specification that can distinguish between the marginal effect of inputs on both the mean and variance of output. then, antle (1983) expanded this technique to account for the impact of production inputs on higher moments of production function (i.e., skewness). later, battese et al. (1997) extended the model proposed by just and pope (1978) to 341dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: a scoping review bio-based and applied economics 11(4): 339-350, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13516 the stochastic frontier production approach developed originally by aigner et al. (1977) and meeusen and van den broeck (1977). according to the authors, the stochastic frontier production function is more consistent with economic theory and reality with regard to the so-called average production function. more recently, kumbhakar (2002) generalized the approach proposed by the previous authors by estimating a model which includes production risk, technical efficiency, and producers’ attitude toward risk. given the inevitable consequence of risk effects on producers’ technical efficiency, risk sources have to be incorporated into the stochastic production frontier to realistically account for and predict producers’ technical efficiency (battese et al., 1997). the primary motivation paving the way for the present study is that, despite its importance, most of the scientific literature on production at the farm level does not account for risk (just, 2003). moreover, it is worth mentioning that one of the central assumptions of the sfa model is that the input variables should be independent of both the error terms (technical efficiency and random error) in the model. it is the general definition of endogeneity, which refers to the correlation between explanatory variables and the error terms. however, it is essential to note that endogeneity may occur for several reasons. for instance, farmers may adjust their inputs according to observed shocks, which usually are included in the random error term. therefore, the correlation between the production inputs and the statistical error term due to the observed shocks would result in endogeneity (latruffe et al., 2017). in addition, a possible endogeneity issue may arise when farmers, being aware they are inefficient, tend to optimize their input use (shee and stefanou, 2014). finally, other endogeneity sources may occur when farmers cope with risk by adopting risk management tools or risk-mitigation practices (vigani and kathage, 2019). the model misspecifications due to the presence of endogeneity leads to erroneous inferences about the assessment of input elasticities and economies of scale, as well as inaccurate and inconsistent estimates of farm technical efficiency (karakaplan and kutlu, 2017). it is worth noting that endogeneity in sfa is often ignored, which could overstate or even undermine the effects of factors on production and, thus, results in key strategies or recommendations that boost farm performance being left out (russo et al., 2022). the impact of the inaccuracy and inconsistency of results may be highly relevant when risk analysis is performed (battese et al., 1997). given the motivations listed above, this paper presents a review of literature that covers agricultural productivity and efficiency analysis. the particular focus is on studies that have adopted the sfa method with the inclusion of risk. the scoping review method has been adopted for the capability to identify and map out evidence and clarify key concepts in agricultural stochastic frontier literature with the inclusion and consideration of risk. specifically, this article aims to provide insights into how risks and risk mitigation strategies have been factored into sfa. the main contribution of the present research relates to analysing the different methods used to deal with endogeneity while aiming to investigate the risk effects on agricultural production within the sfa approach. it is important to highlight these two issues as when they are not considered in modelling, the biased estimates found after analysis may be used to inform policy. this then leaves room for the ineffectiveness of policy interventions as they would be developed without considerations of the complexity of the agricultural production modelling. the exclusion of the effects of risk and riskmitigation practices on studies that aim to investigate farmers’ decision-making would provide inconsistent and irrelevant production guidelines. this review depicts the gaps that researchers need to fill and methods that can be adopted to ensure valid and consistent results that can be used for policy development aimed at ensuring agricultural productivity and efficiency. in the following section, the scoping review methodology, eligibility criteria, and selection process of articles are presented. the results section, presents and illustrates insights of the literature analysed. finally, we discuss the results and provide some conclusions, highlighting the limitations of the study and future research areas. 2. methodology the scoping review method was adopted to conduct the study following the guidelines provided by tricco et al. (2018) in the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (prisma-scr). a scoping review is a form of knowledge synthesis that systematically searches, selects, and synthesizes existing knowledge to map the key concepts, types of evidence, and gaps in research related to a given area or field (colquhoun et al., 2014). the advantage of the scoping review method is that it helps to summarise the existing knowledge used to develop policy or practical recommendations, as well as to provide practical pathways for future research (arksey and o’malley, 2005; piñeiro et al., 2020). compared to the traditional literature review, the scoping method is more rigorous, transparent, and replicable, including steps to reduce the subjectivity bias resulting from 342 bio-based and applied economics 11(4): 339-350, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13516 simone russo, lerato phali, maurizio prosperi the author’s prior knowledge and experience (munn et al., 2018). th e scoping method was thus suitable for this study in exploring how risk has been incorporated into sfa agricultural productivity analysis and how the endogeneity issues have been handled in literature. aft er stating the research question, the subsequent steps of this approach are the identifi cation of relevant studies, study selection, data extraction and charting, and reporting of the results. in order to get a representative sample of the literature, an initial set of articles was identifi ed. th e scopus bibliographic database was used to research the relevant studies, including articles written in english and published in peer-reviewed journals earlier than 30 june 2021. we opted to focus on articles indexed in scopus since it is one of the two most used bibliographic databases, and it includes most (about 99%) of the journals indexed in web of science (singh et al., 2021), particularly in the social sciences topics (mongeon and paul-hus, 2016). th e search was characterized by a combination of three keyword groups included in the paper abstract, title, or keywords. th e following structured query developed using boolean operators and wildcards was used for the research: [“stochastic frontier” or “stochastic production” or “technical efficiency”] and [“risk” or “uncertain*”] and [“ farm*” or “agricultur*” or “ food” or “crop” or “livestock”]. while the fi rst set of keywords included the terms related to the sfa, the second related to the risk, and the third to the agricultural context. th e fi nal set of articles was exported to the mendeley referencing tool for assessment. for consistency purposes, all the authors screened the initial set of articles. we screened the same publications and discussed our chosen studies for review. to be included in the sample, the eligibility criteria used the following: (i) research topic on agricultural production (ii) inclusion of risk and risk management in farm productivity and effi ciency analysis; (iii) the adoption of sfa to model technical effi ciency and agricultural productivity. th e selection process followed several steps which gradually reduced the number of studies according to the eligibility criteria, as shown in figure 1. th e search output initially included 162 peer-reviewed articles. in the first screening step, titles and abstracts were examined, where papers focusing on issues related to risk analysis in the agricultural sector using the sfa approach were retained. th en, the full text of the remaining 94 studies were analysed, excluding 35 articles according to the rejection criteria. finally, in the last screening step, 15 papers were excluded because they utilized a stochastic production function instead of the frontier. however, these papers were examined to consider their insights as regarding endogeneity issues. at the end of the screening process, 44 articles were retained. of the 162 articles, 11 were disqualifi ed because they were not focused on agricultural economics, and 40 for the lack of risk considerations. finally, 67 papers were excluded for their use of methods other than sfa, for instance, stochastic production function (e.g., griffi ths, 1986; eggert and tveteras, 2004; di falco et al., 2007), or non-parametric approaches such as dea (e.g., serra and oude lansink, 2014; chambers et al., 2015; oude lansink et al., 2015), or fuzzy mathematical models (guo et al., 2019; wang et al., 2020). 3. results th e results of the analysis showed that there are several approaches adopted in estimating stochastic production frontiers with risk considerations. figure 2 below presents a histogram of the distribution of the common approaches employed in the retained articles. th e most commonly used methods were those of just and pope (1978), battese and coelli (1995), battese et al. (1997), and kumbhakar (2002). in addition, 15 articles adopted other methods that studied risk in their analysis1. 1 among them, there are the approaches proposed by aigner et al. (1977), antle (1983), blarel et al. (1992), caudill et al. (1995), koop figure 1. prisma-scr flow diagram. source: own elaboration based on tricco et al. (2018). 343dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: a scoping review bio-based and applied economics 11(4): 339-350, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13516 however, not all approaches allow the inclusion of risk within the stochastic production framework, such as battese and coelli (1995). among the techniques that include risk within the production frontier, the most common methods used were the ones proposed by just and pope (1978), battese et al. (1997), and kumbhakar (2002)2. six diff erent thematic groups were identifi ed within the literature analysed, as shown in figure 3. in this analysis, it was found that two articles incorporated risk in the sfa approach by focussing on the relationship between effi ciency, risk aspects, and investment, such as the timing of investment decisions (lambarraa et al., 2016) or the adoption of new technology (ghosh et al., 1994). in addition, nineteen articles investigated the eff ect of farmer risk attitudes, risk mitigation practices, and risk management tools on farm performance. furthermore, six papers examined the impact of agricultural policies on production risk and technical effi ciency. additionally, two studies investigated the diff erences in production risk and technical effi ciency among distinct production technologies, such as intensive or extensive (nguyen et al., 2020) and organic or conventional production (tiedemann and latacz-lohmann, 2013). in addition, four papers investigated the climate eff ect or market volatility on farm performance and/or risk. finally, eleven articles focused on the assessment of the impact of input on production risk and technical effi ciency. in figure 3, the articles that dealt with endogeneet al. (1997), greene (2003, 2005), tsionas (2006), yesuf et al. (2008), o’donnell et al. (2010), power et al. (2011), bravo-ureta et al. (2012), karagiannis and tzouvelekas (2012), kumbhakar et al. (2014), and o’donnell (2016). 2 while all the studies consider risk, not all explicitly include it within the estimated production frontier. some articles assessed it outside the model as a prerequisite or a follow-up step aft er the estimations. ity and those that did not are diff erentiated with colour schemes. th e colour red represents the articles that dealt with endogeneity. as a result, only nine studies out of 44 (about 20%) considered the issue of endogeneity. among them, fi ve articles focused on the risk-management thematic area, two on agricultural policy, one on production technology, and one on input eff ects. th e diff erent methods implemented to account for endogeneity are presented in table 1. among the articles in the risk management thematic area, chang and wen (2011) investigated the off -farm work eff ect on technical effi ciency and production risk in taiwan rice farming, mishra et al. (2019, 2020) examined the impact of contract farming on production risk, technical efficiency, and risk attitudes for diff erent crops in nepal, and rizwan et al. (2020) studied the eff ect of off -farm employment on production risk and technical effi ciency. all these articles developed a stochastic frontier following the model proposed by kumbhakar (2002), accounting for self-selection by separating adopters and nonadopters. khanal et al. (2021) investigated the infl uence of farmers’ climate change adaptations on smallholder farm effi ciency and productivity in nepal rice production. th e authors treated the self-selection endogeneity bias among adopters and non-adopters for observed and unobserved characteristics. in particular, they utilized the propensity score matching (psm) technique to correct for observed heterogeneity, obtaining samples of farmers homogenous in terms of socioeconomic characteristics. th en, they estimated a stochastic frontier using the model proposed by bravo-ureta et al. (2012) to correct for unobserved heterogeneity. in the agricultural policy thematic area, key and mcbride (2014) estimated the effects on production mean and variance caused by the ban of antibiotics on the us hog industry. th ey developed a stochastic fronfigure 2. th eoretical and methodological framework to estimate the production frontier. source: own elaboration. note: th e sum is 45 because one article compared the just and pope and kumbhakar models. figure 3. literature thematic areas accounting for the articles that dealt with endogeneity issues. source: own elaboration. 344 bio-based and applied economics 11(4): 339-350, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13516 simone russo, lerato phali, maurizio prosperi tier following the approach proposed by karagiannis and tzouvelekas (2012). the authors addressed the potential selection bias as the application of antibiotics treatment may be related to other unobserved aspects influencing the production process. in particular, they matched the different treatment effects (antibiotics) to create similar groups based on the observable characteristics. singbo et al. (2020) analysed the impact of the revenue insurance program and environmental regulations on canadian hog farmers’ behaviour and farm performance indicators. the authors addressed the potential endogeneity of input changes related to production shocks by estimating the meta-technology production frontier model developed by o’donnell (2016). within the production technology thematic area, tiedemann and latacz-lohmann (2013) evaluated production risk and technical efficiency in organic and conventional arable crop farms in germany. the authors developed a stochastic frontier approach stemming from the model developed by just and pope (1978). they used the propensity score matching to compare groups, accounting for the self-selection problem due to farm size and soil quality. finally, among the input effects thematic area, the only study that dealt with endogeneity is nauges et al. (2011), who analysed finnish grain production under both inefficiency and risk conditions. they developed a state-contingent production frontier following the model proposed by o’donnell and griffiths (2006). they accounted for the endogeneity of inputs considering the different states of nature. in particular, they considered that farmers allocate inputs differently to manage risk in relation to the meteorological conditions in the relative states of nature. to summarise, seven articles considered endogeneity bias resulting from self-selection, while two considered endogeneity stemming from input use alterations after adverse shocks. in addition to results related to sfa, some other articles which emerged from the search string accounted for endogeneity in the production function. these papers are reported in table 2. all these articles were classified into the risk-management thematic area. among these articles, di falco and chavas (2009) analysed the crop genetic diversity effects on productivity and production risk of ethiopian farmers engaged with barley production, following the antle (1983) approach. the authors estimated the mean function, the variance, and the skewness equations using a threestage least squares (3sls) estimator to correct the selfselection bias, treating biodiversity as endogenous in all equations. following the approach proposed by antle (1983), di falco and veronesi (2014) investigated the influence of climate change adaptations on farm exposure to downside risk for several crops in ethiopia. the decision on whether to adapt or not to climate change is voluntary and may result in self-selection bias. the authors accounted for the endogeneity of the adaptation decision by estimating a switching regression model. by using the same approach, kassie et al. (2015) analysed the effect of sustainable intensification practices on productivity and production risk in maize-legume intercropping production in malawi, while amondo et al. (2019) investigated the impact of using drought-tolerant table 1. articles dealing with endogeneity in the production frontier estimates. category/study frontier theoretical framework endogeneity source methodology risk management chang and wen (2011) kumbhakar (2002) self-selection separating groups mishra et al. (2019) kumbhakar (2002) self-selection separating groups mishra et al. (2020) kumbhakar (2002) self-selection separating groups rizwan et al. (2020) kumbhakar (2002) self-selection separating groups khanal et al. (2021) bravo-ureta et al. (2012) self-selection psm agricultural policy key and mcbride (2014) karagiannis and tzouvelekas (2012) self-selection psm singbo et al. (2020) o’donnell (2016) input endogeneity meta-technology production technology tiedemann and latacz-lohmann (2013) just and pope (1978) self-selection psm input effect nauges et al. (2011) o’donnell and griffiths (2006) input endogeneity state-contingent source: own elaboration. 345dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: a scoping review bio-based and applied economics 11(4): 339-350, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13516 maize varieties on farm productivity, yield variance, and downside risk exposure in zambian maize-growing farms. the research proposed by wang et al. (2018) studied the importance of irrigation infrastructure in enhancing farmers’ ability to adapt to drought and its efficacy in managing drought risk in rice production in china. the authors estimated a production function following the approach proposed by antle (1983). in addition, they implemented a two-stage instrumental variable method to control for the endogeneity of the adaptation decision. finally, following the state-contingent method proposed by quiggin and chambers (2006), mallawaarachchi et al. (2017) estimated the production function of dairy farms in australia to analyse the effect of water allocation on farm performance. they accounted for endogeneity related to the change in the usage of productive inputs under different states of nature according to the productivity shocks. moreover, they proposed a two-stage instrumental variables approach to correct the endogeneity bias due to self-selection. 4. discussion consistent with just (2003), the results of this research confirm the low prevalence of risk-related agricultural production studies, showing the failure of risk researchers in convincing the broader profession of the importance of risk effects on farmers’ decision-making. the vast majority of the articles using sfa in agricultural production did not consider risk despite its relevance in the field. for example, by omitting the keywords related to risk from the search query, the number of articles increases from 162 to 2595. given that risk effects on productivity and technical efficiency are unavoidable, the stochastic production frontier must include risk sources to accurately account for and predict the technical efficiency of producers (battese et al., 1997). however, it was alarming to discover that relatively few articles account for risk by implementing a sfa approach. this may be attributed to the fact that this approach is still in development and the model is rather complex, regarding both the modelling and estimating procedure (kumbhakar et al., 2015). it is worth noting that when the effects of risk are included in the model, the endogeneity sources are often ignored, resulting in biased estimates of parameters. therefore, studies considering risk in the sfa approach seem to fail to represent the complexities of agricultural production modelling, such as accounting for endogeneity issues. despite the methods of dealing with the endogeneity issues in production frontiers being well documented in the recent literature (shee and stefanou, 2014; amsler et al., 2016, 2017; karakaplan and kutlu, 2017; latruffe et al., 2017), most of the studies analysed in this review, do not generally account for endogeneity bias due to the input relationship with production shocks. in addition, other endogeneity sources may arise with the taking up of risk management tools or risk mitigation practices. according to vigani and kathage (2019), there are four possible cases. first, it is necessary to account for the possibility of reverse causality between the choice of adopting risk management instruments and productivity (nelson and loehman, 1987; ramaswami, 1993). more productive farms, for example, are more likely to have the financial and managerial resources for risk mitigation (enjolras et al., 2012; santeramo et al., 2016). in addition, the self-selection problem needs to be addressed to avoid inconsistent estimates of risk mitigation tools on farm results. it is because, generally, the adoption is voluntary, and a particular strategy may be adopted by farms that have more advantages in adopting, i.e., they have different unobservable characteristics that may have an impact on both the adoption decision and performance such as table 2. articles dealing with endogeneity in the function production instead of the frontier. category/study frontier theoretical framework endogeneity source methodology risk management di falco and chavas (2009) antle (1983) self-selection three-stage least squares (3sls) approach di falco and veronesi (2014) antle (1983) self-selection endogenous switching regressor kassie et al. (2015) antle (1983) self-selection endogenous switching regressor mallawaarachchi et al. (2017) quiggin and chambers (2006) self-selection input endogeneity two-stage iv approach state-contingent wang et al. (2018) antle (1983) self-selection two-stage iv approach amondo et al. (2019) antle (1983) self-selection endogenous switching regressor source: own elaboration. 346 bio-based and applied economics 11(4): 339-350, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13516 simone russo, lerato phali, maurizio prosperi risk aversion or perceived barriers to adopting risk management tools (coletta et al., 2018; di falco and veronesi, 2013; giampietri et al., 2020). in addition, another potential source of endogeneity may arise from the substitution effect between risk management practices and input use since the adoption of risk-mitigating practices may change the level of input used (ramaswami, 1992; russo et al., 2022). finally, researchers need to account for omitted variables endogeneity by including the most adopted risk management tools. in fact, the estimates of risk mitigation practice effects may be biased because the total impact of adopting several risk mitigation practices simultaneously might not be equivalent to the sum of the influences when considering each strategy separately (wu and babcock, 1998). however, among the articles within the risk management thematic area, the few that dealt with endogeneity mainly considered the self-selection bias. none of these treated the endogeneity due to the input correlation with production shocks. the lack of studies that deal with endogeneity by using the sfa approach in agricultural economics may be explained as follows. first, the stochastic frontier literature has largely ignored the advances made in the production function framework to control for endogeneity issues (shee and stefanou, 2014). moreover, dealing with endogeneity is relatively more complex in the sfa approach than in the standard regression models. in fact, due to the nature of the error term in the stochastic frontier models, which include both the technical efficiency and statistical error terms, this is a relatively more difficult task (karakaplan and kutlu, 2017), which drastically reduces the number of researchers that are able to deal with these problems. agricultural economists have to push for the advancement of more sophisticated methodologies to account for these issues since farming production is much more complex than other productive sectors. indeed, agricultural production studies have to take into account the biological production cycle and environmental conditions, factors that are less relevant in other sectors. our findings show a gap in the literature in identifying a comprehensive approach capable of dealing with either risk and endogeneity concurrently when assessing farm productivity and technical efficiency in the sfa framework. this apparent deficiency in literature in the field may be related to the lack of consolidated knowledge in terms of standardized methodologies. as emerged in the current analysis, the authors applied different production frontier models by using several strategies to deal with both risk and endogeneity issues. the use of several statistical platforms leads to a situation where the routines are available in a fragmented way. for example, only certain softwares may be more appropriate to treat a specific problem. there is not yet a software where all the estimators are available (kumbhakar et al., 2020). furthermore, despite its widespread use, only the most basic implementations of the sfa are available across the broad array of statistical platforms. as such, the lack of existing routines requires researchers to be able to program or code (e.g., creating new command or algorithms) to develop a frontier that accounts for all these factors. 5. conclusion with the increasing availability of data compared to the past and access to appropriate analytical methods/routines and statistical softwares, sfa may represent a useful approach to yield valuable results that can improve the effectiveness of policies in the agricultural sector. this is also imperative for the future development of well-suited policy instruments. to this end, a scoping literature review was conducted to overview the existing knowledge in farm risk analysis within the sfa framework. in particular, this article aimed to investigate the methods proposed in the literature to deal with endogeneity in sfa risk analysis. the main limitation of this study is related to the inclusion of only peer-reviewed articles published in academic journals. however, this was deemed to be enough to highlight the gap in the literature. therefore, for future studies of this domain, we suggest the review of grey literature as the approaches proposed in the study are still under development. the findings of this research highlight the need for more studies that investigate the farm productivity and efficiency which also account for risk and endogeneity issues. this result is quite critical since the researchers’ goal is often related to providing policy indications to enhance farm performance without focusing on the accuracy of data analysis. neglecting risk and endogeneity in benchmarking studies may yield biased estimates and thus lead to incorrect policy recommendations. a comprehensive approach might help to achieve more accurate estimates that could yield recommendations that ensure improved productivity and technical efficiency of farmers. however, it is plausible to conclude that much still needs to be done in order to get a comprehensive approach to represent the complexity of agricultural production modelling. despite the relevant implications of risk and risk management tools in agricultural decision-making and economic performances, the sfa literature which focus347dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: a scoping review bio-based and applied economics 11(4): 339-350, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-13516 es on these aspects is still underrepresented. research should be focused on measuring the impact of the different sources of risk when assessing 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(2008). the impact of climate change and adaptation on food production in low-income countries: evidence from the nile basin, ethiopia. ifpri discussion paper. volume 11, issue 4 2022 firenze university press systems thinking, mapping and change in food and agriculture domenico dentoni1,*, carlo cucchi2, marija roglic1, rob lubberink3, rahmin bender-salazar4, timothy manyise5 vulnerability and resilience to food and nutrition insecurity: a review of the literature towards a unified framework pierluigi montalbano1, donato romano2,* the local economic impact of climate change mitigation in agriculture cathal geoghegan1,2,*, cathal o’donoghue1,3, jason loughrey2 dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: a scoping review simone russo1,2,*, lerato phali3, maurizio prosperi1 provision of public goods and bads by agriculture and forestry. an analysis of stakeholders’ perception of factors, issues and mechanisms stefano targetti1,*, valentina marconi1, meri raggi2, annette piorr3, anastasio josé villanueva4, kati häfner3, mikko kurttila5, natalia letki6, mihai costica7, dimitre nikolov8, davide viaggi1 bio-based and applied economics bae copyright: © 2023 c.s. onyenekwe, p.i. opata, c.o. ume, d.b. sarpong, i.s. egyir. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6e172 | doi: 10.36253/bae-13436 citation: c.s. onyenekwe, p.i. opata, c.o. ume, d.b. sarpong, i.s. egyir (2023). heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria. biobased and applied economics 12(1): 17-35. doi: 10.36253/bae-13436 received: july 26, 2022 accepted: january 30, 2023 published: june 24, 2023 competing interests: the author(s) declare(s) no conflict of interest. editor: davide menozzi, linda arata. orcid cso:0000-0003-2625-1775 pio: 0000-0001-6829-6125 cou: 0000-0003-2033-0560 dbs: 0000-0001-8288-2999 ise: 0000-0003-2067-8946 heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria chinasa sylvia onyenekwe1, patience ifeyinwa opata1, chukwuma otum ume1,*, daniel bruce sarpong2, irene susana egyir2 1 department of agricultural economics, faculty of agriculture, university of nigeria, nsukka, nsukka, enugu state, nigeria 2 department of agricultural economics and agribusiness, university of ghana, legon, ghana *corresponding author. e-mail: chukwuma.ume@unn.edu.ng abstract. there is overwhelming evidence to suggest that climate shocks undermine food security and livelihood well-being of the climate-impacted niger delta region of nigeria. employing survey data collected from farming and fishing households in the niger delta region of nigeria, the study investigated the range of adaptation practices prevalent in the region, as well as factors influencing the adoption of these adaptation strategies. five hundred and three (503) households (252 fishing households and 251 farming households) were selected using multi-stage sampling techniques. multinomial logit model was used to determine factors affecting the household choice of adaptation strategies. the results show that adaptation strategies adopted by farming households were livelihood diversification (78.5%), crop management (77.7%), and soil and water management (64.5%). factors influencing their choice of adaptation strategies were age, gender, household size, education, extension, and farm size. the adaptation strategies employed by the fishing households were livelihood diversification (83.61%) and intensification [which include the use of improved fishing gears (80.33%), varying fishing locations (67.21%), and expanding area of fishing (40.98%)]. uncovering the heterogeneity in adaptation and resilience aspects to climate shocks has immense practical significance, particularly in providing targeted assistance for the two livelihood groups’ adoption. keywords: climate shocks, crop farmers, fish farming, adaptation strategies, developing nations. jel codes: q13, q22, q54. 1. introduction crop farming and fishing constitute the main economic activity of rural people especially in sub-saharan africa (giller, 2020). it is a source of livelihood for about 70-80% of the population and accounts for 30% of the gdp and 40% of the foreign exchange earnings of most nations in sub-saharan africa (bezner kerr et al., 2019). as climate conditions change all over the http://creativecommons.org/licenses/by/4.0/legalcode 18 bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 chinasa sylvia onyenekwe et al. world, there are increasingly multiple and uneven risks to societies (arfini, 2021). in nigeria for instance, farming and fishing constitute the main livelihood strategy for over 70% of the teaming rural population. in the niger delta region of nigeria, despite the abundance of oil in the region, about 60% of the population of these rural people depend on farming for their life sustenance and livelihood (fund world life, 2018). studies have also shown that the shock and impact are more on farmers in the niger delta region (fund world life, 2018; pedi, 2020), as most of the areas in the niger delta region are coastal areas and as such are bedevilled with a number of environmental challenges and flood-related disasters. also, like in most african countries, there is an over-dependence on rain-fed agriculture in the niger delta region of nigeria, as well as limited adaptive capacity among the farmers (ume, 2017). according to akpoti et al. (2021), over-dependence on the natural environment in the face of climate change, without an adequate safety net, exposes these farmers to climate shocks, which negatively affect productivity and sustainable development. the future sustainability of the agricultural sector and food security in the region will depend on the adaptation strategies adopted by farming and fishing households (bandara & cai, 2014; kahsay & hansen, 2016). this study, therefore, seeks to investigate the range of adaptation practices prevalent in the region, as well as factors influencing the adoption of these adaptation strategies. as developing countries have been projected to be more impacted by climate change, adaptation has been increasingly identified as the policy option to help cope with the negative impact of climate change (ford et al., 2011; lamonaca et al., 2021). according to the ipcc (2001), adaptation is the ability of a system to adjust in response to actual or expected climatic stimuli to reduce harm and cope with the resulting condition. the importance of mainstreaming climate change adaptation into farming activities for sustainable development is evident, and considerable research has investigated the determinants of adoption of climate change adaptation strategies among farmers in the global south, although reviews reveal mixed evidence thus far (bezner kerr et al., 2018; fosu-mensah et al., 2012; ume et al., 2021; zazu & manderson, 2020). for instance, ume et al. (2021) concluded from 14 studies in southeast nigeria that the gender of the farmer has a significant effect on adaptation, while enete & amusa (2010) found an indeterminate influence of socioeconomic factors such as age, education, and gender on adaptation. in ghana fosu-mensah, vlek, & maccarthy (2012) found access to extension services, credit, soil fertility, and land tenure to be the major factors that influenced farmers’ perception and adaptation. the authors suggested a need for more empirical investigations to establish coherence in the literature. in contrast to the large literature on determinants of adaptation among crop farmers in the developing nations, research documenting the range of adaptation practices prevalent in the region is sparse: our literature search identified only three studies. wetende et al., (2018) documented the different climate change adaptation strategies employed by smallholder dairy farmers in the siaya sub-county of western kenya. sinharoy et al. (2018) assessed the determinants of crop farmers’ choice of coping methods to climate change and variability in ethiopia and usefully documented the adaptation method employed by highlands farmers. onyeneke et al. (2018) presented the status of climate-smart agriculture in nigeria, and categorized them into mobility and social networks, adjusting agricultural production systems, diversification on and beyond the farm, farm financial management, and knowledge management and regulations. we expand these available adaptation options in literature by documenting additional adaptation strategies and innovative agroecological farming and fishing methods that farmers in the niger delta region of nigeria employ. this paper contributes to the existing literature in three key ways. first, as highlighted above, very few studies have systematically examined the different adaptation options employed by farmers in developing nations, and these studies did not consider the peculiar vulnerabilities of riverine dwellers and fish farmers. according to the ipcc (2014), vulnerability describes a set of conditions derive from the prevailing cultural, historical, social, political, environmental, and economic contexts. for a long time, the niger delta region has been exposed to various degrees of environmental degradation and conflicts, hence can be referred to as a vulnerable region not only because they are exposed to climate hazards but because of everyday patterns of marginality and neglects experienced by farmers in this region. second, the determinants of adaptation have been extensively covered in the literature. however, empirical evidence in the context of the niger delta region is largely scarce. more so, the underlying drivers of adaptation are complex, and have not been fully understood (bezner kerr et al., 2018). recent studies suggest that they differ from place to place according to locationspecific factors (komba & muchapondwa, 2015; meadows, 2008). furthermore, there is variation in the level of influence of different determinants of adaptation, which makes it difficult to generalize findings (ume et al., 19heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 2020). as stated by fosu-mensah et al. (2012) the determinants of adaptation to climate change among smallholder farmers in the developing economies are still contentious issues, thus, making further empirical study necessary to clarify uncertainties and establish a coherent scholarship. finally, we are not aware of any previous study that examined the different adaptation options for fish farmers in west africa, though the fishery sector is widely acknowledged to have the potential of improving the nutritional status of the rural population. previous research on climate change adaptation among farmers has mostly concerned with crop farmers (amare & simane, 2017; onyeneke et al., 2018; ume et al., 2022), with a few recent studies on dairy and livestock innovations (apata, 2011; wetende et al., 2018). we add another empirical point to this expanding literature with evidence on adaptation options for fish farmers. importantly, the findings from this study can help guide development interventions, on the best way to frame an approach that will engender better climate change adaptation among farmers in the coastal regions in the developing nations and beyond. the rest of the paper is structured as follows: section 2 presents the theories underlying determinants of adaptation strategies, section 3 describes the methodology used in the study, section 4 presents the empirical results, followed by section 5 which details our conclusions and policy implications. 2. theory underlying determinants of adaptation strategies: utility maximization and protection motivation for explaining the choice of adaptation strategies adopted by households, the utility maximization theory is used. households are assumed to be rational beings; hence they choose adaptation options that maximize their expected utility among the available options (amare & simane, 2017; gebrehiwot & van der veen, 2013; menozzi et al., 2015). the limitation of this theory is that in the real world this may not always apply as there are other factors that may affect the behaviour of households. if ui and uj represent the household’s utility for any two adaptation options. following greene (2000) the random utility model can be stated thus: uit=vit+εit, ujt=vjt+εjt 2.1 where uit and ujt are the perceived utility from choosing adaptation options i and j at time t respectively; vit and vjt are the deterministic component and εit and εjt are the error terms of the utility function which are independently and identically distributed. utility cannot be directly observed, it is rather indirectly observed from the choices that households make. choice experiments assume that a household m chooses an option i at time period t, only if this adaptation option generates at least as much utility as any other option for example j, represented as: umit>umjt, j≠i 2.2 the probability of a household m choosing adaptation option i among the available adaptation strategies at time t can then be specified as: pmit=p(umit)>umjt), j≠i 2.3 the second theory, which has been found to be valuable in explaining adaptive behaviours of individuals to climate change is the protection motivation theory (cismaru et al., 2011). the theory of protection motivation was originally postulated by rogers (1975) and applied in the field of health to explain how individuals are motivated to act in a protective manner toward a perceived health risk. however, it has since been adapted and applied in other contexts such as environmental risk and natural hazards. for instance, it has been applied to the studies of natural hazards such as earthquakes in the united states (mulilis & lippa, 1990), and flood in germany and the netherlands (grothmann & reusswig, 2006 and bubeck, botzen, kreibich, & aerts, 2013) and even studies on climate change adaptation (grothmann & patt, 2005; keshavarz & karami, 2016; koerth, vafeidis, hinkel, & sterr, 2013; bockarjova & steg, 2014). this theory postulates that individuals will act to protect themselves against a perceived risk if they perceive that the threat of that hazard, they are exposed to is severe (threat appraisal) and if the coping appraisal is high. threat appraisal is composed of two main components: ‘perceived vulnerability’ (probability) and ‘perceived severity’ (consequences). coping appraisal, on the other hand, consists of three components namely: ‘response efficacy’, ‘self-efficacy’ and ‘response cost’. the coping appraisal is considered high if individuals perceive the protective measures available to be effective i.e., able to mitigate the threat (high ‘response efficacy’), easy i.e., the individuals perception of their ability to implement the required actions (high ‘self-efficacy’) and inexpensive (low ‘response costs’) (floyd et al., 2000). the two appraisal processes inf luence an individual’s protection motivation (maddux & rogers, 1983; opata et al., 20 bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 chinasa sylvia onyenekwe et al. 2021). however, poussin et al. (2014) found that coping appraisal has a far-reaching effect on self-protective bahaviours by individuals than threat appraisal. grothmann & reusswig (2006) in their study concluded that it is just not enough to communicate the threat or risk individuals are exposed to (threat appraisal) but the benefits and cost of precautionary measures (coping appraisal) should also be included. in this study, this theory can be adapted to explain the behaviour of households to act in a protective manner towards the perceived threat to their livelihoods occasioned by environmental and social factors (climate shock, environmental degradation, and conflict). there are two processes. in the first process, ‘threat appraisal’ the household assesses the threat probability for example climate shocks and the severity of the damage that will be done say to their food security or income should they choose not to act. the second process is the ‘adaptation appraisal’ that has three components. the first is the ‘perceived adaptation efficacy’, which is the perception of the effectiveness of the adaptive action in protecting one from the threat (e.g., a judgment that changing crop variety can protect one from climate shocks). the second component is the ‘perceived self-efficacy which refers to the household’s perceived ability to implement the adaptive action (e.g., a household might perceive that they lack the technical skills to implement a particular innovation). the third component is the ‘perceived adaptation cost’, which refers to the cost of taking the adaptive action (such as monetary, time, effort). based on the outcome of these two processes the household responds to the threat. two responses are possible: adaptation and maladaptation, while the former reduces the damage from the threat, the latter increases the damage. some examples of maladaptive responses are denial of the threat and wishful thinking (grothmann & patt, 2005). one major limitation of this theory is that it does not take into account all of the cognitive and contextual factors, including the influence of social norms. 3. methodology 3.1 description of the study area and sampling the study area is niger delta region. it is located at latitudes 4°25’n to 6°00’n and longitudes 5°00’e to 7°5’e (pedi, 2020). it is situated on the atlantic coast of southern nigeria where the river niger divides into many branches (uyigue and agho 2007). it is the second biggest delta in the world having a coastline covering around 450 kilometers which ends at the mouth of imo river (awosika 1995). the region is divided into four ecological zones namely coastal inland zone, mangrove swamp zone, freshwater zone, and lowland rain forest zone. the niger delta region officially comprises nine states namely, abia, akwa ibom, bayelsa, cross river, delta, edo, imo, ondo, and river states. it has about 185 local government areas (lgas) and over 40 ethnic groups in an estimated 3000 communities (pedi, 2020). the region has an estimated population of about 36 million (world meter 2020), and the large majority depend on fishing and farming as a means of livelihood. figure 1 below shows the map of nigeria showing the two states in the niger delta region where data for the study was collected. a multi-stage sampling technique was used in selecting the households used in the study. in the first stage, 2 states were purposively selected out of the nine states due to their dependence on farming and fishing, and the coastal nature of the states which predisposes them to frequent flooding and coastal erosion. in the second stage 13 local government areas (lgas) out of 23 lgas were selected from rivers state purposively due to the predominance of agricultural activities and 4 lgas out of 8 lgas were selected from bayelsa state. in the third stage proportional random sampling was used to select 18 and 8 communities from the selected lgas in rivers and bayelsa states respectively. in the fourth stage, proportional random sampling was used in selecting the 251 farming households and 252 fishing households. a total of 503 household heads were interviewed and where the household head was not available the next available adult was interviewed. we are aware that the choice to interview the next available adult where the household head was not available could have impacted the results in some ways, but we cannot comment on the magnitude of any potential selection bias. figure 1. map of nigeria showing the study area. 21heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 the united nations (2005 p. 44-45) sample size formula (see equation 3.1) was used to determine the number of households selected for the study. using a confidence interval (z) of 95%, 50% default value of prevalence of indicators (r), a sample size of 430 households was required. however, to account for possible missing values and outliers, the sample size was increased to 510. in the end, only 503 of the questionnaires were valid and were used for the analysis. 3.1 where: n= sample size, z = confidence interval (95% level is 1.96), r = estimate of key indicators being measured (default value is 0.5), f = sample design effect (has a default value of 2), k = multiplier accounting for non-response (1.1), p= proportion of the total population accounted for by the target population (0.4), n = mean of household size (5), e = precision level (10% precision level equals 0.01r) 3.2 data collection both secondary and primary data were collected for the study. the secondary data on temperature and rainfall were collected from the nigerian meteorological agency (nimet). the primary data on quantitative information was obtained from households of farmers and fishermen. structured questionnaires were employed. to ensure the reliability and validity of the survey instrument, the survey instrument was given to 3 experts for validation. questionnaires were pre-tested on 10 respondents and modifications were done where necessary before actual data collection (e.g., we modified the framing of some questions that appeared ambiguous to better target the goal of the study). the questionnaires were administered between march and april 2018. the questionnaires had sections on household socio-demographic and institutional characteristics, perceptions of climate shocks and impact, and adaptation strategies (for a detailed description of the type of questions asked see supplementary materials). the secondary data comprises annual temperature and rainfall data for the region for the period between 1982 and 2018. rainfall was measured in millimeters (mm) and temperature in degrees celsius (°c). for ethical considerations, we included an informed consent form to the introductory note on the purpose of the survey and the survey team used it to obtain verbal consent of each respondent’s willingness to participate in the survey. 3.3 econometric estimation to identify adaptation strategies employed by the two livelihood groups descriptive statistics such as percentages were employed. first, the respondents were asked if they’ve experienced any changes in the temperature and rainfall pattern in the last 30 years. where the answer is yes, a follow question is asked on the strategies used to adapt to these changes. some of the respondents reported having been using some of the management practices before the changes but had to intensify their use with the recent changes in climate, while some reported that they only started using the management practices in response to the climate change. in this study the adaptation strategies employed by farmers have been grouped into three namely: soil and water management, crop management and livelihood diversification while adaptation strategies employed by fishermen have been grouped into two: intensification and livelihood diversification. to determine factors influencing choice of adaptation strategies by the two livelihood groups the multinomial logit model was used. the multinomial logit and multinomial probit models are usually used to analyse adoption decisions involving multiple choices such as adaptation decisions that are made jointly (wooldridge, 2002 madalla, 1983). given the myriads of possible drivers of climate change adaptation, zucaro et al., (2021) propose the need for applying multi-criteria analysis to select the most effective climate change adaptation measures. however, the choice of the multinomial logit model over the multinomial probit is because it is computationally easier to calculate the choice probabilities which are expressible in analytical form (tse, 1987). it provides a suitable closed form for underlying choice probabilities, ruling out the need for multivariate integration and this makes it easy to compute choice situations with several alternatives. the computation is also made easier as a result of its likelihood function which is globally concave (hausman & mcfadden, 1984). the limitation of the model is the independence of irrelevant alternatives (iia) property. this assumption states that the ratio of the probabilities of choosing any two alternatives is independent of the attributes of any other alternative in the choice set (hausman & mcfadden, 1984; tse, 1987). specifically, this assumption means that the probability of using a particular adaptation strategy by a household should be independent of the probability of choosing another adaptation strategy. hausman test was used to judge the validity of the assumption. the test is based on the fact that if an alternative is irrelevant, removing an alternative or several alternatives from the model should not change the 22 bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 chinasa sylvia onyenekwe et al. coefficients systematically. the result of the hausman tests of iia assumption (appendix 1 and 2) showed that null hypothesis: odds (outcome-j vs outcome-k) are independent of other alternatives (p>chi2 =.), hence does not violate the assumption that the probability of using a particular adaptation strategy by a household should be independent of the probability of choosing another adaptation strategy. to describe the multinomial logit model let ai denote a random variable representing the adaptation strategy adopted by any household (already identified). we assume that each household faces a set of discrete, mutually exclusive options for adaptation strategies. these strategies are assumed to depend on a number of households, institutional, environmental and other attributes x. the multinomial logit model specifies the relationship between the probability of choosing alternative ai and the set of explanatory variables x as seen in equation 3.2 (greene, 2003): 3.2 in this study the adaptation strategies employed by farmers have been grouped into three namely: soil and water management, crop management and livelihood diversification while adaptation strategies employed by fishermen have been grouped into two: intensification and livelihood diversification. the independent variables used in the model are listed in table 3.1. estimating equation 3.2 gives the j log-odds ratio in equation 3.3. 3.3 the coefficient βj of the multinomial logit model only shows the direction of the effect of the explanatory variable on the dependent variables (adaptation option) and does not provide the actual magnitude of the change or probability. therefore, differentiating equation (3.2) above with respect to the independent variables gives the marginal effects of the independent variables and is stated in equation 3.4: 3.4 marginal effects measure the expected change in the likelihood of a particular adaptation strategy being chosen with respect to a unit change in an explanatory variable from the mean (greene, 2000). the signs of the marginal effects and respective parameter estimates may vary, this is because marginal effects depend on the sign and magnitude of all other parameter estimates. some studies (e.g., amare & simane, 2017; atinkut & mebrat, 2016; deressa, hassan, ringler, alemu, & yesuf, 2009; gunathilaka, smart, & fleming, 2018) have adopted the multinomial logit model to assess the determinants of adaptation strategies employed. 3.4 model specification household socio-economic, institutional, farm level, environmental and location characteristics were hypothesized to influence the choice of adaptation strategies employed. the following explanatory variables were considered in the multinomial model: educational level, household size, age of household head, years of experience in farming/fishing, sex of household head, household income, access to extension services, membership of association, access to information on climate change, access to credit, farm size, perception of shift in temperature, perception of shift in rainfall and location. the empirical model is stated in equation 3.5. adsi=b0+bnsn+bmim+bziz 3.5 where adsi denotes the adaptation strategies employed by farming or fishing households, s, b and i represent the sociodemographic, institutional, and climatic factors, respectively. b0 denotes the intercept; bn, bm and bz denote the parameters estimates for each sociodemographic (n), institutional (m) and climatic (z) factor. a description of the explanatory variables used in the model, the measurement and the apriori expectation has been presented in table 1. 4. results and discussion 4.1 household characteristics and climatic patterns descriptive results are presented in table 2 and figures 2-5. based on the results, about 62% of the sampled households were male-headed households, the majority (77.3%) of them were married and only a few (3%) of them had no formal education. this profile on marital status is higher than the national average, where about 58% of the population are married, but lower in terms of education where literacy rate reached 77.62% in 2021 (statistica, 2022). most (94%) of the households had no access to extension services, no access to credit (about 23heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 88%) and do not belong to any farmer/fisher-based association (89%). this finding corresponded to the national average as reported in emeana (2017) who reported a farmer to extension service ration of 1:10. about 79% of the households had access to health care which is lower than the national average where over 90 percent of nigerian households reported being able to access necessary healthcare (statistica, 2022). about 51% of the households were engaged in off-farm work, this is close to the national average as reported in ume, nuppenau and domptail (2022). on average, the sampled household heads were aged 48 years, had 9 years of schooling, a household size of 7, a farming/fishing experience of 25 years, and farm size (for farming households) of 0.3 hectares. there were no significant differences in the age, experience, household size, access to credit, and access to climate information by farming and fishing households which is evident from the two samples mean comparison test. however, there were significant differences in the gender, years of schooling, membership in social networks, access to extension services, household income, and perception regarding changes in temperature and rainfall by farming and fishing households. responses on farmers’ perception about long-term temperature and rainfall changes (figure 2) show that majority (84.46%) of the surveyed households perceived that the temperature has increased over the last 20 years, 12.75% perceived that it has decreased while the remaining 2.79% did not perceive any change. on the other hand, majority (63.49%) of the respondents perceived that precipitation has decreased, 31.75% perceived that there has been an increase in rainfall while the remaining 4.76% have not observed any change. the perception of households regarding climate shocks has serious implications as to whether to adapt or not and the type of adaptation strategies to adopt. households cannot adapt to what they do not perceive or experience. some studies show that farmers who perceive or experience climate related risks are more likely to plan for adaptation (al-amin et al., 2019; habtemariam et al., 2020; mahmood et al., 2021) furthermore, descriptive analysis presented the annual temperature and rainfall data for the region for the period between 1982 and 2018 as shown in figures 3, 4, and 5. this also validated the local perception of the long-term change in temperature and rainfall. this aligns with the findings of mahmood et al., (2021) who found that the farmers’ perception of the local climate was consistent with historical meteorological trends of temperature and rainfall from 1980 to 2017. the rainfall data showed a large negative deviation compared to their long-term means (dotted lines) for most years particularly between 1982-1983 and 19921998 indicating high rainfall variability (figure 3). the rainfall data revealed that the annual rainfall increased by 2.29 mm every decade. this result does not corroborate the local perception of observed decrease in rainfall. however, the findings are consistent with koomson et table 1. description of explanatory variable and hypothesized signs. variable description measure apriori expectation sociodemographic factors educ years of education continuous (years) + hhsize size of household continuous (number) +/age age of household head continuous (years) +/exp farming/fishing experience continuous (years) +/sex sex of household head dummy (1=male, 0=female) +/hhincome household income continuous (naira) + fsize farm size continuous (hectares) +/institutional factors ext access to extension services dummy (1=yes, 0=no) + asso membership of association dummy (1=yes, 0=no) + info information on climate change dummy (1=yes, 0=no) + cred access to credit dummy (1=yes, 0=no) + climate factors temp perception of shift in temperature dummy (1=yes, 0=no) + rain perception of shift in rainfall dummy (1=yes, 0=no) + state location dummy (1=bayelsa, 0=rivers) +/source: author. 24 bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 chinasa sylvia onyenekwe et al. al. (2020), who showed an overall increase in rainfall in the last decade in eff utu municipality, ghana from 1989 to 2018. as expected, the minimum temperature data showed less dramatic variability over time with overall warming being noticeable, particularly in the middle of the temporal span (figure 4). th e period between 1989 and 2012 had lower temperatures than the annual mean minimum temperature of 22.4°c. th e analysis of the descriptive results further showed that the mean annual minimum temperature increased by 0.01°c every decade. th e annual mean maximum temperature (figure 5) shows a more dramatic variability over time than the annual minimum temperature and is increasing at a faster rate of 0.02°c per decade. th e annual mean temperature shows a less dramatic variability over time than the annual maximum temperature and is increasing at a rate of 0.01°c per decade. th is evidently shows that the days are warming over time. from the analysis of the temporal data, it can be inferred that the local perceptable 2. summary statistics of household characteristics. variables description full sample mean farmers mean fishers mean t-test t-value age age of hh head (years) 47.75 (12.60) 47.48 (13.48) 48.02 (11.68) -0.48 gender gender of hh head (1= male; 0 = female) 0.62 (0.49) 0.39 (0.49) 0.86 (0.35) -12.31*** experience farming/fi shing experience of hh head (years) 24.97 (13.81) 25.19 (14.74) 24.75 (12.86) 0.36 household size number of hh members 7.42 (2.55) 7.41 (2.74) 7.43 (2.36) -0.10 education formal education of hh head (years) 9.07 (4.50) 9.61 (4.63) 8.54 (4.30) 2.69*** access to credit hh had access to credit services (1 = yes, 0 = no) 0.12 (0.33) 0.15 (0.36) 0.10 (0.30) 1.65 social network hh had membership in local organization (1= yes, 0 = no) 0.11 (0.31) 0.15 (0.36) 0.07 (0.25) 2.92*** extension hh had access to extension services (1= yes, 0 = no) 0.06 (0.24) 0.08 (0.28) 0.04 (0.19) 2.28** access to climate information hh had access to information on climate (1=yes, 0=no) 0.49 (0.50) 0.49 (0.50) 0.50 (0.50) -0.13 farm size size of land cultivated (hectare) 0.63 (0.54) household income total hh annual income (n) 821805.2 (718922) 610908.5 (529628) 1031865 (815801) -6.86*** perception of shift in temperature hh perceived that temperature has changed over the last 30 years (1 = yes, 0 = no) 0.78 (0.42) 0.88 (0.33) 0.68 (0.47) 5.25*** perception of shift in rainfall perception of change in rainfall has changed over the last 30 years period (1 = yes, 0 = no) 0.64 (0.48) 0.74 (0.44) 0.53 (0.50) 4.99*** location hh located in bayelsa (1= bayelsa, 0 = otherwise) 2.99 (1.00) 3.00 (1.00) 3.00 (1.00) 0.04 hh located in rivers (1= rivers, 0 = otherwise) 2.99 (1.00) 3.00 (1.00) 3.00 (1.00) 0.04 note: ***, ** and * indicate 1%, 5% and 10% level of signifi cance respectively; 1 usd = n380; values in parenthesis are standard deviations. source: field survey, 2018. figure 2. local perception of long-term temperature and rainfall changes. source: field survey (2018). 25heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 tion of climate variability agreed with the historical data on temperature. 4.2 adaptation strategies to climate shocks the adaptation strategies the farming households employed were grouped into three (3) categories for computational ease. they include soil and water management, crop management, livelihood diversification, and the ‘no adaptation’ option, which was used as the base category in the mnl. in this study, the following adaptation strategies (cover crops, deep tillage, hedging, mulching, ridge cultivation, and run-off harvesting) were grouped into the soil and water management component (swm). crop rotation, crop diversification, agroforestry, changing of planting and harvesting dates, use of improved and drought resistant varieties were grouped under crop management component (cm). engagement in off-farm and non-farm activity was grouped under livelihood diversification component (ld). majority (78.5%) of the surveyed farming households used livelihood diversification as an adaptation option (table 3). this is followed by crop management (77.7%) and soil and water management options (64.5%). however, 10% of the farming households mentioned that they do not use any adaptation strategies. similar studies which have found that farmers adopted some of the above-mentioned strategies are (khanal et al., 2018; mahmood et al., 2021; owusu et al., 2021; shikuku et al., 2017). furthermore, the adaptation strategies the fishing households employed were categorized into two: intensification and livelihood diversification for the purpose of computational ease (table 3). use of improved gears, extension of working hours, varying fishing locations and fishing over large expanses were grouped as intensification. engagement in off-fishing and nonfishing activities were grouped as livelihood diversification. the ‘no adaptation’ option was included in the figure 3. interannual variability in rainfall in the study area between 1982-2018. source: author’s creation from cru climate data. y = 2.2863x + 2200.9 r² = 0.0272 0 500 1000 1500 2000 2500 3000 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018a nn ua l m ea n ra in fa ll (m m ) year rainfall y = 0,0177x + 22,43 r² = 0,3586 21,4 21,6 21,8 22 22,2 22,4 22,6 22,8 23 23,2 23,4 23,6 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 te m pe ra tu re (o c) year tmin figure 4. interannual variability in minimum temperature in the study area between 1982 and 2018. source: author’s own creation from cru climate data. y = 0,0202x + 30,577 r² = 0,511 29,6 29,8 30 30,2 30,4 30,6 30,8 31 31,2 31,4 31,6 31,8 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 20 08 20 10 20 12 20 14 20 16 20 18 te m pe ra tu re (o c ) year tmax figure 5. inter-annual variability in maximum temperature in the study area between 1982 and 2018. source: author’s own creation from cru climate data. y = 0.0191x + 26.477 r² = 0.5363 25,8 26 26,2 26,4 26,6 26,8 27 27,2 27,4 27,6 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 20 08 20 10 20 12 20 14 20 16 20 18 te m pe ra tu re (o c ) year mean temp figure 6. inter annual variability in mean temperature in the study area between 1982 and 2018. source: author’s creation from cru climate data. 26 bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 chinasa sylvia onyenekwe et al. computation of factors influencing choice of adaptation strategies. majority (83.61%) of the surveyed fishing households used livelihood diversification as an adaptation option. this is followed by use of improved gears (80.33%) and varying fishing locations (67.21%) while the least used strategy was fishing over large expanse (40.98%). similar studies which reported fishers using the afore mentioned adaptation strategies are (deb & haque, 2017; galappaththi et al., 2019, 2021; kabisa & chibamba, 2017; mabe & asase, 2020; yanda et al., 2018). 4.3 determinants of choice of adaptation strategies 4.3.1 determinants of choice of adaptation strategies by farming households the decision to choose a certain adaptation strategy is based on several socio-demographic, economic, institutional, and biophysical factors, which are estimated using the multinomial logit model. the results of the multinomial logit model are presented in table 4. the marginal effects of all the explanatory variables have been reported. the results indicate that age of household head positively and significantly affected the probability of adopting soil and water management practices as an adaptation strategy at probability level of 0.05. the magnitude of this effect is 0.003. this suggests that the likelihood of adopting soil and water management practices increases by 0.3% for every year of household head age. a plausible explanation for this result is that older farmers are more experienced and more likely to experience changes in climate and therefore, adopt adaptation strategies to cope with the change. for instance, the study by al-amin et al. (2019) showed that older women were more likely to perceive climate change than younger women. previous studies that reported that age positively affected the adoption of adaptation strategies to climate change include adimassu & kessler (2016); opiyo et al., (2016); alemayehu & bewket (2017) and belay & fekadu (2021), while others like kassim, alhassan, & appiah-adjei (2021) and ali & erenstein (2017) contradicted the results by reporting negative and significant relationship of age with early planting adaptation strategies in ghana and crop management (i.e., adjustment in sowing time, drought-tolerant varieties and shift to new crops) in pakistan. the result shows that gender of household head exerts a positive and significant (p<0.1) influence on the adoption of soil and water management practices. this means that male-headed households are 13% more likely to use soil and water management practices as an adaptation strategy than female headed households. this is probably because male-headed households have better access to resources and information as well as higher decision power to make decisions regarding adaptation. previous studies that corroborate these findings include asfaw & admassie (2004), deressa et al., (2014), deressa et al., (2009) mahmood et al., (2021). on the other hand, gender was found to influence the adoption of livelihood diversification as an adaptation strategy negatively and significantly (p<0.1). the marginal effect of the variable is -0.1841. this means that female-headed households had an 18% higher chance of adopting livelihood diversification as an adaptation strategy. this result is in agreement with the findings of amare & simane (2017) and (kassim et al., 2021) who found that female headed households diversified more and are more likely to engage in off-farm activities. however, it contradicts the findings of asfaw et al., (2017) and rahman & akter (2014) who found that males adopted non-farm livelihood diversification more than females because of their involvement in household chores which leaves them with little or no time to engage in off-farm activities. household size was found to influence the adoption of crop management practices positively and sigtable 3. adaptation strategies employed by farming and fishing households in the study area. adaptation options frequency percentage (%) farmers soil and water management (swm) cover crops deep tillage hedging mulching ridge cultivation run-off harvesting 162 106 130 58 33 69 12 64.54 42.23 51.79 23.11 13.15 27.49 4.78 crop management (cm) crop rotation crop diversification agroforestry changing of planting and harvesting date improved and drought resistant varieties livelihood diversification (ld) 195 118 143 12 168 157 197 77.69 47.01 56.97 4.78 66.93 62.55 78.49 fishing households intensifying fishing efforts using improved fishing gear extending working hours varying fishing location fishing over large expanse livelihood diversification 49 36 41 25 51 80.33 59.02 67.21 40.98 83.61 note: multiple responses indicated. source: field survey (2018). 27heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 nificantly (p<0.01). as household size increases by one the probability that the household will adopt crop management practices increases by 8.9%. this is probably because activities involved in crop management are capital intensive and so only large households size who have household members engaged in other income generating activities that generate extra income to invest in this adaptation option. in addition, it is understandable that large households would like to engage their workforce in different income generating activities and hence are more likely to diversify. another reason could be that most agricultural activities in nigeria are labour intensive due to low mechanization; large household size therefore constitutes a source of labour to enable households engage in adaptation practices and other agricultural practices such as tree planting, soil conservation and other crop management practices. this result is in agreement with the findings of shikuku et al., (2017), ali & erenstein (2017), habtemariam et al., (2020) and diallo, donkor, & owusu (2021) who found that large households are more likely to adopt adaptation strategies such as changing planting date, improved varieties and planting of trees. the variable education exerts a positive and significant effect on farming households’ decision to adopt soil and water management and crop management practices as an adaptation strategy to climate shocks albeit at the 10% and 5% levels. the marginal effects result thus indicates that an increase in the year of schooling by 1 year increases the probability that households will adopt soil and water management and crop management practices by 0.6% and 2.3% respectively. this is expected as education provides more understanding as to the impacts of climate change as well as adaptation methods to table 4. multinomial regression results for determinants of adaptation strategies by farming households. explanatory variables soil and water management marginal effects crop management marginal effects livelihood diversification marginal effects age 0.003** (0.061) -0.002 (0.015) 0.000 (0.019) gender 0.127* (1.164) 0.023 (-0.326) -0.184* (-0.889) household size -0.0135 (-0.048) 0.089*** (0.353) -0.061 (0.002) education 0.006* (0.188) 0.023** (0.142) -0.021 (0.036) access to credit -0.067*** (-0.226) 0.113 (1.672) 0.036 (1.532) social network -0.559** (-0.583) -0.028 (0.619) 0.131 (0.985) extension 0.113 (0.148) 0.037 (-0.849) -0.218*** (-1.983) access to climate information 0.036 (0.689) -0.136* (-0.168) 0.112 (0.422) farm size 0.091** (-0.433) -0.042 (0.867) 0.218*** (1.517) perception of shift in temperature -0.018 (-0.507) 0.036 (-0.177) -0.039 (-0.355) perception of shift in rainfall 0.015 (0.074) 0.181** (0.271) -0.211 -0.661 constant -4.695** -3.684 -0.013 diagnostics number of observations lr(33) prob > chi2 log likelihood pseudo r2 251 128.64 0.0000 -240.00978 0.2113 note: base category: no adaptation; ***, ** and * indicate significance at 1%, 5% and 10% respectively. values in parentheses are the standard errors. source: field survey (2018). 28 bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 chinasa sylvia onyenekwe et al. be adopted to be able to cope with these impacts. this result is in agreement with previous studies such as alauddin & sarker (2014), alam et al., (2016), khanal et al., (2018), belay & fekadu, (2021) mahmood et al., (2021) and kassim et al., (2021) which reported the positive influence of education on adaptation. interestingly, access to extension services was found to exert no significant influence on the adoption of soil and water conservation and crop management but was found to exert a significant (p<0.05) and negative effect on the adoption of livelihood diversification as an adaptation strategy. this means that households with no access to extension services are 21.8% more likely to adopt livelihood diversification as an adaptation strategy. this is contrary to apriori expectations as extension agents are expected to be at the forefront of communicating climate information and innovations in agriculture. a plausible explanation for the negative effect could be that households with no access to extension services are equipped with information on other adaptation strategies such as off-farm activities that they could choose. another plausible explanation could be the weakness of the extension delivery system typically in most african countries as pointed out by oladele & sakagami (2004) and antwi-agyei & stringer (2021) which include poor financial decentralization, inadequate use of alternative extension methods, lack of knowledge on climate change by extension agents, high bureaucratic setting and inadequate cooperation and coordination with other agencies. most previous studies such as (alamin et al., 2019; alemayehu & bewket, 2017; ali & erenstein, 2017; habtemariam et al., 2020; kassim et al., 2021) have often reported positive effects of extension service on adoption of adaptation strategies such as use of improved varieties, soil and water conservation practices. however, our finding is consistent with the findings of owusu et al., (2021) and shikuku et al., (2017) who reported a negative effect of extension services on adoption of adaptation strategies. farm size was found to exert a positive and significant influence on farming household decisions to adopt soil and water management practices and livelihood diversification as an adaptation strategy to climate shocks at 5% and 1% levels respectively. a unit increase in farm size increases the chances of adoption of livelihood diversification and soil and water management practice as an adaptation strategy by 22% and 9.1% respectively. this means that households with larger farm sizes were more likely to diversify more probably to generate additional income for adaptation and expand production. they are more likely to have capacity to invest in climate shock adaptation options. it could also be that farming households with large farm sizes are more worried about the impact of climate shocks since they are more likely to lose a larger proportion of their output compared to those with smaller farm sizes and so are not willing to take the risk. hence, their eagerness to adopt livelihood diversification and soil and water management practices to off-set any adverse effects. this result contradicts the findings of deressa et al., (2011); bazezew et al., (2013) and gebreyesus (2016) that reported that farm size negatively affects the probability of using livelihood diversification as an adaptation measure. however, it agrees with the findings of (al-amin et al., 2019; ali & erenstein, 2017; kassim et al., 2021) who reported positive effect of farm size on adaptation strategies such as upland planting, planting of horticultural crops and improved varieties. another interesting finding is access to credit which was found to exert negative and significant effect on the probability to adopt soil and water conservation as an adaptation strategy at 1%. this means that credit constrained households are 6.7% more likely to take up soil and water conservation as an adaptation strategy. this finding is consistent with the findings of teklewold et al., (2019) who found that households with lack of access to credit are more likely to take up soil conservation practices. however, it contradicts studies such as al-amin et al., (2019), diallo et al., (2021), shikuku et al., (2017), belay & fekadu, (2021) which all argued that households with access to credit are more likely to take up adaptation strategies such as soil and water conservation and crop management practices since access to capital is a major deciding factor in the choice to adopt an innovation and hence required to facilitate adoption of adaptation strategies. again, the variable social network also showed some interesting results. it was found to exert a negative and significant influence on the adoption of soil and water management practices as an adaptation strategy against climate shocks at a 5% level. this means that those who do not belong to any farmer-based organizations or groups are 55.9% more likely to adopt soil and water conservation as an adaptation strategy. this is contrary to apriori expectation since studies such as teklewold et al., (2019), and owusu et al., (2021) have shown that social capital networks positively influence adoption of adaptation strategies and innovation. the reason for the negative influence could be that the farmers belonged to several organizations and received conflicting climate change information from several sources. however, our finding is consistent with the findings of belay & fekadu (2021), diallo et al., (2021) and al-amin et al., (2019) who found that social capital negatively influences farm29heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 ers’ adoption of climate change adaptation strategies such as fertilizer, short duration and drought tolerant varieties. perhaps again somewhat surprisingly, access to information was found to exert negative and significant effect on the adoption of crop management as an adaptation strategy albeit at a 10% level. this means that those who do not have access to climate information are 13.6% more likely to employ crop management as an adaptation strategy. there are mixed findings about the effect of climate information on adaptation strategies. access to climate information has been found by some studies such as kassim et al., (2021), khanal et al., (2018), alam et al., (2016) to promote adoption of adaptation strategies. however, owusu et al., (2021) found no significant impact of the use of climate information on the adoption of adaptation strategies in response to climate change. our finding corroborates the findings of teklewold et al., (2019) who found that climate information negatively influences the adoption of soil conservation as an adaptation strategy. finally, the variable perception of a shift in rainfall showed a positive and significant influence on the adoption of crop management as an adaptation strategy. this means that households who perceived that there has been a change in rainfall are 18.1% more likely to adopt crop management as an adaptation strategy. our finding aligns with other studies like khanal et al., (2018), kassim et al., (2021) al-amin et al., (2019) owusu et al., (2021) who all argue that households who perceive and experience climate change are more likely to adopt adaptation strategies to respond to the adverse effect of climate change. 4.3.2 determinants of choice of adaptation strategies by fishing households studies have shown that farmers’ attitudes perceived behavioral control and past behavior are very important in predicting intentions to adopt the private sustainability schemes (menozzi et al., 2015). this means that the decision to choose a certain adaptation strategy is based on several socio-demographic, economic, institutional and biophysical factors. the results of the multinomial logit model are presented in table 5. the results indicate that education of household heads positively and significantly affected the probability of adopting intensification of fishing efforts and livelihood diversification as an adaption strategy at 1% and 5% levels respectively. this means as years of schooling of household head is increased by one year the probability of adopting intensification increases by 1.5% and livelihood diversification by 0.9%. higher education is associated with greater access to information and skills to adopt adaptation strategies and innovation (belay & fekadu, 2021). this result agrees with previous studies such as sereenonchai & arunrat, (2019) and alam et al., (2016) which reported that education positively influences adaptation choices. access to climate information was found to have a significant negative influence on the choice of intensification as an adaptation strategy by fishing households at a 5% level. this means that fishing households who do not have access to climate information are 7% more likely to adopt intensification as an adaptation strategy. this result is contrary to some studies like mabe & asase, (2020) and sereenonchai & arunrat (2019) which asserts that fishing households with access to climate information are more likely to adopt adaptation strategies to avert the adverse effect of climate change. as expected, the results of the study showed that household income positively and significantly inf luences the probability of adopting livelihood diversification as an adaptation strategy at a 5% significance level. this means that as income increases, the probability of households diversifying their sources of livelihood increases. this may be because of the availability of capital to invest in other non-fishing activities to reduce the risk that climate shock poses to their fishing livelihood. this result is supported by the findings of sereenonchai & arunrat, (2019) who showed that an increasing non-fishing income increases the probability of adopting adaptation strategies. findings from meressa & navrud, (2020) also showed that farmers’ adoption of new varieties could be greatly increased by incorporating traits that are in high demand, suggesting the need for increased income in increasing farmers’ adoption of new technology. the variable perception of shift in rainfall exerts a positive and significant effect on farming households’ decision to adopt both intensification and livelihood diversification as an adaptation strategy at 5% level. this means as fishing households who perceive that there have been changes in rainfall are 9.7% and 9.2% more likely to adopt intensification and livelihood diversification respectively as an adaptation strategy. finally, location was found to exert a negative and significant effect on the adoption of intensification as an adaptation strategy at 1%. this means that fishing households who were located in rivers state were 13.8% more likely to adopt intensification as an adaptation strategy. it is important to note that when compared to bayelsa state, rivers state is more developed and has a weather station. it is possible to fishers located there has more access to climate information than their counterparts thereby making them adopt adaptation strategies. 30 bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 chinasa sylvia onyenekwe et al. location has been found by others studies such as mabe & asase (2020), ali & erenstein (2017) to be an important factor influencing adoption of adaptation strategies. 5. conclusion and policy recommendation the study examined the farmers’ and fishers’ perceptions of the changing climate. they perceived a decrease in rainfall and an increase in temperature which is consistent with the historical meteorological trend from 1982-2018. furthermore, the study investigated the various adaptation strategies employed by farmers and fishers to adapt to climate shocks and factors that affect the adoption of these adaptation strategies. the main adaptation strategies employed by farming households were soil and water conservation practices, crop management practices, and livelihood diversification while fishing households adopted intensification and livelihood diversification as adaptation strategies. livelihood diversification was a common adaptation strategy for both livelihood groups. we used the mnl model to examine the factors inf luencing the adoption of the various adaptation strategies by both livelihood groups and the findings confirm that age, education, farm size, and being a male-headed household are among the important factors that increase the likelihood of farmers to adapt to climate shock using soil and water conservation practices whereas access to credit and social network discourages farmers from using this as an adaptation strategy. our results further show that household size, education, and perception of changes in rainfall exert positive effects on the use of crop management as an adaptation strategy while access to climate information exerts a negative influence. we also find that farm size positively influences farmers to diversify their sources of livelihood whereas female headed households and households who do not have access to extension are more likely to adopt livelihood diversification as an adaptation strategy. on the other hand, factors such as education, household income, and perception of rainfall change positively influence the adoption of livelihood diversification as an adaptation strategy by fishing households. furthermore, the results show that education and perception of changes in rainfall exert positive effects on the use of intensification as an adaptation strategy while fishers who do not have access to climate information and in rivers state are more likely to use intensification. the findings of this study have strong implications for agricultural policy formulation. the heterogeneity in adaptation strategies and determinant suggest that “one size fits all” policies will not work to adapt to climate change. institutional factors such as extension visits, access to credit, social networks, and access to climate information for the farmers should be further investigated as such factors negatively influence the choice of climate change adaptation strategies contrary to the findings of some studies. the empirical findings of this study reinforce the need for policymakers to intensify their efforts in improving the extension service in nigeria. as can be seen from the results only 8% and 4% of the farmers and fishers respectively had access to extension. table 5. multinomial regression results for determinants of adaptation strategies by fishing households. explanatory variables intensification coefficient livelihood diversification coefficient age 0.001 (0.030) -0.001 (0.019) gender 0.018 (0.529) 0.006 (0.139) fishing experience -0.002 (-0.039) 0.001 (0.018) household size -0.004 (-0.104) -0.001 (-0.032) education 0.015*** (0.367) 0.009** (0.196) access to credit 0.030 (0.622) 0.023 (0.416) social network -0.017 (-0.518) -0.028 (-0.700) extension 0.034 (1.077) 0.309 2.4239 access to climate information -0.070** (-1.664) -0.042 (-0.886) household income 2.61e-08 (6.83e-07) 3.71e-08** (7.38e-07) perception of shift in temperature -0.037 (-0.859) -0.058 (-0.980) perception of shift in rainfall 0.097** (2.346) 0.092** (1.867) location -0.138*** (-2.738) 0.003 (-0.099) constant -5.5811*** -4.5704** diagnostics number of observations lr(26) prob > chi2 log likelihood pseudo r2 252 144.03 0.0000 -106.04623 0.4044 note: base category: no adaptation; ***, ** and * indicate significance at 1%, 5% and 10% respectively; location base category: rivers; values in parentheses are the standard errors. source: field survey (2018). 31heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria bio-based and applied economics 12(1): 17-35, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13436 this would facilitate the free flow of information on climate and agricultural innovations to farmers and fishers, especially to those who cannot afford information technology devices. again, membership in associations is another important channel for climate information acquisition that facilitates the adoption of adaptation measures. as can be seen from the study only 15% and 7% of farmers and fishers had membership in any social group. local opinion leaders and other stakeholders should encourage the establishment of farmer and fisher-based organizations in the communities. this could facilitate efficient relay of climate information, and education on the use of climate information in the adoption of adaptation measures. also, the limited access of the farmers (15%) and fishers (10%) to credit could be the reason why they are not adopting the crop management and livelihood diversification as an adaptation strategy. these adaptation strategies could be capital intensive, so policy makers and relevant stakeholders could help ease their liquidity constraints by providing them with affordable credit schemes. in addition, the meteorological services in the region should be improved so that they can educate and provide real-time weather information to enhance the households’ understanding of climatic changes to make strategic adaptation decisions. investing in education is critical for overall development and may thus provide a policy instrument for enhancing their perception of climate change and promoting the use of climate shock adaptation strategies and thereby reducing the vulnerability of both farmers and fishers. finally, since household size and farm size were found to positively influence adoption, the policy implication could be the provide access to farm machinery, which will minimize labour requirements and thereby enable farming households to implement adaptation measures. given the increasing threat from climate change and increase in the demand for food resulting from increasing population, improving adaptation by addressing the aforementioned issues is a fundamental intervention in pursuit of reducing the vulnerability of farming and fishing households thereby improving their livelihoods. more so, since women are mainly responsible for food production in the area, as well as supply majority of the labour used in agriculture, further research could be conducted to examine how climate change might have a differential impact based on gender as well as the determinants of adaptation through gender lenses. as gender has been found to play an important role in decision-making in households. finally, this study was based on cross-sectional data and hence might not provide a robust mechanism for establishing causality, as would have been the case with a time series or panel data. in addition, the data used in this study is not representative of the national demography. we, therefore, recommend future studies using nationally representative panel data to better test addressed the research questions posed in this study. references adimassu, z., & kessler, a. 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(2021). application of multi-criteria analysis selecting the most effective climate change adaptation measures and investments in the italian context. bio-based and applied economics, 10(2), 109-122. https://doi.org/10.36253/bae-9545 appendix 1. hausman tests of iia assumption for farmers. chi square df p>chi square 0 -4.039 24 . 1 -1.943 23 . 2 4.638 24 1.000 3 -14.650 24 . appendix 2. hausman tests of iia assumption for fishermen. chi square df p>chi square no adapt -6.131 13 . intensif -0.533 13 . diversif -7.049 13 . farmers’ motivations and behaviour regarding the adoption of more sustainable agricultural practices and activities linda arata1, davide menozzi2 how do farmers’ pluriactivity projects evolve? how do farmers’ pluriactivity project evolve? clarisse ceriani*, amar djouak, marine chaillard heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria chinasa sylvia onyenekwe1, patience ifeyinwa opata1, chukwuma otum ume1,*, daniel bruce sarpong2, irene susana egyir2 organic cocoa farmer’s strategies and sustainability ibrahim prazeres1,*, maria raquel lucas1, ana marta-costa2, pedro damião henriques3 a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture luzia deißler1,*, kai mausch2, alice karanja3, stepha mcmullin3, ulrike grote1 exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms: evidence from rural rwanda ronja seegers*, etti winter, ulrike grote bio-based and applied economics bae bio-based and applied economics 12(1): 5-15, 2023 | e-issn 2280-6e172 | doi: 10.36253/bae-1344 copyright: © 2023 c. ceriani, a. djouak, m. chaillard. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: c. ceriani, a. djouak, m. chaillard (2023). how do farmers’ pluriactivity projects evolve? how do farmers’ pluriactivity project evolve?. biobased and applied economics 12(1): 5-15. doi: 10.36253/bae-1344 received: july 25, 2022 accepted: march 30, 2023 published: june 24, 2023 competing interests: the author(s) declare(s) no conflict of interest. editor: davide menozzi, linda arata. orcid cc: 0009-0003-3733-6536 ad: 0009-0003-3026-6467 mc: 0009-0009-9871-8021 how do farmers’ pluriactivity projects evolve? clarisse ceriani*, amar djouak, marine chaillard grecat, isa junia, lille, france *corresponding author. e-mail: clarisse.ceriani@junia.com abstract. long criticized, pluriactivity is now perceived as an alternative agricultural strategy and it is becoming a subject of support policies. however, having an off-farm job generates organizational issues that can penalize the viability of this strategy. in this paper, we study the initial motivations of pluriactivity and the strategies developed by farmers over time to handle pluriactivity difficulties and we examine conditions that lead to permanent pluriactivity or not. we use an original qualitative approach interviewing 29 pluriactive farmers in “nord-pas de calais”, region located in northern france. our results show that pluriactivity is a dynamic strategy and farmers develop different strategies to adapt their pluriactivity over time to their farm requirements and time constraints. we find that most of the trajectories lead to a permanent pluriactive status, but pluriactivity lasts longer when both activities adapt to each other. keywords: pluriactivity duration, agriculture, pluriactivity projects, farm management strategies. jel codes: q10, q12, l29. 1. introduction the agricultural sector has experienced several crises in recent years that challenge the conventional production model and encourage farmers to develop income diversification strategies. there is a wide variety of onfarm income diversification that are effective in improving the profitability of the farm such as agricultural output diversification and non-agricultural income diversification (salvioni and al., 2013). the diversification path can also take the direction of an off-farm job. following this strategy, farmers decide to allocate part of their labour forces to off-farm professional activities. farmer’s pluriactivity is an old agricultural strategy but little appreciated by the agricultural world and by the research community which, for a long time, thought that working outside the farm was a marginal strategy. nevertheless, farmers’ pluriactivity presents a set of advantages at the territorial and individual levels. in some respects, this strategy responds to the new requirements of multifunctionality of agriculture, including land use and social networking. often synonymous with part-time salaried employment, pluriactivity can support local development by favouring the reception of new urban populations with specific needs (sport, cultural activities …) and thus meet the new objectives of the agricultural policies that aim to boost rural areas by creating jobs (blanchemanche et al., 2000). some terhttp://creativecommons.org/licenses/by/4.0/legalcode 6 bio-based and applied economics 12(1): 5-15, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-1344 clarisse ceriani, amar djouak, marine chaillard ritories have integrated the economic and social cohesion benefits of pluriactivity and set up new policies to support this strategy (tallon and tonneau, 2012). for farmers, pluriactivity has many different motivations (mage, 1976) but at first, it can compensate for low farm incomes, for their variability, and it can even play a structural role by facilitating investments on the farm (glauden et al., 2006, butault et al. 1999). it can therefore provide an interesting economic answer to new farmers who are more sensitive to “comfort of life” and for whom farm income volatility is an impediment to installation (simon, 2013). on the other hand, having an off-farm job and combining two activities can be hard to handle over time and generates organizational constraints, in particular increasing worktime (keating, 1987) that raises questions about the viability of the project. however, most of the time pluriactivity becomes a permanent path (corsi and salviani , 2017; barlett, 1986) even when it was intended to be transitory and in support of a gradual farm installation (ceriani and djouak, 2018) the agricultural projects of pluriactive farmers are multidimensional and dynamic. they combine both professional projects and family/personal life and must evolve according to the economic and territorial context but also depending on opportunities and organizational constraints (dedieu et al., 1999). initial pluriactivity projects can be short-term, linked to farms’ financial difficulties, or longer-term, due to a desire to conduct several activities. however, initial conditions (socio-economic, organizational, motivation, etc.) can evolve and make pluriactivity permanent or not. the following questions therefore arise: does the duration of pluriactivity depend on the initial project? how do farmers adapt and organize their pluriactivity over time? in this work, we are interested in pluriactive farmers’ trajectories. more specifically, we study the initial motivations of pluriactivity and the management strategies developed by farmers over time to reconstitute the paths that lead to permanent pluriactivity. we use an original qualitative survey with 29 semi-structured interviews of pluriactive farmers in nord-pas de calais (npdc) in france that explores farmers’ life trajectories and expectations about pluriactivity. after presenting our methodology to collect and analyze the farmers’ narratives, we present our results concerning initial pluriactive projects, farm management strategy and duration of pluriactivity. these elements are then cross-compared to reconstitute trajectories and understand better how farmers adapt their organization and expectations to handle their pluriactivity. finally, we discuss our results and conclude. 2. methods 2.1. theoretical typology of the initial project pluriactivity can be considered at different scales; at household level (the household is said to be pluriactive if at least one individual has an off-farm job), or at the farmer level (farmer has an off-farm professional activity). we study farmers’ pluriactivity because we want to focus on pluriactivity as a (new) professional strategy and a farmer is considered pluriactive if he or she has a job outside the farm1. in this paper, we are interested in the organizational strategies developed by pluriactive farmers. many studies have worked on the duration of the agricultural pluriactivity and found that most of the time pluriactivity is a permanent path (barlett, 1986), but they do not compare that long-run situation with the initial expectations of the individual. using a panel of italian family farms, corsi and salviani (2017) have found strong evidence that the off-farm duration is due to farmers’ unobservable characteristics (i.e. risk aversion, preferences…) and to state dependence (e.g. changing status may imply sunk cost because pluriactivity requires time to find a job, to set up the organisation of the farm, allocate the production factors…). recently ceriani and djouak (2018) have studied more than 60 pluriactive farmers’ interviews and found that most of them wanted to be only a farmer when they set up, but the farm was not profitable enough. therefore, for some farmers, pluriactivity was intended to be transitory and in support of a gradual installation but socio-economic constraints or job opportunities have impacted their motivations and expectations. some previous studies also noticed that the “intent” of the operator is an important factor that should be used to discriminate the duration of pluriactivity (boudy, 2009; mage, 1976). indeed, initial pluriactivity motivations are important in pluriactive systems (tallon and tonneau, 2012) and impact the way farmers value their production (income, social ties, environmental criteria…). to analyze the dynamic process of pluriactivity and identify the different strategies farmers can develop and use to adapt their pluriactivity in the long run, we first need to differentiate the initial motivations and expectations of part-time farming. 1 this definition does not include activities of diversification which, being an extension of agricultural activity, does not open up to another status. moreover, diversification is another agricultural strategy that requires different farmers’ skills and generates other organizational constraints that represent a barrier to the adoption for many farms (bartolini and al., 2014). for the same reason, the household’s pluriactivity (companion exercising a profession outside the farm) is not included and analyzed in this article. 7how do farmers’ pluriactivity projects evolve? bio-based and applied economics 12(1): 5-15, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-1344 four initial pluriactivity projects depending on farmers’ motivations and professional situation at the time they set up in agriculture can be defined. like barlett (1986) and mage (1976) before, we consider short run projects when farmers use the off-farm job to invest in the farm to expand it or to save it when it has financial issues. on the other hand, some pluriactive projects are intended to last longer either for strong patrimonial motivations or because farmers are passionate about farming, but do not want to become full-time farmers. table 1 displays more details about this typology of initial pluriactivity projects. 2.2. farm management strategies regardless of the initial motivations and projects of pluriactivity, combining two activities generates various constraints and in particular increases the working time. it implies time constraints and organizational issues. there could be an additional workload even when the other job is a source of well-being and personal fulfillment. according to wilkening (1981), the same number of hours spent in an off-farm job will be more stressful for farmers since it will represent “wasted hours” for their real job as a farmer. the same observation is made by keating (1987) who highlights a feeling of competition between off-farm employment and agricultural activity. these difficulties are variable, directly related to the farm characteristics and the type of off-farm jobs but they can be a source of stress and dissatisfaction (mc coy and filson, 1996, keating, 1987). mc coy and filson (1996) highlight the fact that pluriactivity also impacts the quality of time spent by pluriactive farmers with their families but also limits their own free time. to reduce these constraints and effectively manage their pluriactivity, farmers have to develop strategies according to the farm’s requirement and their motivations. indeed, some farmers will develop strategies to maintain the pluriactivity and to make it more comfortable and others will try to leave this situation. we assume that farmers’ strategies can be analyzed regarding two factors: (i) farm investments and prospects; (ii) socio-economic and organizational constraints. we pay attention to farm projects (the will to develop new production or to find new lands…) and to farmers’ intentions about the pluriactivity (the wish to stay pluriactive or to change in the future). for the organization of pluriactivity, we analyze time constraints related to the offfarm job such as flexibility because agricultural activities must deal with exceptional constraints such as bad weather and livestock surveillance that might affect the organization (dedieu et al., 1999). we also consider the available labour resources (employees, volunteers…) because the labour force is a decisive resource in the management of the farm and it impacts its organization (fiorelli et al., 2007; laurent et al., 1994). 2.3. pluriactive farmers’ trajectories in the last part, we examine the various elements to set up trajectories that lead to a permanent path or not. more specifically, to better understand the conditions that lead to a permanent pluriactivity, we combine the initial project, the strategy on the farm and the “chronological” dimension of pluriactivity (its previous duration on the farm and the future projections of its future existence on the farm). 2.4. data collection and analyses the study was conducted in the nord-pas de calais (npdc), a part of a french region (called hauts de france since 2014) located in the north, bordering beltable 1. initial pluriactivity projects. in iti al p lu ria ct iv ity  p ro je ct set up: farmers already have a job when they set up. they keep the off-farm job to support investments in the farm (new lands, new productions for example) and increase farm income. pluriactivity motivation is essentially economic, and pluriactivity is intended to be transitory because farmers want to be 100% on the farm in the long term (what mage (1976) calls the “aspiring type”). survival: this situation is a necessity; the farm is the main activity and farmers must take another job due to farm or personal occasional financial issues. those farmers did not want to be pluriactive, but it is the only way to continue being a farmer and save the farm (“transitional part-time farmers” for barlett (1986)). passion: the main motivation is passion for agriculture and farm activities. farmers set up in agriculture to live their dream and keep the family farm. farmers already have a full-time job that is important, for income but also for open-mindedness. farmers would have been 100% on the farm when they set up, but farm incomes were not sufficient. one day they might leave the off-farm job to be full-time farmers (“hobby farmers” for mage (1976)) patrimonial: farmers already have a full-time job outside the farm, they want to keep it because it is important to them economically but also socially, so they have no intention to leave it. the main motivation for pluriactivity is the maintenance of the family heritage. pluriactivity is supposed to last. (“investors” for barlett (1986)). 8 bio-based and applied economics 12(1): 5-15, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-1344 clarisse ceriani, amar djouak, marine chaillard gium. agriculture is an important sector that occupies two-thirds of the territory: in 2010, the utilized agricultural area (uaa) represented more than 66% of the total area of the region. agriculture remains highly diversified: field crops, livestock (avesnois and boulonnais dairy), and horticulture (in suburban spaces) (agreste, 2015). pluriactivity is an old phenomenon that tends to increase, but so far, there is still a lack of empirical data and studies on pluriactive farmers in the npdc. to better understand farmers’ paths in a dynamic perspective from their initial project to their current strategy, the richness of a qualitative approach using open questions is required. such research requires at first a deep understanding of farmers’ initial motivations, the reasons for which farmers got an off-farm job, the set-up conditions, the family farm history, and their professional career. to achieve this, we decided to conduct a pilot study selecting 29 pluriactive farmers with a wide variety of personal and professional situations. indeed, an increasingly marked redundancy of collected narratives was observed when we reached this amount, which can be interpreted as the effect of a form of a data saturation relating to the various situations encountered. a summary description of these narratives is detailed in table 1 in the appendix. interviews started with some questions concerning farmers (age when setting up in agriculture, education level, family situation, etc…) and farms (uaa, legal status, production …). then, we asked farmers to tell us about their installation in agriculture and their personal/professional trajectories. next, we asked the farmers to detail their pluriactivity, initial and current motivations, advantages and disadvantages of this double life, and their expectations for the future. at the end of the interview, some questions related to the financial situation of the farm and the workforce were included. a thematic approach was used to analyze the collected qualitative data. this approach enables us to go beyond simply counting words or phrases in the text and to explore explicit and implicit meanings in the data. indeed, with the thematic analysis, we used “themes” (and “sub-themes” to refer to the breakdown of certain themes) to summarize and process the collected material. in short, it is a question of breaking down, recomposing, and associating the main ideas contained in our material, to respond little by little to our main questions: what is fundamental in the farmers interviews to help us see things more clearly? in addition, an empiricalinductive approach was adopted (which is used when there is not much information available related to the problem studied), this is justified by the highly exploratory nature of our investigation, as well as by our need to identify the parameters of aspects relating to the farm management strategies which are truly specific to pluriactivity in agriculture. finally, this general process allowed us to categorize farmers according to their initial pluriactivity project and to identify different farm management strategies developed by farmers. thus, dynamic trajectories could be constructed, which made it possible to make the link between the initial project, the strategy on the farm and the “chronological” dimension of pluriactivity. 3. results 3.1. initial motivations and projects of pluriactivity pluriactivity motivations and expectations depend on family context and job opportunities. the reasons why farmers decided to become pluriactive at first allow individuals to be classified according to four types of initial projects and motivations (table 1, “initial project” line). like barlett (1986), we found that a major motivation is economic, but in different ways. among the 29 farmers, 8 wanted to use pluriactivity as a transitory development project to develop the farm and make it more profitable (set up type). we observed that 7 farmers took an off-farm job because their farm incomes were not enough and so for them being pluriactive was a necessity, a forced choice to compensate for temporary financial difficulties (survival type). passion is very important as well: 8 farmers had a passion for agriculture; they wanted to become a farmer, at least a part-time one and they all grew up in an agricultural environment. according to them, the financial situation of their farm was not bad, but the farm was not big enough to leave the off-farm job and become only a farmer. moreover, the other job was important for them, economically and socially, that is why they decided to combine two activities. pluriactivity was a positive choice when they set up: “yes, it was a desire to be pluriactive, in fact, i did not see myself a full-time farmer… i had a real love and interest in farming, but at the same time i had the desire to have another job activity, physically to be on the move, to be able to travel a little bit… so, farming seemed a little too sedentary to me actually “(passion type). almost all the farmers we interviewed took over the family farm, which implies patrimonial motivations even if the weight of those patrimonial motivations differs among farmers. indeed, we found that 6 farmers had a patrimonial project at first, and even if most of them wanted to become a farmer and work on the family farm, they never intended to be 100% on the farm. they consider agriculture as a secondary or complemen9how do farmers’ pluriactivity projects evolve? bio-based and applied economics 12(1): 5-15, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-1344 tary activity while the off-farm job plays an important role, financially but also for personal identity:” (speaking of agriculture) it is secondary because my offfarm job is really important in my professional life” (individual a7). for these farmers, pluriactivity has imposed itself as the only way to preserve the family farm, perpetuate a family tradition, a commitment undertaken a long time ago by parents, grandparents, etc…but also the only option to enable them to pursue their passion for agriculture. “it is only a family project […] it is the result of the work of generations before us, but it is true that if there had not been children behind, we did not necessarily make it… we would not necessarily have taken the step.” (individual a7) (patrimonial type). eventually, when we asked the farmers if they wanted to be pluriactive when they set up on a farm, a majority (21 farmers) clearly said that they would have been 100% on the farm when they set up in agriculture, if the farm revenues had been sufficient. thus, 72% of the pluriactive farmers we interviewed did not want to be (or stay) pluriactive at first. even if some of those farmers did not try to develop the farm to make it more profitable, this result is important because it means that for many pluriactive farmers, pluriactivity was neither the ideal nor the first choice. 3.2. dynamic farm management strategies the analysis of the organization of pluriactivity included work on the farm, advantages and disadvantages of pluriactivity felt by the farmer, as well as farm investments and projects. we identified 4 different strategies that farmers use to face organizational issues and reach their expectations (see figure 1 “strategy” line). development strategy: some farmers are in a proactive strategy, using pluriactivity to develop farm revenues so as to be able to live on farm incomes only and leave the other job soon. farming is the most important activity. for the moment, farm incomes are not sufficient, and part of the other job income is used to invest in the farm. some of them had a set up motivation and are young farmers (installed for a few years). most of them have livestock farms. sometimes, developing the farm means expanding or creating a new activity on the farm: “being pluriactive has reinforced the development of my angus direct sales workshop for sucklers… as long as i have not reached a sufficient number of cows, i will remain pluriactive”(individual a25). another way to increase farm income can be the transformation of the family farm and its organization; as individual a24 says that this situation permits him to take some risks without pressure: “anyway, we are much more confident in what we do. […] i knew i wanted to do organic vegetables, but i had no idea how to do it, i even complicated things by working with the old varieties of wheat, by working in a local distribution network, etc. …concerning the other profession, it allows me to take more risks in my agricultural activity if necessary”. farmers who belong to this type of strategy are quite satisfied with their pluriactivity because it allows them to set up in agriculture in better conditions, with less risk because the financial security of the other job gives them the opportunity to develop the farm. however, they can be frustrated by not being fully dedicated to the farm, in particular in case of livestock farming: “when you are at the town hall and have a lot of work on the farm, it is annoying because you are not at the right place “ (individual a6). farm disengagement strategy: the farms’ financial situation was quite bad, so farmers took another job to save the farm. the farm had not been organized at first for a pluriactivity because the farm was the only activity of the farmer. due to financial issues, farmers quickly took full-time off-farm jobs that most of the time were not f lexible. a majority are livestock farming which requires intense demand for labour and does not fit easily with pluriactive time constraints, in such a way that farmers have to reduce farm activities. in this category, two subtypes of farmers can be differentiated: – the happy one who wants to keep being pluriactive. those farmers seem to be satisfied with their pluriactivity because they decided/accepted to reduce and simplify farm activities as much as possible. those farmers have less stress, more time such as individual a19 who eventually found his job balance: “the strong points are the simplicity of my work and a lot of free time. i spend very little time on my holding. i don’t want to develop things anymore, i’m too close to retirement”. the other job enables them to increase revenue and reduce risk, and they also find that it enables social contacts and open-mindedness. – unsatisfied farmers who think about leaving agriculture. these farmers do not invest anymore in the farm, but the farm organization remains incompatible with pluriactivity. they highlight tight schedules and working weeks that sometimes exceed 60 hours which leaves little time for leisure and family. there is frustration because the off-farm job appears in competition with their farm activity (keating, 1987). for some farmers, time spent outside the farm may even be perceived as a lost time for the “real” job of farming (wilkening, 1981). agricultural politics clearly impact these farmers and increase their dissatisfaction “we are not compensated for the work 10 bio-based and applied economics 12(1): 5-15, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-1344 clarisse ceriani, amar djouak, marine chaillard we provide… still low milk price and the rise of financial charges!” (individual a12). responsive strategy: some farmers adapt the farm to their off-farm job as they want to be pluriactive. they keep on investing in the farm and keep on developing farm projects, but they want to have another activity outside the farm. for most of them, the other job is qualified, and they like it. they are convinced that their agricultural activity improves their off-farm work efficiency. indeed, it provides entrepreneurial and business skills; it enhances professional networks and gives them a better legitimacy in their work: “(about the farming activity) as part of my job, it brings me a lot of things, both professionally, also socially, somewhere, because i am in contact with other farmers, social networks that are different. i have contacts with my fellow farmers as part of my cuma2, with the new owners. there are many circles of exchange that are, in my opinion, positive, that i would not have if i were only an employee of the chamber of agriculture” (individual a27). some of them have changed farm organisation or production to reduce time constraints, such as individual a4 who oriented the agricultural activity towards automated production which requires less labour and when necessary, gets occasional supports and help from family or friends. others have an agricultural enterprise that requires significant workload and presence on the farm but the off-farm job is flexible so they can free up time when needed such as individual a5, a cattle farmer. this strategy involves reciprocal adaptation of both activities. however, pluriactivity can be constraining and even frustrating: “what is difficult for me is to accept to be locked up when the weather is nice, or to accept when an animal is not fine or maybe i’ll find it dead at night […] it is difficult to handle the fact that if i would be there, i would manage to cure it or i would be at home i would be able to cut wheat because it is ready” (individual a3). these farmers have a positive image of farm work: farmers have their own business, which gives them independence and a freedom to make decisions. farmers have multiple functions and diverse skills: “i am a farmer, a business leader who takes into account different dimensions: technical dimension, economic dimension and then environmental dimension” (individual a20). they think that pluriactivity gives them the possibility to be in « both worlds », it opens their mind. most of those farmers seem to be confident in the future and in their capacities, and most of them are in a proactive entrepreneurial logic: they maintain the family 2 coopérative d’utilisation de matériel agricole, cooperative for the use of agricultural equipment heritage, remain open to possible evolution of their farm and their career without being limited to technical conceptions, or cultural and legal aspects of the profession (lagarde, 2006). “on the heritage side, i am very proud of myself. i have two activities and maintained this farm that may be passed down to my children. i am also very proud to maintain an agricultural business…” (individual a27). still, even when pluriactivity seems to be pleasant, many farmers note work overloads and time constraints: “the disadvantages (of pluriactivity) are double organization, double stress. we combine two different professions and therefore two different stresses. we also have different deadlines.” (individual a26). managerial strategy: other farmers have developed a managerial strategy and have regular employees who manage a large part of the farm work, almost independently. these pluriactive farmers do not consider the farm as their most important activity and most of them became pluriactive for patrimonial motivations. this type of organization of the farm makes the farmer appear as “a manager” who delegates a part of the work to one (or more) trusted person, family members or employees. an essential element of this “managerial” organization of pluriactivity seems to be having someone present on the farm daily. this can be an employee: “today it is the employee who does all the work … for the anecdote?? i address him with the courtesy “vous” because he is my employee, but he knows me from my childhood… i do not need to see him every day, there is trust and he agrees to be autonomous” (individual a1). it can also be a family member who keeps an eye on the farm. the ability to adjust the off-farm work schedule to free up time thanks to its flexibility and the choice of an agricultural enterprise (no livestock farming or vegetables) with fewer time constraints reduce the stress and constraints: “i can easily arrange things with my employee. i make myself available in winter for stand-by duties that i compensate for in the summer at the time of the harvest. so, there are no worries… my tractors return in october to the buildings and come out in february to spread fertilizers. i have six months to disconnect the batteries” (individual a8). this “managerial” governance combined with an optimal organization of the time spent on the farm allows them to consider the future of their pluriactivity with greater serenity. 3.3. reconstitution of individual trajectories having analyzed various elements of the pluriactivity: initial project and strategy; we can now set up trajectories and analyze the link between these different ele11how do farmers’ pluriactivity projects evolve? bio-based and applied economics 12(1): 5-15, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-1344 ments to identify the conditions that lead to permanent pluriactivity (figure 1). we will not detail all the possible trajectories but the most important and frequent ones that apply to 19 farmers. development strategy: some of them had a set up motivation and are young farmers (installed for a few years). others had more survival motivations and succeeded to switch into a development strategy. those farmers do not want to stay pluriactive and think about leaving the off-farm activity as soon as the farm revenue is sufficient “as long as i have not reached a sufficient number of cows, i will remain pluriactive”(individual a25). farm disengagement strategy: due to financial issues or setting up in agriculture, farmers took an off-farm job. diverse constraints forced them to adopt a disengagement strategy towardsthe farm. some are unsatisfied with this situation and might leave agriculture one day because the farm’s financial situation is bad, and they feel they do not belong to the “agriculture world” anymore. other are satisfied and they see their situation as permanently or transitory disengagement, depending mostly on their age. responsive strategy: farmers motivated by a passion to farm, pursuing a responsive strategy. some of the “set up” type found good compatibility between both activities and developed this strategy, too. a majority of these farmers consider their pluriactivity as a long-term strategy. indeed, some of these farmers do not really want to table 2. summary of farm management strategies. st ra te gi es development strategy: farmers work on the farm regularly and farm activities tend to be more important for farmers. farmers work to develop farm activities; they continue to invest in the farm and farm revenues tend to increase. the off-farm job is secondary, and the farm has not been arranged or adapted to the off-farm job. pluriactivity is not well organized and tends to be tough for farmers. farm disengagement strategy: farmers work on the farm regularly without help. the off-farm job is not flexible, but they cannot employ someone because the farm has financial problems. farmers cannot develop the farm; they do not invest anymore in the farm and farm activities tend to become less important for farmers. they keep the off-farm job because it is the only way to maintain the farm and it provides them a constant revenue. responsive strategy: farmers work on the farm regularly thanks to organized pluriactivity which avoids time constraints and organizational issues. either the off-farm job is compatible with the farm work obligations or a salaried labour force is mobilized when needed. farmers still develop and invest in the farm. moreover, pluriactivity is meaningful, and has social and economic advantages for the farmers. managerial strategy: pluriactivity is well organized and most of the farm work is done by employees. pluriactive farmers do not feel pluriactivity is restrictive since they do not have to be on the farm every day. farm revenues are sufficient to at least pay bills. figure 1. dynamic typology of the pluriactivity strategies. 12 bio-based and applied economics 12(1): 5-15, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-1344 clarisse ceriani, amar djouak, marine chaillard stop pluriactivity in the short term, but they still consider pluriactivity as a transition. they seem to be willing to leave their off-farm job one day to become “ just a farmer” because they think the work overload will be too much. “i like it. satisfied, yes. after that, i’m not saying it’s easy every day. some days when you have to run, you run. that’s why i put it into perspective. today, i’m young, it’s okay. maybe ten years from now, i will not be willing to run like this anymore. there is, i think, an evolution over time, with age, which will change priorities. pluriactivity works great for a while, but not forever. i don’t see myself pluriactive until i am 65.” (individual a22). managerial strategy: due to cultural motivations, these farmers all want to stay pluriactive and they are proud of keeping the family farm, despite their other job: “i managed to set up a system that allows me to get my own farm, to manage the farm without limitations, so i am very happy with what i did” (individual a9). they want to manage the farm until its transfer to their children. 4. discussion stable long-term projects: these long-term projects include passion and heritage projects. starting a pluriactive approach with these types of motivations induces specific strategies: responsive in the first case, and managerial in the second. indeed, the necessary conditions to take over the farm were thought out within a framework of managing the farm while being pluriactive, but a certain financial stability was required. moreover, pluriactivity has been conceived and organized from the beginning so that time constraints and work overload are reduced, which facilitates the articulation of the different activities. in the first case, the farm is the place of fulfilment and experimentation, and farmers spend a large part of their time on the farm, whereas in the second case (with more cultural/heritage motivations) farmers manage the farm in a more distant way. time has not changed the initial motivations and projects and farmers are satisfied with their pluriactivity, which brings them a strong complementarity between their two activities. pluriactivity is therefore considered for the long term, for several decades, or until retirement. more flexible transitional projects: initial “set up” and “survival” projects imply strong motivation for agricultural work and a transitional pluriactivity attitude. indeed, we observe that farmers with “setting up” initial motivations and farmers with “survival” initial motivations tend to be following a farm development strategy and a farm disengagement strategy, respectively (in particular, among farmers who do not manage to recover the financial balance of the farm or to set up properly). however, strategies can evolve and so trajectories can be more complex. for instance, we found that some farmers who have reduced their farm engagement (disengagement strategy) have more experience (the majority have been farming for more than 16 years) and tried to develop the farm first (development strategy). however, we also observe that some pluriactive farmers who were in the process of setting up their own businesses have found a certain complementarity and balance between the two activities that allow them to develop a more responsive strategy. in particular, farmers who develop their farm are more likely to continue pluriactivity as long as it brings them advantages, they are in a transitional but dynamic pluriactivity with the aim of leaving the off-farm job someday. on the other hand, when investment in the farm has not been possible, pluriactive farmers are forced to reduce their agricultural activities to handle both activities together. this situation can be experienced as unsatisfactory even if pluriactivity is considered as an opportunity to keep the farm. initial transitional projects enable transitional pluriactivity that allows farmers to develop and (re)invest in the farm to improve the future farming conditions. when the financial and organizational situation do not allow for saving the farm, pluriactivity can be experienced as a failure and farmers can cease agriculture. this type of initial project can also lead to unexpected long-term trajectories, with differentiated investment in the farm, but both activities create a form of complementarity for the pluriactive person. 5. conclusions our research is based on a qualitative approach that allows a deep understanding of farmers’ motivations and trajectories. our results confirm that pluriactivity organisations and expectations tend to change over time depending on the family context, job opportunities and financial situation of the farm; and the way farmers adapt their pluriactivity is usually related to their initial project. however, some unusual trajectories show that farmers’ strategies evolve according to the context and this can also modify motivations and expectations. indeed, finances, organizational constraints and work overload are critical factors that can modify the initial pluriactivity project. it appears that work overload and incompatible schedules might change initial expectations such as individual a14 who was leaving an offfarming job at the time of the interview, even though 13how do farmers’ pluriactivity projects evolve? bio-based and applied economics 12(1): 5-15, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-1344 this job provided additional benefits to their agricultural activity and personal life. our results indicate that pluriactivity is easier for farmers and lasts longer when both activities adapt to each other, for example when the off-farm job requires lots of time, farm production must be less demanding in terms of the workload. also, the possibility of hiring regular or permanent labour makes it easier for farmers. indeed, the presence of a complementary source of labour appears highly significant in terms of the durability of pluriactivity because it allows farmers to be less present on the farm, and it limits not only the workload but also the “competition” between jobs that can generate stress (keating, 1987). the “partial” presence of the farmer on the farm compensated by non-family labour raises the question of the identity of the pluriactive farmer, their managerial skills, and the farmer’s position as executive director (legagneux and olivier-salvagnac, 2017). most of the farmers we interviewed consider themselves farmers-entrepreneurs because their vision of the job is different from that of their parents and grandparents. this new perspective of being a farmer can be related to an increase of the use of salaried workers on farms since the 2000s (legagneux and olivier-salvagnac, 2017) and the restructuring of work and labour organization within the farm (harff and lamarche, 1998). the possibility to hire employees depends on the financial profitability of the farm, which also appears as an important criterion for the initial project’s success. some farmer interviewees expressed the wish to get an employee on the farm, but they cannot afford it. indeed, pluriactive farmers who employ someone on the farm are the only ones who consider that the financial situation of their holding is good. others are often in a precarious financial situation, leading them to increase their working hours in the hope of increasing farm profitability. unfortunately, they rarely see their efforts rewarded and they follow a negative spiral: a bad financial situation requiring them to work more that causes a lot of stress, fatigue, and psychological tension and with results, in general, far from their expectations that can even be a real obstacle for future transfer of the farm to a successor or new entrant. to conclude, this pilot study is the first step in a long-run study about the organization, adaptation, and sustainability of farm pluriactivity. indeed, we believe that farm pluriactivity is becoming more and more common among farmers due to market price fluctuations and agricultural crises that can discourage young farmers to take over the family farm. therefore, pluriactivity can be an interesting strategy that contributes to reduce the income variability and allows the combination of different activities and environments. this strategy however raises specific questions and issues. a deeper reflection on the support of pluriactive farmers requires an integration the characteristics related to their dual profession: time and work management, lack of labour force, organisational difficulties, etc… this consideration is important because it would improve pluriactive farmers’ systems and make this strategy more sustainable and attractive for young farmers who want to set up. 6. references agreste (2015). polyculture-livestock: one out of five farms in the nord-pas-de-calais region. in french. service of statistics, evaluation and agricultural foresight. french ministry of agriculture. barlett p. f. (1986). part-time farming: saving the farm or saving the life-style? rural sociology, 51(3): 289313. bartolini f., andreoli m. and brunori g. (2014). explaining determinants of the on-farm diversification: empirical evidence from tuscany region. bio-based and applied economics 3(2): 137-157 blanchemanche s., laurent c., mouriaux mf., and peskine e. (2000). multifunctionality of agriculture and activity status. économie rurale 260: 41-51. boudy j.f. (2009) live from two jobs. pluriactivity, in french. paris, l’harmattan, 301 p. butault, j.p., delame n., krebs s., and lerouvillois p. (1999). pluriactivity: a corrector for inequalities in farm income. in french. economie et statistiques 329330: 165-180. ceriani, c. and djouak a. (2018). the motivations of the pluriactive farmers of the nord pas de calais region. in french. mondes en développement 182: 115-130. corsi a. and salvioni a. (2017). once part-timer always part-timer? causes for persistence in off farm work state of farmers. bio-based and applied economics 6(2): 159-182 dedieu b., laurent c., and mundler p. (1999). labor organization du travail in complex agricultural systems: interest and limits of the bilan travail method. in french. économie rurale 253: 28-35. fiorelli, c., dedieu b. & pailleux j.y., (2007). explaining diversity of livestock-farming management strategies of multiple-job holders: importance of level of production objectives and role of farming in the household. animal 1: 1209-1218 glauden, t., tietje h., and weiss c. (2006). agricultural on the move: exploring regional differences in farm exit rates in germany. review of regional research, 26: 203-118. 14 bio-based and applied economics 12(1): 5-15, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-1344 clarisse ceriani, amar djouak, marine chaillard harff, y., and lamarche h. (1998). work in agriculture: new demands, new challenges. in french. economie rurale 244: 3-11. keating n. c. (1987). reducing stress of farm men and women. family relations 36: 358-363. lagarde v. (2006). manager profile as an explanatory variable for diversification choices in agriculture. in french. la revue des sciences de gestion 220-221: 31-41. laurent c., chevallier c., jullian p., langlet a., maigrot j.-l., and ponchelet d. (1994). households, agricultural activity, and land use: from local to global through general agricultural censuses. in french. cahiers agriculture 3: 93-107. legagneux b., and olivier-salvagnac v. (2017). what contract labor on farms? at the root of the break-up of the family model? in french. economie rurale 257: 101-116. mage j. a. 1976, « a comparative analyses of part-time farming and full-time farming in ontario-some selected aspects. » in fuller et mage (eds.). part-time farming: problem or resource in rural development. norwich geoabstracts 170-196. mc coy m., and filson g. (1996). working of the farm: impacts of quality life. social indicators research 37: 149-163. salvioni c., ascione e. and henke r. (2013). structural and economic dynamics in diversification italian farms. bio-based and applied economics 2(3): 257275. simon b. (2013). the installation in agriculture. the construction of entrepreneurial intent. in french. economie rurale 334: 23-38. tallon, h., and tonneau j. p. (2012). the pluriactive project between autonomy and precariousness. the challenges of accompaniment. in french. economie rurale 331: 42-55. wilkening e. a. (1981). farm families and family farming. the family in rural society, boulder, colorado: westview press, 27-37. appendix table 1. description of interviewed pluriactive farmers. farmer description a1 female farmer in plfc3 and farm management advisor, field crop farm of 68 ha, 38 years old, installed for 12 years, married with 3 young children a2 male farmer in plfc and sales executive, field crop farm of 62 ha, 37 years old, installed for 5 years, married with 2 young children a3 male individual farmer and mechanical workshop manager, crop-livestock farm of 41 ha, 40 years old, installed for 5 years, married without children a4 male individual farmer and employee in a battery factory, field crop farm of 42 ha, 52 years old, installed for 18 years, married with 2 children over 20. a5 male individual farmer and trader in cattle cooperative, cattle breeding on 35 ha, 36 years old, installed for 8 years, married with 2 young children a6 male individual farmer and gardens-parks manager, croplivestock farm of 20 ha, 45 years old, installed for 15 years, single, 3 children from 5 to 18 years old. a7 female individual farmer and an agricultural advisor, field crop farm of 80 ha, 40 years old, installed for 1 year, married with 2 children of 12 and 18 years old. a8 male individual farmer and hospital employee, field crop farm of 24 ha, 48 years old, installed for 17 years, married with 2 children of 13 and 16 years old. a9 male individual farmer and agricultural union director, field crop farm of 57 ha, 41 years old, installed for 14 years, married with 2 children of 13 and 16 years old. a10 female individual farmer and specialized educator, horse breeding on 10 ha, 34 years old, installed for 6 years, married with 1 children of 5 years old. a11 male individual farmer and manager of a transport company, field crop farm of 50 ha, 52 years old, installed for 22 years, married with 2 children over 20 a12 male individual farmer and works in the construction industry, crop-livestock farm of 52 ha, 60 years old, installed for 21 years, married with 2 children over 20 a13 male individual farmer and machine operator, field crop farm of 31 ha, 35 years old, installed for 8 years, married with 2 children of 5 and 8 years old. a14 male individual farmer and employee in a battery factory, field crop farm of 42 ha, 52 years old, installed for 18 years, married with 2 children over 20. a15 male individual farmer and electromecanician, field crop farm of 98 ha, 35 years old, installed for 5 years, single with 2 young children a16 male individual farmer and gardens-parks manager, field crop farm of 25 ha, 40 years old, installed for 16 years, single, no child. 3 private limited farming company 15how do farmers’ pluriactivity projects evolve? bio-based and applied economics 12(1): 5-15, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-1344 farmer description a17 male individual farmer and teacher, field crop farm of 67 ha, 54 years old, installed for 20 years, married with 3 children between 16 and 26 years old. a18 female individual farmer and worker in industry, cattle farming of 18 cows, farm of 10 ha, 38 years old, installed for 16 years, married with 3 children of 9 and 13 years old. a19 male individual farmer and worker in a medical institute, field crop farm of 36 ha, 60 years old, installed for 35 years, single with 3 children between 12 and 31 years old. a20 male individual farmer and cuma manager, field crop farm of 75 ha, 34 years old, installed for 6 years, married with 2 children of 2 and 4 years old. a21 male individual farmer and computer scientist, field crop farm of 65 ha, 44 years old, installed for18 years, married with 2 children over 20 a22 male individual farmer and teacher, field crop farm of 140 ha, 36 years old, installed for 8 years, married with 2 children between 3 and 6 a23 male individual farmer and farmer employees, field crop farm of 57 ha, 42 years old, installed for 22 years, married with 2 children of 14 and 10 years old. a24 male individual farmer and office designer, field crop farm of 15 ha, 36 years old, installed for 4 years, married without children a25 male individual farmer and teacher, crop-livestock farm of 140 ha, 40years old, installed for 18 years, married without 5 children between 13 and 17 a26 male individual farmer and teacher, crop-livestock farm of 20 ha, 33 years old, installed for 13 years, single without 3 a27 male individual farmer and manager in chamber of agricultural, field crop farm of 40 ha, 45 years old, installed for 12 years, married with 2 children of 14 and 17 years old. a28 male individual farmer and executive manager, field crop farm of 15 ha, 48 years old, installed for 18 years, married with 3 children of 18 and 22 years old. a29 male individual farmer and industrial contract manager, field crop farm of 52 ha, 35 years old, installed for 2 years, married with 1 child aged 1 year farmers’ motivations and behaviour regarding the adoption of more sustainable agricultural practices and activities linda arata1, davide menozzi2 how do farmers’ pluriactivity projects evolve? how do farmers’ pluriactivity project evolve? clarisse ceriani*, amar djouak, marine chaillard heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria chinasa sylvia onyenekwe1, patience ifeyinwa opata1, chukwuma otum ume1,*, daniel bruce sarpong2, irene susana egyir2 organic cocoa farmer’s strategies and sustainability ibrahim prazeres1,*, maria raquel lucas1, ana marta-costa2, pedro damião henriques3 a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture luzia deißler1,*, kai mausch2, alice karanja3, stepha mcmullin3, ulrike grote1 exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms: evidence from rural rwanda ronja seegers*, etti winter, ulrike grote bio-based and applied economics 9(1): 25-52, 2020 issn 2280-6180 (print) © firenze university press issn 2280-6172 (online) www.fupress.com/bae full research article doi: 10.13128/bae-9936 drivers and barriers of process innovation in the eu manufacturing food processing industry: exploring the role of energy policies federica demaria*, annalisa zezza national council for agricultural research and economics – research centre for agricultural policies and bioeconomy, via po, 14 00198 roma, italy abstract. this paper investigates the driving forces that can promote or impede process innovation adoption in the european food manufacturing industry. the study uses a logit model applied to community innovation survey (cis) data containing information on innovation at the industry level for 15 eu member states. results suggest the relevance of many factors, internal and external to the enterprise, such as size and organization of business practices on one hand, and networking activities and cooperation agreements within the supply chain on the other hand. we also focus on energy policy variables as process innovation determinants. energy policies implementation, energy price and the availability of public funds, show a significant impact on process innovation adoption in the european food processing industry. keywords. environmental regulation, energy policy, innovation, cooperation activities, food manufacturing industry, eu. jel codes. l66, o3, q4, q16. 1. introduction in october 2014 the european council agreed on a new 2030 framework for climate and energy, including eu-wide targets and policy objectives. this strategy aims to help the eu achieve a more competitive, secure and sustainable energy system and to meet its long-term 2050 greenhouse gas reductions target. the figures for renewables and energy efficiency have subsequently been increased in the targets for 2030 context including, among others, a 40% cut in greenhouse gas emissions compared to 1990 levels and an indicative target for an improvement of at least 32.5% energy efficiency at eu level. the food processing industry makes a significant and increasing contribution to the overall energy consumption and ghgs emitted in the food chain (oecd, 2017). eurostat considers the total energy consumed by the food processing industry (including bever*corresponding author. e-mail: federica.demaria@crea.gov.it editor: valentina raimondi. 26 federica demaria, annalisa zezza ages and tobacco) to be 29178.5 mtoe in 2015. this amount represents 28% of the energy embedded in the food chain (monfortiferrario et al. 2015), 2.7% of the average final energy consumption and the 10% of the average energy consumption in the manufacturing industry, with denmark and croatia having the larger shares. although the european food processing and beverage sector has gradually improved its energy efficiency – measured as the ratio value added/energy consumption gaining competitiveness and reducing ghgs emissions in the last decades, still it has a strong potential for decreasing energy consumption both as a result of process optimization and plant system improvements (oecd, 2017; ladha-sabur et al, 2019). technical potential energy savings have been estimated to be 22% (compared to 2004) by 2030 (altmann et al, 2010; icf, 2015). energy use reduction and energy recovery from waste are two important methods to reduce production costs in the food processing industry (oecd, 2017; altmann et al. 2010; monfortiferrario et al. 2015). kaminski and leduc (2010) have identified the most important systems and processes where significant energy-efficiency improvements can be achieved in the eu’s food industry: steam, motor and pump, compressed air systems, process cooling and refrigeration, and buildings heating and lighting. the eu lisbon strategy considers innovation as one of the most important factors to enhance productivity, competitiveness, and sustainability in the economy. literature, starting from schumpeter’s studies (1934, 1942) has tried to understand what the internal and external factors influencing process innovation are (cohen, 1996; galende and de la fuente, 2003). many factors determine the firm’s capacity to innovate, ranging from technical capabilities, financial structure, market needs, network relationships, regulations, and subsidies. enterprises, however, can also be deterred from engaging in innovation or fail to bring in new processes or products because existing barriers. recognizing the nature of these barriers is important both from a policy and a management point of view (d’este et al. 2012). literature on innovation has focused mainly been on high tech industries and only a few studies have considered low-tech traditional sectors such as the food processing industry. some specificity characterizes innovation in the food processing industry. firstly, food firms are mainly process innovation oriented (minarelli et al., 2014). secondly, the production of new technologies is usually developed by upstream industries and r&d expenditure is low compared to other sectors (garcia-martinez and briz, 2000; triguero et al., 2013). thirdly, most innovations are incremental rather than radical (garcia-martinez and briz, 2000). fourthly, product typologies and process phases are extremely differentiated and difficult to uniform (capitanio et al., 2010). finally, smes are characteristically prevalent in the agri-food sector. literature shows that smes behave differently with respect to large enterprises relative to innovation adoption, with larger firms being more innovative than smaller ones (galizzi and venturini, 1996). this distinction has important policy implications for policy design. recent research (minarelli et al., 2014) indicates that, in the eu, smes are a very heterogeneous group regarding their innovation profile, particularly in the food sector. the relation between size and innovation is not straightforward, as other factors influence firms’ behaviour as workforce in-house capabilities and the engagement in r&d activities (avermaete et al. 2004). the small average size is considered one of the main barriers to innovation in southern european countries (garcia27drivers and barriers of process innovation martinez and briz, 2000; capitanio et al. 2010). nevertheless, the food processing industry is becoming more technologically intensive to maintain better process control, exploit economies of scale and guarantee food safety and quality, facing market competition through new products and processes development. capitanio et al. (2009) analysing the driving factors of innovation in the italian food processing industry conclude that the determinants can be found both in internal and external factors. among the first, there is the human capital qualification, a clearer orientation to quality products, organizational changes, and relation capacity development. on the external side, factors such as the increasing competition and demand have a relevant role in shaping the innovation process. different determinants emerge when analysing process and products innovation separately. in the first case, the of human capital and market relationships qualities are the most important drivers, while in the of process innovation case, the firm’s financial structure and capital intensity are the most relevant. the firm’s location, meaning socioeconomic context relevance, has a positive and significant impact on process or product innovation adoption. with regard to spain, research has underlined the machinery and equipment suppliers contribution to innovation diffusion (garcia martinez and briz, 2000), together with significant path dependence (triguero et al., 2013). environmental and market factors role seems higher in the food processing industry compared to other manufacturing industries. cleaner technologies1 can improve the production process efficiency by reducing materials and energy consumption, improving firm’s productivity and competitiveness (del rio, 2005; frondel et al., 2007; arimura et al., 2007). regarding the adoption of cleaner processes in the spanish food industry, such as material recycling and water management processes, cuerva et al. (2014) find that firm organizational capabilities i.e. the implementation of an environmental management system are an important driver, while no significant relationship with public support is found. the same result is confirmed in other studies on france and germany (belin et. al. 2011) and for the eu-27 (triguero et al. 2013). small food firms contribute substantially to the food processing industry economic performance and are considered to play a key role in achieving sustainable economic growth in local economies. in the eu, the food and drink sector turnover, in 2017 was 1,192 billion with 294,000 firms. the food processing industry is dealing with several challenges related to the sustainability productive processes, being a large surface water and energy user. however, as previously mentioned, it has a strong potential of decreasing energy consumption both a result of process optimization and plant system improvements (oecd, 2017; ladha-sabur et al, 2019). on the same vein, we focus on the food processing industry investigating for factors that can statistically be associated with the firm’s innovation process. we study the adoption of process innovation in the food processing industry in the eu, considering the role of energy prices and policies2 role. according to the cis 9 survey, 30% of firms in the 1 process innovations are usually grouped in clean technologies and end-of-pipe technologies (kemp and volpi 2008; rave et al, 2011). 2 the main limitation of this study concerns the fact that it does not exploit the endogeneity issues; the panel is too short and the use a lagged variable may overfit the endogenous lagged dependent variable28 federica demaria, annalisa zezza f&b industry that have innovated have reduced their energy consumption while 20% have contributed to reduce energy use or the co2 “footprint” during the consumption or use of a good or service by the end-users. financial measures count for half of the policies addressing energy efficiency in industry. the odysseemure project shows that in many countries the policies in place include a broad mix such as co-operative measures (e.g. agreements among enterprises on energy efficiency), cross-cutting measures with sector-specific characteristics (e.g. industry eco-tax with reduced rates ); fiscal/tariff measures (e.g. tax deduction for energy saving investments in businesses); information/education/training (e.g. advice programs for industry, energy management systems); legislative/informative (e.g. mandatory execution of energy audits in large enterprises); legislative/normative (for e.g. co2 emission fee for large emitters; new market-based instruments. about 10% of overall measures have been introduced under the energy efficiency directive (2012/27/eu), especially measures introduced under article 7 (energy efficiency obligations and/or alternative measures), mandatory audits (article 8) and new certification/qualification schemes. nevertheless, most energy measures are not eu-related but national measures, particularly those rated with a high impact (odyssee-mure, 2015). the main questions addressed in this paper are: i) are networking and cooperation activities between research institutions and food companies relevant in generating and promoting process innovation? ii) is the high energy price a driver for boosting process innovation? iii) does environmental regulation together with policy stringency stimulate the process innovation introduction? iv) if yes, what is the most effective driver? the paper is structured as follows. section 2 presents the hypothesis that will be tested in the model. section 3 introduces the dataset mainly based on the eurostat community innovation survey and the empirical model. section 4 reports the econometric estimates. section 5 shows the main conclusions. 2. conceptual background and hypotheses the empirical literature on the determinants of innovations focuses on four groups of factors impacting production and innovation adoption: technological capabilities, organizational capabilities, market pull and external influences related to the regulatory push/pull and potential existence of networks (cuerva et al., 2014, horbach, 2008, wagner, 2009). technological capabilities refer to knowledge resources, human skills and access to internal or external funds and are common drivers for all kinds of innovation. organizational capabilities have a strong impact on green innovations; for example, the quality management systems adoption is often linked to the implementation of environmental management systems (mazzanti et al. 2008). market pull factors relate to consumers’ preferences or customers’ requirements for new products as well as the search for new niche markets. among external innovation sources, literature considers the regulatory push/pull effect as very relevant (rennings, 2000). regulations have been found to be significantly more important for environmental innovation compared to other innovation (horbach et al. 2012). at the same time, government policies play a role in inducing the creation of new cleaner technologies and also in the adoption of already existing technologies by firms 29drivers and barriers of process innovation (veugelers, 2012). the eu growth strategy europe-2020 seeks to booster innovation and collaboration across actors in the supply and innovation chains and private companies, and to strengthen cooperation among research institutions and firms, in addition to promoting more effective and efficient public financial support for innovation activities. furthermore, the ec green deal communication notes the role of new technologies in providing additional benefits in the transition to a sustainable economy. a recent oecd (2017) study on energy efficiency in the agri-food sector identifies four broad groups of barriers: structural, behavioral, availability and policy. structural barriers encompass issues such as limited know-how on implementing energy-efficiency measures, or fragmented and under-developed supply chains. such barriers prevent an end-user from adopting an energy-efficient technology or practice: for example, low educational attainment and ageing farmers impede the adoption of new potentially energyefficient technologies. behavioral barriers include situations in which limited awareness or end-user inertia inhibit an opportunity pursuit. inertia represents the resistance to change and risk, and the more radical the change, the higher the barrier will be (sorrel et al. 2000). it can lead to preferring interventions with quick and low investments and returns, thus slightly modifying the production system with short pay-back criteria may be explained by risk aversion (jaffe and stavins, 1994). an unfavorable perception or treatment of risks can inflate energy-efficiency projects costs or lead to the underestimation of risks associated with changes in energy prices. uncertainty about energy prices can also limit energy efficiency measures because of higher perceived risks. risks management associated with energy costs and availability in agri-food businesses are largely determined by business size, with larger businesses being more likely to be proactive in managing risks from volatility in energy and commodity prices (oecd, 2017). availability barriers include situations in which the decision-maker is interested in and willing to innovate, but barriers, for example, a lack of capital access might prevent an upgrade to a new heating system or the availability and diffusion of technology and innovations (oecd, 2017). policy barriers are policy-induced market distortions which result in market conditions hindering energy efficiency. for example, energy subsidies can crowd-out public spending and private investment, encourage excessive energy consumption, reduce incentives for investment in renewable energy, and accelerate the depletion of natural resources (mckinsey and company, 2010) i.e. encouraging more fossil fuels or energy usage intensive production . cagno et al. (2013) find that the major perceived barrier for italian manufacturing smes in the food processing industry, regarding the adoption of energy efficiency technologies, is represented by high investment costs. same or also insufficient profitability and low capital availability. the identification of barriers to innovation is crucial for effective policy design. according to the eurobarometer survey of smes in the eu (eurobarometer, 2016), most common barriers are represented by uncertain market demand and returns. other causes are the lack of funds or qualified personnel and in general, low technological capabilities. this barriers typology is expected to be more pervasive for smes, particularly in sectors with non–energy-intensive production processes (fleiter et al., 2012, trianni et al., 2013) such as the food processing industry. 30 federica demaria, annalisa zezza hence, we explore the following research hypotheses: h1a) networking and cooperation activities between research institutions and food companies are positively associated with process innovation. research shows that networking and cooperation effects are unclear, indeed some smes benefit from positive effects from cooperation to achieve innovations (de jong and vermeulen 2006; van gils and zwart, 2004; batterink et al., 2010; omta, 2002); while others experience problems (hoffmann and schlosser, 2001; caputo et al. 2002; kaufmann and todtling, 2002). the importance of cooperation has risen steadily alongside the complexity, risk and cost of innovation activities. innovation cooperation influences innovation activities through the pattern of collaborative relationships and partner type involved (vinding 2002). this relationship is mutually reinforcing external linkages facilitate innovation, and at the same time innovative outputs attract further collaborative ties (powell and grodal 2005). companies that continuously cooperate with different external subjects such as suppliers, customers, competitors, and research organization improve both knowledge sharing and market knowledge acquisition by the firms, resulting in expansion of the firm’s existing knowledge base, which in turn advances a firm’s innovation capability (zhou & li, 2012). such collaboration has been identified in literature as one of the most important external predictors of innovations (alexiev et al., 2016; clauss and kesting, 2017; heirati et al., 2016). in addition, a company may establish collaboration with other business partners, such as technology providers and researchers (bigliardi and galati, 2013). in fact, through networking, a company can extend its range of skills by an effective contractual arrangement (martino and polinori, 2011). vertical cooperation offers more possibilities for innovation smes because cooperation is often used to acquire external know-how, in particular where firms have neither r&d employees nor the special technical requirements necessary to engage in r&d activities (gellynck et al., 2007; gellynck and khüne, 2010; laperche and liu, 2013). collaborative innovation networks are defined when members participate in new product development and innovation processes (alexiev et al., 2016; möller & halinen, 2017). the role of firm network relationships and internationalization has been investigated by cainelli et al. (2011) for italian firms, finding to have a strong effect on the environmental innovations adoption by internationalized firms while being less clear for locally oriented firms. yet, scholars show that when firms cooperate with universities or research institutes, the overall effects on innovation capacity is positive (hájek and stejskal, 2018). research also demonstrates that participating in cooperation networks makes companies more prone to undertaking sustainable oriented innovation (melano-levado, 2020; klewitz and hansen, 2013). resorting to cooperation agreements (e.g. cainelli et al. 2012; demarchi 2012; del río et al. 2013) and external knowledge sourcing (e.g. del río et al. 2013; ghisetti et al. 2015) are thus particularly important and “complement” investments made in organizational and technological capabilities (e.g. horbach 2008; demirel and kesidou 2011; horbach et al. 2012). h1b) information is positively associated with a process innovation. this hypothesis follows the previous one. even in this case, several studies reveal that access to information facilitates the use of scientific knowledge, enhancing innovation and 31drivers and barriers of process innovation increasing the food processing industry competitiveness (ciliberti et al. 2016). however, firm size matters in this regard, where small companies rely on universities or research institutes while larger enterprises might have the capabilities needed to put new ideas into practice (king et al, 2003; ciliberti et al. 2016). lasagni (2012) suggested that innovation performance in smes can be higher when they strongly collaborated with users, customers and suppliers. his results also showed that smes can be more successful in product development when they closely work with research institutes. this suggests that there can be specific types of partners preferred by smes. gomez et al (2016) examine a panel of manufacturing firms in spain to verify the extent to which the use of internal and external sources of information generate product and process innovation. their results show that, although internal sources are influential, external sources of information are key to achieve innovation performance. furthermore, the importance of external sources of information varies depending on the type of innovation (product or process) considered. to generate process innovation, firms mainly rely on suppliers while, to generate product innovation, the main contribution is from customers. h2a) the higher energy price stimulates process innovation. a relatively higher energy price in a country, as a result of country’s energetic structure and energy taxation, will induce a technological change heading to higher energy efficiency. ghisetti and rennings (2014) found that innovation leading toward reductions in the use of energy or materials per unit output positively affect the firm’s competitiveness, while externality-reducing innovations hamper the firm’s competitiveness. cainelli & mazzanti (2013) find that policies targeting the manufacturing sector are likely to induce innovation adoption in the services sector, especially when considering innovation practices aimed at abating co2 emissions and improving energy efficiency. yet, the study of popp (2002), on the standard inducement mechanism, confirms that both energy prices and the quality of knowledge exert a significant and positive effect on patenting. h2b) environmental policy measures stimulate firms’ process innovation adoption. the relevant contribution of porter and van der linde (1995b) has paid attention to “porter hypothesis”, according to which a good environmental innovation can lead to an increase in firms’ performance, for instance through a reduction in energy or materials use. however, since firms are not always aware of the opportunities from eco-innovation, a strict and effective environmental regulation is required in increasing this awareness. therefore, environmental policy seems to be an important eco-innovation driver and deserves specific attention. according to porter and van der linde (1995a), environmental standards can foster innovation but under three well established conditions. firstly, they must create maximum opportunities for eco-innovation, letting the industry choose its own approach to innovation. secondly, regulation should foster continuous improvement in any technology. thirdly, the regulatory process should not leave uncertainty at every stage of implementation. the type of regulation or policy and the way in which it is implemented is important. it could lead firms to effectively address environmental problems. the stringency of the policy and the terms in which it is defined are equally important, since uncertainty depends on these factors. several empirical studies (zew, 2001; rehfeld at al. 2006; reid 32 federica demaria, annalisa zezza et al. 2008; belin at al. 2009) find a positive correlation between innovation and regulations. porter and van der linde, (1995a/b), kemp et al. (2001) and jänicke et al. (2002) show that strict environmental regulations stimulate innovation in several ways, such as advantages created by the development of “green” technologies. however, firms are not able to recognize the environmental innovation cost saving potentials as in the case of energy or materials savings (horbach and rennings, 2007). this leads many of them to believe that an environmentally virtuous behavior is a burden rather than an asset (kemp and andersen, 2004). therefore, regulations and policies can be a catalyst and help them to understand the potential benefits of environmental innovations. popp (2009) argues that in general, market-based policies are thought to provide greater incentives for innovation, as they provide rewards for continuous improvement in environmental quality. conversely, command-and-control policies penalize polluters who do not meet the standard, but do not reward those who do better than mandated as the command-and-control regulations direct a specific level of performance. 3. dataset, variable and estimation methods 3.1 data the dataset used in the analysis is based on the biennial cis surveys carried out from 2010 to 2014 (cis 8 and cis 9). the cis questionnaire addresses several elements of firms such as size, i.e. firm’s size, turnover, employees, cooperation activities, source of information, public financial supports, innovation expenditures, and innovation activities. the cis survey provides information on sectors innovativeness, different types of innovations and various aspects of innovation development. the survey allows to distinguish firms that can easily be categorized into innovating and non-innovating. table 1 reports data on process innovation implemented by enterprises across the eu-28 between 2012 and 2014. the highest proportion of enterprises that have developed process innovation is observed for belgium (46,8%), netherlands (33,4%), portugal (37,2%) and lithuania (42,3%) in 2012/2014, while rates are lower for bulgaria, hungary, romania and slovakia , ranging from 5% to 9%. our panel (cis 8 and cis 9) includes all the in the firms belonging to the food manufacturing industry (nace code c10-12). for data availability reasons, we have restricted the analysis to the following eu countries: bulgaria, cyprus, croatia, czech republic, germany, estonia, hungary, italy, lithuania, portugal, spain, romania, slovenia, slovakia and norway. after removing missing value, the sample contains 4618 observations which are used in the analysis. data on energy policy come from the mure project (mesures d’utilisation rationnelle de l’energie)3 which provides information on energy efficiency policies and their impact assessment in eu countries. the score classifies the eu member states based on scoring energy efficiency policies and trends. it aims to provide comparison indicators and comparable characteristics helping countries to understand whether their policies are comparable or better than in 3 https://www.odyssee-mure.eu 33drivers and barriers of process innovation other countries or whether they can learn from other countries to improve their policies. it ranges between 0 and 5, with 0 meaning “worst” and 5 “best”. countries with a lower score are cyprus, hungary and croatia; conversely, spain, norway and slovenia reported the highest score. the energy price yearly data come from eurostat database available at the following link: “https://ec.europa.eu/eurostat/web/energy/data/database”. prices are provided without taxes, with vat and with all taxes included. a detailed explanation of the variables definition and measurement is reported in appendix (table a.1). 3.2 model and estimation the choice to adopt a process innovation is represented by a binary logit model where the dependent variable (process innovation adoption hereafter proc_inno_adop) is a binary variable (yes=1/no=0) based on the response – at the firm level on the introduction of innovations in the previous three years. let y the dependent variable observed and the latent variable satisfying the single index model (1) even if is not observed, we do observe table 1. enterprises in the food processing industry that have introduced process innovation. 2010/2012 2012/2014 2010/2012 2012/2014 belgium 35.5 46.8 lithuania* 18.4 42.3 bulgaria* 9.6 9.2 luxembourg 43.8 22.6 czechia* 27.2 25.3 hungary* 6.1 7.4 denmark 26.8 19.0 malta : 29.8 germany* 22.3 17.4 netherlands 27.0 33.4 estonia* 37.3 21.9 austria 20.0 26.0 ireland : 51.7 poland 8.1 8.5 greece 29.0 30.3 portugal* 35.8 37.2 spain* 20.0 18.5 romania* 5.1 5.1 france 25.0 26.3 slovenia* 23.1 28.7 croatia* 15.2 21.8 slovakia* 9.1 17.3 italy* 32.6 31.7 finland 38.4 30.1 cyprus* 26.9 : united kingdom 17.0 23.6 latvia 21.2 14.3 the * symbol is indicating the countries used in the analysis. source: eurostat, community innovation survey 34 federica demaria, annalisa zezza (2) (0 for non-innovative firms and 1 for the otherwise). from 1 and 2 we have: (3) where f(x’β) is the cumulative distribution function of -εj. the logit model specifies that cumulative standard logistic is: (4) and the marginal effect is: (5) thus, we estimate the following equation: proc_inn_adopit = α + β1 ln(turnoverit) + β2fundsit + β3lmarketit + β4rmacit + β5gnewmktit + β6orgbupit + β7coit + β8int_info_sourcesit + β9other_info_sourcesit + β10policy_mit + β11epit + fei + fet + εit (6) where i denotes countries, t = 2010, 2012 and fei and fet represent country and time fixed effects respectively. the dependent variable (proc_inn_adop) is a dummy that has been built using three indicators of the cis: a) inpsd which considers the introduction onto the market of a new or significantly improved production method ; b) inpslg which considers a new or significantly improved logistic, delivery or distribution system; c) inpssu that considers the introduction onto the market of a new or significantly improved supporting activities. it takes a value of 1 if a new or significantly improved method of process or distribution has been introduced, 0 otherwise. we have included the following control variables chosen on the basis of their relevance for firm characteristics and strategies: lnturnover, measured as the natural logarithm of the turnover. literature has found that size affects the propensity to innovate, emphasizing the difficulties for small and medium enterprises. indeed, the small average size is considered one of the main barriers to innovation in southern european countries (garcia-martinez and briz, 2000; capitanio et al. 2010). yet, scholars find that farm size has a positive, albeit small, 35drivers and barriers of process innovation effect on innovation, which is in line with the general innovation adoption literature (lapple et al., 2015; feder et al., 1985; sauer and zilberman, 2012). moreover, as highlighted in the diederen et al. (2003) study, agricultural farm size explains differences in adoption. similarly, the work of hashi and stojcic (2012) show that larger firms are more likely to embark, to invest on innovation activities but with decreasing innovation output depending on the firm’s size; funds reflecting the availability of public support to innovation. it takes a value of 1 if enterprises have benefited from public (regional, national, european) support to innovation and 0 otherwise. marzucchi and montresor (2017) note that public funding for innovation generally increases the innovation adoption and environmental innovation particularly. hyytinen and toivanen (2003) analyze the effects of public policy, measured by government funding, on the behavior of privately owned, small and medium sized enterprises (smes) in finland. their findings pointed out that government funding disproportionately helps innovation and growth of firms in industries that are dependent on external finance; lmarket representing firm’s prevalent market. it is a dummy variable equal to 1 if firms operate in the eu/international market and 0 for national/regional market. regarding the access to foreign markets, literature has pointed out that international has been associated with successful innovation development (oliveira and carvalho, 2010; salavisa et al., 2012; de faria et al., 2010; romijn and albaladejo, 2002); orgbup representing organizational practices. it includes new business practices or new method of organizing work responsibilities and external relationships. a part of the literature supports the view that having a structured organization is important in achieving innovation. laursen and foss (2003), find that interdisciplinary teams, quality circles4, employees’ proposals collection system, planned job rotation, delegation of responsibility, integration of functions and performance related significantly lead to innovation. o’connor and demartino (2006) agree that organizational structure and incentive systems are key elements in the innovation success. prester and bozac (2012) in analyzing companies over 20 employees on the european manufacturing innovation survey (emis) in croatia report findings similar to laursen and foss (2003). therefore, organizational practices have impact in achieving innovation. we test our hypothesis by considering the following variables: co is the variable cooperation agreements which includes active participation among companies or institutions on innovation activities. the aim of any cooperation agreement is that of introducing external knowledge to the firms. studies show an uncleared networking and cooperation effect. some smes benefit from positive cooperation effects to achieve innovations (de jong and vermeulen 2006; van gils and zwart, 2004; batterink et al., 2010; omta, 2002); while others experience problems (hoffmann and schlosser, 2001; caputo et al. 2002; kaufmann and todtling, 2002); int_info_sources considers the internal sources of information; it is equal to 1 if its cis score is more than 2 and 0 otherwise. scholars find that innovations are developed by using knowledge from a diverse set of internal and external sources of information 4 jones et al. (2008) define quality circles in a following manner: “the company uses quality circles, defined as regular meetings between employees where they discuss issues related to immediate job tasks and make suggestions to improve production processes”. 36 federica demaria, annalisa zezza (gomez et al., 2016; amara and landry, 2005). furthermore, the influence of each source is different depending on the innovation type. internal sources and suppliers are the main contributors in the case of process innovation (gomez et al. 2016); other_info_sources takes into account external sources of information. it considers information from suppliers, competitors, consultant or from other sources as scientific journal, which allows firms to generate new ideas and developing innovations by merging this kind of information with their internal ones (lefebvre et al., 2015; lee et al., 2010). this variable considers all the information sources other than internal one. it takes a value of 1 if all the information sources report a cis score more than 2 and 0 otherwise; policy_m represents the score attributed to policies and measures at national level in terms of success in achieving energy efficiency in the industry end-use sectors (see data description). regulations and policies can be a catalyst and help firms to understand the potential benefits of environmental innovations. ep refers to yearly energy price data. energy prices are considered together with the policy measures. we include prices with taxes in the model. a relatively higher energy price in a country will induce a technological change in favor of higher energy efficiency. (ghisetti and rennings, 2014; cainelli & mazzanti, 2013; popp, 2002). descriptive statistics on selected variables used in the estimated model are shown in appendix (table a.2). about 64% of the considered firms have introduced process innovations. data show the importance of internal sources of information (68%) and the machinery and equipment acquisition (52%). at the same time, most of the enterprises in the sample operate in the eu markets (89%). only 26% of the firms in the sample are engaged in cooperation activities or received public funds for innovation (26%). energy price (with tax) has high variability in the eu countries ranging from a minimum value of 0.07 and a maximum value of 0.25. finally, the successful policies in energy saving show high variability ranging between 0 and 4.38. the correlation matrix is reported in table 2. correlations are moderate implying that there is a low collinearity risk issues and redundancies in this set of variables. all the control variables are positively correlated with the dependent variable except for “other information sources” and “energy price”. 4. results and discussion we have run three different models with the scope of investigating the energy policy variables effect on process innovation, (table 3). the first one considers the main innovation process drivers and barriers and the successful policy measures (policy_m); the second one takes into account the effect of energy policy variables (ep); while the third one considers the two policy variables effects (ep and policy_m). most of the hypotheses are confirmed by the results with models 1, 2 and 3, satisfying all the tests. the discussion below concerns model 3 in table 3. the variable lnturnover displays a positive and significant coefficient (+0.1288). this result is different from the study of garcia-martinez and briz (2000) or capitanio et al., (2010), where the small average size hamper innovation. as regards to the other control variables we find a positive and sig37drivers and barriers of process innovation ta bl e 2. c or re la tio n m at rix . pr oc es s in no va tio n ad op tio n (in no _p ro c_ ad op ) in te rn al in fo rm at io n so ur ce s ( in t_ in fo _s s) o th er in fo rm at io n so ur ce s (o th er _i nf o_ ss ) a cq ui sit io n m ac hi na ry (r m ac ) fu nd s c oo pe ra tio n (c o) bu sin es s or ga ni za tio n (o rg bu p) tu rn ov er (ln tu rn ov er ) po lic y m ea su re s (p m ) en er gy p ric e (e p) pr oc es s in no va tio n ad op tio n (in no _ pr oc _a do p) 1 in te rn al in fo rm at io n so ur ce s ( in t_ in fo _s s) 0. 13 75 * 1 o th er in fo rm at io n so ur ce s ( ot he r_ in fo _s s) -0 .1 36 3* 0. 24 17 * 1 a cq ui sit io n m ac hi na ry (r m ac ) 0. 31 02 * 0. 09 28 * -0 .0 48 4* 1 fu nd s 0. 16 77 * 0. 07 89 * -0 .0 15 8 0. 12 84 * 1 c oo pe ra tio n (c o) 0. 18 41 * 0. 19 31 * 0. 04 24 * 0. 10 05 * 0. 24 66 * 1 bu sin es s or ga ni za tio n (o rg bu p) 0. 25 85 * 0. 15 57 * -0 .0 26 5* 0. 13 69 * 0. 10 21 * 0. 19 96 * 1 tu rn ov er (ln tu rn ov er ) 0. 20 36 * 0. 07 46 * -0 .2 04 0* 0. 03 57 * 0. 12 69 * 0. 26 78 * 0. 21 34 * 1 po lic y m ea su re s (p m ) 0. 11 19 * 0. 01 51 -0 .0 18 5 -0 .2 21 5* 0. 00 85 -0 .0 12 4 0. 05 83 * 0. 06 39 * 1 en er gy p ric e (e p) -0 .1 53 4* -0 .0 47 9* 0. 20 90 * -0 .0 90 1* 0. 01 88 -0 .0 63 9* -0 .0 35 4* 0. 03 80 * -0 .0 54 1* 1 38 federica demaria, annalisa zezza nificant influence in fostering process innovation adoption for machinery acquisition (rmac), business practices organization (orgbup) and public funds (funds). as regards to the machinery acquisition (rmac +1.8995), our result is in line with the main literature which shows that machinery acquisition foster process innovation adoption. (ciliberti et al 2016). business practices organization reports a positive and statistically significant coefficient (orgbup +0.8891). this result is supported by the following studies laursen and foss (2003); o’connor and demartino (2006); prester and bozac (2012); while silva et al. (2008) find a negative relationship between the propensity to innovate and the organizational rigidities. public financial support for innovation seems to be a process innovation driver (+0.2613) with a positive and statistically significant coefficient. the opening of new markets (gomkt) and market localization (lmarket) report insignificant coefficient. focusing the discussion on the first two hypothesis, results reveal that the presence of cooperation agreements (co +0.3929) and networking activities are positively associated with innovation process. as literature points out, these activities facilitate learning about new opportunities and can improve market access and economies of scale and scope (de faria et al. 2010; cassiman and veugelers, 2002; lopez, 2008). quantitative empirical studies on external knowledge sourcing provide evidence that involving a large number of external sources of knowledge in innovation is a promising choice for large firms (lakhani et al. 2006; laursen and salter 2006). information sources reveal a different pathway depending on their nature. our analysis suggests a relevant role for the internal sources (int_info_sources +0.2622); while the others (other_info_sources -0.5494) are negatively correlated with the process innovation. the impact of various information sources is not straightforward as their use can be public and private – universities, journals, conferences and suppliers among many others which may generate costs that must be considered. in some cases, the over-search of external sources may take too much time and slow down the innovation process. additionally, excessive reliance on external information sources can increase coordination and monitoring costs and could affect the creation of knowledge stocks within the firm. comparing results with existing literature, we find that the information sources affect the generation process innovation as in ciliberti et al 2016. however, this finding is dissimilar to other empirical evidence which shows that firms should always look for external information which can then be embodied into innovation (köhler et al. 2012; costa et al. 2015). in some cases in acquiring external information, companies demonstrate openness and ability to scan the market and identify opportunities which allow them to be more efficient in implementing innovation and decrease the risk of product failure (stewart-knox and mitchell 2003; avermaete et al. 2004; wei and wang 2011). as underlines by tether and tajar 2008; lee et al. 2010, diverse information sources (from suppliers, competitors, consultants) are complementary and, if merged with the existing knowledge, allow to create new knowledge useful for innovation. the third and fourth hypothesis have tested through the impact of the implementation at the country level of energy saving policy measures (policy_m). the positive and statistically significant coefficient of policy_m (+3.3934) indicates that energy policies adopted by eu countries boost innovation. firms innovated, i.e. adopting new or making changes to the organization of the productive process. very similar is the energy price effect (ep +7.8284), which clearly shows that a high energy price is an incentive to modify 39drivers and barriers of process innovation the productive process. our results are also consistent with what is found in rennings and rammer (2009) revealing that process innovations are more strongly aimed at cost reduction, since increasing energy and/or material efficiency are associated with lower costs per unit. on the same vein, popp (2002) exposes the strong and positive impact energy prices on new innovations. similarly, rennings et al. (2008), del rio gonzalez (2005) find that regulation pressure and corporate image are the main drivers for adopting green technologies in the spanish pulp and paper industry. table 4 merges the results of the model with the underlying hypothesis, showing that the model performs reasonably well, with energy policy, cooperation activities, financial table 3. logit regression result from panel data. (1) proc_inn_adoption (2) proc_inn_adoption (3) proc_inn_adoption proc_inn_adoption lnturnover 0.1274*** 0.1288*** 0.1288*** (0.0243) (0.0244) (0.0244) funds 0.2605** 0.2613** 0.2613** (0.0994) (0.0992) (0.0992) lmarket -0.0088 -0.0087 -0.0087 (0.0865) (0.0865) (0.0865) rmac 1.8986*** 1.8995*** 1.8995*** (0.0903) (0.0903) (0.0903) gnewmkt 0.0599 0.0517 0.0517 (0.0376) (0.0381) (0.0381) co 0.3934*** 0.3929*** 0.3929*** (0.1073) (0.1073) (0.1073) orgbup 0.8888*** 0.8891*** 0.8891*** (0.0919) (0.0918) (0.0918) inte_info_sources 0.2620** 0.2622** 0.2622** (0.0896) (0.0896) (0.0896) other_info_sources -0.5122*** -0.5494*** -0.5494*** (0.1208) (0.1237) (0.1237) policy_m 1.9569** 3.2951** (0.8876) (1.133) ep 7.8284* 7.8284* (4.5457) (4.5457) n 4645 4645 4645 adj. r2 0.41 0.40 0.40 chi2 1191.8519 1196.1113 1196.1113 bic 4559.1268 4564.8505 4564.8505 vif 1.20 1.21 1.21 time dummies and country dummies are included in the model. standard errors in parentheses. variable statistically significant at * p < 0.05, ** p < 0.01, *** p < 0.001 respectively. 40 federica demaria, annalisa zezza public supports, and technical capabilities play an important role in creating a favourable environment for processes innovation. table 4. merging hypothesis and results. hypothesis results h1a) networks and cooperation activities promote process innovation yes ⇒ + and significant h1b) sources of information stimulate process innovation internal ⇒ yes ⇒ + and significant other ⇒ yes ⇒ and significant h2a) successful policies measures stimulates firm to introduce process innovation yes ⇒ + and significant h2b) energy price is positively associated with process innovation yes ⇒ + and significant 4.1 robustness check in order to test the robustness of our results, further elaborations are provided (table 5). results were confirmed when we ran the logit model considering the original variables of the cis survey instead of the transformed variables used in section 3. again, cooperation, information sources, policy variables and energy price, are positively associated with process innovation. results were also confirmed when we used variables acting as barriers instead of drivers. the estimated models are consistent; indeed, the impact of most explanatory variables is statistically significant and different from zero. results of the confusion matrix5, which describes how many actual and predicted values exist for different classes predicted by the model, indicate that the model fit quite well with both estimation techniques having a percentage of corrected classified value about 78% (table a.3 in appendix). results from vif test suggest that variables are uncorrelated with each other. tolerance is different from zero and the variance inflation is low. evidence of good fit is reflected in a roc curve (figure 1 in appendix), the area under the roc curve is equal to 0.83 meaning that 83% of the observations are correctly classified. 5. conclusions the food processing industry is a sector mainly constituted by smes, with a low propensity to adopt process innovation. it represents one of the four sectors that consumes more energy in europe although, at a large extent, it is not considered energy intensive and therefore covered by the eu emissions trading system (ets); it also is considered to have a high energy saving potential. a major barrier that literature finds is the low importance attributed to energy consumption in non-energy intensive industries as in the case 5 a confusion matrix is a table used to describe the performance of a classification model on a set of test data for which the true values are known. it tells us how many actual values and predicted values exist for different classes predicted by the model. 41drivers and barriers of process innovation of food processing industry. another barrier is the lack of high expected returns ad short payback times. furthermore, smes have limited access to information, low energy share on their expenditures, too high transaction costs for fund searching, cost disadvantages in table 5. robustness logit estimation. (1) proc_inn_adoption (2) proc_inn_adoption (3) proc_inn_adoption proc_inn_adoption       lnturnover 0.1163*** 0.1100*** 0.1052***   (0.0248) (0.0255) (0.0259) funds 0.0878 0.0881 0.0902   (0.0992) (0.0996) (0.0996) lmarket -0.0453 -0.0343 -0.0449   (0.0885) (0.0889) (0.0893) rmac 1.5594*** 1.5537*** 1.5553***   (0.0914) (0.092) (0.092) gnewmkt -0.0017 0.0011 -0.0001   (0.039) (0.0393) (0.0392) orgbup 0.7801*** 0.7812*** 0.7771***   (0.0929) (0.0936) (0.0937) ssup 0.3931*** 0.3991*** 0.3996***   (0.0431) (0.0433) (0.0433) scom -0.0871 -0.0888 -0.089   (0.0452) (0.0456) (0.0456) sins 0.1445** 0.1416** 0.1395**   (0.0478) (0.0479) (0.048) co 0.2694* 0.2749* 0.2677*   (0.1071) (0.1078) (0.1079) policy_m 4.8399*** 4.5146** 4.4198**   (1.2369) (1.3724) (1.3801) ep 15.4363** 15.3776** 15.0575**   (4.8897) (4.8956) (4.9124) obsprs   0.0219 0.0241     (0.0455) (0.0454) obsfin   -0.0441 -0.0429     (0.0384) (0.0384) empedu     0.1113       (0.1003) n 4425 4398 4398 adj. r-sq  0.346 0.34  0.34  chi2 998.4058 994.3702 999.5503 bic 4358.8634 4345.35 4352.528 time dummies and country dummies are included in the model. standard errors in parentheses. variable statistically significant at * p < 0.05, ** p < 0.01, *** p < 0.001 respectively. 42 federica demaria, annalisa zezza obtaining or developing innovation. all these problems need to be specifically addressed by policy measures. in particular, small food firms contribute substantially to the food processing industry economic performance and are considered to play a role in achieving sustainable economic growth in local economies. nevertheless, the small size of the industries and their less energy intensive use clarifies why it is difficult to apply energy policy measures and, why the optimization process is often not a priority for company managers. being among the largest users of surface water and energy among the manufacturing sectors, the food processing industry needs to reduce its energy consumption and improve its energy efficiency. however, changes in energy efficiency are often difficult because they depend on several factors such as the energy used technical performance, the importance of energy trans-formations, climate conditions, the structure of each economic sector that uses energy (medener, 2013). in this study, we have addressed the following issues: a) the role of cooperation agreements between food processing industries and research institutes in promoting process innovation adoption; b) whether or not the high price of energy encourages process innovation adoption; c) whether or not the environmental regulation and the severity of the policies stimulate process innovation adoption; d) which of these two factors, energy price and successful policy measures, are the most effective tool in promoting process innovation adoption. through our models, we have addressed the issues related to process innovation adoption in the eu food processing industry. as regards to the first two hypotheses, namely the role of cooperation and network activities, on the one hand (h1a) and the role of information (h1b), our study confirms that cooperation agreements encourage smes to adopt new process innovations. networking activities in this regard are relevant because they allow smes to acquire all the knowledges that is unavailable within the firm. the role of different information sources is uncertain. indeed, the internal sources availability seem to encourage the adoption of new processes innovation. external sources information availability shows a negative sign that could be due to the higher costs related to the acquisition of this kind of knowledge and the manager’s or owners’ attitudes. concerning the role energy policy, results confirm the key role of energy prices and energy policies, with the energy price coefficient having a higher weight. furthermore, results confirm the role and the relevance of drivers like financial resources availability at the enterprise level, the presence of new organizational methods, the positive role of r&d firms’ engagement and cooperation activities. these are important findings, in particular, if we consider that smes have limited access to information, low energy share on their expenditures, too high transaction costs for fund searching, cost disadvantages in obtaining or developing innovation. our results support previous research in identifying the main areas for policy action. process innovation adoption in the food processing industry could be enhanced by measures addressed to: information cost reduction in order to support informed choices. one example is the support energy auditing in smes as a tool to track energy consumption and costs throughout a facility and identify opportunities to reduce energy use, increasing entrepreneurs’ awareness. contrasting the low private investment in r&d in smes for process and energy saving innovations through public-funded r&d or promoting enterprises aggregation 43drivers and barriers of process innovation in networks. this action can take several forms research public of financing activities that require partnership with the private sector including technology providers and or facilitating partnership agreements between sectoral entrepreneurs and technology providers. promoting policy coherence in eu policies impacting on energy use and innovation i.e. cap, eu cohesion policies, energy and climate policies as well the bioeconomy and the circular economy strategies. reinforcing the role of partnership tools along the whole supply chain in the agrifood sector enhancing cooperation towards sustainable production, thus creating the necessary conditions by which green labels could deliver and increase the demand for sustainable products. considering that policy instruments are located at different government levels (eu, ms, regional or local) when dealing with the appropriate policy mix, and increase policy coherence through the whole governance system in which policy tools operate (borràs and edquist 2013). acknowledgments this article has been co-funded by the eu project enremilk. grant agreement no.: 613968 (fp7-kbbe-2013-7-single-stage). the views expressed in this article are exclusively those of the authors. we thank the two anonymous referees for their outstanding comments and suggestions. references alexiev, a.s., volberda, h.w., van den bosch, f.a. 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(2012). how knowledge affects radical innovation: knowledge base, market knowledge acquisition, and internal knowledge sharing. startegic management journal, volume 33, issue 9 p.1090-1102. appendix table a.1. variables’ description. variable cis description variable in the model re-coded variable expected sign inpspd introduced onto the market a new or significantly improved method of production dependent variable: process innovation adoption dummy variable: 0=no; 1=yes   inpslg introduced onto the market a new or significantly improved logistic, delivery or distribution system inpssu introduced onto the market a new or significantly improved supporting activities turn total turnover size total turnover in euros + funds public funding from local or regional authorities external financial capacity dummy variable: 0=no; 1=yes + mother local/regional market (within country) lmarket dummy variable: 0=no; 1= yes + rmac acquisition of machinery rmac   + gomkt increase market share gomkt   + orgbup new business practices for organizing work or procedures  orgbup dummy 0= no , 1=yes + co cooperation arrangements on innovation activities cooperation activities dummy variable: 0=no; 1=yes + sentg sources from within the enterprise or enterprise group int_info_sources dummy variable: 0=not important; 1=important + ssup suorces from suppliers of equipment, materials etc other_info_sources dummy variable: 0=not important; 1=important + scom sources from competitors and other enterprises of the same industry sins sources from consultants, commercial labs or private r&d institutes 51drivers and barriers of process innovation variable cis description variable in the model re-coded variable expected sign suni sources from universities or other higher education institutes sclup clients or customers from the public sector scon sources from professional conferences, trade fairs, meetings sjou sources from scientific journals, trade/ scientific publications spro sources from professional and industry associations policy_m energy saving successful policy measures policies 0<score<4.389 + ep energy price (including not-refundable taxation) value of energy price euros/kw + source: cis 2010 2012. table a.2. descriptive statistics of the panel sample. variable obs mean std. dev min max proc_inn_adop 4,651 0.641 0.480 0 1 lmarket 4,646 0.890 0.313 0 1 rmac 4,651 0.526 0.499 0 1 co 4,651 0.258 0.438 0 1 orgbup 4,651 0.344 0.475 0 1 gnewmkt 4,651 2.009 1.105 0 3 turnover 4,651 42400000 141000000 12645 3.10e+09 int_info_sources 4,651 0.697 0.459 0 1 others_info_sources 4,651 0.1163 0.320 0 1 funds 4,651 0.258 0.438 0 1 ep 4,651 0.118 0.036 0.07045 0.2504 policy_m 4,651 3.539 0.939 0 4.389 year 4,651 2010.722 0.961 2010 2012 source: cis 2010 2012. 52 federica demaria, annalisa zezza table a.3. confusion matrix. classified true total d ~d  + 2726 730 3456  254 935 1189 total 2980 1665 4645   classified + if predicted pr(d) >= .5 true d defined as dipend_1 != 0   sensitivity pr(+| d) 91.48% specifity pr(-|~d) 56.16% positive predictive value pr(d| +) 78.88% negative predictive value pr(~d|-) 78.64%   false + rate for true ~d pr(+|~d) 43.84% false rate for true d pr (-| d) 8.52% false + rate for classified + pr(~d|+) 21.12% false rate for classified pr(d| -) 21.36% correctly classified 78.82% figure 1. roc curve. 0. 00 0. 25 0. 50 0. 75 1. 00 s en si tiv ity 0.00 0.25 0.50 0.75 1.00 1 specificity area under roc curve = 0.8288 the role of trust and perceived barriers on farmer’s intention to adopt risk management tools elisa giampietri1, xiaohua yu2, samuele trestini3,* drivers and barriers of process innovation in the eu manufacturing food processing industry: exploring the role of energy policies federica demaria, annalisa zezza step-by-step development of a model simulating returns on farm from investments: the example of hazelnut plantation in italy alisa spiegel1,*, simone severini2, wolfgang britz3, attilio coletta2 the role of group-time treatment effect heterogeneity in long standing european agricultural policies. an application to the european geographical indication policy leonardo cei1, gianluca stefani2, edi defrancesco1 the impact of food price shocks on poverty and vulnerability of urban households in iran ghasem layani1, mohammad bakhshoodeh1, mona aghabeygi2*, yaprak kurstal3, davide viaggi3 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 bio-based and applied economics bae copyright: © 2024 d’alberto, r., raggi, m., & viaggi, d. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: d’alberto, r., raggi, m., & viaggi, d. (2024). innovative contract solutions for the agri-environmental-climate public goods provision: which features meet the farmers’ approval? insights from emilia-romagna (italy). bio-based and applied economics 13(1): 73-101. doi: 10.36253/bae-14016 received: november 30, 2022 accepted: may 05, 2023 published: may 20, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. guest editors: stefano targetti, andreas niedermayr, kati häfner, lena schaller orcid rd: 0000-0002-7227-7485 mr: 0000-0001-6960-1099 dv: 0000-0001-9503-2977 innovative contract solutions for the agrienvironmental-climate public goods provision: which features meet the farmers’ approval? insights from emilia-romagna (italy) riccardo d’alberto1,*, meri raggi2, davide viaggi3 1 dept. of economics, university of verona, via cantarane 24, 37129 verona (vr), italy 2 dept. of statistical sciences “p. fortunati”, alma mater studiorum university of bologna, via delle belle arti 41, 40126 bologna (bo), italy 3 dept. of agricultural and food sciences (distal), alma mater studiorum university of bologna, viale fanin 50, 40127 bologna (bo), italy *corresponding author. e-mail: riccardo.dalberto@univr.it abstract. the agroecological transition promoted worldwide is supported by the european union common agricultural policy towards different strategies and policy tools. the agri-environmental schemes, offering farmers the possibility to adopt environment-friendly practices (thus mitigating negative externalities/providing positive ones) represent a straightforward example. however, there is dissatisfaction about their effectiveness and efficiency, while their improvement is envisaged through a flexible mix of new instruments: novel contract solutions fostering result-based payments, collective implementation, involving value chains and land tenure systems coupled to environmental conditionality. this paper investigates how farmers from emiliaromagna (italy) perceive these innovative contract solutions as “easy to understand”, “applicable”, “economic beneficial”, and their willingness to enroll. the applied ordered logistic regression models include socio-demographic characteristics, structural features of the holdings, and the farmers’ preference(s) for 13 individual contract features. farmers’ perceptions are driven by the previous experience acquired from similar measures, key socio-demographic characteristics/holding structural features, and peculiar contractual elements. keywords: public goods, result-based, collective approach, value chain, land tenure. jel codes: q15, q20, q57. 1. introduction an agroecological transition1 is being promoted worldwide through the un 2030 agenda for sustainable development (united nations, 2015) and 1 agroecological transition corresponds to a systemic transformation generated by the ecologisation of agriculture and food. it concerns multiple actors among farmers, supply chains, natural resource managers, policymakers, etc. and it is characterized by the fact that a deliberate political intention https://doi.org/10.36253/bae-14016 http://www.fupress.com/bae https://doi.org/10.36253/bae-14016 https://orcid.org/0000-0002-7227-7485 https://orcid.org/0000-0001-6960-1099 https://orcid.org/0000-0001-9503-2977 mailto:riccardo.dalberto@univr.it 74 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 d’alberto riccardo et al. in particular in the european union (eu) through its common agricultural policy (cap) and the european green deal (baldock and buckwell, 2021; european commission, 2019). among the cap strategies and policy tools, the most popular instrument is the eco-conditionality embedded in the indirect subsidies (mamine et al., 2020) which makes the payment conditional on the uptake of a set of actions considered appropriate for reducing negative externalities or improving positive ones (hanley et al., 2012; white and hanley, 2016). complementary to that, the agri-environmental schemes (aess) funded by the cap are based on payments to farmers for the uptake of environment-friendly practices and the provision of ecosystem services that go beyond conditionality. aess are a compulsory element of the eu member states rural development plans (rdp) design but are voluntary for farmers. their relevance lies in the mandatory share of funds allocated to co-financing: 30% of cap pillar ii (supposed to grow in the future). a large body of literature considers aess, assessing their agri-environmental-climate effects (see hasler et al., 2022 and the references therein), analyzing their cost-effectiveness and efficiency (ansell et al., 2016; bartolini et al., 2021; blazy et al., 2021; drechsler et al., 2017; pacini et al., 2015), estimating the effects on the agricultural holdings structure and productive choices (arata and sckokai, 2016; bertoni et al., 2020; chabé-ferret and subervie, 2013; d’alberto et al., 2018; mennig and sauer, 2020), and detecting the factors that influence farmers’ uptake decision and behavior (brown et al., 2021; drechsler, 2021; gailhard et al., 2015; raina et al., 2021; vergamini et al., 2020). despite this abundant literature and the knowledge on aess, there is dissatisfaction about their effectiveness and efficiency in delivering agri-environmental-climate public goods (aecpgs2) and in terms of achievements longevity (biffi et al., 2021; bullock et al., 2021). nowadays, aess are largely dominated by action-based approaches addressing individual farmers, while their improvement is envisaged through a flexible mix of new instruments (herzon et al., 2018; olivieri et al., 2021), such as contract solutions fostering result-based payment schemes or collective implementation, and solutions involving value chains and/or implementing new forms of land tenure systems coupled to environmental is willing to bring such a transformation to move towards a more sustainable agricultural and food system (magrini et al., 2019). 2 these are non-rival, non-excludable goods provided by agriculture and forestry with direct implications in terms of (potential) positive externalities for both climate and environment (e.g., carbon sequestration, air and water quality and quantity, soil restoration/ maintenance, etc.) (cooper et al., 2009). conditionality. these novel approaches are expected to provide aecpgs in a more efficient and effective way, being compliant with what is envisaged by the farm to fork strategy and the eu biodiversity strategy for 2030. the former is at the heart of the european green deal that aims at making europe the first climate-neutral continent by 2050. it plans to reduce the environmental and climate footprint of the eu food system by addressing comprehensive challenges in terms of sustainability towards a transition that ensures that the whole food chain has a neutral or positive environmental impact (european commission, 2020a). the latter strongly supports such a transition by acknowledging that it cannot be successfully achieved without restoring the endangered ecosystems, “bringing nature back to agricultural land” (european commission, 2020b). both initiatives strongly support and incentivize the transition to fully sustainable practices. to the best of our knowledge, some of these new incentive approaches have been mainly investigated individually, like the result-based payments – the most studied instrument so far – (birge et al., 2017; russi et al., 2016; sidemo-holm et al., 2018; šumrada et al., 2022, 2021; zabel, 2019) and the collective approaches (el mokaddem et al., 2016; narloch et al., 2017; westerink et al., 2017), while land tenure contracts with environmental clauses and the initiatives along the value chain were seldom addressed by the literature. this paper investigates four novel contract solutions for the aecpgs provision: result-based (rb), collective (co), value chain (vc), and land tenure (lt) contracts. these contract types are analyzed in terms of farmers’ acceptability and willingness to uptake, by assessing: 1) the farmers’ perception of the easiness of understanding related to the innovative contract solution. 2) the farmers’ perception of the contract’s applicability in the farm. 3) the farmers’ perception of the economic benefit deriving from the contract. 4) the farmers’ willingness to enroll. the preferences concerning these points are explained using the socio-demographic characteristics of the farmers/land managers and the structural features of the agricultural holdings. the paper also focuses on the assessment of the influence that 13 individual features that define the contract solutions can play in determining the farmers’ preferences. data are collected by means of an online survey carried out within the eu console project3 among the farmers of emilia-romagna (italy). 3 the console project has received funding from the european union’s horizon 2020 research and innovation programme under grant agreement no. 817949. for further details: https://console-project.eu. https://console-project.eu 75 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 innovative contract solutions for the agri-environmental-climate public goods provision the novelty of the paper lies in 1) the investigation of farmers’ perceptions of four new, incentive contract types that combine a flexible mix of new instruments; 2) the inclusion in the modeling exercise (in addition to the socio-demographic characteristics of the farmer as well as the structural features of the agricultural holding) of the information about the farmers’ preferences for several individual features characterizing these instruments; 3) the application of ordered logistic regression that, to the best of our knowledge, has never been applied to analyze farmers’ preferences for aecpgs contracts.4 ordered logistic regression models are rather solid (agresti, 2019, 2010), but the so-called partial proportional odds/non-parallel lines modelling approach has only recently attained a cohesive formalization (williams, 2006; yee, 2010). the main, recent innovation consisted in their expansion for allowing the relaxation of its key assumption, the “proportionality of the odds” (williams, 2016). the latter states that a respondent operates a proportional shift when evaluating his/ her preferences for the levels depicted by the categorical outcome variable. in other words, the assumption states that the “distance” in terms of individual’s preferences between a lower level of the categorical outcome variable and a higher one, is proportional for all the levels of such a variable. it has been demonstrated that violations of this assumption frequently occur in practice and they have been nimbly disregarded (brant, 1990; long and freese, 2014; xu et al., 2022), hence leading to biased and mis-interpretable results (agresti, 2010). this is not the case of the present work. indeed, we test the proportionality of the odds and relax the assumption when needed. this relaxation allows for avoiding biased estimates by properly depicting the shift of individual’s preferences among the different levels of the categorical outcome variable, applying the partial proportional odds model when there is no proportionality of the odds about the levels of preference. the results hint at the influence that previous experience (acquired from very similar measures), key sociodemographic characteristics, and structural features of the holding play in driving the farmers’ perceptions of the easiness of understanding, applicability, and economic benefit of the contract solutions, as well as their willingness to enroll. in addition, the above-mentioned perceptions can be influenced by peculiar contractual elements, not only those straightforwardly linked to the identification of the contract type. the paper is structured as follows: section 2 presents the research framework, the case study, the data at hand, 4 a similar application (logit modelling), but targeting aess is offered by gailhard and bojnec (2015). and the statistical method. section 3 presents the results, while in section 4 we discuss them. finally, section 5 hosts the conclusions. 2. data and methods 2.1 case study the emilia-romagna region is located in northeastern italy. the southern part is hilly and includes the mountainous areas of the apennines, while the southern part of the po river plain dominates the northern portion of the territory. the plains are characterized by intensive agriculture and arable crops, the hills by vineyards and orchards, and the mountains mainly by grasslands, arable crops, and woods. the plain area is highly urbanized, while the mountainous areas are marginalized and characterized by land abandonment. data on emilia-romagna citizens were collected online, using qualtrics, from may to july 2021 with a questionnaire promoted on the institutional website of the emilia-romagna region dedicated to agriculture (regione emilia-romagna, 2022a) and on the corresponding official facebook page (regione emiliaromagna, 2022b), allowing respondents to freely access the qualtrics link. 559 questionnaires were initiated, of which 305 completely answered questionnaires (55%) are used for the present analysis. table 1 depicts the main descriptive statistics of the sample. 2.2 questionnaire overview the survey questionnaire (d’alberto et al., 2022) is based on two parts: the first collects the socio-demographic characteristics of the respondent and the main characteristics of the agricultural holding he/she manages/owns; the second focuses on the contract solutions. first, we investigated the respondent’s preference(s) for 13 individual features that potentially define a generic environmental programme/contract. secondly, information on the respondent’s preference about the four contract solutions (rb, co, vc, lt) was collected, specified in terms of “understandability”, “applicability” in the farm, and “economic benefit”. finally, the respondent was asked about his/her willingness to enroll. table 2 depicts the 13 individual contract features with their definitions, built on the findings from the scientific literature review on the subject (eichhorn et al., 2020) in combination with the insights gathered from the discussion of such findings among (and with) the 76 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 d’alberto riccardo et al. table 1. descriptive statistics of the sample. explanatory variable nr. of observations percent q1, median, mean, q3 (standard deviation) gender male 264 86.56 % female 41 13.44 % age 18-30 29 9.51 % 31-40 42 13.77 % 41-50 67 21.97 % 51-60 104 34.10 % 61-70 41 13.44 % >71 22 7.21 % educational level primary 74 24.26 % secondary 156 51.15 % university or higher – ba’s, ma’s, ph.d. or equivalent 75 24.59 % membership none 149 48.85 % farmers union 108 35.41 % nature conservation/ environmental organization 48 15.74 % proportion of holding sales – to processor 0 % 213 69.84 % 1-30 % 38 12.46 % 31-60 % 14 4.59 % 61-100 % 40 13.11 % proportion of holding sales – to private wholesaler/retailer 0 % 139 45.57 % 1-30 % 58 19.02 % 31-60 % 25 8.20 % 61-100 % 83 27.21 % proportion of holding sales – to cooperatives 0 % 193 63.28 % 1-30 % 21 6.89 % 31-60 % 21 6.89 % 61-100 % 70 22.95 % proportion of holding sales – direct to final consumer 0 % 228 74.75 % 1-30 % 37 12.13 % 31-60 % 15 4.92 % 61-100 % 25 8.20 % specialization arable 136 44.59 % horticulture 15 4.92 % permanent 84 27.54 % livestock 32 10.49 % mixed 38 12.46 % (continued) 77 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 innovative contract solutions for the agri-environmental-climate public goods provision european stakeholders (viaggi et al., 2020b).5 these features were selected since they potentially characterize, in general, an agri-environmental programme/contract and, at the same time, for being specifically distinctive of one (or more) incentive contract solution. for example, “the payment gets higher, the better your environmental results are” specifically fits to result-based contract solution. however, this contractual element can be part of a collective-based incentive or a solution involving the value chain. therefore, the features are not explicitly linked to a contract type, while each of them can regard a specific aspect of the contract. finally, as per the stakeholders’ suggestions and insights, the 13 features help in framing the general idea of the innovative contract solutions in the most understandable way for the eu farmers/land managers, disregarding their experience(s) with the cap agri-environmental-climate measures (aecms). the features in table 2 were presented to the respondent as general attributes of a hypothetical agrienvironmental contract/programme. before describing rb, co, vc, and lt contract solutions in detail, the respondent was asked: “how much would the following 5 the literature review found and analyzed 58 existing case studies within and outside the eu. a survey among project partners and stakeholders and a workshop addressing 105 stakeholders from 11 eu member states and the united kingdom were held for discussing, selecting, and debating the most promising examples. characteristics of agri-environmental contracts increase or decrease your willingness to enroll to an environmental contract or programme?”. the possible answers (likert scale) were: 1 = “decreases my willingness considerably”, 2 = “somewhat decreases my willingness”, 3 = “no effect on my willingness”, 4 = “somewhat increases my willingness”, 5 = “increases my willingness considerably”. table 3 depicts the descriptions of the four contract solutions offered to the respondent (viaggi et al., 2020a, 2020b). after each short description of the contract, the respondent was asked: “how do you see this contract type? do you agree or disagree with the following statements?”. the three statements were: “easy to understand”, “applicable for my farm”, and “potentially economically beneficial for my farm”. the respondent was asked to express an opinion where 1 = “strongly disagree”, 2 = “disagree”, 3 = “neutral”, 4 = “agree”, 5 = “strongly agree”. finally, for each specific contract solution (rb, co, vc, lt) the respondent was asked: “how likely is that you would enroll in a –name– contract type in the future?” (the answers were 1 = “very unlikely”, 2 = “unlikely”, 3 = “neutral”, 4 = “likely”, 5 = “very likely”). considering the contract features presented in table 2, figure 1 depicts the distribution of the scores that have been given by the respondents to the 13 individual contract features. as per figure 1, there are individual contract features that relevantly influence, in a positive way, the explanatory variable nr. of observations percent q1, median, mean, q3 (standard deviation) organic production no 232 76.07 % yes 73 23.93 % utilized agricultural area owned – in hectares 5.5, 18, 62.41, 40 (191.57) utilized agricultural area rented in – in hectares 0, 9, 49.67, 45 (188.81) direct cap payments no 60 19.67 % yes 245 80.33 % rdp payments – euro no 115 62.30 % yes 190 37.70 % previous experience no 205 67.21 % yes 100 32.79 % note: q1 = 1st quartile; q3 = 3rd quartile. table 1. (continued). 78 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 d’alberto riccardo et al. willingness to enroll in a hypothetical agri-environmental contract/programme, e.g., “self-chosen measures”, “better results, higher payment”, and “annual compensation”. namely, respondents stated that each one of these characteristics contribute in increasing considerably their willingness to enroll in an environmental contract/ table 2. individual contract features. contract feature definition self-chosen measures in the contract, you are free to decide about the management practices to achieve the specified environmental result(s). better results, higher payment the payment gets higher, the better your environmental results are. collective agreement you can collectively agree on environmental targets and measures at landscape-level together with other land managers/forests owners. common payment you and other land managers (farmers/forests owners) receive a common payment. you jointly agree on the distribution of the payment. labelled product you sell your holding’s products labelled as environmentally friendly (e.g., animal welfare products, climate friendly products) when following management measures as prescribed in a processor or retailer contract. paid by customers the contract is not paid by public money, instead the compensation that you get for environmentally friendly production is paid by buyers of your products. reduced land rent you can lease land with a reduced rent, if you agree to follow environmental management clauses as specified in the lease contract. self-monitoring you can do the monitoring of the environmental results yourself (e.g., count specific plants). control by authority the results that you achieve are regularly controlled by the competent authority coming onto your farm, e.g., once per year. free training or advice you are offered free training and advice that enables you to reach the environmental targets. sales guarantee you get a sales guarantee from a processor or retailer in return for implementing environmental measures. annual compensation you get environmental compensation payment on an annual basis. periodical payment you get half of the environmental payment at the beginning of, e.g., the five-year contract, and half at the end of it. table 3. contract solutions descriptions. contract solution description result-based in a result-based contract you receive a payment only for the delivery of environmental or climate results. you are free in your decision about the management practices, e.g., how to contribute to water protection, landscape improvement, biodiversity or to sequester carbon. selected indicators and scoring systems to monitor environmental or climate results are often used, and they will be exactly defined in the contract. you have access to free advice or training when you participate in this contract, and you can voluntarily engage in the monitoring activity. collective you become a member of a group of land managers (farmers or foresters) who applies jointly for compensation in order to implement environmental or climate activities, e.g., water protection, carbon sequestration, biodiversity or landscape improvement. a minimum number of group members (e.g., 5) from your region is required to collaborate in order to get a payment. the group members decide about the implementation and locating the measures, and the distribution of the payment. within the group, peer land managers and advisors share knowledge and support the achievement of the environmental objectives. value chain as a producer, you are part of the value chain (producer, processor, retailer, distributor). you engage in a contract where you commit to deliver environmental or climate benefits connected to the production of selected products, e.g., by carrying out management measures which contribute to water protection, landscape improvement, biodiversity, or carbon sequestration. often these products get a special label. you are paid for it by the market, mainly through a premium price paid by the processor or retailer. land tenure you enter into a land-tenure contract where you commit to give particular attention to environmental aspects beyond legal requirements when producing on the leased land. the landowner accepts a lower lease payment than for comparable land under usual land tenure agreements to compensate your additional efforts. in the contract environmentally friendly management practices on the leased land are prescribed in order to maintain or improve environmental targets, e.g., water protection, landscape and biodiversity improvement or carbon sequestration or alternatively. 79 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 innovative contract solutions for the agri-environmental-climate public goods provision programme. in contrast, a feature like, e.g., “common payment” has a negative influence on the willingness to enroll (i.e., it is expected to somewhat decrease such a willingness). 2.3 methodological approach: proportional odds and partial proportional logit models the socio-demographic characteristics of the respondents, the characteristics of agricultural holdings, and the scores related to the 13 individual contract features are used as explanatory variables in the models (one for each incentive contract solution) where the ordered response variables are 1) the easiness of understanding, 2) the applicability in the farm, 3) the economic benefit, 4) the willingness to enroll. these outcome variables are ordered categorical variables, based on a likert scale. they can be treated by the ordered logit model, also called the proportional odds (po) or parallel lines (pl) model (mccullagh, 1980; winship and mare, 1984). following the notation of agresti (2010), let y be the outcome of interest: an ordinal dependent variable of m categories observed for the i-th individual (i=1,…,n). the generalized ordered logit model can be written as: (1) where j=1,…,m-1. the probabilities that the outcome variable takes on each of the values 1,…,m are equal to: p(yi=1)=1-g(xiβ1), p(yi=j)=g(xiβj-1)-g(xiβj), with j=2,…,m-1 (2) p(yi=m)=g(x_iβm-1). from this generalized framework, special cases can be derived. for example, when m=2, the model in equation 1) equals the logistic regression, while, for m>2, it figure 1. distribution of the scores of the 13 individual contract features. 80 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 d’alberto riccardo et al. becomes equal to a series of binary logistic regressions, one for each pair of categories of the dependent variable. the po/pl model is a further special case that can be written as follows: (3) where j=1,…,m-1. such a model presents β coefficients that do not vary across the values of j, as it is instead in equation 1). therefore, this modelling approach requires that only the α’s do vary across the j values and, hence, it implies that the m-1 regression lines are parallel. this is the key underlying assumption of the po/pl model, usually called “proportionality of the odds”. it states that the relationship between each pair of outcome levels is the same. namely, the shift in individual’s preferences from one level of the categorical variable to the higher/lower one is proportional for all the levels of such a variable. it is well-acknowledged that this cannot always occur in practice. the method has been largely applied by several disciplines in different fields (agresti, 2019), but violations of this fundamental assumption which can frequently occur in practice have been nimbly disregarded (brant, 1990; long and freese, 2014; xu et al., 2022) leading to biased and mis-interpretable results (agresti, 2010). furthermore, this assumption has been discovered to be overly restrictive (williams, 2016). in fact, the po/pl model offers two main pros: 1) it can lead to highly interpretable results (williams, 2016); 2) it benefits from computational efficiency (agresti, 2010). although being very sensitive to violations of the proportionality of the odds, by relaxing the assumption, the aforementioned pros can still be of interest in choosing to apply such a modelling strategy. a successful solution for relaxing the assumption is offered by the partial proportional logit model (ppo) or non-parallel lines model (npl) (mccullagh and nelder, 1989; peterson and harrell, 1990). this alternative modelling strategy has recently gained attention due to the developments proposed by williams (2006) and yee (2010), being a great alternative to the generalized ordered logit model (williams, 2016). relaxing the proportionality of the odds can lead to one or more β’s differing across the values of j, while some other coefficients can still be equal. for the sake of clarity, let x1,x2,x3 be three explanatory variables. the model in equation 3) can be re-written as: (4) where j=1,…,m-1. in the model of equation 4) the β’s for x1,x2 are the same for all the values of j, while the coefficient for x3 can differ. for the sake of simplicity, the unconstrained ppo model proposed by peterson and harrell (1990) and further extended by lall et al. (2002) is adopted here. this model offers a re-parametrization of the model in equation 4) such that, for each explanatory variable, we have a coefficient β and m-2 γ coefficients that indicate a deviation from proportionality. therefore, here we consider po/pl models as the starting point of the analysis, test the proportionality of the odds, and (when needed) eventually relax such an assumption by adopting a properly specified ppo/npl model. the choice of which explanatory variables should be included in the model for the outcome variable of interest is based on the following stepwise approach. first, we included in the po-defined model all the potential explanatory variables. second, we checked for convergence of the model, discarding the explanatory variables that forced convergence to fail. third, we have undergone the assessment of the parallel lines assumption as suggested by long and freese (2014) and williams (2016): if the whole model fails the assumption according to the brant test, a ppo-defined model is run, by relaxing the assumption of proportionality of the odds for the explanatory variables for which the brant test is statistically significant. fourth, we attempted to discard the explanatory variables showing non-statistically significant coefficients but keeping them if their discarding lowered the log-likelihood and the pseudo-r2 of the model, in comparison to the other, newly defined model(s) (i.e., we kept them if the model’s goodness of fit decreased). 3. results in the following, the estimated odds ratios are presented.6 the results are depicted according to the prescriptions of craemer (2009) and williams (2016): when the explanatory variables included in the model meet the parallel lines assumption, the β coefficients are depicted (with the related p-values). in other words, if the coefficients are depicted only for the first category of the 6 for the sake of brevity, only the statistically significant explanatory variables are depicted. please, refer to the supplementary material for the integral version of the results on the models’ coefficients. please, note that we present here only the odds ratios of the statistically significant predictors, although the predictors included in the models were all those depicted in the integral version of the tables in the supplementary material. 81 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 innovative contract solutions for the agri-environmental-climate public goods provision ordinal outcome variable (i.e., only in the second column of the tables) this means that the coefficients are the same for all the categories (since the proportionality of the odds does hold), as per the model in equation 3). when the p-values from the brant test on proportionality are statistically significant, γ coefficients are depicted (with the related p-values), hence identifying the predictors that are not constrained to meet the parallel lines assumption, as per the model in equation 4). in such cases, the γ coefficients are shown for each category of the response variable (i.e., in the other columns of the tables). 3.1 result-based contracts table 4 depicts the odds ratio from the models for the rb contract. they are po/pl models, as per the one depicted in equation 3). in terms of odds, it is worth noticing that for members of nature conservation/environmental organizations, the odds of being more likely to easily understand the rb contracts are almost 2 times greater. by a unit increase in the scoring of self-chosen measures, the odds of being more likely to easily understand the contract is 1.8 times greater. table 4. odds ratio, result-based contract solution. explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* easiness of understanding age (18-30) 31-40 0.922 (0.859) 41-50 ‡ 0.395 (0.030) 51-60 0.540 (0.141) 61-70 0.620 (0.312) >71 0.699 (0.529) membership (none) farmers union 1.361 (0.191) nature conservation/ environmental org. ‡ 1.995 (0.046) self-chosen measures ‡ 1.755 (0.016) applicability in the farm proportion of holding sales – to cooperatives (0%) 1-30 % 1.984 (0.116) 31-60 % 1.616 (0.340) 61-100 % ‡ 2.266 (0.006) organic production (no) yes ‡ 2.301 (0.002) self-chosen measures ‡ 1.687 (0.038) collective agreement ‡ 1.627 (0.004) reduced land rent ‡ 1.917 (0.006) economic benefit better results, higher payment ‡ 1.731 (0.036) willingness to enroll age (18-30) 31-40 1.345 (0.598) 41-50 0.909 (0.850) 51-60 0.664 (0.408) 61-70 0.646 (0.428) >71 ‡ 0.199 (0.016) self-monitoring ‡ 1.659 (0.035) free training ‡ 0.494 (0.029) periodical payment ‡ 1.691 (0.012) note: the reference modality of the explanatory variable is in parentheses. * vu = very unlikely, u = unlikely, n = neutral, l = likely, vl = very likely; p-values in parentheses; ‡ in bold indicates the 0.05 level of statistical significance. 82 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 d’alberto riccardo et al. for the holdings that are largely exposed to cooperatives in terms of sales, the odds of considering “applicable” the rb contract solution is 2.3 times greater. for the holdings producing organic, the odds of being more likely to perceive “applicable” the rb contracts is 2.3 times greater than non-organic holdings. for the higher scoring of self-chosen measures, collective agreement, and reduced rent land, the odds of the perceived applicability in the farm are between 1.6 and 1.9 times greater. for farmers giving higher scores to the possibility that, within the contract, the payment gets higher as much as the achieved environmental results ameliorate, the odds of perceiving as “economic beneficial” the rb contract is 1.7 times greater. with a unit increase in the scoring of free training, the odds of being more likely to enroll in rb contracts decrease, while by a unit increase in the scoring of selfmonitoring and periodical payment, the odds of being willing to enroll in rb contracts are 1.7 times greater. 3.2 collective contracts table 5 depicts the odds ratio from the models for the co contract. the models for the outcome variables easiness of understanding and economic benefit are ppo/ npl models, as per the one depicted in equation 4). the other two models for the outcome variables applicability in the farm and willingness to enroll are po/pl models, as per the one in equation 3). the odds ratio of direct cap payments suggests that for the holdings receiving this payment, the odds table 5. odds ratio, collective contract solution. explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* easiness of understanding direct cap payments (no) yes 0.360 (0.050) 1.749 (0.236) ‡ 5.436 (0.005) ‡ 10.134 (0.046) previous experience (no) yes ‡ 6.189 (0.000) collective agreement ‡ 2.104 (0.022) 0.724 (0.236) 0.705 (0.288) ‡ 0.441 (0.023) applicability in the farm age (18-30) 31-40 ‡ 0.388 (0.040) 41-50 ‡ 0.357 (0.014) 51-60 0.491 (0.075) 61-70 0.748 (0.521) >71 ‡ 0.328 (0.037) utilized agricultural area owned – in hectares ‡ 0.827 (0.024) collective agreement ‡ 1.898 (0.001) common payment ‡ 1.604 (0.006) economic benefit annual compensation ‡ 1.476 (0.038) willingness to enroll age (18-30) 31-40 0.448 (0.117) 41-50 ‡ 0.318 (0.016) 51-60 0.443 (0.075) 61-70 0.552 (0.251) >71 ‡ 0.181 (0.006) collective agreement ‡ 1.527 (0.039) common payment ‡ 1.666 (0.007) self-monitoring ‡ 1.996 (0.003) note: the reference modality of the explanatory variable is in parentheses. * vu = very unlikely, u = unlikely, n = neutral, l = likely, vl = very likely; p-values in parentheses; ‡ in bold indicates the 0.05 level of statistical significance. 83 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 innovative contract solutions for the agri-environmental-climate public goods provision of being “likely” or “very likely” versus the lower categories of easiness of understanding, as well as the odds of being “very likely” (versus the lower categories) are, respectively, 5.4 and 10 times greater than that of the holdings not receiving the payment. having previously experienced collective-alike measures makes the odds of being more likely to easily understand the co contract 6.2 times greater. collective agreement produces asymmetric effects on the easiness of understanding: by a unit increase in the scoring of such contract feature, the odds of being more likely to consider “easy to understand” the co contract is greater, when “very unlikely” is confronted with the upper categories. nevertheless, the odds ratio for the highest level of the response variable (“very likely” versus the lower categories) decreases. by a unit increase in the scoring of the collective agreement, the odds of being more likely to perceive “applicable in the farm” the co contract solution is 1.9 times greater. by a unit increase in the scoring of the contract feature common payment, the odds of being more likely to consider the co contract applicable is 1.6 times greater. for an increase in the scoring of annual compensation the odds of perceiving as “economic beneficial” the co contract solution is 1.5 times greater. for an increase in the scoring of the predictors collective agreement, common payment, and self-monitoring the odds of being more likely to enroll in co contracts are between 1.5 and 2 times greater. 3.3 value chain contracts table 6 depicts the odds ratio from the models for the vc contract. all models are po/pl models, as per the one in equation 3). for the holdings selling to private direct consumers a share between 1% and 60% of the holding product(s), the odds of being more likely to consider “easily understandable” the vc contracts is 2 up to 3.6 times greater. for livestock holdings, the odds of being more likely to perceive “easy to understand” the vc contract is 2.8 greater than that of permanent-specialized farms. by a unit increase of the hectares of uaa rented-in by the holding, the odds of being more likely to consider “understandable” the vc contract solution increases very slightly (it is 1.003 times greater). for respondents who experienced similar measures, the odds of being more likely to “easily understand” the vc contract is almost 8 times greater. concerning the applicability in the farm, the previous experience boosts the odds (for “experienced” respondents the odds ratio is 4.3 times greater). the (potential) economic benefit of vc contracts is negatively influenced by age, while considering the willingness to enroll, for livestock holdings the odds of being more likely to enroll is 3.2 times greater than that of farms specialized in permanent crops. in addition, the previous experience relevantly boosts the odds ratio, while for higher scoring of paid by customers and control by authority, the odds of being more likely to enroll are 1.6 times greater and 1.7 times greater, respectively. 3.4 land tenure contracts table 7 depicts the odds ratio from the models for the lt contract. the model for the outcome variable economic benefit is a ppo/npl model – depicted in equation 4) –, while the others are, all, po/pl models, as per the one depicted in equation 3). in terms of odds ratio, for those who have previously experienced land tenure-alike measures, the odds of being more likely to consider “understandable” the land tenure contract solution is 5.8 greater. by a unit increase in the scoring of self-chosen measures, the odds of being more likely to easily understand the lt contracts is 1.8 times greater. the older the respondent, the lower the odds of perceiving “applicable” the lt contracts, while for an increase in the scoring of control by authority the odds of being more likely to consider “applicable” the lt contract is 1.4 times greater. economic benefit is influenced by the holding exposure to direct consumers (in terms of the amount of sales). asymmetric effects are generated by the increase in the scoring of self-chosen measures, hinting at lower odds of being more likely to consider “economically beneficial” the lt contract. for higher scoring of reduced land rent, the odds of being more likely to perceive the “economic beneficial” of the lt contract is 1.9 times greater. control by authority has also a positive impact on the odds of being more likely to consider “beneficial” the lt contract. by a unit increase in the scoring of sales guarantee, the odds of being more likely to perceive “economically beneficial” the lt contract decreases. willingness to enroll is negatively influenced by age, but it is strongly and positively impacted by the exposure of the holding towards the sales to processors and private wholesalers/retailers, as well as by the increase in the scoring of reduced land rent (odds ratio is 2.3 times greater). 84 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 d’alberto riccardo et al. table 6. odds ratio, value chain contract solution. explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl*vu, u, n, l vs vl* easiness of understanding proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % ‡ 0.285 (0.000) 31-60 % 0.505 (0.150) 61-100 % 1.176 (0.659) proportion of holding’s sales – direct to final consumer (0%) 1-30 % ‡ 2.412 (0.019) 31-60 % ‡ 3.602 (0.025) 61-100 % 0.528 (0.126) specialization (arable) horticulture 1.100 (0.867) permanent 1.122 (0.711) livestock ‡ 2.773 (0.016) mixed 1.108 (0.781) utilized agricultural area rented in – in hectares ‡ 1.003 (0.046) previous experience (no) yes ‡ 7.963 (0.000) applicability in the farm proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % ‡ 0.462 (0.034) 31-60 % 1.034 (0.948) 61-100 % 1.023 (0.951) proportion of holding sales – direct to final consumer (0%) 1-30 % ‡ 2.326 (0.033) 31-60 % 0.699 (0.956) 61-100 % 0.636 (0.284) previous experience (no) yes ‡ 4.311 (0.001) labelled product ‡ 2.318 (0.001) control by authority ‡ 1.538 (0.038) economic benefit age (18-30) 31-40 0.762 (0.534) 41-50 0.652 (0.285) 51-60 ‡ 0.442 (0.032) 61-70 0.551 (0.183) >71 0.766 (0.613) willingness to enroll specialization (arable) horticulture 1.468 (0.660) permanent 0.951 (0.893) livestock ‡ 3.225 (0.050) mixed 0.864 (0.754) previous experience (no) yes ‡ 15.748 (0.001) paid by customers ‡ 1.589 (0.043) control by authority ‡ 1.651 (0.033) note: the reference modality of the explanatory variable is in parentheses. * vu = very unlikely, u = unlikely, n = neutral, l = likely, vl = very likely; p-values in parentheses; ‡ in bold indicates the 0.05 level of statistical significance. 85 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 innovative contract solutions for the agri-environmental-climate public goods provision table 7. odds ratio, land tenure contract solution. explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* easiness of understanding age (18-30) 31-40 0.421 (0.080) 41-50 ‡ 0.361 (0.026) 51-60 ‡ 0.383 (0.030) 61-70 ‡ 0.318 (0.024) >71 0.436 (0.156) previous experience (no) yes ‡ 5.754 (0.000) self-chosen measures ‡ 1.833 (0.019) sales guarantee ‡ 0.560 (0.043) applicability in the farm age (18-30) 31-40 -0.493 (0.113) 41-50 -0.505 (0.085) 51-60 -0.572 (0.136) 61-70 ‡ 0.381 (0.027) >71 ‡ 0.359 (0.041) control by authority ‡ 1.357 (0.047) economic benefit proportion of holding sales – direct to final consumer (0%) 1-30 % 1.561 (0.235) 31-60 % ‡ 3.459 (0.040) 61-100 % 0.907 (0.818) self-chosen measures 2.701 (0.053) ‡ 0.357 (0.019) 0.533 (0.224) 0.397 (0.105) reduced land rent ‡ 1.924 (0.004) control by authority ‡ 1.616 (0.013) sales guarantee ‡ 0.535 (0.029) willingness to enroll age (18-30) 31-40 0.442 (0.163) 41-50 0.411 (0.095) 51-60 0.383 (0.065) 61-70 ‡ 0.235 (0.012) >71 ‡ 0.190 (0.011) proportion of holding sales – to processor (0%) 1-30 % ‡ 3.348 (0.005) 31-60 % 0.058 (0.922) 61-100 % -0.169 (0.680) proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % 0.486 (0.065) 31-60 % ‡ 0.350 (0.036) 61-100 % 0.830 (0.650) reduced land rent ‡ 2.334 (0.001) note: the reference modality of the explanatory variable is in parentheses. * vu = very unlikely, u = unlikely, n = neutral, l = likely, vl = very likely; p-values in parentheses; ‡ in bold indicates the 0.05 level of statistical significance. 86 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 d’alberto riccardo et al. 4. discussion the results suggest different patterns behind the farmers’ perceptions in terms of understandability, applicability, economic benefit, and willingness to enroll related to the four incentive contract solutions investigated. the respondent’s age often plays a key role in determining the overall “acceptability” of the innovative contract solutions, as it is highlighted, e.g., by šumrada et al. (2022) for the result-based schemes in slovenia. we find empirical evidence that being older hints at lower levels of the overall “acceptability” of innovative contract solutions. older farmers often also show a limited willingness to enroll. in line with the research findings of the literature on the subject (see, e.g., gailhard et al., 2015; westerink et al., 2017) we find that the previous experience of similar and/or specific “contract solution-alike” measures has a very strong say in determining the preferences of the farmers/land managers. this is straightforward, for example, in terms of the more solid perception of the easiness of understanding related to both the collective and value chain contracts. some structural characteristics of the holdings play a peculiar role in influencing the respondents’ perceptions, as it is suggested by gailhard and bojnec (2015) and russi et al. (2016). considering the vc contract solution this is particularly evident. indeed, both the holding exposure to certain channels of trade and the amount of sales are relevant boosts of the perceived easiness of understanding and applicability in the farm. in the case of rb contracts, producing organic positively inf luences the perceived applicability in the farm of such contracts, differently from šumrada et al. (2021) which found no evidence of the holdings’ structural characteristics in inf luencing the adoption of resultbased schemes in slovenia. we find also that the perception about the applicability of co contracts is negatively influenced by the size of the farm, in line with the findings of gailhard et al. (2015) on the agri-environmental measures, while, even if limited to rb contracts, other findings show a non-significant influence of the farm’s size in the adoption of potential payment-by-result measures (birge et al., 2017). 5. conclusions we investigated four novel contract solutions that are expected to target more efficiently and effectively the provision of agri-environmental-climate public goods. namely, the result-based (rb), collective (co), value chain (vc), and land tenure (lt) contracts. these contract types have been analyzed in terms of farmers’ acceptability and willingness to uptake, by assessing i) farmers’ perception of the easiness of understanding related to the innovative contract solution; ii) farmers’ perception of the contract’s applicability in the farm; iii) farmers’ perception of the economic benefit deriving from the contract; iv) farmers’ willingness to enroll. the main policy implications concern the fact that farmers show to be rather open toward the contract solutions investigated. at the same time, different farmer’s/farm characteristics may affect acceptance of different contract solutions and this requires careful consideration in the choice of the policy measures and their policy design. by summarizing the main results, with no intention of “profiling” the potential “contractor” of such incentive contract solutions, we can highlight that the general preference of farmers is driven by the following aspects: – rb contracts, the most well-acknowledged instrument, are largely appreciated by the agricultural holdings that produce organic, as well as by farmers being members of nature conservation/environmental organizations. these two aspects act as relevant leverage of the understanding and the perceived applicability of such contract solution (potentially, also as a boost for the perceived importance of the result-based instruments). – co contracts are particularly opposed by big farms which tend to consider them as unapplicable in their agricultural context. in contrast, those receiving direct cap payments tend to be more inclined toward the adoption of such a type of contract. – the most innovative contact solutions (in terms of diffusion and knowledge from the point of view of “contractors”), like vc and lt contracts, are largely influenced by the previous experience of the contractor with respect to “contract solution-alike” measures. being absent such an experience (or perception of experience), vc and lt contract solutions appear to be far more complicated to understand and non-trustworthy. – vc contracts seem to be attractive, above all, for those agricultural holdings that are already exposed to the value chains of the supply system, e.g., in terms of sales to wholesalers/retailers and/or direct consumers. these farms are positively impacted in terms of understandability and applicability related to such contract solutions. in addition, farms that tend to have a more solid tradition in value chains, such as livestock specialized holdings, are far more interested in vc contracts. 87 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 innovative contract solutions for the agri-environmental-climate public goods provision – across the four contract solutions, age has a peculiar (but well-acknowledged in the literature on the subject) role: the older the farmer, the lower the willingness to consider the new contract solution as applicable. the acceptance of contract types is also affected by the perception of individual contract features. as expected, the perceptions of the contractual elements that more evidently characterize each contract solution influence more relevantly the acceptance of farmers about the incentive contract type (e.g., the collective agreement for co contracts or the reduced land rent for lt contracts). however, there are additional contract features that can play a role in impacting the level of acceptance. for 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https://doi.org/10.3390/su13126936 https://doi.org/10.1016/j.agsy.2015.02.004 https://doi.org/10.1016/j.agsy.2015.02.004 https://doi.org/10.2307/2347760 https://doi.org/10.13128/bae-9678 https://agricoltura.regione.emilia-romagna.it https://agricoltura.regione.emilia-romagna.it https://it-it.facebook.com/agricolturacacciaepesca/ https://it-it.facebook.com/agricolturacacciaepesca/ https://doi.org/10.1016/j.landusepol.2016.01.012 https://doi.org/10.1016/j.landusepol.2016.01.012 https://doi.org/10.1016/j.landusepol.2018.05.017 https://doi.org/10.1016/j.landusepol.2018.05.017 https://doi.org/10.1016/j.jnc.2022.126143 https://doi.org/10.1016/j.jnc.2022.126143 https://doi.org/10.1016/j.landusepol.2021.105749 https://doi.org/10.1016/j.landusepol.2021.105749 https://doi.org/10.1016/j.ecolecon.2020.106756 https://doi.org/10.1016/j.landusepol.2017.09.002 https://doi.org/10.1016/j.landusepol.2017.09.002 https://doi.org/10.1007/s10640-016-0002-x https://doi.org/10.1007/s10640-016-0002-x https://doi.org/10.1080/0022250x.2015.1112384 https://doi.org/10.1080/0022250x.2015.1112384 https://doi.org/10.1177/1536867x0600600104 https://doi.org/10.1177/1536867x0600600104 https://doi.org/10.2307/2095465 https://doi.org/10.1177/0049124119882461 https://doi.org/10.1177/0049124119882461 https://doi.org/10.1016/j.landusepol.2018.10.035 https://doi.org/10.1016/j.landusepol.2018.10.035 90 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 d’alberto riccardo et al. supplementary material tables 4, 5, 6, and 7 of the manuscript depict the odds ratio of the statistically significant explanatory variables included in the models considered. here, we present the same tables which, instead, do depict the coefficients of the explanatory variables (same referring models). however, the following tables are presented in their integral version (i.e., the following tables depict the estimated models’ coefficients concerning all the explanatory variables that were included in the models, not only the statistically significant ones). each table is followed by a brief comment about the statistically significant coefficients. the four models in table 4 are po/pl models, as per the one depicted in equation 3) of the manuscript. all the statistically significant variables depicted in table 4 meet the proportionality of the odds assumption. higher values of age make it more likely that the respondent will be in the current (or lower) category of easiness of understanding. being a member of nature conservation/environmental organizations makes it more likely that the respondent will understand the contract more easily. an increase in scoring of self-chosen measures makes it more likely that the respondent will be in a higher category of easiness of understanding. the coefficients of the proportion of holding sales (to cooperatives), organic production, self-chosen measures, and collective agreement positively influence the perceived applicability of rb contracts. better results, higher payment is the only statistically significant predictor for economic benefit in relation to rb contracts. an increase in the scoring of this contract characteristic makes it more likely that the respondent will be in a higher category of economic benefit. being older makes it more likely that the respondent will be at the current level (or lower) of the willingness to enroll in the contract. increases in scoring of self-monitoring and periodical payment make it more likely that the respondent will be in a higher category of willingness to enroll. an increase in the scoring of the contract feature free training makes it more likely that the respondent will be in the current (or lower) level of willingness. the models in table 5 related to the outcome variables easiness of understanding and economic benefit are ppo/npl models, as per the one depicted in equation 4) of the manuscript. in contrast, the models for the outcome variables applicability in the farm and willingness to enroll are po/pl models, as per the one depicted in equation 3) of the manuscript. direct cap payments and collective agreement predictors do fail the test on the proportionality of the odds. receiving direct cap payments boosts the understandability of the collective contract solution, above all with respect to the extreme upper levels of the ordinal outcome variable. collective agreement produces divergent effects on the extreme lower and upper categories. previous experience suggests that having experienced collective-alike measures makes the collective contract more “easily understandable”. being older negatively inf luences the perceived applicability of co contracts. being bigger in terms of holding size makes it more likely that the respondent will perceive “applicable” the co contract. an increase in the scoring of the variables collective agreement and common payment makes it more likely that the respondent will perceive “applicable” the co contract. periodical payment is the only statistically significant predictor influencing (negatively) the economic benefit of co contracts. the willingness to enroll is influenced by age, collective agreement, common payment, and self-monitoring. being older makes it more likely that the respondent will be in the current (or lower) category of willingness to enroll, while the increase in the scoring of the three contract features has a positive effect. the models in table 6 are, all, po/pl models, as per the one depicted in equation 3) of the manuscript. being a holding with a share of sales of 1-30% to private wholesalers/retailers makes it less likely that a respondent will be in a higher category of easiness of understanding. higher values of proportion to holding sales (direct to final consumer) make it more likely that the respondent will be in a higher category (than the current one) of the perceived understandability. being livestock-specialized holding makes it more likely that the vc contracts are more “easily understandable”. the increase in the amount of rented-in land (in terms of hectares of uaa) makes it more likely that the respondent will easily understand the vc contract, as well as having experienced value chain-alike measures. being a holding with a share of sales of 1-30% to private wholesalers/retailers (compared to holdings not exposed to such trades) makes it less likely that the respondent will be in a higher level of applicability in the farm. being a holding exposed for the same share to sales to direct consumers makes it more likely that the respondent will be in a higher category of the applicability of vc contracts. having experienced value chainalike measures makes it more likely that the respondent will perceive “applicable” the vc contract. higher scoring of labelled product and control by authority make it more likely that the respondent will be in a higher category of applicability in the farm. 91 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 innovative contract solutions for the agri-environmental-climate public goods provision table 4. model for result-based contract solution. explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* easiness of understanding age (18-30) 31-40 -0.081 (0.859) 41-50 ‡ -0.929 (0.030) 51-60 -0.616 (0.141) 61-70 -0.478 (0.312) >71 -0.358 (0.529) educational level (primary) secondary 0.072 (0.798) university or higher 0.507 (0.118) membership (none) farmers union 0.308 (0.191) nature conservation/ environmental org. ‡ 0.691 (0.046) proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % -0.305 (0.326) 31-60 % -0.276 (0.531) 61-100 % -0.158 (0.573) self-chosen measures ‡ 0.562 (0.016) better results, higher payment 0.232 (0.346) collective agreement 0.280 (0.079) labelled product 0.097 (0.651) applicability in the farm proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % -0.329 (0.299) 31-60 % 0.363 (0.457) 61-100 % -0.136 (0.661) proportion of holding sales – to cooperatives (0%) 1-30 % 0.685 (0.116) 31-60 % 0.480 (0.340) 61-100 % ‡ 0.818 (0.006) organic production (no) yes ‡ 0.833 (0.002) self-chosen measures ‡ 0.530 (0.038) better results, higher payment 0.404 (0.133) collective agreement ‡ 0.487 (0.004) labelled product 0.236 (0.299) reduced land rent ‡ 0.651 (0.006) self-monitoring 0.184 (0.368) control by authority 0.195 (0.299) free training -0.267 (0.355) sales guarantee -0.335 (0.257) annual compensation 0.441 (0.172) periodical payment 0.271 (0.147) economic benefit self-chosen measures 0.440 (0.082) better results, higher payment ‡ 0.549 (0.036) (continued) 92 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 d’alberto riccardo et al. age produces a negative effect on economic benefit. being specialized in livestock makes it more likely that the respondent will be in a higher level of the willingness to enroll in vc contracts. having experienced value chain-alike measures makes it more likely that the respondent will be in the current (or lower) category of willingness to enroll. an increase in the scoring of paid by customers and control by authority has a positive impact on willingness to enroll. the models in table 7 are, all but the one for the explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* collective agreement 0.163 (0.304) labelled product -0.132 (0.557) reduced land rent 0.027 (0.901) self-monitoring 0.332 (0.103) control by authority -0.177 (0.069) free training -0.068 (0.540) sales guarantee -0.309 (0.805) annual compensation 0.320 (0.335) periodical payment 0.091 (0.623) willingness to enroll age (18-30) 31-40 0.297 (0.598) 41-50 -0.096 (0.850) 51-60 -0.410 (0.408) 61-70 -0.437 (0.428) >71 ‡ -1.612 (0.016) educational level (primary) secondary -0.217 (0.521) university or higher 0.236 (0.547) membership (none) farmers union -0.290 (0.280) nature conservation/environmental org. 0.546 (0.242) proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % 0.418 (0.252) 31-60 % -0.077 (0.874) 61-100 % 0.193 (0.610) previous experience (no) yes 0.041 (0.924) self-chosen measures 0.246 (0.370) better results, higher payment 0.322 (0.277) collective agreement 0.271 (0.180) labelled product 0.310 (0.213) reduced land rent 0.142 (0.563) self-monitoring ‡ 0.506 (0.035) control by authority -0.055 (0.802) free training ‡ -0.705 (0.029) sales guarantee 0.371 (0.228) annual compensation 0.063 (0.862) periodical payment ‡ 0.525 (0.012) note: the reference modality of the explanatory variable is in parentheses. * vu = very unlikely, u = unlikely, n = neutral, l = likely, vl = very likely; p-values in parentheses; ‡ in bold indicates the 0.05 level of statistical significance. table 4. (continued). 93 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 innovative contract solutions for the agri-environmental-climate public goods provision table 5. model for collective contract solution. explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* easiness of understanding membership (none) farmers union -0.085 (0.841) nature conservation/ environmental org. 0.735 (0.496) specialization (arable) horticulture 0.107 (0.835) permanent -0.228 (0.440) livestock 0.184 (0.638) mixed -0.014 (0.971) organic production (no) yes -0.176 (0.505) direct cap payments (no) yes -1.022 (0.051) 0.559 (0.236) ‡ 1.693 (0.005) ‡ 2.316 (0.046) previous experience (no) yes ‡ 1.823 (0.000) self-chosen measures -0.100 (0.693) better results, higher payment 0.088 (0.744) collective agreement ‡ 0.744 (0.022) -0.323 (0.236) -0.350 (0.288) ‡ -0.818 (0.023) labelled product 0.072 (0.751) paid by customers 0.088 (0.646) reduced land rent 0.321 (0.140) self-monitoring 0.255 (0.223) control by authority -0.069 (0.715) free training -0.035 (0.900) sales guarantee -0.038 (0.891) annual compensation 0.257 (0.432) periodical payment 0.254 (0.174) applicability in the farm age (18-30) 31-40 ‡ -0.946 (0.040) 41-50 ‡ -1.029 (0.014) 51-60 -0.711 (0.075) 61-70 -0.291 (0.521) >71 ‡ -1.116 (0.037) proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % -0.190 (0.533) 31-60 % 0.658 (0.125) 61-100 % -0.134 (0.629) utilized agricultural area owned – in hectares ‡ -0.001 (0.024) self-chosen measures 0.220 (0.375) better results, higher payment -0.004 (0.989) collective agreement ‡ 0.641 (0.001) common payment ‡ 0.472 (0.006) reduced land rent 0.352 (0.105) self-monitoring 0.281 (0.153) control by authority -0.107 (0.556) (continued) 94 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 d’alberto riccardo et al. explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* free training 0.087 (0.754) sales guarantee -0.191 (0.461) annual compensation 0.109 (0.718) periodical payment 0.274 (0.130) economic benefit age (18-30) 31-40 -0.588 (0.208) 41-50 -0.368 (0.394) 51-60 -0.386 (0.353) 61-70 -0.077 (0.871) >71 -0.758 (0.168) membership (none) farmers union -0.323 (0.186) nature conservation/ environmental org. 0.209 (0.575) proportion of holding sales – to processor (0%) 1-30 % 0.389 (0.253) 31-60 % -0.240 (0.657) 61-100 % -0.456 (0.244) proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % -0.160 (0.633) 31-60 % 0.474 (0.324) 61-100 % -0.268 (0.507) proportion of holding sales – to cooperatives (0%) 1-30 % -0.227 (0.618) 31-60 % -0.885 (0.071) 61-100 % -0.592 (0.075) previous experience (no) yes 0.373 (0.335) self-chosen measures 0.127 (0.617) better results, higher payment 0.151 (0.561) collective agreement 0.140 (0.587) 0.301 (0.108) 0.229 (0.395) 0.453 (0.232) common payment 0.305 (0.085) labelled product -0.239 (0.314) paid by customers 0.039 (0.847) reduced land rent 0.391 (0.077) self-monitoring 0.361 (0.094) control by authority 0.192 (0.321) free training -0.088 (0.760) sales guarantee 0.003 (0.990) annual compensation -0.060 (0.843) periodical payment ‡ 0.389 (0.038) willingness to enroll age (18-30) 31-40 -0.802 (0.117) 41-50 ‡ -1.146 (0.016) 51-60 -0.813 (0.075) 61-70 -0.595 (0.251) >71 ‡ -1.711 (0.006) table 5. (continued). (continued) 95 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 innovative contract solutions for the agri-environmental-climate public goods provision outcome variable economic benefit that is a ppo/npl model (as the one in equation 4) of the manuscript), po/ pl models, as per the one depicted in equation 3) of the manuscript. in table 7, the predictor self-chosen measures fails to meet the assumption of proportionality of the odds. being older makes it more likely that the respondent will be in the current (or lower) category of easiness of understanding. having previously experienced land tenurealike measures makes it more likely that the respondent will be in a higher category of easiness of understanding. an increase in scoring of self-chosen measures makes it more likely that the respondent will be in a higher level of easiness of understanding, while an increase in scoring of sales guarantee makes it more likely that the respondent will be in the current (or lower) category. being older makes it more likely that the respondent will be in the current (or lower) category of applicability in the farm. higher values of control by authority makes it more likely that the respondent will consider “applicable” the lt contracts. being a holding with a share of 31-60% of sales directly to final consumers makes it more likely that the respondent will be in a higher category of the perceived applicability of lt contracts. an increase in scoring of reduced land rent makes it more likely that the respondent will be in a higher category of the ordinal outcome variable. an increase in scoring of control by authority makes it more likely that the respondent will perceive “applicable” the lt contract solution. a negative impact on the perceived economic benefit of lt contracts is generated by the increase in the scoring of sales guarantee. being older makes it more likely that the respondent will be in the current (or lower) category of willingness to enroll. being a holding with a share of sales of 1-30% to processors (compared to holdings not exposed to such trades) makes it more likely that the respondent will be in a higher category of willingness to enroll. being exposed to the sales to private wholesalers/retailers for a share of 31-60% makes it more likely that the respondent will be in the current (or lower) level of willingness, while it is positively impacted by reduced land rent. explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* membership (none) farmers union -0.406 (0.123) nature conservation/ environmental org. 0.033 (0.937) proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % 0.095 (0.785) 31-60 % -0.137 (0.771) 61-100 % -0.132 (0.709) direct cap payments (no) yes -0.264 (0.398) previous experience (no) yes 0.286 (0.492) self-chosen measures 0.426 (0.117) better results, higher payment -0.257 (0.366) collective agreement ‡ 0.423 (0.039) common payment ‡ 0.510 (0.007) labelled product -0.385 (0.126) paid by customers 0.261 (0.212) reduced land rent 0.428 (0.070) self-monitoring ‡ 0.691 (0.003) control by authority 0.139 (0.488) free training 0.267 (0.374) sales guarantee -0.477 (0.107) annual compensation 0.192 (0.583) periodical payment 0.325 (0.092) note: the reference modality of the explanatory variable is in parentheses. * vu = very unlikely, u = unlikely, n = neutral, l = likely, vl = very likely; p-values in parentheses; ‡ in bold indicates the 0.05 level of statistical significance. table 5. (continued). 96 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 d’alberto riccardo et al. table 6. model for value chain contract solution. explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* easiness of understanding membership (none) farmers union 0.276 (0.270) nature conservation/ environmental org. 0.230 (0.551) proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % ‡ -1.255 (0.000) 31-60 % -0.683 (0.150) 61-100 % 0.162 (0.659) proportion of holding sales – direct to final consumer (0%) 1-30 % ‡ 0.881 (0.019) 31-60 % ‡ 1.281 (0.025) 61-100 % -0.640 (0.126) specialization (arable) horticulture 0.096 (0.867) permanent 0.115 (0.711) livestock ‡ 1.020 (0.016) mixed 0.102 (0.781) utilized agricultural area rented in – in hectares ‡ 0.003 (0.046) direct cap payments (no) yes -0.171 (0.556) previous experience (no) yes ‡ 2.075 (0.000) self-chosen measures 0.022 (0.931) better results, higher payment -0.171 (0.526) labelled product -0.126 (0.606) paid by customers 0.164 (0.422) reduced land rent 0.289 (0.208) self-monitoring 0.343 (0.112) control by authority 0.146 (0.471) free training -0.036 (0.898) sales guarantee -0.005 (0.984) annual compensation 0.328 (0.304) periodical payment 0.121 (0.526) applicability in the farm age (18-30) 31-40 -0.048 (0.926) 41-50 -0.553 (0.247) 51-60 -0.580 (0.201) 61-70 -0.348 (0.508) >71 -0.628 (0.294) membership (none) farmers union -0.023 (0.930) nature conservation/ environmental org. 0.108 (0.792) proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % ‡ -0.773 (0.034) 31-60 % 0.337 (0.948) 61-100 % 0.023 (0.951) (continued) 97 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 innovative contract solutions for the agri-environmental-climate public goods provision explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* proportion of holding sales – direct to final consumer (0%) 1-30 % ‡ 0.844 (0.033) 31-60 % -0.032 (0.956) 61-100 % -0.453 (0.284) specialization (arable) horticulture -0.201 (0.727) permanent -0.208 (0.509) livestock 0.614 (0.159) mixed -0.628 (0.108) utilized agricultural area rented in – in hectares 0.003 (0.090) direct cap payments (no) yes 0.071 (0.816) previous experience (no) yes ‡ 1.461 (0.001) self-chosen measures -0.083 (0.754) better results, higher payment 0.071 (0.788) labelled product ‡ 0.841 (0.001) paid by customers -0.265 (0.209) reduced land rent 0.085 (0.726) self-monitoring 0.195 (0.381) control by authority ‡ 0.430 (0.038) free training 0.221 (0.438) sales guarantee 0.093 (0.748) annual compensation 0.299 (0.362) periodical payment 0.055 (0.783) economic benefit age (18-30) 31-40 -0.272 (0.534) 41-50 -0.482 (0.285) 51-60 ‡ -0.817 (0.032) 61-70 -0.596 (0.183) >71 -0.267 (0.613) proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % -0.366 (0.262) 31-60 % 0.538 (0.231) 61-100 % 0.270 (0.418) proportion of holding sales – direct to final consumer (0%) 1-30 % 0.531 (0.145) 31-60 % -0.035 (0.948) 61-100 % -0.640 (0.121) previous experience (no) yes -0.260 (0.451) self-chosen measures -0.156 (0.524) better results, higher payment 0.195 (0.429) labelled product 0.323 (0.181) reduced land rent 0.162 (0.458) self-monitoring 0.279 (0.195) control by authority 0.269 (0.144) (continued) table 6. (continued). 98 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 d’alberto riccardo et al. explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* free training -0.135 (0.618) sales guarantee 0.135 (0.619) annual compensation 0.309 (0.285) willingness to enroll age (18-30) 31-40 -0.973 (0.187) 41-50 -0.731 (0.300) 51-60 -0.764 (0.255) 61-70 -0.544 (0.461) >71 -1.048 (0.198) membership (none) farmers union -0.247 (0.437) nature conservation/ environmental org. 1.200 (0.188) proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % -0.721 (0.113) 31-60 % -0.673 (0.311) 61-100 % 0.133 (0.837) proportion of holding sales – to cooperatives (0%) 1-30 % -0.707 (0.177) 31-60 % -0.933 (0.126) 61-100 % 0.133 (0.744) proportion of holding sales – direct to final consumer (0%) 1-30 % 0.850 (0.058) 31-60 % 1.420 (0.061) 61-100 % 0.099 (0.843) specialization (arable) horticulture 0.384 (0.660) permanent -0.051 (0.893) livestock ‡ 1.171 (0.050) mixed -0.146 (0.754) utilized agricultural area rented in – in hectares 0.004 (0.097) direct cap payments (no) yes -0.095 (0.791) previous experience (no) yes ‡ -2.757 (0.001) self-chosen measures -0.354 (0.299) better results, higher payment 0.232 (0.528) labelled product 0.478 (0.128) paid by customers ‡ 0.463 (0.043) reduced land rent 0.012 (0.972) self-monitoring -0.150 (0.608) control by authority ‡ 0.501 (0.033) free training 0.333 (0.329) sales guarantee 0.564 (0.151) annual compensation 0.140 (0.759) periodical payment 0.109 (0.656) note: the reference modality of the explanatory variable is in parentheses. * vu = very unlikely, u = unlikely, n = neutral, l = likely, vl = very likely; p-values in parentheses; ‡ in bold indicates the 0.05 level of statistical significance. table 6. (continued). 99 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 innovative contract solutions for the agri-environmental-climate public goods provision table 7. model for land tenure contract solution. explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* easiness of understanding age (18-30) 31-40 -0.866 (0.080) 41-50 ‡ -1.019 (0.026) 51-60 ‡ -0.961 (0.030) 61-70 ‡ -1.146 (0.024) >71 -0.830 (0.156) membership (none) farmers union 0.250 (0.316) nature conservation/ environmental org. 0.592 (0.133) proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % -0.554 (0.125) 31-60 % -0.550 (0.229) 61-100 % -0.234 (0.518) specialization (arable) horticulture 0.944 (0.095) permanent -0.107 (0.725) livestock 0.199 (0.634) mixed -0.045 (0.908) organic production (no) yes 0.017 (0.952) direct cap payments (no) yes -0.320 (0.278) previous experience (no) yes ‡ 1.750 (0.000) self-chosen measures ‡ 0.606 (0.019) better results, higher payment -0.307 (0.270) labelled product -0.248 (0.304) paid by customers -0.059 (0.772) reduced land rent 0.432 (0.058) self-monitoring 0.152 (0.487) control by authority 0.249 (0.197) free training 0.254 (0.370) sales guarantee ‡ -0.580 (0.043) annual compensation 0.463 (0.140) periodical payment 0.295 (0.132) applicability in the farm age (18-30) 31-40 -0.706 (0.113) 41-50 -0.683 (0.085) 51-60 -0.558 (0.136) 61-70 ‡ -0.964 (0.027) >71 ‡ -1.023 (0.041) reduced land rent 0.324 (0.102) control by authority ‡ 0.306 (0.047) annual compensation 0.083 (0.722) periodical payment 0.198 (0.255) (continued) 100 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 d’alberto riccardo et al. explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* economic benefit age (18-30) 31-40 -0.616 (0.208) 41-50 -0.273 (0.539) 51-60 -0.685 (0.110) 61-70 -0.730 (0.140) >71 -1.015 (0.075) membership (none) farmers union 0.165 (0.517) nature conservation/ environmental org. 0.394 (0.296) proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % 0.083 (0.818) 31-60 % -0.946 (0.058) 61-100 % 0.427 (0.286) proportion of holding sales – to cooperatives (0%) 1-30 % -0.059 (0.898) 31-60 % 0.478 (0.355) 61-100 % -0.030 (0.925) proportion of holding sales – direct to final consumer (0%) 1-30 % 0.445 (0.235) 31-60 % ‡ 1.241 (0.040) 61-100 % -0.098 (0.818) specialization (arable) horticulture 0.899 (0.102) permanent -0.555 (0.076) livestock -0.320 (0.454) mixed -0.286 (0.467) previous experience (no) yes 0.761 (0.071) self-chosen measures 0.993 (0.053) ‡ -1.031 (0.019) -0.630 (0.224) -0.924 (0.105) better results, higher payment -0.133 (0.627) labelled product 0.130 (0.590) paid by customers 0.004 (0.985) reduced land rent ‡ 0.655 (0.004) self-monitoring 0.353 (0.110) control by authority ‡ 0.480 (0.013) free training -0.095 (0.736) sales guarantee ‡ -0.625 (0.029) annual compensation 0.370 (0.237) periodical payment 0.010 (0.959) willingness to enroll age (18-30) 31-40 -0.816 (0.163) 41-50 -0.889 (0.095) 51-60 -0.960 (0.065) 61-70 ‡ -1.447 (0.012) >71 ‡ -1.661 (0.011) table 7. (continued). (continued) 101 bio-based and applied economics 13(1): 73-101, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14016 innovative contract solutions for the agri-environmental-climate public goods provision explanatory variable vu vs u, n, l, vl* vu, u vs n, l, vl* vu, u, n vs l, vl* vu, u, n, l vs vl* membership (none) farmers union 0.083 (0.765) nature conservation/ environmental org. 0.768 (0.091) proportion of holding sales – to processor (0%) 1-30 % ‡ 1.208 (0.005) 31-60 % 0.058 (0.922) 61-100 % -0.169 (0.680) proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % -0.721 (0.065) 31-60 % ‡ -1.051 (0.036) 61-100 % -0.187 (0.650) proportion of holding sales – direct to final consumer (0%) 1-30 % 0.373 (0.349) 31-60 % 0.930 (0.125) 61-100 % -0.880 (0.060) specialization (arable) horticulture 0.861 (0.195) permanent -0.394 (0.236) livestock -0.224 (0.625) mixed -0.495 (0.240) direct cap payments (no) yes 0.031 (0.923) previous experience (no) yes 0.759 (0.095) self-chosen measures 0.265 (0.347) better results, higher payment -0.393 (0.195) labelled product -0.058 (0.829) paid by customers 0.275 (0.203) reduced land rent ‡ 0.848 (0.001) self-monitoring -0.263 (0.303) control by authority 0.359 (0.091) free training 0.196 (0.516) sales guarantee -0.373 (0.240) annual compensation 0.407 (0.260) periodical payment 0.325 (0.116) note: the reference modality of the explanatory variable is in parentheses. * vu = very unlikely, u = unlikely, n = neutral, l = likely, vl = very likely; p-values in parentheses; ‡ in bold indicates the 0.05 level of statistical significance. table 7. (continued). _hlk60054323 _hlk104829823 _hlk128742463 _hlk104830529 _hlk127347111 _hlk128647880 _hlk128647635 _hlk128646517 _hlk128649491 new pathways for improved delivery of public goods from agriculture and forestry stefano targetti1,*, andreas niedermayr2, kati häfner3, lena schaller2 assessing the landscape recovery scheme in the uk: a q methodology study in yorkshire, uk emmanouil tyllianakis upscaling environmental incentives in the common agricultural policy: an assessment of the potential of transfers from the first to second pillar fanny le gloux1,2,*, pierre dupraz1 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures: a pestle approach based on stakeholder statements theresa eichhorn1,*, lena schaller1, katri hamunen2,3, tania runge4,* innovative contract solutions for the agri-environmental-climate public goods provision: which features meet the farmers’ approval? insights from emilia-romagna (italy) riccardo d’alberto1,*, meri raggi2, davide viaggi3 the use of innovative contracts to provide agri-environmental public goods: comparing attitudes between ireland and other european countries tracy bradfield1,*, thia hennessy1, riccardo d’alberto2, emmi haltia3 bio -based and a ppl ied economics bae bio-based and applied economics 10(4): 253-264, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-11511 copyright: © 2021 i. dolgopolova, b. li, h. pirhonen, j. roosen. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: i. dolgopolova, b. li, h. pirhonen, j. roosen (2021). the effect of attribute framing on consumers’ attitudes and intentions toward food: a meta-analysis. bio-based and applied economics 10(4): 253-264. doi: 10.36253/ bae-11511 received: july 21, 2021 accepted: december 22, 2021 published: march 31, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: donato romano. discussant: leonardo boncinelli, donato romano orcid id: 0000-0001-7194-1236 bl: 0000-0001-7774-6095 jr: 0000-0002-3032-399x bae 10th anniversary paper the effect of attribute framing on consumers’ attitudes and intentions toward food: a metaanalysis irina dolgopolova, bingqing li, helena pirhonen, jutta roosen* chair of marketing and consumer research, tum school of management, technical university of munich, germany *corresponding author. e-mail: jroosen@tum.de abstract. this paper analyzes the existing literature on the effect of attribute framing on consumers’ attitudes and intentions with regard to food products. attribute framing includes a broader interpretation of gains and losses when a product attribute is presented in a dichotomous way, such as fat vs. lean or harm vs. benefit. meta-analysis results for the whole sample indicate that product attributes framed as gains have a higher effect on attitudes and intentions than product attributes framed as losses. grouping studies by outcome variables, the meta-analysis demonstrates a larger effect size for studies that assess consumer attitude while for studies dealing with consumer intention, the effect size is close to zero and insignificant. we observe from the metaregression results that the gain frame, the use of interaction terms, a specific product, and a student sample significantly influence consumers’ attitudes and intentions. keywords: attribute framing, food products, meta-analysis. jel codes: d91, i12. introduction the framing effect shows that decisions depend on the way in which outcomes are presented. in their seminal contribution, kahneman & tversky (1979) developed the prospect theory that serves the analysis of decisions under risk. according to the evidence they accumulated, choices depend on the gains and losses compared to the current situation rather than to absolute outcomes and the theory postulates that people dislike negative characteristics associated with a choice more than they value positive aspects. that is, the value function in prospect theory is s-shaped and steeper for losses than for gains, meaning that displeasure from a loss is stronger than the pleasure from an equivalent gain (see figure 1) (kahneman & tversky, 1979). thus, people choose differently depending on which characteristic of the choice is emphasized – gains or losses. this discrepancy creates a framing effect initially introduced in kahneman and tversky’s 1981 paper with an example of an asian disease (kahneman & tversky, 1981). in the examhttp://creativecommons.org/licenses/by/4.0/legalcode 254 bio-based and applied economics 10(4): 253-264, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-11511 irina dolgopolova, bingqing li, helena pirhonen, jutta roosen ple, participants of the experiment are confronted with the following problem: “imagine that the u.s. is preparing for the outbreak of an unusual asian disease, which is expected to kill 600 people. two alternative programs to combat the disease have been proposed. assume that the exact scientific estimate of the consequences of the program is as follows”. then, participants in one group are presented with choices a and b: “if program a is adopted, 200 people will be saved. if program b is adopted, there is a 1/3 probability that 600 people will be saved and 2/3 probability that no people will be saved”. and participants in another group are presented with the choices c and d: “if program c is adopted 400 people will die. if program d is adopted there is a 1/3 probability that nobody will die, and 2/3 probability that 600 people will die”. the choices in the original experiment were distributed as follows: program a – 72%, program b – 28%, program c – 22%, program d – 78%. results of the experiment demonstrated that the choice of the program depends on how the outcome is described, in terms of losses (deaths) or gains (survivals), and that people prefer risky outcomes when it comes to losses and certain outcomes when it comes to gains – an effect called loss aversion. the framing effect serves as a means to describe decision anomalies where people seem to deviate from consistent choice behavior because of various framings of outcome, context and goal. presenting or communicating attributes of products in diverse ways came to be referred to as attribute framing, and presenting the goal of behavior in diverse ways – as goal framing (levin et al., 1998). in this study we are particularly interested in attribute framing and thus goal framing is not discussed any further. although the framing effect was initially applied to explain decisions under risk, its meaning extended since then to broader interpretations, for example, when meat is presented as lean or fat (figure 1) (levin et al., 1998). the concept has, according to some, subsequently come to embody a widely understood, generic definition that stands for the conflicting reactions to information presented in disparate ways (braun et al., 1997). several authors stretched the definition and study concepts to merely positive versus negative, strengths versus risks, or even more versus less information framing (levin et al., 1998). the framing effect has been studied in various fields, including medical decisions, auditing evaluations, public health, environmental valuation, marketing, and consumer choice (levin et al., 1998; rothman & salovey, 1997; jin & han, 2014; kragt & bennett, 2012; svenningsen & thorsen, 2021). when applied in a marketing context, attributes are often not presented in bipolar, dichotomous ways and emphasize certain aspects to make them salient. for example, the benefits of meat substitutes that are framed in terms of “societal benefits”, “high tech”, and “same meat” in persuasive appeals are considered as attribute framing (bryant & dillard, 2019). different from risk framing or goal framing which represents loss and gain associated to an expected outcome, attribute framing selects an attribute of a product and describes it in a dichotomous way, such as fat vs. lean, tax vs. offset, and harm vs. benefit. attribute framing, which is usually delivered in the forms of labels, advertisement, and communications, has considerably influenced people’s choice preferences. for example, people are willing to pay more for a burger described as 75% lean than one described as 25% fat (levin & gaeth, 1988). they show a higher preference for 80% fat-free chocolate compared to 20% fat chocolate (braun et al., 1997). in the area of health-related decisions, framing of the choice outcomes provided some controversial results. rothman & salovey (1997) examined a number of framing studies related to the public’s health-related decisionmaking and found evidence of framing effects in hypothetical choice situations. they concluded that the effectiveness of choice frames depends on the illness-detecting or health-affirming function of a message. gallagher & updegraff (2012) in their study of message framing in health communication, found that gain-framed messages are more effective in encouraging prevention behaviors than loss-framed messages. two subsequent meta-analyses of messages regarding disease prevention behaviors also demonstrated conflicting results. o’keefe & jensen (2007) reported that the persuasiveness in disease-prevention communication is higher for gainframed messages than for loss-framed messages. however, their behavior-specific meta-analysis in 2009 reported figure 1. lean-fat framing of meat products with regard to gainloss framing. 255the effect of attribute framing on consumers’ attitudes and intentions toward food: a meta-analysis bio-based and applied economics 10(4): 253-264, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-11511 that loss-framed messages are slightly but significantly more persuasive than gain-framed messages (o’keefe & jensen, 2009). food choices can be associated with potentially negative consequences for health including the development of obesity and other non-communicable diseases. public policy interventions that attempt to influence consumer choices in the food domain use different communication methods to inform consumers about potentially damaging consequences of consuming certain kinds of foods. communicating the nutritional properties of foods can take the form of attribute framing. in this case, product attributes are described in two different ways: a) by emphasizing positive characteristics (e.g., dietary fibers and vitamins), or b) by presenting negative characteristics of foods (e.g., sugar and fat). the effectiveness of different communication strategies is typically measured in the form of consumer behaviors, intentions, or attitudes (gallagher & updegraff, 2012). this paper aims to provide a systematic review of the use of gain-loss attribute framing on food products. we want to investigate and quantify the extent to which other external factors affect the overall framing effect on people’s food choices across different countries. we are specifically interested in what kind of framing results in more positive consumer attitudes and increased intention to purchase healthy and environmentally friendly food products. although our initial approach suggested the inclusion of studies with actual behavior as an outcome, no such studies were identified through our search. we perform a meta-analysis to determine the effectiveness of a gain vs. loss framing and then use a meta-regression to explore study heterogeneity. the remainder of the paper is organized as follows. it starts with a detailed walkthrough of how the literature for the systematic meta-analysis is collected and selected. the next part presents the methods employed to code the data and build the final data frame for analysis. the result section includes the meta and regression analysis results. the major findings and noteworthy points are discussed in the following discussion section and the paper concludes. method a systematic screening of existing literature was first performed to collect and analyze published articles in peer-reviewed journals and conference proceedings via four academic search engines (sciencedirect, web of science, ebsco host, and agecon search), followed by additional random searches on google scholar, resulting in a final set of 25 articles published between 1987 and may 2021. the procedure of publication collection and selection is shown in figure 2. given the broad coverage and the various terms associated with the topic, we used a long boolean search query with an intersection set of “framing”, “food”, and a union set of “consumer behavior”, “consumer decision making”, “consumer choice”, “consumer preference”, “consumer perception”, “consumer willingness to buy”, “consumer willingness to accept”, “consumer willingness to pay”, “consumer buying behavior”, “consumer purchase intention”, and “consumer buying intention”. then, the abstract of each article was first examined in order to include only those articles that cover attribute framing in the food domain. we also set conceptual boundaries to only incorporate dichotomous framings (i.e., promotion vs. prevention, harm vs. benefit) on food choices. in other words, framing effects on other consumer behaviors related to food, such as food-wasting, recycling, and so on, were not considered relevant. furthermore, to systematically analyze the effect sizes of the main intervention (bipolar framing) and other explanatory factors, we needed the mean and standard deviation of each data point. hence, we excluded studies that did not report the means of the dependent variables and whose standard deviations of dependent variables are not computable from the information being reported. the final collection of studies included in this review is listed in table 1. in preparation of the data, besides extracting means and standard deviations, we also target variables that have been reported to influence consumers’ behaviors in the existing systematic reviews in the food realm. the literature review on vegetable consumption by nørnberg et al. (2016) revealed that an overall main effect might not display. the domainand individual-specificities of the figure 2. flowchart of publication gathering and selection process for the meta-analysis 256 bio-based and applied economics 10(4): 253-264, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-11511 irina dolgopolova, bingqing li, helena pirhonen, jutta roosen persuasive effect of loss vs. gain framing are often considered, especially in the food domain (britwum & yiannaka, 2016). due to the lack of evidence of the general effectiveness of the framing effect on consumers’ food choices, we decided to also look into other covariates. frewer et al.’s (2013) systematic review and meta-analysis on genetically modified (gm) food choices suggests that the food type and consumers’ geographic region affect the acceptance and prevalence of gm food. in lusk et al. ‘s (2005) and dannenberg’s (2009) meta-analysis studies table 1. products and attributes of the studies included in the analysis (by year of publication) n author(s) (year) product attribute interaction 1 levin, i. (1987) ground beef lean/fat n/a 2 levin, i., gaeth, g. (1988) ground beef lean/fat n/a 3 loke, w.h., lau, s. (1992) hamburger patty non-fat/fat n/a 4 braun, k. et al. (1997) milk chocolate bar fat-free/fat gender: female vs. male 5 van assema, p. et al. (2001) low-fat diet/fruits and vegetables positive/negative consequences dietary behavior: fat vs. fruit & vegetables 6 levin, i. et al. (2002) ground beef lean/fat n/a 7 orth, u. et al. (2007) apples, bottled water positively/negatively framed advertisements nation 8 kees, j. (2011) healthy/unhealthy food advantages/disadvantages of healthy/unhealthy foods regulatory focus: promotion vs. prevention; time orientation: present vs. future 9 van’t riet, j. et al. (2013) fast food nutrition information n/a 10 jin, h.j., han, d.h. (2014) beef tallow/cow milk food safety prior knowledge: low vs. high 11 abrams, k. (2015) chicken products environmental benefits, animal welfare n/a 12 bosone, l. et al. (2015) healthy diet vitamin and nutrient content regulatory focus: promotion vs. prevention 13 chang, m.-c., wu, c.-c. (2015) organic food environmental benefits environmental motivation: low vs. high 14 de bruijn, g.-j. et al. (2015) fruit fruit intake benefits descriptive majority norm: low intake vs. high intake 15 yan, c. (2015) junk food advantages/disadvantages of junk food attitudinal ambivalence: univalent vs. ambivalent 16 britwum, k., yiannaka, a. (2016) beef products e.coli vaccination media story: included vs. not included 17 chen, m.-y. (2016) high-fiber oat milk health benefits self-construal: independent vs. interdependent; temporal construal: proximal vs. distant 18 koenigstorfer, j., baumgartner, h. (2016) trail mix dietary permitted or dietary forbidden fitness label: included vs. not included; dietary restraint: with vs. without; gender: female vs. male 19 tran, v. et al. (2016) food products benefits/risks of nanotechnology n/a 20 hilverda, f. et al. (2017) organic food advantages/disadvantages of organic foods interaction partner: expert vs. peer vs. anonymous 21 lundeberg, p. et al. (2018) variety of food products healthfulness n/a 22 kuo, k. et al. (2019) fat-free yoghurt, ice cream advantages/disadvantages of yoghurt/ice cream food categorization: virtue vs. vice; regulatory focus: promotion vs. prevention 23 vidal, g. et al. (2019) snack food nutrition information n/a 24 cui, h.j. et al. (2020) ethnic foods advantages/disadvantages of ethnic foods regulatory focus: promotion vs. prevention 25 shan, l. (2020) organic food benefits/losses of buying organic food price anchor: low vs. high; product knowledge: low vs. high 257the effect of attribute framing on consumers’ attitudes and intentions toward food: a meta-analysis bio-based and applied economics 10(4): 253-264, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-11511 on the valuation of gm food, it was found that geographic location of study and food product characteristics significantly influence the percentage premium that participants are willing to pay for non-gm foods. in addition, a meta-analysis on consumer’s willingness to pay for farm animal welfare indicated a significant effect of socio-economic characteristics of participants (lagerkvist & hess, 2011). therefore, our study considers covariates including sample characteristics, geographic location, product characteristics, means of presentation, and control for types of dependent measures the following final list of variables were extracted from the studies: authors and year of publication, product and attribute in question, sample size, treatment type, means and standard deviations of the dependent variable, if the study included interaction terms, the type of attribute communication, if the study was conducted on a student sample, and if it was conducted online. table 2 provides an overview of the variability of products and attributes included in the analysis for the whole sample and for the studies on attitudes and intentions. broadly, the studies discussed attributes related to different health and sustainability issues. health aspects framed in terms of gains and losses included nutritional information and food safety. sustainability aspects included environmental benefits, animal welfare, and organic and ethnic foods. the sample sizes of studies included in the analysis differed between 25 and 433, with 32% of studies having a sample size of less than 100 participants. we observed an almost equal distribution of studies on variables such as the use of frames, outcome variables, interaction terms, specific products, and student samples. thirty-two percent of studies were conducted in the usa, 13% used product labels as means of attribute communication; and 30% of the studies were conducted online. to determine the overall effect of gain vs. loss attribute framing, we performed meta-analysis, using means and standard deviations obtained from the studies. missing standard deviations are a common problem in meta-analysis. we employed the computational method recommended by the cochrane handbook for systematic reviews of interventions (higgins & green, 2011), which enables the calculation of missing standard deviation from the reported t-value and difference in means according to the following formula: σ = difference in means (dm) (1) t to explore between-study heterogeneity, we used mean values of attitudes and intentions as a dependent variable (table 2). attitudes and intentions measured on a 7-point likert scale are included directly. when the outcome variable in a study was measured on a 5-point likert scale, the values were rescaled to the 7-point scale. we use the following random-effects model: table 2. definitions and means and standard deviations of variables included in the analysis. variable definition mean (standard deviation) whole sample n=76 attitude n=40 intention n=36 mean (dependent variable) mean values of attitudes and intentions on a 7-point likert scale 4.40 (0.86) 4.64 (0.64) 4.12 (0.99) frame 1 gain frame, 0 loss frame 0.50 (0.50) 0.50 (0.51) 0.50 (0.51) usa 1 if the study is conducted in the usa, 0 otherwise 0.32 (0.47) 0.50 (0.51) 0.11 (0.32) outcome 1 attitude, 0 intention 0.53 (0.50) interaction 1 interaction term, 0 no interaction 0.51 (0.50) 0.48 (0.51) 0.56 (0.50) product 1 specific product, 0 product category 0.53 (0.50) 0.75 (0.44) 0.28 (0.45) label 1 label is used, 0 other communication forms 0.13 (0.34) 0.25 (0.44) 0.00 (0.00) student 1 student sample, 0 other 0.50 (0.50) 0.45 (0.50) 0.56 (0.50) online 1 online study, 0 other 0.30 (0.46) 0.33 (0.47) 0.28 (0.45) 258 bio-based and applied economics 10(4): 253-264, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-11511 irina dolgopolova, bingqing li, helena pirhonen, jutta roosen meani = β0 + β1 framei + β2 usai + β3 outcomei + β4 interactioni + β5 producti + β6 labeli + β7 studenti + β8 onlinei + ui + εi (2) where meani is the mean value of attitudes and intentions elicited from the studies. the two error terms are ui~n(0,τ2), where τ2 is the between-study variance, and a normally distributed εi~n(0,τ2). results we used the random-effects model to analyze the effect sizes due to the high heterogeneity across participants’ characteristics and methodologies in selected studies. the user-written package “metan” in stata 13 was employed. the forest plot resulting from meta-analysis is presented in figure 3. the overall effect indicated by the standardized mean difference (smd) is positive and significant, indicating that the gain frame results figure 3. meta-analysis results (whole sample). 259the effect of attribute framing on consumers’ attitudes and intentions toward food: a meta-analysis bio-based and applied economics 10(4): 253-264, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-11511 in higher attitudes and intentions than the loss frame. along with the effect sizes, the meta-analysis reports a measure of study heterogeneity which is attributed to variability in the treatment effect rather than to variation in sample sizes (higgins & thompson, 2004). results demonstrated i2 = 98.2%, which calls for further investigation into the studies’ heterogeneity via a metaregression and a sub-group analysis (higgins & green, 2011). the results of a sub-group meta-analysis showed a larger effect size for studies that assess consumer attitude (figure 4) than for studies dealing with consumer intention where the effect size is close to zero and insignificant (figure 5). also, the variation of the effect sizes for studies that measure consumer intention is considerably bigger than those that measure consumer attitudes. whereas a majority of the data points from the attitude subgroup lie on the side favoring the gain framing, the data points from the intention subgroup tend to spread out more evenly. we used a meta-regression specifically designed for meta-analyses (harbord & higgins, 2008), which estimates the between-study variance and coefficients using weighted least squares. stata 13 package “metareg” was used for the estimation. we analyzed the whole sample and then split it into separate estimations for attitudes and intentions. the list of explanatory variables together with coefficients and standard errors is provided in table 3. for the whole sample estimation, residual variation due to heterogeneity equaled 17.84%, while the included covariates explained 72.32% of the between-study variance. the i2 value of 17.84% can be considered as a low level of heterogeneity, according to the classification in higgins et al. (2003). we observed from the results of this regression that the gain frame, the use of interaction terms, a specific product, and a student sample significantly influence consumers’ attitudes and intentions. a gain frame increases consumer attitudes and intentions towards food products with health and environmental figure 4. sub-group meta-analysis results for attitude. 260 bio-based and applied economics 10(4): 253-264, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-11511 irina dolgopolova, bingqing li, helena pirhonen, jutta roosen benefits. when we split our sample into two sub-groups, depending on whether the papers included investigating attitudes or intentions, we observed that the gain frame is significant for attitudes but not for intentions. interestingly, the location of the study in the usa has a different effect depending on the outcome variable. when it comes to consumer intentions, the use of an us sample increases the mean value. on the contrary, attitudes decrease when the study is not conducted in the us. the use of interaction terms positively influences the outcome variable; however, this effect is mainly attributed to the studies about consumer intentions. unlike more general product categories, specific products negatively affect the attitudes to purchase food products with health and environmental benefits. the use of a student sample is marginally significant in the regression for the whole sample; however, this result does not replicate when the sample is split. the use of labels to communicate health and environmental benefits to the consumers is positively associated with consumer attitudes. studies conducted online increase consumer intention to purchase food products; however, the strength of the association is marginal. discussion our results demonstrate that when it comes to food products, the use of gain frames elicits stronger responses from consumers than the use of loss frames. previous research has already indicated that encouraging positive behaviors by evoking loss aversion is not necessarily a guiding principle when it comes to health and environmental benefits (gallagher & upbegraff, 2012), especially in the domain of attribute framing. loss-framed messages are mainly effective when it comes to decisions involving significant risk. food choices usually serve an illness/environmental harm prevention function and are figure 5. sub-group meta-analysis results for intention. 261the effect of attribute framing on consumers’ attitudes and intentions toward food: a meta-analysis bio-based and applied economics 10(4): 253-264, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-11511 not associated with an immediate high level of risk (gallagher & upbegraff, 2012). as expected and congruent with existing literature, both types of framing (gain vs. loss) and interaction factors significantly influence the effectiveness of framing overall. interestingly, the effect of interaction variables is only demonstrated in intentional but not attitudinal measures, whereas it is the opposite for the effect of types of framing. in other words, there is a main overall effect from the framing intervention on people’s attitudes toward food choices, but the main effect of the intervention on people’s intention does not manifest. the significant change in people’s intention of food choices is determined by various moderators, such as individual food product knowledge, regulatory focus, temporal construal, and so on. one possible explanation, according to both the theory of reasoned action (tra) and the theory of planned behavior (tpb), is that all potential external influential factors on intentions and behaviors are thought to be mediated by the attitudes and subjective norms, plus perceived behavioral control for tpb (ajzen & fishbein, 1980). therefore, it is sensible that, as an antecedent factor of intentions, attitudes are more malleable and directly influenced by external interventions than intentions. the effectiveness of framing in changing people’s intentions is more scenarioand individualspecific, which is explained by different interaction terms. consumer attitudes based on beliefs and values often surpass specific products (purhoit, 2012). when it comes to product categories, like fast food or organic food, consumers tend to express their attitudes more readily than when it comes to specific products and labels. as consumers often lack specific knowledge about health and environmental benefits (vermeir & verbeke, 2006), it is easier for them to express their preferences in terms of general product categories. the use of labels as means of conveying the attribute framing compared to other communicative vehicles, such as advertisement and text-based marketing messages, shows a significant increment in people’s attitudes toward making wiser (be it healthier or more environmentally conscious) food choices. it demonstrates that labels employing few words and visuals that concisely communicate the benefits of healthier or more environmentally friendly food (or the harms of the opposite) are more effective in influencing people’s attitudes toward that food. one possible explanation is that consumers are used to obtaining information about food from labels. therefore, they are more f luent in processing information presented with labels. looking at the studies in the meta data by year reveals how the application of attribute framing to food consumption has been gaining research attention and interest. before 2000, the literature on the framing effect related to food choices was, albeit influential, sparse. studies in this domain increased considerably after 2010, in terms of both number and diversity. it was only until recently that researchers mostly focused on how framing could be utilized to influence people’s food choices for the purpose of nutrition and health. three decades after the initial study of the framing effect on food choices, topics have been broadened to cover environmental impact, animal welfare, livestock vaccination, nanotechnology, and so on. the origins of the studies also expanded from the united states to the rest of the world and changed from studies based on student samples to more demographically representative ones. the demographical and topical variability enabled examining other factors that influence the effect of attribute framing on food choices. the benefits and harms resulting from different food choices are not immediate and consequential compared to other decision-making tasks where decisive influences from attribute framing on people’s judgements and decisions have been described, e.g., in lottery and medical treatment decisions. according to temporal discounting theories, people are less sensitive to the losses and gains that manifest themselves later in time. health-benefiting and pro-environmental decisions are typically intertemporal decisions. the disutility that people perceive to experience in the future from eating unhealthy food table 3. results of the meta-regression1. variables coefficients (std. err.) whole sample n=76 attitude n=40 intention n=36 frame 0.27 (0.13)** 0.51 (0.09)*** -0.01 (0.09) usa 0.10 (0.20) -0.74 (0.20)*** 1.01 (0.34)*** interaction 0.30 (0.14)** -0.18 (0.14) 0.33 (0.14)** product -0.32 (0.18)* -0.93 (0.46)* -0.09 (0.44) label 0.30 (0.20) 0.59 (0.12)*** --student -0.57 (0.31)* -0.02 (0.45) -0.71 (0.55) online 0.28 (0.29) 0.83 (0.51) 0.88 (0.49)* intercept 4.60 (0.30)*** 5.27 (0.41)*** 4.14 (0.51)*** adj. r2 72.32% 100.00% 98.74% τ2 0.07 0.00 0.00 i2 17.84% 0.00% 0.00% *, **, *** denotes significance at 0.10, 0.05, and 0.01 level, respectively. 1 in meta-regression r2 indicates the percentage of between-study variance explained by the covariates. the value of 100% indicates that the effect size does not vary substantially across studies. 262 bio-based and applied economics 10(4): 253-264, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-11511 irina 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(2015). persuading people to eat less junk food: a cognitive resource match between attitudinal ambivalence and health message framing. health communication, 30(3), 251–260. https://doi.org/10.10 80/10410236.2013.842525 volume 10, issue 4 2021 firenze university press the effect of attribute framing on consumers’ attitudes and intentions toward food: a meta-analysis irina dolgopolova, bingqing li, helena pirhonen, jutta roosen* rural areas between locality and global networks. local development mechanisms and the role of policies empowering rural actors francesco mantino understanding the bioeconomy: a new sustainability economy in british and european public discourse irene sotiropoulou1, pauline deutz2 sustainable water resources management under population growth and agricultural development in the kheirabad river basin, iran ghasem layani1,*, mohammad bakhshoodeh2, mansour zibaei2, davide viaggi3 the distributors’ view on us wine consumer preferences. a discrete choice experiment lina lourenço-gomes, tânia gonçalves*, joão rebelo 1 the cost of joining forces: collective organisations and the economic performance of dairy farms in south west england authors: paolo nota1, daniele curzi1*, mauro vigani2,3 affiliations: 1 department of environmental science and policy, university of milan, italy. 2 european commission, directorate-general for agriculture and rural development (dg agri), brussels, belgium. 3 countryside and community research institute, university of gloucestershire, cheltenham, uk. * corresponding author daniele.curzi@unimi.it this short communication has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the version of record. please cite this article as: nota p., cruzi d., vigani m., (in press). the cost of joining forces: collective organisations and the economic performance of dairy farms in south west england ,bio-basedand applied economics, just accepted. doi:10.36253/bae-17342 abstract this note highlights the importance of sale agreements for the economic performance of dairy farms in south west england. the reduction in public intervention in milk supply governance led to market coordination through supply chain agreements (scas), resulting in unbalanced power dynamics that reduced farmers’ bargaining power and made them vulnerable to unfair practices. collective organisations are seen as a solution to protect farmers. using coarsened exact matching, the paper estimates the effect of collective organisation participation on selling prices, production costs, and markup in a sample of 200 farms in somerset and devon. the results show that collective mailto:daniele.curzi@unimi.it 2 organisations are associated with 7.3% higher production costs and 23 percentage points lower markup, mainly due to costs associated with milk collection, transport, storage, marketing, and adherence to strict quality standards. however, these costs are counterbalanced by secured market access, stronger negotiating power, and better alignment with market demand. these findings underscore the need to further investigate collective governance in developed agricultural markets. keywords: dairy farming, collective organisations, markup, coarsened exact matching jel codes: q12, q13 1. introduction this note highlights the importance of sales arrangements for the economic viability of farms, analysing the case of dairy farming in somerset and devon, england. with the abolition of milk quotas in 2015, the dairy sector experienced a shift towards a more market-driven model. moreover, since brexit the united kingdom (uk) has moved away from the european union’s common agricultural policy (cap) and developed a new framework centred on the environmental land management (elm) schemes. while elm emphasizes environmental sustainability and productivity, it does not directly address key challenges for small producers, particularly those arising from vertical and horizontal relationships with other companies in the supply chain. the english dairy sector has particularly experienced the effects of these changes. since 2015, the number of dairy farms in england declined by about 3.3% every year1. continued pressure on dairy margins contributed to the decline, with growth of milk-to-feed price ratios of only 0.8% per year insufficient to generate sustainable profits2. in addition, the abolition of the quota system changed the mechanism for aligning milk production to consumer demand, giving a more important role to supply chain agreements (scas). within this evolving supply chain, smaller uk dairy producers deal directly with larger companies – both upstream (e.g., feed and energy suppliers) and downstream (e.g., dairy processors and retailers) – which have greater bargaining power and often set their private quality standards (sexton, 2012). the growing gap in access to market information between farmers and their trading partners can also lead to unfair trading practices, further disadvantaging smaller producers (fałkowski et al., 2017). with the deregulation of the dairy market, reduced policy support, and concentrated market power, competitive pressures on milk producers have intensified. this motivates the development of alternative forms of contractual relationships. collective organisations such as cooperatives can offer 1 calculated on ahdb data extracted on the 18 december 2024 from https://ahdb.org.uk/dairy/gb-producer-numbers 2 calculated on ahdb data extracted on the 18 december 2024 from https://ahdb.org.uk/dairy/milk-to-feed-price-ratio https://ahdb.org.uk/dairy/gb-producer-numbers https://ahdb.org.uk/dairy/milk-to-feed-price-ratio 3 farmers improved bargaining power, better access to affordable inputs, and enhanced price stability. most research on welfare distribution within agricultural supply chains focuses on price transmission (bakucs et al., 2014), with few studies on contracts’ effects on farmer welfare, largely in developing countries (bellemare and bloem, 2018). exceptions include michalek et al. (2018) on slovak farms and verhaegen and van huylenbroeck (2001) in belgium. there are, however, no studies examining collective organisations’ impact on the economic performance of uk dairy farmers. using survey data from 200 dairy farmers in devon and somerset and a coarsened exact matching (cem) approach (porro and iacus 2009; iacus et al. 2012), we estimate the effect of participating in a collective organisation on selling prices, production costs, and markup. additionally, with penalised regression, we explore which agreement and farmer’s characteristics are linked to participating in collective organisations. the findings show that selling through a collective organisation results in 7.3% higher production costs and approximately 23 percentage points lower markups. these increased costs stem from milk collection, transportation, storage, marketing, promotion services, and the production of milk that meets the collective organisation’s strict standards. however, these costs are offset by guaranteed market access, stronger bargaining power with retailers and processors, and better alignment with market demand, helping to prevent oversupply and the price volatility that can come with it. the paper is organized as follows: section 2 provides a conceptual framework for our research question. section 3 describes the cem and the penalised regression methodologies and provides an overview of the data. section 4 shows the results. finally, section 5 provides a discussion of the results and concludes. 2. conceptual framework we adopt a transaction cost framework (williamson 1989) to analyse how farmers’ decisions to either participate in a cooperative or sell through the spot market affect their performance. according to this framework, farmers are assumed to choose the option that minimizes their overall transaction costs. participation in a collective organisation is widely associated with enhanced bargaining power, improved market access, and lower transaction costs – such as those related to searching for buyers, negotiating terms, and enforcing agreements (alho 2015). in addition, collective arrangements can help reduce market uncertainties. however, while transaction costs may be lower within cooperatives, other types of costs can increase. these may include compliance with higher quality standards, coordination of logistics, and administrative overhead. despite these additional costs, cooperatives often benefit from greater bargaining power through the pooling of commodities and resources, which 4 can lead to higher farm-gate prices. based on this framework, we expect that joining a collective organisation have a positive effect on the price received by farmers. this hypothesis is frequently supported in the empirical literature, although the evidence comes primarily from developing countries (grashuis and su 2019). in contrast, the effect on overall costs is less clear, as the reduction in transaction costs may be offset by increased production and coordination expenses. as a result, the net impact on farmers’ markup depends on whether the price premium is sufficient to compensate for potential additional costs. 3. methodology and data to reduces the imbalance in covariates between the treated and control groups and to evaluate the differences in outcomes between them, we use the coarsened exact matching (cem), a counterfactual matching methodology. to match a treated unit with similar control units, the cem first coarsens each variable into substantively meaningful groups and then performs an exact match based on these coarsened data. finally, it retains only the original (uncoarsened) values of the matched data. it is relevant to mention that higher coarsening (higher number of strata) is associated with a lower level of imbalance, which comes however at the expense of a higher drop in observations. after preprocessing data with cem, the analysis can follow using a simple difference in means provided by a linear regression of outcome on treatment (for details see porro and iacus 2009; blackwell et al. 2009, iacus et al. 2012). a limitation of our data is that we observe covariates only for the survey year, and not before the treatment (i.e. selling through a collective organisation). therefore, we cannot ensure covariate balance in the pretreatment period, nor we can confirm their independence from participating in a collective organisation. consequently, this analysis should not be interpreted as causal, but rather as a more accurate estimate compared to a simple comparison between treated and control groups. in the second step, we investigate agreements and farmers’ characteristics associated with participation in a collective organisation using a penalised regression approach. in particular, we apply (logit) lasso, elastic net, and ridge. penalised regressions add a ‘penalty’ to the estimated coefficients, forcing them towards zero (lasso and elastic net) or exactly to zero (ridge), therefore reducing model complexity and allowing for the selection of explanatory variables. we use these methods to select supply chain features and farmers’ characteristics associated with collective organisation agreements among 30 different explanatory variables. 3.1. data description 5 because of the lack of data on scas, we use survey data from a small but representative sample of 200 dairy farmers in devon and somerset (south west england). the sample includes 88 farms from somerset and 112 from devon. the total farm population in the two regions is approximately 1,300. with this sample size, the survey maintains a 5% margin of error (within the commonly accepted threshold ≤10%). in these two counties, there are approximately 20% of all english dairy farmers. the survey was conducted by telephone interviews in november-december 2017 and the covered questions refer to the 2016-17 financial year. the binary treatment variable distinguishes between farms selling milk exclusively through a collective organisation – cooperatives, producer organisations (po), inter-branch organisations (ibos), or farmers’ unions and associations – or through individual businesses with a bilateral contract (e.g., retailers). the outcome variables are milk selling price (€/litre), production costs (€/litre), and markup (%). the markup is calculated as follows: 𝑚𝑎𝑟𝑘𝑢𝑝 = 𝑠𝑒𝑙𝑙𝑖𝑛𝑔 𝑝𝑟𝑖𝑐𝑒 − 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑐𝑜𝑠𝑡𝑠 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑐𝑜𝑠𝑡𝑠 (1) to enhance comparability between farms using different sales channels, we consider only those with exclusive contracts (i.e., towards a single collective organisation or individual business). among the 200 farmers, 173 have an exclusive selling agreement. we considered 4 observations as outliers, i.e. those with a markup lower than the 1st and greater than 99th percentile of the sample distribution. after removing farmers with incomplete data and outliers, the final sample size for the analysis is 169 observations. summary statistics of all the variables are presented in table a1 in the appendix. 43.8% of farms in our sample participate in a collective sale agreement. the average selling price in our sample is about 0.25 euro per litre of milk, while the production cost is about 0.21 €/litre. the average markup is about 36%. we use the cem approach to reduce covariates imbalances among farmers. covariates used for matching are the age (4 categories) and the education of leading farmers (4 categories), total milk production (litres), hectares used for milk production (ha), herd size (number of cows), yield (litre/cow), and full-time workers. in the appendix, we provide a description of the strategy used to select the “preferred” cutpoints combination used for performing the cem. table a2 in the appendix shows the covariates imbalances before and after matching. we focus on the ‘multivariate l1 distance’ that represents the overall imbalance with respect to the full joint distribution (including interactions) of the covariates between the treated and control groups. panel a shows a value of l1 before matching equal to 0.865. it represents a baseline reference for the unmatched data. a good matching solution would produce a reduction in such a statistic. after considering different alternative cutpoints to define the coarsening, we select those that reduce the the value of the multivariate l1 6 distance, preserve statistical power, and minimize the loss of observations (see appendix). panel b shows a value of l1 after matching equal to 0.637, which is about 23 percentage points lower than the unmatched case (with 107 farmers being matched). furthermore, table a3 presents a more standard covariates (average) imbalance and a t-test for the difference in average values that never rejects the hypothesis that they are equal to zero. 4. results table 1 presents our main findings. the estimated coefficient for the treatment variable in column 1 shows that selling milk through a collective organisation rather than to individual businesses is associated with a slightly higher average selling price (0.003 €/litre), although not statistically significant. as shown in column 2, collective sales have statistically significant higher production costs of 0.015 €/litre (about +7.3% of the average costs). this increase in costs leads to a significant reduction in markup of 23 percentage points (column 3).3 a back-of-the-envelope estimate suggests that the sample average farmer selling exclusively through a collective organisation would face a revenue increase of €5,550 and an increase in total costs of €27,750, based on average annual milk production of 1,850,000 litres. robustness checks, such as unmatched ols, the addition of covariates, and a propensity score matching support these findings (see table a4). qualitatively similar results are reached using 150 different combinations of cutpoints in the cem algorithm (see figure a1). to understand why scas with collective organisations imply higher production costs and lower markup, we further investigate five categories of agreements’ characteristics associated with collective organisations using penalised (logit) regression, namely: i) special contract clauses (contract duration, services, producer protections); ii) pricing methods; iii) timing of milk delivery and pricing; iv) costs associated to the agreement; and v) required quality and production standards. given a large number of variables (30 in total), we apply penalised regression methods (namely, ridge, lasso, and elastic net) to select those variables with higher predictive power. table 1. regression results after cem of selling to a collective organisation on farmer’s outcomes (1) (2) (3) selling price €/litre production costs €/litre markup % 3 the sample size decreases from 169 to 107 observations after applying cem. this reduction may raise concerns about generalizability and statistical power (see appendix for further discussion). the statistical (post) power tests for each outcome (price, costs, and markup) are 0.21, 0.72, and 0.82, respectively. 7 collective (binary) 0.003 0.015* -0.233* (0.006) (0.008) (0.126) observations 107 107 107 r2 0.002 0.030 0.038 note: the treatment variable ‘collective’ is = 1 if the exclusive sale is through a collective organisation, 0 otherwise. standard errors in parentheses are robust to heteroskedasticity. significance levels: * p<0.1, ** p<0.05, *** p<0.01. table a4 in the appendix shows the standardised coefficients of the selected variables associated with the lasso and elastic net estimations, while figure a1 shows the absolute value of the standardised coefficients from the ridge estimation. we then run a standard logit model regressing the binary variable that identifies the agreement with a collective organisation on the selected variables. table 2 shows the estimated coefficients.4 dairy farmers selling through a collective organisation are more likely to get paid after the milk delivery and to receive a price that is based on the yearly organisation’s profit. this sca is also more likely to be associated with logistics (collection, storage, transport, handling, etc.), and marketing costs. looking at the overall sample (table 2, column 2), dairy farmers selling through a collective organisation are more likely to comply with higher quality standards for the milk (e.g., taste and nutritional content), increasing production costs. conversely, they are less likely to receive a price based on production costs, therefore, their milk prices do not follow the volatility of input costs. finally, they are less likely to comply with animal welfare standards. table 2. logit model using selected variables from lasso and elastic net describing agreements’ characteristics associated with collective organisations. (1) (2) matched obs. whole sample collective collective payment after delivery 2.365*** 3.195*** (0.826) (0.783) price varies with the share of profits 1.556*** 1.855*** (0.553) (0.481) logistic costs 1.059** 0.918** (0.510) (0.457) marketing costs 0.537 4 note that we are not interested in the magnitude of the coefficients or their causal interpretation. we aim to highlight the presence or absence of relevant features in the case of collective organizations. 8 (0.445) price varies with production costs -0.957* (0.540) quality standards 2.057** (0.883) animal welfare standards -2.393*** (0.853) observations 97 161 pseudo r2 0.25 0.34 note: the dependent variable ‘collective’ is = 1 if the exclusive sale is through a collective organisation, 0 otherwise. the explanatory variables are all binary variables (‘yes’ = 1, ‘no’ = 0). the exact questions are provided in the appendix. eight farmers did not answer all the questions included, therefore the number of observations is lower than in table 1. standard errors in parentheses are robust to heteroskedasticity. significance levels: * p<0.1, ** p<0.05, *** p<0.01. to better understand the reasons why dairy farmers sell through collective organisations, we perform an additional analysis on two groups of variables: i) the farmers’ perception towards the sca; ii) the farmers’ risk-taking attitude. these variables are measured on a 1-5 likert scale (1 = strongly disagree, 5 = strongly agree) and they are analysed using a logit regression. complete questions are available in the appendix. table 3 summarizes the results. farmers who do not have alternative options are not associated with collective organizations. this implies that farmers do have other opportunities to sell their milk. similarly, farmers stating that the sale agreement provides more stable prices from year to year than alternative buyers are less likely to rely on collective organisations. this means that cooperatives/po offer fewer stable prices. however, they offer more opportunities for negotiating prices, an advantage less likely when engaging with individual businesses (e.g., retailers). a negative perception is that farmers who see delays in payments are more likely to be associated with a collective organisation. looking at the risk attitudes, farmers with higher attitudes towards production risks (such as adopting new milking regimes, new fodder, and new breed of cow), marketing, and pricing risks are less likely to be associated with collective organisations. contrary, attitudes toward financial risks (e.g. new investments) increase the propensity to sell through a collective organisation. these results suggest that farmers seek stable market access and protection and at this end, they are willing to accept price volatility, higher costs and financial exposure which can be faced through a higher predisposition to taking financial risks. 5. discussion and conclusion 9 sales agreements are fundamental to the economic viability of farms. using a sample of farmers in somerset and devon, england, in this note we present the results of an empirical analysis showing that dairy farmers face a trade-off between benefits and costs when selling through collective organisations. the benefits consist especially in market coordination, through higher negotiating power with respect to retailers and processors, and alignment with market demand to avoid oversupply that can lead to milk price volatility. without the milk quota system, the pricing mechanism of the collective organisation is the main tool to regulate supply. different types of collective organisations adopt different pricing mechanisms. a&b pricing mechanisms – farmers receive a prices on defined volumes of milk agreed in advance, while any excess of milk delivered is paid a b price discouraging overproduction – are among the most common tools to keep supply and demand tightly controlled. larger cooperatives, such as arla, buy all the milk delivered but rely on their ability to export to tighten the supply reducing the need for price adjustments and allowing farmers to expand. table 3. farmers’ perception and risk attitudes and their association with collective organisations. (1) (2) matched whole sample collective collective no alternative options for selling milk -0.213 -0.156 (0.168) (0.122) higher prices than alternative buyers -0.119 -0.013 (0.231) (0.165) more stable prices than alternative buyers -0.400* -0.497*** (0.242) (0.165) more possibilities for negotiating prices 0.302 0.292* (0.205) (0.154) there are delays in the payments 0.166 0.295 (0.269) (0.224) the costs of this sale agreement are too high 0.278 0.087 (0.237) (0.184) willingness to take production risks -0.266 -0.382** (0.226) (0.173) willingness to take marketing and price risks -0.191 -0.062 (0.248) (0.187) willingness to take financial risks 0.600*** 0.487*** (0.230) (0.168) observations 91 154 10 pseudo r2 0.12 0.12 note: explanatory variables are on a 1-5 likert scale. the exact questions are provided in the appendix. ten farmers did not answer all the included questions, therefore the number of observations is lower than in table 1. standard errors in parentheses are robust to heteroskedasticity. significance levels: * p<0.1, ** p<0.05, *** p<0.01. however, these benefits come with costs that with deregulation have been partially shifted from public support to farms. farmers pay for services such as milk collection, transport and storage, and for marketing and promotion. moreover, farmers make products to the collective organisation’s specifications, owing full responsibility for the competencies and capital needed for the process technology. indeed, farmers selling through collective organisations show a higher propensity to take financial risks, such as taking loans for investments. however, these costs are the price farmers pay for having some confidence that their milk supply has a buyer and a decent price, instead of having to hope for a spot contract most probably not aligned with retailers’ price. the shift of costs from public support to individual farms highlights the need for nuanced policy that account for the dual role of collective associations as both market coordinators and informal insurance mechanisms. while farmers face higher costs and often receive lower prices due to stricter quality requirements, cas offer significant value by reducing exposure to price volatility, thus stabilising farm income and lowering financial risk. policymakers should recognise this implicit insurance function and consider supporting it through targeted instruments, such as subsidies for compliance with quality standards, investment support for required technologies, or risk-sharing mechanisms like income stabilization tools. while this note provides some evidence of the trade-offs farmers face with respect to selling practices and how scas affect their economic performance in a developed country, it has important limitations. first, the estimates cannot be considered causal, as we lack information on pre-treatment periods. second, the results are based on a limited sample of farms from only two counties in england. therefore, the findings cannot be generalized to the national level, nor to other developed countries. finally, we are unable to clearly identify the mechanisms that lead farmers to participate in collective organizations, even when it may result in a lower markup. further research is needed to explore both collective and contractual arrangements in modern agricultural markets. a critical step in this direction would be to improve the current lack of data on scas, with larger samples across multiple sectors and countries to analyse. references 11 alho, e. (2015). farmers’ self-reported value of cooperative membership: evidence from heterogeneous business and organization structures. agricultural and food economics, 3(1). https://doi.org/10.1186/s40100-015-0041-6 bakucs, z., fałkowski, j., ferto, i. (2014). does market structure influence price transmission in the agro-food sector? a meta-analysis perspective. journal of agricultural economics, 65(1): 1– 25. https://doi.org/10.1111/1477-9552.12042 bellemare, m. f., bloem, j.r. (2018). does contract farming improve welfare? a review. world development, 112: 259–271. https://doi.org/10.1016/j.worlddev.2018.08.018 blackwell, m., iacus, s., king, g., & porro, g. (2009). cem: coarsened exact matching in stata. in the stata journal (vol. 9, issue 4). fałkowski, j., c. ménard, r.j. sexton, j. swinnen and s. vandevelde. (2017), unfair trading practices in the food supply chain: a literature review on methodologies, impacts and regulatory aspects, european commission, joint research centre. grashuis, j., & su, y. (2019). a review of the empirical literature on farmer cooperatives: performance, ownership and governance, finance, and member attitude. annals of public and cooperative economics, 90(1), 77–102. https://doi.org/10.1111/apce.12205 iacus, s.m., king, g., porro, g., (2012). causal inference without balance checking: coarsened exact matching. political analisis, 20(1), 1-24. https://doi.org/10.1093/pan/mpr013 michalek, j., ciaian, p., pokrivcak, j. (2018). the impact of producer organizations on farm performance: the case study of large farms from slovakia. food policy, 75: 80–92. https://doi.org/10.1016/j.foodpol.2017.12.009 porro, g., & iacus, s. m. (2009). random recursive partitioning: a matching method for the estimation of the average treatment effect. journal of applied econometrics, 24(1), 163–185. https://doi.org/10.1002/jae.1026 sexton, r.j. (2012). market power, misconceptions, and modern agricultural markets. american journal of agricultural economics, 95(2): 209–219. https://doi.org/10.1093/ajae/aas102 verhaegen, i., van huylenbroeck, g. (2001). costs and benefits for farmers participating in innovative marketing channels for quality food products. journal of rural studies, 17: 443–456. https://doi.org/10.1016/s0743-0167(01)00017-1 williamson, o. e. (1989). transaction cost economics. in handbook of industrial organization (vol. 1, pp. 135–182). elsevier science publishers. https://doi.org/10.1016/s1573-448x(89)01006-x https://doi.org/10.1186/s40100-015-0041-6 https://doi.org/10.1111/1477-9552.12042 https://doi.org/10.1016/j.worlddev.2018.08.018 https://doi.org/10.1111/apce.12205 https://doi.org/10.1093/pan/mpr013 https://doi.org/10.1016/j.foodpol.2017.12.009 https://doi.org/10.1002/jae.1026 https://doi.org/10.1093/ajae/aas102 https://doi.org/10.1016/s0743-0167(01)00017-1 https://doi.org/10.1016/s1573-448x(89)01006-x 12 supplementary materials table a1. summary statistics for outcome, treatment, and control variables n mean sd min max collective sale (dummy) 169 0.438 0.498 0 1 average selling price (€/litre) 169 0.248 0.032 0.18 0.41 production costs (€/litre) 169 0.205 0.050 0.053 0.324 markup (%) 169 0.355 0.731 -0.231 3.348 age (4 categories) 169 2.71 0.876 1 4 education (4 categories) 169 2.793 0.723 1 4 hectares for milk production (ha) 169 141.189 152.642 4 1011 total milk production (litre) 169 1851650 2683897 20000 23000000 herd size (number of cows) 169 234.142 214.657 17 1600 yield (litre/cow) 169 7960.009 1886.203 999 15000 full-time workers (number of) 169 3.698 3.512 0 32 note: authors calculation on survey data. ‘age’ is divided into four categories (1-4): less or equal to 40 years, between 41 and 50, between 51 and 65, and more than 65 years. ‘education’ is divided into four categories (1-4): primarily, lower secondary, higher secondary, and university. the variable ‘hectares for milk production’ refers to the total area cultivated for milk production (including land used for growing fodder crops). 13 table a2. covariates imbalances before (a) and after (b) coarsened exact matching panel a multivariate l1 distance: 0.865 univariate imbalance: l1 mean min 25% 50% 75% max age 0.1778 0.2033 0 1 0 0 0 education 0.0514 0.0078 1 0 0 0 0 full-time workers 0.1932 0.2964 -1 0 0 0 8 herd size 0.1316 24.749 6 -10 27 5 200 hectares 0.0176 17.741 9 -7 -10 0 364 production 0.0815 4.9e+05 55000 -1.2e+05 0 0 8.0e+06 yield 0.1737 -289.73 -2801 -700 -500 0 3000 panel b multivariate l1 distance: 0.637 univariate imbalance: l1 mean min 25% 50% 75% max age 0.0648 -0.0648 0 0 0 0 0 education 0.0833 0.0401 0 0 0 0 0 full-time workers 0.0432 0.1049 0 0 0 0 8 herd size 0.0741 4.292 23 -10 15 -5 200 hectares 0.1512 2.394 9 2 -5 -11 364 production 0.1142 5.9e+05 -18000 41000 0 -2.0e+05 8.0e+06 yield 0.0694 91.88 -300 -3 0 0 3000 note: panel a shows the multivariate and univariate imbalance of covariates without matching observations. panel b shows the multivariate and univariate imbalance after implementing the cem algorithm. the cutpoints for the covariates are: all four categories for ‘age’; category 1 for ‘education’; 4 workers for the variable ‘full-time workers’; 200 cows for ‘herd size’; 141.2 hectares for ‘hectares for milk production’; 800,000 litres for ‘production’; 6666, 8000, and 9000 litre/cow for ‘yield’. 14 table a3. average covariances imbalance before and after cem and t-test on the difference non-matched matched mean difference mean difference individual collective individual collective age (4 categories) 2.62 2.82 -0.20 2.60 2.63 -0.03 (0.14) [0.13] (0.15) [0.87) education (4 categories) 2.79 2.80 -0.01 2.77 2.81 -0.04 (0.11) [0.94] (0.13) [0.75] full-time workers 3.57 3.86 -0.30 3.55 3.83 -0.29 (0.55) [0.59] (0.81) [0.72] ha for milk production 223 248 -25 226 237 -11 (33) [0.46] (48) [0.82] herd size (n. of cows) 133 151 -18 123 133 -11 (24) [0.46] (30) [0.73] milk production (litre) 1637568 2126487 -488918 1748755 2340593 -591838 (415658) [0.24] (636183) [0.35] yield (litre/cow) 8087 7797 290 8273 8267 5 (292) [0.32] (357) [0.99] note: ‘individual’ and ‘collective’ represent farms that sell milk exclusively through individual businesses with bilateral contracts (e.g., retailers) or through collective organizations (e.g., cooperatives). values in curly brackets indicate standard errors from a two-sample t-test, while values in square brackets represent p-values for the alternative hypothesis that the difference is not equal to zero. table a4. alternative results of selling through collective organizations on farmer’s outcomes (1) (2) (3) (4) (5) (6) unmatched ols unmatched ols unmatched ols unmatched ols cem psm dependent variable: average selling price 0.002 0.00001 0.004 0.001 0.003 0.003 (€/litre) (0.005) (0.005) (0.005) (0.005) (0.006) (0.005) production costs 0.010 0.009 0.012 0.011 0.015 0.012* (€/litre) (0.007) (0.008) (0.007) (0.008) (0.009) (0.007) markup (%) -0.236* -0.251* -0.183* -0.204* -0.229* -0.186* (0.132) (0.140) (0.107) (0.107) (0.126) (0.108) observations 196 196 169 169 107 169 exclusive sale only x x x x covariates x x x note: the treatment is the variable ‘collective’ that is a binary variable equal to 1 if the sale is through a collective organisation, 0 otherwise. ‘exclusive sale only’ indicates that farmers sale only through a single agreement. standard errors (in parenthesis) are adjusted for heteroskedasticity in the case of ols and cem, while produced by a bootstrap procedure (100 times) in the case of propensity score matching (psm) to account for the fact that the propensity score is estimated. significance levels: * p<0.1, ** p<0.05, *** p<0.01 15 table a5. lasso and elastic net standardised coefficients of selected variables via crossvalidation (10-folds). (1) (2) (3) (4) lasso elastic net lasso elastic net full sample full sample matched matched payment entirely after delivery (qc26) 0.788 0.759 0.471 0.453 price varies with the share of profits (qc21) 0.545 0.531 0.379 0.368 logistic costs (qc31) 0.243 0.239 0.239 0.235 marketing costs (qc32) 0.098 0.101 price varies with production costs (qc17) -0.165 -0.161 quality standards (qc36) 0.068 0.064 animal welfare standards (qc39) -0.030 -0.028 note: the dependent (dummy) variable is ‘collective’. the definition of each explanatory (dummy) variable is provided in the text below. figure a1. estimated coefficients after different cem combinations. the horizontal axis represents the corresponding l1 statistics. red dots represent point estimates, while blue bars are the 90 and 95 confidence intervals. the red lines are the average of the estimated coefficients (0.002 for price, 0.019 for costs, and -0.278 for markup). 16 figure a2. standardised absolute values of the coefficients from ridge estimation definition of the variables selected by the lasso and elastic net (table 2 and table a5) variable: “payment entirely after delivery” “according to the agreement of this sale/membership rules of a collective organization, when do you get paid? please select one option:” qc.26 entirely after the delivery of products yes no variables: “price varies with the share of profits”; “price varies with production costs” “on what basis is the price of milk determined by the agreement?” qc.21 variable price based on share of organization’s profit yes no qc.17 variable price based on production costs yes no variables: “logistic costs”; “marketing costs” “in this sale agreement, which of the following costs do you incur?” qc.31 collection, storage, transport, handling, etc yes no 17 qc.32 promotional and marketing costs yes no variables: “quality standards”; “animal welfare standards” “does the buyer/collective organization require specific production/quality standards that you have to comply with?” qc.36 standards on the quality of the final product(s) (taste, colour, shape, nutritional content, chemical composition, etc…) yes no qc.39 standards on animal welfare yes no definition of the variables presented in table 3 “on a scale from 1 (strongly disagree) to 5 (strongly agree) how much do you agree with the following statements regarding your satisfaction with respect to this sale agreement?” qc.44 i do not have any alternative options to sell my products qc.45 this sale agreement provides higher prices than alternative buyers qc.46 this sale agreement provides more stable prices from year to year than alternative buyers qc.47 this sale agreement provides more possibilities for negotiating prices qc.48 there are delays in the payments qc.49 the costs associated with this sale agreement are too high (e.g. storage, marketing and promotion, commission on sales) qx.15 i am willing to take more risks than my colleagues with respect to dairy production (e.g. adopt new milking regimes, new fodder, new breed of cow) qx.16 i am willing to take more risks than my colleagues with respect to marketing and pricing risks (e.g. experiment with new sales channels, and new markets) qx.17 i am willing to take more risks than my colleagues with respect to finance (e.g. loans for production facilities, new investments) description of the cem implementation and cutpoints selection to investigate the impact of coarsening cutpoints in the implementation of coarsened exact matching (cem), we run the algorithm multiple times using different sets of cutpoints. specifically, we explore various combinations of cutpoints as follows. first, consider the histograms in figure a3. we observe that most variable distributions are rightskewed, and using too many thresholds would result in many empty or sparsely populated strata. to 18 avoid this, we use the mean, median, mode, and quartiles of each variable's distribution as candidate cutpoints. for categorical variables such as ‘age’ and ‘education’, which take discrete values from 1 to 4, we use the actual values instead of quartiles. figure a3. histograms of the variables used in the cem. in total, we evaluate all 75000 unique combinations of these cutpoints and report the corresponding l1 statistics in figure a4, panel a. for each group of matched observations (e.g., those resulting in 50 matched units), we retain only the combination that yields the lowest l1 statistic. we discard combinations with l1 values greater than 0.86, which corresponds to the l1 value in the unmatched case. this filtering process reduces the number of relevant combinations from 75000 to 151, shown in panel b. as illustrated in both panels of figure a4, there is a clear positive relationship between the l1 statistic and the number of matched observations: lower l1 values tend to correspond to smaller matched samples, and vice versa. this tradeoff has implications for the statistical power and internal validity of our estimates. a smaller number of matched observations typically implies lower statistical power—that is, a reduced probability of rejecting the null hypothesis (β = 0) when the alternative hypothesis (β ≠ 0) is true. moreover, matching on a small subset of the original sample may compromise the internal validity of the estimates, as they are based on a less representative subset. these considerations suggest that relying solely on the cem combination that minimizes the l1 statistic is not optimal in this context. instead, we select our preferred combination by jointly 19 considering the l1 statistic, the number of matched observations, and the statistical power of the resulting estimate. a. b. figure a4. scatter plots showing the relationship between the l1 distance statistic and the number of matched observations after applying cem. panel a displays all 75000 combinations of cutpoints, while panel b shows the subset of combinations that minimize the l1 statistic for each fixed number of matched observations. the dashed line represents the reference l1 value (0.86) obtained without applying any matching. figure a5 illustrates this selection process. panel a displays the l1 statistic, the estimated coefficient (with markup as the dependent variable), and the number of matched observations (represented by bubble size). all but one of the 151 combinations yield negative coefficients; however, those with the lowest l1 values typically correspond to small sample sizes. we select the combination highlighted with a red circle, which substantially reduces the l1 statistic (from 0.865 to 0.637) without excessively reducing the number of matched observations. notably, the estimated coefficient from this combination is close to that obtained from the unmatched ols (red cross), suggesting consistency with the full-sample estimate. in panel b, we demonstrate that this selected estimate has sufficient statistical power (above 80%, indicated by the dashed line). it also outperforms alternatives with fewer observations or coefficients that diverge significantly from the full-sample estimate. importantly, choosing similar combinations yields comparable results (see figure a1), indicating that our findings are robust to reasonable variations in the choice of cutpoints. 20 a. b. figure a5. scatter plots of the estimated markup coefficient against the l1 distance statistic (panel a) and the statistical power (panel b). bubble size is proportional to the number of matched observations. the red circle marks the preferred cem combination, while the red cross represents the unmatched ols estimate. in panel a, the dashed line indicates the reference l1 value (0.865) obtained without applying cutpoints. in panel b, the dashed line marks the commonly used threshold for sufficient statistical power (80%). the l1 statistics are obtained directly from the cem command in stata, while power statistics are calculated using the power oneslope command after each regression. bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 bio-based and applied economics bae copyright: © 2024 le gloux, f. & dupraz, p. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: le gloux, f. & dupraz, p. (2024). upscaling environmental incentives in the common agricultural policy: an assessment of the potential of transfers from the first to second pillar. bio-based and applied economics 13(1): 27-48. doi: 10.36253/bae-14414 received: february 23, 2023 accepted: december 11, 2023 published: may 20, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. guest editors: stefano targetti, andreas niedermayr, kati häfner, lena schaller orcid fl: 0000-0003-3645-4381 pd: 0000-0001-9910-1482 upscaling environmental incentives in the common agricultural policy: an assessment of the potential of transfers from the first to second pillar fanny le gloux1,2,*, pierre dupraz1 1 smart, inrae, rennes, france 2 fanny le gloux’s affiliation moved during the peer review process to: unit research and innovation, directorate-general for agriculture and rural development, european commission, brussels, belgium. the information and views set out in this article are those of the authors and do not necessarily reflect the official opinion of the european commission *corresponding author. e-mail address: legloux.fanny@gmail.com abstract. agri-environment-climate measures and organic farming support have been the main contractual instruments promoting environment-friendly agricultural practices in the european union since the 90s. they are insufficient in reaching significant environmental improvements, partly because underfunded. using french panel data from the farm accountancy data network, we evaluate the impact of a budget transfer from income support to environmental incentives on contract uptake. we apply a generalised tobit model to estimate the adoption probability and the acceptable farmlevel payment triggering this adoption and simulate a transfer from direct payments to organic farming support and agri-environment-climate measures budget. results suggest this mechanism increases adoption. decreasing direct payments affects participation probabilities and acceptable farm-level payments, differently depending on the type of environmental contract, the type of direct payment and the farm technical orientation. we evaluate several transfer scenarios and provide ex-ante elements on how it could help reaching the green deal organic target. keywords: common agricultural policy, tobit model, agri-environment-climate measures, organic farming support. jel codes: q15, q18, q58. 1. introduction the agricultural sector accounted for 10% of the european union’s (eu) greenhouse gas (ghg) emissions for the period 1990 to 2018 and is the second largest contributor after the energy sector (eea, 2020). the continuous intensification of agricultural activities also contributed to natural habitat degradation and dramatic biodiversity decline (dasgupta, 2021). behind the concept of agroecological transition lies the idea of moving away from agrihttps://doi.org/10.36253/bae-14414 http://www.fupress.com/bae https://doi.org/10.36253/bae-14414 https://orcid.org/0000-0003-3645-4381 https://orcid.org/0000-0001-9910-1482 mailto:legloux.fanny@gmail.com 28 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 fanny le glouxa, pierre dupraz cultural practices harming ecosystem services, in particular the systematic use of chemical inputs, towards farming systems maintaining or supporting them (millennium ecosystem assessment, 2005). the eu adopted ambitious environmental targets by 2030 and 2050, in particular on the development of organic farming (of) to reach 25% of organic agricultural land by 2030. many levers at various scales can foster this transition. an important one is better targeting agricultural support to make agroecological farming more profitable than conventional farming (fao et al. , 2021). the common agricultural policy (cap) represented 36% of the 2019 eu’s budget (58.4 billion euros) (ec, 2019) and is the main eu policy supporting environment-friendly farming practices (coderoni, 2023). the cap budget allocated to environmental commitments is low in comparison to income support payments (direct payments of the “first” cap pillar), the latter including little restrictions on agricultural practices (dupraz and guyomard, 2019; european court of auditors, 2017; grethe et al., 2018; matthews, 2013). following the definition of the biodiversity strategy for 2030 and the farm to fork strategy for the agricultural and food sectors, rethinking the design of the cap and its instruments is central to triggering the large-scale agroecological transition of farming systems (ec, 2020a, 2020c). in this study, we develop a farm-based modelling framework to assess a reorientation of the direct payments budget specifically towards environmental contracts in france. in the 2014-2020 cap programming period, environmental incentives were offered in two voluntary 5-year contractual schemes of the rural development pillar (“second” pillar) of the cap: (i) support to of, and (ii) agri-environment-climate measures (aecm). of support are area-based payments to eligible farms undertaking a conversion towards of, or to eligible certified organic farms for maintaining their organic practices. aecm are area-based payments to eligible farms complying with a set of management requirements targeting an environmental objective such as the maintenance of biodiversity or the improvement of water quality. of support has proven to be effective in maintaining the relative competitiveness of of and is a major driver of the sector development (casolani et al., 2021; sanders et al., 2011), while aecm are the cap instruments the most targeted towards public good provision (batáry et al., 2015; dupraz and guyomard, 2019; european court of auditors, 2020; matthews, 2013). in 2019, direct payments accounted for 69% of the cap budget (40.5 billion euros), while 8.6% (3.5 billion euros) was allocated to of support, aecm and natura 2000 sites altogether (ec, 2019). the literature shows that after 30 years of existence, the voluntary environmental schemes of the cap were unsatisfactory to improve the state of the environment. the lack and unbalanced funding, as well as poorly designed instruments, led to insufficient participation and effort to reach environmental thresholds (dupraz et al., 2009; dupraz and pech, 2007; espinosa-goded et al., 2013; targetti et al., 2022; zavalloni et al., 2019). in 2020, only 13% of the eu’s uaa was under an aecm contract, and 6% under an of support contract (ec, 2020b, 2020d). rather than increasing the policy budget to raise environmental incentives, many argued in favour of rebalancing the budget allocation among the various cap instruments (dupraz and guyomard, 2019; matthews, 2013). since the 2014-2020 cap programming period, member states have the flexibility to transfer up to 15% of their direct payments budget to increase support to rural development measures, including of support and aecm (eu, 2013). in france, 7.5% of direct payments have been redirected since 2017 (maa, 2021). for the 2023-2027 cap programming period, it has been decided to dedicate 25% of the direct payments budget to finance a new instrument (ecoschemes) open to all farmers and supporting the voluntary implementation of environment-friendly measures (generally less ambitious than of support or aecm contract requirements) (ec, 2021; runge et al., 2022). although the negotiations ruled out this option, dedicating a higher share of the cap budget to finance more of support and aecm was another potential (complementary) lever to upscale environmental incentives and was preliminarily evaluated by (chatellier et al., 2021). in this paper, we estimate an environmental contract adoption model with observed panel data from the french farm accountancy data network (fadn). we propose a generalised tobit model estimating the adoption decision and the minimum farm-level payment triggering adoption (“acceptable” farm-level payment). we develop a simulation approach to predict the impact of a budget transfer from direct payments towards the implemented environmental contracts during the 2014-2020 cap programming period: support to of and aecm. simulating a budget neutral transfer under ceteris paribus conditions, we decrease the direct payments received by farmers and increase the environmental payments to be distributed to of support and aecm adopters. our farm-based model estimates are used to predict a new contract uptake outcome in 2019. our framework does not integrate the market effects of the simulated budget transfers. it means that we assume that induced farm input and output price changes are negligible. we find that the transfer of an additional 7.5% (reaching the maximum transfer rate of 15% between 29 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 upscaling environmental incentives in the common agricultural policy the two cap pillars) of direct payments towards aecm and of support results in an increase of participation in aecm from 11% to 23%, and in of support from 7% to 15%. the predicted participation rate and uaa under environmental contracts increase linearly with the budget transfer rate simulated. our model suggests that an additional transfer rate of 15.5% to reach 23% of transfer between the two pillars would allow to reach the green deal target of 25% of organic uaa. we observe an indirect effect on farmers’ behaviour of decreasing direct payments. in particular, the probability of participating in aecm significantly increases with the amount of coupled payments for suckler cows received at the farm level (+0.1% per 1,000€). we also estimate a strong positive effect of decoupled direct payments on of support acceptable farm-level payments (+1,039€ per 100€/ ha), such that our model predicts that farms participate in of support for lower farm-level payments after the budget transfer. we identify a differentiated impact of the budget transfer according to the type of farm, with an increased incentive for farms specialised in grazing livestock to contract aecm, and for farms specialised in cereal and field crops, permanent crops, dairy, pigs and poultry or mixed farming with field crops and grazing livestock to contract of support. our first contribution is an ex-ante evaluation method of the transfer mechanism from direct payments to environmental contracts. in particular, we model the impact on adoption. to our knowledge, the effect of such a budget transfer has not yet been assessed at the farm level and for an allocation targeting environmental contracts specifically. previous ex-ante evaluations of the reorientation of direct payments used the capri (common agricultural policy regionalised impact) partial equilibrium model (himics et al., 2020; schroeder, 2021; schroeder et al., 2015), or linear programming (giannakis et al., 2014), to study the impact on environmental indicators aggregated for farm types and eu regions. hence, it remains unsure how effective it can be to significantly increase the voluntary adoption of environment-friendly practices at the farm level, and what are the underlying microeconomic mechanisms. adoption results from the confrontation of the supply of environmental commitments by farmers (farm and farmer characteristics, opportunity costs), and the demand from public authorities (budget, eligibility criteria, technical requirements, payment). our model partly overcomes the absence of information on the diversity of contract characteristics and eligibility rules by controlling for many factors of farm heterogeneity. our second contribution is to capture the effect of direct payments on both the environmental contract adoption decision and the associated acceptable farmlevel payment in france under the 2014-2020 cap framework. beyond a direct positive effect on the participation of an increased budget available to finance environmental contracts, one can expect an indirect effect of the transfer on farmers’ response to environmental incentives, resulting from the decrease of direct payments (lower income support). monetary aspects from different sources, including direct payments, are important drivers of the decision to adopt aecm and of (darnhofer et al., 2019; jaime et al., 2016; sanders et al., 2011; van herzele et al., 2013). allaire et al. (2011) and pufahl and weiss (2009) found different effects of direct payments coupled to production on participation in aecm, with an overall positive effect in germany, and a marginal or negative effect in france for extensive grassland measures. moreover, a positive effect of the decoupling of direct payments on the adoption of of was found in sweden (jaime et al., 2016). this literature proved that both direct payments and environmental payments affect the decision to adopt environment-friendly practices, showing the importance of considering direct and indirect effects when evaluating the potential of a budget transfer in boosting more adoption. in our study, we complement previous studies by looking at the effect of direct payments on not only the adoption decision, but also the amount of payment to allocate to farms to trigger this adoption. the paper is organised as follows. section 2 presents the data, theoretical framework and econometric model of environmental contract adoption, and the procedure to simulate a reorientation of the cap budget. section 3 describes the estimated econometric models and presents the predicted results. section 4 discusses the methodological approach and the findings. finally, section 5 draws some conclusions. 2. materials and methods our methodological approach to simulate a change of cap budget allocation comprises three steps: 1. estimation of the model of voluntary contract adoption under the current budget allocation. 2. prediction of new probabilities and acceptable farmlevel payments with a reduction of direct payments. 3. starting from the farm with the highest probability to participate, allocation of the initial instrument budget plus an additional amount from the direct payments budget to participants, up to their estimated acceptable farm-level payment, until the budget is exhausted. 30 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 fanny le glouxa, pierre dupraz in this section, we present how we applied this methodological approach using observed french data from the 2014-2020 cap programming period with two types of environmental contracts: of support and aecm. 2.1 data the french metropole fadn data for the years 2015 to 2019 were used in the study. the data represent an unbalanced panel of 36,251 farm observations and include information on the total farm-level payment (€) received for aecm contracts on the one hand, and of support contracts on the other hand. the dataset does not include information on the surfaces enrolled in each contract type, nor on the specific measures adopted, but knowing the organic certification and organic conversion status of the farms allows us to identify whether a recipient of of support has a conversion of support contract or maintenance of support contract. the national fadn is designed to be representative of medium and large farms contributing to more than 90% of the gross production and utilised agricultural area (uaa) and covers the scope of 65% of all farms (agreste, 2022). this data source is therefore particularly relevant for ex-ante cap evaluations. from 2015 to 2019, a total of around 1.6 billion was allocated to the farms of our fadn sample for engaging in aecm and of support (table 1). the highest budget was for 2019, with 228 million € to 11% of sample farms for aecm, 66 million € to 1.5% of sample farms for conversion of support and 138 million € to 5% of sample farms for maintenance of support. for that same year (2019), the french government reported allocating a total of 244 million € for aecm, 191 million € for conversion of support and 58 million € for maintenance of support (ddt ariège, 2020). in terms of participation rate, it corresponds to around 11% of metropolitan farms having contracted an aecm, 5% conversion of support and 3% maintenance of support (ddt ariège, 2020; insee, 2022). hence, the fadn sample describes the allocation of 93% of the table 1. common agricultural policy budget and beneficiaries in 2015-20191. year direct payments decoupled direct payments coupled direct payments for suckler cows aecm of support conversion of support maintenance of support budget (million €)2 2015 7,288.4 6,095.6 667.5 165.3 122.5 23.6 99.0 2016 6,955.9 5,781.6 631.5 136.5 123.5 18.7 104.8 2017 7,124.9 5,880.6 651.6 159.4 140.0 19.4 120.6 2018 6,727.5 5,576.2 623.7 189.7 147.2 30.6 116.6 2019 6,676.0 5,561.1 655.0 227.9 203.3 65.7 137.6 beneficiary farms (%) 2015 85.7 84.1 24.9 6.2 5.6 0.9 4.7 2016 85.5 84.2 25.2 6.6 5.8 0.7 5.1 2017 85.1 83.6 25.9 7.8 5.6 0.6 5.0 2018 85.6 84.4 25.8 8.9 5.3 0.8 4.5 2019 85.3 84.1 26.5 10.8 6.9 1.5 5.4 beneficiaries’ uaa (%) 2015 97.9 97.3 34.0 8.8 4.2 0.8 3.5 2016 98.4 98.1 34.1 9.1 4.4 0.7 3.8 2017 98.4 98.0 35.3 10.3 4.9 0.5 4.3 2018 98.4 98.1 35.0 11.9 4.9 0.9 4.0 2019 98.6 98.1 35.8 14.5 6.3 1.6 4.7 aecm: agri-environment-climate measures. of: organic farming. 1 all figures are weighted by the extrapolation coefficient of each observation. 2 to compute the total policy instrument budget for year t, we corrected for delayed payments distributed at year t+1 or t+2. less than 0.2% of the direct payments were distributed at t+1 for 2015, 2016, 2017, 2018 and 2019, and at t+2 for 2015 and 2017. less than 8.0% of the aecm and of support payments were distributed at t+1 for 2018 and 2019. we could not correct for 2019 instrument budgets distributed in 2021 (data not available at the time of the study). source: 2015-2020 french fadn data. 31 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 upscaling environmental incentives in the common agricultural policy aecm budget and 82% of the of support budget to a representative ratio of participants/non-participants in 2019. however, it does not represent well the repartition between conversion of support and maintenance of support and overestimates the allocation of of support to certified farms relative to farms in conversion. yet, we observed the ratio within the of support eligible population (i.e. farms converting to of or already certified in 2019) is well represented in the fadn, at least when it comes to the utilised agricultural area (uaa) (see appendix a1) (agence bio, 2020). 2.2 theoretical model of voluntary adoption of an environmental contract for a given type of environmental contract (aecm on the one hand, and of support on the other hand), we represent the demand for environmental commitments from authorities during a cap programming period by a function θ(m,b,γ) describing a set of measures m (the diversity of technical requirements belonging to the contract type), a total budget b, and policy parameters γ defining exclusion rules. for of support contracts, m includes a diversity of measures designed for specific land use, and either for maintaining organic practices (maintenance of support) or for converting to organic practices (conversion of support). for aecm contracts, m includes a diversity of measures designed for a specific land use and generally an environmental target (water quality, biodiversity…). in france, not all farmers are eligible to aecm contracts and maintenance of support contracts. the exclusion rules are based on the location of the farm and described by γ. the confrontation of demand and supply of environmental commitments results in an uptake equilibrium such that b=∑ipi(m,γi,ai,ki,ei). with pi the farm-level payment allocated to farms, γi whether the farm is eligible to the environmental contract type (location in the eligible area), ai the farm characteristics affecting eligibility to a subset of environmental measures of m (location, land use, organic certification status…), ki other farm and farmer characteristics (economic size, surface, age, education, technical orientation…), and ei the farm economic context (market prices, cap support, etc). we assume the supply of environmental commitments by farmers is driven by the profitability of adoption and eligibility. in practice, the payment for an environmental contract is delivered as a payment per hectare enrolled, and for most measures, the farmer can decide to enrol all or part of his/her farmland. however, the binary adoption decision (participation vs. no participation) is made at the farm level. therefore, we assume the farmer decides based on whether the total farm-level payment received for enrolling his/her profit-maximising amount of farmland in an environmental contract is sufficient to make participation profitable. the decision di* of farmer i to participate and the binary participation di are defined as follows: (1) with mi*∈m the characteristics of the measure(s) adopted by the farm (technical requirements, payment per hectare), φi≥0 the maximum farm-level payment the farm is eligible to for adopting mi* on all eligible surfaces, and pi*>0 the minimum farm-level payment triggering the adoption of mi* (acceptable farm-level payment) by the farmer. mi*=mi*(m,γi,ai,ki,ei) is the optimal contract uptake and the solution to the profit maximisation programme of farm i. if ∀mi∈m,φi(mi,γi,ai,ki,ei)=0 or 0<φi(mi,γi,ai,ki,ei)<pi*(mi,ai,ki,ei) (the farmer is not eligible or participation is not profitable for any contract), then di*<0 and the farm is not participating. if ∃mi∈m,φi(mi,γi,ai,ki,ei)≥pi (mi,ai,ki,ei), the farmer is eligible to at least one contract profitable for him or her, and the farmer decides to participate with the optimal contract uptake mi* such that di*≥0. φi represents the constraint of demand for environmental commitments faced by the farmer (the maximum payment public authorities are willing to allocate for adopting an environmental contract), while pi* represents the constraint of supply (opportunity costs of conventional farming and farm size). in this setting, the farm-level payment allocated to farms pi is: (2) 2.3 empirical model of voluntary adoption of an environmental contract following the theoretical framework, we aim to estimate a model of adoption of environmental contracts during a cap programming period proposing the menu of measures m. due to the censored nature of the farmlevel payment, an estimation of the acceptable farmlevel payment with least squares methods is not applicable. we apply a generalised tobit model (amemiya, 1984; wooldridge, 2010) to simultaneously estimate two dependent variables: the decision to participate (selec32 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 fanny le glouxa, pierre dupraz tion equation) and the acceptable farm-level payment (outcome equation), as functions of observed determinants from a sample of participants and non-participants. we estimate one model for each type of environmental contract: of support and aecm. while both contract types require the implementation of low-input environment-friendly practices, the implications on the farm business are different. on the one hand, adopting an of support contract is associated with the prospect of obtaining or maintaining the organic certification of the farm and accessing the organic market in the long term. it also often implies implementing organic practices on all the farmland. on the other hand, adopting an aecm is associated with a medium-term commitment to low-input farming, and for most measures, on a flexible share of the farmland. for at least those two reasons, it appears relevant to consider that the decision-making process as well as the acceptable farm-level payment triggering the profitability of adoption differ between aecm and of support. with panel data, the decision to participate of farmer i in year t is represented by the latent variable dit* explained by observed covariates zit=(ai,kit,eit) defined in the following paragraphs, environmental contract exclusion criteria γit and an error term εit. to control for individual fixed effects, we rely on the chamberlainmundlak device and control for the individual mean of the subset of time-varying covariates (mundlak, 1978; wooldridge, 2010). α, γ, ξ and ι are the intercept and vectors of parameters to be estimated. the observed participation can be described by a binary random variable dit={0,1} (equation (3)). (3) our outcome of interest is the acceptable farm-level payment pit* triggering participation, which is explained by the observed covariates zit=(ai,kit,eit), environmental contract exclusion criteria γi, the individual mean of the subset of time-varying covariates and an error term uit (equation (4)). β, δ, η and κ are the intercept and vectors of parameters to be estimated. for identification, the outcome equation must include one less explanatory variable than the selection equation. the total farm-level payment pit received by farm i at year t is observed in the data and is only different from zero for participating farms (censored variable at zero). (4) based on the literature on the factors affecting aecm and of adoption and our theoretical approach (allaire et al., 2011; defrancesco et al., 2008; elliott and image, 2018; espinosa-goded et al., 2013; pavlis et al., 2016), we selected a set of variables to model contract uptake. explanatory variables were included to control for factors of eligibility to the diversity of environmental measures (ait) of the set m defined by public authorities in the cap 2014-2020 programming period. we include one dummy variable equal to 1 if the farm is certified organic (organic certification). controlling for organic certification captures the effect of eligibility to maintenance of support or conversion of support, as only certified organic farms can apply to the former. moreover, most aecm contracts are designed specifically for some land use and areas with high natural value. we control for the share of permanent grasslands in the uaa (permanent grasslands), and the load of grazing livestock per hectare (grazing livestock density). we add a dummy equal to 1 if half of the farm’s uaa is located in a natura2000 area (natura2000). accounting for farm and farmer characteristics (kit) captures heterogeneous difficulties in meeting contract requirements and preferences. we control for economic size (standard gross production), uaa (utilised agricultural area), total labour per hectare of uaa (labour), the share of rented land (rented uaa), assets depreciation per hectare of uaa (depreciation) and for the reception of lfa payment (lfa). we account for farm specialisation (1 dummy per technical orientation or group of technical orientations). farmer’s characteristics are age (age) and education (general education and agricultural education). in addition, we control for past participation. to do that we estimate the adoption models with 2016-2019 data (28,967 observations) and use 2015 data to construct a variable equal to 1 if the farm already adopted the environmental contract in 2015, and 0 otherwise (observed participation in aecm in 2015 and observed participation in of support in 2015). in addition, we capture part of the interaction between of support and aecm uptake by controlling for observed participation in aecm (of support respectively) at time t-1 when estimating the decision to participate in of support at time t (aecm respectively) (observed participation in aecm at t-1 and observed participation in of support at t-1). for model identification, we exclude this variable from the simultaneous outcome equation. as we have unbalanced panel data, it has to be noted that information on past participation is missing for observations that were not sampled the year before. 33 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 upscaling environmental incentives in the common agricultural policy regarding the farm economic context (eit), we control for the effect of cap direct payments by including the amount of decoupled direct payments received per hectare of uaa (decoupled payment). we control the amount of direct payments for suckler cows at the farm level (coupled payment for suckler cows) as it is the production receiving the highest coupled support in france. we further control for the cost of land lease per hectare of uaa (land lease), and the observed fuel and lubricant price of the farm ( fuel price), the only variable input price that can be computed with fadn data. fuel price is likely correlated to other farm input prices on the market (mineral fertilisers), and is an indicator of opportunity costs from adopting less input-intensive agricultural practices. when fuel price is not observed for a given observation (8.4% of the sample), we replace it with the mean of the observed fuel prices from the other years for the same farm (3.3% of the sample), or the annual mean of the sample (5.1% of the sample). explanatory variables were included as part of γi to characterise eligibility to the environmental contract types defined by public authorities in the cap 2014-2020 programming period. maintenance of support eligibility depends on the region, with some not proposing those contracts in all or part of their territory after 2017. we therefore account for farm location (1 dummy variable per region) in the model. in practice, location criteria γi also prevent some farms from participating in aecm based on their location. in particular, only farms located in an agri-environment-climate project (with a geographical scale smaller than the region) are eligible. we do not have enough information in the fadn to identify and exclude non-eligible farms in the case of aecm. without information to characterise the exclusion criteria γit, the actual model estimated for aecm is the following one: (5) (6) with vit and wit the error terms. we have an omitted-variable bias on γ equals to in equation (5) and on δ equals to in equation (6). descriptive statistics of the covariates are presented in table 2 and appendix a2. the latent continuous variable dit* is estimated with a probit regression model with the binary variable dit as dependent variable over the sample of participants and non-participants. the acceptable farm-level payment is estimated for each farm of the sample based on the estimation of the outcome equation using the participating farms. we control for year-fixed effects with dummy variables. the individual mean of the time-varying variables controlling for individual-fixed effects are all the covariates included in zi but location in a less favoured or natura2000 area, age, education, farm specialisation, the region, and observed participation in aecm or of support in 2015. we also include the individual mean of the time dummies because we have an unbalanced panel (wooldridge, 2019). we do not impose an upper limit to the estimated acceptable farm-level payments to capture the behaviour of farmers requiring a strong financial table 2. descriptive statistics of the 2016-2019 fadn sample used for the estimations (n=28,967)1. mean standard deviation2 dependent variables participation in aecm 0.09 participation in of support 0.06 aecm payment (€) (d=1) 7,129.68 6,691.92 of support payment (€) (d=1) 8,834.07 9,752.82 independent variables decoupled payment (€/ha) 193.42 379.42 coupled payment for suckler cows (€) 2,179.01 4,552.01 land lease (€/ha) 650.72 3,278.06 fuel price (€/l) 0.63 0.12 standard gross production (€) 173,838.99 194,712.41 utilised agricultural area (ha) 89.14 76.73 labour (awu/ha) 0.24 3.23 share of rented area 0.73 0.36 depreciation (€/ha) 2,006.75 34,780.83 lfa 0.28 age (years) 51.08 9.58 share of permanent grasslands 0.22 0.31 grazing livestock density (lu/ha) 0.55 1.17 natura2000 area 0.04 certified organic 0.08 observed participation in aecm in 2015 0.05 observed participation in of support in 2015 0.04 observed participation in aecm at t-1 0.07 observed participation in of support at t-1 0.05 aecm: agri-environment-climate measure. of: organic farming. awu: annual work unit. lfa: less favoured area. lu: livestock unit. 1 all figures are weighted by the extrapolation coefficient of each observation. 2 standard deviations are reported for the non-dichotomous variables. source: 2015-2019 french fadn. 34 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 fanny le glouxa, pierre dupraz incentive to participate. we impose acceptable farm-level payments that cannot be lower than 300€, which is the minimum required by french public authorities to start a contract (maa, 2020). the tobit regression model provides estimated coefficients of the effect of the explanatory variables on both the decision to participate in an environmental contract and the acceptable farm-level payment triggering participation, as well as the correlation ρ of the error terms of the two equations. the marginal effects of each variable are computed at sample means so that coefficients can be more easily interpreted. 2.4 simulation of cap budget transfer we predict the impact on contract uptake of increasing the budget allocated to aecm and of support while decreasing direct payments in 2019. on the side of the demand for environmental commitments, it corresponds to a change in demand θ, such that the new budget in 2019 is . direct payments distributed to the sample in 2019 (dp19) accounted for 6.7 billion €. the 2019 cap budget already includes a 7.5% transfer to rural development measures (maa, 2021). we first assume an additional transfer of 7.5% to reach 15%, which is the maximum rate allowed under current cap regulations. the additional budget to be allocated is 541 million €. we keep the current budget ratio among the instruments: 53% to aecm ( =286 million €) and 47% to of support ( = 255 million €). the budget to be allocated to sample farms is now =514 million € and =458 million €. in practice, criteria γi prevent some farms from participating in environmental contracts based on their location. because we do not have enough information in the fadn to identify and control for non-eligibility in the case of aecm, our simulation approach is such that all farms of the sample become eligible to aecm under a new budget allocation scenario. another (strong) necessary assumption is that the menu of measures m (technical requirements, area payment) is not affected by a budget transfer so that the estimated effects of the farm and farmer characteristics (ait,kit) and the economic context (eit) on the adoption decision and acceptable farm-level payments can be considered the same with a different budget allocation. in the first stage, model estimates are used to predict farm probabilities and acceptable farm-level payments for enrolling in aecm (of support respectively) in 2019 with a decrease of 7.5% of decoupled payments and coupled payments for suckler cows received. in the second stage, farms are ranked according to decreasing predicted probabilities of adopting aecm (of support respectively). in the third stage, ( respectively) is allocated to farms up to their predicted acceptable farm-level payment, starting with the farm with the highest probability to the lowest, until the budget is exhausted. while keeping the budget ratio among instruments (53% to aecm and 47% to of support), we also conduct additional simulations to identify the rate of budget transfer that would result in enough conversion of support uptake to reach the target of 25% of organic area in france. 3. results 3.1 estimated models of aecm and of support uptake to evaluate the model quality, we compare the observed participation and farm-level payments in 20162019 to the predicted probabilities of participation and acceptable farm-level payments (table 3). the aecm adoption model tends to underestimate the probability of participating in aecm. on average, the estimated acceptable farm-level payments of aecm participants are in the range of their observed farm-level payments, although the standard deviation is lower, suggesting the model does not capture well extreme values. the of support adoption model better captures the probability to participate, on average for the sample and in particular for maintenance of support. the acceptable farmlevel payment of participants is lower than observed farm-level payments on average, particularly for conversion of support. similarly to aecm, the model does not capture well the more extreme values. the difference between estimated and observed data for aecm can be partly explained by an omitted variable bias. in particular, missing data on whether the farm is located in an agri-environment-climate project area (exclusion criteria) may largely explain why the probability of aecm participation is underestimated. similarly, it seems there are important factors explaining participation in conversion of support that the model does not capture. the marginal effects of our covariates of interest on the latent decision to participate and acceptable farmlevel payment are summarized in table 4. the marginal effects and the coefficients of all the model covariates are reported in appendix a3. the estimated effects describe the equilibrium of supply and demand of environmental commitments during the 2016-2019 period. the effect of each factor is a net effect and captures both the 35 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 upscaling environmental incentives in the common agricultural policy effect of the demand θ(m,b,γ) each farm faces (menu of measures and payments each farm is eligible to) and the effect of the characteristics zit of the supplying farms (opportunity costs, fixed costs, number of eligible hectares…). the effect of demand on the one hand, and supply, on the other hand, cannot be isolated. in particular, the effects of the covariates on aecm and of support acceptable farm-level payments are difficult to interpret due to the high heterogeneity of contract requirements, payments per hectare and farm size. a positive effect on the acceptable farm-level payment reveals that ceteris paribus, the participation of a farmer is triggered either for a measure with a higher payment per hectare or for enrolling more hectares. the estimated marginal effects of the explanatory variables on the adoption decision can be more easily confronted to the literature. the correlation estimates ρ of the selection and outcome equations are significant in both models. in particular, the acceptable farm-level payment for adopting aecm decreases with a higher probability of participation (significantly negative ρ), while the acceptable farmlevel payment for adopting of support increases with a higher probability of participation (significantly positive ρ). in other words, farms with a high likelihood of participating in aecm tend to participate for lower farm-level payments than other farms (participation is profitable for lower levels of farm-level payments), and farms with a high likelihood of participating in of support tend to participate for higher farm-level payments than other farms (participation is profitable for higher levels of farm-level payments). this result supports our assumption that farmers behave differently regarding their adoption of aecm or of support contracts, and confirms the relevance of estimating two different models. this difference may be explained by the fact that adopting an of support contract often implies adopting organic practices on all the farmland and tends to be more costly to implement than aecm. we observe that the probability of participating in of support is not significantly affected by the amount of direct payments. regarding aecm, while the effect of decoupled payments is also not significant, the probability of participation significantly increases with the amount of coupled payments for suckler cows received at the farm level (+0.1% per 1,000€). decoupled direct payments have the opposite effect on of support and aecm acceptable farm-level payments. higher decoupled payments tend to increase of support acceptable farm-level payments (+1,039€ per 100€/ha) and decrease aecm acceptable farm-level payments (-93€ per 100€/ha). moreover, the model suggests the effect of coupled direct payments for suckler cows is significantly positive on aecm acceptable farm-level payments (+41€ per 1,000€) and not significant on of support acceptable farm-level payments. we interpret the positive effect of coupled payments on aecm adoption probability as resulting from the large set of aecm contracts designed in france for grazing livestock farming systems, more likely to have suckler cows on the farm (maa, 2020). in the literature, the effect of coupled support on aecm adoption depends on the study (allaire et al., 2011; pufahl and weiss, 2009). our results confirm those of pufahl and weiss (2009) in germany, but we can expect the effect to vary according to the member table 3. comparison between observed and estimated adoption behaviour1. all sample participants agri-environment-climate measures observations 28,967 2,442 observed participation (discrete) 0.09 1.00 estimated participation probability (discrete) 0.05 0.49 observed farm-level payment (€) 7,130 (6,692) estimated acceptable farm-level payment (€) 6,194 (5,728) 7,294 (3,757) of support observations 28,967 1,657 observed participation (discrete) 0.06 1.00 estimated participation probability (discrete) 0.05 0.71 observed farm-level payment (€) 8,834 (9,753) estimated acceptable farm-level payment (€) 10,360 (8,608) 8,236 (6,718) maintenance of support observations 28,967 1,364 observed participation (discrete) 0.05 1.00 estimated participation probability (discrete) 0.05 0.83 observed farm-level payment (€) 8,143 (8,881) estimated acceptable farm-level payment (€) 6,850 (7,563) 7,792 (6,544) conversion of support observations 28,967 293 observed participation (discrete) 0.01 1.00 estimated participation probability (discrete) 0.00 0.03 observed farm-level payment (€) 12,680 (12,963) estimated acceptable farm-level payment (€) 10,659 (8,625) 10,708 (7,124) 1 all figures are weighted by the extrapolation coefficient of each observation. standard deviation in parentheses. source: own elaboration. 36 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 fanny le glouxa, pierre dupraz states and the set of aecm contracts that were designed according to local priorities. the effects of the other covariates controlling for the economic context (fuel price, land lease), and the farm and farmer characteristics are also significant, in particular on participation probabilities. most findings confirm the literature. for instance, the negative effects of age, the cost of land lease and depreciation on aecm adoption probability are coherent with (andreoli et al., 2022; damianos and giannakopoulos, 2002; defrancesco et al., 2018; mack et al., 2020; pavlis et al., 2016; pufahl and weiss, 2009; uthes and matzdorf, 2013; vanslembrouck et al., 2002; zimmermann and britz, 2016). the positive effects of the economic size, uaa, shares of grasslands and rented area, location in a natura2000 area, education and past participation on aecm adoption probability also confirm previous findings (allaire et al., 2011; andreoli et al., 2022; chatzimichael et al., 2014; damianos and giannakopoulos, 2002; defrancesco et al., 2018; giovanopoulou et al., 2011; mack et al., 2020; pavlis et al., 2016; pufahl and weiss, 2009; uthes and matzdorf, 2013; zimmermann and britz, 2016). regarding of support adoption, the negative effect of age and the positive effects of general education and being located in a less favoured area are coherent with other studies (kallas et al., 2010; koesling et al., 2008; läpple and rensburg, 2011). similarly, to the literature (andreoli et al., 2022; koesling et al., 2008; mack et al., 2020; mcgurk et al., 2020), we observe that the farm specialisation and region are significant factors of adoption for both of support and aecm. as expected, we find that a higher fuel price increases the probability of adopting an environmental contract. ceteris paribus, we also see that participation in aecm (of support respectively), significantly decreases if the farm participated in of support (aecm respectively) the year before. we also find some surprising results. we find a negative effect of location in a less favoured area on the probability of participating in aecm, which differs from previous results (allaire et al., 2011; andreoli et al., 2022; mack et al., 2020; zimmermann and britz, 2016). other unexpected results are the negative effect of agricultural education and the positive effect of the grazing livestock load on the probability of participating in of support (koesling et al., 2008; läpple and rensburg, 2011). a finding of this study is that the adoption behaviour of aecm and of support differs. in addition to differences regarding the effects of direct payments, we find opposite effects of some covariates on the probabilities of participation in aecm and of support (agricultural education, location in a natura2000 or less favoured area, economic size, depreciation, cost of land lease and share of grasslands) on the probabilities of participation in aecm and of support. on the supply side (farmers), it can be explained by the fact that the implications of both types of contracts are different. one is the prospect of a long-term commitment to of, while the other is a midterm commitment (5 years). on the demand side (public authorities), the defined eligibility rules result in some contract types and measures not being open to all types of farms, driving or constraining farmers’ behaviour. 3.2 results of the simulations the predicted impact on farmers’ uptake of environmental contracts of a transfer of an additional 7.5% table 4. generalised tobit models estimation: marginal effects at the sample mean. aecm of support participation decision (di*) acceptable farm-level payment (pi*) in 1,000€ participation decision (di*) acceptable farm-level payment (pi*) in 1,000€ decoupled payments (100€/ha) 0.000 (0.000) -0.093* (0.020) -0.000 (0.000) 1.039*** (0.220) coupled payment for suckler cows (1,000€) 0.001*** (0.001) 0.041+ (0.009) 0.000 (0.000) 0.053 (0.011) ρ -0.034*** (0.005) 0.133*** (0.011) σ 5.581*** (0.013) 6.978*** (0.020) number of observations 28,967 2,442 28,967 1,657 log-likelihood -504,317 -318,531 aic 1,008,948 637,376 schwarz criterion 1,010,826 639,254 pseudo-r2 (mcfadden) 0.241 0.378 significance levels: *** p-value <0.001, ** p-value <0.01, * p-value<0.05, + p-value<0.1. standard errors in parentheses. awu: annual work unit. lu: livestock unit. source: own elaboration. 37 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 upscaling environmental incentives in the common agricultural policy (reaching the maximum transfer rate of 15% between the two cap pillars under current regulations) of direct payments to aecm and of support in 2019 in france is presented in table 5. participation in aecm increases from 11% to 23%, and in of support from 7% to 15%. while the aecm budget more than doubles (+126%), participation and the uaa of participants increase proportionally less (+115% and +111% respectively). it suggests decreasing returns of a budget increase and that aecm participants with the new budget allocation tend to have smaller farms. regarding of support, participation (+123%) increases proportionally to the budget increase (+125%), but the uaa of participants increases proportionally more (+142% respectively). contrary to aecm, predicted of support beneficiaries under the new budget allocation tend to have larger farms. in addition, after the budget transfer, the share of the sample participating in both of support and aecm increased from 0.8% to 7.5%. the share of aecm participants with an of support contract increases from 7.7% to 32.1%, while the share of of support participants with an aecm increases from 12.1% to 29.9%. two combined incentives explain this result. first, there is a direct effect of more budget dedicated to financing environmental commitments. more acceptable farm-level payments can be covered and participation becomes profitable for a larger share of farms. this additional budget is taken from 85% of observations receiving direct payments (99.0% of the uaa) and is redistributed to 27.5% of observations (33.0% of the uaa). 19.9% are new adopters of environmental contracts and 7.5% are observed participants in 2019 to which the simulation allocates an additional payment (adoption of additional measures or enrolment of additional hectares). second, there is an indirect effect of the decrease of direct payments on acceptable farm-level payments. the average change of acceptable farm-level payment per farm is -197€ for of support and +8€ for aecm. the “savings” observed for of support contracts contribute to financing the participation of even more farms. we identify a differentiated impact of the budget transfer according to the type of farm (table 6). the farms losing the most income from lower direct payments are specialised in mixed cattle (-3,115 €/farm on average in otexe 47) and in mixed farming with field crops and grazing livestock (-3,015€/farm on average in otexe 83). the less affected farms are specialised in horticulture (-56€/farm on average in otexe 29) and quality wine (-205€/farm on average in otexe 37). on the one hand, the reorientation of the budget particularly incentivises farms specialised in grazing livestock to contract aecm (otexe 45, 46, 47, 48, 73 and 83). this result seems driven by the effect of lower coupled payments for suckler cows which tends to decrease the aecm acceptable farm-level payment. farms specialised in grazing livestock typically receive more coupled payments for suckler cows than other farm types and decide to participate in aecm for lower farm-level payments after the budget transfer. in addition, for farms with grazing livestock, the effect of the amount of coupled payments for suckler cows on the aecm acceptable farm-level payment compensates for the opposite effect of decoupled payments. therefore, contrary to other farm specialisation, aecm acceptable farm-level payments tend to decrease or remain stable for farms specialised in beef (-23€/farm on average in otexe 46), mixed cattle (-8€/farm on average in otexe 47) or mixed farming with field crops and grazing livestock (+0.2€/farm on average in otexe 83). on the other hand, the reorientation of the budget particularly incentivises farms specialised in cereal and field crops, permanent crops, dairy, pigs and poultry or mixed farming with field crops and grazing livestock to contract of table 5. predicted impact of an additional decrease of 7.5% in direct payments in 2019 (n=7,194)1. baseline with a budget transfer aecm of support aecm or of support aecm of support aecm or of support budget (1,000€) 227,862 203,267 431,130 514,752 457,679 972,431 share of farms (%) 10.8 6.9 16.8 23.2 15.3 33.0 total uaa of participants (ha) 3,808,678 1,657,456 5,148,400 8,043,437 4,015,962 10,423,722 share of total uaa (%) 14.5 6.3 19.6 30.7 15.3 39.7 payment of participants (€) (d=1) 7,279 (6,768) 10,238 (12,032) 8,843 (9,758) 7,661 (3,777) 10,348 (8,514) 10,186 (7,900) acceptable farm-level payment (€) 6,473 (5,692) 10,624 (7,918) 6,481 (8,689) 10,427 (7,884) 1 all figures are weighted by the extrapolation coefficient of each observation. standard deviation in parentheses. source: own elaboration. 38 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 fanny le glouxa, pierre dupraz support (otexe 15, 38, 39, 45, 50 and 83). those results are driven by the decrease in acceptable farm-level payments associated with lower decoupled payments (on average -227€/farm in otexe 15, -235€/farm in otexe 45 and -242€/farm in otexe 83). on average, those farm types decide to participate in of support for lower farmlevel payments after the budget transfer. the outputs of simulations in terms of predicted shares of farms and share of uaa participating in environmental contracts under different budget transfer scenarios from the first pillar to aecm and of support (in addition to the 7.5% already transferred from direct payments to the measures of the second pillar since 2017) are presented in figure 1 and figure 21. the share of uaa is calculated from the sum of the uaas of the farms for which we predict participation, divided by the total uaa. we conducted several simulations up to a maximum of 30% of transfer between the two pillars, as the higher the additional transfer compared to the observed situation, the less realistic our prediction becomes. we observe that the participation rate and uaa under environmental contracts increase linearly with the budget transfer rate. in 2019, almost 9% of the uaa was organic (including the total uaa of all farms 1 note that we maintain the budget allocation ratio of 53%/47% between aecm and of support in all our scenarios. certified organic and in conversion, whether they receive of support or not). in the scenario of a 15% transfer between the two pillars (7.5%+7.5%), the uptake of conversion of support is such that the organic uaa doubles. to reach 25% of organic uaa (green deal objective by 2030), our model suggests an additional transfer table 6. allocation of environmental incentives (%) among the types of farms with and without a transfer of an additional 7.5% of the direct payments budget in 2019 (n=7,194)1. technical orientation aecm baseline aecm – budget transfer of support baseline of support budget transfer cereals, oleaginous, protein crops 16.69 9.31 16.46 18.00 other field crops 3.96 2.22 6.35 4.06 vegetable gardening 0.74 0.32 4.93 4.36 horticulture 0.00 0.11 1.76 1.93 wine with quality label 3.75 2.99 14.79 11.77 other wine 0.75 0.38 0.11 0.49 other permanent crops 1.16 0.84 6.35 9.05 dairy farming 17.67 21.14 18.08 19.23 beef farming 24.87 25.86 7.34 7.67 mixed cattle farming 4.79 6.81 1.39 0.95 sheep and goat farming 6.25 8.90 5.57 3.37 pigs and poultry farming 2.60 2.14 2.48 4.65 mixed crops farming 0.78 0.18 2.79 2.76 mixed livestock dominated by grazing livestock 0.18 0.73 1.92 2.03 mixed livestock dominated by granivores 1.09 0.68 1.31 1.22 mixed farming: field crops and grazing livestock 10.37 13.58 3.66 4.81 mixed farming: other combination of crops and livestock 4.31 3.79 4.71 3.66 1 all figures are weighted by the extrapolation coefficient of each observation. source: own elaboration. figure 1. participation in environmental contracts and implementation of organic practices under several scenarios of an additional budget transfer from direct payments to aecm and of support in 2019 (n=7,194). all figures are weighted by the extrapolation coefficient of each observation. aecm: agri-environment-climate measures. of: organic farming. budget allocation assumption: 53% aecm/47% of support. source: own elaboration. 39 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 upscaling environmental incentives in the common agricultural policy rate of 15.5% (to reach 23% of transfer between the two pillars). if we restrict eligibility to of support to noncertified farms (if we allocate the additional of support budget to conventional farms or farms converting to of, as in some french regions since 2017 and now at the national level in the current cap 2023-2027), the additional transfer rate to meet the green deal objective is 10.5% (to reach 18% of transfer between the two pillars). however, this finding needs to be carefully interpreted, as it results from estimations using empirical data for which such eligibility restriction did not exist in a majority of french regions. removing maintenance of support is a strong policy change for which our empirical model would likely no longer fit to represent the uptake behaviour of farms. 4. discussion on the limits of the modelling approach this study proposes a methodological approach to model farmers’ behaviour at a national scale regarding the uptake of environmental commitments within the framework of the cap 2014-2020 in france, applied using fadn data available in all eu countries. we used it to evaluate ex-ante the impact of cap budget allocation changes on the adoption of environmental contracts while capturing the effect of income support instruments on this adoption behaviour. the results can be analysed at the farm level, highlighting a differentiated impact according to farm specialisation. nevertheless, the predicted results need to be interpreted with care, as they depend on the quality of the adoption model estimated. in particular, our model tends to underestimate the probabilities of adoption compared to observed data, in particular for aecm and conversion of support. we identify four main limits to the modelling approach we propose. first, there is insufficient information in the fadn to precisely capture aecm eligibility and the characteristics of the measures adopted by farmers. in particular, not controlling for the diversity of the payments per hectare and surfaces enrolled for the different aecm and of support contracts remains an important limitation of this work, as they represent sources of heterogeneity across farms that we do not capture. to improve this aspect, one possibility is to merge the fadn sample with the dataset on participants to rural development measures collected each year for the annual implementation report (ramo) and collect some of the missing information (surfaces under contract, measure adopted by each farm, municipalities eligible to aecm). second, beyond measure characteristics and contract eligibility, there are additional unobserved factors explaining farmers’ adoption that our model does not capture. aecm and of support payments are typically defined as compensation payments based on income foregone and often do not represent significant economic incentives. as a result, the (unknown) intrinsic motivation due to personal concerns towards the environment is likely to play a major role in explaining the adoption behaviour of a farmer. moreover, in the case of of support adoption, other existing policies to support the organic market such as the tax abatement in france, as well as the demand for organic products expressed by consumers, also drive farmers’ decisions. neighbourhood effects may also determine farmers choice to adopt environment-friendly practices. to correct the matrix of covariances for spatial dependence of observations and allow for spatial correlation of the error terms, applying non-parametric methods based on the definition of an economic distance metric among agents could be envisaged with the relevant data (schlenker and roberts, 2009; conley, 1999). a third important limit to the study is that the reliability of the predictions decreases for higher rates of reduction of direct payments. a transfer of budget from the first pillar to aecm and of support is a significant policy change that would likely have repercussions on agricultural input and output markets, and in particular, affect the price of organic and conventional products. therefore, our simulation approach using marginal effects to model a change in farmers’ behaviour becomes less realfigure 2. uaa of the farms participating in environmental contracts and implementing organic practices under several scenarios of an additional budget transfer from direct payments to aecm and of support in 2019 (n=7,194). all figures are weighted by the extrapolation coefficient of each observation. aecm: agri-environment-climate measures. of: organic farming. uaa: utilised agricultural area. budget allocation assumption: 53% aecm/47% of support. source: own elaboration. 40 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 fanny le glouxa, pierre dupraz istic the larger the budget transfer we simulate. finally, our model could also be subject to a simultaneity bias for some of the covariates, as participation in aecm or of support may affect some farm characteristics such as the standard gross production. 5. concluding remarks aecm and of support are currently the most ambitious environmental contracts in the cap. we evaluated the potential to upscale their adoption without increasing the cap budget, by transferring part of the budget for direct payments with little environmental conditionality to fund additional environmental contracts in france in 2019. our findings suggest this mechanism successfully increases participation by combining two incentives. first, we identify a direct effect of more public money dedicated to financing environmental commitments. second, we identify an indirect effect on farmers’ behaviour of receiving lower direct payments, which tends to decrease the acceptable farm-level payment triggering their decision to participate in of support, making even more money available to finance more environmental commitments. our empirical findings support the relevance of decreasing payments with little environmental conditionality and increasing payments targeted towards the delivery of environmental public goods in the cap. previous evaluation of the reorientation of 15% of direct payments towards rural development measures in the eu28 and in germany with the capri partial equilibrium model identified marginal impacts on environmental indicators (schroeder, 2021; schroeder et al., 2015). another study in greece suggests that 50% transfer would lead to an extensification of farming practices and improve water quality and biodiversity (giannakis et al., 2014). while a transfer from direct payments to environmental incentives with the current regulation (maximum 15%) is unlikely to be sufficient to achieve the farm to fork target of 25% of organic land, our results suggest it can significantly contribute to it. the french government decided to limit eligibility to of support to non-certified farms in the 2023-2027 cap programming period. our predictions show this targeting would theoretically encourage the conversion of new land to organic and facilitate reaching the green deal objective. however, removing maintenance of support can hinder the green deal objective in the long term if keeping organic practices is not profitable through the market. finally, other levers can be applied such as improving environmental contract design to increase their attractiveness and environmental effectiveness, as well as supporting the development of the organic market. the new eco-schemes financed with 25% of the direct payments envelope in the cap for the 2023-2027 programming period for which all eu farmers are eligible, could also contribute to triggering more voluntary adoption. however, a study analysing the french eco-schemes showed that almost all farms would fulfil the technical requirements without changing their current practices, casting doubt on the possibilities to reach significant environmental additionality with this new policy instrument (lassalas et al., 2023). the limitations of the study highlight the need for complementary research to improve the modelling of environmental contract adoption. in particular, the intrinsic motivation and values of farmers, but also locational factors play an important role in the adoption of aecm and of support. they are not sufficiently documented in the fadn. while the upcoming transformation of the fadn into the farm sustainability data network (fsdn) may contribute to facilitate access to a larger set of social, economic, and environmental factors, currently, combining different secondary farm datasets, collecting more data through farmers surveys, and/or using spatial data on pedoclimatic and meteorological conditions would be necessary to better understand farmers adoption behaviour. acknowledgement this research is funded by the horizon 2020 programme of the european union (eu) under grant agreement no. 817949 (console project, https://console-project.eu/). the french fadn data are provided by the “service de la statistique et de la prospective” in the french ministry of agriculture. access to these confidential data has been made possible within a secure environment offered by casd centre d’accès sécurisé aux données (ref. 10.34724/casd). we thank two unknown referees for their valuable comments. references agence bio. 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(2016). european farms’ participation in agri-environmental measures. land use policy, 50, 214–228. https://doi.org/10.1016/j.landusepol.2015.09.019 https://doi.org/10.13128/bae-12179 https://doi.org/10.13128/bae-12179 https://doi.org/10.1016/j.jenvman.2020.110243 https://doi.org/10.1016/j.jenvman.2020.110243 https://www.millenniumassessment.org/en/responses.html#download https://www.millenniumassessment.org/en/responses.html#download https://www.millenniumassessment.org/en/responses.html#download https://www.jstor.org/stable/1913646%0ajstor https://www.jstor.org/stable/1913646%0ajstor https://doi.org/10.1016/j.landusepol.2015.09.024 https://doi.org/10.1016/j.landusepol.2015.09.024 https://doi.org/10.1093/erae/jbp001 https://doi.org/10.1111/1746-692x.12352 https://doi.org/10.1073/pnas.0906865106 https://bonndoc.ulb.uni-bonn.de/xmlui/handle/20.500.11811/8992 https://bonndoc.ulb.uni-bonn.de/xmlui/handle/20.500.11811/8992 https://doi.org/10.1111/1477-9552.12091 https://doi.org/10.1111/1477-9552.12091 https://doi.org/10.36253/bae-12843 https://doi.org/10.1007/s00267-012-9959-6 https://doi.org/10.1007/s00267-012-9959-6 https://doi.org/10.1016/j.jenvman.2013.09.030 https://doi.org/10.1016/j.jenvman.2013.09.030 https://doi.org/10.1111/j.1477-9552.2002.tb00034.x https://doi.org/10.1111/j.1477-9552.2002.tb00034.x https://doi.org/10.2307/j.ctv5rdzwc.1 https://doi.org/10.2307/j.ctv5rdzwc.1 https://doi.org/10.1016/j.jeconom.2018.12.010 https://doi.org/10.1016/j.jeconom.2018.12.010 https://doi.org/10.1007/s10640-018-0237-9 https://doi.org/10.1016/j.landusepol.2015.09.019 https://doi.org/10.1016/j.landusepol.2015.09.019 44 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 fanny le glouxa, pierre dupraz appendices appendix a1. farm accountancy data network sample coverage of farms with organic practices table a1.1. sample coverage of farms with organic practices in 2019. in conversion to organic farming certified organic certified or in conversion to organic farming france number of farms n.a n.a 47,196 share of farms (%) n.a n.a 10.4 uaa (ha) 565,574 1,675,711 2,241,345 share of uaa (%) 1.9 5.8 8.3 sample1 number of farms 5,905 24,805 30,710 share of farms (%) 2.0 8.6 10.6 uaa (ha) 545,601 1,705,243 2,250,844 share of uaa (%) 2.1 6.5 8.6 1 all figures are weighted by the extrapolation coefficient of each observation. sources: 2019 french fadn data, 2019 agence bio data. appendix a2. descriptive statistics of the farm accountancy data network sample table a2.1. education level of the farms of the sample (n=28,967)1. level of education % agricultural none or training of less than 120 hours 6.85 primary agricultural education 12.57 secondary agricultural education (short) 41.27 secondary agricultural education (long) 27.57 agricultural higher education (short) 10.53 agricultural higher education (long) 1.20 general none 7.14 primary school certificate 11.82 secondary education (short) 50.52 secondary education (long) 26.30 non-agricultural higher education 4.22 1 all figures are weighted by the extrapolation coefficient of each observation. source: 2016-2019 french fadn data. table a2.2. regions of the farms of the sample (n=28,967)1. region % ile de france 1.42 champagne-ardenne 6.23 picardie 3.52 haute-normandie 2.20 centre 5.97 basse-normandie 3.90 bourgogne 4.99 nord pas de calais 3.40 lorraine 2.54 alsace 2.25 franche-comté 1.98 pays de la loire 8.16 bretagne 8.31 poitou-charentes 5.69 aquitaine 7.62 midi-pyrénées 8.40 limousin 2.55 rhône-alpes 6.66 auvergne 4.59 languedoc roussillon 5.25 provence-alpes-côte d’azur 3.84 corse 0.54 1 all figures are weighted by the extrapolation coefficient of each observation. source: 2016-2019 french fadn data. table a2.3. technical orientations of the farms of the sample (n=28,967)1. technical orientation otex number % cereals, oleaginous, protein crops 15 18.25 other field crops 16 6.70 vegetable gardening 28 1.73 horticulture 29 2.07 wine with quality label 37 13.81 other wine 38 1.50 other permanent crops 39 2.46 dairy farming 45 14.97 beef farming 46 10.39 mixed cattle farming 47 3.53 sheep and goat farming 48 5.47 pigs and poultry farming 50 5.48 mixed crops farming 61 1.64 mixed livestock dominated by grazing livestock 73 1.20 mixed livestock dominated by granivores 74 1.35 mixed farming: field crops and grazing livestock 83 7.36 mixed farming: other combination of crops and livestock 84 2.10 1 all figures are weighted by the extrapolation coefficient of each observation. source: 2016-2019 french fadn data 45 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 upscaling environmental incentives in the common agricultural policy appendix a3. coefficients and marginal effects of the generalised tobit models table a3.1. estimates of the generalised tobit models for the uptake of agri-environment-climate measures and organic farming support. aecm of support participation decision (di*) acceptable farmlevel payment (pi*) in 1,000€ participation decision (di*) acceptable farmlevel payment (pi*) in 1,000€ intercept -1.149*** (0.047) 0.635 (0.456) -3.585*** (0.071) -10.008*** (0.740) decoupled payments (100€/ha) 0.001 (0.001) -0.119* (0.056) -0.001 (0.001) 1.242*** (0.132) coupled payment for suckler cows (1,000€) 0.014*** (0.004) 0.052+ (0.029) 0.006 (0.007) 0.063 (0.055) fuel price (€/l) 0.453*** (0.034) -0.133 (0.288) 0.168*** (0.048) 0.137 (0.403) land lease (100€/ha) -0.002*** (0.000) -0.009 (0.011) 0.007*** (0.001) 0.046+ (0.027) standard gross production (100,000€) 0.053*** (0.007) -0.232*** (0.061) -0.042*** (0.007) -0.269*** (0.067) labour (awu/ha) -0.003* (0.001) 0.008 (0.245) -0.472*** (0.026) 0.844 (0.848) utilised agricultural area (100ha) 0.277*** (0.024) -0.402* (0.200) 0.455*** (0.039) 10.757*** (0.501) depreciation (10,000€/ha) -0.020*** (0.004) 0.029 (0.099) 0.122* (0.054) -9.692*** (1.167) share of rented land 0.063* (0.032) 1.682*** (0.293) 0.143*** (0.038) -0.697* (0.340) less favoured area -0.031*** (0.007) -0.781*** (0.057) 0.129*** (0.011) -0.085 (0.090) cereals, oleaginous, protein crops, other field crops -0.507*** (0.009) 0.458*** (0.082) 0.014 (0.015) 1.084*** (0.137) vegetable gardening, horticulture -1.208*** (0.026) -0.019 (0.339) 0.136*** (0.023) -0.054 (0.188) wine with quality label, other wine -0.468*** (0.013) -0.139 (0.141) -0.102*** (0.018) 0.713*** (0.159) other permanent crops -0.529*** (0.021) 0.911*** (0.215) 0.741*** (0.020) 2.832*** (0.167) dairy farming -0.100*** (0.009) 1.941*** (0.078) 0.360*** (0.016) 2.879*** (0.135) beef farming 0.041*** (0.010) 0.711*** (0.076) 0.242*** (0.018) -1.638*** (0.149) mixed cattle farming 0.143*** (0.012) 1.347*** (0.092) 0.195*** (0.025) 0.162+ (0.225) sheep and goat farming 0.147*** (0.011) 0.556*** (0.089) 0.034* (0.018) -0.489* (0.147) pigs and poultry farming, mixed livestock dominated by granivores -0.329*** (0.013) -0.331** (0.117) 0.193*** (0.019) 0.299** (0.170) mixed crops farming -0.482*** (0.022) 1.210*** (0.251) 0.326*** (0.024) 2.825*** (0.188) mixed livestock dominated by grazing livestock -0.303*** (0.021) 1.895*** (0.210) 0.518*** (0.029) 0.073 (0.234) mixed farming: field crops and grazing livestock, other combination of crops and livestock baseline age (years) -0.005*** (0.000) 0.061*** (0.002) -0.017*** (0.000) 0.017* (0.003) no general education -0.382*** (0.013) -0.825*** (0.107) -0.545*** (0.018) -0.368*** (0.165) primary school certificate -0.473*** (0.013) -1.344*** (0.113) -0.543*** (0.019) 1.324*** (0.185) secondary education (short) -0.319*** (0.010) -1.828*** (0.089) -0.309*** (0.014) 0.229+ (0.130) secondary education (long) -0.277*** (0.010) -1.584*** (0.087) -0.237*** (0.014) -0.205 (0.131) non-agricultural higher education baseline no agricultural education or training ≤120 h -0.199*** (0.019) -3.090*** (0.159) 0.072** (0.026) 4.018*** (0.236) primary agricultural education -0.208*** (0.019) -3.767*** (0.146) 0.043+ (0.025) 3.903*** (0.228) secondary agricultural education (short) -0.234*** (0.018) -2.845*** (0.135) 0.243*** (0.024) 2.409*** (0.204) secondary agricultural education (long) -0.230*** (0.017) -2.458*** (0.133) -0.004 (0.023) 3.385*** (0.207) agricultural higher education (short) -0.061*** (0.018) -2.186*** (0.135) 0.105*** (0.024) 3.707*** (0.209) agricultural higher education (long) baseline share of permanent grasslands 0.352*** (0.038) -3.235*** (0.322) -0.313*** (0.054) -2.708*** (0.454) density of grazing livestock (lu/ha) 0.135*** (0.014) -0.503* (0.243) 0.396*** (0.034) 0.308 (0.452) natura 0.414*** (0.009) 0.425*** (0.064) -0.012+ (0.016) 2.443*** (0.138) organic certification 0.303*** (0.032) -0.872*** (0.218) 1.208*** (0.024) 2.672*** (0.185) ile de france 0.504*** (0.032) 3.117*** (0.328) 2.421*** (0.055) 15.726*** (0.580) champagne-ardenne 0.053+ (0.030) -0.743** (0.280) 1.605*** (0.058) 7.043*** (0.601) picardie 0.410*** (0.030) 1.407*** (0.287) 2.326*** (0.054) 7.055*** (0.576) (continued) 46 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 fanny le glouxa, pierre dupraz aecm of support participation decision (di*) acceptable farmlevel payment (pi*) in 1,000€ participation decision (di*) acceptable farmlevel payment (pi*) in 1,000€ haute-normandie -0.127*** (0.031) 0.859** (0.301) 2.146*** (0.056) 4.871*** (0.582) centre -0.061* (0.029) 3.680*** (0.284) 2.151*** (0.052) 8.162*** (0.560) basse-normandie -0.518*** (0.029) 2.688*** (0.275) 2.045*** (0.052) 4.771*** (0.551) bourgogne -0.278*** (0.029) 0.501+ (0.277) 2.700*** (0.051) 7.443*** (0.548) nord pas de calais 0.203*** (0.030) -0.874** (0.300) 2.212*** (0.055) 6.374*** (0.587) lorraine 0.018 (0.030) 1.760*** (0.300) 2.697*** (0.053) 13.271*** (0.559) alsace -0.038 (0.033) -0.703* (0.290) 2.524*** (0.054) 8.225*** (0.573) franche-comté -0.394*** (0.031) -3.171*** (0.285) 2.284*** (0.054) 2.790*** (0.564) pays de la loire 0.115*** (0.029) 4.436*** (0.274) 2.609*** (0.051) 5.287*** (0.546) bretagne 0.642*** (0.029) 4.699*** (0.278) 1.862*** (0.052) 4.997*** (0.559) poitou-charentes 0.417*** (0.028) 2.938*** (0.271) 2.350*** (0.052) 8.691*** (0.554) aquitaine -0.180*** (0.028) -1.370*** (0.284) 2.490*** (0.050) 6.601*** (0.543) midi-pyrénées -0.450*** (0.028) -2.529*** (0.282) 2.332*** (0.049) 7.353*** (0.536) limousin -0.336*** (0.030) -1.118*** (0.291) 2.448*** (0.054) 5.751*** (0.567) rhône-alpes 0.066* (0.028) -1.397*** (0.272) 2.553*** (0.050) 5.932*** (0.538) auvergne -0.278*** (0.029) -3.526*** (0.279) 2.410*** (0.052) 5.715*** (0.555) languedoc roussillon 0.149*** (0.028) 0.914** (0.286) 2.176*** (0.050) 5.819*** (0.538) provence-alpes-côte d’azur 0.458*** (0.029) 2.017*** (0.278) 1.756*** (0.051) 3.481*** (0.550) corse baseline observed participation in aecm in 2015 2.512*** (0.007) observed participation in of support at t-1 -0.280*** (0.020) 0.017 (0.165) observed participation in of support in 2015 1.407*** (0.010) observed participation in aecm at t-1 -0.236*** (0.023) 0.684*** (0.207) 2016 -0.363*** (0.009) -0.603*** (0.077) -0.241*** (0.013) -0.168 (0.116) 2017 -0.228*** (0.008) -0.593*** (0.064) -0.325*** (0.011) -0.582*** (0.098) 2018 -0.171*** (0.007) -0.164** (0.057) -0.389*** (0.010) -0.512*** (0.087) 2019 baseline ρ -0.034*** (0.005) 0.133*** (0.011) σ 5.581*** (0.013) 6.978*** (0.020) number of observations 28,967 2,442 28,967 1,657 log-likelihood -504,317 -318,531 aic 1,008,948 637,376 schwarz criterion 1,010,826 639,254 pseudo-r2 (mcfadden) 0.241 0.378 significance levels: *** p-value <0.001, ** p-value <0.01, * p-value<0.05, + p-value<0.1. standard errors in parentheses. awu: annual work unit. lu: livestock unit. source: own elaboration. table a3.1. (continued). 47 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 upscaling environmental incentives in the common agricultural policy table a3.2. generalised tobit models estimation: marginal effects at the sample mean. aecm of support participation decision (di*) acceptable farmlevel payment (pi*) in 1,000€ participation decision (di*) acceptable farmlevel payment (pi*) in 1,000€ decoupled payments (100€/ha) 0.000 (0.000) -0.093* (0.020) -0.000 (0.000) 1.039*** (0.220) coupled payment for suckler cows (1,000€) 0.001*** (0.001) 0.041+ (0.009) 0.000 (0.000) 0.053 (0.011) fuel price (€/l) 0.041*** (0.039) -0.105 (0.022) 0.007*** (0.013) 0.114 (0.024) land lease (100€/ha) -0.000*** (0.000) -0.007 (0.001) 0.000*** (0.001) 0.039+ (0.008) standard gross production (100,000€) 0.005*** (0.005) -0.182*** (0.039) -0.002*** (0.003) -0.225*** (0.048) labour (awu/ha) -0.000* (0.000) 0.006 (0.001) -0.019*** (0.037) 0.706 (0.149) utilised agricultural area (100ha) 0.025*** (0.024) -0.316* (0.068) 0.018*** (0.035) 8.997*** (1.902) depreciation (10,000€/ha) -0.002*** (0.002) 0.023 (0.005) 0.005* (0.009) -8.106*** (1.713) share of rented land 0.006* (0.005) 1.322*** (0.283) 0.006*** (0.011) -0.583* (0.123) less favoured area -0.003*** (0.003) -0.614*** (0.131) 0.005*** (0.010) -0.071 (0.015) cereals, oleaginous, protein crops, other field crops -0.046*** (0.044) 0.360*** (0.077) 0.001 (0.001) 0.906*** (0.192) vegetable gardening, horticulture -0.109*** (0.105) -0.015 (0.003) 0.006*** (0.010) -0.045 (0.010) wine with quality label, other wine -0.042*** (0.040) -0.110 (0.023) -0.004*** (0.008) 0.596*** (0.126) other permanent crops -0.048*** (0.046) 0.716*** (0.153) 0.030*** (0.057) 2.369*** (0.501) dairy farming -0.009*** (0.009) 1.526*** (0.327) 0.015*** (0.028) 2.408*** (0.509) beef farming 0.004*** (0.004) 0.559*** (0.120) 0.010*** (0.019) -1.370*** (0.290) mixed cattle farming 0.013*** (0.012) 1.059*** (0.227) 0.008*** (0.015) 0.135+ (0.029) sheep and goat farming 0.013*** (0.013) 0.437*** (0.094) 0.001* (0.003) -0.409* (0.086) pigs and poultry farming, mixed livestock dominated by granivores -0.030*** (0.028) -0.260** (0.056) 0.008*** (0.015) 0.250** (0.053) mixed crops farming -0.043*** (0.042) 0.951*** (0.204) 0.013*** (0.025) 2.363*** (0.499) mixed livestock dominated by grazing livestock -0.027*** (0.026) 1.489*** (0.319) 0.021*** (0.040) 0.061 (0.013) mixed farming: field crops and grazing livestock, other combination of crops and livestock baseline age (years) -0.000*** (0.000) 0.048*** (0.010) -0.001*** (0.001) 0.014*** (0.003) no general education -0.034*** (0.033) -0.649*** (0.139) -0.022*** (0.042) -0.308*** (0.065) primary school certificate -0.043*** (0.041) -1.057*** (0.226) -0.022*** (0.042) 1.107*** (0.234) secondary education (short) -0.029*** (0.028) -1.437*** (0.308) -0.013*** (0.024) 0.191+ (0.040) secondary education (long) -0.025*** (0.024) -1.245*** (0.267) -0.010*** (0.018) -0.171 (0.036) non-agricultural higher education baseline no agricultural education or training ≤120 h -0.018*** (0.017) -2.429*** (0.520) 0.003** (0.006) 3.361*** (0.710) primary agricultural education -0.019*** (0.018) -2.961*** (0.634) 0.002+ (0.003) 3.264*** (0.690) secondary agricultural education (short) -0.021*** (0.020) -2.237*** (0.479) 0.010*** (0.019) 2.015*** (0.426) secondary agricultural education (long) -0.018*** (0.018) -1.932*** (0.414) -0.000 (0.000) 2.831*** (0.598) agricultural higher education (short) -0.005*** (0.005) -1.718*** (0.368) 0.004*** (0.008) 3.101*** (0.655) agricultural higher education (long) baseline share of permanent grasslands 0.032*** (0.030) -2.543*** (0.544) -0.013*** (0.024) -2.265*** (0.479) density of grazing livestock (lu/ha) 0.012*** (0.012) -0.396* (0.085) 0.016*** (0.031) 0.258 (0.054) natura 0.037*** (0.036) 0.334*** (0.071) -0.000+ (0.001) -2.043*** (0.432) organic certification 0.027*** (0.026) -0.685*** (0.147) 0.049*** (0.093) 2.235*** (0.472) ile de france 0.045*** (0.044) 2.450*** (0.525) 0.098*** (0.187) 13.153*** (2.780) champagne-ardenne 0.005+ (0.005) -0.584** (0.125) 0.065*** (0.124) 5.890*** (1.245) picardie 0.037*** (0.035) 1.106*** (0.237) 0.094*** (0.180) 5.900*** (1.247) haute-normandie -0.011*** (0.011) 0.676** (0.145) 0.087*** (0.166) 4.074*** (0.861) centre -0.005* (0.005) 2.893*** (0.619) 0.087*** (0.166) 6.827*** (1.443) basse-normandie -0.047*** (0.045) 2.113*** (0.452) 0.083*** (0.158) 3.990*** (0.844) bourgogne -0.025*** (0.024) 0.394+ (0.084) 0.110*** (0.209) 6.225*** (1.316) (continued) 48 bio-based and applied economics 13(1): 27-48, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14414 fanny le glouxa, pierre dupraz aecm of support participation decision (di*) acceptable farmlevel payment (pi*) in 1,000€ participation decision (di*) acceptable farmlevel payment (pi*) in 1,000€ nord pas de calais 0.018*** (0.018) -0.687** (0.147) 0.090*** (0.171) 5.331*** (1.127) lorraine 0.002 (0.002) 1.384*** (0.296) 0.109*** (0.209) 11.099*** (2.346) alsace -0.003 (0.003) -0.553* (0.118) 0.102*** (0.195) 6.879*** (1.454) franche-comté -0.036*** (0.034) -2.493*** (0.534) 0.093*** (0.177) 2.334*** (0.493) pays de la loire 0.010*** (0.010) 3.487*** (0.747) 0.106*** (0.202) 4.422*** (0.935) bretagne 0.058*** (0.056) 3.694*** (0.791) 0.076*** (0.144) 4.179*** (0.883) poitou-charentes 0.038*** (0.036) 2.309*** (0.494) 0.095*** (0.182) 7.269*** (1.537) aquitaine -0.016*** (0.016) -1.077*** (0.231) 0.101*** (0.193) 5.521*** (1.167) midi-pyrénées -0.041*** (0.039) -1.988*** (0.426) 0.095*** (0.180) 6.149*** (1.300) limousin -0.030*** (0.029) -0.879*** (0.188) 0.099*** (0.189) 4.810*** (1.017) rhône-alpes 0.006* (0.006) -1.098*** (0.235) 0.104*** (0.197) 4.962*** (1.049) auvergne -0.025*** (0.024) -2.772*** (0.593) 0.098*** (0.186) 4.780*** (1.010) languedoc roussillon 0.013*** (0.013) 0.718** (0.154) 0.088*** (0.168) 4.867*** (1.029) provence-alpes-côte d’azur 0.041*** (0.040) 1.585*** (0.339) 0.071*** (0.136) 2.911*** (0.615) corse baseline observed participation in aecm in 2015 0.226*** (0.217) observed participation in of support at t-1 -0.025*** (0.024) 0.013 (0.003) observed participation in of support in 2015 0.057*** (0.109) observed participation in aecm at t-1 -0.010*** (0.018) 0.572*** (0.121) 2016 -0.033*** (0.031) -0.474*** (0.101) -0.010*** (0.019) -0.141 (0.030) 2017 -0.021*** (0.020) -0.466*** (0.100) -0.013*** (0.025) -0.487*** (0.103) 2018 -0.015*** (0.015) -0.129** (0.028) -0.016*** (0.030) -0.428*** (0.091) 2019 baseline ρ -0.034*** (0.005) 0.133*** (0.011) σ 5.581*** (0.013) 6.978*** (0.020) number of observations 28,967 2,442 28,967 1,657 log-likelihood -504,317 -318,531 aic 1,008,948 637,376 schwarz criterion 1,010,826 639,254 pseudo-r2 (mcfadden) 0.241 0.378 significance levels: *** p-value <0.001, ** p-value <0.01, * p-value<0.05, + p-value<0.1. standard errors in parentheses. awu: annual work unit. lu: livestock unit. source: own elaboration. table a3.2. (continued). _hlk60054323 _hlk104829823 _hlk128742463 _hlk104830529 _hlk127347111 _hlk128647880 _hlk128647635 _hlk128646517 _hlk128649491 new pathways for improved delivery of public goods from agriculture and forestry stefano targetti1,*, andreas niedermayr2, kati häfner3, lena schaller2 assessing the landscape recovery scheme in the uk: a q methodology study in yorkshire, uk emmanouil tyllianakis upscaling environmental incentives in the common agricultural policy: an assessment of the potential of transfers from the first to second pillar fanny le gloux1,2,*, pierre dupraz1 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures: a pestle approach based on stakeholder statements theresa eichhorn1,*, lena schaller1, katri hamunen2,3, tania runge4,* innovative contract solutions for the agri-environmental-climate public goods provision: which features meet the farmers’ approval? insights from emilia-romagna (italy) riccardo d’alberto1,*, meri raggi2, davide viaggi3 the use of innovative contracts to provide agri-environmental public goods: comparing attitudes between ireland and other european countries tracy bradfield1,*, thia hennessy1, riccardo d’alberto2, emmi haltia3 © 2025 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae bio-based and applied economics bae bio-based and applied economics 14(2): 15-30, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17326 citation: rinaldi, g. m., & viaggi, d. (2025). towards the knowledge and innovation system for the bioeconomy?. bio-based and applied economics 14(2): 15-30. doi: 10.36253/bae-17326 received: february, 18, 2025 accepted: july, 17, 2025 published: september 30, 2025 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor in chief: silvia coderoni orcid gr: 0000-0002-1500-4579 dv: 0000-0001-9503-2977 towards the knowledge and innovation system for the bioeconomy? giacomo maria rinaldi*, davide viaggi alma mater studiorum – university of bologna, via zamboni 33, 40126, bologna, italy *corresponding author. email: giacomomaria.rinald2@unibo.it abstract. the bioeconomy is a growing sector in both highand low-income countries, closely linked to innovation. however, knowledge creation and innovation flows remain underexplored due to their complexity. this study aims to introduce the knowledge and innovation system for the bioeconomy (kisb) to analyze sector dynamics. a systematic literature review examined its application, revealing the need for both technologyand collaboration-focused approaches. key findings emphasize the importance of multi-actor and multidisciplinary strategies, with recent research prioritizing collaboration over innovation. ethical and market challenges were noted in commercialization. additionally, the concept of microkisb, operating at an organizational level, offers potential in business and policy research. ultimately, kisb and microkisb serve as tools for policymakers, businesses, and researchers to drive bioeconomy advancements. keywords: bioeconomy, innovation system, knowledge development, innovation flows. jel codes: d85, o31, q57. 1. introduction the bioeconomy represents an important segment of the economy of both high-income and low-income countries (johnson et al., 2022; m’barek and wesseler, 2023), gaining increasing popularity in recent years (m’barek and wesseler, 2023). as pointed out by the systemic literature review in wei et al. (2022), four stages of bioeconomy research can be identified, namely: the infancy stage (1998-2002); the exploring stage (2003-2012); the blooming stage (2013-2017); and the mature stage (2018-to date). hence, the bioeconomy research can be considered in its maturity. moreover, even from a policy perspective, the bioeconomy is considered an established and no longer emerging sector, with more than 60 specific strategies around the world (gbs, 2024). despite this maturity, the concept of bioeconomy is still subject to debate, both in policy and research fields (vogelpohl and töller, 2021; wei et al., 2022), with different points of view that hinder a common vision (johnson et al., 2022; lewandowski, 2018; viaggi et al., 2021). the main issue is that, based on local characteristics, each country (but even each continent) https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae https://doi.org/10.36253/bae-17326 https://doi.org/10.36253/bae-17326 https://orcid.org/0000-0002-1500-4579 https://orcid.org/0000-0001-9503-2977 mailto:giacomomaria.rinald2@unibo.it 16 bio-based and applied economics 14(2): 15-30, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17326 giacomo maria rinaldi, davide viaggi pushes for a different interpretation of the bioeconomy (m’barek and wesseler, 2023). several papers have tried to aggregate the main visions and approaches of the bioeconomy (e.g. bugge et al., 2016; vivien et al., 2019; wei et al., 2022). however, regardless of the vision taken, there are some elements that are transversal and accepted as intrinsic to the bioeconomy. one of these is innovation (viaggi et al., 2021). nevertheless, few studies have focused on the innovative processes that regulate the bioeconomy and, in most cases, they emphasized practical rather than theoretical implications (bröring et al., 2020; faulkner et al., 2024; van lancker et al., 2016). among the few examples of theoretical advancement, one is given by van lancker et al. (2016), who identified five factors and outlined the key characteristics of the innovation process. the five factors, called by the authors “contextual factors” and defined as factors that “impact the implementation and management of innovation development processes in the context of the bioeconomy” (van lancker et al., 2016: 61) are: radical innovation (ri), complex knowledge base (ckb), fragmented policy (fp), challenging commercialisation (cc), and intense cooperation (ic). these elements are considered by the authors as the basis on which innovation development processes are established, but they do not describe the wholeness of the development processes. a methodological approach that allows us to analyse, at the same time, the contextual factors and the development processes is that of innovation systems (is). the is perspective has its roots in the seminal works of lundvall (1985; 1992), nelson (1988; 1993) and dosi (dosi et al., 1988), who started to switch from a technology-based to a knowledge-based approach (godin, 2006), replacing, in this way, the firm-centred vision of innovation with a systemic vision. the concept of is is nowadays well-established (rubach et al., 2017), with extensive literature on the topic (pyka and scharnhorst, 2009). in this framework, the socio-economic context and the relationships among organisations are considered key areas of research (beckenbach et al., 2009; garud et al., 2013). consequently, with the inclusion of new economic and social variables within the innovation processes, the number of disciplines involved in the study of is notably increased, moving the study of innovation into the domain of complexity science (burmaoglu et al., 2019). hence, in the last decades, following the varied backgrounds and the different research interests of the scholars, many different models to visualize innovation have been proposed. one of the first models, widely accepted was outlined by lundvall (1992), who introduced the concept of national innovation systems (nis), shading the light on the impact of national institutions on the development of innovation processes (russo and rossi, 2009). similarly, cooke (1992) introduced the regional innovation systems (ris), underlining the local aspects of innovation and the importance of proximity (boschma, 2004). malerba (2002) focused on the sectoral systems of innovation and production. merging the concepts of national and sectoral systems, spielman and birner (2008) developed a concept for a national agricultural innovation system, further developed by klerkx et al. (2012) in the agricultural knowledge and innovation system (akis). instead, focusing on the typologies of actors that interact within the system, etzkowitz and leydesdorff (2000) identified three main categories, i.e. government, industry and academia, that establish mechanisms, more or less complex, of feedback and support for innovation. referring to the double helix model of dna, the authors metaphorically called this threeactor model triple helix. afterwards, the diffusion of this model in the scientific and political fields brought scholars to consider new categories. hence, carayannis and campbell, first added the media and culture, affirming the quadruple helix model (carayannis and campbell, 2009), and then, introducing the natural environment, proposed the quintuple helix (carayannis and campbell, 2010). despite the academic debate toward these models, these theories have been favourably received by policymakers (aragón et al., 2012). indeed, in the field of innovation policy, the systemic approach has found increasing success, following and proceeding in parallel with the scientific debate (aragón et al., 2012; enger, 2018; protogerou et al., 2010). the aim of this paper is to identify if there is scope for a knowledge and innovation system for the bioeconomy (kisb) framework and which may be its peculiarities. to do so, we decided to first explore what types of is were adopted to describe the bioeconomy, and then to outline the main common characteristics. indeed, to the best of the authors’ knowledge, there are no specific literature reviews that assess the state of the art of is framework in the bioeconomy. the originality of the present systematic literature review lies in its ability to assess, at the same time, the contextual factors of van lancker et al. (2016) and is frameworks that mostly characterize the innovation literature in the bioeconomy. the final results highlight there is no unique is for the bioeconomy – as it happens in other sectors, such as agriculture – and that the contextual factors of van lancker et al. seem to be deficient in describing the complexity of the current innovation context. 17towards the knowledge and innovation system for the bioeconomy? bio-based and applied economics 14(2): 15-30, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17326 the paper is structured in the following way. in section 2, we present the material and methods adopted to carry out this study. in section 3, the results are reported in three main subsections, namely: general information about the papers; contextual factors identified; and categorization of the papers into four groups based on two dichotomies: collaborative-oriented vs. innovationoriented and business-centred vs. policy-centred. these categories were then related to the contextual factors and the type of is approach. in section 4 we discuss the results under the lens of a possible unique knowledge and innovation system for the bioeconomy (kisb), similar to what happens in agriculture with the agricultural knowledge and innovation system (akis). finally, some conclusions are outlined in section 5. 2. material and methods the present paper is conducted following the prisma (preferred reporting items for systematic reviews) approach (moher et al., 2009). this approach foresees several consequential steps. first, the identification of what to investigate (research question), where (sources, databases, etc.) and how to start (keywords, search strings, etc.). second, the authors determine specific preliminary criteria for including or excluding studies, for example, based on the typology of items (articles, reviews, book chapters, etc.) or only publications in a specific range of years. after that, a screening phase is carried out, reading titles and abstracts and identifying the match with the predetermined criteria. the final selection of the eligible articles is made by reading the full papers, rejecting the non-compliant ones that had passed the abstract-based selection. the last phase of the prisma approach is the qualitative review of the selected papers and the presentation of results. our research was conducted in july 2024. based on the research question, we conducted our search in the scopus database1, using as a string: “(bioeconomy or bio-based and economy) and innovation and (system* or network or cluster)”. this first query returned 209 documents (fig. 1). hence, we filtered by subject area, keeping “social sciences”, “business, management and accounting”, “economics, econometrics and finance”, and “multidisciplinary.” based on the document type, we kept only articles and reviews. then, we excluded chinese as a language. finally, according to our research question and the explained background, we selected only papers 1 scopus, elsevier b.v., https://www.scopus.com/, last seen 04/02/2025 figure 1. overview of the process of document selection following the prisma method (moher et al., 2009). https://www.scopus.com/ 18 bio-based and applied economics 14(2): 15-30, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17326 giacomo maria rinaldi, davide viaggi from 2017 to 2024 – the so-called maturity stage of the bioeconomy (wei et al., 2022). in this way, a subtotal of 56 documents was found. based on the research question and the objective of this study, before starting to read titles, abstracts and, eventually, full papers, we defined some criteria: – no papers with no focus/analysis of innovation processes; – no papers on business opportunities/product-oriented (with no specific focuses on innovation systems); – no papers on sustainability assessment; – no papers on technology’s impact on sustainability; – no papers on circular economy with no reference to bioeconomy. after the exclusion of non-compliant papers based on abstracts or full-paper reading or because the document was not findable, we conducted our qualitative research on the final number of 24 papers. the qualitative analysis was conducted through four main steps: i. identif ication of general information, namely: nationality of the institution(s) of the author(s); paper’s topic; sector(s) or subsector(s) of the bioeconomy considered; study reference scale; methodology applied; innovation systems framework adopted; and whether case study or not (if yes, where); ii. identification of the contextual factors (see tab. 1 for the considered criteria); iii. classification of the papers based on four categories, contrasting on the vertical axis the collaborativeoriented and innovation-oriented papers, while on the horizontal axis the business-centred and policycentred ones (fig. 2). the assignment of a paper to one of the categories was concerned primarily with the paper’s research objective. if the research objective was not clear enough, and doubts persisted, the analysis moved to results, discussion and conclusion. however, based on the main focus, none of the papers fell into multiple categories; iv. distribution of is and contextual factors into the four previously identified groups. in greater detail, the criteria listed in tab. 1 are extrapolated by van lancker et al. (2016). hence, to assign one factor to one paper, one or more than one of the criteria must be directly addressed in at least one of the sections of the paper. thus, for example, to assign “challenging commercialisation”, in at least one section there must be the identification of difficulties related to the commercialisation or adoption of bio-based products by other companies (b2b), by the final consumer (b2c) or both. the classification of papers based on the identified four categories represents an original framework developed by the authors. this framework, taking up the original distinction between technology-based and knowledge-based approaches, broadens its scope and contrasts innovation-oriented papers with collaboration-oriented ones. this choice was made to understand whether, in the study of the bioeconomy, linear approaches to innovation persist or whether, given the relatively recent birth of this sector, the collaborative and systemic model is prevalent in the analysis of the sector. similarly, the contrast between firm/business-centred research and policy-centred research was adopted to understand the main point of view of today’s research on the topic of innovation in the bioeconomy. the main scope of this contrast was to understand the distinctions in perspectives between two economic table 1. criteria for selecting contextual factors. contextual factor criteria radical innovation · redesigned business models · reconfigured supply chains · setup new supply chains (new convergences of sectors) complex knowledge base · varieties of sciences and technologies intense cooperation · cooperation between different actors challenging commercialisation and adoption · challenging in b2b · challenging in b2c policy schemes fragmented · different policy schemes · different administrative levels · legal limitations for biobased/ biomass applications figure 2. papers grouped by main orientation (collaboration vs innovation) and research field (business vs policy). 19towards the knowledge and innovation system for the bioeconomy? bio-based and applied economics 14(2): 15-30, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17326 branches (namely, business economics and economic policy) and to identify which of the two prevails when it comes to innovation in the bioeconomy. furthermore, given the importance of these two perspectives, the analysis of the current literature on the topic provides insights in terms of knowledge gaps and future research. hence, by placing these two contrasts on two axes, four different quadrants were identified, and each of them was named depending on the two dimensions involved. the four quadrants are: i) network policy (collaboration-oriented and policy-centred); ii) business environment (collaboration-oriented and business-centred); iii) innovative business (innovation-oriented and business-centred); and iv) innovation policy (innovation-oriented and policy-centred).through these groups, it was possible to better understand the differences in is framework adoption pathways and, focusing on the innovation process, the factors that characterise the bioeconomy context. in the results section, after a general overview (subsection 3.1) and a description of the contextual factors identified (subsection 3.2), the four groups are used as a lens (subsection 3.3) to explore the relationship among them and is frameworks adopted by scholars (subsubsection 3.3.1) and among them and contextual factors emerging from the papers (subsubsection 3.3.2). 3. results 3.1. general overview considering the geographical location of the authors’ institutes, europe has the most prominent role, with twenty papers out of twenty-four that involve only european institutes and two papers that involve european and non-european entities (however, in both cases the first author belongs to a european country). only in two cases, the authors are not european, i.e. in one case from brazil and in the other from brazil and australia. at the country level, the most represented country is germany with 10 contributions, followed by finland with 5 papers. in terms of approach, the large majority of papers are applied research with eighteen of them that consider a case study. lovrić et al. (2020) and bueno et al. (2022) stand out as the sole studies where the authors conducted practical research without analysing a specific case. among the remaining three, two are literature reviews (lang et al., 2023; salvador et al., 2021) and one is a commentary (losacker et al., 2023). moreover, in terms of methodology, the most used methods are qualitative ones, namely focus groups, semi-structured interviews and questionnaires. other methods comprise analysis of research projects, social network analysis, system dynamics and innovation systems approaches. looking at the reference scale, the national perspective is the most addressed, with twelve papers, followed by the global perspective with five papers. other scales, such as regional or continental are addressed as well, but respectively in three and two cases. the municipal and mixed scale (i.e. national plus regional) are referenced in one article each. regarding the bioeconomy sectors or subsectors considered in the papers, the main approach is that of considering the bioeconomy in its general complexity (bogner and dahlke, 2022; chmielińskii and wieliczko, 2022; hurtado and berbel, 2023; lang et al., 2023; losacker et al., 2023; salvador et al., 2021), followed by forestry or wood-based bioeconomy (d’amato et al., 2022; giurca and metz, 2018; laasonen, 2023; lovrić et al., 2020) and green chemistry or biofibre (alfano et al., 2023; kamath et al., 2023; korhonen et al., 2020; loos et al., 2018). less common is the propensity to consider various sectors at the same time (pyka, 2017; scheiterle et al., 2018; torre et al., 2023). 3.2. contextual factors identified identifying the contextual factors as outlined by van lancker et al. (2016), we found that the most common one is intensive cooperation, a concept that emerged in almost all the papers considered (fig. 3). even the complex knowledge base is a widespread factor, discussed or addressed in almost 75% of papers. radical innovation is covered in just over half of the papers, while slightly less than half examines the challenging commercialisation. figure 3. contextual factors identified, in relative numbers, in the papers considered. legend: ri = radical innovations; ckb = complex knowledge base; fp = fragmented policy; cc = challenging commercialisation; ic = intense cooperation. 20 bio-based and applied economics 14(2): 15-30, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17326 giacomo maria rinaldi, davide viaggi finally, the least explored factor is that of fragmented policy, with less than a quarter of the articles focusing on it. however, we also found some elements or critiques that, moving away from van lancker’s definitions, may deepen the knowledge of the innovative context in the bioeconomy. these aspects are further discussed in the following sections. 3.2.1. intense cooperation this factor is the most addressed by different authors and no changes or modifications are reported in the concept: the idea of different actors that intensely cooperate in the bioeconomy innovation processes is widely perceived as one of the main characteristics of the sector. furthermore, this result may suggest that, nowadays, the multi-stakeholder approach is perceived as more distinctive than the multidisciplinary approach (see next section on ckb). bogner and dahlke (2022) underline the importance of empowering and educating heterogeneous actors (different in age, gender, social and educational background) to stay actively engaged and participate in the innovation process with an ex-ante approach rather than an ex-post acceptance approach. however, d’amato et al. (2022) report the difficulty in the finnish wood-based bioeconomy (wbe) to engage in cross-sectoral and cross-discipline knowledge co-production, pointing out the lack of collaborative skills, and organisational differences. similarly, laasonen (2023) highlights the positive effects of welldeveloped relational capabilities, and, on the other hand, the negative impact of their lack on the whole innovation system. a solution to these problems is pointed out by alfano et al. (2023), which observe the role of clusters in aggregating different actors, that could act as intermediaries and help to overcome the collaboration issues. donner and de vries (2023) underline the importance of small-scale initiatives in the circular bioeconomy business models and the role of geographical embeddedness and the relational proximity of actors. in this vein, the local-based innovation and the importance of local actors are pointed out also by torre et al. (2023), in their study on rural development, and by taffuri et al. (2021) in their paper on the urban management of biowaste. in the former, the authors underline the effectiveness of knowledge exchange that the multi-level coordination (from national to local) made and the importance of long-term research programs to keep local actors embedded and aware of how collaborative research works. in the latter, the authors highlight the complex web of stakeholders involved in the cbe paradigm even at the municipality level. however, in some cases, the difference between ic and ckb is blurred. this is the case with some emerging concepts, such as living labs, where, in the case of losacker et al. (2023), they are interpreted as places of interdisciplinary interaction, while in the case of donner and de vries (2023), they are seen, more in general, as “joint systemic co-creation approaches” (donner and de vries, 2023: 13). however, in both cases, the living labs are cited in the “future research” section, underlining the absence of studies in the direction of stable and, more or less, informal collaborations in the bioeconomy. 3.2.2. complex knowledge base although the complex knowledge base of the bioeconomy is widely recognised (e.g. bogner and dahlke, 2022; loos et al., 2018; scheiterle et al., 2018) and still remains one of the peculiarities of this sector, the papers considered a greater tendency to identify this concept with the terms multior interdisciplinarity emerges (see for example chmielińskii and wieliczko, 2022; orozco and grundmann, 2022; torre et al., 2023). chmielińskii and wieliczko (2022) identify interdisciplinary as a way to catch the overall complexity of the bioeconomy and render a holistic vision. this complexity is also pointed out when addressing the issue of lacking knowledge or capacity. for example, the case of loos et al. (2018) points out the lack of capacity of the national system for the implementation of a biomass-based value web that involves several professionals and different know-how. in particular, the authors underline the poor awareness and evaluation of by-products as a resource and the need for coordination and support from public institutions. the latter should facilitate innovation diffusion, support applied r&d, and align institutions toward the commercialisation of plantain fibre (the byproduct analysed in the paper). similarly, drejerska et al. (2020), point out the lack of managerial know-how in implementing circular biowaste management. these two examples demonstrate how, through a systemic analysis, factors emerge that are difficult to identify in a mere technology-oriented or based on a linear approach. in this vein, in recent years some connected concepts are often addressed together with knowledge base, such as skills (e.g. alfano et al., 2023) or education (e.g. chmielińskii and wieliczko, 2022; hurtado and berbel, 2023). 3.2.3. radical innovation in their paper, van lancker et al. (2016) state that “although some existing products and processes may 21towards the knowledge and innovation system for the bioeconomy? bio-based and applied economics 14(2): 15-30, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17326 only need some incremental, gradual innovations, the transition [towards the bioeconomy] will mainly require diverse, radically new and disruptive innovations” (van lancker et al., 2016: 61). this contrast between a more radical and a more moderate approach to innovation often emerges in the papers analysed, although with varying terminology. for example, taffuri et al. (2021) apply an “improvement” perspective, by introducing bio-waste valorisation possibilities within the current metropolitan solid waste management system of a city in northern italy (i.e. turin). kamath et al. (2023) contrast the “path-modernisation” with the “path-creation.” the range of different types of innovation is also part of the work of orozco and grundmann (2022), who outline the variation from incremental to disruptive innovations. this latter concept, gains a wide consensus. indeed, also lovrić et al. (2020), bueno et al. (2022), and losacker et al. (2023) use the term “disruptive” to identify the most radical innovations. however, it is important to underline that, although both radical and disruptive are concepts that imply a deep change, they slightly differ from each other. in fact, the concept of disruptive innovation implies a modification of market dynamics through novel business models and low-end market capture, while the concept of radical innovation is more related to groundbreaking technological advancements (completely new ideas or products) that cause significant organizational transformations within companies (hopp et al., 2018). closer to the concept of “radical” is the concept of “transformative knowledge” explored by bogner and dahlke (2022) in their paper on the german bioeconomy policy. indeed, also in this case the main focus is on the policy side. however, the transition from “innovation” to “knowledge” implies a broadening of the overall vision of the phenomenon, with further dimensions analysed, such as “system knowledge”, “normative knowledge”, “techno-economic knowledge” and “transformative knowledge.” furthermore, this approach reconnects the concept of ri with that of ckb. in the papers analysed, radical innovation is also seen from the business side, as emerged with the concept of disruptive innovation. in this field, giurca and metz (2018) consider the market formation, while lazarevic et al. (2020) consider a niche market. lang et al. (2023) underline the important connection between transformative innovation and the involvement of consumers in bio-based business models. hence, even from a more business-oriented perspective, the importance of a systemic vision may help (e.g. the business model canvas developed by salvador et al., 2021). 3.2.4. challenging commercialisation the aspect of how challenging is the commercialisation of innovations both for b2b and b2c is addressed both directly and indirectly in the papers considered. for example, bogner and dahlke (2022) indirectly address the problem by considering the projects that took place in germany, documenting a strong focus on the market acceptance of products and processes related to the bioeconomy. chmielińskii and wieliczko (2022) underline the difficulties that findings from research encounter in commercialisation. however, in their statement, the authors do not only imply the importance of convincing potential buyers, but they also call for engaging stakeholders across business, scientific, governmental, and consumer sectors and for using better education at all levels. in this way, they mix business and policy recommendations to systematically enhance the national bioeconomy. losacker (2023), more in line with van lancker, refer to “technology legitimization.” however, this concept encompasses social acceptability and broadens the discussion to a legal aspect. moreover, lang et al. (2023) discuss the active role of consumers in influencing business models, while, korhonen et al. 2020 face the problem of the performativity of biomaterials compared to other materials and the importance of this issue in health risks for humans and the environment, stating that in some cases “it makes sense to use the most durable materials available, regardless of the material’s environmental performance.” in other words, due to the large number of ethical challenges that the innovation processes have to face in the bioeconomy, it seems that the specific focus on the commercialisation side limits the capacity of this factor to describe the bioeconomy innovation context. 3.2.5. fragmented policy due to the sectors convergence that characterise the bioeconomy (lazarevic et al., 2020) the optimisation of policies still represents an issue (e.g. korhonen et al., 2020). anyhow, in addition to the low rate of papers that directly address this factor, we found that three policy issues are perceived as more compelling. first, a need for targeted policy interventions (giurca and metz, 2018), that implement the nowadays well-established and structured strategies for the bioeconomy (bogner and dahlke, 2022; hurtado and berbel, 2023). this is the case of the eu, where in addition to the strategy pointed out by the commission (ec, 2018), almost all ms developed their own strategy (joint research centre 22 bio-based and applied economics 14(2): 15-30, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17326 giacomo maria rinaldi, davide viaggi european commission, 2022). an example of successful policy intervention is outlined by lovrić et al. (2020) in the case of finnish wbe. in this context, the incremental change from a forestry to a wood-based bioeconomy policy has been perceived as a success for the stakeholders involved thanks to the reduced policy fragmentation. the second policy issue is the lack of specific funds, considered under several aspects: d’amato et al. (2022) indicate the limited funding opportunities for cooperating in cross-sectoral initiatives as one of the main tension factors in the finnish wbe; in laasonen (2023), the finnish regional and business development agencies and the research and education organisations point out the need for external funding for r&d activities with other partners as one of the element to keep vital collaborations; alfano et al. (2023) show that only a small percentage of the green investments made by italian firms belonging to a biocluster is supported by public funds, with the highest percentage of investments financed by venture capital or traditional bank financing. the third policy issue is a lack of legal frameworks for new technologies or services in the field of the bioeconomy, as emerged in the challenging commercialisation (see specific section). based on these findings, the fragmented policy factor does not describe the overall complexity of policy frameworks in the bioeconomy. 3.3. papers classification the highest number of papers belongs to the category of business environment, with nine papers, followed by network policy with eight (fig. 4). the innovation policy group and innovative business follow with, respectively, four and three papers. hence, looking at the four dimensions considered, we found a higher number of papers directly focused on collaboration (seventeen papers) rather than innovation (seven articles), while between business and policy-centred papers we found a balance of twelve papers each. 3.3.1. groups and innovation systems although the identification of the innovation systems (is) framework for each category did not yield significant results, some remarks can be made. in general, there is a wide range of frameworks adopted by different authors . in the first period (2017-2020) we notice a greater diffusion of innovation systems most known in the literature on innovation, i.e. national innovation systems (nis), regional innovation systems (ris) and technology innovation systems (tis); while in a second phase (2020-2023) we notice a decline in these systems in favour of other frameworks, such as knowledge-based systems (e.g. bogner and figure 4. the selected papers classified based on the four different groups. 23towards the knowledge and innovation system for the bioeconomy? bio-based and applied economics 14(2): 15-30, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17326 dahlke, 2022; d’amato et al., 2022) or stakeholders analysis (taffuri et al., 2021). more in detail, looking at the several is frameworks, the nis was more adopted in the business-centred (loos et al., 2018; scheiterle et al., 2018), tis in policy-centred papers (giurca and metz, 2018; lazarevic et al., 2020) and ris in collaboration-oriented studies (hurtado and berbel, 2023; kamath et al., 2023). no specific is are adopted on the innovation-oriented side. delving into the specific groups, no remarks emerge from the innovative business and innovation policy, while in the case of business environment and network policy, we notice two peculiarities. in the business environment case, there is a tendency to focus with greater detail on sub-groups of the whole is, adopting concepts like networks (bueno et al, 2022) or bioclusters (alfano et al., 2023; kamath et al., 2023). instead, the network policy group’s peculiarity is the broadening of the vision towards a systemic approach of both innovation and knowledge. indeed, only in this group, the concept of knowledge is used as a discriminant. for example, bogner and dahlke (2022) use different knowledge (i.e. transformative knowledge, system knowledge, normative knowledge and techno-economic knowledge) to identify the different types of policies, while d’amato et al (2022) discuss the knowledge co-production within the finnish wbe. finally, chmielińskii and wieliczko (2022) adopt the framework of innovation and knowledge systems, which can be linked to the broad literature on knowledge and innovation systems (kis). 3.3.2. groups and contextual factors apart from intense cooperation, which is the most addressed factor in each category, other contextual factors are mostly in line with the is frameworks outlined above (fig. 5). indeed, complex knowledge base is the most represented factor in collaborative-oriented research, in particular in the network policy group, where it is at the same level of intense cooperation and this confirms the aforementioned interest in the concept of knowledge in this group. instead, in innovation-oriented studies, radical innovation has a prominent role in the innovation policy group, while in innovative business, it shares the same rate with complex knowledge base and challenging commercialisation. in particular, this latter factor characterises innovation-oriented research more than collabfigure 5. contextual factors identified, in relative terms, in the four groups of papers considered. 24 bio-based and applied economics 14(2): 15-30, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17326 giacomo maria rinaldi, davide viaggi orative-oriented one. the fragmented policy is addressed almost only in the policy-centred papers. 4. discussion the variety of frameworks applied to describe the is in the bioeconomy hinders the identification of a singular and unified framework. while this abundance of methodologies allows for the analysis of innovative systems from multiple perspectives, moving toward a unique, widely accepted is may provide some advantages. an example might be provided by one of the most known and successful is, the agricultural knowledge and innovation system (akis) (germundsson and norrman, 2023; ingram and maye, 2020; klerkx and begemann, 2020). rooted in the studies of röling (röling, 1988; röling and wagemakers, 1998), arnold and bell (2001) and spielman and birner (2008), the akis framework was supported by various supranational bodies, such as oecd (2012), world bank (julio and german, 2001), and eu (eu-scar, 2012, 2015, 2019). the latter, in particular, after a gradual introduction of this framework as a policy tool (eu-scar, 2012, 2015, 2019), decided to highlight the role of the akis introducing it in the common agricultural policy (cap) 2023-2027 (european parliament and the council of the european union, 2021) and asking ms to assess how the different actors that compose the national akis interact and support the production and use of knowledge and innovation (eu cap network, 2023). although this concept is still perceived by many political and administrative decision-makers as vague and there is difficulty in fully understanding it (knierim and birke, 2023), a well-functioning akis is seen as a way to strengthen the impact of funds and policy interventions, avoiding duplications and saving costs (eu-scar, 2019). in this sense, a unique is for the bioeconomy, as the knowledge and innovation system for the bioeconomy (kisb) proposed by esposti (2012), might represent a way to determine coherent fund allocations and policy interventions, fulfilling the requests in this direction that we found in this review. in general, this vision might overcome the fragmented and sectorial policy framework that persists in the current bioeconomy. furthermore, such a tool might be useful not only for policymakers but also for all the other components of the system (knierim and birke, 2023). for example, through the analysis of the kisb, several gaps in the system may emerge (e.g. missed brokers or missed technologies) and this may provide to extension services and firms interesting niche markets. nevertheless, it is fundamental to keep in mind that some profound differences persist between akis and kisb. first, due to its modernizing mission and its focus on increasing the sustainability of the rural world, akis’s core components are practitioners, i.e. farmers, foresters, fishers, and food processors (knierim and birke, 2023), seen as implementers of practices that have a direct effect on the environment (schmidt et al., 2022). instead, as we saw in our findings, the current bioeconomy implies a vision that even overcomes van lancker’s complex knowledge base, incorporating knowledgeintensive, high-tech and high organisational and implementation skills. in this sense, the different typologies of practitioners involved in the bioeconomy (e.g. biomaterial producers, bioenergy producers, etc.) expands the audience of stakeholders involved, each with particular needs linked to their own area of interest and reference market. in addition, as we found in this review, the active role of primary producers in the innovation processes of kisb is little explored and, therefore, considered marginal. second, the current akis literature and the actual policy implementation are mainly focused on extension services (amerani et al., 2024; knierim and birke, 2023), especially in their role as innovation brokers. based on the papers we considered, this aspect cannot be focal of kisb nowadays because of the current lack of specific research on advisory services in the bioeconomy innovation process. indeed, to date, research is mainly based on the helix approaches (triple, quadruple and rarely quintuple), considering only the main actors (e.g. business, academia and policymakers) and not connection figures. in this sense, it is not clear whether firms are directly linked to research institutions – with no need for intermediaries –, or if the high-tech innovations in the bioeconomy sector have equipped firms’ in-house r&d with the necessary skills to avoid external advisory services. third, akis can be considered part of kisb if we consider that agriculture is part of the bioeconomy. anyhow, the study of the interactions between these two systems is still in its infancy (chmielińskii and wieliczko, 2022; vilkė and gedminaitė-raudonė, 2020), with several aspects to be further explored, such as the importance of the national akis within a national kisb or the interactions between akis and the other is to form kisb. in particular, can we consider one system overarching the other, or are they synergistic or complementary? fourth, the different roles and importance of consumers. on this aspect, the kisb perspective gives a complexity that the contextual factors identified by van lancker et al. (2016) do not catch completely. indeed, both the challenging commercialisation and intense cooperation do not focus directly on the challenging aspects that characterise the whole innovation process 25towards the knowledge and innovation system for the bioeconomy? bio-based and applied economics 14(2): 15-30, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17326 in the bioeconomy. for example, sustainability and circularity concepts are nowadays considered paramount for the bioeconomy (d’amato and korhonen, 2021; drejerska et al., 2020; lang et al., 2023; salvador et al., 2021). in this vein, the use of biological resources inevitably raises ethical dilemmas (viaggi, 2018; viaggi et al., 2021). an example of this is the possible contrast between food production and the production of other crops (e.g. for biofibres or bioenergy), which is known as the competing dilemma (asada et al., 2020). another example is the well-known debate around genetic modifications (hartung and schiemann, 2014; jacobsen et al., 2013; weisenfeld et al., 2023), which strongly affects the biotechnological component of the bioeconomy (wei et al., 2022). these two examples give an idea of the importance of stakeholders’ engagement in the innovation development, in particular consumers, citizens and end-users. this could be also the reason why the intense cooperation is the most accepted contextual factor as emerged from our results. however, many aspects of this cooperation are still unclear. just to cite some unanswered questions: what are, nowadays, the main drivers? what bottom-up mechanisms characterize cooperation for innovation in the bioeconomy? is this cooperation market-pushed or policy-driven? how does consumer behaviour influence the whole system in the transition towards new bio-products? what is the role and how do local actors contribute to the implementation of new bioeconomic value chains? this latter aspect raises questions regarding the dispute that we found among researchers around the issue of radical innovation. as we saw, researchers are mainly divided between a more moderate and incremental vision of how to implement the bioeconomy (e.g. taffuri et al., 2021) and a more intense and radical one (e.g. bogner and dahlke, 2022). although opposed, from a kisb perspective these two positions can be reconciled. indeed, the path-modernisation and the path-creation (kamath et al., 2023) are both part of the knowledge and innovation processes, with their own actors, mechanisms and characterising factors. hence, both these two streams of research can contribute to a better understanding of the complexity of innovation in the bioeconomy. moreover, all the underlined aspects can benefit both from business-centred and policy-centred research. the business-centred research can largely contribute, through its attitude toward the stakeholder concept (taffuri et al., 2021; korhonen et al., 2020) and the sub-systems description (bioclusters, networks, etc.) (alfano et al. 2023; bueno et al., 2022; kamath et al., 2023). even in this case, the akis literature may provide a framework to explore many of the aspects underlined in the previous questions: microakis (sutherland et al., 2023). this framework focuses on the innovation subset of the whole akis that operates at the farm’s individual level or, using the description provided by sutherland et al. (2022), “the sources of knowledge that farmers personally develop to pursue innovations and to manage their farms” (sutherland et al., 2022: 40). the possibility of exploring the microkisb opens the room to further analysis in the business research, such as new business models, business environment and market creation with a firm-centred systemic perspective. it also allows for considerations in the field of policy-centred research. this stream of research can benefit from the microkisb perspective to draw conclusions about the role of local actors in the transition from national strategies to local implementations. furthermore, the lack of analysis of the mechanisms of knowledge transmission in the whole system and the pressing requests to combine policy interventions and funds allocations – short and medium-term perspective – with bioeconomy strategies – long-term perspective – also questions the wider kisb perspective (more national-oriented). an example is provided by the emerging issue of education and training in the bioeconomy (chmielińskii and wieliczko, 2022; hurtado and berbel, 2023; laasonen, 2023), which represents an interesting point of view for policy considerations to optimize the system’s ability to absorb or generate knowledge (buchmann and pyka, 2015; kurtsal et al., 2024). in this sense, the policy-centred research may merge intense cooperation with fragmented policy, showing that the system perspective can, at the same time, explain the mechanisms and propose pathways, as occurs in the study by hurtado and berbel (2023). hence, both kisb and microkisb can contribute to answering the unanswered questions, combining different levels of research (national, regional, local, etc.), and, at the same time, explaining the mechanisms that regulate all the contextual factors, taken both individually and together. finally, considering the least adopted contextual factors, i.e. cc and fp, we saw that in both cases they were limited in their ability to describe the overall complexity of the innovation development processes in the bioeconomy. this may partly explain why they are less explored by the papers considered. hence, our suggestion is to enlarge both the concepts. the cc should become commercialisation dilemmas (or ethical and market challenges in commercialisation), extending the concept to the ethical aspects of the commercialisation of bioproducts. instead, the fp should become a complex policy and legal framework, underlining the large mix of different levels of policies and norms that characterize the bioeconomy. 26 bio-based and applied economics 14(2): 15-30, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17326 giacomo maria rinaldi, davide viaggi however, this study has some limitations. excluding the linear approach of innovation from research criteria, part of the innovation processes are excluded. in this sense, future research may include this approach to enlarge the vision of the innovation processes. similarly, future research may include contributions provided before 2017, the year we chose as the lower limit of our study. indeed, earlier studies from the infancy and exploring stages of the bioeconomy literature may provide further insights for theoretical advancements in knowledge creation and innovation development in the field of bioeconomy. furthermore, no specific analysis has been conducted in terms of the current level of innovation in the field of the bioeconomy system. specific research on this topic is deemed necessary in the future for a better knowledge of the sector and to understand how the bioeconomy fits into the main modern technological processes (e.g. digitalisation, nature-based solutions, etc.). finally, consulting a single scientific database (i.e. scopus) can be considered a limitation of this research, which future research on the topic could overcome by consulting more databases. 5. conclusion in this study, we conducted a systematic literature review to explore the application of the is framework in the field of the bioeconomy. in particular, the aim was to identify the scope and the characteristics of a kisb framework. we found that a unique framework is nowadays missed. several approaches were adopted, but rarely with the aim of a theoretical advancement for the whole bioeconomy literature. indeed, often the approach adopted was the one best fitting for the purpose of the research, with rare examples of the opposite, i.e. to seek a holistic framework that describes innovation processes within the bioeconomy. however, one of the main results of this study is the possibility of applying and benefiting from a specific kisb. in fact, the mechanisms and dynamics examined in this study go further beyond the simple technology-oriented or linear approach to innovation, as we saw considering the complex amount of skills and professionals needed to implement bioeconomy processes (e.g. in biowaste management). hence, based on the examined papers, some peculiarities should characterise the kisb. first, based on the result that intense cooperation (ic) and complex knowledge base (ckb) are the most common factors, we outlined how the multi-actor and multidisciplinary approaches are fundamental in the bioeconomy innovation processes, and it is not possible to exclude them in the kisb. second, we found a more intense stream of research in the field of collaborations rather than innovations. in this sense, the efforts made by scholars can strongly contribute to outlining a kisb, for example including the analysis of knowledge development. third, even if less represented, the innovation-oriented papers add insights in terms of challenging aspects of commercialisation in the bioeconomy. finally, we found that there is a wide scope for kisb and the connected concept of microkisb (i.e. the innovation subset of the whole kisb that operates at the organisation’s individual level) in both businesscentred and policy-centred research. therefore, kisb and microkisb must be designed in such a way that they can represent an interesting and useful tool for all the actors involved in the bioeconomy innovation process, mainly policymakers, business actors, and researchers. furthermore, similarly to akis in the current cap, even kisb may become a policy objective transversal to all the sectors involved. this would make all the operators aware of the actors involved in the knowledge and innovation system, and, on the other hand, the bioeconomy would benefit from a more systemic promotion and sharing of knowledge. moreover, looking at the contextual factors of van lancker et al. 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(2023). beyond plastic – consumers prefer food packaging derived from genetically modified plants. research policy 52: 104883. reflections and new directions: an editorial retrospective and the launch of our new policy paper section silvia coderoni, fabio bartolini where is the italian agriculture heading? a discussion in light of the prospects for the future cap filippo arfini1*, fabio bartolini2, anna carbone3, tatiana castellotti4, silvia coderoni5, raffaele cortignani6, raffaele d’annolfo4, giovanni dara guccione4, michele donati1, francesca galli7, roberto henke4, giampiero mazzocchi4, alessandro monteleone4, towards the knowledge and innovation system for the bioeconomy? giacomo maria rinaldi*, davide viaggi impact of weather variability on crop yields and land use dynamics in eastern india: shortand long-term effects pratap kumar jena1, kirtti ranjan paltasingh2, souryabrata mohapatra3,,*, ashok mishra4 rebuilding strategies for food self-sufficiency: portugal’s past patterns and future ambitions alexandre macedo joão1, dulce freire2, humberto rocha3, joana dias4 from subsidy to land rent: how production-linked payments shape factor remuneration in agriculture adrian sadłowski expenditure allocation for rural development interventions: main trends and patterns in the choices of the italian regions under the cap 2023-2027 tobia minuzzo, francesco pagliacci* title <arial 13, bold> 1 assessing the social impacts of digital agriculture technology solutions: a practical tool 1 bianchi a.1, corbo c.1, parigi f.1, cesari de maria s.1, renga f. m.1, tumino a.1 2 1 department of management, economics and industrial engineering, politecnico di milano, 3 milano, italy 4 this article has been accepted for publication and undergone full peer review but has not been through 5 the copyediting, typesetting, pagination and proofreading process, which may lead to differences 6 between this version and the version of record. 7 please cite this article as: 8 bianchi a., corbo c., parigi f., cesari de maria s., renga f. m., tumino a. (2025). assessing the 9 social impacts of digital agriculture technology solutions: a practical tool. bio-based and applied 10 economics, just accepted. doi:10.36253/bae-16929 11 12 abstract 13 14 digital agriculture technology solutions (datss) can improve the sustainability of the agricultural 15 sector. while most of the research on the impacts of datss is focused on the economic and 16 environmental dimensions of sustainability, this work aims to understand the social benefits that 17 datss have on farmers. integrating top-down and bottom-up approaches, a social sustainability 18 assessment framework for datss adoption was developed and subsequently applied in the form of 19 a social self-evaluation tool, a questionnaire tested on 60 farmers across 20 european countries, 20 with a heterogeneous composition in terms of sector, types of datss, agronomical context, and socio-21 economic background. the framework and the social self-evaluation tool allowed for a deep 22 investigation of the social impacts of datss in terms of labour evolution, education and learning, 23 and generational change. the results demonstrated the positive effects of datss on the social sphere 24 of sustainability, as well as the importance of integrating this type of social analysis in the evaluation 25 of digital technologies in agriculture. 26 27 keywords: social sustainability, social framework, agriculture 4.0, digital agriculture 28 29 jel classification codes: o330, q12, q160 30 31 1. introduction 32 33 agriculture faces significant economic, environmental, and social challenges and a range of 34 megatrends including climate change, environmental degradation, geopolitical instability, 35 demographic dynamics, changing supply chains and evolving consumer demand are increasingly 36 putting pressure on the sector at both local and global levels. according to fao (2025), between 638 37 and 720 million people may have faced hunger in 2024 (7.8 – 8.8% of the global population), with 38 projections indicating that 512 million people will be chronically undernourished by 2030. the global 39 population is expected to reach 9.7 billion by 2050, demanding increased agricultural productivity 40 while preserving natural resources, ecosystems, and biodiversity (un, 2022). climate change is 41 adversely affecting agriculture, leading to significant economic and productivity losses, complicating 42 efforts to meet human needs (ipcc, 2022). in this scenario, digital technologies can play a pivotal 43 role in increasing the sustainability, productivity, and resilience of agriculture (european 44 commission, 2023a). it has already been extensively highlighted how technologies like internet of 45 things (iot), data analytics and cloud computing, artificial intelligence (ai) and machine 46 learning (ml), satellites, geographic information systems (gis), drones and robots, could enable 47 2 a wide range of digital agriculture technology solutions (datss) with the potential to transform 48 agriculture, increasing productivity while reducing impacts on natural resources and alleviating the 49 labour-intensive work of farmers (papadopoulos et al., 2024; maffezzoli et al., 2022; balafoutis et 50 al., 2020). 51 while a lot of work has been done to assess and prove the environmental and economic 52 sustainability of datss, there is a lack of studies in the literature focusing on the social impacts of 53 digital innovation in agriculture. the research presented in this paper stems from the need to deeply 54 investigate the social impacts of datss adoption on farmers. therefore, this paper aims to present 55 the development, application, and testing of a framework to analyse the benefits and impacts of 56 datss on farmers from a social perspective. 57 this paper is structured as follows. section 2 will briefly introduce the concept of social 58 sustainability and describe its relationship with datss, setting out the background and the objectives 59 of the present work. section 3 will explain the research methodology that led to the framework 60 presented in section 4. a discussion on results is presented in section 5. lastly, section 6 will offer 61 a general assessment of the findings, along with the limitations of the current study and potential 62 future developments. 63 2. theoretical background and objectives 64 2.1. social sustainability and datss 65 despite its frequent use in academic literature and public discourse, the concept of “social 66 sustainability” lacks a universally accepted definition (mcguinn et al., 2020), as most attention is 67 often focused on economic and environmental sustainability (janker & mann, 2020). the 2030 68 agenda for sustainable development frames social sustainability as a multidimensional objective 69 encompassing social equality, poverty eradication, and decent living standards for all (un, 2015). 70 according to the world bank, “social sustainability increases when more people feel part of the 71 development process and believe that they and their descendants will benefit from it”, but the concept 72 remains elusive due to complex socio-cultural factors that are difficult to analyse empirically (barron 73 et al., 2023). the social sustainability of agriculture is increasingly gaining relevance in scientific 74 discussions as well as within institutional and agribusiness sectors, despite the research focus on 75 farming sustainability having been predominantly centred on environmental aspects so far (european 76 commission, 2023; nowak et al., 2019; mcgrath et al., 2023). in agriculture, social sustainability 77 mainly refers to the possibility of maintaining or improving farmers’ lives and working conditions 78 (trivino-tarradas et al., 2019), encompassing various aspects such as fair income, social inclusion, 79 decent living standards, and physical and emotional well-being (zanin et al., 2020). jenker et al. 80 (2019), applying maslow's hierarchy of needs, define social sustainability in agriculture as improving 81 farmers' satisfaction with their physiological, security, social, esteem, and self-actualisation needs. 82 latruffe et al. (2016) distinguish social sustainability at two levels: the farm community level, 83 focusing on farmers' well-being, working conditions, education, and quality of life; and the society 84 level, involving rural development, employment, ecosystem services, quality products, 85 intergenerational continuity, and acceptable agricultural practices. 86 since the 1990s, concepts such as precision farming, digital agriculture, smart agriculture, 87 and, more recently, agriculture 4.0 have emerged, with digitalisation increasingly recognised as a 88 key driver in addressing global and local agricultural challenges, promoting sustainable, inclusive, 89 and equitable agricultural development (bertoglio et al., 2021; schroeder et al., 2021; hernandez et 90 al., 2024). agriculture 4.0, defined as the evolution of precision farming through automated data 91 collection, integration, and analysis from various sources, aims to transform traditional farming 92 systems into digitalised ones, enhancing benefits, reducing costs, and promoting environmental and 93 social sustainability (maffezzoli et al., 2022). the adoption of datss can also improve social 94 sustainability at both the farm and society levels. however, understanding the interactions between 95 technologies, people, and society, along with their associated risks and impacts, remains difficult yet 96 3 essential (gardezi et al., 2022). policy and strategic eu documents and framework, such as the 97 european union's farm to fork strategy, the green deal, the cap, the eu food2030 and the eu 98 vision for agriculture and food, all recognize digitalization and digital connectivity as crucial factors 99 to promote social sustainability, improving quality of life and economic prosperity in rural areas 100 (european commission, 2023a, b, c; european commission, 2025). however, digital innovation in 101 agriculture has lagged due to several interrelated factors such as solution complexity, limited 102 scalability, and structural barriers such as education, technological proficiency, and connectivity 103 (dutta et al., 2019). moreover, farmers often lack clear evidence of the tangible benefits these 104 technologies provide, as well as their actual return on investment, making it difficult to justify their 105 adoption, especially on smaller farms, while larger farms are more likely to adopt datss due to the 106 economies of scale they can leverage (castle et al., 2015). this highlights the need for a thorough 107 investigation of the benefits of datss, particularly in the understudied social dimension. the social 108 impacts, benefits, and risks resulting from digitalisation can be various, depending on the type of 109 datss, the productive sector, the agronomic, cultural, and socio-economic context, and the way each 110 specific technology solution is used and integrated within farm management. among the most 111 immediate and relevant benefits of implementing datss are the reduction of farmers' workloads and 112 working hours, and the substantial increase in work productivity and flexibility (khanna & kaur, 113 2019; sri heera et al., 2019; tsouros et al., 2019), as well as the reduction in heavy labour activities, 114 injuries, and accident rates (balafoutis et al., 2020). a reduction in workload and more flexible 115 working hours could mean a better work-life balance for farmers, allowing for more time with family, 116 friends, or leisure activities (mcgrath et al., 2023). this, combined with the support that datss 117 provide in decision-making, management, monitoring, and labour-intensive tasks, can result in lower 118 work-related stress. however, the use of datss may also generate stress, particularly during the 119 initial phase of technology adoption, due to the steep associated learning curve (gaber et al., 2024), 120 the need to change traditional farm management (butler & holloway, 2016; driessen & heutinck, 121 2015), and issues related to information processing and technology calibration or malfunctioning 122 (balafoutis et al., 2020). 123 naturally, there are also areas of impact that extend beyond the personal realm of individual 124 farmers and concern labour rights, women’s empowerment, gender gaps, social interactions, rural 125 communities, territorial development, youth engagement in farming, and many others (rolandi et al., 126 2021; ali et al., 2016). the technical knowledge, hard skills and training required to implement 127 datss in farms could also impact the local and regional labour market, leading to a higher demand 128 for qualified workers with ict and digital skills, leading to possible digital skills gaps, especially in 129 certain socio-economic contexts (pogorelskaia and várallyai, 2020). when farmers cannot acquire 130 the appropriate technical knowledge and fail to update their skills, a digital divide can arise between 131 those who can take advantage of the benefits of technology and those who cannot, a process further 132 exacerbated by factors such as age, gender, language, or socio-economic background (trendov et al., 133 2019). this digital divide can play not only locally but also globally, accentuating socio-economic 134 differences (fao, 2023) or enhancing power disparities among food systems actors (gardezi et al., 135 2022). according to fao (2023), datss have proven to be capable of reducing the gender gap in 136 agriculture, strengthening women’s livelihoods and empowerment, but only if women’s access to 137 education, financial services, decision-making power and technologies is ensured, all things for which 138 women still lag behind men, particularly in lowand middle-income countries (rodgers and akram-139 lodhi, 2019; ali et al., 2016). datss can also radically change the role and social identity of the 140 farmer, undermining his traditional agronomic techniques and knowledge, and shifting their work 141 from the field to the office, to the extent that a future of farms without farmers can be envisaged 142 (gardezi et al., 2022). as already happened in the history of agriculture with the introduction of 143 disruptive technologies (e.g. the tractor, the combine harvester, chemical pesticides, new genetically 144 modified varieties), the adoption of datss seems to evoke the fear that technology might reduce 145 human labour in both manual and intellectual tasks and lead to a decline of workers in the fields and 146 farms (rotz et al., 2019). on the contrary, datss can solve the problem of labour shortage in 147 4 agriculture, particularly in agricultural systems where matching labour supply and demand is difficult, 148 such as in western and southern europe, the us, and canada. these regions are heavily reliant on 149 seasonal migrant workers, and exploitative and illegal labour practices are frequently reported in both 150 media and academia (caxaj et al., 2023). in this context, datss can be a valuable tool to reduce the 151 reliance on exploited workers, increase supply chain transparency, improve working conditions, and 152 ensure respect for workers' rights. datss could also create new job opportunities and drive the 153 creation of new professional roles and actors involved in the digital transformation of food supply 154 chains (bampasidou et al., 2024). 155 the multiplicity of social aspects, risk factors, and critical issues related to the use of datss 156 makes it necessary to thoroughly investigate how datss change, for better or worse, the lives of 157 farmers, and requires that such assessments are integrated into every analysis on the benefits and costs 158 of digitisation. the assessment of social sustainability in agriculture encompasses various levels of 159 analysis and can be approached through multiple methodologies, often derived from social sciences 160 and based on interviews and surveys for farmers (packer & zanasi, 2023). however, the 161 multidimensional and qualitative nature of social sustainability makes its assessment in farming 162 systems more challenging compared to the economic and environmental dimensions (latruffe et al., 163 2016). through this work, we aim to close this gap and explore the social impacts of datss on 164 farmers, seeking to better understand the benefits, but also the risks, that digital technologies bring to 165 the social sphere of sustainability. 166 2.2. research objectives 167 the objective of this study is to provide a framework for assessing the social sustainability of 168 datss: the social sustainability assessment framework. this framework will serve as the basis for 169 the development of a practical tool the social self-evaluation tool to enable a comprehensive 170 assessment of the social implications of datss adoption by farmers. this objective arises from the 171 recognition that commonly proposed frameworks and indicators in the literature, which often focus 172 on the economic and environmental domains of sustainability, are insufficient to fully examine the 173 social impacts of datss. additionally, the study illustrates a first application of the social self-174 evaluation tool to a sample of farmers adopting datss in order to assess the practical relevance of 175 the framework. however, the utility and applicability of the developed framework and tool extend 176 beyond the scope of this research, offering potential for broader use in future studies and practical 177 applications. the approach and structure underlying the conception and development of the social 178 sustainability assessment framework and the social self-evaluation tool will enable their 179 adaptation to other agricultural contexts and facilitate their use in future practical applications, 180 making them a valuable resource for assessing the social sustainability of digital innovation in various 181 farming systems. 182 3. research methodology 183 184 the research is conducted within the horizon europe quantifarm project, which aims to support the 185 development and adoption of datss across eu countries as a key element for improving the 186 sustainability performance of the agricultural sector. the quantifarm project encompasses 30 test 187 cases (tcs), each involving one or more farmers who have adopted datss on their commercial 188 farms and other stakeholders, such as agronomists and technology providers. as shown in figure 1, 189 the research methodology that led to the development of the social sustainability assessment 190 framework and the social self-evaluation tool integrated a top-down approach through a literature 191 review aimed at examining existing frameworks for the assessment of social aspects in agriculture, 192 particularly those applicable to the impact of digital technologies with a more bottom-up 193 perspective. to this end, semi-structured interviews were conducted with the tcs to incorporate into 194 the framework the most relevant impact areas and social issues related to the implementation of 195 digital solutions on farms. the feedback collected through ongoing interaction with farmers and other 196 5 stakeholders served to refine, improve and validate the framework for its application. this integrated 197 approach led to the development of the social sustainability assessment framework, which was then 198 translated into a questionnaire for farmers, the social self-evaluation tool. the first application of 199 the tool was conducted with the sample of farmers who adopted digital solutions within the tcs of 200 the quantifarm project. the experience gained from this first application, combined with the 201 feedback collected through ongoing interaction with the tcs, served to test the future applicability of 202 the framework and the tool in other agricultural contexts. 203 204 figure 1: an overview of the methodology for the development of the framework 205 206 207 3.1. literature review 208 a literature review was conducted to explore the application of the social sustainability concept 209 in agriculture, particularly in relation to the digital innovation process and the use of agriculture 4.0 210 technologies. more specifically, the goal of the literature review was the identification of the main 211 areas of impact of datss on social sustainability, thanks to the analysis of existing frameworks, tools 212 and kpis for assessing the social impacts in farming. the review provided a deeper understanding of 213 how digital technologies interact with the social aspects of farmers’ lives and daily work activities, 214 including work-life balance, working conditions, skills development, farm management, workplace 215 culture, and workforce development. given the predominantly practical outcomes of the research 216 namely, the development of a framework that could be easily used with farmers a pragmatic 217 approach took precedence over a purely theoretical literature review. therefore, a balance was sought 218 between narrowing the research focus to the main areas of impact and maintaining the ability to 219 account for all the nuances and types of influence that the technology could exert on farmers. 220 consequently, rather than a systematic review, our work more aligns with the concept of 221 scoping/mapping review (paré et al. 2015), or integrative review, defined by torraco (2005) as: “…a 222 form of research that reviews, critiques, and synthesizes representative literature on a topic in an 223 integrated way such that new frameworks and perspectives on the topic are generated.” as underlined 224 by elsbach & knippenberg (2020), an integrative review could consolidate evidence but also generate 225 new insights to advance a specific field of study. 226 for the literature review, the prisma (preferred reporting items for systematic reviews and 227 meta-analyses) guidelines were followed, as described in figure 2 (page et al., 2020). this approach 228 allowed for a transparent and structured identification, selection, and evaluation of relevant studies, 229 6 ensuring the reliability and validity of the review process. thus, a four-step review methodology was 230 followed, consisting of: 1) the definition of an appropriate search strategy, including keywords and 231 databases, 2) the delineation of boundaries and inclusion and exclusion criteria, 3) a first screening 232 and selection of papers, and 4) the full-text analysis of papers and extraction of relevant information. 233 based on the authors’ expertise and previous literature analysis, a set of keywords related to 234 digital technologies in agriculture was selected. the keyword “agriculture 4.0”, which refers to a 235 wide array of digital technologies and solutions used in agriculture (maffezzoli et al. 2022), has been 236 complemented by other keywords frequently used as synonymous terms in academia and industry: 237 “precision farming”, “precision agriculture”, “smart farming”, “smart agriculture” and “digital 238 agriculture”. this allowed for the inclusion of studies published before the widespread adoption of 239 the agriculture 4.0 concept, encompassing digital agricultural technologies not explicitly categorised 240 under the agriculture 4.0 paradigm by the authors. the selected keywords were combined with the 241 keyword “social”: “agriculture 4.0” and “social”, “precision farming” and “social”, “precision 242 agriculture” and “social”, “smart farming” and “social”, “smart agriculture” and “social”, 243 “digital agriculture” and “social”. these queries were considered sufficient to allow the retrieval 244 of research articles relevant to our objective, enabling a thorough investigation of the main areas of 245 social impact of datss. 246 the literature search was undertaken through scopus, the largest abstract and citation database, 247 selected for its international recognition, multidisciplinary coverage, and comprehensiveness. the 248 database search was carried out using each query individually. to narrow the scope of the analysis 249 and ensure that the papers retrieved from scopus were relevant to the most recent digital technologies 250 falling under the umbrella of agriculture 4.0 the search was limited to publications from the last 251 ten years, i.e., from 2013 onwards. moreover, since a preliminary analysis of the papers found in scopus 252 using the first two queries, “agriculture 4.0” and “social” and “precision farming” and “social”, 253 returned many articles without a clear focus on the social dimension, the search strategy for the 254 subsequent queries was refined by applying a “subject area” filter in scopus, limiting results to those 255 in the “social sciences” domain. 256 after the elimination of duplicated papers that is, those retrieved in scopus by more than one query 257 the screening process involved the analysis of the title, keywords and abstract of each paper. this 258 led to the exclusion of the articles that did not have a clear and substantive focus on the social impacts 259 of digital technologies on farmers. the number of papers identified for each query is shown in figure 260 2, along with the total number of papers subjected to full-text analysis after excluding duplicates and 261 those not aligned with the research objectives. 262 from a total of 857 publications found on scopus, 15 were excluded because they were duplicates, 263 while 687 were excluded because they addressed the topic of social impacts only superficially or 264 failed to provide impact areas, indicators, or tools related to the social aspects of digital technologies 265 in agriculture. a total of 155 papers were included in an in-depth analysis to extract relevant 266 information, such as existing frameworks, tools and kpis on social impacts of datss. each paper 267 was thoroughly reviewed, and those demonstrating a direct relationship with social impacts on 268 farming were entered into an excel database, where key information and insights were systematically 269 recorded. 270 271 7 figure 2: flow diagram of literature review (adapted by the authors based on prisma 2020) 272 273 274 the analysis of the scientific literature was complemented by grey literature, specifically focusing on 275 reports from government agencies, ngos, international institutions (e.g. world bank), un agencies 276 (e.g. fao, ifad), and industry associations working on the social aspects of sustainability in 277 farming. these sources were carefully reviewed and cross-referenced with the scientific literature. 278 including grey literature allowed us to capture a broader range of perspectives, enriching the evidence 279 beyond what is available in peer-reviewed journals. this approach also made it possible to integrate 280 context-specific insights and practical knowledge from real-world applications, leading to a more 281 holistic understanding of the social implications of datss adoption. 282 3.2. integration of a bottom-up approach 283 given the practical goal of the research, the top-down literature review was complemented with 284 a bottom-up approach. the importance of combining top-down and bottom-up methods in social 285 science and social sustainability assessment is increasingly recognised (ochsner et al., 2017), not 286 only to improve the comprehensiveness of analyses but also to ensure a more holistic understanding 287 of complex societal dynamics and the inclusion in the assessment process of all the issues that truly 288 matter on the ground (magee et al., 2013). as highlighted in yin's seminal work on case studies 289 (yin, 2017), the value of interviews lies in their ability to provide direct and context-specific insights 290 into complex situations, allowing for the collection of detailed information about the experiences of 291 the interviewees. interviews not only offer an immediate understanding of social dynamics but also 292 enrich the analysis and offer a deeper understanding of the phenomenon under study (yin, 2017). 293 thus, 30 semi-structured interviews were organised with the test cases (1 interview for each tc), 294 involving farmers using datss, but also agronomists and technology providers engaged in each tc. 295 all the interviews were conducted remotely through the microsoft teams platform and lasted 296 approximately one hour each. while the focus of the interviews was on farmers, agronomists and 297 8 technology providers were also involved, as they served as contact points between the farmers and 298 the project and played a crucial role in facilitating communication, as many of the farmers did not 299 speak english, the language of the interviews. moreover, agronomists and technology providers gave 300 valuable insights into the social implications of datss, offering a complementary perspective to that 301 of the farmers. a semi-structured set of open-ended questions was used to conduct the interview in 302 order to balance consistency with the research objectives, but also to ensure enough flexibility for 303 participants to elaborate on their experiences and perspectives. the question addressed: 1) the 304 business and agronomical context of the farms involved in the tc; 2) the reasons behind the datss 305 adoption; 3) the main impacts of dats in terms of agronomic practices and farm management; 4) the 306 barriers, problems and limitations to datss use; 5) the main benefits of dats; 6) the impacts that 307 datss had on the social dimension, including the implication the digitalisation process had on 308 farmers lives and farm management. each interview has been recorded and the audio transcribed to 309 ensure accurate capturing of all information. the transcriptions of all the interviews were analysed 310 using a “manual approach”, due to the relatively small scale of the sample and the need to maintain 311 a more flexible, nuanced understanding of the social impacts and issues mentioned by each 312 stakeholder. as underlined by mattimoe et al. (2021), a manual approach “can facilitate a closeness 313 to the qualitative data” and “facilitate the identification of themes in an organic manner”. also, 314 maher et al. (2018) suggest that a manual approach “encourages more meaningful interaction with 315 the data, compared to a technological approach”. recurring themes, issues and concepts regarding 316 the social impacts of datss were extracted from the text and collected to be compared and integrated 317 with the outputs of the literature analysis. by doing so, the bottom-up process enriched the top-down 318 analysis (literature review), offering direct and concrete insights into the social implications of 319 datss, specifically from the perspectives of farmers and relevant stakeholders involved in field 320 activities. this approach not only ensured that real-world context was incorporated in the research 321 but also helped identify the key aspects around which to structure the framework, focusing on the 322 issues that most directly affect farmers in their daily work and lives. 323 324 3.3. framework application to test cases 325 to test and apply the framework, we used the sample of 30 test cases involved in the quantifarm 326 project, comprising commercial farms operating in 20 european countries, and 7 agricultural sectors, 327 including 20 different crops and animals. each tc includes one or more farmers who have recently 328 adopted one or more categories of datss, providing a valuable sample for the application and testing 329 of the framework and tool. in table 1, the sector of each test case is reported, together with the 330 type of crop or animal production, the category of datss implemented in the farm, the country, the 331 total production managed through technology (expressed in terms of ha or total number of animals), 332 and the number of farmers using datss. the categories of datss adopted across the 30 tcs are: 333 • decision support systems (dss) 334 • farm management systems 335 • variable rate technologies (vrt) 336 • precision irrigation systems 337 • digital pest control systems 338 • automated greenhouses 339 • feeding robots 340 • milking robots 341 • sensors for quality assessment (for the aquaculture sector) 342 • automated monitoring, activity sensors, heat and calving detectors (for the meat and 343 dairy sector) 344 the final sample consists of 60 farmers, as some tcs involved more than one farmer. while the 345 sample size may seem limited, the heterogeneity in agronomic, socio-economic contexts, and 346 9 technological settings represents a strength of this research, allowing for a more comprehensive 347 investigation of the social impacts of datss across diverse backgrounds and environments. 348 moreover, this sample of farmers enabled the evaluation of the framework's and tool's applicability 349 in various agronomic and socio-economic settings, thus paving the way for future applications in 350 different contexts. 351 table 1: overview of the test cases (tc) to which the framework was applied 352 tc sector production datss category country datss management n. of farmers using datss 1 arable potatoes dss greece 0.85 ha 2 2 arable corn precision irrigation system, vrt portugal 29.2 ha 2 3 arable barley, wheat dss spain 30.6 ha 1 4 arable cotton vrt greece 5.1 ha 3 5 arable wheat dss turkey 105 ha 8 6 arable wheat, onion, potatoes dss netherlands 3.5 ha 1 7 arable potatoes dss poland 98 ha 2 8 arable wheat, rapeseed, rye, barley dss latvia 1 silo 1 9 arable corn, wheat dss slovenia 17 ha 1 10 arable wheat dss romania 553 ha 1 11 horticulture olives dss greece 8.6 ha 5 12 horticulture apples dss; digital pest control system poland 1 ha 1 13 horticulture grapevine dss italy 1.1 ha 1 14 horticulture indoor farming strawberries and blueberries dss serbia 3.4 ha 3 15 horticulture olives dss cyprus 5.1 ha 5 16 horticulture apples dss; digital pest control system netherlands 1 ha 1 17 horticulture grapevine dss romania 14 ha 1 18 horticulture tomatoes dss italy 60.5 ha 9 19 horticulture indoor farming tomatoes automated greenhouses netherlands 6 ha 1 20 horticulture bananas precision irrigation system spain 2.2 ha 1 10 21 horticulture indoor farming tomatoes automated greenhouses finland 1.2 ha 1 22 meat poultry farm management system uk 64,000 birds 1 23 meat cows feeding robot; heat and calving detectors france 302 cows 1 24 meat pigs farm management system belgium 682 pigs 1 25 dairy cows feeding robotics + activity sensors france 207 cows 1 26 dairy cows milking robot ireland 180 cows 1 27 dairy cows automated monitoring germany 250 cows 1 28 dairy cows milking robot; feeding robotics romania 803 cows 1 29 apiculture bees automated monitoring lithuania 10 beehives 1 30 aquaculture oysters sensors for quality assessment croatia 5,000.0 m2 1 353 4. results 354 4.1. the social sustainability assessment framework and the social self-355 evaluation tool 356 357 based on the findings from the literature analysis (section 3) and the semi-structured interviews 358 with the test cases (section 3.2), the areas of the social dimension most impacted by the adoption of 359 datss were identified. despite the widespread use and established nature of some questionnaires 360 and indicators for social impacts and working conditions assessment found in literature, such as those 361 proposed by eurofound (2016) or horodnic et al. (2019), there is a notable scarcity of questionnaires 362 tailored to the primary sector and, more specifically, focused on the adoption of innovative 363 technologies by farmers, failing to incorporate certain social indicators that are significantly impacted 364 by the implementation of datss. thus, the first result of this work has been the identification of the 365 9 key social impact areas associated with datss adoption, listed in table 2. 366 367 table 2: key social impact areas of datss 368 social impact area definition some references 11 data every aspect related to data collection, usage, ownership, sharing and privacy rotz et al., 2019; mcgrath et al., 2023; wisemana et al., 2019 food quality/safety the influence of datss on the quality and safety of food delivered to consumers, including traceability and transparency issues guruswamy et al., 2022; food availability the influence of datss on the productivity of farms and the consequent availability of food in local, regional and global contexts benfica et al., 2023 labour evolution every aspect related to the farmers' activities and farm management impacted by datss rotz et al., 2019; salvia, 2019 inclusive growth the influence of datss in creating equitable opportunities for all individuals hernandez et al., 2024 education and learning all the aspects concerning skills, education, and technical competences required to adopt and use datss, but also the impacts that new technologies can have on reskilling processes and learning opportunities for farmers lundström et al., 2018; gardezi et al., 2022 gender equality the impacts that datss could have on the creation of opportunities for women to gain power and leadership in the sector, but also the possible digital and economic divide that new technologies can create ofisi & lukamba, 2020; abdulai 2022; huyer, 2016; hernandez et al., 2024 ruralisation all those processes concerning the revitalisation of rural areas and the impacts that datss could have on the creation of new job opportunities for young people outside urban areas european commission, 2023; rolandi et al., 2021 generational change the role of young farmers in innovation, the attractiveness of farming for young people and the generational change within farm companies kabadzhova, 2022; afere et al., 2019 369 although the adoption of digital technologies has both positive and negative effects on farmers 370 across all the identified social areas, the focus of the framework was placed on those areas that, 371 according to the literature and the information gathered directly from the test cases, can be 372 considered the most directly impacted: labour evolution, education and learning, generational 373 change, and gender equality. food safety is more closely related to analyses involving downstream 374 actors in the food value chain and was not included in the framework. similarly, since the areas 375 related to food security, inclusive growth and ruralisation go beyond impact assessments at the 376 individual farm level and require territorial or regional approaches, they were excluded from the 377 framework. the area of data concerns was also excluded from the framework, as it is more closely 378 related to technical issues rather than social ones. 379 labour evolution is a central topic in the literature on the social impact of datss, representing 380 the area most directly affected by digitalisation and the one in which farmers experience the greatest 381 impact from the transformative role of digital technologies (rotz et al., 2019). a substantial part of 382 the framework’s development focused on this area of impact, within which three specific sub-areas 383 were identified: work dynamics and activities, work-related stress, and work-life balance. 384 work dynamics and activities refer to the role that datss play in transforming the day-to-385 day work of farmers in terms of monitoring, automation, decision-making, resource optimisation and 386 farm management (mcgrath et al., 2023; rotz et al., 2019). in this regard, it is relevant to understand 387 whether datss help the farmer in his daily activities and how his tasks and workload change. 388 12 work-related stress embeds the physiological, psychological, and behavioural responses that 389 individuals may experience when the demands of their job exceed their ability to cope effectively 390 (michie, 2002). nevertheless, the impact of datss on work-related stress remains uncertain 391 according to the scrutinised papers. on one hand, the solutions have the potential to reduce farmers' 392 workload, thereby providing them with more relaxed working schedules. on the other hand, the 393 adoption of new technologies may introduce additional stress and intensify work demands as 394 individuals strive to familiarise themselves with the technology (smith & carayon, 1995). 395 work-life balance does not imply only an equal distribution of time between work and personal 396 life but rather entails the ability to effectively manage and harmonise these two domains, ultimately 397 enhancing both the quality of life and work outcomes. when successfully achieved, work-life balance 398 can generate positive spill-over effects, benefiting not only the individuals directly involved but also 399 all other stakeholders. in this regard, the adoption of digital solutions has shown promise in 400 facilitating this delicate equilibrium by enabling more efficient task completion and promoting 401 conscious utilisation of data (wolor, 2020; esguerra, 2020; čehovin & kohont, 2017). 402 education and learning are a prerequisite for the dissemination of datss, but at the same time, 403 can be promoted by their adoption. agriculture 4.0 technologies require hard skills and technical 404 competencies to fully exploit their potential. this means that a certain learning effort and re-skilling 405 processes are often required from farmers. therefore, it is important to analyse the effort that farmers 406 had to exert to use a new technology, the difficulties related to understanding how it works and the 407 possible stress generated by the learning process. 408 generational change refers to the attractiveness of the agricultural sector for young people and 409 the impacts that new digital technologies could have on the business succession to the new generation 410 of farmers. understanding the perceptions of young individuals regarding agriculture as a viable and 411 appealing career choice is essential for addressing the challenges associated with attracting and 412 retaining young talent and promoting economic development and employment in rural areas. 413 historically, agriculture has struggled to attract young individuals, largely due to perceived factors 414 such as low prestige, manual labour, and limited opportunities for growth and innovation 415 (kabadzhova, 2022; afere et al., 2019). our goal is to investigate whether the integration of digital 416 solutions and the resulting increased entrepreneurial opportunities make the sector more attractive to 417 young people and farmers’ sons. 418 gender equality and the issues related to the gender gap in agriculture encompass the disparities 419 and unequal treatment experienced by men and women within the agricultural sector (oecd, 2018). 420 this gap is apparent in multiple dimensions of agriculture, such as land ownership and tenure, 421 availability of credit and financial services, control over productive assets, involvement in decision-422 making processes, access to education and training, and representation within agricultural 423 organisations and institutions (fremstad & paul, 2020). various social, cultural, economic, and 424 institutional factors contribute to the perpetuation of the gender gap in agriculture (ali et al., 2016). 425 while several studies have examined the impact of gender on technology adoption in agriculture, 426 there remains a lack of research exploring the influence of digital solutions on the gender gap. despite 427 this being a central topic in today's debate, especially in a traditionally male-dominated sector like 428 agriculture, the issue will be explored in more depth during the second year of the project, and it is 429 not included in the framework presented below. 430 building on the identified key social impact areas, the social sustainability assessment framework 431 was developed, as illustrated in figure 3. for each key social impact area, a set of relevant themes 432 and indicators was identified to be included in the framework, the evaluation of which is essential 433 for assessing the real social impact of datss. several studies in the literature (e.g. eurofound 2016; 434 horodnic et al., 2019; boxal & macky, 2014) have provided a robust foundation, having undergone 435 rigorous testing and demonstrating efficacy in identifying relevant social indicators. however, these 436 studies were not specifically designed to assess the impacts of datss. to address this gap and ensure 437 a comprehensive examination of the distinctive aspects of the agricultural sector, the social self-438 evaluation framework incorporates certain indicators already present in the literature with novel 439 13 indicators focusing on work-life balance (wiradendi wolor, 2020; esguerra, 2020), work-related 440 stress (persechino et al., 2013), and the attractiveness of the sector for young individuals (afere et 441 al., 2019). 442 building on the social sustainability assessment framework, the social self-evaluation tool was 443 developed as a questionnaire consisting of 23 rating scale items to be administered to farmers. the 444 rating scale system is used to measure the respondent’s opinion and attitude towards each item. rating 445 scales are frequently employed in the social sciences to measure attitudes. one commonly used 446 instrument is the likert-type scale (tanujaya et al. 2022). likert scales assess respondents’ attitudes 447 by asking them to indicate their level of agreement or disagreement with a series of statements related 448 to a specific topic (croasmun & ostrum, 2011). some researchers argue that increasing the number 449 of points makes the scale more representative and closer to a universal system. however, others 450 believe that increasing the number of items beyond the minimum needed does not significantly 451 improve reliability, and more response options can decrease response quality and consistency due to 452 increased mental effort (croasmun & ostrom, 2011). some research indicates that answer quality 453 declines with more than eleven options and that there are no additional psychometric benefits beyond 454 six options, with the optimal number being between four and six (tanujaya et al., 2022). 455 456 figure 3:the social sustainability assessment framework 457 458 459 a likert-type scale was therefore used for the social self-evaluation tool with an ordinal data 460 type and five answer choices for each question: strongly disagree (sd), disagree (d), neither agree 461 nor disagree (n), agree (a), and strongly agree (sa). the questions are divided as follows: 462 • 15 questions on labour evolution – 10 on work dynamics and activities, 3 on work-463 related stress and 2 on work-life balance 464 • 5 questions on education and learning 465 • 3 questions on generational change 466 14 the social self-evaluation tool was distributed to the sample of farmers for completion, with the 467 data being collected and returned through the contact points of each tc. these contact points 468 facilitated communication and ensured the farmers' participation in the process, allowing for a 469 systematic collection of the social impact data generated through the application of the tool. 470 4.2. first application of the social self-evaluation tool 471 472 the analysis of the data collected through the social self-evaluation tool administered to the 473 farmers shows that digital solutions seem to contribute positively to the social sustainability of 474 farming in line with what has been found on economic and environmental aspects by other researchers 475 (papadopoulos et al., 2024). indeed, considering the responses obtained from farmers from the first 476 application of the tool, it appears that datss positively impact social aspects concerning all three 477 key impact areas identified within the framework: labour evolution, education and learning, and 478 generational change. the overall farmers’ perception of datss seems to be quite positive, even in 479 cases where the technology has been adopted quite recently and farmers are still getting accustomed 480 to it. 481 in figures 4, 5, 6 and 7, the results obtained from the tool application are shown for each item. 482 to facilitate an immediate interpretation of results, data are expressed in terms of the percentage of 483 all the responses for each likert class. concerning the dimension of “labour evolution”, specifically 484 the “work dynamics and activities” in figure 5, the most evident positive impact is the enhanced 485 ability to make decisions more consciously and efficiently, a factor observed in over 90% of cases, 486 considering the sum of the farmers who strongly agree and those who agree with that sentence. this 487 aligns with the fact that more than half of the implemented datss fall into the dss category, hence 488 solutions that are specifically designed to support the decision-making process of farmers. moreover, 489 most of the other technologies incorporate sensors, iot and monitoring systems that can valorise the 490 large amount of data generated in fields, stables or greenhouses and support day-to-day farm 491 management. datss also appear to contribute significantly to tackling complex tasks and addressing 492 unforeseen problems, with 72% and 64% of respondents agreeing or strongly agreeing, respectively. 493 the implementation of new digital technologies in farms has allowed more than half of the farmers 494 to have more time to focus on other tasks, likely due to a reduction in the workload in areas where 495 datss can assist. in nearly half of the cases, farmers perceive that datss reduce the intensity and 496 complexity of work activities, improve their planning, and allow them to better calibrate the speed of 497 execution. despite the occasional perception that digital technologies might replace farmers and their 498 managerial and decision-making roles, only a small portion of respondents (12%) feel that their role 499 is being "replaced" by datss. 500 currently, the perceived contribution of datss to certification and compliance with production 501 standards remains limited. however, this perception is likely to evolve in the coming years, given the 502 increasing importance of certifications related to sustainability, provenance, and traceability for both 503 consumers and food companies. digitalisation has the potential to significantly enhance third-party 504 certification processes, thereby increasing the relevance and utility of datss in this domain. 505 adopting new digital technologies demands that farmers acquire new skills to operate and maintain 506 innovative solutions they are not used to. in addition, steep learning curves and the time required to 507 achieve proficiency with these new technologies can be daunting, particularly for farmers less 508 accustomed to technology and more tied to “traditional” management. this could lead to work-related 509 stress, which in turn can reduce the acceptance of datss and curb their adoption. despite these 510 considerations, it can be seen in figure 4 that most farmers are not affected by stressful factors, from 511 a physical and emotional point of view. instead, for 45% of respondents datss appear to mitigate 512 stress stemming from external factors like climate change. 513 a key component to consider in assessing farmers' well-being in terms of their work and their 514 quality of life, and consequently the impact that datss can have on these areas, is work-life balance 515 (herrera sabillòn et al., 2021). our analysis suggests that farmers perceive datss as increasing their 516 15 available free time, providing more opportunities to spend quality time with family and friends. these 517 results stem not only from having more free time but also more regular working hours, better aligned 518 with those of other types of employment. 519 520 521 522 figure 4: impacts of datss on work-related stress and work-life balance. values represent the percentage of respondents in each class for each item (sample: 60 farmers). figure 5: impacts of datss on work dynamics and activities. values represent the percentage of respondents in each class for each item (sample: 60 farmers). 16 it is well recognised how technical knowledge and skills, both soft and hard, are essential to facilitate 523 the adoption of datss (geng et al., 2024) and can limit their successful use and resulting benefits 524 (trendov et al., 2019). as can be seen in figure 6, more than 4 out of 5 farmers had to learn new 525 things to use datss. nevertheless, farmers perceive that they have engaged in an interesting and 526 motivating learning and upskilling process, and so, for more than 90% of respondents, the need to 527 acquire new knowledge is viewed entirely positively and not as a burden on their work. contrary to 528 common assumptions, the learning process is not experienced as time-consuming, nor is it regarded 529 as complicated or stressful. 530 digitalisation should not be regarded solely as a consequence of generational renewal in 531 agriculture; rather, it ought to be recognised also as a catalyst for this process. by making the sector 532 more appealing to younger generations, digital innovations can actively facilitate and accelerate 533 generational turnover within agricultural enterprises. (farrell et al., 2021; borda et al., 2023). this is 534 also what the farmers involved in this work perceive, as can be seen in figure 7: 65% of them believe 535 that datss are capturing the interest of younger people in working on the farm or in the agricultural 536 sector, thereby facilitating the transition in company management (47% of respondents). all of this 537 helps half of the farmers become less uncertain about the future of their business. 538 539 figure 7: impacts of datss on generational change. values represent the percentage of 540 respondents in each class for each item (sample: 60 farmers). 541 542 figure 6: impacts of datss on education and learning. values represent the percentage of respondents in each class for each item (sample: 60 farmers). 17 5. discussion 543 544 the main contribution of this work lies in the development of a framework for the analysis of 545 social sustainability impacts of digital agriculture solutions. this framework identifies the key areas 546 of social impact and, within each area, delineates specific indicators for assessing social outcomes.. 547 the framework was designed to be operationally usable for assessments and self-assessments, 548 providing a structured approach, simple yet effective, to evaluate the social implications of datss 549 adoption by farmers. to test its practical usability, we developed and implemented a first pilot 550 application in the form of a social self-evaluation tool, which enabled the collection and assessment 551 of social impacts experienced by farmers following the implementation of digital solutions in their 552 farms. existing frameworks and indicators in the literature on social sustainability in agriculture do 553 not specifically focus on the effects of digitalisation, which is a growing area of interest within the 554 primary sector. moreover, among the papers reviewed in the literature analysis that address the social 555 impacts of datss, a lack of structured frameworks emerges for understanding the positive and 556 negative repercussions of digital solution adoption by farmers. some existing frameworks for 557 analysing the social aspects of digitalisation in agriculture, despite their significant scientific and 558 theoretical value, are often distant from the perspective of the individual farmer. these frameworks 559 address important issues, but they focus on broader societal impacts, which are not aligned with the 560 primary objective of this study: to understand the effects of digitalisation on the social sphere more 561 directly related to farmers' daily lives and work. 562 the framework and the corresponding tool were tested on a sample of 60 farmers who adopted 563 different types of datss. this allowed for an assessment of its usefulness and applicability, as well 564 as the collection of valuable insights about the group of farmers involved in the project. the analysis 565 of the results from the first application of the framework has revealed a generally positive perception 566 of datss, with several beneficial impacts identified. datss were found to be effective in reducing 567 workload, increasing work productivity, and simplifying complex tasks. although limited hard skills 568 and technical knowledge are recognised as common barriers to the widespread adoption of datss, 569 farmers were not deterred by the necessity for learning and did not perceive the required training as 570 stressful. conversely, the process of learning to utilise datss was regarded as engaging and 571 intellectually stimulating. furthermore, in most cases, farmers observed that the introduction of 572 digital solutions heightened the interest of younger generations in agriculture, reinforcing the notion 573 that digital innovations can facilitate the generational transition and ensure the continuity of the 574 agricultural sector. the present work attempted to define a tool for assessing the social impacts of 575 datss in real farming conditions, leading to good relevance of the collected data, but posing some 576 challenges. indeed, the practical and time-related requirements of the project have necessitated a 577 compromise between the amount of information that could have been asked from the farmers and the 578 ability to thoroughly investigate and rigorously explain all the social issues related to the adoption of 579 datss. the research methodology, therefore, was focused on reviewing existing literature on the 580 social impacts of datss and subsequently developing a framework that could have been easily 581 interpreted by farmers. since the framework collected responses from datss adopters involved in 582 the quantifarm project, the total number of respondents was only 60, whereas a larger sample size 583 would have allowed for greater robustness. however, it is important to highlight the geographical 584 scope and the internal diversity of this sample. this heterogeneous group of farmers, albeit seemingly 585 not sizable, allowed us to assess the framework in different agronomical contexts, sectors (i.e. arable, 586 horticulture, livestock, apiculture, aquaculture), socio-economic, and technological backgrounds, 587 ensuring a more comprehensive understanding of the social impacts of datss in agriculture. to 588 obtain a more statistically robust analysis of the social impacts of datss, the social self-evaluation 589 framework will be applied throughout the remaining two years of the project. this longitudinal 590 approach will enable an examination of the evolution of social impacts over time, while also allowing 591 farmers to adapt to the technologies, become familiar with their usage, and more accurately assess 592 the perceived experiences and benefits resulting from their adoption. this will lead to a more robust 593 18 evaluation of the social impacts of datss. furthermore, a tool like the social self-evaluation 594 framework could be applied in other contexts with a higher number of potential respondents, such as 595 the annual research on agriculture 4.0 conducted by our research group, the smart agrifood 596 observatory of the politecnico di milano, which involves the participation of hundreds of farmers 597 every year. 598 the tcs and farmers' active involvement with a bottom-up approach facilitated the successful 599 development of the framework and contributed to a robust and inclusive data collection process. this 600 bottom-up approach has allowed for the validation of the questionnaire's robustness and the 601 identification of potential responses to impacts not yet analysed in the literature. additionally, through 602 the continuous interaction with farmers, tcs and other stakeholders involved in the project, the social 603 self-evaluation framework will be integrated with new indicators, primarily focusing on the gender 604 gap and the role of datss in improving the condition of women in agriculture. the qualitative nature 605 of social indicators and the challenge of attributing impacts solely to digital solutions underscore the 606 need for a process of "data normalisation" in subsequent data collection rounds. this normalisation 607 aims to refine the analysis by excluding contingencies and external factors, providing a more accurate 608 view of the true impact of digital technologies on social dimensions. 609 6. conclusions 610 611 this work addressed a critical gap in the existing literature by developing a framework, 612 specifically tailored to the primary sector, to assess the impacts of digitalisation on a diverse range of 613 social aspects. indeed, despite the existence of frameworks in scientific literature focusing on 614 assessing social sustainability and working conditions in companies, there is a lack of social 615 frameworks tailored to the primary sector and specifically focused on datss adoption. for these 616 reasons, the social sustainability assessment framework has been developed to offer an operational 617 tool to evaluate the social implications of datss adoption by farmers. 618 the approach followed to develop the social sustainability assessment framework, with the 619 integration of a literature analysis and a bottom-up process with semi-structured interviews with 620 farmers and other relevant stakeholders, has proven to be effective in identifying the most relevant 621 social issues related to datss adoption. the developed framework offers an opportunity for further 622 exploration of critical aspects that can be impacted by the digital innovation process, such as the 623 evolution of work, generational change, and gender equality in the agricultural sector. in this way, 624 the social sustainability assessment framework not only offers a valuable initial contribution to the 625 social sustainability assessment of datss, but also lays the groundwork for future research aiming 626 to develop robust methodologies for assessing and addressing the social impacts of digital innovations 627 in the agricultural sector. 628 in order to test the practical applicability of the social sustainability assessment framework, 629 we developed and implemented a social self-evaluation tool, which enabled the collection and 630 assessment of social impacts experienced by a sample of 60 farmers following the implementation of 631 digital solutions in their farms. the overall results of the first year demonstrated the real benefits that 632 the adoption of datss could have on farmers and farm management, not only in terms of economic 633 and environmental sustainability (broadly demonstrated by other researchers), but also regarding 634 social impacts. even more importantly, the application of the framework allowed us to demonstrate 635 both its utility and its simplicity in use with farmers in assessing the social sustainability of datss. 636 it is increasingly evident that the need to consider social sustainability arises whenever 637 discussing and working on the digitalisation of agriculture. the social impact areas and issues 638 identified in our social sustainability assessment framework, along with the data collected through 639 the social self-evaluation tool, demonstrate that policymakers, technology providers, 640 agribusinesses, agronomists, and all stakeholders involved in the digital innovation of the primary 641 sector could benefit from using frameworks and tools like the one developed in this study to guide 642 the adoption of digital solutions in agriculture. by focusing on the social impact areas and issues 643 19 identified in this work, policies can help ensure that the digital transformation in agriculture not only 644 drives economic and environmental sustainability but also promotes social benefits, such as inclusive 645 growth and improvements in working conditions on farms. the adoption of such frameworks can be 646 instrumental in the design of targeted policy interventions aimed at addressing social issues in rural 647 areas, ensuring that technological advancements benefit all members of the farming community. 648 from the perspective of technology providers, it is equally crucial to consider the social implications 649 of datss that could be linked to the technical aspects of their development. the usability, ease of 650 use, and reliability of digital tools play a significant role in determining farmers' willingness to adopt 651 and effectively implement these technologies. by integrating social sustainability into the design and 652 development of their solutions, technology providers can ensure that their products promote 653 inclusivity, equity, and social benefits, without causing stress or introducing additional complications 654 to farm management. this includes making sure that digital solutions are accessible to a wide range 655 of farmers, including those with limited digital literacy or technical skills, and that these solutions 656 foster work-life balance, flexibility, better decision-making processes, and simplify the overall 657 management of their farms. 658 659 acknowledgements 660 this study was supported by the project “quantifarm: assessing the impact of digital technology 661 solutions in agriculture in real-life conditions”. project funded by the european union’s horizon 662 europe research and innovation programme under grant agreement no 101059700. additionally, the 663 work was carried out within the activities of the smart agrifood observatory of politecnico di milano 664 and università degli studi di brescia. 665 666 7. references 667 668 abdulai, a. r. 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(2025). rebuilding strategies for food self-sufficiency: portugal’s past patterns and future ambitions. bio-based and applied economics 14(2): 51-65. doi: 10.36253/bae-17226 received: january 29, 2025 accepted: june 26, 2025 published: september 30, 2025 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor in chief: silvia coderoni associated editor: laura garcia herrero orcid amj: 0000-0002-6239-6122 df: 0000-0003-2969-4440 hr: 0000-0002-5981-4469 jd: 0000-0003-2517-7905 rebuilding strategies for food self-sufficiency: portugal’s past patterns and future ambitions alexandre macedo joão1,*, dulce freire2, humberto rocha3, joana dias4 1 center for interdisciplinary studies, university of coimbra, portugal 2 center for interdisciplinary studies, faculty of economics, university of coimbra, portugal 3 centre for business and economics research, faculty of economics, university of coimbra, portugal 4 institute for systems engineering and computers at coimbra, faculty of economics, university of coimbra, portugal *corresponding author. email: j.alexjoao@gmail.com abstract. this study analyses portugal’s wheat productive capacity, exploring the reasons behind its recent steady decline despite achieving self-sufficiency in the 20th century. using multivariate adaptive regression splines (mars), which proved to be highly effective in the development of forecasting models, the research provides valuable insights for countries facing similar challenges in defining production strategies. by employing this approach, decision-makers can improve resource allocation, ensure food security, and foster a resilient agricultural sector. the findings highlight the importance of understanding wheat production dynamics within the european union and aligning national strategies with the union’s goals and policies. the analysis indicates that achieving self-sufficiency is possible, supported by productivity improvements and increased cultivation areas. however, realizing significant production growth demands the adoption of sustainable strategies. this research contributes to shaping informed agricultural policies, enhancing decision-making processes, and promoting a more sustainable and efficient food production system to meet future challenges. keywords: wheat production, self-sufficiency, productivity, forecasting models, policy making. jel codes: e21, e23, e27, q10. abbreviations eec/eu: european economic community/european union mars: multivariate adaptive regression splines cap: common agricultural policy https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae https://doi.org/10.36253/bae-17226 https://doi.org/10.36253/bae-17226 https://orcid.org/0000-0002-6239-6122 https://orcid.org/0000-0003-2969-4440 https://orcid.org/0000-0002-5981-4469 https://orcid.org/0000-0003-2517-7905 mailto:j.alexjoao@gmail.com 52 bio-based and applied economics 14(2): 51-65, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17226 alexandre macedo joão et al. introduction cereal production and consumption have been central issues for the world economy. the main similarity between the subsistence bases of ancient states was the fact that their economy was based on cereal production (scott, 2017). during the second half of the 20th century wheat production and productivity per hectare increased steadily, reflecting its status as the main cereal for human consumption. however, in recent decades, the increases in production of several major cereal crops have slowed down, and in several countries, yields started to stagnate (michel & makowski, 2013). to further aggravate this situation, the armed conflict affecting the ukrainian territory caused international wheat prices to rise to all-time highs. in the case of european countries, this confrontation was especially harsh, given that a lot of countries were dependent on cereals imported from russia and ukraine (zhang et al., 2024). given the strategic relevance that wheat has in societies, how can each state respond to these domestic supply threats? departing from the analysis of portugal, this paper aims to answer a question that is arising in many countries. the main objective of this study is to analyse historical wheat production and consumption data and to project trends for 2022–2032, in order to find out if portugal has the capacity to become self-sufficient in wheat production, something that was already achieved in the 20th century, particularly during the “wheat campaign”. we adopt the multivariate adaptive regression splines (mars) machine learning model, validated in time-series forecasting (nayana et al., 2022). by employing the predictions resulting from mars in scenarios with different cultivated areas, we assessed the implications of land use adjustments on future wheat production. although this study is focused on the portuguese case, the method employed can be applied to other countries. this scenario-based approach provides informed guidance to policymakers, identifying realistic and sustainable strategies to optimize production efficiency, strengthen national food security and support informed planning based on each country’s production potential. portugal was the chosen country because it has been, throughout history, very dependent on the agricultural sector. furthermore, the country has witnessed periods of self-sufficiency and also of very low levels of self-supply in wheat production. but in recent decades, production has dropped to alarming numbers. if in 1989 the degree of self-supply was around 60%, in 2021 this value had dropped to 6.1%. as a result, portugal became a country very dependent on imports. in order to invert this trend, the portuguese government created the national strategy for the promotion of cereal production (nspcp), which set self-supply goals in cereal production for the period between 2018 and 2023. the degree of self-supply defined by the nspcp was 38% for all cereals and 20% for wheat (barreiros, 2018). since the nspcp has already ended, we can confirm that its objectives were not achieved. regardless, and in order to have defined goals with political relevance, its targets will serve as a basis to build one of the three production scenarios discussed in this study. two prediction models were developed, one for the per capita consumption and other for the production per hectare. this study is structured as follows. the next section presents the data collected, as well as the chosen methodology. section 3 discusses the results, drawing three scenarios for production. finally, section 4 presents the main conclusions of this study: self-sufficiency, although difficult to achieve, would still be possible given the area that was once cultivated in portugal. data sources this study is based on quantitative data and qualitative information obtained from scientific studies and relevant historical documents. data is available for the whole portuguese continental territory, but after a preliminary analysis, we observed that 8 districts (out of 18) were responsible for 92% of the national wheat production from 1920 to 2021. these districts are: beja, bragança, évora, faro, lisbon, portalegre, santarém and setúbal (figure 1). considering that these districts contributed the most to national production, it is plausible to assume that this trend will continue and consequently, the analysis focuses on them. unfortunately, some of the data for the districts, like the cultivated area and the climatic variables, were only available from 1943 onwards. so, although at a national level the available data started in 1920, when looking at the district level the available data only considers the period from 1943. as shown by figure 1, we can observe the discrepancies in production between the chosen districts and the rest of the country. the districts that completely make up the alentejo region (beja, évora and portalegre), are the three main contributors and together represent 64% of total production. we collected data on total consumption, per capita consumption, population, total production, productivity per hectare, cultivated area and climatic variables (temperature and precipitation). the data used in this study were produced by several certified national and international entities: national statistics institute (nsi), portu53 bio-based and applied economics 14(2): 51-65, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17226 rebuilding strategies for food self-sufficiency guese institute of the sea and atmosphere (pisa), united nations (un), climate portal (cp) and pordata. production values at district level were calculated by viana et al. (2021). this data required extensive research, since the sources where it was published have different territorial units over the years. therefore, it was necessary to transform the values so that they were all measured using the same territorial unit. this transformation was done by applying an area-weighting technique that allows the transfer of data from a set of units in the territory to a second set of units, with which the first ones overlap. this method assumes a spatially homogeneous relationship between the source unit and the target unit, meaning that the variable is assumed to be equally distributed across the source unit (viana et al., 2021). the evolution of wheat production in portugal has not been consistent over the last century, as can be seen in figure 2. although there have been periods in which production has come close to and, in some cases, even exceeded consumption, the insufficiency of national production has been a constant in portuguese history (pais et al., 1978; freire & lains, 2017). in recent decades, the situation has worsened with production capacity declining at an alarming rate. the cultivated area was also calculated by applying the same method used for production and can also be seen in figure 2. the decrease in cultivated area is pointed out as one of the main reasons why portugal has seen such large production losses in recent decades. the nsi estimated, for the year of 2022, an area of about 103 thousand hectares dedicated to cereal cultivation, the lowest value in the last 100 years. in comparison, 322 thousand hectares of wheat were cultivated in 1989, a year in which the degree of self-supply reached 60%. wheat consumption in portugal has increased over the last century but has stagnated since the beginning of the 21st century. while the consumption registered in 2021 is below its peak, it is still very high when compared to historical values. it’s important to note that the growth in consumption was achieved through an increase in wheat imports and not production (freire & lains, 2017). in our analysis, the consumption values are of total consumption, which include human, animal, and other uses. the evolution of total consumption can also be observed in figure 2. the population of each country constitutes another essential variable in this analysis, as it directly affects the calculation of self-supply. therefore, we collected data on the total portuguese population at the end of each year between 1920 and 2021. for the period 2022 to 2032, we used the population forecasts available in the united nations database (gaigbe-togbe et al., 2022). productivity per hectare is more relevant at district level because it enables us to analyse which districts have the highest yields and where production can be more efficient and profitable. historically, productivity levels in portugal have been low, and experts argue that this is mainly due to poor soil and adverse climatic conditions suitable for increasing productive efficiency in a competitive market. in addition, the soils with productive potential have been progressively occupied by more efficient and profitable crops, such as vineyards, olive groves and almond trees (pais et al., 1978; faisca 2019). the evolution of productivity per hectare is shown in figure 3. climatic variables are essential for understanding the performance of agricultural production, as crops are vulnerable to inter-annual climatic variability (gouveia & trigo, 2008). despite technological advances, modern agriculture is still heavily dependent on weather conditions, which poses a significant risk to production due to short-term variability and future climate changes. jambekar et al. (2018) used data mining techniques to model figure 1. percentage of national production by district (1920-2021). source: own processing based on data from the nsi using arcgis. 54 bio-based and applied economics 14(2): 51-65, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17226 alexandre macedo joão et al. wheat production in india, showing that climatic variables are one of the main predictive factors. rocha & dias (2019) observed that low precipitation and its seasonality distribution can explain as much as 75% of the wheat yield variability. garrido-lestache et al. (2005) concluded that the quality of certain varieties of wheat under mediterranean conditions is mainly affected by precipitation and temperature. we have values for annual temperatures (average, maximum and minimum) and precipitation. these figures cover the period from 1943 to 2021. as we are forecasting future yields, it is also necessary to obtain forecasts for the climatic variables, which are available in the cp database. the data is shown in figures 4 and 5. figure 2. production, consumption and sown area in continental portugal (1920-2021). source: own processing based on data from the nsi. figure 3. productivity of the 8 major wheat producing districts (1943-2021). source: own processing based on data from the nsi. 55 bio-based and applied economics 14(2): 51-65, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17226 rebuilding strategies for food self-sufficiency methodology based on the available data, two different forecasting models were developed: one for production and the other for consumption. it is assumed that future consumption will be influenced mainly by historical consumption and population’s trend. in the case of production, the forecast model includes variables with a direct impact on wheat yield, namely the cultivated area, temperature and precipitation. the method used in both models was mars, developed by friedman, j. (1988). it is similar to linear regression as it aims to model the relationship between the dependent and the independent variables. however, unlike linear regression, which uses a single coefficient to represent the relationship between the variables, mars uses a series of spline functions, with their own set of parameters to model this relationship. each spline function represents a segment of the overall relationship, and the combination of these functions can approximate a figure 4. average, maximum and minimum annual temperature in portugal with forecasts (1920-2032). source: own processing based on data from the pisa and the cp. figure 5. total annual precipitation in portugal with forecasts (1920-2032). source: own processing based on data from the pisa and the cp. 56 bio-based and applied economics 14(2): 51-65, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17226 alexandre macedo joão et al. wide range of non-linear relationships between the predictor variables and the dependent variable. it automatically identifies important interactions in the data which it fits into a model to capture these interactions (nayana et al., 2022). its ability to automatically identify and capture complex relationships in the data, especially for situations where the functional form of the relationship is difficult to specify, as well as its ability to handle large data, make it a very useful tool for data modelling and analysis. the choice of mars over more conventional methods, such as linear regression or arima, is justified by its demonstrated predictive accuracy and robustness in previous studies, as well as its growing momentum as a tool for developing predictive models in complex data mining applications and challenging scenarios (nayana et al., 2022). dias & rocha (2019) tested different modelling approaches for wheat price forecast in the usa, one of which was mars, using only past values of the time series. their study concluded that the most consistent results were obtained with mars. nayana et al. (2022) used mars to predict wheat yield in india. the results showed that mars was an effective approach for predicting wheat yield, achieving high levels of accuracy. jambekar et al. (2018) conducted a study aimed at predicting crop production using different data mining techniques in india, exploring datasets containing information on the production of wheat, climatic variables, and area under cultivation. their results demonstrated that mars was the most effective technique for predicting crop production. overall, these studies highlight the growing relevance of mars in agricultural research, particularly in settings where the underlying data relationships are difficult to specify. its proven effectiveness reinforces its suitability for the present study, which deals with forecasting under structural and historical complexity. by supporting more accurate predictions, mars contributes to better-informed decisions in crop management, resource allocation and policy design. forecasting models per capita consumption the consumption of a given product depends on many variables, such as price, alternatives, availability and per capita income. based on the available data and given the lack of additional information allowing a more detailed analysis of the evolution of consumption, it is assumed that the historical values of the series represent, in an integrated manner, the set of variables that influence consumption. any structural change in the portuguese economy that has occurred during the period from 1920 to 2021, with a significant impact on the variation in consumption, is reflected in the historical records of total consumption. this approach is therefore considered the most suitable for generating viable and realistic forecasts, within the limitations of the available data. these values can therefore be used as a basis for forecasting values. in other words, mars was employed to predict per capita consumption using an autoregressive model, which is a model that uses past values of the dependent variable as forecasting variables. as in any autoregressive model, it is necessary to decide which past values will be considered to estimate the values of the future series (the figure in the future period will be calculated based on known past values from t – n1 to t – n2). the choice of the past values is made automatically by an optimization algorithm that studies the different possibilities, choosing the one that leads to a smaller prediction error considering the data. the sliding window concept is used, in which it is considered that not all the data of the series are known, predicting these values based on the lag interval to be evaluated. as the aim is to estimate the period from 2022 to 2032, while forecasting each year individually, multiple forecasting models were developed, one for each year under analysis. the lagged values that may be the best for predicting consumption figures for next year may not be the best for predicting two years into the future. to create these models, we resorted to existing libraries that implement these methods in the r studio software (dias & rocha, 2019). the predictions of the per capita consumption are represented in figure 6. as expected, the predictions point to an increase in per capita consumption. even with some setbacks and a somewhat uneven evolution, per capita consumption will end 2032 at 156.97 kilos consumed per inhabitant. these values were calculated in kilos per inhabitant, but the total figure is measured in tonnes so that a comparison can be made with production values. obtaining these values for the per capita consumption forecast and combining them with the population forecast for portugal, we calculated the value of national consumption, which is presented in table 1. we can see that the population in portugal will decrease over the period analysed, which is in line with the trend of recent decades. despite this, as we can see from the figures presented, wheat consumption in portugal is expected to continue to increase. in 2021, national wheat consumption was approximately 1 335 million tonnes. this figure, compared to the range of values forecasted up to 2032, shows that despite the decrease in population, total consumption in portugal will increase by around 18% over this period. 57 bio-based and applied economics 14(2): 51-65, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17226 rebuilding strategies for food self-sufficiency productivity per hectare in the case of the model for productivity per hectare, the use of mars was applied differently. as mentioned previously, 8 districts accounted for 92% of the production over the last century. thus, we will estimate the productivity per hectare of these districts individually, providing a way for determining the districts where it is most advantageous to concentrate production. in our first attempt, we only considered climatic variables as independent variables: annual temperature (average, maximum and minimum) and precipitation. however, these climatic variables were found to have little explanatory power with regard to productivity trends. this might be caused by the fact that only annual values of these variables were included, which is not the most optimal level of aggregation, as it does not allow for capturing monthly values and variations that have a significant impact on output. additionally, technological and biotechnological innovations in agriculture significantly influence yields and productivity per hectare, a factor that is not captured by climatic variables, highlighting the importance of considering technological change in analysing dynamics of productivity over time. different categories of innovation, including mechanical, biological, chemical, agronomic, and computer technologies, have profoundly transformed the structure of the agricultural sector (sunding & zilberman, 2001). after the second world war, there were major advances in mechanization and biological innovations, and fertilizer consumption increased significantly around the world (federico, 2010). at the beginning of the 21st century, the sector experienced a new technological revolution, driven by advances in biotechnology and precision agriculture (sunding & zilberman, 2001). however, while innovation is rightly highlighted as a key driver of productivity growth, it is essential to recognize the practical challenges associated with adopting new technologies, particularly in regions with limited financial resources, aging agricultural populations or underdeveloped infrastructures. this is particularly evident in figure 6. per capita consumption (1920-2032). source: own processing based on data from the nsi. table 1. population, per capita consumption and total consumption (2022-2032). year total consumption (ton) population per capita consumption (kg/inh) 2022 1 411 132 10 282 222 137.24 2023 1 508 353 10 259 508 147.02 2024 1 498 097 10 235 702 146.36 2025 1 493 358 10 210 995 146.25 2026 1 593 684 10 185 237 156.47 2027 1 568 546 10 158 975 154.40 2028 1 606 847 10 132 084 158.59 2029 1 471 144 10 104 703 145.59 2030 1 637 633 10 076 502 162.52 2031 1 692 462 10 047 864 168.44 2032 1 572 636 10 018 702 156.97 source: based on data from pordata and un. 58 bio-based and applied economics 14(2): 51-65, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17226 alexandre macedo joão et al. the portuguese case, where several authors have shown the historical and structural difficulties in adopting agricultural innovations (freire & lains, 2017, lains et al., 2024). despite significant transformations in recent decades, portugal continues to face challenges, which make it more difficult to apply advanced solutions (soares, 2005). as such, although innovation is a driver of agricultural productivity growth, it is important to always consider the geographical and infrastructural context in which it is implemented. given that climatic variables alone could not accurately predict future productivity and assuming that, with the constant advances in innovation, average productivity will continue to increase, it was decided to include the year as an explanatory variable. the inclusion of this variable captures the progressive effect of time on productivity and therefore captures the impact of innovations. by including the year as an independent variable, models with acceptable explanatory power were obtained. analysing the results presented in table 2 reveals that climatic variables have good explanatory power over productivity per hectare in certain districts, such as bragança, faro, lisbon and santarém. the forecasts displayed in table 3 indicate a significant increase in productivity per hectare over the period analysed. according to the trends observed, beja, évora, portalegre, santarém and setúbal are the districts with the biggest increases. not coincidentally, they are all neighbours and belong to the ribatejo and alentejo regions, located in the south of portugal. on the other hand, the districts of bragança, faro and lisbon show a slower evolution and seem to be entering a period of stagnation or even decline in productivity, as can be seen in the last three years of the series. production scenarios this section examines whether it is possible for portugal to achieve wheat self-sufficiency over the analysed period. to assess this possibility, three different scenarios of cultivated area are considered: – first scenario: the area in 2021 remains constant; – second scenario: the area corresponds to its maximum value since 1943; – third scenario: the necessary area to reach the 20% target defined in the nspcp. by combining the predicted yields per hectare obtained from mars with the proposed cultivated area in each of these three scenarios, we can determine the potential for wheat production in portugal. table 4 shows the values for the cultivated area for each scenario. in the case of the third scenario, since the sown area was calculated based on the productivity figures predicted for the 2022-2032 period, an average was calculated to provide a single approximate value that represents the table 2. explanatory power of climatic variables. districts explanatory power of climatic variables for predicting productivity beja 74.2% climatic variables are not significant bragança 55.8% maximum and average temperature are significant évora 74.2% climatic variables are not significant faro 76.1% all climatic variables are significant lisbon 84.8% maximum temperature is significant portalegre 64.1% climatic variables are not significant santarém 86.6% maximum temperature is significant setúbal 80.1% climatic variables are not significant source: author’s calculation using rstudio. table 3. prediction of productivity per hectare per district (t/ha) (2021-2032). year beja bragança évora faro lisbon portalegre santarém setúbal 2022 3.14 1.74 3.36 1.13 0.81 2.93 3.58 4.49 2023 3.32 1.81 3.55 1.42 0.53 3.09 3.63 4.84 2024 3.49 1.87 3.74 1.38 0.87 3.25 3.84 5.19 2025 3.67 1.93 3.92 1.58 0.90 3.42 3.92 5.53 2026 3.85 1.99 4.11 1.44 0.93 3.58 4.06 5.88 2027 4.02 2.06 4.30 1.58 0.97 3.74 4.15 6.23 2028 4.20 2.12 4.49 1.37 1.00 3.91 4.28 6.58 2029 4.38 2.18 4.67 1.63 1.03 4.07 4.37 6.93 2030 4.56 2.25 4.86 1.43 1.06 4.23 4.46 7.28 2031 4.73 2.31 5.05 1.50 1.09 4.39 4.48 7.62 2032 4.91 2.37 5.24 1.19 1.02 4.56 4.59 7.97 source: author’s calculation using rstudio. 59 bio-based and applied economics 14(2): 51-65, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17226 rebuilding strategies for food self-sufficiency area needed to achieve 20% self-supply over the entire period. table 4 clearly show that beja stands out as the district with the largest cultivated area across all scenarios, followed by évora and portalegre. lisbon, setúbal, and faro consistently show the smallest cultivated areas. the second scenario, which represents the maximum expansion, presents values significantly higher than the other two. first scenario agricultural production dynamics have undergone significant changes over the past decades. in several countries, the area allocated to agriculture has declined and the workforce in the sector has also fallen significantly (federico, 2010). portugal has followed this trend, as evidenced by the data from the last few decades. increasing production depends largely on expanding the agricultural land and as wheat is considered a very demanding crop in terms of natural resources, namely fertile soils, expanding the cultivated area is a huge challenge. in 2021, the area allocated to wheat production in the 8 districts was 27 374 hectares, corresponding to 95% of the entire cultivated area. this number is one of the lowest of the last century, being only higher than the values recorded in 2018 and 2019. the figures for the cultivated area with wheat in 2021 were used to estimate the amount of output that can be achieved. the production numbers recorded in table 5 show a continuous increase during the series, which can be attributed to productivity gains driven by innovations, as mentioned earlier. despite having the third smallest cultivated area in the last century, the increase in production, even with the land remaining constant, demonstrates portugal’s productive potential. given that the highest selfsupply figure achieved was 8% in 2032, the results show that portugal does not have the capacity to guarantee 20% self-supply, which means that it cannot meet the target set by the nspcp. this indicates that portugal cannot rely exclusively on innovation advances to increase productivity. it is also necessary to look at the possibility of expanding the cultivated area. second scenario in the second scenario, production was calculated based on the largest sown area recorded since 1943. once again, the districts of beja, évora and portalegre recorded the largest areas, while the districts with the smallest were bragança and setúbal. the sum of the figures presented in table 4 corresponds to 814 734 hectares, a figure higher than that recorded at the peak of the wheat campaign, when self-sufficiency was achieved. the importance of mentioning the wheat table 5. production with constant area in tons (2022-2032). year beja bragança évora faro lisbon portalegre santarém setúbal total self-supply 2022 30 730 5 058 23 048 811 625 11 250 4 717 5 271 81 510 5.8% 2023 32 492 5 261 24 351 1 019 409 11 865 4 783 5 682 85 861 5.7% 2024 34 155 5 436 25 655 990 672 12 479 5 059 6 093 90 539 6.0% 2025 35 917 5 610 26 889 1 133 695 13 132 5 165 6 492 95 033 6.4% 2026 37 679 5 785 28 193 1 033 718 13 746 5 349 6 903 99 405 6.2% 2027 39 342 5 988 29 496 1 133 749 14 361 5 468 7 313 103 851 6.6% 2028 41 104 6 163 30 799 983 772 15 013 5 639 7 724 108 197 6.7% 2029 42 866 6 337 32 034 1 169 795 15 628 5 758 8 135 112 722 7.7% 2030 44 627 6 540 33 337 1 026 818 16 242 5 876 8 546 117 013 7.1% 2031 46 291 6 715 34 640 1 076 842 16 857 5 902 8 945 121 268 7.2% 2032 48 054 6 890 35 941 853 787 17 510 6 050 9 357 125 442 8.0% source: author’s calculation using rstudio. table 4. area cultivated in each district for all three scenarios (ha). district first scenario second scenario third scenario beja 9 787 275 363 28 967 bragança 2 907 56 545 9 962 évora 6 859 149 344 20 369 faro 717 65 089 3 065 lisbon 772 71 374 2 861 portalegre 3 840 86 219 11 418 santarém 1 318 64 649 4 872 setúbal 1 174 46 151 3 202 total 27 374 814 734 84 717 source: based on data from the nsi and author’s calculation. 60 bio-based and applied economics 14(2): 51-65, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17226 alexandre macedo joão et al. campaign lies in the fact that it was the period in which the highest levels of self-sufficiency were achieved, demonstrating that it is possible to place portugal in a comfortable situation in terms of domestic supply. following the public policies applied by fascist italy, the portuguese government launched the wheat campaign in 1929. it was established due to two main factors: the insufficiency of national production to meet consumption needs and the implementation of protectionist policies by the countries to which portugal depended on for its supply (freire, 2008). the measures implemented were successful in the sense that, between the years 1931 and 1935, wheat production exceeded domestic consumption and made it possible to create surpluses (pais et al., 1978). the highest degree of selfsufficiency recorded was 161.29% in the year 1934. both testimonies of time and historians recognise that wheat self-sufficiency was difficult to achieve in the following decades. one of the reasons that explains this fall in production in the decades following the wheat campaign is the fact that most of the incentives granted by the portuguese state during the campaign was primarily aimed at increasing the cultivated area (freire, 2008), rather than promoting investment in innovation and new cultivation techniques. however, with the implementation of new technologies that have altered the structure of the agricultural sector and increased productivity, it is relevant to compare the figures recorded during the wheat campaign with those of this second scenario, since self-sufficiency is achieved in both. according to the data presented in table 6, production is significantly higher than consumption forecasts, which suggests that portugal would be able to achieve self-sufficiency if it recuperated the area that was once allocated to growing wheat. in 2022, portugal would reach an output corresponding to 162% of consumption and in 2032, this figure increased to 221%. however, it is unlikely that this land can be repurposed for wheat production due to the occupation of much of it by urban uses, more profitable crops or because it no longer has the required fertility levels (van vliet et al., 2017). during the period in which these maximum areas were recorded, it was clear that they were driven by strong state support, which encouraged producers to occupy their land with wheat. however, given the comparative disadvantages and the fact that the agricultural sector is no longer governed by national policies, but by the common agricultural policy (cap), this type of guideline is no longer available and probably won’t be in the future. third scenario in the third scenario, it was tested the situation in which portugal reaches the 20% target of self-supply. in 2017, the portuguese ministry of agriculture, forestry and rural development announced the creation of the nspcp, which defined several mains goals, like the increase of cereal production, the reduction of external dependence and the achievement of a stronger and more efficient agricultural sector in the period between 20182023. the strategy was based on three pillars: strengthening the role of producer associations, focusing on innovation and transfer of knowledge, and better organization along the production chain (barreiros, 2018). the production of the districts was calculated based on the percentage of total national production achieved by each district in 2021, and is shown in table 7. according to the results, the total wheat production required to meet the self-supply target in 2032 is approximately 10% higher than in 2022, primarily due to the table 6. production with the maximum area in tons (2022-2032). year beja bragança évora faro lisbon portalegre santarém setúbal total self-supply 2022 864 640 98 388 501 796 73 551 57 813 252 622 231 443 207 218 2 287 471 162% 2023 914 205 102 346 530 171 92 426 37 828 266 417 234 676 223 371 2 401 441 159% 2024 961 017 105 739 558 547 89 823 62 095 280 212 248 252 239 524 2 545 208 170% 2025 1 010 582 109 132 585 428 102 841 64 237 294 869 253 424 255 215 2 675 728 179% 2026 1 060 148 112 525 613 804 93 728 66 378 308 664 262 475 271 368 2 789 089 175% 2027 1 106 959 116 483 642 179 102 841 69 233 322 459 268 293 287 521 2 915 968 186% 2028 1 156 525 119 875 670 555 89 172 71 374 337 116 276 698 303 674 3 024 988 188% 2029 1 206 090 123 268 697 436 106 095 73 515 350 911 282 516 319 826 3 159 659 215% 2030 1 255 655 127 226 725 812 93 077 75 656 364 706 288 335 335 979 3 266 447 199% 2031 1 302 467 130 619 754 187 97 634 77 798 378 501 289 628 351 671 3 382 504 200% 2032 1 352 032 134 012 782 563 77 456 72 801 393 159 296 739 367 823 3 476 585 221% source: author’s calculation using rstudio. 61 bio-based and applied economics 14(2): 51-65, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17226 rebuilding strategies for food self-sufficiency projected increase in national consumption. this trend underscores the importance of continuing to invest in strategies to boost wheat production, as the target will continue to rise steadily. in contrast, the required cultivated area gradually decreases over the same period, reflecting the positive impact of innovation-driven gains in productivity. discussion portuguese productive capacity as other european countries, from the second half of the 19th century onwards (federico, 2010; lains et al., 2024), portugal experienced growing state intervention, particularly in the most important agricultural subsectors for human consumption, like cereals. the wheat campaign is part of this trend of national protectionist policies (freire, 2008). in portugal, this kind of policies lasted until the country’s entry into the european economic community (eec), in 1986, where the cap came into effect. the policy changes had a very particular impact on wheat, because in the 20th century it was the crop that deserved the most protection (freire & lains, 2017). the implementation of the cap changed the structure of the portuguese agricultural sector and has been considered detrimental to the sector’s evolution. indeed, it can be observed that agriculture has struggled to generate new dynamics capable of counteracting the structural impacts of the cap reforms, notably failing to devise development strategies, engage new stakeholders, and harness productive potential (cunha, 2010). following the decline in incentives for wheat cultivation, output sharply declined nationwide, since it was state intervention that rendered production economically attractive to farmers. combining these changes in the orientation of incentives with the opening of international markets and the integration into the eec, imports were favoured in order to make up for the national consumption deficit. the findings of the second scenario suggest that portugal could become self-sufficient if it manages to recover a large part of the land once used to produce wheat. in fact, the lower degree of self-supply recorded in this scenario, 159% in 2023, allows us to understand that there is room for production gains through increases in the cultivated area. however, the assumption that the maximum areas historically cultivated can be recovered does not fully consider current constraints, such as urbanization, land abandonment, soil degradation and shifts to more profitable crops (van vliet et al., 2017). these structural changes in land use reduced the feasibility of fully recovering former wheat production areas. this complexity must be acknowledged when interpreting the results. however, the analysis also shows that a consistent and stable increase in cultivated area, combined with productivity gains driven by technological innovation, could significantly improve self-supply. although self-sufficiency is unlikely to be achieved in the short term due to climatic, environmental and political constraints, the third scenario emerges as a more realistic benchmark. to be more specific, the year in which the area necessary to reach 20% is lowest is 2032, and to reach that point it would be necessary to increase the area by 44 300 hectares, an increase of 162% over the 2021 figure. another significant takeaway from the analysis of the results is the identification of the most favourable districts for wheat production. it has already been mentioned that most of the portuguese territory does not have favourable conditions for wheat production and table 7. production required to achieve 20% self-supply in tons (2022-2032). year beja bragança évora faro lisbon portalegre santarém setúbal total 2022 104 573 18 546 78 573 3 929 2 340 38 346 18 221 17 698 282 226 2023 111 779 19 823 83 986 4 200 2 502 40 988 19 476 18 918 301 671 2024 111 019 19 689 83 415 4 171 2 485 40 709 19 343 18 789 299 619 2025 110 668 19 626 83 151 4 158 2 477 40 580 19 282 18 729 298 672 2026 118 102 20 945 88 738 4 437 2 643 43 306 20 578 19 988 318 737 2027 116 239 20 614 87 338 4 367 2 601 42 623 20 253 19 672 313 709 2028 119 078 21 118 89 471 4 474 2 665 43 664 20 748 20 153 321 369 2029 109 021 19 334 81 915 4 096 2 440 39 977 18 995 18 451 294 229 2030 121 359 21 522 91 185 4 559 2 716 44 501 21 145 20 539 327 527 2031 125 422 22 243 94 238 4 712 2 807 45 991 21 853 21 227 338 492 2032 116 543 20 668 87 566 4 379 2 608 42 735 20 306 19 724 314 527 source: author’s calculation using rstudio. 62 bio-based and applied economics 14(2): 51-65, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17226 alexandre macedo joão et al. that only a few regions are capable of meeting the necessary conditions to produce it efficiently (freire & lains, 2017). the alentejo and ribatejo regions, composed by five districts (beja, évora, portalegre, santarém and setúbal), emerge as the most productive zones. setúbal, which is the district with the highest productivity, should have production concentrated mainly in municipalities outside the setúbal peninsula, due to the high residential density in this zone, which are a part of the lisbon metropolitan area. the district of santarém, with the second highest productivity, has historically concentrated production in the municipalities located on the left bank of the tagus river, which is close to the alentejo region. although beja, évora and portalegre have lower productivity levels than these two, they are still the districts with the highest concentration of production and should continue to be prioritised. during several centuries, wheat was the main crop of the alentejo region (faísca, 2019). in fact, “government policies were, since 1899, contributing for the region to consolidate the image of portugal’s granary” (freire, 2008, p. 31). as a result, wheat cultivation has emerged as an activity which exercised a substantial influence on soil degradation and played a significant role in the state of desertification within the region. the scenario based approach and policy context the scenario-based approach used in this study can be easily adapted to other national contexts and crop types, as long as country-specific data on cultivated area, production figures and climatic variables are available. by defining alternative scenarios of land allocation and productivity improvements, the same framework can simulate the evolution of production potential of other cereals such as maize, barley or oats. for example, applying this approach to maize in the southern countries of eastern europe is particularly relevant, as maize is one of the most important cereal crops for this region, occupying a significant share of cultivated land and playing a central role in consumption (grčak et al., 2020). from a political point of view, integrating scenario results with existing agricultural strategies improves decision-making at various levels of governance. in the eu, for example, scenario modelling can serve as a basis for cap interventions and support the “farm to fork” strategy, which aims to make food systems fair, healthy and environmentally friendly (wesseler, 2022). national authorities can then prioritize incentive systems, to encourage farmers to expand cultivation in environmentally sustainable ways or to rotate crops to maintain soil quality. by linking this method directly to policy instruments, future research can provide tailored recommendations that support sustainable intensification, crop diversification and resilience building ensuring that scenario-based planning becomes a relevant indicator for agricultural policymaking. although this study focuses mainly on consumption and production scenarios, it is important to recognize the broader economic context in which is inserted. market dynamics, including price volatility and global imbalances between supply and demand, can have a significant impact on both domestic production incentives and the level of wheat imports. a clear example of this, as mentioned earlier, is the armed conflict on the ukrainian territory. the disruptions in the production cycle and in the supply chain resulting from this conflict have made a significant impact on the market dynamics of wheat on the international stage (zhang et al., 2024). similarly, international trade policies, such as tariffs or subsidies, also play a critical role in shaping these dynamics. in the context of the eu, the cap and the trade agreements with third parties have a profound influence on grain markets (wieliczko, 2017). these factors collectively shape the economic environment in which agricultural decisions are made and must be considered when assessing the viability and sustainability of production strategies. any strategy aimed at achieving national self-sufficiency in wheat must be conceived as a long-term, carefully planned and environmentally sustainable effort. the wheat campaign, although it led to self-sufficiency, proved to be economically and ecologically unsustainable and cannot be repeated. intensive cultivation led to serious erosion and degradation of soil quality due to over-exploitation, particularly in the alentejo, reflecting a lack of planning on the part of policymakers who ignored these factors (freire, 2008). in addition, much of the soil in portugal is unsuitable for growing wheat, resulting in chronically low yields and high production costs due to heavy dependence on fertilizers (faísca, 2019). consequently, any future efforts should prioritize the expansion of wheat into the most suitable areas. to ensure that any increase in production aligns with sustainability goals, expansion must be accompanied by environmental safeguards that mitigate soil degradation, conserve water resources and preserve biodiversity. this implies the adoption of soil conservation practices and the adoption of technologies to optimize the use of production factors. integrating these measures into national policy frameworks will be crucial to balance productivity objectives with long-term ecological resilience and to ensure that the quest for self-sufficiency adheres to the principles of sustainable agriculture. 63 bio-based and applied economics 14(2): 51-65, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17226 rebuilding strategies for food self-sufficiency limitations and further research the work carried out and described in this study has some limitations that should be acknowledged when interpreting the results. one fundamental limitation concerns the relatively weak explanatory power of the climatic variables in the model, particularly in relation to predicting productivity levels. however, this should not be interpreted as proof that climate variables are insignificant in determining agricultural results. nevertheless, aggregating data over large temporal and spatial scales can hide more nuanced interactions between weather patterns and crop output (srikanthan & mcmahon, 2001). by relying on annual figures, the analysis fails to capture the timing and intensity of key climatic events, such as rainfall during the main growth phases or temperature extremes during sensitive periods of the crop cycle, which are known to have substantial effects on yields (gouveia & trigo, 2008). in this regard, the use of weekly, monthly, or even seasonal climatic data would allow for a much more precise understanding of when and how climate variables influence production (gao et al., 2023). as such, the lack of significant explanatory power in the current model probably reflects a limitation in the availability of the data rather than the actual influence of weather on production. future research could benefit significantly from incorporating more granular climate data, both spatially and temporally, to better understand the specific conditions under which climate variability affects agricultural productivity. to resolve this constraint, the model was adjusted to include the year as an explanatory variable. this choice allowed mars to capture the temporal evolution existing in our dataset, which gave it the ability to generate stable and reliable results. more importantly, this inclusion revealed that productivity trends are also significantly influenced by technological innovations, which were not previously considered. by introducing the year into the model, we were able to indirectly reflect the cumulative effect of these innovations over time, from mechanization and the use of fertilizers to the adoption of biotechnology and precision agriculture (federico, 2010). although this approach does not isolate specific technologies, it does incorporate their overall influence on output. the resulting models, which include year as a variable, achieved acceptable levels of explanatory power and improved predictive capacity. conclusion in conclusion, an increase in wheat production not only depends on productivity improvement brought by innovations, but also on the increase of the cultivated area. if it remains constant, the maximum degree of selfsupply that can be achieved is 8% in 2032, a figure not very different from the value of 6,1% recorded in 2021, which is very far from the 20% target. considering the circumstances in which the periods of self-sufficiency occurred, it can be observed that an increase in production would require an extension of the cultivated area, which may become possible if incentives are created. the scenario-based approach used not only provides policymakers with informed guidance on realistic and sustainable strategies for optimizing production efficiency but also provides a form of sensitivity analysis of the mars method used for forecasting. while the mars method has strengths, such as the ability to model non-linear relationships and to select the most efficient variables automatically, it also has limitations, such as sensitivity to missing data and high computational requirements for large data sets. despite these limitations, mars remains a valuable tool due to its ability to produce understandable models and identify influential forecasting factors. however, to ensure reliable and accurate forecasts, it is advisable to complement it with other models and validation techniques. the use of mars in this paper proved very efficient and it emerges as a powerful tool for modelling and predicting wheat production and consumption. it allowed us to work with a very extensive database and, by using its ability to capture nonlinear relationships and interactions among multiple predictors, it provided a robust framework for forecasting. the fact that it identifies and captures complex relationships in data, which would not be possible to unfold if we had used a linear model, was fundamental to this research. for a strategy to be drawn up and implemented successfully in any sector of the economy, it is necessary to carry out studies and analyses of current conditions and past performance in order to establish reliable objectives. based on historical data of variables that directly impact wheat production, our analysis was made to provide us with information on the productive capacity of the main agrarian regions. the fact that we limited ourselves to values that were previously recorded gave us the ability to state that this study is reliable and realistic, because these values have been achieved in the past. as such, this study is of great importance to researchers and policymakers to gain insights into the key factors influencing wheat production, enabling informed decision-making and resource allocation. due to the lack of investment by the portuguese government, which is still not paying enough attention to the problem of decreasing wheat production, it has not yet been possible to record the figures we had 64 bio-based and applied economics 14(2): 51-65, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17226 alexandre macedo joão et al. predicted could be achieved. as agricultural systems face increasing challenges due to climate change and fluctuating market conditions, this paper offers a valuable approach for reliable and accurate wheat production predictions, thereby aiding in the formulation of sustainable agricultural strategies. this becomes more relevant considering the current geopolitical context of the european continent, as it provides countries with information on their productive capacity so that they can design strategies to increase production and achieve self-sufficiency. references barreiros, l. s. 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(2022). the eu’s farm‐to‐fork strategy: an assessment from the perspective of agricultural economics.  applied economic perspectives and policy, 44(4), 1826–1843. wieliczko, b. (2017). the impact of global surrounding on the common agricultural policy. 498, 374–381. zhang, y.-t., li, m.-y., & zhou, w. (2024). impact of the russia-ukraine conflict on international staple agrifood trade networks. foods, 13(13), 2134. reflections and new directions: an editorial retrospective and the launch of our new policy paper section silvia coderoni, fabio bartolini where is the italian agriculture heading? a discussion in light of the prospects for the future cap filippo arfini1*, fabio bartolini2, anna carbone3, tatiana castellotti4, silvia coderoni5, raffaele cortignani6, raffaele d’annolfo4, giovanni dara guccione4, michele donati1, francesca galli7, roberto henke4, giampiero mazzocchi4, alessandro monteleone4, towards the knowledge and innovation system for the bioeconomy? giacomo maria rinaldi*, davide viaggi impact of weather variability on crop yields and land use dynamics in eastern india: shortand long-term effects pratap kumar jena1, kirtti ranjan paltasingh2, souryabrata mohapatra3,,*, ashok mishra4 rebuilding strategies for food self-sufficiency: portugal’s past patterns and future ambitions alexandre macedo joão1, dulce freire2, humberto rocha3, joana dias4 from subsidy to land rent: how production-linked payments shape factor remuneration in agriculture adrian sadłowski expenditure allocation for rural development interventions: main trends and patterns in the choices of the italian regions under the cap 2023-2027 tobia minuzzo, francesco pagliacci* © 2025 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae bio-based and applied economics bae bio-based and applied economics 14(2): 67-78, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16734 citation: sadłowski, a. (2025). from subsidy to land rent: how production-linked payments shape factor remuneration in agriculture. bio-based and applied economics 14(2): 67-78. doi: 10.36253/ bae-16734 received: october 24, 2024 accepted: february 18, 2025 published: september 30, 2025 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor in chief: fabio bartolini associated editor: oriana gava orcid as: 0000-0003-2969-4926 from subsidy to land rent: how productionlinked payments shape factor remuneration in agriculture adrian sadłowski cardinal stefan wyszyński university in warsaw, poland e-mail: a.sadlowski@uksw.edu.pl abstract. the paper aims to recognize the mechanism by which production-linked payments stimulate the inputs of production factors in agriculture and the mechanism for transforming subsidies into remuneration for production factors. the study is theoretical, and the research methods used are economic modeling and marginalist analysis. it was demonstrated that production-linked payments change the allocation of resources compared to the allocation that results from the market mechanism, as well as influence the amount and structure of remuneration for production factors in agriculture. a decomposition of the remuneration of production factors was performed. this comprehensive approach to evaluating the impact of these payments, taking into account the side effects of using this instrument, represents a contribution to the literature. the proposed model can be applied to support the design of agricultural policy instruments, policymaking decisions concerning the selection of tools for achieving established objectives, and academic education in agricultural economics. keywords: agricultural subsidization coefficient, capitalization of direct payments, conversion rate of payments into land rent, financial support for agriculture, production-linked payments. jel codes: h23, q12, q15. 1. introduction with the implementation of the 2003 reform of the common agricultural policy (cap) (the so-called fischler reform), which envisaged the gradual decoupling of direct payments from production (pirzio-biroli, 2008; swinnen, 2010), production-linked payments played an increasingly minor role. they became a kind of remnant in the structure of reformed instruments. gradually, they were converted into so-called historical payments (frascarelli, 2020), i.e., payments linked not to the current production volume but to the volume from a reference period earlier than the year of applying for the payment. after the transition period, they were to cease entirely, and the funds previously paid under production-linked payments were to be added to the budget for decoupled payments. https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae https://doi.org/10.36253/bae-16734 https://doi.org/10.36253/bae-16734 https://doi.org/10.36253/bae-16734 https://orcid.org/0000-0003-2969-4926 mailto:a.sadlowski@uksw.edu.pl 68 bio-based and applied economics 14(2): 67-78, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16734 adrian sadłowski if a broad definition of production-linked payments is adopted, their gradual disappearance was interrupted in 2010, when the so-called special support was introduced (council of the european union, 2009). the amount of financial assistance granted to a farmer under this instrument depended on the area of a given crop in the farm (for crop production sectors) and the number of animals of a given species (for livestock production sectors). similarly, under the cap reform that came into effect in 2015, european union (eu) member states were allowed to allocate part of the available funds to finance payments described as voluntary coupled support (sadłowski, 2018a). the general rules for granting these payments were the same as those established for the aforementioned special support (tangermann, 2011). their use was optional for eu member states and simultaneously subject to various restrictions, including a cap on funding level (potori et al., 2013). the maximum allowable level of funding was expressed as a percentage of the so-called national ceiling, i.e., the amount allocated to a given eu member state for direct payments (sadłowski, 2018b). these instruments were intended to support farmers’ incomes in selected agricultural production sectors. the choice of specific sectors to be supported could be driven by recognizing their particular social sensitivity, environmental importance, or susceptibility to economic crises (anania and d’andrea, 2015; hristov et al., 2020). however, neither the so-called special support nor the so-called voluntary coupled support constituted production-linked payments in the strict sense, understood as payments granted to beneficiaries in amounts proportional to the volume of agricultural products sold. similar solutions were provided for the next programming period (sadłowski, 2019; pilvere et al., 2022). the issue of returning to strictly production-linked direct payments or using such instruments under extraordinary measures (financed either from the eu budget or from the national budgets of eu member states) is raised by the agricultural self-government in discussions on subsequent cap reforms, as well as in cases of extraordinary circumstances that have a strong negative impact on farmers’ incomes. a current example of such circumstances is the increased influx of ukrainian agricultural products, mainly cereals and oil seeds, into the eu market following the temporary liberalization of trade relations between the eu and ukraine (mulyk and mulyk, 2022; hamulczuk et al., 2023; beluhova-uzunova et al., 2024). however, the decisionmaking freedom regarding the use of production-linked payments is limited by the international commitments made by the eu under agreements concluded within the framework of the world trade organization (matthews, 2018; nedumpara et al., 2022). this study aims to identify (i) the mechanism by which strictly production-linked payments stimulate the inputs of production factors in agriculture, and (ii) the mechanism by which subsidies granted in the form of strictly production-linked payments are transformed into remuneration for production factors. a research gap has been identified in the existing literature, particularly in the analysis of the distribution sphere. previous studies have primarily focused on the impact of financial support on production volume (e.g., howley et al., 2009) or the overall efficiency of the agricultural sector (e.g., lankoski and thiem, 2020). the model presented in this article provides a detailed analysis of the impact of production-linked payments not only on the production sphere but also on the size and structure of remuneration for production factors (what falls within the scope of the distribution sphere (see blaug, 1992)) while taking into account the side effect of this instrument – namely, the “capture” of support by landowners. this study therefore proposes a comprehensive approach, uniquely employing ricardo’s theory of land rent, to explain the mechanism by which payments are transformed into the remuneration of production factors. this connection of land rent theory with subsidies has not been done before in theoretical research. furthermore, it should be noted that the existing literature predominantly adopts a macroeconomic perspective, whereas the proposed model considers the specificity of optimization decisions made at the farm level under subsidy conditions. the focus on general analyses and the scantiness of research from a microeconomic perspective may result in insufficient recognition and understanding of complex economic mechanisms, limiting the ability to draw accurate, comprehensive conclusions (compare stiglitz, 2018). the proposed model addresses this gap in the literature and lays the foundation for more precise and multifaceted analyses of agricultural policy in response to current challenges in the sector. by proposing analytical tools for quantifying the effects of production-linked payments, this study also contributes to the standardization of terminology and the development of methodology in this field. it should be noted that – according to the current terminology of eu regulations – so-called coupled payments are a type of financial support that is proportional to the area of a given type of crop (in the case of plant production sectors) or the number of animals of a given species (in the case of animal production sectors), and the definition commonly accepted implicitly in scientific studies is identical to the nomenclature of legal acts. 69from subsidy to land rent: how production-linked payments shape factor remuneration in agriculture bio-based and applied economics 14(2): 67-78, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16734 the subject of relatively numerous studies, the results of which have been reported in the scientific literature, are almost exclusively coupled payments in the sense of the current legal provisions and not production-linked payments in the strict sense of the word, which require further exploration. the article consists of an introduction, a literature review, a methodology section, results, discussion, and conclusions. the “results” presents a model of how production-linked payments affect land use and factor remuneration in agriculture. the “discussion” highlights the model’s advantages and limitations, followed by concluding remarks. 2. literature review the practice of using production-linked payments under the cap has revealed numerous shortcomings of this instrument (beard and swinbank, 2001). their main disadvantage, compared to alternative forms of financial assistance to farmers, is considered to be their stimulating effect on the volume of production in the supported sectors, resulting in the creation (or widening) of a discrepancy between the volume and structure of agricultural production and the volume and structure of demand for agricultural products (howley et al., 2009; oecd, 2020). by rewarding production intensification, production-linked payments intensify the negative effects of agricultural activities on the natural environment (donald et al., 2002; henderson and lankoski, 2019). the environmental damage indirectly caused by this form of support is particularly acute in farming systems where input use was already high at the starting point (lankoski and thiem, 2020). production-linked payments are susceptible to “capture” by next links of agribusiness or by agricultural landowners, which, however, is also a feature (albeit to varying degrees) of other forms of direct support to farmers (góral and kulawik, 2015; sadłowski, 2017; baldoni and ciaian, 2023). in the typical conditions of agricultural markets, with greater bargaining power on the demand side, represented by processors of agricultural products (oleszko-kurzyna, 2007), production-linked payments can be “captured” relatively easily by the next links of agribusiness. this occurs as a result of processors lowering the purchase prices of supported agricultural products. the fewer part of production-linked payments is “captured” by subsequent links in the agribusiness chain (interactions in agricultural product markets), the greater their tendency to capitalize on agricultural land prices and their susceptibility to “capture” by landowners by raising rental rates (interactions in the agricultural land market). these phenomena reduce the effectiveness of direct payments in supporting farmers’ income (latruffe and le mouël, 2009). compared to area-based payments, while production-linked payments show less susceptibility to “capture” by agricultural landowners and greater resistance to capitalization in farmland prices, they are more susceptible to “capture” by buyers of agricultural products (sadłowski, 2017; ciaian et al., 2021). a critical view of the use of production-linked payments has been expressed by tangermann (2011), according to whom a given amount of payment provides the greater economic benefit to the farmer the less it is linked to any requirement, in particular the production of a specific agricultural product. in his view, the decoupled payment is more effective than the coupled payment not only in supporting farmers’ income but also in counteracting abandonment in areas with natural constraints (tangermann, 2011). 3. methodology the theory explaining the mechanism by which production-linked payments influence the production sphere (the level of engagement of agricultural land) and the distribution sphere (the remuneration of production factors) was developed using economic modeling. the remuneration of land as a production factor is interpreted in the model – by ricardo’s (1996) theory of land rent – as the residual amount remaining after paying for the input of the other production factors. the research method used is marginalist analysis, derived from the neoclassical tradition (bartkowiak, 2008). in the model, marginal revenue (mr) is defined not as the increase in total revenue due to an increase in production (and simultaneously sale) by one unit but as the increase in total revenue (tr) resulting from an increase in land input (l) by one unit. unlike a marginal product, which in economic theory is expressed in physical units per unit of variable production factor input (e.g., the measured in tons quantity of “additional” grain produced as a result of increasing input of a specific production factor by one unit), marginal revenue is expressed in monetary units per unit of agricultural land area (e.g., a hectare). similarly, marginal cost (mc) is understood as the rise in total cost (tc) (inputs other than land) due to an increase in land input by one unit (table 1). mc, like mr, is expressed in monetary units per unit of agricultural land area, which allows the relation70 bio-based and applied economics 14(2): 67-78, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16734 adrian sadłowski ship between these two variables and an exogenous variable (land input) to be represented within a single coordinate system. the model adopts the perspective of a farm being a “price taker” (niezgoda, 2009) – both in the market for production factors and in the market for agricultural products. this means that the economic decisions of an individual farm, regarding the size of inputs or the scale of production, do not affect market prices (for agricultural production inputs or products). the issue of the (un)realism of the assumption regarding the independence of price from production volume, as well as the acceptability of adopting unrealistic assumptions, has been widely discussed in theoretical and methodological economic literature (see friedman, 1953; hardt, 2012). in the practical functioning of agricultural markets, the supply side is typically represented by numerous, fragmented producers. from their perspective, the unit price remains the same regardless of the volume of delivery (sale). the presented model focuses on this micro-level perspective. a narrow definition of production-linked payments was adopted (the term “production support” is treated as synonymous), including only those financial support instruments for farmers where the amount of support granted is calculated in proportion to the amount of production sold. the baseline situation, in which production-linked payments are not used (the zero variant), was compared with the situation in which this form of state intervention in agriculture was applied (the alternative variant). this allowed for the determination of the economic effects of the intervention. the identification of the mechanism for converting production-linked payments into remuneration for production factors created a framework for describing and measuring the phenomenon of “capturing” the support provided to farmers by the owners of agricultural land. the essence of the model was presented using a graphical method of visualizing dependencies (charts) and its accompanying descriptive method. the developed model is a tool for analyzing the behavior of a farm as an economic entity; thus, it is a microeconomic model. it enables the determination of the level of land resource usage in a farm that ensures the maximization of economic performance; it is, therefore, an optimization model. at the same time, it is an equilibrium model, as it indicates the functioning of an automatic mechanism that leads the farm to a state of equilibrium, in which the incentives for further changes cease. 4. results 4.1 the impact of production support on the use of agricultural land (production sphere) the analysis is conducted in the first quadrant of the coordinate system (figure 1), as this corresponds to the values of the examined variables that have an economic sense. the horizontal axis represents the amount of agricultural land used (in units of area, e.g., hectares). meanwhile, on the vertical axis, one can read – as the second coordinate of a point located on a given line – the level of mc and mr, expressed in monetary units per unit of agricultural land input, in relation to a homogeneous, unitary plot of land. mc here means the increase in the cost of production, namely the inputs of production factors other than land (i.e. – in the classical approach – labor and capital), resulting from the increase in land input by one unit. mr is understood here as the increase in tr resulting from the increase in the level of land use by one unit. mr₀ is the graph of the mr function under conditions where production-linked payments are not applied, thus it includes only revenue from the sale of agricultural produce. mr₁, on the other hand, refers to the situation where production-linked payments are applied. this table 1. marginal quantities used in the model. variable definitional formula descriptive definition marginal cost increase in total cost (production inputs other than land) due to an increase in land input by one unit. marginal revenue increase in total revenue due to an increase in land input by one unit. source: author’s own elaboration. figure 1. the impact of production-linked payments on the level of agricultural land use. source: author’s own elaboration. 71from subsidy to land rent: how production-linked payments shape factor remuneration in agriculture bio-based and applied economics 14(2): 67-78, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16734 means that mr₁ includes, in addition to revenues from the sale of agricultural produce, revenues from production-linked payments. for an agricultural parcel represented by a given point on the horizontal axis of the coordinate system, the ratio of the vertical distance between the line mr0 and the line mr1 to the vertical distance between the horizontal axis of the coordinate system and the line mr0 corresponds to the relation of the amount of support granted to the value of the sale. in other words, this represents the relationship between remuneration sourced from the state and remuneration sourced from the market. due to the assumption of the independence of the price of the supported agricultural product from the volume of production, this ratio does not change as one moves rightwards along the horizontal axis. sadłowski (2017) demonstrated that the application of production-linked payments leads to an increase in production intensity on land already used for agriculture (even in the absence of support) while simultaneously increasing production extensiveness by bringing previously unused land into agricultural production. in the simplified model presented in this study, the effect of a payment-induced increase in inputs (impact on the course of the mc function graph) and revenues from the sale of agricultural produce (impact on the course of the mr function graph) was omitted in relation to land on which production would be carried out even in the absence of support. the further to the right along the horizontal axis, the less agriculturally useful the land, as the most fertile and accessible plots are used in production first. the graph of the mc function is a downward-sloping line, as the less fertile the land, the lower the amount of labor and capital required to maximize economic outcome (sadłowski, 2017). this statement concerns the inputs of labor and capital that make up the direct costs of production and not the investment outlays (e.g., the costs of building drainage infrastructure) that make it possible to increase the agricultural suitability of the land. the graph of the mr function is also a downwardsloping line. the negative slope of this line reflects the fact that the most productive land, which generates the highest revenue from the sale of agricultural products, is engaged in production first in the pursuit of maximizing economic outcomes. as less and less fertile and increasingly peripherally located land is involved in the production process (moving to the right along the horizontal axis), the mr from each subsequent unit of land area is lower and lower. the area under the mc curve represents the tc level, while the area under the mr curve represents the tr level. the effects of changes in factor input prices would be illustrated by a parallel shift of the mc line, while the effects of changes in the price of the supported agricultural product would be illustrated by a parallel shift of the mr line. an increase/decrease in the prices of agricultural inputs or wages would result in an upward/ downward shift of the mc line, respectively. meanwhile, an increase/decrease in the price of the supported agricultural product would be reflected in an upward/downward shift of the mr line. the optimal level of use of available agricultural land resources when production-linked payments are not applied is determined by the first coordinate of the point where the mc curve intersects the mr₀ curve, i.e., l₀. at this level of land use, the economic outcome, understood as the surplus of tr over tc, is maximized. however, when agricultural production is subsidized by providing farms with financial support proportional to the volume of production, the factors of production engaged in the production process are remunerated not only by the market (in the form of revenues from the sale of agricultural products) but also by the state (in the form of production-linked payments). this is illustrated by the mr function at position mr₁. in this case, the farm’s equilibrium point will be point e₁, which corresponds to a higher level of land use (l₁ > l₀). thus, land that was previously (i.e., in the absence of productionlinked support) unused for agricultural purposes will now be engaged in production. the length of the segment |l₀l₁| reflects the area of this additional land, i.e., land brought into production as a result of the introduction of production-linked payments. they can be equated with marginal lands (see csikós and tóth, 2023); although definitional challenges have not been fully resolved, this concept is relatively frequently used in the literature on the subject. therefore, production-linked support acts as an incentive for farms to increase land use, leading to an overall increase in the agricultural land area utilized in the country. however, if resource management is to be rational, there is no justification for expanding this area for reasons other than an improvement in market conditions in agriculture. 4.2 the impact of production support on the remuneration of production factors (distribution sphere) the remuneration of land, as a resource involved in the production process, is a residual value, representing the surplus of revenues from the sale of agricultural products (in the case of application of production-linked payments, increased by revenues from these payments) 72 bio-based and applied economics 14(2): 67-78, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16734 adrian sadłowski over the production costs, which include inputs of production factors other than land. this definition of land remuneration is equivalent to the economic outcome. based on figure 1, it can be noted that in the case without production-linked payments, the total remuneration of land at the farm’s equilibrium point (e₀) is represented by the area of triangle ae₀b. the value of land rent per unit of land area (homogeneous in terms of agricultural suitability) is symbolized by the vertical distance between the mc curve and the mr₀ curve. the value of land rent decreases as we move rightwards along the horizontal axis, corresponding to the inclusion of land with progressively lower agricultural suitability into the production process. the mc curve lies below the mr₀ curve for land with a sufficient level of agricultural suitability to be profitably involved in production, given the production costs and agricultural product prices. in the case of the use of production-linked payments, land rent consists of two components: one part financed by the market (covered by revenue from the sale of agricultural products) and another part financed by the state (covered by revenue from payments). for a unit of land area (homogeneous in terms of agricultural suitability), the value of the first component is symbolized by the vertical distance between the mc curve and the mr₀ curve, while the value of the second component is represented by the vertical distance between the mr₀ curve and the mr₁ curve. the total remuneration of land at the new equilibrium point (e₁), which, incidentally, corresponds to a greater land input than in the initial situation (l₁ > l₀), is illustrated by the area of the triangle ae₁c. within this area, the market-financed component is represented by triangle ae₀b and the state-financed component by quadrilateral be₀e₁c. to measure the scale of the impact of productionlinked payments on the distribution sphere, the following indicators can be used: – the agricultural subsidization coefficient, – the coefficient of land rent financing by the state, and – the payment-to-land rent conversion coefficient. the presented model allows for a theoretical decomposition of the remuneration of production factors into remuneration from non-land production factors and land rent. for the scenario with production-linked payments, this division can further be separated into the portion financed by the market and the portion financed by the state. the proposed coefficients are structural indicators related to the remuneration of production factors. 4.2.1 agricultural subsidization coefficient the agricultural subsidization coefficient is defined as the ratio of the amount of support granted to the total revenue of the farm, which includes revenue from the sale of agricultural products (sourced from the market) and revenue from various state instruments supporting agriculture financially (in the model case under analysis, state support is provided solely in the form of production-linked payments). therefore, it indicates what portion of the total revenue is derived from state support. in other words, this coefficient shows the percentage of the remuneration of the factors of production involved in agricultural production that is financed by the state. the agricultural subsidization coefficient (caas) is expressed by the formula: (1) where: prv – the production-linked payment rate (expressed in monetary units per unit of mass of the produced (and sold) agricultural product, e.g., in eur/t); v – the volume of supported agricultural products (expressed in units of mass, e.g., in tons); tr₁ – the total revenue from the production of a given mass of agricultural products, including revenue from the sale of those products and revenue from production-linked payments (expressed in monetary units, e.g., in eur); p – the price of the agricultural product (expressed in eur/t). thus, the agricultural subsidization coefficient is a dimensionless value and can take any value from the closed interval between 0 and 100%. the coefficient equals zero when the remuneration of the factors of production is entirely equivalent to the monetary value of the goods produced, which occurs only when the market is the sole source of financing for inputs. in figure 1, this situation corresponds to the zero scenario with e₀ as the equilibrium point. however, in conditions where production-linked payments are applied, the value of this coefficient is greater than zero and, under the assumed conditions (the price of the agricultural product and the payment rate being independent of the farm’s production volume), remains constant as one moves to the right along the horizontal axis of the coordinate system, accompanied by a decrease in the agricultural usefulness of the land. the insensitivity of this coefficient to land productivity is illustrated by the graph shown in figure 2 with a dotted line. 73from subsidy to land rent: how production-linked payments shape factor remuneration in agriculture bio-based and applied economics 14(2): 67-78, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16734 in figure 1, the value of the agricultural subsidization coefficient for a specific homogeneous unit plot is the ratio of the vertical distance between the mr₀ line and the mr₁ line to the vertical distance between the horizontal axis and the mr₁ line. meanwhile, the value of this coefficient for a farm at equilibrium point e₁ (i.e., using an amount of land equal to l₁) is the ratio of the area of quadrilateral bde₁c to the area of trapezoid ol₁e₁c. 4.2.2 coefficient of land rent financing by the state based on figure 1, it can be stated that productionlinked support fully contributes to land remuneration in the case of land that was already being used for agricultural purposes even without this support (up to l₀ inclusive). however, for land that was incorporated into the production process only after the introduction of production-linked payments at rate prv (to the right of l₀, up to and including l₁), production-linked support partially contributes to land remuneration and partially to the remuneration of other production factors. it can be observed that, as one moves along the horizontal axis of the coordinate system to the right of l₀, an increasingly smaller part of the support linked to production goes towards the remuneration of land, while the importance of this support in creating the remuneration of labor and capital is growing. this means that, as land productivity declines, the market’s share in remunerating labor and capital decreases, while the state’s share increases. in the extreme case of the marginal unit plot l₁, productionlinked support fully increases the remuneration of labor and capital while the land rent is zero. to measure what portion of land remuneration is financed by the state, the concept of the coefficient of land rent financing by the state (clrf) can be introduced, expressed by the formula: (2) where: prv – the production-linked payment rate (expressed in monetary units per unit of mass of the produced (and sold) agricultural product, e.g., in eur/t); v – the volume of supported agricultural products (expressed in units of mass, e.g., in tons); tr₁ – the total revenue from the production of a given mass of agricultural products, including revenue from the sale of those products and revenue from productionlinked payments (expressed in monetary units, e.g., in eur); tc – total cost, i.e., the inputs of production factors other than land in relation to a given area of land (expressed in monetary units, e.g., in eur). like the agricultural subsidization coefficient, the coefficient of land rent financing by the state is a dimensionless value and can take any value from the closed interval between 0 and 100%. referring to figure 1, it can be noted that for unit land l₀ and land to the left of it, the state’s share in financing land rent is expressed by the ratio of the vertical distance between the mr₀ line and the mr₁ line to the vertical distance between the mc line and the mr₁ line. this ratio remains constant as one moves to the right along the horizontal axis. for land located to the right of l₀ (up to and including l₁), the state’s share in financing land rent is 100% (since, for this land, both the numerator and the denominator of the fraction expressing this share are the same number corresponding to the vertical distance between the mc line and the mr₁ line), although it does not change the fact that, in absolute terms, land rent decreases as one moves to the right along the horizontal axis of the coordinate system. the graph in the form of a dashed line in figure 2 illustrates how the value of the coefficient of land rent financing by the state changes depending on the agricultural suitability of the land. for the entire farm at equilibrium point e₁ in figure 1, the state’s share in financing land rent is expressed by the ratio of the area of quadrilateral be₀e₁c to the area of triangle ae₁c. 4.2.3 payment-to-land rent conversion coefficient the payment-to-land rent conversion coefficient (clrc) indicates what portion of the financial support provided by the state contributes to the increase in land l clrf caas l1l0 clrc 0% c 100% figure 2. values of the indicators of the impact of productionlinked payments on the distribution sphere, depending on the agricultural suitability of land. source: author’s own elaboration. 74 bio-based and applied economics 14(2): 67-78, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16734 adrian sadłowski rent. this indicator can be expressed by the following formula: (3) where: δlr – the increase in land rent caused by the introduction of production-linked payments (expressed in monetary units, e.g., in eur); prv – the production-linked payment rate (expressed in monetary units per unit of mass of the produced (and sold) agricultural product, e.g., in eur/t); v – the volume of agricultural products supported (expressed in units of mass, e.g., in tons). like the indicators expressed in formulas (1) and (2), the payment-to-land rent conversion coefficient is dimensionless, and its possible values range from 0% to 100%. based on figure 1, it can be stated that for land used agriculturally even in the absence of productionlinked support (up to and including l₀), the value of this coefficient is 100% (both the increase in land rent and the amount of support paid in relation to production generated on a given unit plot are reflected by the vertical distance between the mr₀ line and the mr₁ line, so the quotient of these two values is one). for land that was incorporated into the production process only after the introduction of production-linked payments at rate prv (to the right of l₀, up to and including l₁), this coefficient is expressed by the ratio of the vertical distance between the mc line and the mr₁ line to the vertical distance between the mr₀ line and the mr₁ line. for land within this range, the coefficient is therefore less than 100% and decreases as one moves right along the horizontal axis of the coordinate system, reaching zero for the marginal unit of land l₁. observing the graph in the form of a solid line in figure 2, one can see how this coefficient changes depending on the agricultural suitability of the land. the value of the payment-to-land rent conversion coefficient for all land included in the farm at equilibrium point e₁ in figure 1 can be calculated as the percentage ratio of the area of quadrilateral be₀e₁c to the area of quadrilateral bde₁c. 4.2.4 the phenomenon of “support capture” and its measurement in cases where the land user is not the owner, land rent takes the form of lease rent. a consequence of production-linked payments at least partially converting into land rent is the phenomenon of support being “captured” by landowners through raising lease rent or land sale prices accordingly. in the event of a discrepancy between ownership and use of land, the measure of the degree to which production-linked payments are “captured” by landowners is the payment-to-land rent conversion coefficient (clrc). the “capturing” of financial support granted to farmers (land users) by landowners is manifested through increased lease rent rates and higher prices for agricultural land, i.e., the capitalization of payments. this occurs when the landowner is not the same as the land user, and when the land is subject to market transactions. “capturing” the payments involves incorporating part or all of the support into the lease rent (in the case of leasing) or the land price (in the case of sale), as a consequence of the increased discounted revenues from agricultural land due to the application of financial support instruments for agriculture. the increase in the stream of discounted revenues from production-linked payments (∆disvp) can be calculated using the following formula: (4) where: v – the volume of agricultural products supported (expressed in units of mass, e.g., in tons); clrc – the payment-to-land rent conversion coefficient (a dimensionless quantity); prv – the production-linked payment rate (expressed in monetary units per unit of mass of the produced (and sold) agricultural product, e.g., in eur/t); r – the annual interest rate; (n+1) – the number of years of payment application. the increase in lease rent for a given year as a result of the introduction of production-linked payments corresponds to the increase in the annual revenue stream caused by the introduction of these payments, whereas the entire increase in the future stream of discounted revenue is capitalized in the land price. therefore, the first term on the right-hand side of equation (4) represents the theoretical increase in lease rent during the first year of payment application, while the entire sum represents the theoretical increase in land price, assuming the land was sold at the moment the payments were introduced. the scale and intensity of the “capture” of production-linked payments by landowners depend not only on the predicted future revenue stream from this form of financial support by the potential parties to the agreement (lease or sale). various institutional factors also play a significant role in this context. in particular, the 75from subsidy to land rent: how production-linked payments shape factor remuneration in agriculture bio-based and applied economics 14(2): 67-78, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16734 long-term nature of lease agreements and their inflexibility result in inertia in lease rent rates (góral and kulawik, 2015), and legal restrictions on the sale of agricultural real estate may slow down the process of payment capitalization into land prices (sadłowski, 2017). 5. discussion this study aligns with the theoretical research on the economic effects of using various financial support instruments in agriculture, which includes among others the works of chau and de gorter (2005), kilian and salhofer (2008), and graubner (2018). the issue of use of production-linked payments remains relevant and important, which stems from the need to determine the potential usefulness of this instrument in addressing current agricultural problems – especially as agriculture operates in an increasingly turbulent environment (despoudi et al., 2020; budzyńska and kowalczyk, 2024). this requires recognizing and quantifying the economic effects of using production support, as well as identifying the conditions for its effectiveness and efficiency in achieving the set objectives. the economic effects of using production-linked payments relate to both the production sphere (influence on the level of engagement and directions of use of production factors in agriculture, the volume and structure of agricultural production, and relative prices of agricultural products) and the distribution sphere (influence on the amount and structure of remuneration for production factors). the added value of this study is manifested in three dimensions: cognitive, practical, and methodological. the recognition of the mechanism by which productionlinked payments stimulate the input of production factors in agriculture and the mechanism by which subsidies granted in the form of production-linked payments are transformed into the remuneration of production factors has cognitive value. the model for transforming production-linked payments into the remuneration of production factors can serve as a starting point for econometric research aimed at predicting the economic effects of regulations introduced under agricultural policy (ex-ante evaluation) and measuring the effectiveness and efficiency of agricultural policy instruments (ongoing or ex-post evaluation). the knowledge obtained from such research facilitates the design of agricultural policy tools and the adaptation of instruments to changing socio-economic conditions or revised political objectives. the study also contributes to the development of terminology concerning the economic aspects of direct payments, which promotes the development of methodology and, consequently, the acquisition of more precise and reliable knowledge. the limitations of the research result in particular from its theoretical nature, scope and adopted assumptions. the credibility of the formulated statements results from their methodical derivation while demonstrating logical connections of consequences as part of the ongoing reasoning. however, the conclusions resulting from the model were not included in the form of hypotheses in order to be tested using statistical methods and empirical data. the study was limited to the analysis of the effects of financial incentives, while the motivations for production decisions of farms may be more complex. assumptions about price formation and market structures may preclude the extrapolation of results to agricultural systems with significantly different market realities. 6. conclusions the key conclusions from the theoretical research conducted are as follows: 1. as a result of the application of the direct support system, production factors involved in agriculture generate remuneration exceeding the cash equivalent of agricultural products produced by farms. 2. production-linked payments encourage both more intensive land use and the cultivation of less fertile or more peripherally located land. 3. the agricultural subsidization coefficient measures the relative level of support, remaining constant when payment rate and agricultural product price are independent of production volume. 4. the state’s role in financing land rent grows as land productivity decreases, reaching 100% for marginal land brought into production due to these payments. 5. if payments influence rental rates, landowners “capture” the support, also reflected in land prices; this “capture” is initially limited by rigid rental agreements and legal constraints on land transactions. 6. unlike area-based support, production-linked payments do not strongly drive rental rate increases but are more susceptible to “capture” by buyers in the supply chain. although production-linked payments are not currently used in the cap, the presented model remains valuable for policymaking in the eu, as cap revisions or trade agreement renegotiations remain possible. it enables comparisons with other support tools, helping assess their effectiveness under different conditions. given the increasing instability in agriculture due to eco76 bio-based and applied economics 14(2): 67-78, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16734 adrian sadłowski nomic crises, wars, and rising imports (e.g., from mercosur), 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(2010). the political economy of the most radical reform of the common agricultural policy. german journal of agricultural economics, 59(1): 37–48. https://doi.org/10.52825/gjae.v59i1.1803 tangermann, s. (2011). direct payments in the cap post 2013. european parliamentary research service. belgium. available at: https://coilink.org/20.500.12592/ jmn1cs (accessed 28 december 2024). https://doi.org/10.52825/gjae.v59i1.1803 https://coilink.org/20.500.12592/jmn1cs https://coilink.org/20.500.12592/jmn1cs reflections and new directions: an editorial retrospective and the launch of our new policy paper section silvia coderoni, fabio bartolini where is the italian agriculture heading? a discussion in light of the prospects for the future cap filippo arfini1*, fabio bartolini2, anna carbone3, tatiana castellotti4, silvia coderoni5, raffaele cortignani6, raffaele d’annolfo4, giovanni dara guccione4, michele donati1, francesca galli7, roberto henke4, giampiero mazzocchi4, alessandro monteleone4, towards the knowledge and innovation system for the bioeconomy? giacomo maria rinaldi*, davide viaggi impact of weather variability on crop yields and land use dynamics in eastern india: shortand long-term effects pratap kumar jena1, kirtti ranjan paltasingh2, souryabrata mohapatra3,,*, ashok mishra4 rebuilding strategies for food self-sufficiency: portugal’s past patterns and future ambitions alexandre macedo joão1, dulce freire2, humberto rocha3, joana dias4 from subsidy to land rent: how production-linked payments shape factor remuneration in agriculture adrian sadłowski expenditure allocation for rural development interventions: main trends and patterns in the choices of the italian regions under the cap 2023-2027 tobia minuzzo, francesco pagliacci* bio-based and applied economics bae copyright: © 2023 i. prazeres, m.r. lucas, a. marta-costa, p.d. henriques. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae bio-based and applied economics 12(1): 37-52, 2023 | e-issn 2280-6e172 | doi: 10.36253/bae-13473 citation: i. prazeres, m.r. lucas, a. marta-costa, p.d. henriques (2023). organic cocoa farmer’s strategies and sustainability. bio-based and applied economics 12(1): 37-52. doi: 10.36253/ bae-13473 received: july 30, 2022 accepted: march 20, 2023 published: june 24, 2023 competing interests: the author(s) declare(s) no conflict of interest. editor: davide menozzi, linda arata. orcid ip: 0000-0001-6833-5155 mrl: 0000-0002-5731-767x am-c: 0000-0001-9247-9167 pdh: 0000-0002-9646-3223 organic cocoa farmer’s strategies and sustainability ibrahim prazeres1,*, maria raquel lucas1, ana marta-costa2, pedro damião henriques3 1 cefage center for advanced studies in management and economics, portugal 2 cetrad centre for transdisciplinary development studies, portugal 3 med mediterranean institute for agriculture, environment and development, portugal *corresponding author. e-mail: gibaedy@gmail.com abstract. são tomé and príncipe (stp) is one of the world’s smallest organic cocoa exporting countries, whose product has a positive socio-cultural and economic impact. small producers who ensure it, are associated into two cooperatives that experience several difficulties and dilemmas including climate changes and poverty. diversification of livelihood strategies could lead to wellbeing, poverty and climate mitigation. the aim of this study was to analyse producers’ perception of sustainability related to the organic cocoa production in stp and to explain the influence of different factors on their livelihood strategies (ls). an ordered probit model for disaggregation of factor categories was used for the 2021 period. the results showed that gender, age, family size, members on-farm and off-farm work and professional training courses do not influence livelihood strategies. the important variables for them are education level, perception of social class, insurances and loans and access to services. keywords: households decisions, crop diversity, dependence, ordered probit model, well-being. jel codes: q12, q56, o13. 1. introduction there is an overall consensus about the sensitivity of agriculture to climate neutrality (tol, 2018; piedra-bonilla et al., 2020) and the importance of sustainability to achieve its goals and to meet consumer expectations and farms’ profits (menozzi et al., 2015). however, while the environmental and economic dimensions of sustainability have been theorized more robustly (hovardas, 2021; purvis et al., 2019), the social dimension, which is context-specific and inherently subjective (boyer et al., 2016), has lacked comprehensive approaches, notably in rural areas (gaviglio et al., 2016). according to rasmussen et al. (2017), only 25% of the scientific articles dedicated to sustainability in agricultural production consider the social dimension, and the most used indicators in this http://creativecommons.org/licenses/by/4.0/legalcode 38 bio-based and applied economics 12(1): 37-52, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13473 ibrahim prazeres et al. field are related to the farm labour, quality of life and well-being, and the relationship with the human community (marta-costa et al., 2022). the lack of an approach to social sustainability in studies on developing countries, where poverty is the most serious problem, could compromise the performance of the two others pillars (prazeres et al., 2022a), since the relationships among the three dimensions is generally assumed to be compatible and mutually supportive (boström, 2012; chopin et al., 2021). there are several studies in the literature that reveal the problems and challenges faced by smallholder farmers affecting the production system. these problems come as a result of isolation, small farm size, low levels of technology, innovation and productivity due to farming systems under traditional practices (prazeres et al., 2021; díaz-montenegro et al., 2018), climate changes (piedra-bonilla et al., 2020) and a failure to attract young people and ensure farm succession and/or rejuvenation (anyidoho et al., 2012; henning et al., 2022). additionally, these farmers are constrained by limited financial, natural, health and educational resources, scarce governance and/or organisational support, and pressure to use land with alternative crops or activities, which are more profitable (prazeres & lucas, 2020; prazeres et al., 2021). additionally, they must adapt to severe crop losses due to disease and, very often, they need to consider other activities when making the choices on their livelihood strategies (tittonell, 2014; valbuena et al., 2015; walelign, 2016; walelign & jiao, 2017). thus, the sustainability social pillar makes the search for livelihoods a priority in order to reduce poverty and increase the farms’ wellbeing. in são tomé and principe (stp), agriculture comprises a third of the active population and cocoa activity contributes to over 90% of the national exports, standing out from other export products such as coffee, coconut, flowers, pepper and other spices. in addition to the high amount of cocoa as exported goods (signoret, 2019) and its contribution to the gdp (21%), organic cocoa production (ocp) leads the international country image and guarantees the livelihood of many poor families, by creating jobs and developing local economies (prazeres, 2019). approximately three thousand and three hundred organic small producers are integrated into the existing two cooperatives (cecab and cecac11). there are also organic private companies with their own production, from which satocao and diogo vaz are the most relevant, the latter having its own chocolate factory and shops (prazeres, 2019). the sustainability of ocp in stp matters considering its impact on the agro-ecological system, the social and environmental context of the producing communities, the economic viability of the activity, and the farmer wellbeing, as well as, the viability of the consumer market, which directly relates to consumer trust in the ocp and consecutive willingness to pay a premium for such (prazeres, 2019). this paper attempted to explore the nexus between livelihood strategies and sustainability perception, households’ organic cocoa dependency, and poverty. the livelihood strategies formed the basis for categorising producers based on households’ structure and crop diversification. the paper was organised into five sections. the following section presents background information on sustainability, poverty and livelihood strategies. the third section describes the empirical strategy and econometric specification, while the fourth section exposes and discusses the findings. the final section is dedicated to the conclusions and policy and its practical implications. 2. background sustainable development has become a global pursuit to the agricultural sector due to increasing greenhouse gas emissions and depletion of natural resources needed for agricultural activities (bekun et al., 2019; sarkodie & strezov, 2019; food and agriculture organisation [fao], 2014). these challenges are furthered by the social and economic pressures that arise in a globally competitive environment (iocola et al., 2018; ramos, 2019; santos et al., 2019; vasileiou & morris, 2006; velten et al., 2015), such as rising input prices, labour supply instability, relationships with the end-product market and food safety concerns, which further evidence the need to implement sustainable practices (christ & burritt, 2013). elkington (1994)’s triple bottom line theory is often regarded as the most well-known and comprehensive theoretical model used in the sustainable development approach (hayati, 2017). this theory argues that people, planet and profit are imperative principles of sustainability and promotes the idea that sustainable development occurs when organisations demonstrate responsibility towards environmental health, social equity and economic viability (hayati, 2017; iyer & reczek, 2017). the geographic context takes particular importance in the sustainability paradigm, for which locally configured institutional and biophysical processes shape the criteria and scope of the analyses. therefore, livelihood strategies need to be seen in light of the extent of the resources’ constraints and their availability, which 39organic cocoa farmer’s strategies and sustainability bio-based and applied economics 12(1): 37-52, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13473 support communities in achieving livelihood objectives (chilombo & van der horst, 2021). for instance, the poverty evidenced in rural areas of lowand middle-income countries, that hinders individual and community capacities to meet basic needs, stands out as a multidimensional global challenge to sustainable development (alemie et al., 2022). in these areas, about 90% of the people depend on agriculture for their livelihoods (fao, 2005; ifad, 2011; roser, 2015; mphande, 2016 in alemie et al., 2022), making it urgent to seek strategies that promote the sustainability of agroecological systems and support improvements in the social and environmental context of producing communities (prazeres et al., 2019). the concept of sustainable livelihood appeared in the 1980s (chambers & conway, 1991), and remerged in chilombo and van der horst (2021) and has become a classic paradigm for the study of household livelihoods (kuang et al., 2020). it is focused on coping strategies intertwined with livelihood activities that are linked to the exploitation of land-based resources in rural communities (kuang et al., 2020). several studies have been conducted on the livelihood strategies that affect the interaction of sustainable dimensions, specifically in the african context and the agricultural sector. alemie et al. (2022) identified complex interdependencies between livelihoods and the regulatory supply and cultural ecosystem services, which create bottlenecks to effectively ‘block’ poverty in ethiopia, where 85% of the population are subsistence farmers dependent on local ecosystem services. the research by berhanu et al. (2022) found that an asset-based social policy improves the well-being of poor and vulnerable subgroups and chilombo and van der horst (2021) define assets in terms of human, natural, physical, social and financial capital and capabilities. the capital assets in conjunction with the activity variables and the outcomes, constitute the three closely connected components in which several studies focused on smallholder farmers are concentrated (ellis, 2000; winters et al., 2009; nielsen et al., 2013; walelign & jiao, 2017). empowerment and community involvement play an important role in this context (arroyo, 2013). the achieved livelihood strategies’ outcomes increase income, multidimensional wellbeing and a more sustainable use of natural resources (babulo et al., 2008). however, no single livelihood strategy provided both optimal economic advantages and ecological sustainability (ghazale et al., 2022). even when the households’ choices induced similar livelihood activities, the time or capital used on the diverse livelihood activities may be different (walelign & jiao, 2017). still in this sustainable perspective, deng et al. (2020) forward three determinants of livelihood sustainability – livelihood basis, livelihood acceleration and livelihood environment linked with “starting force”, “driving force” and “supporting force,” respectively, which support different levels of livelihood performance and dynamic processes of livelihood sustainability. the livelihood strategies are changing over time (walelign et al., 2017) originating the livelihood transition or mobility (zhang et al., 2019). according to zhang et al. (2019), the assessment of the factors that affect this transition has strong implications on poverty reducing policies and achieving livelihood sustainability in the long run. since livelihood is composed and conditioned by many factors, including ecology, economy, society and institution (zhao, 2017), sustainable livelihood development is affected by the combined action of many elements (deng et al., 2020). the farmers’ decisions on agricultural production that are based on the livelihood assets, also support families in coping with livelihood vulnerability and risks (fang et al., 2014; liu et al., 2018; jalón et al., 2018; and kuang et al., 2020). in order to deal with natural threats and market risks, farmers try to adjust crop diversity, water and fertiliser management as well as agricultural financial and agrotechnical support (kuang et al., 2020). 3. methods seemingly, cocoa production connects smallholder farmers and their families or representatives in producer countries, to a global value chain and markets, driven by a strong, consistent and increasing demand for chocolate. the global chocolate market size was estimated at usd 113,16 billion in 2021 and is anticipated to grow at a compound annual growth rate (cagr) of 3,7% from 2022 to 2030 (gvr, 2021). the main characteristics of this worldwide value chain are the asymmetric power relations with increasing control by a few (5) corporations which make the big decisions (diaz-montenegro et al., 2018). in reality, there is a great geographic distance between highly atomized producers and the consumption markets, and cocoa producers are ignorant on consumer’s preferences and their choices (prazeres, 2019). additionally, there is price volatility and dependency, albeit no solid connection, on five big companies which control the market and the cocoa supply worldwide. consequently, an asymmetric distribution of value occurs, with cocoa producers receiving only 5% 40 bio-based and applied economics 12(1): 37-52, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13473 ibrahim prazeres et al. of the price paid by the final consumer, while marketing and industry activities seize 25% and sales of retail chocolate capture 70% of the profits (fountain & huetzadams, 2020; squicciarini & swinnen, 2016; abdulsamad et al., 2015). this situation is responsible for several of the problems and challenges faced by producers, one of which is poverty. livelihood strategies are responses to farmer’s decisions to face these problems, which are inf luenced by several factors, such as crop diversification, resources allocation (rahman, 2016), climate changes (rahman, 2016; mu et al., 2018), soil fertility, biodiversity loss, real estate pressure through land use (prazeres, 2019), and trust on farmers’ organisations and their bargaining power (prazeres et al., 2021). in stp, where agriculture comprises a third of the active population, there are two models of cocoa production: conventional with a total yield production of 2,488 tons in 2017, which is very dependent on the prices of the new york stock exchange, and the certified production method (total yield production of 1,065 tons in 2017) as organic or organic plus fair trade (eu, 2021). it is expected that external economic factors, such as market prices and support as well as internal factors such as physical, social, human or natural capital, could influence producer’s decisions to choose cocoa or other crops. prazeres et al. (2022b) identified three livelihood strategies of ocp in stp (organic cocoa mono-crop livelihood strategy, diversified livelihood strategy with two crops organic cocoa and banana or other and, pluriactivity livelihood strategy combining organic cocoa with three or more crops). these livelihood strategies are mainly related to the allocation of capital assets and income variables. families with a low proportion of allocated land had higher income diversification strategies and vice versa. the study also showed that understanding how cocoa producers seek different approaches, could help envisage livelihood strategies as a way of increasing income and producers’ wellbeing, as well as alleviate poverty. also, increases in livelihood can be used by producers for consumption, commercialization or conversion into livelihood assets (zhang et al., 2022). 3.1 statistical model the diversity of livelihood strategies can be compared and the effect of different categories of factor variation can be found without the problem of selection bias. hence, the causal relationship among those factors will be controlled following general models presented in the literature (dusen et al., 2005; benin et al., 2004; piedra-bonilla et al., 2020), in which livelihood strategies election is affected by factors that could be gathered as social, economic and agroecological. thus, an ordered probit model was estimated in which the variable to be studied was the livelihood strategies, measured on a scale of three points (ls1=mono-crop, ls2=bi-crop, ls3=multi-crop). this model can be represented as follows: lsi *=xi’β+εi, εi~nid(0,1) lsi=1 if lsi *≤γ1 lsi=2 if γ1<lsi *≤γ2 (1) lsi=3 if lsi *≥γ2 in which lsi represented the livelihood strategy i and, γ1 e γ2 were parameters to be estimated in conjunction with β. the estimation of the model was based on the maximum probability of occurrence and the interpretation of the coefficient was done in terms of the latent variable or in terms of the effects on the respective probability. for example, βj>0 meant that the latent variable ls*i increase if xij increases. thus, the probability of ls3 (multi-crop) increased while the probability of ls1(mono-crop) decreased. the effect on the intermediate category was however ambiguous as it p (lsi=2 | xi) could increase or decrease. 3.2 data collection a survey was conducted from june to december 2021 on a sample set of 810 farmers involved in the ocp in stp through cooperatives. the selection criteria were both, the cooperative proposals and the availability of the producer to cooperate with the research. compliance with the general data protection regulation was assured throughout. the participants were informed about the use of the information, their rights, and their responses were anonymized. all of the contacted ocp producers were members of one of the two cooperatives (cecab created in 2004, operational from 2005 and autonomous since 2012, and cecac11 created in 2011), which represent the main interface between farmers and the chocolate industry or their representatives or signed a contract with one of the two private companies. both cooperatives are funded by the fund for the development of agriculture (ifad) and the project to support commercial agriculture (papac) and they are supported by various non-governmental organizations as well as the center for agricultural and technological research (ciat). each of the cooperatives brings together different associations organized by geographic zones, which receive the cocoa seed from farmers on two distinct periods (august-september 41organic cocoa farmer’s strategies and sustainability bio-based and applied economics 12(1): 37-52, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13473 and february-march). the training of the farmers and motivation strategies to guarantee the levels and quality of organic cocoa production are carried out by the cooperatives, which also train technicians from the associations that form them and to which the producers belong, these technicians, in turn, then train the farmers. an important role is played by the so-called “sociotechnicians”, who are producers with good performance in the cocoa culture and who monitor other farmers and are remunerated for this task. in reality, these socio-technicians end up replacing the role of the extension services that the state was responsible for ensuring. in addition to strictly agricultural work, the cooperatives develop other actions, such as sociorecreational activities in the communities, inviting specialists who contribute to raising awareness among farmers on various topics (domestic violence, gender equality, alcohol consumption, diseases), financing small social works in the communities and providing support to the neediest (medicines, eyeglasses, coffins). the registration of all information is done manually at the level of the associations and the computerization is done by each cooperative. the study area included the most significant ocp districts and rural communities in stp, namely all the districts in the country, with the exception of caué, pagué and santo antónio – districts in the principe island – because they were not ocp certified members of the cooperatives. as shown in figure 1, the survey was conducted in different steps, starting with 25 preliminary qualitative interviews with 4 cooperatives representatives and other stakeholders (4 distributors and/ or exporters, 2 certification bodies, 3 private companies, 5 sociotechnicians, 2 researchers, 4 government agencies) and the establishment of 10 focus groups of 20 participants (farmers), so to specifically capture the individual and collective perception of the sustainability concept and its main drivers and challenges. then, a questionnaire based on the livelihoods adapted from diaz-montenegro (2019) was applied to the organic cocoa producers, structured in three main sections. the first was dedicated to the characterisation of the household and the farm and incorporated five topics related to: human capital (16 questions on the characterisation of the family and its relation to the farm), natural capital (16 questions on used land and produced crops ), physical capital (4 groups of questions about machinery, equipment and support infrastructures), financial capital (6 questions about financing sources), and social capital (12 questions on partnerships and cooperation and enjoyed benefits);. the second session was devoted to 2) risk perception and attitude and considered the probability of occurrence, their impact severity and degree of control of 19 events identified from both the literature and the country context. this group also included two questions dedicated to the management and tool preferences for risk management, comprising 12 options taken from the literature and the analysis context, and an open question where other options could be considered, namely for the future. the perceive value of joining an ocp cooperative was considered as the last section by including 12 options for assessing the benefit and cost of working with the cooperative. the reduced version of the perval scale (walsh, shiu & hassan, 2014) was explored in this context. this reduced version included 12 items (either observed or manifested variables or indicators, structured from ordinal variables with 7 likert-type response categories, in which 1 meant the highest degree of disagreement and 7 the highest degree of agreement) related to four constructs (or dimensions, latent variables or factors) that underlie the abstract and multidimensional concept of value: functional value, emotional value, social value and monetary value. in the beginning of the questionnaire, a request of participation was highlighted alongside an explanation of the study’s purpose and the guidelines to fulfil the questionnaire, so to prepare and commit the participants to the survey. participants could fill the questionnaire in two ways: direct interview in person or through a paper questionnaire due to return and collect two days after. a total of 838 questionnaires were completed, 180 by paper and the remaining face-to-face. after the removal of 28 incomplete questionnaires, the final sample consisted of 810 respondents. figure 1. analysis design. 42 bio-based and applied economics 12(1): 37-52, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13473 ibrahim prazeres et al. 3.3 sample characteristics figure 2 summarises some of the statistics of surveyed smallholders, by livelihood strategies. table a1, in appendix, presents the description of all the characteristics of the sample set, which was almost equally distributed between the two cooperatives. most of the participants of the sample were male, while 33% of the farmers were females and 52,2% belonged to cecab. the livelihood strategies identified were differentiated by the number and proportion of farmers engaged in growing organic cocoa (with or without other crop combinations), and their ways of allocating resources (14,2% concerned the proportion of farmers who engaged solely in organic cocoa growing, in mono-crop livelihood strategy ls1, 63,5% were involved in a diversified livelihood strategy (ls2) with two crops (organic cocoa and banana), and, 22,2% were engaged in a multi-crop livelihood strategy (ls3), which were combined three or more crops and livelihood activities. the ocp area for the sample was on average 1,95 hectares, with the highest surface value of 12,5 hectares and the lowest value of 0,5 hectares. the average household size varied from 3,6 members in mono-crop to 4,8 in multicrop and 4,2 in bi-crop livelihood strategies. 4. results and discussion the estimation of equation (1) using an ordered probit model yielded the results shown in table 1. the statistical results related the dependent variable livelihood strategy (ls1=mono-crop, ls2=bi-crop, ls3=multi-crop) with the explanatory variables . the explanatory variables were grouped in human, financial and economic, natural, physical and social capital as well as in risk perception and management and perceived value. regarding human capital explanatory variables, the level of education and perception of social classes influence the livelihood strategies. farmers of the mono-crop strategy have higher level of education than multi-crop farmers. in fact, the greater the level of education, the lower the probability of belonging to multi-crops and the greater the probability of belonging mono-crop strategy. as other studies sustained (balogh, 2021, reimers and klasen, 2011; hernández-núñez et l., 2022), probably this is because a higher level of education leads to decisions involving greater productive efficiency, being mono-crop suitable for these choices because it is more efficient than multi-crop. in the specific stp context, sequeira et al. (2022) concluded that improvements into production systems lead to increased family income and help to cross poverty line. in contrast to education level, the livelihood strategy has a positive relation to social class perception. farmers of the multi-crop strategy have a perception of belonging to higher social class than farmers of the mono-crop strategy. this does not seem compatible with the study of irfany et al. (2020) where social class does not influence livelihood strategies. however, the result obtained could be related to the fact that an increased social class perception allows for a belief of being under better economic conditions which is in turn beneficial to the production of organic cocoa in multi-crop (ls3). although not significant, there is a higher probability for mono-crop strategy to have female and younger farmers and a lower number of on-farm family members while family size, professional training courses and number of off-farm family members are higher for multi-crop livelihood strategy. despite ocp being the main activity in the three ls, farmers also engage in different income generating activities, such as off-farm employment. the explanation for that could be related to the fact that which enables them to build better assets, increase economic sustainability and could start becoming integrated production systems (gebru et al., 2018). additionally, off-farm self-employment is one of the variables that significantly improves welfare but has lower probability of existing in mono-crop (irfany et al., 2020). however, in the existing results concerning off-farm the employment, the greater the number of off-farm work members, the greater the probability of selected figure 2. summary of characteristics of the respondents. 43organic cocoa farmer’s strategies and sustainability bio-based and applied economics 12(1): 37-52, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13473 table 1. results of the probit model for livelihood strategies. coefficient standard error z p-value human capital gender (f) -0,236 0,184 -1,286 0,198 age -0,007 0,007 -1,106 0,269 family size 0,054 0,045 1,201 0,230 education level (el) -0,616 0,179 -3,447 0,001*** number of professional training courses 0,026 0,125 0,206 0,837 members on-farm work -0,026 0,117 -0,224 0,823 members off-farm work 0,228 0,147 1,549 0,121 perception of social class (sc) 0,674 0,130 5,182 <0,0001**** financial and economic capital income from agricultural selling 0,007 0,004 1,600 0,110 income from subsidies (human development and others) and remittances from emigrants -0,082 0,360 -0,229 0,819 insurances and loans (il) 0,929 0,239 3,891 <0,0001**** natural capital cocoa area 0,266 0,212 1,256 0,209 cocoa production 0,000 0,000 -0,280 0,779 banana area -0,200 0,195 -1,026 0,305 banana production 0,000 0,000 1,629 0,103 physical capital access to potable water -0,346 0,189 -1,824 0,068* access to electricity -0,217 0,561 -0,387 0,699 access to harvest storage (hs) 1,708 0,651 2,621 0,009*** access to transportation -0,732 0,373 -1,960 0,050** access to roads -0,292 0,187 -1,555 0,120 access to landline 0,316 0,399 0,792 0,428 access to mobile phone (mf) 1,791 0,396 4,520 <0,0001*** access to internet 0,470 0,207 2,272 0,023** access to tv and radio 0,694 0,380 1,825 0,068* access to health center hc) -2,426 0,548 -4,428 <0,0001*** access to schools -0,445 0,258 -1,723 0,085* access to extension services (es) -0,895 0,291 -3,077 0,002*** social capital belong to cecab -0,490 0,217 -2,260 0,024** satisfaction with cooperatives 0,530 0,248 2,131 0,033** trust level in neighbours 0,033 0,125 0,261 0,794 trust level in civil organizations 0,119 0,148 0,804 0,421 trust level in agricultural organizations -0,031 0,101 -0,312 0,755 trust level in district council -0,797 0,622 -1,280 0,201 trust level in local council 1,243 0,614 2,024 0,043** trust level in cooperatives (tc) -0,875 0,243 -3,603 0,000*** trust level in government -0,240 0,259 -0,929 0,353 risk perception and management perception of the likelihood of risks occurring (lr) 0,499 0,161 3,094 0,002*** perception of risk impact severity -0,507 0,221 -2,297 0,022** perception of the degree of self-control of the impact -0,084 0,259 -0,323 0,747 perception of the importance of risk management tools -0,165 0,140 -1,181 0,238 44 bio-based and applied economics 12(1): 37-52, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13473 ibrahim prazeres et al. ls3 (multi-crop) and the lower the probability of having ls1 (mono-crop). in the case of on-farm work, the greater the number of on-farm work members, the lower probability of selected ls3 (polyculture) and greater the probability of having ls1 (mono-crop). this is because mono-crop depend mainly on familiar work than external work. despite external work income being a significant source of income (bjornlund et al. 2019; pritchard et al. 2019), it is associated with greater risks and thus, has a negative impact on the well-being of households (nielsen et al. 2013; bjornlund et al. 2019). concerning economic and financial capital, the results obtained for insurances and loans show that the probability of multi-crop livelihood strategies having insurance and loans is higher than de mono-crop strategies as well as the proportion of income from agricultural sources. in general terms, these results are compatible with those found in irfany et al. (2020)’s study, which displayed that cocoa producers, predominantly males, depended on loans, despite the fact that only a few have accessed formal loans. to ankrah et al. (2023), reducing loan interest rates can foster financial inclusion. in stp, loan interest rates are very high and the ocp have difficulty to access formal banks. this is very important because other significant determinants of livelihood practices were, for instance, access to formal credit for self-employment, among others. also kuang et al. (2020) exposed that farmers’ social, financial and human assets can mitigate their livelihood risks in agricultural production, while their social, natural and physical assets have positive effects on the adoption of the strategies. however, natural and physical assets have the opposite effects in livelihood risks such as the human and financial assets have relatively weak influences in the adaptation strategies (kuang et al., 2020). the livelihood strategies are not related with natural capital explanatory variables, namely, area and production of cocoa and banana. these results were also in line with those found in andres et al. (2016), particularly when dynamic agroforestry systems are introduced on a small scale. for the authors, through mimicking natural forests, these systems offer multiple benefits such as soil fertility enhancement, reduction of pests and disease pressure, erosion control, and revenue diversification. very often, the diversification is induced by income-generating activities to smooth income, accumulate wealth and reduce exposure to risk (sun et al., 2019). physical capital explanatory variables show in a clear way that access to potable water, transportation, health centers, schools and extension services are higher for mono-crop farmers than for multi-crop farmers while access to harvest storage, mobile phone, internet and tv and radio are higher for multi-crop farmers. it is clear that mono-crop farms have better access to statedependent infrastructures, possibly due to the location of agricultural enterprises, while multi-crop farms have better access to services that depend on individual decisions and consumption. according pereira et al. (2022), development programs implemented in stp to improve infrastructure and agricultural production, made a positive contribution to the well-being of rural households. similar results found trigueiros et al (2022) emphasizing the importance of this investments programs to improve socio-economic development and households sustainability. the perception of the importance of this public policies are more valued by male than female (pereira et al, 2022). regarding risk perception and management of events that affect agricultural production and family income, the results show that livelihood strategies are different for the perception of events occurring, being this perception higher for multi-crop than for monocrop farmers and, for severity of events, the mono-crop livelihood strategy have higher severity perception than multi-crop farmers. thereby, adverse events are less perceived by mono-crop which value more the severity of coefficient standard error z p-value perceived value scale (perval) perception of the functional value to joining a cooperative (cfv) -0,589 0,189 -3,109 0,002*** perception of the emotional value joining a cooperative (cev) 0,481 0,180 2,667 0,008*** perception of a social value joining a cooperative (csv) 0,702 0,253 2,773 0,006*** perception of a monetary value joining a cooperative (cmv) 0,271 0,243 1,114 0,265 e log. of likelihood = −249.071 likelihood ratio test: chi-square (44) = 286,245 [0,0000] (*), (**) and (***) significant at 10%, 5% and 1%, respectively. 45organic cocoa farmer’s strategies and sustainability bio-based and applied economics 12(1): 37-52, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13473 impact. it should mention, specifically in stp insular context where climate changes consequences are become severe, that public policies are essential tools to mitigate risk events and impacts (gomes, 2021). concerning the four dimensions of the perceived value of joining a cooperative, the emotional (cev) and the social values (csv) of joining a cooperative, the greater the perceived value, the greater the probability of electing ls3 (multi-crop) and the lower the probability of having ls1 (mono-crop). in the case of the functional value the opposite is observed. from a production stand point, similarly to the results obtained by moreno-miranda et al. (2020) in ecuador, the price paid for product certification is debatable and not perceived as valuable. on the linkage between livelihood strategy and the sustainability at farm level, in addition to the difference between mono-crop vs. multi-crop, it was possible to add other elements. the economic dimension of sustainability, measured by land area and number of income sources, revealed that bi-crop and multi-crop have similar areas (3,7 ha) but greater than mono-crop (2,1 ha) while the number of sources of income are higher for multi-crop (4,2) than for mono and bi-crop (2,2). globally, multi-crop exhibited higher economic sustainability than mono and bi-crop livelihood strategies. the social dimension of sustainability measured by the number of basic services accessed, number of professional training courses and level of trust in institutions, displayed that: mono (8,8) and bi-crop (8,4) have greater access to a higher number of basic services than multicrop (6,7); the number of professional training courses were decreasing from mono (1,3) and bi (1,2) to multicrop (1,1); and the level of trust in institutions was also decreasing from mono (2,6) and bi (2,5) to multi-crop (2,3). overall the mono-crop livelihood strategy was more robust in terms of social sustainability. finally, the environmental dimension of sustainability, measured by the number of crops and productivity levels, disclosed that: as expected multi-crop (3,6) has an average number of crops higher than bi-crop (2) and mono-crop (1) strategies; and cocoa productivity for multi-crop (706 kg/ha) is higher than bi-crop (614 kg/ ha) and mono-crop (479 kg/ha) while banana productivity for multi-crop (918 kg/ha) is higher than bi-crop (435 kg/ha). thus, the multi-crop livelihood strategy is, more environmentally sustainable than mono and bi-crop livelihood strategies. as a whole multi-crop is the most sustainable livelihood system. there is acceptance that certified ocp have a positive sustainability effect (blockeel et al, 2023) as well as crop diversity, as a result of increasing sources of food and income, reducing the risk of adverse events and their impact and having a positive effect on biodiversity. 5. conclusions organic cocoa production is one of the most valued crops in stp and world-wide. the country follows ancient ancestral-style production practices, in which most of the production is in the hands of small-scale producers primarily associated with two cooperatives, which face significant obstacles regarding their sustainability. small scale cocoa production in stp is organized in different livelihood strategies, mono, bi e multi-crop that have similarities and differences among them and represent distinctive production systems. these three strategies have been developed as means of survival of rural households, with dependency of organic cocoa production and, in many cases, incomes still below the poverty line. this is due to the low level of production obtained, which does not allow a better position in the market, and the poor access to technical support. rural cocoa households have been sustained by cocoa cooperatives governance and sociotechicians’ support. cooperative goals are toward inducing and advising farmers to avoid mono-crop in order to achieve greater (bio)diversity and ecosystem services, wellbeing and economic access. these provide enhanced levels of sustainability, climate neutrality transition and market shock prevention which are expected to increase in frequency and intensity. this research shows that globally, multi-crop livelihood strategy have the highest economic sustainability, mono-crop livelihood system was more robust in terms of social sustainability and multi-crop livelihood strategy was the most environmentally sustainable. thus, as a whole, the multi-crop livelihood strategy is the most sustainable livelihood system. the bi-crop and multi-crop livelihood strategies, have the potential to offset environmental and economic risks and consequently improve sustainability and wellbeing. such pathway is relevant for a country like stp which depends economically on its ocp in order to maximize short-term productivity and profitability. nonetheless, cocoa mono-crop has been associated with soil erosion and degradation, biodiversity loss, as well as increased susceptibility to climate change impacts, pests and diseases. the multi-crop livelihood system is the more resilient strategy, because it holds diversified sources of 46 bio-based and applied economics 12(1): 37-52, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13473 ibrahim prazeres et al. income and seems more realistic in terms of management, strategies and in the face of risks. nonetheless, it is less autonomous because it further depends on outside linkages (e.g. off farm labour and cooperatives support). mono-crop farmers are more autonomous because they hold higher levels of education and experience, as well as greater access to technical support, therefore, in the absence of risk events, they can be more successful. on the other hand, in risk events, they suffer greater consequences, thus, they have a better grasp of the impact of events when dealing with severe risks. that is, when the risks are low, mono-crops respond well, when the risks are higher, a multi-crop approach may be more suitable. the results of this study devise crucial policy implications for designing adaptations to organic cocoa national policy, which would involve, for example, better technical assistance, credit, and investment in the development of diversified practices and cocoa plants’ selection, which respond to poverty and climate variability. they can be used to recommend governance measures to lead livelihood strategies to a higher sustainability level in all dimensions and the adoption of climate change adaptations. for instance, the roles of research, knowledge transfers and extension programs in promoting more resilient and sustainable livelihood strategies are vital to promulgating best practices and the ecosystems’ preservation. hence, it is crucial to progress in research, development and innovation (r&d&i) and gather the essential knowledge to be able to move current ocp livelihood strategies to new cleaner circular business models. finally, in terms of practical implications, the research demonstrated several factors with potential to improve organic cocoa livelihoods, but also obstacles, especially in terms of formal credit access, infrastructures scarcity, actions to deal with risk events and trust in institutions and governance practices. these may deter poorer smallholders from diversifying their income sources and improve their social wellbeing. the engagement of producers in social programs and policies that facilitate access to formal finance, could encourage small business livelihood strategies and improve transparency and trust in organic cocoa-dependent communities. acknowledgments this research is 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(2017). sustainable livelihoods research from the perspective of geography: the present status, questions and priority areas. geographical research, 36(10), 1859–1872. https://doi.org/10.11821/ dlyj201710004 51organic cocoa farmer’s strategies and sustainability bio-based and applied economics 12(1): 37-52, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13473 a pp en d ix ta bl e a 1. v ar ia bl es d efi ni tio n an d de sc rip tiv e st at ist ic s. va ria bl e d es cr ip tio n to ta l s am pl e ls 1 m on ocr op ls 2 bi -c ro p ls 3 m ul ticr op o bs . m ea n st an da rd d ev ia tio n m in m ax m ea n st an da rd d ev ia tio n m ea n st an da rd d ev ia tio n m ea n st an da rd d ev ia tio n c ro ps n um be r o f c ro ps 80 9 2, 22 0 0, 92 2 1, 0 7, 0 1, 00 0 0, 00 0 2, 00 0 0, 00 0 3, 62 8 0, 86 5 fe m in in e ge nd er 0= m al e; 1 = fe m al e 80 9 0, 33 0 0, 47 1 0, 0 1, 0 0, 31 3 0, 46 6 0, 37 4 0, 48 4 0, 21 7 0, 41 3 a ge a ge o f f ar m er s 80 9 48 ,4 77 12 ,2 91 15 ,0 88 ,0 52 ,8 96 11 ,6 43 48 ,1 56 12 ,0 42 46 ,5 72 12 ,7 86 fa m ily si ze n um be r o f m al e an d fe m al e m em be rs 81 0 4, 23 1 1, 96 2 1, 0 12 ,0 3, 64 3 1, 89 3 4, 15 6 1, 89 6 4, 82 8 2, 04 9 ed uc at io n le ve l 1= no st ud ie s; 2= pr im ar y; 3= se co nd ar y; 4 = g ra du at e 76 4 2, 20 5 0, 49 2 1, 0 4, 0 2, 16 5 0, 39 6 2, 14 8 0, 46 2 2, 40 1 0, 58 1 n um be r o f p ro fe ss io na l t ra in in g co ur se s n um be r o f t ra in in g co ur se s e nr ol le d 81 0 1, 18 3 0, 72 0 0, 0 6, 0 1, 25 2 0, 59 0 1, 20 2 0, 63 2 1, 08 3 0, 98 0 n um be r o f m em be rs o nfa rm w or k fa m ily m em be rs w ith fa rm w or k 81 0 0, 88 6 0, 73 9 0, 0 3, 0 0, 80 0 0, 82 9 0, 86 8 0, 71 3 0, 99 4 0, 74 4 n um be r o f m em be rs o fffa rm w or k fa m ily m em be rs w ith o fffa rm w or k 81 0 0, 41 2 0, 53 8 0, 0 3, 0 0, 31 3 0, 46 6 0, 39 9 0, 50 6 0, 51 1 0, 64 7 pe rc ep tio n of so ci al c la ss 1= ve ry lo w ; 2 =l ow ; 3 =l ow a ve ra ge ; 4= av er ag e; 5 =h ig h av er ag e; 6 =h ig h 81 0 2, 81 6 0, 73 1 1, 0 6, 0 2, 73 9 0, 86 9 2, 83 7 0, 60 3 2, 80 6 0, 94 0 in co m e fr om a gr ic ul tu ra l s el lin g pe rc en ta ge o f i nc om e fr om ag ric ul tu ra l s el lin g 81 0 54 ,6 40 25 ,5 85 25 ,0 10 0, 0 57 ,5 48 18 ,3 66 58 ,0 35 25 ,4 40 43 ,0 89 26 ,6 90 in co m e fr om su bs id ie s ( hu m an de ve lo pm en t a nd o th er s) a nd re m itt an ce s f ro m e m ig ra nt s 0= do n ot re ce iv e su bs id ie s; 1= re ce iv e su bs id ie s 81 0 0, 03 7 0, 18 9 0, 0 1, 0 0, 00 0 0, 00 0 0, 02 3 0, 15 1 0, 10 0 0, 30 1 in su ra nc es a nd lo an s 0= do n ot h av e in su ra nc e an d lo an s; 1= ha ve in su ra nc e an d lo an s 81 0 0, 07 2 0, 25 8 0, 0 1, 0 0, 05 2 0, 22 3 0, 05 4 0, 22 7 0, 13 3 0, 34 1 c ac ao a re a h ec ta re s 81 0 1, 93 1 0, 81 2 0, 5 12 ,5 2, 08 4 0, 42 2 1, 90 3 0, 87 4 1, 91 5 0, 80 7 c ac ao to ta l p ro du ct io n k ilo s 78 2 11 10 76 3, 06 9 1, 5 96 00 98 3 87 7, 04 0 10 66 62 9, 59 3 13 41 99 2, 54 4 ba na na a re a h ec ta re s 66 2 1, 92 0 0, 83 9 0, 5 12 ,5 1, 62 5 0, 25 0 1, 91 2 0, 83 6 1, 94 7 0, 85 8 ba na na to ta l p ro du ct io n k ilo s 65 8 93 9 12 22 ,1 85 20 ,0 12 00 0 20 00 0, 00 0 72 5 93 2, 66 7 15 20 16 76 ,3 75 a cc es s t o po ta bl e w at er 0= w ith ou t a cc es s; 1= w ith a cc es s 78 6 0, 35 5 0, 47 9 0, 0 1, 0 0, 20 0 0, 40 2 0, 38 9 0, 48 8 0, 36 0 0, 48 2 a cc es s t o el ec tr ic ity 0= w ith ou t a cc es s; 1= w ith a cc es s 78 6 0, 98 3 0, 12 8 0, 0 1, 0 1, 00 0 0, 00 0 0, 98 2 0, 13 2 0, 97 5 0, 15 6 a cc es s t o ha rv es t s to ra ge 0= w ith ou t a cc es s; 1= w ith a cc es s 78 5 0, 76 0 0, 42 7 0, 0 1, 0 0, 93 9 0, 24 0 0, 82 1 0, 38 4 0, 43 8 0, 49 8 a cc es s t o tr an sp or ta tio n 0= w ith ou t a cc es s; 1= w ith a cc es s 78 5 0, 75 3 0, 43 2 0, 0 1, 0 0, 93 9 0, 24 0 0, 81 3 0, 39 0 0, 42 5 0, 49 6 a cc es s t o ro ad s 0= w ith ou t a cc es s; 1= w ith a cc es s 78 5 0, 76 8 0, 42 2 0, 0 1, 0 0, 73 0 0, 44 6 0, 82 1 0, 38 4 0, 62 5 0, 48 6 a cc es s t o la nd lin e 0= w ith ou t a cc es s; 1= w ith a cc es s 78 5 0, 02 8 0, 16 5 0, 0 1, 0 0, 03 5 0, 18 4 0, 02 2 0, 14 6 0, 04 4 0, 20 5 a cc es s t o m ob ile p ho ne 0= w ith ou t a cc es s; 1= w ith a cc es s 78 5 0, 93 0 0, 25 6 0, 0 1, 0 0, 98 3 0, 13 1 0, 90 2 0, 29 8 0, 98 1 0, 13 6 a cc es s t o in te rn et 0= w ith ou t a cc es s; 1= w ith a cc es s 78 5 0, 17 3 0, 37 9 0, 0 1, 0 0, 13 0 0, 33 8 0, 14 5 0, 35 3 0, 29 4 0, 45 7 a cc es s t o tv a nd ra di o 0= w ith ou t a cc es s; 1= w ith a cc es s 78 5 0, 96 8 0, 21 5 0, 0 1, 0 1, 00 0 0, 00 0 0, 95 1 0, 26 5 1, 00 0 0, 00 0 52 bio-based and applied economics 12(1): 37-52, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13473 ibrahim prazeres et al. va ria bl e d es cr ip tio n to ta l s am pl e ls 1 m on ocr op ls 2 bi -c ro p ls 3 m ul ticr op o bs . m ea n st an da rd d ev ia tio n m in m ax m ea n st an da rd d ev ia tio n m ea n st an da rd d ev ia tio n m ea n st an da rd d ev ia tio n a cc es s t o he al th c en tr e 0= w ith ou t a cc es s; 1= w ith a cc es s 78 5 0, 76 4 0, 42 5 0, 0 1, 0 0, 96 5 0, 18 4 0, 82 1 0, 38 4 0, 43 8 0, 49 8 a cc es s t o sc ho ol s 0= w ith ou t a cc es s; 1= w ith a cc es s 78 4 0, 90 2 0, 29 8 0, 0 1, 0 0, 99 1 0, 09 3 0, 91 0 0, 28 7 0, 81 1 0, 39 2 a cc es s t o ex te ns io n se rv ic es 0= w ith ou t a cc es s; 1= w ith a cc es s 78 4 0, 71 1 0, 45 3 0, 0 1, 0 0, 90 4 0, 29 5 0, 78 6 0, 41 1 0, 33 3 0, 47 3 be lo ng to c ec a b 0= do n ot b el on g to c ec a b; 1 = be lo ng to c ec a b 81 0 0, 52 2 0, 50 0 0, 0 1, 0 0, 80 0 0, 40 2 0, 58 9 0, 49 2 0, 15 0 0, 35 8 sa tis fa ct io n w ith c oo pe ra tiv es 1= no ne ; 2 =j us t a li ttl e; 3 =i nd iff er en t; 4= h ig h; 5 =v er y hi gh 75 4 4, 50 7 0, 66 3 2, 0 5, 0 4, 77 4 0, 57 8 4, 49 6 0, 60 6 4, 33 1 0, 83 1 tr us t l ev el in n ei gh bo ur s 1= no ne ; 2 =j us t a li ttl e; 3 =i nd iff er en t; 4= h ig h; 5 =v er y hi gh 79 9 3, 56 4 0, 90 5 1, 0 5, 0 3, 88 7 0, 54 2 3, 67 0 0, 82 1 3, 04 0 1, 10 4 tr us t l ev el in c iv il or ga ni za tio ns 1= no ne ; 2 =j us t a li ttl e; 3 =i nd iff er en t; 4= h ig h; 5 =v er y hi gh 75 4 3, 93 5 0, 59 0 1, 0 5, 0 3, 96 5 0, 47 6 3, 97 4 0, 49 8 3, 77 5 0, 87 8 tr us t l ev el in a gr ic ul tu ra l or ga ni za tio ns 1= no ne ; 2 =j us t a li ttl e; 3 =i nd iff er en t; 4= h ig h; 5 =v er y hi gh 79 9 2, 35 5 0, 92 0 1, 0 5, 0 2, 07 0 0, 55 8 2, 47 0 0, 93 4 2, 20 7 1, 01 0 tr us t l ev el in d ist ric t c ou nc il 1= no ne ; 2 =j us t a li ttl e; 3 =i nd iff er en t; 4= h ig h; 5 =v er y hi gh 79 9 1, 07 0 0, 38 8 1, 0 5, 0 1, 07 0 0, 41 3 1, 04 1 0, 31 4 1, 15 5 0, 53 1 tr us t l ev el in lo ca l c ou nc il 1= no ne ; 2 =j us t a li ttl e; 3 =i nd iff er en t; 4= h ig h; 5 =v er y hi gh 79 9 1, 06 4 0, 35 0 1, 0 5, 0 1, 05 2 0, 29 2 1, 03 5 0, 30 5 1, 15 5 0, 47 4 tr us t l ev el in c oo pe ra tiv es 1= no ne ; 2 =j us t a li ttl e; 3 =i nd iff er en t; 4= h ig h; 5 =v er y hi gh 75 4 4, 52 1 0, 68 2 1, 0 5, 0 4, 86 1 0, 56 0 4, 51 8 0, 62 9 4, 25 4 0, 82 0 tr us t l ev el in g ov er nm en t 1= no ne ; 2 =j us t a li ttl e; 3 =i nd iff er en t; 4= h ig h; 5 =v er y hi gh 79 9 0, 43 1 0, 22 0 1, 0 5, 0 1, 08 7 0, 43 1 1, 02 6 0, 22 0 1, 10 9 0, 53 2 pe rc ep tio n of th e lik el ih oo d of ri sk s oc cu rr in g 1= lo w p ro ba bi lit y … 7 =h ig h pr ob ab ili ty 80 9 2, 59 9 0, 80 4 1, 3 5, 8 2, 25 2 0, 45 4 2, 47 1 0, 72 5 3, 18 5 0, 90 0 pe rc ep tio n of ri sk im pa ct se ve rit y 1= lo w im pa ct … 7 =h ig h im pa ct 80 9 4, 72 9 0, 61 5 1, 8 6, 3 4, 52 2 0, 52 0 4, 73 3 0, 50 3 4, 85 2 0, 87 5 pe rc ep tio n of th e de gr ee o f s el fco nt ro l o f t he im pa ct 1= lo w c on tr ol … 7 =h ig h co nt ro l 80 9 4, 00 4 0, 32 6 1, 6 5, 1 3, 97 5 0, 39 1 4, 02 4 0, 25 5 3, 96 5 0, 44 0 pe rc ep tio n of th e im po rt an ce o f r isk m an ag em en t t oo ls 1= ve ry in ad eq ua te … 7 =v er y ad eq ua te 80 9 4, 91 8 0, 52 4 1, 5 7, 0 4, 97 1 2, 91 7 4, 95 8 1, 50 0 4, 77 1 1, 75 0 pe rc ep tio n of jo in in g a co op er at iv e fu nc tio na l v al ue 1= st ro ng ly d isa gr ee … 7= to ta lly a gr ee 80 9 5, 70 5 0, 54 7 3, 3 7, 0 5, 83 5 0, 31 0 5, 71 7 0, 53 5 5, 58 5 0, 66 7 pe rc ep tio n of jo in in g a co op er at iv e em ot io na l v al ue 1= st ro ng ly d isa gr ee … 7= to ta lly a gr ee 80 9 5, 84 8 0, 64 7 2, 3 7, 0 6, 03 5 0, 28 1 5, 86 7 0, 58 2 5, 67 4 0, 90 4 pe rc ep tio n of jo in in g a co op er at iv e so ci al v al ue 1= st ro ng ly d isa gr ee … 7= to ta lly a gr ee 80 9 5, 95 2 0, 42 1 3, 3 7, 0 6, 02 6 0, 28 3 5, 94 1 0, 40 2 5, 93 5 0, 53 0 pe rc ep tio n of jo in in g a co op er at iv e m on et ar y va lu e 1= st ro ng ly d isa gr ee … 7= to ta lly a gr ee 80 9 5, 94 9 0, 40 5 3, 7 7, 0 5, 97 7 0, 21 0 5, 96 9 0, 34 4 5, 87 4 0, 60 4 farmers’ motivations and behaviour regarding the adoption of more sustainable agricultural practices and activities linda arata1, davide menozzi2 how do farmers’ pluriactivity projects evolve? how do farmers’ pluriactivity project evolve? clarisse ceriani*, amar djouak, marine chaillard heterogeneity of adaptation strategies to climate shocks: evidence from the niger delta region of nigeria chinasa sylvia onyenekwe1, patience ifeyinwa opata1, chukwuma otum ume1,*, daniel bruce sarpong2, irene susana egyir2 organic cocoa farmer’s strategies and sustainability ibrahim prazeres1,*, maria raquel lucas1, ana marta-costa2, pedro damião henriques3 a complex web of interactions: personality traits and aspirations in the context of smallholder agriculture luzia deißler1,*, kai mausch2, alice karanja3, stepha mcmullin3, ulrike grote1 exploring the effectiveness of serious games in strengthening smallholders’ motivation to plant different trees on farms: evidence from rural rwanda ronja seegers*, etti winter, ulrike grote bio-based and applied economics 13(3): 219-223, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-16423 bio-based and applied economics bae © 2024 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae citation: pagliacci, f., raimondi, v., & salvatici, l. (2024). guns, germs and climate: food security and food systems in a risky world. bio-based and applied economics 13(3): 219-223. doi: 10.36253/bae-16423 received: august 5, 20024 accepted: august 6, 2024 published: october 16, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini orcid fp: 0000-0002-3667-7115 vr: 0000-0002-5431-7885 ls: 0000-0003-1791-1663 editorial. guns, germs and climate: food security and food systems in a risky world francesco pagliacci1,*, valentina raimondi2, luca salvatici3 1 department territorio e sistemi agro-forestali (tesaf), università degli studi di padova, italy 2 dipartimento di scienze e politiche ambientali, università degli studi di milano, italy 3 dipartimento di economia e centro rossi-doria, università degli studi roma tre, italy * corresponding author. e-mail: francesco.pagliacci@unipd.it the 2023 aieaa conference in milano emphasized markets’ long-term and recent evolution, especially food, fertilizers and energy. out of the 90 papers presented at the conference, 7 were submitted to be included in this special issue. food inflation reached critical levels in recent years, with double-digit rates recorded in most of the world. though food prices have since fallen from their peaks, the crisis persists and has resulted in expanding numbers of people affected by hunger and malnutrition, especially the poorest, who spend over 60% of their income on food. the number of undernourished people has risen to more than 800 million globally (fao, ifad, unicef, wfp and who, 2022). russia’s february 2022 invasion of ukraine is often cited as the triggering event for these soaring figures, but the truth is that the situation was already dire before the war began. a legacy of high agricultural input costs (i.e., fertilizers, energy, fuel), years of insufficient yield growth, and weather shocks led to low stocks of several vital commodities. it raised international prices, leaving markets susceptible to shocks. the war triggered new disruptions on the supply side, pushing prices further up. in a way, it was just the most recent in a series of crises – conflict, climate change, covid-19, etc. – highlighting the structural drivers underlying the current situation (global network against food crises, 2024). recurrent shocks, enhancing price volatility, are driving up acute food insecurity. food crises around the world are the result of interconnected, mutually reinforcing drivers: conflict and insecurity, economic shocks, and weather extremes. these key drivers were associated with the lingering socioeconomic impacts of covid-19, the knock-on effects of the war in ukraine, and repeated droughts and other weather extremes. in this issue, the article by yalew et al. (2024) (the implications of the russia-ukraine war for african economies: a cge analysis for ethiopia) examines the impact of the 2022 world market price increases for wheat, fuels, and fertilizers on ethiopia’s economy. using a computable general equilibrium (cge) model, the study shows that gdp, wage rates, and househttps://doi.org/10.36253/bae-16423 https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode http://www.fupress.com/bae https://doi.org/10.36253/bae-16423 https://orcid.org/0000-0002-3667-7115 https://orcid.org/0000-0002-5431-7885 https://orcid.org/0000-0003-1791-1663 mailto:francesco.pagliacci@unipd.it 220 francesco pagliacci, valentina raimondi, luca salvatici bio-based and applied economics 13(3): 219-223, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-16423 hold’ consumption in the country decline. the effects of fertilizer and petroleum price changes are particularly notable and unequal across production sectors. crop growing activities tend to substitute inorganic fertilizers with animal manure. the overall effects on urban households are relatively severe compared to the impact on rural households. increasing fertilizer prices tighten the competition for using animal manure as fertilizer (in crop cultivation) and as fuel (by households). to have a proper assessment of the shock impacts, it is essential to take into account the whole value chain. the article by gattone (2024) (participation of farmers in market value chains: a tailored antràs and chor positioning indicator) goes in this direction and presents a micro-level indicator of farmers’ positioning in the market chain based on the conceptual framework outlined by antràs and chor (2013, 2018). the indicator considers the selling location of a farming household and its crop buyers. using panel data from the world bank’s ‘living standards measurement study: integrated surveys on agriculture’ for ethiopia and nigeria, the article empirically applies the proposed indicator and showcases its superior performance compared to the micro-level alternatives. furthermore, by analyzing the dynamics of farmers’ food and total consumption over time and controlling for various household and production characteristics, as well as potential confounding factors, it shows that moving towards a downstream position in the market chain has a positive impact on farmers’ food and total consumption levels. most of the shocks that affect and will affect the agrifood sector are related to climate change, as one of the main environmental problems of the 21st century. consequently, there is an increasing call for efforts directed at detecting best practices of climate change adaptation in agriculture and understanding the factors behind producers’ willingness to implement such adaptation strategies. the article by pagliacci and salpina (2024) (producer, farm, production or perception? what really drives adaptation to climate change in the case of producers of geographical indications?) focuses on the agri-food sector certified productions. it analyses the results of a questionnaire-based online survey administered to 137 producers of agri-food geographical indications in the veneto region (in north-eastern italy) in 2022. using a multinomial logit model, the study highlights the factors explaining adaptation strategies distinguishing three different cases: (i) farmers who have already implemented adaptation strategies; (ii) farmers who are willing to implement them in the future; (iii) farmers who neither have implemented them in the past nor are willing to do so in the future. significant factors revolve around socio-demographic characteristics, farm management and networks, production type, and direct climate change perception. governance mechanisms along the agri-food supply chains are also increasingly important, especially in ecological transition. under the conceptual and analytical lens of neo institutional economics, the article by ciliberti et al. (2024) (exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach) explores farmers’ preferences towards a variety of clauses usually adopted in production contracts. to this purpose, a discrete choice experiment was conducted among 190 durum wheat producers in italy. results from a latent class model show that producers were mainly interested in fixed price formulas and joining shared production rules but revealed little or no interest in compelling sustainable cultivation techniques and providing technical assistance. however, these preferences are heterogeneous across farmers and vary depending on their level of education and previous use of contractual arrangements, with relevant implications for contract design and management. in recent years, agricultural policies have also largely changed in both low-income and high-income countries, being transformed by the demands of a new economy. coupled and decoupled subsidies and trade policies remain centre-stage in many global government initiatives. but digitalization, the green transition, and geopolitical imperatives have multiplied the objectives that agricultural policy is tasked with. this creates inevitable tensions and some trade-offs among economic agents. for example, focusing on products at zero distance to spur local economic development makes the green transition more costly. multiple goals require multiple instruments – a lesson that many governments have yet to internalize (juhász et al., 2023). they also require thinking of agricultural policies in somewhat different ways from what economists are accustomed to. the image that economists have of agricultural policy goes something like this: a group of bureaucrats (a) design some incentives that favored products are to receive (e.g., export subsidies, import protection, etc.), and (b) select the products that are to be incentivized in this fashion. they may then formulate additional rules regarding what kind of farms qualify for the incentives, the specific farm actions or performance criteria on which the incentives are conditioned, and the consequences (or penalties) for non-performance. ideally, the bureaucrats keep lobbies at arms’ length throughout the process and thereafter to provide them with insulation against political manipulation and rent-seeking (juhász et al., 2023). https://doi.org/10.36253/bae-16423 221editorial. guns, germs and climate: food security and food systems in a risky world bio-based and applied economics 13(3): 219-223, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-16423 however, this description of the hard, insulated state does not quite do justice to the reality of economic policy. as juhász et al. (2023) have argued, successful governments combine autonomy from private interest groups with “embeddedness” in social ties that provide “institutionalized channels for the continual negotiation and re-negotiation of goals and policies.” economists might worry that such close relationships with private firms could have made the government more prone to capture. but juhasz et al. (2023) also argue that these links are essential to ensure governments have access to the information needed to design workable policies, adjust to changing circumstances, and prod firms along new technological trajectories in the most effective ways possible. policies aimed at enhancing agricultural productivity growth, such as investments in r&d, strengthening economic incentives for farmers, infrastructure, and rural education and extension, have been found to narrow the yield gap effectively. however, it is also crucial to consider food security, sustainability, and agrifood system resilience as critical elements in productivity growth. in the same vein, access to land is one of the key factors of farm growth, while at the same time, related research is characterised by important gaps, particularly facing the change over time in the nature and role of drivers of the land market. the research in this area can support policymakers in designing policies to promote the survival and growth of farms and facilitate land investment by reducing barriers to land acquisition. in a forthcoming issue, the article by russo et al. (2024) (farm characteristics and exogenous factors inf luencing the choice to buy land in italy) aims to identify the endogenous and exogenous factors that affect the decision to purchase land in italy between 2013 and 2020. probit models are implemented to understand the role of different determinants in land investment decisions. the results show that factors related to capital in machinery and equipment, energy production, the inflation rate and the presence of a successor positively influence the purchase decision, while the cost of capital, the ratio of rented land to utilised agricultural area and of family work units to total work units play the opposite role. the role of utilised agricultural area and gross saleable production per hectare varies depending on the specialisation considered. it is natural, in times of crisis, to respond with some emergency measures, but we should not lose sight of the long term. single, one-off policies will not provide a way out of the current predicament. there is no simple or standard solution to such a complex situation. safeguarding food security and making the food system work will take a whole-system approach. interventions should target the food system, the economic environment, governance, and other key elements in this crisis’s broader, longer-term dimensions. these challenges are complex, but their urgency should not be underestimated; systemic actions should be taken sooner rather than later; otherwise, problems will continue to compound, and the costs of inaction will inevitably increase. recognizing the multi-dimensionality of this crisis and responding to it is imperative to building resilient food systems and future global food security. another important way to mitigate future shocks and promote food security is to step up the fight against climate change and biodiversity loss, both of which featured prominently in discussions for global action, highlighting the critical climate-biodiversity-food nexus. european soils and their status are a matter of concern that has entered the policy arena. a common regulatory framework is currently discussed in the soil monitoring law but has not yet been developed. the soil health narrative has been lately adopted as part of the european union agenda; however, how far such a concept is integrated into current policy instruments is under investigation. the article by winkler et al. (2024) (soils and ecosystem services: policy narratives and instruments for soil health in the eu) is based on content analysis and scoping review and aims to evidence which soil ecosystem services are currently targeted or neglected by the available policy instruments, both regulatory and incentive-based. while primary productivity, nutrient cycles and carbon storage were frequently found, services such as biodiversity, habitat preservation, human well-being, and cultural heritage still appear underrepresented in european soil-related policies. finally, the article by sogari et al. (2024) (intention and behavior toward eating whole grain pasta on a college dining campus: theory of planned behavior and message framing) contributes to individuate gaps and provide relevant information for upcoming policy needs. the consumption of whole grains has several health benefits. however, most us consumers – including young adults – do not meet the recommended intake. a survey based on the theory of planned behavior (tpb) was developed and administered to us college students to understand the underlying factors affecting the intention and consumption of whole-grain pasta. the effects of message interventions on the tpb measures and other variables are examined. 325 participants received different messages on the health benefits of whole grain in the forms of gain(treatment 1) or loss-framed (treatment 2) for four weeks or did not receive any message (control). the authors evaluate variables at two-time points: time https://doi.org/10.36253/bae-16423 222 francesco pagliacci, valentina raimondi, luca salvatici bio-based and applied economics 13(3): 219-223, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-16423 1 (when the first message was received, week 0) and time 2 (one month after the intervention, week 4). the results suggest that attitude, subjective norm, and perceived behavioral control are positively associated with intention, and intention can accurately predict young adults’ behavior. on the other hand, the framing does not affect the tpb variables. the crises we have faced have roots in multiple shocks or long-term pressures – in this case, the slow recovery from the global pandemic, conflicts, and climate-related disasters – and are becoming increasingly common, especially as climate change advances, and more and more intertwined. these crises overlap and amplify disruptive impacts on food production and markets, at different territorial scales. such complex situations will likely drive rising numbers of food-insecure and malnourished people, disrupt farmers’ livelihoods and leave long-lasting implications for well-being. for example, the links between drought, war, and food insecurity are evident in several places. our experience with the current food price crisis offers several key policy lessons. foremost among them is the critical role of trade in ensuring food security. keeping markets open for food and fertilizers – and expanding the number of producers and markets – can reduce price volatility and help ensure the delivery of food where it is needed. grain and vegetable oil supplies can also be increased in the short term by suspending biofuel mandates and avoiding taking land out of food and feed production. when managed well, trade can help improve and strengthen opportunities and choices for producers and consumers, providing alternative sources to secure food supplies and thus stabilizing prices. while trade is essential, it only works well with varied sources of food, feed, and agricultural inputs, both regarding the diversity of products and of producing and exporting countries. yet the war has highlighted an apparent lack of such diversity due to the world’s dependence on imports from ukraine and russia, as grain and fertilizer prices have risen to their highest levels since 2008. putting all your eggs in one basket is never a smart strategy, but neither is shifting all the eggs from one basket to another. providing more options can help to avert such problems when the next shock hits. we need more flexibility in where and how food, feed, and agricultural inputs are produced and consumed. improved diversity, in turn, will increase the resilience of local, national, regional, and global food systems. however, expanding flexibility will require significantly growing public and private investment in research and development to sustainably and rationally expand production, as well as promoting trade strategies that support the diversification of import sources (both in terms of countries and companies) and reducing food loss and waste along supply chains. finally, humanitarian assistance for those most in need and well-targeted social protection, through food or cash transfers, can prevent hunger and malnutrition and deter the devastating long-term impacts of a global food crisis. still, these should not detract from efforts to meet long-term development goals and build resilience to future shocks. in the medium term, countries can invest in increasing sustainable food production. as more countries develop resilient and competitive agricultural systems, importing countries will have access to more trade partners and be able to diversify their sources of imports. references antràs, p., chor, d. (2013). organizing the global value chain. econometrica, 81(6), 2127–2204. antràs, p., chor, d. (2018). on the measurement of upstreamness and downstreamness in global value chains. world trade evolution: growth, productivity and employment, 126–194. taylor & francis group. ciliberti s., frascarelli a., martino g., and marchini a. (2024). exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach. bio-based and applied economics, 13(3), 285-299. https://doi.org/10.36253/ bae-1537 fao, ifad, unicef, wfp, who (2022). the state of food security and nutrition in the world 2022. repurposing food and agricultural policies to make healthy diets more affordable. rome, fao. https:// doi.org/10.4060/cc0639en gattone t. (2024). participation of farmers in market value chains: a tailored antràs and chor positioning indicator. bio-based and applied economics, 13(3), 245-264. https://doi.org/10.36253/bae-15464 global network against food crises (2024). 2023 financing flows and food crises report. juhász, r., lane, n.j., rodrik, d. (2023). the new economics of industrial policy. nber working paper series 31538. http://www.nber.org/papers/w31538 pagliacci, f., salpina, d. (2024). producer, farm, production or perception? what really drives adaptation to climate change in the case of producers of geographical indications? bio-based and applied economics, 13(3), 265-283. https://doi.org/10.36253/bae-15221 russo, s., raggi, m., bimbati, b., povellato, a., viaggi, d. (2024). farm characteristics and exogenous factors https://doi.org/10.36253/bae-16423 https://doi.org/10.36253/bae-1537 https://doi.org/10.36253/bae-1537 https://doi.org/10.4060/cc0639en https://doi.org/10.4060/cc0639en https://doi.org/10.36253/bae-15464 http://www.nber.org/papers/w31538 https://doi.org/10.36253/bae-15221 223editorial. guns, germs and climate: food security and food systems in a risky world bio-based and applied economics 13(3): 219-223, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-16423 influencing the choice to buy land in italy. bio-based and applied economics, just accepted. https://doi. org/10.36253/bae-15548 sogari g., wongprawmas r., andreani g., lefebvre m., pellegrini n., gómez m.i., mora c., menozzi d. (2024). intention and behavior toward eating whole grain pasta on a college dining campus: theory of planned behavior and message framing. bio-based and applied economics, 13(3), 301-316. https://doi. org/10.36253/bae-15456 winkler, g., pagano, l., vergamini, d., bartolini, f. (2024). soils and ecosystem services: policy narratives and instruments for soil health in the eu. biobased and applied economics, just accepted. https:// doi.org/10.36253/bae-15466 yalew, a. w., nechifor, v., ferrari, e. (2024). the implications of the russia-ukraine war for african economies: a cge analysis for ethiopia. bio-based and applied economics, 13(3), 225-243. https://doi. org/10.36253/bae-15364 https://doi.org/10.36253/bae-16423 https://doi.org/10.36253/bae-15548 https://doi.org/10.36253/bae-15548 https://doi.org/10.36253/bae-15456 https://doi.org/10.36253/bae-15456 https://doi.org/10.36253/bae-15466 https://doi.org/10.36253/bae-15466 https://doi.org/10.36253/bae-15364 https://doi.org/10.36253/bae-15364 guns, germs and climate: food security and food systems in a risky world francesco pagliacci1,*, valentina raimondi2, luca salvatici3,* the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia amsalu woldie yalew1,2,*, victor nechifor3, emanuele ferrari3 participation of farmers in market value chains: a tailored antràs and chor positioning indicator tulia gattone adapting to climate change: what really drives the choices of the producers of geographical indications? francesco pagliacci1, dana salpina1,2,* exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach stefano ciliberti, angelo frascarelli*, gaetano martino, andrea marchini intention and behavior toward eating whole grain pasta on a college dining campus: theory of planned behavior and message framing giovanni sogari1, rungsaran wongprawmas1, giulia andreani1, michele lefebvre2, nicoletta pellegrini3, miguel i. gómez4, cristina mora1, davide menozzi1,* 1 farmers’ intention to use agriculture 4.0 in marginal and non-marginal conditions maria sabbagh1, luciano gutierrez1 1department of agricultural sciences, university of sassari, viale italia, 39a, 07100 sassari, ss, msabbagh@uniss.it, lgutierr@uniss.it corresponding author: msabbagh@uniss.it this article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the version of record. please cite this article as: sabbagh m., gutierrez l. (2025). farmers’ intention to use agriculture 4.0 in marginal and non-marginal conditions, bio-based and applied economics, just accepted. doi:10.36253/bae-17229 abstract agriculture 4.0 enhances efficiency, sustainability, and yields while supporting climate change mitigation and adaptation. this study explores the adoption of agriculture 4.0 among 131 durum wheat farmers in sardinia, focusing on differences between marginal and nonmarginal areas. using an extended unified theory of acceptance and use of technology (utaut2) framework, which includes perceived performance risk, the study identifies key factors influencing adoption. facilitating conditions positively impact the adoption intentions, and perceived performance risk has a negative impact. however, performance mailto:msabbagh@uniss.it mailto:@uniss.it mailto:msabbagh@uniss.it 2 expectancy, effort expectancy, social influence and price value don’t significantly affect adoption intentions. policy recommendations include financial support, technical advice access, training programs, and awareness campaigns to promote adoption. these interventions aim to address barriers and foster equitable integration of agriculture 4.0 technologies across diverse farming contexts. keywords: agriculture 4.0, technology adoption; marginal areas, non-marginal areas, utaut2. jel codes: q16; q18; d83 1. introduction marginal areas are territories where farming is challenging due to a confluence of biophysical, socioeconomic, and infrastructural aspects (ahmadzai et al., 2021; alhajj ali et al., 2024; peter et al., 2018; sallustio et al., 2018). these territories face natural and geographic constraints that reduce agricultural competitiveness (ahmadzai et al., 2022; csikós & tóth, 2023; food & nations, 2017; jussila et al., 2019; lal, 2004). on the other hand, non-marginal areas benefit from better natural resources, more established infrastructure, and more access to markets, technology, and research and development (r&d) (coxhead et al., 2002; hidayat et al., 2024; rondinelli, 1992; ruddle, 1991). these areas are often better integrated into regional, national, and worldwide agricultural markets, resulting in increased production and economic benefits (hidayat et al., 2024; jouanjean, 2013; long et al., 2016). 3 farmers in non-marginal areas are generally more willing to accept new technologies due to improved access to credit and extension services, which reduce perceived risks and increase the possibility of successful adoption (pannell et al., 2006; rogers, 2003; yigezu et al., 2018). differently, farmers in marginal areas are more likely to be risk-averse and hesitant to adopt new technologies due to uncertainties about their effectiveness and the potential financial risks involved (girma et al., 2023; marra et al., 2003; wu et al., 2023). these farmers may also lack the technical knowledge and skills required to effectively implement and benefit from new technologies, as well as the necessary support systems for ongoing innovation and r&d (abrol & ramani, 2014; douthwaite et al., 2001; klerkx et al., 2019; scoones et al., 2009). agriculture 4.0 may provide a transformative opportunity to solve these imbalances. agriculture 4.0, an advanced framework that incorporates technologies such as the internet of things (iot), artificial intelligence (ai), robotics, precision farming, and big data analytics, has the potential to transform farming methods in a variety of situations (abiri et al., 2023; fuentes-peñailillo et al., 2024; raj et al., 2021; stupina et al., 2021; wolfert et al., 2017). iot systems enable real-time monitoring of soil, crops, and equipment (e.g., moisture sensors and smart irrigation) (osservatori.net, 2023). precision agriculture tools such as gps-guided machinery and variable-rate technology (vrt) optimize the use of inputs like fertilizers, pesticides, and water (mccormick, 2023) being tools to achieve more sustainable farming systems. remote sensing technologies and drones are destinated to crop health analysis and yield forecasting (maffezzoli et al., 2022). robotics and automation through autonomous tractors, harvesters, and weeding robots help reduce labor requirements (mccormick, 2023; osservatori.net, 2023) , while ai and 4 machine learning offer predictive analytics and decision support (abiri et al., 2023). additionally, blockchain and cloud computing enhance traceability and data management, big data analytics support informed forecasting and strategic planning (maffezzoli et al., 2022), and mobile applications provide farmers with access to weather data, technical assistance, and real-time market prices (agendadigitale, 2023). together, these technologies not only improve efficiency and productivity but also reduce environmental impact and enhance climate resilience. these advances are intended to maximize resource utilization, boost crop yields, and improve overall farm management, being extremely advantageous, especially in marginal areas (abiri et al., 2023; benfica et al., 2023; klerkx et al., 2019; rose & chilvers, 2018; saidakhmedovich et al., 2024). however, whereas nonmarginal areas are well-positioned to adopt these technologies, marginal areas face major barriers (benfica et al., 2023; klerkx et al., 2019; mercure et al., 2021; saidakhmedovich et al., 2024). understanding these constraints is critical to ensure that the benefits of agriculture 4.0 are more widely realized, thereby possibly bridging the development gap between marginal and non-marginal areas (burland & von cossel, 2023; kirk & cradockhenry, 2022; sureth et al., 2023). a complex interaction of elements such as economic situations, information access, social influences, and individual perceptions of risk and benefit impact farmers' attitudes and behaviours regarding new technology adoption (adrian et al., 2005; brick & visser, 2015; rizzo et al., 2024; sabbagh & gutierrez, 2022, 2023). previous studies investigated such elements on smart agriculture technologies in the italian context (caffaro & cavallo, 2019; caffaro et al., 2020; caffaro et al., 2019). 5 to investigate these dynamics,this research utilized the unified theory of acceptance and use of technology 2 (utaut2) model (venkatesh et al., 2012), which provides a comprehensive framework for understanding technology adoption (alghatrifi & khalid, 2019; macedo, 2017; tamilmani et al., 2021). utaut2 expands on the original utaut model, which identifies core factors that influence technology acceptance and use (chang, 2012; venkatesh et al., 2012). utaut2 introduces additional variables such as hedonic motivation, price value, and habit that capture a more comprehensive understanding of consumer and user behaviour in different contexts such as mobile applications, digital communication, e-health, educational tools, banking, agriculture, etc. (an et al., 2016; arain et al., 2019; arenas gaitán et al., 2015; chang, 2012; medeiros et al., 2022; venkatesh et al., 2012; widodo et al., 2019). as well, utaut2 is important in understanding technology adoption since it explains both short-term and longterm technology use (diekmann & theuvsen, 2019). moreover, research has shown that perceived performance risk predicts the intention to adopt a new technology (abikari, 2024; budhi & aminah, 2010; budhi et al., 2022; deng et al., 2018; diekmann & theuvsen, 2019; hasselwander & weiss, 2024; sohn, 2024). for this reason, we extended the utaut2 model to include the variable of perceived performance risk (featherman & pavlou, 2003). we focus our analysis on durum wheat farmers in the sardinia region, considering both marginal and non-marginal conditions. sardinia's unique agricultural landscape, with considerable regional differences, makes it an appropriate case study for investigating these dynamics. some areas of sardinia suffer severe challenges due to low soil quality, water scarcity, and limited infrastructure (fraser-baxter, 2024). durum wheat, a key crop in the 6 region and vital to producing traditional items such as pasta and bread, is inseparably linked to sardinian history and the local economy (mefleh et al., 2019; soddu et al., 2013). furthermore, durum wheat agriculture in sardinia is particularly sensitive to environmental conditions, making it a great indicator of the overall agricultural issues faced across the region (mereu, 2010). agriculture 4.0 technologies may improve durum wheat sowing, monitor soil moisture and nutrient levels in real time, and predict crop diseases before they spread (balyan et al., 2024; güven et al., 2023; shafi et al., 2019; trivelli et al., 2019). the geographical differences in durum wheat yields in sardinia, caused by different soil quality, water availability, and infrastructure, make it a suitable case study for investigating farmers' intentions to implement agriculture 4.0 technologies in marginal and non-marginal areas. this study is pioneering in proposing an expanded utaut2 model to explore the behavioural factors influencing the adoption of agriculture 4.0 technologies in marginal and nonmarginal settings. the implications of this study may extend beyond sardinia, providing significant insights into the broader challenges and opportunities associated with the adoption of agricultural technologies. the findings could help shape agricultural policies that promote sustainable farming practices and economic development in locations with similar agricultural profiles. moreover, it intends to contribute to the global discourse on sustainable agricultural innovation by offering a detailed knowledge of the factors that influence technology adoption, thereby assisting in the transition to more resilient and efficient farming systems. this leads to the central research question: “what are the key 7 factors influencing farmers’ behavioural intention to adopt agriculture 4.0 technologies in sardinia?” this research aligns with several united nations sustainable development goals (sdgs), specifically sdg 2 (zero hunger), sdg 9 (industry, innovation, and infrastructure), and sdg 12 (responsible consumption and production). by investigating the behavioural and structural factors that influence the adoption of agriculture 4.0 technologies, especially in marginal areas, the study contributes to the broader agenda of building resilient food systems and fostering inclusive and sustainable economic growth in rural areas (sdg 8). moreover, promoting the use of resource-efficient technologies directly supports climate action goals (sdg 13) by reducing environmental impact and improving adaptation to climate-related risks. this study contributes to the ongoing discussion on farmers’ motivations and aspirations in agricultural innovation. as noted by arata and menozzi (2023), there is a need for multidimensional approaches that account for both individual drivers and contextual influences on farmer behaviour. while recent contributions, such as deißler et al. (2022), have explored the role of personality traits in shaping aspirations in smallholder contexts, our work adds to this conversation by focusing on behavioural intentions toward agriculture 4.0 use. by drawing on the theory of planned behaviour, our approach emphasizes farmers' perceptions and attitudes as key drivers of decision-making. these are the factors that, while distinct from personality traits, are similarly influential in shaping future-oriented action. this alignment offers a complementary perspective to the journal’s growing body of research on aspirations and innovation adoption. the paper is structured as follows: section 2 outlines the theoretical framework and hypotheses; section 3 details the 8 methodology, including data collection and analysis methods; section 4 presents and discusses the results; section 5 provides conclusions, and section 6 addresses the study's limitations. 2. agriculture 4.0 and behavioural models for the adoption of new technologies. 2.1 agriculture 4.0 in marginal and non-marginal areas agriculture 4.0 represents a transformative shift in farming, leveraging advanced technologies such as precision agriculture, iot, ai, robotics, and big data analytics to enhance efficiency, optimize resource use, and foster sustainable agricultural practices (abiri et al., 2023; wolfert et al., 2017). these technologies have the potential to revolutionize farming in both marginal and non-marginal areas, but their adoption and impact vary significantly due to differences in infrastructure, access to resources, and socioeconomic conditions between the two regions (ahmadzai et al., 2022; klerkx et al., 2019). nonmarginal regions often benefit from stable and predictable weather patterns, ensuring that agriculture 4.0 technologies can function optimally (mana et al., 2024; pechlivani et al., 2023). these tools, which include iot sensors that monitor crop health, soil moisture levels, and pest infestations, empower farmers to make data-driven decisions that enhance productivity, reduce resource consumption, and promote environmental sustainability (fuentes-peñailillo et al., 2024; raj et al., 2021). the availability of advanced farming machinery and technologies, such as ai-driven machinery and variable rate technology 9 (vrt), further contributes to higher productivity, with less environmental impact (shafi et al., 2019; van klompenburg et al., 2020). on the other hand, marginal areas face a host of challenges that hinder the adoption of agriculture 4.0 technologies. marginal areas are often characterized by poor soil quality, limited water resources, geographical isolation, and inadequate infrastructure, which restrict the applicability of advanced farming technologies (ahmadzai et al., 2021; jacobs et al., 2022). these regions are prone to extreme environmental conditions such as drought, floods, heat waves, soil erosion and water scarcity, making it difficult to implement technologies like precision irrigation or smart farming systems that rely on consistent environmental data (akter et al., 2023; cogato et al., 2019; wheaton & kulshreshtha, 2017). the absence of digital literacy and technical support networks in these regions makes it even more challenging for farmers to adopt new technologies (dibbern et al., 2024; ruzzante et al., 2021). as a result, farmers in these areas often lack the knowledge or resources to implement technologies such as iot sensors, ai-driven machinery, and other forms of agriculture 4.0 (douthwaite et al., 2001; klerkx et al., 2019). additionally, the high cost of adopting advanced technologies further exacerbates the divide between marginal and non-marginal areas. while financial support mechanisms such as subsidies and loans are more readily available in non-marginal areas, farmers in marginal regions often have limited access to credit and financial resources, making it difficult for them to invest in expensive technologies like artificial intelligence (ai) driven machinery or vrt (klerkx et al., 2019; yigezu et al., 2018). in marginal areas, where the financial risks of farming are already high due to environmental unpredictability, the upfront investment in 10 advanced technologies can seem discouraging (hurlbert et al., 2019; khan et al., 2024). without sufficient financial backing, many farmers prioritize short-term survival, limiting their ability to make long-term investments in precision farming tools that could potentially enhance productivity (marra et al., 2003). environmental factors, including the vulnerability to climate change, further differentiate the two regions in terms of agriculture 4.0 adoption. in non-marginal areas, stable climatic conditions, fertile soils, and reliable access to water resources make it easier to deploy agriculture 4.0 (javaid et al., 2022; solaw, 2011). technologies that rely on real-time data on soil moisture and weather conditions can significantly enhance water use efficiency and boost agricultural productivity (balyan et al., 2024). however, marginal areas face more unpredictable environmental factors that challenge agriculture 4.0. in these areas, the high variability of environmental conditions means that agriculture 4.0 may not deliver accurate or effective results unless adapted specifically to local conditions (jacobs et al., 2022). social and cultural factors also influence the adoption of agriculture 4.0 technologies, with farmers in non-marginal areas typically more exposed to modern farming practices and educational programs (ahmed & ahmed, 2023; nhuong & truong, 2024). in these regions, farmers often have access to extension services, training programs, and education that promote the adoption of innovative technologies (gardezi & bronson, 2020; raji et al., 2024; ruzzante et al., 2021). their more favourable attitudes towards technology adoption are often supported by governmental and institutional initiatives aimed at integrating new technologies into farming practices (cramb, 2000; tey & brindal, 2012). in contrast, farmers in marginal areas may be more risk-averse, especially when their livelihoods are already 11 precarious due to environmental and financial challenges (scoones et al., 2009). the limited access to education, technical knowledge, and extension services in these regions further limits the willingness and ability of farmers to adopt new technologies, resulting in slower adoption rates compared to non-marginal areas (de rosa & chiappini, 2012; girma et al., 2023; leap, 2023; masi et al., 2023; wu et al., 2023). the differences in the adoption of agriculture 4.0 technologies between marginal and nonmarginal areas highlight the need for tailored interventions. while non-marginal areas focus on optimizing technology and fostering innovation, marginal areas require foundational efforts to improve basic infrastructure, enhance digital literacy, and address the specific environmental and socioeconomic challenges that hinder technology adoption (elsawah et al., 2020; loo et al., 2023; mazzucato & willetts, 2019). the development of affordable, locally tailored technologies and support systems is crucial for ensuring that farmers in marginal areas can benefit from the transformative potential of agriculture 4.0, without exacerbating existing inequalities (jacobs et al., 2022; klerkx et al., 2019). agriculture 4.0 technologies present a stark contrast between marginal and non-marginal agricultural areas due to inherent disparities in natural resources, infrastructure, socioeconomic conditions, and access to technology (ahmadzai et al., 2022; klerkx et al., 2019; saidakhmedovich et al., 2024). understanding these contrasts is critical for developing strategies that ensure equitable access to these technologies and bridge the development gap. 2.2 the unified theory of acceptance and use of technology 2 12 this study utilizes the utaut2 model to explore the factors affecting farmers' intentions to adopt agriculture 4.0 technologies. the utaut2 model, introduced by venkatesh et al. (2012), expands upon the original utaut framework by integrating additional constructs pertinent to consumer-related contexts. the original utaut model emerged from synthesizing eight theoretical frameworks from various disciplines, focusing on technological change and adoption.: innovation diffusion theory idt (rogers, 1962); theory of reasoned action tra (ajzen & fishbein, 1980); theory of planned behaviour tpb (ajzen, 1991); social cognitive theory sct (bandura, 1986); technology acceptance model tam (davis, 1989); model of pc utilization mpcu (thompson et al., 1991); motivational model mm (davis et al., 1992); combined tam-tpb c-tam (taylor & todd, 1995). the main value of this model arises from bringing a historical light on technology use by working around a set of constructs; that is, concepts that encapsulate what is central to the effects of technology use from a user’s intention perspective (yu, 2012). the utaut model centered on four constructs: performance expectancy (pe), effort expectancy (ee), social influence (si), and facilitating conditions (fc) with moderating demographic inputs: gender, age, level of experience, and voluntariness of use (venkatesh et al., 2003). table 1 below illustrates these constructs alongside their theoretical origins, showcasing how each is rooted in one or more of the eight foundational models. building on the theoretical foundation of utaut, venkatesh et al. (2012) introduced the utaut2 model, a pivotal framework that emphasizes the consumer perspective by incorporating three key factors: hedonic motivation, price/value, and habit. this enhancement significantly boosts the model's predictive accuracy for estimating user adoption, reaching up to 74% (venkatesh et al., 2016). the utaut2 model's 13 applicability has been widely recognized as a robust framework within the technology industry. the extensive body of research supporting it underscores its effectiveness in analysing the adoption of new technologies, especially in diverse cultural and social contexts (šumak & šorgo, 2016). several studies, such as those by ena and siewa (2022) toral et al. (2018), have utilized the utaut2 model to investigate the factors influencing farmers' adoption of precision agriculture technologies. 14 table 1. the main constructs of utaut and their origins constructs variables model contributing to constructs performance expectancy perceived usefulness technology acceptance model (tam) (davis, 1989) combined tam-tpb (taylor & todd, 1995) extrinsic motivation motivational model mm (davis et al., 1992) job-fit model of pc utilization mpcu (thompson et al., 1991) relative advantage innovation diffusion theory idt (rogers, 1962) outcome expectations social cognitive theory sct (bandura, 1986) effort expectancy perceived ease of use technology acceptance model (tam) (davis, 1989) complexity model of pc utilization mpcu (thompson et al., 1991) social influence subjective norms theory of reasoned action tra (ajzen & fishbein, 1980) theory of planned behaviour tpb (ajzen, 1991) technology acceptance model (tam) (davis, 1989) combined tam-tpb c-tam (taylor & todd, 1995) social factors model of pc utilization mpcu (thompson et al., 1991) image innovation diffusion theory idt (rogers, 1962) facilitating conditions perceived behavioural control theory of planned behaviour tpb (ajzen, 1991) combined tam-tpb (taylor & todd, 1995) facilitating conditions model of pc utilization mpcu (thompson et al., 1991) complexity innovation diffusion theory idt (rogers, 1962) 15 2.3 selected variables for the study this study engages important variables from the utaut2 (venkatesh et al., 2012) as well as the variable of perceived performance risk (featherman & pavlou, 2003) to cope with the extended research model and better understand the factors influencing farmer acceptance of agriculture 4.0 technologies. each variable indicates a distinct feature that may influence a farmer's willingness to adopt agriculture 4.0 technologies. as a result, the variables chosen for this study are presented below. firstly, performance expectancy (pe) refers to the degree to which individuals believe that using technology will help them achieve gains in job performance (venkatesh et al., 2012).in the context of agriculture 4.0, this construct captures farmers' expectations regarding the improvement in crop yield, efficiency, and overall farm productivity due to the adoption of advanced technologies. previous research has seen this variable for its influence on the adoption of agriculture 4.0 (kolady et al., 2021; paustian & theuvsen, 2017). therefore, based on this, the following research hypothesis is proposed: h1: pe directly and positively influences farmers' intention to adopt agriculture 4.0 technologies. secondly, effort expectancy (ee) is defined as the degree of ease associated with the use of the technology (venkatesh et al., 2012). for farmers, this relates to the perceived ease of learning and using agriculture 4.0 technologies, including iot devices, data analytics tools, and automated machinery. previous research has studied this variable to understand its 16 influence on agriculture 4.0’s adoption (fragomeli et al., 2024; giua et al., 2022). hence, we investigate the research hypothesis that: h2: ee directly and positively influences farmers' intention to adopt agriculture 4.0 technologies. then, social influence (si) refers to the degree to which individuals perceive that important others believe they should use the new technology (venkatesh et al., 2012). in agricultural communities, social influence can come from peers, family members, agricultural advisors, and community leaders. in the context of the study, it is the degree to which a farmer believes that important people support their use of agriculture 4.0 for their daily field tasks. previous studies have provided empirical support that evidences the impact of si on the use of a new technology (moriuchi, 2021). zhai et al. (2020) and harisudin et al. (2023) have studied this variable to examine its influence on the adoption of agriculture 4.0. in this context, our hypothesis is the following: h3: si directly and positively influences farmers' intention to adopt agriculture 4.0 technologies. also, facilitating conditions (fc) are the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the technology (venkatesh et al., 2012). this includes access to necessary resources, such as training programs, technical support and funds. previous research analysed fc from the standpoint of influence on adoption, specifically, agriculture 4.0 (da silveira et al., 2023; giua et al., 2022). thus, our research hypothesis is formulated as follows: 17 h4: fc directly and positively influences farmers' intention to adopt agriculture 4.0 technologies. in addition, the price value (pv) variable has introduced to capture the farmer’s evaluation of whether the benefits of adopting agriculture 4.0 technologies justify the costs (venkatesh et al., 2012). previous studies have evidenced the effect that price/value has on technology adoption, a process that is enhancing in itself, and as such, provides a positive feeling and impact on users (moorthy et al., 2019; palau-saumell et al., 2019). the research hypothesis is formulated as follows: h5: pv directly and positively influences farmers' intention to adopt agriculture 4.0 technologies. finally, perceived performance risk (pr) refers to the potential negative outcomes associated with the use of technology, such as financial loss and crop failure. this construct, introduced by featherman and pavlou (2003), is particularly relevant in the agricultural sector where adopting new technologies often involves significant risks. understanding pr is crucial as it influences farmers' willingness to adopt innovative agricultural technologies like those encompassed in agriculture 4.0. several studies have incorporated pr to predict the adoption of agriculture 4.0 technologies (cook et al., 2022; fragomeli et al., 2024; kendall et al., 2022). for that, the proposed research hypothesis is the following: h6: pr directly and negatively influences farmers' intention to adopt agriculture 4.0 technologies. 18 the extended utaut2 model, with the addition of perceived performance risk, provides a comprehensive framework for understanding the adoption of agriculture 4.0 technologies. the research model is depicted in figure 1. figure 1: the research model 3. methods 3.1. survey design the survey’s questionnaire was divided into three sections. the first section explained the scenario and the research objectives, as well as the definition of agriculture 4.0, its advantages, and the related investments. to ensure participants clearly understood the concept of agriculture 4.0, the questionnaire provided a detailed definition inspired by the international association of precision agriculture. agriculture 4.0 was described as a datadriven farm management strategy where information is collected, processed, and analyzed to guide decisions aimed at improving the efficiency of resource use, productivity, quality, profitability, and sustainability. the definition was accompanied by examples of potential benefits, such as reducing resource waste (e.g., more efficient fertilizer and pesticide use), 19 increasing yields and improving crop quality, enhancing work conditions and efficiency through automation, enabling traceability from production to consumer. furthermore, examples of specific agriculture 4.0 tools and their estimated costs were provided. this allowed respondents to better relate to the technologies under investigation and reflect on their potential adoption. a summary is presented in table 2. the second section included questions about the farmers’ socio-economic characteristics (table 3). this survey section featured the use of nominal and ordinal scales. the third section contained questions about the major constructs included in the utaut2 research model, which are pe, ee, si, fc, pv, pr , and bi. specifically, pe was measured using four items. these items were relative to the respondents’ belief that agriculture 4.0 reduces the use of phytosanitary treatments, increases yield, enhances durum wheat’s quality, and is compatible with other technologies that the farmer already uses to cultivate durum wheat. ee was evaluated using three items related to respondents’ belief that agriculture 4.0 reduces time and workloads and allows for better organization of work, limiting injuries in the cultivation of durum wheat, especially on the most difficult surfaces. si was measured using three items reflecting the usefulness of considering the opinion of other farmers regarding the adoption of agriculture 4.0, the easiness of using agriculture 4.0 if other farmers close to the respondents’ farms utilize it, and the belief of considering the adoption of this technology if farmers’ associations will actively promote it. fc was assessed with three items related to the belief of having the necessary knowledge for the adoption of agriculture 4.0 on durum wheat, the belief of having easy access to technical advice in using this technology as well, as the reliance that the stabilization of a specific measure in the 20 rural development program (rdp) in sardinia region with a capital contribution greater than or equal to 60% would lead respondents to invest in agriculture 4.0. furthermore, the pv construct was assessed with three items related to the belief that agriculture 4.0 could reduce the cost of durum wheat production, obtaining more profits and promoting the efficient work of the farmers as well. pr was measured with three items regarding the possibility that agriculture 4.0 could generate more problems than solutions in managing the farm, tying the farmer as well to external consultants and experts, and creating more administrative work diverting the farmer from fieldwork. the intention to invest in agriculture 4.0 was measured with three items regarding the near future intention of adopting this technology. intentions and attitudes cannot be quantified directly (straub et al., 2004). however, they can be indirectly quantified through observed and measurable indicators using scaling approaches (gefen et al., 2000). to this end, a five-point likert-type scale ranging from “strongly disagree” (-2) to “strongly agree” (2) was used to measure the participants’ attitudes, beliefs, and opinions about the adoption of agriculture 4.0 (see table 4 for the mean and standard deviation of scores). the structural equation model (sem) was used for the analysis of the results since it allows testing all the relationships between the observed and latent variables simultaneously by combining multiple regression with factor analysis and provides general adjustment statistics (iacobucci, 2010). in addition, it can consider the measurement error with the observed variables (hair et al., 2006). table 2: precision agriculture tools: functionalities and investment estimates 21 technology / tool functionality estimated cost 4.0 tractors & implements onboard computer, automatic guidance, automated spraying/fertilization + €5,000 over traditional machinery weather stations & dss (decision support) real-time weather and field monitoring, pest/disease alerts, irrigation/fertilization advice from €1,500 upwards analytics platforms & farm apps integration of field data from sensors, drones, and equipment; decision support €500–€2,500 per year drones aerial imaging, multispectral surveys, application of treatments from €5,000 (excluding pilot license) or €25–€200/ha if outsourced 3.2. data collection an online questionnaire was distributed from november 20th, 2023 to february 26th, 2024, to 217 randomly selected durum wheat farmers in sardinia, italy, with the help of a farmers’ association, coldiretti sardinia. the sample was obtained using a convenience sampling method facilitated by coldiretti. it is not statistically representative of the full sardinian farming population but includes a diverse range of farm sizes and conditions. to better understand the participants' perspectives, we asked whether they believe the land used for cultivating durum wheat meets the criteria for marginal lands. in the questionnaire, we defined marginal lands, according to existing scientific literature (ahmadzai et al., 2022; csikós & tóth, 2023; food & nations, 2017; jussila et al., 2019; lal, 2004), , as areas characterized by poor soil quality, limited rainfall, extreme temperatures, and inadequate access to transportation and communication networks respondents who indicated that 22 their land fit this description were classified as cultivating in marginal conditions, while those who did not were classified as operating in non-marginal conditions. by that, the sample was divided into two groups: farmers located in marginal areas and those in nonmarginal areas. overall, 86 questionnaires were eliminated due to incomplete ones and small duration completion (less than 4 minutes, i.e., less than half the median duration of the interview). in table 3, we present the demographic and socio-economic characteristics of the participants in marginal and non-marginal conditions. the majority of respondents are male in both non-marginal and marginal conditions, with a slightly higher percentage of females in marginal conditions. the age distribution is quite similar between the two groups, with the majority being between 50-64 years old. this indicates that middle-aged farmers form the core demographic in both non-marginal and marginal conditions. education levels are comparable across both conditions, with most respondents having a high school diploma or less. most farms are multi-generational family farms, with a slightly higher presence of firstgeneration farms in marginal conditions (a first-generation farm refers to one where the current farmer is the first in their family to establish or manage a farming business, as opposed to multi-generational family farms passed down through successive generations). there is a notable difference in the likelihood of having a successor between the two conditions. non-marginal farms are more optimistic about having successors compared to marginal farms, where a significant percentage are unlikely to have successors. this is aligned with lobley et al. (2010) who showed that farm succession planning is more prevalent in financially stable farms, where future prospects are more secure and with kimhi 23 and nachlieli (2001) who indicated that farm profitability and stability significantly influence the likelihood of having successors, with marginal farms often facing more uncertainty. moreover, yield levels are higher in non-marginal conditions, with a notable percentage achieving between 2,1-4 tons/ha. marginal conditions show a greater proportion of farms with yields less than 2 tons/ha. this could be due to the fact that yield performance is related to farm management practices and resource availability, which are typically better in nonmarginal conditions (fischer et al., 2014) and as well the fact that non-marginal lands benefit from better soil quality, access to water, and inputs leading to higher yields compared to marginal lands (tilman et al., 2011). 24 table 3 demographic and socio-economics characteristics of the respondents socio-economic variables category non-marginal conditions (n=72) marginal conditions (n=59) frequency % frequency % gender male 63 87.50 49 83.05 female 9 12.50 10 16.95 age 18-49 years 24 33.33 19 32.20 50-64 years 37 51.39 30 50.85 > 65 years 11 15.28 10 16.95 educational level lower than high school diploma 35 48.61 28 47.46 high school diploma 31 43.06 24 40.68 university degree 6 8.33 7 11.86 characteristics of the farm family farm for several generations 62 86.11 49 83.05 first generation family farm 9 12.50 10 16.95 part of a corporate enterprise 1 1.39 0 0 the probability that the farm will have a successor none 8 11.11 6 10.17 unlikely 11 15.28 26 44.08 likely 40 55.56 21 35.59 very likely 5 6.94 3 5.08 certainly 8 11.11 3 5.08 average yield per hectare of the area cultivated with durum wheat < 2 t/ha 5 6.94 8 13.56 2.1 3 t/ha 39 54.17 37 62.72 3.1 4 t/ha 23 31.95 13 22.03 > 4.1 t/ha 5 6.94 1 1.69 experience agriculture 4.0 techniques i have no experience with agriculture 4.0 techniques. 32 44.44 33 55.94 25 table 4. summary statistics of the agriculture 4.0 related items and latent components agriculture 4.0 items and latent components variables non-marginal conditions (n=72) marginal conditions (n=59) mean(m) stdev(sd) mean(m) stdev(sd) performance expectancy (i believe that…) pe 0.93 0.07 0.68 0.09 agriculture 4.0 would help the cultivation of durum wheat by reducing the use of resources such as, for example, fertilizers and phytosanitary treatments. pe1 1.15 0.09 0.80 0.12 thanks to agriculture 4.0, we can increase the yield per hectare of durum wheat. pe2 0.92 0.09 0.54 0.12 agriculture 4.0 allows for a better quality of durum wheat production. pe3 0.85 0.09 0.49 0.13 agriculture 4.0 is compatible with the other technologies i already use to cultivate durum wheat. pe4 0.88 0.09 0.81 0.11 effort expectancy (i believe that …) ee 0.76 0.07 0.58 0.10 i don't use these techniques, but i've seen them used by others and i think i'm somewhat familiar with them. 13 18.06 13 22.03 i use agriculture 4.0 techniques. 27 37.50 13 22.03 26 agriculture 4.0 allows us to reduce time and workload in the cultivation of durum wheat. ee1 0.89 0.10 0.66 0.13 agriculture 4.0 allows for better organization of work in cultivating durum wheat. ee2 0.97 0.08 0.81 0.10 agriculture 4.0 can limit injuries in the cultivation of durum wheat, especially on the most difficult surfaces. ee3 0.46 0.10 0.22 0.14 social influence (i believe…) si 0.83 0.06 0.60 0.11 it is useful to consider the opinions of other farmers regarding the adoption of agriculture 4.0 techniques. si1 0.92 0.08 0.81 0.13 it would be easier to use agriculture 4.0 techniques if other farmers close to my farm also used it. si2 0.69 0.09 0.41 0.13 i would consider adopting agriculture 4.0 techniques if farmers' associations actively promoted their use. si3 0.88 0.08 0.58 0.14 facilitating conditions (i believe …) fc 0.76 0.07 0.51 0.11 i have all the necessary knowledge for the adoption of agriculture 4.0 in the cultivation of durum wheat. fc1 0.26 0.12 -0.03 0.17 the stabilization of a specific measure in the rdp in the sardinia region, with a capital contribution greater than or equal to 60% for companies that invest in agriculture 4.0, would lead me to invest in these new technologies. fc2 1.15 0.09 0.83 0.16 agriculture 4.0 technologies are compatible with those i already use. fc3 0.72 0.10 0.54 0.14 price value (thanks to the use of agriculture 4.0 …) pv 0.87 0.08 0.75 0.13 a reduction in the cost of durum wheat production can be achieved. pv1 0.89 0.09 0.71 0.15 i could work more efficiently. pv2 0.96 0.08 0.88 0.13 i could obtain a greater profit. pv3 0.75 0.09 0.66 0.14 27 perceived performance risk (i believe it is likely that the use of agriculture 4.0 techniques will …) pr 0.00 0.10 0.14 0.12 generate more problems than solutions in managing my farm. pr1 0.24 0.12 -0.34 0.16 tie me to external consultants and experts due to the level of sophistication in applying these techniques. pr2 0.25 0.12 0.46 0.15 create more administrative work, diverting my business from fieldwork. pr3 0.00 0.12 0.29 0.15 behavioural intention bi 0.38 0.11 -0.12 0.14 i will introduce agriculture 4.0 to durum wheat cultivation in the coming months. bi1 0.35 0.11 -0.15 0.16 in the near future, i plan to use agriculture 4.0 techniques in growing durum wheat. bi2 0.58 0.11 0.27 0.16 i have already planned to use agriculture 4.0 techniques on my farm. bi3 0.22 0.12 -0.49 0.16 to explore group differences, pairwise t-tests were performed to assess differences between marginal and non-marginal conditions. to save space, we do not report these t-tests. however, all the pairwise t-tests were significant at the 5% level of confidence. thus, the constructs showed significant differences between the two areas. the analysis of agriculture 4.0-related items (table 4) reveals notable differences in perceptions between non-marginal and marginal farmers. each construct was calculated by taking the average of all related items. non-marginal farmers consistently report higher scores across all utaut2 constructs compared to marginal farmers. they perceive agriculture 4.0 as more beneficial (higher pe and pv), easier to use, and better supported socially and institutionally. in contrast, marginal farmers show greater pr and lower bi to adopt these technologies. 28 3.3. modelling analysis framework due to the limited data available, we had to create a unified model to offer a comprehensive understanding of the factors influencing the adoption intentions of agriculture 4.0 technology. consequently, we merged data from both marginal and non-marginal areas to develop a consolidated model that reflects the overall regional dynamics. a confirmatory factor analysis (cfa) was carried out using ibm spss amos version 26 to evaluate the measurement model's validity, focusing on convergent validity, discriminant validity, and internal consistency of the constructs. convergent validity was assessed by examining the reliability of measurement items (factor loadings), the composite reliability (cr) of each construct, and the average variance extracted (ave) (anderson & gerbing, 1988). standardized factor loadings ranged from 0.58 to 0.96, all exceeding the recommended minimum of 0.50 (gefen et al., 2000). the composite reliability values were consistently above the threshold of 0.70, indicating strong internal consistency of the latent constructs (heinzl et al., 2011). additionally, the ave values, which measure the proportion of variance explained by the latent variables relative to measurement error, ranged between 0.50 and 0.70, exceeding the minimum acceptable value of 0.50 (fornell & larcker, 1981). these results, detailed in table 5, demonstrate high reliability and good convergent validity of the constructs, as they are well-correlated with each other within the model. discriminant validity was evaluated using the heterotrait-monotrait ratio (htmt) (henseler et al., 2015) with coefficients needing to be below 0.90 to confirm that the latent variables 29 are distinct. the results, shown in table 6, indicated that all htmt values were below 0.90, confirming that the constructs are appropriately differentiated. the overall fit of the measurement model was assessed through three key goodness-of-fit indices: the chi-square to degrees of freedom ratio (pcmin/df), the comparative fit index (cfi), and the standardized root mean square residual (srmr). according to established criteria, the model is considered to fit well if the pcmin/df ratio is less than 3, the cfi exceeds 0.90, and the srmr is below 0.08 (hair et al., 2006). the results showed pcmin/df = 2.330, cfi = 0.921, and srmr = 0.080, indicating that the measurement model demonstrates a good fit for the data. 30 table 5. results for the measurement model constructs items loading values cα cr ave performance expectancy pe1 0.74 0.86 0.87 0.53 pe2 0.85 pe3 0.91 pe4 0.58 effort expectancy ee1 0.83 0.79 0.78 0.50 ee2 0.80 ee3 0.62 social influence si1 0.58 0.72 0.75 0.50 si2 0.63 si3 0.91 facilitating conditions fc1 0.60 0.70 0.69 0.50 fc2 0.84 fc3 0.58 price value pv1 0.77 0.87 0.87 0.69 pv2 0.86 pv3 0.86 perceived performance risk pr1 0.60 0.74 0.75 0.50 pr2 0.71 pr3 0.81 behavioural intention bi1 0.83 0.89 0.87 0.70 bi2 0.96 31 bi3 0.88 table 6. heterotrait-monotrait ratio (htmt) results bi ee fc pe pr pv si bi ee 0.523 fc 0.834 0.621 pe 0.632 0.758 0.676 pr 0.422 0.178 0.357 0.315 pv 0.684 0.692 0.769 0.647 0.267 si 0.579 0.513 0.750 0.554 0.184 0.564 3.4. structural model assessment 3.4.1. dataset sample validation with the aim of validating the adequacy of samples collected, hoelter’s n critical index was applied with a significance level of 0.05, equivalent to 95% confidence (bollen & liang, 1988; hoelter, 1983). the size of the sample is131 questionnaires and the hoelter’s n (0.05) is 83 which exceeds the commonly cited minimum threshold of 75, indicating an acceptable sample size for model fit (garson, 2015). 3.4.2. framework model analysis after performing the overall goodness of fit of the research model indicating a good fit to the data (chi-square to degrees of freedom ratio (pcmin/df) of 2.330, comparative fit index 32 (cfi) of 0.921, standardized root mean square residual (srmr) of 0.080), the next step in the analysis involves assessing the explanatory power of the model's dependent variable, measured as r², which reflects how well the independent variables account for variations in the dependent variable. in this study, the r² for behavioural intention was found to be 0.49, meaning that 49% of the variability in behavioural intention is explained by the independent variables in the model (kapoor & singh, 2023; schukat & heise, 2021). the f² values (the change in r² when an exogenous variable is removed from the model) range from 0.09 to 0.16, suggesting a small to medium effect size (cohen, 2013) as indicated in table 7. further analysis involves examining the structural relationships among constructs using the structural equation modelling (sem) approach with the ibm spss amos version 26 software. the results of the path coefficient analysis are shown and detailed in figure 2 and table 8. findings reveal that fc significantly affects behavioural intention (β=0.625, p-value=0.010), while pr negatively impacts behavioural intention (β=-0.315, p-value=0.010). this suggests that participants who perceive higher performance risks are less likely to invest in agriculture 4.0 technologies. the analysis highlights that fc exerts the most substantial influence on the intention to adopt these technologies. conversely, the hypotheses related to pe (β=0.056, p-value=0.729), ee (β=0.039, p-value=0.792), si (β=0.097, p-value=0.686), and pv (β=0.069, p-value=0.685) were not supported, indicating that these factors do not significantly affect farmers' intentions to adopt agriculture 4.0 technologies for durum wheat cultivation. it’s worth noting that demographic variables such as age, education, and previous experience were initially considered for inclusion in the model. however, upon analysis, none of them were statistically significant, and their inclusion resulted in a 33 decrease in the model’s goodness of fit. therefore, to maintain the model’s validity and optimal fit, demographic variables were excluded from the analysis. figure 2. final structural model table 7. f-square results constructs f-square pe → bi 0.09 ee → bi 0.13 si → bi 0.09 fc → bi 0.16 pv → bi 0.11 pr → bi 0.10 table 8. results hypothesis β p-value decision h1: pe→ bi 0.056 0.729 unsupported h2: ee → bi 0.039 0.792 unsupported h3: si → bi 0.097 0.686 unsupported h4: fc → bi 0.625* 0.010 supported h5: pv →bi 0.069 0.685 unsupported h6: pr →bi -0.315* 0.010 supported note: *p-value < 0.05 4. discussion and policy recommendations 34 4.1. differences in impact between marginal and non-marginal areas and their policy implications as emerged from table 4, non-marginal farmers demonstrated higher performance expectancy, effort expectancy, social influence, facilitating conditions, and price value compared to marginal farmers. in this context, non-marginal farmers perceived agriculture 4.0 technologies as beneficial for resource efficiency, yield improvement, reduced effort, and work efficiency. policies and interventions for farmers should aim to reinforce their positive behavioural intentions and help them scale adoption. information provision (hines et al., 1987; stern & dietz, 2002) can focus on showing case studies of successful implementation from peer farmers, inducing a reduction in resource use, increased yield, and efficient work, accompanied by less effort. these campaigns could also be amplified to present, in the form of infographics or videos, how agriculture 4.0 can contribute to sustainability goals by adopting it. additionally, incentives (van valkengoed et al., 2022) such as stabilizing a specific measure within the regional rdp can reward those who adopt these practices. commitment strategies (cialdini, 2009) can motivate farmers to adopt new technologies because people are driven to remain consistent with their actions and beliefs, leading them to feel obligated to fulfil their promises. public commitments are made to try specific technologies, and these pledges can be recognized in public forums through certifications or awards. public recognition inspires individuals and sets positive examples in farming communities, encouraging others to follow suit (cialdini, 2009; schultz et al., 2007). 35 in contrast, marginal farmers expressed hesitancy and a negative behavioural intention due to higher perceived performance risks related to their concerns about being linked to external consultants and lower availability of facilitating conditions, especially for technology knowledge and limited access to financial resources. to increase knowledge and build technological trust, workshops, and training programs can help marginal farmers understand how to efficiently utilize agriculture 4.0 technologies and understand their benefits (kutter et al., 2011; menozzi et al., 2015). implementing pilot programs could enable marginal farmers to test these technologies on their farms for a limited time without longterm commitments. additionally, the government should prioritize providing subsidies or establishing lowinterest loans to facilitate access to agriculture 4.0 technologies. these technologies can lead to more efficient resource use and reduced environmental impact; outcomes that benefit not only farmers but also the broader public through environmental protection, rural development, and climate change mitigation. insurance incentive strategies can help reduce obstacles and ease fears of financial instability by offsetting potential losses during the transition to new technologies (mills, 2007; wreford et al., 2017). policymakers can support marginal farmers by collaborating with local institutions and experts to define small, attainable goals that gradually build trust and familiarity with technology. according to appelbaum and hare (1996), setting clear and realistic objectives—whether individually or through collective initiatives—can strengthen farmers’ self-efficacy and motivation, ultimately supporting more ambitious technological transitions. 36 4.2. the unified utaut2 model results of the unified utaut2 model supported h4 and h6 hypotheses as seen in table 6, showing that facilitating conditions and perceived performance risk significantly influence farmers’ intention to adopt agriculture 4.0 technologies on durum wheat within our convenience sample. the results showed that facilitating conditions significantly impacted farmers’ intentions to use agriculture 4.0 technologies. our findings align with fragomeli et al. (2024), who emphasize that practical and financial support from government initiatives significantly influences the adoption of agriculture 4.0. this support often includes subsidies, training and educational programs, and technical assistance, which help farmers overcome barriers to adopting new technologies. for instance, government-funded training sessions can provide information to improve farmers' understanding of how to use agriculture 4.0 technologies based on iot devices and data analytics platforms, making it easier for them to integrate these technologies into their operations. as well, creating educational programs explaining the challenges in traditional farming practices and the environmental and economic benefits of agriculture 4.0 can also positively induce the adoption of agriculture 4.0. araújo et al. (2021) highlight that having access to essential technological infrastructure such as iot sensors and data analytics tools is critical for successful implementation. when farmers have the necessary resources, infrastructure, and knowledge, they are more likely to adopt and utilize agriculture 4.0 technologies effectively. perceived performance risk had a negative and significant impact on the intention to adopt agriculture 4.0 technologies. perceived performance risk encompasses concerns about the 37 reliability and effectiveness of new technologies. benos et al. (2022) found that if farmers are uncertain about whether agriculture 4.0 will deliver the promised benefits or if they fear potential operational failures or being linked to external consultants, they may be hesitant to adopt these technologies. this concern can stem from previous experiences with technology failures or from insufficient evidence demonstrating the technology’s effectiveness. abikari (2024) further supports this by showing that perceived risks, including those related to technology performance, are crucial in adoption decisions. duong et al. (2019) also highlight that uncertainties about new technologies’ effectiveness can significantly impact farmers’ willingness to adopt them. to mitigate these concerns and build trust, not only clear demonstrations, pilot projects, and empirical evidence of technology benefits should be emphasized but also providing financial incentives, such as subsidies for purchasing agriculture 4.0 technologies or microloans (fragomeli et al., 2024; osorio et al., 2024). it is important to note that financial incentives and public subsidies may strongly influence farmers' awareness and perceived value of agriculture 4.0 technologies. menozzi et al. (2015) indicates that many italian farmers are primarily driven by economic benefits. this pattern could affect how farmers evaluate the usefulness and feasibility of adopting such technologies, especially if some options are more frequently promoted through subsidy programs or public campaigns. additionally, media coverage and 38 institutional promotions often emphasize the availability of tax credits or financial contributions for specific agriculture 4.0 technologies (confagricoltura, 2024; esg360, 2023), which may shape farmer awareness and preferences toward subsidized solutions. contrary to expectations, performance expectancy did not significantly influence the intention to use agriculture 4.0 technologies. although performance expectancy scores were relatively positive in both marginal (0.68) and non-marginal areas (0.96), this construct did not significantly influence behavioural intention in our model. this finding contrasts with im et al. (2008) and araújo et al. (2021), who found that when farmers perceive significant improvements in their operations due to new technologies, they are more inclined to adopt them. a possible explanation for our results could be that, while farmers acknowledge the potential benefits of agriculture 4.0, these benefits alone are not sufficient to drive adoption. this may be due to overriding concerns such as performance risk, limited infrastructure and experience with digital tools, which may weaken the link between perceived performance and the intention to adopt, especially in marginal areas. another possible explanation for our result could be that the perceived benefits of agriculture 4.0 technologies might not align with the specific needs of farmers in sardinia. if farmers do not clearly see how these technologies will enhance their productivity or efficiency, their intention to adopt may not be strongly influenced by performance expectancy (kutter et al., 2011; menozzi et al., 2015). effort expectancy also did not impact on the intention to adopt agriculture 4.0 technologies. this result differs from findings by fragomeli et al. (2024) and abikari (2024), who suggested that technologies perceived as user-friendly and requiring minimal additional effort are more 39 likely to be adopted. our findings are consistent with araújo et al. (2021), which noted that difficulties in integrating agriculture 4.0 technologies with existing systems can act as barriers to adoption. if the technologies are perceived as challenging to integrate, farmers may be discouraged from using them despite their potential benefits. this suggests that high expectancy, or the perception of increased effort and complexity, could negatively impact adoption intentions. social influence did not significantly affect the intention to adopt agriculture 4.0 technologies. this finding is consistent with li et al. (2024) which found that societal norms and peer pressure do not always positively impact the intention to use agriculture 4.0 technologies. farmers may resist adopting new technologies due to scepticism from their community or a preference for traditional methods. yap and al-mutairi (2024) also highlight that negative social perceptions within certain farming communities can hinder technology acceptance. if the broader community holds negative views about agriculture 4.0 technologies, individual farmers may be less inclined to adopt them, even if they recognize potential benefits. price value did not significantly influence the intention to adopt agriculture 4.0 technologies such as 4.0 tractors, weather stations and dss, analytics platforms, farm applications and drones. this result contrasts with findings by araújo et al. (2021) and fragomeli et al. (2024) who highlighted that farmers often justify the initial investment in agriculture 4.0 technologies through anticipated long-term economic returns, such as increased crop yields and improved resource management. the lack of significant impact in our study might 40 suggest that other factors, such as perceived risks or the complexity of technology, overshadow price considerations in the adoption decision-making process. overall, the extended utaut2 framework provides a solid foundation for understanding how facilitating conditions and perceived performance risk influence sardinian wheat farmers' intentions to adopt agriculture 4.0 technologies. designing a supportive choice architecture (thaler & sunstein, 2008) can simplify the adoption process. ensuring easy access to agriculture 4.0 technologies can reduce difficulties. this comprehensive approach, combining education, financial support, social recognition, and accessibility, addresses the barriers to adoption while enhancing farmers’ readiness to embrace agriculture 4.0 technologies. 5. conclusions the study highlighted notable differences in adoption intentions between marginal and nonmarginal farmers of durum wheat in sardinia, driven by disparities in facilitating conditions, perceived benefits, and social influence. non-marginal farmers demonstrated greater readiness and positive intentions toward agriculture 4.0 technologies, while marginal farmers faced barriers such as limited resources and higher perceived risks although they had positive performance expectancy, effort expectancy, social influence, facilitating conditions and price value. combining data from both groups provided a holistic understanding of regional adoption dynamics showing that facilitating conditions and perceived performance risk significantly affect the intention to adopt agriculture 4.0 technologies. facilitating conditions were found to have a positive and substantial impact, 41 highlighting the critical role of support mechanisms such as financial aid, technical training, and access to technological infrastructure in promoting the adoption of these advanced technologies. in contrast, perceived performance risk negatively influenced adoption intentions, reflecting farmers' concerns about the reliability and effectiveness of new technologies. several targeted interventions are recommended to enhance the adoption of agriculture 4.0 technologies. it is essential to focus on providing easy access to technical advice and educational programs through regional extension services. this approach will enable farmers to effectively utilize agriculture 4.0 technologies and reduce barriers to adoption. establishing accessible platforms for technical support will ensure that farmers are well informed about the benefits and functionalities of these technologies. furthermore, improving the educational qualifications of technicians working in regional extension services is necessary to address the knowledge gap related to agriculture 4.0 technologies. this aligns with the findings of caffaro and cavallo (2019) that lower levels of education were linked to higher perceptions of economic barriers, which in turn were negatively correlated with the adoption of smart farming technologies. universities and educational institutions should develop specialized courses or master’s programs focused on these technologies to equip technicians with the skills and knowledge required to support farmers and facilitate successful implementation. overall, by concentrating on enhancing facilitating conditions and addressing perceived performance risks, stakeholders can create a more supportive environment for the adoption of agriculture 4.0 technologies. these interventions will help overcome existing barriers, 42 promote the integration of innovative solutions in durum wheat farming, and ultimately improve productivity and sustainability within the agricultural sector. 6. limitations while this study provides valuable insights into adopting agriculture 4.0 technologies in durum wheat farming, it is important to acknowledge several limitations. the study is constrained by its geographical focus on sardinia, which may limit the generalizability of the findings to other regions with different agricultural contexts or technological infrastructures. additionally, using a convenience sampling method further limits the representativeness of the findings. therefore, the results can be generalised to the wider farming population in sardinia. additionally, the study relies on self-reported data from farmers, which may introduce biases related to respondents’ perceptions or reporting accuracy. the adoption intentions assessed are also based on subjective assessments, which might not fully capture actual technology usage or long-term adoption outcomes. furthermore, the research does not account for all possible variables influencing technology adoption, such as economic fluctuations or policy changes, which could impact the relevance of the findings over time. as highlighted by menozzi et al. (2015), economic incentives often outweigh environmental concerns in italian agricultural decision-making. therefore, farmers may have expressed more favourable opinions toward technologies with known funding opportunities, possibly biasing the intention data. future studies could attempt to control for this effect by comparing knowledge of subsidized vs non-subsidized solutions. also, future research could benefit from a broader geographical scope, longitudinal studies, and 43 a more comprehensive analysis of external factors to enhance the understanding of agriculture 4.0 adoption across diverse agricultural settings. disclaimer the data supporting this study's findings are available as a supplementary file to this paper. references abikari, m. (2024). emotions, perceived risk and intentions to adopt emerging e-banking technology amongst educated young consumers. international journal of bank marketing. abiri, r., rizan, n., balasundram, s. k., shahbazi, a. b., & abdul-hamid, h. 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(2020). decision support systems for agriculture 4.0: survey and challenges. computers and electronics in agriculture, 170, 105256. 1 factors influencing land rental market participation: a case study 1 in northern ireland 2 adewale h. adenugaa and claire jackb 3 agibson institute for land, food and environment, school of biological sciences, queen’s 4 university, belfast, united kingdom 5 beconomics research branch, agri-food and biosciences institute, 18a newforge lane, 6 belfast, bt9 5px, uk 7 corresponding author: a.adenuga@qub.ac.uk 8 this article has been accepted for publication and undergone full peer review but has not been 9 through the copyediting, typesetting, pagination and proofreading process, which may lead to 10 differences between this version and the version of record. 11 please cite this article as: 12 adenuga a.h., and jack c. (2025). factors influencing land rental market participation: a 13 case study in northern ireland, just accepted. doi: 10.36253/bae-16400 14 abstract 15 agricultural land mobility through an efficient land rental market has been shown to contribute 16 to the productive and sustainable utilisation of land, by facilitating the transfer of land from 17 less productive farmers to more productive farmers. however, this is not the case in northern 18 ireland where the sale of agricultural land is limited with a constrained tenanted sector. the 19 objective of this study is to analyse the factors influencing participation in the land rental 20 market in northern ireland. to achieve our objective, data from 1466 farmland owners was 21 analysed using principal component analysis (pca) and multinomial logistic regression model. 22 the results show that land rental market participation is impacted by motivational and 23 socioeconomic factors. the study recommends the development of schemes that support the 24 early and comfortable retirement of older farmers to increase the access of young farmers to 25 land and improve the land rental market. 26 27 keywords: land; rental market; sustainability; conacre; multinomial logit model 28 29 jel classification code : q15 30 31 1. introduction 32 agricultural land mobility has been shown to contribute to the productive and effective 33 utilisation of land as a resource by facilitating the transfer of land from less productive farmers 34 to more productive farmers (bradfield, butler, dillon, & hennessy, 2020; deininger & jin, 35 2008; li, ma, mishra, & gao, 2020; tesfay, 2023). this can be achieved through an efficient 36 land rental market, playing an important role in shaping farmers’ land-use decisions and 37 2 supporting sustainable agricultural production (min, waibel, & huang, 2017; udimal, peng, 38 & guillaume, 2021). a previous study in poland by marks-bielska (2021) has shown that with 39 stable long-term agreements, farming on leased land is comparable to farming on owned land 40 as long as the rights of the lessor and a lessee are protected. 41 land mobility is a significant issue in northern ireland (ni) as historically there have 42 always been strong sentimental and cultural ties to land (adenuga, jack, & mccarry, 2023; 43 bradfield et al., 2023a). the majority of farming in the region is undertaken on owned land and 44 the transfer of land through sale is limited due to high land sales prices. for example, the price of 45 agricultural land in ni ranges between £11500 and £20,000 per acre and less than one percent of the 46 total agricultural land area is offered for sale each year (harris, 2022). this is reflected in the high 47 proportion of farming undertaken on owned land compared to what occurs in other countries. owned 48 land as a percentage of farmed area in ni is 72% which in relative terms is high compared to 49 other countries such as france and germany where the proportion is 38% and 39% respectively 50 and at the eu level, which is 48% (adenuga, jack, & mccarry, 2021; daera, 2023a; 51 eurostat, 2022). this makes the purchase of land for agricultural production in ni less optimal 52 (adenuga, jack, & mccarry, 2021). this is because land ownership requires significant capital 53 investment, and further purchases may not be financially optimal when alternative 54 arrangements, such as renting, provide operational flexibility without the long-term financial 55 burden of land acquisition. additionally, owning a large proportion of land may indicate that a 56 farmer’s operational needs are already met, reducing the necessity for further expansion 57 through purchase. in addition, the average agricultural land area in the region can be regarded 58 as small relative to the other regions of the uk and has become highly fragmented with 59 increased concentration and competition for use among active and intending farmers (adenuga, 60 jack, mccarry, & caskie, 2023; milne et al., 2022). the low level of land mobility, and high 61 fragmentation have a consequential effect on the overall competitiveness and productivity of 62 3 the ni agri-food sector. this potentially constrains opportunities for new entrant farmers to 63 access land (milne et al., 2022). 64 access to land through the rental market provides an avenue for farmers and aspiring 65 farmers to access land and increase their competitiveness since it requires less capital outlay 66 (adenuga, jack, & mccarry, 2021; bradfield et al., 2020; jin & deininger, 2009). with solid 67 legal regulation, the land rental market can be regarded as a rational land management strategy 68 (marks-bielska, 2013). compared to the sale of land, it offers greater flexibility, with an 69 opportunity to design contractual terms to suit both the lessor and the lessee (zhang et al., 2018) 70 it allows farmers to alter farm size, exploit economies of scale, increase operation as well as 71 technical efficiency, and capture technological advances to achieve an optimal level of 72 production (bradfield et al., 2021; bradfield et al., 2020; geoghegan et al., 2021; li et al., 73 2020; zou & luo, 2018). in addition, the land rental market makes it possible for rural 74 households to generate additional income from their land (lan zhang et al., 2018; zou, mishra, 75 & luo, 2020). an efficient and fully functional land rental market that supports optimal 76 allocation and transfer of land is therefore necessary to bring land to its most productive use 77 and provide opportunities to transform the rural economies and improve the welfare of rural 78 households (bradfield et al., 2020; de janvry, gordillo, sadoulet, & platteau, 2001; huy, lyne, 79 ratna, & nuthall, 2016; jin & deininger, 2009). for example, a study by bradfield et al. (2020) 80 for dairy farmers in ireland, showed that farms that rent land generated a higher net margin 81 than farms with no rented land. the study also showed that increased access to land through 82 the land rental market enhanced farming households’ succession plans. 83 given the high cost and limited access to the sale of land in ni, an efficient land rental 84 market provides an avenue for farmers in the region to increase their farmland area. this is 85 particularly important given the shift towards sustainable farming with agricultural support 86 policies now targeted at environment-based payments (adenuga, jack, mccarry, et al., 2023; 87 4 little, lyon, & tsouvalis, 2023). in addition, it can also provide pathways for 88 accessing agricultural land for those who may otherwise have very limited access, for example, 89 young or new-entrant farmers (abay et al., 2021). this will be vital for farmers to meet their 90 environmental commitments. this paper sought to examine the determinants of participation 91 in land rental markets in ni as a mechanism to improve access to land. to achieve our 92 objective, we empirically analysed the motivational and socioeconomic factors influencing 93 land rental market participation in ni. to the best of our knowledge, this study provides the 94 first insights into the complex motivations and behavioural factors underlying farmers’ 95 decisions to participate in the land rental market. while these motivations may also apply to 96 farming in general, understanding, how they influence farmers’ land rental decisions is 97 essential to improving the land market. a previous study in ireland by geoghegan et al. (2021) 98 show that attitudinal factors are a significant predictor of openness to land mobility, both on 99 the supply and demand side of the market this study employs the multinomial logistic 100 regression model which allows us to consider not just farmers who rent-in and rent-out land 101 but also those who neither rent-out nor rent-in land. a previous study by udimal et al. (2021) 102 also employed a similar approach. however, they did not consider motivational factors that 103 could influence participation in the land rental market. it remains unclear how the different 104 farmers’ motivations and behavioural factors influence their decision to participate in the land 105 rental market. results from this study will be useful in providing the requisite evidence base to 106 inform the formulation of policies aimed at encouraging farmers and landowners to engage in 107 the land rental market. 108 2. research methodology 109 in this study, we employed the multinomial logit (mnl) model to analyse the 110 motivational and socioeconomic factors influencing land rental market participation in ni. 111 farms in ni are typically family-owned with a small, tenanted sector compared to other regions 112 5 of the united kingdom. by making use of a couple of attitudinal statements, we derived 113 different farming motivations of farmers using principal component analysis (pca). 114 2.1 data collection 115 116 the dataset used in this study was obtained from a cross-sectional survey of farmers in 117 ni. the sampling frame was the farm census data for ni which consisted of 12 747 farmers for 118 the year 2020 from which we selected 4029 farmers using a stratified random sampling 119 technique. the farmers were classified into six strata based on farmland ownership and rental status: 120 those farming solely on owned land, those farming both on owned and rented land, those farming on 121 owned and rented land while also leasing out land, those farming only on owned but also leasing out 122 land, those farming exclusively on rented land and landowners who lease out all their land. due to the 123 large number of farmers in the “owned land only” and “owned and rented land” groups, we randomly 124 selected 20% of farmers from these two groups. a well-structured questionnaire was developed 125 after a comprehensive literature review and key informant interviews (adenuga, jack, & 126 mccarry, 2021). the questionnaire was organised around some themes. this includes land 127 ownership and rental status and duration, socioeconomic as well as farmers’ attitudinal 128 characteristics. the questionnaire was developed in a hybrid format, making it possible to be 129 completed online and on paper. the snap survey software was used to design the online version 130 of the questionnaire with a qr code that was generated and placed on the front page of the 131 paper version of the questionnaire. respondents either completed the paper questionnaire 132 directly or scanned the qr code with their phone to complete it online. the survey took place 133 between december 2021 and february 2022. we sent two reminders over this period with the 134 qr code included in the letters and the respondents could request a paper copy of the 135 questionnaire if they require a new one. no personally identifiable information was collected, 136 and farmers were assured of their anonymity in reports or publications resulting from the 137 project. to encourage the farmers to complete the questionnaire, each completed and returned 138 6 questionnaire was entered into a prize draw for 1 of 10 £100 e-vouchers. this was for farmers 139 who had indicated their intention to participate in the draw. out of the 4029 questionnaires 140 administered, 1228 paper questionnaires were returned in the pre-paid envelopes sent alongside 141 the questionnaire while 499 questionnaires were completed online. in total, we received 1727 142 responses. this number was reduced to 1466 following the dropping of 91 farmers who both 143 rented out and rented in land, and farmers with no information relating on land ownership or 144 rentals. table 1 gives a summary of the socioeconomic characteristics of the farmers. some of 145 the variables do not add up to 1,466 because some of the farmers did not fully complete the 146 questionnaire, omitting some questions. 147 2.2 empirical model 148 149 the land rental market participation is modeled based on the random utility theory 150 (udimal et al., 2021). the theory assumes that each farmer 𝑖 has different options (𝑘 =151 1,2,3…𝐾) available to them and they chose a particular option (y) that offers the maximum 152 utility by considering the economic and environmental risk associated with the various options 153 (diriye et al., 2022; grant et al., 2019; mcfadden, 1972). to analyse the factors influencing 154 land rental market participation in this study, we employed the multinomial logit model 155 (mnl)(daly, 1987; gensch & recker, 1979). the mnl model has been used extensively in 156 the literature to analyse farmers’ behaviour and choices in decision-making (diriye et al., 2022; 157 duressa, 2021; otieno, 2022; ouattara et al., 2022). the model, unlike the bivariate tobit 158 model employed in previous studies, for example, the study by rahman (2010), can 159 accommodate the non-binary, multivariate nature of the dependent variable (dang & pham, 160 2022; osanya et al., 2020; yasmin et al., 2022). previous studies have also shown that the mnl 161 model performs better than the multinomial probit model (mnp) (dow & endersby, 2004). 162 besides, the mnl model possesses fewer computational problems compared to mnp because 163 the probit likelihood function is often flat near its optimum and it generally requires numerical 164 7 approximation for the multivariate integrals(dow & endersby, 2004). it is also not prone to 165 optimization errors. although it should be acknowledged that mnl model relies on the 166 independence of irrelevant alternatives (iia) assumption, which requires that the relative 167 probabilities of choosing between any two alternatives remain unchanged when other 168 alternatives are introduced or removed. in this study, certain alternatives, such as the sale and 169 purchase of agricultural land, were excluded due to the thin market for land transactions, as 170 discussed in the introduction. while these alternatives may theoretically exist, their practical 171 relevance is limited in this context. nevertheless, we assess the validity of the iia assumption 172 and the results indicate that the exclusion of these alternatives does not significantly influence 173 our findings. 174 generally, our choice of a multivariate dependent variable is based on the need to 175 capture the multiple dimensions of farmers’ land rental behaviour within a single model 176 framework which allows us to identify distinct factors influencing each dimension 177 simultaneously. it should also be acknowledged however that our use of the mnl model 178 means we are not modelling the actual amount of land rented-in or rented-out which could 179 provide additional insight. the choice of our model is in line with the objective of this paper 180 which was mainly to identify factors influencing farmers’ land rental behaviour. in this study, 181 it is assumed that a farmer can choose from any of three alternatives. that is rent-out land; rent-182 in land and neither rent-in nor rent-out land. the observed outcome, 𝑌𝑖𝑘 as a function of the 183 variables 𝑋𝑖𝑘 is presented in equation (1). 184 𝑌𝑖𝑘 = 𝑋𝑖𝑘𝛽𝑘 + 𝜀𝑖𝑘 (1) 185 where 𝑋 is a vector of explanatory variables which include motivational and farmer-specific 186 socioeconomic characteristics that are hypothesised to influence land rental market 187 participation, 𝛽 represents the parameters to be estimated and 𝜀 is the error term or random 188 8 component of the model and it is assumed to be independently and identically distributed 189 (diriye et al., 2022). the choice probability of the 𝑖𝑡ℎ farmer in choosing option k from the list 190 of k options following mellon-bedi et al. (2020) is presented in equation 2. 191 (𝑌𝑖 = 𝑘|𝑋) = exp⁡(𝛽𝑘𝑋𝑖) 1 + ∑ exp⁡(𝛽𝑗𝑋𝑖) 𝐾 𝑗=2 , 𝑓𝑜𝑟⁡𝑘 > 1⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(2) 192 in estimating the model, one of the categories was normalised by equating it to one 193 (equation 3). in our analysis, we used neither renting-out nor renting-in land category as the 194 baseline such that only two equations are estimated. the probability of renting out and renting 195 in land is compared to the probability of neither renting in nor renting out land. 196 𝑃(𝑌𝑖 = 1) = 1 1 + ∑ exp⁡(𝛽𝑗𝑋𝑖) 𝐾 𝑗=2 , 𝑓𝑜𝑟⁡𝑘 = 1⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(3) 197 based on this parameterisation, the jth element of the vector 𝛽𝑘 is interpreted as the 198 increase in the log-odds ratio of the kth category relative to the reference category holding the 199 other explanatory variables constant to their mean values (carpita et al., 2014). 𝑌𝑖 is the 200 dependent variable representing farmers that rent out and rent in land in the two models 201 respectively. the variables hypothesised to influence participation in the land rental market 202 include the age of the farmer measured in years, membership of a business development group 203 (bdg) measured as a dummy variable, having a diversification enterprise (this refers to owning 204 a diversification enterprise on the farm) measured as a dummy variable, off-farm employment 205 (defined as income earned outside of the farm by the primary decision-maker and may include 206 spousal income, which could influence household labour availability and financial 207 stability) measured as a dummy variable, identification of a successor measured as a dummy 208 variable, the enterprise type of the farmer (dairy, beef sheep and others which include arable 209 and horticulture enterprises) , area of farmland owned measured in hectares, level of 210 agricultural qualification measured as a dummy variable, level of formal education, time 211 9 commitment to farming (full-time or part-time) and land type (land classification based on their 212 agricultural conditions as lowland, disadvantage or severely disadvantaged). these variables 213 were obtained from the literature and are considered important factors likely to influence land 214 market participation (adenuga, jack, & mccarry, 2021, 2023; bradfield et al., 2020; che, 215 2016; zou et al., 2020). the descriptive statistics of the variables are presented in table 1. 216 to incorporate the motivational factors into our analysis, principal component analysis 217 (pca) was employed. the pca is a statistical technique that examines the pattern of 218 correlations amongst the explanatory variables and creates a smaller set of uncorrelated linear 219 combinations of the original variables (lapple & kelley, 2010; o’kane et al., 2017). the 220 higher a respondent’s score on each of the factor variables, the higher their associated level of 221 agreement with the specific attitudinal component (howley, 2015; o’kane et al., 2017). the 222 analytical technique was used to reduce sixteen attitudinal statements which represent varying 223 motivations of farmers into four components. the statements included in our principal 224 component analysis were obtained from a comprehensive review of related literature on 225 farmers’ behaviour (adenuga, jack, & mccarry, 2023; howley, 2015; howley et al., 2015). 226 we used the promax rotation to facilitate the interpretation of the components. as is the usual 227 practice, components with an eigenvalue of at least one were retained (o’kane et al., 2017). 228 we retained statements with loadings greater or equal to 0.3 on their target factor. statements 229 that did not load greater or equal to 0.3 on any component were dropped. the four motivational 230 components obtained from the pca include principal components (pc) 1 which shows high 231 loadings for items relating to efficient farm management and technology adoption and was 232 termed “progressive construct”, pc2 loads highly on statements that do not support pro-233 environmental behaviour such as “i am not that concerned about environmental issues” and 234 was termed “environmental apathy” construct., pc3 which loads on the statements that 235 prioritise the protection of the environment was termed “pro-environmental construct ” and 236 10 pc4 which loads highly on statements that relate to being risk averse and was termed “risk 237 averse” construct. two statements that did not align with any of the four components were 238 dropped. the internal consistency of each component was assessed using cronbach’s alpha 239 coefficient which ranged between 0.60 and 0.68, showing good reliability. with a kaiser–240 meyer–olkin measure of sampling adequacy of 0.75, the components represent a significant 241 proportion of the variance in the data (läpple & kelley, 2013). the results of the pca analysis 242 are presented in table a1 in the appendix. the stata spost13 post-estimation command was 243 employed to allow the coefficients of the mnl model to be interpreted in terms of the 244 percentage change in odds for a unit change in the explanatory variable (long & freese, 2005). 245 3. results and discussion 246 3.1 descriptive and socioeconomic characteristics 247 table 1 gives an overview of the socioeconomic and farm characteristics of the 248 respondents. similar to the general farming population in northern ireland, about 90% of the 249 farmers in our sample are livestock farmers (daera, 2023b). our analysis showed that more 250 than half (55%) of the farmers undertake their farming activities in land categorised as either 251 disadvantaged (da) or severely disadvantaged areas (sda). the sda or da (less favoured 252 areas (lfa)) land types refers to land located in parts of the country which, because of their 253 relatively poor agricultural conditions, have been so designated under eu legislation(caskie et 254 al., 2001; daera, 2023b). the majority of respondents are male (91.5%) and 75% of 255 respondents are married. thirty-two percent of the farmers stated that they had no general 256 education qualifications. based on land rental participation status, about 45% of those who rent 257 out land have a diploma or degree level education while it is 35%, of those who rent in land. 258 in terms of agricultural qualification, 33% of the farmers stated that they have formal 259 agricultural qualifications. the value is higher for those that rent in land with 40% of them 260 stating that they have a formal agricultural qualification. only 30% of those who rent out land 261 11 stated that they have formal agricultural qualifications, and it is 33% for those who neither rent 262 in nor rent out land. for 37.9% of respondents, farming was on a full-time basis (these are 263 farmers who work, at least on average, 38.0 hours per week on the farm). twenty-six percent 264 of sheep farmers surveyed reported that they were farming full-time. ninety-eight percent of 265 the farms are family farms held either as sole ownership or partnership. the average years of 266 farming experience is 35 years and 55% of the farmers stated that they have off-farm 267 employment. the proportion of farmers with off-farm employment that rent in land was 61% 268 while it was 47% for those that rent out land and 55% for those that neither rent in nor rent out 269 land. only 13% of the farmers are members of the business development groups (bdgs). 270 nineteen percent of those who rent in land were in the bdg group compared to just 9% for 271 those who rent out land and those who neither rent in nor rent out land. the bdgs is a 272 knowledge transfer scheme developed by the northern ireland college of agriculture, food 273 and rural enterprise (cafre) in march 2016. the scheme employs a group approach aimed 274 at improving the performance of farm businesses through facilitated ‘peer-to-peer’ learning to 275 encourage the fostering of knowledge capital and knowledge exchange between actors (jack et 276 al., 2020). forty percent of the farmers stated that a successor to the farm has been identified. 277 this was 34% among those who rent out land, 41% among those who rent in land, and 42% 278 among those who neither rent out nor rent in land. the modal age group was 55 to 64 years 279 and 68% of the farmers are aged over 55 years. based on land rental market participation, as 280 much as 83% of those who rent out land are older than 55 years, while it is 44% of those who 281 rent in land and 66% of those who neither rent in nor rent out land. this is an indication that 282 those who rent out land are older and more likely to include retired farmers. the average 283 amount of cultivated land owned is 36 hectares. the value is 43 hectares for those that rent in 284 land, 37 hectares for those that rent out land, and 28 hectares for those that neither rent in nor 285 rent out land. 286 12 287 288 289 290 291 table 1: socioeconomic characteristics of respondents 292 variables frequency percentage(%) time commitment (n= 1354) full-time 513 37.9 part-time 841 62.1 land types (n = 1351) lowland 608 45.0 disadvantaged 428 31.7 severely disadvantaged 315 23.3 diversification activities (n=1466) yes 258 17.6 no 1,208 82.4 identification of successor (n=1347) a successor has been identified 536 39.8 successor not yet identified 811 60.2 bdg membership (n = 1466) yes 192 13.1 no 1,274 86.9 age of the farmer (n = 1466) 13 less than 30 49 3.3 30-40 118 8.05 41-54 303 20.7 55-64 400 27.3 65-74 358 24.4 75 or older 238 16.2 education of the farmer (n=1466) no formal qualification 471 32.1 less than 5 gcses 136 9.3 5 gcses or equivalent 219 14.9 a level or equivalent 97 6.6 higher education diploma or equivalent 242 16.5 degree level or higher 301 20.5 agricultural qualifications (n=1447) no formal agricultural qualification 963 66.6 national diploma nvq level 2 or equivalent 171 11.8 national diploma nvq level 3 or equivalent 101 6.9 hnd level or equivalent (just below degree level) 74 5.1 degree level or equivalent 68 4.7 others 70 4.8 off farm employment (n=1466) yes 808 55.1 no 658 44.8 source: own elaboration 293 294 3.2 land market participation 295 296 14 the results of our analysis in terms of land rental characteristics showed a relatively 297 even distribution among the three categories of farmers. we found that 31% of the farmers 298 farmed on owned land only (i.e., neither rent in nor rent out land) while 37% and 31% of the 299 farmers rented in and rented out land respectively. table 2 provides an overview of how land 300 is rented in and rented out in conacre and on long-term lease. the conacre is the predominant 301 form of land rental system in the region. it is a traditional short-term land rental system unique 302 to the island of ireland in which land is let to a farmer nominally for 11 months or 364 days 303 without the need for either party to enter a long-term commitment. specifically, 91% and 78% 304 of the land that is rented out and rented in respectively are in conacre (adenuga, jack, & 305 mccarry, 2023). while the duration for renting land in conacre is 11 months or 364 days, the 306 arrangement between the landlord and the tenant is such that it can be rolled over for several 307 years with no formal contract signed. for example, as shown in table 2, among landlords 308 renting out and farmers renting in land, it can be observed that 48% and 67% have been rented 309 out and rented in respectively for more than 10 years. the long-term lease refers to a land rental 310 arrangement in which a formal contract has been signed between the landlord and the tenant. 311 this proportion of long-term leases among landowners and farmers in ni is still relatively small 312 with a higher proportion on 5-year leases as shown in table 2. 313 table 2: land rental system and duration of rental 314 land rental characteristics renting out land renting in land frequency percentage (%) frequency percentage (%) ways by which land is rented conacre 422 91.1 426 78.0 a long-term lease 22 4.8 66 12.1 a combination of conacre and longterm lease 19 4.1 54 9.9 duration of conacre (years) less than 3 40 9.1 34 7.1 15 3 413 9.3 17 3.5 4 25 5.7 11 2.3 5 34 7.7 28 5.8 more than 5 but less than 10 88 20.0 68 14.2 10 or more years 212 48.2 322 67.1 duration of long-term lease less than 3 2 4.9 9 7.5 3 2 4.9 8 6.7 4 1 2.4 0 0 5 20 48.8 42 35.0 more than 5 but less than 10 5 12.2 15 12.5 10 or more years 11 26.8 46 38.3 315 as presented in table 3, we also analysed the land market participation characteristics 316 of the respondents based on enterprise types. the results show that renting in land was more 317 common among dairy farmers compared to other enterprise types with 79% of the dairy farmers 318 renting in land, in addition to farming on owned land. only 5.5% rented out land. this may 319 reflect the intensive nature of dairy farming in northern ireland (adenuga et al., 2020). it also 320 may have been driven by the relatively higher incomes of dairy farms and their ability to pay 321 higher rents. this is evidenced by the high percentage (84%) of dairy farmers who undertake 322 farming on a full-time basis. although the majority (84%) of the land rented in was in conacre 323 rather than long-term leases. 324 table 3: land rental characteristics by enterprise type 325 enterprises frequency percentage rent out land (%) rent in land (%) neither rent out nor rent in land (%) proportion that are full-time farmers (%) beef suckler 409 32.2 18.3 43.3 38.4 36.3 beef finishing 316 24.9 25.0 39.9 35.1 35.4 16 source: own elaboration 326 3.3 results of econometric analysis 327 328 the results of the parameter estimates resulting from the mnl model are presented in 329 table 3. the likelihood ratio chi-square of 366.61 with a p-value < 0.0001 indicates that the 330 model fits well. a test for the independence of irrelevant alternatives (iia)showed that it was 331 not violated. like other categorical response models, the interpretation of the mnl model is 332 complex because of its nonlinearity. to interpret our mnl model, we have employed the long 333 and freese (2006) spost13 command, listcoef which can provide a single table of the estimates 334 for all the comparisons of outcome categories for each variable included in the model. the 335 coefficients are interpreted in terms of the standardized or percentage change in odds for a unit 336 change in the explanatory variable holding all other variables constant (howley et al., 2015; 337 long & freese, 2006). 338 the results show that motivational and socioeconomic factors influence the likelihood 339 of land rental market participation. in total, fourteen explanatory variables were statistically 340 significant for factors influencing the decision to rent out, land. for the factors influencing the 341 decision to rent in, land, we found seven explanatory variables to be statistically significant 342 compared to the baseline (the decision to neither rent in nor rent out land). we found the 343 progressive construct variable to be a statistically significant factor (p < 0.05) for the decision 344 to rent out and rent in land. in specific terms, one standard deviation increase in the progressive 345 sheep 306 24.1 29.1 35.3 35.6 25.8 dairy 110 8.7 5.5 79.1 15.5 83.6 arable 81 6.4 49.4 27.2 23.5 43.2 poultry 30 2.4 33.3 23.3 43.3 66.7 horticulture 13 1.0 61.5 23.1 15.4 38.5 pig 5 0.3 20.0 60.0 20.0 80.0 17 orientation factor decreases the odds of renting out, land for the average farmer by 15.9%. on 346 the other hand, a standard deviation increase in the progressive orientation factor increases the 347 odds of renting in, land by 18.6%. this implies that farmers, with a progressive and positive 348 mindset and the motivation to maximise profit, have a lesser tendency to rent out land compared 349 to the baseline and are more likely to rent in land. this is understandable as more land is often 350 required to take advantage of economies of scale, adopt new technology, and increase farm 351 incomes (huy et al., 2016; geoghegan et al., 2021). the pro-environment factor was found to 352 have a positive relationship with renting out land, but it was not statistically significant. it was 353 however statistically significant and negatively related to renting-in land. a standard deviation 354 increase in the pro-environment factor reduces the odds of renting in, land by 13.5%. the 355 negative relationship between pro-environmental behaviour and renting in, land may be linked 356 to the predominantly short-term conacre land rental system predominant in ni. this is because 357 environmentally friendly agricultural practices often require long-term investments in soil 358 health and this is difficult to achieve on short-term land rentals without adequate tenure 359 security. besides, farmers with pro-environmental behaviour may avoid renting additional land 360 to retain effective control of their land in line with their values of ensuring proper stewardship 361 of the land. 362 in terms of the socioeconomic characteristics, we found the enterprise type and the time 363 commitment to farming to be statistically significant factors in both the decision to rent out and 364 rent in land but with opposite signs. specifically, farms that are classified as dairy, beef or 365 sheep enterprises are less likely to rent out land and more likely to rent in relative to the baseline 366 enterprises of arable, horticulture, poultry, and pig farms which were categorised as “others”. 367 having a dairy, beef or sheep enterprise reduces the odds of renting out land by 75%, 63% and 368 46% respectively and increases the odds of renting in land by 378%, 68% and 28% 369 respectively. this reflects the intensive nature of land usage in livestock production in ni 370 18 relative to other enterprises with about 79% of the total farmed area used for livestock 371 production (daera, 2023a).on the other hand, the age of the farmer, land type, identification 372 of a successor, the education of the farmer, the area of land owned, and land type were found 373 to be a statistically significant factors influencing the decision to rent out land. membership of 374 the bdg and access to off-farm income were found to be statistically significant factor 375 influencing the decision to rent in land. 376 the identification of a successor reduces the odds of renting out land by 37% relative 377 to the baseline. with a successor already identified, farmers would rather keep their land to 378 themselves to keep the successor on the farm rather than renting out land. a previous study by 379 bradfield et al. (2020) has also shown that with a successor identified, farmers are more likely 380 to keep their land and instead rent in land to provide the successor with immediate employment 381 and skill development. similarly, study by daniele (2024) has shown that the identification of 382 a successor in a farming household significantly influences farmers’ strategic choices. farmers 383 with higher education are more likely to rent out land. a standard deviation increase in degree 384 level or higher education increases the odds of renting out land relative to the baseline by 34%. 385 this may imply that farmers who are highly educated may be spending less time in direct 386 farming activities. this is because higher education tends to increase the opportunity cost of 387 farming due to the increased potential of securing higher-paying jobs outside agriculture. 388 consequently, these farmers may choose to rent out their land instead of farming it themselves. 389 this connection between education and land rental decisions reflects broader economic trends 390 where higher education leads to more diverse career options, thereby influencing land use 391 choices. this result corresponds to that obtained by rahman (2010), bizimana (2011), and 392 zhang et al. (2022) in which they found that farmers with higher levels of education are more 393 likely to rent out land. the result is however in contrast to that obtained by tesfay (2023) in 394 which they found that the less educated farmers are more likely to rent out their land. 395 19 our result also showed that farmers older than 55 years are more likely to rent out land. 396 these are farmers who may be approaching retirement and probably do not have a successor. 397 such farmers will be better off renting out part of their land for additional income. this result 398 corresponds to that obtained by tesfay (2023) and min et al. (2017) in which they found older 399 farmers to be more likely to rent out land. the result is also in line with that obtained by mellon-400 bedi et al. (2020) in their study of smallholder participation in the land rental market in china 401 in which they found that households with a higher share of older people were more likely to 402 participate in the land rental market, it is however in contrast to that obtained by zhang et al. 403 (2022) who found that aged farmers participate less in renting out land. our result also shows 404 that farmers who own larger farm areas are more likely to rent out land. one standard deviation 405 increase in owned land area increases the odds of renting out land by 34% relative to the 406 baseline. a similar result was obtained by vranken and swinnen (2006) in which they found 407 farming households who own more land to be more likely to rent out land. farmers that farm 408 in lands categorised as disadvantaged or severely disadvantaged are less likely to rent out land 409 compared to farming on low land relative to the baseline. this may be because most of the 410 farmers that farm on disadvantaged and severely disadvantaged lands are small beef and sheep 411 farmers who undertake farming usually on a part-time bases. for this group, farming is mostly 412 to keep the family enterprise with a greater attachment to the land. as a result, they are less 413 likely to rent out their land. 414 table 4: determinants of land market participation (n= 1,121) 415 rent-out land rent-in land variables coef. std. err. % %stdx coef. std. err. % %stdx environmentalapathy 0.029 0.052 2.9 4.7 -0.035 0.047 -3.5 -5.6 risk-averse 0.117 0.080 12.4 13.5 0.051 0.068 5.2 5.6 pro-environment 0.079 0.078 8.2 9.3 -0.128* 0.071 -12.0 -13.5 progressives -0.104** 0.052 -9.9 -15.9 0.103** 0.049 10.8 18.6 bdg membership 0.187 0.267 20.5 7.0 0.611*** 0.223 84.1 24.6 off-farm income -0.149 0.195 -13.8 -7.1 0.484*** 0.185 62.3 27.3 successor -0.466*** 0.175 -37.3 -20.5 -0.095 0.159 -9.1 -4.6 dairy enterprise -1.376*** 0.534 -74.7 -32.9 1.566*** 0.375 378.8 57.5 20 beef enterprise -0.989*** 0.231 -62.8 -38.9 0.520** 0.264 68.2 29.6 sheep enterprise -0.609** 0.259 -45.6 -22.1 0.250 0.296 28.4 10.8 owned land area (ha) 0.006*** 0.002 0.6 33.5 0.003 0.002 0.3 17.7 agricultural qualification 0.251 0.210 28.5 12.7 0.021 0.195 2.2 1.0 lower than 5 gcses -0.590* 0.348 -44.6 -15.5 -0.266 0.276 -23.3 -7.3 5 gcses or equivalent 0.070 0.273 7.3 2.6 -0.206 0.238 -18.7 -7.2 a level or equivalent 0.758** 0.352 113.5 21.0 0.251 0.345 28.5 6.5 higher education—diploma or equivalent 0.540* 0.283 71.6 22.7 0.126 0.263 13.4 4.9 degree level or higher 0.724*** 0.253 106.3 33.6 -0.260 0.258 -22.9 -9.9 full-time -0.402* 0.206 -33.1 -17.9 0.804*** 0.184 123.4 48.4 age (greater than 55) 1.142*** 0.225 213.2 70.6 0.003 0.191 0.3 0.1 disadvantaged -0.656*** 0.189 -48.1 -26.4 -0.191 0.180 -17.4 -8.5 severely disadvantaged -0.984*** 0.231 -62.6 -33.8 -0.159 0.194 -14.7 -6.5 source: own elaboration 416 note: % is the percent change in odds for unit increase in our explanatory variable; %stdx is the percent 417 change in odds for a standard deviation change in our explanatory variable; single, double, and triple 418 asterisks (*, **, ***) indicate significance at the 10%, 5%, and 1% level, respectively 419 the result for the factors influencing the decision to rent in land showed that farmers 420 with off-farm income have a positive and statistically significant (p < 0.01) effect on the 421 decision to rent in land. this may imply that the farming households with off-farm income 422 have enough money to invest in and expand their farming activities compared to households 423 with no off-farm income. a previous study by zou et al. (2020) has also found that farmers 424 with part-time, off-farm employment have a greater likelihood of renting in, land. similar result 425 was obtained by geoghegan et al. (2021). however, the studies by vranken and swinnen 426 (2006) and kung (2002) found a contrasting result as they showed that greater availability of 427 off-farm income reduces the probability of renting in land. one possible explanation for the 428 contrasting result is that off-farm income, as defined in this study, may include spousal income. 429 if this is the case, the variable may also serve as a proxy for household labour availability, as 430 married farmers may have additional family members contributing to farm work. this 431 increased labour capacity could, in turn, make land expansion through rental agreements more 432 viable. farmers who are members of the bdgs are also more likely to rent in, land. a previous 433 study by adenuga, jack, ashfield, et al. (2021) has shown that farmers who are members of 434 the bdg operate a more profitable enterprise. their higher profitability and access to 435 21 information through membership of the bdg may contribute to the decision to rent in, more 436 land and earn more income from farming. similarly, farmers who undertake farming on a full-437 time basis are more likely to rent in, land compared to the baseline. this may be linked to the 438 assumption that farmers who undertake farming on a full-time basis are more likely to increase 439 their farm size by renting more land. 440 4. conclusions 441 in this paper, the effect of motivational and socioeconomic factors on farmers’ 442 participation in the land rental market has been analysed using a multinomial mnl model. the 443 use of the mnl model allows for greater flexibility as it is able to incorporate not just the 444 demand side but also the supply side of participation and non-participation in the land rental 445 market simultaneously. an efficient land rental market is essential to increase the efficiency 446 and sustainability of agricultural production through greater access of young and productive 447 farmers to land. our results showed that participation in the land rental market is not only 448 influenced by socioeconomic factors but also motivational factors. most land rentals in ni are 449 still on short-term leases called conacre with a majority of the farmers undertaking farming on 450 owned land. from a policy perspective, it implies that the development of appropriate strategies 451 to encourage land market participation can contribute to the transformation of the rural 452 economy through efficient land use. the enterprise type of the farmer, the identification of a 453 successor and the amount of time devoted to farming are particularly significant factors in the 454 decision to participate in the land rental market. while the result shows that farmers with 455 successors are less likely to rent out land, only 40% of the farmers in our sample already have 456 a successor identified. policies that support succession planning and the transfer of sustainable 457 practices could help maintain the viability of farms in disadvantage areas while programmes 458 that facilitate the transfer of land from farmers without successors to younger and more 459 productive farmers will strengthen the rental market. our results showed that older farmers are 460 22 also more likely to rent out their land compared to younger farmers.. the study recommends 461 the development of policies that encourage the younger generation to engage in farming on a 462 full-time basis through schemes that allow for early and comfortable retirement of the older 463 farmers who are happy to make their land available for rent. an example is the ni land mobility 464 scheme which although has now been replaced with a new scheme (farming for future 465 generations) gave young farmers the opportunity to partner with retiree farmers. this allowed 466 young farmers to learn their trade from those hoping to retire and prove themselves with the 467 eventual aim that they will be able to take over and lease the land in a few years. this is essential 468 to promote generational renewal and the modernisation of agriculture for environmental 469 improvements. 470 another important result of this study is the negative relationship between pro-471 environmental behaviour and renting in, land. while this may be linked to the fact that 472 sustainable agricultural practices often require long-term investment in land, an alternative 473 explanation is that more intensive farming systems, which tend to generate higher profits, 474 create a greater demand for rented land. this is supported by our data in table 3, which shows 475 that dairy farmers, who typically engage in more intensive production, rent the most land. 476 these factors highlight the complex relationship between environmental practices and land 477 rental decisions. this supports the need for policymakers to develop measures aimed at 478 encouraging the adoption of long-term land leasing which provides the tenure security needed 479 to invest in sustainable practices. this could incentivise farmers with pro-environmental 480 behaviour to rent in, land, improving land quality and protecting the environment. financial 481 incentives could also be provided to landowners to rent to less intensive tenants. a previous 482 study by adenuga, jack, mccarry, et al. (2023) has shown that incentives such as income tax 483 incentives to landlords and tenants for sustainable management of agricultural land could 484 encourage long-term land leasing and increase the likelihood of farmers with pro-485 23 environmental behaviour renting in more land. this will enhance long-term productivity and 486 improve the efficiency of land use by allowing more diverse and sustainable practices. 487 our finding that progressive farmers are less likely to rent out land and more likely to 488 rent in land also has important policy implications. by developing a policy framework that 489 encourages the renting of more land by progressive farmers, land will be put to more effective 490 and productive use, leading to a more functional land market. this can be achieved if the 491 government enacts policies that promote long-term flexible arrangements and encourage 492 landowners to lease out their land to more productive farmers if they are not being used 493 efficiently. the creation of an enabling environment that allows progressive farmers to rent in 494 more land will contribute to a more dynamic and competitive land market 495 the fact the majority of the land in the region is located in disadvantaged areas has 496 implications for the rental market with most farmland being fragmented. this supports the need 497 to encourage pro-environmental practices that ensure long-term stewardship of the land. there 498 is also the need to provide targeted financial incentives and infrastructure to make the renting 499 of the land more attractive. a previous study by onofri et al. (2023) also supports the need to 500 provide incentives (tax incentives and subsidies), to drive decision-making of both tenants and 501 landowners. income tax relief provided for renting out land on long-term lease in the republic of 502 ireland has resulted in increased land rental (bradfield et al., 2023b). recently, the ni government 503 has embarked on a soil health nutrient scheme (shns) aimed at testing all soils in ni between 504 2022 and 2026 to improve sustainability and efficiency in the farming sector. this is a good 505 starting point as it provides up-to-date data on the conditions of the land and how it can be 506 improved. this may encourage a more balanced land rental market in which disadvantage areas 507 are not left behind in terms of productivity gains and land market participation. a limitation of 508 our study is that our methodology does not consider the area of land (as a proportion of total 509 land area) rented in or rented out. this should be considered in future research as it has the 510 24 potential to influence landowners and farmers’ motivations to rent out or rent in land. future 511 research should also consider possible interaction between the explanatory variables as this 512 could have an effect on the results. 513 references 514 abay, k. a., chamberlin, j., & berhane, g. 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(2018). how do land rental markets affect 671 household income? evidence from rural jiangsu, pr china. land use policy, 74, 151-165. 672 zou, b., & luo, b. l. (2018). why the uncertain term occurs in the farmland lease market: evidence 673 from rural china [article]. sustainability, 10(8), 15, article 2813. 674 https://doi.org/10.3390/su10082813 675 zou, b. l., mishra, a. k., & luo, b. l. (2020). grain subsidy, off-farm labor supply and farmland leasing: 676 evidence from china. china economic review, 62, article 101293. 677 https://doi.org/10.1016/j.chieco.2019.04.001 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 https://doi.org/10.1016/j.chieco.2019.04.001 28 appendix 697 a. principal component analysis 698 this study employed the principal component analysis (pca) to reduce 14 attitudinal 699 statements to four main motivational constructs which were hypothesized to influence 700 farmers’ participation in the land rental market. the statements and the constructs are 701 included in table a1. 702 table a1: principal components (component loadings) for farming motivations (values > .3 are 703 highlighted in bold) (n= 1,121). 704 variables pc1 pc2 pc3 pc4 progressive (α = 0.68) environmental apathy (α = 0.60) proenvironment (α = 0.63) risk averse (α = 0.66) i am generally keen to adopt new technologies 0.5360 -0.0368 0.0201 -0.2164 i try to find new ways of increasing profit on the farm 0.4888 -0.0139 -0.0174 -0.0547 i find farming rewarding from a quality-of-life perspective 0.3374 -0.1124 -0.0162 0.1251 i think good record keeping is very important in managing a farm business 0.4264 -0.1116 0.0329 0.0461 it is more important to maximize profits than protect the environment -0.0833 0.4775 0.0411 -0.0721 i believe society places too much emphasis on environmental issues -0.0387 0.5274 0.0356 0.0335 i am not that concerned about environmental issues -0.1169 0.4402 -0.0541 -0.0787 i think the media exaggerate the negative impact of agricultural activities on the environment 0.1206 0.3663 0.0557 0.0120 i take some actions to protect the environment when managing my farm because i feel it is the right thing to do 0.0919 0.0829 0.4373 0.0621 | farmers should receive subsidies for protecting the environment and not for the total amount of land farmed -0.0818 0.0928 0.5357 0.0333 in terms of what i produce on my farm, i think it is important to take the environment into consideration, even if it lowers profit 0.0119 -0.0593 0.4794 -0.0226 i am concerned about the loss of biodiversity in our farmed environment 0.0293 -0.0479 0.5210 -0.0420 i try to avoid taking risky farm business decisions -0.1034 -0.0046 -0.0144 0.6889 i try to keep debt levels as low as possible -0.0568 -0.0381 0.0267 0.6628 initial eigenvalues 2.92 2.70 1.38 1.17 705 706 707 708 709 bio-based and applied economics 13(2): 161-170, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14542 bio-based and applied economics bae copyright: © 2024 farsi aliabadi, m.m., & fakari sardehaie, b. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: farsi aliabadi, m.m., & fakari sardehaie, b. (2024). economic sanctions and barley price regime change in iran. bio-based and applied economics 13(2): 161-170. doi: 10.36253/bae-14542 received: march 25, 2023 accepted: march 09, 2024 published: july 25, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: simone cerroni orcid mf: 0000-0002-6650-7344 bf: 0000-0003-3646-3421 economic sanctions and barley price regime change in iran mohammad mehdi farsi aliabadi*, behzad fakari sardehaie water and agricultural strategic research center (nwasrc) of iccima, tehran, iran * corresponding author. e-mail: mm_farsi22@yahoo.com abstract. in iran, barley is considered the second-largest cultivated crop. however, more than 40% of iran’s requirements are imported from the international market. due to the importance of barley in providing livestock feed and food security, its price variation is a critical issue for iranian governments. therefore, in this study, the influence of different determinants of domestic barley price, such as international price, real effective exchange rate variation, price volatility of barley, russian-ukrainian armed conflict, and the existence of economic sanctions, has been investigated by applying the markov-switching model. the main results indicated that in both states, the real effective exchange rate was the primary determinant of the domestic price. moreover, the impact of international price in first state is much more powerful than the second state. also, the results revealed that the persistence of us economic sanctions amplified barley prices in both regimes. according to these findings, the government should eliminate interventions in the barley market by utilizing the preferential exchange rate for importing barley. moreover, pursuing a political agenda to create a stable political condition and lift economic sanctions should be considered the priority for the government to mitigate the barley price upsurge. keywords: barley price, regime change, garch, markov-switching. jel codes: q2, q18, c24. 1. introduction in last decades, agricultural markets witnessed a significant boom-bust cycle and excessive price volatility from 2006 to 2014 (guo and tanaka, 2019; ott and ott, 2014), and this trend was the primary critical economic and food security challenge. moreover, the consequences of food price hikes and exacerbated price volatility can go beyond the economics and food security matters and have social and political repercussions (bhagowalia et al., 2012). periods of high or low prices are not new; however, in recent years, the magnitude of price fluctuation and its geographical expansion have been substantial (bhagowalia et al., 2012). therefore, investigating the trend of increasing market instability for agricultural commodity markets and its impact on commodity prices has become a priority on the international agenda (magrini et al., 2017). https://doi.org/10.36253/bae-14542 http://www.fupress.com/bae https://doi.org/10.36253/bae-14542 https://orcid.org/0000-0002-6650-7344 https://orcid.org/0000-0003-3646-3421 mailto:mm_farsi22@yahoo.com 162 bio-based and applied economics 13(2): 161-170, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14542 mohammad mehdi farsi aliabadi, behzad fakari sardehaie the excessive change in the price of agricultural commodities creates a situation of uncertainty that can have an enormous influence on all the actors, such as consumers, producers, investors, merchants, and government, especially in developing countries (fakari et al., 2013; danehsvar kakhki et al., 2019; mittal and hariharan, 2018). consumers in developing countries spend a considerable share of their income on food; hence, they are sensitive to food price fluctuation (cedrez et al., 2020; farsi aliabadi et al., 2021). on the other hand, the profitability of farming activity and incentives for producers’ investment depend on market prices, and producers’ decisions face a high degree of uncertainty in such a condition (cedrez et al., 2020). additionally, price volatility can generate a higher cost of agricultural commodity trade due to irregularity in the market and inflation pressure (daneshvar kakhkiet al., 2019). therefore, price volatility negatively affects household welfare; layani et al. (2020) indicated that an additional 1.79 percent of urban households drop below the poverty line due to a 9.8 percent increase in food prices. high price variation also imposes substantial pressure on the government to control and stabilize the market prices to satisfy the country’s food security objectives (pieters and swinnen, 2016). due to these negative impacts, it’s essential to identify the nature and reasons for price volatility, which can be helpful in reducing the distractive impact and controlling food prices (fakari et al., 2016). the iranian government has always aimed to prevent price amplification in the agricultural sector due to its negative impact on economic activities. for this purpose, they have implemented a price stabilization policy, where essential commodities such as wheat, sugar, and barley are the central concerns. however, despite the policy, the price of agricultural commodities has increased significantly in recent years due to high inflation, currency weakness, and other macroeconomic difficulties. therefore, it is crucial to identify influential contributors to the rising prices in iran (mehdizadeh rayeni et al., 2022). a vast number of studies focused on investigating and understanding the determinants of agricultural prices (dinku and worku, 2022; iqbal et al., 2022; steen et al., 2023). it’s clear that the joint influence of a plethora of causes generates price variation (santeramo and lamonaca, 2019). biofuel production, energy prices, climate change, condition of financial markets, exchange rate, monetary policies, interest rate, transaction cost, sudden trade restriction, agricultural policies, and increase in food demand are considered as the influential factors that amplified the food prices and its variability (cinar, 2018; eissa and al refai, 2019; lanfranchi et al., 2019, uçak et al., 2022). in the last decade, the influence of exchange rates and international market prices on the dynamics of agricultural food prices in the domestic market has been well documented (mosavi et al., 2014; hájek and horváth, 2016; clapp et al., 2017; braha et al., 2019; lanfranchi et al., 2019; sadiq et al., 2021). while the exchange rate variation affects the price of imported and exported agricultural commodities and also has significant consequences for countries relative prices (adekunle and ndukwe, 2018), the level of the relation among prices in global and regional markets totally depends on market integration and trade policy (brown and kshirsagar, 2015; ganneval, 2016; bekkers et al., 2017; baffes et al., 2019;). therefore, investigating the prices that pass-through exchange rates and international prices for each commodity in each region could be a vital matter for consumers, producers, importers, and policymakers. alongside these traditional deriving forces, political unrest such as sanctions and war has been considered a substantial factor, which leads to food price inflation and fluctuation in international and domestic markets (sohag et al., 2023). since february 2022, the russian invasion of ukraine and this armed conflict have become a driving force of price volatility (nasir et al., 2022). grain production reduction in these predominant producers, trade restrictions, and fuel and fertilizer price spikes are a few reasons that caused agricultural price instability due to the russian-ukrainian conflict (aliu et al., 2023). therefore, this factor also should be taken into consideration. barley crop is the fourth most important cereal in the world, after wheat, corn, and rice. nowadays, barley is consumed as animal feed, and around 70% of barley production is utilized for this purpose, 21% for malting, and less than 6% is directly consumed as human food (tricase et al., 2018). in iran, barley is the second largest crop by area, averaging 1.6 million hectares over the last five years, with production around 3 million tons (motamed, 2017). despite a large amount of production, the domestic production does not meet the country’s requirement; thus, the deficiency is compensated by import (daneshvar kakhki et al., 2019), and in recent years, more than 40% of barley requirements have been imported from international market (awnrc, 2020). due to the importance of barley in providing livestock feed and food security, price variation of barley is a predominant issue for iranian governments. moreover, a strong connection has existed between domestic and international markets due to the high share of imports in providing domestic requirements (sadiq et al., 2021). in this context, the price variation in the global market due to political unrest in major producing countries 163economic sanctions and barley price regime change in iran bio-based and applied economics 13(2): 161-170, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14542 might lead to significant changes in domestic barley prices (mohammadi et al., 2016; daneshvar kakhki et al., 2019). moreover, other factors which have an influence on barley import, such as exchange rate, trade policy and restriction, and international sanctions, might cause price volatility in domestic prices and have a negative impact on food security (mohammadi et al., 2016; hejazi emamgholipour, 2022; zamanialaei et al., 2023). even though some studies have been devoted to investigating the impact of different factors on food price variation, only a few have analyzed the influence of determinants of barley price in the domestic market, and to the best of knowledge no study has paid attention to the possible nonlinear behavior of barley price in iran. therefore, the objective of this study is to investigate the influence of international barley price, exchange rate variation, and local barley price volatility on the possible nonlinear behavior of domestic barley price in the era of maximum pressure campaign and russian-ukrainian military conflict to present a suitable approach for price management. 2. material and methods this study has used time series data to investigate the possible regime change in barley prices under the us maximum pressure camping. for this purpose, a four-step procedure has been developed. in the first step, the time series should be tested to check the presence of the unit root test. for this purpose, we employed the augmented dickey-fuller (adf), phillips-perron, and augmented dickey-fuller with structural break tests. if the series has a unit root, differencing should continue until the series becomes stationary. in the second step, iran’s barley price fluctuation should be extracted. to this end, an arma (autoregressive moving-average model) should be applied to iran’s barley price. then, an lm (lagrange multiplier) test is conducted on the residual of the estimated arma model to check the arch (autoregressive conditional heteroscedasticity) effect (fakari et al., 2013). if the arch effect exists in the residual, the arch/garch (generalized autoregressive conditional heteroscedasticity) models will be applied to extract the domestic barley price volatility. the next step depends on the results of the unit root tests. if the variables are stationary in the first level, we can move to the last step and estimate the markovswitching (ms) model. however, if the variables become stationary only after the first difference, then the johanson co-integration test should be applied to check the existence of the co-integration vector. finally, if a cointegration vector exists, the markov-switching model can be estimated. 2.1. methods 2.1.1. arch/garch methods in order to calculate iran’s barley price volatility, first, the arma model should be estimated. the arma(p,q) (autoregressive moving-average model) general form includes a combination of the autoregressive and the moving average model and has been presented in equation (1). (1) the residual term (et) in equation (1) follows a moving average specification presented in equation (2). constant variance during the time is one of the main assumptions of classic econometric methods. however, in many cases, this assumption is not achievable or logical. in order to overcome this restriction, engle (1982) and bollerslev (1986) presented the arch/ garch model. in this model, two equations are estimated for the mean and variance to model the volatilities. the basic equation for garch (q,p) is presented in equations (3) and (4). it~nid(0,1) (2) εt~nid(0,ht) (3) in the first equation, yt is the conditional mean which depends on explanatory variables that are shown by xi,t, and zt is the residual term. the second equation is the variance equation, and the coefficients should be estimated. equation (4), is a linear function of its past values ( ) and the past values of squared innovations ( ) (engle and bollerslev, 1986). 2.1.2. markov-switching method many economic time series variables exhibit nonlinear behavior associated with the events or abrupt changes in government policies (hamilton, 2018). in recent years, economic variables such as agricultural commodity prices showed a complex and nonlinear behavior, and it is difficult to capture the multiple states correla164 bio-based and applied economics 13(2): 161-170, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14542 mohammad mehdi farsi aliabadi, behzad fakari sardehaie tion that existed between these variables using the linear relationship of a single state (lie et al., 2019; kalligeris et al., 2021). to this end, this paper designed a relationship measurement model based on the markov-switching approach, which can measure the multi-state dependence structure between dependent and independent variables. hamilton (1989) introduced the markov-switching models for time series. it is a powerful method for parameter estimation when economic variable behaves differently in different states of nature or regimes (de la torre-torres et al., 2020). in other words, the ms model permits the time series variables to exhibit periodic shifts in their observed behavior between two regimes. the features of different regimes, such as regime duration and transition possibilities, have been determined endogenously (valera and lee, 2016). this study has assumed that domestic barley price switches between two unobservable states. furthermore, it is supposed that the transition from one state to the other follows a markov process, and the time transition and the duration in each state are random. in this study, the following model is specified: dbpt = cs + xtα + ztβs + ∈st (4) where dbpt represents the barley price in the domestic market, t accounts for time (month), and s represents the unobserved states (s= 1,2). cs is a state dependence intercept, xt is a matrix of state invariant variables, zt is a matrix of state-dependent variables, and ∈st~iidn(0,σ2 s) is the error term. the model also can be written in the following order: αidbpt-i + β11wpt + β21rert + β31vdpt + β41mpct + β51ruct + ∈1t if s = 1 (5) αidbpt-i + β12wpt + β22rert + β32vdpt + β42mpct + β52ruct + ∈2t if s = 1 the conditional transition probability to switch from regime i in the current month to regime j in the next month is presented in the equation (7). pr(st+1 = j|st = i)= pij (6) therefore, the two-state model used in this study will lead to the following probability matrix: (7) with p11 + p12 = 1 and p21 + p22 = 1. 2.2. data the data used in this study consist of the monthly barley price of iran’s domestic barley price, international barley price, the real exchange rate, and barley price volatility in the domestic market from august 2009 to september 2023. this period was chosen because it covers the different us sanction regimes during the agricultural price escalation. moreover, this period includes the international price spike of 2010-2011 and 2019-2020, which might lead to interesting results. the price of barley in the domestic market, the real effective exchange rate, based on the consumer price index, and international barley prices are from the statistical centre of iran (statistical center of iran; price index database, 2023) and imf (imf data base, 2023) respectively. the price volatility of barley in the domestic market is extracted from its time series using the arch/garch method. the index of geopolitical conflict, which can be considered an index of armed conflict between russia and ukraine, has been adapted from the study of caldara and iacoviello (2022). finally, a dummy variable was considered in the analysis to capture the impact of the us maximum pressure campaign on domestic barley prices. descriptions of the variables are presented in table 1. according to the statistics presented in table 2, the prices of domestic barley have experienced significant fluctuations over time. in august 2009, the recorded price for domestic barley was 2004 rials per ton, and it had increased to 111476 by september 2023, showing an average monthly growth rate of 2%. the minimum price for barley was recorded in march 2010, while the maximum price was registered in march 2023. the high standard deviation indicates that the domestic barley price has been extremely unstable. it should be noted that all the variables are transformed to logarithmic form for further investigation. table 1. description of the variables. name definition dbp barley price in iran domestic market wp barley price in international market rer iran real effective exchange rate vdp barley price volatility in iran’s domestic market ruc the average geopolitical risk of russia and ukraine adapted from caldara and iacoviello (2022). mpc dummy variable equal to 1 during maximum pressure campaign and 0 otherwise source: authors definition. 165economic sanctions and barley price regime change in iran bio-based and applied economics 13(2): 161-170, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14542 3. results the results of the adf, and the phillips-perron, and adf with the structural breaks unit root test are presented in table 3. the results of all tests determined that the variables, except the russian-ukrainian conflict, were not stationary at the level. however, the unit root test revealed that the variables were stationary at the first difference. the results of the mean equation, arch effect test, and garch estimation of domestic barley price are presented in tables 4. according to aic (akaike information criterion) and sic (schwarz information criterion) criteria, the arima (2,1,0) was chosen as the best mean specification model. then, the arch effect test was conducted, and its results revealed the existence of heteroscedasticity. therefore, the arch/garch model should apply to capture the domestic barley price volatility. the domestic barley price volatility index is extracted from the garch model and presented in figure (1). the main result indicates that the index experienced significant changes from november 2011 to november 2012 and intensified from may 2020 to may 2022. in iran, the real exchange rate volatility can intensify the volatility of imported commodities such as barley. moreover, since may 2020, the intensification of barley price volatility could be traced back to the impact of the u.s. maximum pressure campaign policy and the elimination of the preferential exchange rate policy. johanson’s co-integration test result indicated that all the variables are co-integrated. therefore, the level variables are employed to estimate the markov-switching model. the results of model estimation for two regimes are presented in table 5. the estimated coefficients for barley world price in both regimes indicated that this variable imposes a positive and statistically significant influence on domestic barley price. according to the results, a percent increase in world barley price increases the domestic price by 0.87 and 0.1 %. this result is to the findings of moghadasi et al. (2011), yousefi and moghadasi (2013), brown and kshirsagar (2015), bekkers et al. (2017), and table 2. statistics of the variables. variables measurement unit mean maximum minimum std. dev. dbp rial per metric ton 24224.86 121353 1777 32699.65 wp us$ per metric ton 151.48 262.95 83.04 52.88 reer index 155.15 502.52 80.89 100.2 ruc index -0.16 1.71 -1.48 0.59 source: authors calculation. table 3. results of unit root tests. adf variables tstatistic result variables tstatistic result dbp -1.38 no stationary δ(dbp) -4.8* stationary wp -1.92 no stationary δ(wp) -8.65* stationary rer -1.9 no stationary δ(rer) -9.53* stationary ruc -4.28* stationary pp dbp -1.84 no stationary δ(dbp) -10.71 stationary wp -1.63 no stationary δ(wp) -8.85 stationary rer -1.95 no stationary δ(rer) -10.24 stationary ruc -3.97* stationary adf with break dbp -2.65 no stationary δ(dbp) -10.34 stationary wp -3.24 no stationary δ(wp) -9.57 stationary rer 0.8 no stationary δ(rer) -11.38 stationary ruc -3.98* stationary source: authors calculation, *, ** and, *** indicate the level of significance for 1, 5 and, 10 percent. table 4. mean equation, heteroscedasticity test and garch estimation of domestic barley price. mean equation: arima(2,1,0) variables intercept ar(1) ar(2) goodness of fit dbp 0.23* 0.17* -0.19* adjusted r2= 0.79 aic= 2.13 sc=2.06 heteroscedasticity test: arch f-statistic= 5.97* obs*r-squared=5.83* arch (1) intercept resid(-1)^2 goodness of fit dbp 0.03* 0.17* adjusted r2= 0.86 aic= 2.33 sc=2.24 source: authors calculation; *, ** and, *** indicate the level of significance for 1, 5 and, 10 percent. 166 bio-based and applied economics 13(2): 161-170, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14542 mohammad mehdi farsi aliabadi, behzad fakari sardehaie daneshvar kakhki et al. (2019). a comparison between the first and second state parameters indicated that the influence of world price declined significantly in the second regime. it is worth noting that state 1 is approximately simultaneous with the absence of economic pressure, and state 2 is virtually concurrent with the intensive economic sanction. in the first state, the government is less sensitive to controlling the prices; therefore, the domestic and international commodity markets are related significantly, and the world price is the significant determinant of domestic prices. however, during the maximum pressure campaign or intensive economic sanction, the government becomes more sensitive to price variation of essential commodities such as barley, and the price transmission from the international to the domestic market is considerably weak. based on the results, a real effective exchange rate has a positive and statistically significant impact on domestic barley prices. to be more specific, a percent increase in real effective exchange rate increases the domestic price by 1.67 and 1.97 % in the first and second regime, respectively. these results are in agreement with the results of mohammadi et al. (2015), ghahremanzadeh et al. (2020), iqbal et al. (2022), and sokhanvar and bouri (2022). the estimated coefficients for dependent variables indicated the contribution of the real exchange rate is considered high in the formation of domestic barley prices in both states. moreover, the influence of this variable in the second state intensified. it is worth noting that the availability of exchange rates through the formal market has become arduous during state 2. moreover, the alternative mechanism for providing the exchange rate with the multiple exchange rate not only does not ease access but also aggravates an extra cost to traders because of the intensification of administrative bureaucracy. therefore, the real exchange rate has a more powerful impact in this state. the domestic price volatility of barley also imposes a positive and statistically significant influence on domestic prices in both states. the results also indicated that a percent increase in barley price volatility results in a growth in domestic barley prices with a magnitude of 0.36 and 0.2 % in the first and second regimes, respectively. comparing the estimated coefficients in different regimes revealed that the barley domestic price volatility imposed a more powerful impact in the first state. as it has been mentioned earlier in the first state, the government was not sensitive to price variation of essential commodities, and price volatility management was not the main administration priority; therefore, the domestic price fluctuation imposed a more powerful impact on barley price. the maximum pressure campaign also has a positive and statistically significant impact on domestic barley prices, which is consistent with the results of ghahremanzadeh et al. (2020). while the influence of the maximum pressure campaign in the first state is not substantial, the impact in the second state is much more influential. during the maximum pressure campaign, the average domestic price of barley was 0.24 % higher than the rest of the period. in other words, in this era and previous economic sanctions from 2012 to 2015, due to the higher cost of imports and excessive difficulty of purchasing from the international market, price management in the domestic market turned into a struggling issue for the government and the domestic market faced higher prices relative to the first state. therefore, lifting the economic sanctions is an essential deriving force that could help to decrease and stabilize the barley price. finally, the russian-ukrainian conflict does not impose a statistically significant influence on domestic 0 0,005 0,01 0,015 0,02 0,025 0,03 0,035 20 09 m 11 20 10 m 05 20 10 m 11 20 11 m 05 20 11 m 11 20 12 m 05 20 12 m 11 20 13 m 05 20 13 m 11 20 14 m 05 20 14 m 11 20 15 m 05 20 15 m 11 20 16 m 05 20 16 m 11 20 17 m 05 20 17 m 11 20 18 m 05 20 18 m 11 20 19 m 05 20 19 m 11 20 20 m 05 20 20 m 11 20 21 m 05 20 21 m 11 20 22 m 05 20 22 m 11 20 23 m 05 figure 1. trend of domestic barley price volatility index. source: authors calculation. table 5. markov-switching estimation results for barley domestic price. dependent variable domestic price variables regime 1 regime 2 intercept 2.01* 3.25* wp 0.87* 0.1*** rer 1.67* 1.91* vdp 0.36* 0.20* mpc 0.1** 0.24* ruc 0.01ns 0.07** goodness of fit aic= -0.88, sc=-0.76, dw=-0.60 source: authors calculation; *, ** and, *** indicate the level of significance for 1, 5 and, 10 percent. 167economic sanctions and barley price regime change in iran bio-based and applied economics 13(2): 161-170, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14542 barley prices in the state 1. throughout the second state, the impact of armed conflict on domestic barley prices in iran is positive and statistically significant; however, its impact is not substantial. this result is predictable because the dependency of iran on ukraine and russia is relatively low (zhang et al., 2023). the properties of the two regimes are presented in table 6, which shows that the transition probability for regime change is significantly low. the regime transition probability from regime 1 to 2 is 0.033, while the likelihood of regime changes from regime 2 to 1 is 0.039. furthermore, regime 1 lasted longer than regime 2. the results indicated that regime 1 lasted 30 months, while the second stat continued for almost 25 months. the transition probability of the first regime is depicted in figure 2. it reveals that state 1 is prevalent from november 2009 to july 2013, and again, it becomes dominant from march 2016 to july 2017. the results indicate that the first regime prevails when the economic sanctions are lifted or not pursued by the us government. 4. conclusion and policy implication this study assessed the impact of main factors on barley prices, including global prices, exchange rates, domestic price volatility, and geopolitical conflict during the us maximum pressure campaign. for this aim, the markov-switching approach has been applied to capture the possible non-linear behavior of the barley price from august 2009 to september 2023. the main results determined that a real effective exchange rate is a dominant deriving factor in domestic barley price formation in both regimes. moreover, the barley price in the international market, domestic price volatility, and maximum pressure camping are driving forces of domestic barley prices. however, the contribution of global barley prices and the domestic price fluctuation has been diminished in the second state, while the influence of maximumpressure camping has been exacerbated. based on the results, the government policy for stabilization of essential commodity prices through utilizing the preferential exchange rate. this policy should also weaken the weak connection between local and international markets. however, the study findings indicated that this policy does not mitigate the price variation in both regimes. therefore, the government should have confined its intervention in the exchange market to the price stabilization proposed. moreover, since the increase in the domestic price volatility led to barley price intensification, the government should design a price volatility and mitigation system based on the facilitation of public procurement and management of governmental reserve to reduce the domestic price fluctuation by securing the supply of the barley in local markets in the case of demand surplus. finally, according to the results, the persistence of us economic sanctions amplified barley prices. therefore, the iranian government should pursue a political agenda to create a stable political condition and lift the economic sanctions by compromising their nuclear program. based on the results, following this program should be considered the main priority for the government to mitigate a price upsurge. this study faced some limitations that could be addressed to provide more precise results. first, there is a data limitation toward a monthly sanction index. in other words, calculating a more accurate sanction index could lead to a more precise assessment. moreover, application of more flexible time series models such as state-space which estimates the yearly coefficients could lead to a more comprehensive assessment. table 6. regime properties for domestic barley price. coefcient standard error transition probabilities p11 0.966 0.019 p12 0.033 0.019 p21 0.039 0.02 p22 0.960 0.02 duration state 1 30.12 17.86 state 2 25.54 13.1 source: authors calculation. 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 0 20000 40000 60000 80000 100000 120000 140000 20 09 m 11 20 10 m 03 20 10 m 07 20 10 m 11 20 11 m 03 20 11 m 07 20 11 m 11 20 12 m 03 20 12 m 07 20 12 m 11 20 13 m 03 20 13 m 07 20 13 m 11 20 14 m 03 20 14 m 07 20 14 m 11 20 15 m 03 20 15 m 07 20 15 m 11 20 16 m 03 20 16 m 07 20 16 m 11 20 17 m 03 20 17 m 07 20 17 m 11 20 18 m 03 20 18 m 07 20 18 m 11 20 19 m 03 20 19 m 07 20 19 m 11 20 20 m 03 20 20 m 07 20 20 m 11 20 21 m 03 20 21 m 07 20 21 m 11 20 22 m 03 20 22 m 07 20 22 m 11 20 23 m 03 20 23 m 07 sanction during presidency of obama nuclear negotiations jcpoapresidency of obama jcpoapresidency of trump maximum pressure campaign presidency of joe biden rials per tons figure 2. transition probability of state 2 for domestic barley price. source: authors calculation. 168 bio-based and applied economics 13(2): 161-170, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14542 mohammad mehdi farsi aliabadi, behzad fakari sardehaie 5. references adekunle, w., and ndukwe, c. i. 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(2025). do agroholdings cope better with the agency problem? empirical evidence from corporate farms in russia. bio-based and applied economics 14(1): 37-47. doi: 10.36253/bae-16199 received: may 27, 2024 accepted: november 8, 2024 published: june 13, 2025 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. associated editor: laura garcia herrero editor in chief: silvia coderoni orcid at: 0000-0001-8026-8848 do agroholdings cope better with the agency problem? empirical evidence from corporate farms in russia alisher tleubayev1,*, yerzhan syzdykov2 1 graduate school of public policy, nazarbayev university, kabanbay batyr avenue 53, 010000 astana, kazakhstan 2 business school, sdu university, 1/1 abylai khan street, kaskelen, almaty, kazakhstan *corresponding author. e-mail: alisher.tleubayev@nu.edu.kz abstract. the agricultural industry in russia demonstrated a notable growth since 2010. russian policymakers strive to further increase agricultural production and set new targets for the industry for the years ahead. while agroholdings are regarded as one of the main driving forces behind the recent success in the agricultural sector, they are also believed to be the main locomotive that will move agriculture towards the set goals. in spite of their growing importance, the literature on agroholdings is still relatively immature and fails to provide clear evidence of their financial efficiency as opposed to non-agroholding farms. the current study utilizes a manually sourced longitudinal dataset of 203 corporate farms in russia and provides a new empirical evidence on the financial performance of agroholding farms through the prism of an agency problem. our findings reveal a significant positive relationship between agroholding membership and financial performance, as indicated by two accounting indicators – return on assets (roa) and return on sales (ros). we further observe that agroholdings face lower agency costs, which to a certain extent, explain their higher financial performance compared to stand-alone farms. the study offers empirical recommendations for policymakers and corporate executives in the russian agricultural sector. keywords: agroholding, corporate farm, agency cost, farm performance, russia. jel codes: m14, q12, q13. 1. introduction the agri-food industry in russia has illustrated profound growth over the past few years. its gross agricultural output jumped more than threefold, from rub 2.46 trillion in 2010 to rub 8.56 trillion in 2022 (rosstat, 2022). during the same period, the export of agricultural products skyrocketed by more than five times, from usd 8.1 billion in 2010 to usd 41.6 billion in 2022 (agroexport, 2023). in the agricultural year of 2021-2022, russia produced a record amount of almost 150 million tonnes of grain, of which 45 https://doi.org/10.36253/bae-16199 https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae https://doi.org/10.36253/bae-16199 https://orcid.org/0000-0001-8026-8848 mailto:alisher.tleubayev@nu.edu.kz 38 bio-based and applied economics 14(1): 37-47, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16199 alisher tleubayev, yerzhan syzdykov million was exported, maintaining its position as the world’s largest grain exporter since 2017 (agroinvestor, 2023). substantial progress can also be seen in the production of poultry and pork. between 2008 and 2017, poultry production increased by over 150%, while pork output nearly doubled (wegren et al., 2019). since the dissolution of the soviet union, russia has become one of the world’s largest importers of agri-food products. however, over the last decade, the import of agri-food products in russia has dropped significantly, by around 67%, from usd 43 billion in 2013 (an all-time high since 1990) to nearly usd 30 billion in 2022, thereby narrowing the negative trade balance in agri-food products (wto, 2023). the substantial fall in the level of agri-food imports was largely caused by the introduction of an import embargo on a list of agri-food products by russia in august 2014 (bobojonov et al., 2018). nowadays, russia is the largest exporter of wheat and beet pulp and ranks among the top-3 exporters of sunflower oil, peas, oil cake, oil meal, flaxseed, and barley in the world (usda, 2018a; uzun et al., 2019). yet, the russian government aims to further increase both the volume and variety of exported agri-food products. in 2018, the president of russia decreed an increase in the export of agri-food products to usd 45 billion and to position russia among the top exporters of agri-food products globally (petukhov, 2018). to achieve these ambitious goals, the russian government introduced a national program for the development of agriculture for 2013-2020, with a total budget of rub 2.28 trillion (usd 76 billion) (usda, 2012). it is commonly believed that agroholdings play a major role in the substantial growth of agri-food production in russia (ryzhova & ivanov, 2023). agroholdings are also considered the driving force for the realization of the ambitious production and export targets set by the government (liefert & liefert, 2015; wegren & elvestad, 2018). russian agroholdings represent a significant concentration of agricultural land, resources, and production (wegren et al., 2023). in 2016, the top-5 agroholdings operated around 3.2 million hectares of agricultural land, the top-20 companies produced nearly half of the total animal feed, and the top-15 companies accounted for 75% of all grain exports (wegren, 2018). a similar pattern can be observed in the meat industry, with nearly half of the country’s meat being produced by the top-25 companies (agroinvestor, 2018b), and approximately 60% of all pork and poultry production accounted for by the top-20 and top-10 companies, respectively (agroinvestor, 2018a; usda, 2018b). furthermore, agroholdings wield significant economic power, with less than a quarter of farms accounting for 93% of all profits (wegren, 2018). as policymakers primarily rely on agroholdings for the realization of the set targets, they have been the main recipients of financial support from the government (wegren & elvestad, 2018). for instance, in 2015, only 248 large agri-food farms (1.2% out of the total number), including agroholdings, received over 40% of all subsidies (uzun et al., 2019). in addition to substantial state support, agroholdings attract significant financial investments from both local and international investors. from 2012 to 2016 alone, the russian agrifood industry received over usd 3 billion of overseas and approximately rub 1 trillion of local investments, with most of these investments going to agroholdings (wegren, 2018). despite the significant growth and increasing role of russian agroholdings in domestic agri-food production, the existing literature on agroholdings is still in its infancy and has certain gaps to be filled (matyukha et al., 2015; visser et al., 2014). first, most of the existing research on agroholdings focuses on their production efficiency (e.g., hahlbrock & hockmann, 2011), with studies concentrating on the financial performance of agroholdings being virtually non-existent. it is worth mentioning that corporate farms in russia are highly vulnerable to financial insolvency, with around 25% of all bankruptcy cases in the country resulting from corporate farms (yastrebova, 2015). understanding the financial efficiency of agroholdings is therefore crucial, especially in light of their significant, often “too big to fail,” roles in the agri-food sector of russia. second, while prior research attempts to identify the production efficiency of agroholdings, it is still not clear which specific attributes and capabilities make agroholdings more efficient compared to other forms of agri-food production. to the best of the authors’ knowledge, this study is a pioneering attempt to understand the financial efficiency of agroholdings through the lens of the agency problem. an agency problem is a conflict of interest between the principals (shareholders) and agents (managers) of an enterprise. a situation of agency conflict may arise due to diverging goals between the shareholder and the manager, or because it is difficult and costly for the shareholder to control and monitor the manager’s actions (berle & means, 1932; eisenhardt, 1989; jensen & meckling, 1976). consequently, this may result in ineffective and inadequate management of companies, which could then negatively impact the company’s financial performance. the remainder of the paper is organized as follows: section 2 provides a theoretical framework and an overview of the literature on agroholdings and their efficiency. section 3 describes the methodology and data employed in the study. this is followed by sec39do agroholdings cope better with the agency problem? empirical evidence from corporate farms in russia bio-based and applied economics 14(1): 37-47, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16199 tion 4, where the results of our empirical analysis are presented and discussed. finally, we present our concluding remarks in section 5. 2. theoretical framework and review of literature agroholdings are specific types of business groups in agriculture that are unique to post-communist countries like kazakhstan, russia, and ukraine (kru). they began to emerge in the kru countries in the 2000s and have been growing considerably since then (rada et al., 2017; ryzhova & ivanov, 2023; visser et al., 2014). although there is no formal definition of agroholdings, there is a consensus in the existing literature that an agroholding is a conglomerate of legally autonomous enterprises, which may include agricultural producers, processors, service providers, and other entities involved in agriculture. the holding company maintains control of the group through ownership of a controlling block of shares (matyukha et al., 2015; spoor et al., 2012; uzun et al., 2021). in this study, we attempt to investigate agroholdings through the lens of an agency problem. an agency problem is a conflict of interest between the shareholders (principals) and managers (agents) of a company, which is regarded as one of the main drawbacks of corporate farms, compared to traditional family farms (hermans et al., 2017). in a family farm setting, there is no separation of ownership and control, and the farms are operated and managed by their owners. this alignment incentivizes managers to work more efficiently, as they are also the owners and thus the residual claimants of the generated revenue. conversely, corporate farms are managed by hired managers, who are not the residual claimants of the profits and therefore have less incentive to maximize farm income (hermans et al., 2017). such a misalignment of interests between owners and managers can lead to agency conflicts (chaddad & valentinov, 2017; valentinov et al., 2015). agency conflicts are common in all corporate farms. however, the extent of the agency problem and related costs might differ from one corporate farm to another. one of the main factors that can enhance a corporate farm’s ability to cope with and minimize potential agency conflicts is the use of advanced and complex management and monitoring technologies. these technologies include performance evaluation systems, incentive compensation programs, formal control mechanisms, and digitized accounting and budgeting systems, among others (chaddad & valentinov, 2017). in comparison to stand-alone corporate farms, agroholdings have better access to resources, both external and internal within the group, which allows them to afford expensive, modern management and monitoring techniques and to implement the best international practices for enhancing their corporate governance mechanisms. indeed, hermans et al. (2017) and petrick (2017) argue that agroholdings employ stimulating performance evaluation systems and offer incentive-based compensation programs. agroholdings also utilize contemporary management practices and advanced technology for monitoring and supervising their workforce and production processes (balmann et al., 2015; liefert et al., 2013). furthermore, gagalyuk & kovalova (2024) note an increasing adoption of digital technologies by agroholdings, which enhances their production and organizational performance. additionally, companies with concentrated ownership structures are believed to have lower agency costs, due to the ability and willingness of large blockholders to more effectively monitor and control the companies’ management (shleifer & vishny, 1997; wang & shailer, 2015). the data used in this study shows considerably higher levels of ownership concentration in agroholding affiliates, with the ownership stake of the largest shareholders averaging 74% as opposed to only 56% in unaffiliated farms. therefore, agroholding affiliates may be better at minimizing their agency costs compared to stand-alone farms. if agroholding members do indeed face lower agency-related costs, then it is reasonable to expect them to demonstrate better financial performance. nonetheless, prior empirical research indicates both positive and negative impacts of agroholding membership on performance (lefebvre, 2023; matyukha et al., 2015; tleubayev et al., 2022; visser et al., 2014). one group of researchers has found that agroholding members have performance advantages over nonmember farms. examples of these advantages for agroholding members in russia include higher land and labor productivity (rylko et al., 2008) and greater scale efficiency (hahlbrock & hockmann, 2011), compared to other forms of agri-food production. furthermore, hahlbrock and hockmann (2011) note that agroholding affiliates also demonstrate higher total factor productivity due to better implementation of contemporary technology. epshtein et al. (2013) report similar findings, revealing that agroholding members achieve better efficiency thanks to their higher use of the latest production technology and strong corporate control mechanisms. another advantage of agroholdings is the existence of internal trade markets that decrease dependence on external suppliers, lower price uncertainties, and significantly reduce transaction costs (hockmann et al., 2011). moreover, 40 bio-based and applied economics 14(1): 37-47, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16199 alisher tleubayev, yerzhan syzdykov the vertical and/or horizontal integration of agroholdings allows their affiliates to benefit from economies of scope and to gain a significant economic advantage over non-affiliated farms (davydova & franks, 2015). lastly, tleubayev et al. (2022) observe that agroholding members show higher technical efficiency. they argue that the agroholding model of agri-food production provides better access to essential resources, such as within-group machinery, equipment, and capital markets, making holding farms more technically efficient. another group of scholars finds a negative relationship between agroholding membership and performance or does not find any significant relationship. for example, hockmann et al. (2005) analyzed 100 large-scale farms in the belgorod region of russia and observed lower levels of efficiency among agroholdings despite their higher use of up-to-date production technology. similar findings were reported by hockmann et al. (2009) in the case of the oryol and belgorod regions of russia. lower levels of production efficiency were also observed among grain-producing holdings in russia. although agroholdings had more investments and technology and used substantially higher levels of fertilizers, the holding farms did not show significantly higher levels of grain yield (uzun et al., 2012). a subsequent investigation by matyukha et al. (2015) also failed to find evidence of economic advantages for agroholding members compared to independent farms. lastly, studies by gataulina et al. (2014) and guriev & rachinsky (2004) also did not find any significant difference in average productivity levels between agroholding affiliates and non-affiliated farms. 3. methodology and data 3.1. models and variables baseline regression model: yit = αit + β1x1it + β2x2it + β3x3it + β4x4it + β5x5it + β6x6it + β7x7it + εit (1) in this model, y represents the financial performance of farms, measured by return on assets (roa) and return on sales (ros). existing research suggests two broader categories of indicators for measuring enterprise performance: indicators based on market value (e.g., tobin’s q) and accounting-based indicators (e.g., return on assets). since the market value-based measures for the farms used in this study are not publicly available, we focus on two widely used accounting-based indicators to measure farm performance: return on assets (roa) and return on sales (ros) (adams & ferreira, 2009; ehrhardt et al., 2003; liu et al., 2014). x1 is the independent variable representing agroholding membership. unfortunately, there is still no formally specified definition for agroholdings. however, there is a consensus among researchers that agroholdings are certain types of business groups in the agri-food sector, consisting of several legally autonomous farms, with controlling stakes in the ownership shares of these farms owned by a holding company (hermans et al., 2017; visser et al., 2012). in this study, we adhere to the above consensus and consider a farm a member of an agroholding if over 50% of the ownership shares of that farm are owned by a holding enterprise. in addition to agroholding membership, which is the main explanatory variable in our model, we also control for a number of board (x2…x4) and farm specific variables (x5…x7). as per board characteristics, we control for the number of directors on the board (x2) (e.g. yermack, 1996), the share of independent directors on the board (x3) (e.g. black & kim, 2012) and gender diversity on the board (x4) (e.g. terjesen et al., 2016). as per farm specific characteristics, we control for farm age (x5) (e.g. reddy et al., 2008), farm size (x6) (e.g. debrah & adanu, 2022), and leverage, the ratio of total debts to total assets (x7) (e.g. garcía-meca & sánchezballesta, 2011). extended regression model: yit = αit + β1x1it + β2x2it + β3x3it + β4x4it + β5x5it + β6x6it + β7x7it + β8x8it + β9x9it + εit (2) in the extended model, we consider the possibility that agroholding members might face lower agency costs, which may potentially result to their higher financial performance, compared to non-agroholding farms. in this model, x8 is an agency cost variable and x9 is an interaction term between agroholding affiliation and agency cost. we measure the agency cost, using two widely used proxies for enterprise level agency costs: operating expense ratio (oer) and asset turnover ratio (ato) (e.g. rashid, 2015; singh & davidson iii, 2003). the other variables are the same as those specified in model (1). table 1 illustrates the farm performance, agroholding affiliation and other control variables employed in this research. 3.2. robustness tests we conduct standard tests to come up with the model that is most suitable for the longitudinal data 41do agroholdings cope better with the agency problem? empirical evidence from corporate farms in russia bio-based and applied economics 14(1): 37-47, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16199 under study. while the f-test, the breusch-pagan lagrangian multiplier test (appendices 2 and 3 respectively) illustrate the significance of fixed and random effects, the results of the hausman test suggest the significance of random effects over the fixed effects (appendix 4). hausman test fails to reject the null hypothesis that the random effects model is consistent and more efficient than the fixed effects model. hence, in this study we employ a random effects model to conduct the regression analyses. conducting a regression analysis with longitudinal data where the number of cross-sectional observations (n) are higher that the number of time-periods (t) may lead to a potential issue of cross-sectional dependence in the error terms (de hoyos & sarafidis, 2006). to control for such a potential issue, we also run our baseline model using the driscoll-kray (dk) robust standard errors, as suggested by (hoechle, 2007). in addition to cross-sectional dependence in the error terms, the driscoll-kray (dk) robust standard errors also control for potential heteroscedasticity and autocorrelation in the model (hoechle, 2007). endogeneity is another problem that may potentially distort the results of the analysis. based on prior research (campbell & mínguez-vera, 2008; doan et al., 2023; marinova et al., 2016), we control for potential endogeneity in the model by employing the 2sls (twostage least squares) method. in a 2sls model, we use the first lag of the explanatory variable as an instrumental variable, as suggested by caramanis & lennox (2008) and garcía-meca & sánchez-ballesta (2011). 3.3. data current research utilizes a manually sourced longitudinal dataset of 203 corporate farms from 27 administrative regions in russia for the years from 2012 to 2017. the sample was chosen through the convenience sampling method, where availability and accessibility are the criteria for the selection of the research sample (etikan, 2016; henry, 1990). because longitudinal data for the majority of russia’s corporate farms are not publicly accessible, our sample consists of 203 corporate farms for which panel data for the variables of interest was publicly available. the data on the variables of interest was sourced from the quarterly and annual reports, as well as the financial statements of those farms. the document sources are publicly accessible through the portal of the “interfax corporate information disclosure center (cidc)1”, which is the agency authorized to release public information on the russian securities market. descriptive statistics of the main variables utilized in this research are described in table 2. the average 1 https://www.e-disclosure.ru/ table 1. variables and descriptions. variables description panel a: dependent variables roa net income / total assets ros net income / sales panel b: explanatory variables agrh_mem dummy variable, which is equal to 1 if more than 50% of the farm is owned by a holding company and 0 otherwise panel c: control variables board characteristics  bsize the total number of directors in the boardroom bod_ind percentage of independent directors in the boardroom bod_div percentage of female directors in the boardroom farm characteristics  fage the number of years since the farm was first registered by the state fsize natural logarithm of the farm’s total assets leverage total debt / total assets agency cost oer operating expenses / sales ato sales / assets source: compiled by authors. https://www.e-disclosure.ru/ 42 bio-based and applied economics 14(1): 37-47, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16199 alisher tleubayev, yerzhan syzdykov agroholding affiliation among the sampled farms is 28%. board of directors, on average, consists of six members. furthermore, around half of the boardrooms are composed of independent directors and female directors represent less than a third of the boards. average farm in this sample is 16 years old and has a size in terms of total assets of around rub 2.3 billion. the average ratio of total debt to total assets is about 47%. lastly, performance indicators such as return on assets (roa) and return on sales are (ros) 4.7% and 5.75%, respectively. 4. results and discussions we begin our analysis with the comparison of the average performance variables of agroholding members versus stand-alone farms. table 3 presents the results of the standard z-test. in terms of both performance measures (roa and ros), agroholding affiliates demostrate better performance, compared to non-member farms. the roa and ros of agroholding members are higher by 1.3% and 4.9% respectively, than those for non-agroholding farms. the differences in both measures are statistically significant at 5% significance level. we continue our analysis by running the random effects (re) regression model with roa and ros as dependent variables and a dummy for agroholding affiliation (agrh_mem) as the main explanatory variable. the first and the second columns of table 4 present the results of the re model. the results suggest a significant positive effect of agroholding affiliation (agrh_mem) on farm performance (both roa and ros). similar with the results of the z-test, agroholding affiliation has a stronger effect on ros, compared to roa. agroholding affiliates illustrate roa and ros that are by 2.6% and 4% higher compared to stand-alone enterprises, respectively. these results are also robust to potential crosssectional dependence (table 4: columns 3 and 4) and potential endogeneity (table 4: columns 5 and 6). while the results of z-test and random effects model reveal better financial performance of agroholding affiliated farms, it is also important to explore what exact characteristics of agroholding affiliates make them financially better off. as mentioned earlier in the paper, we expect that the level of agency cost might to a certain degree, explain the performance differences between agroholding and non-agroholding farms. to test this hypothesis, we proceed to the next step of our analysis and compare the average levels of agency costs between agroholding affiliates and independent companies. as suggested by prior studies (rashid, 2015; singh & davidson iii, 2003), we employ two most widely used proxies for measuring company level agency costs: operating expense ratio (oer) and asset turnover ratio (ato). table 5 illustrates the results of this comparison. the results exhibit a significantly higher agency cost, measured in terms of operating expense ratio (oer), by independent farms (19.23%), as opposed to agroholding members (18.18%). differences in agency costs, measured in terms of asset turnover (ato), are found to be statistically insignificant, hence in our further analyses we proceed with only operating expense ratio (oer) as a measure for agency cost variable (ag_cost). having revealed that agroholding members have lower agency costs, we test for the robustness of this result and extend our baseline regression model by including the interaction term (aghr_memxag_ cost) between the agroholding affiliation variable (agrh_mem) and the agency cost variable (ag_cost). table 6 presents the results of this extended model (2). agency cost (ag_cost) appears to have a significant negative impact on both roa and ros. at the same time, the estimates of the interaction term variable (aghr_memxag_cost) are found to be significantly positive, both for roa (0.44) and for ros (0.87). this suggests that the negative effects of the agency cost on farm performance are significantly lower for agroholding members, compared to stand-alone farms. while a point increase in the agency costs of independent farms leads to a decrease in their roa and ros by 0.93 and 2.24 points respectively, a similar increase in the agency costs of agroholding members leads to only 0.49 and 1.37 points decrease in the levels of their roa and ros, respectively. this implies that agroholding members perform better in dealing with the agency conflict, compared to unaffiliated farms. hence, the agency problem can be regarded as one of the main matters that can to a certain degree, explain the better financial performance table 2. descriptive statistics of key variables. variables obs mean std min max roa 1218 4.7% 0.1 -0.85 0.84 ros 1218 5.75% 0.27 -2.26 2.93 agrh_mem 1218 27.7% 0.45 0 1 bsize 1218 6 1.68 3 15 bod_ind 1218 50.8% 0.38 0 1.8 bod_div 1218 29.27% 0.22 0 1 fage 1218 16 6.16 0 25 fsize 1218 12.92 1.57 7.25 18.87 leverage 1218 47.4% 0.31 0.006 1.83 source: compiled by authors. 43do agroholdings cope better with the agency problem? empirical evidence from corporate farms in russia bio-based and applied economics 14(1): 37-47, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16199 of agroholding affiliates. several factors might provide potential explanations for this. to begin with, agroholdings tend to use stimulating performance evaluation systems and offer attractive compensation contracts to their employees, including the top executive management (hermans et al., 2017; petrick, 2017) . knowing that their efforts actually count and that their income depends on the farm performance, managers would be more likely to work hard for the good of the farm. moreover, when managers receive attractive compensation, they tend to better value their position and try not to risk their top posts in the company. it is therefore less likely that such managers would engage in the expropriation of farm assets for their own benefit, putting personal interests above the interests of the company and its shareholders (florackis, 2008; sajid et al., 2012). in addition, the increasing use of digital technologies by agroholdings enhance their production and organizational performance (gagalyuk & kovalova, 2024). they also tend to implement modern management practices and advanced technology to monitor and supervise their workforce, including the top executive management (hermans et al., 2017; liefert et al., 2013). these practices of agroholdings decrease the agency costs related with the supervision of the hired labor and managetable 3. averages of performance variables, agroholding affiliates vs independent farms. performance variables whole sample agroholding affiliates independent farms difference z-score return on assets (roa) 4.7% 5.6% 4.3% 1.3% 2.25** return on sales (ros) 5.7% 9.3% 4.4% 4.9% 3.33*** *** p<0.01, ** p<0.05, * p<0.1. z-scores are calculated using the methodology suggested by paternoster et al. (1998). source: compiled by authors. table 4. the impact of agroholding affiliation on farm performance (standard errors in parentheses). variables random effects (re) dk robust standard errors 2sls (1) roa (2) ros (3) roa (4) ros (5) roa (6) ros aghr_mem 0.026*** (0.008) 0.040* (0.023) 0.026* (0.012) 0.040** (0.013) 0.034** (0.016) 0.084** (0.042) fage -0.001 (0.001) -0.003** (0.002) -0.001 (0.001) -0.003** (0.001) -0.001 (0.001) -0.003** (0.002) fsize 0.008*** (0.003) 0.035*** (0.007) 0.008 (0.005) 0.035*** (0.008) 0.008** (0.003) 0.03*** (0.008) leverage -0.143*** (0.013) -0.234*** (0.033) -0.143*** (0.011) -0.234*** (0.029) -0.143*** (0.013) -0.232*** (0.033) bsize -0.002 (0.002) 0.004(0.006) -0.002 (0.002) 0.004 (0.006) -0.002 (0.002) 0.005 (0.006) bod_ind 0.025** (0.009) 0.073*** (0.026) 0.025 (0.014) 0.073** (0.027) 0.026** (0.009) 0.075*** (0.026) bod_div 0.053*** (0.016) 0.099** (0.041) 0.053*** (0.012) 0.099*** (0.019) 0.053*** (0.016) 0.104** (0.041) _cons -0.000 (0.038) -0.326*** (0.098) -0.000 (0.085) -0.326* (0.128) 0.006 (0.039) -0.29*** (0.103) r-squared 0.189 0.125 0.189 0.125 0.186 0.120 *** p<0.01, ** p<0.05, * p<0.1. source: compiled by authors. table 5. averages of agency cost variables, agroholding affiliates vs independent farms. agency cost variables whole sample agroholding members independent farms difference z-score operating expense ratio (oer) 18.94% 18.18% 19.23% 1.05% 3.46*** asset turnover (ato) 113% 116% 112% 4% 0.61 *** p<0.01, ** p<0.05, * p<0.1. operating expense ratio (oer) = operating expenses / sales. asset turnover ratio (ato) = sales / assets. source: compiled by authors. 44 bio-based and applied economics 14(1): 37-47, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16199 alisher tleubayev, yerzhan syzdykov ment. finally, yet importantly, agroholdings tend to be the largest shareholders in the ownership structure of their affiliates, holding on average 74% of all shares of their member companies. such huge ownership shares of agroholdings, give them both power and willingness to control that the managers of their affiliated farms act at the best interest of the company and its shareholders (shleifer & vishny, 1997; wang & shailer, 2015), thereby minimizing the expropriation risk by managers. all of the above factors decrease the potential conflicts between the principals (shareholders) and agents (managers) of the companies belonging to agroholdings and therefore minimize their agency costs, which in turn leads to a higher financial performance by holding farms. 5. conclusion russian agri-food industry exhibited noticeable growth during the last decade. while agroholdings accounted for a major part of this growth, they are also expected to be the driving force for reaching the ambitious future goals set for the industry by the government. nonetheless, existing literature on agroholdings is still relatively immature and fails to provide clear evidence on financial efficiency of agroholdings as opposed to non-agroholding farms. the current study utilizes unique farm-level data on the russian corporate agrifood enterprises and provides new empirical evidence on the financial efficiency of agroholding affiliated farms. besides its empirical contribution, this research is also one of the first attempts to offer a theoretical explanation for the emergence and growth of agroholdings through the framework of an agency problem. while agency conflicts are common to all corporate farms, this study proposes that agroholdings perform better at minimizing the agency costs, compared to stand-alone farms. this is because agroholdings have better access to both external and internal resources, which allows them to afford modern and advanced management and monitoring techniques, implement best international corporate governance practices, such as performance evaluation systems, incentive compensation programs and other formal control mechanisms. we therefore hypothesize that the lower agency costs faced by agroholdings make them financially better off, as opposed to non-agroholding farms. indeed, the results of the study indicate that agroholdings have substantially lower agency costs, compared to non-agroholding farms, which translates into to better financial performance (both roa and ros) of the former. the results of this research could appeal to policy makers, executives and shareholders involved in the agricultural sector in russia. for policy makers, this study suggests additional evidence that agroholdings may be more suitable for adapting to current institutional and market conditions compared to other types of agricultural producers, and that they could be the main catalyst for pushing the agricultural sector towards its objectives. however, this does not mean that government support should only be focused on agroholdings. instead, against the backdrop of  evidence on agroholdings’ financial advantages, policy makers should encourage better access to resources for other forms of agricultural producers. this may include stimulating better access to capital, labor and contemporary management and monitoring techniques, including digital technologies, among others. for the shareholders and executives of corporate farms, the findings of this research underscore  the importance of managerial efficiency and encouragement for farm’s financial performance. it is crucial for corporate farms to adopt up-to-date management and monitoring techniques and to introduce stimulating compensation practices that help in aligning management’s interests with those of the shareholders, thus minimizing potential agency conflicts. while this study makes a few contributions to the existing literature, it does have some limitations that should be examined in future research. firstly, the sample selection in this study was driven by data, including only sample farms with publicly available data. this resulted in the sample consisting of mostly larger-sized corporate farms that the average size of corporate farms in the population. therefore, it is important to intertable 6. the impact of agroholding affiliationand agency cost on farm performance (standard errors in parentheses). variables random effects (re) (3) roa (4) ros ag_cost -0.9314*** (0.1595) -2.2374*** (0.4099) aghr_mem 0.1036*** (0.0311) 0.1912** (0.0808) aghr_memxag_cost 0.4431*** (0.1659) 0.8664** (0.4298) fage -0.0016*** (0.0006) -0.0051*** (0.0015) fsize 0.0044* (0.0026) 0.0239*** (0.0063) leverage -0.1248*** (0.0122) -0.1823*** (0.03) bsize -0.0018 (0.0022) 0.0044 (0.0053) bod_ind 0.026*** (0.0095) 0.075*** (0.0236) bod_div 0.0531*** (0.0151) 0.1017*** (0.038) _cons 0.1424*** (0.0393) 0.0704 (0.0961) r-squared 0.2570 0.2206 *** p<0.01, ** p<0.05, * p<0.1. source: compiled by authors. 45do agroholdings cope better with the agency problem? empirical evidence from corporate farms in russia bio-based and applied economics 14(1): 37-47, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16199 pret 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(1996). higher market valuation of companies with a small board of directors. journal of financial economics 40(2): 185-211. building sustainable futures: the bio-based fertilizer case-study carla abitabile, andrea arzeni*, federica cisilino, matteo orlando is there an animal food kuznets curve, and does it matter? vito frontuto1,*, tommaso felici2, vania andreoli3, marco maria bagliani1,#, alessandro corsi1 do agroholdings cope better with the agency problem? empirical evidence from corporate farms in russia alisher tleubayev1,*, yerzhan syzdykov2 farm characteristics and exogenous factors influencing the choice to buy land in italy silvia russo1,*, meri raggi2, barbara bimbati3, andrea povellato3, davide viaggi1 soils and ecosystem services: policy narratives and instruments for soil health in the eu greta winkler1,*, luciano pagano2, daniele vergamini2, fabio bartolini1 bio-based and applied economics bae bio-based and applied economics 12(4): 323-331, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13868 copyright: © 2023 tetere, v., peerlings, j., dries, l. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: tetere, v., peerlings, j., dries, l. (2023). the forest-based bioeconomy in latvia: economic and environmental importance. bio-based and applied economics 12(4): 323-331. doi: 10.36253/bae-13868 received: october 18, 2022 accepted: october 21, 2023 published: december 31, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini orcid vt: 0000-0002-4821-4855 jp: 0000-0002-8984-6226 ls: 0000-0002-1061-1441 the forest-based bioeconomy in latvia: economic and environmental importance vineta tetere1,2,*, jack peerlings1, liesbeth dries1 1 wageningen university, the netherlands, hollandseweg 1, wageningen, 6706 kn 2 latvia university of life sciences and technologies, latvia, svetes 18, jelgava, lv-3001 *corresponding author. e-mail: vineta.tetere@wur.nl abstract. the bioeconomy is considered a means to achieving a climate-neutral economy as aimed for in the eu green deal. for latvia, the forest-based bioeconomy has the potential to contribute to this aim. an operational definition of the forest-based bioeconomy is needed to calculate its size. this research aims to provide such a definition and to determine the contribution of the forest-based bioeconomy to gdp, employment, and greenhouse gas emissions. the direct and indirect contribution of the forest-based bioeconomy to economic indicators and climate change is identified using an input-output model. the results of the model show that the forest-based bioeconomy contributes 6.4% to gdp and 6.6% to total employment in latvia. the contribution to greenhouse gas emissions is 4.9%. furthermore, if co2 sequestration is included, the forest-based bioeconomy becomes climate neutral. keywords: forestry, input-output model, value added, employment, greenhouse gas emissions. jel codes: c67, e01, q23. 1. introduction a strong bioeconomy is a priority in recent eu policies, such as the green deal and the bioeconomy strategy, that strive towards a greener and more resource-efficient economy in the long run (ec, 2012, 2010, n.d.b ). the bioeconomy comprises those parts of the economy that use renewable biological resources from the land and sea – such as crops, forests, fish, animals, and microorganisms to produce food, materials, and energy (ec, n.d.a). major societal challenges such as climate change call for a sustainability transition away from a fossil-based society toward a bioeconomy, in which energy and manufacturing processes are based on sustainable biological resources (ronzon et al., 2015; siebert et al., 2018). in this way, the bioeconomy contributes to the goals of the green deal to transform the eu into a modern, resource-efficient, and competitive economy, by reducing the emissions of greenhouse gases, and by decoupling economic growth from resource use (ec, n.d.b). moreover, by promoting circular and sustainable production systems, the bioeconomy has the potential to contribute to all https://doi.org/10.36253/bae-13868 http://www.fupress.com/bae https://doi.org/10.36253/bae-13868 https://orcid.org/0000-0002-4821-4855 https://orcid.org/0000-0002-8984-6226 https://orcid.org/0000-0002-1061-1441 mailto:vineta.tetere@wur.nl 324 bio-based and applied economics 12(4): 323-331, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13868 vineta tetere, jack peerlings, liesbeth dries dimensions and objectives of the european green deal (ec, 2020). this focus on the potential of the bioeconomy in eu policy narratives, makes it essential to monitor the bioeconomy and to understand its driving forces. an important step in this is to measure the contribution of the bioeconomy and its dimensions to the total economy of countries. there are ongoing efforts to measure this contribution. however, bracco et al. (2018) point out that these efforts focus mainly on the economic importance of the bioeconomy in terms of value added and employment, whereas environmental aspects such as climate change mitigation are often ignored. an exception is lazorcakova et al. (2022) who used input-output analysis to quantify economic as well as environmental indicators to measure the bioeconomy in the visegrad countries. despite studies on the economic importance of the bioeconomy, for many countries and subsectors of the bioeconomy, this information is limited or still missing (wesseler & von braun, 2017). this is especially true for the forest-based bioeconomy, which encompasses the entire forest value chain, from the management and use of natural resources to the delivery of products and services (ladu et al., 2020). lovrić, lovrić, and mavsar (2020) observed a high centralization of forest-based bioeconomy research in a few countries and organizations from north-western europe, while the baltic countries and the countries in central-eastern europe are not adequately represented. current research contributes to closing this knowledge gap by measuring the forestbased bioeconomy (fbb) in latvia. the focus on the forestry sector is especially relevant for latvia, where the forest area covers more than 50% of the total territory. this paper aims to determine the economic and environmental contribution of the fbb to the total performance of the economy in latvia. however, measuring the fbb is not trivial, as there is no unique definition nor set of indicators, no uniform methodology for the assessment of the bioeconomy, and limited data available, especially for partially biobased sectors (ronzon et al. 2017; fao, 2018). fao (2018) summarized the methodologies that can be used to assess the bioeconomy. these methodologies include the value-added/ gdp approach, the input-output model, social accounting matrix multiplier models, computable general equilibrium (cge) models, partial equilibrium models and the use of various disaggregated or composite indices. two of these methods dominate the quantification of the bioeconomy: the value added/gdp approach and inputoutput (io) models. in the value added/gdp approach, biobased shares of various products are estimated by experts and then sectorial statistics are adjusted according to these shares (ronzon et al. 2017; piotrowski, carus, and carrez, 2018). input-output models build on the concept of biomass flows, namely, that individual industries produce biological resources or use inputs from primary biomass producing sectors, and this determines their contribution to the bioeconomy (grealis and o’donoghue, 2015; nordbio, 2017). the io model has advantages over the value added/gdp approach because it automatically includes value added of all industries, and therefore gdp (sum of value added). moreover, the io model includes links between multiple producers and products and allows the integration of economic as well as environmental indicators (gaftea, 2013). in addition to traditional economic indicators (i.e., share in gdp and total employment), this research uses environmental indicators that are connected to climate change. besides total greenhouse gas (ghg) emissions of carbon dioxide (co2), methane (ch4) and nitrous dioxide (n2o) and fluorinated gases (hfc, pfc, sf6, nf3) (see appendix a), we also include a separate measure of co2 emissions as the main greenhouse gas. furthermore, co2 is not only emitted, but also sequestered in forests and harvested wood products. latvia’s forestry sector has the potential to contribute greatly to this. therefore, our research objective is to determine the economic and environmental contribution of the forestbased bioeconomy in latvia. to achieve this objective, the following approach is taken. section 2 provides a review of the characteristics of the forest-based bioeconomy in latvia. the framework of the io model to measure the contribution of the forest-based bioeconomy to gdp, employment, and greenhouse gas emissions, the data used in the io model and 3 scenarios are described in section 3. in section 4, we assess alternative approaches to measure latvia’s forest-based bioeconomy by using different combinations of inputs. the paper concludes with a discussion of the io model results in section 5. 2. the forest-based bioeconomy in latvia latvia is one of the baltic countries situated between lithuania and estonia. it is a country rich in forest resources. in terms of forest area per capita, latvia ranks 4th in the eu (behind finland, sweden, and slovenia), followed by estonia (latvian bioeconomy strategy 2030, 2018). forests occupy on average 33% of the land area in the eu, while in latvia this is 52%, in estonia 50%, and in lithuania 33%. according to the latvian state forest service (2019), the area of forest land was 3.35 million ha in 2018, of which forests occupied 3.04 million ha (91%), https://doi.org/10.36253/bae-13868 325 bio-based and applied economics 12(4): 323-331, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13868 the forest-based bioeconomy in latvia: economic and environmental importance the rest being swamps and forest infrastructure. statemanaged forests covered 1.49 million ha (49%), while 1.55 million ha (51%) were managed by local government and private forest owners in 2018. compared to 1923, when forest land had a share of 23% of the total area, the forest area in latvia has more than doubled (baders et al., 2019). the increase in forest area is expected to continue as a result of purposeful afforestation, as well as through the continued natural growth of forests on abandoned agricultural and non-agricultural lands. additionally, forest biomass is increasing due to sustainable forest management in recent decades (lazdiņš et al. 2019). in 2015, for example, the gross annual increase in biomass was 16.9 million m3, while 10.6 million m3 was harvested (see appendix b for details). latvia’s forests are mostly made up of conifers (53%), but a significant part is also occupied by other species such as birch (30%), white alder (7%) and aspen (7%) (latvian state forest service, 2019). these species are common in all baltic countries. in latvia we see that in 2015 24.27% of the domestically produced forestry products (cpa code a02) are used in the production of wood products (cpa code c16) being the largest user after the production of forestry products itself (39.30%). moreover, 14.29% is exported. although only 21.69% of domestically produced wood products are used by the domestic production of furniture (cpa code c31/c32), most are exported: 65.36%, it is the main variable input for the latter (24.21%). therefore, we see that the production of these three products is vertically linked. however, each of them is also important on its own. we define the forest-based bioeconomy (fbb) as the direct (i.e. the production of forestry, wood and furniture products) and indirect production (i.e. the production of inputs needed in the direct production, e.g. the production of machinery to process wood) needed to enable the final demand of forestry, wood and furniture products. so, we have three ‘sub-complexes’. final demand in io models consists of consumer demand, demand by the public sector (i.e., public institutions), investment demand, and exports. table 1 shows the importance of the forest sector in latvia and the other baltic countries. the forest sector in latvia had a share of 4.8% of gdp in 2017, exports amounted to eur 2.2 billion, or 20.0% of all exports, and employed 46,000 people (5.3% of total employment). these numbers deviate from those previously mentioned because of a different year (2017 instead of 2015). there are 7,000 enterprises in the forest sector, representing 3.8% of the total number of enterprises in latvia (zm, 2019). these companies are often the main pillar of support for rural economies. an important role played by forests is that they sequester co2. forest land and harvested wood products are net sinks of co2. in 2015, they sequestered 3.8 million tons of co2 (table 2), which represents a share of 60.6% in total co2 sequestration in latvia. the rest is sequestered by living biomass in other types of land (skrebele et al., 2020). the amount of sequestered co2 by forest land and wood products represents 35% of the 10.8 million tons of total greenhouse gas emissions in latvia. however, it should be noted that nearly a third of forests in latvia have exceeded their economic maturity age (depending on dominant tree species 41 121 years). the ability of these forests to sequester carbon is lower than that of young and premature forests (latvian state forest research institute silava, 2017), where young forests sequester less than premature forests. the afforestation implemented over previous decades is expected to sequester increasing co2 emissions after 2030 (lazdiņš et al., 2019). old-growth forests serve especially the eu biodiversity strategy 2030 goals. however, there are many risks/shortcomings in that, for example, appearance of invasive species (zute, 2022) and, as mentioned, the intensity of carbon sequestration is lower than that of young forests that grow more table 1. economic indicators (in % of total) for the forest sector in the baltic countries, 2017. country employment exports gdp estonia 5.3 11.9 4.3 latvia 5.3 20.0 4.8 lithuania 4.8 9.9 4.0 source: author`s calculations based on eurostat (2020b); eurostat (2021a); eurostat (2021b). table 2. net ghg emissions by forest land and harvested wood products, 2015. source size net ghg emissions in kiloton co2 equivalent* forest land 3.561 million ha -1,995** harvested wood products 10.626 million m3 -1,850 *see appendix b for composition. **includes sequestration by living biomass and emissions by dead woods, litter, organic soils, and wildfires and controlled burning on forest land. the negative signs represent the net sequestering of ghg emissions. source: skrebele et al. (2020) and csb (2020). https://doi.org/10.36253/bae-13868 326 bio-based and applied economics 12(4): 323-331, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13868 vineta tetere, jack peerlings, liesbeth dries rapidly, removing much more co2 from the atmosphere. a forest management that avoids large emissions from the loss of old trees while rapidly removing co2 from the atmosphere through young forest growth can provide both storage and sequestration benefits. in addition, well-managed forests produce wood products that store carbon long after the trees are harvested. these products provide an added benefit when they are used in place of more energy-intensive ones that require more fossil fuel emissions, such as several building materials (mckinley et al., 2011). 3. materials and methods 3.1 input-output framework section 2 discussed the forest-based bioeconomy (fbb) and its three sub-complexes. next, we quantify the economic and environmental performance of the fbb using an input-output framework that allows the disentanglement of the three sub-complexes. the inputoutput (io) model was developed by leontief in the late 1930s to analyse the economy as a whole and to study the interdependence among the different industries in an economy, since the output of one industry can serve as an input for another industry directly and indirectly (miller and blair, 2009). therefore, a change in the final demand for the products of one industry affects the whole economy via direct and indirect linkages (sink, 2010). cingiz et al. (2021) analysed the value added of the bioeconomy in 28 eu member states using an io model. the input-output model is suitable to track biomass inputs and to determine the contribution of different industries to the fbb and, consequently, the fbb’s contribution to the total economy. the io model is linear as it assumes fixed ratios between inputs and outputs (i.e., io coefficients) and, therefore, is applicable to determine the direct and indirect size of the fbb. the standard io model calculates the vector of product-level output of the industries that is linked to the final demand of products and is given by the following (miller and blair, 2009): x = (i – a)-1f (1) where x is the vector of total output at basic prices, i is the unity (identity) matrix, a is the matrix of io coefficients (the square technical coefficient matrix), f is the vector of final demand of, for example, forest-based products at basic prices (see appendix c). io coefficients give the fixed ratio between the amount of input i used for the production of output j. however, we adjust the model to (e.g. momigliano & siniscalco, 1982; pasinetti, 1973): b = (i – a)-1f ̂ (2) hence, we take the diagonal matrix of ( f ̂ ) and, instead of the vector x, we get the matrix b that shows in each column the production needed in each industry of the economy to make the final demand of each industry’s product possible. consequently, the elements in column k (vector xk) show the production in each industry needed to produce the final demand of products produced by industry k. in this way, we disentangle the three sub-complexes of the fbb. assuming a fixed ratio between economic indicators (i.e., value added and employment) and environmental indicators (i.e., co2 and ghg emissions) with output we get: zkl = b̂l xk (3) where zkl is value added (l=1); employment (l=2); emissions of co2 (l=3) and ghg emissions (l=4) for the subcomplex k of the fbb, b̂l is the diagonal matrix of the fixed ratio of indicator l and the output, and xk is column k of matrix b. 3.2 data description according to oecd (2019), input-output (io) tables describe the sales and purchase relationships of goods and services (i.e. commodities) between producers and consumers within an economy. the table shows the interindustry linkages, final demand, and value added created. therefore, an io table is a numerical overview of an economy. commodities are defined as industry outputs, for example, the product produced by agriculture (industry-by-industry table), or as products, for example, milk (product-by-product table) (oecd, 2019). an io table only includes commodities that have a monetary value, external effects (i.e. non-priced by-products as emissions) or leaves and small branches without economic value are excluded. this research uses the product-by-product io table of 2015 for latvia. there are two versions of this table, one where imports constitute a separate row, implying that intermediate demands are commodities domestically produced and used. the second version is where intermediate demands include imports. given that we are especially interested in domestic production, we decided to use the first table. the original table contains data for 63 goods and services (i.e., products i). however, some rows or columns showing intermediate demands https://doi.org/10.36253/bae-13868 327 bio-based and applied economics 12(4): 323-331, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13868 the forest-based bioeconomy in latvia: economic and environmental importance are empty, that is why they are added to other related products. this results in a table containing 60 goods and services. the io table is developed every five years by the central statistics bureau (csb) of latvia. data are compiled according to the european union statistical classification of products by activity (cpa) and are expressed in basic prices (million euros). industry-by-industry io tables are not provided by csb. io tables for latvia are also provided by oecd but, due to the high level of aggregation, they are not applicable for our purpose. to assess the economic and environmental importance of the fbb for latvia, the following indicators are selected: value added, employment, carbon dioxide (co2) emissions (excluding co2 emissions from biomass combustion) and greenhouse gas (ghg) emissions. value added at basic prices data (see appendix c) are used from the io table, employment data are used from the eu labour force survey of eurostat (2020b), and emissions data are obtained from the air emissions accounts of eurostat (2020a). all data used are from 2015 due to availability of the io table. table 3 gives the value added, employment and co2 and ghg emissions that are directly linked to the production of ‘products of forestry, logging and related services’ (cpa code a02, product i=2), ‘wood and products of wood and cork, except furniture; articles of straw and plaiting materials’ (cpa code c16, i=16) and ‘furniture and other manufacturing such as jewellery, musical instruments, household tools, entertainment articles and other miscellaneous goods that are not covered in other parts of the classification’ (aggregate cpa codes c31 and c32, i=31). in the rest of the paper, we indicate these three product categories as forest products (a02), wood (c16), and furniture (c31/32) products. note that these data are not for the fbb as a whole because they do not include interdependencies with other sectors of the economy. the table shows that the production of the three product categories directly contributes 5% to gdp and 5.8% to total employment in latvia. the contribution to co2 emissions is 3.3% and to greenhouse gas emissions 2.6%, excluding co2 sequestration. 85% of ghg emitted by fbp is co2 (i.e. co2 234,009 ton/ ghg 276,656 ton). 4. scenarios and results 4.1 scenarios we defined the fbb as the direct and indirect production linked to the final demand for forestry products (a02), wood products (c16), and furniture (c31/32). we show the results of the fbb as a whole, but also its decomposition in the three sub-complexes linked to the final demand of the three products mentioned. notice that the calculations imply that if forestry products (a02) are used in the production of wood products (c16) that production, value added, employment and emissions are linked to the sub-complex wood products (c16) and not to the sub-complex forestry products (a02). 4.2 results table 4 shows the size of the fbb and its sub-complexes using 4 indicators. for all four indicators, the sub-complex wood products (c16) is the largest and the sub-complex furniture (c31/32) is the smallest. the overall share in gdp is 6.44%. however, if we include the value added created in the direct production of forestry table 3. value added, employment, and emissions directly related to the production of forestry (a02), wood (c16), and furniture (c31/32) products in latvia, 2015. value added employment co2 emissions ghg emissions million euros % of gdp thousand persons % of total ton % of total ton % of total forestry products (a02)* 356 1.7 18.60 2.2 122,642 1.7 128,945 1.2 wood products (c16) 546 2.6 23.50 2.7 100,586 1.4 136,785 1.3 furniture (c31/32) 139 0.7 7.30 0.9 10,781 0.2 10,926 0.1 total (a02+c16+c31/32) 1,041 5.0 49.4 5.8 234,009 3.3 276,656 2.6 rest of the economy 20,204 95.0 809.6 94.2 6,882,766 96.7 10,501,761 97.4 total 21,245 100 859 100 7,116,775 100 10,778,417 100 note: ghg emissions include co2, n2o in co2 equivalent, ch4 in co2 equivalent, hfc in co2 equivalent, pfc in co2 equivalent, sf6 in co2 equivalent, nf3 in co2 equivalent. * a02, c16 and c31/32 are cpa codes. source: authors’ calculations based on csb (2016), eurostat (2020a and 2020b). https://doi.org/10.36253/bae-13868 328 bio-based and applied economics 12(4): 323-331, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13868 vineta tetere, jack peerlings, liesbeth dries (a02), wood (c16) and furniture (c31/32) products that are used as intermediate inputs in the production of the final demand for other products we get a share of 6.88%. we see a similar increase for the other three indicators. this illustrates that forestry products (a02) are important in the production of wood products (c16) which, in turn, are important for the production of furniture (c31/32). 5. discussion and conclusions we measured the fbb contribution to latvia’s economy using share in gdp, employment and co2 and ghg emissions. we did this using an io model that incorporates the direct and indirect use of intermediate inputs in the production needed to enable the final demand of forestry (a02), wood (c16) and furniture (c31/32) products. these linkages appear to be important, especially forestry products (a02) form an important input in the production of wood products (c16) which, in turn, are important for the production of furniture (c31/32). these linkages determine our definition of the fbb. for another country another definition could apply depending on the linkages present. for example, in other countries like finland the paper industry, which is not present in latvia, could be part of the fbb. the fbb had in 2015 a share of 6.44% in gdp and if we include also the value added created with the production of forestry (a02), wood (c16) and furniture (c31/32) products that are used as intermediate inputs for the production of final demand of non-fbb products, the share equals 6.88%. similar percentages apply for employment (6.58% and 7.12%) and co2 (6.59% and 6.92%). the share of the fbb in total ghg emissions is somewhat lower (4.93% and 5.17%). the outcomes for the fbb are higher than the sum of the value added, employment, co2 and ghg emissions created with the production of forestry (a02), wood (c16) and furniture (c31/32) products, since it includes the indirect use of other products in the production of these products. to our knowledge, this is the first research that takes these linkages into account for the fbb of latvia. the contribution to the emissions of co2 and ghg excludes co2 sequestration. forest land and harvested wood products sequester an estimated 3.8 million tons of ghg emissions. this is 35.2% of total ghg emissions in latvia in 2015. ghg sequestration has increased in recent years due to the expansion of forest land and the annual growth of forest biomass. the eu green deal states that the eu has to become climate neutral by 2050. this requires that eu member states reduce net greenhouse gas emissions to zero. our results show that latvia already achieves this goal set by the eu if we take into account ghg sequestration. furthermore, there is a great potential for further sequestration of ghgs from forest biomass. at the moment, sequestration is not included in the eu emissions trading system, including it would provide opportunities for the latvian economy. table 4. first four rows: value added, employment, co2 and ghg emissions linked to the final demand of forestry products (a02), wood products (c16) and furniture (c30/31) in latvia, 2015. next four rows (i.e. rest): value added, employment, co2 and ghg emissions of forestry products (a02), wood products (c16) and furniture (c30/31) that are linked to the final demand of other products in latvia, 2015. products value added employment co2 emissions ghg emissions million eur % of gdp thousand persons % of the total economy ton % of total co2 equivalent ton % of total linked to final demand of: forestry products a02 211.8 1.00 10.1 1.18 72,854.5 1.02 77,109.9 0.72 wood products c16 953.0 4.49 37.1 4.31 350,815.8 4.93 402,382.1 3.75 furniture c30/31 202.8 0.95 9.4 1.09 45,389.6 0.64 49,657.0 0.46 total 1,367.6 6.44 56.5 6.58 469,059.9 6.59 529,149.0 4.93 rest forestry products a02 46.6 0.22 2.4 0.28 16,046.4 0.23 16,871.2 0.16 wood products c16 34.2 0.06 1.5 0.17 6,296.9 0.09 8,563.0 0.08 furniture c30/31 13.7 0.16 0.7 0.08 1,056.7 0.01 1,070.9 0.01 total rest 94.5 0.44 4.6 0.53 23,400.0 0.33 26,505.1 0.25 total + total rest 1,462.1 6.88 61.1 7.12 492,459.9 6.92 555,654.1 5.17 source: authors’ calculations. https://doi.org/10.36253/bae-13868 329 bio-based and applied economics 12(4): 323-331, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13868 the forest-based bioeconomy in latvia: economic and environmental importance besides the assumed fixed shares between inputs and outputs (i.e., io coefficients), other drawbacks of io models are the absence of a link between income creation and spending, and the assumption of a perfectly elastic supply of factor inputs (i.e., labour and capital) (guerra & sancho, 2014; acemoglu & azar, 2020). these drawbacks are not relevant in this research, as we use the io model for descriptive purposes. moreover, the io model that we use can be applied to any country by using national or regional data sets, statistics of employment, value added, and emissions. in this way, the fbb becomes countryspecific and can form a benchmark and information source for policy formulation to achieve the goals of the green deal because it enables monitoring the bioeconomy and understanding its driving forces. we used the eurostat io table of 2015 to analyse the importance of the forest-based bioeconomy due to the lack of data in recent years. notably, outcomes can differ between years. ideally, we would have information for more years that would enable us to detect and analyse the development of the forest-based bioeconomy over time. a general drawback of the use of the eurostat io table is the high level of aggregation, preferable we would like to have more detail on the products produced in the forest-based bioeconomy. this is especially relevant in case we, for example, would like to formulate product related policies or obtain regional detail. a more specific caveat of the use of the io table of 2015 is that in the light of the green deal and the russian invasion of ukraine, it is expected that latvia will try to increase the use of forest-based biomass for energy production. this potential increase of the fbb cannot be investigated with the present model. despite these drawbacks, this paper gives a first attempt to derive the size of the fbb in latvia using not only economic but also environmental indicators and by including direct and indirect linkages in the economy. references acemoglu, d., & azar, p. d. 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(2010). input-output models. retrieved from https://www.researchgate.net/publication/261175197_ input-output_models skrebele, a., rubene, l., lupkina, l., cakars, i., siņics, l., lazdānemihalko, j., … zustenieks, g. (2020). latvia’s national inventory report 1990 – 2018, submission under unfccc and the kyoto protocol. retrieved from https://unfccc.int/sites/default/files/resource/ lva-2020-nir-11may20.pdf unfccc. (2008). kyoto protocol reference manual on accounting of emissions and assigned amount. retrieved from https://unfccc.int/resource/docs/publications/08_unfccc_kp_ref_manual.pdf wesseler, j., & von braun, j. (2017). measuring the bioeconomy: economics and policies. annual review of resource economics, 9(1), 275–298. https://doi. org/10.1146/annurev-resource-100516-053701 world bank. (2020). what is the difference between purchaser prices, producer prices (vap), and basic prices (vab)? retrieved from https://datahelpdesk.worldbank.org/knowledgebase/articles/114947-what-is-thedifference-between-purchaser-prices-p zm ministry of agriculture. (2019). latvian forest sector in facts and figures 2018. retrieved from https:// www.zm.gov.lv/mezi/statiskas-lapas/buklets-mezanozare-skaitlos-un-faktos-2019-?id=16973#jump. appendix a co2 and ghg emissions information from the national inventory reported to the united nations framework convention on climate change (unfccc) and the convention on long-range transboundary air pollution, as well as data from the central statistical bureau (csb), is used for the calculation of co2 emissions. the ghg emission indicator measures the total national emissions of the so-called ‘kyoto basket’ of greenhouse gases, including carbon dioxide (co2), methane (ch4), nitrous oxide (n2o), and the so-called f-gases (hydrofluorocarbons, perfluorocarbons, nitrogen trifluoride (nf3) and sulphur hexafluoride (sf6)). for each gas’ individual global warming potential (gwp), they are integrated into a single indicator expressed in units of co2 equivalents. emissions data are submitted annually by the eu member states as part of the reporting under the unfccc (unfccc, 2008). appendix b sequestration table b.1. forest land, gross annual increment, potential harvest, and harvested wood products in latvia, 2015-2018. year forest land, 1,000 ha gross annual increment, 1,000 m3 potential harvest, 1,000 m3 harvested wood products, 1,000 m3 2015 3,561 23,637.10 16,927.00 10,626.50 2016 3,561 25,166.92 17,276.44 10,555.81 2017 3,576 26,312.66 17,235.59 11,443.42 2018 3,585 26,480.09 17,584.81 12,861.65 source: latvian state forest service (2019), skrebele et al. (2020), and csb (2020). table b.2. net ghg emissions by forest land and harvested wood products, 2015-2018 (thousand ton co2 equivalents). source 2015 2016 2017 2018 forest land -1,995.01 -3,179.63 -4,905.08 -3,213.87 harvested wood products -1,850.36 -2,129.34 -2,251.33 -2,064.57 total -3,845.46 -5,308.97 -7,156.41 -5,278.44 source: skrebele et al. (2020). appendix c prices the world bank (world bank, 2020) provides the following price definitions: · the basic price is the amount receivable by the producer, exclusive of taxes payable on products, and inclusive of subsidies receivable on products. the equivalent for imported products is the c.i.f. (cost, insurance, and freight) value, that is, the value at the border of the importing country. · the producer price is the amount receivable by the producer inclusive of taxes on products except deductible value added tax and exclusive of subsidies on products. the equivalent for imported products is the c.i.f. value plus any import duties or other taxes on imports (minus any subsidies on imports). producer prices = basic prices + taxes on products (excluding vat) subsidies on products · the purchaser price is the amount payable by the purchaser. this includes trade margins realized by wholesalers and retailers (by definition, their output) as well as transport margins (that is, any transport charges paid separately by the purchaser) and nondeductible vat. purchaser prices = producer prices + trade and transport margins + non-deductible vat https://doi.org/10.36253/bae-13868 https://www.researchgate.net/publication/261175197_input-output_models https://www.researchgate.net/publication/261175197_input-output_models https://unfccc.int/sites/default/files/resource/lva-2020-nir-11may20.pdf https://unfccc.int/sites/default/files/resource/lva-2020-nir-11may20.pdf https://unfccc.int/resource/docs/publications/08_unfccc_kp_ref_manual.pdf https://unfccc.int/resource/docs/publications/08_unfccc_kp_ref_manual.pdf https://doi.org/10.1146/annurev-resource-100516-053701 https://doi.org/10.1146/annurev-resource-100516-053701 https://datahelpdesk.worldbank.org/knowledgebase/articles/114947-what-is-the-difference-between-purchaser-prices-p https://datahelpdesk.worldbank.org/knowledgebase/articles/114947-what-is-the-difference-between-purchaser-prices-p https://datahelpdesk.worldbank.org/knowledgebase/articles/114947-what-is-the-difference-between-purchaser-prices-p https://www.zm.gov.lv/mezi/statiskas-lapas/buklets-meza-nozare-skaitlos-un-faktos-2019-?id=16973#jump https://www.zm.gov.lv/mezi/statiskas-lapas/buklets-meza-nozare-skaitlos-un-faktos-2019-?id=16973#jump https://www.zm.gov.lv/mezi/statiskas-lapas/buklets-meza-nozare-skaitlos-un-faktos-2019-?id=16973#jump modeling conversion to organic agriculture with an eu-wide farm model dimitrios kremmydas*, pavel ciaian, edoardo baldoni a systematic literature review on the rural-urban economic well-being gap in europe cesare meloni1,*, benedetto rocchi2, simone severini1 the forest-based bioeconomy in latvia: economic and environmental importance vineta tetere1,2,*, jack peerlings1, liesbeth dries1 the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia margherita squarcina1,2,*, donato romano1 bioeconomy and resilience to economic shocks: insights from the covid-19 pandemic in 2020 jesús lasarte-lópez1,*, nicola grassano2, robert m’barek1, tévécia ronzon1 acknowledgements bio -based and a ppl ied economics bae bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6e172 | doi: 10.36253/bae-12411 copyright: © 2022 m. mazzocchi, s. capacci, b. biondi. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: m. mazzocchi, s. capacci, b. biondi (2022). causal inference on the impact of nutrition policies using observational data. bio-based and applied economics 11(1): 3-20. doi: 10.36253/ bae-12411 received: december 9, 2021 accepted: may 29, 2022 published: july 22, 2022 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: meri raggi. discussant: gianluca stefani, meri raggi orcid mm: 0000-0002-0498-8031 sc: 0000-0003-4162-682x bb: 0000-0003-0343-1677 bae 10th anniversary papers causal inference on the impact of nutrition policies using observational data mario mazzocchi*, sara capacci, beatrice biondi dipartimento di scienze statistiche, università di bologna *corresponding author. e-mail: m.mazzocchi@unibo.it abstract. we discuss the state-of-the-art in the application of quasi-experimental methods to estimate the impact of nutrition policies based on observational data. this field of application is less mature compared to other settings, especially labour and health policy, as food economists have started to implement widely counterfactual methods only over the last decade. we review the underlying assumptions behind the most prominent methods, when they can be regarded as credible and if/when they can be tested. we especially focus on the problem of dealing with unobserved confounding factors, emphasizing recent evidence on the limitations of propensity score methods, and the hard task of convincing reviewers about the quality of instrumental variables. we discuss the application of difference-in-difference, with an emphasis on its potential in consumer panel data applications, and how results from regression discontinuity design studies should be interpreted. finally, we cover the estimation of counterfactual outcomes using structural methods and provide an overview of recent developments and current gaps. keywords: quasi-experimental methods, policy evaluation, nutrition policy, assumptions. jel codes: c54, c21, q18, i18. 1. introduction the call for evidence-based policy decision has generated an exponential growth in food policy evaluations over the last decade. table 1 shows the counts obtained from a google scholar search for relevant keywords over the last three decades. between 2011 and 2020 the number of hits for the generic term “food policy” is 4.8 times the baseline period, about 229,000 documents compared to 47,400 over the decade 1991-2000. adding the keyword “impact evaluation” highlights a much faster trend. the increase in the number of google scholar hits is almost 25-fold. the proportion of papers with these keywords in relation to the simple “food policy” search results was only 0.63% in the 1990s, and rose to 3.24% in the 2010s. this pattern is confirmed by more specific keyword searches. for example, the additional keyword “causal identification” returns a 67-fold rise over two decades, and when looking for a specific method like “difference-in-difference”, hits grew from almost zero to 2,160, a 108-fold increase. 4 bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 mario mazzocchi, sara capacci, beatrice biondi while this trend is similar in other areas of applied economics like health economics or energy economics, it has brought a small revolution in the agricultural economics field. in the year 2000, the journal food policy was 123th by impact factor within a population of 166 economics journal. in 2019 the journal ranked 28th out of 373 economics journals, and has been regularly the highest ranked agricultural economics journal since 2008. in 2010 the agricultural and applied economics association, formerly known as the american agricultural economics association (same acronym, aaea) decided to rebrand its second-ranked journal, and the review of agricultural economics became applied economics perspectives and policy (aepp). in terms of impact factor, the aepp journal is now the second best in the field of agricultural economics after food policy, ahead of the leading aaea journal, the american journal of agricultural economics. in short, (agricultural and food) policy analysis has become a best seller, and demand and supply of rigorous policy evaluations have grown very rapidly. from an era of paucity of quantitative evaluations, we have moved to abundance. beyond societal interest, this trend has been driven by the amazing progress in data availability, and the evolution in user-friendly econometric software has been equally rapid. as readily available data and software fertilize policy evaluation studies, the academic community needs to set higher methodological standards to defend the credibility and robustness of the findings. without claiming the authority to define those standards, this manuscript has the objective to review the main quantitative methods currently employed in food policy evaluation, more specifically those targeting the causal identification of policies, and explicit the key assumptions they rest on. we restrict our range of applications to the analysis of policies targeting nutrition outcomes. there are not many comprehensive work on impact evaluation methods that are specific to nutrition policies (babu et al., 2016), and not many reviews of the policy evidence consider the credibility of causal inference methods (see e.g. capacci et al., 2012; mazzocchi, 2017) more specifically, the focus of this article is on the application of quasi-experimental methods when secondary data are used for ex-post assessment of food policies. while these “counterfactual” approaches are relatively young within this research field, they are rapidly becoming a minimum standard for causal inference in absence of randomization studies. the 2021 nobel prize in economics has been awarded to david card, joshua angrist, and guido imbens, three key contributors to methodological and empirical research on causal inference with observational data1. as it happens with most social science research objectives, economic policy analysis faces relevant challenges in drawing causal inference from randomized experiments2. even in the less frequent situations where experimental evidence can be collected, the findings can be hardly generalized to be useful in other contexts. thus, economists have historically relied on observational data in their evaluations of public policies, hence the need to address biases from the lack of randomization. the article is structured as follows. we first discuss the opportunities and limitations in the data avail1 see the document on the scientific background for the nobel prize, “answering causal questions using observational data”, https://www. nobelprize.org/uploads/2021/10/advanced-economicsciencesprize2021. pdf 2 still, the application of the experimental approach to economic problems has also generated important results. as one anonymous reviewer points out, the 2019 nobel prize was awarded to esther duflo, abhijit banerjee et michael kremer also in recognition of their application of the experimental approach “to alleviate global poverty”. table 1. food policy and evaluations in google scholar keywords searches over three decades. (a) (b) (c) (c)/(a) (c)/(b) 1991-2000 % 2001-2010 % 2011-2020 % “food policy” 47.4 178 229 4.83 1.29 “food policy” and “impact evaluation” 0.3 0.63 1.89 1.06 7.43 3.24 24.77 3.93 “food policy” and “randomized experiment” 0.02 0.04 0.46 0.26 1.86 0.81 93.00 4.04 “food policy” and “counterfactual” 0.37 0.78 1.86 1.04 5.64 2.46 15.24 3.03 “food policy” and “quasi-experimental methods” 0.006 0.01 0.07 0.04 0.36 0.16 60.00 5.14 “food policy” and “causal identification” 0.003 0.01 0.009 0.01 0.2 0.09 66.67 22.22 “food policy” and “difference-in-difference” 0.02 0.04 0.48 0.27 2.16 0.94 108.00 4.50 source: our search, google scholar accessed on 16/5/2022. 5causal inference on the impact of nutrition policies using observational data bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 able to researchers, especially in relation to the choice of adequate outcome variables to evaluate nutrition policies (section 2). then, we provide a short overview of the main quasi-experimental approaches to identify the causal effect of policies, with an emphasis on the assumptions they rest on, and whether and how they can be tested, as well as some approaches to demonstrate the robustness and validity of the causal findings (section 3). finally, we draw some take-home messages and suggest directions for future research. 2. data and measurement what is the goal of nutrition policy? such question is only apparently trivial, if one thinks what “improving nutrition” means. it is rather obvious that the ultimate aim of the policy is to improve human health, thus evaluations should rely on health outcomes. unfortunately, the cause-effect path between improved nutrition and health outcomes is not immediate, and subject to major uncertainties. hence, it is not surprising that most empirical evaluations of nutrition policies look at their shortto medium-term effects on intermediate outcomes, such as food choices or diet quality indicators, which in turn are health predictors3. the definition of these intermediate outcomes, however, is also subject to a variety of measurement-related issues. food choices not only vary across individuals, but also within individuals. our christmas food choices are likely to differ from those preceding the summer season, we may want to compensate on mondays our week-end eating and drinking choices, and after a heavy lunch we may opt for a light dinner. thus, a first question refers to the time interval which matters to define our baseline outcome indicator. in nutrition science, the gold standard is the dietary record approach (thompson and byers 1994), the amount of food and beverages intake is recorded through a diary kept over a period of few days, normally no more than 7 consecutive days. this minimizes the memory bias, but may generate a fatigue effect (too much effort to keep the diary), and a behavioural bias associated with a “learning-by-doing” effect, as participants become aware of their eating patterns as they record them, and may alter their diets accordingly. an alternative approach rests on 24-hour recalls, which 3 when data allow to do so, causal mediation analysis is a powerful approach which supports the identification of causal chains, i.e. a causal estimate which goes beyond the total effect of the treatment on the outcome, and also identify the indirect effect that occurs due to one or more mediating variables. for a comprehensive overview, see celli (2022). requires the respondent to recall and report all the food and beverages consumed during the previous day. while the task is not particularly burdensome and potentially more accurate, it fails to capture variation between days. this issue may be mitigated by appropriate sampling designs, assuming that heterogeneity across individuals belonging to a specific population group and interviewed at different times reflect – at least on aggregate – the average choices and intertemporal substitutions of individuals in the same group. a third nutrition-focused alternative is the food frequency questionnaire (ffq), which records the “usual” frequency of consumption of a list of food items. ffqs can be acceptable to measure average individual behaviours, but they are usually less accurate in quantifying intakes. despite this, they are cheap and simple, and place a low burden for participants, which made them a commonly used dietary assessment tool (thomson et al. 2003). key food security indicators (e.g. the food consumption score by the world food program) are based on ffqs. although this type of data has become relatively more common in food policy analysis, especially in development studies, economists remain concerned about the quality of measurement tools which depend on some form of self-assessment and have a component of social desirability bias (grimm 2010). for example, lissner (2002) shows that selective underreporting by obese individuals occurs with almost all methods of dietary assessments which rest on self-reports. furthermore, nutrition survey data have a limited coverage of key food policy covariates, often failing to record the prices faced by individuals, their incomes, and consumption of nonfood items. this is why purchase data remain the preferred source of outcome indicators for economic studies, especially in the scanner data era. these large data sets not only allow to monitor individual daily transactions by individual household over several years, but they also have been augmented to provide detailed nutrient information at the level of unique product codes, as well as detailed data on purchase outlets, and household characteristics (muth et al., 2020; biondi et al., 2022). in household budget surveys, households record purchases through oneor two-week diaries, and data suffer by the aforementioned potential biases, although the lack of an explicit nutrition focus should mitigate social desirability biases. in consumer panels based on home scanners, participants scan universal product codes of all products taken home after each shopping trip. point-of-sale scanner data are another rich source and provide measurements of sales volumes and prices, but cannot be related to individual consumer characteristics. 6 bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 mario mazzocchi, sara capacci, beatrice biondi obviously, purchases are only a proxy of actual intakes, and the fact that these measures are at the household level is one serious shortcoming. still, an underused opportunity of large scanner data-set is the possibility to monitor the transaction of one-member households over several years. while this is clearly a selected sample of the overall population, a time series of thousands of high-frequency data for the same individuals could be a unique setting for causal identification for policy evaluations. to show the implications of using different outcome measures and ignoring self-reporting biases, we report in table 2 some aggregate figures on attitudes towards fruit and vegetable consumption, self-assessed intakes and purchase-based intakes in the uk since the start of the 2003 national five-a-day campaign. the data come from nationally representative surveys and were not collected specifically to evaluate the campaign. the first row captures the awareness impact of the information policy. during the first year of the policy, the average perception of what constitutes an optimal consumption was 4.4 portion per day. over time, the campaign has been successful in increasing this target towards the “5-a-day” objective. the second row displays self-assessed intakes and is a clear demonstration of what social desirability means. while in 2003 participants were reporting an intake below the optimal target, in 2007 they were declaring an (average) consumption well above the ultimate policy objective. unfortunately, when assessing intakes based on more objective purchase data, we find that the increase has been quite modest, and well below the perceived optimal intake. clearly, the assessment of the policy effectiveness heavily depends on which outcome we choose to focus on. there is no such thing as the perfect outcome variable and the quality of data is very heterogeneous. rather than an excuse to discard quantitative policy evaluations, this should push researchers to discuss their data sources in great detail, acknowledge any limitation and adopt appropriate countermeasures and robustness checks. a list of secondary data sources potentially relevant for nutrition policy evaluation is provided in table 3. while individually these sources suffer from a variety of shortcomings, some may be addressed by adopting methods which integrate data from different sources, even if they do not pertain to the same subjects. various techniques enable to combine two or more dataset. for example, blundell et al. (2008) match consumer expenditure data from repeated cross-sectional consumer surveys with longitudinal data providing accurate information on incomes. other techniques exist to combine information from different surveys (see the review in lohr and raghunathan, 2017). furthermore, data collected as repeated cross-sections – as is the case for most of national household budget surveys – can be restructured into pseudo-panels by aggregating individual observations into homogeneous groups (e.g. same age group, same gender, same income bracket, etc.) which become the panel unit (deaton 1985). 3. methods for causal inference how do we know that it is rain that leads people to open their umbrella, and not open umbrellas that cause rain? if we had a spreadsheet showing (cross-sectional) data on the presence of rain and open umbrellas, statistics could definitely confirm that the two things are connected, and bring evidence that it is much less likely to find open umbrellas on sunny days. however, without some manipulation, statistics without prior theoretical knowledge is unable to infer causality from mere observational data. one way out in economics (and in life), is the assumption that what happens earlier is more likely to be the cause than the effect, but this reasonable simplification is often useless4. suppose the government lowers vat on healthy foods in year t, and in year t+1 people consume less healthy foods. using again our common sense and theoretical knowledge, we know that lowering vat cannot cause lower consumption, but previous trends or other confounding factors (e.g. prices going up) are messing up with our attempt at causal inference. 4 indeed, estimates from correctly specified structural models drawing from validated economic theories can return good causal estimates. once we know that rain causes open umbrella, and we have enough information to correctly specify our model (e.g. weekday, time of the day, ecc.), we can estimate the relationship between the amount of rain and the density of umbrella, and check that our estimator meets the desired economic properties. our focus is in the (frequent) situation where theory provides insufficient guidance, or lack of information leads to biased estimates. as discussed later in the article, quasi-experimental methods can be a powerful complement to structural models. we are grateful to an anonymous reviewer for soliciting this clarification. table 2. knowledge, self-assessed consumption purchases of fruit and vegetables in the uk, number of portions per person per day (years 2003, 2006, 2007). 2003 2006 2007 optimal intake (reported) 4.4 4.6 4.8 self-assessed intake 3.4 5.2 5.6 assessed intake from purchases 3.7 4 3.9 source: our processing on data from uk consumer attitude survey and expenditure and food survey (various years). 7causal inference on the impact of nutrition policies using observational data bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 this short account does not do justice to centuries of questions about how science should look at causeeffect relationships, since francis bacon, and the logic provided by john stuart mills in 1843 to frame scientific experiments. but the key elements that matter to our treatment are randomization and the potential outcome framework, first formalized by fisher in 1925 and neyman in 1923, respectively (see boring, 1954). interestingly, while these two essential elements behind randomized experimental designs have developed almost simultaneously, their combination to support causal inference with observational data took another half century, until the key contribution of donald rubin (see imbens and rubin, 2015). the rationale behind randomized assignment was that “the validity of the test of significance may be guaranteed against corruption by the causes of disturbance which have not been eliminated’’ (fisher, 1935, p. 19). in other words, randomization as an insurance against confounding factors. meanwhile, neyman had introduced the concept of potential outcomes: “let x denote a possible outcome of the experiment consisting of drawing one ball from the i-th urn” (splawa-neyman et al., 1990). basically, before the draw, an undrawn ball can take any number, just like the health status of schrödinger’s cat is unknown before opening the box. how these philosophical wanderings matter to policy evaluation becomes clearer when one considers the “fundamental problem of causal inference”, also referred to as the neyman-rubin causal model (holland 1986). before any scientific (randomized) or natural (non-randomized) experiments, subjects may expect one of two “future” outcomes, either under treatment or in a nontreatment (“control”) situation. for example, before the government approved the budget law in october 2021, an italian shopper could envisage for the first week of january 2022 one level of sugar-sweetened beverage (ssb) purchases “under the soda tax”, and another level table 3. secondary data sources relevant for nutrition policy evaluation. type of survey description nutrition surveys specifically aimed at monitoring people diet, usually through dietary records, recall or ffq. they usually collect data on key individual characteristics, mostly demographics, but sometimes also on health status and attitudes. health surveys based on interviewing/questionnaires and objective measurements (e.g. blood tests, urine samples, etc.), health surveys record information on people subjective and objective health status. other information often collected: health related behavior, demographic characteristics, lifestyle topics such as smoking habits or dietary habits. household budget surveys their scope is to collect information on household purchases over a period of one or two weeks, based on expenditure diaries and face-to-face interviews. they normally include detailed information on food purchases (at the food item level) and demographic information on the household. in most countries they are run every year. when purchased quantities are provided along with expenditures, it is possible to estimate average prices. scanner data this type of data records expenditure, paid prices and purchased quantities at the most detailed product level (brand and pack size). data are collected either at the point-of-sale through cash registers (retail panels) or at home by household panels equipped with a barcode scanning device (consumer panels). these data are collected by private companies, and in some cases combined with product label information, including nutrition information. households may remain in consumer panels for several years, allowing for longitudinal analyses. opinion/omnibus/ attitude surveys these surveys collect information for multiple purposes, often including measurement of opinions, beliefs, selfreported habits, attitudes, stated preferences, perceived health, lifestyle factors, etc. they may contain self-reported information about eating behaviours and knowledge, and sometimes anthropometric measures. food composition databases food composition tables contain information on the average nutrient content of raw and processed food items available in one country. they are useful in combination with other data-sets to associate food items with their nutrient content. audience measurement data these data are normally used to monitor media consumption (radio, television, newspaper, magazines, websites, social networks). they are conducted by private market research companies, and when combined with purchase data can be useful to explore the exposure to advertising and the impact of advertising regulations. epidemiological studies they provide information on the prevalence and incidence of diseases, morbidity, mortality and related risk factors. they are useful to predict and simulate the ultimate health outcome of a policy based on the estimated impact. they are population-specific and are very heterogeneous in terms of sample sizes and duration. administrative data these data are collected for administrative purposes, but some may be useful for evaluation, e.g. population registers of births, deaths, tax records as well as information on household access to subsidies and financial support. administrative sources may also help quantifying the policy cost. 8 bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 mario mazzocchi, sara capacci, beatrice biondi of ssb purchases “without the soda tax”. for the policy analyst, the perfect evaluation would require to observe both outcomes on the same subject. the outcome difference would be the impact of the ssb tax on that specific individual consumer, and repeating the analysis on many shoppers would return the full distribution of impacts. in absence of parallel words, we have to settle with observing a single outcome. the italian government decided to postpone the introduction of the soda tax for the second consecutive year, and in january 2022 only the “no tax” outcome was observable. 3.1 randomized experiments in experiments, a fisher-style randomized study is the solution to this fundamental problem. the difference between average outcomes from two random samples drawn from the same population, where only one of the two sample is treated, returns an average effect of the treatment. as a matter of facts, in absence of the treatment, two random samples from the same population return average outcomes from the same outcome distribution, and there is no reason why the difference in average outcomes should be significantly different from zero. the above trick works very well with scientific experiments, but several complications emerge when the subjects of the experiment are humans. even in medical randomized controlled trials, external validity of treatment effect estimates is all but granted. designs of experiment for social and policy evaluation studies are even harder to be set up in a meaningful way, for example ensure real randomization, avoid compliance issues, control for a multitude of confounding factors that may act differently between the two groups during the experiment. one key dimension to be considered is what sort of randomization drives the experiment. random assignment to the treatment or control group is the prerequisite of randomized controlled trials. however, this only ensures that the two samples come from the same population, which is not necessarily the actual population of interest and may be self-selected, especially if participation is voluntary. it is hard to think about ethically acceptable trials where participation is compulsory. thus, even a perfect rct may return an estimate of the treatment effect which is affected by a selection bias, when the overall sample of participants is not representative of the target population. an excellent review of the potential limitations of rcts – especially for causal inference in economics – is provided in deaton and cartwright (2018). randomized (food) policy experiments are quite rare. some notable exceptions are the us healthy incentives program (olsho et al. 2016), or the income support progresa program in mexico, where the government – not having enough budget to target all low-income families – randomized the villages where the policy was implemented (gertler 2004). meeting all conditions that make a randomized experiment on food policy able to deliver a reliable estimate of the treatment effect is not a trivial task. thus, it should not be maintained (as it is often the case in public health studies) that randomized experiments are the gold standard, and observational studies are a second-best options to learn about policy effectiveness. however, randomization might be the best route (and possibly the only one) when the objective is not the ex-post quantification of the policy impact, but rather an ex-ante assessment or the ranking of alternative policy instruments addressing the same policy objective. even in cases where the estimate of overall effect sizes cannot be fully trusted, it is possible that the ranking of policy instruments in terms of their cost-effectiveness has an acceptable external validity. see, for example, the randomized experiment on food/ voucher/cash transfer in northern ecuador (hidrobo et al. 2014). 3.2 quasi-experimental methods we now go back to the goal of this article, and discuss how observational non-randomized data can provide ex-post evidence on the impact of a policy. to introduce this class of methods, it may be useful to recall some very standard and light notation. let yit be the potential outcome for unit i if exposed to the policy, and yic the potential outcome for the same unit when not exposed to the policy. suppose that in our target population some units are eventually “treated” and exposed to the policy and other units are not, but assignment to treatment is not necessarily random. this situation where the treated and control group are not the consequence of an explicit randomized design is commonly referred to as a “natural” experiment5. for 5 there is no consistent definition of “natural experiment” in the literature, beyond the common consensus on the lack of explicit randomization. some restrict the definition to those situations where randomization occurs “naturally”, i.e. assignment to treatment is “as if random”, even without the explicit intervention by the researcher. for our discussion, we consider a broader case where the assignment mechanism is unknown and unknowable by the researcher, but there is some external event which allows to regard such mechanism as probabilistic (for a detailed discussion see titiniuk, 2019). 9causal inference on the impact of nutrition policies using observational data bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 example, one may compare soft-drink consumption in a country exposed to a ssb tax (france) with a neighbouring country without the tax (italy), or school fruit schemes where participation of schools to the program is voluntary. we use a binary indicator di to capture exposure to the policy, where di=1 when units are treated and 0 otherwise. at this stage, we consider a situation where we only have a cross-section of units observed in a single time period after the policy implementation, but we can extend later the notation to consider methods that rely on multiple time periods. consider the following identity, where the left-hand term is the average outcome difference between the treated and the control group: e(yit|di=1)-e(yic|di=0)=[e(yit|di=1)-e(yic|di=1)]+ [e(yic|di=1)-e(yic|di=0)] (1) this equation decomposes the difference in means in two parts, the actual average treatment effect on the treated population (att) and the selection bias (sb). on the right-hand side of the equation, the first square bracket [e(yit|di=1)-e(yic|di=1)] is the att, since it compares the average potential outcome under treatment and the average potential control outcome for the same population of individuals, those that are actually treated. thus, the att is the objective of the evaluation, and indicates how much the policy changes the outcome of those that have been exposed to the policy. the second square bracket is the selection bias [e(yic|di=1)-e(yic|di=0)], which is the difference in the average potential control (i.e. without policy) outcome between the treated population and the control population. under perfect randomization, there would be no reason for this difference to be significantly different from zero, as the two samples (treated and controls) would be extracted from the same population. here, however, we deal with observational data. it is hard to think that even without the french ssb tax, france would report the same average soft drink consumption level as italy. thus, in order to get the att it is necessary to purge the outcome difference from our data from a “baseline difference”, intended as the difference between the two groups in absence of the policy. in order to estimate the att, a counterfactual estimate is necessary. one way is to try and estimate e(yic|di=1), which is the outcome we would have observed on the treated group had the policy not been implemented. this would allow to obtain the att directly from the left-hand side of (1). a symmetric route is to try and estimate the sb, and the same counterfactual estimate is needed for this purpose. a first operational step in that direction is the identification of the drivers of the sb. why are the outcomes in the two groups expected to be different in the two group in absence of the policy? why is the french consumption of soft drinks expected to be different from the italian one, even without the ssb tax? we can start by listing those characteristics – other than the tax – that influence soft drink consumption, the many “confounding factors” which are balanced between the two groups when a randomized assignment is possible. the list is long, prices (of soft drinks and substitutes), incomes, levels of advertising, culture and tastes, temperatures and seasonality… having good information on all potential confounders is a very lucky situation, possibly unreal. in many policy situations where subjects may self-select into treatment, namely in voluntary schemes, psychological drivers can play a major role and they are hardly measured in secondary surveys. thus, we complete our notation by defining a vector of subject characteristics x, which is composed by a set of observed variables (or observables) xo and a set of unobserved variables (unobservables) xu. whether a variable ends up in the former or latter set depends on the contents of our dataset. in a randomized setting, the policy impact could be obtained by a very simple regression model, corresponding to a mean comparison test: yi=α+βdi+εi (2) since we have no reason to think that there are other differences than the policy between the two groups, β is a consistent estimate of the att, and the variance of the 0-mean random error captures the variability in outcomes. randomization is expected to balance both xo and xu between the two groups, but the researcher may want to test how well it worked, and test for significant differences in xo. a successful randomization should ensure that none exists. without randomization, we ideally want to control for any confounding factors. thus, the policy model for observational data becomes yi=α+βdi+γxo+δxu+εi (3) which still returns a consistent estimate of the att through β. unfortunately, we do not have information on xu, which leads to an omitted variable problem. quasi-experimental methods try to sort out the issue. 10 bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 mario mazzocchi, sara capacci, beatrice biondi 3.2.1 propensity score matching the class of methods based on propensity score matching (psm) has been popular in health sciences, but it is hardly useful for causal inference without combining it with other quasi-experimental methods6. the reason is simple, the key assumption behind psm (called unconfoundedness) is that there are no variables in xu. any variable which matters to the outcome and is unevenly distributed between those exposed to the policy and those not treated must be either known or highly correlated with a known variable. in other words, an effective matching requires full knowledge of the structural model determining outcomes, or full information about the selection process. in such especially desirable situation, even ols estimates of the model in equation (3) would provide a consistent estimate, even more efficient than psm provided that the linearity assumption holds and there are no heterogeneous treatment effects. not only, but authoritative recent studies have emphasized that improper application of psm could lead to the opposite (and highly undesirable) result of increasing unbalances in unobservables, and lead to larger biases (king and nielsen 2019). nevertheless, psm is widely used, probably because it is effective in reducing dimensionality, it is an intuitive and relatively easy to teach method, and statistical packages offer fast implementation algorithms. without indulging in technical details that are much better described elsewhere (see caliendo and kopeinig, 2008), psm aims at balancing the distribution – or at least the means – of observables between the treated and the control samples. it does so by working on the control sample, by dropping observations, or by applying weights. for example, an observation in the treatment group can be matched with a single observation in the control group, or with a weighted average of observations from the control group. how this matching is accomplished depends on the matching algorithm, and there are many variants: nearest neighbour, radius, kernel and stratification matching being those most commonly implemented. the idea is that rather than matching on the full set of variables xo, a synthetic function of these variables is used, the propensity score. a propensity score is the probability of a unit to end up in the treatment group given its observed characteristic xo, and can be easily estimated via a probit or logit model. matching on the probabilities estimated through these models is easier 6 as pointed out by an anonymous reviewer, propensity scores estimates remain a useful tool to reduce dimensionality, and/or as a complement to other methods. also, psm has advantages when dealing with heterogeneous treatment effects. and more feasible than attempting to match all individual characteristics. the key assumption to exploit psm for causal inference is unconfoundedness, which requires that no relevant unobservable variable exists. can this assumption be tested? not directly, but propensity scores are based on the estimation of a binary dependent variable model, and goodness-of-fit measures for that model, e.g. the pseudor2 or the rate of correct predictions, provide some feedback. even if we find that most of the covariates are relevant (significant) in explaining the assignment-to-treatment process, low goodness-of-fit diagnostics signal that our observables are not enough, and the unconfoundeness assumption is not credible, unless one accepts that unexplained variability only depends on random factors, quite a strong requirement. more sophisticated testing strategies exist, as the rosenbaum bounds or iv-based tests (see diprete and gangl, 2004), but one should be wary of any psm study that does not provide strong evidence that the unconfoundedness assumption is met, as att estimates may otherwise be affected by large biases. beyond this, psm requires overlapping of the propensity scores ranges between the treatment and control group. in a non-random setting we are likely to find higher propensity scores in the target group, and some of them might be too high to find the right match in the control group. in that case, unmatchable observations are dropped from the target group, which means that the estimated att does not refer to the original treated sample, but to the reduced one. this might become a major limitation for the att estimate. imagine that in a voluntary food assistance the poorest individuals are very likely to participate, hence have very high propensity scores, but they are not accounted in the att estimate because no adequate match is found. then, the att will measure the impact of the policy on a population which excludes those who benefit the most. relative to other methods, psm evaluations are less popular in nutrition policy analysis, but several applications can be found in the literature. clark and fox (2009) apply matching methods to investigate the impact of the us school breakfast and national school lunch programs on vitamin, mineral and sodium intakes. the method seems to be more popular among development economists, for example abebaw et al. (2010) use psm to estimate the effects of a food security program in northwestern ethiopia. 3.2.2 instrumental variables provided that one or more “good” instruments are available, iv estimators of the att work on the same 11causal inference on the impact of nutrition policies using observational data bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 data structure of psm, and allow to control for selection effects driven by both observables – through direct inclusion in the estimation equation – and unobservables, the latter through instrumenting. we discuss later the fuzzy concept of “good instrument”, and how authors, reviewers and journal editors tend to diverge in their opinion about the validity of instruments. the interpretation of iv models is straightforward, as it suffices to consider model (3). in absence of information on xu, we face the econometrics textbook problem of omitted variables, so that all coefficient estimates are biased and inconsistent. under an economics viewpoint, a parallel interpretation is that the selection variable di is endogenous, as the probability of being exposed to the policy depends on the outcome level. for example, schools located in high income and education areas where fruit consumption is high, are more likely to participate in school fruit schemes. provided we have one or more adequate instruments w to instrument di, we can control for the selection bias and obtain consistent att estimates, at the cost of giving up some efficiency. statistical packages routinely provide iv-2sls estimators where the first stage regression is again a binary dependent variable model, a probit or a logit. note that the structural policy model (3) still accounts for unbalances in observables , which enter directly the model as they are expected to influence the outcome. instead, instruments should be variables that we would not use as direct explanatory variables for the outcome, and should be exogenous. if we have access to such type of variables, the first stage binary regression would be the same used to estimate propensity scores, with xo as explanatory variables, plus the instruments w which do not belong to xo and do not enter (3). since iv encompasses psm7 and accounts for selection on unobservables, why don’t researchers just rely on iv estimation? the problem is likely to be a familiar one for the experienced reader. first, we struggle to find reasonable instruments in the dataset. second, we struggle to convince reviewers that our instrument choice is a good one. unfortunately, there is no definitive test on the validity of instruments that can convince all actors in the publication process. the issue is a catch-22 one. in order to show that an instrument is exogenous, it must be independent from the residuals of the structural (second stage) equation. however, this test is theoreti7 a caveat is necessary. just like in ols, unbiased estimation through a regression model still requires that the linear specification is appropriate and treatment effects are homogeneous, whereas psm is more flexible. however, there is extensive research to extend iv to deal with heterogeneous treatment effects (see e.g. klein, 2010 and references therein), and propensity score matching can be used in combination with iv estimates, which is why we refer to encompassing here. cally impossible, as we only obtain unbiased estimates of the residuals when we have an exogenous instrument. the empirical solution is to use several instruments, leave one out, estimate the structural equation residuals through the other instruments, then check the correlation between the excluded instrument and the estimated residuals. one can then repeat the procedure leaving out a different instrument each time. while such a strategy may provide some support to the instrument validity claims, it is an empirical one, and it is still grounded on the assumption that the included instrument are exogenous and the residual estimates are unbiased. if many of our instruments are endogenous, the procedure is useless. thus, we still need to be convinced and convince others that the instruments make sense under an economic perspective. the other interesting element is the trade-off between consistency and efficiency. if the instrument are reasonable, exogenous, and obviously significant in the first stage equation, then we can place some trust in the consistency of the att estimate in the second stage equation. however, the att will have a larger standard error, as we rely on predictions of the di variable in the second stage, a sort of propensity score augmented by the instruments. how much larger the standard error depends again on the goodness-of-fit of the first stage probit or logit equation. this time, however, a poor fit does not lead to systematic biases, it just inflates the standard errors, and with large data-sets this is not usually a problem. a list of instruments used in the food policy literature is beyond the scopes of this article, although it would be an interesting reading. for example, hofferth and curtin (2005) investigate the effect of school lunch programs on the bmi of students. participation to the lunch programs is voluntary for schools, and students need to have specific characteristics to be eligible for a free meal. these policy elements are clearly a source of endogenous selection. public school attendance is used as an instrument, as it does not affect bmi directly, but it is strongly associated with the school program participation, since public schools are more likely to be part of lunch programs. an alternative strategy resting on the use of instruments is the control function approach. this approach involves a first stage to model the exposure to the program, and a second stage where the individual probability of exposure is included as an additional variable on the right-hand side of the outcome model, to correct for the selection bias. the heckman two-step estimator is the most widely used control function approach. for example, butler and raymond (1996) explore the impact 12 bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 mario mazzocchi, sara capacci, beatrice biondi of household participation in us food stamp program on nutrient intakes of the elderly, using a variety of instruments, including household assets and distance to a food stamp office. 3.2.3 regression discontinuity designs for some specific policies, eligibility depends on the threshold value for a single continuous variable. typical examples are policies designed around an administrative eligibility criterion based on age or income thresholds to allow access to food assistance programs or other subsidies. when such a sharp classification exists and the variable is known, the division between target and control units is straightforward. as this variable is most likely to be a key determinant for the outcome of interest, this also implies that there is no overlapping and two sub-population are hardly comparable. in these cases, restricting the analysis to those units that are just below or just above the threshold is a potential solution. with a very large sample, the researcher might have a sufficient number of observations even after restricting the data-set. for example, if a policy is targeting subjects aged below 30, and we have a large data set including individuals within 6 months from their 30th birthday, the resulting sample is relatively homogeneous in terms of age, and splitting the sample in two groups through the date of birth is similar to randomizing assignment, and one should not expect major selection biases. a mere mean comparison test between the average outcomes could be a quite good estimate of the att. however, one major caveat accompanies this estimate of the treatment effect, which is certainly valid in the selected neighbourhood of the cut-off point, but not necessarily for data points further away. in our example, we may get good and reliable estimates of the att for those aged 30, but we can say little about the policy effects on those that are aged 20 or 25 relative to those aged 35 or 40. thus, atts estimated through rdd are characterized by limited external validity. furthermore, this threshold analysis commonly runs into two major issues: (1) the number of available observations around the cut-off value is not large; (2) the cut-off point may be associated with a number of confounding events creating discontinuities. for example, if the age cut-off is also the retirement age (e.g. 65), one may think that such an event creates relevant disparities between the target and control groups in variables that may in turn affect the outcome variable. the first problem is addressed by relying on the functional relationship between the outcome and the assignment (running) variable. when such function is identifiable, it can be exploited to expand the sample of interest. to do that, we need to assume continuity, which means that without the policy the outcome would just follow the identified functional relationship with the running variable. the most basic functional form is a simple bivariate linear regression, and the policy impact would be captured by a sharp shift in the intercept as the running variable reaches the cut-off point. by exploiting this linear relationship, one is able to expand the sample and consider units that are further away from the threshold. this brings in a second assumption, linearity, which requires that the linear relationship is valid within the expanded neighbourhood of the cutoff point. although few relationships between the outcome and the running variable are indeed linear, when the neighbourhood under consideration is still relatively small, then the linear approximation performs well and the att estimate becomes more credible (and efficient) for the sample of interest. in other words, its internal validity is higher. clearly this introduces a trade-off between internal validity and efficiency. if we consider a large neighbourhood, we have more observations and a more efficient estimate of the att. however, observations become more heterogeneous, the linearity assumption becomes more influential, and there is less internal validity. rdd deals well with unobservables when these are unlikely to differ substantially between the two groups within a small neighbourhood of the cut-off point. however, the crucial continuity assumption implies that there are no other major “jumps” in relevant outcome determinants at the same cut-off point. there are cases when this assumption is clearly challenged, for example when the cut-off value is one with administrative and legal relevance. for example, age cut-offs at 18 and 65 are common to several economic and health policy measures, or some income eligibility threshold levels can be similar across different policies in the same country, which complicates the attribution of the causal effect to a specific policy. in such situations, the only viable solution seems to be the inclusion in the model of covariates which help to control the confounding effects (frölich and huber 2019). more generally, one should test whether the continuity assumption holds simply by applying the same rdd model to relevant confounding factors, and the expectation is not to find significant discontinuities. the continuity assumption fails to hold when subjects have some control on the assignment variable. for example, one might delay some revenue (job offer) to maintain eligibility for a program based on income thresholds. if these behaviours (“bunching”) are pos13causal inference on the impact of nutrition policies using observational data bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 sible, then the continuity assumption is challenged and rdd becomes less credible8. since the estimation of causal effects through rdd depends on assumptions on the neighbourhood size and the shape of the relationship between the outcome and the running variable, a number of extensions and variants in the estimation procedures exist. first, the optimal size of the window around the cut-off point (the bandwidth) may be also an output of the estimation algorithm. second, non-parametric regressions allow to relax the assumption of a linear relationship, and place different weights on observations depending on how far they are from the cut-off point. third, when the running variable does not determine a sharp cut-off (i.e. all individuals meeting the rule are treated), but only creates a shift in the probability to be treated, then fuzzy rdd better serves for the purpose. this is the case of voluntary policies, where not all eligible individuals are exposed, and/or when there are exceptions allowing participation of subjects that do not meet the cut-off eligibility requirement. including covariates, changing the bandwidth, allowing for non-linear relationships, or opting for a fuzzy design are all choices that may potentially lead to different results, which is why convincing robustness checks are not an optional feature in rdd studies. on the one hand, one may want to show that the estimate of the causal effect is relatively consistent across different choices. on the other hand, falsification tests add credibility to the identification strategy. for example, one may want to show that different cut-off points other than the one relevant to the analysis are not associated with discontinuities. although the range of policies that are suitable to this method is limited, and the aforementioned external validity caveat applies, rdd is considered a relatively powerful causal identification method. sometime researchers have expanded the scope of rdd by considering time as the assignment variable with panel or time series data (see e.g. aguilar et al., 2021). in these exercises, the idea is that comparing outcome just before and after the time of the policy implementation, while exploiting some outcome-time relationship, may lead to the identification of the policy causal effect. however, this also leads to major differences in the requirements for successful identification relative to the standard rdd method, an issue which deserves careful consideration before one chooses “time” rdd over simpler event study models (hausman and rapson 2018). 8 interestingly, this opens the way to relevant behavioural evaluations and estimation which exploit the possibility to identify manipulation (see kleven, 2016). examples of rdd application to nutrition policies include the income-eligibility rule for the us school lunch program (schanzenbach 2009), the removal of vending machines from secondary schools in france (capacci et al., 2018), the impact on nutrition and wellbeing of a new refugee assistance program in kenya (macpherson and sterck 2021), and the effects of microcredit on children nutrition in china (you 2013). 3.2.4 difference-in-differences difference-in-differences (did) has clearly become the most popular and widely applied quasi-experimental method for investigating the causal effects of nutrition policies. the rationale of the method is well known, and it allows to control for selection biases driven by unobserved factors when data from natural experiments are available before and after the policy, provided the appropriate assumptions hold. the did approach follows from the extension of equation (1) to account for multiple time periods. consider its most basic formulation with two time periods, one before (period 0) and one after the policy implementation (period 1). in period 1, by reworking equation (1), one may obtain the att by subtracting the sb from the difference in means: att=[e(y1 it|di=1)-e(y1 ic|di=0)]-[e(y1 ic|di=1)e(y1 ic|di=0)] (4) where the superscript of the outcome variable indicates the time period. assuming that the selection bias does not change between period 0 and period 1, the prepolicy data can be exploited to estimate the selection bias and the att can be rewritten as: att=[e(y1 it|di=1)-e(y1 ic|di=0)]-[e(y0 ic|di=1)e(y0 ic|di=0)] (5) now all terms on the right hand-side of the equation are observable. since there is no policy in period 0 we observe the control outcomes for both the treatment and the control groups. the assumption of constant selection bias is usually referred to as the parallel (or common) trend requirement, since it implies that in without the policy the outcomes evolve at the same pace over time, and could be represented graphically as two parallel lines. such assumption can (and must) be tested when data are available for multiple periods before the policy implementation. one nice feature of the did model is that the att can be estimated through a standard regression model 14 bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 mario mazzocchi, sara capacci, beatrice biondi on the outcomes, which also allows to control for all observed covariates xo: yi=α+βdi+γti+δpi+ϑxo+εi (6) where ti is a binary variable which is 1 in time periods after the policy implementation and 0 otherwise, and pi=diti is another binary variable which is 1 when observation i belongs to the treatment group (di=1) and is observed after the policy implementation (ti=1). the coefficient estimate δ is the att. if equation (6) is based on repeated cross-sections over multiple time periods (t=1,…,k), one could test the parallel trend assumption by allowing (conditional) outcomes to evolve linearly over time before the policy implementation: yit=α+βdit+γt+θ(dit×t)+ϑxoit+εit ∀t∈{tit=0} (7) when θ=0, there are no differential trends between the treated and control groups. when θ≠0 one might still estimate a did model augmenting (6) to allow for divergent linear trends, but such an extension should be supported by credible graphical evidence of a linear evolution of the conditional outcomes. more informative (and efficient) estimates are derived from panel data, where the same units are observed over multiple time periods. there are several advantages in the generalized did model for panel data (a two-way fixed effects panel regression), as (a) the inclusion of cross-sectional fixed effects further controls for constant unobserved heterogeneity across units; (b) the inclusion of time fixed effects allows to control for non-linear heterogeneity across time periods, and extensions to control for differential linear or even non-linear trends are possible; (c) it is possible to allow for different levels of policy intensity (e.g. differential tax rates). consider, for example, the following model: yit=αi+μt+∑n r=1βr(rir×t)+δpit+ϑxoit+uit (8) where the subjects belong to n different groups which may exhibit different linear trends, for example there are n regions or states, and rir=1 when the subject belongs to region r and is 0 otherwise. in this model pit is not necessarily a binary variable, for example it might be a continuous variable between 0 (no policy) and 1 (full policy implementation). in this case, δ estimates the effect of full implementation. with adequate panel data, it is theoretically possible to allow for non-linear differential trends: yit=αi+μt+(τt×dit)+δpit+ϑxoit+uit (9) the above specification allows for differential time fixed effects between treated and control subjects, but identification becomes quite challenging, and even impossible if the policy is implemented at the same time for all treatment units. an alternative is to omit δpit and explore the evolution of the differential time fixed effects τt×dit over time, expecting that they change abruptly relative to their previous pattern for time periods following an effective policy. whatever the specification of the did model, a thorough exploration of pre-existing trends when panel data allow to do so is a necessary but not so trivial task. pre-testing may be affected by low power, and conditioning on pre-existing trends may lead to biases. an interesting review of these issues and a survey of recent papers in leading economics journals is provided in roth (2022). another important note of caution is needed for the estimation of two-way fixed effects panel did models when the policy effects are heterogeneous across groups or time periods, as causal estimates of average treatment effects may be misleading. alternative estimators have been proposed to address the issue (see e.g. de chaisemartin and d’haultfœuille, 2020). the growing availability of panel data, especially commercial consumer panels with a high level of geographical details, has generated an exponential growth of did models applied to the evaluation of the impact of fiscal policies on nutrition outcomes. for example, there is a high number of studies on national, state-level or even city-level taxes on sugar-sweetened beverage (see the review in cawley et al., 2019, or the report by griffith et al. 2019). beyond taxation, the did approach has been applied to a variety of nutrition policies, including nutritional label regulations (variyam 2008), calorie labelling in restaurant menus (vadiveloo, dixon, and elbel 2011), school-based policies (bhattacharya et al., 2006), targeted subsidies (griffith et al., 2018), food assistance programs (rahman 2016), information campaigns (asirvatham et al., 2017) advertising regulations (dhar and baylis 2011). the latter reference contains an example of how did can be reinterpreted in applications lacking the time dimension, and even extended to the situation where multiple control groups can be considered. in dhar and baylis (2011) the impact on fast-food purchases of an advertising ban to children programs applied to tv channels in french-speaking ontario is estimated through a triple did model using post-policy data only. the identification strategy rests on different target-control classifications, as both household without children and household with children in the near eng15causal inference on the impact of nutrition policies using observational data bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 lish-speaking ontario region constitute potential control groups for household with children in quebec, the target group. 3.2.5 strategies based on structural models an alternative approach is needed in situations where there is no natural counterfactual, for instance when a policy potentially acts on the whole population, as in a nationwide a public information campaign. as information policies may be expected to generate behavioural effects beyond the mere change of the average outcome, an option is to generate model-based counterfactual estimates. this approach is especially interesting when the behaviour of interest is well captured by a consolidated economic specification, and it is conveniently applicable when the pre-policy and post-policy data come from different (repeated) cross-sectional samples from the same population9. one may then express the outcome as the function of its determinants in each period: y0 i=f0(x0 io)+ε0 i and y1 i=f1(x1 io)+ε1 i the functions f0 and f1 have the same structural specification, but are characterized by different parameters. for example, f might be a demand function and the parameters represent price and income elasticities. as implied by the lucas critique, a policy is likely to go beyond changing the average level of consumption, and also lead to a change in elasticities, hence the change from f0 to f1. if the policy has no direct impact on the covariates xio, then the two set of estimates allow to evaluate the counterfactual outcome, which is estimated as y1 i=f0(x1 io). in in our example this is the level of consumption that would have been observed in period 1 had the population maintained the preference structure of period 0. the att is f1(x1 io)-f0(x1 io). the approach can be modified to include constraints on behavioural parameters, for example one might require that some of them remain constant between the two time periods. also, if there are variables in x1 io that are significantly affected by the policy, and it is possible to disentangle such effect (e.g. an estimate of the change in public 9 furthermore, structural models and theoretical knowledge are always a valuable complement in the estimation of regression-based quasi-experimental models, as the did, rdd and iv approaches can follow a structural specification. advertising expenditure, or of the price change associated with a tax), one might estimate the counterfactual through f0(x1 io) where the relevant variables in x1 io are purged from the policy effect. when data are organized as panels or relatively long time series, alternative approaches based on structural models may rely on switching and time-varying parameter regressions, intervention or event study analyses. all of these models allow one or more parameters to change in response to the policy. the most basic formulation aims at estimating a sharp step (i.e. an intercept shift as in event studies) at the time of the policy implementation10. when data allow to do so, any parameter in the structural model can potentially change and evolve, either with a pre-determined shape (as in intervention analysis or switching regression) or through random shocks (as in time-varying parameters models). an example of nutrition policy evaluation where the counterfactual is based on a structural model is provided in capacci and mazzocchi (2011), who explore the effects of the 5-a-day information campaign in the uk through a demand system. attanasio et al. (2012) exploit randomisation in the mexican program progresa to discuss how structural models can improve program evaluations even in cases where evidence from experiments is available. kim et al. (2001) estimate a switching regression model to capture the effect of the nutrition labelling and education act on diet quality in the us. 4. concluding remarks, extensions and perspectives this article aims to provide a critical overview of the current state-of-the-art in the field of nutrition policy evaluation using quasi-experimental data. it is not comprehensive in terms of the range of counterfactual methods potentially available to researchers, and by the time it will be published and read it might even be “not-socurrent”. however, until the recent past, nutrition policy evaluation has relied on a much more outdated toolbox relative to other fields, especially compared to labour and health policy analyses. a ranking or a direct comparison of the different methods would not be a wise exercise, as the choice and credibility of quasi-experimental methods is heavily dependent on the plausibility of the underlying assumptions, and the quality and detail of the available data. inevitably, empirical diagnostics on the quality of “counterfactual” causal inference must depend on observed 10 the analogy with regression discontinuity design is considered in section 3.2.3. 16 bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 mario mazzocchi, sara capacci, beatrice biondi variables, which can be outcomes, covariates, or instruments. still, the crucial assumptions refer to unobserved and unobservable variables, and in most cases no conclusive test is available, as discussed in this article. despite this necessary disclaimer, we believe that consolidated and emerging methods will need to deal with the key elements we emphasize. the central and obvious one is that the success of any causal identification depends on the validity of its underlying assumptions. under a technical point of view, this requires validation of findings through appropriate tests for these assumptions – even when they are only suggestive and not conclusive -, together with robustness and falsification checks, and comparisons with alternative identification strategies and possibly even different data. there are several variants of quasi-experimental methods that may improve causal inference. for example, when pre-policy data cover multiple periods and multiple non-treated groups (e.g. regions), the synthetic control method (scm) is a popular option (abadie et al., 2015). consider a situation where only one region is treated, and there are n non-treated regions. the principle is relatively straightforward, instead of using the n controls separately, they are artificially combined into a single control group as a weighted average. the weights are obtained through an optimization algorithm which minimizes – in each time period before the policy – the distance between the outcomes and the observed covariates measured in the target group and those obtained as the weighted average of the n values measured in the multiple control groups. in other words, the scm allows not only to ensure the common trend between the treated region and the artificial control group, but also balances the covariates. then, the weights can be applied in the post-policy period to obtain the counterfactual outcomes. other extensions allow to provide better insights on the impact of a policy by going beyond the average effect and considering characteristics of the att distribution. for example, the difference-in-difference method can be implemented through (panel) quantile regressions, as in the study on the effect of the india public distribution system on nutrient intakes (chakrabarti et al., 2018). recent developments exploit evaluation techniques based on machine learning methods and lasso estimators (belloni et al., 2017). the growing availability of micro-data has brought more emphasis on the identification of heterogenous policy impacts, which poses serious challenges to the interpretation of the average treatment effect (whether ate or att), to the point that in some cases it is not possible to estimate credible average effects. one of these situations is the potential non-compliance to the policy measure by treated subjects, as non-compliers become a third selected group whose members may be systematically different from both treated-compliers and non-treated subjects. a typical example is a policy where compliance is correlated with the treatment effect, for example adherence to nutrition guidelines is likely to depend on the distance between the current diet and the recommended one. one solution might be simply to ignore the compliance issue, consider all those exposed to the policy as the target group, then apply the appropriate method. hence, the resulting estimate will not reflect the actual effect of the treatment, but rather to the average impact on those that are exposed even if they do not “take” the treatment, which is referred to as the average intentionto-treat (itt) effect. alternatively, one may want to consider only those subjects that are exposed and comply with the policy, obviously controlling for the additional selection effect between compliers and non-compliers. the latter approach aims to estimate local average treatment-effects (late), generally through an iv estimator (imbens and wooldridge 2009). more generally, it is not infrequent that the impact of a program varies across subjects just because of the nature of the intervention, for example personalized nutrition actions, hence effectiveness depends on individual subject characteristics. the recent methodological developments are directed at tackling this challenge and capture heterogeneous effects across population subgroups, typically by letting the treatment effect depend on subject characteristics. the developments in data availability and machine learning techniques are especially important to address treatment effects heterogeneity (athey and imbens 2017). under a broader economic evaluation perspective, as researchers we unfortunately face a trade-off between the econometric rigour of the identification strategy and the policy relevance of the findings. a typical example is the focus on immediate (and easier to measure) outcomes which may be distant from the ultimate goal of the policy. do sugar taxes work? typing this question into google scholar returns a little less than 300,000 references at the date we are writing, but we challenge readers to find studies with robust causal inference about their effect on morbidity or mortality. this does not mean that “reasonable” assessments and simulations of the health impact of sugar taxes do not exist, nor that the scarcity of att estimates for health outcomes depends on gaps in the quantitative evaluation toolbox. obviously, the problem lies in the lack of adequate data. the desired effects of many nutrition policies only emerge in the medium-to-long term, and would require prospective cohort studies following people from the cradle to the grave. to the best of our knowledge, no 17causal inference on the impact of nutrition policies using observational data bio-based and applied economics 11(1): 3-20, 2022 | e-issn 2280-6172 | doi: 10.36253/bae-12411 european country is running nutrition studies of this type, and probably the only good example is the cohort study which monitors cardiovascular disease, diet, physical activity on the population of framingham in massachusetts since 1948, now on the third generation of participants (andersson et al., 2019). public investments on more broadly representative and durable prospective cohort studies would generate more knowledge on policy effects than what a century of studies on causal inference has allowed us to do. under this perspective, the “big data” challenge for causal inference, the hot topic in data-rich environments, is less urgent for nutrition policy analysts. instead, we believe that another big methodological challenge of the coming years will become especially relevant to nutrition policy, i.e. the ability to make adequate causal inference from observational data when multiple policies coexist over the data support window. the international history of policy failures in trying to improve diets and reduce obesity, together with a lack of conclusive evidence on longer term outcomes, has favoured the adoption of a “trial and error” policy approach, with a variety of overlapping policies. the coexistence of multiple interventions is clearly an obstacle for the causal inference approaches discussed in this review. on the other hand, a key contribution to nutrition policy evaluation from economists and researchers is related to improving the specification of structural (behavioural) models of food choice. just to mention the most apparent challenge, few evaluation studies succeed in properly modelling dynamic behaviours when using secondary data, which is a requirement to consider habits, intertemporal compensations, discounting, stockpiling. in our discussion, we have underlined that structural models and the proper consideration of prior theoretical knowledge make them an ideal complement rather than an alternative to quasi-experimental methods. finally, causal inference techniques might bring major benefits to the policy evidence-base when combined with other decision support tools that are becoming increasingly popular in nutrition, stochastic microsimulation methods (see e.g. emmert-fees et al., 2021). robust evidence on proximal outcomes from quasiexperimental methods could be valued in combination with simulation methods able to account for longer term effects, dynamic behaviours and heterogeneous impacts. acknowledgements this work was funded by a grant from italy’s ministry of university and research as part of the joint programming initiative (jpi) ‘a healthy diet for a healthy life’, a research and innovation initiative of eu member states and associated countries. the funder had no role 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(2024). lessons learned and policy implications from 20 years of swiss agricultural policy reforms: a review of policy evaluations. biobased and applied economics 13(2): 121-146. doi: 10.36253/bae-14214 received: january 23, 2023 accepted: november 20, 2023 published: july 25, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: meri raggi; silvia coderoni orcid rh: 0000-0003-4545-456x neb: 0000-0002-3379-2425 rf: 0000-0002-0634-5742 lessons learned and policy implications from 20 years of swiss agricultural policy reforms: a review of policy evaluations robert huber1,*, nadja el benni2, robert finger1 1 agricultural economics and policy eth zürich, switzerland 2 federal research station agroscope, tänikon, switzerland *corresponding author. e-mail: rhuber@ethz.ch abstract. learning from the experiences of other countries can support efforts to improve agricultural policies. switzerland provides an interesting case because its policy is exceptionally targeted towards the establishment of sustainable production systems. we describe the history and the current state of swiss agricultural policy, review evaluations of policy reforms, summarise their impact and outline the lessons learned for policy developments in other countries. we discuss four implications: i) some goals have been met, albeit at a high cost, and so, increasing efficiency of policies is key; ii) there is a need for more coherence and coordination regarding the different policy programmes (i.e. in the sense of a ‘food system policy’); iii) cross-compliance measures (i.e. minimum standards for receiving support) have an important leverage effect; and iv) policy differentiation (e.g. by spatial targeting) and increasing farmers’ discretion over how to achieve goals (e.g. by implementing results-based payments) are key for future policies. keywords: agricultural policy, comparative studies, policy comparison, policy evaluation, agriculture and food policy, farm support. jel codes: q01, q18, q57. 1. introduction agricultural policies are essential in achieving a sustainable and resilient farming sector. agricultural policy goals and instruments have high heterogeneity across nations, which reflects the different historical developments of and fundamental differences in societal needs with regard to agricultural policies worldwide (swinnen, 2018). policy learning from the experiences of other countries provides an important entry point for improving agricultural policymaking. switzerland, which is geographically situated in the heart of europe but not part of the european union or the european common agricultural policy (cap), provides an interesting case for policy learning. agricultural policy in switzerland is characterised by its strong governmental support. the producer support estimate for swiss agriculture is about 50%, which implies that half of farmers’ gross receipts are based on public https://doi.org/10.36253/bae-14214 http://www.fupress.com/bae https://doi.org/10.36253/bae-14214 https://orcid.org/0000-0003-4545-456x https://orcid.org/0000-0002-3379-2425 https://orcid.org/0000-0002-0634-5742 mailto:rhuber@ethz.ch 122 bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 robert huber, nadja el benni, robert finger support (oecd, 2022). the total amount of governmental spending is approximately 4 billion swiss francs (chf)1 per year for about 50,000 farms and a total agricultural area of 1.04 million hectares (foag, 2022b). the total cost for taxpayers and consumers in 2022 amounts to roughly chf130,000 per farm per year, or about chf6000 per hectare of agricultural land per year. in addition, swiss agricultural policy has been a forerunner in environmental and animal welfare programmes. in 2022, about 40% of direct payments to swiss farmers are targeted towards biodiversity conservation, landscape maintenance, sustainable production systems and animal welfare. swiss agriculture’s high level of support for environmental and animal welfare programmes, and its unique policy interventions in europe, provides a valuable example for policy learning. this is especially so, given the plans to improve environmental performance in the cap (e.g. via the farm to fork strategy; (e.g., schebesta & candel, 2020) and by the uk as it tries to make its agricultural policies “greener” (e.g., gravey, 2019). in this paper, we present and analyse the goals and instruments of swiss agricultural policy. we also describe the historical development and implementation of the policy and outline its effectiveness by reviewing policy evaluations over the last 20 years. we discuss the lessons learned from swiss agricultural policy to provide insight for other countries, including not only the positive aspects that should be followed but also the negative ones that are better avoided. on this basis, we derive the implications of swiss agricultural policy development that may have promise in other farming contexts and environments. the design and development of swiss agricultural policy has previously only partly been described (e.g., curry & stucki, 1997; el benni & lehmann, 2010; mann, 2003; mann & lanz, 2013; schmid & lehmann, 2000). in its latest review of swiss agricultural policy, in 2015, the oecd focused on recommending how to develop further existing policies on a strategic level (oecd, 2015). since then, no overview has been provided of the most recent reform steps that aim to make swiss agriculture more ecologically sustainable. other agricultural policy reviews and comparisons, such as those between the eu and the us (baylis, peplow, rausser, & simon, 2008; blandford & matthews, 2019) and between the cap and individual countries, such as the uk after brexit (e.g., roederer-rynning & matthews, 2019), have provided insightful descriptions of ongoing policy changes. in this context, countries that want to 1 numbers refer to the year 2021. in 2023 1 swiss franc (chf) equals ca. 1.05 euro and 1.11 us dollar. support more environmentand animal-friendly multifunctional agricultural sectors can gain insights from the experiences drawn from switzerland’s highly complex agricultural policy (e.g. 104 different direct payment measures are currently implemented), its specific policy programmes and their synergies and trade-offs. our analysis presents and discusses the lessons learned from swiss agricultural policy approaches and provides implications for potential agricultural policy development in switzerland and other (european) countries. our contribution focuses on three aspects that extend the current literature on agricultural policy learning. first, we present details and experiences of a wide range of instruments within a multifunctional agricultural landscape and review a (almost) complete set of existing agricultural policy measures that have been applied. such a comprehensive analysis provides a unique perspective on the fact that agricultural policy is more than the sum of its parts. second, the recent shift in swiss agricultural policy towards environmental and animal welfare goals and tailored policy instruments may be exemplary for future european agricultural policy development (schebesta & candel, 2020).2 despite such efforts, switzerland is currently observing an increase in societal discourses that have revealed gaps between societal demand for what agricultural and food systems should deliver, especially in terms of environmental performance and animal welfare, and what the current policies allow them to reach (e.g. huber & finger, 2019). it is likely that this is also emerging in other countries. third, switzerland covers a large gradient of natural environments, from alpine regions to hilly landscapes and highly productive plains, and thus represents an interesting case for analysing the potential of differentiated policy measures within an agricultural policy mix. the results from our analysis provide important entry points for the discussion of policy instruments and the transformation of food and agricultural policies not only for switzerland but also for other countries. the remainder of this paper is structured as follows. we begin by describing the historical development of swiss agricultural policy. in the second section, we provide an overview of the current goals, programmes, and instruments of swiss agricultural policy. in the third section, we provide an overview of the goals achieved from the different policies and discuss the effectiveness and efficiency of the various policy measures, based on a review of swiss agricultural policy evaluations. we then synthesise the impact of the different policies, dis2 we do not provide an explicit comparison between swiss agricultural policy and the cap beyond a short description of their historic development (see the supplementary material) 123lessons learned and policy implications from 20 years of swiss agricultural policy reforms: a review of policy evaluations bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 cuss the lessons learned and present the implications for policy-making and potential learnings to other countryspecific agricultural policies. 2. historical development of swiss agricultural policy to date 2.1. protective policies in the twentieth century governmental regulation of the swiss agricultural sector started at the beginning of the twentieth century. the evolution of a new “food regime” at the start of the previous century, when farmers were increasingly integrated into the industrialising world and dependent on trade as well as mechanical and chemical inputs (tauger, 2020), had triggered various laws aiming to protect swiss farmers from low producer prices due to imports, reduce their debt and maintain their production capabilities. after the world wars, a new constitutional article defined a liberal economic policy in switzerland – albeit with the exception of the agricultural sector. this “exceptionalism” provided a new legal basis for protective policies. the subsequent 35-year phase (1950–1985) was characterised by protective market regulations for grain, milk and sugar, during which switzerland became the greatest supporter of agriculture worldwide (huber & finger, 2019). the producer support estimate pse – that is, the transfer from taxpayers and consumers to farmers – was at about 75% in the mid-1980s. this implies that three-quarters of agricultural gross receipts came from either market protection or other forms of price support (oecd, 2015). 2.2. the era of decoupling the flipside of this massive support until the beginning of the nineties was that the swiss government spent almost chf 2 billion to guarantee high farm-gate prices and sell production surpluses from domestic overproduction on international markets, while increasing environmental awareness brought to light the severe environmental problems of this highly intensive production system. at the same time, the negotiations in the uruguay round of the general agreement on tariffs and trade, and the subsequent foundation of the world trade organization (wto), placed additional pressure on swiss border protection measures and level of support for producers. this domestic and international pressure led to a major change in swiss agricultural policy in the 1990s as switzerland adapted its federal constitution to public and international demands and income and price policies were decoupled. this decoupling was implemented in two successive reform steps. the first of these was in 1992, when switzerland rejected economic integration with the european union but decided to pursue a route of agricultural policy reform combined with bilateral agreements, especially with other european countries (el benni & lehmann, 2010). price support was reduced, and decoupled direct payments were introduced for all farmers without geographical restrictions. in addition, farmers could voluntarily apply to a so-called integrated production programme,3 for which additional payments were provided (finger & el benni, 2013). with the next reform step, in 1999, price guarantees (e.g. for crops and milk) were abolished. governmental spending was converted into direct payments, and tariffrate quotas were introduced that complied with wto rules. direct payments were divided into general (lumpsum area payments) and ecological direct payments. to be eligible for these direct payments, cross-compliance measures were introduced that guaranteed a minimum environmental and social standard across all farms. farmers located in hilly and mountainous regions additionally received payments to compensate for unfavourable production conditions and thus maintained production and concurrent landscape maintenance in remote mountain areas. while the first reform step, in 1992, was legally based on two articles, 31a and 31b, newly introduced into agricultural law, the regulatory change in 1999 was based upon the new article 104 of the federal constitution, which had been accepted in a public vote in 1996. article 104 (see the box in the online supplementary material a) defined multifunctionality as the underlying justification for public support of agriculture (hediger, 2006) and led to a stable political phase between 1999 and 2015. decoupling shifted the financial burden for agricultural support from the consumer (via consumer prices) to the state, and thus the taxpayer (via tax money used for direct payments). switzerland’s new constitutional article explicitly foresaw a periodic examination of the agricultural policy strategy. the annual federal budget for the agricultural sector, amounting to around chf 4 billion (approximately 7% of total governmental spending) had to be approved every four years by the swiss parliament. this recurrent review of the swiss agricultural policy led to four consequent reform steps named after the targeted years of the reforms (ap02, ap07, ap11 and 3 in addition, farmers founded the private food label organisation integrated production (ip suisse) with the goal to align agricultural production with environmental principles such as farm nutrient balance, diversified crop rotation, soil protection and the targeted application of pesticides. 124 bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 robert huber, nadja el benni, robert finger ap14–17). policy developments in this period were in line with the reform agenda, including various deregulation and liberalisation steps, e.g. the bilateral trade agreement on cheese with the eu and abolition of milk quotas (el benni & lehmann, 2010). during this time, the development of agricultural policy was dominated by the administration and the executive (hirschi, widmer, briner, & huber, 2013). overall support and protection decreased slightly, and the producer support estimate amounted to about 50% in 2021, compared to around 18% in the european union (see figure 1). 2.3. increasing societal pressure triggers more environmental sustainability and animal welfare in switzerland, citizens can influence public policy via plebiscites. popular initiatives allow any citizen to launch a proposal to revise the federal constitution. in the period from 2016 to 2022, ten popular initiatives were launched that addressed agricultural policy issues, including food security, food sovereignty, speculation on foodstuffs, fair-trade and animal welfare and pesticides. as a result of these, two opposite societal concerns collided. on the one hand, farmers’ organisations wanted to re-introduce protective measures (e.g. stricter import restrictions, higher governmental market control); on the other, swiss citizens criticised the fact that agriculture had not been meeting its environmental and animal welfare goals. the increase in popular initiatives represented a shift from a government-driven process towards “grass-roots initiatives” that had been developed and articulated outside, or in addition to, the legislative and executive processes. this phenomenon revealed an increasing gap between societal demand and the policies and plebiscites, which could be seen as a barometer of the changes in societal preferences for agriculture and related policies (huber & finger, 2019). while nine out of ten popular initiatives had been rejected by swiss voters, they still had a considerable impact on the development of swiss agricultural policy by putting environmental issues at the top of the agenda (finger, 2021; schmidt, mack, möhring, mann, & el benni, 2019). the pressure led, for example, to the introduction of a new constitutional article (104a) in 2017 that evolved from a counter proposal to a popular initiative that extended the role of agricultural policy towards a more comprehensive “food system policy”. moreover, even though the latest reform process in switzerland had been delayed (ap22+), the public pressure had still led to a strengthening of agricultural laws on pesticide use and nitrogen policies. more precisely, from 2023 onwards, agriculturfigure 1. comparison of producer support estimates (pse) between switzerland and the eu. data from oecd (2022). the different colours refer to the gradient of coupling between the policies and agricultural commodity output. the instruments represented in green are fully decoupled from agricultural production (e.g. a biodiversity conservation programme). light green refers to support that is not linked to current output (e.g. area-based payments for landscape maintenance). red refers to payments coupled to production (e.g. area-based payments for a specific crop, such as sugar beet). blue refers to support that is coupled to commodity outputs or input use. 125lessons learned and policy implications from 20 years of swiss agricultural policy reforms: a review of policy evaluations bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 al policy aims to reduce nitrogen and phosphorus surpluses by 20% until 2030, and the risks associated with the use of plant protection products should be halved by 2027 (foag, 2023).4 swiss agricultural policy and the cap have very similar roots and goals, and they developed on par with respect to the decoupling of income and price policies (see online supplementary information b). however, swiss agricultural policies have on average gone further than those of the eu with respect to aspects of environmental and animal welfare (see e.g., metz, lieberherr, schmucki, & huber, 2020; pe’er et al., 2014). the question is whether and how other countries could learn from the swiss experience to better consider environmental challenges in agricultural policymaking (alons, 2017; pe’er et al., 2020). 4 these targets are, however, still discussed in the ongoing political process of the ap22+. 3. current programmes and instruments in swiss agricultural policy swiss agricultural policy is a sectoral policy at the federal level. the main regulations are concentrated within a few laws with little linkage either to each other or to crosssectoral policy areas such as regional, environmental and climate policy (figure 2). in the following, we summarise the overarching goals of swiss agricultural policy and describe its interventional logic. we then present two key policy instruments of the agricultural law, namely direct payments, and market regulation. details of the other policy programmes in the agricultural law (that is structural support, input regulation and research and education) are presented in the online supplementary material c. 3.1. policy goals and interventional logic the goals of the swiss agricultural policy are derived from the federal constitution (see online supplementary figure 2. overview of swiss agricultural policy, including major legal fundamental agricultural law, federal law on rural land, law on leasehold, spatial planning law and environmental law (grey circles). financial support to farmers is mainly provided through the agricultural law, whereas the other laws include command-and-control regulations. major instrument categories within swiss agricultural law are the direct payment system (green), input regulation (light green), research and consulting (dark green), market regulation and production (red) and structural support (blue). icons reflect the major policy programmes in these areas. the numbers in chf are monetary transfers from consumers and taxpayers to farmers per year, which have been stable since 2010. the figure has been adapted from huber (2022). please note that the bubbles are for illustrative purpose only and do not represent the (monetary) size of the respective law area. 126 bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 robert huber, nadja el benni, robert finger material a). there are two key elements: first, the article defines the multifunctional role of agriculture; that is, the agricultural sector should contribute towards a) the reliable provision of foodstuffs to the population, b) the conservation of natural resources and upkeep of the countryside and c) the decentralised population settlement of the country. this implies that the agricultural sector not only has a role as a producer of food but also as a steward of the environment and a key player in rural development. second, the constitution states that these goals should be achieved by means of a sustainable and market-orientated production policy. in principle, this reflects the main intervention logic5 (see figure 3) and the idea of decoupling income and price support in the agricultural sector; that is, market prices should be based on the principle of economic freedom, whereas the confederation can supplement incomes by means of direct subsidies. it is important to note, however, that market-orientated production does not imply fully lib5 an intervention logic links the objective that needs to be met with the policy options that exist. eralised and deregulated markets. to fulfil the goal of ensuring food supplies, swiss agricultural policy directly and indirectly supports market prices, the competitiveness of the agricultural sector and farm structures and rural infrastructure. article 104 of the federal constitution also predefines four categories of instruments that should be used to achieve these goals (see figure 3). these main policy categories are i) direct payments to support methods of production that are specifically natural and animal friendly; ii) market regulation to protect farm gate prices and declare the production origin and quality of foodstuffs; iii) structural support (i.e. the provision of investment aids and regulation of the consolidation of agricultural property holdings); and iv) input regulation to protect the environment, e.g. against the excessive use of fertilisers, pesticides and other inputs. the article also provides the basis to support agricultural research, counselling, and education, providing the basis of the swiss agricultural knowledge system (obrist, moschitz, home, & 2015). finally, the article provides links to other imporfigure 3. the basic intervention logic summarising the different and overlapping links between the policy goals, main instrument categories, outputs, and indicators in the swiss agricultural law. the goals of article 104 are in green; the additional goals of article 104a are in the white dashed box; instruments with higher impacts on production are in darker blue. other laws as well as research and extension are depicted as basis or supporting categories. sources for indicators and target values: ‡foag (2022a); +foag (2023); †foag, blv, and bafu (2023) *fc (2020) with reference to the year 2021. please note that the bars and arrows are for illustrative purpose only and do not represent the (monetary) size of the respective instrument. formulation of the goals are taken from the original translation of the federal constitution of the swiss confederation (admin.ch). http://admin.ch 127lessons learned and policy implications from 20 years of swiss agricultural policy reforms: a review of policy evaluations bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 tant laws, such as the federal law on rural land and leasehold and the environmental law. the output indicators and the targets of the different policies are set out in various reports of the federal office for agriculture (foag, 2022a) and/or the federal office for the environment (bafu & blw, 2016), although they are constantly debated and revised as part of political processes. article 104a, which was introduced in 2017 through a public vote, strengthens the role of food security formulated in the original 104; that is, it states that the confederation should safeguard the basis for agricultural production by maintaining the extent of agricultural land and guarantee that food production is adapted to local conditions. in addition, the new article also specifies the role of trade in securing food availability by stating that cross-border trade relations should contribute to the sustainable development of the agriculture and food sector. finally, the article also states that food should be used in a way that conserves natural resources (related to food waste, as an important policy goal). the clear setting of the linkage between the objectives and instruments shows that swiss agricultural policies are strongly anchored in the federal constitution. the fact that the swiss public can suggest directly amending the constitution by popular initiatives, and that this democratic tool has been increasingly used in recent years, means that the swiss constitution can be seen as a “social contract” between the agricultural sector and the rest of the society (see e.g., feindt et al., 2019). this brings a high level of legitimacy to the decision-makers on swiss agricultural policy. on the flipside, the federal constitution is a reservoir of conflicting goals6 that have led to many practical trade-offs in the implementation of agricultural policy programmes and instruments, as well as their intended outcomes. this is also shown in the basic intervention logic (see figure 3), illustrating the many overlapping links between the main objectives in the constitution and the four policy categories. 3.2. direct payments at the heart of decoupling income and price policies, as well as incentivising the uptake of more sustainable farming practices, is the substitution of price regulations with direct payments that remunerate farmers for their multifunctional role in society. the swiss agricultural direct payment system has two conceptual pillars. first, payments are conditional on cross-compli6 switzerland does not have a constitutional court, and conflicting articles may be added to the constitution. ance measures. this implies that a farm is only eligible for direct payments if it fulfils minimum environmental requirements (in the so called “proof of ecological performance”) and those of individual farmers (e.g. age, education; see online supplementary material c1 for a detailed description of these standards). second, the conceptual design of the current direct payment system is inspired by the so called tinbergen rule, which states that each individual instrument should address a single goal (mann & lanz, 2013). this implies that there exists a direct payment programme for each specific goal of swiss agricultural policy, namely i) ensuring food supply, ii) the maintenance of cultural landscapes, iii) the promotion of landscape quality, iv) increasing resource efficiency, v) biodiversity conservation and vi) the development of environmentaland animal-friendly production systems. the conceptual alignment of the swiss direct payment programme with the tinbergen rule aims to ensure that the schemes within the corresponding programme are well-targeted to agricultural policy goals (e.g., s. wunder et al., 2018). an overview of these payment schemes, and their budgets can be found in table 1. in addition to the targeting, each of the programmes may consist of different direct payment schemes and measures, which allows the corresponding direct payments to be “tailored” to production regions, farm types or landscape elements, which should ensure the additionality7 of the policy (e.g., guerrero, 2021). for example, the development of a natureand animal-friendly production system contains payments for organic farming, crop production with restricted use of pesticides, animal welfare and reducing concentrated feed in milk and meat production. each of these schemes, in turn, consists of different measures (i.e. payments tailored to crops or livestock units). overall, the swiss direct payment system consists of 104 different payments.8 the design and legal development of direct payments is driven by national authorities, while the responsibility for their administration (control, pay-out, cuts etc.) lies within the swiss cantons. thus, the subsidiarity of swiss agricultural policy is rather low. 3.3. market regulation market regulations in switzerland are based on the following four pillars: i) the regulation of imports, ii) the 7 additionality implies that the direct payment improves environmental outcomes compared to the baseline (e.g., business as usual). 8 note that these payments are often characterised by complex substructures and conditions, so the complexity is even higher than the 104 payment schemes. 128 bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 robert huber, nadja el benni, robert finger table 1. overview of direct payments in swiss agriculture (as of 2022). objective payment for… no. of measures measures tailored to… design budget (million chf) share (2021) ensuring food supply producing food on agricultural land 8 production zones (decreasing with altitude); lower payments for areas under the biodiversity scheme; additional payment for crop rotation area action-based scheme (payment per ha of agricultural land) 1078 39% landscape maintenance cultural landscapes 5 production zones (increasing with altitude; zero for lowlands) action-based scheme (payment per ha of agricultural land) 140 5% steep slopes and very steep slopes 7 different gradients of steepness (and specific payments for grapes) action-based scheme (payment per ha of agricultural land) 149 5% summering pastures 6 specific animals (cattle v sheep) and differentiating between farms that send or receive animals for summering action-based scheme (payment per livestock unit living 100 days on summering pastures) 239 9% biodiversity conservation areas that support biodiversity maintenance 17 production zones and type of biodiversity element or measure (e.g. less intensively used grassland, flowering fallows, trees) action-based scheme (payment per ha; elements like trees are converted on a ha basis) 159 6% areas that support biodiversity of high quality 17 production zones and biodiversity elements. no payments for measures on cropland result-based scheme (payment per ha for a certain quality, i.e. minimal number of rare species found) 163 6% agglomeration bonus 6 production zones and biodiversity elements collaborative payment scheme (payment per ha)* 113 4% landscape quality landscape quality 4 project goals (i.e. ecological elements or land-use types) collaborative payment scheme (payment per ha or livestock unit on summering pastures)* 147 5% sustainable production systems organic agriculture 3 crops (vegetables and grapes, other crops and grassland) action-based scheme (payment per ha) 67 2% extensive production of cereals 1 action-based scheme for crop production without pesticides, except for herbicides (payment per ha) 36 1% grassland-based milk and meat (gmf) 1 action-based scheme that restricts the concentrated use of roughage-consuming animals and the proportion of maize silage from arable land (payment per ha of grassland) 112 4% animal-friendly housing systems 3 animal type (pigs, poultry, cattle and sheep/goats) action-based scheme (payment per livestock unit) 98 3% animals under free-range production systems 7 animal type action-based scheme (payment per livestock unit) 198 7% resource efficiency agricultural practices 19 agricultural practices (direct sowing, precision agriculture techniques, wash-up systems in pesticide applications, reduced nitrogen in feed for pigs) action-based scheme (payment per ha or livestock unit) 43 2% total 104 2’732 100% *farmers receive a bonus payment on top of the action-based payment if they designate land for conservation that is in close proximity to neighbours’ conservation areas. eligibility depends on the project (defined by farmers, cantons, farm advisors and members of ecological planning firms). data are from oecd pse (oecd, 2022). for details of the different payments, refer to the online supplementary material c2. note that in 2023, there have been further adjustments in direct payment schemes (e.g. mack, finger, ammann, & el benni, 2023). 129lessons learned and policy implications from 20 years of swiss agricultural policy reforms: a review of policy evaluations bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 legal principles for the regulation of domestic markets, iii) the regulation of labels and promotion of domestic sales and iv) the specific support of sensitive product markets (crop, wine, cattle, and dairy). these policies create a highly regulated market environment for swiss farmers and other market actors. in the following, we describe the key policies in each of the four domains. border protection was and still is one of the most important instruments in swiss agricultural policy. with the exception of the free trade agreement for cheese between the european union and switzerland (see finger, listorti, & tonini, 2017; irek, 2022), the import of agricultural products is restricted by tariffs and governed by tariff-rate quotas. consequently, almost 40% of the total support for swiss farmers (as measured by the producer support estimate) stems from market price support (see table 2). in contrast to imported food, switzerland does not regulate domestic production under public law. however, it provides a legal basis for private regulations via stakeholders in the food value chain. the federal government delegates market regulations to the members of different food value chains, including producer organisations, food processors, traders, and retailers. these interest table 2. overview of total financial support (border protection and governmental spending) for swiss farmers. objective instrument targeted or tailored to… support (million chf) share pse (2021) market price support tariffs and tariff rate quotas wheat, barley, maize, rapeseed, milk, beef, pig meat, poultry, sheep meat, eggs, other 2447* 41.5% multifunctionality (including environmental goals) direct payments see table 1 2732 46% competitiveness milk price supplement for cheese production milk used to produce cheese 201 9% payments for non-silage feeding of cows milk used to produce raw milk cheese 32 payments for commercial milk milk used for export products (chocolate, biscuits) 149 area payments oilseed cultivation, sugar beet, leguminous crops, grains 77 concession energy prices 65 increase demand for domestic products promotion of domestic agricultural products advertisement of domestic product categories (milk, meat, fruits, vegetables) 67 1% structural support refundable loans stables, young farmer programme, farm diversification 32 2%non-repayable loans stables, residential buildings 3 development and maintenance of infrastructure water and road infrastructure, ameliorations, regional projects to support local value chains 84 support of resource efficiency and sustainability payments for innovative projects (resource programmes) different agricultural practices or technologies 25 0.5% total** 5914 100% governmental spending thereof (i.e. federal budget) 3402 58% data source: oecd (2022) *price support measured in oecd indicator (i.e. market price support); that is, annual monetary value of gross transfers from consumers to agricultural producers arise from policy measures and create a gap between domestic producer prices and the reference prices of a specific agricultural commodity measured at the farm-gate level. **not considered: transition payments (expiring in 2023; chf67 million). total producer support estimate in 2021: chf6008 (oecd, 2022). additional governmental support, namely support by cantons (~chf200 million), research and extension (~chf227 million) and social contributions (~chf60 million); cost of public stockholding (~chf50 million); and administrative costs (~chf60 million). total governmental spending: ~chf4.1 billion. for further details of the different policy programmes, refer to the online supplementary material on c3 (market regulation), c4 (structural support), c5 (input regulation) and c6 (research and extension). 130 bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 robert huber, nadja el benni, robert finger organisations (so-called “branch organisations”) have the right to determine production volumes, target prices and market-clearing measures. the purpose of these “branch organisations” is to countervail market power abuses by input suppliers, the food industry and retailers and guarantee secure food availability for consumers. the government also provides a legal basis for the labelling of agricultural products, such as with respect to type of production (organic) or origin (mountain or alps) and the protected designation of origin (i.e. appellation d’origine protégée, aop, and indication géographique protégée, igp). these geographical indications allow typical specialties from defined areas to be protected and differentiated and support their competitiveness in domestic and foreign markets (maye, kirwan, schmitt, keech, & barjolle, 2016). finally, the swiss government directly regulates and supports specific markets. for example, it subsidises raw milk production that is used for cheesemaking (finger et al., 2017) and funds compensation payments for milk and cereal production for export commodities. this reduces the costs of domestic food processors in highly competitive markets (cheese, chocolate, biscuits etc.). the government also subsidises the production of specific crops (payments for single crops) to increase their availability on domestic markets with payments per hectare. these crops include sugar beets, oilseeds, fodder crops and pulses for human consumption. 4. effectiveness of swiss agricultural policy: what is the evidence? in this section, we summarise the achievements of these regulations with respect to the economic, ecological, and social aims formulated in the constitution, focusing on the main output indicators (see figure 3). we rely on a review of agricultural policy evaluations in switzerland during the last 20 years. our review is based on a systematic search of two sources. first, we systematically searched for agricultural policy evaluations in the administration research actions management information system (aramis) of the swiss federal government. aramis is a database in which the evaluations of the federal administration are stored. we searched the database using the search term ‘agricultural policy’ and found 105 studies from 2002 to 2022. we screened these studies and excluded projects and reports that did not i) focus on agriculture; ii) specifically address a policy instrument (e.g. basic research projects) or iii) evaluated correlations between land-use types e.g. extensively managed grasslands and ecological indicators e.g. bird index without focusing on a specific policy program or measure9. we found 16 relevant evaluations. second, we searched for scientific publications that evaluate swiss agricultural policy instruments. this search in google scholar resulted in additional 17 studies. in total, we included 33 evaluations in our review (see table 3). 4.1. economic performance: production and income with respect to the production and economic goals of the swiss agricultural policy, the outcomes have been mixed. on the one hand, the share of domestic food production of total consumption, (i.e. the degree of self-sufficiency) has been constant10, with some fluctuations, over the last 20 years, despite a growing population (~20% in this period). labour productivity has been steadily increasing, driven mainly by farm structural change and constant re-investment in farm structures and production infrastructure. the corresponding policy targets (i.e. calorie production, productivity increase and re-investment) have been met. farm incomes have also increased on average during the last 12 years (i.e. the period between 2010 and 2022). key elements of this income development are border protection and farm size growth, increasing income from per-hectare direct payments. with respect to border protection, tariff rate quotas are the main instruments, which are highly effective in maintaining high farm-gate prices, as shown in different studies e.g. for meat and vegetables, (loi et al., 2016) or for dairy products, (hillen & von cramon-taubadel, 2019). in addition, the direct payments have become an important source of agricultural income, especially in rural and mountainous regions. average direct payments amount to chf2700 per hectare of all agricultural land in 2021.11 while these payments are targeted towards public goods from agricultural production, they create windfall effects (i.e. increased income), an important and intended side-effect of the direct payment system in switzerland. in particular, payments for ensuring food supplies, which comprise more than one-third of all direct payments, have a high income transfer effect (a. möhring & mann, 2020). on the other hand, the massive support of agricultural production and farm incomes increases economic inef9 please note that we still cite some of these studies in the discussion. 10 average net self-sufficiency between 2015 and 2020 was 58%. net self sufficiency i.e. self-sufficiency corrected for fodder imports, was on average 51%. 11 total support per ha of agricultural land (i.e., including border protection) amounts to ~chf6000 (see introduction). thus, direct payments alone correspond to roughly 46% of the support (see also table 2). 131lessons learned and policy implications from 20 years of swiss agricultural policy reforms: a review of policy evaluations bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 ta bl e 3. p ol ic y ev al ua tio n st ud ie s i n sw itz er la nd 2 00 2– 20 22 . ev al ua tio n in st ru m en t ke y fin di ng s m et ho d so ur ce 1 bi od iv er sit y pr og ra m m e pa ym en ts fo r b io di ve rs ity pa ym en ts in cr ea se d th e bi od iv er sit y co ns er va tio n ar ea . th e co m bi na tio n of ac tio n a nd re su lts -b as ed sc he m es in cr ea se d no t o nl y av er ag e eff ec tiv en es s bu t a lso w in df al l g ai ns . p ay m en ts re su lte d in a p os iti ve re tu rn o f i nv es tm en t (fo r t he p ub lic ). c au sa l i de nt ifi ca tio n (d iff er en ce in d iff er en ce ap pr oa ch ) w ue pp er a nd h ub er (2 02 2) 2 bi od iv er sit y pr og ra m m e pa ym en ts fo r b io di ve rs ity bi od iv er sit y pr om ot io n ar ea s g en er al ly h av e a gr ea te r d iv er sit y of sp ec ie s an d ha bi ta ts th an c on tr ol a re as . q ua nt ita tiv e ta rg et s ( i.e . h a en ro lle d in th e pr og ra m ) a re m et . q ua lit y of b io di ve rs ity a re a (e .g . n um be r o f s pe ci es ) a re no t m et , e sp ec ia lly in th e lo w la nd s. m on ito rin g of bi od iv er sit y, in di ca to r as se ss m en t, re gr es sio n an al ys is e. m ei er e t a l. (2 02 1) 3 re so ur ce p ro gr am m e re so ur ce p ro gr am m e (r p) an d re so ur ce e ffi ci en cy pa ym en ts (r ep s) th e rp is g en er al ly w el l r ec ei ve d by th os e in vo lv ed . h ow ev er , t he o rie nt at io n of re so ur ce e ffi ci en cy c on tr ib ut io ns la ck s a c le ar fo cu s o n th e im pa ct o f t he m ea su re s p ro m ot ed . th is ca lls th e su bs id y in to q ue st io n. r p is m or e effi ci en t th an r ep s. a ss es sm en t o f l eg al an d go ve rn m en ta l do cu m en ts ef k (2 02 1) 4 bi od iv er sit y pr og ra m m e a gg lo m er at io n pa ym en ts th e co lla bo ra tiv e de ve lo pm en t o f a gg lo m er at io n pr oj ec ts is b en efi ci al to in cr ea se th e w ei gh t g iv en to b io di ve rs ity b y co nn ec tin g co ns er va tio n sit es in th e pl an ni ng p ro ce ss o f b on us p ay m en t s ch em es . sp at ia l r eg re ss io n an al ys is h ub er e t a l. (2 02 1) 5 pr ot ec tio n of d om es tic fo od vi a la be lli ng ‘s w iss ne ss ’ r eg ul at io n th e sw iss ne ss re gu la tio n (i. e. re gu la tio n of m in im al st an da rd s t o la be l a pr od uc t “ fr om s w itz er la nd ”) d id n ot a ffe ct d em an d or su pp ly o f d om es tic ag ric ul tu ra l p ro du ct s. a ss es sm en t o f l eg al an d go ve rn m en ta l do cu m en ts , e xp er t su rv ey fe ig e, ri ed er , a nn en , an d ro os e (2 02 0) 6 su st ai na bl e pr od uc tio n sy st em su pp or t f or g ra ss la nd -b as ed m ilk p ro du ct io n (g m f) th e g m f pr og ra m m e re du ce s t he u se o f c on ce nt ra te d fe ed . n o sh or tte rm eff ec t o n ec ol og ic al o ut co m es w as fo un d. e co no m ic o ut co m es im pr ov ed w ith th e pr og ra m m e. a ge nt -b as ed si m ul at io n m od el s w is sl an d m ac k an d ko hl er (2 01 9) 7 m ar ke t i nt eg ra tio n an d effi ci en cy o f s ea so na l t ar iff ra te q uo ta s ta riff ra te q uo ta s ( tr q s) tr q s a re e ffe ct iv e in p ro te ct in g do m es tic p ro du ct io n ag ai ns t c om pe tin g im po rt s b ut le ad to in effi ci en ci es a nd c re at e re nt s f or im po rt er s. re gr es sio n an al ys is (p ar ity b ou nd s m od el ) h ill en (2 01 9) 8 pr ot ec tin g th e sw iss m ilk m ar ke t f ro m fo re ig n pr ic e sh oc ks bo rd er p ro te ct io n pr ic es o f t ar iff -p ro te ct ed d ai ry p ro du ct s a re in flu en ce d by p ric e de ve lo pm en ts in n ei gh bo ur in g co un tr ie s. th is co ul d no t b e ob se rv ed fo r t he li be ra lis ed ch ee se m ar ke t. th e qu al ita tiv e di ffe re nt ia tio n of s w iss p ro du ct s c on tr ib ut es m or e to re du ci ng in te rn at io na l p ric e pr es su re th an p ub lic b or de r p ro te ct io n. pr ic e tr an sm iss io n an al ys is h ill en a nd v on c ra m on -t au ba de l (2 01 9) 9 bo rd er p ro te ct io n an d do w ns tr ea m in du st rie s bo rd er p ro te ct io n th e hi gh m ar ke t p ow er o f u p a nd d ow ns tr ea m in du st rie s r es ul ts in m ar kup s f or a gr ic ul tu ra l i np ut s. m or e co m pe tit io n, le ss b or de r p ro te ct io n an d re gu la to ry o ve rs ig ht c ou ld in cr ea se e ffi ci en cy a lo ng th e va lu e ch ai n. ex pe rt a ss es sm en t w ey a nd g ös se r (2 01 9) 10 bi od iv er sit y pr og ra m m e pa ym en ts fo r b io di ve rs ity th e bi od iv er sit y pr og ra m m e ha s h ad a n eff ec t, bu t s ho rt co m in gs re m ai n (e sp ec ia lly w ith re sp ec t t o th e qu al ity o f t he b io di ve rs ity a re as ). im pl em en ta tio n of th e pr og ra m m e ha s b ee n sa tis fa ct or y, al be it w ith a hi gh a dm in ist ra tiv e bu rd en . e du ca tio n an d tr ai ni ng o f f ar m er s s ho ul d be re in fo rc ed to in cr ea se e ffe ct iv en es s. c oh er en ce w ith o th er p ol ic y m ea su re s sh ou ld b e ch ec ke d. c or re la tio na l a na ly sis , in te rv ie w s, ca se st ud ie s fo nt an a et a l. (2 01 9) (c on tin ue d) 132 bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 robert huber, nadja el benni, robert finger ev al ua tio n in st ru m en t ke y fin di ng s m et ho d so ur ce 11 fo od su pp ly su pp or t pa ym en t f or fo od su pp ly pa ym en ts fo r f oo d su pp ly c on tr ib ut e eff ec tiv el y to c al or ie p ro du ct io n an d in cr ea se fa rm in co m es . e ffi ci en cy c ou ld b e im pr ov ed b y fo cu sin g pa ym en ts on se le ct ed c ro ps a nd fe rt ile so ils . th e eff ec tiv en es s o f t he in st ru m en t cr iti ca lly d ep en ds o n th e de fin iti on o f f oo d se cu rit y. a ge nt -b as ed si m ul at io n m od el s w is sl an d a . m öh rin g, m ac k, zi m m er m an n, m an n, an d fe rja ni (2 01 8) 12 bi od iv er sit y pr og ra m m e a gg lo m er at io n pa ym en ts th er e w as h ig h pa rt ic ip at io n of fa rm er s. th e ag gl om er at io n bo nu s, ho w ev er , d id n ot a llo w th e pr op or tio n of q ua lit at iv el y va lu ab le b io di ve rs ity co ns er va tio n ar ea s t o in cr ea se (a cr os s a ll pr od uc tio n re gi on s) . c om pl ex ad m in ist ra tio n w as o ne im po rt an t b ar rie r. in te rv ie w s, ca se st ud ie s je nn y, st ud er , a nd bo ss ha rd (2 01 8) 13 pr od uc tio n su pp or t o f m ilk pa ym en t f or m ilk p ro ce ss ed in to c he es e a bo ut tw oth ird s o f t he p ay m en ts m ad e be ne fit m ilk p ro du ce rs . th e pa ym en t d ire ct ly a ffe ct s c he es e pr od uc tio n an d ex po rt s b ut a lso h as in di re ct eff ec ts o n ot he r d ai ry p ro du ct s. th e fin di ng s s ug ge st a n et w el fa re g ai n fr om el im in at io n. ve ct or a ut or eg re ss iv e m od el , c a pr i ( pa rt ia l eq ui lib riu m m od el ) fi ng er e t a l. (2 01 7) 14 ec ol og ic al d ire ct p ay m en ts pa ym en ts fo r b io di ve rs ity an d la nd sc ap e th er e is la rg e he te ro ge ne ity in p ro vi sio n co st s f or e nv iro nm en ta l s er vi ce s. ta rg et in g an d ta ilo rin g ha ve th e po te nt ia l t o in cr ea se th e effi ci en cy o f t he cu rr en t d ire ct p ay m en t s ys te m . c os t a cc ou nt in g, in te rv ie w s, ca se st ud ie s h ub er , f lu ry , m ei er , an d m ac k (2 01 7) , 15 su st ai na bl e pr od uc tio n sy st em su pp or t f or g ra ss la nd -b as ed m ilk p ro du ct io n (g m f) th e g m f pr og ra m m e re du ce s n itr og en su rp lu se s, al th ou gh th e eff ec t i s v er y sm al l. a n in cr ea se in p ay m en ts w ou ld h av e lit tle a dd iti vi ty . a ge nt -b as ed si m ul at io n m od el s w is sl an d m ac k an d h ub er (2 01 7) 16 re du ct io n in n itr og en su rp lu se s in st ru m en ts fo r t he ev al ua tio n of n itr og en si ng le p ol ic y in st ru m en ts (m ea t t ax , f er til ise r t ax e tc .) ar e no t s uffi ci en t t o re ac h th e ta rg et ed re du ct io n in n itr og en su rp lu s. a c oh er en t p ol ic y m ix is ne ed ed . a ge nt -b as ed si m ul at io n m od el s w is sl an d sc hm id t e t a l. (2 01 7) ; sc hm id t, m ac k, m an n, a nd s ix (2 02 1) ; sc hm id t, n ec pa lo va , m ac k, m öh rin g, a nd si x (2 02 1) 17 po lic y ev al ua tio n of ta riff ra te q uo ta s bo rd er p ro te ct io n (t rq s) tr q s p ar tly re ac h th ei r p ol ic y ob je ct iv es , a nd th e po lic y ca n th er ef or e be c on sid er ed to h av e be en p ar tly e ffe ct iv e. h ow ev er , t he p ol ic y is cl ea rly in effi ci en t. in a dd iti on to th e vo lu m e of th e tr q s a nd th e siz e of th e ou tof qu ot a du ty , t rq a dm in ist ra tio n m et ho ds a lso h av e an im po rt an t r ol e in th is re sp ec t. ec on om et ric ti m e se rie s a na ly sis lo i e t a l. (2 01 6) 18 ev al ua tio n of la nd sc ap e qu al ity p ay m en ts la nd sc ap e qu al ity p ay m en t (l q p) lq ps h av e pr ov en to b e an e ffe ct iv e to ol to p ay fo r m ai nt ai ni ng a nd pr om ot in g la nd sc ap e qu al ity . h ow ev er , t he re a re c on sid er ab le w in df al l g ai ns by fa rm er s f or m ea su re s t ha t t he y w ou ld n ev er th el es s h av e ap pl ie d. c as e st ud ie s, ex pe rt w or ks ho ps st ei ge r, lü th i, sc hm itt , a nd sc hü pb ac h (2 01 6a ) 19 ru ra l d ev el op m en t ( vi ta lit y an d at tr ac tiv en es s o f r ur al la nd sc ap es ) ru ra l d ev el op m en t in st ru m en ts th er e w as a p os iti ve c or re la tio n be tw ee n m un ic ip al iti es w ith st ro ng ag ric ul tu re a nd v ita lit y. at tr ac tiv en es s s ho w ed o nl y a w ea k ne ga tiv e st at ist ic al co rr el at io n. th e st ud y un de rli ne d th e im po rt an ce o f a gr ic ul tu re a nd ag ric ul tu ra l p ol ic y fo r r ur al a re as . c or re la tio na l a na ly sis , ex pe rt a ss es sm en t su te r e t a l. (2 01 6) 20 in ve st m en t a id in ve st m en t s up po rt be tw ee n a qu ar te r a nd a th ird o f t he su bs id ise d in ve st m en t p ro je ct s w ou ld ha ve b ee n im pl em en te d in e xa ct ly th e sa m e w ay e ve n w ith ou t t he in ve st m en t as sis ta nc e; in th is re sp ec t, th ey h ad n o im pa ct . a ss es sm en t o f l eg al an d go ve rn m en ta l do cu m en ts ef k (2 01 5) (c on tin ue d) ta bl e 3. (c on tin ue d) . 133lessons learned and policy implications from 20 years of swiss agricultural policy reforms: a review of policy evaluations bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 ev al ua tio n in st ru m en t ke y fin di ng s m et ho d so ur ce 21 m ea su re m en t o f f ar m si ze fa rm si ze re gu la tio ns (s ta nd ar d la bo ur fo rc e, sa k ) th e sa k sy st em w as sh ow n to b e eff ec tiv e w he n us ed a s a n en tr y cr ite rio n th ro ug h a th re sh ol d or a s a n ad m in ist ra tiv e de lim ita tio n m ea su re . h ow ev er , w he n us ed a s t he so le se le ct io n cr ite rio n, th e sa k sy st em m us t b e ra te d m or e cr iti ca lly . in te rv ie w s, ex pe rt w or ks ho ps , c as e st ud ie s h ub er , m ei er , a nd fl ur y (2 01 4) 22 eff ec ts o f a gr ic ul tu ra l po lic y re fo rm s a nd fa rm ch ar ac te ris tic s o n in co m e ris k d ire ct p ay m en ts a gr ic ul tu ra l p ol ic y re fo rm s h av e de cr ea se d th e va ria bi lit y of fa rm re ve nu es an d ho us eh ol d in co m es in s w itz er la nd . h en ce , t he c ha ng e fr om m ar ke t su pp or t t o de co up le d di re ct p ay m en ts re du ce s t he in co m e ris k of s w iss fa rm er s. ec on om et ric a na ly sis o f ac co un ta nc y da ta el b en ni , f in ge r, an d m an n (2 01 2) 23 in ve st m en t a id in r ur al de ve lo pm en t in ve st m en t a id s in ve st m en t a id c on tr ib ut es to th e im pr ov em en t o f e co no m ic c on di tio ns in ru ra l a re as , a nd e sp ec ia lly in m ou nt ai n ar ea s. th is eff ec t, ho w ev er , i s o nl y m od er at e, co ns id er in g th e po pu la tio n of a ll pr oc es sin g en te rp ris es . in te rv ie w s, ex pe rt w or ks ho p, c as e st ud ie s fl ur y, g er be r, g iu lia ni , a nd b er ge r (2 01 2) 24 su pp or t o f s um m er in g pa st ur es pa ym en ts a nd re gu la tio ns th e re gu la tio ns fo r s um m er in g pa st ur es a re e ffe ct iv e. pa ym en ts fo r su m m er in g pa st ur es in cr ea se th e ut ili sa tio n of th e a lp s. h ow ev er , t he o ve ra ll co st s o f s um m er in g m ay in cr ea se d ue to a dd iti on al la bo ur a nd in fr as tr uc tu re ne ed s. fa rm su rv ey , de sc rip tiv e an al ys is of c en su s d at a, e xp er t as se ss m en t, ag en tba se d m od el lin g la ub er , c al ab re se , vo n fe lte n, f isc he r, an d sc hu lz (2 01 1) 25 so ci al p ro te ct io n in ve st m en t a id s in st ru m en ts a re e ffe ct iv e. h ow ev er , o nl y ve ry fe w fa rm s n ee d th em , a nd th us , th e effi ci en cy o f t he p ro gr am m e is lo w, g iv en it s h ig h ad m in ist ra tiv e bu rd en . ex pe rt in te rv ie w s a nd as se ss m en t fl ur y an d pe te r (2 01 1) 26 o rd in an ce fo r e co lo gi ca l qu al ity pa ym en t s ch em e a ct io nba se d pa ym en ts w er e fo un d to h av e lo w e ffe ct iv en es s. a re pl ac em en t w ith re gi on al ly ta ilo re d re su lts -b as ed p ay m en ts w as su gg es te d. ex pe rt a ss es sm en t, in te rv ie w s, ca se st ud ie s m an n (2 01 0) 27 a gr i-e nv iro nm en ta l p ol ic y c ro ss -c om pl ia nc e st an da rd s th e ev al ua tio n of th e ‘p ro of o f e co lo gi ca l p er fo rm an ce ’ w ith re sp ec t t o ni tr og en (n ) a nd p ho sp ho ru s ( p) sh ow ed a n ov er al l r ed uc tio n of d iff us e n an d p po llu tio n fr om a gr ic ul tu re . h ow ev er , t he ta rg et s ( -3 3% n a nd -5 0% p ) w er e no t m et . c or re la tio na l a na ly sis h er zo g, p ra su hn , sp ie ss , a nd r ic hn er (2 00 8) 28 fa rm st ru ct ur al c ha ng e re gu la tio n of fa rm su cc es sio n th e on go in g de ve lo pm en t o f s iz e st ru ct ur e is so sl ow th at it re st ric ts th e po te nt ia l r ed uc tio n of p ro du ct io n co st s, w hi ch w ou ld b e im po rt an t t o in cr ea se th e co m pe tit iv en es s o f t he fa rm in g se ct or . m ar ko vch ai n sim ul at io n b. m ei er , g iu lia ni , an d fl ur y (2 00 9) 29 po lic yre la te d tr an sa ct io n co st s o f d ire ct p ay m en ts in sw itz er la nd – a n as se ss m en t o f p ol ic yre la te d tr an sa ct io n co st s i n th e g ris on s a nd z ur ic h ca nt on s s ho w ed th at th es e co st s a m ou nt to 1 .8 % a nd 2 .8 % o f t he o ve ra ll pa ym en ts , r es pe ct iv el y. th us , t he d ire ct p ay m en ts sy st em is c ha ra ct er ise d by re la tiv el y hi gh tr an sf er e ffi ci en cy . in te rv ie w s, ca se st ud ie s bu ch li an d fl ur y (2 00 6) 30 c on ce pt io n of th e sw iss di re ct p ay m en t s ys te m d ire ct p ay m en ts th e ex pe rie nc e fr om d ec ou pl in g sh ow s t ha t s tr uc tu ra l c ha ng e in a gr ic ul tu re is bu ffe re d, th at th e ec ol og ic al q ua lit y of s w iss la nd sc ap es is m ai nt ai ne d or en ha nc ed a nd th at in di vi du al p ro gr am m es a re p ar tly e ffe ct iv e. ex pe rt a ss es sm en t m an n (2 00 3) 31 eff ec t o f d ire ct p ay m en t sy st em d ire ct p ay m en ts d ire ct p ay m en ts h av e ha d th ei r i nt en de d eff ec t. a re aba se d pa ym en t in cr ea se s a nd st ab ili se s f ar m in co m e. c or re la tio na l a na ly sis , se ct or su pp ly m od el m an n an d m ac k (2 00 4) 32 d ec en tr al ise d se ttl em en t o f th e co un tr y a ll in st ru m en ts th e fe de ra l g ov er nm en t c ou ld sp en d ar ou nd c h f7 00 m ill io n le ss e ac h ye ar o n th e go al o f d ec en tr al ise d se ttl em en t. th is im pl ie s t ha t t he c ur re nt ag ric ul tu ra l p ol ic y is no t s uffi ci en tly e ffe ct iv e w ith re ga rd to ta rg et in g de ce nt ra lis ed se ttl em en t a nd so ci al g oa ls. c or re la tio na l a na ly sis , be nc hm ar ki ng ri ed er , b uc hl i, an d ko pa in sk y (2 00 4) 33 ev al ua tio n of m ar ke t su pp or t ( m ilk , m ea t a nd eg gs ) bo rd er p ro te ct io n bo rd er p ro te ct io n ha s p ro ve n eff ec tiv e in p ro te ct in g th e lo ca l g ra in , d ai ry a nd m ea t m ar ke ts . n o eff ec t h ad b ee n fo un d fo r t he e gg m ar ke t. ec on om et ric an al ys is (e qu ili br iu m di sp la ce m en t m od el ) ko ch (2 00 2) ta bl e 3. (c on tin ue d) . 134 bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 robert huber, nadja el benni, robert finger ficiencies along three axes. first, border protection creates high costs for domestic consumers and intermediaries, reducing consumer choice and economic welfare (gray, adenäuer, flaig, & van tongeren, 2017; hillen, 2019). second, the swiss tariff rate quotas are economically inefficient, in the sense that they increase prices along the whole value chain and not only at the farm-gate level (loi et al., 2016); they also create rents to downstream actors that would not exist in the absence of the policy (hillen, 2019). in this context, studies have shown that there could be considerable market power among retailers. an empirical study after the first agricultural reform step in the early 1990s indeed found indications of asymmetric price transmission between produce and retail prices in the pork market (abdulai, 2002) implying that downstream market actors have market power. an analysis focusing on dairy and cheese production between 2004 and 2018, however, did not find such asymmetric price transmissions from producer to consumer (hillen, 2021). even though a direct comparison between these studies is not possible, one potential reason for the absence of asymmetric price transmissions in more recent studies may have been the establishment of “branch organisations” that regulate domestic markets on a private law basis and that lead to very specific levels of protection for products of different types and quality, which reduces asymmetric price transmission (esposti & listorti, 2018; hillen, 2021). third, the regulatory environment also slows resource allocation within the sector to more profitable farms. in fact, the governmental support of approximately chf4 billion is higher than the net sectoral income of roughly chf3 billion. this implies that capital invested by the government into agriculture does not fully trickle down to the farmers. this is, among others, since farmers are compensated for the (often costly) provision of ecosystem services, but it may also reflect that efficiency gains could be achieved by re-allocating governmental spending. overall, the high regulatory environment maintains production levels in swiss agriculture and ensures a certain level of sectoral income at the expense of low competitiveness and high input and consumer prices (gray et al., 2017). 4.2. environmental performance: landscape maintenance, biodiversity, resource efficiency and animal welfare a key characteristic of swiss agricultural policy is that almost 40% of governmental spending is for voluntary agri-environmental direct payment programmes supporting landscape maintenance, biodiversity conservation and sustainable production systems, including programmes for low-input use, animal welfare12 and organic agriculture. in addition, there are important cross-compliance measures for the receipt of direct payments. the introduction of these measures clearly reduced some of the negative environmental effects of the agricultural sector and supported positive ones (e.g., herzog, jacot, tschumi, & walter, 2017). the environmental goals addressed by these payments have been assessed across the following six categories: biodiversity, landscape, greenhouse gas emissions, nitrogen and phosphorus as well as pesticides13 (bafu & blw, 2016). biodiversity: there has been an increase in areas for biodiversity conservation, which has positive associations with flora and fauna. this was observed by several scientific field studies focusing on different taxa, such as vascular plants (aviron et al., 2008; herzog et al., 2005; kampmann et al., 2008; kampmann, lüscher, konold, & herzog, 2012; knop, kleijn, herzog, & schmid, 2006), arthropods (albrecht et al., 2010; aviron et al., 2008), mammals (zellweger-fischer, kéry, & pasinelli, 2011) and birds (birrer et al., 2007; engist, finger, knaus, guélat, & wuepper, 2023; zingg, grenz, & humbert, 2018; zingg, ritschard, arlettaz, & humbert, 2019). in addition, flower strips and other ecological elements have had a positive effect on biodiversity and pest management, as shown by different field and experimental studies (herzog et al., 2017; tschumi et al., 2016; tschumi, albrecht, entling, & jacot, 2015). it is important to note that the swiss direct payment programme to support biodiversity targets quantitative and qualitative goals (see mack, ritzel, & jan, 2020). areas enrolled in the biodiversity programme fulfil the quantitative target of 7% of the utilised agricultural area. of these areas, more than 75% are also enrolled in agglomeration projects. this implies that the quantitative goals (measured in ha) are being met. however, the ecological quality of these areas is still insufficient to reverse or halt biodiversity decline in switzerland (e. meier et al., 2021) and that biodiversity is still not in a good state. for example, engist et al. (2023) showed that there are fewer and less diverse birds in switzerland than in neighbouring countries. in addition, the biodiversity programme also creates windfall gains for farmers (wuepper & huber, 2022). landscape: the maintenance of swiss agricultural landscapes is threatened by two main factors: i) land 12 participation in animal welfare programmes is high. for example, in 2020, 60% of animals were kept in animal-friendly housing systems and 80% were under free-range production systems. 13 soil protection is an additional goal in swiss agricultural policy. however, no monitoring programme has been implemented, and the goal achievement cannot be analysed. 135lessons learned and policy implications from 20 years of swiss agricultural policy reforms: a review of policy evaluations bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 abandonment in mountain regions and ii) the loss of agricultural land to settlement expansion in the lowlands. the explicit goal of the direct payments for landscape maintenance is to reduce annual land abandonment by 1400 hectares, or roughly 20% of the current rate. however, land abandonment is not monitored on a regular basis, and thus, an evaluation of the measures remains difficult. the introduction of the payments, however, stabilised the number of animals sent to summering pastures, despite predictions that the reduction would continue (herzog & seidl, 2018; schulz, lauber, & herzog, 2018). land abandonment is therefore much less eminent, compared to in other european mountain regions (schirpke, tasser, leitinger, & tappeiner, 2022). finally, the evaluation of the landscape quality payments implied that farmers realise windfall gains with little environmental additionality (mann et al., 2023; steiger, lüthi, schmitt, & schüpbach, 2016b). greenhouse gas emissions: the amount of greenhouse gas emissions reduced by 11.5% with the introduction of the direct payment system (7.3 million t co2eq to 6.5 million t co2eq). the main reasons for this were a reduction in the animal herd and decreasing inputs of mineral nitrogen (leifeld & fuhrer, 2005) after the introduction of the cross-compliance standards. since then, emissions have remained stable, despite the goal to reduce agricultural greenhouse emissions by 40% by 2050 compared to the emission level in 1999 (foag et al., 2023). nitrogen and phosphorus: the introduction of cross-compliance measures for all swiss farms reduced the nitrogen and phosphorus pollution of ground and surface water in the first years of the new policy at the beginning of the century (herzog et al., 2008; kupper, bonjour, & menzi, 2015). thus, increasing environmental standards for all farms has had a major effect on the overall ecological performance of the agricultural sector. the main leverage came from the regulation that all farms should comply with the balanced use of nutrients (i.e. the annual nitrogen and phosphorus balance needs to be lower than 110% of crop requirements) to receive direct payments. however, from the initial reduction until about 2005, phosphorus and nitrogen surpluses remained constant. by 2020, the total nitrogen surplus amounted to more than 80,000 t. in certain regions in switzerland with high animal density (see e.g. spörri, el benni, mack, & finger, 2023), the aerial deposition of nitrogen had risen to above 40 kg per ha per year (reutimann, ehrler, & schäppi, 2022). beyond the implementation of cross-compliance measures, political efforts to reduce nutrient load in swiss agriculture have been less successful. for example, the grass-based milk and meat production scheme, which aims to reduce the use of concentrate in roughage-consuming animals, did not reduce nitrogen surpluses but created windfall gains for participating farms (bystricky, bretscher, schori, & mack, 2023; mack & huber, 2017; mack & kohler, 2019). the increased share of sustainable production practices such as organic production (necpalova et al., 2018; nemecek et al., 2011; schader et al., 2013; zimmermann, baumgartner, nemecek, & gaillard, 2011) has also not substantially decreased nutrient load at the sectoral level. the next policy reform targets a reduction of 20% of phosphorus and nitrogen surpluses in swiss agriculture by 2030, compared to the mean emission levels between 2014 and 2016. pesticides: at the beginning of this century, swiss agricultural policies did not focus explicitly on the risks from pesticides, despite their broad application in all major swiss crops (de baan, spycher, & daniel, 2015). policy goals for groundwater pollution (i.e. maximum of 0.1 µg of pesticides per litre of groundwater) have been achieved in the majority of monitoring locations (fc, 2017). in contrast, pesticide loads in small surface water bodies were found to be often above the legal thresholds (spycher et al., 2018). this triggered societal and political debates and finally new political initiatives such as a national action plan and new direct payment programmes that also included public-private cooperation (e.g., mack et al., 2023; n. möhring & finger, 2022; schaub, huber, & finger, 2020). however, the monitoring and evaluation of these efforts remains a challenge, e.g. due to data availability regarding detailed pesticide use (similar to the eu e.g., mesnage et al., 2021) and the complex assessment of health and environmental impacts (n. möhring et al., 2023). the most recent policy goal is to reduce the risks from pesticides by 50% by 2027, compared to the situation in 2012–2015 (finger, 2021; mack et al., 2023). 4.3. social sustainability dimension: decentralised settlement, family farming, income security, administrative burden despite farm structural change, agriculture is still an important pillar of swiss rural economies, especially in the mountain regions (ecoplan & hafl, 2016; flury, huber, & tasser, 2013; rieder et al., 2004). new policy instruments focusing on investment support along the whole rural value chain successfully support the economic viability of many farms (flury, abegg, & jeannerat, 2017). more importantly, while there is a continued discussion about what family farms imply (guarín et al., 2020), the existing policies support continuous re-investment in farm structures. the mean 136 bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 robert huber, nadja el benni, robert finger farm size in switzerland is 21 hectares (foag, 2022b). the dualistic development of farm structures (i.e. an increase in very large and small farms combined with a decrease of mid-sized farms) is much less pronounced in switzerland than in other countries (bokusheva & kimura, 2016). table 4. assessment of policy reforms, policy implications and lessons learned from swiss agricultural policy. evaluations* assessment (what has worked and what not?) lessons learned implication for future reforms [7], [8], [11], [13], [17], [22], [31], [33] stabilisation of farm gate prices and farm incomes policy is effective with respect to maintain farmers’ livelihoods. border protection and direct payments have a high-income transfer effect. increases in efficiency needed [5], [7], [8], [11] self-sufficiency maintained despite growing population; production targets (in calories) are met the farming sector can steadily improve productivity. [20], [21], [28], [32] slowing of structural change public policy maintains small-scale farming structures. [1], [5], [7], [8], [9], [13], [16], [17], [31], [33] high costs for consumer and/or taxpayers there is low efficiency in public support. [19], [21], [24], [25], [31], [32] rural viability is maintained, but only with high public spending [3], [19] many environmental goals with unclear target values or indicators there is a lack of focus on funding. coherence required [14], [16], [30] trade-off between production (in calories) and environmental targets (n, p, ghg etc.) there is the potential to re-allocate funds (i.e. public funding for public goods). [20], [21], [23], [25] continuous re-investment in farm structures re-investment needs to be aligned with environmental goals. [7], [9] rents for upand downstream actors there is a need for coordination between market and policy interests. [6], [16], [27], [30] nitrogen, phosphorus and greenhouse gas emissions stable after an initial decrease with policy reform forcing farmers to comply with minimal standards has a leveraging effect on the results indictors. strengthening crosscompliance [6], [15], [27], [30] environmental targets (i.e. pesticide load or greenhouse gas or ammonia emissions) not met increasing standards can help to achieve environmental targets. [1], [4], [10], [14], [18], [26], [30] biodiversity programme contributes to halting biodiversity loss existing targeting and tailoring provide the basis for effective biodiversity conservation. supporting differentiation [1], [2], [10], [12], [24], [26] most environmental targets are only met quantitatively (i.e. output indicators) and not qualitatively (i.e. result indicators) further efforts are needed to improve the quality of existing biodiversity conservation areas. [1], [11], [15], [18], [26] programmes supporting environmentally friendly farming create windfall effects a shift to results-based payments (i.e. increasing farmers’ discretion) could increase the efficiency of the programmes. [3], [4], [12], [29] high administrative burden digitalisation is needed to reconcile administrative burden and differentiation of policy incentives. *the numbers refer to the evaluation sources in table 3 i.e. [1] wuepper and huber (2022); [2] meier et al. (2021); [3] efk (2021); [4] huber et al. (2021); [5] feige, rieder, annen, and roose (2020); [6] mack and kohler (2019); [7] hillen (2019); [8] hillen and von cramon-taubadel (2019); [9] wey and gösser (2019); [10] fontana et al. (2019); [11] a möhring, mack, zimmermann, mann, and ferjani (2018); [12] jenny, studer, and bosshard (2018); [13] finger et al. (2017); [14] huber, flury, meier, and mack (2017); [15] mack and huber (2017); [16] schmidt et al. (2017), schmidt, mack, mann, and six (2021), schmidt, necpalova, mack, möhring, and six (2021); [17] loi et al. (2016); [18] steiger, lüthi, schmitt, and schüpbach (2016); [19] suter et al. (2016); [20] efk (2015); [21] huber, meier, and flury (2014); [22] el benni, finger, and mann (2012); [23] flury, gerber, giuliani, and berger (2012); [24] lauber, calabrese, von felten, fischer, and schulz (2011); [25] flury and peter (2011); [26] mann (2010); [27] felix herzog, prasuhn, spiess, and richner (2008); [28] b. meier, giuliani, and flury (2009); [29] buchli and flury (2006); [30] mann (2003); [31] mann and mack (2004), [32] rieder, buchli, and kopainsky (2004); [33] koch (2002). 137lessons learned and policy implications from 20 years of swiss agricultural policy reforms: a review of policy evaluations bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 in addition, the restrictive law on rural land has two important implications. first, farm succession in switzerland is almost exclusively restricted to the descendants of farmers. second, farms are kept among families to profit from fiscal rewards, zoning decisions or advantages related to living outside the building zone. thus, most farms that leave the sector are small and at the end of the generational cycle (e.g., zorn & zimmert, 2022). overall, the regulations with respect to structural changes in swiss agricultural policy have led to high investment on a sector level, despite small farm structures and highly regulated land markets, with the consequence being the family-based and continuous, rather than dualistic, development of farm structures. while income inequality in swiss agriculture has increased as a consequence of the decoupling of price and income policies (especially between lowlands and hilly and mountain regions), the introduction of the direct payment system has positively influenced income stability by decreasing the variability of farm revenues and household income in swiss agriculture (el benni & finger, 2013; el benni, finger, & mann, 2012; el benni, finger, mann, & lehmann, 2012). even though direct payments also aim to support farm incomes, the income goals of agricultural policies cannot be considered to have been achieved, and off-farm income is an indispensable diversification strategy of swiss farm households (el benni & schmid, 2021). despite ongoing discussions about the interpretation and measurement of farm incomes (finger & el benni, 2021), the strong governmental support has secured stable farm incomes in swiss agriculture over recent decades. in this context, zimmert and zorn (2022), using a spatial regression discontinuity design, showed that direct payments increased family farm employment. the analysis pointed to not only the economic but also the social side-effects of the current direct payment system because the additional labour force often consists of non-salaried female household members. without a wage, these family members are not sufficiently protected socially, an issue that should gain importance in the discussion on the further development of agricultural policy. finally, a flipside of the enforced conditionality of the swiss direct payments system is that a high administrative burden is placed on both the farmers and the government (mack, ritzel, heitkämper, & el benni, 2021; ritzel, mack, portmann, heitkämper, & el benni, 2020). while the actual costs of monitoring and implementing agricultural policies are less than 5% of the total budget for agriculture, farmers perceive administration to be a burden (el benni et al., 2022; mack, kohler, heitkämper, & el-benni, 2019). 5. discussion: lesson learned and implications for future policy development in this section, we discuss findings from our review with respect to the general lessons learned from switzerland’s experience and the following four implications that may provide entry points for the discussion of specific policy design features that would be transferable also to other countries. first, the economic and social goals have largely been met, but the costs for consumers and taxpayers are high (approximately chf130,000 per farm per year, or ~chf6,000 per hectare of agricultural land per year). thus, increasing the efficiency of swiss agricultural policy is key. second, programmes and instruments need to be more coherently embedded in the food and agricultural sector not only to reconcile the economic and environmental goals but also to improve collaboration along the value chain. third, standards for all farms have increased the overall ecological performance of the agricultural sector. strengthening of crosscompliance measures has the potential to provide valuable leverage and support to the agri-environmental fields that fail to meet their targets. fourth, differentiating targets (e.g. in space) and increasing farmers’ discretion over how to achieve goals provide promising approaches to realise the premise of public funding for public goods. 5.1. increasing efficiency one of the key preconditions for the swiss policy system is its restrictive border protection and generous governmental budget for agriculture. high farm-gate prices and large funds for direct payments have created a system that effectively supports the achievement of some policy targets, such as a food supply, landscape maintenance and contribution to decentralised settlement. the support has also allowed the farming sector to steadily increase labour productivity and to re-invest in smallscale infrastructure (maintaining family-based, peasant farm structures). however, the efficiency of the system is low, including the payments for ensuring that food supplies are effective in increasing calorie production and for maintaining arable land for crop production (a. möhring et al., 2018). up to 25% of these payments could be saved if criteria other than the number of calories produced were considered (e.g. maintaining productive land without calorie targets; (a. möhring & mann, 2020). also, the targeting and tailoring of policies has led to windfall gains for farmers. the design of a biodiversity programme combining different schemes, for example, creates larger windfall effects (wuepper & huber, 2022). 138 bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 robert huber, nadja el benni, robert finger this implies that if the programme has additional environmental benefits, the implementation of the corresponding direct payment comes with high public costs. the restricted farm structural change also implies that farms with low competitiveness remain in the sector (suter et al., 2016). thus, increasing efficiency and reducing the windfall effects of agri-environmental instruments would permit funds to be reallocated to more effective instruments and thus boost the environmental impact of agricultural programmes. in its latest assessment of swiss agricultural policy, the oecd recommended that switzerland further liberalise its border protection and reduce trade barriers while also reducing the overall level of general direct payments (oecd, 2015). this should allow farmers to respond to market signals, increase their competitiveness and bring about greater efficiency in the swiss policy approach. how to align market liberalisation and the support of peasant farm structures or the contribution to decentralised settlement in this context is an important topic for future research. 5.2. improving coherence the acceptance of conflicting goals and trade-offs in agricultural policy-making creates challenges for policy coherence (coderoni, 2023; eyhorn et al., 2019; mann & kaiser, 2023). trade-offs are inherent in the agricultural and food system, and there is no simple strategy that would allow all positive and negative externalities from agricultural production to be disentangled. the key challenge in swiss agricultural policy is the conflicting goals that lead to trade-offs. this involves, for example, the production goals (measured in calories or degree of self-sufficiency), the maintenance of decentralised peasant farm structures and the environmental targets (reductions in emissions and the support of biodiversity conservation areas). given the current inefficiencies in supporting the agricultural sector, reallocating funds, and stronger focusing on the principle of “public funding for public goods” could alleviate the trade-offs between these goals (e.g., bateman & balmford, 2018; s. wunder et al., 2018). this includes, for example, that instruments that promote production include sustainability standards or that support investment should be aligned to environmental or animal welfare goals. a better alignment of policies would not make the inherent trade-offs disappear, but it could certainly improve the efficiency of the public money spent on agriculture. furthermore, some of the windfall gains from agricultural policy support end up in upand downstream companies with a vested interest in maintaining protection. thus, better policy coherence should not only focus on aligning policy instruments but also include the actors along the value chain. in this context, the link between public incentives and private sustainability initiatives (e.g. trough labelling) is key (poppe & koutstaal, 2020). for example, the development of a new, pesticidefree standard for wheat production in switzerland has allowed the creation of synergies between public and private (market) goals, where farmers receive compensation for not using pesticides from governmental direct payments and private price mark-ups (n. möhring & finger, 2022). the political system in switzerland enables partial policy success for different interest groups when negotiating policy reforms (metz et al., 2020). together with public plebiscites on agricultural policy questions (huber & finger, 2019), this can have the effect that the resulting policy has to tolerate certain conflicts in the overall policy. here, the alignment of agricultural policies with more coherent strategies, such as a common food policy that includes a wider range of stakeholders (de schutter, jacobs, & clément, 2020) within specific areas such as pesticides (n. möhring et al., 2020) and nitrogen use (kanter et al., 2020) is important . beyond the integration of stakeholders along value chains, a food system policy could also include demand-side policy instruments for sustainable food consumption (ammann, arbenz, mack, nemecek, & el benni, 2023), consider sustainability standards in global agri-food supply chains (e.g., meemken et al., 2021) or support sustainable public food procurement (e.g., schleiffer, landert, & moschitz, 2022). this could provide the basis to initiate the necessary transformation of the agricultural and food system. in switzerland, the policy goals formulated in article 104a provide a constitutional basis for the future development of such a food policy approach that could also be exemplary for other countries. 5.3. strengthening cross-compliance strict cross-compliance measures provide an effective tool to achieve environmental outcomes. while this had also been discussed in the context of the cap (e.g., pe’er et al., 2019), the swiss example clearly shows that the conditionality of payments is effective in reducing negative environmental externalities and increases the provision of positive externalities in agricultural production. the introduction of the proof of ecological performance as cross compliance measure in switzerland has had a leveraging effect on the environmental performance of swiss agriculture (herzog et al., 2008). stricter conditions for the proof of environmental performance 139lessons learned and policy implications from 20 years of swiss agricultural policy reforms: a review of policy evaluations bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 could, under certain market and production scenarios, actually contribute to the better achievement of environmental targets with little reduction in farm incomes (schmidt et al., 2019). however, there are also critical aspects that need to be discussed in this context. increasing production standards via cross-compliance measures might create leakage effects i.e. some stricter regulations would increase the number of non-complying farms—that is, farms that do not receive direct payments but also do not comply with cross-compliance regulations; (schmidt et al., 2019). while the overall strong support of agriculture in switzerland attenuates this risk to a certain extent, since farms would lose a considerable amount of their income share, this would be more pressing in countries with lower overall support. this implies that command and control instruments could replace cross-compliance measures, but their implementation would certainly create more opposition in the agricultural sector (erjavec & erjavec, 2021). in addition, it could also create leakage of negative environmental effects to other countries if imports were to increase due to the stricter regulation (bystricky, nemecek, krause, & gaillard, 2020). finally, our review does not provide a direct comparison of cross-compliance measures between switzerland and other countries. while some studies have looked at certain commonalities and differences (bafu, 2023; baur & nitsch, 2013; nitsch & osterburg, 2005), the extent to which switzerland, through its experiences with crosscompliance, could serve as a role model for other countries would certainly need additional research. 5.4. supporting differentiation the targeting and tailoring of policy incentives in space, time and across farm types allows for the transparent and efficient support of public goods provided by the farming sector. the swiss case shows the advantages of such policy designs that try to implement the idea of “public funding for public goods”. this allows us to differentiate between regions with different production conditions, which is a prerequisite for the successful support of local public goods provided by agriculture, such as landscape maintenance and biodiversity conservation (gawith & hodge, 2019; navarro & lópez-bao, 2018). in addition, the high degree of targeting and tailoring (in combination with the cross-compliance measures) in the swiss direct payment system enables attenuation of the tendency of adverse selection into voluntary agrienvironmental programmes, which is key for economic incentives for public good provision (e.g., sven wunder, börner, ezzine-de-blas, feder, & pagiola, 2020). a step forward in payment differentiation would be to extend the use of results-based incentives (i.e. paying farmers for achieving targets and not for certain aspects of management). recent studies have shown a promising effect on the effectiveness and efficiency of a more widespread use of such results-based agri-environmental schemes in switzerland (e.g., huber, späti, & finger, 2023; huber et al., 2021; kreft, finger, & huber, 2023; mack et al., 2020; wuepper & huber, 2022). these schemes would also enable farmers to use their own discretion over how to achieve outcome goals (e.g., ehlers, huber, & finger, 2021). the flipside of increasing targeting and tailoring to achieve efficiency gains is more complex systems with potentially high administrative burdens (e.g., el benni et al., 2022). here, the use of digital technologies and the digitalisation of entire agricultural policies plays a key role (ehlers et al., 2022; ehlers et al., 2021). this could not only reduce the administrative burden but also create new opportunities to measure the outcomes of instruments and thus establish results-based or collective policy schemes that do not have to rely on controls on individual farms. 6. conclusion there are four implications from these swiss experiences for policymakers and researchers alike. first, efficiency must be increased to re-allocate funds towards programmes that effectively support the provision of public goods or reduce negative externalities. second, the coherence of different policy programmes is key. increasing funds for public goods might be a necessary condition for a more sustainable agricultural sector, albeit one that is not sufficient. the swiss case shows that the coordination of policies along value chains and across sectoral policies and stakeholders (i.e. in the sense of a “food system policy”) is indispensable for making agriculture and food production more sustainable. third, cross-compliance measures (i.e. minimal economic, environmental, and social standards) for receiving governmental support have an important leverage effect. even though we observed that setting these standards can lead to political conflicts, they have made a decisive contribution to improving the environmental performance of swiss agriculture. fourth, the examination of swiss agricultural policy suggest that some environmental targets can be achieved while allowing for windfall gains from farmers’ provision of environmental public goods. our conclusion is not that other countries should also apply programmes with low additionality, espe140 bio-based and applied economics 13(2): 121-146, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14214 robert huber, nadja el benni, robert finger cially given the fact that they might face much stricter budget constraints, but a carefully differentiated agrienvironmental policy programme that focuses on landscape, biodiversity, animal welfare and ecosystem services should also allow for maintaining economic viability and rural incomes. our review and the derivation of the lessons learned imply two important research gaps. first, more studies that effectively provide scientific evidence for policymakers are needed (el benni, grovermann, & finger, 2023). special emphasis shall be on scientifically sound approaches for policy evaluation, including increased attempts to estimate the causal effect of policies. this is often hampered, however, by the complex regulatory environment and the many interactions between programmes and instruments that are often introduced at the same moment in time. second, future research could focus on the transferability of these lessons, especially with respect to the specific effect of policy mixes and how an integrated policy framework could alleviate trade-offs in the joint provision of food and ecosystem services. our review is context-specific, and we cannot draw direct implications for other countries (e.g. for countries with lower financial resources to support agriculture). however, the implications from the lessons learned in swiss agricultural policy have been mirrored in many ongoing proposals on how to improve the cap (e.g., guyomard et al., 2023; 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(2024). intention and behavior toward eating whole grain pasta on a college dining campus: theory of planned behavior and message framing. bio-based and applied economics 13(3): 301-316. doi: 10.36253/bae-15456 received: november 28, 2023 accepted: march 15, 2024 published: october 16, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: francesco pagliacci, valentina raimondi, luca salvatici orcid gs: 0000-0002-2561-571x rw: 0000-0002-4897-7414 ga: 0000-0001-8660-7732 np: 0000-0002-9178-5274 mig: 0000-0002-3591-5249 cm: 0000-0003-3180-5496 dm: 0000-0002-5241-1587 intention and behavior toward eating whole grain pasta on a college dining campus: theory of planned behavior and message framing giovanni sogari1, rungsaran wongprawmas1, giulia andreani1, michele lefebvre2, nicoletta pellegrini3, miguel i. gómez4, cristina mora1, davide menozzi1,* 1 department of food and drug, university of parma, parco area delle scienze 47/a, 43124 parma, italy 2 director, nutrition management, cornell university, ithaca, ny 14850, usa 3 department of agricultural, food, environmental and animal sciences, university of udine, via sondrio 9 2/a, 33100 udine, italy 4 charles h. dyson school of applied economics and management, cornell university, ithaca, ny 14850, usa * corresponding author. e-mail: davide.menozzi@unipr.it abstract. the consumption of whole grains has several health benefits, however, most us consumers – including young adults – do not meet the recommended consumption intake. to understand the underlying factors affecting the intention and consumption of whole grain pasta, a survey based on the theory of planned behavior (tpb) was developed and administered to us college students. for four weeks, participants (n = 325) either did not receive any information (control) or received weekly messages on the health benefits of whole grain pasta (e.g., high fiber and niacin contents) in the forms of gain(treatment 1) or loss-framed (treatment 2) information. variables of the tpb model and consumers’ perceptions were investigated both at time 1, when the first message was received (week 0), and at time 2, one month after the intervention (week 4). results from the two moments were compared. we found that the tpb measures and perceived usefulness were not influenced by the treatment group; however, the gain-framed message engendered greater message engagement than the loss-framed one. finally, results from the structural equation model showed that attitude, subjective norms, and perceived behavioral control were positively associated with the intention to consume whole grain pasta, and the intention was a strong determinant of participants’ behavior. based on our results, implications and suggestions for future studies are discussed. keywords: dietary fiber, message framing, gain-framed, loss-framed, message engagement. jel codes: i12, d91, d83. 1. introduction substantial socio-environmental changes from adolescence to college can be challenging for many young adults (christoph, ellison, & meador, 2016). https://doi.org/10.36253/bae-15456 https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode http://www.fupress.com/bae https://doi.org/10.36253/bae-15456 https://orcid.org/0000-0002-2561-571x https://orcid.org/0000-0002-4897-7414 https://orcid.org/0000-0001-8660-7732 https://orcid.org/0000-0002-9178-5274 https://orcid.org/0000-0002-3591-5249 https://orcid.org/0000-0003-3180-5496 https://orcid.org/0000-0002-5241-1587 mailto:davide.menozzi@unipr.it 302 bio-based and applied economics 13(3): 301-316, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15456 giovanni sogari et al. in a situation in which young adults are now faced with making their own dietary choices, this transition is often associated with unhealthy eating habits (quick, wall, larson, haines, & neumark-sztainer, 2013; stok, renner, clarys, & deliens, 2018), which can contribute to overweight and obesity and other diet-related diseases (kann et al., 2018; world health organization, 2014). therefore, campus dining programs are working to change the perception of nutrition and healthy eating within their food eateries (franchini, biasini, rosi, & scazzina, 2023). from new and innovative design strategies and different approaches to healthy menus to the inclusion of more produce, many campus dining programs have tested and used health principles and guidelines to nudge customers’ decisions (andreani, sogari, wongprawmas, menozzi, & mora, 2023). one example comes from the us-based menus of change program. menus of change, founded in 2012 by the cia and harvard school of public health, is an initiative to achieve healthy and sustainable menus, with the tagline “the business of healthy, sustainable, and delicious food choices”. menus of change university research collaborative (mcurc) was established with working groups of scholars and campus dining leaders interested in using college and university dining as a platform to establish and accelerate efforts to move campus diners towards healthy menus. healthy eating habits should include high consumption of food considered to be healthy, such as fruit, vegetables, and other high-fiber options, such as whole grains 1 and legumes (u.s. department of health and human services and u.s. department of agriculture & us department of agriculture, 2015). among healthy food choices, whole grain intake is a pivotal aspect to be considered in weight management and overall health of young adulthood, which helps in overweight and obesity prevention (quick et al., 2013). grains, including whole grains, are staple foods in many countries of the world (european commission, 2019) and can be consumed as single foods (e.g., rice, oatmeal), or included as an ingredient in many food products (e.g., breads, cereals, crackers, and pasta) (u.s. department of health and human services and u.s. department of agriculture & us department of agriculture, 2015). evidence showed that higher consumption of whole grains and dietary fiber is inversely asso1 “grains and grain products made from the entire grain seed, usually called the kernel, which consists of the bran, germ, and endosperm. if the kernel has been cracked, crushed, or flaked, it must retain the same relative proportions of bran, germ, and endosperm as the original grain in order to be called whole grain. many, but not all, whole grains are also sources of dietary fiber.” (u.s. department of health and human services and u.s. department of agriculture, 2015, pag. 96). ciated with the risk of obesity and weight gain (maki et al., 2019; slavin, 2005), type 2 diabetes mellitus and cardiovascular disease (ye, chacko, chou, kugizaki, & liu, 2012). because of the health benefits linked to dietary fiber (see jones & engleson, 2010 for a more comprehensive review), governmental institutions and nutritional experts have developed nutrition education and health promotion campaigns to recommend the inclusion of whole grains in the diet (jones & engleson, 2010; marquart, wiemer, jones, & jacob, 2003; shepherd et al., 2012). for instance, the 2015–2020 dietary guidelines for americans suggests that a healthy eating pattern should include grains, at least half of which should be from whole grains (u.s. department of health and human services and u.s. department of agriculture & us department of agriculture, 2015). previous reserach (e.g., wongprawmas et al., 2021) indicates that the availability of whole grain options at comparable prices to conventional ones could be beneficial for students since it may mitigate consumption barriers such as availability and price (meynier, chanson-rollé, & riou, 2020). moreover, another barrier to consuming whole grain products is consumers’ negative perceptions of their sensory attributes (i.e., taste and texture) (bisanz & krogstrand, 2007; dammann, hauge, rosen, schroeder, & marquart, 2013). despite the relevance whole grains have in a healthy diet, limited research (ugunesh, siau, mohd sanip, & koo, 2023; weingarten & hartmann, 2023) has investigated the links between consumer attitudes, intention, and behavior to consume whole grain foods, especially among young adults. therefore, we tested the theory of planned behavior (tpb) (ajzen, 1991) – which is an expectancy-value model of behavior change – to measure the variables influencing the consumption of whole grain pasta. the tpb model postulates that behavioral intention is the central determinant of behavior. previous systematic reviews have demonstrated that the tpb and similar psycho-social theories (e.g. the theory of reasoned action, tra) can serve as reliable tools for predicting sustainable (e.g., biasini et al., 2021) and health-promoting behaviours (e.g., mceachan, conner, taylor, & lawton, 2011), including healthy eating behaviours (e.g., mcdermott et al., 2015). these reviews have shown that, in general, attitude towards the behaviour is the most significant predictor of intention, and intention is the most significant predictor of behaviour (mcdermott et al., 2015; biasini et al., 2021). biasini et al. (2021) observed a wide range of explained variance in intention (7–87%) and/or behaviour (3–81%) across different applied models and study designs. as suggested by these authors, longitudinal studies can provide a prospective 303 bio-based and applied economics 13(3): 301-316, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15456 intention and behavior toward eating whole grain pasta on a college dining campus prediction analysing the causal relationship between dependent and independent variables, which would be otherwise precluded in cross-sectional investigations (mceachan et al., 2011; biasini et al., 2021). based on these considerations, first, the model we tested hypothesizes that the intention to include whole grain pasta in the diet is influenced by the attitude (a person’s favorable or unfavorable evaluation of the behavior), the subjective norms (what other people think one should do), and the perceived behavioral control (the perceived ease or difficulty of performing the behavior). second, we hypothesized that the prospective behavior (actually eating whole grain pasta), measured after four weeks (time 2), is determined by the intention and perceived behavioral control. figure 1 shows the theoretical framework. in addition, past studies suggest that whole grain food consumption could be promoted by using positive information about its health benefits presented at the point of consumption. one study by sogari et al. (2019) found that a psychological health benefit (i.e., vitamin benefits reduce fatigue) related to whole grain foods significantly increased the number of individuals preferring whole grain vs. regular pasta. another study by weingarten and hartmann (2023) showed that repeated exposure to positive information about the health benefits of whole grain increased attitudes and led to higher intentions to consume such products. therefore, the use of health claims and messages to encourage the consumption of whole grain pasta over regular pasta is one communication strategy that could support the shift toward a healthy eating pattern. based on this evidence, it is relevant to understand the effectiveness of different communication strategies on the attitude towards whole grain options in terms of the framing effect, i.e. decisions are influenced by the way the outcomes are presented (dolgopolova, li, pirhonen, & roosen, 2022). meta-analysis results have recently indicated that product attributes framed as gains have a higher effect on attitudes and intentions than product attributes framed as losses (dolgopolova et al., 2022). other researches have indicated that encouraging positive behaviors by evoking loss aversion is not necessarily a guiding principle when it comes to health benefits (e.g., gallagher & updegraff, 2012). dolgopolova et al. (2022) have suggested that loss-framed messages are mainly effective when it comes to decisions involving significant risk, and that food choices are not associated with an immediate high level of risk. thus, a secondary aim of our study is to understand whether providing information on the health benefits of whole grains, under two different framing conditions (gain vs. loss-framed), would influence the tpb measures as well as other variables (e.g., perceived usefulness of eating whole grain pasta). 2. methods 2.1. data collection and the sample data collection was carried out across several dining halls at cornell university, ithaca, ny, us in spring 2019. an online questionnaire was distributed using the qualtrics llc platform (provo, us), and included attitudinal and motivational items derived from the tpb framework, as well as questions on overall eating habits. some survey sections, including the message frames, were revised to improve the clarity of their meaning and reduce the total survey length to approximately 12 minutes. the entire survey was pre-tested with 50 students and faculty staff members. the data collection took place during dinner time in front of the pasta station in a dining setting (time 1). a final sample of 499 college students (female 53.6%, mean age 18.8y), all pasta consumers, participated in this study. participants mostly had a healthy weight range (body mass index between 18.5 and 24.9), were mainly omnivores with a slightly high proportion of flexitarian and vegan or vegetarian, and only 10% had dietary or healthy restrictions. table 1 shows the full set of socio-demographics of the participants. one month after time 1 (time 2), a follow-up questionnaire was sent via email to all the participants in order to evaluate whether any changes in their attitudinal variables occurred and to assess the reported consumption behavior of eating whole grain pasta over the last month. most of the participants returned the electronic questionnaire on the day they received it, and few of them completed it in the following days. a final sample of 325 respondents returned the questionnaire. the full survey flow (time 1 and time 2) is shown in figure a1 in the appendix. the two surveys at the two time figure 1. theoretical framework of the theory of planned behavior (tpb) in time 1 (main survey in week 0) and time 2 (follow-up survey after 4 weeks). 304 bio-based and applied economics 13(3): 301-316, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15456 giovanni sogari et al. points were linked through the student id number. following the completion of the study, participants received a monetary compensation of $5. the study was approved by the institutional review board (irb) of the office of research integrity and assurance of cornell university (protocol number: 1810008359). 2.2. measures the main survey (time 1) consisted of three sections. the first section included the message or framing treatment (control, gain-framed, and loss-framed messages) – details are reported in section 2.3. in the two treatment groups, the participants were asked to carefully read the information provided. the second section was structured to measure the various components of the tpb (ajzen, 1991) and other factors in relation to the participant’s behavior of including whole grain pasta in the diet over the next month (for details see table a1). the tpb survey items and the health claims were based on a review of the existing literature (fishbein & ajzen, 2011) followed by a revision by two nutrition experts as well as three experts in social sciences. finally, the third section of the survey included socio-demographic data (i.e., participants’ age, gender, and body mass index2), self-perception of overall health, physical exercise, eating behavior, and dietary/healthy restrictions. for the tpb section, all measures were assessed using a 7-point scale, from strongly disagree (1) to strongly agree (7). two items measured the perceived behavioral control (pbc), which is related to the control of performing the behavior. three items assessed the subjective norms (sn), which is an individual’s perception of social pressure on the way a person should or should not demonstrate a specific behavior. attitude towards the behavior (att), which represent the degree of a favorable or unfavorable evaluation of a specific behavior, was based on two items about the likelihood that consuming whole grain pasta would result in personal beliefs (i.e., tasty, easy). behavioral intention (int) is the willingness of an individual to perform a specific behavior and it was measured using three items. the factors of the tpb model have prior determinants: att is guided by behavioral beliefs about the likely consequences of performing the behavior, sn is driven by the normative beliefs about the opinions/ expectations of important others, and pbc is influenced 2 the body mass index, abbreviated as bmi, is a measure of a person’s weight relative to height that correlates well with body fat (eurostat, 2017). a person is considered underweight if they have a bmi below 18.5, normal weight between 18.5-24.9, and overweight if they have a bmi greater than or equal to 25. by the control beliefs about barriers and facilitators to perform that behavior (fishbein & ajzen, 2011). all these beliefs (n=12) were measured using a 7-point likert scale from strongly disagree (1) to strongly agree (7). in addition, we asked about the perceived usefulness of whole grain pasta, which measured subjects’ perceptions of performance and effectiveness gains from eating whole grain pasta (e.g., stay in shape, improve work performance) by using three 7-point likert scaled items. two factors were also used to evaluate the quality of the messages provided in the two information conditions. the first factor was the consumer evaluation of the message (hung & verbeke, 2019), which was based on five items with a 7-point likert scale, to measure several characteristics of the health claim, including familiarity, understandability, credibility, interest, and importance. the second factor was the argument quality of the message (bhattacherjee & sanford, 2006), which was used to measure whether the information provided was helpful, valuable, informative, and persuasive, by using four 7-point likert scaled items. four weeks after the initial survey (time 2), participants’ behavior was also assessed by using two measures of reported behavior using a 7-point scale (fishbein & ajzen, 2011). in the first item, respondents were asked to indicate how frequently they consume whole grain pasta, on average, ranging from ‘never’ to ‘almost every day’. in the second item, participants were asked whether they had included whole grain pasta in their diet at least once over the past month. in addition, attitude, intention, and perceived usefulness were measured again in time 2 using the same items as in time 1. note that all canteens on the campus offer whole grain dishes daily; therefore, product availability is not a barrier for the participants. 2.3. intervention with health messages at the beginning of the study, participants were randomly assigned to either a no-information group (control, n=100) or one of the two treatment groups, namely gainframed (n=202) or loss-framed (n=197) messages. students in the gain or loss-framed treatment received four messages about whole grain pasta health benefits. the health benefits were adapted by authorized health claims. in the us, a food-related health claim3 must be approved by public authorities (i.e. the food and drug 3 “health claim means any claim made on the label or in labeling of a food, including a dietary supplement, that expressly or by implication, including “third party” references, written statements (e.g., a brand name including a term such as “heart”), symbols (e.g., a heart symbol), or vignettes, characterizes the relationship of any substance to a disease or health-related condition.” (food and drug administration, 2023). 305 bio-based and applied economics 13(3): 301-316, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15456 intention and behavior toward eating whole grain pasta on a college dining campus administration, fda) and must be supported by a significant body of research showing the relationship between the food/constituent and a health effect in humans. based on this context, four specific health claims related to whole grains were considered (table a2). moreover, following previous works (see deliens et al., 2016 for a systematic review) a media-based approach was used to communicate such expected healthy benefits. in our study, we decided to use health claims in the form of messages considering both general benefits of whole grain foods (e.g., fibers have positive effects on weight management) and more specific ones (e.g., the relationship between fibers and gut health or bowel function) (efsa panel on dietetic products, nutrition, and allergies (nda), 2010). for the two treatment groups, we decided to convey identical information but differently framed in terms of gains or losses associated with an expected outcome (dolgopolova et al., 2022). a gain-framed message might take the form of ‘‘if you perform the advocated action, desirable outcome x will be obtained’’, whereas a lossframed message might be “if you do not perform the advocated action, desirable outcome x will be avoided” (o’keefe & jensen, 2008). the rationale is that one type of framing may be more effective than another at promoting health behavioral change (gallagher & updegraff, 2012). participants in the treatment groups read a similar health message that differentiated for details of either the benefits of including whole grain pasta (gainframed), or the health dangers of not including whole grain pasta (loss-framed). in addition, participants in the two treatment groups received four emails (one per week) that included a different health claim message, still considering the same framework group (gain-framed or loss-framed) and were blinded to the other intervention. thanks to the online platform used to send out personalized emails (mailchimp.com), we were able to electable 1. socio-demographic characteristics, lifestyle variables, and health-related factors reported for the total sample and by the groups at time 1. variables all information treatments p-value control gained-frame loss-frame n 499 100 202 197 % 100 20.0 40.5 39.5 age1 (mean, sd) 18.8 (1.16) 18.6 (1.13) 18.9 (1.16) 18.8 (1.17) 0.267 gender2 0.451 male 44.4 41.0 47.5 42.9 female 53.6 59.0 49.5 55.1 others or prefer not to answer 2.0 0.0 3.0 2.0 bmi1 22.9 (5.79) 22.0 (5.00) 23.1 (5.77) 23.1 (6.16) 0.267 eating behavior2 0.357 omnivore 80.1 79.0 83.2 77.6 vegetarian 6.6 6.0 4.0 9.7 vegan 3.2 3.0 3.0 3.6 flexitarian 8.8 11.0 7.9 8.7 others 1.2 1.0 2.0 0.5 dietary/healthy restrictions2 0.461 yes 10.4 10.0 8.9 12.2 no 87.8 90.0 88.6 85.7 prefer not to answer 1.8 0.0 2.5 2.0 self-perception of overall health3 5.0 (4.0-6.0) 6.0 (4.2-6.0) 5.0 (4.0-6.0) 5.0 (4.0-6.0) 0.145 physical excercise3 4.0 (3.0-5.0) 4.0 (3.0-5.0) 4.0 (3.0-5.0) 4.0 (3.0-5.0) 0.255 note: data are presented as the mean (sds) for continuous variables, as number (%) for nominal variables, and as the median (iqrs) for categorical variables. sds = standard deviations. iqrs = interquartile ranges. bmi: body mass index. n = 498 for age, gender, eating behavior, dietary/healthy restrictions, self-perception of overall health; n=481 for bmi; and n=495 for physical exercise. 1anova. 2pearson chi-square. 3kruskal–wallis test. self-perception of overall health: how healthy do you consider yourself? (from very bad = 1 to very well= 7) physical exercise: how often do you usually engage in physical exercise (30 minutes of exercise)? (from never = 1 to more than 3 times per week = 5. they can choose “i do not want to answer”). http://mailchimp.com 306 bio-based and applied economics 13(3): 301-316, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15456 giovanni sogari et al. tronically assess whether the recipient opened the email with the health claim message. for those who did not open it, a reminder was sent the following day. however, we cannot be sure whether the participants actually read the text incorporated in the email. the information sent via email was different every week to avoid the boredom of reading the same message and the risk of dropping out of the study. the messages were sent to participants in a random order. in this way, the subjects were exposed to all four types of claims (see table a2) in order to have a broader knowledge of the several beneficial roles of eating whole grain food. 2.4. data analysis descriptive statistics were used to report the percentages, median, means, and standard deviations. oneway anova, pearson chi-square, and kruskal-wallis tests for independent samples were performed in order to determine the existence of significant differences between the control and treatment groups regarding the socio-demographic data, lifestyle variables, and healthrelated factors. the internal consistency, validity, and reliability of att, sn, pbc, int, and perceived usefulness (pu) factors were tested using cronbach’s alpha, factor loadings (λ), and composite reliability (cr), respectively, and considering all participants at each time point (time 1 and time 2). discriminant validity was tested by comparing the square root of the ave of each construct with the inter-construct correlation (bagozzi & yi, 2012). then, the internal consistency was assessed for each factor at each time point in all groups. almost all of cronbach’s alphas of each factor at each time point were above the acceptable threshold (α > .60) (van griethuijsen et al., 2015). eleven composite variables were created by averaging the items within each factor (table 2). details of the internal consistency of each factor of the tpb model and other variables in time 1 and time 2 are presented in table a3. one-way anova tests were used to analyze the impacts of different health claim messages as well as the effects of providing information under two different framing scenarios (gain vs. loss-framed) on the tpb measures. repeated measures anova was used to examine the interaction of time and information treatments on attitude, intentions, and perceived usefulness at baseline (week 0) and week 4. the results indicated that there were no different effects between the control and the framings nor differences among health claim messages. therefore, the following structural equation modelling (sem) model analysis was performed on the total sample without separating groups according to the framings. a sem approach was used to test the theoretical framework presented in figure 1. sem allows the specification of a model with both latent (e.g., attitude towards including whole grain pasta in the diet) and observed variables (e.g., the questionnaire items) (kline, 2016). the latent variables, namely the abstract phenomena that cannot be directly measured by the researcher, have been analyzed using confirmatory factor analysis (byrne, 2010). confirmatory factor analysis (cfa), often referred to as the measurement model, is used when the researcher has some knowledge of the underlying latent variable structure or wishes to evaluate a priori hypotheses driven by theory. in our case, to improve the overall goodness-of-fit of the model, we decided to apply the latent variable structure for all tpb variables but pbc, for which we used the observed averaged variable. the goodness-of-fit of the models was assessed using χ2 and their degrees of freedom (df), tucker-lewis index (tli), comparative fit index (cfi), root mean square error of approximation (rmsea) with a 90% confidence interval, and the standardized root mean square residual (srmr). statistical analysis was performed using spss v.28.0 and amos v.27.0 statistical software (ibm corporation, armonk, ny, usa). 3. results 3.1. descriptive statistics table 2 shows the descriptive statistics of the latent and observable variables: the factor loadings of the variables items (λ) above 0.50, cr values above 0.70, cronbach’s α above 0.70 with the only exception of pbc (0.62), and ave values above 0.50 show strong reliability, and convergent validity of all factors in the measurement model. the results demonstrate a moderately positive consumer attitude toward including whole grain pasta in their diet (mean score: 4.75). nevertheless, subjective norms did not show to greatly influence consumers (3.57) whereas they reported relatively strong control over the behavior (5.49). again, consumers exhibited a moderately positive intention to include whole grain pasta in their diet (4.23). in general, participants reported consuming whole grain pasta occasionally (4.63). as shown in table 3, the squared root of the ave of each construct was greater than the spearman’s rankorder correlation (ρ) between the constructs, which also indicates the discriminant validity of the model. we also tested the effects of information (gain vs. loss-framed) on the tpb constructs and other variables in time 1 and time 2 (see details in appendix table 307 bio-based and applied economics 13(3): 301-316, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15456 intention and behavior toward eating whole grain pasta on a college dining campus a3). no significant differences between control, gain and loss-framed groups were found for the tpb measures and pu, neither in time 1 nor time 2. regarding how participants evaluate the type of message and the quality of the argument, significant differences were found between the gainand loss-framed condition. the gain-framed message was found to slightly but significantly engender greater message engagement in terms of overall evaluation (m= 4.86) and quality of the message (m=4.77) than the loss-framed message (overall evaluation: m=4.16, and quality of the message: m = 3.96). interestingly, the results of repeated measures anova (table 4) suggested that time (time 1 vs. time 2) had a positive impact on perceived usefulness (p < 0.001), intention (p < 0.001) and attitude (p = 0.006). nevertheless, there was no significant effect of the interaction of time and treatments (framing) for perceived usefulness (wilks lambda = 0.99, f =2.41, p = 0.092), intention (wilks lambda = 0.99, f =1.10, p = 0.334) and attitude (wilks lambda = 0.99, f =0.42, p = 0.659). the explanation for this finding could be that the request to fill out a follow-up questionnaire in the control group might have positively affected the perceived usefulness of and intention to consume whole grain pasta in time 2. 3.2. effect of beliefs the correlations (ρ) between behavioral, normative, and control beliefs with their relative constructs (attitudes, subjective norms, and pbc, respectively), intention to eat whole grain pasta over the next month, and behavior are reported in table 5. intermediate correlation levels (ρ = 0.40–0.70) are reported for the association of normative beliefs with subjective norms and behavioral beliefs with attitude to table 2. mean values (standard deviation, sd) of single items and tpb constructs, factor loadings (λ), composite reliability (cr), average variance extracted (ave) and cronbach’s α of the total sample (n=499) and follow-up (n=325). n mean (sd) λ cr ave α time 1 attitude (including whole grain pasta in my diet over the next month will be) 499 4.75 (1.48) 0.74 0.59 0.70 difficult/easy 499 4.98 (1.67) 0.59 not tasty/tasty 499 4.51 (1.72) 0.92 subjective norm 499 3.57 (1.41) 0.92 0.79 0.90 most people who are important to me think that i should include whole grain pasta in my diet over the next month 499 3.69 (1.54) 0.95 most people who influence my decisions think that i should include whole grain pasta in my diet over the next month 499 3.61 (1.45) 0.93 it is expected that i should include whole grain pasta in my diet over the next month 499 3.41 (1.65) 0.78 perceived behavioral control 499 5.49 (1.13) 0.84 0.72 0.62 i believe that including whole grain pasta in my diet over the next month is possible 499 5.43 (1.33) 0.85 the decision to include whole grain pasta in my diet over the next month will be only up to me 499 5.56 (1.34) 0.85 intention 499 4.23 (1.55) 0.91 0.77 0.91 i intend to include whole grain pasta in my diet over the next month 499 4.40 (1.64) 0.89 i will try in anyway to include whole grain pasta in my diet over the next month 499 4.25 (1.68) 0.84 i will definitely include whole grain pasta in my diet over the next month 499 4.03 (1.74) 0.89 follow up (time 2) behavior 325 4.63 (1.71) 0.77 0.62 0.76 in the past month, how often have you included a meal with whole grain pasta in your diet? 325 3.84 (1.70) 0.83 i have included whole grain pasta in my diet at least once in the past month 325 5.42 (2.10) 0.75 table 3. spearman’s rank-order correlations (ρ) between the tpb constructs including the squared root of the ave of each construct (reported in bold). att sn pbc int beh att 0.77 0.22*** 0.30*** 0.45*** 0.32*** sn 0.89 n.s. 0.58*** 0.31*** pbc 0.85 0.25*** 0.16** int 0.88 0.55*** beh 0.79 note: att = attitudes; sn = subjective norms; pbc = perceived behavioral control; int = intentions; beh = behavior; *** indicates significance at p<0.001, ** significant at p<0.01, ns=not significant 308 bio-based and applied economics 13(3): 301-316, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15456 giovanni sogari et al. eating whole grain pasta (ρ = 0.40). in particular, parents’ and friends’/partners’ opinions are the two normative beliefs that primarily affect subjective norms and intention. regarding behavioral beliefs, the two most relevant beliefs associated with eating whole grain pasta are a long-term investment for the individual and less dietrelated diseases. control beliefs are negatively associated with pbc, in particular, for the higher costs of whole grain pasta and the perceived lack of availability in the dining halls. these represent the main barriers that decrease the perceived ability of respondents to perform the behavior. finally, the link between control beliefs and intention has positive values, although it is almost non-significant. the effect of the beliefs on behavior is less relevant (ρ ≤ 0.30) and significant only for normative and behavioral beliefs. 3.3. structural equation model results the results of the sem analysis with standardized path coefficients and r2 are reported in figure 2, while the unstandardized coefficients and standard errors are reported in table a4. the sem analysis was performed on the entire sample because framing had no effect on the tpb measures. the results show that there is a satisfactory fit between the hypothesized model and the data (χ2 (df) = 112.61 (37); cfi = 0.975; tli = 0.955; rmsea (90% c.i.) = 0.064 (0.051–0.078)). overall, the tpb model explains 53.2% of the variance for the intention to consume whole grain pasta over the next month (measured in time 1), and 44.5% of the variance in the selfreported behavior measured in time 2. attitude, subjective norms, and perceived behavioral control are significant predictors of the intention to consume whole grain pasta over the coming month. specifically, subjective norms (β = 0.50, p < 0.001) and attitude (β = 0.36, p < 0.001) have a greater influence on the intention than the pbc (β = 0.16, p < 0.001). the intention is also a strong determinant of the behavior to consume whole grain pasta (β = 0.68, p < 0.001), measured after four weeks (self-reported behavior). 4. discussion and conclusions understanding how the behavior towards the inclusion of whole grain products is formed becomes a crucial stage to develop efficient healthy food choice strategies. in our study, the tpb model provides a significant explanation for the variance of the intention to consume whole grain pasta over the next month (r2=0.53), as well as the (self-reported) prospective behavior (r2=0.45). thus, our results of the tpb model show that when individuals have strong attitudes, subjective norms, and perceived behavioral control toward eating whole grain pasta, their intention to eat this product increases, and this higher motivation would be strongly associated with table 4. results of repeated measures anova. variables times wilks lambda f partial eta squared p-valuetime 1 time 2 m sd m sd att (n = 325) 5.20 1.46 5.52 1.34 0.95 7.73 0.05 0.006 pu (n = 325) 4.35 1.10 4.84 1.12 0.86 51.99 0.14 <0.001 int (n = 325) 4.18 1.55 4.41 1.49 0.96 13.70 0.04 <0.001 note: att = attitude; pu = perceived usefulness; int = intentions; m = means; sd = standard deviation. table 5. spearman’s rank order correlations (ρ) between beliefs and their respective direct measure (attitude, subjective norm, and perceived behavioral control – pbc), intention, and behavior. beliefs ρ sig. ρ sig. ρ sig. control beliefs pbc intention behavior conbel1 0.11 ** 0.11 ** 0.03 ns conbel2 -0.28 *** 0.08 * -0.03 ns conbel3 -0.28 *** 0.20 *** 0.06 ns conbel4 -0.09 ** 0.08 * 0.06 ns behavioral beliefs attitude intention behavior behbel1 0.40 *** 0.40 *** 0.24 *** behbel2 0.43 *** 0.38 *** 0.18 ** behbel3 0.45 *** 0.42 *** 0.22 *** normative beliefs subjective norms intention behavior norbel1 0.62 *** 0.41 *** 0.14 ** norbel2 0.66 *** 0.41 *** 0.18 ** norbel3 0.45 *** 0.32 *** 0.08 ns norbel4 0.51 *** 0.35 *** 0.09 * norbel5 0.54 *** 0.33 *** 0.17 ** 309 bio-based and applied economics 13(3): 301-316, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15456 intention and behavior toward eating whole grain pasta on a college dining campus the actual behavior. similar results were found in other studies with regard to healthy dieting; for instance, in studies conducted by hagger et al. (2006), the applied models explained 69% (hagger & chatzisarantis, 2006) and 56% (hagger, chatzisarantis, & harris, 2006) of the intention, with relatively high variability in the explained behavior (66% and 32%, respectively). in line with previous studies (biasini, rosi, scazzina, & menozzi, 2023; sogari et al., 2022), the intention well predicts young adults’ behavior. in particular, subjective norms (i.e., the perceived social influence) affected the intention more than the attitude and pbc (li, long, laubayeva, cai, & zhu, 2020). usually, adolescents or young adults are more influenced by social and peers than other age groups, and this may explain why subjective norms have a stronger influence on intention in the tpb model (barberis, gugliandolo, costa, & cannavò, 2022; friedman et al., 2022). in our case, the effect of behaviors of other students in the canteen (the social context) might affect the participant’s motivation to comply. providing health messages at the point of consumption could, however, steer consumer decisions and be an effective method of delivering strategies to increase healthy eating. a message can be framed either to promote the advantages of consuming a particular food (gain-framed) or to stress the negative outcomes of not consuming that particular food (loss-framed) (gallagher & updegraff, 2012). the success of various message-framing strategies is usually assessed by measuring consumer behaviors, intentions, or attitudes (dolgopolova et al., 2022). our findings show no effect of frame condition on the tpb measures in time 1. this is in line with a review by gallagher & updegraff (2012) that showed no significant effect of framing on attitudes and intentions. moreover, our results align with recent findings by weingarten and hartmann (weingarten & hartmann, 2023), who found that participants did not change their behavior toward whole grain consumption directly after receiving the first messages on the health benefits. ottersen et al., (2022) conducted a study with norwegian consumers to test whether daily mobile phone text message reminders about animal welfare, and the environmental and health consequences of meat would reduce people’s meat consumption. they showed that meat consumption did not change. therefore, simply reminding consumers about these issues may not be enough without further interventions, as eating and dietary habits are strongly entrenched behaviors that are primarily controlled by autonomic processes. our study is one of the few to assess the self-reported prospective behavior change (after four weeks of intervention) as a measure of message framing persuasiveness (gallagher & updegraff, 2012). as suggested by meynier et al., (2020) information provision will more likely lead to a behavioral change if the information is provided on more than one occasion. for instance, weingarten and hartmann (2023) found that providing information over time about the health benefits of whole grain consumption contributed to increasing the positive attitude and behavioral intentions to consume such products. however, in time 2, we found no impact of the informative message (health information) on attitude, intention, and the reported behavior of eating whole grain pasta. this could be also due to the weekly figure 2. results of the tpb model in time 1 (n=499) and in time 2 (n=325). notes: *** indicates a significant difference at p < 0.001, n.s. = not significant. goodness-of-fit statistics: χ2 (df) = 112.61 (37); cfi = 0.975; tli = 0.955, rmsea (90% c.i.) = 0.064 (0.051-0.078). 310 bio-based and applied economics 13(3): 301-316, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15456 giovanni sogari et al. information treatment (once per week), rather than a more intense exposure (daily messages for 14 days, as in the case of weingarten and hartmann (2023)). another possible reason could be that information messages might have a short-lived effect on participants rather than other types of messages. for instance, carfora et al., (2019) showed that participants exposed to emotional messages experienced a more enduring and long-lasting effect than information-type messages. the specific characteristics of the sample (young adults with a healthy status) may be one reason why the health claim message did not have an impact in changing the perception towards whole grain. past studies (e.g., rothman & updegraff, 2011) suggest that gainframed and loss-framed messages may be amplified when the message is of high personal relevance, which might not be our case. another possible reason for the lack of impact from the message is that it did not specifically target consumers’ relevant beliefs (fishbein & ajzen, 2011; weingarten & hartmann, 2023). in our study, we found that the opinions of important others (e.g., parents, friends, and partners) were the strongest normative beliefs influencing the subjective norms (de leeuw, valois, ajzen, & schmidt, 2015); whereas the two most important behavioral beliefs relating to eating whole grain pasta were a personal long-term investment and the possibility of having fewer diet-related illnesses. hence, the messages and interventions should target changing these key beliefs in order to lead to the desired changes. however, gain-framed messages were evaluated in terms of “consumer evaluation” and “argument quality” better than loss-framed ones. the positive message about the health consequences associated with eating whole grain pasta was considered to be more appropriate, helpful, valuable, and persuasive. thus, in line with the literature (dolgopolova et al., 2022; gallagher & updegraff, 2012; rothman, bartels, wlaschin, & salovey, 2006), our results confirm the higher appropriateness of gain-framed health messages when encouraging behavior with ‘little risk’ compared to loss-framed messages (more persuasive with a ‘significant risky’ behavior to perform). several limitations of our study occur. the first limitation is that we collected data only from a single university in the us, with a limited targeted population. therefore, based also on the characteristics of this convenience sample (students enrolled in a us college), generalization of the findings to the broader population may be limited. second, this study used self-report measures about the behavior of eating whole grain pasta which may be subject to response biases or limited memory. third, although we focused our analysis on the individuals who actually opened the emailed messages, we cannot be sure whether the messages were truly read by the participants. despite these limitations, we believe that our work will serve as a stimulus for further investigation on how to better develop communication strategies for the health benefits of whole grain products. future research could explore different types of messages in terms of content and formats, as well as evaluate the results after a longer exposure. if concentrating on young adults, further studies could also consider testing the information across multiple dining halls to evaluate whether results are consistent across different cities. finally, partnerships between nutrition, social scientists, and culinary professionals could support the development of relevant and useful information materials about whole grains consumption benefits. funding this study is part of a wider project called “consumehealth. using consumer science to improve healthy eating habits” and has received funding from the european union’s horizon 2020 research and innovation programme under the marie sklodowska-curie grant agreement no 749514. acknowledgements the authors are grateful to all the staff of the cornell dining venues for their collaboration and help in carrying out this study. additionally, we want to express our gratitude to the students mary gaffney, martina cirelli, and andrew peterson who helped us in the data collection. references ajzen, i. 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(2012). greater whole-grain intake is associated with lower risk of type 2 diabetes, cardiovascular disease, and weight gain. journal of nutrition, 142(7), 1304–1313. https://doi.org/10.3945/jn.111.155325 appendices figure a1. survey flow. https://doi.org/10.3390/nu10060667 https://doi.org/10.3390/nu10060667 http://health.gov/dietaryguidelines/2015/guidelines/ http://health.gov/dietaryguidelines/2015/guidelines/ https://doi.org/10.1108/bfj-10-2021-1152 https://doi.org/10.1108/bfj-10-2021-1152 https://doi.org/10.1007/s11165-014-9438-6 https://doi.org/10.1007/s11165-014-9438-6 https://doi.org/https https://doi.org/https http://doi.org/10.1016/j.appet.2023.106608 https://doi.org/10.3390/ijerph18063173 http://www.searo.who.int/thailand/factsheets/fs0027/en/ http://www.searo.who.int/thailand/factsheets/fs0027/en/ https://doi.org/10.3945/jn.111.155325 314 bio-based and applied economics 13(3): 301-316, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15456 giovanni sogari et al. table a1. constructs and items. codes constructs and items behavioral beliefs (1 = strongly disagree; 7 = strongly agree) (time 1) behbel1 if i include whole grain pasta in my diet over the next month i believe i will live a better quality of life in my old age behbel2 if i include whole grain pasta in my diet over the next month i believe i will have made a long-term investment for myself behbel3 if i include whole grain pasta in my diet over the next month i believe i will have less diet-related diseases in my life normative beliefs (1 = strongly disagree; 7 = strongly agree) (time 1) norbel1 my parents think i should include whole grain pasta in my diet over the next month norbel2 my friends/partner think i should include whole grain pasta in my diet over the next month norbel3 nutritionists think i should include whole grain pasta in my diet over the next month norbel4 my doctor thinks i should include whole grain pasta in my diet over the next month norbel5 chefs think i should include whole grain pasta in my diet over the next month control beliefs (1 = strongly disagree; 7 = strongly agree) (time 1) conbel1 the limited advertising from the dining halls/restaurants i usually go does not encourage me to include whole grain pasta in my diet over the next month conbel2 the higher costs of whole grain pasta stops me from including this product in my diet over the next month conbel3 the lack of availability in the dining halls i usually go stops me from including whole grain pasta in my diet over the next month conbel4 the limited information from public authorities about whole grain benefits does not encourage me to include whole grain pasta in my diet over the next month attitude towards the behavior (time 1 and time 2) for me, including whole grain pasta in my diet over the next month (7-point scale) att1 difficult easy att2 not tasty tasty subjective norm (1 = strongly disagree; 7 = strongly agree) (time 1) sn1 most people who are important to me think that i should include whole grain pasta in my diet over the next month sn2 most people who influence my decisions think that i should include whole grain pasta in my diet over the next month sn3 it is expected that i should include whole grain pasta in my diet over the next month perceived behavioral control (1 = strongly disagree; 7 = strongly agree) (time 1) pbc1 i believe that including whole grain pasta in my diet over the next month is possible pbc2 the decision to include whole grain pasta in my diet over the next month will be only up to me behavioral intention (1 = strongly disagree; 7 = strongly agree) (time 1 and time 2) int1 i intend to include whole grain pasta in my diet over the next month int2 i will try in anyway to include whole grain pasta in my diet over the next month int3 i will definitely include whole grain pasta in my diet over the next month behavior (after one month) (7-point scale) (time 2) beh1 in the past month, how often have you included a meal with whole grain pasta in your diet? never almost always beh2 i have included whole grain pasta in my diet at least once in the past month. false-true consumer evaluation of the claim (1 = strongly disagree; 7 = strongly agree) (time 1) conseval1 i am familiar with the health claim i just read conseval2 i understand this health claim conseval3 this health claim is credible conseval4 this health claim is interesting conseval5 this health claim is important argument quality (1 = strongly disagree; 7 = strongly agree) (time 1) argqua1 the information provided about whole grain pasta is informative argqua2 the information provided about whole grain pasta is helpful argqua3 the information provided about whole grain pasta is valuable argqua4 the information provided about whole grain pasta is persuasive (continued) 315 bio-based and applied economics 13(3): 301-316, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15456 intention and behavior toward eating whole grain pasta on a college dining campus codes constructs and items perceived usefulness (1 = strongly disagree; 7 = strongly agree) (time 1 and time 2) percuse1 including whole grain pasta in my diet will help me to stay in shape (e.g., maintaining my body weight). percuse2 including whole grain pasta in my diet will improve my work performance (e.g., make my working/studying life more productive). percuse3 including whole grain pasta in my diet will make my diet more balanced and healthy (e.g., right amount of fiber intake). table a2. in italic the messages shown to participants. message health benefits of eating whole grain gain framed message (gfm) loss-framed message (lfm) 1 better chance of success in maintaining your body weight (bw) if you include whole grain pasta in your diet, you might have a better chance of success in maintaining your body weight. if you do not include whole grain pasta in your diet, you might not have a better chance of success in maintaining your body weight. 2 its fiber content will contribute to your normal bowel function (bf) if you include whole grain pasta in your diet, its fiber content will contribute to your normal bowel function. if you do not include whole grain pasta in your diet, a lack of fiber content will not contribute to normal bowel function. 3 niacin content (vitamin b3) will contribute to the reduction of tiredness and fatigue (t&f) if you include whole grain pasta in your diet, its niacin content (vitamin b3) will contribute to the reduction of tiredness and fatigue. if you do not include whole grain pasta in your diet, a lack of niacin (vitamin b3) will not contribute to the reduction of tiredness and fatigue. 4 its fiber content will promote your healthy gut (hg) if you include whole grain pasta in your diet, its fiber content will promote your healthy gut. if you do not include whole grain pasta in your diet, a lack of fiber content will not promote your gut health. four different types of health messages were developed, based on the latest scientific opinion on the substantiation of health claims related to (1) whole grain (efsa panel on dietetic products, nutrition and allergies (nda), 2010), (2) wheat bran fibre and increase in faecal bulk (efsa panel on dietetic products, nutrition and allergies (nda), 2010a), and (3) niacin and reduction of tiredness and fatigue (efsa panel on dietetic products, nutrition and allergies (nda), 2010a). table a3. internal consistency of tpb constructs and other variables in time 1 and time 2. variable n. of items control gain frame loss frame p-value a n cronbach’s alpha m sd n cronbach’s alpha m sd n cronbach’s alpha m sd time 1 att 2 100 0.628 4.750 1.319 202 0.717 4.849 1.566 197 0.702 4.655 1.473 0.426 time 2 att 2 77 0.598 4.773 1.344 134 0.748 4.787 1.516 114 0.756 4.956 1.351 0.572 time 1 pu 3 100 0.762 4.443 1.062 202 0.825 4.315 1.141 197 0.847 4.201 1.221 0.225 time 2 pu 3 77 0.837 4.714 1.016 134 0.861 4.925 1.157 114 0.824 4.818 1.140 0.408 time 1 sn 3 100 0.857 3.443 1.311 202 0.905 3.705 1.370 197 0.897 3.504 1.497 0.216 time 1 pbc 2 100 0.559 5.505 1.067 202 0.616 5.505 1.116 197 0.643 5.472 1.185 0.951 time 1 int 3 100 0.902 4.120 1.496 202 0.918 4.297 1.611 197 0.897 4.191 1.517 0.613 time 2 int 3 77 0.933 4.416 1.369 134 0.931 4.368 1.572 114 0.907 4.450 1.479 0.910 time 2 bahavior 2 77 0.732 4.727 1.572 134 0.760 4.493 1.746 114 0.768 4.676 1.803 0.566 time 1 conseval 5 202 0.622 4.857 0.886 197 0.742 4.154 1.127 <0.001 time 1 argqua 4 202 0.859 4.774 1.132 197 0.908 3.956 1.415 <0.001 note: tpb = theory of planned behavior; att = attitude; pu = perceived usefulness; sn = subjective norms; pbc = perceived behavioral control; int = intention; conseval = consumer evaluation of the claim; argqua = argument quality. m = means; sd = standard deviation. a comparison between groups using anova tests. table a1. (continued). 316 bio-based and applied economics 13(3): 301-316, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15456 giovanni sogari et al. table a4. tpb model: unstandardized beta coefficients, standard errors (s.e.), p-values, in time 1 (n=499) and in time 2 (n=325). predictors path coefficients beta s.e. p predictors of behavioral intention (in time 1) att 0.565 0.076 <0.001 pbc 0.220 0.051 <0.001 sn 0.604 0.055 <0.001 predictors of behavior (in time 2) int 0.612 0.057 <0.001 pbc 0.077 0.067 0.250 note: att: attitude towards the behavior; sn: subjective norms; pbc: perceived behavioral control; int: behavioral intention. guns, germs and climate: food security and food systems in a risky world francesco pagliacci1,*, valentina raimondi2, luca salvatici3,* the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia amsalu woldie yalew1,2,*, victor nechifor3, emanuele ferrari3 participation of farmers in market value chains: a tailored antràs and chor positioning indicator tulia gattone adapting to climate change: what really drives the choices of the producers of geographical indications? francesco pagliacci1, dana salpina1,2,* exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach stefano ciliberti, angelo frascarelli*, gaetano martino, andrea marchini intention and behavior toward eating whole grain pasta on a college dining campus: theory of planned behavior and message framing giovanni sogari1, rungsaran wongprawmas1, giulia andreani1, michele lefebvre2, nicoletta pellegrini3, miguel i. gómez4, cristina mora1, davide menozzi1,* bio-based and applied economics 14(1): 75-92, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15466 © 2025 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae bio-based and applied economics bae citation: winkler, g., pagano, l., vergamini, d. & bartolini, f. (2025). soils and ecosystem services: policy narratives and instruments for soil health in the eu. bio-based and applied economics 14(1): 75-92. doi: 10.36253/bae-15466 received: december 6, 2023 accepted: september 2, 2024 published: june 13, 2025 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. associated editor: luca salvatici; valentina raimondi, francesco pagliacci editor in chief: silvia coderoni orcid gw: 0000-0002-3216-5817 lp: 0009-0003-9239-3066 dv: 0000-0002-0721-826x fb: 0000-0002-8946-3110 soils and ecosystem services: policy narratives and instruments for soil health in the eu greta winkler1,*, luciano pagano2, daniele vergamini2, fabio bartolini1 1 department of chemical, pharmaceutical and agricultural sciences (docpas), university of ferrara, italy 2 department of agriculture, food and environment (dafe), university of pisa, italy *corresponding author. e-mail: greta.winkler@unife.it abstract. european soils and their status is a matter of concern that has entered the policy arena and the objective to restore soil health is part of the soil strategy to 2030. aim of this study is to explore the integration of the concept of soil health and the provision of soil ecosystem service by conducting i) a content analysis of eu policies and ii) a scoping review of literature over policy instruments for soil governance. results show a focus on soil fertility, mainly soil organic matter, while services such as conservation of biodiversity or cultural heritage still appear underrepresented. findings are reinforced by the gap in literature, providing little evidence of policy instruments contributing to soil health. a more coordinated effort among policy sectors is required to prioritize soil health in the eu; invesitgating the role of market-based instruments could complement what public policies are lacking. keywords: soil health, ecosystem services, policy instruments, incentives, soil monitoring law. jel codes: q10, q15, q57. 1. introduction soil is a non-renewable and multi-functional resource which contributes significantly to global food security, hosting one of the greatest concentrations of biodiversity on the planet and providing further ecosystem services (es) that include air and water purification, climate regulation and conservation of cultural heritage (fao and itps, 2015). however, soils are under great pressure derived from both bio-physical processes and human-driven processes, such as erosion, floods and landslides, loss of soil organic matter, salinisation, contamination, compaction, sealing, and loss of soil biodiversity (turpin et al., 2017; ipbes, 2018). when those processes impact soils’ status, their ability to provide es is reduced or lost, with an estimated cost of around 50 billion euros yearly in the eu (com 2021/699). therefore, maintaining and enhancing the capacity of soils to provide es can bring economic benefits and is critical to sustain es and ensure human well-being (mea, 2005). https://doi.org/10.36253/bae-15466 https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae https://doi.org/10.36253/bae-15466 https://orcid.org/0000-0002-3216-5817 https://orcid.org/0009-0003-9239-3066 https://orcid.org/0000-0002-0721-826x https://orcid.org/0000-0002-8946-3110 mailto:greta.winkler@unife.it 76 bio-based and applied economics 14(1): 75-92, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15466 greta winkler et al. acknowledging the interconnectedness between soil, plant, animal (humans included) and ecosystem health as framed by the one health concept (van bruggen et al., 2019), soil health can be understood as “the ability of the soil to sustain the productivity, diversity, and environmental services of terrestrial ecosystems”1primarily drawing from soil quality, the adoption of soil health in scientific literature started in the 1990s and developed by recognizing the role of biological processes in soil formation and functioning, identifying soils as ecosystems, mapping soils’ biodiversity and assigning to it an international scale (lehmann et al., 2020). soil health can be a useful metaphor that relates ecosystem functioning to human well-being, but its understanding and interpretation highly depends on the actors, issues, and values at stake in a given context (janzen et al., 2021). recently, the soil health concept has entered the eu policy arena (panagos et al., 2022) as testified by the soil strategy for 2030, included in the european green deal proposal to reach carbon neutrality by 2050. the strategy defines healthy soils as in good chemical, biological and physical condition, and thus able to continuously provide as much es as possible; it also aims at restoring 60 to 70% of european soils, currently considered unhealthy (com 2021/699). there is increasing evidence of the connection between decreasing soil health and loss of es (lehmann et al., 2020; ipbes, 2018); for instance, freshwater availability is affected by a reduction of soil organic matter in agricultural and forest soils (keesstra et al., 2021); soil sealing in urban areas impacts human physical and mental well-being by hindering access to green areas (mcelwee, 2021). however, investigating es when applied to soil requires a deep understanding of chemical, physical and biological soil properties, additionally recognising benefits derived from soil components at different spatial scales (i.e., plot, field, landscape, regional area, countries, global). comprehensive studies on valuation of soil es are rare to be found often addressing methodological limitations derived from need of suitable and comparable indicators (vysna et al., 2022; baveye et al., 2016; dominati et al., 2014). if the current eu soil strategy (com 2021/699) adopts a definition of soil health that heavily relates to es, it can prove useful to understand to what extent this definition is integrated into actual soil governance. by soil governance we mean the mix of different policies and set of instruments available for regulating, incentivizing, and informing about the management of soils (heuser, 2022; rogge and reichardt, 2016; juerges and 1 as defined by the intergovernmental technical panel on soils, available at: towards a definition of soil health (fao.org), accessed july 3rd 2024. hansjürgens, 2018). recent reviews have investigated available policy instruments to soil governance worldwide (juerges and hansjürgens, 2018) and in eu member states (ronchi et al., 2019), however without addressing the connection to soil es. this study aims to explore how far the concept of soil health and the provision of soil es is integrated into current policy instruments in the eu by conducting a content analysis of policy documents. to the best of our knowledge, no such works exist at eu level. moreover, we aim at individuating proof of effective implementation of policy instruments for delivery of es by fostering soil health. the topic of soil health holds growing interest both in the scientific and policy community, considering the upcoming eu soil monitoring law. if the law is implemented it will constitute the first binding instrument for soil regulation at eu level and will require ms to achieve responsive agri-environmental interventions within national public policy. addressing governance gaps and policy needs could contribute in ensuring purposeful action towards soil health. in the next section, the policy narrative of soil health is framed in order to gain an overview of definitions that characterize soil health as well as policy instruments referring to sustainability transition. following the methodology will be presented. results and discussions are structured in two sections, to address both research aims. 2. framing the soil health narrative understanding the concept of soil health requires some clarification: while the previously used attributes of soil fertility and soil quality refer to the local/ regional level and focus on productivity, nutrients, and water cycles (bünemann et al., 2018), soil health is told to encompass a larger range of es. lehman et al. (2022) include in the description of soil health “public” es such as climate mitigation and control, access to recreational and spiritual places to improve human wellbeing and provision of habitat for above and below ground biodiversity. janzen et al. (2021) refer to soil health as a metaphor for an organism in good conditions and suggest considering soil as a (complex) organism, defining its healthy conditions as “[…] the vitality of a soil in sustaining the socio-ecological functions of its enfolding land” (janzen et al., 2021, p. 2). therefore, according to the authors, soil health allows to relate the issue to a broader public of stakeholders – not only farmers and landowners, but also local and national authorities – underlying how far sustainable soil http://fao.org 77soils and ecosystem services: policy narratives and instruments for soil health in the eu bio-based and applied economics 14(1): 75-92, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15466 governance goes beyond the field level thus constitutes a policy concern (juerges and hansjürgens, 2018). the body of literature describes how emerging concepts and needs enter the policy agenda and then into policy cycle. shanahan et al. (2011) claim that policy narratives are central to the policy cycle: being created by a broad set of actors, narratives are the device through which policy programs and the connected values and beliefs are communicated. the process of creating narratives finds its counterpart in the agenda setting, that is the operationalization of (certain) issues into a decisional level, the two phases happen ongoingly and alternately (gonzalez lago et al,. 2019). from a political point of view, as described by montanarella and alva (2015), even though soils are essential to sustainable development, they have never been the specific focus of a multilateral environmental agreement. given the diversity of services that soils can provide, it is no surprise to find a set of policy domains competing for land use, including agriculture, forestry, protected areas, urbanization, and energy production (löbmann et al., 2022). focusing on the eu, after the proposal, discussion, and withdrawal of a thematic strategy for soil protection in 2014, ms have found themselves lacking a comprehensive framework and have been relying on national regulations (heuser, 2022). authors diversely define policy instruments for environmental governance, nuancing from public to market driven and being more or less voluntary or binding, resulting in a quite large range of options (vatn, 2018). for instance, piñeiro et al. (2020) divide incentives for sustainable agricultural practices in regulatory measures, market and non-market based incentives, and cross-compliance between payments and standards. the fao provides a framework to display the numerous instruments available for environmental protection and remuneration, involving individuals, private and public sector, and considering their level of compulsoriness (garrett and neves, 2016). ronchi et al. (2019) refer to measures for soil protection and individuate: regulatory, economic, information, monitoring and research and innovation. juerges and hansjürgens (2018) outline instruments for soil governance as: regulatory, planning, economic, informational, co-operative. for the sake of this study, we adopt the approach of rogge and reichardt (2016), that analyzed sustainability transitions and refer to policy instruments as tools to pursue a goal, dividing them into three catheogories: regulations, incentives, and information. given that pursuit of soil health is currently high on the eu political agenda, we found the approach of linking policy instruments to wider objectives displayed in policy strategies (rogge and reichardt, 2016) as better suiting the purpose of this study. hence, we define regulatory instruments as those applying restrictions, economic as those providing monetary resources and information as those producing and delivering knowledge. table 1 provides an overview of the adopted definitions of policy instruments for soil governance. 3. materials and methods the aim of this study is to analyse the level of integration of the soil health narrative in eu soil governance. this broader objective is pursued firstly by exploring the conceptual integration of keywords related to soil health into policies, which can be done by conducting a content analysis on the explicit use of terminology (neill et al., 2022). secondly, by reviewing literature over available policy instruments that aim at incentivising soil health and the provision of related ecosystem services. to address these aims was adopted a twofold approach, based on i) content analysis of the integration of soil health along different eu policies and ii) narrative review of literature on existing shreds of evidence linking policy instruments to es (grant et al., 2009). the methodology consisting in analysing content of available eu policy instruments and existing evidences over policy instruments’ effect on soil es allowed to explore and understand the topic of soil health and frame it as the current policy narrative (gonzalez lago et al., 2019). 3.1. content analysis of eu instruments in the first phase, grey literature on eu regulatory instruments that tackle soil and land related issues was collected to compile a policy inventory. to do so we used as a reference the recent review by heuser (2022), double checking with laws and strategies related to soil and land as indicated on the eu website (https://environment. ec.europa.eu/topics/soil-and-land_en), and finally integrating further regulations with citation chaining. three main typologies of regulatory instruments were found: i) laws, directives and regulations, ii) environment action plans (eap), that set out goals and legislative proposals for eu environment policy and iii) horizontal strategies, that outline how to integrate sdg into eu policy priorities. a total of 28 regulatory instruments was finally included in the inventory. content analysis of the policy inventory was conducted by deductive coding (saldaña, 2013) with the help of sketchengine (lexical computing, 2003), free software for text analysis that allows to analyse frequency of keywords through large text bodies. sticking to the eu definition of soil health, as the capacity to deliver as https://environment.ec.europa.eu/topics/soil-and-land_en https://environment.ec.europa.eu/topics/soil-and-land_en 78 bio-based and applied economics 14(1): 75-92, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15466 greta winkler et al. many es as possible, we included three main keywords in the coding, namely ‘soil’, ‘soil health’ and ‘ecosystem services’.one further key word, i.e. ‘carbon’, emerged so frequently associated with soil (e.g. soil organic carbon) during the analysis that was finally added (complete overview in appendix i, figure 2). 3.2. review of policy instruments in a second phase, a narrative review of literature was conducted, to explore the connection between delivery of es from healthy soils, and policy instruments to foster these practices. while systematic literature reviews hold a narrow scope and are used for investigating problems that have already been explored in literature, narrative reviews are most frequently applied to explore a topic that has a rather broad coverage and that is evolving through time (byrne, 2016). even if systematic review apply a more rigorous methodology, the flexibility of narrative reviews was beetter suitable for addressing the large range of keywords of incentives to be linked to soil es, as well as the everchanging understanding of the concept of soil health. peer reviewed papers were searched on the scopus database, table 1. definition of policy instruments for soil governance and related keywork search, adapted by the authors from rogge and reichardt (2016) and garrett and neves (2016). instrument category typology instrument definition examples (and sources) keyword search regulation property rights and rights privatization of natural resources. owners can be individuals, private entities, communities, large entities e.g. the state (bartkowkski et al., 2018) soil-health and ecosystem-service* and property or property-use or property-useright prohibition of use and mandatory farm set aside access to land is limited to certain land uses or partly given up for restoration and conservation (bartkowkski et al., 2018) soil-health and ecosystem-service* and prohibition-of-use or prohibition taxes / charges applied to land use or management practices that are not compatible with agro-environmental principles, e.g. taxation on pesticides (ronchi et al., 2019) soil-health and ecosystem-service* and tax or taxes or charge* conservation used to reduce or compensate the costs of conservation of portions of land, e.g. by nonprofit organizations like land trust (vatn, 2018) soil-health and ecosystem-service* and conservation permits and quotas quantified rights to use a natural resource and eventually trade the quotas, e.g. fishing quotas (vatn, 2018) soil-health and ecosystem-service* and permit* or quota* or cap or carbonmarket subsidies governments link compliance to agro-environmental standards with direct payments, e.g. gaec (runge et al., 2022) soil-health and ecosystem-service* and subsid* offsets compensation for land development into (on/off ) site environmental projects, e.g. planting forests (vatn, 2018) soil-health and ecosystem-service* and offset* or offset-program or emissionoffset* information raising awareness and delivering knowledge by means of research, extension services or certification schemes to farmers, advisers, consumers (juerges and hansjürgens, 2018; bampa et al., 2019) soil-health and ecosystem-service* and research or research-program* or advisory or advisory-service* or extension-service* ir label* or certific* or standard* incentive payments for es providers of specific es are compensated for positive outcomes, e.g. farming practices respectful of water bodies (vatn, 2018) soil-health and ecosystem-service and payment* or pes voluntary farm set aside landowners give up part of land for restoration and conservation purposes in exchange of payments, e.g. 4% of non-productive arable land (runge et al., 2022) soil-health and ecosystem-service* and farm-set-aside or set-aside green public procurement public authorities procure goods and services based on environmental requirements, e.g. ecolabel for school canteens (neto et al., 2018) soil-health and ecosystem-service* and green-public-procurement or publicprocurement corporate social responsibility declaration of business strategies to contribute in benefitting the environment, e.g. ngo assessing corporations’ activities (vatn, 2018) soil-health and ecosystem-service and corporate-social-responsibility or crs 79soils and ecosystem services: policy narratives and instruments for soil health in the eu bio-based and applied economics 14(1): 75-92, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15466 combining the keywords “soil-health” and “ecosystemservices” and policy instruments basing on the fao indications over incentives for es (garrett and neves, 2016); the complete overview of keywords’ search is to be found earlier in table 1. searching criteria included search words in title, abstract and keywords, filtering was limited to papers and reviews published until 2023, accessibility and language were first criteria for exclusion . secondly, relevance of the content was based on a first reading of methodologies and results, assessing whether es had only been used as keyword in the abstract or also further addressed. consequently, soil es where counted as 1 every first mention and example were listed and divided into provisioning, regulating, cultural and supporting es, following the mea (2005) classification. 4. results 4.1. content analysis: integration of the soil health concept from a normative and narrative perspective soil in the eu started to be subject of policy almost 40 years ago, often implicitly integrated in different policy instruments during the past decades (heuser et al., 2022; ronchi et al., 2019). the timeline below (figure 1) shows the different types of policy instruments that concern soil governance at eu level. soils don’t figure in the timespan between 1992 and 2002, thus for graphical purposes the decade was represented as a striped rectangle. as can be observed, integration of soils into several policy instruments increases in the latter part of the timeline, marked by the green deal set of policies, that during 2021 and 2022 delivered several strategies aiming to protect, conserve and enhance eu’s natural capital, and safeguard the health and well-being of citizens from environment-related risks and impacts. the soil health strategy to 2030 (com 2021/699) listed objectives that address different policy sectors including agriculture, forestry and urbanization, i.e. to reduce losses of nutrients and use of pesticides, to reduce emissions from land use and land use change and forestry sector (lulucf); and more broadly natural and environmental management, i.e. to combat desertification, restore degraded carbon rich ecosystems; to improve status of water quality; to remediate contaminated sites). in the upcoming paragraph some key findings on conceptual integration of soil health across different policy sectors will be presented, for a complete graphical overview please see appendix i. figure 1. overview of eu policy instruments relating to soils, source: authors’ elaboration. 80 bio-based and applied economics 14(1): 75-92, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15466 greta winkler et al. a. agricultural sector policy instruments have been historically focusing on agricultural soils with two specific objectives: i) to decrease pollution and ii) to protect from erosion and loss of organic matter by enhancing soil fertility. the first directives aimed at protecting human health by preventing and controlling soil pollution from sewage sludge (86/278), nitrates (91/676), pesticides (2009/128) and industrial waste (2010/75). the common agricultural policy (cap), main instrument to support farmers, primarily focused on soil fertility. the past cap (2013/1306) listed farming techniques to enhance good agricultural and environmental conditions (gaec) to be pursued for soils (i.e. reduced tillage and burning, guaranteed minimum soil cover). the current cap (2021/2115) further included crop rotation among the gaec to guarantee soil protection and quality, and protection of wetland and peatlands to ensure organic c storage. soils in the cap are never explicitly linked to es, except for one specific objective aiming at enhancement of es and indicating as impact indicators an increased share of agricultural land covered by landscape features (2021/2116). monitoring of soil conditions is required in the current cap (2021/2116). the organic farming law (848/2018) refers to enhancing soils’ long-term fertility, stability, and biodiversity by reducing tillage and using only allowed fertilizers and conditioners, therefore improving soil es. the farm to fork strategy (com 2020/381) outlines reduction in use of pesticides and fertilizers and increase of organically farmed land but without listing any specific action connected to soil. b. forestry sector the forest strategy (com 2021/572) refers to the contribution of forests to soil stabilization and explicitly relates healthy forest soils to the provision of es, in particular to carbon sequestration, suggesting to-set up an ecosystem-based management approach and related payment schemes. specific target to forests soils is also found in the lulucf regulation (2018/841), addressing soil organic carbon as sink to mitigate ghg emissions, and the current cap (2021/2115) aiming to support forest protection and management of es. the older biodiversity strategy (com 2011/244) related multifunctional forest management to payments for es, albeit not referring their provision to soils. c. energy sector the revised version of the renewable energy directive (2023/2413) contains reference to soils when it comes i) to harvesting forest products by maintaining soil quality (i.e. avoiding compaction) and biodiversity; and ii) to consider improvement of soil carbon and reduced green house gas emission (aka climate mitigation) by measuring or modeling changes in soil carbon amount. d. environmental regulations the habitat directive (91/676) and the environmental liability directive (2004/35) marked the first steps for european natural environmental protection, soils however are almost absent from the text. later on, the environmental action plan (eap), less binding instruments setting the ground for upcoming environmental policies, did target soils. the 6theap (2002/1600) aims at protecting soil from erosion and pollution and calls for a soil strategy, by also mentioning their role as carbon sinks. 7theap (1396/2013) lists water erosion, sealing and contamination to compromise soil es, in particular biodiversity and water cycles, and addresses the need to increase knowledge and data collection on biodiversity to better value es. the 8th eap (2022/591) relates an unspecified loss of es to unsustainable land use management, soil sealing and pollution, and climate change and calls for a soil health law by 2023; furthermore it addresses a full integration on the one health across all policy levels. both latter call for improved natural capital accounting tools and market-based instruments – such as payments for es. the zero pollution action plan (2021/400) aims at reducing soil pollution to levels that are no longer harmful to human health and natural ecosystems, underlining the need to prevent and restore from contamination and regularly assess for their status, specifically agricultural soils for pesticide and nutrient reduction. yet in the same year the climate law (2021/1119), – more legally binding instrument – never explicitly targets to soils, while on the other hand extensively refers to enhancement of carbon sinks by 2030. the recently approved nature restoration law (2022/195) adds a time dimension that is relevant to soil, requiring long-term commitment to address degradation. specific actions refer to increasing stocks of organic carbon in cropland mineral soils and restoring and rewetting organic carbon in peatlands. as for the biodiversity strategy to 2030 (com 2020/380) points out the need to restore soils and terrestrial ecosystems, and related human wellbeing to specific soil es provision: fertility, nutrient cycle, climate regulation. finally, two further regulations very relevant for soils are currently under discussion at eu level. on the one hand the certification for carbon removals (2022) aims at improving soils’ es by increasing the stock of organic c in forest ecosystems and in cropland mineral soils in farming 81soils and ecosystem services: policy narratives and instruments for soil health in the eu bio-based and applied economics 14(1): 75-92, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15466 ta bl e 2. s um m ar y of li te ra tu re fi nd in gs r el at in g es a nd s oi l h ea lth ( nu m be r in b ra ck et s, co lo ur s re fe r to : r ed lo w er th an 1 ; o ra ng e be tw ee n 2 an d 6; y el lo w b et w ee n 7 an d 16 ; g re en hi gh er th an 1 5) . in st ru m en t ty po lo gy su bty po lo gy ex am pl es a nd c ou nt o f e s, d iv id ed a cc or di ng to m ea (2 00 5) pr ov isi on in g re gu la to ry c ul tu ra l su pp or tin g to ta l ( # of e s) in in st ru m en t re gu la tio n pr op er ty ri gh ts an d us e rig ht s fo od fo dd er (m ix ed -g ra zi ng , ro ta tio na l g ra zi ng a nd re fo re st at io n; g ra ss la nd ) bi of ue l (3 ) w at er re te nt io n er os io n c ar bo n sto ra ge (7 ) m ic ro bi al b io di ve rs ity n ut rie nt s’ cy cl es (6 ) 16 c on se rv at io n (e as em en ts a nd co nc es sio ns ) c ro pliv es to ck s ys te m s fo re st p la nt at io n bi of ue l f ro m p as tu re s a nd cr op la nd (9 ) fl oo d pr ot ec tio n c ar bo n se qu es tr at io n pr ot ec tio n of p er i-u rb an g re en ar ea s (1 0) la nd st ew ar ds hi p fo r s oc io cu ltu ra l w el lb ei ng e du ca tio na l an d re cr ea tio na l p ur po se s in di ge no us k no w le dg e (5 ) n ut rie nt s’ cy cl es w at er fi ltr at io n bi od iv er sit y w ild lif e (1 1) 35 pe rm its a nd qu ot as c ar bo n fa rm in g pa stu re s (2 ) c ar bo n se qu es tr at io n er os io n pr ot ec tio n (2 ) ru ra l l iv el ih oo d (1 ) so il or ga ni c ca rb on so il bi od iv er sit y (2 ) 7 su bs id ie s c ro ps c ov er c ro ps bi of ue l (2 ) w at er re te nt io n c ar bo n se qu es tr at io n (2 ) a da pt iv e fa rm er s a nd c iti ze n su pp or t (1 ) n at iv e bi od iv er sit y so il or ga ni c ca rb on (3 ) 8 o ffs et s c ar bo n fa rm in g (1 ) c ar bo n se qu es tr at io n er os io n pr ot ec tio n w at er re te nt io n (3 ) so il or ga ni c ca rb on bi od iv er sit y qu al ity (2 ) 6 in ce nt iv e pa ym en ts fo r e s c ov er c ro ps pa stu re s bi of ue l (3 ) c ar bo n se qu es tr at io n re du ce d em iss io ns w at er re te nt io n er os io n pr ot ec tio n fl oo d pr ot ec tio n (8 ) la nd sc ap e he rit ag e co ns er va tio n (1 ) bi od iv er sit y w at er fi ltr at io n ph os ph or ou s r ec yc lin g so il or ga ni c ca rb on (7 ) 19 to ta l ( # of e s) p er ty po lo gy 20 32 8 31 91 82 bio-based and applied economics 14(1): 75-92, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15466 greta winkler et al. ecosystems. on the other hand the soil monitoring law (2023) provides a definition of soil as vital, limited, nonrenewable and irreplaceable resource and underlines need of monitoring, sustainable management, restoration of soil health and remediation of contaminated sites, but no definition or reference is made to connected es. e. urbanization no legally binding regulations exists at eu level, albeit soil sealing has been mentioned among the first causes of soil degradation in the past and current eap and to minimize soil sealing is among objectives of both the biodiversity (com 2020/380) and soil strategy (com 2021/699) to 2030. 4.2. availability of policy instruments’ relating soil health to es main findings are summarized below (table 2), keywords that gave no results were not included in the table, namely: prohibition of use; farm set asides (both compulsory and voluntary); any information instrument; green public procurement; corporate social responsibility. the cells’ color indicate a different amount of reference for each pair of ecosystem services and instrument typology. overall, papers explicitly bridging soil es to policy instruments for soil health were rather scarce, 23 articles were selected and can be found for a complete overview in appendix ii (since some of theme referered to more than one policy instrument the list comprises a total of 29 references with repetions marked). most literature was published in the last five years, showing an increasing interest in the topic. literature on property rights and land use rights mostly refers to es of healthy soils (16) under different land use types, for instance grassland or forest, crops for food or fuel, but also investigates differences in agricultural practices, e.g. conventional or conservative, and pasture management, mixed or rotational. those instruments highlight the role of regulating (7) and supporting (6) es, and address trade-offs derived from different land use types. conservation instruments show the greatest contribution to all es (35), examples include governmental programs, at regional or local scale, and often refer to landscape level, i.e. forest, grassland. the regulatory and often compulsory character of conservation programs finds little support in terms of incentive instruments that could complement the action, compensating for the costs through governmental incentives or private buyer of es. improvement in market for es was mentioned as key outlook for conservation easements (eastburn et al., 2017). both property right and conservation instrument literature agree that the weak definition of property rights or poor enforcement of property rights in certain regions (such as south america or africa), inequalities in the willingness to appropriate resources when new business opportunities emerge (i.e. biofuels or carbon sequestration), or among different farm typologies (i.e. smallholders and large properties), can lead to overconsumption of common-pool resources. thus, this literature emphasises the importance of improving social capital for enhancing cooperation and common actions (targetti et al., in press), both horizontally among farmers and vertically among supply chain actors (d’alberto, in press) as well as call for new institutions or enhance the capacity of existing institutions to design rules, and conservation strategies for common-pool resources (lant et al., 2008). other regulation categories show lower amount of literature investigating the interplay between instrument and provision of ecosystem service. examples of subsidies for soil health address regulating es (2) and supporting es (3) with the purpose of reducing erosion and increasing soil organic carbon. tradeoffs emerged from provision of fuel instead of food (gomiero, 2018) and from cover crops causing decreased production, having subsidies related to yield losses rather than to improved soil conditions (deines et al., 2022). offsets (6) and quotas (7) don’t relate much to soil health but mostly to carbon farming, understood as a set of practices that increase soil organic carbon stored in farmland and should offset agricultural ghg emissions (keenor et al, 2021). payments for es reward farmers and landowners for improved regulating es (8) and supporting (7) services, acting as compensatory mechanisms for investments, management practices and/or yield reduction. as for sources of financing, public sector is indicated as key funder, i.e. trough the form of direct payments but also private sector, e.g. industry was mentioned as source of finance, through compensation mechanisms (see for instance lal et al., 2020). cultural es are the most neglected from research (8), with the exception of conservation programs that provided some valuable insights (5). a recent study highlights the contribution of relating result-based payments also to cultural es, such as preserving socio-cultural heritage at landscape level (helena guimarães et al., 2023). among supporting services (31), biodiversity is frequently mentioned, though authors lack to specificy if it is below or underground, being the latter more relevant to soil. instruments related to information made no explicit reference to soil health as contributing to ecosystem services. nevertheless, as of certification schemes and mar83soils and ecosystem services: policy narratives and instruments for soil health in the eu bio-based and applied economics 14(1): 75-92, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15466 keting labels, if no literature was found explicitly linking the topic of certification to soil health, it is probably a matter of time as increasing interest in carbon certification schemes is on the go (keenor et al., 2021) and a soil certification is envisioned by the soil monitoring law. 5. discussion of results 5.1. defining the narrative looking at the current policy setting available for soils in the eu, the agricultural sector has paid much attention to preserve soil fertility, showing a shift in narrative from protection against desertification and erosion towards an increase of soil organic matter in the form of organic carbon. policy interventions remain rather designed at farms’ level, as proven by the gaec indicated in the cap, as well as the approach to individual labeling of organic farmers, lacking to embed forms of coordination at territorial level that are more consistent with the scale of soil health (janzen et al., 2021; lehmann et al., 2020). considering the definition of soil health as encompassing a wide range of es, little instruments are to be found integrating the concept to the related loss of es in agriculture, for instance considering the role that ploughing or excessive synthetic fertilizers play on soils’ biodiversity (van bruggen at al., 2019; ingram et al., 2022). moreover, the farm to fork strategy’s attempt to broaden linkages between food security and environmental, soil health included, and at food system level by reducing nutrients’ losses and use of pesticide is encountering notable resistance in its enforcement (coderoni, 2023). the forestry sector has shown greater linkages between sustainable forest management, increased soil es – in the form of water retention, climate regulation and, most of all, carbon sequestration – and related incentives. yet concentrating only on few es, in particular provision of food and fibres or carbon sequestration, might result in insufficient targeting of other es (baveye et al., 2016) thus stepping out of the scale of action and scope of the soil health narrative (lehmann et al., 2020). improved forest management together with peatlands’ maintenance and rewetting are depicted as carbon sinks in forestry strategy, biodiversity strategy, climate law, nature restauration law. those actions all focus prominently on carbon storage, but rarely further provide evidences to improvement of soil health to overall ecosystem health. while acknowledging the important role that carbon plays in key soils’ functions, the risk of this narrow-scoped narrative is to only highlight the marketable and exchangeable value of carbon sequestration, while undermining the wider scope of the economic model for environmental preservation envisioned by es, that greatly focused on biodiversity and habitat preservation in the beginning (gómez-baggethun et al., 2010). instead of tackling single es, approaches both in scientific studies and policy instruments encompassing bundles of es (bartkowski et al., 2018; piñeiro et al., 2020) and minimum level of all es (bouma et al., 2022) could be more relevant to a soil health perspective. our results confirm previous findings claiming that eu policies have kept a rather sectoral approach to soil, mainly focusing on productive sectors and rather neglecting instruments related to natural areas (löbmann et al., 2022; ronchi et al., 2019). moreover, we found that while soil sealing is listed among the causes of loss of soil es (panagos et al., 2021) and its reduction as key objective of recent strategies (e.g. com 2021/699), no binding instruments to target the issue is available yet, while the strategy for a sustainable built environment is being discussed. a more coordinated approach along policy sectors could play a role in ensuring that soil health is pursued. authors recently delving in the topic suggested that policy should focus on preserving and restoring actions, paying higher attention to soil protection in both regulatory (heuser, 2022) and incentive instruments (vysna et al., 2021). this implies also a greater understanding of eu soils’ status and improvement, as aimed by the soil monitoring law proposed in 2023. yet, establishing reasonable and flexible indicators for highly diverse soils remains challenging, for instance when it comes to soil biodiversity (lehmann et al., 2020) or to soil temporal dynamics that can only be over long-time spans (baveye et al., 2016), including effects of degradation and conservation. last but not least, references to cultural es, were almost absent from the current policy instruments, showing that much needs to be done to integrate in the soil health narrative a social dimension that attaches societal valuations and preferences over mere land use and functions (janzen et al., 2021). authors from various subject fields have suggested that a new understanding of the relationship (and reciprocity) between humans and soils could help tackle this issue. for instance, considering soils as natural cultural system to better value cultural es provided by them (costantini, 2023; guimarães et al., 2023), or focusing on soil stewardship (keith et al, 2016) and farmers’ and land users’ relational values (friedrichsen et al., 2021) to further link environmental and social wellbeing to healthy soil management. 5.2. orienting the instruments given the scarcity of results to this narrative review, we would firstly like to address methodological limita84 bio-based and applied economics 14(1): 75-92, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15466 greta winkler et al. tions concerning keyword selection and search databases, nevertheless the exploratory character of this study allows to expand the body of knowledge on soil policy and draw some key reflections that might be useful from an eu perspective. few findings also highlight the little evidence to be found in scientific literature between soil health and related es. this reinforces the previous finding that, despite being soil health part of the scientific discourse since longer (lehmann et al., 2020), a significant gap emerges when it comes to policy instruments. soil health is not at the reach for most of the individuated instruments, while a stronger focus on soil fertility could be detected from this literature search, seen the frequency of carbon sequestration and soil organic carbon among mentioned regulating and supporting es. this is true for public as well as for market driven instruments. in the eu subsidies accessible through the cap are currently the main instrument to incentivize farmers to fulfil environmental requirements, resulting in a hybrid form between regulation and incentive. as for soil they currently include i) direct payments for cross-compliance with gaec concerning soil cover and organic matter, and ii) voluntary eco-schemes that include among areas of action additional “prevention of soil degradation, soil restoration, improvement of soil fertility and of nutrient management and soil biota” (2021/1115 p. 41). in addition, the second pillar, mainly through agrienvironmental-climate schemes (aecs), addresses soil health either directly or indirectly (mantino, 2022; eichhorn et al., 2024a). for example, several rural development programmes (rdps) include measures to increase organic matter in the soil, promote cover crops and conservative agriculture, or invest in reducing soil erosion through repairing or rebuild dry stone walls or other landscape elements. conversely, aecs incentivize measures that indirectly affect soil health, such as organic production or the maintenance and reintroduction of grasslands in mountain areas (vergamini et al., 2024). the current eu common agricultural policy (cap) could serve to test some initial result-based payments in aecs (eichhorn et al., 2024b) and contribute to developing indicators to target ecosystem services (bartkowski, 2018). the combination of command-and-control instruments and market-based instruments in the cap could significantly affect soil health and the delivery of es by farmers. conservation instruments seem more appropriate to the soil health narrative, compared to the field level more common to agricultural subsidies and regulations, for instance es provided by forest soils, permanent grasslands as well as rewetted peatlands could contribute to the matter. regarding payments for es, landowners – most typically farmers – could bekeen on generating es that are marketable, as the case for carbon credits, yet a recent review shows that hybrid incentive mechanisms combining result and action based payments are more likely to be interesting to farmers (raina et al., 2024). payments for es then will need to consider also mechanisms for distribution of benefits and incentives, to reduce the risk of having large landowners as main beneficiaries, attracting land accumulation and lobbing in areas where the land prices are low, thereby excluding small holders (baveye et al., 2016). this is also tightly connected to length and typology of different land tenure arrangements, that might distort the trade-off between profitability and es, given that long-term land tenure contract might also have the positive side effect of increasing commitment towards soil health and thus provisioning es (stevens, 2022). market regulation (i.e. carbon markets) are becoming the main solutions rather than compensation mechanisms parallel to emission reduction actions. private financial institutions appear increasingly interested in mechanisms such as payments for es or offsetting, this on one hand can provide new source of income for supporting those es that are currently neglected. on the other the role of the public sector is relevant in making sure that market-based solutions are still tackling relevant objectives (vatn, 2018), in this case the restoration of healthy soils in the eu by 2050. to overcome this issue key scholars in soil science suggested discussing the option to subsidize farmers based on a minimum level of all es associated to soil health (bouma et al., 2022). level of detail on soil practices contributing to soil health seems for now limited but the concept of bundles of es could further contribute to the discourse (bartkowski et al., 2018). in addition, our results highlight the need for improvements in developing new empirical and theoretical models that enable understanding causal effects between decision-makers’ actions and their impact on multiple dimensions aimed at targeting various ecosystem services (es). this would call the development of bioeconomic models to address both ex-ante and ex-post the complexity of interactions between economic behaviour and soil dynamics at different scales and spatial resolutions. finally, given the scarce results concerning information as a policy instruments, we would like to highlight the important role that this could play in the form of research and dissemination as well as extension services. a recent review by arias-navarro et al. (2023) has summarized key themes of past eu research over soils, primarily on regulating es (erosion protection, soil contamination, soil and water, climate mitigation, car85soils and ecosystem services: policy narratives and instruments for soil health in the eu bio-based and applied economics 14(1): 75-92, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15466 bon storage). moreover soils are one of the five topics included in the eu missions, new instrument to support research and innovation in the period 2021-2027. “a soil deal for europe” aims to reduce several soil degradation processes by addressing four operational objectives including: funding research and innovation; establishing living labs and lighthouses; develop a soil monitoring framework and raising people awareness (for more details on the soil mission objectives please refer to eu mission: a soil deal for europe (european commission, 2023)). this mission has allowed to finance a wide number on soil-related projects that are now running all over europe, assessing their outcomes and contribuitions towards soil health might constitute a topic for future research. on the other hand, extension services mainly focus on soil fertility and show quite heterogenous approaches to advice, mostly lacking the holistic understanding of soil microbiology and chemistry embedded in the understanding of soil health (ingram et al., 2022). lack of information and evidence on which further es might benefit from improved multi-functional soil management might prevent farmers from adopting practices that could benefit soil health, including accessing incentives and markets (schröder et al., 2020). there is a need to move the focus from a soil fertility and carbon-centered discourse to a landscape level understanding of soil uses, that embeds socio-cultural services provided to land users as well as the wider public (guimarães et al., 2023; friedrichsen et al., 2021). 6. conclusions this study contributes to the analysis of policy narratives, understood as the way an environmental problem becomes part of the policy discourse and agenda, by looking at the topic of soil health. it provides an insight over the integration of the soil health concept, intended as the delivery of a broad set of es, throughout current eu policy instruments and scientific literature. the findings of this narrative review open up for a further, more careful investigation of incentives for soil es, to improve the process we suggest some mitigation measures such as narrowing keyword selection to those policy incentives more relevant to soils, broadening search engines, and relying on the prisma flow for a systematic review process. the agricultural sector enlists a set of incentives that promote a correct management of soils to guarantee greater fertility, well managed forest soils are found to improve soil structure and protect from erosion, yet soils are seen predominantly as substrate for primary production and carbon sinks. overall, recognition of the value of es related to soils in policy instruments was identified for primary production – food, fuel and fiber – and carbon storage, thus better fitting the scale of action of soil fertility and soil quality. scientific literature also provided evidences of little availability of instruments explicitly tackling a range of es that is understood for healthy soils. there is a need for integration of broader societal values connected to increased soil health, e.g. in the forms of safeguarded soil biodiversity or land users’ wellbeing, including access and ownership to land. the mix of policies and instruments currently available has so far not been able to tackle the issue of soil degradation, therefore a more coordinated effort among policy sectors is required to prioritize soil health. given the prominent role of the private sector, future research could focus on the role of market instruments in pursuing soil health where more regulatory instruments have failed. aknowledgements the authors gratefully acknowledge funding from the european union’s horizon europe research and innovation program within the project novasoil (grant agreement no 101091268). conflict of interest the authors declare no conflicts of interest. fabio bartolini, co-editor-in-chief of this journal, had no role in the editorial review process and decision-making for this article. references adhikari, k., & hartemink, a. e. 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(2021). payments for ecosystem services within the hybrid governance model: evaluating policy alignment and complementarity on california rangelands.  ecology & society 26(1). 24. ferrarini, a., serra, p., almagro, m., trevisan, m., & amaducci, s. (2017). multiple ecosystem services provision and biomass logistics management in bioenergy buffers: a state-of-the-art review.  renewable and sustainable energy reviews 73: 277–290. 25. (repetition) deines, j. m., guan, k., lopez, b., zhou, q., white, c. s., wang, s., & lobell, d. b. (2023). recent cover crop adoption is associated with small maize and soybean yield losses in the united states. global change biology 29(3): 794–807. 26. peltonen-sainio, p., & jauhiainen, l. (2022). come out of a hiding place: how are cover crops allocated on finnish farms?. sustainability 14(5): 3103. 27. shukla, a., shukla, s., hodges, a. w., & harris, w. g. (2020). valorization of farm pond biomass as fertilizer for reducing basin-scale phosphorus losses. science of the total environment 720: 137403. 28. (repetition) keenor, s. g., rodrigues, a. f., mao, l., latawiec, a. e., harwood, a. r., & reid, b. j. (2021). capturing a soil carbon economy. royal society open science 8(4): 202305. 29. guimarães, m. h., pinto-correia, t., de belém costa freitas, m., ferraz-de-oliveira, i., sales-baptista, e., da veiga, j. f. f., tiago marques, j., pinto-cruz, c., godinho, c., & belo, a. d. f. (2023). farming for nature in the montado: the application of ecosystem services in a results-based model. ecosystem services 61. https://doi.org/10.1016/j.ecoser.2023.101524 https://doi.org/10.1016/j.ecoser.2023.101524 building sustainable futures: the bio-based fertilizer case-study carla abitabile, andrea arzeni*, federica cisilino, matteo orlando is there an animal food kuznets curve, and does it matter? vito frontuto1,*, tommaso felici2, vania andreoli3, marco maria bagliani1,#, alessandro corsi1 do agroholdings cope better with the agency problem? empirical evidence from corporate farms in russia alisher tleubayev1,*, yerzhan syzdykov2 farm characteristics and exogenous factors influencing the choice to buy land in italy silvia russo1,*, meri raggi2, barbara bimbati3, andrea povellato3, davide viaggi1 soils and ecosystem services: policy narratives and instruments for soil health in the eu greta winkler1,*, luciano pagano2, daniele vergamini2, fabio bartolini1 public information instead of vertical integration – exploring “recommended variety lists” in the swiss maize seed market stefan mann*, mauricio benincore and jürg hiltbrunner agroscope tänikon 1, 8356 ettenhausen, switzerland *corresponding author: stefan mann, stefan.mann@agroscope.admin.ch, +41 79 7116026 authors contribution: mauricio benincore was responsible for data collection and conceptualization; stefan mann was responsible for data processing and writing; jürg hiltbrunner was responsible for supervision. this short communication has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the version of record. please cite this article as: mann s., benincore m., hiltbrunner j. (in press). public information instead of vertical integration – exploring “recommended variety lists” in the swiss maize seed market,biobased and applied economics, just accepted. doi:10.36253/bae-16762 abstract institutional economists have suggested vertical integration as a solution in cases of high asset specificity, frequency, and uncertainty. although asset specificity and uncertainty are major issues in the seed market, vertical integration is not an option because farmers and breeders work by different organizational patterns. this study hypothesises that the public provision of information can be an alternative to vertical integration and illustrates this with the “list of recommended maize varieties” in switzerland issued in a private-public partnership based on data generated by the public administration. a survey of maize growers and contractors shows that contractors and farmers in regions ideal for maize production rely strongly on these lists. for users who rely less on maize mailto:stefan.mann@agroscope.admin.ch production, past experience is the most important guide for the choice of variety. it can be concluded that this intervention in the market generates value added for farmers and saves transaction costs. jel-code: q16 1. introduction in our market economy, private goods are mostly traded between private entities, and government intervention in such cases may easily cause misallocations (sadeh et al., 2020). however, this may be different if the public intervention consists in information about the traded goods. while the government’s provision of information is sometimes justified as a necessity (eg. ding, 2014), there is little evidence about the impact that such public engagement may have. the swiss seed market offers a case study that may shed some light on this question. public authorities, in collaboration with partners from the sector, issue lists that recommend certain varieties of cereals, soybeans, potatoes, rapeseed, feed crops and maize, called recommended variety lists (rvls). as switzerland follows one of the most protectionist agricultural policies in the world (nguyen et al., 2021; huber et al., 2023), it is easy to consider this unusual practice as another case for misallocation (see salvatore, 1993, for an extensive debate on the relationship between misallocation and protectionism). as we know from ciliberti et al. (2024) that farmers do not always find technical assistance helpful, it is the objective of this paper to evaluate whether the rvl meets any demand by its potential users. do farmers benefit from the work done by public authorities by selecting varieties according to their benefit for agronomical and technical use compared to others (being not recommended)? is the rvl an important source of information for maize producers? and does this apply to innovative farmers more than to others? these questions are novel, as scholars in the past (turner and bishaw, 2016; mariani, 2021; yang et al., 2024) have mostly described the procedures how new varieties are released to the market, but not critically evaluated them. we use a survey among maize growers to understand the users’ perspective of the maize rvl as an important element of dealing with maize varieties. section 2 sets the empirical and theoretical frame by describing the role of rvls and by exploring transaction cost economics as a potential reason for public intervention. section 3 then introduces the methodology to add empirical evidence of the acceptance of rvls, and section 4 presents the results. the conclusions are drawn in section 5. 2. framework 2.1 case study description every year, research groups of switzerland’s agriculture research station, agroscope, in collaboration with national sector organisations and local partners carry out field experiments with new varieties or candidate varieties of switzerland’s main arable crops, based on which they evaluate the value of cultivation (e.g. yield, ripening behaviour, and tolerances/resistances to pests, diseases and lodging) and use (e.g. digestibility of maize, oil quality of rapeseed, rheological properties of wheat) of single varieties and eventually make suggestions on a selection of varieties that offer a sufficient degree of added value for swiss arable farmers and the rest of the value chain. the sector organisation then invites representatives of farmers and other parts of the value chain to discuss the results, to make suggestions and make adaptations, after which the “recommended variety lists” (rvl) are published prior to the subsequent growing season. this practice distinguishes switzerland from countries that solely decide whether a new variety is sufficiently distinct from the old ones to qualify as a variety of its own but do not interfere in the market beyond that (e.g. heidt et al., 2025). only few other countries like ireland (department of agriculture, food, and the marine, 2024) have decided to recommend certain varieties to their farmers, but to the authors’ knowledge, these lists were never part of a critical evaluation. one of the rvl contains varieties for maize, which is the rvl chosen as our case study. maize is one of the most important arable crops in switzerland, mostly for the feed market, covering approximately 20% of the arable land (foag, 2023) but with a high variability between the different cantons due to climatic differences. there are two main target groups for maize rvl: • farmers growing maize (and deciding themselves which variety they want to grow); • contractors sowing maize for farmers, who delegate the choice of variety to them. additionally, the rvl is also used by extension officers, either from private companies or at agricultural schools. 2.2 theoretical background the “good” that switzerland’s government (supported by private partners) provides for free is an informational good. several economists have examined the market for information. this is a market that is complex due to the intangibility and complexity of information, and undoubtedly one of the most difficult issues in economics. information has been described as a global public good (stiglitz, 1999), which implies that it should be provided by governments. it has also been described as a fully private good that can easily be marketed and therefore can be fully left to the market (kastl et al., 2018). if they are taken as “absolute” answers, both stances are oversimplistic. there is no doubt that ignorance, i.e. the absence of information, can entail high costs on societies (mccoy, 1995), so that information has certainly aspects of public goods. stiglitz and kosenko (2024) have shown that it can be taken for granted that governments improve welfare if they prevent disinformation through fraud laws and disclosure requirements. on the other hand, the literature on intellectual property rights has shown how legal protection concentrates (benkler, 2002) and improves (fang et al., 2017) the production of information. this underlines the aspect of private goods that information undoubtedly has as well. a key to a better understanding of public intervention is transaction cost economics. in particular, williamson (1971, 1979) was first to bring asset specificity, the frequency of transactions and uncertainty into the centre of the institutional discourse, arguing that a high degree of asset specificity and uncertainty and a high frequency of transactions would push companies to organise labour rather in a hierarchical way instead of buying it in the marketplace, leading to vertical integration. it is worthwhile to reflect on whether these conditions apply to the seed market, as presented below. asset specificity has been explored in depth by institutional economists (meirelles des souza filho and varella miranda 2019; richman 2019; cao 2022; grindal 2022). is maize seed a specific asset? dallago (2001), for example, defined asset specificity as the degree to which a good is system specific. maize seed, for example, is only useful for the production of maize and is therefore very system-specific, even more so since there are sub-markets like maize for biogas or maize for nonfood-uses with specific requirements. developing a somewhat stricter definition, lonsdale (2001) defined specificity as “the extent to which the investments made to support a particular transaction have a higher value to that transaction than it would have if they were redeployed for any other purpose”. this definition sets limits to the degree of specificity: indeed, maize seed is only of use for farmers willing to grow maize. however, there is a major number of maize growers; therefore, we should refer to a high relative specificity of a seed, not of absolute specificity, as there is a demand for the seed in a small societal niche with several potential buyers. regarding frequency, crook et al. (2013) emphasised that occasional transactions can easily be governed through contracts, whereas contracts for frequent transactions can cause high transaction costs. in this respect, the seed market is unexceptional. most farmers need seed in spring before sowing, and usually it is part of a crop rotation where this applies every year, so that maize seed is usually traded once per year, except for the few regions where two harvests are possible (mckenzie and pede 2005). with such a frequency, maize seed is situated between goods like staple food which may be bought daily and a house that is maybe bought once per lifetime. uncertainty, however, is a major issue when selecting seeds, aiming for (typically) high yields and a good quality of the crop. the seed market is dynamic. the average life cycle of a specific maize variety in switzerland, to return to this example again, averages around four years. this does not leave ample room for farmers to collect experiences regarding the variety’s characteristics. however, these characteristics are crucial for farmers. they need to know which variety is adapted to the soils and climate of their region, to which diseases the varieties are less susceptible, and which yields and quality to expect. none of these questions can easily be addressed by looking at the product itself. thus, it is always necessary to rely on the information provided. transaction cost economists would therefore most likely recommend that the market for maize seed be integrated (klüppel, 2021; boykin, 2023); on a first sight, this may seem as a promising pathway. while currently breeders usually sell their seed to a cooperative or a private trader from which it is then bought by farmers, a joint company for breeders and farmers has the potential to save transaction costs. however, there are important geographic and organisational obstacles that prevent such an option, at least under current production technologies. the breeding of arable crops is an oligopolistic market in which large multinational companies select and produce in a few places in the world (zhang 2018). the process of concentration continues (bonny 2017). growers, by contrast, are very decentralised and often small enterprises, technologically usually much more diversified and equipped with entirely different technologies as compared to breeders (e.g. kiriti-nganga 2012), and seed is only one of the many inputs they need. this also applies to switzerland, the country in which the rvls are issued and one characterised by family farms with an average size of 20 hectares (lakner et al. 2018). as a company can hardly integrate central structures with such decentral structures, vertical integration is not an option. the impossibility of vertical integration leads farm managers and contractors who sow maize to choose between very high transaction costs or suboptimal decisions. hence, farmers can either rely on past experiences (which they often do according to haden et al., 2012) or word-of-mouth from their social network (another recurrent behavioural pattern according to ma et al., 2022). this strategy will often lead to a less-than-optimal variety choice. or they can undergo major efforts gathering information from competing breeders and perhaps colleagues elsewhere who tested the reliability of the company’s information. this is where public involvement that treats information as a public good can help by lowering transaction costs for approximately 20,000 farm managers who grow arable crops in switzerland. if the federal research station that issues the rvls is perceived as a credible source, farmers that consider themselves innovators will particularly benefit from the list as a transaction cost-saving tool; that is, it is reasonable to assume that the usefulness of the rvls can be measured by the demand for it. this leads to the following two hypotheses: h1: rvls are the most important source of information for the choice of maize varieties among farm managers and contractors. h2: actors who rely on innovation place a higher value on the rvls than actors who rely on their previous experience. 3. methods the two hypotheses require the knowledge about the perceptions of farmers and contractors, so that it became necessary to carry out a survey. the information in the rvls is devoted to a particular crop, and for more concrete analysis and findings, we chose the rvl of maize as a case study. in spring 2024, we sent a survey to 2000 farm managers growing maize (using addresses provided by the federal office of agriculture) and to 375 contractors (distributed by the association of contractors), the majority of whom were also farm managers. reminders were sent once, and a prize draw with vouchers for switzerland’s main agricultural supplier or the swiss railroad served as an incentive to participate. 442 questionnaires were returned, i.e. a response rate of 19 per cent was obtained (21 per cent for farm managers, 8 per cent for contractors). the survey was designed to test the two hypotheses using variables depicted in table 1. two variables were used to describe the position of the rvl in the decision-making system of maize producers and contractors: • one was the degree to which potential users were familiar with the rvl (“knowledge”). • the other (“information”) was a ranking in which respondents familiar with the rvl ranked the importance in comparison with other sources of information about seed (information by breeders, information by traders, prior experience, exchange with colleagues, advice from extension officers). for the test of the first hypothesis, it was sufficient to analyse the results of this ranking, the general knowledge of the rvl, and its use. the first was achieved by comparing the means of the six options of decision support using the wilcoxon matched-pairs signed rank test (o’brien and fleming, 1987) through a descriptive evaluation of the “knowledge" variable. it was more complex to operationalise the degree to which users were innovative in order to test the second hypothesis. for this purpose, the rank of “relying on prior experience” in choosing the maize variety was chosen, even though this may neglect important aspects of innovation such as the use of novel technologies. the underlying assumption was that prior experience was less important for innovative farmers and contractors than for farmers and contractors with a conservative orientation. as a sub-sample of this empirical exercise, only respondents who knew about the existence of the rvl were included. as indicated in table 1, the “importance” variable was the rank of rvl as compared to other information sources such as neighbours or previous experience. it therefore is technically dependent on the other rankings (as respondents could, for example, use rank 1 only once) and therefore could not be used as the dependent variable. instead, the “knowledge” variable was regressed against three information sources for maize varieties, of which one was “experience”, the other two sources used by more innovative actors. farmers not knowing the rvl were, of course, excluded from the sample, as they would be unable to rank the importance of the rvl as an information source. some control variables had to be included to avoid distortions. researchers have claimed, for example, that public actors in the breeding market have a particular responsibility to deliver and promote varieties that deliver high-quality public good characteristics, such as resistance against diseases (mann 2013). as one of the control variables, we checked how important the resistances of a variety were for the respondents. most contractors specialise in arable farming (nye 2020); if farmers contract them, they have a high motivation to choose the best and most adapted maize variety for the farmer as part of their service quality. different to that, most farmers in switzerland also have animals that receive a lot of attention and time; thus, they are likely to devote a smaller part of their time to maize production which is often used as feed for their own animals. therefore, it was necessary to distinguish between the two professional groups for the analysis. many other analyses of the behaviour of swiss farmers have noticed that both the age (karali et al. 2013; baur et al. 2014; ferjani et al. 2015) and gender (mann and van aken 2021; junquera et al. 2022) of farm managers are important predictors of variances. this made it necessary to include these sociodemographic variables in the equation. finally, the heterogeneity of swiss agriculture in terms of topography has also been shown to be decisive (rossier 2012; karali et al. 2014; herzog et al. 2017); therefore, it had to be integrated as another control variable. table 1: variables for the regression analysis explaining the degree of using rvl variable meaning measurement mean min max popularity variables importance degree of using recommended variety list from 1 = most important information source to 6 = least important information source 2.66 1 6 independent variables experience degree of using prior experience for choice of variety from 1 = most important information source to 6 = least important information source 2.09 1 6 exchange degree of using exchange with other farmers/ contractors for choice of variety from 1 = most important information source to 6 = least important information source 3.95 1 6 extension degree of reliance on extension officers for choice of variety from 1 = most important information source to 6 = least important information source 3.84 1 6 resistance importance of disease resistances from 1 = unnecessary to 5 = very important 3.92 1 5 farmer respondent’s occupation 0 = contractor, 1 = farmer 0.93 0 1 gender respondent’s gender 0 = female, 1 = male 0.95 0 1 age respondent’s age no. of years 47.8 23 75 region region’s suitability for maize production 1 = excellent to 4 = marginal 2.46 1 4 4. results figure 1 and table 2 display the numbers needed for the test of hypothesis 1. figure 1 indicates that the rvls for maize were well-known and the most popular external information source. however, table 2 shows that hypothesis 1 still needs to be rejected. prior to the rvl, past experience guided farm managers in selecting maize varieties as the most important factor. nevertheless, the rvl ranked second. importantly, with the exception of the advice of extension officers and other farmers who were similarly unimportant information sources for maize varieties, there was a relatively clear hierarchy in information sources. farmers unequivocally preferred direct information on single varieties by breeders to indirect information provided by input dealers. however, the public information source was considered more important than the less neutral communication by the breeders themselves. figure 1: distribution of answers to the variable “knowledge” table 2: ranking of information sources for maize seed varieties with 1= most important and 5= least important. answer average rank experience 2.08 rvl 2.66 breeders 3.46 extension 3.84 colleagues 3.95 input dealers 4.62 brackets between variables denote insignificant differences at the 95% level. no brackets between adjacent figures next to the table denote statistical differences at the 95 % level. table 3 indicates that hypothesis 2 cannot be rejected. the more farmers relied on their past experience, the less they valorised the rvl. the significance makes it clear that innovative farmers benefit more from the rvl than less innovative farm managers, if innovation can be described negatively through not orienting on last year’s variety. however, the exchange with colleagues was, use regularly use sometimes use once per year for variety planning know it, but don't use it don't know it unexpectedly, an even stronger substitute for the rvl, measured by both the coefficient and the significance (even though, as indicated by table 2, colleagues are, in general, not an important source of information). the substitution effect between the rvl and extension services was much weaker, suggesting beyond the line between innovative farmers (using the rvl) and traditional ones (preferring to relying on their experience), there was a clear distinction between farm managers who preferred horizontal information (such as exchange with their colleagues) and those who preferred sources of information that could be considered superior (such as extension officers and the rvl). table 3: results of the ordered logit analysis explaining “importance” variable coefficient z-value experience -0.351*** -3.60 exchange -0.396*** -5.20 extension -0.189* -2.40 resistance -0.797 -1.17 farmer 0.992** 2.86 gender -0.531 -0.77 age -0.014 -1.39 region 0.335** 2.80 ***p < 0.001; **p < 0.01; *p < 0.05; n = 305, pseudo r2 = 0.06 partly based on their insignificance, the control variables showed that there was no background distortion. this included the lack of significant effects of gender, age, and the perceived importance of resistant varieties. other control variables, however, showed a more significant impact. contractors, for example, relied much more on the rvl than farm managers. the same applies to maize growers in arable regions well adapted to maize production. they also used the rvl more often than farmers in peripheral maize regions. 5. discussion the fact that hypothesis 1 had to be rejected insofar as prior personal experience prove to be the most important information source for variety selection points to the tremendous importance of personal knowledge. in fact, this importance has also been repeatedly emphasized by transaction costs economists like czarniewski (2014) or williams and balasz (2021) and is also prominent in the literature on the behaviour of farmers in general (gebrehivot and van der veen, 2021; duden et al., 2023; ricart et al., 2023). still, the rvl has proven to be the most important external source of information. if one takes the two findings together, it is likely that farmers combine their prior experience and the rvl, checking how happy they are with last year’s choice and whether there is a more promising option on the rvl. it must be admitted that, with an r2 of six per cent, our analysis can only explain a minor share of the differences in importance of the rvl among farmers and contractors. there are certainly soft factors that could not be measured but are of high importance, and maybe some “hard facts” have been overlooked as well. while this is a major limitation of our study, the significant variables still warrant attention. this starts with the role of existing knowledge among farmers, reflected by the “experience” variable. research has demonstrated the strong influence of both explicit and tacit knowledge for farm management (for a review, see tumwebaze et al., 2024). therefore, the diminishing role of prior experience in determining the importance of the rvl should not come as a surprise. similarly, the “exchange” variable reflects the role of social networks. while research clearly highlights the importance of social capital for information generation (raudiliduniene et al., 2021), including in agriculture (yu and gambrah, 2024), our analysis shows how social capital reduces the need for public information. while there is also substitution between extension as a source for variety information and the importance of the rvl, as indicated by the “extension” variable, this effect is only half as strong as for the previous two. this aligns with the views of economists who emphasize the limited impact of extension services under many circumstances (cerdan-infantes et al., 2008; aker, 2011; kasser and alemu, 2017). today, farmers have access to much more information sources than they used to, including social media, which may limit the added value of extension services. the high significance of the “farmer” variable underscores the important role of contractors in agriculture as multipliers. these individuals inform themselves using the rvl and pass their knowledge on to their customers. this aspect has been neglected in the limited body of literature on contracting in agriculture (nye, 2020; bachev, 2024). as the final significant variable, it is no surprise that the rvl plays a larger role in traditional maizegrowing regions, compared to, for example, the hilly region. buzzi et al. (2021) show the vast importance of climatic factors like temperature for the productivity of maize. again, the significance of this variable confirms that the rvl is used if maize production is of great importance for the user. from there, the knowledge trickles down to areas where maize is of less importance. the non-significant variables do not require much further elaboration. the preference for the rvl does not depend on the importance placed on resistant varieties, nor on age or gender. 6. conclusions although the maize rvls seem to be less important for the selection of maize varieties than the farmer’s prior experience, it is still a crucial tool. the results of our survey suggest that there is what widen and steinerova (2019) called a hierarchy of information flows. contractors and farmers in areas highly suitable for maize consult the rvl and are, therefore, likely to transfer their knowledge to other farmers. similar models of information diffusion in other realms of agriculture have been described by rebaudo and dangles (2011) and shaijumon (2018). invariably, we show that the maize rvl plays a more prominent role in this information flow than the information provided by the seed companies themselves. it is likely that two perceptions that public sources have greater credibility and that they have all the information collected control this prominent role of the rvl. the results can also be considered as a justification for the public involvement in providing information on the seed market. on the base of our results, it is likely that using tax money for the rvl may save transaction costs on a societal level as many farmers do not have to collect the information by themselves. therefore, the policy implications of our study are that money invested into the support of farmers in their selection of appropriate varieties is probably well-invested. references aker, j.c. 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(2018) ‘top 20 global seed companies in 2017’, seed special, july 2018: 13–15. data statement: the data is confidential as it contains single-farm-data not ready for publication. conflict of interest: the authors have no conflict of interest to declare. bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 bio-based and applied economics bae copyright: © 2024 rocchi, b., viccaro, m., & sturla, g. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: rocchi, b., viccaro, m., & sturla, g. (2024). an input-output hydroeconomic model to assess the economic pressure on water resources. bio-based and applied economics 13(2): 203-217. doi: 10.36253/bae-14957 received: july 21, 2023 accepted: january 16, 2024 published: july 25, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: matteo zavalloni orcid br: 0000-0002-7545-3093 mv: 0000-0001-9315-4110 gs: 0000-0001-8578-1384 an input-output hydro-economic model to assess the economic pressure on water resources benedetto rocchi1,*, mauro viccaro2,3, gino sturla1 1 department of economics and management, university of florence, italy 2 school of agricultural, forestry, food and environmental sciences, university of basilicata, italy 3 institute of methodologies for environmental analysis-national research council of italy (cnr-imaa), italy * corresponding author. e-mail: benedetto.rocchi@unifi.it abstract. this study develops a hydroeconomic input-output (io) model to evaluate the pressures that economic activities exert on water resources. for a better understanding of the sectoral and total impacts, three innovations are incorporated with respect to previous literature: i) the development of a methodology for disaggregating the extended water demand (blue water plus grey water) by economic sector, ii) the use of the io side of the model to reclassify water demand by “extracting” and “demanding” sectors, and iii) the proposition of an improved indicator of pressure on water resources based on a “feasible” measure of water supply. empirically tested in the tuscany region (italy), our findings reveal significant changes in the structure of economic pressures when adopting the proposed approach. when assessing direct total water withdrawals, agriculture accounts for 61% and manufacture for 20% of regional pressures. however, when considering only the demand for water resources exposed to scarcity reclassified by demanding sectors, agriculture falls to 5% and manufacture rises to 54%. by incorporating grey water in water demand and a “feasible” measure of supply, the regional water exploitation indicator increases from 0.05 to 0.19, and can even reach 0.30 with dry hydrological conditions, beyond the threshold for moderate scarcity (0.20). the unbalance between water supply and demand worsen even more when considering the balance of surface waters only (1.16). the proposed model can support an in-depth analysis of an economy’s water footprint, allowing impacts to be mapped from specific industries to particular water bodies. this information can support decisions about sustainable water management at the national and regional levels. keywords: input-output, extended water demand, feasible water supply, extended water exploitation index, tuscany. jel codes: c67, q25, q50. 1. introduction input-output (io) models have been widely used to quantify the direct and indirect water consumed by industries in order to satisfy the final demand (velazquez, 2006; guan and hubacek 2008; lenzen et al., 2013; ridoutt et al., 2018). a typical use of input-output models extended to water https://doi.org/10.36253/bae-14957 http://www.fupress.com/bae https://doi.org/10.36253/bae-14957 https://orcid.org/0000-0002-7545-3093 https://orcid.org/0000-0001-9315-4110 https://orcid.org/0000-0001-8578-1384 mailto:benedetto.rocchi@unifi.it 204 bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 benedetto rocchi, mauro viccaro, gino sturla resources is for structural analysis. a wide literature has been developed in the last years on the concept of waterenergy-food (wef) nexus, aiming at studying the structural interdependencies among human needs, production activities and natural resources and the related social, technological and environmental constraints (white et al., 2018; xiao et al., 2019; deng et al., 2020; lee et al 2021; meng et al., 2017). a further field of application of environmentally extended io models is the analysis of virtual water flows among countries and the quantification of the water footprint at the regional, national and global scale (feng et al., 2011; duarte et al, 2016; arto et al, 2016; sturla et al 2023 and 2024; wang et al., 2021). io models are also used to assess the water balance of the economy, comparing an estimate of water demand based on economic modelling with a measure of water supply based on hydrological data (cámara and llop, 2020; garcia-hernandez and brouwer, 2021). studies, however, differ on how the demand for water generated by human activities is defined. cámara and llop (2020), for instance, consider the net demand (withdrawals minus discharges) while garcia-hernandez and brouwer (2021) consider only water withdrawals. furthermore, these studies do not consider grey water, i.e. the water required for dilution of pollutants present in water discharges. in a paper on north china, guan and hubacek (2008) use an io model to determine an “extended” demand of water, defined as the net demand (including blue and green water) plus the water required for pollutants dilution (grey water). grey water is estimated based on a mixing model developed by xie (1996), using the chemical oxygen demand (cod) as an indicator of pollution. grey water requirements (and, as a consequence, the extended demand), however, is quantified only for the whole economy. furthermore, in modeling the interactions between the economy and the natural hydrological system, these authors do not quantify any indicator of economic pressure over water resources. in literature, several indicators of pressure on water resources have been proposed. the water exploitation index (wei) corresponds to the ratio between blue water withdrawals and natural availability net of the ecological flow (european environment agency, 2005). an improved version of the wei (wei+) subtracts returns to water bodies, therefore considering the net water demand (faergemann, 2012; european environment agency, 2020; casadei et al., 2020). in other studies, the water availability index (wai) or withdrawals to availability (wta) ratio is defined as the ratio of water withdrawals to renewable water availability (oecd, 2015; garcia-hernandez and brouwer, 2021; pfister et al., 2009). a conventional threshold value of 20% for all the mentioned indicators is used as a water scarcity criterion. this threshold has been recommended to identify the presence of some degree of water stress, while a value of 40% has been proposed to differentiate moderate from severe shortages, without any specific considerations of regulation capacity and extraction feasibility (raskin et al., 1997; alcamo et. al, 2000, pfister et al., 2009, circabc, 2012). based on this background, the objective of this paper is to develop an input-output hydroeconomic model to evaluate the economic pressure on water resources in a more comprehensive way than previous studies. the main innovations of our approach are: i) the development of a methodology for disaggregating the extended water demand (including grey water requirements) by economic sector, ii) the use of the io side of the model to reclassify water demand by “extracting” and “demanding” sectors, and iii) the proposition of an improved indicator of pressure on water resources based on a “feasible” measure of water supply. to calculate the grey water demand for each economic sector, a mixing model is solved that considers the capacity of surface and groundwater to degrade organic matter, not only the standard model based on the mass continuity equation of the dough (hoekstra 2011). we use a modified version of the model proposed by xie et al. (1996) to estimate the requirements of water for dilution by economic sector, considering that water for dilution is supplied by the hydrological system with a given level of pollution. in our model some industries withdraw and return water directly from/to the hydrological system while others do so only through the water supply and the sewerage services. when considering only the direct withdrawals from water bodies, we refer to “extracting industries”. the input-output matrix, through the intermediate flows of goods and services, allows us to reclassify the water demand by “demanding sector”, that is, a new distribution of water uses that considers the direct and indirect pressure of economic sectors on water resources. the indicator of pressure on water resources proposed in this study corresponds to the wei+ indicator but including grey water also in the numerator and considering a feasible measure of supply as a denominator. the groundwater supply considers long-term recharge within a technical range of abstraction. the supply of surface water includes also technical (extraction capacity) and institutional (water concessions) constraints. according to our extended water exploitation index (ewei) the feasible supply depends on hydrology conditions. the more the hydrology is distant from the average year, the more technical and institutional constraints are important. 205an input-output hydro-economic model to assess the economic pressure on water resources bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 we implement the model for the tuscany region of italy. using hydrometeorological information, the water availability is determined, from which the feasible supply is estimated. the mixing model depends on water quality parameters, the effect of water availability on the cod concentration in water bodies and the water discharges from the io hydro-economic model (two-way arrow in figure 1). based on the results of the mixing model (dilution water coefficients), water withdrawal and discharge coefficients and the io regional table, the hydro economic model allows to calculate the extended water demand by extracting industry and reclassify it by demanding industry. finally, based on the extended water demand and the feasible supply, the ewei indicator is obtained. the paper is organized as follows. section 2 presents the structure of the input-output model extended to water resources, including the methodology for estimating water requirements for dilution and the reclassification of the extended demand by demanding industry. section 3 presents the proposed pressure indicator, based on the model’s output and on information about surface and groundwater availability in the region. section 4 describes data and methods used to implement the empirical model for tuscany. section 5 presents the results for the reference year in terms of net and extended water demand classified by industry and water body and an assessment of the overall level of pressure on water resources in tuscany based on the ewei. section 6 presents a discussion of the main results and of methodological limitations of the study. finally, section 7 provides concluding remarks and suggestions for future research. 2. the hydro-economic model 2.1. hydro-economic water flows following guan and hubacek (2008) we consider the extended demand approach, which include the water withdrawals for productive1 uses minus the discharges of water to the hydrological system plus the unavailable water for qualitative balance of water bodies (water requirements to dilute the pollution). the economic system withdraws water from underground and surface sources (blue water) and from rain and soil moisture (green water). after productive uses, 1 in this study, we are interested in water used for production. that is, we assume that water for domestic uses is provided by the water supply industry. actually, there are also direct withdrawals by households from groundwater and surface water bodies whose relevance, however, depends on the case study. in tuscany, this component of the household demand for water does not exceed 3% of total and has not been considered in the analysis. water can be divided into: i) water discharged to surface and groundwater, ii) water incorporated in products and consumed in services, iii) water consumptions by evaporation and transpiration into the atmosphere, and iv) water removed from the immediate water environment (kenny et al., 2019; macknick et al., 2012). figure 1 presents a schematic illustration of the water flows in the hydro-economic system. the productive system extracts water from the hydrological system supply (withdrawals), that is, surface water, groundwater, precipitation and soil moisture (the latter two components associated with agriculture). a part of this water is consumed (goods and services, evaporation and transpiration); the remaining part is discharged with pollution to groundwater and surface water (discharges). by means of physical-chemical processes and fresh water from the hydrological system reserved for quality restoration (dilution requirements), the restored water is available again for use in the production system (in volume and quality). water that returns to the atmosphere is not considered as a recharge within the reference period of the model (one year). it is important to note that the concept of net water demand (withdrawals minus discharges), widely used in the literature to estimate the water exploitation index (wei+) (faergemann, 2012; european environment agency, 2020) considers only the volume of water. the concept of extended water demand used in the present study to calculate the extended water exploitation index (ewei), conversely, considers both water volume and water quality. 2.2. input-output hydro-economic model we consider an economic system with n productive sectors (industries) and a water system with m water figure 1. scheme of the hydro-economic input-output model. source: own elaborations. 206 bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 benedetto rocchi, mauro viccaro, gino sturla sources (or water bodies) to build an environmentally extended io model (miller and blair, 2009). let ad 2 be the matrix of coefficients that represents the structure of intermediate consumptions per unit of output of production activities, calculated from the domestic flows input-output table. the total production of the n industries can be calculated from the following equation: x = (i ad)-1y (1) where x is the vector of gross output of the industries, y is the vector of the final demand and i is the unit matrix. in the hydro-economic approach, the model is expanded to link the level of activation of each industry with exchange flows between production activities and the water bodies composing the hydrological system. let: fk be the (n × 1) vector of the unit water withdrawal coefficients (m3/€) of industries from the water body k. rk be the (n × 1) vector of the unit water discharge coefficients (m3/€) of industries to the water body k. wk be the (n × 1) vector of the unit water for dilution requirement coefficients (m3/€) of industries for the water body k. the extended water demand (n × 1) vector ek for the water body k, disaggregated by industry, is given by: ek = ( + ) (i ad)-1y, k = 1,…,m (2) the hat symbol indicates the diagonalization of the vector. by repeating the operation for the m bodies of water considered in the model it is possible to constitute the (n × m) matrix ed representing the extended water demand of the n productive sectors from the m bodies of water: ed = (f r + w) (3) where the (n × m) matrices f, r and w represent respectively the withdrawal, discharge and dilution requirements coefficients by industry and water body. the total extended demand of water associated with the entire economy, by water source, can be represented by the (m × 1) vector ted: ted = (f r + w)’x (4) 2 for the purposes of this paper, the matrix of direct coefficients for domestic production is calculated following the methodology of weber et al. (2008). this method assumes that each economic sector and final demand category uses imports in the same proportions. where the symbol ’ represents the transposed matrix. the net water demand (nd) can be calculated in an analogous way simply excluding from equations (2) to (4) the terms referring to water requirement for dilution (vectors wk and matrix w). 2.3. water requirements for dilution in this section we show how the (n × 1) vector wk, which was defined in the previous section, is calculated to determine the water requirements for pollutants dilution by economic sector and by water body k. we use a mixing model considering the chemical oxygen demand (cod) parameter based on the model developed by xie (1996) (xie-model, hereafter) and used by guan and hubacek (2008) to estimate the extended demand for the whole economy. this model considers that pollutants are diluted as a result of three effects: mixing with fresh water with a lower concentration, chemical reactions before entering the water bodies and chemical reactions after entering the water bodies. the first component refers to the surface waters and groundwater existing in the discharge areas and the additional water required when this is not enough. this additional water corresponds to grey water. (for more details see appendix a). in this work, we improve the guan and hubacek’s approach as follows: – the water requirement for dilution associated with each production sector is estimated (only for the whole economy in the xie-model); – the dilution water is considered to have a cod concentration similar to the water available for productive use (cod equal to zero in the xie-model); – the worst case is assumed, i.e., when there is no availability of water in the receiving bodies (total natural supply in the xie-model). let assume that vector wk comes from a (m × n) matrix w whose elements wkj represent the coefficients of water for dilution (m3/€) referred to the body of water k and the industry j: wkj = where, ukj (m3/year) is the element of the (m × n) matrix u representing the water required for dilution (including losses) in the water body k by the economic sector j, while xj (€) corresponds to the total output of sector j.3 3 for the case of this study m = 3 (groundwater, surface water and soil moisture), however, the third column of the matrix w (and the matrix u) corresponds to zeros, since the water for dilution is only required to purify water discharged in surface and groundwater bodies. 207an input-output hydro-economic model to assess the economic pressure on water resources bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 the following expression (mixing model) is used to estimate ukj: ukj = ∙ qpkj (6) where: k1k : total reaction rate of pollutants after entering the water body k; k2k : pollution purification rate before entering the water body k; qpkj : discharges into the water body k associated with industry j; cpkj : cod concentration in the discharges to the water body k associated with economic sector j; csk : standard cod concentration in water body k; c0k : cod concentration in water body k. the standard cod concentration csk refers to a low level of pollution associated with good water quality in water bodies. the water used for dilution has a concentration equal to that of the receiving water bodies (c0k ). note that in equation (6) the discharge corresponds to qpkj = rkj ∙ xj, obtained through the hydro-economic input-output model. the cod concentration in water bodies is a parameter that depends on the hydrological system (c0k ), decreasing when water availability is higher and increasing when it is lower. in the case of this study, the concentration associated to an average availability is considered in the base analysis and modified to calculate the water exploitation index in case of dry and wet hydrology. appendix a (supplementary materials) presents the development of the mixing model by explaining in detail the differences between our study and the xie-model. 2.4. reclassification by demanding sectors the input-output matrix, through the intermediate f lows of goods and services, allows to reclassify the net demand and the extended demand of water by “demanding sectors”, that is, according to a new distribution that considers the direct and indirect pressure of each economic sector on the different water bodies of the hydrological system. it is possible to rewrite equation (2) based on (1), ek = ( + ) ∙ x, k = 1,…,m (7) the coefficients in vectors fk, rk and wk are different from zero only for production activities that actually withdraw and return water from/to water bodies. despite all production activities require and discharge water (although to a different extent), the withdrawals and the discharges of water from/to different bodies of the hydrological system are actually carried out only by a limited number of industries (extracting sectors). for example, the largest part of service activities purchase water from the water supply sector and discharges water throughout the sewerage service sector. referring to equation (7) would provide only a partial view of the interdependencies existing between the economy and the hydrological system. it is of interest to know the use of water reclassified by demanding sectors. this was done adding to the total direct use of water of each sector the “virtual” demand of water from other sectors associated with the purchase of intermediate inputs; and subtracting the “virtual” sales of water to other sectors via the supply of intermediate inputs as well. the vector of “virtual” water sales associated with water source k is, sk = ( + ) adx (8) the vector of “virtual” water purchases associated with water source k is, pk = a’d(fk rk + wk) (9) thus, the reclassified water extended demand vector ( ) for the water source k can be written combining equations (7), (8) and (9). = ek sk + ck = ( + )(x adx)+ a’d(fk rk + wk) (10) repeating this procedure for each of the m water sources, the (n × m) matrix red is obtained, representing the extended demand from the m bodies of water reclassified by demanding sector. the reclassified extended water demand (n x m) matrix red can be written as: red = ( + a’d)(f r + w) (11) following a similar procedure, it is possible to find the expressions for the reclassified net demand vector ( ) for the water source k and the (n × m) matrix rnd representing the extended demand from the m water bodies reclassified by demanding sectors. 208 bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 benedetto rocchi, mauro viccaro, gino sturla 3. an indicator of economic pressure on water resources 3.1. water supply in the previous section, the economic demand for water has been defined. an analysis of economic pressures on water resources must also consider water availability. most of the literature has used the natural water supply net of a minimum ecological flow (faergemann, 2012; european environment agency, 2020; oecd, 2015; garcía-hernández and brouwer, 2021; pfister et al., 2009). however, it is not realistic to assume that it is always possible to extract all available surface and groundwater. in practice, in addition to environmental restrictions there are technical and institutional constraints. in the following sections, the natural water supply is characterized based on the hydrological components and a way to correct the natural supply is proposed based on technical and institutional factors. 3.2. natural supply our water supply indicator considers blue water supply and does not include green water (precipitation and soil moisture). to determine the water supply it is necessary to know the components of the hydrological simplified regional balance (braca et al., 2021, 2022) for a year t, which are precipitation (pt), evapotranspiration (et), groundwater recharge (it), runoff (rt) and the variation in soil moisture (δv). the balance equation is: pt = et + it + rt + δvt (12) the annual natural supply of groundwater and surface water ( ) is equal to the sum of the recharge of the aquifers and the runoff: = it + rt (13) this natural supply is variable from year to year, so a long-term natural supply is defined, based on longterm groundwater recharge and average runoff. = i + r (14) for the construction of the wei (european environment agency, 2005), wei+ (faergemann, 2012; european environment agency, 2020), wta (oecd, 2015; pfister et al., 2009) and wai (garcia-hernandez and brouwer, 2021) indicators, a version of the long-term natural supply net of the environmental requirements, i.e. the ecological flow (ef), is used. in our notation we define the natural supply with ecological flow as: s = i + r ef (15) 3.3. feasible supply we define a “feasible” water supply taking into account environmental, technical, and institutional limitations to natural water supply. the management of renewable but limited resources must consider these aspects that constrain the use of water by the economic system. in the following, the feasible supply is characterized in a detailed and formal way. the technical, institutional, and environmental limitations that characterizes the feasible supply for surface water are the following. first of all, although rivers are renewed year after year, not all the runoff of water can be used for economic purposes. on one hand, in the years of high flow, the possibility to capture and accumulate water (hydraulic works) is limited; moreover, it could not be possible to extract all the natural supply of water because the active concessions do not allow it. second, it is not environmentally sustainable to extract all available water as a minimum “ecological” flow is required for the aquatic ecosystem to continue to thrive and provide their services. a “feasible” measure of water supply must take into account that it is possible to withdraw water only up to a certain maximum quantity. the proposed definition of a feasible supply of surface water is based on the following assumptions: – the maximum amount of surface water extraction is defined by the sum of the maximum withdrawals allowed by current concessions; the assumption we make here is that the concessions have been efficiently awarded, considering all technical and hydrological aspects; · the surface water supply is considered to be limited by a minimum “ecological” flow, as a constraint to environmental sustainability; – the maximum concessions levy is defined as m , where m is a factor not necessarily less than 1 and is the average annual runoff; – the minimum ecological flow is defined as e , where e∈(0,1); – the “feasible” annual average runoff is strictly lower than the value. summing up the value of is: (16) 209an input-output hydro-economic model to assess the economic pressure on water resources bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 the technical, institutional, and environmental limitations that affect the feasible supply of groundwater are different. groundwater corresponds to a stock that varies according to the annual recharge; consequently, the extraction annually available depends more on the average annual top-up than on the top-up of the year. unlike surface water, if the recharge in a given year is low, it is still possible to extract a larger quantity (reservoir effect); conversely, when the recharge is high, there are technical and institutional limitations to extraction. the feasible supply can be equal to the average recharge (which ensures sustainability, i.e., a non-decreasing groundwater stock); however, there are some variations that depend on the stock of the resource and the amount of water that infiltrates during the year. in a scenario in which there is no over-exploitation of the aquifers, that is, there are no large variations in the stock, it makes sense to assume that sustainable extraction will be around the average recharge, that is, it will be a little lower in a rainy year and a little higher in a dry year. in general, groundwater concessions are awarded for a slightly higher value than the annual sustainable recharge, since there are years in which it would not be possible to extract the actual recharge (due technical limitations, especially for small users) and other years when it is possible to extract more than the average recharge. the proposed definition of a feasible supply of groundwater is based on the following assumptions: – the sum of the groundwater concessions (d) is the feasible upper supply limit; – the difference between the sum of the concessions and the average annual recharge (d ), defines a share b by which the average recharge can be increased to calculate the feasible supply (b = ) where b∈(0,1) and is the average annual recharge; – the feasible groundwater supply (that can be drawn in one year) will be in the range [ (1 b), (1 + b)]; summing up the value of is: (17) consequently, if the distribution of i is symmetrical around the average, the feasible annual average supply will be equal to the value . the feasible supply for a year t (fst) can be defined as: fst = the long-run feasible supply (fs) corresponds to the average over time (n years): fs = = this correction made to the natural supply of water allows for a more precise approach to the availability of water in the study region. the formulation considers that a series of n years of the hydrological components is available. the longer the series, the more representative of the longterm this defined feasible supply will be. in the next section, an indicator of pressure on water resources is defined considering the proposed measure of water availability. 3.4. an extended water exploitation index we propose a new indicator of economic pressure on water resources, the extended water exploitation index (ewei), comparing the extended demand for groundwater and surface water, and the feasible supply. it basically corresponds to the wei+ indicator (ratio of net demand to natural supply) but including grey water and considering environmental, technical and institutional constraints in the use of water. using equations (3) and (19) the ewei can be written as: ewei = (20) where i is a (1 × n) vector of ones, which allows summing the extended water demand associated with each economic sector. the sum considers groundwater and surface water, k={1,2}. considering equation (1) the ewei can be expressed in terms of the final demand: ewei = (21) the other indicators proposed in the literature assume a perfect substitutability between groundwater and surface water, which is not necessarily true. for this reason, in our analysis we also consider the ewei separately for groundwater and surface water4. 4 the ewei can vary from 0 to values not necessarily lower than 1, that would correspond to an extended demand equal to the feasible supply. as the index is calculated for a whole region and with reference to a one-year period, its value is likely to be largely lower than 1. the intra-annual variability of natural supply as well as the uneven spatial distribution of water resources, however, suggest that situations of water scarcity could exist also in presence of low values of the annual, regional index. this justify the value of the conventional scarcity thresholds adopted in environmental studies (largely lower than 1) and described in section 1. 210 bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 benedetto rocchi, mauro viccaro, gino sturla 4. case study the proposed model was empirically implemented for the tuscany region (central italy). the regional government as well as other agencies involved in various ways in the monitoring and management of regional water resources made available a wide set of data sources to reconstruct the following components of the model: i) an input-output table of the tuscan economy (reference year 2017) properly disaggregated; ii) the water withdrawals (classified by water body) by production activity existing in tuscany (nace classification); iii) the industries’ water discharges to the hydrological system by water body and by level of water quality; iv) the regional hydrological balance and the feasible supply of water. in what follows we provide a summary of the main data used and the assumption made in building the model. a detailed documentation of the empirical implementation can be found in appendix b (supplementary materials). 4.1. the input-output table of tuscany. the model is based on the input-output table (year 2017) of the tuscan economy developed by the regional institute for economic planning of tuscany. the classification of production activities (56 industries) already represented, as separate industries, some of the key sectors in the exchange water flows between the economy and the environment (water supply services, sewerage services, electricity power production and other activities with an intensive use of water). agriculture, an industry that makes an intensive use of water resources for both crop irrigation and livestock rearing, was disaggregated into 8 subsectors corresponding to general farm types defined by the eu regulation 1242/2008 (farms specialized respectively in fieldcrops, horticulture, permanent crops, grazing livestock, granivores, farms with mixed cropping, mixed livestock, mixed crops-livestock). 4.2. water withdrawals and discharge coefficients for each industry, water requirements and discharge coefficients were estimated using different bibliographic and research data. for agriculture, the estimation of irrigation needs was first developed at the municipal level, considering the specific irrigation requirements of each group of crops based on the climate conditions of each municipality. the total withdrawals at the municipal level were divided between underground (wells and springs) and surface sources of supply (reservoirs, lakes, rivers and streams) using the information available in the 2010 general agricultural census at the municipal level. the two sources of supply are substantially balanced at the regional level, representing respectively 49.6% and 50.4% of total withdrawals. the estimates of water withdrawals by crop typology were then reclassified into the eight sub-sectors of agriculture using the data of the census of tuscan agriculture5. the discharge coefficients were quantified as a share of water withdrawals. this amount depends on losses due to inefficiency of irrigation systems (30% of total withdrawals) and natural losses of soil moisture by evaporation (discharges to the atmosphere). natural losses were quantified as a percentage of green water withdrawals, based on technical coefficients from literature. we assumed that the whole amount of discharges due to inefficiency of irrigation systems returns to ground water bodies. the estimation of water use coefficients for livestock production activities was based on technical literature about the needs of water per head of livestock per day. specific coefficients by species and typology of livestock unit (age, production type) were applied to the composition of the regional herd. the estimated total consumption was then distributed among the different fts based on their share in the rearing of livestock units according to standard results from the fadn public database. discharges were quantified as a fixed proportion of withdrawals (13%) and assumed to be returned only to groundwater bodies. for the estimation of the water withdrawal and discharge coefficients in the water supply industry, the information on water billed in the region for the year 2016 was used. secondary data published by istat (2019b) were used to disaggregate water withdrawals between ground and surface sources. the discharges correspond to water losses in the distribution network; we assumed that all of these losses are discharged to groundwater, constitute groundwater recharge and are not contaminated. for the production of the electricity sector, all the existing generators in tuscany and their annual energy production, for the year 2018, were considered at the municipality level (gse, 2022). water consumption corresponds mainly to evaporation in hydroelectric, thermoelectric and geothermal power plants, and was considered as a discharge to the atmosphere. total withdrawals and discharges were considered to be from and towards surface sources. water requirements for manufacture activities have 5 details are provided in supplementary materials, appendix b. 211an input-output hydro-economic model to assess the economic pressure on water resources bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 been quantified using non-published national data provided by istat. starting from the water withdrawals coefficients of the italian economic activities provided by istat, average coefficients were obtained according to the regional composition of the 29 aggregated manufacturing sectors represented in the io table, using the permanent census of manufacturing activities. the implicit assumption is that, different from agriculture, the average water requirements of manufacture are not affected by location. water discharge coefficients were calculated using information from the exiobase database. ratios and shares for italian manufacturing activities resulting from exiobase were applied to the estimated water withdrawals by industry. the distribution of water extraction coefficients between groundwater and surface water was based on secondary data and reasonable ad hoc assumptions. 4.3. quality of discharged water and mixing model water quality is measured based on the chemical oxygen demand (cod, in mg/l). this parameter was assigned to water returned by macro-sectors discharging water directly to water bodies: agriculture, manufacture and sewerage. the water supply industry is not considered because its returns are of water with low cod concentration (losses in aqueducts). a methodology was defined for each macro-sector to properly characterize the quality of its discharges. for the reaction rate of pollutants after entering the water body parameter (k1k ) in equation (6), we consider a value (dimensionless) of 2.80 and 3.64 for groundwater and surface water, respectively. for the pollution purification rate before entering the water body parameter (k2k ) we consider a value (dimensionless) of 0.82 and 1.00 for groundwater and surface water, respectively (guan and hubacek, 2008). the standard cod concentration in water bodies (csk ) is considered equal to 20 mg/l the value for which waters are classified as unpolluted and can be used without prior treatment (rossi and benedini, 2020). the cod concentration in water bodies (c0k ) is assumed to be equal to the standard cod concentration for an average hydrological year. in the sensitivity analysis for wet and dry hydrological years, it is assumed a value of 17.5 mg/l and 22.5 mg/l, respectively. 4.4. hydrological balance and natural supply starting from the information on the hydrological balance for tuscany provided by istat, the average natural supply of surface and groundwater has been calculated as the sum of surface water, groundwater and rainfall directly captured by the agriculture sector. regarding the feasible supply, the total volume of surface water concessions registered by the regional hydrological service (sir, 2021) corresponds to 2,473 mm3. this amount, however, is about 70% of the total, as many of the concession’s records do not include information on the volume. a maximum value of 3,636 mm3 has been estimated by venturi (2014). the average annual runoff is 3,802 mm3, thus the value of parameter m for the calculation of the feasible surface water supply corresponds to 95.6% (3,802 mm3). for the ecological flow, a value of e=20% is considered. this means that surface water bodies will always show a minimum flow rate equivalent to 20% of the average annual flow. this is a rather conservative value (rossi and caporali, 2021). the maximum value of the groundwater concessions is 4,704 mm3, consistent with the interannual regulation of water supply, while the average annual recharge is 4,155 mm3 (sir, 2021). hence, to quantify the groundwater feasible supply, a value of b = = 13% is considered. 5. results 5.1. withdrawals and discharges the volume of water withdrawals and discharges by water-extracting macro-sectors (direct or “not reclassified” water use) is shown in figure 2. the total volume of water withdrawn by the tuscan economic system considering all sources (groundwater, surface water and soil moisture) corresponds to 2,043 mm3. the total volume of discharges is equal to 685 mm3 (33% of withdrawals), with sewerage services representing about 37% of total. the total net demand (withdrawals minus discharges) is equal to 1,359 mm3, corresponding to the volume of water incorporated into products. agriculture, the only sector using green water, represents about 86% of total net demand. the exclusive use of green water by agriculture is ref lected also in the distribution of the net water demand by water source (figure 3). the soil moisture (987 mm3) represents the 73% of total, with groundwater (221 mm3, 16%), surface water (151 mm3, 11%) playing only a minor role. figure 4 shows the net demand reclassified by demanding macro-sectors and divided by water source. services, for example, which neither directly extract nor discharge water from/to water bodies, account for a 212 bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 benedetto rocchi, mauro viccaro, gino sturla reclassified net demand of 158 mm3, since they purchase both water (from the water supply sector) and other inputs from extracting sectors. the component of the net demand supplied by the soil moisture is now distributed among different production activities, with manufacturing “indirectly” using a relevant share of green water. 5.2. water for dilution and extended demand different from guan and hubacek (2008) the demand of water for dilution has been calculated for each industry separately. of the total demand of grey water (974 mm3), 17 mm3 accrue to agriculture (2%), 379 mm3 to manufacturing and constructions (39%) and 578 mm3 to the sewerage sector (59%). the water supply industry discharges water with standard quality while services discharges water through the sewerage network. the breakdown of grey water by industry allows for its reclassification by demanding sector. figure 5 compares direct and reclassified water requirements for dilution by macro-sector. services increase from zero to 129 mm3 in the reclassified case, accounting for a share of grey water requirements of sewerage services and of other industries from which it purchases inputs. also manfigure 2. water withdrawals and discharges by macro sector. tuscany, 2017 mm3. source: own elaborations. figure 3. net water demand by water source. tuscany, 2017 mm3. source: own elaborations. figure 4. net water demand by demanding macro sector and by water source. tuscany 2017 mm3. source: own elaborations. figure 5. water for dilution by extracting and demanding macro sector. tuscany 2017 mm3. source: own elaborations. 213an input-output hydro-economic model to assess the economic pressure on water resources bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 ufacturing increases its demand for grey water (from 379 to 449 mm3). grey water is a major component of water demand of tuscany. the total extended water demand (total net demand plus total water for dilution), is equal to 2,333 mm3 (+72% compared to the net demand). a prominent role is now played by surface bodies (1,094 mm3) that supply 47% of water. groundwater (252 mm3) and soil moisture (988 mm3) supply the extended demand for 11% and 42% respectively. figure 6 shows the extended water demand classified by demanding sectors and water body. manufacturing is the main user of water resources, accounting for 1,144 over 2,333 mm3 (49%) of the extended demand, mostly relying (54%) on surface bodies. a complete breakdown of the components of net and extended demand reclassified by demanding sector for the 56 industries represented in the io is available in appendix c (supplementary materials). 5.3. economic pressure on water resources the extended demand for water for the reference year includes also water requirements supplied by soil moisture to agriculture (green water). to assess the pressures of the economy on regional renewable resources, only the components of demand supplied by surface and ground water bodies (blue and grey water) are considered. in this section the extended demand of groundwater and surface water is compared with the corresponding feasible supply. table 1 provides some summary results for tuscany. the regional extended demand is equal to 1,346 mm3. the natural supply corresponds to 7,958 mm3. the ecological flow corresponds to 761 mm3. the feasible supply amounts to 7,030 mm3, about 88% of the natural supply; the reduction is due to the constraints on supply associated with surface waters. the pressure indicator ewei proposed in this study is compared with the standard indicator wei+, considering only net demand and the natural supply net of the ecological flow. in the reference year of the analysis (2017) the groundwater recharge component was included in the interval assuring the maintenance of the groundwater stock in the long-run. therefore, the feasible supply of groundwater is equal to the natural supply. in the case of surface water, constraints in water exploitation reduce to 2,875 mm3 the “feasible” supply (compared to 3,042 of natural supply). the results show that at the regional level the overall use of water generated by the economy is still compatible with the available resources, also when natural, technical, and institutional constraints to water use are taken into account. when the thresholds proposed in the literature for these indicators are considered (raskin et al., 1997; alcamo et. al, 2000; pfister et al., 2009; circabc, 2012), the wei+ is well below the 20% limit. however, when considering the ewei indicator, the situation in tuscany appears to be close to a moderate scarcity. as explained in section 3, the denominator of the ewei ratio depends on the values assumed by the hydrology in the average year. however, the components of the hydrological balance are random variables that can largely differ from the mean values both upward and downward. it could be interesting to assess what would be the pressure on water resources when natural components of the balance show extreme values. figure 7 shows the results of such a sensitivity analysis, comparing the values assumed by the ewei with a feasible supply calculated with reference to a mean hydrological situation and to two extreme cases corresponding to the years with the best (2010) and the worst (2007) hydrological supply in the reference period (1970 – 2010). when considering the standard thresholds, it is interesting to note that in a dry year, the ewei value figure 6. extended water demand by demanding macro sector and by water source. tuscany 2017 mm3. source: own elaborations. table 1. economic pressure of the economy on water resources. tuscany, 2017 – mm3 and pressure indicators. total groundwater surface water net water demand (mm3) 372 221 151 extended water demand (mm3) 1 346 252 1 094 natural supply minus ecological flow (mm3) 7 197 4 155 3 042 feasible supply (mm3) 7 030 4 155 2 875 wei+ 0.052 0.053 0.05 ewei 0.191 0.061 0.381 source: own elaborations. 214 bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 benedetto rocchi, mauro viccaro, gino sturla (0.3) would indicate that tuscany is in moderate scarcity (0.4 being the limit for severe scarcity). despite this value of the ewei still implies a safety margin between the extended demand and the feasible supply, it should be considered that the regional mean annual value of the ewei hides a wide variability of the hydrological balance at the sub-regional level, with possible critical local situations. moreover, the breakdown by water sources shows relevant differences between ground and surface water. the former faces a quite stable pressure, due to the reservoir effect of the stock. conversely, in the case of surface water, a worsening of the hydrological scenario could lead to a relevant increase of pressures, with a surface water ewei almost three times greater (1.16 vs. 0.38 for the average hydrology scenario). in a critical year the extended demand of surface water in tuscany would exceed by 16% the feasible supply. 6. discussion the model proposed in this study allows a more comprehensive understanding of sectoral economic pressures on water resources. unlike previous studies, which only consider the sectoral disaggregation in blue water uses, this study also allows the identification of grey water associated with each economic activity. along the same lines, this study makes it possible to evaluate the direct and indirect pressures on the different bodies of water, through a reclassification by demanding sectors based on the io model. furthermore, the flexibility of the proposed methodology allows evaluating changes in the pressure structure when considering different approaches. the case study is eloquent regarding the significant changes that the pressure structure can present. when considering withdrawals, a classification of demand by extracting sector and all water sources in the quantification of supply, agriculture represents 61% of regional pressures, manufacturing 20%, and the water supply industry 19%. on the other hand, when considering the extended demand, a classification of demand by demanding sector, and only water sources actually exposed to scarcity (groundwater and surface water), agriculture represents 5%, manufacturing 54%, the water supply industry 10%, sewerage 16%, and services 5%. these differences can be explained by three reasons: i) the high green water component in water demand for agriculture, ii) the high grey water requirements in manufacturing and sewerage (86% of the total), and iii) the relationship between the purchase and sale of intermediate inputs with embodied water, that is positive for manufacturing, sewerage and services. these results show that mapping the sectoral structure is sensitive to the goals pursued in water management. if incentives are to be generated to reduce the direct and indirect pressures of economic activities on the quantity and quality of groundwater and surface water, an approach by demanding sector should be adopted. the developed model also takes care of the role of resources availability in the analysis of the economic impact on water system. specifically, a new indicator (ewei) is proposed, which considers the requirements for blue and grey water (extended demand), and adjusts the natural supply to consider environmental, technical, and institutional restrictions (feasible supply). previous studies, also when including the grey component of water demand, only correct the natural supply for environmental restrictions. once again, the case study exemplifies the differences in the water resource exploitation indicator when aspects not addressed in previous studies are considered. the indicator predominantly used in the literature (wei+) present a value of 0.05; however, the ewei (0.19) indicates that the tuscany region is very close to the threshold of moderate scarcity (0.2) for an average hydrological year. the numerator of the wei+ pressure indicator on water resources compares two quantities of water (withdrawals and discharges) of different quality. as quality is a factor affecting the potential use of water, our results confirm that a correction is necessary, as proposed by guan and hubacek (2008) and replicated in this study. a significant difference is observed when disaggregating pressure indicators for groundwater and surface waters. for surface waters, the proposed indicator has a value of 0.381, significantly higher than the corresponding value of the wei+ indicator. this means that when figure 7. sensitivity analysis of ewei indicator. mean hydrological balance vs. extreme years. source: own elaborations. 215an input-output hydro-economic model to assess the economic pressure on water resources bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 technical and institutional constraints are considered in determining the feasible supply, surface water resources in tuscany show a situation of almost severe scarcity (threshold 0.4). the denominator of the standard wei+ indicator contributes to an underestimation of pressures. to account for variations in climate, this study estimates the ewei for the driest and wettest hydrology within a 40-year period (1971-2010). the results show that tuscany, in case dry hydrology, experiences moderate scarcity (0.30) on average but with huge differences between groundwater (0.07) and surface water (1.16) resources. this suggests that the region’s most significant water management problems, when incorporating water quality requirements and technical and institutional constraints, concern the surface water component of the resource. regarding the limitations and assumptions of the proposed model, the following key elements should be highlighted. first, natural variability also applies within the same year. the annual average values of the hydrological balance components completely conceal different situations within each year in terms of natural and feasible water supply. an annual sustainable average pressure could imply critical situations during periods of the year when the natural water supply is lower. second, it has been assumed that agriculture extracts a certain amount of water for each euro of production directly from soil moisture. however, this assumption is only valid in years with average or aboveaverage hydrology. in the case of dry years, agriculture extracts more from groundwater and surface waters (mainly for irrigation), increasing pressure on these resources. third, both the economy and the hydrological system also exhibit a geographical variability. the distribution of water intakes for irrigation clearly shows that pressures on water resources depend on the location of productive activities and the distribution of water resources in the regional territory. critical local situations could be compatible with a sustainable global balance between extended demand and feasible water supply at the regional level. finally, water resource exploitation indicators, both in the standard version (wei+) and in the extended version proposed in this study (ewei), assume a perfect substitutability between groundwater and surface waters in the economic use. this is not necessarily the case, especially at the regional level, where there are strong geographical constraints on the movement of water resources. for this reason, even considering an average hydrology, tuscany could be exposed to critical situations also at the regional level. 7. conclusions the article proposes a multisectoral and environmentally extended input-output model that represents in detail the links between the economy and the hydrological system. water flows are mapped between economic activities and different components of the hydrological system, considering withdrawals, discharges, and the water requirements necessary to maintain the qualitative balance of the hydrological system (extended demand). a classification by extracting and demanding sectors is used to allocate pressures on water resources considering the both direct and indirect impacts through the purchase and the sale of intermediate inputs. to assess the water balance, an extended water exploitation indicator (ewei) is proposed that considers a correction of the natural supply based on environmental, technical and institutional restrictions. by empirically testing the model in the italian region of tuscany, our results show significant changes in the structure of sectoral pressures when considering the more comprehensive approach proposed. on average, the hydrological system of tuscany is capable of supplying the water needed by the regional economy for medium hydrological conditions. however, the region could present moderate scarcity problems for dry years and serious scarcity problems in the case of surface waters. the developed model can support an in-depth analysis of the water footprint of a regional economy, for example, to map pressures on water resources from specific industries to specific water bodies, and support decisions in water management both at the national and regional level. the identified limitations suggest the direction for further refinement of the model. the interannual and intra-anual variability of the hydrological balance must be modelled. this extension of the model could allow not only to associate a measure of its potential variability with the average results, but also to simulate the impact of climate change scenarios. furthermore, it is necessary to endogenously model the change in the composition of water sources used by agriculture, an activity that in dry years uses a greater amount of groundwater and surface water to make up for the lack of soil moisture. finally, the decomposition of the model at the subregional level could allow an evaluation of the geographical distribution of impacts on water resources and the possible existence of unsustainable local situations also within a sustainable global regional scenario. 216 bio-based and applied economics 13(2): 203-217, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14957 benedetto rocchi, mauro viccaro, gino sturla acknowledgements the paper 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change in iran mohammad mehdi farsi aliabadi*, behzad fakari sardehaie dating common commodity price and inflation shocks with alternative approaches roberto esposti an input-output hydro-economic model to assess the economic pressure on water resources benedetto rocchi1,*, mauro viccaro2,3, gino sturla1 bio-based and applied economics bae bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 copyright: © 2023 a.r. hurtado, j. berbel. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: a.r. hurtado, j. berbel (2023). learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy. bio-based and applied economics 12(2): 141-164. doi: 10.36253/bae-13504 received: august 08, 2022 accepted: may 08, 2023 published: august 05, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini. orcid arh: 0000-0001-8739-1275 jb: 0000-0001-6483-4483 learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy antonio r. hurtado1,2,*, julio berbel2 1 technological corporation of andalusia (cta), seville, spain 2 university of córdoba, córdoba, spain *corresponding author. e-mail: es2rohuc@uco.es abstract. the transition from a linear bio-based economy to a sustainable circular bioeconomy depends not only on the skills that the different actors of the innovation system can find, develop, and exploit internally, but also on the efficiency with which they can access external sources of knowledge and skills related to technologies and markets. in this scenario, understanding the dynamics of learning and knowledge accumulation acquires greater importance due to the bioeconomy’s position at the confluence of several technological areas. therefore, for this study, we apply qualitative system dynamics modelling methods to the analysis of andalusia’s circular bioeconomy, obtaining important insights into its complexity due to the existence of non-linear processes, multiple feedback loops, and time delays. the models thus generated led to the identification of 20 key intervention points where targeted actions by governments and other actors could help overcome the pervasiveness of information asymmetry in the sector. keywords: bioeconomy, system dynamics, knowledge, innovation systems, government. jel codes: o1, o2, o3. 1. introduction a common feature observed in government strategies for the development of a bio-based economy is the belief that a strong innovation system will play a key role in the realization of the sector’s potential. in this context, the dynamics of learning and knowledge accumulation is critical and presents a key challenge as the bioeconomy is largely composed of companies with persistently low levels of digitalization (bacco et al., 2019) and struggling to develop effective business models (reim et al., 2019). for over 30 years, the innovation systems approach (lundvall, 1992; nelson, 1992) has provided an important theoretical framework to explain the complex interactions that take place between the different participants of the innovation process as well as the basis for policymaking in the fields of science, technolhttp://creativecommons.org/licenses/by/4.0/legalcode 142 bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 antonio r. hurtado, julio berbel ogy, innovation, and economic development. this concept has enjoyed vast popularity for the many advantages that it offers over the traditional linear models developed in the previous four decades. however, the inner dynamics of innovation systems remain somewhat unexplored, largely because innovation studies have often pursued a linear thinking approach while the innovation process is known to follow non-linear paths and involve feedback loops across all the stages. on the other hand, while a significant share of publications and government strategies consider the bioeconomy as being intrinsically sustainable (global green growth institute, 2020; motola et al., 2018), various experts have expressed concerns that a linear businessas-usual approach to the bioeconomy can have negative impacts if the principles of a circular economy are not followed (bosch et al., 2015; oecd, 2014; pfau et al., 2014; philp, 2018; reim et al., 2019). in response to these discussions, the term “circular bioeconomy” was created, and some attempts have been made to define the concept but clear guidance for bioeconomy practitioners is still needed (stegmann et al., 2020). a circular economy aims to maintain the value of products, materials, and resources as much as possible while minimising the generation of waste (european commission, 2015), thus requiring interactions across several domains and the involvement of multiple players. however, despite this complexity, most of the analyses conducted until very recently have addressed the processes and components of innovation systems for the development of a circular bioeconomy in isolation. this is changing through the application of system dynamics modelling tools. the system dynamics modelling approach was created during the late 1950s and early 1960s to understand the non-linear behaviour of complex systems and build models that capture their dynamic nature over time (forrester, 1961; meadows, 2009; sterman, 2000). it provides powerful tools to examine cause-and-effect relationships, feedback mechanisms, non-linear effects, time delays and, accordingly, high complexity. applications of system dynamics are increasingly found in a wide range of areas, including manufacturing, construction, infrastructure, software development, healthcare, population studies, waste management, water resources management, ecological and economic systems, and environmental management, among many others (andersson et al., 2002; biroscak et al., 2014; elsawah et al., 2017; guo et al., 2001; hakim et al., 2016; hsieh and chou, 2018; ketzer et al., 2020; kim and andersen, 2012; lai et al., 2017; layani et al., 2021; linnéusson, 2009; magalhães et al., 2018; mahato and ogunlana, 2011; oriama and pyka, 2021; papachristos, 2019; phan et al., 2021; pitoyo et al., 2018; sahin et al., 2017, 2018; soydan and oner, 2012; stave, 2010; stave and kopainsky, 2015; walz et al., 2016; zhou and liu, 2015). this approach has started to find application also in the study of sectoral innovation systems and processes (allas, 2014; allena-ozolina and bazbauers, 2017; aparicio et al., 2016; bergek et al., 2008; candido et al., 2017; grobbelaar, 2005, 2006; maldonado, 2012; milling, 2002; moizer and towler, 2007; rodríguez and navarro chávez, 2011; sixt et al., 2018; suprun, 2018; suprun et al., 2018, 2019; uriona et al., 2012; uriona and grobbelaar, 2016; walrave and raven, 2016; walz et al., 2016), but a recent review of the literature (uriona and grobbelaar, 2019) found that the contribution of the system dynamics approach to research on innovation systems has been limited and that, despite the high value offered by these tools, system dynamics modelling has not yet had the expected scientific impact in this domain. similarly, while there is a growing understanding that the application of linear approaches to analyze the complex mechanisms and interactions that occur in the development of the bioeconomy are often insufficient to get a good grasp of the dynamics governing this transition, systems thinking methods have only recently started to be applied in the study of these pathways (bennich et al., 2018a, 2018b; blumberga et al., 2018; stark et al., 2022). work in this field is just beginning and, as a result, there is a myriad of areas where important research gaps exist. thus, for example, despite the broad recognition that the bioeconomy is a knowledgeintensive sector that depends largely on public policies and programs, the systems thinking approach has found virtually no application to date in the literature about learning, knowledge, and the role of government in the transition to a circular bio-based economy (see methodology, below). against this backdrop, our study seeks to increase the understanding of how the dynamics of innovation systems influence the development of the circular bioeconomy, exploring how knowledge and learning influence the performance of these processes, and identifying points where interventions could enhance the strengths and overcome the weaknesses to promote growth in this sector of the economy. we focus our analysis on the andalusian bioeconomy because it is a key component of the region’s economy, generating an annual turnover of about 29 billion euros and employing around 300.000 people (approximately 9% of the total) (institute of statistics and cartography of andalusia, 2022). the significance of this sector led the regional government of andalusia to release a circular bioeconomy strategy in 2018 and become one of the first regions in spain to 143learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 acknowledge the opportunities that it offers for sustainable growth and competitiveness (regional government of andalusia, 2018). to achieve our objective, we address the following questions: 1) what are the underlying causal structures and feedback mechanisms that interact dynamically in andalusia’s bioeconomy system to shape the transition to a circular bio-based economy in the region? 2) what potential learning and knowledge-related points exist in the system where targeted interventions could have significant impact? 3) what priority actions could be implemented at the identified intervention points that would have the highest probability of positive impact? 4) from a systems thinking perspective, what is the role of government in the transition to a circular bio-based economy? 2. methodology we apply qualitative systems modelling methods (meadows, 2009; sterman, 2000) to analyze andalusia’s circular bioeconomy and to conduct a qualitative assessment of key learningand knowledge-related intervention points to develop this sector. system dynamics models are well-suited for the representation of this type of system as they allow us to analyze complex situations, applying a comprehensive view of the whole and at the same time examining the causal relationships among each of its parts. furthermore, they provide a valuable tool to build theory around behaviours observed within a system and assess the potential impact that management and policy actions could have on it. in this study we use causal loop diagrams (clds) as they are an easy and powerful tool used in system dynamics modelling to provide a visual representation of the elements of a system, their interdependency relationships, and the feedback processes that exist between them. a cld comprises a set of variables that are connected by arrows that are assigned either a positive (+) or negative (-) sign, according to how a dependent variable is affected when an independent variable changes. the connected variables, in turn, can form positive and negative feedback loops, which are at the heart of system dynamics. these loops are positive or “reinforcing” (r) when a change in a variable circulates along the loop in a way that it reinforces the initial variation, generating growth or acceleration and having a destabilizing effect. and they are negative or “balancing” (b) when a change in a variable circulates along the loop in a way that counteracts the initial variation, acting as a stabilizing force. a feedback loop is deemed to have a reinforcing effect when all the relationships are positive or if it contains an even number of negative links, and it has a balancing effect if it contains an odd number of negative links. lastly, the existence of lags in the cause-effect relationships between variables is another key aspect of system dynamics and implies that the effects of a change in a variable become evident not immediately, but after some time. a time delay is indicated in a cld by a perpendicular double line marked in the arrow where it takes place. our methodology comprised four steps, as illustrated in figure 1 and described below. 2.1. literature review the first step consisted of a comprehensive review of the literature related to the application of systems thinking to the study of the development of bio-based sectors and the transition to the bioeconomy, with the objective of identifying the factors that influence performance in these processes and detecting research gaps. for this purpose, we applied an approach based on the preferred reporting items for systematic reviews and meta-analyses (prisma) method (figure 2), using different combinations of the keywords “system dynamics”, “systems thinking”, “bioeconomy”, “bio-based economy”, “transition”, “innovation”, and “innovation system” within article titles, keywords, and abstracts in the scopus database. after several iterations, the searches that yielded a manageable number of relevant results (under 500) were “bio-based economy” and “transition” (148 results), “system dynamics” and “innovation system” (116 results), “bioeconomy” and “transition” and “factors” (65 results), “systems thinking” and “innovation system” (60 results), “bioeconomy transition” (39 results), “system dynamics” and “bioeconomy” (16 results), “systems thinking” and “bioeconomy” (9 results), “bioeconomy” and “transition” and “variables” (8 results), “transition to the bioeconomy” (8 results), “system dynamics” and “bioeconomy” and “transition” (3 results), “transition to the bio-based economy” (2 results), “system dynamics” and “bio-based economy” (2 results), and “systems thinking” and “innovation system” and “clds” (1 result). the duplicates were then discarded, a qualitative screening of the remaining articles was performed through a review of the abstracts followed by a full text review, and a total of 30 were retained due to their relevance for our study (allas, 2014; allena-ozolina and bazbauers, 2017; barisa et al., 2015; bautista et al., 2019; bennich et al., 144 bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 antonio r. hurtado, julio berbel 2018a, 2018b; blumberga et al., 2018; bröring et al., 2020; candido et al., 2017; cavicchi, 2020; chitawo et al., 2018; galanakis, 2006; gottinger et al., 2020; hakim et al., 2016; jin et al., 2019; layani et al., 2021; maldonado, 2012; milling, 2012; oriama and pyka, 2021; raven and walrave, 2020; runge et al., 2017; samara et al., 2012; saryazdi and poursarrajian, 2021; sixt et al., 2018; stark et al., 2022; stern et al., 2015; suprun, 2018; suprun et al., 2019; uriona et al., 2012; uriona and grobbelaar, 2019). furthermore, a search of the scopus database with the keywords “system dynamics” and “bioeconomy” and “knowledge” was conducted and yielded merely two results, of which only one was relevant to our study but was focused on the health sector (oriama and pyka, 2021). th is fi nding revealed an important research gap and led us to the decision of focusing the second part of our study on the identifi cation of learningand knowledge-related leverage points. concurrently, a review of the literature on the andalusian bioeconomy and innovation system was conducted applying the same approach and the keywords “andalusia”, “bioeconomy”, “bio-based economy” and “innovation system”, to gain a perspective of the regional context. a total of fi ve documents were retained aft er expanding the quest to google scholar and the google search engine as the scopus database did not yield any relevant result (agency for innovation and development of andalusia, 2022; ministry of agriculture, fisheries and rural development of andalusia, n.d.; regional government of andalusia, n.d.; vázquez and cohard, 2014; vázquez, 2017). 2.2. preparatory analysis several definitions of circular bioeconomy were found in the literature due to the vast variety of sectors and activities that make up the bio-based economy sector (bugge et al., 2016; giampietro, 2019). th erefore, for the purpose of this study, we decided to focus on the andalusian circular bioeconomy strategy 2030 (acbs) (regional government of andalusia, 2018) and thus adopt the scope therein used to defi ne the circular bioeconomy, i.e., the primary and agro-industrial production of food for human consumption are not included. food products are considered a resource for the circular bioeconomy only if they are deemed unsuitable for human consumption due to non-compliance with regulations or loss of quality during their processing. th e acbs document comprises four strategic lines and four cross-cutting lines of programmes (figure 3). it was developed by the government of andalusia over the course of nearly two years, in a process coordinated by the regional ministry of agriculture, fisheries and rural development that included bilateral meetings with figure 1. research methodology fl owchart. 145learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 various government agencies, the organization of an andalusian circular bioeconomy forum, and consultations with 53 experts from the private sector, universities, and research organizations. for this reason, it was deemed to contain all the relevant variables and infl uencing factors required for at least the initial stages of our analysis. once the scope was defi ned, the strategic lines of the acbs were set as the boundaries of the system for this study and a fi rst group of variables was selected from each one of them, using the 30 articles retained in our literature review as a guiding reference. th ese were subsequently submitted to further refi ning based on the andalusia-related literature and the thematic connections among them was analyzed to further defi ne the boundaries of the system. 2.3. development of clds and refi nement of the models in the next step, the variables selected during the preparatory analysis were used to design a primary conceptual model for each of the four strategic lines, using causal loop diagrams (clds) prepared with vensim ple soft ware (ventana systems, 2022), and links identifi ed in models developed previously for the bio-based sector and other sectoral innovation systems (allas, 2014; bennich et al., 2018a, 2018b; blumberga et al., 2018; bröring et al., 2020; candido et al., 2017; galanakis, 2006; maldonado, 2012; milling, 2012; oriama and pyka, 2021; raven and walrave, 2020; samara et al., 2012; saryazdi and poursarrajian, 2021; suprun, 2018; suprun et al., 2019; uriona et al., 2012; uriona and grobbelaar, 2019). subsequently, taking the primary clds as the starting point, the models were improved iteratively to portray the relationships among the key variables and factors formulated in the andalusian strategy, providing a visual understanding of the causal relationships and the feedback loops that shape the reinforcing and balancing forces between the various components of the system. along this process, several secondary or redundant elements were gradually eliminated to obtain a simple but comprehensive representation of the system with the lowest possible number of elements (sterman, 2000). th e resulting clds were subsequently merged into a series of integrated clds, to provide an overall view of the system. 2.4. identification of intervention points and targeted actions lastly, the strategic lines and programmes of the acbs were revisited for a full text analysis of its 17 prescribed measures to identify specifi c actions related to learning and knowledge that are known for their eff ectiveness in industrial development and that could be implemented to facilitate the transition to a circular biobased economy. th e actions thus identifi ed were subsequently used to fi nd the appropriate intervention points where their implementation could have meaningful impact (meadows, 2009). th roughout the entire process, the literature fi ndings were complemented by the authors’ combined experience of over three decades in the analysis, design, and implementation of public policies, strategies and programs for science, technology, innovation, and economic development in both government and the academic sector in north america and the european union. in addition, preliminary versions of the clds and their intervention points were presented for feedback and comments at the ispim innovation conference 2021 (berlin, germany, june 20-23, 2021) and 30 experts at the xiii agrifood economics congress of the spanish association of agri-food economy (cartagena, spain, september 1-3, 2021), which allowed the collection of further contributions that enriched the results. 3. results th e data gathered from the literature review and analysis of the acbs led to four conceptual clds porfigure 2. overview of the document identifi cation, selection and inclusion process followed in this study (adapted from page et al., 2021). 146 bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 antonio r. hurtado, julio berbel traying the causal relationships among 33 key variables identifi ed from the four strategic lines of the acbs: (1) sustainable generation and availability of biomass resources, (2) infrastructure and logistics management of biomass resources, (3) processing of biomass resources and capacity of industrial production of bioproducts and bioenergy, and (4) development of markets for bioproducts and bioenergy. subsequently, aft er merging them into a series of integrated clds, a total of 20 key learningand knowledge-related interventions points were identifi ed, along with 52 targeted actions that could have meaningful impact on the system. 3.1. sustainable generation and availability of biomass resources the first strategic line formulated in the acbs relates to increasing the availability of biomass resources produced sustainably for their subsequent conversion into bioproducts and bioenergy. during the analysis of the document, several reinforcing feedback loops were identifi ed that would lead to higher “availability of sustainable biomass resources” (capitalized, figure 4). as the main proponent and champion of the regional strategy, government investment in technology and training for sustainable biomass production (“public investment in technology and training i”) drives the development of “skills in sustainable biomass production” as well as the “deployment of sustainable technologies for biomass production” both directly (reinforcing feedback loops r1a, r1b, and r1c) and through the enhancement of private investment in these activities (reinforcing feedback loops r2a, r2b, and r2c). all these together trigger an increase in the “share of land and water used for sustainable biomass production”, which in turn leads to a higher “volume of biomass produced sustainably”. th e higher “availability of sustainable biomass resources” thus achieved consequently induces an increase in both the “use of sustainable biomass in bioindustrial processes” and the “use of biomass figure 3. strategic and instrumental framework of the andalusian circular bioeconomy strategy 2030 (adapted from: regional government of andalusia, 2018). 147learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 transformation and conversion technologies” (reinforcing feedback loops r1d and r2d), resulting in a higher “volume of industrial production of bioproducts and bioenergy” and increased “income from bioproducts and bioenergy” (as well as from related services). lastly, the economic, environmental, and social benefits that accrue from these activities close the loop by prompting more public and private investment in technology and training for sustainable biomass production. the private sector also can promote these cycles (reinforcing feedback loops r3a, r3b, r3c, and r3d), but the impact on the system is lower if it does it alone. three balancing feedback loops were identified. water and land of good quality are often limited resources, but this condition is exacerbated in andalusia due to the region’s geographic characteristics. therefore, the more resources are used to produce biomass for bioproducts and bioenergy, the lower the potential to further expand sustainable biomass production for these purposes (delayed balancing feedback b1). likewise, the more biomass is used in bioindustrial processes, the lower the potential to further expand these activities (delayed balancing feedback b2). and as new entrants are attracted to the region’s circular bioeconomy industry due to increasing income from bioproducts and bioenergy, higher competition for resources would eventually become a limiting factor (delayed balancing feedback b3). 3.2. infrastructure and logistic management of biomass resources the second strategic line described in the acbs relates to optimizing the management and distribution of biomass resources from the points where they are generated to the bioindustries that use them as inputs. several reinforcing feedback loops were identified during share of land and water used for sustainable biomass production skills in sustainable biomass production deployment of sustainable technologies for biomass production volume of biomass produced sustainably public investment in technology and training i private investment in technology and training i + + + + ++ + + availability of sustainable biomass resources use of sustainable biomass in bioindustrial processes + b2 income from bioproducts and bioenergy + + r1a r1b r3a r2a b1+ use of biomass transformation and conversion technologies volume of industrial production of bioproducts and bioenergy + + + + market attractiveness new entrants + b3 + r3c + political commitment + r2b r1d r1c r2c r3b r2d r3d figure 4. dynamics suggested to govern the availability of sustainable biomass resources. 148 bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 antonio r. hurtado, julio berbel the analysis that would lead to higher “economic viability of biomass collection and storage” (capitalized, figure 5). while private investment alone can have a positive impact on the “logistics and transportation infrastructure” available to the sector (reinforcing feedback loop r6a), public investment is a key factor for the success of this strategic line of the acbs through several ways, which include direct contributions to “publicprivate collaboration” initiatives (reinforcing feedback loop r4) as well as financial support and market signals that encourage private investment (reinforcing feedback loops r5a, r5b, and r6b). “public investment in infrastructure and logistics” plays an important role not only on the construction and maintenance of infrastructure and facilities but is seen as essential to improve knowledge about the volume and location of the biomass resources – through the development of inventories of the biomass available for bioindustrial processes in the region. on the other hand, as agricultural and agro-industrial residues and by-products are typically spread across vast areas (for example, biomass from pruning of olive crops is spread throughout 2.5 million hectares of plantations across the entire region of andalusia), “public-private collaboration” is deemed necessary to improve the “economic viability of biomass collection and storage” by “upgrading the logistics and transportation infrastructure” and increasing the “proximity to biomass resources”, which in turn would contribute to boost the “profitability of biomass utilization”. lastly, as the first results of this collaborative work become evident, the model would be replicated across the region through more public and private investment in logistics and transportation infrastructure. and as in the previous model, the attractiveness of the growing market would attract new entrants which, over time, would have a negative impact on returns because of higher competition for the limited resources available (delayed balancing feedback b4). public investment in infrastructure and logistics private investment in infrastructure and logistics public-private collaboration upgrading of logistics and transportation infrastructure proximity to biomass resources economic viability of biomass collection and storage profitability of biomass utilization + + + + + + r4 r6b + + + r5a market attractiveness new entrants + b4 + political commitment r5b r6a + figure 5. causal loop diagram displaying the dynamics hypothesized to govern the economic viability of biomass collection, transportation, and storage. 149learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 3.3. processing of biomass resources and capacity of industrial production of bioproducts and bioenergy the third strategic line defined in the acbs comprises actions to support the development of a bio-based industry that optimizes the use of biomass resources in andalusia, especially through integrated biorefineries. as in the previous cases, several reinforcing feedback loops were identified (figure 6). according to this model, investments in technology and training by the public and private sector would lead to the development of “new models of use of biomass resources and industrial co2”. as synergies are achieved, new companies would be created, new biorefineries would be built, and existing facilities would be reconverted to increase the “industrial use of transformation and conversion technologies” and expand the “volume of industrial production of bioproducts and bioenergy”. as in the previous models, private investment alone can generate positive results (reinforcing feedback loop r9), but public sector involvement can potentiate the system through direct investments, financial support, and positive market signals (reinforcing feedback loops r7 and r8). the income thus generated from bioproducts and bioenergy (as well as from related services) would produce economic, environmental, and social benefits that would in turn encourage more public and private “investment in technology and training” for biomass transformation and conversion. however, as in the previous models, once the biomass processing sector reaches a critical mass, its attractiveness would encourage the entry of new players up to a point where the competition for resources would become a limiting factor (delayed balancing feedback b3). 3.4. development of markets for bioproducts and bioenergy the fourth and last strategic line formulated in the acbs consists of actions aimed at consolidating the markets that already exist in andalusia while promoting and supporting the development of national and international value chains for bioproducts and bioenergy. as described in figure 7, several reinforcing feedback loops public investment in technology and training ii private investment in technology and training ii new models of use of biomass resources and industrial co2 creation of new companies, construction of new biorefineries and reconversion of existing facilities income from bioproducts and bioenergy volume of industrial production of bioproducts and bioenergy use of biomass transformation and conversion technologies + + + + r7 r9 r8 market attractiveness new entrants b3 political commitment + + + + + + + + figure 6. dynamics suggested to govern the development of industrial capacity to process biomass resources and produce bioproducts and bioenergy. 150 bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 antonio r. hurtado, julio berbel were identified that would lead to higher sales of bioproducts, bioenergy, and related bioeconomy services. once again, synergistic “investments in market preparedness” by the public and private sector would lead to enhancements in the collective “market knowledge” about opportunities for bioproducts, bioenergy, and bioeconomy-related services. with this valuable information at hand, both government planning and corporate business plans would be upgraded to support “market development activities” aimed at increasing “market awareness about the benefits of using bioproducts and bioenergy” and triggering the “cultural change in regional, national and international markets” that is needed to switch consumption towards products and energy obtained from sustainable biomass resources. lastly, higher sales of bioproducts, bioenergy, and related bioeconomy services to local, regional, and national customers (reinforcing feedback loops r10a, r11a, and r12a), as well as in international markets (reinforcing feedback loops r10b, r11b, and r12b) would close the loop by increasing the “profitability of circular bioeconomy companies”, which would in turn stimulate more public and private investments in market development activities. as in the previous strategic lines, the finite availability of biomass resources would eventually become a limiting factor as the attractiveness of the new markets leads to the entry of new market players (delayed balancing feedback b5). 3.5. combined causal loop diagrams and key intervention points while the individual clds in figures 4 to 7 depict the dynamics of different dimensions of the system, some interlinkages were identified. figures 8 and 9 display combined clds, highlighting the proposed crossdimension interlinkages, as well as a total of 20 key learningand knowledge-related intervention points identified from the acbs and the literature where targeted actions could have meaningful impact. these were subsequently merged into an integrated cld (figure 10) for the acbs. public investment in market preparedness private investment in market preparedness market knowledge market development activities + ++ + market awareness about benefits of using bioproducts and bioenergy + cultural change in regional, national and international markets exports of bioproducts, bioenergy and bioeconomy services regional/national sales of bioproducts, bioenergy and bioeconomy services + + profitability of circular bioeconomy companies + + + + r10a r12a r11a + market attractiveness new entrants + + b5 political commitment + r10b r11b r12b figure 7. causal loop diagram displaying the dynamics hypothesized to govern the development of markets for bioproducts and bioenergy. 151learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 3.5.1. sustainable generation of biomass resources; development of industrial capacity to process biomass resources and generate bioproducts and bioenergy an analysis of both the acbs and figures 4 to 7 reveals that the strategic lines 1 and 3 of the document have some common variables related to technology and training and, as a result, their respective clds merge as shown in figure 8. a subsequent evaluation led to the identification of 9 key learningand knowledge-related points susceptible of intervention. of these, four (ip1, ip2, ip3, and ip4) are related to public and private investment in technology and training, the development of skills, and the deployment of sustainable technologies, all of which together would lead to an increase of the volume of biomass produced sustainably. subsequently, activities designed to enhance the sharing of knowledge regarding the use of biomass in bioindustrial processes (ip5) would lead to an increase in the volume of industrial production of bioproducts and bioenergy, which is concurrently potentiated by actions designed to promote learning about new models of use of biomass resources and industrial co2 (ip6). lastly, as new companies are created, new biorefineries are built, and existing facilities are reconverted, the andalusian circular bioeconomy would benefit significantly from greater access to the growing stock of knowledge about technologies share of land and water used for sustainable biomass production skills in sustainable biomass production deployment of sustainable technologies for biomass production volume of biomass produced sustainably public investment in technology and training private investment in technology and training + + + + ++ + + availability of sustainable biomass resources use of sustainable biomass in bioindustrial processes + b2 income from bioproducts and bioenergy + + r1a r1b r3a r2a b1 + use of biomass transformation and conversion technologies volume of industrial production of bioproducts and bioenergy + + + + market attractiveness new entrants r1c + political commitment new models of use of biomass resources and industrial co2 creation of new companies, construction of new biorefineries, and reconversion of existing facilities + b3 + + + + + r3d r2b + + r9 r7 r8 r3b r2c r3c r1d r2d ip3 ip4 ip2 ip5 ip6 ip8 ip1 ip9 ip7 figure 8. dynamics suggested to govern the sustainable generation of biomass resources as well as the development of industrial capacity to process biomass resources and obtain bioproducts and bioenergy. 152 bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 antonio r. hurtado, julio berbel for biomass transformation and conversion, the various types of bioproducts and bioenergy generated from these sources, and the rising income from these activities (ip7, ip8, and ip9). above all of these, given that the circular bioeconomy is an emerging sector that will require ongoing government support for some time, political commitment to public investments in technology and training is key for its success. table 1 contains a list of targeted learningand knowledge-related actions identified from the acbs and the authors’ analysis that could be implemented with meaningful impact at these intervention points. 3.5.2. economic viability of biomass collection and storage; development of markets for bioproducts and bioenergy as for the strategic lines 2 and 4 of the acbs, negligible overlap was observed among them and with the others, as shown in figure 9. of the 11 learningand knowledge-related intervention points identified for these two strategic lines of the acbs, four (ip10 to ip13) are related to increasing the economic viability of biomass collection and storage, whereas six (ip14 to ip19) are linked to the commercialization of bioproducts, bioenergy, and bioeconomy services. interestingly, while a significant part of the activities contained in figure 8 involve science, technology, and innovation (sti)-based learning processes, actions aimed at building structures and relationships to enhance the doing, using, and interacting (dui) mode of learning (jensen et al., 2007; thomä, 2017) are likely to have higher impact in these strategic lines. lastly, one intervention point (ip20) was shared by both strategic lines and as noted in the previous figure, political commitment plays a pivotal role due to the emerging nature of the circular bioeconomy sector. table 2 contains a list of targeted learningand knowledge-related targeted actions identified from the acbs and the literature that could be implemented with meaningful impact at these intervention points. 3.6. integrated causal loop diagram figure 10 displays an integrated cld, highlighting the proposed cross-dimension interlinkages, as well as the central role played by government in the development of this emerging sector of the economy. 4. discussion the application of the system dynamics approach to the analysis of andalusia’s circular bioeconomy provides important insights into the complexity of the system due to the existence of non-linear processes, multiple feedback loops, and time delays. likewise, the models generated in this study provide tools for a better understanding of the potential impact that learningand knowledge-related interventions by governments and other actors could have on different parts of the development of the circular bioeconomy. 4.1. learning, knowledge, and innovation a strong innovation ecosystem based on a balance between science and technology push and market and social pull will play a leading role in the realization of the potential presented by the circular bioeconomy and, in realizing this potential, it will be fundamental to keep in mind that an important aspect of the innovation process is its heterogeneity across sectors, industries, and regions. in this regard, assessing and measuring the underlying processes of learning and knowledge accumulation for innovation has been an ongoing challenge for decades (abramovitz, 1956; dosi, 1982; romer, 1990; solow, 1957). this has had important repercussions for technology, innovation, and economic development policymaking, which has strongly relied on a linear r&dbased innovation model. nevertheless, the literature on the topic is increasingly recognizing external knowledge sources as key elements of the innovation process (doloreux et al., 2020; fitjar and rodríguez-pose, 2013; isaksen and nilsson, 2013; jensen et al., 2007; santner, 2018) as well as the systemic nature of the innovation systems framework (uriona and grobbelaar, 2019). one of the most valuable efforts to understand how the process of learning and innovating can differ in firms is the work of jensen et al. (2007), that distinguishes two fundamental forms: the science, technology, and innovation (sti) mode, which focuses on the production and use of codified scientific and technical knowledge; and the doing, using, and interacting (dui) mode, which focuses on experience-based know-how and informal interactive learning. since the proposal of this taxonomy, several studies have explored the extent to which these two modes of innovation can be observed in different economic sectors, providing insights into their links with firm innovativeness on the one hand (e.g. apanasovich et al., 2016; aslesen and pettersen, 2017; doloreux et al., 2020; figueiredo and piana, 2021; gonzález-pernía et al., 2015; isaksen and karlsen, 2010; parrilli and elola, 2012; 153learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 table 1. proposed learningand knowledge-related actions to boost the sustainable generation of biomass resources and enhance the development of industrial capacity to process biomass resources and obtain bioproducts and bioenergy. intervention point variable proposed action related acbs actions ip1 public investment in technology and training · promote knowledge among public sector staff and policy makers regarding the strengths, needs, gaps, opportunities, barriers, and risks faced by the bioeconomy to improve the quality of public investment decisions in technology and training. (1.1.1) (1.1.2) (1.2.1) (1.2.2) (2.1.1) (2.1.2) (3.1.1) (3.1.2) (b.1.1) (b.1.3) (b.3.1) (d.1.2) (d.2.1) · foster knowledge among public sector staff and policy makers about alternative ways of collaborative financing as well as about the role that governments can play as early adopters of innovative solutions through instruments such as public procurement of innovation (ppi). (b.1.2) (c.1.2) ip2 private investment in technology and training · promote knowledge among the private sector regarding the strengths, needs, gaps, opportunities, barriers, and risks faced by the bioeconomy to improve the quality of private investment decisions in technology and training. (1.1.1) (1.1.2) (1.2.1) (1.2.2) (2.1.1) (2.1.2) (3.1.1) (3.1.2) (b.1.1) (b.1.3) (b.3.1) (d.1.2) (d.2.1) · promote knowledge among the private sector about financial instruments available for the circular bioeconomy. (c.1.1) (c.1.2) · upgrade management skills in the private sector to increase the absorptive capacity of companies to receive public funding and improve the quality of their investment decisions in technology and training. (1.2.1) (3.1.2) (3.2.1) (b.3.1) · foster initiatives to disseminate knowledge among local, national, and foreign investors about the region’s competitive advantage in the bioeconomy and specific investment opportunities in r&d, technology, and training. (1.1.1) (2.1.1) (3.2.1) (3.2.2) (c.2.1) (c.2.2) ip3 skills in sustainable biomass production · implement mechanisms and tools to increase the interaction between all levels of the education system and the actors involved in the generation of biomass to promote technical advice, education and training in matters related to sustainable practices. adjust the offer to the needs of the market. (1.2.1) (1.2.2) (b.1.2) (b.1.4) (b.1.5) (b.2.1) (b.3.1) (b.3.2) · develop guidelines and case studies to disseminate knowledge about practices that have shown good results for the sustainability of the generation of biomass resources. (1.2.1) (1.2.2) (b.1.3) (b.1.4) ip4 deployment of sustainable technologies for biomass production · implement mechanisms and tools to increase the interaction between all levels of the education system and the actors involved in the generation of biomass to promote technical advice, education and training in matters related to sustainable technologies. adjust the offer to the needs of the market. (1.2.1) (1.2.2) (b.1.2) (b.1.4) (b.1.5) (b.2.1) (b.3.1) (b.3.2) · prepare technology surveillance reports to disseminate knowledge about current and upcoming technologies available for sustainable biomass production. (1.2.1) (1.2.2) (b.1.3) (b.1.4) · develop guidelines and case studies to disseminate knowledge about technology solutions that have shown good results for the sustainability of the generation of biomass resources. (1.2.1) (1.2.2) (b.1.3) (b.1.4) ip5 use of sustainable biomass in bioindustrial processes · develop and regularly update an inventory of biomass resources in the region to disseminate knowledge about the types and volumes available, their physical and chemical characteristics, their geographic location, and the distribution of their availability over time (seasonality). (1.1.1) · develop and regularly update a georeferenced inventory of potential users of biomass resources. (2.1.1) 154 bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 antonio r. hurtado, julio berbel santner, 2018; thomä, 2017) and with the geography of innovation on the other (fitjar and rodríguez-pose, 2013; parrilli and alcalde-heras, 2016). overall, the learningand knowledge-related actions identified in this study to accelerate andalusia’s transition from a linear bio-based economy to a sustainable circular bioeconomy support the view that an innovation system that encourages a combination of sti and dui activities will have the greatest potential of success as each company will choose the learning mode that best suits its scientific, technological, and geographic context (fitjar and rodriguez-pose, 2013; parrilli and alcalde-heras, 2016). thus, when confronted with the current scenario, the cause-effect relationships and causal cycles herein described suggest that the andalusian innovation system needs greater collaboration and coordination along and across the triple helix to support the development, commercialization, and diffusion of intervention point variable proposed action related acbs actions ip6 new models of use of biomass resources and industrial co2 · foster the incorporation of technical and business skills into the knowledge of people who work in bioindustries to improve the sustainability of companies and increase the addition of value to the region’s biomass resources. (3.1.1) (3.1.2) (b.1.2) (b.1.3) (b.1.4) (b.1.5) · promote collaboration and the generation of synergies within the bioeconomy sector and with other industries to advance new models of use of biomass resource flows and industrial sources of co2. (3.1.3) (b.1.4) (b.1.5) (b.2.1) (b.2.2) · establish and support business clusters, incubators, accelerators, and mentoring programs to promote technology and knowledge transfer among the different innovation system actors interested in the bioeconomy. (b.1.2) (b.1.5) (d.1.1) · develop guidelines and case studies based on regional, national, and international success stories to disseminate knowledge about new models of use of biomass resources and industrial co2. (3.1.2) (b.1.3) (b.1.4) ip7 creation of new companies, construction of new biorefineries, and reconversion of existing facilities · leverage the knowledge and experience acquired in sectors such as the biodiesel industry to promote the development of higher value-adding activities and the conversion of existing facilities to biorefineries. (3.2.2) · develop guidelines and case studies based on regional, national, and international success stories to disseminate knowledge about the planning and implementation of bioindustries and biorefineries. (3.1.2) (b.1.3) (b.1.4) ip8 use of biomass transformation and conversion technologies · implement mechanisms and tools to increase the interaction between all levels of the education system and the bio-based industry to promote technical advice, education and training in matters related to the use of technologies for sustainable biomass transformation and conversion. adjust the offer to the needs of the market. (3.1.1) (3.1.2) (b.1.2) (b.1.4) (b.1.5) (b.2.1) (b.3.1) (b.3.2) · foster collaboration and the establishment of regional, national, and international alliances and multi-actor platforms to facilitate the transfer of knowledge and the adoption of sustainable technologies generated by the r&d and innovation system. (3.1.3) (b.1.4) (b.1.5) (b.2.1) (b.2.2) (d.1.1) · promote the establishment of technology sandboxes and pilot plants for process development and scale-up of sustainable bio-based products and processes. (b.1.2) (authors’ analysis) · prepare technology surveillance reports to disseminate knowledge about current and upcoming technologies for sustainable biomass transformation and conversion. (3.1.1) (b.1.3) (b.1.4) · develop guidelines and case studies to disseminate knowledge about technology solutions that have shown good results for the sustainable transformation and conversion of biomass. (3.1.1) (3.1.2) (b.1.3) (b.1.4) ip9 market attractiveness · develop a portfolio of successful projects, technological innovations, business initiatives, and business models, for each of the links in the value chains associated with the bioeconomy to expand knowledge about the opportunities presented by the bio-based economy across various stakeholder groups. (3.1.1) (3.1.2) (b.1.3) (b.1.4) 155learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 innovative solutions, all of which are necessary for the development of critical mass in the region’s circular bioeconomy. 4.2. the role of government one of the main challenges faced by transition regions such as andalusia in their attempt to close the gap with developed regions is to find the right balance between technological innovation and technological imitation as well as the choice of technologies to be developed and imitated (atkinson and stiglitz, 1969; basu and weil, 1998; lin, 2003). for less developed and transition regions, fulfilling the promise of faster economic growth through technological imitation is not an easy task. studies on technology diffusion have shown that the process of closing the technology gap requires a significant amount of effort and institution building, and that in this process less developed and transition regions generally face larger requirements for capital and other advanced factors than developed regions. thus, in addition to the licensing fees, regions that adopt technologies from leading regions need to incur expenses in the retraining of human capital, organizational restructuring, and so on (stoneman, 1983). furthermore, as the characteristics of new technologies are strongly influenced by the business environments in which they are developed, large differences between regions can represent serious barriers for their transfer. among the factors that have been suggested to play a crucial role in a region’s ability to import technology are its political, commercial, industrial, and financial institutions, as well as national characteristics such as market size and the relative supply of factors of production (abramovitz, 1986; caselli and coleman, 2001; chen and wang, 2021; keller, 2004). technological change is therefore the combined result of innovation and learning activities within domestic organizations, and of the interaction among them and with their environment. it thus becomes obvious that firms, with their different combinations of intrinsic competencies and business strategies, are key players in this process (fagerberg, 1994). as the costs of adoption of new technologies tend to exceed the benefits obtained, these are often not adopted as soon as they become public (stoneman, 1983). the process of technological diffusion then occurs gradually, with a few firms adopting the technology first and the others following in a process that can take more than a decade or even stop before its completion (detragiache, 1998). remarkably, as the followers have the possibility of copying the adaptive efforts of the pioneers and, at the same time, have access to more skilled labor trained by the early adopters, the costs of adoption tend to decrease as more firms import the technology. based on the premise that early adopters create positive externalities for the followers, detragiache (1998) proposed that, once technology adoption starts, less developed regions tend to converge to the level of developed regions as more firms adopt the new technology. accordingly, different degrees of economic convergence among regions are explained by differences in the rate of diffusion of figure 9. causal loop diagram displaying the dynamics hypothesized to govern the economic viability of biomass collection and storage as well as development of markets for bioproducts and bioenergy. market attractiveness new entrants public investment in market preparedness private investment in market preparedness market knowledge market development activities market awareness about benefits of using bioproducts and bioenergy cultural change in regional, national and international markets exports of bioproducts, bioenergy and bioeconomy services regional/national sales of bioproducts, bioenergy and bioeconomy services profitability of circular bioeconomy companies b4 r10a r12a r11a r12b + + + ++ + + + + + + + public investment in infrastructure and logistics private investment in infrastructure and logistics public-private collaboration upgrading of logistics and transportation infrastructure proximity to biomass resources economic viability of biomass collection and storage profitability of biomass utilization + + + + + + r4 r6b r5b + + + r5a political commitment + + + + + + + -b5 r11b r10b r6a ip12 ip13 ip20 ip11 ip16 ip17 ip19 ip10 ip14 + ip15 ip18 156 bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 antonio r. hurtado, julio berbel table 2. proposed learningand knowledge-related actions to potentiate the economic viability of biomass collection and storage and the development of markets for bioproducts and bioenergy. intervention point variable proposed actions related acbs actions ip10 public investment in infrastructure and logistics · improve knowledge about biomass resources and industrial sources of co2 among public sector staff and policy makers in terms of those factors that determine their logistics, as well as about the infrastructure that is required to ensure the supply of biomass to operators, users, and bioindustries. (1.1.1) (1.1.2) (2.1.1) (2.1.2) (2.2.1) (2.2.2) (b.1.3) (b.3.1) (d.1.2) (d.2.1) · promote knowledge among public sector staff and policy makers about potential users of biomass resources and infrastructure gaps in the region. (2.1.1) · foster knowledge among public sector staff and policy makers about alternative ways of collaborative financing and public-private partnerships (ppp). (c.1.2) ip11 private investment in infrastructure and logistics · upgrade technical and management skills in the private sector to increase the absorptive capacity of companies to receive public funding and improve the quality of their investment decisions in infrastructure and logistics for biomass collection, transportation, pretreatment, and storage. (2.1.2) (3.1.2) (b.3.1) · promote knowledge among the private sector about financial instruments available for the circular bioeconomy. (c.1.1) (c.1.2) · promote knowledge within the private sector about alternative ways of collaborative financing and public-private partnerships (ppp). (c.1.2) · foster initiatives to disseminate knowledge among local, national, and foreign investors about the region’s competitive advantage in the bioeconomy and specific investment opportunities in infrastructure and logistics for biomass collection, transportation, pretreatment, and storage. (1.1.1) (2.1.1) (2.2.1) (c.2.1) (c.2.2) ip12 public-private collaboration · develop and regularly update an inventory of biomass resources in the region to improve public and private sector knowledge about the types and volumes available, their physical and chemical characteristics, their geographic location, and the distribution of their availability over time (seasonality). (1.1.1) · improve public and private sector knowledge about both the infrastructure needed and available for the collection, transportation, pretreatment, and storage of the different types of biomass resources. (2.1.2) · develop and regularly update a georeferenced inventory of potential users of biomass resources. (2.1.1) · develop guidelines and case studies to improve public and private sector knowledge regarding best practices in public-private collaboration and about current and upcoming solutions for collection, storage, pretreatment, and transportation of biomass resources. (2.1.2) (b.1.3) (b.1.4) ip13 proximity to biomass resources · promote the transfer of knowledge and the adoption of technologies associated with bioenergy, bioindustries, and small-scale biorefineries established around new local value chains. (1.1.1) (3.1.1) (3.1.2) (3.1.3) (3.2.1) (3.2.2) (b.1.4) (b.1.5) ip14 public investment in market preparedness · improve knowledge among public sector staff and policy makers regarding the needs and gaps faced by the bio-based sector in matters related to market preparedness. (b.3.1) (b.3.2) ip15 private investment in market preparedness · promote knowledge among the private sector about financial instruments available for the circular bioeconomy. (c.1.1) (c.1.2) · upgrade management skills in the private sector to increase the absorptive capacity of companies to receive public funding and improve the quality of their investment decisions in market preparedness. (b.3.1) (b.3.2) 157learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 intervention point variable proposed actions related acbs actions ip16 market knowledge · carry out and disseminate market studies and feasibility analyses to determine supply and demand, prices, and distribution channels for bioproducts, bioenergy and services linked to the circular bioeconomy. (4.1.1) · prepare, regularly update, and disseminate prospective studies on consumption trends and new uses of bioproducts and bioenergy, as well as analyses of areas and/or sectors where there is potential for the introduction of bio-based products in their value chains. (4.1.2) · foster interaction and knowledge exchange among bio-based companies and with actors of other industry sectors. facilitate communication and cooperation among the different agents, especially with the nonrenewable sector, to promote the identification of potential synergies. (3.1.3) (4.1.2) (b.1.5) (b.2.1) (b.2.2) ip17 market development activities · foster knowledge exchange among bio-based companies and with actors of other industry sectors to identify potential synergies and promote cooperation. (b.1.5) (b.2.1) (b.2.2) · promote knowledge among public sector staff and policy makers about the role that governments can play as early adopters of innovative solutions through instruments such as public procurement of innovation (ppi). (b.1.2) (c.1.2) · upgrade international business skills in the private sector to increase the quality of lead generation and conversion in foreign markets. (b.3.1) (b.3.2) (authors’ analysis) · monitor the evolution of the different links that make up the value chains of bioproducts and bioenergy (supply of raw materials, production, and commercialization) and promote knowledge exchange across the bio-based sector to improve efficiencies and ensure its ability to meet market demand in the medium and long term. (3.1.3) (b.1.5) (b.2.1) (b.2.2) (d.1.1) (d.1.2) (d.3.2) ip18 market awareness about benefits of using bioproducts and bioenergy · foster knowledge exchange between the bio-based industry and actors of other sectors of the economy and civil society (such as consumer associations) to promote the differentiating features and added value of bioproducts and bioenergy as well as the positive externalities of the bioeconomy. (4.2.1) (4.2.2) (a.1.1) (a.1.2) · promote the creation of certification and traceability protocols for bioproducts and bioenergy as tools for knowledge transfer across the bioeconomy and to actors of other economic sectors and civil society. (4.2.1) (a.1.2) ip19 cultural change in regional, national and international markets · improve knowledge and business training of public service staff and policy makers in matters related to the bioeconomy, its positive externalities, and the use of assessment tools and instruments such as life cycle analysis and the measurement of the carbon and water footprint of entire value chains. (4.2.2) (a.1.1) (a.1.2) (b.3.1) · improve knowledge and skills of bio-based companies in matters of full cycle sustainability and the efficient use of all the resources destined for the manufacture of their bioproducts or bioenergy, or for the services that they provide. promote the use of assessment tools and instruments such as life cycle analysis and the measurement of the carbon and water footprint of their entire value chains. (4.2.2) (b.3.1) · upgrade the absorptive capacity of the regional economy and society through the introduction of the bioeconomy in the contents of compulsory primary and secondary education, post-compulsory (high school and intermediate vocational training) and higher education (university, continuing education, and advanced vocational training). (b.3.1) (b.3.2) · implement behavioral science (nudging) tools to promote learning and improve decisions about the sustainable use of bio-based products and energy. (authors’ analysis) ip20 market attractiveness · develop a portfolio of successful projects, initiatives, and business models to expand knowledge about the benefits of investing in infrastructure for collection, transportation, pretreatment, and storage of biomass resources. (b.1.3) · develop and disseminate a portfolio of guidelines and case studies about successful initiatives and business models for the introduction of bioproducts, bioenergy, and bioeconomy-related services in regional, national, and international markets. (b.1.3) 158 bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 antonio r. hurtado, julio berbel the imported technologies, whereas convergence failure occurs when the adoption costs are too high or when technology diffusion stops before it is completed. furthermore, this model can successfully account for the fact that economies of transition regions are generally dualistic, i.e., small, traditional firms normally operate along with modern enterprises. in this scenario, it has been argued that when a technology is transferred from a developed region to less developed regions, the discrepancies between the labor skills lead to marked differences in factor productivity and output per capita and, for this reason, governments of less developed and transition regions need to increase their investments in human capital formation (acemoglu and zilibotti, 2001). in this regard, our findings show that the role of government in supporting learning and knowledge-related processes is key for the development of the circular bioeconomy, which can be explained by the pervasiveness of information asymmetries in the sector, its intensity in knowledge and innovation, and its position in the conflub5 profitability of circular bioeconomy companies market development activities market awareness about benefits of using bioproducts and bioenergy public investment in market preparedness exports of bioproducts, bioenergy and bioeconomy services cultural change in regional, national and international markets market knowledge private investment in market preparedness ++ + + + + + + + + regional/national sales of bioproducts, bioenergy and bioeconomy services + + r10ar11a r12a r10b r12b r11b market attractiveness 2 new entrants 2 + political commitment + public investment in infrastructure and logistics private investment in infrastructure and logistics public-private collaboration upgrading of logistics and transportation infrastructure proximity to biomass resources economic viability of biomass collection and storage profitability of biomass utilization r4 r6b r6a r5a b4 + + + + + + + + + + + + share of land and water used for sustainable biomass production skills in sustainable biomass production deployment of sustainable technologies for biomass production volume of biomass produced sustainably public investment in technology and training private investment in technology and training + + + + ++ + + availability of sustainable biomass resources use of sustainable biomass in bioindustrial processes + b2 income from bioproducts and bioenergy + + r1a r1b r3a r2a b1 + use of biomass transformation and conversion technologies volume of industrial production of bioproducts and bioenergy + + + + market attractiveness new entrants r1c + new models of use of biomass resources and industrial co2 creation of new companies, construction of new biorefineries, and reconversion of existing facilities + b3 + + + r1d r2b + + r9 r7 + + r3b r2c r3c r2d r3d r8 r5b ip3 ip4 ip5 ip6 ip8 ip7 ip9 ip12 ip13 ip11 ip16 ip17 ip15 + ip1 ip2 ip10 ip14 ip18 ip19ip20 figure 10. integrated causal loop diagram displaying the main cause-effect relationships and causal cycles involved in the four strategic lines comprised in the andalusian circular bioeconomy strategy. 159learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy bio-based and applied economics 12(2): 141-164, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13504 ence of several technological areas. this is supported by a recent causal mapping analysis of political structures in bioeconomic transitions based on the case of renewable energy political lobbying in six countries (palmer et al., 2022), and is in agreement with the new structural economics framework postulated by lin (2003, 2010), whereby the positive impact of government in this kind of scenario is higher when it seeks to help companies to overcome information and coordination costs about new industries, markets, and technologies; coordinate investment between companies and industries; and internalize the externalities linked to information by compensating pioneering companies through tools such as guarantees and fiscal incentives. furthermore, from the innovation policy perspective, a combination of supply-driven policies aimed at the commercialization of research results will be required to foster the sti mode of learning, along with demand-driven policies aimed at supporting the dui mode of learning for the development of products or services to specific markets (isaksen and nilsson, 2013). lastly, due to the emerging nature of the concept, the circular bioeconomy sector is likely to require ongoing government support for some time, and for this reason political commitment to public investments in technology and training will be key to its success. in andalusia, both the acbs and the bill for the circular economy law of andalusia (leca) that has recently been sent by the government council to the regional parliament for deliberation are the two most important initiatives currently underway in this direction. 5. conclusions and future research the results outlined in this paper provide an initial understanding of the dynamics of andalusia’s bioeconomy and the identification of intervention points where targeted actions could be undertaken to accelerate the transition from a linear bio-based economy to a sustainable circular bioeconomy. the models generated in this study provide tools for a better understanding of the potential impact that interventions by governments and other actors could have on the development of the circular bioeconomy. overall, when confronted with the current scenario, the preliminary cause-effect relationships and causal cycles herein described suggest that the andalusian innovation system needs greater collaboration and coordination along and across the triple helix to support the development, commercialization, and diffusion of innovative solutions, all of which are necessary for the development of critical mass in this emerging sector of the economy. while the clds herein described provide structural insight into the system, an avenue for future research involves the development of quantitative models using stock and flow diagrams to evaluate the sensitivity of the intervention points, using historical data as a reference. references abramovitz, m. 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(2015). the method and index of sustainability assessment of infrastructure projects based on system dynamics in china. journal of industrial engineering and management, 8(3): 10021019. http://dx.doi.org/10.3926/jiem.1496 cap, farm to fork and green deal: policy coherence, governance, and future challenges annalisa zezza key policy objectives for european agricultural policies: some reflections on policy coherence and governance issues silvia coderoni towards a new generation of (agri-) food policies gianluca brunori earliness, phenological phases and yield-temperature relationships: evidence from durum wheat in italy marco tappi1,*, federica carucci2, anna gagliardi1, giuseppe gatta1, marcella michela giuliani1, fabio gaetano santeramo1 does the presence of inner areas matter for the registration of new geographical indications? evidence from italy francesco pagliacci, francesco fasano* learning, knowledge, and the role of government: a qualitative system dynamics analysis of andalusia’s circular bioeconomy antonio r. hurtado1,2,*, julio berbel2 impacts of heat waves on agricultural workers: an analysis of adaptation measures authors: andrea miriana ferroa*, meri raggib, davide viaggia. stefano targettia a department of agricultural and food sciences (distal), alma mater studiorum, university of bologna, viale fannin (50),40127 bologna. b department of statistical science “p. fortunati”, alma mater studiorum, university of bologna, via delle belle arti 41,40126 bologna. * corresponding author: stefano.targetti@unibo.it this article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the version of record. please cite this article as: ferro am., raggi m., viaggi d. targetti s. (2025 impacts of heat waves on agricultural workers: an analysis of adaptation measures, bio-based and applied economics, just accepted. doi: 10.36253/bae-16905 abstract this study evaluates the effectiveness of different farm-level adaptation measures aimed at mitigating the adverse impacts of heat waves on labour productivity. despite the increasing frequency of heat waves, existing literature on occupational heat stress primarily relies on modelled estimates. to address this gap, exploratory interviews and structured questionnaires were employed to identify key challenges posed by heat waves, as well as the perceived benefits and limitations of different adaptation strategies. data were collected from nine farms located in emilia-romagna (northeast italy), all of which were characterized by a long-term commitment to improving working conditions. the analytic hierarchy process was used to evaluate the perceived effectiveness of adaptation measures according to three criteria: acceptability, flexibility, and timeliness. findings indicate that, in the absence of adaptation strategies, productivity losses may reach up to 30%. among the measures mailto:stefano.targetti@unibo.it assessed, shifting work hours was identified as the most effective strategy. the study underscores the need for structured thermal risk assessment protocols and provides recommendations to inform sustainable and worker-centered adaptation policies in the agricultural sector. keywords heat waves, adaptation measures, agricultural workers, productivity loss, analytic hierarchy process (ahp) jel codes: j81, o13 1. introduction worldwide, climate change scenarios point to an intensification of extreme heat events across all regions with growing implications for outdoor workers (ipcc ar6 wgi, 2021). according to the world meteorological organisation (wmo), a heat wave is defined as a period of at least six consecutive days during which the maximum daily temperature exceeds the 90th percentile based on the climatological reference period (1981-2010 or if available 1991-2020). studies, such as de sario et al. (2023), have shown that heat waves have significant economic impacts on society, including increasing healthcare costs (martínez-solanas et al., 2018), and costs for the social security system, that must compensate workers for heat-related injuries (ma et al., 2019). in addition to these social costs, several studies have reported economic costs linked to a reduced worker productivity resulting from high temperature exposure in working environments (morabito et al., 2021; kjellstrom et al., 2009). in sectors such as agriculture, where most activities, are carried out outdoors, heat waves significantly increase the risk of work-related injuries and work productivity reductions (di blasi et al., 2023). thus, in recent years, the necessity to introduce acceptable preventive measures able to reduce the risk of outdoor work-related injuries and excessive heat exposure has become pressing. for instance, in recent years, countries such as italy have introduced regional regulations to improve the working conditions during heat waves. specifically, these regulations limit the working time between 12:30 p.m. and 4:00 p.m. for specific sectors such as agricultural and construction during the summer months and in case of heat waves. in 2021, these measures were implemented in three southern italian regions (puglia, calabria and basilicata). in 2024, similar regulations were adopted by 15 of the 21 italian regions. economic effects linked to heat waves can be classified as direct and indirect impacts. workers’ productivity loss (pl) involves a direct output loss for farms. this can entail relevant indirect effects because of the interdependencies along the value chain, and at a macroeconomic level on household consumption, national output, etc. (zhao et al., 2021). these aspects related to climate changes are often neglected, but the magnitude of their potential impact on workers’ health and productivity underlines the need for case studies providing information on pl in different contexts and activities, and for different economic sectors (kjellstrom et al., 2009). indeed, current knowledge is scarce about the economy-wide effects of heat waves on the productivity of diversified economic sectors that range from outdoor to indoor activities and feature a heterogeneous structure and production orientation, such as agriculture (day et al., 2019). one approach to quantifying heat-related productivity loss is the use of the wet bulb globe temperature (wbgt) index and iso 7243 standards. kjellstrom et al. (2018) developed risk functions based on these metrics to estimate reductions in work capacity under different levels of heat stress. these functions assume that workers self-regulate their workload to avoid severe health consequences, such as heat stroke. accordingly, these models can estimate productivity loss (pl) as a function of heat exposure and work intensity. individuals and firms can reduce the heat-stress implementing specific adaptation strategies able to cope with heat conditions (kjellstrom, holmer, et al., 2009). a range of adaptation options are available and these offer different solutions that can fit to different working conditions and contexts cost-effectively. in general, behavioural adaptations such as the anticipation of working hours to avoid heat peaks, and passive adaptation options such as frequent drink breaks, together with technical equipment are indicated as valuable options for outdoor workers (day et al., 2019). however, the identification of the adaptation measures strictly depends on local contexts and the type of activities. this warns against a generic identification of adaptation solutions and highlights a particular need for heterogeneous economic sectors such as agriculture, for an evaluation carried out at the farm level and able to consider the complexity of adaptation decision-making (day et al., 2019). the effectiveness of the available adaptation measures in reducing the impact of heat waves has a specific relevance to evaluate their soundness as tools to be included in regulations targeting the adaptation of working environments to climate risks. for instance, building on the work of kjellstrom et al. (2018), morabito et al. (2021) applied different heat risk functions to estimate the effectiveness of simple adaptation measures. in this study, wbgt and typical working hours data were combined to compare pl and economic costs related to farming activities in an italian region with and without the adoption of adaptation measures. using the modelled functions, morabito and colleagues estimated a significant effect of the adaptation measures, and thus highlighted a potential positive impact for firms and workers engaged in intensive activities during heat waves, such as agricultural workers. while model-based approaches provide valuable insights, evaluating the perceived effectiveness of adaptation measures and understanding workers’ and managers’ perceptions of climate risks are equally important. risk perception is a critical factor influencing adaptive behavior (madhuri and sharma, 2020), and a range of psychological, economic, and contextual factors may shape the willingness to adopt preventive strategies (d’alberto et al., 2024). despite a robust body of literature focused on modeled projections of productivity loss under heat stress (e.g., orlov et al., 2019; kjellstrom et al., 2009; morabito et al., 2021, empirical data on the perception of heat waves from both the individuals directly affected and those responsible for managing and organizing outdoor working activities during these events are currently limited. moreover, research on the operational feasibility and perceived efficacy of various adaptation strategies is still limited. in particular, studies investigating the views of land managers on the implementation and effectiveness of such measures are, to our knowledge, lacking. yet, understanding these perspectives is important, as land managers are ultimately responsible for adopting and enforcing adaptation strategies at the farm level. the lack of empirical data on the perceived utility and impact of adaptation measures impedes the development of evidence-based policies that ensure worker safety and productivity during heat events. without such insights, efforts to identify and promote tailored, context-specific adaptation measures risk being ineffective or unsustainable (final scientific report of the worklimate project, https://www.worklimate.it/). this study aims to address this research gap by investigating how farm managers in emilia-romagna perceive and evaluate various adaptation measures designed to mitigate the impacts of heat waves on outdoor agricultural workers. the work contribution to the existing literature is twofold: (1) it provides an in-depth analysis of the challenges posed by heat waves within a specific agricultural context; and (2) it evaluates the perceived effectiveness of adaptation strategies based on empirical data collected from nine farms in the region. specifically, the research explores land managers’ assessments of the impacts of heat waves on different types of workers, their evaluation of the advantages and disadvantages of various adaptation strategies, and their perceptions of these strategies’ effectiveness in reducing productivity losses. 2. methodology 2.1 analytical approach in this analysis, the analytic hierarchy process (ahp) method was employed (saaty, 2013). ahp was developed to support decision-making by structuring complex problem into hierarchical framework. ahp organises decision problems into a hierarchy of factors that can be easily interpreted by an expert, thereby facilitating complex judgments and enabling a structured evaluation of multiple alternatives across different criteria. originally developed by mathematician thomas saaty in the 1970s (saaty, 1977), ahp is a widely used multi-criteria decision analysis (mcda) technique. the aim of the ahp is to break down a problem into smaller, more manageable components through pairwise comparisons. at the core of ahp lies the pairwise comparison approach, which evaluate decision elements in pairs, allowing for a more nuanced assessment of their relative importance. the hierarchy itself takes the form of a tree structure with successive levels representing broader decision criteria, specific sub-criteria, and eventually alternatives. these elements are compared pairwise at each level of the hierarchy, from the lowest level (typically comprising the alternatives or specific actions under evaluation) up to the highest-level criterion (saaty, 1987). in this study, the objective was to identify the most effective adaptation measures to mitigate the impacts of heat waves. the ahp process involves assigning numerical values to each pairwise comparisons using a 1-to-9 scale, where 1 indicates equal importance and or relevance of the two elements on the control element and 9 represents extreme importance of one element over another (saaty, 2008) (tab. 1). these comparisons are recorded in pairwise comparison matrices that reflect the relationships between criteria and adaptation measures. after completing the pairwise comparisons, the relative weights (or priority) for each criterion are calculated using mathematical techniques, particularly the eigenvalue methods. this priority is defined as a vector representing the influence (or weight) of each single element within that level of the hierarchy (duke & aull-hyde, 2002). the process entails calculating the normalized values for each matrix to derive weights that reflect the relative importance of each criterion, by means of the eigenvector method and a process of averaging over the normalised columns of the matrix (meade & sarkis, 1999; saaty, 2008). for further details on the calculation of the priority by means of different matrix multiplication methods see in particular (villanueva et al., 2015). subsequent consistency checks are then carried out to ensure the reliability of comparisons. the consistency ratio (cr) is utilised to assess consistency, by comparing the consistency index of the judgments to a random index. a cr value below 0.1 is generally considered acceptable, indicating a reasonable level of consistency in the decisions made during pairwise comparisons. finally, the calculated weights are aggregated to determine the overall priorities of the adaptation measures (liu et al., 2025). however, one of the main limitations of this methodology is that the quality of results heavily depends on the expertise and understanding of the respondents. biases may be introduced if participants are not sufficiently knowledgeable about the subject matter (villanueva et al., 2015). furthermore, ahp does not rely on assumptions regarding known probability distributions (duke & aull-hyde, 2002). as a result, the method is not statistical in nature and typically involves a relatively small number of expert respondents, due to the in-depth knowledge required to complete the questionnaires accurately (which often reduces the number of respondents). table 1saaty’s fundamental scale for pairwise comparison judgments. intensity of importance definition explanation 1 equal importance two activities contribute equally to the objective. 2 weak or slight experience and judgment slightly favor one activity over another. 3 moderate importance experience and judgment slightly favor one activity over another. 4 moderate plus experience and judgment strongly favor one activity over another. 5 strong importance experience and judgment strongly favor one activity over another. 6 strong plus an activity is favored very strongly over another. 7 very strong or demonstrated importance an activity is favored very strongly over another. 8 very, very strong the evidence favoring one activity over another is of the highest possible order of affirmation. 9 extreme importance the evidence favoring one activity over another is of the highest possible order of affirmation. source: ( saaty, 2013). 2.2 identification of target participants as illustrated in figure 1, the survey was organized in three main phases. the first step involved identifying the target group of participants. for our study, the target participants were farm managers of large, well-structured farms with a significant number of workers. given the exploratory nature of the research, we considered appropriate to begin by involving farm managers operating within the same geographical area (emilia romagna). this choice allowed for greater homogeneity and consistency in the data collected, thereby minimising the influence of contextual and structural difference across regiones. in light of the limited number of studies investigating the impact of heatwaves on farm management and the scarcity of empirical data on this issue, the survey specifically targeted farms with a demonstrated commitment to workers’ welfare. indeed, the selected farm managers had already been implementing strategies and measures to mitigate the effects of heat waves on their employees over several years. such an analysis enabled an in-depth analysis of the current implementation status of various adaptation measures. the first part of the survey focused on the cooperative agricole braccianti (cabs) in the province of ravenna. later, the survey was extended to include other large farms in the region with the help of trade associations. collectively, these cooperatives manage approximately 11,500 hectares of utilised agricultural area (uaa). their production is primarily oriented towards fruit trees, vegetables and cereal crops, although some also operate in the livestock and nursery sectors. these cooperatives were of particular interest due to their organisational structure: workers are also cooperative members and are involved in the decision-making process. being directly exposed to the working conditions, the workermembers have developed a deeper awareness of the impacts of heat waves, including suggestions and proposals to more effective prevention and protection measures. figure 1-a flow diagram of the research process. 2.3 identification of adaptation measures and data collection we conducted a literature review to identify the adaptation measures able to reduce the impacts of heat waves on agricultural workers. four adaptation measures were identified through a literature review (day et al., 2019; habibi et al., 2023; kjellstrom, et al., 2009; marinaccio et al., 2022; morabito et al., 2021; spector et al., 2019; zhao et al., 2021): • shifting/anticipating work hours; • setting up shaded areas; • increasing the frequency of work breaks with facilitation of the availability of water; • special equipment (ventilated jackets, clothing in technical, breathable fabric). these measures represent different management options for the organisation of outdoor labour that can improve workers' resilience to heat stress and aim to maintain a safe work environment. therefore, the adoption of the measures also allows to enhance significantly worker performance, productivity, and company profits (habibi et al., 2023). the second phase involved the data collection (figure 1). we started with exploratory interviews with the cab farm managers, aimed at collecting preliminary information on the phenomenon of heat waves and identifying pros and cons related to the potential implementation of adaptation measures at farm level and productivity-related issues or operational problems observed during the last heat wave events. five exploratory interviews were conducted of the seven cab’s in the province of ravenna. each interview lasted on average 40 minutes and was conducted either online or in person. the interview was divided into two main sections: the first section focused on framing the problem and identifying the work problems already encountered during the last heatwaves (summer 20222023). the second section assessed the advantages and disadvantages and the respondents' perceptions of the four selected adaptation measures. then, based on the information gathered in the exploratory interviews, a questionnaire was designed to collect quantitative data on the adaptation measures and assess their effectiveness in reducing the impact of heatwaves on agricultural workers. the questionnaire included 34 questions, and it was implemented on the qualtricsxm (2024) (provo, utah, usa, url https://www.qualtrics.com) platform. the questionnaire was structured in three main sections: the first section was about general information about the respondents and the farms, such as their role, main crops, farm size, the main symptoms observed in workers during the heat waves of the summers of 2022-2023, the activities most affected by the heat waves, and the adaptation measures adopted in these circumstances. the second part of the questionnaire dealt with the assessment of the effectiveness of adaptation measures according to the ahp (saaty, 1987). in order to assess the effectiveness of the identified heat wave protection measures, we included four questions to collect data using the ahp method https://www.qualtrics.com/ (fig. 2). three of these questions focused on specific criteria for assessing the effectiveness of each adaptation measure, namely: 1. worker acceptability, i.e. the expected willingness of outdoor agricultural workers to adopt the measures. 2. timeliness of implementation, i.e. the time needed to implement the measure after a heat wave forecast. 3. flexibility of application, i.e. the possibility to adopt the measure in different types of farms. in addition, a further question concerned the comparison between the selected criteria. these criteria were derived from those identified by day et al. (2019) and subsequently adapted based on the information gathered during the exploratory interviews. this set of questions included four matrices comparing adaptation measures according to each of the criteria listed above, with each pair of principals rated using the ahp 1-9 scale. as an example, we report two of the submitted questions for the two assessment levels: (q11) with respect to the criterion acceptability by workers, which of the following pairs of factors do you think is the preferred adaptation measure by workers? (shifting of working hours vs setting up shaded areas; shifting of working hours vs increased frequency of breaks... etc.) for each pair, express your preference on a scale of 1 to 9, where 1 indicates that the measures are equally preferred and 9 indicates that one measure is extremely more accepted than the other. (q13) which criterion, among the following pairs of criteria, do you think is most important for an effective reduction of workers' productivity loss? (acceptability by the workers vs timeliness of implementation; acceptability by the worker vs flexibility of application…etc) for each pair, express your preference on a scale of 1 to 9, where 1 indicates that the criteria are of equal importance and 9 indicates that one criterion is extremely more important than the other for reducing productivity loss. the final part of the survey focused on estimating the productivity loss of agricultural workers. in the land manager survey, respondents were asked to report any adaptation measures implemented to cope with heat wave events. respondents were then asked to estimate productivity loss in the following ranges in the absence of adaptation measures: 0–10%, 10–20%, 20–30%, and more than 30%. specifically, the questionnaire asked respondents to provide an estimate of the percentage of daily productivity loss estimated for general workers and specialised workers during heat waves and in the absence of adaptation measures. the questionnaire was distributed in june and july, and it was initially distributed to the cabs and to the main regional farmers' associations, who then distributed it to the agricultural land managers in emilia-romagna. the distribution of the questionnaires to emilia-romagna was motivated by the need to obtain data that were as homogeneous as possible. this strategy helped to reduce the variability linked to geographical and structural factors, ensuring greater consistency in the results and facilitating comparisons between the different farm realities analysed. figure 2.hierarchical decision-making structure for selecting the most effective heat wave adaptation measures. 2.4 data processing and analysis the survey collected a total of 15 responses from land managers in emilia-romagna. only 9 questionnaires were completed in all parts and included in the analysis. for the ahp questions, we normalised the comparison matrix data and determined the priorities for each level of the hierarchy. elaborations were performed with r 4.4.1 (2024) (rstudio: integrated development for r. rstudio, pbc, boston, ma url http://www.rstudio.com/). the priority calculation was used to compare the relative contribution of the elements at each level of the hierarchy with an element in the adjacent higher level. we conducted a synthesis of the priorities to calculate a composite weight for each alternative, based on the preferences guided by the comparison matrix. after calculating the composite weight, the relative priority of each adaptation measure was obtained, and a final ranking was developed to identify the best adaptation alternative through the calculation of the average of all the overall priorities that were calculated for each questionnaire (veisi et al., 2022). finally, the consistency ratio (cr) was calculated to verify the consistency of the judgments expressed by the respondents. the cr is suggested as a valid indicator of the cognitive stress of respondents, i.e. a high cr indicates a high complexity level of the questions and thus requires higher efforts in the judgement process. 3. results 3.1 results of the exploratory interviews we conducted five interviews with cab farm managers to get an initial overview of the impacts of the heatwaves, the benefits and challenges of the adaptation measures selected. it emerged that all farms observed issues related to heat waves during the summers of 2022-2023, particularly in orchards during the harvesting of peaches and apricots: “the issue of heat waves is primarily felt in the orchard”. one farm also encountered difficulties in nurseries and in organic http://www.rstudio.com/ fields: “heat waves are also felt in open fields across the 1,200 hectares of organic farmland, which requires frequent tilling to eliminate weeds”. additionally, all managers reported that in the orchards, due to heat waves combined with high levels of humidity, the presence of nets and rows of trees themselves limit air circulation, workers experience signs of fatigue, tiredness, and breathing problems. the farm managers also stated that the productivity of workers decreased during heat waves: “during the hottest hours, fewer kilograms are harvested and in general during heat waves, yields decrease because workers are slower”, “from eleven o'clock onwards, employee performance drops by about 30 per cent”. the interviewed farm managers stated that, to avoid extreme heat during the central hours of the day, they adjust the work schedule. in fact, field workers (who are mostly women) finish their workday by noon and, depending on daylight, they start working about an hour earlier, around 5 a.m. the shifting/anticipating of working hours was implemented by 5 out of 5 farms. for greater clarity, we provide the interviewee's response as follows: “during the summer, it became necessary to start the workday an hour to an hour and a half earlier (depending on daylight) or to leave an hour earlier (by 12 p.m.)”. the anticipation of the working time also contributed to mitigate operational problems related to harvesting and storing the product. indeed, excessive heat also affects the quality of the harvest, especially if it remains under the sun and is not delivered immediately to storage or processing centres. regarding the adaptation measures already implemented by the interviewed farms, in addition to adjusting work hours, five out of five farms also indicated the use of shaded areas represented by break rooms, warehouses, or shelters near the orchards. other shaded areas included seminatural elements like hedges and groves established for instance with the support of agro-environmental measures in the past. the interviewees reported that most employees rely on own water supply. in the five farms however, the interviewees reported that supervisors are always supplied with drinking water as well as with a first aid kit. the availability of water supplies at the farm centre was also indicated by the five interviewed farm managers. regarding the use of special equipment, the purchase of technical clothing to promote better breathability was reported in one interview: “in the summer of 2023, the farm purchased technical fabric shirts that allow workers to work more comfortably. the request originated from the workers”. 3.2 results of the questionnaires the main characteristics of the sample are presented in table 2. the results of the questionnaire indicate that heat waves have a notable impact on farm workers' health. respondents reported significant physical symptoms among workers, including intense fatigue, excessive sweating, drops in blood pressure, muscle cramps, and dehydration. these symptoms typically occur during laborintensive tasks such as fruit and vegetable harvesting, field operations, and manual activities like weed removal. to mitigate the effects of heat waves, most farms have already implemented various adaptation measures. all respondents reported increasing the frequency of breaks and improving access to drinking water. additionally, more than half of the farms have either shifted the start time of work to earlier hours, shortened the duration of work shifts, or established shaded rest areas near the fields. fewer than a quarter of respondents reported the use of specialized equipment during heat waves. among the additional adaptation measures mentioned, several farms have introduced portable shading structures, such as umbrellas, to protect workers during fruit harvesting. table 2 characteristics of respondents. respondent type frequency % entrepreneur 2 22,22% director/manager 5 55,56% technician/foreman 2 22,22% farming system frequency* % cereal farming 7 77,78% horticultural 4 44,44% viticultural 5 55,56% fruit farming 4 44,44% zootechnical 3 33,33% other 2 22,22% symptoms observed during heat waves frequency* % tiredness 9 100,00% pressure drops 1 11,11% muscle cramps 1 11,11% intense sweating 8 88,89% activities affected by heat waves frequency* % fruit and vegetable harvesting 6 66,67% field operations 4 44,44% other (manual weeding) 3 33,33% measures against heat waves frequency* % shifting/anticipating work hours 8 88,89% setting up shaded areas 6 66,67% increasing the frequency of work breaks with facilitation of the availability of water 9 100,00% special equipment 2 22,22% other (e.g. umbrellas) 1 11,11% * each participant had the opportunity to select more than one answer, so the total number of answers may exceed the number of participants. the average of the assigned priorities allowed for the development of a ranking of the most effective adaptation measures based on the selected criteria: worker acceptability, timeliness of implementation, and flexibility of application. as shown in fig. 3, the adaptation measure with the highest ranking is the shifting or anticipation of working hours, with an average priority value of 0.5032. the second-highest ranked measure is increasing the frequency of work breaks and ensuring access to potable water, with a value of 0.3261. considerably lower priority values were assigned to the creation of shaded areas (0.0974) and the use of special equipment (0.0660). the inconsistency ratio for the group of respondents was 0.20. although this value is above the commonly accepted threshold of 0.1, it indicates that the evaluation process posed a considerable cognitive challenge for respondents, likely due to the complexity of comparing multiple criteria and adaptation options. figure 3-priorities of adaptation measures as resulted in the ahp analysis. the third part of the survey asked participants to provide an estimation of the average percentage of daily productivity loss observed among both common and specialized workers in case of heat waves. the results showed that the most relevant productivity losses occurred among common workers, with reduction rates ranging from 20 % to 30% (arithmetic mean: 0.25; interquartile range (iqr): 0.06) 0,2902 0,6835 0,5032 0,0470 0,1136 0,0974 0,2094 0,4774 0,3261 0,0272 0,1591 0,0660 p ri o ri te s 0,0000 0,1000 0,2000 0,3000 0,4000 0,5000 0,6000 0,7000 0,8000 shifting/anticipating work hours setting up shaded areas increasing the frequency of work breaks with facilitation of the availability of water special equipment or more (44 % of respondents). as for specialized workers, such as tractor drivers, most respondents (44%) reported productivity losses between 0% and 10% (arithmetic mean: 0.05; interquartile range (iqr): 0.06). 4. discussion the study, for the first time, explores the perception of land managers regarding heat wave adaptation measures that farms could easily implement to reduce the impact on productivity losses. overall, we found that common workers, especially those involved in fruit harvesting, can have productivity losses of over 30% in the absence of adaptation measures; this is confirmed by evidence in the literature, where productivity losses of this magnitude are reported (morabito et al., 2021). beside this and according to the perception of land managers, the most effective adaptation measure to reduce productivity losses is shifting/anticipating working hours. this evidence is based on the criteria of worker acceptability, timeliness of implementation, and flexibility, and it is also reported in some of the regulations focusing the reduction of heat wave impacts on workers (emilia-romagna region). common workers are the category that is most at risk during heat waves because of their exposure to outdoor activities performed in periods that can be affected by heat waves such as fruit picking. thus, this worker category is exposed to heat and a higher risk of physical stress and reduced productivity. specialised workers, such as tractor drivers, work in more favourable conditions (e.g. air-conditioned cabins) that helps to mitigate the negative effects of high temperatures and preserve work capacity. in this context, a more specific focus on the potential heat-related threats that can affect tractor drivers is necessary as the frequency of getting-on and -off tractors to e.g. operate changes/adjustments on equipment could generate abrupt changes of temperature and impact the workers’ health. we also found that the interviewed land managers believe that the shifting/anticipating of the working hours is the most effective measure to reduce productivity losses, probably because it is easy to use low cost and immediately implementable. the effectiveness of this measure is also supported by morabito et al., (2021), that showed how the adoption of such a measure reduces workers' productivity losses by up to 33% and lowers the economic costs associated with such losses. moreover, earlier working hours would have positive effects on the quality of the harvest, as it would not be exposed to the heat and delivered earlier to the harvest centres nonetheless, as also highlighted by day et al. (2019), changing working hours could impact total working hours, associated income and economic and social costs that should not be underestimated. indeed, anticipating the working schedule involves a range of benefits, but it is important to consider that this change could cause disadvantages and reorganization of workers' routines, especially for those who have long commutations to reach the working place. in addition, the measure would have a significant impact on the various costs that the farm has to bear, including organisational, operational and logistic costs. in fact, 25% of the respondents reported the need to add about one extra working day per week, to compensate for the reduction in working hours. this also involves the need of additional workers or of additional trips to storage points during the week. another challenge for the adoption of this measure is the limited availability of light in the early morning hours, which is essential for fruit picking. it is therefore essential to make workers aware of the importance of this measure and to ensure the smooth running of activities. this is possible thanks to the presence of adequate infrastructure, implemented by companies to enable optimal harvesting even in low light conditions. for example, the installation of appropriate lighting systems can support early operations at dawn, ensuring effective implementation of activities. our results show that other simple adaptation measures such as increasing the frequency of breaks and providing shaded areas are also effective in protecting workers from heat and reducing production losses according to the land managers. shade areas are understood to be groves and hedges or mobile shade structures such as canopy shelters. despite the benefits of these measures, they represent an economic cost that could be significantly higher than the anticipation of the working time because workers are paid even during breaks and because setting up shade zones requires some investments for farms. lastly, the least effective measure for managers is special equipment (ventilated jackets and clothing in technical, breathable fabric) because, beside its cost, it is expected to be hardly acceptable by workers. measures such as the use of special equipment may be rejected if they are considered inconvenient or unnecessarily complex. for this reason, whatever the measure, it is essential to involve workers in the decision-making process and to gather their feedback in order to identify the most practical and valued solutions. the limitations of this study lie in its focus on data from a limited geographic area within italy. moreover, the selection of large farm cooperatives with strong attention towards working conditions and safety allowed to collect the perception of adaptation measures and their effectiveness in farms with relevant awareness of the problem. this sampling approach may have introduced a bias linked to the farms’ greater capacity to implement adaptation strategies. consequently, the findings reflect a localized perspective that may not be representative of the views of entrepreneurs at the national level or in other parts of the world. significant differences could emerge if the research were conducted nationwide or expanded to include a broader range of italian regions and farm types. additionally, the cr of 0.2 points to some cognitive stress. this could be due to the small sample size or respondents' lack of experience with such specific questions on heat waves and adaptation measures. this might have caused difficulties in fully understanding and evaluating the question, affecting the consistency of their responses. 5. conclusions this study presents an overview of how land managers perceive the impacts of heat waves on agricultural workers and assesses the perceived effectiveness of various adaptation measures. the research is based on empirical data collected from nine farms in emilia-romagna, each demonstrating a long-standing commitment to improving working conditions and mitigating heat-related risks in agricultural operations. although the sample size limits the statistical generalizability, our results offer valuable insights to inform future policy interventions. one key recommendation emerging from the study is the potential introduction of targeted incentives to support the adoption of adaptation practices that entail additional costs for farms. such incentives could be designed to provide economic support for more resource-intensive but highly effective measures, such as installing shading infrastructure or rescheduling work to nocturnal hours. another crucial aspect relates to institutional collaboration in developing early warning systems and heat alert tools. these tools could serve both farms and regulatory bodies as a reliable reference for determining the timing and geographic location for the applicability of mandatory or recommended heat adaptation actions. the findings confirm that heat waves have a significant impact on business operations and reduce labor productivity, particularly in open-field tasks such as fruit harvesting. the selected adaptation strategies, many of which are low-cost and easy to implement, provide effective ways to reduce productivity losses and enhance worker protection. among the most effective measures identified are adjusting work schedules to earlier hours and increasing the frequency of breaks with access to potable water. these actions were recognized as widely applicable, simple to implement, and wellaccepted by both managers and workers. beyond improving worker safety and reducing the risk of heat-related accidents, certain adaptation measures can generate operational benefits. for instance, shifting working hours not only reduces exposure to peak temperatures but may also improve the quality of harvest, particularly in sectors such as fruit cultivation. a crucial aspect is the ease of implementation and high acceptance of many adaptation strategies. this enhances their feasibility and potential for widespread adoption. because these measures typically do not require complex organizational changes or significant financial resources, they represent a practical starting point for improving heat adaptation of the agricultural sector. in conclusion, the study underscores the dual benefits of adaptation strategies: they contribute both to worker well-being and to the operational efficiency of farms. by reducing the incidence of heatrelated accidents and minimizing inefficiencies due to loss of labour productivity, targeted adaptation measures can serve as a foundation for sustainable and inclusive agricultural policy under conditions of increasing climate stress. acknowledgement this research is funded by worklimate 2.0 project (bric 2022). references d'alberto, r., targetti, s., schaller, l., bartolini, f., eichhorn, t., haltia, e., harmanny, k., le gloux, f., nikolov, d., runge, t. and vergamini, d., 2024. 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(2024). participation of farmers in market value chains: a tailored antràs and chor positioning indicator. bio-based and applied economics 13(3): 245-264. doi: 10.36253/bae15464 received: november 29, 2023 accepted: april 19, 2024 published: october 16, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: francesco pagliacci, valentina raimondi, luca salvatici orcid tg: 0000-0002-7727-8049 participation of farmers in market value chains: a tailored antràs and chor positioning indicator tulia gattone department of social sciences and economics, sapienza university of rome, italy e-mail: tulia.gattone@uniroma1.it abstract. this study presents a micro-level indicator of farmers’ positioning in the market chain, based on the conceptual framework outlined by antràs and chor (2013, 2018). the indicator considers the selling location of a farming household and its crop buyers. using panel data from the world bank’s ‘living standards measurement study: integrated surveys on agriculture’ for ethiopia and nigeria, this paper applies the proposed indicator empirically and showcases its superior performance in comparison to existing alternatives at the micro-level. furthermore, by analyzing the dynamics of farmers’ food and total consumption over time and controlling for various household and production characteristics, as well as potential confounding factors, this study shows that moving towards a downstream position in the market chain has a positive impact on farmers’ food and total consumption levels. the results are validated through sensitivity analysis and robustness checks. keywords: value chains, economic development, market chain, farming households. jel-codes: q12, o12, o13, c23. introduction the discourse on the effects of farmers’ participation in global markets remains nuanced. one segment of the literature highlights that smallholder farmers’ engagement in traditional markets catalyzes pro-poor outcomes through a cycle of enhanced household income, increased consumption, greater food security, and improved nutrition (bellemare, 2012; montalbano et al., 2018). conversely, another segment postulates that market participation might not significantly benefit those unable to leverage increased market orientation’s advantages (von braun, 1995; carletto et al., 2017). market chain participation encompasses essential activities for food production delivery to consumers, including trading (kaplinsky & morris, 2001). in development scenarios, farmers often find themselves limited to lower-value activities, positioning them at the backward stages of the market value chain, which contrasts with increased employment, better jobs, resources, governance, and food security associated with downstream posihttps://doi.org/10.36253/bae-15464 https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode http://www.fupress.com/bae https://doi.org/10.36253/bae-15464 https://doi.org/10.36253/bae-15464 https://orcid.org/0000-0002-7727-8049 mailto:tulia.gattone@uniroma1.it 246 tulia gattone bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 tioning (minten et al., 2009; cattaneo & miroudot, 2013; african development bank et al., 2014; swinnen, 2014; swinnen & vandeplas, 2014). antràs and chor (2013) offer a foundational model on positioning, illustrating a dependency of downstream stages on upstream activities, yet discussions on the structuring of the most upstream sectors within value chains remain limited. this research merges insights from trade and development literature on value chain positioning, focusing on supplier positioning in global chains as per antràs and chor (2013; 2018), and the commercialization decisions of rural farmers as detailed by migose et al. (2018), minten et al. (2018), and montalbano et al. (2018). it introduces a novel downstreamness measure for rural farmers in market value chains, inspired by antràs and chor’s framework. this study tests the new positioning indicator using the lsms-isa dataset for ethiopian households, selected for its detailed commodity exchange market data, and conducts parallel testing with nigerian lsms-isa data and analyses related to food quantity and market positioning. the indicator outperforms traditional measures in empirical tests. the study examines how farmers’ market positioning affects their consumption levels. findings show that improved positioning significantly boosts farmers’ food and total consumption, supporting existing literature on agricultural commercialization’s impact, validated through extensive sensitivity and robustness checks. the paper is organized as follows: section 2 reviews the literature and theoretical framework. section 3 introduces the market positioning indicator. section 4 details crop value chain structure and methodology. section 5 describes the data and statistics. section 6 discusses the empirical strategy and results. section 7 concludes the study, summarizing key findings and implications. 2. literature review agricultural commercialization is widely regarded as a key mechanism for poverty alleviation in rural settings, underpinned by literature suggesting its positive impact on rural households’ development (von braun & kennedy, 1994; de janvry & sadoulet, 2006). this transition allows smallholder farmers to shift from subsistence farming practices to the cultivation of market-specific crops, facilitating specialization, the adoption of modern agricultural technologies, and ultimately, higher productivity (van asselt & useche, 2022). studies like those of key et al. (2000), yanagizawa (2009). and svensson & jensen (2010) have documented that market participation and positioning are affected by access costs and risk preferences, affirming the benefits of effective market positioning. however, agricultural trade may yield several effects on production constraints, land use, and environmental sustainability (minten et al., 2007), with smallholder farmers facing barriers such as low productivity, stringent standards compliance, and elevated transaction costs that limit market entry (montalbano et al., 2015). vertical market integration turns out to be critically relevant in these contexts characterized by fragmented markets, weak contract enforcement, and political instability (fackler & goodwin, 2001). the nature of the crop buyer significantly influences market positioning, with farmers navigating interactions with intermediaries, large processing firms, and state-managed markets. despite the perception of intermediaries as monopolistic rent-seekers (montalbano et al., 2018), empirical evidence suggests that farmers’ involvement in contract schemes and export chains generally yields positive outcomes for smallholders (minten et al., 2009; barrett et al., 2012; bellemare, 2012; subervie & vagneron, 2013; bellemare & novak, 2017). the interaction between global and local value chains raises questions about the impact of global market participation on local agricultural systems and food consumption. while some argue that global value chains can undermine traditional local markets (ríos guayasamín et al., 2016), others point to the competition for resources that such integration entails (feyaerts et al., 2020). the debate extends to the efficacy of local versus global value chains, with some evidence suggesting local markets may offer better performance or serve as gateways to global chains (d’souza & jolliffe, 2014; wegerif & martucci, 2019). the importance of market positioning within these distribution networks cannot be overstated, yet the lack of comprehensive data and theoretical frameworks for micro-level analysis underscores the complexity of drawing definitive conclusions (feyaerts et al., 2020). selling to immediate social circles is often seen as a strategy of last resort for farmers constrained by high transaction costs or market access issues, highlighting the challenges faced by rural farmers in developing economies (timmer, 1997; key et al., 2000; fackler & goodwin, 2001; fafchamps & hill, 2005). given the disparate nature of existing studies, often limited to specific case studies, this paper aims to bridge the gap by proposing a micro-level measure of market positioning. this contribution seeks to enrich the ongoing discussion on the nuanced relationship between market participation and food consumption, providing a new analytical lens to examine the intricate dynamics at play in agricultural commercialization and its broader socioeconomic impacts. 247participation of farmers in market value chains: a tailored antràs and chor positioning indicator bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 3. the proposed positioning measure value chain downstream positioning, which denotes the proximity of production to final demand, integrates development and trade concepts, highlighting the importance of geographical distance and market access on agricultural decision-making (von thünen, 1966; chamberlin & jayne, 2013; oosting et al., 2014; montalbano & nenci, 2022). this approach reveals the profound effect of location on farming strategies, extending beyond mere physical distance to include factors like travel costs (nanyeenya et al., 2007; duncan et al., 2013). kaplinsky and morris (2001) outline three value chain elements: key buyers, transaction dynamics, and critical factors. montalbano et al. (2018) further refine this by introducing a “positioning dummy”, based on the identity of market outlets, for distinguishing between upstream and downstream positions, highlighting the significance of broader market access. however, the challenge remains in developing a theoretical model that accurately captures value chain participation, especially the volume of sales, a crucial aspect in global value chains (gvcs) discussions (nenci, 2020). traditional input-output (i-o) tables, despite their utility, fall short in detailing the entire value chain network (montalbano & nenci, 2022). antràs and chor (2018; 2022) expand on this by incorporating the sequence of production stages into the analysis, defining upstreamness (u) as the weighted average distance of a stage from final demand, and downstreamness from the proximity to primary production factors. the formula for upstreamness is given by: (1) where represents the dollar amount of each country’s sector needed to produce one dollar’s worth of industry output in another country (i.e., ). downstreamness is similarly defined, focusing on the distance from primary factors, emphasizing the role of value addition in determining chain positioning. applying theoretical models to agricultural value chains reveals challenges, notably with data limitations and the non-linear structure of these chains, which often resemble “flatter” or “spider” configurations, complicating the application of antràs and chor’s (2018) framework. antràs & chor (2019) distill market positioning into the share of output sold directly to consumers, creating a micro-level downstreamness indicator. however, this indicator faces limitations in capturing the intricacies of market chains due to data scarcity. building on the insights from veugelers et al. (2013), giunta et al. (2022), and nenci et al. (2022), who examine value chain participation through the share of imported intermediates, this study proposes a refined indicator for agricultural value chain positioning that accounts for the sequence of intermediaries from farmers to final retailers, emphasizing the critical role of selling positions within the chain. it accounts for the intermediary sequence from farmers to end retailers, highlighting the critical role of selling positions within the chain. this is quantitatively represented as: = selling position n.1 × + selling position n.2 × + ⋯; (2) where the first integer term indicates the selling position number (i.e., the chain positioning of acquiring intermediaries), equals the quantity of crop sold by each household, and y is the total quantity of that crop sold along the crop-selling chain. the current literature reveals numerous shortcomings: the absence of a comprehensive, standalone indicator; incomplete data that lead to partial interpretations; and a neglect of the impact of vertical integration on positioning. these deficiencies underscore the necessity for a refined micro-level downstreamness indicator. this improved indicator should account for the selling position, incorporate the geographical selling location, and consider the multiplicity of buyers. furthermore, it should integrate the welfare effects of positioning, net of geographical distance, and the impact of trade costs on positioning (fafchamps & hill, 2005; mancini et al., 2023). to address these concerns, an enhanced formula is proposed: (3) where equals , equals , equals the quantity of crop sold, and y equals the total quantity of that crop sold along the crop market selling chain. it is important to note that farming households are commonly involved in multiple crop value chains. hence, the resulting positioning value attached to them will be the average of their positioning score in each single crop selling chain. the proposed indicator, following the theory of antràs and chor (2013), incorporates crop demand elasticities as a tuning parameter, suggesting that lower elasticity values (ρ) increase the likelihood of vertical integration 248 tulia gattone bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 in the value chain. this tuning parameter, formulated as 1/(1 ρ), reflects the observation that own-price elasticities are negative for most commodities, as indicated by deaton & muellbauer (1980), and particularly low for crops like maize and sorghum, which exhibit among the lowest values (tafere et al., 2010). finally, the proposed indicator facilitates comparability across different types of value chains and fields by being structured as an index ranging from 0 to 1. the adaptation of antràs and chor’s framework assumes farmers as a type of firm, with the analysis specifically targeted at a singular stage of the chain. the investigation is confined to the dynamics of selling chains, under the premise that scrutinizing solely the farmers’ roles does not encompass the evaluation of added value. additionally, it is assumed that farmers have the capability to engage in multiple crop value chains simultaneously, illustrating a diversified strategy to market participation. the proposed approach integrates elements from development and trade literature, such as “selling position,” “selling location,” and “crop ratio,” while updating the model to reflect non-sequential production stages and the diverse nature of agricultural sales, as suggested by recent insights (davis et al., 2018; antràs & chor, 2022). 4. empirical framework in the establishment of the empirical framework for this analysis, figure 1 systematically delineates the array of market outlets available to smallholder farmers. by illustrating the comprehensive network through which agricultural products transition from production to the end consumer, figure 1 methodically outlines the agricultural value chain, beginning with input suppliers – such as seeds, fertilizers, pesticides, and herbicides – primarily provided by either agricultural development agencies or private entities (audet-bélanger et al., 2013; ugonna et al., 2015; ayele et al., 2021). notably, village collectors often constitute the initial market entry point in countries such as nigeria and ethiopia (ayele et al., 2021; babama’aji et al., 2022), leading to further engagement with agricultural cooperatives and processors. these entities are instrumental in vertical integration, offering essential services like free storage and facilitating transactions with exporters, or local food agencies (gabre-madhin & goggin, 2006; usaid, 2017 additionally, the figure highlights the role of wholesale markets situated in main districts, which acquire crops either directly from farmers or via intermediaries, thereby augmenting access to storage and communication channels (ayele et al., 2021). the significance of private companies in providing downstream positioning benefits is also emphasized, noting their contribution to higher income levels and the facilitation of technology spillovers, which in turn enhance income stability and food security (case, 1992; bandiera & rasul, 2006; matuschke & qaim, 2009; barrett et al., 2017). the analysis further acknowledges the importance of mobile markets and commodity exchange markets as additional, critical conduits connecting smallholders with formal market segments (fao, 2020). the variability in the length of value chains necessitates that farmers engage at various stages, with their positioning influenced by external contingencies such as natural disasters (biggeri et al., 2018). leveraging insights from montalbano et al. (2018), this research assumes that direct sales to primary markets or private entities potentially yield higher profitability, indicative of sophisticated management expertise. consequently, market outlets are classified into seven distinct groups, spanning from upstream positions, characterized by lesser reward, to downstream positions, associated with greater economic benefit. specifically, · outlet n.1: roadside selling position n.1 · outlet n.2: agricultural cooperatives selling position n.2 · outlet n.3: farm-based association figure 1. a standard crop value chain in ethiopia and nigeria. source: author’s adaptation from gabre-madhin & goggin (2006); rashid & negassa (2013), gashaw & kibret (2018); fao (2020); ayele et al. (2021); babama’aji et al. (2022). 249participation of farmers in market value chains: a tailored antràs and chor positioning indicator bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 · outlet n.4: government agencies selling position n.3 · outlet n.5: political leader · outlet n.6: private trader in local market selling position n.4 · outlet n.7: local merchant/grocery · outlet n.8: local market selling position n.5 · outlet n.9: mobile market · outlet n.10: private trader in main market selling position n.6 · outlet n.11: main market · outlet n.12: private company selling position n.7 · outlet n.13: auction market also, a final note must be made for selling locations, whose score scale of 3 is defined, due to limited observations, as follows1: – selling location n.1: selling within the village or near the village – selling location n.2: selling near the town or near the district – selling location n.3: selling outside the district or outside the region 5. data and descriptive statistics this study utilizes the lsms-isa dataset from ethiopia and nigeria, gathered by the ethiopian central statistics agency, the national bureau of statistics of nigeria, and the world bank across three survey waves from 2010 to 2016. the final dataset, nationally representative, comprises approximately 1460 and 1178 observations for ethiopian and nigerian farmers, respectively, commercializing their crops. the analysis draws from household and agricultural data within the lsms-isa dataset, focusing on farmers’ responses about their main crop buyers, encapsulated in a network roster of over 30 actors, allowing identification of primary and secondary commercial partners. variable definitions and descriptive statistics for household variables are detailed in tables a.1 (variable descriptions), a.2 (ethiopia summary statistics), and a.3 (nigeria summary statistics) in the appendix, noting omissions in the nigerian dataset due to missing data. geographical analysis reveals that households are generally located far from main markets, with figure 2 depicting the regional distribution of households in ethiopia and nigeria. selling patterns, as shown in figure a.1 in the appendix, indicate a preference for selling large crop 1 if households resides in the main market, this measure can be bypassed. amounts outside formal markets, particularly with relatives, friends, and neighbors. notably, events like the 2011 floods in ethiopia significantly influenced these trends, with a marked shift in the selling outlets used by farmers. figure 3 and 4 categorize crop sales quantities from figure a.1 by selling position and location, respectively. specifically, as shown in figure 3, ethiopian farmers tend to sell upstream, mainly to agricultural cooperatives and farm-based associations, while nigerian farmers predominantly sell downstream but also through local markets. the distribution of sales by location (see figure 4) shows a majority within or near villages, with a notable portion of nigerian crops sold outside the region before 2012. table 1 presents summary statistics for food and total consumption2,3, alongside food quantity for sensitivity analysis. consumption data in nominal values, adjusted for inflation using the 2010 cpi,4 shows that food consumption constitutes over 70% of total expenditure for households in both ethiopia and nigeria. table 2 shows the downstreamness indicator results, indicating that ethiopian rural households have an average downstreamness score of 0.02, suggesting a predominant upstream positioning within market chains, a trend also observed in nigeria but with more variability. these figures indicate that in the ethiopian sample, the positioning indicator for crop-specific value chains ranges from 0 to 0.7, with rural households having an average downstreamness value of approximately 0.02. in nigeria, there is greater heterogeneity in downstreamness values, with a maximum of 1 in 2011 and a decrease to 0.45 in 2013. these findings support the transition of food supply chains from local and fragmented to longer and geographically connected ones (ifad, 2016). farmers in the market chain predominantly position themselves upstream (montalbano et al., 2018), and the crops they sell exhibit low price elasticity of demand, as demonstrated by studies from which crop elasticities are taken: world bank group (1982), akinleye & rahji (2007), pan et al. (2009), tafere et al. (2010), ashagidigbi (2019), adeniji (2019), and obayelu et al. (2019) moreover, analyzing the data while excluding outliers reveals micro2 following the lsms-isa documentation on the ethiopia socioeconomic survey, consumption total expenditures include three sources: food, non-food and education expenses for each household. 3 as specified in the “basic information document” for the lsms-isa nigeria general household survey, total consumption is calculated as the sum of all food, education, non-food, and imputed rent expenditures. expenditures were calculated and aggregated to household level and converted to per capita terms. 4 available at http://data.worldbank.org/indicator/fp.cpi.totl. http://data.worldbank.org/indicator/fp.cpi.totl 250 tulia gattone bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 trends in market positioning dynamics over the years (figure a.2, a.3 and a.4 in the appendix). 6. identification strategy, results, and sensitivity this section details the identification strategy and results of this study, including analyses of alternative positioning indicators, primary findings for the amended indicator, and subsequent sensitivity and robustness assessments (subsections 6.1 to 6.3). 6.1. identification strategy the empirical strategy tests the correlation between the amended value-chain positioning indicator and the natural log of food and total household consumption, utilizing a semi-logarithmic econometric model. this approach incorporates household and production charethiopia nigeria figure 2. household density per region/state. source: author’s own elaboration from lsms-isa data. ethiopia nigeria figure 3. quantity of crop sold (in kilos) per position. 251participation of farmers in market value chains: a tailored antràs and chor positioning indicator bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 acteristics to control for heterogeneity, following dercon (2004), chaudhuri (2003), and montalbano et al. (2018). the specification employed is: ch,t = αh + βt + ϕ1downh,t + δxh,t + εh,t; (4) where ch,t is alternatively the natural log of household per capita5 of food consumption and total con5 lsms-isa household surveys for nigeria do not provide per adult equivalencies in consumption aggregates. considering the current debate around the likelihood of incurring in mistakes when self-calculating equivalencies (see, deaton & margaret, 1998) and to make estimates across the two samples comparable, the consumption levels for ethiopia are reported in terms of per capita in line with those for nigeria. ethiopia nigeria figure 4. ethiopia quantity of crop sold (in kilos) per selling location. table 1. dependent variables summary statistics. n. of observations mean standard deviation minimum value maximum value et hi op ia food consumption (decimals, etb) 1,394 1,666.08 1891.68 156.24 41,616.74 total consumption (decimals, etb) 1,394 2,021.67 1986.22 188.59 42,073.02 sens. test food quantity (decimals, kg) 1,459 7.15 37.77 0.07 1,004.40 n ig er ia food consumption (decimals, ngn) 1,178 56,075.51 74,259.26 4,751.17 1,672,537 total consumption (decimals, ngn) 1,178 78,349.05 88,541.40 9,334.46 1,699,927 sens. test food quantity (decimals, kg) 1,175 32.9454 156.10 0.04 3268.39 table 2. downstreamness indicator results. n. of obserbations mean standard deviation minimum value maximum value et hi op ia downstreamness in 2011 (decimals) 521 0.02 0.07 0.00 0.70 downstreamness in 2013 (decimals) 1,026 0.02 0.07 0.00 0.70 downstreamness in 2015 (decimals) 883 0.02 0.07 0.00 0.70 n ig er ia downstreamness in 2011 (decimals) 346 0.05 0.12 0.00 1.00 downstreamness in 2013 (decimals) 757 0.00 0.03 0.00 0.45 downstreamness in 2015 (decimals) 515 0.03 0.09 0.00 0.86 252 tulia gattone bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 sumption, downh,t represents the value of the proposed downstreamness indicator, and xh,t is the vector of control variables for household heterogeneity and includes observable household and production characteristic. a non-zero ϕ1 coefficient suggests a significant relationship between market positioning and consumption. the model also accounts for unobserved heterogeneity, time, and location effects, with fixed effects to mitigate timevariant unobserved biases. the study exclusively considers households engaged in value chains to focus on the impact of market chain positioning.6 possible reverse causality between food/ total consumption and market positioning is not expected to impact the estimates because proxies for food consumption and commercialization are measured in different time periods. robustness checks in the appendix include the heckman correction for selection bias and the control function method to address self-selection bias, as suggested by wooldridge (2015). 6.2. main empirics table 3 contrasts the proposed adjusted «à la antràs and chor» (ac) indicator from equation [3] with common downstreamness indicators like the crop share ratio, the geographical distance to the main market and 6 households selling their crop in non-market outlets account for around 7-8% of the final sample for ethiopia. montalbano et al. (2018)’s positioning in terms of crop market outlets. model comparison using adjusted r-squared, aic, and bic coefficients reveals the superior performance of the proposed indicator with respect to traditional market positioning proxies. this finding challenges the commonly used proxies for marketing factors, orientation, and positioning that have been traditionally employed in empirical studies (e.g., inter alia, montalbano et al., 2018; migose et al., 2018; mkuna & wale, 2022). table 4 reports the positive impact of downstream positioning on consumption levels in ethiopia. all estimates were adjusted for household production characteristics to account for additional latent variables that could explain variations in market positioning, effectively reducing potential endogeneity resulting from selectivity bias (fafchamps & hill, 2005). by accounting for timeand geography-related factors, it is observed that ethiopian farmers positioned downstream in the market experience significantly higher per-capita consumption levels compared to farming households with similar characteristics but lower positioning scores. specifically, a 0.01 increase in their market positioning boosts per-capita food consumption by over 50% and total consumption by more than 40%, challenging the view that consumption patterns solely depend on food price shifts. ignoring household and geographic specifics leads to underestimating the “market positioning effect.” the impact table 3. downstreamness indicators comparison – main results for ethiopia. food consumption total consumption (1) (2) (3) (4) (5) (6) (7) (8) proposed indicator (ln) crop share (ln) distance to market market outlets adjusted down. (ln) crop share (ln) distance to market market outlets downstreamness 42.01*** 0.12* -0.20 0.07 35.96*** 0.08 -0.06 0.04 (12.91) (0.07) (1.64) (0.10) (11.01) (0.05) (1.44) (0.09) controls yes yes yes yes yes yes yes yes wave fe yes yes yes yes yes yes yes yes district fe yes yes yes yes yes yes yes yes trends yes yes yes yes yes yes yes yes constant 6.04*** 6.73*** 9.25 6.28*** 6.66*** 7.20*** 8.84+ 6.86*** (0.99) (1.09) (6.58) (1.03) (0.85) (0.94) (5.82) (0.89) n. of observations 1,387 1,387 1,381 1,387 1,387 1,387 1,381 1,387 n. of hh_id 1,097 1,097 1,093 1,097 1,097 1,097 1,093 1,097 r-squared adj. 0.72 0.71 0.64 0.71 0.73 0.72 0.69 0.75 aic -1316.97 -1266.77 -1013.52 -1251.08 -1697.19 -1644.86 -1371.93 -1633.08 bic -615.49 -565.29 -375.39 -549.61 -995.71 -943.38 -733.80 -931.60 standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. 253participation of farmers in market value chains: a tailored antràs and chor positioning indicator bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 is consistent across food and total consumption, with accuracy improving when location controls are included. despite the size of the hypothesized change in positioning score is observed in less than 2% of cases, its significant effect highlights the importance in driving consumption changes among households with varying initial downstream positions. similarly, table 5 presents the nigerian results, mirroring the ethiopian findings. a 0.01 enhancement in positioning indicator value corresponds to approximately 40% and 37% increases in per-capita food and total consumption, respectively. in nigeria, like ethiopia, farmers sell through various channels including local markets, cooperatives, and directly to processors, with a crop range extending to non-food items like cotton. the empirical strategy to nigerian data7 yields results mirroring ethiopia’s: a 0.01 improvement in market positioning leads to roughly a 40% increase in per-capita food consumption and a 37% increase in total consumption. this confirms that better market positioning, after accounting for variables like district characteristics and time trends, significantly enhances consumption levels for farmers in both countries. 7 the variable “crop code” is not controlled for in the case of nigeria, given the few changes in labeling across the years that may have altered the panel dataset combined “crop code” variable. also, interview month is omitted due to several missing observations. consumption data rely on the postharvest surveying visit. data on fertilizer use are from the post-planting questionnaire. 6.3. sensitivity and robustness checks table 6 shows the result of the sensitivity analysis for food quantity in both samples. food quantity is also measured in logarithmic form, just like consumption. results in both countries are very similar. food quantity is positively affected by higher positioning scores for all the specifications provided for both samples. if rural households are able to increase their positioning indicator value by 0.01, on average, and ceteris paribus, they are able to more than double their food quantity level both in ethiopia and nigeria. therefore, impact of increased positioning in value chains on food quantity per household is greater, in terms of magnitude, than the impact on food and total consumption levels per capita. robustness checks are reported in table 7 above for ethiopia and table 9 for nigeria. table 7 shows the results of table 4 replicated with population sampling weights.8 results are robust and consistent with what was previously obtained. as in table 4, results for both food and total consumption show the same dynamics: lower significance for the baseline specification and a downward bias if district dummies are not in the control group but only the wave dummies are considered. similarly, in table 8 above, the results for nigeria (shown in table 5) are replicated with the provided 8 conversely to nigeria, combined population weights are not reported in the lsms-isa ethiopia rural socioeconomic surveys. to avoid mistakenly corrections, population weights were adjusted across the years by attaching the latest weight to the household’s highest surveying wave. table 4. main results for ethiopia – panel fixed effects clustered by household id. food consumption total consumption (1) (2) (3) (4) (5) (6) wave fixed effects district-wave fixed effects district-wave fe hh trends wave fixed effects district-wave fixed effects district-wave fe hh trends downstreamness 31.04** 43.11*** 42.01*** 27.17* 36.13*** 35.96*** (15.03) (12.74) (12.91) (14.31) (11.03) (11.01) controls yes yes yes yes yes yes wave fe yes yes yes yes yes yes district fe yes yes yes yes trends yes yes constant 7.65*** 5.95*** 6.04*** 7.99*** 6.55*** 6.66*** (0.63) (0.99) (0.99) (0.58) (0.85) (0.85) n. of observations 1,387 1,387 1,387 1,387 1,387 1,387 n. of hh_id 1,097 1,097 1,097 1,097 1,097 1,097 r-squared adjusted 0.31 0.72 0.72 0.31 0.73 0.73 standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, + p<0.15. 254 tulia gattone bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 population sampling weights. coefficient estimates for the proposed amended positioning indicator in table 7 and 8 are significant for almost all the specifications provided in both samples. controlling for factors such as time and district fixed effects, including region, district, and village dummies, as well as trends, ethiopian and nigerian households who participate and have a better position in the market chain register, on average and ceteris paribus, have a per-capita equivalent food and total consumption level around 20% times higher than those farming households with the same characteristics and who have a position-indicator score lower than 0.01 unit. to address potential selection bias from excluding about 100 households not commercializing their crops within value chains, this study utilizes the xtheckmanfe stata module by rios-avila (2020) to account for endogeneity and sample selection. the results, adjusted for time effects and heckman correction, are in appentable 5. main results for nigeria – panel fixed effects clustered by household id. food consumption total consumption (1) (2) (3) (4) (5) (6) wave fixed effects district-wave fixed effects district-wave fe hh trends wave fixed effects district-wave fixed effects district-wave fe hh trends downstreamness 31.50*** 33.39*** 33.85*** 26.79** 31.56** 31.46** (11.94) (12.16) (12.50) (10.75) (13.97) (14.19) controls yes yes yes yes yes yes wave fe yes yes yes yes yes yes district fe yes yes yes yes trends yes yes constant 10.75*** 11.45*** 10.93*** 11.08*** 11.49*** 11.03*** (0.24) (0.28) (0.51) (0.28) (0.24) (0.58) n. of observations 1,178 1,178 1,178 1,178 1,178 1,178 n. of hh_id 979 979 979 979 979 979 r-squared adjusted 0.41 0.82 0.82 0.32 0.74 0.74 standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. table 6. sensitivity testing with food quantity. food quantity (ethiopia) food quantity (nigeria) (1) (2) (3) (4) (5) (6) wave fixed effects district-wave fixed effects district-wave fe hh trends wave fixed effects district-wave fixed effects district-wave fe hh trends downstreamness 61.86** 70.51* 81.38** 61.07** 82.03*** 78.18*** (26.55) (36.81) (36.54) (25.31) (26.60) (28.09) controls yes yes yes yes yes yes wave fe yes yes yes yes yes yes district fe yes yes yes yes trends yes yes constant -1.09 7.68*** 7.76*** 2.07*** 2.08*** 2.89*** (1.05) (1.97) (1.92) (0.43) (0.38) (0.77) n. of observations 1,452 1,452 1,452 1,175 1,175 1,175 n. of hh_id 1,121 1,121 1,121 977 977 977 r-squared adjusted 0.13 0.53 0.54 0.59 0.88 0.88 standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. 255participation of farmers in market value chains: a tailored antràs and chor positioning indicator bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 dix table a.4. moreover, the issue whether households’ participation and positioning in markets could be influenced by characteristics affecting both consumption and market position, is addressed using a control function approach. this approach involves adding the residual of a first-stage regression, which predicts the “downstreamness positioning” binary variable, to the main regression as an exclusion restriction. this residual, denoted as ρ, is designed to be uncorrelated with the endogenous variable, thereby providing unbiased estimators in the main equation and mitigating self-selection bias (wooldridge, 2015). table a.5 in the appendix reports the results, showing very consistent outcomes with the previous regressions. 7. discussion and conclusions to summarize, the empirical outcomes indicated that changes in market positioning significantly and table 7. main results with population sampling weights for ethiopia. food consumption total consumption (1) (2) (3) (4) (5) (6) wave fixed effects district-wave fixed effects district-wave fe hh trends wave fixed effects district-wave fixed effects district-wave fe hh trends downstreamness 22.68+ 21.85+ 21.47+ 20.08+ 21.81* 22.39* (15.07) (14.50) (14.61) (13.58) (13.00) (12.97) controls yes yes yes yes yes yes wave fe yes yes yes yes yes yes district fe yes yes yes yes trends yes yes constant 7.24*** 6.91*** 7.05*** 7.61*** 7.56*** 7.68*** (0.62) (1.23) (1.22) (0.58) (1.07) (1.05) n. of observations 1,387 1,387 1,387 1,387 1,387 1,387 n. of hh_id 1,097 1,097 1,097 1,097 1,097 1,097 r-squared adjusted 0.33 0.72 0.73 0.34 0.70 0.71 standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. table 8. main results with population sampling weights for nigeria. food consumption total consumption (1) (2) (3) (4) (5) (6) wave fixed effects district-wave fixed effects district-wave fe hh trends wave fixed effects district-wave fixed effects district-wave fe hh trends downstreamness 15.96 20.52* 21.58** 11.98 18.26 18.56+ (12.26) (10.46) (10.79) (10.89) (13.01) (13.02) controls yes yes yes yes yes yes wave fe yes yes yes yes yes yes district fe yes yes yes yes trends yes yes constant 10.90*** 11.27*** 10.38*** 11.29*** 11.38*** 10.52*** (0.27) (0.23) (0.51) (0.38) (0.23) (0.65) n. of observations 1,172 1,172 1,172 1,172 1,172 1,172 n. of hh_id 973 973 973 973 973 973 r-squared adjusted 0.33 0.83 0.83 0.23 0.76 0.77 standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. 256 tulia gattone bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 consistently matters to increasing the consumption levels of ethiopian farmers selling crops in the market chain. from this perspective, the findings of montalbano et al. (2018) extend to ethiopia and nigeria regarding the positive role of farmers’ market participation in uganda. however, the results contradict the conclusion of montalbano et al. (2018), arguing instead for the significance of market intermediaries. finally, a concern should be sounded concerning the external validity of these findings. since the focus is on investigating market positioning, the overwhelming majority of farmers who produce crops only for home consumption are excluded from the analysis. this gap hampers the ability of the analysis to derive consistent estimates for the entire population of a crop producer. nevertheless, results of the parallel test conducted for nigeria are highly reassuring regarding the proposed amended indicator’s external validity. historically, the examination of farmers’ market decisions traces back to the early 1990s, with seminal works by fafchamps (1992), von braun (1995), and key et al. (2000). yet, a comprehensive analysis of market structure from the farmers’ perspective remains elusive. the motivation behind this work lies on the idea that farmers selling to wholesalers/producers are better off than farmers that sell to the most proximate markets. this work adjusts antràs and chor’s downstreamness indicator to farming households’ selling locations and buyer-market chains. it contributes to the literature by creating a conceptual framework for farmers’ market positioning and a replicable setting for assessing the effects of market positioning on both food security and welfare levels. utilizing national, representative household surveys from ethiopia and nigeria, this paper investigates the relationship between market positioning scores and consumption levels, revealing that farmers positioned further downstream in the value chain experience enhanced food and overall consumption. this study evidences the significant impact of micro-variations in market positioning on rural development and establishes the superiority of the antràs and chor-informed indicator over other alternatives for assessing market positioning’s welfare effects. the findings, robust across various empirical models and further supported by sensitivity analyses focusing on food quantity, underscore the reliability of the research question addressed. this work fills a critical void in existing literature by offering a nuanced, well-validated indicator that assesses farmers’ value chain positioning with a novel emphasis on market outlet identities and selling locations. by incorporating demand elasticity as a pivotal parameter for vertical integration, as suggested by antràs and chor (2013), the indicator not only adheres to but also expands upon the theoretical underpinnings of value chain analysis. empirical validation from ethiopia and nigeria illustrates that slight enhancements in market positioning lead substantial increases in consumption, with 0.01 rise in positioning yielding over a 40% uplift in per-capita consumption levels. the study also acknowledges the challenges in comparing across countries due to incomplete data in existing datasets, especially regarding the network roster for inputs acquisition. it advocates for a broader data collection strategy encompassing trade flows for all actors in the agricultural chain, aiming to elucidate the value added along a farmer’s selling line. this approach promises a more holistic understanding of the agricultural value chain’s dynamics and its implications for farmer welfare. acknowledgments for this paper, i am grateful to my supervisors, prof. pierluigi montalbano and prof. marco letta, for their constant invaluable feedback on this work. i would also like to express my gratitude to the world bank living standards measurement study-integrated surveys on agriculture (lsms-isa) team for providing the data. furthermore, i extend my thanks to prof. carlo pietrobelli. prof. enrico marvasi, prof. marco sanfilippo, prof. donato romano, prof. fabio santeremo, prof. luca salvatici, dr. sara savastano and the participants at the 1st deveconmeet, the sapienza phd meeting 2022, the 12th aieaa conference, ifad & fao “working & networking” workshop on climate change and food systems, the 3rd annual southern phd economics conference (aspec), as well as the 7h nordic development conference (nordev) for their comments on previous versions of this paper. i am also thankful for the doctoral research support received from sapienza university of rome. finally, i express my sincere gratitude to the editors and the anonymous referees for their insightful suggestions. potential errors and views expressed in this paper are those of the author and do not represent those of the institutions with which the author is affiliated. all standard disclaimers apply. references adeniji, f. 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(1982). cocoa handbook. commodity handbook. world bank group, washington, d.c. appendix table a.1. variable definitions and other basic information. variable name definition time period source gender of the household head gender of the household head (binary, 1=female) 2010-2016 world bank lsms-isa ethiopia and nigeria age of the household head (decimals) age years of the household head (decimals) 2011-2015 world bank lsms-isa ethiopia only number of household members in the labor force (decimals) number of household members (binary, 1=female) 2010-2016 world bank lsms-isa ethiopia and nigeria household size (decimals) number of people in the household (decimals) 2010-2016 world bank lsms-isa ethiopia and nigeria average years of education for household adults (decimals, years of schooling) average education level attained by the household adult members (values from 0 to 8) 2011-2015 world bank lsms-isa ethiopia only average years of education for household head (decimals, years of schooling) average education level attained by the household head (values from 0 to 8) 2011-2015 world bank lsms-isa ethiopia only number of household infants (decimals) number of household members in the infant age range (decimals) 2010-2016 world bank lsms-isa ethiopia and nigeria number of household children (decimals) number of household members in the children age range (decimals) 2010-2016 world bank lsms-isa ethiopia and nigeria household years of education (decimals, years of schooling) average education level attained by all household members (values from 0 to 8) 2011-2015 world bank lsms-isa ethiopia only harvest crop (decimals, kg) quantity of crop harvest in the surveying period (decimals, kg) 2011-2015 world bank lsms-isa ethiopia only field size (decimals, ha) average field size in the surveying period (decimals, ha) 2011-2015 world bank lsms-isa ethiopia only free seed event of receiving free seed (binary, 1=no and 2=yes) 2011-2015 world bank lsms-isa ethiopia only seed purchase necessity of purchasing seed (binary, 1=no and 2=yes) 2011-2015 world bank lsms-isa ethiopia only fertilizer use use of fertilizers (binary, 1=no and 2=yes) 2010-2016 world bank lsms-isa ethiopia and nigeria fertilizer purchase purchase of fertilizers (binary, 0=no and 1=yes) 2010-2016 world bank lsms-isa ethiopia and nigeria leftover fertilizer presence of leftover fertilizers (binary, 0=no and 1=yes) 2010-2016 world bank lsms-isa ethiopia and nigeria free fertilizer event of receiving free fertilizers (binary, 0=no and 1=yes) 2010-2016 world bank lsms-isa ethiopia and nigeria https://doi.org/10.1080/21683565.2018.1530716 https://doi.org/10.1080/21683565.2018.1530716 https://doi.org/10.3368/jhr.50.2.420 261participation of farmers in market value chains: a tailored antràs and chor positioning indicator bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 table a.2. households summary statistics for ethiopia. n. of observations mean standard deviation minimum value maximum value gender of the household head (binary, 1=female) 1,460 0.18 0.39 0 1 age of the household head (decimals) 1,460 45.72 14.21 18 97 number of household members in the labor force (decimals) 1,460 2.69 1.38 0 10 household size (decimals) 1,460 5.77 2.19 1 14 average years of education for household adults (decimals, years of schooling) 1,460 1.70 1.83 0 8 number of household infants (decimals) 1,460 0.58 0.80 0 5 number of household children (decimals) 1,460 2.39 1.68 0 10 household years of education (decimals, years of schooling) 1,460 1.70 1.83 0 8 harvest crop (decimals, kg) 1,460 914.13 752.98 0 3,249.61 field size (decimals, m2) 1,460 9030.31 9370.73 0 38,917.46 free seed (binary, 2=yes) 1,459 1.99 0.12 1 2 seed purchase (binary, 2=yes) 1,462 1.94 0.24 1 2 fertilizer use (binary, 2=yes) 1,462 1.81 0.40 1 2 table a.3. households summary statistics for nigeria. n. of observations mean standard deviation minimum value maximum value gender of the household head (binary, 1=female) 1,178 0.20 0.40 0 1 number of household members in the labor force (decimals) 1,178 2.48 2.13 0 13 household size (decimals) 1,178 6.41 3.27 1 28 number of household infants (decimals) 1,178 0.55 0.92 0 6 number of household children (decimals) 1,178 1.90 2.22 0 14 fertilizer purchase (binary, 1=yes)) 1,178 0.33 0.47 0 1 letfover fertilizer (binary, 1=yes) 1,178 0.03 0.17 0 1 free fertilizer (binary, 1=yes) 1,178 0.01 0.10 0 1 fertilizer use (binary, 1=organic) 1,178 1.69 0.46 1 2 ethiopia nigeria figure a.1. quantity of crop sold (in kilos) mean values per selling outlet. 262 tulia gattone bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 figure a.2. kernel density downstreamness positioning indicator ethiopia 2011. figure a.3. kernel density downstreamness positioning indicator ethiopia 2013. 263participation of farmers in market value chains: a tailored antràs and chor positioning indicator bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 figure a.4. kernel density downstreamness positioning indicator ethiopia 2015. table a.4. sample bias – panel fe with the heckman correction. food consumption total consumption (1) (2) (3) (4) heckman fe heckman fe wave fixed effects wave fixed effects wave fixed effects wave fixed effects downstreamness 50.88* 26.41** 48.15+ 22.92** (30.07) (12.26) (30.42) (11.63) controls yes yes yes yes wave fe yes yes yes yes constant 7.57*** 7.79*** 7.64*** 8.15*** (0.42) (0.61) (0.31) (0.57) n. of observations 1,457 1,389 1,457 1,389 n. of hh_id 1,098 1,098 r-squared adjusted 0.26 0.25 standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. note: control variables “household average education level” and “crop code” are excluded as their inclusion in the regression models does not allow convergence in the heckman fixed effect computational tools. bootstrap replications are set to 50. 264 tulia gattone bio-based and applied economics 13(3): 245-264, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15464 table a.5. self-selection bias – control function method. food consumption total consumption (1) (2) (3) (4) (5) (6) wave fixed effects district-wave fixed effects district-wave fe hh trends wave fixed effects district-wave fixed effects district-wave fe hh trends downstreamness 27.23* 43.03*** 41.03*** 24.28+ 36.23*** 35.86*** (15.71) (13.04) (13.07) (14.97) (11.23) (11.20) controls yes yes yes yes yes yes wave fe yes yes yes yes yes yes district fe yes yes yes yes trends yes yes r 0.14 0.01 -0.01 0.10 -0.01 0.01 (0.09) (0.10 (0.10) (0.08) (0.09) (0.09) constant 7.97*** 6.24*** 6.25*** 8.31*** 6.84*** 6.95*** (0.63) (0.99) (0.98) (0.59) (0.85) (0.86) n. of observations 1,387 1,387 1,387 1,387 1,387 1,387 n. of hh_id 1,097 1,097 1,097 1,097 1,097 1,097 r-squared adjusted 0.22 0.71 0.69 0.24 0.72 0.72 standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. guns, germs and climate: food security and food systems in a risky world francesco pagliacci1,*, valentina raimondi2, luca salvatici3,* the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia amsalu woldie yalew1,2,*, victor nechifor3, emanuele ferrari3 participation of farmers in market value chains: a tailored antràs and chor positioning indicator tulia gattone adapting to climate change: what really drives the choices of the producers of geographical indications? francesco pagliacci1, dana salpina1,2,* exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach stefano ciliberti, angelo frascarelli*, gaetano martino, andrea marchini intention and behavior toward eating whole grain pasta on a college dining campus: theory of planned behavior and message framing giovanni sogari1, rungsaran wongprawmas1, giulia andreani1, michele lefebvre2, nicoletta pellegrini3, miguel i. gómez4, cristina mora1, davide menozzi1,* bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 © 2025 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae bio-based and applied economics bae citation: russo, s., raggi, m., bimbati, b., povellato, a. & viaggi, d. (2025). farm characteristics and exogenous factors influencing the choice to buy land in italy. bio-based and applied economics 14(1): 49-73. doi: 10.36253/ bae-15548 received: december 20, 2023 accepted: may 9, 2024 published: june 13, 2025 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. associated editor: luca salvatici, valentina raimondi, francesco pagliacci editor in chief: fabio bartolini orcid mr: 0000-0001-6960-1099 bb: 0000-0002-4826-5003 ap: 0000-0002-6980-4862 dv: 0000-0001-9503-2977 farm characteristics and exogenous factors influencing the choice to buy land in italy silvia russo1,*, meri raggi2, barbara bimbati3, andrea povellato3, davide viaggi1 1 department of agricultural and food sciences, alma mater studiorum, università di bologna, viale fanin 50, bologna, 40127, italy 2 department of statistical sciences “p. fortunati”, alma mater studiorum, university of bologna, via delle belle arti, 41, bo, bologna, 40126, italy 3 crea – research centre for agricultural policies and bioeconomy, legnaro (pd), italy *corresponding author. e-mail: silvia.russo7@studio.unibo.it abstract. access to land is one of the key factors of farm growth. however, related research is characterised by important gaps, in particular, facing the change over time in the nature and role of drivers of the land market. the objective of this paper is to identify the endogenous and exogenous factors that affect the decision to purchase land in italy between 2013 and 2020. five probit regression models were implemented to understand the role of a set of different determinants in land investment decision. the results show that factors related to capital in machinery and plant, energy production and the presence of a successor or young farmer are endogenous factors that positively influence the purchase decision. the ratio of rented land to utilised agricultural area and of family work units to total work units are endogenous factors that negatively affect the purchase decision. exogenous factors related to the cost of capital and inflation rate affect the purchase of land in an opposite way, negatively and positively respectively. the role of utilised agricultural area and value added per hectare varies depending on the specialisation considered. the research can support policymakers in designing policies to promote the survival and growth of farms, as well as to facilitate land investment by reducing barriers to land acquisition. keywords: agricultural land market, land purchase, probit regression model, investment decision, purchase decision. jel codes: q15, q12. 1. introduction land represents a durable, fixed, heterogeneous, and non-reproducible resource and is one of the key productive factors of a farm. the purchase of land is one of the ways through which a farmer can access this fixed productive factor and represents a form of investment in a capital good. compared to other forms of farm size growth, on the one hand, the purchase of land may require a major financial commitment and thus limits the investment in other productive assets (jeong et al., 2022; swinnen et al., 2016). https://doi.org/10.36253/bae-15548 https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae https://doi.org/10.36253/bae-15548 https://doi.org/10.36253/bae-15548 https://orcid.org/0000-0001-6960-1099 https://orcid.org/0000-0002-4826-5003 https://orcid.org/0000-0002-6980-4862 https://orcid.org/0000-0001-9503-2977 mailto:silvia.russo7@studio.unibo.it 50 bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 silvia russo et al. on the other hand, the full transfer of rights allows the new owner to use the land as a collateral asset in order to have greater access to credit (binswanger et al., 1995; bradfield et al., 2023; swinnen et al., 2016). in comparison to investments in other types of on-farm assets, the purchase of land rarely takes place at the same time as it is planned because it is not certain that the farmer will find the supply on the local market meeting his/ her needs/capacity (elhorst, 1993). for the farmer, the availability of land can be one of the main obstacles to the development and growth of the farm (yanore et al., 2024). the land market is characterised by rigid supply and the purchase of land far from the farm centre would lead to increased costs and downtime (cotteleer et al., 2008; schimmenti et al., 2013). for all these reasons, the land market is generally defined as thin and local. the lack of data availability and the absence of wellstructured databases on land transactions, especially in europe, has influenced and limited the research on the land market (de noni et al., 2019). over the years, research mainly focused on identifying the determinants of land value in specific local agricultural land markets or on how agricultural policy payments could influence land value (baldoni et al., 2023; czyzewski et al., 2017; latruffe and le mouël, 2009; michalek et al., 2014; varacca et al., 2022). however, when analysing the literature relating to the investment decision, there appears to be little ex-post empirical research that takes into consideration the investment in land. the objective of this paper is to identify determinants that have influenced the farmer’s decision to purchase land in italy between 2013 and 2020. the work is carried out using fadn data and factors are selected based on a literature review and data availability. the main novelty of the paper is that we use an original analytical framework and a conceptual model developed on the basis of the literature analysis using multiple streams of research, namely structural change in agriculture and the growth of farm size, the investment decision and the land market literature. the paper continues in section 2 with the design of the framework. in section 3 we proceed with the descriptive analysis of the available data and the presentation of the methodology. in section 4, the results of the analysis are presented and will be discussed in section 5. section 6 is dedicated to the conclusions drawn from this study. in order to contextualize this research, a premise is needed. to the best of our knowledge, there are no studies that have identified the factors that may influence the decision of italian farmers to invest in land. consequently, this study and its results should be considered a preliminary exploratory attempt to identify and understand the effects of certain factors selected based on the original analytical framework. 2. theoretical framework land is a factor of production that is strongly connected to and not divisible from three other farm inputs such as machinery, (family) labour and buildings (plogmann et al., 2022). over the years, mechanisation and technological innovation have played an important role in improving farmers’ labour management and replacing the labour force leaving rural areas for better paid non-agricultural work. the adoption of machinery and technological innovation, especially when it is expensive and complex, have stimulated farmers to allocate their managerial skills, capital, and farm assets for the production of a few types of output and, thus, farm specialisation. these three factors have contributed to the development of both economies of scale and size. although technological innovation is accessible to small and large farms, the latter seem to have more financial and managerial capacities, both internal and external, to invest in this factor. thus, the growth in farm size induced by technological innovation seems to be stronger in large farms than in small ones. according to the theoretical literature, these dynamics generate pressures on small farms that might decide to exit the agricultural sector (plogmann et al., 2022). in this regard, researchers have identified “off-farm income” as a factor that could play a dual role in the survival of small farms. on the one hand, the income generated by off-farm activities could represent the first step of the farm’s exit from the sector. on the other hand, this source of income could allow the farmer to remain within the agricultural sector because it could contribute to the stabilisation of the farmer’s income and facilitate access to credit, investment in farm assets, and stimulate the growth of farms managed by young farmers (goddard et al., 1993; hallam, 1991; harrington and reinsel, 1995; key, 2020; neuenfeldt et al., 2019; weiss, 1999; zimmermann et al., 2009). human capital is one of the main factors that can influence a farmer’s investment decision. when talking about human capital, reference is made to demographic characteristics of the farmer and their family. in particular, the age of the farmer and the presence of a potential successor, and the level of education are among the main characteristics that can affect farm size and investment in land. as the age of the farmer increases, the farm enters the so-called maturity and/or decline phase and the farmer 51farm characteristics and exogenous factors influencing the choice to buy land in italy bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 may be more reluctant to increase the farm size (bremmer and oude lansink, 2002). the presence of a potential successor could prevent the farm from entering the decline phase and thus positively affect the investment in a fixed input (huber et al., 2015). furthermore, the purchase of land could entail a major financial commitment and the application for a bank loan. in this regard, the presence of a young farmer or a successor could positively influence the time horizon of the investment and favour the purchase decision (elhorst, 1993; huber et al., 2015; oskam et al., 2009; oude lansink et al., 2001). in addition to these factors, human capital also includes managerial skills that if not possessed by the farmer can be found in the external environment e.g. by turning to advisory and consultancy services (boehlje, 1992). according to the literature, larger farms may have greater economic and financial capacities to access such services. the decision to invest in land is not only influenced by structural and socio-demographic characteristics of the farm, but also by exogenous factors such as the macroeconomic environment, land market regulations, and agricultural policies. the purchase of land represents an investment in a capital good that may require a major financial commitment. in this sense, the cost of capital and the financial position of the farmer could influence the decision and the level of investment. as the interest rate increases, the probability that the farmer is willing to invest and the level of investment decreases. land is not only an important production asset of a farm but also a “safeheaven” asset (schimmenti et al., 2013), attracting the interest of non-farmers who decide to invest in it to protect the capital value from inflation. an increase in the inflation rate leads to an increase in the price of land and vice versa (elhorst, 1993; lawley, 2021; szymańska et al., 2021; thijssen, 1996). policymakers can use land regulation as an instrument to defend the farmers’ position and mitigate potential speculative force in farmland market. each european member state has full decision-making power over its own land regulation. in general, western european countries have a more liberal land regulation than eastern european countries. among the western countries, italy is one of the european countries with the most liberal land regulation (swinnen et al., 2016). with the aim of facilitating access to land for medium-sized farms with financial means, many european countries have provided for the right of pre-emption to be exercised either by local governments, as in france, germany and the netherlands, or by farmers, as in italy (galletto, 2018). in particular, the italian government introduced this instrument to reduce the fragmentation of italian farms, to improve the consolidation of the italian agricultural sector and to facilitate the development of family farms. in italy, art. 8 law n. 590/1965 and art. 7 law n. 817/1971 establish that the italian farmer may exercise the right of pre-emption of land if at least one of three cases occurs: a) he/she is the co-owner of the farm, b) he/she is a professional farmer who directly borders land for sale, c) if he/she has been renting the land for at least two years (legge 590/1965; legge 817/1971).the right of pre-emption has also been extended to agricultural partnerships (as a rule, simple partnerships, and general partnerships) if at least half of the partners are “owner-operator farmer”. subsequently, between 2009 and 2016, the italian state implemented tax concessions to improve the farmer’s position. the law states that: a) the italian farmer with a family farm does not have to pay income tax or land use tax; b) the italian farmer is exempt from paying income tax on the use of the land; c) in case of land purchase, when the buyer is a “owner-operator farmer” or professional agricultural entrepreneur, she/he will pay only 1% of the purchase price as tax, while any other buyer will pay 15%. in 2017, the european parliament called on all member states to review their land regulation in order to ensure fair access to land and to prevent it from being concentrated within a few large farms (european parliament, 2017). in addition to preserving the farmer’s position, land regulation influences the capitalization of subsidies provided by agricultural policies within the value of land and rental rates. stringent land regulation on the land market and land rental market would reduce the capitalisation of subsidies within the land price and rent. the literature presents both theoretical and empirical studies on whether and how much of the subsidies provided through policies are capitalised within the land price value. from a theoretical study, in a perfect market, decoupled direct payments, coupled direct payments, rural development programmes and environmental payments could be capitalised within the land price. however, empirical studies suggest that capitalisation in a real land market is lower than theorised and depends on many factors such as subsidy type, land supply elasticity and farm credit constraints. in addition to influencing land value, subsidies can also influence a farmer’s investment decision and level of investment. subsidies were introduced with the main objective of supporting the farmer’s income and represent a form of income not affected by production risks. consequently, subsidies could positively influence the investment decision and level especially in the presence of an imperfect market. the identified factors are not independent but interact and influence each other (zimmermann et al., 2009). 52 bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 silvia russo et al. in the literature, four empirical studies concerning the farm size growth were identified that adopted a regression model with the farm size as the dependent variable (akimowicz et al., 2013; bremmer and oude lansink, 2002; brenes-muñoz et al., 2016; weiss, 1999). in the literature related to investment decision, two empirical researches were identified that also considered land as a form of investment (elhorst, 1993; oskam et al., 2009). in addition, jeong et al. (2022) identified farm economic characteristics that could affect the decision to buy or lease land in korea by adopting the machine learning algorithm “random forest”. finally, ziemer and white (1981) attempted to better estimate farmland demand in georgia between 1970 and 1978 by accounting for the process underlying the decision to purchase. based on the literature review, factors endogenous and exogenous to the farm that may have an inf luence have been identified and summarised in a conceptual model shown in figure 1. similar to the studies on farm size growth (zimmermann et al., 2009), we do not assume that the identified exogenous and endogenous factors are independent of each other, but that they interact and condition each other. 3. materials and methodology 3.1. data and descriptive analysis the analysis was conducted on italian fadn data of italian farms observed between 2013 and 2020. the data represent an unbalanced panel data consisting of 84610 observations representing 24212 farms. on average, the same farm remains in the sample for about 3 to 4 years. for each farm, there is information on the structural characteristics of the farm, data on the farm’s balance sheet, and data on the socio-demographic characteristics of the farms. of the 24212 farms in the sample, 919 made at least one investment in land during the period in question, of these 176 farms made more than one investment (table 1). around 90% of the sample is characterised by specialised farms in cereals, arable crops, horticulture, fruit economic environment land market policy: european agricultural policies and land regulation purchase of land farmer’s sociodemographics characteristics farm structural characteristics en do ge no us fa ct or s exogenousfactors figure 1. conceptual model developed based on the literature review. 53farm characteristics and exogenous factors influencing the choice to buy land in italy bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 crops, olive growing, viticulture, dairy cattle, herbivores and granivores. the remaining 9.45% by non-specialised farms, of which 9.4% are mixed crop and livestock farms. thirty-two percent of the sample is specialised in annual crops, 29.9% are permanent crops and 27.8% livestock farms (table 2). thirty-nine percent of the land purchases were conducted by farms specialising in permanent crops, followed by farms specialising in annual crops and livestock. in particular, 18% of the recorded transactions were conducted by farms specialising in fruit crops, 16.5% by vineyards, and 12% by farms specialising in arable crops (table 2). in terms of average uaa, specialised livestock farms are the largest, followed by annual crops and permanent crops. among all specialisations, farms specialised in viticulture have the smallest average farm size followed by those specialised in fruit crops and horticulture. there is an important difference in farm size between horticultural farms and those specialised in other annual crops. farms specialised in permanent crops have lower “rent/uaa” ratios than farms specialised in annual crops and livestock (appendix 1). 3.2. empirical model since the investment decision represents a discrete problem (elhorst, 1993), to estimate the probability of participation decision we adopted a probit regression model. the empirical model implemented to conduct the quantitative analysis was developed based on the conceptual model in figure 1 and peculiarities of fadn data. in particular, the characteristics of our database did not allow us to conduct a dynamic analysis, which would be appropriate since investments in capital stock are not annual investments (lefebvre et al., 2015) and generally do not occur at the same time as they are planned (elhorst, 1993). the empirical probit model used is described by the following equation: where: y*i is the binary dependent variable that assumes a value equal to 1 in the year in which the purchase occurs, 0 otherwise. εi is the composite error term. i represents the single observation, xki is the observed value of explanatory variables that described factors linked to farm characteristics, farmer table 1 descriptive analysis: dimension of the unbalanced panel data. full sample buyer % number of observations 84610 1095 1.3 number of farms 24212 919 3.8 table 2. descriptive statistics of the sample based on farm specialization. specialization sample % total observation buyers % total observation n. observations n. observations no specialisation: 7997 9.45 91 8.3 unclassifiable farms 11 0.013 0 0 mixed crops and livestock farming 7986 9.4 91 8.3 annual crops 27796 32.9 312 28.5 cereals 8812 10.4 102 9.3 arable crops 10292 12.2 133 12.15 horticulture 8692 10.3 77 7.03 permanent crops 25305 29.9 432 39.45 fruit crops 10721 12.7 202 18.45 olive growing 4034 4.8 47 4.3 viticulture 10550 12.5 183 16.7 livestock farms 23512 27.8 260 23.75 dairy cattle 7339 8.7 102 9.3 herbivores 12108 14.3 102 9.3 granivores 4065 4.8 56 5.1 tot 84610 100 1095 100 54 bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 silvia russo et al. socio-demographic characteristics and exogenous variables. the effect of xi on is represented by . and are respectively the intercept and the errors for i. the equation is estimated using the ‘glm’ function in rstudio of the ‘stats’ package. the explanatory variables (table 3) introduced in the probit model are listed and defined below. 3.2.1. description of the explanatory variables and expected relation utilized agricultural area it is unclear what effect the initial size of the farm may have on the growth of farm size and on the investment decision. given the nature and characteristic of the data of this variable, it was decided to introduce as an explanatory variable the “uaa sq” which represents the squared value of the total initial uaa of the farm regardless of whether it is owned, leased, or free use. the use of the square variable is able to catch the non-linear effect of it. assuming that farm size can also be a measure of the farm’s ability to generate income (oude lansink et al. 2001), we expect this variable to have a positive effect on the investment decision. value added per hectares this variable was introduced as an explanatory variable representing the productivity of land. through this variable, the aim is to understand whether the productivity per hectare derived from the farm’s activity affects the growth of the farm size through purchase. according to the literature, the farmer is encouraged to buy land when productivity is high (ciaian et al., 2010). therefore, it is assumed that, as productivity per hectare increases, the likelihood of the farmer investing in land increases. value added per total work unit this explanatory variable represents the productivity of farm labour. it is defined as the ratio of value added to total work units. it is assumed that as productivity per labour unit increases, the probability of the farmer purchasing land also increases. production specialisation when not focusing on a single specialization (e.g., the dairy sector), the researchers introduced a categorical variable related to farm specialisation (e.g akimowicz et al., 2013) in order to understand whether the type of farm could influence the farm growth or investment decision. this is probably related to the fact that the type of assets needed by a farm varies according to their specialisation (lefebvre et al., 2015). the data at our disposal include specialised and non-specialised farms. specialisation is defined according to the technical-economic orientation of the fadn database (fadn, 2018). in contrast to this original classification, in this model farms classified as “mixed crop and livestock” are included in the “non-specialised farms”. specialised farms fall into 9 categories: cereal crops, arable crops, horticulture, fruit crops, olive crops, viticulture, dairy cattle, herbivores and granivores. therefore, the explanatory variable was introduced into the model to account for the nine specialisation categories. “no specialisation” is used as the reference category since specialisation is one of the main drivers of the search for economies of scale and farm growth. introducing this variable allows us to understand not only whether specialised farms invest more than non-specialised ones, but also whether the probability of buying land in italy changes as specialisation changes. utilised agricultural area *specialisation the necessary assets of a farm and the “optimal size” vary depending on the type of farming (lefebvre et al., 2015; plogmann et al., 2022). in order to test whether the effect of farm size can vary according to the type of farming, it was decided to combine the two previous variables “uaasq” and “specialisation”., (bremmer and oude lansink, 2002). rent/utilised agricultural area on the one hand, renting allows the farm more flexibility and the possibility to invest its liquidity in other productive assets (swinnen et al., 2016). on the other hand, land managed as property allows the farmer to use it as collateral capital and thus to have greater access to credit (swinnen et al., 2016). it was decided to introduce the ratio of the land managed under rent to the total utilised agricultural area of the farm as an inverse measure of the amount of collateral available (benjamin and phimister, 2002; lefebvre et al., 2015). however, expectations on the direction of the effects of this variable are ambiguous. machinery plant value machinery and plant represent another form of collateral capital for a farm. it is assumed that high values of this variable correspond to a farm’s recent investment in such productive assets that vary proportionally to the farm area (plogmann et al., 2022). furthermore, there is a correlation between the intention to purchase land and investment in other farm assets (lefebvre et al., 2015). 55farm characteristics and exogenous factors influencing the choice to buy land in italy bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 therefore, it is hypothesised that the farm is inclined to purchase with the aim of maximising the productive capacity of the asset in which it has previously invested. common agricultural policy the common agricultural policy has been identified as an exogenous factor that can influence the land price, but also the decision and level of investment. subsidies received and capital financing are not the same for every farm and for this reason it can be considered as an endogenous variable linked to structural characteristics of the farm. it was decided to introduce two continuous variables. the first, the ratio of income subsidies per hectare related to the first pillar of the common agricultural policy and com. the second, the value of the investment subsidies received by the farm between 2013 and 2020 and connected to the measures of the second pillar of the common agricultural policy. pre-purchase investment in land is a planned, long-term investment (elhorst, 1993; oskam et al., 2009; oude lansink et al., 2001). the land market is thin and local, and it could be difficult for a farmer to find the amount of land he wants at one time. (cotteleer et al., 2008; elhorst, 1993). therefore, it may happen that the farmer must make more than one purchase to reach the desired level of investment. the dummy variable “pre_purchase” assumes a value equal to one when the purchasing farm has already made a purchase previously between 2011 and 2020. diversification activities in the literature reviewed, researchers have not considered the role that farm-related activities can have on farm growth and the investment decision. the related activities that can be stimulated by rdp measures allow for a diversification of the farm activity and represent a different form of income for the agricultural firm. three dummy variables were introduced for three agricultural related activities: agrotourism, energy production and contracting. it is expected that conducting agricultural related activities increases the probability that the purchase will occur. family work units family labour can be considered as a fixed input of production within the farm (elhorst, 1993) and elhorst’s research showed that as family labour input increases, investment increases. weiss (1999) and oude lansink et al. (2001) showed that the number of family members affects farm growth and the investment decision. the variable fwu/twu was introduced into the model as a measure of how much the business depends on family labour. it is hypothesised that family farms have a greater interest in investing in the farm and farm growth and thus, as this ratio increases, the probability that the farm invests in land increases. age of farmer and successor the age of the farmer and the presence of the successor can affect the growth of the farm and the investment decision. since there may be several farmers and potential successors with different ages on the same farm, it was decided to create four dummy variables related to the holder and his/her age, and one related to the presence of the successor. in particular, four age ranges were identified to which dummy variables corresponded. each dummy variable relating to the holder takes the value of one if there are no successors for that observation and if the holder or all the holders fall within the range defined by the dummy variable. if the observation corresponds to more than one holder falling in different age groups, all variables related to the holders will have value zero. the variable relating to the presence of a successor will take a value of 1 if there is at least one potential successor between the ages of 1 year and 40 years. a successor was the one who was classified within the dataset as the ‘son’ or ‘grandson’ of the farmer. off-farm income in the literature, it is unclear whether the earning of an off-farm income can be a prelude to leaving the sector or represents a form of income that allows the farm to survive better and not leave the sector (lefebvre et al., 2015; plogmann et al., 2022). based on the available data, a dummy variable was created which takes the value of 1 if the farmer or a member of his or her family who is employed part-time or full-time on the farm earns an off-farm income >2000 euros. 3.2.2. exogenous factors as mentioned before, land is considered an asset that can be used as collateral and a safe investment option. the model introduces two external factors: inflation rate and interest rate. it is assumed that when inflation rates rise, the likelihood of purchasing land also increases. however, buying land may require a significant investment that the farm may need to finance through a bank loan. as interest rates go up, the probability of making such an investment decrease. the inflation rate values, consumer price index-cpi, are obtained from the istat website every december of the reference year, while the 56 bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 silvia russo et al. interest rate is determined by the average annual yield of italian btps (multi-year treasury bonds), which can be found on the website of the italian treasury ministry. 3.2.3. descriptive analysis of explanatory variables table 4 shows the descriptive analysis of the variables included in the model, in particular each variable has two values: one for all farms and one for “buyers” (farms investing in land during the reference period). the average initial area of the sample is 33.7 ha, which increases by about 5 ha if only buyers are taken into account. the value related to value added per hectare (va/ha) exhibits significant variations among the farms. nevertheless, these differences decrease considerably when only the buyers are considered. furthermore, the average value of the variable “va/ha” is lower for the buyers, whereas the median value for buyers is higher than the value within the entire sample. the average value of machinery and plant of the farms that invested in land is more than twice as high as the sample average. table 3 definition of the explanatory variables and expected effects on the decision to buy land. variables specification type of variable expected effect farm structural characteristics uaasq utilised agricultural area square continuous + production specialisation agricultural specialisations considered are: cereals, arable crops, horticulture, fruit crops, olive growing, viticulture, dairy cattle, herbivores, granivores. categorical; non-specialised farms as reference + va/ha ratio between value added (excluding income subsidies and com subsidies) and uaa continuous + va/ twu ratio between value added and total work units continuous + uaasq *production specialisation   continuous*categorical; nonspecialised farms as reference + va/ha*specialisation continuous*categorical; non-specialised farms as reference + va/twu*specialisation continuous*categorical; non-specialised farms as reference + rent/uaa the ratio of the rented uaa to the uaa continuous +/machinery_ plant value value of machinery+ equipment + plant; it represents a proxy variable for level of innovation on farms continuous + income subsidies/ha aid per hectare provided by first pillar and com continuous + investment subsidy investment aid (second pillar) continuous + energy production farm produces renewable energy dummy + subcontracting activities farm carries subcontracting activities dummy + agrotourism farm carries out agrotourism activities dummy + pre_purchase purchases made between 2010-2020 dummy + fwu/twu ratio of family work units to total work units continuous + farmer sociodemographic characteristics farmer_18_39 the farm manager is between 18 and 39 years old dummy + farmer_40_49 the farm manager is between 40 and 49 years old dummy + farmer_50_59 the farm manager is between 50 and 59 years old dummy farmer_over60 the farm manager is aged 60 old or older dummy succ_1_39 there is a potential successor aged between 1 and 39 on the farm dummy + offfarm_income farmer with non-agricultural income >2,000 euro; children/ grandchildren, father-in-law, parent, wife employed part-time or regularly with non-agricultural income >2000 euro dummy + exogenous variables interest rate interest rate recorded for each year on the ministry of the treasury website continuous + inflation_ rate inflation rate taken for each year from the istat website continuous 57farm characteristics and exogenous factors influencing the choice to buy land in italy bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 there are also important differences in both the median value and the standard deviation. for the ratio of rented area to total farm area and of family labour units to total farm hours, there are no important differences between the farms that purchase and the entire sample. it should be noted that the purchasing farms have on average lower fwu/twu than the sample farms. the sample farms received, on average, a higher subsidy/hectare and capital financing than the buying farms. analysing the value of the median and standard deviation of the varitable 4. description of the variables and expected effects. variable min mean median standard deviation max farm structural characteristics uaa 0.01 33.72 15.1 57.7 1754 uaa_buyer 0.23 37.73 19.54 62.17 909.75 uaasq 0 4467.1 227.9 32704.46 3076516.0 uaasq_buyer 0.1 5286 381.8 35908.68 827645 va/ha -209342 7121 2108 32496.66 3792972 va/ha_buyer -2711 6345 3027 10541.81 117597 va/twu -838045 35197 25057 38215.14 1069950 va/twu_buyer -18615 43418 32776 43381.17 468484 machinery and plant value -1628809 34683 6310 114923.9 5450764 machineryand plant value_buyer 0 79280 30239 210057.8 4900435 rent/uaa 0 0.38 0.19 0.41 1 rent/uaa_buyer 0 0.3446 0.1954 0.3785 1 income subsidies/ha 0 260.1 373.8 1071.23 121033.9 income subsidies/ha_buyer 0 386.4 273.6 510.5 6408 investment subsidy 0 583.9 0 6.866.118 639170 capital account_buyer 0 2008 0 15679 435000 energy production 0 0.3679 0 0.18816 1 energy production_ buyer 0 0.075 0 0.264 1 subcontracting activities 0 0.03543 0 0.1848 1 subcontracting activities_buyer 0 0.064 0 0.246 1 agroturism 0 0.04306 0 0.20298 1 agroturism_ buyer 0 0.05 0 0.218 1 pre_purchase 0 0.0026 0 0.051 1 pre_purchase_buyer 0 0.2 0 0.4 1 fwu/twu 0 0.837 1 0.25 1 fwu/twu_buyer 0.017 0.75 0.92 0.29 1 farmer sociodemographic characteristics 18≤farmer≤39 0 0.14 0 0.35 1 18≤farmer≤39_ buyer 0 0.17 0 0.38 1 40≤farmer≤49 0 0.21 0 0.41 1 40≤farmer≤49_ buyer 0 0.25 0 0.43 1 50≤farmer≤59 0 0.23 0 0.42 1 50≤farmer≤59_ buyer 0 0.22 0 0.41 1 farmer>60 0 0.28 0 0.45 1 farmer>60_buyer 0 0.16 0 0.37 1 1≤successor≤39 0 0.09 0 0.29 1 1≤successor≤39_ buyer 0 0.13 0 0.34 1 off_farm income 0 0.16 0 0.36 1 off_farm income_buyer 0 0.24 0 0.43 1 exogenous factors inflation rate -0.20 0.45 0.5 0.42 1.1 interest rate 1.14 1.86 1.81 0.77 3.6 58 bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 silvia russo et al. able for subsidies/ha related to the first pillar, the farms received a higher subsidy and a greater dispersion of values around the mean. for the period taken into account, there are no important differences in the variables related to the exogenous context. 3.4.2. empirical models to the best of our knowledge, there is no research of this type in the literature. therefore, it was decided to run five probit regression models to better understand how different factors might influence the land investment decision: – model 1: the model considers all the variables described above and summarised in table 5 except for the variables “va/ha” and “va/twu”. thus, the model only considers the “uaasq” as the farm size variable. – model 2: same as previous model, but the variable “uaasq” also interacts with specialisation (“uaasq* specialisation”). – model 3: to model 1, the two variables farm productivity per ha (va/ha) and farm productivity per total work unit (va/twu) were included. – model4: same as model 3 adding an interaction between the variable “specialization” and the two variables “uaasq” and “va/ha” (“uuasq* specialisation”, and “va/ha*specialisation”). – model 5: same as model 3 but the “uaasq”, “va/ha” and va/twu variables interact with the specialisation variable. 4. results 4.1. correlation analysis and vif analysis to verify that there is no relationship among the independent variables, a pearson correlation analysis and variance inflation factors (vif) were conducted. the results (appendix 2) show that the indices between the independent variables are far from the threshold values. thus, it can be ruled out that there is multicollinearity between the variables considered in the empirical model developed. 4.2. probit regression models the five implemented models (tables 5, 6, 7, 8, 9) explain between 19 and 20% of the land investment decision of the observed farms between 2013 and 2020. even adding the two variables “va/ha” and “va/twu” and the interaction of the variable “specialisation” with “uaasq”, “va/ ha”, and “va/twu” did not improve the model. as the intercept value also shows, there are other factors that were not considered that influenced the purchase decision. in addition to the value of pseudo r2 not varying, the sign of the independent variables also never changes in the different models implemented. this indicates a good level of robustness of the model. from the analysis and comparison of the five implemented probit regression models, it is evident that the variables that influenced the land investment decision are: the ratio of the rented utilised agricultural area to the utilised agricultural area (rent/uaa), the ratio of family work units to total work (fwu/twu), value of machinery and plant (mechanization_ plant value), production of renewable energy (energy production) and “subcontracting activities”, the age of the farmer, the presence of a successor, “off farm income”, and the two exogenous variables respectively inflation rate and interest rate. these variables are statistically significant in all implemented models despite introducing a new variable and the interaction between variables that differentiate model 1 from the other four models. the results of the five models show that, contrary to what was hypothesized, firm size and land productivity negatively influence the probability of purchasing land, while confirming the positive effect of the variable related to labour productivity. among these three variables, the variable uaasq, although it appears to be less statistically significant, it is the one whose effect remains consistent and stable within the five models despite the introduction of the interaction with the specialisation variable (model 2, model 4 and model 5). in this regard, model 2, model 4 and model 5 show that when the farm specialises in permanent crops such as fruit crops and viticulture, and in the production of horticulture, and herbivore livestock, the initial farm size positively influences the land purchase decision. unlike farm size, the effects of land productivity (va/ha) and labour productivity (va/twu) are lost when these two variables interact with the categorical variable relating to the type of farming (model 3, 4 and 5). specifically, model 4 and 5 show that productivity per hectare increases the probability of buying land when the farm specialises in fruit crops (p<0.05). in contrast to land productivity, labour productivity not only loses its significance when interacting with the specialisation variable but also does not seem to influence the investment decision in any of the specialisation considered. regarding the specialization variable, the results are inconclusive. the impact of the few specialization categories that appear to influence the probability of purchase is neither stable nor consistent across the five 59farm characteristics and exogenous factors influencing the choice to buy land in italy bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 models. specialization affects, not always positively, the dependent variable when considering farms specialized in fruit crops, viticulture, and horticulture. for the latter category, specialization has a negative impact on land acquisition. models 1 and 3 show that farms specializing in permanent crops are more likely to purchase land. the effect of these specialisations changes when the variables land productivity (va/ha) and labour productivity (va/twu) and the interaction between these two variables and specialisation are introduced into the model. the farm is not inclined to purchase as the ratio of rented area to uaa (rent/uaa) and the ratio of fwu to twu (fwu/twu) increases. in all models analysed, these variables are statistically significant (p<0.001) and negatively influence the probability of buying land. consistent with the hypothesis, the variable relating to the value of machinery and plant positively affects the probability of purchase. of the three agricultural-related activities considered, subcontracting activity (p<0.000) and energy production (p<0.5) are statistically signifitable 5. probit regression results based on the model 1 specification. variable estimate std.error statistic p. value   (intercept) -2.02925 0.104478 -19.4227 4.96e-84 *** farm structural characteristics uaasq -1.2e-06 7.1e-07 -1.72448 0.084622 . no specialisation cereals 0.050576 0.061861 0.817562 0.413607 arable crops 0.055266 0.05832 0.947622 0.343322 horticulture -0.13114 0.065892 -1.99019 0.04657 * fruit crops 0.127162 0.055627 2.285978 0.022256 * olive growing -0.01181 0.074624 -0.15824 0.87427 viticulture 0.124953 0.055829 2.238162 0.025211 * dairy cattle 0.039657 0.062406 0.635463 0.525127 herbivores -0.04975 0.05958 -0.835 0.403718 granivores 0.014759 0.074215 0.198868 0.842366 rent/uaa -0.1214 0.033698 -3.60269 0.000315 *** fwu/twu -0.33055 0.049356 -6.69724 2.12e-11 *** machinary_ plant value 3.19e-07 7.02e-08 4.539024 5.65e-06 *** subsidies ue/sau` 1.9e-06 1.06e-05 0.179035 0.85791 capital account 1.74e-06 1.09e-06 1.585145 0.112933 energy production 0.145373 0.05958 2.439938 0.01469 * subcontracting activities 0.211659 0.058079 3.644351 0.000268 *** agrotourism -0.03798 0.061303 -0.61953 0.53557 pre_purchase 7.473506 24.51225 0.304889 0.760451 farm socio-demographic characteristics farmer_18_39 0.223549 0.080916 2.762715 0.005732 ** farmer_40_49 0.176139 0.078891 2.23268 0.02557 * farmer_50_59 0.102417 0.079003 1.296358 0.194852 farmer_over60 -0.04849 0.079899 -0.60692 0.543907 succ_1_39 0.210418 0.082712 2.543965 0.01096 * off_farm income 0.116554 0.032475 3.58899 0.000332 *** exogenous factors inflation rate 0.102711 0.031766 3.233331 0.001224 ** interest rate -0.12116 0.020088 -6.03132 1.63e-09 *** n. observations 84610 n. farms 24212 pseudo r2 0.1969 aic       9449.1   signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1. 60 bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 silvia russo et al. table 6. probit regression results based on the model 2 specification. variable estimate std.error statistic p. value   (intercept) -1.97278 0.106974 -18.4418 6.07e-76 *** farm structural characteristics uaasq -1.8e-05 1.01e-05 -1.76454 0.077641 . no specialisation *ref. cereals 0.014812 0.065264 0.226955 0.820458 arable crops 0.035546 0.061341 0.579489 0.562259 horticulture -0.17526 0.068388 -2.56278 0.010384 * fruit crops 0.080197 0.058392 1.373422 0.169621 olive growing -0.01889 0.079616 -0.2372 0.812499 viticulture 0.081881 0.058412 1.401778 0.160982 dairy cattle 0.016336 0.066267 0.246521 0.805279 herbivores -0.09526 0.062141 -1.53296 0.125286 granivores 0.009716 0.078393 0.123935 0.901367 uaasq*no specialisation *ref. uaasq*cereals 1.63e-05 1.02e-05 1.591816 0.111426 uaa sq*arable crops 1.2e-05 1.05e-05 1.144364 0.252473 uaa sq*horticulture 1.8e-05 1.02e-05 1.76043 0.078335 . uaa sq*fruit crops 2.21e-05 1.06e-05 2.090571 0.036567 * uaa sq*olive growing -2.8e-05 4.34e-05 -0.64604 0.518253 uaa sq*viticulture 2.01e-05 1.05e-05 1.913096 0.055736 . uaa sq*dairy cattle 1.36e-05 1.05e-05 1.304731 0.191985 uaa sq*herbivores 1.72e-05 1.01e-05 1.705913 0.088024 . uaa sq*granivores -1.1e-06 1.61e-05 -0.06829 0.945555 rent/uaa -0.11873 0.033778 -3.51498 0.00044 *** fwu/twu -0.34668 0.050258 -6.8979 5.28e-12 *** machinary_ plant value 3.59e-07 7.3e-08 4.916479 8.81e-07 *** subsidies eu/sau` 1.19e-06 1.1e-05 0.107778 0.914172 capital account 1.66e-06 1.1e-06 1.50566 0.132155 energy production 0.154468 0.059736 2.58584 0.009714 ** subcontracting activities 0.217539 0.058278 3.732783 0.000189 *** agrotourism -0.04088 0.061488 -0.66492 0.506102 pre_purchase 12.8663 40.68004 0.316281 0.75179 farm socio-demographic characteristics farmer_18_39 0.21479 0.081078 2.64919 0.008068 ** farmer_40_49 0.17053 0.079045 2.157388 0.030975 * farmer_50_59 0.09462 0.079167 1.195197 0.23201 farmer_over60 -0.05634 0.080087 -0.70349 0.481748 succ_1_39 0.20331 0.082879 2.453102 0.014163 * off_farm income 0.116426 0.032544 3.577474 0.000347 *** exogenous factors inflation rate 0.103918 0.031817 3.266172 0.00109 ** interest rate -0.12202 0.020131 -6.06119 1.35e-09 *** n. observations 84610 n. farms 24212 pseudo r2 9446.3 aic       0.1986   signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1. 61farm characteristics and exogenous factors influencing the choice to buy land in italy bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 cant in all models and positively influence the probability of the farmer investing in the farmland. all five models show that carrying out agro-tourism activities does not influence the farmer’s decision to invest in land. subcontracting activity and value in machinery and plant are the two variables related to farm characteristics that are most statistically significant (p<0.001) and positively influence the decision to purchase land. from the analyses carried out, the two variables related to agricultural policy subsidies do not seem to influence the decision to invest in land. in all other models the two variables have no effect on the dependent variable. regarding the sociodemographic variables, the presence of the successor aged between 1 and 39 years positively influences the purchase decision in all the models implemented. the age of the farmer/holder also seems to table 7. probit regression results based on the model 3 specification. variable estimate std.error statistic p. value   (intercept) -2.045 0.105 -19.397 8.18e-84 *** farm structural characteristics uaasq -1.5e-06 7.7e-07 -2.00308 0.0451687 * av/ha -1.6e-06 9.46e-07 -1.7294 0.08373801 . av/twu 8.63e-07 3.3e-07 2.616924 0.0088726 ** no specialisation *ref. cereals 0.037215 0.062066 0.599605 0.54876961 arable crops 0.050264 0.058365 0.861204 0.38912569 horticulture -0.10695 0.067537 -1.5835 0.11330853 fruit crops 0.128527 0.055697 2.307599 0.02102146 * olive growing -0.01248 0.074735 -0.16703 0.86734686 viticulture 0.122659 0.056015 2.189751 0.02854231 * dairy cattle 0.025609 0.062677 0.408584 0.68284533 herbivores -0.05549 0.05965 -0.93024 0.35224614 granivores 0.001192 0.076438 0.015592 0.9875601 rent/uaa -0.12716 0.033848 -3.75693 0.00017201 *** fwu/twu -0.33126 0.049916 -6.63638 3.2148e-11 *** machinary_ plant value 2.8e-07 7.26e-08 3.856252 0.00011514 *** subsidies ue/sau` 2.34e-06 1.18e-05 0.197617 0.84334507 capital account 1.77e-06 1.09e-06 1.620226 0.10518384 energy production 0.135872 0.059832 2.270884 0.023154 * subcontracting activities 0.207201 0.058176 3.561636 0.00036855 *** agrotourism -0.03057 0.061388 -0.49806 0.6184439 pre_purchase 7.463387 24.52 0.30438 0.76083871 farm socio-demographic characteristics farmer_18_39 0.223439 0.080945 2.760367 0.00577364 * farmer_40_49 0.173963 0.078917 2.204384 0.02749733 * farmer_50_59 0.100882 0.079033 1.27645 0.20179632 farmer_over60 -0.04797 0.079945 -0.60002 0.54849104 succ_1_39 0.212866 0.082742 2.572654 0.01009221 * off_farm income 0.120334 0.03258 3.693551 0.00022114 *** exogenous factors inflation rate 0.101524 0.031788 3.193758 0.00140434 ** interest rate -0.11983 0.02009 -5.96484 2.4488e-09 *** n. observations n. farms pseudo r2 0.197 aic   9444.2       signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1. 62 bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 silvia russo et al. table 8. probit regression results based on the model 4 specification. variable estimate std.error statistic p. value   (intercept) -1.99374 0.108933 -18.3024 7.92e-75 *** farm structural characteristics uaasq -1.9e-05 1.04e-05 -1.85328 0.063842 . av/ha -7.1e-07 2.63e-06 -0.2676 0.789008 av/twu 1e-06 3.51e-07 2.855578 0.004296 ** no specialisation *ref. cereals 0.072043 0.083218 0.865717 0.386645 arable crops 0.055166 0.066467 0.829975 0.406553 horticulture -0.10531 0.077387 -1.36083 0.173566 fruit crops 0.007115 0.065407 0.108785 0.913373 olive growing 0.068962 0.10743 0.641925 0.520922 viticulture 0.056009 0.063879 0.876809 0.380591 dairy cattle 0.000847 0.077215 0.010964 0.991252 herbivores -0.08485 0.065411 -1.29714 0.194583 granivores 0.007139 0.086704 0.082343 0.934374 uaasq*no specialisation *ref. uaasq*cereals 1.75e-05 1.06e-05 1.649353 0.099075 . uaa sq*arable crops 1.24e-05 1.1e-05 1.134469 0.256598 uaa sq*horticulture 1.9e-05 1.07e-05 1.76849 0.076979 . uaa sq*fruit crops 2.42e-05 1.09e-05 2.223406 0.026188 * uaa sq*olive growing -3.5e-05 4.61e-05 -0.75006 0.453221 uaa sq*viticulture 2.14e-05 1.09e-05 1.967757 0.049096 * uaa sq*dairy cattle 1.5e-05 1.08e-05 1.39341 0.163496 uaa sq*herbivores 1.86e-05 1.04e-05 1.781584 0.074817 . uaa sq*granivores -5.2e-06 1.74e-05 -0.29753 0.76606 va/ha*no specialisation *ref. va/ha*cereals -7.3e-05 5.29e-05 -1.38756 0.165271 va/ha*arable crops -1e-05 8.97e-06 -1.12181 0.261942 va/ha*horticulture -3.7e-06 3.45e-06 -1.08411 0.278315 va/ha*fruit crops 8.52e-06 4.23e-06 2.01371 0.04404 * va/ha*olive growing -3.4e-05 2.67e-05 -1.26886 0.204491 va/ha*viticulture 2.22e-06 3.93e-06 0.564954 0.572105 va/ha*dairy cattle -1.6e-06 8.2e-06 -0.19265 0.847235 va/ha*herbivores -1.1e-05 9.75e-06 -1.13936 0.254554 va/ha*granivores -1.2e-06 3.17e-06 -0.36899 0.712137 rent/uaa -0.12428 0.034028 -3.65222 0.00026 *** fwu/twu -0.3474 0.051617 -6.73025 1.69e-11 *** machinary_ plant value 3.04e-07 7.6e-08 4.001466 6.3e-05 *** subsidies ue/sau` 1.85e-05 1.72e-05 1.07629 0.281797 capital account 1.66e-06 1.11e-06 1.500508 0.133483 energy production 0.147989 0.06 2.46649 0.013644 * subcontracting activities 0.214248 0.058618 3.654989 0.000257 *** agrotourism -0.04545 0.062279 -0.72979 0.465517 pre_purchase 13.97034 39.52004 0.3535 0.723714 farm socio-demographic characteristics farmer_18_39 0.211627 0.081357 2.601209 0.00929 ** farmer_40_49 0.166384 0.079334 2.097254 0.035971 * farmer_50_59 0.090013 0.079462 1.132779 0.257307 (continued) 63farm characteristics and exogenous factors influencing the choice to buy land in italy bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 affect the investment decision. the results from the five models suggest that, in general, being between 18 and 50 years old has a positive effect on the decision to buy land. this probability is even higher if the farmer is aged 18-40. as was hypothesised, the variable on the perception of off-farm income has a positive influence on the purchase decision and is one of the most statistically significant variables (p<0.001). consistent with the hypothesis, the variables relating to the external macroeconomic environment, i.e., the inf lation and interest rates, inf luence the investment decision in opposite direction. as the inflation rate increases, the probability of investment decision increases. as the interest rate increases, the probability that the farmer will invest in land decreases. 5. discussion in comparison to the few empirical studies on the growth of farm size and investment decision including land, this research is conducted on the entire fadn dataset collected at the national level. the analyses are not based on a sample of farms specialising in one type of farming and/or located in a specific and limited geographical area. our data are characterised by 90% observations of farms specialising in 9 different productions and differing in farm and socio-demographic characteristics. this heterogeneity of the analysed sample is due to intrinsic characteristics of the italian agricultural sector. the average surface area of the farms in the sample is about 30 ha above the average uaa recorded in the last istat 2010-2020 census (istat, 2022). this is because the fadn sample is stratified and selects companies with a standard output above 8,000 euro. in any case, the fadn data maintain a certain degree of representativeness of the agricultural sector and represent a useful resource in terms of the amount of data collected in europe (centre for european policy studies, 2008; ciaian et al., 2010). out of the total observations, only 1095 (1.29%) invested in land between 2013 and 2020. the high number of zero-observations can be attributed to the specific characteristics of the land factor and of the land market, as it is unlikely that farms invest in capital goods every year (elhorst, 1993; nilsen and schiantarelli, 2003; oskam et al., 2009). the high number of zero-observations and the complexity of ignoring the heterogeneity effect are some of the reasons why quantitative research using micro-data in the investment decision-making process is challenging (elhorst, 1993). in the empirical studies on the farm size growth and the investment decision, the role of utilised agricultural area is unclear. the five models do not allow to clarify, but to better understand the role of this variable. the initial farm size influences the investment decision negatively but has a different effect depending on farm specialisation. this had already partly emerged in the study conducted by bremmer and oude lansink (2002), which found that uaa had a positive influence on the size growth of arable crops farms and a negative influence on the growth of farms specialized in protected horticulture. in this research, the positive effect of the variable “uaa sq” in the case of farms specialising in fruit crops, viticulture and horticulture can be linked to two different considerations. the first one is linked to characteristics of the fadn data. the mean and median value for permanent crops and horticulture farms is lower than for other crops. this could confirm the hypothesis that when a farm is very large it does not tend to invest in the land input (lefebvre et al., 2015). the second one is related to the intrinsic characteristics of the type of farming. variable estimate std.error statistic p. value   farmer_over60 -0.05757 0.08041 -0.71592 0.474042 succ_1_39 0.197505 0.083187 2.374226 0.017586 * off_farm income 0.116606 0.03276 3.559429 0.000372 *** exogenous factors inflation rate 0.102362 0.031866 3.212269 0.001317 ** interest rate -0.12011 0.020178 -5.95262 2.64e-09 *** n. observations 84610 n. farms 24121 pseudo r2 0.2 aic       9440   signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1. table 8. (continued). 64 bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 silvia russo et al. table 9. probit regression results based on the model 5 specification. variable estimate std.error statistic p. value   (intercept) -1.99498 0.115489 -17.2742 7.36e-67 *** farm structural characteristics uaasq -2e-05 1.11e-05 -1.81586 0.069391 . va/ha -7.9e-07 2.86e-06 -0.27699 0.781788 va/twu 1.32e-06 1.65e-06 0.799903 0.423767 no specialisation cereals 0.07122 0.092882 0.766783 0.443211 arable crops 0.09771 0.082926 1.178278 0.238686 horticulture -0.13821 0.097122 -1.42309 0.15471 fruit crops 0.023628 0.081487 0.28996 0.771847 olive growing 0.051554 0.121524 0.424233 0.671396 viticulture 0.057211 0.077299 0.740133 0.459219 dairy cattle 0.082709 0.093701 0.88269 0.377404 herbivores -0.07159 0.080316 -0.89129 0.372772 granivores -0.03472 0.103522 -0.33543 0.737302 uaasq*no specialisation uaasq*cereals 1.78e-05 1.13e-05 1.576344 0.114946 uaa sq*arable crops 1.49e-05 1.16e-05 1.281408 0.200051 uaa sq*horticulture 1.92e-05 1.16e-05 1.644969 0.099976 . uaa sq*fruit crops 2.51e-05 1.15e-05 2.170636 0.029959 * uaa sq*olive growing -4.5e-05 5.18e-05 -0.86149 0.388966 uaa sq*viticulture 2.2e-05 1.15e-05 1.912193 0.055851 . uaa sq*dairy cattle 1.7e-05 1.14e-05 1.488127 0.136717 uaa sq*herbivores 1.94e-05 1.11e-05 1.747083 0.080623 . uaa sq*granivores -1.1e-05 1.95e-05 -0.56654 0.571024 va/ha*no specialisation va/ha*cereals -8.6e-05 5.8e-05 -1.48766 0.136839 va/ha*arable crops -8e-06 8.82e-06 -0.9121 0.361714 va/ha*horticulture -3.7e-06 3.63e-06 -1.02881 0.303568 va/ha*fruit crops 9.08e-06 4.61e-06 1.968187 0.049047 * va/ha*olive growing -4.3e-05 3.07e-05 -1.41899 0.155902 va/ha*viticulture 1.76e-06 4.38e-06 0.401473 0.688072 va/ha*dairy cattle 6.37e-06 8.45e-06 0.753606 0.451086 va/ha*herbivores -1.1e-05 1.02e-05 -1.0554 0.291243 va/ha*granivores -1.8e-06 3.54e-06 -0.50791 0.611519 av/twu*no specialisation av/twu*cereals 2.36e-07 2.02e-06 0.116892 0.906946 av/twu*arable crops -1.7e-06 2.09e-06 -0.81922 0.412663 av/twu*horticulture 7.66e-07 2.04e-06 0.375434 0.707337 av/twu*fruit crops -7.4e-07 2.09e-06 -0.35518 0.722453 av/twu*olive growing 2.06e-06 3.96e-06 0.519424 0.603465 av/twu*viticulture -5.5e-08 1.86e-06 -0.02988 0.976165 av/twu*dairy cattle -2.7e-06 2.1e-06 -1.29293 0.196035 av/twu*herbivores -5.1e-07 1.9e-06 -0.27174 0.785824 av/twu*granivores 5.28e-07 1.79e-06 0.295245 0.767807 rent/uaa -0.12308 0.034091 -3.6102 0.000306 *** fwu/twu -0.35157 0.051736 -6.79551 1.08e-11 *** machinary_ plant value 3.23e-07 7.85e-08 4.112943 3.91e-05 *** (continued) 65farm characteristics and exogenous factors influencing the choice to buy land in italy bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 unlike annual crops, farms producing permanent crops may prefer to establish new orchards on their own land. obviously, permanent crops require a higher investment cost and return on investment time than annual crops. this could explain why farmers specializing in perennial crops might have an incentive to buy land because it grants them a property right that cannot be guaranteed by the rental contract. this aspect could be particularly relevant in a country like italy where the law allows leases of less than 15 years. this consideration could explain the positive effect exerted by land productivity in the case of companies specialized in fruit crops. with regard to the effect of specialisation, the results showed that specialisation per se does not affect the probability of land purchase of the farms in the sample, contrary to what was assumed on the basis on the theoretical literature. the introduction of interactions of this categorical variable with the variables uaa, va/ha, and va/twu has allowed for a better understanding of the behaviour of these factors. the results of the interactions suggest that the effect of firm size, initiation, and farm productivity may vary according to the specialization. consequently, specialisation plays an important and crucial role in understanding and differentiating the effect of other factors on the probability of land investment. this would confirm what has emerged from the theoretical literature, namely that the factors that can determine farm growth are not independent but interact with each other. the effect of specialisation on farm growth and size had already emerged in the research conducted by akimowicz et al. (2013) according to which specialisation influenced farm size, changes in farm size and growth intensity in the midi-pyrenees region between 2000 and 2007. although theoretically it would be desirable for a farm to have a balance between owned and rented land, as the ratio of rented to total area (rent/uaa) increases, the likelihood of farms increasing their share of owned land decreases. this result could be a confirmation of the findings of the last census of the italian agricultural sector according to which the amount of land managed under lease has increased and this form of management is also becoming established in italy (istat, 2022). the descriptive analysis of the data in annex 1 shows that the farms in the sample specialised in permanent crops have far lower “rent/uaa” ratios than those specialised in annual crops and livestock farming. understanding whether isolating this variable would have a different effect depending on the specialisations would be interesting. the results for the value of capital of machinery and plant confirm what the bremmer et al. (2002), lefebvre variable estimate std.error statistic p. value   subsidies ue/sau` 1.88e-05 1.72e-05 1.093637 0.274114 capital account 1.7e-06 1.11e-06 1.532366 0.125432 energy production 0.147376 0.060086 2.452755 0.014177 * subcontracting activities 0.213639 0.058772 3.635061 0.000278 *** agrotourism -0.04862 0.062315 -0.78028 0.435227 pre_purchase 15.53225 38.49138 0.403525 0.686562 farm socio-demographic characteristics farmer_18_39 0.209433 0.081497 2.569824 0.010175 * farmer_40_49 0.163576 0.079486 2.057915 0.039598 ** farmer_50_59 0.088748 0.079589 1.115078 0.264817 farmer_over60 -0.05863 0.080534 -0.72797 0.46663 succ_1_39 0.194356 0.083337 2.332157 0.019692 * off_farm income 0.113544 0.032812 3.460433 0.000539 *** exogenous factors inflation rate 0.102934 0.031912 3.225551 0.001257 ** interest rate -0.12062 0.020214 -5.96728 2.41e-09 *** n. observations 84610 n. farms 24612 pseudo r2 0.2 aic       9450   signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1. table 9. (continued). 66 bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 silvia russo et al. et al.(2015), and jeong et al. (2022) research had already found. the former had shown how the degree of mechanisation influenced farm growth for arable crops and horticulture in the netherlands. lefebvre et al. (2015)’s study of farmers’ investment intentions in six european countries had shown that farmers are most likely to invest in one asset class after they have already invested in another. the correlation between the intention to invest in two types of assets was also shown between land and the purchase of machinery and machinery replacement. furthermore, research by jeong et al. (2022) had shown that the value of inventory and fixed assets were positively related to land acquisition. to the best of our knowledge, no research in the literature has included variables related to agricultural policies and activities in models to explain farm growth or land investment. the results on subsidies and funding related to european agricultural policies do not confirm the hypothesis that agricultural policies can directly influence the decision to invest in land. rather, subsidies could be seen as a useful tool for the farmer to manage periods of market fluctuations rather than as a form of income to make a long-term investment. the rdp measures do not directly finance land purchases, but they encompass financing for investments in tangible farm assets, innovation, and farm diversification. the correlation analysis allows us to exclude the presence of a relationship between “capital account” and the value of machinery, and of these two variables with the dummy variable relating to subcontracting and energy production. the results for the latter two variables and the forms of income derived from off-farm income lead to the conclusion that the investment in a capital good is supported by forms of income derived from a diversification of the activities carried out by the farmer. the results confirm the conclusions of previous research on the positive effect of the presence of a successor and a young farmer. indeed, in line with the hypothesis, the presence of a young farmer or a farmer under 50 years of age positively influences the probability of purchase. this is probably due to the fact that the age of the holder has an impact on the time horizon of the investment. the results for the introduced exogenous variables confirm the hypothesis. the macroeconomic context influences the investment decision. the inflation rate was not included in the empirical literature analysed on farm size growth and investment decision, while the results regarding the cost of capital confirm what has already found by elhorst (1993) and oskam et al. (2009). it is worth noting that there were no significant changes in interest rates and inflation rates during the considered period. it would be necessary and useful to observe farms over a longer period to fully understand the impact of exogenous factors related to the macroeconomic context, such as those that have occurred in the last two years. the models explain 19% of the land investment decision, suggesting that there are other factors not considered that influence the decision to purchase land. the relative pseudo r2 value is lower than that of other studies on structural change but more in line with studies on investment decision. as in other research (i.e. (akimowicz et al., 2013), the available data and their quality have influenced the choice of explanatory variables and the type of analysis. it was not possible to conduct the analysis on balanced panel data and include explanatory variables related to the financial position of the farm, its local area, and national and municipal land regulation. investments in capital goods could represent a significant investment that may even require a bank loan. these are rational decisions that the farmer makes after analysis of the internal and external business contest. therefore, in order to study and understand this type of investment it would be appropriate to carry out the analysis on farms observed over a long period of time. when testing and implementing the model, we attempted to include the regional variable as a categorical variable. however, this variable reduced the statistical significance of other explanatory variables related to farm structure. the regional variable already contains information related to other variables such as specialisation, uaa, and rent/uaa. this is because the italian territory is highly heterogeneous regarding territorial structure, production, and farm management. for this reason, it was preferred not to include it. furthermore, the land market is thin and local, and the absence of precise geolocation data for farms prevented the consideration of other external factors. farmers tend to buy land near their activity to reduce and avoid downtime (cotteleer et al., 2008). in this regard, the introduction of variables related to the right of pre-emption could be useful in understanding the italian land market, given that such right is provided for within italian legislation. finally, in addition to data availability, the lack of literature has influenced the design of the theoretical framework for developing the conceptual model and the interpretation and discussion of the results. 6. conclusions this research represents a first attempt at an ex-post study using microdata to identify the factors that have influenced the land investment decision in italy by introducing variables related to structural and socio-demo67farm characteristics and exogenous factors influencing the choice to buy land in italy bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 graphic characteristics, economic performances, agriculture policies and the macroeconomic environment. the results showed that more than subsidies provided by agricultural policies, income-generating activities from other on-farm and off-farm activities positively influence land investment. in addition, specialisation appears to be an important factor not so much in the purchase decision, but in understanding and differentiating the effect of other farm structural factors on the likelihood of land investment. the variables rent/uaa and family work units/total work units are the main farm characteristics that negatively influence the probability of purchasing land in italy. as expected, the presence and age of the successor have been confirmed as important sociodemographic characteristics for growth through acquisition. the research shows that the interest rate and inflation rate influence the probability of buying land. the five implemented models explain approximately 20% of the land investment decisions of the analysed farms. therefore, other factors and the interaction between factors can influence farmers’ decisions. the lack of a well-structured database conditioned and limited this research as well as the empirical research analysed in the literature on farm size growth and land investment decisions. in particular, probit analysis on a balanced panel of farms observed over a long period of time was not possible with the available database. investment in land is much less frequent than other types of investment. it is made following a farmer’s consideration of available farm assets, his/her own financial resources, the supply of land on the local land market, and macro-economic factors (i.e. interest rate and inflation rate). for this reason, the analysis of a balanced panel of farms observed for a long time could allow a more accurate analysis of the effect of determinants on the decision to purchase land. in addition, the database influenced the identification and selection of variables that could best capture the determinants that may influence the farmer’s decision and prevented the introduction of variables related to e.g. the financial situation of the farm and land regulation. in the future, the problem of the structured database could be solved by linking the databases available to different italian institutions. the availability of a wellstructured database could be useful to capture and continuously monitor the dynamics and changes within the land market and in farm management. the growth and spread of rented land and the entry into the agricultural sector of young farmers willing to purchase land could require the updating and adaptation of current land policies and regulations that directly and indirectly influence farm management choices and could provide tools, including financial ones, to effectively support generational turnover within the sector by facilitating access to 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2280-6172 | doi: 10.36253/bae-15548 silvia russo et al. ap pe n d ix ap pe nd ix 1 . d es cr ip tiv e an al ys is of sa m pl e fa rm si ze b as ed o n fa rm sp ec ia liz at io n sp ec ia lis at io n m in va /u l m ea n va / t w u m ed ia n va / t w u sd va / t w u m ax va / t w u u a a re n t/ va /h a u a a re n t/ va /h a u a a re n t/ va /h a u a a re n t/ va /h a u a a re n t/ va /h a u a a u a a u a a u a a u a a n o sp ec ia lis ed 0. 01 0 -9 96 4. 8 -2 32 58 9 32 .3 3 0. 38 40 14 .3 26 43 7 15 .7 7 0. 21 13 40 .7 19 54 7 51 .3 9 0. 41 29 37 6. 48 26 15 5. 84 92 0. 1 1 14 86 98 0 57 49 22 an nu al c ro ps 0. 06 0 -7 17 23 -1 53 30 6 36 .1 7 0. 41 93 80 34 72 4 18 0. 28 13 70 24 76 7 60 .2 3 0. 42 35 04 2. 68 36 54 2. 29 17 54 1 13 95 76 4 10 69 95 0 c er ea ls 0. 06 0 -1 32 50 -1 53 30 6 51 .5 1 0. 4 10 39 .6 38 69 4 30 0. 26 86 7. 9 26 34 8 67 .6 0. 41 15 42 39 84 8 12 79 1 10 39 21 .6 50 99 52 ar ab le c ro ps 0. 29 0 -2 03 31 -1 11 45 3 39 .3 0. 44 27 19 .2 32 45 3 21 .3 5 0. 41 10 83 .4 23 38 3 64 .7 0. 42 58 77 .1 38 00 0 17 54 1 12 08 28 .1 10 69 95 0 h or tic ul tu re 0. 07 0 -7 17 23 -1 07 22 7 16 .9 0. 38 25 72 1 33 38 8 4. 5 0. 15 92 82 25 16 2 37 .2 9 0. 42 59 10 8 30 44 7 11 01 .7 5 1 13 95 76 4 61 75 58 pe rm an en t c ro ps 0. 2 0 -3 50 24 -9 61 16 14 .7 3 0. 27 60 33 29 86 4 7. 81 0 39 28 23 01 1 25 .1 3 0. 39 78 20 .8 1 27 84 5 52 6. 87 1 24 89 60 72 87 67 fr ui t c ro ps 0. 2 0 -5 49 7 -2 92 42 14 .0 3 0. 26 65 14 29 66 6 7. 53 0 44 72 23 41 1 24 .5 0. 38 71 82 .5 24 81 0 41 3 1 71 82 .5 41 69 12 o liv e cr op s 0. 85 0 -3 14 8 -2 34 01 18 .8 6 0. 26 29 03 23 26 5 10 .5 5 0 21 99 19 40 9 28 .4 3 0. 39 29 53 .2 18 16 7 39 4 1 67 75 8 20 57 70 vi tic ul tu re 0. 3 0 -3 50 24 -9 61 16 13 .8 5 0. 28 67 41 32 58 9 7. 14 0 41 85 24 34 8 24 .2 5 0. 39 92 99 32 92 4 52 6. 87 1 24 89 60 72 87 67 li ve sto ck se ct or 0. 05 0 -2 09 34 2 -8 38 04 5 51 .7 4 0. 46 66 80 44 47 5 28 0. 44 17 27 31 11 3 73 .4 5 0. 41 44 48 0 49 66 6 16 87 .5 4 1 37 92 97 2 86 30 36 d ai ry c at tle 0. 2 0 -9 22 2 -1 64 89 1 49 .1 3 0. 48 42 92 48 73 6 27 0. 49 29 17 37 91 4 65 .6 6 0. 4 51 19 42 16 3 77 0 1 90 03 9 79 66 55 h er bi vo re s 0. 1 0 -5 28 96 -2 34 75 1 61 .1 5 0. 47 23 47 .2 33 06 6 36 .2 6 0. 44 85 3. 1 24 23 7 83 .4 3 0. 41 12 45 0 36 86 7 16 87 1 40 30 98 58 79 29 g ra ni vo re s 0. 05 0 -2 09 34 2 -8 38 04 5 28 .4 3 0. 44 23 89 8 70 76 8 13 .1 9 0. 37 74 39 52 38 8 43 .2 7 0. 42 10 28 30 76 80 4 51 4. 55 1 37 92 97 2 86 30 36 71farm characteristics and exogenous factors influencing the choice to buy land in italy bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 ap pe nd ix 2 a na ly sis o f r ela tio ns hi ps a m on g in de pe nd en t v ar ia bl es ta bl e a1 . r es ul ts o f p ea rs on c or re la tio n an al ys is (p ar t 1 ). va ria bl e u a as q va /h a va /t w u sp ec ia liz at io n re n t/ u a a fw u /t w u m ac hi ne ry _ pl an t v al ue su bs id ie s u e/ sa u ` c ap ita l ac co un t en er gy pr od uc tio n su bc on tr ac tin g ac tiv iti es ag ro to ur ism pr e_ pu rc h as e fa rm st ru ct ur al ch ar ac te ri sti cs u a as q 1 -0 .0 23 0. 16 7 0. 02 3 0. 04 8 -0 .1 40 0. 20 6 0. 00 0 0. 02 0 0. 04 3 0. 02 7 0. 02 7 0. 00 7 va /h a -0 .0 23 1 0. 12 3 0. 02 8 -0 .0 16 -0 .1 20 0. 01 5 0. 06 8 0. 00 8 0. 01 9 -0 .0 10 0. 01 4 -0 .0 01 va /t w u 0. 16 7 0. 12 3 1 0. 11 9 0. 10 8 -0 .1 52 0. 27 2 0. 10 5 0. 03 8 0. 15 1 0. 04 5 0. 00 4 0. 02 3 sp ec ia liz at io n 0. 02 3 0. 02 8 0. 11 9 1 0. 02 9 -0 .0 28 0. 03 7 -0 .0 04 0. 02 7 0. 05 5 -0 .0 69 0. 00 3 -0 .0 03 re n t/ u a a 0. 04 8 -0 .0 16 0. 10 8 0. 02 9 1 0. 02 3 0. 04 5 0. 01 7 0. 02 5 0. 04 7 0. 06 5 -0 .0 07 -0 .0 05 fw u /t w u -0 .1 40 -0 .1 20 -0 .1 52 -0 .0 28 0. 02 3 1 -0 .1 84 -0 .0 55 -0 .0 64 -0 .0 76 -0 .0 01 -0 .0 60 -0 .0 24 m ac hi na ry _ pl an t v al ue 0. 20 6 0. 01 5 0. 27 2 0. 03 7 0. 04 5 -0 .1 84 1 0. 04 4 0. 12 1 0. 28 1 0. 08 3 0. 03 7 0. 04 1 su bs id ie s u e/ sa u ` 0. 00 0 0. 06 8 0. 10 5 -0 .0 04 0. 01 7 -0 .0 55 0. 04 4 1 0. 02 9 0. 01 5 -0 .0 04 -0 .0 15 0. 00 0 c ap ita l a cc ou nt 0. 02 0 0. 00 8 0. 03 8 0. 02 7 0. 02 5 -0 .0 64 0. 12 1 0. 02 9 1 0. 05 8 0. 00 8 0. 01 7 0. 03 0 en er gy p ro du ct io n 0. 04 3 0. 01 9 0. 15 1 0. 05 5 0. 04 7 -0 .0 76 0. 28 1 0. 01 5 0. 05 8 1 0. 06 0 0. 07 1 0. 01 8 su bc on tr ac tin g ac tiv iti es 0. 02 7 -0 .0 10 0. 04 5 -0 .0 69 0. 06 5 -0 .0 01 0. 08 3 -0 .0 04 0. 00 8 0. 06 0 1 0. 00 0. 01 ag ro to ur ism 0. 02 7 0. 01 4 0. 00 4 0. 00 3 -0 .0 07 -0 .0 60 0. 03 7 -0 .0 15 0. 01 7 0. 07 1 -0 .0 04 1 0. 00 3 pr e_ pu rc h as e 0. 00 7 -0 .0 01 0. 02 3 -0 .0 03 -0 .0 05 -0 .0 24 0. 04 1 0. 00 0 0. 03 0 0. 01 8 0. 01 0 0. 00 3 1 fa rm so ci ode m og ra ph ic ch ar ac te ri sti cs fa rm er _1 8_ 39 2e -0 5 -0 .0 10 0. 00 4 0. 04 2 0. 16 8 0. 00 0 0. 03 3 -0 .0 01 0. 03 8 0. 00 1 0. 00 8 0. 02 1 0. 00 5 fa rm er _4 04 9 4e -0 3 0. 01 4 0. 04 6 0. 04 0 0. 09 9 -0 .0 42 0. 02 0 -0 .0 03 0. 01 3 0. 01 1 0. 01 9 0. 01 3 0. 00 9 fa rm er _5 05 9 -6 e03 0. 00 4 0. 01 5 -0 .0 06 -0 .0 08 -0 .0 17 -0 .0 14 -0 .0 04 -0 .0 17 0. 00 2 0. 00 7 -0 .0 13 -0 .0 02 fa rm er _o ve r6 0 -2 e02 -0 .0 19 -0 .0 83 -0 .1 13 -0 .1 83 0. 02 6 -0 .0 85 -0 .0 08 -0 .0 37 -0 .0 49 -0 .0 53 -0 .0 49 -0 .0 20 su c c _1 _3 9 2e -0 2 0. 01 2 0. 02 3 0. 07 0 -0 .0 25 0. 03 3 0. 06 4 0. 00 2 0. 01 7 0. 04 6 0. 02 3 0. 04 7 0. 01 2 o ff _f ar m in c o m e -3 e03 -0 .0 18 -0 .0 64 -0 .0 15 -0 .0 73 -0 .0 16 0. 00 5 -0 .0 18 -0 .0 03 0. 00 1 0. 00 2 0. 03 7 0. 01 9 ex og en ou s f ac to rs in fla tio n ra te 5e -0 3 0. 00 11 51 0. 00 8 0. 00 1 -0 .0 03 0. 00 6 -0 .0 01 -0 .0 27 -0 .0 08 -0 .0 01 -0 .0 01 -0 .0 03 -0 .0 03 in te re st ra te 6e -0 3 0. 00 87 41 -0 .0 15 0. 00 1 -0 .0 32 -0 .0 04 -0 .0 01 -0 .0 11 0. 01 7 -0 .0 20 -0 .0 13 -0 .0 16 -0 .0 16 72 bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 silvia russo et al. table a1. results of pearson correlation analysis (part 2). variable farmer_ 18_39 farmer_ 4049 farmer_ 5059 farmer_ over60 succ_1_39 off_farm income inflation rate interest rate farm structural characteristics uaasq 0.000 0.004 -0.006 -0.015 0.022 -0.003 0.005 0.006 va/ha -0.010 0.014 0.004 -0.019 0.012 -0.018 0.001 0.009 va/twu 0.004 0.046 0.015 -0.083 0.023 -0.064 0.008 -0.015 specialization 0.042 0.040 -0.006 -0.113 0.070 -0.015 0.001 0.001 rent/uaa 0.168 0.099 -0.008 -0.183 -0.025 -0.073 -0.003 -0.032 fwu/twu 0.000 -0.042 -0.017 0.026 0.033 -0.016 0.006 -0.004 machinery_ plant value 0.033 0.020 -0.014 -0.085 0.064 0.005 -0.001 -0.001 subsidies ue/sau` -0.001 -0.003 -0.004 -0.008 0.002 -0.018 -0.027 -0.011 capital account 0.038 0.013 -0.017 -0.037 0.017 -0.003 -0.008 0.017 energy production 0.001 0.011 0.002 -0.049 0.046 0.001 -0.001 -0.020 subcontracting activities 0.01 0.02 0.01 -0.05 0.02 0.00 0.00 -0.01 agrotourism 0.021 0.013 -0.013 -0.049 0.047 0.037 -0.003 -0.016 pre_purchase 0.005 0.009 -0.002 -0.020 0.012 0.019 -0.003 -0.016 farm socio-demographic characteristics farmer_18_39 1 -0.211 -0.222 -0.256 -0.134 -0.003 -0.010 0.005 farmer_4049 -0.211 1 -0.283 -0.325 -0.170 0.021 0.002 0.014 farmer_5059 -0.222 -0.283 1 -0.342 -0.179 0.019 0.002 -0.014 farmer_over60 -0.256 -0.325 -0.342 1 -0.206 -0.119 0.001 -0.004 succ_1_39 -0.134 -0.170 -0.179 -0.206 1 0.103 0.006 -0.006 off_farm income -0.003 0.021 0.019 -0.119 0.103 1 -0.005 -0.014 exogenous factors inflation rate -0.010 0.002 0.002 0.001 0.006 -0.005 1 0.326 interest rate 0.005 0.014 -0.014 -0.004 -0.006 -0.014 0.326 1 73farm characteristics and exogenous factors influencing the choice to buy land in italy bio-based and applied economics 14(1): 49-73, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-15548 table a2. results of variance inflation factors (vif). variable gvif df gvif^(1/ (2*df )) uaasq 1.307725 1 1.143558 va/ha 1.225749 1 1.107136 va/twu 1.327513 1 1.152178 specialization 1.593554 9 1.026225 rent/uaa 1.120124 1 1.058359 fwu/twu 1.224457 1 1.106552 machinary_ plant value 1.420266 1 1.191749 subsidies eu/sau` 1.053369 1 1.026337 capital account 1.027879 1 1.013844 energy production 1.172852 1 1.082983 subcontracting activities 1.038119 1 1.018881 agrotourism 1.02539 1 1.012615 pre_purchase 1 1 1 farmer_18_39 5.514689 1 2.348338 farmer_4049 6.76246 1 2.600473 farmer_5059 6.554576 1 2.560191 farmer_over60 6.17286 1 2.484524 succ_1_39 4.445275 1 2.108382 off_farm income 1.056985 1 1.028098 inflation rate 1.11444 1 1.05567 interest rate 1.117942 1 1.057328 building sustainable futures: the bio-based fertilizer case-study carla abitabile, andrea arzeni*, federica cisilino, matteo orlando is there an animal food kuznets curve, and does it matter? vito frontuto1,*, tommaso felici2, vania andreoli3, marco maria bagliani1,#, alessandro corsi1 do agroholdings cope better with the agency problem? empirical evidence from corporate farms in russia alisher tleubayev1,*, yerzhan syzdykov2 farm characteristics and exogenous factors influencing the choice to buy land in italy silvia russo1,*, meri raggi2, barbara bimbati3, andrea povellato3, davide viaggi1 soils and ecosystem services: policy narratives and instruments for soil health in the eu greta winkler1,*, luciano pagano2, daniele vergamini2, fabio bartolini1 © 2025 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae bio-based and applied economics bae bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 citation: jena, p. k., paltasingh, k. r., mohapatra, s., & mishra, a. (2025). impact of weather variability on crop yields and land use dynamics in eastern india: shortand long-term effects. bio-based and applied economics 14(2): 31-49. doi: 10.36253/bae-17008 received: december 12, 2024 accepted: june 26, 2025 published: september 30, 2025 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor in chief: silvia coderoni associated editor: laura garcia herrero orcid sm: 0000-0002-3627-8739 impact of weather variability on crop yields and land use dynamics in eastern india: shortand long-term effects pratap kumar jena1, kirtti ranjan paltasingh2, souryabrata mohapatra3,*, ashok mishra4 1 department of economics, maharaja sriram chandra bhanjdeo university, baripada, india 2 department of economics, ravenshaw university, cuttack, india 3 school of liberal arts, indian institute of technology jodhpur, jheepasani, india 4 morrison school of agribusiness, arizona state university, mesa, united states *corresponding author. email: smohapatra@iitj.ac.in abstract. weather variability disrupts food grain production and agricultural sustainability. while existing literature highlights the stationary relationship between weather variables and agricultural outcomes, it often overlooks their bearing on land use changes. this study investigates the dynamic effects of weather variations on crop yields, land use and intensity in odisha, eastern india, using district-level data from 2001-18. by employing a ‘panel auto-regressive distributive lag (p-ardl) model, we assess longand short-term relationships between weather parameters and agricultural yields. results reveal a negative marginal impact of rainfall deviation on yield, ranging from -0.16 for wheat to -0.48 for green gram in the long term. in the short term, however, the marginal impact is positive for some pulses (green gram, black gram) and oilseeds (groundnuts). weather variability has adversely affected the intensity of land use but has induced crop diversification in both the short and long term. keywords: climate change, crop yield response, land use intensity, panel ardl model, odisha, india. jel codes: c33, q15, q18, q54. 1. introduction over the last two decades, the issue of weather fluctuations and their impacts has been debated intensively among policymakers, scientists and academia globally. evidence shows varying effects of long-term changes in weather patterns in various regions, with some areas severely affected and others observed to have had observable positive effects (mohapatra et al., 2023). the impact of weather variability differs across countries’ levels of development. it harms developing countries, whereas it carries possibly low to moderate impacts on developed countries, exacerbating the weather’s impact on inequality (dudu and çakmak, 2018; asogwa et al., 2022; xiang https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae https://doi.org/10.36253/bae-17008 https://doi.org/10.36253/bae-17008 https://orcid.org/0000-0002-3627-8739 mailto:smohapatra@iitj.ac.in 32 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 pratap kumar jena et al. et al., 2022). yet, this pattern is not universal. some developed nations have endured significant damage; for example, the catastrophic floods in japan in 2018 increased vulnerability, drove up healthcare costs and inflicted major losses in the manufacturing sector (lin et al., 2020; yamamoto and naka, 2021; yoshida et al., 2023). similarly, devastating floods in germany in 2021 and spain in 2024 imposed substantial economic burdens on their capitalist economies (martin-moreno et al., 2025). agricultural competitiveness increases the temperature among countries. for example, agrarian competition in developing countries has increased the temperature but declined in developed countries (nugroho et al., 2023). in addition, weather variations have considerably impacted crop production, food availability and quality. the dynamic effects of weather shocks on agricultural output in peru show an adverse impact of weather shocks measured by excess heat or rainfall, which had a delayed negative impact on agricultural production, and its magnitude depends on various factors (crofils et al., 2025). the connectedness and variability effects transmission between weather variables and agricultural productivity in morocco suggest that weather variability increases the spillover effects transmitted to agriculture (belcaid and el ghini, 2020). the dynamic impact of weather changes on vegetable price fluctuations in china observed that the specific vegetable price was affected by changes in particular weather factors, which were timevarying (yang et al., 2022). however, in the case of developing countries like india, the consequence of weather variations is predicted to be harmful, and the impact could be severe in the near future. agricultural productivity will be reduced by 4.5% with a 1°c increase in temperature in india, and it is predicted that the total factor productivity in agriculture will decline across all states by 2050 (pattanayak et al., 2021). several empirical studies have evidenced the deleterious impact of weather variations on farm production and productivity in india and some other countries (arora, 2019; xie et al., 2019; chandio et al., 2020; seven and tumen, 2020; mohapatra et al., 2025). however, research on the issue of weather variations and their dynamic impact on land use patterns and land use intensity is scarce. indeed, this problem has been ignored in the case of indian agriculture1, specifically at the micro level in the agriculture of eastern india. in this paper, we thus attempt to answer the research question of how weather variations influence crop yields and land use dynamics in eastern india. 1 except a recent study by birthal et al. (2021). at the micro level, weather fluctuations significantly affect the exposure and vulnerability of one ecosystem by altering the water supply and food production, damaging infrastructure and causing morbidity and mortality, as noted by the intergovernmental panel on climate change (ipcc, 2014). all the outcomes have implications for land use. in the farm sector, the long-term changes in weather factors affect land use patterns and intensity through their impacts on crops’ comparative advantage: yields or profits (birthal et al., 2021). farmers decide their acreage allocation after carefully analysing their prospects for profit (yield) during the weather shock and their ability to cope with it. again, in developing economies, institutional mechanisms for managing weather risks, such as crop insurance, soil management practices (soil health cards) and so on, are inadequate and inaccessible to resource-poor smallholders. thus, fluctuations in rainfall, a rise in mean temperature and frequent weather extremes severely threaten the food system and food security through changes in the pattern and intensity of land use (opoku mensah et al., 2023; siotra and kumari, 2024). our study bridges the research gap by empirically assessing the dynamic impact of weather variations on land use patterns and intensity, contributing to better micro-level risk management strategies. odisha is a major agriculture-intensive state on the eastern front of india. the farming sector contributes about 9% of the total rice production and 4.22% of the total food grain production of indian agriculture (barik, 2023). the state’s economy and the livelihood of most of its people depend extensively on agriculture and allied activities. the agriculture sector contributes about 20.6% of gross value added to odisha’s economy and supports about half of the state’s total employment, as reported by the government of odisha (goo, 2022). however, being a poor agricultural state on india’s east coast, with about 30% of the population below the poverty line, the state is highly vulnerable to weather shocks (rout, 2021). this case resonates with much of india and the surrounding south and southeast asian economies. furthermore, odisha’s location (extending from 17.31-22.31n latitude and 81.31-87.29e longitude) in the tropical zone makes it susceptible to high temperatures and humidity fluctuations. for instance, in recent decades, odisha has frequently faced weather extremes, such as droughts, floods, storms, tropical cyclones and more (srinivasa rao et al., 2016). a limited number of studies in the literature have evaluated the impact of weather variations on the farm production and productivity of odisha agriculture (hoda et al., 2021; senapati, 2022). however, the effects of weather variations on land use are yet to be examined. hence, the paper discusses 33 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 impact of weather variability on crop yields and land use dynamics in eastern india the dynamic effects of weather variations on crop yields, land use and intensity in eastern india, setting it apart from traditional studies that primarily focus on the impact of climate on yields. the study uses the panel auto-regressive distributed lag (p-ardl) method using district-level data2 for 2001-2018, with 540 total observations. land use is the ratio of land used for agriculture to non-agricultural uses. finally, variables like crop diversification represent land use patterns, and cropping intensity represents land use intensity. the results underscore the significance of dynamic effects, revealing patterns and insights that static analyses would miss. this study makes several significant contributions to the literature. first, it enhances our understanding of how land use patterns and intensity respond to longterm changes in precipitation and temperature, which is crucial for improving crop yields, productivity, food security and livelihood strategies in india and other developing and emerging economies. second, the findings can assist policymakers in formulating incentives to encourage future adaptation strategies in response to increasing weather risks. third, the study employs both short-term and long-term assessments, providing a sophisticated methodological approach. this helps in designing appropriate coping mechanisms to address the adverse impacts of weather variability and limited land. finally, the findings are applicable to various other states in india, including west bengal, bihar, assam and andhra pradesh, which share similar agro-weather zones and agricultural practices. the rest of the paper is organised as follows: section 2 reviews the literature, providing the background and context for the study; section 3 describes the data sources and methodology used in the research, detailing the analytical techniques employed; section 4 discusses the study’s findings, interpreting the results and their implications; section 5 concludes the paper, summarising the key insights and suggesting policy implications based on the findings. 2. brief literature the impact of weather variations is assessed by the effects of weather factors, like precipitation and temperature, on crop yields, and the magnitude of these effects is contingent upon the degree of change in these variables. 2 districts serve as the primary administrative units within indian states. india has approximately 766 districts, each with an average population of around 1.86 million. the state of odisha comprises 30 districts. accordingly, our dataset includes a total of 540 observations spanning an 18-year period. over the years, climate variability has influenced crop production in high-yield and high-technology agricultural areas, particularly in agriculturally based economies in developing countries (lemi and hailu, 2019). the impact of climate change on crop yields was different across crops in thailand. climate change negatively affected longan yield, whereas it positively affected maize and had no significant effect on rice yield. there was no effect of rainfall on crop yields (kyaw et al., 2023). crop yields are highly sensitive to temperature globally, whereas their extent varies in continents (liu et al., 2020). furthermore, the combination of reduced precipitation and elevated temperatures has the potential to result in an escalation of global food prices. to mitigate this issue, farmers must use adaptive strategies to withstand the impacts of weather variations (tuihedur rahman et al., 2018). studies found that compared with rainfall deficit, excess temperature negatively affects agricultural productivity more (taraz, 2018; zampieri et al., 2018). tesfaye and tirivayi (2020) noted a maximum temperature increase in the pre-monsoon period (i.e., april-june). pattanayak and kumar (2021) have estimated that agricultural productivity has reduced by 4.5% with a 1°c increase in temperature in india. in addition, they predicted the total factor productivity in agriculture would decline across all states by 2050. moulkar and peddi (2023) state that the effects of weather variables on crop yield vary in seasons and across crops. generally, the monsoon and winter crop yields are more sensitive to temperature (minimum and maximum) and rainfall. vogel et al. (2019) found that variation in crop yield is associated with temperature-related extremes. climate change has threatened agricultural production in food-insecure regions of asian countries (habib-ur-rahman et al., 2022). in india, the growing threat of climate change – manifested through rising greenhouse gas emissions, erratic weather patterns, and increasing temperature anomalies – poses serious challenges to agricultural sustainability. the ipcc (2007) has underscored the longterm risks of continued fossil fuel reliance, projecting a global temperature rise of up to 6.4°c and a sea level increase of 59 cm by the century’s end if current trends persist. empirical evidence demonstrates a strong nexus between economic growth, energy use, and emissions: a 1% increase in fossil fuel consumption and gdp can raise co₂ emissions by 0.67% and 0.61%, respectively, while a corresponding rise in renewable energy use and agricultural productivity reduces emissions by 3.65% and 0.41% (raihan and tuspekova, 2022). both long-run and short-run relationships exist between agricultural production, economic growth, and co₂ emissions (ali et al., 2019). greenhouse gas emissions tend to rise with the 34 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 pratap kumar jena et al. intensification of agriculture and allied activities but can be mitigated through increased forest cover (ahmed et al., 2025). in the indian context, agricultural expansion and intensification are also linked to rising emissions. a study from bangladesh shows that a 1% increase in agricultural land, crop output, and allied activities contribute to emissions growth of 0.25%, 0.29%, and 0.40%, respectively (raihan et al., 2023). since bangladesh is nearer to odisha and other states in eastern india, the results are more relevant to the present study context. the implications of this growth–energy–agriculture–climate nexus are particularly acute in india’s agrarian economy, where rainfall-dependent farming remains dominant. weather variability – particularly deviations in rainfall and evapotranspiration – has directly undermined crop yields, including key staples and pulses such as groundnut and chickpea. despite being one of the most climate-sensitive sectors, agriculture continues to receive limited policy and investment attention (belford et al., 2022). climate-induced weather extremes, including droughts and heatwaves, disrupt food production, inflate prices, and depress consumption, ultimately worsening household welfare, especially among smallholders and marginal farmers (alvi et al., 2021). in sweden, such extremes cause major yield losses (sjulgård et al., 2023). in india, rainfall and evapotranspiration negatively affect groundnut and chickpea yields. methodologically, studies assessing the agricultural impacts of climate variability in india employ two dominant approaches: general equilibrium and partial equilibrium models. while general equilibrium models offer system-wide analysis, their application is limited in developing contexts due to data and specification issues (deressa, 2007). the partial equilibrium framework – particularly econometric models such as the ricardian approach and crop simulation models – is more prevalent. the ricardian model, grounded in ricardo’s (1817) theory of land rents and later adapted by mendelsohn et al. (1994), estimates the net impact of climate variables on farmland values or productivity. it remains a widely used tool for assessing the welfare effects of climate change, though its application in india often requires careful calibration to account for heterogeneity in climate zones, cropping systems, and socio-economic conditions (paltasingh and goyari, 2015; hashida and lewis, 2022). to address these challenges, india must adopt integrated climate adaptation strategies. these include educating farmers on climate risks, promoting diversified and resilient cropping patterns, strengthening agricultural markets, and improving access to weather forecasts and financial safety nets. as tripathi and mishra (2017) argue, effective adaptation requires locally contextualised measures grounded in an understanding of regional weather variability and its interaction with socio-economic drivers. while existing literature predominantly addresses the climate impact on yields, this paper uniquely explores the dynamic effects of weather variation on crop yields, land utilisation patterns and intensity in eastern india. this study employs a p-ardl model on micro-level data, a methodological innovation compared to previous studies. the chosen methodology allows for an in-depth analysis of dynamic effects, providing insights that static models fail to capture. first, the p-ardl model is particularly well-suited for datasets where variables exhibit a mixed order of integration, i.e., a combination of the i(0) and i(1) series (pesaran and shin, 1998). this flexibility aligns with the nature of our data, as confirmed by the panel unit root tests. in contrast, dynamic panel data models (e.g., arellanobond gmm) and panel cointegration techniques often require all series to be integrated in the same order, typically i(1), which was not the case in our study. second, the p-ardl framework allows for variable-specific lag structures, enabling us to more accurately capture the dynamic relationships among variables with differing temporal responses. conversely, models such as dynamic fixed effects or traditional panel cointegration models generally impose uniform lag lengths across variables, which may lead to model misspecification when applied to heterogeneous datasets like ours. third, the p-ardl model provides a clear and tractable single-equation framework that simultaneously estimates both short-run dynamics and long-run equilibrium relationships (pesaran et al., 1999). this dual capability simplifies interpretation and policy relevance. in contrast, cointegrationbased models (e.g., pedroni, kao, westerlund tests) and system-based approaches often involve more complex estimations, with increased computational burden and interpretation challenges, especially in applied settings. last, p-ardl is well-suited for panels with a moderate time dimension and limited cross-sections, such as ours (t = 18 years; n = 30 districts), and performs robustly in small samples. in contrast, gmm-based dynamic panel data models can suffer from finite-sample bias and overidentification problems, especially when the number of instruments exceeds the number of cross-sectional units. 3. materials and methods 3.1 data sources and variable construction the study uses data from 30 districts of odisha (see figure 1) from 2001 to 2018 from various second35 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 impact of weather variability on crop yields and land use dynamics in eastern india ary sources such as the department of agriculture and farmers’ empowerment (odisha agriculture statistics 2001-18), envis centre of odisha’s state of environment and the crop production statistics information system (cpsis). this study uses specific data on the area, production and yield of nine major crops – rice, wheat, maize, horse gram, green gram, black gram, groundnut, rapeseed and sesamum – these crops are among the major seasonal crops produced in odisha, india3. furthermore, the study uses other control variables, such as fertiliser consumption and agricultural credit, along with a group of weather factors, such as rainfall and maximum and minimum temperatures. we use the rainfall deviation from the normal rainfall ( ) instead of rainfall itself. the expression is standardised as , where is the standard deviation of rainfall (see table 1 for variable description). 3 cereals: rice and wheat – the two primary food grains, forming the staple diet and occupying the largest share of cultivated land. pulses: green gram, black gram, and horse gram – important protein sources, particularly in rainfed and marginal areas. these legumes also contribute to soil nitrogen fixation and improve cropping system sustainability. oilseeds: groundnut, rapeseed, and sesamum – key oil-producing crops suited to odisha’s agro-climatic conditions, cultivated for edible oil and as cash crops. data on weather factors related to the monsoon cropping season are collected from secondary sources, comprising different phenological stages of crop growth, such as sowing and growing. the crop yield of all major crops is taken as the dependent variable in different crop yield response models. the study uses three definitions of agricultural land use patterns: (1) cropping intensity, (2) crop diversification, and (3) ratio of land use to nonfarm land use pattern. cropping intensity is the ratio of gross cropped area to net sown area as , where gca is the gross cropped area, and nsa is the net sown area in odisha’s agriculture, which measures how intensively the land is used for farming purposes. the second definition is crop diversification. crop diversification is a measure that indicates the degree of diverse patterns of land use in a farming system. usually, farmers adopt crop diversification as a traditional strategy to minimise weather risks. this helps stabilise farm income volatility and augments income levels (basantaray et al., 2022). it also helps retain and revive soil health. several diversification measures are available in the literature, but we use the composite entropy index4, def ined as: where cei has two components: distribution and number of crops (n) or diversity. here is the share of th crop in total operational landholding. the value of the cei increases with the rise in the number of crops and decreases in concentration. the value of cei ranges between zero and one, indicating no diversification to perfect diversification. the third variable that captures the land use rate is the rate of agricultural use of land, which is defined again as the ratio of total cultivable (000’ ha) land under agricultural use to non-agricultural use and is formally expressed as where alu stands for the rate of agricultural use of land, is the total land under agricultural use, and lnat is the total land under non-agricultural use. this measure gives us a broad idea of how the cultivable landmass is used for farming purposes and the trend over time. all these variables are gathered annually, and the total observations are 540. all variables and their definitions are described in table a1. 3.2 empirical estimation methods to prepare for the panel ardl estimation, we first conduct descriptive statistics and correlation analysis 4 this cei, a modified version of shannon-weaver index, rectifies its drawbacks and assesses both richness and evenness which makes it possible to compare crop diversity across different places (tesfaye and tirivayi, 2020). so, it is an improved measure of diversification. figure 1. the shaded area within the map of india indicates the study region, odisha. source: authors’ illustration. note: the map delineates the state borders. 36 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 pratap kumar jena et al. to examine the central tendencies, dispersion, and pairwise relationships among the study variables – namely, crop yield, land use, rainfall deviation, and temperature variation. to assess the stationarity of these variables, we apply panel unit root tests, including the levin-lin-chu (llc) test (levin et al., 2002) and the im-pesaran-shin (ips) test (im et al., 2003), both of which are extensions of the augmented dickey-fuller (adf) test and assume cross-sectional independence. the p-ardl model is suitable when the panel data series are either i(0), i(1), or a combination of both, but not i(2). if any variable is found to be integrated with order two (i(2)), we exclude it from the estimation to maintain model validity. the panel adf unit-root test estimates the following model: (1) where in eq. (1), is the random process of one variable, the period t = 1, 2…, t and i = 1, 2,…,n represents the cross-sectional units/groups. if the unit-root test results show that the variables are stationary, either in i(0) or i(1) or mixed order of integration, then the p-ardl model is applied to explore the impact of weather variations and other factors on crop yields, rate, pattern and intensity of land use. otherwise, in the case of non-stationary or stationary in different order, either i(1) or i(2), the todo-yamato causality test and vector error-correction (vec) model are appropriate to measure the effects. we estimated the following baseline model using panel ardl to carry out our objective as we get mixed stationarity conditions of variables: (2) the dependent variable is estimated for all nine major crops and three different forms of land use. the other control variables, such as areas under highyielding varieties (hyva), total fertiliser consumption (fert) and agricultural credit from banks (crd), are also modelled. although the variables are in different units, they are taken in logarithmic values to estimate the log-linear model from eq. (2) for better analysis of results. the coefficients can be directly interpreted as elasticity values. 3.3 p-ardl model specification to term a p-ardl model, we must first determine the optimal lag length. this has been done using the schwarz information criterion (sic), which indicates one lag as the optimum lag for use in the p-ardl model, which estimates both long-term and short-term relationships between variables5. moreover, the dynamic heterogeneous p-ardl model developed by pesaran and shin (1998) and pesaran et al. (1999) can be expressed within the (p,q) lag approach. the period t=1, 2…t and groups i=1, 2…n. it is expressed as: (3) where is the dependent variable (crop yields, cropping intensity and rate of land use), is (k×1) vector of explanatory variables. the parameter vector of the order (k×1) is the coefficient vector of the lagged dependent variable, is the vector of coefficients of all explanatory variables to be estimated, is a unit-specific fixed effect and is an error term. both p and q are optimal lag orders. if the variables in eq. (3) are i(1) and cointegrated, formerly, the error term is an i(0) for all i. a salient feature of cointegrated variables is that they respond to any deviation in the long-term equilibrium relationship. this means that the deviation from the long-term equilibrium captured by the error correction model (ecm) reveals the short-term dynamics of the variables. hence, the short-term relationship between the study variables, the error correction model (ecm), is estimated based on the framework of (p, q) as: (4) where ; ; ; j = 1, 2, …p-1 and ; j=1, 2, 3…q-1. in the above eq. (4), is the ecm coefficient for each unit, and its value indicates the adjustment rate to the long-term equilibrium. the term should be negative and significant. if then we don’t have a long-term relationship. pesaran, shin and smith (1999) developed a pooled mean group (pmg) estimator that combines mean and pooling residuals, and this test incorporates the intercept, short-term coefficients and different error variances across the groups. however, based on this test, 5 this estimation can be conducted using stata, eviews, or any suitable statistical software for time series or panel data analysis. for this study, we employed eviews 13. the underlying code used in the analysis is available from the corresponding author upon reasonable request. 37 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 impact of weather variability on crop yields and land use dynamics in eastern india long-term coefficients are assumed to be equal across the groups, like fixed effect estimators. this p-ardl model can be applied when variables are of the order i (0), i (1) or a mix of both. a flowchart showing related data diagnosis is presented in figure 2. all estimations, including the p-ardl model and diagnostic tests6, were carried out using eviews-13 software. 4. results and discussion 4.1 results of descriptive statistics table 1 reports the descriptive statistics of the study variables7. the results indicate that among the cereal crops, rice yield has the highest mean value (1580.30 kg/ha) and the standard deviation (589.85), followed by wheat and maize yields (1393.79 kg/ha and 1376.35 kg/ ha), and wheat yield has the lowest standard deviation (521.79). the pulse crop yield indicates that horse gram yield has a higher mean value of (317.35 kg/ha) than 6 in the case of omitted variable bias or endogeneity problem, the p-ardl model has advantage as it takes care of endogeneity by selecting the optimal time lag in the model estimation. the problem of serial correlation, normality and heteroscadasticity can be performed in diagnostic tests. 7 the definition of study variables with their units of measurement are shown in table a1. green gram yield (297.08 kg/ha) and black gram yield (290.69 kg/ha), but horse gram has a lower standard deviation (99.10) than other pulse productions. among the oilseeds, groundnut has a higher mean value (1145 kg/ha) and standard deviation (411.85) than rapeseed and sesamum seeds. the skewness values of all crops’ productions are negative, except for maize and rapeseed, and kurtosis values are positive, indicating negatively skewed crop production. similarly, the crop yield statistics indicate that the mean value of rice, wheat, maize and groundnut yields are positive and other crop yields are negative. the standard deviation of maize yield is the highest (802.86 kg/ha), followed by rice yield, whereas the least standard deviation is found in horse gram (99.10). among the weather variables, rainfall has higher mean and standard deviation values (153.15 mm and 2.10mm) than temperatures. the mean values of fertiliser consumption and agriculture land use are (320.83kg/ ha and 0.26). the mean values of cropping intensity and its standard deviation values are positive. the skewness and kurtosis values are also positive and very high. the cross-correlations between study-selected crop yields and other variables are reported in table a2, which indicates that all crop yields are positively influenced by their production, fertiliser consumption, agricultural credit and agricultural land use. among the weather variables, rainfall positively correlates with crop yields, whereas the temperature correlation varies among crops. figure 2. flowchart illustrating data diagnosis. source: authors’ illustration. note: p-ardl specification is excluded. 38 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 pratap kumar jena et al. the maximum temperature negatively correlates with the crop yields, except for the wheat yield. on the other hand, the minimum temperature8 positively correlates with crop yields except for wheat and rapeseed crops. the cropping intensity is positively correlated with rice, wheat, maize and groundnut, and it is harmful to other crop yields. the jarque-beara test statistics and their respective probability values indicate that except for the max_t variable, all variables are significant at a 1% significance level, which means the study variables are normally distributed. 4.2 results of the unit-root test and var lag selection before applying the p-ardl model, it is necessary to test the stationarity condition of variables by using unitroot tests, which will determine the reliability of the subsequent model to determine whether variables are i(0) or i(1) or a mixed order of both, but should not be i(2). the stationarity of all variables is checked using im et al. (2003), and levin et al. (2002), and the results are reported in table a3. table a3 reveals that the crop production and yields are stationary at their level values except for wheat yield, maize, horse gram and sesamum yields, and all of them are stationary at their first difference. 8 the minimum temperature is the minimum annual average temperature. in odisha, for that matter in india, this minimum temperature 23 °c is absolutely normal during monsoon season. the max goes to 40-42 °c here during summer and around 30-35 °c during most of time in a year. other variables like fertiliser consumption, agricultural credit, weather variables like rainfall deviation and temperatures, crop diversification, cropping intensity and rate of agricultural land use are stationary at their level. we find a mixed order of stationarity of variables from the estimated unit-root test results, which suggests the suitability of the p-ardl model. the estimation of the p-ardl model needs an appropriate lag length. the lag selection criteria decide the optimum lag length in model estimation. table a4 reports the results of the optimum lag selection criteria, where the sic suggests that one is the optimum lag, the least lag among all other criteria. we use one lag, as indicated by the sic. 4.3 crop yield response to weather variation table 2 reports both the long-term relationship between the study variables and the error correction results for the short-term relations between the variables. from the long-term equation, it is found that agricultural credit significantly and positively influences all crop yields, which means if agricultural credit increases by 1%, rice yield will increase by 0.04%, wheat yield by 0.13%, horse gram yield by 0.08%, green gram (moong) yield by 0.19%, black gram yield by 0.16%, groundnut yield by 0.08%, rapeseed yield by 0.11% and sesamum seed by 0.09%. similarly, weather variables, such as rainfall deviation and temperatures, have mixed effects on crop yields. if rainfall deviation (both excess or defitable 1. variables tags and descriptive statistics variables mean std. dev. skewness kurtosis jarque-bera probability rice yield (ricey) 1580.30 589.85 0.37 3.15 13.02 0.00 wheat yield (wheaty) 1393.79 521.79 0.15 8.18 606.32 0.00 maize yield (maizey) 1376.35 802.86 2.95 20.12 7376.99 0.00 horse gram yield (hgramy) 317.80 99.10 0.86 6.49 339.92 0.00 green gram yield (mongy) 297.08 114.94 1.03 5.23 207.45 0.00 urad yield (urady) 290.69 109.84 0.97 4.99 174.19 0.00 groundnut yield (gnuty) 1145.17 411.85 1.73 8.97 1069.40 0.00 rapeseed yield (rpseedy) 832.24 144.51 3.17 37.92 6486.00 0.00 sesamum yield (sesay) 255.92 105.98 0.64 3.49 41.95 0.00 credit (cr.) 2.03 0.53 -0.24 2.48 11.04 0.00 fertiliser consumption (fert) 320.83 268.14 20.24 43.73 4615.00 0.00 rainfall deviation (rain_dev) 153.15 2.10 0.88 10.89 1414.87 0.00 maximum temperature (max_t) 35.25 0.03 -0.05 2.58 3.92 0.14 minimum temperature (min_t) 23.00 0.05 -0.43 3.89 33.34 0.00 cropping intensity (cropint) 2.32 2.73 3.77 19.14 6879.20 0.00 rate of agr. land use (alu) 0.26 0.39 0.52 5.18 126.44 0.00 crop diversification (cei) 0.21 0.11 0.29 2.19 21.77 0.00 source: authors’ calculation. note: initial data is parenthesis. 39 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 impact of weather variability on crop yields and land use dynamics in eastern india cit) increases by 1%, yields of wheat decrease by 0.16%, maize by 0.46%, green gram by 0.48%, black gram by 0.06%, rapeseed by 0.28% and sesamum seed by 0.35%, but the yield of horse gram increases by 0.15%. rainfall deviation has no significant impact on the yields of rice and groundnut. this is because rice is a water-guzzling crop, and any positive or negative deviation in rainfall affects its yield the least unless there is a large variation. odisha agriculture is highly dominated by rice, and farmers mostly grow modern varieties that are either droughtor f lood-resistant, depending on the state’s agro-weather zone. similarly, a 1oc increase in maximum temperature significantly increases rice yield by 1.28%, maize by 0.66% and sesamum by 0.90%. on the other hand, a 1oc increase in maximum temperature leads to a decrease in the yields of wheat by 0.48%, horse gram by 0.49%, black gram by 1.54%, groundnut by 1.83% and rapeseed by 0.78%; there is no significant impact on the green gram yield. the minimum temperature increase negatively affects wheat, green gram, rapeseed and sesamum yields but positively impacts rice and black gram yields. except for agricultural credit and weather factors, other variables, such as the area under high-yield-variety seeds and fertiliser consumption, significantly affect crop yields. it is found that the hyva coefficients are significant and positive except for groundnut, which means if the area under high-yield-variety crops increases by 1%, the yield of rice increases by 0.87%, wheat by 0.08%, maize by 0.11%, moong by 0.22%, black gram by 0.15%, rapeseed by 0.05% and sesamum seed by 0.03%. in contrast, groundnut yield decreases by 0.03%. similarly, suppose fertiliser table 2. results of p-ardl model rice wheat maize horse gram moong urad groundnut rapeseed sesa long term elasticities hyva 0.87*** 0.08*** 0.11*** 0.12*** 0.22*** 0.15*** -0.03* 0.05*** 0.03*** (0.02) (0.01) (0.03) (0.02) (0.03) (0.02) (0.02) (0.01) (0.01) fert -0.07** -0.06* 0.16*** 0.15*** 0.07 -0.14*** 0.14** -0.12*** 0.04 (0.04) (0.03) (0.04) (0.03) (0.06) (0.03) (0.05) (0.02) (0.04) crd 0.04*** 0.13*** 0.01 0.08*** 0.19*** 0.16*** 0.08*** 0.11*** 0.09*** (0.01) (0.01) (0.02) (0.01) (0.02) (0.01) (0.01) (0.001) (0.01) rain_dev -0.05 -0.16*** -0.46*** 0.15*** -0.48*** -0.06* 0.06 -0.28*** -0.35*** (0.04) (0.05) (0.09) (0.05) (0.10) (0.03) (0.05) (0.03) (0.04) max_t 1.48*** -0.48* 0.66** -0.49** 0.40 -1.54*** -1.83** -0.78** 0.90** (0.40) (0.27) (0.34) (0.23) (0.53) (0.18) (0.40) (0.30) (0.37) min_t 0.94*** -0.89*** -0.08 0.17 -0.67*** 0.61*** 0.15 -0.87*** -0.48*** (0.15) (0.15) (0.16) (0.12) (0.22) (0.04) (0.11) (0.17) (0.14) short term elasticities ecm(-1) -0.69*** -0.80*** -0.33*** -0.60*** -0.46*** -0.35*** -0.46*** -0.46*** -0.71*** (0.09) (0.08) (0.06) (0.08) (0.05) (0.05) (0.07) (0.10) (0.14) ∆ (hyva) 0.11 0.02 0.17*** 0.23*** 0.15*** 0.27*** 0.15*** 0.20*** 0.16*** (0.09) (0.02) (0.03) (0.03) (0.04) (0.03) (0.03) (0.03) (0.03) ∆(fert) -0.04 -0.11* 0.15 0.04 0.05 0.05 0.11 -0.03 0.01 (0.08) (0.06) (0.11) (0.08) (0.06) (0.06) (0.09) (0.08) (0.13) ∆(cr.) -0.02 -0.04 -0.08 -0.03 0.001 -0.09** -0.03 -0.08 -0.05 (0.06) (0.06) (0.05) (0.06) (0.03) (0.04) (0.03) (0.05) (0.06) ∆(rain_dev) 0.14 0.25*** 0.07 -0.04 0.18*** 0.14*** 0.15*** 0.11 0.18* (0.07) (0.07) (0.09) (0.06) (0.06) (0.06) (0.05) (0.06) (0.11) ∆(max_t) -0.67*** -0.17*** -0.94 0.5 -1.20*** -2.63*** 0.86 0.02 0.81 (0.09) (1.27) (1.01) (0.37) (0.60) (0.74) (0.83) (0.83) (0.84) ∆(min_t) 0.01 0.09 0.3 0.69* -0.28 -0.54 0.41 1.01* -0.31 (0.90) (0.47) (0.73) (0.40) (0.46) (0.51) (0.62) (0.59) (0.72) const. -5.31*** 1.62 0.27*** -0.30*** 0.21*** 0.07*** 1.27*** 0.91 -0.26*** (0.73) (0.16) (0.05) (0.03) (0.03) (0.01) (0.20) (0.20) (0.05) source: authors’ calculation. note: *** p < 0.01%, ** p< 0.05% and * p < 0.10. the coefficients of lagged values of yields are not reported. standard errors in parenthesis. 40 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 pratap kumar jena et al. consumption increases by 1%. in that case, crop yields decrease, such as rice yield decreasing by 0.07%, wheat yield decreasing by 0.06%, black gram yield decreasing by 0.14% and rapeseed yield decreasing by 12%. the results of the short-term equation (eq. 4) show that the ecm coefficients are significantly negative in all crop yields, indicating a need for short-term adjustment for a long-term equilibrium relationship between the study variables. the coefficient of agricultural credit is significantly negative only on the black gram yield, whereas there is no significant effect in all other crop yield models. the rainfall deviation significantly positively impacts green gram, black gram, groundnut, wheat and sesamum yields. since these are mostly pulses and oilseeds grown in rain-fed areas, they consume less water. so, any negative deviation may not harm their yield to a great extent. however, any positive deviation may positively affect their yield because it offers the required amount of moisture rather than creating a flood-like situation. these crops are grown when the monsoon period is over. so, the positive deviation over the normal trend helps the crops rather than creating a flood-like situation and reaps better yields of these dry-area crops. the maximum temperature significantly negatively impacts rice, wheat, green gram (moong) and black gram yields. similarly, the minimum temperature significantly and positively affects the horse gram and rapeseed yield, but it has no significant effect on all other crops. fertiliser consumption is significantly negative only in wheat yield. the high-yielding-variety coefficients are significant and positive for almost all crops, which indicates that if the area under high-yielding varieties increases in the short term by 1%, the yields of maize, horse gram, moong, black gram, groundnut, rapeseed and sesamum seed rise. 4.4 land use response to weather variation table 3 reports the p-ardl results of the impact of weather factors and other control variables on land use patterns and intensity. since we have used three variables representing the rate, pattern and intensity of land use, i.e., rate of land use (alu), crop diversification (cei) and cropping intensity (cropint), we present the results separately. here, we also have both long-term and short-term dynamics. we offer the long-term results first and then the short-term effects. we observed that rainfall deviation significantly harms cropping intensity. more specifically, its elasticity coefficient indicates an additional 1% increase in rainfall deviation, reducing the cropping intensity by 0.16%. it hampers both the gross cropped area and the net sown area. however, it affects the gross cropped area more than the net sown area, reducing the cropping intensity. this is because rainfall deviation, either upward or downward, creates a floodor drought-like situation affecting farming practices and ultimately decreasing the gross cropped area. on the other hand, the net sown area is somewhat determined by the irrigation potential being used. so, it reduces the numerator of the ratio more than the denominator, reducing the cropping intensity. sometimes, delays in the arrival of monsoons also adversely affect soil preparation and sowing/planting of seedlings. this also harms farming by reducing the gross cropped area. naturally, a delayed monsoon will have more devitable 3. results for land use response model, p-ardl, odisha, india cropint alu cei long term elasticities fert 0.08*** (0.02) 0.53*** (0.08) 0.07 (0.06) cred 0.02*** (0.01) 0.19*** (0.02) 0.19** (0.06) rain_dev -0.16*** (0.03) -0.20* (0.11) 0.11** (0.05) max_t -0.68*** (0.16) -1.36** (0.68) 0.29*** (0.01) min_t -0.01 (0.07) -1.06*** (0.29) 0.58 (0.61) short term elasticities ecm (-1) -0.52*** (0.05) -0.66*** (0.08) -61*** (0.06) ∆(croint (-1)) -0.338 *** (0.044) ∆(alu (-1)) -0.449*** (0.042) ∆(cei (-1)) -0.584*** (0.049) ∆(fert) 0.01 (0.02) 0.16 (0.12) 0.21* (0.12) ∆(crd) 0.03* (0.01) -0.05 (0.08) 0.02 (0.05) ∆(rain_dev) -0.01 (0.01) -0.15 (0.11) 0.88*** (0.12) ∆(max_t) -0.14 (0.23) -2.96* (1.59) 1.12* (0.56) ∆(min_t) -0.20* (0.10) -0.28 (1.36) -0.56 (0.41) const. 1.53 (0.14) -2.28*** (0.26) 1.47** (0.65) source: authors’ calculation. note: *** p < 0.01%, ** p < 0.05% and * p < 0.10. variables are naturally log-transformed. in case of rainfall deviation, the absolute value is taken for log transformation. standard errors in parenthesis. 41 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 impact of weather variability on crop yields and land use dynamics in eastern india ations in precipitation by disturbing its spatiotemporal distribution. our result aligns with other studies in african countries, such as those of duku et al. (2018). similarly, as the negative elasticity coefficient of maximum temperature suggests, a 1% increase in maximum temperature (max_t) may reduce the cropping intensity (cropint) by 0.68% over the long term. a plausible explanation is that a rise in temperature reduces the yields of certain crops, discouraging the allotment of land to those crops (birthal and hazrana, 2019). similarly, zampieri et al. (2018) argued that excess temperatures and heatwaves affect crop yield more than drought and rainfall deviation in arid and semi-arid tropical zones. our results are consistent with birthal et al. (2021). similarly, looking into the long-term impact of weather factors on crop diversification (cei) and rate of land use (alu), we observe that most weather factors significantly induce crop diversification but harm the rate of land use in the long term. in fact, the elasticity coefficient of rainfall deviation concerning crop diversification shows that an additional 1% deviation in rainfall induces 0.11% more crop diversification. this is because farmers adopt diversification as an ex-ante coping mechanism to counter the production shock due to weather variations (gouraram et al., 2022). a diversified crop portfolio helps farmers increase the resilience of their farm production system and considerably lower their exposure and vulnerability to the harmful effects of changing environmental conditions (basantaray et al., 2022). even in rainfed agriculture, vertical diversification9 is adopted as an effective risk management strategy (prasada, 2020). similarly, maximum temperature also positively inf luences crop diversification. higher temperatures reduce soil moisture and cause dryness. so, farmers shift the cropping pattern toward pulses, which require less water and little moisture in the soil (zampieri et al., 2018). the elasticity coefficient is 0.29, implying that an additional increase in average temperature by 1oc can induce crop diversification by 29% during the monsoon season on a long-term basis. moniruzzaman (2019) found similar evidence in neighbouring bangladesh. the author simulated that increased temperature increased crop diversification over the baseline scenario of temperature and rainfall during the rainy and summer seasons. however, rainfall deviation (rain_dev) and temperature (both max_t and min_t) adversely affect the rate of land use (alu) in the long term. as observed from the elasticity coefficient, an additional 1% increase in deviation reduces the rate of agricultural use of land by 9 refers to the combination of multiple cropping and industrialization. farmers invest in horticulture, agroforestry, livestock raising, the cultivation of fragrant herbs and more, as part of this type of diversification. 0.20%. rainfall deviations harm the intensity of land use and thereby affect farm production. as stressed above, any deviation from the normal level affects soil preparation and the intensive use of land for cultivation. similarly, if the average temperatures (max_t and min_t) increase by 1oc, agricultural use of land decreases by 1.36% and 1.06%, respectively, which indicates that deviations from normal temperature levels have a severe adverse impact on the rate of agricultural use of land. along with weather factors, we observed that over the long term, fertiliser consumption and agricultural credit affect cropping intensity, crop diversification and rate of agricultural land use. this is because easy and smooth access to credit facilitates the investment in necessary inputs, such as seeds and other equipment, and helps prepare the soil in advance. so, this positively impacts cropping intensity and crop diversification, leading to a rise in land use. regarding the short-term results, we observed that all forms of land use are negatively affected by their lagged values, which is counterintuitive as per the rational expectation theory. observing the weather factors, we find that rainfall deviation does not significantly affect cropping intensity and rate of land use in the short term, though the coefficients are intuitively negative. however, rainfall deviation does induce a substantially higher degree of diversification. the elasticity coefficient indicates that a 1% rise in rainfall deviation brings 0.88% more crop diversification. this may be attributed to the fact that a greater deviation (either positive or negative) has immediate implications for crops. a positive deviation leads to the submergence of crops, while a shortfall of rainfall from the normal level creates a drought-like situation, leading to crop failure. in both cases, it induces more diversification as an immediate strategy in the short term to counter the crop loss caused by weather variation. many empirical studies argue that greater agro-biodiversity contributes to increased crop yield and reduced production risk (di falco and veronesi, 2014). even farmers adopt crop diversification as an ex-ante measure to cope with weatherinduced income shocks (moniruzzaman, 2019). so, rainfall deviation has a positive impact on crop diversification. the minimum temperature significantly affects the rate of agricultural land use and crop diversification, while its elasticity coefficient in the case of cropping intensity is statistically insignificant. however, the maximum temperature adversely affects the rate of agricultural land use, while its impact on crop diversification is positive. among other variables, we observed that credit access significantly impacts cropping intensity in the short term, while fertiliser consumption significantly induces a higher degree 42 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 pratap kumar jena et al. of crop diversification. both elasticity coefficients are statistically significant only at a 10% probability level. 4.5 diagnostic checks the p-ardl model results are diagnosed using the wald diagnostic test. this has the null hypothesis that the covariate’s coefficient is equal to zero. the wald coefficient diagnostic test results of crop yields and land use patterns are reported in tables a5 and a6, which indicate that the estimated f-statistic values are highly significant, meaning that the variables are significant to the model fit. we used different model residual diagnostic tests, such as the serial correlation lm test, normality tests, and heteroscedasticity test, to check the model’s diagnostic. table a7 presents the results of model residual diagnostic tests. all the test results indicate that all null hypotheses of all tests are statistically significant at a 1% level. that means there is a rejection of the null hypothesis concerning land use variables in all three models, which means the models are free from serial correlation and heteroscedasticity and are normally distributed. 5. conclusions the novelty of this study lies in considering, first, the heterogeneous impact of weather variables such as rainfall deviation and temperature, which vary among crops under study, similar to the evidence found by guntukula and goyari (2020). second, rainfall deviation harms the rate and intensity of land use over both the long and short terms, but rainfall deviation induces more crop diversification. so, weather variations may distort the rate and intensity of land use, but the change in land use patterns is favourable. the study findings have significant implications for food security and the long-term viability of the industrial system. because almost all crop yields have been negatively damaged, predictions for food security and the sustainability of food production appear bleak. however, based on the findings, we make policy recommendations to reduce the impact of weather variations on agricultural production in the study area. adaptive policies, strategies and weather financing must be implemented. because temperatures and the timing and amount of necessary rainfall have changed and are expected to vary in the study region, farmers must adopt new crop types and greater diversification techniques to combat weather hazards. by highlighting dynamic effects, this study provides a more nuanced understanding of weather-induced changes in land-use intensity; the policy suggestions hover around land management and efficient use of resources. this implies developing and initiating widespread adoption of modern stress-tolerant cultivars resilient to various weather-induced stresses and making an optimal crop choice – crop diversification strategies. such adoption should be followed by acreage allocation, considering the expected weather shocks. to help farmers, the government could invest in research and development activities to develop crop varieties resilient to extreme weather shocks. policymakers could provide funding for renovating the extension network to disseminate early weather warnings, thus helping sustain the optimal acreage allocation and intensity of land use. crop diversification is an effective ex-ante coping mechanism to counter the harms induced by weather variation. however, government policy has not been conducive to a diversified production system since we somehow promote a monoculture of certain crops that the green revolution initiated. odisha agriculture used to be diversified indigenously. but after the green revolution of the 1960s, rice has become the staple crop grown here at the expense of pulses. though some recent concerted efforts have been made to promote crop diversification and return to indigenous cropping patterns, they have not been widely promoted. the “millet mission” is one such effort by the government of odisha, but we need more such schemes that encourage diversification. we, too, found that weather variations would induce more diversification ex-post, but adopting diversification as an ex-ante strategy will be more effective. agriculture in odisha was traditionally characterised by a highly diversified cropping pattern. however, following the 1960s, it became increasingly concentrated around rice cultivation. a return to diversified farming systems is proposed, encouraging the cultivation of millets, pulses, and other traditional crops to restore balance and resilience in the agricultural landscape. in addition, smallholders must be covered by crop insurance and hedging as part of the formal modern weather risk-mitigating mechanism. the paper’s emphasis on dynamic effects provides a critical contribution to the field, paving the way for future studies to explore these dimensions further. despite the robustness of the empirical strategy and the authenticity of the data sources, this study has certain limitations. the analysis is confined to 30 districts in odisha, which may limit the generalisability of the findings to other indian states or agro-climatic zones. while all variables were sourced from the envis centre of odisha’s state of environment – an authoritative platform supported by the ministry of environment, forest and climate change – certain critical variables such as irrigation infrastructure, mechanisation, and technological adoption 43 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 impact of weather variability on crop yields and land use dynamics in eastern india were not included. these factors can influence crop yield, cropping intensity, and land use, and their omission may lead to potential biases. moreover, the weather variables used – rainfall deviation and average temperature – do not capture the full spectrum of extreme events, intraseasonal variability, or asymmetric effects of droughts and floods. additionally, while the panel ardl framework accommodates dynamic relationships, concerns related to endogeneity and omitted variables, such as input prices or market access, remain. the focus on major crops also excludes the vulnerability of minor crops and allied sectors, while the lack of socio-economic disaggregation restricts the insights for targeted policy interventions. these limitations offer avenues for future research. subsequent studies could incorporate more granular weather data, such as temperature thresholds, rainfall timing, or extreme weather indices, alongside farmlevel primary data to better capture farmer behaviour and adaptation responses. expanding the spatial scope to include other states with similar agro-ecological characteristics would enhance the external validity of the results. furthermore, integrating irrigation access, mechanisation levels, crop insurance coverage, and institutional support would provide a more comprehensive understanding of the drivers of land use decisions and crop productivity under climate stress. the proposed model could also be assessed for its effectiveness in medium-term out-of-sample forecasting using approaches such as the auto-regressive integrated moving average with exogenous variables (arimax) model (kozicka et al., 2018; mantziaris et al., 2024) offering insights into yield responses under future weather variability, which would be of interest to both policymakers and potential investors. future research could also explore gender and caste-based vulnerability to weather shocks, assess the role of policy instruments such as pm-kisan or the millet mission, and apply structural models or simulations to evaluate the effectiveness of proposed adaptation strategies. taken together, the integrated approach provides valuable insights that can inform future agricultural adaptation strategies in weather-sensitive regions, thereby enriching the empirical base for designing climate-resilient agricultural policies. acknowledgment this paper was presented at the odisha research conclave, organised at ravenshaw university, cuttack, in 2023. the authors are grateful to the participants for their valuable feedback. special thanks are extended to amarendra das (niser bhubaneswar) for his insightful suggestions. the authors also acknowledge the handling editor and anonymous reviewers of the journal for their constructive comments, which have substantially improved the quality and clarity of this paper. any remaining errors are the sole responsibility of the authors. funding the authors gratefully acknowledge the financial support provided by the higher education department, government of odisha, india. references ahmed, n., xinagyu, g., alnafissa, m., ali, a., and ullah, h. 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https://doi.org/10.1186/s12889-023-15205-w https://doi.org/10.1088/1748-9326/aaa00d https://doi.org/10.1088/1748-9326/aaa00d 47 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 impact of weather variability on crop yields and land use dynamics in eastern india appendix table a1. description of study variables. variables definitions hypothetical sign dependent variable yld crop yield (kg/hectare) ci cropping intensity is defined as the ratio of gross cropped area to net sown area (index) cei crop diversification is measured by the composite entropy index alu rate of agricultural land use is defined as the ratio of land used for agriculture to non-agricultural use (index) independent variables fert total fertiliser consumption (kg/ha) + hyva area under hyv or modern variety seeds (000’ acres) + crd agricultural credit from banks (rs. billion) + rain_dev standardised deviation of average rainfall during the kharif season (millimetres) from its historical normal value +/max_t average maximum temperature during kharif season (celsius) min_t average minimum temperature during kharif season (celsius) source: authors’ annotation. note: complied from envis centre of odisha’s state of environment, odisha agriculture statistics (various issues) and other sources. table a2. relationship between crops and key variables. cereals pulses oilseeds rice wheat maize horse gram moong urad groundnut rapeseeds sesamum hyva 0.52 0.40 0.22 0.03 0.22 0.32 0.33 0.51 0.08 fert 0.22 0.02 0.26 0.25 0.40 0.34 0.00 0.30 0.14 crd 0.27 0.23 0.49 0.37 0.53 0.47 0.30 0.34 0.35 rain_dev 0.26 0.01 0.02 0.12 0.08 0.13 0.12 0.09 0.06 max_t -0.05 0.00 -0.08 -0.05 -0.06 -0.04 -0.02 -0.05 -0.22 min_t 0.01 -0.06 0.05 0.01 0.01 0.07 0.18 -0.12 0.09 cropint 0.11 0.03 0.03 -0.19 -0.05 -0.14 0.06 -0.15 -0.31 al 0.45 0.07 0.18 0.24 0.29 0.30 0.38 0.12 0.25 source: authors’ estimation. table a3. results of unit root test of study variables. levin, lin & chu t* im, pesaran and shin w-stat intercept intercept with trend intercept intercept with trend level 1st difference level 1st difference level 1st difference level 1st difference ricey -7.84*** -6.49*** -6.65*** -5.12*** ricep -6.36*** -1.86** -7.97*** -5.76*** wheaty -1.09 -8.40*** -2.30** -3.87*** -5.13*** wheatp -3.91*** -4.31*** -3.76*** -3.26*** maizey -2.46** -1.74** -1.31 -13.75** -3.02*** maizep -1.28 -11.38*** -1.05 -8.99*** -2.66*** -1.66* (continued) 48 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 pratap kumar jena et al. levin, lin & chu t* im, pesaran and shin w-stat intercept intercept with trend intercept intercept with trend level 1st difference level 1st difference level 1st difference level 1st difference hgramy -6.43*** -6.46*** -6.04*** -7.21*** hgramp 0.55 -8.64*** -4.01*** 0.3 -13.71** -3.42*** moongy -3.66*** -8.17*** -1.18 -18.08** -6.41*** moongp -5.54*** -3.12*** -6.69*** -3.87*** urady -3.15*** -4.57*** -2.43*** -5.17*** uradp -4.36*** -4.49*** -4.46*** -4.71*** gnuty -5.14*** -6.96*** -3.46*** -4.66*** gnutp -5.14*** -2.33** -3.46*** -0.92 -10.14*** rapeseedy -3.34*** -5.96*** -2.21** -6.19*** rpseedp -6.16*** -7.27*** -6.73*** -7.38*** sesay -4.58*** -6.67*** -6.05*** -7.23*** sesap 1.90 -5.98*** -0.97 -4.07*** 1.03 -11.81*** -0.96 -10.1*** fert -5.38*** -1.08 -1.93** -2.83*** -1.55* cr -5.95*** -3.05*** -0.02 -8.68*** 0.82 -6.92*** rain_dev -4.62*** -1.67 2.25** -7.71*** -5*** max_t -8.53*** -6.92*** -6.96*** -4.76*** min_t -4.95*** -3.46*** -4.78*** -1.86** cropint -5.85*** -2.98*** -4.17*** 0.29 -7.52*** ln -9.55*** -10.90*** -5.84*** -6.87*** source: authors’ estimation. note: *, ** and *** denote 10%, 5% and 1% significance levels, respectively. table a4. wald test results of crop yields. rice wheat maize horse-gram green-gram urad groundnut rapeseeds sesamum f-stat 603.01*** 87.07*** 18.34*** 43.51*** 57.83*** 183.42*** 25.79*** 1130.29*** 1156.39*** chi-sqr 4221.08*** 609.50*** 128.39*** 304.57*** 404.80*** 1283.94*** 180.50*** 7912.02*** 8094.75*** null hypothesis: c (1) = c (2) = c (3) = c (4) = c (5) = c (6) = c (7) = 0 c (1) 0.87 0.08 0.11 0.12 0.22 0.15 -0.03 0.05 0.44 0.02 0.01 0.03 0.02 0.03 0.02 0.02 0.01 0.03 c (2) -0.07 -0.06 0.16 0.15 0.07 -0.14 0.14 -0.12 -0.12 0.04 0.03 0.04 0.03 0.06 0.03 0.05 0.02 0.02 c (3) -0.04 0.13 0.01 0.08 0.19 0.16 0.08 0.11 0.14 0.01 0.01 0.02 0.01 0.02 0.01 0.01 0.00 0.01 c (4) 0.30 0.10 0.07 0.03 -0.01 0.09 0.12 0.04 0.06 0.01 0.02 0.04 0.02 0.04 0.02 0.02 0.01 0.01 c (5) -0.05 -0.16 -0.46 0.15 -0.48 -0.06 0.06 -0.28 -0.29 0.04 0.05 0.09 0.05 0.10 0.03 0.05 0.03 0.02 c (6) 1.48 -0.48 0.66 -0.49 0.40 -1.54 -1.83 -0.78 0.48 0.40 0.27 0.34 0.23 0.53 0.18 0.40 0.30 0.17 c (7) 0.94 -0.89 -0.08 0.17 -0.67 0.61 0.15 -0.87 0.10 0.15 0.15 0.16 0.12 0.22 0.04 0.11 0.17 0.05 source: authors’ estimation. note: *, **, and *** denote 10%, 5% and 1% significance levels, respectively. table a3. (continued). 49 bio-based and applied economics 14(2): 31-49, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-17008 impact of weather variability on crop yields and land use dynamics in eastern india table a5. wald test results of land use. cropint alu cei f-statistic 30.17*** 59.26 33.17*** chi-square 150.83*** 296.28 165.83*** null hypothesis: c (1) = c (2) = c (3) = c (4) = c (5) = 0 c (1) 0.08 0.53 0.09 0.02 0.08 0.01 c (2) 0.02 0.19 0.04 0.01 0.02 0.01 c (3) 0.16 0.20 0.12 0.03 0.11 0.03 c (4) -0.68 1.36 -0.61 0.16 0.68 0.19 c (5) -0.01 1.06 -0.02 0.07 0.29 0.09 source: authors’ estimation. note: *** denotes a 1% significance level. table a6. p-vec model residual diagnostic test. cropint alu cei serial correlation lm tests (h0: no serial correlation at lag order h) lag 1 164.39*** 155.64*** 112.67*** lag 2 189.35*** 197.83*** 129.22*** normality tests (orthogonalisation: cholesky) null hypothesis: residuals are multivariate normal skewness (chi-sq) 334.89*** 399.75*** 418.05*** kurtosis (chi-sq) 11744.56*** 15727.93*** 8789.53*** jarque-bera 12079.44*** 16127.68*** 9207.58*** heteroskedasticity tests: no cross terms chi-square statistics 1075.67*** 1200.50*** 1492.41*** source: authors’ estimation. note: *** denotes a 1% significance level. table a7. p-vec model residual diagnostic test. cropint alu cei serial correlation lm tests (h0: no serial correlation at lag order h) lag 1 164.39*** 155.64*** 112.67*** lag 2 189.35*** 197.83*** 129.22*** normality tests (orthogonalisation: cholesky) null hypothesis: residuals are multivariate normal skewness (chi-sq) 334.89*** 399.75*** 418.05*** kurtosis (chi-sq) 11744.56*** 15727.93*** 8789.53*** jarque-bera 12079.44*** 16127.68*** 9207.58*** heteroskedasticity tests: no cross terms chi-square statistics 1075.67*** 1200.50*** 1492.41*** source: authors’ estimation. note: *** denotes a 1% significance level. reflections and new directions: an editorial retrospective and the launch of our new policy paper section silvia coderoni, fabio bartolini where is the italian agriculture heading? a discussion in light of the prospects for the future cap filippo arfini1*, fabio bartolini2, anna carbone3, tatiana castellotti4, silvia coderoni5, raffaele cortignani6, raffaele d’annolfo4, giovanni dara guccione4, michele donati1, francesca galli7, roberto henke4, giampiero mazzocchi4, alessandro monteleone4, towards the knowledge and innovation system for the bioeconomy? giacomo maria rinaldi*, davide viaggi impact of weather variability on crop yields and land use dynamics in eastern india: shortand long-term effects pratap kumar jena1, kirtti ranjan paltasingh2, souryabrata mohapatra3,,*, ashok mishra4 rebuilding strategies for food self-sufficiency: portugal’s past patterns and future ambitions alexandre macedo joão1, dulce freire2, humberto rocha3, joana dias4 from subsidy to land rent: how production-linked payments shape factor remuneration in agriculture adrian sadłowski expenditure allocation for rural development interventions: main trends and patterns in the choices of the italian regions under the cap 2023-2027 tobia minuzzo, francesco pagliacci* bio-based and applied economics bae © 2024 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae bio-based and applied economics 13(4): 317-331, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14563 citation: ronzon, t., gurria, p., carus, m., cingiz, k., el-meligi, a., hark, n., iost, s., m’barek, r., philippidis, g., van leeuwen, m., & wesseler, j. (2024). assessing the bioeconomy’s contribution to evidence-based policy: a comparative analysis of value added measurements. bio-based and applied economics 13(4): 317-331. doi: 10.36253/bae-14563 received: march 30, 2023 accepted: june 11, 2024 published: december 31, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: silvia coderoni orcid tr: 0000-0001-8554-627x pg: 0000-0002-0420-3273 kc: 0000-0002-8440-2677 ae-m: 0000-0001-7814-9954 si: 0000-0001-5123-3822 rm: 0000-0002-3205-3938 gp: 0000-0003-1727-2240 mvn: 0000-0003-1902-4531 jw: 0000-0002-6009-8543 short communications assessing the bioeconomy’s contribution to evidence-based policy: a comparative analysis of value added measurements tévécia ronzon1,2,*, patricia gurria2, michael carus3, kutay cingiz2, andrea el-meligi1, nicolas hark3, susanne iost4, robert m’barek1, george philippidis5, myrna van leeuwen6, justus wesseler2 1 european commission, joint research centre (jrc), seville, spain 2 agricultural economics and rural policy group, wageningen university, the netherlands 3 nova-institut gmbh, 50354 hürth, germany 4 thünen institute of international forestry and forest economics, hamburg, germany 5 agrifood and technology research centre (cita), zaragoza, spain 6 wageningen economic research, international policy, the hague, the netherlands *corresponding author. e-mail: tevecia.ronzon@gmail.com abstract. this paper reviews the main approaches found in the literature to measure the size of the european bioeconomy. the various estimations published might be confusing at first sight, reporting a value added of the european bioeconomy within the large range of eur 881 billion to eur 2.3 trillion. however, each approach is best suited to measuring a different aspect of the bioeconomy. using the different approaches, we estimate that markets of bio-based products and energy generate eur 730-790 billion of value added, the use of biomass within the european economy generates eur 670 billion of value added, and the sourcing of core bioeconomy industries with goods and services generates eur 270 billion of value added. there is no evidence of an increased use of biomass inputs in eu industries in substitution of fossil resources, nor of a decreasing dependence of traditional bioeconomy industries towards fossil resources over the period 2005-2015. keywords: bioeconomy, value added, europe, input-output tables, bio-based industries, methodologies. jel code: q57. 1. introduction as defined in the european commission’s bioeconomy strategy, the bioeconomy covers all sectors and systems that rely on biological resources, their functions and principles (european commission, 2012, 2018). the bioeconomy promotes the transition to a sustainable economic model derived from the use of biomass and the application of natural sciences, knowledge, and technologies. its relevance is well acknowledged by international organihttps://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode http://www.fupress.com/bae https://doi.org/10.36253/bae-14563 https://doi.org/10.36253/bae-14563 https://orcid.org/0000-0001-8554-627x https://orcid.org/0000-0002-0420-3273 https://orcid.org/0000-0002-8440-2677 https://orcid.org/0000-0001-7814-9954 https://orcid.org/0000-0001-5123-3822 https://orcid.org/0000-0002-3205-3938 https://orcid.org/0000-0003-1727-2240 https://orcid.org/0000-0003-1902-4531 https://orcid.org/0000-0002-6009-8543 mailto:tevecia.ronzon@gmail.com 318 bio-based and applied economics 13(4): 317-331, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14563 tévécia ronzon et al. zations such as the fao (fao, 2021; gomez san juan, harnett, & albinelli, 2022) and the oecd (oecd, 2018). the european union (eu) has also stated its importance for the european economy in its bioeconomy strategy and action plan (european commission, 2012, 2018), recently followed by council conclusions on the opportunities of the bioeconomy in the light of current challenges with special emphasis on rural areas (council of the european union, 2023). together with the development of bioeconomy strategies around the world, the need of tools for quantifying the bioeconomy and monitoring its development has become crucial. however, the bioeconomy is a complex concept, encompassing a broad range of economic activities and their associated workers and consumers, while being dependent on the planet’s ecological boundaries and biomass availability. understanding and analysing such a multidisciplinary phenomenon requires implementing several theoretical and conceptual approaches, using a broad range of methodologies. from global (fao, 2021), macro-regional (european commission, 2022b), to national (federal ministry of food and agriculture (bmel), 2014) and regional level (junta de andalucía, 2018), guidelines and monitoring systems are being developed and implemented. in the case of the eu, the indicators to measure the progress of the european bioeconomy are very broad and numerous (european commission, 2022a; mubareka et al., 2023). however, a smaller number of headline indicators is used by policymakers and stakeholders to analyse and report on the bioeconomy. most prominently among those indicators features (gross) value added, which is the focus of the present study. the european union’s statistical directorate general, eurostat, does not (yet) provide statistics of a specific value-added indicator for the bioeconomy and all its sectors spanning a broad range from primary production (e.g., agriculture), via processing (e.g., wooden products) to services (e.g., restaurants). here, a key scientific challenge relates to the separation of fossil and bio-based production to correctly delimit the bioeconomy (ronzon, piotrowski, m’barek, & carus, 2017). over the last years different methodologies have been developed to fill this gap (for example on the eu: cingiz, gonzalez-hermoso, heijman, and wesseler (2021); iost et al. (2019); iost and weimar (2020); kuosmanen et al. (2020); m’barek et al. (2014); porc, hark, carus, and carrez (2021); ronzon, iost, and philippidis (2022a, 2022b); wesseler and von braun (2017)). however, these methodologies have not been consistently used in bioeconomy policy making for two reasons: (i) the calculation methods are difficult to understand by non-specialists, and (ii) the different methodologies yield very different estimates of the european bioeconomy’s size, which may appear confusing at first sight. the aim of this paper is to bring clarity on the estimates of the bioeconomy’s value added size across different methodologies already published, in order to optimize their use by policy makers and consequently contribute to more evidence-based bioeconomy policies. to do so, the paper clarifies what are the concepts measured by each methodology (section 2) and puts their respective results into perspective (section 3). emphasis is made on pointing to the different aspects of the bioeconomy measured by the different methodologies and on illustrating how each of them can be mobilised to inform on specific policy questions. finally, conclusions are remarked in the final section. 2. presentation of the different methodologies 2.1. overview cingiz, gonzalez-hermoso, et al. (2021) give an overview of the different quantitative approaches for estimating the value added generated by bioeconomic activities from which four types of methodologies match monitoring requirements (i.e., methodologies based on statistical databases that are harmonized across eu member states and updated over time). each type is illustrated in this study by a particular publication that applies to all member states of the eu (cingiz, gonzalez-hermoso, et al., 2021; cingiz, gonzález hermoso, heijman, & wesseler, 2021; kuosmanen et al., 2020; ronzon et al., 2022a, 2022b). all four methodologies are based on industry-level statistics for quantifying the contribution of industry p to the bioeconomy in terms of value added (vp). the bioeconomy being a cross-sectorial concept, the size of its value added is thus the sum of the contribution of all industries represented by nace1 codes in the european system of national accounts that are indexed by p = 1,…,n. they comprise the industries that fully fall within the scope of the bioeconomy indexed by q = 1,…,l, the industries that partly fall within the scope of the bioeconomy indexed by r = l+1,…,m, and the industries that do not fall at all within the scope of the bioeconomy indexed by s = m+1,…,n.2 1 nace is the french acronym for economic activities in the european community. 2 we denote here the industries by letters p, q, r and s to differentiate with the original studies that use the same subscripts i, j, k with diverging definitions. 319assessing the bioeconomy’s contribution to evidence-based policy: a comparative analysis of value added measurements bio-based and applied economics 13(4): 317-331, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14563 the families of methodologies differ on three main aspects: (i) the set of industries q. all methodologies concur in considering the biomass producing industries fully part of the bioeconomy (i.e., agriculture, forestry and fishing). however, divergences occur on the additional industries that complete the set q within the full scope of industries considered (p), see table 1. (ii) the level of the contribution of industries r to the total bioeconomy’s value added. different quantification criteria are considered: the biomass content of products and energy produced or the bioeconomy relevance of the services delivered considering a given policy definition of bioeconomy (see section 2.2.1); the use of biomass; or the provision of inputs to industries q. (iii) the inclusion or exclusion of the industries providing inputs to industries q into the bioeconomy aggregate (p). the different approaches taken regarding points (ii) and (iii) provide distinct measures of the bioeconomy’s value added and inform on a variety of aspects of the bioeconomy. measurement principles are clarified in sections 2.2 to 2.5 while measured aspects are presented in section 3. 2.2. the “output-based” approach 2.2.1. approach the “output-based” approach quantifies the value added generated by an industry p in proportion to the biomass content of tangible (i.e., merchandise) outputs or to the bioeconomy relevance of intangible (i.e., services) outputs. the biomass content is calculated in dry matter content (ronzon et al., 2022a). the ‘bioeconomy relevance’ criterion is derived from a policy definition of the bioeconomy. in the context of the eu bioeconomy strategy, it covers the services associated to a bio-based product (e.g., transport, trade, repair), the marketed ecosystem services (e.g., nature tourism), the generation of knowledge in bioeconomy fields (e.g., research and development in life sciences) or support to bio-based markets (e.g., market research, public administration) (ronzon et al., 2022b). the output-based approach quantifies the value added of the bioeconomy (vbe_o) at a given point in time and space as: vbe_o = ∑q vq + ∑r δr .vr (1) with δr = biomass content share or bioeconomy relevance share of industry r (figure 1). vq and vr are the value added of individual industries q and r (see annex 1). in other terms, the total value added of the bioeconomy is the sum of the value added generated by those industries whose output is biomass (e.g., agriculture, forestry, fisheries, food and beverage manufacturing) or whose output is partially made of biomass (e.g. biobased textile industry, biochemical industry) or whose output is fully or partially bioeconomy relevant (e.g., food services, veterinary activity, research). industries q=1,…,l comprise the biomass producing industries (a01, a02, a03), the manufacturing of food (c10) and beverage (c11), water supply, sewerage and management3 (e36-e38) for their full biomass content, as well as food and beverage service activities (i56) and veterinary activities (m75) for their bioeconomy relevance. industries s=m+1,…,n comprise mining industries (b05-b09), the manufacturing of coke and petroleum products (c19), of mineral or metallic products (c23-c25), of electronic or electrical equipment (c26-c27), of machinery and motor vehicles (c28-c30, c33), the wholesale, retail trade and repair of motor vehicles (g45), the industries of information and communication (j59-j63), of financial, insurance and real estate activities (k64-k66, l68) and of management, employment, human health and social work activities (m70, n78, q86-q88). industries r=l+1,…,m comprise all other nace industries. 2.2.2. data sources the output-based approach builds on a variety of data sources. industry-level data on value added (vq and vr) are retrieved from the eurostat structural business statistics (eurostat, 2020a, 2020b, 2020c) and from eurostat’s national accounts (eurostat, 2020d) for the industries not represented in the former databases. other eurostat databases are mobilised for the computation of the biomass content share or of the bioeconomy relevance share δr (ronzon et al., 2022a, 2022b). in addition to official data, the output-based approach relies on literature, market reports and expert insights for the estimation of the biomass content of the 875 bio-based products listed in the eurostat database on the production of manufactured goods (eurostat, 2021). as δr cannot be quantified with precision with available eurostat data and expert knowledge for all industries r, a minimum and maximum threshold value of δr is determined that consequently generates a minimum and a maximum value of bio-based amount of vr. 3 the dry matter content of water is considered 100% biomass (i.e., organic matter and micro-organisms). 320 bio-based and applied economics 13(4): 317-331, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14563 tévécia ronzon et al. time series data of vr are available from 2008 (the latest revision of the nace classification), up to the most recent common year of data sources used, typically released with a time lag of two years. 2.2.3. data interpretation this approach has been coined “policy-driven” in the sense that the bioeconomy relevance of industries in set p follows the concept of bioeconomy as defined in the eu bioeconomy strategy. indeed, by focusing on the bioeconomy nature of industries’ outputs, the output-based approach provides lower and upper thresholds of domestic bio-based markets (min vbe_o max vbe_o). over time, market developments in the bioeconomy’s valued added, or of an individual bio-based industry’s value added, give insight on progress towards policy objectives of bio-based market uptake. also, the difference between an industry’s bio-based output share δr attained in one country compared with that of another country, or compared with a 100% δr share, gives an indication of the remaining potential for bio-based market development. 2.3. the “input-based” approach 2.3.1. approach the “input-based” approach quantifies the value added generated by an industry p in proportion to its bio-based input cost share. among the different variants of input-based approaches published in the scientific literature (efken, dirksmeyer, kreins, & knecht, 2016; heijman, 2016; iost et al., 2019; iost & weimar, 2020; kuosmanen et al., 2020; meesters, van dam, & bos, 2013; robert, jonsson, chudy, & camia, 2020), only kuosmanen et al. (2020) propose quantifications for the eu aggregate. their methodology, also coined fundamental industry level model (film), is thus proposed here as a benchmark for the families of “input-based” approaches while variations from other input-based approaches are briefly discussed. the film input-based approach relies on the use of monetary flows of input-output tables (iots) for quantifying the value added of the bioeconomy (vbe_i) at a given point in time and space, such as: vbe_i = ∑q vq + ∑r γr .vr (2) with q being the biomass producing industries (agriculture, forestry and fishing) and γr being the biomass input cost share of industry r (figure 1 and equation 3). γr = (3) thus, iqr is the cost of inputs from the set of biomass producing industries q to industry r; ir’r is the cost of inputs from industry r’ to industry r with r’ = l+1,…,m; γr’ is the bio-based input cost share of industry r’ (equation 4); mr is the cost of imported inputs to industry r; γmr is the bio-based input cost share of imported inputs to industry r; and ipr is the cost of inputs from all industries to industry r. note that intra-industry trade is captured when r’ = r. γr’ = (4) that is, the total value added of the bioeconomy is the value added generated from biomass producing activities, and from the use of biomass in all other activity sectors, including from imported products and services. 2.3.2. data sources the film approach is systematic across all industries. the data source is the eurostat iots (eurostat, 2020e) released every five years with some member states also providing annual estimates. this data does not offer a complete coverage of all eu member states but does provide complete data for the eu28 aggregate. 2.3.3. data interpretation by focusing on biomass input cost shares, the film methodology reports on the value added (vbe_i) generated from the use of biomass across all industries of an economy. the 5-year time step evolution of vbe_i gives insight on the increasing (decreasing) mobilisation of biomass – measured in value terms by the economic system considered. the bio-based input cost share γr gives an indication of the degree of dependence of industry r towards non-renewable biological resources: the smaller γr is, the higher the dependence. the development of γr over time indicates progress towards the objective of substituting non-renewable resources with bio-based equivalents. 2.3.4. variation to the film approach while the film approach is homogeneous across all nace industries and employs data from a single source, iost et al. (2019) and iost and weimar (2020) adapt the input-based approach to reflect the bioeconomy concept 321assessing the bioeconomy’s contribution to evidence-based policy: a comparative analysis of value added measurements bio-based and applied economics 13(4): 317-331, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14563 as defined in the previous german bioeconomy strategy (bmel, 2014). first, the delineation of the bioeconomy’s industrial scope is restricted to a selection of bio-based industries that includes only a few bio-based services (i.e., joinery installation and erection of frames and constructional timber works, food and beverage service activities and research and experimental development on biotechnology). second, several sources of german statistics are employed for they offer more precise information than iots (ageb, 2015; destatis, 2018). third, per policy definition, the bio-based share of research industries (m7219) is not determined according to its biomass input cost share but rather from the share of personnel cost incurred in bioeconomy-related research disciplines on total costs (destatis, 2016). data on value added are retrieved from eurostat’s structural business statistics. 2.4. the “weighted input-output based” approach the “weighted input-output based” approach provides a middle ground quantification of the bioeconomy’s value added, taking into account the parameters δp and γp quantified by the output-based and the inputbased approaches (figure 1). it quantifies the value added of the bioeconomy (vbe_w) at a given point in time and country as: vbe_w = ∑p θp.vp (5) where θp is the weighted average of the input-based and output-based coefficients. with that purpose, the output bio-based share δp is weighed with the ratio of value added on gross output, and the input bio-based share γp is weighed with the ratio of total cost of inputs on gross output: θp = (δp.vp + γp.ip) / op (6) the total value added of the bioeconomy is the value generated from the utilization of biomass and bio-based inputs, as well as their conversion into bioeconomy outputs through further processing. the data sources used are the same as those employed in the output-based and input-based approaches (see sections 2.2.2 and 2.3.2). 2.5. the “upstream & downstream” approach 2.5.1. approach the “upstream & downstream” approach quantifies two different aspects of the bioeconomy (figure 1, cingiz, gonzalez-hermoso, et al. (2021)): – ∑r dr, the “downstream effect” of the bioeconomy which corresponds to the value added size of the industries r that use bio-based inputs in proportion to their respective bio-based input cost share βr from industries q. q represents the biomass producing industries, the manufacture of food, beverage, tobacco, wood and paper products, and printing. – ∑r ur, the “upstream effect” of the bioeconomy which corresponds to the value added size of the industries r that source industries q in proportion to their respective output cost share αr. in sum, the “upstream & downstream” approach quantifies the value added of the bioeconomy (vbe_ud) at a given point in time and space as4: vbe_ud = ∑q vq + ∑r (dr + ur) (7) in other words, the total value added of the bioeconomy is the value generated by activities considered core to the bioeconomy (i.e., biomass producing, the manufacturing of food, beverage, tobacco products, wood products, paper and printed products) as well as the value generated by the use of the outputs of the former activities (downstream effect) and the use of inputs by them (upstream effect). similarly to equation (2), dr = βr .vr (8) ur = αr (1βr) vr (9) where αr is the output cost share of industry r to industries q. αr is multiplied by (1βr) to avoid double counting with the downstream effect. αr = (10) where fr denotes the final demand for industry r and er denotes the exports of industry r. 2.5.2. data sources similarly to the film approach, the “upstream & downstream” approach is systematic across all industries. the two effects are computed from the oecd’s iots with annual data series from 2005 to 2015 for the 28 pre-brexit eu member states (oecd, 2021). eu28 data are calculated as the sum of io matrix entries across the 28 countries. 4 notations have been changed compared to cingiz, gonzalezhermoso, et al. (2021) for the sake of harmonization across the various methodologies presented in the paper. 322 bio-based and applied economics 13(4): 317-331, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14563 tévécia ronzon et al. 2.5.3. data interpretation compared to the other three approaches, the “upstream & downstream” method adds information on how much the bioeconomy is integrated with the rest of the economy, in particular to non-bioeconomy sourcing industries. the downstream component ∑r dr provides similar information as the input-based approach (see section 2.3.3). additionally, the output cost share αr used for the quantification of the upstream component ur illustrates the interconnection between industry r and the core bioeconomy industries q. the higher αr is, the larger is the sourcing role of industry r. moreover, the development of the total upstream and downstream effects over time (∑r ur and ∑r dr) informs whether an increasing (decreasing) value creation from the use of renewable biological resources (∑r dr) is concomitant or not with a growth of the economic size of bioeconomy sourcing industries (∑r ur). finally, the ratio of bioeconomy value added on gdp (vbe_ud/gdp) describes how much the bioeconomy is integrated into the whole economy. as a summary, figure 1 graphically illustrates the concepts or f lows quantified in the four approaches and their related equations. table 1 compares the main parameters of the four approaches. 3. results and discussion the four methodologies presented above yield very different estimates of the value added size of the eu bioeconomy in 2015, ranging from eur 881 billion to eur 2.3 trillion5 (figure 2). such a large range may be puzzling at first sight or may even confuse policy makers. in fact, differences in numbers reflect the different aspects of the bioeconomy captured by each approach. this section summarises the main results and illustrates how the specific aspects of each methodology can be mobilised to answer relevant policy questions. 3.1. aggregated results and complementary information on differences in order to provide an overview of main results, we focus hereafter on the comparison of the aggregates of pri5 the eu 2015 is the only common scope of the approaches commented at section 3. the output-based approach from ronzon et al. (2022a and 2022b) provides data at country and eu level from 2008 to 2019, the input based approach published by kuosmanen et al. (2020) provides data for the eu and the year 2015. the upstream and downstream approach published by cingiz et al. (2021a) provides country and eu level data from 2005 to 2019. figure 1. four methodological approaches for determining the bio-based share of industry p. note: i stands for input, o for output and v for value added, all three are measured in monetary terms. 323assessing the bioeconomy’s contribution to evidence-based policy: a comparative analysis of value added measurements bio-based and applied economics 13(4): 317-331, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14563 mary, secondary and tertiary economic sectors6 as illustrated by figure 2. for each of these aggregates, we highlight the reasons leading to differences in value added estimates. the weighted input-output approach is not commented though, as it always provides an intermediate quantification between the input-based and the output-based approaches. all quantifications from the “upstream and downstream” approach are taken from the online database published by cingiz, gonzález hermoso, et al. (2021)7. estimations of value added for the bioeconomy industries of the primary sector are convergent (eur 207 to 216 billion) in spite of methodological differences and slight variations from the different data sources employed by each approach. the output-based approach only considers those industries that produce biomass (eur 216 billion) while the other three approaches also consider a proportion of the bioeconomy value added coming from the mining industries. from an inputbased perspective, eur 1 billion of value added is generated from the use of biological material in mining activities such as for bioleaching. moreover, the upstream effect ur calculated in the “upstream and downstream” approach reveals that eur 1.2 billion of value added are generated from the sourcing of core bioeconomy industries q by mining industries. 6 the primary sector refers to nace sections a and b (biomass production and mining and carrying), the secondary sector to nace c to f (manufacturing), and the tertiary sector to nace g to t (services). 7 although the methodological comments exposed in section 2.5 were derived from cingiz, gonzalez-hermoso, et al. (2021). the value added of the bioeconomy industries operating in the secondary sector differs more from one approach to the other than in the case of primary sector industries: eur 299 billion (input-based approach) to eur 573 billion (output-based approach). the biomass input cost share γr (input-based approach) is systematically smaller than the biomass content δr of the outputs of the manufacturing industries (output-based approach), except for those industries s considered non bio-based in the output-based approach (γr ranging between 0.6% and 2.5%, table 2). as a matter of example, only 55% of the inputs of the manufacturing of food, beverage and tobacco are bio-based inputs while that table 1. summary comparison of the four approaches introduced at sections 2.2 to 2.5. approach “output-based” “input-based” (film) “weighted input-output” “upstream & downstream” quantification criteria biomass content of tangible outputs, bioeconomy relevance of intangible outputs biomass inputs (biomass input cost share) see the two previous columns biomass inputs (biomass input cost share) and sourcing of industries q (output cost share) equations vbe_o = ∑q vq + ∑r δr vr equation (1) vbe_i = ∑q vq + ∑r γr vr equation (2) vbe_w = ∑p θp vp with θp = (δp vp + γp ip) / op equations (5) and (6) vbe_ud = ∑q vq + ∑r (dr + ur) equation (7) industries q (nace codes) a01-a03, c10-c12, e36, i56, m75 a01-a03 a01-a03 a01-a03, c10-c12, c16-c18 industries s (nace codes) b05-b09, c19, c23-c30, c33, g45, k64-k66, l68, m70, n78, q86-q88. none none none data sources expert knowledge and many eurostat sources eurostat’s iots see the two previous columns oecd’s iots interpretation of the results bio-based market size use of biomass middle ground perspective between the previous two columns use of bio-based inputs and integration to the wider economy 500 1.000 1.500 2.000 output input up& downstream weighted inputoutput tertiary sectors (max) tertiary sectors (min) secondary sectors (max) secondary sectors (min) primary sectors figure 2. estimation of the value added size of primary, secondary and tertiary activities of the eu28 bioeconomy according to the four quantitative approaches presented in the study 324 bio-based and applied economics 13(4): 317-331, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14563 tévécia ronzon et al. industry generates fully bio-based outputs (table 2). the proportion (αr (1βr)) of outputs that secondary sector’s industries sell to bioeconomy industries q ranges from 0.3% to 5.3%. the tertiary sector shows a high divergence in terms of value added size estimates from one approach to the other: eur 370 billion (input-based approach) to eur 1,488 billion (output-based approach). the four-fold difference is due to relatively small biomass input cost shares (γr = 1-5%, except for accommodation and food services where γr = 35%) compared with high biomass content or bioeconomy relevance of tertiary outputs (maximum δr = 12-100% in eight out of fourteen tertiary industries, table 2). while the approaches based on iots (input and “up and downstream” approaches) are systematic and precise, the output-based approach suffers from both a lack of clarity about the definition of a bioeconomy service and a lack of informative data table 2. output bio-based shares (a), biomass input cost shares (b), combined upstream and downstream shares (c) and weighted inputoutput shares (d) at the sectorial level and for the eu28 in 2015. nace (a) min – max (b) (c) (d) min– max a01_a03 agriculture, forestry, fishing 100% 100% 100% 100.0% 100% 100% b05_b09 mining and quarrying 0.0% 0.0% 1.4% 3.0% 0.6% 0.6% c10_c12 food, beverages and tobacco products 100% 100% 54.8% 100.0% 66.4% 66.4% c13_c15 textiles, wearing apparel and leather products 35.6% 46.5% 40.9% 6.0% 38.4% 42.8% c16 products of wood and cork 99.7% 99.7% 45.7% 100.0% 61.7% 61.7% c17_c18 paper products and printing 60.8% 98.9% 30.7% 100.0% 37.3% 53.2% c19 coke and refined petroleum products 0.0% 0.0% 0.6% 3.7% 0.5% 0.5% c20_c21 chemicals and pharmaceuticals 25.6% 27.1% 4.3% 8.4% 12.1% 12.6% c22 rubber and plastics products 3.3% 3.9% 4.6% 9.1% 4.1% 4.4% c23 other non-metallic mineral products 0.0% 0.8% 2.5% 5.5% 1.6% 1.9% c24 basic metals 0.0% 0.0% 0.9% 1.5% 0.7% 0.7% c25 fabricated metal products 0.0% 0.0% 1.5% 4.4% 0.9% 0.9% c26 computer, electronic and optical equipment 0.0% 0.0% 1.4% 1.7% 0.9% 0.9% c27 electrical equipment 0.0% 0.0% 1.6% 2.1% 1.0% 1.0% c28 machinery and equipment, nec 0.0% 0.0% 1.1% 2.7% 0.7% 0.7% c29 motor vehicles, trailers and semi-trailers 0.0% 0.0% 1.5% 1.2% 1.1% 1.1% c30 other transport equipment 0.0% 0.0% 1.5% 1.4% 1.1% 1.1% c31_c33 manufacturing nec; repair and installation of machinery and equipment 8.8% 17.8% 8.3% 9.8% 8.4% 11.3% d35_e39 electricity, gas, water supply, sewerage, waste and remediation services 24.0% 25.4% 1.3% 5.6% 10.2% 10.6% f construction 5.3% 5.6% 3.4% 4.4% 3.9% 4.0% g45_g47 wholesale and retail trade; repair of motor vehicles 24.8% 39.8% 3.7% 11.0% 15.0% 23.0% h49_h53 transportation and storage 20.1% 32.2% 0.9% 3.9% 8.9% 14.0% i55_i56 accommodation and food service activities 76.5% 76.5% 34.7% 34.3% 57.7% 56.4% j58_j60 publishing, audiovisual and broadcasting activities 0.0% 32.1% 4.6% 12.3% 2.4% 19.1% j61 telecommunications 0.0% 0.0% 0.9% 2.1% 0.5% 0.5% j62_j63 it and other information services 0.0% 0.0% 0.9% 2.7% 0.5% 0.5% k64_k66 financial and insurance activities 0.0% 0.0% 0.6% 3.3% 0.3% 0.3% l68a real estate activities 0.0% 0.0% 0.9% 2.2% 0.2% 0.2% m69_n82 professional, scientific and technical activities, administrative and support services 4.0% 11.8% 2.1% 4.9% 2.0% 9.8% o84 public administration and defence; compulsory social security 10.5% 15.9% 2.6% 4.7% 0.9% 11.5% p85 education 2.2% 4.9% 4.3% 6.7% 0.9% 3.5% q86_q88 human health and social work activities 0.0% 0.0% 5.4% 5.6% 1.8% 1.8% r90_s96 arts, entertainment and recreation and other service activities 0.2% 47.1% 3.8% 6.9% 1.6% 27.1% t97_t98 activities of households as employers 0.0% 100.0%   0.0% 0.0% 0.0% sources: cingiz, gonzález hermoso, et al. (2021); kuosmanen et al. (2020); ronzon et al. (2022a, 2022b). 325assessing the bioeconomy’s contribution to evidence-based policy: a comparative analysis of value added measurements bio-based and applied economics 13(4): 317-331, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14563 for the quantification of their bioeconomy relevance. in two extreme cases, the bioeconomy relevance of the industries of sport, amusement and recreation activities and of household employerś activities could not be quantified with available data, leading to the very broad assumption of minimum and maximum bioeconomy relevance shares of 0% and 100% (for a discussion of the output-based approach, see ronzon et al. (2022b)). finally, the industries of telecommunication and information technologies, finance, insurance, real estate, human health8 and social work are excluded from the sectorial scope of the bioeconomy in the output-based approach (δr = 0%). nevertheless, they use biomass (γr ranging between 0.6% and 5.4%) and source core bioeconomy industries q with their outputs (αr (1βr) ranging between 0.4% and 2.0%). consequently, they are worth eur 75 billion according to the input-based approach and eur 123 billion in the “upstream & downstream” approach. 3.2. tailoring the right approach to specific policy requirements the recent report from the international advisory council on global bioeconomy on “bioeconomy globalization” (dietz et al., 2024) stresses the monitoring of the bioeconomy as a central piece for the implementation of bioeconomy strategies in many countries around the world. the quantitative methodologies presented above all aim at supporting the monitoring and evaluation of public initiatives related to the bioeconomy, with a specific focus on their economic aspects. taken separately, the different approaches provide insight on fundamental policy questions: (i) what is the size of bio-based markets? what is their potential for development? (ii) what is the size of the economic activities that rely on the use of biomass? (iii) how does the bioeconomy and the rest of the economy interlink? (iv) is the substitution of non-renewable resources by renewable biological resources happening? moreover, sectorial data can also be used to inform on more specific policy questions related with the bioeconomy such as the dependence of the eu economy to fossil resources, the size of the knowledge-based bioeconomy (kbbe) and many others. 8 human health is explicitly excluded from the bioeconomy in the european bioeconomy strategy (european commission, 2018). 3.2.1. size and development of bio-based markets the development of bio-based markets is pivotal in the eu bioeconomy strategies, which have been conceived as engines of green growth. the output-based approach precisely offers the means for monitoring the economic wealth created from the production and selling of bio-based products and bioenergy and from waste treatment (nace sectors a to f). taking the year 2015 as a reference for comparison with the other approaches, the value added size of the eu bio-based markets is estimated between eur 730-790 billion. it has increased by 30-31% in the decade 20092019, which has permitted to maintain their contribution to the eu’s total value added at approximately 5.5-5.9%. european bio-based markets are dominated by food and agricultural commodities (respectively eur 189 billion and eur 183 billion of value added, table 3 (a)). if we follow a stricter definition of “bio-based products” that excludes agricultural, food and feed products, then the largest markets for bio-based products are the ones of paper products and of bio-based pharmaceuticals, with a value added size of eur 45 billion each (table 3 (a)). interestingly, the four industries responsible for the biggest biomass-derived markets – agriculture, food, paper and bio-based pharmaceuticals – were also identified as the main motors of productivity growth in the eu over the last decade by ronzon et al. (2022a), either because these industries have modernised their production processes (agriculture, the manufacture of paper), or because they have attracted workers from less intensive bio-based industries (manufacture of bio-based pharmaceuticals and food products) or both phenomena. their market size has grown by 37-43% over the period 2009-2019, except for the food industry (30% growth). the secondary sector of the eu28 (nace c to f) is 19-21% bio-based in 2015 (δnace c-f). that proportion remains stable over the decade 2009-2019. it is certainly impossible to achieve a fully bio-based secondary sector as some metal, mineral and other non bio-based components of manufactured goods cannot be substituted with biomass. notwithstanding, a 20% share seems low enough to expect some feasible progress. output biobased shares of 35-40% have been achieved by the secondary sectors of latvia and lithuania in 2015, thanks to an important manufacture of wood products and food and beverages (both countries), of wooden furniture (lithuania) and bioenergy industry (latvia). the irish case illustrates a bioeconomy less oriented towards wooden biomass, where the manufacture of bio-based chemicals (δr=31%) together with a strong food and beverage industry drives a 32-33% bio-based secondary sector. 326 bio-based and applied economics 13(4): 317-331, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14563 tévécia ronzon et al. 3.2.2. use of biomass and value added creation side-by-side with market objectives, the two consecutive eu bioeconomy strategies promote the sustainable use of biomass – in particular for industrial purposes – to achieve a bioeconomy transition in europe. a sustainability assessment is out of the scope of the present study. however, the input-based approach developed by kuosmanen et al. (2020) and the downstream component quantified by cingiz, gonzález hermoso, et al. (2021) do provide evidence on the extent to which biomass is used in the different economic sectors of the eu28, and on the ability of each industry to create value added from it. according to kuosmanen et al. (2020), the use of biomass and bio-based products generates eur 670 billion of value added in the eu28 economy, excluding the biomass producing activities9 (2015 data). the primary 9 the industries that produce biomass are fully accounted part of the bioeconomy by kuosmanen et al. (2020) (industries q). as a result, no biomass cost share γr is calculated for those industries and we cannot report on their use of biomass. table 3. top 5 markets according to the different criteria discussed in the text (eu28, 2015). (a) top 5 markets of bio-based products and energy by value added size* industry (nace sector) value added size (vp in million euros) output bio-based share (δp in %) 1 manuf. of food products c10 189,000 100% 2 agriculture a01 183,441 100% 3 manuf. of paper and paper products c17 45,257 45,625 99% 100% 4 manuf. of bio-based pharmaceuticals c21 44,827 49% 5 manuf. of beverages c11 40,890 100% (b) top 5 market industries by value added generated from biomass use industry (nace sector) value added size (vr in million euros) biomass input cost share (γr in %) 1 manuf. of food products, beverage and tobacco products c10_c12 152,458 55% 2 accommodation and food service activ. i55_i56 130,084 35% 3 human health activities q86 29,762 4% 4 education p85 29,226 4% 5 manuf. of textiles, wearing apparel and leather products c13_c15 28,521 41% (c) top 5 industries relying on biomass and bio-based product resources in proportion to their inputs industry (nace sector) value added size (vr in million euros) biomass input cost share (γr in %) 1 manuf. of food products, beverage and tobacco products c10_c12 152,458 55% 2 manuf. of products of wood and cork c16 17,363 46% 3 manuf. of textiles, wearing apparel and leather products c13_c15 28,521 41% 4 manuf. of paper and paper products c17 17,484 37% 5 accommodation and food service activ. i55_i56 130,084 35% (d) top 5 sourcing industries to core bioeconomy industries q, by value added size industry (nace sector) value added size (vr in million euros) output cost share (αr.(1βr) in %) 1 wholesale and retail trade; repair of motor vehicles g45_g47 112,945 8% 2 professional, scientific and technical activities, administrative and support services m69_n82 34,750 2% 3 transportation and storage h49_h53 19,425 3% 4 electricity, gas, water supply, sewerage, waste and remediation services d35_e39 15,221 4% 5 financial and insurance activities k64_k66 14,152 2% * sorted on the maximum estimation of value added size. note: the level of disaggregation varies from one methodology to the other (e.g., the aggregate c10-c12 in (b) is broken down into c10, c11 and c12 in (a)). 327assessing the bioeconomy’s contribution to evidence-based policy: a comparative analysis of value added measurements bio-based and applied economics 13(4): 317-331, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14563 sector, mining and quarrying activities depend on the use of biomass for 1.4% of their input costs, from which they produce eur 1 billion of value added. the secondary sector is more dependent on biomass inputs than the tertiary sector but less efficient at generating value added from it: with a 9% biomass input cost share, the secondary sector generates eur 299 billion compared to a 4% share in the tertiary sector, generating eur 370 billion. the manufacturing of food, beverage and tobacco and accommodation and food services create the largest amounts of value added from biomass usage in the eu28 (eur 152 and 130 billion each, table 3 (b)). more surprisingly, they are followed by human health activities and education (eur 29-30 billion each). human health is excluded from the eu definition of the bioeconomy but it is preponderant in more process-based definitions (e.g., usa, brazil). education uses biomass in the form of paper, wooden desks and furniture and in the form of breakfasts served at school in some member states. the industries that depend more on biomass usages are traditional industrial activities (see the top four industries at table 3 (c), again excluding biomass producing activities8). their sourcing in biomass and bio-based products reaches 37% to 55% of total input costs (γr). tertiary activities come only at the fifth position in the form of accommodation and food services (γr = 35%). 3.2.3. the degree of inclusivity of the bioeconomy within the macroeconomy the scope of the bioeconomy and its penetration into the rest of the economy is another topic of policy interest. the chronological evolution of bioeconomyrelated policy initiatives indeed shows different perceptions of bioeconomy activities. the first policy concept of kbbe put the focus on those scientific and knowledge-productive activities in the domain of life sciences (patermann & aguilar, 2018). in contrast, the first bioeconomy strategy of the eu turned the spotlight onto primary and secondary bio-based production while the second strategy broadened the scope to all types of activities that use biomass, tertiary activities included. the work from cingiz, gonzález hermoso, et al. (2021) applies to all three perceptions and quantifies the interlinkages between the bioeconomy and the rest of the economy. at the eu28 level, the production of biomass contributes 1.6% of the total value added in 2015, which rises to 4.6% if we add the other fully bio-based industries q (food, beverage, tobacco, wood products and paper, see table 1). the trickling down of industries q’s output to partly bio-based manufacturing and service activities permits the generation of an additional 3.9% of the eu28 total value added (eur 511 billion). in addition, cingiz, gonzález hermoso, et al. (2021) claim that bioeconomy industries also depend on the rest of the economy for input provision. that economic link is quantified in the form of a so-called ‘upstream effect’ (equation 9) and is worth 2% of the eu28 total value added. the largest upstream effects are observed from tertiary activities (table 3 (d)), nearly half of the upstream effect being the fact of trade activities (42%) and transportation and storage (7%). in sum, the authors estimate that fully bio-based industries q and the downstream and upstream effect of other industries account for a significant 10.4% of the eu28 value added. regarding the size of the kbbe, the results from cingiz, gonzález hermoso, et al. (2021) are unfortunately not disaggregated enough to inform on the value added generated by the knowledge-productive activities used by the set of industries q (upstream effect of nace m71-m75 and p85). the estimation could be computed with further research though. another approximation could be provided from an output-based perspective, i.e., the value added created by the production of knowledge in bioeconomy fields. unfortunately, available data sources cannot permit a more precise quantification than eur 35-121 billion for the eu28 in 2015. 3.2.4. substitution effect and dependence of the eu bioeconomy to fossil resources the substitution of non-renewable resources in industrial and energy processes is central in the eu bioeconomy strategy for addressing the two objectives of lowering the eu dependence to non-renewable feedstocks and of contributing to climate change mitigation (european commission, 2012 page 5; 2018 page 9). such a substitution effect could be observable from the monitoring of sectorial biomass input cost shares (γr and βr ) over time in the form of increasing usage of biomass input in proportion to total inputs (in value terms). time series are only offered for the biomass input cost shares βr by cingiz, gonzález hermoso, et al. (2021). contrary to the expected upward trend, cingiz, gonzález hermoso, et al. (2021) indicate a reduction of biomass input cost shares from 2.7% to 2.5% in the secondary sector (excluding the set of sectors q) and from 5.1% to 4.4% in the tertiary sector between 2005 and 2015. the authors note, however, that the biomass input cost share of the secondary sector is stabilising since 2010. at the eu28 level, the reduction trend is particularly noticeable in the manufacture of furniture and repair and installation of machinery and equipment 328 bio-based and applied economics 13(4): 317-331, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14563 tévécia ronzon et al. (nace c31-33) and in the industry of publishing, audiovisual and broadcasting (nace j58-60) but trends differ across countries. a note of caution has to be introduced here on the monitoring of biomass inputs in value terms. due to differentials in the relative value of biomass compared to other inputs, a decreasing proportion of biomass inputs in value does not always correlate with a decreasing proportion in quantity. beyond the capacity of a whole economy to use biologically renewable resources in industrial processes and services, some observers question the capacity of the bioeconomy to source itself with less fossil inputs. in that sense, the upstream component of mining and fossilbased industries provides evidence on the link between industries q and fossil resources. cingiz, gonzález hermoso, et al. (2021) estimate that 1.1% of the output of the mining and carrying sector and 3.2% of the output of the manufacturing of coke and refined petroleum products source the core bioeconomy industries q. these proportions have remained fairly steady over the 2008-2015 time period but they vary across eu member states: from 0.3% to 4.4% in the case of mining and carrying activities and from 0.5% to 8.1% regarding the manufacture of coke and petroleum products in 2015. the same logic could apply to examine the use of plastics by industries q although the data source used for the quantification of the upstream component does not disentangle fossil-based from bio-based plastic inputs. 4. conclusion the bioeconomy is considered a strategic subsector of the economy. however, there is no single definition of the bioeconomy, and current policy and research questions address various aspects of the bioeconomy, for which different perspectives are required. this diversity of views makes quantification difficult, but the scientific community has responded well to the challenge of quantifying the economic performance of the eu bioeconomy. as this study demonstrates, a variety of sound methodologies are now implementable to inform on various aspects of value creation in bioeconomy sectors. the challenge lies in understanding, comparing and applying those different methods. this article gives an overview of four of these approaches, and discusses the different results obtained. we conclude that the communication of scientific outcomes to stakeholders could be improved, avoiding the general term “value added of the bioeconomy” without additional clarification of the methodology and sources of data used for its quantification. the output method aligns with the definition of bioeconomy in the eu bioeconomy strategy and is therefore useful for monitoring progress from a policy perspective, both at the country and sectorial level. with this method, we can estimate eur 730-790 billion of value added were created from the aggregated domestic production of biomass, bio-based products and bioenergy (i.e., 5.5-5.9% of the total eu value added) and that it grew by 30-31% over the decade 20092019. the agro-food industry is responsible for half of the eu bio-based market, followed by the paper and bio-based pharmaceuticals industries. services are not yet well captured in the output-based approach. on the other hand, value creation from the use of biomass in the eu is best analysed with the input-based approach. eur 670 billion of value added were created from the use of biomass in all economic sectors, that is, 5% of the total eu value added. this method indicates that the secondary sector is more dependent on biomass inputs than the tertiary sector but less efficient at generating value added from it (eur 299 billion vs. eur 371 billion). finally, the upstream & downstream approach analyses the integration of the bioeconomy into the broader economy well beyond the production of biomass and the manufacturing of products. this approach shows that the bioeconomy contributes 10.5% of the total eu-28 value added: 4.6% from traditional bioeconomy industries, 3.9% from the processing of biomass into other products, and 2% from the use of products and services in the production of biomass and food, wood and paper products. in addition, contrary to the eu bioeconomy strategy’s expectations, there is no clear trend towards an increase of biomass input use in eu industries over the period 2009-2019 that could indicate a substitution of non-renewable resources by bio-based ones. however, such an effect could be masked by a reduction in the relative value of biomass compared to other inputs. the share of mining, coke and petroleum products bought by traditional bioeconomy industries has remained stable between 2009 and 2019. within the bioeconomy, the size of services industries is at least comparable to the size of biomass producing and manufacturing industries. however, it is usually under-estimated because bioeconomy strategies tend to focus on biomass. the approaches commented in this study can provide quantitative evidence to more sectorial questions, related to, among others, the size of paid recreational services, the knowledge bio-based economy or the circular economy. however, some limitations remain to be addressed through further research. refining the estimation of bio-based shares of services and con329assessing the bioeconomy’s contribution to evidence-based policy: a comparative analysis of value added measurements bio-based and applied economics 13(4): 317-331, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14563 structing time series with more sectorial breakdown and more recent data would enhance the methodologies based on iots. monitoring the use of biomass and the bio-based substitution of strategic sectors could also provide additional evidence to the debate on the classification of economic activities into green or brown sectors that has become topical in the context of the publication of a green taxonomy by the eu (bohnenberger, 2022) or provide further tools to assess the degree of “greenness” of each specific activity and their development over time. further research could also address questions of efficient resource/biomass use and the needed transition from linear to circular resource use. frameworks and indicators for measuring circularity are being developed and tested at micro(baratsas, pistikopoulos, & avraamidou, 2022; chrispim, mattsson, & ulvenblad, 2023), regional (bianchi, cordella, & menger, 2023), national or international level (moraga et al., 2019). one of the main challenges is to determine the allocation of impacts to initial biomass use and their subsequent recycled cycles (corona, shen, reike, rosales carreón, & worrell, 2019). sound knowledge of biomass material flows is a prerequisite for determining material biobased inand outputs. pursuing a growing value added from bio-based markets, bio-based feedstock, or bioeconomy inputs should not be the only objective of a functioning bioeconomy. further research is also needed to complement the economic monitoring of the bioeconomy with environmental assessments. only a truly sustainable bioeconomy can support the transformation of the economic system from fossil-based to green growth. the sustainability of production and consumption within the bioeconomy, the health of natural ecosystems and a fair distribution of bioeconomy’s benefits are also central in the policy narrative. the methodologies presented here provide the basis for developing other indicators of the progress of the bioeconomy such as the number of persons it employs (kuosmanen et al., 2020; m’barek et al., 2014; ronzon et al., 2022b; ronzon et al., 2017) or its impact on the greenhouse gas emissions (kuosmanen et al., 2020). however, monitoring sustainability aspects of the bioeconomy requires a much more comprehensive set of indicators 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(2017). measuring the bioeconomy: economics and policies. annual review of resource economics, 9, 275-298. https://doi. org/10.1146/annurev-resource-100516-053701 _ref114578152 _ref114489287 _ref114578337 _ref115429676 _hlk124753738 _hlk124753769 _ref114578159 _hlk125619779 _hlk125620100 _hlk125620116 _hlk168057609 _hlk125617961 _hlk125617978 _heading=h.1fob9te _heading=h.1y810tw _heading=h.3whwml4 _hlk105387355 _hlk162970176 _hlk120503612 _hlk94825625 _hlk101307043 _hlk112165281 _hlk110795819 _hlk163789378 _hlk101572865 _hlk101574469 _hlk162424605 _hlk163657897 _hlk163658986 _hlk163657719 _hlk163657691 _hlk163658456 _hlk163658513 _hlk163658646 _hlk162446713 _hlk125919866 _hlk107164817 _hlk162973538 _hlk162447045 _hlk107174352 _hlk162447083 _hlk124882770 _hlk142394334 _hlk142395728 _hlk142430461 _hlk142337917 _hlk142342729 _hlk142337969 _hlk142337867 _hlk142342461 _hlk142342928 _hlk142338077 _hlk142348528 _hlk164779807 _hlk125467744 _hlk141790649 assessing the bioeconomy’s contribution to evidence-based policy: a comparative analysis of value added measurements tévécia ronzon1,2,*, patricia gurria2, michael carus3, kutay cingiz2, andrea el-meligi1, nicolas hark3, susanne iost4, robert m’barek1, george philippidis5, myrna van leeuwen6, justus wesseler2 predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology lisa baldi1, sara calzolai2, filippo arfini2,*, michele donati1 simulating farm structural change dynamics in thessaly (greece) using a recursive programming model stamatis mantziaris1,*, stelios rozakis2, pavlos karanikolas1, athanasios petsakos3, konstantinos tsiboukas1 analyzing the impact of government subsidies on household welfare during economic shocks: a case study of iran mohammad dehghan1,*, seyyed nematollah moosavi2*, ebrahim zare3 crop production, the pollinator deficit and land use management: uk farm level survey results iain fraser1,*, michelle t. fountain2, john m. holland3 bio-based and applied economics bae copyright: © 2023 krasniqi n., blancard s., gjokaj e., ottaviani aalmo g. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae bio-based and applied economics 12(3): 183-195, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14693 citation: krasniqi n., blancard s., gjokaj e., ottaviani aalmo g. (2023). modelling technical efficiency of horticulture farming in kosovo: an application of data envelopment analysis. bio-based and applied economics 12(3): 183-195. doi: 10.36253/bae-14693 received: may, 9 2023 accepted: august, 1 2023 published: october 15, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: davide menozzi orcid nk: 0000-0003-4570-3239 sb: 0000-0001-5996-2368 eg: 0000-0002-1649-4098 goa: 0000-0001-8357-4971 modelling technical efficiency of horticulture farming in kosovo: an application of data envelopment analysis nol krasniqi1,2,*, stéphane blancard1, ekrem gjokaj2, giovanna ottaviani aalmo3 1 cesaer, inrae, l’institut agro dijon, université bourgogne franche-comté, f-21000, dijon, france 2 faculty of life and environmental sciences, university “ukshin hoti”, prizren, kosovo 3 department of economy and society, norwegian institute of bio-economy research, nibio, ås, norway *corresponding author. e-mail: nol.krasniqi@uni-prizren.com abstract. with a view to integration into the european union, the efficiency and competitiveness of the kosovo’ different sectors (including agriculture) must be improved. this paper assesses the technical efficiency (te) of horticultural farms through data envelopment analysis (dea) applying output orientation. it was founded that the te of these farms is positively affected by their size, with large-size farms presenting overall higher technical efficiency. the research findings indicate that the degree of agricultural education does not have a significant impact on te, whereas public assistance through subsidies and grants has a substantial and negative impact on te, as confirmed by statistical analysis. keywords: technichal efficiency, horticultural farming, data envelopment analysis. jel codes: q10, q18, c14. highlights • with a view to integration into the european union, the efficiency and competitiveness of the kosovo’ different sectors (including agriculture) must be improved • we use a model of technical efficiency of horticulture farming in kosovo with application of dea. • fadn data used on this study are from the years 2015 to 2019, in total 5 years in a row making the total observation 779. • the study’s findings reveals that the majority of farms in the sample show a technical efficiency level below 50%. • it was found that the te of these farms is positively affected by their size, with large-size farms presenting overall higher technical efficiency. http://creativecommons.org/licenses/by/4.0/legalcode https://doi.org/10.36253/bae-14693 https://doi.org/10.36253/bae-14693 https://orcid.org/0000-0003-4570-3239 https://orcid.org/0000-0001-5996-2368 https://orcid.org/0000-0002-1649-4098 https://orcid.org/0000-0001-8357-4971 184 bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14693 nol krasniqi et al. 1. introduction a future integration for kosovo to the european union (eu) raises significant opportunities but also challenges for the country’s economy. one challenge is to improve the competitiveness of several sectors, including agriculture. according to latest agriculture census, kosovo has 1.1 million hectares of land, out of which 53% is agricultural land (from which 54.3% belongs to permanent grasslands, 43.6% arable land, 1.9% permanent crops and 0.3% kitchen garden), 41% is forest, and 6% belongs to other land uses (kas, agriculture census, 2015). kosovo has traditionally supported with direct payment (subsidies) and through investment grants three main agricultural sectors: cereals, horticulture, and livestock, which are divided into 21 subsectors: 11 annual and perennial crops (cereals and horticulture), wine, and organic products, and 10 livestock sectors and milk (kostov et al. 2020). in kosovo, the agriculture sector employs the highest number of people, accounting for 34% of the total employment. this sector also makes a significant contribution to the country’s gross domestic product, which was around 8% in 2019. additionally, agricultural products constitute 17% of the total export value (mafrd, 2020). although, 60% of the population lives in rural areas in kosovo, they do not contribute much to economic growth. according to the kosovo ministry of agriculture, forestry and rural development (mafrd), “only a limited number of farms are currently able to compete and grab a greater share of the eu and foreign market”. the low competitiveness of farms can be attributed to several key structural factors, including the small size of most farms, land fragmentation, outdated building and equipment design, and limited access to financial resources (mafrd, 2014). furthemore, kosovo continues to have a relatively high volume of imported agricultural products, which make up approximately 10% of all imports. in europe, kosovo ranks among the highest importers of food per capita (erp, 2018). in this context and to attain the european standards, improving the competitiveness of the agricultural sector becomes paramount. one way to help agriculture go towards competitiveness in domestic and foreign markets is to improve the technical efficiency (te) of each agricultural sub-sector. technical efficiency refers to the ability to achieve the highest possible output level from a given set of inputs or resources. it measures how effectively inputs are utilized to produce desired outputs within a production process or system. it is a fundamental concept in economics and plays a crucial role in various fields, including agriculture, manufacturing, healthcare, and public services. according to koopmans (1951, as cited in farrell, 1957, p. 255; charnes & cooper, 1985, p. 72) provided a definition of what we refer to as technical efficiency, stating that an input-output vector is technically efficient if increasing any output or decreasing any input can only be achieved by decreasing some other output or increasing some other input. in the context of agriculture, technical efficiency is particularly significant as it directly impacts food production, resource utilization, and sustainability. by measuring and improving technical efficiency, policymakers, farmers, and stakeholders can make informed decisions, allocate resources effectively, and drive agricultural development. in this study we focus on the horticultural farms from fadn data, which includes te for vegetables cultivated indoor in greenhouses and vegetables cultivated outdoor. in comparing the 2019 total share of agricultural crops’s production to 2018, 2019’s vegetables lead with the highest percentage 33.4%, followed by fodder crops, cereals, fruits and others (mafrd 2020). according to the green report from mafrd (2020) the total area cultivated with vegetable during 2019 was 18,911 ha. the crops that dominate the largest area in 2019 were potato (20%), pepper (16%), beans (15%), pumpkin (13%), onion (7%) and watermelon (6%). from the total area with vegetables, the different forms of horticulture in kosovo, with the largest part are produced in open field. in percentage, the main area used for horticulture is in the open field with 83.5% followed by garden with 11.3% and vegetables cultivated in greenhouses with 5.2% (mafrd, 2020). following the introduction, section two presents a comprehensive review of the existing literature. section three provides a detailed explanation of the research methods employed, while section four elaborates on the data utilized for estimating efficiency. moving forward, the fifth section presents the results of the technical efficiency analysis and identifies the factors that influence it. finally, in the sixth section, the paper concludes with a summary of the analysis and discusses the policy implications derived from the findings. 2. literature review despite the fact that there is limited literature that demonstrates the significance of measuring technical efficiency in kosovo’s horticultural sector, there are numerous global studies that explore efficiency in this area, iráizoz et al. (2003) measured the te of horticulhttps://doi.org/10.36253/bae-14693 185modelling technical efficiency of horticulture farming in kosovo: an application of data envelopment analysis bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14693 tural production in a sample of spanish farms. they discovered a significant resemblance between the two technical efficiency estimates. other authors, bozoglu and ceyhan (2007) assessed the technical efficiency of 75 vegetable farms involved in vegetable production and investigated the factors that contribute to technical inefficiency in the samsun region of turkey. this study’s findings indicate that the technical efficiency of the sample vegetable farms ranged from 56% to 95% (82% in average) and was affected by schooling, experience, credit use, participation by women, and that information score negatively affected technical inefficiency. on the other hand, factors such as age, family size, offfarm income, and farm size were positively related to inefficiency. another study conducted by clemente et al. (2015) focused on assessing the technical efficiency of citrus-producing properties in sao paulo state during the years 2009 and 2010. their investigation involved conducting interviews with producers and employing both non-parametric data envelopment analysis (dea) and econometric methods to determine the levels of technical efficiency and identify factors that influenced efficiency. the study’s findings demonstrated that a significant proportion of citrus-producing properties in sao paulo operated below optimal efficiency levels. notably, the factors of “producer schooling” and “experience as a rural producer” emerged as the primary drivers of increased efficiency. the mean technical efficiency score obtained from the study was 0.79, indicating the potential for production growth while maintaining the current input proportions based on the product-oriented model. in a similar vein, a study conducted by irz and stevenson (2012) investigated the potential inverse relationship (ir) between farm size and technical efficiency in philippine brackishwater pond aquaculture. this paper employs a stochastic ray production function to examine the potential inverse relationship in philippine brackishwater aquaculture, utilizing a cross-sectional sample of 127 farms. the distribution of efficiency scores spans the entire range, with an exceptionally low average value of 0.37. farm size explains only 13% of the variability in outputs that are not accounted for by physical inputs, while 73% is attributed to unidentified factors and 14% to random shocks. although the findings of this study are significant for policy formulation, they present a rather negative outcome, as they indicate that variations in efficiency are influenced by unexplained factors. consequently, further investigation and speculation are necessary to uncover the underlying reasons for the subpar average technical performance of farms. previous studies about the technical efficiency in kosovo mainly focused on livestock and the dairy sector. for example, sauer et al. (2015) analysed the effect of migration on farm te and found migration a decreasing effect. more recently, alishani (2019) investigated the effects of public support policies on technical efficiency in kosovo, with 394 farms from fadn year 2014. to the best of our knowledge, few research deals with technical efficiency of horticultural farms in kosovo. frangu et al., (2018) assessed the input efficiency of 136 greenhouse farms growing tomatoes and peppers at both the farm and regional levels. the research utilized a combination of linear regression and dea methods to identify any external factors that impacted efficiency. the study concluded that technical efficiency scores varied between regions, and based on the structural and operational characteristics of the greenhouse farms growing tomatoes and peppers, it was found that there was a possibility for farms and regions with low technical efficiency to enhance their input usage. while larger farm size is essential for achieving sustained higher productivity in the long term, technical efficiency presents the most promising solution for enhancing productivity in the short to medium term and promoting the growth of kosovo’s agricultural sector. vegetable production offers the best opportunity for producing viable incomes on small farms while adding significant value to the national economy. according to statistical data from mafrd, total area for cereals is decreasing, while for vegetables, the area of cultivation is increasing. kosovo has the quality of land to achieve this but experience shows that when the products are grown by large numbers of small farmers acting independently, without irrigation, greenhouses, cool storage, grading and packaging facilities, and sufficient processing capacity there will be a considerable amount of dumping on oversupplied markets at peak supply. this study not only measures efficiency, but it also examines the factors that influence efficiency, and uses this analysis to provide additional recommendations for policy. in order to achieve this objective, we employ a two-step method suggested by simar and wilson (2007). in the first step, we estimate the relative efficiencies using inputs and outputs and then analyse the effects of the exogenous variables on efficiency. as several authors (iráizoz et al. 2003; sauer et al. 2015; wilson, 2001; karimov, 2014; latruffe, 2004; theodoris et al., 2014; gaviglio 2021; morrais, 2021; alishani 2019), the exogenous variables are age, agricultural training of the manager/ holder, gender, irrigation system, altitude, area constrains, total subsidies on crops, rented area (iráizoz et al. 2003; sauer et al. 2015; wilson, 2001; karimov, 2014; latruffe, 2004; theodoris et al., 2014; gaviglio 2021; morrais, 2021; alishani 2019). https://doi.org/10.36253/bae-14693 186 bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14693 nol krasniqi et al. finally, the paper contributes to fill the research gap on efficiency in the horticulture sector in kosovo. we focus on the farms from fadn data, which includes te for vegetables cultivated indoor in greenhouses, and vegetables cultivated outdoor. 3. methodology methodologically, we employ data envelopment analysis (dea) to assess the performance of a group of units. based on the pioneering work of farrell (1957), charnes et al. (1978) developed the dea model under the constant return to scale (crs) assumption, and banker et al. (1984) extended it under the variable returns to scale (vrs) assumption. dea involves creating a production frontier that illustrates the highest attainable output from inputs, and subsequently measuring the distance between each unit and the efficient frontier (blancard and hoarau, 2013). the best performers’ group provides practical observations for constructing this frontier. the most efficient units are those closest to the frontier, and so the furthest from the frontier, the highest is the units’ inefficiency. two approaches can be used to estimate technical efficiency (te): parametric, which includes both stochastic and deterministic methods, and non-parametric, such as dea. in agriculture and farming, each approach has its own advantages and disadvantages when it comes to measuring farm performance. studies comparing parametric and non-parametric methods have revealed disagreements regarding these approaches, particularly in agriculture. coelli (1995) reviewed literature on frontier function estimation and efficiency measurement and suggested potential applications of these methods in agricultural economics. further to this debate sharma (1999) compared two approaches in measuring efficiency of the swine industry in hawaii and the study revealed the dea method is a more robust approach for measuring efficiencies compared to the parametric approach, based on the obtained results. dea is particularly suitable for agriculture because it allows for the assessment of relative efficiencies among multiple decision-making units (dmus) without requiring explicit functional form assumptions or knowledge about the underlying stochastic production function. it considers the best-practice frontier defined by the most efficient units, providing a benchmark for comparing and evaluating the efficiencies of other units. this is beneficial in the agriculture sector, which encompasses a wide range of production systems and practices, where the assumptions of a specific functional form may not hold universally (fare et al. 1994). in our study, we utilized an output-oriented model to estimate te, which was based on both on (variable returns to scale) and (constant returns to scale). the term “decision-making unit” (dmu) is used to refer to any entity that is evaluated based on its ability to transform inputs into outputs. in our study, we use this term to refer horticultural farms. our primary goal is to evaluate efficiency based on the assumption that a dmu can produce a greater amount of output by using the same level of inputs. to achieve this, we use an outputoriented model. we chose output orientation based on the challenges that the horticulture sector in kosovo faces, as described in the first part of the paper. moreover, as following numerous studies, we decomposed technical efficiency (te) into pure technical and scale efficiencies from ccr (charnes et al., 1978) and bcc (banker et al.,1984) models to identify the sources of inefficiencies. let us consider n farms producing s output from m inputs. for the evaluated farm o, the output-oriented dea linear programming is written as follow: max ϕ subject to ∑n j=1 λjxij≤xio i=1,2…,m ∑n j=1 λjyrj≥ϕyro r=1,2…,s ∑n j=1 λj=1(dea–bcc) λj≥0 (dea-ccr ) j=1,2,…n (1) where n, m and s are number of dmus, inputs and outputs, respectively. dmuj consumes xij of input i and produces yrj of output r; λj are the weights assigned by the linear program, ϕ is the calculated efficiency. the summary of the results obtained from the envelopment model interpretation is as follows: if ϕ* = 1, then the dmu under evaluation is a frontier point. i.e., there are no other dmus that are operating more efficiently than this dmu. the dmu under evaluation is inefficient. i.e., this dmu can either increase its output levels or decrease its input levels (zhu, 2014). the results of dea tevrs model represent pure technical efficiency (pte). alternatively, dea tecrs model represents the overall technical efficiency (ote), which consists of two components: scale efficiency and pure technical efficiency. while comparing scores from both dea tecrs and dea tevrs model, if a dmu has a different efficiency score that means that the particular dmu has scale inefficiency. scale efficiency can be obtained by: se = = ote/pte (2) https://doi.org/10.36253/bae-14693 187modelling technical efficiency of horticulture farming in kosovo: an application of data envelopment analysis bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14693 after obtaining the results from the two models, we employed bootstrapping in the nonparametric model to address potential scepticism regarding the use of dea in agriculture. non-parametric efficiency measures are often criticized for lacking a statistical basis. however, simar and wilson (1998) argued that nonparametric efficiency measures do indeed have a statistical basis, and used bootstrapping to analyze the sensitivity of nonparametric efficiency scores to sampling variation. to generate the bootstrap estimates, we utilized the algorithm proposed by simar and wilson (1998) in r studio, which is a statistical computing software. we used b = 2,000 bootstrap replications, and set the bandwidth at h=0.014 based on empirical evidence from simar and wilson (1998) that suggests small values of h provide smooth density estimates that follow the empirical density function, while large values of h yield over-smooth density estimates. 4. data 4.1. data source the study uses data from farms covering the entire kosovo. kosovo has 7 administrative regions, but a nomenclature of territorial units for statistics has not yet been introduced. it is divided into two territorial levels: municipal and settlement level; it currently has 38 municipalities and 1,469 settlements (mafrd 2014). in hydrographic terms, kosovo is divided into river basins: the drini i bardhë, ibri, morava binqës and lepeneci (kas, 2019). this sector of vegetable production in kosovo is one of the main branches of agricultural production whilst in some regions of the dukagjini plain, it represents the main economic activity (mafrd 2014). the predominant approach to horticultural cultivation involves cultivating crops in open fields for the purpose of commercial production. among the various types of crops, vegetable production is typically the most labor-intensive. data employed in this study are extracted from the farm accountancy data network (fadn). the development of a sustainable fadn system in kosovo has been a focus of effort over recent years. funded by the european agency for reconstruction-eu, a fadn pilot project was launched in 2004 involving 50 farms. this network expanded to 159 farms in 2005, increasing the number of farms to 300 in 2008 and 402 in 2013 and 2014. in order to make an adequate selection of the sample, the fadn team applied the stratified simple random sampling. sampling is carried out by following three fundamental criteria, which include economic size, farm type, and region. these criteria conform to the standardized fadn methodology in line with the european commission’s guidelines.1, even though it was simplified to suit the specific situation of the country. the decision was made to include around 1,250 farms, which is roughly 2% of all agricultural holdings, in the fadn survey in order to ensure that the sample is as representative as possible (mafrd, 2020). to account for the possibility of some farms declining to participate in the survey, each entity involved included approximately two additional reserve farms. in order to assess the technical efficiency (te) of the horticultural sector of kosovo, fadn data used are from the years 2015 to 2019, in total 5 years in a row making the total observation 779 (table 2 in appendix). the number of farms is different from year to year, the reason is that some farmers refused to participate in the upcoming years, so there was a number of reserved farms of the same typology which was used in case of refusal, besides this some farms change the category during the five years’ period. 4.2. inputs and output selection to measure the technical efficiency, we retained four inputs and one output. the chosen inputs are widely employed in the literature for measuring technical efficiency. the term “total labor” refers to the amount of work completed in a year, equivalent to a full-time job. this is measured in annual work units (awus), which represent the amount of work performed by a person who is employed full-time on a farm. in kosovo, the minimum annual working hours are considered to be 1,800, which is equivalent to 225 workdays of eight hours each. the second input is land or the utilized agricultural area expressed in hectares. it consists of the land in owneroccupation, rented land and land in share-cropping. the third input is total intermediate consumption, which includes total specific costs (including inputs produced on the farm) and overheads arising from production in the accounting year. the total specific costs included specific crop costs (fertilizers and soil improvers, purchased manure, crop protection products) and other specifics costs (labour and machinery costs and inputs, contract work, and machinery hire, current upkeep of machinery and equipment, motor fuels and lubricants, car expenses). farming overheads include land improvements and buildings, electricity, heating 1 council regulation (ec) no 1217/2009 of 30 november 2009 setting up a network for the collection of accountancy data on the incomes and business operation of agricultural holdings in the european community https://doi.org/10.36253/bae-14693 188 bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14693 nol krasniqi et al. fuels, water, farm insurance, other farming overheads expressed in the euro. finally, we consider one more input which is the average farm capital includes cash & equivalents, receivables, other current assets, inventories, plants, land improvements, farm buildings, machinery and equipment, and intangible assets. output is the total value of the crop products, and of other output expressed in euros, including that of other gainful activities (oga) of the farms. the table 2 (appendix) presents the descriptive statistics of variables for farms together indoor and outdoor in the open fields2. on average, they produced output in value of 29,319 € for the year 2015 with 139 farms in the sample for this year. in 2016 with 150 farms the total output value was 28,404 € while in 2017, 162 farms produced on average 29,678 €. in 2018, 143 farms produced total output on average of 26,516 € while in the year 2019, 185 farms produced total output in value of 25,550 €. 5. results and discussions 5.1. efficiency results in our study vrs, crs and se were evaluated for horticultural farms. the number of farms is different from year to year, the reason is that some farmers refused to participate in the upcoming years, so there was a number from the list of a reserved farm of the same typology which was used in case of refusal, and besides this some farms change the category during the five years’ period. the summary of results is presented in the table 1. the year 2017 showed the highest efficiency score with a pure technical efficiency level of 0.72 for farms horticulture indoor, which means that 28% can increase the output to reach the efficiency frontier. the majority of farms in the sample show a technical efficiency level of above 50%, besides the year 2018, which is with the level of efficiency of 45%. on the contrary, farms operated in the open field have a lower efficiency score, with the largest efficiency level of 0.56 in the year 2017, while similar to horticulture indoor, also at the open field, the year 2018 has the lower efficiency score below the 50%. for the farms, horticulture indoor the highest average score on scale efficiency (0.92) was in the year 2017 while the lowest score (0.77) was in 2019. while for the farms in the open field the highest average score on scale effi2 descriptive statistics for each of inputs and output variables for the three categories horticultural farms indoor, outdoor in open field are presented in table 3 and 4 in appendices. ciency (0.91) in the year 2017 and the lowest in the year 2015 and the lowest score of se (0.74). from the average aggregate results for farms (indoor and open fields together), most of the farms in the sample show a technical efficiency level that is less than 50%. the highest efficiency score with a pure technical efficiency level of 0.50, which means that 50% can increase the output to reach the efficiency frontier. the lowest pure technical efficiency score (0.36) is in the year 2018. the highest average score on scale efficiency (0.94) was in the years 2017 and 2018 while the lowest score (0.87) was in 2015. from the fadn methodology, farms are defined as being commercial only when they pass the standard output of 2,000 euros. this implies that a commercial farm is able to provide the farmer with a sufficient level of income to support the welfare of his family. thus, based on this classification the table 5 in appendices present the technical efficiency score categorized by economic sizes of farms. the large-size farms had overall higher technical efficiency under the category 6 (100,000 < 500,000). 5.2. biased corrected efficiency scores figure 1 to 10 present a graphical illustration of the distribution of farms (in appendices), using box plots to table 1. descriptive results of efficiency estimate for horticultural open field farm. 2015 mean sd min max no of farms pte 0.52 0.28 0.11 1.00 121ote 0.36 0.24 0.10 1.00 se 0.74 0.21 0.10 1.00 2016 pte 0.49 0.27 0.13 1.00 130ote 0.36 0.23 0.12 1.00 se 0.77 0.21 0.28 1.00 2017 pte 0.56 0.26 0.13 1.00 144ote 0.51 0.24 0.12 1.00 se 0.91 0.14 0.31 1.00 2018 pte 0.46 0.28 0.13 1.00 124ote 0.39 0.24 0.10 1.00 se 0.87 0.19 0.21 1.00 2019 pte 0.48 0.28 0.14 1.00 164ote 0.42 0.24 0.12 1.00 se 0.89 0.16 0.17 1.00 source: author’s composition. https://doi.org/10.36253/bae-14693 189modelling technical efficiency of horticulture farming in kosovo: an application of data envelopment analysis bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14693 facilitate the comparison among farms efficiency score in addition to the bias corrected. for each group of farms, the box represents the 50% mid-range values of efficiency scores and biases corrected. the interquartile range (iqr) is depicted by the length of each box, and the natural limits of the distributions are defined by the whiskers (which correspond to the mean ±1.5 (iqr)). any outliers that fall beyond the natural limits are represented by round circles. each group of farms are determined based on the bias corrected scores, the allocation of farms is different than the groups obtained based on the efficiency scores. due to this different scope of the groups, we get these differences that appeared in the charts with red colors. groups of farms are determined based on bias-corrected scores, for example group of farms with bias corrected scores from [0-0.10[ belong to group 1, while [0.10-0.20[ belong to group 2 and the rest until group 10. from the graph its clear the homogeneity of farms in respect to efficiency scores within each group and those to be noted are in group 7 the differences within group in each year under vrs and crs. in addition, there are substantial differences between the two measures. on average, under this determination, in the year 2019 the efficiency score is 0,46, while under the bootstrap pte model, it is only 0.39. further for the same year, the ote average score of te is 0.39, while under the bootstrap ote the score is 0.34. for instance, none of farms found entirely efficient under the pte model and ote model in each year (2015-2019) do not remain so after accounting bias-corrected scores through the bootstrap procedure. in this case, farmers should consider increasing the output while maintaining the same inputs. the results show there is a lot of space for using efficiently the inputs, area, labour, total intermediate consumption and average farm capital. 5.3. determinants used to explain efficiency this section explains the second stage of technical efficiency study. the objective of this stage is to identify shared common characteristics among the most efficient farms. two step procedures are used in the same scenario as irazoz et al. (2003), so ols and analyses of variance are used to determine the link between efficiency and exogenous variables. although the one-stochastic frontier method has a clear technical advantage over the two-step procedures, the two-step procedures may be more logically appealing for policy analysis and decision making because they directly relate the exogenous variables to the observed efficiency performance of the firms. furthermore, identifying the sources of inefficiency may aid in the development of policy recommendations (yu 1998; theodoris 2014). in this case we want to show the effects of exogenous variables in technical efficiency of horticultural production for further policy analysis in national level. running dea and creating a regression model with the dea efficiency scores as the dependent variable and other possible factors as explanatory factors. this is a well-known two-stage technique that has been widely criticized for producing skewed results. however, it is frequently utilized, at the very least, to figure out which determinants are relevant. contrary to a number of authors using tobit, in second stage data envelopment analysis (dea), mcdonald (2003), is not a fan of using this model. in the two articles written by mcdonald (2009, 2010), he describes ols as a better replacement and a sufficient second stage dea model. as he mentioned for many applied researchers, familiar and easy to compute, ols may be the best option. throughout the paper when referring to dea, he dealt with the single output, output-oriented case. after comparing, in a stage 2 analysis, ols, 2lt and 1lt marginal effects were similar. output oriented frontiers are constructed under both the assumptions of variable returns to scale (vrs) and constant return to scale (crs). the effect of the determinants is investigated with ordinary least squares (ols) regressions on each of the three te scores for the period of 2015 to 2019 with total of 779 observations specialized in horticultural farming. this methodology is used by latruffe (2017) to measure effect of subsidies on technical efficiency, contrary to us he used only variable return to scale (vrs) as our purpose is not only to measure the effect of subsidies on technical efficiency, in addition to that also we tend to measure other determinants which effect on technical efficiency on horticultural farming. regarding the determinants that affect the efficiency scores, the most common variable used are farm size, the age of holder, qualifications, experience and specialization of the farmer and combination of inputs (iráizoz et al., 2003). in this study they found limitations to get this information in their sample data, while in our case, we could have accesses to raw data from fadn and get this information. we classified farm level data based on specialization of farms in horticultural (open field and indoor), match them with farm code and efficiency results of each farm. these similar determinants mentioned above were used also from sauer et al., (2015) who investigated the effect of migration on farm technical efficiency in kosovo. another important study is to analyse the managerial drivers and practices due to https://doi.org/10.36253/bae-14693 190 bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14693 nol krasniqi et al. business planning in farm and relation to the technical efficiency. results from the research by wilson on influence of management characteristic on technical efficiency of wheat farmers in eastern england shows that, those farmers who seek information, have more years of managerial experience, and have a large farm are also associated with higher levels of technical efficiency (wilson, 2001). age and education are commonly cited as factors that may impact technical efficiency (karimov, 2014). he stressed the important role formal education (university degree and educational background in agriculture) and informal education such as participating in workshops and seminars of farmers are associated with efficiency-improving results (karimov, 2014). other authors stressed that farmers that are more educated are considered more likely to be efficient farms associated with higher scores of te (latruffe, 2004). and there is a strong significance between agricultural trainings and efficiency (theodoris et al., 2014). farmers who are younger may have a greater tendency to adopt innovative technologies aimed at reducing input usage. in contrast, older farmers may have greater efficiency due to their extensive experience in addressing efficiency-related issues (hadley, 2006). exceptionally to these authors, gaviglio (2021) found in his research that in fact, the level of education does not significantly improve the level of efficiency (gaviglio, 2021). in terms of the socio managerial aspect, we involved the variables age of owner/manager of farm, level of education in agriculture with only practical agricultural experience, basic agricultural training, full agricultural training, with the aim of seeing if the level and type of experience in agriculture affects the inefficiency. other variables were: specialization of farm that produce vegetables indoor and in open field, form of irrigation, irrigation system used on the farm, not applicable (when no irrigation on the farm), surface, sprinkler or drip. in similar research on effects of irrigation in technical efficiency morrais, (2021) results indicated that farms with irrigation had higher average technical efficiency compared to non-irrigators, which implies that irrigation technology has a significant effect on the efficiency gain for those groups. we also included variables on altitude of farms and the location, areas facing natural and other specific constraints. also, we divided regions in two main plains of kosovo, dukagjini plain and kosovo plain, to see which farms are more efficient based on their location, although the plain of dukagjini is well known for cultivating vegetables due to weather conditions, farm experience and tradition etc. however, the other part (mainly, the east part of the kosovo plains) in recent years has benefited from increased investment in this sector based on data from agency for agricultural development of kosovo. following other determinants, we included size to measure this we used the total output expressed in physical units (kg) of vegetables produced by farms. alike iraizoz et al. (2003), they explain that they expected to obtain a positive coefficient for size, because horticultural production could present scale economies, in our case we follow this conclusion. we included the same determinants involving the combination of inputs. additionally, we considered the total output coming from other gainful activities (oga) directly related to the farm such as processing of farm products. we measured this by the share of total oga in total output (%). we want to see if there is higher technical efficiency on farms that diversify their activities, and if large farms operate more efficient with higher share of oga. furthermore, we included as other determinants the share of subsidies to total output. various studies have investigated the effect of subsidies on farms’ technical efficiency, and in general the effect reported is negative. according to minviel and latruffe (2017) direct payments are common negatively associated with farm technical efficiency. in another study of the impact of support policies on technical efficiency of farms in kosovo, subsidies had negative effect on technical efficiency (alishani, 2019). drawing from these related studies, latruffe (2017) conducted a study on the impact of subsidies on technical efficiency with respect to environmental outputs. the study highlights that the policy implications are important, as a farm utilizing subsidies to increase environmental good outputs or reduce environmental bad outputs may have a lower traditional technical efficiency as compared to a farm receiving the same level of subsidies but using them solely for producing marketed outputs. therefore, the effect of subsidies on traditional technical efficiency could be negative for the former farm and positive for the latter farm. however, this case doesn’t necessarily fully apply to for kosovo’s scenario because cross-compliance subsidies are still not introduced in national level support, but it still remains a recommendation from eu commission to kosovo for initiating this form of subsidies. moreover, other determinates we used are the share of paid labour to total awu (annual working unit), and the share of rented land to total uaa (utilized agriculture area). for these two variables results, a study by alishani (2019) found out the paid labour to total awu affects negatively the technical efficiency score, while the determinant of rented land to total uaa was insignificant. https://doi.org/10.36253/bae-14693 191modelling technical efficiency of horticulture farming in kosovo: an application of data envelopment analysis bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14693 finally, we incorporated the factor of machinery and equipment into the analysis, which encompasses various items such as tractors, motor cultivators, lorries, vans, cars, and other farming equipment that are valued in euros. in the study by sauer et al., (2015) results show that physical capital (machinery and farm equipment) decreases technical inefficiency, but this stands mainly because of outdated machinery and equipment. our model with all determinates of inefficiency is presented on the table 8 (appendices). the adjustment shows corrected r squared coefficients of 0.38 for vrs, 0.44 for crs and 0.26 for se. similar results are found by different author, iraizoz et al. (2003) obtained coefficients of 0.31 and 0.68, and in addition, they found similarities to different authors as cited in (parikh ,1995) who obtain a coefficient of 0.214, (sharma, 1999,) with a coefficient of 0.23, and (wadud and white, 2000) with a coefficient of 0.66. concerning the socio managerial aspect in our results, the determinant age of the holder does not have significance with te scores. under the vrs and crs, full agricultural training significantly does not affect the te scores, while under the scale efficiency, there is a strong positive significance of full agricultural training to te scores. these results have relation to different reports that shows either formal or informal education in the field of agriculture remains insufficient compare to eu and neighbouring countries. according to the report from (national research programme of the republic of kosovo from 2010), research and technological development (rtd) in agriculture is still a marginal undertaking in kosovo, despite the fact that agriculture is an important economic sector. compared to other countries in eu and the region, kosovo has the lowest budget allocated for research per gdp, amounting to 0.1%. only 0.19% of budget was allocated (0.05% of gdp), while in 2016, around 0.33% of budget (0.1% of gdp) (kaçaniku, 2018). considering the differences of horticultural farms if they operate more efficiently in open field or indoors in greenhouses, results show not any significance. in terms of irrigation system used on the farm, drip system shows significance on 10% under vrs and crs. this system of irrigation is the most recommended to use in crops, as drip irrigation reduces deep percolation, evaporation and controls soil water status more precisely within the crop root zone (singandhupe, 2003). furthermore, lattrufe and desjeux (2014) indicate that farm size in kosovo increases integration into the output market and that irrigated crop output is more marketable than livestock output. with respect to demographic contents in term of altitude, there is no particular significance. there is strong significance in scale efficiency to altitude below 300m and above 600m. for specific vegetables there are different requirements to produce yields, for example potato according to haverkort (1990) it is shown that is adapted to a wide range of environments and hints are given on further exploitation of its potential in the various ecosystems. for the regional determinant, there is a negative significance under the scale efficiency in dukagjini plain, although it is well known for cultivating vegetables, this confirms our supposition that investment is being increased in the recent years in the east part of the kosovo plain and the area covered by vegetables. as regard to farm size measured as total production in kg, the study shows a positive relationship with technical efficiency under vrs, crs and se, with a strongly significance of 1%, in this case, the most efficient farms produce more in physical units. these results were also found by iraizoz et al. (2003) in horticulture production in spain. with respect to cultivation costs per hectare of land as a determinant relating to a combination of inputs, results show statistically no significant correlation with technical efficiency, contrary to iraizoz et al. (2003) who found negative correlation indicating that higher cultivation costs do not guarantee better results, in terms of efficiency. following other determinants, the partial productivity indices (output per unit of land and output per unit of labour), and the outcome are as expected, because the farms with higher productivity is an indicator for obtaining higher levels of technical efficiency. there is strong statistically significant under vrs, crs and se. with respect to subsidies, as we expected there, is a negative and statistically strong significant correlation between this determinant and technical efficiency under vrs and crs. public expenditure on kosovo’s agriculture and rural development is based on two pillars; grant aid to encourage investments in the means of production (the rural development measures) and payments for quantities of horticulture and livestock produced (direct payments). in term of commercialization, direct payments have positive effects for horticultural and fruits farms (kostov et al. 2020). regarding size, the authors suggest that the impact on commercialization will be more significant if a larger number of semi-subsistence farms receive payments based on their size. in kosovo, eligibility requirements for direct payments related to fruits and vegetables (open field) have lower size thresholds compared to most other payments, making them more attainable for semi-subsistence farmers (kostov et al. 2020). in every year, expenditure on direct payments has exceeded the https://doi.org/10.36253/bae-14693 192 bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14693 nol krasniqi et al. amount contracted for investment grants, and overall accounts for 56% of the total public expenditure in the agricultural sector. at the outset, it should be recognized direct income support has a vital role to play in the management of the transition from a production-oriented to a market-oriented food production sector. last but not least, the lack of producer organization in the fruit and vegetable sector, lack of specialist advice and training, and lack of support for innovation, are not being addressed. continuing the following determinants, utilized agricultural areas rented by the holder does not have any significance on te scores, while paid labour to total annual working unit is statistically significant under vrs only at 10%, contrary to alishani (2019), this determinant affected negatively the technical efficiency score. with respect to other gainful activities in farms concerning the diversification of economic activities, and contrary to what we expected, the results shows this determinant presents negative and statistically significant correlation with te scores, indicating that higher time spending on processing horticultural products does not guarantee a better te score. while as we expected based on results, large farms operate more efficient under vrs and se with higher share of oga. lastly, machinery and equipment’s decrease technical inefficiency on farms, there is a negative and statistically strong significant correlation with te scores under crs and se. this finding are is similar as sauer et al. (2015) on migration and farm technical efficiency evidence from kosovo. this is consistent with our study’s observations regarding the continued use of old technology and machinery by farmers in kosovo. with regards to five years of research data, from the results we can show that the year 2017 is strongly positive correlation with te scores under crs, vrs and se, and it has the highest average te score compare to other year. this mean that farms in horticulture operated more efficiently in the year 2017. 5.3. implications (limitations of our study) a limitation of using the fadn (farm accountancy data network) for measuring technical efficiency is the potential for selection bias. the fadn database collects data from a sample of farms that voluntarily participate in the program. this self-selection process can introduce bias if participating farms differ systematically from non-participating farms in terms of their characteristics or behavior. therefore, the findings based on the fadn data may not be representative of the entire agricultural sector, potentially limiting the generalizability of the results. to address these limitations, we employed appropriate statistical techniques, consider conducted robustness checks. the future research idea is to compare nonparametric methods with parametric methods for measuring technical efficiency scores in the agriculture sector in kosovo. this study will offer valuable scientific insights for researchers and provide assistance to policymakers in addressing the issue of inefficiency. 6. conclusion and policy implication the study’s findings reveals that the majority of farms in the sample show a technical efficiency level below 50%. the insufficient level of sore of te implies that the remaining potential output could not be realized due to technical inefficiency. this means that 50% can increase the output to reach the efficiency frontier. the highest efficiency scores are in the region of prizren and prishtina, the biggest regions in kosovo. concerning the exogenous factors affecting the efficiency scores with respect to subsidies, there is a negative and statistically strong significant correlation between this determinant and technical efficiency under vrs and crs. time spending on processing horticultural products does not guarantee a better te score, although large farms operate more efficient under vrs and se with higher share of oga. the results suggest that farmers should consider increasing the output while maintaining the same inputs. the findings indicate that there is considerable room for improvement for using efficiently the inputs, area, labour, total intermediate consumption and average farm capital. in this respect, policy makers mafrd3 should consider these low results of technical efficiency of farms to focus on a better program for extension services in order to promote better use of inputs. vegetables are produced often in rather small areas and is very labour intensive; this fact fits the current situation with plenty of underemployed family labour and unemployed. however, it seems that sooner than later, the abundance in the workforce will be gone, mainly because young people do not see agriculture as a business but just as an unwanted heritage. the mechanization is again low due to the small parcel sizes, but also due to missing financial means. tractors, ploughs, trailers are old and just bigger farmers can afford machines like good sprayers or e.g., carrot-harvesting machines. the situation is improving when dealing with bigger farmers with modern orchards of 5 ha and more. small farms struggle to access the market and to be commercialized, in this case considering the results from our 3 ministry of agriculture, forestry and rural development. https://doi.org/10.36253/bae-14693 193modelling technical efficiency of horticulture farming in kosovo: an application of data envelopment analysis bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14693 study it was found that the te of these farms is positively affected by their size with large-size farms presenting overall higher technical efficiency. for instance, small farms in horticultural sector should consider gathering in cooperatives. kosovo’s agricultural policy is focused on semicommercial and commercial farmers; 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(2014). quantitative models for performance evaluation and benchmarking data envelopment analysis with spreadsheets. springer. https://doi.org/10.36253/bae-14693 towards a holistic approach to sustainable risk management in agriculture in the eu: a literature review linda arata1, simone cerroni2, fabio gaetano santeramo3, samuele trestini4, simone severini5,* modelling technical efficiency of horticulture farming in kosovo: an application of data envelopment analysis nol krasniqi1,2,*, stéphane blancard1, ekrem gjokaj2, giovanna ottaviani aalmo3 incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon claudiane yanick moukam1,*, calvin atewamba2 farmers’ acceptance of a micro-irrigation system: a focus group study maria sabbagh1,*, luciano gutierrez2 reducing food-related economic loss to improve food security and cattle trade in the sahel: the case of agropastoral systems in senegal abdrahmane wane1,*, aliou diouf mballo2, cabrelle lauriane dzoukou homsi3, pathé diakhaté4, rahimatou memboup5 bio-based and applied economics 10(1): 7-17, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10034 bio -based and a ppl ied economics bae copyright: © 2021 g. guastella, d. moro, p. sckokai, m. veneziani. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: g. guastella, d. moro, p. sckokai, m. veneziani (2021) the capitalisation of decoupled payments in farmland rents among eu regions. bio-based and applied economics 10(1): 7-17. doi: 10.36253/bae-10034 accepted: november 19, 2020 published: july 28, 2021 competing interests: the author(s) declare(s) no conflict of interest. editor: roberto esposti. orcid gg: 0000-0002-1333-4718 dm: 0000-0002-7766-0803 ps: 0000-0001-8278-9663 mv: 0000-0001-6228-9514 the capitalisation of decoupled payments in farmland rents among eu regions gianni guastella1,2, daniele moro1, paolo sckokai1, mario veneziani3 1 università cattolica del sacro cuore, milan, italy 2 fondazione eni enrico mattei, milan, italy 3 università di parma, parma, italy corresponding author: gianni guastella. e-mail: giovanni.guastella@unicatt.it abstract. we study the capitalisation of subsidies in the european union (eu) regions in the years 2006-2008, the first years after the introduction of the common agricultural policy (cap) 2003 reform that decoupled subsidies from production and attached them to land. for this purpose, we use regional aggregated data and estimate the capitalisation rate upon the entire sample and, in a second stage, splitting the sample according to the implementation regime applied by the different eu member states (mss), following the three options introduced by the cap regulations (historical, regional and hybrid model). we find that between 28 and 52 cents per euro of additional subsidy capitalise into land prices in mss that adopted the hybrid and the regional model, respectively. we find as well that subsidies do not capitalise in farmland prices in mss that adopted the historical model. keywords: european union, capitalisation of eu payments, land rental prices, spatial panel econometrics. jel codes: q12, q18. 1. introduction farmland is by far the most valuable input in agricultural production. in the european union (eu), land, alongside permanent crops and quotas, accounts for about 65% of total fixed assets of farms in 2012 and the figure rises to 80% when only farms specialised in field cropping are considered (european commission eu fadn, 2015). accordingly, the theoretical and empirical literature paid much attention to the determinants of farmland prices. following the implementation of the 2003 common agricultural policy (cap) reform, eu subsidies have been decoupled from production and linked to land, increasing the likelihood that these payments get capitalised, in full or in part, in land prices and land rents (ifft, kuethe, & morehart, 2015). the capitalisation of subsidies transfers the money intended to support eu agriculture out of the agricultural sector and, for this reason, the consequences of decoupling and payment harmonisation have recently become http://creativecommons.org/licenses/by/4.0/legalcode 8 bio-based and applied economics 10(1): 8-17, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10034 gianni guastella, daniele moro, paolo sckokai, mario veneziani the focus of academic and policy studies (gocht, britz, ciaian, & paloma, 2013; graubner, 2018; kilian, antón, salhofer, & röder, 2012; klaiber, salhofer, & thompson, 2017; michalek, ciaian, & kancs, 2014). with the recent 2013 reform, the cap moved in the direction of equalising payment across farms, which translated into the reduction of the level of subsidies for most countries and an increase for the few remaining member states (mss) (european commission, 2013). in implementing the 2003 reform, mss could choose between three different implementation schemes, with only two of them guaranteeing a harmonisation of the payments. the first option (historical model) was to assign a farm-specific level of payment reflecting the historical amount of support to that farm during a reference period. in this way, the reform kept unchanged the differences in the levels of payments across farms. the second option (regional model) was to assign a flat payment per hectare to each farm allowing the payment to vary across regions but not among farms in the same region. this second implementation option resulted in the harmonisation of payments at the regional level. the third option (hybrid model) was a combination of both, with a level of payment resulting from the sum of the historical and the regional components, weighing initially more and then progressively less the historical component. although the hybrid model, unlike the regional model, did not realise the harmonisation of payment immediately, it put forward the design of a smooth transition toward this objective. mss that adopted the historical model or the hybrid model without completing the harmonisation of payments are now requested to make a further step in this direction. thus, understanding the consequences of this transition for the capitalisation of the payments in land prices and rents appears of crucial importance. the econometric literature concerned with the capitalisation of coupled and decoupled payments is interested in estimating the capitalisation rate, that is, how much the farmland prices and rents increase following a rise in the payment received. a large part of the literature refers to the us and is relatively less recent, probably because the us introduced decoupled payments earlier than the eu (goodwin, mishra, & ortalo-magné, 2012; kirwan, 2009; lence & mishra, 2003; patton, kostov, mcerlean, & moss, 2008; roberts, kirwan, & hopkins, 2003). with the only exception of lence & mishra (2003), which use county-level data, all studies use farmlevel data. farm-level data are also used at the eu level to investigate the capitalisation of subsidies (breustedt & habermann, 2011; ciaian & kancs, 2012; guastella, moro, sckokai, & veneziani, 2018; klaiber et al., 2017; michalek et al., 2014; o’neill & hanrahan, 2016). the farm-level evidence in the eu is heterogeneous. at the root of such heterogeneity, there is the geographical coverage of the study (the countries and regions analysed, new mss vs old mss), the period of the data (pre-reform vs post-reform), the methodological approach (crosssection analysis, panel data analysis, quasi-experimental approaches) and the type of agricultural support (coupled vs decoupled subsidies). notwithstanding this heterogeneity, there is a broad consensus that payments capitalise in farmland prices. in addition to the farmlevel empirical literature, there is evidence from studies using spatially aggregated data, either at a very aggregate scale, such as the country level (van herck, swinnen, & vranken, 2013) or at a very disaggregated scale such as the municipality level (kilian, antón, salhofer & röder, 2012; nilsson & johansson, 2013). the present work contributes to this empirical literature by exploring the relationship between farmland rental prices and decoupled subsidies in the entire eu. to get comparable results across the eu, we aggregate farm-level data at the regional level and estimate the capitalisation rate using spatial econometric models. in addition, we allow the estimated capitalisation parameter to vary among regions according to the implementation regime adopted by the reference ms. the work aims at contributing to the policy debate as well. in the most recent cap reforms, regions appear to be the designed entities to implement the harmonisation of agricultural payments. hence an investigation at this level is deemed appropriate to understand the potential consequences of this reform for land markets in relation to the harmonisation strategy adopted by each ms. approaching the issue of payment capitalisation with spatially aggregated data has drawbacks and advantages. the gain of farm-level over spatially aggregated data is that with repeated observations over time it is possible to control for unobservable heterogeneity related to the quality of land, undoubtedly among the most important determinants of the farmland price. as a drawback, such heterogeneity likely disappears when data for different land parcels are aggregated at a larger spatial scale. the availability of microdata, however, comes at the price of the limited variation of the dependent variable, farmland rents, over time, when the observed price comes from long-term rental agreements that weakly react to changes in subsidies. hence, an advantage of using spatially aggregated data is the possibility to capture regional trends in land prices that are not subject to price stickiness. another advantage of using aggregated data is the coherence with the policy objective of the 2003 reform, the convergence of agricultural 9 bio-based and applied economics 10(1): 9-17, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10034 the capitalisation of decoupled payments in farmland rents among eu regions subsidies to a fixed per hectare amount at the regional level. as the agricultural subsidy is gradually converging to a fixed regional amount, the comparison across territories, rather than across farms, should be more suitable for understanding and analyse the capitalisation of the single farm payment (sfp). in the remainder of the paper, we briefly introduce in the next section the policy framework framed by the recent reforms and provide a description of the data and the empirical approach in section three. we present the estimation results of the spatial econometric model in section four. a discussion concludes the work. 2. the cap reform: decoupling and harmonisation the cap process of decoupling dates back to 1992, with the macsharry reform that introduced area payments for arable crops, awarded to all farmers sowing cereals, oilseeds and protein crops, provided they were setting aside a fixed share of arable cropland. area payments were based on regional historical yields, thus introducing heterogeneity across the eu, with the intent of keeping support at the pre-reform level. being still linked to production and differentiated by crop, these payments were only partially decoupled. this setting was maintained under the next agenda 2000 reform in 1999. in 2003, under the so-called fischler reform, a radical change of agricultural support was undertaken. area payments converted into an sfp, whose rights were linked to land but progressively decoupled from production. the introduction of the sfp is the key element of the fischler reform. other important innovations are cross-compliance (i.e., payments made conditional to fulfilling a number of requirements concerning land maintenance and other agro-ecological provisions) and modulation (i.e., aiming at transferring support away from the largest farms and finance other voluntary measures of the cap). the reform implementation followed three different schemes. in the historical scheme, the sfp simply ref lected the amount of support historically received by the farm during a reference period (the three years 2000-2002), thus leaving unchanged the differences in the level of support among farms, with no redistribution within a certain area/region. under the regional scheme, within a certain area/region the per-hectare payment was equalised, making it equal to the amount of historical support in that region divided by the eligible land. in other words, the regional scheme allowed to redistribute and harmonise support within each region but keeping differences across regions. finally, the hybrid scheme was a combination of the previous two schemes; at the area/region level the per-hectare payment was made up by two components: the first computed on a historical basis, and the second computed according to the regional model. thus, this option maintained some differences across farms, progressively reduced by transferring support from the historical to the regional component. however, a common feature of the three schemes was to preserve some payment differences across regions in the same ms. in figure 1, we report the distribution of the three implementation schemes of the 2003 cap reform across the eu regions. the 2013 or cioloş reform, while modifying the structure of the direct payments, aimed at a more equitable distribution of support among areas and farmers, to be achieved by mean of both a process of external convergence across mss and a process of internal (full or partial) convergence, across farmers within the same ms or region. through the external convergence process, by 2020 the mss receiving less than 90% of the eu average payment in 2013 will increase such payment in order to close at least one-third of this gap and will not receive less than an agreed minimum payment level (196 euro/ha). payments will thus become more homogenous, although not perfectly equal, across mss. with the internal convergence process, payments are going to be harmonised within each ms, or specific regions inside each ms. three options are available: full convergence, with a flat rate to be reached either by 2015 or by 2019 and partial convergence, in which some differences across farmers are still maintained. figure 1. eu regions adopting different implementation schemes of the cap 2003 reform. 10 bio-based and applied economics 10(1): 10-17, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10034 gianni guastella, daniele moro, paolo sckokai, mario veneziani 3. empirical strategy and data for the empirical analysis, we borrow the theoretical framework from lence & mishra (2003) and use their equation 1 to estimate the capitalisation rate. the dependent variable rit is the average price of land rents in region i at time t. the use of farmland rents instead of values is widespread in capitalisation studies and also brings the advantage that rents more than values can be directly related to market returns, being less sensitive to other location factors (borchers, ifft, & kuethe, 2014). to explain the variation in farmland rents, we use the information on the average productivity including the x matrix, whose j=1,2,…,j columns represent the average productivity of the jth sector weighted by the share of total area farmed in each sector. the set of variables captures the structural differences in agricultural production among european regions that can inf luence farmland rents. mediterranean regions, for instance, characterised by a significant share of land employed in high-value agricultural production from permanent crops, olives, grapes, and related transformed products, are expected, other things being equal, to exhibit higher rental prices. the variable of interest is the average per-hectare amount of subsidies (s) received by farms in region i at time t, and γ is the associated coefficient which expresses the capitalisation rate. the equation also includes region-specific effects βi that account for structural differences among regions due to unobservable factors. z is a matrix of control variables. (1) differently from lence & mishra (2003), who consider two agricultural outputs only, and from eu studies that consider aggregate measures of either productivity (breustedt & habermann, 2011) or market returns (ciaian & kancs, 2012), we consider multiple outputs to capture the considerable heterogeneity in the composition of aggregate agricultural production in the eu regions. more specifically, total production is divided into k=7 output categories, namely, arable crops (including cereals, proteins, potatoes, sugar beet, oilseed and industrial crops), vegetables and flowers, fruits, wines and grapes, olives, forage crops and other crops1. the decoupled payments are measured as the monetary amount disbursed as sfp under the single payment scheme (sps) for the old mss (eu15) and under 1 the aggregation of agricultural activities in sectors is based on the classification scheme provided with the fadn data. the single area payment scheme (saps) for the new mss (eu10). since the amount of the subsidy per hectare is perfectly known, we exclude any endogeneity caused by the problem of expectation errors discussed in lence & mishra (2003) and patton, kostov, mcerlean, & moss (2008). the matrix z includes a list of controls to account for characteristics expected to impact on farmland rent variation. in particular, we control for the average size of farms (size); the average share of family labour (famlab); the average capital per ha (fixass); the animal density (animald); the average share of rented to total utilised agricultural area (uaa) (rentprop). we expect a negative coefficient related to farm size because larger farms have substantially more power to bargain in the land markets. at the same time, it is essential to acknowledge that large farms are more efficient and thus, willing to pay higher land rents. the outcome depends on which effect will prevail, on average. besides farm size, family labour, and fixed assets control for the managerial approach to farming in the regions. in regions where farmers adopt a managerial approach to agricultural activity, the market for land is expected to be more dynamic. consequently, farmland rents should be higher. the animals’ density controls for the higher farmland rents generated by the demand for land for manure spreading, as a result of the nitrate directive. a higher animal density, related to more productive and profitable activities, implies an increase in the demand for land, thus driving up rents. nonetheless, the high density of animals is also a characteristic of regions specialised in livestock production to the largest extent. the share of permanent grassland in these areas can be in fact very high, leading to a spurious negative relationship between farmland rents and animal density due to the unobserved quality of land, that may be lower in regions specialised in livestock production. finally, the theoretical hypothesis that all land is the property of landowners and rented to farmers at the equilibrium rental price might appear simplistic, especially in some eu regions. on average, almost 50% of land used in agriculture is rented, but this figure masks considerable heterogeneity among territories in europe (european commission eu fadn, 2015). the proportion of rented land controls for the increase in the average value of rents due to the limited supply of land for rent. following the discussion presented in the previous section, the likelihood of subsidy capitalisation is higher in case the regional model is adopted compared to the historical model. in the latter case, the extent of capitalisation is determined by the relative abundance of eligible hectares, required to activate the payment, com11 bio-based and applied economics 10(1): 11-17, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10034 the capitalisation of decoupled payments in farmland rents among eu regions pared to the number of entitlements. to assess the structural differences in capitalisation rates among regions in mss that adopted different implementation regimes, the model in equation 1 is modified allowing the capitalisation parameter γ to vary according to the implementation regime in equation 2, where his, hyb and reg are dummy variables indicating whether region i belong to a ms that adopted the historical, hybrid or regional model, respectively. γ = γ1hisi + γ2hybi + γ3regi (2) since the structural characteristics of the implementation regime may also condition the effect of the land market in general, not only of the capitalisation process, we extend the structural heterogeneity approach in equation 2 to all the parameters of the model. both the models in equation 1 and 2 are estimated using linear panel data models. since the observations in the sample are related to spatial units, the standard linear model residuals independence assumptions may be violated. we thus correct the specification assuming a spatial autocorrelation structure of the residuals and estimate a spatial error model (sem, equation 4). (3) alternative spatial econometric model specifications can account for unobserved spatial heterogeneity, omitted spatially correlated variables and spatial spillovers. lesage & pace (2009) provide an extensive review of the possible motivations leading to spatial correlation in data and an overview of testing procedures to select the correct specification based on observed data. in this paper, we take a different perspective. the choice to consider structural heterogeneity in the deterministic part of the model, that is to allow the capitalisation and other parameters to vary depending on the implementation regime, invites to leave the spatial autocorrelation issues in the error term and to exclude the other three prevalent specifications, the spatial autoregressive model (sar), the spatial durbin model (sdm) and the model with the spatial lag of the covariates (sxl). in principle, it is possible to account for structural heterogeneity also in these models. that would result, however, in very complex expressions for the marginal effect, among the others, of subsidies on farmland rents and, hence, of the capitalisation rate. the sem model, differently from the sar, the sdm and the sxl models, is the only one that does not consider spatial processes in the deterministic part of the model and allows a direct interpretation of the coefficients as the marginal effects. this characteristic appears very useful when dealing with structural instability. however, we test the robustness of this choice (and of the results) and estimate the most common alternative to the sem model, the sar model. in both the spatial models without and with the structural heterogeneity, the spatial weight matrix w identifies neighbourhood relationships through its elements wij that express the inverse distance from region i to its neighbour j if the distance is lower than a threshold d* and 0 otherwise2. the dataset used to estimate the capitalisation effect comprises 208 nuts regions belonging to the eu25 countries. more precisely, nuts ii classification is the territorial reference for all countries but the uk and denmark, where nuts i and nuts 0 is used instead, respectively. the choice is consistent with the design of the fadn survey data, which is stratified by regions, agricultural specialisation, and size, and hence returns reliable estimates of the values of interest by aggregating at the regional or higher level3. regional data are available in the fadn database for the whole period 2003-2008 for the eu15 and starting from 2005 for the eu10. however, since some countries implemented decoupling after 2005 only, data for the sps payments are available only from 2006 for the complete set of regions. romania and bulgaria are excluded from the analysis because data collection started in 2007. we compute the distance between each pair of regions based on geographical coordinates available in the reference files of the geographical information system of the eu commission (gisco). since atlantic islands are considered too far for any spatial relation with continental regions to exist, these regions are excluded from the sample. the threshold distance to define contiguity between regions (d*) is 500 km and is appropriate to describe the spatial structure of connectivity links, although arbitrary. in particular, the 500 2 as usual and required, the elements of the matrix are row standardised and the diagonal elements are set to zero to exclude self-contiguity. the choice of the distance-based approach is made to avoid cases of selfcontiguity only. 3 we exclude accordingly the local administrative units (lau, in the eurostat nomenclature) used in kilian, antón, salhofer & röder (2012) and nilsson & johansson (2013) because the fadn aggregates are not representative at this level. we also exclude the country level used in van herck, swinnen, & vranken (2013) because land rents vary significantly across regions of the same country, especially in some member states (mss), and we want to preserve this heterogeneity in our empirical analysis. 12 bio-based and applied economics 10(1): 12-17, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10034 gianni guastella, daniele moro, paolo sckokai, mario veneziani km cut-off distance ensures that every region has at least one neighbour and that, in turn, each row of the weight matrix has at least a non-zero element.4 the 208 regions in our dataset are observed for three years. to build the dataset, we used 153,069 original farm-level observations, excluding the outliers showing unreasonable values of average productivity and payments5. table 1 describes the main variables used for the land rental price model, and table 2 provides some useful descriptive statistics. the average farmland rent, which is the total rent paid by the farmers in the region excluding rent paid for quotas and other things not attached to land over the total rented area, was about 171 euro per hectare in 2006 and increased to 176 in 2008. the largest value of production per ha in 2006 accrued to farms producing vegetables and flowers followed by fruits and wines and grapes and all values changed little during the three years. this substantial heterogeneity indicates that an empirical specification that considers the composition of agricultural production is appropriate in the case of eu regions. the average value of the sfp per ha was 256 euro in 2006 and increased to 298 in 2008. the figure related to the con4 alternative distances have been tested, and the estimate and significance of the capitalisation rate appears not affected. only minor changes can be noticed in relation to the control variables. 5 outliers are not necessarily the result of reporting errors. rather they are closely related to the accountancy nature of the database and appear because some monetary values may be reported in a different accounting year, for instance, in the case of subsidies, because of delayed payments. tribution of family labour is unsurprisingly high, since, on average, almost two over three hours are worked by family members. also, two-thirds of the available uaa in the regions is rented, and the figure looks relatively stable over time. this high proportion of rented land suggests both that an analysis of sfp capitalisation at the eu level is appropriate and that rental prices, rather than sale prices, should be considered for this purpose. 4. results we perform the empirical estimation of the land price model using different estimators and summarise all the results in table 3. the first column of the table reports the estimation results using the pooled ols, from which we get an estimate of the capitalisation rate for subsidies of 26% (26 cents per additional euro). the estimate substantially lowers when introducing in the specification individual effects, either as fixed (second column) or random (third column) effects. assuming fixed effects also leads to an estimate of the capitalisation rate that is not statistically different from zero. this result looks reasonable because both the rental price and the subsidy variables show very limited within variation, partly because of structural rigidities and partly as a consequence of the short time dimension of the panel. in contrast, assuming random effects, we get a significant, although low, capitalisation rate, estimated at 4.3%. we get similar results in the case of spatial models. with both specifications (sar and sem), the use of fixed effects (columns 4 and 6) leads to lower in magnitude and insignificant capitalisation rates, opposite to the random effect specification (columns 5 and 7). the only noticeable difference with the non-spatial model is that the coefficient estimates for the capitalisation rate lower down to 3%. in both spatial and non-spatial models we get positive and statistically significant coefficients for the “arable crops” and “vegetables and flowers” categories, but only in the spatial models, and independently of the specification, we also get positive and statistically significant coefficients for the “fruits” and “wines and grapes” categories. thus, spatial models seem more capable to capture the geographical concentration of specific productions in particular regions of europe, typically the regions of the mediterranean countries. the use of spatial specifications leads to more significant results, also in the case of the control variables. for instance, it is the case of the coefficient of the famlab variable, which is insignificant in the pols model, significant in the fe model and always significant in the table 1. description of the dataset. variable description rent average rent paid (euro/ha) ac output value – arable crops (euro/ha) vf output value – vegetables and flowers (euro/ha) fr output value – fruits (euro/ha) wg output value – wines and grapes (euro/ha) oo output value – olives (euro/ha) fc output value – forage crops (euro/ha) oc output value – other crops (euro/ha) sfp average payment received under saps or sps (euro/ha) size average farm size (ha) famlab share of family to total labour (%) fixass value of fixed assets (machinery and equipment) (euro/ha) animald number of animal units per ha (count in livestock equivalent) rentprop proportion of rented uaa (%) 13 bio-based and applied economics 10(1): 13-17, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10034 the capitalisation of decoupled payments in farmland rents among eu regions table 2. summary statistics of the variables. 2006 2007 2008 mean (sd) [min; max] mean (sd) [min; max] mean (sd) [min; max] rent 171.411 (124.353) [8.25;583.57] 173.611 (123.860) [9.82;609.81] 176.058 (124.688) [12.03;656.35] ac 1287.311 (1514.206) [0;15428.92] 1499.436 (1583.810) [0;15475.54] 1598.579 (2063.647) [0;18560.53] vf 10379.490 (11748.030) [0;96182.79] 10705.340 (11094.790) [0;70163.73] 11880.270 (13328.370) [0;76807.57] fr 5594.956 (6158.491) [0;35745.33] 12143.420 (83190.780) [0;1181033] 9896.156 (44404.570) [0;621233.4] wg 4142.775 (8783.751) [0;63246.45] 5205.113 (13893.260) [0;139150] 5161.718 (11601.920) [0;82500] oo 455.659 (1156.293) [0;10062.25] 479.537 (1212.344) [0;9379.722] 549.385 (1434.236) [0;10297.3] fc 181.831 (285.286) [0;2074.19] 187.035 (245.419) [0;1473.687] 212.475 (284.639) [0;1359.38] oc 29229.110 (111487.700) [0;1249723] 34359.420 (209769.100) [0;2865080] 16822.680 (36829.070) [0;282031.3] sfp 256.108 (128.571) [31.45;626.06] 270.722 (131.702) [36.46;610.11] 298.738 (178.626) [47.711;1367.23] size 10.763 (28.564) [0.05;236.99] 11.254 (30.441) [0.08;210.84] 11.885 (34.100) [0.07;298.81] famlab 0.677 (0.270) [0.02;1.0] 0.672 (0.269) [0.01;1] 0.668 (0.269) [0.01;0.99] fixass 4668.559 (6322.787) [285.67;65261.51] 4976.150 (7887.282) [363.92;80204.6] 5091.916 (8355.090) [483.39;85890.15] animald 1.129 (1.323) [0;12.05] 1.128 (1.379) [0;11.72] 1.152 (1.418) [0;11.22] rentprop 0.674 (0.175) [0.26;0.99] 0.676 (0.177) [0.23;0.98] 0.683 (0.175) [0.17;0.97] table 3. estimates of the rental price equation, eu regions, 2006-2008. pols fe re sar fe sar re sem fe sem re xac 0.114*** 0.037** 0.065*** 0.036*** 0.059*** 0.043*** 0.072*** (0.008) (0.017) (0.011) (0.007) (0.008) (0.008) (0.009) xvf 0.014*** 0.018*** 0.014*** 0.018*** 0.014*** 0.018*** 0.013*** (0.003) (0.005) (0.004) (0.002) (0.003) (0.002) (0.003) xfr 0.014*** 0.006 0.009 0.007* 0.008** 0.008** 0.009** (0.004) (0.012) (0.01) (0.004) (0.004) (0.004) (0.004) xwg 0.016 0.039 0.042 0.039*** 0.038*** 0.033*** 0.026** (0.014) (0.045) (0.035) (0.012) (0.012) (0.011) (0.012) xoo -0.085*** 0.009 0.001 0.012 0.006 0.011 0.000 (0.027) (0.016) (0.01) (0.009) (0.011) (0.009) (0.011) xfc -0.007 -0.020 -0.024 -0.020 -0.007 -0.031 -0.016 (0.028) (0.044) (0.031) (0.019) (0.021) (0.019) (0.023) xoc -0.038* -0.068** -0.065** -0.068*** -0.063*** -0.069*** -0.063*** (0.019) (0.027) (0.026) (0.006) (0.007) (0.006) (0.007) sfp 0.261*** 0.007 0.043** 0.007 0.030** 0.004 0.032** (0.025) (0.022) (0.019) (0.009) (0.012) (0.01) (0.015) size -1.910 -3.707 -9.024* -5.299 -11.100*** -7.806* -16.020*** (3.222) (9.246) (5.178) (4.229) (3.362) (4.437) (4.148) famlab -16.960 -124.900** -44.367 -120.345*** -72.557*** -126.988*** -98.005*** (18.733) (55.197) (31.01) (21.981) (18.662) (21.854) (20.803) fixass 12.470** 14.328 26.844*** 12.928*** 18.333*** 12.644** 21.791*** (5.718) (11.32) (6.689) (4.766) (4.862) (4.883) (5.637) animald 28.176*** 1.439 14.130*** 1.830 10.995*** 2.194 11.304*** (2.804) (5.859) (4.266) (2.437) (2.505) (2.415) (2.629) rentprop -155.691*** -124.286* -134.619*** -121.159*** -127.441*** -126.147 -138.694*** (21.964) (69.663) (37.023) (22.724) (21.196) (22.422) (22.535) 0.206*** 0.557*** (0.062) (0.044) 0.346*** 0.950*** (0.068) (0.018) notes to table: se in parenthesis. ***, ** and * indicate statistical significance at 1%, 5%, and 10% levels respectively. ρ and λ are the spatial parameters in thee equations 2 and 3. 14 bio-based and applied economics 10(1): 14-17, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10034 gianni guastella, daniele moro, paolo sckokai, mario veneziani spatial fe and re models. for the fixass and rentprop variables, the coefficients are weakly significant, or altogether insignificant, when estimated with fe and turn highly significant in all the spatial models. at least for the fixass variable, the fe estimation in both spatial and non-spatial models underestimates the coefficient compared to the re specification. the same happens with the animald and size variables, which are significant in re models only. the more significant results obtained with the use of re specifications in spatial and non-spatial models find justification in the very short time dimension of our panel, which causes structurally low within variation in all variables. building on this evidence, we prefer to resort to re effect specification. moreover, we note from the results in table 3 that all the coefficients have the correct signs and similar magnitude across all specifications. considering the spatial models only, we get positive estimates of the coefficients associated with all the weighted outputs but “other crops” which makes sense, since it is our residual category. we also get a positive estimate of the coefficient related to the sfp variable. the negative coefficient related to size means that larger farms pay, on average, lower rents; the negative coefficient for famlab and the positive one for fixass indicate that more managerial farms pay lower than expected rents, other things equal; the negative coefficient for rentprop is consistent with the hypothesis on the functioning of land markets. this evidence allows concluding against the hypothesis of a severe unobserved heterogeneity bias caused using random effects. confronting the spatial models, both the sar and the sem produce equivalent results, at least regarding estimated coefficients6. in addition, the spatial parameters are always positive and statistically significant. it is possible to notice that the estimated spatial parameter is higher in the re model, and that provides an indication that the spatial component in the re models also accounts for the spatial heterogeneity otherwise accounted for by the fe. one significant advantage of the sem specification over the sar is that it allows easy manipulations of the model in the case of sample splitting, representing the most convenient way to manage the structural instability of the parameters7. we benefit of this property of the sem specification to investigate the extent to which 6 for the sar model the coefficients are not directly interpretable. instead the computation of partial derivatives is necessary, differently from the sem case. however, for the purpose of model comparison, it is sufficient to look at the actual estimates. the estimates of the direct, indirect, and total effects are available upon request. 7 again, primarily because the sem model is the only one allowing the direct interpretation of the estimated parameters. the estimated capitalisation rate varies across groups of regions defined according to the choice made by each ms on the implementation scheme of the sfp. in table 4, we report the estimation results that consider the structural heterogeneity of the parameters across the three groups of regions in figure 1, adopting the regional, hybrid and historical schemes, respectively. in other words, we allow the coefficients β, γ, and δ in equation 3 to vary across schemes (equation 4). we find out less significant results related to the sectoral productivities of regions. the fact that we allow for time-invariant effects across regimes with regimespecific intercepts may explain this evidence, assuming that the differences in productivities among regions in table 4. estimation results by implementation regime, eu regions, 2006-2008. regional hybrid historical intercept -32.130 125.905 63.993 (113.334) (117.884) (54.181) xac 0.028 0.095*** 0.079*** (0.034) (0.023) (0.009) xvf 0.075 0.027** 0.010*** (0.091) (0.012) (0.003) xfr 0.044 0.001 0.022*** (0.18) (0.007) (0.007) xwg 0.048 -0.001 -0.002 (0.158) (0.044) (0.013) xoo 1.971 0.002 -0.010 (1.986) (0.035) (0.011) xfc -0.165 -0.057** (0.12) (0.028) xoc -0.014 0.031 -0.080*** (0.334) (0.071) (0.01) sfp 0.519* 0.284*** 0.017 (0.306) (0.105) (0.013) size -1.383 -16.664 -11.249** (9.614) (10.513) (4.718) famlab -16.619 -49.949 -148.380*** (75.689) (44.21) (29.749) fixass 3.701 5.525 40.090*** (11.315) (13.319) (6.284) animald -7.626 50.313*** 10.022*** (22.161) (15.219) (2.559) rentprop 15.194 -147.330** -168.624*** (96.804) (62.922) (24.307) λ 0.430*** (0.084) notes to table: se in parenthesis. ***, ** and * indicate statistical significance at 1%, 5%, and 10% levels respectively. λ is the spatial parameters in the equation 3. 15 bio-based and applied economics 10(1): 15-17, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10034 the capitalisation of decoupled payments in farmland rents among eu regions the same regime are limited. in general, the estimated coefficients related to productivities are more significant in the historical regime compared to the regional regime. we get similar results estimating the coefficients for the control variables, with overall evidence of a better fit in the case of the historical and hybrid models compared to the regional model. all the coefficients show the expected sign and, with few exceptions, these are also consistent across regimes, although they vary in magnitude, as expected. the most interesting result concerns the capitalisation rates, estimated at 52%, 28% and 2% in case the mss adopted the regional, the hybrid, or the historical model, respectively. the estimated value in the case of mss adopting the historical model is, however, not statistically different from zero. in the case of the hybrid model, we get a very significant result, and in the case of the regional model, the null hypothesis is rejected at the 10% significance level in a standard two-tail test. considering that the capitalisation rate can only be larger than zero, a one-tail test may also be appropriate, and this would reject the null hypothesis at a lower significance level (5%). 5. discussion and conclusion in 2013, the cap reform marked an important step toward the convergence in the level of farm support across the different territories of the eu. the last reform, like the previous ones, has generated a vigorous debate about the possible impact of farm payments on input prices, and in particular on farmland rents. payments decoupled from production and attached to land increase, in fact, the possibility of capitalisation, a side effect which should be taken into account when planning the redistribution of farm support. the existing empirical literature on the capitalisation of agricultural subsidies in farmland rents in europe consistently reports evidence of capitalisation, but the estimated rate varies widely across studies. the geographical coverage of the studies, usually narrow (one region), is among the reasons of such heterogeneity, together with the regime adopted by the reference ms for the 2003 reform for introducing the sfp. this type of payments is, in fact, intrinsically related to land, and this condition is expected to increase the rate of capitalisation compared to coupled subsidies. the extent of the phenomenon is however related to contextual factors such as land market imperfections (ciaian and swinnen, 2006) and the availability of entitlements compared to eligible hectares (ciaian, kancs, and swinnen, 2008). most importantly, the implementation regime could have influenced the rate of capitalisation (kilian and salhofer, 2008; kilian et al., 2012) in the context of the 2003 reform. the three schemes available to implement the 2003 reform differ from each other regarding the perspective harmonisation. almost all the mss applying the regional model are nms and, due to their recent admission to the eu, the regional scheme with harmonised payments was for them the first and unique scheme of payment adopted. only a few mss experienced a direct transition from the coupled payments to a decoupled payment scheme with harmonised payments across farms, while many ms preferred to link the level of decoupled payments to the historical coupled payments. following the 2013 reform, these ms are experiencing a process of gradual harmonisation of payments. some other ms chose the hybrid model that implemented some partial harmonisation of payments during the years preceding the 2013 reform. the regional regime, which foresees an equal payment per hectare among farmers, may have facilitated the capitalisation and the leakage of subsidies out of the agricultural sector. now that the 2013 reform is being implemented a further step in the direction of payment harmonisation is made (ciaian, kancs, & swinnen, 2014). thus, understanding the extent to which the harmonisation is responsible for higher capitalisation becomes even more relevant. this work frames into this stream of theoretical and empirical debate about the influence of the implementation regime on agricultural payment capitalisation. we estimate the capitalisation rate using regional aggregate data from countries that adopted different implementation regimes and show how the estimated capitalisation varies across regimes. consistently with the previous theoretical analysis, we find cross-sectional evidence of structural heterogeneity in the capitalisation rate among regions from member states that implemented the historical, hybrid, and regional regimes. when estimation is conducted on the full sample of european regions, results suggest that 3 cents per additional euro of payment get capitalised into the land price, which is quite a modest result compared to existing evidence: in europe, the capitalisation rate of decoupled subsidies has been previously estimated at between 18 and 20 cents in nms (ciaian and kancs, 2012); between 25 and 77 cents in germany (kilian et al., 2012); between 8 and 76 cents in sweden (nilsson and johansson, 2013). when considering the implementation regimes separately, it is found that as much as 52 cents per euro get capitalised into the land price in ms that adopted the regional regime. only 28 cents per euro are capitalised in mss that adopted 16 bio-based and applied economics 10(1): 16-17, 2021 | e-issn 2280-6172 | doi: 10.36253/bae-10034 gianni guastella, daniele moro, paolo sckokai, mario veneziani the hybrid regime, and there is no evidence of capitalisation in mss that adopted the historical regime. these results add substantial evidence to the hypothesis that an equal payment for all farms in the same region is the scheme producing the highest capitalisation rate. the result is robust to the inclusion of other variables that drive farmland rents and to the use of econometric techniques that explicitly take the geographical position of the region and its neighbouring relationship into account. unfortunately, the data source does not provide additional information about how the scheme has been implemented in each ms, and among regions in each ms. thus, from the evidence in the study, it is not possible to infer any causal effect of the implementation scheme adoption on the capitalisation rate. based on our results, we conclude that in mss that applied the historical model, the decoupled sfp did not capitalise into land prices, but, since these mss are now experiencing the transition toward the full harmonisation of payments, the likelihood that this transition will bring the capitalisation of subsidies is very high. mss that applied the hybrid scheme, in fact, already started the process of harmonisation and the payments have been capitalised since the very first years after the introduction of the sfp, although to a lower extent as compared to mss that implemented the regional scheme. thus, in general, our results emphasise the role of policy design in determining a crucial outcome such as the capitalisation of agricultural subsidies in farmland prices. the application of a general policy objective, such as decoupling subsidies from production and attaching them to land, may lead to very different outcomes depending upon the implementation details. in the case of the cap 2003 reform, the crucial elements of the policy design have been the rules governing the distribution of the payment entitlements and their linkage to the eligible hectares of agricultural land. this is of course extremely relevant for policymakers, in view of any further reform of the policy. funding fadntool integrating econometric and mathematical programming models into an amendable policy and market analysis tool using fadn database, id 265616. references borchers, a., ifft, j., & kuethe, t. 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(2013). capitalisation of direct payments in land rents: evidence from new eu member states. eurasian geography and economics, 54(4), 423–443. https://doi.org/10.10 80/15387216.2013.878230 volume 10, issue 1 2021 firenze university press ten years of bio-based and applied economics: a story of successes, and more to come fabio g. santeramo1, meri raggi2 the capitalisation of decoupled payments in farmland rents among eu regions gianni guastella1,2, daniele moro1, paolo sckokai1, mario veneziani3 contribution of periurban farming systems to local food systems: a systemic innovation perspective rosalia filippini1,2, elisa marraccini3, sylvie lardon2 an investigation into italian consumers’ awareness, perception, knowledge of european union quality certifications, and consumption of agri-food products carrying those certifications niculina iudita sampalean1, daniele rama1, giulio visentin2 wine after the pandemic? all the doubts in a glass daniele vergamini*, fabio bartolini, gianluca brunori public r&d and european agriculture: impact on productivity and return on r&d expenditure michele vollaro1, meri raggi2, davide viaggi1 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 bio-based and applied economics bae copyright: © 2024 eichhorn, t., schaller, l., hamunen, k., & runge, t. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: eichhorn, t., schaller, l., hamunen, k., & runge, t. (2024). exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures: a pestle approach based on stakeholder statements. bio-based and applied economics 13(1): 49-71. doi: 10.36253/bae-14489 received: march 3, 2023 accepted: august 30, 2023 published: may 20, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. guest editors: stefano targetti, andreas niedermayr, kati häfner orcid te: 0000-0002-4286-3272 ls: 0000-0002-8313-8798 kh: 0000-0003-2188-782x tr: 0000-0002-8366-4392 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures: a pestle approach based on stakeholder statements theresa eichhorn1,*, lena schaller1, katri hamunen2,3, tania runge4,* 1 institute of agricultural and forestry economics (afo), department of economics and social sciences, university of natural resources and life sciences, 1180 vienna, austria 2 natural resources institute finland (luke), bioeconomy and environment, yliopistokatu 6 b 80100 joensuu, finland 3 stora enso, keskuskatu 14, 81100 kontiolahti, finland, since august 2022 4 coordination unit climate, soil, biodiversity, thünen institute, braunschweig, germany *corresponding authors. e-mail: tania.runge@thuenen.de; theresa.eichhorn@haup.ac.at abstract. to promote more environmentally friendly and cost-effective agri-environmental-climate measures in the european union, novel approaches such as resultbased and collective schemes are advocated. this study explores macro-environmental factors facilitating or impeding the adoption of such schemes. by means of a pestle analysis and based on a survey of 85 stakeholders from austria and germany, we identify major adoption factors within the political, economic, social, technological, legal, and environmental domains. our results indicate that economic, legal, and social factors are the most influential, with fair payment, clear contract design, and social relations being the most commonly mentioned. moreover, the unpredictability of nature is a major impediment to the adoption of result-based schemes, while social dynamics and farmers’ attitudes are key factors for a successful implementation of collective contracts. overall, the study provides strategic and practical insights that can support the design and implementation of novel agri-environmental-climate measures under the common agricultural policy. keywords: agri-environmental contracts, german and austrian stakeholders, survey, acceptance. jel codes: q15, q18. 1. introduction a more sustainable agricultural system in the european union (eu) is not only a societal demand, but also an ecological necessity to tackle climate change, counteract biodiversity loss, and protect the eu’s natural resources. by providing public funding, the legal framework of the european common agricultural policy (cap) has a prominent role in fostering agriculture’s https://doi.org/10.36253/bae-14489 http://www.fupress.com/bae https://doi.org/10.36253/bae-14489 https://orcid.org/0000-0002-4286-3272 https://orcid.org/0000-0002-8313-8798 https://orcid.org/0000-0003-2188-782x https://orcid.org/0000-0002-8366-4392 mailto:tania.runge@thuenen.de mailto:theresa.eichhorn@haup.ac.at 50 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 theresa eichhorn et al. transition to sustainability. contracts for agri-environment-climate measures (aecms) under the second pillar of the cap are pluriannual commitments, specifically designed to reduce the negative impacts of agriculture on the environment and to mitigate the effects of climate change (european commission, 2017). the main challenge for aecms is to ensure an efficient use of funds in addition to delivering the intended environmental effects. aecms are facing multifaceted criticisms in this regard, such as the lack of empirical evidence supporting their effectiveness, imprecise targeting through insufficient consideration of the heterogeneity of farms and their local circumstances (european court of auditors, 2011), as well as missing (financial) incentives for farmers to produce the best environmental result through their entrepreneurial activity (wbae, 2020). new pathways within the design of aecms are required: approaches such as result-based payments or collective implementation can contribute to a more effective and efficient design of aecms. result-based schemes aim at providing environmental improvement through paying for the achievement of specific environmental objectives instead of prescribing and compensating management practices to farmers. consequently, farmers can flexibly decide how they want to achieve environmental improvement (burton & schwarz, 2013). collective approaches have the objective to activate land managers to jointly provide agri-environmental-climate public goods (aecpgs), often accompanied by formalised cooperation (runge et al., 2022). in fact, resultbased and collective aecms were eligible for receiving eu co-financing within the past cap period (20142022), although they have been applied to a very limited extent in the member states (wbae, 2020). in the new cap strategic plans regulation ((eu) 2021/2115), article 70(5), it is recommended that: “member states may promote and support collective schemes and resultbased payment schemes to encourage farmers or other beneficiaries to deliver a significant enhancement of the quality of the environment at a larger scale or in a measurable way.” (european parliament and the council of the european union, 2021). moreover, recital 71 of regulation (eu) 2021/2115 states that “[s]upport under payments for management commitments may also be granted in the form of (…) result-based interventions”. result-based payment schemes are further specifically mentioned in the eu’s biodiversity strategy 2030 (european commission, 2020). with the new emphasis on environmental performance in the cap, result-based schemes gain importance as a fast-evolving and distinctive approach. for illustration, at the time the survey was conducted, a result-based pilot project for nature conservation (biodiversity) was implemented in austria, which, in the meantime, has been transferred into a fully eligible measure under the austrian agri-environmental programme for the period 2023-2027 (ama, 2023). also in germany, already in the previous cap period some federal states had programmed result-based measures for extensive permanent grassland which now led to the programming of a respective eco-scheme measure targeting flowering species (ble, 2022). as regards collective approaches, they may operate as an extension of many other forms of contracts aiming at a more effective delivery of environmental goods and services, e.g. at a landscape scale. while in the last cap period only the netherlands made extensive use of collective implementation for its agri-environmental schemes, in the new programming period (2023-2027) there are also other countries offering collective measures with cap funding, e.g. ireland (dafm, n.d.) and germany in the federal state of brandenburg (mluk, 2023). still, despite their potential positive impacts on the environmental effectiveness of aecms, several factors can hinder the implementation and uptake of collective and result-based approaches. the implementation of result-based schemes may be impeded by (i) elevated administrative and transaction costs compared to action-based systems due to the requirement for result measurement, limited experience, and often small-scale experimental designs (eichhorn et al., 2022; schwarz et al., 2008), (ii) difficulties in determining accurate indicators for measuring environmental progress (allen et al., 2014; burton & schwarz, 2013); and (iii) potential conflicts with wto regulations (matthews, 2019; melèndez-ortiz et al., 2009). factors hampering farmers’ willingness to participate are (i) the fear among farmers of lacking sufficient knowledge and skills to successfully perform result-based schemes (massfeller et al., 2022), (ii) general scepticism towards novel approaches (stolze et al., 2015), (iii) difficulties in understanding how these contracts work in practice (wezel et al., 2018), (vi) perceived higher risk due to environmental uncertainty, and (vii) no secured remuneration (derissen & quaas, 2013). also collective approaches face hurdles, such as lack of farmers’ willingness to cooperate (franks, 2011), insufficient coordination (olivieri et al., 2021), and missing of pre-existing networks or lack of capacity (prager, 2022). up to now, the state of knowledge on factors supporting or hindering the implementation of novel schemes is largely based either on case studies investigating mostly single or few contract solutions in a specific context (e.g. birge et al., 2017; de sainte marie, 2014; derissen & latacz-lohmann, 2013; prager, 2022; zabel, 2019), on farmers’ surveys mainly addressing farmers’ 51 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures intention to perform such novel schemes (e.g. massfeller et al., 2022; van dijk et al., 2015), or on studies concentrating on contract related factors, such as contract design features (contract length, payment mode etc.) (bredemeier et al., 2022; schulze & matzdorf, 2023). what is still missing, however, is a structured gathering of knowledge about macro-environmental factors influencing the adoption of result-based and collective agri-environmental measures. this is where this study comes in. macro-environmental factors (such as technological, political, natural factors) refer to external forces and conditions that can have a significant impact on a business or organisation’s operations and performance and are beyond the control of the business, but can influence its success or failure (kotler et al., 2018). in our case, we looked at factors, which cannot be influenced by farmers directly, but have an impact on farm business decisions. a pestle analysis framework was used to identify these macro-environmental factors that promote or hinder the implementation of novel contract types in a holistic, structured and multidisciplinary way (yüksel, 2012). our analysis is based on an online survey of 85 stakeholders from austria and germany conducted in spring 2021. within this survey, stakeholders identified a comprehensive set of factors based on six pestle categories (political, economic, social, technological, legal, and environmental). the survey targeted a wide range of stakeholders involved in the promotion, design, implementation and control of aecms, with actors from government agencies, environmental organisations, agricultural associations, and private sector companies. by including policy makers/administrators/advisors from local up to national level, we were able to gather strategic, as well as practical (phenomenological) knowledge (raymond et al., 2010). the importance and originality of this study is that it (1) compares two novel contractual aecm approaches in one analysis, (2) strongly focuses on the opinion of stakeholders on external factors, which are much less examined within the agriculture policy literature and (3) provides a structured analysis of the external factors by applying the pestle approach, a strategic tool from business analysis, for the first time to study aecms. 2. data and method our study aimed at identifying topics potentially affecting the adoption of result-based or collective contracts in their operational environment. for this purpose, the pestle approach was applied. this strategic planning tool is regularly used to analyse external macro-environmental factors that may impact an organisation or industry (gupta, 2013). it is often used in marketing as well as for strategic business decisions (theobald 2019), however, also in other fields the pestle tool (or its previous model pest) is gaining importance (achinas et al., 2019; gupta, 2013; rambaree et al., 2021). “pestle” represents the initial letters of the six factor categories considered, namely political, economic, social, technological, legal, and environmental factors. the main advantages of the pestle approach are, that it (1) enables a holistic, multidisciplinary analysis of the external factors inhibiting or promoting the feasibility of result-based or collective contract solutions before they are put into practice (precondition analysis) (yüksel, 2012), (2) improves decision-making by systematically providing valuable information (in our case phenomenological and strategic stakeholder knowledge) and thereby encourages strategic thinking (nitank & treivdi, 2016) and (3) enhances risk assessment, by identifying potential risks that impact the feasibility and implementation of new types of contracts, thereby helping to take actions to avoid or minimise their effect (nitank & treivdi, 2016). 2.1 questionnaire and data surveys were conducted in austria and germany between end of april and mid-may 20211 to assess stakeholders’ knowledge of external factors impacting the implementation of novel aecms. the surveys were administered online via limesurvey. potential participants were contacted via email and provided with an online-link to access the survey. we aimed to reach key stakeholders and actors (e.g. involved in the promotion, design, implementation and control of aecms), targeting respondents acting in different roles or having different areas of interest from both the public and private sector and with different backgrounds, at local, regional, and state levels. in austria, 80 stakeholders were contacted and 34 questionnaires completed, in germany, 142 persons were contacted and 51 completed surveys were received. this led to a total of 85 surveys considered in the analysis. among the stakeholders, due 1 within the console project, a stakeholder survey with pestle questions about the result-based contract was carried out in 12 countries. however, only germany and austria also conducted a pestle survey for collective contracts. in this contribution, we will, therefore, solely refer to the survey results from germany and austria. for more information on the pestle results for the 12 countries, see hamunen et al. (2023): deliverable 3.3 “synthesis of opinions to implement suggested contract solutions and lessons learned” on the console-website at www.console-project.eu. http://www.console-project.eu 52 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 theresa eichhorn et al. to the still rather experimental nature of such schemes, an overall low level of familiarity with result-based and collective approaches was assumed. connected to this, and for ensuring some common understanding amongst respondents, a short description of result-based as well as collective contract solutions was included in the survey (see appendix a). the questionnaire was structured into three parts: the first part contained questions on the respondent’s backgrounds, such as affiliation, areas of interest, and responsibilities. the pestle approach was then implemented using two blocks of questions (one for result-based and one for collective approaches). the pestle block began with the overarching question of what external factors in the farm environment inhibit or promote the adoption of (a) result-based or (b) collective contracts? for a better understanding, participants were shown the six main pestle categories in a figure (see fig. 1). additionally, pestle categories were described by including short examples/ descriptions: namely 1) environmental factors such as emissions and climate change, 2) political factors such as administration and regulations, 3) economic factors such as purchasing power and income, 4) socio-cultural factors such as demographic development and societal demands, 5) technological factors such as digitalization and innovations, 6) legal factors such as environmental and competition law. the procedure of the survey was then as follows: starting with result-based and in a second round continuing with collective contracts, participants were asked to (1) name 5 particularly important factors impacting on implementation/adoption, which can’t be influenced by farmers directly, but have an impact on farm decisions. as participants were informed about the pestle categories beforehand, they certainly kept them in mind when answering, but they were not asked to name the factor nor to assign their responses to any category. (2) using the symbols “+” or “-”, participants were asked to indicate whether the mentioned factors promote or hinder implementation. (3) in a final ranking exercise, participants were then asked to select the most important factor out of the 5 answers they had given. this resulted in 5 responses for each contract type, of which one each was selected as most important for both result-based and collective contracts. there was no word limit for the free answers but participants were asked to answer in short sentences, supplemented by the note: “the more concrete the information, the better”. in addition, participants were forced to provide five responses, otherwise they were not able to continue the survey. the approach used is illustrated exemplarily in table 1 in appendix a. 2.2 data analysis the analysis of the pestle results was carried out in excel and by using a three-step approach: first, the factors named by the stakeholders were assigned to the 6 pestle categories. this was done separately for the two contract types. (thereby, statements which referred to the design of the contracts themselves, such as contract terms, duration etc. were assigned to the legal category.2) second, factors representing a similar content were grouped, examined and subcategories were built. thereby, a minimum of 3 associated responses were required to form a subcategory. to ensure quality and improve objectivity, the allocation of single factors to the subcategories was conducted via several rounds of exchanges between the austrian and german researchers involved in the study. third, codes and short summarising descriptions were assigned to the subcategories, whereas codes represent the pestle category and a consecutive number (e.g. “ec” for economic factor and “04” for the fourth subcategory). as several identical factors were mentioned by the stakeholders for both types of contracts, subcategories were summarised under identical descriptions where possible. differences in subcategory descriptions between the two contract types are underlined in table 1 for ease of identification and 2 several contract-related responses, in most cases targeting particular contract features, were given by the stakeholders, therefore the legal category was expanded to include them. figure 1. pestle categories; figure showed in the survey. 53 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures – whenever the descriptions differ partly or completely – abbreviations, co for collective and rb for result-based, are added to the respective codes (e.g. ec04rb)). differences in subcategories are often closely related to the specificities of the two contract solutions under consideration.3 when responses couldn’t be assigned to one specific subcategory – either because they highlight interfaces or because they address aspects belonging to two different subcategories – a double-code was given (e.g. ec01/ l06), but they were only assigned (and counted) within the first code. for the case that stakeholders’ answers directly repeated the pre-set pestle category, (e.g. stakeholders stated that economic factors influence adoption), such responses were counted into the respective pestle category, but were not assigned to any subcategory and marked “00”. to reduce the complexity of the interpretation of the external factors we formulated descriptions of the subcategories neutrally or positively. 3. results 3.1 descriptive statistics of stakeholder characteristics among the austrian respondents, most participating stakeholders (64.7%) were active on a national level. in germany mainly regionally (56.3%), and nationally active stakeholders (29.2%) participated in the survey. with regard to the field of activity, “agricultural activity” is in first place in both countries, followed by “environmental protection and nature conservation” and “forestry”. in germany 68.6% and in austria 60.4% of the surveyed stakeholders are employed in these three fields of activity, whereby multiple answers were possible (figure 2). while in austria many participating stakeholders (30%) were representatives of the private sector, a large share of german stakeholders were representing state organisations (22.9%). furthermore, representatives of public companies, non-governmental organisations, scientific institutions, non-profit organisations, associations and civil society took part in the survey. in both countries, “advice or provision of information for farmers” was the most important task or field of interest for the participating stakeholders. thus, 23.3% of the stakeholders in austria and 21.2% in germany were active in this area. for 21.6% and 14.7% of stakeholders in austria and germany, respectively, this task was also the most important field of activity. in austria, “support in the design of contract solutions” (21.4%) and “provision of 3 this is most evident for the social category, where the responses of stakeholders were assigned to different headings with one exception (see table 1). information to the public” (19.4%), were the second and third most important areas of activity, both of which were selected by 20.6% of the respondents. also, in germany, these tasks were named second and third most frequently, however in reverse order (for more details on stakeholder characteristics please see appendix b). 4.2 pestle results for result-based contracts, a total of 376 responses could be assigned to the six pestle categories, of which 147 came from austrian and 229 from german stakeholders. for collective contracts, a total of 3334 responses could be assigned to the six categories (131 from austria, 202 from germany). table 1 summarises the main findings: the title of each section represents the pestle category. subcategories (codes and descriptions) are assigned to each category, with differences between result-based and collective contract types being underlined. for each category, between four and six subcategories have been formed. table 1 also serves as basis for the results presented in chapters 4.2.1 and 4.2.2. a more detailed description of the results is provided in table 4 in appendix c, which, in addition to the descriptions for all subcategories, also indicates the frequency of mentions and the factors selected in the ranking exercise as most important. in addition, it is indicated if the factor mentioned was marked as promoting or hindering. figure 3 shows how the factors identified for resultbased and collective contracts are distributed across the six categories and among the two countries. the total number of responses per country and contract type is 100%, distributed across the six pestle categories. 4 the different amount of answers between collective and result-based schemes can be explained by the fact that only those factors which can’t be influenced by farmers themselves while having an impact on farm decisions were included. furthermore, within the collective part (the third part of the survey) a few participants decided to just write “no additional idea/answer” into the field. 0% 10% 20% 30% 40% 50% food sector training and advice municipal development water management research and development public administration land use policy and planning forestry environmental protection agriculture germany austria figure 2. stakeholders’ fields of activity in austria and germany. 54 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 theresa eichhorn et al. in general, the figure reveals that stakeholders from both countries have given similar preferences to certain pestle categories per contract type, resulting in a similar distribution of the responses. results also show that general differences exist between the importance of specific pestle categories table 1. pestle categories and subcategories built based on the survey responses political economical p01 advice and support to farmers for implementation ec01 payment calculation, appropriate remuneration for participation in the contracts p02 political will to support farmers in delivering environmental services ec02rb availability of sufficient funding for contract payments ec02co availability of sufficient funding for contract payments and for coordination / measure planning p03 low level of bureaucracy and administrative burden ec03 income / revenue security and little financial risk for farmers p04 longer-term stable political framework ec04rb reliability of demand for and value chains to sell the agricultural products ec04co sharing of remuneration between farmers when participating in the contracts p05 assistance in contract implementation by qualified authorities and intermediaries ec05 new income opportunities for farmers by participating in contracts ec06 limited time and financial effort for implementation social technological s01 social appreciation, recognition for the environmental services provided by farmers t01rb t01co existence of appropriate technologies for measuring the results achieved existence of appropriate technologies for measuring the achievements s02rb so2co attitudes of farmers, consideration of cultural norms and traditions attitudes of farmers and sensitivities of farmers t02 determination of appropriate indicators for monitoring s03rb s03co societal and consumers’ demand and interest for environmental services involvement of further stakeholders (interest groups, ...) t03 easy to implement and no time-consuming monitoring / documentation s04rb s04co willingness to work together (interest groups, neighbours, farmers’ associations) content of cooperation t04rb t04co access to technology / machinery, technical practicability access to technology / machinery, distribution of work s05rb s05co farmers’ awareness of environmental topics and knowledge farmers’ awareness of environmental topics and knowledge exchange t05rb sufficient knowledge about the environmental effects of the farming practices s06co group dynamics legal environmental l01rb l01co contract characteristics: voluntariness, flexibility, clear goal(s), possibility of influencing contract characteristics: voluntariness, flexibility, clear goal(s), entry and exit conditions, responsibilities en01 impacts of climate change and perceived need for action l02rb l02co simplicity and comprehensibility of the contract conditions of participation for farmers (number, setting) en02 unpredictability of nature and the limited ability of farmers to have an influence on it l03 clarity and consistency of the legal framework of the contract en03 spatial and regional environmental conditions l04 compatibility of the contract with existing laws, programs and eu policies en04 interplay of action and impacts on nature and environment l05 practical achievability of the contract goals l06 transparent and comprehensible controls and sanctions note: pestle category = title; subcategories including codes (category and a consecutive number e.g. ec01, if different: co= collective; rb= result-based, deviating wording is underlined). 55 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures with respect to specific contract types: for result-based contracts, most stakeholder responses belonging to this contract type fall into the legal category, with 25.8% for germany, respectively 29.3% for austria. the economic category with around 20% is placed second, social third and political fourth. for collective contract solutions, stakeholder responses belonging to the social category take the biggest share with 35.5% of the responses from germany, respectively 33.1% from austria, followed by the legal category on the second, and economic on the third place. differences between both countries amongst categories become obvious only for the political category in respect to collective contracts, which is considered as more important by german than by austrian stakeholders. also, for collective contracts concerning the technological category some variance occurs: here german stakeholders evaluate technological aspects as less important than the austrian respondents do. when looking at the responses which were ranked as most important and their distribution amongst the six pestle categories, differences between the two contract types become even more explicit (see figure 4). for result-based contracts, the economic category received the highest number of responses marked as “most important”. 34% of the german and 31.3% of the austrian stakeholders selected a response belonging to the economic category. for result-based contracts, the legal category follows on the second place with 24% of the german, respectively 25% of the austrian responses ranked first. in sum, more than 50% of the responses ranked as most important for result-based contracts belong to those two categories. for the collective contract the dominance of the social category is outstanding, with 54.2% of the answers from germany and 62.1% of the austrian answers ranked as most important. at a great distance follows the economic category with less than 20% in both countries. 4.2.1 external factors in result-based contracts in the following section, all six pestle categories impacting on result-based contracts are described, following the pestle order5. however, since economic, legal and social factors were mentioned most frequently for result-based contracts and since these are also the factors differing most when comparing the responses for result-based and collective contract solutions, more emphasis is put on these factors. an overview of the most mentioned subcategories in result-based contracts can be found in table 2 at the end of this sub-chapter. – political factors impacting result-based contracts in the political pestle category for result-based contracts, the subcategory low level of bureaucracy and administrative burden (p03) includes 11 individual stakeholder statements. within these 11 statements, the factors “administrative effort”6 and “bureaucracy” have been mentioned four times each. one stakeholder, for example, expressed concerns that result-based contracts would lead to an increase in bureaucracy due to the customization required for each contract to match the specific environment. nine 5 due to the very similar response behaviour of the stakeholders, an evaluation in chapter 4.2.1 and 4.2.2 is carried out without differentiation between the two countries. however, where there are visible differences in response behaviour between austria and germany, this is addressed and highlighted in the result section. 6 all translations of the responses from german to english by the authors. figure 3. breakdown of all stakeholder responses to the six pestle categories per contract type and country. note: the six pestle categories for each contract type by country sum up to 100%. figure 4. breakdown of the responses ranked as most important by the stakeholders to the six pestle categories per contract type and country. note: the six pestle categories for each contract type by country sum up to 100%. 56 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 theresa eichhorn et al. statements were summarised under the subcategory of political will to support farmers in delivering environmental services (p02). one stakeholder stated that the “contents of the contracts must have political consensus”. in the third place, with 8 statements, the subcategory of advice and support to farmers for implementation (p01) emerged. advice, including technical guidance, and access to training are considered promoting factors. one stakeholder for example stated the need for “support in understanding what is worth protecting and why”. – economic factors impacting result-based contracts in the economic pestle category, the subcategory of payment calculation, appropriate remuneration for participation in the contracts (ec01) was built on a sum of 28 statements, representing the subcategory based on most stakeholder responses within the whole pestle analysis for result-based contracts. also, for the factors ranked as most important, this subcategory received the highest number (12 mentions). the payment level itself has been mentioned several times as an important factor for participation (“it must pay off for the farmer”). at the same time, it was critically noted that the payment calculation is “demanding”. also costs and time required for the payment calculation have been mentioned as economic factors. listed as encouraging was that resultbased contracts allow for a “reward for higher environmental standards” and also that the “payments are positively dependent on management/commitment”. specifically, a “ fair design of payment” is called for, and this was made even more explicit in the response that “ fair compensation creates acceptance and reliability.” in this sense, one stakeholder suggests a combination of “basic remuneration plus performance payment (participation + success)”, while another participant advocates “gradations in the achievement of intermediate targets”. in the economic category, 12 statements could be assigned to the subcategory of new income opportunities for farmers by participating in contracts (ec05) placed second. on the third place, out of 10 statements, the subcategory income / revenue security and little financial risk for farmers (ec03) emerged. while the former subcategory focuses on economic opportunities, the latter focuses on the risks associated with result-based contracts. several times, economic profitability was mentioned in ec05, with seven factors being ranked as most important under this subcategory. thereby, the environmental performance to be provided was also considered. one statement explained that “it must be possible to realize a financial and ecological profit that can be economically influenced on the basis of entrepreneurial decisions.” price fluctuations or the price level of the cultivated crops, but also production-related mistakes are mentioned as factors that can put at risk the income for participating farmers. – social factors impacting result-based contracts in the social pestle category compiling factors with relevance for result-based contracts, the subcategory of social appreciation, recognition for the environmental services provided by farmers (s01) is based on 20 statements. thus, it became clear that public perception or appreciation is classified as promoting. one statement in this respect exclaimed “noticeable (!) social recognition” as a factor, and another marked that “the performance should be made visible to the people”. in addition, there were also a few sceptical voices about result-based contracts in the social context namely that “the more differentiated the requirements are, the more difficult it is to argue ‘externally’ the funding level or to explain to the consumer what exactly is being done”. the importance of outreach to improve social recognition was highlighted in four responses. for example, one statement said that “society needs to be made aware of this important work of the farmer through the media”. 15 further statements have been compiled under the subcategory of farmers’ awareness of environmental topics and knowledge (s05rb). as promoting factors, farmers’ own initiative and responsibility in result-based contracts have been mentioned: for example, one statement was that “ farmers are granted expertise/partners in nature conservation”. in the social category, the attitudes of farmers, consideration of cultural norms and traditions (so2rb), but also the willingness to work together (interest groups, neighbours, farmers’ associations) (so4rb) and the societal and consumers’ demand and interest for environmental services (so3rb) further emerged from the stakeholders’ answers. even if result-based contracts are implemented on the level of individual farms, peer pressure or social pressure from other farmers can have both positive and negative effects. an answer expressing positive impacts in this respect was for example that “experience of other farmers with result-oriented contracts influences the acceptance and willingness to participate of interested parties”. – technological factors impacting result-based contracts in the technological pestle category for resultbased contracts, existence of appropriate technologies for measuring the results achieved (t01rb) has emerged as the only subcategory addressed more frequently, assembling 13 responses. participants suggested new technologies, such as drones, remote sensing or aerial photography. one answer says for example that 57 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures “a possible documentation of results by the farmer could be facilitated by an app”, another recognizes that “by all means use digitalization for knowledge creation and control, certainly motivates the majority”. in the technological category, reliability of outcome measurement, the choice of easily measurable indicators, and the availability of technology, both for monitoring and for measure implementation are further crucial factors. one suggestion in this respect was that “easy-to-use tools should be available for documentation/monitoring”. another answer going in the same direction by telling that, “it is also important that technological means are promoted and made available to farmers through appropriate knowledge transfer on advantages and disadvantages”. opportunities are further seen in digitalization and the use of special technology. – legal factors impacting result-based contracts within the legal pestle category, the subcategory of contract characteristics (l01) was highly important for the stakeholders, being represented by 22 statements. here, voluntariness, flexibility and clear goals were named as promoting factors. the importance of achievable goals or a form of co-determination in the setting of goals becomes clear with these two answers: “objective benefit of the goals should be evident to the contracting parties” and “if farmers can influence the selection of the desired ecological goals, this promotes acceptance”. also, “quantitative and qualitative specification of the results (criteria, indicators)” is seen positively. this requirement is closely related to suitable technical feasibility and was coded twice accordingly (l01rb/t02). another subcategory within the legal pestle category, which was built on 20 statements is transparent and comprehensible controls and sanctions (l06). while sanctioning is seen as a factor hindering the implementation of result-based contracts, controls and controllability were rated both positively and negatively. annual fluctuations, especially with regard to biodiversity, insects, etc., are seen as critical for the assessment of results. in this regard, one question raised was “what happens if, for example, no species settle/no results can be shown?” as possible solutions, “easy to control (simple success criteria)” as well as “conciliation in case of differing assessments of success” were mentioned. the third important subcategory within the legal category, with 18 statements as basis, is clarity and consistency of the legal framework of the contract (l03). “legal certainty” and “planning security” were mentioned particularly often with 6, respectively 4 responses. the 2 subcategories of compatibility of the contract with existing laws, programmes and eu policies (l04) and practical achievability of the contract goals (l05) were built on 14 and 12 statements, respectively. in connection with legal regulations, restrictions due to requirements from the fertilizer regulation and the prohibition of double funding were mentioned. demands such as “achieving the agreed results table 2. result-based contracts – subcategories with at least ten mentions. code subcategory sum + 1. p ec s t l en 1 ec01 payment calculation, appropriate remuneration for participation in the contracts 28 21 7 12 2 en02 unpredictability of nature and the limited ability of farmers to have an influence on it 27 3 24 8 3 l01 contract characteristics: voluntariness, flexibility, clear goal(s), possibility of influencing 22 21 1 4 4 l06 transparent and comprehensible controls and sanctions 20 8 12 3 5 s01 social appreciation, recognition for the environmental services provided by farmers 20 17 3 2 6 l03 clarity and consistency of the legal framework of the contract 18 12 6 4 7 s05 farmers’ awareness of environmental topics and knowledge 15 14 1 4 8 l04 compatibility of the contract with existing laws, programmes and eu policies 14 5 9 3 9 t01 existence of appropriate technologies for measuring the results achieved 13 11 2 2 10 ec05 new income opportunities for farmers by participating in contracts 12 10 2 7 11 l05 practical achievability of the contract goals 12 10 2 4 12 p03 low level of bureaucracy and administrative burden 11 4 7 3 13 ec03 income / revenue security and little financial risk for farmers 10 1 9 1 sum = number of responses in total assigned under this heading/factor; + = responses framed positively as well as assigned as promoting factor; – = responses framed negatively as well as assigned as hindering factor; 1. = number of responses, stated as most important factor for result-based contracts by stakeholders in the survey = ranking exercise; categories: p = political ; ec = economical; s = social; t = technological; l = legal; en = environmental. 58 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 theresa eichhorn et al. must not lead to sovereign protection!” or “no obligation to continue after termination of the contract” point to existing legal uncertainties in ecological successes. – environmental factors impacting result-based contracts the environmental pestle category assembles the second most statements for result-based contracts under the subcategory of unpredictability of nature and the limited ability of farmers to have an influence on it (en02). this subcategory was built based on 27 statements in total, and 8 statements ranked as most important. weather conditions and extreme weather events such as lack of precipitation have been mentioned, which can negatively influence the results and thus jeopardize the success of the measures implemented. also addressed are uncertainties in natural processes, population trends, as well as already good ecological status as a baseline, which can make further environmental improvements difficult. from the point of view of the stakeholders, dealing with these uncertainties is crucial for a successful implementation of result-based contracts. this became clear e.g. in the demand that “in case of extreme weather, the farmer must also be compensated” (en02 / ec01) or in the question on “liability in case of non-achievement of goals (capricious weather, ...)” (en02 / l06). the double coding indicates that regulations in this regard are highly relevant from an economic as well as a legal perspective. the subcategory of impacts of climate change and perceived need for action takes a special position within the environmental pestle category (en01). climate change was explicitly mentioned as a factor relevant for result-based schemes, so one statement was for example that “effects of climate change are felt by every farmer and increase the willingness to deal with the topic of soil”. one stakeholder commented that “paid environmental or ecosystem services are farm diversification and increase resilience in climate change.” 4.2.2 external factors in collective contracts for collective contracts, we again structured the results along the six pestle categories. a clear dominance of social factors became obvious for collective contracts. also, legal and economic factors were mentioned often, therefore we describe these three categories in more detail. table 3 at the end of the chapter provides an overview of the twelve subcategories with most statements assigned to collective contracts. – political factors impacting collective contracts as in the case of result-based contracts, within the political pestle category for collective contracts, the subcategory low level of bureaucracy and administrative burden (p03) was the only category to be built on the basis of more than 10 stakeholder statements. hereby arguments concerning efficiency and effort have been raised: stakeholders mention that in collective contract solutions “control effort for [the] authority could be reduced”, and that “public admin costs are reduced and increased within the group, but more efficient”. also mentioned positively was that “administration has fewer individual applications to deal with”. however, there are also a number of responses indicating the risk of even more bureaucracy for this contract type. this shows that the contractual arrangement will be decisive for the amount of bureaucracy. in the ranking process two responses within this category have been selected as most important, namely that “good information in advance about the measure, its practical implementation and about ecological bases” needs to be provided (within subcategory p01) and “political will must be present” (within subcategory p02). – economic factors impacting collective contracts the most important subcategory within the economic pestle category impacting on the adoption of collective approaches is the fair sharing of remuneration between farmers when participating in the contracts (ec04co): many statements take up the issue of fair payment distribution and how this can be organized. one stakeholder for example raised the question “how is the compensation and the distribution within the group realised?” and one respondent put his/her fears in a nutshell as follows: “distribution of payment – when it comes to money, friendship ceases”. other stakeholders suggested a “distribution formula” as well as the “distribution of money via third parties” or a “pre-allocation of the remuneration” in order to avoid disputes. but there were also comments regarding how to consider differences in cost structure amongst participating farms and how to distribute money fairly. besides fair remuneration, two further economic aspects, summarised in the subcategories payment calculation, appropriate remuneration for participation in the contracts (ec01) (13 statements) and new income opportunities for farmers by participating in contracts (ec05) (11 statements) revealed to be of high importance for successful collective contracts. of the 13 answers on payment calculation, 9 came from austria, one of the rare situations with a clear country difference in the response behaviour. as in the case of the result-based contracts, statements on the payment amount and “proper financial incentive” dominate; there is also the demand for “payment also for the additional organisational effort” in the case of collective measures. in the ranking process, 59 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures for collective contracts 16 statements in the economic category were selected as most important, while for the result-based contracts 28 statements in the economic category were chosen. amongst the most important factors were the requests for “financing a coordination function” and “coordination must not be at the expense of remuneration”. – social factors impacting collective contracts in the ranking of all pestle categories, for collective contracts social subcategories take the second to fourth place. this clearly demonstrates the outstanding relevance given to them by the stakeholders inquired (see table 3). 30 statements have been assigned to the subcategory of attitudes and sensitivities of farmers (s02co). moreover, 13 stakeholders ranked statements in this subcategory as the most important, bringing it in the first position. for example, a good neighbourly relationship, “past experiences of cooperation between farmers” and the “alliance of farmers with the same goal” are mentioned as conducive. however, a number of inhibiting factors are also mentioned. for example, the willingness of farmers to cooperate and exchange is doubted, one answer in this respect was that a “ farmer is rather a loner”. but also, envy and jealousy between farmers or “difficulty in finding a group” have been mentioned. trust between farmers, fairness, willingness to communicate and the ability to work in a team are mentioned as prerequisites for the successful implementation of collective contract solutions. the subcategory of content of cooperation (s04co) follows in third place with 29 statements, of which the positive mentions slightly outweigh with 16 answers. 10 responses within this subcategory were ranked as most important by the stakeholders. particularly the setting of common goals was mentioned several times as an important success factor, thereby e.g. two answers stated that “the group focuses on a few or a common goal” and “ farmers can achieve this effectively and on a large scale as a group with a common goal”. coordination and communication efforts within the “collective” are seen as obstacles to be overcome, answers underlying this statement are e.g. that a “common basis for discussion between all participants at equal level” is needed, or, formulated differently, that there must be “no dependencies / power imbalances within the group”. an “equitable distribution of duties and benefits in the collective” is seen as a success factor and it was suggested to offer “mediation and conflict resolution training”. it was noted that “if collective structures already exist, this simplifies the process”. the possibility of exchanging experiences among each other is seen positively, but also that in collective contracts synergies can arise. group dynamics (s06co) were considered as a separate subcategory, as 21 responses explicitly refer to it, 9 of which were ranked as most important. in total, this term was used six times by the respondents, of which it was negatively evaluated five times. for example, group dynamics are described as a “stumbling block” and there is a fear that solutions are endangered “if individual participants crossfire”; there are also questions about how to deal with social conflicts within the group as well as with “difficult characters”. specifically addressed is the concern that “individual interests or political opinions of group members differ greatly or diverge” and “ free-riding” is mentioned as a further problem. promoting factors are if the “group [is] already sufficiently long established”, the presence of group members who have an “exemplary character for other participants” and the emergence of a “we-feeling”. in the case of the subcategory of social appreciation, recognition for the environmental services provided by farmers (s01), with one exception, only promoting factors are mentioned. one statement explains that “as a group it is easier to present interests to the outside world (public, politics)”, and also the “example setting for third parties outside the group” is mentioned. for the success of collective contracts, the involvement of further stakeholders (s03) besides farmers is important. hereby, advisors and agricultural associations were explicitly mentioned, but also cooperation with environmental administrations/ authorities was suggested. with regard to the involvement of nature conservation associations, answers were more reserved with a “distrust of environmentalists” being mentioned and the potential for conflict that this may entail. – technological factors impacting collective contracts within the technological pestle category, even though none of its subcategories was amongst the top twelve, stakeholders raised a number of concrete suggestions: for example, “technical devices that facilitate the application or the implementation of measures” or technical solutions for the “clear breakdown of services and rewards” and for the “verifiability of results and allocation to individual farmers” are seen as beneficial. one stakeholder calls for “suitable (digital) tools for the documentation of the measures implemented”. also, the use of gps “can positively influence coordination within the collective”. – legal factors impacting collective contracts for collective contracts, amongst the legal pestle category, the subcategory contract characteristics: voluntariness, flexibility, clear goal(s), entry and exit 60 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 theresa eichhorn et al. conditions, responsibilities (l01co) was in first place with 34 stakeholder statements belonging to it. the contractual regulation of responsibilities and accountabilities, as well as clear rules and a clear distribution of tasks are named as promoting factors. stakeholders ask questions such as “how is the contract with the agency structured?”, “who selects criteria for performance”, “who evaluates which achievements with distribution of funds?”, “how is the division of labour organized within the group”, or “who is liable if an individual from the collective fails to perform”. several answers in this subcategory refer to the legal protection in case of changes in the group composition or if one or more participants want to leave. the necessity of a clear formulation of goals is further stated, also that this is more difficult in the case of collective contracts as it requires “contractual clarification between farmers”. the risk for disputes is mentioned as an inhibiting factor, either “in the in the contract negotiations”, or because of “unfulfilled requirements of individuals” or regarding “the payout”; in this regard, there is a suggestion to establish an “internal control system in the collective”. 14 stakeholder statements built the legal subcategory of clarity and consistency of the legal framework of the contract (l03). as with result-based contracts, also with collective solutions legal and planning certainty are addressed, and there is the concern about “legal dispute(s) when drafting the contract.” in addition, 10 statements were addressed to the subcategory of transparent and comprehensible controls and sanctions (l06), classified under legal aspects even though being relevant from economic perspective too. central is thereby the question of “how is the cooperation regulated, what happens if repayments would have to be made”. in the same direction goes the statement that the “collective must be secured in terms of a control and sanction system”. there is also concern about the “risk of sanctions or assumption of liability for mistakes made by other farms”. at the same time, another stakeholder points to an advantage of collective contracts with the answer “no feeling as an individual to be at the mercy of the control system”. – environmental factors impacting collective contract solutions in the environmental pestle category for collective contracts, in contrast to the result-based contracts, no subcategory made it into the top twelve. beneficial factors addressed in this category are however the “higher effectiveness of measures” and that “regional concerns can be better addressed”. mentioned is moreover the possibility for implementing measures in a larger area through collective contracts and that the “integration of structures such as wind belts etc. [is] more easily possible”. qualifying, one response reads “suitable only for measures that have a landscape effect and not just an areaspecific effect.” one comment is “if it is clear what characterises a region and what is worth protecting, everyone is on board”. table 3. collective contract solutions – subcategories with at least ten mentions. code subcategory sum + 1. p ec s t l en 1 l01 contract characteristics: voluntariness, flexibility, clear goal(s), entry and exit conditions, responsibilities 34 18 16 4 2 s02 attitudes and sensitivities of farmers 30 9 21 13 3 s04 content of cooperation 29 16 13 10 4 s06 group dynamics 21 8 13 9 5 ec04 sharing of remuneration between farmers when participating in the contracts 18 3 15 4 6 l03 clarity and consistency of the legal framework of the contract 14 8 6 3 7 ec01 payment calculation, appropriate remuneration for participation in the contracts 13 11 2 1 8 ec05 new income opportunities for farmers by participating in contracts 11 10 1 2 9 s03 involvement of further stakeholders (interest groups, ..) 10 8 2 2 10 s01 social appreciation, recognition for the environmental services provided by farmers 10 9 1 3 11 p03 low level of bureaucracy and administrative burden 10 4 6 3 12 l06 transparent and comprehensible controls and sanctions 10 5 5 0 sum = number of responses in total assigned under this subcategory/factor; + = responses framed positively as well as assigned as promoting factor; – = responses framed negatively as well as assigned as hindering factor; 1. = number of responses, stated as most important factor for collective contracts by stakeholders in the survey = ranking exercise; categories: p = political ; ec = economical; s = social; t = technological; l = legal; en = environmental. 61 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures 5. discussion and conclusions this work addresses macro-environmental factors impacting on the success of result-based contracts, and of contracts fostering collective implementation, both aiming for an improved provision of agri-environmental-climate public goods. so far, to the knowledge of the authors, only little literature can be found on hindering or facilitating external factors affecting the implementation of these novel aecms. therefore, this study aimed to investigate political, economic, social, technological, legal as well as environmental factors by using the pestle approach. the analysis was based on a stakeholder survey conducted in germany and austria. the application of the pestle approach has demonstrated its efficacy as a valuable tool for structuring and classifying the varied responses elicited from a diverse set of stakeholders. it could be demonstrated that stakeholders possess the ability to provide input within the established categories/factors. nevertheless, the practical application of the pestle approach within this particular context has encountered certain limitations. first, it is important to recognize that the quality of the results obtained is highly depending on which stakeholders finally participate in the survey. despite successfully engaging a significant number of stakeholders in both countries, it is important to note that the sample is not representative in terms of their regional level (e.g., with a bias towards regional and national stakeholders), background organisation, and other stakeholder characteristics (see appendix b). second, our study specifically addressed factors that are beyond the direct control of farmers, yet exert influence on their business decisions. under the cap, aecm contracts are standardized and not subject to bilateral negotiations, thereby restricting individual contractors from negotiating specific elements of contract design within the legal framework. consequently, various “internal” design elements arise within the “external” legal category (e.g. l01 contract characteristics), rendering the precise differentiation between “external” and “internal” factors somewhat challenging. third, a lack of clear demarcation between external and internal factors was observed within the social category. while there are distinct external social factors such as social appreciation, this category also encompasses subgroups that can be regarded as internal, namely farmers’ awareness and attitudes. taking a broader perspective, it can be argued that the external social environment plays a pivotal role in shaping and influencing farmers’ awareness and attitudes. fourth, in the case of collective contracts, the introduction of a third social interaction in the form of the “group dynamics” of course represents a significant differentiation within the subcategories of collective and results-oriented contracts (e.g. s04co “content of cooperation” and s06co “group dynamics” versus s04rb “willingness to work together (interest groups, neighbours, farmers’ associations”). this has led to significant distinctions within the social category for result-based and collective contract solutions and to different subcategory headings, with one exception. the results of this analysis shall now be discussed along the main external factors revealed for both contract types (see table 2 and 3). starting with the factors/subcategories that exhibit congruence across both contract types, we will conclude with those factors that demonstrate the most significant variations in terms of statements and subcategories. navigating uncertainty in the new cap period – political factors during our pestle analysis, conducted in the midst of the new common agricultural policy (cap) strategic planning discussions, it became evident that the upcoming cap period has resulted in considerable uncertainty among german and austrian stakeholders. the respondents frequently highlighted the importance of having a clear and consistent legal framework for the contracts, as well as ensuring that the contracts are compatible with existing laws, programs, and eu policies. specifically, legal certainty, planning security, ongoing legal changes, and the potential issue of double funding were identified as key concerns. fair payment structures and new income opportunities – economic factors the economic category plays a crucial role in both result-based and collective schemes. the appropriate remuneration for participation and the potential for new income opportunities are perceived highly positive and important for farmers’ engagement among stakeholders in both types of contracts. aecms representing an additional income opportunity is a well-known motivational factor among famers in classical schemes, but was also already confirmed in novel schemes (e.g. barghusen et al., 2021). the calculation of payments, however, is a concern for stakeholders in result-based contracts due to the challenge of compensating appropriately for the environmental improvements achieved. literature recommends tailoring the payment structure to the environmental objective and the level of participation desired (herzon et al., 2018). stakeholders suggest 62 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 theresa eichhorn et al. incorporating intermediate targets or offering graduated payments for various levels of success. the importance of fair economic incentives in introducing existing and novel contract types is widely acknowledged (lastra-bravo et al., 2015; pavlis et al., 2016; ruto & garrod, 2009; wilson & hart, 2000), and should also cover risks in the introductory phase (prager & posthumus, 2010). in collective contracts the fair distribution of payments in line with the management efforts is particularly relevant for adoption. in addition, a “collective bonus” could serve as a reward for the additional effort of the farmers to integrate their business orientation into a specialised (collective) concept (dvl, 2021). beyond money: the power of social recognition in incentivizing environmental services by farmers – social factors in addition to economic incentives, social appreciation and recognition for the environmental services provided by farmers are perceived as strong promoting factors in both result-based and collective schemes. farmers react to societal demand when delivering aecpgs, but this usually goes along with higher / additional workload. making farmers’ work visible, for example through media or public relations work, helps improving the image of agriculture and is perceived as a strong promoting external factor. result-based schemes, in addition, provide an opportunity to report clear environmental results to society. furthermore, farmers themselves have also emphasized the importance of social recognition (russi et al., 2016), which was mirrored by our stakeholder responses. stakeholders expressed concerns about specific contract design elements, with clearly differentiated requirements for result-based and collective contracts – legal factors in line with the reticence and concerns expressed by stakeholders, there are recommendations pertaining to the legal aspects of contract design. while in resultbased schemes, voluntariness, flexibility, and clear goals are key aspects, for collective schemes, entry and exit conditions as well as responsibility issues are particularly relevant. this finding is consistent with previous research suggesting that collective incentive schemes should have clearly defined participation criteria and organisational structures (barghusen et al., 2021; franks, 2011). additionally, stakeholders emphasized the importance of fair distribution of remuneration among farmers participating in collective schemes, and a third-party distribution system or pre-allocation of the remuneration were suggested as means to increase trust and fairness. the legal category also revealed that stakeholders call for legal protection in case of changes in group composition. in literature, result-based schemes promote higher flexibility in farmers’ management decisions (de sainte marie, 2014; klimek et al., 2008; matzdorf & lorenz, 2010; russi et al., 2016; sabatier et al., 2012), and this was also deemed important by the stakeholders. in addition, result-based schemes require clear targeting, which involves a precise definition of the results that farmers can achieve and the ability to influence them, according to our stakeholders. nature’s unpredictability poses a significant hindrance to result-based agri-environmental schemes – environmental factors according to the results of our study, the unpredictability of nature and the limited influence of farmers on it emerged as a major hindering external factor for the adoption of result-based contracts. this issue is very specific to result-based schemes, where linking payments to measurable environmental improvements makes the influence of nature more salient, particularly in direct comparison to the dominating action-based payments. also, for collective contract solutions it was seen as less relevant. already existing literature has identified this issue as a potential risk factor for result-based payments (de snoo et al., 2013; derissen & quaas, 2013; olivieri et al., 2021; wezel et al., 2018), our study provides evidence of its significance in stakeholders’ perception: in the survey, stakeholders identified and mentioned various environmental factors that can influence the ecological outcome, including extreme weather events, seasonal/ regional weather phenomena/conditions, shifts in animal and plant communities, climatic conditions, soil conditions, and the current ecological status. thus, stakeholders acknowledge that the achievement of ecological results is not solely in the hands of farmers. social dynamics and farmer attitudes: key factors in collective contracts – social factors for collective contracts, social relationships between participating farmers and the related difficulties are dominating stakeholders’ perceptions when thinking about hindering and facilitating external factors. this resulted in “attitudes and sensitivities of farmers” being the aspect with the most statements, and it also ranked first in the list of factors rated as most important. farmer-to-farmer relationships and the social dimension of 63 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures such engagements were frequently mentioned. promoting factors included past positive experiences of cooperation between farmers, good neighbourly relations, and an existing basis of trust. hindering factors included a lack of willingness to cooperate, jealousy, traditions, and the perception of farmers as “loners”. the stakeholders’ predominantly pessimistic view of farmers’ willingness to cooperate is also mirrored in a study by rommel et al. (2022). already sutherland et al. (2012) concluded to take farmer co-ordination with caution, especially with regard to social characteristics and assumptions about trust between farmers. they also noted that it seems useful to build on existing structures. franks (2011) stated that the success of collective contracts depends on clubs of like-minded members with similar views and beliefs who are willing to cooperate and have a low level of conflict between the members. stakeholders in our study specifically addressed group dynamics as a crucial factor. they identified difficulties in bundling diverse interests and managing larger groups but saw positive aspects in knowledge exchange, developing a group feeling (“together we protect!”), and possible social control. other studies have shown that farmers are motivated to join a group for knowledge exchange, learning from peers, and socializing with other farmers (prager, 2022). also barghusen et al., (2021) confirmed social norms as an motivation factor. 6. concluding remarks and outlook to sum up, the objective of this study was to investigate the factors that are outside the sphere of influence of the individual farmer impacting the adoption of novel agri-environmental schemes, specifically result-based and collective schemes, using the pestle analysis framework. this approach was conducted to provide a systematic analysis of the macro-environmental factors affecting the implementation of such schemes and to offer indepth insights. the study adopted a stakeholder survey approach and collected precise, multidisciplinary, and holistic insights into most important external factors. the findings of this study can support the decision-making of austrian and german policymakers in the design and implementation of the two novel contract types by considering relevant promoting factors, including practical requirements for result-based and collective contract approaches from the outset. furthermore, the study identified hindering factors that could be used as a basis for risk assessment, and scheme designers could act to minimize or avoid their impact. overall, this study shows the suitability and practicality of the pestle approach for analysing the external factors influencing agri-environmental policy measures. this is becoming even more important under the current cap with its new green architecture that gives greater flexibility at member state level in the choice and design of measures targeting the environment and climate. further research opportunities are seen within the framework of the approach adopted in this study. one pathway to follow in future investigations could be the examination and comparative analysis of responses from further countries and assessing the differences amongst them. moreover, it would be important to quantitatively analyse more in depth the differences between external factors for result-based compared to external factors for collective contracts. another promising area for future research is to look more closely at the stakeholders and actors, their background and their activities at different levels and how this influences their response behaviour. acknowledgment this work was supported by the european commission’s horizon 2020 project console (grant agreement no. 817949). the content of this article does not reflect the official opinion of the european commission. the views expressed in this article are solely those of the authors. references achinas, s., horjus, j., achinas, v., & euverink, g. j. w. 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(2019). biodiversity-based payments on swiss alpine pastures. land use policy, 81(february 2018), 153–159. https://doi.org/10.1016/j.landusepol.2018.10.035 appendix a descriptions of result-based and collective contracts as provided within the pestle survey result-based contract in a result-based contract, land managers receive a payment only for the delivery of environmental or climate results (e.g. water protection, landscape improvement, increasing biodiversity of carbon sequestration). land managers are free to decide about the management practices, with which they want to achieve these environmental or climate improvements. selected indicators and scoring systems to monitor environmental or climate results are often used, and they will be exactly defined in the contract. land managers have access to advice or training when they participate in this contract and they can voluntarily engage in the monitoring activity. contract with collective implementation land managers become members of a group who applies jointly for compensation in order to implement environmental or climate activities, e.g. water protection, carbon sequestration, biodiversity or landscape improvement. a minimum number of group members (e.g. 5) from the region is required to collaborate in order to get a payment. the group members decide about the implementation and locating the measures, and the distribution of the payment. within the group, peer land managers and advisors share knowledge and support the achievement of the environmental objectives. https://doi.org/10.1016/j.landusepol.2014.10.005 https://doi.org/10.1016/j.landusepol.2014.10.005 https://doi.org/10.1016/j.landusepol.2017.10.020 https://doi.org/10.1016/j.landusepol.2017.10.020 https://doi.org/10.1068/a3311 https://doi.org/10.1068/a3311 https://doi.org/10.5539/ijbm.v7n24p52 https://doi.org/10.5539/ijbm.v7n24p52 https://doi.org/10.1016/j.landusepol.2018.10.035 67 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures (1) short introduction into the pestle task (2) overarching question of the pestle survey stated (3) introduction of the six pestle factors including short descriptions and the pestle figure 1 (4) short contract solution descriptions for result-based contract and contract with collective implementation provided (5) participants are asked to name five 5 important aspects, influencing the implementability, in short concrete statements (example given) please name 5 important aspects that, in your view, influence the implementability of rb/co contracts , in short concrete statements. a free text 1 b free text 2 c free text 3 d free text 4 e free text 5 (6) participants are asked to decide for each response given if it is promoting or hindering and to finally select the response considered as most important (example) your list of aspects (transferred from above a e) is the aspect promoting or hindering the adoption? the most important (only one) promoting + hindering 1. a free text 1 þ o o b free text 2 o þ o c free text 3 þ o o d free text 4 o þ o e free text 5 o þ þ table 1. approach used within the pestle survey, exemplarily illustrate for rb schemes. 68 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 theresa eichhorn et al. appendix b – characteristics of the austrian and german stakeholders table 2. characteristics of the austrian and german stakeholders. characteristics germany frequency (%) austria frequency (%) regional level of the respondent national 29,2 64,7 regional 56,3 20,6 local 14,6 0 international 0 14,7 background organisation civil society / private individual 4,2 0 public enterprise 8,3 17,6 non-governmental organisation 8.3 8,8 academic (e.g. university, research institute) 8,3 17,6 non-profit organisation (e.g. foundation, association) 14,6 11,8 private company 18,8 32,4 governmental organisation 22,9 8,8 other (e.g. professional associations) 14,6 2,9 special area of responsibility (multiple answers allowed) agriculture 41,9 28,1 environmental protection / nature conservation 18,1 19,8 forestry 8,6 12,5 land use policy and planning 7,6 4,2 public administration 6,7 5,2 research and development 6,7 6,3 water management 4,8 4,2 community development 2,9 0 training and advice 1,9 9,4 food sector 1,0 10,4 role or areas of interest (multiple answers allowed); in bracket selection of “most important” provider of information/advice to farmers 21,2 (21,6) 23,3 (14,7) provider of information to the public 19,2 (13,7) 19,4 (20,6) assistance for public funding of land management 1,9 (2,0) 4,9 (0) support in the design of contract solutions 14,4 (17,6) 21,4 (20,6) equipment and/or tool provision 7,7 (0) 2,9 (2,9) providing/leasing land to land managers 2,9 (0) 4,9 (5,9) providing finance to land managers/owners/workers 5,8 (2) 3,9 (0) regulation and enforcement 6,7 (5,9) 2,9 (0) lobbying, campaigning 13,5 (17,6) 6,9 (2,9) community leader 1,9 (2) 1,9 (2,9) supervisory authority 1,9 (2) 3,9 (0) product certification body (e.g. organic, …) 0 (0) 0 (0) processor of agricultural products 1 (2) 3,9 (2,9) trade with agricultural products 1,9 (0) 0 (0) no selection “most important” (15,7) (26,5) 69 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures table 3. federal state from which the participants originate (in %). germany stakeholder austria stakeholder baden-württemberg 5,9 burgenland 6,06 bavaria 13,7 lower austria 12,12 berlin 5,9 upper austria 6,06 brandenburg 2,0 salzburg 0 hamburg 3,9 styria 3,03 hesse 5,9 tyrol 0 mecklenburg-western pomerania 5,9 vorarlberg 0 lower saxony 5,9 vienna 30,30 north rhine-westphalia 21,6 across the federal states 42,42 rhineland-palatinate 7,8 saarland 0 saxony 2,0 saxony-anhalt 2,0 schleswig-holstein 7,8 across the federal states 9,8 appendix c table 4. detailed overview of subcategories mentioned within the pestle approach. code subcategory ct sum + 1. p e s t l e p00 political category – without specification rb 5 2 3 1 co 6 0 6 0 p01 advice and support to farmers for implementation rb 8 7 1 1 co 8 8 0 3 p02 political will to support farmers in delivering environmental services rb 9 8 1 1 co 3 3 0 1 p03 low level of bureaucracy and administrative burden rb 11 4 7 3 co 10 4 6 3 p04 longer-term stable political framework rb 6 3 3 0 co 4 2 2 0 p05 assistance in contract implementation by qualified authorities and intermediaries rb 7 6 1 2 co 5 4 1 0 p06rb extensive communication of the measures to the public and to farmers rb 7 7 0 0 ec00 economical category – without specification rb 8 5 3 4 co 7 4 3 4 ec01 payment calculation, appropriate remuneration for participation in the contracts rb 28 21 7 12 co 13 11 2 1 ec02rb availability of sufficient funding for contract payments rb 4 3 1 2 ec02co ... and for coordination / measure planning co 8 6 2 2 ec03 income / revenue security and little financial risk for farmers rb 10 1 9 1 co 6 5 1 3 ec04rb reliability of demand for and value chains to sell the agricultural products rb 6 4 2 1 (continued) 70 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 theresa eichhorn et al. code subcategory ct sum + 1. p e s t l e ec04co fair sharing of remuneration between farmers when participating in the contracts co 18 3 15 4 ec05 new income opportunities for farmers by participating in contracts rb 12 10 2 7 co 11 10 1 2 ec06 limited time and financial effort for implementation rb 8 2 6 1 co 6 2 4 0 s00 social category – without specification rb 7 6 1 0 co 6 4 2 2 s01 social appreciation, recognition for the environmental services provided by farmers rb 20 17 3 2 co 10 9 1 3 s02rb attitudes of farmers, consideration of cultural norms and traditions rb 9 5 4 2 s02co attitudes of farmers and sensitivities of farmers co 30 9 21 13 s03rb societal and consumers’ demand and interest for environmental services rb 8 5 3 2 s03co involvement of further stakeholders (interest groups, ...) co 10 8 2 5 s04rb willingness to work together (interest groups, neighbours, farmers’ associations) rb 5 1 4 1 s04co content of cooperation co 29 16 13 10 s05rb farmers’ awareness of environmental topics and knowledge rb 15 14 1 4 s05co … and knowledge exchange co 8 7 1 2 s06c group dynamics co 21 8 13 9 t00 technological category – without specification rb 7 7 0 1 co 3 3 0 0 t01rb existence of appropriate technologies for measuring the results achieved rb 13 11 2 2 t01co existence of appropriate technologies for measuring the achievements co 8 7 1 1 t02 determination of appropriate indicators for monitoring rb 5 3 2 0 co 1 1 0 0 t03 easy to implement and no time-consuming monitoring / documentation rb 3 2 1 0 co 1 1 0 0 t04rb access to technology / machinery, technical practicability rb 7 3 4 1 t04co access to technology / machinery, distribution of work co 6 4 2 1 t05rb sufficient knowledge about the environmental effects of the farming practices rb 4 2 2 1 l00 legal category – without specification rb 7 1 6 2 co 5 4 1 0 l01rb contract characteristics: voluntariness, flexibility, clear goal(s), possibility of influencing rb 22 21 1 4 l01co … and entry and exit conditions, responsibilities co 34 18 16 4 l02rb simplicity and comprehensibility of the contract rb 9 5 4 0 l02co conditions of participation for farmers (number, setting, ...) co 6 1 5 1 l03 clarity and consistency of the legal framework of the contract rb 18 12 6 4 co 14 8 6 2 l04 compatibility of the contract with existing laws, programmes and eu policies rb 14 5 9 3 co 2 0 2 0 table 4. (continued). (continued) 71 bio-based and applied economics 13(1): 49-71, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14489 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures code subcategory ct sum + 1. p e s t l e l05 practical achievability of the contract goals rb 12 10 2 4 co 3 3 0 2 l06 transparent and comprehensible controls and sanctions rb 20 8 12 3 co 10 5 5 0 en00 environmental category – without specification rb 5 3 2 1 co 4 3 1 0 en01 impacts of climate change and perceived need for action rb 4 2 2 0 co 2 2 0 0 en02 unpredictability of nature and the limited ability of farmers to have an influence on it rb 27 3 24 8 co 5 2 3 0 en03 spatial and regional environmental conditions rb 3 0 3 0 co 7 5 2 1 en04 interplay of action and impacts on nature and environment rb 3 2 1 1 co 3 2 1 0 note: table 4 shows categories and subcategories including sum of all answers, indication of promoting or hindering assessed answers, weighting exercise answers with number of weighted as most important: sum = number of responses in total assigned under this subcategory/factor; + = responses framed positively as well as assigned as promoting factor; – = responses framed negatively as well as assigned as hindering factor; 1. = number of responses, stated as most important factor for collective or result-based contracts by stakeholders in the survey = ranking exercise; categories: p = political ; ec = economical; s = social; t = technological; l = legal; en = environmental. table 4. (continued). _hlk60054323 _hlk104829823 _hlk128742463 _hlk104830529 _hlk127347111 _hlk128647880 _hlk128647635 _hlk128646517 _hlk128649491 new pathways for improved delivery of public goods from agriculture and forestry stefano targetti1,*, andreas niedermayr2, kati häfner3, lena schaller2 assessing the landscape recovery scheme in the uk: a q methodology study in yorkshire, uk emmanouil tyllianakis upscaling environmental incentives in the common agricultural policy: an assessment of the potential of transfers from the first to second pillar fanny le gloux1,2,*, pierre dupraz1 exploring macro-environmental factors influencing adoption of result-based and collective agri-environmental measures: a pestle approach based on stakeholder statements theresa eichhorn1,*, lena schaller1, katri hamunen2,3, tania runge4,* innovative contract solutions for the agri-environmental-climate public goods provision: which features meet the farmers’ approval? insights from emilia-romagna (italy) riccardo d’alberto1,*, meri raggi2, davide viaggi3 the use of innovative contracts to provide agri-environmental public goods: comparing attitudes between ireland and other european countries tracy bradfield1,*, thia hennessy1, riccardo d’alberto2, emmi haltia3 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 bio-based and applied economics bae copyright: © 2024 bradfield, t., hennessy, t., d’alberto, r., & haltia, e. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: bradfield, t., hennessy, t., d’alberto, r., & haltia, e. (2024). the use of innovative contracts to provide agrienvironmental public goods: comparing attitudes between ireland and other european countries. bio-based and applied economics 13(1): 103-120. doi: 10.36253/bae-14444 received: february 28, 2023 accepted: october 25, 2023 published: may 20, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. guest editors: stefano targetti, andreas niedermayr, kati häfner, lena schaller orcid tb: 0000-0001-7203-1509 th: 0000-0002-6137-1249 rd: 0000-0002-7227-7485 eh: 0000-0002-9487-4387 the use of innovative contracts to provide agri-environmental public goods: comparing attitudes between ireland and other european countries tracy bradfield1,*, thia hennessy1, riccardo d’alberto2, emmi haltia3 1 cork university business school, university college cork, ireland 2 department of economics, university of verona, italy 3 natural resources institute finland (luke), finland *corresponding author. e-mail: tracy.bradfield@ucc.ie abstract. results-based, collective action, value chain, and land tenure contracts are means to improve the management of agri-environmental public goods. the objective of this paper is to assess the understandability, applicability, and perceived economic benefit of each of these contract types by land managers and stakeholders in twelve european countries, with a special emphasis on ireland. using survey data, we find that most land managers agree that results-based contracts are understandable, applicable to their farm, and economically beneficial. a lower portion of land managers in ireland than other european countries agree that value chain and land tenure contracts are understandable or applicable to their farms. the results suggest that greater efforts are required to promote collective action contracts across europe as they are paramount to the management of public goods. to increase the adoption of innovative contracts, providing financial certainty and autonomy should be prioritized by policymakers, particularly in ireland. keywords: agri-environmental climate public goods, aecpg, results-based contracts, contract design, environmental policy. jel codes: h41, o13, q28, q58. 1. introduction the provision of agri-environmental-climate public goods (aecpg) such as biodiversity, water and soil quality, and emissions reduction, was very much to the forefront of the european union’s (eu) agenda in the preparation of the latest common agricultural policy (cap) (european commission, 2023a). for example, an aim of both the eu’s green deal and the farm to fork initiatives is for food systems to become environmentally sustainable (european commission, 2023b). therefore, it is important that land managers are encouraged to sustainably manage aecpg. in line with this purpose, the present paper investigates innovative agri-environmental conhttps://doi.org/10.36253/bae-14444 http://www.fupress.com/bae https://doi.org/10.36253/bae-14444 https://orcid.org/0000-0001-7203-1509 https://orcid.org/0000-0002-6137-1249 https://orcid.org/0000-0002-7227-7485 https://orcid.org/0000-0002-9487-4387 mailto:tracy.bradfield@ucc.ie 104 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 tracy bradfield et al. tract types. these are contractual arrangements that incentivise farmers to increase the provision of aecpg alongside private goods (prager et al., 2020) and they are experimental in that they have not been a core feature of traditional agri-environmental schemes (aes) (bredemeier et al., 2022). the analysis explores the perceptions of agricultural and forestry land managers and other stakeholders (advisers, industry representatives, scientists, researchers, etc.) in terms of the understandability, applicability, and the perceived economic benefits of results-based, collective action, value chain, and land tenure contracts, both in ireland and in eleven other european countries. also, we examine the factors that can contribute to the adoption of these innovative agrienvironmental contracts. this paper focuses on the attitudes of irish land managers and other stakeholders towards innovative contract designs for three reasons. firstly, agricultural land managers in ireland play a particularly significant role in the management of aecpg because 72% of land in ireland is used for agriculture, which is the highest portion of land among eu countries (eurostat, 2022). as of 2013, 50% of agricultural land in ireland was under agri-environmental commitments (eurostat, 2023a). however, regardless of this figure, all land managers inf luence aecpg such as biodiversity, water quality and carbon sequestration to some degree. therefore, any efforts to increase the adoption of aes by farmers can help to improve aecpg provision. secondly, agriculture in ireland faces considerable environmental challenges as 37% of total greenhouse gas emissions and over 99% of ammonia emissions arise from agriculture due to the large livestock sector (decc, 2021). additional worries include concerns over biodiversity loss (biodiversity information system, 2022) and unsatisfactory water quality (epa, 2022), all of which can be managed through effective contract designs. attitudes towards the four innovative contract designs discussed here have not been previously assessed in the context of ireland, while a general shortage of debate in relation to the subject exists in the literature (bredemeier et al., 2022), with studies, such as d’alberto et al. (2023), focusing on specific territories and case studies. to advance understanding and to improve the design of innovative agrienvironmental contracts, the eu has funded several projects under the horizon 2020 programme, such as the console project1 (console, 2023) within which the present work was carried out. 1 the project’s full name is contract solutions for effective and lasting delivery of agri-environmental-climate public goods. horizon 2020 grant agreement number: 817949. more information is available at https://console-project.eu/. the remainder of this paper provides an overview of aes in europe, agriculture in ireland, and current environmental challenges in section 2. section 3 describes the data at hand. section 4 includes an analysis of survey data to determine how understandable, applicable, and economically beneficial land managers perceive various contract types to be. section 5 contains an assessment of land managers’ and stakeholders’ recommendations for improving the uptake of aecpg-related contracts. 2. background 2.1 agri-environmental schemes in europe agriculture within the eu is supported by the cap which consists of two pillars. pillar 1 provides financial support to farmers to ensure they have sufficient financial resources to sustain their businesses. this pillar also provides market measures to help support challenges such as input price volatility, financial crises, and climate change. pillar 2 is co-financed by member states and it focuses on rural development. its aims include, among others, the modernisation of farms, employment support, and generational renewal (european council of the eu, 2023). voluntary environmental protection measures have also been traditionally financed by pillar 2 (kelemen et al., 2023). from 2023, these policy tools are managed by member states through their national cap strategic plans (eu cap network, 2023). aes are financed under pillar 2 (kelemen et al., 2023) and they are the primary mechanisms through which land managers are financially rewarded for farming in an environment-friendly manner above that required for the basic payment scheme (teagasc, 2022). the adoption of these schemes was initially voluntary for european countries (burton and schwarz, 2013) and the implementation of aes became compulsory for eu member states in 1992 under ec regulation 2078/92 (cullen et al., 2021). however, their adoption by farmers remains voluntary. in 2013, 26% of the utilised agricultural area of eu countries was under aes (eurostat, 2023a). however, the environmental effects of land managers’ actions are not measurable from this figure, with several authors questioning the effectiveness of aes on biodiversity and/or other aspects of the environment (see, e.g., bartolini et al., 2021; batáry et al., 2015). the reward for the adoption of aes has traditionally been ‘action-oriented’ payments with remuneration being based on a set of prescribed actions rather than the outcome. this focus has been, in part, due to the requirement for compensation to reimburse land managers for the cost of adopting a particular https://console-project.eu/ 105 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 the use of innovative contracts to provide agri-environmental public goods agri-environmental practice (burton and schwarz, 2013). burton and schwarz (2013) provided examples of scenarios where action-based contracts have failed to provide environmental benefits such as the failure of actionoriented approaches to increase hornworts on the swiss plateau, as studied by bisang et al. (2009), and a decline in bird and butterfly numbers in switzerland, as noted by roth et al. (2008). however, action-based approaches continue to be the most common in eu aes despite their limited effectiveness (olivieri et al., 2021). their high acceptability by farmers is due to low risk, as their payments are not affected by external factors such as the weather. action-based contracts are also a suitable option when it is difficult to measure the results of an action, including when monitoring and environmental knowledge is not sufficient (olivieri et al., 2021). issues with the management of any contract can arise through asymmetric information with oliver et al. (2021) noting that it can reduce the provision of public goods through agriculture. this means that one party may have more information than the other(s) and they can use this to their advantage. this asymmetric information can result in contracts being negotiated that might not have been agreed if both parties were fully truthful. adverse selection may arise where land managers choose to include low quality land into aes because it is less productive in other uses (quillérou and fraser, 2010). cullen et al. (2018) noted that extensive farms have tended to enter action-based schemes because their compliance costs are generally lower than intensive farms. in addition, butler et al. (2013) suggested, when referring to the work of butler et al. (2010), that a possible focus of land managers on ease of management rather than ecological benefits can limit the success of action-based schemes. moral hazard emerges when one party acts differently, such as taking on additional risks, because they do not bear the full consequences of their actions. this can lead to free riding where, for example, land managers who receive rewards for collective action decide to rely on the positive actions of others rather than their own. these phenomena need to be considered when developing new, innovative contract types. four innovative contract types assessed by the console project are analysed and discussed in this paper (results-based, collective action, value chain and land tenure contracts). 1. as part of a results-based contract, farmers are paid based on the results of their actions. for example, if the intention of the contract is to improve water quality, such a contract may award payments based on the results of water testing completed at agreed dates throughout the lifetime of the contract. uthes and matzdorf (2013) highlighted that results-based contracts allow farmers to use local knowledge and environmental learning to ameliorate their farm’s results and, hence, they are more efficient than action-based contracts. however, as previously noted, unpredictable external factors such as weather may affect the results land managers achieve and this can discourage them from taking up these contracts. furthermore, it is sometimes difficult to develop and monitor measurable indicators of environmental improvement (olivieri et al., 2021). 2. collective contracts require land managers to become members of a group which applies jointly for compensation in order to implement environmental or climate activities (console, 2023). for example, if the intention is to improve biodiversity, payments may be awarded based on the count of farm birds in an area at specific times within the contract duration. the rewards would be shared amongst the participants in acknowledgement that the results were a collective achievement. prager (2015) explained that some approaches emphasise collaboration, which means that farmers work together. other approaches are based on co-ordination, which implies that farmers work towards the same goal, but in isolation (prager, 2015; reichenspurner et al., 2023). the netherlands is the only eu country to introduce collective aes on a national level. if a land manager wishes to participate in such a scheme, he/she must become a member of an agricultural collective, which is responsible for the contracting and the results measuring (barghusen et al., 2021). olivieri et al. (2021) noted that collective contracts could particularly benefit aecpg such as water quality, biodiversity, and landscapes, which require coordinated buy-in from all land managers in an area. in addition, olivieri et al. (2021) argue that these contracts involve a sharing of knowledge and risks, and issues of moral hazard may be low due to land managers wishing to maintain their reputation instead of free riding. however, it can be difficult to determine the optimum group size and costly to manage a large group (olivieri et al., 2021) with rodríguez-entrena et al. (2019) noting that collective participation leads to a higher degree of uncertainty among the farmers. similarly, villanueva et al. (2015) suggested that farmers’ utility from engagement in collective participation is negatively influenced by the anticipated loss of freedom of their farm management. 3. value chain contracts connect the delivery of aecpg with the production of private goods (console, 106 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 tracy bradfield et al. 2023). the cost of these actions may be paid for by the market, mainly through a premium price. for example, if dairy farmers agree to reduce their greenhouse gas emissions to a certain rate, the reduced emissions levels may be advertised on their bottles of milk and the customer pays a higher price for this guarantee. the contract may be between a land manager and a customer, or it may include many actors along the value chain, such as producers, processors, distributors, and consumers. for example, neumarkter lammsbrau is a german beer producer that engages in value chain contracts with its suppliers of organic raw materials. the agreements are centred around the protection of soil, water, and biodiversity (bredemeier et al., 2022). ireland currently has a quality assurance scheme where sustainable products bear the label ‘origin green’. however, the products that meet the criteria are not charged at a premium price, so the land managers are not reimbursed for their efforts to produce the product sustainably. 4. land tenure contracts mean that a landowner accepts a lower lease payment than for comparable land under usual land tenure agreements, to compensate land managers for their additional efforts to protect the environment (console, 2023). for example, a landowner may contractually require the tenant to comply with certain management requirements like reduced use of pesticides. in addition, long-term and secure contracts often lead to land investments, such as, soil conservation and tree planting which provide benefits for nature and human well-being (bredemeier et al., 2022; robinson et al., 2018). it is worth noting that olivieri et al. (2021) described the current literature on value chain and land tenure contracts as ‘poor’, fostering the need for research on these contract types. it should be highlighted that aes can consist of contracts that combine design and governance characteristics from more than one contract type (bredemeier et al., 2022). for example, aes that aim to enhance biodiversity might involve collective action and land tenure contracts. 2.2 agriculture in ireland and environmental concerns the importance of the livestock sector to agriculture in ireland is highlighted by the fact that, in 2020, 93% of farms were specialist livestock farms (dillon et al., 2022) compared to 22% in all eu countries (eurostat, 2022). 17% of irish farms were dairy farms compared to 5% of farms in the eu (eurostat, 2022). in 2020, the livestock density in ireland was 1.3 livestock units per hectare compared to an eu average of 0.7 (eurostat, 2023b). these relatively high livestock numbers generate environmental challenges, as agriculture produced 37% of greenhouse gas emissions in ireland in 2020 (decc, 2021). more than 80% of agriculture-related greenhouse gas emissions is directly linked to livestock numbers and the management of the manures they produce, while 12% is attributed to chemical fertilisers and the remaining 8% arises from fuel combustion and carbon dioxide from lime usage (decc, 2021). in response to these environmental challenges, the climate action plan 2021 commits to a 22-30% reduction in ireland’s agricultural emissions by 2030, based on 2018 figures (decc, 2021). despite these concerns, livestock numbers continue to rise in ireland with a 0.5% increase in cattle and a 6.4% increase in sheep between 2021 and 2022 alone (cso, 2023). this continued increase may suggest a hesitancy of land managers to reduce means of production in order to provide aecpg. this is supported by the work of cullen et al. (2021) which found that a €1,000 increase in farm income leads to the likelihood of the farmer being an aes participant falling by 1-2%. additional environmental concerns include water quality, with the environmental protection agency noting that agriculture substantially contributes to its decline (decc, 2021). almost one fifth of monitored river water bodies are of ‘poor’ or ‘bad’ status and are severely polluted (epa, 2022). in addition, 85% of habitats in ireland are classified as being of ‘unfavourable status’ and 39% are categorised as ‘bad’ (epa, 2023). as an eu member, ireland is subject to the core targets of the farm to fork strategy which are a 50% reduction in chemical or hazardous pesticide use, a 50% reduction in nutrient loss, and a 20% decrease in fertiliser use by 2030 (european commission, 2020). 2.3 agri-environmental projects in ireland irish aes have evolved since the introduction of the rural environment protection scheme (reps) in 1994. there were three subsequent iterations up to 2009 (mcgurk et al., 2020). farmers received the highest payments for the first 20 hectares, with different rates of declining payments for additional hectares across various iterations of the scheme (cullen et al., 2021). this led to farm size strongly influencing farmers’ decision to participate (hynes and garvey, 2009). the agri-environment options scheme (aeos) replaced reps in 2010. it differed in that the focus was on improving particular landscapes and habitat types (mcgurk et al., 2020; murphy et al., 2014) and scheme 107 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 the use of innovative contracts to provide agri-environmental public goods entry was prioritised for farms with certain features such as land designated as a special areas of conservation2 or special protection area3 (cullen et al., 2021). however, participation was lower than reps due to low payments (dafm, 2017; mcgurk et al., 2020). the green low-carbon scheme (glas) replaced aeos in 2015 and it involved the further targeting of funds to achieve greater scheme results (cullen et al., 2021). glas also had a greater focus on measures aimed at reducing carbon emissions from agriculture. entry into glas was by a three-tier system of priority which considered farms’ ‘priority environmental assets’. the highest tier included farms with natura 2000 sites, important farmland birds, rare breeds, commonages, and high-status waterbodies4. the second tier included those with vulnerable water areas and those choosing to undertake ‘priority actions’ which were low emission slurry spreading, minimum tillage, catch crops, and wild bird cover. the third tier applied to the remaining farms (cullen et al., 2021). all of these schemes have been terminated. a new agri-environmental climate measure called the agri-climate rural environment scheme (acres) was introduced in ireland in january 2023. its objective is to address biodiversity decline mainly in designated regions, while also serving as an income support. this scheme is funded by the irish government and the european agricultural fund for rural development of the eu, under ireland’s cap strategic plan 2023-27 (dafm, 2022). there are two entry points to the acres, with the acres general approach being available nationwide and offering a range of measures for individual land managers. the acres co-operation approach, by contrast, is available to land managers in defined high priority geographical areas where land managers receive resultsbased payments, and a level of co-operation is required amongst participants (dafm, 2022). eco-schemes, funded under pillar 1, are conceptually similar to the aes of cap pillar 2 and may contain the four innovative contract designs discussed in this study. however, land managers are legally entitled to eco-scheme payments, whereas a granting procedure is used to allocate aes payments. member states are free 2 the eu habitats directive lists certain habitats and species that must be protected within special areas of conservation. ‘irish habitats include raised bogs, blanket bogs, turloughs, sand dunes, machair (flat sandy plains on the north and west coasts), heaths, lakes, rivers, woodlands, estuaries and sea inlets’ (national parks and wildlife service, 2023). 3 sites of importance for the conservation or protection of a natural habitat or the population of a species. 4 coastal, transitional, river and lake water bodies that have a high-status objective under the eu water framework directive (environmental protection agency, 2020). to choose eco-scheme measures, as long as they respect the legal requirements in article 31 of the strategic plan regulation5 (runge et al., 2022). this means that their design may benefit greatly from national and local knowledge. in ireland, land managers qualify for ecoscheme payments by undertaking specific agricultural practices on their farms and they have the flexibility to opt in/out of such schemes and/or change the agricultural practices annually (dafm, 2023). 2.4 examples of recent voluntary aes in ireland aside from aes that have been directly designed and funded by the irish government or the eu and ecoschemes, some locally run agri-environmental projects have provided environmental benefits in recent years and, in many cases, they have included innovative contract designs. the burren programme, funded by the irish government, seeks to protect biodiversity in the burren in west ireland, which is an unesco geopark area of exposed limestone. participating land managers may enrol in results-based contracts and five-year environmental targets are agreed between land managers and farm advisors. payments are dependent on land managers implementing plans and performing according to an evidence-based scoring system. the benefits of this project are attributed to the fact that it is locally led, that there are high levels of local engagement and that farms’ assessment is based on scientific evidence (console, 2022a). the biodiversity regeneration in a dairying environment (bride) project, funded by the eu commission and the irish government, also uses results-based contracts and land managers agree to improve the quality of the habitats on their farms. similar to the burren programme, farms are assessed and those with higher scores on habitat quality gain higher payments. the project has benefitted from strong engagement from local land managers showing that the introduction of biodiversity measures contributes to tangible environmental, economic, and social benefits (console, 2022b). the results-based agri-environment payment scheme (rbaps) pilot in ireland, funded by the eu commission and the irish government, aimed to 5 this article establishes that all eu member states must define and provide support for voluntary schemes for the climate, the environment and animal welfare (the ‘eco-schemes’) under the certain conditions that are set out in this article and as further specified in the cap. participants are active farmers or groups of active farmers who make commitments to observe agricultural practices beneficial for the climate, the environment, animal welfare and combatting antimicrobial resistance. further information is available at: https://eur-lex.europa.eu/ legal-content/en/txt/?uri=celex%3a32021r2115 . https://eur-lex.europa.eu/legal-content/en/txt/?uri=celex%3a32021r2115 https://eur-lex.europa.eu/legal-content/en/txt/?uri=celex%3a32021r2115 108 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 tracy bradfield et al. improve biodiversity on 35 farms in an area of high nature value farmland by using results-based payments. small scale, low intensity beef cattle, and sheep farming were targeted (console, 2022c). rbaps was similar to the burren and bride projects in that it focused on improving biodiversity in a small area, which means that collective action is also at play within each project. the carbery greener dairy farms scheme is also results-driven. however, the reward for farmers are the savings they make rather than the receipt of payments. the project was introduced by carbery group6 and teagasc7 to measure, monitor, and optimise resources allocation with regard to environmental sustainability on dairy farms. the programme was based on a previous european project called the dairyman project, where 120 dairy land managers in 10 regions of northwest europe focused on farm resources efficiencies and management (console, 2022d). carbery was the first to start such an endeavour in ireland. various environmental efficiency measures were introduced on each farm to improve performance and achieve financial savings. the benefits from this project are improved carbon footprint of the farms, viability of farms through greater efficiencies, higher quality products, and an evolving ecological mind-set of land managers which spills-over into the wider community (console, 2022d). to improve environmental efficiencies, various capital expenditure was required, such as the introduction of smart meters, plate coolers in milking parlours, and water storage tanks. the funding was provided by carbery, state grants and, in some instances, the land managers themselves (console, 2022d). this is an example of land managers, industry, and the state working together to achieve environmental objectives. 2.5 factors influencing the perceived benefits of innovative agri-environmental contracts in ireland in the literature, the understanding, applicability, and perceived economic benefits of aes by land managers have been shown to influence their adoption. for example, wilson and hart (2000) found that non-familiarity with aes can increase the likelihood of farmers being unable to agree with their benefits. to overcome this, morris et al. (2000) argued that while mass media and generic literature are useful for increasing the under6 a global leader in food ingredients, flavours, and cheese. more information on this organisation is available at https://www.carbery. com/. 7 a state body which provides research, advisory, and training to the agricultural and food sector in ireland. more information on this organisation is available at https://www.teagasc.ie/. standing of aes, personal contact and demonstration are more important for the adoption of environmental measures. similarly, defrancesco et al. (2008) and dessart et al., (2019) found that the more a farmer perceives he/she can easily implement a practice associated with a given agri-environmental contract, the more likely he/she will participate in it. it is indeed crucial that land managers understand aes and the possible contracts that may exist within each scheme, so that they can perceive them as being applicable and beneficial to their land. cullen et al. (2020) studied aes adoption in ireland and they discussed a potential link between understanding and perceived economic benefits. for example, farmers who self-identify as ‘productivists’8 are more likely to participate in aes if there is a potential increase in the profitability of their farm. while monetary incentives already exist in aes, cullen et al. (2020) note that it is important that the added economic benefits of environmental measures are demonstrated to land managers to encourage their adoption. these measures may include optimising nutrient application, increasing pollinator numbers, and improved slurry management. promotion of these measures may also increase the participation of ‘forward-looking farmers’ who are seeking means to enhance the long-term performance of their farms. cullen et al. (2018) also note that the involvement of farmers in the designing of aes will help to ensure that they suit land managers’ interests and practices. kelemen et al. (2023) studied the same four innovative contracts as those outlined in our study. they asked stakeholders in fifteen countries to compare the innovative contract types with existing mainstream actionbased aes and they found that results-based contracts are perceived to require a ‘a broader knowledge base and a more developed infrastructure’ than mainstream aes and the other three innovative contract types. this further highlights the need for land managers to understand the nuances of contract types to increase their adoption. the findings of kelemen et al. (2023) question the applicability of collective contracts as they are considered to be the least suited to existing institutions, and the social and cultural context. one stated reason was the opinion that farmers only collaborate when there is a business interest and that collective contracts might require additional coordination and management. the authors also stress concerns over the perceived economic benefits of results-based contracts. they found that european stakeholders perceive results-based contracts 8 cullen et al. (2020) use the term ‘productivists’ to describe farmers who express a desire to produce more food and maximise income, even if the environment is harmed in the process. https://www.carbery.com/ https://www.carbery.com/ https://www.teagasc.ie/ 109 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 the use of innovative contracts to provide agri-environmental public goods to be ‘more costly to implement’ than mainstream aes and the three other innovative contract types. however, they do not study this on a per country basis. in the study by kelemen et al. (2023), the preferred policies stated by the stakeholders to improve the adoption of the four innovative contract types were formal education, peer-to-peer learning, and financial top-ups. education and learning would help understanding, while the top-ups would help to reduce financial uncertainty. the provision of top-ups allows farmers to retain their flat payment and lose only the top-up if environmental targets are not met (kelemen et al., 2023). 3. data and methodology 3.1 overview of data collection data were collected as part of the eu horizon 2020 funded console project (contract solutions for effective and lasting delivery of agri-environmental-climate public goods). surveys were conducted in twelve european countries (austria, bulgaria, finland, france, germany, ireland, italy, latvia, poland, spain, netherlands, and the united kingdom), with 2,275 land managers and 486 stakeholders surveyed between december 2020 and july 2021 based on non-probability sampling. the irish sample of respondents includes 210 land managers and 16 stakeholders (farm advisors, researchers, and industry experts). the survey questionnaires were designed by means of a common approach by the project partners, in english, and they were then translated to national languages (d’alberto et al., 2022). the questionnaires were disseminated by project partners directly, as well as by non-profit organizations, farmers unions, and local institution boards. the non-probability sampling is due to the fact that the questionnaires, conducted during the covid-19 pandemic restriction period, were distributed mainly via the console project’s website, through local institutions’ mailing lists, and local institutions’ official social media accounts. therefore, respondents were self-selected. 3.2 land manager surveys in ireland, the land manager surveys were distributed by an agency which provides administrative and technical advice to farmers and all surveys were completed online. the characteristics of the irish sample are outlined in table 1, as well as those of all twelve european countries studied. the data of the survey respondents can be compared to nationally representative data which is derived from the farm accountancy data network (fadn, 2021) and eurostat data9 (eurostat, 2016; eurostat, 2021a, eurostat, 2021b)10. farm accountancy data network (2021) data on farm types and data collected by eurostat (2021) on formal agricultural training can be directly compared with the sample of this study. however, eurostat (2021) collected data on farm holders’ ages under categories that differ from those used in our survey. we use data collected by eurostat (2016) on farm holders who describe their main economic activity as being derived from their farm as a proxy variable for farm income being more than 50% of total income. table 2 includes descriptive statistics related to the surveyed land managers’ experiences and opinions of innovative agri-environmental contract designs. as noted in table 2, 30% of surveyed land managers in ireland are currently using results-based contracts, 17% are using collective action, 16% are using value chain, and 3% are using land tenure contracts. between 1 and 5 years is the most preferred contract duration. to the best of the authors’ knowledge, data on the current use of innovative agri-environmental contracts are not currently collected on a level that is representative of the european union or its member states. respondents were asked to select a scoring option on a 5 points likert scale, expressing whether a characteristic of a potential agri-environmental contract would increase their willingness to enrol in such contracts. the options were: increases willingness considerably, somewhat increases willingness, no effect, somewhat decreases willingness and decreases willingness considerably. these characteristics of potential contracts are listed in table 3. then, descriptions of results-based, collective action, value chain, and land tenure contracts (as described in section 2.1) were provided to participants. they were asked whether they strongly agree, agree, are neutral, disagree or strongly disagree with the following statements: 1) ‘the contract type is easy to understand’; 2) ‘the contract type is applicable to my farm’, 3) ‘the contract type is economically beneficial for my farm’. 3.3 stakeholder survey each project partner selected local stakeholders to complete surveys and attend workshops at a local level. 9 data on farm holders’ ages and formal agricultural training in the united kingdom are not reported by eurostat and, therefore, are not included in table 1. 10 this datasets do not include forests. 110 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 tracy bradfield et al. of the total sample, 61% of participants were scientists, 19% represented environmental ngos or advisory services, 10% worked in administration, 7% were farmers, foresters or landowners, and 3% worked in industry. the aim of the workshops was to discuss and select the most promising examples of existing contract solutions among those retrieved by the common literature review. the reasons for the failure or success of these contracts were identified and discussed too. in october 2020, a pan-european web-seminar with 105 participants (excluding the organisers and panellists) was held online (for further details, please refer to viaggi et al., 2020). stakeholders from the local level workshops were called to discuss, together, the results from each country. in addition, stakeholders were asked whether they strongly agree, agree, are neutral, disagree or strongly disagree with the statement that the features of contracts outlined in table 4 would increase the willingness of land managers to enrol in an agri-environmental contract. in addition, stakeholders were asked the following question: ‘in your opinion, for which environmental objective provision would the four contract types be the most suitable? choose only one environmental objective for each contract type.’ the options were landscape and scenery: biodiversity, soil health and quality, carbon storage, and water quality and quantity. table 1. descriptive statistics of surveyed land managers. variable frequency ireland all surveyed countries sample (n=210) nationally representative data sample (n=2,7211) representative data farm type dairy 0.55 0.17 0.14 0.11 cereals 0.01 0.03 0.21 0.19 field crops/permanent crops 0.13 0.16 mixed systems 0.01 0.02 0.11 0.10 cattle 0.21 0.60 0.08 0.10 sheep/goats/poultry 0.17 0.03 0.08 mixed livestock 0.21 0.01 0.02 0.01 forestry 0.01 0.16 fruit/vineyards/horticulture 0.10 0.25 other 0.02 agriculture/forestry education completed 0.82 0.54 0.60 0.65 age 18-30 years 0.06 0.07 31-50 years 0.53 0.42 51-70 years 0.39 0.44 71 years and older 0.02 0.07 < 25 years 0.01 0.01 25-29 years 0.08 0.08 30-34 years 0.07 0.08 35-44 years 0.10 0.11 45-54 years 0.27 0.30 55-64 years 0.24 0.29 65 years and older 0.23 0.13 farm to total income ratio farm income > 50% total income 0.66 0.52 0.88 0.75 1 2,721 valid land manager surveys were considered from the 2,275 units initially retrieved. 111 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 the use of innovative contracts to provide agri-environmental public goods 4. results and discussion 4.1 land managers’ attitudes to innovative contract designs figures 1 details the percentage of land managers who agreed that a characteristic of a contract would increase their willingness to enrol in a hypothetical agrienvironmental contract/programme. the data for ireland is labelled ‘irl’ and the data for the other eleven european countries is noted as ‘others.’ figure 1 suggests that self-chosen measures increase the willingness of most surveyed land managers to enrol in novel aes in ireland and other european countries. this supports the work of the eu in ensuring that each member state develops its own national cap strategic plan in consultation with land managers and other stakeholders. more specifically, it is important that land managers have autonomy over how they manage their land to achieve environmental benefits and the design of aes should allow for this. when the means of the responses11 are calculated for each contract characteristic, the results for ireland and the other countries are very similar. it is important to highlight that common payments are not desired by many respondents in all countries, which plays as a major obstacle for the implementation of collective contracts. a reluctance to share a common payment may be due to increased uncertainty (rodríguez-entrena et al., 2019) or a fear that they will lose autonomy by working collectively (villanueva et al., 2015). they may also fear that others will act as free riders, benefitting from the group’s actions without contributing themselves, which would contradict the perception of olivieri et al. (2021) that the desire of group members to maintain their reputation would reduce the risk of moral hazard. our finding supports the work of kelemen et al. (2023) which noted that collective arrangements are not considered to be the suited to existing institutions, and the social and cultural contexts. as previously mentioned, descriptions of resultsbased, collective action, value chain, and land tenure contracts were then provided to participants who were asked to rate their level of agreement with the understandability, applicability, and economic benefits of these contracts. figure 2 details the percentage of surveyed land managers who agreed that a contract type is easy to understand/applicable/economically beneficial. figure 2 shows that results-based contracts are considered understandable, applicable, and economically beneficial by more land managers in ireland than other european countries. of the four innovative contract 11 based on the points of the likert scale ranging from 1 (decreases willingness considerably) to 5 (increases willingness considerably). table 2. descriptive statistics of surveyed land managers’ experiences. variable frequency ireland (n=210) all surveyed countries (n=2,721) experience of results-based contracts currently using results-based contract 0.30 0.21 never used results-based contract 0.66 0.73 previously used results-based contract 0.04 0.06 experience of collective action contracts currently using collective contract 0.17 0.06 never used collective contract 0.81 0.91 previously used collective contract 0.02 0.03 experience of value chain contracts currently using value chain contract 0.16 0.16 never used value chain contract 0.82 0.81 previously used value chain contract 0.02 0.03 experience of tenure based contracts currently using tenure contract 0.03 0.07 never used tenure contract 0.96 0.90 previously used tenure contract 0.01 0.03 preferred contract length1 < 1 year 0.05 0.23 between 1 and 5 years 0.72 0.66 > 5 up to 10 years 0.23 0.11 1 respondents answered this question regardless of whether they have used entered a contract before or not. table 3. contract characteristics evaluated by land managers. contract characteristic definition self-chosen measures in the contract, the land manager is free to decide about the management practices used to achieve the specified environmental result. common payment a group of land managers receive a common payment and they jointly agree on the distribution of the payment. paid by customers the contract is not paid by public money, instead the compensation that a land manager gets for environmentally friendly production is paid by buyers of products. reduced rent land managers pay reduced rent on land rented in if they agree to follow environmental management clauses as specified in the lease contract. 112 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 tracy bradfield et al. types, the understandability of results-based contracts in ireland scores the highest, at a mean of 4.012, while the 12 based on the points of the likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). mean score of this characteristic for all other countries is 3.8. in relation to results-based contracts, the greatest difference in means between ireland and the other european countries is related to the applicability of results-based contracts. the mean score in ireland is 4.1 compared to table 4. contract characteristics evaluated by stakeholders. contract characteristic definition annual compensation land managers receive compensation payment on an annual basis. authority control the results that land managers achieve are regularly controlled by the competent authority visiting a farm e.g. once a year. self-chosen measures in the contract, the land manager is free to decide about the management practices used to achieve the specified environmental result. better results, higher payment the better the environmental result, the higher the payment. collective agreement land managers can collectively agree on environmental targets and measures at landscape-level together with other land managers. common payment a group of land managers receive a common payment and they jointly agree on the distribution of the payment. free training land managers are offered free training and advice that enables them to reach the environmental targets. labelled product land managers sell their products labelled as environmentally friendly (e.g. climate friendly products) when following management measures as prescribed in a processor or retailer contract. paid by customers the contract is not paid by public money, instead the compensation that a land manager gets for environmentally friendly production is paid by buyers of products. reduced rent land managers pay reduced rent on land rented if they agree to follow environmental management clauses as specified in the lease contract. sales guarantee land managers receive a sales guarantee from a processor or retailer in return for implementing environmental measures. self-monitoring land managers do the monitoring of the environmental results themselves (e.g. count specific plants). periodical payment land managers receive half of the environmental payment at the beginning of the five-year contract period, and half at the end of it. figure 1. the impact of contract characteristics on willingness to enrol in agri-environmental contracts. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% common payment others common payment irl paid by customers others paid by customers irl reduced rent others reduced rent irl self-chosen measures others self-chosen measures irl percentage of respondents increases willingness considerably somewhat increases willingness no effect on willingness somewhat decreases willingness decreases willingness considerably 113 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 the use of innovative contracts to provide agri-environmental public goods 3.6 in other countries. this finding is plausible considering that they are the most common of the four contract types in the sample for ireland. they are also perceived by many to be economically beneficial which follows the connection between familiarity and perceived benefits previously highlighted by wilson and hart (2000). given their relative popularity in ireland13, it is possible that some farmers with no first-hand experience of resultsbased contracts may have gained some insights from those who have personal experience of them. compared to other european countries, agreement with the understandability and applicability of valuechain and land tenure contract types in ireland is relatively low. this is an important finding because, as previously noted, the more a farmer perceives that he/she can easily implement an element of a given agri-environmental contract, the more likely he/she will participate in it (defrancesco et al., 2008; dessart et al., 2019). therefore, our findings call for greater education of these contracts in ireland to increase their adoption. approximately one half of surveyed irish land managers consider value chain contracts to be economically beneficial and, despite their rarity, this suggests there is some interest amongst land managers in ireland to enter this type of contract. as land rental levels in ireland are the second lowest in europe, after portugal, (european commission, 2022), it may be difficult for some irish land managers to imagine that a land tenure contract would be suitable 13 34% of irish farmers in this sample have experience of using these contracts compared to 27% in all twelve surveyed countries. for them. land is also typically rented on eleven-month contract agreements in ireland and the irish government is already encouraging the renting out of land on long term leases through tax incentives (bradfield et al., 2023a). longer contract durations may encourage landowners to include environmental management conditions in their contracts, as the added time may allow them to reap greater benefits from the tenants’ practices. collective contracts are considered to be understandable, applicable, and economically beneficial by the lowest percent of irish and other european land managers. a lack of understanding may be driving the other two factors to be low. it may also be the case that land managers enjoy the autonomy of managing their own farm and do not wish to be contractually linked to other land managers which is supported by respondents being in favour of self-chosen measures (figure 1). previous work by raina et al. (2021) also supports this conclusion as they noted that some studies have found that farmers prefer individual management and discrete compensation. another example is offered by rodríguez-entrena et al. (2019) who stated that collective participation leads to a higher degree of uncertainty among the farmers. farming already bears considerable risk, whether it be financial risk, unpredictable weather or susceptibility to international economic shocks. consequently, land managers may be reluctant to bear more uncertainty through collaborative work (rodríguez-entrena et al., 2019). in summary, of the four innovative contract types presented in this paper, results-based are considered to figure 2. the percentage of land managers who (strongly) agree that a contract type is easy to understand/applicable/economically beneficial. 0% 10% 20% 30% 40% 50% 60% 70% 80% r es ul t-b as ed c ol le ct iv e v al ue c ha in la nd te nu re r es ul t-b as ed c ol le ct iv e v al ue c ha in la nd te nu re r es ul t-b as ed c ol le ct iv e v al ue c ha in la nd te nu re understandable applicable economically beneficial pe rc en ta ge o f r es po nd en ts ireland other countries 114 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 tracy bradfield et al. be understandable, applicable, and economically beneficial by approximately 70% of surveyed land managers in ireland which is higher than in other european countries. this high percentage may be driven by the existing familiarity with this contract type and the fact that they allow for autonomy over both work and its potential results. compared to data from other european countries, the understanding of value chain, collective action and land tenure-based contracts is low in ireland, and further research may explore the reasons why this is the case. 4.2 stakeholders’ attitudes to innovative contract designs a previously mentioned, stakeholders were asked whether they agree that a characteristic increases land managers’ willingness to enrol in an environmental contract/programme and their responses to this question are presented in figure 3. ‘annual compensation’ and ‘sales guarantee’, which both provide financial certainty, were considered by most stakeholders to be particularly important to land managers in both ireland and across europe. a reward system of better results generating higher payments (‘better results, higher payment’) also scored highly. this is also a contract characteristic that supports the desire for financial certainty, as well as environmental benefits. free training is also considered to be important for the uptake of such contracts. when compared to other european countries, land managers in ireland tend to be less in favour of collective action or authority control. this supports our conclusion in section 4.1 that there is a strong desire for autonomy amongst land managers. this may be related to the memory of the fight for independence in the early 1900s to remove authoritative control from english landlords (bradfield et al., 2023b). in ireland, a lower percentage of respondents perceive that the self-monitoring of environmental results encourages enrolment, when compared to other european countries. it may be the case that authority control is thought to be undesirable by land managers in ireland, because it reduces autonomy over land management practices, but the monitoring of environmental outcomes by external agencies is accepted. keleman et al. (2023) highlight that the monitoring of results poses a challenge for innovative contracts with the definition of indicators, use of information technology and farmers’ 0% 20% 40% 60% 80% 100% common payment periodical payment reduced rent paid by customers authority control self-monitoring collective agreement labelled product self-chosen measures free training better results, higher payment sales guarantee annual compensation percentage of respondents ireland other countries figure 3. the percentage of stakeholders who agree that a characteristic increases land managers’ willingness to enrol in an environmental contract/programme. 115 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 the use of innovative contracts to provide agri-environmental public goods expertise being some examples. these may be reasons why farmers are hesitate to self-monitor their actions. all countries perceive periodical payments14 or common payments to be the least likely to increase enrolment which further supports a desire for control over incomes. as the aim of aes and agri-environmental contracts is to improve environmental outcomes, we wish to discover which agri-environmental goods are considered to benefit most from a particular contract type. therefore, stakeholders were asked the following question: ‘in your opinion, for which environmental objective provision would the four contract types be the most suitable? choose only one environmental objective for each contract type.’ figure 4 displays the percentage of respondents who stated that a particular agri-environmental objective would benefit the most from a specific contract type. data for ireland is labelled ‘irl’ and the data for the other eleven european countries is noted as ‘others’. in ireland, most surveyed stakeholders believe that biodiversity would benefit the most from results-based contracts. as previously shown in the description of aes case studies, results-based contracts in ireland have so far mainly targeted improved biodiversity, which may explain why stakeholders in ireland see a link between 14 stated as follows: ‘land managers receive half of the environmental payment at the beginning of the five-year contract period and half at the end of the contract’. these contracts and biodiversity gains. we find that collective action is perceived as the most beneficial for improving water quality amongst respondents in ireland. however, it is the innovative contract type that scores the lowest in terms of understandability, applicability, and perceived economic benefit, and further education may be needed to highlight its potential environmental benefits. a high portion of surveyed stakeholders in ireland feel that value chain contracts are best suited to support carbon storage. fewer of the surveyed stakeholders in other european countries believe that individual contract types can benefit one particular aecpg. this suggests that they perceive the contracts as having a wider range of environmental benefits which is appropriate given that, for example, collective contracts can benefit aecpg such as water quality, biodiversity, and landscapes across large regions (prager, 2015; olivieri et al., 2021). a limitation of this study is a lack of representativeness of the farming population, given the fact that the survey has been carried out on a non-probability sample. however, the large spectrum of respondents questioned, both in ireland and at the european level, support the conclusion that our results remain informative, and the common european survey perspective adds relevance to the comparisons made. further research could include analysis of the factors that cause land managers and stakeholders to either agree or disagree that a contract 0% 20% 40% 60% 80% 100% 120% land tenure others land tenure irl value chain others value chain irl collective others collective irl result-based others result-based irl percentage of respondents landscape and scenery biodiversity soil health and quality carbon storage water quality and quantity figure 4. perceived suitability of contract type for environmental objective (ireland). also available in d’alberto et al. (2022). 116 bio-based and applied economics 13(1): 103-120, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14444 tracy bradfield et al. type is understandable, applicable, and economically beneficial. additionally, more complex experimental research, such as discrete choice experiments, could be utilized to determine the extent to which land managers prefer some characteristics of agri-environmental contracts over others. 5. conclusions understanding the factors influencing farmer decision-making is important for policymakers in their design and promotion of agri-environmental schemes. existing evidence suggests that action-based contracts have not maximized environmental benefits (burton and schwarz, 2013) and, as an alternative, four innovative contract types have been studied by the eu funded console project. this research highlights the perceptions of land managers and stakeholders in terms of the understandability, applicability, perceived economic benefits, and characteristics of such contract forms, which have not been previously studied in the context of ireland despite the challenges the agriculture sector faces in becoming more environmentally sustainable. this research fills a gap in the literature as discussion of these contracts has been limited to date (bredemeier et al., 2022) and it is important that we understand how attitudes differ in ireland compared to other european countries so that further research and the cap can be tailored to fit the local context. our findings show that the understanding of the four innovative contract types, as well as their applicability and economic benefits, could be greatly improved in european countries. this calls for greater promotion and education of these contracts to encourage their adoption. results-based contracts, which are the most common of the four innovative contract types in ireland, are considered understandable, applicable, and economically beneficial by the majority of irish land managers. this suggests that practical experience or the hearing of other people’s experiences can boost understanding and this form of promotion should be encouraged. this is important for land managers in ireland who have relatively low levels of understanding of collective action, value chain, and land tenure contracts due to the fact that they may have had little direct experience of them. with respect to the need for increasing the adoption rate of agri-environmental contracts, self-chosen measures and financial certainty should be the priority, as advised by land managers and stakeholders across europe, and this is particularly the case in ireland. therefore, it is of importance that agri-environmental policies in ireland provide autonomy for farmers. these findings also help to explain why collective contracts are not considered to be economically beneficial by many land managers in this study, as they potentially expose land managers to uncertainty (rodríguez-entrena et al., 2019). training and guidance from expert stakeholders are also considered important for the uptake of such contracts, supporting our conclusion that education about innovative contracts is fundamental. references bartolini, f., vergamini, d., longhitano, d., povellato, a., 2021. do differential payments for agri-environment schemes affect the environmental benefits? a case study in the north-eastern italy. land use policy 107, 104862. https://doi.org/10.1016/j.landusepol.2020.104862 barghusen, r., sattler, c., deijl, l., weebers, c. and matzdorf, b., 2021. motivations of farmers to participate in collective agri-environmental schemes: the case of dutch agricultural collectives, ecosystems and people, 17(1), 539–555. https://doi.org/10.1080/26395 916.2021.1979098. batáry, p., dicks, l.v., kleijn, d., sutherland, w.j., 2015. the role of agri‐environment schemes in conservation and environmental management. conservation biology 29, 1006–1016. https://doi.org/10.1111/ cobi.12536 biodiversity information system for europe, 2022. ireland. available at: https://biodiversity.europa.eu/ countries/ireland”https://biodiversity.europa.eu/countries/ireland. 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(2025). intention to use ai-based camera systems in german pig farming: an extended technology acceptance model, just accepted. doi: 10.36253/bae17220 abstract this study explores the factors influencing german pig farmers' intention to use (itu) ai-based camera systems in livestock farming. this research utilized an extended technology acceptance model. data from 185 farmers were analyzed through structural equation modeling, revealing that ease of use (β=0.276), innovation tolerance (β=0.398) and personal innovativeness (β=0.101) notably impact itu. concerns over data ownership and transparency showed limited effects, and perceived job relevance (β=0.355) enhanced acceptance. expected transparency of ai camera systems had strong influence on perceived ease of use (β=0.419). a gradual integration of the factors showed that perceived usefulness has a strong influence on itu but is superimposed by the factor job relevance in the modelling process. with an mailto:alexander.kuehnemund@hs-osnabrueck.de mailto:alexander.kuehnemund@hs-osnabrueck.de r2 of 0.749, the model has high explanatory and predictive power. these insights underscore the importance of user-centric design and transparency in ai technology deployment in agriculture. although the itu ai camera systems in pig farming depends on its ease of use and transparency, it also depends on the personal characteristics. 1. introduction pig farmers face major challenges in the production and processing of animals. on the one hand, legal requirements for animal health and animal protection in germany increased (german federal ministry of food and agriculture, 2024), e.g. ban on tail docking and requirements for defined husbandry types. on the other hand, pig farmers are faced with societal demands for production like animal rights values (albernazgonçalves et al., 2021). for this reason, the integration of artificial intelligence (ai) into the processes associated with pig farming is needed to improve modern agriculture. therefore, an increasing number of animal behavior monitoring technologies have been developed over the last decade. many of these solutions focus on the combination of visual recordings and artificial intelligence interpretation. in pig farming, these innovations range from live weight detection (wongsriworaphon et al., 2015) and growth (condotta et al., 2018) to behavioral detection (nasirahmadi et al., 2019) and early disease detection (fernández-carrión et al., 2020). as a result, ai technologies can not only increase productivity but also improve overall animal welfare through early disease detection and prevention. however, the adoption of ai systems and the use of intelligent systems in animal husbandry are less common than that of other technologies on farms in germany (rohleder et al., 2020). the aim of our study is to investigate the factors that determine the intention to use ai camera systems in pig farming. in the context of livestock farming, cluster analyses have identified heterogeneity in attitudes toward the agricultural technologies used (schukat & heise, 2021). in addition, various studies on the intention to use (itu) farming technologies have reached different conclusions. michels, bonke, et al. (2020) investigated factors that influence farmers’ use of smartphone apps for crop protection. their analyses revealed that performance expectancy and social norms were among the determining factors for the itu. in contrast, mohr and kühl (2021) investigated the acceptance of ai technologies in agriculture in general and reported that previous factors have no influence on the intention to use them. in their study, for example, the perceived ease of use and the expectation of property rights over business data were decisive factors influencing the intention to use. this finding indicates the importance of analyzing the factors that determine the intended use of specific technologies and target groups. an established method for analyzing the usage intentions of potential target groups is the technology acceptance model (tam) from davis (1985). the tam and various extensions, as well as models based on the original model, are precise means of determining the factors influencing the intention to use and predicting possible utilization (davis & granić, 2024). the model has also been applied to agricultural technologies in different studies (alambaigi & ahangari, 2016; mohr & kühl, 2021; thomas et al., 2023). besides intentional models using the tam there are different other models used in the case of agricultural technologies. for example, the theory of planned behavior (tpb) (ajzen, 1991) have often been used in the context of the implementation of new technologies in the rural economy. sok et al. (2021) identified several articles in the field of animal husbandry that successfully applied to the tpb. in german agriculture this method was applied in study investigates the adoption of mixed cropping (michels, bonke, et al., 2020). in addition, a small number of researchers have examined technologies in agriculture from the perspective of stage-based models (block et al., 2023; lemken et al., 2017), such as the transtheoretical model of behavioral change (ttmc) (prochaska & velicer, 1997). this concept can be used to predict behavioral change and has its origins in the health sciences. applying the model to adaptation is difficult at this stage because similar technologies are not yet available, or are limited, and understanding of the potential benefits can be very narrow. despite the variety of approaches aiming to understand the use intentions of potential target groups, a tambased study is an appropriate choice, especially for technologies in the early stages of development and with low market penetration (davis & granić, 2024). findings from tam and new extensions provide valuable insights for potential technology users and help developers and policymakers set the right course for the adaptation of useful technologies. the differentiation of the technology in question, especially in the field of ai, is necessary to define the research object and draw specific conclusions. in general, ai can be difficult to grasp with respect to the selected target group and application, as there are different perceptions of what ai is and can do. it is therefore useful to design research on the acceptance of technologies according to the object of investigation. another reason to analyze this special issue related to ai technology is that both camera systems and ai that use image data are sensitive cases for potential users (saheb, 2023). since ai-based camera systems are relatively new and the use of this technology in the context of german livestock farming is low, this study on intention to use is essentially a theoretical ex ante model (pierpaoli et al., 2013). against this background, this study analyzes the influence of theoretically derived factors on the utilization intentions of german pig farmers. in addition, the research should help technology developers to adapt their systems to enable better market integration. insights into the relevant characteristics that influence adoption intentions can help to inform farmers about ai camera systems in a targeted way. the findings should also serve to identify potential barriers to adaptation and provide an opportunity for developers and policy makers to take these into account. we use an extended technology acceptance model, which is explained and justified in more detail in the methods section. 2. theoretical framework this investigation uses the tam to analyze the potential adoption behavior of german pig farmers and to explain the intention to use this technology in terms of acceptance (useche et al., 2013). in the context of the technology and the potential users (farmers), we expand this model to the context of pig farmers and the usage of ai camera surveillance, as shown in the following chapter the tam is based on two factors, perceived usefulness (pu) and perceived ease of use (peou), which are decisive for the possible acceptance of new technologies by potential users (davis, 1989). the pu indicates the degree to which a system improves work performance and, according to its founder davis, is a strong influencing factor on the use of technology (davis, 1989). the peou indicates how difficult or simple potential users consider learning and using a system or technology to be (davis, 1989). in the original model, the two factors act as explanatory and predictive variables for the intention to use a new technology. the model in our analysis showed a lack of explanatory power which substantiated the contextual extension. figure 1 illustrates the original tam framework. figure 1: results of the basic technology acceptance model based on (davis, 1989) 2.1. contextual model extension in addition to the pu and the peou, many other factors affect users’ intention to use new technologies (pierpaoli et al., 2013). with the aim of identifying these factors, various extensions of the tam have been made over time and embedded in other concepts to generate independent models that explain the intention to use technologies (davis & granić, 2024). in a systematic overview, granić (2024) presented a total of 17 different models that analyze technology adoption at the individual level. these include, for example, the extended unified theory of acceptance and use of technology (utaut) (venkatesh et al., 2012) and innovation diffusion theory (idt) (rogers, 1975). this resulted in a wide range of possible predictors for the intention to use technologies, whereby different aspects can be categorized in relation to the users, technology, tasks and social factors (davis & granić, 2024). instead of applying one of the existing models to ai-based camera systems, it appears that the special nature of the technology and the task, as well as the users, make an extension necessary that considers these special aspects. in the present research, the combination of surveillance technology and the use of ai, in particular, plays a decisive role in this type of expansion. a literature search in the scopus and springerlink databases during the conception phase of the study led to the factors explained below and, finally, to our extended tam. as part of the modeling process, we assigned the individual constructs to the categories of farmer aspects, technological aspects and social aspects. farmers’ aspects innovation tolerance (it) is a combination of risk attitudes and the expectation of future relevance from the user's perspective. these factors can be well integrated into a behavioral model such as the tam (montes de oca munguia et al., 2021). it is known from the literature that risk aversion has a negative effect on technology adoption (abadi ghadim & pannell, 1997). conversely, seibert et al. (2021) showed in their systematic literature review the positive effect of the willingness to take risks on the intention to use new technologies. a decision under uncertainty involves, in the context of technology adoption, the derivation of the value of the technology in the future. innovators recognize the value of the technology and the future benefits that its use and rapid adaptation offer. they are convinced that utilization will be important in the future to benefit from adoption (rogers, 2003). those who see high potential in new technologies for the future are prepared to use the technology now. this study assumes that the combination of self-perceived risk behavior and the assessment of the importance of using technology in the future is decisive for the intention to use it. personal innovativeness (pi) extends models of technology acceptance by considering individual perceptions and beliefs (agarwal & prasad, 1998). people are described as innovative when they adopt new innovations at an early stage (rogers & shiemaker, 1971). a study on precision agriculture technologies revealed significant correlations between technology acceptance and pi as well as a moderating effect on the itu by influencing the peou (aubert et al., 2012). in her study on the adoption of virtual reality simulations, fagan et al. (2012) reported a significant interaction between pi and peou. in the context of ai and agriculture, mohr and kühl (2021) showed the influence of the pi on the peou. job relevance (jr) describes the extent to which ai-based camera systems are relevant for daily tasks with animals from the user's perspective. farmers are more likely to use an information system if they perceive that the information it conveys is relevant to their job (venkatesh & davis, 2000). in the context of german livestock farmers, the pressure to use technologies to improve their jobs is a factor underlying the behavioral acceptance of farmers. in addition to the direct influence of jr on the intention to use new technologies, (agricultural) studies have highlighted the significant effect of this variable on pu (marangunić & granić, 2015; michels et al., 2021). technological aspects the expectation of property rights (prs) over business data plays an important role in the development of digitalized livestock farming. pr, particularly in the context of ai systems and camera technology, is unclear from a legal perspective (härtel, 2020). the acceptance of ai-based camera systems is linked to the expectation of ownership and the legal certainty of the data created and used in this context (härtel, 2020). another point pertains to the need for ai systems for data-driven learning; for example, camera systems require video and images. currently, it remains unclear who owns the original data and the data processed by the ai system. in relation to the cultural context, german individuals are critical of issues related to data security, especially with regard to the use of surveillance technology (kostka et al., 2021; van heek et al., 2017). a farmer who expects to own the data is assumed to be less willing to use an ai-based camera system. the perceived risk of data abuse (ri) is a crucial factor for the intention to use new ai technologies. the use of ai and camera technology indicates a type of surveillance. fundamental changes in the work environment and people’s trust in ai often lead to irrational worries in german society even at the individual level—a phenomenon that has been called “german angst” (nickl, 2014). in their study, beaudry and pinsonneault (2010) reported that emotions such as anxiety have negative effects on the intention to use and pu of technology. in terms of surveillance characteristics, the ri has an impact on itu camera technology (krempel & beyerer, 2014). with respect to the combination of ai and surveillance technology (park & jones-jang, 2022), acceptance and even pu and peou can be negatively influenced. in terms of the adoption of ai technologies in a professional context, dumbach et al. (2021) identified data protection as the most challenging barrier with respect to ai technology. with respect to surveillance systems, the expected data transparency (tr) of the processed data and the operation of the system itself are important factors in the acceptance of camera technology (krempel & beyerer, 2014). it is difficult or even impossible to understand all aspects of ai systems, even when they are fully transparent. this situation represents a black box that may hinder the development of trust (dam et al., 2018). however, transparency is a major driver of trust, which determines people’s willingness to accept strategic uncertainty (poursabzi-sangdeh et al., 2018; schmidt et al., 2020; zhao et al., 2019). a study by wanner et al. (2022) concluded that transparency on ai-based camera systems affects the pu and peou (wanner et al., 2022). a transparent system is easier to understand; thus, the peou and pu increase because people have more knowledge about the system. social aspects perceived social norm (ps) is based on perceived social pressures, personal feelings of moral obligation and the responsibility to engage in or refuse to engage in a specific behavior (gorsuch & ortberg, 1983). the expectations of behavior created by social pressure influence the intention and actual decision to behave in a certain way (ajzen, 1991). german consumers assess their knowledge about agriculture as rather low (heinke et al., 2017). however, even without sufficient knowledge, many consumers have a critical view of livestock production (heinke et al., 2017). in the past, technological development in agriculture has been viewed critically by the population (gupta et al., 2012; pfeiffer et al., 2021). with respect to animal production, the public opinion of technological development has been accompanied by a negative comparison with natural outdoor husbandry (cardoso et al., 2016; weinrich et al., 2014). the expected view of society for ai-based camera systems therefore seems relevant, as tasks are transferred from farmers and the process of animal husbandry is autonomized. however, meat consumers have expressed a preference for innovation as a solution to potential problems in animal husbandry (schulze et al., 2023). these findings highlight the ambivalent attitudes of the public. table 1 summarizes the factors included in our extended tam. table 1: extended tam constructs category factor source farmers aspects innovation tolerance (it) own creation based on (rogers, 2003; seibert et al., 2021) personal innovativeness (pi) (agarwal & prasad, 1998; aubert et al., 2012; mohr & kühl, 2021) job relevance (jr) (rose et al., 2016; venkatesh & davis, 2000) technological aspects expectancy of property rights over business data (pr) own creation based on (tiwari & tiwari, 2020; van heek et al., 2017) perceived risk of data abuse (ri) (krempel & beyerer, 2014) expected data transparency (tr) (krempel & beyerer, 2014; wanner et al., 2022) social aspects perceived social norm (ps) (ajzen, 1991; gorsuch & ortberg, 1983; heinke et al., 2017; mohr & kühl, 2021; schulze et al., 2023) after the potential explanatory factors were identified, the individual structures were hypothesized in the structural model. appendix 1 shows the list of individual hypotheses. figure 2 shows the hypothesized effect of each factor on the intention of potential users to adopt the technology. figure 2 expanded tam based on davis (1989) 3. study region, data collection and sampling the target population of our investigation was pig farmers in germany, who are decision-makers on their farms. the questionnaire was distributed through an agricultural panel to recruit participants from all federal states of germany. the members of the panel were recruited throughout germany via deutscher landwirtschaftsverlag, a specialized publishing house for agricultural media, which provides panels for various target groups in the german-speaking area.this approach also ensured that farmers who were not involved in the pig industry were not included in the data collection. the survey was conducted online between january and march 2023. the recruitment resulted in a total sample of 185 participants. our sample can be considered a convenience sample, which is useful for studies with a pilot character, such as the present study on the ex-ante intention to use a technology (teddlie & yu, 2007). the participants were contacted via e-mail and initially informed about the study project. before beginning the questionnaire, the participants provided informed consent to participate in the study. the questionnaire (appendix 2) was divided into different parts. the first part of the questionnaire collected sociodemographic and farm-related information. after the sociodemographic questions, the participants were presented with a description of the ai-based camera systems to provide them with a better understanding of the research object. this description was presented in text form. in the second part, farmers were asked to evaluate several statements pertaining to the extended tam. appendix 2 shows the different items, including the questions and descriptive statistics. the survey was administered in german, and the questions were translated into english for this manuscript; however, they were not adapted to the specific cultural context. to assess the statements, the questionnaire used a five-point likert scale ranging from 1 = do not agree to 5 = fully agree. the questionnaire was pretested by two researchers with different groups of farmers to ensure that all the questions could be understood and interpreted unilaterally. these pretests featured two groups of 15 participants. after the test, the participants were asked about their understanding of the survey and its logic, and adjustments were made if they did not understand the statements or the sociodemographic questions. in addition, the intelligibility of the description of the subject matter was assessed by the test group. 4. statistical analysis: structural equation modeling structural equation modeling (sem) is used to model and estimate the relationships among multiple independent and dependent variables concurrently (hair et al., 2021a). this method is particularly useful when the concepts under consideration are unobservable and are measured indirectly through multiple indicators. this research uses the latest approach developed by hair et al. (2021a) with the assistance of the r package seminr (hair et al., 2022). in sem, path models are used to represent the relationships among constructs or latent variables. latent variables cannot usually be measured directly and are therefore created by indicators or manifest variables. the path model visualizes the relationships among all the constructs and depicts the hypotheses that relate the variables via these paths (hair et al., 2021a). a partial least squares (pls) path model consists of two elements. the first element is the structural model, also known as the inner model, which links the constructs. the inner model also represents the hypothesized relationship between the constructs. second, the path model contains a measurement model or outer model. this model represents the relationships between the constructs and the individual indicators. figure 3: structural equation model (hair et al., 2022) figure 3 shows the exemplary inner and outer models for the latent jr in the context of this investigation. the inner model is shown in the center of the figure. the relationships among the elliptical constructs or latent variables are represented by the connecting arrows. the outer model on the left is a formatively measured construct captured by the indicators (jr1, jr2, and jr3). the outer model on the right shows a reflectively measured construct, in this case, the dependent variable itu. in addition to the indicators used to measure the construct, the error terms for the manifest variables are recorded. these error terms represent the unexplained variance when the path model is estimated. however, this description applies only to the manifest variables. in contrast, the formative variables, in which context the relationship leads from the indicator to the construct, have no error terms (sarstedt et al., 2016). minimum sample planning in general, pls-sem is applicable if the sample contains ten times as many participants as independent variables (thompson et al., 1995). however, concerns have been expressed about the simple application of this “ten times” rule in the case of complex structural models. an alternative procedure is represented by the inverse square root method (kock & hadaya, 2018), which is used to calculate the probability that the path coefficient and its standard error are greater than the critical value for a predetermined significance level (hair et al., 2021a). therefore, the minimum sample size (kock & hadaya, 2018) is obtained by the following equation, where pmin is the value of the path coefficient with the minimal magnitude in the pls path model. with a significance level of 5%, nmin > (2.486/pmin)2. since this method is only suitable for ex post analysis, pmin deviates from the value reported in previous studies featuring a similar number of independent variables (michels, fecke, et al., 2020; mohr & kühl, 2021). therefore, a pmin value of 0.185, which indicates a sample size of 180 respondents at a significance level of 0.05, was estimated in this study. statistical requirement verification the results of the pls-sem are evaluated via a two-step process. first, the outer models are analyzed before the structural model (inner model) is evaluated. the decision to measure constructs reflectively or formatively is based on their conceptual nature and causal relationships. reflective constructs (pu, itu, and pi) have highly intercorrelated indicators that reflect the underlying variable, with a focus on internal consistency. formative constructs (peou, jr, tr, ri, it, ps, and prs) are defined by unique, essential indicators that collectively form the construct. the removal of any indicator from formative constructs would significantly alter its meaning, ensuring that all critical dimensions are considered. the analysis of the reflective model reveals that the quality criteria of the indicators are satisfied. the indicator reliability (loadings ≥ 0.7), convergence validity (average variance extracted (ave) ≥ 0.5) and internal consistency (rhoa ≥ 0.6) are satisfactory (see appendix 3) and indicate that the variables of the constructs are appropriate for further analysis (hair et al., 2021b). in addition, the analysis of the heterotrait‒monotrait ratio shows that all values of the reflective factors are below the cutoff value (htmt < 0.9) and are therefore suitable for the analysis (hair et al., 2021b) (see appendix 4). the variance inflation factors (vifs) of the formative variables are less than five, indicating that no critical levels of multicollinearity are observed. the weights (≥ 0.1) and loadings (> 0.5) are satisfactory and significant (hair et al., 2021b) (see appendix 5). variables of the formative constructs that did not meet these values were excluded from further analysis. variables may be included in the analysis if they do not meet the above requirements in part, but the t-statistics indicate that they are significant. the variables listed in appendix 5 contribute to the determination of the formative constructs. explanatory power analysis the structural model represents the hypothesized relationships among different constructs. since the vif indicates a value lower than five, no multicollinearity exists with respect to the variables. some researchers have reported problems with multicollinearity with respect to values ranging between three and five (becker et al., 2015). this criterion is also satisfied for all but one variable, which slightly exceeds three. the model quality regarding multicollinearity is satisfactory. to determine the explanatory power of the model, the r2 of the endogenous constructs is examined (shmueli & koppius, 2011). to assess statistical significance, the bootstrapping approach with 10,000 subsamples was employed, as recommended by streukens and leroi-werelds (2016). the aim of pls-sem is to maximize the r2 value, and values of 0.75, 0.50 and 0.25 indicate substantial, moderate and low levels, respectively (hair et al., 2011). the r2 in our analysis is 0.749, which indicates high explanatory power with regard to the adoption of ai-based camera systems in animal agriculture. predictive power analysis with respect to the analysis of predictive power, however, r2 serves only conditionally (hair & sarstedt, 2021). the plspredict method (shmueli et al., 2016) was used to test the predictive power; accordingly, the model was divided into training samples and holdout samples to evaluate the predictive performance of the model (set.seed 123). the root-mean-square error (rmse) of each indicator of the dependent construct of the structural model was subsequently compared with the rmse of a naive linear regression model (lm) as a benchmark. one quality criterion is that all indicators should have a lower rmse in the structural model than in the lm, in which case the model is reliable and has high predictive power (shmueli et al., 2019). a majority or equal number of lower indicators have moderate predictive power, whereas a minority of lower indicators have weak predictive power (shmueli et al., 2019). the test in this analysis (appendix 6) indicates high predictive power with regard to the dependent indicator of the intention to use. figure 4 shows the full sem and the influence of the indicators after the prerequisite test. legend: variables that influence the object of investigation are shown in bold. figure 4: results of sem 5. results table 2 shows an overview of the descriptive statistics in comparison with the german average. in our sample, farms have a greater number of animals than the german average in each category. the majority of farmers are aged between 35 and 54 (53.1%) and are thus comparable with german farmers (federal ministry of food and agriculture, 2023). in terms of gender, the distribution of the sample is different from the average distribution among german farmers, with one-third of the farmers being female. for our sample, we targeted decision-makers on farms, such as owners or directors. the majority (>99%) of our sample identified themselves as decision-makers on their farms. in this context, the distribution of gender is representative with respect to decision-makers on farms (statistisches bundesamt, 2023). the participants are more highly educated and younger than the average farmer is. table 2: sample description n=185 german average % sex, n (%) female male other 17 (9.2) 166 (89.7) 2 (1.1) 11.25a 88.75a / age [years], mean (range) 43.5 (20-72) 53b vocational education, n (%) no formal agricultural degree vocational or technical school university degree 6.4 49.3 44.3 33.2a 57.5a 9.2a number of fattening pigs, mean (range) 1282.4 (0-8000) / number of sows, mean (range) 138.0 (0-3000) / number of rearing piglets, mean (range) 663.5 (0-16000) / number of acres [hectares], mean (range) 135.0 (0-5000) / a statistisches bundesamt (2023) b german farmers association (2022) the analysis shows that seven out of sixteen hypotheses are supported. we obtain empirical evidence for h2a (β =.276, f2 = .152), h3 (β =.398, f2 = .213), h6a (β = .101, f2 = .035), and h7a (β = .355, f2 = .116), indicating that these constructs are relevant antecedents for the intention to use ai-based camera systems in pig farming. the results for pu and peou support h6b (β = .237, f2 = .083), h7b (β =.833, f2 = 2.037), and h9c (β = .419, f2 = .187). table 3 shows the tested hypotheses, path coefficients, effect size f2 and t statistics of the model. the path coefficients indicate the direct relationships among the hypothesized constructs in sem and can be understood as standardized beta coefficients (hair et al., 2022). in general, the higher the path coefficient is, the greater the relevance of the relationship between the construct and the dependent variable. the analyses revealed that innovation tolerance has the greatest influence on the itu of all the integrated factors. the f² value in sem measures the effect size of an exogenous construct on the explained variance (r²) of an endogenous construct. table 3: results of sem (estimated path co and statistical evaluation measures) hypothesis path coefficient effect size f² 95%ci t-statistics ll ul h1 pu → itu 0.010 0.000 -0.146 0.123 0.138 h2a peou → itu 0.276 0.152 0.150 0.387 2.339 h2b peou → pu 0.08 0.016 -0.008 0.186 1.594 h3 it → itu 0.398 0.213 0.241 0.518 5.632 h4 ps→ itu -0.046 0.005 -0.160 0.035 -0.909 h5 pr → itu -0.110 0.032 -0.168 0.048 -2.078 h6a pi → itu 0.101 0.035 0.020 0.193 2.339 h6b pi → peou 0.237 0.083 0.077 0.404 2.781 h7a jr → itu 0.355 0.116 0.209 0.517 4.477 h7b jr → pu 0.833 2.037 0.756 0.889 24.563 h8a ri → itu 0.004 0.000 -0.074 0.080 0.105 h8b ri → pu 0.035 0.004 -0.036 0.106 0.977 h8c ri → peou -0.196 0.038 -0.381 0.003 -2.001 h9a tr → itu 0.008 0.000 -0.117 0.067 0.153 h9b tr → pu 0.017 0.001 -0.063 0.106 0.409 h9c tr → peou 0.419 0.187 0.233 0.595 4.432 legend: itu: intention to use; it: innovation tolerance; jr: job relevance; peou: perceived ease of use; pi: personal innovativeness; pr: property rights over business data; ps: perceived social norm; pu: perceived usefulness; ri: perceived risk of data abuse; tr: transparency in order to analyze the reliability of the model, a stepwise extension of the original model was performed. the extension showed that both the quality of the model and the influence of the variables changed as a result of the extension. the extension of the classical model showed that the additional factors increased the level of elucidation. the influence on the variance is mainly driven by the factors jr, it and pi. ri shows no additional explanatory contribution. other factors such as ps, pr and tr have a rather marginal explanatory power for itu. figure 5 shows the evolution of the variance explained (r2) by the gradual inclusion of the factors. legend: itu: intention to use; it: innovation tolerance; jr: job relevance; peou: perceived ease of use; pi: personal innovativeness; pr: property rights over business data; ps: perceived social norm; pu: perceived usefulness; ri: perceived risk of data abuse; tr: transparency figure 5: development of r2 across model extensions. the path coefficients were also analysed in the context of stepwise extension. jr, it and peou remain the most important influencing factors after the expansion. the change in the other path coefficients is marginal in the course of extension. an exception is pu, which is outweighed by jr after extension and loses importance as a result of further enlargements. table 4 shows the results in detail. table 4: development of path coefficients number models pu→ itu peou → itu jr→ itu pi → itu ri → itu it→ itu ps → itu pr → itu tr → itu original tam model 0.507 0.383 2 (+ jr) 0.073 0.308 0.557 3 (+ pi) 0.057 0.289 0.543 0.111 4 (+ ri) 0.057 0.290 0.545 0.111 0.005 5 (+ it) -0.006 0.236 0.361 0.098 0.029 0.374 6 (+ ps) -0.005 0.242 0.365 0.095 0.027 0.400 -0.057 7 (+ pr) 0.009 0.252 0.358 0.097 0.027 0.418 -0.041 -0.083 8 (+ tr) 0.010 0.276 0.355 0.101 0.004 0.398 -0.046 -0.110 0.008 legend: itu: intention to use; it: innovation tolerance; jr: job relevance; peou: perceived ease of use; pi: personal innovativeness; pr: property rights over business data; ps: perceived social norm; pu: perceived usefulness; ri: perceived risk of data abuse; tr: transparency 6. discussion the primary objective of this study was to elucidate the factors influencing the intention to use ai-based camera systems in german pig farming. even though our data did not support all the hypotheses, the results showed that user aspects concerning the farmer himself and the perceived ease of use are decisive for the intention to use ai-based camera systems in pig farming. research on acceptance has been conducted to investigate various technologies within the context of agriculture. our results are discussed in light of previous findings on technology acceptance in agriculture. the analyses initially revealed that peou [h2a] is one of the most influential factors in the adoption of ai-based camera systems in german pig farming. previous research confirms these findings. mohr and kühl (2021) reported that the peou and pi, among other factors, influence the acceptance of artificial intelligence among farmers in general. other agriculture studies have confirmed this finding with respect to ease of use and acceptance (michels et al., 2021). the transferability of the results to different agricultural sectors is reinforced by a study related to precision livestock farming, which revealed that visualization and peou influence the acceptance of a system (van hertem et al., 2017). in our study, innovation tolerance [h3] had the greatest impact on the intention to use ai-based camera systems in pig farming. the interpretation of the results of it can be assigned to the person himself or herself, which incorporates a self-image consisting of risk affinity and the estimation of the future importance of this technology. this finding is in consistent with the literature, which states that risk aversion (abadi ghadim & pannell, 1997) or the willingness to take risks (seibert et al., 2021) determines the intention to use a new technology. this construct also supports the assumption that a positive view of the importance of the technology in the future is decisive for the intention to use it (rogers, 2003). although the empirical results show a dominant contribution of it2, while it1 exhibits a low weight and loading. this suggests that the construct is essentially driven by the specific item on ai-related attitudes, and the general trait-based indicator contributes minimally. future research should consider refining the indicators to ensure a more balanced and representative operationalization of the construct. in this study, the influence of personal innovativeness [h6a] on the intention to use aibased camera systems was demonstrated. this construct has a statistically significant positive influence on the acceptance of ai-based camera systems in our sample, indicating that the intention to use increases with increasing innovativeness. although the influence of this construct on the dependent latent variable is low, it can still explain acceptance to some extent. previous studies from agarwal and prasad (1998) and aubert et al. (2012) have identified pi as an influencing variable. this construct serves to identify early adopters as agents of innovation and should be considered an important factor in implementation processes in agriculture. this finding contradicts the results reported by mohr and kühl (2021), who found only an indirect influence of pi on acceptance. this indirect influence [h6b] was also supported by our data. notably, in the case of the cited study, ai was considered in general, and the measurement of pi was made more difficult by a generalization of the subject of the study. the statistical analysis of the survey results revealed another construct that has a statistically significant influence on the itu: the perceived relevance of the technology for the farming profession [h7a]. the influence of jr on acceptance and adoption in the context of agricultural technologies was also demonstrated by michels et al. (2021). the authors analyzed the acceptance of drone technology and demonstrated that jr has the greatest influence on the itu. in conclusion, for practice and the development of new ai-based monitoring systems, it is important to communicate precisely the benefits for everyday working life. although the statistical measurements were not statistically satisfactory overall, this study demonstrated that expectations of data ownership have an effect on the intention to use [h5]. in contrast to other studies, our approach assumed a negative effect of stronger expectations regarding data rights. according to the variables pr2 and pr4 within the final construct and pr1 outside of the construct, the importance of data ownership to farmers determines their intention to use ai-based camera systems. an undefined ownership structure of the data is assumed to lead to rejection of the technology. previous studies have also shown that in the context of german citizens and electronic data, german angst plays a central role in the adoption, acceptance, and design of institutions (akkaya et al., 2012). other constructs (e.g., pu, tr and ri) did not influence the intention to use ai-based camera systems in this sample. this finding contradict the conclusions of krempel and beyerer (2014), whose research on surveillance cameras showed that the transparency of the data processed was one of the most important factors regarding acceptance. this difference may be due to the type of ai surveillance. furthermore, low perceived transparency as a barrier may have an important influence on farmers' intention to use risk management tools (giampietri et al., 2020). while pu [h1] is a crucial factor according to many studies on the acceptance of technology in agriculture (michels, fecke, et al., 2020; michels et al., 2021), it is not relevant in our statistical model or in studies on the acceptance of ai in general (mohr & kühl, 2021). on the one hand, this difference may be because the pu can be accepted or rejected independently of the itu. thus, a rejection of the intention to use is not synonymous with the system’s lack of actual usefulness. on the other hand, the rejection of aibased camera systems despite a perceived high or very high benefit is due to other factors, such as a lack of peou. this finding was not only supported by the full sem, but also by the stepwise inclusion of the factors and the resulting development of the path coefficients. it can be concluded that pu has an influence on the original model, but that is outweighed by, among other things, the introduction of jr. on the one hand, this effect could derive by the fact that both variables measure similar characteristics in the occupational context. on the other hand, there are indications in our model that there is a stronger relationship between jr and itu in the adaptation of technologies by the decision makers, as apparently the relevant professional context is more important than the actual usefulness. an additional consideration in the context of modelling and hypothesis generation is the differentiated role of individual factors, whether as direct determinants, potential mediators, or moderators within the model structure. in the present model, it may be hypothesized that pi exerts a moderating influence on itu, as it reflects, at least in part, trait-like characteristics of the respondents. while the conceptual phase of theorydriven hypothesis development did not provide sufficient justification for including such a moderation effect, theoretical reflections combined with the empirical findings of this study suggest that future analyses should explicitly consider this possibility. besides the findings of our model applying an extended tam, other approaches should be used to investigate the itu of ai camera systems. for example, the tpb could be an appropriate model for further research. in the case of animal husbandry and the monitoring of health and welfare parameters, tpb constructs would help to identify voluntary action by farmers in technology adaptation. an investigation of tpb factors would help to provide important insights for the development of systems and recommendations for policy, particularly in the highly regulated area of agriculture and ai. especially in a policy context where voluntarism is the preferred option for adaptation over regulation. apart from the analysis of behavioral factors further research on the technology itself is also needed. it is equally important to know which economic and technology-specific factors, in addition to behavioral factors, moderate the potential adaptation. for new technologies with a specific field of application, sok and hoestra (2023) used the subject of electrified tractors to show that uncertainty about the economic benefits and cost-effectiveness were the most important factors for the decision of the farmers surveyed. an examination of the economic and technology-specific factors using random utility theory would provide further clarification on the possible adoption or rejection of ai camera systems and help companies and policymakers to create the necessary framework conditions for market integration. since analyses of nonbehavioral factors (e.g. age, education, farm size) have shown little influence on the adaptation of ai camera systems in pig farming (kühnemund & recke, 2024), consideration of the tpb and economic factors could help to explain the variance in the intention to use. our study is limited by the notion that the results must be understood considering the specific types of animal farmers. therefore, these results are only partially applicable to other forms of livestock production. especially in the case of highly integrated value chains that focus on the interests of the integrator, other factors could lead to acceptance or rejection, which were not considered in this study. the results must also be viewed in consideration of the convenience sample and do not constitute a representative analysis of the object of investigation. therefor the findings are not generalizable to the overall population of german pig farmers. for further studies a representative sampling strategy should be applied in order to investigate models like tpb or random utility theory. although germany is one of the largest pig-producing countries in europe and even worldwide, the results cannot be applied uniformly at the international level. cultural idiosyncrasies, the strongly male-dominated agricultural sector and the formal institutions involved in handling the data in this context are only some of the reasons why the results cannot be fully generalized to a european or global context. it is possible that the survey procedure (online survey) causes selection bias because the survey invitation only reached people who were on the mailing list and may also have addressed those who are interested in technology. despite these limitations, this study provides important findings for future research on and the development of ai-based camera systems. this study is characterized by a sample that corresponds to the characteristics of german pig farmers. furthermore, the necessary sample size was achieved, increasing the robustness of the analysis. the model showed satisfactory performance, which emphasizes the significance of the results. knowledge of development and the factors that promote successful implementation are essential for practitioners as well as for policy and regulatory decision-makers. a technology is useful only if it is used by the target group. future research should focus on user-friendly interfaces. in terms of simplicity, it is also important to ensure lowbarrier access to the technology and to create an infrastructure that makes these systems easy to use for all farmers. in addition, it is conceivable that the target group and potential users could be reached through farmers who have already had experience with the system. in addition, the legal component should be explored by investigating the influence of such institutions. the results show that developers should focus on the benefits and application to the farmer's job. the economic relevance of ai-based camera systems, as well as their potential to generate added value at specific stages of the livestock production process, should be more explicitly identified and communicated. their implementation could offer targeted solutions to current challenges, such as the early detection and prevention of tail biting in undocked pigs or the reduction of labor-intensive, legally mandated animal observation tasks that currently lack direct economic return. in addition, attention should be paid to ease of use to ensure successful market integration. the analysis also suggests that ai camera systems should be further developed in collaboration with tech-savvy farmers to address their enthusiasm for innovation. incorporating this technology into an intelligent housing system could lead to successful integration with other solutions such as housing climate and feeding. policy makers should create the basis for such compatibility in order to increase the uptake of technologies. in addition to clear frameworks for transparency and legal certainty of data, policymakers and educational institutions should integrate educational programs into the training of farmers to facilitate the use of new ai technologies. this can lead to future farmers being more open to innovation. 7. conclusion in summary, the perceived ease of use, innovation tolerance, job relevance, and personal innovativeness emerged as influential constructs that shape the intention to use ai-based camera systems in pig farming. understanding the behavior-based acceptance of ai technologies is crucial, and the factors identified in this study can guide the development of ai-based camera systems that are embraced by farmers and offer tangible benefits. in this sample, the 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in pig livestock farming. h2a: the perceived ease of use of ai-based camera systems in pig livestock farming has a positive effect on the intention to use ai-based camera systems in pig livestock farming. h2b: the perceived ease of use of ai-based camera systems in pig livestock farming has a positive effect on the perceived usefulness of ai-based camera systems in pig livestock farming. h3: innovation tolerance has a positive effect on the intention to use ai-based camera systems in pig livestock farming. h4: perceived social norms have a positive effect on the intention to use ai-based camera systems in pig livestock farming. h5: the expectation of property rights over business data has a negative effect on the intention to use ai-based camera systems in pig livestock farming. h6a: the personal innovativeness of farmers has a positive effect on their intentions to use ai-based camera systems in pig livestock farming. h6b: the personal innovativeness of farmers has a positive effect on the perceived ease of use of ai-based camera systems in pig livestock farming. h7a: job relevance has a positive effect on the intention to use ai-based camera systems in pig livestock farming. h7b: job relevance has a positive effect on the perceived usefulness of ai-based camera systems in pig livestock farming. h8a: the perceived risk of data abuse has a negative effect on the intention to use aibased camera systems in pig livestock farming. h8b: the perceived risk of data abuse has a negative effect on the perceived usefulness of ai-based camera systems in pig livestock farming. h8c: the perceived risk of data abuse has a negative effect on the perceived ease of use of ai-based camera systems in pig livestock farming. h9a: expected data transparency has a positive effect on the intention to use ai-based camera systems in pig livestock farming. h9b: expected data transparency has a positive effect on the perceived usefulness of ai-based camera systems in pig livestock farming. h9c: expected data transparency has a positive effect on the perceived ease of use of ai-based camera systems in pig livestock farming. appendix 2: items and descriptive statistics factor name factor description mean sd to what extent do you agree with the following statements? i think that… itu1 … i will additionally observe my animals using cameras. 3.65 1.22 itu2 … i will use cameras in my business in the future. 3.49 1.25 itu3 … i would use cameras on my farm. 3.65 1.22 to what extent do you agree with the following statements? i think that the use of ai-based camera systems… pu1 … allows me to do work in the barn more quickly than before. 2.98 1.23 pu2 … facilitates the work of all employees on my farm. 3.05 1.24 pu3 ... increases the productivity of my business. 3.20 1.16 pu4 ... reduces my overall workload on the farm. 3.05 1.19 pu5 ...gives me more flexibility in terms of my operating processes. 3.23 1.15 to what extent do you agree with the following statements? for me, … peou1 …operating ai cameras to observe animals is easy to learn. 3.79 0.95 peou2 … videos from animal observation cameras are easy to evaluate. 3.25 1.07 peou3 … working with cameras to observe animals in the barn is possible without technical problems. 3.13 1.09 peou4 (r) …it is difficult to operate ai cameras and evaluate videos. 3.67 1.11 to what extent do you agree with the following statements? i think that… jr1 … the use of ai cameras can be relevant to my work. 3.54 1.15 jr2 … the use of ai cameras can have a high degree of relevance for my operations. 3.06 1.17 jr3 … ai cameras are suitable for my business. 3.16 1.15 to what extent do you agree with the following statements? i think that… tr1 … i am well informed about what data are captured by a camera-based image processing system. 2.92 1.15 tr2 … i am well informed about how such a system processes data. 2.76 1.17 to what extent do you agree with the following statements? i think that… ri1 … i could be disadvantaged by errors in the collection or processing of data by the system. 3.10 1.10 ri2 … (image) data could be misused. 3.62 1.23 to what extent do you agree with the following statements? it1 i consider myself to be a risk taker. 3.24 0.93 it2 i think it will be important in the future to use ai cameras for animal observation. 3.25 1.19 to what extent do you agree with the following statements? pi1 i enjoy being around people who are trying out new technologies. 4.03 0.84 pi2 i am very curious about how new agricultural technologies work. 4.07 0.91 pi3 i like to try out new agricultural technologies. 3.84 0.92 pi4 i often determine information about new technologies. 4.10 0.82 to what extent do you agree with the following statements? ps1 the german population has a positive view of modern technology in agriculture. 2.61 0.99 ps2 policy-makers support modern agriculture. 1.84 0.90 ps3 i think that the use of ai camera monitoring in barns is consistent with society's expectations of agriculture. 3.09 1.13 to what extent do you agree with the following statements? pr1 corporate data belongs to the farmers. 4.78 0.55 pr2 stronger regulation for data security reduces the competitiveness of german farmers. 2.40 1.10 pr3 the government should create a data platform for sharing agricultural data. 2.09 1.07 pr4 as long as i receive large benefits from it, i do not care if companies use operational data. 2.11 1.21 pr5 the data flow of visual material should be controlled by farmers. 4.40 1.12 legend: itu: intention to use; it: innovation tolerance; jr: job relevance; peou: perceived ease of use; pi: personal innovativeness; pr: property rights over business data; ps: perceived social norm; pu: perceived usefulness; ri: perceived risk of data abuse; tr: transparency appendix 3: reflective constructs reflective measurement models indicator name indicator reliability loadings convergent validity ave internal consistency rhoa rhoc cronbach’s alpha intention to use ai-based camera systems itu1 0.942 0.898 0.944 0.964 0.943 itu2 0.955 itu3 0.946 perceived usefulness pu1 0.847 0.722 0.907 0.928 0.903 pu2 0.825 pu3 0.876 pu4 0.865 pu5 0.835 personal innovativeness pi1 0.871 0.729 0.827 0.90 0.815 pi2 0.856 pi3 0.834 legend: itu: intention to use; pi: personal innovativeness; pu: perceived usefulness appendix 4: heterotrait–monotrait perceived usefulness personal innovativeness intention to use perceived usefulness . . . personal innovativeness 0.366 . . intention to use 0.742 0.443 . appendix 5: formative constructs formative measurement models indicator name vif weight loadings perceived ease of use peou1 1.723 0.380 0.776 peou2 1.753 0.482 0.849 peou3 1.467 0.446 0.809 peou4 1.342 -0.199 0.329 job relevance jr1 2.943 0.407 0.920 jr2 2.317 0.185 0.817 jr3 2.327 0.509 0.931 perceived risk of data abuse ri1 1.169 0.941 0.992 innovation tolerance it1 1.045 0.079 0.283 it2 1.045 0.981 0.997 perceived social norm ps3 1.057 0.979 0.997 property rights pr2 1.360 0.559 0.797 pr4 1.151 0.379 0.629 transparency tr1 1.196 0.481 0.766 tr2 1.196 0.703 0.898 legend: it: innovation tolerance; jr: job relevance; peou: perceived ease of use; pr: property rights over business data; ps: perceived social norm; ri: perceived risk of data abuse; tr: transparency appendix 6: predictive power itu1 itu2 itu3 rmse (pls) 0.733 0.741 0.768 rmse (lm) 0.761 0.773 0.829 1 towards sustainable business models in the circular bioeconomy: the case of bio-based 1 fertilizers 2 3 viktorija jankuloska*, blagoja mukanov, angela zhivachka, jovana milosavljeva, mario 4 petkovski, tanja popovska 5 agfutura technologies, јurij gagarin 45/1-1, 1000, skopje, republic of north macedonia 6 *corresponding author. e-mail: viktorija.jankuloska@agfutura.com 7 this article has been accepted for publication and undergone full peer review but has not been 8 through the copyediting, typesetting, pagination and proofreading process, which may lead to 9 differences between this version and the version of record.please cite this article as: 10 jankuloska v., mukanov b., zhivachka a., milosavljeva j., petkovski m., popovska t. (in press). 11 towards sustainable business models in the circular bioeconomy: the case of bio-based fertilizers, 12 just accepted. doi: 10.36253/bae-16752 13 14 acknowledgements 15 this paper is carried out within the framework of the european commission bbi ju project ‘bio-16 based fertilising products as the best practice for agricultural management sustainability 17 (bferst)’. h2020-bbi-jti-2018, grant agreement id: 837583. 18 19 20 21 mailto:viktorija.jankuloska@agfutura.com 2 abstract. bio-based fertilizers (bbfs) offer a sustainable solution to the environmental and 22 economic challenges of conventional fertilizers by enhancing nutrient recycling and soil health, 23 aligning with eu strategies. despite the availability of frameworks like the business model 24 canvas (bmc), these models have not been comprehensively applied in the context of bbfs. this 25 paper addresses this gap by using the bmc framework to provide a holistic overview of bbf 26 business models, integrating economic, environmental, and social dimensions. a comprehensive 27 literature review was conducted, focusing on studies examining specific aspects of bbf business 28 models, including value proposition, value creation and delivery, and value capture. the analysis 29 highlights key insights, such as bbfs’ potential to recycle waste, and enhance soil health, 30 alongside challenges like nutrient variability and market adoption barriers. these findings support 31 strategies for advancing sustainable circular bioeconomy practices, offering valuable guidance to 32 stakeholders in the fertilizer and agricultural sectors. 33 34 keywords: circular bioeconomy, bio-based fertilizers, sustainable business model, business 35 model canvas 36 jel codes: m10, q13, q57 37 1. introduction 38 1.1. towards sustainable agriculture: from bio-waste to bio-based fertilizers 39 agricultural production growth has relied heavily on chemical fertilizers and synthetic pesticides 40 (european commission, 2019). however, excessive application of essential nutrients like nitrogen 41 (n) and phosphorus (p), combined with inefficient plant absorption, has caused air, soil, and water 42 pollution and has significantly reduced biodiversity in aquatic ecosystems (juncal et al., 2023; 43 3 yang et al., 2008). similar to healthcare principles that warn against overuse, agronomy advocates 44 for limited fertilizer application to maintain ecological balance (pandian et al., 2024). moreover, 45 fertilizer production relies on finite fossil-based resources, such as natural gas, phosphate rock, 46 and potassium salts, which are depleting (anlauf, 2023; cordell et al., 2009). the european 47 agricultural sector, heavily dependent on imports of these essential raw materials, is vulnerable to 48 supply chain disruptions, threatening food security (smol, 2021; de ridder et al., 2012). this 49 dependency highlights the fertilizer industry’s critical challenge: efficient nutrient recycling 50 (barquero et al., 2024). 51 despite these issues, global chemical fertilizer use increased from 12 million tons in 1961 to over 52 110 million tons by 2018 (rodríguez-espinosa et al., 2023). the european commission (ec) has 53 targeted a 50% reduction in nutrient losses while maintaining soil fertility (heyl et al., 2023; 54 european commission, 2020). the common agricultural policy promotes precision agriculture to 55 improve nutrient efficiency (álvarez salas et al., 2024). additionally, the eu’s farm to fork (f2f) 56 strategy, part of the green deal, aims to reduce fertilizer use by 20% by 2030, promoting a shift 57 to circular bio-based production processes (european commission, 2020). supporting this goal, 58 the 2019 eu fertilizing product regulation (regulation (eu) 2019/2009), effective since may 59 2022, mandates circular and sustainable fertilizer systems by integrating organic and waste-60 derived fertilizers into the market. 61 the bioeconomy, encompassing sectors reliant on biological resources (bröring and vanacker, 62 2022), along with bio-based fertilizers (bbfs) as sustainable alternatives to synthetic fertilizers, 63 plays a crucial role in advancing a circular economy. a prominent initiative is the circular bio-64 based europe joint undertaking (cbe ju), a public-private partnership between the eu and the 65 bio-based europe joint undertaking, with a €2 billion budget for 2021-2031 (donner and de 66 4 vries, 2023). in addition to cbe ju, initiatives such as horizon europe, the european circular 67 bioeconomy fund (ecbf), the european investment bank (eib), and national research and 68 innovation programs provide critical financial and strategic support for advancing bbfs 69 development and commercialization. bbfs offer sustainable alternatives by recycling bio-waste, 70 improving nutrient use efficiency, and reducing environmental impacts compared to traditional 71 fertilizers (álvarez salas et al., 2024). although no standardized definition exists, efforts at 72 european level aim to establish one. bbfs are generally described as materials or products sourced 73 from biomaterials (such as plant, animal, or microbial sources, often including waste, residues or 74 by-products from agriculture, industry or society) containing bioavailable nutrients fit for use as 75 crop fertilizers (arzeni et al., 2024). examples of bbfs include compost-based fertilizers, manure-76 derived fertilizers, biochar, and nutrient-rich products from anaerobic digestion, such as digestate 77 or struvite (kurniawati et al., 2023; chojnacka et al., 2020). 78 1.2. the sustainable business model for bio-based fertilizers 79 academic research on business models, stemming from foundational theories like drucker’s 80 (1985), defines a business model as a structure detailing how a company creates, delivers, and 81 captures value (teece, 2018; osterwalder et al., 2005). the business model canvas (bmc), 82 developed by osterwalder and pigneur (2010), remains the most widely adopted template, 83 comprising nine elements. central to this model is the value proposition, which captures the unique 84 benefits a company offers to its customers. the market component (customer segments, channels, 85 and customer relationships) addresses external elements critical for reaching customers and 86 determining product desirability. the infrastructure component, which includes key activities, 87 resources, and partnerships, defines operational feasibility, while the financial component (cost 88 structure and revenue streams) ensures economic viability. 89 5 the concept of "bioeconomy," introduced by christian patermann, emphasizes using renewable 90 biological resources to meet societal needs with reduced environmental impact (lange, 2022). 91 transitioning to a bioeconomy requires substantial changes to existing business models (reim et 92 al., 2019). although the literature on bioeconomy business models often overlaps with circular 93 economy discussions, there are distinctions (donner and de vries, 2023). the bioeconomy 94 emphasizes replacing fossil resources with renewables, while the circular economy focuses on 95 cascading resource use (bröring and vanacker, 2022; venkatesh, 2022). 96 sustainable business models communicate value propositions that capture economic value while 97 maintaining or regenerating environmental, social, and economic capital (schaltegger et al., 2016), 98 as outlined in the triple layered business model canvas (tlbmc) by joyce and paquin (2016). 99 the tlbmc extends osterwalder and pigneur’s bmc by adding social and environmental layers, 100 providing a holistic view of value creation. while bioeconomy models have sustainability 101 potential, they are not inherently sustainable (de keyser and mathijs, 2023). challenges such as 102 resource use, environmental impact, and social equity must be addressed for these models to 103 realize their sustainability potential. when sustainability is embedded in bioeconomy goals, it can 104 foster positive social and environmental impacts and stimulate economic growth through 105 innovation, particularly in agriculture and food production sectors (pfau et al., 2014). 106 research on sustainable business models, particularly within the agrifood sector, is still sparse 107 (mili and loukil, 2023; bröring and vanacker, 2022; donner and de vries, 2021; salvador et al., 108 2023; reim et al., 2019). while interest in the bmc has been growing, its application within the 109 agrifood sector is still limited. only a few studies (e.g., mili and loukil, 2023; basile, 2021; 110 partalidou et al., 2018) have applied this model within this specific context, and none directly focus 111 on bbfs. the existing literature does touch upon various aspects of bbf business models (e.g., 112 6 álvarez salas et al., 2024; garmendia-lemus et al., 2024; kvakkestad et al., 2023; moshkin et al., 113 2023; cazador et al., 2022; chojnacka et al., 2020; egan et al., 2022; smol, 2021; tur-cardona et 114 al., 2018). however, to the best of the authors' knowledge, no study offers a comprehensive bmc 115 incorporating sustainability-oriented value propositions specific to bbfs. this study contributes 116 to the existing literature by applying the bmc framework to bbfs, offering a comprehensive 117 analysis of their commercialization pathways. although not all elements of the bmc for bbfs are 118 addressed in current literature, broader bioeconomy studies and research on diverse business model 119 components provide valuable insights to guide the development of sustainable bbf business 120 models. customer-related components, however, remain relatively underexplored (hatvani et al., 121 2022). conventional business model frameworks often underemphasize sustainability (frança et 122 al., 2017). this paper seeks to address this by examining key elements for sustainable bbf 123 business models. 124 2. methodology 125 this study employs a qualitative approach, using a literature review as the foundation for analyzing 126 sustainable business models within the circular bioeconomy. the bmc framework was applied as 127 a conceptual framework for this study to evaluate and categorize key components of sustainable 128 business models, with a literature review serving as the primary method to populate and elaborate 129 on its components. while the tlbmc provides a framework to deepen insights into sustainability-130 specific dimensions, this study uses the bmc for its clear structure and broad applicability, serving 131 as a practical starting point for analyzing bbf commercialization. 132 the literature review was conducted using three primary academic databases: scopus, web of 133 science, and google scholar. to ensure a comprehensive search, relevant keywords were used, 134 7 including "circular bioeconomy," "bio-based fertilizers," "sustainable business model," "business 135 model canvas," "recycling-derived fertilizers (rdfs)," "waste-based fertilizers," and "bio-based 136 fertilizer products (bfps)." boolean operators such as and and or were applied to refine and 137 expand the search results. the search primarily targeted peer-reviewed articles, book chapters, and 138 reports published in english, with the majority dating from 2013 to 2024 to reflect recent 139 advancements and industry developments. earlier studies were included when particularly relevant 140 to the research objectives. the selection process followed a systematic three-step approach. first, 141 titles and abstracts were screened to assess relevance to bbf business models and the bmc 142 framework. next, studies passing the initial screening underwent a full-text review to ensure 143 alignment with the research objectives. finally, studies providing insights into at least one of the 144 bmc components—value proposition, value creation and delivery, or value capture—were 145 included for analysis. only high-quality sources, such as peer-reviewed journals and academic 146 publications, were considered to ensure reliability and rigor. to enhance the reliability and 147 robustness of the concepts presented, this study incorporates insights from real-world business 148 models drawn from the b-ferst project, which focuses on integrating bio-waste valorization into 149 fertilizer production, offering practical examples of sustainable business models in the 150 bioeconomy. data extraction was performed manually, with insights mapped to the nine building 151 blocks of the bmc framework: value proposition, customer segments, channels, customer 152 relationships, key activities, key resources, key partners, revenue streams, and cost structure. these 153 elements were further grouped into three main bmc segments: value proposition, value creation 154 and delivery (market and infrastructure), and value capture (financial aspects) (vehmas et al., 155 2024; schaltegger et al., 2016; richardson, 2008). the extracted data were synthesized 156 systematically to identify common themes, opportunities, and challenges associated with bbf 157 8 commercialization. to guarantee the quality of the included studies, criteria such as source 158 credibility, methodological rigor, and relevance to bbf business models were applied during the 159 review process. studies contributing to bbf commercialization, circular bioeconomy frameworks, 160 and sustainability were prioritized, while those lacking sufficient methodological detail or 161 academic rigor were excluded. 162 3. results and discussion 163 the key components of the bmc applied to bbfs are summarized in table 1, providing a 164 structured framework for analyzing their commercialization pathways. this table serves as a 165 foundation for the following discussion, outlining the critical aspects of value proposition, value 166 creation and delivery, and value capture. 167 168 169 9 table 1. business model canvas for bio-based fertilizers: key components and insights 170 key partners suppliers: farmers, waste management companies, and recycling firms that provide essential feedstocks for bbf production. collaborators: researchers, agricultural extension services, policymakers, ngos, and media organizations driving innovation, knowledge dissemination, and regulatory support. logistics partners: transport and distribution partners ensuring efficient supply chain operations and seamless delivery of raw materials and finished products. key activities production: nutrient recovery and fertilizer production. logistics: biomass collection, transport, storage, and quality control. r&d: process optimization, scaling technologies, ensuring compatibility with existing infrastructure, and driving innovation in sustainable production. value proposition economic value: comparable or superior plant growth and yields using recovered nutrients; product-service systems. environmental value: reduce reliance on fossil based resources, recycle biowaste, cut ghg emissions and nutrient leaching, enhance soil health, and promote sustainable nutrient cycling. social value: support local job creation, stimulate rural economies, foster community engagement, ensure fair competition, and improve health and safety through hygienized bbfs. customer relationships co-creation: collaborate with farmers and researchers to improve products through shared expertise and feedback. peer influence: leverage farmer networks to share knowledge and promote sustainable practices. engagement: build trust via education, and online communities. customer segments primary users: farmers and nurserymen seeking sustainable alternatives to conventional fertilizers. target segments: environmentally conscious and premium customers willing to pay for certified, eco-friendly products that align with their values. adoption opportunities: eco-labelling, certification and branding strategies to build trust and market appeal. address knowledge gaps and build confidence through education, technical support and demonstration trials to showcase bbfs effectiveness. key resources tangible resources: biomass, biostimulants, biodegradable coatings. intangible resources: research facilities, advanced technologies, and expertise. human resources: skilled workforce and collaborative teams. channels traditional: print media, agricultural fairs, posters, and field demonstrations. digital: social media, webinars, agricultural apps, and video content. support: efficient logistics, proper product handling, and after-sales technical assistance. cost structure major costs: biomass sourcing, logistics, quality control, regulatory compliance, and navigating lengthy approval processes. key investments: technology development, advanced nutrient recovery methods, strategic marketing, and premium packaging to differentiate bbfs. cost optimization: leverage economies of scale, high-quality concentrated biobased materials, and innovative recovery technologies to streamline production and reduce costs. revenue streams primary revenue streams: fertilizer sales, soil testing, consulting, distribution, specialty products, and licensing. pricing strategies: competitive pricing to drive market adoption and maximize share, or premium pricing to reflect quality, environmental benefits, and customization. additional revenue streams: value-added services (e.g., customer support and loyalty programs), subsidies, and eu funding to support r&d and commercialization efforts. 171 10 3.1. value proposition in sustainable business models for bbfs 172 a value proposition encompasses the benefits a company offers its customers, including not only 173 product/service features that drive sales but also environmental and social values (hatvani et al., 174 2022). studies indicate that plant growth and yield potentials achieved with recovered nutrients 175 are comparable to or even surpass those of conventional fertilizers (barquero et al., 2024). 176 integrating services into business models is vital for the bioeconomy, enhancing value creation and 177 promoting bio-based solutions. product-service systems (pss) combine products with 178 complementary services, tailored to meet customer needs (bröring and vanacker, 2022). in bbf 179 context, service offerings could include nutrient management consulting, application services, 180 monitoring and analysis, product customization, and logistics solutions. 181 the environmental value of bbfs lies in their ability to address the ecological challenges posed 182 by conventional fertilizers. this value can be assessed using the life cycle assessment (lca) 183 approach, which evaluates a product’s environmental impact through indicators such as co₂ 184 emissions, energy use, resource depletion, and water consumption. by recycling bio-waste, bbfs 185 contribute to nutrient recovery, reduce dependence on finite fossil-based resources, and promote 186 circularity in agriculture. furthermore, bbfs help minimize greenhouse gas (ghg) emissions, 187 reduce nutrient leaching, and enhance soil health and quality. however, bbfs also present 188 environmental challenges that must be addressed to maximize their potential. nutrient variability 189 in bbfs can impact their agronomic performance, often leading farmers to favor synthetic 190 fertilizers due to their predictability and immediate results (bonnichsen et al., 2020). bbfs also 191 require mineralization for nutrients to become available to plants (leytem et al., 2024). this 192 process may not always match crop growth cycles, leading to inefficient uptake, nitrate leaching, 193 eutrophication, and potential acidification. additionally, bbfs derived from waste (e.g., manure 194 11 or sewage sludge) may contain pathogens or heavy metals, posing health and ecological risks if 195 not properly treated (kurniawati et al., 2023). effective mitigation strategies are essential to 196 manage these risks (álvarez salas et al., 2024). 197 bbfs also create significant social values related to stakeholder management approach, focusing 198 on indicators such as community engagement, labor conditions, health and safety, and fair 199 competition (vidaurre et al., 2020; rafiaani et al., 2018; joyce and paquin, 2016). by utilizing 200 local bio-waste as feedstock, bbf production supports local job creation and stimulates rural 201 economies (fao, 2018). the involvement of local stakeholders, including farmers and waste 202 processors, fosters community engagement and strengthens local value chains. in terms of health 203 and safety, hygienization-focused bbfs mitigate health risks related to salt and heavy metal 204 accumulation from synthetic fertilizers, which can be absorbed by plants (kurniawati et al., 2023; 205 smol, 2021). bbfs further promote fair competition by offering sustainable alternatives to 206 synthetic fertilizers, empowering farmers to adopt environmentally friendly practices. 207 3.2. value creation and delivery in sustainable business models for bbfs 208 3.2.1. market 209 customers segments 210 market segmentation divides the market into distinct groups with shared needs, enabling targeted 211 marketing strategies. stimulating demand for bbfs requires a deep understanding of behavior 212 influenced by values, ethics, self-interest, product features, and policies. this complexity demands 213 an interdisciplinary approach involving psychology, economics, and sociology (nejadrezaei et al., 214 2024). while farmers are primary bbf users, other groups, like nurserymen, also show interest in 215 adopting waste-based fertilizers, supporting the circular economy (smol, 2021). key product 216 12 attributes influencing bbf adoption among farmers include nutrient content, high organic matter, 217 cost, and ease of application (kvakkestad et al., 2023; egan et al., 2022; tur-cardona et al., 2018). 218 generally, bbfs must offer comparable properties to mineral fertilizers at a competitive price. 219 as sustainable practices gain momentum, bbfs are becoming more desirable, especially among 220 environmentally conscious farmers seeking alternatives to mineral fertilizers (tur-cardona et al., 221 2018). however, a significant barrier is public knowledge and confidence, which can be addressed 222 through effective education and communication on the benefits of bio-economy products (bröring 223 and vanacker, 2022; reim et al., 2019). convincing consumers of bbf advantages remains 224 challenging, as many farmers prefer synthetic fertilizers for their quick results, despite the 225 availability of eco-friendly alternatives at similar prices (ruth et al., 2020). companies struggle to 226 measure and communicate sustainability due to limited data and tools (bröring and vanacker, 227 2022). field trial data on nitrogen release from bbfs could significantly enhance adoption by 228 providing farmers with evidence of their performance and reliability (kvakkestad et al., 2023). 229 research by morgan et al. (2015) highlights that anticipated financial gains and self-efficacy—230 farmers' belief in their ability to achieve desired outcomes—are key drivers for adopting low-231 emission agricultural practices (leap). this insight is highly relevant for bbfs, as improving 232 farmers' self-efficacy through targeted educational programs, technical support, and demonstration 233 trials could encourage their adoption. morgan et al.'s study identifies four farmer categories—non-234 green dismissive, uncommitted, green adopters, and profit-driven adopters—each with distinct 235 attitudes towards climate change, environmental values, time orientation, place attachment, and 236 knowledge self-efficacy. tailored engagement strategies, such as showcasing the economic and 237 environmental benefits of bbfs to profit-driven adopters or emphasizing sustainability to green 238 adopters, could help overcome resistance in different farmer groups. similarly, gazdecki et al. 239 13 (2021) categorize consumers by their sustainability approach into doers, conscious, and reluctant 240 groups. these categories can be linked to bbf adoption by targeting doers and conscious 241 consumers—who are more inclined to adopt sustainable alternatives—through eco-labeling, 242 certification, and branding strategies that emphasize bbfs’ environmental value. addressing the 243 concerns of reluctant consumers through education and communication campaigns could further 244 strengthen market adoption. 245 targeting “premium” segments willing to pay more for environmentally and socially valuable 246 products is essential (reim et al., 2019). studies identify a “green premium” where consumers pay 247 more for bio-based products, and a “certified green premium,” where willingness to pay (wtp) 248 increases for certified bio-based options (morone et al., 2021). standards, certifications, and eco-249 labeling could improve bbf market penetration by verifying sustainability, supporting green 250 purchasing, and reinforcing consumer trust (reim et al., 2019; yenipazarli, 2015). branding also 251 affects bbf acceptance, as terms like “biosolids” over “treated sewage sludge” can positively 252 shape consumer perceptions (álvarez salas et al., 2024). 253 channels 254 market channels describe the pathways through which a company delivers its value proposition to 255 customers, yet they are relatively understudied in the bioeconomy context (reim et al., 2018; 256 coughlan, 2006). channels can be direct, like company websites or stores, or indirect, such as 257 through retailers or distributors, and they comprise five phases: awareness, evaluation, purchase, 258 delivery, and after-sales support (osterwalder and pigneur, 2010). the shift from traditional media 259 to digital platforms has redefined how sustainable agricultural practices are promoted. a 260 comprehensive channel strategy for bioeconomy products should incorporate both traditional and 261 digital media to maximize reach. for farmers with limited digital access, traditional methods like 262 14 print, radio, agricultural fairs, posters, conferences, and field demonstrations remain effective. 263 demonstrations, in particular, allow farmers to engage firsthand with new technologies and 264 practices (sutherland and marchand, 2021). digital marketing, meanwhile, can reach a broader 265 audience via social media, influencer partnerships, email campaigns, webinars, and online 266 advertising. additional tools include agricultural apps and video content, which provide interactive 267 and visual resources ideal for conveying complex information to farmers (bentley et al., 2019). 268 digital training programs that build on existing farmer knowledge and practices are crucial for 269 fostering adoption. these programs facilitate collaboration and knowledge co-creation by 270 integrating traditional, indigenous, and scientific expertise, along with the expertise of farmers 271 (maurel et al., 2022). 272 effective channel management also involves ensuring the timely delivery, proper handling, and 273 optimal storage of products to maintain quality (remondino and zanin, 2022; behzadi et al., 2017; 274 routroy and behera, 2017). efficient logistics and storage are necessary to handle the diverse and 275 bulky bio-based materials, facilitating smooth operations (donner et al., 2021). after-sales 276 support, including technical assistance and application guidance, is essential to build trust and 277 loyalty, encouraging repeat purchases (rebello et al., 2021). training for distributors and retailers 278 is also vital, equipping them to effectively promote and sell bbfs, thus supporting wider adoption 279 among farmers. 280 customer relationships 281 customer relationships outline how a company establishes and maintains connections with specific 282 segments to drive customer acquisition, retention, and sales growth. many consumers are willing 283 to pay a premium for environmentally sustainable, organic, chemical-free, and locally sourced 284 products. companies should identify these “bioeconomy customers” and tailor their offerings 285 15 accordingly (hatvani et al., 2022). consumers play multiple roles in advancing a sustainable 286 circular bioeconomy (lang et al., 2023). as buyers they influence the market by selecting 287 fertilizers, encouraging bbf producers to adapt products to meet customer demands; as lobbyists 288 and influencers, they shape public perception; as partners they contribute to developing standards 289 for bbf usage; and as co-creators they actively participate in value creation, providing feedback 290 and ideas for product improvements. farmers are increasingly recognized as vital co-creators in 291 developing sustainable agricultural practices and innovations. their local knowledge and 292 experience make them essential in designing practical solutions. researchers can also be involved 293 in the co-creation process, identifying gaps that farmers may not have the resources or expertise 294 to address (ruth et al., 2020). 295 effective customer relationship models require integrating information and communication 296 technology (ict) to educate farmers on the benefits of bbfs through high-quality content and 297 examples of successful applications. influencers can also promote sustainable practices by sharing 298 trusted recommendations and educating farmers (vilkaite-vaitone, 2024). however, research 299 shows that "peer farmers" are the most influential information source, as farmers often learn from 300 each other through conversations and by observing practices (e.g., roadside farming) (sutherland 301 and marchand, 2021). peer influence, or the impact of social interactions within a group on 302 individual behavior, is significant in shaping farmers' sustainable behavior (garmendia-lemus et 303 al., 2024; niu et al., 2022; tran-nam and tiet, 2022; bell et al., 2018). translating traditional 304 customer relationships into the virtual marketplace by establishing and maintaining online 305 communities allows farmers to connect and share knowledge (farquhar and rowley, 2006). these 306 communities, a key form of customer relationship alongside personal assistance, dedicated 307 16 personal assistance, self-service, automated services, and co-creation, also help companies better 308 understand their customers (osterwalder and pigneur, 2010). 309 3.2.2. infrastructure 310 key activities 311 key activities encompass essential tasks a business must execute to create, deliver, and sustain 312 value, generate revenue, and achieve its goals. in bbf production, key activities include raw 313 material collection, nutrient recovery, and fertiliser production (granulation stage, and addition of 314 non-microbial plant biostimulant (nmpb) or microbial plant biostimulant (mpb), with the 315 requirement of a coating stage when the biostimulant is mpb) (cazador et al., 2022; b-ferst 316 advanced technology brochure, 2024). biostimulants improve nutrient efficiency, tolerance to 317 abiotic stress, and crop quality traits, independent of their nutrient content (du jardin, 2015). in the 318 circular bioeconomy, particularly bbf production, core activities center on value recovery from 319 waste (waste valorization), converting organic waste into bbfs while maintaining sustainability 320 and resource efficiency. production relies on secondary bio-based raw materials obtained through 321 methods like composting, anaerobic digestion, and fermentation. efficient logistics, storage, and 322 quality control are essential for reusing byproducts and ensuring sufficient biomass supply (reim 323 et al., 2018). logistics and supply chain models for bio-based products are complex due to the 324 seasonal variability, dispersed distribution, and quality inconsistency of biomass sources. 325 challenges include biomass deterioration, diverse conversion technologies, and interdependencies 326 among logistics operations (stellingwerf et al., 2022). resource volumes, particularly feedstocks 327 from agriculture, can vary significantly, posing a challenge for markets like chemicals that are not 328 typically exposed to such fluctuations (hatvani et al., 2022). collection across vast regions with 329 varying quantities necessitates logistical adjustments and strategic planning (bröring and 330 17 vanacker, 2022; donner et al., 2021). furthermore, nutrient recovery technologies in bbf 331 production must address the inherent variability in product qualities, as bbfs need to meet diverse 332 agronomic requirements and cater to a wide range of application contexts. this variability 333 highlights the importance of technological flexibility and adaptability in production processes to 334 ensure consistency and efficacy across different products. future fertilizer plants must 335 accommodate multiple biomass feedstocks using either single or a combination of integrated 336 processes. recycling and waste utilization require careful planning regarding collection, storage, 337 transport, and pretreatment, depending on biomass volume and location (cazador et al., 2022). 338 ongoing r&d is crucial for cost-effective and high-quality production. optimizing bbf 339 production processes ensures efficiency, adaptability, and minimal environmental impact. in the 340 bioeconomy—a sector characterized by high innovation and intensive research—advancements in 341 new product applications, sidestream uses, and innovations toward bio-based and renewable 342 resources play a critical role (salvador et al., 2023). scaling up newly developed technological 343 solutions presents significant challenges. new technologies must be compatible with existing 344 fossil-based infrastructure to ensure value creation. additionally, navigating complex intellectual 345 property issues, which can be costly and time-consuming, is essential for technology development 346 (bröring and vanacker, 2022). transitioning to a bioeconomy also involves internal 347 transformation, requiring a cultural shift within established companies. ongoing innovation and 348 business model evaluation are critical for success (reim et al., 2019). 349 key resources 350 the key resources block identifies essential assets for a business model’s success, encompassing 351 tangible (e.g., financial, physical), intangible (e.g., technology, reputation), and human resources 352 (e.g., skills, expertise) (näyhä, 2020). the european union relies heavily on externally sourced, 353 18 non-renewable raw materials for fertilizer production, with natural gas as the primary energy 354 source (b-ferst advanced technology brochure, 2024). fertilizer production is highly energy-355 intensive, relying on fossil fuels (such as the haber–bosch process for nitrogen fertilizers) or fossil 356 ore deposits (such as phosphate rock) (chojnacka et al., 2020). phosphate rock needed for 357 production of phosphorus-based fertilizers is a finite and irreplaceable resource, concentrated in a 358 few countries worldwide. inefficient phosphorus use has pushed it beyond the planet's safe limits, 359 and continued reliance on primary phosphorus resources threatens agricultural sustainability 360 (magaya et al., 2024). this underscores the urgent need for nutrient recovery from alternative 361 sources. 362 wastes, especially biomass, offer a large reservoir of materials that can be converted into fertilizers 363 through various technologies. high-potential bio-wastes for bbf production include agricultural 364 waste, food waste, sewage sludge, and plant residues (chojnacka et al., 2019; 2020). agricultural 365 by-products like crop residues, plant trimmings and compost provide sources of essential nutrients. 366 animal by-products, such as manure and offal, and agri-food industry waste (e.g., fruit and 367 vegetable peels, pulp, seeds) also serve as valuable inputs. wastewater treatment plant (wwtp) 368 sewage sludge, or the organic material left after wastewater treatment processes, such as ashes and 369 struvite as a source of p and k, is another nutrient source. factors such as quality, regulation, 370 processing feasibility, logistics, product stability, economic feasibility and carbon footprint 371 influence the viability of these biowastes (cazador et al., 2022). 372 bioeconomy business models encounter challenges with biomass quality and availability. the 373 unsuitability or seasonality of raw materials, along with inadequate infrastructure and capacity, 374 can make biomass supply unreliable, disrupting the value generation process. furthermore, various 375 industries compete for the same biomass resources. the heterogeneity of these raw materials, 376 19 particularly when using harvest residues, affects the consistency and quality of the final product 377 (bröring and vanacker, 2022). biostimulants, which enhance plant growth and soil health, are key 378 resources in bbf production (soltaniband et al., 2022). these include both microbial and non-379 microbial biostimulants. biodegradable biopolymer coatings can be used to guarantee the 380 performance of certain biostimulants and enhance the performance of bbfs, ensuring controlled 381 nutrient release and increased agronomic efficiency (cazador et al., 2022; b-ferst advanced 382 technology brochure, 2024). 383 intangible and human resources play a vital role in the shift toward a circular bioeconomy. an 384 innovative, adaptable, motivating organizational culture driven by "power people" and non-385 hierarchical leadership is essential. communication, marketing skills, and team-driven innovations 386 supported by diverse expertise are also critical (näyhä, 2020). many organizations struggle with 387 implementing new business models due to the dominance of established frameworks and a lack of 388 absorptive capacity to acquire new knowledge and skills that may relate to distant sectors. 389 additionally, business models requiring new skills face obstacles such as skilled workforce 390 shortages or limited research facility access (bröring and vanacker, 2022). advanced bio-based, 391 product-oriented technologies are frequently required to facilitate new and complex conversion 392 processes (donner et al., 2021). laboratories are also considered as a key resource or a key 393 partnership, since bioeconomy is a highly innovative and research-intensive sector. laboratories 394 and pilot facilities, essential in the highly innovative and research-intensive bioeconomy sector, 395 are key resources or partnerships for evaluating and scaling diverse nutrient recovery methods to 396 produce high-quality bbfs tailored to varied market demands (hatvani et al., 2022). 397 20 key partners 398 the key partnerships block details the network of suppliers and collaborators essential for effective 399 business operations. partnerships allow companies to optimize operations, reduce risks, and access 400 external resources and capabilities (osterwalder and pigneur, 2010). unlike traditional models 401 focused on shareholders and suppliers, sustainable business models broaden the partnership scope 402 to include stakeholders who add economic, environmental, and social value. the bioeconomy 403 demands high levels of collaboration, with value chains requiring alignment and proactive 404 coordination among diverse stakeholders (bröring and vanacker, 2022). the quintuple helix 405 approach provides a framework for understanding this collaboration by incorporating five key 406 sectors: economic actors, educational and research institutions, policymakers, civil society, and the 407 natural environment (hatvani et al., 2022). key stakeholders in the bbf market include livestock 408 and crop farmers, waste management firms, recycling fertilizer companies, garden owners, 409 horticultural producers, governments, ngos, investors, and scientists, all of whom contribute to 410 raw material supply, technological development, and bbf commercialization (álvarez salas et al., 411 2024; de keyser and mathijs, 2023). 412 securing a reliable biomass supply and managing logistics are vital for bio-based companies, often 413 requiring partnerships across previously unconnected sectors, such as chemical companies and 414 small-scale farmers. primary producers are central to bioeconomy value chains, making 415 collaboration between the agricultural and fertilizer sectors essential for developing sustainable 416 management practices (cazador et al., 2022). strong relationships with farmers, supported by 417 secure payments, timely transport, and financial support for infrastructure, are crucial for success 418 (hatvani et al., 2022). engaging and benefiting biomass producers not only supports the supply 419 chain but is also key to achieving social sustainability in the bioeconomy (lange et al., 2021). 420 21 farmers’ influence on bbf supply and demand varies; livestock farmers are incentivized to use 421 excess manure, while crop farmers hold more power, selectively accepting or rejecting recycled 422 products based on their preferences (álvarez salas et al., 2024). 423 transportation and logistics partners maintain a smooth supply chain, ensuring the delivery of raw 424 materials to production facilities and fertilizers to distributors and retailers. research institutions 425 play a crucial role by collaborating on scientific studies and field research to foster trust and 426 promote new solutions (moshkin et al., 2023). agricultural extension services are vital for 427 supporting farmers in adopting bbfs and ensuring proper application, maximizing their value 428 (kvakkestad et al., 2023). policymakers establish regulatory frameworks for bbf production and 429 marketing, ensuring safety and environmental standards compliance (ruth et al., 2020). media and 430 ngos play a significant role in shaping consumer perceptions. although farmers may be open to 431 using recycled nutrients, skepticism from consumers and the food industry can hinder broader 432 adoption. educating consumers on the environmental benefits of bbfs is therefore essential to 433 increase acceptance (álvarez salas et al., 2024). 434 3.3. value capture in sustainable business models for bbfs 435 3.3.1. finance 436 revenue streams 437 the revenue stream block defines cash flows from each customer segment, essential for 438 profitability (osterwalder and pigneur, 2010). primary revenue streams in the fertilizer industry 439 include fertilizer sales, soil testing, agricultural consulting, distribution, specialty fertilizers, and 440 licensing. for bbfs, revenue relies on pricing strategies and competitive positioning compared to 441 mineral fertilizers, shaped by market demand, product availability, logistics, regulatory conditions, 442 22 and regional fertilization practices (álvarez salas et al., 2024). price reflects product quality and 443 brand positioning, and must match customer expectations and willingness to pay. to facilitate 444 market adoption and maximize market share, bbfs must be competitively priced. research 445 suggests pricing bbfs below mineral fertilizers to attract farmers; tur-cardona et al. (2018) 446 recommend pricing bbfs at approximately 65% of the cost of mineral fertilizers. similarly, 447 bonnichsen et al. (2020) found that farmers would prefer a price reduction of up to 50% compared 448 to current mineral fertilizers. moshkin et al. (2023), however, found that significant price 449 reductions might primarily be needed for rapid market uptake rather than overall revenue 450 maximization. alternatively, pricing bbfs at levels similar to mineral fertilizers could maximize 451 revenue even if initial market penetration is slower, provided marketing strategies effectively 452 address customers' willingness to pay. premium pricing can be an effective approach for bio-based 453 products, especially when highlighting their environmental value, such as reducing reliance on 454 fossil fuels. by positioning waste recovery as a value-added service and reducing production costs 455 through waste inputs, bbfs can justify premium pricing due to their environmental benefits 456 (salvador et al., 2023). market data indicate that while bbfs currently contribute a smaller share 457 of global fertilizer revenue compared to mineral fertilizers, their market share is expected to grow 458 steadily as demand for sustainable agricultural practices increases (joshi and gauraha, 2022). in 459 2023, the global biofertilizers market was valued at approximately usd 2.31 billion and is 460 projected to reach usd 4.77 billion by 2032, reflecting a compound annual growth rate (cagr) 461 of 8.5% (global market insights, 2024). in contrast, the overall global fertilizer market, valued at 462 usd 202.20 billion in 2023, is projected to reach around usd 276.92 billion by 2034, with a 463 cagr of 2.9% from 2024 to 2034 (precedence research, 2024). additionally, environmentally 464 conscious consumers are willing to pay a higher but competitive price if the products not only 465 23 deliver similar functionality but also address their unique demands and preferences (hatvani et al., 466 2022). 467 beyond fertilizer sales, additional revenue streams can be derived from value-added services, such 468 as customer support, loyalty programs, and product optimization services. subsidies can support 469 revenue, but they should underscore long-term benefits, proving bioeconomy products’ 470 profitability beyond public funding reliance (reim et al., 2018). effective use of subsidies and 471 taxes should reflect the socio-economic and environmental advantages not captured in market 472 pricing. larger companies often integrate bio-based products into their broader product range, 473 enabling them to leverage internal financing from other profitable business areas. these firms also 474 benefit from eu funds during the early development stages, supporting essential r&d efforts 475 needed for future industrial-scale investments (cowi, bio-based world news and ecologic 476 institute, 2019). 477 cost structure 478 to maximize growth and profitability, it is essential to understand cost structures, including both 479 fixed and variable costs. companies may adopt cost-driven models focused on efficiency or value-480 driven models that prioritize value creation and high-quality products, even at higher costs. in the 481 bioeconomy, production costs and market feasibility are influenced by several factors, primarily 482 the cost of biomass or feedstock, which must be procured competitively amid competition from 483 other industries. maintaining quality control and regulatory compliance also challenges bbfs’ cost 484 competitiveness with conventional fertilizers, highlighting the need for industry standardization. 485 a major challenge for new bioeconomy business models is the high opportunity cost of substantial 486 initial investments in technology development, with operations often remaining costly due to low 487 production volumes and competition for limited resources (reim et al., 2019). many bio-based 488 24 products lack economies of scale, limiting their competitiveness with fossil-based alternatives and 489 slowing return on investment (bröring and vanacker, 2022). 490 the cost structure of bbfs varies significantly based on the technologies employed, such as 491 composting, thermal processing, nutrient recovery, and advanced coating technologies. each 492 technology presents unique cost drivers, including energy use, material requirements, and 493 regulatory compliance. sourcing large quantities of bio-based materials from diverse locations 494 introduces logistical challenges, complicating supply chains and driving up costs, particularly 495 when production rates at specific sites are limited (e.g., struvite) (bröring and vanacker, 2022). 496 additionally, navigating varied european safety and logistics regulations increases operational 497 expenses, impacting overall economic feasibility. the commercialization of bbfs entails a lengthy 498 approval process, requiring extensive scientific data collection that adds to operational costs 499 (giuliani, 2023). technology development must be complemented by strategic marketing, public 500 relations, and specialized sales to emphasize product benefits, with marketing costs often 501 exceeding development expenses but frequently overlooked in cost analyses (hatvani et al., 2022). 502 packaging costs are also significant, especially when designing premium packaging to differentiate 503 bbfs from conventional fertilizers. on a positive note, efficient production strategies can create 504 economies of scale, reducing per-unit costs and boosting profitability. using high-quality, 505 concentrated biobased materials can streamline production processes, potentially minimizing costs 506 associated with quality control and material waste (cazador et al., 2022). currently, phosphorus 507 recovery is expensive and energy-intensive, involving high-temperature processes and costly 508 reagents (giuliani, 2023). technological innovation, especially in developing cost-effective 509 nutrient recovery methods, such as for phosphorus, can also optimize costs. by optimizing 510 25 production and logistics, companies can reduce costs, enhance profitability, and support 511 competitive pricing. 512 4. conclusions 513 this paper addresses the research gap on sustainable business models for bbfs by applying the 514 bmc framework to provide a comprehensive overview of key elements and considerations related 515 to bbf business models, integrating economic, environmental, and social dimensions. it highlights 516 the significant environmental impact of conventional chemical fertilizers and the need for 517 sustainable alternatives, as underscored by eu strategies like farm to fork and the eu fertilizing 518 product regulation. bbfs offer potential solutions through nutrient recycling and soil health 519 improvement, but a transition to bioeconomy requires substantial business model transformation, 520 with sustainability as a core objective. 521 the bmc framework facilitates a structured analysis of value proposition, value creation and 522 delivery, and value capture specific to bbfs (table 1). the value proposition of bbfs combines 523 economic, environmental, and social benefits. bbfs support comparable or superior plant growth 524 to conventional fertilizers while integrating services like nutrient management and logistics to 525 enhance adoption. environmentally, bbfs reduce reliance on fossil-based resources, recycle bio-526 waste, lower ghg emissions, and improve soil health, addressing ecological challenges in 527 agriculture. socially, bbfs foster local job creation, stimulate rural economies, and enhance health 528 and safety through hygienization processes. however, challenges such as nutrient variability, 529 timing of nutrient availability, and potential health risks require mitigation strategies to maximize 530 their potential as sustainable fertilizer alternatives. 531 26 market blocks, as critical components of value creation and delivery, play a pivotal role in driving 532 the adoption of bbfs. market adoption requires understanding behavioral drivers, including 533 financial motivations, self-efficacy, attitudes toward climate change, and the key product attributes 534 like nutrient content, organic matter, cost, and ease of application. targeted marketing, 535 certification, eco-labeling, and branding are essential for building trust, particularly among 536 environmentally aware consumers. effective channel management, combining traditional and 537 digital media, training programs based on farmers’ existing knowledge and practices, and tailored 538 after-sales support are key to enhancing product awareness, market adoption and customer loyalty. 539 peer influence and online communities also play crucial roles in customer relationship 540 management by promoting knowledge exchange, collaboration and co-creation. key activities like 541 nutrient recovery, ongoing r&d, and logistical management are critical for bbf production, while 542 resources—including biowaste, biostimulants, coatings tailored for controlled nutrient release, 543 flexible nutrient recovery technologies, skilled expertise, research facilities, funds, and innovative 544 organizational culture—are essential for the model's success. diverse and adaptable nutrient 545 recovery methods must address the variability in waste sources and product qualities, ensuring 546 consistent performance across varied agricultural applications. collaborations with stakeholders 547 like farmers, policymakers, researchers, and ngos support resource acquisition, technology 548 development, and bbf commercialization. partnerships between the fertilizer and biomass-549 supplying sectors are crucial for value chain coordination. competitive pricing relative to mineral 550 fertilizers supports market penetration, while premium pricing can be justified by emphasizing 551 bbfs' environmental benefits. additional revenue sources, including value-added services and 552 subsidies, are important for long-term sustainability, helping make bio-based products financially 553 27 viable. to remain competitive, companies must optimize production processes and logistics, use 554 high quality concentrated biobased material and innovate cost-effective nutrient recovery methods. 555 this paper contributes to the literature by presenting the bmc as a comprehensive tool to examine 556 the status of all key aspects and considerations related to commercialization of bbfs, integrating 557 a holistic perspective on value proposition, value creation and delivery, and value capture with 558 sustainability as a central objective. it has potential for providing valuable insights for stakeholders 559 in the fertilizer and agricultural sectors, policymakers, and the research community, providing a 560 foundation for developing strategies to transition towards sustainable circular bioeconomy. future 561 studies should explore the entire tlbmc in the context of bbfs to integrate sustainability 562 principles. since the market-related segments of the bmc are less explored in bioeconomy 563 literature, further research could focus on these areas, leveraging behavioral economics to provide 564 deeper insights into farmer adoption behavior. finally, developing effective education and training 565 programs that build upon farmers' existing knowledge and practices to improve bbf adoption, 566 remains a vital avenue for future exploration. an effective educational approach is necessary not 567 only for farmers but also for the broader public, including policymakers, businesses, and citizens. 568 as christian patermann, widely recognized as the “father of the bioeconomy,” highlights, the 569 greatest challenge in advancing the bioeconomy is its inherent complexity and the need to translate 570 this complexity into accessible understanding (giuliani, 2022). 571 references 572 1. abitabile, c., arzeni, a., cisilino, f., orlando, m .(2024). building sustainable futures: 573 the bio-based fertilizer case-study, bio-based and applied economics, just accepted. 574 doi: 10.36253/bae-15786. 575 28 2. álvarez salas, m., sica, p., rydgård, m., sitzmann, t. j., nyang’au, j. o., 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(2023). bioeconomy and resilience to economic shocks: insights from the covid-19 pandemic in 2020. bio-based and applied economics 12(4): 367-377. doi: 10.36253/bae-14827 received: june 20, 2023 accepted: september 26, 2023 published: december 31, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: silvia coderoni orcid jll: 0000-0002-3627-5466 ng: 0000-0003-1507-7476 rm: 0000-0002-3205-3938 tr: 0000-0001-8554-627x bioeconomy and resilience to economic shocks: insights from the covid-19 pandemic in 2020 jesús lasarte-lópez1,*, nicola grassano2, robert m’barek1, tévécia ronzon1 1 european commission, joint research centre (jrc), edificio expo, calle inca garcilaso, 3, 41092 sevilla, spain 2 department of economics, universidad loyola andalucía, avda. de las universidades s/n, 41704 dos hermanas, spain *corresponding author. e-mail: jesus.lasarte-lopez@ec.europa.eu abstract. using the latest release of employment and value added numbers in the bioeconomy sectors, we conducted an analysis on the performance of the eu bioeconomy during the covid-19 pandemic in 2020. our findings point to a possibly higher level of resilience of the bioeconomy sectors compared to the overall economy. while employment in the bioeconomy registered a similar (but slightly sharper) decrease to the total eu average (-1.7% vs. -1.4%), the value added fell substantially below average (-0.4% vs. -4.0%). the more contemporary biomass-processing sectors (chemicals and pharmaceuticals, as well as bioelectricity) performed better than the more traditional sectors (such as food or textiles). at the member state level, we observe a high degree of heterogeneity in sectoral performance. by discussing these estimates alongside previous qualitative insights from the related literature, we emphasize the relevance of the bioeconomy not only for environmental sustainability but also for socioeconomic resilience. keywords: covid-19 shock, bioeconomy, socioeconomic indicators, european union, green transition, resilience. jel codes: q57, o44. 1. introduction the bioeconomy is composed by all those economic activities that depend on the use of biological resources. this definition includes not only all biomass-producing and processing sectors, but also related services (european commission, 2018). the launch of the eu’s bioeconomy strategy in 20121, along with its update in 20182, positioned the bioeconomy as both a 1 innovating for sustainable growth: a bioeconomy for europe. https://op.europa.eu/en/publication-detail/-/publication/1f0d8515-8dc0-4435-ba53-9570e47dbd51 2 a sustainable bioeconomy for europe: strengthening the connection between economy, society and the environment: updated bioeconomy strategy. https://op.europa.eu/en/publication-detail/-/ publication/edace3e3-e189-11e8-b690-01aa75ed71a1/language-en/format-pdf/source-149755478 https://doi.org/10.36253/bae-14827 http://www.fupress.com/bae https://doi.org/10.36253/bae-14827 https://orcid.org/0000-0002-3627-5466 https://orcid.org/0000-0003-1507-7476 https://orcid.org/0000-0002-3205-3938 https://orcid.org/0000-0001-8554-627x mailto:jesus.lasarte-lopez@ec.europa.eu https://op.europa.eu/en/publication-detail/-/publication/1f0d8515-8dc0-4435-ba53-9570e47dbd51 https://op.europa.eu/en/publication-detail/-/publication/1f0d8515-8dc0-4435-ba53-9570e47dbd51 https://op.europa.eu/en/publication-detail/-/publication/edace3e3-e189-11e8-b690-01aa75ed71a1/language-en/format-pdf/source-149755478 https://op.europa.eu/en/publication-detail/-/publication/edace3e3-e189-11e8-b690-01aa75ed71a1/language-en/format-pdf/source-149755478 368 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14827 jesús lasarte-lópez et al. key enabler and a result of transitioning to a green and fair economy in the eu. as a result, synergies have been identified with the overarching european green deal strategy, which aims to address climate and environmental challenges. specifically, the bioeconomy strategy can help evaluate and address trade-offs between policy objectives and competing resource uses, promoting both environmental sustainability and socioeconomic gains and resilience (european commission, 2022). the significance of the bioeconomy in enhancing resilience to external economic shocks has gained considerable attention in both academic and policy debates, particularly in light of recent major events. in 2020, the covid-19 pandemic strained global supply chains under stress, due to shifts in demand and labour shortages (oecd, 2020; ozdemir et al., 2022; galanakis et al., 2022). more recently, the russian invasion of ukraine led to price increases in basic resources like food and energy products (ramanauske et al., 2022). in this context, the strategic importance of the bioeconomy has become evident in its potential to create shorter and more circular bio-based value chains, thus reducing dependence on imported basic resources (farcas et al., 2020; galanakis et al. 2022; european commission, 2022). an additional step in this direction is the council of the european union’s conclusions on the opportunities of the bioeconomy, approved on april 25, 2023. these conclusions emphasize the potential of the bioeconomy to address challenges such as climate change, fossil fuel dependency and food security, as well as contributing to increased resilience3. despite its recognized strategic importance, the literature examining the role and economic performance of the bioeconomy under the aforementioned events is still scarce and inconclusive. some studies provided qualitative insights into the economic impact of these events on the bioeconomy (see fritsche et al., 2021; galanakis et al., 2022; kulisic et al, 2021 or woźniak & tyczewska, 2021). an ex-ante quantitative assessment was also provided by gonzález-martínez et al. (2020). however, to the extent of our knowledge, an ex-post analysis on the impact of these events on the bioeconomy is still missing in the academic literature. in june 2023, the eu-bioeconomy monitoring system4 (eu-bms, hereinafter) was updated with data on employment and value added in the bioeconomy sector 3 access to the press release and related documents: https://www.consilium.europa.eu/en/press/press-releases/2023/04/25/promoting-a-moresustainable-competitive-and-resilient-europe-and-boosting-rural-areascouncil-approves-conclusions-on-the-opportunities-of-the-bioeconomy/ 4 access to the eubioeconomy monitoring system: https://knowledge4policy.ec.europa.eu/bioeconomy/monitoring_en for 2020. this fact opens the possibility of analysing the performance of the bioeconomy during the pandemic. therefore, this article aims to fill the gap in the literature by using the latest release of the eu-bms to answer the following research questions: – what was the impact of the pandemic on the bioeconomy in the eu and its member states? – did the bioeconomy sectors exhibit greater resilience compared to the overall economy and other sectors? – are there any drivers or common sectoral patterns explaining the performance of the bioeconomy across countries in 2020? this short article is structured as follows. section 2 describes the methodology to estimate jobs and value added in the eu bioeconomy. section 3 presents and discusses the main results. section 4 concludes. 2. data and methodology the sectoral scope of this study comprises all biomass producing and transforming activities, namely the primary sectors and the bio-based manufacturing and electricity ones presents the selected sectors in this study and their contributions to the bioeconomy. the indicators on employment and value added in these sectors from the eu-bms are computed following the methodology proposed by ronzon et al. (2018, 2020, 2022) and lasarte-lópez et al. (2023a). the process involves two main steps. in the first step, data from eurostat is collected and cleaned for sectors falling within the bioeconomy scope defined by the eu’s bioeconomy strategy (european commission, 2018). national accounts data is used for primary sectors (nama_10_a64_e for employment and nama_10_a64 for value added), while structural business statistics (sbs_na_ind_r2) is used for bio-based manufacturing and electricity. in the second step, output bio-based shares are applied to those sectors considered as ‘hybrid’ (their output can contain biomass but also other non-bio-based materials). these shares inform about the proportion of final production by sector made of biomass. therefore, this approach assumes that the quantity of jobs and value added from each sector allocated to the bioeconomy is proportional to its bio-based output. the bio-based shares are initially prepared at the product level (for each item in the prodcom product classification). the proportion of biomass incorporated by all products is estimated using expert knowledge and scientific literature review. this information is then aggregated to determine sectoral bio-based shares at https://doi.org/10.36253/bae-14827 https://www.consilium.europa.eu/en/press/press-releases/2023/04/25/promoting-a-more-sustainable-competitive-and-resilient-europe-and-boosting-rural-areas-council-approves-conclusions-on-the-opportunities-of-the-bioeconomy/ https://www.consilium.europa.eu/en/press/press-releases/2023/04/25/promoting-a-more-sustainable-competitive-and-resilient-europe-and-boosting-rural-areas-council-approves-conclusions-on-the-opportunities-of-the-bioeconomy/ https://www.consilium.europa.eu/en/press/press-releases/2023/04/25/promoting-a-more-sustainable-competitive-and-resilient-europe-and-boosting-rural-areas-council-approves-conclusions-on-the-opportunities-of-the-bioeconomy/ https://www.consilium.europa.eu/en/press/press-releases/2023/04/25/promoting-a-more-sustainable-competitive-and-resilient-europe-and-boosting-rural-areas-council-approves-conclusions-on-the-opportunities-of-the-bioeconomy/ https://knowledge4policy.ec.europa.eu/bioeconomy/monitoring_en https://knowledge4policy.ec.europa.eu/bioeconomy/monitoring_en 369bioeconomy and resilience to economic shocks: insights from the covid-19 pandemic in 2020 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14827 the 2and 4digit levels of the nace classification (see ronzon et al., 2017, for details). the latest release of the eu-bms indicators was conducted with a different data pre-processing than previous releases. this new pre-processing incorporates additional economic information (when available) to estimate missing values. specifically, national accounts estimates on employment and value added by sectors are used as auxiliary variables to compute missing values in structural business statistics (see lasarte-lópez et al., 2023a, for details). 3. results 3.1. general trends in the eu the eu bioeconomy provided 17.16 million jobs in 2020 (8.3% of total employment) and contributed with 664.82 billion euro value added (4.9% of total gdp). these figures reflect a decline in employment above the eu average in comparison to 2019 (-1.7% vs. -1.4%), and a slight decline in the value added with regard to gdp (-0.4% vs. -4.0%). the decline in employment and value added are explained by differences in behaviours by sector. figure 1 illustrates the growth rates of employment and value added and their breakdown by sector. as for employment, most sectors registered negative growth in 2020, with the primary and the traditional biomass-transforming sectors (particularly, food and textiles) explaining a large portion of the total decline. regarding value added, the impacts are mixed; while more traditional bio-based sectors exhibited a negative impact (excluding wood and furniture manufacturing sectors), the bio-based chemicals and pharmaceuticals, plastics, and rubber sectors continued to grow in 2020. 3.2. analysis by member states the heterogeneous behaviour is also manifested in the evolution of the bioeconomy for the 27 eu member states (ms). section 3.2.1 describes the employment dynamics in the bioeconomy sectors by ms, while section 3.2.2 focuses on value added by ms. table 1. sectoral scope and bio-based share by sector. sectors nace codes aggregated bio-based share for the eu27 primary sectors agriculture, forestry and fishing a01, a02, a03 100 food, beverages and tobacco c10, c11, c12 100 bio-based textiles c13, c14, c15 42.0 bio-based manufacturing and electricity wood products and furniture с16, с31 72.4 paper c17 99.5 bio-based chemicals, pharmaceuticals and rubber c20, c21, c22 13.7 bio-based electricity d3511 5.8 own elaboration from lasarte-lópez et al. (2023b). -3,0% -2,5% -2,0% -1,5% -1,0% -0,5% 0,0% 0,5% 1,0% 1,5% 2,0% 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 employment -2,0% -1,0% 0,0% 1,0% 2,0% 3,0% 4,0% 5,0% 6,0% 7,0% 8,0% 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 value added figure 1. growth rate of employment and value added in the eu bioeconomy, and decomposition by sector. source: own elaboration from lasarte-lópez et al. (2023b). https://doi.org/10.36253/bae-14827 370 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14827 jesús lasarte-lópez et al. 3.2.1. employment in the bioeconomy by ms table 2 shows the growth rate of employment in 2020 for the bioeconomy, the total economy, primary sectors, the bio-based manufacturing and electricity sectors, and the total manufacturing sector. despite biobased employment registering a slightly higher decrease than total eu employment, there were 15 ms where employment in the bioeconomy sectors overperformed that of their respective aggregated economies. this is particularly true for finland, france, latvia, poland and slovakia, where employment in the bioeconomy grew while decreasing (or remaining stable) in the overall economy. conversely, luxembourg and malta registered notably better employment growth in the total economy compared to the bioeconomy. splitting the bioeconomy employment into agriculture and bio-based manufacturing and electricity, we see that both subsectors registered a decrease in employment in 9 ms. the consequent overall negative employment performance of the bioeconomy is aligned with the total economy (besides the already mentioned luxembourg). conversely, only finland and latvia registered growth in employment for both subsectors. in the remaining 16 ms, the two subsectors registered variations of opposite signs. in 10 cases, the overall performance of the bioeconomy was driven by the primary sector (4 decreases and 6 increases). in the other 6 cases, the bio-based manufacturing conditioned the sign of the overall bioeconomy (3 increases and 3 decreases). within the eu manufacturing sector, the decrease of bio-based employment in 2020 was less pronounced compared to the overall manufacturing sector. bio-based employment outperformed total manufacturing employment in 17 ms, with 8 ms even experiencing growth in bio-based employment while the overall manufacturing sector witnessed a decrease. however, the remaining 9 ms registered greater declines in bio-based employment than the total manufacturing. only ireland recorded growth in both categories, although the growth rate was lower for bio-based employment. figure 2 decomposes the aggregate growth rate of bio-based manufacturing and electricity sectors. the figure reveals that there are no clear patterns observed across ms. however, two sectors appear to explain most of the growth in the top-performing ms: (1) wood products and furniture (eastern and northern countries such as latvia, slovakia, estonia, finland and slovenia, with the exception of spain) and (2) the manufacturing of food, beverages and tobacco (france, denmark and, to a lesser extent, finland and spain). in contrast, for countries experiencing negative growth in employment, the main drivers are the food, beverages and tobacco sectors, as well as the bio-based textiles sector. notably, bulgaria, portugal and romania show a significant impact from both sectors. food, beverages and tobacco explains most of the negative growth in germany, sweden, and luxembourg, while the bio-based textiles manufacturing sector experienced particularly poor performance in italy. 3.2.2. value added in the bioeconomy by ms the analysis of value added draws a slightly different picture (see table 3). as for the eu, the value added growth of the bioeconomy outperformed that of the overall economy in 22 of the 27 ms. within these countries, the added value of the bioeconomy grew while the total economy decreased in 12 of them; both magnitudes increased in lithuania, bulgaria and denmark and decreased in the remaining 7 ms. in the other 5 ms, the bioeconomy performed worse than the total economy: in luxembourg and ireland by growing less than the total economy; in finland and romania by decreasing more. only in sweden value added in the bioeconomy decrease while it slightly increased in the total economy. the growth of value added in the two main subsectors of the bioeconomy (agriculture and bio-based manufacturing and electricity) exhibited the same sign in 15 ms, with 11 of them experiencing positive growth and being negative in the other 4. in other 4 cases, the value added in primary sectors grew while it decreased in the bio-based manufacturing, resulting in all cases in a negative overall decrease of the bioeconomy, except for spain. as for the remaining 8 ms, the bio-based manufacturing sector recorded growth in valued added while there was a decrease in agriculture. the combined effect of these trends resulted in an overall growth for the bioeconomy, except in hungary, portugal, and sweden. when comparing the evolution of value added between both bio-based and total manufacturing, the direction of value added growth for the two sectors was the same in 11 ms (7 negative and 4 positive). in 15 of the remaining 16 ms, the bio-based sector grew while the total manufacturing decreased. the only exception was greece. it is worth noting that, according to table 3, countries with a more positive or less negative gdp trend tend to be positioned in the upper half of the table when ranked by the overall growth of bioeconomy sectors, particularly in terms of value added. assuming that the decline in a country’s gdp is related with the pandemic’s impact (including lockdown implementation and the effectiveness of measures taken to mitigate the shock), this national https://doi.org/10.36253/bae-14827 371bioeconomy and resilience to economic shocks: insights from the covid-19 pandemic in 2020 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14827 effect on the performance of the bioeconomy partially explains the observed heterogeneity across countries. the dynamics of value added within the bio-based manufacturing and electricity subsectors also present a high degree of heterogeneity. figure 3 shows the contribution by sector to the growth rate of value added. similar to employment, two sectors play a substantial role in driving positive growth in bio-based manufacturing for the top-performing ms: (1) wood products and furniture (latvia, lithuania, estonia, slovakia, slovenia and luxembourg) and food, beverages and tobacco (lithuania, slovakia, bulgaria and the netherlands). among the countries with a lower (negative) growth in value added, the food beverages and tobacco sectors were also important drivers. these countries are located in southern and eastern europe, i.e., croatia, italy, romania, spain and greece. within them, croatia experienced the poorest performance in these sectors, which explain most of the decline in value added within its bioeconomy. in contrast to employment, there is a generalized positive impact across countries from the bio-based chemicals and pharmaceuticals, plastics and rubber, as well as the bio-based electricity sectors. table 2. employment change (%) in the bioeconomy sectors and in the overall economy by ms (2020). note: the categories identified by each typology of arrow are defined following the same classification than mubareka et al. (2023), where a negative performance in 2020 (below -1.0%) is flagged with a red arrow, a stable one (between -1.0% and 1.0%) is remarked with a yellow arrow, and a good performance (above 1.0%) is assigned a green arrow. source: own elaboration from lasarte-lópez et al. (2023b). https://doi.org/10.36253/bae-14827 372 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14827 jesús lasarte-lópez et al. 3.3. sectoral trends across the pandemic the next step is to identify if there were common break in sectoral across countries due to the disruption of the pandemic. for this purpose, we conducted a paired sample t-test to determine whether the growth of bioeconomy sectors by country in 2020 differed significantly from the average growth during the period 20142019, which covers the last expansionary phase of the business cycle in the eu27 before the covid-19 shock. the results are shown in table 4. for the overall bioeconomy, there are no significant breaks in employment trends, which contrast, with the statistically significant negative difference observed in the total employment variation. however, for value added, we identify a statistically significant difference in the growth rate of 2020 compared to the 2014-2019 average, although this difference is of lesser magnitude than the observed for total value added. in the two big sectors of the bioeconomy, a common break in trends is identified for both employment and value added in the bio-based manufacturing and electricity sectors, but not for agriculture, forestry and fishing. the greater relative weight of employment in the primary sector in the bioeconomy can explain the absence of a statistically significant break in total employment within the bioeconomy. as for value added, the lower relative contribution of primary sectors would not offset the negative impact of bio-based industries, therefore explaining the statistically significant break in the total bioeconomy. from a sectoral point of view, the differences in the growth rate of 2020 across countries are statistically significant for the more traditional biomass-processing sectors, namely, the manufacturing of food, beverages and tobacco, bio-based textiles and, only for employment, the paper industry. regarding bio-based chemicals and pharmaceuticals, plastics and rubber, we find no statistically significant differences in the growth trends of this sector across the eu countries. as spotted in section 3.2.2, the contribution of this sector was positive for most counfigure 2. decomposition of growth in employment in the bio-based manufacturing and electricity sectors (2020). source: own elaboration from lasarte-lópez et al. (2023b). -8% -6% -4% -2% 0% 2% 4% 6% 8% 10% lv sk fr ee fi dk es si ie m t hu cy nl at cz be hr lt pl eu 27 el lu se it de ro pt bg bio-based electricity liquid biofuels bio-based chemicals and pharmaceuticals, plastics and rubber (excl. biofuels) paper wood and furniture bio-based textiles food, beverage and tobacco total bio-based manufacturing and electricity https://doi.org/10.36253/bae-14827 373bioeconomy and resilience to economic shocks: insights from the covid-19 pandemic in 2020 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14827 tries in terms of value added. this is probably explained by the crucial role of the bio-based pharmaceuticals sector during the covid-19 pandemic, which was reflected in the economic performance of this sector. the existence of similar breaks in certain sectors suggests that the observed heterogeneity in the overall performance of the bioeconomy across ms could also be partially explained by their sectoral composition. thus, the specialization of some countries in traditional biomass-processing sectors (particularly food and textiles) could have had a negative effect on the aggregate performance of their bioeconomies (e.g., the cases of hungary, italy, france or spain). 3.4. comparison of results with previous studies the covid-19 pandemic and the measures taken to contain it had strong economic consequences in most countries worldwide. in the eu ms, there were widespread falls in production and employment levels (oecd, 2020). besides the effects of restrictions, the collapse of oil prices also hindered the development of the bio-based economy (chulok, 2021). according to fritsche et al. (2021) and galanakis et al. (2022), the effects of the pandemic on the production and distribution of biomass value chains were primarily explained by changes in the demand structure by sector and labour shortages in the supply side, due to mobiltable 3. value added change (%) in the bioeconomy sectors and in the overall economy by ms (2020). note: the criteria that define the orientation of each arrow is the same as in table 2 (see note). source: own elaboration from lasarte-lópez et al. (2023b). https://doi.org/10.36253/bae-14827 374 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14827 jesús lasarte-lópez et al. ity restrictions. however, an expert survey conducted by kulisic et al. (2021) revealed that biomass supply chains demonstrated overall resilience to the pandemic shock: no significant changes in the level of aggregated production were identified for the bio-based industries, and potential productivity gains were anticipated. these findings align with the results in section 3. the decline in value added was less pronounced than that of employment within the bioeconomy, as well as in comparison to gdp. galanakis et al. (2022) highlight that the primary and food manufacturing sectors (nace codes a01, a03, c10 and c11) experienced labour shortages and sharp decreases in demand from food services, which led to income decreases. gonzález-martinez et al. (2020) anticipated a higher resilience in the agriculture sector, and limited impacts on the agri-food sectors overall. these findings are also consistent with our own analysis, as no breaks in trends are found for the primary sectors across ms, and the decline in employment and value added in the agri-food sectors generally remained below the eu average for the total manufacturing sector. the textiles industries (c13, c14 and c15) experienced an increase in demand for protective clothing (e.g., masks) (galanakis et al., 2022). however, according to our analysis, this foreseeable increase did not have a positive impact on the demand of bio-based products, which is affected by the general decline of the sector. this decrease was more pronounced in those countries most affected by the pandemic in the first stage (therefore, implementing stronger restrictions). for instance, the textile sector in italy registered an important decrease in jobs and value added. galanakis et al. (2022) also identified the paper, wood products and furniture industries (c16, c17 and c31) as mainly affected by changes in demand (decrease in wood demand for construction, increase in wood products for home and pallets for distribution). based on our estimates, the net effect on employment and value added was positive, especially in the main woodfigure 3. decomposition of growth in value added in the bio-based manufacturing and electricity sectors (2020). source: own elaboration from lasarte-lópez et al. (2023b). -15% -10% -5% 0% 5% 10% 15% 20% lv lt ee si sk bg lu nl dk pl be hu de m t at ie se cz pt eu 27 fr cy el es ro fi it hr bio-based electricity liquid biofuels bio-based chemicals and pharmaceuticals, plastics and rubber (excl. biofuels) paper wood and furniture bio-based textiles food, beverage and tobacco total bio-based manufacturing and electricity https://doi.org/10.36253/bae-14827 375bioeconomy and resilience to economic shocks: insights from the covid-19 pandemic in 2020 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14827 producing economies (nordic and baltic countries). the increase in the price level of wood as commodity is behind this growth, which conditioned the performance of their overall bioeconomies. the bio-based chemicals and pharmaceuticals, plastics and rubber industries sectors (c20, c21 and c22) witnessed a generalised increase in demand for products such as ethanol and alcohols (for disinfectants). an increased demand for bio-based plastics is also identified, given the higher usage of one-single use plastics products (galanakis et al., 2022, fritsche et al., 2021, woźniak and tyczewska, 2021). these facts, besides the crucial role of the pharmaceuticals sector during the pandemic, are consistent with the superior performance of these sectors in the eu and most ms in 2020. 4. concluding remarks the economic consequences of the major events occurring since 2020 (the covid-19 pandemic and, more recently, the russian invasion of ukraine war) underlined the potential role of the bioeconomy not only to achieve environmental sustainability but also socioeconomic stability. our results shows a higher level of resilience of the eu bioeconomy compared to the overall economy in the initial stage of the pandemic. while employment in the bioeconomy declined similarly to the eu average in 2020 (-1.7% vs -1.4%), value added fell substantially below (-0.4% vs -4.0%). as the primary sectors remained stable, this greater resilience was driven by some bio-based sectors such as chemicals, pharmatable 4. mean and variance of sector growth by period, and results of the t-test for paired two sample for means. employment value added average growth 2014-2019 (variance) change 2020 (variance) p-value average growth 2014-2019 (variance) change 2020 (variance) p-value total bioeconomy -0.0030 -0.0078 0.3277 0.0384 0.0179 0.0335** (0.0003) (0.0005) (0.0002) (0.0025) agriculture, forestry and fishing -0.0142 -0.0123 0.8012 0.0282 0.0271 0.9633 (0.0004) (0.0013) (0.0008) (0.0139) bio-based manufacturing and electricity 0.0125 -0.0049 0.0231** 0.0480 0.0185 0.0070*** (0.0004) (0.0009) (0.0003) (0.0032) food, beverage and tobacco 0.0157 -0.0133 0.0001*** 0.0417 0.0066 0.0044*** (0.0004) (0.0006) (0.0005) (0.0029) bio-based textiles -0.0133 -0.0635 0.0004*** 0.0267 -0.0806 0.0008*** (0.0004) (0.0035) (0.0009) (0.0179) wood and furniture 0.0094 0.0193 0.5213 0.0601| 0.0698 0.8012 (0.0015) (0.0036) (0.0010) (0.0313) paper 0.0136 -0.0060 0.0189** 0.0521 0.0014 0.0546* (0.0006) (0.0011) (0.0009) (0.0147) bio-based chemicals and pharmaceuticals, plastics and rubber 0.0318 0.0376 0.6077 0.0736 0.1059 0.1528 (0.0009) (0.0040) (0.0030) (0.0157) bio-based chemicals and pharmaceuticals, plastics and rubber (excl. biofuels) 0.0339 0.0379 0.7581 0.0854 0.1151 0.1928 (0.0009) (0.0050) (0.0129) (0.0174) liquid biofuels 0.0457 0.4952 0.2420 0.1477 0.5688 0.2406 (0.0454) (3.2358) (0.0793) (3.1269) bio-based electricity 0.1429 0.1352 0.9169 0.1324 -0.1690 0.3203 (0.0300) (0.0866) (0.0351) (2.0624) total economy 0.0168 -0.0120 <0.0001*** 0.0466 -0.0251 <0.0001*** (0.0001) (0.0002) (0.0008) (0.0014) total manufacturing and electricity sectors 0.0119 -0.0216 ≤0.0001*** 0.0539 -0.0368 <0.0001*** (0.0001) (0.0003) (0.0026) (0.0027) note: the null hypothesis is rejected with a significance level of 10% (*), 5% (**) or 1% (***). source: own elaboration from lasarte-lópez et al. (2023b). https://doi.org/10.36253/bae-14827 376 bio-based and applied economics 12(4): 333-366, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14827 jesús lasarte-lópez et al. ceuticals, bioelectricity and wood products, which partially offset the negative impact on more traditional biomass-processing sectors (mainly food, beverages, tobacco, and bio-based textiles). at the ms level, the bioeconomy performance was quite heterogeneous, although a potential effect of the country overall economic performance was identified. furthermore, the disruptions observed in traditional biomass-transforming sectors (food, textiles and paper) were generalised across countries, while bio-based chemicals and bioelectricity kept their positive growth trends in most countries. these findings suggest that the sectoral composition of the bioeconomy could also have an impact on its overall performance at the country level, negatively affecting those countries with higher specialization in the aforementioned traditional biomasstransforming sectors. the lower relative impact of the pandemic shock on the bioeconomy provides empirical evidence for the academic literature and the policy documents supporting the need for the eu to reinforce the bio-based value chains (e.g. farcas et al. 2020, galanakis et al. 2022; european commission, 2018, 2022), so as to fulfil sustainability goals while enhancing socioeconomic resilience to economic shocks and disruptions in the global value chains. these insights are subject to some limitations, caused by the data availability. for 2021 onward, the available information is still scarce, due to long publication delays of some of the required data sources (14 months in the case of the prodcom survey, needed for the product-level bio-based shares; and 21 months for structural business statistics, the main data source for employment and value added in the bio-based manufacturing and electricity sectors). as the year 2020 is the most recent data point available in our dataset, it is still not possible to analyse the full impact of the pandemic on the bioeconomy (i.e., including the recovery in 2021) nor of the russian invasion of ukraine starting in 2022. an additional limitation is that the current composition on the jobs and growth indicators for the bioeconomy only considers biomass-producing and transforming sectors. the bioeconomy services are foreseen to be integrated in the future, following the methodology from ronzon et al. (2022b). these limitations also pave the way for future research. the in-depth analysis of the aforementioned events in 2021 and 2022 would be useful to further support (or not) the hypothesis of a stronger resilience of at least some bioeconomy sectors to economic shocks. the integration of services into the said indicators would also provide an opportunity to analyse the performance of the tertiary sector in comparison to biomass-producing and transforming sectors, as well as their non-biobased counterparts. funding this project received funding from an administrative arrangement between the directorate-general for research and innovation (dg rtd) and the joint research centre (jrc). acknowledgements the authors would like to thank adrian leip (dg rtd) and the colleagues from the knowledge centre of the bioeconomy for their feedback on the manuscript, and saulius tamosiunas for technical support. references chulok, a. 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the case of café de colombia nicola caravaggio (1) and cristina vaquero-piñeiro (2)* (1) department of economics, university of molise via francesco de sanctis, campobasso, italy; nicola.caravaggio@unimol.it; orcid: 0000-0002-9621-4008 (2) department of economics, roma tre university via silvio d’amico 77, rome, italy; cristina.vaqueropineiro@uniroma3.it; orcid: 0000-0002-1378-3361 *corresponding author: cristina.vaqueropineiro@uniroma3.it this paper has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the version of record. please cite this article as: caravaggio n., vaquero-piñeiro c., (in press). are geographical indications contributing to environmental conservation? the case of café de colombia, bio-based and applied economics, just accepted. doi:10.36253/bae-17307 abstract colombia has a rich history of coffee production spanning over 300 years. as one of the world's leading producers, the country dedicates a significant portion of its land to cultivating this vital crop. in the early 2000s, café de colombia received geographical indication (gi) certification, a milestone aimed at strengthening its reputation and competitiveness both nationally and internationally. this paper examines the impact of that recognition on land-use dynamics (i.e., deforestation). using the synthetic control method on a country-level panel dataset from 1992 to 2020, it estimates the effect of the gi certification on colombia’s national deforestation rate. the findings reveal that mailto:nicola.caravaggio@unimol.it https://eur04.safelinks.protection.outlook.com/?url=https%3a%2f%2forcid.org%2f0000-0002-9621-4008&data=05%7c02%7coriana.gava%40crea.gov.it%7c7c03e7a834404f55724808ddf43fd461%7cd59c04b9bde247f7b1b80be26a568618%7c0%7c0%7c638935277505053872%7cunknown%7ctwfpbgzsb3d8eyjfbxb0eu1hcgkionrydwusilyioiiwljaumdawmcisilaioijxaw4zmiisikfoijoitwfpbcisilduijoyfq%3d%3d%7c0%7c%7c%7c&sdata=yf1wchxbu8lkzdjz4hkvd7bb00wbrtgxtctsmy2wrie%3d&reserved=0 mailto:cristina.vaqueropineiro@uniroma3.it https://eur04.safelinks.protection.outlook.com/?url=https%3a%2f%2forcid.org%2f0000-0002-1378-3361&data=05%7c02%7coriana.gava%40crea.gov.it%7c7c03e7a834404f55724808ddf43fd461%7cd59c04b9bde247f7b1b80be26a568618%7c0%7c0%7c638935277505078327%7cunknown%7ctwfpbgzsb3d8eyjfbxb0eu1hcgkionrydwusilyioiiwljaumdawmcisilaioijxaw4zmiisikfoijoitwfpbcisilduijoyfq%3d%3d%7c0%7c%7c%7c&sdata=kwtf0q0uvylqzoeq43h7u%2fgq5p0vbwasixfxywk53j4%3d&reserved=0 mailto:cristina.vaqueropineiro@uniroma3.it 2 deforestation would have been about 0.2 percentage points lower on average without the acknowledgement. based on these results, the paper proposes to adopt a territorial-based and community-led policy mix to convert local peculiarities in strategic assets for sustainable development. funds this research was funded by the european union’s horizon europe research and innovation programme under grant agreement no 101136364. views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the european union and the european research executive agency (rea). neither the european union nor the granting authorities can be held responsible for them. 3 1 introduction colombia, along with brazil and vietnam, is one of the world's top three countries for coffee production, consumption, and export. (fnc, 2011; fao, 2022a). although with a decreasing trend over the last decades, more than 800 thousand hectares of national land, mainly managed by artisanal and family farmers, are still dedicated to this crop that remains a cornerstone of the national economy and identity. after the international coffee agreement ended in 1989, colombia adopted a quality differentiation strategy to strengthen the reputation of its coffee both nationally and internationally. in this effort, coffee’s place of origin became a key factor in defining quality (marescotti and belletti, 2016), making the protection of the origin name so important to certify the café de colombia as a protected geographical indication (pgi) in december 2004 (quiñones-ruiz et al., 2015). following the eu scheme, gi is a sign used on agri-food products that have a specific geographical origin and possess qualities and reputation that are essentially or exclusively due to a particular geographical environment, made of natural and human factors (reg. (eu) 1442/2024). the positive role of certifying a product within the gi scheme was stressed by economic literature that, among benefits, listed: premium pricing (duvaleix et al., 2021), farmers’ income (hughes, 2009; vaquero-piñeiro et al., 2025), consumers’ choices (menapace et al., 2011), trade (curzi and huysmans, 2022; de filippis et al., 2022; giua et al, 2024), and local development (takayama et al., 2021; crescenzi et al., 2022). the presence of gi products in local economies, in fact, generates a set of direct and indirect spill-over effects that become drivers of socio-economic development. consequently, the gi scheme is no longer an issue affecting only agrifood quality differentiation, but, conversely, has assumed a unique economic significance at the local level. more recently, the scheme has been enriched by a potential key role also in supporting the transaction towards more environmentally sustainable production processes (fao and origin, 2024). the new eu gi law (reg. eu no. 1443/2024) introduces, in fact, the possibility of including in the gis’ product 4 specifications sustainable criteria, encompassing environmental, social and economic objectives that go beyond mandatory standards (art. 7), and reporting on them through sustainable reports (art. 8). this reform marked a turning point in the gi market shaping its contribution on environmental sustainability: “the environmental objectives should include […] the conservation and sustainable use of soil, landscape, water and natural resources, the preservation of biodiversity, […] (reg. eu no. 1443/2024). so far, in fact, the gi scheme does not include highly specific production rules or environmental constraints (marescotti and belletti 2016). in this respect, over the last years, the eu has put a lot of effort into financing research projects that will provide evidence-based recommendations to strengthen the contribution of gi to sustainability, while fao proposed a sustainability strategy for gis with specific intervention themes, topics and indicators (fao and origin, 2024) (example of eu projects are: strength2food and gismart horizon projects). most of the existing studies attempting to evaluate the effects on a set of environmental indicators focused on european products (e.g., water and carbon footprint, food miles (caragliu et al., 2024), but some exceptions exist that also look at extra-eu gis (bagal et al., 2013; marescotti and belletti, 2016; bellassen et al., 2022). in this context, this paper aims to contribute to the literature on the environmental sustainability of extra-eu gis by presenting a first reflection on the environmental consequences of the acknowledgement of the café de colombia on national deforestation patterns. the analysis was conducted on a country-level panel dataset over the 1992-2020 period by employing a synthetic control method approach. the effect on forest pattern represents a critical issue for sustainable socio-economic analysis, given that forests, from various perspectives, are recognised as a key ecosystem for sustainable development (blackman et al., 2014). in developing countries, such as latin america, they play an even more crucial role in determining societies’ evolution and community well-being as a source of primary and non-primary goods (ros-tonen and wiersum, 2003; bakkegaard et al., 2017) and fighting climate 5 change (kramer et al., 1997; lugo, 1997; houghton et al., 2000; cunningham et al., 2015; wisniewski and marker, 2019). the acknowledgement of a gi may have a dual consequence, indeed. on the one hand, even if not automatically, but rather based on producers' and institutions' agreements, it may limit the deforestation practice by defining a production area that does not affect forest ecosystems and establishing higher environmental sustainability requirements. on the other hand, however, it could also be true that the economic benefits generated by gi may lead to expansion of agricultural land at the expense of forests, if not well designed and managed. the empirical setting designed for the study is ideal for several reasons. firstly, the study focuses on a pioneering case of extra-eu gi. the extension of the scheme to extra-eu products represents a strategic tool to accomplish trade agreements without making use of tariff measures (unctad, 2019). nowadays, extra-gis are more than 360, and their role in trade agreements will become even more relevant in the future, given the extension of this scheme to manufacturing and craft products. second, the analysis investigates the effects of the gi policy since its inception in colombia in 2005, rather than looking at a specific time subset, and looks at a strategic environmental outcome not only for climate adaptation, but also for the socio-economic development of local communities. conversely to the existing studies, which conducted similar research by using qualitative approaches (ingram et al., 2020), this paper estimates the effect by means of causal empirical methodologies, making the approach replicable. in this context, the paper provides a welcome basis for policy debate on the potential impact of such a policy scheme on the preservation of ecosystems and natural habitats (fao and origin, 2024). the remainder of this paper is organised as follows. section errore. l'origine riferimento non è stata trovata. details the contextual framework guiding this paper, which includes the history of coffee production in colombia, its link with forests and the institutional background of gis. section 6 3 introduces the empirical setting, section 4 presents the results, together with some robustness checks, while section 5 offers some policy implications and advice. eventually, section 6 concludes the manuscript. 2 conceptual framework in the eu, the gi quality scheme was established in 1992 to protect the name of local high-quality agri-food products whose characteristics are linked to the place where they are made, the so-called region of origin (reg. eu no. 2024/1143). even if preserving high-quality local productions and reducing information asymmetry between producers and consumers remain the main aims of this scheme, over the years, several socio-economic territorial externalities that literature demonstrated are associated with gis. among them, papers stressed the capacity of increasing agricultural added value (cei et al., 2018), supporting local development (crescenzi et al., 2022; de simone et al., 2023; vaquero-piñeiro et al., 2025) and internationalisation (giua et al., 2024). at the same time, scholars have demonstrated that gis may support the transition towards more sustainable food systems and diets (galli et al. 2020; gocci and luetge, 2020; vaquero-piñeiro and curzi, 2024), with some positive evidence on the contributions to achieving environmental sustainability targets (arfini et al., 2019). however, a contradictory picture about positive-negative implications of gis remains for different components of environmental protection (falasco et al., 2024). the gi scheme is, in fact, not necessarily linked to preserving natural resources and several can be the transmission channels through which this scheme may affect, positively, negatively or both, environmental performances. the 2024 fao sustainability strategy for gis clearly highlighted the fact that gis can be more sustainable than their conventional counterparts, but not automatically. the sustainability of a gi largely depends on how it is structured and on the extent to which local actors (e.g., producers, institutions, governments) collaborate to invest in sustainable practices. farmers and producers acting collectively, for example, may have more powers than individual producers and take environmental responsibilities (gori and sottini, 2014). environmental objectives could be 7 introduced in the gi product specifications by limiting the region of origin with respect to pre-existing ecosystems or by making environmental production practices mandatory for all the gi producers. among the key environmental challenges faced by gi systems, the fao strategy explicitly highlights forest conservation and regeneration as a specific commitment for gi producers. by increasing the profitability of productions, and consequently the rent for producers and the value of land, the acknowledgement as a gi could constitute a risk for forest preservation. the economic benefits generated by agricultural activities (gonzález-gonzález et al., 2021; armenteras et al., 2013) and gi reputation could enhance land-use changes from forest to agricultural land. to increase production without affecting the quality through the shift toward intensive production processes, producers can operate on the extensive margin by extending the production area at the expense of non-agricultural land, such as forest land. in addition, land use changes may be induced by the free-riding behaviour of producers that offer similar non-certified goods, i.e., colombian coffee without the gi certification. they indirectly benefit from the origin reputation induced by the gi without having any mandatory requirements to respect (menapace and moschini, 2024; mao and görg, 2025). for example, they can use the most recent coffee varieties that, conversely to the autochthonous coffee varieties that grew in the shade of natural forests (beer, 1988), are sun-grown, do not need shade and can be planted instead of forest (fnc, 2008; jha et al., 2011; somarriba and lopez-sampson, 2018; gonzález-gonzález et al., 2021). 2.1 the colombian case study coffee arrived in colombia in the 18th century, introduced by jesuits and over the decades has become the most widespread agricultural production of the country, accounting for 16% of the national agriculture gross domestic product (gdp) (ciravegna et al., 2024). more than 500,000 people are employed in the coffee production, with a great number of small coffee growers (95% smaller than 5 hectares) and a few international corporations working as roasters, traders and distributors (barjolle et al., 2017). from the 1930s, the colombian coffee market was regulated by the national federation 8 of coffee growers, which, twenty years later, introduced a common strategy to differentiate its product and preserve its authenticity. over the years, the claim “colombian coffee” has become a guarantee of quality worldwide, and in the early 1980s, a specific trademark was registered (i.e., juan valdez). in 2005, colombia applied for certified café de colombia as a pgi under the eu gi scheme (quiñones-ruiz et al., 2015), becoming the first non-eu agri-food product recognised by the eu. with the gi acknowledgement, the reputation of café de colombia increased even more, becoming, on the one hand, a crucial production for the economic development, while, on the other hand, a potential risk to local ecosystems, such as the forest. the colombian area is covered, in fact, for more than half (53.3%) of its surface by forest area and hosts 8% of the entire amazon rainforest (fao, 2022a; hansen et al., 2013). the mountain region where coffee plantations are primarily located (armenteraas et al., 2011) was one of the regions that, over the last decades, experienced the largest share of deforestation (63 per cent) (gfw, 2023; gonzález-gonzález et al., 2021). as reported in the product specification: “growing coffee exists in the andean mountain range and its foothills, which run the length of the country”. in this context, it is presumable, therefore, that forests, which are included in the region of origin, are potential targets of expansion for producers seeking to enlarge cultivation areas, generating a negative impact on deforestation, as seen in the case of cafè de colombia. in colombia, in fact, the distance between agricultural areas and forest frontiers is mostly immediate, with a relatively small, if not absent, degraded area (hyde, 2012). 3. data and methodology to investigate what has happened in colombia after the acknowledgement of the café de colombia in 2005, we use a novel dataset at the national level, exploit the synthetic control method over the 1992-2020 period and rely on stata19 software (athey and imbens, 2017). 3.1 data 9 our analysis relies on a dataset arranged by considering different sources of data. regarding forest cover, we considered as a primary source the fao’s 2020 forest resources assessment (fao, 2022b) that represents an official source of forest cover data where countries must follow common guidelines (total forest cover as the sum of natural and planted forest. source: fao, 2018). to have a better cross-country comparison (hyde, 2012; puyravaud, 2003), we use as outcome variable the yearly deforestation rate, whose trend for colombia and the rest of the latin america is depicted by figure 1. figure 1 deforestation trend, colombia vs latin america. notes: the group of latin america is composed by: argentina, bolivia, barbados, belize, brazil, chile, colombia, costa rica, cuba, dominica, dominican republic, ecuador, el salvador, grenada, guatemala, guyana, haiti, honduras, jamaica, mexico, nicaragua, panama, paraguay, peru, uruguay, and venezuela. the dataset embodies several other variables which identify both socio-economic country characteristics and potential drivers of forest cover change and operate as controls in our model (table a1). first, we include the share of agricultural area, the index of agricultural trade openness calculated as the ratio between the value of agricultural exports and imports (hyde, 2012) and cereal yield, generally identified as a proxy to capture agricultural intensification (barbier and burgess, 2001). the agricultural land variable includes hectares dedicated to both crop and livestock. this allows us to control for all possible competing agricultural expansions. second, we control for population density and the share of people living in rural areas, which are considered by the literature as proxies of pressure on natural resources (geist and lambin, 2002; grau 10 and aide, 2008; carr et al., 2009). the variable of gdp per capita represents the core index of economic development for a country, widely used within the forest economics literature, especially through the environmental kuznets curve (ekc) hypothesis (caravaggio, 2020). we retrieved this variable from the world development indicators (wdi) of the world bank (wb, 2022), and it is expressed in constant 2015 us$. trade openness is another core economic variable of a country which may affect in different ways the use of natural and planted forests both negatively (angelsen and kaimowitz, 2001; libman and obydenkova, 2013; lankina et al., 2016; leblois et al., 2017) and positively (meyer et al., 2003; niklitschek, 2007; hyde, 2012). we proxied this variable by using the sum of import and export as share of total gdp as proposed from the wb (2022) from which data has been retrieved. to identify the role of institutions we relied on data provided by freedom house (2022) where the two variables of civil liberties and political rights have been merged within a unique proxy variable which identify the quality of institutions.1 good institutions are generally associated with better forest management, hence less deforestation (bhattarai and hammig, 2001, murtazashvili et al., 2019, cary and bekun, 2021). eventually, to account for the role played by climate change, we considered the variable of temperature change provided by fao (2022a) which aggregates at country level monthly data of mean temperature anomalies with respect of a baseline climatology corresponding to the period 1951-1980. 2.2 synthetic control method the synthetic control method is a counterfactual approach to estimate the impact of a policy on a single unit in panel data settings (abadie et al., 2010). it creates a synthetic control unit for the treated observation (colombia in our case) to simulate via a data-driven approach what its outcome path would be if it did not undergo the policy under analysis. the synthetic unit is obtained by combining and weighting the characteristics of a control group (i.e., donor pool) to construct an artificial control 1 the two variables of the freedom house (2022) both span from 1 to 7 where 1 is the highest level (i.e., high civil liberties and/or high political rights) while 7 the lowest. the variable used in our analysis is a rescaled sum of these two variables which spans from 1 to 10 (where 1 is the lowest institutional quality level and 10 the highest). 11 (not treated) unit that follows similar outcome’s pre-trends of the treated one, but that has not experienced the policy. some studies already exist that used synthetic control method to estimate the effects of different events on forest projections. sills et al. (2015) studied the impacts of local initiatives on the gross deforestation in brazil (i.e., loss of mature forest), rana and sills (2018) the effects of forest management certifications on tropical deforestation, while amador-jimenez et al. (2020) the impact of covid-19 lockdown on forest fires in colombia. furthermore, cappelli et al. (2022) used the synthetic control method to estimate whether the implementation in the national constitution of the buen vivir principles has proved effective in reducing forest losses in bolivia. in this paper, the treated unit is colombia, the policy is the acknowledgment of the gi scheme in 2005, while the pre-period is from 1992 to 2005 and the post-period from 2005 to 2020. the first pretreatment year is 1992 because it was when the eu food gi quality scheme came into force and extraeu countries started to replicate and adopt the same scheme nationally. the control group has been selected based on a set of socio-economic, cultural and environmental factors measured at the country level (table a1) that literature has identified as forest cover change determinants, and the value of the outcome variable averaged over the pre-trend years to help that the synthetic control unit adequately mirrors the treated unit’s deforestation trajectory before treatment (kaul et al., 2015; ferman et al., 2020). we need, in fact, a list of countries that are comparable to colombia, but that have never implemented a gi certification for coffee. the treatment variable has been reconstructed from the product specification downloadable from the eambrosia website and it is a dummy variable that takes the value one for colombia, and zero otherwise. to select it, we started from the entire list of 25 latin american countries and we discard: (i) small islands and tax heaven’ countries because in these areas forest cover might have benefitted or lost from their characteristics unrelated to forest issues (e.g., remote locations or economic conditions); (ii) countries for which relevant data are missing; (iii) dominican republic since in this country there 12 is a similar policy, the cafè of valdesia, which has been acknowledged as a gi in 2016. we are conscious that there are other latin american countries with gis, but in selecting our control group, we do not discard because: (i) they do not regard coffee industry, and therefore we cannot consider them the same policy; (ii) the gi acknowledgment arrived at the end/after the period of analysis and we do not have enough post period treatment; (iii) the gi refers to wine or spirits and therefore follows a different regulation (see table s7 in the supplementary materials – sm). according to the synthetic control method assumptions, to guarantee for the validity of the final control group (16 countries), we checked that the trends of the outcome variable in the treated country and in the donor pool were similar and that the values of pre-treatment predictors in the treated unit was neither the largest or the smallest of the sample (table a2). from the donor pool, the synthetic control method algorithm weighted all the observables for the synthetic control of colombia (table a3). 3 results errore. l'origine riferimento non è stata trovata. depicts the trajectories of treated and synthetic counterparts (figure a1 for p-values [galiani and quistorff, 2017]). figure 2 deforestation trend, colombia vs synthetic colombia. note: the yearly deforestation rate is expressed in hectares. see table a4 for the weights donated by each country of the donor pool. 13 the red line shows the outcome for synthetic colombia estimated as a counterfactual of what would have been observed for the affected unit in the absence of the intervention. before the policy began in 2005, the pre-treatment trends in deforestation were similar for the real and the synthetic colombia. since 2005, a gap has emerged, with colombia showing lower deforestation yearly rates. this suggests that, all else being equal, without the acknowledgement of café de colombia as a gi, the country would have overall experienced a lower deforestation rate. the estimated overall effect is approximately 0.2 percentage points, indicating a higher average deforestation rate over the entire post-treatment period. to quantify the impact of the treatment, we refer to the difference after treatment between the treated and the untreated cohort. it is calculated as the difference between the 2020-2005 difference in colombia and the 2020-2005 difference of synthetic colombia. 3.1 robustness analysis in order to verify the validity of our results, we conduct several robustness checks. first of all, we run a placebo test, commonly used in synthetic control method literature, by iteratively replicating the synthetic control method for every other state that did not certify coffee production as a gi during the sample period (abadie and gardeazabal, 2003; bertrand et al., 2004; abadie et al., 2010). figure 3 shows that the synthetic method provides a good fit for the deforestation trend in colombia before the gi quality scheme. overall, the gap between deforestation in colombia and in its synthetic counterpart during the pretreatment period is smaller than for the states in the donor pool, but it becomes relatively higher after the treatment (figure 3). figure 3 trends in deforestation in colombia (treated country) and placebo gaps. 14 notes: the grey lines represent the difference in deforestation between the respective synthetic control and the state in the donor pool (synthetic – treated); the bold black line represents the gap between colombia and its synthetic country (synthetic – treated). thereafter, we conduct two placebo tests simulating an artificial (placebo) timing of the policy (abadie et al. 2015, heckman and hotz, 1989): (i) we anticipate the treatment year to 2000; (ii) we postpone the treatment year to 2007, when the café de colombia was included in the eu gi list. the first test confirms the impact of the policy on the forest cover in colombia, given that there is no significant effect of the artificial 2000 policy (figure 4 – panel a; figure s1, table s1 in sm) (abadie et al., 2021). the second test validates our results highlighting a similar significant trend of the baseline estimations (figure 4 – panel b; figure s2, table s2 in sm): what influences the deforestation trend is, therefore, the moment when the cafè de colombia began to be discussed as a gi and introduced at the policy level, rather than the exact date of its official inclusion in the eu register, since producers already anticipated that recognition would eventually happen. figure 4 deforestation trend, colombia vs synthetic colombia (placebo timing of the policy). placebo timing of the policy (2000) panel a 15 placebo timing of the policy (2007) panel b note: yearly deforestation rate is expressed in hectares. see table s1 and table s2 in sm for the weights donated by each country of the donor pool. thereafter, we replicate the analysis by excluding brazil from the donor pool, which is the first latin american agriculture and coffee producer. neighbour countries, like colombia, may be affected by brazilian informal cross-border displacement of agri-food productions and exports (meyfroidt et al., 2010). results show that our main results are robust (figure s3, figure s4, and table s3 in sm). findings are mainly coherent and slightly significant, also when we use a different source of data for forest land: climate change initiative land cover (cci-lc) data (esa, 2017; fao, 2022a) (figure s5, figure s6, and table s4 in sm).2 even with this different source of forest cover data, the impact 2 according to the land cover reclassification (un, 2014), we combined tree-covered areas and mangroves. the former category comprises geographical areas dominated by natural tree plants with a coverage of at least 10%, hence both 16 of gi has a detrimental effect on deforestation rates in the first years after the introduction of the policy, while inverts in the long run. we also conduct two other robustness checks by considering (kaul et al., 2015): (i) the value of the outcome 𝑡 − 1 lag averaged over the pre-trend years as a predictor, rather than the outcome at 𝑡; (ii) all the lagged values of the outcome, but without other covariates (ferman et al., 2020). while in the former case, results are in line with the baseline estimations (figure s7 and s8, table s5 in sm for weights donated), in the latter case, some differences in terms of significance emerge (figure s9 and s10, table s6 in sm for weights donated). in the case under analysis, and in line with abadie and gardeazabal (2003) and abadie et al. (2010), covariates are deemed important and should be considered. excluding them is not a first-best solution to simply optimise the pre-treatment fit by using all pre-treatment values of the outcome as separate economic predictors. 5. discussion 5.1 policy implications we found that the gi scheme might have an overall negative impact on deforestation. these results can help policymakers to design policy interventions capable of addressing the characteristics, in terms of the local production system, that have generated this effect. overall, policy interventions should aim at preserving forest ecosystems while guaranteeing the higher income generated by gis (mourao and martinho, 2020). first, our study confirms that more coordinated governance is needed to reduce deforestation (furumo and lambin, 2021). in particular, policy interventions that constrain deforestation should be implemented in parallel with those aimed at enhancing economic revenues. natural and management forests. the latter category includes areas with a woody vegetation with 10% coverage of more but regularly flooded by salt and/or brackish water. data, aggregated at national level, has been retrieved from fao (2022a). 17 a concrete example for the colombian government and institutions to consider is the new eu forest strategy for 2030 (ec, 2021). it sets concrete actions to improve the quantity and quality of eu forests, while supporting the production of sustainable food through different policy interventions, such as a new approach to finance ecosystems restoration and environmental services (cantillon et al., 2025). second, the paper highlighted the need to set shared norms to guarantee that the gi scheme might contribute to enhancing environmental sustainability, given that it is not one of its primary objectives (ec, 2025). the acknowledgement as a gi, increasing the profitability of productions and, consequently, the rent producers and the value of land, might, in fact, constitute a risk for forest preservation. common lines of interventions should be set at the national level for the entire supply chain and all the actors involved, regardless of their business form and economic dimensions (meyfroidt and lambin, 2011). in the case under analysis, the adoption of sustainability strategies is obstructed by the high fragmentation of the supply chain, which is primarily managed by small-scale rural people and family farms (gibson et al., 2011; matricardi et al., 2020). a higher level of power concentration in the upward part of the supply chain may help them to achieve fairer remuneration, increase their market power and avoid the rising costs for marketing and distribution, especially in importing countries, which will decrease the farmers’ share in the coffee retail price. policy makers and practitioners should investigate which production model may be most suitable to strengthen the position of small local producers and promote a reorganisation of local production systems. new entrepreneurial opportunities based on coordination and cooperation among actors along the supply chain (arias and fromm, 2019; ico, 2022; ec, 2024; belso-martinez et al., 2024; vaquero-piñeiro and pierucci, 2025; quiñones-ruiz et al., 2016) and public-private partnerships are, in fact, considered as effective policy mechanisms to scale up impacts (furumo and lambin, 2020). as declared by the 2025 eu vision for agriculture and food, “encouraging [farmers] to join cooperatives and/or associations to reduce costs, increase efficiency and improve prices for the market (ec, 2025: 18 p. 7). in this context, the gi system is, per se, a first step toward cooperation, given its peculiar combination of competition and cooperation among geographically close firms (vaquero-pineiro et al., 2025). however, a governance regime with greater collaboration between environmental and industry stakeholders achieved better environmental outcomes (heilmayr and lambin, 2016). lastly, it should be appropriate for policy interventions managing the gi scheme in tandem with the increasing diffusion of eco-friendly private certifications, such as the rainforest alliance, that pose environmental sustainability as their prime objective (vanderhaegen et al., 2018; oberlack et al., 2023). in this context, the gi might consider the specific standards and requirements established by such certification as a welcome basis and integrate them into the product specification. the information system proposed in the product specification to monitor coffee growers could, for example, record the adoption of private schemes, thereby helping to identify which forest preservation requirements could be most easily incorporated into the specification. 5.2 limitations the study proposed has some limitations that should be discussed. first, the national level could appear too much aggregated and not capable of accounting for regional deforestation patterns. the national level is, however, the only suitable one, given that in the product specification, there are no specific administrative boundaries defining the region of origin. georeferenced data on forest and coffee plantations could potentially be downloadable from satellite databases, even with some limitations in terms of time span (hansen et al., 2013; sulla-menashe et al., 2011; buchhorn et al., 2020) and spatial resolution (liu et al., 2020). however, it will not be sufficient, given that causal impact estimations necessitate a set of socio-economic and demographic contextual information that does not exist at the gridded level. second, the study is limited by the fact that, based on the available data, it is not possible to distinguish between gi and non-gi coffee production. however, given the free-riding effects mentioned before, in this study, we can assume that, at least partially, the effect on land-use change 19 (from forest to coffee plantations) deforestation is, directly and indirectly, driven by the presence of the scheme. third, the model does not isolate the potential presence of other simultaneous events that may have impacted the colombian deforestation patterns in the post-treatment period. an example could be the long-lasting conflict within the revolutionary armed forces of colombia movement and the colombian government, which could represent an obstacle to forest preservation. however, given that it has lasted for almost the entire period under analysis (madr-upra, 2014), it can be considered a sort of contextual condition for the whole country (see sánchez-cuervo et al., 2012; murillo-sandoval et al., 2019; negret et al., 2019; clerici et al., 2020). fourth, the synthetic control method is sensitive to the donor pool selection, which we tested by changing the donor pool in the robustness checks and the assumption of non-spillover effects. the registration of a gi may incentivise the reallocation of non-gi productions in the neighbouring regions that have not been included in the region of origin, generating a new demand for agricultural area, also in those regions that have not been directly targeted by the intervention. territorial spillover effects are, however, more plausible across sub-national areas within the same country than at the national level (cortinovis and van oort, 2018; caragliu and landoni, 2024). 6 conclusions and takeaways does the gi scheme pose risks to the diversity of the ecosystem? by looking at the case of coffee production in colombia, , this paper highlights that for forest ecosystems, such a risk exists. without café de colombia’s gi recognition, deforestation would have been about 0.2 percentage points lower on average, indeed. to preserve local ecosystem services (i.e., forest), while enhancing economic performance, a pre-assessment of the effects of policy intervention is, therefore, required. in the case of gis, in fact, the acknowledgement of a product will enhance product reputation and competitiveness, but it could be at the expense of local ecosystems. policy strategies belonging to 20 different policy areas of interventions (i.e., agrifood, forest management and economic development) should be evaluated complementarily to obtain a comprehensive framework of the effects and design a policy mix capable of supporting sustainable development. in the case of colombia, the socio-economic development of rural and indigenous communities should be promoted through a sustainable forest management approach capable of guaranteeing the preservation of primary and oldgrowth forests. territorial-based interventions, such as the gi scheme, should be integrated into a local development strategy that can blend economic performance with natural resources conservation. this synergy may enhance local expertise, converting into a strategic asset for sustainable development. our analysis needs, however, to be hedged with some caveats, especially due to data limitations. a better harmonised database at the sub-national level for all the regions of the south american countries could help to improve the level of detail of the analysis. at the same time, more exhaustive outcomes accounting for environmental sustainability (e.g., emissions and water use for coffee production) could help to provide a more comprehensive assessment of the scheme. despite these limitations, our analysis offers new evidence to properly assess the environmental sustainability of the gi scheme. to our future agenda, we left investigating the linkages explored in this study in the case of other gi cafès (i.e., café de valdesia in the dominican republic or café antigua in guatemala). 21 appendix figure a 1 p-values – post-treatment period. note: p-values on the y-axis. table a 1 variables’ definitions. variable name definition source agricultural area share of agricultural (farming and livestock) area over total land area. faostat population density total population over land area. faostat people leaving in rural areas share of rural people over total population. faostat trade openness sum of import and export as share of total gdp. wdi cereal yield kilograms per hectare of harvested land. faostat temperature change mean monthly temperature anomalies with respect of a baseline climatology corresponding to the period 1951-1980. faostat gdp per capita gross domestic product per capita at 2015 constant us$. wdi house of freedom index sum of the two variables of civil liberties and political rights rescaled from 1 to 10 (where 1 is the lowest institutional quality level and 10 the highest). authors’ elaboration on freedom house data agricultural trade openness ratio between the value of agricultural exports and imports. authors’ elaboration on faostat data deforestation deforestation rate, calculated on total forest (natural and planted forest) for the reconstructed data and on the sum of tree and mangroves cover for cci-lc data. authors’ elaboration on faostat data note: faostat (source: fao, 2022a); freedom house (source: freedom house, 2022); wdi (source: wb, 2022). 22 table a 2 forest predictors’ means, colombia vs donor pool. agricultur al area populatio n density people leavin g in rural areas trade opennes s cereal yield temperatur e change gdp per capita house of freedo m index agricultur al trade openess argentina 0,45 0,14 0,1 28,67 3873,65 0,47 11722,3 1 7,99 13,82 bolivia 0,34 0,09 0,35 58,42 1682 0,6 2115,75 7,1 1,94 brazil 0,28 0,22 0,17 21,41 2829,03 0,95 7109,65 7,56 6,06 chile 0,21 0,22 0,13 60,55 4586,62 0,35 7806,03 9,34 2,13 colombia 0,4 0,38 0,24 33,48 2950,3 0,6 3974,18 5,86 1,85 costa rica 0,36 0,85 0,33 79,48 3144,67 0,37 7101,63 9,68 3,17 cuba 0.61 1.05 0.24 49.69 2472.74 0.80 4552.82 0.37 0.77 ecuador 0,28 0,56 0,38 42,15 1918,29 0,66 4322,66 6,81 3,21 el salvador 0.62 2.90 0.37 58.44 1879.78 0.80 2748.35 7.29 0.73 guatemala 0,4 1,25 0,53 43,41 1721,85 0,79 2956,13 5,26 2,12 guyana 0,06 0,04 0,72 166,51 3154,61 0,88 3114,65 7,79 2,13 honduras 0,3 0,69 0,49 76,08 1279,29 0,76 1678,16 6,03 1,6 jamaica 0.43 2.46 0.48 91.11 1515.45 0.81 4478.37 7.70 0.53 mexico 0,53 0,55 0,24 27,68 2128,62 0,77 6992,36 6,87 0,87 nicaragua 0,41 0,46 0,43 59,03 1424,42 0,71 1898,17 5,52 1,62 paraguay 0,39 0,14 0,45 51,09 1532,24 0,52 2951,95 6,41 3,93 peru 0,19 0,22 0,25 34,63 2034,98 0,8 3424,98 6,49 0,87 uruguay 0,84 0,19 0,07 34,52 1426,39 0,64 7237,17 9,68 3,83 venezuela 0,24 0,3 0,13 49,64 1658,7 0,66 12971,2 1 4,6 0,11 notes: variables are averaged on the pre-treatment period, from 1992 to 2020. 23 table a 3 pre-treatment forest predictors mean: colombia, synthetic colombia, and donor pool. colombia donor pool real synthetic agricultural area 0.396 0.278 0.386 population density 0.344 0.406 0.686 people leaving in rural areas 0.276 0.282 0.326 trade openness 30.904 42.336 57.417 cereal yield 2539.257 2537.35 2242.21 temperature change 0.409 0.491 0.692 gdp per capita 3535.416 4569.47 5196.75 house of freedom index 5.476 6.998 6.805 agricultural trade openness 2.356 2.371 2.747 outcome variable averaged over the pre-trend years 0.003 0.003 0.002 notes: variables are averaged from 1992 to 2005 (pre-treatment period). pre-trend outcomes from 1992 to 2005. the table summarises the comparison between the pre-treatment average values of covariates used to create the synthetic counterfactual with the average of colombia and the average of the donor pool. table a 4 weights donated by each country of the donor pool, colombia vs synthetic colombia. country synthetic colombia weight argentina 0 bolivia 0 brazil 0.161 chile 0.420 costa rica 0 cuba 0 ecuador 0 el salvador 0 guatemala 0.230 guyana 0 honduras 0 jamaica 0 mexico 0 nicaragua 0.040 perù 0.126 paraguay 0.023 uruguay 0 venezuela 0 notes: countries with positive weights are used as donors. 24 references abadie, a. and gardeazabal, j. 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(2019). the role of soil-protecting forests in reducing soil erosion in young glacial landscapes of northern-central poland. geoderma, 337, 1227-1235. 31 supplementary materials figure s 1 p-values – post-treatment period, placebo timing of the policy (2000). note: p-values on the y-axis. table s 1 weights donated by each country of the donor pool, placebo timing of the policy (2000). country synthetic colombia weight argentina 0.186 bolivia 0 brazil 0 chile 0.298 costa rica 0 cuba 0 ecuador 0 el salvador 0 guatemala 0.068 guyana 0 honduras 0 jamaica 0 mexico 0.349 nicaragua 0.085 paraguay 0.015 perù 0 uruguay 0 venezuela 0 32 figure s 2 p-values – post-treatment period, placebo timing of the policy (2007). note: p-values on the y-axis. table s 2 weights donated by each country of the donor pool, placebo timing of the policy (2007). country synthetic colombia weight argentina 0.060 bolivia 0 brazil 0.107 chile 0.288 costa rica 0 cuba 0 ecuador 0 el salvador 0 guatemala 0.115 guyana 0 honduras 0 jamaica 0 mexico 0.378 nicaragua 0.048 perù 0 paraguay 0.004 uruguay 0 venezuela 0 notes: countries with positive weights are used as donors. 33 figure s 3 deforestation trend, colombia vs synthetic colombia – excluding brazil. note: yearly deforestation rate is expressed in hectares. figure s 4 p-values – post-treatment period, excluding brazil. note: p-values on the y-axis. 34 table s 3 weights donated by each country of the donor pool – excluding brazil. country synthetic colombia weight argentina 0.077 bolivia 0.001 chile 0.333 costa rica 0.021 cuba 0.002 ecuador 0.001 el salvador 0.012 guatemala 0.127 guyana 0.001 honduras 0.001 jamaica 0.001 mexico 0.326 nicaragua 0.087 paraguay 0.009 perù 0 uruguay 0.001 venezuela 0 notes: countries with positive weights are used as donors. figure s 5 deforestation trend, colombia vs synthetic colombia – climate change initiative land cover (cci-lc) data. note: yearly deforestation rate is expressed in hectares. 35 figure s 6 p-values – post-treatment period, climate change initiative land cover (cci-lc) data. note: p-values on the y-axis. table s 4 weights donated by each country of the donor pool, climate change initiative land cover (cci-lc) data. country synthetic colombia weight argentina 0.083 bolivia 0 brazil 0 chile 0 costa rica 0 cuba 0.113 ecuador 0.113 el salvador 0.025 guatemala 0.021 guyana 0 honduras 0 jamaica 0 mexico 0.118 nicaragua 0 paraguay 0 perù 0.639 uruguay 0 venezuela 0 36 figure s 7 deforestation trend, colombia vs synthetic colombia – average of t-1 outcome. note: yearly deforestation rate is expressed in hectares. figure s 8 p-values – post-treatment period, average of t-1 outcome. note: p-values on the y-axis. 37 table s 5 weights donated by each country of the donor pool, average of t-1 outcome. country synthetic colombia weight argentina 0 bolivia 0 brazil 00161 chile 0.42 costa rica 0 cuba 0 ecuador 0 el salvador 0 guatemala 0.230 guyana 0 honduras 0 jamaica 0 mexico 0 nicaragua 0.040 paraguay 0.023 perù 0.126 uruguay 0 venezuela 0 figure s 9 deforestation trend, colombia vs synthetic colombia – all the lagged values of the outcome. note: yearly deforestation rate is expressed in hectares. 38 figure s 10 p-values – post-treatment period, all the lagged values of the outcome. note: p-values on the y-axis. table s 6 weights donated by each country of the donor pool, all the lagged values of the outcome. country synthetic colombia weight argentina 0.006 bolivia 0.0167 brazil 0.013 chile 0.002 costa rica 0.152 cuba 0.001 ecuador 0.017 el salvador 0.010 guatemala 0.086 guyana 0.046 honduras 0.006 jamaica 0.003 mexico 0.009 nicaragua 0 paraguay 0.028 perù 0.433 uruguay 0.001 venezuela 0.019 39 table s 7 other latin america gis and reasons for not discarding countries in the donor pool. gi name product category country year gi type reasons why we do not discard the country from the donor pool ron de guatemala spirit drinks guatemala 2014 pgi different regulations for spirits. tequila spirit drinks mexico 2019 pgi at the end/after the period under analysis no sufficient post policy years. pisco fruit spirit perù 2013 pgi different regulations for spirits. vale dos vinhedos wine brazil 2007 pdo different regulations for wines. demerara rum spirit drinks guyana 2021 pgi at the end/after the period under analysis no sufficient post policy years. camarão da costa negra food brazil applied not registered yet. ron de venezuela spirit drinks venezuela applied not registered yet. cuba spirit drinks cuba 2023 pgi at the end/after the period under analysis no sufficient post policy years. 1 agriculture and environment: the inclusion of italy in the simple and simpleg models cristina vaquero-piñeiro(1), maria laura ojeda(1,2,3), exequiel romero gomez(1,2,3), luca salvatici(1) (1) department of economics, roma tre university, rome, italy (2) universidad de buenos aires. facultad de ciencias económicas. departamento de economía. buenos aires, argentina. (3) conicet-universidad de buenos aires. instituto interdisciplinario de economía política. modelos económicos de simulación. buenos aires, argentina. this article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the version of record. please cite this article as: vaquero-piñeiro c, ojeda ml, gomez er, salvatici l (2025). agriculture and environment: the inclusion of italy in the simple and simple-g models, just accepted. doi:10.36253/bae-16785 abstract the paper presents a methodological contribution to analysing the interactions between economic and environmental factors by using the simplified international model of agricultural prices, land use and environment (simple) model and its gridded version (simple-g). while simple addresses national agricultural trends, simple-g offers a detailed, spatially disaggregated analysis essential for assessing local impacts. we improve these models by building a comprehensive database, capturing aggregate and georeferenced data regarding italy. the use of the models with the extended dataset may support the implementation of adaptation strategies offering boundary conditions for local decision-makers while reflecting the impact of policies on national and global variables. the article outlines the models’ structure, data sources, and calibration process, providing a preliminary application of simple for updating the baseline. 2 keywords: agriculture sustainability, climate change, simulation models, simple, simple-g jel codes: q18, c33, q54, q25 acknowledgements: authors would like to thank all the participants of the 13th aieaa conference who attended the organised session “integrating water and ecosystem services into economic models: insight from the rueesnexus and must4water projects”. special thanks go to arianna di paola (national research council of ital, institute for bioeconomy), the simple research group at purdue university (us), and the crea researchers for their suggestions. funds: this work was funded by the next generation eu – italian national recovery and resilience plan (nrrp), mission 4, component c2, investment 1.1, “fondo per il programma nazionale di ricerca e progetti di rilevante interesse nazionale (prin)” (directorial decree n. 2022/1409) under the project ʺmulti-scale modelization toward socio-ecological transition for water management (must4water) ʺ, n. p2022r8ztw. this work reflects only the authorsʹ views and opinions, neither the ministry for university and research nor the european commission. this work is also part of act4cap27, which is funded by the european union. horizon europe grant agreement no. 101134874. views and opinions expressed are however those of the authors only and do not necessarily reflect those of the european union or the european commission. neither the european union nor the granting authority can be held responsible for them. 3 1. introduction agricultural sustainability and food security face threats from unpredictable climate variations, especially in temperature and precipitation (world bank, 2022). like many other regions, italy has witnessed a discernible rise in severe drought events over the last two decades, profoundly affecting crop yields in some of the most agriculturally crucial areas of the country (spano et al., 2020). these changing and heterogeneous climatic events have distinct impacts on regional water availability, posing location-specific challenges for agricultural production and affecting prices and output both at a national and local level. in this context, tools are crucial to assess the local and global socioeconomic consequences of extreme weather events and shed light on effective adaptation strategies (dorfman et al., 2024). the paper presents a methodological contribution to analysing the interactions between economic and environmental factors by extending the simplified international model of agricultural prices, land use and environment (simple), and its georeferenced gridded version simple-g, to italy. 1 initially developed by baldos & hertel (2013) and later expanded by liu et al. (2017) and baldos et al. (2020b), both simple and simple-g are partial equilibrium models designed to analyse the dynamic interactions between economic and environmental systems. so far, the models are based on 17 regions, and the eu, including all of the member states, is one of them. our main contribution is to extend the resolution of the models and create a database where italy is singled out from the rest of the european union (eu).2 1 grids are spatial units of equal length and width, with the area varying according to the latitude of the grid cell (larger at the equator and smaller moving toward the poles). 2 the database already includes specific countries, such as the us, china, and brazil. in all these cases, gridded data was collected by merging data from different data sources and harmonising the dimensions of the grids at a unique scale. 4 italy is an ideal setting to test the model's validity for an eu country. first, several extreme climatic events have occurred in italy over the last few years, and they most affected agriculture. the number of severe floods, river overflows, record heatwaves in urban and rural areas, landslides, and glaciers retreated is constantly increasing, exacerbating climate stressor effects on agricultural production.3 second, the agrifood sector in italy still accounts for a significant part of the national gdp, much more than in a number of other eu countries. the italian agrifood sector plays a key role in the national economy, mainly thanks to the added value generated by traditional processed products (e.g., tomato industry, pasta and wine). at the same time, italy is the eu country that accounts for the highest number of geographical indications (gis), representing high-quality traditional local production that must be produced in specific areas of the country (reg. (eu) 2024/1143). the gi scheme aims to preserve local production by officially certifying their linkage with the territories of origin and limiting the production to those areas to avoid fraud competition and italian sounding. the production process must be located within the demarcated area (i.e., list of municipalities) defined by the official product specification, and it cannot be delocalised. in this context, evaluating the effects of climatic events at the local level becomes even more relevant to understanding future projections of such productions (tscholl, 2024). for example, as of today, most of the gi wines cannot be irrigated. however, over the past few years, irrigation needs have arisen to avoid production loss due to increasing temperatures. the simple model facilitates the analysis of agricultural production and consumption at an aggregate territorial level (i.e., countries or world regions), offering insights into 3 more information available at climate change knowledge portal, world bank group: https://climateknowledgeportal.worldbank.org/country/italy 5 how crop demand and supply might respond to external factors such as income, population, and technology changes. simple-g provides, instead, a georeferenced extension that focuses on the effects of policies on agricultural production, land exploitation and water use, all within the broader context of regional and global commodity markets (haqiqi & hertel, eds.). in simple-g, while a specific country is considered at the grid level (i.e. local market), the rest of the world is considered at the national or regional world level (i.e., global market). this detailed representation allowsto capture the territorial diversity regarding cropland distribution, crop production, nitrogen application, and surface and groundwater irrigation. by integrating economic theories with environmental sciences, these family of models allows for the assessment of biophysical and economic impacts across various geospatial scales. in doing so, they differ from most previous partial economic models by investigating the global-to-localto-global interactions. the main challenge that researchers must address to run the model is the construction of an ad-hoc dataset for the country of interest. while national aggregate data are used to inform the simple model, georeferenced data sources are therefore needed to create a spatially disaggregated database for simple-g, with a resolution of 5 arcmin grids (approximately 10 km by 10 km) covering the entire country. from the methodological perspective, therefore, the extension provided in this paper contributes to the recent stream of research by creating the simple and simple-g datasets for an eu country. in addition, from the policy perspective, this study provides a new toolbox to investigate the effects of policy interventions and external shocks, such as climate change perspectives, at global and local levels. these results can be used to assist policymakers and practitioners in designing mitigation strategies and policy agendas. 6 the remainder of the paper is organised as follows. section two provides an overview of the simple and simple-g models, focusing on the existing specifications and the models’ structure. section three discusses the details of the italian database construction and an in-depth explanation of model assumptions and calibration. section four presents a first application of simple for italy and, lastly, the conclusions discuss limitations, next steps and future application. 2. simple and simple-g models for agriculture 2.1 existing specifications and applications in recent years, a growing body of literature has aimed at extending equilibrium models to consider the complex interdependencies governing global crop production, land and water use, and food demand. simple and simple-g models go in this direction, given their focus on the agriculture sector. in 2013, baldos & hertel (2013) introduced the simple model to comprehensively capture the main relevant socioeconomic factors influencing cropland use in 15 world regions. the initial simple model was further expanded to integrate additional modules accounting for different issues related to agriculture and agrifood sectors. first, a module accounting for greenhouse gas emissions (ghg) linked to agricultural activities was introduced by lobell et al. (2013) and hertel et al. (2014). secondly, baldos & hertel (2014) extended the simple model by adding a food security module, which analyzed the global and regional effects of climate change and mitigation policies on caloric intake. they found that the model accurately reflects global data. by using this model, baldos & hertel (2015), for example, demonstrated that reducing market barriers could bolster food security worldwide and regionally in the face of climate change challenges to agriculture. more recently, kabir et al. (2023) evaluated the effects of different policy scenarios on food security outcomes in rural and urban niger into 2050. 7 third, this framework has also been extended to research & development (r&d) activities, linking them with total factor productivity (tfp) growth (baldos et al., 2020a), food prices, land use and ghg emissions (hertel et al., 2020, fugile et al., 2022 and baldos, 2023a). despite its computational capacity and projection performance, the simple model lacks a detailed spatial resolution, limiting the analysis at the regional, or at maximum, national level (hertel, 2018). to evaluate sub-national effects, in fact, the model should be based on a more disaggregated territorial level of analysis and include spatial data. the construction of the model requires, in fact, to build a database collecting a huge amount of different data (see the next section for more details), which are not always available and easily to access at the local level. even when available, data comes from different sources, meaning a significant challenge in data collection and harmonisation. in 2017, liu et al. (2017) published a gridded version of the simple model, called simple-g, that divided the world into over 39 thousand grids (30 arc-min,) to capture local patterns in crop supply and demand. as in the case of simple, also in simple-g specific countries can be isolated from the global context and considering for specific local analysis. to do that, a gridded database for the country must be created including all the data needed to run the model. following this idea, baldos et al. (2020b) developed the simple-g model for the usa, while wang et al. (2020) and liu & wang (2021a) for china, and wang et al. (2022a) for brazil.4 additionally, haqiqi et al. (2023a) have employed a worldwide model to evaluate the implications pandemic-weather stress events. 4 the usa was divided into over 75 thousand grids, china in 41 thousand grids by wang et al. (2020) and 88 thousand grids by liu & wang(2021a) and brazil in 50 thousand grids by wang et al (2022a). the rest of the world was aggregated into world regions. 8 several are the modules that theoretically can be added at simple and simple-g databases to capture the intricates global-local-global interactions within economic and environmental systems. however, all the modules are conditioned at data availability, which become even more severe when we look at specific issues. for example, in the case of the us, baldos et al. (2020b) could distinguish at the grid level between rainfed and irrigated water, as well as between ground and surface water, while liu et al. (2022) focus their analysis solely on some specific crops such as corn and soybean. the availability of these data for the us allows, for example, baldos et al. (2020b) and haqaqi et al. (2023b) to study the implications of future population growth trends and groundwater management policies on local crop production and demand. data on water were also available for china, enabling wang & liu (2021b) to analyse the impact of a new water policy, specifically the south-north water transfer project. more recently, wang et al. (2022b) integrated a transportation module to explore the local responses to macro-level policies with a focus on the relationship between transportation costs, agricultural production and grid-level cropland expansion. lastly, ray et al. (2023) incorporated data at the gridded level on agricultural labour market and mobility to simulate the impacts of agricultural policies not only on production, but also on wages and employment rate at both local and global levels. the construction of the database to extend the model at a new country is, therefore, a fulcrum of this research field. the next section presents the general structure of the model, which clarify even more the amount of data required. 2.2 the general structure of the models starting from the simple, the model operates on the assumption of a single aggregate crop produced and consumed across 17 distinct regions (see table a1 in the appendix for the list, baldos et al., 2020b and baldos 2023b). the crop sector encompasses various 9 uses, including consumption as food, feedstock for biofuels, and inputs for livestock and the processed food industry. on the supply side, the model operates under the assumption of a single agricultural sector at a regional level that uses land and non-land inputs (such as labour, capital and purchased materials) to produce homogeneous crops, aiming to minimize costs with a constant return-to-scale technology. the availability of cropland varies region by region and is influenced by the returns on cropland in each area, while non-land inputs also depend on the regional prices given. on the demand side, demand for agricultural products is based on the intuition that exogenous factors such as population growth, income per capita, and biofuel demand -positively influence the quantity of agricultural products demanded. conversely, endogenous changes in agricultural prices negatively impact demand. regarding prices, agricultural prices are defined taking into consideration both potential drivers of food prices and economic responses. demand and supply for crops clear in local and global markets, meaning that consumers and producers face two distinct crop prices, one in the local market and one in the global one. for other commodities, market equilibrium is achieved at the regional level. as a result, processed food, livestock, and non-food products each have unique regional prices. as far as endogenous and exogenous variables are concerned, the model endogenously determines agricultural crop production and prices. when it comes to inputs of production, these are partly influenced by exogenous variables such as land variations due to environmental or urban reasons as well as slack variables. these assumptions go in line with the first version of the model presented by baldos & hertel (2013). the overall structure of simple is described by figure 1 (panel a), while technical details are provided in annex i. 10 figure 1. basic structure of the models. (a) simple (b) simple-g source: own elaboration based on baldos & hertel (2013) and baldos et al. (2020b) 11 simple-g structure is built on simple but enabling crop production to be analysed at the grid level (figure 1, panel b). crop production in simple-g is no longer modelled at a world regional scale, but at a finer grid level.5 since this model is georeferenced, it enables a more detailed representation of the various inputs used in crop production, such as land type, water use, and fertilizer application rates. although this added complexity makes simple-g a more intricate variant of simple, both in modelling and scale, it also makes it a significantly more powerful analytical tool. first, it accounts for human systems' responses to environmental changes, including adjustments in food consumption, agricultural production, land use, and water withdrawals, all influenced by shifts in market prices. second, it offers a framework for measuring spillover effects transmitted between locations through market interactions. the ability to incorporate market-mediated effects and feedback loops between human and natural systems is, in fact, one of the advantages of simple-g over other traditional biophysical models (hertel & haqiqi, eds.). another strength is the multi-scale approach used in simple-g that facilitates the connection between local-to-global changes, as presented in figure 2. at the grid level, there could be a different composition of crop production, driven by differentiated contextual conditions (e.g., agroclimatic conditions). at the national, the overall quantity can be obtained by aggregating the grids’ amount based on assumptions about product differentiation. the procedure is the same to obtain world regional values, starting from aggregating national values. the greater the diversity of crops grown within a region, the lower the elasticity of substitution between these grid-based crop outputs. in this scenario, crop production can be supplied domestically (i.e., consumed within the country of origin) or exported. the decision to export or supply domestically depends on 5 grid cells need to be small enough to allow both the incorporation of available heterogeneity in data and parameters as well as not to influence commodity prices, so they can be treated exogenously. 12 several factors, including the relative price of crops, which is influenced by production costs. figure 2. representation of the local-to-global supply linkages in simple-g source: own elaboration based on haqiqi & hertel (eds.) 3 the italian database in this section, we describe the process followed to single out italy from the macroregional eu dataset, dividing the simple and simple-g world into 18 regions rather than 17 as in baldos (2023b). the simple-g database follows, in fact, the structure and data of the simple model, but with italy no more aggregated at the national level but rather subdivided into 5-arc-minute grids (the remaining 17 regions are aggregated) (baldos et al., 2020b; haqiqi et al., 2023b). the two databases were constructed separately but in tandem. to collect data, and in alignment with the baldos (2023b) 13 database, we referred, reviewed and processed various economic and biophysical information (table 1).6 6 due to heterogeneity in terms of years and geographical coverage, certain assumptions have been done to ensure consistency. due to simple-g's grid-based structure, datasets, typically presented in raster format, have been reviewed and processed using qgis as a geoprocessing tool. 14 table 1. datasets reviewed, base year and geographical dimension. dataset time span geographical dimension links faostat from 1961 to 2023 country level https://www.fao.org/faostat/es/#data earthstat 2000 10km x10 km http://www.earthstat.org/ isimip from 1861 to 2017 50 km x 50 km https://www.isimip.org/ agri4cast 1980-2022 / 2010 25 km x 25 km / 10 km x 10 km https://agri4cast.jrc.ec.europa.eu/dataportal/index.aspx aquastat 2005 10 km x 10 km https://www.fao.org/aquastat/en/ gcwm 1998-2002 10 km x 10 km https://www.uni-frankfurt.de/45217988/global_crop_water_model__gcwm ismea mercati up to 2024 italian regional level https://www.ismea.it/mercati italian farm accountancy data network (fadn) rete di informazione contabile agricola (rica) 2017 italian provincial level https://rica.crea.gov.it/ indagine mercato fondiario crea up to 2022 italian regional level https://www.crea.gov.it/web/politiche-e-bioeconomia/-/indagine-mercatofondiario source: own elaboration. table 2. faostat data sets used to calibrate the simple model. dataset description production provides country-level data on crop and livestock production, including variables such as harvested area, production, yields, and animal stocks. population contains data on each country's total population, disaggregated by gender and by rural and urban areas. macroeconomic statistics offers country-level data on gdp, gross output, value added, and gross capital formation for land-intensive sectors and the food processing industry. land use contains country data for crop area and value of agricultural production. prices presents prices for all crops and livestock at a country level. source: own elaboration. 15 16 the benchmark primary data source was faostat, which provides different datasets at the national level (table 2). however, faostat does not provide georeferenced data, so to create the simple-g model, we needed to consult the earthstat database (ramankutty et al., 2008). this database provides information on cropland, crop production, and fertilizer use by combining agricultural inventory data with satellite-derived land cover data, offering, thereby, information at a 5-minute (~10 km) spatial resolution. the earthstat database was used to determine the structural base of the simple-g model, meaning the number of grids of the model. in doing so, we followed the construction of simple-g brazil (wang et al., 2022a), wherein crop production estimates are formulated by considering only the top 80% of the most significant crops in the country. this approach helps eliminate potential statistical errors associated with minor crop estimates from earthstat (ramankutty et al., 2008).7 the final database is composed of 3,745 grids, which exhibited both cropland and agricultural output (table 3). table 3. grid definition based on earthstat datasets. number of grids output 0 >0 cropland 0 346 328 >0 396 3745 source: own elaboration. however, information coming from earthstat needs to be historically validated. to do that, we used data provided by the inter-sectoral impact model intercomparison project 7 earthstat provides, in fact, reliable estimates only for major crops, leaving minor crops with some biases in the estimations. considering all the crops in the sample would, therefore, imply severe limitations for the application of the model and huge discrepancies with the benchmark aggregate production values reported by faostat. according to faostat, these are: sugarbeet, maize, grapes, tomatoes, wheat, olives, apples, potatoes, oranges, barley and peaches. 17 (isimip), which consists of a time-varying historical land-use (lu) dataset including various land-use categories such as pastures, rangeland, and five distinct crop types.8 the isimip's geographical coverage is lower than earthstat’s, and the geographical disaggregation is less fine (50 km by 50 km grid dimension), which is why we decided to consider earthstat’s data in the model. the same applies to crop data from the agri4cast toolbox (25 km by 25 km grid dimension). although the irrigation data available in the toolbox has a grid dimension equal to the one used (10 km by 10 km), it is outdated, corresponding to 2010, and lacks information on rainfed land. given the current extreme relevance of water issues in the simple-g database for italy, we decided to include the water model, following haqiqi et al. (2023b). this extension allows for the estimation of local demand for agricultural water for irrigation water, which is crucial to modelling the impacts of sustainability policies and climate change shocks. these external events can have, in fact, different effects on irrigated versus non-irrigated (i.e., rainfed) crops due to their varying responses to heat and water stress. this approach means that within each grid cell, two distinct production functions, rainfed and irrigated crops, compete for irrigable land. regarding data on water, we started collecting information from aquastat (source: fao), a global information system focused on hydric resources and agricultural water management. aquastat provides georeferenced information at a 5-minute (~10 km) spatial resolution. information on irrigated and rainfed hectares came from the global crop water model (gcwm) database, developed by siebert & döll (2008) within a 5minute (~10 km) grid dimension. in addition, to obtain information on prices and values 8 this time series spans from 1861 to 2018 and primarily relies on the hyde 3.2 dataset by klein goldewijk et al. (2017). 18 of irrigated vs non-irrigated land and crops, we consulted the italian farm accountancy data network9 (fadn).10 another option for estimating crop prices in italy is using data provided by ismea (instituto di servizi per il mercato agricolo alimentare), which represent market prices from the most representative local markets specific to product varieties and sales conditions.11 however, reliance on this dataset could lead to misleading estimates, given that the dataset provides prices differentiated for crops and regions rather than an aggregated crop price, as needed for the models. simple and simple-g models lack, in fact, differentiation among various crops. in addition, prices are provided only for specific groups of crops, differentiated by regions, which are those crops considered the most relevant for the regional agricultural economic output. this dataset is not, therefore, suitable to generate a representative and comparable regional aggregate crop price, as requested by the model, generating potential estimation biases. finally, we need data on the land market, collected from the dataset provided by the council for agricultural research and economics (crea), to define the average farmland values at regional and national levels. 9 established by the european economic commission in 1965 and conducted in italy since 1968, fadn survey targets professional, market-oriented agricultural companies. it provides representative data across three dimensions: region, economic size, and technical-economic order. the current sample includes approximately 11,000 farms, covering around 95% of the agricultural area, 97% of standard production value, 92% of work units, and 91% of livestock units in italy. the database, organized by thematic area, includes detailed farm-level information on production, input use, costs, and crop types, with identifiers for province and region. 10 it should be noted that the information on prices from fadn has some limitations, as the water cost reported by the survey is defined as the total cost of water without clarifying the potential exclusion of certain types of water. conversely, in the case of water consumption, the metadata file only mentions “water distributed,” suggesting that water from private sources may not be included. we are not aware of any databases providing information on private withdrawals. 11 it is a weekly survey encompassing minimum, maximum, and current price values. 19 to sum up, by utilizing the dataset mentioned above and applying the parameters provided by baldos (2023b) regarding country-specific production shares in global and local markets, as well as income and price elasticities, we were able to build a new database for simple with 18 regions, specifically distinguishing italy. for both simple and simple-g models, the base year is 2017. the choice of the base year is mainly due to the availability of data that would enable the construction of a coherent dataset for both simple and simple-g. table 4 presents the estimated values for italy in 2017 alongside the aggregate values for the remaining 17 regions, covering the key variables required for the model. table 4. key variables estimated for the simple model including italy 2017. variable italy other regions income per capita (in usd) 31,509 278,783 population (in 1000 person) 60,004 7,524,960 crop use: regional biofuels (in 1000 mt: corn eq) 3,013 447,499 crop output (in 1000 mt: corn-eq) 132,549 12,789,222 value of crop output (in 1000 usd) 29,028,232 2,800,839,581 cropland cover (in 1000 ha) 9,218 1,557,064 value of cropland (in 1000 usd) 4,830,646 540,050,675 value of non-land inputs (in 1000 usd) 24,197,586 2,260,788,898 crop use: livestock sector (in 1000 mt: corn eq.) 32,450 2,538,728 crop use: food processing sector (in 1000 mt: corn eq.) 37,698 3,100,306 source: own elaboration based on faostat and baldos (2023b). 3.1 database illustration and calibration figure 3 illustrates the 3,745 grids with cropland and crop production in italy obtained from earthstat database. 20 figure 3. distribution of cropland area and crop production for italy. (a) cropland area (in hectares) (b) crop production (in tons) source: own elaboration based on earthstat database from ramankutty et al. (2008). the data populating the italian dataset come from the data sources described in the previous section, and they are better discussed and visualised here. a first group of variables refers to environmental issues: nitrogen and water use. the simple-g model considers nitrogen fertilizers as one of the key inputs of production function, and, therefore, the amount of nitrogen applications in each grid is needed to calibrate the base year. to achieve this, we used the earthstat database, which collects national and sub-national data on fertilizer application rates for crops and crop groupings from the national institute of statistics (istat). figure 4 depicts the distribution across italy depicting a more intensive use in the northern part of the country, which aligns with results obtained for cropland area and production. 21 figure 4. total nitrogen application distribution for the year 2000in kilograms. source: own elaboration based on earthstat database from ramankutty et al. (2008). in the case of water use, simple-g model incorporates rainfed and irrigated water use as critical inputs for crop production, differentiating between irrigated and rainfed crop supply. in addition, in the irrigated crop production function, the demand for water is subdivided into groundwater and surface water. figure 5 shows the distribution of irrigated (panel a) and rainfed (panel b) cropland hectares in each grid (source: gcwm), while figure 6 depicts the gridded distribution of irrigated land through groundwater (panel a) and surface water (panel b) (source: aquastat).12 figure 5. distribution of irrigated and rainfed cropland hectares. (a) irrigated cropland (b) rainfed cropland 12 aquastat provides data on the percentage of land equipped for irrigation using groundwater and surface water. 22 source: own elaboration based on gcwm data. figure 6. distribution of surface and groundwater extraction on irrigated hectares. (a) surface water (b) groundwater source: own elaboration based on aquastat data. regarding economic variables, the model needs price information, specifically on land, crop, fertilizers and water prices. to estimate the prices of crops and fertilizers, we used the italian fadn database at the nuts3 level and considered the ratio between values and volumes (figure 7). in estimating crop (panel a) and fertilizer (panel b) prices, to be faithful to earthstat database, we considered crops included in the 80% of total 23 production. in the case of water (panel c), prices were also calculated starting from the italian fadn data on total water use in m3.13 13 for estimating the price of water used by farms, the assumption regarding the crops considered is slightly modified. specifically, in nuts3 of caltanissetta, monza e della brianza, rimini, siena, and varese, the restriction of using only the crops considered for production is not applied. while this introduces some inconsistency in the selection of farms in these provinces since crop production was the original eligibility criterion maintaining the criterion would have made obtaining water prices in these areas impossible. the surveyed farms in these provinces do not produce the crops selected to obtain crop production at the grid level, so if the eligibility criteria used for crop and fertilizer price estimations were maintained, there would be no price data for water used in the mentioned provinces. data used are collected in metadata called uso acqua and colture. 24 figure 7. crop, fertilizers and water prices per provinces (a) crop prices (b) fertilizer prices (c) water price note: crop and fertilizer prices expressed in current euros; water prices expressed in current euros/m3 source: own elaboration based on italian fadn rica. we also extracted data on farmers’ expenses from the italian fadn database to estimate the prices and values of non-crop inputs for livestock and processed food (figure 8 and figure 9).14 to calculate the total value of non-crop inputs, we considered water, 14 it should be noted that these expenses are at the general farm level (see bilancio conto economico rica metadata), so they may include expenses generated by other activities carried out in those farms. the farms to be considered are filtered using tables that identify those producing processed food and raising animals. only farms that produce processed foods of non-animal origin are included, indeed. 25 electricity, fuels, veterinary and machinery expenses, commercial and processing expenses, general and land expenses, and salaries. the total values obtained were then divided by their corresponding quantities, distinguishing between the two types of activities to obtain prices.15 figure 8. non-crop inputs values for livestock and processed food per provinces note: expressed in current million euros of 2017. source: own elaboration based on italian fadn rica. 15 in the case of livestock, quantities correspond to the number of adult animals. (a) non-crop input value for livestock (b) non-crop input value for processed food 26 figure 9. non-crop inputs prices for livestock and processed food per provinces (a) non-crop input price for livestock (b) non-crop input price for processed food note: expressed in current euros of 2017. source: own elaboration based on italian fadn rica. finally, calibrating the simple-g model requires determining the cropland values at a grid level. as anticipated in the previous section, we collected data from the crea’s land market database (indagine-mercato-fondiario) and downscaled them at the grid level (figure 10). to do so, we have assumed that all cropland has a uniform value (expressed in euros per hectare) within a nuts2 (italian region) area.16 to ensure time consistency, a final note should be made regarding treating the datasets above. in addition, since most georeferenced datasets are based between 2000 and 2005, it was necessary to update them to 2017 by rescaling the geospecial structures to the values provided by faostat. 16 during data processing, outliers were removed, and weights were applied to scale the results to the population level. this process yields variables at the provincial level. 27 figure 10. cropland value note: expressed in current euros of 2017 per hectare. source: own elaboration based on the land market database (source: crea). 3.2 estimation of the drivers of the model the last step was to estimate the main driver variables needed to simulate a baseline scenario to 2030 for both the simple and simple-g models, with italy as a single country. this scenario will serve as the basis for additional simulations. population growth rates were derived using the population projections developed by samir & lutz (2017), while gdp per capita estimates were based on projections by crespo cuaresma (2017). these projections follow the intermediate shared socioeconomic pathway (ssp2) the international institute for applied systems analysis 28 (iiasa) created. regarding future biofuel demand, they were constructed using estimates provided by iea (2024). the adoption of these databases aligns with the methodology proposed by simple modellers in baldos et al. (2020b), haqiqi et al. (2023b), and baldos (2023b). for the tfp, econometric estimations were performed using a time series model to forecast output and input data for 2030 based on a panel data structure (see annex i for details). table 5 presents the percentage variations of each variable between 2017 and 2030. table 5. estimated percentage variations from 2017 to 2030. population gdp per capita biofuels demand tfp aus_nz 22.01 40.92 6.67 -0.5 bra 9.68 38.59 97.74 18.7 can 15.41 29.57 64.23 13.3 cc_amer 13.48 41.23 58.33 0.9 china 1.32 126.32 19.56 10.3 c_asia 30.61 334.19 224.75 30.2 eu 4.26 39.46 52.86 7.1 e_euro -1.97 59.91 0.00 20.9 ita 0.77 69.70 103.38 1.7 jpn_kr -1.92 9.03 224.75 3.9 m_east 22.40 30.12 331.03 18.3 n_afr 17.28 33.27 0.00 48.3 se_asia 12.15 100.20 224.75 4.3 ssa 38.17 67.84 16243.201 15.9 s_afr 11.90 49.55 0.00 15.2 s_amer 13.51 46.24 58.33 10.8 s_asia 17.80 144.12 0.00 18.2 us 11.83 20.83 24.29 10.1 1note: estimates for the sub-saharan africa region was unavailable in the iea (2024) database. hence, this projection was taken from baldos (2023b). source: own elaboration. 4. a first application of simple-it in the case of simple, the italian database has already been properly structured for integration into the gempack software, and the model is ready to be tested. as a first application, we conducted a baseline simulation to evaluate the expected impacts of the evolution of population, gdp per capita, biofuel demand and crop tfp on agricultural 29 production and land use at the national level projected to 2030. table 6 presents the results showing the percentage impact on crop prices, highlighting the contribution of each driver to the total effect. as shown, the estimated baseline has an overall negative impact on global crop prices (-6.36%) and in most regions, indicating that prices would decrease under the given scenario. this can be explained by the fact that although demand-side drivers (population growth, gdp per capita, and biofuels) exert upward pressure on prices due to increased crop demand (8.85% from population growth, 7.38% from gdp per capita, and 1.32% from biofuels), this effect is more than offset by improvements in sector productivity. overall, the rise in productivity leads to a global price reduction of 17.21%, increasing production enough to counterbalance the higher demand. table 6. crop prices results as percentage changes total effect population gdp per capita biofuels demand crop tfp aus_nz -1.23 8.19 2.11 0.91 -12.43 bra -7.64 5.39 3.08 3.62 -19.73 c_asia -4.15 14.17 12.94 0.42 -31.68 can -6.26 6.47 2.86 1.62 -17.22 cc_amer -1.53 7.05 3.31 0.98 -12.87 china -7.81 1.83 4.56 0.25 -14.45 e_euro -12.57 3.23 3.24 0.65 -19.70 eu -6.57 5.49 2.30 1.17 -15.53 ita -4.62 4.12 1.34 1.56 -11.64 jpn_kr -6.77 3.05 1.94 0.70 -12.46 m_east -8.12 9.68 2.06 0.54 -20.39 n_afr -20.20 8.53 3.47 0.57 -32.77 s_afr -6.96 6.50 3.62 0.75 -17.83 s_amer -5.40 6.85 3.24 0.88 -16.38 s_asia -1.85 10.74 10.47 0.19 -23.25 se_asia 6.91 7.74 8.39 0.50 -9.72 ssa 7.82 18.89 5.41 5.88 -22.36 us -5.33 5.49 1.91 3.85 -16.59 world -6.36 6.33 3.40 1.12 -17.21 source: own elaboration. 30 table 7 illustrates the impact of the simulation on crop production, breaking down the total impact by and the total effect of each driver in the model. analysing the contribution of productivity changes to agricultural output, it is evident that productivity growth leads to an overall global production increase (6.54%). however, in some regions, production declines due to lower comparative competitiveness, as seen in italy, where production decreased by 9.17%. regarding demand-side drivers, all have a positive impact, as expected, with global effects of 8.73% from population growth, 6.19% from gdp per capita, and 1.82% from biofuels. table 7. crop production results as percentage changes total effect population gdp per capita biofuels demand crop tfp aus_nz -2.65 10.22 2.63 1.13 -16.63 bra 33.45 8.30 4.63 5.55 14.97 c_asia 69.06 22.54 18.90 0.67 26.94 can 22.58 9.71 4.19 2.43 6.24 cc_amer 0.20 7.67 3.59 1.07 -12.14 china 12.26 2.14 5.19 0.29 4.65 e_euro 29.49 4.91 4.77 0.99 18.82 eu 7.17 6.42 2.66 1.37 -3.27 ita -1.56 4.48 1.44 1.69 -9.17 jpn_kr 0.90 2.92 1.85 0.67 -4.55 m_east 30.75 13.91 2.89 0.77 13.19 n_afr 70.17 10.43 4.08 0.70 54.97 s_afr 25.32 9.19 5.01 1.06 10.06 s_amer 16.98 8.80 4.10 1.13 2.95 s_asia 38.31 13.57 12.80 0.24 11.69 se_asia 16.57 8.27 8.89 0.53 -1.12 ssa 48.75 24.93 7.02 7.75 9.06 us 15.56 7.09 2.43 4.98 1.06 world 23.28 8.73 6.19 1.82 6.54 source: own elaboration. tables 8 and 9 present the results of cropland use, regarding hectares and land prices. as observed, demand-side drivers increase the need for cropland to meet rising crop demand, leading to a global increase of 7.44% in land use. this, in turn, creates upward pressure on land prices. however, once again, productivity improvements introduce a 31 compensatory effect. as the sector becomes more productive, fewer hectares are required to achieve the same production level, resulting in a global reduction of 2.59% in land use. although this mitigates the need for additional land, it does not fully offset it, leading to a net global increase of 4.86% in land use. at the regional level, however, some areas fully compensate for demand-side drivers through productivity gains—for example, in italy, where total land use decreases by 0.44%. table 8. cropland results as percentage changes total effect population gdp per capita biofuels demand crop tfp aus_nz -1.16 5.48 1.41 0.61 -8.65 bra 6.38 3.89 2.20 2.61 -2.32 c_asia 15.68 10.17 9.04 0.30 -3.84 can 2.24 2.48 1.09 0.62 -1.96 cc_amer -0.22 2.47 1.16 0.34 -4.19 china 0.58 0.66 1.63 0.09 -1.79 e_euro 3.76 2.35 2.32 0.47 -1.38 eu 0.01 0.82 0.34 0.18 -1.33 ita -0.44 0.61 0.19 0.23 -1.47 jpn_kr -0.44 0.44 0.28 0.10 -1.25 m_east 1.25 1.51 0.32 0.08 -0.66 n_afr 2.30 1.31 0.52 0.09 0.38 s_afr 2.53 2.45 1.36 0.28 -1.56 s_amer 3.12 4.67 2.20 0.60 -4.35 s_asia 5.28 3.83 3.71 0.07 -2.33 se_asia 3.81 2.61 2.83 0.17 -1.80 ssa 15.53 12.64 3.60 3.93 -4.64 us 1.48 2.02 0.70 1.42 -2.65 world 4.86 4.15 2.25 1.04 -2.59 source: own elaboration. the changes in land use also explain the observed impact on land prices, which increase globally by 10.33%. since higher land demand drives prices up (with demand-side drivers contributing to a 17.55% global price increase), productivity improvements are unable to fully counteract this effect. while productivity growth reduces land prices by 7.22%, it is not enough to fully offset the upward pressure generated by demand-side factors. 32 table 9. cropland prices results as percentage changes total effect population gdp per capita biofuels demand crop tfp aus_nz -2.05 9.76 2.51 1.08 -15.40 bra 11.68 7.13 4.02 4.78 -4.25 c_asia 29.70 19.47 17.00 0.58 -7.36 can 8.22 9.15 3.99 2.29 -7.21 cc_amer -0.80 8.81 4.13 1.23 -14.96 china 2.09 2.39 5.87 0.32 -6.49 e_euro 6.82 4.26 4.21 0.86 -2.51 eu 0.08 7.48 3.11 1.60 -12.11 ita -3.91 5.42 1.75 2.05 -13.13 jpn_kr -3.93 3.90 2.48 0.90 -11.22 m_east 11.92 14.44 3.03 0.80 -6.35 n_afr 22.96 13.09 5.21 0.88 3.79 s_afr 9.32 9.06 4.99 1.05 -5.78 s_amer 5.64 8.45 3.97 1.09 -7.86 s_asia 20.17 14.76 14.11 0.27 -8.97 se_asia 14.28 9.83 10.60 0.63 -6.78 ssa 29.42 23.98 6.80 7.46 -8.82 us 5.40 7.36 2.54 5.17 -9.67 world 10.33 8.85 7.38 1.32 -7.22 source: own elaboration. conclusion external shocks, such as climate change and extreme events, are shaping the sustainability of the agricultural and agrifood sectors worldwide, and this will become even more relevant due to climate scenario projections. tools for ex-ante analyses are becoming increasingly crucial to understanding how policy design should evolve to address future challenges, such as international competitiveness and food security. in this context, this paper provides, for the first time, the scientific community with the extension of simple and simple-g models to a european country, italy. the paper not only describes the models, discusses the related literature, and documents the work performed to create the database for a new country but also presents: (i) the estimated values used to calibrate the baseline (in both models for 2017); (ii) the 33 exogenous driver projections extending to 2030; (iii) a fist application of simpleit model. the first application of simple, integrating for italy, presented in section 4, demonstrated that agricultural production is projected to increase due to the included drivers (demand and productivity). the observed reduction in production in certain regions, such as italy, is primarily explained by the lower productivity gains in comparison to the other regions, leading to a reallocation of production toward these regions. the overall increase in global crop production is associated with a reduction in agricultural prices. regarding land use, the results exhibit a similar pattern: an increase in the utilized area, except in regions where production decreased or where the increase was negligible. higher agricultural productivity reduces the land requirements to achieve equivalent production levels. however, the rise in demand for agricultural products drives additional production, which in turn requires more land. this explains the increase in utilized hectares, although at a lower rate than the growth in production. moreover, agricultural land prices exhibit an upward trend because of increased demand. additional steps are required to validate the simple-g model presented in this paper before running simulations. specifically, grid-level elasticities of substitution should be defined in the model, following the methodology employed by haqiqi et al. (2023b). once this validation is completed, the model will be ready to conduct location-specific simulations, such as assessing the impacts of local climatic events or policy interventions. both simple and simple-g models can be, in fact, used to simulate policy-oriented shocks, such as changes in agricultural land exploitation, agricultural water use (wastewater), water management strategies (i.e., collective vs private) or in farming 34 processing (e.g., organic). the models incorporate, in fact, market-mediated effects and feedback loops between human and natural systems, guiding heterogeneous economic decisions at different spatial levels (national for simple while gridded for simple-g). they provide a framework for measuring spillover effects transmitted across locations through markets and accounts for tele-coupling distant regions, enabling the analysis of interregional dependencies on a local-global scale. the capacity of simple-g to capture interactions is one of the main strengths of this model, especially for policy design (baldos et al., 2020b). overall, the several potential applications of both of these models will provide unique insights for sustainable policy targeting at the local, national and global levels (hertel & haqiqi, eds.). today, the issue is more relevant than ever, given the current vibrant debate on what will come after the 2030 agenda goals and how to plan sustainable development trajectories in the future within an anticipatory governance framework. 35 references ackerberg, d., benkard, c. l., berry, s., & pakes, a. 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(2022b). transportation cost, agricultural production and cropland expansion in brazil: a multi-scale analysis, conference papers 333501, purdue university, center for global trade analysis, global trade analysis project. world bank. (2022). water in agriculture. understanding poverty. the world bank. 38 appendix table a1. original regional disaggregation included in the simple and simple-g model name region description e_euro eastern europe n_afr north africa ssa sub saharan africa s_amer south america bra brazil aus_nz australia & new zealand eu european union s_asia south asia cc_amer central america and the caribbean s_afr southern africa se_asia southeast asia can canada us united states of america china china and hong kong m_east middle east jpn_kr japan and korea c_asia central asia source: baldos et al. (2020b) and baldos (2023b). 39 annex i technical details of models’ structure (section 2 of the manuscript) agricultural prices are defined based on the following equation (1): (1) 𝑝𝐴 ∗ = ∆𝐴 𝐷 + ∆𝐿 𝑆 − ∆𝐿 𝐷 𝜂𝐴 𝑆,𝐼 + 𝜂𝐴 𝑆,𝐸 + 𝜂𝐴 𝐷 the equation indicates that agricultural prices are positively influenced by the increasing global demand in crop demand (∆𝐴 𝐷) and a shift of the global supply of agricultural land (∆𝐿 𝑆), while negatively affected by variations in the derived global demand for land due to global changes in yields (∆𝐿 𝐷) which, in this model, are exogenously determined. the model accounts for the magnitude of these variations on agricultural prices through three economic responses. first, an intensive margin response, represented by the elasticity of substitution between land and non-land inputs (𝜂𝐴 𝑆,𝐼 ). second, an extensive margin response in land use, captured by the land supply elasticity (𝜂𝐴 𝑆,𝐸). third, a demand response, reflected in the price elasticity of food demand (𝜂𝐴 𝐷). land use for agricultural production is determined based on equation 2: (2) 𝑞𝐿 ∗ = [ ∆𝐴 𝐷 + ∆𝐿 𝑆 − ∆𝐿 𝐷 1 + 𝜂𝐴 𝑆,𝐼 𝜂𝐴 𝑆,𝐸 + 𝜂𝐴 𝐷 𝜂𝐴 𝑆,𝐸 ] − ∆𝐿 𝑆 on the one side, quantity of agricultural land positively depends on increasing in global demand (∆𝐴 𝐷), potentially motivated by an upward demand of biofuels or food. on the other side, it negatively depends on the related global demand for land (∆𝐿 𝐷) that could result from exogenous growth in yields (e.g., due to prior investments in agricultural r&d). additionally, the quantity of agricultural land negatively depends on the potential shifts of the global supply of agricultural land (∆𝐿 𝑆) potentially motivated by different conservation or urbanization policies. finally, as in the case of agricultural prices, the 40 variation in land quantity is influenced by the relative elasticities incorporated in the model. as shown, a higher land global supply elasticity (i.e., the extensive margin response) leads to greater variations in agricultural land use. conversely, higher elasticity of substitution between land and non-land inputs (i.e., the intensive margin response) and higher price elasticity of food demand (i.e., the demand response) result in smaller variations in the quantity of land used. demand for agricultural products is defined as following: (3) 𝑞𝐴 𝐷 = −𝜂𝐴 𝐷𝑝𝐴 + δ𝐴 𝐷 technical details of total factor productivity estimation (section 3 of the manuscript) the first step was to estimate the panel data econometric model (van beveren, 2007) using a fixed effects estimation and the information in “international agricultural productivity”. we apply a logarithmic transformation to linearize the production function, which depends on capital, labor, materials, and the hicksian neutral efficiency level17. the estimated equation was: (4) 𝑦𝑖𝑡 = 𝛽0 + 𝛽𝑘𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 + 𝛽𝑙𝐿𝑎𝑏𝑜𝑟𝑖𝑡 + 𝛽𝑚𝑓𝑒𝑒𝑑𝐹𝑒𝑒𝑑𝑖𝑡 + 𝛽𝑚𝑓𝑒𝑟𝑡𝐹𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟𝑖𝑡 + 𝜔𝑖 + 𝜑𝑡 + 𝑢𝑖𝑡 where capitalit includes the value of the agricultural capital stock for region i at time t, laborit is the number of economically active people in agriculture, feedit and fertilizerit represent the use of materials in agriculture, and uit the error. from this estimation, we 17 while this method can be extended to other production functions if certain additional conditions are met (ackerberg et al., 2007), it was estimated using a cobb-douglas specification for simplicity. 41 obtain the coefficients that can be used to calculate the productivity of the sector, which can be defined as: (5) 𝜔𝑖�̂� = 𝑦𝑖𝑡 − 𝛽�̂�𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 − 𝛽�̂�𝐿𝑎𝑏𝑜𝑟𝑖𝑡 − 𝛽𝑚𝑓𝑒𝑒𝑑 ̂ 𝐹𝑒𝑒𝑑𝑖𝑡 − 𝛽𝑚𝑓𝑒𝑟𝑡 ̂ 𝐹𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟𝑖𝑡 with this equation and the available data, productivity (the exponential of 𝜔𝑖𝑡) could be estimated for each of the regions between 2017 and 2021. to estimate productivity to 2030, this model has to be complemented with a forecast of the output and inputs, so several time series models were estimated.18 18 for each of the variables in every region, an arima model was determined for each series4 and with that model each value between 2022 and 2030 was forecasted. using these forecasts and the parameters estimated in the panel data model, an estimate of productivity in 2030 can be obtained for each of the regions (using equation 5). 1 circular biobased europe joint undertaking: driving innovations towards sustainable 1 agriculture and growth in rural areas. 2 simone maccaferri*, ana ruiz sierra, paola paiano, chloe johnson, elina zicmane, virginia 3 puzzolo, nicoló giacomuzzi-moore 4 circular bio-based europe joint undertaking, white atrium, av de la toison d’or 56-60, 1060 5 brussels, belgium. 6 * simone.maccaferri@cbe.europa.eu 7 this article has been accepted for publication and undergone full peer review but has not been 8 through the copyediting, typesetting, pagination and proofreading process, which may lead to 9 differences between this version and the version of record. 10 please cite this article as: 11 maccaferri s, ruiz sierra a, paiano p, johnson c, zicmane e, puzzolo v, giacomuzzi-12 moore n (2025). circular biobased europe joint undertaking: driving innovations towards 13 sustainable agriculture and growth in rural areas., bio-based and applied economics, just 14 accepted. doi: 10.36253/bae-17237 15 16 abstract: the circular bio-based europe joint undertaking (cbe ju) is a public-private 17 partnership that supports research and innovation fostering the transition towards a competitive, 18 sustainable, and low-carbon economy in europe. cbe ju and its projects represent a significant 19 business opportunity for the primary sector, and in particular for farmers. the deployment of 20 circular bio-based innovations can help diversify farmer´s activities and generate new sources 21 of income. since 2022, about 130 million euros have been made available by cbe ju to 22 develop bio-based solutions with the potential to support sustainable agricultural practices and 23 boost rural economies, and currently 84 entities from the primary sector are participating in the 24 projects. the manuscript aims at critically analysing the major findings associated with primary 25 producers involvement in cbe ju programme and elaborate on the specific actions undertaken 26 to ensure that the primary sector can benefit from being concretely participating to innovative 27 bio-based value chains. 28 mailto:simone.maccaferri@cbe.europa.eu 2 keywords: bioeconomy, primary sector, biorefineries, bio-based industries, public-private 29 partnership 30 1. introduction 31 the unprecedented trend of global warming, climate change, degradation of the natural 32 ecosystems and increased pollution calls for immediate actions. in response, the european 33 union (eu) has put in place the european green deal, a growth strategy that aims to create a 34 fair, prosperous society with a competitive and resource-efficient economy aiming to achieve 35 net-zero greenhouse gas emissions by 2050 (european commission, 2019). 36 to reach these ambitious goals, a transition from a fossilto a bio-based economy is needed. 37 new innovative, sustainable and circular bio-based products, that reach high technical levels of 38 performance and fully meet market requirements, should be developed so that they can 39 successfully replace their current fossil-based counterparts. in addition, the sustainable and 40 local sourcing of biomass, including all types of bio-based side streams, and the reuse, recycling 41 and upcycling of resources will play a key role in ensuring that bio-based systems are 42 sustainable and efficient in the use of natural resources. 43 building on the success of its predecessor, the bio-based industries joint undertaking (bbi 44 ju), circular bio-based europe joint undertaking (cbe ju) is a pivotal instrument to 45 implement this vision at european level as it facilitates a transition away from fossil fuel 46 dependency, aligning with the goals of the eu green deal within the multifaceted policy 47 scenario that govern the bioeconomy and the bio-based sector (european commission, 2021; 48 johnson et al., 2021; mengal et al., 2018; ruiz sierra et al., 2021). 49 policy development in the bio-based sector and in the bioeconomy area at large have received 50 increasing attention in recent years. in april 2023, the eu council endorsed conclusions 51 highlighting the bioeconomy's potential to support the environmental and climate objectives of 52 the european green deal (general secretariat of the council, special committee on 53 3 agriculture, 2023). the bioeconomy is seen as a key factor in enhancing eu competitiveness, 54 reducing fossil fuel dependency, and improving food security. the council stressed the role of 55 bioeconomy in fostering socially and economically viable rural areas, engaging primary 56 producers in climate action, and promoting job creation and equal opportunities across the eu. 57 additionally, it emphasised the need for sustainable solutions in rural regions and the 58 importance of providing diversified incomes for stakeholders in the bioeconomy, including 59 landowners and small businesses, through new value-added chains and business models. 60 more recently, in 2024, the final report of the strategic dialogue on the future of agriculture 61 acknowledged the necessity of collaboration between public and private sectors, as strong 62 public-private partnerships in which rural actors actively participate can help turn niche into 63 norm to support the development and implementation of bioeconomy initiatives (members of 64 the strategic dialogue on the future of eu agriculture, 2024). 65 in addition, in 2024, the european commission (ec) has proposed a series of targeted actions 66 to boost biotechnology and biomanufacturing in the eu, offering innovative solutions to 67 societal challenges (european commission, 2024). biotechnologies which fall into the remit of 68 cbe ju’s field of action hold great promise for modernising not only the bio-based industrial 69 sector but also agriculture, forestry, food and feed industries at european level. 70 altogether, these important political inputs will constitute the basis for the new bioeconomy 71 strategy, that is due for adoption by the end of 2025 and aims to advance innovation and 72 maintain the eu's leadership in the bioeconomy. the new bioeconomy strategy will propose 73 actions to unlock the potential of bioeconomy innovations, so that they can reach the market, 74 and will represent a major step forward in the evolving eu economic and political context. in 75 fact, an increasing number of member states have issued their own national bioeconomy 76 strategies (european commission, 2018). particular attention will be given to the role of the 77 4 primary sector and the significance of biomass sustainability and availability for strengthening 78 and supporting the bio-based sector across europe. 79 in this overall scenario, the objective of this article is to present the activities that cbe ju has 80 performed to ensure that primary sectors benefit from the economic, social, and environmental 81 opportunities offered by circular and bio-based innovations, including the creation and 82 establishment of a dedicated cbe ju working group on primary producers. data derived from 83 the first three years of programme implementation demonstrates how the effort that has been 84 taken contributed to enhance the role of primary producers in circular bio-based systems and 85 value chains. last, the article is presenting cases of projects where the involvement of the 86 primary sector is exemplary and showcase the potential impact of the primary sector in the 87 deployment of bioeconomy, with a specific attention to the first-of-a-kind biorefineries that 88 represent a peculiarity of cbe ju funding strategy. 89 90 2. cbe ju: a public-private partnership to support the green deal 91 objectives 92 the cbe ju is a eur 2 billion public-private partnership between the eu, represented by the 93 ec, and the bio-based industries consortium (bic), a not-for-profit organisation representing 94 the private sector across the bio-based industries. it is established for the 2021-2031 period 95 under horizon europe, the eu’s research and innovation framework programme (european 96 commission, 2021). 97 key objectives of cbe ju are to i) accelerate the innovation process and development of bio-98 based innovative solutions, ii) support the market deployment of mature bio-based innovative 99 solutions and iii) ensure a high level of environmental performance of the bio-based industrial 100 systems. in particular, cbe ju seeks to bridge the gap between the development of new bio-101 based innovations and their market deployment by promoting research and innovation 102 5 collaborative projects which integrate stakeholders throughout bio-based value chains, 103 including the primary sector, as vital biomass suppliers. cbe ju aims at de-risking 104 investments, encouraging collaboration, establishing new value chains, and tackling strategic 105 challenges to build a sustainable and prosperous bioeconomy. it ultimately contributes to 106 boosting european competitiveness on the global scale while at the same time reducing 107 dependency and increasing strategic autonomy. 108 cbe ju also aims to enhance the role of the primary sectors in the european bioeconomy with 109 their involvement in new value chains. this is reflected across all the frameworks and activities 110 that cbe ju is performing, from the council regulation establishing the joint undertakings to 111 the cbe ju strategic research and innovation agenda (sria) and the cbe ju annual work 112 programmes, which are setting the basis for the annual publication of calls for proposals 113 (european commission, 2021; bio-based industries consortium, 2022). 114 cbe ju is a central pillar in establishing europe’s bio-based sector as a global leader, and the 115 overall cbe ju project portfolio is composed of 220 projects, including 138 projects funded 116 by the predecessor bbi ju and 82 projects funded by cbe ju up to now. over 1714 participants 117 from 46 countries have been engaged in cbe ju funded projects, among which 40% are 118 smes. 119 aligned with horizon europe, the cbe ju is funding projects through open calls for proposals. 120 in particular, early-stage development (trls 3–5) is funded by research and innovation actions 121 (rias). innovation actions (ias) are represented by two different type of actions: demonstration 122 projects (ia-demo) that aims at scale up the technology and validate business cases (trl 6–123 7), and flagship projects (ia-flag) that are operating at a pre-commercial scale (trl 8), and 124 aim to support innovations that have already been demonstrated but not yet applied or deployed 125 at industrial and commercial scale in the eu (first-of-a-kind innovation). last, cbe ju is 126 funding coordination and support actions (csas), that address non-technological challenges, 127 6 such societal awareness, capacity building, education and skills, among others. table 1 shows 128 the portfolio of projects funded by cbe ju (and its predecessor bbi ju) divided per type of 129 action and with their associated budget. 130 type of action number of funded projects total budget share of project share of total budget technology readiness level (trl) innovation action – flagship 19 333 m€ 10% 26% 3-5 innovation action – demonstration 76 516 m€ 34% 41% 6-7 research and innovation action 100 383 m€ 45% 30% 8 coordination and support action 25 37 m€ 11% 3% -- total 220 1269 m€ table 1. number of projects funded for type of action and relative share in the overall cbe 131 ju portfolio 132 133 the main application areas covered by the cbe ju projects are bio-based chemicals, 134 biopolymers and bioplastics, food and feed ingredients, packaging, construction materials, crop 135 protection and fertilisation solutions and textile as shown in figure 1. 136 figure 1. number of cbe ju projects by main area of application and type of action. csa 137 projects are not included being horizontal actions to support cbe ju programme. 138 7 139 the main types of feedstocks used in cbe ju projects are agriculture-based feedstock, 140 including residues and by-products from the agri-food industry, followed by forest-based 141 feedstock, including lignocellulosic side streams and wood residues, and finally aquatic 142 feedstock, including aquatic organisms and biomass from fisheries and the aquaculture sector 143 and their residues. other relevant feedstock for cbe ju projects includes side streams from 144 industry, including black liquor from the pulp and paper industry and dairy process side streams, 145 as well as biowaste, including organic fraction of municipal solid waste (ofmsw), and 146 biogenic gases. (see figure 2). 147 figure 2. number of cbu ju projects in relation to their main feedstock divided per type of 148 action. 149 150 151 8 it is therefore noteworthy to underline the importance of primary and secondary biomasses 152 linked to the agricultural, forestry and marine ecosystems in the overall cbe ju portfolio, 153 representing about 90% of the feedstocks addressed by the cbe ju projects. 154 155 3. challenges of the primary sector integration in the biobased 156 industry and opportunities offered by cbe ju to farmers 157 enhancing business opportunities for primary producers in cooperation with the rest of the 158 stakeholders involved in the development of new innovative bio-based value chains and 159 promoting bioeconomy in rural areas are at the core of cbe ju's priorities. 160 farmers are recognised in the context of cbe ju as crucial actors of the value chains as they 161 are providing biorefineries with different types of biomasses (including residues, side streams 162 and by-products) which are converted into high-value bio-based products and materials. the 163 economic activities along the biorefinery processes have the potential to bring new sources of 164 income for primary producers including farmers and therefore boosting local economies in rural 165 areas where they are located. in addition, farmers are also end-users of the innovations 166 delivered by the cbe ju-funded projects which are developing bio-based crop protection and 167 fertilisation solutions with a high environmental performance. these innovations include bio-168 based fertilisers, bio-based pesticides, biostimulants, or biodegradable and bio-based plastics 169 used in agricultural practices. these bio-based products represent a real alternative to 170 conventional products being more sustainable (i.e., having a reduced carbon footprint, 171 minimizing the impact on natural land, protecting soil quality and productivity, minimise the 172 emission of chemicals) and supporting ecosystem restoration, greener agricultural practices 173 (including organic farming), decarbonisation and reduction of eu dependency on imports of 174 raw materials used to produce fertilisers. 175 9 in order to better understand the main challenges and opportunities for primary producers in 176 the bioeconomy sector, a “study on the participation of the agricultural sector in the bbi ju: 177 business models, challenges and recommendations to enhance the impact on rural 178 development” (innovarum, 2019) has been conducted to be used as foundation for the draft of 179 the cbe ju sria. 180 the primary sector plays a fundamental role in the deployment of circular bio-based 181 innovations, significantly influencing the sustainability and success of the broader bio-based 182 economy. as key suppliers of biomass, primary producers provide the essential raw materials 183 needed for bio-based processes. at the same time, they act as both producers and end-users of 184 bio-based products and innovations, making their engagement vital for creating fully integrated, 185 circular value chains. despite their potential to drive sustainability and innovation, several 186 barriers hinder their full participation and the extent to which they can benefit from these 187 opportunities. one of the core challenges is a lack of awareness regarding the opportunities 188 presented by circular bio-based innovations. many primary producers remain uninformed about 189 how these innovations can enhance their operations, improve efficiency, and offer new revenue 190 streams. even when information is available, there is often hesitation to embrace change, 191 stemming from concerns about altering well-established business models. the transition toward 192 bio-based solutions may require significant adjustments to traditional agricultural or forestry 193 practices, leading to uncertainty about economic viability, technical feasibility, and market 194 demand. several communication-related issues further limit primary producers' ability to 195 engage in bio-based innovation (e.g., language used is too complex, inappropriate interlocutors 196 at the primary producer level, insufficient exchanges between primary producers and industry). 197 from a value chain perspective, primary producers are often only marginally integrated into the 198 bio-based value chains and they are facing technological risks and uncertainties as building a 199 10 new supply chain takes many seasons and years, with high risk for the primary sector and with 200 limited short-term results. 201 by addressing these challenges, primary producers can be empowered to take an active role in 202 circular bio-based innovation, ensuring that they do not just remain suppliers of raw materials 203 but also benefit from the transformative opportunities that this growing sector offers. 204 consequently, the cbe ju sria underlined that there is still room for further outreach and 205 mobilisation of primary biomass suppliers from the agriculture sector which had already been 206 identified as a special group that needs to play a bigger and more specific role in the bioeconomy 207 and bio-based value chains (bio-based industries consortium 2022; innovarum, 2019; bio-208 based industries joint undertaking, 2019). 209 among the priorities established in the sria for the primary sector, it is proposed i) to engage 210 and integrate primary producers in sustainable circular bio-based systems and value chains; ii) 211 to improve the primary sector’s involvement in the value chains with the aim of ensuring high 212 quality and quantity of feedstock, while they are rewarded with a proper share of the profit; iii) 213 to revive and revitalise rural, coastal, and peripheral regions through the development of 214 appropriate technological solutions, and inclusive involvement and empowerment of local 215 bioeconomy actors (including the primary sector). 216 in the sria, it is also foreseen that cbe ju actions will implement a value chain approach, 217 and this will ensure that all the relevant actors in the bio-based system, including the supply 218 chain, i.e. primary producers, are appropriately involved in the selected project proposals and 219 are represented to the largest possible extent in the project consortia. 220 the cbe ju has launched in 2024 dedicated communication campaigns in eu countries that 221 are included in the list of widening countries under horizon europe and that were 222 underrepresented through the bbi ju programme implementation, as well as targeted specific 223 stakeholder groups, such as primary producers. communication material is adapted to the local 224 11 language to reach a higher impact. the communication addresses local communities with a 225 message on creating new kinds of jobs and alternative income sources in rural and coastal areas. 226 as a last and relevant operational instrument to foster the primary sector engagement in the 227 biobased industry, cbe ju has created a working group on primary producers (wg pp) that 228 has been officially launched in june 2025. it is an action group and the scope is to propose 229 specific actions to address the challenges faced by the agricultural, forestry and aquaculture & 230 fisheries primary sector and to harness opportunities offered by circular bio-based innovations. 231 the objectives are i) to empower primary producers to play an active and rewarding role in 232 circular bio-based value chains; and ii) ensure they benefit from the economic, social, and 233 environmental opportunities these systems offer (supported by long-term, sustainable, and 234 feasible business models). the wg pp is a group of 64 stakeholders (by june 2024) and consists 235 of organizations representing the agricultural, forestry, fisheries, and aquaculture primary 236 sectors across europe. the wg pp remains open to incorporate additional primary producers 237 who wish to join the initiative in the future, ensuring broader representation across both sectors 238 and regions. among the concrete outcomes expected from the wg pp is the development and 239 implementation of an action plan over the three years period 2025 –2028, with the possibility 240 of extending the work in a later phase, depending on the results achieved and future decisions. 241 the action plan will then be drafted by the members of the working group, being tailor-made, 242 to cover the specific needs and priorities of the primary sector. while the specific actions and 243 the roadmap to achieve them will be defined by the wg pp over the next year, it is already 244 anticipated, based on the swot analysis presented in figure 3 and feedback gathered during 245 the consultative workshop held in february 2024 (circular bio-based europe joint 246 undertaking, 2024), that the actions will focus on different key areas of activity. the first area 247 of activity focuses on raising awareness and improving understanding of the opportunities that 248 circular bio-based innovations offer to primary producers. this includes reaching out to as many 249 12 producers as possible, using the appropriate tools and language to ensure accessibility and 250 impact. to continue, other areas of activities focus on strengthening cooperation of the primary 251 sector with the industry and other stakeholders; deploying feasible business models; and 252 enhancing knowledge exchange on innovation. furthemore, activity focused on creating 253 synergies and collaboration as well as capitalising on existing efforts and building on the work 254 of established networks such as the eu cap network eip agri operational group and 255 various eu-funded projects are also sought. finally, the last area of activity will concentrate on 256 widening participation by mobilising primary producers from regions with unexploited 257 potential and actively engaging them in bio-based value chains (circular bio-based europe 258 joint undertaking, 2025). 259 13 figure 3. swot analysis summarising the main findings of the cbe ju analysis on the problem and opportunities on the participation of primary 260 producers in the biobased innovation development 261 strengths: • primary producers are at the forefront of the bio-based value chains. • direct processing and conversion of biomass at source (primary producers as operators of biorefineries, instead of ‘just’ biomass suppliers). • incremental demand for biomass and increasing demand for circular bio-based products from industry and consumers. • long tradition to cooperate in market-oriented value chains. • possibility to use different forms of cooperation to strengthen the organisation of the primary sector, e.g., farmers' cooperatives (among themselves, and with industry and regional stakeholders), ensuring the direct and indirect participation of their members in the opportunities offered by the circular bioeconomy. • national support (e.g., farmers' support from national advisory services plus their enhanced role under cap). • existence of hubs, training centers, and capacity-building initiatives in rural areas. weaknesses: • primary producers do not always/fully benefit from the opportunities offered by cooperation with the bioeconomy/circular bio-based sector. • mistrust & reluctance about new crops and potential opportunities offered by such crops, including their residues and new farming systems. • insufficient engagement/cooperation of primary producers in the bio-based value chains (missing integration) and difficulty in maintaining motivation over the years. • insufficient innovation capacity. • a volatile market for biomass as input to the bio-based sector. • lack of skills, administrative burden, and language barriers encountered by primary producers to engage in new value chain development through participation in r&i projects. • individual primary producers' involvement in bio-based value chains, in general, is more difficult than if they are represented by the cooperative or other types of organisations serving their interests. • replicability of business models in different regions highly affected by local factors opportunities: • new business opportunities for primary producers based on the efficient use of biomass and valorisation side streams from primary production & crop residues to produce high added-value materials. • novel business models with environmental benefits, e.g., via circulation of nutrients and new sustainable, diversified production systems. • revitalisation of the economic sustainability of unused or abandoned rural areas granting benefits to primary producers that provide ecosystem services. • innovations as a lever for rural regeneration and generational renewal. • benefits of participating in new innovative value chains: reduce the risk and accelerate the work, networking at the eu level, working closely together with other partners in so-called innovation ecosystems. threats: • difficulties in cooperation and an unlevel playing field between large companies/cooperatives and smes/individual primary producers (e.g., feasibility of long-term contracts between the primary sector with industry). • lack of credibility of long-term economic viability of novel bio-based business models and associated technical barriers, e.g., building a new supply chain takes many seasons and years with high risk and very limited short-term results. • too complex or not clearly communicated environmental requirements, including the competing demands for the use of land: biomass use for food and feed production, carbon storage, biodiversity (set-aside), etc. • climate change impact and/or other unexpected threats to primary production. • societal issues, including rural depopulation and aging (the average age of eu farmers is +60 years, and the new generation may not be interested). • policy, legal & regulatory barriers: e.g., validity of ‘new’ crops not included in the actual cap implemented at a national level, difficulty to use abandoned lands, barriers linked to logistics & transport of waste across regions, bio-based sector not considered among the national cap plans and operational groups, fragmented legislative framework, etc. 262 14 4. highlights on farmer participation in cbe ju funded projects 263 the cbe ju annual work programmes are designed to support the primary sector’s to 264 appropriate involvement and representation to the largest possible extent in the project consortia 265 in the selected project proposals. several proposed topics are highly relevant and attractive for 266 the agricultural primary sector (indicative list provided in table 2). also, the expected outcomes 267 envisaged for the projects as described in the topic text envisage positive impacts for farmers 268 and rural areas, such as: engaging with primary producers to ensure that the solutions are 269 meeting their requirements and needs; generating new incomes for farmers; delivering skilled 270 job opportunities in rural areas; supporting the revitalisation of rural areas; fostering 271 cooperation between farmers and bio-based industries; supporting capacity building, training 272 and education for farmers, etc. 273 in addition, cbe ju-funded projects — particularly those at higher trl (6-8, all innovation 274 actions, including flagships) — are requested to implement the multi-actor approach as part 275 of their methodology to ensure an effective engagement with primary producers by involving 276 them in the project activities, including the design and use of the bio-based solutions. this 277 guarantees that the farmers’ requirements and needs are well-considered from the beginning of 278 the development of any innovation and in the building up of a new value chain. synergies with 279 measures and initiatives undertaken in the frame of the common agricultural policy (cap), as 280 for example eip agri and the eip agri operational groups, and with the cap networks are 281 also reflected in several topics, where specific requirements are included in the topic texts and 282 to be implemented in the project. 283 table 2. list of cbe ju topics published in calls for proposals relevant for farmers. 284 cbe ju call type of action and title of the topic 2022 ia-02-cooperative business models for the sustainable mobilisation and valorisation of agricultural residues, byproducts, and waste in rural areas ria-05-sustainable fibres biorefineries feedstock 15 2023 ia-01 small-scale biorefining in rural areas ia-02 production of safe, sustainable, and efficient bio-based fertilisers to improve soil health and quality ria-01 phyto-management; curing soil with industrial crops, utilising contaminated and saline land for industrial crop production 2024 iaflag-01 bio-based value chains for valorisation of sustainable oil crops iaflag-03 bio-based value chains for valorisation of sustainable natural fibre feedstock ia-01 bio-based materials and products for biodegradable in-soil applications ria-03 sustainable, bio-based alternatives for crop protection csa-01 new forms of cooperation in agriculture and the forest-based sector csa-03 supporting the cbe ju deployment group on primary producers to monitor the progress of the cbe ju programme implementation with regard to the strategic 285 participation and integration of feedstock producers and suppliers towards large-scale 286 valorisation of sustainable biomass, a dedicated key performance indicator has been set kpi 287 1.1. number of primary producers, involved as project beneficiaries and/or engaged in value 288 chains at project level. 289 this strategic support framework of cbe ju reaps the benefits. analysing the data gathered 290 during the first three years of operation, it is worth highlighting that since 2022, 84 291 organisations from the agricultural, forestry, and aquatic primary sectors have received a total 292 of about 8.5 million euro in cbe ju funding. 61 out of 84 primary producers are farmers 293 (agricultural primary producers; figure 4, left panel). this means that farmers represent 73% 294 of the primary sector actors involved in cbe ju projects. this is in line with the fact that agri-295 based feedstocks represent the major share of feedstock addressed by cbe ju projects (figure 296 2). 297 primary producers are involved in cbe ju projects, both as beneficiaries of projects (direct 298 participation) or in other ways (indirect participation). indirect participation can take different 299 forms such as contracts as biomass providers, contracts to perform validation services of end-300 products, involvement in advisory bodies, etc. in this regard, an interesting aspect to explore 301 is the comparison between the figures of direct and indirect participation in cbe ju-funded 302 projects as about 40% of farmers are direct participants while 60% are indirect participants. 303 16 this indicates that farmers are interested in participating in cbe ju-funded projects, but not 304 always as direct beneficiaries, and many prefer other forms of involvement that allow them to 305 be engaged in these projects without bearing the administrative burden that come with formal 306 project participation. this approach enables farmers to network and benefit from technical 307 innovations while keeping their focus on their core activities. 308 another aspect analysed in this publication is the involvement of primary producers in the 309 different types of projects that cbe ju is funding (figure 4, right panel). the figures show that 310 the involvement is particularly relevant in innovation actions, including flagships, where the 311 full value chain is covered, and their role is vital to supply biorefineries with locally sourced 312 biomass and/or to test the bio-based solutions as final end-users. in fact, since 2022, 60% of 313 primary producers are involved in innovation actions and flagships, while 25% are in rias and 314 15% in csas. 315 figure 4. participation of primary producers in the cbe ju projects (2022, 2023 and 2024 calls). on 316 the left, distribution of participation among primary sectors as a percentage of total primary producer 317 participation. on the right, distribution of participation per type of funded action as a percentage of total 318 primary producer participation. 319 320 5. examples of cbe ju funded projects significantly involving 321 primary producers 322 61 10 13 agriculture forestry fisheries and aquaculture 60% 25% 15% innovation actions (including flagships) research and innovation actions coordinating and support actions 17 among the different projects selected for receiving cbe ju funding that deliver bio-based 323 innovations that represent new opportunities for farmers, a selection of exemplary cases where 324 the involvement of farmers is particularly relevant is presented. 325 the brilian project has been conceived to support the adoption of sustainable and 326 cooperative business models in rural areas, for the valorisation of agricultural by-products 327 seeking to increase and diversify primary producers’ income (brilian project, 2023). the 328 robocoop-eu project develops circular regional business models for the use of waste 329 streams from three agricultural sectors: grape, olive and stone fruit cultivation (robocoop-330 eu project, 2023). the project will offer new commercial opportunities in rural areas of 331 extremadura, spain; apulia, italy; and west macedonia, greece. another interesting example 332 is manurefinery project that will develop innovative ways to convert livestock manure 333 into bio-based animal feed and fertilisers (manurefinery project, 2024). the project will 334 help reduce the environmental impact of livestock farming and create alternative income 335 sources for farmers on four sites in romania, slovenia and spain. these are only a few 336 examples of innovation actions among many others that are carried out with the support of 337 cbe ju. 338 particularly interesting is the case of the flagships funded by cbe ju. in fact, cbe ju flagship 339 projects have the aim of covering the full value chain from biomass supply to the delivery of 340 end products and considering their location close to the source of biomass. this is particularly 341 relevant in term of creation of economic impact at territorial level. in this context, the role of 342 farmers in the value chains as biomass suppliers but also end-users, together with the expected 343 cooperation that will be forged between primary producers and biorefinery operators, has a high 344 potential to support them by diversifying their business opportunities and sources of income. 345 table 3 summarises the current status of the 19 cbe ju flagships project with a focus on where 346 the biorefinery plant is located and which is the main associated feedstock. 347 18 table 3. summary of the cbe ju flagship and distribution for geographical area, main 348 feedstock and main area of application. 349 name of the project country of location main feedstock area of application cerisea france agri-food bio-based chemicals peference the netherlands agri-food biopolymers and bioplastics first2run italy agri-food bio-based chemicals plenitude the netherlands agri-food food and feed lignoflag romania agri-food other terrific italy agri-food biopolymers and bioplastics sustainext spain agri-food food and feed runfaster4eu italy agri-food bio-based chemicals afterbiochem france agri-food bio-based chemicals farmyng france agri-food food and feed circle the netherlands agri-food bio-based chemicals sylplant france agri-food and forest-based food and feed scale france aquatic food and feed proteus norway aquatic food and feed circular biocarbon spain and italy biowaste biopolymers and bioplastics sweetwoods estonia forest-based construction exilva norway forest-based biopolymers and bioplastics viobond latvia forest-based construction woodcell estonia forest-based biopolymers and bioplastics 350 an example of a flagship project that aims at achieving zero-waste and circular-by-design 351 industrial process in a rural region in europe is sustainext (sustainext project, 2023). 352 the project is turning an existing production plant into a digitalised circular biorefinery to 353 produce healthy plant extracts and functional ingredients from medicinal and aromatic crops – 354 rosemary, camomile and lemon verbena – on disused tobacco fields in extremadura, spain. it 355 also deploys solar panels to enhance soil use and provide clean energy as well as agricultural 356 side streams – olives, cardoons and pomegranates – to showcase how underexploited biological 357 raw materials can be upcycled. the project will generate value and new jobs for the region, 358 19 showing the positive impact of pursuing the european green deal objectives. the project’s 359 model is easily adaptable and replicable and can run on renewable energy. 360 another relevant example is the afterbiochem flagship project (afterbiochem 361 project, 2020). the eu is the world’s largest producer of sugar beet, with about 140 000 sugar 362 beet growers and around 27,000 direct jobs in sugar beet processing. afterbiochem is 363 building the first biorefinery for transforming the sugar industry’s side streams – mainly pulp 364 and non-food waste – into bio-based molecules of industrial interest, for the flavourings, 365 fragrances, hygiene products, pharmaceuticals, antimicrobials and polymers sectors in carling 366 saint-avold, france. this will increase the economic and environmental sustainability of the 367 sugar beet industry. in addition, the process will be flexible enough to adapt to alternative 368 feedstocks in the future. 369 370 6. conclusion 371 analysing the current achievements of the cbe ju projects, some key messages can be drawn 372 on the main elements that are contributing to make a compelling proposal a successful project. 373 in particular, while aligning with the cbe ju objectives and directly contributing to the eu 374 green deal, circular economy and bioeconomy goals, successful projects are demonstrating 375 innovative breakthroughs and, at the same time it has been shown how the involvement of 376 actors across the entire bio-based value chain, from biomass producers to end-users, is a very 377 relevant prerequisite to provide the conditions that make a brilliant ideas a potential market 378 success. smes play a crucial role in the cbe ju programme, accounting for nearly one-third 379 of participants and receiving a corresponding one-third of the total funding. their leadership is 380 also evident, with one in four projects coordinated by an sme, reinforcing their vital 381 contribution to driving innovation and project execution within the bio-based sector. 382 accelerating the innovation process for bio-based solutions and fostering technology transfer 383 20 are part of core objectives of successfully funded cbe ju projects. therefore, it is notewhorty 384 that universities and research centres, representing one-quarter of all cbe ju participants, are 385 key providers of innovative bio-based solutions for the projects. building consortia founded on 386 a strong multi-actor approach with a robust impact potential in term of societal, economic and 387 environmental benefits is also pivotal to drive the market deployment of the biobased sector in 388 europe in a fair and sustainable way. to this regard, successful projects are mastering effective 389 approaches to strengthen the link between circularity, bioeconomy and availability of 390 sustainable biomass in the real life. this is very important to ensure that the farmers play a 391 strategic role in the biobased transformation and are clearly positioned in the biobased value 392 chains. this strong integration between the primary production and the biobased industry is 393 expected to be further enhanced in the longer term, leading to systemic benefit from these 394 collaborations. 395 we are in a transition period: the scope of the bioeconomy, including the opportunities for the 396 primary sector, will be further developed in the revision of the european bioeconomy strategy 397 expected by the end of 2025. 398 as proposed by cbe ju, there is a clear trend of supporting research and innovation aimed at 399 enhancing competitiveness while at the same time ensuring high environmental protection, food 400 security and the decarbonisation of the economy. the circular bio-based innovations developed 401 in cbe ju projects are instrumental in achieving this goal. they will be crucial to make the 402 most of the available sustainable biomass, including wastes, side streams, non-food crops, and 403 cultivations in marginal lands to develop circular bio-based solutions in replacement of fossil-404 based products. these innovative solutions have the potential to protect ecosystems, reduce the 405 utilisation of external inputs, ensure food security, support nature restoration, while diversifying 406 the business models and income streams of the primary sectors. the expected rewarding 407 21 cooperation between farmers and the rest of the biorefinery operators will also be important to 408 ensure a proper and fair share of the profit among all the actors involved in the value chain. 409 the potential of cbe ju to support farmers is high and the partnership’s legal framework is 410 extremely supportive, with measures incentivising their involvement in project activities but 411 also beyond that, empowering farmers and enhancing their role in the circular bio-based value 412 chains, as is proved with the launch of the wg pp. several projects from bbi ju have already 413 finished and demonstrated impacts for the primary sector and rural development. under cbe 414 ju, some of the projects resulting from the first three cycle of funding are already delivering 415 concrete results and impacts. the project outcomes over the next years will be crucial to further 416 understand the challenges and the opportunities for the involvement of farmers in the european 417 circular bio-based value innovations and the impacts of the promotional cbe ju activities on 418 that. 419 altogether, the achievements of cbe ju and the findings related to the integration of primary 420 sector and biobased industry will represent a solid and significant basis, substantiated by 421 concrete outcomes and exploitable results, for the future of the bioeconomy research and 422 innovation support in europe in the next future. 423 references 424 afterbiochem project from h2020-bbi-jti-2019 call (2020). available at: 425 https://www.cbe.europa.eu/projects/afterbiochem . 426 bio-based industries consortium (2022). strategic research and innovation agenda of cbe 427 ju. brussels. 428 bio-based industries joint undertaking (2019). proposal of actions to increase/improve the 429 participation of the agricultural sector in bbi ju and ultimately in the bio-based sector. 430 available at: https://innovarum.es/wp-content/uploads/2020/07/2019.bbiju-study.pdf 431 https://www.cbe.europa.eu/projects/afterbiochem https://innovarum.es/wp-content/uploads/2020/07/2019.bbiju-study.pdf 22 brilian project from horizon-ju-cbe-2022 call (2023). available at: 432 https://www.cbe.europa.eu/projects/brilian 433 circular bio-based europe joint undertaking (2024). report of the workshop engagement with 434 primary producers in circular bio-based value chains. available at: 435 https://www.cbe.europa.eu/system/files/2024-04/report-primary-producers-workshop-2024-436 02-28.pdf 437 circular bio-based europe joint undertaking (2025). available at: 438 https://www.cbe.europa.eu/system/files/2025-01/cbe-ju-wg-pp-concept-note.pdf 439 european commission (2018). a sustainable bioeconomy for europe – strengthening the 440 connection between economy, 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https://innovarum.es/wp-content/uploads/2020/07/2019.bbiju-453 study.pdf 454 johnson, c., ruiz sierra, a., dettmer, j., sidiropoulou, k., zicmane, e., canalis, a., llorente, 455 p., paiano, p., mengal, p., and puzzolo, v. (2021). the bio-based industries joint undertaking 456 https://www.cbe.europa.eu/projects/brilian https://www.cbe.europa.eu/system/files/2024-04/report-primary-producers-workshop-2024-02-28.pdf https://www.cbe.europa.eu/system/files/2024-04/report-primary-producers-workshop-2024-02-28.pdf https://www.cbe.europa.eu/system/files/2025-01/cbe-ju-wg-pp-concept-note.pdf https://innovarum.es/wp-content/uploads/2020/07/2019.bbiju-study.pdf https://innovarum.es/wp-content/uploads/2020/07/2019.bbiju-study.pdf 23 as a catalyst for a green transition in europe under the european green deal. efb bioeconomy 457 journal, 1:100014. 458 manurefinery project from horizon-ju-cbe-2023 call (2024). available at: 459 https://www.cbe.europa.eu/projects/manurefinery 460 members of the strategic dialogue on the future of eu agriculture (2024). strategic dialogue 461 on the future of eu agriculture. a shared prospect for farming and food in europe. brussels. 462 mengal, p., wubbolts, m., zika, e., ruiz, a., brigitta, d., pieniadz, a., and black, s. (2018). 463 bio-based industries joint undertaking: the catalyst for sustainable bio-based economic 464 growth in europe. new biotechnology, 40(a):31–9. 465 robocoop-eu project from horizon-ju-cbe-2022 call (2023). available at: 466 https://www.cbe.europa.eu/projects/robocoop-eu 467 ruiz sierra, a., zika, e., lange, l., ruiz de azúa, p., canalis, a,, mallorquín esteban, p., 468 paiano, p., and mengal, p. (2021). the bio-based industries joint undertaking: a high impact 469 initiative that is transforming the bio-based industries in europe. new biotechnology, 60:105–470 12. 471 sustainext project from horizon-ju-cbe-2022 call (2023). available at: 472 https://www.cbe.europa.eu/projects/sustainext 473 https://www.cbe.europa.eu/projects/manurefinery https://www.cbe.europa.eu/projects/robocoop-eu https://www.cbe.europa.eu/projects/sustainext agriculture 4.0: technological adoption, drivers, benefits and challenges in italy. a descriptive survey pacciani, c. a, catellani, e. a, bacchetti, a. b, corbo, c. a, ciccullo, f. a and ardolino, m. b a department of management engineering, politecnico di milano, via lambruschini, 4b 20156 milano italy (cosimo.pacciani@polimi.it, eleonora.catellani@polimi.it, chiara.corbo@polimi.it, federica.ciccullo@polimi.it) b department of mechanical and industrial engineering, university of brescia, via brianze, 38 25123 brescia italy (andrea.bacchetti@unibs.it, marco.ardolino@unibs.it) this article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the version of record. please cite this article as: pacciani, c., catellani, e., bacchetti, a., corbo, c., ciccullo, f. ardolino, m (2025). agriculture 4.0: technological adoption, drivers, benefits and challenges in italy. a descriptive survey, biobased and applied economics, just accepted. doi:10.36253/bae-17382 abstract: this study aims to examine the current state of awareness regarding agriculture 4.0 (a4.0) among italian agricultural enterprises and to analyse variations in adoption levels, expressed needs, perceived benefits, challenges and barriers to digitalisation. drawing on data from a descriptive survey conducted among italian farms in 2024, this study presents findings from 1,248 respondents. the results indicate varying levels of adoption of a4.0 solutions, with monitoring systems and connected vehicles being the most widely implemented. the primary drivers for a4.0 adoption include farm management, operational control, and the enhancement of production efficiency, all of which are associated with significant perceived benefits. however, challenges such as limited interoperability and skill shortages hinder a4.0 implementation, while financial and structural constraints remain major barriers for farms seeking to transition to a4.0. this study offers valuable insights to inform policymakers, industry stakeholders, and researchers in fostering a more effective and inclusive digital transformation in the italian agricultural sector. keywords: agriculture 4.0, smart farming, digital agriculture, survey jel codes: m15, o32 1. introduction 1 2 agriculture 4.0, also known as “digital agriculture”, “smart farming” or “smart agriculture”, is defined 3 as the integration of advanced digital technologies such as the internet of things (iot), robotics, 4 artificial intelligence (ai), and big data analytics into the agricultural sector (fragomeli et al., 2024). 5 this concept is grounded in the broader framework of industry 4.0, which is responsible for the 6 transformation of manufacturing processes (da silveira et al., 2021). agriculture 4.0 (hereby a4.0) 7 represents a significant departure from both traditional and precision agriculture by leveraging 8 automated, interconnected, and data-driven systems (sharma et al., 2022). 9 the transition to digitalised agricultural systems is increasingly considered as pivotal for addressing the 10 global challenges facing society today. rapid population growth, urbanization, industrialization, loss of 11 arable land, freshwater scarcity, and environmental degradation have escalated concerns regarding food 12 security (abbasi et al., 2022). to meet the rising global demand for food, agricultural practitioners must 13 enhance productivity while minimising pressure on natural resources such as water, land, and energy 14 (sharma et al., 2022). 15 this highlights the urgent need for efficient, data-driven agricultural practices that optimise resource 16 usage and improve productivity (fragomeli et al., 2024). moreover, agriculture is both a major 17 contributor to greenhouse gas emissions and a sector vulnerable to the impacts of climate change (sott 18 et al., 2020). integrating digital technologies offers the potential to mitigate the environmental footprint 19 of agricultural practices while bolstering farmers' resilience to climate change (balasundram et al., 20 2023). 21 technologies such as robotics, smart irrigation and iot sensors can promote more sustainable 22 agricultural practices by reducing emissions, optimising resource use, and enabling real-time monitoring 23 of crop conditions (assimakopoulos et al., 2024). the environmental benefits of a4.0 are closely tied to 24 economic advantages, as digital solutions improve operational productivity, reduce resource waste, and 25 generate cost savings (zul azlan et al., 2024). additionally, from a social perspective, the digitalisation 26 of agriculture empowers farmers by providing better decision-making tools and improving working 27 conditions (zhai et al., 2020). 28 according to papadopoulos et al. (2024), for instance, recording and mapping technologies, combined 29 with guidance and controlled traffic farming technologies, could lead to reductions of up to 80% in 30 fertiliser use. furthermore, vrt (variable rate technologies) could achieve a 60% decrease in fertiliser 31 consumption and an 80% reduction in pesticide use, while also potentially boosting yields by 62%. 32 additionally, robotic systems and smart machines could reduce labour by 97% and diesel consumption 33 by 50%. 34 thus, a4.0 represents a transformative approach that addresses environmental, economic, and social 35 challenges, contributing to the development of more sustainable and resilient agricultural systems 36 (maffezzoli et al., 2022b). 37 despite the promising role that a4.0 solutions could play in mitigating sustainability challenges while 38 improving productivity, their uptake remains limited and fragmented (osrof et al., 2023). literature 39 relates the uneven adoption rate to different factors. 40 recent empirical contributions confirm that farmers’ intentions to adopt new solutions go beyond purely 41 economic considerations and are shaped by a combination of personal attitudes and perceived obstacles. 42 for instance, giampietri et al. (2020) emphasize the role of farmers’ trust, experience and knowledge in 43 the adoption of risk management practices, highlighting the importance of transparency about costs and 44 benefits in adoption incentivization. menozzi et al. (2015) highlight the relevance of farmers’ attitudes 45 and perceived control in adopting sustainable farming practices, stressing the need for better 46 communication and collaboration within the agricultural supply chain to increase a4.0 adoption. 47 meanwhile, data from farm-level surveys show how age, gender, education and farm size remain 48 significant influencing factors for choices regarding, for example, climate change adaptation 49 strategies (onyenekwe et al., 2023). 50 despite the ongoing discussions in the literature regarding the factors that favour or hinder the spread of 51 a4.0, the influence of specific contexts, as countries and types of farms and farmers, remains a 52 compelling area of investigation (fragomeli et al., 2024; da silveira et al., 2023). therefore, the authors 53 emphasise the need for a country-specific investigation on: i) farmers’ awareness of a4.0; ii) the main 54 challenges and barriers in the adoption as well as iii) the sustainability benefits perceived. we believe 55 that building a comprehensive knowledge around the gap between a4.0 technologies, their promised 56 technical advantages and the actual implementation along with the feasibility of realising the related 57 sustainability benefits, is fundamental to inform key decision makers (e.g., policy makers). this 58 knowledge can help in shaping proper strategies which place farmers and their context-specific needs at 59 the centre. 60 to this end, a survey was conducted targeting italian farms to assess the current level of digitalisation in 61 the agricultural sector, with a specific focus on the key dimensions influencing the adoption and 62 implementation of a4.0 solutions. the following research questions were formulated to explore the 63 state-of-the-art of a4.0 in italy: 64 rq1: what is the level of adoption and awareness of a4.0 solutions in italy? 65 rq2: what are the primary factors driving agricultural enterprises to adopt a4.0 solutions? 66 rq3: to what extent have the achieved benefits aligned with the expressed needs? 67 rq4: what are the most significant hindering factors to farmers' adoption of a4.0 solutions? 68 this study reveals that, while a4.0 awareness is high, adoption is uneven, with greater uptake of a4.0 69 solutions such as monitoring systems and connected vehicles. adoption is mainly driven by 70 improvements in farm management rather than operational efficiency. 71 benefits generally meet or exceed expectations, particularly in optimizing technical inputs and water 72 use, which yield both economic and environmental gains. social sustainability effects remain debated, 73 with some evidence of labor market benefits, though concerns persist over potential job displacement. 74 despite the benefits, adoption is hindered by challenges such as interoperability, lack of skills, uncertain 75 return on investments and limited technical support. financial and structural barriers especially for 76 small farms and poor connectivity in remote areas further constrain a4.0 uptake. this study 77 recommends targeted policy support, training, and agrifood supply chains stakeholder collaboration to 78 overcome these barriers and accelerate digital transformation in italian agriculture. 79 the remainder of the paper outlines as follows: the first section develops a review of the existing 80 literature on main a4.0 solutions and applications along with the factors connected to their spread, 81 section 2 presents a literature review covering the evolution of technologies in agriculture, the main 82 driving technologies and their applications, challenges and benefits. section 3 explains the methodology 83 adopted, while results are described in section 4. finally, sections 5 and 6 discuss the main results and 84 draw conclusions from the authors’ work. 85 86 87 88 2. literature review 89 2.1 the evolution of agricultural technologies 90 over the years, agriculture has evolved through distinct technological phases, progressing from 91 agriculture 1.0 to agriculture 4.0 (zhai et al., 2020). traditional agriculture, agriculture 1.0, relied 92 heavily on manual labour and animal power. the transition to agriculture 2.0 began in the 19th century 93 with the introduction of mechanised farming and steam engines, which significantly increased the 94 efficiency of agricultural activities. this second phase was also characterised by an extensive use of 95 chemical fertilizers and pesticides, leading to environmental degradation and resource overexploitation. 96 in the 20th century, agriculture 3.0 emerged, leveraging advancements in computing and electronics to 97 automate processes and enhance precision, also reducing dependency on chemicals. today, a4.0 marks 98 the era of smart farming, integrating digital technologies to create highly interconnected and data-driven 99 agricultural systems (fragomeli et al., 2024). 100 these innovations enable farmers to make real-time, data-informed decisions, improving 101 productivity, sustainability, and resource efficiency while minimising environmental impact. several 102 terms are used to denote a4.0, such as “digital agriculture”, “smart farming” and “smart agriculture” 103 (albiero et al., 2020). 104 as outlined by sponchioni et al. (2019) and maffezzoli et al. (2022b), agriculture 4.0 can be defined 105 as the evolution of precision farming, realised through the automated collection, integration, and 106 analysis of data from the field, equipment sensors, and other third-party sources. while precision 107 farming serves as a management system that aims at optimising crop production inputs at the field 108 level (bongiovanni and lowenberg-deboer, 2004; pierce and nowak, 1999; gebbers and adamchuk, 109 2010), a4.0, facilitated by the smart and digital technologies inherent in industry 4.0, transforms 110 previously isolated data silos into actionable knowledge, supporting farmers in decision-making both 111 within their enterprises and across the broader agrifood supply chain. this shift from traditional to 112 digital systems ultimately aims to enhance cost efficiency and profitability, fostering the transition to 113 more sustainable agricultural systems from an economic, environmental and social perspective. 114 recent advancements in a4.0 are marked by emerging trends that are shaping the future of farming, 115 with a particular focus on enhancing efficiency, sustainability, and resilience. a key forthcoming 116 development is the transition toward agriculture 5.0, which extends the foundations of a4.0 by 117 incorporating human-centric, sustainable, and resilient principles derived from industry 5.0 (abbasi 118 et al., 2022). this shift refines the collaboration between humans and machines, aiming to improve 119 efficiency while reducing environmental impact through circular economy strategies (fragomeli et 120 al., 2024). alongside this evolution, digital twin technology has gained prominence as a tool for 121 optimising agricultural operations (peladarinos et al., 2023; escribà-gelonch et al., 2024), creating 122 real-time virtual replicas of farms that enable monitoring, predictive analytics, and improved system 123 integration (polymeni et al., 2023). by simulating real-world agricultural processes, digital twins can 124 support decision-making in areas such as crop growth, soil composition, and climate adaptability 125 (peladarinos et al., 2023). at the same time, the increasing challenges posed by climate change have 126 accelerated the adoption of climate-smart agricultural (csa) practices, which focus on building 127 resilience against environmental concerns, reducing greenhouse gas emissions, and ensuring long-128 term food security through adaptive resource management (balasundram et al., 2023). 129 130 131 2.2 key technologies and applications 132 there are various ways to categorize the key technologies driving a4.0, as different literature studies 133 highlight several aspects of innovation in the field. internet of things (iot) enables the connection 134 of agricultural devices and machinery, allowing real-time monitoring and automation of farm 135 operations (assimakopoulos et al., 2024; abbasi et al., 2022). sensors and wireless sensor networks 136 collect critical data on soil conditions, weather, and crop health, supporting precision farming (ahmed 137 et al., 2024). artificial intelligence (ai) and machine learning process large datasets to optimise 138 resource use, detect plant diseases, and predict yields, making farming more data-driven and efficient 139 (ahmed et al., 2024; balyan et al., 2024). ai-driven systems are increasingly capable of autonomous 140 decision-making, on-farm reinforcement learning, and real-time adaptation, significantly 141 transforming how decisions are made at the farm level (khanna et al., 2024). robotics and automation 142 include autonomous machines and drones that assist in tasks such as planting, harvesting, and 143 spraying, reducing labour dependency and increasing precision (ahmed et al., 2024; balyan et al., 144 2024). data analytics and big data play a crucial role in processing vast amounts of information 145 collected from farms, offering insights for better decision-making (abbasi et al., 2022). cloud and 146 edge computing ensure that agricultural data is processed efficiently and securely, reducing latency 147 and enabling real-time responses in smart farming systems (abbasi et al., 2022). blockchain 148 technology enhances transparency and traceability in the agricultural supply chain by securely 149 recording transactions and ensuring data integrity (ahmed et al., 2024). 150 while the technologies discussed above form the foundations of a4.0, they are not typically deployed 151 in isolation. instead, they are combined into integrated digital solutions, translating technological 152 capabilities into practical tools for farming and therefore addressing different agricultural needs. such 153 integrated solutions include decision support systems (dss) (araujo et al., 2021), monitoring 154 systems (dayıoğlu and turker, 2021), mapping solutions (karunathilake et al., 2023), variable rate 155 technologies (vrt) (dayıoğlu and turker, 2021), connected vehicles (karunathilake et al., 2023), 156 telemetry systems (papadopoulos et al., 2024), robotics and drones (araujo et al., 2021). these 157 solutions are further described in the methodology section, where their identification, based on a 158 review of scientific and grey literature, forms a key step of the survey design. investigating adoption 159 at the solution level, rather than at the level of individual technologies, better reflects how farmers 160 actually implement digital tools in practice. 161 as with key technologies and solutions, the applications of a4.0 have been categorised in different 162 ways, reflecting the broad range of domains in which digital technologies can support agricultural 163 practices. water and irrigation management involves smart irrigation systems, iot-based sensors, and 164 climate monitoring tools to optimise water use, ensuring efficient irrigation and drought adaptation 165 (ahmed et al., 2024; javaid et al., 2022). soil and crop health monitoring utilizes remote sensing, 166 drones, and ai-driven diagnostics to assess soil fertility, detect diseases, and manage agrochemical 167 and fertilizer use with precision (yousaf et al., 2023). predictive analytics for climate adaptation and 168 yield forecasting apply machine learning and big data analytics to anticipate weather patterns, pest 169 outbreaks, and crop productivity, helping farmers make data-driven decisions to mitigate risks 170 (kumar kasera et al., 2024). autonomous machinery and robotics enhance efficiency by using 171 automated tractors, drones, and harvesting robots to perform tasks such as soil preparation, planting, 172 and harvesting with minimal human intervention (oliveira et al., 2021). controlled-environment 173 agriculture includes greenhouse cultivation, hydroponics, and aquaponics, which optimise growing 174 conditions and reduce dependency on natural weather cycles, ensuring year-round food production 175 (maffezzoli et al., 2022b). livestock monitoring and regulation employs wearable sensors, automated 176 feeding systems, and ai-based health tracking to improve animal welfare, optimise breeding, and 177 prevent diseases (ahmed et al., 2024). finally, supply chain optimisation focuses on product tracking, 178 storage management, and food processing, incorporating blockchain and automation to enhance 179 traceability, reduce waste, and streamline logistics from farm to consumer (kumar kasera et al., 180 2024). 181 to summarise, these technological solutions, applied in a diverse range of domains, can result in a 182 set of improvements for farmers. such improvements, later investigated through a survey, encompass 183 different dimensions. a4.0 solutions can support farmers with improved forecasting capabilities and 184 improved farm management and control; support planning and scheduling activities, while also 185 facilitating and streamlining workforce processes; optimise the use of technical inputs (water, 186 pesticides, fertilizers), enhance efficiency and reduce losses due to pests and diseases. finally, 187 through monitoring and measurement, they enable increased awareness on farm operations and 188 improve the quality of agricultural products. 189 all these enhancements can lead to substantial economic, environmental and social benefits. 190 2.3 sustainability benefits 191 a4.0 yields significant economic, social, and environmental benefits, thereby fostering a profound 192 transformation of the agricultural sector. economically, it enhances resource use efficiency by 193 optimising the application of water, fertilizers, and pesticides, reducing waste, and increasing 194 agricultural yields. this leads to greater profitability for farmers and more cost-effective farming 195 practices (zul azlan et al., 2023; abbasi et al., 2022). additionally, the automation and digitalisation 196 of farm operations, such as harvesting, sowing, and irrigation, result in time and cost savings, 197 improving operational efficiency (pradel et al., 2022). the introduction of predictive models and real-198 time data analysis can help farmers forecast adverse conditions like disease outbreaks or extreme 199 weather, thereby improving the resilience of agricultural systems and ensuring production even in 200 challenging circumstances (zul azlan et al., 2023). from an environmental standpoint, smart farming 201 practices significantly reduce agriculture’s ecological footprint. precision agriculture technologies, 202 ai-driven crop monitoring, and automated machinery facilitate the efficient use of resources, leading 203 to reduced fuel consumption, lower greenhouse gas emissions, and improved water conservation 204 (cambra baseca et al., 2019). moreover, the deployment of technologies such as drones and iot-205 based environmental monitoring systems supports soil health management, optimises nutrient use 206 efficiency, and strengthens climate resilience (abbasi et al., 2022). by minimising waste and 207 promoting environmentally responsible practices, a4.0 emerges as a key driver of sustainable 208 agricultural development (zul azlan et al., 2023). 209 from a social perspective, a4.0 plays a crucial role in enhancing the well-being of rural communities, 210 agricultural workers, and consumers. by promoting more efficient and sustainable farming practices, 211 a4.0 strengthens food security, mitigates food shortages, and reduces waste (jin et al., 2020). 212 furthermore, the integration of advanced technologies equips farmers with improved decision-213 making tools, contributing to higher living standards by lowering labour costs and enhancing working 214 conditions (da silveira et al., 2021). additionally, a4.0 enhances product quality and traceability, 215 ensuring food safety and addressing consumer concerns (zul azlan et al., 2023). the integration of 216 advanced digital monitoring technologies can in fact support the verification of environmental and 217 social standards along the food supply chain (meemken et al., 2024). these systems not only 218 strengthen sustainability management but also offer new avenues for accountability and trust in food 219 systems. however, they further raise important questions about equity and data access, which merit 220 further attention as digital monitoring expands (meemken et al., 2024). despite such promising social 221 benefits, scholars have also drawn attention to the danger of overly optimist narratives that see these 222 innovations as universal solutions. klerkx et al. (2020) emphasize the need to account for the social 223 and ethical implications of a4.0 transitions, particularly in terms of labor displacement, rural 224 depopulation, power concentration, and the marginalization of alternative, potentially more 225 accessible technologies. 226 in fact, while a4.0 promises numerous benefits, its impacts are not unilaterally positive. muhl et al. 227 (2022) stress how digital agriculture may reinforce existing inequalities and that social issues like 228 food insecurity, often driven by broader social injustices, will not be solved by technological 229 development alone. the sustainability debate thus calls for an inclusive and responsible approach to 230 the use and development of these technologies, ensuring accessibility across different contexts (muhl 231 et al., 2022). 232 2.4 challenges and barriers 233 the adoption of a4.0 technologies is hindered by a range of significant challenges and barriers, as 234 highlighted by (assimakopoulos et al., 2024, da silveira et al., 2021; da silveira et al., 2023; 235 fragomeli et al., 2024). an interesting classification of challenges is provided by lezoche et al. 236 (2020), where a distinction is made between organizational, social and technological challenges. 237 among organization challenges, one of the most frequently associated with a4.0 adoption is the high 238 cost connected to the technology adoption, including the initial investment required for the 239 implementation of the components, the ongoing maintenance costs, and the cost of skilled labour (da 240 silveira et al., 2023). these financial challenges are particularly burdensome for small-scale farms, 241 which often lack the necessary capital or access to financing options to invest in such innovations 242 (assimakopoulos et al., 2024). additionally, from a more social perspective, the complexity of 243 modern agricultural technologies and the advanced skills required for their operation present further 244 obstacles (fragomeli et al., 2024). these barriers are not unique to the italian context; similar 245 challenges have been widely observed in other regions, particularly among smallholder farmers. for 246 instance, mhlanga et al. (2023) highlight the digital transformation obstacles in african agriculture, 247 where factors such as limited infrastructure, insufficient digital literacy, lack of funding mechanisms, 248 and farmer resistance significantly constrain adoption. in general, farmers with limited technological 249 proficiency especially older individuals or those with lower levels of formal education may 250 struggle to integrate digital tools into their daily operations (assimakopoulos et al., 2024). it can be 251 stated that, beyond costs, adoption is shaped by a complex interaction of operator characteristics (such 252 as age, education, and digital skills), farm-level attributes (including size, income, and specialization), 253 and the perceived attributes of the technologies themselves such as their trialability, ease of 254 integration, and perceived utility (khanna et al., 2024). 255 from an organizational perspective, uncertain regulatory aspects and complex legal frameworks often 256 hinder adoption (lezoche et al., 2020), highlighting the role of manufacturers and governmental 257 bodies as critical in mitigating these challenges. 258 looking at technological challenges, a further barrier is often recognized in inadequate 259 infrastructures, particularly in rural areas, where poor internet connectivity and restricted access to 260 technical support networks hinder the full utilization of digital technologies (da silveira et al., 2023; 261 fragomeli et al., 2024). moreover, farmers already managing extensive daily responsibilities may 262 perceive these new technologies as overly time-consuming or complex, together with concerns about 263 lack of interoperability and issues about data security and privacy (lezoche et al., 2020). moreover, 264 many farmers report a lack of accessible training programs, technical guidance, and support services, 265 which prevents them from fully understanding and implementing digital tools (da silveira et al., 266 2023). 267 these financial, educational, infrastructural, and institutional barriers underscore the multifaceted 268 challenges associated with adopting a4.0 technologies. addressing these issues through targeted 269 policies, improved infrastructure, and comprehensive training initiatives is essential for promoting 270 widespread and equitable adoption of digital farming solutions. 271 272 3. research methodology 273 274 the primary objective of this research is to assess the current state of digitalisation within the italian 275 agricultural sector, with a specific focus on different key dimensions that shape the adoption and 276 implementation of a4.0 technologies. to evaluate the state-of-the-art of a4.0 in italy, the following 277 research questions have been formulated: 278 rq1: what is the level of adoption and awareness of a4.0 solutions in italy? 279 rq2: what are the primary factors driving agricultural enterprises to adopt a4.0 solutions? 280 rq3: to what extent have the achieved benefits aligned with the expressed needs? 281 rq4: what are the most significant hindering factors to farmers' adoption of a4.0 solutions? 282 to address these research questions, the study examines the following dimensions: 283 adoption and awareness of a4.0 solutions: assessing the extent to which identified a4.0 solutions have 284 been implemented across the sector and the level of awareness that italian farms have regarding these 285 technologies. 286 drivers of digitalisation: identifying the factors motivating farms to explore and implement the proposed 287 a4.0 solutions, highlighting key needs and expectations. 288 benefits achieved: evaluating the advantages achieved through the adoption of a4.0 solutions with 289 regards to the specific needs expressed. 290 challenges encountered by farmers adopting a4.0 technologies: examining obstacles that farms 291 encountered during the adoption and implementation process of a4.0 solutions. 292 inhibiting factors for non-adopting farmers: investigating the underlying reasons for the hesitation or 293 inability of non-user farmers to adopt a4.0 solutions. 294 the last two categories are drawn from the literature on "challenges and barriers", which typically does 295 not distinguish between adopters and non-adopters. however, based on the authors' experience and 296 discussions with farmers and technology providers, this distinction was deemed necessary to better 297 reflect the obstacles faced by italian agricultural enterprises in uptaking and using a4.0 solutions. 298 to address these objectives systematically, the research followed a structured methodology comprising 299 the following steps: 300 sample development. the research referenced data from the 7th general census of agriculture of the 301 italian national institute of statistics (istat)1 to identify a representative sample of italian agricultural 302 enterprises. the sampling framework accounted for critical variables, including farm size, production 303 type, and geographic distribution, ensuring a diverse and comprehensive representation of the italian 304 agricultural sector. the sample was drawn from three perspectives: (1) geographic distribution: italian 305 farms were grouped in four main regions to capture macro-regional variations in farms geographical 306 distribution (table 1). (2) primary crop production: italian farms have been classified based on their 307 primary agricultural products, determined by the proportion of utilised agricultural area (uaa) 308 allocated to specific cultivations (table 2). (3) farm size: italian farms have been categorised according 309 to their uaa size, allowing for an analysis of adoption patterns by operational scale (table 3). a 310 proportionate stratified random sampling approach was employed, whereby the total population, as 311 defined by istat, was divided into mutually exclusive strata. each stratum was sampled in proportion 312 to its representation within the overall population. within each stratum, participants were selected using 313 a random sampling method. 314 315 identification of a set of a4.0 solutions. a tailored set of a4.0 solutions was developed in alignment with 316 the operational characteristics of the agricultural sector based on an analysis of scientific and grey 317 literature on this topic (araújo et al., 2021; dayıoğlu and turker, 2021; karunathilake et al., 2023; 318 papadopoulos et al., 2024). this set comprises the following a4.0 solutions: 319 1 https://www.istat.it/statistiche-per-temi/censimenti/agricoltura/7-censimento-generale/ https://www.istat.it/statistiche-per-temi/censimenti/agricoltura/7-censimento-generale/ dss – decision support systems, that assist farmers in decision-making by optimising management and 320 agronomic choices based on field data, environmental, weather and soil data, and information provided 321 by the farmer. these systems can directly provide both management and agronomic advice to the users. 322 monitoring systems, enabling the monitoring, often remotely and automatically, of environmental 323 conditions or other parameters related to crops. 324 mapping solutions, allowing the mapping of soil and crops, providing spatial variability in soil, crop, 325 and hydrological characteristics, among others. these spatialised datasets can be used for various 326 purposes such as variable rate input applications, agronomic decision-making support, and operational 327 management. 328 variable rate technology (vrt) solutions that enable field operations and the distribution of inputs 329 based on the spatial variability detected in the field and the needs of the soil and crop systems. 330 connected vehicles, i.e. digitally connected machinery that is equipped with integrated digital 331 technologies, such as assisted driving, precision navigation systems, and auto-steering systems. 332 telemetry systems and solutions for vehicle and equipment monitoring, that can locate, monitor and 333 provide assisted control of agricultural machinery, including auto-steering systems and telematic 334 solutions for fleet monitoring, predictive maintenance, and machinery efficiency improvement. 335 robotics, i.e. solutions involving autonomous machinery capable of movement, decision-making, and 336 performing specific tasks and crop operations with little or no operator intervention. 337 drones, i.e. solutions and services involving the use of drones for mapping crops and land through 338 cameras and sensors, monitoring crop health, and applying products or biological control agents. 339 for the purpose of this research, farm management information systems (fmis) have been excluded 340 from the analysis, as they are classified as enabling technologies rather than core components of the 341 a4.0 paradigm. as industry 4.0 evolves and digital technologies continue to expand and mature into 342 practical solutions for farmers, it becomes crucial to distinguish between core components of the 343 paradigm and enabling technologies. while enabling technologies play a vital role in supporting a4.0, 344 they are considered complementary rather than fundamental elements of the paradigm itself. 345 survey design and implementation. an online survey was developed and distributed targeting farms 346 identified through the sampling framework. the online format was chosen for its cost-efficiency, ease 347 of administration, and ability to minimise errors associated with manual data collection, as also reported 348 by van selm and jankowski (2006) and maffezzoli et al. (2022a). 349 this survey consisted of seven sections: 350 1. general information, collecting foundational and demographic details about the respondent and 351 their agricultural enterprise. 352 2. a4.0 awareness and implementation, assessing the level of familiarity and the extent of adoption 353 of the proposed set of a4.0 solutions. 354 3. needs, benefits, and challenges, exploring the specific needs driving the adoption of a4.0 355 solutions, the benefits achieved, and the challenges encountered during their implementation. 356 4. data management capabilities, evaluating the farms’ ability to collect, store, analyse, and utilize 357 data effectively to inform decision-making processes. 358 5. digital skills, assessing the competences and level of expertise of farm operators in relation to 359 a4.0 solutions. 360 6. investments, reviewing past investments, current expenditures, and anticipated future 361 investments in a4.0 solutions. 362 7. inhibiting factors, identifying the barriers and constraints preventing or limiting the adoption of 363 a4.0 solutions. 364 the full set of questions included in each section of the survey is provided in appendix a, located at the 365 end of this manuscript. 366 367 data collection. data collection was conducted from september 2024 to december 2024. the process 368 yielded a total of 1,248 valid responses, providing a robust dataset for detailed analysis. tables 1, 2 and 369 3 report the sample of responses collected according to the critical sampling variables and table 4 370 provides a summary of the main descriptive statistics on collected data. 371 372 table 1. total population and sample size and their distribution across italian regions (number of farms) 373 pop. size distr. pop. size distr. (%) sample size distr. sample size distr. (%) north-west 113,972 10% 304 24% north-east 187,429 16% 319 26% centre 179,230 16% 328 26% south and islands 652,392 58% 297 24% total 1,133,023 100% 1,248 100% 374 table 2. total population and sample size and their distribution across primary crop productions (uaa utilised agricultural area) 375 pop. size distr. pop. size distr. (%) sample size distr. sample size distr. (%) cereal crops 3,054,288 34% 31,923 19% vineyards 742,926 8% 92,693 56% fruit crops 444,805 5% 7,310 4% fodder crops 2,564,217 28% 3,469 2% olive cultivation 1,114,593 12% 4,723 3% vegetable crops 445,966 5% 4,490 3% legumes 85,007 1% 132 0.1% citrus fruits 149,863 2% 21,353 13% industrial plants 477,091 5% 562 0.3% total 9,078,756 100% 166,655 100% 376 table 3. total population and sample size and their distribution across farms’ size (number of farms) 377 pop. size distr. pop. size distr. (%) sample size distr. sample size distr. (%) 0 hectares 12,499 1% 23 1% up to 0.99 hectares 228,481 20% 19 2% from 1 to 1.99 hectares 209,662 18% 61 5% from 2 to 2.99 hectares 128,381 11% 55 4% from 3 to 4.99 hectares 147,320 13% 123 10% from 5 to 9.99 hectares 160,133 14% 209 17% from 10 to 19.99 hectares 109,545 10% 262 21% from 20 to 29.99 hectares 45,118 4% 104 8% from 30 to 49.99 hectares 41,167 4% 109 9% from 50 to 99.99 hectares 32,487 3% 120 10% from 100 onwards 18,230 2% 163 13% total 1,133,023 100% 1,248 100% the tables presented above highlight a discrepancy between the sample distribution and that of the 378 overall population, resulting in an overrepresentation of farms located in northern italy and an 379 underrepresentation of those in the south and islands. this imbalance may introduce a geographical 380 bias into the analysis. moreover, the average utilised agricultural area (uaa) of the sampled farms, 381 amounting to 22 hectares, is notably higher than the national average of 11.1 hectares reported by 382 istat2, indicating a sample skewed toward more structured and capital-intensive farming 383 operations. the sample also includes a disproportionately large share of vineyard farms, a sector 384 typically associated with higher profitability and investment capacity, which may further influence 385 the study's results. 386 however, these deviations do not necessarily compromise the validity of the findings. the research 387 primarily aims to investigate the adoption and perceived benefits of a4.0 solutions, an area where 388 more structured and technologically advanced farms are expected to play a pioneering role (giua, 389 2022). consequently, focusing on more innovative and capitalised enterprises allows for a more 390 detailed understanding of current trends, challenges, and potential impacts, which can serve as a 391 reference point for the broader agricultural sector as it transitions toward digitalisation. 392 prior to presenting the results of the survey data analysis, the authors provide a table outlining the key 393 descriptive statistics of the collected dataset. 394 395 table 4. summary of main descriptive statistics of survey collected data 396 unit mean median std min max farm size ha 22.21 38.50 1,718.2 1 0 40,000 farm annual turnover eur less than €50,000 share 0.35 between €50,000 and €250,000 share 0.38 between €250,000 and share 0.12 2 https://www.istat.it/it/files/2022/06/censimento_agricoltura_gismondi.pdf https://www.istat.it/it/files/2022/06/censimento_agricoltura_gismondi.pdf €500,000 between €500,000 and €1,000,000 share 0.06 over €1,000,000 share 0.09 employees in farm no. 3.69 11.75 6.60 0 950 a4.0 solutions adopted in farm no. 2.68 4.00 1.65 0 8 total amount spent on a4.0 solutions by farm eur less than €5,000 share 0.23 between €5,000 and €15,000 share 0.17 between €15,000 and €30,000 share 0.13 between €30,000 and €50,000 share 0.09 between €50,000 and €75,000 share 0.08 between €75,000 and €100,000 share 0.07 more than €100,000 share 0.23 397 the descriptive statistics in the table above highlight that farm size distribution is skewed. this 398 asymmetry is commonly observed across many countries, as both very large and very small farms 399 coexist, often with significantly different spending capacities, as also noted by the oecd (bokusheva 400 and kimura, 2016). 401 402 4. results 403 404 4.1 a4.0 awareness and adoption level 405 the initial findings of this analysis focus on the current levels of adoption of a4.0 solutions among 406 survey respondents. a summary of these results is presented in figure 1. to assess awareness of a4.0 407 solutions, a four-point scale was employed, ranging from low to high familiarity, following the approach 408 outlined by maffezzoli et al (2022a). this scale effectively distinguishes varying levels of awareness 409 and facilitates cross-tabulation, allowing for the identification of patterns across different respondent 410 groups. the four levels of awareness are defined as follows: (a) unknown, representing a complete lack 411 of familiarity, indicating no awareness of the existence of the proposed solution; (b) known, denoting 412 limited familiarity and implying that the respondent has heard of the solution, but possesses only a 413 superficial understanding; (c) used in the past, not anymore, indicating theoretical familiarity and 414 suggesting that the respondent has a solid understanding of the solution despite no longer using it; and 415 (d) in use, representing practical familiarity, meaning the respondent not only knows about the solution, 416 but also employs it. 417 figure 1. agriculture 4.0 awareness level. sample: 1,248 respondents 418 419 420 the data reveal varying levels of adoption and awareness of a4.0 solutions. key findings show that 421 approximately 26% of respondents currently implement monitoring systems and connected vehicles, 422 making these among the most widely adopted a4.0 solutions. meanwhile, 20% of respondents adopted 423 mapping solutions and 19% employed telemetry systems and solutions for vehicle and equipment 424 monitoring. 425 adoption rates for decision support systems (dss) and variable rate technology (vrt) solutions are 426 notably lower, at 7% and 6% respectively. robotics and drones show the lowest adoption rate, standing 427 at only 3%, likely due to constraints related to cost, technical expertise, or perceived necessity. 428 disaggregated data by farm size reveal that only 23% of farms with less than 10 hectares of uaa 429 have adopted at least one a4.0 solution. similarly, among farms with annual revenues below eur 430 50,000, the adoption rate stands at 21%. however, adoption increases substantially with scale: 66% 431 of farms with a uaa between 100 and 199.9 hectares have adopted a4.0 technologies, and this figure 432 rises to 82% for farms exceeding 200 hectares. a comparable trend is evident with respect to 433 economic size, where adoption reaches 74% among farms with annual revenues between eur 434 500,000 and eur 1,000,000, and rises further to 81% for those with revenues above eur 1,000,000. 435 in contrast, our findings do not reveal substantial differences in a4.0 adoption based on the age or 436 education level of farm managers. the only exception is among managers over the age of 65, who 437 show a lower adoption rate (30%). similarly, post-graduate degree holders are the only educational 438 group exhibiting higher-than-average adoption rates (48%). 439 with regard to agricultural production types, enterprises primarily engaged in cereal cultivation report 440 higher adoption rates of a4.0 solutions (49%), alongside vineyard and fodder farms (both at 43%). 441 the relatively higher a4.0 adoption among cereal and fodder producers can be attributed to the 442 extensive nature of these cropping systems, which can be particularly well-suited to the application 443 of a4.0 solutions in optimizing operations over wide areas. conversely, vineyard enterprises, 444 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% robotics drones variable rate technology (vrt) solutions decision support systems (dss) telemetry systems and solutions for vehicle and equipment monitoring mapping solutions connected vehicles monitoring systems in use used in the past, not anymore known unknown typically characterized by higher revenue margins, tend to possess greater financial capacity to invest 445 in innovation, thereby facilitating the uptake of digital solutions and innovative technologies. 446 447 4.2 needs expressed and benefits perceived from a4.0 implementation 448 449 to comprehensively analyse the key drivers that motivated respondents to adopt and implement a4.0 450 solutions, this study focuses on the specific needs expressed by farmers. these needs reflect both 451 strategic and operational priorities, ranging from farm management and control to the optimisation of 452 input consumption. 453 figure 2 reveals a substantial level of awareness among respondents regarding the broad and 454 multifaceted nature of the a4.0 paradigm. rather than being perceived merely as an extension of 455 precision agriculture whose primary goal is to deploy technological solutions in the field to optimise 456 input consumption and reduce costs a4.0 appears to be increasingly recognised as a comprehensive 457 framework for enhancing overall farm management and control, with positive effects along the overall 458 agrifood supply-chain. this paradigm shift suggests that farmers view a4.0 not only to refine specific 459 agricultural practices, but also as an integral component in fostering a more efficient and data-driven 460 agricultural enterprise. 461 among the ten most frequently expressed needs related to farm management and control, the most 462 prominent include enhancing forecasting capabilities (41%), particularly in areas such as disease 463 outbreaks, crop requirements, plant growth and yield projections, improving control and management 464 processes within the farm enterprise (38%) with a focus on better decision-making and operational 465 efficiency, optimising the planning and scheduling of agricultural activities (32%) and increasing 466 awareness of ongoing farm activities and operations (31%). similarly, in relation to the optimisation of 467 input consumption, respondents identified key areas where a4.0 solutions could bring significant 468 improvements, including optimising the use of technical inputs such as fertilisers and agrochemicals 469 (28%) and enhancing the efficiency of machinery and equipment utilisation (26%), contributing to both 470 cost reductions and operational sustainability. furthermore, respondents expressed the need to 471 streamline and optimise workforce processes (26%), ensuring that operators can perform tasks with 472 efficiency and effectiveness, and to maximize water-use efficiency (24%), which is particularly critical 473 in the context of recent meteorological events in italy: in 2024, the country experienced heavy rainfall 474 in the northern regions, while facing severe droughts in the south3. 475 furthermore, respondents reported adopting a4.0 solutions for additional objectives, including reducing 476 losses due to diseases, pests, and infestations (35%), a critical aspect of maintaining both yield stability 477 and crop health, and improving the quality of the final agricultural product (20%) to meet regulatory 478 requirements. 479 figure 2. needs expressed by respondents. sample: 511 respondents who adopted at least one of the proposed agriculture 4.0 solutions. respondents could 480 choose a maximum of 5 options. 481 3 agro-meteorological monitoring indices (agromind) map on agricultural drought (spei6) (https://wonderful-bush09061f403.5.azurestaticapps.net/agromind.html) https://wonderful-bush-09061f403.5.azurestaticapps.net/agromind.html https://wonderful-bush-09061f403.5.azurestaticapps.net/agromind.html 482 483 figure 3 illustrates the perceived benefits derived from the adoption of a4.0 solutions, as evaluated in 484 relation to the specific needs previously expressed by respondents. the findings indicate that, on average, 485 the implementation of a4.0 technologies resulted in outcomes that aligned with initial expectations for 486 most adopters (74% on average). additionally, a subset of respondents (8% on average) reported that 487 the benefits they experienced exceeded their initial expectations. 488 these results suggest that most farmers who invested in a4.0 solutions perceived their adoption as a 489 successful means of addressing their agricultural needs, with reported benefits generally meeting 490 anticipated outcomes. however, a smaller proportion of respondents indicated that the benefits they 491 obtained were either below their expectations (14% on average) or entirely absent (4% on average), 492 highlighting potential limitations in implementation effectiveness, technology adoption challenges, or 493 contextual constraints that may have hindered the full realisation of expected advantages. 494 furthermore, the analysis reveals that the perceived benefits were more pronounced in activities related 495 to the optimisation of input consumption compared to those associated with farm management and 496 control. specifically, an average of 11% of respondents reported experiencing benefits that exceeded 497 their expectations in the domain of input consumption optimisation. in contrast, only an average of 4% 498 of respondents indicated that benefits surpassed expectations for farm management and control 499 activities. this suggests that a4.0 solutions may be particularly effective in enhancing input efficiency, 500 resource utilisation, and operational streamlining, whereas their impact on broader management and 501 control functions may be more variable or dependent on additional contextual factors. 502 503 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% improve the quality of the final agricultural product maximize water-usage efficiency streamline and optimize worforce processes enhance the efficiency of machinery and equipment optimize the use of technical inputs increase awareness on farm activities and operations optimize the planning and scheduling of activities reduce losses due to diseases, pests, and infestations improve farm control and management processes enhance forecasting capabilities figure 3. benefits perceived by respondents. sample: 511 respondents who adopted at least one of the proposed agriculture 4.0 solutions 504 505 506 moreover, italian farmers who have already adopted a4.0 solutions exhibit a significantly higher 507 propensity to invest further in these technologies compared to non-adopters. specifically, 20% of current 508 users reported their intention to invest more than eur 50,000 in a4.0 technologies within the next year, 509 whereas only 3% of non-users indicated an equivalent investment plan. furthermore, 27% of adopters 510 expected to allocate between eur 5,000 and eur 30,000, compared to just 18% among non-adopters. 511 notably, 55% of non-users were unable to estimate their future investments, in contrast to only 26% of 512 current users. these findings suggest that a4.0 adopters, having already perceived benefits (often 513 exceeding expectations) are more inclined to pursue further technological advancement and exhibit a 514 clearer strategic orientation toward digital transformation. 515 516 4.3 a4.0 implementation challenges and factors inhibiting a4.0 adoption 517 518 this study also aims to assess the challenges encountered by respondents who have adopted at least one 519 of the proposed a4.0 solutions, as well as the barriers faced by those who either could not or chose not 520 to adopt any of these solutions. 521 figure 4 presents the challenges encountered by farms that have implemented a4.0 solutions. the 522 findings indicate that one of the most significant issues reported by 36% of respondents is limited or 523 non-existent interoperability among the adopted solutions. many farmers, indeed, struggle with 524 integrating different digital tools within their existing farm management systems, leading to 525 inefficiencies and operational difficulties (khanna et al., 2024). 526 following interoperability concerns, other notable challenges include the lack of appropriate skills to 527 effectively utilise a4.0 solutions (30%) and the perceived inadequacy of return on investment (26%), 528 suggesting that respondents may not see immediate or sufficient financial benefits from their a4.0 529 investments, potentially discouraging further technological adoption. furthermore, 26% of respondents 530 indicate insufficient or unreliable technical assistance, which further limits a4.0 effectiveness together 531 with operational challenges (20%) and inadequate connectivity (16%). 532 interestingly, only 6% of respondents reported that they did not face any challenges during their a4.0 533 implementation. this finding suggests that most adopters have encountered at least some difficulties in 534 integrating and implementing a4.0 solutions, emphasising the need for targeted interventions to enhance 535 0% 20% 40% 60% 80% 100% improve the quality of the final agricultural product maximize water-usage efficiency streamline and optimize worforce processes enhance the efficiency of machinery and equipment optimize the use of technical inputs increase awareness on farm activities and operations optimize the planning and scheduling of activities reduce losses due to diseases, pests, and infestations improve farm control and management processes enhance forecasting capabilities above the expectations in line with the expectations below the expectations none system compatibility, improve user experience and provide better support mechanisms for farmers 536 transitioning to digital technologies. 537 538 figure 4. challenges encountered by respondents. sample: 511 respondents who adopted at least one of the proposed agriculture 4.0 solutions. 539 respondents could choose more than one option. 540 541 542 figure 5 illustrates the key barriers that have prevented farms from adopting a4.0 technologies. one of 543 the most frequently cited limitations is farm size, with 68% of respondents indicating that their farms 544 are too small to justify investment in a4.0 solutions. this is not surprising, as table 3 shows that in the 545 italian context, most farms (77%) are small or medium-sized. 546 further constraints concern the possible exploitation of a4.0 solutions, with 59% of respondents 547 believing that they would not fully exploit these solutions and 50% stating that their current agricultural 548 technologies and management practices adequately meet their business needs, thereby reducing the 549 perceived necessity of implementing a4.0 solutions. 550 financial concerns also play a significant role, as 45% of respondents believe that the anticipated benefits 551 do not justify the required investment, while 41% struggle to see the potential economic advantages of 552 incorporating digital tools into their operations. additionally, financial constraints further limit adoption, 553 with 38% of respondents citing their inability to spread investment costs over time and 36% highlighting 554 the difficulty of sharing these costs across multiple enterprises. bureaucratic challenges also emerge as 555 a deterrent, as 36% of respondents report difficulties in accessing financial incentives due to stringent 556 requirements and administrative burdens, while 22% point to restricted access to credit lines as a further 557 impediment. 558 while digital skills were identified as a notable challenge among those who have already adopted a4.0 559 solutions, they appear to be a less pressing concern for non-adopters: only 25% of respondents cited a 560 lack of necessary competencies as a barrier, while an equal proportion stated that their collaborators also 561 lacked the required skills. such discrepancy in how digital skills are perceived between adopters and 562 non-adopters reflects an experience gap in a4.0 implementation: non-adopters seem to not yet 563 acknowledge the digital skills challenge because they have not engaged with a4.0 deeply enough, 564 whereas adopters have firsthand knowledge of the difficulties and their impact on agricultural activities. 565 furthermore, 24% of non-adopters indicated that they did not know where to access basic information 566 about a4.0 solutions, underscoring the need for better dissemination of knowledge and educational 567 resources. 568 beyond financial and technical barriers, several other factors have contributed to the reluctance to adopt 569 a4.0 technologies. a lack of applicability to specific agricultural production areas was cited by 34% of 570 0% 5% 10% 15% 20% 25% 30% 35% 40% other no issues detected or identified inadequate or unavailable connectivity operational challenges insufficient or unreliable technical assistance inadequate return on investment lack of appropriate skills and expertise limited or non-existent interoperability respondents, suggesting that certain farming sectors or operational models do not align with the 571 capabilities offered by the proposed a4.0 solutions. connectivity issues also play a role, with 18% of 572 respondents identifying poor internet access as a constraint, particularly where digital infrastructure may 573 be insufficient. additionally, concerns related to data security and privacy were reported by 15% of 574 respondents, indicating a degree of hesitation regarding the management and protection of sensitive farm 575 data in digital systems. 576 these findings highlight the multifaceted nature of the barriers impeding a4.0 adoption, encompassing 577 economic, technical, infrastructural, and informational challenges. addressing these concerns through 578 targeted policies, financial support mechanisms, improved access to training, and enhanced digital 579 infrastructure could facilitate broader adoption and ensure that a wider range of farms can benefit from 580 the efficiencies and advancements offered by a4.0 solutions. 581 figure 5. inhibiting factors faced by respondents. sample: 737 respondents who have not adopted any of the proposed agriculture 4.0 solutions 582 583 584 5. discussion 585 586 this study examines the adoption and awareness levels of agriculture 4.0 (a4.0) solutions, the drivers 587 influencing technological adoption, the benefits obtained, as well as the challenges faced by a4.0 users 588 and the inhibiting factors expressed by a4.0 non-adopters. a comprehensive understanding of these 589 aspects is essential for policymakers, researchers, and industry stakeholders to identify obstacles and 590 develop strategies aimed at facilitating the widespread integration of digital technologies in the 591 agricultural sector. such integration holds the potential to enhance productivity, efficiency, and 592 sustainability within italian agriculture. 593 the findings indicate that while there is widespread awareness of a4.0 solutions among italian farmers, 594 adoption levels vary significantly. these discrepancies are closely associated with the structural 595 0% 20% 40% 60% 80% 100% concerns about data privacy and cybersecurity of a4.0 inability to utilize the solution due to connectivity challenges limited access to credit lines lack of clarity on where to obtain the necessary information insufficient technical or operational expertise insufficient skills among operators to implement 4.0 solutions solutions not applicable to specific production activities incentives hindered by requirements or bureaucracy investment viability relies on cost-sharing across enterprises investment viability relies on spreading costs over time challenges in evaluating the potential investment benefits benefits perceived as insufficient to justify the investment existing tools considered adequate to meet current needs low expected utilization of the solution company size deemed too small to justify the investment agree neither agree nor disagree disagree characteristics of farming enterprises, particularly the size of the utilised agricultural area (uaa) and 596 the level of annual turnover. existing literature has consistently highlighted that the uptake of digital 597 agricultural technologies is contingent upon several structural and socio-economic factors, including 598 farm scale, crop specialization, farmer age, and educational background (giua, 2022). at the national 599 level, our results corroborate this evidence, demonstrating that adoption rates tend to increase 600 proportionally with both the physical and economic size of farms. this trend is further reflected in 601 specific production types such as cereals, fodder crops, and vineyards where the extensive nature of 602 the former two may necessitate technological support, while the relatively higher revenue margins 603 typical of vineyard operations may facilitate investment in a4.0 solutions. certain solutions, such as 604 monitoring systems and connected vehicles, have achieved higher acceptance, whereas others remain 605 unexploited. the primary motivation for adopting a4.0 solutions is predominantly associated with 606 macro-level farm management improvements, including enhanced forecasting capabilities and more 607 effective control and management processes, rather than in-field operational efficiencies, such as 608 optimising technical inputs and increasing machinery and equipment efficiency. 609 the analyses presented in this manuscript, which focus on the italian agricultural sector, are broadly 610 aligned with the findings of international research. for instance, as reported by the united states 611 department of agriculture4, in 2023, 27% of u.s. farms or ranches employed a4.0 solutions for crop 612 management. among the most widely adopted a4.0 solutions for crop management were automated 613 guidance systems (covering 58% of planted acres), yield mapping (44%), variable rate technology 614 (37%), soil maps (22%) and drones and satellite imagery (7%) (united states government 615 accountability office, 20245). similarly, in germany, a survey conducted on bavarian farmers reported 616 that the most widely adopted digital tools included weather and pests forecast models and apps (38%), 617 digital field records (21%), automated steering systems (21%), maps from satellite data (14%), with an 618 overall adoption rate estimated around 62% of the sampled agricultural enterprises (gabriel and 619 gandorfer, 2023). 620 this study also underscored the benefits of a4.0 solutions, which were generally perceived as aligning 621 with expectations, with some exceeding initial anticipations. this suggests a largely successful 622 implementation among adopters. notably, the areas where respondents reported the greatest benefits 623 surpassing expectations were related to the optimisation of technical inputs and water management. 624 consistent with the findings of zul azlan et al. (2023), abbasi et al. (2022), and pradel et al. (2022), 625 a4.0 solutions have demonstrated the potential to assist farmers in reducing input and water 626 consumption, thereby generating both economic advantages through cost reduction and environmental 627 benefits. regarding the potential social sustainability benefits, italian farmers have identified 628 “streamline and optimise workforce processes” among the ones more in line with expectations, with a 629 small share of farmers pointing out that a4.0 solutions disappointed their expectations. the broader 630 social sustainability implications of this perceived benefit remain debated in literature. some studies 631 suggest a positive evolution in the agricultural labour market, potentially improving farmers' livelihoods 632 and creating new employment opportunities (e.g., rotz et al., 2019). other contributions, instead, 633 underline the need for specific studies on the yet unexplored consequences on the agricultural labour 634 market originated from the optimisation of farming activities, potentially reducing the demand for 635 unskilled workers (rotz et al., 2019; rose et al., 2021). 636 nevertheless, despite the perceived benefits of a4.0 solutions, their implementation remains constrained 637 by several challenges. these include interoperability issues, lack of adequate skills, return on investment 638 concerns and technical assistance limitations, which hinder correct a4.0 solutions implementation and 639 their benefits. in addition, several financial and structural constraints emerge as significant deterrents for 640 4 https://downloads.usda.library.cornell.edu/usda-esmis/files/h128nd689/4j03fg187/fj237k64f/fmpc0823.pdf 5 https://www.gao.gov/assets/d24105962.pdf non-adopters. among these, the lack of trust in a4.0 solutions appears to be the most critical barrier. 641 this skepticism is often linked to a perceived low utility of a4.0, a belief that existing tools are sufficient 642 to meet current needs, difficulties in assessing the potential benefits, and the generally small size of 643 agricultural enterprises factors that collectively slow digital adoption in italian agriculture. economic 644 and financial obstacles seem to be less relevant: these include doubts about the feasibility of investments 645 that depend on cost-sharing over time or across multiple farms, as well as limited access to incentives 646 often constrained by bureaucratic complexity (cisilino and licciardo, 2022). these financial constraints 647 pose a fundamental challenge particularly for small and medium-sized farms that may lack the capital 648 required for initial investments in a4.0 solutions. this issue is further exacerbated by the uncertainty 649 surrounding return on investment, making it difficult for farmers to justify the adoption of these solutions 650 without clear and measurable long-term economic benefits. in contrast, technical challenges appear to 651 be less influential: only a minority of non-adopters cite inapplicability to specific production processes, 652 lack of technical skills, or insufficient expertise as reasons for avoiding a4.0 solutions. moreover, 653 connectivity issues emerge as a challenge for non-adopters, especially in marginal areas and on hills 654 across italian regions, thus limiting the implementation of a4.0 solutions, as highlighted by sozzi et al. 655 (2021). as also emphasised by fragomeli et al. (2024) and da silveira et al. (2023), such obstacles 656 significantly impede the broader adoption of a4.0 solutions by limiting both the willingness and ability 657 of farmers to integrate these tools into their production systems. furthermore, as highlighted by 658 gonzales-gemio and sanz-martín (2025), the inequality in access to a4.0 solutions could hinder the 659 adoption of sustainable agricultural practices. digital platforms and monitoring solutions, for instance, 660 have the potential to substantially enhance the efficiency of carbon farming initiatives and contribute 661 more broadly to agricultural sustainability. 662 these findings are consistent with an analysis published by the general secretariat of the council of the 663 european union6, which emphasizes that compared to other sectors the pace of digital adoption in 664 agriculture has been slower. this lag is attributed to several interrelated factors, including inadequate 665 infrastructure, substantial upfront investment requirements, a widespread lack of digital skills, and the 666 inherent complexity of the agricultural sector. the latter includes considerable variability in climate 667 conditions, soil types, crop systems, and farming practices, all of which pose additional challenges to 668 the effective implementation of a4.0 solutions. 669 the findings of this study are also aligned with emerging academic literature on the barriers to a4.0 670 adoption within the italian agricultural sector. for example, addorisio et al. (2025), based on interviews 671 with italian farmers, underscore the critical role of stakeholder cooperation and targeted training 672 initiatives in addressing key impediments to adoption. these include limited interoperability among 673 a4.0 solutions, insufficient digital competencies, and a lack of adequate technical support. similarly, 674 giorgio et al. (2024) explore perceived advantages and challenges associated with digitalisation in 675 northern italy. reported benefits include enhanced environmental sustainability, improved input 676 efficiency, reduced labour requirements, and lower operational costs. however, the study also identifies 677 persistent barriers such as limited digital skills, inadequate data management practices and issues with 678 interoperability. these findings suggest that policies should not only support equipment acquisition, but 679 also promote the development of farmers’ human capital. 680 addressing these challenges through targeted policy interventions, comprehensive training initiatives, 681 and improved system interoperability could substantially enhance a4.0 adoption rates, thereby ensuring 682 that a broader range of agricultural enterprises benefits from the efficiencies and advancements offered 683 by digital innovations. moreover, collaboration among policymakers, technology providers, and industry 684 6 https://www.consilium.europa.eu/media/shxiaxmo/2024_971-art-agriculture-11-02-25.pdf stakeholders is crucial in fostering an ecosystem that supports seamless integration, mitigates adoption 685 barriers, and maximizes the impact of digital agricultural innovations. 686 conclusions 687 this study offers valuable empirical insights into the current state of agriculture 4.0 (a4.0) adoption in 688 italy, shedding light on drivers influencing the uptake of a4.0 solutions, the perceived benefits, the 689 challenges met by farmers who adopted a4.0 solutions and the barriers that prevented other agricultural 690 enterprises from adopting a4.0 solutions. by disaggregating results according to critical variables related 691 to farms (size, primary crop production and geographical localisation), this research contributes to a 692 more nuanced understanding of how the a4.0 paradigm is taking root within the italian agricultural 693 sector. these findings provide a strong empirical foundation for informing public policy, guiding 694 investment strategies and designing initiatives that are tailored to the needs of diverse farming profiles. 695 specifically, the results highlight the importance of structural variables such as farm size, crop 696 production and turnover in shaping adoption patterns, suggesting that public support mechanisms should 697 be differentiated accordingly. small farms, which tend to face greater barriers in terms of investment 698 capacity and technical know-how, may benefit from targeted subsidies, tax incentives, and digital 699 infrastructure improvements, particularly in under-served rural regions. moreover, the limited adoption 700 of certain a4.0 solutions underscores the need for broader outreach, technical assistance and knowledge 701 transfer mechanisms to ensure that innovation diffuses beyond a small subset of more-structured farms. 702 training programs should also be adapted to the varying levels of digital literacy across the sector, with 703 modular content suited to both entry-level and experienced users. 704 moreover, by identifying which types of farms might be most likely to adopt a4.0 solutions and which 705 barriers inhibit the uptake of digital tools, technology providers can refine their product design, 706 marketing strategies, and sales services. companies may, for instance, enhance interoperability and user-707 friendliness to address common usability challenges. 708 a promising avenue for future research would involve a comparative analysis of the levels of a4.0 709 adoption, associated needs, benefits, challenges, and barriers identified in this study with those observed 710 in other european countries and beyond. 711 another potential research direction could focus on examining the impact of a4.0 solutions on 712 economic, environmental, and social sustainability to comprehensively assess the costs and benefits of 713 a4.0 implementation. this analysis could, in turn, contribute to bridging the gap between adopting and 714 non-adopting agricultural enterprises. 715 nonetheless, these contributions should be considered in light of the following methodological 716 limitations arising from the survey administration method and the sample distribution compared to the 717 reference population. as with all computer-assisted web interviewing (cawi) methods, this online 718 survey may exclude individuals without internet access or those less comfortable with technology. 719 additionally, self-selection bias could skew the results, as participants are likely to be those with an 720 interest in the topic or familiarity with online surveys. consequently, adoption rates of a4.0 solutions 721 reported in this study may be overestimated, while the perceived benefits and willingness to invest 722 further in digital technologies could reflect the attitudes of a smaller group of more innovation-oriented 723 farmers. addressing these limitations in the future research would require efforts to reach less digitally-724 involved segments of the italian agricultural sector to enhance the external validity of the findings. 725 moreover, the discrepancy between the sample size distribution and the population size distribution leads 726 to an overrepresentation of farms in the north and an underrepresentation of those in the south and 727 islands, potentially introducing a geographical bias. furthermore, the average uaa (utilised 728 agricultural area) of the sampled farms (22 hectares) is significantly higher than the figure reported by 729 istat7 (11.1 hectares), suggesting a selection of more structured agricultural enterprises. additionally, 730 the greater representation of the vineyard sector, which is characterised by higher-than-average 731 profitability and greater spending capacity, could influence this study’s findings. 732 this study was carried out within the agritech national research center and within the smart agrifood 733 observatory politecnico di milano & 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(2024). 892 harvesting a sustainable future: an overview of smart agriculture’s role in social, economic, and 893 environmental sustainability. journal of cleaner production, 434, 140338. 894 https://doi.org/10.1016/j.jclepro.2023.140338 895 https://doi.org/10.1016/j.jclepro.2023.140338 bio-based and applied economics bae © 2024 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 citation: fraser, i., fountain, m.t., & holland, j.m. (2024). crop production, the pollinator deficit and land use management: uk farm level survey results. bio-based and applied economics 13(4): 397-417. doi: 10.36253/bae-15333 received: november 13, 2023 accepted: august 2, 2024 published: december 31, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: simone cerroni orcid if: 0000-0002-4689-6020 mtf: 0000-0001-9570-290x jmh: 0000-0003-3742-7035 crop production, the pollinator deficit and land use management: uk farm level survey results iain fraser1,*, michelle t. fountain2, john m. holland3 1 school of economics, university of kent, canterbury, kent ct2 7np, uk 2 niab east malling, new road, east malling, kent me19 6bj, uk 3 game and wildlife conservation trust, burgate manor, fordingbridge, hampshire sp6 1ef, uk *corresponding author. e-mail: i.m.fraser@kent.ac.uk abstract. in this paper, we address a gap in the literature concerning pollination management, the pollinator deficit, and conservation objectives. by employing a farm level survey of uk farmers, we examine farmers’ attitudes, understanding and management of pollinators. based on descriptive statistics and regression analysis, we found significant variation in interest and understanding of the impact of pollinators on commercial crops meaning that many respondents did not consider they had a pollinator deficit in terms of crop quality, quantity, or financial impacts. at the same time, many farmers are willing to adopt environmentally beneficial land-use measures if suitable advice and financial incentives are offered. however, there is little evidence of coordination of actions between farms to support wild pollinators. these findings indicate a potential disconnect between a farmer’s understanding of the impact on agricultural output from a pollinator deficit and the agricultural benefits from the adoption of specific environmental measures. keywords: agri-environment policy, bees, wildflower strips, soft fruit, top fruit, arable. jel codes: q15, q576. 1. introduction ensuring sufficient crop pollination is essential if yields are to be maximised. this is particularly the case as we are seeing significant growth in demand for pollinator-dependent crops, at the same time that there is a decline in wild pollinators within the farming environment (jordan et al., 2021; gazzea et al., 2023) with research indicating that many crops may be experiencing a pollination deficit (pd) resulting in sub-optimal levels of production (garibaldi et al., 2016; reilly et al., 2020). for example, as part of an economic analysis of landscape configuration to support pollinators kirchweger et al. (2020) assume that no insect pollination means that the optimal yield for oilseed rape (osr) will be 79% of the maximum with pollination. warnings about the economic impact of sub-optimal levels of crop pollination are frequent in the literature (e.g., cardoso et al., 2020). many studies https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode http://www.fupress.com/bae https://doi.org/10.36253/bae-15333 https://doi.org/10.36253/bae-15333 https://orcid.org/0000-0002-4689-6020 https://orcid.org/0000-0001-9570-290x https://orcid.org/0000-0003-3742-7035 mailto:i.m.fraser@kent.ac.uk 398 bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 iain fraser, michelle t. fountain, john m. holland examine the impact of pollination on the production of specific crops such as perrot et al. (2018) (osr), fountain et al. (2019) (pears), samnegård et al. (2019) (apples), bishop et al. (2020) (faba beans), eeraerts et al. (2019) (sweet cherry) and garratt et al. (2023) for orchards (especially apples). at the same time there are numerous studies examining farm level management options to support wild pollinators (albrecht et al., 2020; fountain, 2022; mchugh et al, 2022; nicols et al., 2019 and 2023). in this literature, pollinator management can refer to measures that support both “managed” and “wild” pollinators. this distinction is important when considering how farmers think about the role of pollination in production. managed pollination services (e.g. bee hives) which can be purchased or rented are equivalent to any other agricultural input and can reduce the uncertainty and risk of relying on wild pollinators. however, in many cases wild pollinators can provide the same or a better service than managed pollinators (e.g. mateos-fierro et al., 2022). in response many governments including the uk have adopted pollinator friendly policy initiatives often embedded in agri-environmental policy (aep) that explicitly aim to reverse the decline in wild pollinators in agricultural landscapes. for example, the uk government published the uk national pollinator strategy (nps) in 2014, a 10-year plan to enhance and improve the status of all pollinating insects in england that includes the wild pollinator and farm wildlife package (defra, 2022). despite all this research and government policy it is somewhat surprising that there is limited research examining the knowledge and understanding that farmers have of the pd in crop production and the associated adoption of appropriate pollinator management activities (hevia et al., 2021; nalepa et al., 2021; osterman et al., 2021). it remains unclear to what extent farmers consider or understand the potential for a pd to exist, and this is unlikely to change anytime soon because farmers rarely monitor the degree of crop pollination unlike yields (garibaldi et al., 2020; gemmill-herren et al., 2021). in this study, our key objective was to understand the degree to which uk farmers consider current levels and quality of pollinator activity and its impact on agricultural production, and to generate evidence on the extent to which farmers consider the pd to be a significant issue. in addition, we wish to examine the mix and type of management activities being implemented to support wild pollinators as well as the level of knowledge about pollinators. we also examine the degree to which aep are enabling on farm management activities to support wild pollinators. to address our research objectives, we developed a survey instrument that examine uk farmers knowledge of pollinator management for crop production together with wider environmental objectives. our survey instrument was developed in collaboration with our project partners (academic and industry) from the north sea region interreg project beespoke.1 in designing the survey, we took a bottom-up approach focussing on farmers to understand their knowledge of the pd as well as the use of aep options. our survey collected data (n=228) on farmers knowledge and understanding of the pd, pollinator habitat and management and aep engagement. it was distributed to farmers growing at least one crop that is pollinator dependent in terms of yield. the survey yielded both qualitative and quantitative data. by undertaking this survey our research contributes to the existing literature in several ways. first, we present evidence on the extent to which uk farmers perceive there to be a pd. understanding farmers views about crop pollination and the associated, quality, quantity and financial implication reveals the extent to which they considered the pd to be important. second, as noted there is limited existing research examining farmer understanding of pollinators and farmers’ needs (e.g., osterman et al., 2021; busse et al., 2021; nalepa et al., 2021). we add to this literature using our survey data for uk farmers. third, within economics, much of the existing research has focussed on generating estimates of the value of pollination services (feuerbacher et al., 2024) or the non-market values society derives from experiencing pollinators (use value), knowing that they exist (non-use existence value) as well as the indirect benefits they provide such wild-flowers and greater biodiversity (moreaux et al., 2023). therefore, there remains a need for more research examining on-farm adoption of pollinator conservation measures. finally, there is a knowledge gap around our understanding of current levels of farm level pollinator management activities and whether this is driven by crop production and/or aep. the paper is structured as follows. in section 2, we briefly review the antecedent literature focussing on the significance of the pd, farmer knowledge and understanding of crop pollination, and aep adoption. next in section 3, we describe our survey instrument and the statistical methods employed to analyse the data collected. next, we present the results of our analysis and in section 5, we discuss implications. finally, in section 6, we conclude. 1 this research was funded by interreg grant: beespoke (benefitting ecosystems through evaluation of food supplies for pollination to open up knowledge for end users) https://northsearegion.eu/beespoke 399crop production, the pollinator deficit and land use management: uk farm level survey results bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 2. literature review 2.1. significance of the pollinator deficit the potential for a pd or pollination limitation to exist in agricultural production has been a reoccurring theme with the literature (garratt et al., 2014; garratt et al., 2023). identification and measurement of the pd has been examined in a wide array of crops in both field studies (reilly et al., 2020) and meta-analysis of existing research (gazzea et al., 2023). economic research on the pd often reports the yield dependence ratios which measures how much of the crop (quality and quantity) is lost if there is no pollination (feuerbacher et al., 2024). when a pd has been identified researchers typically express this in terms of sub-optimal production and consequent reduction in financial returns. for example, garratt et al. (2023) report (for 24 commercial apple orchards in kent, uk), that average pd was 22% in 2018 and 2.6% in 2019 which equated to an average reduction of £15,000 per hectare. the extent of the pd is also highlighted by reilly et al. (2020) who report that five out of seven major pollinated crops in the usa exhibit a pd. and with this potential level of sub-optimal production being identified the economic consequences have also been examined (jordan et al., 2021). however, breeze et al. (2016) and baylis et al. (2021), both note that economically valuing the pd or more generally valuing pollination services has proven to be complicated given the difficulties in identifying key parameters such as the extent to which crop output depends on pollination services. 2.2. farmer knowledge and understanding of pollinators despite the existence of a significant body of research examining and attempting to measure the pd there is far less research that considers the extent to which farmers knowledge and understanding of pollinators or the pd. a particularly relevant study is osterman et al. (2021) who examined the decline of pollinators in agricultural landscapes highlighting the existence of a knowledge gap between understanding the issues around pollinator decline and farmer willingness to adopt science informed land use interventions. they interviewed 560 farmers across 11 countries all growing at least one of four pollinator-dependent crops (including 25 uk osr farmers). osterman et al. (2021) report that many survey participants know about non-bee pollinators via observation in the field but there remains a significant knowledge gap regarding non-bees and crop pollination (rader et al., 2020). in terms of osr and government incentives for aep, they report that 70% of farmers implemented hedgerows when financial incentives are available and 20% without. they found similar results for floral strips. clearly, the motivation for many famers to implement land use interventions such as flower strips is because they receive financial payments. another relevant study is provided by hevia et al. (2021) who surveyed spanish farmers in four areas to understand perceptions about pollinators and practices to promote them. they collected 376 face-to-face questionnaires, although between 59% and 87% of the responses collected are from respondents who are either part-time farmers or non-professional farmers. like osterman et al. (2021) honeybees, then bumble bees and wild bees are the main pollinators with other pollinators not viewed as being as important. respondent attitudes about declines in pollinators informed their views about what needs to be done to reverse the decline. employing stepwise multiple regression hevia et al. (2021) examined what influenced knowledge about pollinators reporting that education, concern about the pollinator crisis and farmer type (i.e. full time) are positively correlated whereas age was negative. they also note that reported actions to promote pollinators are less use of insecticides, crop diversification and fallow fields, and that the level of education is positively correlated with maintaining wild-flowers and reduced spraying. similarly, busse et al. (2021) report that adoption of insect-friendly farming measures, especially integrated pest and pollination management (ippm) (lundin et al., 2021) is only implemented if sufficient financial incentives are available. also, farmers regard insect biodiversity typically in terms of ecosystems services as they relate to agricultural production and not as part of the wider ecosystem. furthermore, farmers appear to implement specific types of agricultural practices without understanding the potential benefits they have on pollinators. for example, flowering catch cropping is used without many farmers realizing the benefits for pollinators. cole et al. (2022) discusses how planting a legume mixture can help support wild pollinators. improving farmers’ understanding of this issue is, as busse et al. (2021) argues, a precondition to the adoption of new land use management techniques that will support pollinators (and insects more generally). other relevant research is presented by eeraerts et al. (2020), who surveyed 24 sweet cherry farmers in flanders, belgium employing semi-structured interviews. they report that the farmers understood the importance of insects as wild pollinators although as is common in the literature there was undue emphasis placed on the importance of specific types of bees. eeraerts et al. (2020) also note that almost all respond400 bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 iain fraser, michelle t. fountain, john m. holland ents pay for honey beehives. this choice can be understood as a short-term solution to their crop pollination requirements whereas making changes to the landscape (e.g., the introduction of wildflower strips) are longer term strategies. more generally, the relationship between wild pollinators and the use of beehives can be understood as a pollination diversification strategy (nalepa et al., 2021). finally, using an online survey of 75 canadian apple growers nalepa et al. (2021) examine the influence of farm characteristics and farmer perceptions about bees and how this influences the adoption of pollinator supporting management practices. employing logistic and poisson regression models they found a positive relationship between grower awareness of pollinators and the number of pollinator supporting practices adopted on-farm. 2.3. aep and pollinator management agricultural production and land-use choices that necessitate the need for aep to support wild pollinators is evidence that agricultural intensification is generally negatively correlated with pollinator diversity and associated services (deguines et al., 2014). increased intensification of crops that require pollination necessitates the need to support pollinators with suitable living habitats in the wider landscape. in addition, kleijn et al. (2015) argue that society cannot rely on crop pollination as motivation for providing meaningful support for wild pollinators. therefore, the importance of aep in promoting and financially supporting wild pollinator management is clear. in the uk, there are a several aep initiatives with specifically designed elements to support pollinators such as the countryside stewardship scheme (css) that offers financial support for undertaking various pollinator supporting activities. defra (2023a) report that popular css on-farm activities that support wild pollinators include management of hedgerows, the provision of winter bird food and flower-rich margins. the flower-rich margins option has been implemented on 32,000 hectares. importantly, aep pollinator options are targeted at conservation objectives and not agricultural production although there can be positive production externalities. when it comes to aep design, mccullough et al. (2021), eeraerts (2023), and pindar and raine (2023) all conclude there needs to be more land maintained as natural/semi-natural habitat. similarly, image et al. (2023) argue that aep needs to complement wildflower strips with other landscape features such as hedgerows and woodland margins. mccullough et al. (2021) suggested that planting small areas may provide some benefits for pollinators (bees) under specific settings but policy, with a focus on the landscape scale, is likely to be more important. wood et al. (2015) also explains that an interaction between landscape features, aep interventions and crops being grown needs to be considered when assessing landscape modifications to support pollinators. gardner et al. (2021) note that wild pollinator populations are more stable in landscapes that have a greater number of boundary features and/or semi-natural features. in terms of explaining adoption of aep (in general) the literature frequently cites opportunity cost (hejnowicz et al., 2016) and the fit of the aep options with existing farm level practices (bartkowski et al., 2023). other explanatory factors identified in the literature as positively influencing adoption include tenure (bartkowski et al., 2023), farm size (wool et al., 2003) and farm type (grassland compared to specialized arable farms) (paulus et al., 2022). in a systematic review of quantitative literature on aep participation canessa et al. (2024) report that binary choice models such as logits and probits are often used to explain adoption, although very few studies examine adoption in relation to biodiversity (7% of models). in these studies, frequently employed independent variables include age, education, farm size, farm type, information sources, and neighbour participation. however, it is noted by tsakiridis et al. (2022) in studies that examine aep and adoption that self-selection bias can be an issue in terms of sample composition. this in turn means that there will likely be higher levels of adoption in sample data such that any statistical signal will be likely stronger and positive. 2.4. summary and key research questions given our review of the antecedent literature and the objectives of our research, the following research questions will be addressed: i. how important do farmers consider the pd to be for crop production? given the existing literature researchers consider the pd to be a significant issue, but it remains unclear if farmers share this view. ii. what types of farm management actions and activities do farmers adopt to support pollinators? the existing literature on the type of actions and activities that farmers adopt to support pollinators is limited and an enhanced understanding will give impor401crop production, the pollinator deficit and land use management: uk farm level survey results bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 tant insights into pollinator management. in particular, understanding the extent to which farmers employ short term (i.e., bee hives) versus long term (i.e., wild pollinators) solutions for pollination services is important. in addition, understanding the extent to which farmers employ aep to supports pollinators and the reasons why. this will also enable us to better understand the degree to which farmers coordinate with neighbours in supporting pollinators. iii. what knowledge do farmers have of pollinators? a reoccurring theme in the literature is the limited knowledge and understanding that farmers appear to have regarding pollinators in terms of types and potential contributions to crop production. iv. what do farmers consider to be their main pollinator management priorities and what advice and information sources will inform these priorities? finally, we examine key priorities in terms of pollinator management and who farmers turn to for advice and information. 3. material and methods 3.1. survey design and implementation our data collection strategy involved the design and implementation of a farm level survey instrument that enabled us to address the research questions raised. the design of our survey enabled us to collect information to address the issues identified in the introduction as well as key themes that emerged from the antecedent literature. in particular, the survey was designed to examine the extent to which farmers understand the required actions and activities to support pollinators and its impact on crop production, knowledge and understanding of pollinators, and appropriate management. the survey (see appendix c) began by requesting information for the most important pollinated crop from each respondent. we wanted to examine attitudes towards crop production and pollination. we asked a series of questions to reveal information regarding farm level production and the pd. the survey then asked about current levels of pollinator land management activities and how these are influenced by participation in aep. we also sought information about farmer knowledge regarding pollinators as well as sources of advice and information used in crop production. given the importance of landscape scale land use decision for wild pollinators, we asked about the extent to which respondents cooperate with neighbouring farmers regarding pollinators.2 the survey instrument was initially trialed by distributing to a small group of farmers involved with the beespoke project who gave feedback. the final version was distributed online in two waves during 2021 and 2022 by an agricultural research company (i.e. map of ag analytics limited https://mapof.ag/). to be included in the survey, we required respondents to grow at least one pollinator dependent crop. survey participation was incentivized yielding 200 responses. in addition, to ensure adequate survey returns from soft and top fruit producers, we also distributed the survey via industry contacts, yielding a final sample of 228 responses. 3.2. descriptive statistics our sample of respondents (n=228) were drawn from farmers across england and scotland with the largest number of responses being recorded for kent (n=32), scotland (n=14), herefordshire and norfolk (both n=13), north yorkshire and suffolk (both n=13), lincolnshire (n=12), shropshire and cambridgeshire (both n=11). by mapping the survey data onto the international territorial levels (itls) adopted by the uk government we could assess the representativeness of our sample of farmers by crop (three most common reported) and region.3 the results are shown in table 1. the results in table 1 reveal that in terms of regional distribution by crop type, our sample of respondents appears to be relatively similar in terms of osr and apples. however, for strawberries our sample maps less well, however, as shown in table 2, that presents sample descriptive statistics, strawberries only account for 7.5% of survey returns. we note, however, that the non-standard composition of the farms being surveyed means that it is difficult to accurately assess the representativeness of our sample. the descriptive statistics shown in table 2 present information for key variables side by side as well as by column. from table 2, we observe that in terms of years of farming experience, it is unsurprising that almost 80% have more than 25 years given the age profile of respondents (median age of over 50 years). the age profile of our respondents is typical for england, although 2 the survey also collected qualitative information using open-ended questions. although these are not referred to in this paper a small selection of responses are provided in appendix b. 3 for details see: https://www.ons.gov.uk/methodology/geography/ ukgeographies/eurostat#south-west-england https://www.ons.gov.uk/methodology/geography/ukgeographies/eurostat#south-west-englandhttps://www.ons.gov.uk/methodology/geography/ukgeographies/eurostat#south-west-england402 bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 iain fraser, michelle t. fountain, john m. holland we have less farmers aged 65 and over compared to recent farm statistics (defra, 2023b). in terms of farm management, 75.4% of respondents are farm owners and 9.2% are tenant farmers, which compares to 54% being owner occupied and 14% being tenanted in 2021 in england (defra, 2023b). in terms of the area of pollinated crops grown, we have an average of 51 hectares with a median of 30 hectares. our sample has 54% of respondents participating in aep. it is difficult to establish if this is high or low compared to national data. within england in 2022, it is reported by defra (2023c) that 34,500 aep agreements were implemented. given that there are almost 200,000 agricultural holdings in england this means 18% are participating, although 80,000 holdings are under 20 hectares and participation amongst small farms is known to be significantly lower. also, the participation rate in our sample is significantly below the levels seen at the peak of earlier aep e.g. entry level scheme had 70% participation. wool et al. (2023) report that in the table 1. percentage of survey respondents by region and three main crops compared against england farm census data for 2021. sample data england census data 20211 region osr2 apples strawberries osr apples strawberries south-east 17 43 6 14 44 50 east of england 19 8 35 25 7 14 west midlands 14 33 12 11 30 21 yorkshire 17 0 0 16 0 1 east midlands 13 0 12 19 2 4 south-west 5 15 6 9 16 9 north-east 4 0 0 6 0 0 north-west 1 3 6 1 1 1 1 source: defra (2024). structure of the agricultural industry in england and the uk at june 2021 www.gov.uk/government/statistical-datasets/structure-of-the-agricultural-industry-in-england-and-the-uk-at-june; 2 osr = oilseed rape. table 2. descriptive data. variable categories percentage variable categories percentage years farming less than 5 5-10 11-15 16-25 more than 25 1.3 3.9 4.4 11.8 78.5 age age under 35 36-45 46-55 56-65 over 65 3.9 12.3 18.4 39.0 26.3 farm management farm owner farm manager tenant farmer other 75.4 11.8 9.2 3.5 farm type top fruit mixed soft fruit livestock arable 15.4 15.5 7.3 3.9 57.9 how crop sold producer organisation contract spot market other 19.3 22.4 45.2 13.2 agri-environmental policy yes no 54.0 46.0 crops grown oilseed rape 59.2 crops grown blackcurrants 0.9 apples 17.5 blueberries 0.9 strawberries 7.5 plums 0.4 cherries 2.6 linseed 0.4 field beans 4.8 spring beans 0.9 pears 1.3 borage 0.9 raspberries 0.9 parsley 0.4 sunflowers 0.4 http://www.gov.uk/government/statistical-data-sets/structure-of-the-agricultural-industry-in-england-and-the-uk-at-june http://www.gov.uk/government/statistical-data-sets/structure-of-the-agricultural-industry-in-england-and-the-uk-at-june 403crop production, the pollinator deficit and land use management: uk farm level survey results bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 humber region of the uk aep adoption rates are relatively low with only 11% of farms adopting. finally, the mix of crops reported in table 2, shows that the most frequent is winter oilseed rape (59.2%), apples (17.5%) and strawberries (7.5%). also, as we would expect, our sample does have a high proportion of arable producers which ref lects current agricultural land use in england (defra, 2021). in 2021, 3.7 million hectares of land was used to grow arable crops with cereals and oilseed crops (various) accounting for 80%. the area used to grow oilseed crops was 313,000 hectares in 2021. in contrast, horticulture accounted for 131,000 hectares of land. the land area devoted to orchards and small fruit was 31,000 hectares (defra 2021) with orchards accounting for almost 70% of this area. 3.3. data analysis, methods, and statistical software we began by examining descriptive statistics for our survey for the set of questions we wished to address. in addition, we implemented a statistical test between pairs of proportions for responses by crop type. we also estimated several regression model specifications to further examine the questions we raised regarding attitudes and knowledge of the pd and pollinators, and farm management and pollinators. in terms of regression model specifications, for example, when we had a binary dependent variable, we employed a logit specification. most data collected by the survey, is either a yes or no responses (e.g., is crop yield lower than expected? see table 5). when employing a binary logit model, it utilises a latent variable approach to determine the probability of an event. this approach retains a linear regression model but utilises a framework to determine the value of a latent or unobserved variable (y*) which in turn determines the outcome observed for the binary dependent variable y. formally, yi * = xikβk + ui where yi = 1 if yi * > 0 yi = 1 otherwise where i = 1,…,n, xik is a i by k data matrix, ß is vector of independent variables (k=1,…,k) to be estimated and ui is the error term assumed to be independently identically distributed with mean zero and constant variance. in contrast, when our dependent variable takes the form of a count variable, we employed a poisson specification. an example is a count of the number of farm management practices adopted to support wild pollinators (see table 7). in this case, the model is specified as f(y|yi) = pr(y = y) = where λ is the poisson distribution parameter. the poisson regression model can be specified in log-linear form: ln λi = β0 + β1x1 + β2x2 + …+ βkxk finally, when the dependent data was an ordered response, we estimated an ordered probit model. all regression models were estimated using limdep version 11 (greene, 2016). our regression analyses do not reveal causality, but potentially important correlations between aspects of farm level activities, crop types and pollinator management. the selection of explanatory variables we employ is informed in part by reference to the antecedent literature. for example, as table 3. explanatory variables used in statistical analysis. variable description units experience number of years farming years* area size of farm hectares aep in an aep scheme supporting pollinators yes = 1, no = 0 records keep records of crop pollination yes = 1, no = 0 soft fruit type of crop produced yes = 1, no = 0 top fruit type of crop produced yes = 1, no = 0 arable type of crop produced yes = 1, no = 0 low yield farmer thinks current levels of pollination are negatively impacting crop yield yes = 1, no = 0 collaborate collaborate with neighbours in supporting wild pollinators yes = 1, no = 0 * in some regression model specifications, we employed experience squared to capture the potential non-linear relationship between experience and the dependent variables. 404 bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 iain fraser, michelle t. fountain, john m. holland noted by canessa et al. (2024), experience, farm type, size and collaboration with neighbours are frequently employed in studies examining adoption of aep. the set of explanatory variables used in the regression analysis are presented in table 3. 4. data analysis 4.1. attitudes about crop pollination we first asked respondents’ questions about their attitudes to current crop pollination (table 4). many of the farmers in our sample do not consider a lack of pollination (i.e. pd) to be an issue that impacts yield, quality, or financial return (table 4). however, by crop type, top fruit growers appear less concerned than either soft fruit growers or arable farmers, illustrating the issues confronting efforts to induce greater on farm pollinator management motivated by economic concerns alone. furthermore, testing the null hypothesis of equality of proportions of responses by crop type, there is a difference at the 10% level of statistical significance between: soft fruit and top fruit (z=2.18, p=0.074) for crop quality; between arable and soft fruit (z=-2.136, p=0.055) for crop quality; and arable and soft fruit (z=2.34, p=0.052) for financial return. however, even for soft fruit producers the highest level of concern was only 25% for financial returns, meaning, either a large proportion of farmers are generally unaware of pd and its impact on production or pd is less important to farmers compared to other aspects of crop production. to further examine the responses reported in table 4, we estimated logit regression models (table 5). farm experience was negatively correlated with a positive response to the questions for crop quality and the impact on finance (table 5), implying that older farmers appear less likely to express concern about aspects of insect pollination on production. in contrast, larger farms were more likely to respond ‘yes’ to the question about the negative impact of insect pollination on yield and crop quality. for crop type (a dummy variable) the excluded category is arable meaning a negative estimate for top fruit (yield and finance) and a positive response for soft fruit (quality) are both relative to arable. these results indicate a table 4. attitudes to pollinator management and crop production (%). question response all crops top fruit soft fruit arable do you believe the yield of your crop is currently lower than it could be because of a lack of insect pollination? no yes 84.2 15.8 92.0 8.0 79.2 20.8 83.2 16.8 do you believe the quality of your crop is currently lower than it could be because of a lack of insect pollination? no yes 92.1 7.9 96.0 4.0 79.2 20.8 92.9 7.1 do you believe the financial return of your crop is currently lower than they could be because of a lack of insect pollination? no yes 86.8 13.2 94.0 6.0 75.0 25.0 86.4 13.6 table 5. is crop yield, quality, or financial return lower than it could be, due to a lack of insect pollination? (yes=1; no=0). low yield low quality low finance variables coeff se p value coeff se p value coeff se p value constant -0.883 0.999 0.377 -1.332 1.220 0.275 -0.731 1.027 0.476 experience -0.313 0.198 0.114 -0.406* 0.245 0.098 -0.369* 0.204 0.071 farm area 0.004* 0.002 0.079 0.005* 0.003 0.064 0.003 0.002 0.214 soft fruit 0.138 0.591 0.816 1.127* 0.644 0.080 0.641 0.563 0.255 top fruit -1.138* 0.636 0.074 -1.281 0.979 0.191 -1.159* 0.704 0.100 aep 0.567 0.411 0.168 0.114 0.546 0.835 0.455 0.431 0.292 records 1.27** 0.546 0.020 1.638** 0.657 0.013 1.177** 0.575 0.041 ll -88.45 -53.21 -80.46 chi2 18.6*** 19.5*** 16.6*** mcfadden pseudo r2 0.10 0.15 0.09 notes: coeff = coefficient; se = standard error; ***, **, * significance at 1%, 5% and 10% level; ll = log-likelihood. 405crop production, the pollinator deficit and land use management: uk farm level survey results bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 mixed response for crop type and how insect pollination impacts crop performance. next, if a farmer keeps pollinator records, then they are more likely to have responded ‘yes’, such that recording crop pollination is likely to heighten awareness of potential issues stemming from crop deficiencies. finally, we note that the mcfadden pseudo r2 for all models is relatively low and as such we should treat these results with a degree of caution. finally, we also asked respondents if they hire contract pollination services. this was confirmed by 35% of respondents. to examine this decision in more detail, we estimated a logit regression model with the results shown in table 6. the results in table 6 reveal that soft fruit and top fruit producers are more likely to employ this type of service compared to arable farmers. 4.2. farm management and wild pollinators next, we asked respondents about farm management practices they employ to encourage and support wild pollinators (table 7). most respondents undertake some type of activity to support wild pollinators (table 7), although these estimates may be subject to a degree of selection bias i.e. responses from farmers interested in pollinators or biodiversity. given the responses on crop quantity, quality and financial returns, the motivation for adoption of practice listed in table 7 are unlikely to be driven by crop production and instead by environmental attitudes, aep requirements, retailer requirements and insecticide container labelling that stipulates how to avoid impacts on pollinators. when we asked respondents their reasons for not adopting practices that support wild pollinators; time (28%), experience (20%) and cost (17%) were the main justifications. to further examine adoption, we estimated a poisson count data regression model by creating a dependent variable for the number of practices adopted/not adopted (see table 7). these results are presented in table 8. the number of adoptions of pollinator beneficial on-farm management activities was only explained by whether a farmer was in aep. in the case of not adopting practices, this was negatively related to farmer experience, if they produced top fruit, and if they were in an aep. more analysis on the proportion of arable and fruit farmers in aep is required. however, there is no published data available and only limited statistics regarding overall land use by farm type. this represents an important information gap. table 6. hire crop pollination services (yes =1/no=0). variables pollinator service coeff se p value constant -2.05** 0.95 0.03 experience 0.07 0.19 0.71 top fruit 1.33*** 0.36 0.00 soft fruit 1.65*** 0.47 0.00 low yield -0.19 0.47 0.69 aep 0.44 0.32 0.17 records 0.26 0.51 0.62 ll -124.3 chi2 23.96*** mcfadden pseudo r2 0.088 notes: coeff = coefficient; se = standard error; ***, **, * significance at 1%, 5%, 10% level; ll = log-likelihood. table 7. farm management supporting wild pollinators (%). farm management practice yes improve management of existing habitats 89.0 establish new flower-rich habitats 73.2 maintain hedgerows by not cutting annually 80.3 time insecticide spraying to reduce impact on pollinators 93.9 time pesticide applications to reduce impact on pollinators 81.6 reduce number of chemical applications to protect beneficial insects 88.2 spot spraying instead of treating an entire crop 46.1 provide nesting and/or overwintering habitat 79.4 table 8. adoption and non-adoption of pollinator supporting activities. adopt not adopt variables coeff se p value coeff se p value constant 1.712*** 0.156 0.000 1.072*** 0.306 0.001 experience -0.002 0.032 0.949 -0.107* 0.063 0.089 soft fruit 0.119 0.085 0.159 -0.105 0.187 0.575 top fruit 0.086 0.065 0.187 -0.693*** 0.175 0.000 low yield 0.062 0.073 0.392 -0.105 0.166 0.527 collaborate 0.107 0.068 0.118 -0.155 0.166 0.350 aep 0.147*** 0.055 0.007 -0.271** 0.119 0.023 ll -458.75 -356.74 chi 2 15.1*** 25.8*** mcfadden pseudo r2 0.016 0.035 notes: coeff = coefficient; se = standard error; p = p value; ***, **, * significance at 1%, 5%, 10% level; ll = log-likelihood. 406 bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 iain fraser, michelle t. fountain, john m. holland we also examined the individual on-farm practices using logistic regression models. these results are presented in table 9. improved management of existing habitats was positively related to being a top fruit producer, and experience, but negative for experience squared, implying that as farmers gain more experience (i.e. years in farming) they have a decreasing likelihood of adoption. for those establishing new flower-rich habitats, experience was positively related, and experience squared negative. farmers who considered levels of pollination to be having a negative impact on crop yield and who collaborated with their neighbours regarding pollinators are positively related. for farmers who maintain hedgerows, experience was positively related, but experience squared was negative. however, being a top fruit producer and considering existing levels of pollination as having a negative impact on crop yield were positively related. the timing of fungicide applications was negatively related for those farmers who consider that existing levels of pollination are having a negative impact on crop yield, but positive for those who collaborate with neighbours. spot spraying was positively related to farmer experience and collaborating with neighbours if producing top fruit but negative for experience squared. finally, when asked about reducing the number of chemical applications to protect beneficial insects, this was positively related to collaboration with neighbours. similarly, farmers who provided nesting and/or overwintering habitat, were also likely to be collaborating with neighbours. our results regarding any aspect of collaboration with neighbours could be a function of pre-existing farm clusters. examining the influence of farm clusters on farm level cooperation warrants further examination. 4.3. knowledge of pollinator types we asked respondents if they knew which pollinators their crops depended on (table 10). most respondents indicated honeybees, bumblebees and solitary bees were the main pollinators. the potential lack of understanding regarding the other pollinators, for example syrphine hoverflies in strawberries (hodgkiss et al., 2018) does indicate a need for greater provision of information for farmers. also, the percentages of respondents indicating the specific type does not vary significantly by crop. examining the average number of pollinator types by crop type reveals little variatable 9. adoption of on-farm pollination supporting activities. variables improve habitata new flowerb hedgec time fungicided spot spraye coeff se p value coeff se p value coeff se p value coeff se p value coeff se p value constant -3.12 2.754 0.257 -6.41** 2.665 0.016 -5.93** 2.515 0.018 1.26 2.585 0.625 -4.89** 2.259 0.030 experience 3.24* 1.879 0.084 5.06*** 1.722 0.003 3.93** 1.564 0.012 0.58 1.559 0.709 2.63** 1.305 0.043 experience2 -0.46* 0.275 0.092 -0.74*** 0.245 0.002 -0.51** 0.219 0.020 -0.12 0.216 0.590 -0.36** 0.176 0.041 soft fruit 1.64** 0.787 0.037 0.27 0.389 0.494 0.85* 0.494 0.084 0.29 0.451 0.518 0.99*** 0.352 0.005 top fruit 1.31 1.057 0.215 0.99 0.665 0.135 0.33 0.605 0.582 0.56 0.661 0.394 0.53 0.462 0.246 low yield 0.87 0.777 0.260 0.94* 0.553 0.091 1.39** 0.691 0.044 -0.82* 0.445 0.065 0.63 0.399 0.116 collaborate 1.03 0.772 0.181 1.08** 0.523 0.039 0.55 0.528 0.299 1.18* 0.639 0.063 0.67* 0.378 0.075 records -0.26 0.814 0.751 0.64 0.667 0.337 0.69 0.794 0.379 0.29 0.680 0.670 0.47 0.503 0.352 ll -70.3 -118.5 -104.1 -103.8 -147.9 chi2 12.72* 25.79*** 18.36** 10.12 18.9*** mcfadden pseudo r2 0.083 0.098 0.081 0.046 0.06 notes: coeff = coefficient; se = standard error; ***, **, * significance at 1%, 5%, 10% level; ll = log-likelihood; a = improve management of existing habitats; b = establish new flower-rich habitats; c =maintain hedgerows by not cutting annually; d = time insecticide spraying to reduce impact on pollinators; e = spot spraying instead of treating an entire crop table 10. which types of pollinators do your crops depend on (%). pollinator group overall yes overall no yes if top fruit yes if soft fruit yes if arable honeybees 80.3 19.7 88.0 87.5 76.6 bumblebees 77.2 22.8 82.0 87.5 74.0 solitary bees 53.9 46.1 80.0 50.0 46.1 hoverflies 28.5 71.5 46.0 25.0 23.4 flies 22.4 77.6 24.0 33.3 20.1 butterflies 25.4 74.6 24.0 16.7 27.3 moths 20.2 79.8 30.0 12.5 18.2 407crop production, the pollinator deficit and land use management: uk farm level survey results bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 tion: top fruit farmers identified 3.74 groups; soft fruit farmers 3.13 groups; and arable farmers 2.86 groups. one result of significance was the importance placed on solitary bees by top fruit producers, suggesting that efforts to increase awareness about the importance of solitary bees in pollinating top fruit is having an impact. for each pollinator type, we ran a logit regression and found that the only positive and statistically significant regressors were either being a top fruit producer or coordinating with a neighbour regarding pollinators, and only for honeybees, solitary bees, and moths. we next asked if respondents undertook any active monitoring of pollinators. results indicated that most respondents relied on crop walks to assess crop pollination requirements (55%). there was also a significant proportion who relied on advice from agronomists and consultants (32%) but most farmers did not monitor pollinators using traps (13%). we also examined on-farm pollination monitoring by employing logit model specifications. these results are presented in table 11. both crop walks and employing traps within crops are positively related with being a top fruit producer, aep participation, and keeping pollinator records. the probability growers who took advice on pollination from agronomists and consultants was more likely if existing pollination levels are low, and they were keeping pollinator records. overall, there was a strong and positive relationship between keeping pollination records and on-farm pollination monitoring activities. we also asked farmers about other aspects of pollination management. 9.6% indicated that they collected records of crop pollination by pollinator type, 17.1% actively managed their farm for wild pollinators in collaboration with neighbours and 49.1% think that they benefit from pollinators by the actions being undertaken by their neighbours. these findings are important given that accurate records of crop pollination are required if changes to production or land use are to be evaluated in terms of supporting wild pollinators. 4.4. pollinator management priorities we next asked respondents their priorities in relation to pollination management (table 12). many respondents indicated “always” or “often” in terms of priorities for pollination management regarding consistent and reliable crop pollination and increased economic returns (table 12). this contradicts the answers reported in table 4 about understanding how crop pollination relates to quantity, quality, and table 11. on-farm pollination monitoring activities (yes =1; no=0). variables traps in crop walk crop agronomist coeff se pvalue coeff se pvalue coeff se pvalue constant -3.02** 1.22 0.01 -0.49 0.84 0.56 -1.57* 0.90 0.08 experience -0.03 0.24 0.89 -0.01 0.17 0.93 0.05 0.18 0.80 top fruit 0.90* 0.49 0.07 1.07*** 0.37 0.00 0.55 0.37 0.13 soft fruit 0.73 0.64 0.26 0.72 0.48 0.13 0.72 0.48 0.14 low yield 0.58 0.54 0.28 -0.25 0.42 0.56 0.91** 0.41 0.03 aep 0.83* 0.47 0.07 0.68** 0.29 0.02 0.11 0.31 0.72 records 1.76*** 0.53 0.00 2.11*** 0.77 0.01 1.71*** 0.52 0.00 ll -74.5 -142.4 -130.4 chi2 24.68*** 29.1*** 25.18*** mcfadden pseudo r2 0.142 0.093 0.088 notes: coeff = coefficient; se = standard error; ***, **, * significance at 1%, 5%, 10% level; ll = log-likelihood. table 12. main priorities for pollination management (%). pollination management main priorities always often maybe never consistent and reliable crop pollination 67.5 18.9 10.5 3.1 increased economic return 62.7 22.4 12.3 2.6 availability of managed pollinators for rental or purchases 14.9 13.2 31.1 40.8 reported declines in wild pollinator populations 20.2 28.5 26.8 24.6 diversification of pollination strategies 22.4 25.9 33.8 18.0 minimising uncertainty and risk in crop pollination 43.0 27.6 22.8 6.6 effectiveness of pollinator species 36.4 24.6 30.7 8.3 408 bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 iain fraser, michelle t. fountain, john m. holland financial returns. these results are also hard to reconcile with data around maintaining records about crop pollination. potentially, it is correlated with crop pollination monitoring and walking the crop, but unless a coherent and meaningful assessment of pollinator presence is related to crop quality/quantity it remains unclear how informative walking a crop can be regarding pollination requirements. thus, whilst most respondents understand the economic significance of crop pollination it is unclear how this is manifesting in current agronomic practices. for the other types of pollination management priorities, there were much lower levels of importance. given the clear correlation between these priorities and the supply of pollinator services either from wild or managed pollinators, these results provide more evidence of inconsistent understanding regarding crop pollination and farm level activity.4 4.5. advice and investment in pollination services our survey revealed that by far the most important source for seeking advice on crop pollination were agronomists and other commercial suppliers (74%). next were published advice (33%) and sources including government, ngos and local environmental groups (at or below 25%), partly relating to the answer about the role 4 we also analysed these responses employing an ordered probit where the dependent variable was coded: never = 0, maybe =1, often = 2 and always =3. all models yielded relatively weak statistical results. see appendix a for details. of agronomists in monitoring pollinators. for the crop pollination information source, we estimated logit model specification. these results are presented in table 13. the results shown in table 13 reveal that there is only weak statistical evidence between the source type of information and the set of explanatory variables. for example, published sources were positively related to reporting a negative yield effect from a lack of pollination. when asked about government sources of information we found only a negative result for experience. for ngos we found a positive relation for aep participation and reporting a negative yield effect. finally, we asked what would make a farmer increase investment in pollination services (table 14). evidence regarding financial benefits, higher payments associated for aep participation and evidence on environmental benefits will all lead to an increase in investment of pollination services (table 14). table 13. sources of advice used by respondents. variables published government ngo local groups coeff se p value coeff se p value coeff se p value coeff se p value constant 3.739 2.447 0.127 5.275* 2.705 0.051 -0.674 1.980 0.734 4.189* 2.275 0.066 experience -1.876 1.424 0.188 -2.916* 1.586 0.066 0.217 1.216 0.858 -2.337* 1.367 0.087 experience2 0.179 0.193 0.353 0.309 0.215 0.151 -0.102 0.170 0.550 0.246 0.189 0.192 low yield 1.011** 0.407 0.013 0.315 0.447 0.481 1.132** 0.418 0.007 -0.333 0.464 0.473 top fruit -0.466 0.522 0.372 0.174 0.526 0.741 -0.131 0.562 0.816 -0.646 0.582 0.267 soft fruit -0.487 0.406 0.231 -0.436 0.467 0.351 0.389 0.411 0.344 -0.692 0.445 0.120 records -0.220 0.539 0.683 0.513 0.546 0.347 0.121 0.538 0.822 1.258** 0.507 0.013 aep 0.452 0.312 0.148 0.133 0.352 0.705 0.767** 0.353 0.030 0.314 0.337 0.351 ll -130.69 -122.42 -127.1 -129.29 chi2 27.45 0.000 22.98 0.000 26.82 0.002 21.54 0.003 mcfadden pseudo r2 0.095 0.094 0.11 0.083 notes: coeff = coefficient; se = standard error; ***, **, * significance at 1%, 5%, 10% level; ll = log-likelihood. table 14. increased investment in pollination services (%). invest in pollination services yes research evidence on financial benefits 63.2 research evidence on environmental benefits 60.5 research evidence on landscape benefits 28.9 farm assurance schemes 31.6 customer assurance schemes 22.8 higher payments for aep 54.8 decrease in natural pollinators 39.0 409crop production, the pollinator deficit and land use management: uk farm level survey results bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 5. discussion 5.1. the significance of the pollinator deficit our survey results indicated that most respondents were not concerned about the financial consequences of inadequate pollination (i.e. the pd). this means that the answer to our first question (how important do farmers consider the pd to be for crop production?) is not very much, there are only low levels of concern about crop pollination and the associated pd. this result is somewhat surprising given the apparent importance of the of the pd within the existing literature. there are several possible explanations for this result. first, any variation in crop yield and/or quality that occurs because of the pd are small and as such considered negligible compared to other factors. within the extensive economic efficiency literature and related farm level benchmarking literature, it was very difficult to identify existing research considering pollination. examples include tariq et al. (2018) and wijayanti et al. (2020) who consider strawberry production and note that variation in pollination as a possible reason for differences in farm level performance. however, the lack of literature on farm level efficiency and productivity that mentions pollination or pollinators likely occurs either because pollination is assumed to be constant, or that the importance of pollination in commercial systems has not been investigated sufficiently to know whether it is optimal and so it has generally been overlooked. second, 35% of our sample of respondents employ crop pollination services (i.e. honeybee hives). as observed by garibaldi et al. (2020), if there are too few pollinators this could be resolved using managed hives in the short term with longer term landscape planning including enhancement and conservation of seminatural habitats and flower strips. in fact, a decision to deploy honeybee hives can be understood as a risk averse approach to pollination, and many farmers see wild pollinators as additional (or secondary) to honeybees (eeraerts et al., 2020), even when honeybees are not the most effective or efficient pollinator. third, there are trade-offs in land use as it relates to agricultural production and pollination management that means that a pd will always occur. micro economic analysis assumes that economic agents will equate net marginal benefits from all activities such as crop production and provision of landscape (e.g. wildf lower strips) to support wild pollinators that in turn enhance crop returns (fezzi and bateman, 2011). assuming we have a single farm, and they can allocate a small land parcel to either production of apples or production of pollinators (e.g., wildflower strips). on this piece of land farmers are equating the return from the crop and the return from supporting pollinators. if the increase in production on the marginal piece of land more than compensates for the reduction in yield from lower pollinator numbers, then the farmer will plant the crop. fourth, our results reveal that the current levels of monitoring and record keeping about crop pollination and pollinators are limited. this means that awareness of the existence of a pd is likely to be low. in addition, this result also answers our third question regarding knowledge of pollinators (what knowledge do farmers have of pollinators?). as noted from the literature, limited knowledge and understanding of pollinators by farmers is frequently reported. in part this could be a result of there being too little monitoring of pollinators, without which it will be difficult for farmers to fully appreciate if existing levels of crop pollination are sufficient. to enable farmers to monitor pollinators requires them to understand how to measure pollination activity as well as be able to identify pollinators. garibaldi et al. (2020) have described a protocol that farmers could employ to assess if current levels of pollination are too low and research projects such as beespoke provide extensive guidance on pollinator identification and land management options.5 the need for this type of protocol is supported by our results in that respondents consider bees to be the most important pollinator, even though many other insects play a significant role in crop pollination. there is a significant body of research demonstrating that insects, in general, are in decline (cardoso et al., 2020; mancini et al., 2023). hall and martins (2020) note that although pollinator decline and its consequences are understood, and bees have played a key role in knowledge enhancement, there is a need enhance understanding to include insects in general. basset and lamarre (2019) and goulson (2019) argue that we require adoption of activities to protect all insects given the rapid declines in population levels being observed. basset and lamarre (2019) also observe that specific species i.e., bees and butterflies have provided an initial focus, but protection needs to go beyond a small group of iconic species. potentially, bees could be a “flagship species” as happened with the short-haired bumblebee project in south-east england (gammans, 2013). conversely, flagship species can mean that other insects are marginalized in terms of conservation efforts 5 beespoke (https://northsearegion.eu/beespoke/) has developed protocols to enable farmers to measure insect pollination by crop type. other examples of research projects supporting farmers in understanding how to count pollinators are the flower-insect timed (fit) counts app (https://fitcount.ceh.ac.uk/) that is part of the uk pollinator monitoring scheme (poms: https://ukpoms.org.uk/). https://fitcount.ceh.ac.uk/ https://ukpoms.org.uk/ 410 bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 iain fraser, michelle t. fountain, john m. holland and understanding other insects that are critical to ecosystem survival. finally, benefits of pollinator monitoring are not confined to individual farmers. there is the need for more general pollinator monitoring such as advocated by breeze et al. (2021). they demonstrated that costs of monitoring are significantly less than losses from poor pollination. identifying the potential economic benefits of monitoring needs to examine if the costs of dealing with sub-optimal levels of pollination are economically meaningful. 5.2. farm level management to support pollinators in relation to our second question (what types of farm management actions and activities do farmers adopt to support pollinators?) our results align with the existing literature. we find that participation in aep is positively correlated with the adoption of pollinator supporting activities such as habitat improvements, establishment of flower strips and hedgerow management. there are several reasons why aep is so important for supporting pollinators on farms. first, when it comes to key farm level priorities to support pollinators financial reward is the most important motivation for adopting appropriate practices. however, the financial driver is unlikely to be because of agricultural production and the pd given our results. that said, our results also reveal that if the financial benefits in terms of crop production from greater levels of pollination can be shown then farmers would invest in pollination services. without this evidence payments offered for aep participation will continue to be the main motivation for adoption. existing research unambiguously demonstrates that higher aep payments attract great levels of participation as there are clearly significant costs involved in creating habitats that support pollinators. if we assume that a farmer creates a wildflower strip, then they will incur costs in terms of soil preparation prior to planting the seed which also needs to be bought as well as ongoing management to ensure the wildflower strips yields sufficient flowering plants that will attract and support pollinators. there may also be an opportunity cost where the land used to produce the flower strip is no longer in conventional production (silva et al., 2023). these costs can reduce the attractiveness of allocating land for pollinators. there may be land that is not currently in production that can be used to support pollinators. in this case, when there is minimal opportunity cost, planting wildflower strips may be an appropriate land use choice, especially if there is also an increase in crop yield (blaauw and isaacs, 2014). second, even though higher rates of payment for aep will likely induce higher participation, not all farmers will participate in aep. as explained by gaines-day and gratton (2017), there may be factors that prevent participation including awareness of policy options, a lack of knowledge to enable participation and a need for farmers’ “to experience a shift in their beliefs, values, or attitudes regarding environmental conservation” (p. 2). nalepa et al. (2021) also argue that increasing farmer awareness and understanding of wild pollinators could see increased levels of adoption of appropriate land use practices. an important element that is required to ensure improved identification of pollinators, is more emphasis on farmer education and extension service to enable them to monitor pollinators and undertake land management practices that support pollinators (nichols et al., 2022). clearly, aep design and implementation needs to recognize the important role that education and extension services play if aep is to be successful. much in the same way that there is growing evidence about the pd, greater efforts are still needed to communicate scientific research findings to farmers such as the importance of specific wildflower seed mixes, appropriate management for floral establishment and on-going management to ensure longevity of the resource (nichols et al., 2022). third, another important finding regarding farm level management emerged when we asked respondents about the extent to which they coordinate activities in support of wild pollinators. only 17% responded positively whereas almost 50% acknowledged that they benefited from the actions of neighbouring farmers. there is significant evidence that many pollinator species are reliant on landscape management and therefore require land-use management at a scale beyond an individual farmers’ control. from a policy perspective, given that many pollinator species are reliant on wider landscape features, and this requires management at a scale beyond an individual farmers’ control, collective action is needed, with policy support. therefore, there is a need to align wild pollinator management with aep design. from an economic perspective, cong et al. (2014) show, using an agent based model (abm), that an individual farmer will have little incentive to manage their farm for wider landscape objectives that can support wild pollinators. a solution to this problem, proposed by economists, is the agglomeration bonus (bareille et al., 2020, 2021). this is a payment, that could be made via aep, that increases as the number of farmers coordinating increases. on a practical level, farm level coordination could be enabled by the development of farm cluster groups and these groups have been growing in importance in the uk (prager, 2022). if the focus of a 411crop production, the pollinator deficit and land use management: uk farm level survey results bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 farm cluster is based purely on pollinators it might not be induce sufficient participation, whereas improving wider biodiversity, and/or pest control, may be more of an incentive. interestingly, the reintroduction of the shorthaired bumble bee (bombus subterraneus) into south-east england does suggest that focusing on a single species can work and in turn yield wider biodiversity benefits (gammans, 2013; sampson et al., 2020). however, the current prescriptive nature of aep design has been noted by arnott et al. (2019) as a limitation enabling longer-term behavioural change. by allowing aep implementation to be more flexible not only might this induce higher levels of participation, but it may also support farm level coordination that in turn generates a landscape that is beneficial for wild pollinators. 5.3. limitations of the current research although our research has revealed important insights into farm level knowledge and understanding of the pd and pollinators within the uk there are several limitations that need to be acknowledged. first, although our regression models yielded interesting results, in general statistical significance is quite low (e.g., low mcfadden pseudo r2 values). therefore, our results do need to be treated cautiously and would be best interpreted as exploratory as opposed to definitive. one way to address this limitation would be to collect a bigger sample of data. it would also be important to ensure that the sample is representative of the type of farm level behaviour and practices we are focussing on. to be able to statistically demonstrate sample representativeness would constitute an important development on the research presented here. second, our sample although does not capture the regional variation in strawberry production as well as that for osr and apples. in part, this limitation could again be addressed by increasing sample size but with a clear emphasis on mapping crop composition by regional production. third, with a different approach to sampling it would be possible to deal with the issues arising from sample selection bias in relation to aep participation. 6. conclusions in this paper, we present findings from a farmlevel survey undertaken to examine farmer knowledge and understanding of the pd, and pollinator management. overall, our results indicate that identifying a pd at the farm level is difficult and is maybe considered less important than other yield limiting factors that can affect output on an annual basis. our findings also indicate that many respondents are actively undertaking farm level management activities that support wild pollinators. therefore, although respondents recognize the importance of pollinators in crop production, they do not seem as concerned with pollination management in relation to crop production, and the pd. this may be because many do not consider there to be issues around crop production and existing levels of crop pollination. or it could be because pollinator monitoring is too difficult or time consuming or that the benefits from monitoring are not understood. there are clearly some crops for which the relationship between crop quantity and quality is positively correlated with levels of pollination, e.g. apples and strawberries. potentially, with an enhanced understanding of crop pollination and provision of simple protocols for assessing levels of pollination (such as those developed by beespoke) farmers might begin to actively monitor pollination of crops. even if the extent of the pd becomes more widely understood, how farmers use their land, and the associated marginal costs and benefits means that increasing pollination levels may not be considered of sufficient economic importance. this land-use trade-off makes pollination decisions more complex than simply looking for margins of improvement in crop production from applying agricultural chemicals. given the existing set of financial incentives determining production there is a negative externality in terms of biodiversity provision which in turn necessitates the need for aep. the challenge in this context is that the land-use activities that support wild pollinators are only part of a wider mix of policies that farmers can adopt. evidence to date suggests farmers are not adopting the right mixes and as such there is an under provision. this implies that the relative “prices” for the mix of land use options is “wrong”. this can be rectified if the relative prices are changed or if some additional benefits from the land use management can be perceived or achieved by farmers. our findings confirm that efforts to inform and provide incentives to farmers to adopt farm management practices that support pollinators will likely be more successful if channeled via aep rather than appealing to the profit motive. if farmers are experiencing pds, then there is likely to be a high correlation with it and potential exit from the industry. however, there is little or no research pointing to a serious pd in crop production and resulting farm level industry exit. until such evidence is forthcoming the desired changes in land-use practices that will support wild pollinators will have to come via aep. 412 bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 iain fraser, michelle t. fountain, john m. holland acknowledgements we thank two reviewers for their extensive and very insightful comments on earlier versions of the manuscript. this research was conducted as part of beespoke, an interreg project supported by the north sea programme of the european regional development fund of the european union. 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(2023). delineating the spatial drivers of agri-environment scheme adoption at field and farm levels. journal of environmental management, 348: 119407. 416 bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 iain fraser, michelle t. fountain, john m. holland appendix a table a1. priorities for pollination management (ordered probit dependent variable: never = 0, maybe =1, often = 2 and always =3) only statistically significant estimates and associated marginal effects are reported. variables reliable cropa coeff/me managed pollinatorsb coeff/me declines pollinatorsc coeff/me diversify managementd coeff/me minimise riske coeff/me effectiveness pollinatorsf coeff/me top fruit y=0 y=1 y=2 y=3 1.07*** -0.03*** -0.01*** -0.15*** 0.28*** 1.36*** -0.38*** -0.12* 0.07*** 0.43*** 0.47* -0.05** -0.12* -0.02 0.18* soft fruit y=0 y=1 y=2 y=3 0.48** -0.02** -0.06** -0.08** 0.16** 0.5*** -0.18*** 0.004 0.06*** 0.12** 0.55*** -0.12*** -0.1*** 0.04*** 0.18*** 0.71*** -0.07*** -0.17*** -0.03 0.27*** aep y=0 y=1 y=2 y=3 0.31 -0.02* -0.04* -0.05* 0.11* 0.25* -0.08* -0.02* 0.03* 0.07* 0.43*** -0.11*** -0.06*** 0.05*** 0.12*** 0.52*** -0.06*** -0.11*** -0.02* 0.2*** records y=0 y=1 y=2 y=3 0.44* -0.09** -0.08 0.03*** 0.14 ll -201.4 -273.5 -311.3 -298.9 -274.6 -292.3 chi2 18.3** 36.4*** 5.89 22.3*** 13.79* 20.28*** mcfadden pseudo r2 0.043 0.062 0.01 0.036 0.025 0.035 notes: coeff = coefficient; ***, **, * significance at 1%, 5%, 10% level; me = marginal effects [y=0,1,2,3]. mes for dummy variables are pr[y|x=1]-pr[y|x=0]; ll = log-likelihood; a=consistent and reliable crop pollination; b=availability of managed pollinators for rental or purchases; c=reported declines in wild pollinator populations; d=diversification of pollination strategies; e=minimising uncertainty and risk in crop pollination; f=effectiveness of pollinator species. in terms of delivery of consistent and reliable crop pollination, top fruit, soft fruit, and aep participation was positively related. for availability of managed pollinators for rental or purchase, and effectiveness of pollinator species, both top fruit and soft fruit growers are positively related. declines in wild pollinator populations and minimizing uncertainty and risk in crop pollination were positively related to aep participation, indicating that either these farmers have an increased awareness of crop pollination or because farmers are more likely to participate in aep if are interested in the environment. for diversification of pollination strategies there was a positive association with soft fruit, aep participation and keeping pollinator records but increased economic return yielded no statistically meaningful results. appendix b: examples of qualitative responses to further investigate this issue, we sought feedback on each of these questions raised. several respondents assess yields (compared to historical levels or national averages) and suggest deficit might be due to a pd, although several noted that measuring a pd is difficult in practice. – “very difficult but think that the crop could always yield better and maybe it is down to not enough pollinators” – “i think it is very difficult to assess how i can say how much the yield is down due to pollinators” – “i don't think you can truthfully” – “very difficult to assess yield deficit” 417crop production, the pollinator deficit and land use management: uk farm level survey results bio-based and applied economics 13(4): 397-417, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15333 – in addition, a few respondents indicated that they employed measures to support pollinators (f loral strips, and honeybee and bumblebee hives) and some examined seed set and flower counts. in terms of the crop quality and pollination the responses were like those for crop production. most respondents provided no specific feedback although a few indicated that it is difficult to assess the pollination/ crop quality relationship and a couple suggested that fruit shape and crop quality indicate issues regarding lack of pollination. – these results echoed many of the qualitative comments for increased investment in pollination services. increased payments for aep for an increase in pollinator beneficial land use management was a frequently articulated response: – “if there was more financial rewards for providing habitats for pollinators, no questions it would be more poplar. money always talks.” – “if there was financial help to allow us to not grow as many crops and put it down to wild flowers.” – “more finical support and advice.” – “help with a grant.” – “grants or financial support.” – “financial incentives to purchase wildflower seed, and schemes to reward farmers for leaving dedicated habitats for wild pollinators.” appendix c: copy of survey instrument attached as separate file. _ref114578152 _ref114489287 _ref114578337 _ref115429676 _hlk124753738 _hlk124753769 _ref114578159 _hlk125619779 _hlk125620100 _hlk125620116 _hlk168057609 _hlk125617961 _hlk125617978 _heading=h.1fob9te _heading=h.1y810tw _heading=h.3whwml4 _hlk105387355 _hlk162970176 _hlk120503612 _hlk94825625 _hlk101307043 _hlk112165281 _hlk110795819 _hlk163789378 _hlk101572865 _hlk101574469 _hlk162424605 _hlk163657897 _hlk163658986 _hlk163657719 _hlk163657691 _hlk163658456 _hlk163658513 _hlk163658646 _hlk162446713 _hlk125919866 _hlk107164817 _hlk162973538 _hlk162447045 _hlk107174352 _hlk162447083 _hlk124882770 _hlk142394334 _hlk142395728 _hlk142430461 _hlk142337917 _hlk142342729 _hlk142337969 _hlk142337867 _hlk142342461 _hlk142342928 _hlk142338077 _hlk142348528 _hlk164779807 _hlk125467744 _hlk141790649 assessing the bioeconomy’s contribution to evidence-based policy: a comparative analysis of value added measurements tévécia ronzon1,2,*, patricia gurria2, michael carus3, kutay cingiz2, andrea el-meligi1, nicolas hark3, susanne iost4, robert m’barek1, george philippidis5, myrna van leeuwen6, justus wesseler2 predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology lisa baldi1, sara calzolai2, filippo arfini2,*, michele donati1 simulating farm structural change dynamics in thessaly (greece) using a recursive programming model stamatis mantziaris1,*, stelios rozakis2, pavlos karanikolas1, athanasios petsakos3, konstantinos tsiboukas1 analyzing the impact of government subsidies on household welfare during economic shocks: a case study of iran mohammad dehghan1,*, seyyed nematollah moosavi2*, ebrahim zare3 crop production, the pollinator deficit and land use management: uk farm level survey results iain fraser1,*, michelle t. fountain2, john m. holland3 1 farmers, experts and students’ subjective probability distributions on methane emission 1 reductions in livestock farming: an experimental comparison across elicitation methods 2 claudia magnaperaa,c,1, austeja kazemekaitytea, roberta raffaellia, simone cerronia,b 3 adepartment of economics and management, university of trento, trento, italy 4 bcentre agriculture food environment (c3a), university of trento, trento, italy 5 cinstitute of natural resource sciences (iunr), zurich university of applied sciences 6 (zhaw), wädenswil, switzerland 7 1 corresponding author, e-mail address: claudia.magnapera@unitn.it 8 this article has been accepted for publication and undergone full peer review but has not been 9 through the copyediting, typesetting, pagination and proofreading process, which may lead to 10 differences between this version and the version of record. 11 please cite this article as: 12 magnapera c., kazemekaityte a., raffaelli r., cerroni s. (2025). farmers, experts and 13 students’ subjective probability distributions on methane emission reductions in livestock 14 farming: an experimental comparison across elicitation methods. bio-based and applied 15 economics, just accepted. doi:10.36253/bae-17310 16 17 abstract 18 subjective probabilities are important determinants of economic choice behaviour, and their 19 elicitation is not trivial. different methods are available in the literature. this paper compares 20 three of them using an economic experiment with farmers, other experts (animal nutrition and 21 production scientists, vets, as well as animal feed, dairy, and meat company representatives), 22 and agricultural students: the frequency method (fm), the interval method (im), and the 23 quadratic scoring rule method (qsr). these methods vary in the degree of complexity and 24 saliency of the incentive scheme. elicited subjective probability distributions refer to methane 25 emissions reductions that can be achieved by changing animal diets in livestock farming. the 26 study investigates whether these methods produce consistent results across methods and 27 mailto:claudia.magnapera@unitn.it 2 participant groups. subjective probability distributions do not significantly differ across 28 methods and participant groups. overall, these results support that the use of less complex 29 methods such as the fm and im may be preferable. 30 31 introduction 32 33 subjective beliefs help in understanding economic agents’ behaviour, particularly in 34 contexts characterised by risk and uncertainty (manski, 2004; 2018), and the agri-food sector 35 is a fitting example (cerroni and rippo, 2023). eliciting stakeholders’ beliefs along the agri-36 food supply chain is essential to disentangle the role these beliefs play in their decision-making 37 processes (hardaker and lien, 2010). there is a vast literature showing that farmers’ beliefs 38 are correlated with farmers’ decisions in different contexts: contract farming (e.g., cerroni, 39 2020; cerroni et al., 2023), insurance uptake (e.g., arata et al., 2023; čop et al., 2023), risk 40 management (e.g., meraner and finger, 2014; van winsen et al., 2014), technology adoption 41 (e.g., purvis et al., 1995; tjernström et al., 2021), and adoption of sustainability practices (e.g., 42 trujillo-barrera et al., 2016; dessart et al., 2019). therefore, knowing farmers’ beliefs can help 43 policymakers designing information-based interventions that create awareness in the farming 44 community and facilitate a shift towards production patterns that maximize societal welfare 45 (manski, 2018; cerroni and rippo, 2023). 46 however, eliciting beliefs is not a trivial task. a wide array of methods has been used 47 in the literature, and there is no consensus on the methods that provide the most reliable 48 estimates in mainstream economics (e.g., trautmann and van de kuilen, 2015; charness et al., 49 2021). the literature eliciting beliefs in agricultural economics, and more specifically farmers’ 50 and experts’ beliefs, is limited and very few elicitation methods have been employed to retrieve 51 expectations (cerroni and rippo, 2023). 52 3 this paper contributes to this literature by comparing farmers’ and experts’ beliefs 53 elicited via three different methods, the frequency method (fm) (hurley and shogren, 2005), 54 the interval method (im) (dufwenberg and gneezy, 2000), and the quadratic scoring rule 55 (qsr) (brier, 1950) using a framed field experiment (harrison and list, 2004). a similar 56 exercise has been proposed on farmers’ risk preferences by reynaud and couture (2012). 57 most attempts to elicit farmers’ beliefs use likert scales that elicit qualitative judgments 58 regarding the probability and/or the magnitude of an event (e.g., meraner et al., 2019; feyisa 59 et al., 2023). despite the likert scales requiring a low cognitive effort from participants, 60 qualitative judgments cannot be incorporated into economic models of decision-making under 61 risk and uncertainty like the subjective expected utility model (seut, savage, 1954). more 62 sophisticated methods can be used to retrieve farmers’ beliefs expressed in a probabilistic 63 fashion. these methods can be paired with the use of monetary incentives (see cerroni et al., 64 2023 for a review), and, if properly designed, they allow eliciting subjective probability 65 distributions that can be incorporated in standard models of decision making under uncertainty 66 (e.g., manski 2004; cerroni et al., 2012). the literature on the elicitation of farmers' subjective 67 probability distributions is relatively limited, especially regarding direct comparison of 68 different incentivised methods (cerroni and rippo, 2023). 69 several incentive-compatible methods can elicit truthful subjective probability 70 distributions. some studies have explored whether different elicitation methods produce 71 different subjective probability distributions (trautmann and van de kuilen, 2015), and we are 72 not aware of any study that has compared farmers’ subjective probability distributions elicited 73 via different incentivized and incentive-compatible mechanisms while linking potential 74 differences to complexity and saliency of the incentive scheme. complexity refers to the 75 cognitive cost of the elicitation mechanism for participants (charness et al., 2021). saliency of 76 the incentive scheme refer to the potential influence that experimental payoffs have on subjects’ 77 4 actions in the experiment (charness et al., 2016; kleinlercher and stöckl, 2017). this study 78 aims to fill this methodological gap by specifically comparing the fm (hurley and shogren, 79 2005), im (dufwenberg and gneezy, 2000), and qsr (brier, 1950). qsr has been already 80 used to elicit farmers’ subjective probabilities distributions in the literature (e.g., cerroni, 2020; 81 cerroni et al., 2023; čop et al., 2023). while the fm and im were used as well (e.g., menapace 82 et al., 2013; čop et al., 2023), these methods were never paired with a proper monetary 83 incentive scheme. the fm asks participants to report how frequently different outcomes of a 84 random variable occur. if the reported frequency matches exactly the empirical frequency 85 (known a priori), participants receive a fixed prize x. the im is equivalent to the fm, except 86 that, in the im participants are paid a fixed prize x if the reported frequency approximately 87 matches the empirical frequency. this implies that the saliency of pay-off is higher in the im 88 than in the fm. according to charness et al. (2021) fm and im are less complex than qsr. 89 in particular, our study focuses on farmers and other experts’ beliefs about the potential 90 impact of adopting a new agricultural practice to improve the sustainability of food production. 91 specifically, it elicits dairy farmers and other experts’ beliefs about the methane reductions that 92 can be achieved when introducing specific feed additives (i.e., essential oils) to bovine diets. 93 other experts involved in our study are animal nutrition and production scientists, vets, as well 94 as animal feed, dairy, and meat company representatives. we also included a group of 95 postgraduate agricultural students (i.e. master-level), both as future sector participants and to 96 contribute to the ongoing debate on the use of student proxies in experiments. including 97 agricultural students provides an additional test of whether farmer and agricultural students 98 behaviour are consistent in economic experiments (e.g., grüner et al., 2022; höhler et al., 99 2024). some evidence finds that students and farmers can behave similarly in generic risk tasks 100 (e.g., grüner et al., 2022), whereas other work warns that lack of domain expertise may lead to 101 divergent responses (e.g., höhler et al., 2024). students offer practical advantages for 102 5 experiments due to their accessibility and learning capacity (grüner, 2022), but their 103 differences from farmers in age, income, and sector-specific experience may limit how well 104 their responses generalize to the farming population (e.g, belot et al., 2015). testing whether 105 student-elicited distributions align with those of practitioners therefore provides valuable 106 evidence on the external validity of experimental results in the agricultural context. 107 the emphasis on zootechnical feed additives, specifically essential oils, and their 108 impact on reducing methane emissions from livestock production stems from the fact that 109 livestock contributes 14.5% of the total greenhouse gas emissions (ghges) from 110 anthropogenic activities (fao, 2017). most livestock-related methane emissions are due to 111 enteric fermentation, and the use of essential oils in animal diets can help reduce the sector's 112 carbon footprint (hristov et al., 2013; honan et al., 2021). this aligns with eu policies, 113 including the eu green deal, the farm-to-fork strategy, and the planned revision of the 114 industrial emission directive, which are pushing livestock farmers to lower their carbon 115 footprint. consequently, farmers are encouraged to adopt innovations that support this goal 116 (block et al., 2024). the introduction of feed additives such as essential oils is a readily 117 available and cost-effective option to reduce ghges at the farm level (belanche et al., 2020). 118 119 in our experiment, each participant provides a subjective probability distribution three 120 times, each time facing a different elicitation mechanism. the order of exposure is randomized. 121 results show that farmers and other experts’ subjective probability distributions do not differ 122 across methods, suggesting that it is preferable to implement simpler methods over complex 123 ones. farmers, other experts, and students’ subjective probability distributions are similar. 124 this paper is structured as follows: we start with a review of the elicitation of beliefs in 125 the agricultural sector and a description of the three methods employed in this study. next, we 126 6 present the empirical application, describe our sample, the experimental design, and the main 127 findings. finally, we discuss the implications of these findings. 128 1. literature review 129 1.1. elicitation of farmers’ subjective probability distributions 130 farmers’ probabilistic beliefs can be elicited using many different approaches. most 131 studies elicit probabilistic qualitative judgments about the occurrence of given events using 132 likert scales. among others, events refer to climatic adverse events, loss in production or 133 income, and efficacy of an innovative risk-reducing technology or insurance product in 134 reducing production or income losses (see meraner et al., 2019; feyisa et al., 2023; villacis et 135 al., 2023). a typical question would be asking how likely an x% loss in production due to 136 climatic events is on a five-point scale from 1 (very unlikely) to 5 (very likely). probabilistic 137 qualitative judgments are relatively easy to elicit as they require a relatively low design effort 138 from a researcher’s perspective and a relatively low cognitive effort from a participant’s 139 perspective (e.g., delavande et al., 2011). however, these strengths are counterbalanced by 140 some limitations. likert scales are not incentive-compatible, meaning that they do not induce 141 respondents to truthfully reveal their beliefs. in addition, probabilistic qualitative judgments do 142 not facilitate interand intra-personal comparisons (e.g. manski, 2004). finally, they cannot be 143 incorporated into decision making models of decision making under risk and uncertainty (e.g. 144 cerroni et al., 2012). 145 a useful way to circumvent some of these disadvantages is the use of direct elicitation 146 methods (or direct introspection). participants are directly asked to state the probability of an 147 event to occur. if several direct questions are asked about different states of the world, it is 148 possible to map the entire probability distribution of an outcome variable. this approach has 149 been widely used to elicit farmers’ subjective probability in the literature in both developing 150 and developed countries (see hardaker and lien, 2010; cerroni, 2020; cerroni and rippo, 2023 151 7 for reviews). direct methods are generally paired with the use of visual aids that facilitate 152 farmers’ understanding of the elicitation task. an example would be asking farmers to allocate 153 a given number of tokens (generally 100) among different states of the world that may 154 characterize a given outcome variable, suggesting that each token is equivalent to a probability 155 point. čop et al. (2023) and fezzi et al. (2021) used this approach to elicit farmers’ subjective 156 probability distributions regarding farm income losses in croatia and regarding loss in 157 production due to extreme climatic events in italy. direct methods are widely used to elicit 158 subjective probability distributions in developing countries (delavande et al., 2011; cerroni 159 and rippo, 2023). while direct introspection is relatively straightforward to implement for 160 researchers, it might be cognitively demanding and challenging for respondents who are not 161 always able and willing to express the likelihood of events in a probabilistic way (manski, 162 2004). new web interfaces were recently created to facilitate farmers’ understanding of direct 163 elicitation methods (see crosetto and de haan, 2023). direct methods are not generally 164 incentive-compatible; hence, they do not overcome the issue of the potential of misreported 165 probabilities (trautmann and van de kuilen, 2015; cerroni, 2020; charness et al., 2021). 166 to overcome the latter issue and elicit truthful beliefs, it is possible to use indirect 167 methods. if associated with a proper incentive scheme, these methods are incentive compatible. 168 these methods ask respondents to make choices between lotteries and allow retrieving 169 subjective probabilities distributions based on respondents’ gambling behaviour. for a 170 comprehensive review of these methods, we refer to cerroni and rippo (2023). to date, only 171 a very few studies have used these methods to elicit farmer’s subjective probability 172 distributions. all these studies have used proper scoring rules. linear scoring rules have been 173 used by grisley and kellong (1983) and smith and mandac (1995) in developing countries. 174 quadratic scoring rules have been used by cerroni (2020) in scotland, cerroni et al. (2023) in 175 zimbabwe, and čop et al. (2023) in croatia. these methods are not free from criticism. a 176 8 recent study by danz et al. (2022) argued that individuals’ actual behaviour in response to 177 incentives may lead respondents to deviate from truth-telling when eliciting beliefs. 178 1.2. comparing methods for eliciting subjective probabilities distributions 179 reliable subjective probability distributions are often needed in economic modelling to 180 explain or predict human behaviour. hence, it is essential to have reliable measures of 181 individuals’ subjective beliefs. several methods are available and choosing the right one for 182 eliciting truthful beliefs is a relevant issue (charness et al., 2021). the reliability of elicited 183 subjective probability distributions is contingent upon the methodology employed and the 184 participants’ comprehension of it. 185 charness et al. (2021) ranked the methods available to elicit beliefs according to a scale 186 based on complexity, which is the subjective burden a rule places on a decision-maker (oprea, 187 2020). in our study, complexity pertains to the extent of the understanding required to generate 188 utility-maximising belief reports. some methods, such as the fm and the im, have a low degree 189 of complexity. in contrast, some others, such as qsr, entail a higher degree of complexity and 190 may not be fully understood by participants at the cost of truthful belief reports. 191 to the best of our knowledge, there is limited research that compares complex methods 192 to simpler ones, and this paper aims to fill the gap. trautmann and van de kuilen (2015) 193 compared subjective probabilities related to whether a participant accepted or rejected an 194 allocation submitted by a proposer in a simple two-player ultimatum game using different 195 elicitation techniques. specifically, they tested non-incentivised direct method (i.e., direct 196 introspection) against incentivised and incentive compatible methods such as outcome-197 matching, probability matching, and qsr with or without corrections for deviations from risk 198 neutrality. internal validity was investigated by testing for additivity and consistency of players’ 199 beliefs with their allocation decisions during the game, while external validity was investigated 200 9 by testing whether elicited beliefs matched objective probability measured at the end of the 201 game (i.e. accuracy). results suggest that incentivised and incentive-compatible methods 202 predict better respondents’ behaviour during the game than non-incentivised introspection 203 without necessarily having better performances in terms of additivity and accuracy. schlag et 204 al. (2015) and charness et al. (2021) discuss the strengths and limitations of different elicitation 205 mechanisms and conclude that more research in this direction is needed. for a comprehensive 206 discussion of the most suitable methods to elicit subjective probability distributions for 207 agricultural research, which particularly focuses on developing countries, we suggest reading 208 cerroni and rippo (2023). we contribute to this literature by comparing three subjective 209 probability distribution methods that differ in terms of complexity, and saliency of the incentive 210 scheme. 211 212 1.3. frequency method (fm), interval method (im), and quadratic scoring rules (qsr) 213 the fm is among the least complex methods. participants are asked to guess the 214 empirical frequency of a random variable, either when the random variable has two possible 215 outcomes (hurley and shogren, 2005) or multiple ones (schlag and tremewan, 2021). in both 216 cases, participants are rewarded with a fixed prize x if and only if their reported frequencies 217 match exactly the empirical frequencies. this rewarding mechanism can be applied either when 218 empirical frequencies are observable or not yet observable. in the latter case, a few options to 219 match unobservable frequencies would be by asking experts about it, or by using forecasts 220 based on historical data. the main advantage of the method is that it is very easy to implement 221 and easy to understand, in fact, it does not require any mathematical calculations, and natural 222 frequencies are processed better by individuals than other formats (e.g., numbers, percentages) 223 (schlag and tremewan, 2021). these characteristics (i.e., better understanding, fast 224 responding, and less likelihood of choosing focal points) make the frequency method fm 225 10 theoretically robust to deviations from risk neutrality and therefore more likely to be 226 empirically incentive compatible (schlag and tremewan, 2021). nevertheless, when faced with 227 situations where there are many possible outcomes and unfamiliar topics, people may feel that 228 their chances of achieving a reward are low because they must make correct predictions for 229 each potential outcome. as a result, they may tend to misreport their beliefs, as they perceive 230 the likelihood of being rewarded is tied to accurate predictions. 231 this major disadvantage of the fm can be alleviated by the im, which is de facto a 232 variation of the latter. it is still ranked as a simple method. yet, it mitigates the low likelihood 233 of being rewarded by allowing participants to guess correctly when their reported frequencies 234 are approximately equal to the underlying ones. the primary challenge is in establishing an 235 appropriate margin of error to not incentivize underand over-reporting. while this approach 236 significantly reduces complexity by retaining similar characteristics to the previous method, 237 the literature reflects a scarcity of applications utilising the im (e.g., dufwenberg and gneezy, 238 2000; charness and dufwenberg, 2006). a similar approach is eliciting beliefs using most-239 likely intervals (mli) (schlag and van der weele, 2015). the interval method aims at providing 240 a probability distribution within a certain percentage range of the underlying distribution, while 241 mli consists of specifying an interval that contains the most likely outcomes. this specification 242 changes how the reward is calculated. while in the im, participants are rewarded based on how 243 well their entire probability distribution aligns with the underlying probability distribution 244 within a certain margin of error, in the mli participants are typically rewarded based on the 245 width of the interval they provide, with narrower intervals being rewarded more favourably. 246 moving to more complex methods, qsr is a frequently used incentive-compatible 247 method to elicit beliefs, which first appeared in brier (1950). it is a specification of the family 248 of the proper scoring rules, which assign a numerical score to the individuals’ probabilistic 249 predictions regarding future outcomes, rewarding them for the accuracy of their reports, while 250 11 having severe punishments if they miss the true allocation in that interval (harrison et al., 251 2017). the payoff score is defined as 252 𝑆 = (2 × 𝑟𝑘) − ∑ (𝑟𝑖) 2 𝑖=1,..,𝐾 (eq .1) 253 where 𝐾 corresponds to the number of intervals, 𝑟𝑘 are the reports of the likelihood that the 254 event (i.e., methane reduction) falls in the interval k = 1, … , k. each 𝑟𝑘 ≥ 0 , ∀𝑘 and 255 ∑ (𝑟𝑖)𝑖=1,..,𝐾 = 1 (matheson and winkler, 1976). the method is incentive-compatible under the 256 assumption of risk neutrality and expected utility maximisation (kadane and wrinkler, 1988; 257 schlag and van der weele, 2015; winkler and murphy, 1970). under these assumptions, an 258 individual who is sufficiently risk averse would be drawn to report a probability distribution 259 close to a uniform one, or report probabilities equal to 50% in the case of binary events 260 (andersen et al., 2014). it is good practice to accompany the task with measurements of 261 individuals’ risk attitudes, to control for the initial assumption of risk neutrality (andersen et 262 al., 2014; offerman et al., 2009). however, harrison et al. (2017) demonstrated that having a 263 large enough set of intervals (i.e. states of the world k) allows a reliable elicitation of subjective 264 probability distributions without undertaking calibration for risk attitudes. qsr is classified as 265 a complex elicitation method by charness et al. (2021). participants may have difficulties in 266 understanding the multiple layering of the qsr, which, accompanied by a substantial aversion 267 to complexity (oprea, 2020), might impair truthful reports. 268 269 12 2. empirical application 270 the global methane pledge at a global level, the eu green deal, the farm to fork 271 strategy, and the eu methane strategy at a european level, are among the variety of initiatives 272 currently underway to mitigate the significant climate impact of methane (ch4) from one of its 273 predominant sources, that is the agricultural sector. mitigating methane emissions would allow 274 for short-term effects on the climate, due to the evidence that ch4 has about 82 times the 275 climate-changing impact of carbon dioxide (co2) over 20 years (smith et al., 2021). in the 276 agricultural sector, animal production is a significant contributor, particularly through 277 ruminants’ enteric fermentation, which alone accounted for 69% of ch4 emissions within the 278 sector at the european level (eea, 2020). 279 there is a vast range of mitigation technologies available in agriculture, such as 280 production intensification, dietary and rumen manipulation, and selection of low-ch4-281 producing animals (beauchemin et al., 2022). some of these may be available at low cost and 282 bring additional benefits for farms, but barriers such as insufficient knowledge and expertise 283 and lack of financial incentives (long et al., 2016) need to be addressed since their uptake is 284 uneven across the european union. 285 different methane-reducing technologies have different potential for mitigation (arndt 286 et al., 2022). in our empirical application, we consider the wide class of ch4-reducing essential 287 oils that can be categorised as zootechnical feed additives for animal diets. including essential 288 oils in bovine feed is a practice that is effective in the short term, and studies suggest it is 289 leading to a positive impact on feed efficiency and hence milk production (wells, 2024). 290 essential oils, derived through a steam distillation process of various parts of plant species, act 291 as rumen modifiers and can cause an inhibition of the methanogenesis process in ruminants 292 (glasson, 2022). many studies show that, even though there is a substantial reduction in enteric 293 ch4, the results are variable (honan et al., 2021; hristov et al., 2013). arndt et al. (2022) built 294 13 a database reporting the effects of various mitigation strategies, including 115 studies on 295 additives. feed additives, such as essential oils, have a mean value of the reduction in daily 296 ch4 emissions of -8.3%, with a 95% confidence interval (-9.8%, -6.8%). the variation depends 297 on several factors such as the environment, the animal’s diet and health, and others. 298 3. methods 299 3.1. sample characteristics and data collection 300 the sample consists of 60 individuals, evenly distributed between two groups: 20 dairy 301 farmers from various italian regions, 10 livestock experts, and 30 master’s students from 302 various agri-food faculties.1 for simplicity, the group of farmers and experts is going to be 303 called the “dairy sector” throughout the paper. the sample was recruited using a snowball 304 sampling method, a non-probabilistic approach where initial participants were asked to refer 305 others who fit the study criteria. data was gathered during the summer and the autumn of 2023 306 primarily through one-to-one with farmers and experts or group experimental sessions with 307 students on the zoom platform.2 participants were asked to report their probability on the web 308 interface generated via the software o-tree (chen et al., 2016). ethical clearance was granted 309 by the ethical review board at the university of trento (italy).3 310 311 1the 10 experts consulted are: 3 professionals from a public unit for forage resources and livestock production (including a technologist, a technician, and a department head), 2 veterinarians, 2 experts in animal feed development and commercialization, 1 head of a dairy consortium, 1 food processing technologist, and 1 agronomist. 210 sessions in total (4 participants in session 1, 3 participants in session 2, 3 participants in session 3, 3 participants in session 4, 1 participant in session 5, 6 participants in session 6, 1 participant in session 7, 4 participants in session 8, 4 participants in session 9, 1 participant in session 10). while the procedures and instructions were identical across settings, we acknowledge different settings might have introduced subtle differences, resulting in a minor limitation of the study design. 3we did not collect demographic data, as the primary aim was to compare elicitation methods. this was intended to reduce respondent burden and keep participants focused on the tasks. 14 3.2. experimental design 312 in this study, farmers, experts, and students were asked to report their subjective 313 probability distribution regarding the reduction (in percentage value) of enteric methane 314 emissions which could be achieved using essential oils in bovine diets in a dairy farm across 315 three consecutive tasks. specifically, each participant completed the same task three times, each 316 time under different elicitation methods: fm, im, and qsr. the only difference among the 317 three tasks was how the monetary reward was attributed. to control for order effects, the order 318 of the task was randomized. participants were divided into two groups. specifically, one group 319 was exposed to tasks in the following order: i) fm, ii) im, and iii) qsr, and the other group as 320 follows: i) qsr, ii) fm, and iii) im. the fm was always presented before the im to facilitate 321 the understanding of the im, which is a variation of the fm. 322 before the elicitation of the subjective probability distributions, participants were 323 provided with the following standardised information: i) a description of policy initiatives 324 aiming at reducing methane emissions from the agricultural sector, ii) a description of strategies 325 to reduce methane emissions in dairy farming with a focus on essential oils, and iii) a 326 description of benefit and opportunities of using essential oils. experimental instructions are 327 available in appendix a. 328 regardless of the elicitation mechanism, participants were informed that the reduction 329 in methane emissions can fall in any percentage value between -6.6% and -10% given the 330 information retrieved from the literature (arndt et al., 2022). using the mean and confidence 331 interval reported in arndt et al. (2022), we approximated an empirical benchmark distribution 332 by fitting a normal distribution, against which participants’ subjective probabilities were 333 compared. the normal distribution can be a good approximation because it provides a 334 continuous, two‐parameter reference distribution for comparing participants’ subjective 335 probabilities without further assumptions. participants were instructed to allocate 70 tokens 336 15 across seven predefined intervals each representing a range of methane emission reduction 337 percentages using the interface depicted in figure 1. these intervals included the following 338 percentages of reduction: (-10%, -9.6%), (-9.5%, -9.1%), (-9%, -8.6%), (-8.5%, -8.1%), (-8%, 339 -7.6%), (-7.5%, -7.1%), and (-7%, -6.6%). they were informed that the number of tokens 340 allocated to each interval should reflect their perceived likelihood of the corresponding methane 341 emission reduction occurring in the future. a higher allocation of tokens to a particular interval 342 indicates a higher subjective likelihood of that reduction range according to the participant’s 343 beliefs. 344 345 346 figure 1. example of the interface used in the economic experiment. 347 348 the experiment was incentivised. participants did not receive any participation fee, but 349 they could get a reward of up to €25, knowing that, at the end of the session, one of the three 350 tasks would have been randomly extracted and used to determine their final payoff. each 351 elicitation method has a different incentivisation scheme. the fm pays €25 if and only if the 352 probability distribution matches exactly the empirical distribution, otherwise it pays €0. 353 matching exactly meant that the number of tokens allocated by the participant perfectly 354 corresponded to the probabilities defined by the empirical distribution. the im pays €25 if and 355 only if the subjective probability distribution matches the empirical distribution with an error 356 margin equal to 10% in each interval, otherwise it pays €0. the payment scheme of the qsr is 357 completely different as the qsr allows for a more continuous spectrum of payoffs as explained 358 in eq. 1. 359 16 4. results and discussion 360 4.1. comparisons across elicitation methods 361 figure 2 shows the subjective probability distributions across elicitation methods 362 when the sample is pooled (dairy sector plus students). 363 364 365 figure 2 within-group average subjective probability distributions per task 366 367 we test differences in subjective probability distributions across elicitation methods 368 using the wilcoxon signed-rank test for paired samples.4 results suggest no statistically 369 significant differences across methods when the sample is pooled (dairy sector plus students). 370 results hold when we estimate a generalized linear model with an underlying binomial 371 distribution for our dependent variable and a logistic link function a la papke and woolridge 372 4results from the non-parametric tests are available in table 4 in the appendix b. 17 (1996) (see table 1). this modelling approach is particularly well suited when the state space 373 of the dependent variable ranges from 0 to 1 as is the case in our study (e.g. cerroni et al., 374 2012). our dependent variable 𝑝𝑖,𝑘 is the probability associated with each participant 𝑖 to each 375 interval 𝑘 (i.e. state of the world) shown in figure 2.5 our discrete independent variables are 376 associated with the elicitation mechanism: 𝐹𝑀𝑘 equals 1 if the probability was elicited using 377 the fm, and 𝐼𝑀𝑘 equals 1 if the probability was elicited using the im. the variable 𝑄𝑆𝑅𝑘 is 378 used as a baseline and equals 1 if the probability was elicited using the qsr.6 standard errors 379 are clustered at the individual level. the model is specified as follows: 380 381 𝑃𝑖,𝑘 = 𝛼 + 𝛽𝐹𝑀𝐹𝑀𝑖 ,𝑘 + 𝛽𝐼𝑀𝐼𝑀𝑖 ,𝑘+ 𝜀𝑖,𝑘 (eq. 2) 382 383 table 1. effect of elicitation methods on subjective probabilities (for each interval) dep. var.: (𝑃𝑖,𝑘 ) 1 2 3 4 5 6 7 fm 0.255 0.157 0.012 -0.130 0.294 0.156 -0.355 (0.619) (0.554) (0.486) (0.499) (0.500) (0.531) (0.518) im 0.053 0.130 0.144 0.083 0.061 -0.011 -0.477 (0.643) (0.557) (0.476) (0.481) (0.518) (0.516) (0.532) constant -2.358*** -2.031*** -1.592*** -1.596*** -1.803*** -1.757*** -1.576*** (0.459) (0.403) (0.344) (0.345) (0.370) (0.364) (0.343) observations 180 180 180 180 180 180 180 ll -33.676 -28.225 -39.625 -34.450 -37.143 -36.065 -51.568 note: *p<0.1; **p<0.05; ***p<0.01 384 ll stands for log-likelihood 385 robust standard error in brackets 386 387 388 389 5in our study, seven possible states of the world were introduced. 6qsr serves as the baseline category, and thus it is not explicitly included as a variable in the equation. 18 these results suggest that different methods elicit equivalent subjective probability 390 distributions. we can conclude that less complex (i.e. cognitively demanding) elicitation 391 methods (fm and im) are performing equivalently to a complex elicitation method such as the 392 qsr. subjective probability distributions are stable regardless of the use of completely 393 different incentive schemes (fm and im vs qsr). in addition, the difference in the saliency of 394 payoffs between fm and im does not produce any significant effect on the elicited subjective 395 probability distributions. therefore, we conclude that the use of less cognitively demanding 396 techniques like im and if might be a viable approach for the elicitation of subjective probability 397 distributions. we acknowledge that failure to detect differences across methods within subjects 398 may be because subjects tend to confirm their beliefs over time, showing a sort of confirmatory 399 bias (e.g., rabin and schrag, 1999; zappalà, 2023). we evaluated the presence of confirmatory 400 bias by taking advantage of the task randomization. more specifically, we are comparing 401 subjective probability distributions elicited via the fm and the qsr when these were the first 402 methods faced by our participants. this between-subject analysis rules out the presence of 403 confirmatory bias. results from the kolmogorov-smirnov test suggest that there is no 404 difference in subjective probabilities across groups, indicating that subjective probability 405 distributions are similar across methods even when confirmatory bias does not play any role.7 406 results hold when estimating generalised linear models (eq. 3) (see table 2), as above. in this 407 case 𝐹𝑀_𝑓𝑖𝑟𝑠𝑡𝑘 equals 1 if the probability was elicited using the fm as a first task, and 408 𝑄𝑆𝑅_𝑓𝑖𝑟𝑠𝑡𝑘 equals 1 if the probability was elicited using the qsr as the first task. the latter 409 is used as a baseline. 410 411 𝑃𝑖,𝑘 = 𝛼 + 𝛽𝐹𝑀𝑓𝑖𝑟𝑠𝑡𝑘 𝐹𝑀𝑓𝑖𝑟𝑠𝑡𝑘 + 𝜀𝑖,𝑘 (eq. 3) 412 413 414 7results from the non-parametric tests are available in the appendix b in table 5. 19 table 2. effect of confirmatory bias on subjective probabilities (for each interval) dep. var.: (𝑃𝑖,𝑘 ) 1 2 3 4 5 6 7 fm_first -0.308 -0.589 0.313 0.459 0.112 0.056 -0.286 (0.509) (0.451) (0.406) (0.423) (0.418) (0.437) (0.435) constant -2.080*** -1.622*** -1.729*** -1.893*** -1.746*** -1.844*** -1.678*** (0.368) (0.311) (0.323) (0.342) (0.325) (0.336) (0.317) observations 180 180 180 180 180 180 180 ll -33.843 -27.684 -39.304 -34.388 -37.762 -36.414 -51.831 note: *p<0.1; **p<0.05; ***p<0.01 415 ll stand for log-likelihood 416 robust standard error in brackets 417 418 419 420 4.2. comparisons across groups 421 figure 3 and figure 4 show subjective probability distributions across elicitation 422 methods for the dairy sector (farmers plus experts) and students, respectively. these provide 423 insights into probabilistic beliefs of dairy farmers, experts, and students across different 424 outcomes. it is noticeable that, overall, the probability mass is concentrated in the three central 425 intervals, suggesting that these groups generally expect moderate to average outcomes of future 426 reduction in methane emissions in dairy farming. this is a typical stance when evidence or 427 confidence in the topic is neither very strong nor very weak. however, it is interesting to note 428 that students assign a moderate probability (around 10%) to the scenario with the lowest 429 reduction in methane emissions. this probability rises to approximately 15% and 20% when 430 assessed by farmers and experts, indicating a degree of scepticism about the efficacy of 431 essential oils in reducing methane emissions, and signalling a need for further research on the 432 topic. 433 434 20 435 436 437 figure 3. dairy sector’s average subjective probability distributions per task 438 439 440 441 figure 4. students’ average subjective probability distributions per task 442 443 444 21 differences in subjective probability distributions across groups (dairy sector vs 445 students) were assessed using the kolmogorov-smirnov test for the between-subjects 446 comparison.8 results indicate that there are no significant differences in token allocation across 447 groups. equivalent results are obtained when we estimate a generalized linear model (binomial 448 distribution and logistic link function as above) where the dependent variable 𝑝𝑖,𝑘 is the 449 probability associated with each participant 𝑖 to each interval 𝑘 (i.e., state of the world) shown 450 in figure 1 (see table 3). our discrete independent variable is 𝐷𝑎𝑖𝑟𝑦_𝑆𝑒𝑐𝑡𝑜𝑟𝑘 that equals 1 if 451 the probability was elicited from a farmer or an expert (otherwise = 0), while students were 452 used as a baseline. standard errors are clustered at the individual level. the model is specified 453 as follows: 454 455 𝑃𝑖,𝑘 = 𝛼 + 𝛽𝐷𝑎𝑖𝑟𝑦_𝑆𝑒𝑐𝑡𝑜𝑟𝐷𝑎𝑖𝑟𝑦_𝑆𝑒𝑐𝑡𝑜𝑟𝑖,𝑘 + 𝜀𝑖,𝑘 (eq. 4) 456 457 table 3. effect of being a farmer or expert (dairy sector) on subjective probabilities (for each interval) dep. var.: (𝑃𝑖,𝑘 ) 1 2 3 4 5 6 7 dairy sector -0.157 -0.339 0.210 0.106 -0.537 0.179 0.461 (0.509) (0.453) (0.392) (0.400) (0.419) (0.430) (0.441) constant -2.175*** -1.775*** -1.648*** -1.663*** -1.436*** -1.904*** -2.088*** (0.348) (0.299) (0.286) (0.288) (0.268) (0.314) (0.336) observations 180 180 180 180 180 180 180 ll -33.884 -27.904 -39.335 -34.399 -37.208 -36.064 -51.289 note: *p<0.1; **p<0.05; ***p<0.01 458 ll stands for log-likelihood 459 robust standard error in brackets 460 461 462 8results from the non-parametric tests are available in table 6 in the appendix b. 22 these results may be driven by the fact that agricultural students are very aware of 463 climate change mitigation strategies that can be implemented at the farm level and hence they 464 are as knowledgeable and aware as farmers and experts regarding the specific topic under 465 investigation. on the other hand, it might be possible that, given the novelty of the topic and 466 the high degree of uncertainty related to the efficacy of introducing feed additives to reduce 467 methane emissions from livestock production, both students, farmers and experts have little 468 knowledge and awareness of the problem. 469 470 5. conclusion 471 this study compares three indirect methods —fm, im, and qsr—to elicit farmers’, 472 other experts’ and students’ subjective probability distributions about a specific agricultural 473 outcome using a framed economic experiment. the outcome variable considered is the methane 474 emission reduction which can be achieved by the introduction of zootechnical feed additives to 475 bovine diets. feed additives, more specifically essential oils, are readily available and cost-476 effective solutions to reduce the carbon footprint of animal production (belanche et al., 2020). 477 as the eu is designing policies that require farmers to reduce their ghges (e.g., the green 478 deal, the farm-to-fork strategy, and the industrial emission directive), understanding farmers’ 479 and other stakeholders’ beliefs regarding the effectiveness of ghge-reducing innovations can 480 help anticipate adoption behaviour. 481 the three elicitation methods differ in two main features: the level of complexity (i.e., 482 the cognitive cost of the elicitation mechanism for participants) and the saliency of the incentive 483 scheme (i.e., the ability of the incentive scheme to alter behavior). following charness et al. 484 (2021), we classify the qsr as complex, and the fm and im as simple. similarly, the im and 485 the qsr have more salient incentive schemes than the fm. the qsr has been recently used to 486 elicit farmers’ subjective probability distributions using proper incentive schemes (cerroni et 487 23 al., 2020; cerroni et al., 2023; čop et al., 2023), while the fm and im have been applied without 488 incentivization in earlier studies (e.g., menapace et al., 2013; čop et al., 2023). 489 in our study, each participant was asked to provide a subjective probability distribution 490 when exposed to these three different methods. the order of the tasks was randomized to 491 mitigate order effects. our sample consists of farmers, other experts (i.e., animal nutrition and 492 production scientists, veterinaries as well as feed, dairy, and meat company representatives), 493 and postgraduate agricultural students. our results indicate that, on average, the subjective 494 probability distributions elicited using the three methods do not differ significantly, implying a 495 consistent elicitation through tasks. this leads us to suggest that less complex elicitation 496 methods (fm and im) may be preferable when feasible in experimental studies. no difference 497 has been highlighted between groups. in fact, subjective probability distributions were elicited 498 consistently across the dairy sector (i.e., farmers and experts), and agricultural students. dairy 499 farmers, experts, and students generally expect moderate to average reductions in methane 500 emissions from essential oils. however, while students see a moderate chance of achieving the 501 lowest methane reduction possible, dairy farmers and experts are more sceptical, highlighting 502 the need for further research on the efficacy of essential oils. 503 little variation across groups may suggest that agricultural students are as 504 knowledgeable as experts and farmers about reductions in methane emission that can be 505 achieved using essential oils. nevertheless, an alternative explanation is that, given the high 506 degree of uncertainty surrounding the outcome variable, both the dairy sector (farmers and 507 experts) and students form similar uncertain beliefs. 508 in this regard, as the exercise focused on a highly uncertain outcome, that is methane 509 reductions achievable through the use of essential oils, about which even experts hold wide-510 ranging opinions, it remains an open question whether method performance would remain 511 24 equivalent when eliciting beliefs about more familiar or less ambiguous outcomes (for example, 512 crop yields or weather events). 513 although our primary objective was methodological (comparing fm, im, and qsr) 514 rather than producing broadly representative estimates, the modest sample size may still cast 515 doubt on external validity. to reinforce and extend these findings, future research should 516 replicate the experiment with larger, stratified samples spanning different regions, crop 517 systems, and farm structures. 518 in practical terms, our results offer clear guidance for researchers in agricultural and 519 environmental economics: simpler, low-burden elicitation methods can be as effective as 520 complex scoring rules, easing implementation without compromising data quality. to build on 521 this work, future research should (1) replicate the comparison across different domains of 522 uncertainty, (2) employ larger, stratified samples to bolster statistical power, and (3) explore 523 alternative interfaces or incentive schemes. such extensions will help sharpen our 524 understanding of how methodological choices shape the elicitation of subjective probabilities 525 in decision-making under uncertainty. 526 while this study is primarily methodological, understanding how farmers and other 527 stakeholders form beliefs may indirectly support the design of information-based interventions. 528 for instance, clearer communication (e.g., informational workshops, webinars or farmer-529 friendly explanatory materials) or demonstration activities (such as on-farm trials where 530 farmers can directly observe outcomes) could help reduce scepticism toward innovations such 531 as feed additives. nevertheless, such applications fall beyond the scope of this study and would 532 require further targeted research. 533 references 534 535 andersen, s., fountain, j., harrison, g. w., et al. 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(2025). agritech policy landscape: insights from 5 relevant stakeholders on policy issues and strategic plans in italy, bio-based and applied 6 economics, just accepted. doi:10.36253/bae-17356 7 abstract 8 agricultural practices face growing challenges, including climate change, resource 9 constraints, meeting sustainability goals and food security. this study examines 10 stakeholder perspectives on smart farming technologies and their integration into policy 11 frameworks. a mixed-method approach, using triangulation of qualitative and quantitative 12 data, combines an online survey (targeting experts from academia, industry, and 13 policymaking) distributed through the agritech project network and face-to-face 14 interviews (engaging key stakeholders with in-depth knowledge of agricultural policy and 15 technology implementation). key findings reveal significant optimism about the potential 16 of smart technologies to enhance efficiency, sustainability, and productivity in agriculture. 17 however, widespread adoption is hindered by barriers such as high initial investment costs 18 mailto:ahmed.moussaoui2@unibo.it and a lack of technical knowledge. the study identifies policy gaps and provides actionable 19 recommendations, including financial incentives, capacity-building initiatives, and 20 improved infrastructure, to support the integration of these technologies. the findings 21 underscore the critical need for adaptive policies that align with the evolving landscape of 22 agricultural innovation, ensuring equitable access and long-term sustainability. 23 keywords 24 agritech, technology adoption, european agricultural policy, sustainability, stakeholders’ 25 perspectives. 26 jel codes : q16, q18 1. introduction 27 the global agricultural sector faces increasing challenges in balancing productivity, 28 sustainability, and environmental responsibility. climate change and resource constraints 29 are putting increasing pressure on agricultural systems, whereas food security remains a 30 multifaceted challenge that goes beyond production. ensuring stable access to affordable, 31 nutritious food also depends on market structures, distribution networks, and social 32 inclusion (fao, 2021). while technological innovation can support more efficient and 33 sustainable production, it must be embedded within broader strategies that address 34 systemic barriers to food security (fao, 2021; ipcc, 2023). given the limitations of arable 35 land and the growing demand for sustainable food production, smart agriculture 36 technologies are gaining recognition as a key driver of transformation. these technologies, 37 encompassing sensor-based systems, iot configurations, ai applications, and renewable 38 energy solutions, offer advanced tools for precision farming, real-time monitoring, and 39 resource optimization (basso and antle, 2020; finger et al., 2019; knierim et al., 2019). 40 however, their adoption remains low and uneven despite their potential, primarily due to 41 high initial costs, limited technical knowledge, and inadequate infrastructure (akimowicz 42 et al., 2021). these barriers are particularly pronounced for small and medium-sized farms, 43 which often lack the necessary resources and institutional support to implement such 44 technologies effectively. 45 recent research by menozzi et al. (2023) also highlights that farmers’ decisions to engage 46 in sustainability practices are shaped not only by economic incentives but also by 47 behavioral drivers, such as perceived control and peer influence. in the case of digital 48 agriculture, these behavioral aspects, especially regarding trust in digital systems and ease 49 of use, are equally important and deserve policy attention. 50 complementing this view, giampietri et al. (2020) emphasize the role of trust in 51 intermediaries and institutional transparency in shaping farmers’ willingness to adopt 52 cap-subsidized risk management tools. while their study addresses instruments like 53 insurance and mutual funds, our work extends this behavioural framing to digital 54 agriculture, where trust also involves confidence in data systems and algorithm-based 55 decision-making. while these behavioral dynamics were not the primary focus of our 56 empirical study, they provide a valuable conceptual lens through which to interpret 57 stakeholder concerns around adoption. 58 a well-structured policy environment is critical in facilitating the adoption of smart 59 agriculture technologies. policies that support financial incentives, training programs, and 60 rural infrastructure development can significantly enhance accessibility and encourage 61 broader implementation among diverse farming operations (détang-dessendre et al., 62 2018). while existing frameworks, such as the common agricultural policy (cap), the 63 green deal, and the farm to fork strategy, emphasize the role of innovation in agricultural 64 sustainability, they exhibit notable gaps in addressing key adoption barriers. for instance, 65 the cap’s current funding mechanisms primarily benefit large-scale farms with greater 66 financial capacity, leaving smallholders with limited access to grants and subsidies 67 necessary for adopting high-cost digital technologies (lovec et al., 2020). additionally, 68 despite the green deal and farm to fork strategy highlighting the need for sustainable 69 agriculture, they fall short in prioritizing investments in rural digital connectivity, an 70 essential component for integrating smart technology, particularly in remote agricultural 71 regions (ehlers et al., 2022). there is a need for proactive and adaptive policy approaches 72 that address both financial and technical barriers while fostering stakeholder collaboration 73 and long-term sustainability. 74 this study aims to examine stakeholder perspectives on the adoption challenges and 75 opportunities of smart agriculture technologies and identify policy interventions that can 76 facilitate their broader integration. using a mixed-method approach, the research combines 77 qualitative interviews with key stakeholders and a quantitative online survey to gather 78 diverse insights on the policy landscape, adoption barriers, and potential solutions. the 79 analysis applies triangulation between the qualitative and quantitative findings to 80 strengthen the interpretation of results and ensure that policy recommendations are 81 grounded in multiple sources of evidence. the findings contribute to the existing literature 82 by bridging the gap between technological advancements and policy implementation, 83 providing evidence-based recommendations to enhance the diffusion of technology in 84 agriculture. 85 this study is part of the agritech project, a national research initiative funded by the italian 86 national recovery and resilience plan (pnrr) that brings together universities, research 87 institutions, and industry stakeholders to foster innovation in precision agriculture, ai, and 88 sustainable farming. conducted within spoke 3, which focuses on policy frameworks and 89 governance for smart agriculture adoption, this research builds on prior project activities 90 that mapped key actors in the innovation ecosystem and developed targeted engagement 91 strategies (agritech, 2023). the stakeholder database, created in the framework of the 92 project, enabled the distribution of our questionnaires through a trusted and well-informed 93 network, ensuring policy-relevant insights from diverse, experienced participants across 94 academia, industry, and policymaking. 95 the paper first describes the methodological framework, detailing the qualitative and 96 quantitative data collection and analysis approaches. it then presents key findings, 97 highlighting stakeholder perspectives on the benefits and challenges of smart agriculture 98 technologies. the discussion explores the broader implications for policy and practice, 99 focusing on the need for strategic policy interventions to overcome adoption barriers. 100 finally, the study concludes with recommendations for future research and actionable 101 policy measures to foster a more supportive environment for smart agriculture innovation. 102 2. methodology 103 2.1. overview 104 to comprehensively assess stakeholder perspectives on smart agriculture technologies, this 105 study employed a mixed-method approach, integrating qualitative and quantitative data 106 collection techniques. this methodological choice is well-suited for exploring complex 107 issues such as technology adoption in agriculture, as it allows for in-depth insights from 108 expert stakeholders while also capturing broader trends in the sector (creswell & clark, 109 2017; fielke et al., 2020). the combination of qualitative interviews and a structured online 110 survey aims to strengthen the study's analytical depth by triangulating stakeholder 111 perceptions across different backgrounds and levels of expertise. 112 given the exploratory aim of this research and considering the quantitative sample size, 113 the survey quantitative data primarily serve to identify general trends and perceptions 114 rather than provide statistically robust conclusions. this quantitative approach is 115 complemented by the qualitative interviews, which offer deeper, context-rich insights. by 116 combining both qualitative and quantitative data, we follow an established methodological 117 practice known as triangulation, enhancing the reliability and validity of our findings 118 through cross-verification (fetters et al., 2013). 119 the review of the existing literature revealed that previous research has often examined 120 technology adoption in agriculture from either a purely economic or behavioral 121 perspective. the focus of this study is to integrate policy dimensions and directly involve 122 stakeholders from multiple sectors, including academia, technology providers, policy 123 institutions, and farmers' associations. this holistic approach, which explicitly links 124 technological innovation with policy development, represents a novel contribution to the 125 existing body of literature. 126 the study focused on stakeholders in italy. while emilia-romagna, one of italy’s most 127 technologically advanced agricultural regions, was the starting point of the stakeholders’ 128 mapping, the survey distribution and interviews also involved participants from other key 129 agricultural areas such as puglia, lombardia, and veneto. this broader geographical 130 engagement allowed the research to capture a more representative view of the national 131 smart agriculture policy landscape. 132 both qualitative and quantitative components of the study shared a common core of 133 thematic focus, centering on: 134 • the barriers and drivers of smart agriculture technology adoption. 135 • the role of existing policies in shaping adoption trajectories. 136 • the perceived needs for policy innovation to facilitate broader uptake. 137 these dimensions were used both to frame the design of the survey and interviews and to 138 guide the interpretation of findings in the results and discussion sections. rather than 139 formal hypotheses, they function as thematic pillars for an exploratory investigation into 140 how policy, behavior, and technology interact in the current agricultural innovation 141 landscape. 142 this methodological design aims to ensure a holistic assessment of the policy landscape 143 surrounding smart agriculture technologies, while providing valuable insights for both 144 academic discourse and policy formulation. 145 2.2. qualitative data collection 146 the qualitative phase focused on gathering comprehensive insights from experts with 147 extensive knowledge of smart agriculture technologies and policies. it was essential to 148 understanding the barriers and opportunities surrounding the adoption of these 149 technologies. a semi-structured interview format was used to ensure a structured approach, 150 allowing for a mix of predefined questions and open-ended discussions. this approach 151 provided a comprehensive view of stakeholder experiences, enabling the identification of 152 key themes related to technology adoption and policy needs. 153 in-depth qualitative interviews were conducted with carefully selected experts in smart 154 agriculture technologies and policy. these interviews were designed to elicit rich, detailed 155 insights from highly experienced individuals. although the final sample comprised five (5) 156 participants, the decision to proceed with these interviews was taken based on the principle 157 of thematic saturation, that is, the point at which no substantially new insights emerge from 158 additional interviews (guest et al., 2006). given the specificity and expertise of our 159 respondents, the interviews provided consistent and robust information across key themes. 160 this approach aligns with accepted qualitative research standards, where small, 161 purposively selected samples are typical and appropriate for exploratory, expert-based 162 investigations (creswell, 2013). 163 the questionnaire was designed based on the agricultural knowledge and innovation 164 system (akis) framework, which highlights the importance of multi-actor collaboration 165 in agricultural innovation. it was structured into five main sections: (1) the respondent’s 166 background and expertise, (2) their perspectives on smart agriculture technologies, (3) 167 challenges related to adoption, (4) awareness and evaluation of current policies, and (5) 168 recommendations for improving policy support. this structured design ensured that 169 responses covered both technical and policy-related dimensions, making this phase a 170 crucial foundation for the overall study. 171 participants were selected through a purposive sampling approach, ensuring that only 172 individuals with significant expertise and direct involvement in the field were included. 173 the selection process was based on a stakeholder mapping exercise carried out earlier in 174 the agritech project. experts were identified from three key groups: public sector 175 representatives involved in agricultural policy, academic researchers specializing in 176 precision agriculture and rural policy, and industry professionals working with smart 177 agriculture technologies and farmer cooperatives. this targeted selection process ensured 178 a diverse yet highly relevant sample, strengthening the credibility of the findings. 179 interviews were carried out face-to-face whenever possible, allowing for detailed 180 discussions and clarifications. in cases where in-person meetings were not feasible, remote 181 interviews were held. five key experts participated in this qualitative survey. thematic and 182 textual analysis was used to process the responses, identifying recurring themes and key 183 insights. the results from this phase informed the refinement of the quantitative survey in 184 the next stage of data collection, ensuring that the study captured both broad trends and in-185 depth perspectives. 186 2.3. quantitative data collection 187 the second data collection phase involved an online questionnaire to capture broad 188 stakeholder perspectives on smart agriculture technologies, their adoption, perceived 189 benefits, policy awareness, and associated challenges. 190 this structured survey was designed to complement the qualitative insights gathered in the 191 first phase by providing quantifiable data to identify patterns and validate expert opinions. 192 the integration of both qualitative and quantitative methods was an attempt to ensure a 193 comprehensive and balanced understanding of the key factors influencing the adoption of 194 smart agriculture technologies. 195 the online questionnaire was adapted from the qualitative questionnaire, and structured 196 into multiple sections, each addressing a critical aspect of technology adoption and policy 197 implications. the first section focused on general respondent information, including their 198 professional background, sector of activity, and geographic location, allowing for an 199 analysis of how perspectives varied across different stakeholder groups. the second section 200 examined familiarity and involvement with smart agriculture technologies, prompting 201 respondents to indicate their level of knowledge and direct engagement with specific 202 technologies, such as robotics, iot, ai, renewable agri-systems, and spectral technologies. 203 the third section examined the perceived contributions of these technologies, evaluating 204 opinions on their potential to improve agricultural productivity, resource efficiency, 205 environmental sustainability, and labor optimization. 206 a key component of the questionnaire was its focus on policy awareness and barriers to 207 adoption. respondents were asked whether they were aware of existing policies that 208 support smart agriculture technologies, providing insights into the effectiveness of current 209 policy communication and identifying gaps where improved dissemination of information 210 might be needed. additionally, the survey investigated major obstacles preventing the 211 widespread adoption of these technologies, including financial constraints, technical 212 knowledge gaps, regulatory barriers, and infrastructure limitations. the final section 213 solicited policy recommendations, encouraging respondents to suggest changes to existing 214 policies or propose new policy instruments that could facilitate the integration of smart 215 agriculture technologies into mainstream agricultural practices. 216 the questionnaire was strategically distributed across multiple channels to ensure a high-217 quality and representative dataset. it was shared within the agritech project network, 218 reaching academics and researchers with expertise in agricultural policy, technology, and 219 innovation. it was also circulated among stakeholders from the previously established 220 project stakeholders’ network, including policymakers, industry representatives, farmers' 221 associations, and technology developers, potentially reaching over 90 persons. this 222 distribution strategy was designed to maximize diversity in respondent backgrounds while 223 maintaining a high level of expertise in the responses collected. 224 the sampling approach was purposive, targeting individuals with direct experience and 225 informed perspectives on adopting smart agriculture technologies. rather than aiming for 226 a large random sample, the focus was on obtaining high-quality responses from 227 knowledgeable stakeholders whose input could provide valuable insights into policy needs 228 and adoption challenges. a total of 35 responses were collected, and after applying validity 229 criteria, 20 responses were retained for final analysis. while this sample size may appear 230 modest for a quantitative survey, it is consistent with expert-elicitation methods in policy 231 and innovation research, where depth of knowledge and professional insight are prioritized 232 over statistical representativeness (baker et al., 2013). 233 the criteria for inclusion ensured that responses were complete, internally consistent, and 234 provided by individuals with relevant expertise in the field of smart agriculture. validity 235 was assessed based on completeness, consistency, and relevance to the research topic. 236 responses that were incomplete, contained inconsistencies, or came from participants with 237 no clear connection to smart agriculture were excluded. both the online questionnaire and 238 the qualitative interviews were conducted in parallel in the same period of time. 239 rather than claiming statistical generalizability, the primary goal of the quantitative data 240 is to highlight general patterns, stakeholder perspectives, and areas needing policy 241 attention. these quantitative insights are therefore exploratory and are critically supported 242 and contextualized through the qualitative findings obtained from in-depth expert 243 interviews, ensuring that the interpretations are robust and contextually meaningful. 244 while the sample size of five qualitative interviews and 20 valid quantitative responses 245 may appear limited, it is justified by the methodological rigor applied in the selection and 246 analysis processes. the qualitative interviews were conducted with carefully selected key 247 stakeholders representing different sectors of agriculture, including policy, research, and 248 industry, ensuring expert-driven insights. thematic saturation was reached, as no 249 significantly new themes emerged in later interviews, suggesting that the core challenges 250 and opportunities had been effectively captured (baker et al., 2013). 251 for the quantitative survey, although the response count is modest, it reflects targeted 252 participation from experienced stakeholders within the agritech project network and a pre-253 established stakeholder database. the respondents’ expertise ensured high-quality, 254 informed perspectives, making the findings valuable for understanding adoption trends and 255 policy needs. future research could expand the sample size to further validate the findings. 256 2.4. data analysis 257 the analysis of the collected data followed a structured multi-step approach, integrating 258 both qualitative and quantitative methodologies to ensure a comprehensive interpretation 259 of stakeholder perspectives on the adoption of smart agriculture technology and policy 260 needs. given the mixed-methods nature of the study, different analytical strategies were 261 applied to the qualitative and quantitative datasets to maximize the depth and reliability of 262 insights. 263 the qualitative data obtained from face-to-face interviews were manually analyzed using 264 a combination of textual synthesis and thematic analysis. this approach was chosen to 265 extract detailed insights from expert responses while maintaining the depth and context of 266 qualitative feedback. in particular, thematic analysis involved identifying recurring 267 patterns in the responses related to technology adoption, policy gaps, financial constraints, 268 and regulatory needs (kiger & varpio, 2020). while the analysis was primarily descriptive, 269 it provided structured insights into the challenges and opportunities surrounding each 270 specific smart technology developed in the agritech project. the responses were 271 synthesized into key themes aligned with the study’s focus, ensuring stakeholders’ 272 perspectives on technology diffusion, policy barriers, and suggested interventions were 273 effectively captured. 274 to ensure a structured interpretation of the qualitative data, insights were categorized into 275 two main dimensions. the first focused on technology-specific insights, where each smart 276 technology of the agritech project, namely: iot, ai, sensor-based systems, and robotics, 277 was examined separately. responses highlighted perceived benefits, adoption challenges, 278 and policy needs unique to each innovation. the second dimension analyzed the broader 279 policy environment, capturing stakeholder views on existing policy frameworks, gaps in 280 regulatory support, and recommendations for improving policy measures. this approach 281 ensured that the qualitative findings were systematically organized, aiming to understand 282 stakeholder perspectives. 283 given the exploratory purpose and the sample size, the quantitative data obtained from the 284 online survey were analyzed in xlstat using basic descriptive statistical methods 285 (frequencies, percentages, and cross-tabulations) to highlight general trends and 286 stakeholder perceptions regarding smart technology adoption, rather than conducting in-287 depth statistical tests. frequency distributions were used to summarize categorical 288 variables such as familiarity with specific technologies, perceived benefits, policy 289 awareness, and adoption challenges. cross-tabulations were applied to compare 290 stakeholder perspectives across different professional sectors. additionally, mean and 291 standard deviation calculations were used to analyze responses on likert-scale questions, 292 assessing attitudes toward policy effectiveness, investment challenges, and knowledge 293 dissemination needs. 294 the findings from the quantitative analysis provided a broad overview of key trends in 295 technology adoption and policy perceptions. these insights were cross-referenced with the 296 qualitative findings to ensure that the study’s conclusions were supported by both in-depth 297 expert opinions and a wider range of stakeholder perspectives. 298 3. results 299 the presentation of results follows the dual structure of our research design, distinguishing 300 between general (cross-cutting) trends observed across stakeholders from the online survey 301 (section 3.1) and technology-specific insights derived from expert qualitative interviews 302 (section 3.2). 303 3.1. cross-cutting perspectives on smart technology adoption 304 3.1.1. geographic distribution and professional sectors of the online survey 305 the geographic distribution of online respondents shows a balanced representation from 306 italy’s major agricultural regions (figure 1), with the highest representation from emilia 307 romagna (46%), followed by puglia (36%), and smaller contributions from lombardia 308 and veneto (9% each). this distribution indicates a blend of perspectives from key 309 agricultural areas, offering insights into potential regional variations in technology 310 adoption and policy needs within the smart technologies sector. 311 312 figure 1: geographic distribution of stakeholders 313 in terms of professional sectors, the respondents represented a broad spectrum within the 314 agricultural and smart technologies domains (figure 2). approximately 33.33% of 315 participants were involved in agricultural technology, including roles related to software 316 development and research in precision agriculture. another 33.33% came from academic 317 backgrounds, emphasizing the importance of research-driven insights in advancing smart 318 technologies solutions. direct farming operations accounted for 12% of respondents, 319 ensuring representation of the practical, on-ground perspective crucial to understanding 320 adoption barriers. the remaining participants were involved in diverse areas, including 321 professional training, technological transfer, manufacturing, and viticulture. this 322 multifaceted representation highlights the need for cross-sectoral collaboration to create 323 comprehensive and inclusive smart technology adoption policies. 324 46% 36% 9% 9% emilia romagna puglia lombardia veneto 325 figure 2: professional sector of the stakeholders 326 327 the level of involvement with specific smart agriculture technologies varied among online 328 respondents (figure 3). sensor-based technologies emerged as the most familiar, with 329 31.82% of respondents indicating familiarity. autonomous systems, ai, iot, and nature-330 based renewable systems each garnered attention from 13%-18% of respondents, reflecting 331 a broad interest in diverse smart agricultural innovations. novel spectral interface 332 technologies were the least familiar, with only 4.55% of respondents indicating 333 involvement or interest, which could be attributed to limited applications or high 334 implementation costs. 335 33% 33% 12% 22%agricultural technology companies academic research direct farming operations others 336 figure 3: key stakeholders' familiarity with agritech project innovative technologies 337 338 online respondents identified several primary contributions of smart technologies to the 339 agricultural sector (figure 4). the leading perceived benefit was resource waste reduction, 340 cited by 25.81% of participants as a crucial advantage. closely following was the potential 341 for reducing environmental impact, highlighted by 22.58% of respondents as a key benefit. 342 improved crop yields were also a prominent contribution, recognized by 19.35% of 343 participants as a fundamental outcome of adopting smart technologies. enhanced pest, as 344 well as disease detection and increased labor efficiency were both identified as significant 345 benefits, with each selected by 16.13% of respondents. interestingly, none of the 346 respondents chose the “others” option, suggesting that the primary contributions listed 347 were comprehensive enough to cover stakeholders’ perceptions of the benefits of smart 348 technologies. 349 18.18% 13.64% 18.18% 31.82% 13.64% 4.55% autonomous and robotic systems iot technologies artificial intelligence / machine learning and modelling sensor-based technologies/ remote sensing/geospatial technologies nature-based/innovative renewable agri-systems / water, soil, wastewater, and nutrients reuse / organic agriculture novel spectral interface technologies 350 figure 4: contributions of innovative technologies to the agricultural sector, according to key stakeholders 351 352 3.1.2. policy awareness and integration 353 the survey revealed varied levels of policy awareness among respondents. a substantial 354 portion, 50%, expressed uncertainty regarding whether smart agriculture technologies are 355 acknowledged within existing policy frameworks, suggesting a need for clearer 356 communication on policy provisions. in contrast, 37.50% of respondents believed that 357 relevant policies do exist, while 12.50% indicated an absence of any supportive policy. 358 several specific frameworks were noted among those who confirmed policy awareness, 359 including pac 2023-27, agenda 2030, and precision farming policies. additionally, 360 respondents mentioned partial policy alignment with broader frameworks such as the 361 green deal, farm to fork, and soil and biodiversity strategies. this feedback highlights 362 a fragmented policy environment where existing frameworks recognize the importance of 363 innovation in agriculture but lack specific support for smart agriculture technologies. 364 19% 26% 16% 16% 23% improved crop yields reduction of resource waste enhanced pest/disease detection increase in labor efficiency reduction of environmental impact survey participants identified significant barriers impacting the adoption of smart 365 agriculture technologies, primarily focusing on high initial investment costs and limited 366 technical knowledge. 45% of respondents cited each of these factors, emphasizing the need 367 for financial strategies and educational initiatives to address these challenges. additionally, 368 10% of respondents noted limited infrastructure as an obstacle, highlighting the importance 369 of developing robust infrastructure to support connected technologies like iot. none of the 370 respondents considered regulatory barriers an issue, suggesting that financial and 371 knowledge-based obstacles are the most immediate concerns. these findings imply that 372 while policies supporting smart agriculture technologies exist, they are not tailored to 373 alleviate farmers' specific challenges, particularly small and medium-sized operations with 374 limited capital and expertise. 375 participants offered a range of recommendations for policy adjustments that could facilitate 376 the adoption of specific smart agriculture technologies. for autonomous and robotic 377 systems, respondents suggested financial incentives, such as non-repayable grants, and the 378 diddemination of broader information to raise awareness. iot technologies were identified 379 as requiring targeted training programs, while ai and machine learning would benefit from 380 a structured data-sharing framework and technical support to aid users in navigating 381 complex algorithms. sensor-based technologies require policies that focus on transforming 382 raw data into actionable information, enabling farmers to make informed decisions based 383 on real-time insights. for renewable agri-systems, respondents suggested training vouchers 384 and regulatory adjustments to support organic and sustainable practices. these policy 385 recommendations emphasize the importance of tailoring support mechanisms to the 386 distinct requirements of each smart agriculture technology, thus enhancing both 387 accessibility and usability. 388 online survey respondents prioritized several key research questions to guide future policy 389 development regarding smart agriculture technologies. approximately 44.44% of 390 participants identified “how can government policies foster innovation in agriculture?” as 391 the most pressing question, signaling strong interest in government's direct role in driving 392 technological advancements. equally prioritized was “how can smart agriculture 393 technologies be integrated into the existing agricultural system?” indicating that the 394 practicalities of implementing new technologies within current systems are of critical 395 concern alongside policy considerations. the importance of understanding the impact of 396 existing policies on the adoption of smart agriculture technologies was also noted, with 397 11.11% ranking it as the primary concern and 44.44% ranking it as the second most 398 important concern. lastly, the collaboration between government and private sector 399 stakeholders was noted as an area for future exploration, even if with lower priority. the 400 diversity of opinions on this question suggests a balanced focus on government-led and 401 collaborative initiatives. 402 the online survey also identified key stakeholders essential to the development of smart 403 agriculture technologies policy, including farmers and academia (each cited by 25% of 404 respondents), smart technologies companies (17.86%), public agencies, and large retailers 405 (14.29% each). this distribution underscores the necessity of engaging diverse participants 406 to create policies that address practical needs, market demands, and technological 407 feasibility. 408 3.2. technology-specific insights 409 the qualitative data gathered from the qualitative expert interviews provide a deeper 410 understanding of stakeholder perspectives on specific smart agriculture technologies, their 411 potential contributions, and the barriers that may hinder their adoption. the insights gained 412 through these interviews underscore the diversity of challenges and recommendations 413 within the smart agriculture technologies domain, offering nuanced perspectives that 414 supplement the survey findings. 415 3.2.1. perspectives on robotic systems 416 stakeholders frequently highlighted the transformative potential of robotic systems in 417 addressing labor shortages, a pressing issue particularly in labor-intensive areas such as 418 fruit and vegetable production. robotic technologies allow for precise management of 419 tasks, from field crop monitoring to harvesting, which can significantly improve efficiency 420 while reducing reliance on manual labor. this technological precision supports a shift 421 toward sustainable practices, as robots can optimize resource allocation, minimize wastage, 422 and even carry out tasks with environmental sensitivity in mind. however, stakeholders 423 pointed out that the high costs associated with robotic systems pose substantial barriers to 424 adoption, especially for small and medium-sized farms. the financial outlay required for 425 these technologies and their technical complexity presents a formidable challenge for 426 farmers without specialized knowledge or resources to support this transition. 427 to address these issues, stakeholders suggested targeted financial incentives, such as non-428 repayable grants or tax relief for farms adopting robotic systems. furthermore, they 429 advocated for broader policy adjustments to ease the learning curve associated with these 430 technologies. suggestions included on-site training programs, community equipment-431 sharing initiatives, and educational workshops that demystify the use of robotics in 432 farming. from a policy perspective, interviewees indicated that while overarching 433 strategies like the green deal and farm to fork acknowledge the importance of agricultural 434 innovation, they lack specific provisions to support the adoption of robotics. by expanding 435 precision farming policies to include robotics, policymakers could foster a more 436 comprehensive approach to integrating these technologies into agricultural systems. 437 3.2.2. iot for resource optimization 438 iot technologies were recognized by stakeholders as essential for optimizing resource use, 439 particularly in water management. by integrating iot-enabled devices, farmers can collect 440 real-time data on soil moisture, crop health, and environmental conditions, allowing for 441 precise irrigation adjustments that conserve water and reduce costs. beyond individual 442 farm benefits, stakeholders noted that the data generated by iot systems could support 443 broader agricultural analytics, improving forecasting and resource management on a 444 regional or even national level (weersink et al., 2018). 445 despite these advantages, stakeholders expressed concerns over the cost and 446 interoperability of iot systems, which can make adoption challenging, particularly for 447 smaller farms. the lack of standardized protocols for data sharing among different iot 448 devices presents another barrier, as farmers often require an integrated view of data across 449 multiple devices and systems. to address these issues, stakeholders recommended policy 450 interventions to promote data-sharing standards and compatibility protocols to enable 451 seamless integration across iot platforms. additionally, they advocated for reducing 452 bureaucratic complexities surrounding iot implementation, which could encourage more 453 farms to adopt iot configurations and benefit from their potential efficiencies. 454 3.2.3. sensor platforms and remote sensing technologies 455 sensor technologies, particularly those designed for unmanned or automated 456 configurations, were identified as having significant potential to enhance agricultural 457 efficiency. these technologies allow for precise management of resources like water and 458 nutrients and provide real-time monitoring that supports effective disease control and 459 overall crop health management. for example, by using soil moisture sensors, farmers can 460 optimize irrigation schedules, reducing water use without compromising crop quality. 461 additionally, the environmental benefits of sensor-based systems are considerable, as they 462 minimize the need for excess inputs, thereby lowering the environmental footprint of 463 agricultural operations. 464 however, stakeholders noted that sensor platforms face barriers similar to those of other 465 advanced technologies, including high installation costs, technical limitations, and the need 466 for specialized training. furthermore, respondents pointed out that the absence of a unified 467 data platform for sensor integration complicates data interpretation, making it challenging 468 for farmers to convert raw data into actionable insights. to support the adoption of sensor 469 technology, stakeholders suggested policy adjustments that include infrastructure 470 investments, such as broadband expansion to rural areas and establishing public-private 471 partnerships for data platform development. these initiatives could facilitate real-time data 472 aggregation and analysis, allowing farmers to maximize the benefits of sensor platforms 473 for sustainable agriculture. 474 3.2.4. role of artificial intelligence and machine learning in agriculture 475 artificial intelligence (ai) and machine learning (ml) technologies hold transformative 476 potential for agriculture, enabling real-time analysis and predictive insights that enhance 477 decision-making and resource allocation. ai-driven applications allow farmers to monitor 478 crop health, predict yield outcomes, and optimize input use, making farm management 479 more efficient and responsive. stakeholders believe that ai could streamline processes 480 across the agricultural value chain, from planning and planting to harvest and market 481 delivery, thereby adding value at each production stage. 482 despite this promise, ai adoption in agriculture is restricted by several challenges. first, 483 the high costs associated with ai solutions can be prohibitive, particularly for smaller 484 operations. second, data interoperability presents technical challenges, as different ai 485 applications often require diverse data inputs that may not be readily compatible with each 486 other. lastly, stakeholders highlighted the complexity of using ai solutions, which often 487 require advanced technical knowledge that may be inaccessible to many farmers. 488 recommendations for policy interventions included establishing open data systems, which 489 could facilitate data sharing across ai platforms, and government-supported training 490 programs that simplify the use of ai. additionally, respondents advocated for technical 491 support mechanisms to help farmers navigate ai applications and fully realize their 492 potential benefits. 493 3.2.5. nature-based solutions and renewable agriculture 494 stakeholders emphasized the growing importance of nature-based solutions, such as water 495 and soil reuse, nutrient recycling, and organic farming practices, as essential components 496 of sustainable agriculture. these renewable systems reduce environmental impact by 497 reducing reliance on synthetic inputs and fostering a more balanced relationship between 498 agriculture and the environment. nature-based solutions promise healthier soils, improved 499 crop resilience, and long-term sustainability, making them an attractive alternative for 500 farmers aiming to minimize their ecological footprint. 501 however, the transition to renewable agri-systems is not without challenges. stakeholders 502 noted that high initial investment costs, limited expertise, and regulatory inconsistencies 503 are significant barriers. to address these challenges, respondents recommended that 504 policies provide financial incentives, such as subsidies for transitioning to organic farming 505 and grants for infrastructure investments. training programs focused on sustainable 506 farming practices and more robust certification systems were also suggested to ensure 507 market recognition of organic and nature-based products. by supporting these transitions, 508 policymakers can promote a more sustainable agricultural model that aligns with 509 environmental goals. 510 3.2.6. novel spectral interface technologies 511 while novel spectral interface technologies, including microwave and thz radiation 512 applications, were less familiar to many respondents, some stakeholders acknowledged 513 their potential for non-invasive agricultural monitoring. these technologies allow for 514 detailed analysis of crop health, soil composition, and other critical indicators without 515 physical contact, which could prove valuable for precision agriculture. however, the 516 application of spectral technologies faces unique challenges, including high costs, safety 517 concerns related to radiation use, and the need for specialized expertise to interpret 518 complex data. 519 stakeholders recommended targeted policy interventions to address these challenges. 520 suggestions included funding for research focused on agricultural applications of spectral 521 technologies, safety standards to ensure that radiation use does not pose health risks, and 522 farmer training programs to build competence in spectral data interpretation. additionally, 523 respondents expressed interest in exploring integrating spectral data with ai, which could 524 improve data analysis and support more efficient agricultural decision-making. 525 4. discussion 526 the findings of this study reinforce the well-documented potential of smart agriculture 527 technologies to address pressing challenges in the agricultural sector, such as resource 528 efficiency, climate adaptation, and sustainability. these technologies, when the right 529 conditions are met, also play a growing role in building food system resilience by 530 improving productivity and reducing losses, particularly under climate stress, as reported 531 by gemtou et al., (2024). despite this potential, adoption remains limited due to financial, 532 technical, and infrastructural constraints. these results align with previous research, which 533 emphasizes that economic barriers and knowledge gaps are among the most significant 534 obstacles to the adoption of technology in agriculture (basso & antle, 2020; finger et al., 535 2019). however, the findings also highlight a critical gap in policy awareness, which has 536 received less attention in the existing literature but emerged as a key concern among 537 stakeholders in this study. 538 one of the particularities of this research lies in its mixed-methods approach, which 539 combines qualitative depth with exploratory quantitative insights. while the number of 540 responses in the survey is modest, the alignment between the survey trends and the 541 interview narratives provides a form of triangulation that enhances the robustness of the 542 results. this integration allowed us to validate emerging patterns, ensuring that the insights 543 are not reliant on a single data source but are reflected across multiple forms of stakeholder 544 engagement (fetters et al., 2013; creswell & plano clark, 2017). the triangulation design 545 was particularly valuable for assessing the adoption barriers and policy dynamics around 546 smart technologies, where numerical trends were consistently reinforced by expert 547 perspectives. 548 this convergence of evidence across the two methods strengthens confidence in the 549 relevance of the results. one of the most striking of these results is the widespread lack of 550 clarity regarding the role of existing policies in supporting smart agriculture technologies. 551 many respondents expressed uncertainty about whether current frameworks, such as the 552 common agricultural policy (cap) 2023–2027, the green deal, and farm to fork, 553 sufficiently address the specific needs of technological adoption in agriculture. this 554 reflects findings from previous studies indicating that while sustainability and innovation 555 are often mentioned in high-level policies, their implementation at the farm level is often 556 fragmented and unclear (candel, 2022; rose et al., 2021). a key implication of this study 557 is that policymakers must improve communication strategies to ensure that farmers, 558 technology developers, and other stakeholders are well-informed about existing policy 559 instruments and funding opportunities. 560 this lack of clarity is also linked to a broader issue of trust and how farmers perceive these 561 policies. for instance, giampietri et al. (2020) show that trust in intermediaries plays a 562 critical role in adoption of cap-subsidized risk management tools. our findings suggest 563 that in the context of smart farming, this trust must extend to digital service providers and 564 data systems, highlighting the need for transparency, digital literacy, and certification 565 mechanisms that can build farmers’ confidence in technological tools. 566 consistent with earlier research (long et al., 2016; weersink et al., 2018), this study also 567 confirms that high initial investment costs remain a fundamental barrier to technology 568 adoption. this is particularly problematic for small and medium-sized farms, which 569 struggle to access capital for automation, ai-driven decision support tools, and iot-enabled 570 monitoring systems. the exploratory quantitative results highlighted the widespread 571 concern about financial and technical barriers, and likewise, these survey insights were 572 strongly supported by qualitative findings, where experts repeatedly emphasized similar 573 barriers such as high upfront costs, limited access to financial resources, and difficulties 574 accessing technical support. this cross-analysis between survey data and expert interviews 575 strengthens the validity of our observations and highlights the need for targeted policy 576 responses that directly address these barriers. while financial incentives, such as grants, 577 tax credits, and low-interest loans, are already part of some policy frameworks, 578 stakeholders expressed concerns that these incentives are often complex, difficult to access, 579 or insufficient to offset adoption costs. policymakers should consider simplifying 580 administrative procedures for funding applications and targeting financial assistance 581 toward the most impactful technologies identified in this study, such as sensor-based 582 monitoring, ai-driven decision-making, and precision irrigation systems. 583 additionally, as reinforced by both datasets, cost-sharing and infrastructure emerged as 584 cross-cutting themes, underscoring their significance regardless of methodological lens. 585 stakeholders recommended public-private partnerships to support cost-sharing initiatives, 586 particularly for expensive infrastructure investments, such as rural broadband expansion. 587 these findings reinforce recent discussions on the role of co-financing mechanisms and 588 innovation clusters in mitigating the risk associated with technology adoption for farmers 589 (ehlers et al., 2022). 590 a consistent finding across both data sources was the importance of technical knowledge 591 and training in shaping adoption outcomes, consistent with previous studies (charatsari & 592 lioutas, 2013; lovec et al., 2020). smart agriculture technologies often require specialized 593 skills, yet many farmers have limited access to training programs that could help them 594 integrate these innovations effectively. stakeholders emphasized the need for structured, 595 hands-on training initiatives that focus on technology usability, data interpretation, and 596 integration into existing farming systems. 597 this highlights an important policy gap: while some funding exists for technology 598 development, there is often insufficient investment in farmer education and capacity 599 building. policymakers should consider expanding agricultural extension services to 600 provide in-person training, online courses, and demonstration farms where farmers can 601 experience the benefits of digital agriculture firsthand. knowledge transfer partnerships 602 between research institutions and farming communities could also play a crucial role in 603 reducing this barrier. this aligns with menozzi et al. (2023), who emphasize that perceived 604 behavioural control and attitudes are pivotal in shaping adoption decisions, especially when 605 practices are unfamiliar or technically demanding. similarly, our respondents stressed the 606 difficulty of using ai or iot platforms, reinforcing the need for support measures that go 607 beyond finance to include training, usability, and peer-to-peer learning networks. 608 the study also highlights infrastructure limitations, particularly concerning internet 609 connectivity in rural areas. technologies such as iot-based monitoring, remote sensing, 610 and ai-driven decision support tools rely on high-speed internet and cloud computing, yet 611 many agricultural regions lack the necessary broadband infrastructure. this issue is 612 consistent with prior research, which emphasizes that the digital divide between urban and 613 rural areas is a significant barrier to the diffusion of technology (ehlers et al., 2022). 614 a broader finding from this study is that smart agriculture policies must be adaptive, 615 responsive, and inclusive. stakeholders reported that existing policies often fail to 616 differentiate between the needs of different types of farmers, particularly smallholders 617 versus large-scale agribusinesses. one-size-fits-all policy approaches may not be effective 618 in promoting equitable adoption, suggesting the need for targeted support mechanisms. 619 additionally, stakeholder engagement must be prioritized in policy design and 620 implementation. the findings of the qualitative survey suggest that many policy 621 frameworks lack farmer representation in the decision-making process, leading to 622 misalignment between policy objectives and on-the-ground realities. to improve this, 623 policymakers should, according to the key expert stakeholders, incorporate participatory 624 approaches, such as co-design workshops, multi-actor innovation networks, and regional 625 consultation forums. 626 while this study aims to provide valuable insights into the adoption barriers and policy 627 needs of smart agriculture technologies, using triangulation, combining exploratory survey 628 findings with detailed expert interviews, to provide a balanced and credible approach, in 629 an attempt to make the insights more robust, certain limitations should be acknowledged. 630 the sample size, particularly for the qualitative interviews, was relatively small, which 631 may limit the generalizability of some findings. additionally, the reliance on self-reported 632 data introduces the possibility of response biases, as participants' perceptions may not 633 always reflect objective realities. however, it is important to note that the study 634 purposefully targeted key stakeholders, namely: policy experts, researchers, and 635 technology developers, identified through a structured stakeholder mapping within the 636 agritech project. as such, the participants likely represent some of the most informed 637 individuals on smart agriculture policy and technology in italy, enhancing the relevance 638 and depth of the insights gathered. future research should explore larger and samples to 639 validate these findings across different agricultural systems and geographic regions. 640 comparative studies examining policy effectiveness in multiple countries could offer 641 deeper insights into best practices for supporting smart agriculture adoption. 642 5. conclusion and policy implications 643 this study highlights the importance of policy frameworks in facilitating the adoption of 644 smart agriculture technologies while revealing key barriers hindering their widespread 645 implementation. the results emphasize stakeholders' strong optimism regarding these 646 technologies' role in improving agricultural efficiency, sustainability, and resilience. 647 however, the study also identifies three major obstacles: high investment costs, technical 648 knowledge gaps, and inadequate infrastructure, all of which must be addressed through 649 targeted policy interventions. 650 a critical takeaway from this research is the necessity for policy alignment and 651 accessibility. while existing frameworks acknowledge innovation, a disconnect exists 652 between policy provisions and stakeholder awareness. this highlights the need for 653 simplified policy regulations, better communication strategies, and stronger engagement 654 with the farming community. policies should be designed to be practical, transparent, and 655 adaptable, ensuring that they effectively support farmers and technology adopters in 656 different agricultural settings. 657 another key implication is the urgent need for financial instruments tailored to the realities 658 of smart agriculture, such as differences in farm sizes, digital readiness and access to 659 broadband infrastructure, among others. policies must focus on incentives such as 660 subsidies, tax relief, and low-interest loans to lower the entry barriers for farmers, 661 particularly small and medium-sized operations. at the same time, public-private 662 partnerships should be expanded to create co-financing models that distribute investment 663 risks across multiple stakeholders. 664 the role of education and technical training also emerges as a fundamental aspect of 665 successful adoption. smart agriculture technologies require specialized skills that many 666 farmers currently lack. to address this, agricultural extension services should integrate 667 digital training programs, on-field demonstration projects, and mentorship initiatives. 668 collaboration between universities, policymakers, and industry leaders can create 669 structured knowledge-sharing platforms that provide ongoing support to farmers. 670 finally, this study underscores the importance of an inclusive and adaptive policy-making 671 approach. engaging diverse stakeholders, from farmers to technology developers and 672 policymakers, is essential for crafting policies grounded in real-world needs. multi-actor 673 governance structures, such as stakeholder consultation groups, regional innovation hubs, 674 and participatory policy platforms, should be institutionalized to ensure that agricultural 675 policies evolve in tandem with technological advancements. 676 in conclusion, smart agriculture technologies represent a transformative opportunity for the 677 agricultural sector; however, their full potential can only be realized with robust, well-678 coordinated, and forward-thinking policies. policymakers can accelerate the transition 679 toward a more sustainable, productive, and resilient agricultural system by addressing 680 financial constraints, bridging the knowledge gap, expanding digital infrastructure, and 681 improving stakeholder engagement. beyond economic and technological advancements, 682 the successful integration of these innovations has profound implications for long-term 683 sustainability and global food security. by improving resource efficiency, reducing 684 environmental degradation, and enhancing adaptive capacity to climate change, smart 685 agriculture technologies contribute to more resilient food systems that can meet the 686 demands of a growing population. however, ensuring equitable access to these 687 technologies is essential to prevent the widening of disparities between large-scale and 688 smallholder farmers. future policy efforts should focus on fostering inclusive innovation, 689 integrating sustainability goals into technology adoption strategies, and aligning digital 690 agriculture with broader climate and food security policies. by doing so, agricultural 691 technologies can evolve in ways that not only drive economic growth but also ensure 692 environmental sustainability and food system resilience. 693 acknowledgements 694 this study was carried out within the agritech national research center and received 695 funding from the european union next-generationeu (piano nazionale di 696 ripresa e resilienza (pnrr) – missione 4 componente 2, 697 investimento 1.4 – d.d. 1032 17/06/2022, cn00000022). this manuscript reflects 698 only the authors’ views and opinions, neither the european union nor the european 699 commission can be considered responsible for them. 700 6. references 701 akimowicz, m., del corso, j.p., gallai, n., and képhaliacos, c. 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(2018). opportunities and 780 challenges for big data in agricultural and environmental analysis. annual review of 781 resource economics, 10: 19-37. doi: 10.1146/annurev-resource-100516-053654 782 1 the social and economic determinants of farm succession in 1 ireland 2 mika wakamatsu shin1 *, jason loughrey2 , emma dillon2 and anne kinsella2 3 4 affiliation: 5 1 currently freelance. former affiliate: agricultural economics and farm surveys 6 department, teagasc rural economy and development programme, teagasc, athenry, 7 co. galway, ireland 8 *corresponding author email: wkmtmk0127@gmail.com orcid: 0000-0001-7259-9 4862 10 11 2 agricultural economics and farm surveys department, teagasc rural economy and 12 development programme, teagasc, athenry, co. galway, ireland 13 email: jason.loughrey@teagasc.ie orcid: 0000-0001-9658-0801, 14 email: emma.dillon@teagasc.ie orcid: 0000-0002-3712-9575, 15 email: anne.kinsella@teagasc.ie orcid: https://orcid.org/0000-0001-5670-9025 16 17 18 this article has been accepted for publication and undergone full peer review but has 19 not been through the copyediting, typesetting, pagination and proofreading process, 20 which may lead to differences between this version and the version of record. 21 please cite this article as: 22 shin mw, loughrey j, dillon e (2025). the social and economic determinants of farm 23 succession in ireland, bio-based and applied economics, just accepted. doi: 24 10.36253/bae-16403 25 26 abstract 27 28 this study explores the multifaceted factors influencing farm succession in ireland, 29 emphasising the interaction among economic, social, and environmental aspects. with 30 an ageing farm population, the need for effective succession strategies is critical to 31 ensuring sustainable agricultural practices. we analyse the impact of drivers and 32 barriers to succession, highlighting the importance of considering social factors along 33 with economic factors using a probit model to examine these relationships. our findings 34 reveal that while farm size and dairy farming status show complex relationships with 35 the likelihood of presence of a successor, social factors such as excessive workload 36 impact decision-making. our findings confirm expected relationships while offering 37 mailto:anne.kinsella@teagasc.ie 2 new insights into farm succession and the farmer’s life cycle. beyond profitability, 38 social factors—such as workload and its perception by the next generation—play a 39 crucial role in successor identification. highlighting these dynamics, our study 40 underscores the importance of social sustainability in securing farming’s future. 41 jel code j10, q12, q18 42 keywords farm succession, social factors, farmer ageing, probit model, 43 endogenous succession cycle 44 acknowledgement 45 funding provided by the department of agriculture, food and marine under the project 46 entitled renew2050 rural generational renewal 2050 2019r414. 47 48 1. introduction 49 improving the sustainability of agriculture, across economic, environmental, and 50 social dimensions, is recognised as central to delivering the key objectives of the 51 common agricultural policy (cap). one such objective is to support generational 52 renewal. an ageing of the farm population is evident in ireland (meredith and crowley, 53 2018) and across europe (bertolozzi-caredio et al., 2020, may et al., 2019). the share 54 of farm holders aged 65 or over is now almost one-third in ireland compared to one-55 fifth in 1991 (cso, 2021), underlining the extent of the challenge in the irish context. 56 farm succession, the transfer of managerial control of the farm, is critical to continued 57 farm sustainability (leonard et al., 2017a,b, russell, et al., 2020) with previous 58 literature highlighting its importance from both the farm household and rural 59 community perspectives. farm succession is significant for the uptake of innovation, 60 efficient and effective farm management (nuthall and old, 2017). most farms in ireland 61 are family-owned, and entry into farming sector is primarily through inheritance 62 (hennessy and rehman 2006; deming et al 2019). 63 3 the complex nature of farmer decision making around succession and inheritance is 64 multifaceted, with a broad range of economic, personal and social factors at play 65 (conway et al., 2016, 2021, 2022; leonard, et al., 2017a, b; góngora et al., 2019). these 66 factors can be classified as drivers and barriers to succession. previous research has 67 highlighted the significance of individual farm circumstances (conway et al., 2016; 68 eistrup et al., 2019; schlesser 2021; rech et al., 2021). conway et al. (2021, 2022) also 69 highlight the limited capacity of financial incentives alone in facilitating generational 70 renewal and land mobility and contend that policy also needs to address the emotional 71 and social wellbeing of older farmers, as well as their sense of purpose in later life. an 72 improved understanding of these influential factors is vital for the design of effective 73 policy to support generational renewal in the context of the new cap. 74 to secure the farm for the next generation, it is also important to consider the 75 environmental performance, which is increasingly recognised as being integral to the 76 sustainability of agriculture (barral and detang-dessendre 2023). much recent research 77 points that younger farmers in europe are more aware of environmental issues, quicker 78 to adopt eco-friendly technologies, and more adaptable to policy changes (perez et al., 79 2020; läpple and kelley, 2015). despite the significant role of younger farmers in 80 sustainability, studies indicate that there remain barriers to new entrants taking over a 81 farm (hartarska et al., 2021; schlesser et al., 2021; zagata et al., 2017). 82 regarding the relationship between succession and sustainability, potter and lobley 83 (1996) coined the term ‘succession, successor and retirement effects’ to describe the 84 processes whereby an identified successor or lack thereof, can significantly influence 85 the farm holder’s level of interest and investment in the farm when approaching what 86 should be their own retirement from farming. thus, the sustainability of the farm 87 business is influenced by the presence of successors. furthermore, leonard et al. (2017) 88 4 emphasises economic concerns, whether a farm can generate enough income to support 89 both the farmer and their successor, as well as the farmer's residual income if they 90 transfer the farm prior to death. transfer decisions and procedures are often difficult 91 and stressful and may relate to the valuation of the farm as retiring farmers seek to sell 92 farmland at the highest possible price (jeanneaux et al 2022). 93 despite widespread recognition of the connection between socio-economic factors and 94 farm succession, there is considerable space in the academic literature for further 95 exploration of the potential association between social aspects and farm succession. 96 previous research acknowledges the limited capacity of financial incentives alone in 97 facilitating generational renewal. through this study, we expect to make 98 recommendations to policy makers on how social aspects should be considered in the 99 design of future generational renewal policies. this paper identifies the endogenous 100 nature of farm succession and examines the relationship between farm-level economic 101 and social factors and the identification of a successor. the factors are intricately 102 intertwined, making it challenging to consider these intertwined factors and the 103 presence of a successor. this paper is innovative in that it conceptualises farm 104 succession within the framework of the farm life cycle, elucidating the endogenous 105 factors. the significance lies not in listing barriers and drivers, but in analysing the 106 relationship between these factors within the farm life cycle and the presence or absence 107 of a successor. the objective of our study is to explore some of the drivers and barriers 108 to farm succession in the irish context, with a particular emphasis on relevant social 109 and economic factors. 110 the next section discusses potential drivers and barriers to farm succession based on 111 the literature. section 3 provides an overview of the data and methods used in this paper. 112 5 section 4 contains the results of the data analyis, section 5 provides some discussion 113 and conclusions. 114 2. literature review 115 the general process of farm inter-generational transfer is well described in the existing 116 literature, which distinguishes between three distinct but inter-related concepts. 117 inheritance, the legal transfer of ownership of farm business assets including land; 118 retirement, the withdrawal of the existing manager from active managerial control 119 and/or involvement in manual work on the farm and: succession, the transfer of 120 managerial control over the use of these assets (errington, 2002). 121 there exists particular policy instruments and financial incentives to encourage early-122 stage inter-generational farm transfer, however, delayed succession remains a concern 123 throughout the world (lobley et al 2010). the literature identifies barriers to land 124 transfer to non-family members relating to the value that farmers place on identity, 125 occupation, control, and status in the community (duesberg et al. 2017). schlesser et 126 al. (2021) investigate social and economic factors affecting farm succession using focus 127 groups, highlighting the complex challenges for both the older generation and 128 successors. understanding these obstacles is crucial for our study to improve insights 129 into the issue. 130 2.1 economic factors 131 farm succession may be anticipated by current farmers’ behaviour in making additional 132 investment to expand their farm business (calus et al., 2008). farmers with identified 133 successors are more likely to invest or expand in anticipation of a takeover of the farm 134 by a son or daughter than those who do not have a successor, which is known as the 135 succession effect. the prospect of a successor often leads incumbent farmers to make 136 management decisions they might not otherwise make, resulting in substantial 137 6 improvements to the farm's productivity and sustainability (leonard et al., 2017; lobley 138 et al., 2010; uchiyama et al., 2008). on the other hand, they describe the ‘retirement 139 effect’, which generally has a negative impact on farms such that the farmer who has 140 not identified a successor attempts no additional investment and scales down the farm 141 business. similarly, carreira and teixeira (2011) investigate that firms that exit the 142 market appear to exhibit a noticeable productivity disadvantage compared to those that 143 survive, not only in the year preceding exit but also for a significant number of years 144 leading up to the exit. this means that farmers who do not secure a successor will not 145 engage in investment activities for several years before the actual retirement. much 146 economic research has dealt with the question of farm succession and non-succession 147 by highlighting important explanatory factors at the farm level such as farm holder’s 148 age, off-farm employment, farm size, economic viability, farmer’s education and 149 household composition (kimhi and lópez, 1999; stiglbauer and weiss, 2000; glauben 150 et al., 2006). 151 the economic viability of the farm is well documented as playing a central role in the 152 succession process (glauben et al., 2009; hennessy and rehman 2007; zagata and 153 sutherland, 2015). farm size is found to have a positive stable effect on farm succession 154 in the case of farming in italy (bertoni and cavicchioli, 2016; cavicchioli et al., 2018). 155 successors on larger family farm (both in physical (area) and economic size) are more 156 likely to enter the farm as full-time. 157 farmland ownership is also discussed as influencing the farm succession process. 158 ireland has low land mobility as older farmers accumulate capital to secure their future 159 financial situation and are unwilling to transfer their farm assets (leonard et al. 2017). 160 the major farm transfers occur with non-market arrangements, usually inheritance. 161 7 irish farmers influence agricultural land markets as most farm transfers occur within 162 the family and this is attributed to the strong emotional attachment to land in ireland 163 (bradfield et al., 2023). rented land (both conacre, the short-term, 11-month land 164 leasing and long-term leasing) accounts for only 18% of utilised agricultural area 165 (uaa) in ireland, that is the second lowest in eu (european commission 2021). policy 166 measures to facilitate land transfer to younger generations have had limited success 167 (bika, 2007; geoghegan and o’donoghue, 2018; geoghegan et al., 2021). 168 2.2 social factors 169 in addition to economic factors, the absence of successors is associated with older 170 farmers’ unwillingness to retire, which stems from emotional attachment to the farm 171 (conway et al. 2022). farming is a way of life for many older farmers throughout the 172 world and retirement from their daily routines and social circles in the farming 173 community may affect their emotion negatively. studies show that existing retirement 174 incentives bring limited success in encouraging farmers to retire at or before pension 175 age and seem not to change the traditional family farm transfer (gilmore 1999; contzen 176 et al., 2017). bika (2007) writes that in addition to financial reasons, irish farmers’ 177 reticence in using the early retirement scheme1 could have been due to ‘sentimental 178 bonds with the land’. indeed, policy around generational renewal can often involve 179 strategies to encourage older farmers to ‘step aside’ to facilitate new entrants while 180 farmers often wish to remain ‘rooted in place’ on the farm (conway et al., 2022) and 181 work for as long as possible (uchiyama et al. 2008). this farmers’ ‘never retire’ attitude 182 is associated with reduced likelihood of a successor being identified in several countries 183 1 the early farm retirement scheme (efrs) was introduced to encourage older farmers to retire and to attract younger farmers into the industry. in ireland, there have been three rounds to the efrs scheme, in 1993, 2000 and 2007 but it ceased in 2009. these schemes enabled farmers to retire early (from age 55) and provided them with a pension (up to €15,000 a year for a maximum of 10 years) provided they retired from farming completely by transferring, selling or leasing their farm to a young trained farmer (hayden et al 2021). 8 (lobley et al., 2010). thus, retirement in farming is not only influenced by economic 184 factors, but also emotional and social status. 185 one such consideration is the desire for social inclusion and the avoidance of isolation. 186 social inclusion, driven by the concept of social capital, plays a crucial role in farmers' 187 decision-making processes (shortall 2008). this social capital is composed of resources 188 from networks of relationships based on social structure and acknowledged as 189 important component in farmers’ decision-making (arnott et al.2021; cofré-bravo et 190 al. 2019). this facilitates discussions and the designation of a successor on the farm 191 (abdala et al. 2022)2. by maintaining these positive relationships, older farmers can 192 navigate the complexities of succession planning more effectively, ensuring the 193 continuity of their agricultural operations. 194 social factors such as stress or anxiety, and excessive workload are also among the 195 factors that can be considered as barriers to farm succession. due to the rapid expansion 196 of the dairy sector following the removal of eu milk quotas in 2015, dairy farmers 197 experience an increase in labour intensity in ireland. brennan et al. (2021) state that 198 workload related stress was the second highest source of stress among farmers in 199 ireland, next to poor weather. this stress not only affects the mental well-being of older 200 farmers but also influences the perspectives of potential successors, deterring them 201 from considering a future on the family farm (brennan et al. 2022). conversely, 202 strategies such as increasing human capital through workload and information sharing 203 among family members and neighbours can help alleviate this problem, incentivizing 204 potential successors to remain in the agricultural sector (bertoni and cavicchioli, 2016). 205 2 abdala et al (2022) refer to social capital as the willingness of individuals and groups to obtain information, influence, and nurture solidarity with other social actors through the structure and content of existing relationships (adler and kwon, 2002) 9 human capital here is considered according to the oecd definition as the productive 206 wealth embodied in labour, skills and knowledge (tan, 2014) or any stock of 207 knowledge or the innate/acquired characteristics a person has that contributes to his or 208 her economic productivity (garibaldi, 2006). given the increasing focus on quality of 209 life issues (contzen and häberli, 2021), there is a strong case for more emphasis on 210 collaborations that enable a more sustainable farm workload. 211 although a wider range of social factors have emerged in the discussion of generational 212 renewal (brennan et al., 2021; 2022), there are very few recent studies explicitly 213 accounting for social capital and farm succession, except for abdala et al. (2022), which 214 identifies some relationships between farm succession and social factors in the case of 215 brazil. abdala et al. (2022) found that farmers with social capital, such as access to 216 information or networks with customers and suppliers, are more likely to identify a 217 successor. such social capital includes agricultural education, which can enhance 218 farmers' ability to manage a farm more effectively (läpple et al., 2015). 219 engaging with farm advisory services through formal advisory contracts is another 220 form of social capital, as it involves leveraging networks to acquire knowledge and 221 information (cofré-bravo et al., 2019). however, despite recognition of the importance 222 of social aspects, studies addressing these factors remain insufficient. 223 the pursuit of better agricultural knowledge can be an indicator of a higher possibility 224 of farm succession as it may indicate willingness to improve and continue the farm 225 business. 226 agri-environmental scheme (aes) participation is recognised as one of the strategies 227 for pursuing sustainable agriculture and these types of strategies may be translated into 228 a more sustainable agricultural business for future generations. farmers with a 229 10 sustainable strategy tend to participate in aes. various factors contribute to the 230 decision to participate in aes, including economic concerns, the educational level of 231 farmers, and the environmental features on farms. according to cullen et al. (2020), 232 farmers who are having a "forward-looking" self-identity are more likely to participate 233 in aes. this type of identity influences aes participation, aligning with research that 234 aes participation is favoured as one possible survival strategy when foreseeing the 235 sustainability of agriculture (ingram et al., 2013; cullen et al., 2021). in this sense, 236 motivations to join aes are shaped by the desire to continue the farm, which may be 237 associated with the presence of a successor. in other words, the lack of a successor can 238 be a reason not to enter aes due to a winding down and poor availability of labour with 239 an inability to meet the aes management requirements (riley, 2006). 240 2.3 life cycle 241 the above mentioned drivers and barriers to succession are intertwined and emerge 242 under different conditions at the farm level. understanding this complexity can be 243 facilitated by conceptualizing farm succession within a farm life cycle approach. 244 succession processes in family farming must be appreciated for their long duration 245 (fischer and burton, 2014). in ireland, where most new entrants inherit farms, the 246 identity of successors fostered during the life cycle shapes the structure of farming. 247 potential successors develop their farm business skills by gradually getting involved in 248 farming, influencing the current farm manager's behavior towards succession, such as 249 making additional investments. this life-cycle approach shows that succession is an 250 endogenous process, where practical involvement on the farm increases successor 251 identification and vice versa (fischer and burton, 2014). negative experiences can 252 deter potential successors even if the farm is viable. 253 11 socio-economic studies suggest that factors such as the presence of a male heir and the 254 number of children influence farm transfer decisions (o’gráda, 1980; banovic et al., 255 2015; kennedy, 1991). in northern ireland, jack et al. (2019) found that succession 256 decisions are shaped by the gender of children, with a preference for male farm 257 employees. econometric studies from various countries further indicate that a larger 258 number of children increases the likelihood of farm transfer while also delaying farm 259 closure (väre, 2006; banovic et al., 2015). additionally, having more family members 260 present on the farm enhances the probability of family succession (stiglbauer and 261 weiss, 2000). however, regional differences exist, as cavicchioli et al. (2015; 2018) 262 found both weak positive and negative associations between the number of children and 263 farm succession. 264 from these studies, we hypothesize that farmers with large land ownership and capital 265 assets are more likely to identify successors; those with less social interaction are less 266 likely; aes participation is associated with a higher likelihood of identifying 267 successors; and farmers with identified successors are more likely to invest in their farm 268 in anticipation of succession. 269 3. data and method 270 3.1 data 271 the main dataset for this research is the teagasc national farm survey (nfs) data 272 from 20183. this dataset contains detailed information about farms in terms of their 273 economic, social and environmental performance in ireland. the choice of time period 274 is based on data availability as the 2018 data includes information about farm 275 3 the teagasc nfs is part of the eu farm accountancy data network (fadn), this data was collected in addition to the core fadn dataset. 12 succession and whether or not farm holders have identified a successor. the agricultural 276 sector in 2018 was affected by unusual weather with abnormal rainfall levels in spring 277 and drought in the summer. the drought had a more significant impact on the south and 278 east nuts34 region relative to the border, midlands and west region (falzoi et al., 279 2019). some of the survey responses may reflect the difficult conditions of this year 280 (particularly regarding incidence of farm related stress). 281 in the teagasc nfs, a farm accounts book is recorded for each year for a nationally 282 representative survey of farms throughout ireland, selected on the basis of a stratified 283 random sample using information provided by the central statistics office. the sample 284 size is approximately 900 farms in recent years. this sample of farms is randomly 285 sampled each year to represent approximately 90,000 farms in ireland. each farm is 286 assigned a weighting factor based on the farm system and farm size so that the results 287 of the survey are representative of the national population of farms. the analysis 288 focuses on farms with holders over 50 years old, a critical age for succession decisions. 289 farmers are actively shaping the future trajectory of their farms. this approach allowed 290 us to examine the factors influencing farm succession decisions while accounting for 291 the gradual and nuanced nature of the succession process. for instance, the decision-292 making process for farm succession can begin several years before the actual succession 293 event takes place (calus et al. 2008). moreover, even at an earlier stage, forward-294 looking farmers may undertake new investments to keep a fragile business afloat if they 295 pursue farm transfer to a known successor, while others may prefer to sell the business. 296 such farmer’s behaviour is observed and influenced by various factors even before 297 retirement age (paroissien et al. 2021). examining this age group allows for a larger 298 4 the nomenclature of territorial units for statistics (nuts) 3 corresponds to border, west, mid-west, south east, south-west, dublin, mid-east, and midlands of ireland. 13 and more representative sample for robust econometric analysis. this provides a sample 299 of 538 farmers representing approximately 58,000 farms nationally. 300 succession decisions are made with factors influencing over a long-time frame (lobley 301 et al, 2010). teagasc nfs data from 2013 is used in our model to provide a lagged value 302 for the presence of young people in farm households. this variable is included to 303 examine the importance of family networks, which affect the number of farmers who 304 choose successors as of 2018. young adults (aged 20 to 44 years) in the farm household 305 may move out of the farm household to enter third-level education or employment 306 elsewhere, but they may still be part of a family network and interested in contributing 307 to the farm. thus, the presence of young adults in 2013 might be related to the status of 308 successor identification in 2018. attrition is present in the teagasc nfs data but most 309 farms participating in the 2018 survey provided data as part of the 2013 survey. the 310 year 2013 is chosen as important changes in household composition can take place in a 311 five-year period and the choice of this year limits the amount of attrition. in the 2018 312 dataset, 538 farms were available for analysis. to construct the lagged variable for the 313 presence of a young adult in the household, we used data from 2013. among these, 371 314 farms could be matched across both years and had relevant information on young adults. 315 in addition, due to missing values—particularly arising from the creation of interaction 316 variables—the final sample size for model 2 was reduced to 349. for model 1, which 317 does not include the lagged variable but includes interaction terms, the sample size was 318 reduced to 501. this reduction is likely due to listwise deletion in cases with missing 319 values introduced by the interaction variables. 320 3.2 methods 321 14 in order to analyse the drivers and barriers of farm succession, we use a probit model 322 with the dependent variable being the presence of a chosen successor as a binary 323 variable where successor =1, otherwise=0. 324 i) econometric model 325 the probit model is used to test the potential relationship between a number of farm, 326 farmer and farm household characteristics (economic and social) with the presence of 327 a chosen successor. note that due to the absence of adequate long-term survey data to 328 conclusively verify whether the farmer in nfs has indeed undergone farm succession, 329 the identification a successor is employed as a dependent variable for the analysis. the 330 choice of explanatory variables is based on the need to account for particular farm, 331 family and social factors. farmer age, land quality, and an interaction between the 332 presence of a dairy enterprise and the size of land ownership are included because much 333 research indicates a positive relationship between farm size and farm succession 334 (uchiyama et al., 2008; glauben et al., 2009; wheeler et al., 2012). average dairy farm 335 income is higher than the income of other systems in ireland (donnellan et al., 2020). 336 we therefore expect that higher farm income attracts more successors and thus we use 337 the presence of a specialist dairy farm as an independent variable in this model. 338 a variable representing investment is included among the independent variables since 339 investment indicates a willingness of the farm holder to further develop the farm. at 340 the same time, investment is another factor that increases a potential successor’s 341 willingness to take over the farm business (calus et al., 2008). the investment variable 342 is based on net new investment, which is defined as all capital expenditure during the 343 year less capital sales and grants. the cost of major repairs to farm buildings, plant and 344 machinery as well as land improvements is also included. it does not include 345 investments in land purchases (dillon et al., 2021, p.89). 346 15 many of the independent variables tend not to vary much in value over time and this 347 reduces the likelihood of reverse causality. we attempt to account for the influence of 348 past household composition by the inclusion of a lagged independent variable using 349 data from the 2013 teagasc nfs survey. we create a lagged variable indicating the 350 presence of young adults aged 20 to 44 years old in the household in 2013. the nfs 351 2018 data provides information about social factors including isolation (defined as 352 those farmers living alone) and the presence of workload stress/pressure 5 . these 353 variables were ascertained as part of an additional special survey of teagasc nfs farms 354 in autumn 2018. these social variables are likely to be endogenous but are an important 355 consideration given that they are likely to be closely linked to the construction of 356 successor availability and identification. however, to avoid unnecessary sample loss 357 and preserve statistical power, we presented model 1, which focuses on the full 2018 358 dataset. 359 farm income and farm viability (defined as the farm receiving at least the minimum 360 wage for family labour and a 5% return on non-land assets) are likely to be associated 361 with farm succession (leonard et al., 2017). both income and viability variables are 362 likely to be highly endogenous in terms of their relationship with farm succession. 363 potential successors may play a very important role in influencing the current farm 364 income and viability due to the labour provided on the farm. at the same time, higher 365 farm income and farm viability may incentivise potential successors to seek to take over 366 the farm business at some point in the future. farm income has a number of components 367 that are exogenous or weakly endogenous including soil quality, land ownership and 368 5 farmers are asked if they have experienced stress/anxiety from any aspect of their farm business in the last 5 years. ten possible sources of stress/anxiety are listed in the questionnaire and farmers are requested to list the top three sources. those who chose “workload e.g. work life balance, seasonal demands (calving, lambing)” as one of the three sources are included in this variable. 16 agricultural training. in the econometric analysis, we therefore include these variables 369 rather than the farm income or farm viability variables individually. 370 a binary variable representing whether farmers have formal advisory (extension) 371 service contract is included since it could be an indicator of the willingness to improve 372 farming practices, thus, continuation of the farm business. 373 participation in an agri-environment scheme (aes) is included since the motivations 374 for participating in aes could be tied to the presence of a potential successor. 375 conversely, the absence of a successor can be associated with less likelihood of aes 376 participation, as it may indicate a winding-down of the farm business operations, 377 limited availability of labour, and an inability to meet the management requirements set 378 by aes. 379 figure 1 shows the average and median age of the farm holder by farm type in ireland. 380 it indicates that certain farm system faces more severe aging problem than others. 381 fig. 1 average and median age of the farm holder by farm type in 2020 382 source: cso census of agriculture 2020 383 384 17 3.3 summary statistics 385 we describe the summary statistics for the variables used in the econometric analysis. 386 using the teagasc nfs 2018 sample of farmers of at least 50 years old, the statistics 387 show that 57 per cent of farmers have identified a successor at that point in time (table 388 1). among them, 34% were dairy farmers. the proportion with a successor varies 389 between farming systems with 53.8 per cent in the case of sheep farms and 58.6 per 390 cent for dairy farms. for the purposes of the econometric modelling and due to the 391 limited sample size, we concentrate the succession analysis on farm holders aged over 392 50 years. 393 table 1 394 farmers who identified successor (over 50 years old) 395 farm system share of successor farms in each system (%) dairy 58.6 cattle rearing 55.7 cattle other 57.2 sheep 53.8 tillage 54.3 total 56.7 source: teagasc national farm survey 2018 396 the descriptive statistics are provided in table 2. for this particular sample, the average 397 age is 63 years old. the average age of farm holders with a chosen successor is 65 years 398 old and without a successor is 60.4 years old. dairy farms are similarly represented in 399 both succession and non-succession groups. dairy farms account for 16 per cent of the 400 farms with a successor and 17 per cent of the farms without a successor. as expected, 401 the proportion of farms in the best soil quality category is higher for those with a 402 successor (32 per cent) relative to those without a successor (26 per cent). 403 the average net new investment is higher on farms with a chosen successor relative 404 to those farms without a successor (€8,085 per annum versus €5,777 per annum). this 405 18 suggests that investment is associated with the probability of farm succession although 406 the direction of causality may not be clear. the presence of an excessive workload or 407 evidence of farm related stress (as reported by the farm holder) is more common on 408 farms without a successor (35 per cent) relative to farms with a successor (24 per cent). 409 there is a notable difference between the two groups in terms of the share of farm 410 holders living alone. table 2 shows that 32 per cent of farm holdings without a chosen 411 successor are living alone. this is much lower at 15 per cent for holdings where a 412 successor is chosen. 413 table 2 414 descriptive statistics of variables used in the analysis 415 all farm holdings (n=538) farm holders with a chosen successor (n= 305) farm holders without chosen successor (n=233) expec ted result6 variable description unit mean sd mean sd mean sd chosen successor whether or not the current farm holder has chosen a successor (0 = no,1 = yes) 0.56 0.50 1.00 0.00 n/a n/a independe nt variables dairy farm farm is specialist dairy farm (0 = no,1 = yes) 0.16 0.37 0.16 0.37 0.17 0.37 + land ownership amount of farmland owned hectares 37.15 28.92 37.30 30.25 36.95 27.16 + interaction of dairy and land ownership interaction between dairy farm and land ownership variables hectares 6.66 17.63 8.65 22.39 7.78 20.46 farmer age age of the farm holder years 63.01 7.79 65.00 7.37 60.44 7.57 + best soil category farm is located on soils with wide range of use (0 = no,1 = yes) 0.30 0.46 0.32 0.47 0.26 0.44 + number of household members age 20 to 44 in 2013* household members in this age category in 2013 number of people 0.53 0.82 0.54 0.83 0.17 0.44 + 6 this indicates the expected direction of influence each variable has on the probability of having a chosen successor, based on prior literatures mentioned earlier section. 19 all farm holdings (n=538) farm holders with a chosen successor (n= 305) farm holders without chosen successor (n=233) expec ted result6 variable description unit mean sd mean sd mean sd isolation of living alone farm holder is living alone (0 = no,1 = yes) 0.23 0.42 0.15 0.36 0.32 0.47 excessive workload self-reported presence of excessive workload (0 = no,1 = yes) 0.29 0.45 0.24 0.43 0.35 0.48 net new investment capital expenditure less capital sales and grants euro 7,080 21,683 8,085 24,783 5,777 16,797 + formal advisory contact farm holder has a formal contract for advisory (0 = no,1 = yes) 0.44 0.50 0.43 0.50 0.46 0.50 + participatio n in aes farm participates in an agrienvironment scheme (see adas 2020) (0 = no,1 = yes) 0.37 0.48 0.36 0.48 0.37 0.48 + source: teagasc national farm survey 2018 416 sd – standard deviation 417 *available for 371 observations 418 419 4. result 420 in this section, we show the results of the probit model in table 3. the lagged variable 421 representing the presence of a young adult within the household is available only for 422 371 observations. this is because the creation of the lagged variable relied on 2013 423 data, and the number of farms that existed in both 2013 and 2018 was smaller than those 424 present only in 2018, leading to a reduced sample size in model 2 compared to the full 425 sample. however, this remains an important variable for examining the potential role 426 of household composition in influencing succession. we therefore present the results 427 of the probit model both excluding and including the lagged variable (shown as model 428 1 and model 2, respectively). this way allows us for greater statistical power in 429 20 estimating the core relationships. marginal effects are calculated to show the change in 430 the probability of succession due to a change in the value of the independent variables. 431 farm income has components that are exogenous or weakly endogenous including soil 432 quality, land ownership and agricultural training. in addition, land ownership is 433 included since irish farm succession mostly occurs via inheritance. the area of owned 434 land could be associated positively with identification of a successor. in this model, we 435 use these variables rather than the reported farm income. 436 we analyse whether the presence of a dairy farm or the farm size influences the 437 likelihood of succession. we expected that the presence of a dairy farm would increase 438 the likelihood of succession due to the relatively higher farm income on dairy farms 439 relative to non-dairy farms (donnellan et al., 2020). however, our model finds land 440 ownership (as opposed to rent) is not statistically significant as shown in table 4 in 441 appendix, even though these factors are considered positive indicators for identifying 442 a successor. this is not consistent with previous studies showing a positive relationship 443 between farm size and the probability of farm succession (kimhi and bollman, 1999; 444 breustedt and glauben, 2007; morais et al., 2018). 445 the absence of a significant relationship between land ownership size and succession 446 and between dairy farming and succession motivated us to explore the possible 447 interaction of dairy farming and (owned) farm size. we therefore add an interaction 448 variable for the presence of a dairy farm with land ownership (hectares). as a result of 449 this interaction, farm size is not significant for non-dairy but is significant for dairy 450 farms. this indicates that only larger dairy farms are associated with a higher 451 probability of farm succession. these results are presented in table 3, which we regard 452 as our main results because the model better captures system-specific succession 453 dynamics and reveals patterns that were not evident in the initial specification shown 454 21 in table 4. the results from the 2020 cso census of agriculture point to an increase 455 in the average size of dairy farms with a reduction in the number of farms classified as 456 specialist dairy. this indicates a rising concentration within dairy farming. the 457 inclusion of the interaction term in table 3 provides greater explanatory power and 458 offers a more nuanced understanding of the conditions under which farm size matters. 459 for this reason, we present table 3 as our main model. 460 the age of the farm holder is positive and statistically significant. this is expected given 461 that older farmers are more likely to have reached the point of identifying a successor 462 (may et al., 2019). the marginal effect indicates that the probability of having a 463 successor increases by approximately 2 per cent per year. stiglbauer and weiss (2000) 464 also found a positive relation between the age of the farm holder and farm succession. 465 the soil quality variable is positive and statistically significant in the farm succession 466 decision. it indicates that better soil quality brings higher productivity, that provide 467 sufficient income to support two generations during succession process. 468 table 3 469 probit model results: determinants of farm succession in 2018 470 471 model 1 model 2 variables coef. sig marginal effect coef. sig marginal effect interaction of dairy farm and land ownership 0.0095 ** 0.0092 * farmer age 0.0535 *** 0.02 0.0585 *** 0.02 best soil category 0.2139 * 0.08 0.198 0.07 excessive workload -0.262 ** -0.09 -0.160 -0.06 isolation of living alone -0.13 0.05 -0.109 -0.04 net new investment (€) 0.004 0.001 0.002 0.0009 formal advisory contract 0.0170 0.006 0.025 0.009 participation in aes 0.317 ** 0.11 0.226 0.08 number of household members age 20 to 44 in 2013** 0.0003 0.0001 constant -3.318 *** -3.645 *** observations 501 349 *** p<0.01, ** p<0.05, * p<0.1 472 473 22 the results in table 3 point to the importance of social factors, including excessive 474 workload. farmers who report excessive workload and related stress are less likely to 475 have identified a successor. while this may seem intuitive, its importance lies in the 476 broader context of succession as a long-term, endogenous process. may et al. (2019) 477 found that farmers experiencing hardship may be reluctant to encourage their children 478 to pursue farming, reinforcing the idea that such stress has cumulative intergenerational 479 effects. succession is a gradual process shaped by accumulated experiences, evolving 480 identities, and intergenerational interactions (fischer and burton, 2014). continuous 481 exposure to workload stress as observed by the next generation, can alter perceptions 482 of farming as a viable or desirable career. such social aspects therefore warrant 483 consideration alongside economic drivers to inform more holistic policy and advisory 484 strategies. 485 isolation is not statistically significant in both model (with/without lagged variable) and 486 has a negative coefficient as expected. it is noteworthy that a substantial proportion of 487 the sampled farmers, approximately 23%, live alone, and these farmers tend to be less 488 likely to identify successors. the marginal effect of isolation is relatively substantial at 489 -5%, compared to other binary variables. while this finding does not reach statistical 490 significance, it aligns with previous research by dudek (2016), which emphasises the 491 importance of household composition in farm succession based on panel data from 492 poland. one possible reason for the lack of significance in our case could be sample 493 size limitations. nonetheless, the results suggest that social factors, particularly 494 household structure, may play an important role in farm succession decisions. 495 therefore, incorporating social dynamics into succession models remains a relevant 496 avenue for further research. 497 23 investment was expected to have a strong association with the probability of succession 498 as reported in a previous study in belgium (calus et al., 2008). here it is found to be 499 positive and not significant. investment could be potentially endogenous given that the 500 presence of a successor could motivate the current holder to invest more. however, we 501 include investment in our model considering its importance, and the fact that farm 502 succession itself has been verified as an endogenous cycle (fischer and burton, 2014). 503 although the investment was not statistically significant in our results, previous 504 research has established it as a key indicator of farm succession. for instance, calus et 505 al. (2008) found that succession intentions begin influencing farm investment decisions 506 up to ten years before the actual transfer. this suggests that even if this variable does 507 not appear significant in our model, it remains a crucial factor in succession planning, 508 reinforcing the need for early successor identification to ensure farm continuity. the 509 lack of statistical significance in our findings may be partly attributed to sample size 510 limitations. further research with larger datasets would be beneficial to fully capture 511 the role of investment in farm succession dynamics. 512 we expected that formal advisory (extension) contract could be an indicator of a higher 513 possibility of farm succession since it may indicate openness or willingness to improve 514 farming activities. while it is not statistically significant, it showed a positive 515 coefficient. including this variable is valuable, as research on farm succession has often 516 placed less emphasis on social factors. access to advisory services can be seen as a 517 form of social capital, potentially influencing farm succession decisions by providing 518 guidance and strategic planning support as abdala et al., (2022) investigated. the lack 519 of statistical significance in our model could be due to heterogeneity in how advisory 520 services are utilised, such as the timing of advisory service use may vary, with some 521 24 farmers engaging with advisors too early or too late in the succession process for 522 measurable effects to be captured within our dataset. 523 farmer participation in an aes is found to be positive and statistically significant in 524 model 1. this supports the conclusion of cullen et al. (2020) that participation in such 525 schemes has a positive association with the willingness to continue farming. 526 participation in environmental schemes can also provide economic incentives to 527 younger farmers. 528 finally, the lagged independent variable for the presence of young adults in the farm 529 household is positive and not significant, which is only included in model 2. although 530 this lagged variable is not statistically significant, it is retained in model 2 due to its 531 conceptual importance in succession literature and to test robustness. while this result 532 does not provide strong evidence in our model, the presence of young adults is often 533 considered a key factor in succession decisions. the use of this lagged variable allows 534 us to capture long-term household dynamics that may influence successor availability. 535 the lack of significance in our results could be due to sample size, timing differences 536 in succession planning, or unobserved factors influencing young adults’ career choices 537 around the age categories. nevertheless, as previous research of fischer and burton 538 (2014) suggests, early exposure to farm life and involvement in decision-making 539 processes play a crucial role in the eventual transfer of farm management. 540 5. discussion and conclusion 541 in this study, we use econometric methods to explore the drivers and barriers to farm 542 succession, incorporating both social and economic factors. while profitability and land 543 ownership are often key drivers of succession decisions (pitson et al., 2020), our 544 findings indicate that social factors, such as workload stress and participation in agri-545 25 environmental schemes (aes), also play a significant role in shaping succession 546 outcomes. these insights suggest the importance of considering social sustainability 547 alongside economic factors in the farm succession process. 548 our results reveal that economic characteristics, particularly land ownership size and 549 soil quality, are key determinants in identifying successors associated with greater 550 successor potential. however, social factors also influence succession decisions by 551 shaping both the current farmer’s management practices and the potential successors’ 552 perceptions of the farm’s long-term viability. for example, workload stress can affect 553 how farmers operate day-to-day, while participation in aes may reflect a more future-554 oriented outlook, which may appeal to younger generations. 555 succession should not be viewed as a one-time decision but as a continuous process, 556 influenced by ongoing actions and experiences. the decisions of the current farm 557 manager, such as improving soil quality or expanding land, directly affect how potential 558 successors view the farm’s future and their potential role. these accumulated 559 experiences contribute to the successor’s identity formation and long-term engagement 560 with the farm. 561 in line with calus (2009), our findings challenge the traditional view that succession is 562 driven solely by economic incentives. instead, we show that succession unfolds 563 gradually, shaped by evolving social and familial dynamics. social and economic 564 elements interact over time, making succession a dynamic process rather than a fixed 565 event. supporting this view, findings from a cluster analysis of irish farm types further 566 confirm that succession decisions are nuanced and shaped by farm-specific economic 567 viability and household or workload-related social conditions (loughrey et al., 2025). 568 notably, our study contributes to the growing body of literature by quantitatively 569 examining social factors (abdala et al., 2022; špička and berg, 2022) and providing 570 unique insights into their role in farm succession dynamics. high work intensity, 571 26 especially in demanding systems like dairy farming, can contribute to farm exits 572 (ferjani et al., 2015), further influencing succession dynamics. in addition, succession 573 decisions evolve gradually, shaped by long-term experiences rather than isolated events 574 (lobley, 2010). the long-term impact of witnessing parental stress from farming can 575 influence the decisions of potential successors, underlining the importance of resilience 576 across both social and material dimensions (darnhofer, 2020). 577 further supporting this perspective, bertolozzi-caredio (2024) confirms that the 578 presence of a successor influences the behaviour of incumbent farmers, reinforcing the 579 idea that farm succession is a dynamic and mutual process. to facilitate smoother 580 transitions, targeted strategies are needed to support both successors and incumbents. 581 one such approach includes fostering farmer partnership agreements or hiring 582 additional labour that have proven beneficial but remain underutilized (conway et al., 583 2017; garcia et al., 2023; shin et al., 2023). these measures not only alleviate excessive 584 workloads but also enhance the attractiveness of farming as a viable career path for the 585 next generation. 586 farms with lower incomes can seek to improve farm viability through participation in 587 aes schemes for example as an economic incentive, particularly on less intensive 588 farms, to stabilise farms. cullen et al. (2020) notes that a forward-looking self-identity 589 is linked to greater aes participation, ensuring farm continuity for future generations. 590 furthermore, innovative environmental practices, such as organic farming, may attract 591 younger farmers with new financial incentives through the cap (farrell et al., 2022). 592 policy should broaden its focus to enable succession not only within families but also 593 through hired labour or non-relatives. our findings imply that policies should address 594 the pathways for both new entrants and incumbents, potentially facilitating partnerships 595 27 to share workload, knowledge, and profits, thereby ensuring the sustainable 596 continuation of farming enterprises. 597 a limitation of this study is that that it relies on farmers’ self-reports about whether 598 they have identified a successor, rather than tracking actual succession events. in 599 addition, capturing farm succession over a longer period would be beneficial. the 600 endogenous cycle of farm succession highlights the link between farmers' identity 601 development and the farm business ladder, which could be better understood using a 602 panel data framework, as in dudek and pawłowska (2022). this would help establish 603 the cause-and-effect relationship over time and provide more evidence on the impact of 604 succession on farm continuity, productivity, and sustainability. 605 this study provides valuable insights into farm succession by utilizing a nationally 606 representative dataset. while the dataset used is from 2018, making it one of the most 607 recent sources available for analyzing long-term succession trends, it does not capture 608 the potential impacts of recent economic and social changes, such as the covid-19 609 pandemic or the ongoing inflationary crisis. these events may have influenced 610 succession decisions by altering economic stability, labour availability, and 611 generational attitudes toward farming. future research should incorporate more recent 612 data to assess these effects. 613 the statistical significance of some results is limited, partly due to the sample size 614 constraints. expanding the dataset or complementing quantitative findings with 615 qualitative approaches, such as interviews with farmers and advisors, could provide 616 deeper insights into the role of social capital in succession planning. 617 despite these limitations, this study highlights the importance of understanding both 618 economic and social factors in farm succession. future research should continue 619 28 exploring these aspects, particularly in the context of evolving economic conditions and 620 policy changes that shape generational renewal in agriculture. 621 622 acknowledgement 623 funding provided by the department of agriculture, food and the marine under the 624 project entitled renew2050 rural generational renewal 2050 2019r414. 625 references 626 abdala, r. g., binotto, e., and borges, j. a. r. 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(2015). deconstructing the ‘young farmer problem in 904 europe’: towards a research agenda. journal of rural studies, 38, 39-51. doi: 905 https://doi.org/10.1016/j.jrurstud.2015.01.003 906 907 https://doi.org/10.1016/j.jrurstud.2015.01.003 39 appendix 908 table 4 909 probit model results: determinants of farm succession in 2018 910 model 1 model 2 coef. sig marginal effect coef. sig marginal effect dairy farm 0.3019 0.12 0.3504 * 0.12 land ownership -0.0008203 0.00 -0.000959 0.00 farmer age 0.05858 *** 0.02 0.05407 *** 0.02 best soil category 0.1991 0.07 0.2050 0.07 isolation of living alone -0.1683 -0.05 -0.1717 -0.06 excessive workload -0.1421 -0.05 -0.2589 * -0.09 net new investment 0.000000949 8 0.00 0.0000008113 0.00 formal advisory contract 0.01844 0.01 0.008074 0.00 participation in aes 0.1900 0.07 0.2910 * 0.1 number of household members age 25 to 44 in 2013** -0.005908 -0.00 constant -3.736 *** -3.443 *** number of obs 349 501 pseudo r-squared 0.161 0.124 *** p<.01, ** p<.05, * p<.10 911 912 do economic performance and innovation have a relationship? evidence from operational groups in the italian agri-food sector francesco mazzulla and meri raggi francesco mazzulla: corresponding author, dept. of statistical sciences, university of bologna, via belle arti 41, 40126, bologna, francesco.mazzulla@unibo.it meri raggi: dept. of statistical sciences, university of bologna, via belle arti 41, 40126, bologna, meri.raggi@unibo.it this article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the version of record. please cite this article as: mazzulla f., raggi m. (2025). do economic performance and innovation have a relationship? evidence from operational groups in the italian agri-food sector, just accepted. doi: 10.36253/bae16801 highlights • better economic performance for farms located in regions with ogs. • farm performance is enhanced by a stimulating environment. • innovation linked to farm economic performance. this study was carried out within the agritech national research center and received funding from the european union next-generationeu (piano nazionale di ripresa e resilienza (pnrr) – missione 4 componente 2, investimento 1.4 – d.d. 1032 17/06/2022, cn00000022). this paper reflects only the authors’ views and opinions, neither the european union nor the european commission can be considered responsible for them. abstract this study aims to investigate the potential mutual interdependence between an environment that fosters and encourages innovation and the economic performance of agricultural businesses operating in the sector. specifically, it seeks to determine whether the economic performance of farms in mailto:francesco.mazzulla@unibo.it mailto:meri.raggi@unibo.it regions with established operational groups (og) is better than that of farms located in regions where ogs have not yet been implemented, using the european innovation partnership for agricultural productivity and sustainability (eip-agri). we combine data on og’s collected from the innovarurale website, with financial information from the orbis database for the period 2013-2022, to assess farm performance. our estimation strategy employs three staggered difference-in-differences (did) models and an event-study to validate the parallel trends assumption. the results show a positive association between the presence of ogs in a region and an improved economic performance. our findings suggest that the diffusion of innovation tends to be related to the characteristics of the local economic environment, which should be a critical factor in future policy discussions. keywords: eip-agri; staggered difference-in-differences model; operational groups; economic performance evaluation jel codes: q18; o30 1. introduction a study on the 7th italian agricultural census1 (henke and sardone, 2022), reveals that the italian agri-food sector is undergoing significant structural changes. between 2010 and 2020, the number of farms declined drastically, with 380,000 out of 500,000 missing farms being smaller than 2 hectares. meanwhile, larger farms expanded, with those over 100 hectares growing by 17% and those between 50-100 hectares increasing by 11%. despite this consolidation, micro and small farms face challenges due to a lack of entrepreneurial skills, limiting their adaptability. additionally, the expected generational transition has not materialized, raising concerns about the sustainability of small-scale 1 https://www.istat.it/statistiche-per-temi/censimenti/agricoltura/7-censimento-generale/ https://www.istat.it/statistiche-per-temi/censimenti/agricoltura/7-censimento-generale/ farming in italy. innovation trends from the census further highlight sectoral shifts. over 50% of farms, particularly in the north, have adopted mechanization for planting, tillage, and irrigation, enhancing efficiency. digitalization has risen sharply, with 15.8% of farms using it compared to just 3.8% in 2010. however, diversification efforts remain limited, hindered by farm size and generational gaps. regional disparities persist, with northern farms embracing technology more rapidly than those in the south. these findings indicate that while innovation is progressing, barriers to equitable and widespread adoption remain. in the agri-food sector, according to golubev et al. (2021), knowledge dissemination in agriculture could increase productivity, adapting to environmental and market changes, and improving decisionmaking. it helps farmers adopt innovative practices, manage resources efficiently, and make informed choices, boosting financial stability. it fosters continuous learning through shared experiences and provides access to resources like financial aid and training. promoting sustainable farming practices ensures environmental conservation and long-term agricultural viability, contributing to food security and broader sustainable development goals for future generations. to improve this aspect in the italian agri-food sector, the eip agri2 (european innovation partnership for agricultural productivity and sustainability) has been implemented with the aim of improving agricultural productivity and sustainability through collaborative efforts among various stakeholders, including farmers, researchers, advisors, and sector representatives. the eip agri promotes a multi-actor and interactive approach, as a method of work, and this is applied in the implementation of ogs and of multi-actor research projects and thematic networks (under horizon 2020, and horizon europe). this approach highlights the active participation of diverse stakeholders during the project, enhancing collaboration and integrating their knowledge into the innovation process. the interactive approach facilitates a reciprocal exchange of ideas, merging scientific knowledge with practical insights from stakeholders, thus expediting the implementation of new 2 https://ec.europa.eu/eip/agriculture/en/node.html https://ec.europa.eu/eip/agriculture/en/node.html concepts. knowledge sharing is central to this approach, encompassing both formal education and informal peer exchanges, which can improve learning outcomes and yield more effective solutions. the main aim is to identify, develop, and implement innovative solutions tailored to the specific needs of farmers and rural communities (european commission, 2014), and places emphasis on the importance of knowledge exchange and sharing as a practice among stakeholders to enhance the overall effectiveness of agricultural practices. this is achieved through various dissemination activities, including workshops, training sessions, and the use of digital platforms. the european commission study (2024) looks at the results and effects of eip-agri support in order to get ready for the ex-post review scheduled for 2026. the study provides information on the effectiveness of og calls and possible areas for improvement by evaluating how member states' answers to these calls have impacted the achievement of results. the parameters set by og calls have been shown to promote the co-generation of innovative solutions, indicating a collaborative approach to problem-solving. the results indicate that these calls have increased the likelihood that innovative ideas will be embraced more broadly, perhaps leading to improved stakeholder engagement and the development of new partnerships. arzeni et al. (2023) study the implementation of eip-agri in italy to analyse the effectiveness of ogs. the authors conduct an online survey finding that the implementation of ogs has been effective in capturing the real issues faced by farmers and rural entrepreneurs. this effectiveness is attributed to the collaborative nature of the groups, which fosters relationships among partners and enhances the innovation process. the study identified also low levels of both internal and external communication within ogs, which hindered the dissemination of results and reduced overall effectiveness. as a result, the authors discuss how future ogs should focus on improving their capacity to address the needs of larger groups of farmers and enhance their communication strategies to better share innovative solutions. the aim of this paper is to use the implementation of eip-agri, combined with financial data, as a tool to identify the italian regions that favour innovation through the financing of ogs and thus investigate the mutual interdependence between an environment that supports and encourages innovation and the economic performance of agricultural businesses operating in the sector. using an estimation strategy suitable to analyse a policy characterised by a staggered implementation over time (staggered did) we show that farms located in regions where ogs are present appear to experience a positive and significant increase in their economic performance. in the first section there will be a review of the literature related to the topic discussed in this study; in the second section we examine the data and the main variables used in the analysis and in the third section we offer an overview of the methodological approach implemented in this study. finally, we present the results (fourth section) and discuss the main implications (fifth section). 2. background innovation in the agrifood sector is often limited due to a combination of several factors. for example, manning (2024) explains how farmers face challenges like socio-technical barriers, such as external factors (market dynamics, bureaucratic hurdles) and internal factors (employee relations, financial constraints). moreover, both public and private sector investments in agrifood innovation have decreased, limiting research and development opportunities (carlberg, 2024). finally, a lack of awareness about food security and the benefits of digital innovations affects farmers' intentions to adopt new technologies (aboagye-darko and mkhize, 2025). the literature has emphasised that to foster innovation in the agrifood sector could be necessary several different strategies. to enhance innovation in the agrifood sector, it is essential to implement a diverse range of strategies tailored to different dimensions of the innovation ecosystem. .a significant strategy is to engage stakeholders inclusively, ensuring that farmers, entrepreneurs, and researchers can collaborate meaningfully in digital co-innovation initiatives (gouthon et al., 2024).another strategy is to increase public investment in agricultural r&d,with the goal of allocating at least 0.10 percent of gdp to drive scientific advancement and technological progress (carlberg, 2024).furthermore, promoting sustainability-oriented innovation requires policies that support eco-innovation through environmental education, capacity building, and robust institutional frameworks (chaparro-banegas et al., 2024). at the european level, innovation in the agrifood sector is supported through a combination of policies, strategic frameworks, and funding instruments. the eu research and innovation policy provides the overarching framework for initiatives such as horizon europe, a key funding programme that serves as an instrument to support research and technological development. within horizon europe, cluster 6 ("food, bioeconomy, natural resources, agriculture and environment") finances a wide range of projects related to sustainable agriculture, including but not limited to precision farming, agricultural robotics, biotechnology, circular bioeconomy, and climate-resilient farming systems. strategic documents such as the european green deal and the farm to fork strategy guide the eu’s long-term vision for sustainability and food system transformation. while not policies in themselves, these strategies shape legislative and funding priorities. they emphasize the reduction of pesticide and fertilizer use, support the development of sustainable business models, and promote innovative practices that enhance both environmental and economic performance in agriculture. crucially, the common agricultural policy (cap) remains the central eu policy for supporting agricultural innovation. through mechanisms such as operational groups (ogs) under the european innovation partnership for agricultural productivity and sustainability (eip-agri) and the strengthening of the agricultural knowledge and innovation system (akis), the cap facilitates knowledge exchange, co-creation, and the uptake of innovative solutions among farmers, researchers, advisors, and other stakeholders. in this context, the eip-agri also fits in, promoting collaboration between farmers, researchers and businesses to accelerate the adoption of innovations and financing operational groups to develop innovative solutions in the agricultural sector. this paper uses eip-agri as a tool to analyse the link between innovation and economic performance. this relationship is explored in literature from two perspectives: innovation as a driver of economic performance, and vice versa. to examine which studies have found that innovation in the agri-food sector can lead to improved economic performance, we can mention ponta et al. (2022) that describe a positive relationship between sustainability-oriented innovation outputs and production value growth and ebitda (earnings before interest, taxes, depreciation, and amortization) growth in farms. moreover, research across the eu-28 showed that countries with higher investment in r&d and agricultural education achieved better agricultural performance (coca et al., 2017). technological eco-innovation and r&d spending were identified as key conditions benefiting company performance in the spanish agri-food sector (rabadán et al., 2019). additionally, regional clusters were found to play a role in spreading innovation among associated farms, particularly for small and medium-sized ones that face difficulties innovating individually (finco et al., 2018). these studies collectively highlight the importance of innovation, sustainability-oriented practices, and collaborative efforts in driving economic growth and competitiveness in the agri-food sector. the literature also proposes a cyclical pattern in which innovation might result in better economic performance, which in turn enables additional investment in innovation (francois and shi, 1999; baláž et al., 2023; lucchese and pianta, 2011; brusoni et al., 2006). the regional performance of the agri-food sector significantly influences new investment in innovation. studies show that organizational factors, including management, strategy, structure, culture, climate, and market orientation help to achieve innovative performance in regional agri-food companies (corchuelo martínez-azúa et al., 2020). regional context affects both the capacity and nature of innovation, with marketing innovation in farms linked to prior ict experience (peón and martínez-filgueira, 2020). small farms face challenges in achieving innovation individually, but regional clusters can help develop innovation strategies and increase competitiveness (finco et al., 2018). public funding can also support innovation, with regional funding being more accessible to agri-food companies compared to state funding (alarcón and arias, 2018). therefore, creating an enabling environment could help stimulate for agricultural innovation. to the best of our knowledge, no previous studies have examined this link using farm-level financial data and a european policy instrument, such as eip-agri, as a tool to identify the farms that are located in an area where there are ogs promoting the dissemination of innovative knowledge in the sector. this paper aims to fill the gap in the literature using an estimation method that provides robust results when the policy is implemented at a staggered level. 3. data we combine data from two different sources. we collected data on the year of first implementation, the duration of the project and the total number of ogs per region from the official italian eip-agri website (innovarurale3) (table 1). as can be seen there are some regions in our sample that do not yet have ogs (abruzzo, lazio, molise, sardegna, valle d'aosta). some of these regions (lazio and molise) are currently in the selection phase. others (abruzzo and sardegna) began forming their first ogs in 2023. finally, valle d'aosta has not activated this measure. the second dataset used is the orbis4 database (bureau van dijk). orbis provides financial data on corporations for the past 10 years. the data are limited first of all to consider only active farms providing financial data in the period 2013-2022. table 1: number of new ogs by italian region and by year region 2016 2017 2018 2019 2020 2021 2022 total abruzzo 0 0 0 0 0 0 0 0 basilicata 0 5 6 0 0 0 0 11 calabria 0 0 0 0 0 2 2 4 campania 0 0 0 34 6 11 0 51 emilia-romagna 51 37 5 44 43 33 0 213 friuli v. g. 0 0 0 8 0 0 0 8 lazio 0 0 0 0 0 0 0 0 liguria 0 0 0 0 15 6 0 21 lombardia 0 0 0 25 0 0 0 25 3 https://www.innovarurale.it/it/pei-agri/i-gruppi-operativi-italia 4 https://login.bvdinfo.com/r0/orbis marche 0 0 4 26 12 0 16 58 molise 0 0 0 0 0 0 0 0 piemonte 0 0 5 3 20 4 0 32 puglia 0 0 0 0 47 1 0 48 sardegna 0 0 0 0 0 0 0 0 sicilia 0 0 0 5 45 4 7 61 toscana 0 0 0 48 4 0 0 52 trentino a. a. 3 8 4 0 2 0 0 17 umbria 1 1 11 0 1 0 0 14 valle d'aosta 0 0 0 0 0 0 0 0 veneto 0 3 19 39 0 0 0 61 total 55 54 54 232 195 61 25 676 note: the italian ogs started to be formed in 2016. source: authors’ calculation using innovarurale data. then, according to the nace classification code: limited to the following codes: 01 (crop and animal production, hunting and related service activities); 02 (forestry and logging); 03 (fishing and aquaculture). from 2013 to 2022, we derive farm-level financial data to add the economic performance of the agri-food sector to our analysis. in table 2 are summarized the descriptive statistics for the economic performance of selected variables. table 2: descriptive statistics of the economic performance variables variable obs mean std. dev. min max operating revenues 111779 1.986 5.947 0 44.716 total assets 111779 3.572 7.628 .006 52.254 net income 111773 .017 .191 -.727 1.118 cash flow 93241 .132 .392 -.479 2.653 shareholders’ funds 111779 1.221 3.341 -.194 23.172 solvency ratio 110788 31.173 31.044 -34.992 99.268 source: authors’ estimation from orbis database. 4. estimation strategy we assign the farmers that are located in a region affected by the presence of ogs to a treatment group and those not affected by that to a control group. therefore, a method that could be used to analyse this reform is the did model, because it is appropriate to focus on the impact of the policy. as not all regions started to implement the ogs in the same year, there are more than two time periods and units are treated at different points in time. roth et al. (2023) stated that using the 2x2 did (2 groups, 2 periods) could lead to potential bias in the estimated result. as a result, a staggered did model might be the best choice in this case. we use three different recently developed estimation techniques. callaway and sant'anna (2021) develop a methodology characterized by the concept of "group-time average treatment effect", that is the average treatment effect for group g at time t, including information regarding the unit's first year of treatment: 𝐴𝑇𝑇(𝑔, 𝑡) = 𝔼[𝑌𝑖,𝑡(𝑔) − 𝑌𝑖,𝑡(∞)|𝐺𝑖 = 𝑔] ( 1) the average treatment effect on the treated (att) can be interpreted as the difference between the potential outcome: the operating revenues (for example) of the i region in period t if there have been affected by og implementation at time g, and if there has not been influenced by time t. the difference between the expected change in operating revenues for group g between periods g-1 and t and the result for the control group that was not yet treated at period t is known as the att (roth et al., 2023). the final estimation of att is: 𝐴𝑇�̂�(𝑔, 𝑡) = 1 𝑁𝑔 ∑ [𝑌𝑖,𝑡 − 𝑌𝑖,𝑔−1 𝑖:𝐺𝑖=𝑔 ] − 1 𝑁𝐺𝑐𝑜𝑚𝑝 ∑ [𝑌𝑖,𝑡 − 𝑌𝑖,𝑔−1] 𝑖:𝐺𝑖𝜖𝑐𝑜𝑚𝑝 ( 2) where 𝑁𝑔 is the number of regions where there has been new og implementation at time g. 𝑁𝐺𝑐𝑜𝑚𝑝 is the number of regions in the control group, not-yet-treated by time t. the difference in parentheses represents the difference between the dependent variable of the treated regions at year t and before the policy occurred (g – 1). the main drawback of this approach is that the control period is limited to the period immediate prior the intervention. consequently, to compute the att, we use also the imputation approach of borusyak et al. (2024), that consider the entire pre-treatment period as the reference period. the authors start from a twfe model: 𝑌𝑖,𝑡(∞) = 𝛾𝑖 + 𝛿𝑡 + 휀𝑖,𝑡 ( 3) considering only regions that are not-yet treated during the periods. after that they use the predicted value from this regression to calculate the never-treated outcome for each treated region, to estimate the treatment effect for each treated unit and then aggregate them to compute the att. finally, the model proposed by de chaisemartin and d'haultfœuille (2020), with a binary treatment and two periods, consists of a weighted average of two did estimators, the first one compares the evolution of the dependent variable between the “switchers in” observations (those transitioning from untreated to treated) and the never treated ones between period 1 and period 2, the second estimator compares the evolution of the dependent variable between the always treated observations and the “switchers out” (that go from treated to untreated) between period 1 to period 2. the final estimator, once derived from the previous calculations, could be applied also to analyse staggered policies that have been affected by with more than two periods. the method is basically the same, but the comparison is for each pair of consecutive time periods and then computing the average of these estimators across time. furthermore, de chaisemartin and d'haultfœuille (2024) proposed another type of estimator that can deal with a non-binary treatment variable, that is suitable for analysing treatment intensity. in fact we will use this model to introduce in our study the number of ogs for each year and region as the treatment variable, instead of working with the binary variable considered in the other settings. since different parallel trends assumptions characterize each of these approaches (marcus and sant’anna, 2021), we perform more than one estimation and, as roth et al. (2023) recommend when there are multiple pre-treatment periods, we test for pre-trends using the event study plot developed by borusyak (2023). 5. results the total number of farms in our sample is 11496. the treatment group consist of 9607 farms, while 1889 are located in regions where ogs have not yet been implemented. in order to verify the parallel trend assumptions needed for each of the three approaches we start by looking at the average trends of the dependent variables (figure 1). since 2016 is the first year that ogs start to form, we selected it as the graph's cut-off year. for any specification of the dependent variable, it is quite evident that the trends exhibit a parallel pattern prior to the policy's implementation (except for solvency ratio). figure 1: average trends of the dependent variables in the treatment group and in the control group using an event-study analysis in figures 2, we offer a visual assessment of the pre-existing patterns in accordance with the pre-testing methodology (roth et al., 2023). since at least two models out of three shows not-significant pre-trend estimates (all of the intervals related to the pre-treatment include the zero value) the parallel trend assumption appears to be true and, consequently, we cannot reject the null hypothesis that the parallel trend was satisfied during the pre-treatment phase, confirming the validity of our design. figure 2: comparison of the staggered did estimators using the event-study plot having shown that the data support the parallel trend hypothesis, we can proceed to investigate in detail the differences between the two groups. table 3 reports the average treatment effects on the treated (atts) estimated using the three staggered difference-in-differences (did) models outlined in the previous section. the positive and statistically significant coefficients indicate that the farms located in the treated regions exhibit improved economic performance compared to those in regions where og was not yet implemented. although the estimated effects vary in magnitude across the different model specifications, all results consistently point to a positive and statistically significant impact. these findings provide empirical evidence supporting the hypothesis that the presence of ogs is associated to an increase in economic performance. table 3: staggered did estimation results (atts) operating revenues total assets net income cash flow shareholders funds solvency ratio callaway and sant’anna (2021) borusyak et al. (2024) de chaisemartin and d'haultfoeuille (2020) 0.142*** 0.102*** 0.135*** 0.176*** 0.107*** 0.165*** 0.003* 0.006*** 0.003* 0.015*** 0.016*** 0.014*** 0.112*** 0.115*** 0.104*** 0.639** 1.091*** 0.561** legend: * p<.1; ** p<.05; *** p<.001. variables are expressed in millions (except for solvency ratio). finally, we assess the effect of treatment intensity (measured by the number of ogs), on economic performance, as reported in table 4. table 4: staggered did estimation results (atts) using the intensity to treatment (number of ogs), according to de chaisemartin and d'haultfoeuille (2024) operating revenues total assets net income cash flow shareholders funds solvency ratio de chaisemartin and d'haultfoeuille (2024) 0.002*** 0.003*** 0.00007* 0.0002*** 0.002*** 0.011** legend: * p<.1; ** p<.05; *** p<.001. variables are expressed in millions (except for solvency ratio). this analysis employs the estimator proposed by de chaisemartin and d'haultfoeuille (2024) as a robustness check, giving its suitability for handing non-binary treatment variables. the application of this model adds further depth to our empirical strategy. the results reinforce our previous findings: the positive and statistically significant coefficients suggest that a higher number of ogs in treated areas is associated with a significant improvement in farms’ economic performance. 6. discussion the results indicate the existence of a reciprocal relationship between farms’ economic performance and an environment that encourages and supports innovation. this relationship emerges consistently across all three model specifications and when considering six different economic performance variables. several possible interpretations may explain this finding. one plausible explanation, supported by finco et al. (2018), is that farms located in areas where ogs are active benefit from the diffusion of innovation fostered by the european innovation partnership (eip), which provides funding and resources aimed at supporting innovation uptake. in this view, the presence of ogs encourages farms to adopt innovative practices, thereby improving their economic performance. an alternative hypothesis, as suggested by corchuelo martínez-azúa et al. (2020), asserts that certain italian regions possess distinctive socioeconomic characteristics that both facilitate the emergence of ogs and provide a fertile ground for the development of innovative networks. these pre-existing regional conditions could, in turn, enhance the dissemination and effectiveness of innovation initiatives. this study has two main limitations. first, our study does not constitute a formal impact evaluation of the eip-agri, as it is not possible to precisely identify which of the farms in our sample are directly involved in ogs therefore, we are unable to attribute the observed effects to one explanatory theory over the other. second, the orbis database, while rich in financial information, is not fully representative of the italian agri-food sector. in particular, it underrepresents smes, which are highly prevalent in the italian context. despite these constraints, our findings offer valuable insights into the dynamics of innovation support in agriculture and provide a preliminary understanding of how the ogs have been implemented within the italian agri-food sector. 7. conclusions this study investigates the relationship between the economic performance of the italian agri-food sector and the presence of an innovation-friendly environment. we apply an estimation strategy that compares three distinct staggered did models employing recently developed methodological frameworks to assess this association. to the best of our knowledge, this is the first study to use the eip-agri as an analytical tool to explore the relationship between regional economic performance in the agri-food sector and an innovation-friendly environment. the main findings suggests that farms located in regions where ogs have been recently established show significantly better financial performance compared to those in regions without the presence of ogs. however, the data also reveal regional disparities in the effectiveness of this dynamic, specifically the expected virtuous circle of innovation, improved economic performance and increased regional investment in innovation, appears more evident in some regions than in others. these findings could carry implications for policymakers. to promote the diffusion of innovation and improve local farm performance, targeted awareness campaigns and better-targeted incentive mechanisms may be necessary in regions where such dynamics are less evident. future research should focus on refining the identification of farms that are involved in the ogs, which would allow for the application of causal inference techniques to more precisely evaluate the impact of eip-agri. additionally, expanding the dataset to include non-corporate farms particularly smes would contribute to a more comprehensive analysis of the sector. furthermore, understanding the temporal dimension of policy impacts, namely the lag between project completion and observable impacts could improve the evaluation of innovation policies. this study offers an overview of the implementation of the eip-agri across the italian regions, providing insights into how the regional socioeconomic environment is connected with the diffusion of innovation and how the policy could be more consistently and effectively implemented nationwide. references aboagye-darko, d., & mkhize, p. 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(2023). what’s trending in difference-indifferences? a synthesis of the recent econometrics literature. journal of econometrics, 235(2), 2218-2244. towards digital farming: exploring technological integration in agricultural practices of a 1 sample of italian livestock farms. 2 ogochukwu felicitas, okoye *1; selene, righi 1; colomba, sermoneta 2; gianluca, brunori 1; michele, 3 moretti1. 4 1 university of pisa/department of agriculture, food and environment, pisa, italy 5 2 national institute of statistics (istat), italy 6 * corresponding author: ogochukwu felicitas okoye (felicitas.okoye@phd.unipi.it) 7 this article has been accepted for publication and undergone full peer review but has not been 8 through the copyediting, typesetting, pagination and proofreading process, which may lead to 9 differences between this version and the version of record. 10 11 please cite this article as: 12 okoye of, righi s, sermoneta c, brunori g, moretti m (2025). towards digital farming: 13 exploring technological integration in agricultural practices of a sample of italian livestock 14 farms, just accepted. doi: 10.36253/bae-16777 15 16 17 abstract 18 despite the rapid rise of digital technologies in agriculture, their application remains more 19 prominent in crop farming than in the livestock sector. recognizing this gap, our study explores 20 the current state and determinants of digital technology adoption across italian livestock farms, 21 examining key factors and broader trends in the industry. using national agricultural census, and 22 national statistical programme data, we applied a logistic regression model to assess the 23 likelihood of adoption of technology. findings reveal that large ruminant farms, particularly dairy 24 cattle and buffalo, are more likely to integrate digital tools like decision support systems, cloud 25 services, and monitoring devices. in contrast, meat cattle, small ruminants, and pig farms lag. 26 key determinants include broadband connectivity, ownership structure, education, and age, with 27 additional factors influencing specific technology categories. our results establish a foundation 28 for future policy and investment, underscoring the need to build digital infrastructure and promote 29 an inclusive model. 30 keywords: digital agriculture, livestock farming, technology adoption. 31 jel codes: o33, q16, c83. 32 33 mailto:felicitas.okoye@phd.unipi.it 1 introduction 34 the livestock industry has undergone significant transformations driven by evolving human 35 needs, changes in consumption (righi et al., 2023), and technological advancements (subach & 36 shmeleva, 2022). traditionally, livestock farming relied on subjective and less quantifiable but 37 holistic approaches to animal welfare and management (buller et al., 2020). however, a shift 38 towards modern, data-driven processes characterized by interconnectivity and efficiency marks 39 a significant change. often referred to as digital farming, this shift includes concepts such as 40 smart farming, precision agriculture, and agriculture 4.0, rooted in the sustainability discourse 41 and bolstered by advancements in information technology (zhou et al., 2022). this shift is driven 42 by concerns over food insecurity, economic factors, climate change, market dynamics and 43 sustainability (thornton et al., 2014). 44 digital technologies advance agricultural practices by enabling diagnosis, intelligent perception, 45 decision-making, and improved production processes (zhou et al., 2022; finger, 2023). applied 46 along the entire value chain, they enhance resource management, trade promotion, operational 47 efficiency, and knowledge exchange (barnes et al., 2019; elijah et al., 2018), promoting 48 sustainability, resilience, and overall competitiveness (finger, 2023). however, adoption 49 challenges persist. finger (2023) notes that technologies with more potential benefits are least 50 profitable and less widely adopted by small scale farmers due to their high investment costs and 51 limited returns. large and capital-intensive farms tend to benefit disproportionately from these 52 technologies, exacerbating existing inequalities (hackfort, 2021). additional challenges include 53 data ownership and power distribution within the farming community (morrone et al., 2022; 54 neethirajan & kemp, 2021; wolfert et al., 2017). 55 despite the growing integration of digital technologies in agriculture, livestock farming remains 56 one of the least digitalized sectors globally (neethirajan & kemp, 2021). meanwhile, adoption 57 is diverse and limited to basic aspects of livestock management (guntoro et al., 2019). barriers 58 to adoption include social factors such as low levels of trust in new technologies, digital illiteracy, 59 and resistance to change (eastwood et al., 2021; fao, 2022), as well as technological challenges 60 like infrastructure limitations and system integration, compatibility, and interoperability (abeni 61 et al., 2019; tuyttens et al., 2022). economic factors include high cost of technology, uncertainty 62 about the return on investment, and misaligned business models (groher et al., 2020). 63 additionally, the complexity of agroecosystems, compounded by difficulties in collaboration 64 among stakeholders, agreeing on common goals and farming practices represent barriers to 65 successful digitalization (grivins & kilis, 2023). these combined factors create a complex 66 environment that limits widespread adoption of digital technologies in livestock farming (cui & 67 wang, 2023). moreover, digital technology development is dominated by a small number of 68 powerful multinational corporations, prioritizing digital innovations that support their market 69 dominance, potentially misaligning with the practical needs of smaller farms (hackfort 2021). 70 research related to digitalisation in the livestock sector has focused more on certain livestock 71 species, particularly cattle, with less emphasis on small ruminants like sheep and goats (morrone 72 et al., 2022; tzanidakis et al., 2023) and other livestock such as pigs and buffaloes. this disparity 73 has implications for animal welfare, leading to speciesor livestockspecific differences 74 (tuyttens et al., 2022). therefore, it is crucial to comprehend the extent of digital technology 75 usage in livestock production and management (fuentes et al., 2022). in italy, the livestock sector 76 is vital not only economically but also for its cultural heritage (pulina et al., 2017). local breeds, 77 adapted through selective breeding, support sustainable husbandry and rural communities. by 78 leveraging breed diversity and innovative technologies, the sector can address challenges like 79 greenhouse gas emissions and resource management, contributing to both productivity and 80 sustainability. 81 the primary goal of this research is to explore the extent of digitalization among italian livestock 82 farms and identify the factors influencing digital technology adoption. this research contributes 83 to existing literature in several ways. first, the study provides a comprehensive analysis across 84 all livestock categories, including small ruminants and monogastric species, thereby addressing 85 a critical gap in digitalization research within the livestock sector, predominantly focused on 86 dairy cattle and large ruminants. second, by leveraging a large-scale, nationally representative 87 datasets, we offer robust empirical evidence on the determinants of digital technology adoption, 88 moving beyond the commonly acknowledged availability of digital solutions to assess the actual 89 uptake and utilization of these technologies at the farm level. third, this study introduces a new 90 economic dimension by examining the role of marketing channels and their influence on digital 91 investments, shedding light on how different marketing sales strategies may either facilitate or 92 hinder the adoption of technological innovations. the findings of this research provide valuable 93 insights for policymakers, technology providers, and researchers seeking to foster a more 94 inclusive and effective digital transformation in livestock farming. specifically, it seeks to 95 answer: what is the current extent of digital technology usage in livestock production and 96 management? what factors influence the adoption these technologies? the paper goes on with 97 a literature review on how digital technologies have been integrated into the livestock sector over 98 time. in section 3, the material and methods are explained. the results of the analysis are 99 presented and discussed in section 4. we conclude with reflections on policy implications and 100 recommendations. 101 2 digitalization in the livestock sector 102 digitalization in the livestock sector encompasses a broad array of technological innovations 103 reliant on digital infrastructures and networks. these components enhance the creation, storage, 104 and exchange of data, improving the functionality of various digital tools. initially, information 105 and communication technologies (ict) were pivotal in precision agriculture (cox, 2002), but 106 today’s digital solutions leverage the internet, big-data technology, cloud computing, and the 107 internet of things (iot) through sensors and wireless communication networks (zhou et al., 108 2022). 109 technologies in this digital shift, as recorded in the early 2000s, involve basic data management 110 systems, including electronic identification systems such as radio frequency identification 111 (rfid) tags, tracking collars, and wearable biosensors, enabling farmers to monitor individual 112 animal health, behaviours, and movement (eastwood et al., 2021). these tools have improved 113 animal welfare and productivity through early detection of health issues (zhou et al., 2022). in 114 europe, farm management information systems (fmis), barn cameras, and sensors are 115 increasingly integrated into livestock management (gabriel & gandorfer, 2023). this phase also 116 introduced simple automation for feeding, milking, and climate control systems. 117 building on these early innovations, the mid-to-late 2000s saw the development of automated 118 farm management technologies, increased development of robotic systems, and the integration 119 of real-time data collection tools. these advancements laid the groundwork for further 120 digitalization, allowing farms to transition from basic monitoring to more interactive, data-driven 121 decision making. 122 in the last decade, digitalization advanced with integrated systems combining data analytics, real-123 time monitoring and advanced sensors to enhance precision and decision making (wolfert et al., 124 2017; chavan et al., 2024). technologies at the forefront include artificial intelligence (ai)-125 driven analytics, iot-based monitoring systems, blockchain technology, advanced automation, 126 and robotics, which promise improvement in sustainability and traceability (calcante et al., 2014; 127 alonso et al., 2020a; d’agaro et al., 2021; alshehri, 2023; alipio & villena, 2023). however, 128 blockchain technology is in its early stage of development and requires further validation and 129 adoption (neethirajan & kemp, 2021). further innovations such as gene editing and advanced 130 biometrics offer further potential for improving animal welfare and sustainability (eastwood et 131 al., 2021). as shown in figure 1, these digital tools address species-specific challenges and 132 benefits various livestock production systems commodity based, nature-based, subsistence, and 133 value-seeking systems (kraft et al., 2022) and tailored technological solutions are necessary for 134 each system as they face unique challenges (baker et al., 2022). for instance, a commodity-based 135 system might struggle with regulatory issues, while a value-seeking system may face challenges 136 related to skill shortages and data management. despite these advancements, concerns remain 137 that the focus on high-tech solutions may neglect simpler, more accessible innovations that could 138 benefit a wider range of farmers, particularly small-scale operations (barrett & rose, 2022). to 139 address issues of exclusion, alonso et al., (2020b) suggest creating an integrated technology 140 ecosystem — a coordinated network — where iot, edge computing, ai, and blockchain 141 technologies are bundled together. this approach is intended to lower the costs associated with 142 adopting these technologies individually. however, while such an ecosystem could enhance 143 accessibility, it may also introduce privacy and data ownership concerns and limit widespread 144 adoption. promoting individual components of precision livestock farming technologies can be 145 unsustainable (banhazi et al., 2012). 146 digitalization presents both optimistic prospects and contentious challenges. precision livestock 147 farming technologies can enhance animal welfare monitoring, but increased automation risks 148 detaching farmers from direct animal care (buller et al., 2020; tuyttens et al., 2022), and 149 technologies like drones may also cause stress to animals (alanezi et al., 2022). ethical concerns 150 persist around factory farming practices and environmental impacts (neethirajan & kemp, 2021). 151 meanwhile the prioritization of economic efficiency could undermine broader sustainability 152 goals (macpherson et al., 2022). while these tools can drive sustainability, their deployment 153 must prioritize ethical standards and resource efficiency. 154 moreover, digitalization is reshaping farm business models, facilitating direct marketing and 155 mitigating price effects and risks. however, it also alters farm operations, work content, and 156 interactions among value chain stakeholders (weber et al., 2022), requiring support for farmers as 157 they navigate these complexities. while the adoption of digital technologies is inevitable, 158 particularly as they become more affordable, akin to the widespread adoption of smartphones 159 (morrone et al., 2022), farmers adoption decisions are influenced by socio-demographic, 160 institutional, and economic factors, which complicate the process (vecchio et al., 2020). 161 the future of digital technologies in livestock farming requires a balanced approach that 162 recognizes current capabilities while learning from past lessons. stakeholders must integrate 163 digital advancements with practical applications and ethical considerations to build a sustainable 164 and competitive future (eastwood et al., 2021). 165 figure 1. livestock production systems: technologies and impacts. adapted from baker et al., (2022). 166 167 168 3 material and methods 169 3.1 data 170 this study utilized secondary data from two sources: the 7th general census on agriculture 171 conducted by the national institute of statistics (istituto nazionale di statistica-istat) for the 172 agricultural year 2019-2020, specifically covering the period from november 1st, 2019, to 173 october 31st, 2020. the reference date for farm herd size in this dataset was december 1st, 2020. 174 this census provided structural data on farms at national, regional, provincial, and municipal 175 levels. the istat survey was structured into sections covering general farm information, land 176 use, consistency of farms, farm management methods, related activities and company managers, 177 labor, and additional information on product destination, revenues/subsidies, marketing, 178 investment in innovations, and memberships in associations or organizations. this dataset 179 contributed to most of the socio-demographic variables used for this study. the target population 180 comprised italian farms, with at least 20 ares (2,000 m²) of utilized agricultural area (uaa). 181 the national statistical programme (ist-00173) survey (nsp) aimed to survey the number of 182 cattle, buffaloes, pigs and sheep and goats owned on june 1st and december 1st of each calendar 183 year. in the december 2020 edition, additional questions were introduced to survey the diffusion 184 of technologies in livestock holdings. the livestock farms are selected by stratified random 185 sampling; the stratification is by region and livestock classes, based on the total number of 186 animals owned on the farm. both datasets were collected via online questionnaire and telephone 187 interviews. 188 the datasets initially included 246,161 observations from istat and 7,587 from the nsp 189 survey. after merging based on identification number and cleaning the datasets, the sample size 190 was reduced to 4,133 livestock farms. the following exclusion criteria were further applied, that 191 is, farms raising multiple types of livestock (due to the ambiguity of digital technology 192 application), and those raising only horses, rabbits, ostriches, or poultry. the final sample size 193 was 2,412 observations. the variables used in the analysis, summarized in table 1, were 194 categorized based on their relevance to technology adoption, as highlighted in the existing 195 literature. 196 table 1. variables selected from the datasets. 197 variable type description socio-demographic characteristics age continuous individual age; min. age: 21 & max. age: 94 gender categorical 1: male; 2: female education categorical 1: no educational qualification. 2: primary school leaving certificate/ certificate of final assessment. 3: secondary or vocational school-leaving certificate (obtained no later than the year 1965) /secondary education diploma. 4: professional education/training qualification related to agriculture that does not permit enrolment in university. 5: professional education/training qualification not related to agriculture that does not permit enrolment in university. 6: upper secondary education related to agriculture that allows enrolment in university. 7: upper secondary education not related to agriculture that allows enrolment in university. 8: bachelors/master’s or equivalent qualification in agriculture. 9: bachelors/master’s or equivalent qualification not in agriculture. area categorical 1: central; 2: northeast; 3; northwest; 4: south. farm operations & endownment labor continuous total number of employees on the farm. livestock unit (uba) continuous sum of each livestock unit. total agricultural area (sat) continuous total area in hectares type of ownership (legal form) categorical 1: entrepreneur or sole proprietorship or family business. 2: partnerships 3: capital companies 4: cooperative societies 5: state administration or public body. 6: other private entities 7: collective ownership 8: consortia production system categorical organic=1, conventional=2 type of livestock (dairy cattle, meat cattle, buffaloes, sheep, goat, pig) categorical 1: large ruminants; 2: small ruminants; 3: monogastric number of livestock continuous total number of each livestock in the farm. membership of association binary 0: no association; 1: yes association association with producer organization association with network of enterprises association with other organization/companies public subsidies (%) continuous the amount of subsidy received in percentage of the farms total gross revenues. marketing channels for animal products binary 0: no sales on this marketing channel. 1: yes, sales in this marketing channel. direct sales on the farm off-farm direct sales sales to other farms sales to industrial companies in free market sales to industrial companies with multi-annual agreements sales to commercial enterprises in free market sales to commercial enterprises with multi-annual agreements sales or contribution to association bodies other remunerative activities binary 0: no other activities, 1: yes, other activities behavioural attribute agricultural training (learning orientation) binary 0: never attended agricultural training. 1: attended agricultural training. regional infrastructures fixed broadband connectivity binary 1: yes, the farm uses at least one fixed broadband internet connection, 0: no digitalization . binary 0: no, i do not use this technology. 1: yes, i use this technology. decision support software data cloud computing digital devices for animal monitoring social network/website precision animal husbandry system/machinery 198 199 3.2 methodology 200 the study draws on various explanatory variables established in the literature to explore the 201 adoption of digital technologies in livestock farming. tey & brindal, (2012) classify factors 202 influencing adoption into six categories: socio-economic, agro-ecological, institutional, 203 informational, perceptual, and technological. based on these, a tailored set of variables were 204 selected for this research. 205 the analysis categorized livestock into large ruminants, small ruminants and monogastric 206 animals because they have distinct management requirements, economic values and level of 207 environmental impacts that shape the development of digital technology. variations in the 208 availability and adoption of digital technologies across different livestock categories have been 209 documented in the literature (thomann et al., 2023). research on digital technologies has 210 predominantly focused on dairy cattle reflecting their intensive management systems and 211 economic importance (marino et al., 2023). similarly, buffalo farming follows specialized 212 intensive management strategies, particularly in regions where it plays a significant economic 213 role, such as italy, due to the high demand for mozzarella di bufala campana pdo (trapanese 214 et al., 2024). while digitalization efforts often focus on ruminants due to their large land use and 215 contributions to greenhouse gas (ghg) emissions (pulina et al., 2017), this study considers 216 adoption across all livestock categories to assess variation. 217 in assessing the status of digitalization in farms, the study considered the data utilization 218 (meteorological, satellite, and drone-collected data), websites and social networks, cloud 219 computing, decision support systems (dss), digital devices for individual animal monitoring, 220 and precision husbandry systems or machinery. the percentage frequency of digital technology 221 adoption was then calculated for each livestock category. 222 to ensure robustness and comparability across livestock categories in analyzing the determinants 223 of technology adoption, the study further identified specific tools within digital devices for 224 animal monitoring and precision animal husbandry systems/machinery. these technologies were 225 analyzed as individual tools, rather than being grouped under broader categories. only 226 technologies applicable to all livestock types were retained in the analysis. species-specific 227 technologies, such as milking robots and milking parlors equipped with online milk quality 228 measurement systems (which are exclusively used in dairy farming), were excluded to maintain 229 consistency and enable cross-category comparisons. similarly, social networks and websites 230 were not considered core digital tools, as they do not serve a direct farm management function. 231 technologies were classified into three categories: highly adopted (adopted by at least 15% of 232 sampled farms), somewhat adopted (adopted by less than 15% of sampled farms) and no digital 233 technologies. accordingly, livestock farms were grouped into farms using highly adopted digital 234 technologies, farms using somewhat adopted digital technologies, and farms using no digital 235 technology. this classification enabled a detailed assessment of digital technology adoption 236 across farms by capturing variations in uptake. including the ‘somewhat adopted’ category 237 allowed us to distinguish farms with low but notable level of adoption, enabling a more focused 238 analysis of the factors influencing digital technology adoption. this approach helped identify 239 specific drivers or barriers associated with each category. this categorization formed the 240 dependent variable in the econometric analysis, where the outcome variable 𝑌𝑖 was binary, taking 241 a value of 1 if the farm adopted any form of digital technology (highly or somewhat adopted) 242 and 0 if no digital technology was adopted. 243 3.2.1 model specification 244 a binary choice framework was used to model the decision to adopt digital technologies. a 245 logistic regression model was employed to estimate the probability of adoption based on a set of 246 independent variables. this approach, estimated via the maximum likelihood estimation (mle), 247 is preferred over multinomial logistic regression, as the categorization of digital technology use 248 is not based on a distinct choice between unordered categories, but rather a binary decision of 249 adoption. the logistic regression model is specified as: 250 log ( (pr( 𝑌𝑖=1)) 1−pr(𝑌𝑖 =1) ) = 𝛽0 + 𝛽1𝑋1 + ⋯ + 𝛽𝑛 𝑋𝑛 (1) 251 where: 252 pr( 𝑌𝑖 = 1) represents the probability that farm 𝑖 adopts at least one digital technology within 253 each category. 𝑋1, … , 𝑋𝑛 are the independent variables hypothesized to influence adoption. 𝛽0 is 254 the intercept, and 𝛽𝑛 (for each 𝑛) are the estimated coefficients indicating the change in log-odds 255 with a unit change in each variable. the independent variables include, total area of holding, 256 state aid subsidy received, livestock unit (uba), age, labour, total number of livestock, fixed 257 broadband connectivity, area, type of livestock, type of ownership (legal form), production 258 system, other remunerative activities, gender, education, agricultural training, membership of 259 association, and marketing channels. 260 3.2.2 marginal effects 261 marginal effects were calculated to provide an intuitive understanding of how each variable 262 impacts the likelihood of adopting digital technologies. these effects translate the logistic 263 regression coefficients into changes in the probability of adoption. 264 the marginal effect of each variable 𝑋𝑗 on the probability of adoption 𝑃𝑟(𝑌 = 1|𝑋) is given by: 265 𝜕pr(𝑌 = 1|𝑋) 𝜕𝑋𝑗 = 𝑃𝑟(𝑌 = 1|𝑋) . (1 − 𝑃𝑟(𝑌 = 1|𝑋)) . 𝛽𝑗 (2) 266 where: 267 𝜕pr(𝑌 = 1|𝑋) 𝜕𝑋𝑗 is the marginal effect of variable 𝑋𝑗 on adopting probability. 268 𝑃𝑟(𝑌 = 1|𝑋) is the predictive probability of adoption. 269 1 − 𝑃𝑟(𝑌 = 1|𝑋) is the probability of non-adoption. 270 𝛽𝑗 is the estimated coefficient of 𝑋𝑗. 271 marginal effects were computed for both highly adopted and somewhat adopted technologies to 272 observe differential impacts across varying levels of adoption. 273 4 results and discussions 274 4.1 descriptive results 275 table 2 presents the descriptive analysis of the sampled livestock farms. the average age varied 276 across livestock types, ranging from an average of 47 years for buffalo farmers to an average of 277 56 years for pig farmers, with an overall average age of 53 years. this reflects the typical 278 demographic trend of middle-aged individuals managing farms. gender distribution shows a 279 predominance of male farm managers (85%), consistent with traditional gender roles in 280 agriculture. 281 labor input, measured as the number of permanent employees, also varied across livestock 282 categories, with buffalo farms requiring the highest average labor (4.6 employees), while other 283 types, such as meat cattle and sheep farms, operated with fewer workers. the scale of operations, 284 measured in livestock units, showed significant variation, with buffalo farms having the largest 285 average livestock units per farm (321.8), indicating a high scale of operations that requires more 286 labor force. there are also variations in the total area of holdings (sat). 287 public subsidies, an essential component of agricultural support, varied across livestock types. 288 these variations could be attributed to policy priorities or the socio-economic focus of subsidies. 289 ownership structures leaned heavily towards sole proprietorship and family-owned businesses, 290 which likely offer flexibility in decision-making processes that are less bureaucratic than other 291 ownership forms. in terms of education, 41.3% of farmers had secondary education diplomas, 292 and only 17% had formal higher education in agriculture. however, 64.9% of farmers 293 participated in agricultural training programs, indicating a reliance on non-formal education to 294 compensate for the lower levels of formal education. 295 conventional farming remained the most predominant type of production system (95.2%), while 296 organic farming remained relatively limited (5%). the limited adoption of organic methods may 297 be due to the challenges of organic livestock production, as farmers rely on organic crops and 298 avoid conventional feed additives, hormones, and medicines, making organic farming both costly 299 and complex. engagement in other remunerative activities varied by livestock type, with goat 300 farmers (34.52%) participating more frequently in these activities compared to other livestock 301 types. a suggestion that income diversification may be a crucial strategy for economic resilience. 302 marketing channels for final products were influenced by farm scale and final products 303 (processed vs. non-processed), and 45.4% of respondents were affiliated with companies or 304 organizations, with 28.2% participating in producer associations, reflecting a differentiated 305 approach to market engagement. 306 table 2. descriptive statistics of socio-economic characteristics across various livestock types. 307 dairy cattle (n=1091) meat cattle (n=435) buffalo (n=49) goat (n=84) sheep (n=329) pig (n=424) total (n=2412) age (mean & sd) 54 (13.4) 53 (14.2) 47 (13.2) 49.6 (15.5) 48.6 (14.4) 55.7 (14.0) 53.0 (14.0) gender male 987 355 43 62 266 350 2063 female 104 80 6 22 63 74 349 labour (mean & sd) 3.2 (3.7) 1.6 (2.4) 4.6 (3.7) 1.6 (2.1) 1.9 (5.6) 2.5 (5.5) 2.6 (4.2) type of ownership (legal form) entrepreneur or sole proprietorship or family business 525 356 30 70 259 273 1513 partnerships 543 69 12 11 63 132 830 capital companies 17 7 7 2 4 16 53 cooperative societies 6 3 1 3 3 16 state administration or public body other private entities collective ownership consortia education no educational qualification 4 5 5 4 18 primary school leaving certificate/ certificate of final assessment 90 67 9 35 65 266 secondary education diploma 458 183 16 39 145 155 996 professional education/training qualification related to agriculture 77 14 1 9 14 115 professional education/training qualification not related to agriculture 57 18 2 3 10 26 116 upper secondary education related to agriculture 156 44 6 13 31 45 295 upper secondary education not related to agriculture 167 63 19 11 73 79 412 bachelor’s/master’s or equivalent qualification in agriculture 41 12 1 1 9 15 79 bachelor’s/master’s or equivalent qualification not in agriculture 41 29 5 7 12 21 115 livestock unit (uba) (mean & sd) 196.0 (231.9) 81.4 (247.6) 321.8 (250.9) 20.1 (30.8) 44.6 (50.5) 248.9 (697.2) 160.4 (358.3) production system organic 33 45 4 4 19 10 115 conventional 1058 390 45 80 310 414 2297 total no of livestock (mean & sd) 128.5 (155.2) 66.0 (295.7) 342.6 (262.0) 115.1 (194.3) 398.8 (480.2) 868.9 (2236.2) 288.2 (1010.2) total area of holding (sat) ha. (mean & sd) 68.9 (80.1) 84.9 (169.8) 45.3 (50.6) 35.3 (52.4) 81.6 (160.2) 50.4 (229.8) 68.7 (145.5) membership of association producer association 398 99 16 19 54 93 679 network of enterprises 45 11 5 3 12 15 91 association with companies/organization 579 160 22 23 130 181 1095 public subsidies (%) 13.2 (17.3) 225.1 (24.5) 8.0 (12.0) 18.0 (26.9) 30.2 (28.8) 13.3 (20.4) 17.7 (22.4) marketing channels direct sales on the farm 135 776 10 18 60 112 411 off-farm direct sales 59 32 4 4 31 43 173 sales to other farms 65 78 1 10 19 32 205 sales to industrial companies in free market 220 39 14 11 69 59 412 sales to industrial companies with multiannual agreements 75 11 1 2 9 9 107 sales to commercial enterprises in free market 328 179 12 14 91 91 715 sales to commercial enterprises with multiannual agreements 33 7 3 1 0 3 47 sales or contribution to association bodies 398 31 6 8 48 17 508 remunerative activities 215 62 8 29 44 132 490 agricultural trainings (learning orientation) 827 231 36 40 173 258 1565 regional infrastructures fixed broadband connectivity (%) 64.5 37.5 63.3 45.2 28.9 51.2 51.7 source: own elaboration. 308 4.2 extent of digital technology adoption 309 table 3 illustrates significant variation in digital technology adoption across livestock types, 310 revealing distinct trends within the italian livestock sector. decision support systems (dss) are 311 adopted by 28.32% of farms, indicating their role in optimizing farm management and decision-312 making. similarly, cloud computing services are utilized by 26.16% of farms, with the highest 313 adoption rates observed among buffalo (48.98%) and dairy cattle farms (33.09%), emphasizing 314 their role in managing complex, large-scale operations. in contrast, meat cattle, sheep, goat, and 315 pig farms display relatively lower adoption rates. the limited uptake in these sectors may reflect 316 a low perceived value of investment in digital technologies, as these farms may not require the 317 same level of operational complexity as dairy or buffalo farms. 318 however, pig farms (24.06%) report a higher adoption of cloud services compared to meat cattle 319 (17.47%), suggesting a moderate level of digitalization in this sector. among small ruminants, 320 goat farms (23.81%) exhibit a higher adoption rate of cloud services compared to sheep farms 321 (14.59%). 322 in the sample, 29.98% of the farms use any type of digital devices, particularly sensors for 323 individual animal monitoring that is widely adopted among dairy (37.21%) and buffalo farms 324 (28.57%), where real-time tracking of animal production and health is essential in high-output 325 dairy operations. in contrast, meat cattle (20.69%), sheep (19.15%), and goat farms (17.86%) 326 show moderate adoption, while pig farms report the lowest uptake (9.91%). detectors for 327 individual animal production are primarily employed in buffalo (16.33%) and dairy cattle farms 328 (15.12%), whereas behavior image analyzers show minimal adoption across all species (3.69%). 329 precision animal husbandry systems remain underutilized, with an overall adoption rate of 330 1.78%. their adoption is virtually nonexistent among small ruminants, with no recorded uptake 331 in goat and sheep farms. among large ruminants, dairy cattle (2.84%) and buffalo farms (2.04%) 332 report some adoption, while meat cattle and pig farms have minimal use (1.38% and 1.18%, 333 respectively). according to bucci et al., (2019), italy continues to lag behind other eu countries 334 in the adoption of precision agriculture technologies (pats). additional precision systems, 335 such as information management tools (0.95%), machines with online food quality analysis 336 (0.12%), remote animal identification (0.54%), and robotic systems for ration management and 337 stable cleaning (0.21%), remain rare across all livestock categories. the limited use of precision 338 livestock farming (plf) technologies in small ruminants and pig farms suggests significant 339 untapped potential for digital transformation in these sectors. studies by abeni et al., (2019) and 340 vaintrub et al., (2021) indicate that advanced management tools are predominantly adopted in 341 dairy-intensive regions, where they enhance operational efficiency and reduce labor costs, 342 although this trend remains less prevalent in the italian livestock sector. 343 the adoption of meteorological and satellite data follows a similar pattern, with dairy cattle farms 344 (26.58%) being the primary users, followed by meat cattle (18.39%) and buffalo farms (14.29%). 345 small ruminant and pig farms demonstrate relatively low adoption rates, indicating a reduced 346 reliance on environmental data for operational decision-making. large, commercially oriented 347 farms tend to perceive greater value in meteorological data, whereas smaller-scale and traditional 348 operations may not prioritize these digital tools. 349 social networks and websites, which are frequently used for marketing and supply chain 350 engagement, show the highest adoption rates among goat (27.38%) and buffalo farms (26.53%). 351 in contrast, dairy cattle (12.37%), meat cattle (7.36%), sheep (8.51%), and pig farms (19.34%) 352 exhibit lower engagement on these platforms, which may reflect sector-specific market structures 353 and differences in digital literacy. 354 in summary, the results reveal clear disparities in digital technology adoption across livestock 355 sectors. dairy cattle and buffalo farms lead in the adoption of dss (43.26% and 44.90%, 356 respectively) and cloud computing (33.09% and 48.98%, respectively), indicating higher levels 357 of digital transformation in these intensive production systems. conversely, meat cattle, small 358 ruminants, and pig farms demonstrate limited digital engagement, highlighting areas of untapped 359 potential and the need for targeted strategies to promote digital technology adoption. 360 table 3. adoption of digital tools across different livestock types in percentages. 361 digital tools dairy cattle (n=1091) meat cattle (n=435) buffalo (n=49) goat (n=84) sheep (n=329) pig (n=424) total (n=2412) decision support system 43.26 16.55 44.90 11.90 9.12 18.16 28.32 use of data 26.58 18.39 14.29 11.90 12.46 13.44 20.11 cloud services 33.09 17.47 48.98 23.81 14.59 24.06 26.16 social network/website 12.37 7.36 26.53 27.38 8.51 19.34 12.98 digital devices 41.61 23.45 40.82 17.86 23.40 12.97 29.98 specific digital devices common to various livestock types. sensors on the limbs neck or ear tags 37.21 20.69 28.57 17.86 19.15 9.91 26.12 detectors of individual production 15.12 1.84 16.33 2.38 0.61 1.89 8.00 behavior image analyzers 7.15 0.92 4.08 0.00 0.91 0.47 3.69 others 1.01 2.76 6.12 0.00 8.21 0.71 2.32 precision animal husbandry system 2.84 1.38 2.04 0.00 0.00 1.18 1.78 specific precision animal husbandry system/machinery common to various livestock types information system for livestock management 1.92 0.46 0.00 0.00 0.00 0.00 0.95 machines equipped with online analysis of food quality 0.18 0.23 0.00 0.00 0.00 0.00 0.12 milking robots 0.37 0.00 2.04 0.00 0.00 0.00 0.21 milking parlor equipped with online milk quality measurement system 0.73 0.00 2.04 0.00 0.00 0.00 0.37 remote animal identification management 0.92 0.23 2.04 0.00 0.00 0.24 0.54 systems sensors for detecting the productive and reproductive activity of the live 1.83 0.46 0.00 0.00 0.00 0.24 0.95 stock robotic systems for ration management and stable cleaning 0.09 0.23 0.00 0.00 0.00 0.71 0.21 others 0.18 0.00 0.00 0.00 0.00 0.71 0.21 source. authors own elaboration 362 4.3 determinants of digital technology adoption in livestock production 363 the logistic regression model was used to examine factors influencing the likelihood of adopting 364 digital innovations among livestock farms. it explicitly distinguishes between farms with highly 365 adopted and somewhat adopted digital innovations. the output of the marginal effects analysis 366 is summarized in table 4. 367 fixed broadband connectivity significantly impacts both highly and somewhat adopted digital 368 innovations. sozzi et al. (2021) agree that poor internet coverage impacts adopting digital 369 agriculture (da) tools, particularly in italy’s marginal and hilly agricultural areas. thus, targeted 370 investments in rural internet infrastructure are critical to advancing agricultural modernization. 371 the variable area captures regional effects on digital innovation adoption, and it is significant 372 only for highly adopted digital innovations. compared to the central region, the northeast region 373 and northwest region have a 9% and 8% likelihood of adoption of digital technology, 374 respectively. this regional disparity mirrors findings by altamore et al., (2024), who suggest that 375 policy support and socio-economic differences shape diverse regional practices. just as structural 376 characteristics such as governance and historical influences drive varying agricultural practices 377 across northern and southern italy, similar regional factors may influence digital innovation 378 adoption in our context. this suggests that adoption patterns may be shaped by present-day 379 policies and longstanding regional characteristics that impact economic and social development. 380 similarly, for the highly adopted digital innovations, the type of livestock variable shows that 381 farmers managing monogastric animals are less likely (by approximately 14%) to adopt digital 382 technologies. additionally, managing small ruminants is associated with a reduced likelihood 383 (by 8%) of digital technologies adoption compared to the large ruminant animals. the 384 implication is that the digital technologies available may be more applicable to certain livestock 385 systems than others and reflect specific technological needs or economic considerations in 386 different livestock types. the type of ownership (legal form) positively influences high and 387 somewhat adopted digital innovations, with a probability of 10% and 5% respectively, 388 particularly within family-owned enterprises, where streamlined decision-making processes 389 support the adoption of new technologies. meanwhile, for somewhat adopted digital innovations, 390 other remunerative activities significantly increase the likelihood of adoption by 14%, 391 suggesting that farmers with additional income streams may be more inclined to explore these 392 digital solutions, as diversified income streams provide additional financial flexibility. however, 393 these activities have no significant effect on highly adopted innovations, indicating that diversified 394 income streams do not necessarily impact the adoption of broader farm management systems like 395 dss or cloud services. this provides evidence that although technology adoption is widespread, 396 it is often limited to basic aspects of livestock management, with more sophisticated systems 397 concentrated in specialized sectors. education is a significant predictor for high and somewhat 398 adopted digital innovations, with each additional year of schooling increasing the likelihood of 399 adopting digital innovations by 1% in each case. educated farmers are expected to be more 400 receptive to technological advancements, given potential improvements in understanding and 401 implementing new digital tools. agricultural training positively impacts highly adopted digital 402 innovations (5% increase), highlighting the role that agricultural knowledge and innovation 403 systems (akis) play in facilitating digital transformation. in italy, the coordination of 404 knowledge exchange and training initiatives falls under the jurisdiction of the regional authorities 405 (birke et al., 2022). this decentralized approach allows akis to tailor training and knowledge-406 sharing programs for different regions' specific needs and contexts. such regionalized systems 407 align with the common agricultural policy’s cross cutting objective of modernizing agriculture 408 by fostering knowledge sharing, innovation and digitalization through specialized training 409 programs (an, 2024). state aid subsidies received show a negative impact on somewhat adopted 410 digital innovations. altamore et al., (2024) argues that italian farms primarily use subsidies to 411 supplement income. for instance, in southern regions of italy where subsidies makeup a 412 substantial portion of farm revenue, farms may prioritize essential operational costs over 413 technology investment. while some eu aid targets agricultural innovation, the lack of subsidies 414 specifically for digital technology suggests that generalized aid does not effectively drive digital 415 adoption, underscoring a need for targeted incentives. being part of a producer association is 416 positively associated with highly adopted digital innovations, increasing the likelihood of 417 adoption by 6%. this supports the notion that community networks facilitate knowledge 418 exchange and collective learning, making innovation adoption easy. a structured network 419 encourages technology use more than independent farms by fostering a focus on product quality 420 and accountability (chen et al., 2021). the amount of livestock unit (uba) has a strong positive 421 effect on highly adopted digital innovations, particularly for large farms where technologies such 422 as dss and cloud services offer benefits for streamlining operations and managing complex farm 423 activities. this effect aligns with findings in literature, where herd size is often linked to greater 424 investment in management tools that support efficiency at scale (abeni et al., 2019). age has an 425 inverse relationship with highly and somewhat adopted digital innovations. younger farmers are 426 generally more likely to adopt digital innovations, possibly because they tend to have higher 427 digital literacy and a longer-term outlook, giving them more time to benefit from these 428 technologies. this trend is consistent with findings in the technology adoption literature (barnes 429 et al., 2019; michels et al., 2020). labor shows a positive impact on somewhat adopted digital 430 innovations, with each additional worker associated with a 2% increase in the likelihood of 431 adopting these technologies like precision animal husbandry systems and digital devices (e.g., behavior 432 analyzers and production detectors, etc.), which require continuous monitoring and data interpretation. 433 conversely, highly adopted digital technologies like dss and cloud services, designed to automate 434 tasks and reduce labor dependency post-implementation, may appeal more to farms with fewer 435 employees. marketing channels play a critical role in shaping digital technology adoption. direct 436 on-farm sales have been found to positively influence somewhat adopted digital innovations, 437 while sales through association bodies are more strongly linked to highly adopted innovations. 438 however, the structure of the supply chain affects the extent to which producers benefit from 439 price fluctuations and market stability, which in turn influences their ability to invest in digital 440 technologies. empirical evidence suggests that farmers represent a weak link in the value chain, 441 meaning that increases in retail market prices often do not translate into significant financial 442 benefits for producers (goodwin et al., 2024). this price asymmetry may limit farmers' financial 443 capacity to adopt digital tools, particularly when market uncertainty discourages long-term 444 investments. 445 table 4. determinants of digital innovation adoption in the livestock sector: marginal effects analysis. 446 variables marginal effects (highly adopted digital innovations) marginal effects (some what adopted digital innovations) fixed broadband connectivity 0.16*** (0.02) 0.14*** (0.02) area northeast 0.09** (0.04) 0.02 (0.03) northwest 0.08* (0.04) -0.01(0.03) south -0.01(0.04) -0.04 (0.03) type of livestock monogastric -0.14*** (0.03) -0.01 (0.02) small ruminants -0.08** (0.04) 0.03 (0.03) type of ownership (legal form) 0.10*** (0.02) 0.05*** (0.02) total area of holding (ha) -0.00 (0.00) -0.00 (0.00) production system conventional -0.05 (0.05) -0.06(0.04) other remunerative activities 0.01 (0.03) 0.14*** (0.02) gender female 0.03 (0.03) -0.00 (0.02) education 0.01** (0.01) 0.01*** (0.00) agricultural training 0.05* (0.02) 0.03 (0.02) state aid subsidy received -0.00 (0.00) -0.00* (0.00) membership of association producer association 0.06** (0.03) 0.01(0.02) network of enterprises 0.09 (0.07) 0.02(0.04) association with companies/organization -0.00 (0.02) 0.02(0.02) livestock unit (uba) 0.00*** (0.00) -0.00 (0.00) age -0.00*** (0.00) -0.00** (0.00) labor 0.00 (0.00) 0.02*** (0.00) total no. of livestock -2.69e-06 (0.00) -9.57e-06 (0.00) marketing channels -direct sales on the farm -0.02 (0.03) 0.04* (0.02) -off-farm direct sales 0.00 (0.04) 0.01(0.03) -sales to other farms -0.02 (0.04) 0.00 (0.03) -sales to industrial companies in free market 0.05 (0.03) -0.03 (0.02) -sales to industrial companies with multi-annual agreements 0.04 (0.06) 0.03(0.04) -sales to commercial enterprises in free market 0.03 (0.03) -0.01(0.02) -sales to commercial enterprises with multi-annual agreements -0.07 (0.08) 0.06 (0.05) -sale or contribution to association bodies 0.09***(0.03) 0.02 (0.02) source: own elaboration. standard error values are inside parentheses, *p<0.10, **p<0.05, ***p<0.01 447 5 conclusion 448 this study highlights the variations in the adoption of digital technologies in livestock farming 449 across italy, with dairy and buffalo farms leading in digital engagement, while meat cattle, 450 sheep, goat, and pig farms are lagging. this disparity in digital adoption is shaped by differences 451 in operational scale and economic structure, which collectively impact animal welfare across 452 species. key socio-economic determinants, such as broadband connectivity, farm ownership 453 structure, education, and age, significantly influence digital technology. younger farmers and 454 those with higher education levels are generally more inclined toward technology adoption, 455 suggesting that digital literacy and training could potentially bridge adoption gaps for other farm 456 demographics. additionally, fostering a bottom-up, participatory co-design approach in the 457 development of digital tools can play a crucial role in aligning technology with farmers' specific 458 needs and contexts. engaging farmers actively in the co-design process allows for the tailoring 459 of digital tools to address practical challenges, usability concerns, and operational requirements 460 from the perspective of end-users. this approach not only enhances relevance but also promotes 461 a sense of ownership and empowerment among farmers. by incorporating feedback from 462 farmers into the design of digital tools, technology providers and developers can ensure that 463 these innovations are accessible, adaptable, and directly responsive to the needs of those who 464 will implement them in the field. 465 highly adopted digital innovations are influenced by the location, livestock type, participation 466 in agricultural training, livestock unit size, membership in producer associations, and 467 engagement in sale or contributions to association bodies. in contrast, somewhat adopted 468 innovations are driven by engagement in other income-generating activities, state aid subsidies 469 received, labor availability, and involvement in direct sales marketing. improving broadband 470 infrastructure remains crucial to fostering widespread digital adoption, and targeting younger, 471 digitally literate farmers is key. integrating digital literacy into agricultural training programs 472 can further ensure broad and effective engagement. the gap between traditional agricultural 473 education and the skills needed for digital farming highlights the need for modernized curricula 474 to align with evolving demands of the industry. 475 the common agricultural policy (cap) presents an opportunity to promote digital 476 transformation by providing targeted funding for rural broadband infrastructure and digital 477 literacy training. future cap reforms could focus on promoting digital technologies that 478 enhance sustainability and efficiency, with a particular focus on small and mid-sized farms. 479 addressing disparities in access is essential to ensure that the benefits of digital agriculture 480 extend to farms of all sizes and types. 481 these economic constraints with regards to the market structure reinforce the need for targeted 482 policies that support both digital innovation and income stabilization measures. ensuring that 483 farmers have more predictable market conditions and access to stable contracts may enhance 484 their ability to invest in digital technologies, fostering greater digital transformation in the 485 livestock sector. 486 looking ahead, further research should explore the long-term effects of digital technologies on 487 farm productivity, economic outcomes, and sustainability. additionally, it is important to 488 examine the specific barriers faced by small and medium-sized farms in adopting digital tools 489 and assess how policies such as the cap can promote digital inclusion across the sector. 490 understanding the adaptation of digital solutions to different farm sizes and livestock species is 491 essential for advancing precision livestock farming and ensuring technological advancements 492 benefit the entire sector, not just a select few. 493 broader implications for the livestock sector were also noted, particularly in underrepresented 494 areas such as plf for small ruminants and pigs. reducing barriers to adoption in these areas is 495 critical for fully realizing the potential of digital technologies across all livestock types. 496 collaborative efforts among technology providers, agricultural organizations, and policymakers 497 are essential to fostering a sustainable and equitable transformation in livestock farming, 498 benefiting a diverse range of farm types and sizes. 499 in summary, this study provides valuable insights into the current state of digital adoption in 500 italian livestock farming, yet limitations such as unaccounted regional differences that may 501 affect the generalizability of the findings and the dataset, which was collected up until 2021, 502 needs to be updated to reflect more recent developments, suggest avenues for ongoing research. 503 bridging the digital divide in livestock farming will require targeted, well-informed strategies 504 to achieve a comprehensive digital transformation across the sector, equipping farmers with the 505 tools needed to thrive in an increasingly digital agricultural landscape. 506 references 507 abeni, f., petrera, f., & galli, a. 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(2025). addressing needs for the diffusion of digital greenhouse 32 farming. insights from living labs in the mediterranean basin, bio-based and applied economics, 33 just accepted. doi:10.36253/bae-17755 34 mailto:oriana.gava@crea.gov.it mailto:sara.sturiale@phd.unipi.it mailto:mgallard@ual.es mailto:ecebacalan@akdeniz.edu.tr mailto:laarif.asma@iresa.agrinet.tn mailto:thameurbouslama@gmail.com mailto:alejandra.navarrogarcia@crea.gov.it mailto:oriana.gava@crea.gov.it 2 abstract 35 agriculture 4.0 represents a huge opportunity for the transformation of agrifood sectors. however, 36 its adoption (and diffusion) in real-world farming contexts faces multiple challenges. this study 37 focuses on greenhouse farming within the mediterranean basin. it aims to assess the needs of actors 38 involved in the uptake of agriculture 4.0 and define enabling conditions to support achieving these 39 needs, focusing on the introduction of an innovative decision support system in real-world 40 greenhouses for tomato production. a qualitative and comparative approach is implemented, using 41 participatory data collection methods with cross-disciplinary experts from four case studies across 42 the mediterranean basin. data are collected through one-to-one open discussions, supported using 43 context and swot analyses to stimulate reflection and recall. the findings highlight the need to 44 improve digital literacy among farmers and advisors, build trust through tailored education 45 conditions and mentorship, and support young farmers with financial incentives and training. 46 market dynamics are relevant as well, pinpointing the need for stronger product images and 47 increasing consumer awareness through certification and labelling. great interest and technology 48 potential emerges from the possibility to enable (partial) remote work thereby benefiting a work-49 life balance. simplifying bureaucratic processes and enhancing policy support for cooperation and 50 farmer unions are also essential for encouraging farmers to adopt digital technology. 51 keywords: decision support system (dss), agricultural digitalisation, qualitative research, multi-52 actor engagement, actor needs, enabling 53 54 3 1. introduction 55 1.1 background 56 agriculture 4.0, also known as digital agriculture, leverages precision and data-driven 57 technologies such as the internet of things, data analytics, artificial intelligence, and machine 58 learning and is viewed as a promising approach to sustainably enhance food production. the use of 59 these technologies is particularly relevant in intensive farming systems like greenhouse production 60 (maffezzoli et al., 2022; mondejar et al., 2021). however, while technology-driven solutions can be 61 appealing, resources may be wasted if technologies are not developed responsibly by aligning with 62 the actual needs of actors1 directly involved in technology adoption and diffusion (rose et al., 2021). 63 large literature acknowledge this issue and suggest that a variety of needs exist ranging from 64 developing user-friendly digital applications, reducing access costs and enhancing digital literacy, to 65 improving policy and governance (klerkx et al., 2019; mcfadden et al., 2022; unesco, 2018; wolfert 66 et al., 2017; yuan and sun, 2024). these needs often cannot be met, and enabling conditions should 67 be created to facilitate the successful implementation and adoption of specific actions, policies, or 68 technologies (huber-stearns et al., 2017). creating enabling conditions for agriculture 4.0 requires 69 technical and financial measures and coherent governance frameworks that reconcile productivity, 70 environmental sustainability, and social objectives (coderoni, 2023). it also requires adopting a 71 responsible research and innovation (rri) approach through the engagement of a variety of actors 72 (e.g., farmers, researchers, policymakers) directly involved in the technology adoption and its 73 diffusion, prioritising their actual needs and considering the diversity of contextual features at the 74 territorial level (eastwood et al., 2019; rose et al., 2021). these actors should be part of the 75 innovation process to ensure that agriculture 4.0 technologies are implemented in a way is socially 76 1 the term "actor" is used consistently throughout this study (instead of “stakeholder”) to ensure homogeneity of wording and to better reflect the actor-centred nature of the presented research. 4 beneficial, inclusive, and equitable to all the affected actors while minimising negative impacts 77 (fielke et al., 2022; klerkx et al., 2019; mcgrath et al., 2023; rose and chilvers, 2018). this 78 engagement can also lead to improved design of agricultural policies in the frame of agriculture 4.0 79 that foster fair and equitable working conditions (da silveira et al., 2021; maffezzoli et al., 2022). 80 living labs (lls) are increasingly recognised as suitable settings for operationalising rri, by serving 81 as collaborative platforms for inclusive, reflexive, and context-sensitive innovation and enabling 82 research approach centred on the perspectives of those directly involved in agriculture and 83 innovation (campos and marín-gonzález, 2023; owen et al., 2012). 84 research centred on the actors’ needs that respond to priority issues at the territorial level 85 requires significant improvement and expansion, especially developed towards lls (mgendi, 2024; 86 ogunyiola et al., 2024). understanding these needs would yield grounded recommendations to 87 bridge the research-practice gap while supporting the improvement of existing interventions to 88 foster responsible and sustainable agricultural digitalisation (mcfadden et al., 2022; wanner et al., 89 2018). also, bridging the research–practice gap is essential for ensuring that digitalisation 90 contributes to sustainability, a challenge long recognised in agricultural policy research (matthews, 91 2021). the literature shows that agriculture 4.0 can help mitigate the strain on limited resources, 92 address climate change, reduce water and agrochemical usage, improve soil health, and boost 93 biodiversity, while maintaining or increasing yields, lowering input costs, and enhancing food safety 94 through better traceability (macpherson et al., 2022). however, the observed impacts are not 95 without controversy, and there is an ongoing debate regarding the social implications of agricultural 96 digitalisation, with some studies highlighting benefits such as improved working conditions and 97 community well-being, while others point to issues like social inequality, data privacy concerns, and 98 the potential to widen the gap between large and small-scale farmers (carolan, 2024; klerkx et al., 99 2019). to maximize the positive impacts while minimising the negative consequences and fully 100 5 realize the potential of agriculture 4.0, it is essential to expand and deepen knowledge about the 101 perspectives and requirements of actors on the ground to understand how to sustain them in 102 addressing the issues they experience (ingram et al., 2022). the research implications need to be 103 practically useful beyond the case study level by offering a broader perspective on the researched 104 problem (yin, 2014), although there is a notable lack of research that provides such a varied 105 perspective. particularly, more studies are needed that incorporate evidence from a wide range of 106 geographical and socio-economic contexts (fasciolo et al., 2024; hinson et al., 2019; klerkx and 107 rose, 2020; maffezzoli et al., 2022). 108 1.2 aim and contribution of the research 109 against this background, the aim of this study is to assess actor needs and propose enabling 110 conditions to foster the diffusion of agriculture 4.0 in the mediterranean basin, through qualitative 111 research. the research adopts a qualitative approach framed within a rri framework, 112 operationalised through lls established at the project level in each case study. the four rri 113 dimensions (anticipation, reflexivity, inclusion, and responsiveness) guided research activities and 114 actor engagement from the project’s inception through to the generation of findings and policy 115 recommendations. lls supported innovation implementation and adoption through processes of 116 mutual learning and co-creation across domains of expertise and disciplinary boundaries. their 117 composition reflects the diversity of actor perspectives in real-world agricultural contexts and 118 constitutes the sample for this study. the research follows a stepwise approach involving: (i) the 119 prioritisation of key socio-economic issues affecting the adoption and diffusion of the dss (and more 120 broadly, agriculture 4.0 innovations) in the greenhouse sector; (ii) the identification of priority actor 121 needs, i.e. those directly linked to the identified issues; and (iii) the elaboration of enabling 122 conditions to support the fulfilment of these needs. a real-world situation is examined, i.e. the 123 introduction of an innovative decision support system (dss) in greenhouses of the mediterranean 124 6 basin. four case studies are considered, i.e. almería (spain), antalya (turkey), monastir (tunisia), 125 and tuscany (italy), hosting each of them a real-world pilot farm for testing the dss in commercial 126 greenhouses (sturiale et al., 2024a). all case studies are important players in the international 127 greenhouse vegetable market are representatives for the greenhouse sector at the territorial level 128 (sturiale et al., 2024a). the data were collected from cross-disciplinary experts engaged within lls. 129 this research advances knowledge in several ways. first, it centres on actors and their needs, 130 which are essential for identifying priority issues and enabling conditions for digital technology 131 adoption (soriano et al., 2023). second, it bridges the science-practice gap by offering grounded yet 132 theoretically sound recommendations, consistent with responsible research and innovation 133 principles (lajoie-o’malley et al., 2020; macpherson et al., 2022). third, it provides a cross-country 134 perspective across the mediterranean, a region marked by shared greenhouse technologies and 135 climate concerns but diverse socio-economic contexts, allowing for implications beyond the case 136 study level (bocean, 2024). 137 the findings offer a holistic view of agriculture 4.0 challenges and opportunities, based on 138 diverse mediterranean case studies (xu et al., 2024). they clarify actors’ prioritised needs and the 139 enabling conditions for digital agriculture uptake. although focused on mediterranean greenhouses 140 and a specific dss, the insights are relevant to broader agricultural sectors globally. the inclusion of 141 varied case studies enriches the analysis and extends its relevance (bocean, 2024). the 142 operationalisation of rri through living labs (lls) shaped knowledge co-production and 143 interpretation, revealing socially relevant dynamics and innovation trajectories beyond standard 144 metrics (stilgoe et al., 2013). lls facilitated joint reflection, contextual adaptation, and integration 145 of diverse knowledge systems, including those of smallholders, women, and migrant workers 146 (campos and marín-gonzález, 2023; ehlers et al., 2025). this responsiveness aligned innovation with 147 evolving societal needs and values (kokotovich et al., 2021), positioning lls as boundary 148 7 infrastructures where technical, social, and normative dimensions are negotiated. finally, the 149 research highlights the social implications of agricultural digitalisation, particularly equity and 150 inclusion, which merit greater attention in academic and policy debates (hundal et al., 2023; 151 maffezzoli et al., 2022). 152 153 2. theoretical framework 154 actor needs are the requirements, expectations, and preferences of those who have an interest 155 or stake in a particular project, process, or system, including a wide range of operational, economic, 156 social, and environmental aspects that are deemed critical for ensuring the successful adoption and 157 implementation of innovations. identifying and addressing key actor needs is essential for aligning 158 project outcomes with the interests and priorities of all involved parties, thereby enhancing the 159 overall effectiveness and sustainability of the initiative (feng et al., 2024; littau et al., 2010). these 160 needs respond to issues experienced not only by farmers but also by other actors, such as e.g., 161 advisors, which are generally context-specific and can negatively affect the uptake and widespread 162 use of digital agriculture in rural areas (dibbern et al., 2024). research indicates that real-world 163 issues are barriers to agriculture 4.0 and can create lock-in situations that hinder the achievement 164 of sustainability goals through digital transformation. especially, these issues can prevent the full 165 adoption and integration of digital technologies in agriculture, thereby limiting the potential 166 benefits in terms of productivity, profitability, and sustainability (da silveira et al., 2023a, 2023b). 167 the literature identifies drivers and barriers of agriculture 4.0 (da silveira et al., 2021; dibbern 168 et al., 2024). drivers include, e.g., the potential for increased productivity, profitability, and viability 169 of farming through the optimisation of resource use, cost reduction, and enhancement of crop 170 yields (fragomeli et al., 2024). other drivers encompass education, age, and farm size; for instance, 171 younger and more educated farmers managing larger, capital-intensive enterprises are more likely 172 8 to adopt agriculture 4.0 technologies (kroupová et al., 2024). barriers include economic constraints, 173 such as the high initial costs and limited access to capital, which can deter adoption, particularly 174 among small and medium-sized farms (dibbern et al., 2024). other examples of barriers are the lack 175 of technical literacy and insufficient information about the benefits and profitability of digital 176 agriculture that hinder farmers' willingness to invest in new technologies (kroupová et al., 2024). 177 identifying enabling conditions to support the realisation of actors’ needs is of particular relevance 178 to improve the sustainability of farming through digital tools, by removing the barriers and then 179 overcoming lock-in situations (da silveira et al., 2023b). 180 enabling conditions include financial support, technological infrastructure, policy frameworks, 181 and capacity-building initiatives that collectively create a conducive environment to harness the 182 potential of digital tools for enhancing farmers' productivity, resource efficiency, and decision-183 making capabilities. for instance, financial support through subsidies and incentives can reduce the 184 initial cost burden, making these technologies more accessible to smaller farms (fragomeli et al., 185 2024). public or private support to investment in physical assets in rural areas can address 186 inadequate infrastructure, facilitating the effective and widespread use of agriculture 4.0 187 technologies (derakhti et al., 2023). implementing training programs to enhance technical expertise 188 among farmers can bridge the knowledge gap and ease the integration of digital tools on farm 189 (wang et al., 2020). additionally, creating knowledge-sharing initiatives and fostering a culture of 190 innovation can help overcome resistance to change and build social trust in agriculture 4.0 191 (ganeshkumar et al., 2023). 192 193 3. methodology and data 194 3.1 the living lab approach in a rri framework 195 9 this study is grounded in a broader project that adopts a rri approach to balance economic, 196 socio-cultural, and environmental dimensions in addressing complex societal challenges (owen et 197 al., 2012). rri offers a normative framework for guiding innovation toward socially desirable 198 outcomes, structured around four interrelated dimensions: anticipation, reflexivity, inclusion, and 199 responsiveness. rri principles call for early and continuous involvement of diverse actors to ensure 200 that innovation processes align with societal values and needs (gremmen et al., 2019). lls have 201 emerged as a promising methodology for operationalising rri in agricultural digitalisation. they 202 provide collaborative, real-world environments where diverse stakeholders (e.g., farmers, 203 researchers, policymakers, civil society) can co-create, test, and evaluate technologies (campos and 204 marín-gonzález, 2023; ehlers et al., 2025). lls are particularly suited to addressing the social 205 dimensions of agriculture 4.0, enabling dialogue, trust-building, and the negotiation of trade-offs 206 between technological promise and lived experience (cascone et al., 2024; compagnucci et al., 207 2021; gardezi et al., 2022). the ll approach enables the integration of multiple knowledge systems 208 and interests that both shape and are shaped by digital agricultural transitions (kamilaris et al., 209 2017; wolfert et al., 2017). the engagement of locally embedded experts can provide grounded 210 insights into local needs and priorities as and the potential impacts of agriculture 4.0, supporting 211 knowledge exchange across diverse socio-economic and cultural settings (da silveira et al., 2021; 212 regan, 2019; zhai et al., 2020). 213 lls were established in 2021 using a socio-technical systems approach, which recognises 214 that technological innovation is embedded in broader institutional, economic, and cultural contexts 215 (rijswijk et al., 2021). they are implemented in four mediterranean regions, i.e. almería (spain), 216 antalya (turkey), monastir (tunisia), and tuscany (italy), each hosting a commercial-scale pilot farm 217 for testing a decision support system (dss) for tomato greenhouse production, i.e. the studied 218 innovation. the dss was specifically developed to optimise input use (water and nutrients), support 219 10 integrated pest and disease management, and enhance productivity in tomato greenhouses. it uses 220 climate and cultivation data to run simulation models for fertigation and outbreak prediction. 221 accessible online via wi-fi and managed through a mobile app, the dss is low-cost and compatible 222 with existing farm infrastructure. it provides farmers with tailored guidance on irrigation and 223 fertilisation schedules, along with alerts for pest and disease development to support timely 224 biological control interventions. 225 the case studies were selected as they meet the criteria of typicality (mediterranean-type 226 greenhouses, generally low-tech, well-developed greenhouse sector, important market position) 227 and diversity (contextual specificity: socio-economic, cultural, geographical) with respect to a series 228 of relevant sustainability issues related to the low diffusion of agriculture 4.0 in greenhouse farming 229 (gong and tan, 2021; sovacool, 2011) (table 1).230 11 231 table 1. implementation of living labs in the case studies and key case study features. 232 case studies living lab participants dss modules economic social environmental level of digital technology agribusiness knowledge creation/transfer policy almería (spain) 13 8 7 water, fertiliser, pest management high labour costs, decreasing margins, competition from other countries predominantly immigrant workforce, labour conditions, specialization, contract stability limited adoption of advanced techniques (e.g. closed-loop systems), use of biological control and drip irrigation moderate antalya (turkey) 16 4 1 water, fertiliser high production costs, insufficient government support predominantly immigrant workforce, labour conditions, specialization, contract stability limited adoption of advanced techniques, lack of data on sustainable systems low to moderate monastir (tunisia) 4 7 3 water, fertiliser, pest management low financing capacity, misuse of inputs, lack of control over costs and prices national, predominantly unqualified workforce, reluctance of older farmers, fragmented ownership high chemical use, limited adoption of sustainable practices and advanced techniques low tuscany (italy) 5 28 3 water, fertiliser, pest management high labour costs, low market power, poor generational turnover predominantly immigrant workforce, low confidence in new technologies public concerns about food naturalness, taste, and environmental impact low to moderate 233 12 the selection rationale informed the ll design and actor engagement strategies, ensuring 234 that the innovation process was locally relevant and socially responsive. for instance, almería faces 235 high labour costs and market competition; antalya struggles with limited government support and 236 high input costs; monastir is affected by low financing capacity and fragmented farm structures; and 237 tuscany deals with poor generational turnover and low confidence in digital tools. these contextual 238 differences also shaped the implementation decisions made by farmers regarding dss modules. 239 participants were selected through purposive sampling based on their capacity to offer 240 informed insights into the specific challenges and dynamics surrounding digital technology adoption 241 (patton, 2023; potters et al., 2022). the sampling strategy aimed to engage individuals 242 knowledgeable about the innovation and committed to sustainability improvements in their local 243 greenhouse sectors. actor selection was guided by local knowledge and aimed to ensure 244 representation across the agricultural value chain, including producers, advisors, policymakers, 245 technology providers, and civil society. willingness and capacity to engage across all ll phases, i.e. 246 from problem framing to evaluation, were also considered. actors participating in each ll constitute 247 the sample for this research (figure 1). 248 13 249 figure 1. living lab actor demographics. software: python libraries matplotlib (hunter, 2007), seaborn (waskom, 2021). 250 ll actor composition across the case studies shows diversity in terms of gender, age, 251 education level, and agricultural background. however, some cases reveal uneven representation 252 in specific categories, such as a predominance of male participants or limited variation in education 253 levels, reflecting local stakeholder networks, actor availability, and broader socio-institutional 254 dynamics. 255 lls were established at the project level as vehicles for embedding rri principles throughout 256 the innovation process. the established lls brought together a diverse range of actors operating at 257 both farm and territorial levels. while actor representation differed across case studies, reflecting 258 local technical, social, and cultural contexts, the categorisation of participants aimed to balance 259 inclusivity with operational feasibility, ensuring comparability across cases. 260 14 ll structure and activities were explicitly aligned with the four rri dimensions (anticipation, 261 reflexivity, inclusion, responsiveness), ensuring that the development and diffusion of the dss were 262 technically sound, ethically grounded, and socially responsive (ehlers et al., 2025; stilgoe et al., 263 2013) (table 2). 264 table 2. implementation of living labs (ll) under the dimensions of responsible research and innovation. 265 *commitment letters are confidential. **project deliverables report across rri dimensions. 266 engagement anticipation reflexivity inclusion responsiveness actors practice partners (farmers) research team and ll actors ll actors research team and ll actors type of involvement early engagement iterative learning and feedback actor mapping and continuous engagement adaptation of methods and approaches; identification of impact indicators timing since project proposal throughout project throughout project throughout project activities commitment letters*; codefinition of focal questions harmonised guidelines; joint interpretation of impact results activity protocols; ethical/legal compliance; diverse representation context analysis, swot, needs assessment (and other sustainability assessment exercises); co-creation and sharing sessions (workshops, training), policy recommendations process documentation** (bartolini et al., 2021; incrocci et al., 2024; laarif et al., 2024a, 2024b; navarro garcia and lupu, 2021; sturiale et al., 2024c, 2024b) 267 15 268 anticipation was embedded from the proposal stage, with early engagement of practice 269 partners to co-define focal questions and explore potential impacts and trade-offs of dss adoption 270 days (see (bartolini et al., 2021; fernández et al., 2024)). reflexivity was fostered through iterative 271 learning cycles, joint interpretation of impact results, and continuous reflection on the assumptions 272 and values shaping the innovation process experiences (see (laarif et al., 2024b; sturiale et al., 273 2024b)). inclusion was ensured by mapping and engaging a diverse set of actors across agribusiness, 274 policy, and knowledge domains, with attention to ethical and legal compliance and the 275 representation of marginalised voices (see (navarro garcia and lupu, 2021)). responsiveness was 276 demonstrated through the adaptation of methods, indicators, and engagement strategies based on 277 contextual feedback, informing both dss implementation and policy recommendations see 278 (bartolini et al., 2021; incrocci et al., 2024; laarif et al., 2024b, 2024a; sturiale et al., 2024c)). 279 280 3.2 data collection process and analysis 281 all data collection and reporting activities were designed and conducted by the research 282 team, with local members operating within their respective lls. activities were supported by 283 centrally harmonised guidelines, jointly agreed upon and prepared. these included methodological 284 instructions and templates for data collection and reporting, reflecting best practices in lls that 285 emphasize structured actor engagement, harmonized protocols, and context-sensitive 286 implementation. case study-specific findings were initially analysed by the lead author and 287 subsequently reviewed by all co-authors, with the final output discussed and validated collectively. 288 this collaborative and iterative approach aligns with established ll methodologies that promote 289 inclusive and responsible innovation through interdisciplinary co-creation and collective validation 290 (forbat et al., 2025; gardezi et al., 2022; hossain et al., 2019) 291 16 actor engagement was facilitated through one-to-one open discussions aimed at prioritising 292 context-specific issues and identifying corresponding needs and enabling conditions. these 293 interviews were conducted via video call, allowing participants to interact with visual materials and 294 texts as they were developed during the conversation. 295 the discussions were informed by in-depth context analyses conducted at the case study 296 level as part of related research activities (see (sturiale et al., 2024c)). these analyses framed the 297 unique circumstances of each agricultural setting and helped identify the factors influencing the 298 adoption and effectiveness of digital technologies (rijswijk et al., 2021). they included a broad range 299 of information: physical and technological attributes of greenhouse farming (klerkx et al., 2019); 300 economic aspects such as financial performance, cost structures, and incentives (metta et al., 2022); 301 social dimensions including workforce demographics, labour conditions, and public perceptions 302 (eastwood et al., 2019); and environmental considerations related to sustainability practices and 303 impacts (rose et al., 2021). before the interviews, respondents received the context analysis along 304 with a clear explanation of the exercise’s aims and procedures. the sessions employed swot 305 analysis (see supplementary materials) as a boundary object, leveraging its accessibility and 306 familiarity to facilitate structured dialogue (spee and jarzabkowski, 2009). this approach enabled 307 experts with diverse perspectives to collaboratively identify barriers and drivers of digital 308 technology uptake and to prioritise issues relevant to local contexts (helms and nixon, 2010; pagot 309 and andrighetto, 2024). respondents were explicitly invited to elaborate through recall and 310 brainstorming with research team members, following a three-step process: 311 1) reflect on priority issues that should be addressed in the greenhouse farming sector at 312 the territorial level to foster agricultural digitalisation, based on their experience in the 313 ll and knowledge of the dss, but not limited to it; 314 17 2) identify barriers and drivers to solving these issues, derived from swot items—315 specifically, barriers from weaknesses and threats, and drivers from strengths and 316 opportunities(pagot and andrighetto, 2024); 317 3) highlight priority needs that could help overcome barriers or leverage drivers to address 318 the identified issues. 319 enabling conditions for these priority needs were defined through discussion during the final 320 project workshop, which included all scientific partners and ll actors. these conditions were 321 informed by the presentation of project outcomes and refined through collective input. 322 323 4. results and discussion 324 findings indicate similarities among case studies, particularly regarding needs related to 325 knowledge, farmers’ behaviour and bargaining power, and remote work. however, contextual 326 differences highlight specific territorial needs to foster the uptake and diffusion of the dss and, 327 more broadly, to enable the wider use of digital tools in agriculture (table 3).328 18 table 3. prioritised needs and enabling conditions related to priority issues and swot items in the case studies. 329 case studies priority issues swot items priority needs enabling conditions almería knowledge and practical skills unskilled labour improving technical skills of farmers and advisors create and/or improve education and foster knowledge transfer about digital tools tuscany unskilled labour antalya low level of knowledge monastir low level of specialisation tuscany reluctance to change propensity to innovate; aging farmers building acceptability and trust create and/or improve education and foster knowledge transfer about digital tools almería aging farmers antalya aging agricultural population support for young farmers’ entrepreneurship monastir low profitability monastir abandonment of farming activities farm exit; low profitability reducing farm exits create and/or improve education and knowledge, and foster knowledge transfer about digital tools; support for young farmers’ entrepreneurship tuscany farm exit; economic viability almería high market competition and low consumer awareness market competition creating product identity product branding antalya market conditions monastir too low margin of product sale market competitiveness increasing farmer margins certification and labelling schemes; policy support for sustainable products almería unfair distribution of value added along the value chain weak bargaining power; many middlemen increasing farmer bargaining promote collective approaches (e.g., cooperatives, unions); organising demand-driven production antalya many middlemen monastir lack of collective organisation tuscany low bargaining power; level of cooperation tuscany slow and complex bureaucracy for public incentives burdensome bureaucracy simplifying bureaucracy simplified paperwork for public incentives antalya low profitability production-support policy 19 case studies priority issues swot items priority needs enabling conditions monastir insufficient supply of greenhousegrown food water shortages developing land and crop production planning antalya high production costs rising energy costs; high input costs increasing liquidity for new technology uptake support for investment in digital technology monastir high production costs tuscany heavy workload and difficult work-life balance work-life balance; climate change facilitating remote farming operations education and knowledge transfer; public/private investment in broadband infrastructure almería workload; work-life balance antalya many working hours monastir difficult management of personal life 330 331 332 20 4.1 improving technical skills of farmers and advisors and knowledge transfer 333 the widespread deficiency in knowledge and practical skills related to digital tools among 334 agricultural workers presents a significant issue, which can become a barrier to the effective 335 implementation of digital solutions. this was consistently observed across the case studies. advisors 336 often possess skills in digital technologies, but they lack the time to test or explain them to farmers. 337 this disconnect between research and practice hampers the adoption of agriculture 4.0 338 technologies and the dss under study, potentially leading to suboptimal farm management and 339 productivity. 340 these findings suggest a pressing need for comprehensive educational reforms and targeted 341 training programs at both national and international levels. interviewees emphasised the 342 importance of integrating digital skills into agricultural education to ensure that current and future 343 generations of farmers are equipped to use agriculture 4.0 technologies effectively. they also 344 highlighted the need for robust knowledge transfer mechanisms to bridge the gap between research 345 and practice. this includes fostering partnerships between research institutions and agricultural 346 practitioners to facilitate the dissemination of innovative practices and technologies. 347 these findings are supported by the literature, which similarly identifies the lack of digital 348 literacy as a systemic issue in agriculture. studies indicate that enhancing technical skills through 349 targeted educational programs is essential for bridging the gap between research and practical 350 application (dibbern et al., 2024; fragomeli et al., 2024). the need for improved knowledge transfer 351 mechanisms is also emphasised, as effective communication of research findings can lead to better 352 farm management practices (rose et al., 2021). furthermore, peer-to-peer learning initiatives are 353 recognised as valuable tools for fostering a supportive environment for skill development, enabling 354 farmers to leverage digital tools effectively (da silveira et al., 2023b). 355 356 21 4.2 building acceptability and trust 357 all case studies emphasize the general lack of acceptability and trust in digital technology 358 among farmers. farmers, particularly older ones, may be reluctant to adopt digital tools due to 359 resistance to change, perceived risks, and a lack of digital literacy. those who have relied on 360 traditional methods for decades may be sceptical about the benefits of agriculture 4.0 and prefer 361 to stick with familiar practices. they may see the initial investment and learning curve associated 362 with digital tools as risky. providing tailored education and training, including mentorship programs 363 that demonstrate the tangible benefits and offer hands-on sessions with digital tools, can help build 364 trust and encourage adoption. 365 the literature suggests that tailored education and mentorship programs can alleviate 366 farmers’ aversion towards new technologies by demonstrating their tangible benefits (da silveira et 367 al., 2023b; ganeshkumar et al., 2023). building trust through hands-on training and engagement is 368 crucial for overcoming scepticism and encouraging adoption (da silveira et al., 2023a). additionally, 369 more support for young farmers' entrepreneurship can drive innovation and the adoption of digital 370 tools, as younger farmers may be more open to integrating the dss and other agriculture 4.0 371 technologies into their practices. younger farmers tend to be more receptive to digital innovations, 372 indicating that fostering entrepreneurship among youth can drive broader acceptance of agriculture 373 4.0 technologies (klerkx and rose, 2020). 374 the reluctance of older generations to adopt digital tools highlights the need for tailored 375 educational initiatives that address specific concerns and barriers. policymakers should consider 376 implementing mentorship programs that pair experienced farmers with younger, tech-savvy 377 individuals to foster trust and facilitate knowledge exchange. the role of young farmers as change 378 agents in the adoption of digital technologies should be recognised and supported through targeted 379 entrepreneurship programs (bocean, 2024; shamshiri et al., 2024). furthermore, cultivating 380 22 communities of support through collaborative platforms can empower all farmers, including those 381 beyond the greenhouse sector. these platforms encourage peer-to-peer learning, creating a 382 collaborative environment that is also beneficial for enhancing trust and confidence in technology 383 use (derakhti et al., 2023; gumbi et al., 2023; petraki et al., 2025) (derakhti et al., 2023; gumbi et 384 al., 2023; petraki et al., 2025). 385 386 4.3 reducing farm exits 387 in monastir and tuscany, a key issue is the gradual abandonment of farming activities, 388 primarily due to low profitability and very limited generational turnover. this trend poses a 389 significant threat to the agricultural sector, as it may hinder the adoption and diffusion of agriculture 390 4.0 technologies, which are essential for modernising farming practices and improving productivity. 391 therefore, there is a need to reduce farm exits. supporting young farmers' entrepreneurship 392 through tailored policy initiatives, such as access to training programs, financial incentives, and 393 mentorship opportunities, is vital for revitalising the sector (derakhti et al., 2023). by making 394 farming more attractive to younger generations, the sector can ensure a continuous influx of new 395 entrants and ideas, which is essential for the adoption of innovative practices (eastwood et al., 396 2019). 397 to reduce farm exits, especially by attracting and retaining young farmers to ensure a 398 continuous influx of new entrants and ideas, several enabling conditions should be established. 399 providing financial support through subsidies, grants, and low-interest loans can reduce the initial 400 cost burden for young farmers, making farming more attractive and viable (derakhti et al., 2023). 401 implementing training programs that focus on new digital tools and how they can support 402 sustainable practices can enhance the technical skills of young farmers, enabling them to adopt and 403 integrate agriculture 4.0 technologies effectively (eastwood et al., 2019). establishing mentorship 404 23 programs where experienced farmers guide and support young farmers can facilitate knowledge 405 transfer and build confidence. 406 the trend of farm abandonment due to low profitability and limited generational turnover 407 is a critical issue that requires urgent attention. attracting and retaining young farmers is essential 408 for the sustainability of the agricultural sector, including its modernisation through digital tools. 409 incentives, such as access to affordable land, financial support, and training programs focused on 410 digital tools, can create a conducive environment for youth by making farming more appealing to 411 younger generations. in turn, the agricultural sector can benefit from fresh ideas and innovative 412 practices that are needed for the uptake and widespread use of agriculture 4.0 technologies 413 (macpherson et al., 2022; petraki et al., 2025). 414 415 4.4 creating product identity 416 meeting market requirements is perceived as a major issue in almería and antalya, 417 highlighting the importance of a strong product image to stand out against competitors. the 418 emerging need is for product differentiation in the market and greater consumer awareness, 419 especially by creating a unique identity for greenhouse-grown vegetables, distinguishing them from 420 other horticultural products, e.g. grown elsewhere or using different practices. this involves 421 developing elements that resonate with consumers, such as e.g. product denomination and origin 422 and logo, as well as emphasising the environmental and human health benefits of agricultural 423 products, while ensuring transparency in the production system. this can be achieved by 424 highlighting unique attributes of the products, especially focusing on eco-friendly practices achieved 425 through dss use, to attract consumers who are increasingly concerned about environmental 426 sustainability and health. greater consumer awareness is essential to inform and educate the public 427 about sustainability attributes, thus driving specific demand. 428 24 related research highlights the importance of transparency and sustainability in agricultural 429 practices, which can be achieved through digital tools like the dss examined in this study, enhancing 430 consumer trust and demand (fragomeli et al., 2024; maffezzoli et al., 2022). certification and 431 labelling schemes play a critical role in communicating the value of sustainably produced goods, 432 thereby attracting consumers who prioritise environmental and health benefits (da silveira et al., 433 2023b). certification provides formal recognition of adherence to specific standards, such as organic 434 farming or sustainable practices, which can enhance the credibility and marketability of the 435 products. labelling schemes offer a clear and accessible way for consumers to identify and trust 436 these certified products. this aligns with the need for greater consumer awareness regarding the 437 attributes of agricultural products (da silveira et al., 2023a). 438 the importance of a strong product image in meeting market demands is a key finding that 439 has implications for marketing strategies and consumer education. farmers should prioritise 440 transparency and sustainability in their practices to enhance consumer trust and demand. this can 441 be achieved through the widespread uptake of effective certification and labelling schemes that 442 communicate the environmental and health benefits of agricultural products. however, initiatives 443 that promote consumer awareness regarding sustainable practices are needed as well to drive or 444 enhance demand for responsibly produced goods (mcfadden et al., 2022; xu et al., 2022). 445 446 4.5 increasing farmer margins 447 actors in monastir highlight the issue of low profit margins in agricultural sales, which 448 discourages investment in new technology. to address this, there is a critical need to allow for a 449 price premium on agricultural products. this can be achieved by differentiating products based on 450 their sustainability and quality attributes, such as environmental and health benefits, appealing to 451 consumers willing to pay more for sustainably produced goods (derakhti et al., 2023). 452 25 enabling conditions for this need include robust certification processes that ensure 453 transparency and trust in the sustainability claims. certification and labelling schemes can play a 454 crucial role in communicating the value of sustainable products to consumers, justifying the price 455 premium. effective marketing campaigns are also essential to educate consumers about the 456 benefits of sustainable food and to increase their willingness to pay for it. additionally, financial 457 mechanisms like subsidies, grants, or other incentives can support farmers in adopting new 458 technologies by offsetting the costs of dss uptake and related changes in sustainable agricultural 459 practices and inputs, thereby enhancing their economic viability (eastwood et al., 2019). 460 the importance of a strong product image in meeting market demands is a key finding that 461 has implications for marketing strategies and consumer education. farmers should prioritise 462 transparency and sustainability in their practices to enhance consumer trust and demand. this can 463 be achieved through the widespread uptake of effective certification and labelling schemes that 464 communicate the environmental and health benefits of agricultural products. however, initiatives 465 that promote consumer awareness regarding sustainable practices are needed as well to drive or 466 enhance demand for responsibly produced goods (mcfadden et al., 2022; xu et al., 2022). 467 468 4.6 increasing farmer bargaining 469 all case studies highlight the issue of unfair value distribution along the food value chain. 470 this imbalance results in farmers receiving a disproportionately small share of the profits compared 471 to other downstream actors, such as distributors and retailers. in some regions, like e.g. almería 472 and antalya, this problem is exacerbated by the relatively high number of intermediaries. inequity 473 in value distribution can lead to financial instability for farmers, discouraging the adoption of 474 innovations such as the dss and sustainable practices, and may result in low market responsiveness. 475 26 strengthening farmers' bargaining power is then crucial for achieving sustainability objectives 476 through digitalisation. 477 key enabling conditions to address this need involve the promotion of collective approaches, 478 such as cooperation initiatives, including second tier-cooperatives, and fostering stronger farmer 479 unions. additional benefits can be realised by enhancing efficiency through the organisation of 480 demand-driven production. by organising into cooperatives or unions, farmers can pool their 481 resources, share knowledge, and collectively negotiate better prices and terms with downstream 482 actors (ganeshkumar et al., 2023; klerkx and rose, 2020). example cooperation initiatives include 483 marketing cooperatives that help farmers sell their products collectively or supply cooperatives that 484 enable farmers to purchase inputs at lower costs. for example, second-tier cooperatives, i.e. union 485 of smaller, first-tier cooperatives, proved successful especially in almeria, working together to 486 provide services, support, and resources (including training) to their cooperative members 487 (giagnocavo et al., 2014). also, farmer unions can advocate for policies that support fairer farmer 488 prices, provide legal assistance, and offer entrepreneurial training. these collective actions among 489 farmers can lead to improved market access and enhanced resilience against market fluctuations 490 (rose et al., 2021). the organisation of demand-driven production can be achieved through the 491 adoption of dedicated digital tools, such as predictive technology and analytics. these tools link 492 supply with demand, helping growers mitigate unexpected risks and challenges by predicting 493 market demand and maximising productivity (eastwood et al., 2017; suksa-ngiam and bechor, 494 2024). the dss developed in this research represents a farm-level step towards organising demand-495 driven production. it equips greenhouses with sensors and iot devices that provide on-site 496 information useful for predictive models. however, dedicated tools for market predictions are still 497 needed. cooperation initiatives may help distribute the costs of these additional technologies, 498 enabling their widespread adoption at the territorial level. 499 27 strengthening farmers’ bargaining power is essential for improving their economic viability 500 and enabling the adoption of agriculture 4.0 technologies. promoting collective approaches, such 501 as cooperatives and unions, and organising demand-driven production are key strategies. these 502 approaches empower farmers to negotiate better terms, access markets more effectively, and share 503 the costs and benefits of digital innovation. 504 505 4.7 simplifying bureaucracy 506 in tuscany, stakeholders emphasise that slow and complex bureaucracy often discourages 507 farmers from applying for public incentives, hindering the sustainable upgrade of farm practices, 508 including the adoption of new digital tools. the complexity and lengthy processes involved in 509 paperwork can be particularly daunting, leading to frustration and disengagement among farmers. 510 complex bureaucratic processes can deter farmers from applying for public incentives (mcfadden 511 et al., 2022). to address these issues, there is a need for simpler bureaucracy. 512 enabling conditions for this simplification include implementing streamlined application 513 processes that reduce unnecessary bureaucratic steps and increase assistance to farmers and 514 advisors throughout the application process (eastwood et al., 2019). simplified application 515 procedures and targeted support can enhance farmer engagement and participation, making it 516 easier for them to access the support they need for adopting digital tools and sustainable practices. 517 the complexity of bureaucratic processes can prevent farmers from accessing public 518 incentives. simplifying these processes is crucial for enhancing farmer engagement and 519 participation in programs aimed at promoting digital agriculture. policy improvement should 520 prioritise the streamlining of application procedures and the provision of targeted technical support 521 to farmers throughout the bureaucratic process, thereby facilitating access to the resources needed 522 for adopting new technologies (martens and zscheischler, 2022; monda et al., 2023). 523 28 524 4.8 developing land and crop production planning 525 findings from antalya indicate that the current supply of greenhouse-grown food is 526 insufficient to meet both domestic and foreign market demand. this production gap challenges the 527 region's agricultural sector, potentially leading to missed economic opportunities and reduced 528 competitiveness in both domestic and international markets. therefore, strategic land and crop 529 production planning is needed to optimise the use of available agricultural land, ensuring that the 530 right crops are grown in the right quantities to meet market demands. this approach can stabilise 531 the market and ensure a steady supply of greenhouse-grown food. 532 implementing policies that support effective production strategies is crucial for optimising 533 resource use and enhancing market competitiveness (derakhti et al., 2023). actors identify 534 production-support policies as crucial enabling conditions as they can provide the necessary 535 framework and resources to assist farmers in implementing effective land and crop production 536 strategies. these policies can encourage farmers to adopt best practices and invest in dss or other 537 digital tools that enhance productivity and sustainability (dibbern et al., 2024). 538 addressing production gaps through strategic planning is essential for ensuring food system 539 resilience and competitiveness. policy frameworks that support land and crop planning can help 540 align production with market needs, reduce inefficiencies, and promote the adoption of digital tools 541 that support data-driven decision-making in agriculture. 542 543 4.9 increasing liquidity for new technology uptake 544 farmers in monastir and antalya are struggling with rising production costs, making it 545 difficult to sustain their operations. in tunisia, for instance, this issue arises because equipment like 546 greenhouses and agricultural inputs such as seeds, pesticides, and fertilisers are imported. to 547 29 overcome this issue, better access to liquidity is needed to invest in new technologies that can 548 enhance efficiency and productivity. access to liquidity is critical to encourage farmers’ uptake of 549 agriculture 4.0 technologies, such as the dss, which can help reduce costs and increase yields in 550 greenhouses and other farming systems. 551 specific support mechanisms for agriculture 4.0, including grants and subsidies, can 552 encourage the modernisation of farm production and the adoption of agriculture 4.0 (eastwood et 553 al., 2019; ganeshkumar et al., 2023). support from public and private institutions is an important 554 enabling condition to help farmers adopt new digital technologies alongside more sustainable 555 practices. this financial backing is essential for enabling farmers to transition to more efficient and 556 sustainable practices (klerkx and rose, 2020). 557 addressing liquidity constraints is essential for enabling farmers to invest in digital tools and 558 transition toward sustainable agricultural practices. public and private financial support 559 mechanisms, such as subsidies, grants, and low-interest loans, can reduce the initial cost burden 560 and make digital technologies more accessible, particularly for small and medium-sized farms. 561 562 4.10 facilitating remote farming operations 563 the heavy workload and difficulty in achieving a work-life balance for the workforce across 564 the case studies underscore the need for technology that facilitates remote farming operations. 565 these issues are particularly sensitive for women and young parents, who often juggle multiple 566 responsibilities. by reducing the need for constant physical presence on the farm, remote farming 567 operations can significantly alleviate the physical and time burdens on farmers, allowing them to 568 manage their greenhouses more efficiently and effectively from a distance (finger, 2023; lajoie-569 o’malley et al., 2020). the dss under study is designed to enable remote monitoring of greenhouse 570 conditions, such as climate and soil, and to perform tasks like fertilisation, irrigation, and diseases 571 30 onset and development. the need to facilitate remote farming operations closely aligns with the 572 overarching objective of the study and is intrinsically linked to other needs, such as improving the 573 technical skills of farmers and advisors, and knowledge transfer and building acceptability and trust. 574 addressing the need for remote work likely requires most previously mentioned enabling 575 conditions. however, two more specific enabling conditions can be identified that complement 576 those already discussed. first, targeting public and private investments to establish robust digital 577 infrastructure in rural areas to ensure reliable internet connectivity is crucial. while some 578 specialised greenhouse districts in the investigated case studies may already have this 579 infrastructure, a digital divide still exists that must be bridged to enable agricultural digitalisation. 580 (gumbi et al., 2023; rose et al., 2021). second, collaborative efforts are increasingly recognised as 581 vital for digital transformations in agriculture (martens and zscheischler, 2022; wang et al., 2020). 582 fostering a culture of knowledge sharing and collaboration among farmers can greatly improve their 583 ability and confidence to operate remote farming tasks. peer-to-peer learning and mentorship 584 programs can play a significant role in enabling farmers to operate remote farming systems 585 confidently. this can be facilitated by developing collaborative platforms that foster a community 586 of support, where farmers can share resources, knowledge, and best practices (fasciolo et al., 2024; 587 fragomeli et al., 2024; jayasiri et al., 2024). 588 bridging the gap between urban and rural areas is closely linked to rural improvement: 589 investments in digital infrastructure are expected to offer manifold benefits, by enhancing individual 590 farms and improving the overall quality of life in rural communities. improved internet connectivity 591 facilitates access to digital tools, which can enrich education, healthcare, and economic 592 opportunities for rural residents (finger, 2023; fragomeli et al., 2024). an additional important 593 aspect is the potential to offer partial remote work opportunities through the diffusion of dss that 594 enable monitoring and operating tasks without the need to be on-site. this aligns the modality of 595 31 agricultural work with those seen in other economic sectors, making it more appealing. the 596 flexibility enabled by remote farming tasks reduces the physical toll typically experienced by farmers 597 and prioritises the balance between work and personal life, fostering inclusivity for women and 598 young parents in the agricultural workforce (gabriel and gandorfer, 2023; gumbi et al., 2023; 599 shamshiri et al., 2024; yuan and sun, 2024). 600 601 4.11 critical assessment of the research 602 the limitations of the research should be acknowledged to support informed interpretation 603 and guide future research improvements. 604 the research is geographically limited to four case studies in the mediterranean basin. 605 although these regions represent significant players in the global greenhouse vegetable market, the 606 findings may not be fully generalisable to other regions worldwide. 607 ll actor selection aimed to ensure representation across the agricultural value chain, 608 however the number of participants per category varied by case study. for instance, in antalya, only 609 one policy representative was involved, which may have constrained the diversity of policy 610 perspectives relevant to both the local territorial context and turkey more broadly. 611 the study relies on qualitative data collected through participatory methods involving a 612 diverse group of actors with interdisciplinary expertise. the sample size and composition of engaged 613 actors may not capture the full diversity of views within each region, and the findings might be 614 influenced by the perspectives and biases of the participants. 615 data collection and reporting is based on internally developed procedures and protocols, 616 tailored to the ll approach and the relatively small sample size. in contexts where such internal 617 management is not feasible (e.g., studies involving randomised sampling, large sample sizes, 618 saturation-based sampling) widely recognised tools for reporting qualitative research should be 619 32 considered. for example, the coreq checklist (tong et al., 2007) ) offers a structured framework 620 for ensuring transparency and rigour in qualitative research. 621 findings emphasise the importance of technical skills, trust, market dynamics, and policy 622 frameworks in fostering the adoption of digital technologies. however, other potential factors that 623 may also play an important role in technology adoption were not extensively explored, e.g., cultural 624 attitudes, social networks, economic incentives. 625 626 5. recommendations for the science-policy-society interface 627 findings highlight how technical skills, trust, market dynamics, and policy frameworks 628 interact to shape the adoption of agriculture 4.0 technologies across mediterranean regions. 629 focusing on greenhouse farming, where remote management is relatively more feasible, this study 630 suggests that successful implementations may offer scalable models for broader agricultural 631 applications (bocean, 2024; yuan and sun, 2024). these findings support the generation of 632 recommendations for the science-policy-society interface, emphasising the need for integrated 633 approaches to unlock the full potential of digital tools in agriculture. 634 there is a critical need for wider and enhanced collaboration among scientists, policymakers, 635 and agricultural practitioners to foster the successful adoption of agriculture 4.0 technologies 636 (matthews, 2021). encouraging interdisciplinary research that integrates insights from agricultural 637 science, social sciences, and technology studies can provide a holistic understanding of the 638 challenges and opportunities associated with digital agriculture (finger, 2023; rotz et al., 2019). 639 developing policy frameworks that are informed by empirical research and stakeholder input can 640 ensure that interventions are relevant and effective. achieving synergies between digitalisation, 641 sustainability, and food security requires policy coherence and governance models that integrate 642 environmental and economic objectives through participatory, goal-based approaches coderoni 643 33 (2023). policy enhancement should include the engagement of farmers and agricultural advisors to 644 co-create policies that address their specific needs and concerns (derakhti et al., 2023; gabriel and 645 gandorfer, 2023). raising public awareness about the benefits of digital agriculture and the 646 importance of sustainable practices is essential for garnering societal support for agricultural 647 innovations. educational campaigns should target not only farmers but also consumers, fostering a 648 culture of sustainability and responsible consumption (gouroubera et al., 2025; rose et al., 2021). 649 establishing mechanisms for monitoring and evaluating the impact of digital agriculture initiatives 650 can provide valuable insights into their effectiveness and inform future policy decisions. continuous 651 feedback loops between research, policy, and practice can enhance the adaptability and 652 responsiveness of agricultural interventions towards digitalisation (fragomeli et al., 2024; yang et 653 al., 2024). 654 the successful adoption of digital technologies extends beyond individual farms; it 655 empowers communities and enhances their economic resilience. as these technologies become 656 more widespread, rural areas may experience significant transformations that address longstanding 657 rural-urban disparities (fragomeli et al., 2024; yang et al., 2024). as the agricultural landscape 658 evolves, significant potential emerges from the adoption of remote farming technologies. these 659 technologies can improve work-life balance, foster community support, and create new job 660 opportunities, although strategic investment in digital infrastructure is still needed. this appeal can 661 enhance workforce diversity, ensuring that agriculture remains competitive and relevant in the 662 rapidly changing job market. agricultural digitalisation serves not only ecological sustainability but 663 also uplifts rural communities by fostering a more equitable and diverse agricultural community 664 (rose et al., 2021; wolfert et al., 2017). 665 666 6. conclusions 667 34 this study identifies key enabling conditions for the effective implementation of agriculture 4.0 668 technologies in mediterranean greenhouse farming. by following a rri approach, findings from 669 participatory research across lls case studies suggest that efforts should focus on improving digital 670 literacy, building trust in technology, leveraging market dynamics, and facilitating remote farming 671 operations. these strategies can support the digital transformation of agriculture while promoting 672 social inclusion, equity, and improved workforce conditions, particularly for women and youth. 673 to support evidence-based decision-making, the following policy recommendations are proposed: 674 • invest in digital literacy and training: tailored educational programs and knowledge transfer 675 mechanisms are essential to bridge the gap between research and farm-level application; 676 • support inclusive technology adoption: initiatives should consider generational and socio-677 economic differences to avoid inadvertently excluding older or less digitally literate farmers; 678 • strengthen market incentives: certification schemes, consumer awareness campaigns, and 679 simplified bureaucratic processes can enhance product value and encourage investment in 680 digital tools; 681 • promote social equity and cooperation: policies should reinforce farmer unions and 682 collaborative initiatives to improve bargaining power and ensure fair value distribution; 683 • enable remote farming solutions: digital tools that improve work-life balance and 684 operational efficiency can foster sustainability and attract new entrants to the sector. 685 key limitations of this study include its context-specific nature and reliance on the socio-686 institutional dynamics of each territorial ll, which should be carefully considered when interpreting 687 the findings and assessing their broader applicability. future research should expand the 688 geographical coverage, integrate quantitative methods, and explore additional factors, such as 689 cultural attitudes and social networks, that influence technology adoption. also, in the context of 690 35 lls, integrating participatory action research principles could offer additional value, particularly in 691 enhancing actor agency and long-term impact, given the strong emphasis placed on collective action 692 and transformation led by participants. 693 disclosure of potential conflicts of interest 694 authors declare that they have no known competing financial interests or personal 695 relationships that could have appeared to influence the work reported in this paper. the funder had 696 no role in study design, data collection and analysis, decision to publish, or preparation of the 697 manuscript. 698 699 data availability statement: data will be made available upon request 700 acknowledgements 701 this research was funded under the eu partnership for research and innovation in the 702 mediterranean area (iguess-med – ‘innovative greenhouse support system in the mediterranean 703 region efficient fertigation and pest management through iot based climate control’; grant 704 agreement number: 1916; section 1, topic 1.2.2: “sustainability and competitiveness of 705 mediterranean greenhouse and intensive horticulture”). the funder had no role in study design, 706 data collection and analysis, decision to publish, or preparation of the manuscript. the authors 707 thank all stakeholders in the living lab activities of the iguess-med project for their contributions. 708 709 references 710 bartolini, f., incrocci, l., and buendía guerrero, d. 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(2020). decision support systems for 1002 agriculture 4.0: survey and challenges. computers and electronics in agriculture, 170: 105256. 1003 https://doi.org/10.1016/j.compag.2020.105256 1004 1005 bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 bio-based and applied economics bae copyright: © 2024 esposti, r. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: esposti, r. (2024). dating common commodity price and inflation shocks with alternative approaches. bio-based and applied economics 13(2): 171-201. doi: 10.36253/bae-14060 received: december 9, 2022 accepted: november 2, 2023 published: july 25, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: simone cerroni orcid re: 0000-0002-1656-0331 dating common commodity price and inflation shocks with alternative approaches roberto esposti department of economics and social sciences, università politecnica delle marche, ancona, italy e-mail r.esposti@staff.univpm.it abstract. this paper investigates the occurrence of common price shocks (co-exceedance) across different commodities. imf monthly price series of 11 commodities are considered over the 1980-2021 period. the analysis considers two alternative stochastic processes. the first looks for common volatility clusters using individual garch models to detect whether and when respective clusters overlap. through an appropriate battery of tests, the second alternative looks for a common bubble generating process (bgp) by searching for individual explosive roots and then dating them to identify the possible overlaps and first movers. evidence emerging about these shock generating processes is linked to the analogous behaviour of the us consumer price index (cpi) to assess to what extent inflation shocks can be associated to the observed commodity price spikes. results show that the detection of temporary bubbles and volatility clusters only partially agrees on the episodes of exuberance, on the first-moving commodities and on the involvement of the cpi. this provides helpful suggestions on the development of a real-time surveillance tool supporting policy intervention in periods of commodity price turbulence. keywords: commodity prices, price volatility, explosive roots, garch models. jel codes: q11, c32. 1. introduction the large and rapid surge of most commodity prices that started in 2021 and lasted for the whole of 2022 points to two stylised facts that have been repeatedly investigated in previous episodes of price spikes: commodity prices move together; the rise of commodity prices transmits, somehow, to the consumer price index (cpi). the consequent inflation rate rush largely impacts economies and societies and usually induces a quite vigorous policy response (ider et al., 2023). nonetheless, the explanations of these price dynamics are still to be fully understood. the literature on the common movement (or co-movement) of commodity prices is vast (byrne et al., 2020). one limit of this literature is that it implicitly assumes that the communality of price dynamics has to be intended as the existence of a common data generation process (dgp), usually represented https://doi.org/10.36253/bae-14060 http://www.fupress.com/bae https://doi.org/10.36253/bae-14060 https://orcid.org/0000-0002-1656-0331 mailto:r.esposti@staff.univpm.it 172 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 via some variant of vector auto-regression (var) or vector error correction (vec) models or through more sophisticated representation of the underlying common drivers (for instance, common latent factors) (esposti, 2021). but this may contrast with empirical evidence that suggests substantially different fundamentals across very diverse commodities, thus questioning the presence of common real determinants to justify commonality. in general terms, most representations of the common dgp and of the consequent price transmission process (like the conventional granger causality, for instance) may be too simplistic to capture the real underlying interdependence across commodities, if any, thus providing misleading evidence on the actual causal linkages. however, a specific strand of the recent empirical literature stresses that a common dgp is not strictly needed for a common temporary behaviour to be observed (zhao et al., 2021, p. 781; mutascu et al., 2022). in particular, commonality may only occur within the periods of exuberance, also referred to as co-exceedance. when the price spike expires each series reverts to its own (possibly different) normal-time dgp. this hypothesis can be also transferred to the second stylised fact, that of the cpi response: for a transmission of shocks to the cpi to occur we do not need a common dgp with the commodity prices, but only some co-exceedance with them. the presents paper aims to contribute to this body of studies by proposing an original methodological approach which then leads to a novel policy tool. the main originality of the approach consists in juxtaposing and combining two alternative stochastic processes generating co-exceedance. the first resides in the occurrence of common (but not interdependent, that is, multivariate) volatility clusters whose behaviour is here modelled through appropriate generalised auto-regressive conditional heteroskedasticity (garch) models. the second consists in the occurrence of common bubbles (a common bubble generating process, bgp), that is, temporary explosive roots within the individual series but whose timing largely corresponds across commodities. individual price series of very diverse commodities are thus separately investigated in order to assess whether and when volatility clusters (first) and temporary explosive roots (second) are found. although these methodological approaches have already been adopted in previous empirical studies (otero and baum, 2021; phillips and shi, 2020; zhao et al., 2021), this paper proposes a combination of these techniques to assess the co-exceedance of commodity prices without relying on some arbitrary and unreliable common dgp. monthly series of 11 commodity prices and the respective price indexes released by the international monetary fund (imf) over the 1980-2021 period are considered. co-exceedance is assessed by confronting the occurrence of these events across series. if some overlapping is observed, it supports the existence of some contagion (or transmission) across prices. the sequence of the events across prices can finally suggest the direction of this possible contagion. the same analysis is then repeated on the us cpi. the interest for this methodological approach eventually lies in its application to design a suitable policy tool. instead of concentrating on complex and possibly misleading causation processes, the proposed empirical strategy aims to identify when periods of rapid price rises occur and assesses whether they are common across commodities. therefore, it allows to develop a real-time surveillance tool guiding a prompt policy response in the right direction, in particular by distinguishing interventions that can be confined to the sectoral context from interventions that require an economy-wide spectrum of actions. in order to be easily interpretable also by non-technical users, this tool is aimed to transfer results into a sort of periodically updatable dashboard visualizing the critical information under investigation: if a bubble is occurring for a given commodity, when it started, whether other commodities are involved by the same bubble, who moved first and, finally, if and to what extend this price surge is also reflected in the cpi. contributing to the definition of such a policy tool represents a further objective of the present study. the rest of the paper is structured as follows. section 3 overviews the recent empirical literature in the field while section 3 presents the adopted dataset and the main stylised facts. section 4 details the adopted methodological approach, the results of which are illustrated in section 5. in section 6 these results are discussed and juxtaposed with the evidence emerging from more conventional methodologies about the investigation of commodity price dynamics. section 7 draws some policy implications and concludes. 2. the common movement of commodity prices: literature and evidence the paper by wang and tomek (2007) may represent the first study that explicitly and extensively discussed the sequence of empirical issues to be tackled in investigating the actual dgp of commodity prices. though their main attention was on the stationarity properties of agricultural commodities, their conclusions can be extended to other commodities and properties of the unknown dgp. the main argument is that, due to 173dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 their market fundamentals (on both the supply and the demand side), commodity prices are expected to be mean reverting, with the long-term mean value possibly moving along a deterministic trend. so, prices are expected to follow a stationary dgp around a drift or a trend. the fact that in the empirical literature the presence of a unit root is only occasionally rejected has to be attributed to the characteristics of the respective tests and/or to their misspecification. in particular, other characteristics of a stationary dgp can make it similar to a unit-root process. one is that these prices often show long memory (that is, fractional integration) making it possible for a close-to-(but-lower-than)-one root to be confounded with a unit root. another is the presence of a structural break that may shift the long-term value upward or downward and can itself generate a potential confusion as evidence of nonstationarity: the presence of a structural break within a stationary series may lead to accepting the presence of a unit root, thus wrongly concluding that the series is non-stationary (baum, 2005; glynn et al., 2007). a consistent body of recent studies concentrates on several different stochastic processes to explain the complex (i.e., non-linear) commodity price dynamics and the possible underlying co-movement. they are, in particular, fractional integration and structural breaks. a recent example, though concerning stock market indices and not commodity prices is caporale et al. (2020). based on an approach originally proposed by cuestas and gilalana (2016), they argue that fractional integration is very much related to non-linearities.1 the possibility of structural breaks is also considered since many studies argue that fractional integration might be artificially generated by the presence of breaks in the data that have not been taken into account. in fact, the presence of structural breaks within commodity price series was already considered by wang and tomek (2007). however, it must be noticed that fractional integration and/or structural breaks can hardly explain the behaviour of commodity prices and, in particular, the abovementioned co-exceedance, that is, their recurrent episodes of temporary exuberance as also emerging by simple visual inspection (see next section). they remain interesting and possibly relevant processes in the investigation of individual dgp since they may significantly interfere with the investigation of temporary bubbles and/or garch effects. therefore, although the approach here adopted considers other dgps, the presence of structural breaks can not be excluded at least for some of 1 another interesting strand of empirical literature on commodity price dynamics, and strongly linked to non-linearities and fractional integration, consists in the so-called fractal approach (cromwell et al., 2000). these commodities (esposti, 2021) and will be considered here for comparative purposes (see section 6). concentrating on the stationarity properties these studies overlook another major characteristic of these price series that clearly emerges from a simple visual inspection: the presence of temporary exuberance. therefore, their dgp is expected to also generate selfextinguishing periods of particularly high or low values. most of the literature in the last 15 years has essentially focused on this issue also as a consequence of the 20072008 price spike and of the following turbulent period. a lot of theoretical and empirical research has tried to investigate the origins of these price nonlinearities, jumps and spikes, as well as to put forward testing procedures to assess their presence. we can summarize this research effort in three main directions and, then, in their possible combination. the first strand of research explains the observed price spikes and jumps as the consequence of a temporary increase in their variability (or volatility). it is the formation of volatility clusters that eventually generates the observed highly irregular price dynamics. in most applications, this idea is implemented by specifying and estimating garch regression models possibly admitting asymmetric effects and non-stationary processes for the price level. see li et al. (2017), baur and dimpfl (2018) and esposti (2021), just to mention a few, for the application of different variants of garch modelling to commodity prices. within the second body of studies the origin of the episodes of price turbulence is the formation of temporary bubbles. several tests have been originally proposed to detect temporary price bubbles within mean-reverting, thus stationary, processes (gürkaynak, 2008). more recently, the presence of temporary bubbles has been admitted, and tested, within possibly non-stationary processes, that is, as temporary explosive roots emerging within unit-root processes (phillips et al., 2011, 2015; phillips and shi, 2020). gharib et al. (2021) and zhao et al. (2021) have recently used this battery of tests to assess the co-exceedance of some commodity prices and to date the respective bubbles. co-existence of both processes is also possible. this is considered helpful for two complementary reasons. on the one hand, as already anticipated, it is always difficult to clearly distinguish between the outcome of these two processes (gürkaynak 2008, pp. 182-183; chang, 2012). on the other hand, none of the two alternative processes may totally capture all the features of the observed price dynamics. to reconcile these two alternative processes, chang (2012) adopts an autoregressive jump-intensity(arji)-garch model. originally pro174 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 posed by chan and maheu (2002), this model is in principle able to generate both temporary bubbles and volatility clusters within stationary processes. the third strand of empirical research in the field differs from the conventional time-series approaches as it is grounded on the spectral analysis and in time–frequency approaches. for the evolution of market prices, wavelet analysis has emerged as a useful and powerful tool in assessing price co-movement cycles. without resorting on any theoretical causation (price transmission) process, it allows to explore how the series of prices are related at different frequencies admitting nonlinearities like structural breaks.2 mutascu et al. (2022) provide a valuable example of this kind of approach by investigating the co-movements of gasoline and diesel prices in different countries at different frequencies. though this approach is relatively new, interesting and promising, it is still based on the assumption of a permanent interdependence between prices although flexible and not-linear. in the present study, as anticipated, we do not want to admit any persistent co-movement but only co-exceedance, therefore prices moving together only in specific periods of price spikes. nonetheless, the combination of the co-exceedance analysis here proposed with wavelet analysis can open interesting developments for future research in this area. here, the aim is to investigate the commodity price dynamics following the first two relative recent strands of research by pointing to commodity price co-exceedance rather than co-movement. in particular, unlike chang (2012) and zhao et al. (2021) the objective is not to estimate the parameters of the actual dgp but to date the episodes of price turbulence by confronting, in this respect, two competing processes: garch within stationary processes (volatility clusters) and temporary explosive roots within non-stationary processes (bubbles). moreover, unlike zhao et al. (2021) here we do not adopt granger causality testing to assess the direction of the possible transmission of the price shocks across commodities.3 by dating these periods individually, we provide evidence on this transmission by solely juxtaposing the timing of the individual episodes. this is done not only on commodity prices and price indexes but also on the cpi series. while the empirical literature on the commodity price properties and behaviour is vast and follows the abovementioned directions, 2 we wish to thank an anonymous reviewer for helpful suggestions on this aspect. 3 granger causality tests imply a common linear dgp across series (var or vec models) (zhao et al., 2021, p. 783). but both commonality and linearity may not hold in the present case. nonetheless, for the sake of comparison and robustness check of results, in section 6 we will present granger causality tests. the investigation of the cpi dynamics (and its growth rate, the inflation rate) mostly follows other directions. it mainly concentrates on the common movement and possible interdependence with other macroeconomic variables and is only occasionally connected to commodity prices (garzón and hierro, 2022; ider et al., 2023). garch effects possibly occurring in the cpi or inflation rate series has been extensively analysed (engle, 1982), but we are not aware of studies assessing the presence of temporary bubbles within these series. in fact, visual inspection seems to suggest quite different properties of cpi compared to commodity prices (see next section). nonetheless, if a transmission from commodity prices to cpi is expected, especially in periods of price turbulence, this should imply some form of co-exceedance between these series. but there is a final original aspect of the present contribution with respect to the recent literature in the field. it concerns the policy implications of the proposed empirical approach. in previous studies either these implications are overlooked or they concentrate on the possible effect of policy interventions on the nature and scope of commodity price co-movement or co-exceedance like, for instance, the fuel tax system (mutascu et al., 2022) or import tariffs (esposti and listorti, 2018). if the main objective of a policy in this context is to minimize the negative impact of a generalized rise of commodity prices, knowing the possible underlying causation and transmission process, that is the structural linkages generating co-movement, might not be so critical. what seems important is rather a quick understanding that a price “bubble” is forming and whether or not it is just sectoral (so it involves a limited number of commodities) or it is generalized across all markets, that is, it is a co-exceedence. sectoral interventions to neutralize a momentary price surge are present in many contexts and are usually rapidly activated (in the case of agricultural commodities, for instance, the agricultural market-crisis interventions represent an interesting example (fao et al., 2011)). when occurring on firstmoving prices, these prompt sectoral responses may help to prevent a generalized “bubble”. understanding if and when this latter is, in fact, occurring then becomes critical to promptly activate system-wide actions, particularly intended to prevent or slow-down downstream impact on inflation rate surges (ider et al., 2023). this real-time surveillance tool able to provide such an early warning, as well as the generality and the first movers of the “bubble”, seems to be particularly helpful for a prompt policy response. 175dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 3. price series under scrutiny the present analysis concerns the price of a selection of 11 commodities belonging to three different categories: 4 agriculture commodities (corn, wheat, soybean, beef); 3 energy commodities (crude oil, natural gas, coal); 4 metals (aluminium, copper, zinc; nickel).4 all price series are taken from the imf commodity price dataset.5 all prices are monthly and cover the period january 1980 (1980m1)-december 2021(2021m12) (504 observations) with the only exception of natural gas whose series starts in 1985m1 (444 observations). together with individual commodity prices, the imf dataset also contains aggregate price indexes for groups of commodities. here, three monthly price indexes are considered: food price index (foodind) covering the period 1991m1-2021m12; metals price index (metind) covering the period 1980m1-2021m12; fuel (energy) index (eneind) covering the period 1992m1-2021m12. annex 1 provides details about which product quality these prices refer to, where they have been collected and on which aggregates respective indexes have been defined. table a1 also reports the respective descriptive statistics which include the conventional distributional indices suggesting that commodity prices depart from the normal distribution mostly for a longer right tail depending on the exceptionally high prices observed during temporary bubbles. the dynamics of commodity prices is investigated in combination with the evolution of the overall consumer price index (cpi). unfortunately, no worldwide (or global) cpi is available. moreover, many available cpi are usually collected and released at a quarterly or yearly basis. here, the us monthly cpi series is used (see annex 1 for more details).6 this series seems suitable in the present analysis not only for the concordant frequency, but also because the us still represents the largest economy worldwide, so any impact of the global commodity prices on inflation can be consistently assessed on this series. it must also be noticed that, as detailed in annex 1, several price series concern us markets and, in any case, all prices are expressed in us $. therefore, using the us cpi does not incur the risk of downscal4 selected commodities are the most important worldwide (in terms of value) within the respective categories. in fact, nickel is the fifth in the list of metals after lead. but for this latter a sufficiently long series is not available. 5 these price series are proprietary and can not be made available within the paper’s material. however, they can be freely downloaded at https://data.imf.org/?sk=471dddf8-d8a7-499a-81ba-5b332c01f8b9 or requested at https://www.imf.org/en/research/commodity-prices. 6 this data can be freely downloaded from https://fred.stlouisfed.org/ series/cpiaucsl. ing (if not neutralizing) the transmission of commodity price shocks to the cpi due to the exchange rate adjustment (garzón and hierro, 2022). unlike many previous studies (esposti, 2021), commodity prices, as well the three price indexes, are not deflated. as here we want to investigate the possible impact of commodity price spikes on the cpi, it does not seem appropriate to purge inf lation from these series. the same strategy is followed for the possible presence of seasonality: no seasonal adjustment is performed on price series and indexes. the logic behind this choice is twofold. on the one hand, we prefer to analyse the price series that economic agents really confront with. on the other hand, as stressed by wang and tomek (2007) and corradi and swanson (2006), any data transformation has to be taken with care as it could introduce artefacts within the series under investigation. however, we consider as appropriate a data transformation that is supported by the theory (corradi and swanson, 2006, p. 222). this is the case of the logarithmic transformation of the price levels. this transformation is largely used in empirical literature (listorti and esposti, 2012; esposti and listorti, 2013) and has two main motivations. first of all, price logarithms are more likely to show a normal distribution than price levels, and normality is usually required by the estimation and inference approaches. in other words, the log-normal statistical distribution of price levels has to be considered as a main regular feature of these series (listorti and esposti, 2012; esposti and listorti, 2013). secondly, the logarithmic transformation finds a robust theoretical justification in deriving the commodity price dynamics as geometric brownian motions (gbm) (diba and grossman, 1988; gürkaynak, 2008; su et al., 2017). this tradition also includes the idea of “rational bubbles”, that is, periods of price exuberance entirely justified by agent’s expectations about commodity fundamentals (diba and grossman, 1988). empirically, this hypothesis implies that price logarithms might take the form of mean reverting processes (due to market fundamentals) plus a random walk, a mean-reverting non-constant volatility (garch) and, possibly, temporary explosive roots.7 according to ibrahim et al. (2021), a gbm can generate a stochastic process that assumes normally distributed price level growth rates (therefore, difference in the logarithms) while admitting both unitroot (with drift and/or deterministic trend) and garch effects (volatility clusters).8 however, these recent studies 7 actually, diba and grossman (1988) exclude that, within this logic, a rational bubble can actually start: if it is observed it must always have existed. 8 see also agustini et al. (2018) for a similar derivation. https://data.imf.org/?sk=471dddf8-d8a7-499a-81ba-5b332c01f8b9 https://data.imf.org/?sk=471dddf8-d8a7-499a-81ba-5b332c01f8b9 https://www.imf.org/en/research/commodity-prices https://fred.stlouisfed.org/series/cpiaucsl https://fred.stlouisfed.org/series/cpiaucsl 176 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 do not admit temporary bubbles. taking into account pros and cons of the logarithmic transformation (corradi and swanson, 2006; wang and tomek, 2007), the present paper considers both the price levels and the logarithm of price levels and in parallel repeats the analysis for these two cases in order to assess which results are robust across the transformation. annex 2 displays the time evolution of the three aggregate price indexes (figure a1), the 11 individual commodity prices (figures a2-a4) and the logarithms of these individual prices (figures a5-a7) over the 1980m1-2021m12 period.9 visual inspection points to some general characteristics of the price dynamics. within each group, commodity prices seem to show some common movements: periods of exuberance as well as collapses substantially correspond across different commodities. this is only partially confirmed across groups: metals and agricultural commodities tend to share the same periods of rise and fall, while energy commodity prices seem more stable and less volatile at least until the very last years of the period under consideration. however, if aggregate price indexes instead of individual series are considered, it emerges that the three series largely overlap with a substantial correspondence of positive and negative spikes. what is common across commodities is also that price turbulence seems to sharply increase in the second half of the period under consideration and, in particular, from 2005 onwards. from this simple visual inspection, therefore, the hypothesis of common movement seems largely supported. for all commodities, periods of temporary exuberance are recurrently observed. during these periods, prices rapidly increase and then rapidly collapse to a level that does not differ much from the pre-exuberance level. therefore, despite these “bubbles”, prices still seem to behave like mean-reverting processes. this does not exclude changes in the long-term mean level or a longterm trend in this respect (esposti, 2021). but these changes or trends seem mild and are overshadowed by the large short-term instability. as could be expected, the logarithmic transformation does not change the general behaviour of the series. qualitatively, price levels and their logarithms are similar even though the latter are obviously smoother and this seems particularly evident for the energy commodity prices. at the same time, major differences emerge between commodity price series and the cpi series. figure a8 (annex 2) reports the cpi, its monthly growth rate (i.e., 9 the logarithmic transformation is not considered here for the price indexes and cpi. it would rather require a different aggregation of the elementary prices into the index and this would simply generate another kind of index possibly introducing a further artefact. the inflation rate) together with the oil price which arguably is one of its major drivers, but it is also one of the most stable commodity prices. the difference is evident. oil price seems to follow a mean reverting process possibly with an increase of volatility in the second part of the period and an upward shift of the long-term mean value. cpi is much more stable, also in the second half of the period, and apparently moves along a deterministic trend. it follows that the inflation rate seems to behave like a mean-reverting process around an almost-zero long-term value with a limited, though appreciable, increase in the variability in the second half of the period. this purely visual inspection gives rise to the two key research questions underlying the present study. on the one hand, commodity prices seem to move together at least during periods of turbulence, but this would suggest a common stochastic process whose properties, however, are not self-evident. most price series show some characteristics of mean-reverting processes, and this would indicate they are stationary processes around drifts or trends. but the large and quick shocks, though temporary, do not seem consistent with this kind of processes. there should be some other underlying stochastic process, that may differ across prices but still admits their common movement at least in the periods of turbulence. on the other hand, the research challenge about the linkage between commodity prices and the cpi is quite the opposite. they apparently behave as very different stochastic processes, so commonality should be excluded. nonetheless, strong economic arguments, as well as an abundant empirical evidence (garzón and hierro, 2022), suggest that a common movement of many critical commodity prices has to be transferred, somehow, to the cpi. 4. the methodological approach the common theoretical framework of the investigation of commodity price dynamics consists in price formation mechanisms (or equations), that is, reduced-form models expressing the respective underlying market equilibrium.10 price formation equations represent the dynamic stochastic process as a mean-reverting or nonstationary process eventually generating the price level and volatility. these reduced form models have the further advantage of allowing a compact representation of cross-commodity price dynamics in the form of multiple 10 fackler and goodwin (2001) provide a common template based on linear excess demand functions embracing all dynamic regression models from which an estimable reduced-form model can eventually be derived. 177dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 simultaneous equation models that may explain both comovement and co-exceedance. the theoretical justification of these cross-commodity price transmission mechanisms, however, is not univocal. the prevalent explanation is that also very different commodities (for instance oil and corn) may display interdependence in the respective fundamentals (i.e., demand and supply). for instance, on the supply side, one commodity (e.g., oil) may enter as an input (thus, a cost) in the production process or supply chain of another commodity (e.g., corn and, consequently, beef). on the demand side, consumption of one commodity may be directly (through substitution effect) or indirectly (through income effect) affected by the price of another commodity (dawson et al., 2006; listorti and esposti, 2012; esposti and listorti, 2013). sometimes, however, this interdependence through the fundamentals can be so indirect and remote that it seems more reasonable to provide another theoretical justification of price co-movement and co-exceedance: though prices are not interdependent, they still all respond to the same underlying (often latent) common factors (stigler, 2011; byrne et al., 2020; esposti, 2021). the research question underlying the present study, however, comes before these theoretical representations of price interdependence, that is nature and forms of price co-movement and co-exceedance. it rather looks for empirical support on the evidence of co-exceedance, its possible temporary nature and its dating. therefore, we work on univariate models and not on multivariate models. on these premises, consider n commodities whose price is observed over t time periods (months in the present case). on the basis of rational agent’s expectation or efficient markets theory (zhao et al., 2021), assume that for any i-th commodity there exists an unobserved fundamental price depending on the real market drivers (supply, demand, storage, expectations). the natural constraints applying to these drivers should make this market fundamental price nonexplosive. the actual (i.e. observed) price moves around this fundamental level but it usually deviates from it according to some underlying stochastic dgp expressed by the following univariate price formation equation: pit = αi + δit + bipit-1 + uit, ∀i∈n, ∀t∈t s<t (1) where pit is the i-th commodity price (or the logarithm of price) at time t; αi expresses the drift while δi the deterministic trend coefficient. αi, δi, bi thus are commodity specific unknown parameters to be estimated. αi and δi indicate the long-term fundamental price level or the long-term deterministic trend, respectively, to which the actual price is expected to revert. the error term uit is usually assumed to be normally, independently and identically distributed, that is uit~nid(0,σ2). however, as autocorrelation in these disturbance terms is very likely to occur, (1) can be augmented to account for a transient dynamics: ∆pit = αi + δit + βipit-1 + θis∆pit-s + εit, ∀i∈n, ∀t∈t s<t (2) where βi = (βi-1) and θis are further commodity specific unknown parameters to be estimated. the error term is now correctly assumed to be εit~nid(0,σ2). (2) is the typical adjusted dickey-fuller (adf) regression and may admit different dgps depending on the value of βi. in particular, the price series is stationary, possibly around a drift (αi) or a trend (βit), whenever βi < 0. if βi = 0, the price series contains a unit root and it thus follows a non-stationary process (a random walk) possibly with a drift (αi) or a trend (βit). finally, whenever βi > 0, the price series has an explosive root implying a permanent and progressive departure from the fundamental price level unless it is temporary (a “bubble”). in practice, such process would contradict the actual existence of a fundamental price level. based on (2), distinct dgps can be considered to represent the observed deviation of prices from the alleged fundamental level. firstly, a generalized autoregressive conditional heteroskedasticity effect on εit can be included to capture the presence and persistence of volatility clusters. this is obtained by reformulating (2) as follows (garch(p,q)) regression model): ∆pit = αi + δit + βipit-1 + θis∆pit-s + εit = γi + ρip -pεit + ωiq -q, ∀i∈n,∀t∈t s,p,q<t (3) where εit = σitzit with zit~nid(0,1). is the it-h commodity price error term variance at time t, and ρip and ωiq are further commodity specific unknown parameters to be estimated. together, parameters ρip (also called arch terms) and ωiq (called garch terms) express the overall degree of persistence of volatility. it is usually assumed that ρip + ωiq < 1 (with p=q), indicating that volatility is mean reverting. otherwise, we would be faced with a persistent volatility, i.e., volatility behaving as a random walk (or non-stationary) process (engle, 1982; agustini 178 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 et al., 2018).11 once the garch model parameters have been estimated on the basis of the observed series, it is possible to assess whether and when volatility clusters occur. to do this, in-sample predictions of variance (i.e., ) are generated. then, on the basis of some pre-determined threshold (see below) clusters are found in those periods when this limit is exceeded.12 but a garch process is just one of the possible dgps consistent with the observed irregular commodity price dynamics. as stressed by engle (1982), a garch regression like (3) can be just an approximation to a more complex regression with non-arch disturbances. so, the garch specification might be picking up the effect of some relevant omissions from the estimated model. for this reason, we want here to make (3) compete with a second, and alternative, stochastic process generating a similar price behaviour. it consists of a dgp admitting temporary (or periodically collapsing) bubbles in the price levels. this dgp can be represented as a variant of the adf regression (2) as follows: ∆pit = +δit + pit-1 + ∆pit-s + εit, ∀i∈n, ∀t∈tr1, r2∈t s>t (4) where r1 and r2 denote the starting and ending points, respectively, of the possible temporary bubble. r1 and r2 are expressed as fractions of t so that r2 = r1 + rw, where rw is the window size of the regression, also expressed as a fraction of t. the number of observations to estimate (4) is tw = [trw], where [·] is the floor function which gives the integer part of the argument (otero and baum, 2021). for series showing temporary bubbles we should observe explosive roots for some sub-periods, that is, some [r1,r2] interval. this can be assessed through tests where the null hypothesis is h0: = 0, implying that the series shows a unit root, against the alternative hypothesis h0: > 0, implying that the series shows an explosive root in the [r1,r2] interval. a key contribution to a consistent formulation and implementation of this kind of tests was originally made by phillips et al. (2011), then improved by phillips et al. (2015) and phillips and shi (2020). the basic version of the test is the right-tailed adf statistic based on 11 this is also called integrated garch (igarch) process/model (campbell et al., 1996; chan, 2010). 12 although their validity in generating reliable predictions is largely questioned, arch/garch models are usually quite successful in generating in-sample projections (taleb, 2009). the full range of observations, r1 = 0 and r2 = 1 (i.e., rw = 1), denoted . as it applies to the whole period of observations, this statistic may fail in detect short-time temporary bubbles. therefore, a second statistic is based on the supremum t-statistic (sadf) that results from a forward recursive estimation of (4): sadf(r0) = (5) also this statistic may fail in the case of multiple temporary bubbles within the series. a third statistic can be thus computed. it is the generalised supremum adf (gsadf) test: gsadf(r0) = (6) based on these statistics, it is firstly possible to asses if one or more temporary bubbles occur. secondly, a backward testing procedure (backward sadf, or bsadf, statistics) allows dating these bubbles over the period t (phillips et al., 2011; 2015). for any particular observation, i.e. the i-th commodity observed at time r2, it is possible to test whether it belongs to a phase of explosive behaviour by performing a sadf test on a sample sequence where the endpoint is fixed at time r2, and expands backwards to the starting point, r1, which varies between 0 and (r2 − r0). this backward sadf statistic is defined as: bsadfr2(r0) = (7) a further refinement of these tests has been recently proposed by phillips and shi (2020) and takes into account both the presence of heteroskedasticity and the multiplicity issue in recursive testing. they thus recommend a wild bootstrap approach to compute the critical values of the abovementioned tests.13 the methodological approach followed here can thus be summarised as follows. firstly, we look for the stochastic properties of the individual commodity price series and the cpi. in particular, the presence of a unitroot (with or without a drift or a trend) and of arch effects is investigated. secondly, on the basis of the firststep evidence, garch effects are considered as the possible explanation of the observed periods of price turbulence. garch regression models like (3) are estimated on individual series and in-sample volatility predictions are generated to assess and date the volatility clusters. 13 one limit of these tests is that they do not allow breaks in levels or time trends. as discussed, neither a trend nor a structural break can explain by itself the observed irregular price behaviour. however, they can not be excluded at least from some commodities (see table 1) and might affect both the statistics and the critical values of these explosive root tests. 179dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 thirdly, as an alternative to garch processes, we consider the formation of temporary bubbles as expressed by (4), therefore as a momentary departure from the fundamental process either stationary or nonstationary. , sadf and gsadf tests are performed on individual series and the temporary bubbles, if any, are consequently dated by performing the bsadf test. finally, the beginning and the end of volatility clusters and of temporary bubbles are confronted both across the two alternative processes and among commodities (and cpi) in order to assess similarities and differences, as well as the presence of possible contagion effects. thus the analysis of co-exceedance simply consists in seeing whether volatility clusters or bubbles are common (i.e., overlap) or not. in case of a positive answer, it is then legitimate to ask, and to assess, whether a contagion effect can be deduced, that is, which series (i.e. price) moves first possibly driving the movement of the others. clearly, this investigation can not be confused with a formal causality assessment or testing. usual timeseries causality assessment in a multivariate context is performed via granger causality testing. this latter, however, assumes a linear relationship across commodities and does not seem consistent with the observed stochastic properties of these series and bubble formation. in this respect, some recent developments in the field seem promising for future research (shahzad et al., 2021; esposti, 2022). it is worth stressing, however, that assessing causality is not so essential for the main policy implication of interest here. investigating which commodities show a bubble formation earlier than others remains useful to build that real-time warning policy tool mentioned in previous sections. 5. results14 5.1. stochastic properties of the series table 1 reports the battery of unit root tests and of the arch tests on (2) for all series under investigation. in the case of price indexes, cpi included, it emerges that all series are stationary. the selected specification15 includes a drift in the case of the three commodity price 14 all testing and estimation procedures have been performed with software stata 17. in particular: garch models have been estimated using the command arch with arch(1) garch(1) specification; explosive roots have been tested using the radf command; the structural break tests have been performed using commands zandrews and clem; pairwise granger causality tests have been performed by using, the var and vargranger commands. 15 the best specification has been selected following enders (1995, p. 256-260). indexes and a trend in the case of cpi. at the same, all indexes here show an arch effect except foodind. consequently, all indexes behave as mean-reverting processes (with the mean moving along a deterministic trend in the case of cpi) possibly with volatility clustering. table 1. unit root (adf) and conditional heteroskedasticity (arch) tests on commodity price indexes and commodity prices (1980m1-2021m12)a. series adfb (w/o drift&trend) adf (with drift) adf (with trend) archc price indexes foodindd 0.475 -1.224† -2.439 0.126 metind -0.088 -1.404† -2.721 82.74* eneinde 0.150 -0.915† -3.138 106.9* cpi 6.988 0.539 -2.492† 72.91* price levels oil -0.502 -1.594† -2.906 92.63* coal -0.327 -1.599† -3.415 160.9* gasf 0.581 -0.409† -2.249 228.6* aluminium -0.416 -3.063* -4.010†* 65.84* copper 0.522 -0.621 -2.487† 112.8* zinc -0.229 -2.023†* -3.804* 106.9* nickel -1.041 -2.628†* -3.368 84.09* wheat -0.375 -2.816†* -3.349 108.9* corn -0.214 -1.872†* -2.891 29.86* soy -0.322 -2.089†* -3.288 67.44* beef 1.004 0.049 -1.739† 64.76* logarithm of price levels oil -1.199 -1.202† -2.631 78.04* coal 0.429 -1.501† -2.919 79.51* gasf -1.737 -1.445† -2.647 68.6* aluminium 0.178 -3.169†* -4.322* 65.86* copper 0.784 -0.858† -2.710 40.29* zinc 0.693 -1.827†* -3.478* 19.18 nickel 0.354 -2.008†* -3.102 19.62 wheat 0.252 -2.410† -3.045 28.32* corn 0.315 -1.755†* -2.855 7.38 soy 0.212 -2.032†* -3.243 24.67* beef 0.887 -0.167† -1.839 42.35* *statistically significant at 5% confidence level. † selected specification according to enders (1995, p. 256-260). a the test specification in terms of lags included has been selected case by case on the basis of the aic. b 5% critical value of the three adf test specifications, respectively: -1.95; -1.65; -3.42. c lagrange multiplier (lm) test performed on the residuals of the adf unit-root test equations; 5% critical value: 21.03. d 1991m1-2021m12. e 1992m1-2021m12. f 1985m1-2021m12. 180 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 regarding the individual commodity price series, however, a differentiated picture emerges across the commodity groups. if the levels are considered, energy commodities are all stationary around a drift. metals, on the contrary, show non-stationarity around a drift in the case of zinc and nickel, non-stationarity around a trend in the case of aluminium and stationarity in the case of copper. finally, all agricultural commodities, except for beef, are non-stationary around a drift while beef is stationary around a deterministic trend. despite these difference, all commodity prices show an arch effect. interestingly enough, the logarithmic transformation changes the evidence emerging from the tests only for four commodities and only in one case (wheat) does this change concern stationarity properties. aluminium remains non-stationarity but now around a drift. also copper and beef downscale from a trend to a drift while maintaining stationarity. wheat shows the most significant change passing from non-stationarity around a drift to a stationarity around a drift. thus, unlike the respective price level, the logarithm of the wheat price seems to behave like a mean-reverting process. the key point, here, is that while visual inspection of both price indexes and price series would indicate some common movement, tests indicate that such commonality may occur for price indexes but not for individual prices where four different dgps are observed, and this happens also within the same commodity group. this makes the hypothesis of common movement hardly tenable, at least over the whole time period. at the same time, however, visual inspection also reveals the presence of common periods of exuberance that are not necessarily compatible with the dgps emerging from tests. the limited reliability of the dgps emerging from the tests when compared to the actual price dynamics is confirmed by generating in-sample predictions from the estimated adf regressions. figure 1 compares these predictions with the real series for two cases that should express different dgps: a stationary series around a drift (mean reverting) (oil) figure 1. oil (left scale) and aluminium (right scale) prices: observed series and in-sample predicted series from respective adf model estimation (1980m1-2021m12) (see annex 1 for units of measure). 181dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 and a non-stationary series around a trend (aluminium). the two predicted series are quite similar, despite the different dgps, and, above all, in both cases these predictions largely diverge from the actual series especially in the last third of the observed period. evidently, there is something more in the stochastic process generating these series and this has to do more with temporary effects than with constant properties of the series. as the arch test is concordant across all series (except for foodind), the presence of volatility clusters can be a serious candidate to explain these temporary processes. but also temporary explosive roots (bubbles) could be considered as they are compatible with both stationary and non-stationary series over the whole period (diba and grossman 1988, p. 529). 5.2. volatility clusters table 2 reports the estimates of parameters ρ and ω of the garch regression model (3) (with a garch(1,1) specification) for the different series in both price levels and logarithms. two main facts emerge. first of all, with the only exception of foodind, in all series both estimated ρ and ω are statistically significant (corn is the only case where ρ is not statistically different from 0). this confirms what was already obtained with arch tests presented in table 2: volatility clusters occur in all series except for foodind. secondly, many series violate the assumption of temporary clusters: for the price indexes eneind and cpi, and price levels of natural gas, aluminium, zinc, wheat, corn, beef, we can not reject the hypothesis of ρ+ω =1. therefore, in these cases volatility follows a non-stationary process thus making clusters permanent rather than temporary as expected. logarithms of prices partially confirm this evidence but some differences are worth noticing: non-stationary volatility is observed also for oil and nickel while it is now excluded for aluminium, zinc and beef. contradictory evidence emerges about the reliability of these garch processes as generators of the observed price dynamics. on the one hand, the existence of volatility clusters is consistent with the observed large variability, or instability, of the commodity prices in specific periods of time. on the other hand, however, in several cases these processes support permanent volatility shocks thus becoming less compatible with the observed temporary episodes of turbulence. as discussed, once estimated, standard error in-sample predictions for these garch models can be generated. figure 2 shows these predictions for the three price indexes and figures 3a-3b for the individual price levels and logarithms, respectively. figure a9 (panel a)) reports the same predictions for the cpi and its growth rate (i.e., inflation rate). as expected, volatility clusters do not emerge for foodind, while a significant increase of volatility can be appreciated in the second part of the period of (starting around 2005) for both metind and eneind. for these indexes, this volatility dynamics seems consistent with the increased price turbulence observed in the same period as shown in figure a1. in the case of individutable 2. garch(1,1) model estimation and persistency test on commodity price indexes and commodity prices (1980m1-2021m12) (estimated standard errors in parenthesis)a. series ρ ω test ρ+ω =1 (χ2(1)) price indexes foodindb 0.036 (0.049) -0.171 (0.717) 3.96* metind 0.247 (0.049)* 0.675 (0.032)* 4.24* eneindc 0.373 (0.081)* 0.641 (0.058)* 0.16 cpi 0.116 (0.022)* 0.879 (0.021)* 0.22 price levels oil 0.343 (0.041)* 0.726 (0.031)* 10.4* coal 0.494 (0.062)* 0.664 (0.026)* 13.4* gasd 0.304 (0.051)* 0.625 (0.044)* 3.52 aluminium 0.276 (0.046)* 0.706 (0.038)* 0.39 copper 0.241 (0.032)* 0.812 (0.019)* 8.74* zinc 0.210 (0.031)* 0.815 (0.020)* 2.03 nickel 0.427 (0.049)* 0.700 (0.027)* 19.6* wheat 0.150 (0.022)* 0.865 (0.014)* 1.93 corn 0.090 (0.013)* 0.901 (0.012)* 2.15 soy 0.241 (0.030)* 0.718 (0.032)* 3.83* beef 0.315 (0.043)* 0.699 (0.031)* 0.82 logarithm of price levels oil 0.441 (0.050)* 0.617 (0.038)* 3.26 coal 0.214 (0.039)* 0.755 (0.036)* 3.95* gasd 0.520 (0.063)* 0.491 (0.043)* 3.66 aluminium 0.179 (0.041)* 0.748 (0.052)* 4.28* copper 0.065 (0.023)* 0.878 (0.035)* 10.51* zinc 0.064 (0.021)* 0.896 (0.028)* 6.35* nickel 0.196 (0.029)* 0.799 (0.031)* 0.76 wheat 0.062 (0.015)* 0.933 (0.013)* 0.58 corn 0.016 (0.010) 0.942 (0.039)* 1.75 soy 0.113 (0.031)* 0.676 (0.086)* 9.35* beef 0.155 (0.049)* 0.613 (0.039)* 9.39* *statistically significant at 5% confidence level. a only estimates of parameters ρ and ω are reported. other model parameter estimates are available on request. b 1991m1-2021m12. c 1992m1-2021m12. d 1985m1-2021m12. 182 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 al series, predictions show huge volatility variations for oil and for all mineral and agricultural commodities. clusters seem to be relatively rare and quite temporary in the first part of the period, while they become more frequent and longer, thus possibly permanent, from 2005 onwards. this seems even more true for cpi and therefore, but less intensively, for the inflation rate. cpi volatility sharply rises in 2005 and remains higher than in the previous period with only a drastic drop during years 2013-2014. the question is whether the magnitude of this volatility clustering is consistent with the actual price turbulence or whether, in fact, we should look for alternative explanations. 5.3. temporary bubbles table 3 reports the sequence of tests for the presence of temporary bubbles as expressed by equations (5) and (6). as discussed, moving from to gsadf the tests improve in terms of recursiveness and flexibility, therefore in precision, in detecting the temporary explosive roots.16 the presence of a temporary bubble is excluded in all cases (price indexes, individual price levels and logarithms of individual price levels) when the search of the bubble extends to the whole period ( ). something emerges with sadf with a temporary explosive root observed for metind and eneind, and for the price level of all energy commodities, all minerals, and wheat. in the case of the logarithm of prices a bubble is detected only for oil. the generalised occurrence of temporary bubbles is eventually indicated by the gsadf test. with the only exclusion of beef (both the price level and its logarithm), at least one temporary explosive root is found in all the series.17 16 it is worth noticing that the test in table 3 (second column) corresponds to the adf test with drift in table 1 (third column) as the explosive bubble tests associated to equation (4) may include a drift but not a deterministic trend. however, strictu sensu, they are not the same test since the former is a right-tailed statistics so the critical values are different. the statistics itself slightly differs in some cases because the adopted specifications (i.e., lag structure) are not always the same. 17 notice that the difference between the sadf and gsadf tests are larger here than what was presented in previous studies (see gharib et al., 2021, p. 5, in particular) arguably because, despite the number figure 2. garch(1,1) model standard error in-sample prediction for commodity price indexes (2000m1=1) (1992m1-2021m12). 183dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 a) b) c) figure 3a. garch(1,1) model standard error in-sample prediction for energy commodities (a), metals (b) and agricultural commodities (c) price levels (2000m1=1) (1980m1-2021m12). 184 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 a) ) b) c) figure 3b. garch(1,1) model standard error in-sample prediction for energy commodities (a), metals (b) and agricultural commodities (c) logarithm of prices (2000m1=1) (1980m1-2021m12). 185dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 in order to better appreciate how many bubbles occur and when, the bsadf tests (equation (7)) are computed. results (with the critical values) are reported in figure 4 for the three price indexes and in figures 5a,b for the commodity price levels and logarithms, respectively. it appears that, for indexes, bubbles very sporadically emerge before and after the 2005-2008 of observations, the period covered here is quite long (more than 40 years). period. on the contrary, over these four years the tests exceed the critical values several times for all the indexes. metind is the index for which this exceedance is more often observed. in the case of individual price levels significant differences are found across the three groups. for energy prices, only in period 2005-2008 we observe one or more bubbles shared by the three prices. in the case of metals, beside that period, a common bubble is also observed in the mid-eighties. agricultural commodities present a more composite situation: bubbles are more frequent and occur in the mid-eighties, mid-nineties, 2007-2008 and in the last decade. but they are often individual bubbles and, again, only in 2007-2008 we observe a bubble shared by most (except for beef) agricultural commodities. qualitatively, results obtained with the price logarithms are similar even though, as could be expected, the bubbles are less frequent and, consequently, also the commonality of bubbles is more sporadic. 5.4. first movers and contagion as discussed in previous sections, the focus of the present study is not on commodity price interdependence but on investigating the formation of temporary bubbles within individual price series in order to allow a real-time monitoring tool to inform on the formation of temporary bubbles, on the possible involvement of several commodities and on the first moving prices. the combination of the two alternative approaches here proposed allows to present their results in a form that permits an intuitive visualization of all this information about if and how co-exceedance is occurring. figures 6a,b aim to provide this easily interpretable visualization by displaying the periods of exceedance (volatility clusters or bubbles) for price levels and logarithms, respectively. bubbles are dated on the basis of the bsadf tests. in the case of volatility, following engle (1982, p 1003), exceedance is found any time the predicted volatility exceeds the double (in the case of price indexes) or the triple (in the case of individual commodity prices) of the average predicted volatility (i.e., standard error) over the two subperiods 1980m1-2000m12 and 2001m1-2021m12. together with table 4, these figures are also intended to provide an example of how the proposed approach can contribute to a real-time surveillance tool through an easily interpretable and periodically updatable dashboard visualization. to summarize this evidence and better interpret it in terms of co-exceedance, table 4 reports the beginning (the “exuberance date” to use the term of gharib et al., 2021, p. 6) and the end (the “collapsing date”) months of table 3. temporary explosive root tests on commodity price indexes and commodity prices (1980m1-2021m12)a. series sadf gsadf price indexes foodindb -1.289 0.0487 3.644* metind -1.285 3.303* 7.170* eneindc -1.167 4.907* 5.092* cpi 0.288 0.854 3.189* price levels oil -2.352 3.843* 4.417* coal -1.166 8.177* 8.762* gasd -0.456 4.164* 6.619* aluminium -2.524 2.405* 5.132* copper -0.883 2.972* 4.750* zinc -2.937 3.871* 5.908* nickel -2.656 3.426* 5.491* wheat -2.517 3.360* 3.795* corn -2.151 0.357 3.462* soy -2.478 -0.553 2.981* beef -0.134 0.937 1.211 logarithm of price levels oil -1.199 1.871* 2.275* coal -1.500 0.414 2.511* gasd -1.745 -0.135 2.891* aluminium -3.168 -0.270 -2.816* copper -0.858 -0.128 3.124* zinc -1.827 0.057 3.583* nickel -2.008 1.361 2.989* wheat -2.410 1.120 3.098* corn -1.756 0.513 2.306* soy -2.032 -1.200 2.271* beef -0.167 0.665 1.794 *statistically significant at 5% confidence level with bootstrap critical values computed with 200 repetitions. a all test specifications include 6 lags. b 1991m1-2021m12. c 1992m1-2021m12. d 1985m1-2021m12. 186 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 periods with at least three different commodities showing at least two consecutive months of exceedance, thus a co-exceedance in terms of volatility or common bubble for at least two consecutive months (phillips et al., 2011). the table also reports: commodities showing this coexceedance (second column); the commodity that can be identified as the first mover (third column), that is, the price whose exceedance started first before the period of co-exceedance; whether or not also cpi shows exceedance in the same period, and whether or not cpi can be considered the first mover (forth column). in the case of price levels, it emerges that bubbles concentrate in the 2005-2008 period though in different moments involving different commodities. basically, we can identify two main episodes. the first goes from 2005m8 to 2006m9. it only involves energy commodities and metals with oil and copper as first movers. cpi is itself involved in the bubble, as could be expected, but surprisingly it behaves as the first mover. the second episode is shorter and goes from 2008m2 to 2008m8. it involves all energy commodities wheat and corn but no minerals. again, oil behaves as the first mover. this latter commodity actually seems to experience a single bubble from mid-2005 to the end of 2008. cpi is also involved but not as the first mover. volatility clusters emerging from the garch regressions show a significant difference compared to the bubbles. a first episode is found from mid-1988 to mid1989, it concerns some minerals and agricultural commodities but no energy commodities. wheat seems to be the first mover. other four episodes, in fact behaving as a single one, can be detected from 2006m8 to 2009m8. in the first part of this period, the cluster exclusively involves metals and wheat. then, other prices enter the group included energy commodities and, finally, also oil. in the very last part of this episode, the volatility clusters involve most (9 out of 11) commodities. if we consider this whole period as a single episode, the first mover seems to be nickel which sounds a little surprising. in the second part of the period, wheat and natural gas emerge as other possible candidates. two other volatility clusters can be found in the last decade of the period under investigation. one concerns a very short period (two months in mid-2012) and only involves agricultural commodities. the other concerns the very last months of the period of observation (2021m9-2021m12); it is short simply because it continues beyond the period of observation. this period of exceedance is not identified with the bubble testing arguably because the bubble has still to collapse. future figure 4. bsadf tests for indexes for commodity price indexes (1992m1-2021m12). 187dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 a) b) c) figure 5a – bsadf tests for indexes for energy commodities (a), metals (b), agricultural commodities (c) price levels (1980m1-2021m12). 188 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 a) b) c) figure 5b. bsadf tests for indexes for energy commodities (a), metals (b), agricultural commodities (c) logarithm of price levels (1980m1-2021m12). 189dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 19 80 m 1 19 80 m 12 19 81 m 11 19 82 m 10 19 83 m 9 19 84 m 8 19 85 m 7 19 86 m 6 19 87 m 5 19 88 m 4 19 89 m 3 19 90 m 2 19 91 m 1 19 91 m 12 19 92 m 11 19 93 m 10 19 94 m 9 19 95 m 8 19 96 m 7 19 97 m 6 19 98 m 5 19 99 m 4 20 00 m 3 20 01 m 2 20 02 m 1 20 02 m 12 20 03 m 11 20 04 m 10 20 05 m 9 20 06 m 8 20 07 m 7 20 08 m 6 20 09 m 5 20 10 m 4 20 11 m 3 20 12 m 2 20 13 m 1 20 13 m 12 20 14 m 11 20 15 m 10 20 16 m 9 20 17 m 8 20 18 m 7 20 19 m 6 20 20 m 5 20 21 m 4 oil coal gas aluminium copper zinc nickel wheat corn soy beef en er gy m et al s fo o d 19 80 m 1 19 80 m 12 19 81 m 11 19 82 m 10 19 83 m 9 19 84 m 8 19 85 m 7 19 86 m 6 19 87 m 5 19 88 m 4 19 89 m 3 19 90 m 2 19 91 m 1 19 91 m 12 19 92 m 11 19 93 m 10 19 94 m 9 19 95 m 8 19 96 m 7 19 97 m 6 19 98 m 5 19 99 m 4 20 00 m 3 20 01 m 2 20 02 m 1 20 02 m 12 20 03 m 11 20 04 m 10 20 05 m 9 20 06 m 8 20 07 m 7 20 08 m 6 20 09 m 5 20 10 m 4 20 11 m 3 20 12 m 2 20 13 m 1 20 13 m 12 20 14 m 11 20 15 m 10 20 16 m 9 20 17 m 8 20 18 m 7 20 19 m 6 20 20 m 5 20 21 m 4 oil coal gas aluminium copper zinc nickel wheat corn soy beef en er gy m et al s fo o d a) b) figure 6a. dating of explosive roots (a) and volatility clusters (b) for all commodity price levels (1980m1-2021m12) (see table 4 for details). 190 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 19 80 m 1 19 80 m 12 19 81 m 11 19 82 m 10 19 83 m 9 19 84 m 8 19 85 m 7 19 86 m 6 19 87 m 5 19 88 m 4 19 89 m 3 19 90 m 2 19 91 m 1 19 91 m 12 19 92 m 11 19 93 m 10 19 94 m 9 19 95 m 8 19 96 m 7 19 97 m 6 19 98 m 5 19 99 m 4 20 00 m 3 20 01 m 2 20 02 m 1 20 02 m 12 20 03 m 11 20 04 m 10 20 05 m 9 20 06 m 8 20 07 m 7 20 08 m 6 20 09 m 5 20 10 m 4 20 11 m 3 20 12 m 2 20 13 m 1 20 13 m 12 20 14 m 11 20 15 m 10 20 16 m 9 20 17 m 8 20 18 m 7 20 19 m 6 20 20 m 5 20 21 m 4 oil coal gas alum copper zinc nickel wheat corn soy beef en er gy m et al s fo o d 19 80 m 1 19 80 m 12 19 81 m 11 19 82 m 10 19 83 m 9 19 84 m 8 19 85 m 7 19 86 m 6 19 87 m 5 19 88 m 4 19 89 m 3 19 90 m 2 19 91 m 1 19 91 m 12 19 92 m 11 19 93 m 10 19 94 m 9 19 95 m 8 19 96 m 7 19 97 m 6 19 98 m 5 19 99 m 4 20 00 m 3 20 01 m 2 20 02 m 1 20 02 m 12 20 03 m 11 20 04 m 10 20 05 m 9 20 06 m 8 20 07 m 7 20 08 m 6 20 09 m 5 20 10 m 4 20 11 m 3 20 12 m 2 20 13 m 1 20 13 m 12 20 14 m 11 20 15 m 10 20 16 m 9 20 17 m 8 20 18 m 7 20 19 m 6 20 20 m 5 20 21 m 4 oil coal gas alum copper zinc nickel wheat corn soy beef en er gy m et al s fo o d a) b) figure 6b. dating of explosive roots (a) and volatility clusters (b) for all commodity logarithms of price levels (1980m1-2021m12) (see table 4 for details). 191dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 investigations will confirm the nature and scope of the current period of exuberance. the cluster identified here suggests that it concerns both energy prices and metals but it is likely driven by the former and, in particular, by natural gas. two major facts seem to emerge from this analysis of co-exceedance. first of all, the correspondence between bubbles and volatility cluster detection is limited. periods correspond in the case of the major episode that occurred between 2005 and 2008. but for the rest of the sample, the detection of the episodes of co-exceedance does not correspond. also the involved commodities significantly differ and, consequently, the first movers. bubble detection seems to stress more the dynamics of energy commodities, and oil in particular, while volatility clusters point more to metals and agricultural table 4. dating of temporary explosive roots and volatility clusters for commodity price levels and logarithm of levels (1980m1-2021m12). period commodities first moverc (date) cpi: y/n; first mover (y/n & date)d price levels bubblesa 2005m8-2005m10, 2006m1, 2006m3, 2006m6 oil, copper, zinc oil, copper (2005m6) y; y (2005m3) 2006m2, 2006m4-2006m5 oil, aluminium, copper, zinc 2006m7, 2006m9 oil, copper, zinc nickel 2006m10-2006m12 oil, copper, zinc 2008m2-2008m7 oil, coal, gas, wheat, corn oil (2007m3) y; n 2008m5, 2008m8 oil, coal, gas, corn volatility clustersb 1988m7-1988m10 copper, wheat, beef wheat (1988m4) n 1988m11-1989m7 copper, zinc, nickel, wheat 2006m8-2006m11 copper, zinc, nickel, wheat nickel (2006m5) n 2007m4-2007m12 copper, zinc, nickel, wheat 2008m3-2008m8 gas, copper, wheat, corn wheat (2006m8) n 2008m9-2009m8 oil, gas, coal, aluminium, copper, zinc, corn, soy, beef gas (2008m3) y; n 2012m7-2012m8 corn, soy, beef soy (2012m8) n 2021m9-2021m12 gas, aluminium, copper gas (2021m7) y; n/y(2021m7) logarithm of price levels bubblesa period commodities first mover (date) cpi: y/n; first mover (y/n & date) 2005m12-2006m10 aluminium, copper, zinc zinc (2005m9) y; y (2005m3) 2008m6-2008m7 oil, coal, wheat, corn wheat (2008m2) y; n 2015m12-2016m6 oil, gas, copper copper (2015m8) n volatility clustersb period commodities first mover (date) cpi: y/n; first mover (y/n & date) 1988m4-1989m1 aluminium, nickel, soy aluminium (1987m12) y; n 2008m11-2009m3 oil, coal, aluminium, copper, nickel coal (2008m3) y; n a the dating of the bubble corresponds to periods when at least 3 commodities show explosive roots, that is bsadf test significant at 5% confidence level with bootstrap critical values computed with 200 repetitions. only periods with at least two consecutive months of exceedance are reported. b the dating of the volatility clusters corresponds to periods when predicted volatility (i.e., standard error) is larger than three times the subperiod (1980m1-2000m12; 2001m1-2021m12) average volatility. only periods with at least two consecutive months of exceedance are reported. c the first mover is the price of the group whose exceedance started first before the period of co-exceedance. d the first y/n indicates whether or not also cpi shows exceedance in the same period; the second y/n indicates whether or not cpi can be considered the forst mover (in parenthesis the date). 192 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 commodities. a final difference concerns the involvement of cpi that seems very limited in the case of clusters while it is more relevant for bubbles. the second notable fact is the difference between price levels and price logarithms. as the logarithmic transformation re-scales the data and, therefore, scales down their variability, respective results are expected to make the more robust evidence emerge: the number of individual episodes may slightly decline and the number of common episodes is expected to substantially reduce. it turns out that the number of episodes of co-exceedance detected on price logarithms is lower, as expected, for both bubbles and volatility clusters. but the nature of these episodes does not necessarily correspond with what is observed on price levels and this lack of robustness passing from levels to logarithms seems more evident for bubbles than for volatility clusters. the involved commodities are not necessarily the same, as well as the first movers, and also the involvement of the cpi shows some difference. in general terms, when the logarithms are considered, it seems more difficult to find some general pattern in the results, especially in terms of a key role of some commodities like the energy ones. looking for regularities, two special cases are worth noticing here. the first concerns the oil price. properties and behaviour of this commodity price emerging in the present work confirm the bubble detection and dating reported in previous studies, particularly in su et al. (2017) and zhao et al. (2021).18 it could also be argued that oil price has to behave as a sort of upstream price since it enters as a production cost in most downstream production processes, included farming and mining activities. but this role of oil as first mover is not generally observed and seems to emerge only in bubble testing with price levels. the second interesting role is that played by agricultural commodities. on the one hand, they can be considered as downstream prices compared to energy commodities and metals. but, for this reason, they can severely impact on cpi dynamics. from our results, it emerges that agricultural commodity prices seem to be a little more “stable” in the sense that episodes of exuberance (bubbles or volatility clusters) are less frequent and shorter. at the same time, while energy commodities and metals are apparently more interdependent, agricultural prices seem to follow more autonomous patterns and are less likely to act as first movers and, thus, to be suitable candidates to drive the other commodity prices and of the cpi. 18 su et al. (2017, p. 6) conclude that “there are explosive multiple bubbles in the wti oil market in 1990, 2005, 2006, 2008 and 2015. generally, oil bubbles mostly occur during the period of price volatility”. what can we finally conclude about the evidence on the linkage between commodity prices and cpi? while results tend to confirm some stochastic properties of the cpi that may explain periods of exuberance, the evidence that these periods are the consequence of analogous episodes in commodity prices is poor. the major episode of price exuberance between 2005-2008 confirms, as could seem obvious, a connection between commodity prices and cpi, maybe because this episode involves a large number of commodities, though in different times. in fact, this connection seems quite weak beyond this period. and also within this 2005-2008 period it is not clear whether commodity price exuberance induced a cpi response or if it is actually the other way round. this lack of evidence should not be surprising and evidently asks for further investigation. other very recent empirical investigations (lian and freitag, 2022), for instance, suggest that oil price shocks do not always imply a shock on cpi and sometime this latter may move independently and also precede the former. 6. comparison with other stochastic processes and approaches for the sake of comparison and in order to validate the results here obtained, it is worth investigating the commonality of the commodity price dynamics also with more conventional approaches. rather than focusing on co-exceedance, as in the present study, these approaches look for the commonality of the stochastic generation processes (i.e., co-movement and the consequent price interdependence) under the typical hypothesis of either stationary or non-stationary linear dgp, possibly with a drift and/or a trend (esposti, 2021). as already discussed in section 2, in order to capture the complexity and non-linearity of these series a further occurrence that can be considered consists in admitting that series undergo, in one or more points in time, a structural break in either the drift or the trend (or in both) (baum, 2005). in principle, under multiple breaks, these stochastic processes could explain the presence of periods of extremely high (or low) prices (the “bubbles”) as a sequence of two structural breaks with the latter eventually compensating the former and thus making its effect only temporary. table a2 (annex 3) reports a battery of tests specifically designed to assess whether these more conventional stochastic processes represent suitable alternatives to the two co-exceedance processes here considered. four tests are reported. they all confront a unit-root process (the null hypotheses of the tests) with a stationary process 193dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 presenting one or two structural breaks in some terms of the process itself.19 all tests admit endogenous breaks, thus not only do they test their presence but they are also able to date these breaks. the first two tests consist in two specifications of the zivot-andrews (za) unit root test (zivot and andrews, 2002) admitting only one structural break in either the intercept or both intercept and trend. test results largely accept unit-root processes without a break against the presence of a structural break within stationary series. the only exceptions are coal and soy in the price levels and only soy in the logarithms of prices. the break date is similar, late 2006-early 2007, and corresponds to one of the major periods of co-exceedance identified in previous sections. not only is the break accepted for only two commodities but, more importantly, it can not explain why and how, once started, the period of turbulence then comes to an end since the structural break introduces a permanent change in the process. the other two tests, consisting in two variants of the clemente-montañés-reyes unit root test (cmr) (clemente et al., 1998), can be helpful in this respect. in this case, the statistical significance of the breaks themselves can be assessed as they enter in the test specification as time dummies with the respective coefficients. more importantly, this test admits two structural breaks within the stationary process thus allowing the combination of the two breaks to capture a temporary change in the process, like in the case of periods of price surge. the test can be performed under two different natures of the breaks: a sudden change in the series (the additive outliers, or ao, model) or a gradual shift in the mean of the series (the innovational outliers, or io, model). evidently, the former, much more than the latter, is expected to capture the short periods of price turbulence. although cmr tests confirm how difficult it can be, over such a long period of time, to univocally identify clear stable stochastic processes for any given commodity and, even more, commonality across commodities in this respect, they still provide some interesting indications. one the one hand, the cmr test under the ao model seems to confirm the main evidence emerging from the za test results without any relevant difference between price levels and logarithms: for most commodity prices (oil being the only exception) a non-stationary process is accepted against a stationary process with structural breaks. when the io model is considered, however, the cmr test indicates that for many commodities (all energy prices, aluminium, zinc, corn and foodind itself) a mean-reverting process under two structural breaks 19 for more details on the za and cmr tests, also see baum (2005). is accepted. even more interestingly, this test indicates that, for both ao and io cases, the structural breaks are always statistical significant (with only one exception). in some cases, the interval between the two breaks is too wide (more than three years) to really capture a period of price exuberance (see the cpi case, for instance). in other cases, however, the time window between the two breaks seems quite consistent with the periods of exceedance here identified, as shown in figures 6a,b. this is the case, in particular, of coal and all metals. as already discussed, with respect to the purpose of the present study, the introduction of structural breaks may seem an unnatural way to capture co-exceedance: it still maintains the linear specification of the dgp possibly with a permanent change while here the intention is to identify a dgp with temporary non-linearities. it follows that admitting structural breaks within the stochastic process representation may still confound shortterm and long-term dynamics within the price series. nonetheless, present results suggest that multiple breaks within an appropriate specification eventually constitute a sort of spline process capable to proxy temporary nonlinearities. even though not considered further here, this kind of approach, together with the introduction of multiple structural breaks within non-linear processes (bai and perron, 2003; caporale et al., 2020), can represent a promising alternative empirical strategy in future research in the field.20 there is a final aspect to be considered about the introduction of structural breaks as a valid alternative to capture co-exceedance. it concerns the identification of the first-moving commodities and the possible consequent contagion process. as shown, within the proposed approach, this identification is made only qualitatively by identifying and then visualizing when, commodity by commodity, the periods of exuberance start and end (see figures 6a,b). very often, however, within the empirical literature this identification is formally pursued using granger causality testing (esposti and listorti, 2013). this approach must satisfy the prerequisite that series under investigation show the same stochastic properties (they are all either i(0) and i(1)), and then it requires the estimation of multiple-equation linear models (in the form of var or vec models, respectively) representing the common movement from which direction and nature of price interdependence (or transmission) can be assessed. within this representation one or more struc20 the use of international or global commodity prices, as well as the widely heterogenous dating of these structural breaks across commodities, makes it hard to speculate on the possible linkage between them and external shocks like, for example, policy regime changes. however, this investigation may represent a further direction of research for the future. 194 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 tural breaks can be included (as time dummies) to possibly capture some changes in the linear relationships, thus admitting temporary non-linearities. results here presented demonstrate how the prerequisite of this empirical strategy to detect first movers and contagion can be challenging. a common dgp is impossible to find when all price series are considered. but even concentrating on individual commodity groups, tables 1 and 5(??) suggest there is always at least one commodity showing a different underlying stochastic process compared to the others. apparently, an interesting case is that of energy commodities under the io specification of the cmr test: when two structural breaks are admitted they all behave as stationary processes with a drift and a deterministic trend. therefore, an attempt to perform ganger causality testing can be made here by estimating a var model with four endogenous variables: the three energy prices (oil, coal and gas prices) and the cpi, since it quite robustly emerges as a i(0) process. the var specification also includes a drift, a deterministic trend and two time dummies representing the two breaks at 2003m11 and 2013m5. table a3 (annex 3) reports the results of the respective granger causality tests and the estimated coefficients of the two structural breaks.21 as often occurs with granger causality testing, results are not easily interpretable. however, they confirm some of the evidence obtained with our proposed approach. it seems hard to identify an indisputable driving price and, in particular, this does not seem the case of the oil price. when price levels are considered, oil and coal show a reciprocal granger causation, while natural gas is only granger caused by the coal price. oil and coal price both granger-cause the cpi response, while cpi itself does not granger-cause any of the energy prices as could be expected. coal rather than oil seems to be the driving price, if any, and this seems to be reinforced when the logarithm of prices are considered instead of the levels. the presence of structural breaks, though suggested by tests reported in table a2, is not confirmed by var estimation coefficients associated to respective time dummies are mostly not statistically different from zero. compared to the approach here proposed, which is based on the search of co-exceedance periods (thus admitting non-linearities in the dgp) rather than on linear price interdependence, these more conventional stochastic processes do not seems to provide any helpful additional information. on the contrary, they seem to fail in the search of common periods of exuberance over a large group of commodities, thus they do not seem 21 for the sake of space limitation, the var model estimates are not reported here but are available upon request. appropriate for designing a real-time surveillance dashboard informing a prompt policy response. nonetheless, even in these approaches recent contributions have opened new interesting perspectives that may deserve careful consideration in future research. for instance, the implementation of non-linear granger causality testing seems particularly promising (shahzad et al., 2021). 7. conclusive remarks periods of commodity price exuberance raise political concerns particularly for their possible impact on the inflation rate. timely interventions by the appointed institutions are often invoked but do not necessarily prove to be effective in preventing or neutralizing these episodes. after all, common price spikes (thus, coexceedance) might not imply a common policy response since for some commodities exuberance tends to be motivated by real drivers while in other cases financial phenomena are prevalent. understanding the mechanisms underlying generation, transmission and, then, collapse of co-exceedance remains relevant to design the proper, possibly differentiated, policy response. but in the shorter term an appropriate policy response may just need a timely detection of the price surge and of the degree of diffusion across commodities. the present paper aims to develop a single methodological approach, albeit based on alternative stochastic processes, that does not assume common movement and price interdependence but only co-exceedance, thus commonality occurring only within the periods of exuberance. this approach is able to detect whether such a period occurs, when it starts and when it ends, the degree of diffusion across commodities, the possible presence of driving prices and, eventually, the transfer to the inflation rate. on this basis, the proposed methodology is intended to offer an easily interpretable visualization of the critical information it generates. results presented indicate that the different approaches considered (bubbles and volatility cluster detection in both price levels and logarithms) are able to provide clear indications on when the exceedance occurs, on its overlapping across commodities and on possible first movers. however, this evidence is not concordant or, at least, robust across the different approaches making the final outcome of the analysis, and the policy implication itself, severely dependant on the analyst’s choices in this respect. results do not even agree on the involvement of the cpi in these episodes of exuberance, therefore on the transmission of commodity price spike to inflation rate. 195dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 on the basis of this discrepancy, it seems wise to develop the abovementioned policy tool in a way that prudently admits both processes and elaborates information from a combination of them. at the same time, this discrepancy points to room for further methodological improvements. after all, both competing representations of the origin of exceedance, volatility clusters and temporary bubbles, show pros and cons and this makes it difficult to draw a general preference for one or the other. garch modelling seems to represent more permanent changes in volatility rather than short periods of exuberance. furthermore, it hardly combines volatility clusters with a non-stationary process in the price levels. at the same time, bubble detection applies well to positive bubbles, therefore periods of exuberance then followed by a collapse, but it does not necessarily succeed in case of negative bubbles, that is episodes that start with a price crash (gharib et al., 2021, pp. 3-4).22 in fact, bubble detection can only by applied ex post, therefore when the bubbles have already collapsed. this substantially limits the actual applicability of the approach by analysts and policy makers. moreover, currently available tests only apply to univariate bubble detection. multivariate bubble testing has not yet been proposed and this prevents a direct investigation of contagion across commodities. regarding all these aspects, results obtained in the present study also suggest the extension of the adopted tool to other stochastic processes, 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(2021). identifying bubbles and the contagion effect between oil and stock markets: new evidence from china. economic modelling 94, 780–788. zivot, e., andrews, d.w.k., 2002. further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. journal of business economics and statistics 20(1), 25–44. 197dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 annex 1 description of the data used in the analysis individual commodity prices (source: imf) oil: crude oil (petroleum), price index, 2005 = 100, simple average of three spot prices; dated brent, west texas intermediate, and the dubai fateh gas: natural gas, russian natural gas border price in germany, us$ per million metric british thermal unit coal: australian thermal coal, 12,000btu/pound, less than 1% sulfur, 14% ash, fob newcastle/port kembla, us$ per metric ton aluminium: 99.5% minimum purity, lme spot price, cif uk ports, us$ per metric ton copper: grade a cathode, lme spot price, cif european ports, us$ per metric ton zinc: high grade 98% pure, us$ per metric ton nickel: melting grade, lme spot price, cif european ports, us$ per metric ton wheat: no.1 hard red winter, ordinary protein, kansas city, us$ per metric ton corn: u.s. no.2 yellow, fob gulf of mexico, u.s. price, us$ per metric ton soy: u.s. soybeans, chicago soybean futures contract (first contract forward) no. 2 yellow and par, us$ per metric ton beef: australian and new zealand 85% lean fores, cif u.s. import price, us cents per pound table a1. descriptive statistics on commodity price indexes and commodity prices (1980m1-2021m12). series obs minimum maximum mean standard deviation skewness kurtosis price indexes foodinda 372 76.04 194.1 121.9 60.20 -0.384 1.965 metind 504 44.16 256.2 101.3 53.69 -0.084 2.556 eneindb 360 22.07 257.3 99.75 67.76 -0.656 2.244 cpi 504 40.79 146.2 92.17 28.09 -0.005 1.799 price levels oil 504 18.44 249.6 84.09 56.86 0.877 2.534 coal 504 24.09 240.7 57.66 34.01 1.584 5.816 gasc 444 1.444 32.91 5.398 4.199 1.979 10.42 aluminium 504 918.8 3578 1712 468.1 0.778 3.325 copper 504 1272 10308 3882 2520 0.742 2.116 zinc 504 597.4 4381 1541 786.4 1.019 3.277 nickel 504 3433 51783 11394 7270 1.867 8.130 wheat 504 88.55 403.8 168.5 54.33 1.235 4.396 corn 504 65.35 332.9 142.7 57.14 1.420 4.542 soy 504 158.31 622.9 292.1 105.4 1.036 3.204 beef 504 74.26 272.2 130.5 44.44 1.026 3.111 price logarithms oil 504 2.914 5.520 4.207 0.671 0.202 1.755 coal 504 3.178 5.483 3.913 0.510 0.610 2.358 gasc 444 0.367 3.493 1.461 4.250 1.255 6.286 aluminium 504 6.823 8.182 7.409 0.265 0 .158 2.542 copper 504 7.148 9.240 8.056 0.642 0.289 1.566 zinc 504 6.392 8.385 7.222 0.477 0.370 1.948 nickel 504 8.141 10.85 9.173 0.566 0.330 2.406 wheat 504 4.483 6.000 5.081 0.296 0.489 2.882 corn 504 4.179 5.807 4.894 0.353 0.663 2.868 soy 504 5.064 6.434 5.619 0 .332 0.520 2.277 beef 504 4.307 5.606 4.820 0.313 0.568 2.316 a 1991m1-2021m12. b 1992m1-2021m12. c 1985m1-2021m12. 198 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 aggregate commodity price indexes (source: imf) foodind: food price index, 2016 = 100, includes cereal, vegetable oils, meat, seafood, sugar, and other food (apple (non-citrus fruit), bananas, chana (legumes), fishmeal, groundnuts, milk (dairy), tomato (veg)) price indices metind: metals price index, 2005 = 100, includes copper, aluminium, iron ore, tin, nickel, zinc, lead, and uranium price indices eneind: fuel (energy) index, 2005 = 100, includes crude oil (petroleum), natural gas, and coal price indices overall consumer price index (source: us federal reserve) cpi: federal reserve economic data, economic research division, federal reserve bank of st. louis. cpiaucns consumer price index for all urban consumers: all items in u.s. city average, index 1982-1984=100, monthly, not seasonally adjusted. the inflation rate is computed as the monthly growth rate of this cpi. annex 2 – commodity price dynamics 0 50 100 150 200 250 300 19 80 m 1 19 80 m 12 19 81 m 11 19 82 m 10 19 83 m 9 19 84 m 8 19 85 m 7 19 86 m 6 19 87 m 5 19 88 m 4 19 89 m 3 19 90 m 2 19 91 m 1 19 91 m 12 19 92 m 11 19 93 m 10 19 94 m 9 19 95 m 8 19 96 m 7 19 97 m 6 19 98 m 5 19 99 m 4 20 00 m 3 20 01 m 2 20 02 m 1 20 02 m 12 20 03 m 11 20 04 m 10 20 05 m 9 20 06 m 8 20 07 m 7 20 08 m 6 20 09 m 5 20 10 m 4 20 11 m 3 20 12 m 2 20 13 m 1 20 13 m 12 20 14 m 11 20 15 m 10 20 16 m 9 20 17 m 8 20 18 m 7 20 19 m 6 20 20 m 5 20 21 m 4 foodind metind enelind figure a1. commodity price indexes (2005m1=100) (1980m1-2021m12). 0 100 200 300 400 500 600 700 19 80 m 1 19 80 m 12 19 81 m 11 19 82 m 10 19 83 m 9 19 84 m 8 19 85 m 7 19 86 m 6 19 87 m 5 19 88 m 4 19 89 m 3 19 90 m 2 19 91 m 1 19 91 m 12 19 92 m 11 19 93 m 10 19 94 m 9 19 95 m 8 19 96 m 7 19 97 m 6 19 98 m 5 19 99 m 4 20 00 m 3 20 01 m 2 20 02 m 1 20 02 m 12 20 03 m 11 20 04 m 10 20 05 m 9 20 06 m 8 20 07 m 7 20 08 m 6 20 09 m 5 20 10 m 4 20 11 m 3 20 12 m 2 20 13 m 1 20 13 m 12 20 14 m 11 20 15 m 10 20 16 m 9 20 17 m 8 20 18 m 7 20 19 m 6 20 20 m 5 20 21 m 4 oil coal gas figure a2. energy commodities prices (2005m1=100) (1980m1-2021m12). 0 50 100 150 200 250 300 350 400 19 80 m 1 19 81 m 1 19 82 m 1 19 83 m 1 19 84 m 1 19 85 m 1 19 86 m 1 19 87 m 1 19 88 m 1 19 89 m 1 19 90 m 1 19 91 m 1 19 92 m 1 19 93 m 1 19 94 m 1 19 95 m 1 19 96 m 1 19 97 m 1 19 98 m 1 19 99 m 1 20 00 m 1 20 01 m 1 20 02 m 1 20 03 m 1 20 04 m 1 20 05 m 1 20 06 m 1 20 07 m 1 20 08 m 1 20 09 m 1 20 10 m 1 20 11 m 1 20 12 m 1 20 13 m 1 20 14 m 1 20 15 m 1 20 16 m 1 20 17 m 1 20 18 m 1 20 19 m 1 20 20 m 1 20 21 m 1 aluminium copper nickel zinc figure a3. metals prices (2005m1=100) (1980m1-2021m12). 0 50 100 150 200 250 300 350 400 19 80 m 1 19 81 m 1 19 82 m 1 19 83 m 1 19 84 m 1 19 85 m 1 19 86 m 1 19 87 m 1 19 88 m 1 19 89 m 1 19 90 m 1 19 91 m 1 19 92 m 1 19 93 m 1 19 94 m 1 19 95 m 1 19 96 m 1 19 97 m 1 19 98 m 1 19 99 m 1 20 00 m 1 20 01 m 1 20 02 m 1 20 03 m 1 20 04 m 1 20 05 m 1 20 06 m 1 20 07 m 1 20 08 m 1 20 09 m 1 20 10 m 1 20 11 m 1 20 12 m 1 20 13 m 1 20 14 m 1 20 15 m 1 20 16 m 1 20 17 m 1 20 18 m 1 20 19 m 1 20 20 m 1 20 21 m 1 beef corn soy wheat figure a4. agricultural commodities prices (2005m1=100) (1980m1-2021m12). 199dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 20 40 60 80 100 120 140 160 180 200 220 19 80 m 1 19 80 m 12 19 81 m 11 19 82 m 10 19 83 m 9 19 84 m 8 19 85 m 7 19 86 m 6 19 87 m 5 19 88 m 4 19 89 m 3 19 90 m 2 19 91 m 1 19 91 m 12 19 92 m 11 19 93 m 10 19 94 m 9 19 95 m 8 19 96 m 7 19 97 m 6 19 98 m 5 19 99 m 4 20 00 m 3 20 01 m 2 20 02 m 1 20 02 m 12 20 03 m 11 20 04 m 10 20 05 m 9 20 06 m 8 20 07 m 7 20 08 m 6 20 09 m 5 20 10 m 4 20 11 m 3 20 12 m 2 20 13 m 1 20 13 m 12 20 14 m 11 20 15 m 10 20 16 m 9 20 17 m 8 20 18 m 7 20 19 m 6 20 20 m 5 20 21 m 4 oil coal gas 80 85 90 95 100 105 110 115 120 19 80 m 1 19 81 m 1 19 82 m 1 19 83 m 1 19 84 m 1 19 85 m 1 19 86 m 1 19 87 m 1 19 88 m 1 19 89 m 1 19 90 m 1 19 91 m 1 19 92 m 1 19 93 m 1 19 94 m 1 19 95 m 1 19 96 m 1 19 97 m 1 19 98 m 1 19 99 m 1 20 00 m 1 20 01 m 1 20 02 m 1 20 03 m 1 20 04 m 1 20 05 m 1 20 06 m 1 20 07 m 1 20 08 m 1 20 09 m 1 20 10 m 1 20 11 m 1 20 12 m 1 20 13 m 1 20 14 m 1 20 15 m 1 20 16 m 1 20 17 m 1 20 18 m 1 20 19 m 1 20 20 m 1 20 21 m 1 aluminium copper nickel zinc 90 95 100 105 110 115 120 125 130 19 80 m 1 19 81 m 1 19 82 m 1 19 83 m 1 19 84 m 1 19 85 m 1 19 86 m 1 19 87 m 1 19 88 m 1 19 89 m 1 19 90 m 1 19 91 m 1 19 92 m 1 19 93 m 1 19 94 m 1 19 95 m 1 19 96 m 1 19 97 m 1 19 98 m 1 19 99 m 1 20 00 m 1 20 01 m 1 20 02 m 1 20 03 m 1 20 04 m 1 20 05 m 1 20 06 m 1 20 07 m 1 20 08 m 1 20 09 m 1 20 10 m 1 20 11 m 1 20 12 m 1 20 13 m 1 20 14 m 1 20 15 m 1 20 16 m 1 20 17 m 1 20 18 m 1 20 19 m 1 20 20 m 1 20 21 m 1 beef corn soy wheat figure a5. logarithms of the energy commodities prices (2005m1=100) (1980m1-2021m12). figure a6. logarithms of the metals prices (2005m1=100) (1980m1-2021m12). figure a7. logarithms of the agricultural commodities prices (2005m1=100) (1980m1-2021m12). 0 0.00001 0.00002 0.00003 0.00004 0.00005 0.00006 0 2 4 6 8 10 12 14 16 18 19 80 m 1 19 80 m 12 19 81 m 11 19 82 m 10 19 83 m 9 19 84 m 8 19 85 m 7 19 86 m 6 19 87 m 5 19 88 m 4 19 89 m 3 19 90 m 2 19 91 m 1 19 91 m 12 19 92 m 11 19 93 m 10 19 94 m 9 19 95 m 8 19 96 m 7 19 97 m 6 19 98 m 5 19 99 m 4 20 00 m 3 20 01 m 2 20 02 m 1 20 02 m 12 20 03 m 11 20 04 m 10 20 05 m 9 20 06 m 8 20 07 m 7 20 08 m 6 20 09 m 5 20 10 m 4 20 11 m 3 20 12 m 2 20 13 m 1 20 13 m 12 20 14 m 11 20 15 m 10 20 16 m 9 20 17 m 8 20 18 m 7 20 19 m 6 20 20 m 5 20 21 m 4 cpi inflation rate (right scale) -5 -4 -3 -2 -1 0 1 2 3 4 5 19 80 m 1 19 81 m 1 19 82 m 1 19 83 m 1 19 84 m 1 19 85 m 1 19 86 m 1 19 87 m 1 19 88 m 1 19 89 m 1 19 90 m 1 19 91 m 1 19 92 m 1 19 93 m 1 19 94 m 1 19 95 m 1 19 96 m 1 19 97 m 1 19 98 m 1 19 99 m 1 20 00 m 1 20 01 m 1 20 02 m 1 20 03 m 1 20 04 m 1 20 05 m 1 20 06 m 1 20 07 m 1 20 08 m 1 20 09 m 1 20 10 m 1 20 11 m 1 20 12 m 1 20 13 m 1 20 14 m 1 20 15 m 1 20 16 m 1 20 17 m 1 20 18 m 1 20 19 m 1 20 20 m 1 20 21 m 1 cpi inflation rate 90% cv 95% cv a) b) figure a8. oil price (2005=100), cpi (2005=100) and inflation rate (1980m1-2021m12). figure a9. garch(1,1) model standard error in-sample prediction (a) and bsadf test for cpi and inflation rate (1980m1-2021m12). 200 roberto esposti bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 annex 3 – unit-root and granger-causality tests with structural breaks table a2. testing with structural breaks within an adf specification (unit-root testing) for commodity price indexes, price levels and logarithm of levels (1980m1-2021m12). series za (break month)e cmr (b.reak months)f intercept intercept&trend ao io price indexes foodinda -4.780 -4.932 -3.844 (1990m2*; 2006m3*) -7.408* (1990m2*; 2006m3*) metind -3.826 -4.048 -5.176 (2005m6*; 2014m3*) -5.406 (2004m9*; 2013m7*) eneindb -4.199 -4.305 -4.736 (1991m2*; 2004m9*) -4.831 (1990m11*; 2003m11*) cpi -3.461 -3.730 -3.079 (1992m5*; 2007m10*) -2.570 (1985m11*; 2002m11*) price levels oil -4.731 -4.755 -6.299* (2005m9*; 2015m2*) -6110* (2003m11*; 2013m5*) coal -5.245* (2006m11) -5.253* (2006m11) -2.869 (2007m3*; 2008m1*) -5.791* (2006m9*; 2007m6*) gasc -2.337 -2.454 -2.462 (2004m11*; 2014m5*) -1.718* (2003m8*; 2013m11*) aluminium -4.532 -4.399 -3.447 (1987m4*; 2004m4*) -5.603* (2004m8*; 2007m6*) copper -4.471 -4.464 -3.589 (2005m6*; 2014m3*) -4.131 (2004m4*; 2013m6) zinc -4.236 -4.236 -4.209 (2005m6*; 2006m10*) -5.614* (2004m10*; 2007m4*) nickel -4.027 -4.626 -4.155 (2005m9*; 2007m1*) -6.867 (2005m2*; 2006m4*) wheat -3.663 -3.673 -3.747 (2007m4*; 2013m10*) -5.190 (2009m11*; 2013m1*) corn -4.572 -4.577 -5.119 (2009m11*; 2013m1*) -5.688* (2009m5*; 2012m6*) soy -5.091* (2006m10) -5.102* (2007m5) -4.749 (2007m3*; 2013m11*) -6.061 (2006m3*; 2013m3*) beef -4.009 -4.574 -4.244 (2009m5*; 2018m8*) -4.532 (2008m9*; 2018m9*) logarithm of price levels oil -4.188 -4.363 -5.055 (1985m4*; 2003m9*) -4..888 (1998m1*; 2003m11*) coal -4.273 -4.597 -3.476 (2007m3*; 2008m1*) -4..753 (2002m9*; 2005m9*) gasc -2.598 -4.706 -4.327 (1993m10*; 2003m8*) -6..451* (1993m11*; 2003m9*) aluminium -4.349 -4.639 -4.366 (1987m4*; 2003m4*) -5.307* (2004m5*; 2007m6*) copper -4.584 -4.699 -4.392 (1987m1*; 2005m6*) -4.820* (1985m2*; 2002m8*) zinc -4.701 -4.708 -4.580 (2005m6*; 2007m1*) -5.333 (1986m8*; 2004m6*) nickel -3.958 -4.204 -4.401 (1987m1*; 2006m3*) -4.586 (1986m2*; 2002m3*) wheat -3.972 -3.969 -3.456 (2006m10*; 2013m10*) -4.270 (2004m10*; 2013m4*) corn -4.750 -4.772 -5.037 (2006m3*; 2013m10*) -4.969 (2005m7*; 2012m4*) soy -5.343* (2006m10) -5.390* (2006m10) -4.472 (2007m3*; 2013m11*) -5.638* (2005m8*; 2013m3*) beef -4.039 -4.886 -4.432 (1993m1*; 2009m5*) -4.169 (2002m4*; 2008m9*) *statistically significant at 5% confidence level. a 1991m1-2021m12. b 1992m1-2021m12. c 1985m1-2021m12. e zivot andrews (za) unit-root test with one endogenous structural break in the intercept or in both the intercept and the deterministic trend; lags selected with aic between 6 and 12 months; only statistically significant breaks are reported. f clemente, montanes and reyes (cmr) unit-root test with two endogenous breaks (mean shifts) and deterministic trend; lags selected with aic between 6 and 12 months; ao=additive outlier and io=innovational outlier specifications; only statistically significant breaks are reported. 201dating common commodity price and inflation shocks with alternative approaches bio-based and applied economics 13(2): 171-201, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14060 table a3. granger causality test (χ2) of var model estimates with oil, coal, natural gas and cpi as endogenous variables (1985m1-2021m12)a,b price levels logarithms of price levels crude oil coal 13.51* 8.529* gas 3.558 9.221* cpi 5.138 8.354* structural break dummies: 2003m11; 20013m5 4.955*; 1.273 -0.069*; -0.021 coal crude oil 16.09* 8.6054* gas 7.41 0.01853 cpi 3.251 0.77946 structural break dummies: 2003m11; 20013m5 2.131; -0.375 0.039*; 0.004 gas crude oil 0.739 1.253 coal 56.59* 3.353 cpi 5.204 6.060 structural break dummies: 2003m11; 20013m5 -0.257; -0.052 -0.034; -0.035 cpi crude oil 67.25* 47.25* coal 17.18* 15.91* gas 5.573 0.876 structural break dummies: 2003m11; 20013m5 0.067; 0.043 0.001; 0.000 *statistically significant at 5% confidence level. a the period considered depends on natural gas data availability. b the var model specification includes a drift, a deterministic trend and lags decided on the basis of aic. _hlk149729446 _hlk135142798 _hlk135142780 _hlk152021616 _hlk136884351 _hlk140394422 _hlk149850987 _hlk149823827 _hlk116460470 _hlk125295920 _hlk125296237 _hlk125295754 _hlk94009644 lessons learned and policy implications from 20 years of swiss agricultural policy reforms: a review of policy evaluations robert huber1,*, nadja el benni2,3, robert finger1 the political economy determinants of agri-environmental funds in the european rural development programmes francesco pagliacci1, matteo zavalloni2,* economics sanction and barley price regime change in iran mohammad mehdi farsi aliabadi*, behzad fakari sardehaie dating common commodity price and inflation shocks with alternative approaches roberto esposti an input-output hydro-economic model to assess the economic pressure on water resources benedetto rocchi1,*, mauro viccaro2,3, gino sturla1 1 the importance of wooden biomass in the transition to a bioeconomy in latvia 1 vineta tetere 1,2 *, jack peerlings 1, liesbeth dries 1 2 * corresponding author, vineta.tetere@wur.nl 3 1 wageningen university, the netherlands 4 2 latvia university of life sciences and technologies, latvia 5 this article has been accepted for publication and undergone full peer review but has not been 6 through the copyediting, typesetting, pagination and proofreading process, which may lead to 7 differences between this version and the version of record. 8 please cite this article as: 9 tetere v., peerlings j., dries l. (2025). the importance of wooden biomass in the transition 10 to a bioeconomy in latvia, bio-based and applied economics, just accepted. 11 doi:10.36253/bae-16107 12 abstract 13 the eu green deal advocates decarbonising the eu’s energy sector, largely by transitioning 14 to renewable sources. latvia aims to increase the share of renewable energy production in total 15 energy production to 50% by 2030 (it was 39% in 2017), prioritising biomass from forests and 16 wood for bioenergy. this paper evaluates increasing the tax on non-biobased energy use 17 alongside implementing a subsidy for biobased energy use, particularly from wood biomass, to 18 promote the substitution of the first by the latter as a step towards climate neutrality and energy 19 self-sufficiency. furthermore, it examines technological advancements in the bioenergy sector 20 as an alternative instrument. using an applied general equilibrium model and 2015 supply and 21 use data, the study allows for substitution between domestic and imported inputs and between 22 the non-biobased and biobased energy product. given latvia’s heavy reliance on imported 23 fossil fuels, these measures could lead to a 58% increase in bioenergy production compared to 24 2015, reducing co2 emissions by 0.3 – 1.7%, and reducing non-biobased energy imports by 25 2.5-4.2%. 26 keywords: biobased energy, non-biobased energy, product tax, subsidy 27 jel codes: c68, e17, q23 28 mailto:vineta.tetere@wur.nl 2 1. introduction 29 the transition from a fossil-based economy to a biobased economy is considered a priority to 30 mitigate the effects of climate change in the european union (eu). the eu green deal states 31 that the eu has to become climate neutral by 2050 (ec b, n.d.). this requires the eu to reduce 32 net greenhouse gas (ghg) emissions to zero. carbon dioxide (co2) remains the main 33 greenhouse gas emitted through human activities, and most co2 emissions come from the 34 energy sector: electricity, heating, and transport. therefore, one of the main actions proposed 35 in the green deal is to decarbonise the eu’s energy sector, largely through the transition of the 36 generation of power from fossil-based to renewable sources. the latvian government follows 37 this action with the intention to increase the production of energy from renewable sources to 38 50% of the total energy production in 2030 (it was 39% in 2017). to achieve this goal, an 39 emphasis will be placed on sourcing biomass from the forest and wood industry to be used for 40 the production of biobased energy (em, 2019). the reason for this focus is that the production 41 and use of other renewable energy products are small. for example, in 2015 91.7% of the use 42 of renewable energy was from biomass from forests and wood, for hydropower, this was 7.7% 43 and for wind 0.6%. 44 as a reaction to the russian invasion of ukraine, the european commission introduced the 45 repowereu plan in march 2022 outlining measures to drastically reduce russian gas imports 46 and achieve independence from russian fossil fuels before the end of the decade. the key 47 elements in this plan are diversifying supplies, reducing demand, and increasing the production 48 of green energy in the eu. this is expected to accelerate the green transition by reducing ghg 49 emissions, reducing dependency on imported fossil fuels, and protecting the eu against price 50 hikes on the energy market (eu commission, 2022). 51 economic instruments like taxes on fossil fuels and subsidies on biobased energy production 52 or use can contribute to achieving the goals of the green deal and the repowereu plan. since 53 fossil fuels are non-renewable and ghg emissions are harmful to the environment, a product 54 3 tax can help in reducing its demand and supply, as it increases the net price demanders pay and 55 decreases the price suppliers receive. product subsidies for biobased energy have the opposite 56 effect. moreover, product taxes and subsidies can stimulate the development and use of more 57 sustainable technologies (wolfson & koopmans, 1996). 58 a tax on fossil energy can in addition to the reduction in emissions – lead to an increase in 59 welfare if it reduces the tax distortions caused by other taxes (“second best effect”). if this 60 happens, then we speak of a ‘double dividend’ (see de mooij, 2002 and goulder, 1995). a 61 potential double dividend can be an extra incentive to introduce a tax on fossil energy use. 62 in latvia, the forest sector is one of the cornerstones of the economy. forestry, wood processing 63 and furniture manufacturing contributed 5.1% to gdp, 5.4% to total employment and 20.7% to 64 exports in 2018 (am ministry of agriculture, 2022). furthermore, the forest area covers 52% 65 of the country’s territory and this is expanding. it has doubled since 1935 due to farm 66 abandonment that resulted in the conversion of cropland fields into young forests (fonji & taff, 67 2014). the increase in forest area is expected to continue because of purposeful afforestation, 68 as well as through the continued natural overgrowth of forest on non-agricultural lands. 69 moreover, wooden biomass is increasing annually due to more sustainable forest management 70 in recent decades (lazdiņš et al., 2019). this opens possibilities for further increase in the use 71 of wooden biomass in the production of biobased energy. 72 the aim of this paper is to investigate the potential of taxes, subsidies, and technological change 73 to increase the share of biobased energy production in latvia. more specifically, it assesses the 74 effect of an increase in the tax on non-biobased energy use and the implementation of a subsidy 75 on biobased energy use, especially from wooden biomass, to facilitate the substitution of the 76 first for the latter as a step in the transition of latvia’s economy towards climate neutrality and 77 self-sufficiency of energy. moreover, the paper assesses the effects of technological change in 78 the industry producing biobased energy. such a technological change in the production of 79 biobased energy is instrumental for a successful transition of the energy sector. to this end, the 80 4 eu and the latvian government will stimulate technological change as part of the green deal 81 using € 4.4 billion from eu funds for latvia between 2021 and 2027 (fm, n.d.). 82 the paper uses an applied general equilibrium (age) model based on the model developed by 83 komen and peerlings (1999) to achieve the aim. their model included greenhouse gas 84 emissions and policy scenarios to reduce these emissions. however, it did not distinguish 85 between non-biobased and biobased energy, nor did it include biomass. especially in the 1980s 86 and 1990s age models were used to assess different policy issues, e.g., agricultural policy 87 reform, environmental taxation, etc. (for an overview, see bergman, 1990; gunning & keyzer, 88 1995; robinson, 1989). policy issues simulated with age models reflect relatively large 89 shocks to an economy, as age models explicitly model the economy as a whole. calculating 90 the effects of large shocks cannot be done using a partial equilibrium model given that these 91 models assume too many variables (e.g. wages, interest, etc.) exogenous. the transition towards 92 climate neutrality and energy self-sufficiency can be considered as a large shock to the latvian 93 economy. data come from the supply and use tables for 2015 and national accounting data from 94 the latvian central statistics bureau (csb). it is assumed that a nested production structure 95 allows for explicit imperfect substitution between domestic and imported inputs in energy 96 production to account for latvia’s current dependence on imported fossil fuels. by increasing 97 the use of (domestically produced) wooden biomass in the energy sector through a tax on the 98 use of non-biobased products and a subsidy on the use of the biobased products, the amount of 99 co2 emissions from fossil energy and the dependence on fossil energy imports are expected to 100 reduce. the technological change is expected to lead to similar effects. to the best of our 101 knowledge, this is the first application of an age model to latvia and the first age analysis 102 to investigate the effects of the green deal. 103 the remainder of the paper is structured as follows. section 2 describes the energy and forestry 104 sectors and policies of latvia. section 3 presents the age model. the data are described in 105 5 section 4. section 5 presents and discusses the results of the model. section 6 concludes and 106 provides a general discussion. 107 108 2. energy and forestry sectors, and policies 109 latvia is highly dependent on imports of fossil fuels. table 1 shows that oil products and natural 110 gas are not produced in the country. electricity is produced mostly domestically, partly from 111 fossil fuels and partly from renewable energy sources. 112 table 1. energy production, imports, exports and domestic consumption in latvia, 2020 113 product production imports exports domestic consumption oil products, thousand euro1 726.56 145.64 542.4 natural gas, million euro 433.74 0.00 433.74 electricity, million euro 181.86 137.71 84.02 235.55 1: production plus imports does not add up to domestic consumption and exports because of changes in stocks and 114 statistical issues. 115 source: csb, 2021a, 2021b, 2021c 116 to meet the objectives set by the eu in the green deal and international commitments (see 117 table 2), the latvian government drafted the national energy and climate plan 2021 – 2030. 118 the long-term goal of the plan is to promote the development of a climate-neutral economy in 119 a sustainable, competitive, cost-effective, secure, and market-based way by improving energy 120 security and public welfare. to achieve this goal, it is necessary: ‘1) to promote the efficient 121 use of resources, as well as their self-sufficiency and diversity; 2) to ensure a significant 122 reduction in the consumption of resources, in particular fossil and unsustainable resources, 123 6 and a simultaneous transition to sustainable, renewable and innovative use of resources, 124 ensuring equal access to energy for all sections of society; 3) to stimulate research and 125 innovation that contributes to the development of a sustainable energy sector and the mitigation 126 of climate change’ (em ministry of economics, 2019). 127 table 2. eu and latvia`s energy policy indicators and targets 128 indicator/target eu’s target, 2030 latvia’s actual value in 2017 latvia’s target, 2030 latvia’s target, 2050 reducing ghg emissions (% to 1990) (lulucf* excluded) -40 -57 -65 climate neutrality (irreducible ghg emissions are compensated by lulucf sector) reducing ghg emissions (% to 1990) (lulucf included) -38 energy produced from res**, share of gross final consumption (%) 32 39 50 share of imports in gross domestic energy consumption (%) 44.1 30-40 * land use, land use change and forestry 129 ** renewable energy sources 130 source: em, 2019 131 one of the goals of the national energy and climate plan is to increase the share of renewable 132 energy sources in latvia. the plan includes the so-called ‘tax greening’ (“polluter pays 133 principle”), where the focus is on taxes such as excise, value added, vehicle, electricity, and 134 natural resource taxes. however, to our knowledge, these have not been implemented by the 135 beginning of 2025. 136 7 the transition from a fossil-based economy to a biobased economy is especially relevant for 137 the forest sector in latvia. the forest sector is expected to contribute to this transition through 138 the replacement of fossil fuels and non-renewable products with forestry-based products 139 (kröger & raitio, 2017). in addition to being used in the production of traditional wood-based 140 products, such as furniture, wooden biomass is increasingly being used in energy generation 141 and in the production of textiles, bioplastics, chemicals, and intelligent packaging, and is also 142 contributing to the construction sector (hetemäki et al., 2017; hurmekoski et al., 2018). 143 one fifth of the forest stands in latvia is in the age of mature and old-growth (csb, 2021d). 144 the co2 sequestration capacity of old trees is relatively low, hindering the fulfilment of the 145 green deal targets making them a potential feedstock to produce biobased energy. 146 according to data from the eu bioeconomy monitoring system dashboard (ec, n.d.), 58.5% 147 of wooden biomass in latvia is used in the production of bioenergy and 41.5% is used as 148 materials in manufacturing in 2015. the largest share of wooden biomass in energy production 149 was taken by firewood (30% of the total consumption of energy sources) in 2018, followed by 150 briquettes, pellets, wood scraps, and wood chips. the largest consumers of wooden biomass are 151 households followed by the energy transformation sector (figure 1). 152 153 figure 1. wooden biomass consumption in energy production in latvia 2016 – 2019 (%) 154 8 155 source: am, 2022 156 157 3. model 158 this section describes the age model developed and applied in this paper. the model is based 159 on the model of komen and peerlings (1999). their model included greenhouse gas emissions 160 and policy scenarios to reduce these emissions. however, it did not distinguish between non-161 biobased and biobased energy. we present the model in section 3.1 and discuss the modelling 162 of taxes, subsidies, and technological change in section 3.2. a full description can be found in 163 appendix a. 164 165 3.1 general description 166 an age model describes an economy as a whole and is therefore useful to analyse large shocks 167 to the economy that affect, through market linkages, all the economy’s actors (i.e. industries, 168 households, government). it mainly consists of demand and supply functions of commodities 169 27 25 27 26 37 35 35 33 30 33 32 35 7 7 5 6 0 20 40 60 80 100 120 2016 2017 2018 2019 % year industry and construction households energy transformation sector other 9 and factor inputs, and income formation and distribution equations (dervis et al., 1982). the 170 developed model contains 60 commodities and 60 industries including both a non-biobased and 171 biobased energy commodity and industry. however, one industry can produce more than one 172 commodity, and one commodity can be produced by more than one industry. industries are 173 assumed to minimise costs as they face a constant returns to scale nested constant elasticity of 174 substitution (ces) production function (see, e.g. arrow et al., 1961; sato, 1967). 175 figure 2 shows that for industry b, the intermediate energy intermediate inputs (𝐼𝑁𝑏,𝑔 ∀𝑔 ∈176 𝑆𝑒𝑛), material intermediate inputs (𝐼𝑁𝑏,𝑔 ∀𝑔 ∈ 𝑆𝑚𝑎𝑡) and primary inputs (𝑃𝑅1, 𝑃𝑅2) are 177 aggregated into 3 aggregate inputs respectively. this is done using 3 ces functions, each with 178 their own substitution elasticity. the aggregate inputs are then aggregated into an aggregate 179 output (𝑌𝑏) using a ces function with again its own substitution elasticity. the aggregated 180 output is then divided into different outputs (𝑌𝑌𝑏,𝑔) using a leontief transformation function 181 (i.e., using fixed ratios). 182 183 figure 2. production of industry b 184 185 where: 186 inputs aggregated inputs aggregated output outputs yyb,g ∀g ∈ g yb (ces, leontief) aenb (ces) inb,g ∀g ∈ sen ainb (ces) inb,g ∀g ∈ smat aprb (ces) pr1,pr2 10 inb,g ∀g ∈ sen: use of energy commodity g as an intermediate input in industry b. commodities 187 are in the set sen of energy intermediate inputs. 188 inb,g ∀g ∈ smat: use of commodity g as an intermediate input in industry b. commodities are in 189 the set smat of non-energy intermediate inputs. 190 pr1,pr2: labour (j=1) and capital (j=2) used in industry b. 191 aenb, ainb, and aprb: aggregate energy, aggregate intermediate and aggregate primary input 192 use, respectively, in industry b. 193 𝑌𝑏: aggregate output in industry b. 194 𝑌𝑌𝑏,𝑔: output g of industry b. 195 196 source: authors` elaboration 197 198 at the highest level, outputs are produced by an aggregate energy input, an aggregate 199 intermediate input, and an aggregate factor input. at the lowest level, the aggregate energy 200 input is composed of a biobased and a non-biobased energy input. the aggregate intermediate 201 input is composed of 58 non-energy intermediate inputs. the aggregate factor input is 202 composed of labour and capital. cost minimisation leads to the demand for energy intermediate 203 inputs, non-energy intermediate inputs, labour and capital. 204 figure 3 shows that in the next step of the model, the outputs produced by different industries 205 are aggregated commodity by commodity. aggregation gives domestic production (𝐷𝑃𝑔) of a 206 commodity g. domestic production competes with imports of the same commodity (𝐼𝑀𝑔). this 207 competition can be seen as an aggregation into total supply (𝑆𝑃𝑔) using a ces production 208 function. the total supply is then disaggregated using a cet transformation function into 209 domestic use (𝐷𝑈𝑔) and exports (𝐸𝑋𝑔). ces production and cet transformation functions 210 imply that with profit maximisation relative prices determine demand and supply, respectively. 211 11 domestic use equals the sum of intermediate demand (∑ 𝐼𝑁𝑏,𝑔𝑏∈𝐵 ), private household demand 212 (𝑋𝑔 𝑐𝑜𝑛), public household demand (𝑋𝑔 𝑔𝑜𝑣 ) and investment demand (𝑋𝑔 𝑖𝑛𝑣). 213 214 figure 3. supply and use of commodity g 215 216 217 where: 218 dpg : domestic production of commodity g. 219 img : imports of commodity g. 220 spg : total supply of the commodity g. 221 dug : domestic use of the commodity g. 222 exg : exports of commodity g. 223 inb,g : intermediate input demand of commodity g in industry b. 224 xg con: private household demand of commodity g. 225 xg gov: public household demand of commodity g. 226 domestic production and imports domestic supply domestic use and exports 𝐷𝑈𝑔 = ∑b∈b (in b,g+ xg con + xg gov + xg inv ) dug spg (ces/cet) dpg = ∑𝑏=1 𝐵 𝑌𝑌𝑏,𝑔 img exg 12 xg con: investment demand of commodity g. 227 228 source: authors` elaboration 229 230 the model includes one private household that supplies labour and capital to the industries and 231 receives income in return. capital and labour are assumed to be imperfectly mobile between 232 industries. we also assume one aggregated public household (i.e., government). consumption 233 of commodity g by the private and public household follows from maximising a ces direct 234 utility function given an income constraint. the ces utility function used implies an income 235 elasticity of one. in addition, as a consumer, the public household imposes taxes and 236 redistributes income. a fixed share of both private and public household income is saved. 237 savings together with the (minus) surplus on the balance of trade equal investment. investment 238 demand is modelled using a leontief production function implying that the demand for an 239 individual commodity is proportional to total investment (komen & peerlings, 2001). 240 the model also includes greenhouse gas emissions that are proportionally linked to the 241 production of an industry (𝑌𝑏). 242 243 3.2 taxes, subsidies, and technological change 244 all transactions in the model can be potentially taxed or subsidised. taxes can be divided into 245 product and non-product taxes (including subsidies). the latter are levied on income, the first 246 on transactions of commodities. product taxes drive a price wedge between the demand and the 247 supply price. in the model, we use ad valorem taxes on demand (see equation 1). 248 249 𝑃𝑑𝑒𝑚𝑎𝑛𝑑 = (1 + 𝑡𝑎𝑥𝑟𝑎𝑡𝑒)𝑃𝑠𝑢𝑝𝑝𝑙𝑦 (1) 250 251 13 a tax increases the price demanders must pay and decreases the price suppliers receive; a 252 subsidy does the opposite. 253 this paper also examines the effects of hicks neutral technological change in the biobased 254 energy industry. hicks neutral technological change implies that with the same level and ratio 255 between (all) inputs, more biobased energy can be produced. equation 2 shows a ces input 256 demand function. in the case of a hicks neutral technological change, the exogenous scale 257 parameter 𝛤 increases. we include the hicks neutral technological change in the aggregate 258 demand functions (see figure 2) of the biobased energy industry. hicks neutral technological 259 change implies that input demand (𝑥𝑛) and cost of production decrease given a level of output 260 ceteris paribus. however, in the age model, the ceteris paribus assumption does not hold, as 261 hicks neutral technological change lowers the price of wooden biomass as less is needed to 262 produce biobased energy, making it more attractive to demand. therefore, technological change 263 in the production of biobased energy leads to an increase in the demand for wooden biomass 264 and a lower price for biobased energy. in addition, the lower price of biobased energy leads, 265 because of substitution, to a reduction in the demand for non-biobased energy by all demanders. 266 the degree of substitution between biobased energy and non-biobased energy depends on the 267 degree of substitution (i.e. substitution elasticity) between both in the different industries. 268 𝑥𝑛(. . ) = 𝑦. 𝛤−1. 𝛼𝑛 𝜎. 𝑤𝑛 −𝜎 . (∑ 𝛼𝑛 𝜎𝑁 𝑛=1 . 𝑤𝑛 1−𝜎) 𝜎 1−𝜎 n = 1,..,n (2) 269 where: 𝑥𝑛 conditional demand for input n, 𝑦 output, 𝑤𝑛 − price of input n, 𝜎substitution 270 elasticity, 𝛤 scale parameter and 𝛼𝑛 distribution coefficient of input n. 271 272 4. data 273 the model uses the supply and use tables (sut) at the basic prices from latvia`s central 274 statistical bureau for 2015 (csb latvia, 2016). a supply table shows in its columns the supply 275 of commodities by the different industries and by imports in an economy for a given period. a 276 14 use table shows in its columns the use of commodities by type of use. therefore, the use table 277 reveals in its columns the input structure of each industry and the demand for individual 278 commodities by the different final demand categories (eurostat, 2008). due to a lack of data, 279 some industries and commodities are aggregated. the data set used in modelling contains 60 280 commodities and 60 industries (see appendices b and c). 281 furthermore, we use latvia’s ‘energy sut’ in terajoules from eurostat’s 2015 physical energy 282 flow accounts (eurostat, 2021) with energy commodities supplied/used by industries to split 283 commodity electricity, gas, steam and air-conditioning of the sut in non-biobased and 284 biobased electricity, gas, steam and air-conditioning, respectively. we consider energy 285 commodities wood, wood waste and other solid biomass, liquid fuels, and biogas as biobased 286 energy commodities. other energy commodities are fossil-based or renewable energy sources 287 that are not biobased. this implies, for example, that electricity and heat are non-biobased 288 energy products, but they can be produced using both the biobased and non-biobased product. 289 the two energy commodities are used to calculate the shares of biobased and non-biobased 290 energy commodities in the commodity and industry ‘electricity, gas, steam and air-291 conditioning’ in the sut, respectively. according to the data of physical energy flow 292 accounts, 7.47% of the total energy supplied and 5.88% of the total energy used comes from 293 biobased energy commodities. 294 295 5. scenarios and results 296 5.1 scenarios 297 in the base scenario, the model calculates back the actual situation of the latvian economy in 298 2015. this includes a product tax of 17% for all energy commodities – biobased and non-299 biobased – since all energy producers pay the tax. 300 301 15 scenario i 302 in scenario i, we introduce an arbitrary 25% tax on non-biobased energy demand and a subsidy 303 of 10% for biobased energy demand replacing the 17% tax on both products in the base scenario 304 (see equation 1 and equations a.29-a.33 in appendix a). 305 306 scenario ii 307 in scenario ii, a 25% hicks neutral technological change in the biobased energy production 308 industry is introduced in the base scenario, where the scale parameter 𝛤 in eq. (2) is increased 309 by 25%. this scenario reflects the technological change in new technologies producing 310 biobased energy that is partially stimulated by government investment from eu funds. the 25% 311 is selected because it leads to a similar increase in the production of biobased energy as in 312 scenario 1. 313 314 5.2 results 315 table 3 shows the outcomes of both scenarios. it is important to note that all price changes are 316 relative to the price numeraire chosen, which is the exchange rate in our case. 317 318 scenario i 319 table 3 shows that due to the switch to the subsidy (10%) on biobased energy, its production 320 increases with 57.8%. table 3 also shows that due to the tax (25%) on non-biobased energy, 321 the price of non-biobased energy increases for buyers (8.3%). moreover, production in the non-322 biobased energy industry decreases (-6.0%). this leads to a reduction in the value added (-323 2.9%) of this industry. non-biobased energy is substituted by biobased energy in all industries 324 where the degree of substitution depends on the substitution elasticity between non-biobased 325 and biobased energy. in the model, we assume that this substitution elasticity is large (𝜎 = 1.5), 326 implying that the degree of substitution is large (see appendix d). in all industries, we see 327 16 therefore a reduction in the demand of non-biobased energy and an increase in the use of 328 biobased energy. overall, the use of energy falls between 1-3% (a reduction of aenb; see 329 figure 2). the increase in biobased energy production (57.8%) leads to a reduction in imports 330 of biobased energy products (i.e. natural gas and oil) of 4.2%, making latvia less dependent on 331 energy imports. however, the subsidy on non-biobased energy increases the imports of this 332 product (64.4%). however, these imports are still very small. table 3 shows a 1.7% reduction 333 in co2 emissions assuming co2 emissions from the biobased energy product to be zero (i.e. 334 being climate neutral). the reduction largely follows from the reduced use of non-biobased 335 energy products (5.7%). despite this reduction, the target of 50% energy from biobased sources 336 is not reached. 337 overall, there is a welfare gain (63.5 million euros) in scenario i, where we measured welfare 338 as private, public, and investment demand changes in prices of the base year. the welfare gain 339 results from a reduction in already existing distortions by introducing the subsidy for biobased 340 products (replacing the 17% tax) and increasing the tax on non-biobased products (from 17% 341 to 25%). therefore, there is a double dividend. however, whether the double dividend exists 342 depends on the level of tax and subsidy. sensitivity analyses show that larger taxes and 343 subsidies create welfare losses and that especially the subsidy helps to reduce already existing 344 distortions. table 3 shows that the increase in tax revenue from the product-related tax on non-345 biobased energy production (117.4 million euro) is larger than the cost of the switch from the 346 tax to the subsidy for the biobased product (45.6 million euro). 347 348 scenario ii 349 table 3 shows that hicks neutral technological change of 25% (scenario ii) results in a 350 reduction in the price of biobased energy (-28.1%) and therefore, an increase in production 351 (57.9%) and value added (10.3%) in the biobased energy industry. this leads to a substitution 352 away from non-biobased energy and a reduction in the production (-1.2%) and value added (-353 17 0.8%) in the non-biobased energy industry. also, in this scenario, import of the biobased energy 354 product fall (-2.5%). the lower price of the biobased energy product decreases imports of the 355 biobased energy product (-12.4%). again, imports are very small. compared to scenario i, the 356 reduction in co2 emissions is smaller (0.3% versus 1.7%) because the price and production of 357 non-biobased products changes less, and therefore, less substitution takes place. the welfare 358 increase is similar to the welfare increase in scenario i (61.9 million euros). this welfare gain 359 results from the fact that fewer inputs are needed in the production of biobased energy. this 360 shows the attractiveness of technological change. however, in this scenario, technological 361 change is ‘free’, and this is, of course, not true. 362 overall, one can conclude that the effects for the latvian economy are not large. important 363 reasons for this are the fact that the biobased energy industry is small and even a large growth 364 in production (57.8% and 57.9% in scenario i and ii respectively) does not create a substantial 365 change. another reason is that in the age model factor inputs are mobile between industries 366 making that labour and capital moving out of industries affected negatively by the scenarios 367 can be used elsewhere in the economy leading there to higher production and value added. 368 finally, the age model allows for substitution because of relative price change, again 369 smoothing the effect for the economy as a whole. 370 371 table 3. scenario results compared to initial values (i.e., base scenario) 372 initial values scenario i: tax and subsidy (% change) scenario ii: technological change (% change) production* and value added** in million euro of the base year production of non-biobased energy industry (𝑌𝑏) 1,766.3 -6.0 -1.2 18 production of biobased energy industry (𝑌𝑏) 110.3 57.8 57.9 forestry production (𝑌𝑏) 938.9 -0.3 -0.1 value added in non-biobased energy industry (𝐴𝑃𝑅𝑏) 663.6 -2.9 -0.8 value added in biobased energy industry (𝐴𝑃𝑅𝑏) 34.2 27.7 10.3 value added in forestry (𝐴𝑃𝑅𝑏) 352.8 -0.2 -0.1 prices (index, so no unit) price of non-biobased energy production (price of 𝐷𝑃𝑔) 1.00 1.2 -0.8 price of biobased energy production (price of 𝐷𝑃𝑔) 1.00 9.4 -28.1 price of non-biobased energy demand (price of 𝐷𝑈𝑔) 1.17 8.3 -0.8 price of biobased energy demand (price of 𝐷𝑈𝑔) 1.17 -16.1 -28.8 co2 emissions*** in 1000 tons co2 emissions in non-biobased energy industry 1,757,841 -6.0 -1.2 total co2 emissions 6,937,629 -1.7 -0.3 tax revenue in million euro (nominal) (m euro) (m euro) product tax paid on non-biobased energy product 293.7 411.1 287.7 product tax paid on biobased product 23.7 -21.9 24.5 welfare in million euro of the base year (m euro) (m euro) laspeyers index 63.5 61.9 * note: quantities are expressed in million euros for the base year 2015. this implies that initial supply prices 373 (indices) are equal to 1; the initial price for energy demanders is equal to 1.17 due to the 17% tax on energy 374 demand. 375 ** value added equals the value of capital and labour. 376 19 *** excluding co2 emissions from biobased energy commodities that are assumed to be climate neutral. 377 source: authors` elaboration 378 379 6. conclusions and discussion 380 this paper aims to assess the effect of a tax on non-biobased energy demand and a subsidy on 381 biobased energy demand replacing a lower tax on both to facilitate the substitution of the first 382 for the latter as a step in the transition of latvia’s economy towards climate neutrality and 383 energy self-sufficiency. furthermore, the effects of hicks neutral technological change in the 384 biobased energy industry are examined. the paper uses an applied general equilibrium (age) 385 model to assess the effects of a tax, subsidy, and technological change given the expected 386 economy-wide effects and interest in national emissions and welfare. 387 the paper finds that a tax in combination with the subsidy indeed has the expected effects. the 388 green deal proposed decarbonisation of the economy by transitioning from fossil-based to 389 renewable sources in energy production. latvia aims to increase the share of renewable energy 390 production in total energy production to 50% by 2030 (it was 39% in 2017), focussing on the 391 use of wooden biomass in energy production. according to the results of the model, the supply 392 of the biobased energy commodity has increased by 57.8%. however, measures are insufficient 393 to deliver the target, climate neutrality, and energy self-sufficiency. however, there is an overall 394 welfare gain in both scenario i and scenario ii. so, scenario i reduces existing distortions in 395 latvia’s economy (i.e., double dividend). scenario ii (technological change in the biobased 396 energy industry) leads to a similar increase in the production and use of biobased energy. 397 because in scenario ii the prices of non-biobased energy are affected less than in scenario i, 398 it`s production and use fall less leading to a lower reduction in co2 emissions. although the 399 welfare gain is similar to the gain in scenario i, scenario ii ignores the costs of technological 400 change and does not explicitly include the incentives needed to implement it. 401 20 to our knowledge, there are no prior studies on the use of wooden biomass for bioenergy 402 production in latvia. while there are studies of the age model on energy taxes, they are not 403 recent. for example, komen and peerlings (1999) analysed the effect of an energy tax on small 404 users in the netherlands using 1990 data. they only find a double dividend in the case of small 405 tax rates. goulder (1995) discusses the double dividend in more detail, coming to the same 406 conclusion. welfare effects found by komen and peerlings (1999) are also small, like in our 407 case. this is largely due to the substitution possibilities economy-wide and fixed endowments 408 of labour and capital in combination with factor mobility in the models used. 409 this study has three main caveats. first, latvia devised an action plan in 2019, and is currently 410 undergoing upgrading procedure, although the exact measures are largely unknown. therefore, 411 it is not possible to calculate the effects of actual policies. this research can contribute to the 412 formulation of such policies. second, data on energy use and supply are largely aggregated and 413 had to be disaggregated for this study. this involved arbitrary choices. this emphasises the 414 importance of data collection. related to this, in the base year there is hardly and use of other 415 renewable energy sources than biomass from forests and wood. wind and solar energy are 416 negligible, although there is some hydropower. it is to be expected that the share of wind and 417 solar energy will grow, requiring that in future research they must be considered separate energy 418 products. finally, an age model is a powerful tool to analyse the economy-wide effects of 419 policies but also comes at a price. for example, the level of aggregation is high, for example, it 420 distinguishes not between, e.g. electricity and heat production. despite these caveats, this study 421 contributes to the discussion of the transition of the latvia`s economy towards climate 422 neutrality and 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(1996). regulatory taxation of fossil fuels: theory and 510 policy. ecological economics, 19(1), 55–65. 511 512 513 25 appendices 514 appendix a model description 515 516 demand and supply equations 517 aggregate output in industry b (𝑌𝑏) is composed of a hypothetical aggregate energy input 518 (𝐴𝐸𝑁𝑏), aggregate intermediate input (𝐴𝐼𝑁𝑏) and aggregate factor input (𝐴𝑃𝑅𝑏) according a 519 ces production function with constant returns to scale (see glossary at the end of this appendix 520 for overview of variables, coefficients and sets). intermediate inputs g in industry b (𝐼𝑁𝑏,𝑔) are 521 transformed into an aggregate energy and aggregate intermediate input according to the ces 522 production functions with constant returns to scale. labor (𝑃𝑅𝑏,1) and capital (𝑃𝑅𝑏,2) in 523 industry b are transformed into the aggregate factor input, using a ces production function 524 with constant returns to scale. cost minimization yields ces demand functions for the 525 aggregate energy (a.1), aggregate intermediate (a.2), aggregate factor (a.3), energy (a.4), 526 intermediate (a.5) and factor inputs (a.6): 527 528 𝐴𝐸𝑁𝑏 = 𝑓𝐴𝐸𝑁𝑏 𝐶𝐸𝑆 (𝑌𝑏 , 𝑊𝐴𝐸𝑁𝑏 , 𝑊𝐴𝐼𝑁𝑏 , 𝑊𝐴𝑃𝑅𝑏) ∀𝑏 ∈ 𝐵 (a.1) 529 530 𝐴𝐼𝑁𝑏 = 𝑓𝐴𝐼𝑁𝑏 𝐶𝐸𝑆 (𝑌𝑏 , 𝑊𝐴𝐸𝑁𝑏 , 𝑊𝐴𝐼𝑁𝑏 , 𝑊𝐴𝑃𝑅𝑏) ∀𝑏 ∈ 𝐵 (a.2) 531 532 𝐴𝑃𝑅𝑏 = 𝑓𝐴𝑃𝑅𝑏 𝐶𝐸𝑆 (𝑌𝑏, 𝑊𝐴𝐸𝑁𝑏 , 𝑊𝐴𝐼𝑁𝑏 , 𝑊𝐴𝑃𝑅𝑏) ∀𝑏 ∈ 𝐵 (a.3) 533 534 𝐼𝑁𝑏,𝑔 = 𝑓𝐸𝑁𝑏,𝑔 𝐶𝐸𝑆 (𝐴𝐸𝑁𝑏 , 𝑾𝑰𝑵𝒈) ∀𝑏 ∈ 𝐵, ∀𝑔 ∈ 𝑆𝑒𝑛 (a.4) 535 536 𝐼𝑁𝑏,𝑔 = 𝑓𝐼𝑁𝑏,𝑔 𝐶𝐸𝑆 (𝐴𝐼𝑁𝑏 , 𝑾𝑰𝑵𝒈) ∀𝑏 ∈ 𝐵, ∀𝑔 ∈ 𝑆𝑚𝑎𝑡 (a.5) 537 538 26 𝑃𝑅𝑏,𝑗 = 𝑓𝑃𝑅𝑏,𝑗 𝐶𝐸𝑆 (𝐴𝑃𝑅𝑏 , 𝑾𝑷𝑹𝒃,𝒋) ∀𝑏 ∈ 𝐵, ∀𝑗 ∈ 𝐽 (a.6) 539 540 supply of output g by industry b (𝑌𝑌𝑏,𝑔) is proportional to the aggregate output (𝑌𝑏) by industry 541 b (a.7). aggregation of outputs over industries gives domestic production (𝐷𝑃𝑔) of commodity 542 g (a.8): 543 544 𝑌𝑌𝑏,𝑔 = 𝛿𝑏,𝑔 𝑌 × 𝑌𝑏 ∑ 𝛿𝑏,𝑔 𝑌 𝑔∈𝐺 = 1 ∀𝑏 ∈ 𝐵, ∀𝑔 ∈ 𝐺 (a.7) 545 546 𝐷𝑃𝑔 = ∑ 𝑌𝑌𝑏,𝑔 𝐵 𝑏=1 ∀𝑔 ∈ 𝐺 (a.8) 547 548 domestic production (𝐷𝑃𝑔) and imports (𝐼𝑀𝑔) are aggregated into total supply of commodity 549 g (𝑆𝑃𝑔) using a ces production function with constant returns to scale. this implies that the 550 armington assumption is adopted (see dervis et al., 1982, p.221). the total supply is then 551 divided into domestic use (𝐷𝑈𝑔) and exports (𝐸𝑋𝑔) using a cet product transformation 552 function with constant returns to scale. cost minimization yields ces demand equations for 553 domestic production (a.9) and imports (a.10) and revenue maximization yields cet supply 554 equations for domestic use (a.11) and exports (a.12): 555 556 𝐷𝑃𝑔 = 𝑓𝐷𝑃𝑔 𝐶𝐸𝑆(𝑆𝑃𝑔, 𝑊𝐷𝑃𝑔 , 𝑊𝐼𝑀𝑔) ∀𝑔 ∈ 𝐺 (a.9) 557 558 𝐼𝑀𝑔 = 𝑓𝐼𝑀𝑔 𝐶𝐸𝑆(𝑆𝑃𝑔, 𝑊𝐷𝑃𝑔 , 𝑊𝐼𝑀𝑔) ∀𝑔 ∈ 𝐺 (a.10) 559 560 𝐷𝑈𝑔 = 𝑓𝐷𝑈𝑔 𝐶𝐸𝑇(𝑆𝑃𝑔, 𝑊𝐷𝑈𝑔, 𝑊𝐸𝑋𝑔) ∀𝑔 ∈ 𝐺 (a.11) 561 562 𝐸𝑋𝑔 = 𝑓𝐸𝑋𝑔 𝐶𝐸𝑇(𝑆𝑃𝑔, 𝑊𝐷𝑈𝑔, 𝑊𝐸𝑋𝑔) ∀𝑔 ∈ 𝐺 (a.12) 563 27 564 total labour (j=1) and total capital (j=2) available in the economy (𝑇𝑃𝑅𝑗) are divided into 565 supply of labour (𝑃𝑅𝑏,1) and capital (𝑃𝑅𝑏,2) by industry using cet product transformation 566 functions with constant returns to scale. revenue maximisation yields supply functions for 567 labor and capital, respectively (a.13): 568 569 𝑃𝑅𝑏,𝑗 = 𝑓𝑝𝑟𝑏,𝑗 𝐶𝐸𝑇 (𝑇𝑃𝑅𝑗 , 𝑾𝑷𝑹𝒃,𝒋) ∀𝑏 ∈ 𝐵, ∀𝑗 ∈ 𝐽 (a.13) 570 571 maximization of the ces utility functions yields ces demand equations for the private 572 household (a.14) and public household (a.15): 573 574 𝐶𝑂𝑁𝑔 = 𝑓𝑐𝑜𝑛𝑔 𝐶𝐸𝑆 (𝐸𝑋𝑃𝑐𝑜𝑛, 𝑾𝑪𝑶𝑵𝒈) ∀𝑔 ∈ 𝐺 (a.14) 575 576 𝐺𝑂𝑉𝑔 = 𝑓𝑔𝑜𝑣𝑔 𝐶𝐸𝑆 (𝐸𝑋𝑃𝑔𝑜𝑣, 𝑾𝑮𝑶𝑽𝒈) ∀𝑔 ∈ 𝐺 (a.15) 577 578 the demand for investment goods (𝑋𝑔 𝑖𝑛𝑣) is given by (a.16): 579 580 𝐼𝑁𝑉𝑔 = 𝛿𝑔 𝑖𝑛𝑣 × 𝐼𝑁𝑉 ∑ 𝛿𝑔 𝑖𝑛𝑣 = 1𝑔∈𝑔 ∀𝑔 ∈ 𝐺 (a.16) 581 582 zero-profit conditions 583 the value of the disaggregated outputs by industry is equal to the value of the aggregate output 584 produced by industry (a.17). the value of aggregate output equals the value of the aggregate 585 energy input, aggregate intermediate input and aggregate factor input (a.18): 586 587 ∑ 𝑊𝐷𝑃𝑔𝑔∈𝐺 𝑌𝑏,𝑔 = 𝑊𝑌𝑏 × 𝑌𝑏 ∀𝑏 ∈ 𝐵 (a.17) 588 28 589 𝑊𝑌𝑏𝑌𝑏 = 𝑊𝐴𝐸𝑁𝑏 × 𝐴𝐸𝑁𝑏 + 𝑊𝐴𝐼𝑁𝑏 × 𝐴𝐼𝑁𝑏 + 𝑊𝐴𝑃𝑅𝑏 × 𝐴𝑃𝑅𝑏 ∀𝑏 ∈ 𝐵 (a.18) 590 591 the value of the aggregate energy input is equal to the value of the energy inputs by industry 592 (a.19). the value of the aggregate intermediate input equals the value of the intermediate inputs 593 by industry (a.20). the value of the aggregate factor input equals the value of labour and capital 594 by industry (a.21): 595 596 𝑊𝐴𝐸𝑁𝑏 × 𝐴𝐸𝑁𝑏 = ∑ 𝑊𝐼𝑁𝑏,𝑔 × 𝐼𝑁𝑏,𝑔𝑔∈𝑆𝑒𝑛 ∀𝑏 ∈ 𝐵 (a.19) 597 598 𝑊𝐴𝐼𝑁𝑏 × 𝐴𝐼𝑁𝑏 = ∑ 𝑊𝐼𝑁𝑏,𝑔 × 𝐼𝑁𝑏,𝑔𝑔∈𝑆𝑚𝑎𝑡 ∀𝑏 ∈ 𝐵 (a.20) 599 600 𝑊𝑁𝐴𝑃𝑅𝑏 × 𝐴𝑃𝑅𝑏 = ∑ 𝑊𝑃𝑅𝑏,𝑗 × 𝑃𝑅𝑏,𝑗 2 𝑗=1 ∀𝑏 ∈ 𝐵 (a.21) 601 602 the value of total supply (𝑆𝑃𝑔) equals the sum of the value of domestic production and imports 603 (a.22) and the sum of the value of domestic use and exports by commodity (a.23): 604 605 𝑊𝑆𝑃𝑔 × 𝑆𝑃𝑔 = 𝑊𝐷𝑃𝑔 × 𝐷𝑃𝑔 + 𝑊𝐼𝑀𝑔 × 𝐼𝑀𝑔 ∀𝑔 ∈ 𝐺 (a.22) 606 607 𝑊𝑆𝑃𝑔 × 𝑆𝑃𝑔 = 𝑊𝐷𝑈𝑔 × 𝐷𝑈𝑔 + 𝑊𝐸𝑋𝑔 × 𝐸𝑋𝑔 ∀𝑔 ∈ 𝐺 (a.23) 608 609 the value of the supply of labour and capital equals the value of the total availability of labour 610 and capital, respectively (a.24). 611 612 ∑ (𝑊𝑃𝑅𝑏,𝑗 × 𝑃𝑅𝑏,𝑗)𝐵∈𝑏 = 𝑊𝑇𝑃𝑅𝑗 × 𝑇𝑃𝑅𝑗 ∀𝑗 ∈ 𝐽 (a.24) 613 614 29 the value of the demand for individual investment goods equals the expenditure on investment 615 (a.25): 616 617 ∑ (𝑊𝐼𝑁𝑉𝑔 × 𝐼𝑁𝑉𝑔)𝑔∈𝐺 = 𝑊𝐼𝑁𝑉 × 𝐼𝑁𝑉 (a.25) 618 619 margins 620 the value of demand/supply for margins per commodity (𝑀𝐴𝑅𝑔) is assumed to form a share of 621 the value of the different demand categories and is given by (a.26). 622 623 𝑀𝐴𝑅𝑔 = 𝑚𝑔 𝑠𝑝 × 𝑊𝐷𝑈𝑔 × 𝐷𝑈𝑔 + 𝑚𝑔 𝑠𝑝 × 𝑊𝐸𝑋𝑔 × 𝐸𝑋𝑔 gg (a.26) 624 625 the total value of the margins is zero (a.27): 626 627 ∑ 𝑀𝐴𝑅𝑔𝑔∈𝐺 = 0 (a.27) 628 629 equilibrium conditions for commodities 630 total domestic use equals intermediate, private household, public household and investment 631 demand (a.28): 632 633 𝐷𝑈𝑔 = ∑ 𝐼𝑁𝑏,𝑔 + 𝐶𝑂𝑁𝑔 + 𝐺𝑂𝑉𝑔 + 𝐼𝑁𝑉𝑔𝑏∈𝐵 gg (a.28) 634 635 price equations 636 indirect taxes and margins drive a wedge between the demanders’ and suppliers’ price of 637 domestic use. this applies to commodities consumed by the private household (a.29), the 638 public household (a.30), as investment commodities (a.31), as intermediate inputs (a.32) and 639 as exports (a.33): 640 30 641 𝑊𝐶𝑂𝑁𝑔 = (1 + 𝑚𝑔 𝑠𝑝 + 𝑡𝑔 𝑠𝑝) × 𝑊𝐷𝑈𝑔 (a.29) 642 643 𝑊𝐺𝑂𝑉𝑔 = (1 + 𝑚𝑔 𝑠𝑝 + 𝑡𝑔 𝑠𝑝) × 𝑊𝐷𝑈𝑔 (a.30) 644 645 𝑊𝐼𝑁𝑉𝑔 = (1 + 𝑚𝑔 𝑠𝑝 + 𝑡𝑔 𝑠𝑝) × 𝑊𝐷𝑈𝑔 (a.31) 646 647 𝑊𝐼𝑁𝑔 = (1 + 𝑚𝑔 𝑠𝑝 + 𝑡𝑔 𝑠𝑝) × 𝑊𝐷𝑈𝑔 (a.32) 648 649 𝑊𝐺𝐸𝑋𝑔 = (1 + 𝑚𝑔 𝑠𝑝 + 𝑡𝑔 𝑠𝑝) × 𝑊𝐸𝑋𝑔 (a.33) 650 651 the price received for exports (a.34) and paid for imports (a.35) are equal to the world market 652 price times the exchange rate: 653 654 𝑊𝐺𝐸𝑋𝑔 = 𝑊𝑃𝐸𝑋𝑔 ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ × 𝐸𝑅̅̅ ̅̅ (a.34) 655 𝑊𝐼𝑀𝑔 = 𝑊𝑃𝐼𝑀𝑔 ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ × 𝐸𝑅̅̅ ̅̅ (a.35) 656 657 non-product related taxes are levied on the total value of labour and capital used by industry 658 (a.36): 659 660 𝑊𝐴𝑃𝑅𝑏 = (1 + 𝑡𝑏 𝑎𝑝𝑟) × 𝑊𝑁𝐴𝑃𝑅𝑏 (a.36) 661 662 income formation and distribution 663 tax revenue from product (𝑃𝑇𝑋) and non-product related taxes (𝑁𝑃𝑇𝑋) and social contribution 664 (𝑆𝑂𝐶𝐶𝑂𝑁) is given by equations a.37, a.38 and a.39, respectively: 665 31 666 𝑃𝑇𝑋 = ∑ (𝑡𝑔 𝑠𝑝 × 𝑊𝐷𝑈𝑔 × 𝐷𝑈𝑔 + 𝑡𝑔 𝑠𝑝 × 𝑊𝐸𝑋𝑔 × 𝐸𝑋𝑔)𝑔∈𝑆 (a.37) 667 668 𝑁𝑃𝑇𝑋 = ∑ (𝑡𝑏 𝑎𝑝𝑟 × 𝑊𝑁𝐴𝑃𝑅𝑏 × 𝐴𝑃𝑅𝑏)𝑏∈𝐵 (a.38) 669 670 𝑆𝑂𝐶𝐶𝑂𝑁 = 𝑟𝑠𝑜𝑐𝑐𝑜𝑛 × 𝐿𝐴𝐵𝐼 (a.39) 671 672 labour income (𝐿𝐴𝐵𝐼), capital income (𝐶𝐴𝑃𝐼) and capital depreciation (dep) are given by 673 equation a.40, a.41 and a.42 respectively: 674 675 𝐿𝐴𝐵𝐼 = 𝑊𝑇𝑃𝑅1 × 𝑇𝑃𝑅1 (a.40) 676 677 𝐶𝐴𝑃𝐼 = 𝑊𝑇𝑃𝑅2 × 𝑇𝑃𝑅2 (a.41) 678 679 𝐷𝐸𝑃 = 𝑟𝑐𝑎𝑝𝑑𝑒𝑝 × 𝐶𝐴𝑃𝐼 (a.42) 680 681 the income (𝐼𝑐𝑜𝑛), expenditure 𝐸𝑋𝑃𝑐𝑜𝑛 and savings (𝑆𝐴𝑉𝑐𝑜𝑛) of the private household are 682 given by equations a.43, a.44 and a.45 respectively: 683 684 𝐼𝑐𝑜𝑛 = (1 − 𝑟𝑠𝑜𝑐𝑐𝑜𝑛) × 𝐿𝐴𝐵𝐼 + (1 − 𝑟𝑐𝑎𝑝𝑑𝑒𝑝) × 𝐶𝐴𝑃𝐼 (a.43) 685 686 𝐸𝑋𝑃𝑐𝑜𝑛 = (1 − 𝑠𝑐𝑜𝑛) × 𝐼𝑐𝑜𝑛 (a.44) 687 688 𝑆𝐴𝑉𝑐𝑜𝑛 = 𝑠𝑐𝑜𝑛 × 𝐼𝑐𝑜𝑛 (a.45) 689 690 32 the income (𝐼𝑔𝑜𝑣), expenditure (𝐸𝑋𝑃𝑔𝑜𝑣) and savings (𝑆𝐴𝑉𝑔𝑜𝑣) of the public household are 691 given by equations a.46, a.47 and a.48, respectively: 692 693 𝐼𝑔𝑜𝑣 = 𝑃𝑇𝑋 + 𝑁𝑃𝑇𝑋 + 𝑆𝑂𝐶𝐶𝑂𝑁 (a.46) 694 695 𝐸𝑋𝑃𝑔𝑜𝑣 = (1 − 𝑠𝑔𝑜𝑣) × 𝐼𝑔𝑜𝑣 (a.47) 696 697 𝑆𝐴𝑉𝑔𝑜𝑣 = 𝑠𝑔𝑜𝑣 × 𝐼𝑔𝑜𝑣 (a.48) 698 699 total savings (a.49) equal the sum of the savings of the private household, public household 700 and capital depreciation minus the surplus on the balance of trade (bbar). as the balance of 701 trade is expressed in foreign price it must be multiplied by the exchange rate (er): 702 703 𝑆𝐴𝑉 = 𝑆𝐴𝑉𝑐𝑜𝑛 + 𝑆𝐴𝑉𝑔𝑜𝑣 + 𝐷𝐸𝑃 − 𝐵𝐵𝐴𝑅 × 𝐸𝑅̅̅ ̅̅ (a.49) 704 705 the surplus on the balance of trade (a.50) equals: 706 707 𝐵𝐵𝐴𝑅 = ∑ (𝑊𝑃𝐸𝑋̅̅ ̅̅ ̅̅ ̅̅ �̅� × 𝐸𝑋𝑔)𝑔∈𝐺 − ∑ (𝑊𝑃𝐼𝑀̅̅ ̅̅ ̅̅ ̅̅ �̅� × 𝐼𝑀𝑔)𝑔∈𝐺 (a.50) 708 709 the value of investment (𝐼𝑁𝑉𝑇) equals the value of the savings (a.51) but also the value of the 710 investment commodities (a.52). due to walras’ law, one equation must be dropped to keep the 711 model identified. in the operational model this is equation a.52. 712 713 𝐼𝑁𝑉𝑇 = 𝑆𝐴𝑉 (a.51) 714 715 𝐼𝑁𝑉𝑇 = 𝑊𝐼𝑁𝑉 × 𝐼𝑁𝑉 (a.52) 716 33 717 price numéraire 718 an applied general equilibrium model is homogenous of degree zero. this model selects the 719 exchange rate as price numéraire. 720 721 environment 722 co2, ch4, n2o emissions of an industry (𝐸𝑀𝐼𝑆𝑏) equals the share (amount) of emissions 723 (𝛿𝑏 𝑒𝑚𝑖𝑠) emitted by the industry(𝑌𝑏 𝑜𝑙𝑑). 724 725 𝛿𝑏 𝑒𝑚𝑖𝑠 = 𝐸𝑀𝐼𝑆𝑏 𝑜𝑙𝑑/𝑌𝑏 𝑜𝑙𝑑 (a.53) 726 727 𝐸𝑀𝐼𝑆𝑏 = 𝛿𝑏 𝑒𝑚𝑖𝑠 × 𝑌𝑏 (a.54) 728 729 welfare change 730 as a welfare measure, the laspeyres index of real income change is taken. this index compares 731 commodity bundles between two equilibria (e.g. before and after a policy change), using the 732 prices of the initial equilibrium (a.55). this welfare measure allows for the calculation of the 733 welfare effects of savings other than the private savings of which the underlying optimizing 734 behaviour is not modelled explicitly (i.e., capital depreciation, government deficit and the 735 balance of trade). since savings are equal to investments, the bundle of investment commodities 736 represent welfare derived from saving. 737 738 𝑊𝐸𝐿𝐹 = 739 ∑ (𝑊𝐶𝑂𝑁𝑔 𝑜𝑙𝑑 × 𝐶𝑂𝑁𝑔 𝑜𝑙𝑑)𝑔∈𝑆 +∑ (𝑊𝐺𝑂𝑉𝑔 𝑜𝑙𝑑 × 𝐺𝑂𝑉𝑔 𝑜𝑙𝑑)𝑔∈𝑆 + ∑ (𝑊𝐼𝑁𝑉𝑔 𝑜𝑙𝑑 × 𝐼𝑁𝑉𝑔 𝑜𝑙𝑑) −𝑔∈𝑆 740 ∑ (𝑊𝐶𝑂𝑁𝑔 𝑜𝑙𝑑 × 𝐶𝑂𝑁𝑔 ) −𝑔∈𝑆 ∑ (𝑊𝐺𝑂𝑉𝑔 𝑜𝑙𝑑 × 𝐺𝑂𝑉𝑔 )𝑔∈𝑆 − ∑ (𝑊𝐼𝑁𝑉𝑔 𝑜𝑙𝑑 × 𝐼𝑁𝑉𝑔 )𝑔∈𝑆 (a.55) 741 742 34 glossary 743 variables: 744 quantities 745 𝑌𝑏 aggregate output of industry b 746 𝐴𝐸𝑁𝑏 aggregate intermediate energy input in industry b 747 𝐴𝐼𝑁𝑏 aggregate intermediate materials input in industry b 748 𝐴𝑃𝑅𝑏 aggregate factor input in industry b 749 𝐼𝑁𝑏,𝑔 intermediate input g in industry b 750 𝑃𝑅𝑏,𝑗 labour (j=1) and capital (j=2) in industry b 751 𝑌𝑌𝑏,𝑔 output g of industry b 752 𝐷𝑃𝑔 domestic production of commodity g 753 𝐼𝑀𝑔 import of commodity g 754 𝑆𝑃𝑔 total supply of commodity g 755 𝐷𝑈𝑔 domestic use of commodity g 756 𝐸𝑋𝑔 export of commodity g 757 𝑇𝑃𝑅𝑗 ̅̅ ̅̅ ̅̅ ̅ labour (j=1) and capital (j=2) endowment in the economy 758 𝐶𝑂𝑁𝑔 consumer demand for commodity g 759 𝐺𝑂𝑉𝑔 government demand for commodity g 760 𝐼𝑁𝑉𝑔 demand of investment good g 761 𝐼𝑁𝑉 aggregate investment good 762 𝐸𝑀𝐼𝑆𝑏 emissions of industry b 763 764 prices (quantity symbols plus a w) 765 𝑊𝐴𝐸𝑁𝑏 price of aggregate intermediate energy input in industry b 766 𝑊𝐴𝐼𝑁𝑏 price of aggregate intermediate materials input in industry b 767 35 𝑊𝐴𝑃𝑅𝑏 price of aggregate factor input including tax in industry b 768 𝑊𝑁𝐴𝑃𝑅𝑏 price of aggregate factor input excluding the tax in industry b 769 𝑊𝐼𝑁𝑔 price of intermediate input g 770 wprb,j price of labour (j=1) or capital (j=2) used in industry b 771 𝑊𝐼𝑀𝑔 price of import of commodity g 772 𝑊𝑃𝐼𝑀𝑔 ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ world market price of import of commodity g 773 𝑊𝐷𝑃𝑔 price of domestic production of commodity g 774 𝑊𝑌𝑏 price of aggregate output of industry b 775 𝑊𝑆𝑃𝑔 price of total supply of commodity g 776 𝑊𝐷𝑈𝑔 price of domestic use of commodity g 777 𝑊𝐸𝑋𝑔 price of export excluding taxes and margins of commodity g 778 𝑊𝐺𝐸𝑋𝑔 price of export including taxes and margins of commodity g 779 𝑊𝑃𝐸𝑋𝑔 ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ world market price of export of commodity g 780 𝑊𝐶𝑂𝑁𝑔 price of commodity g demanded by the private household 781 𝑊𝐺𝑂𝑉𝑔 price of commodity g demanded by the public household 782 𝑊𝑇𝑃𝑅𝑗 price of labour (j=1) and capital (j=2) endowment in the economy 783 𝑊𝐼𝑁𝑉𝑔 price of investment good g 784 𝑊𝐼𝑁𝑉 price of aggregate investment good 785 786 other 787 𝐸𝑅̅̅ ̅̅ exchange rate 788 𝑀𝐴𝑅𝑔 value of demand/supply of margins by commodity g 789 𝐿𝐴𝐵𝐼 labour income 790 𝐶𝐴𝑃𝐼 capital income 791 𝐷𝐸𝑃 capital depreciation 792 36 𝐼𝑐𝑜𝑛 income of private household 793 𝐼𝑔𝑜𝑣 income of public household 794 𝐸𝑋𝑃𝑐𝑜𝑛 expenditure of private household 795 𝐸𝑋𝑃𝑔𝑜𝑣 expenditure of public household 796 𝑆𝐴𝑉𝑐𝑜𝑛 expenditure of private household 797 𝑆𝐴𝑉𝑔𝑜𝑣 expenditure of public household 798 𝑆𝐴𝑉 total savings 799 𝐵𝐵𝐴𝑅 surplus on the trade balance (in foreign prices) 800 𝐷𝐸𝑃 capital depreciation 801 𝐼𝑁𝑉𝑇 value of the investment 802 𝑃𝑇𝑋 value of the product related taxes 803 𝑁𝑃𝑇𝑋 value of the non-product related taxes 804 𝑆𝑂𝐶𝐶𝑂𝑁 social contributions 805 welf welfare change 806 807 fixed coefficients 808 𝛿𝑏 𝑒𝑚𝑖𝑠 coefficient dividing aggregate emissions by industry b 809 𝑚𝑔 𝑠𝑝 margins for commodity g 810 𝑡𝑔 𝑠𝑝 tax rate for product related taxes on commodity g 811 𝑡𝑏 𝑎𝑝𝑟 tax rate for non-product related taxes in industry b 812 𝛿𝑏,𝑔 𝑌 coefficient dividing aggregate output into outputs g in industry b 813 𝛿𝑔 𝑖𝑛𝑣 coefficient dividing aggregate investment good into investment good g 814 𝑠𝑐𝑜𝑛 savings rate of the private household 815 𝑠𝑔𝑜𝑣 savings rate of the public household 816 𝑟𝑠𝑜𝑐𝑐𝑜𝑛 share of labour income that goes to social contributions 817 37 𝑟𝑐𝑎𝑝𝑑𝑒𝑝 capital depreciation as share of capital income 818 819 sets and subsets: 820 g: goods, g = 1 to 60 821 b: industries, b = 1 to 60 822 j: factors, j = 1 (labour) j = 2 (capital) 823 :gsen  subset energy commodities: g = 24,25 824 :gsmat  subset materials: g = 1,…,23,26,…,60 825 826 miscellaneous 827 gov = public household; con = private household; inv = investment; en = energy; old = base 828 year value; ces = constant elasticity substitution; cet = constant elasticity of 829 transformation 830 831 bold printed variables represent a vector; variables with a bar represent exogenous variables. 832 833 appendix b aggregation level of commodities and industries in supply-use tables 834 cpa/nace code commodity/industry a01 crop and animal production, hunting and related service activities a02 forestry and logging a03 fishing and aquaculture b mining and quarrying c10_12 manufacture of food products, beverages and tobacco products c13_15 manufacture of textiles, wearing apparel and leather products 38 c16 manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials c17 manufacture of paper and paper products c18 printing and reproduction of recorded media c19 manufacture of coke and refined petroleum products c20 manufacture of chemicals and chemical products c21 manufacture of basic pharmaceutical products and pharmaceutical preparations c22 manufacture of rubber and plastic products c23 manufacture of other non-metallic mineral products c24 manufacture of basic metals c25 manufacture of fabricated metal products, except machinery and equipment c26 manufacture of computer, electronic and optical products c27 manufacture of electrical equipment c28 manufacture of machinery and equipment n.e.c. c29 manufacture of motor vehicles, trailers and semi-trailers c30 manufacture of other transport equipment c31_32 manufacture of furniture; other manufacturing c33 repair and installation of machinery and equipment d35-1 biobased energy d35-2 non-biobased energy e36 water collection, treatment and supply e37_39 sewerage; waste collection, treatment and disposal activities; materials recovery; remediation activities and other waste management services f construction 39 g wholesale and retail trade h49 land transport and transport via pipelines h50_51 water and air transport h52 warehousing and support activities for transportation h53 postal and courier activities i accommodation and food service activities j58 publishing activities j59_60 motion picture, video and television program production, sound recording and music publishing activities; programming and broadcasting activities j61 telecommunications j62_63 computer programming, consultancy and related activities; information service activities k64 financial service activities, except insurance and pension funding k65 insurance, reinsurance and pension funding, except compulsory social security k66 activities auxiliary to financial services and insurance activities l68 real estate activities m69_70 legal and accounting activities; activities of head offices; management consultancy activities m71 architectural and engineering activities; technical testing and analysis m72 scientific research and development m73 advertising and market research m74_75 other professional, scientific and technical activities; veterinary activities n77 rental and leasing activities n78 employment activities 40 n79 travel agency, tour operator reservation service and related activities n80_82 security and investigation activities; services to buildings and landscape activities; office administrative, office support and other business support activities o public administration and defense; compulsory social security p education q86 human health activities q87_88 social work activities r90_92 creative, arts and entertainment activities; libraries, archives, museums and other cultural activities; gambling and betting activities r93 sports activities and amusement and recreation activities s94 activities of membership organizations s95 repair of computers and personal and household goods s96_t other personal service activities 835 appendix c industries ranked according to the largest shares in supply and use of biobased 836 energy commodities 837 suppliers of energy commodities* users of energy commodities** forestry and logging manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials fishing and aquaculture manufacture of chemicals and chemical products manufacture of machinery and equipment n.e.c. residential care activities and social work activities without accommodation 41 rental and leasing activities sewerage, waste management, remediation activities manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials manufacture of chemicals and chemical products manufacture of furniture; other manufacturing retail trade, except of motor vehicles and motorcycles * 90%< 838 **30%< 839 source: author`s calculations based on eurostat, 2021 840 841 appendix d substitution (sigma) and transformation (omega) elasticities 842 symbol function value sigmay (b) ces for output 0.4 sigmapr (b) ces for aggregate primary input 0.4 sigmae (b) ces for aggregate energy input 1.5 sigmax (b) ces for aggregate intermediate input 0.4 sigmadoms (g) ces for domestic supply 1.5 omegadomd (g) cet for domestic demand -1.5 843 844 © 2025 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae bio-based and applied economics bae bio-based and applied economics 14(2): 3-4, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-18640 editorial reflections and new directions: an editorial retrospective and the launch of our new policy paper section fabio bartolini, silvia coderoni over the past two years, bio-based and applied economics (bae) has consolidated the journal role as a platform for rigorous and innovative research at the intersection of economics, sustainability, agriculture, and biobased systems. in a period marked by significant global transitions, ranging from climate change and biodiversity loss to disruptive technological shifts and geopolitical instabilities, our community of scholars has provided crucial insights into how bio-based economies can adapt, evolve, and contribute to sustainable development. as a result of this commitment, we also received two important recognitions of the quality of the works published by the journal: bae is now ranked q1 in several subject categories and has been included in the list of “class a” journals for economics and political economy scientific sectors in italy. this editorial aims to offer both a retrospective overview of the journal’s main contributions over the last two years and a prospective look at new directions. since our appointment as editors in chief, bae has hosted a rich variety of contributions, spanning theoretical analyses, empirical applications, and interdisciplinary approaches. among the most recurring themes we want to mention: 1. sustainability and agricultural economics – numerous papers have examined how farming systems can balance productivity with ecological stewardship. topics such as agroecological transitions, carbon farming, and sustainable business models have been central. 2. circular and bio-based economies – research has increasingly focused on the transition towards circular models, with attention to waste valorisation, renewable bio-resources, and innovations in supply chains. 3. technological innovation and agriculture 4.0 – several studies have addressed the adoption and socio-economic impacts of precision agriculture, digitalisation, and smart farming technologies, especially in mediterranean and european contexts. 4. food systems and consumer behaviour – the journal has published relevant work on the transformation of food systems, including consumer attitudes towards sustainability labels, alternative proteins, and value chain governance. 5. climate and resource economics – contributions have deepened our understanding of climate change mitigation and adaptation strategies, the economics of soil and water management, and policy instruments to support resilience. 6. trade and food security – contribution on investigating how the evolving trade dynamics, supply chain vulnerabilities, and geopolitical tensions shape the capacity of agri-food systems to ensure availability, accessibility, and stability of food supplies, with particular attention to both global interdependencies and local resilience. 7. agricultural and rural development policies –contributions have emphasised the role of agricultural and rural development policies in balancing productivity and sustainability, and supporting farmers’ livelihoods within the broader transformation of rural areas. collectively, these research lines illustrate the breadth and relevance of bae’s mission: fostering knowledge that bridges economic science with pressing societal challenges. beyond thematic contributions, bae has also been a space for methodological innovation. authors have employed diverse approaches, from advanced econometric models to participatory scenario building, from experimental economics to multi-criteria decision analyses. these methods have enriched our ability to capture the complexity of bio-based and agricultural systems. equally important has been the increasing internationalisation of the journal. in the past two years, bae has attracted submissions from across europe, latin america, asia, and africa, reflecting the global relevance of bio-based transitions. the diversity of contexts-from european union policy frameworks to local farmhttps://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae https://doi.org/10.36253/bae-18640 4 bio-based and applied economics 14(2): 3-4, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-18640 fabio bartolini, silvia coderoni ing practices in emerging economies-has offered fertile ground for comparative analysis and cross-learning. the growing engagement of early-career researchers has further expanded the vibrancy of our community. their contributions, often interdisciplinary and problem-oriented, have underscored the evolving character of applied economics in addressing twenty-first-century challenges. introducing the new policy paper section in bio-based and applied economics the global challenges surrounding climate change, biodiversity loss, food security, and sustainable rural development have increasingly highlighted the need for a stronger dialogue between academic research and policymaking. bae has traditionally provided a platform for scholarly contributions that advance our understanding of agricultural, food, resource, and environmental economics. as these fields continue to expand and evolve, also the channels through which research is communicated, interpreted, and applied should. with this issue, we are pleased to launch a new section of the journal dedicated to policy papers. this section is designed to bridge the gap between academic research and policy relevance, offering a space for concise, rigorous, and practice-oriented contributions that speak directly to current policy debates. the section will provide a venue for evidence-based, timely, and practice-oriented analyses with direct implications for decision-making. while traditional research articles remain the backbone of scholarly communication, their structure and orientation often limit their accessibility to policymakers, practitioners, and wider stakeholders. policy papers provide a complementary format, explicitly tailored to highlight the implications of economic research for policy design and evaluation. they aim not only to report findings, but also to translate them into actionable insights that can inform real-world decision-making processes. the agricultural and bio-based sectors are at the forefront of major societal transitions: the decarbonisation of economies, the digitalisation of production systems, the sustainable management of natural resources, and the pursuit of food system resilience. in all these areas, evidence-based policy support is indispensable. policy papers published in bae will provide an opportunity for researchers to directly engage with these pressing challenges, while ensuring that scientific rigour remains the foundation of the journal’s reputation. scope and characteristics policy papers are expected to be shorter than research articles, typically ranging from 4,000 to 6,000 words, and should prioritise clarity, accessibility, and relevance over technical detail. while empirical evidence and methodological robustness are valued, the primary focus lies in the articulation of policy problems, the synthesis of available evidence, and the identification of feasible policy options. submissions may take different forms, including: – policy analyses, which assess the effectiveness of existing policies and highlight opportunities for reform. – evidence syntheses, which bring together insights from multiple studies to inform ongoing policy debates. – forward-looking perspectives, which explore new policy challenges emerging from technological, environmental, or social transitions. – comparative evaluations, which examine how different policy approaches perform across contexts and jurisdictions. by launching the policy paper section, bio-based and applied economics reinforces its mission of supporting both academic excellence and societal impact. we view this initiative as a contribution to a broader knowledge exchange ecosystem, in which researchers, decision-makers, and practitioners engage in a productive dialogue. our aim is to stimulate a culture of evidence-informed policymaking, grounded in applied economics but open to interdisciplinary collaboration. we hope that our readership will find in the policy paper section a source of inspiration, practical knowledge, and critical reflection on the directions of agricultural, environmental, and bio-based policy. in establishing this new section, we reaffirm the belief that research must not remain confined to academic circles but must actively contribute to shaping a more sustainable, fair, and resilient future. the policy paper initiative represents a concrete step in this direction, and we look forward to the contributions and debates it will generate in the coming years. the first paper of the section is published in this same issue and is the synthesis of the study day, which took place in rome on 3rd april 2025, when more than twenty researchers have discussed the relevance and the implications of the commission’s communication a vision for agriculture and food” for italy (arfini et al., 2025). as we look back with pride at the journal’s recent trajectory and forward with ambition, we remain grateful to our authors, reviewers, and readers. their dedication and engagement are the pillars of bae’s success. we invite the community to embrace this new phase, contributing not only new research but also impactful policy insights for a sustainable bio-based future. https://doi.org/10.36253/bae-18640 reflections and new directions: an editorial retrospective and the launch of our new policy paper section silvia coderoni, fabio bartolini where is the italian agriculture heading? a discussion in light of the prospects for the future cap filippo arfini1*, fabio bartolini2, anna carbone3, tatiana castellotti4, silvia coderoni5, raffaele cortignani6, raffaele d’annolfo4, giovanni dara guccione4, michele donati1, francesca galli7, roberto henke4, giampiero mazzocchi4, alessandro monteleone4, towards the knowledge and innovation system for the bioeconomy? giacomo maria rinaldi*, davide viaggi impact of weather variability on crop yields and land use dynamics in eastern india: shortand long-term effects pratap kumar jena1, kirtti ranjan paltasingh2, souryabrata mohapatra3,,*, ashok mishra4 rebuilding strategies for food self-sufficiency: portugal’s past patterns and future ambitions alexandre macedo joão1, dulce freire2, humberto rocha3, joana dias4 from subsidy to land rent: how production-linked payments shape factor remuneration in agriculture adrian sadłowski expenditure allocation for rural development interventions: main trends and patterns in the choices of the italian regions under the cap 2023-2027 tobia minuzzo, francesco pagliacci* inequality and concentration in farmland production and size: a regional analysis for the european union from 2010 to 2020 simone boccaletti 1, paolo maranzano 2*, miguel viegas 3 1 department of economics, management and statistics, university of milano-bicocca, milano, italy & osservatorio o-fire, university of milano-bicocca, milano, italy; orcid: https://orcid.org/0000-0002-69725005 2 department of economics, management and statistics, university of milano-bicocca, milano, italy & fondazione eni enrico mattei, milano, italy; orcid: https://orcid.org/0000-0002-9228-2759 3 research unit on governance, competitiveness and public policies (govcopp), university of aveiro, portugal, orcid: https://orcid.org/0000-0003-1390-4992 * corresponding author at paolo.maranzano@unimib.it corresponding author: a.adenuga@qub.ac.uk this article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the version of record. please cite this article as: boccaletti s., maranzano p., viegas m. (2025). inequality and concentration in farmland production and size: a regional analysis for the european union from 2010 to 2020, just accepted. doi: 10.36253/bae-17797 abstract this article investigates the phenomenon of market concentration in the european agricultural sector from 2010 to 2020 at the regional level. to this end, we exploit the spatiotemporal dynamics of the gini concentration index for farmland and production. first, we examine the variability within and between countries to assess whether the industry has suffered from increasing concentration during the study period. the next objective is to identify the empirical relationship between the two indicators and determine whether spatial spill-overs occur in terms of market concentration across regions. our findings confirm the fragmented picture of the european agricultural sector, which is characterized by a high degree of heterogeneity within and across countries as concerns both land concentration and production concentration. in addition, we confirm the existence of a positive association between the two concentration measures. lastly, the remarkable spatial autocorrelation we observe supports the hypothesis that adjacent regions tend to register similar levels of concentration, generating clusters of regions with either high or low values. https://orcid.org/0000-0002-6972-5005 https://orcid.org/0000-0002-6972-5005 https://orcid.org/0000-0002-9228-2759 https://orcid.org/0000-0003-1390-4992 mailto:paolo.maranzano@unimib.it 1. introduction understanding the dynamics of industry concentration in the agricultural sector is crucial for policymakers to determine where and how policies aimed at supporting small-scale farms might be most effective. as a matter of fact, the agricultural sector is a key sector that may help withstand and recover from the impact of economic downturns, especially in rural areas (e.g., see giannakis & bruggeman, 2018). at worldwide level, the picture is very fragmented and, consequently, still opaque. lowder et al. (2021) provide an overview of the number of farms by size at global scale, pointing out that small farms (less than 5 hectares) represent the vast majority of firms, but have less than 20% of the overall farmland. giller et al. (2021) provide a similar picture, although they recognize that, since production costs and selling prices are determined by large-scale markets, the danger in the next decades is the increase in the marginalization of smallholder farmers, and additional and excessive dependence on very large farms. as of today, the picture of the market is the same as at the beginning of the millennium: the distance described in von braun (2005) between the “marginal” farm (small, with low level of sustainability and disconnected to science) and the “dominant” farm (large, more sustainable and users of advanced science) has not closed. in the european agricultural sector, two large bodies of evidence have emerged. first, research has shown that farm and company structures vary considerably across and within regions and countries (guarín et al., 2020). a comprehensive review of the scholarship reveals that numerous historical, cultural, geographical, economic, and political factors influence these disparities (zimmermann et al., 2009). second, the european union’s (eu) agricultural sector has undergone a significant transformation over the last decades, marked by a steady concentration of agricultural holdings. because larger farms have the potential to achieve economies of scale, which can reduce production costs and, thus, increase overall production, this concentration may, in theory, increase efficiency. nonetheless, the intensive agricultural practices associated with larger farms can give rise to issues of soil degradation, water pollution, and biodiversity loss (fassò et al., 2023). moreover, the decline of small farms can have adverse effects on rural communities through job losses and the deterioration of social cohesion. between 2010 and 2020, the number of eu farms fell from 12 to 9 million, while output rose from €304 to €360 billion (eurostat, 2024a), indicating growing concentration and structural change. note. figure 1 shows values referring only to countries for which data is available for both 1990 and 2020. figure 1 visually represents the concentration of agricultural sectors within the eu. from 1990 to 2020, the aggregate number of farms fell substantially in most eu countries. importantly, the average area per farm expanded significantly. several factors contribute to this trend. global competition and volatile market prices push smaller farms towards consolidation or closure. meanwhile, larger farms can often afford advanced machinery and automation, leading to increased efficiency and productivity. in parallel, younger generations are less likely to pursue careers in agriculture, leading to a decline in the available workforce for smaller farms. lastly, eu policies, while not directly aimed at concentration, affect land distribution and might inadvertently favor larger farms or smaller ones. this raises questions about how the structure (e.g., the type of farming and average size) of farms has changed and whether this change has been uniform or heterogeneous in europe. in this paper, we investigate the phenomenon of market concentration in the eu agricultural and livestock farming industry between 2010 and 2020. to this end, we use regional (nomenclature of territorial units for statistics classification [nuts-2]) and national (nuts-0) level data from eurostat (eurostat, 2024a) to examine the spatiotemporal dynamics of concentration measures for agricultural standard output (as a proxy for economic size) and farmland (as a proxy for physical size) of european farms. our research focuses on the evolution of the gini index (giorgi & gigliarano, 2017) within and between countries to assess whether the european agricultural market has seen increased concentration of power in fewer but larger farm holdings during the past decade. the overarching aim of the study can be declined into two research questions: 1) what temporal and spatial patterns characterize the levels of concentration of farmland and agricultural production? specifically, is there a common trajectory between (i.e., at the national level) and within (i.e., at the regional level) european countries in terms of concentration measures between 2010 and 2020? 2) what empirical relationship exists between the concentration of agricultural farmland and the concentration of standard output? specifically, are the two concentration measures positively correlated, such that an increase in the physical concentration of agricultural businesses is associated with an increase in their economic concentration, and vice versa? to the best of our knowledge, the literature on territorial disparity in the european agricultural sector is still developing. one early notable paper relevant to our analysis was authored by vollrath (2007) and showed that land inequality is inversely related to productivity. in other words, a decrease in the gini index of land distribution substantially increases land productivity. this is because farms that operate with family labor benefit from productivity advantages, and more equal land distribution equalizes the marginal product of labor across farms. in this regard, the existence of a sizeable effect of land inequality on output is a symptom of economic inefficiencies in the agricultural sector. the present paper contributes to the literature in several important ways. first, whereas prior studies have examined land and output concentration at the national or coarse regional levels, our analysis exploits regional data for the eu27 countries over a decade (2010–2020) to offer a finergrained and more spatially detailed picture of market concentration trends. second, unlike most of the existing research, we examine both farmland and output (production) concentration jointly, which allows us to explore the relationship between physical and economic size concentration. third, we incorporate exploratory spatial data analysis techniques to assess the presence of spatial dependence and clustering in concentration patterns, an approach largely overlooked in the current literature. finally, we provide a temporal and spatial mapping of concentration trends, enabling a dynamic assessment of how concentration evolved over the post–financial crisis decade and in the context of ongoing common agricultural policy (cap) reforms. the remainder of this paper is organized as follows. in section 2, we briefly synthesize relevant issues raised in the agricultural economics literature and directly related to market concentration. in particular, we focus on territorial heterogeneities and the unequal distribution of production, profits, and resources among farms, as well as their economic consequences. in section 3, we discuss the role of the gini index in quantifying market concentration and the statistical tools we used to analyze spatial patterns of concentration. we also describe the regionalized eurostat database on agricultural indicators we drew from to compute the gini index for production (as a proxy for the economic size) and farmland (as a proxy for physical size) of eu farm holdings. in section 4, we discuss the empirical findings derived from the data analysis of available data on european regions from 2010 to 2020. concluding remarks are provided in section 5. 2. background on distributional issues in the european agricultural sector disparities in the agricultural sector, particularly in land ownership and productivity, have long been recognized as a major factor shaping economic and social outcomes in rural areas. unequal access to productive land contributes to broader divisions in income, opportunity, and regional development (wegerif & guereña, 2020). in recent decades, the growing consolidation of farmland has renewed concerns about equity, efficiency, and sustainability in european agriculture (van der ploeg et al., 2015). the structure of agricultural holdings in the eu reflects a complex mix of historical, geographical, economic, and institutional factors. post-war land reforms in eastern europe (e.g., poland, east germany, and czechoslovakia) broke up large estates, while countries like italy implemented milder redistributive measures in their southern regions (bonanno, 1988; mathijs, 2018; van der ploeg et al., 2009). these legacies continue to shape farm size and ownership patterns today. in many western european countries, family-owned farms dominate, while parts of eastern and southern europe show more dualistic or fragmented structures. natural conditions also contribute to heterogeneous land concentration. soil quality, terrain, and climatic suitability influence not only the types of crops or livestock but also the potential for economies of scale (lobley & winter, 2009). demographic pressures, such as rural depopulation and aging farmer populations, further affect the viability of smaller holdings (eurostat, 2022). economic and institutional drivers also play a key role. technological change, globalization, and eu policies, though not directly aimed at consolidation, may have disproportionately benefited larger farms. notably, investments in capital-intensive machinery and access to financial markets tend to favor actors with greater scale and bargaining power, and policy instruments such as areabased subsidies may inadvertently accelerate concentration processes (nolte & ostermeier, 2017). several studies have linked land inequality to productivity gaps. vollrath (2007) found that more equal land distribution tends to increase land productivity, particularly in systems that rely on family labor. in his cross-country analysis, a 0.16 reduction in the farmland gini coefficient was associated with an 8.5% increase in productivity. this relationship reflects the inefficiency of highly unequal land distribution, which often fails to allocate resources to their most productive use. other research has explored spatial patterns of productivity. ezcurra et al. (2008) documented significant spatial dependence in agricultural performance across european nuts-2 regions, finding that neighboring regions tend to exhibit similar productivity levels. giannakis and bruggeman (2018) added a classification of agricultural systems (e.g., field crops, permanent crops, livestock) and revealed considerable heterogeneity within and between these systems, providing evidence that specialization and geography play intertwined roles. recently, tóth (2023) insisted on the importance of territorial capital in shaping land use outcomes, while guarín et al. (2020) proposed new typologies of small farms to better understand the diversity and challenges facing this segment of the sector. these works converge on the idea that agricultural disparity is a multi-scalar issue, visible both between countries and within regions, and requires nuanced measurement and analysis. in this context, our study adds to the literature by jointly analyzing the spatiotemporal dynamics of land and output concentration, using disaggregated nuts2 level data from across the eu. while much of the existing research focuses either on productivity or land-use patterns, we emphasize distributional concentration and its spatial interdependence, which have been underexplored in empirical assessments of european agriculture. 3. exploring agricultural market concentration patterns in european regions using the gini index and exploratory spatial data analysis we considered regionalized data about the agricultural market in europe provided by eurostat (2024b) according to the 2010 nuts. in particular, we examined the regional (nuts-2) and national (nuts-0) level information on eu farms contained in the “main farm indicators by nuts 2 regions” open database (eurostat, 2024a). the database features indicators on the agricultural industry in europe for 2010, 2013, 2016, and 2020.1 among the full set of available data, we extracted a subsample concerning the following regional quantities: • overall number of agricultural holdings or farms (measured as headcount) • utilized agricultural land (in hectares) • standard output of agricultural production (in euros) given the exploratory nature of this paper, we did not consider subclassifications of farms based on their productive specialization, such as organic producers or livestock farms. nevertheless, subsequent research must incorporate this information to expand the findings and provide a more comprehensive characterization of the market concentration dynamics in the agricultural sector. because our objective was to consider the largest possible spatiotemporal sample, we included european regions with complete (non-missing) information for the entire period under study. although more recent classifications were available, we selected the nuts 2010 nomenclature due to its comprehensive coverage of the 27 eu member states during the 2010–2020 period. the selected dataset contains comprehensive data from 236 regions. 1 as of august 2025, the “main farm indicators by nuts 2 regions” open database also contains information for 2010, 2013, 2016, 2020, and 2023. for 2023, a dedicated analysis of the effects of the major geopolitical events that occurred in previous years on the european agricultural market would be required. such events constitute insightful research opportunities that should be seized. we computed concentration indices for both farmland (i.e., utilized agricultural area) and production (i.e., standard output of farming), considering the stratification of agricultural farms into 𝐾 = 11 classes2 of economic size based on increasing standard output values. taking advantage of the number of farms and the cumulated standard output for each stratum in a region, we calculated the gini indices for production and farmland following cerqueti et al. (2024), which rely on the gini index specification for grouped data described in brown (1994). specifically, let 𝑑 be the index for production (𝑑 = 𝑃) and farmland (𝑑 = 𝐿), and let 𝑗 = 1, … , 𝐾 be the index for the economic strata. we then computed the gini index for each region 𝑠 = 1, … , 236 as follows: 𝑮𝒊𝒏𝒊𝒅𝒔 = 𝑵𝒔 𝑵𝒔 − 𝟏 [𝟏 − ∑[(𝑸𝒅𝒋 + 𝑸𝒅𝒋−𝟏) × (𝑭𝒅𝒋 − 𝑭𝒅𝒋−𝟏)] 𝑲 𝒋=𝟏 ] where 𝑑 = {𝑃, 𝐿} represents the production (𝑃) or farmland (𝐿) values, 𝑁𝑠 is the total number of farms in region 𝑠, 𝑄𝑑𝑗 = ∑ 𝑞𝑑𝑖 𝑗 𝑖=1 is the cumulative proportion of production or farmland up to the 𝑗-th ordered class (with 𝑞𝑑𝑖 being the regional share of production or farmland associated with the 𝑖-th ordered class over the total), and 𝐹𝑑𝑗 = ∑ 𝑓𝑑𝑖 𝑗 𝑖=1 is the cumulative proportion of farm holding up to the 𝑗-th ordered class (with 𝑓𝑑𝑖 = 𝑁𝑠𝑖 𝑁𝑠 being the regional share of farms associated with the 𝑖-th ordered class over the total number of farms in the region under the constraint ∑ 𝑁𝑠𝑖 𝐾 𝑖=1 = 𝑁𝑠). in short, 𝐺𝑖𝑛𝑖𝑃𝑠 corresponds to the gini index for the production (standard output) in region 𝑠, while 𝐺𝑖𝑛𝑖𝐿𝑠 is the gini index for farmland in region 𝑠. given that we utilized yearly data for 2010, 2013, 2016, and 2020, we computed a gini index for each year and region. 2 the eurostat database used to build the area-level concentration measures refers to a large set of farm indicators by organic farming, utilized agricultural area, economic size (i.e., standard output), and type of agricultural holding aggregated at the regional (nuts-2) level. eurostat classifies farms by standard output as follows: 0 euros; over 0 euros to less than 2,000 euros; from 2,000 to 3,999 euros; from 4,000 to 7,999 euros; from 8,000 to 14,999 euros; from 15,000 to 24,999 euros; from 25,000 to 49,999 euros; from 50,000 to 99,999 euros; from 100,000 to 249,999 euros; from 250,000 to 499,999 euros; 500,000 euros or over. from a statistical perspective, the gini index can be interpreted as a measure of either statistical dispersion (i.e., variability) or statistical concentration (giorgi & gigliarano, 2017). whereas the latter measures the agricultural market’s concentration in terms of the economic capacity of farms, the latter gauges it in terms of land owned by farm holding (i.e., physical size). by definition, the gini index is a normalized metric lying between 0 and 1, or, equivalently, between 0 and 100 if rescaled as a percentage (giorgi & gigliarano, 2017). a gini index value equal to 0 is expected when all farms have the same standard output or the same hectares of land (i.e., the perfect equal distribution scenario). conversely, a value close to 1 (or 100) represents a situation of high concentration in which almost all the land or standard output is owned by a very restricted number of farm holdings, leaving only a very small amount to the remaining companies. in the extreme case of a gini index value of exactly 1 (or 100), the entire agricultural land (standard output) of a region would be owned (produced) by a single farm holding (i.e., the full concentration scenario). in this paper, we describe the spatial and temporal evolution of agrobusiness concentration by comparing the two gini indices to establish an empirical relationship between the concentration of production (as a proxy for the farms’ economic size) and the concentration of farmland (as a proxy for the farms’ physical size). to do so, we employed a sample covering a time frame subsequent to that of ezcurra et al. (2008), namely, 2010 to 2020, and all regions of the eu27 countries. we performed an exploratory analysis by studying the evolution through space and time of the linear correlation between the concentration of agricultural land and the concentration of agricultural production. we also investigated the temporal dynamics of spatial autocorrelation measures that can describe the influence of the gini index recorded in neighboring regions on that observed in each european region to assess the presence of spatial spillovers in market concentration across regions. although our study does not directly imply any causal relationship between physical concentration and economic concentration, the next paragraphs will thoroughly examine how these two measures may evolve simultaneously in the eu agricultural market, without assuming any causal relationships. when examining the correlation between concentration measures, a direct and positive relationship is generally expected; however, careful consideration is still required. clearly, if the gini index value of farmland is equal to 1 (i.e., one firm owns all the land in a region), a production gini index value of 1 is directly implied. on the contrary, a gini index of farmland close to 0 does not directly imply a low gini index value of production. two points support this view. first, even if the land is homogeneously distributed, some of it could be “inactive,” leading to an uneven distribution of standard output. second, even if most of the land is productive, two regions with the same land gini index value could have higher or lower levels of production concentration for different reasons: for instance, farms in one region could have higher productivity levels due to differences in land-use productivity (e.g., organic vs. non-organic production, crops vs. livestock, etc.) but also due to the cost structure and market characteristics of the farms. finally, the link between the two levels of inequality depends primarily on the possible presence of economies of scale, scope, and capacity in the agricultural sector. table 1 displays the conceptual framework, highlighting how different structural configurations of this sector may arise. table 1: conceptual framework high concentration/disparity in the standard output low concentration/disparity in the standard output high concentration/ disparity in land distribution there is substantial variation in both farm size and standard output. higher output levels are associated with greater production capacity. there may be economies of scale and scope, with larger farms driving total sector output. disparity in standard output is not driven by unequal land size. this may be due to capacity constraints unrelated to the total size of the farm, or to industry and regional specialization, or to economies of scope favoring smaller farms (while economies of scale may be neither relevant nor present). low concentration/ disparity in land distribution although the size of the land is homogeneous, productivity and standard output vary substantially. this may reflect differentiation within the sector and thus farm specialization. although farms have the same land size, they produce and focus on different products, resulting in different standards of output. scale and farm capacity do not drive the disparity in the standard output. the region has farms of similar size that produce a similar standard output. this may indicate productivity homogeneity within the region. as shown in table 1, we anticipated that, at the nuts-2 regional level, most regions would fall into either the north–west configuration (i.e., high disparities in both land concentration and standard output) or the south–east configuration (i.e., low disparities in both indicators). the geographical configuration we considered distinguishes between 236 regions in the eu, and each region may cover a substantial amount of land. by contrast, the north–east configuration (i.e., low disparity in standard output combined with high disparity in land concentration) and the south configuration (i.e., high disparity in standard output but low disparity in land concentration) are likely to occur in smaller regions, where specialization may play a more significant role. therefore, we expected a positive association between the two disparity indicators given that our analysis concerns large regions (defined at the regional level). this is in line with the possible dynamics of both gini indices. a decline in the gini index value for farmland could mean either that some micro farms are acquiring other small plots of land and becoming larger or that a large area of land has been sold to a number of other farms. the possible change in the gini index for standard output could be driven by potential economies of scale following this reduction in land concentration. if economies of scale are relevant, a decrease in land concentration could lead to a decrease in the heterogeneity of standard output as farms become more similar in terms of physical size. the spatial dependence analysis revealed local patterns in the spatial distribution of the agricultural market, which result in the coexistence of sub-areas with nonhomogeneous characteristics (see, for instance, ezcurra et al., 2008). ezcurra et al.’s (2008) findings regarding agricultural productivity suggest at least two sub-areas: north-central europe, oriented toward animal farming, with a relatively large share of cereal and forage crops, and southern europe, specializing in the production of vegetables and permanent crops. this confirms the hypothesis of dualism in the european agricultural market discussed by kearney (1991) and gutierrez (2000), which also provide evidence of spatial dependence among regions between 1980 and 2001; that is, neighboring regions tended to register similar levels of gross value added per worker in the agricultural sector. to make our findings comparable with the existing literature, we employed exploratory spatial data analysis techniques (elhorst, 2010; lesage, 2008) designed to measure the degree of spatial dependence of agricultural concentration and its temporal evolution. in particular, we estimated the dependence between regions using moran’s statistic for both global and local spatial autocorrelation (anselin, 1995). 4. empirical results 4.1 national and regional patterns of farmland and production concentration we begin the empirical analysis by presenting a combination of country-level and regionallevel evidence regarding the spatiotemporal dynamics of the gini indices for production and farmland. the primary findings are outlined in table 2, which synthesizes the evolution of the phenomenon between 2010 and 2020, emphasizing country-specific characteristics with respect to temporal and territorial (i.e., intra-country) dynamics. a close examination of the available data revealed significant variations among european countries. specifically, farmland exhibited lower concentrations than standard output. this suggests that overall land productivity was characterized by significant heterogeneity both between and within countries. table 2: summary of the main empirical results on the gini index for farmland and production farmland gini index production gini index country time variation intra-country differences time variation intra-country differences austria (at) constant after 2013 some differences detected, but province heterogeneity decreases over time constant after 2013 minor differences detected belgium (be) negligible minor differences detected negligible minor differences detected bulgaria (bg) decreasing in the time horizon considered minor differences detected increasing until 2016, then slightly decreasing between 2016 and 2020 minor difference detected, but province heterogeneity decreases over time cyprus (cy) increasing until 2016, then slightly decreasing between 2016 and 2020 / slight increase over time / czech republic (cz) constant over time minor differences detected slight increase over time minor differences detected germany (de) slight increase over time some differences detected, especially between east and west germany increase over time some differences detected, especially between east and west germany denmark (dk) increasing over time / increase over time / estonia (ee) considerable time variations / constant until 2016, then slightly decreasing / greece (el) considerable decrease between 2013 and 2016 some differences detected small time variations detected; jump in 2020 minor differences detected spain (es) slight increase until 2016 considerable differences, especially between northern regions and southern regions constant over time minor/negligible differences finland (fi) increase over time (jump in 2020) minor/negligible differences slight increase over time minor/negligible differences france (fr) slightly increasing until 2016, then sharply decreasing; large volatility minor differences detected, but province heterogeneity seems to decrease over time slightly decrease over time minor differences detected croatia (hr) considerable time variations considerable differences, but province heterogeneity seems to decrease over time increasing over time considerable differences detected hungary (hu) constant until 2016, then sharply decreasing minor/negligible differences small time variations detected, but very similar 2010 and 2020 minor/negligible differences ireland (ie) small time variations detected minor/negligible differences small time variations detected, but very similar 2010 and 2020 minor/negligible differences italy (it) sharp decrease between 2010 and 2013 regional differences detected, but not differentiated between northern and southern italy time variations detected, but very similar 2010 and 2020 levels regional differences detected, but not differentiated between northern and southern italy lithuania (lt) slightly increasing over time / slightly increasing over time / luxembourg (lu) constant over time / slightly increasing over time / latvia (lv) strongly increasing over time / slightly increasing over time / malta (mt) strongly decreasing over time; large drop in 2020 / decreasing until 2016, then increasing / netherlands (nl) small time variations detected; noticeable drop between 2013 and 2016 minor/negligible differences decreasing until 2016, then constant minor/negligible differences poland (pl) constant over time considerable differences detected slightly increasing over time minor differences detected portugal (pt) constant over time considerable differences detected slightly increasing over time minor differences detected romania (ro) large and positive time variations considerable differences detected increasing over time considerable differences detected sweden (se) increasing over time negligible differences increasing over time negligible differences slovenia (si) small time variations detected negligible differences constant until 2016, then slightly increasing negligible differences slovakia (sk) slightly decreasing over time negligible differences slightly decreasing over time negligible differences figure 2 shows the minimum, average, and maximum gini index at the country level for both standard output (left panel) and agricultural farmland (right panel). we computed the yearly average gini index by country according to the following two-step procedure. first, we summed the annual number of farm holdings, hectares, and standard output of all regions (nuts-2) in a given country (nuts-0) along the available dimensions, excluding economic size (i.e., organic farming, utilized agricultural area, and farm type of agricultural holding). then, we calculated the national gini index by aggregating the economic size classes as described in section 3. the minimum and maximum values represent the lowest and highest concentration values recorded among the regions in a given country. for a more detailed view, table a1 of the appendix displays the numerical value of the average gini index by country and year for both standard output and hectares, and table a2 reports countryand year-specific intra-country variability. the latter was computed as the root mean squared error of the available regional concentration measures from the national average in table a1, which acted as the barycenter. note: gini index is reported in a 0-100 scale. by rows are reported the minimum (min), the average (mean), and the maximum (max) gini index by country and year computed aggregating the available regional values. the yearly average gini index by country is computed as follows. first, the annual number of farm holdings, the hectares (ha), and the standard output (so) of all the regions (nuts-2) belonging to a given country (nuts-0) are summed across the available dimensions excluding the economic size (i.e., organic farming, utilized agricultural area, and farm type of agricultural holding). then, the national gini index is computed by aggregating the economic size classes as described in section 3. the minimum and maximum values represent the lowest and highest concentration values recorded among the regions belonging to a given country. regarding lithuania (lt), the last available information from eurostat regards 2016. thus, gini index and the corresponding descriptive statistics for 2020 are not available (grey region). although the average values remained broadly stable over time, some country-specific patterns emerged. the two gini indices had similar values only for bulgaria, croatia, hungary, romania, and slovakia. we also identified a group of countries with relatively low levels of the gini index in central europe. specifically, france, finland, the netherlands, belgium, and austria exhibited an average farmland gini index value below 40%, with a production gini index ranging between 50% and 65%. germany and slovenia displayed relatively homogeneous market concentration as well. figure 3 shows the gini index at the regional nuts-2 level for both farmland size (first row) and production (second row) in the four years considered. note: gini index is reported in a 0-100 scale. regarding lithuania (lt), the last available information from eurostat regards 2016. thus, gini index for 2020 is not available (grey region). note: variations of the gini index is computed as the difference between the regional (nuts-2) gini index for 2020 and the gini index for 2010. as the gini index are computed on a 0-100 scale, also the variations are in the same scale. regarding lithuania (lt), the last available information from eurostat regards 2016. thus, gini index and the corresponding variation is not available (grey region). note: relative variations of the gini index are computed as the ratio of the difference between the regional (nuts-2) gini index for 2020 and the gini index for 2010 and the regional gini index for 2010. thus, it does coincide with the ratio of the raw (absolute) variation and the gini in 2010. relative variations are reported in a percentage scale. regarding lithuania (lt), the last available information from eurostat regards 2016. thus, gini index and the corresponding relative variation is not available (grey region). figure 4 and figure 5 show the raw change (i.e., the net difference) and the relative change in the indices between 2010 and 2020, respectively. the analysis provides a more granular and informative picture of the european agricultural sector, capturing dynamics that are lost at the aggregate level. notably, the gini index for standard output invariably exceeded that of farmland in all nuts-2 regions. despite the inherent physical limitations imposed by land size, this outcome was not entirely unexpected. a more homogeneous distribution of land does not guarantee a more homogeneous standard output (table 1); however, a higher gini index of standard output suggests a considerable concentration of land ownership compared to the actual distribution of land. this phenomenon may be attributed to several factors, primarily related to the efficiency of the production process. first, economies of scale can generate a higher standard output at a growing rate: even a moderate increase in the available land surface may result in a more than proportional increase in standard output due to improved land-use efficiency. second, smaller farms may have access only to comparatively cheap machinery and equipment, which may be outdated, potentially reducing overall productivity; in contrast, larger farms may be able to use more advanced and efficient machinery to maximize the use of available land. lastly, capital investment tends to be more efficient for larger farms. we identified france and the netherlands as the countries with the lowest gini index values observed, both for standard output and farmland. between 2010 and 2020, france experienced a visible decline in the gini index, with the southern region constituting a notable exception as it saw either no change or a modest increase. the decline is predominantly attributable to the jump occurred between 2016 and 2020, as illustrated in table 2. furthermore, the gini index appears to have decreased slightly over time, with a more pronounced decline observed in farmland concentration. however, the region of provence-alpes-côte d’azur in the south saw its hectare index value rise— an exception to the overall trend. overall, the evidence suggests a decline in regional heterogeneity, defined by variations in land size, over time. germany and spain exhibit highly distinctive patterns. we observed a discrepancy in the levels of the gini index in german regions, with lower indices in western and southern regions than in eastern ones. this discrepancy was consistently confirmed over the period considered and reflects historical dualism in the national development process, which was only partially resolved by the reunification of the country in the 1990s. there is also some evidence—albeit modest—of this difference for standard output. meanwhile, the situation in spain was somewhat different. in 2010, northern regions had a lower degree of concentration than southern regions as concerns size. however, in the period under consideration, the northern regions saw a substantial increase in the hectare gini index, indicating an increase in concentration, on average, while southern regions demonstrated negligible or minimal change. the latter pattern was not observed for standard output. in spanish regions, the gini index value of standard output was relatively high, with minimal variation between areas. moreover, the pattern in the southern spanish regions was similar to that of portuguese regions, in terms of both hectares and standard output. we observed relevant differences between western and eastern areas in poland as well. the western region had larger gini indices than eastern ones, especially areas bordering germany and the czech republic. between 2010 and 2020, this gap reduced thanks to increased concentration in eastern regions, although the average production gini index at the country level slightly rose during this period. the two scandinavian countries included in the sample (finland and sweden) had markedly divergent gini index values, with finland receiving lower values than sweden for both standard output and hectares. furthermore, we observed an increase in the gini index in both countries between 2010 and 2020. however, no substantial disparities emerged for either country at the nuts-2 level. next, we thoroughly examined gini index patterns for austria and italy. austria had both a relatively low farmland gini index and a low production gini index, comparable to the averages of western germany. interestingly, between 2010 and 2020, the farmland gini index increased in western austria (i.e., vorarlberg, tirol, and salzburg regions), with no relevant change in standard output concentration; instead, standard output concentration decreased in kärnten and steiermark, regions in which the farmland gini index slightly increased in the same period. in italy, the time patterns of the two gini indices appeared to be regionally specific. we observed a substantial decline in the farmland gini index in most regions, with notable values recorded in sardinia, lazio, and abruzzo. conversely, slight increases were evident in emilia-romagna and marche. the production gini index seemed relatively stable over time, with slight decreases in certain regions and negligible increases in others. consequently, the size and output concentration of these two countries were still marked by significant heterogeneity and regional specificity. in contrast to the east–west dualism characteristic of germany and poland, italy showed a classical but less pronounced north–south differential. in addition, the country’s homogeneous values in both land and production suggested a lack of divergence in these domains. the time patterns in greece’s gini index values are of particular interest. in 2010, greece’s average gini index was similar to that of western germany. the largest absolute decrease in our sample was observed in most greek regions over the 10 years under study. however, during the same period, we observed different variations in the production gini index: while land size became more homogeneous, the standard output became much more concentrated. this phenomenon was particularly salient in the ipeiros region, where the production gini index saw the most substantial increase, alongside a pronounced decline in the farmland gini index. some countries in central and eastern europe had remarkably high gini index values, although the patterns were heterogeneous. in 2010, bulgaria exhibited one of the highest farmland gini indices on record. this index declined throughout the nation’s territory over the subsequent decade. concurrently, the production gini index rose modestly, particularly in the southwestern, south central, and northwestern regions. a similar pattern was noted in hungary and slovakia, which differed only in the minimal or negligible increase in standard output concentration. meanwhile, ireland, the netherlands, and belgium had similarly low gini indices with little variation over the decade. eventually, some important differences in border areas should be highlighted due to their similar physical characteristics (e.g., mountainous areas between italy and austria and between france and spain) and weather conditions, especially in terms of rain, wind, and solar irradiance. accordingly, we observed similarities in agricultural land concentration in some cross-border areas: the portugal–spain border, a large part of central europe comprised of france, the netherlands, belgium, austria, and western germany, a smaller part of central europe including eastern germany, western poland, and the czech republic, and, lastly, hungary and slovakia. for other cross-border areas, the picture remained fragmented, and common patterns cannot be easily defined. 4.2 common and index-specific dependence patterns in farmland and output concentration as illustrated in figures 6 and 7, a moderately strong linear correlation (i.e., the r value indicates the pearson’s correlation coefficient) existed between the two indices at the year level, as well as between the change in the two indices between 2010 and 2020. the graphs reveal a direct and unambiguous relationship between the farmland gini index and the production gini index. figure 6 indicates an increasing positive linear correlation between the concentration index for hectares and standard output during the decade. this is corroborated by the growth rates documented in figure 7, which demonstrate a positive correlation between the increments in the gini index for production and for farmland concentration, proving that the two concentration measures consistently varied simultaneously over time for most regions. furthermore, figure 6 reveals that although the range of the gini index for standard output (horizontal axis) remained constant, the range of the farmland gini index (vertical axis) narrowed slightly due to a decline in the maximum gini index values and a concurrent rise in the minimum levels. further, the spatial dependence analysis (see appendix, table a3) demonstrated the presence of a strong positive autocorrelation between eu regions for both production and farmland concentration. this finding supports the hypothesis that adjacent regions tend to register similar levels of statistical concentration, generating clusters of regions with either similarly high or similarly low values. in this regard, spatial clusters transcend national borders, forming contiguous clusters of regions that can be traced back to historical-political events and processes. once again, the evidence supports the hypothesis of a dichotomy between north-central and southeastern europe. the results presented in figure 8 are consistent with those of ezcurra et al. (2008), which highlight the european dualism characterized by the presence of distinct geographical regions on the continent: regions in southern europe, such as spain and italy, and eastern europe, including romania and hungary, exhibit notably high production and farmland concentrations, while regions in the netherlands, belgium, and france have significantly lower levels. a very similar clustering structure was also confirmed by cerqueti et al. (2024). extending the notion of spatial autocorrelation within a clusterbased framework, the authors ascertained that european regions can be categorized into three macroregions (e.g., germany, benelux, and the northeastern french regions form a homogeneous cluster) with group-specific determinants of agricultural production concentration. figure 9 shows the robustness of these results, where spatial autocorrelation remained positive for a considerable number of spatial lags and values increased from 2010 to 2020 note: lisa stands for local indicator of spatial autocorrelation (anselin, 1995). regions are grouped into five non-overlapping clusters or quarters, that is, high-high (hh) group (i.e., regions with high concentrations are surrounded by highly-concentrated neighbors); low-low (ll) group (i.e., regions with low concentrations are surrounded by lowlyconcentrated neighbors); high-low (hl) group (i.e., regions with high concentrations are surrounded by lowly-concentrated neighbors); low-high (lh) group (i.e., regions with low concentrations are surrounded by highly-concentrated neighbors); non-significant area in which local autocorrelation index is not statistically significant. . 4.3 discussion and further considerations the present study offers novel evidence of the fragmentation of the european agricultural sector, a phenomenon characterized by remarkable heterogeneity as concerns both farmland and production concentration. spatial heterogeneities are evident within and between countries, with noticeable clusters of neighboring highly concentrated regions contrasting with clusters of adjacent regions with significantly lower concentrations. such clusters transcend national boundaries and often correspond to areas with known common historical and political processes (e.g., former soviet bloc countries). we identified various time patterns that, however, vary significantly on both a national and an intra-national scale. furthermore, we employed spatial autocorrelation analysis to identify cross-country areas exhibiting analogous patterns of land and output concentration. the cross-country analysis revealed that, at the aggregate level, a more equal distribution (or less unequal) distribution of land and standard output was found in the area delimited to the west by austria and western germany, to the east by france, to the north by the netherlands, and to the south by southern france and austria. in contrast, central (from eastern germany) and eastern europe seemed to have a more unequal distribution in both indicators, especially the czech republic, hungary, romania, bulgaria, and slovakia. at the end of the 20th century, central and eastern european countries underwent significant changes in their agricultural sector, in particular in their approach to land distribution and migration from rural areas. specifically, as van vliet et al. (2015) argue, land use was strongly impacted by the shift to post-socialism, given that under socialism, most land was collectivized following “optimization” schemes run at the central level. the decollectivization of lands and the return to private holdings, therefore, had significant effects on land usage and the migration from very poor rural areas. this could explain why these countries are now characterized by a highly unequal distribution of land, as large firms may have accrued monopolistic power and also acquired many small firms. levers et al. (2018) showed that between 1990 and 2006, low-intensity and de-intensifying land systems dominated in europe’s east, in stark contrast to the dynamics in western europe. additionally, some countries in southern europe (namely, spain, italy, portugal, and croatia) seemed to have, on aggregate, a similar level of disparity, slightly higher than france and western germany or the benelux countries. mediterranean european areas have different characteristics in terms of soil, degradation related to climate change, and other factors such as farmland abandonment (a common issue in europe) compared to central europe (e.g., see garcíaruiz & lana-renault, 2011; malek et al., 2018). our analysis revealed interesting results within countries, often connected to the european historical paths. a key finding is represented by the marked differences in the national orientation toward land management. for instance, the difference between western and eastern germany is connected to the country’s historical dualism. in spain, we found clear differences in terms of hectares disparity between northern and southern regions. this may result from differences in the climate, geography, and soil quality, which have consequences for the type of farming and the need for smaller or bigger firms, in some cases. further, land abandonment has differed between the rural areas of regions like the pyrenees and those of other regions (garcía-ruiz, 2010). in contrast to these results, we did not find evidence of a clear difference in land distribution between northern and southern italy (for a specific insight on italy we refer the reader to corti et al., 2013, which present an overview of the soil management practices through the time and in the different italian physiographic districts, analysing their effects on soil conservation and fertility). the development of rural areas remains one of the primary challenges of the eu’s cap strategy (viaggi, 2008; viegas, 2021). the capacity of the member states and the eu to facilitate the revitalization of rural areas and, thereby, counteract the ongoing process of desertification, enables enhanced social and territorial cohesion while also serving as a response to the substantial climaterelated challenges confronting the region. implementing the agroforestry mosaic, as one of the main eco-schemes3 provided for in current cap regulations, requires understanding the advantages of 3 eco-schemes provide support to farmers who implement agricultural practices beneficial to the environment and climate. these measures reward and incentivize farmers for acting towards more sustainable farm and land management with the objective of preserving public goods. ensuring the viability of a scale of production that has been relegated to secondary importance for decades. small and medium-sized farms recognize the productive potential of vast abandoned areas, which can be allocated to forestry and plant or animal production, feeding short marketing circuits, boosting local economies, and helping prevent the fires that have devastated much of europe’s forests in recent years. 5. concluding remarks the academic agenda on regional disparities among european farms is still evolving. the literature so far has largely focused on the influence of spatial disparity on productivity levels across regions. in contrast, the present analysis contributes to the scientific debate by providing a clear map of the statistical concentration of agricultural land size and production at the regional level for the eu27 countries. furthermore, we presented novel evidence concerning the temporal evolution of the agricultural sector’s concentration during the 2010–2020 decade. this work calls for follow-up research. first, although the present analysis does not explicitly consider farm typologies, the evolution of the distribution of agricultural systems likely plays a role in shaping land and output concentration. according to eurostat typology data, the past decade has seen notable shifts across eu regions, including the decline of small-scale grazing livestock holdings in southern and eastern europe and the consolidation of field crop and granivore systems in more capital-intensive areas. these alterations may offer a partial explanation for the higher concentration in production we observed, particularly in regions where high-productivity systems (e.g., granivores, horticulture) have expanded. concurrently, land concentration may have increased in regions where permanent crop systems, which are frequently associated with substantial estates, have gained ground. these structural shifts appear to align with broader cap dynamics favoring scale and capital investment, and they may reinforce spatial inequality between farming systems. therefore, a more detailed disaggregation contrasting organic and non-organic producers, as well as crops and livestock farmers, could enrich the literature on the drivers of concentration. it would also help determine whether the substantial heterogeneity in output can be attributed to these specific agricultural enterprises. furthermore, policy changes have contributed to the evolution of land and output concentration over the past decade. in particular, the reforms of the cap (including the decoupling of direct payments, the introduction of greening measures, and the promotion of competitiveness and modernization) may have disproportionately benefited larger and more capitalized farms. these farms frequently possess a strategic advantage in meeting subsidy requirements and absorbing compliance costs, which may indirectly accelerate consolidation trends. although the present analysis does not formally isolate the effects of policy, these institutional dynamics can reasonably be expected to interact with market forces in shaping structural inequality. accordingly, future research could further explore the causal impact of specific cap instruments on concentration patterns at the regional level. lastly, given the growing importance of sustainable agronomic practices, researchers should examine whether changes in regional farmland and output concentrations are associated with changes in pollution levels. this could assist policymakers in assessing whether a more equitable land distribution could contribute to a concrete reduction in the environmental impact of the agricultural industry and support climate-change mitigation policies. declarations • funding: part of the authors were funded by the university of milano-bicocca (italy) through the project “scarface sequestering carbon through forests, agriculture, and land use” (2024ateqc-0048). • data availability and codes: data used in the paper are public (source eurostat). since we use only public data, no special permission is need to use copyrighted material from other sources (including the internet). all results presented in this paper can be reproduced using the r software. the codes were developed 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(2025). is there an animal food kuznets curve, and does it matter? bio-based and applied economics 14(1): 19-35. doi: 10.36253/bae-16172 received: may 24, 2024 accepted: september 23, 2024 published: june 13, 2025 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. associated editor: oriana gava editor in chief: silvia coderoni orcid vf: 0000-0002-0627-9511 tf: 0000-0002-3779-4225 va: 0000-0001-9163-011x mmb: 0000-0003-4918-8852 ac: 0000-0002-8519-6721 is there an animal food kuznets curve, and does it matter? vito frontuto1,*, tommaso felici2, vania andreoli3, marco maria bagliani1,#, alessandro corsi1 1 department of economics and statistics, università degli studi di torino, torino 10153, italy 2 utrecht university school of economics (u.s.e), kriekenpitplein 21-22, 3584 ec utrecht, the netherlands 3 school of biological sciences, university of western australia, 35 stirling hwy, crawley, wa 6009, australia # sadly, prof. marco maria bagliani, passed away in july 2024 while this paper was under revision. we wish to dedicate this paper to a dear colleague and friend. *corresponding author. e-mail address: vito.frontuto@unito.it abstract. proteins from animal sources, including meat, and plant-based foods are essential for a healthy human diet. however, animal-based proteins have significantly higher environmental impacts (e.g., greenhouse gas emissions, deforestation, and water usage) and health risks (e.g., obesity, type 2 diabetes, kidney stones and cardiovascular diseases) compared to plant-based proteins. the consumption patterns of these proteins are strongly influenced by income levels. this study introduces the concept of an animal food kuznets curve by systematically analyzing the relationship between income and animal-based protein consumption. utilizing a novel panel dataset spanning 28 years and covering 79 countries, we uncover an inverted u-shaped relationship between income and the consumption of animal-based and meat proteins. our findings indicate that the turning points occur around 43,000-45,000 us$, corresponding to the 90th and 95th percentiles of the per capita income distribution in the sample. at these income levels, protein consumption is estimated at approximately 25 g/ day for meat and 52 g/day for animal-based proteins, as compared to recommended total protein intake of 45-56 g/day. these insights highlight the critical need for targeted policy interventions, such as taxes, nudges, and informational campaigns to promote sustainable dietary choices across all income levels. our study provides empirical evidence for the importance of integrating economic and environmental policies to enhance global food sustainability. keywords: protein consumption, consumption drivers, environmental kuznets curve, mixed effects model, panel data. jel codes: q54, q56, c23. 1. introduction over the past 50 years, the global consumption of animal-based proteins, including meat, eggs, dairy, and seafood, has significantly increased in https://doi.org/10.36253/bae-16172 https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae https://doi.org/10.36253/bae-16172 https://orcid.org/0000-0002-0627-9511 https://orcid.org/0000-0002-3779-4225 https://orcid.org/0000-0001-9163-011x https://orcid.org/0000-0003-4918-8852 https://orcid.org/0000-0002-8519-6721 mailto:vito.frontuto@unito.it 20 bio-based and applied economics 14(1): 19-35, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16172 vito frontuto et al. both absolute and per capita terms (bonnet et al., 2020; marques et al., 2018; pais et al., 2021). this growth has been mainly driven by increased meat consumption (bonnet et al., 2020; sans and combris, 2015). according to oecd and fao (2023), global per capita meat consumption has nearly doubled, rising by 87 percent from about 23 kg per person per year in 1961 to 43 kg per person per year in 2021. similarly, other animalbased foods have seen increases, with milk consumption by 16 percent and egg consumption by 129 percent. this tendency is in accordance with the nutrition transition featuring increasing demand for animal-based foods when income rises (popkin, 1993). however, diets rich in animal-based protein have been linked to adverse health and environmental outcomes (tilman and clark, 2014), while diets with a higher composition of plantbased proteins are associated with less damaging impacts (galli and moretti, 2024). several studies have called for urgency in shifting protein consumption from animalbased sources to plant-based sources (willett et al., 2019), especially in upper-middle income countries with sustained economic growth rates (duro et al., 2020). indeed, the increase in global meat consumption (kg/year per capita) between 1961 and 2021 has been driven mainly by countries with rapid economic growth such as south korea (1,935 percent), china (1,774 percent) and indonesia (398 percent). several studies have demonstrated that the first global protein transition, marked by a significant increase in demand for animal-based protein over the last century, was closely linked to changes in real income (sans and combris, 2015). the more recent second nutrition transition, characterized by a stabilization or decline in animal-based protein consumption, particularly meat (godfray et al., 2018; marques et al., 2018; vranken et al., 2014), may also be attributed to similar factors. economic growth has initially promoted animal-based consumption and then it has slowed it down. this brought some scholars to claim the existence of an environmental kuznets curve (ekc) for animal-based food consumption, which could be named animal food kuznets curve (afkc). according to the ekc original theory, the environmental impact of economic growth increases in the first phase and subsequently declines (grossman, 1995; grossman and krueger, 1991). if such a trend proved true for animal-based food consumption, it would decrease the urgency of policies aiming at curbing its consumption since income growth would automatically lead to its decline. nevertheless, the existence of an afkc is to be empirically verified, and its actual effect on global consumption is to be assessed. this paper aims at investigating interactions between protein consumption and income over the last 30 years. the research uniquely analyses protein intake from animal-based, meat and plant-based sources to understand the dynamics of change and the predominant factor of variation, i.e., income. while the existing literature has predominantly focused on meat consumption and its correlation with income (york and gossard, 2004; vranken et al., 2014), there is a noticeable gap concerning the consumption of protein from different sources. this paper aims to bridge this gap by comprehensively exploring differences in protein consumption across animal-based, meat and plant-based sources using a global panel dataset covering 28 years and 79 countries. the originality of this study is further highlighted by the application of the linear mixed effect model. this methodological advancement addresses cross-sectional dependence in errors within large panel datasets, thus enhancing the accuracy of parameter estimates compared to conventional fixed effects models. 2. negative impacts of animalbased protein consumption animal-based products are an essential source of nutrients – proteins, among others – to humans. however, among protein-rich foods, those of animal-based sources produce higher greenhouse gas (ghg) emissions (dyer and desjardins, 2022; errickson et al., 2021), use more land (van zanten et al., 2018) and water (mekonnen and gerbens-leenes, 2020), cause more acidification and eutrophication (godfray et al., 2018; poore and nemecek, 2018). among animal-based foods, meat has a higher environmental damage potential than those derived from eggs, milk and seafood (de vries and de boer, 2010). among meats, beef proteins have the highest impact on the environment (de vries and de boer, 2010; gaillac and marbach, 2021). there is an urgent need for transitioning to more sustainable protein sources, such as protein of vegetal sources – pulses, legumes and novel protein-rich foods (mcclements and grossmann, 2021) – which have a lower environmental impact (mazac et al., 2022). plantbased diets can reduce ghg emissions by 49%, land use by 76%, scarcity-weighted freshwater withdrawals by 19%, acidification by 50% and eutrophication by 49% (poore and nemecek, 2018). another reason to reduce consumption of animalbased products, particularly meat, is related with the potential adverse effects of its excessive consumption on human health. a higher availability of animal-based protein consumption would benefit food-insecure countries, where fewer alternatives are available to access nutrients 21is there an animal food kuznets curve, and does it matter? bio-based and applied economics 14(1): 19-35, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16172 and micronutrients. here, a higher animal-based protein consumption would increase food and nutritional security. by contrast, the developed world, if anything, consumes an excessive amount of proteins (aiking and de boer, 2020). for instance, while the lancet commission on healthy diets suggests that an “adequate protein intake for adults is 0.8 g/kg bodyweight, which is 56 g/ day for a 70-kg individual” (willet et al., 2019) and the european food safety authority (efsa) sets an average requirement intake of 46 g protein per capita per day (agostini et al., 2012), protein intake in the eu is around 82 g per day, of which 49 g from animal-based sources and 33 g from plant-based sources (simon et al., 2024). this aspect highlights substantial inequalities of the food systems between developing and developed world, and also represents an increased risk for human health. meat consumption contributes to global obesity (you and henneberg, 2016), higher risks of type 2 diabetes (malik et al., 2016), kidney stones (asoudeh et al., 2022), cardiovascular disease mortality (zheng et al., 2022), cancer mortality (huang et al., 2021) in the specific, colorectal, breast and prostate cancer (cellura et al., 2022; gonzalez et al., 2020) and more generally all-cause mortality (sun et al., 2021). conversely, diets rich in plant-based proteins, such as legumes, nuts and seeds, while sufficient to achieve full protein adequacy in the developed world (mariotti and gardner, 2019), seem to confer protection against the incidence of cancers (gonzalez et al., 2020) and to reduce global mortality (springmann et al., 2016). increasing the share of plant-based proteins will provide significant health and environmental co-benefits (bonnet et al., 2020; stylianou et al., 2021). this study aims to assess the relationships between income and different protein sources to highlight potential differences that can be useful to understand the impact of policies. the paper will discuss the relationship between food consumption and income using existing literature, which, however, rarely took into consideration protein sources other than meat, and explains the theory behind the model in section 3. we will then outline the data and the econometric strategy we chose to apply to describe this relationship in section 4. the results of the estimated models are presented in section 5 and their implications are discussed in section 6 and 7. 3. the relationship between animal-based protein consumption and income rising real gross domestic product (gdp at constant prices) over the last century has been identified as the root-cause of a global nutrition transition. the transition encompasses a shift towards animal-based sourced proteins in general (gerbens-leenes et al., 2010; sans and combris, 2015) and proteins from meat in particular (milford et al., 2019; york and gossard, 2004). as income increases, consumers tend to shift their dietary preferences toward more resource-demanding foods (tilman and clark, 2014). this transition is taking place at different stages and paces worldwide (gerbensleenes et al., 2010). consumption of animal-based foods is much higher in developed countries than in developing and least developed countries. however, the upward trend is more pronounced in developing countries (henchion and zimmermann, 2021), where the rise above the poverty line occurs at a faster pace than it did in developed countries (sans and combris, 2015; drewnowski and poulain, 2018). meanwhile, in higher income countries a “second nutrition transition” seems to occur (pais et al., 2021; vranken et al., 2014). in these countries, the consumption of animal-based proteins, especially from meat, seems to stagnate or decline when reaching a high level of income. vranken et al. (2014) and cole and mccoskey (2017) have therefore found evidence of an inverted u-shape relationship between meat consumption and income, indicating that the consumption of unsustainable proteins could reach a maximum and then decline. therefore, these studies suggested that meat protein consumption follows an ekc. arguably, the reasons for an afkc differ from those of the ekc. the latter is justified by the increasing environmental impact of the shift from an agricultural to an industrial economy, followed by a decreasing impact due to resources-saving technological progress and increasing environmental awareness. in the case of the afkc, the same reasons do not apply, and the determinants have to be ascribed to the factors mentioned above. the reasons behind this decline can be attributed to several factors: i) increasing awareness of the health risks associated with high meat consumption, ii) concerns about the environmental impact of meat production, including greenhouse gas emissions, deforestation, and water usage, iii) growing awareness of animal welfare, iv) the rising availability and popularity of plant-based meat alternatives, v) the spread of popular dietary trends, such as vegetarianism, veganism, and flexitarianism. we therefore present a model of animal-based food consumption that incorporates the above reasons for an inverted u-shaped consumption-income pattern for animal-based food (af) consumption. the theoretical model sheds light on past trends in af consumption and the reasons that render possible an afkc. nevertheless, while the model may justify the existence of an afkc, it does not predict it unequivocally. 22 bio-based and applied economics 14(1): 19-35, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16172 vito frontuto et al. the model (for a formal presentation see appendix 1) assumes that utility from af consumption has two components. the first one directly stems from its consumption per se, due to its taste and appetite value. utility is therefore a positive function of animal-based food consumption, so that its marginal utility is positive, but decreasing, due to increasing satiation: additional af consumption provides less and less additional utility. the second component is the nutritional and health one. according to the nutritional literature, consumption of animal-based proteins has initially a positive effect on nutrition and health (receding from famine, mortality declines, see e.g., mathijs, 2015) but, at higher levels, it brings several adverse health effects (e.g., cardiovascular risks, obesity-related issues). hence, if consumers are aware of and care about the negative impacts of high animal-based food consumption on health, this component of utility has an inverted u-shape. in addition, as mentioned above, concern for animal welfare and for the environment can be reasons for a lower utility associated with large animal-based food consumption (frank, 2008). in this model, for simplicity we include these effects in the health one. the model assumes that a consumer maximizes his/ her utility subject to a budget constraint. the equilibrium condition states that the marginal utility from af consumption per se, plus the marginal utility stemming from the variation in nutrition-health due to the effect on consumers’ health of an additional af consumption, equals the additional utility that could be drawn from other goods that could be purchased with the animal-based food price, i.e., the marginal opportunity cost of af. the marginal utility of af consumption per se decreases when af increases, and reaches a lower bound at zero for satiation, when further consumption provides no additional utility. the marginal utility from nutritional-health benefits also decreases with af consumption and remains positive as long as the marginal health benefit is positive, then it becomes negative. when the marginal health benefits, at high consumption levels, become negative, they may determine a decrease in overall utility if disutility from health damages prevails over utility due to taste. in this case, an inverted u-shape of the income-consumption relationship results. the model implies that a decrease in the af price relative to all other prices (i.e., a decrease in real af price) leads to higher af consumption. this explains what actually happened in the past (fao, 2009) when the relative price of af declined with reference to other food prices. the crucial question for the existence of an afkc is nevertheless the shape of the relationship between income and af consumption. among necessities, animal-based food is more expensive than plant-based food. at low-income levels, a higher income allows a shift from cheap staple food to animal-based food, as empirically observed in all countries in the initial stages of development and as a general trend in the recent decades (sans and combris, 2015; delgado et al., 2009; among others). however, the model cannot unambiguously predict a priori whether a further income growth leads to an increase or decrease of af consumption, because the resulting equilibrium will depend on how the marginal utilities of af of other consumptions and of nutritionhealth react to income, and on their interrelationships. the model allows for the existence of an afkc, but does not imply its necessity. the form of the incomeaf consumption relationship has therefore to be determined empirically. plant-based protein consumption also increases with income at the initial stages of development. however, its increase is presumably slower than the one of animal-based proteins, since income growth allows consumption of the more expensive animal-based proteins, so that in the diet the share of animal-based proteins grows. if consumption of animal-based proteins declines at high income levels, it is possible that plant-based protein consumption will increase as a substitute. the relationship between plant-based protein consumption and income must also be determined empirically. 4. materials and methods 4.1. variables and data we employ a balanced panel dataset covering 79 countries from 1991 to 2018 (table a.1 in appendix 2). we draw on data from the food and agriculture organization (fao) new balance sheets (nbss; fao, 2021), where food supply quantities are used as proxies for consumption (cole and mccoskey, 2017; you and henneberg, 2016). these quantities are measured in grams per capita per day and reflect food reaching consumers, with the caveat that actual consumption may be lower due to waste and spoilage during preparation. the study classifies protein consumption into three types: “meat protein” from poultry, pork, goat, mutton and bovine; “animal-based protein” encompassing all animal products including dairy and eggs; and “plant-based protein” derived from cereals, vegetables, fruits, beans, nuts, seeds, roots and spices. we explore potential determinants of protein consumption across three principal dimensions: economic, socio-cultural, and land use. in the economic dimen23is there an animal food kuznets curve, and does it matter? bio-based and applied economics 14(1): 19-35, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16172 sion, the primary focus is income expressed by gdp per capita (p.c.) at chained purchasing power parities (ppps) measured in million constant 2017 us$. the data are collected from the penn world table (feenstra et al., 2015), a set of national-accounts data to measure real gdp across countries and over time. in the presence of an inverted u-shape, i.e. kuznets curve, we expect positive estimated coefficients for the linear terms and negative coefficient for the quadratic terms. in addition to income p.c., we recognize the substantial influence of food prices on protein consumption patterns. to capture this influence, we build national price indexes using data from faostat (fao, 2022b). specifically, we select the price of the most consumed item within each of the three protein sources (meat, animal-based and plant-based) for every country and year under study and build an index using the first year of the time series (1991) as base year. this approach aims to quantify how variations in food prices across different protein sources impact dietary choices and consumption behaviors globally. indeed, our hypothesis is to observe a negative coefficient for the price index meaning that an increase in prices determines a reduction in protein consumption. in addition to own price for each protein source, we have also tested relative prices. in fact, as suggested by fao (2009) over the last 50 years there has been a decline in the prices of livestock products relative to those of other products, making consumption of animalbased and plant-based foods more affordable than meat even without rising income. a third economic variable used in our empirical application is the trade openness, built as the ratio of imports and exports over national gdp. our hypothesis is to observe a positive effect of trade on the three proteins consumption due to the likely larger availability of different products and thus protein sources. beyond economic factors, social and cultural influences could also shape protein consumption patterns. we integrate several key variables to explore these dimensions. first, the religious beliefs were incorporated by using the percentage of population adhering to islam as a proxy to understand dietary restrictions that may affect consumption preferences, for example by reducing meat consumption and increasing plant-based protein intakes. second, we integrate the percentage of women participating in the labour force as an indicator of evolving food preparation practices. third, the percentage of adults with tertiary education levels is used to capture the influence of educational attainment on dietary preferences and awareness of nutritional choices, potentially affecting protein intake patterns. we hypothesize that more educated people tend to prefer diets with more plant-based food for both health and environmental concerns. however, we are aware that education is strongly correlated with income levels. finally, to further explore other contextual conditions likely influencing protein consumption, the study includes two proxies of land use: the harvested area per capita as a measure for the relevance of the agricultural sector for self-provision of proteins and the percentage of the population living in urban areas. these variables are used to examine the impact of urbanization on dietary habits and access to diverse food options, including protein sources. table 1 provides a comprehensive list of variables used in the study, and their descriptive statistics and sources. unlike the typical practice in environmental kuznets curve (ekc) literature, the study uses variables in their original levels instead of logarithmic transformations, aligning with findings by hasanov et al. (2021).1 4.2. econometric strategy since the mid-1950s, scholars testing the kuznets curve (kc) hypothesis on various environmental and non-environmental indicators have primarily used crosssectional data and longitudinal data with fixed effects estimators (e.g., vranken et al. (2014) for consumption of meat protein). however, traditional panel data estimators assume cross-sectional independence, basing the models on homogeneous coefficients and yielding inconsistent estimated parameters (heck and thomas, 2020). indeed, cross-sectional units may exhibit shared characteristics, such as spatial effects, omitted common factors, or socioeconomic networks interaction leading to cross-sectional dependence, calling for estimators that account for intercepts and slopes heterogeneity. the literature on heterogeneous panels has evolved along two main strands: i) the application of mean group (mg) estimators (pesaran and smith, 1995) and subsequent modifications (augmented mg and common correlated effects mg; teal and eberhardt, 2010), ii) the application of multilevel or mixed effect models to panel data (mcculloch et al., 2001). the key distinction between panel models (such as mg estimators) and mixed effects models lies in the treatment of the independent variables. in mixed effects 1 hasanov (2021) argues that in non-linear logarithmic environmental kuznets curves (ekc), the signs of estimated coefficients and the statistical significance of lower-order polynomial terms can vary arbitrarily based on the units of measurement chosen for the independent variables. consequently, hasanov suggests that researchers should first study the ekc in levels considering the potential issues with the logarithmic specification. 24 bio-based and applied economics 14(1): 19-35, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16172 vito frontuto et al. models, independent variables are treated as non-random variables, whereas in panel data models, they are always assumed to be random. another significant difference is in estimating the average effects (invariant between individuals) and individual (or random) effects. in the case of mg estimators, individual-specific ordinary least-squares (ols) regressions are estimated then the individual-specific parameters are averaged across the panel to determine an overall effect. in the case of mixed effect models, the estimated parameters are the common effect with the random effects representing individual deviations from this average, inferred from estimated variances and covariances (dinda, 2004). a meta-analysis conducted by saqib and benhmad (2021) on more than five hundred studies concluded that the econometric strategy does not significantly impact the test of the ekc hypothesis. however, they highlighted the greater reliability of longitudinal data and the robustness of methods that deal with heterogeneous panels such as mg estimators and mixed effect models. in this paper, we employ a mixed effect model because of our focus on the variation in regression coefficients rather than a global behaviour as an average of country-specific dynamics. country-specific estimates, limited by the income ranges, cannot properly identify the curvature of a general function. to account for intercept and slopes heterogeneity in parameters the unknown parameters are decomposed in a fixed term γ (constant across countries) and a random term δ (specific for each country). thus, the relationship between protein consumption per capita (animal-, plant-based and meat) and gdp per capita is modelled as: (proteins/p)it = (γs0 + δsi0) + (γs1 + δsi1) (gdp/p)sit + (γs2 + δsi2) (gdp/p)2 sit + ∑j j=3 βsijxsitj + εit (1) where s= a,m,p identifies the protein source (animal-, plant-based and meat), i=1,…,n indicates the countries, t=1,…,t the time periods, gdp is defined as above and p is population, xj the j-covariates. note that represents the potential non-linear effect of gdp per capita on proteins consumption and it is used in the kuznets framework to check the inverted u-shaped curvature of the relation. this model has been estimated using maximum likelihood estimators for the three sources of protein (rabe-hesketh and skrondal, 2008) and likelihood-ratio tests have been employed to compare different models and to validate the use of random coefficients. moreover, the models are first estimated with an unstructured random-effects covariance matrix, which allows for distinct variances and covariances between all randomtable 1. list of variables with descriptive statistics. variable description mean std. min max source dependent variables meatprot per capita meat-based protein consumption (g/day) 16.4 11.1 1.2 46.9 fao (2021) animalprot per capita animal-based protein consumption (g/day) 35.2 21.4 3.2 79.7 fao (2021) plantprot per capita plant-based protein consumption (g/day) 44.0 10.1 22.9 82.7 fao (2021) independent variables gdppc per capita expenditure-side real gdp at chained ppps (000 us$) 16.6 16.3 0.4 90.3 penn world table (feenstra et al., 2015) gdppc2 squared gdppc 541.7 880.9 0.2 8154.2 penn world table (feenstra et al., 2015) price index animal-based products 0.99 0.16 -0.88 2.65 fao (2022b) meat products 0.98 0.17 -0.45 2.50 plant-based products 1.11 0.52 -2.73 4.67 trade (imports+exports) / gdp (%) 68.7 34 13.8 227.4 world bank (2022a) education share of post-secondary education (%) 10.2 8.9 0.15 48.3 world bank (2022b) permus share of muslims over population (%) 23.4 35.7 0 99.8 arda (2022) perfemwork share of female employment (%) 40.2 9.3 10.7 56 world bank (2022c) urbanization share of people living in urban areas (%) 58.6 22.2 5.5 95.3 world bank (2022d) harvarea harvested area/population (per capita ha) 0.172 0.2 0 1.4 fao (2022a) n. obs. 2212 n. groups 79 sources: fao, penn world table, world bank, arda and own calculation. 25is there an animal food kuznets curve, and does it matter? bio-based and applied economics 14(1): 19-35, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16172 effects covariates. however, inconsistent estimations for the plant-based protein model necessitated an identity covariance structure, assuming equal variances. according to the literature on testing the nature of the time-series to select the appropriate panel estimator (perman and stern, 2003; eberhardt, 2012), the model of equation [1] was tested relative to: i) cross-sectional dependence; ii) presence of unit roots (i.e., stationarity); iii) long-run relationship (i.e., cointegration). to select the appropriate test for investigating unit roots, we initially checked the cross-sectional dependence of the series using the pesaran test (pesaran, 2021) under the null hypothesis of cross-sectional independence. most variables exhibited cross-sectional dependence (except for trade) (see table a.2 in appendix 3). subsequently, we tested the stationarity of the series by implementing the modified pcadf test (costantini and lupi, 2013) which consider cross-sectional dependence under the null hypothesis of non-stationarity. the results suggested that the null hypothesis of non-stationarity can be rejected only after transforming the series in their first differences except for the urbanization rate and the education (table a.3 in appendix 3). then, we checked the cointegration assumption to prevent the regression from providing biased statistical evidence of the relationship among variables. cointegration was investigated through various tests, including the phillips-perron, the modified phillips-perron, the augmented dickey-fuller tests (pedroni, 1999; pedroni, 2004) and the so-called westerlund test (westerlund, 2005) by assuming the presence of cross-sectional dependences (table a.4 in appendix 3). the rejection of the null hypothesis of all these tests indicates that our models are cointegrated. the findings support the selection of the mixed effect model as appropriate to estimate heterogenous coefficients for intercepts and slopes. 5. results table 2 presents the estimated coefficients of the model of meat animal-based and plant-based protein consumption which exhibit overall significance. likelihood-ratio tests have been applied to compare different models and different covariates. education and urbanization rate turned out to be non-stationary even when transformed in their first differences and were therefore not used to avoid spurious estimated coefficients (see table a.3 in the appendix 3). prices for the three protein sources are not statistically significant and hence are not included in our preferred specification in table 2 (see table a.5 for the estimated coefficients of model including prices).2 table 2 also reports the estimated standard deviations for the intercept, the gdp per capita and the gdp2 per capita coefficients. all of these standard deviations are statistically significant, indicating the intercept and slopes heterogeneity and thus supporting the use of the mixed effects model. the most important determinant of meat protein consumption is per capita income, with both its estimates of the linear and the quadratic term highly significant. the estimates indicate that a thousand dollar increase in per capita income induces a 0.725 g/ day increase in meat protein consumption. notably, the negative sign of the squared term suggests that meat protein consumption does increase with income, but at a decreasing pace. among the variables aside from income, the trade and the percentage of muslims are significant. specifically, every additional percentage point in the ratio of imports plus exports over gdp implies a 0.008 g/day increase in the average meat protein consumption. a percentage point increase in the share of muslims over the population translates into a 0.11 g/day decrease in the average meat protein consumption, ceteris paribus. these results are consistent with andreoli et al. (2021) and milford et al. (2019). female participation, however, does not show statistical significance, as in milford et al. (2014). the positive sign of the gdp parameter and the negative sign of the gdp2 parameter, both significant, suggest the existence of an inverted u-shaped relationship, thus supporting the existence of an afkc where meat protein consumption increases with per capita income up to a maximum before decreasing. a crucial point for assessing the policy implications of the afkc is nevertheless determining the level of the turning point. this can be calculated as where is the estimated parameter of per capita income and the estimated parameter of its square.3 this simple calculation results in a turning point of 42,923 us$,4 located between the 90th and the 95th percentiles of the per capita income distribution in the whole sample, and above the 80th percentile of the income distribution in the last year of the panel (2018). it could be argued that the turning point should also be estimated consid2 we used version 18 of stata for windows to carry out the analysis of the data in this paper. the mixed command has been used to estimate the mixed models presented in table 2. 3 the formula for the maximum income in the estimated second-degree equation is obtained by setting the derivative of the equation to zero and solving for the income variable. 4 to present a more concise table of results, the coefficients have been rounded to three decimal places. consequently, the turning point value derived from rounded coefficients differs from the one presented in the text, which uses estimated coefficients to six decimal places. 26 bio-based and applied economics 14(1): 19-35, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16172 vito frontuto et al. ering the variation of the estimated parameters. unfortunately, the turning point results from the ratio of two normal random variables, which results in a cauchy distribution, whose expected value and variance are undefined. however, its mode and median are defined, and the distribution is symmetrical. we therefore perform a monte carlo simulation of the median turning point. we randomly draw couples of and parameters from a bivariate normal distribution, calculate the turning point, repeated for 1000 draws, and individuate the median turning point of these simulations. by repeating the procedure 10,000 times we obtain an empirical distribution of the medians, from which we calculate their mean and standard deviation. the result of 42,891 us$ is sensibly similar to the simple calculation from the estimated parameters. the standard deviation is relatively modest, 318 us$, and the range went from a minimum of 41,867 to a maximum of 44,159 us$. the minimum value is around the 90th percentile. comparisons with previous studies reveal similar turning point estimates, i.e. 46,000-66,000 constant 2017 international us$ p.c. (andreoli et al., 2021); 36,37549,848 constant 2005 us$ p.c. (cole et al., 2013); 35,00053,000 constant 2005 international us$ p.c. (vranken et al., 2014), indicating consistency across analyses. however, employing mixed effects models alongside monte carlo simulation produces more efficient estimates with reduced variability. furthermore, the results allow us to predict the meat protein consumption corresponding to the turning point, by using the estimated parameters and the per capita income of the turning point and setting the other variables at their mean. to appreciate the variation of the prediction, we also calculate the predicted consumption when the other variables are taken at the minimum and maximum of their observed values,5 and when they are taken at their mean plus/minus their standard deviation. table 3 presents the results. the calculated meat protein consumption at the income turning point and the mean of the other variables is 24.61 g/day, slightly below the 75th percentile. the maximum value (29.57 g/day) is between the 85th 5 when calculating the maximum and minimum consumption, variables with a negative parameter were taken as positive, so to identify the maximum possible range. table 2. results of the models of protein consumption. indep. variables dependent variables meat protein animal-based protein plant-based protein coef. std.err. coef. std.err. coef. std.err. gdppc 0.725*** 0.135 1.255*** 0.206 1.506*** 0.419 gdppc2 -0.008*** 0.003 -0.015*** 0.004 -0.003 0.002 trade 0.008*** 0.003 0.019*** 0.005 0.032*** 0.005 harvarea 1.097 1.317 1.930 1.863 -15.479*** 1.739 permus -0.105*** 0.0281 -0.177*** 0.484 0.212*** 0.421 perfemwork -0.008 0.033 0.058 0.048 0.113** 0.046 constant 11.092*** 2.191 25.517*** 3.689 30.675*** 2.613 sd(gdppc) 1.086 0.124 1.686 0.161 3.644 0.361 sd(gdppc2) 0.021 0.006 0.030 0.004 0.005 0.002 sd(constant) 13.364 1.414 25.170 2.378 12.892 1.120 sd(residual) 1.541 0.028 2.167 0.035 2.093 0.034 n. obs. 2212 2212 2212 n. groups 79 79 79 wald chisq(6) 78.33*** 108.10*** 194.50*** log likelihood -4533.46 -5302.99 -5300.83 *, **, *** indicate that statistics are significant at the 10%, 5% and 1% level of significance respectively. table 3. predicted protein consumption at the turning points. meat protein animal-based protein mean 24.61 51.62 min 16.16 35.02 max 29.57 61.96 mean-sd 20.45 43.79 mean+sd 28.76 59.45 27is there an animal food kuznets curve, and does it matter? bio-based and applied economics 14(1): 19-35, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16172 and the 90th percentile, while the minimum (16.16 g/ day) is between the 55th and the 60th percentile. the results of the model of animal-based protein consumption (table 2) are similar to the ones of meat protein consumption, as meat constitutes about 30 percent of total animal-based protein intake. the trade variable has a significant and positive impact on consumption, higher than for meat (every additional percentage point in the ratio of imports plus exports over gdp implies a 0.019 g/day increase in the average meat protein consumption). similarly, the share of muslims over the population is significant and negative, suggesting that a 1 percent increase in the share generates 0.17 g/day decrease in animal-based protein consumption. both per capita income and its square estimated parameters are significant, with larger absolute values than the respective parameters of meat consumption, thus suggesting a more rapid increase but also a faster slowing down of the growth. the turning point is located at 41,928 us$, slightly lower than the turning point of meat consumption, but still within the 90th and the 95th percentile of income distribution. strictly considered, the results indicate that the consumption of animal-based products other than meat start declining at a lower income level than meat consumption. however, the small difference in the turning point, along with the likely variation in the estimates, suggest that in practice there is no appreciable difference in the behaviour of meat relative to the other animal-based proteins. animal-based protein consumption at the turning point, calculated as above (table 3), is 51.62 g/day, falling between the 70th and 75th percentile, for the whole panel and 2018. the maximum (61.95 g/day) and the minimum (35.02 g/day) values are located over the 80th percentile and between the median and the 60th percentile, respectively, for both the panel and the 2018 distribution. also, it should be considered that the adequate total protein intake for average adults suggested by the lancet commission on healthy diets is 56 g/day (willet et al., 2019) and the average requirement intake set by the efsa is 46 g total protein per capita per day (agostoni et al., 2012). in contrast, the results of the model for plant-based proteins (table 2) differ from the previous ones mainly in the fact that the quadratic term of gdp turns out not to be statistically different from zero, meaning that the afkc hypothesis is not confirmed in this case and that plant-based protein consumption increases linearly with income. among the other estimated coefficients, the openness to international trade positively inf luences plant-based protein consumption, possibly due to the exposure to consumption models or via their increased availability. every additional percentage point in the ratio of imports plus exports over gdp implies a 0.03 g/day increase in the average plant-based protein consumption. the per capita harvested area negatively impacts plant-based protein consumption, as one per capita hectare more induces a decrease of consumption of 15.5 g/day. nevertheless, one per capita hectare is more than 5-fold the average (0.17), so the size of the estimated parameter should be related to the one of the marginal effects in the covariate. a possible -admittedly questionableexplanation of this counterintuitive finding is that when more land is available it is mainly devoted to cereal crops rather than pulses. consistently with the negative effect on animal-based protein consumption, the share of muslims over the population has a positive and significant effect, as a one percent increase of their share induces a 0.21 g/day increase in the average plant-based protein consumption. since the squared per capita income parameter, although exhibiting a negative sign, is non-significant, no turning point can be consistently predicted, with plant-based protein consumption increasing linearly with income, at a rate of 1.50 g/day increase for every additional thousand dollars. 6. discussion the empirical results suggest the existence of an inverted u-shaped relationship between animal-based and meat protein consumption and per capita income and a linear relationship between per capita income and plant-based protein consumption. both models of animal-based and meat protein consumption capture an initial increase in the amount of protein from these sources as income grows. taste, appetite and the need to increase protein consumption for optimal nutrition can also be considered responsible for this initial increase. as consumers have a rising purchasing power from a growing income, they diversify their bundle of goods and increase their consumption of foods rich in proteins, as also observed by the theory of nutrition transition (popkin, 1993). in particular, within the diet composition, the animal-based food proportion increases and the plant-based one decreases, as shown by all historical records. however, the historical experience of developed countries shows that consumption keeps increasing until it reaches an amount that may cause negative externalities, consistently with the theoretical model presented. nevertheless, we found that the inversion of the trends is predicted at very high-income levels. this is consistent with both the assumption of the positive effect 28 bio-based and applied economics 14(1): 19-35, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16172 vito frontuto et al. of income on the taste-appetite driver of consumption and with the negative health effects at high income levels. our investigation also found a linear increase of plant-based protein consumption with income. the increase of plant-based protein consumption is lower than that of animal-based and meat but it is continuous. it is possible in fact that when animal-based and meat protein consumption decline plant-based proteins act as substitutes. in fact, the popularity of novel protein consumption with plant-based origins has been recently observed (mcclements and grossmann, 2021). a somewhat counterintuitive result is that meat protein consumption actually starts declining at a slightly higher income level than animal-based protein consumption. a tentative explanation can relate these trends to a composition effect of rising incomes. at low-income levels, a rising income allows consumption of “non-meat” animal-based proteins (eggs, dairy, fish, etc., generally cheaper than meat) in addition to plant-based ones, as also suggested by the higher income parameter of animal-based than meat protein consumption. as income further rises, meat consumption becomes affordable, and substitutes for “non-meat” animal-based protein consumption, up to the point that the latter starts to decline. this has been empirically observed before (e.g., see akpalu and okyere, 2022) and is consistent with the theory of nutrition transition (dagevos and voordouw, 2017). the high level of the turning points, especially the one of meat protein consumption, have important sustainability implications related to the environment and health, with significant consequences for policy makers. even though the consumption of unsustainable protein reaches a peak and decreases, the peak is at a very high level of income. the majority of the world population is positioned well below the income turning point and still has a long way to go before it reaches the level that, according to our results, decreases the consumption of unsustainable proteins. thus, the global level of meat and animal-based protein consumption is expected to grow for at least the near future and with that, the impacts on health and the environment. hence, income growth does not warrant a decrease in animal-based protein consumption sufficient to curb its environmental impact. 7. conclusions and policy implications this study analysed how animal-based protein consumption is determined by per capita income. we modelled this relationship empirically through a panel of 79 countries over 28 years, distinguishing between meat and more generally animal-based proteins. in addition, we also modelled the relationship between plant-based proteins and per capita income. our main goal was determining whether an animal food kuznets curve (afkc) exists, according to which animal-based protein consumption increases with income and then declines. our results suggested that an afkc exists, since the estimates show an initial increasing and then decreasing significant trend of animal-base food (af) consumption relative to real income. it was also possible to calculate the per capita income level at which af protein consumption starts to decline, corresponding to 42-43,000 us$, i.e., over the 90th percentile of the per capita income distribution. by contrast, plant-based protein consumption monotonically increases with income. some limitations of this study are acknowledged. we tried to build indexes for prices using the price of the most consumed item in every type of protein source, but they turned out to be non-significant, so we were forced to proxy them with variables whose relationship with prices could be weak. other explanatory factors, in particular income, have had a much more pronounced effect on animal-based foods consumption than prices, resulting in the limited influence of prices on protein consumption found in this study and previous literature (inter alia mildford et al., 2019). we adopted mildford et al.’ (2019) argument that in addition to income, natural conditions can be an important determinant of protein consumption. we therefore included per capita harvested area as a control, like cole and mccoskey (2017). the socio-cultural determinants of diets are arguably important and, even if we tested several, most were correlated with income and others were not significant. this may be due to the inadequacy of those variables to represent the actual socio-cultural determinants. despite these limitations, this study is consistent with previous literature and has important policy implications. the policy interest in detecting an afkc is because such a trend, in principle, would decrease the concern for the environmental and health impacts of animal-based food (af) consumption. if a rising income would curb af consumption, policies aiming at reducing it would be less urgent. unfortunately, the income levels at which we found that af starts to decline are so high that it is unlikely that this trend can cope with the environmental and health impacts that the growing consumption is creating. more so, because most of the predictable growth of animal-based protein consumption will take place in developing countries. for these countries, the path for reaching income levels determining an inversion of the trend is still long. the inescapable policy implications that the negative environmental impacts of animal-based food consumption must be tackled direct29is there an animal food kuznets curve, and does it matter? bio-based and applied economics 14(1): 19-35, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16172 ly. interventions can be envisaged on the production side from a technical point of view, since for instance some techniques allow lower ghg emissions from bovines (thomson and rowntree, 2020). changing the production mix could also help since the environmental impact of poultry and pigs is lower than that of bovines. however, supply is driven by demand, and this calls for interventions on consumers both regarding the type of animal-based products and the quantity of consumption. the regulation of meat and animal-based consumption is one of the major challenges that countries must face in the coming decades (willett et al., 2019) with the goal of a protein transition reducing the share of animal-based proteins in human diets (simon et al., 2024). bonnet et al. 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[a2]. the result is nevertheless a complex function of the second direct and cross derivatives of a, h, c, and its sign cannot be unambiguously determined, it can be positive or negative. appendix 2 table a.1. list of countries analysed. country iso code algeria dza argentina arg australia aus austria aut bangladesh bgd bolivia bol brazil bra country iso code belize blz cameroon cmr canada can cabo verde cpv sri lanka lka chile chl china chn colombia col congo cog cyprus cyp denmark dnk dominican republic dom ecuador ecu egypt egy el salvador slv finland fin france fra gambia gmb germany deu ghana gha greece grc guinea gin honduras hnd hungary hun india ind indonesia idn iran (islamic republic of ) irn ireland irl israel isr italy ita côte d’ivoire civ japan jpn jordan jor kenya ken cambodia khm republic of korea kor lao people’s democratic republic lao lebanon lbn madagascar mdg malaysia mys mali mli mauritius mus mexico mex morocco mar mozambique moz namibia nam nepal npl netherlands nld new zealand nzl nicaragua nic niger ner nigeria nga 34 bio-based and applied economics 14(1): 19-35, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16172 vito frontuto et al. country iso code norway nor pakistan pak panama pan paraguay pry peru per philippines phl poland pol portugal prt rwanda rwa saudi arabia sau south africa zaf spain esp sweden swe switzerland che togo tgo turkey tur united kingdom gbr united states of america usa burkina faso bfa uruguay ury appendix 3 table a.2. test of cross-sectional dependence of variables. variable# pesaran test animalprot 2.678*** meatprot 2.398** plantprot 2.798*** gdppc 9.247*** gdppc2 16.342*** trade 0.158 harvarea 5.03*** perfemwork 3.804*** urbanization 1.948* education 85.837*** animal-based price index 12.883*** meat price index 6.536*** plant-based price index 24.181*** #the percentage of muslim (permus) has not been tested because time invariant. *, **, *** stand for the significance level of 10%, 5% and 1% respectively. the null hypothesis is the absence of cross-sectional dependence. table a.3. unit root test on variables and their first difference. variable pcadf test variable pcadf test animalprot 2.965 ∆animalprot -8.341*** meatprot 1.408 ∆meatprot -10.136*** plantprot 3.616 ∆plantprot -19.977*** gdppc 5.426 ∆gdppc -3.426*** gdppc2 6.295 ∆gdppc2 -1.592* trade 4.052 ∆trade -7.158** harvarea 2.232 ∆harvarea -7.700*** perfemwork 1.631 ∆perfemwork -2.565*** urbanization 4.302 ∆urbanization 6.272 education 11.310 ∆education 3.016 animal-based price index -12.225*** ∆animal-based price index -20.497*** meat price index -12.825*** ∆meat price index -5.473*** plant-based price index -16.698*** ∆plant-based price index -9.583*** *, **, *** stand for the significance level of 10%, 5% and 1% respectively. the null hypothesis is non-stationarity. table a.4. cointegration test assuming cross-sectional dependence. test name animalprot meatprot plantprot pedroni modified phillips–perron t 4.590*** 3.697*** 1.88** phillips–perron t -7.509*** -8.279*** -9.710*** augmented dickey–fuller t -8.99*** -10.195*** -10.324*** westerlund variance ratio -2.579 *** -2.751*** -1.6434* *, **, *** indicate that statistics are significant at the 10%, 5% and 1% level of significance respectively. the null hypothesis is no-cointegration. 35is there an animal food kuznets curve, and does it matter? bio-based and applied economics 14(1): 19-35, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-16172 table a.5. results of the full models of protein consumption. indep. variables dependent variables animal-based protein meat protein plant-based protein coef. std.err. coef. std.err. coef. std.err. gdppc 1.256*** 0.206 0.725*** 0.135 1.505*** 0.419 gdppc2 -0.015*** 0.004 -0.008** 0.003 -0.002 0.001 trade 0.018*** 0.005 0.008** 0.003 0.032*** 0.004 harvarea 1.190 1.862 1.087 1.317 -15.486*** 1.738 permus -0.177*** 0.048 -0.105*** 0.028 0.211*** 0.421 perfemwork 0.057 0.047 -0.008 0.033 0.111** 0.046 price index (animal-based) 0.304 0.289 price index (meat) 0.065 0.200 price index (plant-based) -0.184 0.148 constant 25.245*** 3.697 11.033*** 2.199 30.910*** 2.619*** sd(gdppc) 1.686 0.161 1.087 0.125 3.644 0.361 sd(gdppc2) 0.029 0.003 0.021 0.006 0.005 0.002 sd(constant) 25.17 2.378 13.369 1.415 12.894 1.119 sd(residual) 2.167 0.035 1.541 0.028 2.092 0.033 n. obs. 2212 2212 2212 n. groups 79 79 79 wald chisq(6) 109.25*** 78.31*** 196.14*** log likelihood -5302.43 -4533.41 -5300.07 *, **, *** indicate that statistics are significant at the 10%, 5% and 1% level of significance respectively. building sustainable futures: the bio-based fertilizer case-study carla abitabile, andrea arzeni*, federica cisilino, matteo orlando is there an animal food kuznets curve, and does it matter? vito frontuto1,*, tommaso felici2, vania andreoli3, marco maria bagliani1,#, alessandro corsi1 do agroholdings cope better with the agency problem? empirical evidence from corporate farms in russia alisher tleubayev1,*, yerzhan syzdykov2 farm characteristics and exogenous factors influencing the choice to buy land in italy silvia russo1,*, meri raggi2, barbara bimbati3, andrea povellato3, davide viaggi1 soils and ecosystem services: policy narratives and instruments for soil health in the eu greta winkler1,*, luciano pagano2, daniele vergamini2, fabio bartolini1 1 how milk quotas and the cap affect gi systems: the case of parmigiano reggiano pdo alberto giannecchini1, lisa baldi2, michele donati2, and filippo arfini1 1 department of economics and management, university of parma 2 department of chemistry, life sciences and environmental sustainability, university of parma this paper has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the version of record. please cite this article as: giannecchini a., baldi l., donati m. and arfini f. (in press). are milk quotas beneficial or detrimental for rural development in geographical indication systems? the case of parmigiano reggiano cheese, bio-based and applied economics, just accepted. doi:10.36253/bae-18285 abstract this article investigates the application of the milk quota system to geographical indications (gis), using the parmigiano reggiano pdo cheese as a case study. firstly, an overview on the empirical context focuses on the evolution of cheese production under the rules set by the gi-group. afterwards, an ex-ante analysis evaluates the impacts of the milk quota rules under the cap 2023-2030 scheme on the parmigiano reggiano pdo production system. the latter assessment is addressed through an agent-based model based on the positive mathematical programming approach. the results indicate that the quota system leads to a concentration of production in larger dairy farms, with environmental and social effects that negatively impact the sustainable rural development model advocated by the common agricultural policy. keywords: agent-based modeling, positive mathematical programming, agricultural policy, milk quota, geographical indication jel codes: q12, r11, r58 2 1 introduction in 1984, the european union (eu) introduced the milk quotas regime to curb the overproduction of milk, which had placed a large financial strain on the european agricultural guarantee fund (petit, 1987). this policy represented one of the most controversial decisions in the common agricultural policy (cap 2023-2030), and it has contributed to justify the later reforms (barbero et al., 1984; oskam et al., 2010). eu milk quotas were gradually loosened until their final abolishment starting from april 1, 2015, in order to give farmers a higher flexibility in responding to the growing demand for milk and dairy products in the european market (jongeneel et al., 2023). this policy decision made the eu dairy sector more competitive by increasing exports, but it also introduced additional volatility in milk prices and, consequently, in farmers’ incomes. however, this provision didn’t completely eliminate production quotas, but a new scheme tailored to geographical indication (gi) production groups was created instead. more precisely, for gi dairy products (e.g. parmigiano reggiano, comté, roquefort) there was no continuation of the old eu quotas, but many gi groups and producer organizations introduced their own private or collective supply management rules (sometimes called self-regulation or consortium-based production controls). differently from the old centralized system, these schemes constitute consortium-agreed market regulation measures or planning mechanisms to balance supply with demand, maintain quality, and stabilize prices. this article aims to offer a novel contribute to the existing literature by addressing milk quotas under the modified policy framework concerning gi products, specifically with reference to the parmigiano reggiano (pr) pdo cheese value chain, which was chosen because of its high economic relevance at regional level (the area of production represents 18% of all the milk produced in italy (clal, 2025a)) and the availability of rich and updated data. the importance of this type of case study is supported by donati et al. (2020), who demonstrated that gi products make a significantly higher contribution to the local economy compared to their standard counterparts. the assessment of the quota policy is elaborated by employing an agent-based modelling approach which allows to evaluate comprehensively the three dimensions of sustainability: i) the economic performance, assessed through the gross margin generation, ii) the social impacts (referred to the effects on less profitable activities, i.e. the structural changes in the production system), and iii) the environmental outcomes in terms of greenhouse gases emissions. the objective is to answer to the 3 following research question: how do milk quotas affect the sustainability of the pr pdo rural system? different simulation scenarios will be compared to assess whether the scheme has positive or negative impacts and to identify potential trade-offs across the aforementioned dimensions. to make the analysis closer to reality, it also includes one of the cap 2023-2030 greening measures, so that the results will account for environmental constraints and european subsidies too, likewise in the real market, where farmers not only must respect the rules established by their own gi consortium with regard to inputs and processes, but they are also subject to the greening constraints that affect productive structures, costs, and revenues. the paper is structured as follows: section 2 focuses on the legislative background and relevant literature, and the description of the evolution of the pr pdo quota system according to the rules of the pr-gi group. section 3 describes the methodology of the agent-based model. section 4 presents the results, while section 5 discusses them and draws the conclusions of the study. 2 contextual background 2.1 the legislative framework the removal of milk quotas was performed gradually to avoid market setbacks. a significant milestone is represented by the regulation (eu) 261/2012 on contractual relationships in the milk and dairy sector, that sought to prepare the dairy market for the new context following the cap 2023-2030 shift from the enforcement of direct market instruments to indirect interventions with the objective of stabilizing agricultural prices and ensuring stable incomes for farmers (pieri and rama, 2017; chiodini, 2016). specifically, the abolishment of milk quotas in 2015 was first announced in the regulation (eu) 72/2009, whereas regulation 8, which at the same time deferred a discussion on the consequences of this decision for pdo cheese producers (art. 4, amendment 36 to regulation (ec) 1234/2007). the regulation (eu) 261/2012, article 17, stated that member states should be authorized to establish production control measures for gi cheeses, because of their importance (especially for vulnerable areas) and to guarantee their quality and value added. the italian government applied this provision through the decree of 12 october 2012 (art. 5, par. 1), which recognizes the gi producer groups’ authority to regulate their cheese supply and establish the procedures and conditions for submitting production control plans. after these reforms, the current eu no longer enforces quotas on milk and dairy products, thus granting 4 producers organizations (pos), interbranch organizations (ios), and other groups (as defined under the regulation (eu) 1151/2012) the freedom to autonomously regulate production based on their specific performances and characteristics. the regulation (eu) 261/2012 was issued in connection with the regulation (eu) 1151/2012 (repealed by the regulation (eu) 1143/2024), establishing the gi system (protected designation of origin, protected geographical indication, and traditional specialty guaranteed, commonly referred to as pdos, pgis, and tsgs) to ensure fair remuneration for producers, consistent protection of product names across the eu, and clear consumer information regarding the characteristics that enhance the value of gi products (regulation (eu) 1151/2012, art. 4). the latter regulation also defined pos and ios as entities responsible for ensuring product quality, reputation, and legal protection, as well as promoting value-added attributes (regulation (eu) 1151/2012, art. 45). however, the innovative provisions of this policy framework, intended to enhance market efficiency, appeared to be incomplete as they did not permit all gis to adjust supply in response to changing market demand. notably, only gis in dairy, fruit, and vegetable sectors were authorized to manage production volumes (regulations (eu) 261/2012 and (ec) 1234/2007). only recently, the regulation (eu) 1143/2024 expanded this right to almost all food products recognized as gis (art. 33, par. 3, let. c). such regulatory framework causes the coexistence of two parallel production systems: the non-gi market, where milk is traded without constraints, and the gi market, where it can be indirectly regulated by imposing a threshold on the final product volumes. in other words, the aforementioned evolution finally led to a surreptitious reintroduction of the abolished system of milk quotas, which is intended to secure higher prices for producers, but that also establishes a system of asset capitalization in milk production, thus transforming it into an exclusive right that generates barriers to entry and rent-seeking effects. however, unlike the previous quotas regime, the current policy is markedly different as each gi group can adopt a tailored scheme for the governed pdo/pgi. regarding the italian context, the two most reputable “pdo grana cheeses” (parmigiano reggiano and grana padano pdo) have two different quota systems: the rules of parmigiano reggiano pdo assign the milk quotas to farmers in the form of milk volumes (tons), while the rules for grana 5 padano pdo assign the quotas to dairies in the number of cheese wheels (dongo, 2022; prg, 2014). as highlighted by pieri and rama (2017) and carletti and giangiulio (1998), the system governing the allocation of production rights exhibited rent-seeking dynamics that facilitated the concentration of milk production among larger farms, with significant implications for both rural development and farm management. in terms of rural development, the monetization of quotas by farmers who exited the sector altered the regional development trajectory by removing smaller producers from the supply chain. from a farm management perspective, the consolidation of production among remaining dairy farms often necessitated the assumption of debt, thereby exacerbating financial and operational pressures. this, in turn, undermined the competitiveness and resilience of these farms. the parmigiano reggiano cheese consortium indicates a growing proportion of farms experiencing economic losses between 2019 and 2023, a trend partly attributable to the inherent complexity of implementing effective management control systems in agriculture, which constrains operational efficiency (campiotti, 2024). 2.2 impacts of milk quotas in europe: evidence from the literature there is an extensive stream of literature analyzing the effects of milk quotas both at national and regional levels in the eu context, but it only addresses the old european regime, especially regarding the potential implications of its abolition. before delving into the discussion, it is essential to emphasize that the present study will refer to two distinct types of “efficiency”: i) economic efficiency (or profitability), which is the ability to maximize profits and thereby generate a higher gross margin, and ii) productive efficiency (or productivity), understood as the ability to produce a greater amount of output given the same input level. with respect to the policy outcomes, the article by corbett (1992) argues that the quota scheme discouraged production efficiency and competitiveness, led to market polarization (large producers becoming larger and small farms increasing in number), and caused a stagnation of the dairy industry across the whole european level. however, baldock et al. (2008) highlighted how milk quotas had a positive impact on maintaining production in italian less favored areas (lfas), primarily through the redistribution of unused quotas, thus mitigating the ongoing phenomenon of concentration of production from mountainous lfas to more profitable farms (registered from 1995 to 2004); this came with positive environmental effects, as the maintenance of agricultural activities in less productive, marginal regions is essential for the preservation of soil, biodiversity, and landscape. regarding the scenarios of quotas abolition, lips and rieder (2005) estimated how milk output and price 6 would have changed in the eu at the country level. in particular, it was found through a computable general equilibrium model that raw milk prices decreased on average by 22% and those of dairy products by 12.6%, in response to modest average output increases of 2.9% and 1.8%, respectively. this led to the conclusion that the removal of quotas would have overall minor effects on the european milk production, even though the output changes are heterogeneous for each country. baptiste et al. (2012), using a bioeconomic model based on positive mathematical programming, found that, without quotas, milk production in western france is expected to increase and to concentrate in more competitive regions, thus altering the geographical distribution of supply. the predictions presented in the study by markus et al. (2011) for the removal of quotas, made through the capri model, indicate an overall increase in milk production by 4.4% and a reduction in price by 1.6%, while agricultural income is expected to register a total loss of 2% (nearly 4.7 billion €). as dairy herds are simulated to increase, nitrogen emissions are estimated to grow accordingly, even though the increase is relatively moderate (0.66-1.41%). on the other hand, no considerable changes in arable land use were found. boere et al. (2015) focused on farm dynamics in terms of land use changes in the netherlands. their results show that quotas hamper land expansion for milk production, which becomes faster towards the time period in which they are removed, while land decreases are faster in the presence of quotas because they can be sold (thus making it easier for farms to exit dairy farming). moreover, the model adds to the analysis various socio-economic characteristics of farm holders: a higher education is correlated with more substantial marginal changes in both increases and decreases of land, having successors makes land expansion faster and its reduction slower, full-time labor involvement reduces land changes, and older farm operators are less inclined to alter land use too. more recently, philippidis and waschik (2019) applied the monopolistic melitz model to simulate the changes in the eu milk market from 2020 to 2030 in two scenarios: the baseline, where quotas remain, and the abolition case. the authors found that in the baseline, under the hypothesis of perfect competition, milk prices are expected to increase on average by 31.9 index points by 2030, and dairy prices, due to price transmission effects, by 11.5 index points; meanwhile, downstream dairy production remains static. when quotas are removed, both milk and dairy prices fall (respectively by 39.1 and 10.9 index points) while production volumes grow (more or less markedly depending on the country). an imperfect competition experiment reveals similar results, but it gives additional information about the supply-chain structure, where a "shakeout" of small farms occur as production concentrates into fewer, larger farms. madau et al. (2017) and bórawski et al. (2020) found that the removal of milk quotas fostered the expansion of production, higher 7 efficiency in the use of inputs, and an increase of milk yields. the same phenomenon was confirmed by náglova et al. (2021), who presented several cases in northern europe where the abolition of quotas was correlated with greater milk productive efficiency; notably, eu milk production grew steadily between 2014 and 2018, while experiencing a reduction of about 900,000 cows in 22 eu countries (particularly in germany, france, and italy). from the environmental point of view, läpple et al. (2022) found that the absence of milk quotas promotes increased greenhouse gases (ghg) and nitrogen emissions, exacerbating environmental degradation and contributing to climate change; conversely, quotas can help limit ghg emissions but may lead to the concentration of nitrate production on larger farms. 2.3 the evolution of the milk quotas system for parmigiano reggiano pdo the area of production established by the product specification of the parmigiano reggiano pdo group (prg), considered in this analysis, is strictly limited to the provinces (nuts 3 level) of parma, reggio-emilia, modena, bologna, and mantua (prg, 2025a); specifically, milk, cows, and feed must all originate in these areas and respect specific quality criteria. the regulation of milk production by a quota system was first introduced in 2014 to align milk supply and demand, ensuring that overproduction did not result in price reductions, thus destabilizing farmers’ incomes. other objectives, aligned with the regulation (eu) 1308/2013, include the support to vulnerable areas, quality assurance, and the strengthening of producers’ bargaining power (prg, 2014). the prg effectively maintained the milk price above that in the non-gi market (figure 1). the pr production plan (prg, 2014) defines the supply control mechanisms: at the start of every year, the prg determines a total equilibrium production level and assigns each farmer a milk quota proportional to its production capacity. milk produced beyond this quota does not lose its suitability for transformation into pr cheese. still, if at the end of the year the total amount of cheese produced exceeds the equilibrium level, farmers will have to pay an additional contribution proportional to the amount by which they exceeded their quotas. more precisely, this policy is not directly applied to farmers but indirectly, as the contribution is levied on dairies, which then apportion it among farmers who supplied them with milk, based on each farmer's surplus production. 8 figure 1: comparison of milk prices. source: own elaboration on clal (2025b) and clal (2025c). as a last remark, the production plan defines quotas as "intangible assets", meaning that they can also be sold or rented to other farmers; to safeguard farmers producing in lfas, the quotas located in mountain areas cannot be bought by farmers in plain or hill areas, and vice versa. moreover, a discount on additional contributions is applied for mountain farms. the cheese equilibrium level, the quantity of assigned quotas, and the amount of extra contributions are revised every year based on market conditions. detailed information on the characteristics of farmers supplying milk for pr cheese production is provided in appendix a. at the macro level, pr cheese production steadily grew from 3,298,000 wheels in 2014 to 4,079,000 in 2024, at an average annual rate of 1.6% (clal, 2025a). conversely, active farms decreased from 3,186 in 2014 to 2,606 in 2020, with a 27% reduction in farms below 800 tons/year of production and a 19% increase in farms above 800 tons/year (prg, 2022). according to deserti and bertolini (2021), this occurred because elderly farmers were unable to keep up with innovations and closed their operations, with the allocation of milk quotas accelerating this trend. indeed, quotas were transferred from minor to more extensive, competitive dairy farms. in particular, bertolini and giovannetti (2020) explained how farmers’ innovations increased milk protein content, resulting in better curdling yield, as between 2015 and 2018, the milk needed to produce one cheese wheel was reduced by 5.4%. while a single cow produced, on average, 14 forms/year in 2005, production per cow had increased to 18.5 forms/year by 2021 (prg, 2023). the quota scheme has a substantial impact, as the production threshold has been exceeded increasingly over the years, thereby burdening farmers with growing contributions to be paid (figure 2). moreover, while the additional contribution for surplus production increased, the volumes of exchanged quotas decreased over the years (figure 3). 9 figure 2: comparison between milk transformed into pr cheese and the production threshold. source: own elaboration on clal (2025a) and prg annual production updates (prg, 2025b). figure 3: traded milk quotas and total turnover. source: own elaboration on prg (2025b). finally, the elasticity of produced milk with respect to the assigned quotas is almost unitary. in contrast, the price elasticity is lower, suggesting that farmers adjust production more reactively to quotas than to price (table 1). 10 year total milk (’000 q) total quotas (’000 q) ∆milk/∆quotas ∆milk/∆price 2016 1,823.1 1,824.1 2017 1,907.1 1,835.5 1.01 0.61 2018 1,919.9 1,843.9 0.97 0.07 2019 1,937.2 1,863.6 0.97 0.04 2020 2,017.4 1,876.2 0.97 0.19 2021 2,086.5 1,899.8 0.94 0.60 2022 2,031.2 1,921.9 0.92 0.49 2023 2,071.4 1,930.0 0.95 0.95 table 1: elasticity of processed milk to assigned quotas and to milk price. source: own elaboration on clal (2025a) and prg annual production updates (prg, 2025b). in summary, the quota scheme has contributed to the renewal of the pr system by concentrating production towards farms with higher capacity and productivity, which represents a relevant structural change in the supply chain. this process occurred despite quota purchases (or leases) and contribution payments, which certainly had an impact on the economic and financial management of farms. although quotas are not binding, resulting in milk production consistently exceeding the equilibrium threshold, the price guarantee associated with them, coupled with the prg promotional activity, allowed pr producers to invest in their businesses. 3 the ex-ante assessment of the milk quota system to address the research questions stated in section 1, the study enhances the farm-based model agrisp (agricultural regional integrated simulation package) to analyze the following aspects: i) how trading quotas impact farmers’ economic performance, ii) to what extent this policy favors or hampers marginal farms, and iii) what are the environmental effects in terms of co2 emissions. 3.1 data the sample counts 79 farms covering the pr cheese production area, extracted from the national italian 2023 fadn-rica database and restricted to those specialized in milk production (identified by the farmtype code "450"). further details on the dataset are provided in table 2. the fadn database, through the rica at the italian level, provides data at the individual farm level including detailed information on land utilization, subsidies paid to farmers, crops prices, and family structure of farms. 11 province < 10 ha 11 20 ha 21 50 ha 51 100 ha total parma 7 6 4 1 18 bologna 2 0 1 0 3 modena 1 2 7 1 11 reggio-emilia 5 4 10 0 19 mantua 8 9 10 1 28 total 23 21 32 3 79 table 2: structure of the sample: # of dairy farms. source: rica database (2023). it is assumed that farmers maximize their gross margin (gm) subject to constraints related to environmental practices and production quotas, as described in the next paragraph. a comparative statics approach is used: different equilibrium points are compared based on data from a single year and that all agents are simulated simultaneously. in particular, the granularity of the data allows to simulate the exchange of quotas and land, as less efficient farms in terms of gm transfer their quotas to more efficient agents, consequently also changing their sizes (if production levels decrease, the non-utilized land will also be transferred). while economic and sociodemographic data can be directly extracted from the rica for each farm, additional metrics to measure the emission levels and estimate environmental outcomes are needed. specifically, nitrogen emissions produced by cows are based on data provided by the national regulation 15/12/2017 of the emilia-romagna region, which considers 82.8 kg of no2 (nitrogen dioxide) per dairy cow and 36.0 kg of no2 per rebreeding cow. the amount of emitted co2 (carbon dioxide) is also computed for each activity by following the ipcc (intergovernmental panel on climate change) guidelines (ipcc, 2008; solazzo et al., 2016); the respective values per hectare of production are showed in table 3. 3.2 the model agrisp is an agent-based model (abm) based on positive mathematical programming (pmp), built in gams (general algebraic modeling system)3, introduced by arfini et al. (2005) and further developed over the years to include a more detailed representation of farm relationships (arfini et al., 2008; gigante et al., 2014), environmental metrics, including co2 emissions and water consumption (donati et al., 2013), and policy scenarios, such as the cap 2023-2030 greening measures (solazzo et al., 2014; baldi et al., 2024). 3gams is a software used for mathematical programming capable of solving large-scale linear, nonlinear, and mixed-integer optimization problems through computer code. 12 cereals animal feed industrial crops dairy milk durum wheat 1.33 alfalfa 0.50 beetroot 1.45 milk 5.50 soft wheat 1.55 protein crops 1.04 potato 2.27 sorghum 1.33 pastures 2.24 tomato 2.24 rice 8.50 other forages 0.67 soja 0.81 barley 0.99 oleaginous 0.82 other cereals 1.33 sunflower 0.82 maize 3.52 vegetables 2.21 table 3: tons of co2 emissions per hectare. source: own computations on ipcc guidelines and fadn data, as in baldi et al. (2023). the approach underlying the agrisp model is based on the three elements listed here: 1. development of individual cost functions to address farmers’ heterogeneity; 2. calibration for each farm reproducing their observed activity levels using the so called "selfselection", a rule representing the agents’ willingness to adopt the activities that satisfy their strategy while being aware of all those available (paris and arfini, 2000); 3. introduction of abm rules to simulate the exchange of resources (here the production quotas) by using the common cost function matrix, which provides farmers with the information on those activities not included in their production plan but that could be adopted. the general objective function to be maximized is defined as follows: 𝑚𝑎𝑥 𝐺𝑀 = ∑ (𝑝𝑛,𝑗𝑥𝑛,𝑗) − 1 2 ∑ (𝑥′𝑛,𝑗q̂𝑛,𝑗𝑥𝑛,𝑗 + û𝑛,𝑗𝑥𝑛,𝑗)𝑗𝑗 (1) subject to: anxn ≤ bn (2) 13 where p represents the market prices for each activity j and farm n, and x is the vector of production levels. q constitutes the non-linear part of the pmp equation; it is a symmetric positive semi-definite matrix, ensured through the cholesky factorization, while u is the vector of marginal cost deviations per farm with respect to the frontier cost function qx. in equation 2, a is the matrix of technical coefficients of limiting production factors (here constituted by land), and b is the vector of their availability. a limitation of the model lies in the x′qx matrix as, due to computational constraints, the many activities practiced by the agents have to be aggregated into macro-categories (for instance, single crop species are grouped into broader classifications such as "cereals", "oilseeds", and so on); while this reduces the dimensions of the matrix, it may slightly hamper the model accuracy. two groups of constraints (equations 3-4, where λ is the vector of differential marginal costs) are introduced to implement the ‘self-selection’ process, providing the necessary structure to represent farmers’ production decisions. this mechanism not only enables the model to distinguish between activated and non-activated practices, but also extends beyond basic pmp by allowing the simulation of farmers’ potential access to all available production activities and their choice of those that are most economically convenient from the full set of available options. mcn,k|xk > 0 : λ̄n,k + c¯n,k = qkxn + un,k , k = 1, ..., jn (3) mcn,k|xk = 0 : λ̄k + c¯k ≤ qkxn + un,k , k = 1, ..., j – jn. (4) the first constraint (equation 3) implies that realized productions have a marginal cost (mc) equal to that of all the existing productions j across the frontier. the second one (equation 4) regards the estimation for non-activated productions, based on the relation mc(x) ≡ λ̄ + c̄ = q x̄ , where the common element of the cost function for all farms is obtained from the average marginal cost observed in the sample (arfini et al., 2005). the parameters of the costs matrix q are estimated through the maximum entropy approach, which consists in maximizing the level of uncertainty relative to the probability distribution of each element of the matrix, as explained in detailed in paris and howitt (1998). differently from other methods, like the least squares, the maximum entropy can be used when variable costs are known as in this case and is more consistent with the "inertia" characterizing economic decisions in agricultural businesses (paris and howitt, 1998). the abm is strongly based on this estimation, as the willingness of agents to exchange resources among them depends on their specific marginal costs. 14 in agrisp, dairy production relies on the following assumptions: i) milk price covers the costs of production (equation 5) and ii) the livestock is linked to the available land through the produced fodder crops (equation 6, where y is the feed required per unit of milk). pn,milkxn,milk ≤ cn,milkxn,milk (5) yn,fodderxn,milk – xn,fodder ≤ 0 (6) the market price for fodder crops is set equal to 0 as it is assumed that farmers do not sell it, but reuse it entirely for milk production; feed includes pastures, alfalfa, and other forages, while silage maize is excluded as it is prohibited from the pr cheese product specification (prg, 2025a). to regulate the mechanisms of quota exchange, the following abm constraints are developed in gams. firstly, each farmer has the option to either sell or buy production quotas but, as established in the pr production plan to protect activities in marginal areas (prg, 2014), only farmers in the same agrarian regions can interact among them (i.e., quotas in mountain areas cannot be sold to plain and hill areas, and vice-versa). the price of quotas is exogenous and fixed at the average level observed in 2022, which is equal to 130 €/ton (prg, 2025b). in particular: ∑𝑥𝑛,𝑚𝑖𝑙𝑘 ≤ 𝑥 𝑛,𝑚𝑖𝑙𝑘 + 𝑍𝑛 − 𝑉𝑛 ∀𝑛 𝑗 (7) ∑𝑍𝑛 𝑛 −∑𝑉𝑛 = 0 ∀𝑛 (8) 𝑛 where it is ensured that the total produced milk (∑𝑥𝑛,𝑚𝑖𝑙𝑘) does not exceed the initial level of production x̄ plus the rented quotas zn and minus the sold quotas vn. equation 8 ensures that the exchange of quotas is consistent with the available level. to avoid that farmers sell and buy quotas at the same time, the following control point for each agent is introduced: znvn = 0 (9) meaning that either zn, vn, or both must be equal to zero for each farm, and therefore situations where both have values different from 0 are avoided. finally, the following constraints are introduced to trace transfers for each pair of farms: 𝑍𝑛 = ∑𝑍𝑍𝑛,𝑚 𝑚 ∀𝑛 (10) 15 𝑉𝑚 = ∑𝑉𝑉𝑛,𝑚 𝑛 ∀𝑚 (11) 𝑍𝑍𝑛,𝑚 − 𝑉𝑉𝑛,𝑚 = 0 ∀𝑛∀𝑚 (12) where zzn,m and vvn,m are the matrices for pairs of farms n and m respectively buying and selling quotas. specifically, equation 12 ensures that the quotas bought buy one farm are equal to those sold by the other farm. finally, it is assumed that farmers older than 65 and with no successors do not participate in the trading of quotas and receive a pension equal to 12,000 €. in fact, it is reasonable to assume that older conductors with no future perspectives for their activity would be less inclined to make investments and significantly change the structure of their farm; this tendency is also confirmed by the analysis of boere et al. (2015) (section 2). when a farm reduces its production or exits the market (because production levels become zero), its land has to be transferred to another farm in order to keep the total uaa constant in the model. consequently, constraints analogous to those written for milk quotas are also imposed on the land factor to enable its exchange among agents (baldi et al., 2023). this relationship is represented as: ∑(𝐴𝑛,𝑗𝑥𝑛,𝑗) ≤ 𝑏𝑛 + 𝑅𝑛 − 𝑇𝑛 ∀𝑛 𝑗 (13) where r and t are respectively the transferred and acquired hectares of land by each farm, subject to the same control points and behavioral rules as those listed above for quotas. unlike the quota-related constraints, which are activated only in the scenario simulating the scheme, the land-related ones remain active across all scenarios. 3.2.1 environmental constraints in addition to the previous behavioral constraints, also environmental aspects are included in the model. the cap 2023-2030 greening measures, more specifically the gaec 7, aims at maintaining soil fertility and biodiversity by encouraging farmers to rotate different crops. in agrisp, this standard is implemented through requirements on crop diversification and crop rotation on arable land (european commission, 2013). crop diversification is developed as follows: sn,j ≤ 0.75a (14) sn,j + tn,j ≤ 0.95a (15) where s is the main crop, which cannot occupy more than 75% of the utilized land if the farm owns 16 more than 10 ha of utilized agricultural area (uaa); in the second constraint, applied to farms with more than 30 ha of uaa, the two main crops (s and t) cannot occupy more than 95% of the surface. these constraints ensure a diversified use of land as stated by the cap 2023-2030 greening measures. crops rotation, which requires changes between botanical groups, is introduced through the following equation: xn,alfalfa +xn,proteics +xn,soja +xforages +xn,beet +xn,tomato = xn,wheat +nn,maize +nn,barley +xn,sorgum (16) since agrisp is a static, non-recursive model for comparative static analysis, equation 16 simplifies the condition on annual rotation by assuming that the same share of land is used for wheat, legumes and forages. similar simplifications are also commonly used in dynamic mathematical programming models, where the farm is divided into roughly equal parts and the acreage of each crop is maintained constant, so that crop rotation is treated as a land-share parameter (cortignani and dono, 2020). finally, greenhouse gas emissions are computed for each activity as described in solazzo et al. (2016), based on the guidelines provided by the regional legislation and the ipcc (section 3.1). 3.3 simulation scenarios the milk quotas scheme is assessed by simulating four policy scenarios, described in table 4. name description baseline no quotas policy is applied, and farmers can produce without production constraints. baseline_price an increase in milk price by 7% is applied. quota_trade the abm constraints for the exchange of quotas among farmers are introduced and, therefore, the additional costs and revenues from buying and selling are added to the general equation (1). quota_trade_price the scenario quota_trade is modified by setting an increase in milk price by 7%. table 4: simulated policy scenarios. source: own elaborations on simulation results all simulation scenarios include the cap 2023-2030 greening constraints and payments, as well as those allowing land transfers, described in section 3.3. in the following sections, the changes in economic, structural, and environmental performances will be examined by analyzing the outcomes of introducing quotas into a free market (baseline vs. quota_trade), and how farms respond to a price 17 increase under both the free market (baseline vs. baseline_price) and the constrained market (quota_trade vs. quota_trade_price) scenarios. since agrisp is a static model, all discussed values are always referred to one year of activity. 4 results the simulated impacts of milk quotas are compared with the reference scenario (baseline) and are described in this section. figure 4 depicts the policy effects on crop production, while figure 5 presents the changes in milk production. figure 4: comparison of changes in land utilization. source: own elaborations on simulation results as expected, the productive structure of farms remains mostly unchanged, as they specialize in milk production and therefore always dedicate most of their land to fodder crops. on the other hand, the policy has more visible impacts on milk production. compared to the baseline scenario, the implementation of quotas does not limit milk production very effectively. figure 5: comparison of changes in milk production. source: own elaborations on simulation results however, when the milk price rises, production grows less markedly than in the absence of quotas. in particular, production is highly responsive to price in the non-constrained market, as it nearly doubles 18 (figure 5); even though its elasticity remains more than proportional to price when quotas are imposed, this reaction – previously more uncontrolled – is now dampened (table 5). compared scenarios elasticity baseline vs baseline_price 6.1 quota_trade vs quota_trade_price 2.4 table 5: elasticity of milk production to price. source: own elaborations on simulation results to illustrate the structural effects of the policy, in figures 6 and 7 the number of farms is categorized by altimetric areas and uaa classes, respectively. the variations in their numbers reflect the activation and deactivation of sample farms as they expand or contract their land. when farmers’ production levels fall to zero, they exit the market. the total number of farms is slightly lower when the policy is not enforced. as shown in figure 6, between baseline and baseline_price, there is a slight increase in farms across all three areas, whereas when milk prices rise due to the policy's effect, there is an incentive to enter the market only for mountain and hill farms. figure 7 shows that small producers remain mostly stable across the scenarios, while the activity of medium-large farms (21-50 ha) is more strongly promoted when quotas are applied. figure 6: dynamic number of farms per area and policy scenario. source: own elaborations on simulation results 19 figure 7: dynamic number of farms per uaa class and policy scenario. source: own elaborations on simulation results the economic impacts are shown in figure 8, where the average gross saleable product (gsp) and the gross margin (gm) per farm are plotted. while production volumes grow by 14% without quotas and by 4% with them in place, profits increase by 4% and 6%, respectively. in table 6, farm dimensions and their productivity (average milk production level per cow) are compared. even though milk production (and consequently the gsp) is higher in the quota_trade scenario than in the baseline, the gm is slightly worse. conversely, when milk price rises (in quota_trade_price), there is an improvement in quota_trade, but the gm is always lower than in the scenario where price increases without quotas being enforced (baseline_price). on the other hand, table 6 highlights how in both quota scenarios there are more cows but concentrated in less farms. the quota trading mechanism fosters productivity, albeit not to a greater extent than the increase in milk price alone, simulated in baseline_price. figure 8: economic results per policy scenario. source: own elaborations on simulation results 20 scenarios cows number cows per farm tons of milk per cow baseline 2,346 34 5.01 baseline_price 3,111 41 5.38 quota_trade 2,832 37 5.19 quota_trade_price 3,214 41 5.35 table 6: farms productivity per policy scenario. source: own elaborations on simulation results environmental outcomes are displayed in figure 9. results show that the policy is not very effective in reducing total greenhouse gases (ghg) emissions. however, when milk price rises, emissions increase by a lower degree than in the absence of quotas. figure 9: ghg emission levels (co2 and no2) per policy scenario. source: own elaborations on simulation results 5 discussion after analyzing the key findings on the implementation of the pr production plan, this study aims to evaluate the actual economic, structural, and environmental impacts of milk supply management under the policy framework implemented by the prg. from a policy perspective, the main concern is the establishment of new farm assets associated with the right to produce milk for pr cheese, which expanded farms' production capacity and economic dimensions (table 6). the quotas policy fostered the conditions for production growth; in fact, it is not strictly binding, but farms can exceed their allocated production threshold and pursue economies of scale by trading milk quotas. in the scenarios, while a higher milk production is incentivized in all areas (mountain, hill, and plain) when quotas are in place, the market becomes much less reactive to price changes (figure 5 and table 5). moreover, this mechanism does not constitute a strong entry barrier, 21 as there are more farms in the market than the baseline scenarios (figure 7); additionally, the rule establishing that quotas cannot be traded between different agrarian regions prevents plain and hill farms – characterized by a much higher production capacity – from competing with mountain farms, thus supporting production and development in such lfas. however, since quotas represent an additional cost for farmers who want to expand their production, an increase in productivity is not followed by an improved economic efficiency: although farms intensify their production in an attempt to cover their costs (table 6), thereby also generating a higher gsp, gross margins in absolute terms remain on average lower compared to those generated in the free market (figure 8). this aligns with bouamramechemache et al. (2008), where it is stated how production quotas are attributable to decreases in farmers’ gross margins (which are even stronger when production grows). this means that gains from higher production quantities are not able to fully compensate for investments in quotas, and further price increases would be needed to achieve the same profits. indeed, in quota_trade_price they rise by a slightly greater extent in percentage terms, but the result is still worse than in baseline_price. while the existing literature agrees on the positive effects of quotas on profitability, here it is highlighted how, in a supply chain with different and stricter cost structures typical of gi products, enhanced productivity does not necessarily come with better economic results. on the contrary, the expansion of costs generated by the quotas scheme is not offset by the current price premium guaranteed to farmers. bigger farms, with stronger capital bases, may absorb these costs more easily, while smaller activities might experience sharper constraints, which in the long term could reinforce structural inequalities within the sector. therefore, even if quotas do not seem to stop farmers from entering the market, they can hamper competitiveness when economic margins are systematically eroded. indeed, looking at the social dimension, even though both small farms and activities in marginal areas are preserved as they do not exit the market, there is a slight tendency to concentrate production towards larger farms when quotas are traded (table 6). a limitation of the model lies in the absence of dynamic elements, which prevents us from considering how this dimension would evolve across multiple years. however, the empirical evidence (appendix a) confirms that the concentration observed in the simulations, when comparing the baseline and quota_trade scenarios, is bound to become increasingly exacerbated. while this may represent an advantage when it strengthens farmers’ ability to invest in more efficient production technologies, it simultaneously undermines social sustainability, as it excludes small farmers from the supply chain – those very actors that gis are, by definition, meant to support. at the same time, the policy does not improve the environmental outcomes, since ghg emissions are 22 at the lowest level in the baseline scenario (figure 9). this stems from the fact that, as mentioned above, the quotas are not strictly binding and an expansion in land allocated to forage production and livestock is observed, so that environmental impacts are not curbed either. on the other hand, accordingly to the analysis of baldock et al. (2008), the policy as implemented by the pr consortium is actually effective in preserving marginal mountain areas, which, in an overly competitive context, would otherwise be abandoned with negative consequences for soil and ecosystems. in this regard, the environmental issue is particularly relevant in light of the art. 7 of regulation (eu) 1143/2024, allowing gi groups to draft sustainability reports on their production system. 6 conclusions of the study from a methodological perspective, the pmp approach is employed to calibrate model parameters and assess production costs, replicating observed practices and outcomes. abms complement this approach by enabling the social characterization of individual agents and their interactions within the economic environment through rule-based asset exchanges. the integration of pmp and abm enables broader, more realistic scenario simulations by accounting for economic and non-economic parameters. the overall result is a more detailed analysis of impacts, ranging from sectoral to regional scales, and an improved understanding of the phenomena observed as individual policy measures vary. in summary, with respect to the research question stated in the study's introduction, the findings suggest that milk quotas have mixed effects on the sustainability dimensions of the pr cheese rural system, thereby allowing for the evaluation of both the pros and cons of the policy. in particular: i) farmers’ profits (economic aspect) are slightly eroded, ii) activities in marginal areas (social aspect) are preserved, but there is a general tendency of polarization towards larger farms, and iii) ghg emission levels (environmental aspect) are not reduced, but the increased number of farms operating in mountain areas has a positive role on the preservation of ecosystems and landscapes. when drawing conclusions, it has to be acknowledged that pmp and abms, like any other economic modelling approach, imply simplifications and assumptions on agents’ behaviors and market dynamics. in the first place, even though the behavioral assumptions are realistic and supported by the literature, they remain quite simplistic with respect to reality; the decision on whether to participate or not in quotas investments may depend not only on age and future business prospects, but also on risk aversion, "myopic" or long term planning, access to market information, and volatility of milk price. 23 however, such data cannot be incorporated into the model because it is not currently available in either national or regional databases. thus, future research may improve the model by enriching it with additional elements from behavioral economics theories. further studies may also focus on creating recursive structures capable of modelling individual agents for several years, with the aim of studying the evolution of the system at a high level of detail over time. as a closing remark, the supply control system for eu gis is a significant tool for safeguarding farmers’ incomes; nevertheless, its application extends beyond pdo and pgi systems’ management, influencing rural development dynamics toward selecting commercial activities based on market competitiveness and impacting the relationship between agriculture and the environment. therefore, it is crucial to understand the effects of these supply chain management tools and transform them into effective tools for production, social and environmental planning at the territorial level. this development should occur through collaborations between local stakeholders in coordination with the rural development program. 24 references allevaweb (2025). parmigiano reggiano 2016-2024 (e oltre): la filiera raccontata dai suoi numeri. available at: https://allevaweb.it/parmigiano-reggiano-la-filiera-raccontata-dai-suoi-numeri/. last accessed: october 2025. arfini, f., donati, m., petriccione, g. and solazzo, r. 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(2014). a pmp model for the impact assessment of the common agricultural policy reform 2014-2020 on the italian tomato sector. new medit. xiii. 9-19. solazzo, r., donati, m., tomasi, l. and arfini, f. (2016). how effective is greening policy in reducing ghg emissions from agriculture? evidence from italy. science of the total environment, 573:1115–1124. https://backend.parmigianoreggiano.com/uploads/disciplinare_parmigiano_reggiano_1a92c5e19a.pdf https://backend.parmigianoreggiano.com/uploads/disciplinare_parmigiano_reggiano_1a92c5e19a.pdf https://registro.parmigianoreggiano.it/qlpr/home.do https://registro.parmigianoreggiano.it/qlpr/home.do 29 appendix a milk tons per farm 2014 2015 2016 2017 2018 2019 2020 2021 2022 0 200 1,086 1,070 949 812 801 712 598 548 497 200 400 840 797 774 756 717 673 652 600 572 400 800 654 637 647 660 618 614 632 629 609 800 2.000 476 479 495 501 514 520 517 533 516 2.000 4.000 100 103 115 134 138 144 156 166 159 > 4.000 30 26 33 37 40 43 51 56 57 total 3,186 3,112 3,013 2,900 2,828 2,706 2,606 2,532 2,410 table 7: number of farms by production class. source: prg (2022). altimetric area 2016 2017 2018 2019 2020 2021 2022 2023 2024 mountain 1,085 1,044 1,018 975 940 912 865 825 800 plain and hill 1,928 1,865 1,810 1,734 1,670 1,620 1,544 1,466 1,421 total 3,013 2,900 2,828 2,709 2,610 2,532 2,413 2,291 2,221 table 8: number of farms by geographical distribution. source: allevaweb (2025). enabling technologies in citrus farming: a living lab approach to 1 agroecology and sustainable water resource management 2 giuseppe timpanaro 1, giulio cascone 1*, vera teresa foti 1 3 1 department of agriculture, food and environment, university of catania, via s. sofia 100, 95123, 4 catania, italy. giuseppe.timpanaro@unict.it; giulio.cascone@phd.unict.it; v.foti@unict.it 5 6 this article has been accepted for publication and undergone full peer review but has not been through 7 the copyediting, typesetting, pagination and proofreading process, which may lead to differences 8 between this version and the version of record. 9 please cite this article as: 10 timpanaro g., cascone g., foti vt (2025). t enabling technologies in citrus farming: a living 11 lab approach to agroecology and sustainable water resource management, bio-based and applied 12 economics, just accepted. doi:10.36253/bae-17357 13 14 highlights 15 • enabling technologies accelerate agroecological transition in inland agriculture. 16 • sensors, dss, and digital tools reduce water consumption on citrus farms. 17 • digital technologies boost yield per hectare and increase net profit. 18 • living labs foster knowledge transfer, reducing resistance to innovation. 19 • monte carlo simulation reveals key drivers affecting economic outcomes. 20 21 abstract 22 this study examines the role of enabling technologies in the agroecological transition, focusing on 23 sustainable water management in citrus farming through the participatory approach of a living lab 24 in the inner area of calatino in sicily. the analysis is based on a comparison of two citrus farms: 25 one equipped with advanced digital tools (sensors, decision support systems, and real-time 26 monitoring), and one with a traditional management approach. through the joint application of 27 mailto:giuseppe.timpanaro@unict.it mailto:giulio.cascone@phd.unict.it mailto:v.foti@unict.it economic analysis, monte carlo simulation and sensitivity analysis, it was possible to estimate the 28 effects of technology adoption. findings reveal that enabling technologies reduce water consumption 29 by 33%, increase yield per hectare by 16%, and boost net profit by 25% (+€2,780/ha), enhancing 30 resource efficiency and lowering operational costs. additionally, the living lab facilitated 31 knowledge transfer, fostered collaboration, and mitigated resistance to innovation, highlighting the 32 need for targeted training and institutional support to promote broader adoption. 33 these results provide valuable insights for policymakers and stakeholders, demonstrating how digital 34 solutions can drive sustainability, economic viability, and resilience in agriculture, but also for 35 farmers, providing operational tools to improve farm efficiency and profitability. 36 37 keywords 38 agroecology, enabling technologies, living lab, water management, citrus farming 39 40 1. introduction 41 in recent decades, agroecology has become a key strategy to tackle sustainability challenges in 42 agriculture. it combines ecological, economic, and social principles to address problems like soil 43 degradation, biodiversity loss, climate change, and economic inequality this paradigm not only 44 protects the environment but also offers economic advantages by fostering local markets, short supply 45 chains, and more equitable and resilient food systems (van der ploeg et al., 2019; d’annolfo et al., 46 2017; poux and aubert, 2018). 47 agroecology successfully integrates environmental sustainability with agricultural productivity 48 through practices that enhance soil fertility, promote crop diversification, and reduce reliance on 49 chemical inputs. studies have demonstrated that agroecological systems can achieve yields 50 comparable to those of conventional agriculture while delivering significant benefits in terms of lower 51 environmental impact and increased resilience to climate change (d’annolfo et al., 2017; poux and 52 aubert, 2018). moreover, adopting agroecological practices improves the quality of food produced, 53 contributing to human health and the well-being of farming communities (belliggiano and conti, 54 2019). 55 other studies have highlighted how agroecological systems can generate economic benefits for 56 farmers by reducing dependence on external inputs and increasing long-term profitability (van der 57 ploeg et al., 2019; d’annolfo et al., 2017). however, the agroecological transition requires adequate 58 support from public policies, including instruments that promote the adoption of agroecological 59 practices and facilitate market access for small-scale producers (gava et al., 2022; schiller et al., 60 2020). agroecology not only promotes more sustainable and resilient farming practices but also 61 represents a comprehensive approach to agri-food governance, fostering farmers' autonomy, food 62 sovereignty, and social justice (van der ploeg et al., 2019). 63 a key factor in accelerating the agroecological transition is the integration of key enabling 64 technologies (kets), such as digital tools, internet of things (iot) sensors, artificial intelligence, 65 and precision agriculture systems, which optimize resource management and reduce waste (chollet 66 et al., 2023; bellon-maurel et al., 2022). these technologies provide real-time data on soil and crop 67 status, boosting efficiency and reducing environmental impact (fischetti et al., 2025; ewert et al., 68 2023). by adapting practices to local conditions, kets offer agroecology a practical path to greater 69 sustainability (ewert et al., 2023). 70 71 however, the integration of kets into agroecology has sparked debate within the agroecological 72 community, dividing the sector into two opposing perspectives. traditionalists argue that 73 agroecology should preserve traditional practices and local knowledge, avoiding reliance on 74 technological tools that could disrupt the ecological and social balance of agricultural systems. 75 modernizers see innovation as an opportunity to improve sustainability and efficiency. they support 76 the responsible integration of new technologies to make farming models more resilient (bertoglio et 77 al., 2021; menozzi et al., 2015; arata and menozzi, 2023). 78 despite these concerns, the synergy between agroecology and enabling technologies offers significant 79 potential for sustainable development, particularly in inner areas. these territories can benefit from 80 agroecological innovation to revitalize agricultural activity and enhance local natural resources (gava 81 et al., 2025; verharen et al., 2021). moreover, inner areas offer unique opportunities for 82 agroecological innovation due to the presence of traditional farming systems and the availability of 83 high-quality natural resources (verharen et al., 2021). the integration of modern technologies into 84 agroecological production systems—through decision-support tools, knowledge-sharing platforms, 85 and mobile applications for farm management (espelt et al., 2019; emeana, 2021)— represents a 86 concrete opportunity to facilitate the transition to more sustainable models. these tools can help 87 reduce barriers to the adoption of agroecological practices and strengthen producers' competitiveness 88 in the market (maurel and huyghe, 2017). 89 in this context, living labs emerge as essential tools for promoting an integrated system that 90 combines technology and agroecology. these participatory innovation spaces engage farmers, 91 researchers, policymakers, and other agri-food system stakeholders, fostering the experimentation of 92 innovative solutions and facilitating knowledge transfer at the local level (larbaigt et al., 2024; 93 berghez et al., 2019; giampietri et al., 2020; ouattara et al., 2024). living labs serve as a bridge 94 between scientific research and agricultural practice, allowing technologies to be tailored to specific 95 territorial needs, thereby improving farmers' acceptance of new practices and enhancing the 96 effectiveness of transition strategies (giagnocavo et al., 2022; belliggiano and conti, 2019). 97 a concrete example of such integration is the experimental initiative focused on citrus farming in the 98 inner area known as the "calatino," aimed at demonstrating its economic feasibility. this territory 99 encompasses nine municipalities in central-eastern sicily (caltagirone, grammichele, licodia eubea, 100 mazzarrone, mineo, mirabella imbaccari, san cono, san michele di ganzaria, and vizzini) all within 101 the metropolitan city of catania. the area represents 1.6% of the regional population and spans 102 approximately one thousand square kilometres. 103 in this living lab a range of integrated systems have been installed, incorporating weather stations, 104 sensors, and decision-support systems, with the aim of optimising water usage. this initiative is 105 expected to enhance resource use efficiency, while concurrently improving the resilience and 106 economic viability of the production system (fischetti et al., 2025; ewert et al., 2023; rocchi et al., 107 2024). 108 citrus farming was selected for this study because it represents one of the most relevant agricultural 109 sectors in sicily, with more than 30 % of national citrus production, and oranges covering more than 110 60 % of the total supply (scuderi et al., 2022). while remaining a leading global player, italy has lost 111 leadership in the last decade due to structural criticalities in strategic areas such as sicily (rapisarda 112 et al., 2015), which nevertheless maintains 55 % of the national area dedicated to citrus (about 61 000 113 ha) (istat, 2022). 114 the research was based on the hypothesis that adopting an integrated system (weather station, 115 sensors, and decision-support system) enables a more sustainable management of water resources, 116 reducing waste (water consumption) and environmental costs while positively impacting operational 117 costs, revenues, and farm economic efficiency. 118 therefore, the following research questions were formulated: 119 • q1. how can the integration of enabling technologies accelerate the agroecological transition 120 in inner areas? 121 • q2. what are farmers' perceptions and resistances regarding the adoption of digital tools and 122 precision agriculture systems in the agroecological context? 123 • q3. what economic and environmental impacts result from combining agroecological 124 practices with innovative technologies, particularly in the citrus sector? 125 • q4. to what extent do living labs facilitate the creation of an integrated system that merges 126 technology and agroecology, fostering sustainability in inner areas? 127 128 2. materials and methods 129 2.1. study area 130 the inner area of calatino covers approximately 982 km² and includes nine municipalities in the 131 province of catania: caltagirone, grammichele, licodia eubea, mazzarrone, mineo, mirabella 132 imbaccari, san cono, san michele di ganzaria, and vizzini. the area has a population of 133 approximately 70,606 inhabitants. it is characterized by an economy strongly linked to agriculture, 134 with a significant presence of farms and specialized crops, as well as artisanal activities primarily 135 related to ceramics and small-scale industry. 136 the utilized agricultural area (uaa) of the inner area of calatino amounts to 56,330 hectares, of 137 which approximately 4% is allocated to organic farming. organic production is particularly 138 concentrated in the municipalities of san cono (11%) and vizzini (9.9%). overall, the calatino 139 region hosts 279 organic farms, primarily cultivating citrus fruits, vineyards, olive groves, and 140 herbaceous crops, representing a growing sector. 141 one of the most representative sectors in terms of income and employment in calatino is citrus 142 production, particularly concentrated in the municipality of mineo, which hosts vast plantations 143 dedicated to the cultivation of oranges and mandarins (table 1). 144 145 table 1. agricultural land and crops in the calatino region. 146 municipality area (km²) farms utilised agricultural area (ha) citrus groves (ha) vineyards (ha) olive groves (ha) herbaceous crops (ha) caltagirone 383,37 2.368 20.437 615 892 1.469 10.659 grammichele 32,07 511 1.698 480 21 176 665 licodia eubea 112,45 823 6.132 68 956 342 2.660 mazzarrone 34,78 352 1.905 17 865 160 375 mineo 245,27 1.859 15.423 3.000 30 952 5.573 municipality area (km²) farms utilised agricultural area (ha) citrus groves (ha) vineyards (ha) olive groves (ha) herbaceous crops (ha) mirabella imbaccari 15,3 214 990 4 9 117 419 san cono 6,63 100 278 1 4 33 58 san michele di ganzaria 25,81 217 904 4 45 139 535 vizzini 126,75 463 8.563 170 48 296 4.080 total calatino 982 6.907 56.330 4.359 2.870 3.684 25.024 source: elaboration on istat data, 2022. 147 148 additionally, other municipalities in the area, such as caltagirone and vizzini, also feature extensive 149 citrus orchards, although integrated with other agricultural productions. mazzarrone is renowned for 150 its pgi table grapes, while san cono stands out for its pdo prickly pear (figure 1). 151 152 153 figure 1. production characteristics of the study area (our elaboration). 154 155 local agriculture is characterized by a combination of herbaceous crops (cereals, legumes, forages) 156 and tree crops (vineyards, olive groves, citrus orchards, and fruit trees), with a huge portion of the 157 area dedicated to organic or transitioning farming methods. 158 the university of catania has launched a living lab with the aim of fostering the transition towards 159 sustainability and a circular economy. the initiative involves farmers, local institutions, 160 environmental organisations and consumers, and is focused on establishing the calatino bio-district. 161 among the various crops present, citrus cultivation was chosen as the focal crop for the living lab 162 project because of its significant economic weight in the calatino area and its sensitivity to water 163 resource management issues. citrus fruits represent one of the main sources of local agricultural 164 income and require particularly efficient water management, making them an ideal case for 165 experimenting with innovative strategies in line with agroecological principles. 166 the primary objectives are to promote: 167 • the transition to organic farming and organic certification to enhance the competitiveness of 168 local products; 169 • the adoption of sustainable agricultural practices, such as crop rotations, organic fertilizers, 170 and integrated pest management, in line with agroecological principles; 171 • short supply chains, through local markets and the creation of a food hub for the distribution 172 and valorization of organic products; 173 • social inclusion and cooperation among producers, processors, and distributors. 174 through these strategies, the bio-district aims to enhance the environmental sustainability of local 175 agriculture and promote economic development based on circularity and biodiversity, positioning 176 calatino as a model for agroecological transition in sicily. 177 2.2. study design 178 the calatino living lab serves as a participatory platform where farmers, researchers, technical 179 experts, and institutional representatives collaborate to facilitate the agroecological transition of the 180 region. this large-scale transition is often hindered by regulatory constraints, economic challenges, 181 and technological limitations (toffolini et al., 2021; beaudoin et al., 2022; potters et al., 2022; 182 yousefi and ewert, 2023; timpanaro et al., 2024; gardezi et al., 2024). in sicily, the recent regional 183 legislation on agroecology (regional law no. 21 of 29/07/2021, "provisions on agroecology, 184 biodiversity protection, sicilian agricultural products, and technological innovation in 185 agriculture") establishes strict criteria for farms, highlighting the need for an in-depth analysis of its 186 practical implications and potential areas for improvement. 187 the methodological approach adopted is summarized in figure 2. the establishment of a 188 collaborative ecosystem is imperative for the co-design of innovative solutions for sustainable water 189 resource management, agroecology, and the adoption of enabling technologies by farmers, 190 institutions, researchers, businesses, and consumers. a preliminary study involved the identification 191 of key stakeholders and the definition of local challenges. this was followed by structuring the living 192 lab as a participatory platform for research and experimentation. stakeholders were selected using a 193 targeted approach, favoring organic or in-conversion farmers operating in the citrus sector who 194 expressed interest in adopting agroecological practices and innovative technologies. institutional 195 representatives, technicians and local associations with a key role in promoting agricultural 196 sustainability in the calatino area were also involved. stakeholder engagement was achieved through 197 preliminary meetings, thematic focus groups, interactive workshops, and demonstration visits to pilot 198 farms, with invitations disseminated via email, social media, and local networks. 199 although this targeted selection ensured the active participation of motivated and competent actors, 200 it is important to recognise that it may have introduced a certain degree of bias into the selection. 201 specifically, the inclusion of stakeholders already inclined towards innovation and sustainability may 202 limit the generalisability of the results to broader agricultural populations that may be more hesitant 203 or resistant to adopting digital technologies. 204 205 206 figure 2. methodological framework adopted in the calatino living lab. 207 208 the first step of the living lab was an in-depth analysis of regional regulations to understand the 209 criteria for recognizing agroecological farms and the potential barriers to their adoption. through 210 participatory discussions among stakeholders several critical issues were identified, including: 211 • high initial requirements, such as the obligation to allocate 20% of farmed land to native 212 varieties and to replant 20% of the area with indigenous tree species; 213 • management difficulties, due to the requirement for complex environmental certifications and 214 the high costs of compliance; 215 • limited technological support, as no incentives are provided for adopting innovative tools that 216 could facilitate the agroecological transition; 217 • commercial constraints, including the obligation to sell 20% of production in local markets, a 218 requirement that could disadvantage farms located in more remote areas. 219 the stakeholder discussions within the living lab also highlighted a shared need to leverage 220 technological innovations to support farms in resource management, improve production efficiency, 221 and ensure economic sustainability. a key concern among stakeholders was water resource 222 management, one of the main challenges for sicilian agriculture. multiple focus groups were 223 organized to explore issues such as: 224 • how can water management be improved in agroecological farms? 225 • which technologies can promote water conservation without compromising productivity? 226 • what strategies can be adopted to make irrigation more efficient and less dependent on 227 intensive water use? 228 the focus groups revealed that many organic farms lack advanced tools for water monitoring, relying 229 instead on empirical practices that often lead to waste or water shortages. 230 based on the discussions and emerging needs, two organic citrus farms in the calatino region were 231 selected as pilot cases to assess the impact of enabling technologies applied to irrigation management 232 (one implementing key enabling technologies and the other without kets). these farms align with 233 the agroecological principles defined by fao (2018) and were equipped with (table 2): 234 • weather stations for real-time monitoring of temperature, humidity, and precipitation; 235 • soil sensors to measure moisture levels and optimize irrigation; 236 • decision support systems (dss) based on climatic and agronomic data to enhance resource 237 management. 238 the choice of these technologies was guided directly by the critical issues identified during the focus 239 groups. soil sensors and weather sheds allow accurate monitoring of environmental parameters, 240 enabling more efficient irrigation management tailored to actual crop needs. the dss system 241 provides farmers with decision support based on objective data, reducing uncertainty in irrigation 242 planning and helping to limit water wastage. table 2 summarizes the comparison between the 243 principles of agroecology (fao, 2018), the corresponding enabling technologies, and their practical 244 application in traditional agroecology, precision agriculture, and the two pilot farms within the living 245 lab. the structure of the table allows for a direct comparison of how different approaches integrate 246 technology to address agroecological goals. reading across each row, one can observe the 247 progressive transition from traditional practices to precision and digitally-supported agroecological 248 farming. each principle – such as biodiversity, resource efficiency or co-creation of knowledge – is 249 linked to specific digital tools (e.g. soil sensors, dss platforms) and corresponding practices observed 250 in the field. for example, while the traditional approach relies on experience-based decisions, the 251 digitised farm uses real-time data to manage irrigation and nutrient input more precisely. this 252 alignment between agroecological objectives and enabling technologies illustrates how innovation 253 can improve sustainability and productivity without compromising ecological integrity. 254 255 256 257 table 2. comparison between agroecology, precision agriculture and the two pilot citrus farms for experimentation 258 within the calatino living lab. 259 fao principles enabling technologies agroecology precision agriculture farm with technologies farm without technologies 1. diversity gis (geographic information systems) biodiversity mapping irrigation and fertilization zoning mapping cover crops and water retention traditional cultivation without mapping 2. synergy big data local agroecological planning optimization of production efficiency weather and soil data analysis for crop synergy experiencebased management and traditional rotations 3. efficiency iot (internet of things) sensors for water conservation automated irrigation and fertilization targeted irrigation sensors and dss for water management scheduled irrigation without monitoring 4. resilience drones monitoring of natural resources detection of infestations and targeted irrigation decision-support system for mitigating water and climate stress reactive response to climate change without predictive tools 5. recycling sensors natural measurement of soil nutrients advanced soil and crop monitoring nutrient monitoring to reduce chemical inputs fertilizers and compost application based on experience 6. knowledge sharing big data and digital platforms shared access to environmental and agricultural data ai-driven process optimization software for comparison between agroecological farms limited knowledge exchange within local cooperatives 7. human and social values mobile applications for farmers digital training for social inclusion agricultural workforce automation decision-making support based on digital data dependence on personal experience and manual labor 8. food traditions blockchain for traceability protection of local production monitoring of production chains traceability of farm sustainability traditional sales without digital certification 9. responsible governance open data and gis active participation in agricultural management. automated data collection for agricultural policies use of platforms for farm monitoring participation limited to local cooperatives 10. circular economy iot and ai for agricultural waste management recycling and reuse of agricultural byproducts waste reduction through optimization crop residue recovery and reuse of wastewater traditional disposal without optimization 260 261 262 2.3. elaboration method 263 the comparison between citrus farming with and without innovative technologies was based on the 264 analysis of total costs and net benefits for each system, including water savings, production yield, and 265 profitability increase, as extensively explored in the literature (alston, 2010; pardey et al., 2010; 266 lubell et al., 2011; alston et al., 2021; medici et al., 2021; jamil et al., 2021). 267 the baseline assumptions for the comparison are reported in table 3. the analyzed parameters 268 highlight the potential impact of digital innovations on irrigation, climate monitoring, decision-269 making processes, water-use efficiency, management costs, and agronomic yield. 270 271 table 3. comparison parameters adopted in the evaluation of kets in citrus fruit growing (*) aspect farm with technology farm without technology irrigation uses precise data (soil moisture, weather forecasts) to optimize water requirements irrigation based on experience and traditional fixed irrigation cycles (not optimized) climate monitoring weather station and sensors provide realtime data on temperature, wind, and rainfall based on visual observations and generic weather forecasts decision-making user-friendly application suggests irrigation timing and quantity subjective decisions based on intuition and experience water efficiency greater water control with reduced waste high risk of water excess or deficit, leading to higher-thannecessary consumption management costs initial investment in technology, but lower variable costs (e.g., energy for irrigation) constant costs due to inefficient resource use agronomic yield optimized water requirements and reduced plant stress, leading to higher productivity yield affected by irrigation mismanagement or unexpected climatic conditions *our elaboration. 272 as for the total costs (c) for each agricultural system, these are calculated as the sum of the costs of 273 water, fertiliser, labour, cover crops and technology (for the innovative system only), as shown in 274 table 4. 275 276 table 4. data determination methodology for evaluating the cost-effectiveness of adopting kets technology for water 277 savings. 278 variables farm with technology farm without technology total costs (c) 𝐶𝑡 = 𝐴 ∗ (𝑊𝑡 ∗ 𝐶𝑤 + 𝐶𝑓 + 𝐶𝑝 + 𝐶𝑡 + 𝐶𝑒 + 𝐶𝑐𝑐 + 𝐶𝑜𝑡ℎ𝑒𝑟) 𝐶𝑐 = 𝐴 ∗ (𝑊𝑐 ∗ 𝐶𝑤 + 𝐶𝑓 + 𝐶𝑝 + 𝐶𝑒 + 𝐶𝑐𝑐 + 𝐶𝑜𝑡ℎ𝑒𝑟) total revenue (r) 𝑅𝑡 = 𝐴 ∗ 𝑃𝑡 ∗ 𝑝 𝑅𝑐 = 𝐴 ∗ 𝑃𝑐 ∗ 𝑝 net profit (π𝑐) π𝑡 = 𝑅𝑡 − 𝐶𝑡 = 𝐴 ∗ (𝑃𝑡 ∗ 𝑝 − (𝑊𝑡 ∗ 𝐶𝑤 + 𝐶𝑓 + 𝐶𝑝 + 𝐶𝑡 + 𝐶𝑒 + 𝐶𝑐𝑐 + 𝐶𝑜𝑡ℎ𝑒𝑟)) π𝑐 = 𝑅𝑐 − 𝐶𝑐 = 𝐴 ∗ (𝑃𝑐 ∗ 𝑝 − (𝑊𝑐 ∗ 𝐶𝑊 + 𝐶𝑓 + 𝐶𝑝 + 𝐶𝑒 + 𝐶𝑐𝑐 + 𝐶𝑜𝑡ℎ𝑒𝑟)) the variables considered were the following: a = cultivated area (ha); pc = production per hectare in agriculture without innovative water-saving technologies (t/ha); pt = production per hectare in agriculture with innovative watersaving technologies (t/ha); p = sales price per tonne (€/t); wc = water consumption per hectare in agriculture without innovative water-saving technologies (m³/ha); wt = water consumption per hectare in agriculture with innovative water saving technologies (m³/ha); cw = water cost per m³ (€/m³); cf = fertiliser cost per hectare (€/ha); cp = pesticide cost per hectare (€/ha); ct = technology cost (installation + maintenance per hectare) (€/ha); ccc = cover crop cost per hectare (€/ha); ce = energy cost per hectare (€/ha); cother = other costs (€/ha). 279 280 the additional benefit of farming with innovative technologies over conventional farming is given 281 by: 282 δ𝐵 = π𝑡 − π𝑐 283 expanding 284 δ𝐵 = 𝐴 ∗ ((𝑃𝑡 − 𝑃𝑐) ∙ 𝑝 − [(𝑊𝑡 − 𝑊𝑐) ∗ 𝐶𝑤 + 𝐶𝑡 + 𝐶𝑐𝑐]) 285 where: 286 (pt pc) * p = represents the increase in profitability due to increased production. 287 (wt wc) * cw = represents the water savings in terms of costs. 288 ct + ccc are the additional costs for the adoption of technologies and cover crops. 289 if: 290 δb>0 → adoption of the technologies is cost effective. 291 δb<0 → the additional costs outweigh the benefits, making the transition uneconomic without 292 incentives. 293 δb≈0 → profitability is similar in the two models, but there may be indirect environmental benefits. 294 the economic evaluation was completed with a sensitivity analysis, hypothesising alternative 295 scenarios on a possible rent for the kets plant and equipment (necessary to have up-to-date and 296 enhanced decision support systems with links to meteorological databases), and with a monte carlo 297 modelling to focus the analysis on the other variables (water consumption, operating costs, 298 production) that present uncertainty and that most influence the difference in profit between the two 299 pilot companies. 300 monte carlo modelling assumes that: 301 ∆π𝑖 = π𝑖 𝑡𝑒𝑐ℎ − π𝑖 𝑛𝑜𝑛𝑡𝑒𝑐ℎ 302 at the end of n iterations we estimate 303 • the average profit for each company 304 π̅𝑡𝑒𝑐ℎ = 1 𝑁 ∑ π𝑖 𝑡𝑒𝑐ℎ𝑁 𝑖=1 and π̅𝑛𝑜𝑛𝑡𝑒𝑐ℎ = 1 𝑁 ∑ π𝑖 𝑛𝑜𝑛𝑡𝑒𝑐ℎ𝑁 𝑖=1 305 • the average difference 306 δπ̅̅ ̅̅ = 1 𝑁 ∑ δπ̅̅ ̅̅ 𝑖 𝑁 𝑖=1 307 • the distribution (and dispersion) of ∆π, which makes it possible to assess the probability that 308 the technology will lead to a higher profit. 309 the final monte carlo model used was as follows: 310 δπ = [400 ∗ 𝑄𝑡𝑒𝑐ℎ − (𝑤 ∗ 𝑐𝑤 𝑡𝑒𝑐ℎ + 𝑐𝑐𝑜𝑣𝑒𝑟 𝑡𝑒𝑐ℎ + 𝑐𝑓𝑒𝑟𝑡 𝑡𝑒𝑐ℎ + 𝑐𝑝𝑒𝑠𝑡 𝑡𝑒𝑐ℎ + 𝑐𝑒𝑛𝑒𝑟𝑔𝑦 𝑡𝑒𝑐ℎ + 𝑐𝑡𝑒𝑐ℎ + 𝑐𝑜𝑡ℎ𝑒𝑟)]311 − [400 ∗ (𝑤 ∗ 𝑐𝑤 𝑛𝑜𝑛𝑡𝑒𝑐ℎ + 𝑐𝑐𝑜𝑣𝑒𝑟 𝑛𝑜𝑛𝑡𝑒𝑐ℎ + 𝑐𝑓𝑒𝑟𝑡 𝑛𝑜𝑛𝑡𝑒𝑐ℎ + 𝑐𝑝𝑒𝑠𝑡 𝑛𝑜𝑛𝑡𝑒𝑐ℎ + 𝑐𝑒𝑛𝑒𝑟𝑔𝑦 𝑛𝑜𝑛𝑡𝑒𝑐ℎ + 𝑐𝑜𝑡ℎ𝑒𝑟)] 312 where each uncertain parameter is sampled from a specified distribution. repeating this calculation 313 for many iterations yields the profit difference distribution, which provides a comprehensive 314 assessment of the economic sensitivity to the adoption of the innovative technology. 315 3. results 316 3.1. living lab approach and case study characteristics 317 the two citrus farms analyzed were identified as pilot sites within the living lab of the calatino 318 inner area, a collaborative ecosystem aimed at testing and validating innovative solutions for 319 regenerative citrus farming and sustainable water resource management. the objective is to develop 320 scalable strategies for other farms seeking to integrate regenerative practices with technological 321 innovations. 322 the selection of the farms (table 5) was based on: 323 • representation of the citrus sector within the region and the study area. 324 • diversity in management practices, as one farm adopted enabling technologies, while the 325 other relied on a traditional agroecological approach. 326 • entrepreneurs’ willingness to engage in the co-experimentation and training process. 327 the two pilot farms are in mineo (catania province) and share the same production identity (5 328 hectares of blood oranges, organic certification, and a commitment to regenerative agriculture). their 329 differing agricultural management approaches make them suitable case studies for assessing the 330 impact of enabling technologies compared to a system based solely on traditional agronomic 331 experience. 332 333 table 5. structural characteristics of the pilot sites. information farm with technology farm without technology localization mineo mineo uau, ha 5 5 production address blood orange blood orange organic certification yes yes regenerative agriculture cover crops + advanced water management cover crops with traditional management water use sensor monitoring + dss manually programmed irrigation nutrient management soil analysis + targeted fertilisation experience-based fertilisation pest control biological strategies + data monitoring biological strategies without monitoring market selling to local supply chains and quality markets selling to local supply chains and quality markets *our elaboration 334 the farm utilizing innovative technology has integrated sensors, a decision support system (dss), 335 and advanced soil analysis to optimize irrigation and plant nutrition. the goal is to achieve more 336 efficient water use, a more targeted nutrient management strategy, and continuous pest monitoring, 337 thereby reducing input usage and maximizing productivity. 338 the farm without innovative technology follows a more traditional approach, with manually 339 scheduled irrigation and fertilization based on the farmer’s experience. while it employs cover crops 340 and organic farming strategies, it lacks tools for real-time monitoring of soil and water conditions, 341 which can result in less precise management and higher resource consumption. 342 the intersection of three key elements—organic farming (a low-impact agricultural management 343 model aligned with agroecological principles, aiming for balanced and resilient production systems 344 while reducing dependency on external inputs), regenerative agriculture (cover crops contribute to 345 reducing erosion, improving water retention, and increasing soil organic matter, fostering a healthier 346 and more productive ecosystem in the long term), and enabling technologies (agroecology does not 347 exclude technology but leverages it to enhance sustainable resource management)—is represented by 348 agroecology. this guiding principle unites the two pilot farms of the living lab in the calatino. 349 this integrated approach improves the sustainability, productivity, and resilience of agricultural 350 systems, turning environmental and economic challenges into opportunities for innovation (niggli, 351 2015; gascuel-odoux et al., 2022; bless et al., 2023; domínguez et al., 2024). 352 3.2. issues related to the management of irrigation resources 353 the discussion among stakeholders on the water emergency in citrus farming has highlighted how it 354 is the result of a combination of climatic, institutional and economic factors that negatively affect 355 production and farm sustainability. figure 3 represents a visualization of the relationships between 356 the main factors characterizing this crisis, as they emerged during the focus group. the structure was 357 elaborated using maxqda software, through the exploration of co-occurrences between thematic 358 codes applied to text segments. the figure is organized hierarchically, starting from the main cause 359 (climate change) at the top, branching downward into its effects on water availability and plant health, 360 and further into institutional and economic consequences. arrows represent causal links, while 361 mitigation strategies are shown as side branches connected to the specific problems they address. no 362 color coding was used; the structure is entirely based on logical connections and thematic clusters. 363 this approach made it possible to clearly highlight the connections between climatic, institutional 364 and economic variables, as well as the mitigation strategies adopted by citrus growers and sector 365 experts. 366 367 figure 3. cause-effect relationships in irrigation water management issues in citrus farming 368 369 the central element of the water crisis, as emerged from the discussion, is climate change, which 370 manifests through alterations in rainfall patterns. this results in two opposing but equally damaging 371 situations: water scarcity, caused by reduced precipitation and rising temperatures that intensify 372 evaporation and increase plant water demand, or water excess, with sudden and intense rainfall 373 leading to floods, water stagnation, and root damage. 374 these issues are compounded by institutional inefficiency, which worsens water resource 375 management. the lack of maintenance of watercourses, poor planning in water distribution, and the 376 bureaucratic rigidity of reclamation consortia make it difficult for citrus growers to access water when 377 they need it most. additionally, the absence of a consumption-based pricing system leads to waste 378 and inefficient resource use. 379 to address the water crisis, citrus growers have adopted various technological and agronomic 380 solutions. these include innovations in irrigation, such as surface and subsurface micro-irrigation 381 systems to reduce water waste, or the use of regulated deficit irrigation systems to optimize water use 382 according to plant growth stages. farmers have also experimented with alternative water resources, 383 such as treated wastewater, through phytoremediation processes, to reduce dependence on 384 conventional water sources. a common strategy is the selection of rootstocks resistant to water stress, 385 as well as the use of raised beds to improve drainage and controlled cover cropping. 386 according to stakeholders, a coordinated territorial approach involving public institutions, 387 reclamation consortia, and producer organizations is lacking. additionally, a revision of irrigation 388 tariffs based on actual consumption could encourage more responsible water use, while increased 389 digitalization in water resource management (sensors, weather stations) could enable more precise 390 irrigation planning. 391 these results highlight not only the complexity of the water crisis, but also the proactive role of 392 farmers in experimenting with feasible solutions. the issues and strategies discussed in this section 393 have been translated into the visual structure shown in figure 3, which helps to summarise the entire 394 problem-solving framework in a single view. this makes the figure particularly useful for better 395 understanding where to intervene and how to support adaptation efforts more effectively. 396 3.3. cost-effectiveness assessment of kets deployment 397 the calculations clearly show the positive impact of ket adoption on farm management, with 398 benefits reflected in water efficiency, operating costs, productivity and overall profitability. 399 table 6 shows that the adoption of enabling technologies results in a significant improvement in farm 400 management, with water consumption reduced by 33% and a consequent annual saving of 420 €/ha, 401 without penalizing productivity. this implies greater sustainability in resource use and reduced 402 production costs. 403 the cost of energy for water withdrawal is reduced by 31%, confirming how energy efficiency is an 404 additional economic benefit of technological innovation. 405 productivity increases by 6 tons/ha (+16%), translating into a revenue increase of €2,400/ha. this 406 result underscores how technological adoption not only improves efficiency, but also directly 407 contributes to strengthening the company's competitiveness. 408 at the same time, there is a reduction in the use of fertilizers (-15%) and a drastic decrease in 409 pesticides (-71%), reflecting the improvement in agronomic management and less dependence on 410 external inputs, with clear economic and environmental benefits. 411 table 6. parameters for comparing citrus fruit farms with and without kets (*) parameter farm with technology farm without technology difference % annual water consumption (m³/ha) 2,800 4,200 -33% average cost of water (€/m³) 0.3 0.3 0% water saving (€/ha) 420 € 0 € -- water saving (%) 33% 0 -- production per hectare (t/ha) 44 38 16% sale price (€/t) 400 € 400 € 0% revenues per hectare (€/ha) 17,600 € 15,200 € 16% cost cover crops (€/ha) 250 € 250 € -- fertiliser costs (€/ha) 720 € 850 € -15% pesticide cost (€/ha) 40 € 140 € -71% energy cost for irrigation (€/ha) 520 € 750 € -31% technology investment (€/ha) 500 € 0 € -- other cost 1,570€ 1,990€ -21% total cost (€/ha) 3,600 € 3,980 € -10% *our elaboration. 412 413 despite an initial investment of €500/ha, the innovative company achieves a net profit of €14,000/ha, 414 compared to €11,220/ha for the traditional company, with a 25% increase in profitability (+€2,780/ha) 415 (table 7). this highlights how the economic benefits far outweigh the costs of technology adoption. 416 417 table 7. comparison of economic benefits and adoption convenience between citrus farms with and without kets (*) parameter farm with technology farm without technology difference % revenues r (€/ha) 17,600 € 15,200 € 16% total costs c (€/ha) 3,600 € 3,980 € -10% net profit π (€/ha) 14,000 € 11,220 € 25% change in benefits (δb) +2,780 *our elaboration. 418 419 3.4. sensitivity analysis 420 considering three scenarios based on complete enabling technologies to be acquired by annual 421 subscription, a sensitivity analysis can also be developed (table 8): 422 • 200 €/ha/year → basic package (sensors + basic software); 423 • 400 €/ha/year → intermediate (sensors + advanced dss + local weather) 424 • 600 €/ha/year → advanced (sensors + advanced dss + weather integrated with weather 425 databases such as sias, ispra, swissmetnet, etc.). 426 sensitivity analysis on the different levels of technology subscription shows that even with a higher 427 fee (600 €/ha/year), the positive margin remains substantial (+2,180 €/ha compared to the farm 428 without technology). the intermediate package (400 €/ha/year) emerges as the one most balanced 429 between investment and economic benefit, suggesting a sustainable option for maximizing farm 430 profitability. 431 432 433 434 435 436 table 8. profit sensitivity with technology rent (*) rental scenario annual cost per hectare (€) net new profit (€/ha) difference vs. farm without technology (€) convenience compared to the traditional model rent 200 €/ha/year 200.00 € 13,800.00 2,580.00 very affordable rent 400 €/ha/year 400.00 € 13,600.00 2,380.00 still profitable rent 600 €/ha/year 600.00 € 13,400.00 2,180.00 advantageous but low margin *our elaboration. 437 438 to assess how net profit (€/ha) responds to key economic drivers, a monte carlo simulation was 439 conducted. the goal was to compare the farm adopting innovative technology with the one that does 440 not, highlighting how variations in certain parameters can either amplify or reduce the benefits 441 derived from technology adoption. 442 the model assumed that the product’s selling price (400 €/t) and non-specific fixed costs (e.g., general 443 expenses, logistics) remain constant, while variations in production and costs influenced by 444 technology were analyzed. analysis considered water costs, expenses for cover crops, fertilizers, 445 pesticides, irrigation energy, and, for the technology-adopting farm, the technological investment. 446 the monte carlo simulation involves repeated iterations, where in each cycle, random values are 447 drawn for each parameter according to predefined distributions. in this study, uniform distributions 448 around baseline values were assumed. in particular, the unit cost of water was varied between 0.3 and 449 0.5 €/m³, while water consumption for the technological farm ranged between 2,520 and 3,080 m³/ha, 450 and for the non-technological farm, between 3,780 and 4,620 m³/ha. similarly, production per hectare 451 and operating costs were defined within specific intervals to reflect real-world variability and 452 simulate a wide range of scenarios. 453 table 9 shows that, on average, the farm adopting technology achieves a net profit of approximately 454 14,000 €/ha, while the non-technological farm reaches around 11,220 €/ha, resulting in an average 455 difference of +2,780 €/ha. these results indicate a significant average economic benefit from 456 adopting innovative technology. the standard deviations, 1,200 €/ha and 1,400 €/ha respectively, 457 highlight considerable variability. this suggests that while the average benefit is positive, in some 458 scenarios, the advantage may be lower or even more pronounced. 459 table 9. monte carlo simulation results (*) statistics farm with technology (€/ha) farm without technology (€/ha) difference (tech nontech, €/ha) average profit 14.000 € 11.220 € 2.780 € standard deviation 1.200 € 1.400 € 1.300 € minimum profit 11.000 € 8.500 € 2.500 € maximum profit 17.000 € 15.500 € 3.500 € median 14.100 € 11.300 € 2.800 € *our elaboration. 460 461 the economic advantage is primarily driven by savings in operational costs. the technology enables 462 a substantial reduction in water consumption, leading to lower water expenses, and decreases costs 463 associated with fertilizers and pesticides, due to more efficient and sustainable farming practices. 464 these savings, combined with a potential increase in yield per hectare, contribute to a higher net 465 profit. 466 the simulation also highlights the model’s sensitivity to various parameters. for instance, an increase 467 in the unit cost of water shifts total costs to higher values, making water savings even more critical. 468 similarly, variations in yield per hectare directly affect revenue and, consequently, net profit. the 469 ability to adjust multiple parameters simultaneously helps identify key drivers of economic success 470 and potential sources of risk. 471 the monte carlo simulation comparing farms with and without innovative technology demonstrates 472 that adopting technology leads to a significant average increase in net profit per hectare. these 473 findings provide essential support for strategic decision-making in a competitive and dynamic 474 environment, where operational efficiency and innovation are crucial for success. 475 4. discussion 476 the analysis conducted within the living lab of the calatino inner area has enabled an exploration 477 of the impact of enabling technologies on the agroecological transition in inner areas, highlighting 478 economic, environmental, and organizational benefits. starting from the research questions, the 479 findings clearly show that the integration of enabling technologies enhances the efficiency of resource 480 management, particularly in terms of water and nutrient use, helping to improve productivity and 481 keep costs down. these findings align with those reported by bellon-maurel et al. (2022) and maurel 482 and huyghe (2017), who highlight how digital tools contribute to resource optimization and improved 483 sustainability in agricultural systems. furthermore, ajena et al. (2022) emphasize that digitalization 484 can break down traditional barriers fostering innovation in rural sectors, particularly in inner areas 485 where challenges are more pronounced. therefore, the integration of technology accelerates the 486 agroecological transition by providing farmers with real-time data and decision-making tools that 487 enhance precision and sustainability in farm management. regarding the second research question, 488 the comparison between the two pilot farms revealed a significant gap in farmers' perceptions. the 489 farm that adopted the innovative technology reported tangible benefits, such as reduced operational 490 costs and improved productivity. in contrast, the farm following a traditional approach relied on well-491 established methods and expressed skepticism toward digital tools. this resistance stems from a 492 perception of greater reliability associated with traditional methods, combined with limited 493 familiarity with innovative technologies and concerns about high initial costs and a steep learning 494 curve. these aspects are consistent with the findings of anderson and maughan (2021) and schiller 495 et al. (2020), who describe the existing gap between innovation and tradition in agriculture. literature 496 suggests that the lack of specific training and institutional support represents a major barrier to the 497 adoption of digital technologies (timpanaro et al., 2023). 498 in this context, living labs serve as co-experimentation and training spaces that facilitate knowledge 499 transfer and help overcome initial resistance (scuderi et al., 2023). active participation and dialogue 500 among farmers, researchers, and technical experts contribute to demystifying new technologies and 501 highlighting their potential in sustainable resource management. living labs show that they can 502 function as catalysts for change, fostering an agroecological transition that is not only technologically 503 advanced, but also socially inclusive (cascone et al., 2024; beaudoin et al., 2022). 504 the third research question led to a deeper analysis and reflection on the economic outcomes through 505 monte carlo simulation. from an economic perspective, the farm integrating enabling technologies 506 achieves higher per-hectare revenues due to increased production and more efficient cost 507 management. these findings align with the studies of alston (2010) and pardey et al. (2010), which 508 emphasize how agricultural innovation can generate substantial economic benefits. 509 from an environmental perspective, the adoption of innovative technologies promotes more 510 sustainable resource management and a reduction in chemical input use. the decrease in water 511 consumption and pesticide application, for example, contributes to minimizing environmental impact 512 and fostering more regenerative agricultural practices. these results are consistent with the evidence 513 provided by domínguez et al. (2024) and d’annolfo et al. (2017), who highlight the potential of 514 combining agroecological practices with technological innovation to promote sustainable and 515 resilient agriculture. thus, the integration of technologies not only enhances economic efficiency but 516 also represents a successful approach to reducing environmental impact by encouraging a more 517 responsible use of resources. 518 finally regarding q4, the living lab model implemented in the calatino context has proven to be an 519 effective environment for the co-creation and experimentation of innovative solutions. the two pilot 520 farms, despite sharing the same production identity and organic certification, differ in their 521 management approach: one integrates enabling technologies, while the other follows a traditional 522 method. this strategic choice has highlighted how the presence of digital technologies is not 523 contradictory to agroecological principles but rather enhances their effectiveness, improving the 524 sustainable management of resources and the resilience of the production system. 525 living labs play a crucial role in bridging the gap between technological innovation and traditional 526 agricultural practices. they provide a space where farmers, researchers, technologists, and 527 institutional stakeholders can experiment, exchange experiences, and validate solutions in real time 528 (scuderi et al., 2024). in our case, the adoption of digital tools has improved irrigation monitoring 529 and management, leading to more efficient water use and lower operational costs. these results, 530 combined with the integration of regenerative practices such as the use of cover crops and targeted 531 nutrient management, contribute to creating an integrated system that addresses the environmental 532 and economic challenges of inner areas. moreover, the active participation of farmers in living labs 533 fosters a bottom-up approach that stimulates responsible innovation and the dissemination of best 534 practices. 535 for example, during one of the demonstration sessions, an organic farmer had the opportunity to test 536 a low-cost soil moisture monitoring system, immediately noting its usefulness in reducing water 537 waste. this kind of direct experience helped turn initial prejudice into interest and openness. in 538 another case, a young farmer who initially showed skepticism toward the use of digital data for crop 539 management changed his perspective after sharing his needs with a group of experts within the living 540 lab and receiving support in interpreting the data collected. the opportunity to learn by doing, in a 541 nonjudgmental and co-creation-oriented context, proved essential to reduce cognitive barriers and 542 build confidence toward innovation. recent studies (gascuel-odoux et al., 2022; potters et al., 2022) 543 also highlight how collaboration and the engagement of local actors are essential for achieving 544 effective and sustainable agroecological transitions. 545 a critical issue that deserves attention concerns the economic implications related to the costs of 546 adopting enabling technologies, especially in vulnerable rural settings. while these technologies can 547 generate efficiency and reduced operating costs, they often entail significant upfront investments, the 548 need for technical maintenance, and increasing dependence on external suppliers. this can lead to an 549 imbalance in bargaining power between farms, which are often small or medium-sized, and 550 technology providers, which operate according to industrial and centralized market logics. 551 in the absence of adequate support and regulatory measures, this imbalance can produce regressive 552 effects: farms with greater economic capacity will be able to access technologies more easily and take 553 competitive advantage of them, while the more fragile realities risk being excluded from the 554 innovation process (bissadu et al., 2025). 555 for this reason, it is crucial to accompany technology adoption with targeted policy strategies capable 556 of ensuring affordability, technical training, systems interoperability and open innovation models. 557 living labs, represent a possible lever to rebalance power dynamics through co-design and direct 558 involvement of farmers in technology selection and testing processes. to effectively address these 559 power imbalances and promote a more inclusive adoption of enabling technologies, several targeted 560 policy actions should be considered. such measures can help rebalance contractual relationships 561 between farmers and technology providers, in line with the principles of responsible innovation 562 (bellon-maurel et al., 2022; beaudoin et al., 2022; gava et al., 2025). 563 first, public incentives for technology adoption should be conditional on the use of open standards 564 and interoperable systems to avoid technological lock-in, as discussed by ditzler and driessen (2022) 565 and clapp and ruder (2020). this approach strengthens farmers' autonomy and prevents dependence 566 on proprietary technologies controlled by a few large suppliers (bissadu et al., 2025). 567 second, it is essential to promote the creation of farmer-led cooperatives or technology consortia to 568 strengthen collective bargaining power in the purchase and negotiation of technology services. this 569 is in line with recommendations to strengthen agricultural innovation systems (potters et al., 2022) 570 and enable bottom-up governance models (gava et al., 2025). 571 thirdly, the creation of public platforms dedicated to the collective procurement of technologies, 572 supported by technical advisory services and independent consultants, can further protect farmers 573 from unfavourable contractual conditions. the provision of advisory vouchers for access to third-574 party technical expertise would complement this strategy. 575 furthermore, regulatory frameworks should explicitly recognise farmers' ownership of agricultural 576 data generated by digital systems, ensuring that technology providers cannot appropriate or monetise 577 such data without informed consent (clapp and ruder, 2020; bellon-maurel et al., 2022). 578 living labs themselves can be institutionalised as territorial “technology brokers”, acting as 579 independent intermediaries to ensure equitable access to innovation and promote co-created solutions 580 tailored to local needs (beaudoin et al., 2022; gardezi et al., 2024). this model of participatory 581 innovation is in line with the agroecological governance structures advocated by gascuel-odoux et 582 al. (2022), which support equitable access to technological innovation in rural areas. 583 by adopting these integrated strategies, policymakers can help reduce asymmetries in bargaining 584 power, protect the interests of smallholder farmers, and promote an inclusive, resilient, and 585 participatory agroecological transition. 586 in summary, our research findings indicate that: 587 • the integration of enabling technologies accelerates the agroecological transition by 588 improving resource management and increasing profitability. 589 • farmers’ perceptions are influenced by direct experience and the support provided by living 590 labs, which help overcome resistance to innovation. 591 • the combination of agroecological practices and innovative technologies generates positive 592 economic and environmental impacts, as evidenced by increased productivity and reduced 593 operational costs. 594 • living labs play a key role in facilitating the integration of technology and agroecology, 595 fostering the creation of integrated and sustainable systems in inner areas. 596 these findings not only confirm the existing literature but also provide an operational framework to 597 guide strategic decisions in complex agricultural contexts, where sustainability and innovation need 598 go hand in hand. the integrated and participatory approach promoted by living labs thus emerges 599 as an effective response to current and future challenges, helping to transform environmental and 600 economic challenges into opportunities for innovation and sustainable development. 601 602 5. conclusions 603 the study conducted within the living lab of the calatino inner area highlights how the integration 604 of enabling technologies can play a crucial role in accelerating the agroecological transition in rural 605 areas. the results, derived from a comparative analysis of two pilot citrus farms—one adopting 606 advanced digital tools and the other maintaining a traditional approach—demonstrate economic, 607 environmental, and managerial benefits, confirming the transformative potential of such innovations. 608 the farm that integrated sensors, decision support systems (dss), and other digital technologies 609 achieved significant operational efficiency, including a 33% reduction in water consumption and a 610 16% increase in yield per hectare, leading to a 25% improvement in profitability. these findings not 611 only underscore the importance of more precise resource management but also confirm that the 612 adoption of enabling technologies can enhance environmental sustainability by reducing chemical 613 inputs and improving irrigation efficiency. the study also highlights some critical issues and concrete 614 challenges to be addressed. among these, the affordability of technologies is a major obstacle, 615 especially for small companies with limited liquidity. similarly, the technical complexity of the 616 systems and the costs associated with maintenance, software updates and staff training may limit 617 widespread adoption. furthermore, the scalability of the tested solutions remains to be verified in 618 different contexts due to soil and climate conditions, farm size and crop type. 619 however, this study has some limitations. first, the small number of cases analyzed may limit the 620 generalizability of the results. given the diversity of agronomic and socio-economic contexts, further 621 large-scale studies are needed to confirm the replicability of the observed benefits. additionally, 622 while the methodology integrates an in-depth economic analysis and a monte carlo simulation, it 623 could be enriched by further long-term measurements to assess the economic and environmental 624 sustainability of these technologies over time. 625 another limitation concerns the analysis of farmers’ perceptions. while the comparison between the 626 innovative and traditional groups highlighted resistance and scepticism toward digital tools, a more 627 extensive qualitative investigation—such as in-depth interviews or focus groups with a broader 628 sample of producers—could provide further insights into the dynamics of adoption and the training 629 needs required to support the transition. 630 based on these considerations, several future research directions emerge. expanding the living lab 631 model to other rural areas in sicily and different agricultural sectors could help determine whether 632 enabling technologies can generate similar benefits in different contexts. future studies could 633 implement comparative pilot projects in different production systems, such as viticulture or olive 634 growing, and monitor key indicators like water use efficiency, yield performance, and farmer 635 adoption rates over at least three growing seasons. 636 further research could also explore the long-term impact of adopting digital tools, analyzing, for 637 example, how economic and environmental benefits evolve over multiple production cycles and 638 under changing climatic and market conditions. longitudinal studies should be conducted, integrating 639 detailed farm accounting records, soil and water monitoring data, and farmer surveys, to track both 640 economic returns and resource use efficiency over a 5–10 year horizon. another key area of interest 641 involves the development of training programs and institutional support mechanisms to facilitate the 642 dissemination of these technologies among farmers. future initiatives should design modular, 643 practice-oriented training programs focused on digital literacy, irrigation management, and precision 644 agriculture tools, targeting different farmer profiles (smallholders, young farmers, cooperatives), 645 possibly through partnerships with vocational training institutes and local cooperatives. 646 collaborations with universities and research centers to design dedicated training programs could 647 help overcome learning curve challenges and promote greater adoption of digital systems. 648 finally, the study highlights the importance of targeted policy actions to mitigate power asymmetries 649 between farmers and technology providers. by introducing conditional incentives, promoting 650 collective procurement mechanisms, supporting open innovation models, and formalising the role of 651 living labs as technology intermediaries, policymakers can help ensure that the digital 652 transformation in agriculture promotes autonomy, inclusiveness, and long-term sustainability (clapp 653 and ruder, 2020; bellon-maurel et al., 2022; gava et al., 2025). these measures are essential to 654 enable a fair and balanced agroecological transition, particularly in vulnerable rural contexts. 655 this study demonstrates that the integration of enabling technologies, supported by a participatory 656 model such as the living lab, represents a fundamental driver in accelerating the agroecological 657 transition in rural areas. despite certain limitations, the findings provide a strong scientific and 658 operational contribution, suggesting that the combination of digital innovation and agroecological 659 practices can not only enhance economic efficiency and environmental sustainability but also foster 660 cultural and organizational change toward a more resilient and inclusive agricultural system. future 661 research and targeted policy interventions will be essential to facilitate the broader adoption of these 662 models and contribute decisively to the transformation of the agri-food system. 663 664 funding: this study was carried out within the agritech national research center and received 665 funding from the european union next-generationeu (piano nazionale di ripresa e 666 resilienza (pnrr) – missione 4 componente 2, investimento 1.4 – d.d. 1032 667 17/06/2022, cn00000022). this manuscript reflects only the authors’ views and opinions, neither 668 the european union nor the european commission can be considered responsible for them. 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(2023). agroecology, technology, and stakeholder awareness: 872 implementing the un food systems summit call for action. iscience, 26(9). 873 https://doi.org/10.1016/j.isci.2023.107510 874 https://doi.org/10.1016/j.agsy.2023.103661 https://doi.org/10.1186/s42055-023-00060-9 https://doi.org/10.1186/s42055-023-00060-9 ________________________________________________________________________________________________ 1 bridging the gap: the impact of compensatory measures on 1 mountain farming in piedmont 2 moino francesca1, giacardi alessandro2 , califano giovanbattista3 and cagliero roberto1 3 1 crea pb, turin, italy 4 2 università di roma la sapienza, rome, italy 5 3 università degli studi di napoli federico ii, naples, italy 6 corresponding author: francesca moino (francesca.moino@crea.gov.it) 7 8 this article has been accepted for publication and undergone full peer review but has not been through 9 the copyediting, typesetting, pagination and proofreading process, which may lead to differences 10 between this version and the version of record. 11 please cite this article as: 12 moino f., giacardi a., califano g., cagliero r. (2025). bridging the gap: the impact of 13 compensatory measures on mountain farming in piedmont, bio-based and applied economics, 14 just accepted. doi:10.36253/bae-16852 15 16 acknowledgments 17 this work was funded by the european union next-generationeu: (call prin 2022 italian ministry of university 18 and research (mur) recipe cod. 20224zhnxe). 19 highlights 20 21 ● an income gap exists between mountain and non-mountain cattle farms in piedmont 22 ● rural development program support helps reduce but does not fully eliminate this economic 23 disparity 24 ● addressing income inequalities would require more than doubling per-hectare compensatory 25 allowance payments 26 ● redistribution of support should prioritize farm type and labor over land area 27 28 29 abstract 30 31 this study examines the impact of the rural development program (rdp) on reducing income 32 disparities between farms in mountainous and non-mountainous areas in piedmont, italy. using 33 farm accounting data network data from 2012–2022, the analysis focuses on cattle, sheep and 34 goats, and fruit farms, with 525 farms (3,171 observations; 36% in mountainous areas). a pooled 35 ________________________________________________________________________________________________ 2 multivariate regression assesses income disparities excluding rdp support, rdp’s effectiveness in 36 mitigating gaps, and the role of compensatory allowance. 37 the findings indicate that significant income disparities are primarily observed in small farms 38 specialized in cattle and sheep and goats, with mountain farms facing a net shortfall of €1,319 and 39 €2,384 per hectare, respectively. while compensatory allowance support helps reduce this gap—by 40 8.93% for cattle farms and 5.28% for sheep and goat farms—a substantial disparity remains. 41 bridging the gap entirely would require doubling compensatory payments to €340 per hectare, 42 though alternative strategies are discussed. 43 44 keywords: fadn, compensatory allowance, mountain areas, income gap 45 46 jel classification codes: c18, c54, q18, r58 47 48 1. introduction 49 mountain agriculture in europe faces critical challenges due to the natural constraints of these 50 regions, often classified as areas with natural constraints (ancs) under regulation (eu) no. 51 1305/2013, art. 32.1 previously termed less favoured areas (lfas) under the common 52 agricultural policy (cap), these regions face challenges such as steep slopes, adverse climates, and 53 poorer soil fertility, which collectively restrict arable land and require labour-intensive farming 54 practices. these limitations lead to higher production costs and reduce profitability, causing many 55 farmers in mountainous regions to abandon agricultural activities (giannakis & bruggeman, 2015; 56 strijker, 2004). such abandonment contributes to depopulation, economic decline, and the loss of 57 agro-biodiversity and traditional landscapes (cesaro & marongiu, 2013). to counteract these adverse 58 trends, the european union (eu) has historically provided targeted support to anc within the cap 59 1 see regulation (eu) no 1305/2013 of the european parliament and of the council of 17 december 2013 on support for rural development by the european agricultural fund for rural development (eafrd) and repealing council regulation (ec) no 1698/2005. oj l 347, 20.12.2013, p. 487. ________________________________________________________________________________________________ 3 framework, aiming to mitigate the biophysical disadvantages and sustain agricultural livelihoods in 60 ancs. 61 the effectiveness of anc and lfa support measures has been widely studied, with mixed 62 findings (romagnoli et al., 2021). for instance, borsotto et al. (2010) questioned the effectiveness of 63 these subsidies across different european contexts, while ferto et al. (2022) and klima et al. (2020) 64 observed heterogeneity in outcomes based on regional applications. oxousi (2012) conducted a 65 comparative analysis on farm performance, finding that while anc support plays a key role in the 66 profitability of mountain and other disadvantaged farms, mountainous farms still struggle compared 67 to those in less favoured, but more productive, areas. similarly, wieliczko et al. (2018) identified 68 significant disparities in production efficiency and economic performance between farms receiving 69 anc support and those that do not, indicating that while anc payments partially help mitigate 70 economic disadvantages, they do not fully compensate for them. 71 as the anc policy evolved, the 2014–2020 rural development program (rdp) introduced 72 more refined eligibility criteria to direct support toward regions with genuine biophysical constraints. 73 anc support remains a crucial tool for maintaining agricultural activities, preserving rural 74 landscapes, and preventing land abandonment in vulnerable regions (whitaker, 2024; veveris et al., 75 2014). in mountainous regions, these payments significantly contribute to rural sustainability, by 76 retaining population, enhancing business viability, and preserving environmental and territorial 77 integrity (cooper et al., 2006; dax et al., 2021). 78 while the impact of anc support has been widely studied, limited research has assessed its 79 effectiveness in reducing economic disparities between mountainous and non-mountainous farms in 80 specific regional contexts. this study aims to address this gap by analyzing the adequacy of 81 compensatory allowance (ca) in mitigating income inequalities in mountain agriculture, focusing on 82 the piedmont region. unlike previous research, which has often relied on cross-country or aggregated 83 assessments (poláková, 2019), this study adopts a context-specific approach using farm accountancy 84 data network (fadn) data. by evaluating the financial resources required to ensure that 85 ________________________________________________________________________________________________ 4 compensatory measures effectively offset the economic disadvantages faced by mountain farmers, 86 this research provides a robust policy assessment and contributes to a more precise understanding of 87 rural development interventions (romagnoli et al., 2021). 88 the choice of piedmont, a region in northern italy, as a case study is justified by its diverse 89 agricultural landscape, which includes both mountain and lowland areas, allowing for a meaningful 90 comparison between farms operating under different environmental constraints. covering over 52% 91 mountainous territory, piedmont includes both the apennine and alpine ranges, where depopulation, 92 agricultural abandonment, and the difficulties faced by small, isolated communities are pronounced 93 (ferlaino, 2019). livestock farming, central to piedmont’s mountainous areas, relies on limited 94 resources and small, family-run farms, which differ substantially from lowland agriculture (istat, 95 2020). eu programs have increasingly addressed these issues, although the region’s administrative 96 fragmentation, characterised by numerous small municipalities, complicates local development 97 management. this makes piedmont a compelling case for understanding the complexities of 98 mountain development and the critical role of targeted policies in fostering sustainability. 99 to properly interpret the methodology and findings, it is essential to understand the structure 100 of the ca under the rdp, established under regulation (ec) no. 1698/2005. during the 2007–2013 101 programming period, the ca was implemented through measure 211, while in the 2014–2020 period, 102 it was integrated into measure 13.1, both aimed at providing direct financial support to farmers in 103 mountainous areas to offset the economic disadvantages imposed by challenging terrain and climate 104 conditions. measure 13.1 allocated approximately 60 million euros annually to mountain farmers in 105 piedmont, succeeding measure 211, which had provided 52.5 million euros in the previous 106 programming period (cagliero et al., 2018; nuval piemonte, 2016). these measures play a crucial 107 role in sustaining agriculture, supporting socio-economic stability, and preserving landscapes in 108 disadvantaged areas (ferlaino, 2019; regione piemonte, 2016). however, the compensatory 109 allowance is not automatically granted to all farmers but is subject to eligibility criteria based on land 110 use and farm characteristics. the amount of support varies depending on the agricultural system and 111 ________________________________________________________________________________________________ 5 the level of land disadvantage. additionally, to prioritize small and medium-sized farms, payments 112 decrease as farm size increases: farms with less than 20 hectares receive 100% of the allowance, those 113 between 20 and 40 hectares receive 70%, those between 40 and 70 hectares receive 40%, those 114 between 70 and 1000 hectares receive 10%, while farms over 100 hectares are not eligible for support. 115 this tiered approach reflects the strategic importance of maintaining agricultural activity in 116 mountainous areas and ensuring that support is directed toward those most affected by structural 117 disadvantages (regione piemonte, 2016). 118 the results of this study are specific to the piedmont region, as they reflect the particular 119 agricultural, economic, and policy context of this territory. while the per-hectare compensatory 120 allowance is a common feature of anc support across the eu, its implementation varies in structure, 121 budget allocation, and eligibility criteria, depending on national and regional regulations (e.g., 122 differences in budget distribution, farm eligibility rules, and administrative procedures). 123 consequently, the findings may not be directly replicable in other regions, as variations in policy 124 design could influence the effectiveness of ca in addressing economic disparities. however, this 125 study offers a methodological framework that can be adapted for analyzing other mountain regions, 126 particularly within the alpine arc, where similar agricultural and economic constraints exist. 127 expanding the analysis to additional regions would facilitate a broader evaluation of anc policy 128 effectiveness and identify potential areas for policy refinement. 129 the paper is organised as follows: section 2 presents the data and methodology, including analytical 130 frameworks; section 3 reports the results of the policy impact analysis; section 4 discusses findings 131 with an emphasis on policy implications; and section 5 concludes, suggesting directions for future 132 research and policy improvements. 133 ________________________________________________________________________________________________ 6 2. data and research methodology 134 2.1. data collection 135 the analysis is based on data from the fadn, which collects standardised data annually, 136 offering microeconomic insights into income trends and the structural economic dynamics of farms 137 across europe. for this study, we used data from the italian survey, which includes approximately 138 11,000 farms each year2. 139 from this dataset, we selected farms located in the piedmont region with a standard output3 140 value exceeding €8,000, in order to focus on commercial farms. we further restricted the sample to 141 those farms engaged in farming types (ft)4 typical of the piedmont mountainous area, specifically 142 farms specialised in cattle, sheep and goats, and fruit production. the analysis covers a ten-year 143 period, from 2012 to 2022. 144 to distinguish between farms in mountainous and non-mountainous areas, we used the receipt 145 of the ca as a classification criterion. farms receiving this support were classified as located in 146 mountainous areas, while those not receiving it were considered non-mountainous. this approach 147 was adopted since the fadn database defines farm altitude based on the farm center, which may not 148 always correspond to the actual location of the arable land. using ca support as a proxy provided a 149 more reliable way to identify mountain farms. moreover, this classification appears consistent, as 150 available data show that, on average, 88% of the total eligible regional hectares received the payment 151 during the years considered5. in addition, farms larger than 100 hectares were excluded from the 152 2 https://rica.crea.gov.it/index.php?lang=en 3 the standard output is the average monetary value of the agricultural output at the farm-gate price of each agricultural product (crop or livestock) in a given region. it is calculated by member states per hectare or per head of livestock, by using basic data for a reference period of 5 successive years. 4 classify farms based on their typological affinities, which reflect the similarity of each agricultural activity to others. the tf are determined by the relative significance of various enterprises within each farm. this relative significance is quantitatively measured as the proportion of each enterprise's standard output to the total standard output of the farms. 5 https://servizi.regione.piemonte.it/catalogo/anagrafe-agricola-piemonte-dati-sintesi; psr piemonte – dati di sintesi | servizionline https://servizi.regione.piemonte.it/catalogo/anagrafe-agricola-piemonte-dati-sintesi https://servizi.regione.piemonte.it/catalogo/psr-piemonte-dati-sintesi https://servizi.regione.piemonte.it/catalogo/psr-piemonte-dati-sintesi ________________________________________________________________________________________________ 7 analysis, as they are not eligible for ca payments. including them would have led to a 153 misclassification of their geographical location and distorted the estimation. 154 based on this criterion, the final sample consists of an unbalanced panel dataset of 525 farms, 155 resulting in a total of 3,171 observations, of which 36.14% are located in mountainous areas. table 1 156 presents the distribution of the sample by ft, farm scale and geographic location. the majority of the 157 sample comprises farms specialising in cattle, followed by those engaged in fruit production, and 158 lastly, sheep and goat farming. farms specialised in sheep and goats are the only group with a higher 159 number of observations in mountainous areas compared to plains. 160 161 table 1. sample size categorised by farming type, area and farm scale. 162 farm scale cattle sheep and goats fruit total non mountain area small 364 60 546 970 large 853 21 181 1,055 mountain area small 262 39 228 529 large 486 67 64 617 total 1,965 187 1,019 3,171 note: small-scale = uaa < 20 ha; large-scale = uaa between 20 and 100 ha 163 2.2. analytical framework 164 the rationale behind the ca support measure assumes that farms located in mountainous areas 165 face a significant income disparity compared to those in non-mountainous areas. the first step of our 166 analysis aimed to empirically test whether this income disparity exists when excluding the effects of 167 the ca. subsequently, we assessed the extent to which the ca narrows the income gap between 168 mountain and non-mountain farms. 169 to conduct this analysis, we used a pooled multivariate regression model. this model was 170 selected because it allows for the simultaneous estimation of the effects of several explanatory 171 variables on multiple dependent variables over time, making it particularly suitable for unbalanced 172 ________________________________________________________________________________________________ 8 panel data such as ours (baltagi, 2008). the analysis was structured around two key dependent 173 variables: net income (ni) per hectare of utilized agricultural area (uaa) excluding ca support, and 174 overall ni per hectare of uaa. this distinction enabled us to isolate the specific contribution of ca 175 payments in narrowing income disparities. 176 looking at the descriptive statistics in table 2, we observe that, on average, the ni excluding 177 ca support is 41.16% lower for mountain farms compared to non mountain farms. however, when 178 considering the overall ni, the income gap narrows to 37.76%. 179 180 table 2. descriptive statistics of dependent variables 181 ni excluding ca/ha ni/ha non mountain mountain non mountain mountain farm scale m (€) sd m (€) sd m (€) sd m (€) sd cattle small 3,228 7,556 1,115 2,182 3,228 7,556 1,251 2,193 large 2,310 2,655 1,140 1,830 2,310 2,655 1,251 1,832 sheep and goats small 2,079 9,849 1,006 3,124 2,079 9,849 1,142 3,132 large 355 356 793 1,016 355 356 908 1,047 fruit small 4,421 4,826 4,344 3,985 4,421 4,826 4,488 3,992 large 5,129 5,594 3,878 3,350 5,129 5,594 4,016 3,365 overall 3,265 5,120 1,906 2,925 3,265 5,120 2,032 2,936 note: small-scale = uaa < 20 ha; large-scale = uaa between 20 and 100 ha 182 for the independent variables, we included the farm’s geographical location that distinguishes 183 whether the farm is in a mountainous or non-mountainous area, as this is the primary focus of our 184 study. additionally, we considered several control variables, selected based on previous studies that 185 identified key characteristics from the fadn database as significant determinants of farm income 186 (andrejovská & glova, 2022; kryszak et al., 2021; ryś-jurek, 2019; średzińska, 2018; strzelecka et 187 al., 2018). these variables include a time variable to control for yearly trends, farm characteristics 188 such as economic dimension (ed) of the farm, measured in terms of standard output, the number of 189 farm labour units, the percentage of family labour, the hectares of uaa, the specific ft, the organic 190 certification and some personal characteristics of the farmer such as age, gender and level of education 191 ________________________________________________________________________________________________ 9 (table 3 and 4). an interaction term between ft, farm location and farm scale were included to 192 capture any differential effects that may arise from the combination of these three factors. this 193 approach enables us to consider how the limitations imposed by mountainous terrain may affect each 194 ft and farm scale differently. 195 the regression model assumes that the error terms for the two dependent variables are identical, 196 reflecting the premise that residual farm income, after controlling for the relevant variables, should 197 behave similarly across the two income measures. given that the same farms are observed across 198 multiple years, we addressed the potential issue of autocorrelation by clustering the standard errors 199 at the farm level. this adjustment corrects for any non-independence of observations within each 200 farm over time, enhancing the robustness of our estimates. 201 additionally, we addressed selection bias through inverse probability weighting (ipw). first, 202 we defined the propensity score as p(xᵢ) = pr(wᵢ = 1 | xᵢ, β), where wᵢ is an indicator equal to 1 if a 203 farm is a “mountain farm” (i.e., receiving ca support) and 0 otherwise. the vector β represents the 204 parameters, and xᵢ is a vector of observed covariates. the propensity score reflects the probability 205 that a farm receives ca support and is modelled as f[h(xᵢ)], where f is typically the logistic or 206 normal cumulative distribution function (guo & fraser, 2010). in this study, the propensity scores 207 were estimated using a logit model. inverse probability weights were then computed by taking the 208 reciprocal of the estimated probability of the observed treatment status. for a treated unit (“mountain 209 farm”), the weight is 1/p(xᵢ), while for a control unit (“non mountain farm”) the weight is 1/(1 − 210 p(xᵢ)). these weights adjust for differences in covariate distributions between the two groups. finally, 211 the computed weights were used in the multivariate regression (see bellon et al., 2015) to estimate 212 the effect of being in a mountainous area on net income. this weighted regression aims to balance 213 the sample, mimicking a randomized experiment and providing less biased estimates (guo & fraser, 214 2010). 215 ________________________________________________________________________________________________ 10 after estimating the regression models, we conducted pairwise comparisons to identify 216 statistically significant differences in ni between mountain and non-mountain farms across the 217 various ft. 218 to further explore the specific role of the ca in reducing the income gap, we estimated the 219 required financial magnitude of ca payment to effectively address income disparities between 220 mountain and non-mountain farms. to achieve this, we conducted a pooled regression analysis, where 221 the dependent variable was net income per hectare of uaa, and the independent variables included 222 the ca payment per hectare of uaa, along with the same control variables used in the previous 223 analysis. using the model estimates, we calculated how the marginal increase in ca payment would 224 affect the income of mountain farms, identifying the per-hectare payment needed to fully bridge the 225 income gap with non-mountain farms. 226 2.3. variables description 227 the description of the variables reveals that, on average, farms in the dataset employ slightly 228 more than two workers, with an average of 2.07 labour units (lu), and rely predominantly on family 229 labour, which represents approximately 92.2% of the total workforce. the average age of the farm 230 managers is 53.9 years (table 3). 231 regarding ed, the majority of farms fall into the mid-range, with 40.24% of farms classified 232 within the ed category, having a standard output between €100,000 and €500,000 per year. farms 233 with a standard output between €50,000 and €100,000 represent 24.38% of the dataset, while those 234 with an output between €25,000 and €50,000 account for 17.85%, and 13.88% fall within the €8,000–235 €25,000 range. only a small proportion of farms exceed €500,000 in standard output (3.66%). in 236 terms of farm specialisation, 61.97% of farms focus on cattle farming, 32.13% specialise in fruit 237 production, and 5.97% are dedicated to sheep and goat farming. the majority of farms (63.56%) are 238 located in non-mountainous areas, while 36.14% operate in mountain regions. 239 ________________________________________________________________________________________________ 11 for farm scale, farms are classified as small (< 20 hectares) and large (20 to 100 hectares). 240 this classification reflects the structure of the compensatory allowance (ca), which is granted in 241 full to farms under 20 hectares, while farms above this threshold receive a proportionally reduced 242 payment. the sample is nearly evenly distributed, with 47.27% of farms classified as small and 243 52.73% as large. 244 in terms of farm management, 86.03% of the farm managers are male. farm diversification is 245 relatively limited, with only 18.46% of farms engaged in activities beyond primary agricultural 246 production. similarly, organic farming is a niche practice, with only 11.51% of farms certified as 247 organic. education levels vary among farm managers, with 62.37% having completed secondary 248 school, and 22.31% holding a high school diploma. a smaller proportion has attained higher 249 education, with 1.58% holding a bachelor's degree and 0.06% an associate degree, while 13.68% have 250 primary or no formal education. the data spans multiple years, from 2012 to 2022, with a fairly 251 balanced distribution of observations across these years (table 4). 252 253 table 3. description of the continuous independent variables 254 description unit of measure m sd lu farm’s labour unit n 2.07 1.96 flu percentage of family labour % 92.2 1.97 age farmer’s age n 53.9 12.03 255 256 257 table 4. description of the discrete allowance variables 258 description n % ed € of standard output 8,000 – 25,000 440 13.88 25,000 – 50,000 566 17.85 50,000 100,000 773 24.38 100,000 – 500,000 1,276 40.24 ________________________________________________________________________________________________ 12 500,000 – 1,000,000 108 3.41 > 1,000,000 8 0.25 farm scale small (< 20 ha) 1,499 47.27 large (20 to 100 ha) 1,672 52.73 ft cattle 1.965 61.97 sheep and goats 187 5.97 fruit 1.019 32.13 farm location non mountain area 2,025 63.56 mountain area 1,146 36.14 gender woman 443 13.97 man 2,728 86.03 diversification yes 588 18.46 no 2,583 81.46 organic yes 365 11.51 no 2.806 88.49 level of education no formal education 70 2.22 primary school 362 11.46 secondary school 1,971 62.37 high school diploma 705 22.31 associate degree 2 0.06 bachelor’s degree 50 1.58 year 2012 316 9.97 2013 326 10.28 2014 316 9.97 2015 309 9.74 2016 314 9.90 2017 305 9.62 2018 304 9.59 2019 188 5.93 2020 186 5.87 2021 307 9.68 2022 300 9.46 259 3. results 260 the findings from the ipw-adjusted multivariate regression model, as outlined in table 5, show 261 that the control variables are significant in the expected direction across the two equations. 262 specifically, an increase in the economic dimension positively influences the dependent variables, 263 as does a marginal increase of uaa. moreover, farms specialised in fruit production scored higher 264 on the dependent variables when compared to cattle farms. focusing on the variable that distinguishes 265 ________________________________________________________________________________________________ 13 between farms located in mountainous areas and those not situated in mountainous areas, we observe 266 that in the two cases the coefficient is significant and negative. this suggests that there is a gap in net 267 income between farms located in mountainous areas and those not located in mountainous areas that 268 the ca support is unable to bridge. 269 ________________________________________________________________________________________________ 14 270 table 5. results of the ipw-adjusted multivariate regression model. 271 ni excluding ca/ha ni/ha coefficient robust se coefficient robust se location (mountain) -1319.44** 603.97 -1201.49** 605.79 ft (vs cattle) sheep & goats 1737.47* 1003.23 1741.02* 1005.25 fruit 2419.50*** 883.81 2421.13*** 884.60 ft × location × farm scale cattle × mountain × large -2080.62** 821.62 -2065.50** 822.79 cattle × mountain × small -832.43* 481.14 -867.59* 483.10 sheep & goats × mount × large -4283.34*** 944.87 -4282.66*** 945.98 sheep & goats × mount × small -1064.76 1018.55 -1057.00 1022.25 sheep & goats × non m × small -2620.65*** 996.31 -2662.57*** 1001.69 fruit × mountain × large -2069.54** 801.21 -2063.17** 800.91 fruit × mountain × small -1513.35 939.55 -1513.56 947.54 fruit × non mountain × small 86.07 927.07 91.64 930.49 lu 248.43** 116.99 247.35** 117.11 flu 1179.13 1090.93 1161.00 1090.79 ed 1070.17*** 260.69 1060.88*** 260.84 woman -298.93 299.37 -299.62 299.48 diversification -18.91 426.15 -22.89 426.63 organic 39.21 475.92 57.18 477.77 education 16.50 209.12 10.93 208.72 age (years) 24.56** 12.35 24.28** 12.30 year (vs 2012) 2013 -66.35 353.20 -83.99 357.96 2014 145.63 405.84 141.99 405.14 2015 444.99 331.13 494.20 329.88 2016 1391.10** 690.63 1384.50** 690.78 ________________________________________________________________________________________________ 15 2017 374.81 428.84 367.44 428.68 2018 1072.66** 490.55 1068.52** 487.95 2019 325.40 583.24 320.91 584.57 2020 520.55 555.68 499.98 554.84 2021 103.94 395.84 107.05 394.49 2022 -156.24 386.01 -150.14 384.57 constant -3252.46** 1416.46 -3188.35** 1413.39 note: * p<0.10, ** p<0.05, *** p<0.01. 272 post-hoc pairwise analyses indicate that, in the two regressions, the gap is not uniformly evident 273 across all fts and farm scales. from the first regression, which has ni excluding the amount of ca 274 support received as the dependent variable, a negative income gap exists for small farms specialised 275 in cattle and in sheep and goats, while large farms in these same categories and those specialised in 276 fruit do not show a statistically significant gap. focusing on small cattle farms, the estimated 277 magnitude of the gap excluding ca support is -€1,319.44 per hectare, while the estimated ni gap is 278 -€1,201.49 per hectare. for small farms specialised in sheep and goats, the estimated magnitude of 279 the gap excluding ca support is -€2,384.19 per hectare, which decreases to -€2,258.49 when 280 considering the estimated ni gap (table 6). 281 282 table 6. results of the post-hoc analyses. 283 ni excluding ca ni χ² p-value scale gap (€/ha) robust se gap (€/ha) robust se cattle small -1319.44 ** 603.97 -1201.49** 605.79 159.88 < .0001 large -71.24 305.14 -3.586 301.425 68.93 < .0001 sheep & goats small -2384.19 *** 867.49 -2258.49 *** 870.54 101.85 < .0001 large 343.25 386.87 418.60 397.00 9.31 0.002 fruit small -1233.37 754.99 -1109.84 757.62 147.01 < .0001 large -763.25 946.99 653.55 953.66 0.48 0.486 note: ni = net income; ca = compensatory allowance; gap = estimate of the operating income difference between mountain and 284 non-mountain farms; ** p < .05, *** p<0.01. the last two columns refer to the wald test between the two estimated gaps, where h0: 285 ni excluding ca = ni. 286 287 ________________________________________________________________________________________________ 16 our analysis further proceeded to determine the required financial magnitude of ca payment 288 to effectively address income disparities between farms situated in mountainous and non-289 mountainous areas. the analysis was conducted exclusively on small farms specialising in cattle and 290 sheep and goats, as these were the farming types where the income gap was found to be negative and 291 statistically significant. as shown in figure 1, mountain farms currently receive an average ca 292 payment of around €135/ha. to fully close the income gap between farms in mountainous areas and 293 those in non-mountainous areas, the ca payment would need to more than double, reaching 294 approximately €340/ha. 295 figure 1. estimated increase in ca required to bridge the net income gap between mountain and 296 non-mountain farms. the shaded area represents a 95% confidence interval. the dashed line depicts 297 the average ca received by mountain farms. 298 299 ________________________________________________________________________________________________ 17 4. discussion 300 the findings of this study provide a deeper understanding of the economic challenges faced 301 by farms in piedmont’s mountainous areas, especially concerning income disparities when compared 302 to farms in non-mountainous regions. in addition, the study highlights the role of public support in 303 addressing these challenges, particularly evaluating the effectiveness of compensatory measures such 304 as the ca. 305 our results confirm the existence of an income gap between farms situated in mountainous 306 and non-mountainous areas. this disparity is largely driven by socio-ecological challenges such as 307 steep terrain, adverse climate conditions, and limited competitiveness, which collectively increase the 308 risk of land abandonment in mountain regions (dax, 2021). additional challenges, including an 309 ageing farming population, limited technical training, and the prevalence of low-input systems, 310 further undermine the economic sustainability of agriculture in these areas (giannakis & bruggeman, 311 2015; strijker, 2004). these vulnerabilities appear to be particularly pronounced in smaller farms, 312 which tend to have fewer resources to adapt to structural constraints. 313 ________________________________________________________________________________________________ 18 moreover, our study demonstrates that this income gap is not uniformly distributed across different 314 types of farming, confirming that distinct mountain farming types result in different economic 315 outcomes (papić milojević & bogdanov, 2023). the income gap was found to be statistically 316 significant, and negative in small farms specialised in cattle and sheep and goats. importantly, these 317 differences are further influenced by farm scale: small-scale farms within these specialisations show 318 a marked income disadvantage, whereas large farms within the same types often do not present a 319 significant income gap. the reason for this phenomenon could be attributed to the high fixed costs 320 borne by small farms specialized in animal breeding, which, combined with lower production 321 volumes, result in a decrease in profitability (kuhl et al., 2019). at the same time, the absence of a 322 significant income gap for other farming types raises important policy considerations. if certain 323 sectors do not experience substantial economic disadvantages due to mountain constraints, this might 324 suggest that ca support should not be uniformly distributed, but rather adjusted according to the 325 specific needs of different farming types and scales. current eligibility rules already distinguish 326 between small and large farms—granting full ca support only to those under 20 hectares—but our 327 findings show that this differentiation, while appropriate in principle, does not go far enough in 328 correcting income disparities. 329 furthermore, our results indicate that the income gap in small farms specialized in animal 330 breeding is only partially bridged by ca support, which covers only a small portion of this gap. this 331 finding echoes previous concerns regarding the effectiveness of policies aimed at supporting 332 mountain farms in italy (whitaker, 2024). however, ca support remains insufficient to fully close 333 the gap. previous studies have shown that the impact of anc support is variable, depending on 334 context (namiotko et al., 2017). while some research aligns with our findings, demonstrating anc 335 supports’ limited role in bolstering mountain farm income (ferto et al., 2022; wieliczko et al., 2018), 336 other studies have highlighted its effectiveness in other agricultural types, such as cereals (klima et 337 al., 2021), or in boosting income for organic farms, especially small-scale (veveris et al., 2014). 338 ________________________________________________________________________________________________ 19 even though current eligibility criteria consider factors such as the agricultural system, the level 339 of land disadvantage, and farm scale (regione piemonte, 2016), they do not fully capture the 340 economic heterogeneity across those factors and different farming types. this limitation suggests that 341 a more differentiated approach in the allocation of ca support could enhance its effectiveness. in 342 particular, redistributing resources from less-affected sectors to those that bear the highest costs of 343 mountain farming could provide a more targeted use of funds. 344 notably, the current policy framework already recognises the importance of farm scale by 345 modulating ca support, however our findings indicate that this differentiation is not sufficient to 346 fully compensate for the income gap observed in small mountain farms. 347 to further reduce the income disparities, a multifaceted strategy should be considered. in 348 addition to revising ca distribution, policies aimed at enhancing productivity and market access for 349 mountain farms could be beneficial. investments in infrastructure, technological innovation, and 350 training programs tailored to the needs of mountain farmers could improve efficiency and economic 351 resilience (dax & fischer, 2018; pezzini, 2001). furthermore, fostering cooperatives and producer 352 organizations may strengthen the bargaining power of mountain farmers, enabling them to capture a 353 larger share of market value and thereby reducing income disparities (knickel et al., 2018). 354 nonetheless, the income gap for farms specialised in animal breeding is not fully addressed 355 by ca support, and achieving full compensation would require more than doubling the per-hectare 356 payment. while increasing financial support could help, it is essential to evaluate its cost-357 effectiveness. a simple increase in payments may not be the most efficient approach, as it could lead 358 to budgetary constraints without necessarily addressing structural inefficiencies. instead, a 359 combination of increased financial aid and complementary measures, such as targeted investments in 360 modernization, innovation, and value-chain integration, could yield better long-term results. future 361 policy adjustments should consider the balance between financial sustainability and the actual impact 362 of interventions on mountain farm incomes. 363 ________________________________________________________________________________________________ 20 it is essential to consider a redistribution of support payments that reflects the varied economic 364 outcomes across different mountain ft, underscoring the need for sectoral policies tailored to these 365 differences (papić milojević & bogdanov, 2023). a more tailored distribution of ca support, based 366 not only on land characteristics but also on the specific economic challenges of different farming 367 types, could lead to a more equitable and effective support mechanism. in particular, an equitable 368 distribution of ca support may be challenging to achieve with a flat per-hectare payment structure. 369 allocating contributions based on labour units rather than area could offer a more equitable approach, 370 and presents a promising avenue for future research. additionally, first-pillar direct payments could 371 play a critical role in this redistribution by facilitating a convergence process that reallocates resources 372 in favour of mountain areas (tantari et al., 2017). 373 it is important to acknowledge a broader structural disadvantage that our analysis does not capture: 374 the limited range of viable production options in lfas. mountain farms are often constrained not 375 only by higher production costs but also by ecological and climatic factors that restrict crop and 376 livestock choices, limiting their ability to diversify or switch to more profitable activities. this implies 377 an additional layer of opportunity cost, which is not addressed through intra-sectoral profitability 378 comparisons alone. capturing such constraints would require integrating agronomic feasibility 379 assessments and opportunity cost modeling into a broader analytical framework, an important, though 380 currently out-of-scope, direction for future research. however, we believe recognizing this limitation 381 is essential when designing compensation schemes and rural development strategies that aim to fully 382 reflect the multi-dimensional nature of disadvantage in lfas. 383 beyond public support, narrowing the income gap between mountain and non-mountain farms 384 might also require consumer recognition and willingness to pay a price premium for mountain-origin 385 products. this added value would create a more sustainable revenue stream for mountain farms, 386 helping to offset higher production costs and lower yields, and thus supporting the long-term viability 387 ________________________________________________________________________________________________ 21 of agriculture in these challenging areas (cei et al., 2023; staffolani et al., 2023; mazzocchi & sali, 388 2021). 389 it is important to acknowledge, however, that the empirical strategy employed in this study 390 does not allow for causal inference. although we apply statistical techniques to adjust for observable 391 differences between farms, unobserved confounding factors may still influence the results. as such, 392 the findings should be interpreted as associations rather than causal effects. policy implications 393 should be considered exploratory, offering indications rather than definitive prescriptions. 394 395 5. conclusions 396 this study aimed to assess the effectiveness of rdp’s measures to bridge the income gap 397 between farms located in mountainous and non-mountainous areas in piedmont. the results show 398 that a statistically significant and negative income gap exists only in small farms specialized in cattle 399 and sheep and goats. net of the ca support provided through the rdp, the average shortfall amounts 400 to €1,319.44 per hectare for cattle farms and €2,384.19 per hectare for sheep and goat farms. 401 compensatory allowance support helps reduce this gap by 8.93% for cattle farms and 5.28% for sheep 402 and goat farms, though a substantial disparity remains. 403 while increasing compensatory allowances (cas) could help narrow income disparities in 404 mountain areas, our findings suggest that this measure alone is neither financially sustainable nor 405 sufficient to address the broader challenges these regions face. a more promising approach involves 406 embedding cas within integrated territorial development strategies that tackle structural and 407 systemic constraints. for example, policy experiences in france during the early 2000s highlight the 408 value of bundled interventions, whereby cas were linked to participation in agri-environmental or 409 organic farming schemes—promoting both economic viability and environmental stewardship. at 410 the regional level, the strategia per le montagne del piemonte (dgr 27/02/2023) offers a concrete 411 ________________________________________________________________________________________________ 22 illustration of how multi-sectoral action plans can support youth entrepreneurship, workforce 412 training, and the valorization of ecosystem services alongside traditional income support (regione 413 piemonte, 2022; 2023). additionally, shifting from per-hectare to per-labor-unit payments could 414 enhance the equity and effectiveness of support, better aligning aid with actual farm effort and 415 viability—though this would require overcoming significant administrative and wto-related 416 hurdles. ultimately, advancing the cost-effectiveness and legitimacy of rural policy in mountain areas 417 calls for a transition from a logic of compensation to one of strategic investment, in line with the 418 broader vision of the strategia nazionale per le aree interne, recognizing mountain farming as a 419 cornerstone of territorial resilience and social cohesion 420 in general, the findings indicate that ca support does not fully bridge the income gap, even 421 when supplemented by contributions from other rdp measures. to completely close this gap, ca 422 support would need to more than double, increasing from €135/ha to €340/ha, which would require 423 a substantial rise in allocated resources. given the limited economic feasibility of this approach, a 424 more viable solution would involve not only increasing contributions but, more importantly, 425 redistributing them. all in all, mountainous areas constitute approximately 45% of piedmont’s total 426 land area and 30% of its agricultural land, making them crucial for the region’s economic and 427 environmental sustainability. the study serves as an exploratory analysis, with the intention of 428 expanding the research to additional alpine regions in future studies. 429 430 acknowledgments 431 this work was funded by the european union next-generationeu: (call prin 2022 italian ministry of 432 university and research (mur) recipe cod. 20224zhnxe). also, we would like to thank the two 433 anonymous reviewers for their thoughtful comments and efforts towards improving the initial draft of our 434 manuscript. 435 436 ________________________________________________________________________________________________ 23 references 437 andrejovská, a., & glova, j. 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(2018). economic component of 541 anc payments. example of the farms in poland. research for rural development, 2. 542 543 © 2025 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae bio-based and applied economics bae bio-based and applied economics 14(1): 93, 2025 | e-issn 2280-6172 acknowledgements the editorial board of bio-based and applied economics thanks all reviewers for their support for volume 13, issues 1-4, published in 2024. their contribution has been essential in maintaining the quality of the journal. thank you all. reviewers assaker georges bareille françois bazzani claudia beyhan zeynep biondi beatrice boncinelli fabio borrego-marín maría del mar bournaris thomas bozzola martina bradfield tracy cacchiarelli luca cavallo carla corrado alessandra cortignani raffaele curzi daniele d’alberto riccardo donati michele fasolino nunzia franco silvio fusacchia ilaria galioto francesco gava oriana huber robert irek judith jambor attila jurkenaite nele macrì maria carmela magazzino cosimo marino davide matthews alan mazzoli enrico obi chinedu olper alessandro ordonez melania salazar pagano luciano pellegrini emilia prosperi paolo rac ilona raggi meri righi selene rocchi benedetto sacchi giovanna schulze christoph selvaggi roberta senni saverio sogari giovanni stranieri stefanella tassinari gianmaria tremma ourania tyllianakis emmanouil varacca alessandro vecchio riccardo veneziani mario vergamini daniele viaggi davide zeilinger julian zezza annalisa https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae building sustainable futures: the bio-based fertilizer case-study carla abitabile, andrea arzeni*, federica cisilino, matteo orlando is there an animal food kuznets curve, and does it matter? vito frontuto1,*, tommaso felici2, vania andreoli3, marco maria bagliani1,#, alessandro corsi1 do agroholdings cope better with the agency problem? empirical evidence from corporate farms in russia alisher tleubayev1,*, yerzhan syzdykov2 farm characteristics and exogenous factors influencing the choice to buy land in italy silvia russo1,*, meri raggi2, barbara bimbati3, andrea povellato3, davide viaggi1 soils and ecosystem services: policy narratives and instruments for soil health in the eu greta winkler1,*, luciano pagano2, daniele vergamini2, fabio bartolini1 economic and policy analysis of technologies uptake for the smart management of agricultural systems davide viaggi1, giulia maesano1, davide menozzi2 1 department of agricultural and food sciences, alma mater studiorum university of bologna. viale fanin, 50 40127 bologna, italy 2 department of food and drug, university of parma. parco area delle scienze 47/a, 43124 parma, italy introduction this special issue of bio-based and applied economics “economic and policy analysis of the adoption of smart agriculture technologies” stems from the growing diffusion of innovative digital technologies as strategic solutions for the development of the agricultural sector. agriculture is undergoing a profound transformation thanks to the integration of new technologies (vishnoi and goel, 2024; aijaz et al., 2025), with a view to the sustainable development of the sector (norman and macdonald, 2004; nica et al., 2025). the combined economic and environmental benefits of technology adoption in agriculture are widely recognized in the literature (giorgio et al., 2024; papadopoulos et al., 2025). to illustrate, technologies in agriculture help address current interconnected challenges related to productivity, cost reduction, agri-food safety, natural resource conservation, animal welfare, worker safety, and, more generally, the achievement of sustainable development goals (castillo-díaz et al., 2025; finger, 2023; basso and antle, 2020; musa and basir, 2022; sridhar et al., 2023). in this context, technological innovations have enabled significant improvement of various agricultural processes through the introduction of different tools, such as the internet of things (iot), sensors, robotics, drones, blockchain, and artificial intelligence (sharma and shivandu, 2024). the diffusion of innovative digital tools in agriculture is growing (shang et al., 2021), but their take up still varies significantly across countries, farm types, and production systems (eastwood et al., 2019; rose and chilvers, 2018; shepherd et al., 2020). this uneven pattern highlights the need to understand the mechanisms underlying the adoption of these technologies and suggests that digital transformation in agriculture is not just about technology, but also depends on social structures, institutions, and interactions between networks and governance systems (roberts et al., 2017; jia, 2021), as well as farmers’ personal attitudes and traits (deißler et al., 2022). this special issue contributes to the ongoing debate on how digitalization is reshaping agriculture. combining behavioral theories, such as the theory of planned behavior (ajzen, 1991), the technology acceptance model (davis, 1989), and the unified theory of technology acceptance and use (venkatesh et al., 2012), with economic and policy analyses, the articles examine in detail the factors that help or hinder farmers in adopting new technologies (maesano et al., 2025; cozzi et al., 2025; moussaoui et al., 2025). presentation of the special issue the articles collected in this special issue aim to offer a broad and multifaceted view of the dynamics linked to the diffusion of innovative digital technologies in the agricultural sector, considering the behavioral, economic, and political dimensions that influence the intention to adopt them. kühnemund and recke (2025), drawing on the technology acceptance model (tam) framework, investigate the determinants that drive german pig farmers to introduce ai-based camera systems into livestock production. their findings indicate that perceived ease of use, openness to innovation, and individual innovativeness are the main factors influencing adoption intention. concerns about data ownership and privacy, however, play a lesser role in driving behavior. overall, the authors argue that farmers place significant importance on the reliability and functionality of technology. however, trust and transparency are essential determinants of technology adoption. these findings underscore the importance of user-centered design and clear communication regarding how intelligent technologies are implemented in practice. cozzi et al. (2025) conduct a study in the italian horticultural sector, to analyze the adoption of watersmart technologies. based on data from a survey of 251 farmers in italy, using an extended tam3 framework, the authors find that perceived usefulness and social norms strongly influence adoption intentions. the results also show that ease of use is less influential in driving intentions. their analysis highlights how social interaction and perceived benefits outweigh usability or socioeconomic characteristics in shaping farmers’ behavior. from this perspective, the findings suggest that participatory and peer-learning environments can serve as effective channels to accelerate the diffusion of innovation. the findings are consistent with those of sabbagh and gutierrez (2025) and kühnemund and recke (2025), both of which emphasize the key role of social capital in linking technological potential to actual behavioral change. sabbagh and gutierrez (2025) extend the unified theory of acceptance and use of technology framework to analyze the adoption of agriculture 4.0. the authors identify the main determinants of adoption by comparing marginal and non-marginal areas. their findings reveal that facilitating conditions, such as access to infrastructure and technical support, and social influence are the main predictors of adoption. furthermore, according to the study’s findings, perceived performance risks have been shown to be barriers to adoption. the authors conclude that adoption intentions depend not only on individual motivation, but also on social and territorial structures that enable knowledge exchange and reduce perceived risk. these findings echo previous work on the rural digital divide, highlighting the need for context-specific policies (rose and chilvers, 2018; eastwood et al., 2019). timpanaro et al. (2025) contribute to the literature debate by analyzing the methods of introducing digital tools and their effects in sicilian citrus farming. using a living lab approach, the authors demonstrate that digital technologies can increase yield per hectare, improve profitability, and enhance water efficiency on citrus farms. their findings also indicate that participatory innovation processes promote knowledge exchange and collaboration, helping to reduce farmers' resistance to change. the study highlights the need for targeted training and institutional support to ensure that digitalization is effective and inclusive. this participatory perspective resonates with the call for innovation ecosystems that integrate technology into local socioeconomic contexts and sustainability goals. maesano et al. (2025) examine the factors influencing italian consumers' intentions to purchase organic pasta traced using blockchain technology. extending the theory of planned behavior (tpb) framework (ajzen, 1991), the authors assess the potential of blockchain in preventing and detecting food fraud. their findings suggest that subjective norms, perceived behavioral control, and attitudes toward technology are the main predictors of purchase intention, while trust in traditional quality certifications plays a limited role. therefore, consumers place greater trust in digital traceability tools than in conventional certification systems. however, from a consumer perspective, uncertainty remains about the practical benefits of these technologies, highlighting the need for a credible and transparent environment in which innovation provides clear added value. pacciani et al. (2025) evaluate digitalization levels, perceived benefits, needs, and barriers on a sample of 1,248 italian farms. the results show that monitoring systems and connected machinery are the most used technologies. in addition, efficiency gains in farm and production management, improved operational control, and perceived benefits are key drivers of adoption, while financial and structural limitations remain significant obstacles. the authors call for coordinated policy measures to support the digital transition, combining advisory services, investment in infrastructure, and human capital development. their conclusions are consistent with those of sabbagh and gutierrez (2025) and timpanaro et al. (2025), who also emphasize the importance of governance coordination, training, and connectivity in promoting technology diffusion. finally, moussaoui et al. (2025) employ a mixed-methods design, combining surveys and in-depth interviews to gather stakeholder perspectives on smart agriculture technologies and their policy integration. the results of the study show a broad agreement on the potential of technologies to improve agricultural efficiency, sustainability, and productivity; nonetheless, it also identifies persistent barriers, including high upfront costs and limited technical expertise. the authors highlight the need for financial incentives, capacity-building initiatives, and stronger infrastructure to encourage adoption. the conclusion of this study supports adaptive, multi-level governance frameworks that link top-down policy design with bottom-up innovation processes to ensure greater policy coherence. in line with pacciani et al. (2025), their findings reinforce the view that digital transformation depends as much on systemic governance reform as on technological progress. cross-cutting insights and policy implications this special issue offers different perspectives on the dynamics of technology adoption and the governance of digital transformation in agriculture. the evidence confirms that technology adoption is not merely a technical or economic process (though these aspects are very important), but it is a socio-institutional transition, that depends on mental constructs, social norms, and collective learning mechanisms, and is strongly influenced by the external conditions in which innovations are embedded. behavioral models indicate that perceived usefulness and social influence are the main determinants of farmers' acceptance of innovations. conversely, perceived risk, high costs, and institutional uncertainty remain the main barriers. from a policy perspective, the findings highlight that monetary incentives alone will not ensure a successful digital transition unless they are part of coherent and flexible governance arrangements that align public and private resources, promote interoperability, and leverage synergies within the sector (wolfert et al., 2017; klerkx et al., 2019; viaggi, 2019). the articles in this special issue suggest that effective strategies must combine investment with the development of digital infrastructure and educational programs to build long-term innovation capacity. more generally, the integration of behavioral and economic policy analysis in these articles demonstrates how interdisciplinary science can inform evidence-based solutions to ensure the deployment of smart technologies in the context of resilient agri-food systems. acknowledgements the guest editors thank all authors for their valuable contributions, the anonymous reviewers for their constructive feedback, and the bio-based and applied economics editorial team for their support in publishing this special issue. this special issue was inspired by agritech national research center (spoke 3 – enabling technologies and sustainable strategies for the smart management of agricultural systems and their environmental impact), which also funded six out of seven papers. this special issue also saw the participation of our colleague maurizio canavari, who passed away prematurely at the beginning of 2025, far too soon. he was a curious, generous, and committed scholar, deeply engaged in the fields of agri-food economics and consumer behavior. his collaborative spirit enriched all those who had the privilege to work with him. maurizio contributed to the conception, design, and realization of one of the studies included in this special issue, namely cozzi et al. (2025). he leaves an indelible memory among his colleagues and students. all guest editors contributed equally to the conception and writing of this editorial. references aijaz, n., lan, h., raza, t., yaqub, m., iqbal, r., & pathan, m. s. (2025). artificial intelligence in agriculture: advancing crop productivity and sustainability. journal of agriculture and food research, 101762. ajzen, i. (1991). the theory of planned behavior. organizational behavior and human decision processes, 50(2), 179-211. basso, b., & antle, j. (2020). digital agriculture to design sustainable agricultural systems. nature sustainability, 3(4), 254-256. castillo-díaz, f. j., belmonte-ureña, l. j., gálvez-sánchez, f. j., & camacho-ferre, f. (2025). sustainable technological innovations in agriculture: financial challenges and investment priorities cozzi, e., menozzi, d., maesano, g., canavari, m., & mora, c. 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(2024). climate smart agriculture for sustainable productivity and healthy landscapes. environmental science & policy, 151, 103600. bio-based and applied economics bae © 2024 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae bio-based and applied economics 13(4): 387-396, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15105 citation: dehghan, m., moosavi, s.n., & zare, e. (2024). analyzing the impact of government subsidies on household welfare during economic shocks: a case study of iran. bio-based and applied economics 13(4): 387-396. doi: 10.36253/bae-15105 received: september 8, 2023 accepted: september 2, 2024 published: december 31, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: fabio bartolini orcid md: 0000-0002-7824-0472 snm: 0000-0001-5645-1976 ez: 0000-0002-2787-5507 analyzing the impact of government subsidies on household welfare during economic shocks: a case study of iran mohammad dehghan1,*, seyyed nematollah moosavi2,*, ebrahim zare3 1 agricultural economics, marvdasht branch, islamic azad university, fars province, iran 2 department of agricultural economics, extension and education, marvdasht branch, islamic azad university, fars province, iran 3 agricultural economics, fars agricultural and natural resources research and training center, fars, iran *corresponding authors. e-mail: dehghan191@gmail.com; seyed_1976mo@yahoo.com abstract. the study evaluates the effectiveness of iranian government subsidies for households by comparing the welfare impact of food price shocks with the subsidy payments they receive. this helps us assess the government’s efforts to reduce poverty in iran. the household income and expenditure survey in 2020 was used to calculate compensated price elasticities using the quadratic almost ideal demand system (qaids). results showed negative and less than 1 own-price elasticities for all food items, with a sensitivity to changes in income greater than one for demand of cereals, cooking oil, and fruits. then, compensated variations (cv) welfare index was used to evaluate the effectiveness of government support payments in reducing household vulnerability due to food price increases. the results showed that the cv fluctuates between 8.05-80.46 $ under different scenarios. in other words, consumers are in a worse situation in terms of welfare and their expenditure increases. the vulnerability index of low-income households, after applying different food price scenarios, is in the range of 1.46-14.67%, which is reduced to 1.35-14.53% by implementing the cash-targeted subsidy policy. in other words, the effectiveness of the government’s subsidy policy of $19 per person could reduce the vulnerability of these households by only 0.14%. keywords: poverty, vulnerability, welfare, subsidies, food, demand system. jel codes: d12, d60, i3, i32, q18. introduction social protection programs like subsidies aim to prevent poverty and social crisis and promote justice, even if it means sacrificing some economic efficiency (barr, 2020). however, poor subsidy payment methods can cause inefficiency and injustice. there is currently a heated debate among economists and policymakers about the link between targeting subsidies and poverty reduction (amegashie, 2006). price changes wield considerable influence over resource allocation and consumer behavior, thereby shaping the https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode http://www.fupress.com/bae https://doi.org/10.36253/bae-15105 https://doi.org/10.36253/bae-15105 https://orcid.org/0000-0002-7824-0472 https://orcid.org/0000-0001-5645-1976 https://orcid.org/0000-0002-2787-5507 mailto:dehghan191@gmail.com mailto:seyed_1976mo@yahoo.com 388 bio-based and applied economics 13(4): 387-396, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15105 mohammad dehghan, seyyed nematollah moosavi, ebrahim zare implementation of economic welfare policies (khodaparast shirazi et al., 2018). the surge in food prices, both domestically and globally, has become a pressing global concern (fao, 2021). although the covid-19 pandemic exacerbated the issue by disrupting supply chains and inflating prices further (elleby et al., 2021), the trend of rising food prices predates the pandemic, originating in 2018 (panzone et al., 2021). in iran, where food accounts for a substantial portion of household consumption, the anticipated impact of food inflation on household welfare is pronounced (layani et al., 2020). iran’s persistent struggle with high inflation underscores the gravity of the situation, with efforts ongoing to identify and address underlying causes (ilias, 2010). notably, between 2010 and 2019, iran witnessed an increase in spending on essential items like food and housing, exacerbating the economic strain on households (salehi isfahani, 2020). despite the reformed social protection policy in iran, the adverse effects of food price shocks could disproportionately affect low-income households in due to their vulnerable economic structures (pawlak and kołodziejczak, 2020). to contextualize the analysis of price shocks on household vulnerability and welfare across diverse countries, it is imperative to understand the intricate dynamics of consumer behavior and demand systems. for instance, studies utilizing sophisticated models such as translog, almost ideal demand system (aids), and quadratic almost ideal demand system (qaids) offer valuable insights into the responsiveness of consumers to price changes. take, for example, deaton and mulbaer’s (1980). there has been a growing body of literature regarding the impact of price shocks on household vulnerability and welfare effects across various countries in recent years (e.g. aziz et al., 2016; arfini & aghabeygi, 2018; adekunle et al., 2020; lugo et al., 2022). karagiannis et al.’s (2000) analysis on greece, abdulai’s (2002) examination of switzerland, mazzocchi et al.’s (2004) investigation on italy, tefera’s (2010) exploration of ethiopia, ahn et al.’s (2018) study on korea, yuzbashkandi and mehrjo’s (2020) research on iran and abdullah and mohammed (2023) study on iraq. specifically, ivanic and martin (2008) and ivanic et al. (2012) researched the correlation between global food price escalation and poverty in low-income countries, considering world and local prices’ impact on poverty. arfini and aghabeygi (2018) found that increasing food import prices in italy affected the welfare index by 1061.48 billion usd in the entire food group. the meat group was most affected and the fruit group was least affected. a recent study (layani et al. 2020) found that rising food prices in iran have a significant impact on rural households, with 10.63% of them falling below the poverty line. anindita et al. (2022) used various methods to examine the impact of price and income changes on demand and welfare in urban indonesia. they found that households substitute some food items as a coping mechanism. rossen et al. (2022) have conducted a study that examines the effect of price shocks on different household groups based on income and age. their research found that households with lower income and older individuals experience more significant welfare losses and a decrease in tax burdens compared to lower-income households with younger individuals. these studies provide valuable insights into the demand system and price elasticities of goods and offer useful implications for policymakers and practitioners in the field. however, these prior studies have solely focused on analyzing the welfare impacts that arise from changes in prices. however, they fail to address a fundamental question: to what extent can the government’s welfare policies, such as cash transfers, effectively mitigate the decrease in welfare and prevent households from falling below the poverty line. therefore, this current research aims to address this research gap by assessing the impact of the iranian government’s subsidy policy for poor households. in 2017 year, the iranian government introduced a new social protection program by replacing universal subsidies with targeted subsidies while implementing support policies for vulnerable groups as a way to reduce poverty (hosseini et al., 2017). a subsidy reform program replaced energy subsidies with direct cash payments. recently, to protect iranian households from vulnerability caused by price liberalization, the government provides additional subsidies to consumers and eliminates cash subsidies for high-income groups. although the amount of cash subsidy increased significantly from 2011 to 2019, the share of this cash payment of household average income decreased from 22% to 5%. this government assistance includes official transfers from the ministry of health, labour and welfare and komite emdad. although the poor benefited significantly from the monthly transfers, their real value diminished rapidly due to high inflation. the reformed social protection programs are targeted, particularly prioritizing poor households headed by women, using a targeting algorithm developed jointly with the world bank. government welfare payments decreased from $35.4 in 2018 to $19.8 per person in 2019 due to economic sanctions and reduced revenue, resulting in less support for people in need (salehi isfahani, 2020). the study calculates the welfare effects of changes in food prices and evaluates the effects of these changes 389analyzing the impact of government subsidies on household welfare during economic shocks: a case study of iran bio-based and applied economics 13(4): 387-396, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15105 on the poverty line and the number of poor households in iran. unlike previous studies, this research considers the vulnerability index, which has received insufficient attention in assessing the effects of price shocks on consumer behavior. the paper is divided into distinct sections. firstly, the theoretical fundamentals and materials, and methods are presented. secondly, the results of calculating the price and income elasticities of food demand for iranian households are reported, along with an examination of the effects of various food price increase scenarios. finally, the fourth section provides conclusions and suggestions. methodology data in this study, to estimate the demand system and calculate the price and income elasticities of food items, the latest cost-income data of 2878 iranian households supported by the government, which was published by the iranian statistics center, was used. these data include the amount of consumption of each food item and their corresponding price. also, to estimate the aids system, the size of the household, and the education level of the head of the household were used in the estimation of the demand system. table 1 presents the socio-economic characteristics of the sample under study. the average age of the head of the household in government-supported households is 52.32 years, while the average number of years of education of the head of the household is 5.27 years. the average size of households in the sample was 3.89 individuals. the government-supported households included in the study are categorized as low-income households, with an average monthly income of $53.65. it has been observed that the group of households under consideration here has an average per capita food expenditure of $14.05 per month, which constitutes around 26% of the per capita income. welfare analysis the evaluation of the efficacy of providing subsidies to low-income households is determined by comparing the ratio of the compensated variation (cv) index to the per capita income with the ratio of cash subsidies to monthly income (eq. 1). after cash subsidy cv monthly income monthly income vulnerability index subsidy policy = (1) where cv is the household’s welfare index as a result of different price shocks. the ratio of the compensated variation (cv) index to the monthly income is known as the vulnerability index (azzam and rettab, 2012). e v y wea lfl ay ri ed ex fb ff er cn tu ss od fl po ri iu ci et sr he oe ce kb m y o l nt e b i l n o s i i p c h y ncom = (2) by following khodadad kashi et al. (2005), arshadi and karimi (2013) and layani et al. (2020), the 66 percent of the average household food expenditure is defined relative poverty line: poverty line=66 percent × (average food expenditure) (3) after computing the poverty line, we can divide urban households into two groups: the households that have a food expenditure higher than poverty line (above the poverty line), and the households that have a food expenditure lower than poverty line (below the poverty line). the reason for this is because poverty lines are highly elastic to relative food prices (bresciani and valdes, 2007), and changes in food prices result in variations of poverty prevalence. furthermore, we then compute a new poverty line, after accounting for the rise in food prices (rodriguez-takeuchi and imai, 2013): secondary poverty line= poverty line + welfare index-subsidy policy (4) different indexes measure welfare changes due to policy implementation. economic conditions like price changes can affect consumer utility rates. to determine the impact of economic conditions on consumer utility, criteria like consumers surplus (cs), compensated variation (cv), and equivalent variation (ev) are used. we use cv to determine the minimum amount that iranian consumers are willing to accept to tolerate higher food prices. studies suggest that the cv is the most suitable criterion for our analysis (tefera, 2012 and cranfield table 1. socio-economic characteristics of the studied households. variables average age of household head (year) 52.32 education of household head (year) 5.27 family size 3.89 per capita food expenditure ($) 14.05 per capita income per month ($) 53.65 * source: iranian statistics center in 2020 (1$=208000 rial). 390 bio-based and applied economics 13(4): 387-396, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15105 mohammad dehghan, seyyed nematollah moosavi, ebrahim zare 2007). compensated variation was utilized in the study, as indicated by research conducted by azzam and rettab (2012), tefera (2012), layani et al. (2020), and roosen et al. (2022). (5) where pi 0 and xi 0 correspond to price and quantities before price shock and dxi * is the compensated quantity change in demand following the price shock using the compensates elasticities. the percentage change of xi * is not available. however, by the total differential of the hicksian demand functions xi *(.) for i = 1, 2,…, n i.e., an approximation of the change is obtained. (6) where εij h is the hicksian price elasticity for i = 1, 2,…, n and j = 1,2,…, n. to estimate the hicksian price elasticities as shown in (6), we estimate a qaids model for n commodities by imposing the usual restrictions: adding-up, homogeneity, and symmetry. the qaids model developed by banks et al. (1997), which has budget shares that are quadratic in log total expenditure, is an example of the empirical demand systems that have been developed to allow this expenditure nonlinearity. (7) where si is the share of food group i in total expenditure on the n food groups, for i=1,2,..,n; and pj is a vector of prices; m is total expenditure and z vector of statistical variables dependent on household characteristics. also, f(p) is the laspeyres price index defined by logf(p)* = σisilogpi. the restrictions are: ∑n i=1αi = 1.∑n i=1γij = 0.∑n i=1βi = 0 .γij = γji i.j = 1.2…n (8) the formulae for the elasticities in the quaids are given by banks, blunbell and lewbel (1997). they are obtained by first differentiating equation (7) with respect to logm and logpj, respectively, to obtain: (9) (10) the expenditure elasticities are then derived as ei = mi/si + 1. the uncompensated or marshallian price elasticities are given by em ij = mi/si – dij, where dij is the kronecker delta, which is equal to one when i=j, otherwise dij = 0. using the slutsky equation, em ij = em ij + sjei, the compensated or hicksian elasticities can be calculated and used to assess the symmetry and negativity conditions by examining the matrix with elements , which should be symmetric and negative semi-definite in the usual way. definition food price shocks there are several methods to define the price increase scenario. the first method is to use previous studies. another method is to use time series data for food. for this purpose, the price of food taken and the price growth during the studied years were first calculated for each group, and the average rate of change of price growth was calculated and defined as the scenario for food price change. the scenarios of increasing food prices in this study are shown in table 2. in addition to food price fluctuations, global statistics were also considered according to the statistics center of iran. the first scenario studied is food price changes based on the reports of the statistics center of iran (2021). it can be seen that meat has the highest price fluctuations and vegetables have the lowest price fluctuations. the second scenario in this study is the price fluctuations of food imports to iran. on this basis, information on food import prices was collected, and the average annual fluctuations in food import prices were used as the basis table 2. different food prices shock scenarios (%). food first scenario second scenario third scenario fourth scenario cereals 34 15.31 9.64 6.07 meat 124.4 16.26 4.33 3.83 dairy 56.7 15.56 3.63 6.88 oil and fat 54.4 9.60 26.27 8.22 fruit 27.5 11.96 9.87 5.38 vegetables 25.4 30.22 8.22 5.35 sugar 28.5 19.03 13.15 8.08 tea and coffee 31 10.09 9.87 5.38 391analyzing the impact of government subsidies on household welfare during economic shocks: a case study of iran bio-based and applied economics 13(4): 387-396, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15105 for the price scenario. finally, the third and fourth scenarios were defined based on the fao predicted price changes. accordingly, the food price index reported by fao was examined for the period 2003-2021. the annual changes in the food price index were calculated, and the average changes in the price index during 2019-2021 were used as the third scenario and the average changes in the price index during 2003-2019 were used as the fourth scenario. results table 3 displays the average food expenditure and expenditure share of eight main food groups. cereals have the highest expenditure share, while tea and coffee have the lowest. on average, the expenditure allocated to tea and coffee is $4.50 per month, and the expenditure allocated to cereals is $41.70 per month. meat has the second-highest food expenditure share among all food groups, accounting for 23.52% of the total expenditure. according to layani et al.’s (2020) study on urban households, the average monthly expenditure on food indicates that cereals and meat are the top two priorities, with respective spending of $43.69 and $41.01. additionally, households allocated an average of $4.69 on tea and coffee. a comparative analysis with the current study suggests that these households face challenges in maintaining a healthy nutritional status and are more vulnerable to price fluctuations. these findings highlight the need to address the nutritional inadequacies of these households and to develop interventions that support better dietary practices. this study focuses on the iranian agricultural market and aims to measure the impact of price changes on household expenditures. specifically, we seek to answer the question: how will a price shock in the agricultural market affect the expenses of iranian households that receive government support? this research explores whether payment of cash subsidies can effectively compensate for the reduction of welfare caused by such price shocks. to address the given query, it is imperative to compute the changes in the consumption patterns of various food products that ensue due to fluctuations in their prices. this can be accomplished by calculating the own-price and cross-price elasticities of the different food categories. the following section presents the results of the price and income elasticities. price and income elasticities of food after estimating the coefficients of the systems of equations based on the equations presented in the previous section, the price and income elasticities were obtained (table 4). the compensated own-price and cross-price elasticities of food are shown in table 4. as can be seen, all compensated elasticities of the studied food are negative as expected, and this is consistent with the behaviour that maximizes the utility of rational consumers. in terms of absolute values, the highest own-price elasticity is related to oil, and the lowest own-price elasticity is related to dairy. the own-price elasticity of cereals is -0.398%. therefore, 1% increase in cereal prices, assuming other conditions are constant, can reduce demand for this commodity by 0.398%. the compensated ownprice elasticity of meat is calculated as -0.529%. actually, the demand for meat and cereals are inelastic. it is worth noting that the own-price elasticity of oil and fat is (-0.729%) and the own-price elasticity of dairy products is (-0.006%). in other words, with a 1 % increase in the price of oil and fat (or dairy) assuming other conditions are constant, the demand for this food item decreases by 0.729% (or 0.006%). own-price elasticity of fruits, vegetables and sugar are very close in terms of absolute value as -0.634 %, -0.608 % and -0.633 %, respectively. according cross-price elasticities, there is a poor complementary relationship between cereals and other food groups. however, the effect of changes in cereal prices on demand for other foods is more pronounced. this result may be due to the higher importance of cereal for the poor households or the higher expenditures share of cereal. for instance, the effect of rising dairy prices on cereal demand is negative. in other words, with a 1 % increase in dairy prices, the demand for cereals decreases by 0.093 %, and this indicates a complementary relationship between the two prodtable 3. average food consumption expenditure and share of food expenditure food average monthly food expenditure ($) food expenditure share (%) cereals 41.699 29.68 meat 33.045 23.52 dairy 16.238 11.56 oil and fat 8.591 6.12 fruit 12.506 8.90 vegetables 16.297 11.60 sugar 7.600 5.41 tea and coffee 4.504 3.21 * source: iranian statistics center in 2020 (1$=208000 rial). 392 bio-based and applied economics 13(4): 387-396, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15105 mohammad dehghan, seyyed nematollah moosavi, ebrahim zare ucts. however, these households add cereals to their food portfolio as a substitute for dairy products. this result is expressed based on the elasticity coefficient of 0.177%. although other foods are considered meat substitutes for households, the effect of meat price change on the demand for oil and fat, fruit and tea, and coffee is negative and is equal to -0.106%, -0.026% and -0.429%, respectively, and this indicates the complementarity of meat for these food groups. the cross-elasticity between oil and fat and other food groups such as meat, dairy, fruits, vegetables, and sugar are negative and it shows the existence of a complementary relationship between oil and fat with other food. however, the increase in oil and fat prices leads to an increase of 0.048 %, 0.065 %, 0.021 %, 0.068% and 0.043 % demand for meat, dairy, fruits, vegetable, and sugar, respectively. the cross-elasticity of other commodities with oil and fat suggests a substitution relationship between them. the highest substitute for fruit is tea and coffee (cross price elasticity is 0.560). also, the highest complementary relationship between fruit and dairy products was obtained (cross price elasticity is -0.385). for households supported by the relief committee, compensated cross-sectional elasticity of vegetables indicates the existence of a substitution relationship between vegetables and other food groups (except tea and coffee). indeed, if the decision is made to include foods such as cereals, dairy products, meat, oil and fat in the consumption basket containing vegetables, these foods are added to the poor households’ consumption basket as a substitute. the highest and lowest substitution relationships are for vegetables-meat (0.199%) and vegetables-sugar (0.067%), respectively. it is worth to mention that vegetables themselves are considered as a complementary commodity for oil and fat (elasticity 0.048 %), fruits (elasticity 0.054 %). in other words, increasing the price of vegetables reduces the demand for oil, fat and fruits. interestingly, vegetables are considered as a complementary commodity for oil and fat (cross-price elasticity is -0.048 %), fruits (crossprice elasticity is -0.054 %). in other words, increasing the price of vegetables reduces the demand for oil, fat and fruits. the estimated total income elasticities presented in table 4 have the expected positive signs in all eight commodities. the values for cereals (e=1.303), oil cooking (e=1.210), and fruits (e=1.392) are much greater than others. this implies a fairly large response of demand for these food groups to changes in total food expenditure. actually, the demand for cereals, oil cooking, and fruits are elastic with respect to total food expenditure. the estimated income elasticities of meats, dairy, vegetables, sugar and tea and coffee are less than unity, so these goods are fairly inelastic with respect to total food expenditure. welfare effects of food price shocks evaluating the impact of price shocks on consumer welfare can provide valuable insights into the effectiveness of government support policies aimed at reducing poverty and vulnerability. table 5 shows the effect of food prices shock on household expenditures. as shown, under the first price scenario, cv welfare index fluctuates between 0.98-29.15%. the highest cv index is related to meat and the lowest welfare index is related to tea and coffee. the total compensated variations index in this scenario is 57.28%. in other words, as a result of changes in food prices, the food expenditure of households supported by the government will increase by $80.461. therefore, if the government-supported households want to choose and consume the same food basket before the price change, their expenditure will increase by 57%. under the second price scenario, the total weltable 4. price and income elasticities for each food groups. cereals meats dairy oil cooking fruits vegetables sugar tea and coffee cereals -0.398 0.086 -0.093 0.073 0.065 0.016 0.005 0.321 meats 0.196 -0.529 0.175 0.048 0.084 0.119 0.061 -0.071 dairy 0.177 0.715 -0.006 0.065 0.203 0.523 0.367 -1.044 oil cooking 0.285 -0.106 -0.382 -0.729 -0.001 -0.048 -0.082 0.565 fruits 0.258 -0.026 -0.385 0.021 -0.633 -0.054 -0.060 0.560 vegetables 0.144 0.199 0.188 0.068 0.069 -0.608 0.068 -0.035 sugar 0.159 0.236 0.284 0.043 0.088 0.175 -0.633 -0.219 tea and coffee 0.206 -0.147 -0.429 0.011 -0.071 -0.117 -0.093 -0.243 income elasticities 1.303 0.918 0.136 1.210 1.392 0.998 0.774 0.836 * source: authors’ calculations. 393analyzing the impact of government subsidies on household welfare during economic shocks: a case study of iran bio-based and applied economics 13(4): 387-396, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15105 fare index of compensated variations was $23.523, which is 16.74% of the baseline food expenditure of households. the cv index of food items in this scenario fluctuates between 0.32-4.60%. with the simultaneous change of food prices based on the third and fourth scenarios, cv welfare index was equal to $12.327 and $8,058 respectively, which is 8.78% and 5.74% of household food expenses in the base year, respectively. the highest cv index in the third scenario is related to cereals (2.90%) and the lowest is related to tea and coffee (0.32%). in the fourth scenario, the welfare index of food items fluctuates in the range of 0.17-1.82%. the vulnerability index of poor households f luctuates between 1.46-14.67% in different price scenarios. the highest vulnerability index was obtained after applying the first price scenario and the lowest was obtained as a result of the fourth price scenario. given that the average monthly income of poor households is 548.31$, the total welfare loss due to rising food prices is equivalent to 14.67% of average household income in first scenario, which is an indicator of the vulnerability of households as a result of multiple food price shock. this index decreases to 1.46% in the fourth scenario. in order to support low-income households and establish social justice, the iranian government pays a cash subsidy of about $19 per person per month to the head of the household’s account. the amount of cash subsidy received by the households is equivalent to 0.14% of the average monthly income of the households. in other words, the iranian government has only been able to reduce the vulnerability of low-income households by 0.14% by implementing this policy. therefore, after the implementation of the targeted subsidy policy and supporting the low-income groups, the vulnerability index of households will be in the range of 1.35-14.53% in different price scenarios. finally, table 6 presents the secondary poverty line after the price increase and the subsidy policy. as can be seen, the secondary food poverty line varies between $26.88-99.29 under the different price shock scenarios. the results show that the subsidy policy was not efficient and some low-income households are still at risk of food poverty. the highest number of households below the poverty line will occur in the first price scenario. table 5. welfare effect of multiple meat price shocks. food groups expenditure scenario 1 scenario 2 scenario 3 scenario 4 cv ($) cv (%) cv ($) cv (%) cv ($) cv (%) cv ($) cv (%) cereals 41.699 14.502 10.32 6.457 4.60 4.079 2.90 2.563 1.82 meats 33.045 40.947 29.15 5.417 3.86 1.436 1.02 1.279 0.91 dairy 16.238 9.172 6.53 2.526 1.80 0.568 0.40 1.111 0.79 oil cooking 8.591 4.642 3.30 0.824 0.59 2.239 1.59 0.702 0.50 fruits 12.506 3.453 2.46 1.497 1.07 1.229 0.88 0.672 0.48 vegetables 16.297 4.194 2.99 4.910 3.50 1.334 0.95 0.878 0.63 sugar 7.600 2.169 1.54 1.441 1.03 0.991 0.71 0.611 0.44 tea and coffee 4.504 1.381 0.98 0.450 0.32 0.451 0.32 0.242 0.17 total 140.478 80.461 57.28 23.523 16.74 12.327 8.78 8.058 5.74 vulnerability index before subsidy policy 14.67% 4.28% 2.29% 1.46% vulnerability index after subsidy policy 14.53% 4.14% 2.15% 1.35% * source: authors’ calculations (1$=208000 rial). table 6. effect of price shock on poverty line. scenario 1 scenario 2 scenario 3 scenario 3 secondary food poverty line 99.29 42.35 31.15 26.88 % of households above poverty line 37.18 49.57 58 61 % of households below the poverty line 62.82 50.43 42 39 * source: authors’ calculations. 394 bio-based and applied economics 13(4): 387-396, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15105 mohammad dehghan, seyyed nematollah moosavi, ebrahim zare discussion economic welfare measurement is crucial for policy-making. demand analysis and consumption patterns help predict future situations. it’s crucial to assess the impact of economic policies like subsidies and price changes on food security, health, and consumer welfare. we can gauge their effectiveness by observing consumer behaviour. in this study, an attempt was made to investigate the effectiveness of the policy of paying subsidies to poor households on reducing the vulnerability of poor households. for this purpose, use the household expenditure and income survey of households supported by the government (under the support of the komite emdad) and the quaids model and cv welfare index. the cv showed that the lost welfare of the low-income households in iran under different price shock scenario. the welfare index of compensated variations of low-income households fluctuates between 8.05-80.46 $ under different scenarios. in other words, consumers are in a worse situation in terms of welfare and their expenditure increases. this finding was also reported by arfini and aghabeygi (2018) for italian consumers and layani et al. (2020) for iranian urban households. the largest decline in household welfare due to price changes is related to two groups of cereals and meat. the cv index for the cereal fluctuates between 10.32-1.82% under different price scenarios. for meat, cv is between 0.91-29.15%. roosen et al. (2022) showed that a general rise in the value-added meat tax from 7% to 19% leads to a welfare loss of 0.83 euros per household per month in germany. based on the results, the degree of welfare lost by the studied households in 2020 as a result of different price shock scenarios, considered on average about 14.67%, 4.28%, 2.29% and 1.46% of their income in this year. comparison of the findings with those of other studies (e.g. layani et al., 2020) confirms that the vulnerability of low-income households is more than others. the results indicated that a significant number of households have lower food expenditures than the estimated food poverty line, and they suffer from malnutrition. therefore, the government’s support policies (including the payment of cash subsidies to the head of the household) have not been able to eliminate the vulnerability of low-income households caused by food price inflation, and some of these households are still below the poverty line. the government has tried to play an effective role by supporting vulnerable households with appropriate assistance programs or paying subsidies to offset the impact of price increases. the results of this study show that cash subsidy payments offset only a small portion of the welfare loss. thus, if the government’s goal is to support vulnerable households, regulating the market for these products can play an important role in food security and support implementation. iran’s goods and services subsidy policy has been criticized for being inefficient despite being a consumer-supportive policy for the past 40 years. the poverty index is still high and standard welfare is not achieved for households. it is costly, potentially distorts the market, and benefits some groups that do not require support. currently, the iranian government provides a uniform subsidy to all individuals irrespective of their distinctive characteristics. however, empirical evidence demonstrates that the vulnerability of different individuals varies based on their demand structure. therefore, undertaking such studies can aid the government in providing targeted subsidies based on the income of each person and minimizing the adverse effects of price shocks. for instance, individuals with lower income exhibit different behavioral patterns than those with higher income. thus, the subsidy granted to them should be calculated based on their demand and welfare effects. this study is a significant step towards targeted subsidies, reducing governmental resource wastage, and promoting efficient allocation of resources. the current study was conducted on low-income households that are supported by the government, commonly known as relief committee member households. the findings of this study suggest that the government’s existing support packages require a redesign to enhance the living conditions of these households. to achieve the desired outcome of improving the livelihoods of these households, policymakers are advised to consider increasing the amount of cash subsidy provided to these individuals. alternatively, policymakers may also consider implementing sound policies that create stable employment opportunities for these individuals, which may lead to an improvement in their income status. on the other hand, the demand for various types of meat, cereals, dairy products, and other food products will increase for various reasons, including population growth, which can be met by domestic production or foreign sources. considering the significant results and effects that changes in global prices can have on household expenditure, the most logical policy is to support domestic production. more specifically, if food production does not keep pace with population growth, per capita food production will decrease as a result. therefore, increasing demand should be met by increasing food imports or reducing exports, or resorting to both measures, which may affect domestic food prices. the increase in food imports definitely leads to greater dependence on foreign sources. this leads not only to 395analyzing the impact of government subsidies on household welfare during economic shocks: a case study of iran bio-based and applied economics 13(4): 387-396, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15105 a financial burden, but also to a number of economic, social, and political problems, including the impact of global price fluctuations on the domestic market. considering the increasing trend of global food prices in recent years, and taking into account the welfare losses due to this price increase as an indirect tax imposed on consumers, it is possible to accurately identify vulnerable households and pay support. the cost played a more effective role in offsetting the impact of the price increase and supporting them. given the impact of the rise in food prices on the well-being of the population and the need to respond to the increase in demand for food resulting from the rise in prices and to pay attention to food security, it may be important to improve the quality of people’s diets through measures such as increasing the production of appropriate foods and creating diversity in food production, especially for foods that account for a significant portion of household food expenditures. conclusion this study set out price elasticities of eight food groups to evaluate the impact of food price changes on iranian households. using the quadratic almost ideal demand system (qaids), we investigated how increasing food prices affects the welfare of iranian urban consumers and the poverty line. the estimated price and expenditure elasticities align with expectations, with own elasticities being negative and expenditure elasticities being positive. the research has also shown that the rise in food prices has led to a decline in the purchasing power of households, resulting in a loss of welfare. the findings of a cost-of-living analysis indicate that the consumer welfare of different scenarios varied between $8.05 to $80.46. this is equivalent to approximately 5.74% to 57.28% of the total food expenditure of eight food groups in 2020. while the impact of food price changes varied across food groups, the majority of households experienced significant difficulties in accessing food due to such price changes. it is noteworthy that after food price shocks, there was an increase in the number of households that fell below the poverty threshold. therefore, the government can play a crucial role in supporting the vulnerable households and households below the poverty line, considering the increasing trend of food prices in recent years with appropriate support programs or by paying subsidies to compensate for the effect of the price increase. however, the findings of the present study indicate that the extant cash subsidy payment policy is insufficient to compensate for the decline in welfare resulting from the surge in food prices in recent years. the outcome of such research endeavors can significantly contribute to the policymakers’ ability to develop comprehensive and targeted support packages for households susceptible to economic vulnerabilities. by incorporating the results of these studies into their policy formulation process, policymakers can design effective measures to address the needs of vulnerable households and facilitate their economic stability and overall well-being. however, there are some limitations in the present study that should be considered to make appropriate policy. this limitation is using the main food groups instead of using food separately in this study. further research can also focus on calculating welfare effect separately for each food item and for different income deciles in order to determine exactly the extent of government support for vulnerable and poor households. the application of price elasticities, segmented by income, age, and education, can provide an accurate framework to 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(2020). towards agricultural demand for the main energy carriers in iran: application of linear approximate almost ideal demand system (la-aids) analysis.  journal of the saudi society of agricultural sciences, 19(5), 313-318. _ref114578152 _ref114489287 _ref114578337 _ref115429676 _hlk124753738 _hlk124753769 _ref114578159 _hlk125619779 _hlk125620100 _hlk125620116 _hlk168057609 _hlk125617961 _hlk125617978 _heading=h.1fob9te _heading=h.1y810tw _heading=h.3whwml4 _hlk105387355 _hlk162970176 _hlk120503612 _hlk94825625 _hlk101307043 _hlk112165281 _hlk110795819 _hlk163789378 _hlk101572865 _hlk101574469 _hlk162424605 _hlk163657897 _hlk163658986 _hlk163657719 _hlk163657691 _hlk163658456 _hlk163658513 _hlk163658646 _hlk162446713 _hlk125919866 _hlk107164817 _hlk162973538 _hlk162447045 _hlk107174352 _hlk162447083 _hlk124882770 _hlk142394334 _hlk142395728 _hlk142430461 _hlk142337917 _hlk142342729 _hlk142337969 _hlk142337867 _hlk142342461 _hlk142342928 _hlk142338077 _hlk142348528 _hlk164779807 _hlk125467744 _hlk141790649 assessing the bioeconomy’s contribution to evidence-based policy: a comparative analysis of value added measurements tévécia ronzon1,2,*, patricia gurria2, michael carus3, kutay cingiz2, andrea el-meligi1, nicolas hark3, susanne iost4, robert m’barek1, george philippidis5, myrna van leeuwen6, justus wesseler2 predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology lisa baldi1, sara calzolai2, filippo arfini2,*, michele donati1 simulating farm structural change dynamics in thessaly (greece) using a recursive programming model stamatis mantziaris1,*, stelios rozakis2, pavlos karanikolas1, athanasios petsakos3, konstantinos tsiboukas1 analyzing the impact of government subsidies on household welfare during economic shocks: a case study of iran mohammad dehghan1,*, seyyed nematollah moosavi2*, ebrahim zare3 crop production, the pollinator deficit and land use management: uk farm level survey results iain fraser1,*, michelle t. fountain2, john m. holland3 1 towards a new policy narrative for agriculture: capturing social sustainability issues 1 jesús antón1*, masayasu asai2 and francesco vanni1 2 1: organisation for economic co-operation and development (oecd) 3 2: division of natural resource economics, graduate school of agriculture, kyoto 4 university, kyoto, 606-8502, japan 5 *corresponding author 6 this article has been accepted for publication and undergone full peer review but has not been 7 through the copyediting, typesetting, pagination and proofreading process, which may lead to 8 differences between this version and the version of record. 9 please cite this article as: 10 anton j., asai m., vanni f. (2025). towards a new policy narrative for agriculture: capturing 11 social sustainability issues, just accepted. doi: 10.36253/bae-17303 12 13 highlights: 14 • climate change analysis has created a new agri-environmental policy narrative. 15 • tackling social and inequality issues has gained increasing policy relevance. 16 • missing definition and metrics of social sustainability in agriculture is a bottleneck 17 (92 characters) 18 • measuring income inequalities could trigger change towards inclusive transition. 19 abstract: 20 awareness about issues related to inequality and well-being in agriculture is increasing, with 21 some evidence of inequalities affecting e.g. women, youth, and migrant farmworkers, that 22 hinder their access to income, land, health, education, and training. despite the increasing 23 2 policy interest around social sustainability, tackling social issues in agriculture is complex due 24 to lack of consensus in definition, contextual specificities, data gaps and needs to apply non-25 sectoral policies. two decades ago, environmental sustainability faced similar challenges but 26 is now mainstreamed in agricultural policy making. climate change measurement and analysis 27 played a pivotal role in creating a new agri-environmental policy narrative. expanding 28 agricultural sustainability from the green transition towards a just transition will require a game 29 changer that is measurable and highly correlated with main social issues. could an investment 30 in measuring income inequalities play this role and facilitate a new social sustainability 31 perspective in agricultural policies? 32 keywords: social sustainability, green transition, income inequalities, inclusiveness, well-being 33 1. introduction 34 the goal of sustainability over time is recognised as one of the most fundamental principles 35 in global policy making, typically covering three pillars: economic, environmental and social 36 sustainability (giddings, et al., 2002). to advance sustainable development, the agricultural 37 sector thus needs to contribute to all three dimensions (janker & mann, 2020; fao, 2022). 38 traditionally, the sustainability debate in agriculture has focused mainly on economic aspects 39 and, more recently, on the environment. economic sustainability, building competitiveness 40 and productivity growth, has been prominent in agricultural policies. over the past two 41 decades, together with other environmental concerns, climate change and its effects on 42 economic growth and environmental outcomes have come to the forefront of global 43 agriculture policy dialogues (olesen & bindi, 2002; howden, et al., 2007). the need to 44 accelerate a green transition in agriculture has led to an increased focus of agriculture and 45 food systems policies on climate-smart strategies to move farms and rural communities 46 towards net zero emissions and better management of the environment (asai, et al., 2023). 47 3 the income gap between agriculture and other economic sectors has been a long-lasting 48 argument to justify support to farmers, in particular in the early times after the second world 49 war (gardner, 1992). recent data show that farm income in the eu member states has been 50 increasing, even if there may still be in some cases a gap compared to other sectors 51 (matthews, 2024). on the other hand, the lack of economic opportunities for the farming 52 sector, declining services and lower well-being standards in rural areas remained prominent, 53 witness the farmer protests that emerged in europe in 2023-2024 (finger, et al., 2024; 54 matthews, 2024). 55 in recent decades, the social aspects have been rarely discussed as main policy drivers in 56 agriculture and are seen as a cause or a consequence of environmental or economic 57 problems, rather than a stand-alone goal. however, recent evidence shows that farmers, 58 farmworkers and their families in rural areas of oecd countries are facing a diversity of social 59 issues that are of an increasing concern for policy makers (asai & antón, 2024). for instance, 60 in switzerland, female farm family workers work around 75-80 hours a week, but only about 61 half of them (55%) are paid for their work (moser & saner, 2022). in the united kingdom, over 62 50% of workers in agriculture, forestry and fishing were suffering from work-related 63 musculoskeletal disorders (msds) (hse, 2023), while in australia one farmer dies by suicide 64 every 10 days, a rate 59% higher than non-farmers (sartor, 2021). in the united states, the 65 net farm income of african american farmers is 10% of the average of other farmers (collins, 66 et al., 2023). 67 most of these issues are related to inequality and quality of life (e.g. physical and mental 68 health) that are not a new phenomenon in agriculture. however, people’s awareness of the 69 related risks is increasing. for instance, more frequent extreme weather events result in farm 70 4 income losses, which may be perceived as critical risks by famers, exacerbating the 71 uncertainty on the sustainability of the sector and, potentially, impacting mental illness and 72 higher rates of suicide (daghagh yazd, et al., 2019; riethmuller, et al., 2023). social issues are 73 returning from a new lens: skewed distribution of income and of low-income risk among 74 farmers and farmworkers reflect inequalities and potential social exclusion, which is a concern 75 for citizens and policy makers. 76 tackling social issues has gained increasing policy importance, also in agriculture, as reflected 77 in the food systems approach (oecd, 2021). however, the lack of data and evidence has been 78 identified as a constraint to identify and address some social issues, including related to 79 gender, illness and injuries in the farm, and immigrant farmworkers (giner, et al., 2022; 80 merisalu, et al., 2019; antonioli, et al., 2023). accordingly, there is no widely acknowledged 81 methodology for quantifying and analysing the social dimension of sustainability, neither on 82 the criteria to be used when assessing the concept (saleh & ehlers, 2023; janker & mann, 83 2020). 84 the overall goal of this paper is to identify opportunities to advance towards social 85 sustainability goals in agriculture when designing, implementing and monitoring policies1. 86 how can the agriculture and food policy community develop a narrative and the required 87 evidence to respond to existing social sustainability issues? we first review the green 88 transition in agriculture according to recent agricultural policy trends in oecd countries. in 89 particular, we assess critical conditions that transformed the policy narrative by 90 mainstreaming environmental sustainability, led by climate change and the efforts to 91 1 this paper is mainly built on the findings from the recent oecd works on agricultural policy reviews (oecd, 2023; oecd, 2024) and social issues in agriculture (asai & antón, 2024), and on a keynote presentation at the aieaa conference in bari (italy) in july 2024 (antón, et al., 2024). 5 measure its linkages to agriculture. second, we explore the main dimensions of social issues 92 in agriculture, and their data and measurement challenges that impede further understanding 93 and analysing social sustainability concerns. finally, we explore the role of income as potential 94 catalyst to advance on the social sustainability agenda. income is measurable and could be 95 analysed from a new social sustainability perspective, focused on income inequalities and 96 well-being, facilitating the advancement of the policy agenda from a necessary green 97 transition to a green and inclusive transition in agriculture. 98 2. how the environment became a main driver in recent agricultural policy trends 99 agricultural policies were significantly reformed in the 1990´s and 2000s in the united states, 100 the european union and in other oecd countries. for instance, the reforms of the eu´s 101 common agricultural policy (cap) prior to the mid-2000s were successful in reducing 102 producer support, notably market price support, while progressively “decoupling” support 103 from production, with payments per hectare that do not require any specific production and 104 are more effective in transferring support to farmers. the main goal of these reforms was of 105 an economic nature: reducing the distortions associated to the government support to the 106 sector and reaching farmers more effectively. 107 a shift on composition and level of support was observed not only in the european union, but 108 across oecd countries, where successive reforms have led to increased market orientation 109 and more efficient forms of support. it is also reflected in the share of the most production110 and trade-distorting forms of support, which has also decreased. given that such support 111 (market price support, coupled direct payments and input support) potentially also 112 contributes to negative environmental outcomes, these reforms also contributed to improve 113 environmental sustainability, even if this was not the main objective (bureau & antón, 2022) 114 6 since these reforms took place, there has also been an increasing scope of environmental 115 requirements attached to the cap payments (figure 1). since 2010, the european union’s 116 producer support estimate (pse) level and composition have remained almost unchanged, 117 though increasingly with input constraints attached to payments, reflecting a greater 118 integration of environmental and climate objectives (oecd, 2023; oecd, 2024). 119 120 figure 1 integration of policy instruments with environmental and climate objectives in the 121 common agricultural policy in the european union 122 (bureau & antón, 2022)to reflect this evolution of agricultural policy objectives and impacts, a 123 variety of agri-environmental indicators has been developed by countries and international 124 organisations to track the environmental performance of the farming sector, particularly 125 during the last two decades. for instance, the oecd agri-environmental database (oecd, 126 2023) shows trends and levels of a broad range of indicators, including on agricultural land 127 use change, fertiliser use, water abstraction, on-farm energy consumption, ghg emissions 128 and nutrient balances (figure 2). these indicators were selected on the basis of data 129 availability, and environmental and policy relevance. they provide an accurate comparable 130 measurement of the main environmental pressures associated with agricultural activities. the 131 oecd agri-environmental database allows to assess performance comparing trends across 132 countries and between agricultural output growth and environmental outcomes. for 133 7 instance, in the last three decades oecd countries significantly increased output while, at the 134 same time, reduced nutrient balances. trends in other environmental outcomes such as ghg 135 emissions and farmland bird index are less promising. 136 137 figure 2 oecd agri-environmental database 138 source: oecd agri-environmental indicator data base (oecd data explorer) 139 regardless of the performance of each country, the development of agri-environmental 140 indicators has been an integral part of a new narrative that has increased the focus of 141 agricultural policies on environmental sustainability. the measurement of these sustainability 142 outcomes helps to develop a common understanding of the environmental goals and their 143 links with agricultural production, practices and policies. these indicators have also inspired 144 and informed attempts to combine economic and environmental performance into an 145 environmentally sustainable productivity index in agriculture (cobourn, et al., 2024). 146 climate change has been a global game changer or “catalysts” in the environmental policy 147 agenda and, to a great extent, also in the agricultural sustainability debate. indeed, climate 148 change is a shared environmental concern and a global public good that has contributed to 149 growing awareness on environmental sustainability (figure 3) reflected in the european 150 https://data-explorer.oecd.org/vis?tm=agri-environmental%20other%20indicators&pg=0&snb=48&vw=ov&df%5bds%5d=dsdisseminatefinaldmz&df%5bid%5d=dsd_agri_env%40df_aei&df%5bag%5d=oecd.tad.arp&df%5bvs%5d=1.0&pd=2012%2c&dq=.a.totagr_land....&ly%5bcl%5d=time_period&ly%5brs%5d=ref_area&to%5btime_period%5d=false 8 green deal egd. each country’s and each sector´s ghg emissions contribute cumulatively to 151 the increase of the overall concentration of ghgs in the atmosphere, and then mitigating 152 climate change through reduced emissions is a common goal for which there are already 153 comparable methods to measure, and relevant indicators have been developed accordingly. 154 climate change also brings multiple related agri-environmental issues together because there 155 are significant correlations among them. for instance, there are links between different 156 emissions, water quality and nutrient imbalance, and between emissions and biodiversity. 157 the work of the international pannel on climate change (ipcc) has informed policymaking 158 and international negotiations, including the unfcc and the paris agreement, and has 159 triggered and embedded a large body of research on measuring and understanding the 160 environmental impacts of different economic activities and alternative policies (guerrero, 161 2021) (lankoski, 2016) (oecd, 2022) (deboe, 2020). the analysis of climate change and of its 162 relations to the agricultural sector not only has contributed to a new narrative that 163 increasingly puts farmers in the driving seat of the contribution of agriculture to the 164 environment, but it has also stimulated the development of a broad range of agri-165 environmental policies and regulations. 166 167 figure 3 climate change as a game changer in environmental sustainability 168 9 3. what are the policy challenges to advance towards social sustainability? 169 the food systems approach to policy making has incorporated not only agri-environmental 170 concerns, but also consumer concerns and social issues (oecd, 2021), resulting in a growing 171 concern for policy makers and research communities to improve well-being of farmers and 172 their communities (asai & antón, 2024). well-being of farmers is affected by a broad range 173 of factors, which can be classified in four main groups: (1) factors affecting farmers’ economic 174 well-being (such as income and wealth); (2) factors affecting the quality of life, including work 175 and job quality; (3) factors affecting the well-being of the community; and (4) factors 176 affecting the well-being of women, indigenous peoples and specific social groups. 177 as regards as the economic factors, regional inequalities and the urban-rural divide challenge 178 the well-being of rural areas (meloni, et al., 2024; oecd, 2020). based on the analysis of 179 household disposable income in 25 european countries, meloni et al. (2024) found that the 180 income of rural households is lower than that of non-rural households. the proximity to urban 181 centres plays an important role in shaping well-being of rural residents, including farmers 182 (oecd, 2020). rural places situated in closer proximity to urban centres exploit benefits from 183 infrastructure development (e.g. hospitals and schools) and transportation because of 184 improved access to human capital, external markets, and a wide array of services and 185 environmental amenities. remote areas, in contrast, face the largest challenges regarding 186 connectivity, causing higher costs for transportation, infrastructure and service provision that 187 affect the well-being of residents in these areas (oecd, 2020; oecd/ec-jrc, 2021). 188 given that agricultural sector faces double challenges of aging and rural depopulation, 189 encouraging generational renewal is a top priority for many countries. nevertheless, young 190 farmers encounter multiple obstacles both prior to entry and once in the sector (campi, et al., 191 10 2024). these obstacles include capital constraints, regulatory complexities, access to land and 192 housing, lower access to services compared to other jobs, and lack of the networks needed 193 to access resources. negative social views of farming due to e.g. hard-working conditions, 194 degrade the attractiveness of the profession and discourage new entrants (campi, et al., 195 2024). furthermore, a ‘brain drain’ of young talents from rural areas challenges generational 196 renewal (kalantaryan, et al., 2021; zagata & sutherland, 2015). other studies also show that 197 farms in more isolated regions are less prone to be inherited by the following generation 198 (aldanondo ochoa, et al., 2007). 199 as for the factors affecting the quality of life, agriculture is known for one of the most 200 hazardous sectors worldwide, with numerous studies reporting elevated levels of 201 occupational fatalities, injuries, and illnesses (who, 2004). as regards the working conditions, 202 farmers may face long working hours, in particular during peak production seasons and under 203 labour shortages (marlenga, et al., 2010; hostiou, et al., 2020). it was recently found that 204 farmers working longer than 40 hours per week may be at higher risk for fatigue-related injury 205 and illness (elliott, et al., 2022). in many cases farmers and those working in agriculture are 206 also exposed to chemical pesticides, and this is linked to chronic illnesses such as cancer, and 207 heart, respiratory and neurological diseases (dhananjayan & ravichandran, 2018). 208 occupational stress, associated with longer working hours, compliance with increasing 209 government regulations, weather volatility, and financial pressures is another factor that may 210 have negative effects on quality of life and in some cases it can lead to mental health issues 211 for farmers and their families (farm management canada, 2020; brennan, et al., 2021; 212 daghagh yazd, et al., 2019). a range of ongoing occupational stressors associated with 213 farming may contribute to place farmers at an elevated risk of suicide (purc-stephenson, et 214 11 al., 2023): evidence from australia, france and the united states shows higher suicide rates 215 of farmers than those working in other sectors (miller & rudolphi, 2022; page & fragar, 2002; 216 bossard, et al., 2016; hostiou, et al., 2020). 217 securing equal opportunities to work in safe conditions and the same access to care and 218 health services is highly important for the individual well-being. the literature shows that in 219 the farming sector such conditions are not always met and are challenged by climate change 220 and structural transformations. studies in canada highlight three barriers for providing 221 mental care services for farmers: accessibility of health services in rural areas; stigma around 222 mental health in the agricultural community; and lack of health professionals who are familiar 223 with the agricultural context (farm management canada, 2020; hagen, et al., 2019). 224 social capital is another important dimension of social sustainability and is key for higher 225 community well-being. inclusiveness may be achieved through better connections between 226 people and in particular cultural events and leisure activities can lead to a higher sense of 227 civic engagement for farmers and improved co-operation with other members of the 228 community (halstead, et al., 2021; rivera, et al., 2018). moreover, community involvement, 229 trust and support can help people tackle challenges and opportunities, and contribute to 230 improve individual well-being and resilience, helping individuals and communities to recover 231 from, and more successfully adapt and transform in response to adverse events (aldrich & 232 meyer, 2014; adger, 2010). in contrast, rural crime, discrimination and social isolation lead to 233 distrust among community members and lack of a sense of belonging, adversely impacting 234 community well-being (deller & deller, 2010; smith, 2020). the ongoing ageing and 235 depopulation trends in rural areas may exacerbate this negative phenomenon. 236 12 finally, there are unique challenges often faced by women, indigenous peoples, and specific 237 social groups, such as migrant farmworkers and people with disabilities, due to social and 238 economic barriers and biases that hinder their access to income, land, food, health, education 239 and training, and other services (oecd/fao, 2016; todd, et al., 2024; ilo, 2023). women tend 240 to encounter longer unpaid working hours more often than men and have lower social 241 security entitlements (fao, 2020). in the european union, only 31.6% of farm managers were 242 female in 2020 (oecd, 2023), while in the united states, 7% of all farms were operated solely 243 by women in 2017-2020 (todd, et al., 2024). these figures imply that women’s role in 244 agricultural decision making, and farm and land ownership remains relatively modest. 245 for indigenous peoples the main inequalities concern their access to land (including land that 246 was taken from their ancestors), education and training, as well as capital, which remains a 247 significant barrier for indigenous entrepreneurs and business owners (oecd, 2019). migrant 248 farmworkers often are (informally) hired on a casual, piecework or seasonal basis, and their 249 work often involves long hours and difficult conditions under high risk of illnesses and injuries, 250 while being insufficiently covered by social security (un, 2009; martin, 2016). 251 the actors and territories involved on these social issues are very heterogeneous and the 252 challenges facing farmers and farmworkers are diverse. different social circumstances may 253 require different policies and tools and need targeted analysis. furthermore, the bargaining 254 position of farmers and farmworkers differs across locations and sectors and is a main source 255 of inequalities both along the agrifood value chain and within the farming sector. 256 in the past decade, several oecd countries have incorporated social issues in the policies and 257 programmes led by their respective ministries of agriculture. for example, generational 258 renewal and social conditionality on employment conditions and on-farm safety and health 259 13 are part of the goals and measures included under the european union’s common 260 agricultural policy 2023-27 (oecd, 2023). both canada and new zealand implement specific 261 agricultural measures for indigenous communities, while, in the united states, the 262 department of agriculture administers programmes that benefit the so defined “socially 263 disadvantaged farmers and ranchers” (todd, et al., 2024; asai & antón, 2024). in italy and 264 japan, the ministries fund “social farming” initiatives to create more inclusive opportunities 265 for vulnerable groups at community level, such as promoting agricultural employment for 266 persons with disabilities (guirong & oba, 2023; borsotto & giarè, 2020). 267 table 1 presents an overview of five case studies from oecd countries with examples on how 268 governments have approached issues of inequality and other social issues in agriculture: the 269 definition of the issue, the policy rationale and the specific policy measures. across these 270 policy examples, policy makers have looked beyond traditional sectoral policies and seek to 271 target social issues from a broader policy perspective, as agricultural policies are often not 272 designed for the purpose of tackling these issues. the main types of policies in the toolbox 273 applied in these examples are targeted measures on health, skills, training, social protection, 274 legal reforms, research and data. existing agricultural policies are not targeted to identified 275 social issues and they are used only as accompanying measures (switzerland) or potential 276 sources of funding (italy). 277 table 1 policy examples and their policy interventions to address social issues 278 country social issues at stake rationale for policy interventions main policy instruments canada (case 1) increasing number of farmers suffering from mental health problems. mitigate factors of farmers’ stress that could affect mental health support farmer mental health research promote mental health literacy in agricultural communities switzerland (case 2) many family members (e.g. wives) who work on the farm receive no financial renumeration and social protection. equal treatment across workers in agriculture and with other sectors from 2027, extend social protection coverage to partners on farms as a precondition for direct payment 14 italy (case 3) lack of effective social and health services in some rural areas, and limited care services for vulnerable groups. inclusiveness opportunities for vulnerable groups through the social farming (sf) practices set-up networks for diversification of agricultural activities, (e.g. healthcare, education) and to carry sf new zealand (case 4) economic pressures, demographic and social changes, and mental health challenge well-being of farmers and other citizens in remote rural communities. support “rural community hubs” to build social relationships and rural resilience start-up funding to help establish the “rural community hub” where people meet, discuss issues, have workshops etc. japan (case 5) limited job opportunities for people with disabilities while agricultural sector faces an acute shortage of labour force. equal access to jobs and sources of income for persons with disabilities. reduce labour shortages in agricultural sector provide training courses and support to develop user-friendly facilities that reduce barriers to employment faced by persons with disabilities. source: based on asai & anton (2024) 279 note: information covers a selection of case studies from table 2 collected from governments and experts in those case 280 study countries/regions in the period of june 2023 feb 2024. 281 4. measuring social sustainability performance 282 the lack of appropriate data is a further challenge to advance in the social sustainability 283 agenda in agriculture, making important social issues invisible to both policy makers and 284 citizens. greater understanding of issues around inequality and inclusiveness and the best 285 policy approaches to address them requires appropriate data, indicators and measurement 286 (asai & antón, 2024; giner, et al., 2022). , which is challenging due to complexity, a missing 287 social sustainability framework, lack of data and unstandardized indicators (brennan, et al., 288 2020; janker & mann, 2020). figure 5 summarises the three main challenges associated with 289 measuring social sustainability performance in agriculture: the lack of a clear and agreed 290 definition of social sustainability; the data gaps to define and identify social issues; and the 291 challenge to quantify social issues in indicators. even if agri-environmental sustainability faces 292 similar challenges, there has been a significant advancement in the last two decades as 293 reflected in the set of agreed oecd agri-environmental indicators in figure 2. 294 15 295 figure 4 schematic presentation of the challenges for the measurement of social 296 sustainability in agriculture 297 despite the increasing interest, the common understanding of what constitutes social 298 sustainability and how it might be achieved is limited (janker, et al., 2019; asai & antón, 2024; 299 nowack, et al., 2022). social sustainability is still considered as subjective and there is no 300 consensus on the different aspects it should entail (janker & mann, 2020; saleh & ehlers, 301 2023). a universal definition is lacking and there is no widely acknowledged methodology for 302 quantifying and assessing the social dimension of sustainability. indicators on contracts, 303 gender gaps and socioeconomic characteristics of the farming population are a good starting 304 point. however, janker & mann (2020) performed an analysis of 87 farm-related social 305 sustainability assessment tools finding a diversity of approaches: some tools are based on 306 human rights and working rights according to the un and ilo conventions and look for 307 working conditions indicators, while others assess farmers’ perceptions of their quality of life. 308 social issues may remain hidden if there is no data able to identify and define them. evidence 309 suggests that there is still a large gap between agricultural sector-specific (e.g. the census of 310 agriculture) and economy-wide data on social issues (asai & antón, 2024). in many oecd 311 countries, farmers represent a very small share of the total population and they are often 312 under-sampled in general surveys that tend to be non-representative of the farmer 313 16 population. for instance, although the eu’s income and living conditions survey (eu-silc) 314 was not created for the assessment of farmers’ welfare, it allows for the identification of 315 farmers and farm households (marino, et al., 2023). an attempt to analyse the income gaps 316 between farm and non-farm households in eu member states using eu-silc was confronted 317 to too small samples of farm households to allow a representative distributional analysis 318 (rocchi, et al., 2020; marino, et al., 2021; marino, et al., 2023)2. 319 having a small sample size poses a critical limitation on the use of general datasets for 320 exploring social issues in agriculture, especially when focusing on smaller sub-groups within 321 farming populations, notably those that are disadvantaged or vulnerable. considering that 322 the family farm remains the most common type of farm in many countries, women often 323 engage in family unpaid labour that might not be recorded in statistics, which makes it 324 difficult to acknowledge and assess (giner, et al., 2022). regarding the racial and ethnic 325 minorities in agriculture, some countries like the united states have a questionnaire on racial 326 and ethnic, underor un-reported cases are frequent due to incomplete survey responses 327 with respect to race and ethnicity information (lacy, 2023). 328 the surveys regularly conducted in the agricultural sector, including the farm accountancy 329 data network (fadn) in europe and the agricultural resource management survey (arms) 330 in the united states, are primarily intended for economic purposes. although there are 331 ongoing initiatives to expand the scope of these surveys (e.g. from fadn to farm 332 sustainability data network (fsdn) reflecting the cap’s evolution towards sustainability), 333 they may still not be well-suited to analyse social issues. furthermore, most of the existing 334 2 eu-silc is a harmonised household survey that collects multidimensional microdata on income, poverty, social exclusion and living conditions in europe. 17 sectoral surveys focus on farmers, yet there are substantial data gaps regarding farmworkers, 335 especially migrant and seasonal farmworkers, despite their important role in the agricultural 336 sector in many countries (ryan, 2023; ramos, et al., 2020). in economy-wide household 337 surveys farm households are under-sampled, and migrant and seasonal farmworkers are not 338 captured because they normally focus on the resident population (kalantaryan, et al., 2021). 339 some countries like italy and the united states collect some data on seasonal foreign 340 farmworkers (antonioli, et al., 2023; castillo, et al., 2022). 341 the self-employed status of many farmers is likely resulting in the under-reporting of 342 incidents (e.g. accidents, injuries, illness and suicides). studies from european countries found 343 that farmers and farmworkers are unlikely to report injuries if they do not have an incentive 344 such as insurance benefits (merisalu, et al., 2019). in areas such as mental health, it is difficult 345 to ask sensitive questions on personal health or social relations through a survey (brennan, et 346 al., 2020). several studies highlight that a large share of actual cases of mental illness or 347 suicide may be underreported due to social stigma in rural areas (purc-stephenson, et al., 348 2023; miller & rudolphi, 2022). finally, there are personal and social sensitivities that are 349 country specific and make data collection on social issues particularly challenging. for 350 instance, some countries such as finland, norway and sweden, explicitly forbid the collection 351 of statistics on ethnic identity (oecd, 2019). these data gaps make it harder to develop 352 indicators to monitor and tackle social issues and to identify target groups. 353 because social issues are complex and vary across countries and regions, context-specific data 354 and analytical methods are used, requiring more qualitative indicators than for environmental 355 and economic issues. such indicators are subject to a high degree of subjectivity (kelly, et al., 356 2018) and are difficult to harmonise. the choice of social sustainability indicators is not only 357 18 the result of a neutral scientific analysis, but also of societal choice reflecting a diversity of 358 views. 359 finally, another challenge is identifying drivers that hinder some aspects of well-being in a 360 manner that is specific for farmers or their communities (asai & antón, 2024). this analysis is 361 critical to identify the need for policies that specifically tackle social sustainability in an 362 agricultural context. information regarding these driving factors and causal relations is 363 frequently limited. there is a risk of a vicious circle between the shortage of data for 364 identifying policy demands and the lack of clear policy priorities for funding data initiatives. 365 5. looking for a catalyst on social sustainability 366 therefore, despite the increasing policy interest around the dimensions that affect the well-367 being of farmers, their families and farmworkers, and that of the communities in which they 368 live, defining and tackling social issues in agriculture is complex. there are four main 369 bottlenecks summarised in figure 5. first, there is no consensus on what constitutes a social 370 issue. the nature of social sustainability includes social processes and interactions that 371 emerge within a community and makes it difficult to identify a coherent, clear and utilisable 372 definition (eizenberg & jabareen, 2017). moreover, subjectivity often comes into play in 373 people’s judgments that a particular state of affairs constitutes a social issue (kulik, 2023). 374 this is frequent in any analysis of agriculture, but in the case of social issues the driving factors 375 go beyond complex production conditions into personal, health and community linkages. 376 19 377 figure 5 – the four main bottlenecks in addressing social issues 378 second, social issues are often context specific and addressing them requires considering 379 different perspectives and sensitivities of stakeholders. urban-rural inequalities play an 380 important role in shaping well-being of rural residents, including farmers (oecd, 2020; meloni, 381 et al., 2023). thus, social issues can benefit from a place-based approach because they are 382 associated with a specific location. possible solutions often derive from the local context, and 383 policy interventions are often away from the traditional agricultural policy areas (oecd, 2020; 384 asai, et al., 2023). 385 third, tackling social issues requires policies that go beyond traditional sectoral programmes. 386 agricultural policies focus mainly on economic and environmental outcomes of the sector, 387 often leaving social objectives and implications to other policy areas. however, the 388 agricultural sector is only a small player for social policy partners and its specificities and policy 389 context may be overlooked. as confirmed by the five policy examples in table 1, defining and 390 tackling social issues in agriculture requires policies beyond traditional sectoral programmes 391 (asai, et al., 2023; janker & mann, 2020; saleh & ehlers, 2023). 392 finally, as discussed in section 4, social issues are often poorly measured due to the lack of 393 data and data infrastructure, and subsequent unstandardised indicators. the trade-offs 394 20 between social and economic sustainability are, therefore, difficult to assess. for instance, 395 how better working conditions affect productivity. 396 considering these bottlenecks, a game changer seems necessary to advance on the social 397 sustainability agenda in agriculture, similarly to what climate change measurement and 398 analysis represented in the context of the green transition. this does not mean that social 399 sustainability must come after environmental sustainability in a sequential manner. policy 400 trends towards environmental and social sustainability may have the same policy roots, but 401 they may need different triggers to effectively become main drivers of policy changes and 402 impacts. 403 the policy agenda for a more inclusive transition could benefit from an indicator that is easily 404 measurable and highly correlated with social sustainability issues, and that allows cross-405 comparison among countries, regions and social groups. income inequality has a good 406 potential to play a catalyst role on social sustainability, since it meets several critical 407 conditions. although not perfect, income inequality is a widely social concern and affects all 408 the population, and it is also correlated to many dimensions that are currently characterising 409 the social sustainability debate in agriculture, including health, gender, marginalized groups, 410 decent work and social capital. of course, a complete analysis of social issues should also 411 include access to public services and infrastructure that also contribute to well-being. 412 together with wealth, income largely determines the ability of individuals to meet their basic 413 needs (e.g. food, housing, healthcare, transportation, education) and to make choices that 414 contribute to security, satisfaction and personal fulfilment (meloni, et al., 2024; oecd, 2020; 415 meloni, et al., 2023). thus, addressing income-related inequalities is critical to achieve overall 416 economic well-being. in the agricultural policy debate, such issues have been discussed for a 417 21 long time to justify policy support aiming to address the assumption of lower income in 418 agriculture business as compared to other production activities (rocchi, et al., 2020; katchova, 419 2008). however, the social sustainability debate would benefit from a broader perspective on 420 income, by looking not only at the level of farm income, but also looking at: the farm 421 household income and income of those working in farming and food sector; the income 422 distribution differences by gender and with other sectors; and the differences among 423 agricultural, rural and non-rural households. it should also entail by focusing on policies 424 tackling income inequalities and their impacts on low household income and poverty among 425 those making their living from agriculture, rather than focusing solely on increasing farm 426 income (oecd, 2023; oecd, 2003). 427 recent studies show that in the european union farm household incomes on average are not 428 particularly lower compared to non-farm household incomes (rocchi, et al., 2020; marino, et 429 al., 2021; mittenzwei, et al., 2024), while others have shown that income inequality and 430 poverty are greater in the farm community compared to the non-farm community (de frahan, 431 et al., 2017). however, the lack of data is the main constraint for an accurate assessment. 432 administrative, political, and technical obstacles hinder the collection of comprehensive farm 433 household data and currently there is no reliable system to allow income comparisons among 434 farmers, farm workers and those in other sectors of the economy (hill & bradley, 2015; eca, 435 2016; oecd, 2023). 436 improving the understanding of the income distribution issues related to farms, farm 437 households and rural households could help to move forwards the social sustainability agenda 438 also from an agricultural policy perspective. as showed in figure 6, in the context of the cap, 439 direct payments to farmers decoupled from production, which represent an important part 440 22 of farm income, have been increasingly linked to several environmental requirements under 441 conditionality. however, direct payments are distributed to households based on the amount 442 of land used rather than on their overall household income. a full sustainable (social and 443 environmental) transition would lead to a shift in the policy mix towards more targeted 444 payments to farm households suffering from low-income, and to result-based agri-445 environmental payments (oecd, 2023). 446 447 figure 6 policy pathways towards a green and inclusive transition in agriculture 448 in addition to these targeted payments, other eu and national agricultural policies could 449 contribute to the inclusive transition. for example, eu rural development policy includes a 450 range of measures some of which may increase the attractiveness of rural areas and promote 451 agricultural entrepreneurship. the provision of public services such as education, health and 452 transport is particularly relevant to improve wellbeing and social sustainability. social 453 conditionality was also introduced in the cap 2023-27, with the overall objective of linking 454 farmer payments to compliance with certain labour laws. although all these policy tools have 455 potential to improve, among other, the well-being and working conditions of farmers and the 456 agricultural labour force, they are not targeted to income distribution issues. 457 stronger evidence on disposable income could allow to have a better understanding of the 458 standard of living of farmers, since income is strongly interlinked with key dimensions of well-459 being including, among other, job quality, housing, health and work-life balance. thus, 460 23 improving the availability and access to micro-economic datasets for the assessment of the 461 income aspect of policies not only at farm level but at the household level could be a very 462 important step in monitoring and tackling social sustainability issues in agriculture. 463 such a data investment would provide policy makers with a proxy for the well-being of farm 464 households and then a tool to better define the rationale of income support and to target it 465 to legitimate social objectives (oecd, 2023). a more accurate measurement of total farm 466 household income would also allow to assess the potential impact of agriculture policies as 467 compared to non-sectoral policies such as social policies on income and ensuring livelihoods, 468 as well as to contributing to other social sustainability objectives. data availability and needed 469 investments to measure farm household income deserves a separate in-depth analysis. 470 6. conclusions 471 social issues are gaining momentum in research and policy discussions on agricultural 472 sustainability. this is the result of multiple drivers, including increasing anecdotal evidence of 473 inequalities and quality of life issues that are specific to the agricultural sector. similarly to 474 environmental issues twenty years ago, social sustainability today lacks a clear and shared 475 definition, and a common and well-established metrics to tackle its complexity and its 476 multiple and interrelated dimensions. measuring and analysing climate change, together with 477 other agri-environmental indicators, has contributed to create a new agri-environmental 478 policy narrative based on metrics related to the environmental sustainability of agriculture. 479 recently, governments have made efforts to focus their policies on achieving agricultural 480 “sustainable productivity growth” (spg) (oecd, 2024). the concept of spg is based on the 481 idea of increasing productivity while reducing the pressures on the environment. the need to 482 also cover the social aspects of sustainability has emerged in the discussion on measuring the 483 24 spg (oecd, 2024). the main difficulty of measuring social sustainability performance is its 484 many dimensions and context-specificity (asai & antón, 2024; janker & mann, 2020) 485 despite this limitation and other existing bottlenecks in addressing social sustainability, an 486 increasing number of governments has started to approach the issues of inequality, 487 inclusiveness and other social issues in agriculture. since agricultural policies are often not 488 designed for the purpose of tackling social issues, seeking for cross-sectoral approaches and 489 collaboration with other policy areas and stakeholders can help to design policy mixes 490 targeted to the sector’s social concerns. however, the lessons from agri-environmental 491 sustainability show that to advance on the social sustainability agenda a new narrative is 492 needed based on clear definitions and metrics. the design and implementation of suitable 493 policy mixes needs an evidence-based approach to respond to the most pressing social issues. 494 in a context where available statistical tools are not sufficient to measure the well-being of 495 farm households and farm workers, measuring income inequalities could be a catalyst to 496 advance on the research and policy agenda on social sustainability through both a new 497 narrative and a new set of indicators. in particular, more reliable data on agricultural 498 household income could be an important first step to design more effective and targeted 499 income support that responds to social sustainability concerns. investing on data to build a 500 new evidence-based narrative on the sustainable transition of agriculture, that needs to be 501 greener, but also more inclusive and socially sustainable. 502 acknowledgements 503 this paper does not represent the views of the oecd or of its member countries. the opinions 504 expressed and arguments employed are those of the authors. 505 25 this paper builds on the findings from the recent oecd works on agricultural policy reviews 506 (oecd, 2023; oecd, 2024) and social issues in agriculture (asai & antón, 2024), and on a 507 keynote presentation at the aieaa conference in bari (italy) in july 2024 (antón, et al., 2024). 508 the authors wish to acknowledge the valuable supports made by helena rasch and leonie 509 gollisch during their internship at the oecd. the authors are also grateful to members of the 510 oecd farm-level analysis network and country experts who provided case 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(2023). towards a holistic approach to sustainable risk management in agriculture in the eu: a literature review. bio-based and applied economics 12(3): 165-182. doi: 10.36253/bae-14492 received: march, 4 2023 accepted: september, 11 2023 published: october 15, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: donato romano discussant: roberta raffaeli, paolo sckokai orcid la: 0000-0002-3011-5381 sc: 0000-0001-9231-1591 fgs: 0000-0002-9450-4618 st: 0000-0002-9828-8262 ss: 0000-0001-5501-3552 bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14492 bae 10th anniversary papers towards a holistic approach to sustainable risk management in agriculture in the eu: a literature review linda arata1, simone cerroni2, fabio gaetano santeramo3, samuele trestini4, simone severini5,* 1 università cattolica del sacro cuore, piacenza, italy 2 university of trento, trento, italy 3 university of foggia, foggia, italy 4 university of padova, padova, italy 5 university of tuscia, viterbo, italy *corresponding author. e-mail: severini@unitus.it abstract. agriculture is one of the sectors most exposed to a plethora of risky phenomena such as weather, pests and diseases, changes in prices and government policies, instability of global markets. we review the literature on risk management (rm) in agriculture focusing on five key issues: i) why evidence on rm is often controversial; ii) how farmers behave in selecting among available rm instruments; iii) why some of these instruments are underutilised; iv) how to assess the impacts of innovative rm tools to (further) improve their design; v) how agricultural policy measures aimed at increasing the environmental sustainability of the sector could affect rm choices. we address all these issues to get a holistic vision of rm, and point at areas where further analyses are needed. keywords: risk management choices, behavioural factors, adoption of risk management tools, use of chemicals, feasibility studies. jel code: q12, d81, d83, o31. 1. introduction although risk concerns all economic activities, agriculture is one of the most concerned sectors, due to its exposure to a plethora of risky phenomena such as weather, pests and diseases, changes in prices and government policies, instability of global markets, and other factors (moschini and hennessy, 2001; hardaker et al., 2015; komarek et al., 2020). furthermore, the multifaceted risks farmers must cope with are very likely to occur simultaneously, producing a compounded negative effect (hardaker et al., 2004). risk in agriculture causes wide volatility in farmers’ income and wellbeing and in turn it influences the decision-making process. experiencing negative events reduces farmers’ willingness to invest and innovate (sckokai and moro, 2009). this, in turn, may negatively affect farms’ productivity and http://creativecommons.org/licenses/by/4.0/legalcode https://doi.org/10.36253/bae-14492 https://orcid.org/0000-0002-3011-5381 https://orcid.org/0000-0001-9231-1591 https://orcid.org/0000-0002-9450-4618 https://orcid.org/0000-0002-9828-8262 https://orcid.org/0000-0001-5501-3552 https://doi.org/10.36253/bae-14492 166 bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14492 linda arata et al. competitiveness (vigani and kathage, 2019) and push farms out of the business. the negative consequences may also be reflected in the value chain (cafiero, 2008) and transferred to all stakeholders of the agro-food system. major and unexpected events such as the covid-19 pandemic and the food/energy crises induced by the war in ukraine have unrevealed the vulnerability of the global food supply. by threatening the status of global food security, these major shocks have induced unprecedented policy responses in all advanced economies, as well as in developing countries (european parliament, 2022; oecd, 2020; santeramo and kang, 2022). over the years, risk in agriculture has been increasing in width and depth, unveiling the need for improving risk management (rm), as recognized by the european commission (2017) “[...] it is important to set up a robust framework for the farming sector to successfully prevent or deal with risks and crises, with the objective of enhancing its resilience and, at the same time, providing the right incentives to crowd-in private initiatives”. rm refers to the actions taken to manage potential problems induced by risky events, to reduce their detrimental consequences, and to increase the chances of success of the business (kahan, 2013). in this sense, rm can be a key factor in enhancing the resilience of farms and related farming systems (spiegel et al., 2020) and several scholars call for improving and enlarging the scope of rm to do so (finger et al., 2022). unfortunately, the state of knowledge on rm in agriculture is still incomplete, and the current approaches to rm are too simple, partial, and inappropriate to successfully help cope with multi-faced global challenges: changes in climate, more frequent extreme weather events, unstable and volatile markets, food security and food safety threats. improving the state of knowledge on rm is important: successfully managing risks helps in finding the right balance among productivity, environmental care, market resilience to climate change, and capability to secure safe and quality food. this paper reviews the extant literature on the analyses of agricultural rm, highlights progress and gaps, and advices on promising areas of research. this exercise is per se a very useful contribution to developing a holistic approach to analysing rm. more generally, we hope this piece will stimulate the debate on this relevant topic. while we are aware that some recent literature reviews exist, especially on specific topics (e.g., komarek et al., 2020), we believe that our paper makes a twofold contribution to the extant debate. first, our overview of the literature focuses on five research questions: i) why evidence on rm is often controversial; ii) how farmers behave in selecting among available rm instruments; iii) why some of these instruments are underutilised; iv) how to assess the impacts of innovative rm tools to (further) improve their design; v) how agricultural policy measures aimed at increasing the environmental sustainability of the sector could affect risk and, consequently, rm choices. these questions are answered in the subsequent sections. this review also highlights areas where further analyses are needed. second, we use a holistic approach to the topic. since rm in agriculture is a complex phenomenon, several rm actions are available, and farmers’ decisions are affected by spatially and temporally heterogeneous factors, a holistic approach seems needed (figure 1). rm decisions are strongly influenced by the context in which farmers operate. several dimensions are relevant to define the context, including not only farm structural and productive characteristics, but also the markets and the environment in which farmers operate. regarding the markets, the complexity and interconnection of the global agri-food sector have imported new risks into the sector or emphasized old ones. regarding the environment, there is a vast literature pointing out the effect of climate change on the risks farmers are facing (e.g., sorvali et al., 2021). a growing body of literature has also shown that farmer’s behavioural factors do affect the farmer’s rm choices and therefore such factors cannot be ignored. furthermore, the farm sector in the eu is heavily affected by policies. on the one hand, eu rural development policies support the adoption of specific rm tools providing subsidies to reduce the cost of adoption. on the other hand, farm production is constrained by pieces of legislation aimed at reducing the use of inputs with a harmful effect on the environment. however, often these inputs (e.g., pesticides in the case of pests, and irrigation in the case of drought) have also an effect on agricultural risk, thus their imposed reduction is likely to influence rm choices. the policy context is evolving in this area: the recently released farm-to-fork strategy (f2fs) and the cap reform (european commission, 2018) have set very ambitious environmental targets for eu agriculture (reduction of 50% and 20% in the use of pesticides and fertilisers respectively, by 2030). this will have consequences on the risk faced by farmers because the use of chemicals is intimately related to risk in agriculture and its management (möhring et al., 2020). studying the impact of policies targeted to environmental objectives on the farmer’s risk and the uptake of rm tools is worthy to be addressed. farmers are the ultimate decision-makers in terms of risk management strategies. as economic agents they can take several actions to manage risk including the adoption of specific rm tools (santeramo, 2019; cai, de janvry and sadoulet, 2020), changes in production mix and diversification, subscription of https://doi.org/10.36253/bae-14492 167towards a holistic approach to sustainable risk management in agriculture in the eu: a literature review bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14492 production contracts, use of risk decreasing input such as pest control chemicals and irrigation (cerroni, 2020). their actions are however influenced by risk preferences (iyer et al., 2020), and other behavioural factors. the literature on the influence of other behavioural factors (i.e., subjective probabilities, risk perception and preferences, ambiguity attitudes, loss aversion and time preferences) on farmers’ decisions to uptake rm tools (colen et al., 2016) is scant (coletta et al., 2018; cerroni, 2020; čop et al., 2023). similar considerations apply to the attitude toward innovations and the ability to gather and process information. in the end, this oversimplified framework (and logical flow) advocates for a holistic approach to the analysis of rm also realizing that the current state of agricultural rm is constantly evolving, and it needs to be adapted to novel challenges. our literature review is an attempt to approach the study of rm by adopting a holistic view: the methods adopted in the analysis of rm in agriculture, the behavioural factors affecting rm adoption, innovative rm tools, the relationship between agricultural risk and input use and between different policies directly or indirectly affecting risk and rm in agriculture. figure 1. graphical representation of the environment in which rm takes place. source: own elaboration. https://doi.org/10.36253/bae-14492 168 bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14492 linda arata et al. these topics were selected because they are important in influencing the choice of rm strategies. furthermore, on these issues there have been significant advances in the literature but still some aspects require further study. although the emphasis is on eu rm, the review considers analyses carried out also in non-eu countries. these are included to report approaches of analyses that could be replicated in the eu context and to better position the possible strategic choices of the eu with respect to the international context. section 2 gives an overview of the methods available in the literature to study the adoption of rm in agriculture and it also summarises risk types whose frequency is increasing in the agricultural sector. section 3 focuses on the behavioural factors, specifically the subjective probabilities, risk and uncertainty preferences, affecting the farmer’s decision to adopt insurance products (either traditional insurance or weather-based index insurance). section 4 provides a picture of innovative rm tools, such as the mutual funds for catastrophic events (introduced by the last cap reform) and the weatherbased index, together with their pros and cons compared to traditional insurance products. section 5 reports on the studies related to the impact of pesticide and fertiliser use on agricultural risk as well as on the effect of insurance product adoption on this use. the section also highlights potential synergies and trade-off among different eu agricultural policies. the last section concludes by summarizing the main points raised by the literature review. here, special attention is paid to identify the areas where further improvements in the research on farm risk management are needed. 2. explaining risk management choices to cope with the large array of risks the farm sector is facing, the european union (eu) decided to emphasize the role of new rm tools (meuwissen et al., 2018) by structurally supporting not only crop insurance products, but also mutual funds (mf). these can cover yield losses and, by means of the income stabilisation tool (ist), can help farmers cope with income drops (el benni et al., 2016), enlarging the type of risks covered by subsidized tools. despite the pervading exposure to risks for farmers (trestini et al., 2017), the advantages that these instruments provide to farms (enjolras et al., 2014; severini et al., 2019) and the confirmed trend in the reduction of decoupled direct payments, the expenditure foreseen since 2014 in the cap for the risk management toolkit involves only 12 over 28 member states (chartier et al., 2017). among these, italy, the leading country in terms of allocated budget, still records a limited uptake of risk management tools (ismea, 2022). the application of cap is further pushing in the direction to improve the development and the support for risk management solutions by confirming actual tools and introducing in the italian strategic plan of the cap, from 2023, the new catastrophic risk coverage called agricat, built as a mutual fund. all this offers farmers the opportunity to get access to a wide set of rm solutions. this availability of innovative tools (i.e. mutual funds, ist and agricat) together with the limited diffusion of traditional ones (i.e. insurance), impose understanding determinants of the diffusion of both traditional and innovative rm tools to allow farmers to maintain and improve their resilience and competitiveness under the new orientations of cap. understanding the factors that influence the adoption of rm tools allows, from the point of view of policy maker, to evaluate the effectiveness of rm policies and to guide their design. while, from the perspective of insurance company or mutual fund, it provides a better understanding of farm preferences by driving the development of rm tools that can promote farm resilience. the review of the research methodologies applied to understanding the adoption of rm tools by farmers allows to identify research gaps and suggest potential future studies. the adoption of risk management tools is extensively investigated in the literature (harrison and ng, 2019), yet the behavioural factors of this adoption is often neglected (see section 3 for a detailed discussion on this point). in the eu, a growing body of studies about yield insurance and mutual fund adoption is observable (enjolras and sentis, 2011; liesivaara and myyrä, 2017; meuwissen et al., 2018; santeramo, 2018; santeramo et al., 2016; was and kobus, 2018). indexbased insurance tools, marginally adopted in the eu, are mainly investigated in developing countries (e.g., bucheli et al., 2021; jensen et al., 2018) with some applications in the eu (vroege et al., 2019). in order to understand rm behaviour and assess the probability of farmers’ adoption of available and innovative tools, it is worth considering different determinants simultaneously within an effective conceptual framework. in fact, the determinants of the adoption of rm tools are widely discussed in the literature, whereas practical application aiming to understand the interaction of different determinants is much less explored (holt and laury, 2002; franken et al., 2017). indeed, risk and ambiguity preferences may affect risk behaviour directly (menapace et al., 2013; čop et al., 2023). furthermore, risk attitude explains risk behaviour, being https://doi.org/10.36253/bae-14492 169towards a holistic approach to sustainable risk management in agriculture in the eu: a literature review bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14492 indirectly affected by socio-economic and individual characteristics (dohmen et al., 2011; donkers et al., 2001). complex interrelations can be simplified by multivariate statistical analysis such as the so-called structural equation modelling (sem) (ullman and bentler, 2003). sem allows for testing complex models that imply both direct and indirect effects, allowing to solve limitations of traditional regression models. furthermore, sem allows researchers to distinguish between observed and latent variables, testing a wider variety of hypotheses compared with most traditional approaches (kline, 1998). this approach is quite recent among agricultural economists, with one of the first examples incorporating risk components proposed by pennings and leuthold’s (2000). in recent applications, risk behaviour has been investigated focusing on risk perception and risk attitude (van winsen et al., 2016), and on farm socio-economic and individual characteristics of risk attitude (franken et al., 2017), also incorporating the role of trust and perceived barriers (giampietri et al., 2020). a first attempt to apply a defined framework to understand the participation to rm tools has been applied by rippo and cerroni (2023) using the unified theory of use and acceptance (venkatesh et al., 2003). beside these attempts to build a framework able to understand and support the diffusion of available and innovative tools, any shared conceptual framework is, to our knowledge, applied in the literature. literature proposing the analysis of determinants of diffusion and/or adoption of innovation at smes including farms is extensive and well formalized. many other methodologies should be tested also for the case of rm tools adoption. to do so, we should consider the organisational profile of farms and the role of individuals, especially in family farms. at first glance, when evaluating family farm choices, the theory of planned behaviour (tpb) (ajzen, 1985; 1991) and technology acceptance model (tam) (davis, 1986) appear to be the most appropriate frameworks. this is because the two frameworks include constructs such as “subjective norms”, in the case of the tpb, or the perceived usefulness, in the case of the tam, which strictly refer to the individual evaluation of the choice or to the perceived social pressure to engage or not to engage in a behaviour. when the farm adopts a structure in which wage labour becomes prevalent by assuming a corporate structure with division and delegation of responsibilities, the process of choice moves from being individual or dependent on family needs and relationships to being the result of an organizational choice. in this case, diffusion of innovation theory (doi) (rogers, 1995), and the technology– organization–environment (toe) framework (tornatzky and fleischer, 1990) are suitable. these methodologies analyse the adoption process at the organizational level, including among determinants of specific adoption choice variables like the compatibility with the company, in the case of doi, and formal/informal organisational link, in the case of toe. besides deterministic approaches, machine learning solutions start to be applied to further understand factors affecting the adoption of rm strategies. few applications can be retrieved from the current literature considering application of insurance contracts in romania and italy (mare et al., 2022; biagini et al., 2022a) and mutual funds for pest diseases in the north of italy (höschle et al., 2023). at present, there is a growing interest in the management of new and/or growing risks: among these, it is worth to mention the need to manage growing systematic abiotic risks (e.g., drought and frost) and emerging biotic threats (pests and diseases). as regards the latter, the need for better-tailored risk management tools becomes more urgent given the orientation of agricultural policy towards the significant improvement of production environmental standards (e.g., f2f strategy), often not sufficiently supported by alternative solutions in pest management. in the case of extreme and systematic weather risks, a country-wide event cannot be covered under indemnity insurance schemes because the costs for the physical damage assessment in the field often outweigh the benefit for the insured farm (vroege et al., 2019) and the systemic nature of the event may expose insurance sector to unsustainable costs. concerning biotic threats, the availability of insurance is rare as it is often unsuitable for the insurance market, due to both their unpredictable spread, linked to an epidemic dynamic, and agents’ behavioural reasons, mainly moral hazard (norton et al., 2016). to face the limitations of insurance, some pioneering initiatives of mutual funds to manage such risks have been locally developed in italy, both with private and public support (giampietri et al., 2020; höschle et al., 2023), but the availability of such tools is below the expected demand. to design a better-tailored rm tool offering protection against pests and diseases that are appealing to farmers, the first important step is to investigate farmers’ preferences for the characteristics of such innovative tools. until now, farmers’ preferences for insurance contract characteristics remain mostly unaddressed, despite being of utmost importance for designing new insurance contracts, extending them to other crops, and increasing participation rates. to this purpose, the discrete choice experiment (dce) approach has proved to be useful. based on the lancaster’s (1966) theory of https://doi.org/10.36253/bae-14492 170 bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14492 linda arata et al. consumer and the random utility theory, demand is defined over the characteristics of goods, rather than over goods themselves. in dce respondents are thus asked to choose between different bundles of goods (e.g., rm tools) described in terms of their characteristics (e.g., price, level of maximum indemnity). dce has been largely employed to elicit consumers’ preferences and policy design (colombo et al., 2005). more recently, this approach has also been used to investigate farmers’ preferences for agro-environmental scheme designs (ruto and garrod, 2009) and contract farming configurations (abebe et al., 2013). furthermore, discrete choice experiments have been used to investigate farmers’ preferences for insurance contract characteristics. while there are several dce carried out in developing countries addressing farmers’ preferences for insurance characteristics (akter at al., 2016; reynaud, nguyen, aubert, 2017; ward and makhija, 2018; ali et al., 2021; tang et al., 2022), there are only few that concern european farmers (mercadé et al 2009; liesivaara and myyrä, 2017; möllmann et al., 2019; doherty et al., 2021; čop et al., 2023) and there are no applications on italian farmers. in conclusion, the literature review highlights the lack of a general framework to support the development of effective policies to promote rm solutions in agriculture. a comparison of different frameworks can improve the understanding of farmers’ behaviour and evaluate the most suitable approach depending on the organizational profile. these studies should support policy design based on the joint study of farmers’ preferences and behaviour towards rm strategies. furthermore, there is a need to carry out policy impact assessments in terms of farmer uptake of rm innovations and effects in reducing risks and increasing farm resilience. finally, it may prove useful, as rm solutions for farmers increase, to better understand the interactions and possible trade-offs between the different tools. 3. behavioural factors influencing farmers’ adoption of risk management tools from a behavioural perspective, the economic framework that is generally used to understand and predict farmers’ decisions to cope with agricultural risks is rooted in expected utility theory (eut) (von neumann and morgenstern, 1947). however, some fairly recent empirical applications have demonstrated that farmers’ decisions to insure their production often depart from standard eut. non-standard economic theories, such as prospect theory, explain farmers’ choice behaviour more parsimoniously (e.g., babcock, 2015; dalhaus et al., 2020; feng et al., 2021). these empirical findings determined the development of a small but growing research that investigates the extent to which behavioural factors such as farmers’ probabilistic beliefs, probability weighting, risk and uncertainty preferences, and loss aversion influence farmers’ decisions to purchase an insurance product (e.g., fezzi et al., 2021) and participate to mutual funds (rippo and cerroni, 2023; čop et al., 2023). these behavioural factors are generally elicited using experimental methods, while their ability to explain and predict farmers’ choice behaviour is tested by combining data from economic experiments with primary data obtained using stated preference surveys or available secondary data on actuarial farmers’ purchasing decisions (iyer et al., 2019). in this section, we mainly focus on the literature related to subjective probabilities, risk and uncertainty preferences. subjective probabilities are considered to be important predictors of farmers’ behaviour because farmers, like any other economic agent, base their decisions on their beliefs or expectations when the decision context is highly uncertain. if expressed in a probabilistic fashion, these beliefs or expectations are defined as subjective probabilities (e.g., hardaker and lien, 2010). the literature looking at the role of subjective probabilities in explaining farmers’ behaviour is scant, and only a very small number of studies examined how subjective probabilities influence farmers’ decision to purchase an insurance product (see cerroni, 2020; čop et al., 2023; cerroni and rippo, forthcoming for recent reviews). there are a couple of noticeable exceptions. čop et al. (2023) found that subjective probabilities are important predictors of farmers’ decisions to enrol on a sector-specific ist related to grapevine. fezzi et al. (2021) found that farmers’ subjective probabilities regarding production losses due to extreme climatic events are not in line with objective measures of risk. hence, policy interventions geared to reduce this gap could have important policy implications regarding insurance subsidization. one potential drawback of these studies is the elicitation of subjective probabilities using hypothetical methods that are not incentive-compatible and therefore do not induce farmers to elicit truthful beliefs. however, the literature on decision analysis provides several incentive-compatible methods that are able (in theory) to elicit truthful beliefs under a proper incentive scheme. these methods could be used to elicit more accurate subjective probabilities related to uncertain agricultural outcomes (see cerroni and rippo, forthcoming for a review). more accurate probability assessments should have in theory a higher degree of external validity and explain farmers’ choice behaviour more parsimoniously. https://doi.org/10.36253/bae-14492 171towards a holistic approach to sustainable risk management in agriculture in the eu: a literature review bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14492 risk preferences have been shown to be an important driver of farmers’ decision-making processes, especially those related to the adoption of new technology and crops (e.g., liu, 2013; barham et al., 2016). however, only a few studies have investigated whether these preferences can play a role in explaining farmers’ decision to purchase insurance products. recent research indicates that risk preferences are poorly correlated with the decision to purchase traditional insurance products (menapace et al., 2016; coletta et al., 2018; rommel et al., 2019; čop et al., 2023). these results may be driven by some confounding factors that have been recently identified in the related literature. first, risk preferences appear to be highly context-dependent (finger et al., 2022) and therefore their ability to explain farmers’ choice behaviour may be context-dependent too. second, a wide range of approaches exists to elicit risk preferences (see cerroni, 2020, and cerroni et al., forthcoming for recent reviews), and, unfortunately, empirical evidence suggests that different elicitation techniques provide inconsistent measures (e.g., reynaud and couture, 2012). once again, the elicitation technique used may have an impact on the ability of elicited preferences to explain choice behaviour. some practitioners advocate that adding an agricultural context to standard monetary lotteries can improve the external validity of elicited preferences, thus boosting the predictive power of elicited risk preferences. on the other hand, contextualization may lead farmers to use heuristics that undermine the internal validity of experimental data (see cerroni, 2020 for an application of contextualized field experiments and a discussion on strengths and limitations). if farmers’ risk preferences are extensively researched in the related literature, uncertainty and ambiguity preferences are not. there are only very few studies eliciting farmers’ uncertainty and ambiguity preferences (e.g., beharam et al., 2014, bougherara, 2017; cerroni, 2020). none of these studies attempt to use such preferences to explain farmers’ behaviour when purchasing insurance products. in this section, we use the terms uncertainty and ambiguity interchangeably, however, we have to acknowledge that the distinction between risk, uncertainty and ambiguity is far from being clear in the decision analysis literature (see cerroni and rippo, forthcoming for a discussion). the most popular approach to disentangling these concepts is the frequentist. here, risk refers to situations where definite numerical probabilities are known and can be objectively measured, while uncertainty refers to situations where definite numerical probabilities are unobservable (knight, 1921). however, other paradigms exist, such as the subjectivist, under which subjective probabilities play a key role under both conditions of risk and uncertainty (ramsey, 1931; de finetti, 1931, savage, 1954). furthermore, there are other schools of thought that try to differentiate uncertainty from ambiguity. for example, according to harrison (2011), uncertainty refers to situations when the agent can form a unique and well-defined subjective probability distribution, while ambiguity refers to situations when the agent is not capable of doing so. this brief discussion on the role that subjective probabilities, risk and uncertainty preferences can have on farmers’ decisions to use risk management tools allows to highlight a few key points relevant in the case of the use of insurances. first, the literature exploring the extent to which these behavioural factors affect these decisions is almost non-existent. the literature focusing on standard agricultural insurances and mutualistic solutions is limited. while common sense suggests that subjective probabilities, risk, and uncertainty preferences can affect the uptake of risk management tools, the extent of these impacts and the underlying behavioural mechanisms are unclear and under researched. second, there is still an open discussion in the decision analysis literature regarding the most appropriate way to elicit these behavioural factors. many methods are available to elicit subjective probabilities, risk and uncertainty preferences in the literature and empirical evidence suggests different methods lead to different results. this may have an influential impact on the role these behavioural factors play in explaining farmers’ insurance decisions. the horse race to truthful probabilistic beliefs, risk and uncertainty preferences is not over yet. exploring the internal and external validity of results obtained via different elicitation methods appears to be the only strategy to shed light on these issues. third, behavioural factors can be useful also to predict farmers’ choice behaviour. a new stream of research is emerging that seeks to incorporate these behavioural factors into simulation models to enhance their ability to explain and predict choice behaviour (e.g., huber et al., 2022). this line of research definitively contributes to build a more holistic view about. sustainable risk management in agriculture. 4. innovative risk management tools the previous sections have focused on rm behaviour and choices of rm tools mainly referring to already existing tools such as crop insurance schemes that have a long history in italy, and date back to the early 2000s (cafiero et al., 2007; santeramo and ramsey, 2017). the transition has been motivated by drawbacks associathttps://doi.org/10.36253/bae-14492 172 bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14492 linda arata et al. ed with the ex-post compensation, such as its financial unsustainability (goodwin and smith, 1995; goodwin and mahul, 2004; mahul and stutley, 2010; santeramo et al., 2016), as compared to crop insurance and revenue insurance schemes. despite this, the public crop insurance scheme has not been a story of success, as testified by low and heterogeneous participation and retention (santeramo et al., 2016). this has been motivated by the lack of tradition with subsidized schemes, as well due to the necessity to serve a relatively little market, with many (highly differentiated) crops and a majority of small-size firms (santeramo et al, 2016) and has suggested to implement ameliorative reforms to overcome the complexity of the policy environment (severini et al., 2017). two major reforms were implemented in 2013 and 2015; the former removed subsidies to the mono-risk insurance contracts; the latter replaced the multiand pluri-risks contract schemes with “packages” covering a set of adversities (santeramo et al., 2022). both reforms had negligible effects on insured acreage (as high as three percent) and insured values (estimated to be lower than one percent), casting doubts on their effectiveness. finally, farmers are often coping with risks which are not covered by traditional insurance schemes, or that are extraordinary in terms of expected damages (i.e., due to so called catastrophic events). because of these reasons, it seems relevant to consider innovative rm tools because these may overcome the issues encountered by the traditional insurance products. indeed, the new common agricultural policy is continuing to reform by enlarging the support for innovative risk management interventions and strategies. here we focus on two of themes: mutual funds for the so-called catastrophic events and the index-based insurance schemes. these options are precisely meant to complement traditional insurance schemes but are not the only innovative instruments1. while the ex-post approach tries to limit the potential additional damages that may occur after a catastrophic event, and to promote the restoration of the damaged structures, alternative mechanisms may help share the costs associated with extreme events and catastrophes. the use of insurance tools to cope with extreme and catastrophic events is dated (michel-kerjan, 2010) but still very debated, especially in agriculture (bucheli et al., 2020). the rationale is simple: due to the increasing amount of available data on weather conditions and the higher frequency of extreme events and natural disasters (both 1 for sake of brevity, we do not discuss other innovative instruments such as, for instance, the income stabilization tool (cfr. giampietri et al., 2020, and zinnanti et al., 2022, for recent assessments). systemic in nature and with high impacts on the sector), coping more directly with these events is not only possible, but also necessary to avoid the default of many farms. the catastrophic bonds and the catastrophic reinsurance may help cope with disasters as they are bet on the occurrence of a disaster, in which case an indemnity is paid. another possibility, being explored in italy, is the use of a mutual fund to cover losses from high impactful events. in italy the new risk management interventions have been defined by the national strategic plan (nsp) 20232027 of the cap. besides confirming support to (production) insurance schemes, (production) mutual funds (for plants, animal production, farm structures and livestock farms), and (income) mutual funds (for selected sectors such as poultry, sugar beet, durum wheat, cow and sheep milk, olive, fruit and vegetable, rice, and pig), it also establishes a mutual fund (the agricat) for catastrophic event. the latter covers farmers against specific weather events (frost, drought, and flood) defined as potentially catastrophic. the indemnities are triggered by production losses due to one of the three events, as certified by randomly executed expert reports. the economic sustainability of the newly established fund is unclear, and depends on the design of the fund, on the rating of the premia, and on the effectiveness of the damage reports. in 2022 the ministry of agriculture has started a pilot study in thirteen provinces (both in the north and in south), for twelve products (apples, pears, durum wheat, corn, almonds, oranges, apricots, actinidia, wine grapes, oil olives, peaches, and industrial tomatoes). ismea has released preliminary results on peaches, concluding that about sixty percent of insured farms have incurred in losses below the (20%) damage or (30%) deductible thresholds, whereas the remaining share of farms has incurred in losses as high as fifty percent in one out of four cases. the findings are of undoubted interest, but also worrisome compared to the us system, in which indemnities worth 14.9% of insured liability are considered to be excessively high in that the median value is as low as 2% of crop insurance liabilities (delay et al., 2022). such a high level of indemnities points at precise future goals, which can be summarised in four priorities: i) improve the accuracy of existing data to map and monitor high-impacts weather events; ii) increase the penetration of the program, and farm retention; iii) mitigate and reduce repetitive losses to lower operating expenses; iv) strengthen the financial sustainability of the program by designing optimal participation rates. these points translate into research questions worth investigation. another innovation, barely adopted in developed countries, is the use of index-based insurances: the https://doi.org/10.36253/bae-14492 173towards a holistic approach to sustainable risk management in agriculture in the eu: a literature review bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14492 scheme indemnifies farmers, who have likely incurred losses, when the index exceeds a threshold (abdi et al., 2022). a practical example may help in understanding the rationale behind this scheme. assume a set of farms are exposed to potentially detrimental events (e.g., excessive rain): when the event occurs with a certain magnitude (e.g., the daily volume of precipitation is three times larger than the average precipitation) the likelihood that the farm experiences a loss is high. in this situation, while collecting data on precipitation for each single farm may be costly and inappropriate to determine the incurred losses, relying on the index may be a second-best solution. the operation of an index-based insurance consists of indemnifying all farms when the index exceeds a threshold. the scheme of an index-based insurance has pros and cons, and is not the ultimate solution (carter et al., 2017): on one hand it may reduce (or eliminate) moral hazard and adverse selection issues; on the other hand, it may be ineffective if the correlation between triggered pay-outs and the occurrence of loss events is rather low. this potential fallacy is referred to as “basis risk” and defined as “the risk that a protection buyer’s own losses exceed the payments under a risk transfer mechanism structured to hedge against these losses.” (ross and williams, 2009). the basis risk has serious impacts on the functioning of index insurance (clement at el., 2020) and calls for a deep understanding of the phenomena aimed to be coped against. the basis risk is a three-dimensional concept, defined by time, space and design of the index. the three dimensions correspond to the temporal, spatial and design basis risks, which are inversely correlated with the informative content of the information being used. for instance, an index-insurance built on hourly data for weather events provides lower temporal basis risks with respect to the same index built on annual data. similarly, an index built on state level data will have higher spatial basis risk than a similar index relying on municipality level data. as for the design basis risk, the lower the flexibility (and complexity) of the index, the higher the design basis is likely to be. differently, highly informative datasets allow good performance, in terms of correlation between agricultural data and weather statistics (e.g., cheng et al., 2017). in a recent paper, stigler and lobell (2023) decompose, from a theoretical point of view, the basis risk of the index insurance schemes. their empirical analysis uses linear and quantile regressions, coupled with richly informative datasets, to derive effective indexes. index-based insurance is still underutilized in developed countries, whereas there are several applications in developing countries: “more than fifteen developing countries have offered individual-level index insurance schemes […], and some twenty have offered it at the institutional or geographical level.” (carter et al., 2017, p. 423). the low uptake of index-based insurance calls for a better understanding of the challenges that prevent participation. in particular, carter et al. (2017) indicate four areas of improvement on (a) the design of the contract; (b) the measurement of risks; (c) the quality of insurance schemes, and (d) the use of other risk coping interventions. these areas of improvement should be approached by promoting empirical studies that explore the informative content of the large datasets, through a holistic lens capable of merging knowledge from different disciplines (e.g. climatology, agronomy, statistics, economics, management) . for instance, promising research may be conducted by analysing the correlation in the tails of the yield and weather data distribution2 (e.g., copula-based models, quantile regressions) as well as using quantitative methods capable of synthesising large sets of variables3 (e.g., machine learning, shrinkage estimators). however, none of those techniques would be sufficient without a better understanding of the fundamentals of the economics and management of risks, topics that should remain a priority in the research agenda. 5. agricultural risk, input use and related policies the previous sections have considered the potential role of rm strategies and tools without accounting for the fact that other policies exist and affect farmers’ behaviour. indeed, some policies not targeted to risk in agriculture may have an unintended effect on rm behaviour and choices. a holistic view to the risk analysis cannot ignore the synergies and trade-off across policies directly or indirectly affecting the agricultural risk. this section refers to the literature that has shown that there exists a relation between the farmer’s use of fertilisers and pesticides and the risk farmer faces. thus, any policy aimed at constraining the use of chemicals in agriculture has an indirect effect on the risk the farmers must cope with. this branch of analysis fits with the eu f2f strategy, which has defined a set of objectives and guidelines to drive the european agri-food system toward a fair, 2 see for instance applications by goodwin and hungerford (2015), conradt et al. (2015), bokusheva (2018), among others. 3 a good discussion on recent methodological advances to model insurance is provided by ali et al. (2020) who discuss the potentiality of machine learning, as well as of artificial intelligence. https://doi.org/10.36253/bae-14492 174 bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14492 linda arata et al. healthy, and environmental-friendly transition. among the targets of the strategy, two are specifically addressed to fertilisers and pesticide use. the strategy aims at reducing the overall application of chemical pesticides in agriculture by 50% as well as of the more hazardous ones by 50% by 2030. the strategy also envisages a reduction in fertiliser application of 20% by 2030 and a reduction of the nutrient losses in the soil of 50% over the same time horizon. along this line, in june 2022 the commission made a proposal (european commission, 2022) to revise the directive on the sustainable use of pesticides (2009/128/ec) and to switch the legal framework from a directive to a regulation. the proposal aims at meeting the f2f strategy targets in terms of pesticides and has received criticism from many member states which particularly blame the “flat rate” pesticide cut proposed by the commission. in order to implement the fertiliser goal of the strategy, a new digital tool is being developed, the farm sustainability tool for nutrients (fast). fast will combine data from different sources and will provide detailed ad hoc recommendations on the application of crop fertilisation and plant protection products. this should improve the efficiency in fertiliser use and, consequently, should help to comply with the target on the fertiliser use reduction and losses. finally, measures related to the pesticides and fertilisers targets are contained in the national strategic plans of the common agricultural policy (cap) 2023-2027, specifically in the eco-schemes and in the enhanced conditionality of the first cap pillar as well as in the agrienvironmental-climate measures of the second cap pillar. the effect of a policy targeted at the reduction of chemicals is likely to affect farm input decisions and the corresponding farmers’ expected utility in two ways. first, it would directly constrain the amount of chemical application in the farmer’s decision process. second, as literature has shown that pesticides and fertilisers use often changes the level of agricultural risk and farmers are usually risk-averse, the policy imposed on these inputs alters the level of risk the farmers face and, as a consequence, is likely to affect farmer’s decision on the use of other inputs related to the chemicals (may they be substitutes or complementary inputs). the use of agricultural inputs, including pesticides and fertilisers, is affected by the degree of risk aversion of the farmer (bontemps et al., 2021). for example, according to the model adopted (either the expected utility or the cumulative prospect theory (tversky and kahneman, 1992)) it has been shown that from 4% to 19% of the pesticide expenditure on farms is explained by farmer risk aversion behaviour (bontemps et al., 2021). therefore, the higher the risk aversion of the farmers the higher will be the impact on farmer’s decisions and, in turn, on the effectiveness of a policy that restricts chemicals use. the estimation of the farm risk aversion behaviour is partially dependent on the theoretical model adopted. for instance, rommel et al. (2022) in a study on farmer’s risk preferences in 11 eu countries have shown that the cumulative prospect theory explains the preferences better compared to the expected utility framework. möhring et al. (2020a) provide a deep literature review on the relationship between pesticide use and farm risk. they show that around half of the papers that so far have assessed this issue find a risk-increasing effect of the pesticides (e.g., serra et al., 2006 and 2008), around half report a risk-decreasing effect (e.g., koundouri et al., 2009; antle, 2010; gardebroek et al., 2010) and only one paper finds no effect (hurd, 1994). besides the heterogeneity of the agricultural products and of the countries analysed in the papers, another reason to explain such opposite effects found in the literature is the heterogeneity of the indicators used to measure pesticide application across papers. möhring et al. (2020a) show that the impact of pesticide use on farm risk depends on the pesticide indicators and on the pesticide type considered. unfortunately, in most of the countries the national fadn datasets contain data of poor quality related to pesticide quantity and this prevents from using that type of information in this type of research. if no better-quality data are available, the only option is to use pesticide expenditure as a proxy for pesticide quantity. indeed, most of the studies cited above use pesticide expenditure. improving the data quality concerning pesticides in the eu is of paramount importance to inform evidence-based policy making. the f2f strategy also acknowledges the need to overcome the data gaps by changing the 2009 regulation concerning statistics on pesticides. in addition, the commission announced its intention to convert the fadn dataset into the fsdn (farm sustainability data network) which would include more detailed information on the environmental practices of the farms and would introduce data on their social practices (european commission, 2021). when it comes to fertilisers, literature (paulson and babcock, 2010; sriramaratnam et al., 1987) agrees that fertilisers are risk-increasing and it highlights the “fertilisers paradox”. indeed, although fertilisers are riskincreasing, risk averse farmers oversupply them. this happens because, under production uncertainty, due to for example unpredictable weather conditions or uncertainty in the amount of nutrients available to the crops, the overapplication of fertilisers is used by farmers as https://doi.org/10.36253/bae-14492 175towards a holistic approach to sustainable risk management in agriculture in the eu: a literature review bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14492 a form of self-protection (babcock, 2001; paulson and babcock, 2010). therefore, when modelling farmer fertiliser use it is important to account for the uncertain conditions the agricultural production faces which affect farmer’s decisions about fertilisers. when analysing the relationship between chemical use and agricultural risk some issues must be addressed from a methodological viewpoint. first, there may be some simultaneity in the chemical use decision and agricultural risk. indeed, usually chemicals are applied multiple times in the cropping seasons and the number of applications as well as the amount of chemicals applied in each application are decided by the farmers based on how the season is going in terms of weather and pests and how the crop is growing. if throughout the cropping season, the farmer observes an increase in agricultural risk compared to his initial expectation, he may adjust the planned fertiliser and pesticide application consequently. in addition, the past year agricultural risk is likely to affect the current year input decisions. this consideration makes relevant the use of a sequential decision-making production model as proposed by antle (1983a) to account for the possible feedback effect, i.e., the farmer adjusts his decision on variable input use based on the output observed or on the adjustment in the output expectations. second, the analysis of risk in agriculture needs to account not only for the mean and variance of the crop yields and of farmer’s revenue, but also for higher moments of the yield and revenue distribution (finger et al., 2018) as farmers are often downside risk averse (di falco and chavas, 2006). indeed, it is likely that farmer decisions are more affected by variations of crop yield and farm revenue below the average than by variations above the average. specific econometrics approaches exist to address higher moments of the distribution such as the moment-based approach (antle, 1983b) recently applied to italian farm data by biagini et al. (2022b) and its updated version that uses partial moments (antle, 2010). another important topic only partially addressed in the literature is the relationship between rm tools adoption and chemical application. rm tools change the agricultural risk faced by farmers and consequently, they may impact the use of risk-increasing and riskdecreasing input when the farmer is not risk-neutral. for example, the adoption of insurance may induce farmers to adopt moral hazard behaviour which in turn influences the pesticide and fertiliser use decisions (mishra, 2005). as stated earlier, pesticides are risk-increasing or risk-decreasing according to the type of crop, the country and the pesticide indicator considered, and therefore the direction of the relationship between pesticides and farm insurance remains an empirical question. second, insurance may induce farmers to change land allocation among crops, e.g. by growing more risky crops, and this also affects chemical use. analysing whether insurance and chemicals are substitutes, complements or independent goods from the producer’s perspective is a worthy issue. indeed, this assessment would outline the interaction and the spillover effects among two policies: the one pointing at reducing chemicals application in agriculture and the one pointing at increasing the adoption of rm tools among farmers. although this research branch is rather explored in the us where studies reveal a positive, a negative and a zero effect of insurance uptake on input use, möhring et al. (2020b) is one of the few examples applied to european agriculture. the study is focused on two countries (france and switzerland) and it shows that the adoption of insurances increases the application of pesticides. 6. conclusions the agricultural sector is, by its nature, exposed to several risks, and farmers have a long-lasting tradition in coping with them. yet the rising complexity and interconnection of the global agri-food sector have introduced new risks into the sector, calling for more and more frequent policy interventions. in the european union, the interventions on risk management have been fragmented and managed at the national level and slow is the introduction of innovative tools. in the context of a cap with a declining budget, oriented toward environmentally-friendly and sustainable production models, risk management becomes a relevant strategy to lower income uncertainty and favour the resilience of the agri-food system. given the low uptake rate of rm tools in the european union, studying the determinants of the uptake referring to both the farm and farmer’s characteristics as well as to the rm tool design is crucial to set up effective innovative rm tools and to refine the traditional ones. studies on the determinants of farmers’ behaviour towards risk management tools are rather limited in number. equally, literature on innovative tools, such as index-based and the catastrophic insurance schemes is scant. the role of behavioural factors in explaining farmers’ risk management decisions is often neglected in the literature. however recent studies suggest that behavioural factors should be incorporated into models to explain and predict farmers’ adoption and use of risk management tools (e.g., babcock, 2015; dalhaus et al., 2020; feng et al., 2020; tack and yu, 2021). those studhttps://doi.org/10.36253/bae-14492 176 bio-based and applied economics 12(3): 165-182, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-14492 linda arata et al. ies have stimulated a growing stream of research that has mainly focused on the elicitation of farmers’ risk preferences using experimental methods. empirical results show that further research is needed in several dimensions. first, preference elicitation methods appear to have an important impact on the magnitude of elicited preferences, therefore further research is needed to identify the methods that provide more robust measures in terms of internal and external validity. second, empirical evidence indicates that risk preferences are context-dependent. hence, it is still unclear whether risk preferences elicited using monetary lotteries are fully able to explain farmers’ choice behaviour, or whether practitioners should move to the use of contextualized lotteries that may improve the external validity of elicited preferences. third, while farmers’ risk preferences have been widely investigated in the literature, there are only a few studies focusing on the role that subjective beliefs, ambiguity attitudes and time preferences may have on farmers’ decisions in general, and more specifically, regarding the uptake of different rm tools. finally, a holistic approach to the study of risk management cannot ignore the interrelation between farmer’s input not specifically targeted to risk management and the agricultural risk. this is key to discover possible synergies and trade-offs among different policies. the policies aiming at reducing the fertiliser and pesticide use in agriculture indirectly affect the agricultural risk because fertilisers and pesticide impact the risk level. results reported by the literature on this impact are controversial and depend on the crop, country and indicator considered and there is often the issue of poor data quality. a more sustainable use of chemicals in agriculture and a better management of risk in the sector are two forefront topics in agricultural economics. hence, efforts towards gathering better quality data are required. in addition, given the importance of framing consistent and effective eu policies, the investigation of the relationship between the rm tools adoption and the farm application of chemicals needs to be addressed. this investigation would shed light on whether two apparently independent policies, namely the policy restricting the use of chemicals in agriculture and the one promoting the adoption of rm tools, have the same or opposite direction. besides briefly mentioning what the literature has already provided and which methods have been investigated, we conclude this paper with a few priorities to orient future research: efforts should be devoted to improve the use of the large amount of available data, to improve the financial mechanisms that may ensure the financial stability of the rm schemes, and to increase the interconnection (and complementarity) of the rm instruments. in short, the search for innovation in rm should be not only oriented toward a sophistication of the strategies, but also (and mainly) toward a better exploitation of the informative content of the existing data, as well as of the holistic nature of the approaches. in addition, the results of this literature review suggests that the theoretical framework used so far in the literature for understanding farmer behaviour in terms of rm is not unique: different models have been applied sparsely, and often without accounting for the complex nature of the issue at stake. therefore, it seems necessary to commit research efforts to carrying out a comparative evaluation of methods and hypotheses used in empirical analyses, also including behavioural variables towards risk. this latter calls for developing new tools to investigate farmers’ preferences, with particular attention to the characteristics of the tools and their interaction with other strategies. similarly, because farmers’ behaviour is affected by several policies, including those aimed at reducing the use of potentially harmful inputs, it seems important to analyse rm choices under a more articulated policy scenario. fostering the analyses in these directions is expected to better understand how farmers select among available rm instruments including the most innovative ones on which they have not a large experience. the results of these analyses could provide insights that could be used to increase the uptake of already existing rm tools, facilitate the design and introduction of the new ones and, indirectly, allow the eu farm sector to become more resilient. acknowledgement this work has been developed within the prin project 20205l79r9 funded by the italian ministry of university and research (mur), and within the agritech national research center and received funding from the european union next-generationeu (piano nazionale di ripresa e resilienza (pnrr) – missione 4 componente 2, investimento 1.4 – d.d. 1032 17/06/2022, cn00000022). this manuscript reflects only the authors’ views and opinions; neither the 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(2022). simulating risk management strategies for specialized farming systems: the potential impact of the eu income stabilization tool. agricultural finance review, 83(2): 264-285. https://doi. org/10.1108/afr-11-2021-0146 https://doi.org/10.36253/bae-14492 towards a holistic approach to sustainable risk management in agriculture in the eu: a literature review linda arata1, simone cerroni2, fabio gaetano santeramo3, samuele trestini4, simone severini5,* modelling technical efficiency of horticulture farming in kosovo: an application of data envelopment analysis nol krasniqi1,2,*, stéphane blancard1, ekrem gjokaj2, giovanna ottaviani aalmo3 incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural cameroon claudiane yanick moukam1,*, calvin atewamba2 farmers’ acceptance of a micro-irrigation system: a focus group study maria sabbagh1,*, luciano gutierrez2 reducing food-related economic loss to improve food security and cattle trade in the sahel: the case of agropastoral systems in senegal abdrahmane wane1,*, aliou diouf mballo2, cabrelle lauriane dzoukou homsi3, pathé diakhaté4, rahimatou memboup5 bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 bio-based and applied economics bae © 2024 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae citation: pagliacci, f., & salpina, d. (2024). adapting to climate change: what really drives the choices of the producers of geographical indications?. biobased and applied economics 13(3): 265-283. doi: 10.36253/bae-15221 received: october 2, 2023 accepted: april 9, 2024 published: october 16, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: valentina raimondi, luca salvatici orcid fp: 0000-0002-3667-7115 ds: 0000-0001-9663-2456 adapting to climate change: what really drives the choices of the producers of geographical indications? francesco pagliacci1, dana salpina1,2,* 1 department of land, environment, agriculture and forestry, university of padua, via dell’università 16, 35020 legnaro (pd) italy 2 euro-mediterranean center on climate change, porta dell’innovazione building 2nd floor via della libertà 12 , 30175 venice (ve), italy *corresponding author. e-mail: dana.salpina@cmcc.it abstract. in an era of rapid climate change, there is an increasing call for the efforts directed at detecting best practices of climate change adaptation in agriculture and understanding the factors behind producers’ willingness to implement adaptation strategies. many studies consider solely traditional agriculture and specific sectors (e.g., wine), while little attention has been paid to certified and high-quality products, as a whole. to fill this knowledge gap, in 2022 a questionnaire-based online survey was administered to 137 producers of agri-food geographical indications in the veneto region (north-eastern italy). using a multinomial logit model, this study highlights the factors explaining adaptation strategies distinguishing three cases: (i) farmers who have implemented adaptation strategies; (ii) farmers intending to implement them in the future; (iii) farmers neither having implemented nor willing to do so. results suggest that socio-demographic characteristics, particularly education, matter, with producers holding a high school degree in agriculture showing a greater willingness to adapt. also, being full-time farmer couples with higher probability of having already implemented adaptation strategies. lastly, also a direct observation of climate change in the production area affects farmers’ adaptation decisions. keywords: climate change, adaptation, pdos, pgis, producers’ survey. jel codes: q1, q15, q54. 1. introduction one of the major recommendations of the united nations climate change conference cop27 (in 2022) is the recognition of the importance of sharing best adaptation practices among public and private key stakeholders, while adjusting them to country-specific context (unfccc, 2022). in such a setting, national governments have the direct responsibility of detecting best practices, highlighting the main factors behind climate change adaptation. for the agri-food sector, both incremental and transformational climate change adaptation strategies have a paramount importance (howden et al., https://doi.org/10.36253/bae-15221 https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode http://www.fupress.com/bae https://doi.org/10.36253/bae-15221 https://orcid.org/0000-0002-3667-7115 https://orcid.org/0000-0001-9663-2456 mailto:dana.salpina@cmcc.it 266 bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 francesco pagliacci, dana salpina 2010; ingram, 2012; fedele et al., 2019). although incremental adaptation strategies alone are commonly considered insufficient to achieve the zero-hunger target of sustainable development goal 2 (sdg2) and address the impacts of climate change (fao, 2018), they can indeed contribute to national and regional transformative adaptation processes, especially in the case of local level strategies (rahman et al., 2021). however, adaptation to climate change for agrifood geographical indication (gi) systems is even more complex process, due to the legislative and institutional framework characterising them. indeed, for each gi, a product specification (hereinafter, ps) defines the delimited area of production as well as production rules (e.g., plant varieties, harvest dates, size and colour). according to the world trade organisation (wto, 1994), gis are indications aimed at identifying goods as produced in a given geographical area, whose quality and reputation are attributable to the geographical origin itself. in practice, gis are considered as a sort of social constructions (belletti et al., 2017), which play a crucial role in fostering endogenous rural development, hence contributing to the preservation of the traditional agri-food systems and related social networks (vandecandelaere et al., 2010), and thus, to socio-economic and environmental sustainability of the concerned rural areas (owen et al., 2020). however, to contribute to this goal, gi management must be implemented effectively (giacomini and mancini, 2015) and gi regulations put producers under obligation to comply with the respective ps. the complex policy and socio-economic processes on which gis rely on (thompson and scoones, 2009) often makes the modification of their ps complex, even if for the urgent purpose of climate change adaptation. in particular, the introduction of such changes requires an agreement among the involved producers, which can be concerned with long and costly authorisation processes (belletti et al., 2015; quiñones-ruiz et al., 2018) on the one hand, and with the product’s quality and reputation at stake, on the other hand. all these reasons explain why agri-food gis are quite vulnerable to climate change. in this setting, gis adaptation to climate change depends on the capacity of agents and institutions to innovate, hence finding new solutions. information on already-existing adaptation practices and a better understanding of the drivers behind the willingness of gi agents to adapt is crucial in informing public policies. indeed, policies can foster anticipatory adaptation strategies within the agri-food sector. this is particularly important when self-investment for adaptation is insufficient, also due to the existence of major financial constraints, both in high-income and low-income countries (ignaciuk, 2015; deressa et al., 2009). for the last decade, the studies addressing climate change adaptation of farmers have increased, especially in low-income countries. they have focused either on specific territories, e.g., the “char” islands in bangladesh (ahmed et al., 2021), the amazon basin (bauer et al., 2022), laikipia district in kenya (ogalleh et al., 2012); or on specific productions, such as tea (muench et al., 2021), coffee (bro, 2020), honey (vercelli, et al. 2021). in fact, only a few studies focused on the nexus of climate change adaptation and gis in high-income countries. according to marescotti et al. (2020), safeguarding protected designations of origins (pdos) and protected geographical indications (pgis) from the effects of climate change is a rather new topic mostly disregarded by international literature. some studies addressed climate change perception of wine producers (e.g., lereboullet, 2013; lamonaca et al., 2021), while there is paucity of studies focusing on agri-food gis, specifically. to this regard, a recent study by henry (2023) suggested the chance for agricultural supply relocation as an option to adapt to climate change, even in the case of gi labels. although this chance is currently excluded outside of the boundaries of the designed geographical area of production, it is still true that – at least in principle changes in the geographical area of production are admitted as non-minor amendments by regulation (eu) no 1151/2012 (article 53). however, according to an analysis of the amendments in the fruit and vegetable sector in the eu by marescotti et al. (2020), only one out of 81 non-minor amendments until 2018 affected the area of production, justifying the need to enlarge the production area with climate change. actually, henry (2023) also stressed the existence of expected negative impact on quality of products, limiting similar relocations. this research aims to shed new light on local adaptation strategies in the case of agri-food gis in the veneto region (north-eastern italy), i.e., one of the regions with the highest climate change risk in italy (arpav, 2017). in particular, its objective is to highlight the main factors influencing the decision of producers to counter climate change impact. the main research questions of this study are: what are the main adaptation practices used by producers of agri-food gis? and what are the main factors influencing the willingness of agri-food gi producers to adapt? in order to answer these questions, the study is based on a structured online survey, targeted to agrifood gi producers in the case-study area. with the help of primary data, it highlights the main adaptation practices in place as well as the factors influencing the will267adapting to climate change: what really drives the choices of the producers of geographical indications? bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 ingness of farmers to implement them (either currently or in the future). adaptation revolves around the complex interplay of socio-demographic characteristics (e.g., age, education), management characteristics and networking activity, production type, altitude as well as climate change perception itself. to estimate these factors, a multinomial logit model is used. findings suggest that despite a generalised awareness of climate change, this has not yet turned into widespread decision to implement adaptation measures. rather, developing peer-topeer learning practices among farmers and fostering collaborations among those gi systems that face similar risks is of utmost importance. the paper is structured as follows. section 2 provides the theoretical background on adaptation to climate change and on the factors affecting adaptation. section 3 discusses the materials and methods used, by briefly describing the case study area, the sample, data collection and analysis. section 4 presents the results of the study, while section 5 discusses them. section 6 explores the main policy implications of this study and section 7 concludes. 2. theoretical background: factors of climate change adaptation within climate change literature, adaptation is defined as a process of adjustment to current or future climate and its effects, so as to reduce harm or take advantage of some positive opportunities (ipcc, 2014). in agriculture, there are many climate change adaptation measures (e.g., technological and behavioural, reactive and anticipatory; tactical and strategical) (ingram, 2012). adaptation options can be grouped into the following categories: cultivars and breed improvements; changing management practices; switching crops, breeds and farming systems; managing water; diversifying agricultural systems; managing fisheries and supply chain options (fao, 2018). however, in the case of gis, adaptation is somehow hindered by the pss, given that they define bounded production areas and well-codified production rules (thompson and scoones, 2009). for example, many gis include the specifications on crop varieties and breeds (salpina and pagliacci, 2022a), hence adaptation requires a modification of the code of practices, turning into long and costly authorisation processes (belletti et al., 2015; quiñones-ruiz et al., 2018). however, despite the extensive literature on gi products, just a few studies provide insights into pss amendments justified by climate change (e.g., marescotti et al., 2020; belletti et al., 2015). thus, according to marescotti et al. (2020), compliance with the pss might be more difficult to attain due to climate change, which would limit adaptation options. overall, scholars distinguish between two types of adaptation processes (i.e., incremental and transformational), based on the expected complexity of their implementation, their costs, expected risks, and the number and heterogeneity of the different stakeholders who are engaged (howden et al., 2010). incremental adaptation refers to short-term measures implemented at local level based on farmers’ knowledge and experience (e.g., introduction of rain covers). transformational adaptation refers to long-term measures implemented at a larger spatial scale (region, state), suitable when impact intensity is high (e.g., changes in the boundaries of the production area). fao (2007) also distinguishes between 1) autonomous or on-farm adaptation, i.e., the reaction of a single farmer to climate change; and 2) planned adaptation, as policy options or response strategies, which modify adaptive capacity or ease the introduction of given adaptation strategies. being gis “social constructions” (belletti et al., 2017), both types of adaptation matter, involving both single producers and broader managing authorities. in particular, the understanding of both incremental (autonomous) and transformational (planned) methods of adaptation is crucial for the agri-food sector (ingram, 2012; fedele et al., 2019). the transformational or planned adaptation is usually influenced by the socioeconomic and political structure of a given country or region. however, at farm level, the factors behind climate change adaptation can vary considerably. in the case of gis, these factors can be grouped into four areas: socio-demographic characteristics of producers, farm management and networks; product characteristics; climate change magnitude and its perception. socio-demographic characteristics. studies on climate change adaptation usually claim that a number of sociodemographic variables influence the development and the transmission of innovations at the farm level, including age (morel and cartau, 2023), sex (zamasiya et al., 2017) and education level (guo et al. 2021). these factors affect the absorptive capacity of farmers towards innovations and the introduction of new agricultural practices, including adaptation to climate change, making easier the acquisition, assimilation, use, and transformation of external knowledge in the decision making process (asrat and simane, 2018; abdala et al., 2022). farm management and networks. besides sociodemographic characteristics of producers, other characteristics of the farm management matter, as well as the networks in which a farm is involved (below et al. 2012; khan et al., 2020; gao et al., 2022). with regard to 268 bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 francesco pagliacci, dana salpina management practices, the difference between part-time and full-time farmers is important. although declining in europe (shahzad and fischer, 2022), part-time farming is still present, hence affecting the decisions about climate change adaptation. full-time farmers are more likely to have more information and knowledge on changes in climatic conditions than part-time farmers, hence the former being more prone to adaptation (maponya and mpandeli, 2012). this can also be associated with less time dedicated to farm-related activities by a part-time farmer. also, the presence of formal and informal networks as a part of social capital (akahoshi & binotto, 2016), within and outside each single gi system, can explain diffusion of innovation, hence encouraging agri-food gi producers to innovate (wang et al., 2021). according to ingram & kirwan (2011), informal relationships can facilitate the formation of joint ventures for information exchange and business partnerships, and thus accelerate the adaptation in agriculture. product characteristics. adaptation to climate change can be also affected by some characteristics related to the type of production as well as regulatory issues. firstly, the effects of climate change – hence, the adaptation practices – differ considerably among, e.g., cropbased or animal-based productions (fao, 2018). secondly, and in the case of gis, type of the certification (i.e., either a pdo or a pgi) may influence adaptation to climate change. actually, the restrictions imposed by each denomination, particularly in terms of size of production area, provenance of raw materials and production processes, are different, with the former being tighter than the latter. magnitude and perception of climate change. when addressing climate change-related risks, several studies have claimed that adaptation decisions can depend both on the (measurable) magnitude of climate change and on individual perceptions. therefore, in the context of adapting to climate change at the farm level, the effectiveness of adaptation measures can depend both on the overall increase of temperature and on farmers’ ability to perceive climate-related hazards, evaluating their impact on production. according to many theories aimed at explaining the risk-reducing behaviour of economic agents against natural hazards, different perception of climate change can emphasise subjective aspect in assessing the risks associated with it. for instance, according to the protection motivation theory (pmt), rooted in the theory of planned behaviour (fishbein and ajzen, 1975; grothmann and reusswig, 2006), individuals’ decisions to engage in a protective response against natural hazards are driven, among others, by threat appraisal (also known as ‘risk perception’), which encompasses perceived probability and perceived consequences that an individual associates with a certain hazard (fishbein and ajzen, 2010; fahad et al., 2020; ahmed et al. 2021; talanow et al., 2021). similarly, in the case of climate change adaptation strategies, guo et al. (2021) claimed that perceived temperature change can have a significant impact on farmer’s adaptive behaviour. however, it should also be noticed that farmers’ perception of climate change is usually aligned with observed real climatic trends in specific regions (ogalleh et al. 2012; alam et al., 2017; bauer et al., 2022). 3. methods and data 3.1. study area the veneto region is located in the north-east of italy. it is characterised both by several pdos and pgis produced in the area, and by large climate change hazard, making this area perfectly suitable for studying adaptation to climate change and for obtaining insights which might be expanded to agricultural areas in other temperate regions of the eu. veneto is among the first italian regions in terms of the economic impact of food gi, which amounted to € 433m in 2021, including about 800 economic agents. in the region, gis represent 48% of the total agri-food sector (well above the national average, which is equal to 21%) (ismea-qualivita, 2022). moreover, in the veneto region, 36 different agri-food gis (18 pdos and 18 pgis, respectively) can be produced, according to the pss that set the boundaries for the production of each gi. among them, there are some of the gis with the highest production value in italy, e.g., grana padano cheese, asiago dop cheese. moreover, both crop-based gis and animal-based gis are produced. among crop gis, there are fruits and berries (e.g., cherries, chestnuts), vegetables (e.g. radicchio chicory, asparagus), and olive oil. animal-based gis include processed meats (e.g., ham), cheeses, and honey. at sub-regional level, the production areas mainly concentrate in the nuts3 regions (province, in italian) of treviso, verona, and vicenza, where some municipalities are eligible for the production of more than 9 different gis each (fig. 1). in addition to the widespread diffusion of gis, the veneto region is also highly prone to climate change (pagliacci and salpina, 2022), having experienced a rapid increase in average temperatures (regione veneto, 2021), since the 1990s. in particular, when comparing the decades 1961–1970 and 2009–2018, temperature 269adapting to climate change: what really drives the choices of the producers of geographical indications? bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 increase has been large across the entire veneto region, ranging from a minimum increase of less than +1°c in the plains near the adriatic sea to an increase of more than +3°c in some areas of the alpine region (pagliacci and salpina, 2022). in terms of rainfall, there has been a significant change in precipitation patterns. although the region’s annual total precipitation has not changed widely, there has been an increase in both the maximum annual values of short-term rainfall, on the one hand (sofia et al., 2017), and in the frequency, length, and severity of droughts and related heat spells, on the other (bonzanigo et al., 2016). in general terms, it can be observed that climate variability has increased, with a large number of extreme events (e.g., heavy rainfall, strong winds, hailstorms…) observed in almost every municipality of the region, in the decade 2010– 2020. given these characteristics, climate change hazard can be considered high across the whole region, with almost all agri-food sectors being largely affected (pagliacci and salpina, 2022). 3.2. data collection to answer the research questions, primary data was collected using a questionnaire-based online survey administered to agri-food gi producers in the study area. consortia or producer organisation (pos) helped in identifying respondents. as not all of them agreed upon providing a full list of producers, due to privacy reasons, they were asked to send the questionnaire directly to their members/producers, or alternatively information available online were considered. after a pilot phase (december 2021), the entire survey was administered between january and august 2022. comprehensively, 183 responses were collected, with 46 of them being discarded after the first data cleaning. thus, the final database includes data from 137 producers that answered all the questions necessary for the analysis. among the respondents, 29 producers of animalbased gis1 and 108 producers of crop-based gis participated in the survey. it is approximately 18% of the overall population of gi producers located in the region2, excluding the producers of the raw materials. the sample size – ranging from 5 to 15% – can be considered as adequate for a household survey (bartlett et al., 2001; alam et al., 2017). all agri-food gis factually produced in the region were considered in this study. to further classify them, the current analysis refers to the clusters of agri-food gis of the veneto region identified by salpina and pagliacci (2022a) on a broad set of variables (i.e., type of gi, category, total revenue, decade of registration, share of production occurring in the region). their classification returned six clusters of gis. three of them include pdos only, distinguished according to revenue, territorial concentration at the local level and decade of registration (“little revenue pdos”; “large-scale pdo cheeses”; “second-generation pdos”). the remaining clusters include pgis (the “unexploited opportunities”, namely gis for which the production in veneto is actually nil; “firstgeneration crop pgis”, i.e., early pgis, with higher revenue; “second-generation crop pgis with little revenue”, i.e., pgis with little turnover, more territorially concentrated, and registered more recently) (salpina and pagliacci, 2022a). in the current analysis, all the clusters were considered, with the only exception of the ‘unexploited opportunities’, given that the four meat-based pgis included are not produced in veneto. actually, the focus on clusters, rather than on single products, enables us to provide information that can be useful for gi products 1 in the case of animal-based gis, producers of the final products (e.g., cheesemakers) were surveyed, asking them to report also details about their suppliers. except for a few large dairy companies, often cheesemakers were also milk-producers, hence able to provide first-hand information. moreover, the answers of a few cheesemakers producing 2 gis were duplicated. 2 the total number of producers for all gis is not available. according to the authors’ estimations based on data of qualivita and numbers provided by consortia, there are approximately 800 producers of agri-food gis in the region (around 700 in the case of crop-based, and around 100 for animal-based gis), excluding the producers of raw materials, i.e., only milk or meat producers. figure 1. distribution of the gi production areas across the region. 270 bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 francesco pagliacci, dana salpina with similar characteristics (e.g., size of production area, total turnover). with regard to the contents of the survey, the questionnaire addressed a broad series of topics: farm management and networks, gi production, perception about climate change, implementation of adaptation measures, barriers to adaptation, additional socio-demographic information of the respondents. some of those questions were used to retrieve some core variables for the econometric model (see the following section 3.3). this includes the questions on socio-demographic characteristics, farm management and network, product characteristics, and climate change perception and adaptation decisions (i.e., those mentioned in the theoretical background section). additional questions included in the survey were instead used as ancillary variables to enhance the primary findings of the study, through some additional descriptive statistics. among others, they include the questions on the impact of extreme weather events on agri-food gis, on adaptation measures implemented or planned to be implemented by producers and on cost-effectiveness evaluation of these measures, as well as on barriers to adaptation. in particular, the adaptation practices proposed in the questionnaire are based on the results of salpina and pagliacci (2022b), who had used semi-structured interviews and focus-group discussions involving managers of consortia and pos in the same study area to understand how agents involved in agri‐food gis production are adapting to climate change. 3.3. the econometric model firstly, preliminary descriptive statistics are analysed, considering: observations on climate change, extreme events and adaptation practices, distinguishing between crop and animal-based gis. secondly, the main drivers of adaptation of gis producers to climate change are analysed through econometric models. as a dependent variable, the analysis considers the implementation of adaptation strategies by gi producers. in particular, three alternative situations are distinguished: (i) the one in which producers have already implemented adaptation strategies at the farm level; (ii) the one in which producers are willing to implement them in the next future; (iii) the one in which producers neither have implemented them in the past nor are willing to do so in the future. a set of covariates is considered to analyse the occurrence of the different situations (table 1). the table bases on the theoretical background presented in section 2, thus distinguishing the core variables in terms of socio-demographic characteristics of the producers, farm management and networks, production characteristics, magnitude and perception of climate change. lastly, a control variable (altitude) is considered as well, by distinguishing farms in the lowlands, hills, and mountains. all these factors affect farmers’ decision to implement adaptation strategies. to test this hypothesis, a comprehensive multinomial model is used. in particular, we estimate five different models: y = βpp + βaa + ε (1) y = βff + βaa + ε (2) y = βdd + βaa + ε (3) y = βcc + βaa + ε (4) y = βpp + βff + βdde + βcc + βaa + ε (5) where: – y is the (n x 3) matrix, where n = 137 respondents, indicating the alternatives decisions about adaptation (no, yes, yes in the future), and assuming the unwillingness to implement any adaptation strategies (both in the past and in the future) as the reference baseline. – p is the (n x 3) matrix of the proxies of the sociodemographic characteristics of producers (including age range, sex and education level) and is the (3 x 1) vector of respective unknown parameters. – f is the (n x 2) matrix of the proxies for farm management (i.e., full-time/part-time activity) and the number of farm adhesions, or networks the farm is involved in, such as pos and associations (e.g., cia – confagri) and is the (2 x 1) vector of respective unknown parameters. – d is the (n x 3) matrix of the proxies for the product characteristics (type of certifications, clusters, and type of product) and is the (3 x 1) vector of respective unknown parameters. – c is the (n x 3) matrix of the proxies for climate change variables, encompassing both variations in mean temperature between 2009-2018 and 19611970, and producer perception of climate change and extreme events in the production area. additionally, is the (3 x 1) vector of respective unknown parameters. – a is the (n x 1) vector of control variables about altitude and is the (1 x 1) unknown parameter. – ε is the (n x 1) vector of error terms. 271adapting to climate change: what really drives the choices of the producers of geographical indications? bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 the implementation of the models was performed by using the software r (r core team, 2021). 4. results 4.1. characteristics of respondents all the statistics about the socio-demographic characteristics of the producers, farm management, and product type characteristics under consideration in this study are shown in table 2 and commented in this subsection. the following subsection 4.2 will focus on magnitude and perception of climate change. with regard to the geographical distribution of the respondents, most of them are from the nuts-3 regions of treviso (30%), verona (23%), and vicenza (19%) which also host the largest number of agri-food gis in the region (fig. 2). in terms of socio-demographic characteristics of the producers, most of the respondents are young producers. the rate of female respondents (23%) is low, but somehow similar to the share of women who are agricultural holders in the veneto region (26% of the total, according to istat, 2022). the largest share of respondents has a diploma of non-agrarian high school, followed by those with a university degree in non-agricultural field. with regard to farm management and networks, respondents are mostly full-time farmers, with only 38 out of 137 being part-time farmers. they are small-size farms, 50% of the total cases being family-run. moreotable 1. classification of the core variables considered for the analysis of the factors of adaptation. factor label levels (when categorical) socio-demographic characteristics age 0 = less than 35 1 = 35-44 2 = 45-54 3 = 55-64 4 = more than 64 sexa 1 = male education level 0 = elementary school 1 = middle school 2 = high school (agrarian) 3 = high school (non-agrarian) 4 = university degree (agrarian) 5 = university degree (non-agrarian) farm management and networks farm management 1 = part-time nr. of adhesions (memberships to different networks) continuous product characteristics cluster, according to salpina and pagliacci (2022a) 0 = custer “little revenue pdos” 1 = custer “large-scale pdo cheeses” 2 = custer “second-generation pdos” 3 = custer “first-generation crop pgis” 4 = custer “second-generation crop pgis with little revenue” certification type (pdo vs. pgi) 1 = pgi type of the product 1= crop-based climate change climate change observation in the production area 1 = yes observation of extreme events in the production area 1 = yes long-term temperature change (difference in °c of the mean temperature of the period 2009-2018 and the mean temperature of the period 1961-1970)b continuous control factor altitude 0 = mountains 1 = hills 2 = lowlands a the research uses a binary sex categorisation (male/female), as a set of biological attributes associated with physical and physiological features. b data refers to the municipality where the producer is located. data retrieved and adapted by https://climatechange.europeandatajournalism.eu/en/about (see ferrari and gjergji, 2020, for further methodological details). https://climatechange.europeandatajournalism.eu/en/about https://climatechange.europeandatajournalism.eu/en/about 272 bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 francesco pagliacci, dana salpina ver, they are also members of only 1.66 networks on average. when considering the characteristics of the produced gis, most respondents are crop-based gi producers (108 out of 137), and pgi producers (63 out of 137). with regard to the 6 gi cluster classification by salpina and pagliacci (2022a), most of the respondents belong to the cluster of “second-generation crop pgis”, “little revenue pdos”, and “second-generation pdos”, which include the largest number of agri-food gis. 4.2. climate change magnitude and perception and adaptation practices with regard to climate change, data on long-term temperature change provided by ferrari and gjergji (2020) and some of the ancillary variables collected in the survey help to better characterise the sample. on average, the set of the municipality in which the respondents are located have experienced an increase of +2.7 °c, when comparing the period 1961-1970 and the period 2009-2018. thus, 95% of the respondents producing crop-based gis and 86% of the respondents producing animal-based gis had direct experience of climate change in the production area. the main concern for both groups is an increased irregularity of precipitation (80%, on average), followed by temperature increase (72%, on average) (fig 3). in terms of extreme weather events, 73% of the respondents have directly observed them in their production areas, over the last decade. on average, the impact of the extreme events under consideration in this research is evaluated as medium, except for frost, which seem to have the lowest effect among the respondents. producers of animal-based gis reported also a high impact of drought (table 3). figure 2. geographical distribution of the sample. 273adapting to climate change: what really drives the choices of the producers of geographical indications? bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 more specifically, among the effects of climate change on crop-based gis, respondents pointed out the effect on the volume of production and water availability. conversely, the perceived effect on soil quality is relatively lower. among animal-based gi producers, the major concern is heat stress, affecting cattle and milk production during summer, with negative consequences in terms of both product quality and quantities produced. despite the large and direct experience of climate change, only 24% of the respondents have already adopted some types of adaptation measures to cope with climate change. moreover, 33% of them are planning to adopt them in the next future. among the managerial measures, which are implemented by both crop-based gi producers and animalbased gi producers, insurances (45%) are the most popular anticipatory measures of adaptation, followed by the use of advisory services and training. among more technical measures (namely, those specific to either cropbased gis or animal-based gis), introduction of new crop varieties (49%), followed by increased efficiency of pests (46%), irrigation (45%) and crop rotation (45%) are the ones most mentioned by crop-based gi respondents. as for the producers of animal-based gis, they mostly opt for barn cooling systems to deal with heat stress of animals (34%), followed by importing forage from outside the production area (21%) (fig. 4). in terms of costs and effectiveness of adaptation measures, the ranking for managerial and technical adaptation measures of crop-based gis is quite heterogeneous. for crop-based gis, introduction of new irrigation systems and increased efficiency of pesticides were attributed the highest scores in terms of cost/effectiveness ratio (3.6/5.0), whereas pest increase received the lowest score (2.1/5.0). for animal-based gis, the lowest table 2. producer, farm, and production characteristics of the respondents (137 total respondents). factor label levels (when categorical) value missing values socio-demographic characteristics age 0 = less than 35 17 24 1 = 35-44 25 2 = 45-54 30 3 = 55-64 19 4 = more than 64 22 sex 1 = male 81 25 education level 0 = elementary school 1 24 1 = middle school 20 2 = high school (agrarian) 11 3 = high school (non-agrarian) 43 4 = university degree (agrarian) 10 5 = university degree (non-agrarian) 29 farm management and networks farm management 1 = part-time 38 38 nr. of adhesions (memberships to different networks) average number (std. dev.) 1.66 (1.15) 0 product characteristics cluster, according to salpina and pagliacci (2022a) 0 = custer “little revenue pdos” 29 0 1 = custer “large-scale pdo cheeses” 18 2 = custer “second-generation pdos” 27 3 = custer “first-generation crop pgis” 16 4 = custer “second-generation crop pgis with little revenue” 48 certification type (pdo vs. pgi) 1 = pgi 63 0 type of the product 1= crop-based 108 0 figure 3. observations on climate change. 274 bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 francesco pagliacci, dana salpina score is for pasture management plans (1.0/5.0), while the highest one is for barn cooling systems (3.8/5.0) (table 4). 4.3. drivers affecting climate change adaptation the factors influencing farmer willingness to implement adaptation measures are analysed under models (1)-(5) admitted in this study. table 5 returns the results of these models. in (1), which includes sociodemographic variables, education plays an important role. as expected, the respondents with a high school degree in agriculture show a greater willingness to adapt (either in terms of already-implemented adaptation strategies or in terms of future adaptation). conversely, age is never significant. in (2), part-time management negatively affects adaptation decisions, while larger number of adhesions to associations and other sectoral networks couples with a higher probability of having already introduced some forms of adaptation practices. when considering production features, in (3), no covariates are significant. in (4), direct perception of the effects of climate change plays a major role in driving adaptation decisions, while, as an unexpected result, an increase in average temperature in the production areas shows a negative coefficient. lastly, when considering all covariates jointly, in (5), education level remains the main factor influencing on-farm adaptation to climate change. in particular, education in agrarian field is positively associated with adaptation strategies. it is also confirmed that part-time farmers are less willing to undertake adaptation measures. as for gi products, “large-scale pdo cheeses” (cluster 2) show negative coefficients, in table 3. average impact of extreme weather events on agri-food gis (as evaluated by producers, scale from 1 to 5) impact (yes) drought frost hailstorm heavy rainfall/ flood insects/ diseases outbreaks crop-based gis 84/108 3.1 2.6 3.3 3.0 3.4 animal-based gis 16/29 3.7 2.3 3.5 3.1 3.3 average 100/137 3.2 2.5 3.3 3.0 3.4 figure 4. adaptation methods implemented or planned to be implemented by producers of agri-food gis. 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% sh or tte rm fo re ca st se as on al fo re ca st w ar ni ng sy st em s in su ra nc e co op er at io n tr ai ni ng s ad vi so ry se rv ic es n ew cr op v ar ie tie s ch an ge d da te s o f p la nt in g di ve rs ifi ed cr op v ar ie tie s cr op ro ta tio n so il co ns er va tio n sh ad in g su pp le m en ta ry ir ri ga tio n n ew ir ri ga tio n sy st em in cr ea se d ef fic ie nc y of p es ts pe st s i nc re as e im po rt ed fo ra ge st or ag e ca pa ci ty (f or ag e) pa st ur e m an ag em en t p la ns co ol in g sy st em s f or b ar ns n ew a ni m al b re ed s le ss a ni m al s p er b ar n managerial (average for cropand animal-based gis) technical (crop-based gis) technical (animal-based gis) 275adapting to climate change: what really drives the choices of the producers of geographical indications? bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 terms of both current and future adaptation to climate change. similarly, when considering the type of gis, producers of crop-based gis are less willing to adapt than those of animal-based gis, both when considering already existing adaptation strategies and future ones. nevertheless, adaptation to climate change remains significant among producers that do observe climate change in their production areas. in addition, altitude of the production areas only shows a small effect, suggesting a negative relation between flatland locations and adaptation strategies. table 5 also shows the results of the mcfadden test (hausman and mcfadden, 1984), the akaike information criterion (sakamoto et al., 1986), and the bayesian information criterion (schwarz, 1978), computed for each model. although the computed tests do not point to the full model (5), however it is the one with the largest accuracy ratio. 5. discussion this study offered important insights into the extent of adaptation to climate change in the case of the high quality agri-food gis of the veneto region (italy). the results show that agri-food gi producers are highly aware of climate change, having experienced both its direct and indirect impacts. in the case of animal-based gi productions, mainly indirect impacts of climate change are observed (e.g., alteration in fodder quality and quantity). in the case of crop-based products, the spectrum of direct impacts seems to be larger. however, although producers are perfectly aware of climate change and of its effects on gi production, adaptation has not reached its full potential among them. only 50% of the respondents have already adapted to climate change or are expressing their willingness to do so in the next future. in particular, their decisions seem to be driven by a large number of factors. all the different types of admitted drivers (i.e., socio-demographic characteristics of producers, farm management, type of product, climate change observation) matter in predicting adaptation measures at the farm level. producers with an educational degree related to agriculture, who adhere to sectoral networks, and who perceive more directly climate change in their production area tend to be more willing to adapt to climate change. these findings are consistent with previous studies that claim the critical role played by risk perceptions (fishbein and ajzen, 2010; grothmann and reusswig, 2006; menapace et al., 2015; hasan and kumar, 2019; zagaria et al., 2021), involvement in social networks (bairagi et al., 2021; bazzana et al., 2022) and education (muench et al., 2021; guo et al. 2021), when explaining adaptation attitudes. the counterintuitive negative relationship between magnitude of climate change and willingness to implement adaptation strategies (as observed in just one of the selected models) might be explained with the intuition that further decreases in economic profitability, due to global warming, could make any adaptation investments too costly compared to any potential future benefits. however, among the most interesting findings, rigidity of the ps deserves specific attention. indeed, it can be observed that some of the adaptation practices implemented by conventional farmers, either in italy (bonzanigo et al., 2016) or elsewhere (song et al., 2019; antwi-agyei et al., 2021; nor diana et al., 2022), are also adopted by some of the producers of agri-food gis in the veneto region. this is the case, for example, of some varietal improvements as well as by the introduction of barn cooling systems. the main difference in adaptable 4. average score of adaptation measures (as evaluated by producers, scale from 1 to 5). managerial methods of adaptation (average) short-term forecast 3.0 seasonal forecast 2.7 warning systems 2.9 insurance 3.2 cooperation 2.7 trainings 3.4 advisory services 3.0 involvement of external actors 2.7 adaptation measures for crop-based gis new crop varieties 2.8 changed dates of planting 2.6 diversified crop varieties 3.5 crop rotation 3.5 soil conservation 2.8 shading 2.5 supplementary irrigation 3.6 new irrigation system 3.6 increased efficiency of pests 3.6 pests increase 2.1 adaptation measures for animal-based gis imported forage 2.3 storage capacity (forage) 2.2 pasture management plans 1.0 cooling systems for barns 3.8 new animal breeds 2.8 less animals per barn 2.0 276 bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 francesco pagliacci, dana salpina ta bl e 5. r es ul ts o f t he m od el s. (1 ) (2 ) (3 ) (4 ) (5 ) ye s fu tu re ye s fu tu re ye s fu tu re ye s fu tu re ye s fu tu re se x (m al e) 0. 89 0 0. 24 6 1. 73 1 0. 64 9 (0 .6 33 ) (0 .5 75 ) (1 .0 97 ) (0 .8 61 ) a ge (3 544 ) -0 .1 55 0. 87 2 1. 46 8 1. 49 1 (0 .9 38 ) (0 .9 69 ) (1 .3 75 ) (1 .2 30 ) a ge (4 554 ) 0. 24 5 1. 59 2 0. 80 3 1. 90 0 (0 .9 56 ) (0 .9 83 ) (1 .3 93 ) (1 .2 62 ) a ge (5 564 ) -0 .1 18 0. 76 8 1. 30 2 0. 78 2 (0 .9 83 ) (1 .0 23 ) (1 .6 08 ) (1 .4 43 ) a ge (o ve r6 4) -0 .4 93 -0 .1 56 0. 03 4 0. 05 5 (0 .8 93 ) (1 .0 23 ) (1 .4 94 ) (1 .2 32 ) ed uc at io n (m id dl e) 15 .0 73 ** * 12 .5 02 ** * (0 .6 78 ) (0 .6 53 ) ed uc at io n (h ig h no nag ra ria n) 16 .0 46 ** * 13 .4 03 ** * 2. 40 3* 1. 79 2 (0 .4 56 ) (0 .4 58 ) (1 .4 17 ) (1 .1 72 ) ed uc at io n (h ig h ag ra ria n) 50 .9 49 ** * 49 .2 97 ** * 41 .6 79 ** * 42 .2 09 ** * (0 .3 61 ) (0 .3 61 ) (0 .6 52 ) (0 .6 52 ) ed uc at io n (u ni ve rs ity n on -a gr ar ia n) 14 .8 30 ** * 12 .6 54 ** * 1. 02 1 2. 38 0* (0 .5 43 ) (0 .5 03 ) (1 .6 01 ) (1 .3 28 ) ed uc at io n6 (u ni ve rs ity a gr ar ia n) 17 .0 94 ** * 14 .8 83 ** * 1. 84 0 2. 80 4 (0 .9 76 ) (0 .9 66 ) (1 .8 41 ) (1 .7 26 ) fa rm m an ag em en t ( pa rt -t im e) -2 .5 98 ** * -1 .6 77 ** * -3 .7 24 ** * -2 .4 00 ** (0 .7 55 ) (0 .6 43 ) (1 .1 73 ) (1 .0 33 ) n r. of a dh es io ns 0. 56 4* 0. 24 4 0. 51 4 -0 .0 30 (0 .3 01 ) (0 .2 83 ) (0 .3 84 ) (0 .3 75 ) c lu st er s c l2 0. 99 4 1. 31 6 -1 3. 67 1* ** -1 2. 40 5* ** (1 .1 64 ) (1 .2 30 ) (1 .5 88 ) (1 .5 43 ) c lu st er s c l3 0. 22 0 0. 19 1 -0 .6 11 1. 23 2 (0 .7 50 ) (0 .7 48 ) (1 .5 80 ) (1 .4 91 ) c lu st er s c l5 0. 19 0 0. 36 2 -1 .0 94 -0 .4 52 (0 .6 09 ) (0 .5 61 ) (0 .8 70 ) (0 .8 55 ) c lu st er s c l6 -0 .5 97 -0 .4 44 0. 21 1 0. 41 9 (0 .4 58 ) (0 .4 12 ) (0 .7 39 ) (0 .6 73 ) c er tifi ca tio n ty pe (p g i) -0 .4 08 -0 .0 82 -0 .8 83 -0 .0 33 (0 .4 68 ) (0 .4 37 ) (0 .7 60 ) (0 .6 60 ) (c on tin ue d) 277adapting to climate change: what really drives the choices of the producers of geographical indications? bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 (1 ) (2 ) (3 ) (4 ) (5 ) ye s fu tu re ye s fu tu re ye s fu tu re ye s fu tu re ye s fu tu re ty pe (c ro p) 0. 95 0 1. 43 1 -1 0. 46 7* ** -1 0. 93 9* ** (0 .8 68 ) (0 .9 50 ) (1 .4 19 ) (1 .4 16 ) c lim at e ch an ge o bs er va tio n (y es ) 1. 88 9* 0. 73 1 7. 08 8* ** 4. 50 6* ** (1 .0 47 ) (1 .0 02 ) (1 .6 75 ) (1 .5 19 ) o bs er va tio n of e xt re m e ev en ts (y es ) -0 .0 07 0. 76 7 -0 .0 62 1. 33 4 (0 .7 09 ) -0 .7 83 (1 .3 55 ) (1 .3 26 ) lo ng -t er m te m pe ra tu re c ha ng e -1 .4 71 ** -0 .6 24 -1 .4 94 0. 14 6 (0 .7 35 ) (0 .6 95 ) (1 .2 38 ) (1 .1 06 ) a lti tu de (h ill s) -0 .3 12 -0 .1 21 -1 .1 36 -1 .0 06 -0 .4 63 -0 .3 99 0. 27 8 -0 .2 66 -1 .2 77 -1 .5 45 (0 .8 56 ) (0 .8 01 ) (0 .9 37 ) (0 .8 20 ) (0 .8 60 ) (0 .8 20 ) (0 .8 70 ) (0 .7 71 ) (1 .4 62 ) (1 .3 42 ) a lti tu de (l ow la nd s) -0 .0 94 -0 .0 93 -1 .7 46 * -1 .4 45 0. 27 6 0. 01 5 0. 06 1 -0 .3 51 -2 .5 72 * -1 .9 20 (0 .8 80 ) (0 .8 29 ) (1 .0 20 ) (0 .9 17 ) (0 .8 40 ) (0 .8 04 ) (0 .8 40 ) (0 .7 58 ) (1 .3 88 ) (1 .2 80 ) c on st an t -1 6. 06 3* ** -1 3. 91 0* ** 1. 38 2 1. 81 4* -0 .6 06 -0 .8 72 1. 88 9* 0. 73 1 7. 08 8* ** 4. 50 6* ** (1 .0 33 ) (1 .0 12 ) (1 .1 09 ) (1 .0 21 ) (1 .0 85 ) (1 .1 37 ) (1 .0 47 ) (1 .0 02 ) (1 .6 75 ) (1 .5 19 ) o bs . 10 6 93 11 5 99 86 a ic 25 3. 56 19 6. 86 27 3. 61 22 9. 16 21 8. 17 bi c 32 2. 81 22 2. 18 31 7. 53 25 5. 11 32 1. 25 m cf ad de n 0. 13 0. 13 0. 04 0. 04 0. 29 a cc ur ac y 53 .7 7 49 .4 6 49 .5 7 42 .4 2 63 .9 5 n ot es : s ta tis tic al ly si gn ifi ca nt * p< 0. 1; * *p <0 .0 5; * ** p< 0. 01 . ta bl e 5. (c on tin ue d) . 278 bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 francesco pagliacci, dana salpina tation strategies between agri-food gi producers and conventional ones is the existence of regulative barriers imposed by pss. however, the fact that the certification type (i.e., producing either a pdo or a pgi) is never significant might suggest that rigidity of code of practices (i.e., more stringent pss in the case of pdos than in the case of pgis) is not a big issue in climate change adaptation for gi producers. this finding seems to be supported also by the analysis of the main barriers, according to the respondents’ perspectives (table 6). indeed, the restriction imposed by pss is one of the least perceived concerns by producers, who are worried much more by the lack of financial resources or by difficulties in having access to public funds (e.g., those of the rural development policy). moreover, information issues seem to play a key role in the adaptation process. similarly, to what is observed across europe (simonet and leseur, 2019) or elsewhere (alam et al., 2017; belay et al., 2022), the economic aspect of adaptation is proved to matter, as on-farm adaptation mostly relies on producers’ own resources. on top of that, there is an issue of uncertainty, associated with the high cost of investments, and with the uncertain long-term benefits. in other words, uncertain future costs of climate risks compared to the certain and immediate costs of adaptation measures together with uncertain expected returns on investment represent one of the major barriers to climate change adaptation (lefebvre et al., 2014), also in the case of agri-food gi producers. the barriers discussed above couple with external factors, mainly involving policy and governance issues: observed complexity in having access to public funds, a lack of technical assistance in obtaining such help, market dynamics and the current geo-political conditions. in this context, climate change adaptation, which is of utmost importance given the impacts already affecting gi farmers and producers, seems to require specific policy interventions. 6. policy implications the results of this study represent an important contribution, not only to inform policymakers at regional level (i.e., in the veneto region), but also for national and eu policymakers and stakeholders. indeed, the results of this study are highly generalizable in terms of suggested approach and adopted empirical strategy. in particular, the suggested strategy, distinguishing three alternative situations (farmers who have implemented adaptation strategies; farmers intending to implement them in the future; and farmers neither having implemented nor willing to do so in the future) holds promise for delivering a relatively elevated degree of accuracy and interpretability, also when implemented in other case studies. moreover, the results suggest that the main policy instruments for high-quality agri-food products might be largely improved across the eu. firstly, a more targeted support within the new common agricultural policy (2023–2027) will largely help. this is true also in a region such as veneto, where in the 2014-2020 programming period just 1.5% of the total funds of the rural development programme was earmarked to the measure aimed at supporting quality schemes (i.e., measure 03). besides a larger public fund allocation, in this context, reliability of new technologies and clear information regarding their effectiveness might help. this will provide new incentives to the producers of agri-food gi, when considering their options of investing in new adaptation measures to climate change. moreover, it could also be helpful developing peer-to-peer learning practices among producers together with fostering further collaborations among gi systems that face similar risks. indeed, the role of public policies is not limited to allocation of financial resources to prevent the financial barriers of adaptation, but it can also ease knowledge transfer (ignaciuk, 2015), fostering collaborations between farms and consortia, and across sectors (e.g., table 6. barriers to climate change adaptation, as perceived by producers of agri-food gis (as evaluated by producers, scale from 1 to 5). barriers (number of respondents) adaptation no (40) yes (33) yes_ future (45) total (118) lack of financial resources 3.6 3.1 3.9 3.6 high cost of investments and long-term benefits 3.9 3.2 3.5 3.5 accession to rdp funds 3.6 3.9 3.8 3.7 long waiting time for the accession rdp funds 3.8 3.5 3.9 3.7 lack of technical assistance 3.4 3.0 3.4 3.3 lack of information on effectiveness of certain adaptation measures 3.8 3.5 3.8 3.7 restriction imposed by pss 3.5 3.1 3.2 3.3 land property 2.8 2.4 2.8 2.6 lack of local and production networks 2.1 2.8 2.5 2.5 lack of producers’ representation in the decision-making process 3.3 3.3 3.3 3.3 279adapting to climate change: what really drives the choices of the producers of geographical indications? bio-based and applied economics 13(3): 265-283, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15221 public and private). to this regard, the framework of agricultural knowledge and innovation systems (akis) could be strengthened, in terms of a multi-stakeholder process (cruz maceín et al., 2023). analogously, also the functions of consortia and pos could be strengthened to better facilitate the coordination among the stakeholders for the implementation of adaptation strategies at gi level. these entities, which frequently offer advisory support, can serve as innovation intermediaries, cooperating with research organisations (salpina and pagliacci, 2022b) and facilitating horizontal and vertical diffusion of information. thus, consortia and pos can play a pivotal role in sustaining adaptation efforts, and guide farmers in investing in new adaptation measures. lastly, the findings of this empirical study hold the potential to contribute significantly to international discourse surrounding food policy, by providing an indepth examination of climate change adaptation practices within the gi agri-food sector, the policy area that has thus far received limited attention within academic circles. 7. conclusion this study aimed at analysing climate change adaptation strategies in the case of high-quality agri-food sector, shedding light on the main factors inf luencing the decision of producers to adapt. in the past, this topic was largely neglected in the literature. actually, to the authors’ best knowledge, only a few other studies have already focused on the topic of climate change adaptation, taking agri-food gis into consideration. the key findings of the research suggest that despite a generalised (and high) awareness of climate change among gi farmers and producers, this has not yet turned into widespread adoption of adaptation measures. the main factors influencing the willingness of producers are confirmed to revolve around the complex interplay of sociodemographic characteristics (e.g., age, education), farm management and networks, and production characteristics, in addition to the perception of climate change. despite the potential limitations of any online surveys (e.g., some bias in respondents’ characteristics, in favour of younger and more educated ones), further studies could eventually replicate the questionnairebased survey in other countries and regions, making use of the same methods proposed here. moreover, it should be noticed that this study encompassed the certified agri-food sector in general. thus, future works, focusing on a specific sector (e.g., only cheese products), would allow for a more targeted examination of key variables affecting climate change adaptation. one additional limitation of this study is the absence of a comparison between farmers operating within gi schemes and the ones operating outside such schemes. however, such a limitation was due to the complexity of such a comparison and mostly to the data collection process, which was primarily done through consortia and pos. future research will eventually address this gap, providing valuable insights into this phenomenon. moreover, future lines of research will also involve the analysis of the drivers contributing to the adoption of specific adaptation measures and will consider additional and more sophisticated proxies for climate change perception. acknowledgement the authors gratefully acknowledge the financial support from the project “what if the terroir moves under our feet? addressing the effects of climate change on the use of geographical indications for agri-food products in veneto, italy” (pagl_bird20_05 bird 3 2020/2022 research grant). project financed with bird 2020 funds, department tesaf, university of padova italy. the authors thank the editor and the anonymous referees for their precious suggestions that have led to a 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(2024). predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology. bio-based and applied economics 13(4): 333-351. doi: 10.36253/bae-14592 received: april 6, 2023 accepted: march 27, 2024 published: december 31, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: simone cerroni orcid lb: 0000-0002-7505-1174 sc: 0000-0001-9719-2392 fa: 0000-0002-5179-2541 md: 0000-0002-3957-842x predicting the effect of the common agricultural policy post-2020 using an agentbased model based on pmp methodology lisa baldi1, sara calzolai2, filippo arfini2,*, michele donati1 1 department department of chemistry, life sciences and environmental sustainability, university of parma, italy 2 department of economics and management, university of parma, italy *corrisponding author. e-mail: filippo.arfini@unipr.it abstract. the objective of this study is to perform an ex-ante assessment of the potential impacts of agro-environmental measures included in the post-2020 common agricultural policy (cap), by estimating farmers’ responsiveness in adopting organic agricultural practices and an eco-scheme that incentivises extensive forage systems. this research is conducted by means of an agent-based model (abm), based on positive mathematical programming (pmp), implemented in gams. the abm facilitates the simulation of interaction among farmers, allowing for an analysis of farm heterogeneity. the pmp methodology adds a non-rational dimention to the farmers’ economic drivers. the model is calibrated using 2019 farm accountancy data network (fadn) data specific to the emilia romagna region in italy. our findings reveal significant impacts on land use, with a notable decrease in cereal cultivation in favour of protein and fodder crops. moreover, structural shifts are observed, notably a decrease in the number of small-scale farms. we also assess environmental and economic implications, observing a modest reduction in co2 equivalent emissions per hectare, an increase in water demand, and an overall economic stability among farms, as indicated by changes in gross margin per hectare. keyword: cap reform, agent base model, land use, structural change, co2 emission. jel codes: c61, q15, q18, q52. 1. introduction since its first implementation in the early 1960s, the common agricultural policy (cap) has greatly impacted european union agriculture, driving farm behavior through subsidies, direct and indirect payments, production constraints, and trade regulations. the cap objectives have gradually moved from strengthening agricultural production to providing public goods through different reforms. however, despite the environmental principles embedded in the cap regulations, as from the fischler reform in 2003, the intensification of agricultural practices has progressively eroded several critihttps://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode http://www.fupress.com/bae https://doi.org/10.36253/bae-14592 https://doi.org/10.36253/bae-14592 https://orcid.org/0000-0002-7505-1174 https://orcid.org/0000-0001-9719-2392 https://orcid.org/0000-0002-5179-2541 https://orcid.org/0000-0002-3957-842x 334 bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 lisa baldi, sara calzolai, filippo arfini, michele donati cal environmental components such as climate, water quality, pollination, biodiversity, physical and psychological well-being, as well as cultural heritage (european environmental agency 2019; nègre, 2022). these developments have had significant repercussions on the provisioning of ecosystem services. the “greening” measures introduced in the 2014-2020 cap reform proved inadequate to meet social demand for an eu agriculture more aware of its role in enhancing regulatory and cultural services (cortignani & dono, 2019; alons, 2017). the cap post-2020 reform aimed to redress past failures in meeting eu green deal objectives and following targets established by the farm-to-fork and biodiversity strategies. the new green cap architecture is based on eco-schemes, one of the most important innovations introduced by the cap post-2020 reform, which obliges member states to allocate at least 25% of first pillar payments to measures beneficial for the environment and the climate. strategic plan regulations limit eco-schemes to active farmers, which can apply voluntarily (european commission 2020). during the last decade, several economic models have been developed to help policymakers and stakeholders to evaluate cap greening mechanisms from an ex-ante perspective. the main results provided by capri, pasma, and ifm-cap models suggested that the cap measures generating environmental benefits are not as effective as expected (solazzo et al., 2016). a recent ex-post analysis confirmed these results (bertoni et al., 2021). this empirical evidence supports the idea that economic modelling is a useful decision tool for designing more effective agricultural policies, increasing researcher and policymaker interest in in-depth impact assessment of agricultural policies at the farm scale (kremmydas et. al 2018). the aim of this paper is to present an abm, based on positive mathematical programming, for conducting an ex-ante impact evaluation of the agri-environmental measures incorporated into the post-2020 common agricultural policy (cap). this evaluation entails a comparative static exercise, whereas we equate the baseline scenario with two simulated scenarios wherein farmers receive the organic payments or the payment for extensive forage systems, if economically viable. the baseline scenario also represents the counterfactual scenario, enabling the evaluation of the impacts of a particular policy (where farmers receive basic coupled and decoupled payments) against alternative policies. the model employed is static in the sense that it evaluates the initial sample at a particular moment in time and compares it to the same sample where farmers have altered their behaviour to maximize their utility function due to different payment conditions. the quadratic functions, commonly used in dynamic models to capture temporal dynamics, are introduced, in this study, with the pmp to represent nonlinear relationships between variables at a specific point in time. positive mathematical programming is widely used in agricultural policy assessment (howitt, 1995; britz et al., 2012; solazzo et al., 2014; reidsma et al., 2018; matthews, 2022). a distinctive feature of pmp is its ability to recover important entrepreneurial decision variables, such as hidden costs related to past farming experience, risk attitude, and production expectations, useful for simulating more realistic behaviours, not solely driven by economic rationale. in this research, the pmp model is an agent-based model (abm) which can capture interactions between farms in the use of scarce resources. abms are better suited to fulfilling important disaggregated specifications, to capturing farm heterogeneity at the regional level, and considering interaction between farmers in the use of scarce resources. they bring substantial innovations to mathematical programming models (reidsma et al. 2018; berger & troost 2014). integrating positive mathematical programming (pmp) techniques within abms provides a rigorous framework for modelling agents’ decision-making processes, particularly with respect to optimising their behavior subject to constraints and policy incentives. pmp helps in simulating how agents respond to policy changes based on economic principles represented by explicit and implicit variable cost. moreover, the integrated methodology of abms and pmp enables the assessment of ex-ante agricultural policies by examining their potential effects on farmers’ behaviour related to agricultural production choices, land use, structural adjustments, as well as their environmental and economic impacts, supporting policymakers in making informed decisions while considering farms heterogeneity. that said, implementing abms with pmp requires detailed data on agent characteristics, preferences, decision rules, and interactions, which can be challenging to obtain, especially at fine spatial scales. limited or inaccurate data may lead to uncertainty and biases in model outcomes. abms can become highly complex, particularly when modelling large-scale agricultural systems with numerous interacting agents and processes. calibrating such models to real-world data and ensuring their validity and reliability can be time-consuming and computationally intensive. with over one million hectares of uaa (8.6% of national uaa), in 2016 emilia romagna accounts for respectively 10.9% (€3,221.91 million) and 15.17% (€2,292.83 million) of italian crop production and ani335predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 mal value, making this region one of the most productive agricultural areas in italy. moreover, for the same reference year, 55% of agricultural land is under high intensity input agricultural practices, 37% under medium intensity and 8% under low intensity input practices. agricultural activities have a strong climate-change impact, accounting in europe for 10% of total greenhouse gases emission (eurostat 2022). italy is the fifth largest contributor, after france, germany, spain and poland, emitting 8% of total agricultural ghgs. not surprisingly, the high level of agricultural productivity and related impacts, as well as the consolidated presence of industrial and logistic infrastructures, heavy urbanization, and the peculiar geographical conformation of the po valley, make emilia romagna, together with the other three regions of the valley – lombardia, piemonte and veneto, the most polluted and impacted areas in italy (raffaelli et al. 2020). this study is organised as follows. the materials and methods section presents the characteristics of the farm sample and discusses how pmp is particularly suitable for developing abm models. the policy scenario section describes the main agricultural policy instruments used in the simulation, and the results are discussed in the last section. 2. methodology and data 2.1. agent-based models and pmp a key feature of abms is their capacity to evaluate the interactions between agents (farms) and to describe the impact on land use and structural change according to the structure, productivity, efficiency, and spatial heterogeneity of the agents in their territory (reidsma et al., 2018). agents can represent different individual farms, entrepreneurs, or aggregated entities, such as farm types. the ability of abms to capture the interactions between farmers can be leveraged under the assumption of non-full rationality in production preferences. this can be done because farmers tend to maximize their utility function, rather than their profit function (nolan et al., 2009; kremmydas et. al 2018). this is plausible only if agents represent individual farm-households, in which family structure and other individual characteristics are particularly important in determining transaction costs affecting the economic objective to be maximized. decisions are based on production factor endowment and level of technological knowledge, as well as the perception of economic and technical risks.the literature provides some attempts to measure the effect of cap provisions through abm-type models, such as agripolis (happe et. al 2004), mp-mas (schreinemachers and berger, 2011), and regmas (lobianco & esposti, 2010), however none of them is associated with the pmp. linking agent-based models (abms) with mathematical programming (mp) models offers the advantage of creating micro-level models that can depict technological variations based on the structural characteristics of farms. for more insights into the different types of abms, kremmydas et al. (2018) have conducted a systematic literature review on abms for evaluating agricultural policies. the integration between abm and pmp models enables the optimization of the cost function for each farm within the sample. this optimization takes into consideration the unique characteristics and behaviors of individual farmers, starting from the observed optimal scenario. the cost function is hypothesized to be a quadratic functional form in output quantities: c(x) = x’qx/2, where the q matrix is symmetric and positive semidefinite. additionally, this integration allows for the simulation of structural and technological changes, such as changes in farm size or the potential abandonment of farm activities. an abm based on pmp can estimate these choices by simulating land exchange, the introduction of new activities and changes in agricultural management practices. aggregating these results can provide a useful and solid insight into the general trend of the agricultural sector at regional, national, and international levels. pmp is generally used as a straightforward calibration technique as seen in the capri model, where specific technical coefficients are applied. in this study, the pmp methodology employed for calibration is based on farm marginal costs, which consider accounting costs c and the marginal implicit cost λ, intended as “transaction costs”, or socio-economic costs (anderson et al., 1985), perceived by the farmers. these costs are estimated under economic constraints using the dual property of a profit maximisation problem implicit in the model. this results in shadow prices linked to production activities that precisely equate to the combined total of the estimated accounting cost and the estimated differential marginal costs. the estimated accounting cost corresponds to the farm accounting values, whereas the estimated differential marginal costs can be viewed as the opportunity cost linked to each activity. the estimated differential marginal cost, usually referred as hidden cost, represents the portion of the estimated total marginal cost not documented in the farm accounting sheet but taken into account by farmers when formulating production plans (cesaro and marongiu, 2013). the hidden costs refer to the specific and individual opportunity costs that each farmer considers when deciding whether to introduce a given crop in the production 336 bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 lisa baldi, sara calzolai, filippo arfini, michele donati plan. these hidden costs incorporate the specific and individual opportunity costs that each farmer weighs when determining whether to incorporate a particular crop into the production plan. these costs are important not only for the marginal cost calculation but also for the calibration. it is for this reason that the pmp guarantees that the cost estimates obtained can be used for reproducing the basic production situation, enabling the assessment of each farm’s response within the sample to the policy measures implemented. although there is no theoretical rationale requiring a specific functional form for farmers’ reactions, the quadratic form is employed in this study because it is widely used in agricultural economics and inherently represents the cost function. additionally, the cholesky decomposition ensures to obtain a symmetric and positive semidefinite matrix. 2.2. the model structure agrisp (agricultural regional integrated simulation package), the model described in this paper, is a supply abm, based on the pmp approach, which models farm-holders as agents and analyzes the impact of new cap measures on agents’ behaviours related to land use, gross margin, carbon emission, and water consumption. agrisp is implemented in gams (gams 2023) and articulated in a calibration module and a simulation module, depicted in figure 1. the exact production level for each farm is estimated with the “self-selection”. a detailed explanation of self-selection rules and a comparison between the farm and frontier cost functions can be found in paris and arfini (paris & arfini, 2000). leveraging on the self-selection process, in agrisp, agents belonging to a specific regional farm sample can exchange production techniques or adopt new agricultural practices, if experimental research makes technical information available. this is accomplished through the use of a common frontier-cost function, shared at the regional level, estimated using the pmp and which incorporates the costs associated with all crops and cultivation techniques, and the deviation of each individual farm from this function (arfini and donati 2012). the common frontier-cost function serves as a link among the farms in the sample. the deviation from the common cost function is regarded as a basis for comparing costs and profitability among the farms included in the sample. the introduction of a subsidy or a tax, which triggers changes in output prices of variable costs, leads farms to different cost-efficiency crop or techniques combination, as result of the optimization run in the simulation phase. this can be viewed as a form of “social learning process” or, more accurately, as an exchange of technical and economic information made available, because observed, in the sample. the interconnectedness stems from the fact that all farms are aware of the potential techniques available. the latent technologies or crops are those options that agents could potentially adopt but remain “unused” by a farm due to their lack of economic viability within a particular simulated scenario.. supports coupled to a specific technique or tied to the acreage can alter the economic ratios among various production plans. as a result, farm holders may choose to adopt a new crop or technology from the array of agronomic techniques practiced by the farms in the sample, originally latent in their production plan, and their decision is influenced not only by the accounting cost but also by the utility cost unique to each farm. following calibration, the simulation module assesses the repercussions of alternative policy scenarios by leveraging the positive information embedded in the nonlinear cost function and employing a set of hypothetical behavioural rules. these agent-based rules offer a more realistic representation of the interactions among farms, encompassing resource exchange, as well as the choices made by the farmers regarding different agricultural practices, taking into account the specific social and family characteristics. more specifically, as argued by möhring et al., farm dynamism is correlated with the farm holder’s age and successors’ presence (möhring et al. 2016). the authors of this study make the assumption that once farmholders reach the age of 65, they are more inclined to reduce farm activity rather than expand it. likewise, it is assumed that farms with holders aged 65 or older, without successors, are unlikely to lease additional hectares or opt for the conversion of farms from conventional to organic practices. farmers over 65 are more likely to rent out their land, totally or partially. in the model, the complete rental of land is regarded as equivalent to abandoning the farming activity. on the other hand, if the holder is younger than 65 or the possibility of a generational renewal exists, they may consider expanding the farming activity by leasing additional land from neighboring farms or transitioning from conventional to organic practices.. it is important to highlight that all these decisions are contingent upon cost-effectiveness. therefore, the economic cost function that needs to be optimized incorporates factors such as the cost of land rental and the supplementary expenses associated with converting and sustaining organic crops. the equations associated with the key characteristics of the model are outlined below, and more details 337predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 on the implementation of the policy instruments can be found on the appendix 1. the interactions between agents (1-3), related to the adoption of a specific production plan, are given by sharing the same frontiercost function (q) plus a deviation (u) and the adoption of the self-selection rule (4-5) by the nth farm. the self-selection allows for the replication of the observed production plan through a comparison between the marginal cost of the current activity (or technology) and the average cost of a new activity or technology, which is defined within the q matrix as latent activity or technology. figure 1. model structure. source: authors’ own elaboration. 338 bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 lisa baldi, sara calzolai, filippo arfini, michele donati (p’nxn – 1/2x’nq̂nxn – unxn) (1) anxn ≤ bn (2) xn ≥ 0 (3) to simulate the fact that not all farms in the sample cultivate all the crops encountered in the region two sets of constraints are postulated. the first set deals with the crops, which are produced, and, thus, the marginal cost relation is an equation: mcnk | xrk > 0 λnk + cnk = qkxrn + unk if the k-th activity is produced, k=1, …, jn (4) where mcnk is the marginal cost for the n-th farm associated with the k-th activity. the second set of constraints deals with the activities which are not produced by the n-th farm, in which case the marginal cost relation is a weak inequality with respect to the level of the frontier-cost function: mcnk | xrk = 0 : λ_nk + c_nk ≤ qkxrn + unk if the k-th activity is not produced, k = 1,…,jn (5) r is the level of production observed for activity k and the vector unk assumes the role of indexing the cost function with the farm n specific characteristics. λ represents the implicit component of the marginal cost associated to the production of the activity k by the farm n. restrictions (4) and (5) enable farmers also to select possible production activities from all activities present in the region among the activities observed in the first phase of the pmp (paris and arfini, 2000). in the case of conversion to organic farming, equations (1-3) are replaced by equations (6-8). p’cxc + p’gxg – 1/2 [xc xg] qcg [xc xg] (6) s.t. acxc + agxg ≤ b (7) ancxnc ∙ angxng ≤ 0 (8) any farm using conventional technology (c) can convert to organic technology (g) if it is more profitable. in the italian fadn, information regarding the agronomic management practice (organic or conventional) is provided for each farm. from this information the average costs, yield and output prices of the organic production are extrapolated. when a farm converts to organic farming those values are applied for the crops included in its production plan. appendix 1 explains the operational implementation of the conversion from conventional to organic agriculture in the simulation phase. the objective function, with the non-linear cost component, takes advantage of the self-selection property, allowing the substitution of technology or crops based on the cost information provided in the qcg matrix.consequently, farms that decide to convert to organic farming change their production plan and cost structure. equations (9-14) represent and rules related to the exchange of the land factor between agents. setting j activities, n and m farm holdings exchange land between each other. equation 9 indicates that the available utilised area is equal to the available area plus the rented-in land minus the rented land. equations (9 14) indicate that a farmer can either rent or rent out land, and that the total amount of rented land must be equal to the total rented-out land at the agrarian region level.. more precisely constraint (9) requires that the total land allocated to the different crops j (j = 1, … ,j), must be less than or equal to the observed total available land at the j farm level, bn, plus the land rented (zn) minus the land rented out (vn). anjxn ≤ bn + zn – vn (9) the land rented is represented as: zn = ∑ m zznm (10) and the land rented out is represented as: vm = ∑ n vvnm (11) where zznm and vvnm are the matrix tracing the transfer of land for each pair of farms for renting and renting out, respectively. furthermore, for each pair of farms, the land rented by one farm must be equal to the land out by the other, as follows: zznm – vvnm = 0 ∀n≠m (12) to avoid a given farm renting and renting out land at the same time, a specific constraint has been added: zn ∙ vn = 0 (13) finally, to ensure that the exchange of land is consistent with the total available land at regional level, we establish that the total land rented must be equal to the total land rented out: 339predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 ∑ n zn = ∑ n vn (14) therefore, we assume that the exchange of land is limited to the farms located in the same agrarian region. each farm has a marginal cost level, estimated with the pmp, beyond which acquiring additional land provides no further advantage. introducing a price shock or a policy incentive can lead to a change in the shadow price of land for a specific farm. however, the land rental price remains constant, as it is treated as exogenous to the model and is assumed to be uniform throughout the emilia-romagna region. agents’ interactions are regulated by the behavioural rules already mentioned in the previous section and here summuarised: i) conventional farmers older than 65 and without successors cannot move to organic practices; ii) farms are only allowed to exchange land within the agrarian regions where they operate; iii) farmers older than 65 and without successors cannot rent land. the input level is calculated based on the spending on purchased inputs, both for crops and livestock, per hectare of uaa. the inputs are purchased fertilizers and soil improvers, plant protection products, other means for protection, bird scarers, anti-hail shells, frost protection and purchased feed. to provide environmental impact assessment, we integrated the italian fadn data with environmental information on greenhouse gas (ghg) emission factors and water consumption for the different crops. ghg emissions from agricultural activities were estimated by applying the icaai methodology (impronta carbonica dell’azienda agricola italiana), developed by crea-pb, following the guidelines provided by the ipcc for establishing a national inventory of greenhouse gas emissions (coderoni and vanino, 2022; ipcc, 2008). this procedure, already implemented by solazzo et al. (2016) assumes that the amount of atmospheric emissions is linearly related to the level of economic activity, and the emission factors considered for the agricultural sector are carbon dioxide, methane and nitrous oxide, expressed in ton co2eq per hectare or head of livestock. the conversion factors referred to the 100-year global warming potential and are provided by the fourth assessment report of the ipcc (2007), following equation (15): co2eq = co2 + 298 ∙ n2o + 25 ∙ ch4 (15) more in detail, carbon dioxide emissions comprise emissions due to mechanical cropping operations (ribaudo 2011) and soil organic carbon (soc) estimation; methane emissions are due to livestock enteric fermentation and rice cultivation; nitrogen emissions include animal manure management, synthetic fertilizer application and atmospheric deposition (solazzo et al. 2016). the water consumption measurement uses the water footprint network, based on the extensive work of mekkonnen and hoekstra (mekonnen and hoekstra 2010) that estimates the water footprint of 147 crops and over 200 products, and which also calculates the water footprint at national and sub-national level of each crop worldwide. the concept of water footprint was previously introduced by hoekstra in 2002 in order to assess the direct and indirect use of freshwater resources along a production chain (hoekstra and hung 2002), as a sum of i) blue water, surface water or groundwater for irrigation; ii) green water, the water naturally embedded in the rhizosphere and available for plant assimilation; iii) grey water, the volume of water necessary to dilute ecotoxic compounds (mainly used in crop protection) to restore specific quality standards. results are analised using the aggregation depicted in table 1. 2.3. data the economic agents in the model are the individual farms included in the “rete di informazione contabile agricola” (rica or fadn) database, which has been operational in italy since 1968. this database is managed by crea and provides data for the year 2019. the initial sample is specific to the emilia-romagna (nuts2) region and comprises 739 farms out of the nearly 11,000 sampled farms across italy. since rica assigns a sample weight to each farm to ensure it is representative of the entire population, the weighted sample corresponds to a total of 40,753 farms. table 2 illustrates the distribution of farms based on their size class (measured in hectares) and their management practices, which can be either conventional or organic. the set of farm data includes information on geographical location (region, province, altitude, agrartable 1. crop aggregation. macrocategory aggregated crops cereals wheat, barley, rice, sorghum, other cereals forages alfalfa, forage maize, other forages proteic/oilseeds sunflower, soja, protein crops, other oilseeds maize maize meadows pastures meadows and pastures indutrial tomato industrial tomato other industrial crops beetroot, potato 340 bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 lisa baldi, sara calzolai, filippo arfini, michele donati ian zone), agricultural practices (conventional, organic), household characteristics (age and gender of the farm holder, number of potential farm holder’s successors), land use, specific production costs per crop (cost of seeds, fertilizers, pesticides, energy, water), gross total product, and cap payments. table 3 depicts the heterogeneity of the sample based on class of age, per farm size and percentage of organic farms, that represent almost 15% of the farms population in emilia romagna. within the sample, the average age of the landholders is 61 for conventional farms and 54 for organic farms. the “agrarian region” spatial definition is a peculiarity of the fadn and it further segments italian provinces (nuts3) based on geographical location and altitude range. although similar to the european sampling, the italian fadn is notably more comprehensive, considering over 2,500 variables for each sampled farm, in contrast to the european fadn, which only takes into account approximately 1,000 variables (crea-pb 2021). table 4 detailed the observed land use in emilia romagna region in the year 2019. the prevailing land use relates to cereals (33.26% of the total utilized agricultural area (uaa)) followed by forage (33.17%); meadows and pastures count for 10.54% of the regional uaa. 2.4. policy scenarios to model how farmers respond to the adoption of organic agricultural practices and eco-scheme, two scenarios are implemented in agrisp and evaluated through a comparative static analysis. more specifically, we compare the baseline scenario, represented by the calibrated fadn data for the year 2019, wherein farmers receive the basic coupled and decoupled payments, with the simulated scenario. greening measures of the previous cap reform: crop diversification, maintenance of permanent grassland, and the establishment of ecological focus areas are simulated (european commission 2017) are also included in the baseline. the two cap post-2020 scenarios implemented in the simulation module of agrisp are: 1. the “organic” scenario, where payments are made to encourage farm holders to adhere to organic agricultural practices in order to increase the area table 2. number of farms according to size class (ha) and management practices. size (ha) conventional farms organic farms total initial sample weighted sample initial sample weighted sample initial sample weighted sample < 10 246 17,312 23 1,397 269 18,710 10-20 120 7,714 17 1,950 137 9,664 20-50 152 5,975 34 1,610 186 7,585 50-100 68 2,197 25 964 93 3,160 100-300 47 1,249 3 92 50 1,342 > 300 1 51 3 61 4 112 total 634 34,499 105 6,074 739 40,573 table 3. farms per age and size class, based on management practices. holder’s age conventional farms organic farms total % organic farms ≤40 41–64 ≥65 ≤40 41–64 ≥65 size class % age class/farm type 6.81 46.55 46.64 15.02 64.31 20.67 < 10 1,122 7,470 8,721 159 1,034 205 18,710 3.44% 10-20 230 3,350 4,134 283 1,293 375 9,664 4.81% 20-50 525 3,235 2,215 130 1,001 478 7,585 3.97% 50-100 439 1,112 645 340 481 142 3,160 2.37% 100-300 34 842 374 52 41 1,342 0.23% > 300 51 46 15 112 0.15% total 2,350 16,059 16,089 913 3,906 1,255 40,573 14.97% 341predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 under organic agriculture to 25%, according to the farm to fork strategy target (appendix 1). regional payments for organic crops are listed in the rdp of emilia romagna (dg agri 2021). in this scenario farmers will opt for organic farming if economically convenient, considering transition costs, organic yield and prices for organic products. 2. the “eco-scheme” scenario simulates the 4th ecoscheme in the italian national strategic plan (masaf 2022). it envisages incentives in the form of additional payment of 110 €/ha added to the basic payment, for an extensive forage system. in our model, we consider the crop category “meadows and pastures” as eligible for this payment. the “ecoscheme” scenario is added to the subsidy foreseen to support the conversion to the organic agronomic management practice. the abm rules and the pmp methodology integrated in the agrisp model trigger farm owners’ decisions on farm organisation, including factors such as land endowment and utilisation. this is achieved by optimising the individual utility functions of each farm, which subsequently influence the environmental impact and the overall regional gross margin. other models can be used to perform similar comparative analysis, such as partial equilibrium models based on farm types (e.g. capri), providing a macroeconomic perspective by analysing the interactions between supply and demand in agriculture. however, these models can offer insights into how policies affect market equilibrium, prices and production but do not consider the farms heterogeneity. as noted above (equations 9 14), in both scenarios farmers can exchange land according to specific agent-based constraints that trace a one-to-one relationship between all the farms included in the sample, in the sense that each farm has the option to rent or rent out arable land with the other farms located in the same agrarian region. farmers exchange land as a way of making optimal use of their resources. farmers can adopt different structural strategies, such as leasing out their land and exiting the market entirely, or alternatively, they may choose to lease out only a portion of their land while continuing their farming activities. the rental price for land is not resulting from a land market equilibrium but is assumed to remain fixed at 690€/ha. this price is derived from the “survey on the land market” conducted by crea-pb (2019) in emilia romagna. 3. discussion of results the “organic” scenario and the “eco-scheme” scenario are executed using the calibrated 2019 italian fadn data, and subsequently compared to the baseline, which does not incorporate any agent-based or policy constraints. the main emerging phenomena are: (i) the impact on land use, including technological changes for conversion to organic farming; (ii) the structural changes recorded in total number of farms per sized-class and in terms of farm holder age ; (iii) the environmental impact related to the carbon emissions and water consumption; (iv) the impacts on farmers’ gross margin. 3.1. impacts on land use the impact of the two scenarios on land use has been analysed both in total hectares allocated and as a percentage (table 5). cereals, the less profitable crops, decrease overall by 13.74% in the organic scenario and by 13.90% in the eco-scheme one respectively. meadows and pastures experience a modest decrease in the organic scenario, but the eco-scheme subsidy helps bring production back up slightly. all other crop categories show an increase. among them, protein/oleaginous crops reveale the highest rise, with an increase of 8.58% for the organic scenario and 8.59% for the eco-scheme scenario. the greening requirement leads to land set-aside of 0.28% on “organic” and 0.27% in “eco-scheme” farms. additional elaboration is provided for each class of dimension concerning the four crops that exhibit higher table 4. land use in thousands of hectars. land use (1000 ha) cereals forages proteic/ oilseeds maize meadows pastures industrial tomato other industrial crops total conventional 263 230 52 81 45 23 35 729 organic 46 78 10 5 53 3 4 199 total 309 308 62 87 98 26 39 928 % 33.26% 33.17% 6.64% 9.33% 10.54% 2.82% 4.24% 342 bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 lisa baldi, sara calzolai, filippo arfini, michele donati variation: cereals, forage, protein/oleaginous crops, and industrial tomatoes (figure 2). delving into the results we notice that the decrease in cereal production is mostly accentuated in the small medium-sized farms (under 100 hectares), whereas the decrease is of lower intensity in farms between 100 and 300 ha and almost not relevant in farms over 300 ha. this could be explained with the fact that cereals are typically grown on large plots of land, and they tend to require less labor and inputs per unit of land compared to other crops. large-scale cereal farms may have specialised equipment and processes optimised for extensive agriculture, making it less practical or economical to switch to different crops or practices and they may have more stable market contracts or subsidies that incentivise the continuation of existing cereal production methods. in smaller to medium-sized farms, the decrease in cereal production could be more pronounced when switching to organic or eco-schemes due to the relative increase in labor and management required for these practices. smaller farms might not benefit from economies of scale in the same way larger operations do and may feel the shifts in practice more acutely. for farms under 50 hectares there is no incentive to increase the production of forage. this is probably due to the relatively low amount of the subsidy for conversion to organic (only 120€/ha for alfalfa and other forage) that the eco-scheme scenario is not able to counterbalance. however, for larger farms (50 hectares and above), the trend reverses, with the forage under organic and eco-scheme scenarios having more allocated land than in baseline, with the largest increases seen in the 100300 hectares size class. this could also be driven by the concentration of the dairy farms in class 3-5 (86.33%), which may have further interest in forage. strong positive shift towards protein/oleaginous crops production is reported in both the organic and the eco-scheme scenarios consistently across all farm sizes, suggesting that farmers find agroecological practices economically viable for these products, notably more profitable. this might be due to more favorable subsidies for these crops (351€/ha) or higher market price for organic products. the percentage increase in land allocation is higher in larger farms, especially in those over 300 hectares. this could be due to the greater financial resilience of larger farms, allowing them to take on the risk of transition and the associated costs more readily than smaller farms. also for these crops, data suggests significant economies of scale for larger farms, more likely to distribute the costs and labor required for organic farming more efficiently. the total increase of around 129% for both organic and ecoscheme scenarios is particularly notable. it underscores a widespread and significant adoption of these practices across the sector. a remarkable increase is depicted for smaller farms (<10 hectares) in the organic and eco-scheme scenarios for industrial tomatoes, which might be due to the high subsidy of 427€/ha. this makes it financially attractive for smaller operations to switch to these practices. medium-sized farms (10-50 hectares) also show substantial increases for both scenarios. this could suggest that the subsidy is sufficient to cover the additional costs of transitioning and that the market for organic or eco-friendly tomatoes is strong. there’s a notable decrease in land allocation for farms larger than 300 hectares, where there’s no activity in the organic and eco-scheme scenarios. this stark contrast to other size classes might be influenced by several factors, including the possibility that farms producing industrial tomatos practice more intensive farming and consequently have relatively smaller size. tomato cultivation typically involves higher costs for seeds, fertilizers, pesticides, and water. they also require careful management and more labor for tasks like pruning, table 5. impact on land use, per crop in hectares and in %. crops land allocation in hectares per crop land allocation in % per crop baseline organic eco-scheme baseline organic eco-scheme cereals 308,691.60 181,205.60 179,665.30 33.27 19.53 19.36 forages 307,796.00 331,914.00 333,666.00 33.17 35.77 35.96 protein/oleaginous 61,604.60 141,340.40 141,267.40 6.64 15.23 15.22 maize 86,561.00 90,632.00 88,917.00 9.33 9.77 9.58 meadows pastures 97,817.00 93,449.00 97,454.00 10.54 10.07 10.50 industrial tomato 26,184.00 32,444.00 33,143.00 2.82 3.50 3.57 other industrial crops 39,318.80 54,361.80 51,379.50 4.24 5.86 5.54 greening 2,620.70 2,475.40 0.00 0.28 0.27 total 927,973.00 927,967.50 927,967.60 343predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 trellising, and harvesting. intensive crops like tomatoes are often grown in smaller areas with a higher yield per hectare and are more labor-intensive than extensive crops. the increase in organic and eco-scheme scenarios for the 50-100 and 100-300 hectares classes is lower compared to the smaller farms, and this could be because these larger operations might already be producing at scale, and the relative benefit of the subsidy is lower compared to their overall operations. despite the differences in subsidies, the overall trend shows that there is a significant move towards organic and eco-scheme practices across most crop types and farm sizes. the data for the industrial tomato crop, especially the impressive increases in the smaller size classes, shows that when subsidies are perceived as significant and worthwhile, they can be a powerful motivator for changing farming practices. however, for larger farms, especially those over 300 hectares, the current subsidy rates and perhaps other factors related to scale, market dynamics, or the specifics of tomato cultivation may not provide enough incentive for a shift to organic or eco-scheme practices. overeall organic land increases significantly in the organic scenario (+43% at aggregated level) but is lower at (+35% at aggregated level) for the eco-scheme. looking at the impact of the two payments schems per class of dimension (figure 3) we notice that the more reactive are the medium size farms, particularly those in the class 100-300 ha. it’s worth mentioning that the significant rise in organic surface area within this category might be attributed to the absence of a cap on the subsidies that farms can request. 3.2. structural changes the impact of the scenarios on the number of farms, in terms of farm size, is illustrated in figure 4. 20.000 40.000 60.000 80.000 100.000 <10 10-20 20-50 50-100 100-300 >300 ha c ul tiv at ed w ith c er ea ls farms size class cereal (ha) baseline organic eco-scheme 20.000 40.000 60.000 80.000 100.000 <10 10-20 20-50 50-100 100-300 >300 ha c ul tiv at ed w ith fo ra ge farms size class forage (ha) baseline organic eco-scheme 10.000 20.000 30.000 40.000 50.000 <10 10-20 20-50 50-100 100-300 >300 ha c ul tiv at ed w ith p ro te ic /o ils ee d farms size class proteic/oilseed (ha) baseline organic eco-scheme 5.000 10.000 15.000 20.000 <10 10-20 20-50 50-100 100-300 >300 ha c ul tiv at ed w ith in du st ria l to m at os farms size class industrial tomatos (ha) baseline organic eco-scheme figure 2. land use per crop and per scenario. 344 bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 lisa baldi, sara calzolai, filippo arfini, michele donati overall, there is a noticeable decline in the weighted figures, showing a drop of 5,381 units for the organic scenario and a drop of 5,325 units for the eco-scheme scenario. (table 6). the farms appearing to be the most affected are the smaller ones, with a decrease of 18% in farms smaller than 10 hectares, a 13% decrease in the class with a uaa of 10-20 hectares, and a 10% decrease for farms smaller than 50 hectares altogether (figure 4). the activation of the land exchange constraints, allowing for land rental, as highlighted in figure 5, emerges as the primary trigger for this structural transformation in the scenarios. however, there is an exception with very small farms (less than 10 hectares), where the incentives for organic conversion and eco-scheme 4 do not seem adequate to support them. these phenomena might be explained with the fact that small farm holders are more likely to leave the market, while big farms tend to consolidate. for small farms, with shadow prices lower than market prices, it becomes more economically efficient to lease out their land rather than continue farming. we can make the assumption that larger farms exhibit greater resilience, as they can capitalize on their economies of scale, as well as on the subsidies tied to their larger land holdings. from an age-based analysis, and considering the initial agronomic practices of the sample, results reveal (figure 6) that young farm holders (aged below 40), who represent only a small portion, experience a slight increase in the size class of less than 10 hectares, in conventional farming, due to the impact of the land exchange rules. however, their overall decrease remains relatively stable. within the organic compart the decline is perceivable in the smaller size class 20.000 40.000 60.000 80.000 100.000 120.000 140.000 160.000 180.000 <10 10-20 20-50 50-100 100-300 >300 ha c ul tiv at ed w ith o rg an ic c ro ps farm size class organic (ha) baseline organic eco-scheme figure 3. changes in hectares cultivated under organic farming per scenario and size class. table 6. impact of scenarios on number of farms (weighted). farm size class baseline organic eco-scheme < 10 18710 15368 15297 10-20 9664 8465 8387 20-50 7585 6852 6852 50-100 3160 3053 3159 100-300 1342 1342 1342 > 300 112 112 112 0 10000 20000 30000 40000 50000 baseline organic eco-scheme n um be r o f f ar m s structural changes per scenario < 10 10-20 20-50 50-100 100-300 > 300 figure 4. structural changes according to farm size in ha. -4000 -3500 -3000 -2500 -2000 -1500 -1000 -500 0 500 < 10 10-20 20-50 50-100 100-300 > 300 n um be r o f f ar m s farm size class impact of land exchange land organic eco-scheme figure 5. effect of land exchange on number of farms per size class (in ha) compared to baseline. 345predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 (less than 10 hectares) and in the 10-20 hectare range, primarily influenced by land exchange. in the 50-100 hectare class instead, incentives have a minor but still positive effect. in the age range of 41-64, the land exchange rules contribute to a decrease in the number of very small farms, while subsidies help retain some of the 10-20 hectare farms in the market. for organic farms in this same age range, subsidies appear to be beneficial in the 20-50 hectare size class, although the impact of land exchange still remains a significant driver in reducing the number of small farms. farmers aged 65 or older, constituting 43% of the initial sample, appear to be the less responsive to change triggered by subsidies, with a slight exception for conventional farms in the 10-50 hectare range. the primary factor leading to the decrease in the number of very small conventional farms is the opportunity to exchange land. if the total utilized agricultural area (uaa) is assumed constant, the average farm size increases from the 31 hectares in the “baseline” to the 41 and 40 hectares in “organic” and “eco-scheme” scenarios. this result is consistent with the ongoing trend according to the 7th general census of agriculture (istat 2022). 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 < 10 10-20 20-50 50-100 100-300 > 300 n um be r o f f ar m s farm size class age under 40 conventional farms baseline land organic eco-scheme 0 200 400 600 800 1000 1200 1400 < 10 10-20 20-50 50-100 100-300 > 300 n um be r o f f ar m s farm size class age under 40 organic farms baseline land organic eco-scheme 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 < 10 10-20 20-50 50-100 100-300 > 300 n um be r o f f ar m s farm size class age 41-64 conventional farms baseline land organic eco-scheme 0 200 400 600 800 1000 1200 1400 < 10 10-20 20-50 50-100 100-300 > 300 n um be r o f f ar m s farm size class age 41-64 organic farms baseline land organic eco-scheme 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 < 10 10-20 20-50 50-100 100-300 > 300 n um be r o f f ar m s farm size class age above 65 conventional farms baseline land organic eco-scheme 0 200 400 600 800 1000 1200 1400 < 10 10-20 20-50 50-100 100-300 > 300 n um be r o f f ar m s farm size class age above 65 organic farms baseline land organic eco-scheme figure 6. variation in number of farms per size class and age range. 346 bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 lisa baldi, sara calzolai, filippo arfini, michele donati census results depict an overall decrease in the number of farms, while across all regions of italy, farm sizes are increasing, which confirms that incentives to counter the disappearance of small farms need to be well-planned. 3.3. environmental impacts the environmental impact of the cap post-2020 reform on climate change can be evaluated in terms of ghg emissions per agricultural activity. ghg emissions are measured in co2 equivalent. implementing subsidies to support organic agriculture, in this research, leads to a total reduction of almost 6% of tons of co2 equivalent emitted at the regional level, resulting in a total reduction of 1,294 thousand and 1,297 thousand respectively for scenario organic and eco-scheme at the regional level (table 7), confirming that organic practices impact less on the climate than conventional ones (holka et. al 2022). in line with these results is the average carbon emission per hectare (figure 7). carbon footprint aggregated per crop shows that the reduction in emissions is mainly due to the reduction of cereal cultivation (-11%), while there is a slight increase in emissions related to forage, protein crops and oilseeds. the per farms-size analysis of the evolution of the ghg emissions across scenarios depicts (figure 8) how the implemented policies generally lead to a significant reduction in co2 equivalent emissions across most farm sizes, with the exception of the largest farm size category (above 100 hectares), where emissions actually increase. this suggests that while subsidy-driven policies can effectively reduce ghg emissions in smaller to midsized farms, their impact on larger farms may require additional considerations or tailored approaches. the results underline the importance of carefully designing agricultural subsidies to ensure they achieve desired environmental outcomes across all farm sizes. it may also point towards the need for diversified strategies that cater specifically to the operational and environmental conditions of different farm sizes. unlike carbon emission, water resources are in general strongly affected by the transition to organic production. water consumption in the organic scenario increases by 9,4% (figure 9), which is mainly due to the decrease in cereal production, offset by an increase in oilseeds and protein crops. forage cultivation consumes the most water of all crops, accounting for over 60% of the regional water footprint. the result is coherent with the fact that alfalfa is one of the most widespread crops in emilia romagna (solazzo et al. 2016). however, if we delve further in the results per farm size, we note that for farms smaller than 20 hectares, both subsidy scenarios lead to a reduction in water consumption, suggesting that the adoption of organic and eco-friendly practices can effectively decrease water usage in smaller scale operations. for farm sizes larger than 20 hectares, both subsidy scenarios result in an table 7. carbon emission in 1,000 tco2 equivalent aggregated per crop. baseline organic eco-scheme cereals 493.22 290.93 290.67 forages 184.15 196.02 198.88 proteic/oilseeds 54.05 126.34 126.31 maize 305.00 319.34 313.30 meadows pastures 219.08 209.29 218.26 industrial tomato 55.34 68.57 70.04 other industrial crops 62.90 83.81 79.58 total 1,373.75 1,294.30 1,297.04 1,48 1,39 1,4 0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 1,60 baseline organic eco-scheme tc o 2e q/ ha carbon emission per hectare figure 7. average carbon emission (tco2eq) per hectare. 0 50.000 100.000 150.000 200.000 250.000 300.000 350.000 400.000 <10 10-20 20-50 50-100 100-300 >300 1, 00 0 tc o 2 eq . farm size class ghg emissions baseline organic eco-scheme figure 8. ghg emissions (ton of co2 equivalent) per class of farm size. 347predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 increase in water consumption. this trend is especially pronounced in the largest farm size category (100-300 hectares), which could reflect the more water-intensive nature of some organic and eco-friendly practices, or possibly the increased water requirements for these practices to be effective at a larger scale. the results indicate that while subsidy-driven policies can support water conservation in smaller farms, they may exacerbate water use in larger operations. this could have significant implications for water resources management, especially in regions facing water scarcity. these findings underscore the importance of designing agricultural subsidies and practices that are tailored to farm size and local water availability conditions. policies should consider the varying impacts of organic and ecofriendly practices on water consumption across different farm sizes to ensure sustainable water use. the increased water consumption under both scenarios for larger farms highlights the need for comprehensive environmental assessments of subsidy programs. ensuring that efforts to reduce one form of environmental impact do not inadvertently increase another is crucial for the overall sustainability of agricultural practices. 3.4. economic results gross margin per hectare increases in both “organic” and “eco-scheme” scenarios. the increase of 8.8% in the “organic” scenario, corresponding to 81€/ha, can be attributed to the implementation of subsidies for organic farming conversion. adding to these subsidies the payment for extensive forages leads to an overall increase in gross margin of 9.2% (85€/ha) (figure 10). looking at gross margin relative variation according to size class (figure 11), less economically efficient farms are those with an uaa over 300 ha, followed by those between 50 and 100 ha. all the other classes show an increase in the gross margin per hectare. 4. discussion and conclusion in this study, the application of the agent-based methodology within the agrisp model has proven to be an effective tool for quantifying the supply-side impacts of cap measures. methodologically, agrisp introduces unique features to capture the diverse characteristics of farms, their decisions, and interactions within their economic and social contexts. it facilitates predictions of the effects of cap reform at a granular level, including individual farms, and enables analysis at both territorial and sectoral levels. the social variables, such as family structure and farmers’ age, are taken in consideration in the model through the definition of specific rules, to characterise the behaviour of the entrepreneur. the choice of the social variables and the socio-structur4.248 4.646 4.673 4.000 4.100 4.200 4.300 4.400 4.500 4.600 4.700 4.800 baseline organic eco-scheme m ill io ns o f m 3 water consumption figure 9. water consumption (m3) per hectare. 918 999 1003 0 200 400 600 800 1000 1200 baseline organic eco-scheme gr os s m ar gi n (€ /h a) gross margin per hectare figure 10. gross margin variation. 100 11 6 -15 9 -25 9 100 14 8 -16 8 -26 9 -40 -20 0 20 40 60 80 100 <10 10-20 20-50 50-100 100-300 >300 total gm /h a % v ar ia tio n scenario gross margin variation per hectare organic eco-scheme figure 11. gross margin variation per ha according to size class compared to baseline scenario. 348 bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 lisa baldi, sara calzolai, filippo arfini, michele donati al rules in this paper was made to assess how the cap strategies may benefit young farmers, however, other socio-structural rules linked to the characteristics of the agricultural family business can be included. another innovative feature is the capabilities of simulating the farmers’ attitude to change their production plans or their production factors endowment. in order to model farmers’ willingness to make changes, the pmp methodology was employed to calculate the marginal cost of individual agricultural productions and the constraining factor, represented by the availability of land. comapring costs with alternative options acts as a benchmark for farmers when considering the adoption of new technologies and adjustments to their farm structure. furthermore, the pmp methodology coupled with the self-selection process, enables agents to adapt their production plans by broadening their decision-making options, incorporating production methods and technologies employed by other farms in the sample, as well as considering new production technologies that may emerge due to policy interventions. consequently, farmers can introduce new processes or modify production intensity, when these choices prove to be more advantageous. using this approach, agrisp enabled the simulation of the transition to organic farming in response to the introduction of additional payments and the eco-scheme 4. the analysis of the model results may highlight which farm categories are advantaged and which are penalised when policy measures are implemented, whether they are designed for specific production categories or are applicable to all farms across the agricultural region. micro-based farm models, capable of simulating farmers’ behaviour and their aptitude to change production plans under economic, market, technological and environmental scenarios, are becoming increasingly important, however supply-side farm models, while accurately simulating the entrepreneur’s strategies, have the limitation of assuming the farm as a “closed” production system whose decisions consider only the production resources available. nontheless, the exchange of production factors between farmers, particularly land, in order to adjust to fluctuations in their marginal value, allows the sample’s dynamics to be brought closer to reality. the results illustrated in this paper showing how less efficient farmers rent out land to more productive ones, enabling the latter to expand their operations and leverage economies of scale and scope, well reproduce the decline in number of farms depicted in the most recent italian agricultural census. furthemore, our preliminary results show that the ambitious objectives of the new cap reform would have significant impacts on land use as well as non-negligible effects on climate change mitigation and water resource consumption. the complexity of the new cap, due to potential contradicting objectives such as competitiveness and environment sustainability, requires careful ex-ante evaluation of the possible outcome. this study reveals that the subsidies allocated to organic farming conversion and the italian eco-scheme 4, applied to the emilia-romagna fadn sample (2019), may lead to: 1. a considerable decrease in the number of small farms, 2. a shift from cereal cultivation towards protein and feed crops, 3. a substantial economic stability among farms, measured by changes in gross margin per hectare, 4. a modest reduction in co2 equivalent emissions per hectare, and 5. an increased demand for water resources. overall, the effect appears to be positive in terms of co2 reduction. however, concerns are raised by the further increase of capital-intensive agriculture at detriment of small farms. this work presents some results aggregated at the regional level, but further analysis could be done to highlight findings at the sub-regional level, to suggest more targeted actions able to consider the individual characteristics of different rural areas, allowing, for instance, different payment scheme better calibrated to the territorial conditions and specific regional policy objectives. to conclude, it is noteworthy that like any modeling approach, abms with pmp involve simplifications and assumptions about agents’ behavior, market dynamics, policy implementation, and other factors. these assumptions may not always hold true in practice, leading to potential limitations in the model’s predictive accuracy and generalizability across different contexts. integrating variuos modeling approaches could provide a comprehensive assessment of agricultural policies, taking into account farm heterogeneity, farmers’ cost and risk perceptions, and the dynamic nature of production decisions and techniques. collaboration between interdisciplinary teams of researchers and stakeholders is essential to develop and apply these models effectively in policy analysis and decision-making processes. ackowledgments this research was funded by the european union’s horizon 2020 research and innovation programme 349predicting the 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k = j: index of crop parameters pcnj: output prices for conventional crops pbnj: output prices for organic crops shnj: specific crop payment (€/ha) shbnj: specific payment for organic crops (€/ha) sfpn: single farm payment including basic and greening payments r: rent price for land (€/ha) qjk: matrix q unj: farm deviations abnj: technical coefficients for organic crops acnj: technical coefficients for conventional crops variables gmn: gross margin 351predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology bio-based and applied economics 13(4): 333-351, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14592 xhnj: land use xhcnj: land use for convention crops xhbnj: land use for organic crops xcnj: production for conventional crops xbnj: production for organic crops vn: land rented zn: land leased list of relevant equations: 1) constraint linking land allocation to conventional and organic practices xhcnj + xhbnj = xhnj ∀n [conventional and ((with farm owner ≤ 65 years) or (with farm owner > 65 years and with successor))]: δj 2) constraint ensuring the total conversion by crop xhcnj · xhbnj = 0 ∀n [conventional and ((with farm owner ≤ 65 years) or (with farm owner > 65 years and with successor))]: δj 3) constraint linking organic land allocation and organic production abnj · xbnj = xhbnj ∀n [conventional and ((with farm owner ≤ 65 years) or (with farm owner > 65 years and with successor))]: δj 4) constraint linking conventional land allocation and conventional production acnj · xcnj = xhcnj ∀n [conventional and ((with farm owner ≤ 65 years) or (with farm owner > 65 years and with successor))]: δj 5) objective function at the farm level ∀n [conventional and ((with farm owner ≤ 65 years) or (with farm owner > 65 years and with successor))]: δj _ref114578152 _ref114489287 _ref114578337 _ref115429676 _hlk124753738 _hlk124753769 _ref114578159 _hlk125619779 _hlk125620100 _hlk125620116 _hlk168057609 _hlk125617961 _hlk125617978 _heading=h.1fob9te _heading=h.1y810tw _heading=h.3whwml4 _hlk105387355 _hlk162970176 _hlk120503612 _hlk94825625 _hlk101307043 _hlk112165281 _hlk110795819 _hlk163789378 _hlk101572865 _hlk101574469 _hlk162424605 _hlk163657897 _hlk163658986 _hlk163657719 _hlk163657691 _hlk163658456 _hlk163658513 _hlk163658646 _hlk162446713 _hlk125919866 _hlk107164817 _hlk162973538 _hlk162447045 _hlk107174352 _hlk162447083 _hlk124882770 _hlk142394334 _hlk142395728 _hlk142430461 _hlk142337917 _hlk142342729 _hlk142337969 _hlk142337867 _hlk142342461 _hlk142342928 _hlk142338077 _hlk142348528 _hlk164779807 _hlk125467744 _hlk141790649 assessing the bioeconomy’s contribution to evidence-based policy: a comparative analysis of value added measurements tévécia ronzon1,2,*, patricia gurria2, michael carus3, kutay cingiz2, andrea el-meligi1, nicolas hark3, susanne iost4, robert m’barek1, george philippidis5, myrna van leeuwen6, justus wesseler2 predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology lisa baldi1, sara calzolai2, filippo arfini2,*, michele donati1 simulating farm structural change dynamics in thessaly (greece) using a recursive programming model stamatis mantziaris1,*, stelios rozakis2, pavlos karanikolas1, athanasios petsakos3, konstantinos tsiboukas1 analyzing the impact of government subsidies on household welfare during economic shocks: a case study of iran mohammad dehghan1,*, seyyed nematollah moosavi2*, ebrahim zare3 crop production, the pollinator deficit and land use management: uk farm level survey results iain fraser1,*, michelle t. fountain2, john m. holland3 bio-based and applied economics 13(3): 285-299, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15374 bio-based and applied economics bae © 2024 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae citation: ciliberti, s., frascarelli, a., martino, g., & marchini, a. (2024). exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach. bio-based and applied economics 13(3): 285-299. doi: 10.36253/bae15374 received: november 20, 2023 accepted: may 22, 2024 published: october 16, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: francesco pagliacci, valentina raimondi, luca salvatici orcid sc: 0000-0001-7833-9547 af: 0000-0002-8910-7044 gm: 0000-0001-6827-3185 am: 0000-0001-7358-9133 exploring preferences for contractual terms in a scenario of ecological transition for the agrifood sector: a latent class approach stefano ciliberti, angelo frascarelli*, gaetano martino, andrea marchini department of agricultural, food an environmental sciences, university of perugia, italy *corresponding author. e-mail: angelo.frascarelli@unipg.it abstract. governance mechanisms along the agri-food supply chains are increasingly important in a scenario of ecological transition. under the conceptual and analytical lens of the transaction cost economics, we explored farmers’ preferences towards a variety of clauses usually adopted in production contracts. to this purpose, a discrete choice experiment among 190 durum wheat producers in italy was conducted. results from a latent class model showed that producers were mainly interested in fixed prices formula and to join shared rules of production but revealed little or no interest for compelling sustainable cultivation techniques and the provision of technical assistance. however, these preferences are heterogeneous across farmers and vary depending on their level of education and previous use of contractual arrangements, with relevant implications for contract design and management. keywords: contracts, transition, nie, latent class analysis, cereals. jel codes: q13, d23, l14. introduction there is consensus that the global food system is not delivering as needed on several key metrics, including addressing excessively high rates of hunger and malnutrition, agriculture-driven environmental footprint, unequal distribution of welfare along supply chains, among others (mcgreevy et al., 2022). a more recent movement has called attention to the fact that such problems may be better addressed when implementing an ecological transition in food system to respond to shocks and crises stemming from conventional food systems. cholez et al. (2017) posit that an examination of contractual frameworks is pivotal during this transition, as they can adeptly navigate uncertainties and simultaneously provide clear demarcations of property and decision rights in emerging supply chains. taken as a whole, this literature highlights the importance of governance considerations for the agro-ecological transition. over the last decade production contracts have become increasingly important to enhance coordination along the agri-food supply chain (machttps://doi.org/10.36253/bae-15374 https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode http://www.fupress.com/bae https://doi.org/10.36253/bae-15374 https://doi.org/10.36253/bae-15374 https://orcid.org/0000-0001-7833-9547 https://orcid.org/0000-0002-8910-7044 https://orcid.org/0000-0001-6827-3185 https://orcid.org/0000-0001-7358-9133 mailto:angelo.frascarelli@unipg.it 286 bio-based and applied economics 13(3): 285-299, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15374 stefano ciliberti, angelo frascarelli, gaetano martino, andrea marchini donald 2015; vassalos et al., 2016). they can connect farmers with buyers, reduce uncertainty in prices and demand, provide risk sharing against natural disasters and climate related shocks, and in some cases, provide access to inputs technical assistance (fao, 2017). however, there are at least two main different types of contracts at stake (marketing and production contracts) which differ for several reasons (dubbert et al., 2021). while in marketing contracts farmers control their assets and production inputs independently by usually determining price, quantity and delivery conditions to secure sales on market (soullier and moustier, 2018), production contracts entail the provision of resources – such as production input supply (e.g. seedlings and fertilizer), credit, and other support like extension services or transport of harvest – and quite often they impose a particular production method or input regime to farmers (otsuka et al., 2016). production contracts represent an organizational solution which has been extensively discussed regarding its potential to resolve market limits. they allow farmers to be integrated into modern agricultural value chains by reducing transaction costs and being provided with inputs, technical assistance and assured against price fluctuations (schipmann & qaim, 2011; swinnen and maertens, 2007). this type of contracts increasingly aims to engage farmers in delivering high quality products and contributing to environmental sustainability by reducing the use of chemical fertilisers and pesticides. however, in many situations, farmers are hesitant to use written contracts, likely due the fact that existing informal contracts are deeply rooted in traditional social norms (jäckering et al., 2021). moreover, farmers may be reluctant because of the high enforcing costs, especially when formal institutions are not well developed (michler and wu, 2020). to sum up, participating in a contract entails tradeoff between incentives and costs (bogetoft and olesen, 2002). for this reason, if the contract design does not include price incentives and provision of inputs, farmers may be discouraged from participating in the arrangements because they must comply with quality and sustainability requirements and other costly specifications (abebe et al., 2013; pancino et al., 2019). moreover, producers may have different views on and experiences with the advantages and disadvantages related to contracts (widadie et al., 2020). consequently, two research questions arise: which contractual terms can lead farmers to adopt production contracts in a scenario of ecological transition? do farms and farmers’ characteristic affect acceptance of contractual terms? in this background, the first aim of this study is to investigate farmers’ preferences towards a wide variety of contractual terms usually adopted in production contracts in the context of the italian durum wheat sector. the second aim is to determine which and whether farmers and farms characteristics affect the probability of accepting the above-mentioned clauses. in doing so, our paper contributes to filling a knowledge gap on the role of heterogeneous farmers’ preferences in affecting contract design, offering insights on the potential acceptance of contractual terms in a scenario of ecological transition. this latter imposes a reduction of chemical inputs and a gradual shift from fossil fuels to cut net greenhouse gas emissions in agriculture. accordingly, we first elaborate a conceptual and analytical framework about the effects and the potential acceptance for specific clauses in the agri-food context. material and methods are then described in detail, mainly revolving around a discrete choice experiment carried out among italian farmers. lastly, results from latent class logit estimations are presented and discussed in the lights of the existing literature before final remarks and policy recommendations are delivered. 2. study context we focus on a staple food crop of strategic importance for italy and for many countries bordering the mediterranean, such as durum wheat. italy produces half of the durum wheat grown in the eu-28 (uk included) and it is leader both in the per capita consumption of pasta and in its production (bux et al., 2022). durum wheat represents the main cereal crop in italy covering about 44% of the total cereal area. cultivation is widespread in southern italy, in marginal areas at risk of abandonment, characterized by few employment alternatives in other economic sectors and in which it is difficult to find an alternative crop. in 2020, 1.2 million hectares (about 10% of the total utilized agricultural area) were sown to durum wheat in italy for a total production of about 4 million tons. apulia, with a production of about 760,000 tons, is still italy’s main producer overtaking sicily, marche, and emiliaromagna (ismea, 2022). durum wheat is at the base of a national supply chain of considerable importance, with first and second processing industries generating a turnover of about 5.6% of total italian agribusiness (ismea, 2023). italy is the undisputed leader in the pasta industry, accounting for more than 73% of the eu turnover, with an average production of around 5.3 million tons per year which is a quarter of the total world production (ismea, 2023). in terms of market outlets, semolina pasta is one of the most important components of italian 287exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach bio-based and applied economics 13(3): 285-299, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15374 agrifood exports (4.6%), which have grown steadily in recent years and contribute positively to the eu’s agrifood trade balance (crea, 2022). the italian supply chain of pasta has evolved over the last decade thanks to the growth in demand for “100% italian” and high-quality pasta, in order to add value to the national production pasta. as far as quality is concerned, the protein content is traditionally considered the main quality parameter. as for the origin of pasta, despite the increase in the cultivation of national durum wheat, the annual requirement of the italian milling and pasta making industries is around 6 million tons, against a national production of 4 million tons (istat, 2024; italmopa, 2023). being far away from self-sufficiency, the supply chain is persistently dependent on import (especially from non-eu countries) as a consequence. in order to improve the degree of self-sufficiency and the quality of the provision of durum wheat, a national fund (named “fondo grano duro”) has been established since 2017 incentivizing farmers to sign longterm production contracts with pasta makers (ciliberti et al., 2019). last but not least, in order to contain emissions and increase the environmental sustainability of pasta, both processors and pasta companies promote the adoption of environmental-friendly cultivation techniques, practices and methods (bux et al., 2022; stanco et al., 2020). in this regard, the share of utilised agricultural area dedicated to organic durum wheat is particularly high in southern italy, with basilicata at the first place (22.8%), followed by molise (13.5%), apulia (13.5%) and sicily (9.6%). lastly, marche (6.4%) is the first region in center-north italy (sinab, 2023). because of the increasing request for high quality and sustainable productions and due also to public interventions, the number of contractual arrangements between main semolina and pasta producers and farmers (or their organizations) has widely increased all over the country in the last years (rossi et al., 2023). 3. conceptual framework recent advancements in transaction cost economics have revealed that hybrid governance mechanisms are largely widespread, with contracts being their primary form (ménard, 2004). these latter play a pivotal role in fostering ecological transition, aiming to coordinate the actions of a diverse set of actors and integrate different dimensions of sustainability, as noted by cholez and magrini (2023). contractual frameworks are crucial for this transition path, since they can have direct consequences on the use of input and dedicated investments to achieve certain environmental threshold in agri-food systems. under the lens offered by tce, a flourishing literature has analysed contracts as governance structures affected by transactional attributes such as asset specificity and uncertainty (anh et al., 2019; cai and ma, 2015; key and runsten, 1999; mao et al. 2022; minten et al., 2009; ochieng et al., 2017; ola and menapace, 2020; permadi et al., 2017; widadie et al., 2020). evidence reveals that, on the one hand, some contractual requirements can be associated with high transaction costs, therefore representing a major obstacle for choosing contracts. on the other hand, these latter flourish in presence of collective actions, transparent conditions and trust which help farmers to reduce transaction costs. ménard (2018) underscored the importance of assessing contracts based on the allocation of rights between transacting parties as a negotiation process. this refreshed viewpoint facilitates an analysis emphasizing how contracts can help alleviate sources of uncertainty and asset specificity surrounding novel technologies and knowledge and distinctly delineate the rights and responsibilities regarding the benefits stemming from the ecological transition. consequently, contracts raise crucial questions about the collective strategies that go beyond individual interests and include varied modes of organization, besides market forces. in other words, implementing effective governance is contingent upon the alignment of individual interests with these collective strategies, expanding beyond market-driven relations and incorporating diversified organizational modes, where hybrid coordination and the role of contracts are key (ménard, 2004). such a governance perspective examines the logic behind the adoption of coordination mechanisms to support the relationships among a multitude of agents involved in the ecological transition along the agrifood supply chain. in this paper, we follow previous works dealing with production contracts (abebe et al., 2013; polinori and martino, 2019; oliveira et al., 2021) matching the econometric rationale of choice experiments, where individuals derive utility from the different characteristics a good possesses, with aspect of contract design. in this approach, contractual terms affect the value (utility) each farmer gain from the choice, which is the difference between revenues and costs (i.e. the profit). moreover, according to the discriminating alignment principle of williamson (1991), each contractual term is expected to affect not only production costs but also transaction costs related to transactional attributes 288 bio-based and applied economics 13(3): 285-299, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15374 stefano ciliberti, angelo frascarelli, gaetano martino, andrea marchini (mainly asset specificity and uncertainty) associated with contractual conditions chosen. to better capture this effect, we therefore explicitly decompose the value (utility) associated to contractual choices in two components: a positive (i.e. revenue) and a negative one (i.e. production and transaction costs). as a consequence, we see this expected value as the profit for the farmer i (i = 1, 2, 3 . . .n) from each contractual terms t (t = 1, 2, 3. . .), which we decompose as follows: πit = rit (cit + tit) (1) with πit being the profit, rit the revenue the farmers get from each contractual terms, while cit and tit respectively represent related production and transaction costs. it follows that since each contractual term brings its own revenues as well as production and transaction costs, alternative combinations of different contractual terms lead to different expected profit configurations. consequently, all other things being equal, insertion/ removal of a contractual term affects both revenues and costs involved, as follows: ∑j jβzijkwi = wirijt wi(cijt + tijt) (2) where zijk is an index for the alternative j from a choice situation k of contractual terms which are chosen in a contract from an ith farmer, whose individual (and farms’) characteristics are represented by a vector w, while β expresses the magnitude of the acceptance of each term. reasonably, a farmer asked to choose among alternatives is willing to accept a contract including combinations of contractual terms which maximizes his/her expected profit. 3.1. contractual terms, individual characteristics and farmers’ preferences henceforth, inspired by previous studies in this field for similar (soullier and moustier, 2018) or identical crops (biggeri et al., 2018; carillo et al., 2017; ciliberti et al., 2019; 2022; 2023; oliveira et al., 2021; pancino et al., 2019; rossi et al., 2023; viganò et l., 2022; weituschat et al., 2023), we conceptualize both the role of selected but highly relevant contractual terms (related to production techniques, technical assistance, quality requirements and payment solutions) and confounding variables referred to individual (farms and farmers’) characteristics. accordingly, we elaborate research hypotheses to be tested. rules for sustainable production the fact that a farmer chooses a production contract implies the willingness to commit resources to comply with certain production rules (ciliberti et al., 2019). this seems to contradict basic behavioural assumptions, but in some cases farmers may want to demonstrate their commitment and may prefer a trader that values such an individual effort (given the fact that buyers are able to measure individual commitments, at least after the transaction occurred). another driver is that farmers’ engagement and reputation could lead to higher price premium (carriquiry and babcock, 2007; mccluskey and loureiro, 2005). moreover, farmers may also believe that opting for a less strict buyer will lead some of them to take opportunistic actions; such an occurrence in turn could contribute to damaging potential common benefits of building a collective reputation (stanco et al., 2020). in this work, we propose farmers three contractual terms generically referred to production rules: shared and agreed rules, imposed rules or no rules of production. based on previous literature we elaborate a following research hypothesis (rh 1): durum wheat producers prefer to commit on contractual terms introducing production rules. moreover, in a scenario of ecological transition there is increasing evidence that some contractual terms require farmers for the adoption of environmental-friendly practices (pancino et al., 2019; rossi et al., 2023). however, adoptions of sustainable cultivation techniques imposing strict restrictions on pesticides, fertiliser or natural resources uses can represent a disincentive for farmers to enter a contract, since this would lead to lower yields and higher unit costs of production (weituschat et al., 2023). here, we focus on three specific sustainability requirements related to the durum wheat production cycle: a fractioned supply of nitrogen (that is the most important fertilizer for cereals), the adoption of a cultivation technique that promotes minimum soil disturbance (i.e. no-tillage), and lastly a joined combination of these two practices. based on previous evidence, we elaborate a research hypothesis (rh 2), as follows: durum wheat producers prefer contractual terms establishing mild sustainable cultivation techniques, rather than strict and costly commitments. provision of technical support the need to access information and assistance on technology, production rules and quality requirements may 289exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach bio-based and applied economics 13(3): 285-299, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15374 motivate farmers toward production contract (oliveira et al., 2021). in this paper we explore preferences towards three specific contractual clauses on this subject: no technical assistance, the provision of direct technical support thanks to advisors, the provision of remote support by means of a remote decision support system (dss). the buyer could provide all the required technical assistance so that farmers can benefit of updated and timely researchbased information (rossi et al., 2010). in a scenario of ecological transition, forms of technical assistance provided by buyers can help farmers to understand the reasoning for limiting pesticide and fertilizers use and the benefits of applying a more precise dosage, therefore fostering the adoption of sustainable production techniques (ciliberti et al., 2022; šūmane et al., 2018). therefore, we formulate a research hypothesis (rh 3) related to this type of clause: durum wheat producers prefer contractual terms establishing the provision of technical assistance. quality requirements maintaining and improving the quality production and ensuring compliance with food safety requirements is crucial in modern agricultural settings. such an issue is associated with the ability to comply with formal or informal quality standards for farmers (biggeri et al., 2018; carillo et al., 2017; soullier and moustier, 2018). however, quality remains the main challenge in situations where the agri-food markets do not incentivize it, as farmers may be reluctant to invest their time and energy to improve quality. it follows that related requirements are a major source of uncertainty in agri-food transactions for buyers (frascarelli et al., 2021). usually, farmers may choose between low quality requirements, with small incentive to improve quality but low risk of product rejection, and a high-quality option, with higher incentive but larger risk of product rejection. farmers therefore tend to prefer contract with low quality requirements, all other things being equal , given the uncertainty of farmers about meeting quality standards and due to the lower risk of product rejection (oliveira et al., 2021). here we want to test farmers’ preferences for different and increasingly demanding quality requirements referred to various thresholds of protein content in durum wheat: in more details, a lower level (>12.5%), a medium-high level (13.5%), and a very high level (14.5%) of proteins. based on the existing literature a research hypothesis (rh 4) is elaborated as follows: durum wheat producers prefer contractual terms setting in advance lower quality standards and requirements. price and payment formulas the general assumption in the literature is that farmers’ motivation to participate in contractual arrangements is primarily to manage market uncertainty with pre-established price formula. these latter refer to the payment conditions farmers agree with, in exchange for delivering an agreed product quality and quantity. since the mid-2000s price volatility has been a typical feature of prices of grain commodity, driven by several factors as a consequence of increasing linkages among food, energy, and financial markets (ott, 2014; santeramo e lamonaca, 2019; tadesse et al., 2014). to this regard, the adhesion to properly designed contracts is expected to reduce sources of market uncertainty (oliveira et al., 2021). this governance solution applies also to the durum wheat supply chain, characterised by strong price instability and asymmetric price transmission along the value chain, which mainly penalise farmers (viganò et al., 2022). in this paper we want to test farmers’ preferences for three different price formulas: fixed, variable (that is, market) and a mixed price option (50% fixed and 50% market price). thus, based on previous evidence, a research hypothesis (rh 5) is elaborated, as follows: durum wheat producers prefer contractual terms establishing price formula alternative to variable market price. as for payment modality, fearing opportunistic behaviours, farmers do not like delays and want to avoid issues with payments since they increase uncertainty, particularly when buyers are not trusted (ciliberti et al., 2023). moreover, farmers prefer immediate payment over delayed payment to address market uncertainty, also because they need money for purchasing inputs for the next production cycle (oliveira et al., 2021). in this paper we test farmers’ preferences for three different clauses related to payment modality: payment on delivery, deferred payment, and payments in instalments on a monthly basis. accordingly, another research hypothesis (rh 6) comes out: durum wheat producers prefer contractual terms setting immediate payment. lastly, the relationship between contractual terms and farmers’ utility and preferences can be affected by some characteristics we intended to control for. the emerging literature on the determinants of farmers’ preferences towards contractual terms in the durum wheat sector suggests several of those individual characteristics which must be checked for (frascarelli et al., 2020; rossi 290 bio-based and applied economics 13(3): 285-299, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15374 stefano ciliberti, angelo frascarelli, gaetano martino, andrea marchini et al., 2023; weituschat et al., 2023a;2023b). we decided to select some of the most representative and relevant, focusing on age, education, experience, size, participation in cooperative, and previous use of contracts. all that said and considered, figure 1 graphically illustrates and resumes the hypothesized causal relationship we conceptualized between specific contractual terms and farmers’ utility and preferences, which can be affected by confounding variables related to individual farms and farmers’ characteristics. 4. materials and methods 4.1. experimental design, sampling strategy and data collection discrete choice experiments are frequently performed in economic literature in order to establish individual preferences across items, such as good, services or in our case, contracts (hensher et al., 2005; louviere et al., 2010). the experimental design for a choice experiment relies on the identification of a set of relevant characteristics (attributes), which in our case relate to different type of contractual terms and their corresponding levels. to this purpose, after analysing real production contracts adopted in the durum wheat supply chain over the last years (see ciliberti et al., 2022 for more details), we also conducted a focus group discussion with key stakeholders to gain a better understanding of which clauses are more relevant for durum wheat producers1. these activi1 the focus group included 8 participants among representatives of ties helped us to evaluate the relevance of some contractual terms for farmers, so as to decide which attributes and levels to include in our discrete choice experiment. therefore, based on this evidence, we selected six attributes with three levels each, which are reported in table 1. afterwards, we decided to adopt an efficient design using the software stata so that contractual attributes and their levels were randomly distributed into 18 choice sets, containing three contracts with six attributes each. then, choice sets were arranged into 6 blocks and each respondent was submitted to one block with three choice sets only, so as to reduce the number of contracts to evaluate. in detail, for each choice set, each farmer was allowed to specify his preference towards one out of three contracts plus an opt-out option (i.e. “none of the previous contract”). a structured questionnaire (including the choice experiment and an additional section with general information on farmers and farms’ characteristics) was then realized to investigate farmers’ preferences over contractual terms (see supplementary material). it was pretested and validated across a small sample of almost two dozens of randomly selected durum wheat producers. as a final step, in order to collect data and information from our study population, consisting of farmers producing durum wheat in italy, we adopted a purposive sampling strategy. to this aim, trained interviewers durum wheat producers, input providers, buyers (processors, manufacturers) and experts (agronomists and technical advisors). the aim was to discuss the following questions: which are the main contractual terms included in production contracts? how are they negotiated between producers and buyers? what are the main (emerging) clauses related to environmental sustainability, if any? figure 1. the causal pathway between contractual terms and farmers’ preferences. 291exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach bio-based and applied economics 13(3): 285-299, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15374 directly submitted the survey among farmers attending several technical workshops and seminars in central and southern italy (where durum wheat production is mostly located), between late 2018 and early 2020 (until national authorities imposed the lockdown due to the covid-19 pandemics). as a consequence, the composition of the sample mainly depended on farmers’ attendance to these workshops and their willing and ability to correctly fill out the questionnaire in all its sections. results are based on a sample of 190 completed questionnaires collected among durum wheat producers. no protests from respondents were observed and reported. table a in the appendix reports detailed descriptive statistics related to respondents’ characteristics. comparing information with those available for the reference population (ismea, 2023b; istat 2024), it comes out that the average size of the sampled farms is way larger than the national one in 2021 (that was 11.1 hectares). however, apart from some respondents located in central and northern italy (marche and emilia-romagna), about 75% of the interviewed farmers came from southern italy (with a large share from apulia, followed by basilicata), where most of the production (76%) and cultivated areas for durum wheat (69%) were located in 2020 (ismea, 2023b). lastly, farmers with less than 45 years represents 13% of the total at national level. only one out of ten has a degree, whereas almost 60% own a secondary school diploma (istat, 2024). 4.2. econometric analysis in this paper, we follow pacifico and yoo (2013) and yoo (2020) to run a latent-class conditional logit (lcl), which extends the conditional logit by incorporating a discrete representation of unobserved preference heterogeneity across decision makers. specifically, lcl assumes that there are c distinct types, or “classes” of decision makers and that each class c makes choices consistent with its own conditional logit model with utility coefficient vector βc. suppose that the probability that decision maker i belongs to class c is given by a fractional multinomial logit specification: (2) where zi is a row vector of decision maker n’s characteristics and the usual constant regressor (that is, 1); θc is a conformable column vector of membership model coefficients for class c, with θc normalized to 0 for identification; and θ = (θ1, θ2,…, θc−1) denotes a collection of the c − 1 identified membership coefficient vectors. under lcl, the joint likelihood of decision maker n’s choices is given by (3) where b = (β1, β2,…, βc) denotes a collection of the c utility coefficient vectors and each pn(βc) is obtained by evaluating β = βc. in more detail, the model is estimated using an expectation-maximization (em)-algorithm (bhat, 1997). such a model simultaneously estimates preference coefficients for different classes and the probability of an individual to belong to a class based on choice patterns and individual covariates. it therefore extends the previous analysis by incorporating a discrete representation of unobserved preference heterogeneity. as a result, we are able to further check for preference heterogeneity among farmers, since latent class model identifies unobserved groups of individuals with homogenous preferences by using a discrete mixing distribution (swait, 1994). lastly, econometric analyses were run using the software stata 14.2 implementing usual optimization methods for maximum likelihood estimation. 5. results and discussion latent class analyses were performed in order to identify classes of durum wheat producers with similar table 1. attributes and related levels selected for the discrete choice experiment. attributes levels production rules not established arranged with the buyer compelled by the buyer sustainability requirements fractioned use of nitrogen (fun) minimum soil disturbance (msd) joined adoption of fun and msd technical support not provided provided by technical advisors remotely provided thanks to a dss software quality requirements medium grain protein content (> 12.5%) medium-high grain protein content (> 13.5%) high grain protein content (> 14.5%) price formula fixed price market price mixed (50% market – 50% fixed) price payment modality on delivery deferred payment monthly payments 292 bio-based and applied economics 13(3): 285-299, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15374 stefano ciliberti, angelo frascarelli, gaetano martino, andrea marchini preferences towards contractual attributes. we computed different models with 2 and 3 classes and used information criteria measures to test goodness-of-fit (yang, 2006). the number of classes was chosen with regard to the akaike information criterion (aic), the consistent aic (caic) and the bayesian information criterion (bic). we opted for a latent model with 2 classes which minimizes most criteria, in our case caic (1207.79 vs 1250.97) and bic (1174.79 vs 1197.97), revealing the best goodness-of-fit. table 2 reports the differences of durum wheat producers and their farms across the 2 classes, focusing on relevant control variables referred to individual characteristics. looking at table 2 we are able to identify main differences among members of the two classes of respondents. on the one hand, class 1 group less experienced farmers with lower education and smaller cultivated areas, but with a higher attitude to join collective arrangement and sign contracts to sell durum wheat. on the other hand, class 2 encompasses durum wheat producers with opposite features, therefore more experienced and educated, less collaborative and with bigger farms. however, by using a nonparametric mann-whitney u test for continuous data and a chi-square test for dummy variables, statistically significant differences between the two classes emerged for age, high level of education and the use of production contracts. looking at the results of the latent class analysis, the majority of contractual terms show significant coefficients in both classes, highlighting relevant preferences towards attributes (table 3), even if some interesting differences among classes. first and foremost, we focus on the “no-choice” variable, which was selected in 123 out of 570 “no-choice” situations faced by the respondents2. results reveal a sig2 in detail, the “no-choice” variable was selected at least in one choice set out of three by 21 respondents, in two choice sets out of three by nificant but contrasting interest for production contracts across classes. in class 1, the negative coefficient (-1.310) suggests that farmers were significantly keen to reject the “no-choice” option in favour of one of the production contracts they were proposed. this latter was therefore considered more beneficial and reliable than the status quo in order to overcome spot market imperfections and reduce transaction costs, in line with van den broeck et al. (2017). on the other hand, the positive coefficient in class 2 (+6.528) shows a significant preference for the “no-choice” option and so against the proposed contractual solutions as a whole, in accordance with previous findings from schipmann and qaim (2011) and blandon et al. (2010). with regard to production rules, positive and significant coefficients for both terms highlight that farmers in both classes are highly reluctant to rules unilaterally imposed by the processing industry (i.e., the reference variable), but with some interesting differences. always taking as reference the base level, farmers in the first class prefer shared rules (+0.476) more than no rules at all (+0.369), while in the second class the opposite is true with producers largely preferring a free production process (+2.581) over rules agreed with buyers (+1.950). with all that said, the first research hypothesis is partially confirmed, in line with earlier evidence from gelaw et al. (2016), showing that farmers usually choose to join contracts since they are willing to commit resources in order to comply with certain production rules and gain reputation. however, at the same time, farmers tend to refuse contractual terms unilaterally imposing techniques and production rules, since they are traditionally concerned and suspicious of any attempt of limiting their decisional autonomy (ciliberti et al., 2023; vaissiere et al., 2018). when asked to reveal preferences towards specific contractual terms setting rules for a more environmental-friendly and sustainable production, farmers reveal heterogeneous preferences across the two classes. while in the first class clauses are not significant, vis à vis a combined use of no-tillage and a fractioned supply of nitrogen (the reference level), farmers in class 2 show a clear and significant preference for a minimum mechanical soil disturbance (+1.256), but also a noteworthy and larger aversion to a lower use of nitrogen as fertilizer (-2.076). this is a signal that, in absence of specific incentives, farmers still look at this type of clauses with low enthusiasm and a certain suspect. they only accept to reduce soil disturbance since – compared to a 15 respondents and in all the three choice sets by 24 respondents, for a total of 60 respondents out of 190 (31.6%) which selected the “nochoice” option at least once. table 2. individual characteristics for each class (mean and standard deviations) and differences. main characteristics class 1 class 2 difference age (n.) 47.03 (14.22) 48.34 (11.83) -1.31* exp_y (n.) 26.63 (14.51) 27.28 (12.38) -0.65 educ_h (%) 81.50 (38.83) 93.02 (25.50) -11.52*** coop_m (%) 40.42 (49.08) 39.02 (48.83) 1.40 contr_p (%) 63.88 (48.04) 48.83 (50.03) 15.05*** size (ha) 121.97 (217.27) 305.64 (715.99) -183,67 ***, **, * denote that mean values of class 1 farmers are significantly different from class 2 farmers at p < .01, p < .05, and p < .10, respectively. 293exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach bio-based and applied economics 13(3): 285-299, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15374 fractioned use of fertilizers – it can ensure a reduction of costs, but with a limited impact on yields and production. as a consequence, the second hypothesis can be confirmed, substantiating the fact that farmers’ commitment in environmentally sustainable production is still partial, as it is perceived as a source of disadvantage when compared with farmers’ returns from conventional agricultural production (chèz et al., 2020). the primary reason is that the cost of environmentally sustainable production is considerably higher and that the yield is relatively lower than that of conventional agriculture (wang et al., 2019). as far as technical support is concerned, it is interesting to observe that only durum wheat producers in class 1 showed a slightly significant and positive interest (+0.270) for a contractual term introducing such a service (against the reference level “no technical support”), provided that it is offered on field by buyers’ trusted technicians and advisors. no significant preferences occurred in class 2 instead. therefore, even with some caveats, the third hypothesis can be confirmed in the light of the evidence on the acceptance of technical assistance. this result contributes to confirming farmers’ interest for support services aimed to foster both innovation uptake and compliance with contractual requirements (cholez et al., 2023; martino et al., 2017). in the durum wheat sector, these ancillary services are usually provided when signing a contract, so that farmers can get support from expert agronomists in order to improve grain quality, production yields and profitability (viganò et al., 2022). our results confirm that relational contracting fosters process innovation in agri-food chains (martino et al., 2017). however, a possible interpretation of the results could be that frequent on farm visits or solutions for remote assistance could be seen, by the most dynamic and independent farmers, as a subtle attempt of table 3. parameter estimates for the latent class model attribute level class 1 class 2 coeff. p>|z| se coeff. p>|z| se production rules arranged 0.476 ** 0.154 1.950 ** 0.968 none 0.369 ** 0.157 2.581 ** 1.003 sustainability requirements msd 0.027 0.149 1.256 * 0.671 fun 0.049 0.148 -2.076 * 1.199 technical support advisors 0.270 * 0.151 1.090 0.676 dss 0.250 0.153 0.004 0.738 quality requirements protein > 12.5% 0.290 * 0.150 1.366 ** 0.642 protein > 13.5% 0.210 0.154 -1.231 0.797 price formula fixed price 0.680 *** 0.160 2.269 ** 0.796 mixed price 0.419 ** 0.166 0.397 0.962 payment modality on delivery 0.083 0.153 0.031 0.745 deferred payments 0.131 0.153 0.650 0.633 no-choice : -1.310 *** 0.486 6.528 *** 1.601 class share 0.723 0.277 log likelihood -504.833 aic 1075.667 bic 1256.799 control variables (reference: class 2) variables coeff. p>|z| se age 0.018 0.032 : : : contr_p 0.923 ** 0.454 : : : coop_m 0.019 0.473 : : : educ_h -2.771 ** 1.133 : : : exp_y -0.034 0.032 : : : size -0.001 0.000 : : : significance levels: *** 1% ** 5% * 10% 294 bio-based and applied economics 13(3): 285-299, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15374 stefano ciliberti, angelo frascarelli, gaetano martino, andrea marchini controlling their activities and performances, therefore limiting the acceptance of this type of clauses. looking at clauses related to quality requirements, results clearly allow to confirm the fourth hypothesis highlighting significant and positive preference for these terms in both classes, but only to a limited extent. it is not by chance that farmers in class 1 and 2 prefer terms imposing the lowest possible qualitative threshold (of protein content) for their product (coefficients are respectively +0.290 and +1.366) vis à vis the most compelling one (that is, protein more than 14.5%). these results are fully in line with previous indications highlighting that these clauses are accepted by farmers because deemed able to reduce source of behavioural and technological uncertainty for farmers, since buyers’ requirement are known in advance. however, as expected, farmers tend to opt for less stringent clauses confirming previous indication from blandon et al. (2010), oliveira et al., 2021). when clauses related to price formula are considered, farmers’ preference reveal a strong and significant interest in both classes for clauses offering fixed instead of market price. taking into account this latter option as reference level, in class 2 there is a stronger interest for a guaranteed minimum price than in class 1 (coefficients are respectively +2.269 and +0.680). moreover, in class 1 durum wheat producers are also significantly attracted by mixed price (+0.419) compared to the base level. that said, empirical evidence corroborates the fifth research hypothesis in accordance with previous empirical studies which highlighted that, all other things being equal, farmers prefer a fixed price option over a variable one (miyata et al., 2009). price stability is therefore confirmed to be a major driver of participating in contracts, since it can shield farmers against the volatility which has largely affected cereals since the mid-2000s due to the several circumstances (maertens and vande velde, 2017; santeramo e lamonaca, 2019). however, contradicting the common credence that farmers are risk averse, wang et al. (2011) also showed that based on their characteristics, farmers may have different risk preferences and entrepreneurial attitude, so that a mixed pricing strategy based on certain performance criteria can be sometime preferred to a minimum guaranteed price. very interestingly, farmers reveal no significant preference to any type of payment modality compared to the reference level (fractioned monthly payment). therefore, they make no distinction between payment on delivery and other solutions establishing payments in instalments or delayed. so, the sixth hypothesis must be rejected, in line with the work of oliveira et al. (2021), but against earlier evidence revealing negative preference for delayed payment (cai and ma, 2015). lastly, results reveal that only a few control variables can explain differences among the two groups of respondents and their preferences towards contractual terms. in line with previous works, they refer to previous use of contracts and the level of education. on the one hand, earlier experiences with production contracts make farmers more likely to belong to class 1, so more confident and relying on production contracts, as already demonstrated by van den broeck et al. (2017). on the other hand, higher level of education (i.e., high school diploma or higher qualification) increase the likelihood of going into class 2, with a significant but negative effect on contract participation in contrast with widadie et al. (2020) but perfectly in line with findings of ren et al. (2021) and miyata et al. (2009). 6. conclusions implementing innovative and effective governance mechanisms along the agri-food supply chain is of key importance in a scenario of ecological transition, so as to better coordinate actions of a multitude of economic actors in an uncertain context. adopting the conceptual lens of the transaction cost economics, the present work contributed to the burgeoning literature in this field, investigating whether and how production contracts may play a key role in fostering a better alignment of individual interests with broader collective goals and strategies, integrating also social and environmental dimensions. focusing on a highly strategic agri-food production in the italian context, such as durum wheat, we conducted a discrete choice experiment to analyse farmers’ preferences for a selected and relevant number of contractual terms, which differently affect source of production and transaction costs. moreover, applying a latent class analysis we also detected the role played by some individual characteristics questioning the homogeneity of these preferences. findings indicated that the path towards the use of contracts able to match both private and public goals is still long for at least two reasons. first, farmers show a strong interest for clauses protecting against market and behavioural uncertainty (fixed price and shared rules of production) but are still hesitant in joining compelling quality and environmental requirements if not properly incentivized or supported. moreover, technical support provided by the buyer is sometimes seen as a form of control and therefore unwelcome. second, results are not homogenous across respondents, reveal295exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach bio-based and applied economics 13(3): 285-299, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-15374 ing that there is need to better take into account the heterogeneity of preferences, overcoming one-size fits all approach to contract design and implementation. to this regard, attention must be paid to the fact that respondents sometimes preferred to not make a choice. this fact signals the existence of a not negligible share of farmers who have different opinions and preferences from other producers as well as different expectations and needs which shall be somehow addressed by stakeholders. as a consequence, interesting policy and managerial implications follow. in line with the approach of this paper, the importance of implementing an evidencebased and more participatory approach to contract design, negotiation and adoption is noteworthy. such an action could allow to better tailor contractual terms on producers’ characteristics and to reduce their suspicion over such a governance solution, which is often seen as a subtle form of exploitation promoted by buyers to reduce their decisional autonomy over land. empirical evidence also reveals that another key and central point in a context of ecological transition is to identify and define types of (monetary or non-monetary) incentives to promote the adoption of terms related to sustainable cultivation practices and the adoption of environmental certification. even if they still play a limited role in the italian cereal sector, cooperatives, producers’ organizations, and interbranch organizations can also play a decisive role along this path, reducing transaction costs related to the negotiation and the enforcement of production. lastly, technical support provided by contract should be better promoted across durum wheat producers, highlighting the strategic role of knowledge and innovation transfer for improving both quality and sustainability of production. all that said, it must be also considered that this work has some limitation. first, since results were based on a purposive and biased sample of a few hundred durum wheat producers they cannot be generalized, if not with some caution. in this regard, investigating farmers’ preferences for contractual terms in a given period of time for a specific production in a certain context at least allowed to reduce potential sources of exogenous heterogeneity. moreover, another caveat is related to the fact that the empirical analysis relied on a discrete choice model approach, so on stated rather than on observed preferences. lastly, experimental design imposed to select only a limited number of contractual terms to be analysed, leaving room for future research in this area to evaluate further and different clauses. funding sources this work was supported by the national operational programme on research and innovation 2014-2020 (fse react-eu action iv.6 “green”) of the italian ministry of university and research (md 1062/2021). the content of this article does not reflect the official opinion of the funding institutions. the views expressed in this paper are solely those of the authors. acknowledgements the authors 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(2020). lclogit2: an enhanced command to fit latent class conditional logit models. the stata journal, 20(2): 405–425. https://doi.org/10.1177/1536867x20931003 appendix a table a. characteristics of the sampled durum wheat producers and their farms (n=190). variable name variable description mean sd min max age years of the farmers (n.) 47.24 13.70 18 85 contr_p use of production contract (y/n) 0.62 0.48 0 1 coop_m member of a cooperative (y/n) 0.40 0.49 0 1 educ_h high school or higher qualification (y/n) 0.83 0.36 0 1 exp_y years of experience as farmers (n.) 26.84 14.01 2 60 size hectares of farming areas (n.) 164.87 399.82 2.56 3680 https://doi.org/10.1016/j.csda.2004.11.004 https://doi.org/10.1016/j.csda.2004.11.004 https://doi.org/10.1177/1536867x20931003 guns, germs and climate: food security and food systems in a risky world francesco pagliacci1,*, valentina raimondi2, luca salvatici3,* the implications of the russian invasion of ukraine for african economies: a cge analysis for ethiopia amsalu woldie yalew1,2,*, victor nechifor3, emanuele ferrari3 participation of farmers in market value chains: a tailored antràs and chor positioning indicator tulia gattone adapting to climate change: what really drives the choices of the producers of geographical indications? francesco pagliacci1, dana salpina1,2,* exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach stefano ciliberti, angelo frascarelli*, gaetano martino, andrea marchini intention and behavior toward eating whole grain pasta on a college dining campus: theory of planned behavior and message framing giovanni sogari1, rungsaran wongprawmas1, giulia andreani1, michele lefebvre2, nicoletta pellegrini3, miguel i. gómez4, cristina mora1, davide menozzi1,* © 2025 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae bio-based and applied economics bae bio-based and applied economics 14(2): 5-14, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-18042 citation: arfini, f., bartolini, f., carbone, a., castellotti, t., coderoni, s., cortignani, r., d’annolfo, r., dara guccione, g., donati, m., galli, f., henke, r., mazzocchi, g., monteleone, a., raggi, m., pesce, a., pupo d’andrea, m. r., rocchi, b., romano, d., sardone, r., sotte, f., targetti, s., zumpano, c., & ciliberti, s. (2025). where is the italian agriculture heading? a discussion in light of the prospects for the future cap. bio-based and applied economics 14(2): 5-14. doi: 10.36253/bae18042 received: june 15, 2025 accepted: july 23, 2025 published: september 30, 2025 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: oriana gava. policy paper where is the italian agriculture heading? a discussion in light of the prospects for the future cap filippo arfini1,*, fabio bartolini2, anna carbone3, tatiana castellotti4, silvia coderoni5, raffaele cortignani6, raffaele d’annolfo4, giovanni dara guccione4, michele donati1, francesca galli7, roberto henke4, giampiero mazzocchi4, alessandro monteleone4, meri raggi8, alessandra pesce4, maria rosaria pupo d’andrea4, benedetto rocchi9, donato romano9, roberta sardone4, franco sotte10, stefano targetti8, catia zumpano4 1 università di parma, italy 2 università di ferrara, italy 3 unimercatorum, italy 4 crea – centro di ricerca politiche e bioeconomia, italy 5 università di teramo, italy 6 università della tuscia, italy 7 università di pisa, italy 8 università di bologna, italy 9 università di firenze, italy 10 socio emerito aieaa, italy *corresponding author. e-mail: filippo.arfini@unipr.it abstract. in early 2025, the european union launched a new phase of dialogue on the future of agricultural and food policies, aiming to move beyond the sustainability-centred narratives of the green deal and farm to fork strategy. the initiative, grounded in the “strategic dialogue on the future of eu agriculture” and the commission’s communication “a vision for agriculture and food,” reframes the role of agriculture within a broader geopolitical and socio-economic context. the italian council for agricultural research and analysis of the agricultural economy and the italian association of agricultural and applied economics convened a study day to examine the relevance and the implications of the eu’s vision for italy. this paper presents a synthesis of the discussions and reflections, structured along four thematic pillars: economic, environmental, social, and institutional sustainability. the analysis highlights the structural weaknesses of italian agriculture, the need for circular and diversified agricultural systems, the integration of agroecological and climate resilience strategies with competitiveness, the need for generational and social renewal, and the necessity for political reflection on the adequacy of the italian agricultural policy governance system. by capturing the perspectives of researchers and academics, the paper contributes to the national debate on reshaping eu agricultural policy beyond 2027. keywords: italian agriculture, cap reform, sustainability, multiannual financial framework. jel codes: q01, q18. https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae https://doi.org/10.36253/bae-18042 https://doi.org/10.36253/bae-18042 https://doi.org/10.36253/bae-18042 mailto:filippo.arfini@unipr.it 6 bio-based and applied economics 14(2): 5-14, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-18042 f. arfini et al. 1. introduction between the end of last year and the beginning of 2025, the european union launched a new phase of debate around the future of policies for the agricultural and agrifood sectors. this latest phase aims to carry the strongly sustainability-focused approach which had inspired the green deal and the farm to fork strategy into a different perspective, in which the sectoral challenges are placed in a drastically changed global context and pursue the ambition of making the agricultural sector more attractive and responsive to the expectations of stakeholders. the guidelines and recommendations for this new phase were outlined in the “strategic dialogue on the future of eu agriculture”, a document resulting from a working group comprising approximately 30 european stakeholders from the agri-food sector, civil society, rural communities, and academia. the requests that emerged were taken up by the eu commission with the publication of a strategic document, “a vision for agriculture and food. shaping together an attractive farming and agri-food sector for future generations”, which placed the issue of agricultural policy renewal within a more ambitious agenda for food and the future of rural areas. a renewal program, based on further in-depth papers related to many unresolved issues, will be introduced in the coming months of 2025, with new emerging themes added. the strategic vision document closes with an exhortation from the eu commission, which “…invites the european parliament, the council, the european economic and social committee, the committee of the regions, the social partners and all stakeholders to actively contribute to the development and delivery of the initiatives in this communication.”. crea – research centre for agricultural policies and bioeconomy and aieaa (associazione italiana di economia agraria e applicata) jointly took up this idea and organised a study day, which took place in rome on 3rd april 2025. more than twenty researchers, both academic and non-academic, experts in the various topics at the centre of the recent documents, actively participated in the event. the work began with two general overview speeches: the first provided an in-depth analysis of the specificities of the italian production system, drawing on the detailed sectoral analysis carried out by crea pb in its yearbook of italian agriculture (crea, 2024); the second offered a reasoned summary of the contents of the eu strategic vision document. then, the discussion was organised into four thematic tables, each focused on a dimension of sustainability — economic, environmental, social, and institutional — with as many coordinators as needed to guide the participants through a structured discussion on the issues of most significant relevance to italy’s national context. the results and reflections arising from the debate are briefly reported in the following sections, which represent a first contribution to the internal discussion on the future of agricultural and food policies, by a component of the italian research world. 2. the cap post-2027 in the vision of the european commission on february 19, 2025, the european commission presented the communication “a vision for agriculture and food,” outlining a roadmap to 2040 that ensures future policies align with this vision (european commission, 2025a). the document sets the direction and outlines principles closely aligned with the recommendations of the strategic dialogue (2024), while also being strongly influenced by other strategic documents regarding the european union’s (eu) competitiveness, its repositioning in the changing global geo-economic and geopolitical context, and its capacity to respond to crises (draghi, 2024; niinistö, 2024; letta, 2023; spain’s national office of foresight and strategy, 2023). the document was highly anticipated, as it traditionally outlines the commission’s orientations for the future of the common agricultural policy (cap) at the mid-point of the programming period. this was also the case in 2017, when the communication (european commission, 2017) paved the way for the new delivery model and cap national strategic plans (nsp). in that document, the cap was the focus, but agriculture and the broader agri-food system were largely absent from the debate on the future of the eu, except in budgetary issues. in 2025, by contrast, agriculture and food production are at the heart of the eu’s political agenda, as they are considered strategic for maintaining economic and social stability, ensuring food security in times of crisis, and guaranteeing european food sovereignty. the vision is therefore dedicated to securing their long-term competitiveness and sustainability, with the cap being just one of several policies contributing to these goals, often not even the most important one. the document focuses on four fundamental priority areas, which correspond to the three classic pillars of sustainability – economic, environmental, and social (the latter enriched by the food component) – alongside a fourth area focused on the sector’s competitiveness and resilience. generational renewal and innovation 7where is the italian agriculture heading? a discussion in light of the prospects for the future cap bio-based and applied economics 14(2): 5-14, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-18042 are cross-cutting themes throughout the communication, with the former being a long-term priority due to the ageing farming population, and the latter a supporting element to facilitate a sustainable transition. regarding sustainability, the document emphasises the need to integrate both economic challenges and ensure a socially just transition into the ecological transition, highlighting the importance of circular sustainability. according to this approach, environmental and economic sustainability enable the sector to remain competitive and meet society’s expectations regarding food safety, food security, quality, vitality of rural areas, preservation of local cultures and traditions, animal welfare, and other related concerns. in the priority area dedicated to economic sustainability, the most significant references to the cap can be found. the document confirms the need to continue providing farmers with income support that should be more targeted and fairer, capable of attracting young and new farmers. support should be more focused on farmers actively engaged in food production (with priority given to the production of agricultural products essential for the eu’s strategic autonomy and resilience), on the economic vitality of farms, and on environmental protection. furthermore, the document emphasises the need to streamline and simplify payments for ecosystem services, as well as to simplify conditionality by shifting from conditions to incentives, rewarding farmers who exceed mandatory requirements. however, there are not enough details to clarify how all this will impact the green architecture of the current cap (which is not even mentioned in the document) or the resources required to remunerate farmers. the document also touches on the issue of flexibility – both for farmers, in defining practices best suited to their farms and contexts, and for member states, in achieving the objectives of the post-2027 cap. the second priority area, focused on competitiveness, aims to ensure european food sovereignty by reducing critical dependencies (such as proteins, raw materials, and fertilisers), promoting fairer global competition, avoiding situations where european standards on food safety and sustainability place the eu at a disadvantage and lead to a loss of competitiveness, and strengthening eu’s ability to respond to crises. the priority area dedicated to environmental sustainability outlines the agricultural sector’s contribution to the eu’s 2040 climate target, considering its specific characteristics and the need to ensure both competitiveness and food security. in the fourth priority area, focused on social sustainability, the document highlights the need to strengthen synergies and complementarities between the cap and other policies, including the cohesion policy, to provide adequate support and tangible impact in rural areas through integrated planning and implementation efforts. this aspect becomes particularly relevant when considered in light of the communication on the future multiannual financial framework (mff) (european commission, 2025b). in that document, the current budget structure, based on spending programs rather than policies, is shown to cause delays in planning and expenditure, as well as overlaps and gaps due to the lack of coordinated strategies for cross-cutting priorities. therefore, the mff communication proposes a country-level plan focused on common priorities, including promoting economic, social, and territorial cohesion, as well as implementing key reforms and investments. reading the two documents together reveals a desire for greater integration between rural development policy and cohesion policy, although the extent of such integration, particularly in terms of policy autonomy, funding, and the role of public administrations, remains to be determined. the vision does not propose solutions but provides a broad overview of the transformations agriculture needs, promoting ongoing dialogue among stakeholders, institutions, and civil society, along with a combination of policies and institutional levels. it implicitly calls for the need, without explicitly naming it, for horizontal governance (among institutions at the same level with responsibilities over different policies) and vertical governance (among several institutions with responsibilities over the same policy) (coderoni, 2023). 3. points of view about economic sustainability the economic sustainability of the entire italian agri-food system depends on both macro and micro aspects of the national system, including the structural characterisation of italian agriculture and the strong trade interconnections within and outside europe. these aspects depend on the ability to guarantee income, adequately remunerate production factors, ensure competitiveness, and employ workers. among the various aspects that determine and influence economic sustainability, those relating to the international scenario and risk management are worth closer examination. the economic sustainability of the entire italian agri-food system strongly depends on the evolution of the international scenario in two interconnected aspects: one external and one internal to the italian country sys8 bio-based and applied economics 14(2): 5-14, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-18042 f. arfini et al. tem. on the external side, italian agriculture finds itself in the peculiar situation of being dependent on foreign markets for specific strategic production inputs (such as chemical inputs, soy, etc.). at the same time, the food industry exports high-quality, simple, and processed products, such as those with geographical indications, whose production cannot be outsourced (crea, 2024). this situation has been achieved thanks to the advantages derived from the european single market, as well as a general climate of institutional and market stability, with the world’s leading countries considered italy’s commercial partners. it is evident that situations of financial instability linked to exchange rates -, economic instability linked to tariffs -, or institutional instability tied to unclear or no longer perceived as clear market governance rules lead to repercussions that result in increased production costs, strain on the domestic market, and a decrease in prices and agricultural incomes. on the internal side, within the italian country system, the economic variables of the primary sector highlight that the profitability of land and labour has remained almost stable over the last decade, with only slight increases during the post-covid years. these weak increases are less significant, especially on smallsized farms, due to the tensions recorded on international price markets, confirming that, despite the national production model’s backbone being found in smallsized farms, the latter continue to be more vulnerable. the economic sustainability of the agricultural system, therefore, is closely linked to the structural dimension of farm holders’ companies. addressing this challenge also includes promoting generational turnover initiatives. in our country, the process of ageing has not suffered any setbacks in recent years, with a group of entrepreneurs over 60 years of age that largely exceeds that of entrepreneurs under 40 (crea, 2024). considering these structural aspects of the agricultural production system, the organisational and coordination capacity of value chains is becoming increasingly important not only to define production quantities and selling prices, but especially to define quality levels aligned with the global market and to bring in financial and human resources capable of supporting innovation processes and the management of commercial strategies in both domestic and international markets (crea, 2024). from this perspective, the italian agri-food system is highly complex, encompassing businesses that vary in terms of ownership, corporate form, and strategy. cooperative enterprises, family-owned companies, and multinationals compete in national and international markets. these latter companies have acquired all or part of the corporate structure of many italian food companies, influencing the behaviour of the value chains they are part of, including their internationalisation strategies. the economic sustainability of the italian agri-food system increasingly depends on developing an efficient and modern industrial relations system, capable of providing timely guidance to supply chains and their operators. in this regard, forms of supply chain management related to inter-professional organisations would guarantee a management capacity suitable to face the economic challenges stemming from market instability and those arising from climate change, which, in turn, are embedded in international dynamics. in a context marked by extreme weather events, market crises, and geopolitical instability, strengthening the resilience of italian farms has become a priority. two strategic levers in this direction are diversification and circularity. diversification involves two main strategies. first, expanding the range of cultivated crops, for example, by introducing legumes or oilseeds such as sunflowers and rapeseed, can help better cope with the effects of climate change. second, developing alternative sources of income for farmers, such as renewable energy production, agritourism, and direct sales, to help stabilise incomes during periods of market volatility. at the same time, promoting nutrient circularity is essential to reduce farm costs and mitigate the environmental impact of chemical fertilisers. encouraging the reuse of nitrogen-rich livestock manure, adopting precision agriculture techniques, and integrating agroecological practices into production cycles can enhance farm sustainability and reduce reliance on imported fertilisers. investing in diversification and circularity means building a more resilient and sustainable agricultural system that cannot only cope with external shocks but also adapt and evolve. 4. points of view about environmental sustainability the environmental dimension of sustainability is, in some respects, the most delicate as it implies negotiation and interaction between several actors (farmers and citizens) in managing different aspects that impact the environment and society itself. even though the cap in the past has introduced actions that go in the direction of creating a more environmentally sustainable production model, there are still numerous areas of intervention that include the adoption of more sustainable agricultural practices, the maintenance of high levels of biodi9where is the italian agriculture heading? a discussion in light of the prospects for the future cap bio-based and applied economics 14(2): 5-14, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-18042 versity, the reduction of greenhouse gases, and the maintenance of certain limiting production factors (i.e. water, soil, inputs). the vision document foresees achieving a higher level of environmental sustainability as a function of science’s ability to provide answers and develop interventions in several areas, including technological innovation, the evolution of agricultural production models, the development of supporting infrastructures, and increased consumer awareness. the ongoing decline in biodiversity and accelerating climate change constitute one of the most pressing environmental challenges facing society. despite significant financial resources allocated to environmental objectives, the effectiveness of eu agri-environmental and climate schemes in mitigating agriculture’s impact on biodiversity remains questionable (pe’er et al., 2022). in italy, this situation highlights the need for innovative contractual solutions to improve policy efficiency. among the most promising approaches are result-based schemes, in which farmers receive payments contingent upon achieving environmental outcomes, and collective approaches, in which groups of farmers commit to shared targets (targetti et al., 2024). nevertheless, key considerations include their capacity to attract private investment, the availability of enabling technologies, and the complexity they may entail. in italy, the agroecological transition requires a strong commitment from farmers, supported by robust institutional frameworks. beyond the mere adoption of agroecological practices at farm and food system levels, it is essential to invest in training, advisory services, and knowledge exchange networks (wezel, 2015). reinforcing territorial governance mechanisms, such as bio-districts, and integrating local knowledge systems are also crucial (dara guccione et al., 2024). in light of the water crisis, agroecology presents a pivotal strategy for enhancing climate resilience. therefore, full integration of agroecology within italy’s cap nsp, with targeted support for bio-districts and sustainable resource management, is essential. despite the great emphasis on agroecology and bio-districts and their potential contribution to a more sustainable agriculture, it must be admitted that this is a residual system in the italian agricultural landscape, still far from becoming a reference model for many italian farmers. although agricultural greenhouse gas (ghg) emissions in italy have declined by 19% since 1990 (crea, 2024), this reduction is mainly attributable to decreased production levels (baldoni et al., 2017). greater ambition in mitigation efforts is therefore required to attain climate neutrality without compromising productivity (coderoni, 2023). beyond the cap, innovative policy instruments are being considered. the eu regulation on carbon removals and carbon farming establishes quality criteria for certifying carbon credits generated from agricultural soils and forests, potentially stimulating voluntary carbon markets through private finance. similarly, the introduction of an agricultural emission trading system, although highly questioned (copa-cogeca, 2024), could apply the polluter-pays principle within the sector, reducing emissions cost-effectively. in this context, italy’s availability of farm-level ghg estimates from fadn data (coderoni & vanino, 2022) could facilitate the identification of mitigation hot spots for targeted interventions, such as those supported by the agrifood just transition fund. soil health, a long-standing concern, has recently regained prominence through the eu’s soil strategy, particularly via the soil deal and soil mission, which aims to reverse degradation currently affecting approximately two-thirds of eu soils. in italy, pressing concerns include soil erosion, depletion of organic matter, biodiversity loss, and nutrient runoff. however, significant obstacles persist, including the dispersion of incentives across cap measures, structural transformations within the sector, and institutional inadequacies (winkler et al., 2025). dairy livestock farming represents a key sector in the decarbonisation agenda and is undergoing substantial transformation due to evolving consumption patterns and growing demand for sustainable dairy products (coderoni, 2023). although climate-smart innovations, such as robotic feeding systems, are enhancing efficiency, challenges remain concerning production standards and reliance on imported feed. additionally, there is concern regarding the potential redistribution of costs along the supply chain under emerging policy regimes (huber, 2024). agriculture is inherently circular, traditionally reusing by-products such as manure to maintain and enhance soil fertility. beyond internal recycling, the sector holds significant potential to strengthen circularity through cross-sectoral synergies. fertiliser use remains a primary environmental concern, accounting for approximately one-third of agriculture’s co₂ emissions and depending heavily on scarce and unevenly distributed natural resources. in response, the eu regulation 2019/1009, which entered into force in 2022, promotes the use of organic and waste-derived fertilisers as part of a broader strategy to support sustainable agriculture. nevertheless, adopting such alternatives remains limited, hindered by perceived high costs, concerns regarding potential contaminants, and cultural resistance (ronzon et al., 2024). facilitating this transition requires the 10 bio-based and applied economics 14(2): 5-14, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-18042 f. arfini et al. development of industrial symbiosis initiatives, supported by policy instruments such as the eu’s integrated nutrient management action plan (abitabile et al., 2025). strengthening agricultural knowledge and innovation systems (akis) to enhance information dissemination and farmer skills, alongside improved monitoring through tools such as the farm sustainability data network (fsdn), is crucial for fostering a more circular and resilient agricultural sector. 5. points of view about social sustainability social sustainability lastly entered the debate on the european union’s agricultural policies. it is encouraging that this issue is now being addressed more concretely. in the strategic dialogue, seven principles address social sustainability, a significant step forward. additionally, the vision emphasises the importance of this topic, particularly in the context of generational renewal, which is seen as essential for the vitality of agriculture and rural areas. this is welcome news in italy, where the issue is particularly acute (carbone et al., 2024). it is also promising that the focus shifts from young to new entrants. many young beneficiaries would likely enter farming anyway, while others seek to enter later in life, bringing valuable skills, capital, and networks. new entrants, regardless of age, face land access issues, especially in densely populated italy. therefore, the mention of a european observatory on farmland is a positive development. lack of infrastructure and services also prevents entries; thus, the broader, non-sectoral approach is a welcome development. we now await the generational renewal strategy, as promised by 2025. for italy, promoting and enhancing social sustainability involves engaging with various aspects of agriculture and the food chain. knowledge and skills are among the challenges recognised in the dialogue as an opportunity to expand farmers’ lifelong learning and revitalise extension services. moreover, another challenge in the vision document concerns “building an attractive sector that ensures a fair standard of living and leverages new income opportunities”. a focus on generational and entrepreneurial renewal should also consider the social diversity of the italian agricultural system. farming income contributes to the welfare of diverse entrepreneurs to varying extents. farmers managing large holdings often belong to the highest income deciles. small and medium farms, conversely, typically represent only one among several income sources for farming families, rather than being the primary one (marino et al., 2024). small and medium-sized activities still involve a significant number of people. in some rural contexts, they play a relevant social role, providing employment. their support is likely to generate valuable social outcomes. however, the attractiveness of agriculture for small and medium-sized farms, as well as for young people and new entrants, strongly depends on the rural context in which they operate. more than direct farm income support, these farmers would need measures targeted at promoting farm business diversification, enabling household livelihood strategies based on «pluri-activity», simplifying bureaucracy in farm management, and promoting horizontal cooperation in marketing farm produce. social sustainability in italian agriculture also requires a critical acknowledgement and systematic response to the economic and social inequalities embedded throughout the agri-food supply chain. these disparities disproportionately affect women and migrant labourers and are often neglected or tacitly accepted, despite constituting deep-rooted structural challenges (zumpano,2020; corrado and zumpano, 2021). thus far, the cap has largely overlooked the social dimension, offering only broad, non-binding recommendations concerning gender equality, without establishing enforceable commitments (zumpano, 2021). in the domain of labour rights, intervention has been limited to sanction-based mechanisms, which have proven insufficient and largely ineffective (canfora & leccese, 2022). the analysis of recent eu policy documents reveals little progress on these issues, particularly in terms of proposals. persisting in this limited approach risks exacerbating rural decline, as individuals increasingly disengage from agricultural work and abandon rural territories. building on the advances made in the cap’s environmental dimension, there is a need to support methodological frameworks that embed social sustainability into agricultural policy through the implementation of fairness schemes. another topic focused on the strategic dialogue is “making the healthy and sustainable choice the easy one.” this topic extends beyond the agricultural sector and encompasses the broader food system, aligning with the european commission’s recommendations (sapea, 2023). appealing to consumers’ rationality is not enough. different dimensions of the “ food environment” need to be addressed to promote sustainable consumption. from a systemic perspective, four key aspects are of central importance: nutrition and diet, consumer information, public food procurement and the response to food poverty. regarding the first aspect, italy can valorise the heritage value of the mediterranean diet (dernini & capone, 2024). however, it must deal with the decline in adherence and the rise in obesity, which raises the 11where is the italian agriculture heading? a discussion in light of the prospects for the future cap bio-based and applied economics 14(2): 5-14, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-18042 question of who should lead the change and with what incentives. in terms of information, the main challenge for sustainability labelling is to strike a balance between simplicity and comprehensiveness, considering the various social dimensions of sustainability (ranging from nutritional value to supply chain equity to animal welfare, etc.) (sanye mengual et al., 2024). public procurement of food plays a strategic role in education and market orientation; however, the key issue remains defining effective sustainability criteria, which is the subject of ongoing debate (european commission, 2024). italy is widely recognised for its excellence in this area through the cam (minimum environmental criteria), which integrates environmental, territorial, and social sustainability criteria into public catering tenders. a widely shared call is to strengthen food literacy, meaning navigating a highly complex food environment. finally, the importance of solidarity networks, such as food banks, is recognised to actively support food systems in addressing emergency food insecurity situations, provided that such networks are supported by appropriate policies (galli et al., 2018). however, the role of agriculture and rural areas is often nuanced or neglected (mazzocchi et al., 2023). the reference to food waste remains rather vague: in the vision, it is mentioned only once, without any specific target, merely as a general commitment to continue existing initiatives. this is problematic because the commercial dynamics that drive food waste behaviours are not recognised. the introduction of elements that lead to considering agriculture in its social aspects, along with explicit measures, is a novelty that should be welcomed in the italian agricultural landscape. however, the concrete impact of these measures depends on elements that require an evident willingness on the part of national policymakers to implement them. 6. points of view about institutional sustainability both the strategic dialogue and the vision have highlighted some common elements that may influence the future policy governance for the agricultural sector and rural areas. first, budget simplification of the multiannual financial framework (mff) may require establishing a single fund for development policies and a plan for each country, which would contain key reforms and investments focused on common priorities. second, cap is still a central tool for achieving the objectives of competitiveness and sustainability of the agricultural sector and rural areas. however, it should improve coordination with other policies to achieve a synergistic and more effective contribution (coderoni, 2023). third, cap’s strategic approach to programming is still valid. however, some implementation mechanisms need to be simplified, while at the same time strengthening a target approach and the responsibility of member states to ensure achievement of the set targets. finally, cooperation with stakeholders needs to be improved at all stages of the programming cycle. the discussion on institutional sustainability, however, must start with an analysis of the governance of programming, management and evaluation of the three main policy instruments that directly or indirectly affect the agricultural sector and rural areas in the 2023-2027 programming period: the cap nsp, the national recovery and resilience plan (nrrp) and the partnership agreement for cohesion policy. the cap nsp, which introduced unitary and national “program” for pillar i and pillar ii and influenced the way interventions are programmed, consulted and approved, opening a broad debate on the role of the ministry of agriculture, the regions and the paying agencies as managing authorities and in monitoring and evaluation responsibilities, necessitating the setting up of new coordinating “bodies”. at the same time, the new objectives introduced with the “farm to fork” strategy, new instruments (eco-schemes and social conditionality), the strengthening of bottom-up approaches, and mechanisms for performance assessment have introduced new actors and new “institutional” relationships. the nrrp provided for “agricultural” interventions managed directly by the ministry of agriculture and other national administrations, firmly integrated with the nsp, but with different implementation and performance evaluation modalities and not always fully coordinated with cap interventions. finally, the partnership agreement for cohesion policy provides for several national and regional interventions complementary to the cap, in particular with regard to the development of inner areas, the promotion of human capital and environmental protection. nevertheless, no formal coordination mechanism has been foreseen to ensure effective integration at territorial level. a crucial aspect highlighted by the documents under the scanner is the stakeholders’ dialogue: a process innovation tested for the first time in the cap nsp through the partnership table (henke et al., 2024). italy is rich in experiences in this regard, carried out by local administrations collaborating with research institutions, the third sector, and private operators, through public par12 bio-based and applied economics 14(2): 5-14, 2025 | e-issn 2280-6172 | doi: 10.36253/bae-18042 f. arfini et al. ticipation mechanisms such as food councils, explicitly mentioned in the vision. on all these aspects, the progress of these new programming tools, their coherence, and integration capacity need to be monitored. a comparison at the eu level of the member states’ capacity to respond to the unitary programming inherent in the psp would also be valuable and desirable. the thematic discussion on institutional sustainability highlighted some assessments for possible reform scenarios. a first element concerns the cap’s separation from other policies. from a strategic point of view, the single fund hypothesised in the budget reform could make it possible to improve the integration of the agricultural sector into the economic system on fundamental issues such as food, environment, land, and food security, where the complementary action of policies could be fundamental. the issue of the single fund is central, both because of the risks of resource loss for the sector and due to its effects on delivery mechanisms and performance assessment, which are already complex and impact policies in various ways. participants in the discussion emphasised the need to change the approach and orient the cap and future policies towards: i) tailored and targeted policies, given the heterogeneity of the recipients, with the need to accompany these processes with practical tools for evaluating results rather than inputs and performance; and ii) forward-looking aid oriented towards rewarding behaviour that can generate structural changes in the system, overcoming backwards-looking payments that tend to sustain the status quo and widen inequalities. the other evidence that emerges from the discussion is the gap between the vision of agriculture, the relationship with traditional challenges (environmental sustainability, generational change, innovation) and that with the new challenges (food, health, labour, trade) and the role of incentive and regulation policies as opposed to “softer” forms of policies that are more suited to interventions in the more downstream components of the food system (education, information, transparency, addressing a proper food literacy, as advocated in the strategic dialogue). the tendency is to focus solely on the cap, but it is necessary to discuss policies more broadly, to consider possible new beneficiaries, how to avoid conflicts between different objectives, and how to leverage synergies between actors. given the above scenario, especially for italy, it becomes crucial to discuss the role of institutional actors involved and how these new processes can be governed within the already complex governance of policies due to the requirement of the italian constitution, which considers the administrative regions as responsible for setting up their regional policy for agriculture. thus, in terms of institutional sustainability, there emerges the need to question how the national system should organise itself at the central level to interpret, measure and evaluate the system proposed to us by the eu, in terms of: i) integration and coherence of policies, in particular by looking at the programming tools that we have used in this programming, also with a comparison at the european level, and of the possible tools that may be proposed; ii) analysis of the trade-offs between the different objectives inclusiveness, sustainability, productivity, resilience and the visions of the different stakeholders; and iii) systematic implementation of mechanisms for evaluating policies, to allow real learning on the effectiveness and efficiency of the various interventions to achieve the set objectives. the new european agricultural policy is undoubtedly more complex in 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(2021), la nuova politica di sviluppo rurale deve essere un’opportunità anche di genere, pianetapsr, n.107. https://www.pianetapsr.it/flex/cm/ pages/serveblob.php/l/it/idpagina/2642 https://doi.org/10.1111/conl.12901 https://doi.org/10.1111/conl.12901 https://doi.org/10.36253/bae-14563 https://doi.org/10.2760/90191 https://doi.org/10.2760/90191 https://doi.org/10.5281/zenodo.8031939 https://doi.org/10.5281/zenodo.8031939 https://agriculture.ec.europa.eu/overview-vision-agriculture-food/main-initiatives-strategic-dialogue-future-eu-agriculture_en https://agriculture.ec.europa.eu/overview-vision-agriculture-food/main-initiatives-strategic-dialogue-future-eu-agriculture_en https://agriculture.ec.europa.eu/overview-vision-agriculture-food/main-initiatives-strategic-dialogue-future-eu-agriculture_en https://doi.org/10.36253/bae-15862 https://doi.org/10.36253/bae-15862 https://doi.org/10.1080/21683565.2015.1115799 https://doi.org/10.36253/bae-15466 https://doi.org/10.36253/bae-15466 https://www.reterurale.it/flex/cm/pages/serveblob.php/l/it/idpagina/21203 https://www.reterurale.it/flex/cm/pages/serveblob.php/l/it/idpagina/21203 https://www.pianetapsr.it/flex/cm/pages/serveblob.php/l/it/idpagina/2642 https://www.pianetapsr.it/flex/cm/pages/serveblob.php/l/it/idpagina/2642 reflections and new directions: an editorial retrospective and the launch of our new policy paper section silvia coderoni, fabio bartolini where is the italian agriculture heading? a discussion in light of the prospects for the future cap filippo arfini1*, fabio bartolini2, anna carbone3, tatiana castellotti4, silvia coderoni5, raffaele cortignani6, raffaele d’annolfo4, giovanni dara guccione4, michele donati1, francesca galli7, roberto henke4, giampiero mazzocchi4, alessandro monteleone4, towards the knowledge and innovation system for the bioeconomy? giacomo maria rinaldi*, davide viaggi impact of weather variability on crop yields and land use dynamics in eastern india: shortand long-term effects pratap kumar jena1, kirtti ranjan paltasingh2, souryabrata mohapatra3,,*, ashok mishra4 rebuilding strategies for food self-sufficiency: portugal’s past patterns and future ambitions alexandre macedo joão1, dulce freire2, humberto rocha3, joana dias4 from subsidy to land rent: how production-linked payments shape factor remuneration in agriculture adrian sadłowski expenditure allocation for rural development interventions: main trends and patterns in the choices of the italian regions under the cap 2023-2027 tobia minuzzo, francesco pagliacci* 1 land, women and development: a systematic review of causal evidence valeria strusia∗, sara balestrib a università cattolica del sacro cuore, department of international economics, institutions and development (diseis), largo gemelli, 1, milan, 20123, italy b university of perugia, department of economics, perugia, italy ∗corresponding author. e-mail address: valeriapaola.strusi@unicatt.it this article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the version of record. please cite this article as: strusi v., balestri s. (2025). land, women and development: a systematic review of causal evidence, just accepted. doi: 10.36253/bae-17846 abstract this article systematically reviews the causal literature on the impacts of women’s land rights on development in the global south, asking how they affect different development outcomes, and what patterns emerge across types of rights, geographies, and methodological approaches. following the prisma protocol, we screen over 800 records and retain 96 studies, covering 46 countries and 202 effects classified in 11 outcomes. findings show consistently positive effects on empowerment, education, and health, though the latter are less studied. evidence on investment, financial inclusion, and welfare is more ambiguous, while natural resource management and intrahousehold dynamics remain too underexplored for meaningful conclusions. most research focuses on formal ownership, overlooking the broader ‘bundle’ of rights; regions outside africa and south asia are critically underrepresented. by mapping causal patterns and gaps, this review establishes a systematic evidence map and guides future evaluations addressing thematic and geographic blind spots. keywords: land, land rights, women, development, systematic review, prisma jel: o13, p48, q15 1. introduction gender-based discrimination in land access is a well-documented issue in empirical literature. women often have inferior rights to natural resources (otsuka & place, 2001), and when they do 2 hold rights to land, these often apply to smaller or lower-quality plots than those allocated to men (fao, 2011). this disparity arises not only from discrimination in the allocation of agricultural land but also from unequal access to complementary inputs such as fertilizer, pesticides, and extension services (burke et al., 2018; burke & jayne, 2021; chu et al., 2023). even with formal landownership, effective access and use can be limited by weak institutions that fail to enforce rights. for poor rural women, pursuing justice entails both financial and social costs, especially when family or clan dependence discourages challenging these power structures (polavarapu, 2020). women’s land claims often remain weak despite access to formal justice, due to poor enforcement of statutory rights, elite-driven land dispossession, and overlapping legal systems. customary courts – the main dispute-resolution venues in developing countries – often reflect patriarchal norms that discriminate against women and reinforce gender inequalities (behrman et al., 2012; dell’angelo et al., 2017; polavarapu, 2020). legal discrimination at the intersection of land, marriage, and inheritance laws further limits women’s rights as their claims often depend on relationships with men, leaving them vulnerable when these ties change or dissolve. economic theory and empirical case studies suggest that land access can reduce the gender asset gap, strengthening women’s economic security and social status (bhaumik et al., 2016) and contributing to broader household and community development (agarwal, 1995; doss, 2006: duflo & udry, 2004; luke & munshi, 2011; quisumbing & maluccio, 2003). more recently, land rights (lrs) have also been linked to improved climate adaptation and societal resilience (laura et al., 2023). yet, the relationship between land access, women’s agency, and development remains poorly understood. empirical findings are heterogeneous across geographies, outcomes and research designs, and much of the discourse continues to rely on fragmented case studies rather than systematic evidence. consequently, generalized claims – such as the portrayal of land access as a ‘panacea for household welfare problems’ (bhaumik et al., 2016, p. 249) – risk overstating the case in the absence of a comprehensive appraisal of the scientific literature. in this context, a systematic review is both timely and necessary. as global development agendas increasingly prioritize gender equality, lrs, and climate resilience, there is an urgent need for a rigorous synthesis of causal evidence to inform policy and future research. to our knowledge, this is the first systematic review on the land-women-development nexus that applies a standardized protocol of analysis as the preferred reporting items for systematic reviews and meta-analyses (prisma) (page et al., 2021), ensuring transparency and methodological consistency. a natural point of reference to this work is the earlier review of meinzen-dick et al. (2019), which mapped 11 pathways linking women’s land rights (wlrs) to poverty reduction, and narratively synthesized the qualitative and quantitative evidence up to 2017. building on that foundation – but restricting inclusion to studies adopting inferential methods – this review also innovates by: (1) documenting geographic, temporal and methodological trends in the literature, (2) introducing a typology of lrs to assess how different forms of access are explored in the empirical literature, and (3) including publications through late 2024, thereby broadening the temporal coverage of meinzen-dick et al. (2019). methodologically, our approach also aligns with lawry et al. (2017) in (1) prioritizing causal identification through quantitative studies that go beyond descriptive analysis, (2) organizing outcomes in ten classes that partly overlap with ours (i.e., investment, productivity, access to credit, 3 consumption, gender equality), and (3) emphasizing effect heterogeneity across countries and regions. our review, however, differs in three respects. first, gender is the central organizing principle rather than a subgroup dimension: we restrict inclusion to studies identifying effects of wlrs and track outcomes directly tied to women’s agency in households and communities (e.g., empowerment, health, education). second, we introduce a typology of lrs, an angle not systematically pursued by lawry et al. (2017). finally, to provide a full picture of the evidence base, we also include carefully controlled observational designs, while acknowledging the tradeoffs for internal validity (see section 4.4). therefore, by cutting through the complexity and fragmentation of existing evidence, this review provides a solid grounding for advancing scholarly understanding and policy debates around wlrs and their development implications. 2. theoretical perspectives on securing wlrs along the traditional measurement of the gender asset gap, awareness of its implications for development has gradually emerged, with scholars and policymakers linking its reduction to poverty alleviation (de janvry & sadoulet, 2016; meinzen-dick et al., 2019). rooted in institutional theory, this idea highlights the role of property rights in economic development. when farmers enjoy secure property rights and ownership protection, they invest more in their land, enhancing agricultural productivity, natural resource management (nrm), access to credit, and, ultimately, long-term growth in output and income (de janvry & sadoulet, 2016). recognized rights also allow land rental or sales to more efficient users, potentially generating gains from trade. additionally, better rights make it easier to collateralize land, further enabling investment (de soto, 2000; besley, 1995). several influential studies provide empirical evidence for these claims, particularly in africa (goldstein & udry, 2008; ali et al., 2011; deininger et al., 2008). although early property rights theory established a connection between landownership and development, it did not incorporate an explicit gender perspective. yet, it already recognized that a ‘male monopoly over property’ (folbre, 1996, p.10) undermines efficiency by limiting women’s full participation in economic activities. during the mid-2000s, policymakers’ early enthusiasm for formal, individual land titles (de soto, 2000) shifted toward recognizing informal and communal rights (ostrom, 1990) as more effective tools for protecting vulnerable groups from ‘land grabs’ (de schutter, 2015). at the same time, many economists moved away from the unitary model of the household (becker, 1974), which treats the family as a single utility-maximizing entity, in favor of collective frameworks (chiappori, 1992), particularly intra-household bargaining models (agarwal, 1997a), which acknowledge that intra-household inequality and different preferences – often gender-based – shape household spending and resource allocation. within a bargaining framework, agarwal (1994) presents three arguments for why wlrs matter for development: efficiency, empowerment and welfare. efficiency refers to the productive use of land: in line with property rights theory, women with secure land access tend to invest more in productive and sustainable agricultural practices (goldstein & udry, 2008) and freely decide what to grow, consume or sell, enhancing food security (duflo & udry, 2004; fao, 2011). empowerment involves enhancing ‘women’s agency and voice’ (sen, 1999, p. 193). land, as a 4 source of wealth and social status (de janvry & sadoulet, 2016), can enhance women’s bargaining power and lessen dependence on male relatives. economic independence, in turn, can mitigate the risk of gender-based violence (polavarapu, 2020), decrease reliance on transactional sex, and improve women’s ability to negotiate safer sex, helping prevent stis (conrad & doss, 2008). these benefits extend to household and community welfare: landownership increases women’s influence over nutrition, education and healthcare (agarwal, 1995) and serves as collateral for such expenses (menon et al., 2014; agarwal, 2003). critics of the unitary model (ward-batts, 2008; lundberg et al., 1997; armand et al., 2020; alderman et al., 1995) show that family expenditure patterns depend on who controls assets: women typically allocate resources more equitably and with long-term perspective (udry, 1996; deininger et al., 2013; burrone & giannelli, 2023; thomas, 1990; mcelroy, 1990), benefiting children’s well-being and especially girls’ human capital. access to land is also expected to encourage women’s participation in collective action at local and broader societal levels, potentially enhancing institutional inclusivity. their involvement in managing village commons, in particular, can strengthen social equity and community resilience, while gender-equitable management of common-pool resources can prevent resource conflict and the ‘tragedy of the commons’ (meinzen-dick et al., 1997; pandolfelli et al., 2008; ratner et al., 2013). recent research explores how different lr typologies influence development. moving beyond traditional ‘ownership,’ scholars increasingly refer to a ‘bundle of rights,’ a continuum of land-related actions held jointly or individually (schlager & ostrom, 1992)1. women rarely enjoy a complete ‘bundle,’ and different rights affect outcomes differently. emerging approaches also highlight the distinction between individual and joint rights as a key measure of women’s land autonomy (bhatla et al., 2010). individual rights, particularly those backed by formal titles, are often more empowering (agarwal, 2003), as they reduce dependence on others. however, since empowerment depends on what is culturally perceived as legitimate and appropriate (jackson, 2003), individual ownership can disrupt power relations, triggering conflict and ‘male backlash’ as predicted by the relative resource theory of domestic violence (atkinson et al., 2005). 3. research aim and methods building on such theoretical arguments, a growing body of empirical research has tested the landwomen-development nexus, yet causal relationships remain inconclusive. findings vary significantly across regions, types of rights, and methodological approaches. while development practitioners promote land tenure security, there is still limited understanding of its broader 1 schlager & ostrom (1992) identify rights of access, withdrawal, management, exclusion and alienation. empirical studies have adapted this framework to fit case studies, often mapping them into access, use and ownership/control (meinzen-dick et al., 2019). 5 benefits and the contextual factors, such as gender norms and intra-household dynamics, that mediate its impact (higgins et al., 2018; lawry et al., 2017). we therefore propose a systematic review to take stock of such empirical evidence. specifically, we asked: “how do wlrs affect development outcomes, and what patterns emerge across different types of rights, geographical contexts, and methodological approaches?” this research question guided our effort to synthesize findings, identify evidence gaps, and trace the evolution of research in this domain. we identified well-studied and underexplored outcomes from a gender perspective, and evaluated the direction, consistency, and geographical distribution of findings as well as the methods used to estimate causal effects. additionally, we examined the extent to which empirical literature distinguishes between (1) individual and joint rights, and (2) different rights within schlager & ostrom (1992)’s ‘bundle’. this review applied the prisma 2020 statement, renowned for setting standards for transparent and reproducible systematic reviews (page et al., 2021).2 our protocol incorporated strategies from seminal works (e.g., pickering & byrne, 2014) on systematic reviews in social sciences and was preregistered online3. while systematic reviews have been widely applied to land research, they rarely adopt a gender perspective, which we instead propose in this review.4 3.1.eligibility criteria we included peer-reviewed articles or reputable working papers in english that provided causal evidence through observational (with at least multivariate regression), quasi-experimental, or experimental approaches. further criteria were inspired by the pico framework. eligible studies focused on populations (p) from lowto upper-middle-income countries. in (quasi-)experimental studies, interventions (i) encompassed laws, policies, and land-use changes affecting women’s ability to benefit from land. in observational studies, outcome comparisons based on the presence/absence of rights or varying levels of tenure security for women were used as interventions. comparisons (c) required baseline groups where women lacked rights or had insecure tenure (using temporal, spatial, or betweengroup designs). outcomes (o) were not predefined, to capture the full range of potential impacts as they naturally emerged from the review (page et al., 2021). 3.2.search strategy we searched web of science, scopus, and econlit, recognized for their coverage of peer-reviewed development research. we applied a stepwise refinement approach to progressively narrow the search queries (appendix a). the final query focused on three core themes: land, rights, and women, with minor adjustments to align with databases’ search syntax (figure 1). in line with the strategy outlined in section 3, it avoided mentioning specific outcomes (e.g., ‘education’). 2 the wide range of outcomes, heterogeneous indicators, and differences in wlrs coding made a formal meta-analysis unfeasible. a systematic review instead enables a structured synthesis that accommodates methodological variation. 3 methodological details are available in the protocol at 10.17605/osf.io/45qkb. 4 see de jong et al. (2021), higgins et al. (2018), lawry et al. (2017), murken & gornott (2022). https://doi.org/10.17605/osf.io/45qkb 6 figure 1: search query additionally, we manually screened the reference lists of retrieved studies to identify relevant publications not captured by the initial queries5. as a result, we included 14 additional publications (appendix b), which underwent the same eligibility screening and data extraction procedures as those retrieved from the original search. since studies often report multiple effects across countries and outcomes, we treated effects as units of analysis rather than entire articles, following the approach adopted by higgins et al. (2018). effects were recorded in a spreadsheet with their corresponding attributes (e.g., country, statistical significance) (pickering & byrne, 2014; ogundari, 2022). multiple estimates for the same effect – derived from different model specifications, robustness checks or heterogeneity analyses – were consolidated based on reviewers’ judgment. the prisma flowchart (figure 2) illustrates the workflow of screening, retrieving, and selecting articles, resulting in 96 studies and 202 effects after snowballing (appendix c). 5 while database searches remain the core of systematic reviews, prisma permits supplementary backward snowballing to enhance comprehensiveness, provided that its use is cautious, transparent, and reproducible (page et al., 2021). 7 figure 2: prisma flowchart although no outcomes were specified in the search query, a working definition of “development outcomes” guided screening to ensure consistency. these are understood as measurable, objective dimensions of socio-economic progress at the individual, household, or community level, including, but not limited to, agricultural productivity, household income and expenditure, health and nutrition, education, and women’s empowerment. consequently, outcomes not directly 8 indicative of development were deemed “ineligible”. for example, migration was excluded due to its ambiguity, as it may imply either an adaptation strategy or a decline in living standards6. thus, the review focuses on outcomes clearly linked to development impacts attributable to wlrs. 3.3 limitations given the complexity and context-specific nature of land-related impacts, especially gendered ones, we included only empirical studies that moved beyond descriptive correlations and addressed selection bias and confounding factors using inferential methods. this methodological filter ensures internal validity and supports evidence-based policy recommendations grounded in robust causal inference. however, this choice entails limitations. excluding purely observational or descriptive studies, which offer valuable qualitative insights or rich contextual detail, risks overlooking important dimensions of the wlr-development nexus. it may bias findings toward data-rich settings, underrepresenting marginalized groups, lesser-studied regions, and informal tenure systems. moreover, causal studies often emphasize measurable outcomes, neglecting longer-term or intangible effects better captured by qualitative research. therefore, while enhancing findings’ internal validity, our approach may limit breadth and external validity. a fuller understanding of wlrs’ impacts would benefit from complementary syntheses incorporating mixed-method and ethnographic studies to capture social norms, power dynamics, and lived experiences influencing tenure security and development. 4. main findings of the systematic literature review the final sample of 96 publications reflects the richness and fragmentation of the empirical literature on wlrs and development. geographically, the evidence is highly concentrated in subsaharan africa and south asia, with scattered coverage in other regions. thematically, it spans 11 outcome areas attributable to development processes, including women’s empowerment, household welfare, agricultural productivity, financial inclusion, and health. methodologically, most studies rely on observational and, to a lesser extent, quasi-experimental designs, often exploiting policy changes to identify causal effects7. building on this evidence, this section presents the main findings of the systematic review, focusing on how wlrs influence various development outcomes. we first examine wlrs’ operationalization in the literature, emphasizing the elements of the ‘bundle’ and how these distinctions shape observed effects (section 4.1). to facilitate comparison, outcomes were grouped into broader domains reflecting the primary focus of reported effects, with empirical insights synthesized for each (section 4.2). the analysis then considers studies’ geographical and temporal coverage (section 4.3) and, subsequently, their methods and data (section 4.4), offering a 6 see, for example, kudo (2015). 7 appendix d reports the distribution of studies across regions, methods, and outcomes, together with detailed information for each included study. 9 systematic overview of not only what outcomes are affected by wlrs but also how, where, and with which methodological approaches these effects have been assessed. 4.1.operationalizing the ‘bundle of rights’ contemporary economic theory (schlager & ostrom, 1992) highlights the importance of women’s concrete actions regarding land. to assess whether empirical literature reflects this, we propose a preliminary classification of the lrs mentioned as explanatory variables in reviewed studies (figure 3). figure 3: distribution of rights mentioned as explanatory variables most effects (190) are explicitly associated with ownership or inheritance, typically self-reported or documented via a land title. however, authors often omit details on the specific claims these rights entail or how they were validated in surveys. although ownership is often assumed to grant the complete ‘bundle of rights,’ even titling does not ensure women can exercise any or all rights. shwachman kaminaga & sheldon (2022, p. 1200) observe that ‘[t]he interplay between de facto and de jure legal systems […] [affects] whether a title (physical or assumed) is recognized in practice.’ if ownership simply reflects de jure rights, our understanding of de facto rights (and their heterogeneous impacts) is impaired. further, studies rarely assess different rights separately, often combining them into a single explanatory variable. other rights in the ‘bundle’ are scarcely represented: alienation appears only in 25 effects; access/use rights are present in 18 effects (based on actual access or land-use certificates); management and withdrawal are evaluated in only 14 and eight effects, respectively. no studies explicitly mention exclusion rights; though five cases report women’s inability to maintain exclusive land access (e.g., the fear of land loss noted in deininger et al., 2019). this gap is notable, 10 as schlager & ostrom (1992) argue that individual rights of exclusion promote investment and productivity. only 20 studies consider multiple dimensions of the ‘bundle.’ notably, 55% are concentrated in sub-saharan africa, indicating a strong regional focus that will be examined in more detail in the following sections. 4.2.development outcomes the analysis identified 11 outcome categories attributable to development processes. among these, women’s empowerment emerged as the most investigated one. others derived from the theoretical literature (investment and environmental sustainability, agricultural productivity, nrm, and financial inclusion), scholars’ attention to ‘mediated’ impacts (welfare, health, and education), and the latest developments in this field (intra-household dynamics, social capital and collective action, and social stability). greater consistency of findings emerges from specific areas of empowerment (namely, decisionmaking and economic empowerment), health, and education, where wlrs are connected to robust and positive impacts. more heterogeneous or scattered findings emerge on the remaining outcomes. 4.2.1. women’s empowerment with 45 studies, empowerment emerges as the most examined outcome, slightly more based on observational studies (51%) and with broad country coverage, especially in south asia and subsaharan africa. we identified four main dimensions: decision-making, economic empowerment, family planning, and intimate partner violence (ipv). decision-making is the most popular theme in this area, with most studies confirming a positive relationship. particularly coherent findings emerge from quasi-experimental studies, where nearly every intervention demonstrated a positive impact (ajefu et al., 2024; biswas et al., 2024; grabe, 2010; grabe, 2015; melesse et al., 2018; mishra & sam, 2016; persha et al., 2017; santos et al., 2014; schling & pazos, 2024; wiig et al., 2011). a few observational studies, however, have found insignificant effects in specific countries (amir-ud-din et al., 2024; doss et al., 2014; kumar et al., 2020; yokying & lambrecht, 2020). decision-making power is usually measured in the household, except for grabe (2015) on women’s participation in community meetings. thresholds for considering a woman ‘empowered’ vary widely, from having (some) input in (key) family decisions, to full independence. findings on ‘economic empowerment’8 are largely positive, particularly on employment and earnings in sub-saharan africa (ajefu et al., 2024; peterman, 2011; efobi et al., 2019) and vietnam (menon et al., 2017). nevertheless, negative effects on employment were identified in india’s patrilineal societies (bahrami-rad, 2021), where the combination of property inheritance and social stigma against women working outside the home reduced their economic participation, 8 economic empowerment includes employment, entrepreneurship, influence on household expenditure, and similar outcomes. 11 particularly in agriculture. similarly, in china, reforms granting land leasing rights increased men’s off-farm employment more than women’s, leaving them with greater housework and childcare responsibilities (shi et al., 2024). wlrs were also found to increase household expenditure/savings on healthcare, education and women’s and girls’ goods, and to decrease those in traditional men’s goods (brulé, 2010; muchomba, 2017; nguyen & le, 2023; quisumbing & maluccio, 2003). four studies on india’s inheritance reform, however, reveal troubling family-planning outcomes: landed women were more likely to marry paternal cousins to keep property within male lineages (bahrami-rad, 2021) and showed a stronger preference for male offspring (bose & das, 2024). related evidence indicates increased female foeticide (bhalotra et al., 2020) and child mortality (rosenblum, 2015). overall, these findings suggest that – despite pro-women land reform – social norms adapt to preserve traditional inheritance patterns, as households seek more sons to maintain male control over family assets. a small evidence base explores women’s experiences with ipv, with mixed findings. while some studies show protective effects of wlrs (biswas et al., 2024; muchomba et al., 2014; panda & agarwal, 2005), others report increased ipv under specific conditions (peterman et al., 2017; shwachman kaminaga & sheldon, 2022; ward & harlow, 2021), particularly in south asia (anderson & genicot, 2015; murshid, 2017). 4.2.2. investment and environmental sustainability the review identified 19 studies on investment and environmental sustainability9, mainly concentrated on sub-saharan africa (80%) and mostly based on observational evidence. one experimental study – the only randomized-controlled trial (rct) in this review – found that land formalization in benin promoted long-term investments, with particularly strong effects among female-headed households (goldstein et al., 2018). quasi-experimental studies show mixed findings. in rwanda, land regularization increased soil conservation, particularly among female-headed households (ali et al., 2014). similarly, in zambia, widows’ inability to inherit land reduced fertilizer use, fallowing and intensive tillage (dillon & voena, 2018). in india, adding women’s names to land titles boosted agricultural investment and the use of higher-quality inputs (santos et al., 2014). nevertheless, other studies found no significant effects. in nicaragua, for example, titling alone had no effect, suggesting the need for complementary support for vulnerable groups (de la o campos et al., 2023). observational studies present similarly mixed findings, though most support a positive relation. for example, in ghana, insecure wlrs reduced fallowing (goldstein & udry, 2008), while in kenya, tenure insecurity under patrilineal inheritance systems discouraged long-term investments among male heirs in male-headed households (though this effect did not extend to women) (linkow, 2019). likewise, in malawi, stronger women’s tenure security in uxorilocal communities 9 these outcomes were grouped as the reviewed investments involve adopting sustainable fuels or agriculture, often with conservation measures. 12 promoted investment (benjamin, 2020), but reduced men’s tenure security, discouraging their long-term investments (lovo, 2016). interestingly, several studies explored how different rights across the ‘bundle’ shape investment. for example, in ghana, alienation reduced investments in female-owned plots, while other rights showed no significant effects (aikins et al., 2021). conversely, in malawi, alienation was linked to increased investment in organic manure, whereas other rights were not (deininger et al., 2021). adoption of climate-smart agriculture was more likely when management rights aligned with ownership and economic rights, although results varied across countries, right types, and land managers’ gender (teklewold, 2023). these mixed results could be explained by variations in how investments are defined and measured, or by their nature and timeframe (short-term or long-term), which, carrying different risks, are likely to elicit different responses to varying levels of tenure security. 4.2.3. agricultural productivity the review identified 13 studies on agricultural productivity, mainly concentrated on sub-saharan africa (84%) and based on observational studies (61%). an experimental approach found that benin’s land demarcation increased long-term investment among female-headed households but did not improve agricultural yields in the short term, suggesting that productivity benefits may take longer to materialize (goldstein et al., 2018). some quasi-experimental studies evaluated the productivity impacts of government land policies. in ethiopia, land certification correlated positively with higher agricultural output value, particularly for female-headed households (bezabih et al., 2016; holden & ghebru, 2011). likewise, in vietnam, individual and joint land-use certificates improved rice yields (newman et al., 2015). however, in malawi, redistribution only improved productivity among male-headed households (mendola & simtowe, 2015). observational studies particularly show the relationship between women’s tenure security and productivity. in malawi, for instance, the fear of land loss only reduced productivity among female farmers, but not among their male counterparts (deininger et al., 2019). similarly, in ghana, women’s lower productivity was attributed to insecurity over retaining land while it lay fallow (goldstein & udry, 2008). in kenya, women farmers with titles were more productive than those without (owoo & boakye-yiadom, 2015). further, in nigeria, access and ownership rights were associated with higher productivity (daudu et al., 2022), while in rwanda, the ability to sell or collateralize land increased productivity, especially for households that acquired farmland through purchase, loans or leases (kamande & bahati, 2019). this aligns with findings from ethiopia, where rental market participation increased productivity among female-headed households (bezabih et al., 2016; holden & ghebru, 2011). studies employed various measures of productivity, including physical yields (e.g., per hectare/acre) (benjamin, 2020; daudu et al., 2022; mendola & simtowe, 2015; owoo & boakyeyiadom, 2015), and monetary measures, such as revenue (bezabih et al., 2016; deininger et al., 2019; goldstein et al., 2018; holden & ghebru, 2011) and profits (goldstein & udry, 2008; masterson, 2007). 4.2.4. natural resources management 13 the review identified only three studies on nrm. all of them focus on ethiopia and investigate a single aspect (land rental decisions), providing preliminary support for the positive effects of wlrs. akpalu & bezabih (2015) show that female farmers often avoid renting out their land due to fears of losing it to male tenants. together with findings on productivity, this evidence suggests that improving institutional support for wlrs and dispute resolution could make land markets more efficient. quasi-experimental evidence reinforces this point. specifically, holden & ghebru (2011) found that paper-based certificates increased the likelihood that female-headed households would rent out their land. however, upgrading to digitized certificates had little additional effect (persha et al., 2017). future research should extend beyond ethiopia, whose land laws differ from those in most other developing countries and even within sub-saharan africa, limiting findings’ generalizability. for instance, land sales and collateralization are illegal, and in some areas, non-residents risk losing their lrs (stevens et al., 2020). thus, pro-women land policies in more typical land markets could yield different nrm outcomes. 4.2.5. financial inclusion the review identified eight studies on financial inclusion. the evidence is limited but evenly distributed across regions and across observational and quasi-experimental designs (50% each). findings point to a generally positive relationship, supported by four studies (balasubramanian et al., 2019; nguyen & le, 2023; persha et al., 2017; santos et al., 2014). in vietnam, for instance, land-use certificates allowed women to collateralize their land, reducing dependence on costly informal credit markets (nguyen & le, 2023). access to credit also enabled investment in education and health, generating spill-over effects. notably, this study is one of the few in the broader literature on lrs to identify clear impacts on financial inclusion (deininger et al., 2019). nevertheless, benefits may depend on men’s involvement. for instance, while landed women are more likely to have formal savings and accounts, they are also more likely to obtain credit when owning land jointly with a male relative (balasubramanian et al., 2019). furthermore, land interventions may not benefit women’s financial inclusion as much as men’s. in ethiopia, for example, digitized certificates increased credit-based business transactions, but gains were smaller among female-headed households (persha et al., 2017). not all studies observed significant effects. in nicaragua, titling had no significant impact on credit (de la o campos et al., 2023). in peru, the relationship between landownership and ‘credit resources’ was also insignificant (schling & pazos, 2024). similarly, in tanzania, women living in communities with stronger property and inheritance rights were no more likely to own a savings account (peterman et al., 2017). indicators of financial inclusion vary from access to formal credit and microcredit to bank account ownership and reliance on informal loans. some aspects of financial inclusion remain unexplored, such as access to digital payment services, remittances, and (crop) insurance. 14 4.2.6. welfare the review identified 11 studies on welfare10, primarily in sub-saharan africa (82%) and observational (64%). findings are mixed, with some highlighting negative impacts under specific conditions. quasi-experimental studies consistently demonstrate that pro-women land policies can improve welfare for both men and women. in ethiopia, for instance, longer land certificate tenure increased consumption, particularly among female-headed households (holden & ghebru, 2011). similarly, land redistribution in malawi boosted wealth and welfare, especially in matrilineal communities (mendola & simtowe, 2015), while in vietnam, women’s individual certificates increased household expenditures and reduced poverty (menon et al., 2017). another study in malawi (asfaw & maggio, 2018) found that temperature shocks reduced household consumption more severely when women managed land alone; however, these effects were weaker in matrilineal districts, where women’s stronger tenure security and greater investment in agricultural technologies helped buffer the impact. findings from observational studies are more ambiguous. in tanzania, for example, women in communities with strong property and inheritance rights enjoyed higher expenditures and savings (peterman, 2011). similarly, in burkina faso, women’s landownership was associated with higher income among livestock-keepers (montcho et al., 2023). however, where women lack market access, complementary inputs, or rights enforcement, the land-welfare nexus weakens. in such contexts, welfare may even improve when men retain control over assets (bhaumik et al., 2016). for example, in malawi, women’s landownership correlated negatively with income from high-value agriculture (bhaumik et al., 2016). this aligns with the argument that tenure security alone cannot drive socio-economic development without institutional support and access to complementary inputs (de janvry & sadoulet, 2016). similarly, deere et al. (2004) observed that wlrs often benefit off-farm income more than farm income. welfare has been measured through different proxies, including consumption, income (farm/offfarm), expenditure, savings, asset value, poverty lines, and self-reported poverty status. most studies understand welfare as access to material goods, as is traditional in economics. future studies could explore alternative measures capturing life satisfaction and relative deprivation, such as subjective well-being, experienced preferences, and the indexes inspired by sen (2008)’s ‘capability approach’.11 4.2.7. health 10 while these studies focus on income, consumption and savings, their authors generally use them as proxies for welfare. 11 see, for example, the human development index and the oecd’s better life index. 15 the review identified 20 studies on health12, evenly distributed between sub-saharan africa and south asia and between quasi-experimental and observational methods. findings strongly suggest that wlrs improve health outcomes, particularly for children. quasi-experimental studies link wlrs to improved child anthropometric measures. for example, in nigeria, joint landownership improved women’s caloric intake and reduced child stunting and underweight rates (aderemi, 2021). in india and kenya, inheritance reforms were associated with better child anthropometric measures (ajefu et al., 2022, 2024) through better education and household bargaining power (ajefu et al., 2022). however, not all studies find direct effects. in india, kumar et al. (2020) observed that landownership alone was insufficient to mitigate child stunting; instead, women’s education and household wealth were more critical. when women control crop choices, they prioritize food availability and dietary diversity. in rwanda, for example, female landownership improved both (kamande & bahati, 2019), though the effect of joint ownership alone was inconclusive. similarly, in vietnam, management and use rights in de facto female-headed households were linked to improved food security (bairagi et al., 2022). women’s income and decision-making are important mediators: observational studies from ghana (doss, 2006) and india (rehman et al., 2019), for instance, showed that women’s asset ownership directs spending toward food, while in nepal, women landowners had greater decision-making, which correlated with better child anthropometric measures (allendorf, 2007). most studies focus on household or child-level outcomes, with only aderemi (2021) examining women’s nutrition. indicators also vary, with most relying on anthropometric scores or caloric intake, and only two assessing nutritional diversity (kamande & bahati, 2019; burrone & giannelli, 2023). two studies assess health beyond nutrition-related measures. in vietnam, female-only land-use rights led to a larger reduction in child illness rates and a greater increase in insurance coverage than male-only or jointly held rights (menon et al, 2014), suggesting a shift toward child health expenditures as predicted by intra-household bargaining theory. in kenya, individual landownership reduced hiv rates among those women most at risk of survival sex by providing economic security and alternatives to risky sexual behaviors (muchomba et al., 2014). 4.2.8. education the review identified eight studies on education, mainly in india (50%) and based on quasiexperimental methods. evidence consistently shows that wlrs improve educational outcomes, especially for women and girls. for example, in vietnam, women’s land-use certificates increased children’s school 12 most studies (90%) focus on nutritional outcomes, typically measured through anthropometric indicators. these are categorized under the health domain, as stunting, wasting, or underweight rates reflect not only food intake or security but also broader health factors (de haen et al., 2011), including disease prevalence, sanitation, and healthcare access (fao, 1999). 16 enrollment (menon et al., 2014). in india and the philippines, pro-women inheritance reforms raised higher educational attainment for women (bose & das, 2017) and their daughters (deininger et al., 2013; roy, 2015; quisumbing et al., 2003). consistent with intra-household bargaining theory, wlrs can shift household expenditure toward education. in rural ghana, for example, households where women owned more farmland spent more on education (doss, 2006). interestingly, while wlrs improve outcomes for girls, effects on boys are mixed: in india, bose & das (2017) found reduced attainment, while deininger et al. (2013) (on the same indian reform) and quisumbing et al. (2003) (on the philippines) found positive effects only for girls. 4.2.9. intra-household dynamics the review identified only one quasi-experimental study on intra-household dynamics13, limiting the empirical evidence to india. family conflict is often modeled through bargaining models that include separation or divorce as potential exit strategies. anderson & genicot (2015) extended this framework by including suicide as an extreme exit option and found that pro-women inheritance reform increased suicide rates for both sexes due to marital conflict. despite its robust causal design, the study’s findings may not generalize beyond india, where divorce and separation are extremely rare (anderson & genicot, 2015). where marriage dissolution is more accessible, wlrs may influence household dynamics differently, fostering either conflict or cooperation. 4.2.10. social capital and collective action the review identified three observational studies on social capital and collective action14, mainly in sub-saharan africa. despite their small number, most report positive effects. as social capital cannot be directly measured (meinzen-dick et al., 2004), studies use proxies like participation in organizations or social events. in uganda, wlrs increased women’s involvement in coffee cooperatives and collective marketing, especially in gender-equal or joint-ownership households (meier zu selhausen, 2016). in vietnam, women’s share of family land increased their (non-)productionrelated group membership and attendance at social events (nguyen & le, 2023). however, in ghana, yokying & lambrecht (2020) found no significant differences in agricultural group membership between landed and landless women. 4.2.11. social stability 13 by ‘intra-household dynamics,’ we refer to the ways in which wlrs affect family relationships, fostering either cooperation or conflict, particularly between spouses. 14 collective action refers to coordinated land management through common property regimes or inter-farms collaboration (meinzen-dick et al., 2004). social capital underpins collective action, and it is often analyzed jointly (krishna, 2004). 17 the review identified only one quasi-experimental study on social stability, which found no significant effects of digitized certificates on land disputes in ethiopia (persha et al., 2017). two important considerations, however, contextualize this finding. first, the study focused on the marginal benefits of digitized certificates over paper-based ones, rather than assessing the overall impact of certification. second, the low incidence of disputes limited statistical power (persha et al., 2017). 4.3.geographic and time coverage the review uncovered effects across 46 countries, plus two multi-country studies.15 however, evidence availability varies considerably across and within regions (figure 4). figure 4: distribution of effects, by country of analysis sub-saharan africa (123 effects, 49 articles) and south asia (37 effects, 26 articles) dominated the literature, reflecting their high levels of rural poverty (castañeda et al., 2018) and the substantial policy relevance of wlrs. in contrast, europe & central asia (one effect) and the middle east & north africa (three effects, two articles) are critically underrepresented. east asia & pacific (19 15 balasubramanian et al. (2019) and sommer et al. (2024) aggregate data from multiple developing economies. 18 effects, 12 articles) and latin america & the caribbean (17 effects, 10 articles) have a scattered evidence base, insufficient to generalize findings at the regional level. the analysis paints a modest picture of outcomes’ coverage outside the two focal regions (figure 5). research on empowerment is common to all regions but mostly concentrated in sub-saharan africa, as are studies on investment and productivity. south asia, despite ranking second in terms of geographic coverage and extensively relying on traditional agriculture, holds limited evidence on the latter two outcomes. on the other hand, health is well-represented in both focal areas, while receiving no attention elsewhere. figure 5: distribution of effects, by region and outcome even within well-covered regions, research concentrates on specific countries. research in subsaharan africa focuses mostly on tanzania, ethiopia and malawi, while research in south asia appears overwhelmingly clustered on india. this trend appears driven by data availability and the possibility of exploiting natural experiments for analysis. countries undergoing legal or policy changes tend to attract scholarly interest since they offer unique opportunities to identify generalizable effects. notable instances include ethiopia’s and benin’s land certification programs (started in 1998 and 1993, respectively), india’s inheritance reform (2005), and vietnam’s land law (1993), which serve as sources of 19 discontinuity in multiple articles in this review.16 broadly speaking, the main land programs covered by the reviewed studies occurred in the late 1990s and early 2000s, and were accompanied by extensive documentation and standardized data collection efforts, largely supported by the international community. most studies draw on periodical data from the early 2000s to 2015, with no significant regional differences in temporal coverage. the most recent evidence is from nigeria, with data extending to 202117. the temporal distribution of data does not reveal specific patterns in terms of outcomes or geographies; however, recent studies tend to consider a broader range of rights within the ‘bundle’ compared to earlier research. when examining how different rights in the ‘bundle’ intersect with geographical coverage, subsaharan africa and south asia still dominate the evidence base, especially for ownership/inheritance rights. while this geographic focus carries over to other dimensions, east asia is relatively better represented in access/use and alienation rights. this suggests that, in this region, specific local contexts – such as legal frameworks and customary practices – and sociocultural dynamics may shape wlrs in ways that differ from other regions. data availability from major land reforms (e.g., vietnam) likely also drives scholarly focus. 4.4.research methods and data most studies adopt observational (142 effects from 57 articles) or quasi-experimental designs (57 effects from 37 articles) (figure 6), with only two studies (three effects) employing experimental approaches. among these, only goldstein et al. (2018) (on benin) used an rct – the “gold standard” in causal inference (higgins et al., 2018) – leveraging a major land reform in the country. 16 appendix d presents further details on programs/reforms covered in the reviewed studies. 17 see daudu (2022). 20 figure 6: distribution of effects, by country of analysis and study design common identification strategies include instrumental variables, differences-in-differences, and their combination with matching methods. matching methods and inverse probability weighting are often used in observational studies to address confounding bias. key data sources include the living standards measurement study, the household living standards survey, the integrated household panel survey, and the demographic and health survey.18 most studies (72%) rely on cross-sectional data, single or repeated (153 effects, 69 studies), while only a few use panel data (49 effects, 27 studies), largely limited to china, india, vietnam and selected countries in sub-saharan africa (benin, ethiopia, ghana, malawi, nigeria, rwanda and tanzania). longitudinal data enable the observation of long-term outcomes (e.g., productivity, education), the use of fixed-effects estimators, and the study of dynamic processes (e.g., lagged effects). however, large-scale longitudinal surveys with land modules remain rare. 18 details in appendix d. 21 recent studies increasingly use individualand plot-level analyses,19 as already noted by meinzendick et al. (2019), enabling estimation of local effects with high internal validity. nevertheless, studies where the parcel is the explicit unit of analysis remain relatively niche20. first appearing in goldstein & udry (2008), plot-level studies in our sample account for only 15 studies (18%), mostly on investment (nine studies, or 60% within this domain), productivity (seven studies, or 54%), financial inclusion and nrm (one study each), with mainly positive effects. apart from de la o campos et al. (2023), kelly et al. (2019), and newman et al. (2015) – on nicaragua, haiti and vietnam, respectively – all plot-level analyses refer to sub-saharan africa (80%). while this focus on local-level analyses provides rich insights, it has reduced attention to ‘macrolevel’ and cross-country analyses. furthermore, few studies differentiate outcomes by women’s characteristics (e.g., marital status), except for research on family and inheritance law reforms. figure 7: distribution of articles, by type of rights analyzed despite extensive research on individual rights, only 12 studies explicitly compared the effects of individual and joint rights, largely due to data limitations (figure 7). even when data is available, however, many studies aggregate these categories to maintain statistical power, with half providing combined analyses. when differentiated, individual rights improved decision-making over joint rights in sub-saharan africa (doss et al., 2014). both lr types were beneficial compared to landlessness; however, in india, individual rights showed no effect, and joint rights had negative effects, reflecting heterogeneous local outcomes. in vietnam, instead, both types of rights increased women’s self-employment (menon et al., 2017), but female-only rights improved health and education outcomes more than male-only or joint rights (menon et al., 2014). in kenya, both increased vulnerability to ipv (ward & harlow, 2021). further research is needed to confirm 19 given our focus on gendered impacts, included studies provide either individual-level data (on women) or plot-level data (with gender-specific information), even when the household is the observation unit. 20 data measured at different scales can be integrated, for instance, plot-level variables analyzed at individual or household levels. here, however, the measurement level refers specifically to the outcome variable. 22 whether individual rights are more empowering for women, as suggested by agarwal (2003) and bhatla et al. (2010), among others. 5. discussion of the main findings and research gaps 5.1. wlrs’ effect on development outcomes synthesizing such a diverse literature is challenging due to the wide range of outcomes and the heterogeneity of the indicators used to measure them. to provide a structured and interpretable overview, we followed approaches similar to higgins et al. (2018) and, partly, lawry et al. (2017), reporting the number—and, in our case, the proportions—of positive and statistically significant effects by outcome21. this approach uncovers outcome patterns and identifies areas of abundant or limited evidence (figure 8). empirical evidence remains uneven across outcomes and geographies. it is larger in the area of empowerment and, to a smaller extent, productivity, health, and investment, whereas other outcomes warrant increased focus. figure 8: proportion of positive effects, by region and development outcome 21 the diversity of outcomes and measurement indicators, along with multiple regressions using alternative proxies for the same outcome within individual studies, renders a meta-analysis impractical and a structured summary of effect sizes challenging to interpret. 23 the strongest, most coherent evidence of positive effects is on empowerment (figure 8). lrs consistently improve women’s decision-making and economic empowerment; though impacts on ipv and family planning, particularly in south asia, vary. as meinzen-dick et al. (2019) note, there is also coherent, albeit limited, evidence of positive impacts on productivity, financial inclusion, and social capital. although limited in number, the evidence on education and health is unambiguously positive. future research could explore potential long-term, intergenerational benefits of wlrs, such as whether children of landed women become healthier, more productive adults, contributing to poverty reduction. greater attention is also needed on nutritional diversity – a key but unexplored aspect of food security (fao, 1996). evidence on investment and welfare is moderate but inconsistent, with some studies reporting negative effects, partly contrasting meinzen-dick et al. (2019), who noted a high level of agreement on ‘technology adoption,’ and lawry et al. (2017), who found relatively consistent positive effects of broader lr recognition on long-term investment22. the evidence on intra-household dynamics, nrm, and social stability is insufficient to draw definitive conclusions. at the same time, discussions on climate adaptation and resilience remain largely absent from the literature, with only asfaw & maggio (2018) addressing them. future research should examine how wlrs contribute to such outcomes across different climatic and institutional contexts. further, as in lawry et al. (2017), existing evidence on nrm focuses exclusively on land rental, while other practices affecting economic efficiency (e.g., use, sales, purchases, or gifting) remain unexplored. 5.2. progress with respect to previous reviews while our outcome classification does not directly align with meinzen-dick et al. (2019)’s or lawry et al. (2017)’s, several points are worth mentioning. first, by extending the timeframe, this review adds over 30 recent studies,23 reflecting the surge in research following transnational large-scale land acquisitions and the russian invasion of ukraine, which intensified land pressures in the global south (dell’angelo et al., 2023). second, this broader scope enables inclusion of additional outcomes (education, health, intrahousehold dynamics, social stability, social capital and collective action) beyond those covered by previous reviews. more specifically, while meinzen-dick et al. (2019) recognized potential impacts on women’s bargaining power and decision-making over consumption, human capital and intergenerational transfers, this review assesses the ‘mediated’ outcomes themselves, rather than bargaining power in these domains. it also expands the ‘credit’ category (present, in different capacities, in both previous reviews) to encompass broader financial inclusion indicators. 22 these discrepancies stem from different eligibility criteria and our inclusion of more recent studies. lawry et al. (2017) had a broader scope, with many reviewed studies lacking a gender lens. 23 in terms of number of reviewed studies, the closest comparison is meinzen-dick et al. (2019), which also focused on wlrs. lawry et al. (2017) covered broader lr recognition and is less directly comparable. 24 the review finds no progress in resilience and limited gains beyond agricultural productivity, health, and investment. third, this review proposes two preliminary classifications of lr typologies considered in the empirical literature. drawing on schlager & ostrom (1992), the first shows an almost exclusive focus on ownership/inheritance; the second, contrasting individual and joint rights, highlights a predominant focus on individual rights or combined analyses. therefore, despite theoretical expectations (agarwal, 2003), studies examining heterogeneous impacts across different lr typologies remain scarce. 5.3.further considerations beyond our outcome classification, no eligible studies addressed several potential outcomes suggested by economic theory (stevens et al., 2020), theoretical frameworks (meinzen-dick et al., 2019), or qualitative research (higgins et al., 2018; lawry et al., 2017). these include displacement, extension services, democracy and governance, public infrastructure, human development, inequality, post-conflict recovery, poverty reduction, sustainable landscapes and biodiversity conservation. macro-level outcomes (e.g., democracy and governance, inequality) are largely absent, likely due to authors’ focus on local effects with strong internal validity. yet, institutions influence both individual experiences and broader socio-economic systems: if rights’ recognition affects women and communities, national-level impacts are also plausible. future research should explore whether wlrs produce systemic effects. observed trends and gaps appear largely driven by data availability and uneven opportunities to exploit natural experiments, leading to over-representation of certain countries and outcomes, and few studies on different lr typologies. in particular, the intuitive relationship between rights and agency has enriched research on empowerment, but also overshadowed outcomes not directly tied to gender equality. 6. conclusion despite recent growth in empirical studies, evidence on the land-women-development nexus remains fragmented across regions, outcomes, and methods. building on meinzen-dick et al. (2019), this review advances the field by applying the prisma protocol and systematizing 202 causal effects from 96 publications across 46 countries. this approach enhances transparency and reproducibility, while clarifying areas of consensus, contradictions, and evidence gaps. specifically, the review (1) codifies effects’ direction and significance, (2) proposes an original classification of 11 outcomes related to wlrs’ recognition, and (3) critically synthesizes empirical evidence, identifying patterns across outcomes and geographies. the most consistent findings concern empowerment, particularly decision-making and economic empowerment, with positive spillovers to education and health. these results support longstanding claims that secure lrs can strengthen women’s agency and shift household investments toward human capital (meinzen-dick et al., 2019). however, contradictions remain: gender-equitable land reforms may improve women’s bargaining power but also trigger violence ‘backlash’ or regressive family planning shifts, particularly under entrenched patriarchal norms. likewise, evidence on investment, productivity, financial inclusion, and welfare is highly heterogeneous, confirming that 25 tenure security alone is insufficient without complementary access to inputs, markets, and effective lrs enforcement (de janvry & sadoulet, 2016). important gaps remain. research disproportionately focuses on sub-saharan africa and south asia, especially where legal or policy reforms have generated natural experiments. methodologically, reliance on observational and cross-sectional data limits insights into long-term impacts. moreover, studies tend to privilege ownership and inheritance rights, with less attention to other dimensions of the ‘bundle’ – despite recent literature beginning to move in this direction. neglected outcome areas include nrm, social stability, climate resilience, and broader macrolevel processes like governance and inequality. our findings highlight clear policy priorities: land interventions can promote development and women’s empowerment, but effectiveness requires policies that account for local gender norms, guard against unintended backlash, and are supported by agricultural services, financial infrastructure, and lrs’ enforcement. the research agenda should expand geographically and thematically, moving beyond decisionmaking and individual ownership to explore how women benefit from land and how these pathways affect different outcomes. advancing this agenda requires the systematic collection of longitudinal micro-data, including the mode of acquisition (e.g., inheritance, purchase), rights’ typologies (individual or joint), women’s characteristics, and specific dimensions of the ‘bundle.’ such efforts should follow standardized, gender-sensitive protocols and include macro-level indicators capturing local institutional settings. addressing these gaps will enable a more comprehensive understanding of how wlrs contribute to inclusive and sustainable development, and their role in climate adaptation and resilience at local and systemic levels. declaration of competing interest the authors have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. data availability the review protocol is accessible at 10.17605/osf.io/45qkb. effects’ classification is available upon request. acknowledgements we are grateful to s. beretta, f. perali, f. cerniglia, and two anonymous referees for their insightful comments on an earlier version of this paper. we also thank the seeds annual conference 2024 participants for their valuable feedback. references abay, k. a., breisinger, c., glauber, j., kurdi, s., laborde, d., & siddig, k. 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(2020). landownership and the gender gap in agriculture: insights from northern ghana. land use policy, 99, 105012. https://doi.org/10.1016/j.landusepol.2020.105012 https://doi.org/10.1016/j.wdp.2023.100486 https://doi.org/10.2307/145670 https://doi.org/10.1086/262050 https://doi.org/10.1136/bmjopen-2020-046069 https://doi.org/10.3368/jhr.43.2.325 https://doi.org/10.1016/j.landusepol.2020.105012 consumer intentions to purchase organic pasta with blockchainbased traceability giulia maesano1, seyyedehsara sadrmousavigargari1, alessandra castellini1 1department of agricultural and food sciences. alma mater studiorum university of bologna, viale fanin 50, bologna, italy *corresponding author: giulia.maesano2@unibo.it this article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the version of record. please cite this article as: maesano g., sadrmousavigargari s., castellini a. (2025). consumer intentions to purchase organic pasta with blockchain-based traceability, just accepted. doi: 10.36253/bae-17195 highlights • subjective norms strongly influence purchase intentions for blockchain-traceable pasta. • perceived control impacts adoption by facilitating access to blockchain-verified products. • trust in certifications does not significantly impact blockchain-based purchase intentions. • positive attitudes toward the technology drive blockchain adoption in food traceability. • attitude towards traceability does not positively influence the purchase intention of pasta traced with blockchain technology abstract the increasing complexity of global food supply chains has heightened consumer concerns about food safety, quality and authenticity, and triggered a growing demand for transparency-enhancing technologies such as blockchain. this study examines the factors influencing consumers’ intention to purchase organic pasta with blockchain-based traceability using an extended theory of planned behaviour (tpb) framework. in addition to the traditional tpb constructs, the study incorporates trust in quality certifications and attitudes towards blockchain technology to provide a comprehensive analysis of decision-making processes. the data was collected via an online survey of 190 italian respondents and analysed using partial least squares structural equation modelling (pls-sem). the results show that subjective norms, perceived behavioural control and attitudes towards technology significantly influence purchase intentions, while trust in quality certifications and attitudes towards the traceability of blockchain do not significantly influence purchase intention.. these findings suggest that while blockchain technology is recognised for its potential to improve transparency, its practical benefits are not yet fully understood or appreciated by consumers. this study contributes to the literature on consumer behaviour in the agri-food sector and provides practical insights for policy makers and marketers to promote blockchain-based traceability systems. mailto:giulia.maesano2@unibo.it keywords: consumer purchase intention; theory of planned behaviour (tpb); organic pasta; blockchain-based traceability; food fraud, technology 1. introduction in the food sector, issues such as traceability and food safety have become central to the supply chain, with producers increasingly prioritising these aspects over other objectives (alshehri, 2023). this shift goes hand in hand with an emerging paradigm shift in consumer demand. consumers are now showing an increasing preference for products that are perceived as safer (mahsun et al., 2023). this is evidenced by the fact that more and more consumers are expressing concerns about food safety and quality and, therefore, favour foods whose labels provide clear and accurate information about product characteristics (lewis & grebitus, 2016; sadílek, 2019; moruzzo et al. 2020; kaczorowska et al. 2021). the european parliament and the council have also established quality certification for organic agri-food products through regulation (eu) no 2018/848. according to this regulation, organic products have been developed to respond to a specific market where consumers demand products whose production respects the environment and animal welfare, preserves biodiversity and contributes to rural development (sampalean, et al., 2021). however, consumers cannot verify credence attributes and must therefore rely on the reliability of the manufacturer's or retailer's claims (plasek and temesi 2019). credence attributes refer to product characteristics that consumers cannot directly verify before purchase and must rely on external assurances to assess their validity (plasek and temesi, 2019; lassoued and hobbs, 2015). in the context of food products, these attributes include factors such as organic certification, geographical origin, sustainability claims, and production methods (fernqvist and ekelund, 2014) the credibility of these parties also depends on consumer trust in the food system, including the regulatory authorities responsible for ensuring food safety and compliance with food labelling regulations (fernqvist and ekelund 2014; lassoued and hobbs 2015; meijer et al. 2021). trust is a multi-layered concept that is shaped by several factors, including the geographical and temporal distance between the parties involved, cultural norms, the institutional environment and historical events that influence perceptions of food safety and quality (berg, 2004). currently, consumer trust in the food system is uncertain, particularly in relation to transparency and authenticity (frewer, 2017; wu et al., 2021; menon et al., 2021) and more generally in relation to perceptions of food safety (macready et al., 2020; meijer et al., 2021). the main cause of this trend is the inherent complexity of the food supply chain, which involves a multitude of parties and processes (hassoun et al., 2020; reitano et al., 2024) and can lead to food safety issues (meijer et al., 2021). this decline in consumer confidence has significant consequences, such as the limited effectiveness of certifications and consequently a decrease in potential demand for products with credible attributes, such as origin, production process characteristics and product properties (marozzo et al., 2022). from a public interest perspective, low trust has negative implications for sustainable development and public health policies that rely on traditional forms of certification to inform consumers about the nutritional and ethical value of products kjærnes, 2006; sapp et al, 2009; hobbs and goddard, 2015; kaiser and algers, 2017). considering the above-mentioned characteristics of the agri-food production system, it is essential to develop a coherent management system adapted to its specific needs (gardeazabal et al., 2023). in response to the prevailing concerns in the agri-food sector, a number of technological innovations have emerged to improve and strengthen food traceability. among these, blockchain technology (bct) has attracted much attention (reitano et al., 2024). the emergence of cryptocurrencies has led to the popularisation of bct, which can be defined as a decentralised and immutable register of information (gupta and sadoghi, 2019; krzyzanowski, guerra & boys, 2022). in such a system, all subjects in the chain can access the recorded information at any time, but without the possibility to change a record (tian, 2017; zhao et al., 2019; wünsche and fernqvist, 2022). this function is suitable for meeting the specific requirements of the food industry and creating a reliable system for tracking the path of a food product from production to consumption. this will make it easier to ensure food safety (saurabh & dey, 2021; mónica martínez castañeda & fejoo, 2023) and has the potential to combat problems such as label tampering, counterfeiting of designations of origin and the introduction of substandard products (ayan et al., 2022; serra-majem et al., 2020). in the food sector, bct seems to be a promising solution that could enable more transparency (see javaid et al., 2021; aldrighetti et al., 2021; singh & sharma, 2022; vern et al., 2024). it is already being used to record all transactions between actors involved in the supply chain to ensure the transparency and traceability of products (see kamilaris et al., 2019; galvez et al., 2018). however, despite its potential, a fundamental factor is the understanding of the benefits attributed by consumers, as emphasised by feng and colleagues (2020). indeed, the widespread adoption of this technology depends on consumer perception and acceptance (albertsen et al., 2020). as singh et al. (2023) argue, the success of any technological innovation in the food sector is inextricably linked to consumer acceptance. in the consumer market, there is a growing willingness among consumers to adopt innovative technologies that facilitate access to comprehensive data on supply chain operations (cozzio et al., 2023). in line with this premise, a study by osei et al. (2021) hypothesises that consumers will adopt bct technology if it can demonstrably improve food safety and quality. numerous studies have shown that bcts have a positive impact on consumer purchasing decisions (sander et al., 2018; violino et al., 2019; polenzani et al., 2020; lin et al., 2022). however, other authors have pointed to a discrepancy between consumer perception and the actual value attributed to technology-specific information confirming that food has been traced with bcts (shew et al., 2022). liu et al. (2023) investigated the relationship between consumer trust in the agri-food system and certification and showed a positive influence of high levels of trust on preferences for products with traceability and the use of bcts. the influence of bcts on purchasing decisions, especially for certified food has a significant impact on demand and thus contributes to the success of bct-based systems. the comprehensive traceability information that this technology provides along the entire food supply chain represents significant added value for consumers. contini et al. (2023) have shown that bct promotes a positive attitude towards consumer preferences and perceptions, thus increasing trust in the system due to satisfaction with the perceived quality of the certified products. as mazzù et al. (2021) note, bct-based traceability also requires the involvement of certification and regulatory bodies in the supply chain system. this helps to increase consumer confidence in the reliability of the information provided, while facilitating access to comprehensive food information, including declarations from food supply chain actors, such as organic certification, chemicals used and agricultural practises. although the technological potential of bct has been demonstrated in previous studies (kamilaris et al., 2019; galvez et al., 2018), there is still little research on consumer perceptions and intentions. in particular, there is a need to investigate how consumers evaluate bct-enabled traceability in combination with established constructs such as trust, attitudes and perceived ease of use. in recent literature, theoretical frameworks such as the theory of planned behaviour (tpb) have been used to analyse consumer intentions to adopt blockchain in food systems. the studies by dionysis et al. (2022) and lin et al. (2021), for example, highlighted the importance of subjective norms and perceived behavioural control. however, the results regarding attitudes towards bct were inconclusive. contini et al. (2023) emphasised the potential of bct to increase trust, but their results show a discrepancy between consumer trust in traditional certifications and the added value of blockchain traceability. to fill this gap, this study investigates which factors influence consumers’ intention to buy organic pasta with blockchain-based traceability. we conducted an online questionnaire with a sample of 190 italian respondents to investigate their behaviour towards organic pasta, as it already plays an important role in several practical applications of bct. using the extended tpb model, we were able to identify the factors that influence consumption. constructs such as attitude, subjective norms and perceived behavioural control were complemented by trust in quality certifications and attitudes towards technology to increase the predictive power of the model. partial least squares structural equation modelling (pls-sem) was used to analyse the relationships between the constructs and validate the research hypotheses. 2. theoretical framework and research hypotheses development the theory of planned behaviour (tpb) is a theoretical model from the field of psychology with particular significance for predicting and changing human behaviour, especially in connection with the use of technology (ajzen, 2020; fleiß et al., 2024; cudjoe et al., 2023). the tpb postulated by ajzen (1980) is based on the assumption that individual behaviour depends on three basic elements: the individual's attitude, subjective norms or social pressure and perceived behavioural control. the tpb has been used in the consumer decision-making literature in a variety of contexts (lin, 2007), including in the context of food choice, where it has been used to identify the motivational factors underlying the choice of one product over another (nardi et al., 2019; sogari et al., 2024) and to predict consumers' behaviour and intentions towards organic products (armitage and conner, 2001). the tpb is based on the idea that a person's behaviour depends on their intention to perform that behaviour. behavioural intention is the result of the interaction of three factors: 1) attitude (att): represents a person's inclination to perform a certain action. it is a person's opinion or judgement about adopting or performing a particular behaviour based on their values, beliefs and previous experiences with that behaviour. a positive attitude leads to a greater likelihood of behaving consistently with one's intention. 2) subjective norms (sn): refers to the influence of other people's thoughts and attitudes towards a particular behaviour. in other words, it is the social pressure to perform or avoid a certain action, which may result from the expectations, encouragement or opinions of others. 3) perceived behavioural control (pbc): refers to the perception of a person's ability to perform an action or the perception of the difficulty or ease of a particular behaviour depending on certain factors. several studies have investigated consumers' intention to buy products tracked with a blockchainbased system. in the study by dionysis et al. (2022), the factors influencing the purchase intentions of coffee consumers considering coffee products that can be tracked with a blockchain-based tracking system are analysed using the tpb model. the original tpb model was extended to include additional constructs such as trust, past habits and environmental protection. the study contributes to the literature by providing insights into the factors that influence consumers' purchase intentions and shows that attitude towards coffee that is traceable with a blockchain-based traceability system, subjective norm and perceived behavioural control are positively associated with purchase intention. the study by lin et al. (2021) also utilised the tpb to investigate the factors influencing chinese consumers' intentions towards blockchain food traceability technology to ensure the food safety and quality of chinese organic food. the study proposed an integrated conceptual framework combining two established theoretical models: the tpb and the informational success model (iss). the study found that attitude and perceived behavioural control significantly and positively influence intention to use blockchain adoption, while subjective norms are positively but not significantly correlated with intention to use. the work of menozzi et al. (2015) analyses consumer attitudes and behaviour towards traceable food to explain the intention to buy traceable food using tpb. the results show that the predictive power of the tpb model increases significantly when new variables are added: habits, trust, past behaviours and socio-demographic variables. the results show that attitudes and trust influence the purchase intention for traceable food products. prisco et al. (2022) present an integrated approach that combines the tam (technology acceptance model) and the tpb (theory of planned behaviour) and adds as benefits the additional factors "efficiency and safety", "reduced costs" and "quality of customer service" perceived by companies adopting blockchain technology. the results show that attitude and perceived behavioural control are the most important predictors of intention to adopt blockchain, while perception of benefits is the most important predictor of attitude. in addition, subjective norms were found to have a positive effect on behavioural intention, while the effect of perceived ease of use on attitude was not significant. in their study, liu et al. (2023) explored the association between consumer trust in agricultural and food systems and the impact of certifications. their results showed a positive correlation between high consumer trust and a preference for products with certificates of origin and the use of bcts. the influence of bcts on consumer purchasing decisions, especially for certified food, is an important factor influencing demand and thus the success of bct-based systems. when investigating the relationship between trust in the food system and certifications, it was found that a high level of trust positively influences preferences for pdo and bcts, while it has a less pronounced effect on preferences for organic certifications (contini et al., 2023). the absence of a notable interaction between the degree of trust in the food system and the preference for organic certification can be attributed to the finding that such a preference does not rely on the degree of trust in the food system in general. rather, it is determined by the alignment of values among the various actors involved in the organic supply chain (thorsøe, 2015). this trust is reinforced by consumer satisfaction with the quality of the products (ladwein and romero, 2021) and is linked to the organic certification logo (janssen and hamm, 2012). based on the analysis of previous literature, the tpb (ajzen, 1991) was chosen as the conceptual model for this study. however, this study aims to improve the predictive power of the tpb. in addition to the original items of the tpb, such as attitude, subjective norms and perceived behavioural control, additional constructs are introduced: trust in quality certification and attitude towards technology. based on the above literature and theory, the following hypotheses are formulated. to avoid verbosity, the indicators in the table are presented in capital letters. see table 1 below for details. table 1. hypotheses and paths hypotheses path h1: subjective norms positively affects the intention to purchase pasta traced with blockchain technology (sn) sn→int h2: perceived behavioral control positively affects the intention to purchase pasta traced with blockchain technology (pbc) pbc→int h3: attitude towards traceability positively affects the intention to purchase pasta traced with blockch2ain technology (att) att→int h4: trust in quality certifications positively affects the intention to purchase pasta traced with blockchain technology (tqc) tqc→int h5: attitude towards technology positively affects the intention to purchase pasta traced with blockchain technology (tec) tec→int 3. data and method 3.1. data collection the data collection tool consists of an online questionnaire developed on the qualtrics platform to explore consumer intentions regarding organic pasta tracked through an innovative traceability system. the design of the questionnaire is based on the tpb presented in the previous section. the tpb approach effectively identifies factors influencing decision-making and perceived risk, making it suitable for the focus of this study on traceable products. the questionnaire aims to capture the determinants influencing consumer preferences and behaviour by incorporating the key tpb constructs. the questionnaire was divided into several sections, each designed to collect specific information related to the objectives of the study. 1) introduction: this section provided a general overview of the study and ensured that participants kept their responses confidential. 2) tpb constructs: this section explored participants' intentions and the key dimensions of the tpb model: attitude, subjective norms and perceived behavioural control. the intention construct captures the likelihood that consumers will consider purchasing pasta with blockchain traceability once it is available. the subjective norms construct measures the influence of social factors, including family, academia, media, and retail, on consumers' decision to purchase pasta with blockchain traceability. the construct of perceived behavioural control assesses consumers' perceptions of the ease or difficulty of accessing and using products with blockchain traceability. this includes finding such products in shops and using the relevant technology, which is critical to understanding potential barriers to adoption. the attitudinal construct captures consumer perceptions of the benefits associated with using blockchain technology for food traceability and focuses on aspects such as safety, transparency, authenticity and production standards. the design of these questions was guided by previous research such as dang & tran (2020), dionysis et al. (2022) and menozzi et al. (2015) to ensure that all key variables were comprehensively addressed. a 5-point likert scale was used, ranging from 'strongly disagree' to 'strongly agree'," so that participants could express a nuanced opinion on each statement. 3) consumer trust in quality certification: trust in quality certification is an important factor that influences consumers' confidence in the safety and authenticity of products. this construct assesses the extent to which consumers trust the quality certification information provided by companies. this block focused on assessing trust in organic food producers and sellers, drawing on the work of li et al. (2023). 4) attitudes towards technology: the questions in this section were organised based on the technology readiness index (tri), a scale validated by parasuraman (2000). this index measures consumer attitudes toward technology in four dimensions: optimism (opt), innovativeness (inn), discomfort (dis), and insecurity (ins). by including these dimensions, the survey was able to assess how technological readiness influences consumer acceptance of traceable systems. respondents rated their level of agreement on a 5-point scale, which allowed for an in-depth analysis of their comfort and adaptability to new technological applications. 5) socio-demographic questions: in the last section, demographic information such as age, gender, education level and income were collected. the scales for the tpb constructs and the technology readiness index were adopted from previous studies to ensure their validity and reliability. the use of established scales in the study ensured that the constructs measured accurately reflected the concepts they were intended to assess. table 2 shows the latent variables and items in detail. variable items intention (int) 1. when blockchain-traceable pasta becomes available, i intend to buy it 2. when blockchain-traceable pasta becomes available, i will look for it and consider buying it 3. when blockchain-traceable pasta is available, i am inclined to buy it subjective norms (sn) 1. i would buy pasta tracked via blockchain technology because my partner, family and friends approve it 2. i would buy pasta tracked via blockchain technology because scientists are in favour 3. i would buy pasta tracked via blockchain technology because the media (tv radio, social media) is in favour 4. i would buy pasta tracked via blockchain technology because the food manufacturers and supermarkets promote it perceived behavioural control (pbc) 1. i feel able to find blockchain-tracked food products in shops easily 2. i think it is easy to use apps or online tools to verify food traceability via blockchain 3. i think it is easy for me to follow the food production chain thanks to blockchain attitude toward bct (att) 1. with the use of blockchain, organic pasta traceability information is more secure 2. the origin of organic pasta tracked with blockchain traceability is always transparent 3. organic pasta information with blockchain traceability is more authentic 4. organic pasta with blockchain traceability will meet higher production standards trust toward quality certifications (tqc) 1. companies always comply with quality certification regulations 2. companies provide consumers with transparent information on quality certification 3. quality-certified product information is always truthful 1. i am optimistic about the innovative impact of technology attitude toward technology (tec) 2. i feel at ease to become familiar with technology 3. i believe that the adoption of technology can generate a significant improvement in transaction and information security 4. i find innovative technology to be mentally stimulating the online questionnaire was administered to a sample of italian respondents to gain insight into the factors that influence consumer behaviour. the survey was distributed online via the most popular social networking platforms (whatsapp, instagram and facebook) to maximise reach and engagement. these platforms facilitated efficient data collection across all social networks and allowed for broader geographic and demographic representation. the survey was available on social media platforms from 30 october 2023 to 28 february 2024. during this period, participants were able to complete the questionnaire at their leisure. a total of 251 responses were collected, of which 190 were completed. a widely used procedure for estimating the minimum sample size in plssems is the “tenfold rule” (hair et al., 2011), which assumes that the sample size should be greater than 10 times the maximum number of inner or outer model terms that point to a latent variable in the model. pls-sem is advantageous as it does not impose strict assumptions about data distribution and can provide reliable results even when working with limited sample sizes by maximizing explained variance and minimizing estimation bias (russo & stol, 2021). a combination of a random and snowball system was used to recruit participants. this approach was chosen for its practicality, as it enabled the efficient collection of responses from easily accessible individuals and facilitated the expansion of the research area and access to larger social networks. the random sample initially enabled rapid distribution of the survey, with the questionnaire accessible and fillable online and a particular focus on social media users. 3.2. data analysis the data analysis was conducted using the softwere stata 18.5. structural equation modelling (sem) was used to examine the extended theoretical framework and test the hypotheses. sem combines various multivariate analysis methods that facilitate the investigation of multiple interactions between several latent variables (berki-kiss & menrad, 2022). it is widely used in the social sciences, especially in the field of psychology. in this study, the partial least squares (pls) structural equation model (sem) was utilised. pls-sem is a statistical tool that has gained popularity among researchers who use it to analyse empirical data and evaluate different relationships simultaneously (hair et al., 2019). the applications of covariance-based sem (cb-sem) and partial least squares sem (plssem) are complementary, rather than competitive (marcoulides & saunders, 2006). pls-sem is more effective than cb-sem for analysing complex cause-effect relationships between multiple latent variables (sarstedt et al., 2016). in addition, pls-sem provides reliable results even with relatively small sample sizes compared to covariance-based sem. furthermore, hair et al. (2011) suggested that pls-sem is the optimal approach when research aims to identify causal relationships with unidentified potential variables that influence individuals' multidimensional behaviour and intentions. the process consists of two steps. these include the structural model (inner model) and the measurement model (outer model). the structural model evaluates the development of theories and hypotheses, while the reliability and validity of the constructs are evaluated using the measurement model (russo & stol, 2021). 4. results table 3 contains the most important socio-demographic indicators. in the study sample, men (41%) and women (48%) were almost equally distributed. the largest age groups were 30-39 (33%) and 4049 (29%), followed by those over 60 (22%). the youngest group comprised only 16% of participants. it is noteworthy that there were no people between the ages of 50 and 59. the survey participants have a high level of education: the vast majority (84%) have a university or postgraduate degree. only a small percentage (14%) have a high school diploma, and even fewer (2%) have completed middle school. none of the respondents reported having completed primary school. most respondents (46%) were white-collar workers, followed by those working in businesses and public institutions (31%). a smaller proportion (16%) were unemployed and only 7% were students. in terms of income, the majority of participants (60%) reported an income of between €0 and €26,000. a smaller percentage (21%) earned more than 26,001 euros. interestingly, 18% of participants stated that they had no income. table 3. socio-demographic characteristics detail of respondents percentage (%) gender male 41 female 48 other genders 6 prefer not to answer 5 age 19-29 16 30-39 33 40-49 29 50-59 0 over 60 22 education elementary school 0 middle school 2 high school 14 college degree 31 post-degree (master, phd..) 53 occupation enterprise and public institution 31 employee 46 not employed 7 unemployed 4 retired 5 student 7 income level (euro / month) 0 € 18 from 0 to 10.000 € 33 from 10.001 to 26.000€ 27 from 26.001 to 55.000€ 7 from 55.001 to 75.000€ 4 from 75.001 to 120.000€ 2 >120.000€ 8 the measurement model was assessed on the basis of convergent and discriminant validity. convergent validity refers specifically to the extent to which the indicators of the variables accurately indicate and measure them and to which other measures of the same variables correlate appropriately (bani-khalid et al., 2022). to determine the convergent validity of the measurement model, we assessed the loadings of the indicators, the average variance extracted (ave) and the composite reliability (cr) as well as cronbach's alpha. according to the literature, the values for cronbach's alpha and composite reliability (cr), average variance extracted (ave) and the loadings of the indicators must be higher than 0.70, 0.70, 0.5 and 0.70, respectively (khan et al., 2023; lin et al., 2021; rubel et al., 2021). accordingly, the loadings of the indicators were examined at in the first stage.as shown in table 4 in the final measurement model, all indicator loadings exceed the threshold of 0.70. it means that the construct explains over half of the variance of the indicator. therefore, acceptable item reliability is provided. moreover, cronbach’s alpha and composite reliability are typically used to evaluate internal consistency reliability (hair et al., 2019). as table 4 shows all composite reliability and cronbach α values are higher than 0.70, as it suggests that the elements of the same latent variable are similar. the total mean of the squared loadings of the items associated with the construct is represented by the average variance extracted (ave) (russo & stol, 2021) was used to evaluate convergent validity. the table 4 displays that theaverage variance extracted (ave) from each latent variable is higher than 0.5. it means that the construct explains more than half of the variance of its items. in summary, table 4 demonstrates that the standardized loadings, cronbach’s alpha , cr , ave are all higher than the values recommended by the literature. therefore, convergent validity was confirmed based on the results. table 4. reliability and validity tests latent construct items standardize d loadings cronbach's alpha cr ave intention (int) int1 0.898 0.834 0.901 0.753 int2 0.932 int3 0.764 subjective norms (sn) sn1 0.873 0.869 0.910 0.717 sn2 0.858 sn3 0.814 sn4 0.840 perceived behavioural control (pbc) pbc1 0.783 0.814 0.890 0.731 pbc2 0.892 pbc3 0.885 attitude toward bct (att) att1 0.882 0.893 0.926 0.757 att2 0.841 att3 0.900 att4 0.856 trust toward quality certifications (tqc) tqc1 0.908 0.904 0.940 0.839 tqc2 0.929 tqc3 0.911 attitudes toward technology (tec) tec1 0.916 0.916 0.947 0.856 tec2 0.929 tec3 0.930 discriminant validity shows the extent to which the items represent the target construct and whether a latent variable measures a separate construct (russo & stol, 2021). in this study discriminant validity assessed with the heterotrait-monotrait ratio of the correlations (htmt). the heterotrait-monotrait ratio of correlations (htmt) is defined as the average of the correlations between items measuring different constructs (heterotrait correlations) relative to the geometric mean of the average correlations for items measuring the same construct (monotrait correlations) (hair et al., 2019). the result of table 5 illustrates that all heterotrait-monotrait ratio of correlations (htmt) are below the threshold value of 0.90recommended by (hair et al., 2019), which confirms the sufficient discriminant validity of the individual constructs. it can therefore be concluded that the measurement model fulfils the required criteria for validity and reliability (reliability as well as convergent and discriminant validity). table 5. results of the discriminant validity heterotraitmonotrait ratio of correlations (htmt) int sn pbc att tqc tec int sn 0.782 pbc 0.762 0.680 att 0.690 0.695 0.856 tqc 0.404 0.494 0.487 0.389 tec 0.730 0.563 0.798 0.772 0.270 we evaluate the structural model in terms of variance explained (r2), effect size (f2), predictive relevance (q2), path coefficient (β), and hypotheses testing. the structural model is employed for the purpose of investigating the impact of exogenous variables on endogenous variables. the results of the hypotheses developed are shown in table 6. the adjusted r2 of 0.58 indicates that subjective norms, perceived behavioural control, and attitudes toward technology explain a substantial portion of the variance in consumers' intentions to purchase traced pasta using blockchain technology. effect size (f2) was calculated to measure the magnitude of the significant effects. as cohen (1988) suggested, in the structural model, f2 values of 0.02 indicate small effects. 0.15 indicates medium effects, and 0.35 indicates large effects (bani-khalid et al., 2022). table 5 shows that subjective norms have a medium effect size, and perceived behavioural control and attitude toward technology have a small effect size. in this step, the q2 value is calculated to evaluate the pls path model's predictive accuracy. the approach relies on the blindfolding technique that eliminates individual points from the data matrix. these omitted points are then imputed using the mean, followed by estimating the model parameters. thus, the q² does not exclusively represent out-of-sample prediction; it reflects a combination of outof-sample predictive ability and in-sample explanatory power. the blindfold procedure predicts the missing data points for each variable using these estimated parameters as inputs. small discrepancies between the original and predicted values result in a higher q2 value, indicating higher prediction accuracy (hair et al., 2019). based on the result of table 6, the q2 value for the endogenous latent construct is greater than zero. the conclusions were drawn based on p-values (see table 6), which led to the decision to accept or reject the hypotheses taken in the study. to answer h1: "subjective norms positively affects the intention to purchase pasta traced with blockchain technology", the results show that sn have a statistically significant positive effect on the int to purchase blockchain-traceable products. therefore, the h1 is accepted. the coefficient of 0.403 indicates that social influence plays a significant role in shaping consumer behaviour. to answer hypothesis h2 "perceived behavioural control positively affects the intention to purchase pasta traced with blockchain technology", it was also found to have a positive and significant effect on intention. however, the effect size (0.032) was smaller than that of sn. thus, h2 is accepted. in response to h3 "attitude towards traceability positively affects the intention to purchase pasta traced with blockchain technology", contrary to expectations, att did not significantly affect intention. the very low coefficient and the high p-value (0.969) indicate that the attitude towards blockchain-traceable products does not directly influence the purchase intention in this context. therefore, h3 is rejected. hypothesis h4, "trust in quality certifications positively affects the intention to purchase pasta traced with blockchain technology", was not supported, as indicated by the non-significant coefficient (0.006) and high p-value (0.913). to answer h5 "attitude towards technology positively affects the intention to purchase pasta traced with blockchain technology", tec has a significant and positive influence on purchase intention with a coefficient of 0.306. therefore, the h5 is accepted. table 6 result of the hypothesis testing hypothesis no. relationship coefficient p-value decision r2_a q2 f2 h1 sn -> int 0.403 0.000*** confirmed 0.582 0.439 0.216 h2 pbc -> int 0.187 0.017** confirmed 0.032 h3 att-> int 0.003 0.969 unconfirmed 0.000 h4 tqc -> int 0.006 0.913 unconfirmed 0.000 h5 tec -> int 0.306 0.000*** confirmed 0.099 note: ** p < 0.05, *** p < 0.01 5. discussion this study provides empirical evidence on the determinants influencing consumers' intention to purchase blockchain-enriched products, with a focus on pasta. the results highlight important factors influencing consumer behaviour and offer practical implications for marketers and policy makers seeking to promote the adoption of blockchain technology in the food industry. these include subjective norms, perceived behavioral control, and attitudes toward technology, which significantly influenced consumers’ purchase intentions for blockchain-traceable organic pasta. the results confirm that technology readiness is an important determinant of consumers' willingness to purchase pasta with blockchain-based traceability. result indicates that consumers who have a positive attitude towards technological innovation are more likely to have the intention to purchase blockchaintraceable products. this is consistent with the technology readiness index (tri), which postulates that optimism and familiarity with technology can facilitate the adoption of new technological solutions (parasuraman, 2000). the significance of this relationship suggests that fostering a positive attitude towards the benefits of technology, such as increased transparency and safety in the food supply chain, may encourage consumers to adopt products that utilise blockchain traceability. this emphasises the importance of education and technological awareness in marketing strategies. this result is consistent with the findings of lin et al. (2021), who also found a positive correlation between consumers' technology readiness and their willingness to purchase technology-enabled products. the positive impact of tec suggests that individuals with an optimistic attitude towards the benefits and simplicity of technological products are more willing to accept products that incorporate blockchain for traceability. this finding emphasises the importance of technological awareness and educational initiatives. concrete examples of educational initiatives include awareness campaigns to educate the public on how blockchain improves food traceability and safety; interactive digital tools, such as mobile apps or qr codes on packaging, that allow consumers to access transparent supply chain data; and workshops and online courses aimed at consumers and food professionals to improve understanding and trust in blockchain-based certifications. this result provides a valuable opportunity for companies to develop marketing campaigns that emphasise the transparency, security and innovation of blockchain technology. in this way, companies can gain consumer trust and encourage adoption. for example, educating consumers about how blockchain technology guarantees authenticity and traceability could appeal to technologically people who value innovation and transparency in their food. the study found that att does not have a significant impact on purchase intent. this result may be explained by the specificity of blockchain technology, where consumers may not fully understand or prioritise the benefits even if they have a positive attitude towards it. alternatively, external factors such as lack of trust could also have a stronger influence on purchasing decisions, thus obscuring the effect of attitude. this result is in line with the findings of previous studies by dang & tran (2020) and prisco et al. (2022), which found that general attitudes towards a product do not always translate into purchase behaviour, especially in contexts where consumers do not fully understand or appreciate the perceived benefits. however, this finding contradicts the results of dionysis et al. (2022), who postulated that a positive attitude towards traceability and transparency in the food industry is a good predictor of purchase intention. the divergence in results may be attributed to contextual differences or the presence of features of blockchain technology that consumers have not yet fully understood. even if consumers are in favour of the concept of traceability, this does not necessarily mean that they are motivated to buy pasta with blockchain traceability. this suggests a disconnect between attitudes and actions, with consumer attitudes not always translating into actual purchasing behaviour. further research could explore how this gap can be bridged by linking blockchain traceability to more directly perceived benefits such as food safety, quality assurance and environmental sustainability. pbc was identified as an important predictor of purchase intention, suggesting that consumers who believe they have the ability and resources to identify and utilise blockchain-traceable pasta products are significantly more likely to express a purchase intention. this result is consistent with the tpb framework, which states that consumers who feel able to find and use blockchain traceable products are more likely to have the intention to purchase them. this finding emphasises the importance of ease of access and use for technology-driven innovations such as blockchain. improving the level of control perceived by consumers through intuitive applications and clearer information can increase the likelihood of adoption. moreover, the finding is consistent with the results of studies by lin et al. (2021), dang & tran (2020), dionysis et al. (2022) and prisco et al. (2022), which have shown that pbc plays a central role in influencing consumer intentions, especially in the context of new technology adoption. the significant role of pbc suggests that ease of access and use are key factors for consumers. if consumers perceive blockchain-traceable pasta as easily accessible and verifiable, they are more likely to express interest in purchasing it. therefore, companies should prioritise the development of user-friendly and accessible blockchain-based traceability solutions. one possible solution is the development of straightforward applications or digital resources that allow consumers to effortlessly verify the traceability of products, improving their perceived control over the purchasing process. despite the inconsistency of sn as a predictor in different studies, the results of this research context show its importance. this result is consistent with the theory of planned behaviour, which postulates that the approval and support of significant others, e.g. family, friends and social networks, can strongly influence a person's behavioural intentions (ajzen, 1991). this suggests that opinions, recommendations and social pressure from peers, family, media and credible authorities are critical to consumers' intention to purchase pasta with blockchain traceability. this finding contradicts the discrepancies observed in other studies, but highlights an important aspect of social influence on consumer behaviour. the importance of subjective norms in this study suggests that social acceptance and approval can be effective in driving the adoption of products with blockchain traceability. incorporating social evidence, such as endorsements from influencers, experts, and food industry leaders, into marketing strategies could effectively generate consumer interest. in addition, the implementation of educational initiatives that spread knowledge about the benefits of blockchain technology, supported by authoritative figures such as scientists and food safety professionals, could help to reinforce societal expectations of purchasing such products. finally, the hypothesis that trust in quality certifications directly influences consumers' intention to buy products with blockchain traceability was not confirmed. this result indicates that trust in existing quality certifications does not necessarily lead to a higher purchase intention for blockchaintraceable products. one possible explanation for this is that while consumers trust conventional quality certifications, they do not perceive traceability via blockchain as directly linked to these traditional certifications or do not see it as an added value. the lack of significant results could also be due to a knowledge gap or a lack of perceived relevance between quality certifications and blockchain technology. this result is in line with the result reported by contini et al. (2023). they also found that trust in traditional quality certifications is not necessarily transferable to new technological applications. this can be attributed to the fact that there is no recognisable link in consumers' minds between blockchain traceability and existing quality measures. the lack of emphasis on the role of trust suggests that consumers may not perceive blockchain technology as a natural extension of existing quality certification systems. an alternative explanation is that respondents may have a high level of trust in traditional certifications, but do not perceive the value of blockchain technology as being enhanced by them. this emphasises the need for clear communication about how blockchain can complement and enhance quality certification by providing additional layers of transparency and authenticity beyond traditional systems. 6. conclusion this study provides new insights into the factors that influence consumers' intention to buy blockchain-labelled pasta. the study highlights that while attitudes towards the technology positively influence consumer purchase intentions, general attitudes towards products with blockchain traceability and trust in existing quality certifications were not found to be significant predictors. this suggests that successful marketing strategies should focus on educating consumers about the benefits of blockchain, simplifying the user experience, and leveraging social influences to drive adoption of blockchain-based traceability. these findings have important implications for both policy makers and producers in the agri-food sector. for policy makers, the study suggests that blockchain technology can be an important tool to combat food fraud and ensure food safety and quality. there is therefore a need for supportive policies and regulations that promote the adoption and implementation of blockchain throughout the food supply chain. governments can incentivize blockchain adoption to improve trust in food certifications through targeted policies and financial support. first, governments could launch consumer education initiatives, such as awareness campaigns and digital tools, to improve public understanding of how blockchain enhances food safety and authenticity. finally, regulators could develop clear and enforceable standards for blockchain traceability, ensuring that certified products meet high standards of transparency and accountability. for producers, the results of this study can help develop effective marketing and communication strategies to promote products with blockchain traceability. by emphasising benefits such as authenticity, traceability and sustainability, producers can gain consumer trust and increase the appeal of products with blockchain traceability. while blockchain can potentially increase trust in existing quality signals, the challenge is to effectively communicate its benefits to consumers. by recognising the importance of social norms, attitudes towards technology and perceived behavioural control, stakeholders can promote transparency, accountability and sustainability in the agri-food industry, creating a more efficient and competitive environment. although we acknowledge the limitation of our sample size, the use of pls-sem ensures the robustness of our results, as this method is suitable for studies with relatively small samples (hair et al., 2011; sarstedt et al., 2016). this method allows us to work with small sample sizes, maximize explained variance, and minimize estimation bias (russo & stol, 2002). moreover, the combination of snowball and random sampling is effective for data collection, it is important to recognise the inherent limitations of these techniques. first and foremost, there is the possibility of selection bias in non-probability sampling. for future studies, it would be beneficial to consider the use of random sampling to minimise bias. acknowledgements this study was carried out within the agritech national research center and received funding from the european union next-generationeu (piano nazionale di ripresa e resilienza (pnrr) – missione 4 componente 2, investimento 1.4 – d.d. 1032 17/06/2022, cn00000022) for the author g.m. this paper reflects only the authors' views and opinions, neither the european union nor the european commission can be considered responsible for them. the study received funding from the european union's he research and innovation program under grant agreement no 101084188, for the authors a.c. and s.s.. views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the european union or the european research executive agency (rea). neither the european union nor the granting authority can be held responsible for any use that may be made of the information the document contains. a special thanks goes to professor maurizio canavari for his valuable guidance and supervision throughout this work references ajzen, i. 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(2025). weathering the storm: a systematic review of climate change adaptation in agriculture. methods, metrics, and impacts, biobased and applied economics, just accepted. doi:10.36253/bae-16753 abstract much attention has been paid to the impact of the agriculture sector on the environment and its contribution to climate change. however, the sector is also vulnerable to the impacts of climate extremes. thus, a growing number of studies have focused on adapting to these changes. we conducted a systematic literature review and assessment of the evidence gathered from 124 studies on the impacts of and adaptation to climate change in the agriculture sector in oecd member countries. results highlight a significant knowledge gap in understanding the full economic effects of climate change as the impacts of climate change on input costs is not extensively studied in the way that impacts on farm output is. additionally, there is a need to understand the indicators used to assess climate impacts in agriculture for easier comparison across studies. we recommend targeted research and funding to close this knowledge gap including conducting long term 2 analyses to evaluate the costs and benefits of adaptation strategies as well as capacity building and knowledge exchange. key words: systematic review, climate change, adaptation, agriculture, impacts jel codes: c10, 013, q12 1. introduction agricultural production is sensitive to weather and therefore directly affected by climate change (nelson et al, 2014; malhi et al, 2021). with rising temperatures, shifting rainfall patterns, and the increasing prevalence of extreme weather events such as floods and droughts, not to mention the growing scale of potential climate impacts, it is critical to assess the risks posed to the sector and evaluate the effectiveness of adaptive practices (world bank, 2010) to aid decision-makers in resource allocation. given that budgets are often constrained (mysiak et al, 2018), understanding the risks and potential adaptation strategies within the agriculture sector can allow policymakers to understand where funds and resources are most needed and can be most effective. challenges for effective adaptation policy for agriculture include financial constraints, lack of coordination and knowledge gaps. these factors often lead to maladaptation whereby the sector becomes more vulnerable to climate change (oecd, 2023). to minimise the risk of maladaptation, adaptation policies that are flexible and suitably robust across a range of climate scenario outcomes should be prioritised (ignaciuk, 2015). to aid policymakers in avoiding maladaptation in this way, this review aims to present a range of climate risks faced by farmers and the effectiveness of adapting to these risks with specific adaptation strategies. furthermore, understanding the impacts of climate change on agriculture and how detrimental effects can be mitigated by investing in appropriate adaptation strategies is crucial not only for the sector itself but also for the wider economy (gallic & vermandel, 2020), and for global food security. historically, much attention has been paid to the agriculture sector’s impact on the environment and its contribution to climate change. however, there is now a growing body of literature on the impacts of climate change on agriculture and how adapting to climate change affects farm businesses (burke & emerick, 2016; cogato et al, 2019; chatzopoulos et al, 2020). where previously the economic literature has rarely included weather or climate extremes in analysis, there is now development of literature on 3 the effect of climate extremes on agriculture. this shift in focus reflects the increasing awareness and acknowledgment of agriculture being severely impacted by climate change and not just a driver of climate change. as farmers attempt to cope with the growing impacts of climate change on their enterprises, it has become more pertinent to explore the impacts of this in the literature. while numerous empirical analyses exist in the literature, the methods, metrics, and case studies are highly heterogeneous, making it challenging to compare findings across studies and formulate consistent recommendations for decision-makers. therefore, one of the aims of this review paper is to identify and summarize the methods and metrics used, highlighting gaps and the latest advancements in the literature. we scrutinize a large number of studies that meet our inclusion criteria, synthesizing and discussing the diversity in metrics used to capture climate change, the variety of adaptation strategies, and the range of outcome variables included in models to assess impacts. this approach allows us to identify less-studied areas, observe methodological trends in the literature driven, among other factors, by the availability of new and more sophisticated data, and recommend directions for future research. finally, we outline the economic costs and benefits associated with climate change adaptation strategies, as well as comparing the impacts of climate change on agriculture with and without such adaptation. unlike previous reviews, our study examines both climate change impacts and adaptation, with a focus on oecd member countries. this broader scope enables a more comprehensive assessment of agriculture in relatively comparable socio-economic contexts, improving the potential for cross-study and cross-country comparisons. such an approach supports policymakers in making decisions based on the capacity and resilience of agricultural systems, rather than prescribing specific adaptation strategies—a best-practice approach given the challenges of determining which strategies are most effective at the local level (oecd, 2023). in addition, easier comparison between studies strengthens the findings surrounding the effectiveness of adaptation strategies. by focusing on both impacts and adaptation, we add value to previous analyses, as it is well known that the impact of climate change heavily depends on the effectiveness of adaptation strategies employed. furthermore, a key aspect in formulating policy recommendations is the need for a deeper understanding of the costs and benefits of adaptation strategies. 4 ultimately, our work aims to explore the following areas; a) the methods and metrics used in the literature to assess the impact of climate change on agriculture and the effectiveness of climate change adaptation measures; b) the effects of changes in the climate system; c) the adaptation strategies employed to reduce the effects of climate change; and d) the effectiveness of these strategies in reducing different types of detrimental socio-economic losses. by assessing the effectiveness of these measures, we provide an indication of the potential economic savings of adaptation to boost farm resilience. the findings and recommendations of this review are addressed to the scientific community to inform future research and efforts to identify the adaptation strategies that are the most appropriate under different conditions. this is perceived as useful also for stakeholders and policymakers who can design, and finance measures aimed at encouraging the use of the most promising strategies. the review continues as follows. in section 2, we outline the methodology employed to identify, process and extract relevant studies conducted. in section 3, we present the results of the review including a bibliometric analysis and outline of the effectiveness of adapting to climate change in the agriculture sector. finally, in section 4 we conclude with a summary of the main findings and their implications for policy and future research. 2. methodology in order to gain a better understanding of the climate impacts affecting agriculture and how farmers are adapting to these changes, we conducted a systematic review of the literature according to prisma guidelines (page et al, 2021). the framework facilitated transparent and complete reporting of the findings (fig. 1). inclusion criteria for the review was designed to identify studies that analyse the impacts of climate change and adaptation in the agriculture sector. the population of interest was the agri-food sector in oecd countries. as mentioned in section 1, we focus on oecd countries to enable a broader examination of agriculture in settings with more comparable socio-economic conditions, environments, and policy frameworks, making cross-country and cross-study comparisons easier. the outcomes considered in the review were determinants (i.e. weather and climate shocks such as drought and flood events, changes in precipitation patterns and increases in temperature), impacts (i.e. impact 5 on profit/farm income, yield, land value), model estimation of impacts (e.g. long difference approach, fixed effects panel approach, ricardian approach), adaptation strategies employed (e.g. drought resistant crops, irrigation), and the effectiveness of these adaptation strategies. full details of the inclusion criteria can be found in the supplementary materials. we included empirical research published worldwide in english up to november 2024. paper retrieval was conducted from april 2024 to june 2024 and again in october 2024 to check for further relevant papers published after this time. only empirical research papers were considered for inclusion in the review, i.e. not letters to editors, commentaries, responses and so forth. the web of science and scopus electronic databases were searched to identify relevant literature. details of the full search strategy can be found in the supplementary materials including the search queries and keywords employed. identified studies were screened in two steps according to best practice guidelines: firstly, by titles and abstracts, and secondly by the full text. potential biases were addressed by using more than one reviewer during the screening process as recommended by prisma guidelines. publication information, study characteristics, and findings from included studies were recorded in a standardised data extraction form using covidence™ (veritas health innovation, 2016), a software programme used to organise and manage systematic and scoping literature reviews. extracted data gathered information on three main areas: impacts, adaptation, study characteristics. data on impacts included the type of weather or climate shock analysed, the impact type analysed, timing of the impact, and quantification of the impact. data on adaptation included the tool or strategy implemented, timing of the strategy, and effectiveness of the strategy. in terms of study characteristics, we gathered information on publication year, country of origin, study objectives, currency units, farm type, level of analysis, and model employed for analysis. we employ a narrative synthesis approach as this is better suited to reviews where the subject area is broad (el chami et al, 2022). 6 fig. 1. prisma flow diagram the database searches yielded 12,389 records. after removing duplicates this was reduced to 10,831 records which were screened according to the methods outlined above. robust screening of the records resulted in 124 studies being included in the review. this process was carefully and transparently documented and has been schematically summarised for simplification according to the diagram above (fig. 1). the reasons for excluding studies from the final review as outlined in fig 1 can be defined as follows. ‘irrelevant outcome’ refers to studies that do not indicate an impact of either climate change or adaptation 7 practices. ‘access issues’ refers to studies that we were unable to gain access to within the timeframe of the review process. ‘irrelevant population’ refers to studies that focused on other aspects of the agri-food sector such as trade or did not focus on an area within the scope of oecd countries. ‘grey literature’ refers to working papers, commentaries, conference abstracts or other non-empirical papers. ‘non-english studies’ refers to studies conducted and published in a language other than english. 3. results: the state of evidence on impacts and strategies in this section we begin with a bibliometric analysis to outline the temporal and spatial trends in the literature. we then examine the climate parameters explored in the literature and the impacts of these changes in the climate system. we follow this with an examination of how these impacts can be adapted to and indicate the effectiveness of these strategies. 3.1 bibliometric analysis the first relevant study which captures the research questions of this review was published in the mid-nineties. the number of studies by publication year (fig. 2) shows that interest in both climate impacts and adaptation has fluctuated over the years, however adaptation studies are lagging behind climate impact studies despite a more recent increase in interest. it is most likely that adaptation studies are simply fewer in number rather than there being slower in development or impact. given that the impacts of climate change need to be understood before adaptation measures can be planned or implemented, it is not surprising that there are more studies on impacts than adaptation. relevant studies did not cover all oecd member countries with 13 countries not represented in this review (austria, costa rica, denmark, estonia, iceland, ireland, israel, latvia, lithuania, luxembourg, norway, slovak republic and slovenia). this does not indicate that studies are not available in these regions, but rather that they did not meet our robust inclusion criteria specific to this review. for example, some studies in these countries have focused less on observed impacts in the climate system and therefore were excluded from this review. the united states offers the most publications (n=36). many studies are also conducted across europe 8 without focus on one specific country (n=12). germany offers 9 studies, australia and spain offer 7 studies each and türkiye offers 6 studies. the countries which are covered the least with only one study each are colombia, czechia, japan, new zealand, poland and portugal (fig 3). the publications can be divided into four subject areas (fig. 4). the most common is economics (n=62), followed by agronomy and agricultural sciences (n=34) and climate and environmental sciences (n=26). the remainder are classified as biology/plant/soil sciences (n=4). it is unsurprising that the majority of studies have been conducted within the economics field as the financial impacts of climate change and adapting to climate change are crucial for farmers and their farm enterprises and thus this aligns with perceived needs. fig. 2. number of studies by publication year 9 fig. 3. number of studies by oecd country fig. 4. number of studies by subject area 3.2 impacts of climate change a total of 89 studies explored climate change impacts on agriculture. in terms of the farm types that are explored, arable farms tend to dominate (n=64). nine studies examine dairy farms, and four studies examine other types of livestock farms. three studies are based on mixed crop/livestock farms and the remaining nine studies either cover all farm types or do not specify. the studies also analyse impacts across a range of levels 46% 29% 22% 3% economics agronomy/agricultural sciences climate & environmental sciences biology/plant/soil science 10 (table 1). most studies were conducted at the region or county level (n= 42). many studies were also conducted at the farm level (n= 17) and at the national level (n= 15). the remaining studies were conducted at the individual plot or crop level (n= 9) or other levels, namely the european level (n= 7). table 1. level of analysis of climate impact studies level of analysis n of studies references individual crop/plot 9 ahmed et al, 2022; alayon-gamboa, 2020; gulino et al, 2023; ruiz et al, 2019; vanongeval et al, 2023; alidoost et al, 2019; bencze et al, 2010; klatt et al, 2021; schlenker et al, 2009 individual farm 17 chatzopoulos et al, 2015; ahmed et al, 2022; blanco-penedo et al, 2020; bozzola et al, 2018; durant et al, 2018; mukherjee et al, 2013; papadopoulou et al, 2021; pourzand et al, 2020; schauberger et al, 2021; wang et al, 2021; dalhaus et al, 2020; eakin et al, 2012; hill et al, 2014; huitu et al, 2020; perez-uribe et al, 2024; skoufias et al, 2013; vroege et al, 2023 region/county level 42 akbar et al, 2020; beillouin et al, 2020; burke & emerick, 2016; demirdogen et al, 2024; dono et al, 2013; espe et al, 2017; gonzalez et al, 2008; harkness et al, 2023; himanen et al, 2013; huszvai et al, 2020; jiang et al, 2020; kaufmann et al, 1997; lee et al, 2018; li et al, 2018; linnenleucke et al, 2020; maqsood et al, 2020; mendelsohn et al, 1996; meng et al, 2017, mourtzinis et al, 2016; nguyen et al, 2023; ortizbobea et al, 2019; polsky et al, 2001; ray et al, 2018; wang et al, 2016; zalud et al, 2017; albers et al, 2017; changnon et al, 2000; di poala et al, 2023; feng et al, 2018; galushko et al, 2022; hamed et al, 2021; heil et al, 2023; lobell et al, 2011; murray-tortarolo et al, 2019; plastina et al, 2021; quiroga et al, 2009; schaub et al, 2020; sharma et al, 2019; thomasz et al, 2020; van oort et al, 2012; zipper et al, 2016; finger et al, 2018 national level 15 benito-verdugo et al, 2023; vaakan et al, 2024; chandio et al, 2020; dogan et al, 2010; himanen et al, 2013; karahasan et al, 2023; mayer et al, 1999; nasrullah et al, 2021; chavas et al, 2017; kalbarczyk et al, 2015; powell et al, 2015; reinermann et al, 2019; sheng et al, 2019; villa-falfan et al, 2023; deschenes et al, 2007 other (e.g. european level) 7 massetti et al, 2018; quiédeville et al, 2022; reidsma et al, 2007; reidsma et al, 2009; van passel et al, 2017; wu et al, 2017; fabri et al, 2022 we build on the work by ortiz-bobea (2021) to classify the methods used in the existing literature to analyse climate impacts. they are categorised as follows: biophysical approaches (n= 2), ricardian approach (n= 8) (e.g., mendelsohn et al, 1996; van passel et al, 2017; massetti et al, 2018; fabri et al, 2022), standard panel approaches (n= 57) (e.g., powell et a;, 2015; zipper et al, 2016; ortiz-bobea et al, 2019; pourzand et al, 11 2020; gulino et al, 2023; karahasan et al, 2023; demirdogen et al, 2024), time series approaches (n= 2) (hamed et al, 2021; vanongeval et al, 2023), joint estimation of short and long run responses (n= 5) (e.g., chandio et al, 2020; çakan et al, 2024) , mixed statistical and biophysical approaches (n= 1), machine learning approaches (n= 4) (e.g., feng et al, 2018; beillouin et al, 2020) and other econometric approaches (n= 10) (e.g., durant et al, 2018; ray et al, 2018; wang et al, 2021). while these categories are not an exhaustive list of all empirical approaches, they are useful for classifying the methods identified in this review. for a more in-depth discussion of some of these approaches, see ortiz-bobea (2021). standard panel approaches are the most commonly used method when analysing the impacts of climate change on agriculture. this result is unsurprising given that there is a long tradition of using longitudinal data to analyse weather conditions in agricultural economics, with a surge in interest within the scope of climate change following deschênes & greenstone’s (2007) seminal work on the impacts of climate change in the agricultural sector. jiang et al (2020) use a multiple linear regression model to quantify the relationships between corn yield and meteorological factors in the usa. they find that on average, extreme heat (kdd) is estimated to be the most stressful meteorological factor in most districts. corn yield reduces by 0.011, 0.020, 0.022, and 0.008 mg/ha for one unit of kdd increase in four growth phases, respectively. increasing precipitation reduces yield by 0.002 mg/ha per mm at the early vegetative stage, yet additional precipitation promotes yield by 0.006 and 0.005 mg/ha per mm at the grain filling stage and 0.001 mg/ha per mm at the late dry-down stage. using a standard panel approach has allowed for the examination of climate impacts on corn yields over the course of the growing season. in a similar vein, demirdogen et al (2024) estimate the impact of climate change on wheat production (yield and land) in türkiye over the past 25 years using a panel data model with fixed effects. results indicate that a 1c increase in temperature is associated with a yield loss of approximately 6% and the observed 1.5c temperature increase over the past 25 years has led to a yield loss of nearly 9%. they also find that temperature changes and drought events during may have a particularly significant impact on wheat yields. an increase of 1c in may temperatures results in a reduction of approximately 4% in wheat yields and contribute to a decrease in the size of the harvested area, exceeding 6%. 12 in most of the models in environmental economics, weather and climate measurements are solely based on temperature records. in agricultural economics these measurements are often supplemented by rainfall observations in order to characterise agricultural returns patterns (gallic & vermandel, 2020). the climate changes analysed in the literature can be categorized as changes in weather patterns (i.e. fluctuations in temperature and precipitation), and extreme weather events (e.g. drought, heatwaves, floods etc) (lucas et al, 2021). it is important to note that some studies consider more than one metric for instance, many examine both temperature and precipitation changes, or precipitation changes and drought. both temperature and precipitation are widely explored in the literature (n= 50 and 53 respectively). given that agriculture is dependent on weather conditions it is unsurprising that marginal changes in temperature and precipitation have been extensively studied. we observe a pattern among studies examining these changes that increases in precipitation in the short term tend to bring positive impacts for crop yields (although not in the long term), whereas crop yields tend to be negatively impacted by increases in temperature (burke & emerick, 2016; çakan et al, 2024; chandio et al, 2020; dogan et al, 2018). among extreme weather events, drought is the most widely studied (n=14) (e.g., zipper, 2016; sheng, 2019; schaub, 2020; nguyen, 2023) followed by heatwaves and heat stress (n= 10) (e.g., mukherjee, 2013; finger, 2018; schauberger, 2021; fabri, 2022), storms/hurricanes (n= 5) (e.g., durant, 2018; huitu, 2020; klatt, 2021), flooding (n= 3) (e.g.,klatt, 2020; wang, 2021; pérez-uribe, 2024) and frost events (n= 1) (e.g., dalhaus, 2020). these studies suggest that drought has an overwhelming negative impact on agriculture in terms of yield and economic output (benito-verduga et al, 2023; nguyen et al, 2023; quiroga et al, 2009; pérez-uribe et al, 2024; schaub et al, 2020; sheng et al, 2019). where drought may have the opposite effect on yield, other factors such as crop type (ray et al, 2018) or location (zipper et al, 2016) are at play. far less attention has been paid to extreme weather events such as flooding, storms and hurricanes, and frost events. this lack of information on the potential impacts of these shocks represents a gap in knowledge and impedes preparation for adaptation. the majority of studies assessed the impact of climate change on production (yield change, variability, or loss, including milk yields on dairy farms) (n= 81). a considerable proportion also assessed the impact of climate change on economic output (farm profitability, income, crop prices) (n= 27). only one study examines the 13 impact of climate change on input costs, for example fertiliser costs, which is also important for understanding the full financial impact of climate change and are also affected by changes in weather patterns and climate change (schaub et al, 2020). additional outcome variables include changes in farmland value (n= 10), herd size on livestock farms (n= 3), damage payments or subsidies (n= 2), and changes in product quality (n= 5). these results are summarised in fig 5 below. as the main focus in the literature is on arable farming, there is also a gap in information on the livestock sector, in which understanding climate impacts and the need for adaptation is pivotal. livestock systems face distinct climate risks—including heat stress, altered disease dynamics, and feed shortages—that require tailored adaptation strategies (rojas-downing et al, 2017). neglecting this sector undermines food security, especially for smallholders and pastoralists who depend heavily on animal production and often have limited adaptive capacity. moreover, livestock and crop systems are interdependent in many mixed farming contexts, meaning insufficient attention to livestock resilience can compromise whole-farm adaptation. in addition, a wide range of output variables have been used to convey the impacts of climate change on agriculture. nevertheless, there is no standardised set of indicators to compare and contrast these impacts, or the climate metrics involved, making it difficult to quantify the exact economic consequences of changes in weather and climate conditions. we also classify the climate impacts by timingshort, medium and long term. we define these as seasonal impacts (short term) (n= 48), annual impacts (medium term) (n= 28), and multi-annual impacts (long term) (n= 24). some studies, especially those which employ long difference methods and joint estimation approaches, can be classified in more than one category. long term impacts are the least studied within the literature and tend to focus on climate impacts on outcomes such as land value (mendelsohn et al, 1996; gonzàlez et al, 2008; bozzola et al, 2018), which require analysis over a longer time period and/or use methodologies which lend themselves to analysing longer term impacts such as the ricardian approach (chatzopoulos et al, 2015; massetti et al, 2018; fabri et al, 2022). on the other hand, short term impact studies tend to focus on changes in yields as would be expected with seasonal impacts (wu et al, 2017; beillouin et al, 2020; benito-verdugo et al, 2023). 14 fig. 5. types of impacts explored *some studies examine more than one impact 3.3 adaptation strategies a total of 47 studies explored adaptation in the agriculture sector. as is the case for climate impact studies, the majority of adaptation studies focus on arable farms (n=36) while dairy and livestock farms are less studied (5 studies respectively). the remaining studies explore mixed crop/livestock farms (n=1) or cover all farm types (n=5). in terms of the level of analysis conducted in these studies (table 3), the largest proportion was conducted at the region or county level (n= 20), followed by at the farm level (n= 12), and the individual crop or plot level (n= 8). fewer studies were conducted at the national level (n= 3) or european level (n= 4). table 3. level of analysis of the adaptation studies level of analysis n of studies references individual crop/plot 8 fernandez-ortega et al, 2023; petonen-sainio et al, 2014; 0 10 20 30 40 50 60 70 80 90 inputs damage payments, subsidies etc. herd size product quality land value economic output yields inputs damage payments, subsidies etc. herd size product quality land value economic output yields number of studies 1 2 3 5 10 27 81 15 quiroga et al, 2014; ruiz et al, 2019; wittwer et al, 2023; bowles et al, 2020; alayon-gamboa et al, 2011; domingos et al, 2021 individual farm 12 auci et al, 2023; bouttes et al, 2019; di falco et al, 2014; gafsi et al, 1997; jordan et al, 2021; láng et al, 2023; mukherjee et al, 2013; pexas et al, 2021; vanschoenwinkel et al, 2018; perezuribe et al, 2024; skidmore et al, 2022; zhang et al, 2018 region/county level 20 akbar et al, 2020; burke & emerick, 2016; harkness et al, 2023; kaugmann et al, 1997; lee et al, 2016; li et al, 2020; lu et al, 2020; parker et al, 2017; patanita et al, 1029; polsky et al, 2011; smith et al, 2021; wang et al, 2016; wang et al, 2021; jacobs et al. 2022; kath et al, 2018; whitbread et al, 2015; zhang et al, 2015; karapinar et al, 2020; wheeler et al, 2014; won et al, 2024 national level 3 michalopoulos et al, 2020; harkness et al, 2021; matsushita et al, 2016 other (e.g. european level) 4 reidsma et al, 2007; reidsma et al, 2009a; reidsma et al, 2009b; reidsma et al, 2010 in terms of the methods used in the studies examining the effectiveness of adaptation, a range of approaches have been implemented. the studies employed the ricardian approach (n= 2) (polsky et al, 2001; vanschoenwinkel et al, 2018), standard panel approaches (n= 29) (e.g., reidsma et al, 2010; difalco et al, 2014; wang et al, 2016; karapinar et al, 2020; wittwer et al, 2023), time series approaches (n= 1) (matsushita et al, 2016), joint estimation of short and long run responses (n= 4) (e.g., di falco et al, 2008; foudi et al, 2011), machine learning approaches (n= 1) (akbar et al, 2020), and other econometric approaches such as sensitivity analysis and principal-agent models (n= 12) (e.g., zhang et al, 2018; pexas et al, 2021). as is the case for impact studies, standard panel data approaches have been the most used methods throughout the included literature. joint estimation of short and long run responses as introduced by moore & lobell (2014) combine elements of panel approaches and cross-sectional methods to jointly estimate the shortand longterm impacts of climate change and adaptation (ortiz-bobea, 2021). di falco et al (2008) investigate the effects of rainfall shocks on agroecosystems productivity and how increasing the level of spatial crop diversity can mitigate negative impacts in italy using the joint estimation approach. they find that crop biodiversity ensures that the agroecosystem remains productive when facing low or scarce rainfall both over the short and long term. these findings indicate crop diversification can be a long-term adaptation strategy that can continue to bring benefits to farmers beyond a seasonal timeframe. this can encourage investment in this strategy and 16 increase adoption. while several of the included studies employ this method there is scope for this approach to be utilised further to conduct longer term experiments and better understand the costs and benefits of adapting to climate change. an array of adaptation tools and strategies have been adopted to cope with the impact of climate change. in response to droughts, irrigation (e.g. auci et al, 2023; jordan et al, 2021; lu et al, 2020), water storage (e.g. smith et al, 2021; wheeler et al, 2014) and crop diversification (e.g. di falco et al, 2008; bowles et al, 2020) have been studied. in response to more frequent heat waves, some economic agents have installed cooling systems (e.g. li et al, 2020; mukherjee et al, 2013; pexas et al, 2021). additionally, certain strategies are policy-driven rather than driven by the autonomous decisions of farmers, such as participation in agrienvironment schemes and other government strategies (e.g. won et al, 2024; aláyon-gamboa et al, 2011). on livestock farms, steps have been taken to reduce herd sizes (which also allows for meeting climate change mitigation goals) or change to breeds that are more resilient and better suited to new weather conditions (e.g. akbar et al, 2020; skidmore et al, 2022). other strategies include changing cropping patterns and tillage systems (whitebread et al, 2015; ruiz et al, 2019; peltonen-sainio et al, 2014), participating in insurance schemes (e.g. kath et al, 2018; wang et al, 2021), leasing additional acreage (e.g. zhang et al, 2018), adopting or changing fertilization techniques and/or quantities (e.g. peréz-uribe et al, 2024; reidsma et al, 2009a; reidsma et al, 2009b), and converting to organic farming (e.g. bouttes et al, 2019; reidsma et al, 2007). see fig 6 below for a complete overview of the adaptation strategies identified in the literature. 17 fig. 6 adaptation strategies explored in the literature although adaptation may seem like a simple concept of adjusting behaviours, actions, and decisions within biological, social, and built systems in response to climatic changes (smit & pilifosova, 2001), it has proven difficult to reach consensus within the literature on a definition. robert et al (2016) classify adaptation strategies through timing, temporal and spatial scopes. timing refers to whether adaptation is proactive (anticipatory) or reactive (a response after a shock). temporal refers to whether adaptation is strategic (long term adjustments) or tactical (short term adjustments). spatial refers to whether adaptation is localised (e.g. a single crop) or widespread (e.g. farm system). adaptation can also be autonomous (a decision made solely by the farmer at the farm level) or dependent (based upon changes in policies or subsidies) (forsyth & evans, 2013). the most used classification in the literature is reactive versus proactive adaptation (engler et al, 2021). however, despite the agreement on the definition of reactive and proactive adaptation, the strategies that can be included in each category can vary depending on the case study. for example, the widely used practice of changing the timing of sowing and harvesting has been classified as both proactive and reactive by different scholars. therefore, an adaptation strategy could be both depending on if it is a response (reactive) or a coping mechanism (proactive) (carman & zint, 2020). 0 2 4 6 8 10 12 14 16 18 leasing acreage specialisation reducing or changing stock cooling systems conversion to organic farming insurance participation crop diversification business & government strategies fertilisation & other inputs changing cropping patterns irrigation & water storage leasing acreage speciali sation reduci ng or changin g stock cooling system s conver sion to organic farming insuran ce particip ation crop diversif ication busines s & govern ment strategi es fertilis ation & other inputs changi ng croppin g pattern s irrigati on & water storage number of studies 1 1 2 3 3 4 4 9 11 12 17 18 proactive refers to anticipated adjustments, also referred to as anticipatory or ex ante adaptation. of the included studies, 29 referred to proactive adaptation including crop diversification and insurance participation. reactive refers to adaptation performed after a shock and is also referred to as responsive or ex-post adaptation. as expected, more studies have focused on reactive adaptation such as changing stock sizes or cropping patterns (n= 34). localised adaptation is usually conducted at the plot scale whereas widespread adaptation usually concerns the entire farm. of the studies included in the present review, 32 studies explore localised adaptation. examples include installing cooling systems in barns or changing cropping patterns. twenty-eight studies examine widespread adaptation including conversion to organic farming. strategic adaptation is the capacity to adapt in the long term (years). a total of 24 studies discuss adaptation that can be classified as strategic such as changing livestock breeds or leasing acreage. tactical adaptation is instantaneous short-term adjustments over a seasonal to daily timescale, for example, changing cropping patterns or fertiliser use. a greater proportion of the studies discuss tactical or short-term adaptation (n= 40) (table 2). table 2. adaptation dimensions timing temporal spatial proactive reactive tactical strategic localised widespread number of studies 29 34 40 24 32 28 given the uncertainty of climate change and its impacts, it is difficult for farmers to anticipate a shock and adapt accordingly in a proactive manner. this is exacerbated by lack of knowledge or insufficient information on climate change (robert et al, 2016). however, to reduce the impacts of climate change, it is crucial for farmers to take more proactive, anticipatory steps to minimise damages. to encourage farmers to undertake proactive steps to adapt, it is crucial that information on long term climate impacts is distributed. within the included studies, there is also a pattern of autonomous adaptation dominating over planned, dependent adaptation. autonomous adaptation has been a controversial topic within the literature, with economists arguing that this type of adaptation is inefficient and likely reduces the urgency surrounding necessary, planned interventions from governments or other key stakeholders (stern, 2007; eisenack, 2009; forsyth & evans, 19 2013). therefore, more evidence is needed to identify how autonomous adaptation might connect with planned adaptation and there is greater need for governments to step up with efficient interventions to reduce the burden on individual farms. 3.4 effectiveness of adaptation practices in this section, we present the effectiveness of adaptation practices reported in the included studies. the majority of the studies reported negative effects of climate change (n= 60). only five studies reported positive effects while 23 studies reported both positive and negative effects of climate change. the majority of studies assessing the effectiveness of adaptation strategies report positive impacts (n= 34). only four studies reported solely negative effects of adapting (wang et al, 2021; skidmore, 2022) while the remainder reported both positive and negative effects (n= 12) depending on other conditions such as crop types or timing, and of course how the authors define “positive” or “negative” impacts. in an example of a negative impact of adapting, wang et al (2021) demonstrate that participation in a crop insurance programme led to more detrimental impacts on crop yields as farmers did not feel the need to implement other adaptation strategies owing to their insurance participation. however, in australia, kath et al (2018) found that participating in an excessive rainfall index insurance scheme could make sugar cane farmers better off by 269.85 aud/ha on average in years with excessive rainfall. irrigation was the most commonly studied adaptation measure and has proven to have a largely positive effect by enhancing revenue through increased yields (polsky et al, 2001). for example, in the usa both li et al (2020) and lu et al (2020) find that irrigation leads to yield increases. li et al (2020) find that yields are increased by 12.28% per acre while lu et al (2020) find that crop yields increase by 147%, 139%, 132% and 124% for spring wheat, hay, corn silage and corn grain respectively. changing cropping patterns is another commonly studied adaptation strategy and has also led to generally positive results. in hungary, làng et al (2021) demonstrate how when combining barley and sweet corn yields during double cropping the overall crop yield was 18.5 tonnes/ha compared to single cropping which yields 5.6 tonnes/ha. however, in switzerland wittwer et al (2023) find pronounced and consistent yield reductions due to experimental drought events for all cropping systems. in table 4 below, we further compare the effects of climate change with the impacts of 20 adaptation in order to demonstrate the effectiveness of adaptation in boosting resilience in the agriculture sector. adaptation is only efficient if the cost of making an adjustment is less than the resulting benefits. results suggest that adaptation will reduce damages caused by climate change. however, while efficient adaptation responses can reduce the overall impacts associated with climate change, inefficient adaptation can increase costs (mendelsohn, 2006), and as such maladaptation should be avoided. the example outlined in wang et al (2021) where participating in insurance schemes led to a decrease in planned adaptation demonstrates how adaptation strategies can sometimes not have the desired effect. a similar effect can be observed in the mitigation literature where some strategies such as carbon removal can deter or delay mitigation efforts (mclaren, 2016). therefore, this mitigation deterrence theory could also be transferred to the adaptation literature as ‘adaptation deterrence’. this is particularly pertinent for insurance schemes, government strategies and subsidy payments which can prevent other adaptation strategies that aid in reducing or preventing further damage. careful planning and further research into the effectiveness of climate adaptation can prevent maladaptation from increasing the damages associated with climate change in the agriculture sector. 21 table 4. examples of net benefit of adaptation by strategy main adaptation strategies studies country examples of net benefit of adaptation irrigation & water storage akbar et al, 2020; auci et al, 2023; foudi et al, 2011; jordan et al, 2021; li et al, 2020; lu et al, 2020; patanita et al, 2019; pérez-uribe et al, 2024; polsky et al, 2001; quiroga et al, 2014; reidsma et al, 2009a; reidsma et al, 2009b; reidsma et al, 2010; smith et al, 2021; vanschoenwinkel et al, 2018; wang et al, 2016; zhang et al, 2015 usa; italy; france; chile; usa; usa; portugal; colombia; usa; spain; europe; europe; europe; usa; europe; usa; usa irrigation leads to a yield increase of 12.28% per acre (li et al, 2020). irrigation plays an important role in boosting the crop yield, with the highest increases for spring wheat (147%), hay (139%), corn silage (132%), and corn grain (124%) (lu et al, 2020). arid regions with access to stored water avoided the 13% losses in crop value experienced in irrigated areas with more limited storage during droughts (smith et al, 2021). irrigation serves as a self-insurance. irrigating farmers have higher means but lower variance of profits than non-irrigating farmers. however, for already irrigating farmers using more water volumes increases the variance of outcome (foudi et al, 2011). crop diversification burke et al, 2016; bowles et al, 2020; di falco et al, 2008; matsushita et al, 2016 usa; usa; italy; japan more diverse rotations increased maize yields (28.1% on average), including in favorable conditions (22.6%). notably, more diverse rotations also showed positive effects on yield under unfavorable conditions, whereby yield losses were reduced by 14.0%, and 89.9% in drought years (bowles et al, 2020). cooling systems li et al, 2020; mukherjee et al, 2013; pexas et al, 2021 usa; usa; sweden cooling systems contribute to an annual increase in gross revenues of between $106,830 and $162,631(mukherjee et al, 2013). farm profitability improved by +6.79% with showers and +3.37% with increased air velocity (pexas et al, 2021). business & government strategies alayón-gamoa et al, 2011; gafsi et al, 1997; harkness et al, 2021; harkness et al, 2023; mexico; france; uk; uk; usa; europe; europe; europe; usa monetary surpluses have increased considerably thanks to the high level of mw subsidies given to farms (140000ff on average) and to bonuses from the european union (cap) and from the french state. the 22 polsky et al, 2001; reidsma et al, 2009a; reidsma et al, 2009b; reidsma et al 2010; won et al, 2024 increase in monetary surplus has enabled the farmers to increase their private expenditure by 90% on average and to make large investments (gafsi et al, 1997). an increase in agrienvironment payments per hectare decreases the variability of income for mixed farms by 6% whereas increases the variability income by 3% for cereal farms. subsidies therefore have a smaller relative effect on the variability of income, in comparison to the farming practices examined in this study (harkness et al, 2023). counties with higher levels of government program participation have higher land values (polsky et al, 2001). reducing or changing stock akbar et al, 2020; skidmore et al, 2022 usa; brazil the hybrid breed is expected to be smaller, travel longer distance for water, use a wider variety of forage and would be more resilient to temperature; but it will take at least a decade to find out whether the hybrid was a success for the southern utah landscape and climatic conditions or not (akbar et al, 2020). specialisation harkness et al, 2021; harkness et al, 2023 uk; uk for general cropping farms specialisation has a large relative effect; increasing specialisation by 1 standard deviation increases the variability of income by 13% (harkness et al, 2023). leasing acreage zhang et al, 2018 usa a one-inch precipitation increase reduces leased land by 44 acres. projections estimate that by 2050, leased acreage on average will decline by 23% under rcp 4.5 and 29% under rcp 8.5 (zhang et al, 2018). changing cropping patterns domingos et al, 2021; fernández-ortega et al, 2023; jacobs et al, 2022; karapinar et al, 2020; làng et al, 2023; lee et al, 2016; michalopoulos et al, 2020; parker et al, 2017; peltonen–sainio et al, 2014; england, spain, usa; türkiye; hungary; south korea; greece; germany; finland; spain; australia; switzerland date and cultivar adaptation increase yields of adapter households by 16% and 31% respectively (karapinar et al, 2020). by combining barley and sweet corn yields during double cropping, the overall crop yield was 18.5 tonnes/ha; compared to single cropping 5.6 tonnes/ha (làng et al, 2023). 23 ruiz et al, 2019; whitbread et al, 2015; wittwer et al, 2023 results indicate an increase of 46 kg ha for every day earlier that the crop is planted (parker et al, 2017). when early sowing occurred with late maturity, yields were at their highest for all cereals (barley 5710kgha) (oat 5930kgha) (wheat 4700kgha). for barley, the yield was enhanced by a combination of early sowing and late maturity by 0.15, in oat by 0.19 and in wheat by up to 0.24. in oat and wheat, such combinations were also associated with high single grain weight (peltonen-sainio et al, 2014). insurance participation di falco et al, 2014; kath et al, 2018; wang et al, 2021 italy; australia; usa index insurance could make farmers better off by $269.85 aud/ha on average in years with excessive rainfall (kath et al, 2018). fertilisation & other inputs fernàndez-ortega et al, 2023; harkness et al, 2021; harkness et al, 2023; kaufmann et al, 1997; michalopoulos et al, 2020; patanita et al, 2019; pèrez-uribe et al, 2024; reidsma et al, 2007; reidsma et al, 2009a; reidsma et al, 2009b; reidsma et al, 2010; wang et al, 2016 spain; uk; uk; usa; greece; portugal; colombia; europe; europe; europe; europe; usa using fertilizer has the most consistent association with yields as it increases yields by 0.8, 0.02, and 0.24 t/h for plantains, coffee, and cassava, respectively (pèrez-uribe et al, 2024). fertilizer intensive farmers can largely reduce the negative effects of higher temperatures in the uk, france and italy, while in other regions, the effect is small or even negative (reidsma et al, 2010). on average, a $10/acre increase in fertilizer use can lead to a yield increase around 9 kg/acre for cotton, and about 0.12 ton/acre for hay (wang et al, 2016). conversion to organic farming bouttes et al, 2019; di falco et al, 2008; wittwer et al, 2023 france; italy; switzerland following conversion to organic farming, farm self-sufficient milk productivity in 2012 ranged from 2558 to 4932 kg milk/ha/year, economic efficiency in 2012 ranged from 1.5 to 6.5, and profitability per worker in 2012 ranged from 12 to 84 eur/year (bouttes et al, 2019). source: own elaboration 24 4. conclusion in this systematic review, our aim was to evaluate the impact of climate change on the agriculture sector and examine the effectiveness of adaptation strategies responding to these impacts. examining 124 studies, we sought insights into the methods and metrics used to assess the impacts of climate change on agriculture, what the effects of changes in the climate system are, which adaptation strategies are employed, and how effective these strategies are. in terms of the metrics used within the literature, marginal changes in temperature and precipitation have been studied most frequently. this is unsurprising given that agriculture is highly dependent on weather patterns. yet it is important to note that climate change will also exacerbate extreme weather events and therefore these should be examined further. drought has been extensively studied in the literature however other extreme events such as storms and frost events have not. while a range of methods have been identified in this review, standard panel approaches have been employed most often. we find that there is scope for further studies employing joint estimation of short and long run responses to better understand the long-term costs and benefits of adaptation. in examining climate impacts on agriculture, it is evident that there are overwhelmingly negative effects across the sector, including decreasing yields, reductions in product quality, and loss of income. given that the majority of studies focused on arable farming, changes in yield were most frequently examined in the literature. the least studied impact was changes in input costs, indicating a gap in knowledge surrounding the full economic costs of climate impacts on agriculture. a wide range of adaptation strategies have been identified in the literature. those which have received more attention have been irrigation, changing cropping patterns, and fertilisation and other inputs. conversely, leasing acreage, specialisation and reducing or changing stock have been identified as the least studied strategies. overall, adaptation tends to be reactive, short term and localised. thus, there is a lack of forward planning where climate change is concerned within the sector, leaving farmers 25 vulnerable to future threats from a changing climate. nonetheless, we find that adaptation has a generally positive role to play in reducing the negative impacts of climate change. given these findings, this review highlights several important implications for future research. while there is abundant data on the impact of climate change on farm output, the effects on input costs such as fertilizer, water, and labour, have not been extensively studied. understanding these cost variations is crucial for a more comprehensive assessment of the economic impacts of climate change, particularly given that fertilisation and other inputs have been identified as a widely used and effective adaptation strategy. in terms of adaptation strategies studied, there is a lack of research into organic farming, diversification, specialisation, leasing acreage and cooling strategies while irrigation, government strategies, fertilisation and changing crop patterns have been widely studied in the literature. additionally, the literature suggests a need for long term experiments to evaluate the costs and benefits of adaptation strategies as current research predominantly focuses on short term investments and impacts therein. another key recommendation is the need to understand the pros and cons of the various methods and indicators used to assess climate impacts on agriculture, as well as the opportunities and challenges that new data and technologies present to the industry and researchers. this understanding would facilitate easier comparison across studies and help in developing more effective management strategies for both farmers and policymakers. there are also other factors at play in the adoption and effectiveness of adaptation strategies that have not been the focus of this review such as extension services, land tenure security and farmer perceptions. these provide other potential avenues for future interdisciplinary research. while every effort was made to make this review as comprehensive as possible, it is limited by the decision to exclude grey literature and non-english studies, particularly in underrepresented contexts and these would provide further detail in future reviews. this review also formulates a number of policy implications. for instance, while the majority of studies assess impacts on arable agriculture, there is also considerable evidence that climate change impacts and the need for adaptation is pivotal also in the livestock sector (aside the well-known importance of climate change mitigation). capacity building and knowledge exchanges through participatory approaches will prove invaluable. the assessment of effectiveness and associated costs and benefits of specific impacts 26 and adaptation strategies can contribute to forward planning both on the part of farmers and policymakers. references ahmed, h., tamminen, l.m. and emanuelson, u. 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(2016) drought effects on us maize and soybean production: spatiotemporal patterns and historical changes. environmental research letters, 11(9), p.094021. doi 10.1088/1748-9326/11/9/094021 https://doi.org/10.3390/atmos14050778 https://doi.org/10.1016/j.agsy.2023.103731 https://doi.org/10.2166/wcc.2015.060 https://doi.org/10.1111/risa.13623 https://doi.org/10.1093/erae/jbab034 https://doi.org/10.1016/j.agwat.2013.10.015 https://doi.org/10.1071/cp14136 https://doi.org/10.1016/j.agsy.2023.103721 https://doi.org/10.1111/ajae.12396 https://doi.org/10.3389/fevo.2017.00126 https://doi.org/10.1016/j.agwat.2017.04.007 https://doi.org/10.1016/j.landusepol.2018.07.030 https://doi.org/10.1016/j.scitotenv.2014.12.004 1 input interactions and structural dynamics in greek agriculture productivity zisis c. mandanas, phd, school of agriculture and food science, university of the peloponnese, zisismandanas@gmail.com dimitrios p. petropoulos, doctor, associate professor, school of agriculture and food science, university of the peloponnese, d.petropoulos@uop.gr nikolaos apostolopoulos, doctor, assistant professor, school of economy and technology, university of the peloponnese, anikos@uop.gr corresponding author: zisis c. mandanas: zisismandanas@gmail.com, this article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the version of record. please cite this article as: mandanas zc, petropoulos dp, apostolopoulos n(2025). input interactions and structural dynamics in greek agriculture productivity, bio-based and applied economics, just accepted. doi:10.36253/bae-16399 abstract this study examines the impact of fixed capital, variable inputs, and labor on agricultural output in greece from 1981 to 2021 using a cobb-douglas equation. it also investigates changes over time through the bai-perron structural changes method, confirming a mailto:zisismandanas@gmail.com mailto:d.petropoulos@uop.gr mailto:anikos@uop.gr mailto:zisismandanas@gmail.com 2 structural break in 2006. from 1981 to 2005, labor and fixed capital had positive elasticities, contributing positively to agricultural production but with decreasing returns to scale. post-2006, labor and fixed capital exhibited negative elasticities, indicating inefficiencies, while variable inputs retained positive elasticity. this shift suggests an increased influence of variable inputs on production after 2006. the findings highlight the need for continuous monitoring and analysis of agricultural factors to enhance sector competitiveness and sustainability, providing insights for policy design and implementation aimed at agricultural development. keywords: agriculture productivity growth; input-output analysis; cobb douglas function, greek agriculture 1. introduction agricultural production processes require not only the optimal use and combination of inputs, but also the effective utilization of other aspects such as technological innovations, the experience and ability of those employed in the agricultural sector, and consideration of elements related to the economic circumstances that characterize economies both individually and globally. moreover, unprecedented economic, social, and environmental conditions, particularly financial crises, income inequalities among farmers and consumers, and climate change, have shaped a complex environment for agricultural production. based on the above reasoning, decisions are formulated regarding the appropriate quantities and types of production factors, with the ultimate goal of achieving economic efficiency in the agricultural sector. decisions about input use are not purely technical; they also reflect agriculture’s continuous endeavors to adapt strategically to 3 shifting economic, environmental, and technological conditions. as farmers face extensive changes in market conditions, technological capabilities, and political regulations, the selection of production factors must be performed while considering their interaction. in this context, capital is a decisive factor for agricultural production, fulfilling a dual role as a source of financing and as a shaper of production processes. technological progress and innovation require frequent investments in fixed capital, such as equipment and machinery. these investments aim to improve efficiency along with the use of and promote sustainable production methods (crespi & zuniga, 2012; gann, 2000) in most sectors, hence also in including agriculture. in addition, labor productivity is a fundamental determinant for the efficient implementation of agricultural activities, influencing output quantity and quality, as well as the associated production costs. the latter are also affected by the quality of inputs utilized (akram et al., 2019), such as fertilizers, pesticides and seeds, as well as land rental expenses, payments to dependent labor (jayachandran, 2006), and intermediate consumption elements in general. the selection of the amount of labor and variable inputs used in the production process, is influenced by the potential provided by existing fixed capital, highlighting the interaction between these elements. the cobb-douglas production function is considered a useful tool for modeling the interaction between factors of production, as it expresses the relationship between the quantity of production outputs and the inputs used in it. the efficiency of the cobb-douglas function, lies in its ability to reliably represent the production process. it estimates the contribution of each input to the total output, the returns to scale, the marginal productivity of production factors, and their marginal rate of substitution (fuss & mcfadden, 1978). the application of the cobb-douglas function to agricultural production has been intensely 4 researched, as it captures the relationship between inputs and output, while simultaneously offering simplicity and flexibility in modeling. to better understand the suitability of the cobb-douglas production function for analyzing agricultural productivity and the influence of different production factors on output, a number of empirical studies are reviewed. within this framework, armagan & ozden (2007) argue that the cobb-douglas production function estimation is suitable for the efficient analysis of agricultural activities, as it is characterized by ease of calculations and the capability of statistical testing of production flexibilities, pointing out that this approach addresses the concept of productivity and efficient use of inputs, through the integration of the output function based on the inputs used. faruq-uz-zaman (2021) further endorses the cobb-douglas production function, highlighting its practical value in estimating marginal productivity and assessing input efficiency. using data related to crop production in bangladesh, the author employed inputs such as land area under cultivation, the proportion of the population engaged in agriculture, household spending as a proxy for capital input, fertilizer usage per hectare, and total cultivated land under irrigation to estimate impacts on agricultural output. the results indicated inefficiency in input use, with negative returns to scale (elasticity of -0.977), primarily due to the negative marginal productivity of cultivated land and labor, highlighting the non-rational use of the aforementioned production factors. echevarria (1998) confirmed the negative effect of labor growth on agricultural production using data from 1971 to 1991 for canada. by employing the cobb-douglas equation, she compared the agricultural sector with services and industry, determining a reduced need for labor in agriculture and the particular importance of using technological 5 innovations, as they positively affect productivity. mundlak et al. (1999) used panel data from 1967 to 1992 for 57 countries, suggesting that agricultural production should rely to a greater extent on its extensive industrialization. they argued that output is relatively less influenced by labor changes than by capital, and that efficient production methods reduce labor needs, indicating a shift towards labor-reducing technology. yuan (2011) used data from 1999 to 2008 for hebei province, china. the study estimated a cobb-douglas production function with independent variables including cultivated land area, effective irrigation area, chemical fertilizer usage, agricultural machinery power, rural electricity consumption, and manpower. effective irrigation area was identified as the most significant contributor to agricultural output, exerting a positive sign, followed by chemical fertilizer usage and farm labor. cultivated land area, electricity consumption, and farm machinery power also showed a positive impact on output, though to a lesser extent, with overall input elasticity indicating decreasing returns to scale, with a value of 0.56. dawson & lingard (1982), investigate the relationship between various input factors and total revenue in agricultural output using a cobb-douglas production function. the study employs ordinary least squares and covariance analysis to estimate the elasticities and marginal productivity of labor, total wage-bill, machinery costs, livestock costs, crop costs, general farming costs, rent, and land area, which are set as independent variables, using total revenue as the dependent variable. the results emphasize the special role of labor and raw materials, as their increase leads to improved productivity, while a similar but less important effect is observed with investments in fixed capital. nevertheless, 6 decreasing returns on the scale of variable inputs are observed, indicating a relatively reduced efficiency in the use of production factors. a study by ghoshal & goswami (2017), referring to the efficiency of agricultural production in regions of india using stochastic frontier analysis with the use of the cobbdouglas production function, in panel data for the period from 2005 to 2014, found matching results. the average annual efficiency ranged from 0.376 to 0.882 across different regions of the country, also indicating inefficiency in the use of production inputs, with the fertilizers and pesticides being the most important variable inputs for increasing output. additionally, the presence of a comprehensive public infrastructure network, specifically the total state road length per unit of cultivated area, had a positive impact on output. this finding is consistent with that of mamatzakis (2003), who studied the greek agricultural sector from 1960 to 1995 using the i3sls method to estimate a translog cost function. the study indicated that improvements in public infrastructure, reduce marginal variable costs and positively impact productivity advancement. the present study investigates how the main production factors, namely, fixed capital, variable inputs and labor, have jointly shaped the growth of greek agricultural output over the past four decades amid evolving technological advances, macroeconomic shocks, and common agricultural policy regimes. to this end, a modified cobb-douglas specification, incorporating capital-labor and capital-intermediate input interactions, is estimated and subjected to sequential structural-break analysis to identify and date any endogenous shifts in factor productivity. at the second level, the resulting factor elasticities are compared to chart the evolution of input returns over time, to derive evidence-based 7 insights into how structural shifts reallocate productivity patterns, which can inform the design of policy measures to strengthen sectoral efficiency and resilience. the paper is organized as follows. section 2 discusses the evolution of greek agriculture in relation to cap common agricultural policy reforms and macroeconomic shocks. section 3 provides an overview of the data employed and details the empirical methodology adopted. section 4 summarizes the estimation results derived from the empirical analysis. section 5 derives policy implications from the results, and section 6 presents concluding comments. 2. competitiveness and productivity challenges in greek agriculture greece’s entry into the european economic community in 1981 and the immediate adoption of the common agricultural policy, is widely credited with transforming greek agriculture. previously characterized by small-scale, family subsistence farming, the greek rural economy began shifting towards market-oriented production after the accession (caraveli, 2000). tangible improvements in farm incomes and rural development were evident by the late 1970s following greece's eu membership, as cap price guarantees initially boosted agricultural revenues (christou & sarris, 1980). today, the cap continues to shape and control the framework of operation and activity in the agricultural sector. over approximately four decades of cap implementation, significant changes have been observed in the greek rural economy. the most noteworthy of these changes concern employment, production structure, prices, and rural incomes. common agricultural policy implementation resulted in substantial improvements in producer incomes through subsidies and played a key role in rural development. however, according to haniotis 8 (2024), cap revisions have often exacerbated structural issues in greek agriculture, including uneven distribution of subsidies, a lack of a functional farm advisory system, and slow adaptation to market-oriented reforms, which have limited greece's ability to align production with market demands and compete internationally. the 1980s, according to historical data from the hellenic statistical authority (elstat, 1980–2006), stand out as the most prosperous decade for greek agriculture, marked by high real producer prices and strong overall production value. following this prosperous period, production volumes stabilized in the 1990s, while real prices gradually and steadily declined. building on the changes, the 2003 reform of the common agricultural policy, which decoupled subsidies from actual output, brought a significant shift to the greek agricultural landscape by weakening the link between support and production. as noted by psaltopoulos et al. (2006), policy adjustments altered the rural economic environment and had uneven effects across regions, adding a layer of instability to production outcomes. the global financial crisis of 2008 compounded these challenges, and by 2011, the value of agricultural production had fallen to its lowest level in decades (eurostat, 2024). at the same time, falling consumer demand and financial pressure on households caused a deeper contraction in agricultural output, revealing the level that agricultural sector was exposed to wider economic disruptions (konstantinidis, 2016). as reported by the greek gsee-adedy labor institute (2002), labor productivity during the 1980s increased at an annual rate of 4.9%. in the 1990s, it stabilized at slightly higher levels, despite a significant reduction in the workforce from 31.6% of the total personnel in greece in 1980 to 18.0% in 2000. between 2000 and 2010, the agricultural labor force in greece experienced a cumulative decline of 22.1%, equivalent to a 9 compound annual decrease of 2.5%. over the same period, the branch’s gross value added receded from €7.49 billion to €6.52 billion, a drop of 13.0% (1.4% per year) (elstat, 2024). simultaneously, the 2007 financial shock, coupled with the decrease in private investments in agriculture, resulted in low overall performance of the primary sector. although agriculture still absorbed 11.0% of national employment, it generated barely 3.5% of gdp, a mismatch that steadily eroded its competitiveness (kassimis & papadopoulos, 2013). these challenges have persisted in the long term, leading the country to exhibit a consistent trade deficit in agricultural products, which ranged from -1.5 to -2.8 billion euros annually throughout the mid-2010s and up to date (elstat, 2014-2023). as a result, in 2024, greek agriculture contributes only 3.8% to the country's national gdp, with an average of approximately 3.6% for the 2020s, which is significantly lower than the 6.9% contribution recorded in 1995 and the average of around 4.6% during the 2000s (eurostat, 2024), suggesting the sector's reduced competitiveness within the national economy. as productivity trends downward and a significant portion of the workforce departs the agricultural sector, further contraction of production is likely. kalfas et al. (2024) emphasize that addressing productivity and workforce challenges requires substantial investment in infrastructure, supportive policies, and farmer education to facilitate the adoption of modern practices and enhance sector performance. hence, enhancing competitiveness in the agricultural sector remains a key challenge for the future of greek agriculture, as these factors determine its ability to penetrate international markets. 3. materials & methods 10 data were collected from the european commission's directorate general for economic and financial affairs (ameco) database on an annual basis, covering the period 1981–2021. the regression model used is based on a production function that reflects the impact of labor and production cost on agricultural output. these factors interact separately with capital, allowing for a detailed analysis of their combined effects on the production process. this relationship is formulated as a modified cobb–douglas production function, expressed by the following equation: 𝑌 = 𝐴 ⋅ (𝐿𝐾)𝑎𝐿 ⋅ (𝐶𝐾)𝑎𝐶 (1), where y defines agriculture output in real prices, l represents labor in terms of employed persons, c defines agricultural sector’s variable costs as the aggregate of land rental expenses, hired-labor wages and intermediate consumption, k stands for the agriculture sector fixed capital in real prices and a captures total factor productivity. the above functional form implies that capital acts not as an isolated input but as a productivity amplifier, influencing the effectiveness of labor and cost via the lk and ck interaction terms. the parameters αl and αc therefore represent the output elasticities with respect to the labor–capital and cost–capital interactions respectively, as specified below: 휀𝐿 = 𝜕𝑌 𝜕𝐿 ⋅ 𝐿 𝑌 = 𝐴𝛼𝐿 ⋅ (𝐿𝐾)𝛼𝐿−1 ⋅ (𝐶𝐾)𝛼𝐶 ⋅ 𝐾 ⋅ 𝐿 𝐴⋅(𝐿𝐾)𝛼𝐿⋅(𝐶𝐾)𝛼𝐶 = 𝛼𝐿 (2) 휀𝐶 = 𝜕𝑌 𝜕𝐶 ⋅ 𝐶 𝑌 = 𝐴𝛼𝐶 ⋅ (𝐿𝐾)𝛼𝐿 ⋅ (𝐶𝐾)𝛼𝐶−1 ⋅ 𝐾 ⋅ 𝐶 𝐴⋅(𝐿𝐾)𝛼𝐿⋅(𝐶𝐾)𝛼𝐶 = 𝛼𝐶 (3) since (1) can be rewritten as: 𝑌 = 𝐴 ⋅ 𝐿𝑎𝐿 ⋅ 𝐶𝑎𝐶 ⋅ 𝐾𝑎𝐿+𝑎𝐶 (4), the output elasticity with respect to capital 휀𝐾 is presented as the aggregate of the interaction elasticities: 휀𝐾 = 𝜕𝑌 𝜕𝐾 ⋅ 𝐾 𝑌 = 𝐴 ⋅ 𝐿𝛼𝐿 ⋅ 𝐶𝛼𝐶 ⋅ (𝛼𝐿 + 𝛼𝐶) ⋅ 𝐾𝛼𝐿+𝛼𝐶−1 ⋅ 𝐾 𝐴𝐿𝛼𝐿⋅𝐶𝛼𝐶 ⋅𝐾𝛼𝐿+𝛼𝐶 = 𝛼𝐿 + 𝛼𝐶 (5) 11 to estimate the impact of labor, production cost and capital on agricultural output via ordinary least squares, the modified cobb–douglas production function is transformed to a log-linear model: ln(𝑌) = 𝛽0 + 𝛽1 ⋅ (𝑙𝑛𝐿 + 𝑙𝑛𝐾) + 𝛽2 ⋅ (𝑙𝑛𝐶 + 𝑙𝑛𝐾) + 𝑢 (6), where u is the disturbance term, 𝛽1 = 𝛼𝐿, 𝛽2 = 𝛼𝐶 and 𝛽1 + 𝛽2 = 𝛼𝐿 + 𝛼𝐶. hence, the parameter estimates directly reflect the output elasticities of the labor– capital and cost–capital interactions, illustrating how shifts in capital intensity condition the productivity of both labor and intermediate inputs and thus affect overall output. to accommodate non‐constant elasticities and endogenous complementarity, the specification follows thompson (2014) in treating capital as a productivity amplifier. in the form given in (1), this entails that both the marginal product of labor, 𝜕𝑌 𝜕𝐿 , and the marginal product of intermediate inputs, 𝜕𝑌 𝜕𝐶 , are functions of the capital stock k, thereby endogenizing substitution elasticities and capturing evolving input complementarities. the traditional cobb–douglas form imposes two restrictive assumptions that are crucial for understanding its limitations: (i) unit elasticity of substitution (σ=1) between every input pair; and (ii) full separability, meaning the marginal product of each factor does not depend on the level of any other factor (arrow et al., 1961). induced-innovation theory, however, challenges these assumptions by predicting that when relative factor prices change, firms will direct technical change toward the now-cheaper factor, thereby endogenously altering the productivity of both capital and labor (acemoglu, 2002). thus, a standard cobb–douglas cannot capture complementarity or allow its intensity to adjust, which is necessary to account for economic changes such as mechanization, cap-subsidy 12 regimes, and rising production costs that have fundamentally transformed agriculture over the past decades. by contrast, the ces specification of arrow et al. (1961) relaxes the unit‐ substitution assumption by permitting σ≠1, yet it still constrains that elasticity to remain constant over time. the translog function, as introduced by christensen et al. (1973), affords full flexibility, but at the expense of estimating 𝑛(𝑛+3) 2 parameters, a complexity that induces high covariance among regressors, leading to multicollinearity and unstable estimates (berndt & christensen, 1973). semi-flexible, interaction-augmented specifications provide a balanced alternative to the rigid cobb–douglas and the overly complex translog forms. thompson (2014) evaluated three competing models: a log-linear cobb–douglas, a full translog, and a twointeraction 'physical production function', which is specified as follows: 𝑌 = 𝐴 ⋅ (𝐿𝐾)𝑎𝐿 ⋅ (𝐸𝐾)𝑎𝐸 (7), where l denotes labor, k capital, e energy, and a total factor productivity, demonstrating that this specification outperforms both the log-linear cobb–douglas and the translog by treating capital as a productivity amplifier, thereby introducing spill-over effects to labor and energy that vary with the level of the capital stock. based on the above, the study adopts thompson’s (2014) specification by replacing the energy term (e) with sector-specific variable costs (c). this approach preserves loglinearity, allows for the direct interpretation of each exponent as an output elasticity, endogenizes substitution elasticities, and captures evolving capital–input complementarities. 13 additionally, given the frequent complex changes in the relationship between agricultural outputs and production inputs in the agricultural sector, due to factors such as rapid technological progress, climate change, and other environmental factors, as well as the application of differentiated policies and changing demand needs, it is necessary to study and detect the presence of structural changes in this sector. in this direction, the baiperron multiple structural changes linear estimation (bai & perron, 2003) is applied, which detects the presence of endogenous structural breaks, under the following model specification: 𝑦𝑡 = 𝑥𝑡 ′𝛽𝑗 + 𝑧𝑡 ′𝛿 + 𝑢𝑡, for 𝑡 = 𝑇𝑗−1 + 1, … , 𝑇𝑗 , 𝑗 = 1, … 𝑚 + 1 (8), where 𝑦𝑡 denotes the dependent variable at time t, 𝑥𝑡 represents a vector of covariates with regime-specific coefficients 𝛽𝑗, 𝑧𝑡 comprises covariates with time-invariant coefficients 𝛿 and the error term 𝑢𝑡 captures stochastic disturbances. the model partitions the data into 𝑚 + 1 distinct regimes, defined as intervals (𝑇𝑗−1, 𝑇𝑗] between structural breakpoints, where each regime 𝑗 maintains constant parameters 𝛽𝑗, while allowing coefficients to shift across regimes. initially, the hypothesis of zero versus a fixed number of up to five breaks, and zero versus an undetermined number of up to five breaks (bai & perron, 1998) is tested, followed by testing 𝑙 versus 1 + 𝑙 breaks (bai & perron, 2003), a procedure that is particularly suitable for small samples, as outlined in detail by antoshin et al. (2008). the test statistics are calculated as follows: 𝑇𝑒𝑠𝑡 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐: 𝑅𝑆𝑆0−𝑅𝑆𝑆1 𝜎2 (9), 14 where 𝑅𝑆𝑆0 is the residual sum of squares under the null hypothesis (fewer breaks), 𝑅𝑆𝑆1 is the residual sum of squares under the alternative hypothesis (more breaks) and 𝜎2 is the variance of the residuals. for sequential testing of breaks, the approach involves two steps. in the first stage, the null hypothesis of no structural breaks versus the alternative of m breaks is tested, using the 𝑠𝑢𝑝𝐹(𝑚) test, defined as the maximum f-statistic across all possible break points 𝑇1 through 𝑇𝑚: 𝑠𝑢𝑝𝐹(𝑚) = 𝑚𝑎𝑥{𝐹𝑇1 , 𝐹𝑇2,…,𝐹𝑇𝑚 } (10) subsequently, the hypothesis of 𝑙 versus the alternative of 𝑙 + 1 structural breaks is examined using the 𝑠𝑢𝑝𝐹(𝑙 + 1|𝑙) statistic, which evaluates whether the inclusion of an additional break significantly improves the model fit, defined as: 𝑠𝑢𝑝𝐹(1 + 𝑙|𝑙) = 𝑇−𝑙(𝑝+𝑞) 𝑞 ∙ 𝑅𝑆𝑆𝑙−𝑅𝑆𝑆1+𝑙 𝑅𝑆𝑆1+𝑙 (11), where 𝑝 is the number of parameters in 𝛽, 𝑞 is the number of parameters in 𝛿, 𝑇 is the sample size, 𝑅𝑆𝑆𝑙 is the residual sum of squares with 𝑙 breaks and 𝑅𝑆𝑆1+𝑙 is the residual sum of squares with 1 + 𝑙 breaks. the structural change points were detected using the bayesian information (bic) (yao, 1998) and modified schwarz (lwz) (liu, 1997) criteria, in order to achieve the optimal choice of the number of structural changes incorporated in the regression model. the final selection of the optimal model is achieved based on the criterion that provides the lowest critical value threshold, and then the linear regression model is estimated for each possible period of structural change. 4. results 15 in figure 1, time trends of output, fixed capital, employed persons, and variable costs for the greek agriculture sector (1980–2020) are presented. initially, a significant increase in agricultural output and fixed capital of the greek agriculture sector occurred during the 1980s and up to 1995. this was likely related to the introduction of new technologies and improvements in the management of european resources, which indicate the adoption of strategies for the development of the agricultural sector and the industrialization of production, leading to the transformation of the capital/labor ratio. to provide more detail, the average growth rate of agricultural output for the period 1981– 1995 is 2.24%, with an overall percentage change of 34.69% in 1995 compared to 1981. additionally, the average and total increase in fixed capital for the same period is 1.84% and 28.21%, respectively. this suggests a potential relationship between invested capital and agricultural output, possibly indicating increasing returns to scale. the trend of increasing capital intensity and decreasing labor intensity in agricultural production is evident. this is confirmed by a decrease in the number of individuals employed in the agriculture sector at an average rate of 2.24% per year, resulting in an overall reduction of 27.80% in the total number of farmers, equivalent to 292,200 individuals. in contrast to the trends in output and capital, production costs increased significantly by 3.09% per year and 50.30% overall by 1995 compared to 1981. during the 1996–2006 period, agricultural output growth reversed, resulting in a total decline of 10.22%. this was mainly due to a 14.41% reduction in agricultural product in 2006 compared to 2005, and an average negative change of 0.95%. in contrast, while capital experienced a decelerated average annual growth of 1.34% and a total growth of 16.95%, employment decreased significantly by 27.35% from 1996 to 2006, at a constant 16 annual rate of 2.90%. on the contrary, production costs continued their upward trend, increasing by 35.11% in 2006 compared to 1996. in 2007, as the global economic crisis emerged, a decline in agricultural output to its average levels during the 1990s and a total reduction of 6.04% is observed. during the years 2007–2009, fixed capital increased by 11.52% compared to the pre-crisis period. however, variable costs decreased less proportionally than the reduction in output, reflecting the negative effects of the crisis on the agricultural sector. after the crisis period, agricultural output is observed to recover with a slow growth rate of 0.95% per year, failing to reach pre-crisis levels. these recovery efforts are accompanied by a reduction in fixed capital by 11.66% in 2021 compared to 2009, with an annual decrease of 1.12%, and an average increase in production costs of 1.26% annually. thus, the compensation of fixed capital and variable inputs of production is linked to the stabilization of agricultural output. finally, the number of individuals employed in the agricultural sector shows relative stability after the negative economic conditions of the period 2007-2009, decreasing by 15,500 individuals in 2021 compared to 2007 at a rate of 2.92%, demonstrating the long-term effect of the economic crisis on employment. figure 1. time trends 17 the spearman correlation matrix (table 1) reveals statistically significant linear relationships between agricultural production, fixed capital, employment, and variable costs, providing insights into the dynamics of agricultural productivity. the positive correlation between agricultural output and capital (rho = 0.338, p = 0.031) suggests that an increase in capital contributes to the growth of agricultural productivity. nevertheless, it appears that the increase in agricultural output is also associated with an increase in variable production costs (rho = 0.447, p = 0.003) and a decrease in the number of individuals employed in the agricultural sector (rho = -0.432, p = 0.005). the negative correlation between capital and labor (rho = -0.663, p < 0.001) confirms the inversion of the capital-labor ratio. the results suggest that technological development and automation of the production process, bring about a steady increase in production. moreover, improvements in equipment and know-how allow labor to be more productive, regardless of the number of workers, with production costs increasing given increased investment in 18 equipment (rho = 0.653, p < 0.001). simultaneously, as the number of people employed in the agricultural sector decreases, production costs increase (rho = -0.783, p < 0.001), as a result of changes in the production process, with the integration of more industrialized but also costly practices driving part of the rural population to abandon the agricultural sector. table 1. spearman correlation matrix output capital labor cost output 1 capital 0.338 (0.031) 1 labor -0.432 (0.005) -0.663 (<0.001) 1 cost 0.447 (0.003) 0.653 (<0.001) -0.783 (<0.001) 1 numbers in parentheses denote p-values stationarity must be established as a prerequisite in time-series analysis to avoid spurious regression relationships (granger & newbold, 1974; phillips, 1986) and ensure consistent parameter estimates (phillips & perron, 1988). thus, adf and phillips-perron unit root tests are applied, using the mackinnon critical values of -3.441 and -2.866 for a 1% and 5% significance level with a constant term, and -3.974 and -3.417 for a 1% and 5% significance level with both a constant term and trend (mackinnon, 1996). as observed at table 2, for all the variables, the assumption of stationarity is accepted at levels. 19 table 2. unit root tests adf phillips-perron constant without trend constant with trend constant without trend constant with trend lny -2.897* -2.928* -2.958* -3.421* lnl+lnk -4.514* -4.652** -3.724** -3.962* lnc+lnk -3.358* -3.639* -4.787** -5.105** * denotes stationarity at 5% ** denotes stationarity at =1% the confirmed stationarity of all variables ensures that the modified cobb–douglas elasticities can be interpreted without the risk of invalidating standard inference procedures due to stochastic trends. the estimation results (table 3) show that the coefficient β1 is statistically significant with a value of 0.105 (p = 0.025), indicating a positive combined effect of capital and labor on agricultural output. additionally, the interaction between fixed and variable cost signifies a positive impact on agricultural output (β2=0.103, p = 0.038). based on the estimated coefficients, output elasticity to labor equals 0.105 and the elasticity of output with respect to variable inputs equals to 0.103, with the output elasticity to capital being equal to 0.208. consequently, a 1% increase in fixed capital raises agricultural output by 0.208%, while equivalent increases in labor and variable inputs lead to 0.105% and 0.103% increases in agricultural production, respectively, underscoring the relatively greater importance of fixed capital., the production function exhibits decreasing returns to scale. the modest r² of 0.282 reflects that the modified cobb–douglas specification— by design—captures only the elasticities of labor, fixed capital, and variable inputs, excluding broader determinants of output such as policy subsidies, climatic shocks, or technological change. consequently, while the estimated coefficients are informative about 20 input-output relationships, the low goodness-of-fit underscores the need to interpret the results within the context of an inherently multifaceted agricultural system in which many important drivers lie outside the production-function framework. table 3. ols estimation results dependent variable: lny b s.e t p constant 6.972 3.121 2.234 0.031 * lnl+lnk 0.105 0.046 2.333 0.025 * lnc+lnk 0.103 0.048 2.142 0.038 * r2 0.282 f (p) 7.673 (0.002) * significant at 5% in table 4, structural breaks testing is presented. τhe initial test examines the hypothesis of no structural breaks against the alternative of up to 5 separate breaks in the relationship between production inputs and agricultural output. the corresponding sup fts signal the presence of structural changes, as the null hypothesis of no structural breaks is in any case rejected. the wald-wolfowitz test, using the wd max criterion, also rejects the null hypothesis, supporting the presence of up to five structural breaks. having established the presence of structural breaks in the relationship between production inputs and agricultural output, the determination of their exact number follows by testing the null hypothesis of l versus the alternative of l + 1 breaks. this procedure identified one significant break based on the supft(1|0) statistic, while all other supf(l+1|l) were insignificant. the presence of a structural break is confirmed by the bic and lwz information criteria, which identify one break occurring in 2006. 21 table 4. estimated structural breaks 0 vs unknown number of breaks 0 vs fixed number of breaks l versus the alternative of l + 1 information criteria estimated break dates wdmax number of breaks sup ft number of breaks sup ft(l+1|l) number of breaks bic criterion lwz criterion 134.226* (15.590) 1 44.741* (13.980) 1|0 44.741* (13.980) 1 -6.479* -6.123* 2006 2 30.056* (11.990) 2|1 3.972 (15.720) 2 -6.433 -5.856 1990, 2006 3 26.732* (10.390) 3|2 2.882 (16.830) 3 -6.408 -5.591 1987, 1994, 2006 4 20.230* (9.050) 4|3 0.970 (17.610) 4 -6.152 -5.072 1987, 1994, 2006, 2015 5 14.800* (7.460) 5|4 0.219 (18.140) 5 -5.820 -4.442 1987, 1993, 1999, 2006, 2015 * significant at 5% numbers in parentheses denote bai-perron critical values given the presence of a structural break dated in 2006, the regression model is reestimated using standard regression techniques, after dividing the sample of observations into two sub-periods with the first covering the 1981 to 2005 and the second 2006 to 2021. the results presented in table 5, show the existence of significant dissimilarities in the relationship between production inputs and agricultural output for the two sub-periods. 22 table 5. ols estimation results under structural breaks dependent variable: lny 1981 2005 b s.e t p constant 1.225 0.450 2.722 0.011 * lnl+lnk 0.161 0.077 2.090 0.044 * lnc+lnk 0.304 0.024 12.461 0.000 ** 2006 2021 b s.e t p constant 10.161 0.931 10.903 0.000 ** lnl+lnk -0.411 0.059 -6.996 0.000 ** lnc+lnk 0.304 0.056 5.429 0.000 ** r2 0.852 f (p) 40.514 (<0.001) * significant at 5% ** significant at 1% regarding the 1981-2005 period, the coefficient of capital and labor is statistically significant (p = 0.044), equal to 0.161. for the combination of capital and variable inputs, the corresponding coefficient is 0.304 (p < 0.001). therefore, it is indicated that the combined increase of fixed capital and labor and fixed capital and variable inputs of production by 1% implies an increase in agricultural output by approximately 0.161% and 0.304% respectively, expressing the contribution of combined efficiency to productivity. τhe presence of decreasing returns to scale, with scale is evident, as the elasticities the labor and variable cost are equal to 0.161 and 0.304, respectively, while the capital elasticity attributed to the sum of the aforementioned elements is 0.465. 23 as for the 2006-2021 period, results are strongly differentiated compared to those of the 1981-2005 and even compared to those of the entire 1981-2021 period. the most significant result is the negative sign of the coefficient of capital and labor, which equals 0.411 (p < 0.001), indicating that their combined increase leads to a decrease in agricultural output. in contrast, the coefficient for the combination of capital and variable inputs remains at 0.304 and is significant (p < 0.001), indicating an increasing effect on agricultural production. the negative elasticity of labor indicates its detrimental effect on production, while capital also shows inelastic behavior with an elasticity of -0.107. in contrast, the positive elasticity of variable production inputs confirms their beneficial effect on agricultural output. 5. discussion analyzing agricultural production in greece and examining its relationship with production factors opens new perspectives for understanding the driving forces that shape the agricultural economy of the country. the period after greece's accession to the european union in 1981 and the implementation of the common agricultural policy marked an era of change in the agricultural sector, coinciding with an increase in productivity following the general european trend. however, this trajectory was not stable, as the present study clearly established certain distinct features, providing insight into the evolution of agricultural production and revealing consequent changes in output, employment, fixed capital, and variable inputs. more specifically, the results highlight a pronounced shift in how labor, fixed capital, and intermediate inputs drive output, with a clear structural break around the mid2000s. in the 1981–2005 sub-period, the log-linear estimates under the modified cobb 24 douglas production function, reveal that labor services and variable inputs both made positive contributions to real output, alongside a robust role for fixed capital. the aggregate of the elasticities remained below unity, indicating decreasing returns to scale. this pattern is often seen in agrarian settings with small, family-run plots, where increasing inputs leads to coordination costs and underutilization rather than proportional production increases (cornia 1985; rizov et al. 2013). as latruffe (2010) emphasizes, when scale elasticities lie below unity further productivity improvements must come from gains in technical efficiency, that is, from using the existing technology and resources more effectively. the structural break in 2006, marks a radical reversal in factor contributions. negative labor and capital elasticities are highly atypical, but may occur in cases of severe misallocation of production factors. for example, zorya et al. (2003) and bezlepkina & oude lansink (2003), document negative land and capital elasticities under transition-era credit schemes in post-soviet republics, attributing these to mismanaged inputs. additionally, rapid mechanization can lead to a labor surplus, which may depress output and cause marginal labor products to approach zero or become slightly negative (echevarría, 1998). moreover, when physical networks and advisory capacities do not expand in tandem with inputs, it can lead to coordination breakdowns and idle resources, further compounding misallocation and reversing marginal productivity (yuan, 2011). according to the above the greek case can be considered an outlier. two main mechanisms may explain this anomaly. first, cap’s area-based payments eased credit constraints for many smallholders, who often lacked the scale to justify such investments. this prompted them to acquire tractors and other machinery they could not fully employ, resulting in under-utilized capital assets as idle equipment failed to contribute to output 25 (o’toole & hennessy, 2015; di corato & zormpas, 2022). second, labor quality may have deteriorated as younger, more productive workers migrated to urban sectors due to the 2007 crisis (cavounidis, 2013), leaving a workforce skewed toward older, part-time farmers. as li & sicular (2013) demonstrate, such demographic shifts can sharply erode the marginal productivity of those who remain, as older farmers tend to adopt new technologies and practices more slowly, which is relevant to the greek case. by contrast, variable inputs retain positive output elasticities throughout the full period. this pattern aligns with evidence from the green revolution, where greater use of fertilizers, irrigation water, and high-yielding seeds underpinned most of the dramatic yield increases of the late twentieth century (tilman et al., 2002). for instance, adoption of modern agrochemicals and high-yield varieties can serve as a proxy for rapid productivity growth even as land and labor contributions flatten (fan et al., 2012). likewise, extensionsupported fertilizer programs in sub-saharan africa, have repeatedly demonstrated that well-targeted variable inputs can deliver positive returns despite systemic constraints on scale (ricker-gilbert & jayne, (2012). the 2006 structural break in greek agriculture closely corresponds with the implementation effects of the 2003 cap ‘fischler’ reform, which greece opted to apply in fully decoupled form immediately after its adoption. by converting subsidies into areabased entitlements, decoupling dismantled the incentive to deploy labor and capital efficiently, allowing farms—many of which were small, fragmented, and operated by aging farmers—to receive support irrespective of production (konstantinidis, 2016). consequently, agricultural output gains remained modest, as traditional inputs delivered diminishing returns within a structurally constrained production environment. eu evidence 26 suggests that decoupling can enhance farm productivity in certain contexts (rizov et al., 2013). however, greece’s structural limitations seem to have hindered effective resource reallocation when market signals replaced production-linked support. as a result, productivity gains failed to materialize, and after 2006, total factor productivity growth in greece remained weak, reflecting the limited responsiveness of both labor and capital inputs. beyond decoupling, other cap instruments likely reinforced the structural shift. the introduction of cross-compliance in 2005, obliged farmers to adhere to environmental and land-management standards, such as soil conservation, crop rotations, and buffer zones, to qualify for payments. while these measures aim to safeguard long-term productivity, they diverted labor toward non-productive compliance tasks, raising costs and constraining intensive practices (barnes et al., 2013). similarly, agri-environment schemes launched in 1992 and broadened in 2003, along with compulsory and later voluntary, setaside payments, compensate for land withdrawal and low-input management, which have been shown to lower total-factor productivity (mary, 2013) while sometimes prompting costly input re-allocation (chakir & thomas, 2022). in greece, farm-level evidence shows that higher per-hectare organic-farming subsidies, granted without tying support to output, are associated with lower technical efficiency, implying that such payments can depress factor productivity rather than expand production (nastis et al., 2012). the 2013 cap reform added a “greening” layer, implemented from 2015, that ties part of each farm’s direct payment to compliance with crop-diversification rules, the establishment of ecological-focus areas, and the preservation of permanent grassland. ciaian et al. (2018) show that these obligations can reduce land profitability and curb 27 intensive cultivation, although the predicted output and productivity losses are limited and vary by region. in greece, where most holdings cultivate fewer than five hectares (european commission, 2024), the compliance with greening obligations leads to a shift from cash crops to set-aside and low-input uses, resulting in reduced farm income and a reallocation of labor and capital toward compliance measures rather than productivityfocused activities (mantziaris et al., 2017). finally, the 2007-2013 and 2014-2020 rural development programmes channeled cap pillar ii funds into on-farm modernization, training, advisory services, and agrienvironmental measures. dudu and smeets kristkova (2017) show that across eu regions, spending on physical-capital investments, human-capital support, and environmental actions raises total-factor productivity. however, in greece, the absorption of the 20072013 pillar ii budget was only 41%, severely restricting opportunities to upgrade irrigation networks, acquire precision machinery, and expand extension services (reziti & zangelidis, 2019). moreover, recurrent administrative fragmentation and ministerial turnover undermined strategic continuity (karantininis, 2017). as a result, despite pillar ii’s potential to restore labor and capital productivity, under-utilization of these funds left greek agriculture trapped in an input-intensive, subsidy-driven equilibrium, reinforcing the negative elasticity observed after 2006. policy implications from our findings are clear. to address the decline in labor and capital productivity, greece's agriculture policy must shift from general calls for technology uptake to specific actions for efficient use of common agricultural policy (cap) funds and strengthening national institutions. it is essential to allocate a substantial share of rural development (pillar ii) resources to productivity-enhancing activities, such 28 as precision planting technology, drip-irrigated systems, cold-chain storage, and rural infrastructure upgrades, while adjusting co-financing rates based on farm size and business case viability. along with financial support for investment in capital, establishing an agricultural knowledge and innovation system (akis) is essential for efficiently turning innovations into sustainable productivity improvements. plans are underway to set up regional akis centers to bring universities, research institutes, extension networks, and producers' associations together into an organized system to provide field-relevant skills pertaining to precision agriculture methods, integrated management methods, and digital farm management materials. through the creation of specific lines for akis budgeting within pillar i and pillar ii, greece is seeking to ensure continuity for advisory services despite ministerial leadership instability and to improve support for experienced producers in embracing intricate innovations. also, realigning pillar i direct payments to include performance components is expected to enhance the link between subsidies and efficiency improvements. linking a portion of basic payments to measurable improvement, for example, per labor unit yield increases or and rising return on invested capital, while ensuring compliance with environmental standards, will create a culture of continuous improvement without undermining income certainty. finally, ongoing monitoring of input elasticities and returns to scale at the regional level can serve as an early-warning system for inefficiency, allowing policymakers to adapt support measures dynamically. 29 6. conclusions the present study was conducted to evaluate the joint contributions of fixed capital, labor and variable inputs to greek agricultural output. a log-linear cobb–douglas production function, augmented with interaction terms for capital and labor and capital and variable inputs, was estimated through ordinary least squares, and the bai–perron multiple-break procedure was employed to detect structural shifts in the input–output relationships. the results of the study demonstrate that the increase in capital and labor, and their interaction, contribute to the increase in agricultural production, aligning with classical production theory. the study also finds a two-way positive relationship between investments in fixed capital and the quality and quantity of production materials, enhancing the productivity of agricultural holdings. however, decreasing returns to scale indicate potential under-utilization of inputs or over-investment in productive factors. this result shows significant changes over time, particularly after 2006, when the efficiency of labor and capital decreased, possibly influenced by the economic crisis, signifying shifts in production patterns. the negative elasticity of labor is intriguing and complex to explain, potentially attributed to factors such as technological progress and automation. as agricultural technology advances, less labor may be required, yet productivity can still increase. conversely, the inelasticity of capital suggests diminished returns on investments in fixed equipment, poor management, or inadequate agricultural infrastructure. in contrast, the positive elasticity of variable production factors highlights their crucial role in agricultural output, especially when effectively combined with capital. 30 given that greek agriculture operates within the framework of the cap, which emphasizes support for agriculture while enforcing compliance with environmental and quality standards, there is an opportunity for greek agriculture to transition towards higher quality and environmentally friendly production methods. achieving increased and sustainable agricultural production will require extensive research and innovation efforts across all levels, ensuring that significant innovations are implemented at the necessary 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(2003). interdependencies between agriculture and macroeconomics in ukraine. georg-august-universitaet goettingen. 1 digital technology adoption among italian farmers: an extended technology 1 acceptance model approach in the horticultural sector 2 elena cozzi1, e-mail: elena.cozzi@unipr.it 3 davide menozzi1*, orcid: 0000-0002-5241-1587, e-mail: davide.menozzi@unipr.it 4 giulia maesano2, orcid: 0000-0001-9622-1067, email: giulia.maesano2@unibo.it 5 maurizio canavari2, orcid: 0000-0003-0573-7880, email: maurizio.canavari@unibo.it 6 cristina mora1, orcid: 0000-0003-3180-5496, e-mail: cristina.mora@unipr.it 7 1 department of food and drug, university of parma, parco area delle scienze 47/a, 43124 8 parma, italy 9 2 department of agricultural and food sciences, alma mater studiorum, università di 10 bologna, via zamboni, 33, 40126 bologna, italy 11 * corresponding author 12 13 this article has been accepted for publication and undergone full peer review but has not been 14 through the copyediting, typesetting, pagination and proofreading process, which may lead to 15 differences between this version and the version of record. 16 please cite this article as: 17 cozzi e, menozzi d, maesano g, canavari m, mora c (2025). digital technology adoption 18 among italian farmers: an extended technology acceptance model approach in the 19 horticultural sector, bio-based and applied economics, just accepted. doi: 10.36253/bae-20 17364 21 highlights 22 • a structured survey conducted with 251 italian horticultural farmers 23 • the extended tam3 explains 18% of the variance in the behaviour (the adoption of 24 water-smart technologies), and 65% of the variance in intention 25 • behavioural intention is a significant predictor of the behaviour 26 • perceived usefulness and social norms have a significant effect on adoption intention 27 • perceived ease of use has no influence on adoption intentions 28 abstract 29 the adoption of digital technologies in agriculture is essential for enhancing sustainability, 30 productivity, and resource efficiency. this study investigates the factors influencing italian 31 mailto:elena.cozzi@unipr.it mailto:davide.menozzi@unipr.it mailto:giulia.maesano2@unibo.it mailto:maurizio.canavari@unibo.it mailto:cristina.mora@unipr.it 2 horticultural farmers' adoption of innovative water-smart agricultural technologies using an 1 extended technology acceptance model (tam3). the research employs a structured survey 2 conducted with 251 italian farmers, analysing their perceptions of technology usefulness, ease 3 of use, social norms, and sustainability outcomes. structural equation modelling (sem) 4 confirms that perceived usefulness significantly influences adoption intentions, while perceived 5 ease of use plays a limited role. social norms and sustainability-related benefits also emerge as 6 critical determinants. results also indicate the impact of farm size and workforce on adoption 7 behaviour. these findings highlight the need for targeted policies, training programs, and 8 financial incentives to overcome adoption barriers. the study provides insights for 9 policymakers, technology developers, and agricultural stakeholders to foster digital innovation 10 in the horticultural sector, contributing to sustainable water management practices. 11 keywords: digital agriculture; farmer adoption; technology acceptance model (tam); 12 horticultural sector; water-smart sustainable farming. 13 14 3 1. introduction 1 the agricultural sector is facing many unprecedented challenges. given the increasing pressure 2 on agricultural systems, effective measures are needed to reduce agriculture's impact on natural 3 resources, in line with the european green deal and the united nations 2030 agenda 4 (montanarella and panagos, 2021). in this context, digital technologies and smart solutions are 5 key to improving efficiency, productivity, and sustainability in agriculture (yigezu et al., 2018), 6 with irrigation practices playing a particularly important role (asadi et al., 2020). water scarcity 7 and drought are now recognised as a global problem of the utmost importance (ermolieva et 8 al., 2022; ungureanu et al., 2020), exacerbated by climate change (kapsdorferová, 2024). smart 9 water technologies offer a double benefit: help to save water and use it more efficiently; and 10 reduce operating costs and improve productivity by maximising output per unit of water 11 consumed (gemtou et al., 2024). innovations such as soil moisture sensors, automatic irrigation 12 systems and predictive modelling can effectively address the challenges of water scarcity and 13 climate variability (adeyemi et al., 2017), as well as energy savings (patle et al., 2019). a major 14 challenge in smallholder agriculture is the limited adoption of innovative technologies, 15 resulting in low overall technology penetration in the sector (senyolo et al., 2018). 16 indeed, despite efforts to promote a fair transition to digital agriculture, the adoption of smart 17 technologies remains uneven, shaped by a complex mix of individual, technological, and 18 contextual factors (shang et al., 2021). previous studies have shown that there are significant 19 differences in adoption rates among farmers (paustian and theuvsen, 2017). farmers' decision-20 making processes is shaped by perceived benefits, ease of use and external pressures, making 21 4 it a key factor in understanding the adoption landscape (cimino et al., 2024; schulze schwering 1 et al., 2022). given the limited technological penetration of the agricultural sector and the 2 potential benefits offered by digital technologies, it is crucial to investigate the farmers' 3 behaviour, their willingness to adopt smart solutions, the determinants of smart technology 4 adoption and identifying strategies to promote broader adoption of water technologies (gemtou 5 et al., 2024). 6 although prior research has examined farmers' adoption patterns, it has often focused on large-7 scale farms or specific regions with advanced technological infrastructures (paustian and 8 theuvsen, 2017). additionally, studies have highlighted barriers such as limited digital literacy, 9 financial constraints, and a lack of institutional support for small and medium-sized farms 10 (senyolo et al., 2018; shang et al., 2021). despite the increasing research on technology 11 adoption in agriculture, there are still notable gaps. firstly, little attention has been paid to the 12 adoption of water-saving technologies in the horticultural sector, which is crucial for 13 sustainable agriculture, in contrast to the more commonly studied large-scale cereal production 14 (adeyemi et al., 2017). secondly, while factors like such as farm size and socio-demographic 15 characteristics have been investigated, the influence of sustainability motivations and social 16 norms on technology adoption has not yet been sufficiently researched (gemtou et al., 2024). 17 finally, the adoption of digital technologies in italian agriculture, which is characterised by 18 fragmented land ownership, regional diversity and varying levels of technological readiness, 19 has been insufficiently studied (baldoni et al., 2018). 20 5 this study aims to fill these gaps by analysing the factors that influence italian farmers in 1 adopting digital technologies for better water management and the barriers they face, with a 2 focus on horticultural crops. horticulture has been considered for some reasons: first, because 3 of the importance of this sector in the italian agricultural system; secondly, for the relevance of 4 the irrigation in this cropping system (patle et al., 2019); third because of the relevance of smart 5 precision in horticulture (adeyemi et al., 2017). the technologies studied relate to smart water 6 management through a three-stage technology complexity: the first (basic) stage is represented 7 by the introduction of soil moisture sensors, which proceeds to a system that combines sensors 8 with an automatic irrigation system, and in the last stage the sensors are connected to an 9 automated system, which in turn is connected to and dialogs with predictive models1. 10 understanding farmers' perceptions and adoption of water-efficient innovations is essential for 11 shaping targeted policies and effective incentives, offering valuable insights for policymakers 12 and stakeholders aiming to promote sustainable agriculture. 13 14 2. literature review and theoretical background 15 as the existing literature shows, the process of adopting new technologies is inherently complex 16 and dynamic (montes de oca munguia et al., 2021). in particular, the decision-making process 17 is influenced by various factors that affect the rate of technology adoption by farmers (gemtou 18 1 specifically, automatic irrigation systems are connected to sensors that monitor soil moisture and activate valves wirelessly; instead, predictive modelling integrates the first two solutions (soil moisture sensors and automatic irrigation systems) into predictive models that merge real-time data with historical data, analyse it, and make autonomous irrigation decisions thanks to water delivery schedules that optimize dosing based on specific crop requirements and environmental conditions. 6 et al., 2024; osrof et al., 2023). although the existing literature has explored the mechanisms 1 of innovation diffusion, there does not seem to be a unified set of theories or models that could 2 explain the phenomenon. some authors have highlighted the specificity of theories in modelling 3 different aspects of the technology adoption process (dissanayake et al., 2022; osrof et al., 4 2023), while others have expressed doubts about the generalist ability of theories to represent 5 different technologies and practices (montes de oca munguia et al., 2021). indeed, there is still 6 confusion about the methods of analysis and the choice of explanatory variables that should be 7 used to model the adoption process (de oca munguia and llewellyn, 2020). to illustrate, shang 8 et al. (2021) argue that the mechanisms of adoption and diffusion of digital agricultural 9 technologies need to be understood at both the farm level and the system level. they also 10 suggest that the focus in determining technology diffusion should be on system interactions in 11 combination with individual characteristics. given the evidence presented in the literature, it 12 can be assumed that the categories of individual, technological, social and economic factors 13 influencing technology adoption can describe the entire decision-making process (dissanayake 14 et al., 2022). there is a clear lack of convergence and consistency in the results regarding the 15 impact and statistical significance of the individual factors assessed in the adoption models (de 16 oca munguia and llewellyn, 2020). this discrepancy can be attributed to the fact that most 17 adoption studies do not include variables on technologies or practices. it is recognized that the 18 use of multiple paradigms in modelling technology adoption and diffusion can increase the 19 explanatory power of the models. however, it is important to consider the factors and their 20 7 interactions in a way that is consistent with the objectives and context of the study within a 1 specific food system (dentoni et al., 2023). 2 literature offers a large range of theoretical frames exploring the intention to adopt; among 3 others the theory of planned behaviour (tpb) (ajzen, 1991; fishbein and ajzen, 2010), the 4 social cognitive theory (bandura, 1986), and the unified theory of acceptance and use of 5 technology (utaut) and its further extensions (venkatesh et al., 2003). in the present work 6 we applied the technology acceptance model (tam) (davis, 1989) for measuring the 7 intention of italian farmers to adopt innovative smart technologies. the tam model has been 8 chosen for designing the model since this theory was precisely conceived focusing on 9 technological solutions, hereby offering the ideal setting to place the research. the other 10 theories might be adapted to a technology frame, whereas the tam, being already tailored on 11 the observed object of the analysis, better suits the purpose of the current investigation. 12 according to the tam paradigm, two dispositions towards a new technology (perceived 13 usefulness and ease of use) determine a person's attitude towards using that technology and 14 influence their desire to use it. perceived usefulness is the extent to which a person believes 15 that job performance can be enhanced by using the new technology, whereas perceived ease of 16 use is the extent to which a person believes that using the new technology is effortless. some 17 extensions of the original tam conceptualization have been proposed, such as the tam3 18 version (venkatesh and bala, 2008). the tam3 extension introduces new constructs and 19 determinants that affect the core variable perceived ease of use and proposes new relationships 20 between the constructs. the factors influencing perceived ease of use in the tam3 version are 21 8 computer self-efficacy, perception of external control, computer anxiety, computer playfulness, 1 perceived enjoyment, and objective usability, whereas perceived usefulness is affected by 2 subjective norm, image, relevance to work, output quality and demonstrability of results. other 3 innovations introduced by this extension include: (i) the correlation between perceived ease of 4 use and perceived usefulness, (ii) the correlation between perceived ease of use and intention, 5 and (iii) the concept of anxiety. the latter factor, which expresses the degree of emotional fear, 6 apprehension, nervousness, or stress experienced when interacting with a new technology, is 7 supposed to negatively affect the perceived ease of use. the more anxiety a person feels, the 8 less likely they are to perceive the technology as easy to use. 9 the tam3 conceptualization was adopted since flexible adjustments to context specific needs 10 compared to the original tam version are possible. indeed, venkatesh and bala (2008) added 11 some constructs (i.e. experience, subjective norm, and image) which resulted to be key factors 12 in the exploratory phase: farmers decision rely on peer recommendation as well as on previous 13 experience on digital tools. therefore, only an extended version of the very first 14 conceptualization could confirm or not these clues. furthermore, in order to comprise some 15 small adjustments to the basic model (like the sustainability dimension), an extended version 16 with more variables seemed to be more appropriate than the earliest edition. minor adjustments 17 were made to the original tam3 version by venkatesh and bala (2008) to better suit the 18 purpose and context of the analysis. first, all constructs were considered in the context-specific 19 environment, i.e. the adoption of new water-smart agricultural technologies by italian 20 horticultural farms. moreover, some aspects were evaluated as very important and emerged 21 9 explicitly from the exploratory phase with the participants, such as technology self-efficacy and 1 quality of outcomes. other tam3 characteristics, such as (computer) playfulness or perceived 2 enjoyment, do not apply to the context of the current research and were therefore excluded from 3 the model design. then, the need to include sustainability dimensions when dealing with digital 4 technologies in the agri-food sector is pressing (alexandrova-stefanova et al., 2024; gemtou 5 et al., 2024; khanna, 2021; mcfadden, 2022). consequently, a category based on with the 6 sustainability assessment of food and agriculture systems (safa) aspects (fao, 2014) was 7 included in the model since this method represents a comprehensive and international reference 8 for assessing sustainability along agri-food value chains. more specifically, the themes inspired 9 by the safa indicators, were (i) the reduced water-used thanks to the optimization of the 10 irrigation system, (ii) the improved skills the employees and the holder/farmer need to reach to 11 use the technology, and (iii) new employees recruited thanks to their technological skills. 12 therefore, we tested the following main hypotheses on the factors influencing the adoption of 13 new water-smart agricultural technologies by italian horticultural farms (figure 1): 14 h1: perceived usefulness is positively affected by output quality (h1a), by sustainability 15 outcomes measured by safa indicators (h1b), and by subjective norms (h1c); 16 h2: perceived ease of use is positively affected by technology self-efficacy (h2a), and is 17 negatively affected by anxiety (h2b); 18 h3: perceived ease of use has a positive impact on farmers' intention to adopt new technologies 19 (h3a), and is positively affecting the perceived usefulness of new technologies (h3b); 20 h4: perceived usefulness has a positive impact on farmers' intention to adopt new technologies; 21 10 h5: subjective norms have a positive impact on farmers' intention to adopt new technologies; 1 h6: the farmers' intention to adopt new technologies is positively affecting the behaviour, i.e. 2 the new technology adoption. 3 4 figure 1. model testing the factors affecting the adoption of water-smart agricultural new 5 technologies by italian horticulture farms. 6 7 8 moreover, individual factors, such as socio-demographic and organizational characteristics, 9 which determine the natural and structural conditions of the farm, have been found to correlate 10 with farmers’ decisions. in particular, farmers’ education level, gender, age, technology 11 literacy, were among the individual drivers more frequently included in studies investigating 12 the smart farming technologies adoption (osrof et al., 2023). farmers differentiate for their 13 level of human capital (i.e. experience, education, and other relevant training) (huffman, 2001; 14 okoye et al., 2025). these individual-level differences shape their technology adoption: the 15 11 higher the human capital, the more likely the technologies are adopted (mcfadden, 2022). farm 1 size, mostly expressed in total acreage farmland, is a prominent factor among the organizational 2 ones, since larger farm size is consistently seen as pivotal for achieving economies of scale 3 when adopting smart farming technologies (okoye et al., 2025). farm income is another key 4 element, as farmers with a higher income are more willing to invest in new technologies (osrof 5 et al., 2023). farm location is also a notable barrier within this theme, showing mixed effects 6 in past studies. some research indicates that it might negatively affects farmers’ motivation to 7 adopt smart technology, particularly if farms face unfavourable climate conditions or soil 8 quality (paxton et al., 2011). in italy, farms in the northern regions are generally more 9 competitive, due to larger farm sizes, advanced mechanisation, and stronger market integration. 10 in contrast, farms located in central and southern regions often face structural constraints, 11 including smaller farms and lower productivity (baldoni et al., 2018; okoye et al., 2025). other 12 studies emphasize the importance of social factors and access to information for the adopting 13 of innovative smart technologies (blasch et al., 2022). in this context, being a member of a 14 farmers’ associations or a producer organizations (pos), where knowledge transfer is one of 15 the main objectives, might facilitate adoption (okoye et al., 2025). therefore, we controlled the 16 main endogenous variables of the model, i.e. perceived usefulness, adoption intention and 17 behaviour, with individual factors, namely farmers’ age, education level and years of 18 experience in the agricultural sector, and organizational ones, including farm size, farm location 19 (expressed by the latitude of the province where the farm is located), number of employees, and 20 membership in a cooperative or a producer organization (figure 1). 21 12 1 3. data and method 2 3.1. data collection 3 the data collection consisted in two phases: first we conducted a preliminary exploratory 4 phase with qualitative, unstructured interviews. we interviewed 10 stakeholders, including 5 agronomists, technicians from producer organizations (pos), representatives of the tomato 6 interbranch organization (oi pomodoro nord italia), farmers, and vegetable/tomato 7 producers. the aim of the exploratory interviews was to identify relevant aspects to be 8 included in the final model and to highlight those that could be omitted. in this way, relevant 9 points such as the output quality and self-efficacy were included in the final surveys. the 10 questions focused on previous experience with smart technologies, skills in using them, public 11 financial support for the adoption of technical solutions and the farm structure, as well as 12 farmers’ previous personal background. in the second phase, we conducted a survey among a 13 sample of italian horticultural farms. an initial pilot phase (n=21 interviews) was carried out 14 to test the questionnaire. in this pilot phase the model was tested on 21 farms equally 15 distributed in the country (10 in centre and southern italy, 11 in the northern regions). most 16 of them cultivate tomatoes, while a smaller group (n=4) grows only other fresh vegetables, 17 such as pumpkin, celery, fell, broccoli, etc. the majority of the farms interviewed do not use 18 any kind of technology; only a few rely on autonomous driving systems (n=3 farms), drones 19 (n=1 farm), and sensors (n= 4 farms). word-of-mouth, peer recommendations, and previous 20 experience are the preferential ways the technologies are adopted. 21 13 once the pilot phase was completed, the main study was conducted in the period from october 1 to november 2024 by an international market research company using the cati (computer 2 assisted telephone interview) method. the survey lasted approximately 30 minutes. the final 3 sample consisted of 251 italian farmers. it included both tomato growers (50% from northern 4 italy and 50% from the south) and farmers cultivating other fresh vegetables, such as carrots, 5 peppers, eggplants, lettuce, etc., distributed across northern, central and southern italy (30%, 6 17%, and 53%, respectively). the geographical distribution was designed to be representative 7 of horticultural farms, based on data from the italian national institute of statistics (istat). 8 the inclusion of multiple administrative regions across the country ensures a comprehensive 9 understanding of cultivation practices and highlights the heterogeneity of technological 10 adoption. 11 12 3.2. measures 13 together with the socio-demographic information and the descriptive indicators, the 14 questionnaire was designed to test the model hypotheses. overall, it included 14 constructs, 15 with a total of 45 items. the constructs included in the final model (figure 1) were aimed to 16 understand the drivers for the adoption of innovative water-smart agricultural technologies by 17 italian horticultural farms. all tam3 items were measured on a 7-point scale (from ‘strongly 18 disagree’ to ‘strongly agree’) (see annex table a1). 19 subjective norm was assessed by three items (e.g., “many producers i know have already 20 adopted this innovation”) and was adapted by fishbein and ajzen (2010). this construct 21 14 refers to the perceived social pressure to perform or not perform a particular behavior. these 1 social influences shape intentions and actions, and, in this study, the construct was explored 2 considering peer adoption, which could lead to direct recommendations or serve as a concrete 3 example (kutter et al., 2011). the same construct considered also the customers’ perception, 4 which is more and more important, especially if linked to sustainability issues (such as water 5 saving practices). 6 we measured the perceived usefulness with four items (e.g., “this innovation could improve 7 my productivity”) and perceived ease of use with two items (e.g., “this technology should be 8 easy to use”) (davis, 1989). output quality, i.e. the perception of the quality of the technology 9 in performing the task, was measured by four items (e.g., “using this technology will improve 10 the quality of my products”), each adapted from venkatesh and bala (2008). safa-based 11 aspects (i.e. the sustainability-related outcomes of the new technology adoption) were assessed 12 by four items (e.g., “by using this innovation, i could help reduce water consumption”). 13 technology self-efficacy, i.e. the belief in how well someone can perform actions to achieve 14 performance outcomes, was measured by three items (e.g., “i would use this innovation easily 15 if i had technical support”), whereas anxiety was assessed by three items (e.g., “new 16 technologies make me feel uncomfortable”). all these latter items were adapted from 17 venkatesh and bala (2008). finally, we used three items to assess behavioural intention (e.g., 18 “i intend to use this technology in the near future”) (fishbein and ajzen, 2010). 19 the study focused on the three levels of water-smart technologies described above: level 1) – 20 soil moisture sensors, level 2) – a system combining sensors with an automatic irrigation 21 15 system, and level 3) – sensors connected to an automated system, which in turn is connected 1 to and interacts with predictive models. if a farmer indicated they have adopted a certain level 2 of the technology, the items were framed for the next level. for instance, if a respondent have 3 already adopted soil moisture sensors, we asked about the intention to adopt the sensors 4 connected to an automated system. if no adoption was reported, we asked about their intention 5 to adopt soil moisture sensors (level 1), whereas when they reported the highest level of 6 adoption, we asked about the intention to adopt more advanced predictive models. thus, the 7 behaviour was assessed with a single item, ranging from 1 to 4, considering the different 8 adoption levels (1=no technology; 2=level 1; 3=level 2; 4=level 3). we acknowledge that in 9 tam3 formulations intention and behaviour typically refer to the same “future-oriented” action 10 (venkatesh and bala, 2008). given the cross-sectional nature of our study, we adopted a 11 graduated behavioural scale to capture the level of adoption by farmers of increasingly 12 sophisticated water-smart technologies. this decision was necessary given the multi-stage 13 nature of the technology adoption in precision agriculture (finger, 2023), including smart-water 14 management technologies. indeed, in many cases farmers adopt these solutions incrementally, 15 in a progression from basic systems (in our case, soil moisture sensors) to more complex 16 systems (such as predictive models). accordingly, behavioural intention was framed toward the 17 next logical step in technology adoption, while behaviour captured the current level of adoption. 18 therefore, the intention-behaviour link in this study, tested in h6 (figure 1), should be 19 interpreted as a sequential progression in the adoption pathway, as measured by the behavioural 20 scale, and not as a direct action-intention relation. 21 16 1 3.3. data analysis 2 we performed the statistical analysis using spss v.29.0 and amos v.29.0 statistical software 3 (ibm corporation, armonk, ny, usa). means, standard deviations, median and interquartile 4 range (iqr) were calculated for each questionnaire item and its related construct. structural 5 equation modelling (sem) was used to test hypotheses h1–h6 and the theoretical framework 6 in figure 1. sem allows models to be specified with both latent (e.g., perceived usefulness) 7 and observed variables (e.g., farm size) (kline, 2016). 8 to improve our understanding of farm-level attitude and structural variables, we have 9 considered two models using the same dataset: in model 1 we included only the variables of 10 the extended-tam3 model, i.e. behaviour, behavioural intention, subjective norm, perceived 11 usefulness, perceived ease of use, output quality, sustainability-related constructs (safa), 12 technology self-efficacy and anxiety. then, we controlled for the effects of individual factors 13 (i.e., farmers’ age, educational level, and years of experience in the agricultural sector) and 14 organizational factors (i.e., farm size, farm location, number of employees, membership in a 15 cooperative or a producer organization) on the endogenous variables (i.e., perceived usefulness, 16 behavioural intention, and behaviour), building a second model, which is an extension of model 17 1. the full model, containing all factors and their interactions, was fitted to the data; then, the 18 least significant parameters were removed through a backwards deletion method, using 19 akaike’s information criterion (aic) for assessing the improvement in model fit (power et al., 20 2013). in general, the aic declines as the model fit increases (kline, 2016). where the model 21 17 fit was not significantly improved by the removal of a parameter, it remained in the final model. 1 this process was repeated until only two significant variables remained in the model 2: number 2 of employees and farm size (expressed in hectares of utilised agricultural area, uaa). these 3 additions are theoretically relevant and empirically meaningful for explaining farmers' digital 4 adoption behaviour in a real-world context (mcfadden, 2022; okoye et al., 2025; osrof et al., 5 2023). 6 convergent validity of the model variables was assessed using average variance extracted 7 (ave), cronbach’s α coefficient, and composite reliability (cr). discriminant validity was 8 tested by comparing the square root of the ave of each construct with the inter-construct 9 correlation (bagozzi and yi, 2012). the goodness-of-fit of the models was assessed using the 10 χ2 and their degrees of freedom (df), the tucker-lewis index (tli), the comparative fit index 11 (cfi), the root mean square error of approximation (rmsea) with a 90% confidence interval, 12 and the standardised root mean square residual (srmr) (kline, 2016). since model 1 and 13 model 2 are not nested, due to additional parameters, we evaluated their relative performance 14 using multiple comparative indicators, as recommended in the sem literature, such as cfi, 15 tli, rmsea, and aic. the coefficient of determination (r2) was used to measure the 16 explained variance of the endogenous variables. we applied the maximum likelihood 17 estimation routine (byrne, 2010). 18 19 4. results 20 4.1. descriptive statistics 21 18 the overall sample consisted of 251 respondents who were responsible for farm’s decisions 1 (78% always, 14% often, and 8% sometimes). most respondents were male (92%), had 2 completed upper secondary education (53%), had an average age of 53 years, and a median of 3 30 years of experience in the agricultural sector (table 1). most farms were located in southern 4 italy and on the islands (51.4), had a median utilised agricultural area (uaa) of 15 ha, 5 employed less than 10 people (68%), with a median turnover of €200.000. the most frequently 6 cultivated vegetables were tomatoes, both for fresh consumption (44%) and for the processing 7 industry (41%), followed by peppers (16%) and zucchinis (11%). 8 most of the sampled farmers had not yet adopted any of the proposed technologies (n=175, 9 69.7%). those who have deployed any of these technologies relied on level 1 (i.e. soil moisture 10 sensors, n=43, 17.1%), and a few were already using automated irrigation systems (level 2) or 11 predictive models (level 3), accounting for 6.4% (n=16) and 6.8% (n=17) respectively (table 12 1). in light of these findings, it is important to understand the motivation for the adoption of 13 new technologies and the factors that hamper their introduction. 14 15 table 1. description of the sample: farms characteristics and socio-demographics data of 16 farmers (n=251). 17 variables sample n % age of the respondent age (years, mean and sd) 52.8 (11.9) gender male 231 92.0 female 20 8.0 others or prefer not to answer 0 0.0 educational level 19 primary 8 3.2 secondary lower 57 22.7 secondary higher 132 52.6 tertiary 54 21.5 geographical area of the farm north-west 28 11.2 north-east 66 26.3 center 28 11.2 south and islands 129 51.4 farm size uaa (ha, median and iqr) 15.0 (4.0-60.0) farms by uaa classes < 2 ha 20 8.0 2 – 4.99 ha 47 18.7 5 – 19.99 ha 69 27.5 20 – 49.99 ha 45 17.9 > 50 ha 70 27.9 most cultivated vegetables tomato (for fresh consumption) 110 43.8 tomato (for the processed industry) 104 41.4 peppers 40 15.9 zucchinis 27 10.8 eggplants 13 5.2 lettuce 13 5.2 potatoes 12 4.8 melons 9 3.6 cauliflowers 8 3.2 enterprise n. employee category micro (1-9 employees) 171 68.1 small (10-49) 64 25.5 medium 1 (50-99) 12 4.8 medium 2 (100-249) 4 1.6 large (≥250) 0 0.0 farm’s turnover turnover (.000 euro, median and iqr) 200 (90-650) farmer’s years of experience in agriculture years of experience (median and iqr) 30 (20-40) levels of water-smart technologies a no technological innovation 175 69.7 level 1 43 17.1 level 2 16 6.4 level 3 17 6.8 notes: data are presented as the mean (sd) for continuous variables for which the hypothesis of normal 1 distribution cannot be rejected at p<0.05, as median (iqr) otherwise, or as number (%) for nominal variables. sd 2 = standard deviation. iqr = interquartile range. uaa = utilised agricultural area. a levels of water-smart 3 20 technologies: level 1) – soil moisture sensors, level 2) – a system combining sensors with an automatic irrigation 1 system, and level 3) – sensors connected to an automated system, which in turn is connected to and interacts with 2 predictive models. 3 4 table 2 shows the descriptive statistics of the latent and observable variables, as well as the 5 tests conducted on the constructs. the factor loadings of the variable items (λ) exceeded 0.50, 6 the cronbach’s α and cr values were above 0.70, and the ave values exceeded 0.50; these 7 results, with the only exception of perceived ease of use, demonstrated strong reliability, as well 8 as convergent and discriminant validity of all factors in the measurement model. we opted to 9 retain the perceived ease of use construct, first, because the construct is grounded in the tam, 10 where plays a fundamental role. secondly, while the cronbach’s alpha is slightly low, the 11 construct shows acceptable composite reliability and standardized factor loadings. the ave is 12 slightly below the conventional 0.50 threshold, but is still within an acceptable range, 13 particularly when supported by theoretical significance and other reliability indicators. 14 discriminant validity was further confirmed by verifying that the square root of the ave for 15 each construct, as shown in table 3, was greater than the correlations between the constructs 16 (bagozzi and yi, 2012). 17 overall, the results in table 2 show a moderately positive perceived usefulness of water-smart 18 agricultural new technologies (mean score: 4.81), which means in particular that farmers 19 moderately agree that by using this technology they could reduce water consumption and 20 improve productivity by using this technology. the results also show a moderately positive 21 21 perceived ease of use (4.69) and output quality (4.59). furthermore, important others had no 1 significant influence (3.63), and there was relatively low anxiety about applying new 2 technologies (3.16). the results indicated a positive evaluation of the sustainability aspects 3 related to the new technology (e.g., reduced water consumption, enhanced technical skills, etc., 4 mean score: 5.03), as well as positive technology self-efficacy (5.12). in particular, respondents 5 stated that they would use this innovation easily if they had technical support. furthermore, 6 farmers exhibited a moderately positive intention to adopt innovative water-smart agricultural 7 technologies (4.58). 8 9 table 2. mean values (standard deviation, sd) and median values (interquartile range, iqr) of 10 single items and constructs, factor loadings (λ), composite reliability (cr), average variance 11 extracted (ave) and cronbach’s α of the sample (n=251). 12 mean (sd) median (iqr) λ cr ave α perceived usefulness 4.81 (1.06) 5.00 (4.25-5.25) 0.84 0.56 0.84 pu1 4.80 (1.25) 5.00 (4.00-5.00) 0.78 pu2 4.61 (1.34) 5.00 (4.00-5.00) 0.75 pu3 4.86 (1.20) 5.00 (4.00-5.00) 0.79 pu4 4.95 (1.37) 5.00 (4.00-6.00) 0.67 perceived ease of use 4.69 (0.92) 4.50 (4.00-5.00) 0.61 0.45 0.61 peu1 4.98 (1.06) 5.00 (4.00-5.00) 0.59 peu2 4.40 (1.10) 5.00 (3.00-5.00) 0.75 output quality 4.59 (0.98) 4.75 (4.00-5.00) 0.83 0.56 0.84 oq1 4.57 (1.15) 5.00 (4.00-5.00) 0.70 oq2 4.52 (1.25) 5.00 (4.00-5.00) 0.78 oq3 4.80 (1.16) 5.00 (4.00-5.00) 0.85 22 note: all items were measured on a 7-point scale (from ‘strongly disagree’ to ‘strongly agree’), where median 1 value 4 indicates ‘neither agree or disagree’. a levels of water-smart technologies: level 1) – soil moisture sensors, 2 level 2) – a system combining sensors with an automatic irrigation system, and level 3) – sensors connected to 3 an automated system, which in turn is connected to and interacts with predictive models. 4 5 4.2. drivers of digital innovation 6 model 1 showed a good fit with the collected data: χ2 (df) = 461.975 (280), cfi = 0.950, 7 rmsea = 0.051 (90%ci 0.043 – 0.059), tli = 0.942 and srmr = 0.054. the standardized 8 oq4 4.48 (1.20) 5.00 (4.00-5.00) 0.64 safa 5.03 (1.00) 5.00 (4.67-5.67) 0.78 0.55 0.79 safa1 5.12 (1.21) 5.00 (5.00-6.00) 0.68 safa2 4.87 (1.17) 5.00 (4.00-5.00) 0.72 safa3 5.10 (1.20) 5.00 (5.00-6.00) 0.81 anxiety 3.16 (1.18) 3.00 (2.67-3.67) 0.85 0.66 0.85 anx1 3.28 (1.33) 3.00 (3.00-4.00) 0.74 anx2 3.10 (1.33) 3.00 (2.00-3.00) 0.88 anx3 3.10 (1.37) 3.00 (2.00-3.00) 0.81 technology self-efficacy 5.12 (1.11) 5.00 (4.67-6.00) 0.92 0.80 0.92 tse1 5.07 (1.22) 5.00 (5.00-6.00) 0.87 tse2 5.20 (1.18) 5.00 (5.00-6.00) 0.93 tse3 5.08 (1.18) 5.00 (5.00-6.00) 0.88 subjective norms 3.63 (1.09) 3.67 (3.00-4.33) 0.76 0.53 0.74 sn1 3.84 (1.31) 4.00 (3.00-5.00) 0.84 sn2 3.52 (1.41) 3.00 (3.00-5.00) 0.51 sn3 3.53 (1.31) 3.00 (3.00-5.00) 0.78 behavioural intention 4.58 (1.35) 4.67 (4.00-5.33) 0.91 0.77 0.91 bi1 4.41 (1.51) 5.00 (4.00-5.00) 0.93 bi2 4.54 (1.47) 5.00 (4.00-5.00) 0.89 bi3 4.80 (1.41) 5.00 (4.00-6.00) 0.80 behaviour a 1.50 (0.89) 1.00 (1.00-2.00) 23 path coefficients and their significance levels are shown in table 4, whereas the unstandardized 1 coefficients and standard errors are shown in the appendix table a2. 2 3 table 3. spearman’s rank-order correlations (ρ) between the constructs including the squared 4 root of the ave of each construct (reported in bold on the main diagonal). 5 pu peu oq safa anx tse sn bi beh pu 0.75 0.31*** 0.63*** 0.52*** −0.28*** 0.40*** 0.36*** 0.57*** 0.36*** peu 0.67 0.24*** 0.31*** −0.17** 0.34*** 0.14* 0.28*** 0.15* oq 0.75 0.46*** −0.21*** 0.34*** 0.43*** 0.60*** 0.35*** safa 0.74 −0.23*** 0.63*** 0.16* 0.39*** 0.19** anx 0.81 −0.16** −0.15* −0.29*** −0.17** tse 0.89 n.s. 0.39*** n.s. sn 0.73 0.44*** 0.36*** bi 0.88 0.40*** note: pu = perceived usefulness; peu = perceived ease of use; oq = output quality; safa = sustainability 6 assessment of food and agriculture systems; anx = anxiety; tse = technology self-efficacy; sn = subjective 7 norms; bi = behavioural intentions; beh = behaviour; sign.: *** p<0.001, ** p<0.01, * p<0.01, n.s. = not 8 significant. 9 10 overall, model 1 shows r2 values of 0.65 for the intention and 0.16 for the behaviour in 11 adopting a new water-smart technology. this means that, respectively, 65.1% of the variance 12 in intention and 16.4% of the variance in behaviour can be explained by the tested variables. 13 the results suggest that the intention to adopt an innovative water-smart technology 14 significantly influences the actual behaviour (i.e., the adoption of the technology itself), as 15 postulated by h6 (p<0.001). behavioural intention, in turn, is positively influenced by 16 24 perceived usefulness with p<0.001, which is one of the two core variables of the tam3 (h4 1 accepted). 2 3 table 4. tam3-extended models: coefficient of determination (r2), standardised coefficients 4 (β), p-values, research hypotheses and model fit indices (n=251). 5 model 1 model 2 r2 β p hypotheses r2 β p hypotheses pu 0.791 0.822 peu → pu 0.133 0.044 h3b accepted 0.121 0.060 h3b accepted oq → pu 0.658 <0.001 h1a accepted 0.708 <0.001 h1a accepted safa → pu 0.199 0.027 h1b accepted 0.152 0.081 h1b accepted sn → pu 0.045 0.512 h1c rejected 0.035 0.607 h1c rejected emp → pu 0.183 <0.001 uaa → pu 0.040 0.351 peu 0.305 0.303 tse → peu 0.487 <0.001 h2a accepted 0.488 <0.001 h2a accepted anx → peu −0.169 0.041 h2b accepted −0.163 0.048 h2b accepted bi 0.651 0.652 pu → bi 0.601 <0.001 h4 accepted 0.602 <0.001 h4 accepted peu → bi 0.069 0.295 h3a rejected 0.060 0.351 h3a rejected sn → bi 0.278 <0.001 h5 accepted 0.268 <0.001 h5 accepted emp → bi 0.034 0.473 uaa → bi 0.086 0.065 beh 0.164 0.179 bi → beh 0.404 <0.001 h6 accepted 0.370 <0.001 h6 accepted emp → beh 0.037 0.534 uaa → beh 0.153 0.009 model fit indices χ2 (df) 461.975 (280) 500.298 (323) cfi 0.950 0.952 tli 0.942 0.944 aic 657.561 722.298 25 rmsea (90% c.i.) 0.051 (0.043 – 0.059) 0.047 (0.039 – 0.055) srmr 0.054 0.058 note: pu = perceived usefulness; peu = perceived ease of use; oq = output quality; safa = sustainability 1 assessment of food and agriculture systems; anx = anxiety; tse = technology self-efficacy; sn = subjective 2 norms; bi = behavioural intentions; emp = number of employees; uaa = average farm size (utilised agricultural 3 area); beh = behaviour. 4 5 perceived ease of use does not significantly affect the intention to adopt technologies, therefore 6 not supporting h3a; however, it positively affects perceived usefulness of new technologies 7 with p<0.05, confirming h3b. h5 is also supported since subjective norm has a positive effect 8 on the intention to adopt a technology (p<0.001), showing that perceived social pressure has an 9 influence on the farmers’ motivation to adopt a new technology. the construct of anxiety shows 10 a negative effect on the perceived ease of use (p<0.05, h2b accepted), a property that is 11 stimulating and that could open up new ways of designing and conceptualizing modern 12 technologies. perceived ease of use, on the other hand, is positively influenced by the self-13 efficacy of the technology, with p<0.001, supporting h2a. in turn, perceived usefulness is 14 influenced by the quality of the output (i.e., the perceived quality of the outcomes achieved 15 through the use of the technology, p<0.001) and by the safa indicators (p<0.05), thereby 16 supporting h1a and h1b, respectively. however, h1c is not supported, as the effect of 17 subjective norms on perceived usefulness is not significant. 18 overall, model 2 shows good fit with the data (χ2 (df) = 500.298 (323), cfi = 0.952, rmsea 19 = 0.047 (90%ci 0.039 – 0.055), tli = 0.944 and srmr = 0.058). despite a higher aic for 20 model 2 as compared to model 1, due to the additional parameters, the extended model shows 21 26 slight improvements in global fit (cfi, tli, rmsea), while also improving the explained 1 variance of dependent variables, in particular behavioural outcome (up to 17.8%) (table 4). 2 the overall path and the tested hypotheses are confirmed, and the inclusion of structural 3 variables adds value to the explanatory power of the model without substantially compromising 4 parsimony. in particular, the number of employees positively influences respondents’ perceived 5 usefulness (p<0.001), indicating that decision-makers in larger farms, in terms of workforce, 6 find the innovative technology capable of enhancing farm performance. in turn, the average 7 farm size in uaa positively influences the behaviour (p<0.001) and behavioural intentions 8 (p<0.10). in other words, respondents working in larger farms are more willing to adopt the 9 new technologies or have already adopted them. 10 11 5. discussion 12 this study provides empirical evidence on the factors influencing the intention and behaviour 13 of italian farmers to adopt digital water-saving technologies, with a focus on the horticultural 14 sector. the results show that about 70% of the farmers surveyed have not adopted any of the 15 proposed technologies, emphasising the persistent gap between technological innovation and 16 practical implementation in the field. this finding confirms the limited uptake of smart farming 17 solutions in italy and emphasises the importance of understanding the barriers to adoption and 18 the enablers of adoption. this is in line with previous research highlighting the slow pace of 19 digitalisation in italian agriculture compared to other european countries (addorisio, spadoni, 20 et al., 2025; cimino et al., 2024; okoye et al., 2025; timpanaro et al., 2024). 21 27 using the extended tam3 model, the study explains about 18% of the variance in the actual 1 adoption of water-smart technologies, and 65% in adoption intention. as expected, behavioural 2 intention is a significant predictor of the behaviour, indicating that farmers motivation in 3 adopting the innovative technologies affect the actual adoption. the applied model further 4 assumes that the effect of other variables (e.g., self-efficacy) on behavioural intention is 5 mediated by perceived usefulness and perceived ease of use. consistent with prior research 6 (davis, 1989; venkatesh and davis, 2000) perceived usefulness proved to be a key factor in 7 willingness to adopt, emphasising the importance farmers attach to tangible benefits such as 8 improved efficiency, performance or sustainability. consistent with other studies, which 9 suggest that perceived usefulness significantly influences technology adoption when it does not 10 significantly increase production costs (pierpaoli et al., 2013), our results show that economic 11 viability remains a key concern, especially in a sector dominated by small to medium sized 12 farms with limited investment capacity. the importance of perceived benefits is also supported 13 by studies of adoption in agriculture across europe, including those by paustian and theuvsen 14 (2017) and shang et al. (2021), who argue that demonstrable improvements in performance are 15 essential to overcome innovation scepticism. in general and specifically in the italian context, 16 where high input costs and fragmented land ownership remain systemic barriers to 17 modernisation, benefits are often associated not only with profitability, but also with the ability 18 to ensure compliance with environmental standards and receive subsidies under the eu's 19 common agricultural policy (cap). the perceived benefits of digital tools by farmers are 20 28 therefore determined by a complex interplay of market incentives, legal requirements and 1 evolving sustainability expectations. 2 furthermore, in horticulture, a labour-intensive and water-dependent sub-sector, the perceived 3 benefits of smart technologies are particularly relevant. with climate change exacerbating water 4 scarcity in regions such as southern italy, precision irrigation and monitoring systems are 5 increasingly recognised as essential climate adaptation tools (lakhiar et al., 2024). however, 6 despite their potential, the perceived benefits of these tools often depend on farmers' ability to 7 access relevant knowledge, trust the data provided and integrate new practises into existing 8 routines – all factors mediated by the socio-cultural and institutional dynamics of the italian 9 agricultural landscape (addorisio, casolani, et al., 2025). 10 in contrast to the predictions of the original tam3 model (venkatesh and bala, 2008), 11 including those of its specific application (kühnemund and recke, 2025), this study found that 12 perceived ease of use did not significantly influence italian farmers’ intention to adopt digital 13 water-smart technologies. this deviation from theoretical expectations can be explained by the 14 structural and cultural characteristics of italian agriculture. the sector is dominated by small 15 family farms, many of which, especially in the southern regions, face a pronounced digital 16 divide due to limited access to broadband infrastructure, digital skills and technical training 17 (addorisio, casolani, et al., 2025). similar findings have been found in other studies (for an 18 overview, see osrof et al., 2023), including research on the italian fruit and wine sector, where 19 ease of use was also found to be insignificant (canavari et al., 2021). as suggested by schulze 20 schwering et al. (2022), perceived ease of use may play a minor role when farmers rely more 21 29 on external support or adopt collaborative practises, suggesting that social norms may override 1 ease of use concerns. indeed, social norms had a significant influence on adoption intentions in 2 this study, mirroring the findings of senyolo et al. (2018) and dissanayake et al. (2022). these 3 findings suggest that peer influence and visibility of successful cases may help to foster a 4 culture of innovation and support wider adoption. 5 the study also shows that sustainability-related factors — such as improved water management 6 and skills development— increase perceived usefulness. this supports the arguments of montes 7 de oca munguia et al. (2021), who emphasise that the integration of sustainability goals can 8 increase the perceived value of smart technologies. this is particularly relevant in the italian 9 mediterranean climate context, where water scarcity, soil degradation and the effects of climate 10 change pose an acute challenge to horticultural productivity and environmental resilience. the 11 perceived alignment of digital technologies with sustainability goals therefore not only 12 improves acceptance, but also strengthens farmers’ motivation by linking environmental 13 benefits with economic performance (paustian and theuvsen, 2017). this last point is thought-14 provoking when it comes to examining the role of farmers and their commitment to 15 sustainability, as well as their awareness of the use of smart devices to promote more sustainable 16 practices. in the face of climate change and the pressure that agriculture is putting on 17 environmental resources, only the direct and committed involvement of farmers can promote a 18 more conscious and widespread use of smart technologies with the aim of reaping their benefits 19 (menozzi et al., 2015). linking sustainability to usefulness may also increase perceived 20 profitability, which strengthens farmers’ interest in adoption. 21 30 technology self-efficacy was found to have a significant effect on perceived ease of use, 1 suggesting that farmers who have confidence in their technical abilities are more likely to 2 perceive smart technologies as manageable. this supports previous findings highlighting the 3 relevance of perceived behavioural control for the adoption of sustainable practises (menozzi 4 et al., 2015). in the italian context, where a significant part of the agricultural population 5 consists of small farmers or ageing operators with limited participation in formal ict training, 6 self-efficacy becomes a crucial factor for the adoption of digital practises. italian agriculture is 7 characterised by a dual structure: on the one hand, there are highly mechanised and export-8 oriented companies, and on the other, traditional small farms that often lack digital 9 infrastructure. therefore, strengthening the confidence and skills of farmers through training is 10 crucial to bridging this technological gap. the results also shed light on important barriers, 11 including limited digital skills and poor access to information, which are in line with the 12 findings of other studies (osrof et al., 2023; sabbagh and gutierrez, 2023; yigezu et al., 2018). 13 the negative correlation between anxiety and perceived ease of use further underscores the 14 need for user-friendly technologies that reduce cognitive and operational complexity. targeted 15 training initiatives and inclusive measures are needed to address these barriers, especially for 16 farmers with limited resources. 17 the structural model also examined the influence of individual and organizational 18 characteristics. among these, only farm size and number of employees significantly affected 19 the model’s endogenous variables, while other factors such as education level, age, and farm 20 location showed no consistent impact. these findings reflect the mixed evidence in the literature 21 31 (osrof et al., 2023) and may be partially explained by the evolving role of education and age in 1 farming. for instance, highly educated individuals may pursue careers outside agriculture 2 (michels et al., 2020), while smart technologies are increasingly designed to be accessible 3 regardless of users’ educational background (wachenheim et al., 2021). similarly, although 4 older age is often associated with lower adoption rates, several studies have shown no 5 significant age effect (garcía-jiménez et al., 2022). larger farms—measured by uaa 6 acreage—were more likely to adopt or intend to adopt digital water-smart solutions. this 7 confirms the role of scale economies in overcoming the high investment costs associated with 8 advanced technologies (osrof et al., 2023). the significant effect of the number of employees 9 on perceived usefulness may be interpreted in two ways: first, these technologies may be seen 10 as reducing labour needs and costs; second, they may be valued for their potential to enhance 11 employees’ technical capabilities and improve workforce productivity. the latter interpretation 12 aligns more closely with the observed positive relationship between sustainability orientation 13 and perceived usefulness. 14 in summary, this study contributes to the understanding of digital technology adoption in italian 15 agriculture by validating core constructs of the tam3 model while highlighting context-16 specific dynamics. this study not only confirms existing theoretical frameworks, but also 17 highlights the importance of adapting digital transformation strategies to the italian agricultural 18 reality, characterized by structural constraints, strong social networks and a growing, yet 19 uneven, momentum towards sustainable innovation. by identifying key drivers and barriers to 20 adoption, the findings provide valuable guidance for the development of tailored policies, 21 32 training programs, and user-oriented technologies that can support the digital transformation of 1 the horticultural sector. promoting the integration of sustainability goals, addressing capacity 2 gaps, and leveraging social norms can collectively enhance adoption and support the transition 3 toward a more sustainable and resilient agricultural system. 4 5 6. conclusion 6 the adoption of digital technologies in the italian horticultural sector is a multifaceted challenge 7 influenced by a variety of individual, technological, social and contextual factors. these same 8 factors are of utmost importance for policy makers that needs to design appropriate measures 9 for promoting the adoption of water-saving solutions. identifying both the motivations and the 10 barriers to innovation might indeed inform policies by suggesting models that are suited to the 11 challenges faced by farmers. likewise, these findings can inform all interested stakeholders — 12 from solution and technology developers to producer organizations — about the key aspects 13 needed to promote the adoption of digital technologies within the agricultural sector. 14 this study shows that individual intention is an important determinant of the actual adoption of 15 innovative water-saving technologies and highlights the crucial role of farmer motivation in 16 decision-making. perceived usefulness of these technologies has a significant effect on 17 adoption intention, while perceived ease of use requires further investigation due to its limited 18 relevance in the current context. social norms were identified as an important determinant of 19 farmers’ intentions, highlighting the importance of community influence and external support 20 in promoting the adoption of digital technologies. to close the observed adoption gap, targeted 21 33 interventions should be developed to address barriers such as digital literacy, infrastructure and 1 accessibility of technology. furthermore, the regional and culture-specific nuances observed in 2 this study should be taken into account when developing customised strategies. 3 the results highlight important policy and business implications, suggesting that government 4 agencies, agricultural cooperatives, and technology developers should emphasize the economic 5 and environmental benefits of digital irrigation technologies. the identification of appropriate 6 measures, such as the creation of networks, outreach activities, knowledge-sharing initiatives 7 and technical assistance, can also accelerate adoption. by addressing these research and 8 knowledge gaps, this study contributes to both the academic literature and practical policy 9 making. it provides a refined theoretical model to understand technology adoption in smalland 10 medium-sized farms and offers practical insights to promote sustainable and efficient water 11 management in agriculture. further exploration of constructs that have negative correlates, such 12 as anxiety, could lead to more user-centred technology design that reduces barriers to 13 technology adoption and improves usability. policy, and in particular the agricultural 14 knowledge and innovation systems (akis) under the cap, can play a crucial role in 15 addressing these aspects by facilitating multi-actor collaborations between farmers, researchers, 16 advisors, and technology developers, as well as strengthening the dissemination of best 17 practices and provide continuous support (esposti, 2012). 18 some limitations of this study should be mentioned. first, the study reflects not only a specific 19 context, such as the horticultural sector, but also national characteristics, which can vary greatly 20 from country to country due to different regulatory and incentive frameworks, cultural practises 21 34 and, most importantly, technological infrastructures. nevertheless, the sample is not 1 representative of italian farmers. this must be taken into account when interpreting the results 2 and deriving consequences for corporate management. an extension of the sample and a 3 repetition of the study in other countries could therefore be interesting to test the validity of all 4 the hypotheses put forward in the original theory. extending the analysis to other crops farming, 5 as well as to livestock farming, and expanding the sample size could provide more accurate 6 insights over the phenomenon. eu cross-regional studies might also be very interesting to point 7 out differences in the adoption rate to be linked to cultural specificities, but also to normative, 8 political and infrastructural settings. the results might in this way serve policy makers and 9 institutions to design ad hoc policies to meet the desired technology level. 10 second, we did not consider prospective behaviour, i.e., we did not measure actual behaviour 11 in the future (i.e., future adoption of the innovative technologies), but only current behaviour. 12 although this approach is quite common in similar cross-sectional studies (see, e.g., (li et al., 13 2023) on smart agricultural technology adoption), it might have limited the compatibility of 14 behaviour with its antecedents (fishbein and ajzen, 2010). for instance, it has been noticed 15 that relations between intention and behaviour might be stronger for studies measuring 16 concurrent behaviour than those measuring behaviour prospectively (mceachan et al., 2011). 17 however, we decided to use this multiple-level measurement of behaviour in order to capture, 18 better than a binary variable, the progressive adoption from basic systems (in our case soil 19 moisture sensors) to more complex systems (such as predictive ones). thus, the intention-20 behaviour link in this study should be interpreted as a sequential progression in the adoption 21 35 pathway, rather than a direct action-intention relation. future studies employing longitudinal 1 data collection could better report this linkage between intention and prospective adoption. 2 finally, this study used self-report measures about the behaviour which may be subject to 3 response biases. however, the cati method can help with complex or sensitive questions 4 by allowing the interviewer to clarify questions and guide the respondent, thus reducing 5 misinterpretation and encouraging more accurate responses (dillman et al., 2014). 6 despite these limitations, this study is, to our knowledge, one of the first aimed at 7 investigating the relative importance of behavioural precursors in explaining the intention 8 to adopt innovative water-smart technologies in italian horticultural farms. 9 10 acknowledgements 11 this study was carried out within the agritech national research center and received funding 12 from the european union next-generationeu (piano nazionale di ripresa e 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(2018). enhancing adoption of agricultural technologies requiring high 8 initial investment among smallholders. technological forecasting and social change 9 134: 199–206. 10 44 appendices 1 2 table a1. constructs and items. 3 codes items perceived usefulness pu1 this innovation would make my work easier pu2 this technology would make my work faster pu3 this innovation could improve my productivity pu4 by using this technology, i could reduce water consumption in my company perceived ease of use peu1 this technology should be easy to use peu2 using this technology will not require much effort output quality oq1 i expect that the results of using this technology will be excellent oq2 using this technology will improve the quality of my products oq3 by using this system, i would increase the efficiency of my work oq4 by using this innovation, i would increase the quantity of product in the field safa safa1 by using this innovation, i could help reduce water consumption safa2 with the introduction of this technology, employees could receive training and enhance their knowledge and technical skills safa3 by introducing this innovation, i could receive training and improve my technical skills anxiety anx1 i get nervous when working with new technologies anx2 new technologies make me feel uncomfortable anx3 i am afraid of applying new technologies technology self-efficacy tse1 i would use this technology easily if someone showed me how to use it tse2 i would use this innovation easily if i had technical support tse3 i would use this innovation easily if i were familiar with the system 45 note: all items were measured on a 7-point scale (from ‘strongly disagree’ to ‘strongly agree’). 1 2 subjective norms sn1 people whose opinions matter to me think that i should use this technology sn2 many producers i know have already adopted this innovation sn3 my customers think that i should use this technology behavioural intention bi1 i will definitely use this technology in the near future bi2 i intend to use this technology in the near future bi3 if there were no significant barriers, i would use this system in the near future 46 table a2. tam3-extended model: unstandardized beta coefficients and standard errors (s.e.) 1 (n=251). 2 model 1 model 2 beta s.e. beta s.e. peu → pu 0.202* 0.100 0.180# 0.096 oq → pu 0.796*** 0.136 0.848*** 0.136 safa → pu 0.236* 0.106 0.179# 0.102 sn → pu 0.040 0.060 0.030 0.059 emp → pu 0.267*** 0.065 uaa → pu 0.000 0.000 tse → peu 0.308*** 0.064 0.308*** 0.064 anx → peu -0.098* 0.048 -0.095* 0.048 pu → bi 0.862*** 0.110 0.866*** 0.112 peu → bi 0.149 0.142 0.130 0.139 sn → bi 0.351*** 0.082 0.334*** 0.082 emp → bi 0.072 0.101 uaa → bi 0.000# 0.000 bi → beh 0.256*** 0.039 0.235*** 0.039 emp → beh 0.049 0.080 uaa → beh 0.000** 0.000 note: pu = perceived usefulness; peu = perceived ease of use; oq = output quality; safa = sustainability 3 assessment of food and agriculture systems; anx = anxiety; tse = technology self-efficacy; sn = subjective 4 norms; bi = behavioural intentions; emp = number of employees; uaa = average farm size (utilised agricultural 5 area); beh = behaviour. sign.: *** p<0.001, ** p<0.01, ** p<0.05, # p < 0.10. 6 7 bio-based and applied economics bae © 2024 author(s). open access article published, except where otherwise noted, by firenze university press under cc-by-4.0 license for content and cc0 1.0 universal for metadata. firenze university press | www.fupress.com/bae bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 citation: mantziaris, s., rozakis, s., karanikolas, p., petsakos, a., & tsiboukas, k. (2024). simulating farm structural change dynamics in thessaly (greece) using a recursive programming model. bio-based and applied economics 13(4): 353-386. doi: 10.36253/ bae-14790 received: june 3, 2023 accepted: september 2, 2024 published: december 31, 2024 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: matteo zavalloni simulating farm structural change dynamics in thessaly (greece) using a recursive programming model stamatis mantziaris1,*, stelios rozakis2, pavlos karanikolas1, athanasios petsakos3, konstantinos tsiboukas1 1 department of agricultural economics and rural development, agricultural university of athens, greece 2 school of chemical and environmental engineering, technical university of crete, chania crete, greece 3 department of performance, innovation and strategic analysis for impact, bioversity international, rome, italy *corresponding author. e-mail: sta.athens@hotmail.com) abstract. although the policy impacts on farms accumulate year by year, most farm decision models focus on short-term decisions, evaluating policies based on snapshots. structural changes are gradually built; therefore, farm decision models should consider the sequences within the period under study. multiyear data from the arable sector in thessaly, greece, have fed a newly developed farm-level recursive linear programming model mainly to simulate farm structural change dynamics. the proposed model incorporates new evidence on the strategic decision of arable crop farms regarding their remaining in the production system and farm expansion. results reveal an evident gradual farmland concentration in relatively large farms, accompanied by a gradual expansion of the most profitable cropping activities, verifying the real-world survival strategy of farms. keywords: farm structural change, land use change, recursive linear programming model, arable production system, greece. jel codes: c61, q12, q18. 1. introduction the declining number of surviving farms over time and the increase in average farm size generally signal the evolutionary process of structural change in the agricultural sector of developed economies (plogmann et al., 2022), implying changes in the farm size distributions (zimmermann and heckelei, 2012; saint-cyr et al., 2019). agricultural economists have shown great interest in describing structural change dynamics and understanding its drivers (plogmann et al., 2022). structural change is driven by various economic factors (neuenfeldt et al., 2019), environmental factors and social drivers (rirdc, 2007). neverthehttps://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode http://www.fupress.com/bae https://doi.org/10.36253/bae-14790 https://doi.org/10.36253/bae-14790 https://doi.org/10.36253/bae-14790 mailto:sta.athens@hotmail.com 354 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. less, some authors (wiborg, 1998; plogmann et al., 2022) consider farm economic performance the primary driver of structural change since it somehow encloses all the above factors. structural change is a normal evolutionary process in an economy (goddard et al., 1993). over time, rising agricultural productivity enabled the transfer of productive factors required for the development of other sectors of the economy (balmann and valentinov, 2016). however, structural change in the agricultural sector is usually correlated with public concerns, which are mainly expressed through public debates in two terms, firstly as “dying peasants” and secondly as “factory farming” (balmann and valentinov, 2016). highlighting the first public concern, this may be because, generally, structural change hardly leads to pareto superior states (balmann and valentinov, 2016). from this perspective, cochrane (1958) concludes that increased agriculture productivity positively affects only a limited number of innovative farms, while most farmers are affected negatively due to the following drop in agricultural commodity prices. suppose we analyze this reasoning from the point of view of public policy; in that case, structural change may reduce the problem concerning the profitability of remaining farms but, on the other side, reduce the number of small farms and thus counters the equity goals of public society (finger and benni, 2021). within this context, some authors consider the significant role of public policy in mitigating the consequences of structural change by pointing out that “much of the public policy agenda has clearly been established on a premise of optimality of a family farm structure” (goddard et al., 1993: 486). however, implementing appropriate policy interventions presupposes providing detailed information (by policy analysts) on structural change in agriculture through evidence-based policy-relevant research to support evidence-based agricultural policy decision-making. the european common agricultural policy (cap) marks essential shifts in the context where farms operate, with significant reforms attempted every decade. policy impacts on farms accumulate year after year, affecting the farm structures and, by extension, the well-being of rural communities, creating a ripple effect on the local economy. in this framework, modeling the dynamics of structural change adjustment (i.e., the change over time of farm numbers and farm size distribution) is highly desirable because it can provide policymakers and stakeholders with possible alternative scenarios of structural change adjustments, but it is still not widely used in policy analysis (ciaian et al., 2013; espinosa et al., 2016). modeling exercises such as dynamic appraisals can support policy analysts in formulating public policies to obtain the “desired farm structure” considering the societal demands for equity (finger and benni, 2021). two main methodological approaches incorporate structural change in agriculture: econometrics and simulation models (which aim to analyze farm structural change endogenously) (espinosa et al., 2016; zimmermann et al., 2009). econometric models include markov chains (zimmermann and heckelei, 2012) and various other regression approaches (zimmermann et al., 2009). simulation models include recursive programming models (e.g., wiborg, 1998; guinde et al., 2005; henningsen et al., 2005; offermann and margarian, 2014; djanibekov and finger, 2018; mittenzwei and britz, 2018) and agent-based models (e.g., balmann, 1997; berger, 2001; happe et al., 2008; freeman et al., 2009; bert et al., 2011; troost and berger, 2016; beckers et al., 2018; sun et al., 2022; donati et al., 2024). as simulation models can endogenously capture farm structural change, they are considered suited to analyzing policy changes’ allocative and distributive effects on an agricultural production system (guinde et al., 2005; happe et al., 2008; espinosa et al., 2016). although agent-based models such as agripolis (balmann, 1997) are considered by various modelers the most comprehensive attempt at analyzing the impact of policies on structural change (e.g., zimmermann et al., 2009), are characterized by greater complexity (e.g., zimmermann et al., 2009), and they are very demanding in terms of parameterisation (e.g., zimmermann et al., 2009; rowan et al., 2011; kremmydas et al., 2023) and calibration (e.g., zimmermann et al., 2009). in addition, the preference for simpler processbased models1 should not be ignored (troost and berger, 2020). therefore, while capturing structural change endogenously and providing meaningful insights into the allocative and distributional effects of various exogenous factors, the farm-level recursive programming models can also be manageable regarding the degree of complexity and data requirements compared to other simulation models such as agent-based models. based on the above discussion, the main objective of this research is to investigate the impacts of policy experiments on farm structural change dynamics in greece through an endogenous modeling approach based on a newly developed farm-level recursive linear programming model. while primarily aimed at simulating the impact of policy experiments on the evolutionary process of farm structural change, the proposed simulation model is also secondarily used to simulate the effect 1 process-based models include models such as simulation models and systems dynamics models. 355simulating farm structural change dynamics in thessaly (greece) using a recursive programming model bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 on land use change while analyzing its relationship with structural change adjustment. in the context of structural changes, the strategic decision of farms is summarized through the phrase “grow or go” (plogmann et al., 2022), implying the aspects of (i) farm viability and (ii) farm growth/expansion. through the proposed modeling approach, we integrate the farm’s economic performance as the main driver of this decision (e.g., wiborg, 1998; paroissien et al., 2021; plogmann et al., 2022). in more detail, in addition to traditional monetary value criteria to determine a surviving/viable farm, we introduce a novel viability criterion, assuming that farmers may compare their economic performance to societal consumption benchmark, in the sense that the agent (in our case, realworld individual farm) must achieve a minimum level of profitability, allowing entry into the “rat race” according to “keeping up with the joneses” (kuj) preferences (e.g., barnett et al., 2010; lombardo, 2021; paroissien et al., 2021). regarding farm expansion, the proposed modeling approach introduces a further novel element through the concept of relative optimal farm growth in equity to reallocate/allocate resources between neighboring surviving farms. the proposed model can also be characterized as a one-way communication model where the information flows from the econometric model to the recursive programming farm model (huang et al., 1980). in particular, the autoregressive integrated moving average (arima) models are used to forecast the values of the exogenously determined parameters of interest to conduct out-of-sample simulations. additionally, arima stochastic process estimates express the agents’ quasi-rational expectations regarding agricultural commodity prices and crop yields (nerlove and bessler, 2001; siegle et al., 2024). for the empirical application of the proposed simulation model, a representative sample of arable crop farms (in terms of farm structure) of the region of karditsa (nuts-3 level), thessaly, is chosen. the priority of empirical application given to the arable production system is justified by the fact that greek arable farming is characterized by a comparatively higher rate of structural change concerning the other main types of farming (other permanent crops, other grazing livestock) (fadn public database). from a general perspective, with this analysis, we attempt to contribute to the debate on dynamic assessments of the multidimensional effects in the context of policy reforms. additionally, more specific contributions to literature are expressed through at least four ways: first, we add knowledge by integrating evolutionary and social psychology elements to define a farm as viable based on kuj preferences. second, we simulate resource reallocation based on the criterion of relative optimal farm growth in equity as an alternative farm expansion/growth criterion to traditional criteria such as the shadow values of resources (e.g., guinde et al., 2005; hennessy, 2007; espinosa et al., 2016). third, the utilization of the arima stochastic process for time series forecasting of the values of the exogenously determined parameters (such as agricultural commodities prices, input prices, and crop yields) is an addition to the existing literature since in similar simulation models; these values are mainly determined either from secondary data sources (e.g., wiborg, 1998; hennessy, 2007; offermann and margarian, 2014) or through assumptions/ scenarios (e.g., guinde et al., 2005; henningsen et al., 2005; troost and berger, 2016; mittenzwei and britz, 2018) or simplified trend models (e.g., happe et al., 2008; bert et al., 2011; beckers et al., 2018). fourth, despite the great importance of the arable production system for the greek agricultural sector and the comparatively higher rate of structural change than the other main production systems, to our knowledge, farm-level recursive programming models have not been used to provide a “bottom-up” simulation of structural change of greek arable production system. the rest of the paper is organized as follows. section 2 describes the applied methodology, the data used to apply the methodology, and the policy experiments. the empirical results are presented in section 3, section 4 discusses them, and concludes. 2. methodology and data 2.1. recursive programming models for impact assessment in agriculture recursive programming models have already been introduced in the 1960s to represent dynamic adjustments of production capabilities at the farm level, and then with the study of day and cingo (1978) regional interdependence and structural elements were incorporated (espinosa et al., 2016). indicatively, recursive programming farm models have been utilized for the development of farm firm growth models (e.g., chien and bradford, 1976; cittadini et al., 2008; dowson et al., 2019) to investigate the economic consequences due to farmers’ adaptability to different water availability scenarios (e.g., iglesias et al., 2003; rowan et al., 2011; robert et al., 2018; dowson et al., 2019), to assess the impacts of various policy reform and price scenarios on farm income and investment behavior (e.g., viaggi et al., 2010; viaggi et al., 2011; davis et al., 2013; britz et 356 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. al., 2016) and to analyze the impact of policies on farm structural change (e.g., wiborg, 1998; guinde et al., 2005; henningsen et al., 2005; offermann and margarian, 2014; djanibekov and finger, 2018; mittenzwei and britz, 2018). the main structural elements of a recursive programming model correspond to a constrained optimization model and a data generator, where the data generator, given the optimal value or solution in period t, reinitializes the parameters of period t+1, including a set of constraints that relates the feasible values of current variables to past values of variables and exogenous events (mccarl and spreen, 1997). following chien and bradford (1976) and mccarl and spreen (1997), the general formulation of the recursive programming farm model is as follows: max e{πt} = ∑ j e{cj,t}t xj,t (1) subject to: ∑ j ai,j,t xj,t ≤ bi,t ∀i (2) xj,t ≥ 0 ∀j (3) where e{ } denotes the expectation operator; e{πt} is farm’s expected gross profit in eur which is maximized in year t ; e{cj,t} is the vector of expected gross profit in eur/hectare (ha) of the j cropping activity in period t ; xj,t is the vector of the decisions variables that denotes the level of the j cropping activity (hectares for crops) in period t; ai,j,t are the resource i usages by the j cropping activity per ha in period t; bi,t is the vector of available resources i in period t, functionally dependent upon lagged phenomena (kay, 1971; mccarl and spreen, 1997). the reinitialization of the vector of available resources (bi,t) is conducted through farm firm growth rules such as the endogenous feedback mechanism (efm) (e.g., kay, 1971; chien and bradford, 1976; mccarl and spreen, 1997; cittadini et al., 2008; davis et al., 2013; robert et al., 2016). although efm has been applied with some variations, the general mathematical formulation is as follows: bi,t = f(bi,t-1, xi,t-1*, vi,t)) (4) where the vector of available resources (bi,t) in period t is determined by the vector of available resources in the previous period (bi,t-1), the optimal decisions in the previous period (xi,t-1) and by the vector vi,t that allows for external changes in the resource restrictions due to exogenous events that will occur in the period t which are rather determined by external economic and environmental factors (kay, 1971; mccarl and spreen, 1997; davis et al., 2013; robert et al., 2016). since the proposed model is used for structural change analysis, three more basic structural elements are included to determine (i) farm viability, (ii) farm growth/expansion, and (iii) capital stock evolution at the farm level. a detailed description of these structural elements of the model is carried out in subsequent sections. 2.2. arima modeling for economic forecasting in agriculture the usefulness of such a simulation model, which is optimized sequentially within a dynamic framework, lies in the ability to provide results outside the reference period (out-of-sample forecasts). therefore, to conduct out-of-sample simulations, the forecasted values of the exogenously determined parameters of the farm are required. various modelers have used arima models to forecast exogenously determined parameters such as agricultural commodity prices (e.g., mao et al., 2022), crop yields (e.g., petsakos et al., 2016), cost of production factors (e.g., hloušková et al., 2018) and supply of various resources (e.g., the total amount of agricultural land, total amount of pesticides) (costache et al., 2021). arima models are fitted utilizing the information in the series itself to predict future points in the series (christodoulos et al., 2010; garnier, n.d.), and therefore the independent variables are lagged values of the series. more specifically, the future values of the dependent variable can only be described through their probability distribution rendering the series a stochastic process2 (pardoe, n.d.). in this vein, several modelers consider that the use of arima models is appropriate for economic forecasting in agriculture, especially in cases of lack of well-developed theory or limited information (petsakos et al., 2016); as a result, the forecasting of exogenous variables often present problems for econometric model users (oliveira et al., 1979). within this context, the arima stochastic process is utilized for estimating the values of exogenously determined parameters of interest (in our case, agricultural commodity prices, crop yields, costs, interest rate, total arable land, and total circulating capital) to perform out-of-sample forecasts in the medium term. in 2 details on arima modeling framework are provided in part a: conceptual framework of arima modeling in the supplementary material. 357simulating farm structural change dynamics in thessaly (greece) using a recursive programming model bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 addition, arima models are utilized to estimate the values for random/stochastic parameters, such as agricultural commodity prices and crop yields, to express agents’ quasi-rational expectations mechanism (nerlove and bessler, 2001; siegle et al., 2024). 2.3. simulation model specification and assumptions 2.3.1. model’s basic structure the initial endowments with production factors are specified before the sequential simulation starts (in our case, arable land, irrigated land, circulating capital, capital stock, and borrowed capital) (happe et al., 2008) (see figure 1). to simulate farms’ productive decisions through the proposed farm-level recursive linear programming model, we assume that farms optimize the expected gross profit (e.g., rowan et al., 2011) for each year t given the farm’s resource, policy, and flexibility constraints. to elaborate more, resource constraints contain: (i) arable land constraint; (ii) irrigated land constraint; and (iii) circulating capital constraint. policy constraints contain: (i) 2013 cap reform constraints (greening obligations); (ii) cap post-2020 reform scenario constraints; (iii) nitrate pollution reduction program constraints; and (iv) organic farming program constraint. flexibility constraint corresponds to the constraint of multiannual contract farming3. each sub-model (based on representative individual real-world farm) optimized recursively4 for a sequence of 15 years (from 2012 to 2026). time progresses in discrete time intervals, symbolizing the commencement of a growing season at time t (see figure 1). to perform outof-sample simulations (i.e., outside the reference period, specifically after 2019), mainly arima models are used to forecast the values of the exogenously determined parameters of interest (see figure 1). 2.3.2. farm agents’ expectations specification and model validation various authors (e.g., femenia et al., 2017) consider naïve and quasi-rational expectations (arima modeling), both based on past observations, to be the most frequent expectation mechanisms5 in some types of 3 a detailed description of the objective function and constraints is provided in part b: structure of the model’s objective function and constraints in the supplementary material. 4 the model is written in gams language. 5 a detailed description of farm agents’ expectations mechanisms is provided in nerlove and bessler (2001), haile et al. (2016), femenia et al. (2017), and siegle et al. (2024). farming. influenced by this finding, we emphasize these two mechanisms of expectations regarding agricultural commodity prices and crop yields in the present study, considering that they will be representative of sample farms and the information available to them (mainly based on past observations). more specifically, we have formulated two alternative models; one referred to as the quasi-rational expectations (qr) model and the other as the naïve and quasi-rational expectations (nv&qr) model. in more detail, in the qr model case, the agent’’ expectations are expressed through quasi-rational expectations (arima modeling) for agricultural commodity prices and crop yields (e.g., narayana and parikh, 1981; nerlove and bessler, 2001; siegle et al., 2024). in the nv&qr model case, the agent’’ expectations are expressed through naïve price expectations for agricultural commodity prices (e.g., nerlove and bessler, 2001; robert et al., 2018; siegle et al., 2024) and through quasi-rational expectations for crop yields. then the two proposed models are validated for their capability to reproduce activities allocation (gómez-limón et al., 2016), the number of surviving farms (beckers et al., 2018), and the farm size distribution (freeman et al., 2009; beckers et al., 2018). 2.3.3. determining farm viability usual approaches to defining farm viability are based on the opportunity cost of farming (e.g., loughrey et al., 2022) and the poverty line (e.g., miller et al., 1981; loughrey et al., 2022). other approaches to defining farm viability focus on monetary returns, where the farm income should ensure long-term farm growth in equity, or at least the equity should remain stable into the future (e.g., bright et al., 2007; barnes et al., 2015). another interesting approach to defining farm viability from a socio-economic perspective is based on the “keeping up with the joneses” (kuj) preferences (miller et al., 1981; paroissien et al., 2021). farmers may compare their profits to the overall standard of living (average living expenditures/average consumption level) of socially close reference group (neighboring farms), which is considered the societal consumption benchmark or social reference point of consumption level (paroissien et al., 2021). from this perspective, agents that stand below their societal reference point (in the sense of not being able to finance this level of consumption) are forced to stay out of the “rat race of keeping up with the joneses” (barnett et al., 2010), may experience lower life satisfaction and professional well-being, a situation which may 358 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. create incentives to exit the system (paroissien et al., 2021; nguyen and herron, 2021). therefore, a farm must achieve a minimum level of profitability, allowing entry into the “rat race” (lombardo, 2021) according to kuj preferences (i.e., keeping up with a benchmark proportional to the average level of consumption of the socially close reference group (barnett et al., 2010) such as neighboring farms). the influences for this hypothesis come from evolutionary and social psychology, where various researchers assume that the quest for status – frequently referred to in this context as “keeping-up-with-the joneses”– depends on the social norms related to a benchmark consumption level such as the average consumption level of the socially close reference group (fisher and heijdra, 2009; lombardo, 2021; mageli et al., 2022). based on the above reasoning, various researchers assume that the quest for social status can be linked to the striving to survive (mageli et al., 2022). notably, since social groups can distribute resources among their members, an agent’s chances to survive and reproduce are greatly enhanced if she/he belongs to a group and if she/he holds a relatively high social rank within the group, in the sense that an agent’s relative position may give her/ him a survival advantage through access to material and reproductive resources (mageli et al., 2022). alternatively, farm viability can be defined according to a combination of monetary value and socio-ecoinitial conditions : number of neighboring surviving farms, farm size distribution, available arable land at farm level, available circulating capital at farm level, capital stock at farm level, borrowed capital at farm level farm-level data (field survey) farm-level optimization model : expected farm gross profit maximization under resource, policy and flexibility constraints farm agents' expecations quasi-rational agents’ expectations for prices and crop yields or naive agents’expecations for prices and quasi-rational agents’ expectations for crop yields out-of-sample simulations arima models: forecasting values of costs, prices, crop yields,interest rate, total arable land, and total circulating capital linear trend model: forecasting value of living expenditures index farm viability algorithm: i) optimal farm net profit after tax ≥ simulated societal consumption benchmark of neighboring farms and ii) optimal farm growth in equity ≥ 0 number of neighboring surviving farms farm size distribution share of farmland by farm size classes regional land use change dynamics economic performance by farm size classes environmental impact assessement re-initialization of resources & farm firm growth rules of surviving farms: -endogenous feedback mechanism (efm) for resources (land, circulating capital) coupled with: relative optimal farm growth in equity to reallocate/allocate resources (abandoned/relesased land available for rent, regional availability of circulating capital) between neighboring surviving farms n ex tg ro w in g se as on (t =t +1 ) post-solution module of economic indicators : i) optimal farm net profit after tax ii) optimal farm growth in equity post-solution module of means-based environmental indicators: i) fertilizer use ii) pesticide use iii) water use capital stock evolution (investement module): -perpetual inventory method (pim) coupled with: -leontief production relationship between capital stock and land actual prices; actual crop yields determining required borrowing capital: i) required borrowing circulating capital ii) required borrowing investment capital updated information on initial conditions optimal cropping activities allocation regional crop supply number of neighboring surviving farms farm size distribution times series data (statistical authority, rural institutions, fadn) policy scenarios figure 1. conceptual diagram of the proposed modeling framework. notes: a post-solution module of means-based environmental indicators enables the model to estimate the environmental performance of farms. however, to limit the size of this paper, the environmental impact assessment will not be presented here. source: authors 359simulating farm structural change dynamics in thessaly (greece) using a recursive programming model bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 nomic criteria (bert et al., 2011; mittenzwei and britz, 2018; seidel and britz, 2019). in the present modeling approach, a sample farm is considered viable/surviving by satisfying two viability criteria: (i) the criterion of societal consumption benchmark of neighboring farms (nbf)6 according to the kuj preferences and, (ii) the criterion of non-negative optimal farm growth in equity. at this point, we would like to mention that, following similar simulation models (bert et al., 2011; offermann and margarian, 2014; mittenzwei and britz, 2018; seidel and britz, 2019) we simulate only farm exit according to the farm exit module considering economic and socioeconomic criteria. consequently, we do not model the life cycle of agents who enter farming, get old, and retire (bert et al., 2011). therefore, following each discrete optimization time-step (annual), every neighboring farm nbf decides whether to remain in the system or exit (see also figure 1). specifically, a neighboring farm is considered viable and remains in the production system when at the end of the year t meets both viability criteria, i.e., (i) the optimal farm net profit after tax (fnpat*nbf,t) should be at least equal to the simulated average living expenditures of neighboring farms in year t (lenbf,t sim), and (ii) optimal farm growth in equity (fge*nbf,t) should be at least equal to zero. 6 the literature on whom agents compete with for social status, i.e., who the joneses are, is relatively limited (mageli et al., 2022). nevertheless, it is conceivable that agents compare more intensely with agents who are socially proximate to them (mageli et al., 2022). for example, society serves as a socially distant reference group, whereas colleagues are socially close reference groups (mageli et al., 2022). in this framework, we could consider a socially close reference group to each agent (individual real-world farm), farms with the same productive specialization located in the same region, i.e., neighboring farms (nbf) correspond to arable crop farms of the regional unit of karditsa (nuts-3 level). in particular, farmers of this reference group could be considered colleagues due to their similar professional goals and intense professional interactions, which are expressed through their professional collective bodies, such as trade union bodies, groups of producers, and cooperatives, which are mainly made up of farmers of common productive specialization. from this perspective, the intense professional and, consequently, social interactions may provide each agent of the reference group (neighboring farm) with a comparatively better level of information about the economic performance of its neighbors and the livelihood level (consumption level, particularly for visual commodities that are connected to income or wealth, e.g., cars and houses) (mageli et al., 2022) than for socially distant reference groups (i.e., farms with different productive specializations compared to the agent). consequently, this comprehensive information signals the process of forming social norms based on which a social group’s social status or position is determined. in our case, the quest for social status is reflected in kuj preferences (fisher and heijdra, 2009; lombardo, 2021; mageli et al., 2022). finally, we also relied on a strict definition of neighboring farms for this selection based on the relevant literature (paroissien et al., 2021), where only farms with the same specialization located in the same region are included in the socially close reference group (neighboring farms). as regards the mathematical formulations of the specific profitability measures are as follows considering the relevant literature (grdc, 2015): fnpat*f,t = π*f,t – (depf,t + lrcf,t + sfncf,t + lfncf,t + sicf,t + fptxf,t) (5) fge*f,t = fnpat*f,t – lef,t (6) where fnpat*f,t is the optimal farm net profit after tax f in year t; π*f,t is the optimal gross profit of farm f in year t; depf,t is the depreciation of machinery of farm f in year t; lrcf,t are the land rental costs7 of farm f in year t; sfncf,t are the short-term finance costs which correspond to the interest paid for short-term loans of farm f in year t; lfncf,t are the long-term finance costs which correspond to the interest paid for long-term loans of farm f in year t; sicf,t are the social insurance contributions paid by farm f in year t; fptxf,t is the farm profit tax paid by farm f in year t; fge*f,t is the optimal farm growth in equity of farm f in year t; lef,t are the living expenditures8 of farm f in year t. 2.3.4. re-initialization of resources and farm firm growth rules the annual re-initialization of resources required for the farms’ operation and growth/expansion process is conducted through the εndogenous feedback mechanism (efm) (whose general structure has been presented in the 2.1 section). an essential part of the literature indicates that growth in equity determines the prospects for growth/expansion of the farm (e.g., painter, 2005; cittadini et al., 2008; bert et al., 2011; grdc, 2015), that is, that the acquisition of resources will be determined through this profitability measure. hence, we consider that optimal farm growth in equity could be used as an alternative criterion of farm expansion/growth to tra7 in case that farm rents out part of owned farmland, then receives land rental income lrincf,t. consequently the equation (5) is adapted as follows: fnpat*f,t = (π*f,t + lrincf,t) – (depf,t + sfncf,t + lfncf,t + sicf,t + fptxf,t), indicating that a farm cannot simultaneously rent in and rent out farmland, a condition we also find in similar simulation models (e.g., donati et al., 2024). 8 the estimation of living expenditures following the base year (2012) is carried out by utilizing the living expenditures index (lei) of households in rural areas (elstat, 2021). that is, heterogeneity between farms in the living expenditures in the base year (2012) is captured, but its evolution over time is based on the exogenously determined living expenditures index (lei). since the available time series of the living expenditures index (lei) does not meet the minimum required time horizon of 16 data points of the arima model (christodoulos et al., 2010), we use a linear trend model instead of the arima model to make post-sample forecasts. 360 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. ditional criteria such as the shadow values of resources (e.g., land, circulating capital) (guinde et al., 2005; hennessy, 2007; espinosa et al., 2016). however, given resource constraints, especially land, farm expansion is possible when neighboring farms decide to downsize or abandon agricultural production (plogmann et al., 2022). essentially, the process of structural change drives the reallocation of the resources required for expansion, where the resources of non-viable neighboring farms (e.g., land) are reallocated to viable ones (see also figure 1). various modelers (e.g., bert et al., 2011; sheng et al., 2015; herrera et al., 2022; sun et al., 2022) highlight the role of relative profitability as a criterion/mechanism for the reallocation/allocation of resources between surviving farms. within this context, our concern was how optimal farm growth in equity could be expressed as a criterion/mechanism for resource reallocation among viable farms and integrated into the efm. to model this mechanism, we adapted the concept of efficient allocation (ayerst et al., 2020; chen et al., 2022). according to the proposed adaptation, we replace relative farming productivity with relative farm growth in equity. we consider this adjustment to be reasonable since foster et al. (2008) found that “firms’ self-selection behavior (in choosing an operating scale, or to enter or exit) is made based on firm profitability rather than firm productivity and consequently resource reallocation may not always align with firm productivity growth, particularly in the short run” (sheng et al., 2015: 75). by incorporating the proposed resource reallocation/allocation mechanism into the efm, each farm’s annual level of resource is determined by the available level of the resource at the beginning of the previous growing season, the relative optimal growth in equity at the end of the previous growing season (indicating the optimal decisions), and by exogenous events9 that will occur in the current growing season. since we have ensured (from the viability determination assumptions) that a viable farm will not reveal negative optimal growth in equity, the mathematical formulation of the share of any resource r ∈ {al,crc} allocated or reallocated is as follows: ωsim rvf,nbf,t = , for t = 1… t, 0 ≤ ωsim rvf,nbf,t (7) 9 we assume that exogenous events are expressed through successive differences in the aggregate level of resources where the relative optimal growth in equity of the previous growing season allocates these positive or negative differences across farms. where ωsim rvf,nbf,t is the simulated share of resource r allocated/reallocated to viable neighboring farm in year t; fge*vf,nbf,t is the optimal farm growth in equity of viable neighboring farm in year t; fge*vf,nbf,t is the aggregate optimal farm growth in equity of viable neighboring farms in year t. essentially the simulated share of resource r allocated to viable neighboring farm in period t (ωsim rvf,nbf,t ) expresses the part of efm which corresponds to optimal decisions (xjt*) while considering the interdependence of optimal decisions of viable neighboring farms, indicating competitiveness for resources. it is also worth noting that the simulated share (ωsim rvf,nbf,t ) remains the same for each resource allocated/reallocated. (i) arable land therefore, considering the above, the efm mechanism for the resource of arable land will be formulated as follows: alvf,nbf,t = alvf,nbf,t-1 + ωsim alvf,t-1 [ alnvf,nbf,t-1 sim + (talnbf,t – talnbf,t-1)], for t=2…t (8) where alvf,nbf,t is the available arable land of viable neighboring farm in year t; alnvf,nbf,t-1 is the available arable land of viable neighboring farm at the beginning of year t-1; ωsim alvf,nbf,t-1 is the simulated share of arable land reallocated to viable neighboring farm at the end of the year t-1, that is, following the annual optimization; alnvf,nbf,t-1 sim is the simulated aggregate arable land of non-viable neighboring farms at the end of the year t-1, that is, following the annual optimization; talnbf,t is the actual total arable land of neighboring farms in year t; talnbf,t-1 is the actual total arable land of neighboring farms in year t-1. essentially, the product ωsim alvf,nbf,t-1 (talnbf,t – talnbf,t-1) corresponds to the vector vit of efm that allows for external changes in the resource restrictions due to exogenous events, and probably reflects the competition for resources with other types of farms or non-agricultural sectors which operate within the same region. however, competitive pressures are likely to lead to an unfavorable situation, i.e., talnbf,t – talnbf,t-1 < 0 and consequently to a decrease of available arable land for the viable neighboring farms, which will be reallocated among them utilizing the inverse form of the simulated share of arable land (ωsim alvf,nbf,t-1 -1), that is, less profitable albeit viable farms will abandon proportionately more of their arable land. as can be easily understood by the reader, the above procedure is also applied to the available irrigated land 361simulating farm structural change dynamics in thessaly (greece) using a recursive programming model bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 (ilvf,nbf,t), which is expressed as a share of the total arable land and is assumed to be constant at the base year level and equal to 80%. based on relevant literature (e.g., bert et al., 2011; djanibekov and finger, 2018; donati et al., 2024), the farmland is reallocated only on a rental basis through farmland rental arrangements between tenants and landowners, and the land rental price is exogenously determined 10. (ii) circulating capital similarly, we apply the efm in the case of determining the available circulating capital on an annual basis. the noticeable difference lies in the fact that the circulating capital of non-viable neighboring farms is not reallocated to viable neighboring farms as in the case of arable land. crcvf,nbf,t = crcvf,nbf,t-1 + ωsim crcvf,nbf,t-1 (tcrcnbf,t – crcvf,nbf,t-1 sim), for t = 2…t (9) crcvf,nbf,t is the available circulating capital of viable neighboring farm in year t; crcvf,nbf,t-1 is the available circulating capital of viable neighboring farm at the beginning of year t-1; ωsim crcvf,nbf,t-1 is the simulated share of circulating capital allocated to viable neighboring farm at the end of the year t-1, that is, following the annual optimization; crcvf,nbf,t-1 sim is the simulated total circulating capital of viable neighboring farms at the end of the year t-1, that is, following the annual optimization; tcrcnbf,t is the actual total circulating capital of neighboring farms in year t. as before (in the case of available arable land), the product ωsim crcvf,nbf,t-1 (tcrcnbf,t – crcvf,nbf,t-1 sim) ref lects the effect of the external economic factors that can form the availability of financial resources at farm level, such as the financial system, the tax system, macroeconomic conditions (e.g., level of inflation), etc. these factors may create a healthy financial situation or financial stress. financial stress could therefore lead to an unfavorable situation, i.e., tcrcnbf,t – crcvf,nbf,t-1 sim < 0 and consequently to a decrease of the available circulating capital for the viable neighboring farms which will be allocated to them utilizing the inverse form of the simulated share of circulating capital (ωsim crcvf,nbf,t-1 -1), that is, less profitable, albeit viable farms, will lose proportionately more of their circulating capital11. 10 detailed information concerning land rental costs/land rental income estimation is provided in part c: land rental costs/land rental income estimation in the supplementary material. 11 detailed information concerning required borrowing circulating 2.3.5. capital stock evolution at the farm level (investment module) the intertemporal evolution of capital stock at the farm level is assessed utilizing the perpetual inventory method (pim) where the capital stock (machinery and equipment) of the farm f in year t is equal to the nondepreciable capital stock of the year t-1 plus gross investment in fixed assets that will be made through the year t (weyerstrass, 2016). the mathematical formulation of pim is as follows: kf,t = (kf,t-1 – depf,t-1) + if,t (10) where kf,t is the capital stock of farm f in year t; kf,t-1 is the capital stock of farm f in year t-1; depf,t-1 is the depreciation of farm f in year t-1, which is obtained from the equation depf,t-1 = δkf,t-1, where δ is the fixed depreciation rate equal to 5% (weyerstrass, 2016; femenia et al., 2017), and if,t is the gross investment on fixed asset of farm f in year t. gross investment in fixed assets includes annual cash expenditures for the maintenance of capital stock due to economic depreciation and the acquisition of required investment capital for farm expansion (net investment on fixed assets) (smale et al., 1986). following similar modeling approaches (kay, 1971; freeman et al., 2009), we assume a leontief production relationship between capital stock and land. it is therefore assumed that the capital stock remains constant per hectare of arable land at the base year level ( , so that the amount charged for depreciation in year t-1 (depf,t-1) is constantly reinvested in new capital stock (or gross investment on fixed assets) in year t(if,t) (freeman et al., 2009). essentially, the constant intertemporal relationship between capital stock and arable land renders the investment process a continuous process of investment or disinvestment (britz et al., 2016) determined by the arable land acquired or abandoned12. 2.4. farm data description and specification for the empirical application of the proposed simulation model, a representative sample of arable crop farms capital & short-term finance costs estimations is provided in part d: borrowed capital & finance costs estimations /d1. borrowed circulating capital & short-term finance costs estimations in the supplementary material. 12 detailed information concerning required borrowing investment capital and long-term finance costs estimations is provided in part d: borrowed capital & finance costs estimations/d2. borrowed investment capital & long-term finance costs estimations in the supplementary material. 362 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. in karditsa (nuts-3 level) is chosen. the regional unit of karditsa is one of the five regional units of the region of thessaly (nuts-2 level) located southwest of it. this study utilizes farm-level data provided by a research project that thoroughly investigated the perspective of a sample of farms of the regional unit of karditsa that specialized in “other fieldcrops/general field cropping” (according to the tf14 classification of fadn) to cultivate alternative crops such as energy crops. initially, 70 farms were selected by stratified random sampling, and detailed data on production, revenues, fixed assets, and subsidies for 2005 and 2006 were collected through personal interviews. two field surveys followed (after 2006) to update mainly data on production, revenues, fixed assets, and subsidies through personal interviews. through these two follow-up surveys, we collected additional socio-economic information such as living expenditures and how agricultural subsidies were spent (e.g., living expenses, investments, production costs, loans). the first follow-up field survey was conducted in 2012, where data from 48 remaining farms were updated (from the initial 70), and the second was in 2019, where data from 31 remaining farms (out of 48 in 2012) were updated. for the empirical application of the simulation model, the data of the most recent period (2012-19) are utilized to manage the complexity of the model at a computable level. the sample represents at a satisfactory level the farm structure of 6,272 farms specializing in “other fieldcrops/ general field cropping” in the regional unit of karditsa for 2012. specifically, based on a comparison of our sample with the farm accountancy data network (fadn) data for the base year (2012), we found a significant degree of similarity in terms of farm size distribution, where the finger–kreinin (fk) similarity index (finger and kreinin, 1979) stands at 90.2% (see also table 1). consequently, although the farm sample size can be considered relatively small compared to the population, it sufficiently reflects the heterogeneity in farm structure13. cotton and durum wheat are the main activities regarding total farmland area shares. all observed activities (i.e., cotton, processing vegetables, tobacco, maize, alfalfa) except durum wheat and set-aside require irrigation. the production of processing vegetables and tobacco is conducted through annual contracts with the industry, while for the activity of alfalfa (seed production), the farmers conclude a ten-year contract. 13 using a relatively small sample of farms is not unusual for relevant in-depth analyses in the context of farm-level mathematical programming models (e.g., iglesias et al., 2003; viaggi et al., 2010; viaggi et al., 2011; djanibekov and finger, 2018; lairez et al., 2023). since field survey through personal interviews is a very costly and slow process (khanal and omobitan, 2020), collecting data on an annual basis during the interim years of the period 2012-19 was not possible. this fact created the need to fill in the gaps in the time series of the model parameters. model parameters were estimated for the period considered utilizing the available national times series setting 2012 as the base year. in addition, the available national times series provided the necessary input data for the arima and linear trend models. the national time series are provided by various exogenous data sources14. however, it should be noted that for the activities cultivated under contract farming, we assume that prices remain constant at the base year levels for all simulation periods since sample farmers stated that they remained almost invariable for the period 2012-19. 2.5. policy experiments simulation experiments for two alternative policy scenarios were performed. additionally, we ran simulations for a combined (policy and geopolitical) scenario. business as usual (bau) scenario: we assume that the baseline policy implemented from 2015 to 2022 (2013 cap reform), will continue to be implemented until 2026. expressly, we assume that decoupled and coupled payments will remain stable at the levels of 2022, as well as the greening obligations related to crop diversification and the ecological focus area (efa) to receive decoupled payments (greek ministry of rural development and food, 2014). 14 for more details see the part e: historical dataset and forecasting method of exogenously determined parameters in the supplementary material. table 1. farm size distributions comparison of farms specialized in “other fieldcrops/general field cropping” (according to the tf14 classification of fadn) in the region of karditsa, 2012 farm size class (ha) characterization sample farms fadn farms (%) farms (%) <10 very small 37.46 36.28 10-<30 small 43.75 53.68 30-<50 medium 12.5 7.94 50-<100 large 6.25 2.23 ≥100 very large notes: the determination and characterization of farm size classes is based on happe et al. (2008), and huettel & margarian (2009). source: authors, based on sample data and fadn. 363simulating farm structural change dynamics in thessaly (greece) using a recursive programming model bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 cap post-2020 scenario: according to the greek strategic plan proposal for the cap 2023-27 (greek ministry of rural development and food, 2022), the provisions of the cap post-2020 reform scenario apply from the year 2023. in the period 2023-26, internal full convergence will be implemented, i.e., the convergence of the value of payment entitlements at a single unit value (flat rate) at the agronomic region level15(greek ministry of rural development and food, 2022). the value of the payment entitlements in the agronomic region of interest, i.e., arable land, will equal 231.4 eur/ha in 2026. farms with available arable land of more than 10 hectares are obligated to apply ecological focus area to 4% of it to receive decoupled payments. it should be mentioned that is maintained the measure of diversification of crops for farms with available arable land larger than 10 hectares to receive decoupled payments, valid from 2015 in the context of the 2013 cap reform. the proposed strategic plan also includes implementing the redistributive payment mechanism during the period 2023-27. specifically, relatively small farms with available arable land between 2 and 11 hectares, will be considered beneficiaries of the redistributive support, equal to 117 εur/ha. in addition, the proposed national strategic plan aims to improve the environmental performance of arable crop farms by adopting voluntary environmental measures referred to as eco-schemes. one of the main measures is considered to be the extension of the application of the ecological focus areas, where farms with available arable land less than 10 hectares can apply ecological focus area to 5% of it, receiving an average ecoscheme payment equal to 200 eur/ha. additionally, farms with available arable land more than 10 hectares can apply ecological focus area to 10% of it receiving an average eco-scheme payment equal to 240 eur/ha (greek ministry of rural development and food, 2022). cap post-2020 & long war of attrition (lwa) scenario: this combined scenario is a variant of the previous one, integrating the serious possibility that russia’s invasion of ukraine will become a long war of attrition (modern war institute, 2022) with severe and prolonged consequences for the global economy. given the emerging upward trends in grain prices (maize, wheat) due to russia’s invasion of ukraine and uncertainty over the future of the black sea grain initiative (european council, 2022), we assume a high grain price scenario for the period 2022-26 combined with the provisions of cap post-2020 reform scenario described 15 since the 2013 cap reform, the process of payments convergence has started in the form of partial convergence. above. in particular, we consider the upper bound of the prediction intervals for durum wheat and maize prices provided by arima model forecasts. 3. results 3.1. validation of the simulation model the validation results presented in table 2 confirm the ability of both models to reproduce the evolution of activities allocation to at least a satisfactory level (percentage absolute deviation (pad index): 8.05-29.6%; finger–kreinin (fk) similarity index (fk index): 85.296%) according to the relevant literature16 (e.g., gómezlimón et al., 2016), providing a good representation of reality. however, the nv&qr model is significantly superior in the base year. validations of the models on their ability to reproduce the actual farm size distribution and the actual number of farms are carried out for the year 2019 as it is the only year of observations available after the base year. in this context, both models simulate to at least a satisfactory level the evolution of farm size distributions (pad index = 17.58%; fk index = 91.2%) and the number of viable farms (absolute percentage error (ape) = 6.4%)17 without revealing any difference in terms of forecasting accuracy (see also table 3). as can be seen, both models slightly overestimate the rate of structural change, that is, the percentage change in the number of surviving farms, in the reference period (simulated: 39.6% (from 48 to 29 farms) vs. actual: 35.42% (from 48 to 31 farms)). although both models are characterized by satisfactory forecasting accuracy, we will choose the best fitting model, the nv&qr model, to assess the impact of policy and combined scenarios on structural change and land use change. 3.2. forecasting models accuracy after estimating the best-fitting arima models for the exogenously determined parameters of interest (i.e., 16 although there is no commonly accepted threshold in the international literature for these two indicators, gómez-limón et al. (2016) consider the values of pad index = 33.2% and fk index = 83.4% satisfactory. 17 although there is no commonly accepted threshold for mape (mean absolute percentage error) in the international literature; however, some authors consider that a model is characterized by good forecasting accuracy (or goodness-of-fit) when mape (ape, in our case, due to a single year of observations available after the base year) does not exceed 20%, whereas when it does not exceed 10%, the forecasting accuracy is characterized as high or perfect (e.g., quartey-papafio 2021). 364 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. costs, prices, crop yields, total arable land, total circulating capital, and interest rate), we measured their forecasting accuracy by in-sample forecasts according to the mape measure. most arima models are characterized by high forecasting accuracy; the mape does not exceed 10%, while the other models are characterized by good forecasting accuracy18 (e.g., quartey-papafio et al., 2021). a high forecasting accuracy also characterizes the uti18 for more details, see the part f: arima and linear trend models estimations in the supplementary material. lized linear trend model for the rural households’ living expenditure index (lei). 3.3. simulated structural change figure 2 depicts the evolution of the number of viable/surviving farms and the average farm size over time19. the simulated number of farms decreases by 19 the initial number of farms is normalized to 100. table 2. actual and simulated land allocation. activity actual 2012 area (ha) nv&qr model 2012 area (ha) qr model 2012 area (ha) actual 2019 area (ha) nv&qr model 2019 area (ha) qr model 2019 area (ha) cotton 467.9 454.36 365.48 305.1 296.52 266.15 tobacco (virginia) 58.6 83.74 94.02 82 109.15 105.56 maize 27 11.82 11.82 27.15 14.83 48.95 processing tomato 31 23.11 23.11 52 55.57 55.53 processing pepper 30 25.66 37.06 68.8 70.95 71.33 alfalfa (hay) 66.5 63.75 68.43 96.6 101.7 10 alfalfa(seed production) 58.5 45.47 45.47 durum wheat 139 163.13 217.85 236.75 236.95 236.88 set-aside 27.2 21.62 29.41 17.9 13.81 14.89 total area (ha) 847.2 847.2 847.2 944.8 944.8 944.8 pad index (%) 11.6 29.6 8.05 11.7 fk index (%) 94.2 85.2 96 94.15 note: nqr model: naïve and quasi-rational expectations model; qr model: quasi-rational expectations model source: authors, based on sample data. table 3. actual and simulated farm size distribution and number of farms. farm size class (ha) actual 2019 farms (%) actual 2019 farms (%) nv&qr model 2019 farms (%) nv&qr model 2019 farms (%) qr model 2019 farms (%) qr model 2019 farms (%) <10 22.57 7 13.79 4 13.79 4 10-<30 45.15 14 48.27 14 48.27 14 30-<50 12.9 4 13.79 4 13.79 4 50-<100 16.12 5 17.24 5 17.24 5 ≥100 3.22 1 6.89 2 6.89 2 total number of sample farms (n) 31 29 29 ape (%) 6.4 6.4 pad index (%) 17.58 17.58 fk index (%) 91.2 91.2 note: nv&qr model: naïve and quasi-rational expectations model; qr model: quasi-rational expectations model. the determination of farm size classes is based on happe et al. (2008), and huettel & margarian (2009). source: authors, based on sample data. 365simulating farm structural change dynamics in thessaly (greece) using a recursive programming model bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 39.6% for the reference period 2012-19, while the average farm size increases from 17.65 hectares to 32.58 hectares. as we can see, the process of structural change continues after 2019, when the simulation model forecasts a further reduction in the number of viable farms. according to the bau scenario, for the period 20192026, a decrease in the number of farms by 41.4% and an increase in the average farm size from 32.58 hectares to 62.76 hectares are foreseen. for the cap post2020 reform and cap post-2020 & lwa scenarios, the simulation model forecasts a comparatively higher rate of structural change. specifically, for 2019-26 the number of farms decreases by 48.3%, and the average farm size increases from 32.58 hectares to 70.35 hectares. this simulation result almost coincides with the estimates of some farmers in the sample, who consider that by 2026 the studied farms will be reduced by 50% compared to 2019 (when the most recent survey was conducted). therefore, regardless of the scenario, the model predicts an increase in the rate of structural change compared to that simulated in the period 2012-19. examining the dynamics of structural change from the perspective of farm size distribution, we observe a decrease over time in the percentage of very small (farm size class: <10 hectares) and small farms (farm size class: 10-< 30 ha) (see figure 3). a decline over time is also foreseen for the share of the farmland area of these farms. on the contrary, for the large (farm size class: 50-<100 ha), and very large farms (farm size class ≥100 ha), an increase in the shares of the farms and farmland area is foreseen. medium-sized farms (farm size class: 30-<50 ha) show a weak upward trend in the share of farms and a weak downward trend in the share of farmland area. a very high concentration of farmland in very large farms (farm size class ≥100 ha) is foreseen since, according to all examined scenarios, almost only 10% of farms will concentrate about 50% of the total farmland area. it is worth noting that the cap post-2020 and cap post2020 & lwa scenarios (although they do not show substantial differences in the rate of structural change), compared to the bau scenario, negatively impact the viability of small and very small farms. the above findings are in line with the estimates of the sample farmers who claim that in the region of karditsa will gradually prevail, arable crop farms with a size of at least 30 hectares since such a farm size can ensure a decent standard of living for the rural household as well as growth prospects. regarding the evolution of farm profitability, the simulation results depicted in figure 4 reveal a gradual increase in the average farm net profit after tax (fnpat) for all scenarios. this development can be considered reasonable since, through the structural change, the comparatively less profitable farms exit and release resources such figure 2. simulated number of farms and average farm size by scenario. note: the provisions of the cap post-2020 scenario apply from the year 2023. source: authors, based on sample data. 366 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. as land, which the comparatively more profitable farms acquire. in this framework, surviving and consequently growing farms tend to make more efficient use of available resources, allocating them to comparatively more profitable productive activities, as we will see below. although there are no significant differences between the scenarios, in the last two years of the simulation (2025-26), a clear distinction is simulated in favor of the cap post-2020 and cap post-2020 & lwa scenarios, which is probably due to the higher rate of structural change. further analyzing the evolution of average profitability by farm size class, the simulation results provided in table 4 show an increase in profitability for farms with a size of at least 30 hectares, explaining the claim of the sample farmers that shortly the arable crop farms with a size of at least 30 hectares will be able to remain in the production system. even more, implementing the cap post-2020 and cap post-2020 & lwa figure 3. share of farms and farmland area by farm size classes and scenario. note: the provisions of the cap post-2020 scenario apply from the year 2023. source: authors, based on sample data. 367simulating farm structural change dynamics in thessaly (greece) using a recursive programming model bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 scenarios is projected to enhance the profitability of these farms further. it is also worth noting that between these two scenarios, no substantial differences can be found in the evolution of profitability. 3.4. simulated land use change as regards the simulated land use change dynamics illustrated in figure 5, the main change can be seen in the progressive expansion of the processing vegetable area and especially for processing pepper. this finding thoroughly verifies farmers’ expectations for the further expansion of these crops. in particular, the processing pepper farmers of the sample state that their export activity will increase significantly in the coming years since they receive more than double commodity prices compared to domestic prices. processing tomato farmers aspire to a significant expansion of their productive activity due to the positive growth prospects of the local tomato processing industry, as they also consider the role of the local group of processing tomato farmers to be particularly beneficial. an increasing trend in the processing vegetable area is simulated for both scenarios. still, a more significant upward trend is simulated for the cap post-2020 reform scenario, possibly due to the increased rate of structural change leading to more efficient use of resources, in the sense that surviving farms tend to allocate farmland area to comparatively more profitable activities20. conversely, we simulated a significant gradual decrease in the cotton and tobacco areas. in fact, for the cap post-2020 reform scenario, we observe a fur20 details are provided in table a1 in the appendix. figure 4. evolution of simulated average farm net profit after tax (fnpat) by scenario. note: the provisions of the cap post-2020 scenario apply from the year 2023. source: authors, based on sample data. table 4. simulated mean farm net profit after tax (fnpat) in eur by farm size classes (2012-2026). farm size class in ha (characterization) 2012 2019 2026 (bau scenario) 2026 (cap post-2020 scenario) 2026 (cap post-2020 & lwa scenario) <10 (very small) 18,156 13,706 12,196 10-<30 (small) 41,931 26,707 25,989 24,987 25,010 30-<50 (medium) 59,829 68,250 98,608 104,309 107,964 50-<100 (large) 110,370 69,918 115,803 122,013 126,539 ≥100 (very large) 695,181 1.738,668 1.855,216 1.865,563 aggregate 39,526 84,644 271,183 323,692 327,622 note: the determination and characterization of farm size classes is based on happe et al. (2008), and huettel & margarian (2009). source: authors, based on sample data. 368 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. ther reduction of the cotton and tobacco areas. durum wheat area increases significantly over time for the bau scenario, while for the cap post-2020 reform scenario, a decrease after 2022 is foreseen due to the set-aside applied by the vast majority of sample farms (more than 90%) in the context of eco-scheme payments. based on this finding, we conclude that farms have a strong incentive to adopt eco-schemes since the majority exceed 10 hectares and, therefore, would be required to implement set-aside on 4% of arable land without extra payment. in the cap post-2020 & lwa scenario, an expected increase is simulated for the area of the grain (durum wheat, maize), especially maize, due to the possible increase and maintenance of farm gate prices at high levels due to the ukrainian crisis. accordingly, a further reduction in cotton and tobacco areas is simulated. 4. discussion and conclusions dynamic modeling methodologies are deemed crucial for comprehending the evolution of economic agents’ behaviors in response to shifts in the economic environment or policies (gardebroek and oude lansink, 2008). considering the volatile economic environment in which farms operate due to recent international developments, such assessments gain significant weight when using simulation models like the one we propose herein since they can support policy analysts in formulating and specifying the appropriate policy measures. in this context, this study described the conceptual framework of a newly developed farm-level recursive linear programming model primarily aiming at simulating the impact of policy reform on structural change in the arable production system of the region of karditsa (nuts-3 level), one of the central growing regions of arable crops in greece. while managing to capture mainly endogenously the dynamics of structural change adaptation, the proposed simulation model can simultaneously be characterized by a comparatively low level of modeling complexity compared to other simulation models, such as agent-based models. from a general perspective, this paper seeks to contribute to the debate on dynamic assessments of the multidimensional effects in the context of the cap post2020 reform while considering recent geopolitical developments in the context of the ukrainian crisis. validation results demonstrate satisfactory performance of the simulation model in reproducing past changes. therefore, we can use the model to assess the effects of various scenarios on the agricultural production system. by carrying out policy experiments for two different policy scenarios and a combined scenario (policy and geopolitical) we estimated an increased rate of structural change compared to the reference period (2012-19), and especially for the cap post-2020 and cap post-2020 & long war of attrition (lwa) scenarios. the proposed model simulated an evident gradual concentration of farmland in relatively large farms (farm size ≥50 ha), accompanied by a decrease in the number of relatively small farms (farm size < 30 ha), making these findings consistent with the results obtained from simulation models (e.g., happe et al., 2008; bert et al., 2011; donati et al., 2024) and other dynamic modeling approaches (herrera et al., 2022; schuh et al., 2022). (a) bau scenario (b) cap post-2020 scenario (c) cap post-2020 & lwa scenario figure 5. simulated arable land allocation by scenario. note: the provisions of the cap post-2020 reform scenario apply from the year 2023. source: authors, based on sample data. 369simulating farm structural change dynamics in thessaly (greece) using a recursive programming model bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 regardless of the examined scenario, the simulated average farm profitability shows a gradual increase, which is partly explained by the fact that relatively more profitable farms remain in the production system confirming previous findings obtained from simulation models (happe et al., 2008; bert et al., 2011) and other dynamic modeling approaches (herrera et al., 2022; schuh et al., 2022). obviously, the surviving farms which achieve growth in equity tend to allocate their growing resources (such as farmland, circulating capital and fixed assets) more efficiently, i.e., to relatively more profitable productive activities (in our case, processing vegetables), further enhancing average farm profitability (bert et al., 2011). however, a downward trend is simulated for the average profitability of relatively small farms (farm size < 30 ha). in terms of land use change dynamics, regardless of the scenario, our model simulated an increasing trend of the land allocated to food crops such as processing vegetables and a simultaneous decreasing trend of the farmland allocated to industrial crops such as cotton and tobacco. the rationale explains this result discussed earlier, namely that surviving farms tend to expand productive activities with comparatively higher profitability, a finding that is also consistent with findings obtained from a simulation model applied to the agricultural system of the argentine pampas (bert et al., 2011). additionally, bert et al. (2011) consider that this behavior of the farms is interpreted by their survival strategy. considering the above, it could be said that a correlation of land use change with structural change emerges, in the sense that the viability of farms is strongly dependent on the land use chosen (bert et al., 2011) and is expressed through their survival strategy to allocate their farmland area and capital to the most profitable cropping activity gradually. focusing on the paper’s main finding – namely, the agricultural production concentration in relatively large farms (farm size ≥ 50 ha) – it is found that this has some significant policy implications. in particular, an intensifying continuation of pressures towards fewer but larger farms (i.e., an increasing rate of structural change) could lead to a breakdown of social cohesion, a prerequisite for addressing rural communities’ challenges (knutson et al., 1986). from this perspective, appropriate policy measures could focus, for example, on the enhancement of farmers’ market access since small and medium-sized farms have issues accessing markets, achieving a proper share in the eu food chain, including value-added processing, and maintaining bargaining power (schuh et al., 2022). in this vein, cooperatives are one way to improve farmers’ access to markets and strengthen bargaining power, primarily through vertical integration, which can often play a significant role in increasing the economic benefits of farmers (schuh et al., 2022). therefore, it is essential to prioritize examining exemplary cooperative practices and supporting the adoption of similar operational models through policy actions (schuh et al., 2022). even if essential insights were gained, this modeling exercise is characterized by several caveats, where we will focus on the main ones. first, although the proposed recursive linear programming model utilizes input data of representative individual real-world farms, effectively capturing the heterogeneity in farm structure and replicating varied farm behavior, it does not explicitly capture the interaction between individual farms in the sense of not incorporating an endogenous price formation mechanism for the market of locally available resource like land (berger, 2001; troost and berger, 2015; kremmydas, 2019). additionally, it does not fully consider spatial relationships, overlooking the imperfect land allocation among farms by disregarding internal transport costs and the physical immobility of land (berger, 2001; troost and berger, 2015; kremmydas, 2019). in this context, the determination of the regional level at which farms can be regarded as competitors for the farmland offered is left to the subjectivity of the modeler. although administrative units are often used as a realistic approach (in our case, the regional unit of karditsa (nuts-3 level)), ideally, the regional level could be defined by the viewpoint of active farmers who operate the land (plogmann et al., 2022). consequently, these weaknesses of the proposed model limit its ability to fully capture interactions between farms and spatial dynamics, limiting its explanatory power in policy analysis. especially, the model cannot provide detailed insights into the impacts of policy scenarios/options on farm structure due to their effects on local resource markets (kremmydas, 2019). furthermore, the incomplete incorporation of spatial dynamics curtails the model’s explanatory capacity regarding policy effects on the environment, where spatial aspects hold considerable importance (kremmydas, 2019). second, although the proposed simulation model considers the differences in profits among neighboring farms cultivating different farmland areas in the base year, providing a reasonable representation of the farm growth process, it does not consider economies of scale in an intertemporal context. the capture of economies of scale at a longitudinal level by the proposed model was not carried out to maintain its computational complexity. however, a more detailed model that considers this dimension could enhance the representation of farm heterogeneity and, consequently, policy representation towards a more realistic framework. therefore, future developments of the proposed simulation model could incorporate cost reductions 370 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. as a function of farm expansion and/or technological progress (happe et al., 2008; bert et al., 2011). third, due to the lack of farm-level data for the interim years of the reference period, we were forced to use the available national-level time series for parameters of interest to bridge the time series data gap at the farm level. however, various authors have highlighted and documented the statistical differences between regional/national and farm-level time series data associated with underestimation of variability (e.g., debrah and hall, 1989). in particular, aggregated data tends to underestimate the variability of parameters such as prices and yields at the farm level (debrah and hall, 1989), which may lead to a less adequate representation of reality regarding farms’ behavior and adaptation. this modeling exercise has identified many avenues for further research, highlighting only a few. first, the geographical and sectoral coverage should be expanded. second, it is of particular importance to run simulations using alternative allocation/reallocation mechanisms of resources, such as relative shadow values of resources. third, an interesting avenue for further research is to conduct an environmental impact assessment by utilizing meanand effect-based indicators (lebacq et al., 2013; donati et al., 2024) but also to incorporate social indicators, allowing us to assess sustainability performance at the farm level (e.g., lairez et al., 2023). finally, further research could be conducted on the investigation of farm viability using alternative monetary and socioeconomic viability criteria. to conclude, although our modeling results may not represent all greek regions, they may be particularly informative for trends that may emerge due to structural and land-use changes in rural areas with similar arable production systems, not only in the country but also in the wider mediterranean area. acknowledgements this research is co-financed by greece and the european union (european social fund-esf) through the operational programme “human resources development, education and lifelong learning” in the context of the project “strengthening human resources research potential via doctorate research” (mis-5000432), implemented by the state scholarships foundation (ικυ). references ayerst, s., brandt, l., and restuccia, d. 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christodoulos et al., 2010). an important class of stochastic models for describing time series are called stationary models or autoregressive-moving average (arma) models varying about a fixed constant mean level and with constant variance (box et al., 2016). an arma (p,q) model is formulated as follows: yt = φiyt-i + εt – θjεt-j, (a1) where φ1 .…, φp are the autoregressive (ar) parameters to be estimated, θ1 ,…,θq are the moving average (ma) parameters to be estimated, and ε1…εt are a series of unknown random “shocks” (or residuals) that are assumed to follow a normal distribution (pardoe, n.d.). the model can be simplified by introducing the box-jenkins backward shift operator21 where biyt = yt-i 21 the backward shift operator is a useful notational device expressing and bjεt = εt-j; y1,…,yt is any time series ; p<t and q<t (pardoe, n.d.). substituting backward shift operators in equation (a1), we obtain the following form: (1 – φibi)yt = (1 – θjbj)εt (a2) which is often reduced further to (pardoe, n.d.): φp(b)yt = θq(b)εt (a3) many series encountered in industry or business reveal nonstationary behavior 22and do not vary about a fixed mean, showing a stochastic trend (box et al., 2016). we should therefore convert a non-stationary time series to a stationary one by differencing the arma (p,q) model. then the arma (p,q) model can be extended and written using differences δyt = (1 – β)dyt = ∇dyt as follows: φp(b)(1 – β)dyt = θq(b)εt (a4) where d is the order of differencing. replacing in the arma model with the differences above, we obtain the formal arima p,d,q) model (pardoe, n.d.). to detect non-stationarities, we utilize one of the most well-known tests, which corresponds to the augmented dickey-fuller (adf) test (asteriou & hall, 2007; mahan et al., 2015; box et al., 2016). the identification of possible model orders (p,q) is approached through the utilization of autocorrelation function (acf) and partial autocorrelation function (pacf) plots (mahan et al., 2015; box et al., 2016; garnier, n.d.) while trying to keep the model orders at low levels (≤ 2) for most of the estimated models (gottardi & scarso, 1994). after estimating several models, we test whether the condition of invertibility (asteriou & hall, 2007; garnier, n.d.) and statistical significance of the ar and ma parts of the model are satisfied (mossad & alazba, 2015). the estimated models are then compared according to the akaike information criterion (aic) by selecting the model with the lowest value (mahan et al., 2015; box et al., 2016; garnier, n.d.). the diagnostic check of the model is then performed, which is applied to residuals to detect whether they exhibit the length of previous data the model uses to provide forecasts (christodoulos et al., 2010). 22 arima modeling requires that the time series be stationary (schaffer et al., 2021). a stationary series is characterized by three properties: a constant mean, constant variance, and constant covariance that depends only on the time intervals (schaffer et al., 2021). time series with trends or changing variance is non-stationary. 376 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. autocorrelation, utilizing the breusch-godfrey lagrange multiplier (lm) test (mahan et al., 2015; weyerstrass, 2016; ayele et al., 2017). the null hypothesis of the lm test is that there is no autocorrelation in the residuals series up to the pre-determined lag order (p=2 in our analysis) at the 5% level of significance (weyerstrass, 2016; ayele et al., 2017). regarding the measurement of the forecasting accuracy of arima models, there is no universally preferred measure; however, according to various modelers (gottardi and scarso, 1994; christodoulos et al., 2010), particular emphasis is given to the measure of mean absolute percentage error (mape). at this point, we would like to point out that there is no commonly accepted threshold for mape in the international literature; however, some authors consider that a forecasting model is characterized by good forecasting accuracy (or goodness-of-fit) when mape does not exceed 20%, whereas when it does not exceed 10%, the forecasting accuracy is characterized as high or perfect (e.g., quartey-papafio et al., 2021). estimates and statistical tests of arima models were performed using eviews statistical package. part a: references asteriou, d., & hall, s. (2007). applied econometrics: a modern approach. palgrave macmillan, new york. ayele, a. w., gabreyohannes, e., & tesfay, y. y. (2017). macroeconomic determinants of volatility for the gold price in ethiopia: the application of garch and ewma volatility models. global business review, 18(2), 308–326. https://doi. org/10.1177/0972150916668601 box, g., jenkins, g., reinsel, g., & ljung g. (2016). time series analysis: forecasting and control, 5th edition, by george e. p.  box, gwilym m.  jenkins, gregory c.  reinsel and greta m. ljung, 2015. published by john wiley and sons inc. christodoulos, c., michalakelis, c., & varoutas, d. (2010). forecasting with limited data: combining arima and diffusion models. technological forecasting and social change, 77(4), 558–565. https://doi. org/10.1016/j.techfore.2010.01.009 garnier, h. (n.d.). introduction to time series analysis and forecasting. university of lorraine. https://w3.cran. univ-lorraine.fr/perso/hugues.garnier/enseignement/ tsaf/c-tsaf-box-jenkins_method.pdf gottardi, g., & scarso, e. (1994). diffusion models in forecasting: a comparison with the box-jenkins approach. european journal of operational research, 75(3). https://doi.org/10.1016/0377-2217(94)90300-x mahan, m., chorn, c., & georgopoulos, a. (2015). white noise test: detecting autocorrelation and nonstationarities in long time series after arima modeling. proceedings of the 14th python in science conference, 97–104. https://doi.org/10.25080/majora-7b98e3ed-00f mossad, a., & alazba, a. a. (2015). drought forecasting using stochastic models in a hyper-arid climate. atmosphere, 6(4), 410–430. https://doi.org/10.3390/ atmos6040410 pardoe, i. (n.d.). course notes for  stat 501: regression methods: t.2.5.1 arima models. department of statistics, pennsylvania state university. https:// online.stat.psu.edu/stat501/lesson/t/t.2/t.2.5/t.2.5.1arima-models quartey-papafio, t. k., javed, s. a., & liu, s. (2021). forecasting cocoa production of six major producers through arima and grey models. grey systems, 11(3), 434-462. https://doi.org/10.1108/gs-04-20200050 schaffer, a.l., dobbins, t.a. & pearson, sa. (2021). interrupted time series analysis using autoregressive integrated moving average (arima) models: a guide for evaluating large-scale health interventions. bmc medical research methodology, 58(21), https://doi. org/10.1186/s12874-021-01235-8 weyerstrass, k. (2016). a tool for supporting economic policy-making in the former yugoslavia. documentation and applications of a macroeconomic multi-country model. osteuropa: geschichte, wirtschaft, politik, 49. wien: lit-verlag. 224 p. part b: structure of the model’s objective function and constraints the following describes the objective function’s structure and the constraints typical to each sub-model. the objective function of the expected gross profit of the farm f in year t is defined as follows: max e{πf,t} = xt f,j,t [e{pf,j,t} e{yf,j,t} – vcf,j,t + lsj,t + ef,tecop1f,j,t + εf,tecop2f,j,t] + dpf,tdlf,t + bf,tnpf,tnlf,t + βf,t opf,tolf,t + rf,trpf,talf,t (b1) subject to: arable land constraint xf,j,t = alf,t ,for t = 1,…,t, j ∈ j (b2) irrigated land constraint xf,wj,t ≤ ilf,t ,for t = 1,…,t, wj ∈ wj, wj ⊆ j (b3) circulating capital constraint xf,j,tvcf,j,t ≤ crcf,t ,for t = 1,…,t, j ∈ j (b4) xf,j,t ≥ 0 ,for t = 1,…,t (b5) https://doi.org/10.1177/0972150916668601 https://doi.org/10.1177/0972150916668601 https://doi.org/10.1016/j.techfore.2010.01.009 https://doi.org/10.1016/j.techfore.2010.01.009 http://w3.cran.univ-lorraine.fr/perso/hugues.garnier/enseignement/tsaf/c-tsaf-box-jenkins_method.pdf http://w3.cran.univ-lorraine.fr/perso/hugues.garnier/enseignement/tsaf/c-tsaf-box-jenkins_method.pdf http://w3.cran.univ-lorraine.fr/perso/hugues.garnier/enseignement/tsaf/c-tsaf-box-jenkins_method.pdf https://doi.org/10.1016/0377-2217(94)90300-x https://doi.org/10.25080/majora-7b98e3ed-00f https://doi.org/10.3390/atmos6040410 https://doi.org/10.3390/atmos6040410 https://online.stat.psu.edu/stat501/lesson/t/t.2/t.2.5/t.2.5.1-arima-models https://online.stat.psu.edu/stat501/lesson/t/t.2/t.2.5/t.2.5.1-arima-models https://online.stat.psu.edu/stat501/lesson/t/t.2/t.2.5/t.2.5.1-arima-models https://doi.org/10.1186/s12874-021-01235-8 https://doi.org/10.1186/s12874-021-01235-8 377simulating farm structural change dynamics in thessaly (greece) using a recursive programming model bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 where e{ } denotes the expectation operator; e{πf,t} denotes the expected gross profit of the farm f which is maximized in year t ; xf,j,t is the j x 1 vector of decision variables and denotes the level of cropping activity j (hectares for crops) of the farm f in year t; e{pf,j,t} denotes the j x j diagonal matrix of expected price of the output from cropping activity j in eur/kg of the farm f in year t; e{yf,j,t} denotes the j x 1 vector of expected yield of cropping activity j in kg/ha of the farm f in year t; vcf,j,t is the j x 1 vector of variable cost of cropping activity j in eur/ha of the farm f in year t23, lsi,t is the j x 1 vector of land subsidy of cropping activity j in eur/ ha in year t. ecop1j,t is the j x 1 vector of potential eco-scheme payment of cropping activity j in eur/ha of the farm f with a size of less than or equal to 10 hectares in year t under the cap post-2020 reform; ecop1f,j,t is the j x 1 vector of potential eco-scheme payment of cropping activity j in eur/ha in year t of the farm f with a size greater than 10 hectares in year t under the cap post2020 reform24; ef,t denotes the binary variable that corresponds to the farm f in year t and is equal to 1 when the farm adopts the eco-schemes25 and the size of the farm does not exceed 10 hectares, while it gets the value 0 when the farm does not adopt the eco-schemes or when it exceeds 10 hectares; εf,t denotes the binary variable that corresponds to the farm f in year t and is equal to 1 when the farm adopts the eco-schemes and the size of the farm exceeds 10 hectares , while it gets the value 0 when the farm does not adopt the eco-schemes or when it does not exceed 10 hectares (obligations concerning eco-schemes adoption are explained in constraints (b9)-(b12)). dpf,t is the entitlement value of decoupled payments in eur/ha of the farm f in year t; dlf,t is the eligible farmland area of decoupled payments in hectares of the farm f in year t; npf,t is the agri-environmental payment in eur/ha of the nitrate pollution reduction programme of the farm f in year t; nlf,t is the farmland area in hectares included in the nitrate pollution reduction programme of the farm f in year t; opf,t is the agri-envi23 where vcf,j,t = icf,j,t + hlcf,j,t + mrcf,j,t; icf,j,t denotes the input cost ic of cropping activity j in eur/ha of the farm f in year t; hlcf,j,t denotes the cost of hired labour hlc of cropping activity j in eur/ha of the farm f in year t ; mrcf,j,t denotes the machinery rental costs of cropping activity i in eur/ha of the farm f in year t. 24 the provisions concerning the voluntary measures of eco-schemes are included in the greek strategic plan proposal for the cap 2023-2027 (https://ead.gr/wp-content/uploads/2022/01/cap_sp_ proposal_30_12_2021.pdf). 25 to determine which farms are likely to adopt the eco-schemes (based only on economic criteria) for 2023-26, we estimate the average annual difference in the optimal farm net profit after tax (fnpat*) for each farm due to adopting the eco-schemes. therefore, the farm will adopt the eco-schemes if this annual average difference is positive. ronmental payment in eur/ha of the organic farming programme of the farm f in year t; olf,t is the farmland area in hectares included in the organic farming program of the farm f in year t; bf,t denotes the binary variable that corresponds to the farm f in year t, and is equal to 0 when the farm does not participate in the nitrate pollution reduction programme, while it gets the value 1 when it participates26; ßf,t denotes the binary variable that corresponds to the farm f in year t and is equal to 0 when the farm does not participate in the organic farming programme, while it gets the value 1 when it participates27. in addition, when the binary variable bf,t takes the value 1, the binary value ßf,t will take the value 0 and vice versa, indicating that a farm cannot simultaneously participate in the two different agri-environmental measures of pillar b of the common agricultural policy28 (obligations concerning agri-environmental measures are explained in constraints (b13)-(b16)). rf,t denotes the binary variable that corresponds to the farm f in year t, and is equal to 0 when the size of the farm exceeds 11 hectares or when it is less than 2 hectares ; rpf,t is the redistributive payment in eur/ha of the farm f in year t under the cap post-2020 reform; alf,t is the available arable land in hectares of the farm f in year t; j is the set of potential activities29; xf,wj,t is the level of irrigated cropping activity wj in hectares of the farm f in year t; wj is the set of potential irrigated activities30 ilf,t is the available irrigated land in hectares of the farm f in year t; crcf,t is the total available circulating capital in eur of the farm f in year t. the remaining constraints are specific to the farm and correspond to policy and flexibility constraints: 2013 cap reform constraints (greening obligations) crop diversification obligation for farm f with total available arable land (alf,t) > 10 hectares: xf,j,t hf,t ≤ hf,t 0.75 alf,t for t = 2015,…,t (b6) 26 a farm’s participation in the nitrate pollution reduction programme (agri-environmental measure of the rural development programme) is determined through a priori information provided from sample farms. 27 a farm’s participation in the organic farming programme (agrienvironmental measure of the rural development programme) is determined through a priori information provided from sample farms. 28 of course, it may be true that bf,t = ßf,t = 0, which indicates the nonmandatory nature of the specific agri-environmental policy measures. 29 where j= {cotton(ct); tobacco(tb); maize(mz); pr. tomato(pt); pr. pepper(pp); alfalfa(aa); alfalfa-seed(aasd); durum wheat(dw); setaside(st)}, if bf,t = 0 then st ∉ j 30 where wj = {cotton(ct); tobacco(tb); maize(mz); pr. tomato(pt); pr. pepper(pp); alfalfa(aa); alfalfa-seed (aasd} https://ead.gr/wp-content/uploads/2022/01/cap_sp_proposal_30_12_2021.pdf https://ead.gr/wp-content/uploads/2022/01/cap_sp_proposal_30_12_2021.pdf 378 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. where hf,t denotes the binary variable that corresponds to the farm f in year t, and is equal to 0 when the available arable land (alf,t) ≤ 10 hectares, while it gets the value 1 when the available arable land (alf,t) > 10 hectares. ecologic focus area obligation for farms f with total available arable land (alf,t) > 15 hectares: 0.7⟦ xf,lgj,t⟧ + xf,st,t ≥ gf,t0.05alf,t for t = 2015,…,t, lgj ∈ lgj, lgj ⊆ j (b7) where xf,lgj,t is the level of legume crops (lgj) in hectares of the farm f in year t; lgj = {alfalfa-hay (aa); alfalfaseed (aasd)}; gf,t denotes the binary variable that corresponds to the farm f in year t, and is equal to 0 when the available arable land (alf,t) ≤ 15 hectares, while it gets the value 1 when the available arable land (alf,t) > 15 hectares. crop diversification obligation for farm f with total available arable land (alf,t) > 30 hectares: [xf,l1 j,t * + xf,l2 j,t *]uf,t ≤ uf,t 0.95alf,t) for t = 2015,…,t, l1 j ∈ j, l2 j ∈ j (b8) where xf,l1 j,t * the optimal level of cropping activity in hectares, to which the largest share (l1 j) of the available arable land (alf,t) of farm f in year t is allocated; xf,l2 j,t * the optimal level of cropping activity in hectares, to which the second largest share (l2 j) of the available arable land (alf,t) of farm f in year t is allocated; uf,t denotes the binary variable that corresponds to the farm f in year t, and is equal to 0 when the available arable land (alf,t) ≤ 30 hectares, while it gets the value 1 when the available arable land (alf,t) > 30 hectares. cap post-2020 reform scenario constraints crop diversification obligation for farm f with total available arable land (alf,t) > 10 hectares: xf,j,t ≤ hf,t 0.75 alf,t for t = 2023,…,t, j ∈ j (b9) cap post-2020 reform scenario constraints(adoption of eco-schemes) eco-schemes adoption: extension of efa application by farm f with total available arable land (alf,t) ≤ 10 hectares: xf,st,t = ef,t 0.05 alf,t for t = 2023,…,t, st ∈ j (b10) eco-schemes adoption: extension of efa application by farm f with total available arable land (alf,t) > 10 hectares: xf,st,t = εf,t 0.1 alf,t for t = 2023,…,t, st ∈ j (b11) cap post-2020 reform scenario constraints(non-adoption of eco-schemes) efa application by farm f with total available arable land (alf,t) > 10 hectares: xf,st,t = εf,t 0.04 alf,t for t = 2023,…,t, st ∈ j (b12) nitrate pollution reduction program constraints (agri-environmental measure of the rural development programme): xf,nwj,tbf,t ≥ bf,t 0.75 nlf,t, for t = 1,…,t, nwj ∈ nwj, nwj ⊆ j (b13) where xf,nwj,t is the level of irrigated cropping activity included in the nitrate pollution reduction program (nwj) in hectares of the farm f in year t; nwj = {cotton(ct); maize(mz); pr. tomato(pt); pr. pepper(pp) } xf,ndj,t ≥ bf,t 0.2 nlf,t, for t = 1,…,t, ndj ∈ ndj, ndj ⊆ j (b14) where xf,ndj,t is the level of non-irrigated cropping activity included in the nitrate pollution reduction program (ndj) in hectares of the farm f in year t; ndj = {durum wheat (dw)} xf,st,t ≥ bf,t 0.05 nlf,t, for t = 1,…,t, st ∈ i (b15) where xf,st,t is the level of set-aside (st) included in the nitrate pollution reduction program (hectares) of the farm f in year t. we want to point out that from the year 2018 onwards, the vast majority of sample farms implemented the nitrate pollution reduction program as follows: the share of 0.75 of constraint (b13) was set to 0.7; the share of 0.2 of constraint (b14) was set to 0.3, and the share of 0.05 of constraint (b15) was set to 0. organic farming program constraint (agri-environmental measure of the rural development programme): xf,orj,t ≥ olorgf,tβf,t, for t = 1,…,t, orj ∈ orj, orj ⊆ j (b16) 379simulating farm structural change dynamics in thessaly (greece) using a recursive programming model bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 where xf,orj,t is the level of organic cropping activity included in the organic farming program (orj) in hectares of the farm f in year t; orj = {alfalfa (aa)}. flexibility constraint of multiannual contract farming 0.85 clf,t cf,t ≤ xf,aasd,t cf,t ≤ 1.15 clf,t cf,t, for t = 2015,…,t, aasd ∈ ι (b17) where xf,aasd,t is the level of alfalfa-seed (aasd) in hectares of the farm f in year t; clf,t is the available land of the farm f in year t included in the multiannual contract farming program; cf,t denotes the binary variable that corresponds to the farm f in year t, and is equal to 0 when the farm does not participate in the program of multiannual contract farming , while it gets the value 1 when it participates. part c: land rental costs/land rental income estimation as mentioned in the main text, land is reallocated only on a rental basis through farmland rental arrangements between tenants and landowners. laporte et al. (2020) state that “the most popular and frequently used farmland rental arrangement is a fixed cash rent agreement, where the landowner receives a predetermined fee to be paid by the tenant regardless of agricultural commodity price or crop yield” (p. 1). this type of landowners’ rental agreement is also maintained for the case under consideration, where the farmers pay after harvesting and selling the agricultural commodities in the market. the following is an estimate of land rental costs for each year after the initial one, where lrcvf,nbf,t are the land rental costs of viable neighboring farm in year t; lrcvf,nbf,t-1 is the rented land of viable neighboring farm in year t-1; lrit is the land rental price index in year t; lrpnbf,t=1 is the average land rental price per land unit (eur/ha) in base year (t=1) applicable to the region where the neighboring farms operate; lrpnbf,t is the average land rental price per land unit (eur/ha) in year t applicable to the region where the neighboring farms operate; ωsim alvf,nbf,t-1 is the simulated share of arable land reallocated to viable neighboring farm at the end of the year t-1, that is, following the annual optimization; alnvf,nbf,t-1 is the simulated aggregate arable land of non-viable neighboring farms at the end of the year t-1, that is, following the annual optimization; talnbf,t is the actual total arable land of neighboring farms in year t; talnbf,t=1 is the actual total arable land of neighboring farms in year t-1; alvf,nbf,t is the available arable land of viable neighboring farm in year t. for t = 2…t, rlvf,nbf,t ⊆ alvf,nbf,t (c1) the average land rental price (applicable to the region where the neighboring farms operate) (lrpnbf,t) was used as a single land rental price for all farms to simplify the modeling process, considering that the observed differences in payable land rental prices between farms are negligible. since the land rental price is exogenously determined in this model version31, updating its variance for each year after the base year is conducted using the land rental price index (lrit) (elstat, 2019b). additionally, we must mention that product rlvf,nbf,t-1 lrpnbf,t indicates that land rental prices are renegotiated every cropping cycle. to simplify the presentation of the estimation of land rental costs on an annual basis, we did not separate the land into irrigated and non-irrigated. it is worth mentioning that the average land rental price of nonirrigated land is about 50% lower. to make post-sample forecasts in the medium term, the exogenously identified average land rental price per land unit (lrpnbf,t) is estimated through arima stochastic process. although rare in our analysis, there is the case of viable farms that rent out part of owned land because the estimated reduction of the land attributed to them due to exogenous reasons32 [ωsim alvf,nbf,t-1 -1 (talnbf,t – talnbf,t-1)] exceeds (i) the previous year rented land (rlvf,nbf,t-1) and (ii) the land that accumulated endogenously, i.e., the released land available for rent, derived from 31 following similar simulation models (bert et al., 2011; djanibekov & finger, 2018; donati et al., 2024), the land rental price is exogenous in the suggested model. unfortunately, this version of the model does not fully consider the interaction between farms and the spatial relationships to include a land rental market with the endogenous formation of the rental price through an auction mechanism (bert et al., 2011) as it is usually applied in agent-based models. however, land rental price endogeneity could be approximated to some extent through shadow values, for example, using the distribution of shadow value for the land of viable farms based on the exogenously determined land rental price, but this aspect requires further investigation. 32 competitive pressures from other farm types or non-agricultural sectors are likely to lead to an unfavorable situation, i.e., talnbf,t – talnbf,t-1 < 0 and consequently to a decrease of available arable land for the viable neighboring farms, which will be reallocated among them utilizing the inverse form of the simulated share of arable land (ωsim alvf,nbf,t-1 ), that is, less profitable albeit viable farms will abandon/ release proportionately more of their arable land. 380 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. non-viable neighboring farms [ωsim alvf,nbf,t-1 ( alnvf,nbf,t-1 sim)]. in this case, land rental costs are negative (lrcvf,nbf,t < 0), equal to land rental income for the viable neighboring farm (lrincvf,nbf,t > 0). consequently, the equation fnpat*f,t = π*f,t – (depf,t + lrcf,t + sfncf,t + lfncf,t + sicf,t + fptxf,t) (5) (in section 2.3.3.determining farm viability of the main text) is adapted as follows: fnpat*f,t = (π*f,t + lrincf,t) – (depf,t + sfncf,t + lfncf,t + sicf,t + fptxf,t) (c2) indicating that a farm cannot simultaneously rent in and rent out farmland, a condition we also find in similar simulation models (e.g., donati et al., 2024). part c: references bert, f. e., podestá, g. p., rovere, s. l., menéndez, á. n., north, m., tatara, e., laciana, c. e., weber, e., & toranzo, f. r. (2011). an agent-based model to simulate structural and land use changes in agricultural systems of the argentine pampas. ecological modelling, 222(19), 3486–3499. https://doi.org/10.1016/j. ecolmodel.2011.08.007 djanibekov, u., & finger, r. (2018). agricultural risks and farm land consolidation process in transition countries: the case of cotton production in uzbekistan. agricultural systems, 164, 223–235. https://doi. org/10.1016/j.agsy.2018.03.009 donati, m., calzolai, s., baldi, l., & arfini, f. (2024). predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology. bio-based and applied economics. retrieved from https://oaj.fupress.net/index.php/ bae/article/view/14592 elstat (2019b). costs for the factors of agricultural and livestock production (costs indices), 2019. hellenic statistical authority. https://www.statistics.gr/en/statistics/-/publication/dkt33/2019 happe, k., balmann, a., kellermann, k., & sahrbacher, c. (2008). does structure matter? the impact of switching the agricultural policy regime on farm structures. journal of economic behavior and organization, 67(2), 431–444. https://doi.org/10.1016/j. jebo.2006.10.009 laporte, j., mackellar, b., & pennington, d. (2020). farmland rent considerations part 3: farmland rental agreements and arrangements. michigan state university.https://www.canr.msu.edu/news/farm_ land_rental_agreements_and_arrangements part d: borrowed capital & finance costs estimations d1. borrowed circulating capital & short-term finance costs estimations farm growth in equity is the surplus income available to put back into the business by either purchasing assets or debt repayment (hofstrand, 2009; bert et al., 2011; grdc, 2015). therefore, the current level of short-term borrowing will be determined by the optimal farm growth in equity of the year t-1 minus the sum of the existing debt and the required circulating capital of the current year. more specifically, if the optimal farm growth in equity of the previous year is enough to serve: 1) the scheduled principal repayment of existing debt of farm f in year t-1 (dprpf,t-1) which consists of (i) the borrowed circulating capital of farm f (bcrcf,t) to be repaid within the same year received and (ii) the borrowed investment capital of farm f (binvcf,t) to be repaid within a predetermined duration of years (tl)33 2) and the required circulating capital of farm f of the current year (crcf,t), then the farm f will not take out a short-term loan, otherwise the farm will be led to shortterm borrowing. the mathematical formulation of the condition is as follows: for t = 2,…,t (d1) where bcrcf,t is the borrowed circulating capital of farm f in year t; dprpf,t-1 is the principal repayment of existing debt of farm f in year t-1. in the case of a short-term loan, the level of borrowed circulating capital will be calculated as follows: bcrcf,t = (crcf,t + dprmf,t-1) – fge*f,t-1 (d2) respectively the short-term finance costs will be estimated as follows: sfncf,t = bcrcf,t sirf,t (d3) where sfncf,t are the short-term finance costs of farm f in year t and sirf,t is the short-term interest rate in year t. according to the greek banking system, the shortterm interest rate is based on the bfr (basic rate for farmers). 33 we assume an equal annual repayment which corresponds to the ratio https://doi.org/10.1016/j.ecolmodel.2011.08.007 https://doi.org/10.1016/j.ecolmodel.2011.08.007 https://doi.org/10.1016/j.agsy.2018.03.009 https://doi.org/10.1016/j.agsy.2018.03.009 https://oaj.fupress.net/index.php/bae/article/view/14592 https://oaj.fupress.net/index.php/bae/article/view/14592 https://www.statistics.gr/en/statistics/-/publication/dkt33/2019 https://www.statistics.gr/en/statistics/-/publication/dkt33/2019 https://doi.org/10.1016/j.jebo.2006.10.009 https://doi.org/10.1016/j.jebo.2006.10.009 https://www.canr.msu.edu/people/jonathan_laporte https://www.canr.msu.edu/people/bruce_mackellar https://www.canr.msu.edu/people/dennis_pennington https://www.canr.msu.edu/news/farm_land_rental_agreements_and_arrangements https://www.canr.msu.edu/news/farm_land_rental_agreements_and_arrangements 381simulating farm structural change dynamics in thessaly (greece) using a recursive programming model bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 d2. borrowed investment capital & long-term finance costs estimations farm growth in equity is the surplus income available to put back into the business by either purchasing assets or debt repayment (hofstrand, 2009; bert et al., 2011; grdc, 2015), and hence the current level of longterm borrowing will be partially determined by the optimal farm growth in equity. therefore, the following conditions determine the need or not for borrowed investment capital in year t (binvcf,t): for t = 2,…,t (d4) in case the sum of optimal farm growth in equity of year t-1 (fge*f,t-1) and depreciation of year t-1 (depf,t-1) exceeds the sum of scheduled principal repayment of existing debt in year t-1 (dprpf,t-1), the required level of circulating capital of year t crcf,t and the required gross investment on fixed assets in year t (if,t), then the farm will not take out a long-term loan. alternatively, the farm will have to take out a long-term loan. in the case of a long-term loan, the level of borrowed investment capital (binvcf,t) will be calculated as follows: binvcf,t = (crcf,t + dprpf,t-1 + if,t) – (fge*f,t-1 + depf,t-1) (d5) respectively the long-term finance costs will be estimated as follows: lfncf,t + lirt (d6) where lfncf,t are the long-term finance costs of farm f in year t and lirt is the long-term interest rate in year t. based on literature (dafwa, 2014), we consider that the repayment duration of borrowed investment capital (tl) should be equal to 15 years. the long-term interest rate is based on the bfr (basic rate for farmers) according to the greek banking system. part d: references bert, f. e., podestá, g. p., rovere, s. l., menéndez, á. n., north, m., tatara, e., laciana, c. e., weber, e., & toranzo, f. r. (2011). an agent-based model to simulate structural and land use changes in agricultural systems of the argentine pampas. ecological modelling, 222(19), 3486– 3499. https://doi.org/10.1016/j.ecolmodel.2011.08.007 dafwa (2014). generating more profit from you farm business. department of agriculture and food and the state of western australia (dafwa). https:// www.agric.wa.gov.au/sites/gateway/files/generating%20more%20profit%20from%20your%20farm%20 business_print%20version%2027aug14.pdf grdc (2015). how do i measure the financial performance of my farm business? grains research and development corporation (grdc). https://grdc. com.au/resources-and-publications/all-publications/ publications/2015/01/farming-the-business-manual/ fb_manual_document_3rd-edition_jul19-web72dpi070-149.pdf hofstrand, d. (2009). building equity in your farm business. ag decision maker, file c3-60, iowa state university. https://www.extension.iastate.edu/agdm/ wholefarm/html/c3-60.html https://doi.org/10.1016/j.ecolmodel.2011.08.007 https://www.agric.wa.gov.au/sites/gateway/files/generating more profit from your farm business_print version 27aug14.pdf https://www.agric.wa.gov.au/sites/gateway/files/generating more profit from your farm business_print version 27aug14.pdf https://www.agric.wa.gov.au/sites/gateway/files/generating more profit from your farm business_print version 27aug14.pdf https://www.agric.wa.gov.au/sites/gateway/files/generating more profit from your farm business_print version 27aug14.pdf https://grdc.com.au/resources-and-publications/all-publications/publications/2015/01/farming-the-business-manual/fb_manual_document_3rd-edition_jul19-web-72dpi070-149.pdf https://grdc.com.au/resources-and-publications/all-publications/publications/2015/01/farming-the-business-manual/fb_manual_document_3rd-edition_jul19-web-72dpi070-149.pdf https://grdc.com.au/resources-and-publications/all-publications/publications/2015/01/farming-the-business-manual/fb_manual_document_3rd-edition_jul19-web-72dpi070-149.pdf https://grdc.com.au/resources-and-publications/all-publications/publications/2015/01/farming-the-business-manual/fb_manual_document_3rd-edition_jul19-web-72dpi070-149.pdf https://grdc.com.au/resources-and-publications/all-publications/publications/2015/01/farming-the-business-manual/fb_manual_document_3rd-edition_jul19-web-72dpi070-149.pdf https://grdc.com.au/resources-and-publications/all-publications/publications/2015/01/farming-the-business-manual/fb_manual_document_3rd-edition_jul19-web-72dpi070-149.pdf https://grdc.com.au/resources-and-publications/all-publications/publications/2015/01/farming-the-business-manual/fb_manual_document_3rd-edition_jul19-web-72dpi070-149.pdf https://www.extension.iastate.edu/agdm/wholefarm/html/c3-60.html https://www.extension.iastate.edu/agdm/wholefarm/html/c3-60.html 382 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. part e: historical dataset and forecasting method of exogenously determined parameters table e1. data sources of times series and forecasting method of exogenously determined farm model parameters farm model parameter of interest range data source forecasting method hired labor costs (hlcf,j,t) [2001-2018 ] elstat (2019b) arima model input costs (icf,j,t) [2000-2019 ] elstat (2019c) arima model machinery rental costs (mrcf,j,t) [2000-2019 ] elstat (2019b) arima model land rental price (lrpnbf,t) [2000-2018 ] elstat (2019b) arima model interest rate (sirt; lirt) [2000-2020 ] elstat (2019b) arima model cotton yield (yf,ct,t) [1961-2017 ] greek ministry of rural development and food; arima model d. wheat yield (yf,dw,t) [1961-2017 ] greek ministry of rural development and food; greek ministry of rural development and food (2019) arima model tobacco yield (yf,tb,t) [1979-2017 ] greek ministry of rural development and food; greek ministry of rural development and food (2019) arima model pepper yield (yf,pp,t) [1961-2007 ] greek ministry of rural development and food arima model tomato yield (yf,ptm,t) [1961-2007 ] greek ministry of rural development and food arima model legumes crops yield (yf,aasd,t; yf,aa,t) [2000-2017 ] greek ministry of rural development and food (2019) arima model maize yield (yf,mz,t) [1981-2017 ] greek ministry of rural development and food; greek ministry of rural development and food (2019) arima model cotton price (pf,ct,t) [2000-2019 ] elstat (2019c) arima model d. wheat price (pf,dw,t) [2000-2019 ] elstat (2019c) arima model legume crops price (pf,aasd,t; pf,aa,t) [2000-2019 ] elstat (2019c) arima model maize price (pf,mz,t) [2000-2019 ] elstat (2019c) arima model total arable land (talnbf,t) [2004-2019 ] fadn public database* arima model total circulating capital (tcrcnbf,t) [2004-2019 ] fadn public database* arima model living expenditures (lef,t) [2008-2020 ] elstat (2021) linear trend model notes: * the available farm accountancy data network (fadn) time series were filtered to include greek farms specialized in “other fieldcrops” (according to the tf14 classification of fadn), utilizing the parameters of arable land (se026) and other circulating capital (se480), which were multiplied by the parameter farms represented (sys02) to obtain values at an aggregate level. source: elstat (2019b), elstat (2019c), elstat (2021), fadn public database, greek ministry of rural development and food, greek ministry of rural development and food (2019). part e: references elstat (2019b). costs for the factors of agricultural and livestock production (costs indices), 2019. hellenic statistical authority. https://www.statistics.gr/en/statistics/-/publication/dkt33/2019 elstat (2019c). input and output price indices in agricultural and livestock production, 2019. hellenic statistical authority. https://www.statistics.gr/en/statistics/-/publication/dkt30/2019-m12 elstat (2021). household budget survey (after 2008), 2020. hellenic statistical authority. https://www.statistics.gr/en/statistics/-/publication/sfa05/2020 fadn public database. farm accountancy data network public database. directorate-general for agriculture and rural development. https://agridata.ec.europa.eu/ extensions/fadnpublicdatabase/fadnpublicdatabase.html greek ministry of rural development and food. statistical data-time series (in greek). http://wwww.minagric.gr/ greek/agro_pol/3.htm greek ministry of rural development and food (2019). statistics data of areas and production of plant products (in greek). http://www.minagric.gr/index.php/el/ the-ministry-2/statistikes-tekmhrioshs/8510-statistikaekt-parag-fytikonproionton https://www.statistics.gr/en/statistics/-/publication/dkt33/2019 https://www.statistics.gr/en/statistics/-/publication/dkt33/2019 https://www.statistics.gr:443/en/statistics?p_p_id=documents_war_publicationsportlet_instance_qdq8fbkko4ln&p_p_lifecycle=2&p_p_state=normal&p_p_mode=view&p_p_cacheability=cachelevelpage&p_p_col_id=column-2&p_p_col_count=4&p_p_col_pos=1&_documents_war_publicationsportlet_instance_qdq8fbkko4ln_javax.faces.resource=document&_documents_war_publicationsportlet_instance_qdq8fbkko4ln_ln=downloadresources&_documents_war_publicationsportlet_instance_qdq8fbkko4ln_documentid=405241&_documents_war_publicationsportlet_instance_qdq8fbkko4ln_locale=en https://www.statistics.gr:443/en/statistics?p_p_id=documents_war_publicationsportlet_instance_qdq8fbkko4ln&p_p_lifecycle=2&p_p_state=normal&p_p_mode=view&p_p_cacheability=cachelevelpage&p_p_col_id=column-2&p_p_col_count=4&p_p_col_pos=1&_documents_war_publicationsportlet_instance_qdq8fbkko4ln_javax.faces.resource=document&_documents_war_publicationsportlet_instance_qdq8fbkko4ln_ln=downloadresources&_documents_war_publicationsportlet_instance_qdq8fbkko4ln_documentid=405241&_documents_war_publicationsportlet_instance_qdq8fbkko4ln_locale=en https://www.statistics.gr/en/statistics/-/publication/dkt30/2019-m12 https://www.statistics.gr/en/statistics/-/publication/dkt30/2019-m12 https://www.statistics.gr/en/statistics/-/publication/sfa05/2020 https://www.statistics.gr/en/statistics/-/publication/sfa05/2020 https://agridata.ec.europa.eu/extensions/fadnpublicdatabase/fadnpublicdatabase.html https://agridata.ec.europa.eu/extensions/fadnpublicdatabase/fadnpublicdatabase.html https://agridata.ec.europa.eu/extensions/fadnpublicdatabase/fadnpublicdatabase.html http://wwww.minagric.gr/greek/agro_pol/3.htm http://wwww.minagric.gr/greek/agro_pol/3.htm http://www.minagric.gr/index.php/el/the-ministry-2/statistikes-tekmhrioshs/8510-statistika-ekt-parag-fytikonproionton http://www.minagric.gr/index.php/el/the-ministry-2/statistikes-tekmhrioshs/8510-statistika-ekt-parag-fytikonproionton http://www.minagric.gr/index.php/el/the-ministry-2/statistikes-tekmhrioshs/8510-statistika-ekt-parag-fytikonproionton 383simulating farm structural change dynamics in thessaly (greece) using a recursive programming model bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 pa rt f : a ri m a a nd li ne ar tr en d m od els e sti m at io ns ta bl e f1 . a ri m a m od el s o f e xo ge no us ly d et er m in ed p ar am et er s o f i nt er es t εx og en ou sly d et er m in ed pa ra m et er o f f ar m m od el (y t) ti m e se rie s d at a po in ts [p er io d] a ri m a m od el (p ,d ,q ) φ 1 φ 2 φ 3 θ 1 θ 2 θ 3 μ m a pe (% ) a ic au gm en te d d ic ke yfu lle r tst at ist ic br eu sc hg od fr ey s er ia l c or re la tio n lm t es t [p ro b. x 2 (p )] h ire d la bo r p ric e in de x 19 [2 00 120 18 ] (2 ,0 ,1 ) 1. 64 ** * (0 .0 5) -0 .8 2** * (0 .0 4) -0 .9 9** * (0 .0 9) 92 .7 9** * (0 .5 9) 0. 95 3. 84 -3 .5 5* pr ob . x 2 (2 )= 0. 05 9 in pu t p ric e in de x 20 [2 00 020 19 ] (1 ,1 ,1 ) 0. 87 ** * (0 .0 6) -0 .9 9** * (0 .1 2) 3. 78 6. 19 -4 .2 9** pr ob . x 2 (2 )= 0. 44 m ac hi ne ry re nt al p ric e in de x 20 [2 00 020 19 ] (0 ,2 ,1 ) -0 .5 0** (0 .2 1) 1. 29 4. 09 -7 .0 0** * pr ob . x 2 (2 )= 0. 92 la nd re nt al p ric e in de x 19 [2 00 020 18 ] (1 ,0 ,1 ) 0. 53 ** (0 .2 0) 0. 99 ** * (0 .0 6) 99 .0 5** * (1 .5 7) 1. 05 3. 76 -4 .0 1** pr ob . x 2 (2 )= 0. 40 in te re st ra te in de x 21 [2 00 020 20 ] (0 ,2 ,1 ) -0 .9 1** * (0 .0 7) 5. 51 6. 49 -6 .9 2* ** pr ob . x 2 ( 2) =0 .9 9 c ot to n yi el d (k g/ h a) 57 [1 96 120 17 ] (1 ,0 ,1 ) 0. 92 ** * (0 .0 1) -0 .9 7** * (0 .0 3) 29 9. 23 ** * (5 .3 6) 7. 55 9. 21 -4 .2 1** * pr ob .x 2 (2 )= 0. 54 d . w he at y ie ld (k g/ 0 .1 h a) 57 [1 96 120 17 ] (2 ,0 ,1 ) 0. 50 ** * (0 .1 5) 0. 43 ** * (0 .1 4) -0 .6 2** * (0 .1 4) 27 8. 04 ** * (5 1. 51 ) 11 .3 0 9. 83 -3 .4 7** pr ob .x 2 (2 )= 0. 98 to ba cc o yi el d (v irg in ia ) (k g/ 0. 1 h a) 39 [1 97 920 17 ] (1 ,0 ,3 ) 0. 87 ** * (0 .0 2) -1 .0 0** * (0 .1 5) 0. 48 ** (0 .2 1) -0 .4 6** * (0 .1 5) 33 6. 52 ** * (8 .1 7) 6. 73 9. 40 -4 .2 1** * pr ob .x 2 (2 )= 0. 35 pe pp er y ie ld (k g/ 0. 1 h a) 47 [1 96 120 07 ] (1 ,0 ,2 ) 0. 98 ** * (0 .0 9) -0 .6 6** * (0 .1 4) -0 .2 9** (0 .1 4) 43 96 .7 2** * (1 07 9. 63 ) 6. 28 13 .2 1 -4 .1 0** * pr ob .x 2 (2 )= 0. 81 to m at o yi el d (k g/ 0. 1 h a) 47 [1 96 120 07 ] (1 ,1 ,0 ) -0 .4 4** * (0 .1 4) 81 .2 7** (3 1. 97 ) 5. 5 14 .3 4 -7 .2 1** * pr ob .x 2 (2 )= 0. 36 le gu m es c ro ps y ie ld [a lfa lfa (h ay & se ed )] (k g/ 0. 1 h a) 18 [2 00 020 17 ] (0 ,0 ,2 ) 0. 31 ** * (0 .0 8) 0. 93 ** * (0 .0 2) 74 0. 24 ** * (2 6. 53 ) 4. 69 10 .8 3 -4 .8 2** * pr ob .x 2 (2 )= 0. 10 m ai ze y ie ld (k g/ 0. 1 h a) 37 [1 98 120 17 ] (2 ,0 ,0 ) 0. 54 ** * (0 .1 6) 0. 33 ** * (0 .1 6) 10 86 .8 5** * (1 22 .1 9) 3. 77 10 .6 7 -4 .1 8** pr ob .x 2 (2 )= 0. 36 c ot to n pr ic e (e u r/ kg ) 20 [2 00 020 19 ] (1 ,0 ,1 ) 0. 85 ** * (0 .0 6) -0 .9 6** * (0 .0 4) 0. 48 ** * (0 .0 48 ) 15 .1 7 -2 .1 9 -3 .3 9* pr ob .x 2 (2 )= 0. 16 d . w he at p ric e (e u r/ kg ) 20 [2 00 020 19 ] (3 ,0 ,0 ) 0. 84 ** * (0 .2 3) -0 .6 2** (0 .2 9) 0. 44 * (0 .2 2) 0. 19 ** * (0 .0 2) 10 .8 1 -4 .0 8 -3 .1 9* pr ob .x 2 (2 )= 0. 32 le gu m e cr op s p ric e (a lfa lfa -h ay ) (e u r/ kg ) 20 [2 00 020 19 ] (1 ,1 ,0 ) -0 .4 3* (0 .2 2) 6. 00 -6 .3 6 -4 .3 6** pr ob .x 2 (2 )= 0. 80 m ai ze p ric e (e u r/ kg ) 20 [2 00 020 19 ] (1 ,0 ,1 ) 0. 83 ** * (0 .0 6) -0 .9 9** * (0 .1 0) 0. 18 ** * (0 .0 0) 8. 48 -4 .7 0 -3 .4 0* pr ob .x 2 (2 )= 0. 08 (c on tin ue d) 384 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. εx og en ou sly d et er m in ed pa ra m et er o f f ar m m od el (y t) ti m e se rie s d at a po in ts [p er io d] a ri m a m od el (p ,d ,q ) φ 1 φ 2 φ 3 θ 1 θ 2 θ 3 μ m a pe (% ) a ic au gm en te d d ic ke yfu lle r tst at ist ic br eu sc hg od fr ey s er ia l c or re la tio n lm t es t [p ro b. x 2 (p )] to ta l a ra bl e la nd in de x 16 [2 00 420 19 ] (0 ,1 ,3 ) -1 .2 6** * (0 .2 8) 1. 17 ** * (0 .2 2) -0 .8 2** * (0 .1 4) 3. 19 ** * (0 .8 9) 3. 67 6. 98 -7 .7 5** * pr ob .x 2 (2 )= 0. 64 to ta l c irc ul at in g ca pi ta l i nd ex 16 [2 00 420 19 ] (0 ,1 ,1 ) -0 .9 3** * (0 .0 6) 29 . 7 ** * (2 .3 1) 10 .9 1 10 .2 4 -3 .7 5** pr ob .x 2 (2 )= 0. 19 n ot es : ∇ d y t = μ + ϕ 1∇ d y t1 + ⋯ ϕ p∇ d y t-p + ε t − θ 1ε t1 − ⋯ − θ qε tq; φ 1, . . . , φ p: au to re gr es siv e (a r) m od el p ar am et er s of o rd er p ; θ 1, … , θ q: m ov in g av er ag e (m a ) m od el p ar am et er s of o rd er q (m ar tín ez -a co st a et a l., 2 02 0) ; ε t is w hi te n oi se ; μ= a co ns ta nt e qu al to th e m ea n of th e se rie s if d = 0 (n ar ay an a & p ar ik h, 1 98 1) ; * in di ca te s sig ni fic an ce a t 0 .1 le ve l, ** in di ca te s sig ni fic an ce a t 0 .0 5 le ve l, ** * in di ca te s sig ni fic an ce a t 0 .0 1 le ve l; th e nu ll hy po th es is h 0 of th e b re us ch -g od fr ey s er ia l c or re la tio n lm t es t i s th at th er e is no a ut oc or re la tio n in th e re sid ua ls se rie s up to p re -d et er m in ed la g or de r (p =2 in o ur an al ys is) a t t he 0 .0 5 le ve l o f s ig ni fic an ce (w ey er st ra ss , 2 01 6) . so ur ce : a ut ho rs , b as ed o n el st at ( 20 19 b) , e ls ta t (2 01 9c ), fa d n p ub lic d at ab as e, g re ek m in ist ry o f r ur al d ev el op m en t a nd f oo d, g re ek m in ist ry o f r ur al d ev el op m en t a nd fo od (2 01 9) . ta bl e f2 . l in ea r t re nd m od el re gr es sio n st at ist ic s o f r ur al h ou se ho ld s’ liv in g ex pe nd itu re in de x (l ei ) va ria bl e c oe ffi ci en t st d. e rr or tst at ist ic pr ob .   c 0. 95 47 77 0. 01 96 44 48 .6 03 63 0. 00 00 @ tr en d -0 .0 23 92 5 0. 00 27 78 -8 .6 12 05 7 0. 00 00 rsq ua re d 0. 87 08 43 m ea n de pe nd en t v ar 0. 81 12 26 a dj us te d rsq ua re d 0. 85 91 01 s. d . d ep en de nt v ar 0. 09 98 46 s. e. o f r eg re ss io n 0. 03 74 79 a ka ik e in fo c rit er io n -3 .5 89 45 3 su m sq ua re d re sid 0. 01 54 51 sc hw ar z cr ite rio n -3 .5 02 53 8 lo g lik el ih oo d 25 .3 31 45 h an na nq ui nn c rit er . -3 .6 07 31 8 fst at ist ic 74 .1 67 53 d ur bi nw at so n st at 0. 64 28 14 pr ob (f -s ta tis tic ) 0. 00 00 03 so ur ce : a ut ho rs , b as ed o n el st at (2 02 1) d at a. ta bl e f1 . ( c on tin ue d) . 385predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) (m) (n) (o) (p) (q) (r) (s) figure f1. arima and linear trend models of the exogenously determined parameters of interest. notes: the horizontal axis indicates the year; 0.1 ha (hectare) =1 stremma is the greek unit of land area. (a) hired labor price index; (b) input price index; (c) machinery rental price index; (d) land rental price index; (e) interest rate index; (f) cotton yield (kg/0.1 ha); (g) durum wheat yield (kg/0.1 ha); (h) tobacco yield (kg/0.1 ha); (i) pepper yield (kg/0.1 ha); (j) tomato yield (kg/0.1 ha); (k) legume crops yield (kg/0.1 ha) including alfalfa (hay & seed); (l) maize yield (kg/0.1 ha); (m) cotton price (eur/kg); (n) durum wheat price (eur/kg); (o) alfalfa (hay) price (eur/kg); (p) maize price (eur/kg); (q) total arable land index; (r) total circulating capital index; (s) living expenditures index. source: authors, based on elstat (2019b), elstat (2019c), elstat (2021), fadn public database, greek ministry of rural development and food, greek ministry of rural development and food (2019). 386 bio-based and applied economics 13(4): 353-386, 2024 | e-issn 2280-6172 | doi: 10.36253/bae-14790 stamatis mantziaris et al. part f: references elstat (2019b). costs for the factors of agricultural and livestock production (costs indices), 2019. hellenic statistical authority. https://www.statistics.gr/en/statistics/-/publication/dkt33/2019 elstat (2019c). input and output price indices in agricultural and livestock production, 2019. hellenic statistical authority. https://www.statistics.gr/en/statistics/-/publication/dkt30/2019-m12 elstat (2021). household budget survey (after 2008), 2020. hellenic statistical authority.https://www.statistics.gr/en/statistics/-/publication/sfa05/2020 martínez-acosta, l., medrano-barboza, j. p., lópezramos, á., lópez, j. f. r., & lópez-lambraño, á. a. 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(2016). a tool for supporting economic policy-making in the former yugoslavia. documentation and applications of a macroeconomic multi-country model. osteuropa: geschichte, wirtschaft, politik, 49. wien: lit-verlag. 224 p. _ref114578152 _ref114489287 _ref114578337 _ref115429676 _hlk124753738 _hlk124753769 _ref114578159 _hlk125619779 _hlk125620100 _hlk125620116 _hlk168057609 _hlk125617961 _hlk125617978 _heading=h.1fob9te _heading=h.1y810tw _heading=h.3whwml4 _hlk105387355 _hlk162970176 _hlk120503612 _hlk94825625 _hlk101307043 _hlk112165281 _hlk110795819 _hlk163789378 _hlk101572865 _hlk101574469 _hlk162424605 _hlk163657897 _hlk163658986 _hlk163657719 _hlk163657691 _hlk163658456 _hlk163658513 _hlk163658646 _hlk162446713 _hlk125919866 _hlk107164817 _hlk162973538 _hlk162447045 _hlk107174352 _hlk162447083 _hlk124882770 _hlk142394334 _hlk142395728 _hlk142430461 _hlk142337917 _hlk142342729 _hlk142337969 _hlk142337867 _hlk142342461 _hlk142342928 _hlk142338077 _hlk142348528 _hlk164779807 _hlk125467744 _hlk141790649 assessing the bioeconomy’s contribution to evidence-based policy: a comparative analysis of value added measurements tévécia ronzon1,2,*, patricia gurria2, michael carus3, kutay cingiz2, andrea el-meligi1, nicolas hark3, susanne iost4, robert m’barek1, george philippidis5, myrna van leeuwen6, justus wesseler2 predicting the effect of the common agricultural policy post-2020 using an agent-based model based on pmp methodology lisa baldi1, sara calzolai2, filippo arfini2,*, michele donati1 simulating farm structural change dynamics in thessaly (greece) using a recursive programming model stamatis mantziaris1,*, stelios rozakis2, pavlos karanikolas1, athanasios petsakos3, konstantinos tsiboukas1 analyzing the impact of government subsidies on household welfare during economic shocks: a case study of iran mohammad dehghan1,*, seyyed nematollah moosavi2*, ebrahim zare3 crop production, the pollinator deficit and land use management: uk farm level survey results iain fraser1,*, michelle t. fountain2, john m. holland3 bio-based and applied economics bae bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 copyright: © 2023 kremmydas, d., ciaian, p., baldoni, e. open access, article published by firenze university press under cc-by-4.0 license. firenze university press | www.fupress.com/bae citation: kremmydas, d., ciaian, p., baldoni, e. (2023). modeling conversion to organic agriculture with an eu-wide farm model. bio-based and applied economics 12(4): 261-304. doi: 10.36253/ bae-13925 received: november 08, 2022 accepted: october 09, 2023 published: december 31, 2023 data availability statement: all relevant data are within the paper and its supporting information files. competing interests: the author(s) declare(s) no conflict of interest. editor: matteo zavalloni orcid dk: 0000-0002-4444-1698 pc: 0000-0003-2405-5850 eb: 0000-0002-5296-1212 modeling conversion to organic agriculture with an eu-wide farm model dimitrios kremmydas*, pavel ciaian, edoardo baldoni european commission, joint research center (jrc), seville, spain  *corresponding author. e-mail: dimitrios.kremmydas@ec.europa.eu abstract. this paper analyses the impacts of the farm to fork strategy (f2f) target of 25% organic farmland by 2030 in the eu using a farm level model. two approaches are deployed to model conversion to organic agriculture. the first one, the endogenous approach, operates under the assumption that farm conversions to organic production result from assessing the utility difference between organic and conventional production systems. the exogenous approach relies on econometric estimation of the likelihood of farms to convert to organic driven by a combination of monetary and non-monetary drivers. the simulated impacts of the f2f target at the eu level vary depending on the chosen methodology. gross income changes range from +3.8% under the endogenous approach to -1.3% under the exogenous approach. both approaches forecast decreased production (-0.5% to -15%) for most crops and animal products upon achieving the organic target. keywords: organic farming, farm model, ifm-cap, farm to fork strategy, eu green deal, eu. jel codes: q12, q57. 1. introduction the farm to fork (f2f) strategy of the eu green deal (european commission, 2019, 2021) aims to stimulate the transition to a sustainable food system that is fair, healthy, and environmentally friendly. among other proposed solutions, such as nutrient surplus reduction, pesticide risk reduction, antimicrobial use reduction, or increase of biodiversity, one of the key tools to achieve the transition is to promote the expansion of organic farming. the f2f strategy sets the target of 25% of the eu’s agricultural area under organic farming by 2030 (european commission, 2020). currently, only 9% of the utilized agricultural area is under organic farming in the eu. therefore, to achieve the f2f goal, a sizable agricultural area (17%) would need to convert from conventional to organic agriculture. organic farming is significantly different from conventional farming, particularly regarding management practices and productivity (alvarez, 2021; baker et al., 2020; bonfiglio et al., 2022; reganold & wachter, 2016; watson c.a. et al., 2002). for this reason, the conversion of a large share of the agrihttps://doi.org/10.36253/bae-13925 http://www.fupress.com/bae https://doi.org/10.36253/bae-13925 https://doi.org/10.36253/bae-13925 https://orcid.org/0000-0002-4444-1698 https://orcid.org/0000-0003-2405-5850 https://orcid.org/0000-0002-5296-1212 mailto:dimitrios.kremmydas@ec.europa.eu 262 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni cultural area to organic farming may have a significant effect on the eu agri-food system. more specifically, while organic farming is generally perceived to have positive environmental impacts, concerns exist about potential decreases in food production when shifting from conventional to organic farming methods (meemken & qaim, 2018; reganold & wachter, 2016; seufert & ramankutty, 2017; timsina, 2018). the potential production decrease associated with reaching the f2f target raises the issue of food security both in the eu and globally, given that eu is a major food producer and exporter. the main contribution of this paper is to shed light on these issues by developing (individual) farm level modeling of eu-wide organic conversion in order to bring quantitative insights into the potential production effects of reaching the 25% organic target in the eu. four main modeling approaches have been applied in the literature to simulate the impacts of conversion to organic farming: (i) spatially explicit agronomic/biophysical models, (ii) partial equilibrium agro-economic models, (iii) individual or representative agro-economic farm models1, and (iv) non-conventional models. in the first approach, the interplay between nutrient inputs, spatially explicit biophysical characteristics and outputs are explored to analyze the impacts of the conversion to organic production on the whole food system. the geographic scope of this approach spans from the regional level to world coverage by applying different spatial resolution depending on the study objectives (barbieri et al., 2019; jones & richard crane, 2014; lee et al., 2020; muller et al., 2017). the second approach relies on partial equilibrium models, which depict the behavioral interactions of economic agents within the agriculture sector at the regional, country or global level (barreiro hurle et al., 2021; bremmer et al., 2021). in the third approach, the study scale is either the individual (acs et al., 2007, 2009; kerselaers et al., 2007) or representative farms (smith et al., 2018), where the allocation of activities is 1 the main distinction between ‘representative’ and ‘individual’ farm modelling considered in this paper refers to the representation of production and endowments structure of farms. the ‘representative’ farm model considers a virtual farm aggregating the production and endowments of several farms. it represents production and endowments structure averaged over all farms across considered dimensions (e.g. by production specialization, farm size, regional level). the ‘individual’ farm model refers to the production and endowments of a real (individual) farm. note that in statistical terms when representative sampling is deployed, an individual farm included in the sample is representative of the larger farm population from which it is drawn in a way that it reflects the characteristics of the farm population (so that the sample can accurately represent the whole population). thus, the farms used in the model are individual farms that represent the eu farming population. however they are not average ‘representative’ farms that are used in models that aggregate many farms into one (e.g. the capri model). usually modeled as a constrained optimization problem. this approach captures more disaggregated behavioral choices. finally, the last approach relies on non-conventional modeling methods like agent-based modeling and system dynamics (rozman et al., 2013; xu et al., 2018). each of these modeling approaches has several limitations in modelling organic conversion. the main limitation of the agronomic/biophysical models is that they do not consider the economic dimension of conversion, neither at the farm level nor at the aggregate regional or country level. hence, they cannot capture the organic conversion of specific farms. they usually assume full conversion of the modeled food system and then compare it with the situation before the conversion (barbieri et al., 2019; muller et al., 2017). although partial equilibrium agro-economic models consider the economic dimension of organic conversion by construction they do not capture micro behavior at the farm level. instead, they attempt to model organic production and input relationships by adjusting general productivity parameters (e.g., yields, input use) and/or introducing organic-related aggregate production constraints. representative farm models suffer from similar limitations as the food system and partial equilibrium agroeconomic models. however, they can capture in greater detail some organic farm practices and their differences across farm types. they also usually assume full conversion to organic production of all modeled farm types (smith et al., 2018). finally, regarding the nonconventional models, agent-based models can capture the organic conversion and specific aspects of organic farm practices in more detail. however, they are not applied at a larger geographical scale due to their high data requirements (kremmydas et al., 2018). in contrast, system dynamic models may represent well the interactions between the elements of the system and provide answers to strategic decisions, but they cannot model details of organic conversion and organic farm practices (richardson, 2011). applying an individual farm-level model for modeling organic conversion has several advantages. first, since organic conversion choice and organic production practices are farm-specific, applying an individual farm-level approach can offer a more accurate representation of organic farming without imposing strong assumptions on farmers’ behavior. for example, detailed agronomic and behavioral constraints representing the technological differences between the two systems (conventional and organic) can be introduced. second, individual farm models incorporate individual farms and technology representation, enabling the selection of specific farms that are more likely to convert. a https://doi.org/10.36253/bae-13925 263modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 third advantage is their effectiveness in modeling policy incentives, especially those targeting environmental and organic production. indeed, the common agricultural policy (cap), among others, includes farm-specific environmental measures (including support for organic production) which aim to improve the environmental and climate performance of the eu farming sector. finally, an individual farm-level model can provide distributional effects across the farm population, allowing for more nuanced impact analyses for policy making (buysse et al., 2007; ciaian et al., 2013). however, the individual farm models applied in the literature to simulate conversion to organic production exhibit several limitations. first, they rely solely on expert knowledge, which restricts their applicability to a broader geographical scale, such as the entire eu. indeed, they are either applied to a single farm (acs et al., 2007) or a single country (kerselaers et al., 2007). moreover, these models do not develop a methodology for selecting specific farms to undergo conversion; instead, they assume the conversion of all farms. this paper aims to fill the gap in the existing literature on individual farm modelling of organic conversion. specifically, it focuses on the challenges of adjusting an eu-wide model – ifm-cap (individual farm model for common agricultural policy analysis) – to account for changes in farm performance and management practices associated with organic production. achieving these model adjustments requires conducting several econometric estimations to identify the difference in performance between organic and conventional production across individual farms in all eu countries. this is due to the scarcity of readily available expert knowledge for such a wide geographic area encompassing a heterogeneous range of production systems. to fully leverage the farm-level model, we consider behavioral constraints that are relevant to organic farming such as crop rotation, nitrogen management, maximum stocking density, feed self-sufficiency and minimum share of fodder in the diet, respecting the heterogeneity across the eu farms. additionally, to simulate the effects of the f2f organic target on farm income, production (quantities and value) and production costs, we consider two alternative approaches to select specific farms for conversion to organic production. this differs from the modeling approaches applied in the existing literature, which typically assume 100% conversion. the first approach, referred to as ‘endogenous’ approach, is based on profitability (utility maximization) differences between organic and conventional production systems. under this approach, the subset of the most profitable farms are assumed to convert to organic farming. the second approach, referred to as ‘exogenous’ approach, employs a probabilistic framework to econometrically estimate the likelihood of farms converting to organic production. the underlying idea is that conventional farms sharing characteristics similar to organic farms are more likely to convert to organic farming. in econometric estimation, we take into account both monetary (e.g. subsidies, intensity of input use) and non-monetary factors (e.g. farm structural characteristics) that are often found in the literature to affect the likelihood of farmers adopting organic agriculture (canavari et al., 2022; sapbamrer, 2021; serebrennikov et al., 2020; willock et al., 1999).2 using farm accountancy data network (fadn), we conduct a comparative assessment of multiple probability models to identify the best-performing approach, which is then utilized for the selection of a subset of farms converting to organic production. the paper is structured as follows. the next section describes the methodology of modelling organic production in the ifm-cap. section 3 presents the methodology applied for the selection of converting farms to organic production. section 4 describes the simulated results, while section 6 concludes. 2. modeling organic production in the ifm-cap model the ifm-cap model is a static positive mathematical programming model, which solves a set of microeconomic models reproducing the behavior of individual farms (kremmydas et al., 2022). the model assumes that farmers maximize their expected utility of income subject to technical and policy constraints related to resource endowments, production relationships, and cap policy. ifm-cap models 81,107 individual farms from the 2017 fadn database3, covering all 27 member states (ms). its calibration against the 2017 fadn data is performed with a positive mathematical programming (pmp) approach. the ifm-cap model has been used in various past studies for ex-ante cap policy assessments at the eu level (european commission, 2018a; louhichi et al., 2017, 2018; petsakos et al., 2022). 2 for more details see supplementary material part a. 3 the fadn is a european system of farm surveys that take place every year and collect structural and accountancy information on eu farms, such as farm structure and yield, output, land use, inputs, costs, subsidies, income, and financial indicators. the fadn data is unique in the sense that it is the only source of harmonized and representative farmlevel microeconomic data for the whole european union. farms are selected to take part in the survey based on stratified sampling frames established for each eu region. https://doi.org/10.36253/bae-13925 264 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni the generic mathematical formulation for an individual farm that follows conventional production system is as follows:4 (1) subject to: (2) where i ∈ set of “animal activities” (3) where i,j indices denote the agricultural (crop and livestock) activities, m denotes marketable commodities (i.e., feed purchased and farm output sold in the market or used as animal feed),5 t represents the resource and policy constraints related to activities (e.g., agricultural land, greening obligations), while ν denotes animal feeding constraints and n the different types of nutrients or energy requirements. regarding the decision variables, xi is the level of activity i (hectares and head) and ζi,m is the amount of feed m given to animal activity i (tons per head). regarding the rest of the elements, e[gmi] is the expected gross margin for activity i (eur/ha or eur/ head), e denotes decoupled payments (eur), di is the intercept of the activity-specific behavioural (implicit cost) function (the linear pmp terms), qi,j is its slope (the nonlinear pmp terms a diagonal positive semi-definite matrix), df i,m is the linear term of the behavioural function related to animal feeding, qf i,m is the nonlinear part of the same function (a diagonal positive semi-definite matrix), φ is the farmer’s constant absolute risk aversion (cara) coefficient and ωij is the covariance matrix of activity revenues per hectare or per head. inequality (2) represents the general structure of the animal feeding constraints, where af n,m,ν is a matrix of coefficients representing the content of nutrient n in feed m, while bf i,n,ν is the quantity limit of nutrient n given to animal i (lower 4 the optimization problem is specific to each farm. however, for simplicity we have suppressed the index for farms, f, in all equations. 5 mathematically this means that the set of feeds in ifm-cap, and the set of farm outputs, some of which can be used as feeds themselves, are subsets of the set of all marketable commodities included in the model. or upper, or satisfied as equality),6 and θf i,n,ν is the shadow price of the ν-th feeding constraint. at,i are coefficients for resource and policy constraints, bt are available resource levels and upper bounds for policy constraints, while θt are their corresponding shadow prices. the expected activity gross margin is defined as: (4) where yi,m is the expected yield of output from activity i, pm denotes the expected price for commodity m (including for feed and young animals), ξm are estimated production losses, vi are coupled payments linked to activity i, and ci are the accounting variable costs. the calculation of variable costs differs between crop and animal activities. for crops, ci = ∑kci,k, k are intermediate inputs (i.e. fertilizer, seeds, crop protection, etc.) and ci,k are the per hectare costs of each input type. for animals, ci = ∑m ∈ feedpmζi,m, feed m given to animal activity i is evaluated at price pm. the model formulation for organic production system changes as follows (the changes are highlighted in bold letters): (5) where: (6) for crops, (7) for animals, (8) subject to: 6 this equation ensures that animal-specific nutrient demands (requirements) are met from on-farm produced or purchased feed (supply). balancing feed supply (availability) and demand (requirements) is done through nutrient values. additionally, we set lower and upper thresholds for feed in animal diets for each animal category to align feed allocation with animals’ physiological requirements and prevent overuse or underuse of specific feeds in the diet (kremmydas et al., 2022). https://doi.org/10.36253/bae-13925 265modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 (9) where i ∈ set of “animal activities” (10) the following are the main model differences between conventional and organic management: the parameters pg m, yg i,m, cg i,k and af,g n,m,ν capture percentage differences between conventional and organic farming in prices, yields, costs and the content of nutrients in feeds, respectively. a modified set of technical constraints, t’, is considered in equation (10), which adds farm practices specific to organic farming, namely crop rotation, nitrogen management, maximum stocking density, feed self-sufficiency and minimum share of fodder in the diet. additionally, the cap greening constraints are removed because organic farms are exempted from complying with the greening requirements. the next sections provide a more detailed description of these model changes introduced in ifm-cap for organic farming. 2.1 output prices and yields of organic crops the findings from the literature indicate that in general, organic farms tend to achieve lower crop yields and to obtain price premiums compared to conventional farms (alvarez, 2021; de ponti et al., 2012; offermann & nieberg, 2000; seufert et al., 2012). to account for these effects, we apply a log-linear econometric specification to estimate the relative difference in the expected output prices and yields of crops between organic and conventional production systems. the advantage of the econometric approach is that we can control for a series of factors potentially affecting prices and yields, which can bias the estimated results if not accounted for. as covariates, we use a set of farm structural characteristics such as farm specialization, farm size, altitude of the farm, presence of natural constraints, the share of irrigated land and time dummy. to isolate the effect of organic farming on yields and prices, we do not include proxies of input use in the econometric estimations due to their high correlation with the organic status of the farm. their inclusion in the estimated equation would likely bias downwards the estimates (particularly yield gaps).7 7 for more details on the summary statistics of costs, prices and yields, distribution of organic farms, and econometric models see supplementary material part b. the estimations are based on fadn data for 20072016, covering the whole eu. we perform estimations for main crop products and for different geographical regions (fadn regions) to account for heterogeneity in technology, local characteristics, and farming systems. the estimated price and yield differences are then pooled together by five macro-regions: central europe north, central europe south, northern europe, southern europe and uk & ireland. the median values8 are extracted for each macro-region and used as price, pg m, and yield, yg i,m, differences between conventional and organic farming in the ifm-cap model. overall, the estimated results show that organic farms attain higher output prices and lower yields than conventional farms. for most crops and macro-regions, the difference in prices varies between around 10% and 60%, while for yields, between -5% and -45%. the highest absolute difference in prices and yields is observed in uk & ireland and central europe north, while the smallest differences tend to be in southern europe.9 2.2 variable cost of organic crop production due to different technologies applied by organic and conventional farms, variable crop production costs are expected to differ between the two farming systems. therefore, we conduct econometric estimations for four types of variable cost categories (per-hectare) – seeds, fertilizers, crop protection, and other crop-specific costs – to identify the differences induced by different technologies applied by the two farming systems. a linear econometric model was used to estimate these differentials between organic and conventional farms. the estimations are based on fadn data for 2007-2016, covering the whole eu.10 given that technologies and production mixes are expected to differ between farm types and regions, we econometrically estimate cost differences for each fadn region and for each production specialization separately. the estimated percentage difference in costs, cg i,k, between organic and conventional farms for each cost category, region, and farm specialization are then used to adjust the costs for converted farms in ifm-cap. overall, the estimates indicate that organic farms generally have lower variable costs than conventional farms across most farm specializations and cost categories. this is particularly the case for fertilizers and 8 the median price and yield differences between conventional and organic farming are expected to be robust against potential data outliers and model misspecification. 9 for more information see table a1 and table a2 in appendix. 10 for more details, see the part of ‘part b: econometric estimations’ in the supplementary material. https://doi.org/10.36253/bae-13925 266 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni crop protection costs. however, more mixed results are obtained for seeds and other crop-specific costs, where higher values for organic farms than conventional farms are more common across different farm specializations.11 2.3 organic livestock output and feed prices, yields and feed efficiency similar as in the case of crops, for dairy milk, we estimated the differences in prices and yields between organic and conventional farming using fadn data for 2007-2016, covering the whole eu. data for other livestock activities are not directly available in the fadn. these activities are derived from the livestock module in ifm-cap (kremmydas et al., 2022). thus, for other livestock activities, we performed an econometric analysis of yield and price differences between conventional and organic farms using derived data from the livestock module in ifm-cap for the period 2012-2016. as in the case of crops, the estimations were done by using the log-linear regression models of livestock yields and prices (for different fadn regions) by accounting for a set of explanatory variables relating to farm characteristics and to the characteristics of the operating environment. note that in some cases (e.g. poultry meat) when data did not allow to conduct econometric estimations (e.g. small sample size), we relied on literature estimates from the meta-analysis conducted by gaudaré et al. (2021). their study compared the evidence from literature on the productivity and feed-use efficiency between conventional and organic livestock animals. overall, organic livestock farms have higher output prices and lower yields than conventional farms. for most crops and macro-regions, the difference in prices varies between around 5% and 50%, while for yields, between -1% and -25%. the highest absolute difference in prices seem to be in northern europe, while the smallest differences tend to be in central europe south, southern europe and uk & ireland. for yields, there is no clear pattern across macro-regions.12 ifm-cap models explicitly animal feed in terms of its physical quantity and nutrient value by balancing feed demand (determined by animal nutrient requirements) and feed supply/availability (determined by on-farm produced and purchased feed and its feed nutrients content). the utility maximization problem then determines endogenously the most cost-efficient selection of specific feeds in each animal’s diets (kremmydas et al., 2022)13. 11 for more information see table a3 in appendix. 12 for more information see table a4 and table a5 in appendix. 13 livestock costs and feed requirements per head in ifm-cap are derived based on fadn data and external data sources. this was applied because fadn does not contain all relevant information needed in line with the prerequisite to use organic feeds in organic livestock farms, we use price differences between organic and conventional feed, pg m, estimated for crops in the previous section for organic purchased feeds. since most organic crop prices are usually higher than conventional crop prices, the cost of purchased feed is expected to be greater in organic than in conventional farms. further, according to the gaudaré et al. (2021), organic livestock farming shows lower feed efficiency by between 6% and 20% as compared to conventional farms. following this evidence, we apply a 13% decrease in organic feed efficiency in ifm-cap, af,g n,m,ν, by reducing nutrient content in the organic feed as compared to conventional feed. the lower feed efficiency for organic farms may be explained, among others, by differences in feeding strategies (e.g., a higher share of rough fodders in animal diets in organic compared to conventional farming) and differences in herd management practices as compared to conventional farms (e.g., more extended resting period between lactations for dairy). 2.4 behavioral constraints of organic farms as indicated in equation (10), we consider five behavioral constraints in ifm-cap identified in the literature to characterize the organic production system and differentiate it from the conventional system: crop rotation, nitrogen management, maximum stocking density, feed self-sufficiency, and minimum share of fodder in the diet (barbieri et al., 2017; reimer et al., 2020; gaudaré et al., 2021). crop rotation in organic farming, crop rotation is used to manage the nutrient balance in the soil, address weed problems and prevent soil diseases and insect pests. it also facilitates farmers to substitute for chemical fertilizers and to parameterize the feed in ifm-cap (in contrast to crop activities). fadn contains only aggregated economic data on feed availability and costs at farm level. the disaggregated feed data such as feed use by each animal category, nutrient content of feed, animal nutrient requirements are not available in fadn. the high posterior density (hpd) estimation approach was used to estimate animal-level feed data by combining fadn and external data, where external data are used only as prior information in the estimation approach. the estimation approach combines these different data sources by taking into consideration the minimization of deviation of estimated data values from the available prior information, the minimization of feed costs, balancing between feed nutrient requirements of livestock and feed availability, and data constraints to ensure that the sum of animal-level feed costs is as close as possible to the aggregated cost values reported in fadn. for more details see (kremmydas et al., 2022). https://doi.org/10.36253/bae-13925 267modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 plant protection, which is strictly limited in the organic production system (reganold & wachter, 2016; baker et al., 2020). ideally, modeling crop rotation requires a multi-annual model with detailed agronomic information at the plot level (castellazzi et al., 2008; dury et al., 2012). since the ifm-cap model is a comparative static model and does not consider time dynamics, we model differences in crop rotation between organic and conventional management indirectly by introducing empirically estimated farm-specific flexibility cropping constraints for main crops as follows:14 sc org ≤ (1 + rc) ∙ sc conv ∀ c (11) where sc org is the share of main crop c in total area of farm converted to organic production, sc conv is the observed share of main crop c on conventional farm, and rc is a crop-specific coefficient representing the reduction of the main crop share due to the farm converting to organic. the motivation for applying flexibility constraint (11) comes from the observation that organic rotations are more complex and diversified than conventional ones. for example,(barbieri et al., 2017) based on a meta-analysis of literature evidence comparing crop rotation differences between organic and conventional farming, barbieri et al. (2017) estimated that, on average, at the global scale, organic rotations last for 4.5 ± 1.7 years. this duration is approximately 15% longer than their conventional counterparts and include 48% more crop categories. the f lexibility cropping constraints (11) represent the extensification of the main crops’ area allowed under the organic production system in ifm-cap. it sets the crop specific maximum thresholds that a crop can represent in the total farm area such that to replicate the distribution observed on organic farms. this modeling of crop rotation means that the most frequent crops of the rotation will be cultivated less frequently by organic farms than by conventional farms reflecting the observed distribution. the rc coefficient is estimated based on fadn data15 aiming to shift the distribution of the area shares of the crops of the converted farms towards the distribution of area shares empirically observed among organic farms. 14 we introduce the flexibility constraint for the following main crops: soft wheat, durum wheat, barley, grain maize, fodder maize, rape seed, sugar beet, sun flower, potatoes. 15 for more details on the estimation methodology, see supplementary material part c. nitrogen management the organic farm’s nitrogen management is expected to impact the area devoted to the cultivation of nitrogenfixing crops. organic farms are expected to cultivate more nitrogen-fixing crops than conventional farms, primarily to maintain land fertility through the ability of these crops to fix nitrogen from the air and thus provide a source of nitrogen that could serve as a substitute for inorganic fertilizers (barbieri et al., 2017). additionally, the eu organic regulation 848/2018 requires the cultivation of leguminous crops by organic farms to maintain the soil’s fertility and biological activity. farms can also use other practices for nitrogen management, such as green and animal manure, leaving land fallow or grassland (chmelíková et al., 2021; lin et al., 2016). modeling the farm’s nitrogen management is relatively complex and requires information unavailable in fadn (küstermann et al., 2010; thomas, 2003). moreover, this is further complicated because nitrogen management practices could be very heterogeneous across organic farms, with some not using nitrogen-fixing crops. indeed, according to fadn data, around 40% of organic farms did not cultivate nitrogen-fixing in the eu in 2017, varying between 19% and 77% across different farm specializations. instead, according to fadn data, organic farms without nitrogen-fixing crops have a significantly higher share of fallow land and grassland in the total land than farms that cultivate nitrogen-fixing crops. this higher share is likely explained by the fact that the farms without nitrogen-fixing crops maintain land fertility through animal manure, fallow land, or grassland management. to model nutrient management in ifm-cap, we apply a simplified approach to model nitrogen management. we combine the agronomic knowledge with a data-driven approach to approximate the changes that converted farms need to undertake in their area allocation to account for nutrient management practices. more specifically, we assume that farms that convert to organic farming will cultivate a more significant share of their arable area with nitrogen management related crops16 determined by the following flexibility constraint: (sc org) ≥ (1 + η) ∙ (sc conv) (12) where, n is the set of the crops related to nitrogen management, sc org and sc conv are the area shares of crop 16 the nitrogen related crops in ifm-cap are soybean, pulses, other fodder, permanent grassland and fallow land. https://doi.org/10.36253/bae-13925 268 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni c in total farm area when in the organic and conventional status, respectively, and η is a farm specific coefficient representing the increase of nitrogen related area in organic farming compared to conventional ones. the constraints (12) defines the minimum area share of nitrogen related crops that organic farms need to maintain on farm. these minimum area shares are farm specific and are defined in such a way that the distribution of the nitrogen-fixing, fallow and grassland area shares of the converted farms shifts such that to resemble the observed ones on organic farms in fadn.17 maximum stocking density requirements the eu organic regulation (european commission, 2018) requires that the total stocking density does not “exceed the limit of 170 kg of nitrogen per year and hectare”. the regulation also indicates the number of livestock units (lsu) per hectare. based on this, we introduce the maximum stocking density constraint in the ifm-cap for organic farms specifying that the total livestock units multiplied by the maximum number of hectares allowed per one livestock unit18 across all animal categories of the farm cannot exceed the total farm area. this constraint requires the converted farms to adjust their number of animals to the available farm area such that to respect the maximum thresholds set by the eu organic regulation. feed self-sufficiency the organic production system is characterized by a high degree of self-sufficiency of animal feed to reduce the risks of uncertain availability of organic feed on the market (especially for fodder). it also allows to sustain a better nutrient management at the farm level (lampkin et al., 2017). to account for this aspect of an organic production system, we consider a feed self-sufficiency constraint in ifm-cap. the constraint is based on the requirement set by the eu organic regulations regarding the animals’ feed sourcing. the legislation requires a minimum percentage of the animal’s feed to come from on-farm production: 60% for bovine and ovine and caprine and 30% for porcine and poultry (european commission, 2008, 2018b). in ifm-cap, we constraint the maximum share of purchased feed at the farm level in line with the thresholds provided in the eu organ17 for more details on the estimation of the minimum shares see the supplementary material part c. 18 for more information see table a6 in appendix. ic regulations (e.g., 40% for bovines). the constraint ensures that the purchased feed (expressed in dry matter terms) does not exceed the maximum share of the total feed use at the farm level. minimum share of fodder in diet organic farms usually use a higher proportion of fodder in animal feed due to the lower possibility of acquiring organic concentrate feed on the market (lower diversity and higher prices than for conventional feed) and the rules set by the eu organic regulation (flaten & lien, 2009; gaudaré et al., 2021; lampkin et al., 2017). the eu organic regulations (european commission, 2008, 2018b) require that all animals should have access to roughage. for bovine, ovine, and caprine animals, the percentage of dry matter that should come from roughage, fresh or dried fodder, or silage is 60%. however, this percentage may be reduced to 50% for female animals in milk production for a maximum period of three months in early lactation. in addition, the regulation specifies that roughage, fresh or dried fodder, or silage should be added to the daily ration for porcine and poultry, but without providing a specific minimum share. following the eu organic regulations, we introduce a constraint in ifm-cap that defines the minimum share of fodder in the animal diet (represented in dry matter) for each farm animal. we use a minimum share 57.5% fodder for bovine, ovine and caprine animals19 and 0.5% for porcine and poultry animals20. 3. the selection of converting farms alongside modelling the effects of organic conversion at the farm level, the selection of specific farms that convert to organic production system needs to be considered in an individual farm model. this is particularly relevant for policies that aims to achieve a partial conversion to organic such as the f2f strategy which sets the 25% area target. to the best of our knowledge, there is no consistent theoretical framework available in the literature that would provide modelling framework for selecting the farm that will convert. we consider two alternative selection approaches that build on different 19 this share is calculated as follows: [60% for nine months]*(9/12) + [50% for three months]*(3/12) 20 note that the 0.5% share for porcine and poultry is set ad-hoc since a specific value is not provided in the regulation. this share is based on literature findings indicating that porcine and poultry in organic farms often have a proportion of their diet in form of roughage (e.g. hermansen et al., 2004; sossidou et al., 2015). https://doi.org/10.36253/bae-13925 269modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 grounds. one is based on ifm-cap modeling results (utility maximization) and is referred to as ‘endogenous’ approach. the second one is based on external drivers affecting organic conversion determined outside the ifm-cap model referred to as ‘exogenous’ approach. 3.1 endogenous selection in the endogenous approach, we assume that the propensity to convert is proportional to the utility difference between conventional production system and organic production system. the endogenous selection approach solely relies on the ifm-cap model simulation results. first, we simulate the utility obtained with the conventional farming practices in place by solving the utility maximization problem outlined in equations (1) to (4), uconv = e[u]. second, we run the utility maximization problem of organic production provided in equations (5) to (10), uf org = ef[u]’. finally, we order farms in decreasing order in terms of utility difference between organic and conventional farming obtained for each farm, ∆u = uorg uconv. the best-performing farms are selected to convert to the organic production system. the number of selected converting farms depends on the simulated scenario (e.g. on the organic area target considered). 3.2 exogenous selection the exogenous approach is based on estimation of the likelihood of individual farms converting to the organic farming using fadn data. this approach does not rely on ifm-cap model simulation results but is exogenously introduced in the model based on results obtain from econometric estimations. our main assumption is that the likelihood of conversion depends on the similarity of conventional farms with respect to organic ones: conventional farms that are more similar to organic ones − in terms of farm characteristics, performance, behavior and the environment in which they operate − are assumed to be more likely convert to organic farming. farms that are already similar to organic ones will find it less costly to make additional changes to their production methods to make it in line with the organic farming requirements. using probability models, we estimate the conversion likelihood for all farms included in the ifm-cap base year (i.e., for fadn farms in 2017). we apply seven different probability models commonly used in the literature to estimate organic farm conversion: (i) linear probability model (lp), (ii) the linear probability model with stepwise selection algorithm (lp + ssa),21 (iii) the logit model (logit), (iv) the logit model using the covariates of model lp + ssa (logit + ssa), (v) the probit model (probit), (vi) the probit model using the covariates of model lp + ssa (probit + ssa), and (vii) the random forest algorithm (random forest) (basnet et al., 2018; burton et al., 1999; chatzimichael et al., 2014; chmielinski et al., 2019; djokoto et al., 2016; genius et al., 2006; hattam & holloway, 2005; läpple & rensburg, 2011; lohr & salomonsson, 2000; malá & malý, 2013; parra lópez & calatrava requena, 2005; serebrennikov et al., 2020). the dependent variable used in all models is binary taking value of 1 if the farm is organic and 0 if the farm is conventional (non-organic). the choice of explanatory variables used in these models has been guided by previous empirical literature that suggested that several drivers may impact farmers’ decision to convert to organic farming. these drivers include quantifiable monetary factors, such as subsidies and input expenditures, as well as non-monetary factors, such as structural characteristics, access to farm organic buyers/markets, and farmer believes and attitudes towards the environment22 (canavari et al., 2022; sapbamrer, 2021; serebrennikov et al., 2020; willock et al., 1999)23. the set of selected covariates have been constructed using fadn data for 2014-2017 period to proxy these monetary and non-monetary drivers24. we compare the results obtained from all estimated probability models and choose the predictions generated by the model with the best prediction accuracy. fadn farms (in each ms or at the eu level, depending 21 a stepwise selection algorithm based on the aic criterion is applied to the full specification of the lp model. this selection algorithm allows reducing the number of covariates used in the estimation phase and, possibly, increasing the accuracy (goodness of fit) of the predictions. this reduced equation is then used to re-estimate the linear model, the logit and the probit model. 22 for more details see supplementary material part a. 23 note that unlike studies typically done in the literature on adoption of organic farming (bravo-monroy et al., 2016; darnhofer et al., 2005; fairweather, 1999; hattam & holloway, 2005; kallas et al., 2009; lohr & salomonsson, 2000; parra lópez & calatrava requena, 2005; yu et al., 2014), our approach is a prediction exercise. our aim is to assign a probability of conversion to fadn farms rather than apply an explanatory model of conversion (shmueli, 2010). 24 more specifically, the monetary covariates considered in the estimations capture the amount of subsidies received, the performance of organic farms in the region relative to conventional ones, regional land prices, input expenditure. on the other hand, non-monetary covariates capture different farm characteristics such as the structural characteristics of the farm, production specialization, the characteristics of the geographical location in which farm operates, the type of farm activities, crop biodiversity index, yield gaps, labor use, and the presence of organic farming in the region. for the full list of covariates, see part d of the supplementary material. https://doi.org/10.36253/bae-13925 270 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni on the type of simulated policy target)25 are then ranked according to their estimated likelihood of converting to the organic status, and those with the highest probability are assumed to convert to organic production.26 this implies that the selection of farms that convert to organic production in the exogenous approach is not necessarily those that gain the most in terms of profit (utility) but instead, those are estimated to be most likely converting determined by the various monetary and nonmonetary related factors considered in the estimations. the prediction accuracy of the seven estimated models varies between 0.51 and 0.99, with most models across ms and eu having an accuracy greater than 0.8.27 for the majority of ms, as well as for the eu as a whole28, the random forest algorithm outperformed the other six models in terms of prediction accuracy. exceptions are luxemburg and ireland, for which the logit model and the logit model with a stepwise selection algorithm have shown a higher prediction accuracy, respectively. the prediction accuracy for the selected model is greater than 0.88 across ms and eu. 4. results we apply the modified ifm-cap model defined by equations (5) (10) to simulate the 25% target set in the f2f strategy. we consider the implementation of the target both at the ms and eu levels. the ‘ms level’ implementation considers reaching the 25% target for each eu ms. the ‘eu level’ implementation means that the 25% target is set at the eu level and thus, some ms may have an organic area share lower or greater than 25%. we use those two scenarios because the actual policy implementation seems not to be clearly defined. while the target is set at the eu level, member states have the primary obligation to implement it, but the target is not mandatory for them (european commission, 2020). thus, the two considered scenarios represent bounds within which the impact of the target is expected to lie. the simulated impact of the organic target were compared against a reference, or ‘baseline’ scenario which represents the base year situation without organic 25 the estimated ms conversion probabilities are more appropriate when modeling the policy target set at the ms level. in contrast, the eu level conversion probabilities are more appropriate when modeling the policy target set at the eu level. 26 for more details see supplementary material part d. 27 the performance metric of the seven models and the best performing model for ms and eu level estimations are reported in table a8 in appendix. 28 due to its computation complexity, the stepwise selection algorithm is not performed with the sample of eu as whole. conversion (i.e. 2017). the baseline simulations are based on equations (1)-(4). 4.1 comparison of the farms selected in the endogenous and exogenous approaches table 1 shows the share of farms ranked in the first two quantiles (q1 and q2) of the distribution selected for organic conversion that overlaps in both the endogenous and exogenous approaches. in general, the two selection approaches select different farms to convert. in both the endogenous and exogenous approaches, there is only 5% overlap of farms selected for conversion in the first quantile (q1), and only 25% overlap in the first two quantiles (q1q2) of the distribution. the discrepancy in these results arise from the selection criteria used by the two approaches. the profit maximization rule in the endogenous approach selects the most performant farms for organic conversion, most of which, as shown in table 1, are different from the farms selected in the exogenous approach where the selection is based on the similarity of farms in monetary and non-monetary characteristics, such as farm structural characteristics. when we break down the converting farms by farm specialization, we find that only for a few farm specializations, most farms selected in both approaches overlap (more than 60% in q1 and q2), namely specialist olives, specialist wine and permanent crops combined. this implies that drivers considered in the exogenous approach are relatively well aligned with the performance related rule in the endogenous approach for these farm groups. on the contrary, in specialist orchards, specialist granivores, specialist milk, mixed crops and livestock and specialist cereals, oilseed, protein crops, the majority of farms selected in one approach are generally not selected in the other approach and vice versa (more than 80% in q1 and q2). in other farm specializations, there is a 30% to 50% overlap in the selected farms between the two approaches for q1 and q2 quantiles. a similar pattern holds when we break down the converting farms by economic size. for all economic size classes, farms selected in one approach are generally not selected in the other: only between 21% and 31% of selected farms in q1 and q2 overlap in both approaches (table 1). additionally, as reported in table a9 in appendix29, the endogenous approach tends to select for organic conversion mainly farms specialized in field crops, specialist horticulture and mixed crops and livestock, while 29 table a9 in appendix shows the share of the selected farms by specialization, economic size and selection approach. https://doi.org/10.36253/bae-13925 271modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 the exogenous approach makes a more balanced selection, although it still favours certain farm types, such as farms specializing in permanent crops, field crops, specialist milk and mixed livestock farms over other specializations (particularly when compared to specialist other field crops, specialist cattle and other mixed crops). in terms of economic farm size, both approaches tend to select primarily small farms. 4.2 the economic impacts of the 25% organic target simulation results show that the aggregate farm income30 in the eu increases compared to baseline in the endogenous approach and decreases in the exogenous approach (table 2).31 these results are expected because 30 farm income is calculated as the difference between total revenues (output value and subsidies, excluding organic payments) and variable costs (e.g., fertilisers, pesticides, seeds, feeding). 31 note that the farm income change does not include organic payments for the converted farms. this implies that a decrease in income reprethe endogenous approach selects farms for conversions based solely on profitability, resulting in only the bestperforming farms converting and thus leading to higher farm income as compared to the baseline scenario. in contrast, the exogenous approach selects farms for conversion based on factors not always directly related to profitability (particularly non-monetary ones), meaning that the converting farms may not necessarily be the most profitable ones. for the target set at the eu level, the aggregate farm income in the eu increases compared to the baseline by 3.8% in the endogenous approach and decreases by 1.2% in the exogenous approach. for the targets set at ms level, the farm income change is slightly smaller (3.6% in the endogenous approach and -1.3% in the exogenous approach) compared to the eu-level target (table 2). the income effects are determined by changes in the output value and production costs. in the endogenous approach, both f2f target scenarios lead to an increase sents a proxy for the minimum budgetary support required to offset the income loss. table 1. share of same farms in the endogenous and exogenous approaches ranked top of the conversion selection list in the eu by farm specialization and economic farm size. share of selected farms overlapping in both approaches in q1 (%) share of selected farms overlapping in both approaches in q1 and q2 (%) farm specialization specialist cereals, oilseed, protein crops (15) 1% 18% specialist other field crops (16) 3% 32% specialist horticulture (20) 6% 38% specialist wine (35) 26% 74% specialist orchards fruits (36) 1% 7% specialist olives (37) 49% 97% permanent crops combined (38) 12% 67% specialist milk (45) 1% 10% specialist sheep and goats (48) 6% 34% specialist cattle (49) 0% 10% specialist granivores (50) 1% 7% mixed crops (60) 8% 49% mixed livestock (70) 5% 15% mixed crops and livestock (80) 2% 14% economic farm size small farms 5% 31% medium sized farms 5% 26% large farms 4% 21% total 5% 25% notes: the table shows the share of overlapping farms ranked in q1 and q2 in both endogenous and exogenous approaches. q1 and q2 refer to the first and second quantile of the ordered distribution of the two approaches. the farms that belong to the top two quantiles are likely to be selected to convert to organic farming. small farms: includes commercial farms with a standard output of less or equal to 25,000 euros; medium farms: standard output greater than 25,000 euros and less or equal than 100,000 euros; large farms: standard output greater than 100,000 euros. https://doi.org/10.36253/bae-13925 272 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni in the aggregate output value compared to the baseline: 2.9% for the eu target and 2.8% for the ms target. the output value increases is driven by the organic price premium, which more than offsets the reduction in the output quantity resulting from the switch to organic production. in contrast, the exogenous approach results in a decrease in the aggregate output value for both ms and eu level targets: -2.3% for the eu target and -2.2% for the ms target. this implies that, in the exogenous approach, the organic price premium does not fully offset the reduction in the output quantity caused by the switch to organic production (table 2). regarding production costs, they generally decrease across the simulated scenarios compared to the baseline. the exception is livestock feed costs for the endogenous approach, which show a slight increase (table 2). the cost reduction across simulated scenarios is primarily driven by lower expenditure on fertilizers and plant protection in the organic production system. in the endogenous approach, the cost reduction reinforces the increase in output value thus contributing to an improvement in farm income in both f2f target scenarios. the production cost reduction in the exogenous approach is not sufficient to offset the decrease in output value, resulting in lower farm income in these scenarios compared to the baseline. overall, the eu-level target results in slightly more favourable aggregate income change (either more positive or less negative) for farms compared to the ms target, with a stronger effect observed in the endogenous approach. this outcome can be attributed to the differences in the farm selection process for conversion between the two scenarios: the eu target selects from a combined pool of all eu farms, whereas the ms target involves farm selection split by ms sub-pools. in other word, the eu target allows a more profitable allocation of organic land, enabling countries in which organic farming is more profitable to exceed the 25% target, while other countries remain below this threshold. when considering farm income across farm specializations, the impacts of the f2f target are relatively highly heterogeneous, with effects varying in magnitude and direction. for example, the specialist wine, specialist other field crops, and mixed livestock tend to perform better than the other specializations. similar to above, when comparing the income performance between the endogenous and exogenous approaches, the former approach generally yields more favourable results across different farm specializations, but both approaches result in heterogeneous impacts across farm groups. on the other hand, the income effects are more consistent in magnitude and direction across economic size classes. under the endogenous approach, all economic size classes experience an improvement in income, while the exogenous approach results in negative impacts. small and/or large farms tend to be more affected than medium-sized farms (table 3). these income effects across farm types depend on a combination of performance-related factors that undergo change when farms convert to the organic production system. these factors encompass changes in yields, organic price premiums, and variable costs. the estimations provided in section 2 reveal that they vary across regions, products, and farm types. the actual income effect of the f2f target is, therefore, contingent on the importance of specific product and cost types within different farm groups. additionally, the proportion of farmers selected for conversion within a specific group plays a significant role. specifically, farms groups with organic conversion resulting in lower yield reductions (e.g. permanent crops, fodder crops), higher price premiums (e.g. vegetables, sugar beet, pork, poultry and sheep/goats meat), greater cost reductions (e.g. specialist other field crops, specialist cereals, oilseed, protein crops, mixed livestock) and smaller proportion of converted farms (e.g. mixed crops, specialist cattle, large farms) tend to experience lower adverse impact or achieve more favorable income effects compared to other farm groups. however, the varying importance of these factors and the offsetting effects between them (e.g. reduction in variable costs versus reduction in yields) across farm groups determine the actual income outcome, which makes it complex to identify more specific income patterns across farm groups. table 2. simulated impacts of ms and eu organic targets on aggregate farm income, output value and costs in the eu (% change to the baseline) targets set at eu level targets set at ms level endogenous exogenous endogenous exogenous farm income (excl. organic payments) +3.8% -1.2% +3.6% -1.3% output value +2.9% -2.3% +2.8% -2.2% crops specific costs -5.3% -2.7% -4.9% -3.7% livestock feed costs +0.7% -5.1% +0.7% -4.0% https://doi.org/10.36253/bae-13925 273modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 figure 1 shows more disaggregated results on the distribution of the farm income change among converted farms for both the ms and eu organic targets, as well as for the two conversion selection approaches. the distribution of farm income change in the endogenous approach is shifted to the right, with most farms (more than 90% of converted farms) experiencing an improvement in income in both targets. in contrast, the distribution for the exogenous approach is shifted to the left and the negative income change tends to predominate among converted farms (for more than 50% of converted farms) in both targets. as discussed previously, these results are explained by the fact that the endogenous approach selects better-performing farms for conversion, whereas the exogenous approach considers both monetary and non-monetary factors, resulting in the selection of less profitable farms, as shown in figure 1. table 4 shows more detailed results on the changes in aggregate production quantity for the main crop and animal products. as expected, the production quantity decreases for most crop and animal products (between -0.5% and -15%) in the simulated scenarios compared to table 3. simulated impacts of ms and eu organic targets on aggregate farm income in the eu by farm specialization and economic size (% change to the baseline). targets set at the eu level targets set at μs level endogenous exogenous endogenous exogenous farm specialization specialist cereals, oilseed, protein crops (15) 4.7% -0.4% 4.2% -0.3% specialist other field crops (16) 6.4% 0.1% 5.8% 0.0% specialist horticulture (20) 2.9% 1.1% 2.8% 0.2% specialist wine (35) 10.1% 4.5% 9.4% 4.7% specialist orchards fruits (36) 0.1% -5.1% 0.1% -5.7% specialist olives (37) 2.4% 2.5% 2.3% 1.3% permanent crops combined (38) 1.6% 0.5% 1.5% -0.3% specialist milk (45) 0.3% -2.0% 0.3% -1.7% specialist sheep and goats (48) 2.2% -3.8% 1.7% -2.4% specialist cattle (49) 0.1% -2.2% 0.0% -3.3% specialist granivores (50) 7.6% -5.6% 7.4% -6.0% mixed crops (60) 4.2% 0.2% 3.7% -0.6% mixed livestock (70) 7.4% 0.2% 7.3% 0.3% mixed crops and livestock (80) 10.6% -1.4% 10.5% -1.1% economic farm size small farms 3.70% -0.60% -1.50% 3.40% medium sized farms 3.30% -1.10% -1.00% 3.00% large farms 4.10% -1.40% -1.30% 3.90% figure 1. probability density of the farm income change of converted farms in the eu in the ms and eu organic targets (% change to the baseline). https://doi.org/10.36253/bae-13925 274 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni baseline due to the generally lower yields achieved following farm conversion to an organic production system. these changes tend to be more pronounced for the ms target than for the eu target, contributing to the more adverse income effects observed for the ms target compared to the eu target reported in table 2. production effects are somehow different between the endogenous and exogenous approaches, with permanent crops and animal products having smaller decreases in the former than the latter approach. this result is expected because, by design, the endogenous approach selects better-performing farms for conversion compared to the exogenous approach. for arable crops, the results are mixed between the endogenous and exogenous approaches, although the production changes tend to be greater in the former than the latter approach (table 4). these differences in production changes are driven by the types of farms selected in a given approach. in the endogenous approach, farms specialized in some arable crops (e.g. field crops) are selected to a greater extent than in the exogenous approach. the reverse is valid for some permanent crops and animal activities (e.g. specialist wine and specialist milk), where a greater share of farms tend to be selected in the exogenous than in the endogenous approach. additionally, the exogenous approach selects farms for conversion that share similar non-monetary characteristics with organic farms, including factors related to production structure. consequently, they are expected to be less affected by certain organic requirements, such as crop rotation and nitrogen management, resulting in a smaller adjustment in arable crop area and overall production levels. in contrast, the endogenous approach selects the best-performing farms for conversion, which may not necessarily resemble organic farms in these non-monetary characteristics. this, among other factors, is expected to have a less adverse impact on the economic variables of these farms (e.g., potentially resulting in lower yield reductions). however, it leads to a more significant adjustment in the allocation of arable crop area (and thus overall production levels) to ensure compliance with crop rotation and nitrogen management requirements. table 4. simulated impacts of ms and eu level organic targets on aggregate production quantity in the eu (% change to the baseline). targets set at the eu level targets set at μs level endogenous exogenous endogenous exogenous soft wheat -8.7% -3.5% -7.8% -5.8% barley -9.0% -3.4% -9.2% -5.2% other cereals -1.0% -2.3% -2.6% -3.5% grain maize -6.2% -3.5% -4.8% -4.8% soybean 0.4% 0.6% 0.7% 0.5% pulses -3.8% -2.5% -4.1% -5.3% sunflower seed 2.7% -1.2% 4.3% -2.6% rape seed -2.2% -1.7% -1.1% -6.4% potatoes -11.7% -5.6% -12.0% -9.8% vegetables -4.9% -4.7% -5.2% -6.7% fodder maize -1.4% -4.2% -2.8% -6.5% fodder other 1.4% -0.5% 1.5% -0.2% permanent grass -0.3% -1.9% -1.2% -3.1% table wine -10.2% -5.0% -9.4% -3.9% apples and pears -0.4% -8.4% -0.5% -13.1% berry species -0.7% -6.4% -1.0% -22.2% citrus fruits -0.4% -8.9% -0.4% -5.7% olive oil -4.2% -5.2% -3.9% -2.2% cow milk for sales -0.2% -2.8% -0.7% -3.3% beef -0.9% -4.3% -1.6% -5.7% sheep & goat milk -0.7% -16.8% -0.4% -11.4% sheep & goat meat -0.1% -7.1% -0.2% -6.2% pork meat 0.0% -4.2% -0.3% -5.2% poultry meat -0.4% -12.6% -0.4% -7.8% eggs -3.9% -9.0% -3.8% -10.5% https://doi.org/10.36253/bae-13925 275modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 among specific products, only soybean, sunflower and other fodder exhibit production increases in at least endogenous approach. these positive effects are largely driven by the rotation requirement in organic farming to replace main crops with smaller ones, such as soybean and sunflower. additionally, the feed self-sufficiency condition requires a higher proportion of on-farm feed production for animals, such as soybean or other fodder, in organic farming. in contrast, most other products experience a decrease in production quantity across all scenarios. in the case of the animal sector, all products are negatively affected, with less heterogeneity observed compared to the crop sector (table 4). this reduced variability in production changes across animal products may result from lower variation in the organic production-related parameters across different animal activities, especially yield decreases in organic animal production. furthermore, organic behavior constraints may have a less differential impact across animal categories compared to crops32. 5. discussion and conclusions this paper presents the modelling of organic farm conversion in an individual farm-level model (ifmcap) aiming to study the methodological challenges related to modelling specific farm selection into organic production and the parametrization of the converted farms. the developed model is applied to simulate economic impacts of the organic area targets adopted in the eu’s f2f strategy. the paper’s main contribution to the literature lies in providing a framework for modeling organic farm conversion within an eu-wide individual farm model (ifm-cap) and bringing quantitative insights into the potential income and production effects of reaching the 25% organic target in the eu. the results show that the simulated economic impacts based on individual farm model for the f2f organic target strongly depends on modelling assumptions, with implications that appear to be more significant than whether the organic target is set in the eu or ms level. model simulations of the f2f organic target using the exogenous approach – under which the combination of monetary and non-monetary drivers determine farm conversion – result in more adverse aggregate farm income effects and a greater decrease in aggregate production value compared to the endogenous approach – under which 32 for example, the maximum stocking density requirement imposes constraints on all animal categories (represented in lsu), while the nitrogen management requirement affects only specific crops, namely nitrogen-fixing crops. profitability drives the farm conversion. these divergent result are driven by the fact that each approach tends to select different farms for conversion. in the endogenous approach, conversion to organic production significantly increases farm income for many farms that undergo conversion (for more than 90% of converted farms). conversely, the exogenous approach shows negative income change for most converted farms (for over 50% of converted farms). while the f2f target may not necessarily have an adverse effect on the aggregate production value (especially in the endogenous approach) due to the organic price premiums offsetting the impact, the lower yields in organic production systems are expected to lead to a decrease in production quantity for most eu crop and animal products, ranging from -0.5% to -15%. the literature on the profitability of organic farms presents mixed findings, often suggesting that organic farms have similar profitability levels to conventional farms. this implies that price premiums of organic products may offset the higher costs and lower yields associated with organic production (alvarez, 2021; de ponti et al., 2012; offermann & nieberg, 2000; seufert et al., 2012). hence, the positive income effect simulated in the endogenous approach raises the question about its accuracy in modeling farmers’ conversion decisions. moreover, the fact that farms are conventional in the baseline, yet the organic production is profitable in the endogenous approach, further highlights concerns about potential inaccuracies in capturing farmers’ conversion decisions. this may suggest that certain behavioral effects of organic conversion, such as non-monetary factors that entail costs and benefits for converting farms (e.g., farmers’ education and experience, willingness to adopt new technologies, access to organic markets), may not have been adequately accounted for. in contrast, the exogenous approach aligns more closely with the literature’s findings on simulated income changes and the role of non-monetary factors as essential drivers of farm conversion decisions to organic production (canavari et al., 2022; sapbamrer, 2021; serebrennikov et al., 2020; willock et al., 1999). however, the exogenous approach may reduce the role of profitability in influencing farmers’ conversion decisions, as conversion probabilities are estimated based on both non-monetary and monetary factors. consequently, this approach leads to lower responsiveness of the organic conversion to changes in profit-related incentives such as organic price premiums or subsidies. for instance, scenario simulations run with varying levels of organic payments is expected to yield a relatively minor response in terms of organic conversion under the exogenous approach, while the endogenous approach demonstrates https://doi.org/10.36253/bae-13925 276 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni a more significant impact. additionally, the exogenous approach does not consider endogenous conversion choice, within the model which limits its applicability for policy impact simulations involving various types of subsidies (e.g. different types of environmentally related subsidies relevant to the cap and f2f strategy) and their interactions. overall, both the endogenous and exogenous approaches may have limitations in accurately capturing farmers’ conversion decisions. the two approaches represent different ways of modeling the organic conversion decision. while the former assumes farm conversion solely based on profitability, the latter relies on exogenously introduced non-monetary and monetary drivers. an approach that combines both non-monetary and monetary factors in an endogenous manner appears more promising. such an approach would require linking unobserved costs and benefits associated with nonmonetary drivers to observed costs and benefits (profits). however, deriving these unobserved costs and benefits presents a significant theoretical and empirical challenges when integrating the two approaches (esposti, 2022; kuminoff & wossink, 2010). while we have implemented the organic conversion selection in an individual farm model, it is important to note that this issue is relevant to other modeling methods as well. for instance, when modeling the organic target with a partial equilibrium model, it becomes necessary to introduce appropriate supply shocks. this process involves implicit assumptions about the share of different activities that will switch to organic production, along with the magnitude of yield and cost changes for each activity. essentially, this assumption indirectly represents the farm selection process in an individual farm model. in essence, the selection approach used in an individual farm model explicitly determines which types of farms are more likely to convert to organic production. however, this is not an additional assumption compared to more aggregated models; instead, it offers greater transparency. therefore, modelling organic targets in aggregated models may benefit from integration with individual farm models to enhance the accuracy of organic conversion modeling. the findings of this paper have also some policy implications. the simulations show that a considerable share of farms experience a positive income change when converting to organic production (including in the exogenous approach). this result aligns with the findings of kerselaers et al. (2007) for belgium, who estimate a sizable positive ‘economic conversion potential’33 33 they define ‘economic conversion potential’ as ‘the potential difference in individual farm income obtained under the current conventioncompared to the conventional production system. these findings indirectly suggest the presence of non-monetary factors that may constrain farms from converting. therefore, in the context of the f2f strategy’s objective of promoting organic production, it may be necessary for the policy mix to address non-monetary factors (e.g., training, networking, and market access) in addition to providing monetary incentives. this approach could enhance the f2f strategy’s effectiveness in achieving its goal of reaching 25% organic area in the eu. the paper’s findings suggest that the f2f organic target could have significant implications for food security. simulations indicate a potential substantial decrease in the production of major crop and livestock products within the eu. to fully assess its impact on global food security – including the overall supply of agricultural commodities, market impacts, and access to food for vulnerable consumers – conducting further analysis using global market models is essential. this becomes particularly important in the current global context marked by food inflation and the ongoing war in ukraine (european commission, 2023). when drawing conclusions from our findings, it is necessary to recognize the assumptions inherent in our model. first, our simulation results are conditional on the assumption that the organic price premiums over conventional products remain unchanged from the current (pre-target) level. however, an increased supply of organic products could potentially lead to a decrease in the price premiums, potentially impacting farm income more adversely than simulations suggest. second, our model assumes a fixed farm structure, meaning that farms’ production specialization and size remain unchanged following conversion to organic production. in reality, converted farms may make more significant adjustments in production structure and scale than model accounts for. a third potential caveat is that our analysis does not include market price feedback effects. the substantial production decrease simulated for the f2f organic target is expected to raise market prices, impacting farm income. consequently, our model may understate income increases in the endogenous approach and overstates income decreases in the exogenous approach. fourth, the exogenous approach in our study only considers factors affecting farm organic conversion that were observed in fadn. however, as literature suggests, there are several other drivers not available in the fadn that may impact organic conversion decisions, such as farmers’ knowledge and skills about organic production methods, access to organic markets, or organic al production mode and an estimated income under organic production mode’. https://doi.org/10.36253/bae-13925 277modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 certification costs. these factors would need to be incorporated into future analyses when data become available. finally, our analysis focuses solely on the economic impacts of the organic targets. future research needs to extend the analysis to include environmental impacts. this would allow for a more comprehensive investigation of the trade-offs between economic and environmental effects in the transition of the eu farming sector towards greater adoption of organic production. addressing these limitations and conducting further research will enhance the robustness of our results and provide a more complete understanding of the eu-wide impacts of organic targets set in the f2f strategy. disclaimer the authors are solely responsible for the content of the paper. the views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the european commission. references acs, s., berentsen, p. b. m., & huirne, r. b. m. 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https://doi.org/10.17221/82/2013-agricecon 281modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 appendix table a5. estimated median percentage difference in the expected crop prices between organic and conventional farming in the eu. central europe north central europe south northern europe southern europe uk & ireland wheat +60% +45% +20% +6% +72% maize +59% +39% +35% +8% +35% other cereals +48% +26% +11% +5% +57% oilseeds +31% +27% +13% +8% +20% sugar beet +2% +100% +51% +51% +51% vegetables +56% +114% +30% +19% +98% fruits +39% +34% +37% +11% +13% other permanent crops +30% +49% +8% +20% +16% fodder crops +24% +5% +5% +1% +5% source: own econometric estimations. note: of the 463 organic price coefficients estimated at fadn region level, 68% are statistically significant at 90% confidence level. central europe north: belgium, luxemburg, netherlands, germany, poland. central europe south: austria, czech republic, france, hungary, slovakia, romania. northern europe: sweden, finland, estonia, lithuania, latvia, denmark. southern europe: bulgaria, croatia, cyprus, greece, italy, malta, portugal, spain, slovenia. table a6. estimated median percentage difference in the expected crop yields between organic and conventional farming in the eu. central europe north central europe south northern europe southern europe uk & ireland wheat -44% -34% -41% -12% -56% maize -32% -22% -20% -5% -20% other cereals -43% -34% -32% -16% -45% oilseeds -57% -32% -42% -11% -35% vegetables -42% -44% -41% -11% -76% sugar beet -2% -22% -12% -12% -12% fruits -51% -57% -36% -22% -64% other permanent crops -9% -21% -5% -12% -4% fodder crops -16% -5% -10% -4% -9% source: own econometric estimations. note: of the 550 organic yield coefficients estimated at fadn region level, 77% are statistically significant at 90% confidence level. https://doi.org/10.36253/bae-13925 282 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni table a7. ranges of percentage differences in estimations variable crop production costs between organic and conventional farms by farm specialization and region in the eu. seeds/ha fertilizers/ha crop protection/ha other costs/ha max min max min max min max min per farm specialization specialist cop (15) -4% +18% -91% -31% -88% -18% -24% +57% specialist other field crops (16) -15% +78% -71% -17% -99% -13% -26% +9% specialist horticulture (20) -25% -2% -15% -4% -29% +2% -7% +82% specialist wine (35) -30% +3% -19% +25% -21% -13% +1% +16% specialist orchards fruits (36) -24% +31% -47% -14% -41% -19% -24% +8% specialist olives (37) -3% -3% -7% -7% -19% -19% +2% +2% permanent crops combined (38) -11% +5% -31% -8% -13% -12% -37% +12% specialist milk (45) -10% +13% -52% -11% -54% -12% -5% +35% specialist sheep and goats (48) -9% +22% -81% -16% -33% +2% -10% +21% specialist cattle (49) -14% +42% -60% -5% -50% -3% -10% -2% specialist granivores (50) -32% +5% -39% -20% -67% +18% -17% +98% mixed crops (60) -19% -1% -45% -17% -40% -18% -46% +4% mixed livestock (70) -8% +2% -46% -18% -52% -21% -56% +39% mixed crops and livestock (80) -10% +5% -80% -16% -70% -16% -13% +6% per region central europe north -32% +5% -49% +25% -52% -13% -17% +57% central europe south -17% +31% -63% -4% -67% -12% -46% +82% northern europe -25% +19% -52% -11% -54% +2% -56% 0% southern europe -30% +9% -41% -3% -41% +18% -9% +98% uk & ireland -24% +78% -91% -15% -99% -22% -24% +35% source: own econometric estimations. note: estimations performed by region, type of farming, and cost item. given the numerous cost combinations estimated and to facilitate result visualization, the table presents minimum and maximum median values for each cost group. of the 1,748 organic coefficients estimated, 55% are statistically significant at 90% confidence level. table a8. estimated median percentage difference in the expected livestock price between organic and conventional farming in the eu. central europe north central europe south northern europe southern europe uk & ireland beef meat +5% +7% +15% +4% +4% dairy milk for sale +26% +12% +8% +4% +22% eggs/laying hens +44% +7% +16% +32% +25% pork meat +93% +29% +113% +78% +78% poultry meat +45% +45% +45% +45% +45% sheep/goats milk for sale +4% +8% +4% +1% +4% sheep/goats meat for fattening +29% +29% +29% +29% +29% source: own econometric estimations. note: for milk, 65% of the 60 estimated coefficients for prices are significant at 90% confidence level. for other livestock activities, approximately 52% of the estimated coefficients for prices were significant at 90% confidence level. https://doi.org/10.36253/bae-13925 283modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 table a9. estimated median percentage difference in the expected livestock yields between organic and conventional farming in the eu central europe north central europe south northern europe southern europe uk & ireland beef meat -26% -29% -15% -10% -18% dairy milk for feeding -9% -15% -6% -10% -10% dairy milk for sale -20% -18% -10% -8% -14% eggs/laying hens -0.1% -7% -10% -7% -6% pork meat -3% -18% -32% -18% -18% poultry meat -10% -10% -10% -10% -10% sheep/goats milk for feeding -14% -14% -14% -14% -14% sheep/goats milk for sale -14% -14% -14% -14% -14% sheep/goats meat for fattening -10% -10% -10% -10% -10% female calves -1% -1% -1% -1% -1% male calves -1% -1% -1% -1% -1% source: own econometric estimations, except poultry meat (gaudaré et al., 2021). note: for milk, 65% of the 60 estimated coefficients yields are significant at 90% confidence level. for other livestock activities, approximately 32% of the estimated coefficients for yields were significant at 90% confidence level. table a10. thresholds of livestock units per hectare provided in the eu organic regulation 2018/848. animal activity regulation threshold (lsu per ha) land usage coefficient (ha per lsu) dairy cows 2 0.5 other cows 2.5 0.4 breeding heifers 2.5 0.4 cull dairy cows 2 0.5 calves for fattening 5 0.2 ewes 13.3 0.075188 pigs for fattening 14 0.071429 breeding sows 6.5 0.153846 laying hens 230 0.004348 table chickens 580 0.001724 source: eu organic regulation 2018/848 and own calculations (last column). table a7. covariates used in the prediction of the likelihood to convert. name type description class frequency/summary statistics mean std. dev. region class fadn region dummies 10,20,30,…,862 tf14 class dummies for the 14 fadn classes of type of farming 15(0.168), 16(0.105), 20(0.055), 35(0.051), 36(0.049), 37(0.015), 38(0.013), 45(0.173), 48(0.050), 49(0.086), 50(0.051), 60(0.033), 70(0.026), 80(0.118) activities numeric share of the total agricultural area by production activity. additionally, the share of cereals is interacted with all other activities. in total 24 activities 0.27 0.44 livestock class dummy for the presence/absence of livestock activities 0.58 0.49 max share crop detailed numeric maximum share of the major crop according to fadn activities 0.59 0.24 https://doi.org/10.36253/bae-13925 284 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni name type description class frequency/summary statistics mean std. dev. max share crop aggregate numeric maximum share of the major crop according to ifmcap activities 0.71 0.21 shannon numeric shannon index of crop biodiversity 0.98 0.57 share uaa owned numeric share of owned utilized agricultural area 0.53 0.38 regional land rent numeric regional average rental price of agricultural land per hectare 202.99 192.05 uaa numeric total utilized agricultural area 101.62 283.92 siz6 class classes of economic size 1(0.046), 2(0.173), 3(0.178), 4(0.193), 5(0.321), 6(0.087) typown class type of ownership of the farm 1(0.809), 2(0.114), 3(0.072), 4(0.003) altitude class altitude class of the holding 1(0.654), 2(0.230), 3(0.091), 4(0.023) anc3 class classes of areas with natural constraints 1(0.482), 2(0.367), 3(0.150) total awu ha numeric total annual working units per hectare 2.95 8.35 share unpaid awu numeric share of awu of family workers 0.80 0.30 lu/ha numeric livestock density 8.46 769.03 irrsys class type of irrigation system 0(0.792), 1(0.047), 2(0.060), 3(0.087), 4(0.011) fixed assets/ha numeric fixed assets per hectare in eur 28,930.85 1,634,160.85 mfp numeric multifactor productivity measured as total output value divided total input costs 1.26 0.82 decoupled/ ha numeric decoupled payments per hectare 269.17 1,298.88 coupled/ha numeric coupled payments per hectare 103.65 1,712.24 environment/ha numeric environmental payments per hectare 60.71 3,695.92 lfa/ha numeric payments for least favoured areas per hectare 37.94 186.54 other/ha numeric other rdp payments per hectare 32.69 4,927.54 investments/ha numeric payments for investments per hectare 86.77 9,779.52 organic wheat yield ratio numeric ratio between the yield of wheat for organic and for conventional farms in the fadn region 0.62 0.35 organic maize yield ratio numeric ratio between the yield of maize for organic and for conventional farms in the fadn region 0.61 0.39 organic milk yield ratio numeric ratio between the yield of milk for organic and for conventional farms in the fadn region 0.72 0.36 regional share organic numeric share of organic farms in the region 0.10 0.09 fertilizers/ha numeric expenditure per hectare in fertilizers 352.58 5,285.69 pesticides/ha numeric expenditure per hectare in pesticides 232.45 1,490.94 relative fertilizers/ha numeric expenditure per hectare in fertilizers relative to the expenditure of farms of the same organic status, tf14 and region 1.13 4.56 relative pesticides/ha numeric expenditure per hectare in pesticides relative to the expenditure of farms of the same organic status, tf14 and region 1.10 3.13 note: for more information about fadn classes, please refer to the fadn farm return. for more information about the choice of indicators, please refer to supplementary material part d. for class variables, except region, the code of the classes is presented together with its relative frequency in parenthesis. https://doi.org/10.36253/bae-13925 285modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 table a8. comparisons of the prediction accuracy metric of estimated models in the exogenous approach. lp lp + ssa logit logit + ssa probit probit + ssa random forest maximum prediction accuracy selected model belgium 0.8096 0.8053 0.9014 0.8017 0.8709 0.8066 0.9411 0.9411 random forest cyprus 0.8102 0.8148 0.8497 0.8443 0.8504 0.8435 0.8993 0.8993 random forest czechia 0.8563 0.8556 0.9424 0.9213 0.8191 0.9082 0.9653 0.9653 random forest germany 0.9273 0.9275 0.9301 0.9282 0.9293 0.9291 0.9725 0.9725 random forest greece 0.7228 0.7224 0.7543 0.7449 0.6187 0.5984 0.914 0.914 random forest spain 0.7597 0.7583 0.7683 0.7664 0.7691 0.7676 0.928 0.928 random forest estonia 0.8305 0.828 0.8029 0.9354 0.7705 0.7399 0.9653 0.9653 random forest france 0.7067 0.7054 0.7253 0.5933 0.7241 0.7225 0.9251 0.9251 random forest croatia 0.8499 0.849 0.8526 0.8462 0.8482 0.8416 0.9139 0.9139 random forest hungary 0.7498 0.7434 0.7922 0.7607 0.6312 0.7714 0.8781 0.8781 random forest ireland 0.8366 0.839 0.8831 0.9843 0.8523 0.9841 0.9526 0.9843 logit + ssa lithuania 0.9676 0.9679 0.9762 0.9743 0.9511 0.9697 0.9801 0.9801 random forest luxemburg 0.9389 0.9404 0.9905 0.9816 0.8846 0.9783 0.9802 0.9905 logit latvia 0.8983 0.8967 0.8848 0.9361 0.9212 0.9322 0.9835 0.9835 random forest italy 0.8032 0.8017 0.7232 0.8036 0.7173 0.8019 0.8972 0.8972 random forest netherland 0.7476 0.755 0.7993 0.778 0.7951 0.7728 0.9507 0.9507 random forest austria 0.9007 0.9001 0.9006 0.9134 0.9003 0.907 0.9472 0.9472 random forest poland 0.8662 0.8655 0.9377 0.9353 0.6075 0.9293 0.9692 0.9692 random forest portugal 0.7636 0.7622 0.7776 0.7748 0.5243 0.7627 0.9411 0.9411 random forest romania 0.7314 0.7284 0.5263 0.7617 0.6627 0.7576 0.9022 0.9022 random forest finland 0.9186 0.9154 0.8801 0.9288 0.872 0.9248 0.9801 0.9801 random forest sweden 0.804 0.8021 0.7745 0.7417 0.8374 0.852 0.9561 0.9561 random forest slovakia 0.8053 0.796 0.845 0.836 0.835 0.5953 0.8997 0.8997 random forest slovenia 0.9162 0.9172 0.9456 0.9439 0.917 0.9375 0.9636 0.9636 random forest bulgaria 0.7483 0.7512 0.6083 0.6291 0.6631 0.5099 0.8783 0.8783 random forest denmark 0.9668 0.9668 0.9775 0.9759 0.9766 0.9747 0.984 0.984 random forest eu 0.7288 0.573 0.5449 0.9367 0.9367 random forest https://doi.org/10.36253/bae-13925 286 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni table a9. the distribution of selected farms for conversion in the exogenous and endogenous approaches in the eu and ms organic targets in the eu by farm specialization and economic farm size (% of farms by farm specialization and size).   targets set at eu level  targets set at ms level endogenous exogenous endogenous exogenous farm specialization specialist cereals, oilseed, protein crops (15) 17% 11% 16% 10% specialist other field crops (16) 5% 3% 7% 4% specialist horticulture (20) 20% 10% 19% 6% specialist wine (35) 0% 10% 0% 8% specialist orchards fruits (36) 10% 12% 11% 6% specialist olives (37) 2% 6% 3% 2% permanent crops combined (38) 1% 6% 3% 10% specialist milk (45) 4% 9% 3% 10% specialist sheep and goats (48) 0% 8% 1% 10% specialist cattle (49) 2% 1% 2% 1% specialist granivores (50) 7% 7% 6% 6% mixed crops (60) 2% 1% 2% 2% mixed livestock (70) 12% 9% 12% 13% mixed crops and livestock (80) 17% 9% 16% 11% total 100% 100% 100% 100% economic farm size small farms 63% 59% 62% 64% medium sized farms 22% 28% 24% 23% large farms 15% 13% 15% 13% total 100% 100% 100% 100% https://doi.org/10.36253/bae-13925 287modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 supplementary material part a: literature review on drivers and impacts of organic conversion regarding the literature relevant to the methodological challenges of modeling organic production in an individual farm model, we recognize two main strands of analysis. the first strand deals with the drivers of conversion to organic farming. its findings are relevant to designing the approach to model farm conversion from conventional to the organic production system. the second strand compares the organic farm performance and organic farm management practices with the conventional ones. the findings from this strand of literature are relevant for the parametrization of converted organic farms in terms of yields, price, input costs, and management practices differences from conventional farms. 6.1 drivers of conversion to organic farming the economic literature has primarily applied empirical analyses to identify the main drivers of organic farming conversion; theoretical literature is minimally used or not widely applied. the main reasons explaining this choice are (i) the complexity of modeling theoretically the process of adoption and diffusion of organic farming due to significant differences in the types of farming technologies applied across different farm types and regions, and (ii) the difficulties in accounting for less quantifiable drivers critical in explaining farmers’ conversion decision, such as believes and attitudes towards the environment (serebrennikov et al., 2020; willock et al., 1999). in order to study the likelihood of conversion to organic farming, the empirical literature has heavily relied on the use of probability models (basnet et al., 2018; burton et al., 1999; chatzimichael et al., 2014; chmielinski et al., 2019; djokoto et al., 2016; genius et al., 2006; hattam & holloway, 2005; läpple & rensburg, 2011; lohr & salomonsson, 2000; malá & malý, 2013; parra lópez & calatrava requena, 2005; serebrennikov et al., 2020). these models use a set of covariates to determine the conditional probability of adopting organic farming. they are typically used to investigate the causal effect of these covariates on the probability of conversion. there are a wide variety of available probability models applied to estimate drivers of organic farm conversion, such as the linear probability model, nonlinear probability models, such as logit and probit, and machine-learning approaches (e.g., decision trees and their applications)34. for investigating the likelihood of converting to organic farming, non-linear probability models have been the most widely used empirical tools (serebrennikov et al., 2020). an essential aspect of many studies on the adoption of organic farming is that they have often relied on tailored surveys with a relatively narrow geographical scope (bravo-monroy et al., 2016; burton et al., 1999; darnhofer et al., 2005; fairweather, 1999; hattam & holloway, 2005; kallas et al., 2009; lohr & salomonsson, 2000; parra lópez & calatrava requena, 2005; yu et al., 2014). this limited scope is likely because the drivers of adoption are highly site-specific and specific to the agricultural farming system and agricultural technology considered, as well as linked to farmers’ perceptions and attitudes that may also have a local dimension (sapbamrer, 2021; serebrennikov et al., 2020; willock et al., 1999). the findings from this literature suggest that although profit maximization (costs and benefits) impacts farmers’ decision to convert to organic farming, they are not necessarily the sole or primary drivers. instead, some key factors determining the adoption of organic farming are farm characteristics − such as farm size, production specialization, age of farmer –, access to organic buyers/markets, and farmer beliefs and attitudes towards the environment. overall, the main implication of the literature findings is that the conversion modeling cannot rely solely on profit maximization assumption, i.e., by considering only the costs and benefits of organic production and its difference from conventional farming. instead, it also needs to consider other non-profit maximization factors affecting farmers’ behavior. 6.2 performance and management practices of organic farming there is abundant literature analyzing the differences between organic and conventional production systems. many studies often use detailed micro datasets to analyze the performance difference empirically (e.g., yields, profitability) between organic and conventional farms (brenes-muñoz et al., 2016; froehlich et al., 2018; gillespie & nehring, 2013; kuminoff & wossink, 2010; kuosmanen et al., 2021; tiedemann & latacz-lohmann, 2013; uematsu & mishra, 2012; würriehausen et al., 2015; yu et al., 2014). another relatively large body of literature relies on case studies (i.e., using a small sample size) to identify differences between organic and conventional systems. some focus on management practices 34 these include bagging, random forest, and boosting (james et al., 2013). https://doi.org/10.36253/bae-13925 288 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni (bilsborrow et al., 2013; dobbs & smolik, 1997; greer et al., 2008; krause & machek, 2018; shah et al., 2017; white et al., 2019), and others on environmental aspects (chmelíková et al., 2021; hoffman et al., 2018; meier et al., 2015; perego et al., 2019; reimer et al., 2020). given the abundance of the literature, some other studies use meta-analysis techniques to quantify the differences between organic and conventional agriculture. several aspects have been examined, like yields (de ponti et al., 2012; seufert et al., 2012), crop rotations (barbieri et al., 2017), livestock management (gaudaré et al., 2021), productivity (alvarez, 2021), environmental impacts (mondelaers et al., 2009; tuomisto et al., 2012) and nutrient budgets (reimer et al., 2020), are examined. overall, the literature findings indicate that organic farms show lower performance in obtained crop yields, although results are highly heterogeneous across studies. similar findings hold for livestock productivity, although the gap seems to be lower than in the case of crop yields. organic products are usually found to receive price premia compared to conventional products. the findings regarding profitability are less conclusive, and organic farms are often found to show similar profitability levels as conventional farms implying that price premia of organic products may offset higher costs and lower yields of organic production (alvarez, 2021; de ponti et al., 2012; offermann & nieberg, 2000; seufert et al., 2012). a significant difference between organic and conventional farming is in the applied management practices. studies find that organic farms usually apply more crop rotations with longer duration, higher crop diversity, and evener crop species distribution (barbieri et al., 2017). also, livestock management is based on more farm-produced feed, a lower proportion of concentrate, and lower feed-use efficiency (gaudaré et al., 2021). part a: references alvarez, r. 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β1, β2 and β3 are parameters to be estimated. in the yield gap analysis the list of variables contained xit include organic status of the farm, year dummies, farm specialization, farm size, altitude of the farm, presence of natural constraints, the share of irrigated land. for livestock activities, we include the percentage differences in expected value of the performance indicator between organic and conventional farms can be obtained from the estimate of parameter to see how, equation (1) can be written as follows: (2) where , , and are the estimated parameters and is the expected value of the logarithm of the performance indicator. the difference between the logarithm of performance indicator between organic (orgit = 0) and conventional farms (orgit = 1) can be written as: (3) the logarithmic difference of equation (3) is only an approximation to the percentage difference in expected values between the organic and conventional farms. for an exact calculation of this percentage difference, the following transformation can be used (hill et al., 2011): (4) equation (4) is a non-linear function of the coefficient estimate and it has been used as percentage difference in yields and prices between organic and conventional farms. unit costs of crop production in contrast with prices and yield estimations, for unit costs we use a linear estimation model. this is a more appropriate approach than the log-linear one because several organic farms are associated with zero expenditure on some of the cost categories considered. the estimation has been conducted for the four types of variable cost categories k (k=1,…,4) used in the model. these categories are seeds, fertilizers, crop protection, and other crop specific costs, all expressed on a per-hectare basis. the model is represented as follows: ck,it = β1 + β2orgit + β’3xit + εit (5) https://doi.org/10.36253/bae-13925 293modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 where ck,it is the cost per hectare for input category k for farm i at time t, orgit is an indicator variable that takes value 1 if the farm is a fully organic at time t and zero otherwise, xit is a matrix that contains a set of explanatory variables, and εit is the error term of the equation; β1, β2 and β3 are parameters to be estimated. the list of variables contained in xit includes the organic status of the farm, year dummies, altitude class, areas with natural constraints, output value per hectare, share of unpaid labor in total labor, assets value per hectare, share of irrigated land, size in terms of hectares and livestock units. the percentage differences in expected value of the unit costs per hectare between organic and conventional farms can be obtained in a different way with respect to the methodology described in equation (4) the starting point is given by the following equation: (6) where , , and are the estimated parameters and is the expected value of the unit cost per hectare for input category k. the percentage difference between organic (orgit = 1) and conventional farms (orgit = 0) for this unit cost can be then obtained as follows: (7) where is a vector made of the averages of the variables contained in xit. mapping of econometric estimation categories to ifm-cap categories table b.3. mapping of fadn crop groups with crops and feed in ifm-cap used in estimations product fadn ifm-cap crop ifm-cap feed cereals all cereals excluding rice (kcer) rye (ryem), barley (barl), oats (oats), other cereals for the production of grain (ocer), rice (pari) distillers dried grains with solubles (ddgs) fruits fruits and berry orchards and citrus orchards (kfru) apples and pears (appl), citrus fruits (citr), peaches and nectarines (peac), berries (berr), nuts (nuts), other fruits (ofru) maize grain maize (cmz) maiz non-fruit permanent crops olive groves + vines+ permanent crop under glass + nurseries + other permanent crops + growth of young plantation (kopc) table wine (twin), table grapes (tagr), table olives (tabo), olive oil (oliv) oilseeds rapes (crape )+ sunflower (csnfl ) + soya (csoya ) + linseed (clinsed) + other oilseeds (ccrpoiloth) other oil (ooil), rapeseed (rape), sunflower (sunf), soya (soya), pulses (puls), other industrial crops (oind) soya cake (soyc), rapeseed cake (rapc), sunflower cake (sunc), rapeseed oil (rapo), soya oil (soyo), sunflower oil (suno) vegetables fresh vegetables melons and strawberry open field (cvegof) + fresh vegetables melons and strawberry market gardening (cvegmg) + fresh vegetables melons and strawberry under glass (cvegug) vegetables marketing garden (vgmg), vegetables open field (vgof), vegetables under glass (vgug), potatoes (pota) wheat common wheat (cwhtc) soft wheat (swhe), durum wheat (dwhe) grass grasses (kgra) other crops (ocro), maize for fodder (maif), fodder root crops (roof), other fodder crops (ofar) sugar beet sugar beet (csugbt) sugar beet (sugb) https://doi.org/10.36253/bae-13925 294 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni table b.4. mapping of region groups used in estimations and nuts0. peseta group (econometric estimation) nuts0 code central europe north be,lu, nl, de, pl central europe south at, cz, fr, hu, sk, ro northern europe se, fi, ee, lt, lv, dk southern europe bg, hr, cy, el, it, mt, pt, si, es uk & ireland ir part b: references hill, r.c. and griffiths, w.e. and lim, g.c. (2010). principles of econometrics, 4th edition, john wiley & sons, incorporated, isbn 9781118136966. part c: behavioral constraints crop rotations from an agronomic point of view, in order to substitute for no reliance on chemical fertilizers and plant protection, organic farming requires crop rotations (reganold & wachter, 2016; baker et al., 2020). indeed, barbieri et al. (2017), based on meta-analysis, found that on average at the global scale, organic rotations last for 4.5 ± 1.7 years, which is 15% more than their conventional counterparts, and include 48% more crop categories. below, we describe how we use this finding to elicit values for the flexibility constraints of the crop rotations in the ifm-cap model. first, we argue that the observed share of crop acreage35 is related to the duration of the crop rotation and the frequency that a crop appears, as follows: 1. for a given crop duration, the share of crop acreage is proportional to the frequency that the crop appears in the rotation (the less times the crop appears, the lower the acreage share). 2. for a given frequency that a crop appears in the rotation, the crop acreage is inversely proportional to the duration of the rotation (the more years the rotation cycle, the less the acreage share). 35 we need to define the following related concepts, as used by dury et al. (2012): crop acreage, refers to the area on a farming land normally devoted to one or a group of crops every year (e.g. x hectares of wheat, y hectares of winter barley). ifm-cap models crop acreage. crop allocation, is the assignment of a particular crop to each plot in a given piece of land. ifm-cap does not model in plot level, so crop allocation is not relevant. crop rotation is defined as the practice of growing a sequence of plant species on the same land. it is characterized by a cycle period. again, ifm-cap does not contain explicit plot level information and thus crop rotation, as defined here, cannot be represented. in order to establish the above arguments, we start from a farm that has a single 1-ha plot and follows a 2-year rotation where a crop appears once every two years. the probability of finding this crop in a random year will be 1/2, as in figure c.2. when we consider a farm that has more than one plot, we can deduce the relation between crop rotation and expected share of crop acreage by means of a binomial distribution36. for a specific year, the binomial’s independent experiment is checking a plot for a crop and the ‘success’ event is finding this crop. as shown above, the probability of success for a single plot is p=1/2. thus, for n independent experiments (i.e. n plots), as shown in figure c.3, the expected number of successes equals to [p]*[n], where p=1/2. the expected share of the central crop to the total utilized agricultural area, assuming 1-ha plots, equals to [p]*[n]/ [n]=[p]=1/2. 36 the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own boolean-valued outcome: success (with probability p) or failure (with probability q = 1 − p). figure c.2. the probability to find a crop in a 2-year fixed rotation that alternates with another crop. figure c.3. schematic of a farm with many plots and the relation of the crop acreage and the rotation frequency and length. https://doi.org/10.36253/bae-13925 295modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 we can generalize this finding for the case of fixed rotation lengths. the expected share of crop acreage will equal to where, pc the expected share of crop acreage of crop c, fc the number of appearances of the crop (frequency) and d the length (duration) of the rotation. in table c.1 we show the share of acreages for different combinations of crop duration and crop frequency. as mentioned above, organic farms have longer and more diversified rotations (barbieri et al., 2017). we interpret “longer and more diversified” rotation as rotations that have longer duration and with crops that are less frequently in the rotation. according to the argumentation already presented, both mean reduced acreage shares of crops, when the farm converts. however, there is a lower limit on the reduction of the acreage, related to the crop appearing at least one in the rotation the fadn data also supports the connection of “longer and more diversified” rotation to the crop acreage. in table c.3, we show the differences between the mean acreage shares between the organic and conventional fadn farms (and the corresponding 95% confidence interval). the cash crops in organic farms have a lower acreage share than in the conventional ones. we model the extensification of the rotation as a reduction on the current share of a crop. more specifically, we will introduce the following flexibility constraint to the farms that convert. where is the share of crop c in farm f when converted to organic, is the observed share of crop c in farm f when it is conventional and rf,c is a crop and farm-specific coefficient of share reduction related to the “longer and more diversified” rotation of a converted farm. for estimating, rf,c we consider that the crops with an area share at 20% or smaller of the total uaa, are already cultivated extensively (20% correspond to a rotation of once every five year,37 see table c.2). thus, a farm that converts to organic does not need to change the relative acreage allocation of those crops. only farms that have for some crops a share greater than 20% will need to reduce the area of these crops. thus, for a farm that belong to farm type tf (we use the notation of tf(f); i.e. the tf of f) , rf,c equals to: where, diffc,tf is the difference between the mean acreage shares between the organic and conventional fadn farms of the farm type (tf) that the farm belongs (as in table c.3; for non-significant differences, we set diffc,tf to zero); sharec,tf is the average share of crop c in the conventional farms of the tf farm type (as in table c.4). in table c.5, we give the for each farm type. for farm types where we did not see statistically significant differences, we set r = 0. since rf,c is farm specific, in table c.6 we give the percentage of farms that rf,c > 0, so that the reader knows the impact of this constraint. 37 this is in line with findings of barbieri et al. (2017). table c.1. characteristics of different crop rotations. duration of rotation (d) frequency of a crop (fc) frequency to duration share of acreage (pc) 3 1 1/3 0.33 2 2/3 0.66 4 2 2/4 0.50 3 3/4 0.75 5 2 2/5 0.40 3 3/5 0.60 4 4/5 0.80 6 2 2/6 0.33 3 3/6 0.50 4 4/6 0.66 5 5/6 0.83 7 2 2/7 0.28 3 3/7 0.42 4 4/7 0.57 5 5/7 0.71 table c.2. expected share of acreage for a crop that appears once in the rotation. duration of rotation (d) frequency of a crop (fc) share of acreage (pc) 3 1 0.33 4 1 0.25 5 1 0.20 6 1 0.16 7 1 0.14 8 1 0.12 https://doi.org/10.36253/bae-13925 296 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni nitrogen management nitrogen management is different between organic and conventional farms. in the conventional methods, inorganic/mineral fertilizers compensate for the soil nutrients removed through production. in organic farm management inorganic fertilizers are prohibited, and thus, soil fertility is maintained partially with adding organic fertilizers (mainly manure) and with crop rotation schemes, mainly green manure and nitrogen fixation from leguminous crops (chmelíková et al., 2021; lin et al., 2016; reganold & wachter, 2016). chongtham et al. (2017) using a structured interview survey, found that the majority arable farmers used perennial clover and grass crops as green manure (referred as ‘ley’) in their rotation. the ley crops were undersown in annual cereal crops and remained for at least one more year during which they were cut regularly to table c.3. difference between means of acreage shares; organic and conventional farms. soft wheat durum wheat barley grain maize fodder maize rape seed sugar beet sun flower potatoes specialist cop (15) -11.3%*** +9.8%** +2.2%ns -10.1%*** -1.1%ns -4.9%ns -5.5%ns -4.7%ns -0.4%ns specialist other field crops (16) -10.0%*** -2.5%ns -3.3%* -9.7%*** -6.2%ns -0.9%ns -7.1%*** -8.7%*** -7.4%*** specialist horticulture (20) -11.0%* na -2.9%ns -24.5%* na na na -16.0%** -5.4%** specialist wine (35) -4.0%ns -4.8%ns -4.0%* -10.7%* -9.4%ns +15.8%ns na na -0.5%ns specialist orchards fruits (36) -7.2%* -14.6%*** -5.1%ns -5.3%ns -7.5%ns na na -6.0%ns -0.1%ns specialist olives (37) -1.0%ns +9.8%ns +6.6%ns na na na na na +0.7%ns permanent crops combined (38) +2.6%ns -10.6%* -1.6%ns -3.1%ns na na na +13.5%ns -1.8%* specialist milk (45) -5.0%*** +2.9%ns -3.8%*** -10.5%*** -13.9%*** -4.3%*** -3.5%ns -9.3%** -1.2%*** specialist sheep and goats (48) -5.3%*** -5.6%** -7.0%*** -10.1%** -10.2%*** na na na -2.0%*** specialist cattle (49) -5.7%*** -4.5%ns -4.3%*** -10.5%*** -16.8%*** -2.7%*** na -8.2%*** -1.5%*** specialist granivores (50) -7.6%** -0.4%ns -2.7%ns -14.7%** -10.1%ns -6.8%* na +8.8%ns -4.0%ns mixed crops (60) -8.9%*** +0.9%ns -3.4%ns -14.7%*** na na na -7.4%ns -6.2%*** mixed livestock (70) -6.0%*** +5.3%ns -7.1%*** -19.3%*** -11.4%ns na na -8.3%ns -2.2%*** mixed crops and livestock (80) -7.7%*** +2.0%ns -3.9%*** -11.4%*** -3.4%ns -8.5%*** -4.9%* -14.1%*** -2.9%*** notes: the significance of the mean difference is based on a two-sided welch’s t-test. regarding the significance levels in the superscript: (ns) means a non-significant value; (*),(**) and (***) are 95%, 99% and 99.9% significance levels. na means that there was not enough number of observations to get a mean difference. table c.4. average shares of certain crops in conventional farms. soft wheat durum wheat barley grain maize fodder maize rape seed sugar beet sun flower potatoes specialist cop (15) 35.7% 36.1% 20.9% 29.4% 10.0% 22.0% 10.0% 26.0% 1.8% specialist other field crops (16) 29.3% 28.9% 18.3% 22.7% 21.8% 16.8% 18.4% 19.0% 21.9% specialist horticulture (20) 29.8% 0.0% 29.2% 32.7% 0.0% 0.0% 0.0% 21.8% 16.6% specialist wine (35) 20.5% 26.1% 17.8% 21.3% 20.9% 14.6% 0.0% 0.0% 3.8% specialist orchards fruits (36) 19.4% 28.3% 19.2% 19.9% 18.0% 0.0% 0.0% 14.0% 4.3% specialist olives (37) 15.3% 27.4% 23.9% 0.0% 0.0% 0.0% 0.0% 0.0% 1.2% permanent crops combined (38) 22.5% 29.4% 19.4% 18.2% 0.0% 0.0% 0.0% 15.7% 3.2% specialist milk (45) 13.8% 15.0% 11.9% 17.6% 23.6% 9.8% 8.9% 12.6% 2.4% specialist sheep and goats (48) 13.7% 17.4% 18.7% 19.7% 15.8% 0.0% 0.0% 0.0% 2.6% specialist cattle (49) 12.8% 17.0% 11.6% 14.4% 24.0% 9.0% 0.0% 11.6% 2.2% specialist granivores (50) 30.0% 32.5% 27.1% 35.3% 31.8% 17.4% 0.0% 24.5% 10.4% mixed crops (60) 26.0% 31.6% 23.0% 26.9% 0.0% 0.0% 0.0% 23.3% 14.3% mixed livestock (70) 18.2% 17.7% 16.2% 24.5% 20.0% 0.0% 0.0% 13.7% 4.1% mixed crops and livestock (80) 23.6% 22.4% 16.8% 23.6% 12.6% 16.2% 11.8% 18.9% 6.4% https://doi.org/10.36253/bae-13925 297modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 control weeds, and in some cases to sell hay or silage to neighboring farms. for dairy farmers, they report that ley was two or three years of ley followed by two years of cereals. this was a common scheme for beef and sheep farmers too. the same finding is present in watson c.a. et al. (2002). he says that in mixed systems, the rotations are most commonly based on ley/ arable rotations, where fertility is built during the ley phase, in which grazing and fodder production provide an economic return. finally, barbieri et al. (2017) finds through meta-analysis that at the global scale, organic rotations have fewer cereals and more temporary fodders. in addition, they find that organic rotations have 2.8 times more temporary fodder crops (such as alfalfa, clover, clover-grass, italian ryegrass, etc.) than conventional systems, which generally occupy land for an entire year. finally, for livestock systems, the use of permanent grassland (pastures and meadow) is also common (watson c.a. et al., 2002). modeling the farm’s nitrogen management is quite complex and requires information that is not available in fadn (küstermann et al., 2010; thomas, 2003). for this, we will not explicitly model the underlying mechanism of plot-level nutrient management. instead, we will focus on the increase of the share of nitrogen fixing crops through a data driven approach. the first step is to focus on the crops that relate to the nitrogen management decision of the farm. for ifmcap, these activities are: 1. soya (code: soya) 2. puls that is the aggregation of the following three fadn activities: ‘peas, field beans and sweet lupines’, ‘lentils, chickpeas and vetches’ and ‘other protein crops’. 3. ofar that is the aggregation of the following fadn activities: ‘temporary grass’, ‘green maize’ and ‘leguminous plants’. 4. fall that is the fallow land. 5. pgra that is the permanent grassland activity, corresponding to pasture and meadows that exist in the same plot for at least 5 years. when we compare the share of land devoted to these five activities between organic and conventional farms, we see statistically significant differences. table c.5. . reduction of share of crops when a conventional farm converts to organic (rtf,c). soft wheat durum wheat barley grain maize fodder maize rape seed sugar beet sun flower potatoes specialist cop (15) -31.8% 27.2% -34.5% specialist other field crops (16) -34.1% -17.9% -42.8% -38.6% -46.0% -33.6% specialist horticulture (20) -36.9% -75.0% -73.2% -32.5% specialist wine (35) -22.2% -50.2% specialist orchards fruits (36) -37.3% -51.4% specialist olives (37) permanent crops combined (38) -36.0% -57.6% specialist milk (45) -36.0% -31.8% -59.5% -59.1% -43.8% -74.1% -48.9% specialist sheep and goats (48) -38.8% -32.0% -37.5% -51.4% -64.4% -75.6% specialist cattle (49) -44.1% -36.9% -72.7% -70.2% -29.9% -71.2% -66.2% specialist granivores (50) -25.2% -41.6% -38.9% mixed crops (60) -34.4% -54.7% -43.0% mixed livestock (70) -32.9% -43.8% -79.0% -53.9% mixed crops and livestock (80) -32.8% -23.2% -48.1% -52.3% -41.8% -74.3% -45.3% notes: for empty cells, no reduction is applied, since the differences between organic and conventional farms were not significant. table c.6. percentage of farms with rtf,c > 0. soft wheat durum wheat barley grain maize fodder maize rape seed sugar beet sun flower potatoes 59.0% 25.9% 15.6% 49.2% 28.5% 8.0% 17.1% 8.8% 15.6% https://doi.org/10.36253/bae-13925 298 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni thus, we model the change in nitrogen management by means of flexibility constraint that is active in the case that the farm converts: where, n is the set of nitrogen fixing crops of the model (puls, ofar, soya,pgra and fall), and are the shares of crop c in farm f when in the organic and conventional status respectively, and nf is a farm specific coefficient related to the type of farming that the farm belongs. we calculate it as follow. where difftf(f) is the last column of table c.5 and sharetf(f) is the second column. part c: references barbieri, p., pellerin, s., & nesme, t. (2017). comparing crop rotations between organic and conventional farming. nature scientific reports, june, 1–11. https:// doi.org/10.1038/s41598-017-14271-6 chmelíková, l., schmid, h., anke, s., & hülsbergen, k. j. (2021). nitrogen-use efficiency of organic and conventional arable and dairy farming systems in germany. nutrient cycling in agroecosystems, 119(3), 337–354. https://doi.org/10.1007/s10705-021-10126-9 chongtham, i. r., bergkvist, g., watson, c. a., sandström, e., bengtsson, j., & öborn, i. (2017). factors influencing crop rotation strategies on organic farms with different time periods since conversion to organic production. biological agriculture and horticulture, 33(1), 14–27. https://doi.org/10.1080/0144876 5.2016.1174884 dury, j., schaller, n., garcia, f., bergez, a. r., & eric, j. (2012). models to support cropping plan and crop rotation decisions . a review. agronomy for sustainable development. https://doi.org/10.1007/s13593011-0037-x küstermann, b., christen, o., & hülsbergen, k. j. (2010). modelling nitrogen cycles of farming systems as basis of siteand farm-specific nitrogen management. agriculture, ecosystems and environment, 135(1–2), 70–80. https://doi.org/10.1016/j.agee.2009.08.014 lin, h. c., huber, j. a., gerl, g., & hülsbergen, k. j. (2016). nitrogen balances and nitrogen-use efficiency of different organic and conventional farming systems. nutrient cycling in agroecosystems, 105(1), 1–23. https://doi.org/10.1007/s10705-016-9770-5 reganold, j. p., & wachter, j. m. (2016). organic agriculture in the twenty-first century. nature plants, 2(february), 15221. https://doi.org/10.1038/ nplants.2015.221 thomas, a. (2003). a dynamic model of on-farm integrated nitrogen management. european review of table c.7. difference of acreage share for nitrogen management related crops between organic and conventional farms. conventional mean organic mean % difference organic-conventional specialist cop (15) 13.6% 35.0% +21.4%*** specialist other field crops (16) 21.9% 49.5% +27.5%*** specialist horticulture (20) 30.1% 29.9% -0.20%ns specialist wine (35) 58.1% 79.2% +21.1%*** specialist orchards fruits (36) 62.4% 74.7% +12.3%*** specialist olives (37) 46.3% 63.3% +16.9%*** permanent crops combined (38) 50.0% 59.4% +9.30%ns specialist milk (45) 64.5% 85.2% +20.7%*** specialist sheep and goats (48) 79.3% 87.3% +8.0%%*** specialist cattle (49) 74.3% 92.1% +17.8%*** specialist granivores (50) 23.6% 58.2% +34.6%*** mixed crops (60) 26.8% 46.7% +19.9%*** mixed livestock (70) 38.9% 75.5% +36.6%*** mixed crops and livestock (80) 30.2% 61.4% +31.2%*** notes: the significance of the mean difference is based on a two-sided welch’s t-test. regarding the significance levels in the superscript: (ns) means a non-significant value; (*),(**) and (***) are 95%, 99% and 99.9% significance levels. https://doi.org/10.36253/bae-13925 https://doi.org/10.1038/s41598-017-14271-6 https://doi.org/10.1038/s41598-017-14271-6 https://doi.org/10.1007/s10705-021-10126-9 https://doi.org/10.1080/01448765.2016.1174884 https://doi.org/10.1080/01448765.2016.1174884 https://doi.org/10.1007/s13593-011-0037-x https://doi.org/10.1007/s13593-011-0037-x https://doi.org/10.1016/j.agee.2009.08.014 https://doi.org/10.1007/s10705-016-9770-5 https://doi.org/10.1038/nplants.2015.221 https://doi.org/10.1038/nplants.2015.221 299modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 agricultural economics, 30(4), 439–460. https://doi. org/10.1093/erae/30.4.439 watson c.a., atkinson, d., gosling, p., jackson, l. r., & rayns, f. w. (2002). managing soil fertility in organic farming systems. soil use and management, 18(3), 239–247. https://doi.org/10.1079/sum2002131 part d: estimating conversion probabilities in the exogenous approach the proposed exogenous approach is based on estimation of the likelihood to convert to organic farming of individual farms. our main assumption is that the likelihood of conversion depends on the similarity of conventional farms with respect to organic ones: conventional farms that are more similar to organic ones are more likely to convert to organic farming. this assumption is consistent with the idea that farms that are already similar to exiting organic farms would need to make smaller adjustments to transition to organic production methods and at the same time capitalize on output price premiums and cap organic support. unlike exercises typical of the literature on adoption of organic farming (bravo-monroy et al., 2016; yu et al., 2014; kallas et al., 2009; parra lópez and calatrava requena, 2005; darnhofer et al., 2005; hattam and holloway, 2005; lohr and salomonsson, 2000; fairweather, 1999; burton et al., 1999), this is a prediction exercise38. our aim is to assign a probability of conversion to fadn farms and our focus is on all farms included in the base year of ifm-cap (i.e., for farms in fadn in 2017). therefore, the scope of our exercise is much broader than typical case-studies that analyze the drivers of conversion to organic farming. here, we aim to cover different eu regions and types of farms in terms of size and specialization. the economic literature has primarily applied empirical approaches to analyze drivers of organic conversion; theoretical models are usually not applied due to the complexity of drivers affecting organic farming decisions (serebrennikov et al., 2020; willock et al., 1999). furthermore, applying theoretical models is complicated by the heterogeneity of farming systems across the whole eu. therefore, an empirical predictive approach based on econometric estimations of the likelihood of adopting organic farming seems to be more appropriate in our context. regarding the estimation framework, we rely on the use of probability models. we compare the performance 38 see shmueli (2010) for a comparison between predictive and explanatory models. of multiple probability models and select the best performing one. we apply seven different prediction models to estimate the probability of conversion to organic farming: (i) the linear probability model (lp), (ii) the linear probability model with a stepwise selection algorithm (lp + ssa),39 (iii) the logit model (logit), (iv) the logit model using the covariates of model lp + ssa (logit + ssa), (v) the probit model (probit), (vi) the probit model using the covariates of model lp + ssa (probit + ssa), and (vii) the random forest algorithm (random forest). the latter one is a tree-based classification/regression tool able to handle large numbers of regressors, robust to overfitting, and that does not require distribution assumptions (biau and d’elia, 2011; james et al., 2013). for further details on treebased methods and on the random forest algorithm, please refer to james et al. (2013). the model selection criterion is solely based on the ability of the models to predict the status of the current fadn farms correctly. in other words, the in-sample prediction accuracy40 is the performance metric used to compare models and select the most performant one out of the seven considered. the performance metric of each model is calculated as the (non-weighted) average of the share of correct in-sample predictions of the conventional farms (0s) and the organic ones (1s). for example, a model that correctly predicts 90% of the conventional farms and 80% of the organic ones has a performance metric of 85%. using a non-weighted average implies assigning equal importance to the predictive ability of the farms’ conventional and organic status. the dependent variable used in all models is binary taking value of 1 if the farm is organic and 0 if the farm is conventional (non-organic). each model is fed with covariates chosen based on literature review and that relate to different monetary and non-monetary related factors such as the structural characteristics of the farm, the geographical location, the types of farm activities, the amount of subsidies received, the presence of organic farming in the region of activity, the performance of organic farms in the region relative to conventional ones, regional land prices, costs, and revenue information. the list of covariates used in the estimations is presented in table d.1. 39 a stepwise selection algorithm based on the aic criterion (implemented in r with the function step (r foundation for statistical computing, 2022) is applied to the full specification of the lp model. this selection algorithm reduces the number of covariates used in the estimation phase and, possibly, increases the accuracy (goodness of fit) of the predictions. this reduced equation is then used to re-estimate the linear, logit, and probit models. 40 out-sample accuracy is also evaluated with fadn data between 2014 and 2016 used as a test set and fadn data for the year 2017. https://doi.org/10.36253/bae-13925 https://doi.org/10.1093/erae/30.4.439 https://doi.org/10.1093/erae/30.4.439 https://doi.org/10.1079/sum2002131 300 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni the covariates in table d.1 have been constructed using available fadn data to capture the structural characteristics of the farm, production specialization, the characteristics of the geographical location in which it operates, the type of farm activities, crop biodiversity index, yield gaps, labor use, the amount of subsidies received, the presence of organic farming in the region of activity, the performance of organic farms in the region relative to conventional ones, regional land prices and input expenditure. the choice of these variables was guided by findings from previous empirical literature suggesting that structural features of the farm such as size, specialization, livestock density, ownership, family contribution to farm activities, and geographical location (genius et al., 2006; canavari et al., 2008; peter silas, 2008; koesling et al., 2008; khaledi et al. 2010; läpple, 2010; kaufmann, 2011; malá and malý, 2013; haris et al., 2018; serebrennikov et al., 2020; sapbamrer, 2021), production choices (anderson et al., 2005; kisaka-lwayo, 2007; malá and malý, 2013; knowler and bradshaw, 2007; métouolé méda et al., 2018), subsidies (genius et al., 2003; läpple, 2010; malá and malý, 2013; chmielinski et al., 2019; yanakittkul and aungvaravong, 2020), presence of organic farming in the region (läpple, 2010; läpple and rensburg, 2011; saoke, 2011; sriwichailamphan and sucharidtham, 2014; haris et al., 2018), land ownership and assets (kaufmann et al., 2011; chmielinski et al., 2018), farm performance indicators (parra lópez and calatrava requena, 2005; malá and malý, 2013; lu and cheng, 2019; liu et al., 2019) as well as other non-monetary drivers (e.g., believes and attitudes towards health and the environment) (egri, 1999; canavari et al., 2008; koesling et al., 2008; läpple, 2010; läpple and rensburg, 2011; mzoughi, 2011; wollni and andersson, 2014; haris et al., 2018; nguyen et al., 2020) may impact farmers’ decision to convert to organic farming. estimations and comparisons of the performance of the seven considered models are carried out for each ms and the eu.41 the in-sample predicted organic conversion probabilities obtained with the best performing model are then used in ifm-cap. that is, ifm-cap farms (in each ms or at the eu level, depending on the type of simulated policy target)42 ranked according to their likelihood of being organic, and those with the 41 the models are estimated using 2014-2017 data. a data cleaning procedure is applied before estimation. data for italy, denmark, and bulgaria prior 2016 have been removed due to the very low number of organic farms compared to 2017. 42 the estimated ms conversion probabilities are more appropriate when modeling the policy target on the share of organic land that needs to be converted at the ms level. in contrast, the eu level conversion probabilities are more appropriate when modeling the policy target set at the eu level. highest probability are selected to convert. this implies that the selection of farms that convert to organic production in the exogenous approach are not necessarily those that gain the most in terms of profit-maximizing behaviour but those estimated to be most likely converting, determined by various monetary and non-monetary related factors. this is in contrast to the endogenous approach, where the sole driver is profit maximization behavior, i.e., the utility gain from the conversion. performance results and model selection table d.2 presents the performance metric of the seven models and the best performing model for ms and eu level estimations. the prediction accuracy varies between 0.51 and 0.99, with most models across ms and eu having an accuracy greater than 0.8. for the majority of ms, as well as for the eu as a whole43, the random forest algorithm outperformed the other six models in terms of prediction accuracy. exceptions are luxemburg and ireland, for which the logit model and the logit model with stepwise selection algorithm have shown a higher prediction accuracy, respectively. the prediction accuracy for the selected model is greater than 0.88 across ms and eu. table d.3 presents a more detailed performance metric for the best performing model by indicating the in-sample confusion matrices which includes the percentages of correct and incorrect predictions generated by the selected models, together with the number of observations for the conventional and organic status. the in-sample confusion matrix shows the share of correct predictions both for the conventional status (the 0-s) and for the organic status (the 1-s), as well as the share of the incorrect predictions (i.e., 0for organic and 1 for conventional). as shown in table d.3, the prediction performance of the selected model is relatively high. for the ms-based models, the share of correct predictions varies between 85% and 99% for the non-organic farms and between 84% and 99% for the organic ones. for the eu, the random forest algorithm also performs pretty well, with a prediction accuracy of approximately 94% for both organic and non-organic farms. part d: references anderson, j., jolly, d., green, r. (2005). determinants of farmer adoption of organic production methods 43 due to its computation complexity, the stepwise selection algorithm is not performed with the full eu sample. https://doi.org/10.36253/bae-13925 301modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 in the fresh-market produce sector in california: a logistic regression analysis. in 2005western agricultural economics association annual meeting. biau, o., and d’elia, a. (2012). euro area gdp forecasting using large survey datasets. a random forest approach. euroindicators working papers, issn 1977-3331, eqp 2011/02, publications office of the european union, 2012. bravo-monroy, l., potts, s. g., and tzanopoulos, j. (2016). drivers influencing farmer decisions for adopting organic or conventional coffee management practices, food policy, 58: 49-61. burton, m., rigby, d., and young, t. (1999). analysis of the determinants of adoption of organic horticultural techniques in the uk, journal of agricultural economics, 50(1): 47-63. table d.1. covariates used in the prediction of the likelihood to convert in the exogenous approach. name type category description region class nm fadn region dummies tf14 class nm dummies for the 14 fadn classes of type of farming activities numeric nm share of the total agricultural area by production activity. additionally, the share of cereals is interacted with all other activities. in total 24 activities livestock class nm dummy for the presence/absence of livestock activities max share crop detailed numeric nm maximum share of the major crop according to fadn activities max share crop aggregate numeric nm maximum share of the major crop according to ifm-cap activities shannon numeric nm shannon index of crop biodiversity share uaa owned numeric nm share of owned utilized agricultural area regional land rent numeric m regional average rental price of agricultural land per hectare uaa numeric nm total utilized agricultural area siz6 class nm classes of economic size typown class nm type of ownership of the farm altitude class nm altitude class of the holding anc3 class nm classes of areas with natural constraints total awu ha numeric nm total annual working units per hectare share unpaid awu numeric nm share of awu of family workers lu/ha numeric nm livestock density irrsys class nm type of irrigation system fixed assets/ha numeric m fixed assets per hectare in eur mfp numeric m multifactor productivity measured as total output value divided total input costs decoupled/ ha numeric m decoupled payments per hectare coupled/ha numeric m coupled payments per hectare environment/ha numeric m environmental payments per hectare lfa/ha numeric m payments for least favored areas per hectare other/ha numeric m other rdp payments per hectare investments/ha numeric m payments for investments per hectare organic wheat yield ratio numeric nm ratio between the yield of wheat for organic and for conventional farms in the fadn region organic maize yield ratio numeric nm ratio between the yield of maize for organic and for conventional farms in the fadn region organic milk yield ratio numeric nm ratio between the yield of milk for organic and for conventional farms in the fadn region regional share organic numeric nm share of organic farms in the region fertilizers/ha numeric m expenditure per hectare in fertilizers pesticides/ha numeric m expenditure per hectare in pesticides relative fertilizers/ha numeric m expenditure per hectare in fertilizers relative to the expenditure of farms of the same organic status, tf14 and region relative pesticides/ha numeric m expenditure per hectare in pesticides relative to the expenditure of farms of the same organic status, tf14 and region notes: m: monetary variable; nm: non-monetary variable https://doi.org/10.36253/bae-13925 302 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni canavari, m., cantore, n., lombardi, d. 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(2018). the decision to adopt organic practices in malaysia: a mix-method approach. in proceedings of the 92 annual conference, coventry, uk, 16–18 april 2018 hattam, c. e.,  and  holloway, g. j.  (2005). adoption of certified organic production: evidence from mexico. paper at: researching sustainable systems intertable d.2. comparisons of the prediction accuracy metric of estimated models. lp lp + ssa logit logit + ssa probit probit + ssa random forest maximum prediction accuracy selected model belgium 0.8096 0.8053 0.9014 0.8017 0.8709 0.8066 0.9411 0.9411 random forest cyprus 0.8102 0.8148 0.8497 0.8443 0.8504 0.8435 0.8993 0.8993 random forest czechia 0.8563 0.8556 0.9424 0.9213 0.8191 0.9082 0.9653 0.9653 random forest germany 0.9273 0.9275 0.9301 0.9282 0.9293 0.9291 0.9725 0.9725 random forest greece 0.7228 0.7224 0.7543 0.7449 0.6187 0.5984 0.914 0.914 random forest spain 0.7597 0.7583 0.7683 0.7664 0.7691 0.7676 0.928 0.928 random forest estonia 0.8305 0.828 0.8029 0.9354 0.7705 0.7399 0.9653 0.9653 random forest france 0.7067 0.7054 0.7253 0.5933 0.7241 0.7225 0.9251 0.9251 random forest croatia 0.8499 0.849 0.8526 0.8462 0.8482 0.8416 0.9139 0.9139 random forest hungary 0.7498 0.7434 0.7922 0.7607 0.6312 0.7714 0.8781 0.8781 random forest ireland 0.8366 0.839 0.8831 0.9843 0.8523 0.9841 0.9526 0.9843 logit + ssa lithuania 0.9676 0.9679 0.9762 0.9743 0.9511 0.9697 0.9801 0.9801 random forest luxemburg 0.9389 0.9404 0.9905 0.9816 0.8846 0.9783 0.9802 0.9905 logit latvia 0.8983 0.8967 0.8848 0.9361 0.9212 0.9322 0.9835 0.9835 random forest italy 0.8032 0.8017 0.7232 0.8036 0.7173 0.8019 0.8972 0.8972 random forest netherland 0.7476 0.755 0.7993 0.778 0.7951 0.7728 0.9507 0.9507 random forest austria 0.9007 0.9001 0.9006 0.9134 0.9003 0.907 0.9472 0.9472 random forest poland 0.8662 0.8655 0.9377 0.9353 0.6075 0.9293 0.9692 0.9692 random forest portugal 0.7636 0.7622 0.7776 0.7748 0.5243 0.7627 0.9411 0.9411 random forest romania 0.7314 0.7284 0.5263 0.7617 0.6627 0.7576 0.9022 0.9022 random forest finland 0.9186 0.9154 0.8801 0.9288 0.872 0.9248 0.9801 0.9801 random forest sweden 0.804 0.8021 0.7745 0.7417 0.8374 0.852 0.9561 0.9561 random forest slovakia 0.8053 0.796 0.845 0.836 0.835 0.5953 0.8997 0.8997 random forest slovenia 0.9162 0.9172 0.9456 0.9439 0.917 0.9375 0.9636 0.9636 random forest bulgaria 0.7483 0.7512 0.6083 0.6291 0.6631 0.5099 0.8783 0.8783 random forest denmark 0.9668 0.9668 0.9775 0.9759 0.9766 0.9747 0.984 0.984 random forest eu 0.7288 0.573 0.5449 0.9367 0.9367 random forest https://doi.org/10.36253/bae-13925 303modeling conversion to organic agriculture with an eu-wide farm model bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 national scientific conference on organic agriculture, adelaide, australia, september 21-23, 2005. james, g., witten, d., hastie, t., and tibshirani, r. 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(2013). the determinants of adopting organic farming practices: a case study in the table d.3. confusion matrices of the selected models by ms and for the eu as a whole. belgium hungary portugal c ̅ o ̅   c ̅ o ̅ c ̅ o ̅ c 0.948 0.052 c 0.904 0.096 c 0.954 0.046 o 0.066 0.934 o 0.148 0.852 o 0.071 0.929 cyprus ireland romania c ̅ o ̅   c ̅ o ̅ c ̅ o ̅ c 0.852 0.148 c 0.984 0.016 c 0.937 0.063 o 0.054 0.946 o 0.015 0.985 o 0.132 0.868 czechia lithuania finland c ̅ o ̅   c ̅ o ̅ c ̅ o ̅ c 0.978 0.022 c 0.986 0.014 c 0.98 0.02 o 0.047 0.953 o 0.026 0.974 o 0.02 0.98 germany luxemburg sweden c ̅ o ̅   c ̅ o ̅ c ̅ o ̅ c 0.978 0.022 c 0.994 0.006 c 0.959 0.041 o 0.033 0.967 o 0.013 0.987 o 0.047 0.953 greece latvia slovakia c ̅ o ̅   c ̅ o ̅ c ̅ o ̅ c 0.898 0.102 c 0.989 0.011 c 0.944 0.056 o 0.07 0.93 o 0.022 0.978 o 0.144 0.856 spain italy slovenia c ̅ o ̅   c ̅ o ̅ c ̅ o ̅ c 0.92 0.08 c 0.917 0.083 c 0.976 0.024 o 0.064 0.936 o 0.122 0.878 o 0.049 0.951 estonia netherlands bulgaria c ̅ o ̅   c ̅ o ̅ c ̅ o ̅ c 0.947 0.053 c 0.958 0.042 c 0.909 0.091 o 0.016 0.984 o 0.056 0.944 o 0.153 0.847 france austria denmark c ̅ o ̅   c ̅ o ̅ c ̅ o ̅ c 0.938 0.062 c 0.941 0.059 c 0.982 0.018 o 0.088 0.912 o 0.047 0.953 o 0.014 0.986 croatia poland eu c ̅ o ̅   c ̅ o ̅ c ̅ o ̅ c 0.909 0.091 c 0.971 0.029 c 0.938 0.062 o 0.081 0.919 o 0.032 0.968 o 0.064 0.936 notes: c: conventional; o: organic; : predicted conventional; : predicted organic. https://doi.org/10.36253/bae-13925 304 bio-based and applied economics 12(4): 261-304, 2023 | e-issn 2280-6172 | doi: 10.36253/bae-13925 dimitrios kremmydas, pavel ciaian, edoardo baldoni czech republic. agricultural economics czech, 59, 19-28. métouolé méda, y.j., egyir, i., zahonogo, p., jatoe, j., atewamba, c. 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(2014). farmers’ willingness to switch to organic agriculture: a non-parametric analysis, agric. econ. czech, 60 (6): 273-278. https://doi.org/10.36253/bae-13925 https://doi.org/10.1111/j.1477-9552.1999.tb00814.x https://doi.org/10.1111/j.1477-9552.1999.tb00814.x modeling conversion to organic agriculture with an eu-wide farm model dimitrios kremmydas*, pavel ciaian, edoardo baldoni a systematic literature review on the rural-urban economic well-being gap in europe cesare meloni1,*, benedetto rocchi2, simone severini1 the forest-based bioeconomy in latvia: economic and environmental importance vineta tetere1,2,*, jack peerlings1, liesbeth dries1 the impact of covid-19 on household income and participation in the agri-food value chain: evidence from ethiopia margherita squarcina1,2,*, donato romano1 bioeconomy and resilience to economic shocks: insights from the covid-19 pandemic in 2020 jesús lasarte-lópez1,*, nicola grassano2, robert m’barek1, tévécia ronzon1 acknowledgements